pulumi.Input[str]:\n return pulumi.get(self, \"function_name\")","def function_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"function_name\")","def name(self):\r\n pass","def custom_actions(self, form_wizard_entry, request=None):","def _form(self, r, widget, **attr):\n\n widget_get = widget.get\n\n label = widget_get(\"label\", \"\")\n # Activate if-required\n #if label and isinstance(label, str):\n if label:\n label = current.T(label)\n icon = widget_get(\"icon\", \"\")\n if icon:\n icon = ICON(icon)\n\n context = widget_get(\"context\", None)\n tablename = widget_get(\"tablename\", None)\n resource, context = self._resolve_context(r, tablename, context)\n\n # Widget filter option\n widget_filter = widget_get(\"filter\", None)\n if widget_filter:\n resource.add_filter(widget_filter)\n\n record = resource.select([\"id\"], limit=1, as_rows=True).first()\n if record:\n record_id = record.id\n else:\n record_id = None\n\n if record_id:\n readonly = not current.auth.s3_has_permission(\"update\", tablename, record_id)\n else:\n readonly = not current.auth.s3_has_permission(\"create\", tablename)\n\n sqlform = widget.get(\"sqlform\", None)\n if not sqlform:\n sqlform = resource.get_config(\"crud_form\")\n if not sqlform:\n from ..ui import S3SQLDefaultForm\n sqlform = S3SQLDefaultForm()\n\n get_config = current.s3db.get_config\n if record_id:\n # Update form\n onvalidation = get_config(tablename, \"create_onvalidation\") or \\\n get_config(tablename, \"onvalidation\")\n onaccept = get_config(tablename, \"create_onaccept\") or \\\n get_config(tablename, \"onaccept\")\n else:\n # Create form\n onvalidation = get_config(tablename, \"create_onvalidation\") or \\\n get_config(tablename, \"onvalidation\")\n onaccept = get_config(tablename, \"create_onaccept\") or \\\n get_config(tablename, \"onaccept\")\n\n form = sqlform(request = r,\n resource = resource,\n record_id = record_id,\n readonly = readonly,\n format = \"html\",\n onvalidation = onvalidation,\n onaccept = onaccept,\n )\n _class = self._lookup_class(r, widget)\n\n # Render the widget\n output = DIV(H4(icon,\n label,\n _class = \"profile-sub-header\",\n ),\n DIV(form,\n _class = \"form-container thumbnail\",\n ),\n _class = _class,\n )\n\n return output","def form(self):\n\t\treturn self._form","def name(self):\r\n return self.__class__.__name__","def get_form_class():\n return RazorPaymentForm","def name(self) -> str:\n ...","def name(self) -> str:\n ...","def get_function_name(self):\n return self.__function","def name(self):\n return self.__name__","def name(self):\n return \"action_news_search\""],"string":"[\n \"def name(self):\\n # type: () -> Text\\n\\n return \\\"advice_form\\\"\",\n \"def form_name(self):\\n \\n # Unpack\\n request = self.request\\n context = request.context\\n \\n # Prepare\\n first_btn = len(self.buttons) and self.buttons[0]\\n btn_is_create = first_btn and first_btn.lower() == u'create'\\n action = u'Create new' if btn_is_create else u'Edit'\\n name = self.__class__.__name__\\n \\n # Try to use the ``request.context``.\\n if context:\\n name = getattr(context, 'name', context.__name__)\\n \\n # Return concatenated, e.g. `Edit Foo`.\\n return u'{0} {1}'.format(action, name)\",\n \"def name(self): \\n return \\\"search_form\\\"\",\n \"def name(self) -> Text:\\n\\n return \\\"user_form\\\"\",\n \"def Wraith_Form(self):\\t\\t\\n\\t\\tprint(self.name.Title() + \\\"Wraith\\\")\",\n \"def name(self) -> Text:\\n\\n return \\\"transact_search_form\\\"\",\n \"def method_name(self):\\n pass\",\n \"def get_method_name(self):\\n\\t\\treturn self.method_name\",\n \"def method(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"method\\\")\",\n \"def name(self) -> Text:\\n\\n return \\\"elicitation_form\\\"\",\n \"def name(self):\\n return \\\"order_form\\\"\",\n \"def name(self) -> str:\\n return f\\\"{self.class_object.__name__}.{self.method_str}\\\"\",\n \"def method_name(self) -> str:\\n if isinstance(self.view_func, str):\\n return self.view_func\\n return self.view_func.__name__\",\n \"def name(self) -> Text:\\n\\n return \\\"transfer_form\\\"\",\n \"def saveButtonMethod(self):\\n return AbstractAction(\\\"Save\\\")\",\n \"def name(self):\\n # type: () -> Text\\n\\n return \\\"form_suggestion\\\"\",\n \"def get_method_name(self):\\n return 'lipisha'\",\n \"def __str__(self) -> str:\\n\\n return self.display_form()\",\n \"def render_form():\",\n \"def name(self, value):\\n\\t\\tself.form.setObjectName(value)\",\n \"def make_form(self):\",\n \"def __str__(self):\\n return '#{}: {}'.format(self.circle.slug_name, self.code)\\n return 'Form {} aswered by {} at {}'.format(\\n self.form,\\n self.answered_by,\\n self.answered_at\\n )\",\n \"def get_unlink_form_name(self):\\r\\n return self.provider.NAME + '_unlink_form'\",\n \"def name(self):\\n\\t\\treturn self._func_name\",\n \"def logic_program_form(self):\\n #TODO\\n return ''\",\n \"def forms(self):\\n edit = EquipmentChownForm\\n return {\\n 'edit': edit,\\n }\",\n \"def name(self) -> Text:\\n\\n return \\\"cc_payment_form\\\"\",\n \"def get_edit_template_name(self, request=None):\\n if not self.edit_template_name_ajax:\\n return self.edit_template_name\\n elif request and request.is_ajax():\\n return self.edit_template_name_ajax\\n else:\\n return self.edit_template_name\",\n \"def action_type(self):\",\n \"def logic_program_form(self):\\r\\n #TODO\\r\\n return ''\",\n \"def saveAsButtonMethod(self):\\n return AbstractAction(\\\"Save As\\\")\",\n \"def action_name(self) -> \\\"str\\\":\\n return self._attrs.get(\\\"actionName\\\")\",\n \"def _getMethodName(self):\\n return self.id().split('.')[-1]\",\n \"def name(self):\\n name = self.function_name\\n\\n # Feature type is based on additional data that used\\n # for example if insight is for Healthsites Facilities\\n # than feature type is Healthsites Facilities\\n\\n if self.feature_type:\\n name = '%s for %s' % (name, self.feature_type)\\n return name\",\n \"def name(self):\\n return \\\"action_news_headline_au\\\"\",\n \"def __str__(self):\\n return _action_args_dict[self.action].name\",\n \"def full_name(self) -> Union[str, None]:\\n if not self.view_func:\\n return None\\n\\n prefix = self.view_func.__module__\\n if self._controller_cls:\\n module_name = self._controller_cls.__module__\\n class_name = self._controller_cls.__name__\\n prefix = f'{module_name}.{class_name}'\\n return f'{prefix}.{self.method_name}'\",\n \"def get_name(cls):\\n return 'text'\",\n \"def __getattr__(self, name):\\n if len(name) > 4 and name[:4] == 'set_':\\n return partial(self._set_control, name[4:].replace('_control', ''))\",\n \"def edit_template(self):\\n return '{}/{}.html'.format(self.object_name, self.edit_endpoint)\",\n \"def __str__(self):\\n return str(self.get_action_display())\",\n \"def get_form_class(self):\\n return self.form_class\",\n \"def name(self):\\n return \\\"action_news_headline_india\\\"\",\n \"def _repr_(self):\\n description = \\\"1-form \\\"\\n if self._name is not None:\\n description += \\\"'%s' \\\" % self._name\\n return self._final_repr(description)\",\n \"def _repr_(self):\\n description = \\\"1-form \\\"\\n if self._name is not None:\\n description += \\\"'%s' \\\" % self._name\\n return self._final_repr(description)\",\n \"def get_js_component(self):\\n return 'View';\",\n \"def custom_actions(self, form_entry, request=None):\",\n \"def name(self) -> Text:\\n\\n return \\\"new_product_application_form\\\"\",\n \"def name(cls):\\n return arg.s()(cls.func).func.__name__\",\n \"def name(self):\",\n \"def name(self):\",\n \"def name(self):\",\n \"def name(self):\",\n \"def show_name(self):\\n return self.name\",\n \"def method(self):\\n return self._method\",\n \"def method(self):\\n return self._method\",\n \"def name(self):\\n return \\\"action_news_headline_us\\\"\",\n \"def permission_codename(cls):\\n return f'{cls.app_label}_{cls.name}_action'\",\n \"def name(self):\\n ...\",\n \"def name(self) -> str:\\n return self._formal_name\",\n \"def get_basic_form(self):\\n return self.basic_form\",\n \"def name(self) -> str:\",\n \"def name(self) -> str:\",\n \"def name(self) -> str:\",\n \"def name(self) -> str:\",\n \"def name(self) -> str:\",\n \"def get_method(self) -> MethodStr:\\n return METHODS.inverse[self.method()]\",\n \"def form_tweaks(self):\\n pass\",\n \"def get_html_name(form, name):\\n return form.add_prefix(name)\",\n \"def getTemplateName(self, plotType):\\n\\n # TODO ?\\n return self.getGraphicsMethodName(plotType)\",\n \"def name(self):\\n return \\\"action_news_cnn\\\"\",\n \"def edit_form():\\n return template (\\\"edit\\\")\",\n \"def display_name(self):\",\n \"def name(self):\\n return self.__class__.__name__\",\n \"def name(self):\\n return self.__class__.__name__\",\n \"def name(self):\\n return self.__class__.__name__\",\n \"def name(self):\\n return self.__class__.__name__\",\n \"def name(self):\\n return self.__class__.__name__\",\n \"def plugin_name(self):\",\n \"def css_class(self):\\n css_type = self.widget_type\\n css_title = normalizer.normalize(self.data.title)\\n return ('faceted-checkboxtree-widget '\\n 'faceted-{0}-widget section-{1}').format(css_type, css_title)\",\n \"def css_class(self):\\n css_type = self.widget_type\\n css_title = normalizer.normalize(self.data.title)\\n return ('faceted-checkboxtree-widget '\\n 'faceted-{0}-widget section-{1}').format(css_type, css_title)\",\n \"def display(self):\\n return self.name\",\n \"def widget(cls,\\n r,\\n label = \\\"Anonymize\\\",\\n ajaxURL = None,\\n _class = \\\"action-lnk\\\",\\n ):\\n\\n T = current.T\\n\\n default = \\\"\\\"\\n\\n # Determine target table\\n if r.component:\\n resource = r.component\\n if resource.link and not r.actuate_link():\\n resource = resource.link\\n else:\\n resource = r.resource\\n table = resource.table\\n\\n # Determine target record\\n record_id = S3Anonymize._record_id(r)\\n if not record_id:\\n return default\\n\\n # Check if target is configured for anonymize\\n rules = resource.get_config(\\\"anonymize\\\")\\n if not rules:\\n return default\\n if not isinstance(rules, (tuple, list)):\\n # Single rule\\n rules[\\\"name\\\"] = \\\"default\\\"\\n rules = [rules]\\n\\n # Check permissions to anonymize\\n if not S3Anonymize.permitted(table, record_id):\\n return default\\n\\n # Determine widget ID\\n widget_id = \\\"%s-%s-anonymize\\\" % (table, record_id)\\n\\n # Inject script\\n if ajaxURL is None:\\n ajaxURL = r.url(method = \\\"anonymize\\\",\\n representation = \\\"json\\\",\\n )\\n script_options = {\\\"ajaxURL\\\": ajaxURL,\\n }\\n next_url = resource.get_config(\\\"anonymize_next\\\")\\n if next_url:\\n script_options[\\\"nextURL\\\"] = next_url\\n cls.inject_script(widget_id, script_options)\\n\\n # Action button\\n translated_label = T(label)\\n action_button = A(translated_label, _class=\\\"anonymize-btn\\\")\\n if _class:\\n action_button.add_class(_class)\\n\\n # Dialog and Form\\n INFO = T(\\\"The following information will be deleted from the record\\\")\\n CONFIRM = T(\\\"Are you sure you want to delete the selected details?\\\")\\n SUCCESS = T(\\\"Action successful - please wait...\\\")\\n\\n form = FORM(P(\\\"%s:\\\" % INFO),\\n cls.selector(rules),\\n P(CONFIRM),\\n DIV(INPUT(value = \\\"anonymize_confirm\\\",\\n _name = \\\"anonymize_confirm\\\",\\n _type = \\\"checkbox\\\",\\n ),\\n LABEL(T(\\\"Yes, delete the selected details\\\")),\\n _class = \\\"anonymize-confirm\\\",\\n ),\\n cls.buttons(),\\n _class = \\\"anonymize-form\\\",\\n # Store action key in form\\n hidden = {\\\"action-key\\\": cls.action_key(widget_id)},\\n )\\n\\n dialog = DIV(form,\\n DIV(P(SUCCESS),\\n _class = \\\"hide anonymize-success\\\",\\n ),\\n _class = \\\"anonymize-dialog hide\\\",\\n _title = translated_label,\\n )\\n\\n # Assemble widget\\n widget = DIV(action_button,\\n dialog,\\n _class = \\\"s3-anonymize\\\",\\n _id = widget_id,\\n )\\n\\n return widget\",\n \"def _action_handler_name(self, action):\\n\\n return '%s_handler' % action\",\n \"def name(self):\\n\\n return self.__class__.__name__\",\n \"def get_form(self):\\n return self.form\",\n \"def get_form(self):\\n return self.form\",\n \"def function_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"function_name\\\")\",\n \"def function_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"function_name\\\")\",\n \"def name(self):\\r\\n pass\",\n \"def custom_actions(self, form_wizard_entry, request=None):\",\n \"def _form(self, r, widget, **attr):\\n\\n widget_get = widget.get\\n\\n label = widget_get(\\\"label\\\", \\\"\\\")\\n # Activate if-required\\n #if label and isinstance(label, str):\\n if label:\\n label = current.T(label)\\n icon = widget_get(\\\"icon\\\", \\\"\\\")\\n if icon:\\n icon = ICON(icon)\\n\\n context = widget_get(\\\"context\\\", None)\\n tablename = widget_get(\\\"tablename\\\", None)\\n resource, context = self._resolve_context(r, tablename, context)\\n\\n # Widget filter option\\n widget_filter = widget_get(\\\"filter\\\", None)\\n if widget_filter:\\n resource.add_filter(widget_filter)\\n\\n record = resource.select([\\\"id\\\"], limit=1, as_rows=True).first()\\n if record:\\n record_id = record.id\\n else:\\n record_id = None\\n\\n if record_id:\\n readonly = not current.auth.s3_has_permission(\\\"update\\\", tablename, record_id)\\n else:\\n readonly = not current.auth.s3_has_permission(\\\"create\\\", tablename)\\n\\n sqlform = widget.get(\\\"sqlform\\\", None)\\n if not sqlform:\\n sqlform = resource.get_config(\\\"crud_form\\\")\\n if not sqlform:\\n from ..ui import S3SQLDefaultForm\\n sqlform = S3SQLDefaultForm()\\n\\n get_config = current.s3db.get_config\\n if record_id:\\n # Update form\\n onvalidation = get_config(tablename, \\\"create_onvalidation\\\") or \\\\\\n get_config(tablename, \\\"onvalidation\\\")\\n onaccept = get_config(tablename, \\\"create_onaccept\\\") or \\\\\\n get_config(tablename, \\\"onaccept\\\")\\n else:\\n # Create form\\n onvalidation = get_config(tablename, \\\"create_onvalidation\\\") or \\\\\\n get_config(tablename, \\\"onvalidation\\\")\\n onaccept = get_config(tablename, \\\"create_onaccept\\\") or \\\\\\n get_config(tablename, \\\"onaccept\\\")\\n\\n form = sqlform(request = r,\\n resource = resource,\\n record_id = record_id,\\n readonly = readonly,\\n format = \\\"html\\\",\\n onvalidation = onvalidation,\\n onaccept = onaccept,\\n )\\n _class = self._lookup_class(r, widget)\\n\\n # Render the widget\\n output = DIV(H4(icon,\\n label,\\n _class = \\\"profile-sub-header\\\",\\n ),\\n DIV(form,\\n _class = \\\"form-container thumbnail\\\",\\n ),\\n _class = _class,\\n )\\n\\n return output\",\n \"def form(self):\\n\\t\\treturn self._form\",\n \"def name(self):\\r\\n return self.__class__.__name__\",\n \"def get_form_class():\\n return RazorPaymentForm\",\n \"def name(self) -> str:\\n ...\",\n \"def name(self) -> str:\\n ...\",\n \"def get_function_name(self):\\n return self.__function\",\n \"def name(self):\\n return self.__name__\",\n \"def name(self):\\n return \\\"action_news_search\\\"\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6674236","0.65364265","0.6175527","0.59753966","0.5915975","0.58678293","0.5806205","0.5771903","0.5759645","0.57106274","0.5694327","0.5591224","0.55854917","0.5552111","0.5548712","0.5472779","0.54639256","0.54545367","0.5444614","0.54379505","0.54134136","0.53910995","0.53818125","0.53774655","0.53635144","0.5340729","0.5330842","0.53282446","0.53250283","0.53191775","0.531827","0.5317587","0.5273923","0.5269147","0.5256489","0.5239594","0.523817","0.522551","0.5196027","0.51878166","0.517746","0.51769453","0.5168979","0.51590186","0.51590186","0.5138869","0.5126777","0.51090175","0.5098626","0.50938904","0.50938904","0.50938904","0.50938904","0.5083108","0.50819516","0.50819516","0.5077883","0.50764644","0.5076346","0.50683707","0.505642","0.50518674","0.50518674","0.50518674","0.50518674","0.50518674","0.50278413","0.5018974","0.5013178","0.5012562","0.5011497","0.5000719","0.49932498","0.4981961","0.4981961","0.4981961","0.4981961","0.4981961","0.49816543","0.49705416","0.49705416","0.49703956","0.49699607","0.49655774","0.495802","0.4953882","0.4953882","0.4953362","0.4953362","0.49512798","0.4950103","0.49445313","0.4943095","0.494308","0.4941675","0.49398822","0.49398822","0.49369946","0.49276212","0.49261034"],"string":"[\n \"0.6674236\",\n \"0.65364265\",\n \"0.6175527\",\n \"0.59753966\",\n \"0.5915975\",\n \"0.58678293\",\n \"0.5806205\",\n \"0.5771903\",\n \"0.5759645\",\n \"0.57106274\",\n \"0.5694327\",\n \"0.5591224\",\n \"0.55854917\",\n \"0.5552111\",\n \"0.5548712\",\n \"0.5472779\",\n \"0.54639256\",\n \"0.54545367\",\n \"0.5444614\",\n \"0.54379505\",\n \"0.54134136\",\n \"0.53910995\",\n \"0.53818125\",\n \"0.53774655\",\n \"0.53635144\",\n \"0.5340729\",\n \"0.5330842\",\n \"0.53282446\",\n \"0.53250283\",\n \"0.53191775\",\n \"0.531827\",\n \"0.5317587\",\n \"0.5273923\",\n \"0.5269147\",\n \"0.5256489\",\n \"0.5239594\",\n \"0.523817\",\n \"0.522551\",\n \"0.5196027\",\n \"0.51878166\",\n \"0.517746\",\n \"0.51769453\",\n \"0.5168979\",\n \"0.51590186\",\n \"0.51590186\",\n \"0.5138869\",\n \"0.5126777\",\n \"0.51090175\",\n \"0.5098626\",\n \"0.50938904\",\n \"0.50938904\",\n \"0.50938904\",\n \"0.50938904\",\n \"0.5083108\",\n \"0.50819516\",\n \"0.50819516\",\n \"0.5077883\",\n \"0.50764644\",\n \"0.5076346\",\n \"0.50683707\",\n \"0.505642\",\n \"0.50518674\",\n \"0.50518674\",\n \"0.50518674\",\n \"0.50518674\",\n \"0.50518674\",\n \"0.50278413\",\n \"0.5018974\",\n \"0.5013178\",\n \"0.5012562\",\n \"0.5011497\",\n \"0.5000719\",\n \"0.49932498\",\n \"0.4981961\",\n \"0.4981961\",\n \"0.4981961\",\n \"0.4981961\",\n \"0.4981961\",\n \"0.49816543\",\n \"0.49705416\",\n \"0.49705416\",\n \"0.49703956\",\n \"0.49699607\",\n \"0.49655774\",\n \"0.495802\",\n \"0.4953882\",\n \"0.4953882\",\n \"0.4953362\",\n \"0.4953362\",\n \"0.49512798\",\n \"0.4950103\",\n \"0.49445313\",\n \"0.4943095\",\n \"0.494308\",\n \"0.4941675\",\n \"0.49398822\",\n \"0.49398822\",\n \"0.49369946\",\n \"0.49276212\",\n \"0.49261034\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5890521"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":89385,"cells":{"query":{"kind":"string","value":"Update the value we match against."},"document":{"kind":"string","value":"def edit( self, value=None, operator=None ):\n if value is None:\n self._clear()\n else:\n if type( value ) == type( '' ):\n value = value.split('\\n')\n self.value = tuple( value )\n\n if not operator:\n operator = None\n\n self.operator = operator"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["async def updated(self, value):\n pass","def _update_value(self, value):\n old_hash = get_hash(self._value)\n new_hash = get_hash(value)\n self._value = value\n if old_hash is None or new_hash is None or (old_hash != new_hash):\n self.is_dirty = True","def update_val(self, val):\n self.in_val = val","def updateValue(self):\n self.value = self.var.get()","def updateItem(self, value):\n self.value = value\n self.age = 0\n self.freq += 1","def value(self, value):\n self._update_value(value)","def update( self, dval ):\n self.val[:] += dval[:]\n return","def update_value(self, reward):\n\t\tval = self.value\n\t\tval = val + ((reward - val)/self.visited)\n\t\tself.value = val","def update(self, value):\n # If the value has not already been set, set it.\n if self.value is None:\n self.value = value\n else:\n # Calculate the new value.\n self.value = ((1-self.weight) * self.value + self.weight * value)\n return self.value","def update(self, v, r):\n pass","def update(self, val, feats):\n raise NotImplementedError","def update(self, tree_path, value):\n\t\traise NotImplementedError","def update(self, key, new_value):\n raise NotImplementedError","def _update_value(self):\n args = [] # todo: pyscript support for list comprehension\n for s in self._upstream:\n args.append(s())\n value = self._call_func(*args)\n self._set_value(value)","def update_variable(value):\n return value","def update(self)->None:\n database.cursor.execute(\"UPDATE votes SET value = %s WHERE id = %s\", (\n self.value,\n self.id\n ))\n database.connection.commit()","def update(self, data):\n pass","def update(self, data):\n pass","def update(self, data):\n pass","def update(self, data):\n pass","def value(self, new_value):\n self._value = self.sanitize(new_value)","def _update_field_value(browser, field_name, operator, value):\n browser.login('mgr')\n browser.keyword_search(KEYWORD, apply='Update')\n browser.getControl('field').displayValue = [field_name]\n browser.getControl('Next').click()\n assert '' == browser.getControl('new value', index=0).value\n browser.getControl('new value', index=0).value = value\n browser.getControl('operation').displayValue = [operator]\n browser.getControl('Next').click()","def update(self, v_input):\n\n self.v = v_input","def update():","def update():","def test_update_value(self):\n n = Node(inputs=2)\n # Override weights to static value for reproducibility\n n.weights = [1, 1]\n n.update_value([2, 3])\n\n self.assertEqual(round(n.value, 3), 0.993)","def _update_value_at(self, index, value):\n node = self._get_node_at(index)\n if node is None:\n raise IndexError('List index out of range.')\n node.value = value","def value(self, value):\n\t\toldvalue = self._value\n\t\tself._value = value\n\t\tif oldvalue != value:\n\t\t\tself.changed()","def update(self, matcher):\n self._comparator = matcher._comparator\n self._allowedStatus = matcher._allowedStatus","def update_proxy(self, instance, value):\n self.value = value","def _update(self):\n pass","def update_score():\n pass","def update(self):\n try:\n value = self.get_value()\n except:\n self.send_connection_state(False)\n return\n self.send_connection_state(True)\n self.send_new_value(value)","def update_values(self):\n for key in self.inputs.keys():\n value = self.inputs[key]['entry'].get()\n self.inputs[key]['value'] = value","def update(self, x):\n pass","def update(self, val):\n self.current_val = val\n self.redraw()","def update(self, value, newValue):\n # get node to be updated\n node = self.find(value)\n\n # node not found: abort\n if node == None:\n print('Node update from ', value, ' to ', newValue, ' failed: ',\n 'Node with value ', value, ' not found')\n return\n\n # node found: update it\n self.remove(value)\n self.add(newValue)","def _update_value(self) -> int:\n\n value_list = [card.value if card.value <= 10 else 10 for card in self]\n hand_value = sum(value_list)\n\n # Checks to see if any Aces can be worth 11 points instead of 1 point\n while value_list.count(1) > 0 and (21 - hand_value) >= 10:\n value_list[value_list.index(1)] = 11\n hand_value = sum(value_list)\n\n self._value = hand_value","def updateValue(self, state):\n return self.getQValue(state, self.policy[state[0], state[1]])","def set_val(self, input):\n return","def update(self, leaf_value):\n # Count visit.\n self._n_visits += 1\n # Update Q, a running average of values for all visits. wtf ??? this line is rigth but kind of wired !\n self._Q += 1.0 * (leaf_value - self._Q) / self._n_visits","def update(self, value: Opt[bytes], wal: bool = True):\n self.value = value\n self.wal = wal","def Update(self, value):\n self.SetValue(self.GetValue() + tf.cast(value, self.dtype))","def Update(self, value):\n self.SetValue(self.GetValue() + tf.cast(value, self.dtype))","def update(self, values):\n pass","def apply_update(self, control, value):\n for pattern, func in self.routes:\n match = pattern.match(control)\n if match is None:\n continue\n self.state[control] = value\n if func is not None:\n # Things like names don't need update functions.\n func(val=value, **match.groupdict())\n return True\n # No match.\n return False","def update(self) -> None:\n ...","def update(self, key, value):\n hash_key = hash(key) % self.length\n bucket = self.array[hash_key]\n if not bucket:\n raise ValueError('Key does not exist')\n for key_val_pair in bucket:\n if key_val_pair[0] == key:\n key_val_pair[1] = value\n break","def update( ):\r\n pass","def update(self, data):\n return self._data.update(data)","def change_value(self,val):\n self.val = val","def update(self, value):\n if value < self.min:\n self.min = value\n if value > self.max:\n self.max = value\n self.total += value\n self.instances += 1\n self.values.append(value)","def _update(self, count=True, forced=False):","def update(self, key, new_val):\n\n s = self.get(key)\n s.value = new_val\n s.update()\n return s","def update_timeval(self):\n self.timeval = self.get_timeval()","async def _update_values(self, model: Model):\n\n raise NotImplementedError","def update(self):\n self._data.update()\n\n self._state = self._data.get_value(self._type)","def _update_checksum(self, field, tag_id, value):\n # pylint: disable=unused-argument\n if tag_id != 10:\n self._checksum = checksum(field, self._checksum) + 1","def update(self, key, value):\n if key in self.map:\n self.map[key] = value","def set_value(self, field, value):\n field = self.find_first(field)\n if field is not None:\n field.value = value","def update(self, value):\n log_gui.debug(\"update value of field %s with : %s\", repr(self._name), value)\n wid = self._store_widget\n wid.setProperty(\"python-object\", value)\n wid.emit(self._sig)","def updateScore(self, score):\n self.__score += score","async def update_one(self, where, data):\n\n pass","def update(self):\n\n pass","def update(self, leaf_value):\n # Count visit.\n self._n_visits += 1\n # Update Q, a running average of values for all visits.\n self._Q += 1.0 * (leaf_value - self._Q) / self._n_visits","def value(self, value):\n self._value = value\n self.is_dirty = True","def update_action_value(self, state, action, value):\n self.value_function[to_table_index(state, action)] = value","def update(self):\n self.value = self.sensor.update()","def update(self, leaf_value):\n # Count visit.\n self._n_visits += 1\n # Update Q, a running average of values for all visits.\n self._Q += 1.0*(leaf_value - self._Q) / self._n_visits","def update_variable(value):\n return value + 1","def update(self, leaf_value):\n # Count visit.\n self._n_visits += 1\n # Update Q, a running average of values for all visits.","def update(self, data):\n self.data.update(data)","def update_value(attrib_key, attrib_val):\r\n \r\n if attrib_key == 'addr:street':\r\n attrib_val = update_street(attrib_val)\r\n \r\n elif attrib_key == 'addr:postcode':\r\n attrib_val = update_postcode(attrib_val)\r\n\r\n elif attrib_key == 'phone':\r\n attrib_val = update_phone(attrib_val)\r\n\r\n else:\r\n print(\"Missed value: check update_errors_dict\")\r\n update_errors_dict[attrib_key] = attrib_val\r\n\r\n return attrib_val","def value(self, value):\n if self.value == value: # case where we are setting at the same value\n return\n if (not self.has_data) or self.is_unknown or self.is_byte:\n if not ida_bytes.patch_byte(self.ea, value):\n raise RuntimeError(\"Unable to patch value: {}\".format(self))\n elif self.is_word:\n if not ida_bytes.patch_word(self.ea, value):\n raise RuntimeError(\"Unable to patch value: {}\".format(self))\n elif self.is_dword:\n if not ida_bytes.patch_dword(self.ea, value):\n raise RuntimeError(\"Unable to patch value: {}\".format(self))\n elif self.is_qword:\n if not ida_bytes.patch_qword(self.ea, value):\n raise RuntimeError(\"Unable to patch value: {}\".format(self))\n else:\n raise RuntimeError(\"Unable to patch value: {}\".format(self))","def update(self) -> None:\n pass","def update(self) -> None:\n pass","def update(self, value):\n orig = get_nested_default(self._request.session, self._path)\n orig.update(value)\n set_nested(self._request.session, self._path, orig)\n # self._value = get_nested_default(self._session, self._path)\n self.save()","def update(self, answer):\n position = self.find(answer)\n\n if position is None:\n raise ValueError(\"Answer instance does not exist in store\")\n else:\n self.answers[position]['value'] = answer.value","def update_metric(self, metric, value):\n if self.is_number(value):\n self.logger.debug(\"Collected raw metric: %s = %s\" % (metric, value))\n self.raw_metrics[metric] = value","def update(self, idx, value):\n idx = self.__capacity - 1 + idx\n self.__tree[idx] = value\n self.__update(idx)","def update(self):\n raise NotImplementedError","def _update_node(node, value):\n node.N += 1\n node.W += value\n node.Q = node.W / node.N","def update(self, *args, **kw):\n pass","def update_reference_value(self, reference_value: float):\n self.__reference_value = reference_value","def parse_and_set_value(self, new_value_str):\n self.value = self.parse(new_value_str)","def update(self, node, key, value):\n item = self._Item(key, value)\n self.update_node(node, item)","def update_cell(self, x, y, value):\n x1, y1 = self.transpose_coordinates(x, y)\n if self.is_in_field(x1, y1):\n self._cells[y1][x1] = value\n return True\n return False","def update(self) -> None:\n self.data.update()\n self._state = round(self.data.rate[\"rates\"][self._target], 3)","def update(self, data):\n return data","def value(self, value):\n\n\t\tself.__value = value","def update_data():\n pass","def update_value(self, value):\n try:\n value = int(value)\n except:\n pass\n logger.debug(\"Wemo update_value: {value}\", value=value)\n\n if self.yombo_device is None:\n logger.info(\"Cannot update device state, no attached Yombo device.\")\n return\n\n # We try to guess if the status update was a result of a command request.\n last_device_command = None\n\n if self.last_request_id is not None and self.last_request_id in self.device_commands:\n last_device_command = self.device_commands[self.last_request_id]\n if time() - last_device_command.broadcast_at > 2:\n self.last_request_id = None\n\n self.update_status(value, last_device_command)","def update(self)->None:\n pass","def update_score_other_rounds(self, match):\n self.match_views.update_score(match)","def update_score(self, score: int) -> int:\n self.score += score\n return self.score","def update_score(self):\n self.score = TurboMQ.calculate_fitness(self.result, self.graph)","def update_object ( self, event ):\n self.value = self.control.GetValue()","def result(self, value):\n self.set_local(0, value)","def _refresh(self):\n self._value = int(self._service.get_value(self._entry_point, self._path))","def test_update_values(self):\r\n m0 = TestUpdateModel.create(count=5, text='monkey')\r\n\r\n # independently save over a new count value, unknown to original instance\r\n m1 = TestUpdateModel.get(partition=m0.partition, cluster=m0.cluster)\r\n m1.count = 6\r\n m1.save()\r\n\r\n # update the text, and call update\r\n m0.update(text='monkey land')\r\n self.assertEqual(m0.text, 'monkey land')\r\n\r\n # database should reflect both updates\r\n m2 = TestUpdateModel.get(partition=m0.partition, cluster=m0.cluster)\r\n self.assertEqual(m2.count, m1.count)\r\n self.assertEqual(m2.text, m0.text)","def set_curr_value(self, val):\n # only goal that is in progress can have it's current value changed\n if self._status != EGoalStatus.IN_PROGRESS:\n raise NotImplementedError('Cannot set value to finished or not started goal')\n # try cast to int - mainly for QuantifiedGoal representation\n val = self.fw.types.try_float_cast(val)\n # update both in the stages object and in raw data\n self._values[EStage.CURRENT] = self._data_process(val)\n self._skeleton.curr_value = val\n # use progressor to update the database\n self._progressor.dump_to_database(self)"],"string":"[\n \"async def updated(self, value):\\n pass\",\n \"def _update_value(self, value):\\n old_hash = get_hash(self._value)\\n new_hash = get_hash(value)\\n self._value = value\\n if old_hash is None or new_hash is None or (old_hash != new_hash):\\n self.is_dirty = True\",\n \"def update_val(self, val):\\n self.in_val = val\",\n \"def updateValue(self):\\n self.value = self.var.get()\",\n \"def updateItem(self, value):\\n self.value = value\\n self.age = 0\\n self.freq += 1\",\n \"def value(self, value):\\n self._update_value(value)\",\n \"def update( self, dval ):\\n self.val[:] += dval[:]\\n return\",\n \"def update_value(self, reward):\\n\\t\\tval = self.value\\n\\t\\tval = val + ((reward - val)/self.visited)\\n\\t\\tself.value = val\",\n \"def update(self, value):\\n # If the value has not already been set, set it.\\n if self.value is None:\\n self.value = value\\n else:\\n # Calculate the new value.\\n self.value = ((1-self.weight) * self.value + self.weight * value)\\n return self.value\",\n \"def update(self, v, r):\\n pass\",\n \"def update(self, val, feats):\\n raise NotImplementedError\",\n \"def update(self, tree_path, value):\\n\\t\\traise NotImplementedError\",\n \"def update(self, key, new_value):\\n raise NotImplementedError\",\n \"def _update_value(self):\\n args = [] # todo: pyscript support for list comprehension\\n for s in self._upstream:\\n args.append(s())\\n value = self._call_func(*args)\\n self._set_value(value)\",\n \"def update_variable(value):\\n return value\",\n \"def update(self)->None:\\n database.cursor.execute(\\\"UPDATE votes SET value = %s WHERE id = %s\\\", (\\n self.value,\\n self.id\\n ))\\n database.connection.commit()\",\n \"def update(self, data):\\n pass\",\n \"def update(self, data):\\n pass\",\n \"def update(self, data):\\n pass\",\n \"def update(self, data):\\n pass\",\n \"def value(self, new_value):\\n self._value = self.sanitize(new_value)\",\n \"def _update_field_value(browser, field_name, operator, value):\\n browser.login('mgr')\\n browser.keyword_search(KEYWORD, apply='Update')\\n browser.getControl('field').displayValue = [field_name]\\n browser.getControl('Next').click()\\n assert '' == browser.getControl('new value', index=0).value\\n browser.getControl('new value', index=0).value = value\\n browser.getControl('operation').displayValue = [operator]\\n browser.getControl('Next').click()\",\n \"def update(self, v_input):\\n\\n self.v = v_input\",\n \"def update():\",\n \"def update():\",\n \"def test_update_value(self):\\n n = Node(inputs=2)\\n # Override weights to static value for reproducibility\\n n.weights = [1, 1]\\n n.update_value([2, 3])\\n\\n self.assertEqual(round(n.value, 3), 0.993)\",\n \"def _update_value_at(self, index, value):\\n node = self._get_node_at(index)\\n if node is None:\\n raise IndexError('List index out of range.')\\n node.value = value\",\n \"def value(self, value):\\n\\t\\toldvalue = self._value\\n\\t\\tself._value = value\\n\\t\\tif oldvalue != value:\\n\\t\\t\\tself.changed()\",\n \"def update(self, matcher):\\n self._comparator = matcher._comparator\\n self._allowedStatus = matcher._allowedStatus\",\n \"def update_proxy(self, instance, value):\\n self.value = value\",\n \"def _update(self):\\n pass\",\n \"def update_score():\\n pass\",\n \"def update(self):\\n try:\\n value = self.get_value()\\n except:\\n self.send_connection_state(False)\\n return\\n self.send_connection_state(True)\\n self.send_new_value(value)\",\n \"def update_values(self):\\n for key in self.inputs.keys():\\n value = self.inputs[key]['entry'].get()\\n self.inputs[key]['value'] = value\",\n \"def update(self, x):\\n pass\",\n \"def update(self, val):\\n self.current_val = val\\n self.redraw()\",\n \"def update(self, value, newValue):\\n # get node to be updated\\n node = self.find(value)\\n\\n # node not found: abort\\n if node == None:\\n print('Node update from ', value, ' to ', newValue, ' failed: ',\\n 'Node with value ', value, ' not found')\\n return\\n\\n # node found: update it\\n self.remove(value)\\n self.add(newValue)\",\n \"def _update_value(self) -> int:\\n\\n value_list = [card.value if card.value <= 10 else 10 for card in self]\\n hand_value = sum(value_list)\\n\\n # Checks to see if any Aces can be worth 11 points instead of 1 point\\n while value_list.count(1) > 0 and (21 - hand_value) >= 10:\\n value_list[value_list.index(1)] = 11\\n hand_value = sum(value_list)\\n\\n self._value = hand_value\",\n \"def updateValue(self, state):\\n return self.getQValue(state, self.policy[state[0], state[1]])\",\n \"def set_val(self, input):\\n return\",\n \"def update(self, leaf_value):\\n # Count visit.\\n self._n_visits += 1\\n # Update Q, a running average of values for all visits. wtf ??? this line is rigth but kind of wired !\\n self._Q += 1.0 * (leaf_value - self._Q) / self._n_visits\",\n \"def update(self, value: Opt[bytes], wal: bool = True):\\n self.value = value\\n self.wal = wal\",\n \"def Update(self, value):\\n self.SetValue(self.GetValue() + tf.cast(value, self.dtype))\",\n \"def Update(self, value):\\n self.SetValue(self.GetValue() + tf.cast(value, self.dtype))\",\n \"def update(self, values):\\n pass\",\n \"def apply_update(self, control, value):\\n for pattern, func in self.routes:\\n match = pattern.match(control)\\n if match is None:\\n continue\\n self.state[control] = value\\n if func is not None:\\n # Things like names don't need update functions.\\n func(val=value, **match.groupdict())\\n return True\\n # No match.\\n return False\",\n \"def update(self) -> None:\\n ...\",\n \"def update(self, key, value):\\n hash_key = hash(key) % self.length\\n bucket = self.array[hash_key]\\n if not bucket:\\n raise ValueError('Key does not exist')\\n for key_val_pair in bucket:\\n if key_val_pair[0] == key:\\n key_val_pair[1] = value\\n break\",\n \"def update( ):\\r\\n pass\",\n \"def update(self, data):\\n return self._data.update(data)\",\n \"def change_value(self,val):\\n self.val = val\",\n \"def update(self, value):\\n if value < self.min:\\n self.min = value\\n if value > self.max:\\n self.max = value\\n self.total += value\\n self.instances += 1\\n self.values.append(value)\",\n \"def _update(self, count=True, forced=False):\",\n \"def update(self, key, new_val):\\n\\n s = self.get(key)\\n s.value = new_val\\n s.update()\\n return s\",\n \"def update_timeval(self):\\n self.timeval = self.get_timeval()\",\n \"async def _update_values(self, model: Model):\\n\\n raise NotImplementedError\",\n \"def update(self):\\n self._data.update()\\n\\n self._state = self._data.get_value(self._type)\",\n \"def _update_checksum(self, field, tag_id, value):\\n # pylint: disable=unused-argument\\n if tag_id != 10:\\n self._checksum = checksum(field, self._checksum) + 1\",\n \"def update(self, key, value):\\n if key in self.map:\\n self.map[key] = value\",\n \"def set_value(self, field, value):\\n field = self.find_first(field)\\n if field is not None:\\n field.value = value\",\n \"def update(self, value):\\n log_gui.debug(\\\"update value of field %s with : %s\\\", repr(self._name), value)\\n wid = self._store_widget\\n wid.setProperty(\\\"python-object\\\", value)\\n wid.emit(self._sig)\",\n \"def updateScore(self, score):\\n self.__score += score\",\n \"async def update_one(self, where, data):\\n\\n pass\",\n \"def update(self):\\n\\n pass\",\n \"def update(self, leaf_value):\\n # Count visit.\\n self._n_visits += 1\\n # Update Q, a running average of values for all visits.\\n self._Q += 1.0 * (leaf_value - self._Q) / self._n_visits\",\n \"def value(self, value):\\n self._value = value\\n self.is_dirty = True\",\n \"def update_action_value(self, state, action, value):\\n self.value_function[to_table_index(state, action)] = value\",\n \"def update(self):\\n self.value = self.sensor.update()\",\n \"def update(self, leaf_value):\\n # Count visit.\\n self._n_visits += 1\\n # Update Q, a running average of values for all visits.\\n self._Q += 1.0*(leaf_value - self._Q) / self._n_visits\",\n \"def update_variable(value):\\n return value + 1\",\n \"def update(self, leaf_value):\\n # Count visit.\\n self._n_visits += 1\\n # Update Q, a running average of values for all visits.\",\n \"def update(self, data):\\n self.data.update(data)\",\n \"def update_value(attrib_key, attrib_val):\\r\\n \\r\\n if attrib_key == 'addr:street':\\r\\n attrib_val = update_street(attrib_val)\\r\\n \\r\\n elif attrib_key == 'addr:postcode':\\r\\n attrib_val = update_postcode(attrib_val)\\r\\n\\r\\n elif attrib_key == 'phone':\\r\\n attrib_val = update_phone(attrib_val)\\r\\n\\r\\n else:\\r\\n print(\\\"Missed value: check update_errors_dict\\\")\\r\\n update_errors_dict[attrib_key] = attrib_val\\r\\n\\r\\n return attrib_val\",\n \"def value(self, value):\\n if self.value == value: # case where we are setting at the same value\\n return\\n if (not self.has_data) or self.is_unknown or self.is_byte:\\n if not ida_bytes.patch_byte(self.ea, value):\\n raise RuntimeError(\\\"Unable to patch value: {}\\\".format(self))\\n elif self.is_word:\\n if not ida_bytes.patch_word(self.ea, value):\\n raise RuntimeError(\\\"Unable to patch value: {}\\\".format(self))\\n elif self.is_dword:\\n if not ida_bytes.patch_dword(self.ea, value):\\n raise RuntimeError(\\\"Unable to patch value: {}\\\".format(self))\\n elif self.is_qword:\\n if not ida_bytes.patch_qword(self.ea, value):\\n raise RuntimeError(\\\"Unable to patch value: {}\\\".format(self))\\n else:\\n raise RuntimeError(\\\"Unable to patch value: {}\\\".format(self))\",\n \"def update(self) -> None:\\n pass\",\n \"def update(self) -> None:\\n pass\",\n \"def update(self, value):\\n orig = get_nested_default(self._request.session, self._path)\\n orig.update(value)\\n set_nested(self._request.session, self._path, orig)\\n # self._value = get_nested_default(self._session, self._path)\\n self.save()\",\n \"def update(self, answer):\\n position = self.find(answer)\\n\\n if position is None:\\n raise ValueError(\\\"Answer instance does not exist in store\\\")\\n else:\\n self.answers[position]['value'] = answer.value\",\n \"def update_metric(self, metric, value):\\n if self.is_number(value):\\n self.logger.debug(\\\"Collected raw metric: %s = %s\\\" % (metric, value))\\n self.raw_metrics[metric] = value\",\n \"def update(self, idx, value):\\n idx = self.__capacity - 1 + idx\\n self.__tree[idx] = value\\n self.__update(idx)\",\n \"def update(self):\\n raise NotImplementedError\",\n \"def _update_node(node, value):\\n node.N += 1\\n node.W += value\\n node.Q = node.W / node.N\",\n \"def update(self, *args, **kw):\\n pass\",\n \"def update_reference_value(self, reference_value: float):\\n self.__reference_value = reference_value\",\n \"def parse_and_set_value(self, new_value_str):\\n self.value = self.parse(new_value_str)\",\n \"def update(self, node, key, value):\\n item = self._Item(key, value)\\n self.update_node(node, item)\",\n \"def update_cell(self, x, y, value):\\n x1, y1 = self.transpose_coordinates(x, y)\\n if self.is_in_field(x1, y1):\\n self._cells[y1][x1] = value\\n return True\\n return False\",\n \"def update(self) -> None:\\n self.data.update()\\n self._state = round(self.data.rate[\\\"rates\\\"][self._target], 3)\",\n \"def update(self, data):\\n return data\",\n \"def value(self, value):\\n\\n\\t\\tself.__value = value\",\n \"def update_data():\\n pass\",\n \"def update_value(self, value):\\n try:\\n value = int(value)\\n except:\\n pass\\n logger.debug(\\\"Wemo update_value: {value}\\\", value=value)\\n\\n if self.yombo_device is None:\\n logger.info(\\\"Cannot update device state, no attached Yombo device.\\\")\\n return\\n\\n # We try to guess if the status update was a result of a command request.\\n last_device_command = None\\n\\n if self.last_request_id is not None and self.last_request_id in self.device_commands:\\n last_device_command = self.device_commands[self.last_request_id]\\n if time() - last_device_command.broadcast_at > 2:\\n self.last_request_id = None\\n\\n self.update_status(value, last_device_command)\",\n \"def update(self)->None:\\n pass\",\n \"def update_score_other_rounds(self, match):\\n self.match_views.update_score(match)\",\n \"def update_score(self, score: int) -> int:\\n self.score += score\\n return self.score\",\n \"def update_score(self):\\n self.score = TurboMQ.calculate_fitness(self.result, self.graph)\",\n \"def update_object ( self, event ):\\n self.value = self.control.GetValue()\",\n \"def result(self, value):\\n self.set_local(0, value)\",\n \"def _refresh(self):\\n self._value = int(self._service.get_value(self._entry_point, self._path))\",\n \"def test_update_values(self):\\r\\n m0 = TestUpdateModel.create(count=5, text='monkey')\\r\\n\\r\\n # independently save over a new count value, unknown to original instance\\r\\n m1 = TestUpdateModel.get(partition=m0.partition, cluster=m0.cluster)\\r\\n m1.count = 6\\r\\n m1.save()\\r\\n\\r\\n # update the text, and call update\\r\\n m0.update(text='monkey land')\\r\\n self.assertEqual(m0.text, 'monkey land')\\r\\n\\r\\n # database should reflect both updates\\r\\n m2 = TestUpdateModel.get(partition=m0.partition, cluster=m0.cluster)\\r\\n self.assertEqual(m2.count, m1.count)\\r\\n self.assertEqual(m2.text, m0.text)\",\n \"def set_curr_value(self, val):\\n # only goal that is in progress can have it's current value changed\\n if self._status != EGoalStatus.IN_PROGRESS:\\n raise NotImplementedError('Cannot set value to finished or not started goal')\\n # try cast to int - mainly for QuantifiedGoal representation\\n val = self.fw.types.try_float_cast(val)\\n # update both in the stages object and in raw data\\n self._values[EStage.CURRENT] = self._data_process(val)\\n self._skeleton.curr_value = val\\n # use progressor to update the database\\n self._progressor.dump_to_database(self)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6900959","0.66881514","0.662792","0.6566491","0.6534293","0.639026","0.62795675","0.6251601","0.6240275","0.6180104","0.61638635","0.6101243","0.60290325","0.60176915","0.60130274","0.6004048","0.59539163","0.59539163","0.59539163","0.59539163","0.5949472","0.59451115","0.5945096","0.5932123","0.5932123","0.5930735","0.59110224","0.5896543","0.5877031","0.58743185","0.5857929","0.5825709","0.58195704","0.581068","0.5800037","0.57809794","0.57754093","0.57630956","0.57628155","0.57619566","0.57617646","0.57615167","0.57552844","0.57552844","0.5752321","0.57510966","0.5731736","0.5699904","0.5695976","0.56905466","0.5683748","0.56779134","0.5676354","0.5672036","0.5668821","0.5666779","0.5664856","0.5655746","0.5642201","0.56378204","0.5635322","0.5626115","0.5623442","0.56162804","0.56141734","0.56130797","0.5611404","0.56105864","0.5601789","0.5592969","0.5592884","0.5589815","0.55858284","0.55712605","0.55702966","0.55702966","0.55620384","0.5558811","0.55489683","0.55484504","0.5548395","0.5544697","0.5540826","0.5537169","0.55349636","0.5533767","0.5530169","0.5524144","0.55209523","0.5520799","0.55099654","0.5509184","0.55068237","0.550232","0.54993963","0.54965144","0.5496331","0.54958135","0.5487179","0.54856753","0.54816943"],"string":"[\n \"0.6900959\",\n \"0.66881514\",\n \"0.662792\",\n \"0.6566491\",\n \"0.6534293\",\n \"0.639026\",\n \"0.62795675\",\n \"0.6251601\",\n \"0.6240275\",\n \"0.6180104\",\n \"0.61638635\",\n \"0.6101243\",\n \"0.60290325\",\n \"0.60176915\",\n \"0.60130274\",\n \"0.6004048\",\n \"0.59539163\",\n \"0.59539163\",\n \"0.59539163\",\n \"0.59539163\",\n \"0.5949472\",\n \"0.59451115\",\n \"0.5945096\",\n \"0.5932123\",\n \"0.5932123\",\n \"0.5930735\",\n \"0.59110224\",\n \"0.5896543\",\n \"0.5877031\",\n \"0.58743185\",\n \"0.5857929\",\n \"0.5825709\",\n \"0.58195704\",\n \"0.581068\",\n \"0.5800037\",\n \"0.57809794\",\n \"0.57754093\",\n \"0.57630956\",\n \"0.57628155\",\n \"0.57619566\",\n \"0.57617646\",\n \"0.57615167\",\n \"0.57552844\",\n \"0.57552844\",\n \"0.5752321\",\n \"0.57510966\",\n \"0.5731736\",\n \"0.5699904\",\n \"0.5695976\",\n \"0.56905466\",\n \"0.5683748\",\n \"0.56779134\",\n \"0.5676354\",\n \"0.5672036\",\n \"0.5668821\",\n \"0.5666779\",\n \"0.5664856\",\n \"0.5655746\",\n \"0.5642201\",\n \"0.56378204\",\n \"0.5635322\",\n \"0.5626115\",\n \"0.5623442\",\n \"0.56162804\",\n \"0.56141734\",\n \"0.56130797\",\n \"0.5611404\",\n \"0.56105864\",\n \"0.5601789\",\n \"0.5592969\",\n \"0.5592884\",\n \"0.5589815\",\n \"0.55858284\",\n \"0.55712605\",\n \"0.55702966\",\n \"0.55702966\",\n \"0.55620384\",\n \"0.5558811\",\n \"0.55489683\",\n \"0.55484504\",\n \"0.5548395\",\n \"0.5544697\",\n \"0.5540826\",\n \"0.5537169\",\n \"0.55349636\",\n \"0.5533767\",\n \"0.5530169\",\n \"0.5524144\",\n \"0.55209523\",\n \"0.5520799\",\n \"0.55099654\",\n \"0.5509184\",\n \"0.55068237\",\n \"0.550232\",\n \"0.54993963\",\n \"0.54965144\",\n \"0.5496331\",\n \"0.54958135\",\n \"0.5487179\",\n \"0.54856753\",\n \"0.54816943\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":89386,"cells":{"query":{"kind":"string","value":"Return a tuple of query elements to be passed to the catalog (used by 'Topic.buildQuery()')."},"document":{"kind":"string","value":"def getCriteriaItems( self ):\n # filter out empty strings\n result = []\n\n value = tuple( filter( None, self.value ) )\n if not value:\n return ()\n result.append( ( self.field, self.value ), )\n\n if self.operator is not None:\n result.append( ( '%s_operator' % self.field, self.operator ) )\n\n return tuple( result )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def query(self):\n if bool(self._query_parameters): # ApiGateway set key value None in case of absence params, not an empty dict\n payload = {inflection.underscore(k): v for k, v, in self._query_parameters.items()}\n else:\n payload = dict()\n QueryTuple = namedtuple('QueryTuple', sorted(payload))\n the_tuple = QueryTuple(**payload)\n return the_tuple","def _ATTopic_query(self, **kw):\n del kw['path'] # don't want to limit to context as w/ folders\n return self.context.queryCatalog(self.context.REQUEST, \n False, # no batch here\n None, # no b_size here\n False,\n **kw)","def getQueries(self):\n return sorted( self.qryDocs.keys() )","def build_query_structure(self):\n query_list = list()\n filter_list = list()\n for key, val in self.q_dict.items():\n if key in self.es_query_keys:\n query_list.append(\n {\"match\": {\".\".join(key.split(\"_\")): val[0]}})\n elif key in self.es_date_keys:\n filter_list.append(\n {\"range\": {\".\".join(key.split(\"_\")): val}})\n elif \":\" in val[0]:\n #for handling queries like dd_dct=gte:1\n range_val = val[0].split(\":\")\n filter_list.append({\"range\": {\".\".join(key.split(\"_\")): {\n range_val[0]: int(range_val[1])}}})\n else:\n filter_list.append(\n {\"terms\": {\".\".join(key.split(\"_\")): val}})\n return query_list, filter_list","def query(self, page) -> [str, dict]:","def queryThis(query: Query) -> list:\n q = str(query) # extra line for clarity. calls the __str__ magic function\n\n return list(client.query(q).result())","def query_nodes(self):\n query_nodes = []\n for node in self.nodes:\n if 'querynode' == node.get('infos').get('type'):\n query_nodes.append(node)\n return query_nodes","def build_inner_query(request, tags):\n # the inner query builds a list of streams matching all the\n # clauses which we can then select from\n clauses = []\n uuid_clause = \"true\"\n for (k, v) in tags:\n if k == 'uuid': \n if v != None:\n uuid_clause = \"s.uuid = %s\" % escape_string(v)\n continue\n if v != None:\n clauses.append(\"hstore(%s, %s)\" % (escape_string(k),\n escape_string(v)))\n else: break\n\n if len(clauses) == 0: clauses = [\"hstore(array[]::varchar[])\"]\n inner_query = \"\"\"\n (SELECT s.id FROM stream s, subscription sub\n WHERE s.subscription_id = sub.id AND (%s) AND (%s) AND s.metadata @> (%s))\n\"\"\" % (build_authcheck(request), uuid_clause, ' || '.join(clauses))\n\n return inner_query, clauses","def elements(self):\n return self.q","def _get_queries(args):\n if args.mode == '2DSEQ':\n queries = [\"@type=='2dseq'\", \"@is_spectroscopy==True\", \"@is_complex==True\"]\n elif args.mode == 'FID':\n queries = [\"@type=='fid'\", \"@is_spectroscopy==True\"]\n return queries + args.query","def get(self):\n return [\n [a, v]\n for a, v in sorted(self.__dict__.items())\n if a != \"query_name\" and len(v)\n ]","def build_query(db, request, tags):\n inner_query, clauses = build_inner_query(request, tags)\n if len(tags) and tags[-1][0] == 'uuid':\n # if we select uuid as the trailing tag we have to be special\n query = \"\"\"\nSELECT DISTINCT s.uuid \nFROM stream AS s\nWHERE s.id IN \"\"\" + inner_query\n elif len(tags) and (tags[-1][1] == None or tags[-1][1] == ''):\n # odd-numbered clasues, so we print matching values of tags\n t = escape_string(tags[-1][0])\n query = \"\"\"\nSELECT DISTINCT metadata -> %s AS svals FROM stream\nWHERE id IN %s AND metadata ? %s\nORDER BY svals ASC\"\"\" % (t, inner_query, t)\n else:\n # otherwise we print all tags matching the restriction\n query = \"\"\"\nSELECT DISTINCT skeys\nFROM (\n SELECT skeys(metadata) FROM stream\n WHERE id IN %s\n) AS skeys ORDER BY skeys ASC\"\"\" % inner_query\n\n log.msg(query)\n d = db.runQuery(query)\n d.addCallback(log_time, time.time())\n return d","def query(cursor, query):\n out = []\n c = cursor.execute(query)\n out.append(c.fetchall())\n # will return list of tuples for each query\n return out","def subscribedQueries(self):\n return map(Query.get, self.subscriptions)","def query3() :","def list_named_queries(verbose):\n if not verbose:\n rows = [[r] for r in named_queries.list()]\n headers = [\"Name\"]\n else:\n headers = [\"Name\", \"Query\"]\n rows = [[r, named_queries.get(r)]\n for r in named_queries.list()]\n\n if not rows:\n status = named_queries.usage\n else:\n status = ''\n return [(rows, headers, status, '', False)]","def _get_catalog_results(self, featured=False, **kw):\n if 'context' in kw.keys():\n kw['path'] = {'query': '/'.join(kw['context'].getPhysicalPath())}\n\n types = ('Article', 'Blog Entry', )\n states = ('published', )\n sort = 'Date'\n \n results = self.qrymethod(portal_type=types,\n review_state=states,\n is_featured=featured,\n sort_on=sort, \n sort_order='descending',\n **kw)\n\n return results","def driver_queries(self) -> Iterable[Dict[str, str]]:\n return self.svc_queries","def query_schema(self, name, param):\n\n alias, name, need_list = self.parse_entry(name)\n\n if not name:\n result = self.process_multiple_query(need_list, param)\n else:\n result = self.process_single_query(name, need_list, param)\n return alias, result","def namedtuplefetchall(query, param_list):\r\n\twith connection.cursor() as cursor:\r\n\t\tcursor.execute(query, param_list)\r\n\t\tdesc = cursor.description\r\n\t\tnt_result = namedtuple('Result', [col[0] for col in desc])\r\n\t\treturn [nt_result(*row) for row in cursor.fetchall()]","def get_query_specs(self, *args, **keyword_args):\n\n lookup_table_creation_query = {\n cdr_consts.QUERY:\n LOOKUP_TABLE_CREATION_QUERY.render(\n project_id=self.project_id,\n sandbox_dataset=self.sandbox_dataset_id,\n dataset_id=self.dataset_id,\n lookup_table=EXPLICIT_IDENTIFIER_CONCEPTS)\n }\n\n sandbox_query = {\n cdr_consts.QUERY:\n SANDBOX_EXPLICIT_IDENTIFIER_RECORDS.render(\n project_id=self.project_id,\n dataset_id=self.dataset_id,\n sandbox_dataset_id=self.sandbox_dataset_id,\n observation_table=OBSERVATION,\n intermediary_table=self.get_sandbox_tablenames()[0],\n lookup_table=EXPLICIT_IDENTIFIER_CONCEPTS,\n concept_fields=get_concept_id_fields(OBSERVATION))\n }\n\n suppress_query = {\n cdr_consts.QUERY:\n SUPPRESS_EXPLICIT_IDENTIFIER_RECORDS.render(\n project_id=self.project_id,\n dataset_id=self.dataset_id,\n sandbox_dataset_id=self.sandbox_dataset_id,\n intermediary_table=self.get_sandbox_tablenames()[0],\n observation_table=OBSERVATION)\n }\n\n return [lookup_table_creation_query, sandbox_query, suppress_query]","def extract_queries(self, path_topics=\"../topics-rnd5.xml\"):\n \n topic_queries = []\n with open(path_topics, \"r\") as f:\n for line in f:\n match = re.match(\".*<query>([^<]*)<\\/query>.*\", line)\n if match:\n topic_queries.append(match.group(1))\n if len(topic_queries) != 50:\n sys.exit(\"There should be 50 topics, found {}\".format(\n len(topic_queries)))\n \n return topic_queries","def query_many(self, *q, read_as=None):\n raise NotImplementedError","def qargs(self):\n return self._qargs","def setup(query):\n\n results = []\n return results","def setup(query):\n\n results = []\n return results","def setup(query):\n\n results = []\n return results","def setup(query):\n\n results = []\n return results","def form_cand_queries_amongvocab(self, batch_input, gram):\n N = batch_input['pair_objects'].size(0)\n tensor_type = batch_input['pair_objects'].long().data.type()\n\n M = len(self.vocab[gram])\n cats = self.idx_to_vocab[gram].type(tensor_type)\n queries = cats.unsqueeze(0) \n labels = batch_input['labels_'+gram].type(queries.data.type()) #(N,M)\n\n return (queries, labels)","def make_complex_query_set(self):\n\n query = self.request.GET.get(\"q\")\n category = self.request.GET.get(\"category\")\n title = self.request.GET.get(\"title\")\n\n q_object = Q()\n\n if query:\n q_object.add((\n Q(category__category_name__icontains=query) |\n Q(title__icontains=query) \n ), Q.OR)\n\n else:\n if category:\n q_object.add(\n Q(category=category),\n Q.AND)\n if title:\n q_object.add(\n Q(title__icontains=title),\n Q.AND)\n\n return q_object","def __generateQuery(self, query):\n if query == None:\n return [\"1=1\"]\n elif type(query) is not list:\n return [query]\n else:\n return query","def query(self):\n if IATTopic.providedBy(self.context) or ICollection.providedBy(self.context):\n return self.context.queryCatalog(batch=False)\n elif IFolderish.providedBy(self.context):\n return self.context.getFolderContents(batch=False)","def make_complex_query_set(self):\n\n query = self.request.GET.get(\"q\")\n program_id = self.request.META.get('HTTP_X_SVMS_PROGRAM_ID')\n category = self.request.GET.get(\"category\")\n title = self.request.GET.get(\"title\")\n level = self.request.GET.get(\"level\")\n description = self.request.GET.get(\"description\")\n status = self.request.GET.get(\"status\")\n job_tag = self.request.GET.get(\"job_tag\")\n\n q_object = Q()\n\n if query:\n q_object.add((\n Q(program_id=query) |\n Q(category=query) |\n Q(title__icontains=query) |\n #Q(category__category_name__icontains=query) |\n Q(description__icontains=query) |\n Q(job_tag__tag__in=str(query).split(\",\"))\n ), Q.OR)\n\n if query.isnumeric():\n q_object.add(\n Q(level__icontains=int(query)), Q.OR)\n\n q_object.add(Q(status=strtobool(query)), Q.OR) if query in [\n \"true\", \"True\", \"False\", \"false\"] else None\n\n else:\n if program_id:\n q_object.add(\n Q(program_id=program_id),\n Q.AND)\n\n if category:\n q_object.add(\n Q(category=category),\n Q.AND)\n if title:\n q_object.add(\n Q(title__icontains=title),\n Q.AND)\n\n if description:\n q_object.add(\n Q(description__icontains=description), Q.AND)\n\n if job_tag:\n q_object.add(\n Q(job_tag__tag__in=str(job_tag).split(\",\")),\n Q.AND)\n\n if level:\n if level.isnumeric():\n q_object.add(\n Q(level__icontains=int(level)),\n Q.AND)\n else:\n raise Exception(\n ErrorMessage.WRONG_FIELD_TYPE.value.format(\"level\",\n \"numeric\"))\n\n q_object.add(Q(status=strtobool(status)), Q.AND) if status in [\n \"true\", \"True\", \"False\", \"false\"] else None\n\n return q_object","def query(self, query):","def query_subject(self, s):\n self.setQuery(\"\"\"\n Select ?p ?o where {\n %s ?p ?o\n } ORDER BY (?p)\"\"\" % (s))\n\n try:\n rval = self.query()\n g = rval.convert()\n return [(x['p']['value'], x['o']['value']) for x in g['results']['bindings']]\n except:\n print \"Select failed\"\n traceback.print_exc(file=sys.stdout)","def collect_tracks_query(query, collection_type):\n search_result = SP.search(query, 1, 0, collection_type)\n\n item = search_result[collection_type + 's']['items'][0]\n name = item['name']\n\n return name, collect_tracks(item, collection_type)","def queryComponent(type=None, filter=None, all=0):","def parse_query(query):\n qlist = []\n splitted = query.split(\"&\")\n for entry in splitted:\n cmd, arg = entry.split(\"=\")\n qlist.append((cmd, arg))\n return qlist","def query(self):","def getQueries(self):\r\n\t\tp=[]\r\n\t\tfor pa in self.session.query(Query):\r\n\t\t\tp.append(pa)\r\n\t\treturn p","def get_query_fields(cls):\n ...","def _query_range_get(self):\n return (self.query_start, self.query_end)","def __iter__(self):\n\t\tfields = 'fieldname', 'text', 'docfreq', 'indexfreq'\n\t\tcur = self.index.collection.find(fields=fields).sort('fieldname')\n\t\treturn (tuple(rec[field] for field in fields) for rec in cur)","def get_qs(self):\n if self.qs == None:\n # Get the Lemma PKs\n qs = self.get_queryset()\n else:\n qs = self.qs\n return qs","def get_qs(self):\n if self.qs == None:\n # Get the Lemma PKs\n qs = self.get_queryset()\n else:\n qs = self.qs\n return qs","def build_tag_query(db, request, tags):\n inner_query, clauses = build_inner_query(request, tags)\n query = \"\"\"\nSELECT s.metadata || hstore('uuid', s.uuid)\nFROM stream s\nWHERE s.id IN \"\"\" + inner_query + \"\"\"\nORDER BY s.id ASC\"\"\"\n log.msg(query)\n d = db.runQuery(query)\n d.addCallback(log_time, time.time())\n return d","def get_questions_of_topic(topic):\n\n dynamodb = boto3.resource(\"dynamodb\", region_name=\"eu-central-1\")\n question_table = dynamodb.Table(\"Questions\")\n\n fe = Attr(\"TopicId\").eq(topic.get(\"TopicId\"))\n response = question_table.scan(FilterExpression=fe)\n questions = response.get(\"Items\")\n return questions","def make_query(self):","def query(self, q):\n for key in self.metadb.query(q):\n yield key, self.datadb[key]","def query(self):\n return self.details[KEY_QUERY]","def get_catalog_query(self):\n # Implemented from kitosid template for -\n # osid.resource.BinQuerySession.get_bin_query_template\n return self._get_provider_session('catalog_query_session').get_catalog_query()","def queries(self):\n return self.__queries","def get_resource_query(self):\n pass","def get_resource_query(self):\n pass","def build_query(table, attr_converter, page=0):\n where_statement, values = build_where_statement(attr_converter)\n query = \"SELECT DISTINCT {} FROM {} {} LIMIT {} OFFSET {};\".format(\n ', '.join([attr_converter[x]['column'] for x in attr_converter]),\n table, # db table\n where_statement, # various statements to narrow search results\n PG_LIMIT, # number of rows to return\n int(PG_LIMIT)*page # number or rows to skip\n )\n return query, values","def query(self, **kwargs):","def terms(self) -> Tuple[Term, ...]:\n ...","def as_tuple(self) -> tuple[typing.Any, ...]:\n return tuple(getattr(self, field.name) for field in Schema)","def query_all(self):\n return multisearch.queries.QueryAll().connect(self)","def query(self, queries):\n final_result = []\n results = self.__return_query('query', queries)\n if results == None:\n return None\n else:\n if len(results) > 1:\n for result in results:\n final_result.append(result['data'])\n else:\n final_result = results\n return final_result","def extract_special_queries(queries):\n specials = {}\n dc = queries.copy()\n for i in queries:\n if i.startswith('__') and i in FILTERS_LIST:\n specials[i] = queries[i]\n del dc[i]\n return (dc, specials)","def get_query_specs(self):\n\n person_ext_table_query = {\n cdr_consts.QUERY:\n PERSON_EXT_TABLE_QUERY.render(project=self.project_id,\n dataset=self.dataset_id)\n }\n return [person_ext_table_query]","def query_results(self):\n return self.details[KEY_QUERY_RESULTS]","def _process_query(self, sql_query):\n # MSSQLDB expects params to be a tuple. \n # Overwriting the default implementation to convert params to tuple.\n paramstyle = getattr(self, 'paramstyle', 'pyformat')\n query = sql_query.query(paramstyle)\n params = sql_query.values()\n return query, tuple(params)","def query(self):\n \n return self._query","def get_query(self):\n q = db.Query(self.KIND,keys_only=self.KEYS_ONLY)\n for prop, value in self.FILTERS:\n q.filter(\"%s =\" % prop, value)\n if self.ancestor:\n q.ancestor(self.ancestor)\n q.order(self.ORDER_BY)\n return q","def get_query_list():\n prov_list = QueryProvider.list_data_environments()\n\n print(\"Generating documentation for for the following providers\")\n print(\", \".join(list(PROVIDERS)))\n print(\"Skipping the following providers\")\n print(\", \".join(list(set(prov_list) - set(PROVIDERS))))\n env_providers = {prov: QueryProvider(prov) for prov in tqdm.tqdm(PROVIDERS)}\n\n query_series = []\n for env, env_queries in env_providers.items():\n query_names = env_queries.list_queries()\n for query_name in query_names:\n q_group, q_name = query_name.split(\".\")\n qry = env_queries.query_store.get_query(q_group + \".\" + q_name)\n if \"table\" in qry.default_params:\n q_table = qry.default_params[\"table\"].get(\"default\", \"na\").split()[0]\n elif \"table\" in qry.required_params:\n q_table = qry.required_params[\"table\"].get(\"default\", \"na\").split()[0]\n else:\n q_table = \"-\"\n q_dict = {\n \"Environment\": env,\n \"QueryGroup\": q_group,\n \"Query\": q_name,\n \"Description\": qry.description,\n \"Req-Params\": \", \".join(\n sorted(\n [\n f\"{param} ({p_data.get('type')})\"\n for param, p_data in qry.required_params.items()\n ]\n )\n ),\n # \"OtherParams\": \", \".join([f\"{param}\" for param in qry.default_params]),\n \"Table\": q_table,\n }\n query_series.append(pd.Series(q_dict))\n print()\n return pd.DataFrame(query_series).sort_values(\n [\"Environment\", \"QueryGroup\", \"Query\"]\n )","def get_subscription_ids(self, topic: str) -> Tuple[int, ...]:\n try:\n return tuple(self.__sub_ids[topic])\n except KeyError:\n return ()","def query(self):\r\n raise NotImplementedError","def similar_qs(self):\n pass","def __tuple(self):\n return (self.__app, self.__namespace, self.__pairs)","def make_complex_query_set(self):\n\n query = self.request.GET.get(\"q\")\n category_name = self.request.GET.get(\"category_name\")\n o_net_soc_code = self.request.GET.get(\"o_net_soc_code\")\n description = self.request.GET.get(\"description\")\n job_title = self.request.GET.get(\"job_title\")\n level = self.request.GET.get(\"level\", '')\n\n q_object = Q()\n\n if query:\n q_object.add((\n Q(category__category_name__icontains=query) |\n Q(category__o_net_soc_code__icontains=query) |\n Q(category__description__icontains=query) |\n Q(category__job_title__description__icontains=query) |\n Q(category__job_title__title__icontains=query)\n ), Q.OR)\n\n if query.isnumeric():\n q_object.add(\n Q(category__job_title__level__icontains=int(query)), Q.OR)\n\n else:\n if category_name:\n q_object.add(\n Q(category__category_name__icontains=category_name),\n Q.AND)\n\n if o_net_soc_code:\n q_object.add(\n Q(category__o_net_soc_code__icontains=o_net_soc_code),\n Q.AND)\n\n if description:\n q_object.add((\n Q(category__description__icontains=description) |\n Q(\n category__job_title__description__icontains=description)\n ), Q.AND)\n\n if job_title:\n q_object.add(\n Q(category__job_title__title__icontains=job_title),\n Q.AND)\n\n if level:\n if level.isnumeric():\n q_object.add(\n Q(category__job_title__level__icontains=int(level)),\n Q.AND)\n else:\n raise Exception(\n ErrorMessage.WRONG_FIELD_TYPE.value.format(\"level\",\n \"numeric\"))\n\n return q_object","def prepare_queries(self):\n self.log.info(\"preparing queries ...\")\n self.prepared_queries = {}\n self.prepared_query_to_str = {}\n initNs = {\"rdfs\": RDFApi.RDFS}\n get_classes = \"\"\"\n SELECT ?class\n WHERE {\n ?class rdf:type rdfs:Class .\n }\n \"\"\"\n self.add_prepared_query(\"get_classes\", get_classes, initNs)\n\n get_properties = \"\"\"\n SELECT ?property\n WHERE {\n ?property rdf:type rdf:Property .\n }\n \"\"\"\n self.add_prepared_query(\"get_properties\", get_properties, None)\n\n get_term_to_label = \"\"\"\n SELECT ?term ?label\n WHERE {\n ?term rdfs:label ?label\n }\n \"\"\"\n self.add_prepared_query(\"get_term_to_label\", get_term_to_label, initNs)\n\n get_term_to_desc = \"\"\"\n SELECT ?term ?desc\n WHERE {\n ?term rdfs:comment ?desc\n }\n \"\"\"\n self.add_prepared_query(\"get_term_to_desc\", get_term_to_desc, initNs)\n\n get_ancestors = \"\"\"\n SELECT ?class\n WHERE {\n ?subject rdfs:subClassOf* ?mid .\n ?mid rdfs:subClassOf* ?class .\n }\n group by ?class\n order by count(?mid)\n \"\"\"\n self.add_prepared_query(\"get_ancestors\", get_ancestors, initNs)","def query(self):\n return self.__query","def query(self):\n return {\n 'name': self.NAME,\n 'statistics_queries': self._statistics_queries\n }","def query(self, page) -> [str, dict]:\n params = {'size': self.max_page_size,\n 'sort': 'displayLabel', 'page': page, 'query': self.querystring}\n url = '{base_url}/v1/stations/rsql'.format(base_url=self.base_url)\n return [url, params]","def generate_query(self):\n return","def search_info() -> tuple:\r\n search_tag = input(\"You can search by author, title, publisher, shelf, category, and subject.\\n\"\r\n \"How do you want to search?\")\r\n search_keyword = input(\"Please enter the keyword you are looking for:\")\r\n\r\n return search_tag.capitalize(), search_keyword","def query_predicate(self, p):\n self.setQuery(\"\"\"\n Select ?s ?o where {\n ?s %s ?o\n } ORDER BY (?s)\"\"\" % (p))\n\n try:\n rval = self.query()\n g = rval.convert()\n return [(x['s']['value'], x['o']['value']) for x in g['results']['bindings']]\n except:\n print \"Select failed\"\n traceback.print_exc(file=sys.stdout)","def _query(self):\n request = self._createRequest()\n try:\n opener = urllib2.build_opener()\n opener.addheaders.append(('Cookie', self.cookies))\n response = opener.open(request)\n return (response, self.returnFormat)\n except urllib2.HTTPError, e:\n if e.code == 400:\n raise QueryBadFormed()\n elif e.code == 404:\n raise EndPointNotFound()\n elif e.code == 500:\n raise EndPointInternalError(e.read())\n else:\n raise e\n return (None, self.returnFormat)","def _statistics_queries(self):\n queries = {}\n for stats in self._statistics:\n queries[stats.COLLECTION_NAME] = stats.query\n return queries","def iParseQuery(queryResults):\n iPaths = []\n results = queryResults.get_results()\n\n for item in results:\n for k in item.keys():\n if k.icat_key == 'DATA_NAME':\n name = item[k]\n elif k.icat_key == 'COLL_NAME':\n coll = item[k]\n else:\n continue\n iPaths.append(coll+'/'+name)\n return iPaths","def _get_query(request):\n query = request.GET.get(\"query\", \"\")\n date = request.GET.get(\"date\", \"\")\n timestamp = request.GET.get(\"timestamp\", None)\n sort = request.GET.get(\"sort\", \"top\").lower()\n filter = request.GET.get(\"filter\", \"following\").lower()\n\n if timestamp:\n t = parse(timestamp, ignoretz=True)\n timestamp = pytz.utc.localize(t)\n else:\n timestamp = timezone.now()\n\n start_time = ''\n end_time = ''\n\n if date:\n start_time, end_time = DateRangeParser().parse(date)\n\n get_dict = {\n \"query\": query,\n \"filter\": filter,\n \"sort\": sort,\n \"start_time\": start_time,\n \"end_time\": end_time,\n \"username\": request.GET.get(\"username\", \"\"),\n \"orderBy\": request.GET.get(\"orderBy\", \"start_time\"),\n \"direction\": request.GET.get(\"direction\", \"\"),\n \"template\": request.GET.get(\"template\", \"\"),\n \"type\": request.GET.get(\"type\", \"\"),\n \"page\": request.GET.get(\"page\", 1),\n 'timestamp': timestamp,\n }\n\n return get_dict, query, date, sort, filter","def get_query_specs(self, *args, **keyword_args):\n sandbox_zips_with_whitespace = {\n cdr_consts.QUERY:\n SANDBOX_ZIPS_WITH_WHITESPACE.render(\n project_id=self.project_id,\n sandbox_dataset_id=self.sandbox_dataset_id,\n whitespace_sandbox=ZIPS_WITH_WHITESPACE_SANDBOX,\n dataset_id=self.dataset_id,\n obs_table=OBSERVATION)\n }\n\n sandbox_invalid_zip_codes = {\n cdr_consts.QUERY:\n SANDBOX_INVALID_ZIP_CODES.render(\n project_id=self.project_id,\n sandbox_dataset_id=self.sandbox_dataset_id,\n sandbox_table=self.sandbox_table_for(OBSERVATION),\n dataset_id=self.dataset_id,\n obs_table=OBSERVATION,\n pipeline_tables=PIPELINE_TABLES,\n zip3_lookup=ZIP3_LOOKUP)\n }\n\n clean_zips_of_whitespace = {\n cdr_consts.QUERY:\n CLEAN_ZIPS_OF_WHITESPACE.render(\n project_id=self.project_id,\n dataset_id=self.dataset_id,\n obs_table=OBSERVATION,\n sandbox_dataset_id=self.sandbox_dataset_id,\n whitespace_sandbox=ZIPS_WITH_WHITESPACE_SANDBOX)\n }\n\n update_zip_codes = {\n cdr_consts.QUERY:\n UPDATE_INVALID_ZIP_CODES.render(\n project_id=self.project_id,\n dataset_id=self.dataset_id,\n obs_table=OBSERVATION,\n sandbox_dataset_id=self.sandbox_dataset_id,\n sandbox_table=self.sandbox_table_for(OBSERVATION))\n }\n\n return [\n sandbox_zips_with_whitespace, clean_zips_of_whitespace,\n sandbox_invalid_zip_codes, update_zip_codes\n ]","def get_params(self):\n params = super(JoinQuery, self).get_params()\n params.append(('q', self._query))\n\n return params","def query(func, tuple_=False):\n\n def structures(self, *args, **kwargs):\n objects = func(self)\n original = {s: n for n, s in enumerate(objects.structures)}\n if len(args) == 1:\n return {objects.get(args[0])} if args[0] in objects.ids else set()\n for k, v in kwargs.items():\n objects = filter_objects(objects, k, v)\n if tuple_:\n return tuple(sorted(\n objects.structures, key=lambda s: original[s]\n ))\n else:\n return set(objects.structures)\n return structures","def extract_query(pkt):\n request = DNSRecord.parse(pkt)\n\n # Extract the hostname\n qname = request.q.qname\n hostname = str(qname)\n # Remove tailing period (artifact of the dnslib)\n if hostname[-1] == '.':\n hostname = hostname[:-1]\n\n # Record type\n qtype = QTYPE.get(request.q.qtype)\n\n return (hostname, qtype)","def get_items_for_query(self, query_str):\n raise NotImplementedError()","def query(self):\n ret = libxml2mod.xmlURIGetQuery(self._o)\n return ret","def author_queries(case, author):\n query_set = {\n \"0\": [],\n \"1\": [1],\n \"2\": [4,1],\n \"3\": [2,1],\n \"4\": [3,2,1],\n \"5\": [5,4,3,2,1],\n \"6\": [6,5,4,3,2,1]\n }\n qs = query_set[str(case)]\n query_list = []\n for q in qs:\n query_list.append(author_case_query(q, author))\n return query_list","def parameter_values(self) -> List[Tuple[str, Any]]:\n pvs = [(param, getattr(self, variable))\n for variable, param in self.variable_name_to_query_param.items()]\n return [(p, v) for p, v in pvs if v is not None]","def query(self):\n return self.event.get('queryStringParameters', dict())","def typenames(self):\n return tuple((item[0] for item in self))","def query(self):\n return self._query","def query(self):\n return self._query","def query(self):\n return self._query","def _getQuery(self, keys, any):\n operator = 'or' if any else 'and'\n def _and(lhs, rhs): return (lhs & rhs)\n def _or(lhs, rhs): return (lhs | rhs)\n join = {'and': _and, 'or': _or}[operator]\n iter = keys.iteritems()\n key, val = iter.next()\n query = (where(key) == val)\n for key, value in iter:\n query = join(query, (where(key) == value))\n return query","def _query_by_es_query(self, body: str, **kwargs) -> typing.Optional[typing.List[dict]]:\n return self.qm.search(body=body, **kwargs)","def precomp_target_queries(self, triplet_queries):\n self.eval()\n\n triplet_queries_idx = np.zeros((len(triplet_queries),3), dtype=np.int)\n queries_sro = Variable(torch.zeros(len(triplet_queries),3)).long()\n\n for count,triplet_query in enumerate(triplet_queries):\n\n subjectname, predicate, objectname = triplet_query.split('-')\n sub_cat = self.classes.word2idx[subjectname]\n obj_cat = self.classes.word2idx[objectname]\n rel_cat = self.predicates.word2idx[predicate]\n\n triplet_queries_idx[count,0] = sub_cat\n triplet_queries_idx[count,1] = rel_cat\n triplet_queries_idx[count,2] = obj_cat\n\n queries_sro[count,0] = self.idx_to_vocab['s'][sub_cat]\n queries_sro[count,2] = self.idx_to_vocab['o'][obj_cat]\n queries_sro[count,1] = self.idx_to_vocab['r'][rel_cat]\n\n if torch.cuda.is_available():\n queries_sro = queries_sro.cuda() \n\n\n return queries_sro, triplet_queries_idx","def retrieve_object_info(session, queries, sortkey):\n results = []\n\n for query in queries:\n expquery = query[\"expanded_query\"]\n qtype = query[\"expanded_query_type\"]\n\n if qtype == \"collection\":\n queryresults = []\n queryresults.extend(get_direct_subcollections(session, expquery))\n queryresults.extend(\n get_dataobjects_in_collection(\n session, expquery))\n elif qtype == \"dataobject\":\n queryresults = get_dataobject_info(session, expquery)\n else:\n exit_with_error(\n \"Internal issue - illegal query type in retrieve_object_info: \"\n + qtype)\n\n query[\"results\"] = sort_object_info(queryresults, sortkey)\n results.append(query)\n\n return results"],"string":"[\n \"def query(self):\\n if bool(self._query_parameters): # ApiGateway set key value None in case of absence params, not an empty dict\\n payload = {inflection.underscore(k): v for k, v, in self._query_parameters.items()}\\n else:\\n payload = dict()\\n QueryTuple = namedtuple('QueryTuple', sorted(payload))\\n the_tuple = QueryTuple(**payload)\\n return the_tuple\",\n \"def _ATTopic_query(self, **kw):\\n del kw['path'] # don't want to limit to context as w/ folders\\n return self.context.queryCatalog(self.context.REQUEST, \\n False, # no batch here\\n None, # no b_size here\\n False,\\n **kw)\",\n \"def getQueries(self):\\n return sorted( self.qryDocs.keys() )\",\n \"def build_query_structure(self):\\n query_list = list()\\n filter_list = list()\\n for key, val in self.q_dict.items():\\n if key in self.es_query_keys:\\n query_list.append(\\n {\\\"match\\\": {\\\".\\\".join(key.split(\\\"_\\\")): val[0]}})\\n elif key in self.es_date_keys:\\n filter_list.append(\\n {\\\"range\\\": {\\\".\\\".join(key.split(\\\"_\\\")): val}})\\n elif \\\":\\\" in val[0]:\\n #for handling queries like dd_dct=gte:1\\n range_val = val[0].split(\\\":\\\")\\n filter_list.append({\\\"range\\\": {\\\".\\\".join(key.split(\\\"_\\\")): {\\n range_val[0]: int(range_val[1])}}})\\n else:\\n filter_list.append(\\n {\\\"terms\\\": {\\\".\\\".join(key.split(\\\"_\\\")): val}})\\n return query_list, filter_list\",\n \"def query(self, page) -> [str, dict]:\",\n \"def queryThis(query: Query) -> list:\\n q = str(query) # extra line for clarity. calls the __str__ magic function\\n\\n return list(client.query(q).result())\",\n \"def query_nodes(self):\\n query_nodes = []\\n for node in self.nodes:\\n if 'querynode' == node.get('infos').get('type'):\\n query_nodes.append(node)\\n return query_nodes\",\n \"def build_inner_query(request, tags):\\n # the inner query builds a list of streams matching all the\\n # clauses which we can then select from\\n clauses = []\\n uuid_clause = \\\"true\\\"\\n for (k, v) in tags:\\n if k == 'uuid': \\n if v != None:\\n uuid_clause = \\\"s.uuid = %s\\\" % escape_string(v)\\n continue\\n if v != None:\\n clauses.append(\\\"hstore(%s, %s)\\\" % (escape_string(k),\\n escape_string(v)))\\n else: break\\n\\n if len(clauses) == 0: clauses = [\\\"hstore(array[]::varchar[])\\\"]\\n inner_query = \\\"\\\"\\\"\\n (SELECT s.id FROM stream s, subscription sub\\n WHERE s.subscription_id = sub.id AND (%s) AND (%s) AND s.metadata @> (%s))\\n\\\"\\\"\\\" % (build_authcheck(request), uuid_clause, ' || '.join(clauses))\\n\\n return inner_query, clauses\",\n \"def elements(self):\\n return self.q\",\n \"def _get_queries(args):\\n if args.mode == '2DSEQ':\\n queries = [\\\"@type=='2dseq'\\\", \\\"@is_spectroscopy==True\\\", \\\"@is_complex==True\\\"]\\n elif args.mode == 'FID':\\n queries = [\\\"@type=='fid'\\\", \\\"@is_spectroscopy==True\\\"]\\n return queries + args.query\",\n \"def get(self):\\n return [\\n [a, v]\\n for a, v in sorted(self.__dict__.items())\\n if a != \\\"query_name\\\" and len(v)\\n ]\",\n \"def build_query(db, request, tags):\\n inner_query, clauses = build_inner_query(request, tags)\\n if len(tags) and tags[-1][0] == 'uuid':\\n # if we select uuid as the trailing tag we have to be special\\n query = \\\"\\\"\\\"\\nSELECT DISTINCT s.uuid \\nFROM stream AS s\\nWHERE s.id IN \\\"\\\"\\\" + inner_query\\n elif len(tags) and (tags[-1][1] == None or tags[-1][1] == ''):\\n # odd-numbered clasues, so we print matching values of tags\\n t = escape_string(tags[-1][0])\\n query = \\\"\\\"\\\"\\nSELECT DISTINCT metadata -> %s AS svals FROM stream\\nWHERE id IN %s AND metadata ? %s\\nORDER BY svals ASC\\\"\\\"\\\" % (t, inner_query, t)\\n else:\\n # otherwise we print all tags matching the restriction\\n query = \\\"\\\"\\\"\\nSELECT DISTINCT skeys\\nFROM (\\n SELECT skeys(metadata) FROM stream\\n WHERE id IN %s\\n) AS skeys ORDER BY skeys ASC\\\"\\\"\\\" % inner_query\\n\\n log.msg(query)\\n d = db.runQuery(query)\\n d.addCallback(log_time, time.time())\\n return d\",\n \"def query(cursor, query):\\n out = []\\n c = cursor.execute(query)\\n out.append(c.fetchall())\\n # will return list of tuples for each query\\n return out\",\n \"def subscribedQueries(self):\\n return map(Query.get, self.subscriptions)\",\n \"def query3() :\",\n \"def list_named_queries(verbose):\\n if not verbose:\\n rows = [[r] for r in named_queries.list()]\\n headers = [\\\"Name\\\"]\\n else:\\n headers = [\\\"Name\\\", \\\"Query\\\"]\\n rows = [[r, named_queries.get(r)]\\n for r in named_queries.list()]\\n\\n if not rows:\\n status = named_queries.usage\\n else:\\n status = ''\\n return [(rows, headers, status, '', False)]\",\n \"def _get_catalog_results(self, featured=False, **kw):\\n if 'context' in kw.keys():\\n kw['path'] = {'query': '/'.join(kw['context'].getPhysicalPath())}\\n\\n types = ('Article', 'Blog Entry', )\\n states = ('published', )\\n sort = 'Date'\\n \\n results = self.qrymethod(portal_type=types,\\n review_state=states,\\n is_featured=featured,\\n sort_on=sort, \\n sort_order='descending',\\n **kw)\\n\\n return results\",\n \"def driver_queries(self) -> Iterable[Dict[str, str]]:\\n return self.svc_queries\",\n \"def query_schema(self, name, param):\\n\\n alias, name, need_list = self.parse_entry(name)\\n\\n if not name:\\n result = self.process_multiple_query(need_list, param)\\n else:\\n result = self.process_single_query(name, need_list, param)\\n return alias, result\",\n \"def namedtuplefetchall(query, param_list):\\r\\n\\twith connection.cursor() as cursor:\\r\\n\\t\\tcursor.execute(query, param_list)\\r\\n\\t\\tdesc = cursor.description\\r\\n\\t\\tnt_result = namedtuple('Result', [col[0] for col in desc])\\r\\n\\t\\treturn [nt_result(*row) for row in cursor.fetchall()]\",\n \"def get_query_specs(self, *args, **keyword_args):\\n\\n lookup_table_creation_query = {\\n cdr_consts.QUERY:\\n LOOKUP_TABLE_CREATION_QUERY.render(\\n project_id=self.project_id,\\n sandbox_dataset=self.sandbox_dataset_id,\\n dataset_id=self.dataset_id,\\n lookup_table=EXPLICIT_IDENTIFIER_CONCEPTS)\\n }\\n\\n sandbox_query = {\\n cdr_consts.QUERY:\\n SANDBOX_EXPLICIT_IDENTIFIER_RECORDS.render(\\n project_id=self.project_id,\\n dataset_id=self.dataset_id,\\n sandbox_dataset_id=self.sandbox_dataset_id,\\n observation_table=OBSERVATION,\\n intermediary_table=self.get_sandbox_tablenames()[0],\\n lookup_table=EXPLICIT_IDENTIFIER_CONCEPTS,\\n concept_fields=get_concept_id_fields(OBSERVATION))\\n }\\n\\n suppress_query = {\\n cdr_consts.QUERY:\\n SUPPRESS_EXPLICIT_IDENTIFIER_RECORDS.render(\\n project_id=self.project_id,\\n dataset_id=self.dataset_id,\\n sandbox_dataset_id=self.sandbox_dataset_id,\\n intermediary_table=self.get_sandbox_tablenames()[0],\\n observation_table=OBSERVATION)\\n }\\n\\n return [lookup_table_creation_query, sandbox_query, suppress_query]\",\n \"def extract_queries(self, path_topics=\\\"../topics-rnd5.xml\\\"):\\n \\n topic_queries = []\\n with open(path_topics, \\\"r\\\") as f:\\n for line in f:\\n match = re.match(\\\".*<query>([^<]*)<\\\\/query>.*\\\", line)\\n if match:\\n topic_queries.append(match.group(1))\\n if len(topic_queries) != 50:\\n sys.exit(\\\"There should be 50 topics, found {}\\\".format(\\n len(topic_queries)))\\n \\n return topic_queries\",\n \"def query_many(self, *q, read_as=None):\\n raise NotImplementedError\",\n \"def qargs(self):\\n return self._qargs\",\n \"def setup(query):\\n\\n results = []\\n return results\",\n \"def setup(query):\\n\\n results = []\\n return results\",\n \"def setup(query):\\n\\n results = []\\n return results\",\n \"def setup(query):\\n\\n results = []\\n return results\",\n \"def form_cand_queries_amongvocab(self, batch_input, gram):\\n N = batch_input['pair_objects'].size(0)\\n tensor_type = batch_input['pair_objects'].long().data.type()\\n\\n M = len(self.vocab[gram])\\n cats = self.idx_to_vocab[gram].type(tensor_type)\\n queries = cats.unsqueeze(0) \\n labels = batch_input['labels_'+gram].type(queries.data.type()) #(N,M)\\n\\n return (queries, labels)\",\n \"def make_complex_query_set(self):\\n\\n query = self.request.GET.get(\\\"q\\\")\\n category = self.request.GET.get(\\\"category\\\")\\n title = self.request.GET.get(\\\"title\\\")\\n\\n q_object = Q()\\n\\n if query:\\n q_object.add((\\n Q(category__category_name__icontains=query) |\\n Q(title__icontains=query) \\n ), Q.OR)\\n\\n else:\\n if category:\\n q_object.add(\\n Q(category=category),\\n Q.AND)\\n if title:\\n q_object.add(\\n Q(title__icontains=title),\\n Q.AND)\\n\\n return q_object\",\n \"def __generateQuery(self, query):\\n if query == None:\\n return [\\\"1=1\\\"]\\n elif type(query) is not list:\\n return [query]\\n else:\\n return query\",\n \"def query(self):\\n if IATTopic.providedBy(self.context) or ICollection.providedBy(self.context):\\n return self.context.queryCatalog(batch=False)\\n elif IFolderish.providedBy(self.context):\\n return self.context.getFolderContents(batch=False)\",\n \"def make_complex_query_set(self):\\n\\n query = self.request.GET.get(\\\"q\\\")\\n program_id = self.request.META.get('HTTP_X_SVMS_PROGRAM_ID')\\n category = self.request.GET.get(\\\"category\\\")\\n title = self.request.GET.get(\\\"title\\\")\\n level = self.request.GET.get(\\\"level\\\")\\n description = self.request.GET.get(\\\"description\\\")\\n status = self.request.GET.get(\\\"status\\\")\\n job_tag = self.request.GET.get(\\\"job_tag\\\")\\n\\n q_object = Q()\\n\\n if query:\\n q_object.add((\\n Q(program_id=query) |\\n Q(category=query) |\\n Q(title__icontains=query) |\\n #Q(category__category_name__icontains=query) |\\n Q(description__icontains=query) |\\n Q(job_tag__tag__in=str(query).split(\\\",\\\"))\\n ), Q.OR)\\n\\n if query.isnumeric():\\n q_object.add(\\n Q(level__icontains=int(query)), Q.OR)\\n\\n q_object.add(Q(status=strtobool(query)), Q.OR) if query in [\\n \\\"true\\\", \\\"True\\\", \\\"False\\\", \\\"false\\\"] else None\\n\\n else:\\n if program_id:\\n q_object.add(\\n Q(program_id=program_id),\\n Q.AND)\\n\\n if category:\\n q_object.add(\\n Q(category=category),\\n Q.AND)\\n if title:\\n q_object.add(\\n Q(title__icontains=title),\\n Q.AND)\\n\\n if description:\\n q_object.add(\\n Q(description__icontains=description), Q.AND)\\n\\n if job_tag:\\n q_object.add(\\n Q(job_tag__tag__in=str(job_tag).split(\\\",\\\")),\\n Q.AND)\\n\\n if level:\\n if level.isnumeric():\\n q_object.add(\\n Q(level__icontains=int(level)),\\n Q.AND)\\n else:\\n raise Exception(\\n ErrorMessage.WRONG_FIELD_TYPE.value.format(\\\"level\\\",\\n \\\"numeric\\\"))\\n\\n q_object.add(Q(status=strtobool(status)), Q.AND) if status in [\\n \\\"true\\\", \\\"True\\\", \\\"False\\\", \\\"false\\\"] else None\\n\\n return q_object\",\n \"def query(self, query):\",\n \"def query_subject(self, s):\\n self.setQuery(\\\"\\\"\\\"\\n Select ?p ?o where {\\n %s ?p ?o\\n } ORDER BY (?p)\\\"\\\"\\\" % (s))\\n\\n try:\\n rval = self.query()\\n g = rval.convert()\\n return [(x['p']['value'], x['o']['value']) for x in g['results']['bindings']]\\n except:\\n print \\\"Select failed\\\"\\n traceback.print_exc(file=sys.stdout)\",\n \"def collect_tracks_query(query, collection_type):\\n search_result = SP.search(query, 1, 0, collection_type)\\n\\n item = search_result[collection_type + 's']['items'][0]\\n name = item['name']\\n\\n return name, collect_tracks(item, collection_type)\",\n \"def queryComponent(type=None, filter=None, all=0):\",\n \"def parse_query(query):\\n qlist = []\\n splitted = query.split(\\\"&\\\")\\n for entry in splitted:\\n cmd, arg = entry.split(\\\"=\\\")\\n qlist.append((cmd, arg))\\n return qlist\",\n \"def query(self):\",\n \"def getQueries(self):\\r\\n\\t\\tp=[]\\r\\n\\t\\tfor pa in self.session.query(Query):\\r\\n\\t\\t\\tp.append(pa)\\r\\n\\t\\treturn p\",\n \"def get_query_fields(cls):\\n ...\",\n \"def _query_range_get(self):\\n return (self.query_start, self.query_end)\",\n \"def __iter__(self):\\n\\t\\tfields = 'fieldname', 'text', 'docfreq', 'indexfreq'\\n\\t\\tcur = self.index.collection.find(fields=fields).sort('fieldname')\\n\\t\\treturn (tuple(rec[field] for field in fields) for rec in cur)\",\n \"def get_qs(self):\\n if self.qs == None:\\n # Get the Lemma PKs\\n qs = self.get_queryset()\\n else:\\n qs = self.qs\\n return qs\",\n \"def get_qs(self):\\n if self.qs == None:\\n # Get the Lemma PKs\\n qs = self.get_queryset()\\n else:\\n qs = self.qs\\n return qs\",\n \"def build_tag_query(db, request, tags):\\n inner_query, clauses = build_inner_query(request, tags)\\n query = \\\"\\\"\\\"\\nSELECT s.metadata || hstore('uuid', s.uuid)\\nFROM stream s\\nWHERE s.id IN \\\"\\\"\\\" + inner_query + \\\"\\\"\\\"\\nORDER BY s.id ASC\\\"\\\"\\\"\\n log.msg(query)\\n d = db.runQuery(query)\\n d.addCallback(log_time, time.time())\\n return d\",\n \"def get_questions_of_topic(topic):\\n\\n dynamodb = boto3.resource(\\\"dynamodb\\\", region_name=\\\"eu-central-1\\\")\\n question_table = dynamodb.Table(\\\"Questions\\\")\\n\\n fe = Attr(\\\"TopicId\\\").eq(topic.get(\\\"TopicId\\\"))\\n response = question_table.scan(FilterExpression=fe)\\n questions = response.get(\\\"Items\\\")\\n return questions\",\n \"def make_query(self):\",\n \"def query(self, q):\\n for key in self.metadb.query(q):\\n yield key, self.datadb[key]\",\n \"def query(self):\\n return self.details[KEY_QUERY]\",\n \"def get_catalog_query(self):\\n # Implemented from kitosid template for -\\n # osid.resource.BinQuerySession.get_bin_query_template\\n return self._get_provider_session('catalog_query_session').get_catalog_query()\",\n \"def queries(self):\\n return self.__queries\",\n \"def get_resource_query(self):\\n pass\",\n \"def get_resource_query(self):\\n pass\",\n \"def build_query(table, attr_converter, page=0):\\n where_statement, values = build_where_statement(attr_converter)\\n query = \\\"SELECT DISTINCT {} FROM {} {} LIMIT {} OFFSET {};\\\".format(\\n ', '.join([attr_converter[x]['column'] for x in attr_converter]),\\n table, # db table\\n where_statement, # various statements to narrow search results\\n PG_LIMIT, # number of rows to return\\n int(PG_LIMIT)*page # number or rows to skip\\n )\\n return query, values\",\n \"def query(self, **kwargs):\",\n \"def terms(self) -> Tuple[Term, ...]:\\n ...\",\n \"def as_tuple(self) -> tuple[typing.Any, ...]:\\n return tuple(getattr(self, field.name) for field in Schema)\",\n \"def query_all(self):\\n return multisearch.queries.QueryAll().connect(self)\",\n \"def query(self, queries):\\n final_result = []\\n results = self.__return_query('query', queries)\\n if results == None:\\n return None\\n else:\\n if len(results) > 1:\\n for result in results:\\n final_result.append(result['data'])\\n else:\\n final_result = results\\n return final_result\",\n \"def extract_special_queries(queries):\\n specials = {}\\n dc = queries.copy()\\n for i in queries:\\n if i.startswith('__') and i in FILTERS_LIST:\\n specials[i] = queries[i]\\n del dc[i]\\n return (dc, specials)\",\n \"def get_query_specs(self):\\n\\n person_ext_table_query = {\\n cdr_consts.QUERY:\\n PERSON_EXT_TABLE_QUERY.render(project=self.project_id,\\n dataset=self.dataset_id)\\n }\\n return [person_ext_table_query]\",\n \"def query_results(self):\\n return self.details[KEY_QUERY_RESULTS]\",\n \"def _process_query(self, sql_query):\\n # MSSQLDB expects params to be a tuple. \\n # Overwriting the default implementation to convert params to tuple.\\n paramstyle = getattr(self, 'paramstyle', 'pyformat')\\n query = sql_query.query(paramstyle)\\n params = sql_query.values()\\n return query, tuple(params)\",\n \"def query(self):\\n \\n return self._query\",\n \"def get_query(self):\\n q = db.Query(self.KIND,keys_only=self.KEYS_ONLY)\\n for prop, value in self.FILTERS:\\n q.filter(\\\"%s =\\\" % prop, value)\\n if self.ancestor:\\n q.ancestor(self.ancestor)\\n q.order(self.ORDER_BY)\\n return q\",\n \"def get_query_list():\\n prov_list = QueryProvider.list_data_environments()\\n\\n print(\\\"Generating documentation for for the following providers\\\")\\n print(\\\", \\\".join(list(PROVIDERS)))\\n print(\\\"Skipping the following providers\\\")\\n print(\\\", \\\".join(list(set(prov_list) - set(PROVIDERS))))\\n env_providers = {prov: QueryProvider(prov) for prov in tqdm.tqdm(PROVIDERS)}\\n\\n query_series = []\\n for env, env_queries in env_providers.items():\\n query_names = env_queries.list_queries()\\n for query_name in query_names:\\n q_group, q_name = query_name.split(\\\".\\\")\\n qry = env_queries.query_store.get_query(q_group + \\\".\\\" + q_name)\\n if \\\"table\\\" in qry.default_params:\\n q_table = qry.default_params[\\\"table\\\"].get(\\\"default\\\", \\\"na\\\").split()[0]\\n elif \\\"table\\\" in qry.required_params:\\n q_table = qry.required_params[\\\"table\\\"].get(\\\"default\\\", \\\"na\\\").split()[0]\\n else:\\n q_table = \\\"-\\\"\\n q_dict = {\\n \\\"Environment\\\": env,\\n \\\"QueryGroup\\\": q_group,\\n \\\"Query\\\": q_name,\\n \\\"Description\\\": qry.description,\\n \\\"Req-Params\\\": \\\", \\\".join(\\n sorted(\\n [\\n f\\\"{param} ({p_data.get('type')})\\\"\\n for param, p_data in qry.required_params.items()\\n ]\\n )\\n ),\\n # \\\"OtherParams\\\": \\\", \\\".join([f\\\"{param}\\\" for param in qry.default_params]),\\n \\\"Table\\\": q_table,\\n }\\n query_series.append(pd.Series(q_dict))\\n print()\\n return pd.DataFrame(query_series).sort_values(\\n [\\\"Environment\\\", \\\"QueryGroup\\\", \\\"Query\\\"]\\n )\",\n \"def get_subscription_ids(self, topic: str) -> Tuple[int, ...]:\\n try:\\n return tuple(self.__sub_ids[topic])\\n except KeyError:\\n return ()\",\n \"def query(self):\\r\\n raise NotImplementedError\",\n \"def similar_qs(self):\\n pass\",\n \"def __tuple(self):\\n return (self.__app, self.__namespace, self.__pairs)\",\n \"def make_complex_query_set(self):\\n\\n query = self.request.GET.get(\\\"q\\\")\\n category_name = self.request.GET.get(\\\"category_name\\\")\\n o_net_soc_code = self.request.GET.get(\\\"o_net_soc_code\\\")\\n description = self.request.GET.get(\\\"description\\\")\\n job_title = self.request.GET.get(\\\"job_title\\\")\\n level = self.request.GET.get(\\\"level\\\", '')\\n\\n q_object = Q()\\n\\n if query:\\n q_object.add((\\n Q(category__category_name__icontains=query) |\\n Q(category__o_net_soc_code__icontains=query) |\\n Q(category__description__icontains=query) |\\n Q(category__job_title__description__icontains=query) |\\n Q(category__job_title__title__icontains=query)\\n ), Q.OR)\\n\\n if query.isnumeric():\\n q_object.add(\\n Q(category__job_title__level__icontains=int(query)), Q.OR)\\n\\n else:\\n if category_name:\\n q_object.add(\\n Q(category__category_name__icontains=category_name),\\n Q.AND)\\n\\n if o_net_soc_code:\\n q_object.add(\\n Q(category__o_net_soc_code__icontains=o_net_soc_code),\\n Q.AND)\\n\\n if description:\\n q_object.add((\\n Q(category__description__icontains=description) |\\n Q(\\n category__job_title__description__icontains=description)\\n ), Q.AND)\\n\\n if job_title:\\n q_object.add(\\n Q(category__job_title__title__icontains=job_title),\\n Q.AND)\\n\\n if level:\\n if level.isnumeric():\\n q_object.add(\\n Q(category__job_title__level__icontains=int(level)),\\n Q.AND)\\n else:\\n raise Exception(\\n ErrorMessage.WRONG_FIELD_TYPE.value.format(\\\"level\\\",\\n \\\"numeric\\\"))\\n\\n return q_object\",\n \"def prepare_queries(self):\\n self.log.info(\\\"preparing queries ...\\\")\\n self.prepared_queries = {}\\n self.prepared_query_to_str = {}\\n initNs = {\\\"rdfs\\\": RDFApi.RDFS}\\n get_classes = \\\"\\\"\\\"\\n SELECT ?class\\n WHERE {\\n ?class rdf:type rdfs:Class .\\n }\\n \\\"\\\"\\\"\\n self.add_prepared_query(\\\"get_classes\\\", get_classes, initNs)\\n\\n get_properties = \\\"\\\"\\\"\\n SELECT ?property\\n WHERE {\\n ?property rdf:type rdf:Property .\\n }\\n \\\"\\\"\\\"\\n self.add_prepared_query(\\\"get_properties\\\", get_properties, None)\\n\\n get_term_to_label = \\\"\\\"\\\"\\n SELECT ?term ?label\\n WHERE {\\n ?term rdfs:label ?label\\n }\\n \\\"\\\"\\\"\\n self.add_prepared_query(\\\"get_term_to_label\\\", get_term_to_label, initNs)\\n\\n get_term_to_desc = \\\"\\\"\\\"\\n SELECT ?term ?desc\\n WHERE {\\n ?term rdfs:comment ?desc\\n }\\n \\\"\\\"\\\"\\n self.add_prepared_query(\\\"get_term_to_desc\\\", get_term_to_desc, initNs)\\n\\n get_ancestors = \\\"\\\"\\\"\\n SELECT ?class\\n WHERE {\\n ?subject rdfs:subClassOf* ?mid .\\n ?mid rdfs:subClassOf* ?class .\\n }\\n group by ?class\\n order by count(?mid)\\n \\\"\\\"\\\"\\n self.add_prepared_query(\\\"get_ancestors\\\", get_ancestors, initNs)\",\n \"def query(self):\\n return self.__query\",\n \"def query(self):\\n return {\\n 'name': self.NAME,\\n 'statistics_queries': self._statistics_queries\\n }\",\n \"def query(self, page) -> [str, dict]:\\n params = {'size': self.max_page_size,\\n 'sort': 'displayLabel', 'page': page, 'query': self.querystring}\\n url = '{base_url}/v1/stations/rsql'.format(base_url=self.base_url)\\n return [url, params]\",\n \"def generate_query(self):\\n return\",\n \"def search_info() -> tuple:\\r\\n search_tag = input(\\\"You can search by author, title, publisher, shelf, category, and subject.\\\\n\\\"\\r\\n \\\"How do you want to search?\\\")\\r\\n search_keyword = input(\\\"Please enter the keyword you are looking for:\\\")\\r\\n\\r\\n return search_tag.capitalize(), search_keyword\",\n \"def query_predicate(self, p):\\n self.setQuery(\\\"\\\"\\\"\\n Select ?s ?o where {\\n ?s %s ?o\\n } ORDER BY (?s)\\\"\\\"\\\" % (p))\\n\\n try:\\n rval = self.query()\\n g = rval.convert()\\n return [(x['s']['value'], x['o']['value']) for x in g['results']['bindings']]\\n except:\\n print \\\"Select failed\\\"\\n traceback.print_exc(file=sys.stdout)\",\n \"def _query(self):\\n request = self._createRequest()\\n try:\\n opener = urllib2.build_opener()\\n opener.addheaders.append(('Cookie', self.cookies))\\n response = opener.open(request)\\n return (response, self.returnFormat)\\n except urllib2.HTTPError, e:\\n if e.code == 400:\\n raise QueryBadFormed()\\n elif e.code == 404:\\n raise EndPointNotFound()\\n elif e.code == 500:\\n raise EndPointInternalError(e.read())\\n else:\\n raise e\\n return (None, self.returnFormat)\",\n \"def _statistics_queries(self):\\n queries = {}\\n for stats in self._statistics:\\n queries[stats.COLLECTION_NAME] = stats.query\\n return queries\",\n \"def iParseQuery(queryResults):\\n iPaths = []\\n results = queryResults.get_results()\\n\\n for item in results:\\n for k in item.keys():\\n if k.icat_key == 'DATA_NAME':\\n name = item[k]\\n elif k.icat_key == 'COLL_NAME':\\n coll = item[k]\\n else:\\n continue\\n iPaths.append(coll+'/'+name)\\n return iPaths\",\n \"def _get_query(request):\\n query = request.GET.get(\\\"query\\\", \\\"\\\")\\n date = request.GET.get(\\\"date\\\", \\\"\\\")\\n timestamp = request.GET.get(\\\"timestamp\\\", None)\\n sort = request.GET.get(\\\"sort\\\", \\\"top\\\").lower()\\n filter = request.GET.get(\\\"filter\\\", \\\"following\\\").lower()\\n\\n if timestamp:\\n t = parse(timestamp, ignoretz=True)\\n timestamp = pytz.utc.localize(t)\\n else:\\n timestamp = timezone.now()\\n\\n start_time = ''\\n end_time = ''\\n\\n if date:\\n start_time, end_time = DateRangeParser().parse(date)\\n\\n get_dict = {\\n \\\"query\\\": query,\\n \\\"filter\\\": filter,\\n \\\"sort\\\": sort,\\n \\\"start_time\\\": start_time,\\n \\\"end_time\\\": end_time,\\n \\\"username\\\": request.GET.get(\\\"username\\\", \\\"\\\"),\\n \\\"orderBy\\\": request.GET.get(\\\"orderBy\\\", \\\"start_time\\\"),\\n \\\"direction\\\": request.GET.get(\\\"direction\\\", \\\"\\\"),\\n \\\"template\\\": request.GET.get(\\\"template\\\", \\\"\\\"),\\n \\\"type\\\": request.GET.get(\\\"type\\\", \\\"\\\"),\\n \\\"page\\\": request.GET.get(\\\"page\\\", 1),\\n 'timestamp': timestamp,\\n }\\n\\n return get_dict, query, date, sort, filter\",\n \"def get_query_specs(self, *args, **keyword_args):\\n sandbox_zips_with_whitespace = {\\n cdr_consts.QUERY:\\n SANDBOX_ZIPS_WITH_WHITESPACE.render(\\n project_id=self.project_id,\\n sandbox_dataset_id=self.sandbox_dataset_id,\\n whitespace_sandbox=ZIPS_WITH_WHITESPACE_SANDBOX,\\n dataset_id=self.dataset_id,\\n obs_table=OBSERVATION)\\n }\\n\\n sandbox_invalid_zip_codes = {\\n cdr_consts.QUERY:\\n SANDBOX_INVALID_ZIP_CODES.render(\\n project_id=self.project_id,\\n sandbox_dataset_id=self.sandbox_dataset_id,\\n sandbox_table=self.sandbox_table_for(OBSERVATION),\\n dataset_id=self.dataset_id,\\n obs_table=OBSERVATION,\\n pipeline_tables=PIPELINE_TABLES,\\n zip3_lookup=ZIP3_LOOKUP)\\n }\\n\\n clean_zips_of_whitespace = {\\n cdr_consts.QUERY:\\n CLEAN_ZIPS_OF_WHITESPACE.render(\\n project_id=self.project_id,\\n dataset_id=self.dataset_id,\\n obs_table=OBSERVATION,\\n sandbox_dataset_id=self.sandbox_dataset_id,\\n whitespace_sandbox=ZIPS_WITH_WHITESPACE_SANDBOX)\\n }\\n\\n update_zip_codes = {\\n cdr_consts.QUERY:\\n UPDATE_INVALID_ZIP_CODES.render(\\n project_id=self.project_id,\\n dataset_id=self.dataset_id,\\n obs_table=OBSERVATION,\\n sandbox_dataset_id=self.sandbox_dataset_id,\\n sandbox_table=self.sandbox_table_for(OBSERVATION))\\n }\\n\\n return [\\n sandbox_zips_with_whitespace, clean_zips_of_whitespace,\\n sandbox_invalid_zip_codes, update_zip_codes\\n ]\",\n \"def get_params(self):\\n params = super(JoinQuery, self).get_params()\\n params.append(('q', self._query))\\n\\n return params\",\n \"def query(func, tuple_=False):\\n\\n def structures(self, *args, **kwargs):\\n objects = func(self)\\n original = {s: n for n, s in enumerate(objects.structures)}\\n if len(args) == 1:\\n return {objects.get(args[0])} if args[0] in objects.ids else set()\\n for k, v in kwargs.items():\\n objects = filter_objects(objects, k, v)\\n if tuple_:\\n return tuple(sorted(\\n objects.structures, key=lambda s: original[s]\\n ))\\n else:\\n return set(objects.structures)\\n return structures\",\n \"def extract_query(pkt):\\n request = DNSRecord.parse(pkt)\\n\\n # Extract the hostname\\n qname = request.q.qname\\n hostname = str(qname)\\n # Remove tailing period (artifact of the dnslib)\\n if hostname[-1] == '.':\\n hostname = hostname[:-1]\\n\\n # Record type\\n qtype = QTYPE.get(request.q.qtype)\\n\\n return (hostname, qtype)\",\n \"def get_items_for_query(self, query_str):\\n raise NotImplementedError()\",\n \"def query(self):\\n ret = libxml2mod.xmlURIGetQuery(self._o)\\n return ret\",\n \"def author_queries(case, author):\\n query_set = {\\n \\\"0\\\": [],\\n \\\"1\\\": [1],\\n \\\"2\\\": [4,1],\\n \\\"3\\\": [2,1],\\n \\\"4\\\": [3,2,1],\\n \\\"5\\\": [5,4,3,2,1],\\n \\\"6\\\": [6,5,4,3,2,1]\\n }\\n qs = query_set[str(case)]\\n query_list = []\\n for q in qs:\\n query_list.append(author_case_query(q, author))\\n return query_list\",\n \"def parameter_values(self) -> List[Tuple[str, Any]]:\\n pvs = [(param, getattr(self, variable))\\n for variable, param in self.variable_name_to_query_param.items()]\\n return [(p, v) for p, v in pvs if v is not None]\",\n \"def query(self):\\n return self.event.get('queryStringParameters', dict())\",\n \"def typenames(self):\\n return tuple((item[0] for item in self))\",\n \"def query(self):\\n return self._query\",\n \"def query(self):\\n return self._query\",\n \"def query(self):\\n return self._query\",\n \"def _getQuery(self, keys, any):\\n operator = 'or' if any else 'and'\\n def _and(lhs, rhs): return (lhs & rhs)\\n def _or(lhs, rhs): return (lhs | rhs)\\n join = {'and': _and, 'or': _or}[operator]\\n iter = keys.iteritems()\\n key, val = iter.next()\\n query = (where(key) == val)\\n for key, value in iter:\\n query = join(query, (where(key) == value))\\n return query\",\n \"def _query_by_es_query(self, body: str, **kwargs) -> typing.Optional[typing.List[dict]]:\\n return self.qm.search(body=body, **kwargs)\",\n \"def precomp_target_queries(self, triplet_queries):\\n self.eval()\\n\\n triplet_queries_idx = np.zeros((len(triplet_queries),3), dtype=np.int)\\n queries_sro = Variable(torch.zeros(len(triplet_queries),3)).long()\\n\\n for count,triplet_query in enumerate(triplet_queries):\\n\\n subjectname, predicate, objectname = triplet_query.split('-')\\n sub_cat = self.classes.word2idx[subjectname]\\n obj_cat = self.classes.word2idx[objectname]\\n rel_cat = self.predicates.word2idx[predicate]\\n\\n triplet_queries_idx[count,0] = sub_cat\\n triplet_queries_idx[count,1] = rel_cat\\n triplet_queries_idx[count,2] = obj_cat\\n\\n queries_sro[count,0] = self.idx_to_vocab['s'][sub_cat]\\n queries_sro[count,2] = self.idx_to_vocab['o'][obj_cat]\\n queries_sro[count,1] = self.idx_to_vocab['r'][rel_cat]\\n\\n if torch.cuda.is_available():\\n queries_sro = queries_sro.cuda() \\n\\n\\n return queries_sro, triplet_queries_idx\",\n \"def retrieve_object_info(session, queries, sortkey):\\n results = []\\n\\n for query in queries:\\n expquery = query[\\\"expanded_query\\\"]\\n qtype = query[\\\"expanded_query_type\\\"]\\n\\n if qtype == \\\"collection\\\":\\n queryresults = []\\n queryresults.extend(get_direct_subcollections(session, expquery))\\n queryresults.extend(\\n get_dataobjects_in_collection(\\n session, expquery))\\n elif qtype == \\\"dataobject\\\":\\n queryresults = get_dataobject_info(session, expquery)\\n else:\\n exit_with_error(\\n \\\"Internal issue - illegal query type in retrieve_object_info: \\\"\\n + qtype)\\n\\n query[\\\"results\\\"] = sort_object_info(queryresults, sortkey)\\n results.append(query)\\n\\n return results\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6507253","0.642685","0.59497595","0.590914","0.5836972","0.5767123","0.57669634","0.57419294","0.5726026","0.56799763","0.55797607","0.55152404","0.54958004","0.54945856","0.5494529","0.5469922","0.5443926","0.54248416","0.54075605","0.53540444","0.53506386","0.53417015","0.533703","0.53351355","0.53219575","0.53219575","0.53219575","0.53219575","0.531785","0.5314892","0.53050643","0.52965206","0.5295981","0.5290705","0.528785","0.5275612","0.5259272","0.52490485","0.52112466","0.52075666","0.51957065","0.51853496","0.5178922","0.5161661","0.5161661","0.51517487","0.51484096","0.5146905","0.51448274","0.5144392","0.514254","0.51349556","0.5134538","0.5134538","0.5119322","0.51065856","0.5093127","0.5083987","0.50825137","0.508182","0.5079378","0.5071822","0.5064355","0.5044867","0.5044439","0.5043765","0.50435966","0.50344193","0.50274354","0.5019633","0.50144345","0.5011642","0.5007747","0.50057673","0.50054175","0.49980518","0.4996026","0.49913412","0.4987337","0.4987303","0.4979773","0.4972759","0.4960521","0.49594438","0.49545515","0.4947278","0.4942638","0.49424553","0.49421653","0.49397045","0.49339363","0.49283594","0.49262774","0.49229395","0.49229395","0.49229395","0.492198","0.49180982","0.4917214","0.49155188"],"string":"[\n \"0.6507253\",\n \"0.642685\",\n \"0.59497595\",\n \"0.590914\",\n \"0.5836972\",\n \"0.5767123\",\n \"0.57669634\",\n \"0.57419294\",\n \"0.5726026\",\n \"0.56799763\",\n \"0.55797607\",\n \"0.55152404\",\n \"0.54958004\",\n \"0.54945856\",\n \"0.5494529\",\n \"0.5469922\",\n \"0.5443926\",\n \"0.54248416\",\n \"0.54075605\",\n \"0.53540444\",\n \"0.53506386\",\n \"0.53417015\",\n \"0.533703\",\n \"0.53351355\",\n \"0.53219575\",\n \"0.53219575\",\n \"0.53219575\",\n \"0.53219575\",\n \"0.531785\",\n \"0.5314892\",\n \"0.53050643\",\n \"0.52965206\",\n \"0.5295981\",\n \"0.5290705\",\n \"0.528785\",\n \"0.5275612\",\n \"0.5259272\",\n \"0.52490485\",\n \"0.52112466\",\n \"0.52075666\",\n \"0.51957065\",\n \"0.51853496\",\n \"0.5178922\",\n \"0.5161661\",\n \"0.5161661\",\n \"0.51517487\",\n \"0.51484096\",\n \"0.5146905\",\n \"0.51448274\",\n \"0.5144392\",\n \"0.514254\",\n \"0.51349556\",\n \"0.5134538\",\n \"0.5134538\",\n \"0.5119322\",\n \"0.51065856\",\n \"0.5093127\",\n \"0.5083987\",\n \"0.50825137\",\n \"0.508182\",\n \"0.5079378\",\n \"0.5071822\",\n \"0.5064355\",\n \"0.5044867\",\n \"0.5044439\",\n \"0.5043765\",\n \"0.50435966\",\n \"0.50344193\",\n \"0.50274354\",\n \"0.5019633\",\n \"0.50144345\",\n \"0.5011642\",\n \"0.5007747\",\n \"0.50057673\",\n \"0.50054175\",\n \"0.49980518\",\n \"0.4996026\",\n \"0.49913412\",\n \"0.4987337\",\n \"0.4987303\",\n \"0.4979773\",\n \"0.4972759\",\n \"0.4960521\",\n \"0.49594438\",\n \"0.49545515\",\n \"0.4947278\",\n \"0.4942638\",\n \"0.49424553\",\n \"0.49421653\",\n \"0.49397045\",\n \"0.49339363\",\n \"0.49283594\",\n \"0.49262774\",\n \"0.49229395\",\n \"0.49229395\",\n \"0.49229395\",\n \"0.492198\",\n \"0.49180982\",\n \"0.4917214\",\n \"0.49155188\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5601633"},"document_rank":{"kind":"string","value":"10"}}},{"rowIdx":89387,"cells":{"query":{"kind":"string","value":"Compute a superset of the true target tract with kNN."},"document":{"kind":"string","value":"def compute_superset_with_k(true_bundle, kdt, prototypes, k=1000, distance_func=bundles_distances_mam):\n true_bundle = np.array(true_bundle, dtype=np.object)\n dm_true_bundle = distance_func(true_bundle, prototypes)\n D, I = kdt.query(dm_true_bundle, k=k)\n superset_idx = np.unique(I.flat)\n # recompute the gt streamlines idx\n I = kdt.query_radius(dm_true_bundle, 10e-4)\n gt_idx = []\n for arr in I:\n for e in arr:\n gt_idx.append(e)\n gt_idx = list(set(gt_idx))\n superset_idx = list(set(superset_idx))\n print('gt length: %d' % len(gt_idx))\n additional_idx = list(set(superset_idx) - set(gt_idx))\n\n return additional_idx, gt_idx"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def compute_superset(true_tract, kdt, prototypes, k=1000, distance_func=bundles_distances_mam):\n true_tract = np.array(true_tract, dtype=np.object)\n dm_true_tract = distance_func(true_tract, prototypes)\n D, I = kdt.query(dm_true_tract, k=k)\n superset_idx = np.unique(I.flat)\n return superset_idx","def __init__(self, training_set, k=5):\n super(Knn, self).__init__(training_set)\n self.k = k","def __init__(self, trainset, k=3):\n self._trainset = trainset\n self.k = k","def knn(p, k, x, t):\r\n\r\n # Number of instances in data set\r\n N = x.shape[0]\r\n\r\n Euclidean_Distance = numpy.square(x - p) #Euclidean distance\r\n dis = numpy.sum(Euclidean_Distance, axis=1) #sum of the euclidean distance\r\n inds = numpy.argsort(dis)[:k] #sort the indices of the distance array\r\n tgt_cat = Counter([t[i] for i in inds]) #count the times of equivalent target labels\r\n top_class = max(tgt_cat, key= tgt_cat.get) #top class among the k nearest points\r\n\r\n\r\n #top_class = 0\r\n\r\n return top_class","def k_nearest_neighbors(x_test, df_training, k):\n\n return np.argpartition(distance_to_each_training_point(x_test,\n df_training), k-1)[:,0:k]","def kNN_train(self, x_train, y_train, x_test, k = 5, processing = None, distMethod = \"Manhattan\"):\n y_test = list()\n\n if processing == \"Scalar\":\n # print(\"Preprocessing = Scalar\")\n stdScalar = preprocessing.StandardScaler().fit(x_train)\n x_train = stdScalar.transform(x_train)\n x_test = stdScalar.transform(x_test)\n\n elif processing == \"MinMax\":\n\n # print(\"Preprocessing = MinMax\")\n mmScalar = preprocessing.MinMaxScaler()\n x_train = mmScalar.fit_transform(x_train)\n x_test = mmScalar.fit_transform(x_test)\n\n elif processing == \"None\":\n self.true = True\n # print(\"No Preprocessing\")\n\n else:\n print(\"wrong processing\")\n exit()\n\n for i in range(0, len(x_test)):\n y_test_temp = list()\n zeroCount = 0\n oneCount = 0\n\n # find distance of a instance in test test to all instances in training set\n for j in range(0, len(x_train)):\n if distMethod == \"Manhattan\":\n y_test_temp.append(self.manhattan(x_train[j], x_test[i]))\n elif distMethod == \"Euclidean\":\n y_test_temp.append(self.euclidean(x_train[j], x_test[i]))\n else:\n print \"something wrong with distance calculation\"\n exit()\n\n # take indices of k nearest points\n # print y_test_temp\n temp = np.asarray(y_test_temp).argsort()[:k]\n # check class of each of k nearest points\n for tmp in temp:\n if y_train[tmp] == 0:\n zeroCount += 1\n elif y_train[tmp] == 1:\n oneCount += 1\n else:\n print(\"something wrong in counting\")\n\n # classify\n if zeroCount >= oneCount:\n y_test.append(int(0))\n elif oneCount > zeroCount:\n y_test.append(int(1))\n else:\n print(\"somethign wrong\")\n\n # print y_test\n return y_test","def __call__(self, output, target):\n maxk = max(self.topk)\n batch_size = target.size(0)\n\n _, pred = output.topk(maxk, 1, True, True)\n pred = pred.t()\n correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n res = []\n for k in self.topk:\n correct_k = correct[:k].view(-1).float().sum(0)\n res.append(correct_k.mul_(100.0 / batch_size))\n return res","def choose_k_away(k, node, precomputed=True):\n if precomputed:\n sps = np.load('./short_paths/' + str(node) + '.npy')\n else:\n sps = shortest_path(node)\n nodes = []\n for index, x in enumerate(sps):\n if x == k and index in poss_targets:\n nodes.append(index)\n return nodes","def knnSame(k, Xtrain, Ytrain):\n d = euclidean_distances(Xtrain, squared=True)\n np.fill_diagonal(d, np.inf)\n nnc = Ytrain[np.argsort(d)[..., :k].flatten()].reshape(Xtrain.shape[0], k)\n pred = [max(nnc[i], key=Counter(nnc[i]).get) for i in range(nnc.shape[0])]\n return np.array(pred)","def correct(output, target, topk=(1,)):\n maxk = max(topk)\n _, pred = output.topk(maxk, 1, True, True)\n pred = pred.t()\n correct = pred.eq(target.view(1, -1).expand_as(pred))\n res = []\n for k in topk:\n correct_k = correct[:k].view(-1).sum(0, keepdim=True)\n res.append(correct_k)\n return res","def correct(output, target, topk=(1,)):\n maxk = max(topk)\n _, pred = output.topk(maxk, 1, True, True)\n pred = pred.t()\n correct = pred.eq(target.view(1, -1).expand_as(pred))\n res = []\n for k in topk:\n correct_k = correct[:k].view(-1).sum(0, keepdim=True)\n res.append(correct_k)\n return res","def subsample_training_set(self, k, seed=None):\n if seed is not None:\n rng_state = th.random.get_rng_state()\n with th.random.fork_rng():\n th.manual_seed(seed)\n self._subsample_training_set(k)\n if any(th.random.get_rng_state() != rng_state):\n raise ValueError(\"Bad RNG state\")\n else:\n self._subsample_training_set(k)","def get_target_root_partitions_subset(self) -> PartitionsSubset:\n partitioned_asset_keys = {\n asset_key\n for asset_key in self.target_subset.asset_keys\n if self.target_subset.asset_graph.get_partitions_def(asset_key) is not None\n }\n\n root_partitioned_asset_keys = (\n AssetSelection.keys(*partitioned_asset_keys)\n .sources()\n .resolve(self.target_subset.asset_graph)\n )\n\n # Return the targeted partitions for the root partitioned asset keys\n return self.target_subset.get_partitions_subset(next(iter(root_partitioned_asset_keys)))","def check_subset(P1,P2,k=-1):","def evalute_subset(X_train, X_test, y_train, y_test):\r\n clf = KNeighborsClassifier(n_neighbors=3)\r\n clf.fit(X_train, y_train) \r\n y_pred = clf.predict(X_test)\r\n return accuracy_score(y_test, y_pred)","def build_full_trainset(self):\n\n return self.construct_trainset(self.raw_ratings)","def __init__(self, k: int, training_set: np.ndarray):\n self._k = k\n self._training_set = training_set","def knn0(pnts, p, k):\r\n p = np.asarray(p)\r\n pnts = np.asarray(pnts)\r\n diff = pnts - p[np.newaxis, :]\r\n d = np.einsum('ij,ij->i', diff, diff)\r\n idx = np.argsort(d)[:k]\r\n# s = [i.tolist() for i in pnts[idx]]\r\n return pnts[idx].tolist()","def knn(k, Xtrain, Ytrain, Xtest):\n d = euclidean_distances(Xtest, Xtrain, squared=True)\n nnc = Ytrain[np.argsort(d)[..., :k].flatten()].reshape(Xtest.shape[0], k)\n pred = [max(nnc[i], key=Counter(nnc[i]).get) for i in range(nnc.shape[0])]\n return np.array(pred)","def fit_full(self, K):\r\n pass","def KS_algorithm(dataset_x, dataset_y, train_size = 0.7, return_dataset = True):\n #Decide number of total samples\n n = dataset_x.shape[0]\n \n #Turn into train_size to float\n try: \n train_size = float(train_size)\n except Expection as e:\n print(e)\n \n #Jusge if the train_size is by ratio or quantity\n if 0 < train_size < 1: \n num_of_samples = round(n*train_size)\n elif 1 < train_size < 2: \n return \"Train size must be between 1 to 0 or 2 to number of total samples\" \n elif train_size == n or train_size == 1:\n return [x for x in range(n)]\n elif 2 <= train_size < n:\n num_of_samples = round(train_size)\n elif train_size > n:\n return \"Num_of_samples must be less than or equal to original number of samples\"\n \n #Create original sample index\n samples = [x for x in range(n)]\n \n #Create distance matrix\n print('Building distance matrix')\n dist_matrix = create_dist_matrix(dataset_x)\n\n #Pick first two furthest away data points\n print('Picking two furthest away samples')\n max_array = np.argmax(dist_matrix == np.max(dist_matrix), axis = 1)\n ind1 = max_array[max_array != 0][0]\n ind2 = max_array[ind1]\n\n #Initiate chosen sample list, append first two samples and remove from remaining sample list\n chosen_samples = [ind1, ind2]\n samples.remove(ind1)\n samples.remove(ind2) \n remaining_samples = num_of_samples - 2\n\n #Continue picking samples if not yet reaching target number of samples\n while remaining_samples > 0:\n sub_array = np.min(dist_matrix[:,chosen_samples], axis=1)\n chosen = np.argmax(sub_array)\n chosen_samples.append(chosen)\n samples.remove(chosen)\n remaining_samples -= 1\n \n #See if this function should return train and test set or simply index of chosen samples\n if return_dataset == True:\n not_chosen_samples = [x for x in range(n) if x not in chosen_samples]\n return dataset_x.iloc[chosen_samples,:], dataset_x.iloc[not_chosen_samples,:], dataset_y.iloc[chosen_samples,:], dataset_y.iloc[not_chosen_samples,:] \n else:\n return chosen_samples","def findknn(xTr,xTe,k):\n\n # YOUR CODE HERE\n if k > len(xTr):\n k = len(xTr)\n \n D=l2distance(xTe, xTr)\n (m,n) = D.shape\n \n indices = []\n dists = []\n for i in range(m):\n smallest_indices = np.argsort(D[i])\n ind = smallest_indices[:k]\n dis = D[i,smallest_indices[:k]]\n indices.append(ind)\n dists.append(dis)\n \n indices = np.transpose(np.array(indices))\n dists = np.transpose(np.array(dists))\n return indices, dists","def get_neighbors(training_set, \r\n labels, \r\n test_instance, \r\n k, \r\n distance=distance):\r\n distances = []\r\n for index in range(len(training_set)):\r\n dist = distance(test_instance, training_set[index])\r\n distances.append((training_set[index], dist, labels[index]))\r\n distances.sort(key=lambda x: x[1])\r\n neighbors = distances[:k]\r\n return neighbors","def __init__(self, top_k):\n super(MinibatchTopkAccuracy, self).__init__(\n reset_at=\"iteration\", emit_at=\"iteration\", mode=\"train\", top_k=top_k\n )\n self.top_k = top_k","def knn(trainingSetData, testSetData, k):\n trainingSet = trainingSetData.drop([14], axis=1) # drop income\n testSet = testSetData.drop([14], axis=1) # drop income\n\n distances = {}\n # this will store the distances re-sorted in ascending/descending order\n sort = {}\n # income band results (>=50k or <50K)\n incomePredictions = []\n\n # Calculating euclidean distance between each row of training data and test data instance\n for testInstance in range(len(testSet)): # len(testSet)\n \n # Store current test Point:\n testInstance = testSet.iloc[testInstance] \n \n distances = euclideanDistanceRow(testInstance, trainingSet)\n\n # sort the distances in order of smallest first:\n sorted_d = sorted(distances.items(), key=lambda x: x[1], reverse=False)\n\n neighbors = []\n\n # Extracting top k neighbors\n for x in range(k):\n neighbors.append(sorted_d[x])\n\n\n classVotes = {}\n\n # Calculating the most freq class in the neighbors\n results = {\"lessThan50\": 0, \"moreThan50\": 0}\n\n # creating a dataframe to which we will add the income values:\n\n for x in range(len(neighbors)):\n if (trainingSetData.iloc[neighbors[x][0]][14] == 0.0):\n results[\"lessThan50\"] += 1\n elif (trainingSetData.iloc[neighbors[x][0]][14] == 1.0):\n results[\"moreThan50\"] += 1\n\n print('results',results)\n\n if (results[\"lessThan50\"] > results[\"moreThan50\"]):\n incomePredictions.append(0.0)\n elif (results[\"lessThan50\"] < results[\"moreThan50\"]):\n incomePredictions.append(1.0)\n\n return incomePredictions","def fit(self, trainingFeatures, trainingTargets):\r\n\r\n \"\"\" Implement kNN learning below. \"\"\"\r\n\r\n self._fitCalled = True\r\n self.labels = trainingTargets\r\n self.data = trainingFeatures","def k_shortest_paths(\n self,\n G,\n source,\n target,\n k=1,\n weight='weight',\n ):\n\n if source == target:\n return ([0], [[source]])\n\n (length, path) = nx.single_source_dijkstra(G, source, target,\n weight=weight)\n if target not in length:\n raise nx.NetworkXNoPath('node %s not reachable from %s' % (source,\n target))\n\n lengths = [length[target]]\n paths = [path[target]]\n c = count()\n B = []\n\n # Is deep copy really required?\n # Fails due to embedded Ctype objects which can not be pickled\n # # G_original = G.copy()\n # Swapping with shallow copy...will it work?\n\n G_original = G\n if nx.is_directed(G_original):\n G = nx.DiGraph(G_original)\n else:\n G = nx.Graph(G_original)\n\n ######################################\n #TODO: wrap this up somehow\n print ''\n print term.move_up + term.move_up\n ######################################\n print 'getting K:{} paths...'.format(k),\n for i in range(1, k):\n with term.location():\n print i\n for j in range(len(paths[-1]) - 1):\n spur_node = paths[-1][j]\n root_path = (paths[-1])[:j + 1]\n\n edges_removed = []\n for c_path in paths:\n if len(c_path) > j and root_path == c_path[:j + 1]:\n u = c_path[j]\n v = c_path[j + 1]\n if G.has_edge(u, v):\n edge_attr = G.edge[u][v]\n G.remove_edge(u, v)\n edges_removed.append((u, v, edge_attr))\n\n for n in range(len(root_path) - 1):\n node = root_path[n]\n\n # out-edges\n\n for (u, v, edge_attr) in G.edges_iter(node, data=True):\n\n # print 'lala1: {} -> {}'.format(u,v)\n\n G.remove_edge(u, v)\n edges_removed.append((u, v, edge_attr))\n\n if G.is_directed():\n\n # in-edges\n\n for (u, v, edge_attr) in G.in_edges_iter(node,\n data=True):\n\n # print 'lala2: {} -> {}'.format(u,v)\n\n G.remove_edge(u, v)\n edges_removed.append((u, v, edge_attr))\n\n (spur_path_length, spur_path) = nx.single_source_dijkstra(G,\n spur_node, target, weight=weight)\n if target in spur_path and spur_path[target]:\n total_path = root_path[:-1] + spur_path[target]\n total_path_length = self.get_path_length(G_original,\n root_path, weight) + spur_path_length[target]\n heappush(B, (total_path_length, next(c), total_path))\n\n for e in edges_removed:\n (u, v, edge_attr) = e\n G.add_edge(u, v, edge_attr)\n\n if B:\n (l, _, p) = heappop(B)\n lengths.append(l)\n paths.append(p)\n else:\n break\n\n return (lengths, paths)","def dp_kendall_feature_selection(features, labels, k, epsilon):\n # Remove intercept feature\n features = features[:, :-1]\n _, d = features.shape\n split_eps = epsilon / k\n selected_indices = []\n # selected_correlations tracks correlations between features and previously\n # selected features\n selected_correlations = np.zeros((k - 1, d))\n label_coefficients = kendall(features, labels)\n # Sensitivity is 3/2 in the first round and 3 after\n sensitivity = 3/2\n for j in range(k):\n diffs = label_coefficients\n if selected_indices:\n last_idx = selected_indices[-1]\n old_feature = features[:, last_idx]\n selected_correlations[j - 1, :] = kendall(features, old_feature)\n diffs = diffs - (\n np.sum(selected_correlations, axis=0) / len(selected_indices)\n )\n sensitivity = 3\n new_idx = top_k.basic_peeling_mechanism(\n item_counts=diffs,\n k=1,\n epsilon=split_eps,\n l_inf_sensitivity=sensitivity,\n monotonic=False,\n )[0]\n selected_indices.append(new_idx)\n label_coefficients[new_idx] = -np.inf\n # Add back intercept feature\n selected_indices.append(d)\n return np.asarray(selected_indices)","def build_knn(coords, k=6, **kwargs):\n \n tree = BallTree(coords, **kwargs)\n _, ind = tree.query(coords, k=k+1) # the first k is \"oneself\"\n pairs = pairs_from_knn(ind)\n return pairs","def fetch_unlabelled(self, k, show_progress = False):\n \n self.candidates = self.get_unseen()\n if self.subsample and (self.subsample < len(self.candidates)):\n self.candidates = np.random.choice(self.candidates, self.subsample, replace = False).tolist()\n if len(self.candidates) < k:\n k = len(self.candidates)\n \n entropy = AppendedConditionalEntropy(self)\n steps = trange(k) if show_progress else range(k)\n for it in steps:\n \n if self.parallelized and (it > 0):\n with Pool(initializer = _init_pool, initargs = (entropy,)) as p:\n ce = p.map(_parallel_ce, self.candidates)\n else:\n ce = [entropy(i) for i in self.candidates]\n \n min_ind = np.argmin(ce)\n entropy.append(self.candidates[min_ind])\n del self.candidates[min_ind]\n \n return entropy.ret","def train(self, train_set) -> None:\n super().train(train_set)\n # split into data and target\n xlist, y = zip(*train_set)\n x = sparse.vstack(xlist)\n self._classifier.fit(x, y)","def selectFeatures(k_features=5, *args):\n X, y = args\n skb = SelectKBest(k=k_features)\n return skb.fit_transform(X, y)","def top_k_1_5(output: torch.Tensor, target: torch.Tensor, topk=(1, 5)) -> List[torch.FloatTensor]:\n with torch.no_grad():\n # ---- get the topk most likely labels according to your model\n # get the largest k \\in [n_classes] (i.e. the number of most likely probabilities we will use)\n # max number labels we will consider in the right choices for out model\n maxk = max(topk)\n batch_size = target.size(0)\n\n # get top maxk indicies that correspond to the most likely probability scores\n # (note _ means we don't care about the actual top maxk scores just their corresponding indicies/labels)\n # _, [B, n_classes] -> [B, maxk]\n _, y_pred = output.topk(k=maxk, dim=1)\n # [B, maxk] -> [maxk, B] Expects input to be <= 2-D tensor and transposes dimensions 0 and 1.\n y_pred = y_pred.t()\n\n # - get the credit for each example if the models predictions is in maxk values (main crux of code)\n # for any example, the model will get credit if it's prediction matches the ground truth\n # for each example we compare if the model's best prediction matches the truth. If yes we get an entry of 1.\n # if the k'th top answer of the model matches the truth we get 1.\n # Note: this for any example in batch we can only ever get 1 match (so we never overestimate accuracy <1)\n # [B] -> [B, 1] -> [maxk, B]\n target_reshaped = target.view(1, -1).expand_as(y_pred)\n # compare every topk's model prediction with the ground truth & give credit if any matches the ground truth\n # [maxk, B] were for each example we know which topk prediction matched truth\n correct = (y_pred == target_reshaped)\n # original: correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n # -- get topk accuracy\n list_topk_accs = [] # idx is topk1, topk2, ... etc\n for k in topk:\n # get tensor of which topk answer was right\n ind_which_topk_matched_truth = correct[:k] # [maxk, B] -> [k, B]\n # flatten it to help compute if we got it correct for each example in batch\n # [k, B] -> [kB]\n flattened_indicator_which_topk_matched_truth = ind_which_topk_matched_truth.reshape(\n -1).float()\n # get if we got it right for any of our top k prediction for each example in batch\n tot_correct_topk = flattened_indicator_which_topk_matched_truth.float().sum(\n dim=0, keepdim=True) # [kB] -> [1]\n # compute topk accuracy - the accuracy of the mode's ability to get it right within it's top k guesses/preds\n topk_acc = tot_correct_topk / batch_size # topk accuracy for entire batch\n list_topk_accs.append(topk_acc)\n # list of topk accuracies for entire batch [topk1, topk2, ... etc]\n return list_topk_accs","def in_top_k(predictions, targets, k, name=None):\n with ops.name_scope(name, \"in_top_k\"):\n return gen_nn_ops.in_top_kv2(predictions, targets, k, name=name)","def extended_training(X,Y):\n trainSz = 4000\n \n X_, Y_ = extend_training_set(X[:trainSz],Y[:trainSz])\n \n lda = LDA()\n lda.fit(X, Y)\n X2 = lda.transform(X)\n X_2 = lda.transform(X_)\n \n clf = neighbors.KNeighborsClassifier()\n clf.fit(X_2, Y_)\n \n predict = clf.predict(X2)\n return predict, lda, clf","def _kneighbors(self, y):\n if self.__x is None:\n raise ValueError(\"Input dataset not fitted yet! Call .fit() first!\")\n if self.__device and self.tools.device(y) != self.__device:\n raise ValueError(\"Input dataset and query dataset must be on same device\")\n if len(y.shape) != 2:\n raise ValueError(\"Query dataset must be a 2D tensor\")\n if self.__x.shape[-1] != y.shape[-1]:\n raise ValueError(\"Query and dataset must have same dimensions\")\n if self.__normalise:\n y = y / self.tools.repeat(self.tools.norm(y, 2, -1), y.shape[1]).reshape(\n -1, y.shape[1]\n )\n y = self.tools.contiguous(y)\n y_labels = self.__assign(y)\n\n y_ranges, _, _ = cluster_ranges_centroids(y, y_labels)\n self.__y_ranges = y_ranges\n y, y_labels = self.__sort_clusters(y, y_labels, store_x=False)\n x_LT = self.__LazyTensor(self.tools.unsqueeze(self.__x, 0))\n y_LT = self.__LazyTensor(self.tools.unsqueeze(y, 1))\n D_ij = self.__distance(y_LT, x_LT)\n ranges_ij = from_matrix(y_ranges, self.__x_ranges, self.__keep)\n D_ij.ranges = ranges_ij\n nn = D_ij.argKmin(K=self.__k, axis=1)\n return self.__unsort(nn)","def find_partitions(V,k):\n k_subs = k_subset(V,k)\n k_subs = uniq_subsets(k_subs)\n\n return k_subs","def trainSet(self):\r\n self.currIdx = 0\r\n random.shuffle(self.trainSamples)\r\n self.samples = self.trainSamples[:self.numTrainSamplesPerEpoch]","def runmaxmin(self):\n import random\n random.seed(self.seed)\n mindist_ptolandmarkset = np.full(self.pointcloud.size, np.inf)\n self.subsetindices = []\n for i in xrange(self.subsetsize):\n if i == 0:\n selected_index = random.randint(0, self.pointcloud.size - 1)\n # update min for all the rest indices\n # update min for this index to 0.\n for z in xrange(self.pointcloud.size):\n # if z == selected_index:\n # mindist_ptolandmarkset[z] = 0.0\n # else:\n mindist_ptolandmarkset[z] = self.pointcloud.distmat[selected_index][z]\n else:\n selected_index = np.argmax(mindist_ptolandmarkset)\n # update minimum distance for all points\n for z in xrange(self.pointcloud.size):\n mindist_ptolandmarkset[z] = min(mindist_ptolandmarkset[z],\n self.pointcloud.distmat[selected_index][z])\n\n self.subsetindices.append(selected_index)\n\n self.subsetpointcloud = pc.PointCloud(self.pointcloud.points[self.subsetindices])","def generate_superset(self, number):\n \n superset = []\n for i in range(0, 2**(self.dim)):\n if (number & i)==number:\n superset.append(i)\n return superset","def knn(self,query_vec,k, stdev=False):\n\n sims = []\n if k > len(self.terms):\n k = len(self.terms)\n sims = np.matmul(self.vectors, query_vec.vector)\n if stdev:\n sims = zscore(sims)\n indices = np.argpartition(sims, -k)[-k:]\n indices = sorted(indices, key=lambda i: sims[i], reverse=True)\n results = []\n for index in indices:\n results.append([sims[index], self.terms[index]])\n return results","def _automatic_training_set(self, n_cutouts=100):\n dstl = Load_DSTL()\n np.random.seed(42)\n for ii in range(n_cutouts):\n # Get region around a shape\n triples, mask, ind_shape, img_dim = dstl.extract_region(object_class=self.class_type, image_id='6120_2_2', buffer_size=10)\n if self.radius is not None:\n triples, mask = self._sliding_window(triples.reshape(*img_dim, 3), mask.reshape(img_dim), window_radius=self.radius)\n # Add to Feature Matrix\n if ii == 0:\n features = triples\n labels = mask\n else:\n features = np.vstack([features, triples])\n labels = np.hstack([labels, mask])\n return features, labels","def select_seeds(self, graph, k):\n raise NotImplementedError\n return set()","def generate_k_folds(dataset, k):\n\n # TODO: finish this.\n folds = []\n dataset = np.concatenate((dataset[0], np.array(dataset[1]).reshape(-1,1)), axis=1)\n dataset_shape = dataset.shape\n shape_test_set = int(round(dataset_shape[0]/k,0))\n split_dataset = np.array_split(dataset,k,axis=0)\n for i in range(k):\n test_set = split_dataset[i]\n c = [k for j,k in enumerate(split_dataset) if j!=i]\n training_set = np.concatenate(c,axis=0)\n if test_set.shape[0] != shape_test_set:\n step = test_set.shape[0] - shape_test_set\n test_set = test_set[:-step,:]\n training_set = np.concatenate((training_set, test_set[-step:,:]), axis=0)\n r_test_set = (test_set[:,:-1], list(test_set[:,-1]))\n r_train_set = (training_set[:,:-1], list(training_set[:,-1]))\n folds.append((r_train_set, r_test_set))\n return folds","def num_correct(output, target, topk=(1,)):\n with torch.no_grad():\n maxk = max(topk)\n batch_size = target.size(0)\n\n _, pred = output.topk(maxk, 1, True, True)\n pred = pred.t()\n correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n res = []\n for k in topk:\n correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)\n res.append(correct_k)\n return res, batch_size","def Get(self,k:int): \n ### get partitions depending on the partition schemes C that depends on k!\n return subsets_k(list(range(self._n)),k)","def S(k):\n\n from scipy.special import binom as binomial\n\n TrueHset = [0]\n if k > 1:\n for j in np.arange(k, 0, -1, dtype=int):\n TrueHset = list(set(TrueHset) | set([binomial(j, 2) + x for x in S(k - j)]))\n return TrueHset","def existsSubset(S,t,k): \n\tn = len(S)\n\t# M[n+1][t+1][k+1]\n\tM = [[[False for _ in range(k+1)]for _ in range(t+1)]for _ in range(n+1)]\n\t\n\t# Base: if k is 0 and t is 0 \n\t# For any n, we can achieve 0 with size 0 subset \n\t# Otherwise, every other cell is False \n\tfor i in range(n+1): \n\t\tM[i][0][0] = True\n\n\t# Fill M[n][t][k]\n\t# The run-time of this algorithm is O(ntk)\n\t# because at worst we are filling in a 3D array \n\t# of size n*t*k\n\tfor i in range(1, n + 1):\n\t\tfor j in range(1, t + 1):\n\t\t\tfor l in range(1, k + 1):\n\t\t\t\t# Total target so far is less than array element \n\t\t\t\tif(j < S[i-1]):\n\t\t\t\t\tM[i][j][l] = M[i-1][j][l]\n\t\t\t\t# Total target so far is greater (or equal) than array element \n\t\t\t\telif(j >= S[i-1]):\n\t\t\t\t\t# Leave \n\t\t\t\t\tleave = M[i-1][j][l]\n\t\t\t\t\t# Take \n\t\t\t\t\ttake = M[i-1][j - S[i-1]][l-1]\n\t\t\t\t\t# Only False or False will be False \n\t\t\t\t\tM[i][j][l] = leave or take \n\n\t# If possible, back track \n\t# We are still basing back-tracking \n\t# off of the array of size ntk \n\t# so run-time remains at O(ntk)\n\tif(M[n][t][k]):\n\t\t# Initialize subset array \n\t\tsubset = [] \n\t\t# Initialize i, j, and l \n\t\ti, j, l = n, t, k\n\t\t# While i, j, and l are all greater than 0 \n\t\twhile(i != 0 and j != 0 and l != 0):\n\t\t\t# Check if M[i][j][l] == take \n\t\t\tif(M[i][j][l] == M[i-1][j - S[i-1]][l-1]):\n\t\t\t\t# Add to subset \n\t\t\t\tsubset += [S[i-1]]\n\t\t\t\t# Decrement relevant indices \n\t\t\t\tj -= S[i-1]\n\t\t\t\ti -= 1 \n\t\t\t\tl -= 1\n\t\t\telse:\n\t\t\t\ti -= 1\n\t\t# Since we backtracked, reverse the subset\n\t\tsubset.reverse()\n\t\treturn subset \n\t# If not possible, return False \n\treturn M[n][t][k]","def set_uniform_Kk(self, clip=True):\n\t\t\n\t\tKk1_los = random_matrix([self.Mm], params=[self.lo_Kk1_hyper_lo, \n\t\t\t\t\t\t\tself.lo_Kk1_hyper_hi], sample_type='uniform',\n\t\t\t\t\t\t\tseed=self.seed_Kk1)\n\t\tKk1_his = random_matrix([self.Mm], params=[self.hi_Kk1_hyper_lo, \n\t\t\t\t\t\t\tself.hi_Kk1_hyper_hi], sample_type='uniform',\n\t\t\t\t\t\t\tseed=self.seed_Kk1)\n\t\tKk2_los = random_matrix([self.Mm], params=[self.lo_Kk2_hyper_lo, \n\t\t\t\t\t\t\tself.lo_Kk2_hyper_hi], sample_type='uniform',\n\t\t\t\t\t\t\tseed=self.seed_Kk2)\n\t\tKk2_his = random_matrix([self.Mm], params=[self.hi_Kk2_hyper_lo, \n\t\t\t\t\t\t\tself.hi_Kk2_hyper_hi], sample_type='uniform',\n\t\t\t\t\t\t\tseed=self.seed_Kk2)\n\t\t\n\t\tself.Kk1 = random_matrix([self.Mm, self.Nn], [Kk1_los, Kk1_his], \n\t\t\t\t\t\t\t\tsample_type='rank2_row_uniform', \n\t\t\t\t\t\t\t\tseed = self.seed_Kk1)\n\t\tself.Kk2 = random_matrix([self.Mm, self.Nn], [Kk2_los, Kk2_his], \n\t\t\t\t\t\t\t\tsample_type='rank2_row_uniform', \n\t\t\t\t\t\t\t\tseed = self.seed_Kk2)\n\t\t\n\t\tif clip == True:\n\t\t\tarray_dict = clip_array(dict(Kk1 = self.Kk1, Kk2 = self.Kk2))\n\t\t\tself.Kk1 = array_dict['Kk1']\n\t\t\tself.Kk2 = array_dict['Kk2']","def run_knn(\n features: List[List[float]],\n labels: List[Optional[bool]],\n k: int = 1,\n) -> List[bool]:\n # Filter out the features that are already clustered\n features_l, labels_l = zip(*[(f, l) for f, l in zip(features, labels) if isinstance(l, bool)])\n\n # Fit a nearest neighbour algorithm\n neighbours = KNeighborsClassifier(\n n_neighbors=k,\n ).fit(features_l, labels_l)\n\n # Predict all the features' labels\n return neighbours.predict(features) # type: ignore","def k_truss(G, k):\n H = G.copy()\n\n n_dropped = 1\n while n_dropped > 0:\n n_dropped = 0\n to_drop = []\n seen = set()\n for u in H:\n nbrs_u = set(H[u])\n seen.add(u)\n new_nbrs = [v for v in nbrs_u if v not in seen]\n for v in new_nbrs:\n if len(nbrs_u & set(H[v])) < (k - 2):\n to_drop.append((u, v))\n H.remove_edges_from(to_drop)\n n_dropped = len(to_drop)\n H.remove_nodes_from(list(nx.isolates(H)))\n\n return H","def NDCG_binary_at_k_batch(logits, y_true, k=10):\n n = logits.shape[0]\n dummy_column = np.arange(n).reshape(n, 1)\n\n idx_topk_part = bn.argpartition(-logits, k, axis=1)[:, :k]\n topk_part = logits[dummy_column, idx_topk_part]\n idx_part = np.argsort(-topk_part, axis=1)\n # X_pred[np.arange(batch_users)[:, np.newaxis], idx_topk] is the sorted topk predicted score\n idx_topk = idx_topk_part[dummy_column, idx_part]\n # build the discount template\n tp = 1. / np.log2(np.arange(2, k + 2))\n\n DCG = (y_true[dummy_column, idx_topk] * tp).sum(axis=1)\n IDCG = np.array([(tp[:min(n, k)]).sum()\n for n in y_true.getnnz(axis=1)])\n\n # sanity check\n assert (IDCG - DCG >= 0).all()\n return DCG / IDCG","def narration_target(self):","def nDCG_at(prediction, target, k=10):\n pass","def __init__(self, K=10, base_learner=DecisionTreeClassifier(), S=200, cv=5,\n epsilon=3, cost=90, fraud_label=1, t=3):\n\n # let the parent do the stuffs\n super().__init__(K=K, base_learner=base_learner, S=S, cv=cv)\n\n # init statistics of bins (i,k) each epsilon bin has an array k\n self.bins = epsilon * [K * [{'mean': 0.0, 'var': 0.0, 'num': 0}]]\n\n # a cost (because it is a cost sensitive classifier)\n self.cost = cost\n\n # the fraud label as indicated by the users\n self.fraud_label = fraud_label\n\n # confidence level\n self.t = t\n\n # count all the samples predicted in order to get the average of the k classifiers used\n self.samples = 0","def cls_acc(output, target, topk=(1,)):\r\n maxk = max(topk)\r\n batch_size = target.size(0)\r\n\r\n _, pred = output.topk(maxk, 1, True, True)\r\n pred = pred.t()\r\n correct = pred.eq(target.view(1, -1).expand_as(pred))\r\n\r\n res = []\r\n for k in topk:\r\n correct_k = correct[:k].view(-1).float().sum(0)\r\n res.append(correct_k.mul_(100.0 / batch_size))\r\n return res","def getNegativeSamples(target, dataset, K):\n\n indices = [None] * K\n for k in xrange(K):\n newidx = dataset.sampleTokenIdx()\n while newidx == target:\n newidx = dataset.sampleTokenIdx()\n indices[k] = newidx\n return indices","def getNegativeSamples(target, dataset, K):\n\n indices = [None] * K\n for k in xrange(K):\n newidx = dataset.sampleTokenIdx()\n while newidx == target:\n newidx = dataset.sampleTokenIdx()\n indices[k] = newidx\n return indices","def getNegativeSamples(target, dataset, K):\n\n indices = [None] * K\n for k in xrange(K):\n newidx = dataset.sampleTokenIdx()\n while newidx == target:\n newidx = dataset.sampleTokenIdx()\n indices[k] = newidx\n return indices","def train(self,trainset):\n \n self.n_classes = len(trainset.metadata['targets'])\n if self.n_classes > 2:\n raise ValueError('Invalid. Should have 2 classes.')\n \n features = np.zeros((len(trainset),trainset.metadata['input_size']))\n labels = np.zeros((len(trainset)),dtype='int')\n for i,xy in enumerate(trainset):\n x,y = xy\n features[i] = x\n labels[i] = y\n\n if self.criterion == 'information_gain':\n def criterion_fcn(labels0, labels1):\n return libmilk.supervised.tree.information_gain(labels0, labels1, include_entropy=self.include_entropy)\n elif self.criterion == 'z1_loss':\n def criterion_fcn(labels0, labels1):\n return libmilk.supervised.tree.z1_loss(labels0, labels1)\n else:\n raise ValueError('Invalid parameter: '+self.criterion+'. Should be either \\'information_gain\\' or \\'z1_loss\\'')\n\n learner = libmilk.supervised.tree_learner(criterion=criterion_fcn,min_split=self.min_split,return_label=True)\n #self.subsample = subsample\n #self.R = R\n model = learner.train(features, labels)\n \n self.tree = model","def do_knn(x_data):\n return True","def k_shortest_paths(G, source, target, k=1, weight='weight'):\n\tif source == target:\n\t\treturn ([0], [[source]]) \n\t \n\tlength, path = nx.single_source_dijkstra(G, source, target, weight=weight)\n\tif target not in length:\n\t\traise nx.NetworkXNoPath(\"node %s not reachable from %s\" % (source, target))\n\t\t\n\tlengths = [length[target]]\n\tpaths = [path[target]]\n\tc = count()\t\t\n\tB = []\t\t\t\t\t\t\n\tG_original = G.copy()\t\n\t\n\tfor i in range(1, k):\n\t\tfor j in range(len(paths[-1]) - 1):\t\t\t\n\t\t\tspur_node = paths[-1][j]\n\t\t\troot_path = paths[-1][:j + 1]\n\t\t\t\n\t\t\tedges_removed = []\n\t\t\tfor c_path in paths:\n\t\t\t\tif len(c_path) > j and root_path == c_path[:j + 1]:\n\t\t\t\t\tu = c_path[j]\n\t\t\t\t\tv = c_path[j + 1]\n\t\t\t\t\tif G.has_edge(u, v):\n\t\t\t\t\t\tedge_attr = G.edge[u][v]\n\t\t\t\t\t\tG.remove_edge(u, v)\n\t\t\t\t\t\tedges_removed.append((u, v, edge_attr))\n\t\t\t\n\t\t\tfor n in range(len(root_path) - 1):\n\t\t\t\tnode = root_path[n]\n\t\t\t\t# out-edges\n\t\t\t\tfor u, v, edge_attr in G.copy().edges_iter(node, data=True):\n\t\t\t\t\tG.remove_edge(u, v)\n\t\t\t\t\tedges_removed.append((u, v, edge_attr))\n\t\t\t\t\n\t\t\t\tif G.is_directed():\n\t\t\t\t\t# in-edges\n\t\t\t\t\tfor u, v, edge_attr in G.in_edges_iter(node, data=True):\n\t\t\t\t\t\tG.remove_edge(u, v)\n\t\t\t\t\t\tedges_removed.append((u, v, edge_attr))\n\t\t\t\n\t\t\tspur_path_length, spur_path = nx.single_source_dijkstra(G, spur_node, target, weight=weight)\t\t\t\n\t\t\tif target in spur_path and spur_path[target]:\n\t\t\t\ttotal_path = root_path[:-1] + spur_path[target]\n\t\t\t\ttotal_path_length = get_path_length(G_original, root_path, weight) + spur_path_length[target]\t\t\t\t\n\t\t\t\theappush(B, (total_path_length, next(c), total_path))\n\t\t\t\t\n\t\t\tfor e in edges_removed:\n\t\t\t\tu, v, edge_attr = e\n\t\t\t\tG.add_edge(u, v, edge_attr)\n\t\t\t\t\t \n\t\tif B:\n\t\t\t(l, _, p) = heappop(B)\n\t\t\tlengths.append(l)\n\t\t\tpaths.append(p)\n\t\telse:\n\t\t\tbreak\n\t\n\treturn (lengths, paths)","def k_shortest_paths(G, source, target, k=1, weight='weight'):\n\tif source == target:\n\t\treturn ([0], [[source]]) \n\t \n\tlength, path = nx.single_source_dijkstra(G, source, target, weight=weight)\n\tif target not in length:\n\t\traise nx.NetworkXNoPath(\"node %s not reachable from %s\" % (source, target))\n\t\t\n\tlengths = [length[target]]\n\tpaths = [path[target]]\n\tc = count()\t\t\n\tB = []\t\t\t\t\t\t\n\tG_original = G.copy()\t\n\t\n\tfor i in range(1, k):\n\t\tfor j in range(len(paths[-1]) - 1):\t\t\t\n\t\t\tspur_node = paths[-1][j]\n\t\t\troot_path = paths[-1][:j + 1]\n\t\t\t\n\t\t\tedges_removed = []\n\t\t\tfor c_path in paths:\n\t\t\t\tif len(c_path) > j and root_path == c_path[:j + 1]:\n\t\t\t\t\tu = c_path[j]\n\t\t\t\t\tv = c_path[j + 1]\n\t\t\t\t\tif G.has_edge(u, v):\n\t\t\t\t\t\tedge_attr = G.edge[u][v]\n\t\t\t\t\t\tG.remove_edge(u, v)\n\t\t\t\t\t\tedges_removed.append((u, v, edge_attr))\n\t\t\t\n\t\t\tfor n in range(len(root_path) - 1):\n\t\t\t\tnode = root_path[n]\n\t\t\t\t# out-edges\n\t\t\t\tfor u, v, edge_attr in G.copy().edges_iter(node, data=True):\n\t\t\t\t\tG.remove_edge(u, v)\n\t\t\t\t\tedges_removed.append((u, v, edge_attr))\n\t\t\t\t\n\t\t\t\tif G.is_directed():\n\t\t\t\t\t# in-edges\n\t\t\t\t\tfor u, v, edge_attr in G.in_edges_iter(node, data=True):\n\t\t\t\t\t\tG.remove_edge(u, v)\n\t\t\t\t\t\tedges_removed.append((u, v, edge_attr))\n\t\t\t\n\t\t\tspur_path_length, spur_path = nx.single_source_dijkstra(G, spur_node, target, weight=weight)\t\t\t\n\t\t\tif target in spur_path and spur_path[target]:\n\t\t\t\ttotal_path = root_path[:-1] + spur_path[target]\n\t\t\t\ttotal_path_length = get_path_length(G_original, root_path, weight) + spur_path_length[target]\t\t\t\t\n\t\t\t\theappush(B, (total_path_length, next(c), total_path))\n\t\t\t\t\n\t\t\tfor e in edges_removed:\n\t\t\t\tu, v, edge_attr = e\n\t\t\t\tG.add_edge(u, v, edge_attr)\n\t\t\t\t\t \n\t\tif B:\n\t\t\t(l, _, p) = heappop(B)\t\t\n\t\t\tlengths.append(l)\n\t\t\tpaths.append(p)\n\t\telse:\n\t\t\tbreak\n\t\n\treturn (lengths, paths)","def get_next_train_valid(X_shuffled, y_shuffled, itr, k):\n X_valid = X_shuffled[itr*len(X_shuffled)//k:(itr+1)*len(X_shuffled)//k,:]\n Y_valid = y_shuffled[itr*len(y_shuffled)//k:(itr+1)*len(y_shuffled)//k]\n \n X_train = np.delete(X_shuffled, np.s_[itr*len(X_shuffled)//k:(itr+1)*len(X_shuffled)//k:1], 0)\n Y_train = np.delete(y_shuffled, np.s_[itr*len(y_shuffled)//k:(itr+1)*len(y_shuffled)//k:1], 0)\n \n \n return X_train, Y_train, X_valid, Y_valid","def run_knn(k, train_data, train_labels, valid_data):\n\n dist = l2_distance(valid_data.T, train_data.T)\n nearest = np.argsort(dist, axis=1)[:,:k]\n\n train_labels = train_labels.reshape(-1)\n valid_labels = train_labels[nearest]\n\n # note this only works for binary labels\n valid_labels = (np.mean(valid_labels, axis=1) >= 0.5).astype(np.int)\n valid_labels = valid_labels.reshape(-1,1)\n\n return valid_labels","def determine_k(dataset, range_k, n_seed=30):\r\n range_Ks = np.arange(0,range_k,1,dtype=int) #range of delays to study\r\n h_K=np.zeros((10,range_k))\r\n \r\n for i in range(10):\r\n for k, K in enumerate(range_Ks):\r\n traj_matrix= embed.trajectory_matrix(dataset, K=K)\r\n labels= cl.kmeans_knn_partition(traj_matrix, n_seed)\r\n h= op_calc.get_entropy(labels)\r\n h_K[i,k]=h\r\n \r\n return(h_K)","def get_folds(X, y, k):\n # temporarily change the 1/-1 nature of y to 1/0\n _y = (y + 1) / 2\n # partition the examples into postive and negative sets\n positive_indices = np.where(_y)[0]\n negative_indices = np.where(_y - 1)[0]\n assert len(positive_indices) + len(negative_indices) == len(y)\n\n # shuffle both lists\n np.random.shuffle(positive_indices)\n np.random.shuffle(negative_indices)\n\n # create k buckets of indices of (approximately) equal size\n positive_folds_indices = \\\n np.array(np.array_split(positive_indices, k))\n negative_folds_indices = \\\n np.array(np.array_split(negative_indices, k))\n\n train_X, train_y, test_X, test_y = [], [], [], []\n for i in range(k):\n train_folds = np.concatenate((np.arange(0, i), np.arange(i+1, k)))\n pos_train_indices = np.concatenate(positive_folds_indices[train_folds])\n neg_train_indices = np.concatenate(negative_folds_indices[train_folds])\n pos_test_indices = positive_folds_indices[i]\n neg_test_indices = negative_folds_indices[i]\n\n train_X.append(\n np.concatenate((X[pos_train_indices], X[neg_train_indices]))\n )\n train_y.append(\n np.concatenate((y[pos_train_indices], y[neg_train_indices]))\n )\n test_X.append(\n np.concatenate((X[pos_test_indices], X[neg_test_indices]))\n )\n test_y.append(\n np.concatenate((y[pos_test_indices], y[neg_test_indices]))\n )\n\n return zip(train_X, train_y, test_X, test_y)","def compute_superset_with_r(true_bundle, kdt, prototypes, r=1, distance_func=bundles_distances_mam):\n true_bundle = np.array(true_bundle, dtype=np.object)\n dm_true_bundle = distance_func(true_bundle, prototypes)\n I = kdt.query_radius(dm_true_bundle, r)\n superset_idx = []\n for arr in I:\n for e in arr:\n superset_idx.append(e)\n superset_idx = list(set(superset_idx))\n # recompute the gt streamlines idx\n I = kdt.query_radius(dm_true_bundle, 10e-4)\n gt_idx = []\n for arr in I:\n for e in arr:\n gt_idx.append(e)\n gt_idx = list(set(gt_idx))\n print('gt length: %d' % len(gt_idx))\n additional_idx = list(set(superset_idx) - set(gt_idx))\n\n return additional_idx, gt_idx","def getNegativeSamples(target, dataset, K):\n\n indices = [None] * K\n for k in range(K):\n newidx = dataset.sampleTokenIdx()\n while newidx == target:\n newidx = dataset.sampleTokenIdx()\n indices[k] = newidx\n return indices","def subsets(x, k):\n sub_set = set()\n for i in x:\n sub_set = sub_set.union(set(combinations(i, k)))\n return list(sub_set)","def top_k(self, k = 1):\n\t if self.shapley_rank == {}:\n\t \treturn []\n\n\t n = self.nodes\n\t topknodes = []\n\t i = 0\n\t count = 0\n\t while count < k and not i == n:\n\t if self.shapley_rank[i][0] not in topknodes and not self.is_adj(self.shapley_rank[i][0], topknodes):\n\t topknodes.append(self.shapley_rank[i][0])\n\t count += 1\n\t i += 1\n\t i = 0\n\t if not count == k:\n\t while not count == k:\n\t if self.shapley_rank[i][0] not in topknodes:\n\t topknodes.append(self.shapley_rank[i][0])\n\t count += 1\n\t i += 1\n\t return topknodes","def _extend_label_set(self, data, trainer_cls, session):\n # Select which nodes to label next, and predict their labels.\n selected_samples, selected_labels = self._select_samples_to_label(\n data, trainer_cls, session)\n # Replace the labels of the new nodes with the predicted labels.\n if selected_samples.shape[0] > 0:\n data.label_samples(selected_samples, selected_labels)\n return selected_samples","def smote(X, y, N=100, K=5):\n\n seed = 1\n np.random.seed(seed)\n\n # Step 1. Define minority and majority class examples, and minority class features\n ms = int(sum(y))\n min_cls = X[0:ms, :]\n\n # Step 2. If N is less than 100, then only a random percent will be smoted.\n if N < 100:\n np.random.shuffle(min_cls)\n ms = int((N / 100) * ms)\n N = 100\n\n syn_ex = int(N / 100) * ms\n\n # Step 3. Compute the k-NN for each minority class\n clf = neighbors.KNeighborsClassifier()\n clf.fit(X, y)\n\n neighborhoods = []\n for i in range(ms):\n xi = X[i, :].reshape(1, -1)\n neighbours = clf.kneighbors(xi, n_neighbors=K, return_distance=False)[0]\n # Skip itself in the neighborhood\n neighbours = neighbours[1:]\n\n # Find all the minority examples\n neighborhood = []\n for index in neighbours:\n if index <= ms - 1:\n neighborhood.append(index)\n\n neighborhoods.append(neighborhood)\n\n # Step 4. Determine the amount of SMOTE examples to develop per neighbourhood.\n\n num_ex = int(syn_ex / len(neighborhoods))\n\n # Step 5. Generate SMOTE examples\n syn_data = []\n for i in range(ms):\n xi = X[i, :].reshape(1, -1)\n for j in range(num_ex):\n # if the neighbourhood is not empty\n if neighborhoods[i]:\n index = np.random.choice(neighborhoods[i])\n xzi = X[index, :].reshape(1, -1)\n si = xi + (xzi - xi) * np.random.uniform(0, 1)\n syn_data.append(si)\n\n # Build the data matrix\n data = []\n for values in syn_data:\n data.append(values[0])\n\n print(\"{} amount of minority class samples generated\".format(len(data)))\n\n # Step 6. Re-build the data set with synthetic data added\n\n # Concatenate the positive labels with the newly made data\n labels = np.ones([len(data), 1])\n data = np.concatenate([labels, data], axis=1)\n\n # Concatenate with old data\n org_data = np.concatenate([y.reshape(-1, 1), X], axis=1)\n data = np.concatenate([data, org_data])\n\n # Test the new generated data\n test = []\n for values in syn_data:\n a = clf.predict(values)\n test.append(a)\n\n print(\"Using the old classifier, {} out of {} would be classified as minority.\".format(np.sum(test), len(syn_data)))\n\n return data, neighborhoods","def test_sparsity_topk_mask_dykstra(self, l=1e-2):\n n = int(1e3)\n seed = np.random.RandomState(0)\n y = jnp.array(seed.randn(n))\n k = 10\n out_top_k_dykstra = sparse_soft_topk_mask_dykstra(y, k, l=l)\n self.assertAlmostEqual(\n y.argsort()[::-1][:k].sort().sum(),\n out_top_k_dykstra.argsort()[::-1][:k].sort().sum(),\n )\n self.assertAlmostEqual(len(jnp.where(out_top_k_dykstra > 0)[0]), k)","def get_training_data(self):\n\n # this actually never was a set\n # src_set = self.target['src'].values\n # dst_set = self.target['dst'].values\n\n # train_negative = self.get_negative_edges(src_set, dst_set, self.train_ind.shape[0]) # * self.K)\n # test_negative = self.get_negative_edges(src_set, dst_set, self.test_ind.shape[0])\n\n train_positive = self.target.iloc[self.train_edge_ind].values\n test_positive = self.target.iloc[self.test_edge_ind].values\n\n # # print(train_positive.shape, train_negative.shape, test_positive.shape, test_negative.shape)\n # print(f\"Working with {train_positive.shape[0]} positive and {train_negative.shape[0]} negative samples in the train set, {test_positive.shape[0]} and {test_negative.shape[0]} - in test set\")\n\n X_train = train_positive\n X_test = test_positive\n\n y_train = np.ones((self.train_edge_ind.shape[0],))\n y_test = np.ones((self.test_edge_ind.shape[0],))\n\n # X_train = np.vstack([\n # train_positive,\n # train_negative\n # ])\n\n # X_test = np.vstack([\n # test_positive,\n # test_negative\n # ])\n\n # y_train = np.concatenate([np.ones((self.train_ind.shape[0],)), np.zeros((self.train_ind.shape[0]),)]) # self.train_ind.shape[0]) * self.K\n # y_test = np.concatenate([np.ones((self.test_ind.shape[0],)), np.zeros((self.test_ind.shape[0],))])\n\n assert X_train.shape[0] == y_train.shape[0]\n assert X_test.shape[0] == y_test.shape[0]\n\n def shuffle(X, y):\n ind_shuffle = np.arange(0, X.shape[0])\n np.random.shuffle(ind_shuffle)\n return X[ind_shuffle], y[ind_shuffle]\n\n self.X_train, self.y_train = shuffle(X_train, y_train)\n self.X_test, self.y_test = shuffle(X_test, y_test)\n\n print(f\"Splitting into {self.X_train.shape[0]} train and {self.X_test.shape[0]} test samples\")\n\n # return X_train, X_test, y_train, y_test","def get_knn_all(self, query_idxs, k=None):\n k = self.args.k if k is None else k\n assert get_rank() == 0\n in_query_mask = np.isin(self.idxs, query_idxs)\n assert np.sum(in_query_mask) == query_idxs.size\n in_query_X = self.X[in_query_mask]\n _, I = self._build_and_query_knn(self.X, in_query_X, k+1)\n remap = lambda i : self.idxs[i]\n v_remap = np.vectorize(remap)\n I = v_remap(I)\n return I[:,1:]","def evaluate(self, b_size, k_max, df, source_is_head=True):\n\t\t\n\t\tself.k_max = k_max\n\n\t\tcross_Triplets_dataset = crossDomain_Triplets(self.kg_s, self.kg_t, df, source_is_head)\n\n\t\tdataloader = DataLoader(cross_Triplets_dataset, batch_size=b_size, shuffle=True, )\n\n\t\trank_true_entities = torch.empty(size=(df.shape[0],), device=self.device).long()\n\n\t\tfor i, batch in enumerate(dataloader):\n\t\t\t\n\t\t\t#print('batch device: ', batch[0].device)\n\t\t\t#print('batch[0] : ', batch[0], '\\n*****************************************')\n\t\t\t#print('batch[1] : ', batch[1], '\\n******************************************')\n\t\t\t#print('batch[2] : ', batch[2], '\\n*************************************')\n\n\t\t\th_idx, t_idx, r_idx = batch[0].to(self.device), batch[1].to(self.device), batch[2].to(self.device)\n\t\t\t#h_idx, t_idx, r_idx = batch[0], batch[1], batch[2]\n\n\t\t\trk_true_ents = self.lp_helper(h_idx, t_idx, r_idx, source_is_head)\n\n\t\t\trank_true_entities[i*b_size: (i+1)*b_size] = rk_true_ents\n\n\t\tself.rank_true_entities = torch.cat((self.rank_true_entities, rank_true_entities), dim=0)\n\n\t\tself.evaluated = True","def fold(self, k):\n if not (0 < k < 5):\n raise ValueError('almazan cross-evaluation proposes a k=4 k-cross evaluation, {} out of range'.format(k))\n test_idcs = [idx for idx, fold in enumerate(self.fold_idcs) if fold == k]\n train_idcs = list(set(range(len(self))).difference(set(test_idcs)))\n return self.sub_set(train_idcs), self.sub_set(test_idcs)","def kNN(k, testTfidf, trainTfidf, test_file, train_label, weight=True):\r\n test_y = [] \r\n \r\n # iterate through all lines in the test reviews and classify them\r\n for index, line in enumerate(test_file):\r\n # cosine similarity\r\n cos_similarity = linear_kernel(testTfidf[index:index+1], trainTfidf).flatten()\r\n \r\n if weight == True:\r\n # get the indices of nearest neighbors based on k parameter \r\n neighbor_indices = cos_similarity.argsort()[:-k:-1]\r\n # similarities\r\n similarities = cos_similarity[neighbor_indices]\r\n # get a list of labels from the neighbors and sum the list\r\n labels_list = train_label[neighbor_indices].tolist()\r\n\r\n # make cosine similarity value negative or positive based on\r\n # its label and sum the cosine similarities\r\n my_list = [] \r\n for s, l in zip(similarities, labels_list):\r\n if l == -1:\r\n my_list.append(-s)\r\n else:\r\n my_list.append(s) \r\n \r\n label_sum = sum(my_list)\r\n #classify based on label_sum\r\n if label_sum > 0:\r\n test_y.append(\"+1\")\r\n else:\r\n test_y.append(-1)\r\n\r\n else:\r\n # get the indices of nearest neighbors based on k parameter \r\n neighbor_indices = cos_similarity.argsort()[:-k:-1]\r\n # get a list of labels from the neighbors and sum the list\r\n labels_list = train_label[neighbor_indices].tolist()\r\n label_sum = sum(labels_list)\r\n\r\n # classify based on label_sum\r\n if label_sum > 0:\r\n test_y.append(\"+1\")\r\n else:\r\n test_y.append(-1)\r\n \r\n print(index)\r\n \r\n return pd.DataFrame(test_y)","def p_y_x_knn(y, k):\n number_of_classes = 4\n resized = np.delete(y, range(k, y.shape[1]), axis=1)\n summed_with_zero = np.vstack(np.apply_along_axis(np.bincount, axis=1, arr=resized, minlength=number_of_classes + 1))\n summed = np.delete(summed_with_zero, 0, axis=1)\n return summed / k","def training_set(self):\n return self._training_set","def find_k_most_uncertain(nn, sess, X, batch_size=None, k=2,\n pool_size=None):\n\n # Initialization\n results = []\n i = -1\n\n if batch_size is None:\n _batch_size = X.shape[0]\n else:\n _batch_size = batch_size\n\n # Create the pool\n order = np.arange(X.shape[0])\n\n if pool_size is not None:\n order = np.random.choice(X.shape[0], \n min(X.shape[0], pool_size),\n replace=False)\n X_pool = X[order, :].copy()\n else:\n X_pool = X.copy()\n\n # Loop over the batches\n for i in range(X_pool.shape[0]/_batch_size):\n\n feed_dict = {}\n feed_dict[nn.input_tensor] = X_pool[i * _batch_size:\n (i+1) * _batch_size]\n\n # Predict the batch\n pred = sess.run(nn.prediction, feed_dict=feed_dict)\n\n # The most uncertain is the closest to 0.5\n pred = np.abs(0.5 - pred).reshape(-1)\n\n # Sort it by uncertainty\n batch_order = np.argsort(pred)\n pred = pred[batch_order]\n\n # Create associated indices\n to_zip = order[range(i * _batch_size, i * _batch_size + pred.shape[0])]\n to_zip = to_zip[batch_order]\n\n results = kmin(results, zip(pred, to_zip), k)\n\n # Last uncomplete batch\n feed_dict = {}\n feed_dict[nn.input_tensor] = X_pool[(i+1) * _batch_size:]\n\n # Predict the last batch\n pred = sess.run(nn.prediction, feed_dict=feed_dict)\n\n # Sort it by uncertainty\n pred = np.abs(0.5 - pred).reshape(-1)\n batch_order = np.argsort(pred)\n pred = pred[batch_order]\n\n # Create associated indices\n to_zip = order[(i + 1) * _batch_size:]\n to_zip = to_zip[batch_order]\n\n results = kmin(results, zip(pred, to_zip), k)\n\n return [i[1] for i in results]","def train_knn(training_data):\n return knnclassifier(training_data, keys, 3)","def KNN(x_train, x_test, y_train, k=3):\n knn = KNeighborsClassifier(n_neighbors=k)\n knn.fit(x_train, y_train)\n y_pred = knn.predict(x_test)\n return y_pred","def forward(self, x, target, N_k):\n elbo = 0\n phi = self.base(x)\n def loglik(sample): #currently hardcoded for binary classification\n return -torch.log(1 + torch.exp(-target * sample))\n mu = self.mu\n cov = self.L @ self.L.T\n means = phi @ mu\n variances = torch.diagonal(phi @ cov @ phi.T, 0)\n elbo += (self.quadr(loglik, means, variances)).sum()\n elbo /= x.shape[0] #mean\n \n kls = 0\n kls -= kl_divergence(self.w_distr, self.w_prior)\n\n for i in range(len(self.prev_tasks_distr)):\n phi_i = self.base(self.prev_tasks_tensors[i])\n cov_i = phi_i @ phi_i.T\n p_u = MultivariateNormal(torch.zeros(cov_i.shape[0]).to(self.device),\n covariance_matrix=cov_i)#cov_i * self.sigma_prior)\n kls -= kl_divergence(self.prev_tasks_distr[i], p_u)\n elbo += kls / N_k\n\n return -elbo","def __topK_train(self, k):\n f = open(\"train_info.csv\")\n reader = csv.DictReader(f, delimiter=\",\")\n artists = []\n for line in reader:\n artists.append(line['artist'])\n freqs = defaultdict(int)\n for artist in artists:\n freqs[artist] += 1\n\n sorted_freqs = sorted(freqs.items(), key=operator.itemgetter(1))\n final_list = list(reversed(sorted_freqs))\n res = []\n for pair in final_list[:k]:\n res.append(pair[0])\n if not os.path.exists(os.path.join(TEST_DIR, pair[0])):\n os.mkdir(os.path.join(TEST_DIR, pair[0]))\n return res","def knn(x, x_train, y_train, k=1):\n y_pred = np.zeros(len(x), dtype=np.int8)\n for i, sample in enumerate(x):\n # Calculate distance from this sample to every training sample\n dist = [np.linalg.norm(sample-train) for train in x_train]\n\n # Find the k nearest training samples\n k_nearest_labels = []\n for j in range(k):\n closest = np.argmin(dist)\n k_nearest_labels.append(y_train[closest])\n dist.pop(closest)\n\n # This sample's label the one the appears most frequently in\n # the k nearest, or the first nearest if all appear equally\n labels, counts = np.unique(k_nearest_labels, return_counts=True)\n y_pred[i] = labels[np.argmax(counts)]\n return y_pred","def _decimate_k(self):\n # Setting the seed here once so as to get the same set\n # independent of filters.\n random.seed(1)\n k_sorted = sorted(self.kvec.keys())\n k_selected = []\n for knorm in k_sorted:\n nkmax = len(self.kvec[knorm])\n k_selected.append(random.sample(list(range(nkmax)), min(self.nk, nkmax)))\n return k_sorted, k_selected","def get_k_shortest_paths(env: RailEnv,\n source_position: Tuple[int, int],\n source_direction: int,\n target_position=Tuple[int, int],\n k: int = 1, debug=False) -> List[Tuple[Waypoint]]:\n\n # P: set of shortest paths from s to t\n # P =empty,\n shortest_paths: List[Tuple[Waypoint]] = []\n\n # countu: number of shortest paths found to node u\n # countu = 0, for all u in V\n count = {(r, c, d): 0 for r in range(env.height) for c in range(env.width) for d in range(4)}\n\n # B is a heap data structure containing paths\n # N.B. use OrderedSet to make result deterministic!\n heap: OrderedSet[Tuple[Waypoint]] = OrderedSet()\n\n # insert path Ps = {s} into B with cost 0\n heap.add((Waypoint(source_position, source_direction),))\n\n # while B is not empty and countt < K:\n while len(heap) > 0 and len(shortest_paths) < k:\n if debug:\n print(\"iteration heap={}, shortest_paths={}\".format(heap, shortest_paths))\n # – let Pu be the shortest cost path in B with cost C\n cost = np.inf\n pu = None\n for path in heap:\n if len(path) < cost:\n pu = path\n cost = len(path)\n u: Waypoint = pu[-1]\n if debug:\n print(\" looking at pu={}\".format(pu))\n\n # – B = B − {Pu }\n heap.remove(pu)\n # – countu = countu + 1\n\n urcd = (*u.position, u.direction)\n count[urcd] += 1\n\n # – if u = t then P = P U {Pu}\n if u.position == target_position:\n if debug:\n print(\" found of length {} {}\".format(len(pu), pu))\n shortest_paths.append(pu)\n\n # – if countu ≤ K then\n # CAVEAT: do not allow for loopy paths\n elif count[urcd] <= k:\n possible_transitions = env.rail.get_transitions(*urcd)\n if debug:\n print(\" looking at neighbors of u={}, transitions are {}\".format(u, possible_transitions))\n # for each vertex v adjacent to u:\n for new_direction in range(4):\n if debug:\n print(\" looking at new_direction={}\".format(new_direction))\n if possible_transitions[new_direction]:\n new_position = get_new_position(u.position, new_direction)\n if debug:\n print(\" looking at neighbor v={}\".format((*new_position, new_direction)))\n\n v = Waypoint(position=new_position, direction=new_direction)\n # CAVEAT: do not allow for loopy paths\n if v in pu:\n continue\n\n # – let Pv be a new path with cost C + w(u, v) formed by concatenating edge (u, v) to path Pu\n pv = pu + (v,)\n # – insert Pv into B\n heap.add(pv)\n\n # return P\n return shortest_paths","def knn_prediction(X, y, x, k):\n ## homework:start\n result = \n ## homework:end\n return result","def find_k(self, kpt):\n kpt = np.array(kpt)\n ns = np.linalg.norm(self.kpts - kpt[None, :], axis=1)\n ik = np.argmin(ns)\n return ik","def __init__(self, top_k):\n\n super(EpochTopkAccuracy, self).__init__(\n reset_at=\"epoch\", emit_at=\"epoch\", mode=\"train\", top_k=top_k\n )\n self.top_k = top_k","def get_concentrated_mask(class_weights, topk):\n\n mask_topk = torch.zeros(class_weights.shape).to(device)\n\n # TODO: can we cache this somehow?\n seq_tensor = torch.LongTensor([i for i in range(class_weights.shape[0])])\n\n if topk > 0:\n _, topk_domain = torch.topk(class_weights, topk)\n\n for i in range(topk):\n mask_topk[seq_tensor, topk_domain[:, i]] = 1\n else:\n topk_domain = None\n\n return mask_topk, topk_domain, seq_tensor","def __call__(self, graph: Data, n_min: int, nodes_to_keep: List[int] = None, exhaustive: bool = False):\n nodes_to_keep = nodes_to_keep if nodes_to_keep is not None else []\n mcts = self._get_mcts(graph, n_min, nodes_to_keep, exhaustive)\n\n for iteration in range(self.m):\n mcts.search_one_iteration()\n\n explanation = mcts.best_leaf_node()\n\n return explanation.node_set, mcts","def get_coreset(self, pool_features, labelled_features):\n new_batch = []\n self.update_distances(pool_features, labelled_features, reset_dist=True)\n for _ in range(self.num_query):\n # choose furthest point\n ind = np.argmax(self.min_distances)\n # New examples should not be in already selected since those points\n # should have min_distance of zero to a cluster center.\n assert ind not in new_batch\n # update distances with this point\n self.update_distances(pool_features, pool_features[ind, :].reshape(1,-1), reset_dist=False)\n new_batch.append(ind)\n print(f\"Maximum distance from cluster centers is {max(self.min_distances)}\")\n\n return new_batch","def feature_select(T_train, y_train, k=3):\r\n from sklearn.metrics import roc_auc_score\r\n\r\n T_principle_index = list() # The index of Principle features\r\n AUC = list()\r\n\r\n # Forward Stage-wise algorithm\r\n for j in range(k):\r\n auc = list()\r\n Dist = list()\r\n\r\n for i in range(len(T_train.T)):\r\n if i in T_principle_index:\r\n auc.append(0)\r\n Dist.append(0)\r\n continue\r\n T_temp_index = T_principle_index + [i]\r\n T0 = T_train.T[T_temp_index].T # transient statistics\r\n C0 = gen_center(T0, y_train)\r\n dist = util.distance(T0, C0) # distance\r\n Dist.append(dist)\r\n auc.append(roc_auc_score(y_train, -dist))\r\n\r\n m = np.argmax(auc)\r\n T_principle_index = T_principle_index + [m]\r\n AUC.append(auc[m])\r\n\r\n dist = Dist[m]\r\n T_principle_index = np.array(T_principle_index)\r\n T_principle = T_train.T[T_principle_index].T\r\n\r\n return T_principle, T_principle_index, np.array(dist), AUC","def set_subset(self):\r\n if self._random_subset:\r\n perm = torch.randperm(len(self._indices))\r\n self._subset = self._indices[perm][:self._subset_size]\r\n else:\r\n self._subset = torch.Tensor(self._indices[:self._subset_size])","def shortest_tips(neuron):\n (branch_index,) = np.where(neuron.branch_order[neuron.n_soma:] == 2)\n (endpoint_index,) = np.where(neuron.branch_order[neuron.n_soma:] == 0)\n selected_index = np.union1d(endpoint_index + 1,\n branch_index + 1)\n selected_index = np.append(0, selected_index)","def _get_targets_single(self, mask_preds: Tensor,\n gt_instances: InstanceData,\n positive_info: InstanceData):\n gt_bboxes = gt_instances.bboxes\n device = gt_bboxes.device\n gt_masks = gt_instances.masks.to_tensor(\n dtype=torch.bool, device=device).float()\n\n # process with mask targets\n pos_assigned_gt_inds = positive_info.get('pos_assigned_gt_inds')\n scores = positive_info.get('scores')\n centernesses = positive_info.get('centernesses')\n num_pos = pos_assigned_gt_inds.size(0)\n\n if gt_masks.size(0) == 0 or num_pos == 0:\n return mask_preds, None, 0\n # Since we're producing (near) full image masks,\n # it'd take too much vram to backprop on every single mask.\n # Thus we select only a subset.\n if (self.max_masks_to_train != -1) and \\\n (num_pos > self.max_masks_to_train):\n perm = torch.randperm(num_pos)\n select = perm[:self.max_masks_to_train]\n mask_preds = mask_preds[select]\n pos_assigned_gt_inds = pos_assigned_gt_inds[select]\n num_pos = self.max_masks_to_train\n elif self.topk_masks_per_img != -1:\n unique_gt_inds = pos_assigned_gt_inds.unique()\n num_inst_per_gt = max(\n int(self.topk_masks_per_img / len(unique_gt_inds)), 1)\n\n keep_mask_preds = []\n keep_pos_assigned_gt_inds = []\n for gt_ind in unique_gt_inds:\n per_inst_pos_inds = (pos_assigned_gt_inds == gt_ind)\n mask_preds_per_inst = mask_preds[per_inst_pos_inds]\n gt_inds_per_inst = pos_assigned_gt_inds[per_inst_pos_inds]\n if sum(per_inst_pos_inds) > num_inst_per_gt:\n per_inst_scores = scores[per_inst_pos_inds].sigmoid().max(\n dim=1)[0]\n per_inst_centerness = centernesses[\n per_inst_pos_inds].sigmoid().reshape(-1, )\n select = (per_inst_scores * per_inst_centerness).topk(\n k=num_inst_per_gt, dim=0)[1]\n mask_preds_per_inst = mask_preds_per_inst[select]\n gt_inds_per_inst = gt_inds_per_inst[select]\n keep_mask_preds.append(mask_preds_per_inst)\n keep_pos_assigned_gt_inds.append(gt_inds_per_inst)\n mask_preds = torch.cat(keep_mask_preds)\n pos_assigned_gt_inds = torch.cat(keep_pos_assigned_gt_inds)\n num_pos = pos_assigned_gt_inds.size(0)\n\n # Follow the origin implement\n start = int(self.mask_out_stride // 2)\n gt_masks = gt_masks[:, start::self.mask_out_stride,\n start::self.mask_out_stride]\n gt_masks = gt_masks.gt(0.5).float()\n pos_mask_targets = gt_masks[pos_assigned_gt_inds]\n\n return (mask_preds, pos_mask_targets, num_pos)","def generate_mnist_datasets(\n datapoints_per_task,\n K_list,\n cir_inner_loop_list, \n test_task_idx, \n val_task_idx,\n n_finetune_sets):\n\n # arbitrarily chosen, class-imbalance rate in outer and inner training loops\n cir_outer_loop = 0.5\n cir_inner_loop = 0.5\n # class-imbalance rate in the test sets of the test and validation tasks\n cir_test = 0.5\n # arbitrarily chosen, percentage of data that will be used in the inner training loop\n percent_data_inner_loop = 0.5\n\n percent_data_finetune_val = 0.8\n\n n_test_set = 4000\n\n test_task_idx, val_task_idx = test_task_idx, val_task_idx\n\n finetune_sets_per_K_cir = {}\n test_task_test_set, val_task = {}, {}\n \n\n train_task_list_inner, train_task_list_outer = [], []\n\n train_tasks_idxs = [i for i in range(0,10) if i not in [val_task_idx, test_task_idx]]\n\n base_path = '/home/USER/Documents'\n if (not (os.path.exists(base_path))):\n base_path = '/home/ubuntu/Projects'\n train_images, train_labels = loadlocal_mnist(\n images_path= base_path + '/MAML/raw_data/MNIST_data/train-images-idx3-ubyte', \n labels_path= base_path + '/MAML/raw_data/MNIST_data/train-labels-idx1-ubyte')\n\n test_images, test_labels = loadlocal_mnist(\n images_path= base_path + '/MAML/raw_data/MNIST_data/t10k-images-idx3-ubyte', \n labels_path= base_path + '/MAML/raw_data/MNIST_data/t10k-labels-idx1-ubyte')\n\n\n train_images, test_images = train_images.reshape((-1,28,28))/255.0, test_images.reshape((-1,28,28))/255.0\n images = np.concatenate((train_images, test_images))\n labels = np.concatenate((train_labels, test_labels))\n\n test_task_normal_indexes, val_task_normal_indexes = list(np.nonzero(labels == test_task_idx)[0]), list(np.nonzero(train_labels == val_task_idx)[0])\n test_task_X_normal, val_task_X_normal = images[test_task_normal_indexes],train_images[val_task_normal_indexes]\n test_task_Y_normal, val_task_Y_normal = np.zeros_like(labels[test_task_normal_indexes]), np.zeros_like(train_labels[val_task_normal_indexes])\n\n\n # val and test task have anomalies (samples of other numbers) that are not used for training\n # besides the two sets of anomalies (one for val task and one for test task are disjoint)\n test_task_anomalous_indexes = list(np.nonzero(test_labels[:5000] != test_task_idx)[0])\n val_task_anomalous_indexes= [index for index, element in enumerate(list(test_labels[5000:])) if element not in [val_task_idx, test_task_idx]]\n\n\n test_task_X_anomalous, val_task_X_anomalous = test_images[:5000][test_task_anomalous_indexes],test_images[5000:][val_task_anomalous_indexes]\n test_task_Y_anomalous, val_task_Y_anomalous = np.ones_like(test_labels[:5000][test_task_anomalous_indexes]), np.ones_like(test_labels[5000:][val_task_anomalous_indexes])\n\n test_task_X, val_task_X = np.concatenate((test_task_X_normal, test_task_X_anomalous)), np.concatenate((val_task_X_normal, val_task_X_anomalous))\n test_task_Y, val_task_Y = np.expand_dims(np.concatenate((test_task_Y_normal, test_task_Y_anomalous)),-1), np.expand_dims(np.concatenate((val_task_Y_normal, val_task_Y_anomalous)),-1)\n\n\n train_tasks_X_list, train_tasks_Y_list = [], []\n for task_idx in train_tasks_idxs:\n train_task_normal_indexes = list(np.nonzero(train_labels == task_idx)[0]) \n train_task_anomalous_indexes = [index for index, element in enumerate(list(train_labels)) if element not in [task_idx, val_task_idx, test_task_idx]]\n assert(len(np.nonzero(train_labels[train_task_anomalous_indexes] == val_task_idx)[0]) == 0)\n assert(len(np.nonzero(train_labels[train_task_anomalous_indexes] == test_task_idx)[0]) == 0)\n train_task_X_normal, train_task_X_anomalous = train_images[train_task_normal_indexes], train_images[train_task_anomalous_indexes]\n train_task_Y_normal, train_task_Y_anomalous = np.zeros_like(train_labels[train_task_normal_indexes]), np.ones_like(train_labels[train_task_anomalous_indexes])\n train_task_X, train_task_Y = np.concatenate((train_task_X_normal, train_task_X_anomalous)), np.concatenate((train_task_Y_normal, train_task_Y_anomalous))\n train_tasks_X_list.append(train_task_X)\n train_tasks_Y_list.append(np.expand_dims(train_task_Y,-1))\n\n\n\n # building test task sets of data\n normal_indexes, anomaly_indexes = list(np.nonzero(test_task_Y == 0)[0]), list(np.nonzero(test_task_Y == 1)[0])\n n_test_set_normal = int(n_test_set*cir_test)\n test_set_normal_indexes = random.sample(normal_indexes, n_test_set_normal)\n test_set_anomaly_indexes = random.sample(anomaly_indexes, n_test_set - n_test_set_normal)\n test_set_indexes = []\n test_set_indexes += test_set_normal_indexes\n test_set_indexes += test_set_anomaly_indexes\n\n test_task_test_set['test_X'], test_task_test_set['test_Y'] = test_task_X[test_set_indexes], test_task_Y[test_set_indexes]\n\n\n #shuffle\n s_test = np.arange(test_task_test_set['test_X'].shape[0])\n np.random.shuffle(s_test)\n test_task_test_set['test_X'], test_task_test_set['test_Y'] = test_task_test_set['test_X'][s_test], test_task_test_set['test_Y'][s_test]\n\n rest_normal_indexes = [index for index in normal_indexes if index not in test_set_normal_indexes]\n rest_anomaly_indexes = [index for index in anomaly_indexes if index not in test_set_anomaly_indexes]\n\n\n for K in K_list:\n finetune_sets_per_cir = {}\n for cir in cir_inner_loop_list:\n\n rest_normal_indexes = [index for index in normal_indexes if index not in test_set_normal_indexes]\n rest_anomaly_indexes = [index for index in anomaly_indexes if index not in test_set_anomaly_indexes]\n \n finetune_sets_list = []\n\n disjoint = False\n if(cir*K*n_finetune_sets<len(rest_normal_indexes)):\n disjoint = True\n\n n_finetune_normal = int(K*cir)\n n_finetune_anomaly = K - n_finetune_normal\n for i in range(n_finetune_sets):\n # if enough for disjoint do that\n # else sample randomly\n # store in a dict with keys cir_K\n finetune_normal_indexes = random.sample(rest_normal_indexes, n_finetune_normal)\n finetune_anomaly_indexes = random.sample(rest_anomaly_indexes, n_finetune_anomaly)\n finetune_indexes = []\n finetune_indexes += finetune_normal_indexes\n finetune_indexes += finetune_anomaly_indexes\n finetune_set = {}\n finetune_set['finetune_X'], finetune_set['finetune_Y'] = test_task_X[finetune_indexes], test_task_Y[finetune_indexes]\n\n #shuffle\n s_finetune = np.arange(finetune_set['finetune_X'].shape[0])\n np.random.shuffle(s_finetune)\n finetune_set['finetune_X'], finetune_set['finetune_Y'] = finetune_set['finetune_X'][s_finetune], finetune_set['finetune_Y'][s_finetune]\n\n finetune_sets_list.append(finetune_set)\n \n if(disjoint):\n rest_normal_indexes = [index for index in rest_normal_indexes if index not in finetune_normal_indexes]\n rest_anomaly_indexes = [index for index in rest_anomaly_indexes if index not in finetune_anomaly_indexes]\n\n finetune_sets_per_cir[str(cir)] = finetune_sets_list\n\n finetune_sets_per_K_cir[str(K)] = finetune_sets_per_cir\n\n\n #building val task sets of data\n normal_indexes, anomaly_indexes = list(np.nonzero(val_task_Y == 0)[0]), list(np.nonzero(val_task_Y == 1)[0])\n n_val_finetune = int(percent_data_finetune_val*datapoints_per_task)\n n_val_test_set = datapoints_per_task - n_val_finetune\n n_val_test_set_normal = int(n_val_test_set*cir_test)\n val_test_set_normal_indexes = random.sample(normal_indexes, n_val_test_set_normal)\n\n\n val_test_set_anomaly_indexes = random.sample(anomaly_indexes, n_val_test_set - n_val_test_set_normal)\n val_test_set_indexes = []\n val_test_set_indexes += val_test_set_normal_indexes\n val_test_set_indexes += val_test_set_anomaly_indexes\n val_task['test_X'], val_task['test_Y'] = val_task_X[val_test_set_indexes], val_task_Y[val_test_set_indexes]\n\n\n rest_normal_indexes = [index for index in normal_indexes if index not in val_test_set_normal_indexes]\n rest_anomaly_indexes = [index for index in anomaly_indexes if index not in val_test_set_anomaly_indexes]\n\n n_val_finetune_normal = int(n_val_finetune*cir_inner_loop)\n val_finetune_normal_indexes = random.sample(rest_normal_indexes, n_val_finetune_normal)\n val_finetune_anomaly_indexes = random.sample(rest_anomaly_indexes, n_val_finetune - n_val_finetune_normal)\n val_finetune_indexes = []\n val_finetune_indexes += val_finetune_normal_indexes\n val_finetune_indexes += val_finetune_anomaly_indexes\n\n val_task['finetune_X'], val_task['finetune_Y'] = val_task_X[val_finetune_indexes], val_task_Y[val_finetune_indexes]\n\n #shuffle\n s_val_finetune = np.arange(val_task['finetune_X'].shape[0])\n s_val_test = np.arange(val_task['test_X'].shape[0])\n np.random.shuffle(s_val_finetune)\n np.random.shuffle(s_val_test)\n\n val_task['finetune_X'], val_task['finetune_Y'] = val_task['finetune_X'][s_val_finetune], val_task['finetune_Y'][s_val_finetune]\n val_task['test_X'], val_task['test_Y'] = val_task['test_X'][s_val_test], val_task['test_Y'][s_val_test]\n\n\n\n # building sets of data of the training tasks\n for task_X, task_Y in zip(train_tasks_X_list, train_tasks_Y_list):\n normal_indexes, anomaly_indexes = list(np.nonzero(task_Y == 0)[0]), list(np.nonzero(task_Y == 1)[0])\n\n n_inner_loop = int(percent_data_inner_loop*datapoints_per_task)\n n_inner_loop_normal = int(n_inner_loop*cir_inner_loop)\n n_outer_loop = datapoints_per_task - n_inner_loop\n n_outer_loop_normal = int(n_outer_loop*cir_outer_loop)\n \n inner_loop_normal_indexes = random.sample(normal_indexes, n_inner_loop_normal)\n inner_loop_anomaly_indexes = random.sample(anomaly_indexes, n_inner_loop - n_inner_loop_normal)\n inner_loop_indexes = []\n inner_loop_indexes += inner_loop_normal_indexes\n inner_loop_indexes += inner_loop_anomaly_indexes\n\n train_task_inner_X, train_task_inner_Y = task_X[inner_loop_indexes], task_Y[inner_loop_indexes]\n\n rest_normal_indexes = [index for index in normal_indexes if index not in inner_loop_normal_indexes]\n rest_anomaly_indexes = [index for index in anomaly_indexes if index not in inner_loop_anomaly_indexes]\n\n \n outer_loop_normal_indexes = random.sample(rest_normal_indexes, n_outer_loop_normal)\n outer_loop_anomaly_indexes = random.sample(rest_anomaly_indexes, n_outer_loop - n_outer_loop_normal)\n outer_loop_indexes = []\n outer_loop_indexes += outer_loop_normal_indexes\n outer_loop_indexes += outer_loop_anomaly_indexes\n\n train_task_outer_X, train_task_outer_Y = task_X[outer_loop_indexes], task_Y[outer_loop_indexes]\n\n\n s_inner = np.arange(train_task_inner_X.shape[0])\n s_outer = np.arange(train_task_outer_X.shape[0])\n np.random.shuffle(s_inner)\n np.random.shuffle(s_outer)\n train_task_list_inner.append([train_task_inner_X[s_inner],train_task_inner_Y[s_inner]])\n train_task_list_outer.append([train_task_outer_X[s_outer],train_task_outer_Y[s_outer]])\n\n\n\n train_tasks_inner_X = np.stack([train_task_list_inner[i][0]\n for i in range(len(train_task_list_inner))], 0)\n train_tasks_inner_Y = np.stack([train_task_list_inner[i][1]\n for i in range(len(train_task_list_inner))], 0)\n train_tasks_outer_X = np.stack([train_task_list_outer[i][0]\n for i in range(len(train_task_list_outer))], 0)\n train_tasks_outer_Y = np.stack([train_task_list_outer[i][1]\n for i in range(len(train_task_list_outer))], 0)\n\n \n train_tasks = {'X_train_inner': train_tasks_inner_X,\n 'Y_train_inner': train_tasks_inner_Y,\n 'X_train_outer': train_tasks_outer_X,\n 'Y_train_outer': train_tasks_outer_Y\n }\n\n\n return train_tasks, val_task, test_task_test_set, finetune_sets_per_K_cir"],"string":"[\n \"def compute_superset(true_tract, kdt, prototypes, k=1000, distance_func=bundles_distances_mam):\\n true_tract = np.array(true_tract, dtype=np.object)\\n dm_true_tract = distance_func(true_tract, prototypes)\\n D, I = kdt.query(dm_true_tract, k=k)\\n superset_idx = np.unique(I.flat)\\n return superset_idx\",\n \"def __init__(self, training_set, k=5):\\n super(Knn, self).__init__(training_set)\\n self.k = k\",\n \"def __init__(self, trainset, k=3):\\n self._trainset = trainset\\n self.k = k\",\n \"def knn(p, k, x, t):\\r\\n\\r\\n # Number of instances in data set\\r\\n N = x.shape[0]\\r\\n\\r\\n Euclidean_Distance = numpy.square(x - p) #Euclidean distance\\r\\n dis = numpy.sum(Euclidean_Distance, axis=1) #sum of the euclidean distance\\r\\n inds = numpy.argsort(dis)[:k] #sort the indices of the distance array\\r\\n tgt_cat = Counter([t[i] for i in inds]) #count the times of equivalent target labels\\r\\n top_class = max(tgt_cat, key= tgt_cat.get) #top class among the k nearest points\\r\\n\\r\\n\\r\\n #top_class = 0\\r\\n\\r\\n return top_class\",\n \"def k_nearest_neighbors(x_test, df_training, k):\\n\\n return np.argpartition(distance_to_each_training_point(x_test,\\n df_training), k-1)[:,0:k]\",\n \"def kNN_train(self, x_train, y_train, x_test, k = 5, processing = None, distMethod = \\\"Manhattan\\\"):\\n y_test = list()\\n\\n if processing == \\\"Scalar\\\":\\n # print(\\\"Preprocessing = Scalar\\\")\\n stdScalar = preprocessing.StandardScaler().fit(x_train)\\n x_train = stdScalar.transform(x_train)\\n x_test = stdScalar.transform(x_test)\\n\\n elif processing == \\\"MinMax\\\":\\n\\n # print(\\\"Preprocessing = MinMax\\\")\\n mmScalar = preprocessing.MinMaxScaler()\\n x_train = mmScalar.fit_transform(x_train)\\n x_test = mmScalar.fit_transform(x_test)\\n\\n elif processing == \\\"None\\\":\\n self.true = True\\n # print(\\\"No Preprocessing\\\")\\n\\n else:\\n print(\\\"wrong processing\\\")\\n exit()\\n\\n for i in range(0, len(x_test)):\\n y_test_temp = list()\\n zeroCount = 0\\n oneCount = 0\\n\\n # find distance of a instance in test test to all instances in training set\\n for j in range(0, len(x_train)):\\n if distMethod == \\\"Manhattan\\\":\\n y_test_temp.append(self.manhattan(x_train[j], x_test[i]))\\n elif distMethod == \\\"Euclidean\\\":\\n y_test_temp.append(self.euclidean(x_train[j], x_test[i]))\\n else:\\n print \\\"something wrong with distance calculation\\\"\\n exit()\\n\\n # take indices of k nearest points\\n # print y_test_temp\\n temp = np.asarray(y_test_temp).argsort()[:k]\\n # check class of each of k nearest points\\n for tmp in temp:\\n if y_train[tmp] == 0:\\n zeroCount += 1\\n elif y_train[tmp] == 1:\\n oneCount += 1\\n else:\\n print(\\\"something wrong in counting\\\")\\n\\n # classify\\n if zeroCount >= oneCount:\\n y_test.append(int(0))\\n elif oneCount > zeroCount:\\n y_test.append(int(1))\\n else:\\n print(\\\"somethign wrong\\\")\\n\\n # print y_test\\n return y_test\",\n \"def __call__(self, output, target):\\n maxk = max(self.topk)\\n batch_size = target.size(0)\\n\\n _, pred = output.topk(maxk, 1, True, True)\\n pred = pred.t()\\n correct = pred.eq(target.view(1, -1).expand_as(pred))\\n\\n res = []\\n for k in self.topk:\\n correct_k = correct[:k].view(-1).float().sum(0)\\n res.append(correct_k.mul_(100.0 / batch_size))\\n return res\",\n \"def choose_k_away(k, node, precomputed=True):\\n if precomputed:\\n sps = np.load('./short_paths/' + str(node) + '.npy')\\n else:\\n sps = shortest_path(node)\\n nodes = []\\n for index, x in enumerate(sps):\\n if x == k and index in poss_targets:\\n nodes.append(index)\\n return nodes\",\n \"def knnSame(k, Xtrain, Ytrain):\\n d = euclidean_distances(Xtrain, squared=True)\\n np.fill_diagonal(d, np.inf)\\n nnc = Ytrain[np.argsort(d)[..., :k].flatten()].reshape(Xtrain.shape[0], k)\\n pred = [max(nnc[i], key=Counter(nnc[i]).get) for i in range(nnc.shape[0])]\\n return np.array(pred)\",\n \"def correct(output, target, topk=(1,)):\\n maxk = max(topk)\\n _, pred = output.topk(maxk, 1, True, True)\\n pred = pred.t()\\n correct = pred.eq(target.view(1, -1).expand_as(pred))\\n res = []\\n for k in topk:\\n correct_k = correct[:k].view(-1).sum(0, keepdim=True)\\n res.append(correct_k)\\n return res\",\n \"def correct(output, target, topk=(1,)):\\n maxk = max(topk)\\n _, pred = output.topk(maxk, 1, True, True)\\n pred = pred.t()\\n correct = pred.eq(target.view(1, -1).expand_as(pred))\\n res = []\\n for k in topk:\\n correct_k = correct[:k].view(-1).sum(0, keepdim=True)\\n res.append(correct_k)\\n return res\",\n \"def subsample_training_set(self, k, seed=None):\\n if seed is not None:\\n rng_state = th.random.get_rng_state()\\n with th.random.fork_rng():\\n th.manual_seed(seed)\\n self._subsample_training_set(k)\\n if any(th.random.get_rng_state() != rng_state):\\n raise ValueError(\\\"Bad RNG state\\\")\\n else:\\n self._subsample_training_set(k)\",\n \"def get_target_root_partitions_subset(self) -> PartitionsSubset:\\n partitioned_asset_keys = {\\n asset_key\\n for asset_key in self.target_subset.asset_keys\\n if self.target_subset.asset_graph.get_partitions_def(asset_key) is not None\\n }\\n\\n root_partitioned_asset_keys = (\\n AssetSelection.keys(*partitioned_asset_keys)\\n .sources()\\n .resolve(self.target_subset.asset_graph)\\n )\\n\\n # Return the targeted partitions for the root partitioned asset keys\\n return self.target_subset.get_partitions_subset(next(iter(root_partitioned_asset_keys)))\",\n \"def check_subset(P1,P2,k=-1):\",\n \"def evalute_subset(X_train, X_test, y_train, y_test):\\r\\n clf = KNeighborsClassifier(n_neighbors=3)\\r\\n clf.fit(X_train, y_train) \\r\\n y_pred = clf.predict(X_test)\\r\\n return accuracy_score(y_test, y_pred)\",\n \"def build_full_trainset(self):\\n\\n return self.construct_trainset(self.raw_ratings)\",\n \"def __init__(self, k: int, training_set: np.ndarray):\\n self._k = k\\n self._training_set = training_set\",\n \"def knn0(pnts, p, k):\\r\\n p = np.asarray(p)\\r\\n pnts = np.asarray(pnts)\\r\\n diff = pnts - p[np.newaxis, :]\\r\\n d = np.einsum('ij,ij->i', diff, diff)\\r\\n idx = np.argsort(d)[:k]\\r\\n# s = [i.tolist() for i in pnts[idx]]\\r\\n return pnts[idx].tolist()\",\n \"def knn(k, Xtrain, Ytrain, Xtest):\\n d = euclidean_distances(Xtest, Xtrain, squared=True)\\n nnc = Ytrain[np.argsort(d)[..., :k].flatten()].reshape(Xtest.shape[0], k)\\n pred = [max(nnc[i], key=Counter(nnc[i]).get) for i in range(nnc.shape[0])]\\n return np.array(pred)\",\n \"def fit_full(self, K):\\r\\n pass\",\n \"def KS_algorithm(dataset_x, dataset_y, train_size = 0.7, return_dataset = True):\\n #Decide number of total samples\\n n = dataset_x.shape[0]\\n \\n #Turn into train_size to float\\n try: \\n train_size = float(train_size)\\n except Expection as e:\\n print(e)\\n \\n #Jusge if the train_size is by ratio or quantity\\n if 0 < train_size < 1: \\n num_of_samples = round(n*train_size)\\n elif 1 < train_size < 2: \\n return \\\"Train size must be between 1 to 0 or 2 to number of total samples\\\" \\n elif train_size == n or train_size == 1:\\n return [x for x in range(n)]\\n elif 2 <= train_size < n:\\n num_of_samples = round(train_size)\\n elif train_size > n:\\n return \\\"Num_of_samples must be less than or equal to original number of samples\\\"\\n \\n #Create original sample index\\n samples = [x for x in range(n)]\\n \\n #Create distance matrix\\n print('Building distance matrix')\\n dist_matrix = create_dist_matrix(dataset_x)\\n\\n #Pick first two furthest away data points\\n print('Picking two furthest away samples')\\n max_array = np.argmax(dist_matrix == np.max(dist_matrix), axis = 1)\\n ind1 = max_array[max_array != 0][0]\\n ind2 = max_array[ind1]\\n\\n #Initiate chosen sample list, append first two samples and remove from remaining sample list\\n chosen_samples = [ind1, ind2]\\n samples.remove(ind1)\\n samples.remove(ind2) \\n remaining_samples = num_of_samples - 2\\n\\n #Continue picking samples if not yet reaching target number of samples\\n while remaining_samples > 0:\\n sub_array = np.min(dist_matrix[:,chosen_samples], axis=1)\\n chosen = np.argmax(sub_array)\\n chosen_samples.append(chosen)\\n samples.remove(chosen)\\n remaining_samples -= 1\\n \\n #See if this function should return train and test set or simply index of chosen samples\\n if return_dataset == True:\\n not_chosen_samples = [x for x in range(n) if x not in chosen_samples]\\n return dataset_x.iloc[chosen_samples,:], dataset_x.iloc[not_chosen_samples,:], dataset_y.iloc[chosen_samples,:], dataset_y.iloc[not_chosen_samples,:] \\n else:\\n return chosen_samples\",\n \"def findknn(xTr,xTe,k):\\n\\n # YOUR CODE HERE\\n if k > len(xTr):\\n k = len(xTr)\\n \\n D=l2distance(xTe, xTr)\\n (m,n) = D.shape\\n \\n indices = []\\n dists = []\\n for i in range(m):\\n smallest_indices = np.argsort(D[i])\\n ind = smallest_indices[:k]\\n dis = D[i,smallest_indices[:k]]\\n indices.append(ind)\\n dists.append(dis)\\n \\n indices = np.transpose(np.array(indices))\\n dists = np.transpose(np.array(dists))\\n return indices, dists\",\n \"def get_neighbors(training_set, \\r\\n labels, \\r\\n test_instance, \\r\\n k, \\r\\n distance=distance):\\r\\n distances = []\\r\\n for index in range(len(training_set)):\\r\\n dist = distance(test_instance, training_set[index])\\r\\n distances.append((training_set[index], dist, labels[index]))\\r\\n distances.sort(key=lambda x: x[1])\\r\\n neighbors = distances[:k]\\r\\n return neighbors\",\n \"def __init__(self, top_k):\\n super(MinibatchTopkAccuracy, self).__init__(\\n reset_at=\\\"iteration\\\", emit_at=\\\"iteration\\\", mode=\\\"train\\\", top_k=top_k\\n )\\n self.top_k = top_k\",\n \"def knn(trainingSetData, testSetData, k):\\n trainingSet = trainingSetData.drop([14], axis=1) # drop income\\n testSet = testSetData.drop([14], axis=1) # drop income\\n\\n distances = {}\\n # this will store the distances re-sorted in ascending/descending order\\n sort = {}\\n # income band results (>=50k or <50K)\\n incomePredictions = []\\n\\n # Calculating euclidean distance between each row of training data and test data instance\\n for testInstance in range(len(testSet)): # len(testSet)\\n \\n # Store current test Point:\\n testInstance = testSet.iloc[testInstance] \\n \\n distances = euclideanDistanceRow(testInstance, trainingSet)\\n\\n # sort the distances in order of smallest first:\\n sorted_d = sorted(distances.items(), key=lambda x: x[1], reverse=False)\\n\\n neighbors = []\\n\\n # Extracting top k neighbors\\n for x in range(k):\\n neighbors.append(sorted_d[x])\\n\\n\\n classVotes = {}\\n\\n # Calculating the most freq class in the neighbors\\n results = {\\\"lessThan50\\\": 0, \\\"moreThan50\\\": 0}\\n\\n # creating a dataframe to which we will add the income values:\\n\\n for x in range(len(neighbors)):\\n if (trainingSetData.iloc[neighbors[x][0]][14] == 0.0):\\n results[\\\"lessThan50\\\"] += 1\\n elif (trainingSetData.iloc[neighbors[x][0]][14] == 1.0):\\n results[\\\"moreThan50\\\"] += 1\\n\\n print('results',results)\\n\\n if (results[\\\"lessThan50\\\"] > results[\\\"moreThan50\\\"]):\\n incomePredictions.append(0.0)\\n elif (results[\\\"lessThan50\\\"] < results[\\\"moreThan50\\\"]):\\n incomePredictions.append(1.0)\\n\\n return incomePredictions\",\n \"def fit(self, trainingFeatures, trainingTargets):\\r\\n\\r\\n \\\"\\\"\\\" Implement kNN learning below. \\\"\\\"\\\"\\r\\n\\r\\n self._fitCalled = True\\r\\n self.labels = trainingTargets\\r\\n self.data = trainingFeatures\",\n \"def k_shortest_paths(\\n self,\\n G,\\n source,\\n target,\\n k=1,\\n weight='weight',\\n ):\\n\\n if source == target:\\n return ([0], [[source]])\\n\\n (length, path) = nx.single_source_dijkstra(G, source, target,\\n weight=weight)\\n if target not in length:\\n raise nx.NetworkXNoPath('node %s not reachable from %s' % (source,\\n target))\\n\\n lengths = [length[target]]\\n paths = [path[target]]\\n c = count()\\n B = []\\n\\n # Is deep copy really required?\\n # Fails due to embedded Ctype objects which can not be pickled\\n # # G_original = G.copy()\\n # Swapping with shallow copy...will it work?\\n\\n G_original = G\\n if nx.is_directed(G_original):\\n G = nx.DiGraph(G_original)\\n else:\\n G = nx.Graph(G_original)\\n\\n ######################################\\n #TODO: wrap this up somehow\\n print ''\\n print term.move_up + term.move_up\\n ######################################\\n print 'getting K:{} paths...'.format(k),\\n for i in range(1, k):\\n with term.location():\\n print i\\n for j in range(len(paths[-1]) - 1):\\n spur_node = paths[-1][j]\\n root_path = (paths[-1])[:j + 1]\\n\\n edges_removed = []\\n for c_path in paths:\\n if len(c_path) > j and root_path == c_path[:j + 1]:\\n u = c_path[j]\\n v = c_path[j + 1]\\n if G.has_edge(u, v):\\n edge_attr = G.edge[u][v]\\n G.remove_edge(u, v)\\n edges_removed.append((u, v, edge_attr))\\n\\n for n in range(len(root_path) - 1):\\n node = root_path[n]\\n\\n # out-edges\\n\\n for (u, v, edge_attr) in G.edges_iter(node, data=True):\\n\\n # print 'lala1: {} -> {}'.format(u,v)\\n\\n G.remove_edge(u, v)\\n edges_removed.append((u, v, edge_attr))\\n\\n if G.is_directed():\\n\\n # in-edges\\n\\n for (u, v, edge_attr) in G.in_edges_iter(node,\\n data=True):\\n\\n # print 'lala2: {} -> {}'.format(u,v)\\n\\n G.remove_edge(u, v)\\n edges_removed.append((u, v, edge_attr))\\n\\n (spur_path_length, spur_path) = nx.single_source_dijkstra(G,\\n spur_node, target, weight=weight)\\n if target in spur_path and spur_path[target]:\\n total_path = root_path[:-1] + spur_path[target]\\n total_path_length = self.get_path_length(G_original,\\n root_path, weight) + spur_path_length[target]\\n heappush(B, (total_path_length, next(c), total_path))\\n\\n for e in edges_removed:\\n (u, v, edge_attr) = e\\n G.add_edge(u, v, edge_attr)\\n\\n if B:\\n (l, _, p) = heappop(B)\\n lengths.append(l)\\n paths.append(p)\\n else:\\n break\\n\\n return (lengths, paths)\",\n \"def dp_kendall_feature_selection(features, labels, k, epsilon):\\n # Remove intercept feature\\n features = features[:, :-1]\\n _, d = features.shape\\n split_eps = epsilon / k\\n selected_indices = []\\n # selected_correlations tracks correlations between features and previously\\n # selected features\\n selected_correlations = np.zeros((k - 1, d))\\n label_coefficients = kendall(features, labels)\\n # Sensitivity is 3/2 in the first round and 3 after\\n sensitivity = 3/2\\n for j in range(k):\\n diffs = label_coefficients\\n if selected_indices:\\n last_idx = selected_indices[-1]\\n old_feature = features[:, last_idx]\\n selected_correlations[j - 1, :] = kendall(features, old_feature)\\n diffs = diffs - (\\n np.sum(selected_correlations, axis=0) / len(selected_indices)\\n )\\n sensitivity = 3\\n new_idx = top_k.basic_peeling_mechanism(\\n item_counts=diffs,\\n k=1,\\n epsilon=split_eps,\\n l_inf_sensitivity=sensitivity,\\n monotonic=False,\\n )[0]\\n selected_indices.append(new_idx)\\n label_coefficients[new_idx] = -np.inf\\n # Add back intercept feature\\n selected_indices.append(d)\\n return np.asarray(selected_indices)\",\n \"def build_knn(coords, k=6, **kwargs):\\n \\n tree = BallTree(coords, **kwargs)\\n _, ind = tree.query(coords, k=k+1) # the first k is \\\"oneself\\\"\\n pairs = pairs_from_knn(ind)\\n return pairs\",\n \"def fetch_unlabelled(self, k, show_progress = False):\\n \\n self.candidates = self.get_unseen()\\n if self.subsample and (self.subsample < len(self.candidates)):\\n self.candidates = np.random.choice(self.candidates, self.subsample, replace = False).tolist()\\n if len(self.candidates) < k:\\n k = len(self.candidates)\\n \\n entropy = AppendedConditionalEntropy(self)\\n steps = trange(k) if show_progress else range(k)\\n for it in steps:\\n \\n if self.parallelized and (it > 0):\\n with Pool(initializer = _init_pool, initargs = (entropy,)) as p:\\n ce = p.map(_parallel_ce, self.candidates)\\n else:\\n ce = [entropy(i) for i in self.candidates]\\n \\n min_ind = np.argmin(ce)\\n entropy.append(self.candidates[min_ind])\\n del self.candidates[min_ind]\\n \\n return entropy.ret\",\n \"def train(self, train_set) -> None:\\n super().train(train_set)\\n # split into data and target\\n xlist, y = zip(*train_set)\\n x = sparse.vstack(xlist)\\n self._classifier.fit(x, y)\",\n \"def selectFeatures(k_features=5, *args):\\n X, y = args\\n skb = SelectKBest(k=k_features)\\n return skb.fit_transform(X, y)\",\n \"def top_k_1_5(output: torch.Tensor, target: torch.Tensor, topk=(1, 5)) -> List[torch.FloatTensor]:\\n with torch.no_grad():\\n # ---- get the topk most likely labels according to your model\\n # get the largest k \\\\in [n_classes] (i.e. the number of most likely probabilities we will use)\\n # max number labels we will consider in the right choices for out model\\n maxk = max(topk)\\n batch_size = target.size(0)\\n\\n # get top maxk indicies that correspond to the most likely probability scores\\n # (note _ means we don't care about the actual top maxk scores just their corresponding indicies/labels)\\n # _, [B, n_classes] -> [B, maxk]\\n _, y_pred = output.topk(k=maxk, dim=1)\\n # [B, maxk] -> [maxk, B] Expects input to be <= 2-D tensor and transposes dimensions 0 and 1.\\n y_pred = y_pred.t()\\n\\n # - get the credit for each example if the models predictions is in maxk values (main crux of code)\\n # for any example, the model will get credit if it's prediction matches the ground truth\\n # for each example we compare if the model's best prediction matches the truth. If yes we get an entry of 1.\\n # if the k'th top answer of the model matches the truth we get 1.\\n # Note: this for any example in batch we can only ever get 1 match (so we never overestimate accuracy <1)\\n # [B] -> [B, 1] -> [maxk, B]\\n target_reshaped = target.view(1, -1).expand_as(y_pred)\\n # compare every topk's model prediction with the ground truth & give credit if any matches the ground truth\\n # [maxk, B] were for each example we know which topk prediction matched truth\\n correct = (y_pred == target_reshaped)\\n # original: correct = pred.eq(target.view(1, -1).expand_as(pred))\\n\\n # -- get topk accuracy\\n list_topk_accs = [] # idx is topk1, topk2, ... etc\\n for k in topk:\\n # get tensor of which topk answer was right\\n ind_which_topk_matched_truth = correct[:k] # [maxk, B] -> [k, B]\\n # flatten it to help compute if we got it correct for each example in batch\\n # [k, B] -> [kB]\\n flattened_indicator_which_topk_matched_truth = ind_which_topk_matched_truth.reshape(\\n -1).float()\\n # get if we got it right for any of our top k prediction for each example in batch\\n tot_correct_topk = flattened_indicator_which_topk_matched_truth.float().sum(\\n dim=0, keepdim=True) # [kB] -> [1]\\n # compute topk accuracy - the accuracy of the mode's ability to get it right within it's top k guesses/preds\\n topk_acc = tot_correct_topk / batch_size # topk accuracy for entire batch\\n list_topk_accs.append(topk_acc)\\n # list of topk accuracies for entire batch [topk1, topk2, ... etc]\\n return list_topk_accs\",\n \"def in_top_k(predictions, targets, k, name=None):\\n with ops.name_scope(name, \\\"in_top_k\\\"):\\n return gen_nn_ops.in_top_kv2(predictions, targets, k, name=name)\",\n \"def extended_training(X,Y):\\n trainSz = 4000\\n \\n X_, Y_ = extend_training_set(X[:trainSz],Y[:trainSz])\\n \\n lda = LDA()\\n lda.fit(X, Y)\\n X2 = lda.transform(X)\\n X_2 = lda.transform(X_)\\n \\n clf = neighbors.KNeighborsClassifier()\\n clf.fit(X_2, Y_)\\n \\n predict = clf.predict(X2)\\n return predict, lda, clf\",\n \"def _kneighbors(self, y):\\n if self.__x is None:\\n raise ValueError(\\\"Input dataset not fitted yet! Call .fit() first!\\\")\\n if self.__device and self.tools.device(y) != self.__device:\\n raise ValueError(\\\"Input dataset and query dataset must be on same device\\\")\\n if len(y.shape) != 2:\\n raise ValueError(\\\"Query dataset must be a 2D tensor\\\")\\n if self.__x.shape[-1] != y.shape[-1]:\\n raise ValueError(\\\"Query and dataset must have same dimensions\\\")\\n if self.__normalise:\\n y = y / self.tools.repeat(self.tools.norm(y, 2, -1), y.shape[1]).reshape(\\n -1, y.shape[1]\\n )\\n y = self.tools.contiguous(y)\\n y_labels = self.__assign(y)\\n\\n y_ranges, _, _ = cluster_ranges_centroids(y, y_labels)\\n self.__y_ranges = y_ranges\\n y, y_labels = self.__sort_clusters(y, y_labels, store_x=False)\\n x_LT = self.__LazyTensor(self.tools.unsqueeze(self.__x, 0))\\n y_LT = self.__LazyTensor(self.tools.unsqueeze(y, 1))\\n D_ij = self.__distance(y_LT, x_LT)\\n ranges_ij = from_matrix(y_ranges, self.__x_ranges, self.__keep)\\n D_ij.ranges = ranges_ij\\n nn = D_ij.argKmin(K=self.__k, axis=1)\\n return self.__unsort(nn)\",\n \"def find_partitions(V,k):\\n k_subs = k_subset(V,k)\\n k_subs = uniq_subsets(k_subs)\\n\\n return k_subs\",\n \"def trainSet(self):\\r\\n self.currIdx = 0\\r\\n random.shuffle(self.trainSamples)\\r\\n self.samples = self.trainSamples[:self.numTrainSamplesPerEpoch]\",\n \"def runmaxmin(self):\\n import random\\n random.seed(self.seed)\\n mindist_ptolandmarkset = np.full(self.pointcloud.size, np.inf)\\n self.subsetindices = []\\n for i in xrange(self.subsetsize):\\n if i == 0:\\n selected_index = random.randint(0, self.pointcloud.size - 1)\\n # update min for all the rest indices\\n # update min for this index to 0.\\n for z in xrange(self.pointcloud.size):\\n # if z == selected_index:\\n # mindist_ptolandmarkset[z] = 0.0\\n # else:\\n mindist_ptolandmarkset[z] = self.pointcloud.distmat[selected_index][z]\\n else:\\n selected_index = np.argmax(mindist_ptolandmarkset)\\n # update minimum distance for all points\\n for z in xrange(self.pointcloud.size):\\n mindist_ptolandmarkset[z] = min(mindist_ptolandmarkset[z],\\n self.pointcloud.distmat[selected_index][z])\\n\\n self.subsetindices.append(selected_index)\\n\\n self.subsetpointcloud = pc.PointCloud(self.pointcloud.points[self.subsetindices])\",\n \"def generate_superset(self, number):\\n \\n superset = []\\n for i in range(0, 2**(self.dim)):\\n if (number & i)==number:\\n superset.append(i)\\n return superset\",\n \"def knn(self,query_vec,k, stdev=False):\\n\\n sims = []\\n if k > len(self.terms):\\n k = len(self.terms)\\n sims = np.matmul(self.vectors, query_vec.vector)\\n if stdev:\\n sims = zscore(sims)\\n indices = np.argpartition(sims, -k)[-k:]\\n indices = sorted(indices, key=lambda i: sims[i], reverse=True)\\n results = []\\n for index in indices:\\n results.append([sims[index], self.terms[index]])\\n return results\",\n \"def _automatic_training_set(self, n_cutouts=100):\\n dstl = Load_DSTL()\\n np.random.seed(42)\\n for ii in range(n_cutouts):\\n # Get region around a shape\\n triples, mask, ind_shape, img_dim = dstl.extract_region(object_class=self.class_type, image_id='6120_2_2', buffer_size=10)\\n if self.radius is not None:\\n triples, mask = self._sliding_window(triples.reshape(*img_dim, 3), mask.reshape(img_dim), window_radius=self.radius)\\n # Add to Feature Matrix\\n if ii == 0:\\n features = triples\\n labels = mask\\n else:\\n features = np.vstack([features, triples])\\n labels = np.hstack([labels, mask])\\n return features, labels\",\n \"def select_seeds(self, graph, k):\\n raise NotImplementedError\\n return set()\",\n \"def generate_k_folds(dataset, k):\\n\\n # TODO: finish this.\\n folds = []\\n dataset = np.concatenate((dataset[0], np.array(dataset[1]).reshape(-1,1)), axis=1)\\n dataset_shape = dataset.shape\\n shape_test_set = int(round(dataset_shape[0]/k,0))\\n split_dataset = np.array_split(dataset,k,axis=0)\\n for i in range(k):\\n test_set = split_dataset[i]\\n c = [k for j,k in enumerate(split_dataset) if j!=i]\\n training_set = np.concatenate(c,axis=0)\\n if test_set.shape[0] != shape_test_set:\\n step = test_set.shape[0] - shape_test_set\\n test_set = test_set[:-step,:]\\n training_set = np.concatenate((training_set, test_set[-step:,:]), axis=0)\\n r_test_set = (test_set[:,:-1], list(test_set[:,-1]))\\n r_train_set = (training_set[:,:-1], list(training_set[:,-1]))\\n folds.append((r_train_set, r_test_set))\\n return folds\",\n \"def num_correct(output, target, topk=(1,)):\\n with torch.no_grad():\\n maxk = max(topk)\\n batch_size = target.size(0)\\n\\n _, pred = output.topk(maxk, 1, True, True)\\n pred = pred.t()\\n correct = pred.eq(target.view(1, -1).expand_as(pred))\\n\\n res = []\\n for k in topk:\\n correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)\\n res.append(correct_k)\\n return res, batch_size\",\n \"def Get(self,k:int): \\n ### get partitions depending on the partition schemes C that depends on k!\\n return subsets_k(list(range(self._n)),k)\",\n \"def S(k):\\n\\n from scipy.special import binom as binomial\\n\\n TrueHset = [0]\\n if k > 1:\\n for j in np.arange(k, 0, -1, dtype=int):\\n TrueHset = list(set(TrueHset) | set([binomial(j, 2) + x for x in S(k - j)]))\\n return TrueHset\",\n \"def existsSubset(S,t,k): \\n\\tn = len(S)\\n\\t# M[n+1][t+1][k+1]\\n\\tM = [[[False for _ in range(k+1)]for _ in range(t+1)]for _ in range(n+1)]\\n\\t\\n\\t# Base: if k is 0 and t is 0 \\n\\t# For any n, we can achieve 0 with size 0 subset \\n\\t# Otherwise, every other cell is False \\n\\tfor i in range(n+1): \\n\\t\\tM[i][0][0] = True\\n\\n\\t# Fill M[n][t][k]\\n\\t# The run-time of this algorithm is O(ntk)\\n\\t# because at worst we are filling in a 3D array \\n\\t# of size n*t*k\\n\\tfor i in range(1, n + 1):\\n\\t\\tfor j in range(1, t + 1):\\n\\t\\t\\tfor l in range(1, k + 1):\\n\\t\\t\\t\\t# Total target so far is less than array element \\n\\t\\t\\t\\tif(j < S[i-1]):\\n\\t\\t\\t\\t\\tM[i][j][l] = M[i-1][j][l]\\n\\t\\t\\t\\t# Total target so far is greater (or equal) than array element \\n\\t\\t\\t\\telif(j >= S[i-1]):\\n\\t\\t\\t\\t\\t# Leave \\n\\t\\t\\t\\t\\tleave = M[i-1][j][l]\\n\\t\\t\\t\\t\\t# Take \\n\\t\\t\\t\\t\\ttake = M[i-1][j - S[i-1]][l-1]\\n\\t\\t\\t\\t\\t# Only False or False will be False \\n\\t\\t\\t\\t\\tM[i][j][l] = leave or take \\n\\n\\t# If possible, back track \\n\\t# We are still basing back-tracking \\n\\t# off of the array of size ntk \\n\\t# so run-time remains at O(ntk)\\n\\tif(M[n][t][k]):\\n\\t\\t# Initialize subset array \\n\\t\\tsubset = [] \\n\\t\\t# Initialize i, j, and l \\n\\t\\ti, j, l = n, t, k\\n\\t\\t# While i, j, and l are all greater than 0 \\n\\t\\twhile(i != 0 and j != 0 and l != 0):\\n\\t\\t\\t# Check if M[i][j][l] == take \\n\\t\\t\\tif(M[i][j][l] == M[i-1][j - S[i-1]][l-1]):\\n\\t\\t\\t\\t# Add to subset \\n\\t\\t\\t\\tsubset += [S[i-1]]\\n\\t\\t\\t\\t# Decrement relevant indices \\n\\t\\t\\t\\tj -= S[i-1]\\n\\t\\t\\t\\ti -= 1 \\n\\t\\t\\t\\tl -= 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\ti -= 1\\n\\t\\t# Since we backtracked, reverse the subset\\n\\t\\tsubset.reverse()\\n\\t\\treturn subset \\n\\t# If not possible, return False \\n\\treturn M[n][t][k]\",\n \"def set_uniform_Kk(self, clip=True):\\n\\t\\t\\n\\t\\tKk1_los = random_matrix([self.Mm], params=[self.lo_Kk1_hyper_lo, \\n\\t\\t\\t\\t\\t\\t\\tself.lo_Kk1_hyper_hi], sample_type='uniform',\\n\\t\\t\\t\\t\\t\\t\\tseed=self.seed_Kk1)\\n\\t\\tKk1_his = random_matrix([self.Mm], params=[self.hi_Kk1_hyper_lo, \\n\\t\\t\\t\\t\\t\\t\\tself.hi_Kk1_hyper_hi], sample_type='uniform',\\n\\t\\t\\t\\t\\t\\t\\tseed=self.seed_Kk1)\\n\\t\\tKk2_los = random_matrix([self.Mm], params=[self.lo_Kk2_hyper_lo, \\n\\t\\t\\t\\t\\t\\t\\tself.lo_Kk2_hyper_hi], sample_type='uniform',\\n\\t\\t\\t\\t\\t\\t\\tseed=self.seed_Kk2)\\n\\t\\tKk2_his = random_matrix([self.Mm], params=[self.hi_Kk2_hyper_lo, \\n\\t\\t\\t\\t\\t\\t\\tself.hi_Kk2_hyper_hi], sample_type='uniform',\\n\\t\\t\\t\\t\\t\\t\\tseed=self.seed_Kk2)\\n\\t\\t\\n\\t\\tself.Kk1 = random_matrix([self.Mm, self.Nn], [Kk1_los, Kk1_his], \\n\\t\\t\\t\\t\\t\\t\\t\\tsample_type='rank2_row_uniform', \\n\\t\\t\\t\\t\\t\\t\\t\\tseed = self.seed_Kk1)\\n\\t\\tself.Kk2 = random_matrix([self.Mm, self.Nn], [Kk2_los, Kk2_his], \\n\\t\\t\\t\\t\\t\\t\\t\\tsample_type='rank2_row_uniform', \\n\\t\\t\\t\\t\\t\\t\\t\\tseed = self.seed_Kk2)\\n\\t\\t\\n\\t\\tif clip == True:\\n\\t\\t\\tarray_dict = clip_array(dict(Kk1 = self.Kk1, Kk2 = self.Kk2))\\n\\t\\t\\tself.Kk1 = array_dict['Kk1']\\n\\t\\t\\tself.Kk2 = array_dict['Kk2']\",\n \"def run_knn(\\n features: List[List[float]],\\n labels: List[Optional[bool]],\\n k: int = 1,\\n) -> List[bool]:\\n # Filter out the features that are already clustered\\n features_l, labels_l = zip(*[(f, l) for f, l in zip(features, labels) if isinstance(l, bool)])\\n\\n # Fit a nearest neighbour algorithm\\n neighbours = KNeighborsClassifier(\\n n_neighbors=k,\\n ).fit(features_l, labels_l)\\n\\n # Predict all the features' labels\\n return neighbours.predict(features) # type: ignore\",\n \"def k_truss(G, k):\\n H = G.copy()\\n\\n n_dropped = 1\\n while n_dropped > 0:\\n n_dropped = 0\\n to_drop = []\\n seen = set()\\n for u in H:\\n nbrs_u = set(H[u])\\n seen.add(u)\\n new_nbrs = [v for v in nbrs_u if v not in seen]\\n for v in new_nbrs:\\n if len(nbrs_u & set(H[v])) < (k - 2):\\n to_drop.append((u, v))\\n H.remove_edges_from(to_drop)\\n n_dropped = len(to_drop)\\n H.remove_nodes_from(list(nx.isolates(H)))\\n\\n return H\",\n \"def NDCG_binary_at_k_batch(logits, y_true, k=10):\\n n = logits.shape[0]\\n dummy_column = np.arange(n).reshape(n, 1)\\n\\n idx_topk_part = bn.argpartition(-logits, k, axis=1)[:, :k]\\n topk_part = logits[dummy_column, idx_topk_part]\\n idx_part = np.argsort(-topk_part, axis=1)\\n # X_pred[np.arange(batch_users)[:, np.newaxis], idx_topk] is the sorted topk predicted score\\n idx_topk = idx_topk_part[dummy_column, idx_part]\\n # build the discount template\\n tp = 1. / np.log2(np.arange(2, k + 2))\\n\\n DCG = (y_true[dummy_column, idx_topk] * tp).sum(axis=1)\\n IDCG = np.array([(tp[:min(n, k)]).sum()\\n for n in y_true.getnnz(axis=1)])\\n\\n # sanity check\\n assert (IDCG - DCG >= 0).all()\\n return DCG / IDCG\",\n \"def narration_target(self):\",\n \"def nDCG_at(prediction, target, k=10):\\n pass\",\n \"def __init__(self, K=10, base_learner=DecisionTreeClassifier(), S=200, cv=5,\\n epsilon=3, cost=90, fraud_label=1, t=3):\\n\\n # let the parent do the stuffs\\n super().__init__(K=K, base_learner=base_learner, S=S, cv=cv)\\n\\n # init statistics of bins (i,k) each epsilon bin has an array k\\n self.bins = epsilon * [K * [{'mean': 0.0, 'var': 0.0, 'num': 0}]]\\n\\n # a cost (because it is a cost sensitive classifier)\\n self.cost = cost\\n\\n # the fraud label as indicated by the users\\n self.fraud_label = fraud_label\\n\\n # confidence level\\n self.t = t\\n\\n # count all the samples predicted in order to get the average of the k classifiers used\\n self.samples = 0\",\n \"def cls_acc(output, target, topk=(1,)):\\r\\n maxk = max(topk)\\r\\n batch_size = target.size(0)\\r\\n\\r\\n _, pred = output.topk(maxk, 1, True, True)\\r\\n pred = pred.t()\\r\\n correct = pred.eq(target.view(1, -1).expand_as(pred))\\r\\n\\r\\n res = []\\r\\n for k in topk:\\r\\n correct_k = correct[:k].view(-1).float().sum(0)\\r\\n res.append(correct_k.mul_(100.0 / batch_size))\\r\\n return res\",\n \"def getNegativeSamples(target, dataset, K):\\n\\n indices = [None] * K\\n for k in xrange(K):\\n newidx = dataset.sampleTokenIdx()\\n while newidx == target:\\n newidx = dataset.sampleTokenIdx()\\n indices[k] = newidx\\n return indices\",\n \"def getNegativeSamples(target, dataset, K):\\n\\n indices = [None] * K\\n for k in xrange(K):\\n newidx = dataset.sampleTokenIdx()\\n while newidx == target:\\n newidx = dataset.sampleTokenIdx()\\n indices[k] = newidx\\n return indices\",\n \"def getNegativeSamples(target, dataset, K):\\n\\n indices = [None] * K\\n for k in xrange(K):\\n newidx = dataset.sampleTokenIdx()\\n while newidx == target:\\n newidx = dataset.sampleTokenIdx()\\n indices[k] = newidx\\n return indices\",\n \"def train(self,trainset):\\n \\n self.n_classes = len(trainset.metadata['targets'])\\n if self.n_classes > 2:\\n raise ValueError('Invalid. Should have 2 classes.')\\n \\n features = np.zeros((len(trainset),trainset.metadata['input_size']))\\n labels = np.zeros((len(trainset)),dtype='int')\\n for i,xy in enumerate(trainset):\\n x,y = xy\\n features[i] = x\\n labels[i] = y\\n\\n if self.criterion == 'information_gain':\\n def criterion_fcn(labels0, labels1):\\n return libmilk.supervised.tree.information_gain(labels0, labels1, include_entropy=self.include_entropy)\\n elif self.criterion == 'z1_loss':\\n def criterion_fcn(labels0, labels1):\\n return libmilk.supervised.tree.z1_loss(labels0, labels1)\\n else:\\n raise ValueError('Invalid parameter: '+self.criterion+'. Should be either \\\\'information_gain\\\\' or \\\\'z1_loss\\\\'')\\n\\n learner = libmilk.supervised.tree_learner(criterion=criterion_fcn,min_split=self.min_split,return_label=True)\\n #self.subsample = subsample\\n #self.R = R\\n model = learner.train(features, labels)\\n \\n self.tree = model\",\n \"def do_knn(x_data):\\n return True\",\n \"def k_shortest_paths(G, source, target, k=1, weight='weight'):\\n\\tif source == target:\\n\\t\\treturn ([0], [[source]]) \\n\\t \\n\\tlength, path = nx.single_source_dijkstra(G, source, target, weight=weight)\\n\\tif target not in length:\\n\\t\\traise nx.NetworkXNoPath(\\\"node %s not reachable from %s\\\" % (source, target))\\n\\t\\t\\n\\tlengths = [length[target]]\\n\\tpaths = [path[target]]\\n\\tc = count()\\t\\t\\n\\tB = []\\t\\t\\t\\t\\t\\t\\n\\tG_original = G.copy()\\t\\n\\t\\n\\tfor i in range(1, k):\\n\\t\\tfor j in range(len(paths[-1]) - 1):\\t\\t\\t\\n\\t\\t\\tspur_node = paths[-1][j]\\n\\t\\t\\troot_path = paths[-1][:j + 1]\\n\\t\\t\\t\\n\\t\\t\\tedges_removed = []\\n\\t\\t\\tfor c_path in paths:\\n\\t\\t\\t\\tif len(c_path) > j and root_path == c_path[:j + 1]:\\n\\t\\t\\t\\t\\tu = c_path[j]\\n\\t\\t\\t\\t\\tv = c_path[j + 1]\\n\\t\\t\\t\\t\\tif G.has_edge(u, v):\\n\\t\\t\\t\\t\\t\\tedge_attr = G.edge[u][v]\\n\\t\\t\\t\\t\\t\\tG.remove_edge(u, v)\\n\\t\\t\\t\\t\\t\\tedges_removed.append((u, v, edge_attr))\\n\\t\\t\\t\\n\\t\\t\\tfor n in range(len(root_path) - 1):\\n\\t\\t\\t\\tnode = root_path[n]\\n\\t\\t\\t\\t# out-edges\\n\\t\\t\\t\\tfor u, v, edge_attr in G.copy().edges_iter(node, data=True):\\n\\t\\t\\t\\t\\tG.remove_edge(u, v)\\n\\t\\t\\t\\t\\tedges_removed.append((u, v, edge_attr))\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif G.is_directed():\\n\\t\\t\\t\\t\\t# in-edges\\n\\t\\t\\t\\t\\tfor u, v, edge_attr in G.in_edges_iter(node, data=True):\\n\\t\\t\\t\\t\\t\\tG.remove_edge(u, v)\\n\\t\\t\\t\\t\\t\\tedges_removed.append((u, v, edge_attr))\\n\\t\\t\\t\\n\\t\\t\\tspur_path_length, spur_path = nx.single_source_dijkstra(G, spur_node, target, weight=weight)\\t\\t\\t\\n\\t\\t\\tif target in spur_path and spur_path[target]:\\n\\t\\t\\t\\ttotal_path = root_path[:-1] + spur_path[target]\\n\\t\\t\\t\\ttotal_path_length = get_path_length(G_original, root_path, weight) + spur_path_length[target]\\t\\t\\t\\t\\n\\t\\t\\t\\theappush(B, (total_path_length, next(c), total_path))\\n\\t\\t\\t\\t\\n\\t\\t\\tfor e in edges_removed:\\n\\t\\t\\t\\tu, v, edge_attr = e\\n\\t\\t\\t\\tG.add_edge(u, v, edge_attr)\\n\\t\\t\\t\\t\\t \\n\\t\\tif B:\\n\\t\\t\\t(l, _, p) = heappop(B)\\n\\t\\t\\tlengths.append(l)\\n\\t\\t\\tpaths.append(p)\\n\\t\\telse:\\n\\t\\t\\tbreak\\n\\t\\n\\treturn (lengths, paths)\",\n \"def k_shortest_paths(G, source, target, k=1, weight='weight'):\\n\\tif source == target:\\n\\t\\treturn ([0], [[source]]) \\n\\t \\n\\tlength, path = nx.single_source_dijkstra(G, source, target, weight=weight)\\n\\tif target not in length:\\n\\t\\traise nx.NetworkXNoPath(\\\"node %s not reachable from %s\\\" % (source, target))\\n\\t\\t\\n\\tlengths = [length[target]]\\n\\tpaths = [path[target]]\\n\\tc = count()\\t\\t\\n\\tB = []\\t\\t\\t\\t\\t\\t\\n\\tG_original = G.copy()\\t\\n\\t\\n\\tfor i in range(1, k):\\n\\t\\tfor j in range(len(paths[-1]) - 1):\\t\\t\\t\\n\\t\\t\\tspur_node = paths[-1][j]\\n\\t\\t\\troot_path = paths[-1][:j + 1]\\n\\t\\t\\t\\n\\t\\t\\tedges_removed = []\\n\\t\\t\\tfor c_path in paths:\\n\\t\\t\\t\\tif len(c_path) > j and root_path == c_path[:j + 1]:\\n\\t\\t\\t\\t\\tu = c_path[j]\\n\\t\\t\\t\\t\\tv = c_path[j + 1]\\n\\t\\t\\t\\t\\tif G.has_edge(u, v):\\n\\t\\t\\t\\t\\t\\tedge_attr = G.edge[u][v]\\n\\t\\t\\t\\t\\t\\tG.remove_edge(u, v)\\n\\t\\t\\t\\t\\t\\tedges_removed.append((u, v, edge_attr))\\n\\t\\t\\t\\n\\t\\t\\tfor n in range(len(root_path) - 1):\\n\\t\\t\\t\\tnode = root_path[n]\\n\\t\\t\\t\\t# out-edges\\n\\t\\t\\t\\tfor u, v, edge_attr in G.copy().edges_iter(node, data=True):\\n\\t\\t\\t\\t\\tG.remove_edge(u, v)\\n\\t\\t\\t\\t\\tedges_removed.append((u, v, edge_attr))\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif G.is_directed():\\n\\t\\t\\t\\t\\t# in-edges\\n\\t\\t\\t\\t\\tfor u, v, edge_attr in G.in_edges_iter(node, data=True):\\n\\t\\t\\t\\t\\t\\tG.remove_edge(u, v)\\n\\t\\t\\t\\t\\t\\tedges_removed.append((u, v, edge_attr))\\n\\t\\t\\t\\n\\t\\t\\tspur_path_length, spur_path = nx.single_source_dijkstra(G, spur_node, target, weight=weight)\\t\\t\\t\\n\\t\\t\\tif target in spur_path and spur_path[target]:\\n\\t\\t\\t\\ttotal_path = root_path[:-1] + spur_path[target]\\n\\t\\t\\t\\ttotal_path_length = get_path_length(G_original, root_path, weight) + spur_path_length[target]\\t\\t\\t\\t\\n\\t\\t\\t\\theappush(B, (total_path_length, next(c), total_path))\\n\\t\\t\\t\\t\\n\\t\\t\\tfor e in edges_removed:\\n\\t\\t\\t\\tu, v, edge_attr = e\\n\\t\\t\\t\\tG.add_edge(u, v, edge_attr)\\n\\t\\t\\t\\t\\t \\n\\t\\tif B:\\n\\t\\t\\t(l, _, p) = heappop(B)\\t\\t\\n\\t\\t\\tlengths.append(l)\\n\\t\\t\\tpaths.append(p)\\n\\t\\telse:\\n\\t\\t\\tbreak\\n\\t\\n\\treturn (lengths, paths)\",\n \"def get_next_train_valid(X_shuffled, y_shuffled, itr, k):\\n X_valid = X_shuffled[itr*len(X_shuffled)//k:(itr+1)*len(X_shuffled)//k,:]\\n Y_valid = y_shuffled[itr*len(y_shuffled)//k:(itr+1)*len(y_shuffled)//k]\\n \\n X_train = np.delete(X_shuffled, np.s_[itr*len(X_shuffled)//k:(itr+1)*len(X_shuffled)//k:1], 0)\\n Y_train = np.delete(y_shuffled, np.s_[itr*len(y_shuffled)//k:(itr+1)*len(y_shuffled)//k:1], 0)\\n \\n \\n return X_train, Y_train, X_valid, Y_valid\",\n \"def run_knn(k, train_data, train_labels, valid_data):\\n\\n dist = l2_distance(valid_data.T, train_data.T)\\n nearest = np.argsort(dist, axis=1)[:,:k]\\n\\n train_labels = train_labels.reshape(-1)\\n valid_labels = train_labels[nearest]\\n\\n # note this only works for binary labels\\n valid_labels = (np.mean(valid_labels, axis=1) >= 0.5).astype(np.int)\\n valid_labels = valid_labels.reshape(-1,1)\\n\\n return valid_labels\",\n \"def determine_k(dataset, range_k, n_seed=30):\\r\\n range_Ks = np.arange(0,range_k,1,dtype=int) #range of delays to study\\r\\n h_K=np.zeros((10,range_k))\\r\\n \\r\\n for i in range(10):\\r\\n for k, K in enumerate(range_Ks):\\r\\n traj_matrix= embed.trajectory_matrix(dataset, K=K)\\r\\n labels= cl.kmeans_knn_partition(traj_matrix, n_seed)\\r\\n h= op_calc.get_entropy(labels)\\r\\n h_K[i,k]=h\\r\\n \\r\\n return(h_K)\",\n \"def get_folds(X, y, k):\\n # temporarily change the 1/-1 nature of y to 1/0\\n _y = (y + 1) / 2\\n # partition the examples into postive and negative sets\\n positive_indices = np.where(_y)[0]\\n negative_indices = np.where(_y - 1)[0]\\n assert len(positive_indices) + len(negative_indices) == len(y)\\n\\n # shuffle both lists\\n np.random.shuffle(positive_indices)\\n np.random.shuffle(negative_indices)\\n\\n # create k buckets of indices of (approximately) equal size\\n positive_folds_indices = \\\\\\n np.array(np.array_split(positive_indices, k))\\n negative_folds_indices = \\\\\\n np.array(np.array_split(negative_indices, k))\\n\\n train_X, train_y, test_X, test_y = [], [], [], []\\n for i in range(k):\\n train_folds = np.concatenate((np.arange(0, i), np.arange(i+1, k)))\\n pos_train_indices = np.concatenate(positive_folds_indices[train_folds])\\n neg_train_indices = np.concatenate(negative_folds_indices[train_folds])\\n pos_test_indices = positive_folds_indices[i]\\n neg_test_indices = negative_folds_indices[i]\\n\\n train_X.append(\\n np.concatenate((X[pos_train_indices], X[neg_train_indices]))\\n )\\n train_y.append(\\n np.concatenate((y[pos_train_indices], y[neg_train_indices]))\\n )\\n test_X.append(\\n np.concatenate((X[pos_test_indices], X[neg_test_indices]))\\n )\\n test_y.append(\\n np.concatenate((y[pos_test_indices], y[neg_test_indices]))\\n )\\n\\n return zip(train_X, train_y, test_X, test_y)\",\n \"def compute_superset_with_r(true_bundle, kdt, prototypes, r=1, distance_func=bundles_distances_mam):\\n true_bundle = np.array(true_bundle, dtype=np.object)\\n dm_true_bundle = distance_func(true_bundle, prototypes)\\n I = kdt.query_radius(dm_true_bundle, r)\\n superset_idx = []\\n for arr in I:\\n for e in arr:\\n superset_idx.append(e)\\n superset_idx = list(set(superset_idx))\\n # recompute the gt streamlines idx\\n I = kdt.query_radius(dm_true_bundle, 10e-4)\\n gt_idx = []\\n for arr in I:\\n for e in arr:\\n gt_idx.append(e)\\n gt_idx = list(set(gt_idx))\\n print('gt length: %d' % len(gt_idx))\\n additional_idx = list(set(superset_idx) - set(gt_idx))\\n\\n return additional_idx, gt_idx\",\n \"def getNegativeSamples(target, dataset, K):\\n\\n indices = [None] * K\\n for k in range(K):\\n newidx = dataset.sampleTokenIdx()\\n while newidx == target:\\n newidx = dataset.sampleTokenIdx()\\n indices[k] = newidx\\n return indices\",\n \"def subsets(x, k):\\n sub_set = set()\\n for i in x:\\n sub_set = sub_set.union(set(combinations(i, k)))\\n return list(sub_set)\",\n \"def top_k(self, k = 1):\\n\\t if self.shapley_rank == {}:\\n\\t \\treturn []\\n\\n\\t n = self.nodes\\n\\t topknodes = []\\n\\t i = 0\\n\\t count = 0\\n\\t while count < k and not i == n:\\n\\t if self.shapley_rank[i][0] not in topknodes and not self.is_adj(self.shapley_rank[i][0], topknodes):\\n\\t topknodes.append(self.shapley_rank[i][0])\\n\\t count += 1\\n\\t i += 1\\n\\t i = 0\\n\\t if not count == k:\\n\\t while not count == k:\\n\\t if self.shapley_rank[i][0] not in topknodes:\\n\\t topknodes.append(self.shapley_rank[i][0])\\n\\t count += 1\\n\\t i += 1\\n\\t return topknodes\",\n \"def _extend_label_set(self, data, trainer_cls, session):\\n # Select which nodes to label next, and predict their labels.\\n selected_samples, selected_labels = self._select_samples_to_label(\\n data, trainer_cls, session)\\n # Replace the labels of the new nodes with the predicted labels.\\n if selected_samples.shape[0] > 0:\\n data.label_samples(selected_samples, selected_labels)\\n return selected_samples\",\n \"def smote(X, y, N=100, K=5):\\n\\n seed = 1\\n np.random.seed(seed)\\n\\n # Step 1. Define minority and majority class examples, and minority class features\\n ms = int(sum(y))\\n min_cls = X[0:ms, :]\\n\\n # Step 2. If N is less than 100, then only a random percent will be smoted.\\n if N < 100:\\n np.random.shuffle(min_cls)\\n ms = int((N / 100) * ms)\\n N = 100\\n\\n syn_ex = int(N / 100) * ms\\n\\n # Step 3. Compute the k-NN for each minority class\\n clf = neighbors.KNeighborsClassifier()\\n clf.fit(X, y)\\n\\n neighborhoods = []\\n for i in range(ms):\\n xi = X[i, :].reshape(1, -1)\\n neighbours = clf.kneighbors(xi, n_neighbors=K, return_distance=False)[0]\\n # Skip itself in the neighborhood\\n neighbours = neighbours[1:]\\n\\n # Find all the minority examples\\n neighborhood = []\\n for index in neighbours:\\n if index <= ms - 1:\\n neighborhood.append(index)\\n\\n neighborhoods.append(neighborhood)\\n\\n # Step 4. Determine the amount of SMOTE examples to develop per neighbourhood.\\n\\n num_ex = int(syn_ex / len(neighborhoods))\\n\\n # Step 5. Generate SMOTE examples\\n syn_data = []\\n for i in range(ms):\\n xi = X[i, :].reshape(1, -1)\\n for j in range(num_ex):\\n # if the neighbourhood is not empty\\n if neighborhoods[i]:\\n index = np.random.choice(neighborhoods[i])\\n xzi = X[index, :].reshape(1, -1)\\n si = xi + (xzi - xi) * np.random.uniform(0, 1)\\n syn_data.append(si)\\n\\n # Build the data matrix\\n data = []\\n for values in syn_data:\\n data.append(values[0])\\n\\n print(\\\"{} amount of minority class samples generated\\\".format(len(data)))\\n\\n # Step 6. Re-build the data set with synthetic data added\\n\\n # Concatenate the positive labels with the newly made data\\n labels = np.ones([len(data), 1])\\n data = np.concatenate([labels, data], axis=1)\\n\\n # Concatenate with old data\\n org_data = np.concatenate([y.reshape(-1, 1), X], axis=1)\\n data = np.concatenate([data, org_data])\\n\\n # Test the new generated data\\n test = []\\n for values in syn_data:\\n a = clf.predict(values)\\n test.append(a)\\n\\n print(\\\"Using the old classifier, {} out of {} would be classified as minority.\\\".format(np.sum(test), len(syn_data)))\\n\\n return data, neighborhoods\",\n \"def test_sparsity_topk_mask_dykstra(self, l=1e-2):\\n n = int(1e3)\\n seed = np.random.RandomState(0)\\n y = jnp.array(seed.randn(n))\\n k = 10\\n out_top_k_dykstra = sparse_soft_topk_mask_dykstra(y, k, l=l)\\n self.assertAlmostEqual(\\n y.argsort()[::-1][:k].sort().sum(),\\n out_top_k_dykstra.argsort()[::-1][:k].sort().sum(),\\n )\\n self.assertAlmostEqual(len(jnp.where(out_top_k_dykstra > 0)[0]), k)\",\n \"def get_training_data(self):\\n\\n # this actually never was a set\\n # src_set = self.target['src'].values\\n # dst_set = self.target['dst'].values\\n\\n # train_negative = self.get_negative_edges(src_set, dst_set, self.train_ind.shape[0]) # * self.K)\\n # test_negative = self.get_negative_edges(src_set, dst_set, self.test_ind.shape[0])\\n\\n train_positive = self.target.iloc[self.train_edge_ind].values\\n test_positive = self.target.iloc[self.test_edge_ind].values\\n\\n # # print(train_positive.shape, train_negative.shape, test_positive.shape, test_negative.shape)\\n # print(f\\\"Working with {train_positive.shape[0]} positive and {train_negative.shape[0]} negative samples in the train set, {test_positive.shape[0]} and {test_negative.shape[0]} - in test set\\\")\\n\\n X_train = train_positive\\n X_test = test_positive\\n\\n y_train = np.ones((self.train_edge_ind.shape[0],))\\n y_test = np.ones((self.test_edge_ind.shape[0],))\\n\\n # X_train = np.vstack([\\n # train_positive,\\n # train_negative\\n # ])\\n\\n # X_test = np.vstack([\\n # test_positive,\\n # test_negative\\n # ])\\n\\n # y_train = np.concatenate([np.ones((self.train_ind.shape[0],)), np.zeros((self.train_ind.shape[0]),)]) # self.train_ind.shape[0]) * self.K\\n # y_test = np.concatenate([np.ones((self.test_ind.shape[0],)), np.zeros((self.test_ind.shape[0],))])\\n\\n assert X_train.shape[0] == y_train.shape[0]\\n assert X_test.shape[0] == y_test.shape[0]\\n\\n def shuffle(X, y):\\n ind_shuffle = np.arange(0, X.shape[0])\\n np.random.shuffle(ind_shuffle)\\n return X[ind_shuffle], y[ind_shuffle]\\n\\n self.X_train, self.y_train = shuffle(X_train, y_train)\\n self.X_test, self.y_test = shuffle(X_test, y_test)\\n\\n print(f\\\"Splitting into {self.X_train.shape[0]} train and {self.X_test.shape[0]} test samples\\\")\\n\\n # return X_train, X_test, y_train, y_test\",\n \"def get_knn_all(self, query_idxs, k=None):\\n k = self.args.k if k is None else k\\n assert get_rank() == 0\\n in_query_mask = np.isin(self.idxs, query_idxs)\\n assert np.sum(in_query_mask) == query_idxs.size\\n in_query_X = self.X[in_query_mask]\\n _, I = self._build_and_query_knn(self.X, in_query_X, k+1)\\n remap = lambda i : self.idxs[i]\\n v_remap = np.vectorize(remap)\\n I = v_remap(I)\\n return I[:,1:]\",\n \"def evaluate(self, b_size, k_max, df, source_is_head=True):\\n\\t\\t\\n\\t\\tself.k_max = k_max\\n\\n\\t\\tcross_Triplets_dataset = crossDomain_Triplets(self.kg_s, self.kg_t, df, source_is_head)\\n\\n\\t\\tdataloader = DataLoader(cross_Triplets_dataset, batch_size=b_size, shuffle=True, )\\n\\n\\t\\trank_true_entities = torch.empty(size=(df.shape[0],), device=self.device).long()\\n\\n\\t\\tfor i, batch in enumerate(dataloader):\\n\\t\\t\\t\\n\\t\\t\\t#print('batch device: ', batch[0].device)\\n\\t\\t\\t#print('batch[0] : ', batch[0], '\\\\n*****************************************')\\n\\t\\t\\t#print('batch[1] : ', batch[1], '\\\\n******************************************')\\n\\t\\t\\t#print('batch[2] : ', batch[2], '\\\\n*************************************')\\n\\n\\t\\t\\th_idx, t_idx, r_idx = batch[0].to(self.device), batch[1].to(self.device), batch[2].to(self.device)\\n\\t\\t\\t#h_idx, t_idx, r_idx = batch[0], batch[1], batch[2]\\n\\n\\t\\t\\trk_true_ents = self.lp_helper(h_idx, t_idx, r_idx, source_is_head)\\n\\n\\t\\t\\trank_true_entities[i*b_size: (i+1)*b_size] = rk_true_ents\\n\\n\\t\\tself.rank_true_entities = torch.cat((self.rank_true_entities, rank_true_entities), dim=0)\\n\\n\\t\\tself.evaluated = True\",\n \"def fold(self, k):\\n if not (0 < k < 5):\\n raise ValueError('almazan cross-evaluation proposes a k=4 k-cross evaluation, {} out of range'.format(k))\\n test_idcs = [idx for idx, fold in enumerate(self.fold_idcs) if fold == k]\\n train_idcs = list(set(range(len(self))).difference(set(test_idcs)))\\n return self.sub_set(train_idcs), self.sub_set(test_idcs)\",\n \"def kNN(k, testTfidf, trainTfidf, test_file, train_label, weight=True):\\r\\n test_y = [] \\r\\n \\r\\n # iterate through all lines in the test reviews and classify them\\r\\n for index, line in enumerate(test_file):\\r\\n # cosine similarity\\r\\n cos_similarity = linear_kernel(testTfidf[index:index+1], trainTfidf).flatten()\\r\\n \\r\\n if weight == True:\\r\\n # get the indices of nearest neighbors based on k parameter \\r\\n neighbor_indices = cos_similarity.argsort()[:-k:-1]\\r\\n # similarities\\r\\n similarities = cos_similarity[neighbor_indices]\\r\\n # get a list of labels from the neighbors and sum the list\\r\\n labels_list = train_label[neighbor_indices].tolist()\\r\\n\\r\\n # make cosine similarity value negative or positive based on\\r\\n # its label and sum the cosine similarities\\r\\n my_list = [] \\r\\n for s, l in zip(similarities, labels_list):\\r\\n if l == -1:\\r\\n my_list.append(-s)\\r\\n else:\\r\\n my_list.append(s) \\r\\n \\r\\n label_sum = sum(my_list)\\r\\n #classify based on label_sum\\r\\n if label_sum > 0:\\r\\n test_y.append(\\\"+1\\\")\\r\\n else:\\r\\n test_y.append(-1)\\r\\n\\r\\n else:\\r\\n # get the indices of nearest neighbors based on k parameter \\r\\n neighbor_indices = cos_similarity.argsort()[:-k:-1]\\r\\n # get a list of labels from the neighbors and sum the list\\r\\n labels_list = train_label[neighbor_indices].tolist()\\r\\n label_sum = sum(labels_list)\\r\\n\\r\\n # classify based on label_sum\\r\\n if label_sum > 0:\\r\\n test_y.append(\\\"+1\\\")\\r\\n else:\\r\\n test_y.append(-1)\\r\\n \\r\\n print(index)\\r\\n \\r\\n return pd.DataFrame(test_y)\",\n \"def p_y_x_knn(y, k):\\n number_of_classes = 4\\n resized = np.delete(y, range(k, y.shape[1]), axis=1)\\n summed_with_zero = np.vstack(np.apply_along_axis(np.bincount, axis=1, arr=resized, minlength=number_of_classes + 1))\\n summed = np.delete(summed_with_zero, 0, axis=1)\\n return summed / k\",\n \"def training_set(self):\\n return self._training_set\",\n \"def find_k_most_uncertain(nn, sess, X, batch_size=None, k=2,\\n pool_size=None):\\n\\n # Initialization\\n results = []\\n i = -1\\n\\n if batch_size is None:\\n _batch_size = X.shape[0]\\n else:\\n _batch_size = batch_size\\n\\n # Create the pool\\n order = np.arange(X.shape[0])\\n\\n if pool_size is not None:\\n order = np.random.choice(X.shape[0], \\n min(X.shape[0], pool_size),\\n replace=False)\\n X_pool = X[order, :].copy()\\n else:\\n X_pool = X.copy()\\n\\n # Loop over the batches\\n for i in range(X_pool.shape[0]/_batch_size):\\n\\n feed_dict = {}\\n feed_dict[nn.input_tensor] = X_pool[i * _batch_size:\\n (i+1) * _batch_size]\\n\\n # Predict the batch\\n pred = sess.run(nn.prediction, feed_dict=feed_dict)\\n\\n # The most uncertain is the closest to 0.5\\n pred = np.abs(0.5 - pred).reshape(-1)\\n\\n # Sort it by uncertainty\\n batch_order = np.argsort(pred)\\n pred = pred[batch_order]\\n\\n # Create associated indices\\n to_zip = order[range(i * _batch_size, i * _batch_size + pred.shape[0])]\\n to_zip = to_zip[batch_order]\\n\\n results = kmin(results, zip(pred, to_zip), k)\\n\\n # Last uncomplete batch\\n feed_dict = {}\\n feed_dict[nn.input_tensor] = X_pool[(i+1) * _batch_size:]\\n\\n # Predict the last batch\\n pred = sess.run(nn.prediction, feed_dict=feed_dict)\\n\\n # Sort it by uncertainty\\n pred = np.abs(0.5 - pred).reshape(-1)\\n batch_order = np.argsort(pred)\\n pred = pred[batch_order]\\n\\n # Create associated indices\\n to_zip = order[(i + 1) * _batch_size:]\\n to_zip = to_zip[batch_order]\\n\\n results = kmin(results, zip(pred, to_zip), k)\\n\\n return [i[1] for i in results]\",\n \"def train_knn(training_data):\\n return knnclassifier(training_data, keys, 3)\",\n \"def KNN(x_train, x_test, y_train, k=3):\\n knn = KNeighborsClassifier(n_neighbors=k)\\n knn.fit(x_train, y_train)\\n y_pred = knn.predict(x_test)\\n return y_pred\",\n \"def forward(self, x, target, N_k):\\n elbo = 0\\n phi = self.base(x)\\n def loglik(sample): #currently hardcoded for binary classification\\n return -torch.log(1 + torch.exp(-target * sample))\\n mu = self.mu\\n cov = self.L @ self.L.T\\n means = phi @ mu\\n variances = torch.diagonal(phi @ cov @ phi.T, 0)\\n elbo += (self.quadr(loglik, means, variances)).sum()\\n elbo /= x.shape[0] #mean\\n \\n kls = 0\\n kls -= kl_divergence(self.w_distr, self.w_prior)\\n\\n for i in range(len(self.prev_tasks_distr)):\\n phi_i = self.base(self.prev_tasks_tensors[i])\\n cov_i = phi_i @ phi_i.T\\n p_u = MultivariateNormal(torch.zeros(cov_i.shape[0]).to(self.device),\\n covariance_matrix=cov_i)#cov_i * self.sigma_prior)\\n kls -= kl_divergence(self.prev_tasks_distr[i], p_u)\\n elbo += kls / N_k\\n\\n return -elbo\",\n \"def __topK_train(self, k):\\n f = open(\\\"train_info.csv\\\")\\n reader = csv.DictReader(f, delimiter=\\\",\\\")\\n artists = []\\n for line in reader:\\n artists.append(line['artist'])\\n freqs = defaultdict(int)\\n for artist in artists:\\n freqs[artist] += 1\\n\\n sorted_freqs = sorted(freqs.items(), key=operator.itemgetter(1))\\n final_list = list(reversed(sorted_freqs))\\n res = []\\n for pair in final_list[:k]:\\n res.append(pair[0])\\n if not os.path.exists(os.path.join(TEST_DIR, pair[0])):\\n os.mkdir(os.path.join(TEST_DIR, pair[0]))\\n return res\",\n \"def knn(x, x_train, y_train, k=1):\\n y_pred = np.zeros(len(x), dtype=np.int8)\\n for i, sample in enumerate(x):\\n # Calculate distance from this sample to every training sample\\n dist = [np.linalg.norm(sample-train) for train in x_train]\\n\\n # Find the k nearest training samples\\n k_nearest_labels = []\\n for j in range(k):\\n closest = np.argmin(dist)\\n k_nearest_labels.append(y_train[closest])\\n dist.pop(closest)\\n\\n # This sample's label the one the appears most frequently in\\n # the k nearest, or the first nearest if all appear equally\\n labels, counts = np.unique(k_nearest_labels, return_counts=True)\\n y_pred[i] = labels[np.argmax(counts)]\\n return y_pred\",\n \"def _decimate_k(self):\\n # Setting the seed here once so as to get the same set\\n # independent of filters.\\n random.seed(1)\\n k_sorted = sorted(self.kvec.keys())\\n k_selected = []\\n for knorm in k_sorted:\\n nkmax = len(self.kvec[knorm])\\n k_selected.append(random.sample(list(range(nkmax)), min(self.nk, nkmax)))\\n return k_sorted, k_selected\",\n \"def get_k_shortest_paths(env: RailEnv,\\n source_position: Tuple[int, int],\\n source_direction: int,\\n target_position=Tuple[int, int],\\n k: int = 1, debug=False) -> List[Tuple[Waypoint]]:\\n\\n # P: set of shortest paths from s to t\\n # P =empty,\\n shortest_paths: List[Tuple[Waypoint]] = []\\n\\n # countu: number of shortest paths found to node u\\n # countu = 0, for all u in V\\n count = {(r, c, d): 0 for r in range(env.height) for c in range(env.width) for d in range(4)}\\n\\n # B is a heap data structure containing paths\\n # N.B. use OrderedSet to make result deterministic!\\n heap: OrderedSet[Tuple[Waypoint]] = OrderedSet()\\n\\n # insert path Ps = {s} into B with cost 0\\n heap.add((Waypoint(source_position, source_direction),))\\n\\n # while B is not empty and countt < K:\\n while len(heap) > 0 and len(shortest_paths) < k:\\n if debug:\\n print(\\\"iteration heap={}, shortest_paths={}\\\".format(heap, shortest_paths))\\n # – let Pu be the shortest cost path in B with cost C\\n cost = np.inf\\n pu = None\\n for path in heap:\\n if len(path) < cost:\\n pu = path\\n cost = len(path)\\n u: Waypoint = pu[-1]\\n if debug:\\n print(\\\" looking at pu={}\\\".format(pu))\\n\\n # – B = B − {Pu }\\n heap.remove(pu)\\n # – countu = countu + 1\\n\\n urcd = (*u.position, u.direction)\\n count[urcd] += 1\\n\\n # – if u = t then P = P U {Pu}\\n if u.position == target_position:\\n if debug:\\n print(\\\" found of length {} {}\\\".format(len(pu), pu))\\n shortest_paths.append(pu)\\n\\n # – if countu ≤ K then\\n # CAVEAT: do not allow for loopy paths\\n elif count[urcd] <= k:\\n possible_transitions = env.rail.get_transitions(*urcd)\\n if debug:\\n print(\\\" looking at neighbors of u={}, transitions are {}\\\".format(u, possible_transitions))\\n # for each vertex v adjacent to u:\\n for new_direction in range(4):\\n if debug:\\n print(\\\" looking at new_direction={}\\\".format(new_direction))\\n if possible_transitions[new_direction]:\\n new_position = get_new_position(u.position, new_direction)\\n if debug:\\n print(\\\" looking at neighbor v={}\\\".format((*new_position, new_direction)))\\n\\n v = Waypoint(position=new_position, direction=new_direction)\\n # CAVEAT: do not allow for loopy paths\\n if v in pu:\\n continue\\n\\n # – let Pv be a new path with cost C + w(u, v) formed by concatenating edge (u, v) to path Pu\\n pv = pu + (v,)\\n # – insert Pv into B\\n heap.add(pv)\\n\\n # return P\\n return shortest_paths\",\n \"def knn_prediction(X, y, x, k):\\n ## homework:start\\n result = \\n ## homework:end\\n return result\",\n \"def find_k(self, kpt):\\n kpt = np.array(kpt)\\n ns = np.linalg.norm(self.kpts - kpt[None, :], axis=1)\\n ik = np.argmin(ns)\\n return ik\",\n \"def __init__(self, top_k):\\n\\n super(EpochTopkAccuracy, self).__init__(\\n reset_at=\\\"epoch\\\", emit_at=\\\"epoch\\\", mode=\\\"train\\\", top_k=top_k\\n )\\n self.top_k = top_k\",\n \"def get_concentrated_mask(class_weights, topk):\\n\\n mask_topk = torch.zeros(class_weights.shape).to(device)\\n\\n # TODO: can we cache this somehow?\\n seq_tensor = torch.LongTensor([i for i in range(class_weights.shape[0])])\\n\\n if topk > 0:\\n _, topk_domain = torch.topk(class_weights, topk)\\n\\n for i in range(topk):\\n mask_topk[seq_tensor, topk_domain[:, i]] = 1\\n else:\\n topk_domain = None\\n\\n return mask_topk, topk_domain, seq_tensor\",\n \"def __call__(self, graph: Data, n_min: int, nodes_to_keep: List[int] = None, exhaustive: bool = False):\\n nodes_to_keep = nodes_to_keep if nodes_to_keep is not None else []\\n mcts = self._get_mcts(graph, n_min, nodes_to_keep, exhaustive)\\n\\n for iteration in range(self.m):\\n mcts.search_one_iteration()\\n\\n explanation = mcts.best_leaf_node()\\n\\n return explanation.node_set, mcts\",\n \"def get_coreset(self, pool_features, labelled_features):\\n new_batch = []\\n self.update_distances(pool_features, labelled_features, reset_dist=True)\\n for _ in range(self.num_query):\\n # choose furthest point\\n ind = np.argmax(self.min_distances)\\n # New examples should not be in already selected since those points\\n # should have min_distance of zero to a cluster center.\\n assert ind not in new_batch\\n # update distances with this point\\n self.update_distances(pool_features, pool_features[ind, :].reshape(1,-1), reset_dist=False)\\n new_batch.append(ind)\\n print(f\\\"Maximum distance from cluster centers is {max(self.min_distances)}\\\")\\n\\n return new_batch\",\n \"def feature_select(T_train, y_train, k=3):\\r\\n from sklearn.metrics import roc_auc_score\\r\\n\\r\\n T_principle_index = list() # The index of Principle features\\r\\n AUC = list()\\r\\n\\r\\n # Forward Stage-wise algorithm\\r\\n for j in range(k):\\r\\n auc = list()\\r\\n Dist = list()\\r\\n\\r\\n for i in range(len(T_train.T)):\\r\\n if i in T_principle_index:\\r\\n auc.append(0)\\r\\n Dist.append(0)\\r\\n continue\\r\\n T_temp_index = T_principle_index + [i]\\r\\n T0 = T_train.T[T_temp_index].T # transient statistics\\r\\n C0 = gen_center(T0, y_train)\\r\\n dist = util.distance(T0, C0) # distance\\r\\n Dist.append(dist)\\r\\n auc.append(roc_auc_score(y_train, -dist))\\r\\n\\r\\n m = np.argmax(auc)\\r\\n T_principle_index = T_principle_index + [m]\\r\\n AUC.append(auc[m])\\r\\n\\r\\n dist = Dist[m]\\r\\n T_principle_index = np.array(T_principle_index)\\r\\n T_principle = T_train.T[T_principle_index].T\\r\\n\\r\\n return T_principle, T_principle_index, np.array(dist), AUC\",\n \"def set_subset(self):\\r\\n if self._random_subset:\\r\\n perm = torch.randperm(len(self._indices))\\r\\n self._subset = self._indices[perm][:self._subset_size]\\r\\n else:\\r\\n self._subset = torch.Tensor(self._indices[:self._subset_size])\",\n \"def shortest_tips(neuron):\\n (branch_index,) = np.where(neuron.branch_order[neuron.n_soma:] == 2)\\n (endpoint_index,) = np.where(neuron.branch_order[neuron.n_soma:] == 0)\\n selected_index = np.union1d(endpoint_index + 1,\\n branch_index + 1)\\n selected_index = np.append(0, selected_index)\",\n \"def _get_targets_single(self, mask_preds: Tensor,\\n gt_instances: InstanceData,\\n positive_info: InstanceData):\\n gt_bboxes = gt_instances.bboxes\\n device = gt_bboxes.device\\n gt_masks = gt_instances.masks.to_tensor(\\n dtype=torch.bool, device=device).float()\\n\\n # process with mask targets\\n pos_assigned_gt_inds = positive_info.get('pos_assigned_gt_inds')\\n scores = positive_info.get('scores')\\n centernesses = positive_info.get('centernesses')\\n num_pos = pos_assigned_gt_inds.size(0)\\n\\n if gt_masks.size(0) == 0 or num_pos == 0:\\n return mask_preds, None, 0\\n # Since we're producing (near) full image masks,\\n # it'd take too much vram to backprop on every single mask.\\n # Thus we select only a subset.\\n if (self.max_masks_to_train != -1) and \\\\\\n (num_pos > self.max_masks_to_train):\\n perm = torch.randperm(num_pos)\\n select = perm[:self.max_masks_to_train]\\n mask_preds = mask_preds[select]\\n pos_assigned_gt_inds = pos_assigned_gt_inds[select]\\n num_pos = self.max_masks_to_train\\n elif self.topk_masks_per_img != -1:\\n unique_gt_inds = pos_assigned_gt_inds.unique()\\n num_inst_per_gt = max(\\n int(self.topk_masks_per_img / len(unique_gt_inds)), 1)\\n\\n keep_mask_preds = []\\n keep_pos_assigned_gt_inds = []\\n for gt_ind in unique_gt_inds:\\n per_inst_pos_inds = (pos_assigned_gt_inds == gt_ind)\\n mask_preds_per_inst = mask_preds[per_inst_pos_inds]\\n gt_inds_per_inst = pos_assigned_gt_inds[per_inst_pos_inds]\\n if sum(per_inst_pos_inds) > num_inst_per_gt:\\n per_inst_scores = scores[per_inst_pos_inds].sigmoid().max(\\n dim=1)[0]\\n per_inst_centerness = centernesses[\\n per_inst_pos_inds].sigmoid().reshape(-1, )\\n select = (per_inst_scores * per_inst_centerness).topk(\\n k=num_inst_per_gt, dim=0)[1]\\n mask_preds_per_inst = mask_preds_per_inst[select]\\n gt_inds_per_inst = gt_inds_per_inst[select]\\n keep_mask_preds.append(mask_preds_per_inst)\\n keep_pos_assigned_gt_inds.append(gt_inds_per_inst)\\n mask_preds = torch.cat(keep_mask_preds)\\n pos_assigned_gt_inds = torch.cat(keep_pos_assigned_gt_inds)\\n num_pos = pos_assigned_gt_inds.size(0)\\n\\n # Follow the origin implement\\n start = int(self.mask_out_stride // 2)\\n gt_masks = gt_masks[:, start::self.mask_out_stride,\\n start::self.mask_out_stride]\\n gt_masks = gt_masks.gt(0.5).float()\\n pos_mask_targets = gt_masks[pos_assigned_gt_inds]\\n\\n return (mask_preds, pos_mask_targets, num_pos)\",\n \"def generate_mnist_datasets(\\n datapoints_per_task,\\n K_list,\\n cir_inner_loop_list, \\n test_task_idx, \\n val_task_idx,\\n n_finetune_sets):\\n\\n # arbitrarily chosen, class-imbalance rate in outer and inner training loops\\n cir_outer_loop = 0.5\\n cir_inner_loop = 0.5\\n # class-imbalance rate in the test sets of the test and validation tasks\\n cir_test = 0.5\\n # arbitrarily chosen, percentage of data that will be used in the inner training loop\\n percent_data_inner_loop = 0.5\\n\\n percent_data_finetune_val = 0.8\\n\\n n_test_set = 4000\\n\\n test_task_idx, val_task_idx = test_task_idx, val_task_idx\\n\\n finetune_sets_per_K_cir = {}\\n test_task_test_set, val_task = {}, {}\\n \\n\\n train_task_list_inner, train_task_list_outer = [], []\\n\\n train_tasks_idxs = [i for i in range(0,10) if i not in [val_task_idx, test_task_idx]]\\n\\n base_path = '/home/USER/Documents'\\n if (not (os.path.exists(base_path))):\\n base_path = '/home/ubuntu/Projects'\\n train_images, train_labels = loadlocal_mnist(\\n images_path= base_path + '/MAML/raw_data/MNIST_data/train-images-idx3-ubyte', \\n labels_path= base_path + '/MAML/raw_data/MNIST_data/train-labels-idx1-ubyte')\\n\\n test_images, test_labels = loadlocal_mnist(\\n images_path= base_path + '/MAML/raw_data/MNIST_data/t10k-images-idx3-ubyte', \\n labels_path= base_path + '/MAML/raw_data/MNIST_data/t10k-labels-idx1-ubyte')\\n\\n\\n train_images, test_images = train_images.reshape((-1,28,28))/255.0, test_images.reshape((-1,28,28))/255.0\\n images = np.concatenate((train_images, test_images))\\n labels = np.concatenate((train_labels, test_labels))\\n\\n test_task_normal_indexes, val_task_normal_indexes = list(np.nonzero(labels == test_task_idx)[0]), list(np.nonzero(train_labels == val_task_idx)[0])\\n test_task_X_normal, val_task_X_normal = images[test_task_normal_indexes],train_images[val_task_normal_indexes]\\n test_task_Y_normal, val_task_Y_normal = np.zeros_like(labels[test_task_normal_indexes]), np.zeros_like(train_labels[val_task_normal_indexes])\\n\\n\\n # val and test task have anomalies (samples of other numbers) that are not used for training\\n # besides the two sets of anomalies (one for val task and one for test task are disjoint)\\n test_task_anomalous_indexes = list(np.nonzero(test_labels[:5000] != test_task_idx)[0])\\n val_task_anomalous_indexes= [index for index, element in enumerate(list(test_labels[5000:])) if element not in [val_task_idx, test_task_idx]]\\n\\n\\n test_task_X_anomalous, val_task_X_anomalous = test_images[:5000][test_task_anomalous_indexes],test_images[5000:][val_task_anomalous_indexes]\\n test_task_Y_anomalous, val_task_Y_anomalous = np.ones_like(test_labels[:5000][test_task_anomalous_indexes]), np.ones_like(test_labels[5000:][val_task_anomalous_indexes])\\n\\n test_task_X, val_task_X = np.concatenate((test_task_X_normal, test_task_X_anomalous)), np.concatenate((val_task_X_normal, val_task_X_anomalous))\\n test_task_Y, val_task_Y = np.expand_dims(np.concatenate((test_task_Y_normal, test_task_Y_anomalous)),-1), np.expand_dims(np.concatenate((val_task_Y_normal, val_task_Y_anomalous)),-1)\\n\\n\\n train_tasks_X_list, train_tasks_Y_list = [], []\\n for task_idx in train_tasks_idxs:\\n train_task_normal_indexes = list(np.nonzero(train_labels == task_idx)[0]) \\n train_task_anomalous_indexes = [index for index, element in enumerate(list(train_labels)) if element not in [task_idx, val_task_idx, test_task_idx]]\\n assert(len(np.nonzero(train_labels[train_task_anomalous_indexes] == val_task_idx)[0]) == 0)\\n assert(len(np.nonzero(train_labels[train_task_anomalous_indexes] == test_task_idx)[0]) == 0)\\n train_task_X_normal, train_task_X_anomalous = train_images[train_task_normal_indexes], train_images[train_task_anomalous_indexes]\\n train_task_Y_normal, train_task_Y_anomalous = np.zeros_like(train_labels[train_task_normal_indexes]), np.ones_like(train_labels[train_task_anomalous_indexes])\\n train_task_X, train_task_Y = np.concatenate((train_task_X_normal, train_task_X_anomalous)), np.concatenate((train_task_Y_normal, train_task_Y_anomalous))\\n train_tasks_X_list.append(train_task_X)\\n train_tasks_Y_list.append(np.expand_dims(train_task_Y,-1))\\n\\n\\n\\n # building test task sets of data\\n normal_indexes, anomaly_indexes = list(np.nonzero(test_task_Y == 0)[0]), list(np.nonzero(test_task_Y == 1)[0])\\n n_test_set_normal = int(n_test_set*cir_test)\\n test_set_normal_indexes = random.sample(normal_indexes, n_test_set_normal)\\n test_set_anomaly_indexes = random.sample(anomaly_indexes, n_test_set - n_test_set_normal)\\n test_set_indexes = []\\n test_set_indexes += test_set_normal_indexes\\n test_set_indexes += test_set_anomaly_indexes\\n\\n test_task_test_set['test_X'], test_task_test_set['test_Y'] = test_task_X[test_set_indexes], test_task_Y[test_set_indexes]\\n\\n\\n #shuffle\\n s_test = np.arange(test_task_test_set['test_X'].shape[0])\\n np.random.shuffle(s_test)\\n test_task_test_set['test_X'], test_task_test_set['test_Y'] = test_task_test_set['test_X'][s_test], test_task_test_set['test_Y'][s_test]\\n\\n rest_normal_indexes = [index for index in normal_indexes if index not in test_set_normal_indexes]\\n rest_anomaly_indexes = [index for index in anomaly_indexes if index not in test_set_anomaly_indexes]\\n\\n\\n for K in K_list:\\n finetune_sets_per_cir = {}\\n for cir in cir_inner_loop_list:\\n\\n rest_normal_indexes = [index for index in normal_indexes if index not in test_set_normal_indexes]\\n rest_anomaly_indexes = [index for index in anomaly_indexes if index not in test_set_anomaly_indexes]\\n \\n finetune_sets_list = []\\n\\n disjoint = False\\n if(cir*K*n_finetune_sets<len(rest_normal_indexes)):\\n disjoint = True\\n\\n n_finetune_normal = int(K*cir)\\n n_finetune_anomaly = K - n_finetune_normal\\n for i in range(n_finetune_sets):\\n # if enough for disjoint do that\\n # else sample randomly\\n # store in a dict with keys cir_K\\n finetune_normal_indexes = random.sample(rest_normal_indexes, n_finetune_normal)\\n finetune_anomaly_indexes = random.sample(rest_anomaly_indexes, n_finetune_anomaly)\\n finetune_indexes = []\\n finetune_indexes += finetune_normal_indexes\\n finetune_indexes += finetune_anomaly_indexes\\n finetune_set = {}\\n finetune_set['finetune_X'], finetune_set['finetune_Y'] = test_task_X[finetune_indexes], test_task_Y[finetune_indexes]\\n\\n #shuffle\\n s_finetune = np.arange(finetune_set['finetune_X'].shape[0])\\n np.random.shuffle(s_finetune)\\n finetune_set['finetune_X'], finetune_set['finetune_Y'] = finetune_set['finetune_X'][s_finetune], finetune_set['finetune_Y'][s_finetune]\\n\\n finetune_sets_list.append(finetune_set)\\n \\n if(disjoint):\\n rest_normal_indexes = [index for index in rest_normal_indexes if index not in finetune_normal_indexes]\\n rest_anomaly_indexes = [index for index in rest_anomaly_indexes if index not in finetune_anomaly_indexes]\\n\\n finetune_sets_per_cir[str(cir)] = finetune_sets_list\\n\\n finetune_sets_per_K_cir[str(K)] = finetune_sets_per_cir\\n\\n\\n #building val task sets of data\\n normal_indexes, anomaly_indexes = list(np.nonzero(val_task_Y == 0)[0]), list(np.nonzero(val_task_Y == 1)[0])\\n n_val_finetune = int(percent_data_finetune_val*datapoints_per_task)\\n n_val_test_set = datapoints_per_task - n_val_finetune\\n n_val_test_set_normal = int(n_val_test_set*cir_test)\\n val_test_set_normal_indexes = random.sample(normal_indexes, n_val_test_set_normal)\\n\\n\\n val_test_set_anomaly_indexes = random.sample(anomaly_indexes, n_val_test_set - n_val_test_set_normal)\\n val_test_set_indexes = []\\n val_test_set_indexes += val_test_set_normal_indexes\\n val_test_set_indexes += val_test_set_anomaly_indexes\\n val_task['test_X'], val_task['test_Y'] = val_task_X[val_test_set_indexes], val_task_Y[val_test_set_indexes]\\n\\n\\n rest_normal_indexes = [index for index in normal_indexes if index not in val_test_set_normal_indexes]\\n rest_anomaly_indexes = [index for index in anomaly_indexes if index not in val_test_set_anomaly_indexes]\\n\\n n_val_finetune_normal = int(n_val_finetune*cir_inner_loop)\\n val_finetune_normal_indexes = random.sample(rest_normal_indexes, n_val_finetune_normal)\\n val_finetune_anomaly_indexes = random.sample(rest_anomaly_indexes, n_val_finetune - n_val_finetune_normal)\\n val_finetune_indexes = []\\n val_finetune_indexes += val_finetune_normal_indexes\\n val_finetune_indexes += val_finetune_anomaly_indexes\\n\\n val_task['finetune_X'], val_task['finetune_Y'] = val_task_X[val_finetune_indexes], val_task_Y[val_finetune_indexes]\\n\\n #shuffle\\n s_val_finetune = np.arange(val_task['finetune_X'].shape[0])\\n s_val_test = np.arange(val_task['test_X'].shape[0])\\n np.random.shuffle(s_val_finetune)\\n np.random.shuffle(s_val_test)\\n\\n val_task['finetune_X'], val_task['finetune_Y'] = val_task['finetune_X'][s_val_finetune], val_task['finetune_Y'][s_val_finetune]\\n val_task['test_X'], val_task['test_Y'] = val_task['test_X'][s_val_test], val_task['test_Y'][s_val_test]\\n\\n\\n\\n # building sets of data of the training tasks\\n for task_X, task_Y in zip(train_tasks_X_list, train_tasks_Y_list):\\n normal_indexes, anomaly_indexes = list(np.nonzero(task_Y == 0)[0]), list(np.nonzero(task_Y == 1)[0])\\n\\n n_inner_loop = int(percent_data_inner_loop*datapoints_per_task)\\n n_inner_loop_normal = int(n_inner_loop*cir_inner_loop)\\n n_outer_loop = datapoints_per_task - n_inner_loop\\n n_outer_loop_normal = int(n_outer_loop*cir_outer_loop)\\n \\n inner_loop_normal_indexes = random.sample(normal_indexes, n_inner_loop_normal)\\n inner_loop_anomaly_indexes = random.sample(anomaly_indexes, n_inner_loop - n_inner_loop_normal)\\n inner_loop_indexes = []\\n inner_loop_indexes += inner_loop_normal_indexes\\n inner_loop_indexes += inner_loop_anomaly_indexes\\n\\n train_task_inner_X, train_task_inner_Y = task_X[inner_loop_indexes], task_Y[inner_loop_indexes]\\n\\n rest_normal_indexes = [index for index in normal_indexes if index not in inner_loop_normal_indexes]\\n rest_anomaly_indexes = [index for index in anomaly_indexes if index not in inner_loop_anomaly_indexes]\\n\\n \\n outer_loop_normal_indexes = random.sample(rest_normal_indexes, n_outer_loop_normal)\\n outer_loop_anomaly_indexes = random.sample(rest_anomaly_indexes, n_outer_loop - n_outer_loop_normal)\\n outer_loop_indexes = []\\n outer_loop_indexes += outer_loop_normal_indexes\\n outer_loop_indexes += outer_loop_anomaly_indexes\\n\\n train_task_outer_X, train_task_outer_Y = task_X[outer_loop_indexes], task_Y[outer_loop_indexes]\\n\\n\\n s_inner = np.arange(train_task_inner_X.shape[0])\\n s_outer = np.arange(train_task_outer_X.shape[0])\\n np.random.shuffle(s_inner)\\n np.random.shuffle(s_outer)\\n train_task_list_inner.append([train_task_inner_X[s_inner],train_task_inner_Y[s_inner]])\\n train_task_list_outer.append([train_task_outer_X[s_outer],train_task_outer_Y[s_outer]])\\n\\n\\n\\n train_tasks_inner_X = np.stack([train_task_list_inner[i][0]\\n for i in range(len(train_task_list_inner))], 0)\\n train_tasks_inner_Y = np.stack([train_task_list_inner[i][1]\\n for i in range(len(train_task_list_inner))], 0)\\n train_tasks_outer_X = np.stack([train_task_list_outer[i][0]\\n for i in range(len(train_task_list_outer))], 0)\\n train_tasks_outer_Y = np.stack([train_task_list_outer[i][1]\\n for i in range(len(train_task_list_outer))], 0)\\n\\n \\n train_tasks = {'X_train_inner': train_tasks_inner_X,\\n 'Y_train_inner': train_tasks_inner_Y,\\n 'X_train_outer': train_tasks_outer_X,\\n 'Y_train_outer': train_tasks_outer_Y\\n }\\n\\n\\n return train_tasks, val_task, test_task_test_set, finetune_sets_per_K_cir\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6441935","0.61719203","0.58573407","0.57999724","0.5726697","0.57163185","0.5672652","0.56291544","0.5529054","0.55143523","0.55143523","0.5510025","0.5475415","0.54626054","0.5443","0.54321223","0.5406091","0.53836495","0.53636736","0.5355484","0.53139937","0.52942836","0.52879745","0.52759","0.52719074","0.5267622","0.5208054","0.5197417","0.5167682","0.51541597","0.515285","0.51497316","0.514392","0.5141453","0.5138802","0.5136998","0.5133355","0.51239586","0.5122241","0.5111605","0.5094194","0.509353","0.5089818","0.5086925","0.5084973","0.5082094","0.5080297","0.5078086","0.5074232","0.50689864","0.50663793","0.5057371","0.5051284","0.5047746","0.50287634","0.5027662","0.50239664","0.50239664","0.50239664","0.50213385","0.5016704","0.50115895","0.50115895","0.5010421","0.50086015","0.5008392","0.5008029","0.5007365","0.50039166","0.4998839","0.49981654","0.49939403","0.4986714","0.4983877","0.49756664","0.49753475","0.4972276","0.49712494","0.4968937","0.4964004","0.49610433","0.49575603","0.49574053","0.49522844","0.49480906","0.49402773","0.4939621","0.49380597","0.4934312","0.4934044","0.49296808","0.4928732","0.49239105","0.49228162","0.49198222","0.4916868","0.49116823","0.49104464","0.49058807","0.49016187"],"string":"[\n \"0.6441935\",\n \"0.61719203\",\n \"0.58573407\",\n \"0.57999724\",\n \"0.5726697\",\n \"0.57163185\",\n \"0.5672652\",\n \"0.56291544\",\n \"0.5529054\",\n \"0.55143523\",\n \"0.55143523\",\n \"0.5510025\",\n \"0.5475415\",\n \"0.54626054\",\n \"0.5443\",\n \"0.54321223\",\n \"0.5406091\",\n \"0.53836495\",\n \"0.53636736\",\n \"0.5355484\",\n \"0.53139937\",\n \"0.52942836\",\n \"0.52879745\",\n \"0.52759\",\n \"0.52719074\",\n \"0.5267622\",\n \"0.5208054\",\n \"0.5197417\",\n \"0.5167682\",\n \"0.51541597\",\n \"0.515285\",\n \"0.51497316\",\n \"0.514392\",\n \"0.5141453\",\n \"0.5138802\",\n \"0.5136998\",\n \"0.5133355\",\n \"0.51239586\",\n \"0.5122241\",\n \"0.5111605\",\n \"0.5094194\",\n \"0.509353\",\n \"0.5089818\",\n \"0.5086925\",\n \"0.5084973\",\n \"0.5082094\",\n \"0.5080297\",\n \"0.5078086\",\n \"0.5074232\",\n \"0.50689864\",\n \"0.50663793\",\n \"0.5057371\",\n \"0.5051284\",\n \"0.5047746\",\n \"0.50287634\",\n \"0.5027662\",\n \"0.50239664\",\n \"0.50239664\",\n \"0.50239664\",\n \"0.50213385\",\n \"0.5016704\",\n \"0.50115895\",\n \"0.50115895\",\n \"0.5010421\",\n \"0.50086015\",\n \"0.5008392\",\n \"0.5008029\",\n \"0.5007365\",\n \"0.50039166\",\n \"0.4998839\",\n \"0.49981654\",\n \"0.49939403\",\n \"0.4986714\",\n \"0.4983877\",\n \"0.49756664\",\n \"0.49753475\",\n \"0.4972276\",\n \"0.49712494\",\n \"0.4968937\",\n \"0.4964004\",\n \"0.49610433\",\n \"0.49575603\",\n \"0.49574053\",\n \"0.49522844\",\n \"0.49480906\",\n \"0.49402773\",\n \"0.4939621\",\n \"0.49380597\",\n \"0.4934312\",\n \"0.4934044\",\n \"0.49296808\",\n \"0.4928732\",\n \"0.49239105\",\n \"0.49228162\",\n \"0.49198222\",\n \"0.4916868\",\n \"0.49116823\",\n \"0.49104464\",\n \"0.49058807\",\n \"0.49016187\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.670607"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":89388,"cells":{"query":{"kind":"string","value":"Compute a superset of the true target tract with kNN."},"document":{"kind":"string","value":"def compute_superset_with_r(true_bundle, kdt, prototypes, r=1, distance_func=bundles_distances_mam):\n true_bundle = np.array(true_bundle, dtype=np.object)\n dm_true_bundle = distance_func(true_bundle, prototypes)\n I = kdt.query_radius(dm_true_bundle, r)\n superset_idx = []\n for arr in I:\n for e in arr:\n superset_idx.append(e)\n superset_idx = list(set(superset_idx))\n # recompute the gt streamlines idx\n I = kdt.query_radius(dm_true_bundle, 10e-4)\n gt_idx = []\n for arr in I:\n for e in arr:\n gt_idx.append(e)\n gt_idx = list(set(gt_idx))\n print('gt length: %d' % len(gt_idx))\n additional_idx = list(set(superset_idx) - set(gt_idx))\n\n return additional_idx, gt_idx"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def compute_superset_with_k(true_bundle, kdt, prototypes, k=1000, distance_func=bundles_distances_mam):\n true_bundle = np.array(true_bundle, dtype=np.object)\n dm_true_bundle = distance_func(true_bundle, prototypes)\n D, I = kdt.query(dm_true_bundle, k=k)\n superset_idx = np.unique(I.flat)\n # recompute the gt streamlines idx\n I = kdt.query_radius(dm_true_bundle, 10e-4)\n gt_idx = []\n for arr in I:\n for e in arr:\n gt_idx.append(e)\n gt_idx = list(set(gt_idx))\n superset_idx = list(set(superset_idx))\n print('gt length: %d' % len(gt_idx))\n additional_idx = list(set(superset_idx) - set(gt_idx))\n\n return additional_idx, gt_idx","def compute_superset(true_tract, kdt, prototypes, k=1000, distance_func=bundles_distances_mam):\n true_tract = np.array(true_tract, dtype=np.object)\n dm_true_tract = distance_func(true_tract, prototypes)\n D, I = kdt.query(dm_true_tract, k=k)\n superset_idx = np.unique(I.flat)\n return superset_idx","def __init__(self, training_set, k=5):\n super(Knn, self).__init__(training_set)\n self.k = k","def __init__(self, trainset, k=3):\n self._trainset = trainset\n self.k = k","def knn(p, k, x, t):\r\n\r\n # Number of instances in data set\r\n N = x.shape[0]\r\n\r\n Euclidean_Distance = numpy.square(x - p) #Euclidean distance\r\n dis = numpy.sum(Euclidean_Distance, axis=1) #sum of the euclidean distance\r\n inds = numpy.argsort(dis)[:k] #sort the indices of the distance array\r\n tgt_cat = Counter([t[i] for i in inds]) #count the times of equivalent target labels\r\n top_class = max(tgt_cat, key= tgt_cat.get) #top class among the k nearest points\r\n\r\n\r\n #top_class = 0\r\n\r\n return top_class","def k_nearest_neighbors(x_test, df_training, k):\n\n return np.argpartition(distance_to_each_training_point(x_test,\n df_training), k-1)[:,0:k]","def kNN_train(self, x_train, y_train, x_test, k = 5, processing = None, distMethod = \"Manhattan\"):\n y_test = list()\n\n if processing == \"Scalar\":\n # print(\"Preprocessing = Scalar\")\n stdScalar = preprocessing.StandardScaler().fit(x_train)\n x_train = stdScalar.transform(x_train)\n x_test = stdScalar.transform(x_test)\n\n elif processing == \"MinMax\":\n\n # print(\"Preprocessing = MinMax\")\n mmScalar = preprocessing.MinMaxScaler()\n x_train = mmScalar.fit_transform(x_train)\n x_test = mmScalar.fit_transform(x_test)\n\n elif processing == \"None\":\n self.true = True\n # print(\"No Preprocessing\")\n\n else:\n print(\"wrong processing\")\n exit()\n\n for i in range(0, len(x_test)):\n y_test_temp = list()\n zeroCount = 0\n oneCount = 0\n\n # find distance of a instance in test test to all instances in training set\n for j in range(0, len(x_train)):\n if distMethod == \"Manhattan\":\n y_test_temp.append(self.manhattan(x_train[j], x_test[i]))\n elif distMethod == \"Euclidean\":\n y_test_temp.append(self.euclidean(x_train[j], x_test[i]))\n else:\n print \"something wrong with distance calculation\"\n exit()\n\n # take indices of k nearest points\n # print y_test_temp\n temp = np.asarray(y_test_temp).argsort()[:k]\n # check class of each of k nearest points\n for tmp in temp:\n if y_train[tmp] == 0:\n zeroCount += 1\n elif y_train[tmp] == 1:\n oneCount += 1\n else:\n print(\"something wrong in counting\")\n\n # classify\n if zeroCount >= oneCount:\n y_test.append(int(0))\n elif oneCount > zeroCount:\n y_test.append(int(1))\n else:\n print(\"somethign wrong\")\n\n # print y_test\n return y_test","def __call__(self, output, target):\n maxk = max(self.topk)\n batch_size = target.size(0)\n\n _, pred = output.topk(maxk, 1, True, True)\n pred = pred.t()\n correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n res = []\n for k in self.topk:\n correct_k = correct[:k].view(-1).float().sum(0)\n res.append(correct_k.mul_(100.0 / batch_size))\n return res","def choose_k_away(k, node, precomputed=True):\n if precomputed:\n sps = np.load('./short_paths/' + str(node) + '.npy')\n else:\n sps = shortest_path(node)\n nodes = []\n for index, x in enumerate(sps):\n if x == k and index in poss_targets:\n nodes.append(index)\n return nodes","def knnSame(k, Xtrain, Ytrain):\n d = euclidean_distances(Xtrain, squared=True)\n np.fill_diagonal(d, np.inf)\n nnc = Ytrain[np.argsort(d)[..., :k].flatten()].reshape(Xtrain.shape[0], k)\n pred = [max(nnc[i], key=Counter(nnc[i]).get) for i in range(nnc.shape[0])]\n return np.array(pred)","def correct(output, target, topk=(1,)):\n maxk = max(topk)\n _, pred = output.topk(maxk, 1, True, True)\n pred = pred.t()\n correct = pred.eq(target.view(1, -1).expand_as(pred))\n res = []\n for k in topk:\n correct_k = correct[:k].view(-1).sum(0, keepdim=True)\n res.append(correct_k)\n return res","def correct(output, target, topk=(1,)):\n maxk = max(topk)\n _, pred = output.topk(maxk, 1, True, True)\n pred = pred.t()\n correct = pred.eq(target.view(1, -1).expand_as(pred))\n res = []\n for k in topk:\n correct_k = correct[:k].view(-1).sum(0, keepdim=True)\n res.append(correct_k)\n return res","def subsample_training_set(self, k, seed=None):\n if seed is not None:\n rng_state = th.random.get_rng_state()\n with th.random.fork_rng():\n th.manual_seed(seed)\n self._subsample_training_set(k)\n if any(th.random.get_rng_state() != rng_state):\n raise ValueError(\"Bad RNG state\")\n else:\n self._subsample_training_set(k)","def get_target_root_partitions_subset(self) -> PartitionsSubset:\n partitioned_asset_keys = {\n asset_key\n for asset_key in self.target_subset.asset_keys\n if self.target_subset.asset_graph.get_partitions_def(asset_key) is not None\n }\n\n root_partitioned_asset_keys = (\n AssetSelection.keys(*partitioned_asset_keys)\n .sources()\n .resolve(self.target_subset.asset_graph)\n )\n\n # Return the targeted partitions for the root partitioned asset keys\n return self.target_subset.get_partitions_subset(next(iter(root_partitioned_asset_keys)))","def check_subset(P1,P2,k=-1):","def evalute_subset(X_train, X_test, y_train, y_test):\r\n clf = KNeighborsClassifier(n_neighbors=3)\r\n clf.fit(X_train, y_train) \r\n y_pred = clf.predict(X_test)\r\n return accuracy_score(y_test, y_pred)","def build_full_trainset(self):\n\n return self.construct_trainset(self.raw_ratings)","def __init__(self, k: int, training_set: np.ndarray):\n self._k = k\n self._training_set = training_set","def knn0(pnts, p, k):\r\n p = np.asarray(p)\r\n pnts = np.asarray(pnts)\r\n diff = pnts - p[np.newaxis, :]\r\n d = np.einsum('ij,ij->i', diff, diff)\r\n idx = np.argsort(d)[:k]\r\n# s = [i.tolist() for i in pnts[idx]]\r\n return pnts[idx].tolist()","def knn(k, Xtrain, Ytrain, Xtest):\n d = euclidean_distances(Xtest, Xtrain, squared=True)\n nnc = Ytrain[np.argsort(d)[..., :k].flatten()].reshape(Xtest.shape[0], k)\n pred = [max(nnc[i], key=Counter(nnc[i]).get) for i in range(nnc.shape[0])]\n return np.array(pred)","def fit_full(self, K):\r\n pass","def KS_algorithm(dataset_x, dataset_y, train_size = 0.7, return_dataset = True):\n #Decide number of total samples\n n = dataset_x.shape[0]\n \n #Turn into train_size to float\n try: \n train_size = float(train_size)\n except Expection as e:\n print(e)\n \n #Jusge if the train_size is by ratio or quantity\n if 0 < train_size < 1: \n num_of_samples = round(n*train_size)\n elif 1 < train_size < 2: \n return \"Train size must be between 1 to 0 or 2 to number of total samples\" \n elif train_size == n or train_size == 1:\n return [x for x in range(n)]\n elif 2 <= train_size < n:\n num_of_samples = round(train_size)\n elif train_size > n:\n return \"Num_of_samples must be less than or equal to original number of samples\"\n \n #Create original sample index\n samples = [x for x in range(n)]\n \n #Create distance matrix\n print('Building distance matrix')\n dist_matrix = create_dist_matrix(dataset_x)\n\n #Pick first two furthest away data points\n print('Picking two furthest away samples')\n max_array = np.argmax(dist_matrix == np.max(dist_matrix), axis = 1)\n ind1 = max_array[max_array != 0][0]\n ind2 = max_array[ind1]\n\n #Initiate chosen sample list, append first two samples and remove from remaining sample list\n chosen_samples = [ind1, ind2]\n samples.remove(ind1)\n samples.remove(ind2) \n remaining_samples = num_of_samples - 2\n\n #Continue picking samples if not yet reaching target number of samples\n while remaining_samples > 0:\n sub_array = np.min(dist_matrix[:,chosen_samples], axis=1)\n chosen = np.argmax(sub_array)\n chosen_samples.append(chosen)\n samples.remove(chosen)\n remaining_samples -= 1\n \n #See if this function should return train and test set or simply index of chosen samples\n if return_dataset == True:\n not_chosen_samples = [x for x in range(n) if x not in chosen_samples]\n return dataset_x.iloc[chosen_samples,:], dataset_x.iloc[not_chosen_samples,:], dataset_y.iloc[chosen_samples,:], dataset_y.iloc[not_chosen_samples,:] \n else:\n return chosen_samples","def findknn(xTr,xTe,k):\n\n # YOUR CODE HERE\n if k > len(xTr):\n k = len(xTr)\n \n D=l2distance(xTe, xTr)\n (m,n) = D.shape\n \n indices = []\n dists = []\n for i in range(m):\n smallest_indices = np.argsort(D[i])\n ind = smallest_indices[:k]\n dis = D[i,smallest_indices[:k]]\n indices.append(ind)\n dists.append(dis)\n \n indices = np.transpose(np.array(indices))\n dists = np.transpose(np.array(dists))\n return indices, dists","def get_neighbors(training_set, \r\n labels, \r\n test_instance, \r\n k, \r\n distance=distance):\r\n distances = []\r\n for index in range(len(training_set)):\r\n dist = distance(test_instance, training_set[index])\r\n distances.append((training_set[index], dist, labels[index]))\r\n distances.sort(key=lambda x: x[1])\r\n neighbors = distances[:k]\r\n return neighbors","def __init__(self, top_k):\n super(MinibatchTopkAccuracy, self).__init__(\n reset_at=\"iteration\", emit_at=\"iteration\", mode=\"train\", top_k=top_k\n )\n self.top_k = top_k","def knn(trainingSetData, testSetData, k):\n trainingSet = trainingSetData.drop([14], axis=1) # drop income\n testSet = testSetData.drop([14], axis=1) # drop income\n\n distances = {}\n # this will store the distances re-sorted in ascending/descending order\n sort = {}\n # income band results (>=50k or <50K)\n incomePredictions = []\n\n # Calculating euclidean distance between each row of training data and test data instance\n for testInstance in range(len(testSet)): # len(testSet)\n \n # Store current test Point:\n testInstance = testSet.iloc[testInstance] \n \n distances = euclideanDistanceRow(testInstance, trainingSet)\n\n # sort the distances in order of smallest first:\n sorted_d = sorted(distances.items(), key=lambda x: x[1], reverse=False)\n\n neighbors = []\n\n # Extracting top k neighbors\n for x in range(k):\n neighbors.append(sorted_d[x])\n\n\n classVotes = {}\n\n # Calculating the most freq class in the neighbors\n results = {\"lessThan50\": 0, \"moreThan50\": 0}\n\n # creating a dataframe to which we will add the income values:\n\n for x in range(len(neighbors)):\n if (trainingSetData.iloc[neighbors[x][0]][14] == 0.0):\n results[\"lessThan50\"] += 1\n elif (trainingSetData.iloc[neighbors[x][0]][14] == 1.0):\n results[\"moreThan50\"] += 1\n\n print('results',results)\n\n if (results[\"lessThan50\"] > results[\"moreThan50\"]):\n incomePredictions.append(0.0)\n elif (results[\"lessThan50\"] < results[\"moreThan50\"]):\n incomePredictions.append(1.0)\n\n return incomePredictions","def fit(self, trainingFeatures, trainingTargets):\r\n\r\n \"\"\" Implement kNN learning below. \"\"\"\r\n\r\n self._fitCalled = True\r\n self.labels = trainingTargets\r\n self.data = trainingFeatures","def k_shortest_paths(\n self,\n G,\n source,\n target,\n k=1,\n weight='weight',\n ):\n\n if source == target:\n return ([0], [[source]])\n\n (length, path) = nx.single_source_dijkstra(G, source, target,\n weight=weight)\n if target not in length:\n raise nx.NetworkXNoPath('node %s not reachable from %s' % (source,\n target))\n\n lengths = [length[target]]\n paths = [path[target]]\n c = count()\n B = []\n\n # Is deep copy really required?\n # Fails due to embedded Ctype objects which can not be pickled\n # # G_original = G.copy()\n # Swapping with shallow copy...will it work?\n\n G_original = G\n if nx.is_directed(G_original):\n G = nx.DiGraph(G_original)\n else:\n G = nx.Graph(G_original)\n\n ######################################\n #TODO: wrap this up somehow\n print ''\n print term.move_up + term.move_up\n ######################################\n print 'getting K:{} paths...'.format(k),\n for i in range(1, k):\n with term.location():\n print i\n for j in range(len(paths[-1]) - 1):\n spur_node = paths[-1][j]\n root_path = (paths[-1])[:j + 1]\n\n edges_removed = []\n for c_path in paths:\n if len(c_path) > j and root_path == c_path[:j + 1]:\n u = c_path[j]\n v = c_path[j + 1]\n if G.has_edge(u, v):\n edge_attr = G.edge[u][v]\n G.remove_edge(u, v)\n edges_removed.append((u, v, edge_attr))\n\n for n in range(len(root_path) - 1):\n node = root_path[n]\n\n # out-edges\n\n for (u, v, edge_attr) in G.edges_iter(node, data=True):\n\n # print 'lala1: {} -> {}'.format(u,v)\n\n G.remove_edge(u, v)\n edges_removed.append((u, v, edge_attr))\n\n if G.is_directed():\n\n # in-edges\n\n for (u, v, edge_attr) in G.in_edges_iter(node,\n data=True):\n\n # print 'lala2: {} -> {}'.format(u,v)\n\n G.remove_edge(u, v)\n edges_removed.append((u, v, edge_attr))\n\n (spur_path_length, spur_path) = nx.single_source_dijkstra(G,\n spur_node, target, weight=weight)\n if target in spur_path and spur_path[target]:\n total_path = root_path[:-1] + spur_path[target]\n total_path_length = self.get_path_length(G_original,\n root_path, weight) + spur_path_length[target]\n heappush(B, (total_path_length, next(c), total_path))\n\n for e in edges_removed:\n (u, v, edge_attr) = e\n G.add_edge(u, v, edge_attr)\n\n if B:\n (l, _, p) = heappop(B)\n lengths.append(l)\n paths.append(p)\n else:\n break\n\n return (lengths, paths)","def dp_kendall_feature_selection(features, labels, k, epsilon):\n # Remove intercept feature\n features = features[:, :-1]\n _, d = features.shape\n split_eps = epsilon / k\n selected_indices = []\n # selected_correlations tracks correlations between features and previously\n # selected features\n selected_correlations = np.zeros((k - 1, d))\n label_coefficients = kendall(features, labels)\n # Sensitivity is 3/2 in the first round and 3 after\n sensitivity = 3/2\n for j in range(k):\n diffs = label_coefficients\n if selected_indices:\n last_idx = selected_indices[-1]\n old_feature = features[:, last_idx]\n selected_correlations[j - 1, :] = kendall(features, old_feature)\n diffs = diffs - (\n np.sum(selected_correlations, axis=0) / len(selected_indices)\n )\n sensitivity = 3\n new_idx = top_k.basic_peeling_mechanism(\n item_counts=diffs,\n k=1,\n epsilon=split_eps,\n l_inf_sensitivity=sensitivity,\n monotonic=False,\n )[0]\n selected_indices.append(new_idx)\n label_coefficients[new_idx] = -np.inf\n # Add back intercept feature\n selected_indices.append(d)\n return np.asarray(selected_indices)","def build_knn(coords, k=6, **kwargs):\n \n tree = BallTree(coords, **kwargs)\n _, ind = tree.query(coords, k=k+1) # the first k is \"oneself\"\n pairs = pairs_from_knn(ind)\n return pairs","def fetch_unlabelled(self, k, show_progress = False):\n \n self.candidates = self.get_unseen()\n if self.subsample and (self.subsample < len(self.candidates)):\n self.candidates = np.random.choice(self.candidates, self.subsample, replace = False).tolist()\n if len(self.candidates) < k:\n k = len(self.candidates)\n \n entropy = AppendedConditionalEntropy(self)\n steps = trange(k) if show_progress else range(k)\n for it in steps:\n \n if self.parallelized and (it > 0):\n with Pool(initializer = _init_pool, initargs = (entropy,)) as p:\n ce = p.map(_parallel_ce, self.candidates)\n else:\n ce = [entropy(i) for i in self.candidates]\n \n min_ind = np.argmin(ce)\n entropy.append(self.candidates[min_ind])\n del self.candidates[min_ind]\n \n return entropy.ret","def train(self, train_set) -> None:\n super().train(train_set)\n # split into data and target\n xlist, y = zip(*train_set)\n x = sparse.vstack(xlist)\n self._classifier.fit(x, y)","def selectFeatures(k_features=5, *args):\n X, y = args\n skb = SelectKBest(k=k_features)\n return skb.fit_transform(X, y)","def top_k_1_5(output: torch.Tensor, target: torch.Tensor, topk=(1, 5)) -> List[torch.FloatTensor]:\n with torch.no_grad():\n # ---- get the topk most likely labels according to your model\n # get the largest k \\in [n_classes] (i.e. the number of most likely probabilities we will use)\n # max number labels we will consider in the right choices for out model\n maxk = max(topk)\n batch_size = target.size(0)\n\n # get top maxk indicies that correspond to the most likely probability scores\n # (note _ means we don't care about the actual top maxk scores just their corresponding indicies/labels)\n # _, [B, n_classes] -> [B, maxk]\n _, y_pred = output.topk(k=maxk, dim=1)\n # [B, maxk] -> [maxk, B] Expects input to be <= 2-D tensor and transposes dimensions 0 and 1.\n y_pred = y_pred.t()\n\n # - get the credit for each example if the models predictions is in maxk values (main crux of code)\n # for any example, the model will get credit if it's prediction matches the ground truth\n # for each example we compare if the model's best prediction matches the truth. If yes we get an entry of 1.\n # if the k'th top answer of the model matches the truth we get 1.\n # Note: this for any example in batch we can only ever get 1 match (so we never overestimate accuracy <1)\n # [B] -> [B, 1] -> [maxk, B]\n target_reshaped = target.view(1, -1).expand_as(y_pred)\n # compare every topk's model prediction with the ground truth & give credit if any matches the ground truth\n # [maxk, B] were for each example we know which topk prediction matched truth\n correct = (y_pred == target_reshaped)\n # original: correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n # -- get topk accuracy\n list_topk_accs = [] # idx is topk1, topk2, ... etc\n for k in topk:\n # get tensor of which topk answer was right\n ind_which_topk_matched_truth = correct[:k] # [maxk, B] -> [k, B]\n # flatten it to help compute if we got it correct for each example in batch\n # [k, B] -> [kB]\n flattened_indicator_which_topk_matched_truth = ind_which_topk_matched_truth.reshape(\n -1).float()\n # get if we got it right for any of our top k prediction for each example in batch\n tot_correct_topk = flattened_indicator_which_topk_matched_truth.float().sum(\n dim=0, keepdim=True) # [kB] -> [1]\n # compute topk accuracy - the accuracy of the mode's ability to get it right within it's top k guesses/preds\n topk_acc = tot_correct_topk / batch_size # topk accuracy for entire batch\n list_topk_accs.append(topk_acc)\n # list of topk accuracies for entire batch [topk1, topk2, ... etc]\n return list_topk_accs","def in_top_k(predictions, targets, k, name=None):\n with ops.name_scope(name, \"in_top_k\"):\n return gen_nn_ops.in_top_kv2(predictions, targets, k, name=name)","def extended_training(X,Y):\n trainSz = 4000\n \n X_, Y_ = extend_training_set(X[:trainSz],Y[:trainSz])\n \n lda = LDA()\n lda.fit(X, Y)\n X2 = lda.transform(X)\n X_2 = lda.transform(X_)\n \n clf = neighbors.KNeighborsClassifier()\n clf.fit(X_2, Y_)\n \n predict = clf.predict(X2)\n return predict, lda, clf","def _kneighbors(self, y):\n if self.__x is None:\n raise ValueError(\"Input dataset not fitted yet! Call .fit() first!\")\n if self.__device and self.tools.device(y) != self.__device:\n raise ValueError(\"Input dataset and query dataset must be on same device\")\n if len(y.shape) != 2:\n raise ValueError(\"Query dataset must be a 2D tensor\")\n if self.__x.shape[-1] != y.shape[-1]:\n raise ValueError(\"Query and dataset must have same dimensions\")\n if self.__normalise:\n y = y / self.tools.repeat(self.tools.norm(y, 2, -1), y.shape[1]).reshape(\n -1, y.shape[1]\n )\n y = self.tools.contiguous(y)\n y_labels = self.__assign(y)\n\n y_ranges, _, _ = cluster_ranges_centroids(y, y_labels)\n self.__y_ranges = y_ranges\n y, y_labels = self.__sort_clusters(y, y_labels, store_x=False)\n x_LT = self.__LazyTensor(self.tools.unsqueeze(self.__x, 0))\n y_LT = self.__LazyTensor(self.tools.unsqueeze(y, 1))\n D_ij = self.__distance(y_LT, x_LT)\n ranges_ij = from_matrix(y_ranges, self.__x_ranges, self.__keep)\n D_ij.ranges = ranges_ij\n nn = D_ij.argKmin(K=self.__k, axis=1)\n return self.__unsort(nn)","def find_partitions(V,k):\n k_subs = k_subset(V,k)\n k_subs = uniq_subsets(k_subs)\n\n return k_subs","def trainSet(self):\r\n self.currIdx = 0\r\n random.shuffle(self.trainSamples)\r\n self.samples = self.trainSamples[:self.numTrainSamplesPerEpoch]","def runmaxmin(self):\n import random\n random.seed(self.seed)\n mindist_ptolandmarkset = np.full(self.pointcloud.size, np.inf)\n self.subsetindices = []\n for i in xrange(self.subsetsize):\n if i == 0:\n selected_index = random.randint(0, self.pointcloud.size - 1)\n # update min for all the rest indices\n # update min for this index to 0.\n for z in xrange(self.pointcloud.size):\n # if z == selected_index:\n # mindist_ptolandmarkset[z] = 0.0\n # else:\n mindist_ptolandmarkset[z] = self.pointcloud.distmat[selected_index][z]\n else:\n selected_index = np.argmax(mindist_ptolandmarkset)\n # update minimum distance for all points\n for z in xrange(self.pointcloud.size):\n mindist_ptolandmarkset[z] = min(mindist_ptolandmarkset[z],\n self.pointcloud.distmat[selected_index][z])\n\n self.subsetindices.append(selected_index)\n\n self.subsetpointcloud = pc.PointCloud(self.pointcloud.points[self.subsetindices])","def generate_superset(self, number):\n \n superset = []\n for i in range(0, 2**(self.dim)):\n if (number & i)==number:\n superset.append(i)\n return superset","def _automatic_training_set(self, n_cutouts=100):\n dstl = Load_DSTL()\n np.random.seed(42)\n for ii in range(n_cutouts):\n # Get region around a shape\n triples, mask, ind_shape, img_dim = dstl.extract_region(object_class=self.class_type, image_id='6120_2_2', buffer_size=10)\n if self.radius is not None:\n triples, mask = self._sliding_window(triples.reshape(*img_dim, 3), mask.reshape(img_dim), window_radius=self.radius)\n # Add to Feature Matrix\n if ii == 0:\n features = triples\n labels = mask\n else:\n features = np.vstack([features, triples])\n labels = np.hstack([labels, mask])\n return features, labels","def knn(self,query_vec,k, stdev=False):\n\n sims = []\n if k > len(self.terms):\n k = len(self.terms)\n sims = np.matmul(self.vectors, query_vec.vector)\n if stdev:\n sims = zscore(sims)\n indices = np.argpartition(sims, -k)[-k:]\n indices = sorted(indices, key=lambda i: sims[i], reverse=True)\n results = []\n for index in indices:\n results.append([sims[index], self.terms[index]])\n return results","def select_seeds(self, graph, k):\n raise NotImplementedError\n return set()","def generate_k_folds(dataset, k):\n\n # TODO: finish this.\n folds = []\n dataset = np.concatenate((dataset[0], np.array(dataset[1]).reshape(-1,1)), axis=1)\n dataset_shape = dataset.shape\n shape_test_set = int(round(dataset_shape[0]/k,0))\n split_dataset = np.array_split(dataset,k,axis=0)\n for i in range(k):\n test_set = split_dataset[i]\n c = [k for j,k in enumerate(split_dataset) if j!=i]\n training_set = np.concatenate(c,axis=0)\n if test_set.shape[0] != shape_test_set:\n step = test_set.shape[0] - shape_test_set\n test_set = test_set[:-step,:]\n training_set = np.concatenate((training_set, test_set[-step:,:]), axis=0)\n r_test_set = (test_set[:,:-1], list(test_set[:,-1]))\n r_train_set = (training_set[:,:-1], list(training_set[:,-1]))\n folds.append((r_train_set, r_test_set))\n return folds","def num_correct(output, target, topk=(1,)):\n with torch.no_grad():\n maxk = max(topk)\n batch_size = target.size(0)\n\n _, pred = output.topk(maxk, 1, True, True)\n pred = pred.t()\n correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n res = []\n for k in topk:\n correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)\n res.append(correct_k)\n return res, batch_size","def Get(self,k:int): \n ### get partitions depending on the partition schemes C that depends on k!\n return subsets_k(list(range(self._n)),k)","def existsSubset(S,t,k): \n\tn = len(S)\n\t# M[n+1][t+1][k+1]\n\tM = [[[False for _ in range(k+1)]for _ in range(t+1)]for _ in range(n+1)]\n\t\n\t# Base: if k is 0 and t is 0 \n\t# For any n, we can achieve 0 with size 0 subset \n\t# Otherwise, every other cell is False \n\tfor i in range(n+1): \n\t\tM[i][0][0] = True\n\n\t# Fill M[n][t][k]\n\t# The run-time of this algorithm is O(ntk)\n\t# because at worst we are filling in a 3D array \n\t# of size n*t*k\n\tfor i in range(1, n + 1):\n\t\tfor j in range(1, t + 1):\n\t\t\tfor l in range(1, k + 1):\n\t\t\t\t# Total target so far is less than array element \n\t\t\t\tif(j < S[i-1]):\n\t\t\t\t\tM[i][j][l] = M[i-1][j][l]\n\t\t\t\t# Total target so far is greater (or equal) than array element \n\t\t\t\telif(j >= S[i-1]):\n\t\t\t\t\t# Leave \n\t\t\t\t\tleave = M[i-1][j][l]\n\t\t\t\t\t# Take \n\t\t\t\t\ttake = M[i-1][j - S[i-1]][l-1]\n\t\t\t\t\t# Only False or False will be False \n\t\t\t\t\tM[i][j][l] = leave or take \n\n\t# If possible, back track \n\t# We are still basing back-tracking \n\t# off of the array of size ntk \n\t# so run-time remains at O(ntk)\n\tif(M[n][t][k]):\n\t\t# Initialize subset array \n\t\tsubset = [] \n\t\t# Initialize i, j, and l \n\t\ti, j, l = n, t, k\n\t\t# While i, j, and l are all greater than 0 \n\t\twhile(i != 0 and j != 0 and l != 0):\n\t\t\t# Check if M[i][j][l] == take \n\t\t\tif(M[i][j][l] == M[i-1][j - S[i-1]][l-1]):\n\t\t\t\t# Add to subset \n\t\t\t\tsubset += [S[i-1]]\n\t\t\t\t# Decrement relevant indices \n\t\t\t\tj -= S[i-1]\n\t\t\t\ti -= 1 \n\t\t\t\tl -= 1\n\t\t\telse:\n\t\t\t\ti -= 1\n\t\t# Since we backtracked, reverse the subset\n\t\tsubset.reverse()\n\t\treturn subset \n\t# If not possible, return False \n\treturn M[n][t][k]","def S(k):\n\n from scipy.special import binom as binomial\n\n TrueHset = [0]\n if k > 1:\n for j in np.arange(k, 0, -1, dtype=int):\n TrueHset = list(set(TrueHset) | set([binomial(j, 2) + x for x in S(k - j)]))\n return TrueHset","def set_uniform_Kk(self, clip=True):\n\t\t\n\t\tKk1_los = random_matrix([self.Mm], params=[self.lo_Kk1_hyper_lo, \n\t\t\t\t\t\t\tself.lo_Kk1_hyper_hi], sample_type='uniform',\n\t\t\t\t\t\t\tseed=self.seed_Kk1)\n\t\tKk1_his = random_matrix([self.Mm], params=[self.hi_Kk1_hyper_lo, \n\t\t\t\t\t\t\tself.hi_Kk1_hyper_hi], sample_type='uniform',\n\t\t\t\t\t\t\tseed=self.seed_Kk1)\n\t\tKk2_los = random_matrix([self.Mm], params=[self.lo_Kk2_hyper_lo, \n\t\t\t\t\t\t\tself.lo_Kk2_hyper_hi], sample_type='uniform',\n\t\t\t\t\t\t\tseed=self.seed_Kk2)\n\t\tKk2_his = random_matrix([self.Mm], params=[self.hi_Kk2_hyper_lo, \n\t\t\t\t\t\t\tself.hi_Kk2_hyper_hi], sample_type='uniform',\n\t\t\t\t\t\t\tseed=self.seed_Kk2)\n\t\t\n\t\tself.Kk1 = random_matrix([self.Mm, self.Nn], [Kk1_los, Kk1_his], \n\t\t\t\t\t\t\t\tsample_type='rank2_row_uniform', \n\t\t\t\t\t\t\t\tseed = self.seed_Kk1)\n\t\tself.Kk2 = random_matrix([self.Mm, self.Nn], [Kk2_los, Kk2_his], \n\t\t\t\t\t\t\t\tsample_type='rank2_row_uniform', \n\t\t\t\t\t\t\t\tseed = self.seed_Kk2)\n\t\t\n\t\tif clip == True:\n\t\t\tarray_dict = clip_array(dict(Kk1 = self.Kk1, Kk2 = self.Kk2))\n\t\t\tself.Kk1 = array_dict['Kk1']\n\t\t\tself.Kk2 = array_dict['Kk2']","def run_knn(\n features: List[List[float]],\n labels: List[Optional[bool]],\n k: int = 1,\n) -> List[bool]:\n # Filter out the features that are already clustered\n features_l, labels_l = zip(*[(f, l) for f, l in zip(features, labels) if isinstance(l, bool)])\n\n # Fit a nearest neighbour algorithm\n neighbours = KNeighborsClassifier(\n n_neighbors=k,\n ).fit(features_l, labels_l)\n\n # Predict all the features' labels\n return neighbours.predict(features) # type: ignore","def k_truss(G, k):\n H = G.copy()\n\n n_dropped = 1\n while n_dropped > 0:\n n_dropped = 0\n to_drop = []\n seen = set()\n for u in H:\n nbrs_u = set(H[u])\n seen.add(u)\n new_nbrs = [v for v in nbrs_u if v not in seen]\n for v in new_nbrs:\n if len(nbrs_u & set(H[v])) < (k - 2):\n to_drop.append((u, v))\n H.remove_edges_from(to_drop)\n n_dropped = len(to_drop)\n H.remove_nodes_from(list(nx.isolates(H)))\n\n return H","def NDCG_binary_at_k_batch(logits, y_true, k=10):\n n = logits.shape[0]\n dummy_column = np.arange(n).reshape(n, 1)\n\n idx_topk_part = bn.argpartition(-logits, k, axis=1)[:, :k]\n topk_part = logits[dummy_column, idx_topk_part]\n idx_part = np.argsort(-topk_part, axis=1)\n # X_pred[np.arange(batch_users)[:, np.newaxis], idx_topk] is the sorted topk predicted score\n idx_topk = idx_topk_part[dummy_column, idx_part]\n # build the discount template\n tp = 1. / np.log2(np.arange(2, k + 2))\n\n DCG = (y_true[dummy_column, idx_topk] * tp).sum(axis=1)\n IDCG = np.array([(tp[:min(n, k)]).sum()\n for n in y_true.getnnz(axis=1)])\n\n # sanity check\n assert (IDCG - DCG >= 0).all()\n return DCG / IDCG","def narration_target(self):","def nDCG_at(prediction, target, k=10):\n pass","def __init__(self, K=10, base_learner=DecisionTreeClassifier(), S=200, cv=5,\n epsilon=3, cost=90, fraud_label=1, t=3):\n\n # let the parent do the stuffs\n super().__init__(K=K, base_learner=base_learner, S=S, cv=cv)\n\n # init statistics of bins (i,k) each epsilon bin has an array k\n self.bins = epsilon * [K * [{'mean': 0.0, 'var': 0.0, 'num': 0}]]\n\n # a cost (because it is a cost sensitive classifier)\n self.cost = cost\n\n # the fraud label as indicated by the users\n self.fraud_label = fraud_label\n\n # confidence level\n self.t = t\n\n # count all the samples predicted in order to get the average of the k classifiers used\n self.samples = 0","def cls_acc(output, target, topk=(1,)):\r\n maxk = max(topk)\r\n batch_size = target.size(0)\r\n\r\n _, pred = output.topk(maxk, 1, True, True)\r\n pred = pred.t()\r\n correct = pred.eq(target.view(1, -1).expand_as(pred))\r\n\r\n res = []\r\n for k in topk:\r\n correct_k = correct[:k].view(-1).float().sum(0)\r\n res.append(correct_k.mul_(100.0 / batch_size))\r\n return res","def getNegativeSamples(target, dataset, K):\n\n indices = [None] * K\n for k in xrange(K):\n newidx = dataset.sampleTokenIdx()\n while newidx == target:\n newidx = dataset.sampleTokenIdx()\n indices[k] = newidx\n return indices","def getNegativeSamples(target, dataset, K):\n\n indices = [None] * K\n for k in xrange(K):\n newidx = dataset.sampleTokenIdx()\n while newidx == target:\n newidx = dataset.sampleTokenIdx()\n indices[k] = newidx\n return indices","def getNegativeSamples(target, dataset, K):\n\n indices = [None] * K\n for k in xrange(K):\n newidx = dataset.sampleTokenIdx()\n while newidx == target:\n newidx = dataset.sampleTokenIdx()\n indices[k] = newidx\n return indices","def train(self,trainset):\n \n self.n_classes = len(trainset.metadata['targets'])\n if self.n_classes > 2:\n raise ValueError('Invalid. Should have 2 classes.')\n \n features = np.zeros((len(trainset),trainset.metadata['input_size']))\n labels = np.zeros((len(trainset)),dtype='int')\n for i,xy in enumerate(trainset):\n x,y = xy\n features[i] = x\n labels[i] = y\n\n if self.criterion == 'information_gain':\n def criterion_fcn(labels0, labels1):\n return libmilk.supervised.tree.information_gain(labels0, labels1, include_entropy=self.include_entropy)\n elif self.criterion == 'z1_loss':\n def criterion_fcn(labels0, labels1):\n return libmilk.supervised.tree.z1_loss(labels0, labels1)\n else:\n raise ValueError('Invalid parameter: '+self.criterion+'. Should be either \\'information_gain\\' or \\'z1_loss\\'')\n\n learner = libmilk.supervised.tree_learner(criterion=criterion_fcn,min_split=self.min_split,return_label=True)\n #self.subsample = subsample\n #self.R = R\n model = learner.train(features, labels)\n \n self.tree = model","def do_knn(x_data):\n return True","def k_shortest_paths(G, source, target, k=1, weight='weight'):\n\tif source == target:\n\t\treturn ([0], [[source]]) \n\t \n\tlength, path = nx.single_source_dijkstra(G, source, target, weight=weight)\n\tif target not in length:\n\t\traise nx.NetworkXNoPath(\"node %s not reachable from %s\" % (source, target))\n\t\t\n\tlengths = [length[target]]\n\tpaths = [path[target]]\n\tc = count()\t\t\n\tB = []\t\t\t\t\t\t\n\tG_original = G.copy()\t\n\t\n\tfor i in range(1, k):\n\t\tfor j in range(len(paths[-1]) - 1):\t\t\t\n\t\t\tspur_node = paths[-1][j]\n\t\t\troot_path = paths[-1][:j + 1]\n\t\t\t\n\t\t\tedges_removed = []\n\t\t\tfor c_path in paths:\n\t\t\t\tif len(c_path) > j and root_path == c_path[:j + 1]:\n\t\t\t\t\tu = c_path[j]\n\t\t\t\t\tv = c_path[j + 1]\n\t\t\t\t\tif G.has_edge(u, v):\n\t\t\t\t\t\tedge_attr = G.edge[u][v]\n\t\t\t\t\t\tG.remove_edge(u, v)\n\t\t\t\t\t\tedges_removed.append((u, v, edge_attr))\n\t\t\t\n\t\t\tfor n in range(len(root_path) - 1):\n\t\t\t\tnode = root_path[n]\n\t\t\t\t# out-edges\n\t\t\t\tfor u, v, edge_attr in G.copy().edges_iter(node, data=True):\n\t\t\t\t\tG.remove_edge(u, v)\n\t\t\t\t\tedges_removed.append((u, v, edge_attr))\n\t\t\t\t\n\t\t\t\tif G.is_directed():\n\t\t\t\t\t# in-edges\n\t\t\t\t\tfor u, v, edge_attr in G.in_edges_iter(node, data=True):\n\t\t\t\t\t\tG.remove_edge(u, v)\n\t\t\t\t\t\tedges_removed.append((u, v, edge_attr))\n\t\t\t\n\t\t\tspur_path_length, spur_path = nx.single_source_dijkstra(G, spur_node, target, weight=weight)\t\t\t\n\t\t\tif target in spur_path and spur_path[target]:\n\t\t\t\ttotal_path = root_path[:-1] + spur_path[target]\n\t\t\t\ttotal_path_length = get_path_length(G_original, root_path, weight) + spur_path_length[target]\t\t\t\t\n\t\t\t\theappush(B, (total_path_length, next(c), total_path))\n\t\t\t\t\n\t\t\tfor e in edges_removed:\n\t\t\t\tu, v, edge_attr = e\n\t\t\t\tG.add_edge(u, v, edge_attr)\n\t\t\t\t\t \n\t\tif B:\n\t\t\t(l, _, p) = heappop(B)\n\t\t\tlengths.append(l)\n\t\t\tpaths.append(p)\n\t\telse:\n\t\t\tbreak\n\t\n\treturn (lengths, paths)","def k_shortest_paths(G, source, target, k=1, weight='weight'):\n\tif source == target:\n\t\treturn ([0], [[source]]) \n\t \n\tlength, path = nx.single_source_dijkstra(G, source, target, weight=weight)\n\tif target not in length:\n\t\traise nx.NetworkXNoPath(\"node %s not reachable from %s\" % (source, target))\n\t\t\n\tlengths = [length[target]]\n\tpaths = [path[target]]\n\tc = count()\t\t\n\tB = []\t\t\t\t\t\t\n\tG_original = G.copy()\t\n\t\n\tfor i in range(1, k):\n\t\tfor j in range(len(paths[-1]) - 1):\t\t\t\n\t\t\tspur_node = paths[-1][j]\n\t\t\troot_path = paths[-1][:j + 1]\n\t\t\t\n\t\t\tedges_removed = []\n\t\t\tfor c_path in paths:\n\t\t\t\tif len(c_path) > j and root_path == c_path[:j + 1]:\n\t\t\t\t\tu = c_path[j]\n\t\t\t\t\tv = c_path[j + 1]\n\t\t\t\t\tif G.has_edge(u, v):\n\t\t\t\t\t\tedge_attr = G.edge[u][v]\n\t\t\t\t\t\tG.remove_edge(u, v)\n\t\t\t\t\t\tedges_removed.append((u, v, edge_attr))\n\t\t\t\n\t\t\tfor n in range(len(root_path) - 1):\n\t\t\t\tnode = root_path[n]\n\t\t\t\t# out-edges\n\t\t\t\tfor u, v, edge_attr in G.copy().edges_iter(node, data=True):\n\t\t\t\t\tG.remove_edge(u, v)\n\t\t\t\t\tedges_removed.append((u, v, edge_attr))\n\t\t\t\t\n\t\t\t\tif G.is_directed():\n\t\t\t\t\t# in-edges\n\t\t\t\t\tfor u, v, edge_attr in G.in_edges_iter(node, data=True):\n\t\t\t\t\t\tG.remove_edge(u, v)\n\t\t\t\t\t\tedges_removed.append((u, v, edge_attr))\n\t\t\t\n\t\t\tspur_path_length, spur_path = nx.single_source_dijkstra(G, spur_node, target, weight=weight)\t\t\t\n\t\t\tif target in spur_path and spur_path[target]:\n\t\t\t\ttotal_path = root_path[:-1] + spur_path[target]\n\t\t\t\ttotal_path_length = get_path_length(G_original, root_path, weight) + spur_path_length[target]\t\t\t\t\n\t\t\t\theappush(B, (total_path_length, next(c), total_path))\n\t\t\t\t\n\t\t\tfor e in edges_removed:\n\t\t\t\tu, v, edge_attr = e\n\t\t\t\tG.add_edge(u, v, edge_attr)\n\t\t\t\t\t \n\t\tif B:\n\t\t\t(l, _, p) = heappop(B)\t\t\n\t\t\tlengths.append(l)\n\t\t\tpaths.append(p)\n\t\telse:\n\t\t\tbreak\n\t\n\treturn (lengths, paths)","def get_next_train_valid(X_shuffled, y_shuffled, itr, k):\n X_valid = X_shuffled[itr*len(X_shuffled)//k:(itr+1)*len(X_shuffled)//k,:]\n Y_valid = y_shuffled[itr*len(y_shuffled)//k:(itr+1)*len(y_shuffled)//k]\n \n X_train = np.delete(X_shuffled, np.s_[itr*len(X_shuffled)//k:(itr+1)*len(X_shuffled)//k:1], 0)\n Y_train = np.delete(y_shuffled, np.s_[itr*len(y_shuffled)//k:(itr+1)*len(y_shuffled)//k:1], 0)\n \n \n return X_train, Y_train, X_valid, Y_valid","def get_folds(X, y, k):\n # temporarily change the 1/-1 nature of y to 1/0\n _y = (y + 1) / 2\n # partition the examples into postive and negative sets\n positive_indices = np.where(_y)[0]\n negative_indices = np.where(_y - 1)[0]\n assert len(positive_indices) + len(negative_indices) == len(y)\n\n # shuffle both lists\n np.random.shuffle(positive_indices)\n np.random.shuffle(negative_indices)\n\n # create k buckets of indices of (approximately) equal size\n positive_folds_indices = \\\n np.array(np.array_split(positive_indices, k))\n negative_folds_indices = \\\n np.array(np.array_split(negative_indices, k))\n\n train_X, train_y, test_X, test_y = [], [], [], []\n for i in range(k):\n train_folds = np.concatenate((np.arange(0, i), np.arange(i+1, k)))\n pos_train_indices = np.concatenate(positive_folds_indices[train_folds])\n neg_train_indices = np.concatenate(negative_folds_indices[train_folds])\n pos_test_indices = positive_folds_indices[i]\n neg_test_indices = negative_folds_indices[i]\n\n train_X.append(\n np.concatenate((X[pos_train_indices], X[neg_train_indices]))\n )\n train_y.append(\n np.concatenate((y[pos_train_indices], y[neg_train_indices]))\n )\n test_X.append(\n np.concatenate((X[pos_test_indices], X[neg_test_indices]))\n )\n test_y.append(\n np.concatenate((y[pos_test_indices], y[neg_test_indices]))\n )\n\n return zip(train_X, train_y, test_X, test_y)","def run_knn(k, train_data, train_labels, valid_data):\n\n dist = l2_distance(valid_data.T, train_data.T)\n nearest = np.argsort(dist, axis=1)[:,:k]\n\n train_labels = train_labels.reshape(-1)\n valid_labels = train_labels[nearest]\n\n # note this only works for binary labels\n valid_labels = (np.mean(valid_labels, axis=1) >= 0.5).astype(np.int)\n valid_labels = valid_labels.reshape(-1,1)\n\n return valid_labels","def determine_k(dataset, range_k, n_seed=30):\r\n range_Ks = np.arange(0,range_k,1,dtype=int) #range of delays to study\r\n h_K=np.zeros((10,range_k))\r\n \r\n for i in range(10):\r\n for k, K in enumerate(range_Ks):\r\n traj_matrix= embed.trajectory_matrix(dataset, K=K)\r\n labels= cl.kmeans_knn_partition(traj_matrix, n_seed)\r\n h= op_calc.get_entropy(labels)\r\n h_K[i,k]=h\r\n \r\n return(h_K)","def getNegativeSamples(target, dataset, K):\n\n indices = [None] * K\n for k in range(K):\n newidx = dataset.sampleTokenIdx()\n while newidx == target:\n newidx = dataset.sampleTokenIdx()\n indices[k] = newidx\n return indices","def subsets(x, k):\n sub_set = set()\n for i in x:\n sub_set = sub_set.union(set(combinations(i, k)))\n return list(sub_set)","def top_k(self, k = 1):\n\t if self.shapley_rank == {}:\n\t \treturn []\n\n\t n = self.nodes\n\t topknodes = []\n\t i = 0\n\t count = 0\n\t while count < k and not i == n:\n\t if self.shapley_rank[i][0] not in topknodes and not self.is_adj(self.shapley_rank[i][0], topknodes):\n\t topknodes.append(self.shapley_rank[i][0])\n\t count += 1\n\t i += 1\n\t i = 0\n\t if not count == k:\n\t while not count == k:\n\t if self.shapley_rank[i][0] not in topknodes:\n\t topknodes.append(self.shapley_rank[i][0])\n\t count += 1\n\t i += 1\n\t return topknodes","def _extend_label_set(self, data, trainer_cls, session):\n # Select which nodes to label next, and predict their labels.\n selected_samples, selected_labels = self._select_samples_to_label(\n data, trainer_cls, session)\n # Replace the labels of the new nodes with the predicted labels.\n if selected_samples.shape[0] > 0:\n data.label_samples(selected_samples, selected_labels)\n return selected_samples","def smote(X, y, N=100, K=5):\n\n seed = 1\n np.random.seed(seed)\n\n # Step 1. Define minority and majority class examples, and minority class features\n ms = int(sum(y))\n min_cls = X[0:ms, :]\n\n # Step 2. If N is less than 100, then only a random percent will be smoted.\n if N < 100:\n np.random.shuffle(min_cls)\n ms = int((N / 100) * ms)\n N = 100\n\n syn_ex = int(N / 100) * ms\n\n # Step 3. Compute the k-NN for each minority class\n clf = neighbors.KNeighborsClassifier()\n clf.fit(X, y)\n\n neighborhoods = []\n for i in range(ms):\n xi = X[i, :].reshape(1, -1)\n neighbours = clf.kneighbors(xi, n_neighbors=K, return_distance=False)[0]\n # Skip itself in the neighborhood\n neighbours = neighbours[1:]\n\n # Find all the minority examples\n neighborhood = []\n for index in neighbours:\n if index <= ms - 1:\n neighborhood.append(index)\n\n neighborhoods.append(neighborhood)\n\n # Step 4. Determine the amount of SMOTE examples to develop per neighbourhood.\n\n num_ex = int(syn_ex / len(neighborhoods))\n\n # Step 5. Generate SMOTE examples\n syn_data = []\n for i in range(ms):\n xi = X[i, :].reshape(1, -1)\n for j in range(num_ex):\n # if the neighbourhood is not empty\n if neighborhoods[i]:\n index = np.random.choice(neighborhoods[i])\n xzi = X[index, :].reshape(1, -1)\n si = xi + (xzi - xi) * np.random.uniform(0, 1)\n syn_data.append(si)\n\n # Build the data matrix\n data = []\n for values in syn_data:\n data.append(values[0])\n\n print(\"{} amount of minority class samples generated\".format(len(data)))\n\n # Step 6. Re-build the data set with synthetic data added\n\n # Concatenate the positive labels with the newly made data\n labels = np.ones([len(data), 1])\n data = np.concatenate([labels, data], axis=1)\n\n # Concatenate with old data\n org_data = np.concatenate([y.reshape(-1, 1), X], axis=1)\n data = np.concatenate([data, org_data])\n\n # Test the new generated data\n test = []\n for values in syn_data:\n a = clf.predict(values)\n test.append(a)\n\n print(\"Using the old classifier, {} out of {} would be classified as minority.\".format(np.sum(test), len(syn_data)))\n\n return data, neighborhoods","def test_sparsity_topk_mask_dykstra(self, l=1e-2):\n n = int(1e3)\n seed = np.random.RandomState(0)\n y = jnp.array(seed.randn(n))\n k = 10\n out_top_k_dykstra = sparse_soft_topk_mask_dykstra(y, k, l=l)\n self.assertAlmostEqual(\n y.argsort()[::-1][:k].sort().sum(),\n out_top_k_dykstra.argsort()[::-1][:k].sort().sum(),\n )\n self.assertAlmostEqual(len(jnp.where(out_top_k_dykstra > 0)[0]), k)","def get_training_data(self):\n\n # this actually never was a set\n # src_set = self.target['src'].values\n # dst_set = self.target['dst'].values\n\n # train_negative = self.get_negative_edges(src_set, dst_set, self.train_ind.shape[0]) # * self.K)\n # test_negative = self.get_negative_edges(src_set, dst_set, self.test_ind.shape[0])\n\n train_positive = self.target.iloc[self.train_edge_ind].values\n test_positive = self.target.iloc[self.test_edge_ind].values\n\n # # print(train_positive.shape, train_negative.shape, test_positive.shape, test_negative.shape)\n # print(f\"Working with {train_positive.shape[0]} positive and {train_negative.shape[0]} negative samples in the train set, {test_positive.shape[0]} and {test_negative.shape[0]} - in test set\")\n\n X_train = train_positive\n X_test = test_positive\n\n y_train = np.ones((self.train_edge_ind.shape[0],))\n y_test = np.ones((self.test_edge_ind.shape[0],))\n\n # X_train = np.vstack([\n # train_positive,\n # train_negative\n # ])\n\n # X_test = np.vstack([\n # test_positive,\n # test_negative\n # ])\n\n # y_train = np.concatenate([np.ones((self.train_ind.shape[0],)), np.zeros((self.train_ind.shape[0]),)]) # self.train_ind.shape[0]) * self.K\n # y_test = np.concatenate([np.ones((self.test_ind.shape[0],)), np.zeros((self.test_ind.shape[0],))])\n\n assert X_train.shape[0] == y_train.shape[0]\n assert X_test.shape[0] == y_test.shape[0]\n\n def shuffle(X, y):\n ind_shuffle = np.arange(0, X.shape[0])\n np.random.shuffle(ind_shuffle)\n return X[ind_shuffle], y[ind_shuffle]\n\n self.X_train, self.y_train = shuffle(X_train, y_train)\n self.X_test, self.y_test = shuffle(X_test, y_test)\n\n print(f\"Splitting into {self.X_train.shape[0]} train and {self.X_test.shape[0]} test samples\")\n\n # return X_train, X_test, y_train, y_test","def get_knn_all(self, query_idxs, k=None):\n k = self.args.k if k is None else k\n assert get_rank() == 0\n in_query_mask = np.isin(self.idxs, query_idxs)\n assert np.sum(in_query_mask) == query_idxs.size\n in_query_X = self.X[in_query_mask]\n _, I = self._build_and_query_knn(self.X, in_query_X, k+1)\n remap = lambda i : self.idxs[i]\n v_remap = np.vectorize(remap)\n I = v_remap(I)\n return I[:,1:]","def evaluate(self, b_size, k_max, df, source_is_head=True):\n\t\t\n\t\tself.k_max = k_max\n\n\t\tcross_Triplets_dataset = crossDomain_Triplets(self.kg_s, self.kg_t, df, source_is_head)\n\n\t\tdataloader = DataLoader(cross_Triplets_dataset, batch_size=b_size, shuffle=True, )\n\n\t\trank_true_entities = torch.empty(size=(df.shape[0],), device=self.device).long()\n\n\t\tfor i, batch in enumerate(dataloader):\n\t\t\t\n\t\t\t#print('batch device: ', batch[0].device)\n\t\t\t#print('batch[0] : ', batch[0], '\\n*****************************************')\n\t\t\t#print('batch[1] : ', batch[1], '\\n******************************************')\n\t\t\t#print('batch[2] : ', batch[2], '\\n*************************************')\n\n\t\t\th_idx, t_idx, r_idx = batch[0].to(self.device), batch[1].to(self.device), batch[2].to(self.device)\n\t\t\t#h_idx, t_idx, r_idx = batch[0], batch[1], batch[2]\n\n\t\t\trk_true_ents = self.lp_helper(h_idx, t_idx, r_idx, source_is_head)\n\n\t\t\trank_true_entities[i*b_size: (i+1)*b_size] = rk_true_ents\n\n\t\tself.rank_true_entities = torch.cat((self.rank_true_entities, rank_true_entities), dim=0)\n\n\t\tself.evaluated = True","def fold(self, k):\n if not (0 < k < 5):\n raise ValueError('almazan cross-evaluation proposes a k=4 k-cross evaluation, {} out of range'.format(k))\n test_idcs = [idx for idx, fold in enumerate(self.fold_idcs) if fold == k]\n train_idcs = list(set(range(len(self))).difference(set(test_idcs)))\n return self.sub_set(train_idcs), self.sub_set(test_idcs)","def kNN(k, testTfidf, trainTfidf, test_file, train_label, weight=True):\r\n test_y = [] \r\n \r\n # iterate through all lines in the test reviews and classify them\r\n for index, line in enumerate(test_file):\r\n # cosine similarity\r\n cos_similarity = linear_kernel(testTfidf[index:index+1], trainTfidf).flatten()\r\n \r\n if weight == True:\r\n # get the indices of nearest neighbors based on k parameter \r\n neighbor_indices = cos_similarity.argsort()[:-k:-1]\r\n # similarities\r\n similarities = cos_similarity[neighbor_indices]\r\n # get a list of labels from the neighbors and sum the list\r\n labels_list = train_label[neighbor_indices].tolist()\r\n\r\n # make cosine similarity value negative or positive based on\r\n # its label and sum the cosine similarities\r\n my_list = [] \r\n for s, l in zip(similarities, labels_list):\r\n if l == -1:\r\n my_list.append(-s)\r\n else:\r\n my_list.append(s) \r\n \r\n label_sum = sum(my_list)\r\n #classify based on label_sum\r\n if label_sum > 0:\r\n test_y.append(\"+1\")\r\n else:\r\n test_y.append(-1)\r\n\r\n else:\r\n # get the indices of nearest neighbors based on k parameter \r\n neighbor_indices = cos_similarity.argsort()[:-k:-1]\r\n # get a list of labels from the neighbors and sum the list\r\n labels_list = train_label[neighbor_indices].tolist()\r\n label_sum = sum(labels_list)\r\n\r\n # classify based on label_sum\r\n if label_sum > 0:\r\n test_y.append(\"+1\")\r\n else:\r\n test_y.append(-1)\r\n \r\n print(index)\r\n \r\n return pd.DataFrame(test_y)","def p_y_x_knn(y, k):\n number_of_classes = 4\n resized = np.delete(y, range(k, y.shape[1]), axis=1)\n summed_with_zero = np.vstack(np.apply_along_axis(np.bincount, axis=1, arr=resized, minlength=number_of_classes + 1))\n summed = np.delete(summed_with_zero, 0, axis=1)\n return summed / k","def training_set(self):\n return self._training_set","def find_k_most_uncertain(nn, sess, X, batch_size=None, k=2,\n pool_size=None):\n\n # Initialization\n results = []\n i = -1\n\n if batch_size is None:\n _batch_size = X.shape[0]\n else:\n _batch_size = batch_size\n\n # Create the pool\n order = np.arange(X.shape[0])\n\n if pool_size is not None:\n order = np.random.choice(X.shape[0], \n min(X.shape[0], pool_size),\n replace=False)\n X_pool = X[order, :].copy()\n else:\n X_pool = X.copy()\n\n # Loop over the batches\n for i in range(X_pool.shape[0]/_batch_size):\n\n feed_dict = {}\n feed_dict[nn.input_tensor] = X_pool[i * _batch_size:\n (i+1) * _batch_size]\n\n # Predict the batch\n pred = sess.run(nn.prediction, feed_dict=feed_dict)\n\n # The most uncertain is the closest to 0.5\n pred = np.abs(0.5 - pred).reshape(-1)\n\n # Sort it by uncertainty\n batch_order = np.argsort(pred)\n pred = pred[batch_order]\n\n # Create associated indices\n to_zip = order[range(i * _batch_size, i * _batch_size + pred.shape[0])]\n to_zip = to_zip[batch_order]\n\n results = kmin(results, zip(pred, to_zip), k)\n\n # Last uncomplete batch\n feed_dict = {}\n feed_dict[nn.input_tensor] = X_pool[(i+1) * _batch_size:]\n\n # Predict the last batch\n pred = sess.run(nn.prediction, feed_dict=feed_dict)\n\n # Sort it by uncertainty\n pred = np.abs(0.5 - pred).reshape(-1)\n batch_order = np.argsort(pred)\n pred = pred[batch_order]\n\n # Create associated indices\n to_zip = order[(i + 1) * _batch_size:]\n to_zip = to_zip[batch_order]\n\n results = kmin(results, zip(pred, to_zip), k)\n\n return [i[1] for i in results]","def train_knn(training_data):\n return knnclassifier(training_data, keys, 3)","def KNN(x_train, x_test, y_train, k=3):\n knn = KNeighborsClassifier(n_neighbors=k)\n knn.fit(x_train, y_train)\n y_pred = knn.predict(x_test)\n return y_pred","def forward(self, x, target, N_k):\n elbo = 0\n phi = self.base(x)\n def loglik(sample): #currently hardcoded for binary classification\n return -torch.log(1 + torch.exp(-target * sample))\n mu = self.mu\n cov = self.L @ self.L.T\n means = phi @ mu\n variances = torch.diagonal(phi @ cov @ phi.T, 0)\n elbo += (self.quadr(loglik, means, variances)).sum()\n elbo /= x.shape[0] #mean\n \n kls = 0\n kls -= kl_divergence(self.w_distr, self.w_prior)\n\n for i in range(len(self.prev_tasks_distr)):\n phi_i = self.base(self.prev_tasks_tensors[i])\n cov_i = phi_i @ phi_i.T\n p_u = MultivariateNormal(torch.zeros(cov_i.shape[0]).to(self.device),\n covariance_matrix=cov_i)#cov_i * self.sigma_prior)\n kls -= kl_divergence(self.prev_tasks_distr[i], p_u)\n elbo += kls / N_k\n\n return -elbo","def __topK_train(self, k):\n f = open(\"train_info.csv\")\n reader = csv.DictReader(f, delimiter=\",\")\n artists = []\n for line in reader:\n artists.append(line['artist'])\n freqs = defaultdict(int)\n for artist in artists:\n freqs[artist] += 1\n\n sorted_freqs = sorted(freqs.items(), key=operator.itemgetter(1))\n final_list = list(reversed(sorted_freqs))\n res = []\n for pair in final_list[:k]:\n res.append(pair[0])\n if not os.path.exists(os.path.join(TEST_DIR, pair[0])):\n os.mkdir(os.path.join(TEST_DIR, pair[0]))\n return res","def knn(x, x_train, y_train, k=1):\n y_pred = np.zeros(len(x), dtype=np.int8)\n for i, sample in enumerate(x):\n # Calculate distance from this sample to every training sample\n dist = [np.linalg.norm(sample-train) for train in x_train]\n\n # Find the k nearest training samples\n k_nearest_labels = []\n for j in range(k):\n closest = np.argmin(dist)\n k_nearest_labels.append(y_train[closest])\n dist.pop(closest)\n\n # This sample's label the one the appears most frequently in\n # the k nearest, or the first nearest if all appear equally\n labels, counts = np.unique(k_nearest_labels, return_counts=True)\n y_pred[i] = labels[np.argmax(counts)]\n return y_pred","def _decimate_k(self):\n # Setting the seed here once so as to get the same set\n # independent of filters.\n random.seed(1)\n k_sorted = sorted(self.kvec.keys())\n k_selected = []\n for knorm in k_sorted:\n nkmax = len(self.kvec[knorm])\n k_selected.append(random.sample(list(range(nkmax)), min(self.nk, nkmax)))\n return k_sorted, k_selected","def get_k_shortest_paths(env: RailEnv,\n source_position: Tuple[int, int],\n source_direction: int,\n target_position=Tuple[int, int],\n k: int = 1, debug=False) -> List[Tuple[Waypoint]]:\n\n # P: set of shortest paths from s to t\n # P =empty,\n shortest_paths: List[Tuple[Waypoint]] = []\n\n # countu: number of shortest paths found to node u\n # countu = 0, for all u in V\n count = {(r, c, d): 0 for r in range(env.height) for c in range(env.width) for d in range(4)}\n\n # B is a heap data structure containing paths\n # N.B. use OrderedSet to make result deterministic!\n heap: OrderedSet[Tuple[Waypoint]] = OrderedSet()\n\n # insert path Ps = {s} into B with cost 0\n heap.add((Waypoint(source_position, source_direction),))\n\n # while B is not empty and countt < K:\n while len(heap) > 0 and len(shortest_paths) < k:\n if debug:\n print(\"iteration heap={}, shortest_paths={}\".format(heap, shortest_paths))\n # – let Pu be the shortest cost path in B with cost C\n cost = np.inf\n pu = None\n for path in heap:\n if len(path) < cost:\n pu = path\n cost = len(path)\n u: Waypoint = pu[-1]\n if debug:\n print(\" looking at pu={}\".format(pu))\n\n # – B = B − {Pu }\n heap.remove(pu)\n # – countu = countu + 1\n\n urcd = (*u.position, u.direction)\n count[urcd] += 1\n\n # – if u = t then P = P U {Pu}\n if u.position == target_position:\n if debug:\n print(\" found of length {} {}\".format(len(pu), pu))\n shortest_paths.append(pu)\n\n # – if countu ≤ K then\n # CAVEAT: do not allow for loopy paths\n elif count[urcd] <= k:\n possible_transitions = env.rail.get_transitions(*urcd)\n if debug:\n print(\" looking at neighbors of u={}, transitions are {}\".format(u, possible_transitions))\n # for each vertex v adjacent to u:\n for new_direction in range(4):\n if debug:\n print(\" looking at new_direction={}\".format(new_direction))\n if possible_transitions[new_direction]:\n new_position = get_new_position(u.position, new_direction)\n if debug:\n print(\" looking at neighbor v={}\".format((*new_position, new_direction)))\n\n v = Waypoint(position=new_position, direction=new_direction)\n # CAVEAT: do not allow for loopy paths\n if v in pu:\n continue\n\n # – let Pv be a new path with cost C + w(u, v) formed by concatenating edge (u, v) to path Pu\n pv = pu + (v,)\n # – insert Pv into B\n heap.add(pv)\n\n # return P\n return shortest_paths","def knn_prediction(X, y, x, k):\n ## homework:start\n result = \n ## homework:end\n return result","def __init__(self, top_k):\n\n super(EpochTopkAccuracy, self).__init__(\n reset_at=\"epoch\", emit_at=\"epoch\", mode=\"train\", top_k=top_k\n )\n self.top_k = top_k","def find_k(self, kpt):\n kpt = np.array(kpt)\n ns = np.linalg.norm(self.kpts - kpt[None, :], axis=1)\n ik = np.argmin(ns)\n return ik","def __call__(self, graph: Data, n_min: int, nodes_to_keep: List[int] = None, exhaustive: bool = False):\n nodes_to_keep = nodes_to_keep if nodes_to_keep is not None else []\n mcts = self._get_mcts(graph, n_min, nodes_to_keep, exhaustive)\n\n for iteration in range(self.m):\n mcts.search_one_iteration()\n\n explanation = mcts.best_leaf_node()\n\n return explanation.node_set, mcts","def get_concentrated_mask(class_weights, topk):\n\n mask_topk = torch.zeros(class_weights.shape).to(device)\n\n # TODO: can we cache this somehow?\n seq_tensor = torch.LongTensor([i for i in range(class_weights.shape[0])])\n\n if topk > 0:\n _, topk_domain = torch.topk(class_weights, topk)\n\n for i in range(topk):\n mask_topk[seq_tensor, topk_domain[:, i]] = 1\n else:\n topk_domain = None\n\n return mask_topk, topk_domain, seq_tensor","def get_coreset(self, pool_features, labelled_features):\n new_batch = []\n self.update_distances(pool_features, labelled_features, reset_dist=True)\n for _ in range(self.num_query):\n # choose furthest point\n ind = np.argmax(self.min_distances)\n # New examples should not be in already selected since those points\n # should have min_distance of zero to a cluster center.\n assert ind not in new_batch\n # update distances with this point\n self.update_distances(pool_features, pool_features[ind, :].reshape(1,-1), reset_dist=False)\n new_batch.append(ind)\n print(f\"Maximum distance from cluster centers is {max(self.min_distances)}\")\n\n return new_batch","def feature_select(T_train, y_train, k=3):\r\n from sklearn.metrics import roc_auc_score\r\n\r\n T_principle_index = list() # The index of Principle features\r\n AUC = list()\r\n\r\n # Forward Stage-wise algorithm\r\n for j in range(k):\r\n auc = list()\r\n Dist = list()\r\n\r\n for i in range(len(T_train.T)):\r\n if i in T_principle_index:\r\n auc.append(0)\r\n Dist.append(0)\r\n continue\r\n T_temp_index = T_principle_index + [i]\r\n T0 = T_train.T[T_temp_index].T # transient statistics\r\n C0 = gen_center(T0, y_train)\r\n dist = util.distance(T0, C0) # distance\r\n Dist.append(dist)\r\n auc.append(roc_auc_score(y_train, -dist))\r\n\r\n m = np.argmax(auc)\r\n T_principle_index = T_principle_index + [m]\r\n AUC.append(auc[m])\r\n\r\n dist = Dist[m]\r\n T_principle_index = np.array(T_principle_index)\r\n T_principle = T_train.T[T_principle_index].T\r\n\r\n return T_principle, T_principle_index, np.array(dist), AUC","def set_subset(self):\r\n if self._random_subset:\r\n perm = torch.randperm(len(self._indices))\r\n self._subset = self._indices[perm][:self._subset_size]\r\n else:\r\n self._subset = torch.Tensor(self._indices[:self._subset_size])","def shortest_tips(neuron):\n (branch_index,) = np.where(neuron.branch_order[neuron.n_soma:] == 2)\n (endpoint_index,) = np.where(neuron.branch_order[neuron.n_soma:] == 0)\n selected_index = np.union1d(endpoint_index + 1,\n branch_index + 1)\n selected_index = np.append(0, selected_index)","def _get_targets_single(self, mask_preds: Tensor,\n gt_instances: InstanceData,\n positive_info: InstanceData):\n gt_bboxes = gt_instances.bboxes\n device = gt_bboxes.device\n gt_masks = gt_instances.masks.to_tensor(\n dtype=torch.bool, device=device).float()\n\n # process with mask targets\n pos_assigned_gt_inds = positive_info.get('pos_assigned_gt_inds')\n scores = positive_info.get('scores')\n centernesses = positive_info.get('centernesses')\n num_pos = pos_assigned_gt_inds.size(0)\n\n if gt_masks.size(0) == 0 or num_pos == 0:\n return mask_preds, None, 0\n # Since we're producing (near) full image masks,\n # it'd take too much vram to backprop on every single mask.\n # Thus we select only a subset.\n if (self.max_masks_to_train != -1) and \\\n (num_pos > self.max_masks_to_train):\n perm = torch.randperm(num_pos)\n select = perm[:self.max_masks_to_train]\n mask_preds = mask_preds[select]\n pos_assigned_gt_inds = pos_assigned_gt_inds[select]\n num_pos = self.max_masks_to_train\n elif self.topk_masks_per_img != -1:\n unique_gt_inds = pos_assigned_gt_inds.unique()\n num_inst_per_gt = max(\n int(self.topk_masks_per_img / len(unique_gt_inds)), 1)\n\n keep_mask_preds = []\n keep_pos_assigned_gt_inds = []\n for gt_ind in unique_gt_inds:\n per_inst_pos_inds = (pos_assigned_gt_inds == gt_ind)\n mask_preds_per_inst = mask_preds[per_inst_pos_inds]\n gt_inds_per_inst = pos_assigned_gt_inds[per_inst_pos_inds]\n if sum(per_inst_pos_inds) > num_inst_per_gt:\n per_inst_scores = scores[per_inst_pos_inds].sigmoid().max(\n dim=1)[0]\n per_inst_centerness = centernesses[\n per_inst_pos_inds].sigmoid().reshape(-1, )\n select = (per_inst_scores * per_inst_centerness).topk(\n k=num_inst_per_gt, dim=0)[1]\n mask_preds_per_inst = mask_preds_per_inst[select]\n gt_inds_per_inst = gt_inds_per_inst[select]\n keep_mask_preds.append(mask_preds_per_inst)\n keep_pos_assigned_gt_inds.append(gt_inds_per_inst)\n mask_preds = torch.cat(keep_mask_preds)\n pos_assigned_gt_inds = torch.cat(keep_pos_assigned_gt_inds)\n num_pos = pos_assigned_gt_inds.size(0)\n\n # Follow the origin implement\n start = int(self.mask_out_stride // 2)\n gt_masks = gt_masks[:, start::self.mask_out_stride,\n start::self.mask_out_stride]\n gt_masks = gt_masks.gt(0.5).float()\n pos_mask_targets = gt_masks[pos_assigned_gt_inds]\n\n return (mask_preds, pos_mask_targets, num_pos)","def generate_mnist_datasets(\n datapoints_per_task,\n K_list,\n cir_inner_loop_list, \n test_task_idx, \n val_task_idx,\n n_finetune_sets):\n\n # arbitrarily chosen, class-imbalance rate in outer and inner training loops\n cir_outer_loop = 0.5\n cir_inner_loop = 0.5\n # class-imbalance rate in the test sets of the test and validation tasks\n cir_test = 0.5\n # arbitrarily chosen, percentage of data that will be used in the inner training loop\n percent_data_inner_loop = 0.5\n\n percent_data_finetune_val = 0.8\n\n n_test_set = 4000\n\n test_task_idx, val_task_idx = test_task_idx, val_task_idx\n\n finetune_sets_per_K_cir = {}\n test_task_test_set, val_task = {}, {}\n \n\n train_task_list_inner, train_task_list_outer = [], []\n\n train_tasks_idxs = [i for i in range(0,10) if i not in [val_task_idx, test_task_idx]]\n\n base_path = '/home/USER/Documents'\n if (not (os.path.exists(base_path))):\n base_path = '/home/ubuntu/Projects'\n train_images, train_labels = loadlocal_mnist(\n images_path= base_path + '/MAML/raw_data/MNIST_data/train-images-idx3-ubyte', \n labels_path= base_path + '/MAML/raw_data/MNIST_data/train-labels-idx1-ubyte')\n\n test_images, test_labels = loadlocal_mnist(\n images_path= base_path + '/MAML/raw_data/MNIST_data/t10k-images-idx3-ubyte', \n labels_path= base_path + '/MAML/raw_data/MNIST_data/t10k-labels-idx1-ubyte')\n\n\n train_images, test_images = train_images.reshape((-1,28,28))/255.0, test_images.reshape((-1,28,28))/255.0\n images = np.concatenate((train_images, test_images))\n labels = np.concatenate((train_labels, test_labels))\n\n test_task_normal_indexes, val_task_normal_indexes = list(np.nonzero(labels == test_task_idx)[0]), list(np.nonzero(train_labels == val_task_idx)[0])\n test_task_X_normal, val_task_X_normal = images[test_task_normal_indexes],train_images[val_task_normal_indexes]\n test_task_Y_normal, val_task_Y_normal = np.zeros_like(labels[test_task_normal_indexes]), np.zeros_like(train_labels[val_task_normal_indexes])\n\n\n # val and test task have anomalies (samples of other numbers) that are not used for training\n # besides the two sets of anomalies (one for val task and one for test task are disjoint)\n test_task_anomalous_indexes = list(np.nonzero(test_labels[:5000] != test_task_idx)[0])\n val_task_anomalous_indexes= [index for index, element in enumerate(list(test_labels[5000:])) if element not in [val_task_idx, test_task_idx]]\n\n\n test_task_X_anomalous, val_task_X_anomalous = test_images[:5000][test_task_anomalous_indexes],test_images[5000:][val_task_anomalous_indexes]\n test_task_Y_anomalous, val_task_Y_anomalous = np.ones_like(test_labels[:5000][test_task_anomalous_indexes]), np.ones_like(test_labels[5000:][val_task_anomalous_indexes])\n\n test_task_X, val_task_X = np.concatenate((test_task_X_normal, test_task_X_anomalous)), np.concatenate((val_task_X_normal, val_task_X_anomalous))\n test_task_Y, val_task_Y = np.expand_dims(np.concatenate((test_task_Y_normal, test_task_Y_anomalous)),-1), np.expand_dims(np.concatenate((val_task_Y_normal, val_task_Y_anomalous)),-1)\n\n\n train_tasks_X_list, train_tasks_Y_list = [], []\n for task_idx in train_tasks_idxs:\n train_task_normal_indexes = list(np.nonzero(train_labels == task_idx)[0]) \n train_task_anomalous_indexes = [index for index, element in enumerate(list(train_labels)) if element not in [task_idx, val_task_idx, test_task_idx]]\n assert(len(np.nonzero(train_labels[train_task_anomalous_indexes] == val_task_idx)[0]) == 0)\n assert(len(np.nonzero(train_labels[train_task_anomalous_indexes] == test_task_idx)[0]) == 0)\n train_task_X_normal, train_task_X_anomalous = train_images[train_task_normal_indexes], train_images[train_task_anomalous_indexes]\n train_task_Y_normal, train_task_Y_anomalous = np.zeros_like(train_labels[train_task_normal_indexes]), np.ones_like(train_labels[train_task_anomalous_indexes])\n train_task_X, train_task_Y = np.concatenate((train_task_X_normal, train_task_X_anomalous)), np.concatenate((train_task_Y_normal, train_task_Y_anomalous))\n train_tasks_X_list.append(train_task_X)\n train_tasks_Y_list.append(np.expand_dims(train_task_Y,-1))\n\n\n\n # building test task sets of data\n normal_indexes, anomaly_indexes = list(np.nonzero(test_task_Y == 0)[0]), list(np.nonzero(test_task_Y == 1)[0])\n n_test_set_normal = int(n_test_set*cir_test)\n test_set_normal_indexes = random.sample(normal_indexes, n_test_set_normal)\n test_set_anomaly_indexes = random.sample(anomaly_indexes, n_test_set - n_test_set_normal)\n test_set_indexes = []\n test_set_indexes += test_set_normal_indexes\n test_set_indexes += test_set_anomaly_indexes\n\n test_task_test_set['test_X'], test_task_test_set['test_Y'] = test_task_X[test_set_indexes], test_task_Y[test_set_indexes]\n\n\n #shuffle\n s_test = np.arange(test_task_test_set['test_X'].shape[0])\n np.random.shuffle(s_test)\n test_task_test_set['test_X'], test_task_test_set['test_Y'] = test_task_test_set['test_X'][s_test], test_task_test_set['test_Y'][s_test]\n\n rest_normal_indexes = [index for index in normal_indexes if index not in test_set_normal_indexes]\n rest_anomaly_indexes = [index for index in anomaly_indexes if index not in test_set_anomaly_indexes]\n\n\n for K in K_list:\n finetune_sets_per_cir = {}\n for cir in cir_inner_loop_list:\n\n rest_normal_indexes = [index for index in normal_indexes if index not in test_set_normal_indexes]\n rest_anomaly_indexes = [index for index in anomaly_indexes if index not in test_set_anomaly_indexes]\n \n finetune_sets_list = []\n\n disjoint = False\n if(cir*K*n_finetune_sets<len(rest_normal_indexes)):\n disjoint = True\n\n n_finetune_normal = int(K*cir)\n n_finetune_anomaly = K - n_finetune_normal\n for i in range(n_finetune_sets):\n # if enough for disjoint do that\n # else sample randomly\n # store in a dict with keys cir_K\n finetune_normal_indexes = random.sample(rest_normal_indexes, n_finetune_normal)\n finetune_anomaly_indexes = random.sample(rest_anomaly_indexes, n_finetune_anomaly)\n finetune_indexes = []\n finetune_indexes += finetune_normal_indexes\n finetune_indexes += finetune_anomaly_indexes\n finetune_set = {}\n finetune_set['finetune_X'], finetune_set['finetune_Y'] = test_task_X[finetune_indexes], test_task_Y[finetune_indexes]\n\n #shuffle\n s_finetune = np.arange(finetune_set['finetune_X'].shape[0])\n np.random.shuffle(s_finetune)\n finetune_set['finetune_X'], finetune_set['finetune_Y'] = finetune_set['finetune_X'][s_finetune], finetune_set['finetune_Y'][s_finetune]\n\n finetune_sets_list.append(finetune_set)\n \n if(disjoint):\n rest_normal_indexes = [index for index in rest_normal_indexes if index not in finetune_normal_indexes]\n rest_anomaly_indexes = [index for index in rest_anomaly_indexes if index not in finetune_anomaly_indexes]\n\n finetune_sets_per_cir[str(cir)] = finetune_sets_list\n\n finetune_sets_per_K_cir[str(K)] = finetune_sets_per_cir\n\n\n #building val task sets of data\n normal_indexes, anomaly_indexes = list(np.nonzero(val_task_Y == 0)[0]), list(np.nonzero(val_task_Y == 1)[0])\n n_val_finetune = int(percent_data_finetune_val*datapoints_per_task)\n n_val_test_set = datapoints_per_task - n_val_finetune\n n_val_test_set_normal = int(n_val_test_set*cir_test)\n val_test_set_normal_indexes = random.sample(normal_indexes, n_val_test_set_normal)\n\n\n val_test_set_anomaly_indexes = random.sample(anomaly_indexes, n_val_test_set - n_val_test_set_normal)\n val_test_set_indexes = []\n val_test_set_indexes += val_test_set_normal_indexes\n val_test_set_indexes += val_test_set_anomaly_indexes\n val_task['test_X'], val_task['test_Y'] = val_task_X[val_test_set_indexes], val_task_Y[val_test_set_indexes]\n\n\n rest_normal_indexes = [index for index in normal_indexes if index not in val_test_set_normal_indexes]\n rest_anomaly_indexes = [index for index in anomaly_indexes if index not in val_test_set_anomaly_indexes]\n\n n_val_finetune_normal = int(n_val_finetune*cir_inner_loop)\n val_finetune_normal_indexes = random.sample(rest_normal_indexes, n_val_finetune_normal)\n val_finetune_anomaly_indexes = random.sample(rest_anomaly_indexes, n_val_finetune - n_val_finetune_normal)\n val_finetune_indexes = []\n val_finetune_indexes += val_finetune_normal_indexes\n val_finetune_indexes += val_finetune_anomaly_indexes\n\n val_task['finetune_X'], val_task['finetune_Y'] = val_task_X[val_finetune_indexes], val_task_Y[val_finetune_indexes]\n\n #shuffle\n s_val_finetune = np.arange(val_task['finetune_X'].shape[0])\n s_val_test = np.arange(val_task['test_X'].shape[0])\n np.random.shuffle(s_val_finetune)\n np.random.shuffle(s_val_test)\n\n val_task['finetune_X'], val_task['finetune_Y'] = val_task['finetune_X'][s_val_finetune], val_task['finetune_Y'][s_val_finetune]\n val_task['test_X'], val_task['test_Y'] = val_task['test_X'][s_val_test], val_task['test_Y'][s_val_test]\n\n\n\n # building sets of data of the training tasks\n for task_X, task_Y in zip(train_tasks_X_list, train_tasks_Y_list):\n normal_indexes, anomaly_indexes = list(np.nonzero(task_Y == 0)[0]), list(np.nonzero(task_Y == 1)[0])\n\n n_inner_loop = int(percent_data_inner_loop*datapoints_per_task)\n n_inner_loop_normal = int(n_inner_loop*cir_inner_loop)\n n_outer_loop = datapoints_per_task - n_inner_loop\n n_outer_loop_normal = int(n_outer_loop*cir_outer_loop)\n \n inner_loop_normal_indexes = random.sample(normal_indexes, n_inner_loop_normal)\n inner_loop_anomaly_indexes = random.sample(anomaly_indexes, n_inner_loop - n_inner_loop_normal)\n inner_loop_indexes = []\n inner_loop_indexes += inner_loop_normal_indexes\n inner_loop_indexes += inner_loop_anomaly_indexes\n\n train_task_inner_X, train_task_inner_Y = task_X[inner_loop_indexes], task_Y[inner_loop_indexes]\n\n rest_normal_indexes = [index for index in normal_indexes if index not in inner_loop_normal_indexes]\n rest_anomaly_indexes = [index for index in anomaly_indexes if index not in inner_loop_anomaly_indexes]\n\n \n outer_loop_normal_indexes = random.sample(rest_normal_indexes, n_outer_loop_normal)\n outer_loop_anomaly_indexes = random.sample(rest_anomaly_indexes, n_outer_loop - n_outer_loop_normal)\n outer_loop_indexes = []\n outer_loop_indexes += outer_loop_normal_indexes\n outer_loop_indexes += outer_loop_anomaly_indexes\n\n train_task_outer_X, train_task_outer_Y = task_X[outer_loop_indexes], task_Y[outer_loop_indexes]\n\n\n s_inner = np.arange(train_task_inner_X.shape[0])\n s_outer = np.arange(train_task_outer_X.shape[0])\n np.random.shuffle(s_inner)\n np.random.shuffle(s_outer)\n train_task_list_inner.append([train_task_inner_X[s_inner],train_task_inner_Y[s_inner]])\n train_task_list_outer.append([train_task_outer_X[s_outer],train_task_outer_Y[s_outer]])\n\n\n\n train_tasks_inner_X = np.stack([train_task_list_inner[i][0]\n for i in range(len(train_task_list_inner))], 0)\n train_tasks_inner_Y = np.stack([train_task_list_inner[i][1]\n for i in range(len(train_task_list_inner))], 0)\n train_tasks_outer_X = np.stack([train_task_list_outer[i][0]\n for i in range(len(train_task_list_outer))], 0)\n train_tasks_outer_Y = np.stack([train_task_list_outer[i][1]\n for i in range(len(train_task_list_outer))], 0)\n\n \n train_tasks = {'X_train_inner': train_tasks_inner_X,\n 'Y_train_inner': train_tasks_inner_Y,\n 'X_train_outer': train_tasks_outer_X,\n 'Y_train_outer': train_tasks_outer_Y\n }\n\n\n return train_tasks, val_task, test_task_test_set, finetune_sets_per_K_cir"],"string":"[\n \"def compute_superset_with_k(true_bundle, kdt, prototypes, k=1000, distance_func=bundles_distances_mam):\\n true_bundle = np.array(true_bundle, dtype=np.object)\\n dm_true_bundle = distance_func(true_bundle, prototypes)\\n D, I = kdt.query(dm_true_bundle, k=k)\\n superset_idx = np.unique(I.flat)\\n # recompute the gt streamlines idx\\n I = kdt.query_radius(dm_true_bundle, 10e-4)\\n gt_idx = []\\n for arr in I:\\n for e in arr:\\n gt_idx.append(e)\\n gt_idx = list(set(gt_idx))\\n superset_idx = list(set(superset_idx))\\n print('gt length: %d' % len(gt_idx))\\n additional_idx = list(set(superset_idx) - set(gt_idx))\\n\\n return additional_idx, gt_idx\",\n \"def compute_superset(true_tract, kdt, prototypes, k=1000, distance_func=bundles_distances_mam):\\n true_tract = np.array(true_tract, dtype=np.object)\\n dm_true_tract = distance_func(true_tract, prototypes)\\n D, I = kdt.query(dm_true_tract, k=k)\\n superset_idx = np.unique(I.flat)\\n return superset_idx\",\n \"def __init__(self, training_set, k=5):\\n super(Knn, self).__init__(training_set)\\n self.k = k\",\n \"def __init__(self, trainset, k=3):\\n self._trainset = trainset\\n self.k = k\",\n \"def knn(p, k, x, t):\\r\\n\\r\\n # Number of instances in data set\\r\\n N = x.shape[0]\\r\\n\\r\\n Euclidean_Distance = numpy.square(x - p) #Euclidean distance\\r\\n dis = numpy.sum(Euclidean_Distance, axis=1) #sum of the euclidean distance\\r\\n inds = numpy.argsort(dis)[:k] #sort the indices of the distance array\\r\\n tgt_cat = Counter([t[i] for i in inds]) #count the times of equivalent target labels\\r\\n top_class = max(tgt_cat, key= tgt_cat.get) #top class among the k nearest points\\r\\n\\r\\n\\r\\n #top_class = 0\\r\\n\\r\\n return top_class\",\n \"def k_nearest_neighbors(x_test, df_training, k):\\n\\n return np.argpartition(distance_to_each_training_point(x_test,\\n df_training), k-1)[:,0:k]\",\n \"def kNN_train(self, x_train, y_train, x_test, k = 5, processing = None, distMethod = \\\"Manhattan\\\"):\\n y_test = list()\\n\\n if processing == \\\"Scalar\\\":\\n # print(\\\"Preprocessing = Scalar\\\")\\n stdScalar = preprocessing.StandardScaler().fit(x_train)\\n x_train = stdScalar.transform(x_train)\\n x_test = stdScalar.transform(x_test)\\n\\n elif processing == \\\"MinMax\\\":\\n\\n # print(\\\"Preprocessing = MinMax\\\")\\n mmScalar = preprocessing.MinMaxScaler()\\n x_train = mmScalar.fit_transform(x_train)\\n x_test = mmScalar.fit_transform(x_test)\\n\\n elif processing == \\\"None\\\":\\n self.true = True\\n # print(\\\"No Preprocessing\\\")\\n\\n else:\\n print(\\\"wrong processing\\\")\\n exit()\\n\\n for i in range(0, len(x_test)):\\n y_test_temp = list()\\n zeroCount = 0\\n oneCount = 0\\n\\n # find distance of a instance in test test to all instances in training set\\n for j in range(0, len(x_train)):\\n if distMethod == \\\"Manhattan\\\":\\n y_test_temp.append(self.manhattan(x_train[j], x_test[i]))\\n elif distMethod == \\\"Euclidean\\\":\\n y_test_temp.append(self.euclidean(x_train[j], x_test[i]))\\n else:\\n print \\\"something wrong with distance calculation\\\"\\n exit()\\n\\n # take indices of k nearest points\\n # print y_test_temp\\n temp = np.asarray(y_test_temp).argsort()[:k]\\n # check class of each of k nearest points\\n for tmp in temp:\\n if y_train[tmp] == 0:\\n zeroCount += 1\\n elif y_train[tmp] == 1:\\n oneCount += 1\\n else:\\n print(\\\"something wrong in counting\\\")\\n\\n # classify\\n if zeroCount >= oneCount:\\n y_test.append(int(0))\\n elif oneCount > zeroCount:\\n y_test.append(int(1))\\n else:\\n print(\\\"somethign wrong\\\")\\n\\n # print y_test\\n return y_test\",\n \"def __call__(self, output, target):\\n maxk = max(self.topk)\\n batch_size = target.size(0)\\n\\n _, pred = output.topk(maxk, 1, True, True)\\n pred = pred.t()\\n correct = pred.eq(target.view(1, -1).expand_as(pred))\\n\\n res = []\\n for k in self.topk:\\n correct_k = correct[:k].view(-1).float().sum(0)\\n res.append(correct_k.mul_(100.0 / batch_size))\\n return res\",\n \"def choose_k_away(k, node, precomputed=True):\\n if precomputed:\\n sps = np.load('./short_paths/' + str(node) + '.npy')\\n else:\\n sps = shortest_path(node)\\n nodes = []\\n for index, x in enumerate(sps):\\n if x == k and index in poss_targets:\\n nodes.append(index)\\n return nodes\",\n \"def knnSame(k, Xtrain, Ytrain):\\n d = euclidean_distances(Xtrain, squared=True)\\n np.fill_diagonal(d, np.inf)\\n nnc = Ytrain[np.argsort(d)[..., :k].flatten()].reshape(Xtrain.shape[0], k)\\n pred = [max(nnc[i], key=Counter(nnc[i]).get) for i in range(nnc.shape[0])]\\n return np.array(pred)\",\n \"def correct(output, target, topk=(1,)):\\n maxk = max(topk)\\n _, pred = output.topk(maxk, 1, True, True)\\n pred = pred.t()\\n correct = pred.eq(target.view(1, -1).expand_as(pred))\\n res = []\\n for k in topk:\\n correct_k = correct[:k].view(-1).sum(0, keepdim=True)\\n res.append(correct_k)\\n return res\",\n \"def correct(output, target, topk=(1,)):\\n maxk = max(topk)\\n _, pred = output.topk(maxk, 1, True, True)\\n pred = pred.t()\\n correct = pred.eq(target.view(1, -1).expand_as(pred))\\n res = []\\n for k in topk:\\n correct_k = correct[:k].view(-1).sum(0, keepdim=True)\\n res.append(correct_k)\\n return res\",\n \"def subsample_training_set(self, k, seed=None):\\n if seed is not None:\\n rng_state = th.random.get_rng_state()\\n with th.random.fork_rng():\\n th.manual_seed(seed)\\n self._subsample_training_set(k)\\n if any(th.random.get_rng_state() != rng_state):\\n raise ValueError(\\\"Bad RNG state\\\")\\n else:\\n self._subsample_training_set(k)\",\n \"def get_target_root_partitions_subset(self) -> PartitionsSubset:\\n partitioned_asset_keys = {\\n asset_key\\n for asset_key in self.target_subset.asset_keys\\n if self.target_subset.asset_graph.get_partitions_def(asset_key) is not None\\n }\\n\\n root_partitioned_asset_keys = (\\n AssetSelection.keys(*partitioned_asset_keys)\\n .sources()\\n .resolve(self.target_subset.asset_graph)\\n )\\n\\n # Return the targeted partitions for the root partitioned asset keys\\n return self.target_subset.get_partitions_subset(next(iter(root_partitioned_asset_keys)))\",\n \"def check_subset(P1,P2,k=-1):\",\n \"def evalute_subset(X_train, X_test, y_train, y_test):\\r\\n clf = KNeighborsClassifier(n_neighbors=3)\\r\\n clf.fit(X_train, y_train) \\r\\n y_pred = clf.predict(X_test)\\r\\n return accuracy_score(y_test, y_pred)\",\n \"def build_full_trainset(self):\\n\\n return self.construct_trainset(self.raw_ratings)\",\n \"def __init__(self, k: int, training_set: np.ndarray):\\n self._k = k\\n self._training_set = training_set\",\n \"def knn0(pnts, p, k):\\r\\n p = np.asarray(p)\\r\\n pnts = np.asarray(pnts)\\r\\n diff = pnts - p[np.newaxis, :]\\r\\n d = np.einsum('ij,ij->i', diff, diff)\\r\\n idx = np.argsort(d)[:k]\\r\\n# s = [i.tolist() for i in pnts[idx]]\\r\\n return pnts[idx].tolist()\",\n \"def knn(k, Xtrain, Ytrain, Xtest):\\n d = euclidean_distances(Xtest, Xtrain, squared=True)\\n nnc = Ytrain[np.argsort(d)[..., :k].flatten()].reshape(Xtest.shape[0], k)\\n pred = [max(nnc[i], key=Counter(nnc[i]).get) for i in range(nnc.shape[0])]\\n return np.array(pred)\",\n \"def fit_full(self, K):\\r\\n pass\",\n \"def KS_algorithm(dataset_x, dataset_y, train_size = 0.7, return_dataset = True):\\n #Decide number of total samples\\n n = dataset_x.shape[0]\\n \\n #Turn into train_size to float\\n try: \\n train_size = float(train_size)\\n except Expection as e:\\n print(e)\\n \\n #Jusge if the train_size is by ratio or quantity\\n if 0 < train_size < 1: \\n num_of_samples = round(n*train_size)\\n elif 1 < train_size < 2: \\n return \\\"Train size must be between 1 to 0 or 2 to number of total samples\\\" \\n elif train_size == n or train_size == 1:\\n return [x for x in range(n)]\\n elif 2 <= train_size < n:\\n num_of_samples = round(train_size)\\n elif train_size > n:\\n return \\\"Num_of_samples must be less than or equal to original number of samples\\\"\\n \\n #Create original sample index\\n samples = [x for x in range(n)]\\n \\n #Create distance matrix\\n print('Building distance matrix')\\n dist_matrix = create_dist_matrix(dataset_x)\\n\\n #Pick first two furthest away data points\\n print('Picking two furthest away samples')\\n max_array = np.argmax(dist_matrix == np.max(dist_matrix), axis = 1)\\n ind1 = max_array[max_array != 0][0]\\n ind2 = max_array[ind1]\\n\\n #Initiate chosen sample list, append first two samples and remove from remaining sample list\\n chosen_samples = [ind1, ind2]\\n samples.remove(ind1)\\n samples.remove(ind2) \\n remaining_samples = num_of_samples - 2\\n\\n #Continue picking samples if not yet reaching target number of samples\\n while remaining_samples > 0:\\n sub_array = np.min(dist_matrix[:,chosen_samples], axis=1)\\n chosen = np.argmax(sub_array)\\n chosen_samples.append(chosen)\\n samples.remove(chosen)\\n remaining_samples -= 1\\n \\n #See if this function should return train and test set or simply index of chosen samples\\n if return_dataset == True:\\n not_chosen_samples = [x for x in range(n) if x not in chosen_samples]\\n return dataset_x.iloc[chosen_samples,:], dataset_x.iloc[not_chosen_samples,:], dataset_y.iloc[chosen_samples,:], dataset_y.iloc[not_chosen_samples,:] \\n else:\\n return chosen_samples\",\n \"def findknn(xTr,xTe,k):\\n\\n # YOUR CODE HERE\\n if k > len(xTr):\\n k = len(xTr)\\n \\n D=l2distance(xTe, xTr)\\n (m,n) = D.shape\\n \\n indices = []\\n dists = []\\n for i in range(m):\\n smallest_indices = np.argsort(D[i])\\n ind = smallest_indices[:k]\\n dis = D[i,smallest_indices[:k]]\\n indices.append(ind)\\n dists.append(dis)\\n \\n indices = np.transpose(np.array(indices))\\n dists = np.transpose(np.array(dists))\\n return indices, dists\",\n \"def get_neighbors(training_set, \\r\\n labels, \\r\\n test_instance, \\r\\n k, \\r\\n distance=distance):\\r\\n distances = []\\r\\n for index in range(len(training_set)):\\r\\n dist = distance(test_instance, training_set[index])\\r\\n distances.append((training_set[index], dist, labels[index]))\\r\\n distances.sort(key=lambda x: x[1])\\r\\n neighbors = distances[:k]\\r\\n return neighbors\",\n \"def __init__(self, top_k):\\n super(MinibatchTopkAccuracy, self).__init__(\\n reset_at=\\\"iteration\\\", emit_at=\\\"iteration\\\", mode=\\\"train\\\", top_k=top_k\\n )\\n self.top_k = top_k\",\n \"def knn(trainingSetData, testSetData, k):\\n trainingSet = trainingSetData.drop([14], axis=1) # drop income\\n testSet = testSetData.drop([14], axis=1) # drop income\\n\\n distances = {}\\n # this will store the distances re-sorted in ascending/descending order\\n sort = {}\\n # income band results (>=50k or <50K)\\n incomePredictions = []\\n\\n # Calculating euclidean distance between each row of training data and test data instance\\n for testInstance in range(len(testSet)): # len(testSet)\\n \\n # Store current test Point:\\n testInstance = testSet.iloc[testInstance] \\n \\n distances = euclideanDistanceRow(testInstance, trainingSet)\\n\\n # sort the distances in order of smallest first:\\n sorted_d = sorted(distances.items(), key=lambda x: x[1], reverse=False)\\n\\n neighbors = []\\n\\n # Extracting top k neighbors\\n for x in range(k):\\n neighbors.append(sorted_d[x])\\n\\n\\n classVotes = {}\\n\\n # Calculating the most freq class in the neighbors\\n results = {\\\"lessThan50\\\": 0, \\\"moreThan50\\\": 0}\\n\\n # creating a dataframe to which we will add the income values:\\n\\n for x in range(len(neighbors)):\\n if (trainingSetData.iloc[neighbors[x][0]][14] == 0.0):\\n results[\\\"lessThan50\\\"] += 1\\n elif (trainingSetData.iloc[neighbors[x][0]][14] == 1.0):\\n results[\\\"moreThan50\\\"] += 1\\n\\n print('results',results)\\n\\n if (results[\\\"lessThan50\\\"] > results[\\\"moreThan50\\\"]):\\n incomePredictions.append(0.0)\\n elif (results[\\\"lessThan50\\\"] < results[\\\"moreThan50\\\"]):\\n incomePredictions.append(1.0)\\n\\n return incomePredictions\",\n \"def fit(self, trainingFeatures, trainingTargets):\\r\\n\\r\\n \\\"\\\"\\\" Implement kNN learning below. \\\"\\\"\\\"\\r\\n\\r\\n self._fitCalled = True\\r\\n self.labels = trainingTargets\\r\\n self.data = trainingFeatures\",\n \"def k_shortest_paths(\\n self,\\n G,\\n source,\\n target,\\n k=1,\\n weight='weight',\\n ):\\n\\n if source == target:\\n return ([0], [[source]])\\n\\n (length, path) = nx.single_source_dijkstra(G, source, target,\\n weight=weight)\\n if target not in length:\\n raise nx.NetworkXNoPath('node %s not reachable from %s' % (source,\\n target))\\n\\n lengths = [length[target]]\\n paths = [path[target]]\\n c = count()\\n B = []\\n\\n # Is deep copy really required?\\n # Fails due to embedded Ctype objects which can not be pickled\\n # # G_original = G.copy()\\n # Swapping with shallow copy...will it work?\\n\\n G_original = G\\n if nx.is_directed(G_original):\\n G = nx.DiGraph(G_original)\\n else:\\n G = nx.Graph(G_original)\\n\\n ######################################\\n #TODO: wrap this up somehow\\n print ''\\n print term.move_up + term.move_up\\n ######################################\\n print 'getting K:{} paths...'.format(k),\\n for i in range(1, k):\\n with term.location():\\n print i\\n for j in range(len(paths[-1]) - 1):\\n spur_node = paths[-1][j]\\n root_path = (paths[-1])[:j + 1]\\n\\n edges_removed = []\\n for c_path in paths:\\n if len(c_path) > j and root_path == c_path[:j + 1]:\\n u = c_path[j]\\n v = c_path[j + 1]\\n if G.has_edge(u, v):\\n edge_attr = G.edge[u][v]\\n G.remove_edge(u, v)\\n edges_removed.append((u, v, edge_attr))\\n\\n for n in range(len(root_path) - 1):\\n node = root_path[n]\\n\\n # out-edges\\n\\n for (u, v, edge_attr) in G.edges_iter(node, data=True):\\n\\n # print 'lala1: {} -> {}'.format(u,v)\\n\\n G.remove_edge(u, v)\\n edges_removed.append((u, v, edge_attr))\\n\\n if G.is_directed():\\n\\n # in-edges\\n\\n for (u, v, edge_attr) in G.in_edges_iter(node,\\n data=True):\\n\\n # print 'lala2: {} -> {}'.format(u,v)\\n\\n G.remove_edge(u, v)\\n edges_removed.append((u, v, edge_attr))\\n\\n (spur_path_length, spur_path) = nx.single_source_dijkstra(G,\\n spur_node, target, weight=weight)\\n if target in spur_path and spur_path[target]:\\n total_path = root_path[:-1] + spur_path[target]\\n total_path_length = self.get_path_length(G_original,\\n root_path, weight) + spur_path_length[target]\\n heappush(B, (total_path_length, next(c), total_path))\\n\\n for e in edges_removed:\\n (u, v, edge_attr) = e\\n G.add_edge(u, v, edge_attr)\\n\\n if B:\\n (l, _, p) = heappop(B)\\n lengths.append(l)\\n paths.append(p)\\n else:\\n break\\n\\n return (lengths, paths)\",\n \"def dp_kendall_feature_selection(features, labels, k, epsilon):\\n # Remove intercept feature\\n features = features[:, :-1]\\n _, d = features.shape\\n split_eps = epsilon / k\\n selected_indices = []\\n # selected_correlations tracks correlations between features and previously\\n # selected features\\n selected_correlations = np.zeros((k - 1, d))\\n label_coefficients = kendall(features, labels)\\n # Sensitivity is 3/2 in the first round and 3 after\\n sensitivity = 3/2\\n for j in range(k):\\n diffs = label_coefficients\\n if selected_indices:\\n last_idx = selected_indices[-1]\\n old_feature = features[:, last_idx]\\n selected_correlations[j - 1, :] = kendall(features, old_feature)\\n diffs = diffs - (\\n np.sum(selected_correlations, axis=0) / len(selected_indices)\\n )\\n sensitivity = 3\\n new_idx = top_k.basic_peeling_mechanism(\\n item_counts=diffs,\\n k=1,\\n epsilon=split_eps,\\n l_inf_sensitivity=sensitivity,\\n monotonic=False,\\n )[0]\\n selected_indices.append(new_idx)\\n label_coefficients[new_idx] = -np.inf\\n # Add back intercept feature\\n selected_indices.append(d)\\n return np.asarray(selected_indices)\",\n \"def build_knn(coords, k=6, **kwargs):\\n \\n tree = BallTree(coords, **kwargs)\\n _, ind = tree.query(coords, k=k+1) # the first k is \\\"oneself\\\"\\n pairs = pairs_from_knn(ind)\\n return pairs\",\n \"def fetch_unlabelled(self, k, show_progress = False):\\n \\n self.candidates = self.get_unseen()\\n if self.subsample and (self.subsample < len(self.candidates)):\\n self.candidates = np.random.choice(self.candidates, self.subsample, replace = False).tolist()\\n if len(self.candidates) < k:\\n k = len(self.candidates)\\n \\n entropy = AppendedConditionalEntropy(self)\\n steps = trange(k) if show_progress else range(k)\\n for it in steps:\\n \\n if self.parallelized and (it > 0):\\n with Pool(initializer = _init_pool, initargs = (entropy,)) as p:\\n ce = p.map(_parallel_ce, self.candidates)\\n else:\\n ce = [entropy(i) for i in self.candidates]\\n \\n min_ind = np.argmin(ce)\\n entropy.append(self.candidates[min_ind])\\n del self.candidates[min_ind]\\n \\n return entropy.ret\",\n \"def train(self, train_set) -> None:\\n super().train(train_set)\\n # split into data and target\\n xlist, y = zip(*train_set)\\n x = sparse.vstack(xlist)\\n self._classifier.fit(x, y)\",\n \"def selectFeatures(k_features=5, *args):\\n X, y = args\\n skb = SelectKBest(k=k_features)\\n return skb.fit_transform(X, y)\",\n \"def top_k_1_5(output: torch.Tensor, target: torch.Tensor, topk=(1, 5)) -> List[torch.FloatTensor]:\\n with torch.no_grad():\\n # ---- get the topk most likely labels according to your model\\n # get the largest k \\\\in [n_classes] (i.e. the number of most likely probabilities we will use)\\n # max number labels we will consider in the right choices for out model\\n maxk = max(topk)\\n batch_size = target.size(0)\\n\\n # get top maxk indicies that correspond to the most likely probability scores\\n # (note _ means we don't care about the actual top maxk scores just their corresponding indicies/labels)\\n # _, [B, n_classes] -> [B, maxk]\\n _, y_pred = output.topk(k=maxk, dim=1)\\n # [B, maxk] -> [maxk, B] Expects input to be <= 2-D tensor and transposes dimensions 0 and 1.\\n y_pred = y_pred.t()\\n\\n # - get the credit for each example if the models predictions is in maxk values (main crux of code)\\n # for any example, the model will get credit if it's prediction matches the ground truth\\n # for each example we compare if the model's best prediction matches the truth. If yes we get an entry of 1.\\n # if the k'th top answer of the model matches the truth we get 1.\\n # Note: this for any example in batch we can only ever get 1 match (so we never overestimate accuracy <1)\\n # [B] -> [B, 1] -> [maxk, B]\\n target_reshaped = target.view(1, -1).expand_as(y_pred)\\n # compare every topk's model prediction with the ground truth & give credit if any matches the ground truth\\n # [maxk, B] were for each example we know which topk prediction matched truth\\n correct = (y_pred == target_reshaped)\\n # original: correct = pred.eq(target.view(1, -1).expand_as(pred))\\n\\n # -- get topk accuracy\\n list_topk_accs = [] # idx is topk1, topk2, ... etc\\n for k in topk:\\n # get tensor of which topk answer was right\\n ind_which_topk_matched_truth = correct[:k] # [maxk, B] -> [k, B]\\n # flatten it to help compute if we got it correct for each example in batch\\n # [k, B] -> [kB]\\n flattened_indicator_which_topk_matched_truth = ind_which_topk_matched_truth.reshape(\\n -1).float()\\n # get if we got it right for any of our top k prediction for each example in batch\\n tot_correct_topk = flattened_indicator_which_topk_matched_truth.float().sum(\\n dim=0, keepdim=True) # [kB] -> [1]\\n # compute topk accuracy - the accuracy of the mode's ability to get it right within it's top k guesses/preds\\n topk_acc = tot_correct_topk / batch_size # topk accuracy for entire batch\\n list_topk_accs.append(topk_acc)\\n # list of topk accuracies for entire batch [topk1, topk2, ... etc]\\n return list_topk_accs\",\n \"def in_top_k(predictions, targets, k, name=None):\\n with ops.name_scope(name, \\\"in_top_k\\\"):\\n return gen_nn_ops.in_top_kv2(predictions, targets, k, name=name)\",\n \"def extended_training(X,Y):\\n trainSz = 4000\\n \\n X_, Y_ = extend_training_set(X[:trainSz],Y[:trainSz])\\n \\n lda = LDA()\\n lda.fit(X, Y)\\n X2 = lda.transform(X)\\n X_2 = lda.transform(X_)\\n \\n clf = neighbors.KNeighborsClassifier()\\n clf.fit(X_2, Y_)\\n \\n predict = clf.predict(X2)\\n return predict, lda, clf\",\n \"def _kneighbors(self, y):\\n if self.__x is None:\\n raise ValueError(\\\"Input dataset not fitted yet! Call .fit() first!\\\")\\n if self.__device and self.tools.device(y) != self.__device:\\n raise ValueError(\\\"Input dataset and query dataset must be on same device\\\")\\n if len(y.shape) != 2:\\n raise ValueError(\\\"Query dataset must be a 2D tensor\\\")\\n if self.__x.shape[-1] != y.shape[-1]:\\n raise ValueError(\\\"Query and dataset must have same dimensions\\\")\\n if self.__normalise:\\n y = y / self.tools.repeat(self.tools.norm(y, 2, -1), y.shape[1]).reshape(\\n -1, y.shape[1]\\n )\\n y = self.tools.contiguous(y)\\n y_labels = self.__assign(y)\\n\\n y_ranges, _, _ = cluster_ranges_centroids(y, y_labels)\\n self.__y_ranges = y_ranges\\n y, y_labels = self.__sort_clusters(y, y_labels, store_x=False)\\n x_LT = self.__LazyTensor(self.tools.unsqueeze(self.__x, 0))\\n y_LT = self.__LazyTensor(self.tools.unsqueeze(y, 1))\\n D_ij = self.__distance(y_LT, x_LT)\\n ranges_ij = from_matrix(y_ranges, self.__x_ranges, self.__keep)\\n D_ij.ranges = ranges_ij\\n nn = D_ij.argKmin(K=self.__k, axis=1)\\n return self.__unsort(nn)\",\n \"def find_partitions(V,k):\\n k_subs = k_subset(V,k)\\n k_subs = uniq_subsets(k_subs)\\n\\n return k_subs\",\n \"def trainSet(self):\\r\\n self.currIdx = 0\\r\\n random.shuffle(self.trainSamples)\\r\\n self.samples = self.trainSamples[:self.numTrainSamplesPerEpoch]\",\n \"def runmaxmin(self):\\n import random\\n random.seed(self.seed)\\n mindist_ptolandmarkset = np.full(self.pointcloud.size, np.inf)\\n self.subsetindices = []\\n for i in xrange(self.subsetsize):\\n if i == 0:\\n selected_index = random.randint(0, self.pointcloud.size - 1)\\n # update min for all the rest indices\\n # update min for this index to 0.\\n for z in xrange(self.pointcloud.size):\\n # if z == selected_index:\\n # mindist_ptolandmarkset[z] = 0.0\\n # else:\\n mindist_ptolandmarkset[z] = self.pointcloud.distmat[selected_index][z]\\n else:\\n selected_index = np.argmax(mindist_ptolandmarkset)\\n # update minimum distance for all points\\n for z in xrange(self.pointcloud.size):\\n mindist_ptolandmarkset[z] = min(mindist_ptolandmarkset[z],\\n self.pointcloud.distmat[selected_index][z])\\n\\n self.subsetindices.append(selected_index)\\n\\n self.subsetpointcloud = pc.PointCloud(self.pointcloud.points[self.subsetindices])\",\n \"def generate_superset(self, number):\\n \\n superset = []\\n for i in range(0, 2**(self.dim)):\\n if (number & i)==number:\\n superset.append(i)\\n return superset\",\n \"def _automatic_training_set(self, n_cutouts=100):\\n dstl = Load_DSTL()\\n np.random.seed(42)\\n for ii in range(n_cutouts):\\n # Get region around a shape\\n triples, mask, ind_shape, img_dim = dstl.extract_region(object_class=self.class_type, image_id='6120_2_2', buffer_size=10)\\n if self.radius is not None:\\n triples, mask = self._sliding_window(triples.reshape(*img_dim, 3), mask.reshape(img_dim), window_radius=self.radius)\\n # Add to Feature Matrix\\n if ii == 0:\\n features = triples\\n labels = mask\\n else:\\n features = np.vstack([features, triples])\\n labels = np.hstack([labels, mask])\\n return features, labels\",\n \"def knn(self,query_vec,k, stdev=False):\\n\\n sims = []\\n if k > len(self.terms):\\n k = len(self.terms)\\n sims = np.matmul(self.vectors, query_vec.vector)\\n if stdev:\\n sims = zscore(sims)\\n indices = np.argpartition(sims, -k)[-k:]\\n indices = sorted(indices, key=lambda i: sims[i], reverse=True)\\n results = []\\n for index in indices:\\n results.append([sims[index], self.terms[index]])\\n return results\",\n \"def select_seeds(self, graph, k):\\n raise NotImplementedError\\n return set()\",\n \"def generate_k_folds(dataset, k):\\n\\n # TODO: finish this.\\n folds = []\\n dataset = np.concatenate((dataset[0], np.array(dataset[1]).reshape(-1,1)), axis=1)\\n dataset_shape = dataset.shape\\n shape_test_set = int(round(dataset_shape[0]/k,0))\\n split_dataset = np.array_split(dataset,k,axis=0)\\n for i in range(k):\\n test_set = split_dataset[i]\\n c = [k for j,k in enumerate(split_dataset) if j!=i]\\n training_set = np.concatenate(c,axis=0)\\n if test_set.shape[0] != shape_test_set:\\n step = test_set.shape[0] - shape_test_set\\n test_set = test_set[:-step,:]\\n training_set = np.concatenate((training_set, test_set[-step:,:]), axis=0)\\n r_test_set = (test_set[:,:-1], list(test_set[:,-1]))\\n r_train_set = (training_set[:,:-1], list(training_set[:,-1]))\\n folds.append((r_train_set, r_test_set))\\n return folds\",\n \"def num_correct(output, target, topk=(1,)):\\n with torch.no_grad():\\n maxk = max(topk)\\n batch_size = target.size(0)\\n\\n _, pred = output.topk(maxk, 1, True, True)\\n pred = pred.t()\\n correct = pred.eq(target.view(1, -1).expand_as(pred))\\n\\n res = []\\n for k in topk:\\n correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)\\n res.append(correct_k)\\n return res, batch_size\",\n \"def Get(self,k:int): \\n ### get partitions depending on the partition schemes C that depends on k!\\n return subsets_k(list(range(self._n)),k)\",\n \"def existsSubset(S,t,k): \\n\\tn = len(S)\\n\\t# M[n+1][t+1][k+1]\\n\\tM = [[[False for _ in range(k+1)]for _ in range(t+1)]for _ in range(n+1)]\\n\\t\\n\\t# Base: if k is 0 and t is 0 \\n\\t# For any n, we can achieve 0 with size 0 subset \\n\\t# Otherwise, every other cell is False \\n\\tfor i in range(n+1): \\n\\t\\tM[i][0][0] = True\\n\\n\\t# Fill M[n][t][k]\\n\\t# The run-time of this algorithm is O(ntk)\\n\\t# because at worst we are filling in a 3D array \\n\\t# of size n*t*k\\n\\tfor i in range(1, n + 1):\\n\\t\\tfor j in range(1, t + 1):\\n\\t\\t\\tfor l in range(1, k + 1):\\n\\t\\t\\t\\t# Total target so far is less than array element \\n\\t\\t\\t\\tif(j < S[i-1]):\\n\\t\\t\\t\\t\\tM[i][j][l] = M[i-1][j][l]\\n\\t\\t\\t\\t# Total target so far is greater (or equal) than array element \\n\\t\\t\\t\\telif(j >= S[i-1]):\\n\\t\\t\\t\\t\\t# Leave \\n\\t\\t\\t\\t\\tleave = M[i-1][j][l]\\n\\t\\t\\t\\t\\t# Take \\n\\t\\t\\t\\t\\ttake = M[i-1][j - S[i-1]][l-1]\\n\\t\\t\\t\\t\\t# Only False or False will be False \\n\\t\\t\\t\\t\\tM[i][j][l] = leave or take \\n\\n\\t# If possible, back track \\n\\t# We are still basing back-tracking \\n\\t# off of the array of size ntk \\n\\t# so run-time remains at O(ntk)\\n\\tif(M[n][t][k]):\\n\\t\\t# Initialize subset array \\n\\t\\tsubset = [] \\n\\t\\t# Initialize i, j, and l \\n\\t\\ti, j, l = n, t, k\\n\\t\\t# While i, j, and l are all greater than 0 \\n\\t\\twhile(i != 0 and j != 0 and l != 0):\\n\\t\\t\\t# Check if M[i][j][l] == take \\n\\t\\t\\tif(M[i][j][l] == M[i-1][j - S[i-1]][l-1]):\\n\\t\\t\\t\\t# Add to subset \\n\\t\\t\\t\\tsubset += [S[i-1]]\\n\\t\\t\\t\\t# Decrement relevant indices \\n\\t\\t\\t\\tj -= S[i-1]\\n\\t\\t\\t\\ti -= 1 \\n\\t\\t\\t\\tl -= 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\ti -= 1\\n\\t\\t# Since we backtracked, reverse the subset\\n\\t\\tsubset.reverse()\\n\\t\\treturn subset \\n\\t# If not possible, return False \\n\\treturn M[n][t][k]\",\n \"def S(k):\\n\\n from scipy.special import binom as binomial\\n\\n TrueHset = [0]\\n if k > 1:\\n for j in np.arange(k, 0, -1, dtype=int):\\n TrueHset = list(set(TrueHset) | set([binomial(j, 2) + x for x in S(k - j)]))\\n return TrueHset\",\n \"def set_uniform_Kk(self, clip=True):\\n\\t\\t\\n\\t\\tKk1_los = random_matrix([self.Mm], params=[self.lo_Kk1_hyper_lo, \\n\\t\\t\\t\\t\\t\\t\\tself.lo_Kk1_hyper_hi], sample_type='uniform',\\n\\t\\t\\t\\t\\t\\t\\tseed=self.seed_Kk1)\\n\\t\\tKk1_his = random_matrix([self.Mm], params=[self.hi_Kk1_hyper_lo, \\n\\t\\t\\t\\t\\t\\t\\tself.hi_Kk1_hyper_hi], sample_type='uniform',\\n\\t\\t\\t\\t\\t\\t\\tseed=self.seed_Kk1)\\n\\t\\tKk2_los = random_matrix([self.Mm], params=[self.lo_Kk2_hyper_lo, \\n\\t\\t\\t\\t\\t\\t\\tself.lo_Kk2_hyper_hi], sample_type='uniform',\\n\\t\\t\\t\\t\\t\\t\\tseed=self.seed_Kk2)\\n\\t\\tKk2_his = random_matrix([self.Mm], params=[self.hi_Kk2_hyper_lo, \\n\\t\\t\\t\\t\\t\\t\\tself.hi_Kk2_hyper_hi], sample_type='uniform',\\n\\t\\t\\t\\t\\t\\t\\tseed=self.seed_Kk2)\\n\\t\\t\\n\\t\\tself.Kk1 = random_matrix([self.Mm, self.Nn], [Kk1_los, Kk1_his], \\n\\t\\t\\t\\t\\t\\t\\t\\tsample_type='rank2_row_uniform', \\n\\t\\t\\t\\t\\t\\t\\t\\tseed = self.seed_Kk1)\\n\\t\\tself.Kk2 = random_matrix([self.Mm, self.Nn], [Kk2_los, Kk2_his], \\n\\t\\t\\t\\t\\t\\t\\t\\tsample_type='rank2_row_uniform', \\n\\t\\t\\t\\t\\t\\t\\t\\tseed = self.seed_Kk2)\\n\\t\\t\\n\\t\\tif clip == True:\\n\\t\\t\\tarray_dict = clip_array(dict(Kk1 = self.Kk1, Kk2 = self.Kk2))\\n\\t\\t\\tself.Kk1 = array_dict['Kk1']\\n\\t\\t\\tself.Kk2 = array_dict['Kk2']\",\n \"def run_knn(\\n features: List[List[float]],\\n labels: List[Optional[bool]],\\n k: int = 1,\\n) -> List[bool]:\\n # Filter out the features that are already clustered\\n features_l, labels_l = zip(*[(f, l) for f, l in zip(features, labels) if isinstance(l, bool)])\\n\\n # Fit a nearest neighbour algorithm\\n neighbours = KNeighborsClassifier(\\n n_neighbors=k,\\n ).fit(features_l, labels_l)\\n\\n # Predict all the features' labels\\n return neighbours.predict(features) # type: ignore\",\n \"def k_truss(G, k):\\n H = G.copy()\\n\\n n_dropped = 1\\n while n_dropped > 0:\\n n_dropped = 0\\n to_drop = []\\n seen = set()\\n for u in H:\\n nbrs_u = set(H[u])\\n seen.add(u)\\n new_nbrs = [v for v in nbrs_u if v not in seen]\\n for v in new_nbrs:\\n if len(nbrs_u & set(H[v])) < (k - 2):\\n to_drop.append((u, v))\\n H.remove_edges_from(to_drop)\\n n_dropped = len(to_drop)\\n H.remove_nodes_from(list(nx.isolates(H)))\\n\\n return H\",\n \"def NDCG_binary_at_k_batch(logits, y_true, k=10):\\n n = logits.shape[0]\\n dummy_column = np.arange(n).reshape(n, 1)\\n\\n idx_topk_part = bn.argpartition(-logits, k, axis=1)[:, :k]\\n topk_part = logits[dummy_column, idx_topk_part]\\n idx_part = np.argsort(-topk_part, axis=1)\\n # X_pred[np.arange(batch_users)[:, np.newaxis], idx_topk] is the sorted topk predicted score\\n idx_topk = idx_topk_part[dummy_column, idx_part]\\n # build the discount template\\n tp = 1. / np.log2(np.arange(2, k + 2))\\n\\n DCG = (y_true[dummy_column, idx_topk] * tp).sum(axis=1)\\n IDCG = np.array([(tp[:min(n, k)]).sum()\\n for n in y_true.getnnz(axis=1)])\\n\\n # sanity check\\n assert (IDCG - DCG >= 0).all()\\n return DCG / IDCG\",\n \"def narration_target(self):\",\n \"def nDCG_at(prediction, target, k=10):\\n pass\",\n \"def __init__(self, K=10, base_learner=DecisionTreeClassifier(), S=200, cv=5,\\n epsilon=3, cost=90, fraud_label=1, t=3):\\n\\n # let the parent do the stuffs\\n super().__init__(K=K, base_learner=base_learner, S=S, cv=cv)\\n\\n # init statistics of bins (i,k) each epsilon bin has an array k\\n self.bins = epsilon * [K * [{'mean': 0.0, 'var': 0.0, 'num': 0}]]\\n\\n # a cost (because it is a cost sensitive classifier)\\n self.cost = cost\\n\\n # the fraud label as indicated by the users\\n self.fraud_label = fraud_label\\n\\n # confidence level\\n self.t = t\\n\\n # count all the samples predicted in order to get the average of the k classifiers used\\n self.samples = 0\",\n \"def cls_acc(output, target, topk=(1,)):\\r\\n maxk = max(topk)\\r\\n batch_size = target.size(0)\\r\\n\\r\\n _, pred = output.topk(maxk, 1, True, True)\\r\\n pred = pred.t()\\r\\n correct = pred.eq(target.view(1, -1).expand_as(pred))\\r\\n\\r\\n res = []\\r\\n for k in topk:\\r\\n correct_k = correct[:k].view(-1).float().sum(0)\\r\\n res.append(correct_k.mul_(100.0 / batch_size))\\r\\n return res\",\n \"def getNegativeSamples(target, dataset, K):\\n\\n indices = [None] * K\\n for k in xrange(K):\\n newidx = dataset.sampleTokenIdx()\\n while newidx == target:\\n newidx = dataset.sampleTokenIdx()\\n indices[k] = newidx\\n return indices\",\n \"def getNegativeSamples(target, dataset, K):\\n\\n indices = [None] * K\\n for k in xrange(K):\\n newidx = dataset.sampleTokenIdx()\\n while newidx == target:\\n newidx = dataset.sampleTokenIdx()\\n indices[k] = newidx\\n return indices\",\n \"def getNegativeSamples(target, dataset, K):\\n\\n indices = [None] * K\\n for k in xrange(K):\\n newidx = dataset.sampleTokenIdx()\\n while newidx == target:\\n newidx = dataset.sampleTokenIdx()\\n indices[k] = newidx\\n return indices\",\n \"def train(self,trainset):\\n \\n self.n_classes = len(trainset.metadata['targets'])\\n if self.n_classes > 2:\\n raise ValueError('Invalid. Should have 2 classes.')\\n \\n features = np.zeros((len(trainset),trainset.metadata['input_size']))\\n labels = np.zeros((len(trainset)),dtype='int')\\n for i,xy in enumerate(trainset):\\n x,y = xy\\n features[i] = x\\n labels[i] = y\\n\\n if self.criterion == 'information_gain':\\n def criterion_fcn(labels0, labels1):\\n return libmilk.supervised.tree.information_gain(labels0, labels1, include_entropy=self.include_entropy)\\n elif self.criterion == 'z1_loss':\\n def criterion_fcn(labels0, labels1):\\n return libmilk.supervised.tree.z1_loss(labels0, labels1)\\n else:\\n raise ValueError('Invalid parameter: '+self.criterion+'. Should be either \\\\'information_gain\\\\' or \\\\'z1_loss\\\\'')\\n\\n learner = libmilk.supervised.tree_learner(criterion=criterion_fcn,min_split=self.min_split,return_label=True)\\n #self.subsample = subsample\\n #self.R = R\\n model = learner.train(features, labels)\\n \\n self.tree = model\",\n \"def do_knn(x_data):\\n return True\",\n \"def k_shortest_paths(G, source, target, k=1, weight='weight'):\\n\\tif source == target:\\n\\t\\treturn ([0], [[source]]) \\n\\t \\n\\tlength, path = nx.single_source_dijkstra(G, source, target, weight=weight)\\n\\tif target not in length:\\n\\t\\traise nx.NetworkXNoPath(\\\"node %s not reachable from %s\\\" % (source, target))\\n\\t\\t\\n\\tlengths = [length[target]]\\n\\tpaths = [path[target]]\\n\\tc = count()\\t\\t\\n\\tB = []\\t\\t\\t\\t\\t\\t\\n\\tG_original = G.copy()\\t\\n\\t\\n\\tfor i in range(1, k):\\n\\t\\tfor j in range(len(paths[-1]) - 1):\\t\\t\\t\\n\\t\\t\\tspur_node = paths[-1][j]\\n\\t\\t\\troot_path = paths[-1][:j + 1]\\n\\t\\t\\t\\n\\t\\t\\tedges_removed = []\\n\\t\\t\\tfor c_path in paths:\\n\\t\\t\\t\\tif len(c_path) > j and root_path == c_path[:j + 1]:\\n\\t\\t\\t\\t\\tu = c_path[j]\\n\\t\\t\\t\\t\\tv = c_path[j + 1]\\n\\t\\t\\t\\t\\tif G.has_edge(u, v):\\n\\t\\t\\t\\t\\t\\tedge_attr = G.edge[u][v]\\n\\t\\t\\t\\t\\t\\tG.remove_edge(u, v)\\n\\t\\t\\t\\t\\t\\tedges_removed.append((u, v, edge_attr))\\n\\t\\t\\t\\n\\t\\t\\tfor n in range(len(root_path) - 1):\\n\\t\\t\\t\\tnode = root_path[n]\\n\\t\\t\\t\\t# out-edges\\n\\t\\t\\t\\tfor u, v, edge_attr in G.copy().edges_iter(node, data=True):\\n\\t\\t\\t\\t\\tG.remove_edge(u, v)\\n\\t\\t\\t\\t\\tedges_removed.append((u, v, edge_attr))\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif G.is_directed():\\n\\t\\t\\t\\t\\t# in-edges\\n\\t\\t\\t\\t\\tfor u, v, edge_attr in G.in_edges_iter(node, data=True):\\n\\t\\t\\t\\t\\t\\tG.remove_edge(u, v)\\n\\t\\t\\t\\t\\t\\tedges_removed.append((u, v, edge_attr))\\n\\t\\t\\t\\n\\t\\t\\tspur_path_length, spur_path = nx.single_source_dijkstra(G, spur_node, target, weight=weight)\\t\\t\\t\\n\\t\\t\\tif target in spur_path and spur_path[target]:\\n\\t\\t\\t\\ttotal_path = root_path[:-1] + spur_path[target]\\n\\t\\t\\t\\ttotal_path_length = get_path_length(G_original, root_path, weight) + spur_path_length[target]\\t\\t\\t\\t\\n\\t\\t\\t\\theappush(B, (total_path_length, next(c), total_path))\\n\\t\\t\\t\\t\\n\\t\\t\\tfor e in edges_removed:\\n\\t\\t\\t\\tu, v, edge_attr = e\\n\\t\\t\\t\\tG.add_edge(u, v, edge_attr)\\n\\t\\t\\t\\t\\t \\n\\t\\tif B:\\n\\t\\t\\t(l, _, p) = heappop(B)\\n\\t\\t\\tlengths.append(l)\\n\\t\\t\\tpaths.append(p)\\n\\t\\telse:\\n\\t\\t\\tbreak\\n\\t\\n\\treturn (lengths, paths)\",\n \"def k_shortest_paths(G, source, target, k=1, weight='weight'):\\n\\tif source == target:\\n\\t\\treturn ([0], [[source]]) \\n\\t \\n\\tlength, path = nx.single_source_dijkstra(G, source, target, weight=weight)\\n\\tif target not in length:\\n\\t\\traise nx.NetworkXNoPath(\\\"node %s not reachable from %s\\\" % (source, target))\\n\\t\\t\\n\\tlengths = [length[target]]\\n\\tpaths = [path[target]]\\n\\tc = count()\\t\\t\\n\\tB = []\\t\\t\\t\\t\\t\\t\\n\\tG_original = G.copy()\\t\\n\\t\\n\\tfor i in range(1, k):\\n\\t\\tfor j in range(len(paths[-1]) - 1):\\t\\t\\t\\n\\t\\t\\tspur_node = paths[-1][j]\\n\\t\\t\\troot_path = paths[-1][:j + 1]\\n\\t\\t\\t\\n\\t\\t\\tedges_removed = []\\n\\t\\t\\tfor c_path in paths:\\n\\t\\t\\t\\tif len(c_path) > j and root_path == c_path[:j + 1]:\\n\\t\\t\\t\\t\\tu = c_path[j]\\n\\t\\t\\t\\t\\tv = c_path[j + 1]\\n\\t\\t\\t\\t\\tif G.has_edge(u, v):\\n\\t\\t\\t\\t\\t\\tedge_attr = G.edge[u][v]\\n\\t\\t\\t\\t\\t\\tG.remove_edge(u, v)\\n\\t\\t\\t\\t\\t\\tedges_removed.append((u, v, edge_attr))\\n\\t\\t\\t\\n\\t\\t\\tfor n in range(len(root_path) - 1):\\n\\t\\t\\t\\tnode = root_path[n]\\n\\t\\t\\t\\t# out-edges\\n\\t\\t\\t\\tfor u, v, edge_attr in G.copy().edges_iter(node, data=True):\\n\\t\\t\\t\\t\\tG.remove_edge(u, v)\\n\\t\\t\\t\\t\\tedges_removed.append((u, v, edge_attr))\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif G.is_directed():\\n\\t\\t\\t\\t\\t# in-edges\\n\\t\\t\\t\\t\\tfor u, v, edge_attr in G.in_edges_iter(node, data=True):\\n\\t\\t\\t\\t\\t\\tG.remove_edge(u, v)\\n\\t\\t\\t\\t\\t\\tedges_removed.append((u, v, edge_attr))\\n\\t\\t\\t\\n\\t\\t\\tspur_path_length, spur_path = nx.single_source_dijkstra(G, spur_node, target, weight=weight)\\t\\t\\t\\n\\t\\t\\tif target in spur_path and spur_path[target]:\\n\\t\\t\\t\\ttotal_path = root_path[:-1] + spur_path[target]\\n\\t\\t\\t\\ttotal_path_length = get_path_length(G_original, root_path, weight) + spur_path_length[target]\\t\\t\\t\\t\\n\\t\\t\\t\\theappush(B, (total_path_length, next(c), total_path))\\n\\t\\t\\t\\t\\n\\t\\t\\tfor e in edges_removed:\\n\\t\\t\\t\\tu, v, edge_attr = e\\n\\t\\t\\t\\tG.add_edge(u, v, edge_attr)\\n\\t\\t\\t\\t\\t \\n\\t\\tif B:\\n\\t\\t\\t(l, _, p) = heappop(B)\\t\\t\\n\\t\\t\\tlengths.append(l)\\n\\t\\t\\tpaths.append(p)\\n\\t\\telse:\\n\\t\\t\\tbreak\\n\\t\\n\\treturn (lengths, paths)\",\n \"def get_next_train_valid(X_shuffled, y_shuffled, itr, k):\\n X_valid = X_shuffled[itr*len(X_shuffled)//k:(itr+1)*len(X_shuffled)//k,:]\\n Y_valid = y_shuffled[itr*len(y_shuffled)//k:(itr+1)*len(y_shuffled)//k]\\n \\n X_train = np.delete(X_shuffled, np.s_[itr*len(X_shuffled)//k:(itr+1)*len(X_shuffled)//k:1], 0)\\n Y_train = np.delete(y_shuffled, np.s_[itr*len(y_shuffled)//k:(itr+1)*len(y_shuffled)//k:1], 0)\\n \\n \\n return X_train, Y_train, X_valid, Y_valid\",\n \"def get_folds(X, y, k):\\n # temporarily change the 1/-1 nature of y to 1/0\\n _y = (y + 1) / 2\\n # partition the examples into postive and negative sets\\n positive_indices = np.where(_y)[0]\\n negative_indices = np.where(_y - 1)[0]\\n assert len(positive_indices) + len(negative_indices) == len(y)\\n\\n # shuffle both lists\\n np.random.shuffle(positive_indices)\\n np.random.shuffle(negative_indices)\\n\\n # create k buckets of indices of (approximately) equal size\\n positive_folds_indices = \\\\\\n np.array(np.array_split(positive_indices, k))\\n negative_folds_indices = \\\\\\n np.array(np.array_split(negative_indices, k))\\n\\n train_X, train_y, test_X, test_y = [], [], [], []\\n for i in range(k):\\n train_folds = np.concatenate((np.arange(0, i), np.arange(i+1, k)))\\n pos_train_indices = np.concatenate(positive_folds_indices[train_folds])\\n neg_train_indices = np.concatenate(negative_folds_indices[train_folds])\\n pos_test_indices = positive_folds_indices[i]\\n neg_test_indices = negative_folds_indices[i]\\n\\n train_X.append(\\n np.concatenate((X[pos_train_indices], X[neg_train_indices]))\\n )\\n train_y.append(\\n np.concatenate((y[pos_train_indices], y[neg_train_indices]))\\n )\\n test_X.append(\\n np.concatenate((X[pos_test_indices], X[neg_test_indices]))\\n )\\n test_y.append(\\n np.concatenate((y[pos_test_indices], y[neg_test_indices]))\\n )\\n\\n return zip(train_X, train_y, test_X, test_y)\",\n \"def run_knn(k, train_data, train_labels, valid_data):\\n\\n dist = l2_distance(valid_data.T, train_data.T)\\n nearest = np.argsort(dist, axis=1)[:,:k]\\n\\n train_labels = train_labels.reshape(-1)\\n valid_labels = train_labels[nearest]\\n\\n # note this only works for binary labels\\n valid_labels = (np.mean(valid_labels, axis=1) >= 0.5).astype(np.int)\\n valid_labels = valid_labels.reshape(-1,1)\\n\\n return valid_labels\",\n \"def determine_k(dataset, range_k, n_seed=30):\\r\\n range_Ks = np.arange(0,range_k,1,dtype=int) #range of delays to study\\r\\n h_K=np.zeros((10,range_k))\\r\\n \\r\\n for i in range(10):\\r\\n for k, K in enumerate(range_Ks):\\r\\n traj_matrix= embed.trajectory_matrix(dataset, K=K)\\r\\n labels= cl.kmeans_knn_partition(traj_matrix, n_seed)\\r\\n h= op_calc.get_entropy(labels)\\r\\n h_K[i,k]=h\\r\\n \\r\\n return(h_K)\",\n \"def getNegativeSamples(target, dataset, K):\\n\\n indices = [None] * K\\n for k in range(K):\\n newidx = dataset.sampleTokenIdx()\\n while newidx == target:\\n newidx = dataset.sampleTokenIdx()\\n indices[k] = newidx\\n return indices\",\n \"def subsets(x, k):\\n sub_set = set()\\n for i in x:\\n sub_set = sub_set.union(set(combinations(i, k)))\\n return list(sub_set)\",\n \"def top_k(self, k = 1):\\n\\t if self.shapley_rank == {}:\\n\\t \\treturn []\\n\\n\\t n = self.nodes\\n\\t topknodes = []\\n\\t i = 0\\n\\t count = 0\\n\\t while count < k and not i == n:\\n\\t if self.shapley_rank[i][0] not in topknodes and not self.is_adj(self.shapley_rank[i][0], topknodes):\\n\\t topknodes.append(self.shapley_rank[i][0])\\n\\t count += 1\\n\\t i += 1\\n\\t i = 0\\n\\t if not count == k:\\n\\t while not count == k:\\n\\t if self.shapley_rank[i][0] not in topknodes:\\n\\t topknodes.append(self.shapley_rank[i][0])\\n\\t count += 1\\n\\t i += 1\\n\\t return topknodes\",\n \"def _extend_label_set(self, data, trainer_cls, session):\\n # Select which nodes to label next, and predict their labels.\\n selected_samples, selected_labels = self._select_samples_to_label(\\n data, trainer_cls, session)\\n # Replace the labels of the new nodes with the predicted labels.\\n if selected_samples.shape[0] > 0:\\n data.label_samples(selected_samples, selected_labels)\\n return selected_samples\",\n \"def smote(X, y, N=100, K=5):\\n\\n seed = 1\\n np.random.seed(seed)\\n\\n # Step 1. Define minority and majority class examples, and minority class features\\n ms = int(sum(y))\\n min_cls = X[0:ms, :]\\n\\n # Step 2. If N is less than 100, then only a random percent will be smoted.\\n if N < 100:\\n np.random.shuffle(min_cls)\\n ms = int((N / 100) * ms)\\n N = 100\\n\\n syn_ex = int(N / 100) * ms\\n\\n # Step 3. Compute the k-NN for each minority class\\n clf = neighbors.KNeighborsClassifier()\\n clf.fit(X, y)\\n\\n neighborhoods = []\\n for i in range(ms):\\n xi = X[i, :].reshape(1, -1)\\n neighbours = clf.kneighbors(xi, n_neighbors=K, return_distance=False)[0]\\n # Skip itself in the neighborhood\\n neighbours = neighbours[1:]\\n\\n # Find all the minority examples\\n neighborhood = []\\n for index in neighbours:\\n if index <= ms - 1:\\n neighborhood.append(index)\\n\\n neighborhoods.append(neighborhood)\\n\\n # Step 4. Determine the amount of SMOTE examples to develop per neighbourhood.\\n\\n num_ex = int(syn_ex / len(neighborhoods))\\n\\n # Step 5. Generate SMOTE examples\\n syn_data = []\\n for i in range(ms):\\n xi = X[i, :].reshape(1, -1)\\n for j in range(num_ex):\\n # if the neighbourhood is not empty\\n if neighborhoods[i]:\\n index = np.random.choice(neighborhoods[i])\\n xzi = X[index, :].reshape(1, -1)\\n si = xi + (xzi - xi) * np.random.uniform(0, 1)\\n syn_data.append(si)\\n\\n # Build the data matrix\\n data = []\\n for values in syn_data:\\n data.append(values[0])\\n\\n print(\\\"{} amount of minority class samples generated\\\".format(len(data)))\\n\\n # Step 6. Re-build the data set with synthetic data added\\n\\n # Concatenate the positive labels with the newly made data\\n labels = np.ones([len(data), 1])\\n data = np.concatenate([labels, data], axis=1)\\n\\n # Concatenate with old data\\n org_data = np.concatenate([y.reshape(-1, 1), X], axis=1)\\n data = np.concatenate([data, org_data])\\n\\n # Test the new generated data\\n test = []\\n for values in syn_data:\\n a = clf.predict(values)\\n test.append(a)\\n\\n print(\\\"Using the old classifier, {} out of {} would be classified as minority.\\\".format(np.sum(test), len(syn_data)))\\n\\n return data, neighborhoods\",\n \"def test_sparsity_topk_mask_dykstra(self, l=1e-2):\\n n = int(1e3)\\n seed = np.random.RandomState(0)\\n y = jnp.array(seed.randn(n))\\n k = 10\\n out_top_k_dykstra = sparse_soft_topk_mask_dykstra(y, k, l=l)\\n self.assertAlmostEqual(\\n y.argsort()[::-1][:k].sort().sum(),\\n out_top_k_dykstra.argsort()[::-1][:k].sort().sum(),\\n )\\n self.assertAlmostEqual(len(jnp.where(out_top_k_dykstra > 0)[0]), k)\",\n \"def get_training_data(self):\\n\\n # this actually never was a set\\n # src_set = self.target['src'].values\\n # dst_set = self.target['dst'].values\\n\\n # train_negative = self.get_negative_edges(src_set, dst_set, self.train_ind.shape[0]) # * self.K)\\n # test_negative = self.get_negative_edges(src_set, dst_set, self.test_ind.shape[0])\\n\\n train_positive = self.target.iloc[self.train_edge_ind].values\\n test_positive = self.target.iloc[self.test_edge_ind].values\\n\\n # # print(train_positive.shape, train_negative.shape, test_positive.shape, test_negative.shape)\\n # print(f\\\"Working with {train_positive.shape[0]} positive and {train_negative.shape[0]} negative samples in the train set, {test_positive.shape[0]} and {test_negative.shape[0]} - in test set\\\")\\n\\n X_train = train_positive\\n X_test = test_positive\\n\\n y_train = np.ones((self.train_edge_ind.shape[0],))\\n y_test = np.ones((self.test_edge_ind.shape[0],))\\n\\n # X_train = np.vstack([\\n # train_positive,\\n # train_negative\\n # ])\\n\\n # X_test = np.vstack([\\n # test_positive,\\n # test_negative\\n # ])\\n\\n # y_train = np.concatenate([np.ones((self.train_ind.shape[0],)), np.zeros((self.train_ind.shape[0]),)]) # self.train_ind.shape[0]) * self.K\\n # y_test = np.concatenate([np.ones((self.test_ind.shape[0],)), np.zeros((self.test_ind.shape[0],))])\\n\\n assert X_train.shape[0] == y_train.shape[0]\\n assert X_test.shape[0] == y_test.shape[0]\\n\\n def shuffle(X, y):\\n ind_shuffle = np.arange(0, X.shape[0])\\n np.random.shuffle(ind_shuffle)\\n return X[ind_shuffle], y[ind_shuffle]\\n\\n self.X_train, self.y_train = shuffle(X_train, y_train)\\n self.X_test, self.y_test = shuffle(X_test, y_test)\\n\\n print(f\\\"Splitting into {self.X_train.shape[0]} train and {self.X_test.shape[0]} test samples\\\")\\n\\n # return X_train, X_test, y_train, y_test\",\n \"def get_knn_all(self, query_idxs, k=None):\\n k = self.args.k if k is None else k\\n assert get_rank() == 0\\n in_query_mask = np.isin(self.idxs, query_idxs)\\n assert np.sum(in_query_mask) == query_idxs.size\\n in_query_X = self.X[in_query_mask]\\n _, I = self._build_and_query_knn(self.X, in_query_X, k+1)\\n remap = lambda i : self.idxs[i]\\n v_remap = np.vectorize(remap)\\n I = v_remap(I)\\n return I[:,1:]\",\n \"def evaluate(self, b_size, k_max, df, source_is_head=True):\\n\\t\\t\\n\\t\\tself.k_max = k_max\\n\\n\\t\\tcross_Triplets_dataset = crossDomain_Triplets(self.kg_s, self.kg_t, df, source_is_head)\\n\\n\\t\\tdataloader = DataLoader(cross_Triplets_dataset, batch_size=b_size, shuffle=True, )\\n\\n\\t\\trank_true_entities = torch.empty(size=(df.shape[0],), device=self.device).long()\\n\\n\\t\\tfor i, batch in enumerate(dataloader):\\n\\t\\t\\t\\n\\t\\t\\t#print('batch device: ', batch[0].device)\\n\\t\\t\\t#print('batch[0] : ', batch[0], '\\\\n*****************************************')\\n\\t\\t\\t#print('batch[1] : ', batch[1], '\\\\n******************************************')\\n\\t\\t\\t#print('batch[2] : ', batch[2], '\\\\n*************************************')\\n\\n\\t\\t\\th_idx, t_idx, r_idx = batch[0].to(self.device), batch[1].to(self.device), batch[2].to(self.device)\\n\\t\\t\\t#h_idx, t_idx, r_idx = batch[0], batch[1], batch[2]\\n\\n\\t\\t\\trk_true_ents = self.lp_helper(h_idx, t_idx, r_idx, source_is_head)\\n\\n\\t\\t\\trank_true_entities[i*b_size: (i+1)*b_size] = rk_true_ents\\n\\n\\t\\tself.rank_true_entities = torch.cat((self.rank_true_entities, rank_true_entities), dim=0)\\n\\n\\t\\tself.evaluated = True\",\n \"def fold(self, k):\\n if not (0 < k < 5):\\n raise ValueError('almazan cross-evaluation proposes a k=4 k-cross evaluation, {} out of range'.format(k))\\n test_idcs = [idx for idx, fold in enumerate(self.fold_idcs) if fold == k]\\n train_idcs = list(set(range(len(self))).difference(set(test_idcs)))\\n return self.sub_set(train_idcs), self.sub_set(test_idcs)\",\n \"def kNN(k, testTfidf, trainTfidf, test_file, train_label, weight=True):\\r\\n test_y = [] \\r\\n \\r\\n # iterate through all lines in the test reviews and classify them\\r\\n for index, line in enumerate(test_file):\\r\\n # cosine similarity\\r\\n cos_similarity = linear_kernel(testTfidf[index:index+1], trainTfidf).flatten()\\r\\n \\r\\n if weight == True:\\r\\n # get the indices of nearest neighbors based on k parameter \\r\\n neighbor_indices = cos_similarity.argsort()[:-k:-1]\\r\\n # similarities\\r\\n similarities = cos_similarity[neighbor_indices]\\r\\n # get a list of labels from the neighbors and sum the list\\r\\n labels_list = train_label[neighbor_indices].tolist()\\r\\n\\r\\n # make cosine similarity value negative or positive based on\\r\\n # its label and sum the cosine similarities\\r\\n my_list = [] \\r\\n for s, l in zip(similarities, labels_list):\\r\\n if l == -1:\\r\\n my_list.append(-s)\\r\\n else:\\r\\n my_list.append(s) \\r\\n \\r\\n label_sum = sum(my_list)\\r\\n #classify based on label_sum\\r\\n if label_sum > 0:\\r\\n test_y.append(\\\"+1\\\")\\r\\n else:\\r\\n test_y.append(-1)\\r\\n\\r\\n else:\\r\\n # get the indices of nearest neighbors based on k parameter \\r\\n neighbor_indices = cos_similarity.argsort()[:-k:-1]\\r\\n # get a list of labels from the neighbors and sum the list\\r\\n labels_list = train_label[neighbor_indices].tolist()\\r\\n label_sum = sum(labels_list)\\r\\n\\r\\n # classify based on label_sum\\r\\n if label_sum > 0:\\r\\n test_y.append(\\\"+1\\\")\\r\\n else:\\r\\n test_y.append(-1)\\r\\n \\r\\n print(index)\\r\\n \\r\\n return pd.DataFrame(test_y)\",\n \"def p_y_x_knn(y, k):\\n number_of_classes = 4\\n resized = np.delete(y, range(k, y.shape[1]), axis=1)\\n summed_with_zero = np.vstack(np.apply_along_axis(np.bincount, axis=1, arr=resized, minlength=number_of_classes + 1))\\n summed = np.delete(summed_with_zero, 0, axis=1)\\n return summed / k\",\n \"def training_set(self):\\n return self._training_set\",\n \"def find_k_most_uncertain(nn, sess, X, batch_size=None, k=2,\\n pool_size=None):\\n\\n # Initialization\\n results = []\\n i = -1\\n\\n if batch_size is None:\\n _batch_size = X.shape[0]\\n else:\\n _batch_size = batch_size\\n\\n # Create the pool\\n order = np.arange(X.shape[0])\\n\\n if pool_size is not None:\\n order = np.random.choice(X.shape[0], \\n min(X.shape[0], pool_size),\\n replace=False)\\n X_pool = X[order, :].copy()\\n else:\\n X_pool = X.copy()\\n\\n # Loop over the batches\\n for i in range(X_pool.shape[0]/_batch_size):\\n\\n feed_dict = {}\\n feed_dict[nn.input_tensor] = X_pool[i * _batch_size:\\n (i+1) * _batch_size]\\n\\n # Predict the batch\\n pred = sess.run(nn.prediction, feed_dict=feed_dict)\\n\\n # The most uncertain is the closest to 0.5\\n pred = np.abs(0.5 - pred).reshape(-1)\\n\\n # Sort it by uncertainty\\n batch_order = np.argsort(pred)\\n pred = pred[batch_order]\\n\\n # Create associated indices\\n to_zip = order[range(i * _batch_size, i * _batch_size + pred.shape[0])]\\n to_zip = to_zip[batch_order]\\n\\n results = kmin(results, zip(pred, to_zip), k)\\n\\n # Last uncomplete batch\\n feed_dict = {}\\n feed_dict[nn.input_tensor] = X_pool[(i+1) * _batch_size:]\\n\\n # Predict the last batch\\n pred = sess.run(nn.prediction, feed_dict=feed_dict)\\n\\n # Sort it by uncertainty\\n pred = np.abs(0.5 - pred).reshape(-1)\\n batch_order = np.argsort(pred)\\n pred = pred[batch_order]\\n\\n # Create associated indices\\n to_zip = order[(i + 1) * _batch_size:]\\n to_zip = to_zip[batch_order]\\n\\n results = kmin(results, zip(pred, to_zip), k)\\n\\n return [i[1] for i in results]\",\n \"def train_knn(training_data):\\n return knnclassifier(training_data, keys, 3)\",\n \"def KNN(x_train, x_test, y_train, k=3):\\n knn = KNeighborsClassifier(n_neighbors=k)\\n knn.fit(x_train, y_train)\\n y_pred = knn.predict(x_test)\\n return y_pred\",\n \"def forward(self, x, target, N_k):\\n elbo = 0\\n phi = self.base(x)\\n def loglik(sample): #currently hardcoded for binary classification\\n return -torch.log(1 + torch.exp(-target * sample))\\n mu = self.mu\\n cov = self.L @ self.L.T\\n means = phi @ mu\\n variances = torch.diagonal(phi @ cov @ phi.T, 0)\\n elbo += (self.quadr(loglik, means, variances)).sum()\\n elbo /= x.shape[0] #mean\\n \\n kls = 0\\n kls -= kl_divergence(self.w_distr, self.w_prior)\\n\\n for i in range(len(self.prev_tasks_distr)):\\n phi_i = self.base(self.prev_tasks_tensors[i])\\n cov_i = phi_i @ phi_i.T\\n p_u = MultivariateNormal(torch.zeros(cov_i.shape[0]).to(self.device),\\n covariance_matrix=cov_i)#cov_i * self.sigma_prior)\\n kls -= kl_divergence(self.prev_tasks_distr[i], p_u)\\n elbo += kls / N_k\\n\\n return -elbo\",\n \"def __topK_train(self, k):\\n f = open(\\\"train_info.csv\\\")\\n reader = csv.DictReader(f, delimiter=\\\",\\\")\\n artists = []\\n for line in reader:\\n artists.append(line['artist'])\\n freqs = defaultdict(int)\\n for artist in artists:\\n freqs[artist] += 1\\n\\n sorted_freqs = sorted(freqs.items(), key=operator.itemgetter(1))\\n final_list = list(reversed(sorted_freqs))\\n res = []\\n for pair in final_list[:k]:\\n res.append(pair[0])\\n if not os.path.exists(os.path.join(TEST_DIR, pair[0])):\\n os.mkdir(os.path.join(TEST_DIR, pair[0]))\\n return res\",\n \"def knn(x, x_train, y_train, k=1):\\n y_pred = np.zeros(len(x), dtype=np.int8)\\n for i, sample in enumerate(x):\\n # Calculate distance from this sample to every training sample\\n dist = [np.linalg.norm(sample-train) for train in x_train]\\n\\n # Find the k nearest training samples\\n k_nearest_labels = []\\n for j in range(k):\\n closest = np.argmin(dist)\\n k_nearest_labels.append(y_train[closest])\\n dist.pop(closest)\\n\\n # This sample's label the one the appears most frequently in\\n # the k nearest, or the first nearest if all appear equally\\n labels, counts = np.unique(k_nearest_labels, return_counts=True)\\n y_pred[i] = labels[np.argmax(counts)]\\n return y_pred\",\n \"def _decimate_k(self):\\n # Setting the seed here once so as to get the same set\\n # independent of filters.\\n random.seed(1)\\n k_sorted = sorted(self.kvec.keys())\\n k_selected = []\\n for knorm in k_sorted:\\n nkmax = len(self.kvec[knorm])\\n k_selected.append(random.sample(list(range(nkmax)), min(self.nk, nkmax)))\\n return k_sorted, k_selected\",\n \"def get_k_shortest_paths(env: RailEnv,\\n source_position: Tuple[int, int],\\n source_direction: int,\\n target_position=Tuple[int, int],\\n k: int = 1, debug=False) -> List[Tuple[Waypoint]]:\\n\\n # P: set of shortest paths from s to t\\n # P =empty,\\n shortest_paths: List[Tuple[Waypoint]] = []\\n\\n # countu: number of shortest paths found to node u\\n # countu = 0, for all u in V\\n count = {(r, c, d): 0 for r in range(env.height) for c in range(env.width) for d in range(4)}\\n\\n # B is a heap data structure containing paths\\n # N.B. use OrderedSet to make result deterministic!\\n heap: OrderedSet[Tuple[Waypoint]] = OrderedSet()\\n\\n # insert path Ps = {s} into B with cost 0\\n heap.add((Waypoint(source_position, source_direction),))\\n\\n # while B is not empty and countt < K:\\n while len(heap) > 0 and len(shortest_paths) < k:\\n if debug:\\n print(\\\"iteration heap={}, shortest_paths={}\\\".format(heap, shortest_paths))\\n # – let Pu be the shortest cost path in B with cost C\\n cost = np.inf\\n pu = None\\n for path in heap:\\n if len(path) < cost:\\n pu = path\\n cost = len(path)\\n u: Waypoint = pu[-1]\\n if debug:\\n print(\\\" looking at pu={}\\\".format(pu))\\n\\n # – B = B − {Pu }\\n heap.remove(pu)\\n # – countu = countu + 1\\n\\n urcd = (*u.position, u.direction)\\n count[urcd] += 1\\n\\n # – if u = t then P = P U {Pu}\\n if u.position == target_position:\\n if debug:\\n print(\\\" found of length {} {}\\\".format(len(pu), pu))\\n shortest_paths.append(pu)\\n\\n # – if countu ≤ K then\\n # CAVEAT: do not allow for loopy paths\\n elif count[urcd] <= k:\\n possible_transitions = env.rail.get_transitions(*urcd)\\n if debug:\\n print(\\\" looking at neighbors of u={}, transitions are {}\\\".format(u, possible_transitions))\\n # for each vertex v adjacent to u:\\n for new_direction in range(4):\\n if debug:\\n print(\\\" looking at new_direction={}\\\".format(new_direction))\\n if possible_transitions[new_direction]:\\n new_position = get_new_position(u.position, new_direction)\\n if debug:\\n print(\\\" looking at neighbor v={}\\\".format((*new_position, new_direction)))\\n\\n v = Waypoint(position=new_position, direction=new_direction)\\n # CAVEAT: do not allow for loopy paths\\n if v in pu:\\n continue\\n\\n # – let Pv be a new path with cost C + w(u, v) formed by concatenating edge (u, v) to path Pu\\n pv = pu + (v,)\\n # – insert Pv into B\\n heap.add(pv)\\n\\n # return P\\n return shortest_paths\",\n \"def knn_prediction(X, y, x, k):\\n ## homework:start\\n result = \\n ## homework:end\\n return result\",\n \"def __init__(self, top_k):\\n\\n super(EpochTopkAccuracy, self).__init__(\\n reset_at=\\\"epoch\\\", emit_at=\\\"epoch\\\", mode=\\\"train\\\", top_k=top_k\\n )\\n self.top_k = top_k\",\n \"def find_k(self, kpt):\\n kpt = np.array(kpt)\\n ns = np.linalg.norm(self.kpts - kpt[None, :], axis=1)\\n ik = np.argmin(ns)\\n return ik\",\n \"def __call__(self, graph: Data, n_min: int, nodes_to_keep: List[int] = None, exhaustive: bool = False):\\n nodes_to_keep = nodes_to_keep if nodes_to_keep is not None else []\\n mcts = self._get_mcts(graph, n_min, nodes_to_keep, exhaustive)\\n\\n for iteration in range(self.m):\\n mcts.search_one_iteration()\\n\\n explanation = mcts.best_leaf_node()\\n\\n return explanation.node_set, mcts\",\n \"def get_concentrated_mask(class_weights, topk):\\n\\n mask_topk = torch.zeros(class_weights.shape).to(device)\\n\\n # TODO: can we cache this somehow?\\n seq_tensor = torch.LongTensor([i for i in range(class_weights.shape[0])])\\n\\n if topk > 0:\\n _, topk_domain = torch.topk(class_weights, topk)\\n\\n for i in range(topk):\\n mask_topk[seq_tensor, topk_domain[:, i]] = 1\\n else:\\n topk_domain = None\\n\\n return mask_topk, topk_domain, seq_tensor\",\n \"def get_coreset(self, pool_features, labelled_features):\\n new_batch = []\\n self.update_distances(pool_features, labelled_features, reset_dist=True)\\n for _ in range(self.num_query):\\n # choose furthest point\\n ind = np.argmax(self.min_distances)\\n # New examples should not be in already selected since those points\\n # should have min_distance of zero to a cluster center.\\n assert ind not in new_batch\\n # update distances with this point\\n self.update_distances(pool_features, pool_features[ind, :].reshape(1,-1), reset_dist=False)\\n new_batch.append(ind)\\n print(f\\\"Maximum distance from cluster centers is {max(self.min_distances)}\\\")\\n\\n return new_batch\",\n \"def feature_select(T_train, y_train, k=3):\\r\\n from sklearn.metrics import roc_auc_score\\r\\n\\r\\n T_principle_index = list() # The index of Principle features\\r\\n AUC = list()\\r\\n\\r\\n # Forward Stage-wise algorithm\\r\\n for j in range(k):\\r\\n auc = list()\\r\\n Dist = list()\\r\\n\\r\\n for i in range(len(T_train.T)):\\r\\n if i in T_principle_index:\\r\\n auc.append(0)\\r\\n Dist.append(0)\\r\\n continue\\r\\n T_temp_index = T_principle_index + [i]\\r\\n T0 = T_train.T[T_temp_index].T # transient statistics\\r\\n C0 = gen_center(T0, y_train)\\r\\n dist = util.distance(T0, C0) # distance\\r\\n Dist.append(dist)\\r\\n auc.append(roc_auc_score(y_train, -dist))\\r\\n\\r\\n m = np.argmax(auc)\\r\\n T_principle_index = T_principle_index + [m]\\r\\n AUC.append(auc[m])\\r\\n\\r\\n dist = Dist[m]\\r\\n T_principle_index = np.array(T_principle_index)\\r\\n T_principle = T_train.T[T_principle_index].T\\r\\n\\r\\n return T_principle, T_principle_index, np.array(dist), AUC\",\n \"def set_subset(self):\\r\\n if self._random_subset:\\r\\n perm = torch.randperm(len(self._indices))\\r\\n self._subset = self._indices[perm][:self._subset_size]\\r\\n else:\\r\\n self._subset = torch.Tensor(self._indices[:self._subset_size])\",\n \"def shortest_tips(neuron):\\n (branch_index,) = np.where(neuron.branch_order[neuron.n_soma:] == 2)\\n (endpoint_index,) = np.where(neuron.branch_order[neuron.n_soma:] == 0)\\n selected_index = np.union1d(endpoint_index + 1,\\n branch_index + 1)\\n selected_index = np.append(0, selected_index)\",\n \"def _get_targets_single(self, mask_preds: Tensor,\\n gt_instances: InstanceData,\\n positive_info: InstanceData):\\n gt_bboxes = gt_instances.bboxes\\n device = gt_bboxes.device\\n gt_masks = gt_instances.masks.to_tensor(\\n dtype=torch.bool, device=device).float()\\n\\n # process with mask targets\\n pos_assigned_gt_inds = positive_info.get('pos_assigned_gt_inds')\\n scores = positive_info.get('scores')\\n centernesses = positive_info.get('centernesses')\\n num_pos = pos_assigned_gt_inds.size(0)\\n\\n if gt_masks.size(0) == 0 or num_pos == 0:\\n return mask_preds, None, 0\\n # Since we're producing (near) full image masks,\\n # it'd take too much vram to backprop on every single mask.\\n # Thus we select only a subset.\\n if (self.max_masks_to_train != -1) and \\\\\\n (num_pos > self.max_masks_to_train):\\n perm = torch.randperm(num_pos)\\n select = perm[:self.max_masks_to_train]\\n mask_preds = mask_preds[select]\\n pos_assigned_gt_inds = pos_assigned_gt_inds[select]\\n num_pos = self.max_masks_to_train\\n elif self.topk_masks_per_img != -1:\\n unique_gt_inds = pos_assigned_gt_inds.unique()\\n num_inst_per_gt = max(\\n int(self.topk_masks_per_img / len(unique_gt_inds)), 1)\\n\\n keep_mask_preds = []\\n keep_pos_assigned_gt_inds = []\\n for gt_ind in unique_gt_inds:\\n per_inst_pos_inds = (pos_assigned_gt_inds == gt_ind)\\n mask_preds_per_inst = mask_preds[per_inst_pos_inds]\\n gt_inds_per_inst = pos_assigned_gt_inds[per_inst_pos_inds]\\n if sum(per_inst_pos_inds) > num_inst_per_gt:\\n per_inst_scores = scores[per_inst_pos_inds].sigmoid().max(\\n dim=1)[0]\\n per_inst_centerness = centernesses[\\n per_inst_pos_inds].sigmoid().reshape(-1, )\\n select = (per_inst_scores * per_inst_centerness).topk(\\n k=num_inst_per_gt, dim=0)[1]\\n mask_preds_per_inst = mask_preds_per_inst[select]\\n gt_inds_per_inst = gt_inds_per_inst[select]\\n keep_mask_preds.append(mask_preds_per_inst)\\n keep_pos_assigned_gt_inds.append(gt_inds_per_inst)\\n mask_preds = torch.cat(keep_mask_preds)\\n pos_assigned_gt_inds = torch.cat(keep_pos_assigned_gt_inds)\\n num_pos = pos_assigned_gt_inds.size(0)\\n\\n # Follow the origin implement\\n start = int(self.mask_out_stride // 2)\\n gt_masks = gt_masks[:, start::self.mask_out_stride,\\n start::self.mask_out_stride]\\n gt_masks = gt_masks.gt(0.5).float()\\n pos_mask_targets = gt_masks[pos_assigned_gt_inds]\\n\\n return (mask_preds, pos_mask_targets, num_pos)\",\n \"def generate_mnist_datasets(\\n datapoints_per_task,\\n K_list,\\n cir_inner_loop_list, \\n test_task_idx, \\n val_task_idx,\\n n_finetune_sets):\\n\\n # arbitrarily chosen, class-imbalance rate in outer and inner training loops\\n cir_outer_loop = 0.5\\n cir_inner_loop = 0.5\\n # class-imbalance rate in the test sets of the test and validation tasks\\n cir_test = 0.5\\n # arbitrarily chosen, percentage of data that will be used in the inner training loop\\n percent_data_inner_loop = 0.5\\n\\n percent_data_finetune_val = 0.8\\n\\n n_test_set = 4000\\n\\n test_task_idx, val_task_idx = test_task_idx, val_task_idx\\n\\n finetune_sets_per_K_cir = {}\\n test_task_test_set, val_task = {}, {}\\n \\n\\n train_task_list_inner, train_task_list_outer = [], []\\n\\n train_tasks_idxs = [i for i in range(0,10) if i not in [val_task_idx, test_task_idx]]\\n\\n base_path = '/home/USER/Documents'\\n if (not (os.path.exists(base_path))):\\n base_path = '/home/ubuntu/Projects'\\n train_images, train_labels = loadlocal_mnist(\\n images_path= base_path + '/MAML/raw_data/MNIST_data/train-images-idx3-ubyte', \\n labels_path= base_path + '/MAML/raw_data/MNIST_data/train-labels-idx1-ubyte')\\n\\n test_images, test_labels = loadlocal_mnist(\\n images_path= base_path + '/MAML/raw_data/MNIST_data/t10k-images-idx3-ubyte', \\n labels_path= base_path + '/MAML/raw_data/MNIST_data/t10k-labels-idx1-ubyte')\\n\\n\\n train_images, test_images = train_images.reshape((-1,28,28))/255.0, test_images.reshape((-1,28,28))/255.0\\n images = np.concatenate((train_images, test_images))\\n labels = np.concatenate((train_labels, test_labels))\\n\\n test_task_normal_indexes, val_task_normal_indexes = list(np.nonzero(labels == test_task_idx)[0]), list(np.nonzero(train_labels == val_task_idx)[0])\\n test_task_X_normal, val_task_X_normal = images[test_task_normal_indexes],train_images[val_task_normal_indexes]\\n test_task_Y_normal, val_task_Y_normal = np.zeros_like(labels[test_task_normal_indexes]), np.zeros_like(train_labels[val_task_normal_indexes])\\n\\n\\n # val and test task have anomalies (samples of other numbers) that are not used for training\\n # besides the two sets of anomalies (one for val task and one for test task are disjoint)\\n test_task_anomalous_indexes = list(np.nonzero(test_labels[:5000] != test_task_idx)[0])\\n val_task_anomalous_indexes= [index for index, element in enumerate(list(test_labels[5000:])) if element not in [val_task_idx, test_task_idx]]\\n\\n\\n test_task_X_anomalous, val_task_X_anomalous = test_images[:5000][test_task_anomalous_indexes],test_images[5000:][val_task_anomalous_indexes]\\n test_task_Y_anomalous, val_task_Y_anomalous = np.ones_like(test_labels[:5000][test_task_anomalous_indexes]), np.ones_like(test_labels[5000:][val_task_anomalous_indexes])\\n\\n test_task_X, val_task_X = np.concatenate((test_task_X_normal, test_task_X_anomalous)), np.concatenate((val_task_X_normal, val_task_X_anomalous))\\n test_task_Y, val_task_Y = np.expand_dims(np.concatenate((test_task_Y_normal, test_task_Y_anomalous)),-1), np.expand_dims(np.concatenate((val_task_Y_normal, val_task_Y_anomalous)),-1)\\n\\n\\n train_tasks_X_list, train_tasks_Y_list = [], []\\n for task_idx in train_tasks_idxs:\\n train_task_normal_indexes = list(np.nonzero(train_labels == task_idx)[0]) \\n train_task_anomalous_indexes = [index for index, element in enumerate(list(train_labels)) if element not in [task_idx, val_task_idx, test_task_idx]]\\n assert(len(np.nonzero(train_labels[train_task_anomalous_indexes] == val_task_idx)[0]) == 0)\\n assert(len(np.nonzero(train_labels[train_task_anomalous_indexes] == test_task_idx)[0]) == 0)\\n train_task_X_normal, train_task_X_anomalous = train_images[train_task_normal_indexes], train_images[train_task_anomalous_indexes]\\n train_task_Y_normal, train_task_Y_anomalous = np.zeros_like(train_labels[train_task_normal_indexes]), np.ones_like(train_labels[train_task_anomalous_indexes])\\n train_task_X, train_task_Y = np.concatenate((train_task_X_normal, train_task_X_anomalous)), np.concatenate((train_task_Y_normal, train_task_Y_anomalous))\\n train_tasks_X_list.append(train_task_X)\\n train_tasks_Y_list.append(np.expand_dims(train_task_Y,-1))\\n\\n\\n\\n # building test task sets of data\\n normal_indexes, anomaly_indexes = list(np.nonzero(test_task_Y == 0)[0]), list(np.nonzero(test_task_Y == 1)[0])\\n n_test_set_normal = int(n_test_set*cir_test)\\n test_set_normal_indexes = random.sample(normal_indexes, n_test_set_normal)\\n test_set_anomaly_indexes = random.sample(anomaly_indexes, n_test_set - n_test_set_normal)\\n test_set_indexes = []\\n test_set_indexes += test_set_normal_indexes\\n test_set_indexes += test_set_anomaly_indexes\\n\\n test_task_test_set['test_X'], test_task_test_set['test_Y'] = test_task_X[test_set_indexes], test_task_Y[test_set_indexes]\\n\\n\\n #shuffle\\n s_test = np.arange(test_task_test_set['test_X'].shape[0])\\n np.random.shuffle(s_test)\\n test_task_test_set['test_X'], test_task_test_set['test_Y'] = test_task_test_set['test_X'][s_test], test_task_test_set['test_Y'][s_test]\\n\\n rest_normal_indexes = [index for index in normal_indexes if index not in test_set_normal_indexes]\\n rest_anomaly_indexes = [index for index in anomaly_indexes if index not in test_set_anomaly_indexes]\\n\\n\\n for K in K_list:\\n finetune_sets_per_cir = {}\\n for cir in cir_inner_loop_list:\\n\\n rest_normal_indexes = [index for index in normal_indexes if index not in test_set_normal_indexes]\\n rest_anomaly_indexes = [index for index in anomaly_indexes if index not in test_set_anomaly_indexes]\\n \\n finetune_sets_list = []\\n\\n disjoint = False\\n if(cir*K*n_finetune_sets<len(rest_normal_indexes)):\\n disjoint = True\\n\\n n_finetune_normal = int(K*cir)\\n n_finetune_anomaly = K - n_finetune_normal\\n for i in range(n_finetune_sets):\\n # if enough for disjoint do that\\n # else sample randomly\\n # store in a dict with keys cir_K\\n finetune_normal_indexes = random.sample(rest_normal_indexes, n_finetune_normal)\\n finetune_anomaly_indexes = random.sample(rest_anomaly_indexes, n_finetune_anomaly)\\n finetune_indexes = []\\n finetune_indexes += finetune_normal_indexes\\n finetune_indexes += finetune_anomaly_indexes\\n finetune_set = {}\\n finetune_set['finetune_X'], finetune_set['finetune_Y'] = test_task_X[finetune_indexes], test_task_Y[finetune_indexes]\\n\\n #shuffle\\n s_finetune = np.arange(finetune_set['finetune_X'].shape[0])\\n np.random.shuffle(s_finetune)\\n finetune_set['finetune_X'], finetune_set['finetune_Y'] = finetune_set['finetune_X'][s_finetune], finetune_set['finetune_Y'][s_finetune]\\n\\n finetune_sets_list.append(finetune_set)\\n \\n if(disjoint):\\n rest_normal_indexes = [index for index in rest_normal_indexes if index not in finetune_normal_indexes]\\n rest_anomaly_indexes = [index for index in rest_anomaly_indexes if index not in finetune_anomaly_indexes]\\n\\n finetune_sets_per_cir[str(cir)] = finetune_sets_list\\n\\n finetune_sets_per_K_cir[str(K)] = finetune_sets_per_cir\\n\\n\\n #building val task sets of data\\n normal_indexes, anomaly_indexes = list(np.nonzero(val_task_Y == 0)[0]), list(np.nonzero(val_task_Y == 1)[0])\\n n_val_finetune = int(percent_data_finetune_val*datapoints_per_task)\\n n_val_test_set = datapoints_per_task - n_val_finetune\\n n_val_test_set_normal = int(n_val_test_set*cir_test)\\n val_test_set_normal_indexes = random.sample(normal_indexes, n_val_test_set_normal)\\n\\n\\n val_test_set_anomaly_indexes = random.sample(anomaly_indexes, n_val_test_set - n_val_test_set_normal)\\n val_test_set_indexes = []\\n val_test_set_indexes += val_test_set_normal_indexes\\n val_test_set_indexes += val_test_set_anomaly_indexes\\n val_task['test_X'], val_task['test_Y'] = val_task_X[val_test_set_indexes], val_task_Y[val_test_set_indexes]\\n\\n\\n rest_normal_indexes = [index for index in normal_indexes if index not in val_test_set_normal_indexes]\\n rest_anomaly_indexes = [index for index in anomaly_indexes if index not in val_test_set_anomaly_indexes]\\n\\n n_val_finetune_normal = int(n_val_finetune*cir_inner_loop)\\n val_finetune_normal_indexes = random.sample(rest_normal_indexes, n_val_finetune_normal)\\n val_finetune_anomaly_indexes = random.sample(rest_anomaly_indexes, n_val_finetune - n_val_finetune_normal)\\n val_finetune_indexes = []\\n val_finetune_indexes += val_finetune_normal_indexes\\n val_finetune_indexes += val_finetune_anomaly_indexes\\n\\n val_task['finetune_X'], val_task['finetune_Y'] = val_task_X[val_finetune_indexes], val_task_Y[val_finetune_indexes]\\n\\n #shuffle\\n s_val_finetune = np.arange(val_task['finetune_X'].shape[0])\\n s_val_test = np.arange(val_task['test_X'].shape[0])\\n np.random.shuffle(s_val_finetune)\\n np.random.shuffle(s_val_test)\\n\\n val_task['finetune_X'], val_task['finetune_Y'] = val_task['finetune_X'][s_val_finetune], val_task['finetune_Y'][s_val_finetune]\\n val_task['test_X'], val_task['test_Y'] = val_task['test_X'][s_val_test], val_task['test_Y'][s_val_test]\\n\\n\\n\\n # building sets of data of the training tasks\\n for task_X, task_Y in zip(train_tasks_X_list, train_tasks_Y_list):\\n normal_indexes, anomaly_indexes = list(np.nonzero(task_Y == 0)[0]), list(np.nonzero(task_Y == 1)[0])\\n\\n n_inner_loop = int(percent_data_inner_loop*datapoints_per_task)\\n n_inner_loop_normal = int(n_inner_loop*cir_inner_loop)\\n n_outer_loop = datapoints_per_task - n_inner_loop\\n n_outer_loop_normal = int(n_outer_loop*cir_outer_loop)\\n \\n inner_loop_normal_indexes = random.sample(normal_indexes, n_inner_loop_normal)\\n inner_loop_anomaly_indexes = random.sample(anomaly_indexes, n_inner_loop - n_inner_loop_normal)\\n inner_loop_indexes = []\\n inner_loop_indexes += inner_loop_normal_indexes\\n inner_loop_indexes += inner_loop_anomaly_indexes\\n\\n train_task_inner_X, train_task_inner_Y = task_X[inner_loop_indexes], task_Y[inner_loop_indexes]\\n\\n rest_normal_indexes = [index for index in normal_indexes if index not in inner_loop_normal_indexes]\\n rest_anomaly_indexes = [index for index in anomaly_indexes if index not in inner_loop_anomaly_indexes]\\n\\n \\n outer_loop_normal_indexes = random.sample(rest_normal_indexes, n_outer_loop_normal)\\n outer_loop_anomaly_indexes = random.sample(rest_anomaly_indexes, n_outer_loop - n_outer_loop_normal)\\n outer_loop_indexes = []\\n outer_loop_indexes += outer_loop_normal_indexes\\n outer_loop_indexes += outer_loop_anomaly_indexes\\n\\n train_task_outer_X, train_task_outer_Y = task_X[outer_loop_indexes], task_Y[outer_loop_indexes]\\n\\n\\n s_inner = np.arange(train_task_inner_X.shape[0])\\n s_outer = np.arange(train_task_outer_X.shape[0])\\n np.random.shuffle(s_inner)\\n np.random.shuffle(s_outer)\\n train_task_list_inner.append([train_task_inner_X[s_inner],train_task_inner_Y[s_inner]])\\n train_task_list_outer.append([train_task_outer_X[s_outer],train_task_outer_Y[s_outer]])\\n\\n\\n\\n train_tasks_inner_X = np.stack([train_task_list_inner[i][0]\\n for i in range(len(train_task_list_inner))], 0)\\n train_tasks_inner_Y = np.stack([train_task_list_inner[i][1]\\n for i in range(len(train_task_list_inner))], 0)\\n train_tasks_outer_X = np.stack([train_task_list_outer[i][0]\\n for i in range(len(train_task_list_outer))], 0)\\n train_tasks_outer_Y = np.stack([train_task_list_outer[i][1]\\n for i in range(len(train_task_list_outer))], 0)\\n\\n \\n train_tasks = {'X_train_inner': train_tasks_inner_X,\\n 'Y_train_inner': train_tasks_inner_Y,\\n 'X_train_outer': train_tasks_outer_X,\\n 'Y_train_outer': train_tasks_outer_Y\\n }\\n\\n\\n return train_tasks, val_task, test_task_test_set, finetune_sets_per_K_cir\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.670574","0.6441951","0.61715454","0.58573383","0.58003443","0.5726443","0.5716722","0.56723654","0.56273323","0.5529329","0.5513812","0.5513812","0.5510223","0.5474325","0.5462067","0.5443153","0.54321855","0.54066193","0.5383252","0.5364684","0.535411","0.5313234","0.5294072","0.52886987","0.5275952","0.52722687","0.52682084","0.520776","0.5198296","0.5166159","0.5154693","0.5152909","0.5149385","0.51436615","0.5140495","0.5140203","0.51360697","0.5132468","0.5124879","0.5121204","0.5110535","0.50950193","0.50935423","0.5090069","0.5086956","0.5084045","0.508174","0.5079113","0.5078909","0.5073351","0.50689405","0.50657845","0.5057752","0.50522715","0.5048436","0.5029765","0.5027712","0.50244975","0.50244975","0.50244975","0.50216717","0.50155866","0.501132","0.501132","0.5011142","0.5008429","0.5008369","0.50080836","0.50044537","0.4998409","0.49971935","0.49947062","0.4986661","0.4982808","0.49769706","0.4974014","0.4972891","0.49712783","0.49692273","0.4964156","0.4961033","0.49572152","0.49555844","0.49521005","0.49482688","0.49402243","0.49402237","0.49375114","0.49339026","0.49337536","0.49288148","0.49282476","0.4922232","0.49221912","0.49203297","0.4918532","0.4911253","0.49097297","0.4906021","0.49016464"],"string":"[\n \"0.670574\",\n \"0.6441951\",\n \"0.61715454\",\n \"0.58573383\",\n \"0.58003443\",\n \"0.5726443\",\n \"0.5716722\",\n \"0.56723654\",\n \"0.56273323\",\n \"0.5529329\",\n \"0.5513812\",\n \"0.5513812\",\n \"0.5510223\",\n \"0.5474325\",\n \"0.5462067\",\n \"0.5443153\",\n \"0.54321855\",\n \"0.54066193\",\n \"0.5383252\",\n \"0.5364684\",\n \"0.535411\",\n \"0.5313234\",\n \"0.5294072\",\n \"0.52886987\",\n \"0.5275952\",\n \"0.52722687\",\n \"0.52682084\",\n \"0.520776\",\n \"0.5198296\",\n \"0.5166159\",\n \"0.5154693\",\n \"0.5152909\",\n \"0.5149385\",\n \"0.51436615\",\n \"0.5140495\",\n \"0.5140203\",\n \"0.51360697\",\n \"0.5132468\",\n \"0.5124879\",\n \"0.5121204\",\n \"0.5110535\",\n \"0.50950193\",\n \"0.50935423\",\n \"0.5090069\",\n \"0.5086956\",\n \"0.5084045\",\n \"0.508174\",\n \"0.5079113\",\n \"0.5078909\",\n \"0.5073351\",\n \"0.50689405\",\n \"0.50657845\",\n \"0.5057752\",\n \"0.50522715\",\n \"0.5048436\",\n \"0.5029765\",\n \"0.5027712\",\n \"0.50244975\",\n \"0.50244975\",\n \"0.50244975\",\n \"0.50216717\",\n \"0.50155866\",\n \"0.501132\",\n \"0.501132\",\n \"0.5011142\",\n \"0.5008429\",\n \"0.5008369\",\n \"0.50080836\",\n \"0.50044537\",\n \"0.4998409\",\n \"0.49971935\",\n \"0.49947062\",\n \"0.4986661\",\n \"0.4982808\",\n \"0.49769706\",\n \"0.4974014\",\n \"0.4972891\",\n \"0.49712783\",\n \"0.49692273\",\n \"0.4964156\",\n \"0.4961033\",\n \"0.49572152\",\n \"0.49555844\",\n \"0.49521005\",\n \"0.49482688\",\n \"0.49402243\",\n \"0.49402237\",\n \"0.49375114\",\n \"0.49339026\",\n \"0.49337536\",\n \"0.49288148\",\n \"0.49282476\",\n \"0.4922232\",\n \"0.49221912\",\n \"0.49203297\",\n \"0.4918532\",\n \"0.4911253\",\n \"0.49097297\",\n \"0.4906021\",\n \"0.49016464\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5006581"},"document_rank":{"kind":"string","value":"68"}}},{"rowIdx":89389,"cells":{"query":{"kind":"string","value":"Compute the dissimilarity representation of the tract and build the kdtree."},"document":{"kind":"string","value":"def compute_kdt_and_dr(tract, num_prototypes=None):\n tract = np.array(tract, dtype=np.object)\n print(\"Computing dissimilarity matrices...\")\n if num_prototypes is None:\n num_prototypes = 40\n print(\"Using %s prototypes as in Olivetti et al. 2012.\"\n % num_prototypes)\n else:\n print(\"Using %s prototypes\" % num_prototypes)\n t0 = time()\n distance = partial(parallel_distance_computation,\n distance=bundles_distances_mam)\n dm_tract, prototype_idx = compute_dissimilarity(tract,\n distance,\n num_prototypes,\n prototype_policy='sff',\n verbose=False)\n print(\"%s sec.\" % (time() - t0))\n prototypes = tract[prototype_idx]\n print(\"Building the KD-tree of tract.\")\n kdt = KDTree(dm_tract)\n return kdt, prototypes, dm_tract"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def generate_kdtree(self):\n if self.method==2:\n coordinates = self.unassigned_data[0:3,:]\n else:\n coordinates = self.unassigned_data[0:2,:]\n tree = cKDTree(coordinates.T)\n\n return tree","def compute_kdtree_and_dr_tractogram(tractogram, num_prototypes=None):\n tractogram = np.array(tractogram, dtype=np.object)\n print(\"Computing dissimilarity matrices...\")\n if num_prototypes is None:\n num_prototypes = 40\n print(\"Using %s prototypes as in Olivetti et al. 2012.\"\n % num_prototypes)\n print(\"Using %s prototypes\" % num_prototypes)\n dm_tractogram, prototype_idx = compute_dissimilarity(tractogram,\n num_prototypes=num_prototypes,\n distance=bundles_distances_mam,\n prototype_policy='sff',\n n_jobs=-1,\n verbose=False)\n prototypes = tractogram[prototype_idx]\n print(\"Building the KD-tree of tractogram.\")\n kdt = KDTree(dm_tractogram)\n return kdt, prototypes","def CreateTree(d,list_L):\r\n \r\n d_new = defaultdict(dict)\r\n \r\n #labels corresponding to minimum evolutionary distance in Matrix\r\n a,b = minInDict(d)\r\n\r\n n = ['(',a,',',b,')']\r\n new_cluster = ''.join(n)\r\n \r\n #remove the entries in newly formed cluster from our list\r\n for k in list_L:\r\n if k == a:\r\n list_L.remove(k)\r\n break\r\n for p in list_L:\r\n if p == b:\r\n list_L.remove(p)\r\n break\r\n\r\n #to remove an existing cluster with those entries\r\n for j in list_L:\r\n if a in j or b in j:\r\n list_L.remove(j)\r\n\r\n #Base case of our recursive function \r\n if len(d) == 1:\r\n return new_cluster\r\n\r\n #calculating distances from our new cluster(eg: (a,b))to other labels/clusters\r\n for q in list_L:\r\n \r\n if b in d:\r\n if q in d[a] and q in d[b]:\r\n d_new[new_cluster][q] = (d[a][q] + d[b][q])/2\r\n continue\r\n if q in d:\r\n if b in d[q] and q in d[a]:\r\n d_new[new_cluster][q] = (d[a][q] + d[q][b])/2\r\n continue\r\n if b in d:\r\n if a in d[q] and q in d[b]:\r\n d_new[new_cluster][q] = (d[q][a] + d[b][q])/2\r\n continue\r\n if a in d[q] and b in d[q]:\r\n d_new[new_cluster][q] = (d[q][a] + d[q][b])/2\r\n \r\n list_L.insert(0,new_cluster)\r\n \r\n #deleting row and column corresponding to a and b\r\n del d[a]\r\n if b in d:\r\n del d[b]\r\n \r\n for p in d:\r\n for q in d[p]:\r\n if q != b and q != a:\r\n d_new[p][q] = d[p][q]\r\n\r\n return CreateTree(d_new,list_L)","def _getKDTree(self, index=None):\n\n if self._n_csets:\n if index is None:\n index = self._acsi\n kdtree = self._kdtrees[index]\n if kdtree is None:\n kdtree = KDTree(self._coords[index])\n self._kdtrees[index] = kdtree\n return kdtree\n else:\n return None","def create_tree():\n\tdtree = {}\n\n\tdtree['stats'] = None\n\tdtree['libs'] = {'fort':\n\t\t\t\t\t\t\t {'integers': None, 'floats': None, 'data': None}\n\t\t\t\t\t ,'cache blocking': None}\n\tdtree['grid'] = {'size' :\n\t\t\t\t\t\t\t {'nxgb': None, 'nygb': None, 'nzgb': None}\n\t\t\t\t\t ,'geom' :\n\t\t\t\t\t\t\t {'Lx' : None, 'Ly' : None, 'Lz' : None\n\t\t\t\t\t\t\t ,'dx' : None, 'dy' : None, 'dz' : None\n\t\t\t\t\t\t\t ,'x' : None, 'y' : None, 'z' : None\n\t\t\t\t\t\t\t ,'xloc': None, 'yloc': None, 'zloc': None}}\t\t\t\t\t\t\t \t\t\t\t\t \t\t \n\tdtree['eqns'] = {'qvec' : \n\t\t\t\t\t\t\t {'nvars': None, 'solved': None, 'stored': None, 'views': None}\n\t\t\t\t\t ,'coeff': None\n\t\t\t\t\t ,'time' : None\n\t\t\t\t\t ,'ndim' : None}\n\n\tdtree['misc'] = {'verbose': None, 'working precision': None}\n\tdtree['mpi'] = {'split': \n\t\t\t\t\t\t\t {'nxpr': None, 'nypr': None, 'nzpr': None}\n\t\t\t\t\t ,'dMpi' : None}\t\t \t\n\tdtree['num'] = {'hlo' : None\n\t\t\t\t\t ,'deriv': \n\t\t\t\t\t\t\t {'order': None, 'stencil': None, 'hlo': None} \n\t\t\t\t\t ,'filtr': \n\t\t\t\t \t {'order': None, 'stencil': None, 'hlo': None,'eps': None}\n\t\t\t\t\t ,'tint' : \n\t\t\t\t\t {'tstep': None, 'cfl': None, 'itn': None}}\n\n\tdtree['bc'] = {'wall': \n\t\t\t\t\t\t\t {'isoT': None, 'zeroQ': None, 'slip': None}}\t\n\n\tdtree['usr'] = None\n\tdtree['ios'] = None\n\t\n\tfrom rhsinfo import dim, stencil, order, coefficients, varname, varsolved, varstored, varbc, wp,hlo_rhs\n\n\tdtree['eqns']['qvec']['solved'] = []\n\tdtree['eqns']['qvec']['stored'] = []\n\tdtree['eqns']['qvec']['bcs'] = {'face':{'i' :[],'j' :[],'k' :[]},\n\t\t\t\t\t\t\t\t\t 'edge':{'ij':[],'jk':[],'ik':[]}}\n\n\tfor v in varsolved:\n\t\tdtree['eqns']['qvec']['solved'].append([v,varname[v]])\t\n\n\tfor v in varstored:\n\t\tdtree['eqns']['qvec']['stored'].append([v,varstored[v]['ind']])\n\n\tfor v in varbc:\n\t\tfor bcloc in ['face','edge']:\n\t\t\tif bcloc in varbc[v]:\n\t\t\t\tloctype = ''.join(sorted(varbc[v][bcloc].replace('1','').replace('max','')))\n\t\t\t\tdtree['eqns']['qvec']['bcs'][bcloc][loctype].append([v,varbc[v]['ind']])\n\n\n\tdtree['eqns']['coeff'] = []\n\tfor v in coefficients:\n\t\tdtree['eqns']['coeff'].append([v,coefficients[v]])\t\n\n\tdtree['eqns']['qvec']['nvars'] = len(varname)#+len(dtree['eqns']['qvec']['stored'])\t\n\tdtree['num']['deriv']['stencil'] = stencil\n\tdtree['num']['deriv']['hlo'] = hlo_rhs #int((stencil-1)/2)\n\tdtree['num']['deriv']['order'] = order\n\t\n\t# if dtree['num']['filtr']['hlo'] != None:\n\t# \tdtree['num']['hlo'] = max(dtree['num']['deriv']['hlo'],dtree['num']['filtr']['hlo'])\n\t# else:\n\t# \tdtree['num']['hlo'] = dtree['num']['deriv']['hlo']\t\n \n\tdtree['num']['hlo'] = hlo_rhs\t\t\t\n\t\n\tdtree['eqns']['ndim'] = dim\n\tdtree['misc']['working precision'] = wp\n\tdtree['misc']['verbose'] = True\n\n\t# dtree['libs']['cache blocking'] = [256,2,6] # good for 11 pts 3D, div. forme of N.S.\n\t\n\tdtree['libs']['cache blocking'] = [2560000,2,6]\n\n\t# recover BCs info:\n\t\n\ttry:\n\t from rhsinfo import bc_info\n\texcept: \n\t\tbc_info = [{},{}]\n\n\tdtree['bc']\t = {'allbc':bc_info[1],'mybc':[]} # OVERWRITE predefined 'bc' key.\n\n\treturn dtree","def distanceToTree_deprec(self,taxonomic_forest,update_inner_attributes=True):\n #Thankfully the distance is symmetric by definition so order doesn't matter.\n Jacobian = []\n logger.warning('[FOR DEVELOPER] This is not optimized. Should be necessary to use attr_t1 and attr_t2 with name_id (integer)')\n for i in settings.TAXONOMIC_TREE_KEYS:\n F1 = []\n for j in settings.TAXONOMIC_TREE_KEYS:\n d = self.forest[i].robinson_foulds(taxonomic_forest[j],unrooted_trees=True)[0]\n F1.append(float(d))\n Jacobian.append(F1)\n if update_inner_attributes:\n self.JacobiM = np.matrix(Jacobian)\n self.Jacobi = np.linalg.det(self.JacobiM)\n return np.matrix(Jacobian)","def implement_tree(X, y, labels, k, alpha_range, criterion, d):\r\n\r\n kf = KFold(n_splits=k)\r\n min_avg_error = math.inf\r\n best_alpha = None\r\n avg_time_build_tree = 0\r\n for alpha in alpha_range:\r\n total_error = 0\r\n for train_idx, test_idx in kf.split(X):\r\n X_train, X_test = X[train_idx], X[test_idx]\r\n y_train, y_test = y[train_idx], y[test_idx]\r\n train = Tree(criterion, X_train, y_train, labels)\r\n test = Tree(criterion, X_test, y_test, labels)\r\n start = time.time()\r\n train_tree = train.build_tree(train.root, train.X, train.y, 0, d, train.labels, train.root.X_idx, None)\r\n test_all(train)\r\n end = time.time()\r\n train.is_valid(train_tree)\r\n period = end - start\r\n avg_time_build_tree += period\r\n pruned_tree, pruned_alpha, error = train.prune_tree(train_tree, alpha, test.X, test.y, test.labels)\r\n total_error += error\r\n avg_error = total_error / k\r\n if avg_error < min_avg_error:\r\n min_avg_error = avg_error\r\n best_alpha = alpha\r\n avg_time_build_tree = avg_time_build_tree / (k*len(alpha_range))\r\n\r\n return min_avg_error, best_alpha, avg_time_build_tree","def discretize(self,dt):\n self._skip_deriv = True\n res = self.discretize_state(dt)\n self._skip_deriv = False\n n = self.geodesic.extrinsicDimension()\n return GeodesicTrajectory(self.geodesic,res.times,[m[:n] for m in res.milestones])","def set_kd_tree(self, reset=False):\n if reset or self.tree is None:\n isoxy = np.column_stack((np.sum(self.meshx[self.ikle2],\n axis=1)/3.0,\n np.sum(self.meshy[self.ikle2],\n axis=1)/3.0))\n self.tree = cKDTree(isoxy)","def problem3(self, s):\n\n points = 0\n\n # Test KDTNode.__init__ (can only hold np.ndarrays; 2 points)\n try:\n s.KDTNode(\"This is not a numpy array\")\n self.feedback += \"\\n\\tKDTNode(x) failed to raise a TypeError \"\n self.feedback += \"for x not a numpy array (np.ndarray)\"\n except:\n points += 2\n\n # Test KDTNode.__sub__ (euclidean distance; 2 points)\n x = np.random.random(10); y = np.random.random(10)\n A = KDTNode(x); B = KDTNode(y)\n C = s.KDTNode(x); D = s.KDTNode(y)\n points += 2*self.numTest(A-B, C-D, \"\\n\\tKDTNode.__sub__ failed\")\n\n # Test KDTNode.__eq__ (1 Point)\n D = s.KDTNode(1.5*x)\n if not (C == D):\n points += 1\n else:\n self.feedback += \"\\n\\tKDTNode.__eq__ failed on nonequal\"\n\n # Test KDTNode.__lt__ and KDTNode.__gt__ (5 points)\n x = s.KDTNode(np.array([3,1,0,5], dtype=np.int)); x.axis = 0\n y = s.KDTNode(np.array([1,2,4,3], dtype=np.int)); y.axis = 1\n if x < y: points += 1\n else: self.feedback += \"\\n\\tKDTNode.__lt__ failed\"\n if y < x: points += 1\n else: self.feedback += \"\\n\\tKDTNode.__lt__ failed\"\n\n x.axis = 2; y.axis = 3\n if x > y: points += 1\n else: self.feedback += \"\\n\\tKDTNode.__gt__ failed\"\n if y > x: points += 2\n else: self.feedback += \"\\n\\tKDTNode.__gt__ failed\"\n\n return points","def get_featured_tree(self):\n\n for t in self.tree.get_terminals():\n t.sample_series = self.feature_table[t.name]\n self.feature_tree = self.recursion_tree(self.tree.root)\n for clade in self.feature_tree.find_clades(order='level'):\n clade.depth = 1+len(self.feature_tree.get_path(clade))\n \n #i = 0\n #for clade in self.feature_tree.find_clades(order='level'):\n # clade.ID_num = i \n #clade.abu = np.mean(clade.sample_series.values)\n #clade.domain_otu = clade.sample_series.idxmax()","def test_nti(self):\r\n # using the input of the distance matrix generated from this tree\r\n # '(((sp1:.06,sp2:.1)A:.031,(sp3:.001,sp4:.01)B:.2)AB:.4,((sp5:.03,sp6:.02)C:.13,(sp7:.01,sp8:.005)D:.1)CD:.3)root;'\r\n # using this tree and this clumping we get almost exact agreement\r\n # between phylocom and relatedness.py\r\n distmat = array(\r\n [[0., 0.16, 0.292, 0.301, 0.951, 0.941, 0.901, 0.896],\r\n [0.16,\r\n 0.,\r\n 0.332,\r\n 0.341,\r\n 0.991,\r\n 0.981,\r\n 0.941,\r\n 0.936],\r\n [0.292,\r\n 0.332,\r\n 0.,\r\n 0.011,\r\n 1.061,\r\n 1.051,\r\n 1.011,\r\n 1.006],\r\n [0.301,\r\n 0.341,\r\n 0.011,\r\n 0.,\r\n 1.07,\r\n 1.06,\r\n 1.02,\r\n 1.015],\r\n [0.951,\r\n 0.991,\r\n 1.061,\r\n 1.07,\r\n 0.,\r\n 0.05,\r\n 0.27,\r\n 0.265],\r\n [0.941,\r\n 0.981,\r\n 1.051,\r\n 1.06,\r\n 0.05,\r\n 0.,\r\n 0.26,\r\n 0.255],\r\n [0.901,\r\n 0.941,\r\n 1.011,\r\n 1.02,\r\n 0.27,\r\n 0.26,\r\n 0.,\r\n 0.015],\r\n [0.896, 0.936, 1.006, 1.015, 0.265, 0.255, 0.015, 0.]])\r\n\r\n seed(0)\r\n obs_nti = nti(distmat, list(arange(8)), [0, 1, 3, 6], 1000)\r\n assert_almost_equal(-1.2046544711672049, obs_nti)","def copyAndCleanTree (self):\n\t\t# TODO: Need to do several things here:\n\t\t# - NoNames\n\t\t# - copy support scores to internal branch names\n\n\t\t## Main:\n\t\t# Copy the tree so as not to damage original\n\t\tete_tree = deepcopy (self.data)\n\n\t\t# set root branch to zero, make change later\n\t\tete_tree.dist = 0.0\n\n\t\t# find max / min branchlength for diagnostic purposes\n\t\t# doesn't use negative or zero branch lengths\n\t\t# Also clean names\n\t\tmax_bl = None\n\t\tmin_bl = None\n\t\tfor n in ete_tree.traverse (\"postorder\"):\n\t\t\tif (0.0 < n.dist):\n\t\t\t\tif (max_bl is None) or (max_bl < n.dist):\n\t\t\t\t\tmax_bl = n.dist\n\t\t\t\tif (min_bl is None) or (n.dist < min_bl):\n\t\t\t\t\tmin_bl = n.dist\n\t\t\tclean_name = n.name.strip()\n\t\t\tif (clean_name[0] == \"'\") and (clean_name[-1] == \"'\"):\n\t\t\t\tclean_name = clean_name[1:-1]\n\t\t\tn.name = clean_name\n\n\t\t# set all branches to be at least 1/100 of the largest or 1/10 the\n\t\t# smallest, whichever is larger\n\t\tdefault_bl = max (max_bl / 100, min_bl/10)\n\t\tfor n in ete_tree.traverse (\"postorder\"):\n\t\t\tif (n.dist <= 0.0):\n\t\t\t\tn.dist = default_bl\n\n\t\t# get support values on tree by setting supprt as name\n\t\tfor n in ete_tree.traverse (\"postorder\"):\n\t\t\t# if an internal node\n\t\t\tif (not n.is_leaf()):\n\t\t\t\tn.name = config.SUPPORT_FMT % n.support\t\n\n\t\t# very hacky - calc appropriate scale bar size and stick on root\n\t\tmagn = int (floor (log10 (max_bl)))\n\t\tscale_size = 10**magn\n\t\tete_tree.scale_size = scale_size\n\n\t\t## Postcondtions & return:int ( floor ( log10 (x)))\n\t\treturn ete_tree","def get_kd(model, X_train, Y_train, X_test, X_test_noisy, X_test_adv, dataset='mnist'):\r\n # Get deep feature representations\r\n print('Getting deep feature representations...')\r\n X_train_features = get_deep_representations(model, X_train,\r\n batch_size=batch_size)\r\n X_test_normal_features = get_deep_representations(model, X_test,\r\n batch_size=batch_size)\r\n X_test_noisy_features = get_deep_representations(model, X_test_noisy,\r\n batch_size=batch_size)\r\n X_test_adv_features = get_deep_representations(model, X_test_adv,\r\n batch_size=batch_size)\r\n # Train one KDE per class\r\n print('Training KDEs...')\r\n class_inds = {}\r\n for i in range(Y_train.shape[1]):\r\n class_inds[i] = np.where(Y_train.argmax(axis=1) == i)[0]\r\n kdes = {}\r\n warnings.warn(\"Using pre-set kernel bandwidths that were determined \"\r\n \"optimal for the specific CNN models of the paper. If you've \"\r\n \"changed your model, you'll need to re-optimize the \"\r\n \"bandwidth.\")\r\n print('bandwidth %.4f for %s' % (BANDWIDTHS[dataset], dataset))\r\n for i in range(Y_train.shape[1]):\r\n kdes[i] = KernelDensity(kernel='gaussian',\r\n bandwidth=BANDWIDTHS[dataset]) \\\r\n .fit(X_train_features[class_inds[i]])\r\n # Get model predictions\r\n print('Computing model predictions...')\r\n preds_test_normal = model.predict_classes(X_test, verbose=0,\r\n batch_size=batch_size)\r\n preds_test_noisy = model.predict_classes(X_test_noisy, verbose=0,\r\n batch_size=batch_size)\r\n preds_test_adv = model.predict_classes(X_test_adv, verbose=0,\r\n batch_size=batch_size)\r\n # Get density estimates\r\n print('computing densities...')\r\n densities_normal = score_samples(\r\n kdes,\r\n X_test_normal_features,\r\n preds_test_normal\r\n )\r\n densities_noisy = score_samples(\r\n kdes,\r\n X_test_noisy_features,\r\n preds_test_noisy\r\n )\r\n densities_adv = score_samples(\r\n kdes,\r\n X_test_adv_features,\r\n preds_test_adv\r\n )\r\n\r\n print(\"densities_normal:\", densities_normal.shape)\r\n print(\"densities_adv:\", densities_adv.shape)\r\n print(\"densities_noisy:\", densities_noisy.shape)\r\n\r\n ## skip the normalization, you may want to try different normalizations later\r\n ## so at this step, just save the raw values\r\n # densities_normal_z, densities_adv_z, densities_noisy_z = normalize(\r\n # densities_normal,\r\n # densities_adv,\r\n # densities_noisy\r\n # )\r\n\r\n densities_pos = densities_adv\r\n densities_neg = np.concatenate((densities_normal, densities_noisy))\r\n artifacts, labels = merge_and_generate_labels(densities_pos, densities_neg)\r\n\r\n return artifacts, labels","def build(self, dist_matrix, class_map, cluster_naming_function):\n # Update attributes\n self.orig_dist_matrix = dist_matrix \n self.class_map = class_map \n self.work_dist_matrix = dist_matrix\n\n # Get number of elements\n n = dist_matrix.shape[0]\n\n if PROGRESS:\n print 'Starting tree build now!'\n\n # Loop through n-3 elements & add nodes in tree\n for i in range(n - 3):\n\n if DEBUG:\n print 'Distance Matrix'\n pprint(self.work_dist_matrix)\n print\n\n # Calculate q_matrix matrix from distances\n q_matrix = _calculate_q_matrix(self.work_dist_matrix)\n \n if DEBUG:\n print 'Q matrix:'\n pprint(q_matrix)\n print\n\n # Find pair of elements (i,j) where q_matrix(i,j) has the lowest value\n (min_col, min_row) = _find_min_pair(q_matrix)\n\n # Add nodes i,j, and cluster node of i and j to this tree\n # And update working distance matrix accordingly\n new_cluster_name = cluster_naming_function(min_row, min_col, self.cluster_map)\n self.cluster_leaves(min_row, min_col, new_cluster_name) \n\n if DEBUG:\n print 'Tree:'\n pprint(nx.clustering(self.tree))\n pprint(self.cluster_dictionary)\n print '\\n\\n'\n \n # View graph after each step for debugging\n if VIEW_ALL:\n labels = {i[0]: i[0]+'/'+i[1]['c'] for i in njt.tree.nodes(data=True)}\n layout = nx.spring_layout(njt.tree)\n nx.draw_networkx(njt.tree, pos=layout, with_labels=True, labels=labels) #class labels\n plt.show()\n\n if PROGRESS:\n print str(i + 1) + \" down, \" + str(n-i-4) + \" to go...\"\n \n # Add remaining branch lengths and nodes from working distance matrix to this tree \n previous_cluster = new_cluster_name\n mid_edge_length = 0.5 * (self.work_dist_matrix.iat[0, 1]\n + self.work_dist_matrix.iat[0, 2]\n - self.work_dist_matrix.iat[1, 2])\n (node1, node2) = (self.work_dist_matrix.columns[0], self.work_dist_matrix.columns[1])\n new_cluster = cluster_naming_function(node1, node2, self.cluster_map)\n self.cluster_leaves(node1, node2, new_cluster)\n # Viz only scales based on a weight attribute, so we set that as the length\n self.tree.add_edge(previous_cluster, new_cluster, length=mid_edge_length, weight=mid_edge_length)\n\n if DEBUG:\n print 'Final tree:'\n pprint(nx.clustering(self.tree))\n pprint(self.cluster_dictionary)","def _CalculateDissimilarities(self):\n print 'Calculating dissimilarities'\n num_bins = len(self._mix_bins)\n\n for left_cluster_id, left_bin_collection in (self\n ._left_bin_collection_by_cluster_id.iteritems()):\n for right_cluster_id, right_bin_collection in (self\n ._right_bin_collection_by_cluster_id.iteritems()):\n # This operation can be easily parallelized via multiprocessing.\n d = self._CalculateDissimilarityBetweenClusters(\n left_cluster_id, left_bin_collection, right_cluster_id,\n right_bin_collection)\n print 'Left cluster: %s, Right cluster: %s, dissimilarity: %s' % (\n d.left_cluster_id, d.right_cluster_id, d.dissimilarity_score)\n self._dissimilarities.append(d)\n\n print 'Dissimilarities are calculated'","def decision_tree(df, dt_dict, curr_node,\r\n prev_attr = None, align_dir = None,\r\n depth = -1, no_data = False,\r\n ensemble = None):\r\n \r\n class_count = get_class_count(df)\r\n # get the class label counts for the given dataframe\r\n leaf_node_bool = check_leaf_node(df)\r\n # this function helps to check if we have a leaf node\r\n if leaf_node_bool:\r\n # if its leaf node\r\n curr_node[align_dir] = df['class'].values[0]\r\n # assign the leaf node value\r\n elif no_data:\r\n # if we are out of data points\r\n class_counts = df['class'].value_counts()\r\n # get the class counts\r\n curr_node[align_dir] = np.argmax(class_counts)\r\n # assign the majority class of prev node\r\n else:\r\n entropy_values_series = impurity.entropy_calc(df, ensemble = ensemble)\r\n # calculate the entropy values for each feature\r\n info_gain_dict = {}\r\n # empty dict for information gain\r\n for feature in entropy_values_series.index:\r\n # iterate over each features\r\n impurity.information_gain_calc(df, feature, info_gain_dict)\r\n # function call for information gain calculation\r\n for f in entropy_values_series.index:\r\n # iterate over each feature\r\n information_gain = entropy_values_series[f] - info_gain_dict[f][1]\r\n # calculation of information gain\r\n info_gain_dict[f] = (info_gain_dict[f][0], information_gain)\r\n # update the information gain dict\r\n best_feature = sorted(info_gain_dict, key = lambda x: info_gain_dict[x][1])[-1]\r\n # get the best feature on which to be splitted.\r\n #print(best_feature)\r\n node_value = (best_feature, info_gain_dict[best_feature], class_count[0],\r\n class_count[1])\r\n # get the node value\r\n \r\n if not leaf_node_bool and align_dir:\r\n # growing the tree\r\n if depth == 0:\r\n if node_value[2] > node_value[3]:\r\n node_value = 0\r\n else:\r\n node_value = 1\r\n curr_node[align_dir] = node_value\r\n return 0\r\n else:\r\n curr_node[align_dir] = {node_value:{}}\r\n curr_node = curr_node[align_dir][node_value]\r\n else:\r\n dt_dict[node_value] = {}\r\n curr_node = dt_dict[node_value]\r\n \r\n data_split(df, best_feature, info_gain_dict, \r\n dt_dict, curr_node, depth)\r\n # function call for data split\r","def dijkstra_tree(self, s):\n l = []\n dist = {}\n prev = {}\n discovered = {}\n g = Graph(attr={DIRECTED: True})\n g.add_vertex(vertex.Vertex(s, {\"WEIGHT\": 0}))\n for v in self.get_vertices():\n dist[v] = float('inf')\n prev[v] = None\n discovered[v] = False\n dist[s] = 0\n l.append((s, dist[s]))\n while len(l) != 0:\n u = min(l, key=lambda x: x[1])\n l.remove(u)\n u = u[0]\n discovered[u] = True\n for v in self.get_adjacent_vertices_by_vertex(u):\n if not discovered[v]:\n alt = dist[u] + self.get_edge((u, v)).attr[\"WEIGHT\"]\n if alt < dist[v]:\n dist[v] = alt\n prev[v] = u\n l.append((v, dist[v]))\n g.add_vertex(vertex.Vertex(v, {\"WEIGHT\": dist[v]}))\n g.add_edge(edge.Edge(u, v, {\"WEIGHT\": dist[v]}))\n\n return g","def compute_tree(self,\n verbose=True):\n\n # Tree structure in format {leaf_id: node()}\n self.tree = {}\n # A path is list of integers in (-1, 0, 1) indicating the set of\n # decisions to take through the tree (lower, null, higher)\n # based on the specified labels and cutoff of the nodes.\n paths = [[]]\n path_idx = 0\n start_time = time()\n\n # Each path will point to a leaf that is not yet in the tree.\n while path_idx < len(paths):\n if verbose:\n string = f'{path_idx}/{len(paths)} ({time()-start_time:.0f} s)'\n sys.stdout.write('\\r'+string[:40]+' '*(40-len(string)))\n sys.stdout.flush()\n path = paths[path_idx]\n self.compute_path(path)\n leaf = node(path_idx)\n if self.sub_y_data.size == 0:\n raise NameError('No data on the leaf error')\n if len(path) < self.max_tree_depth or self.max_tree_depth <= 0:\n cutoffs = []\n for split_label in self.labels:\n cutoff, value = self.best_cutoff(split_label)\n cutoffs.append([split_label, cutoff, value])\n cutoffs = sorted(cutoffs, key=lambda x: -x[2])\n split_label, cutoff, value = cutoffs[0]\n leaf.value = value\n if value > self.value_threshold:\n leaf.label = split_label\n leaf.cutoff = cutoff\n leaf.id_lower = len(paths)\n paths.append(path+[-1])\n leaf.id_higher = len(paths)\n paths.append(path+[1])\n if np.isnan(self.sub_split_data[split_label]).any():\n leaf.id_null = len(paths)\n paths.append(path+[0])\n else:\n leaf.is_leaf = True\n ys_with = self.sub_y_data[self.sub_bin_data]\n ys_without = self.sub_y_data[self.sub_bin_data]\n leaf.n_data_with = len(ys_with)\n leaf.n_data_without = len(ys_without)\n if ys_with.size == 0 or ys_without.size == 0:\n leaf.effect = 0\n else:\n leaf.effect = ys_with.mean() - ys_without.mean()\n self.tree[leaf.id] = leaf\n path_idx += 1\n\n if verbose:\n string = f'{path_idx}/{len(paths)} ({time()-start_time:.0f} s)'\n sys.stdout.write('\\r'+string[:40]+' '*(40-len(string)))\n sys.stdout.flush()\n print()","def classify_treeNN(self, query_name):\n # 1) Find set of closest neighbors & their class names\n # ie. leaves with at most neighborhood_max_edges edges between itself \n # and the query node\n neighborhood_classes = self.getNeighborhoodClasses(query_name)\n print \"neighborhood \" , neighborhood_classes\n\n # 2) Find aggregate similarity score for each class\n # Use minimum operator for distance measure & maximum for similarity measure\n # EQ 6.1 in Chapt 6, Busa-Fekete et al\n R = {}\n for c,ids in neighborhood_classes.iteritems():\n sim_score = min([nx.shortest_path_length(self.tree, source=query_name, \n target=i, weight='length') for i in ids])\n if DEBUG: print \"\\tCLASS / SIM_SCORE: \", c, sim_score\n R[sim_score] = c # distance measure\n\n min_score = min(R.keys())\n if DEBUG: print \"MIN_SCORE: \", min_score\n\n return R[min_score] #class of minimum distance score","def build_kruskal_concept_tree(csvsegstack, outputdir):\n tree_struct = Tree()\n nodes, slidenames = get_leaves_from_segment_stack(csvsegstack)\n for nodeid, desc in nodes.items():\n tree_struct.descriptor[nodeid] = desc\n tree_struct.slides[nodeid] = set()\n tree_struct.slides[nodeid].add(slidenames[nodeid])\n edges = knn_edges(nodes)\n krusk = WeightedKruskal()\n last_cpt_id = max(nodes.keys()) + 1\n for kedge in krusk.spanning_tree(edges):\n last_cpt = get_root(tree_struct, last_cpt_id)\n n1, n2, w = kedge\n rn1 = get_root(tree_struct, n1)\n rn2 = get_root(tree_struct, n2)\n tree_struct.parents[rn1] = last_cpt\n tree_struct.parents[rn2] = last_cpt\n tree_struct.children[last_cpt] = (rn1, rn2)\n tree_struct.weights[last_cpt] = w\n tree_struct.population[last_cpt] = tree_struct.population[rn1] + tree_struct.population[rn2]\n tree_struct.descriptor[last_cpt] = (tree_struct.population[rn1] * tree_struct.descriptor[rn1])\\\n + (tree_struct.descriptor[rn2] * tree_struct.population[rn2])\n tree_struct.descriptor[last_cpt] /= tree_struct.population[last_cpt]\n tree_struct.slides[last_cpt] = tree_struct.slides[rn1] | tree_struct.slides[rn2]\n last_cpt_id += 1\n\n with open(os.path.join(outputdir, \"full_tree.p\"), \"wb\") as f:\n pickle.dump(tree_struct, f)\n return tree_struct","def get_kdtree(x: np.ndarray, y: np.ndarray):\n combined_x_y_arrays = np.dstack([y.ravel(), x.ravel()])[0]\n\n tree = KDTree(combined_x_y_arrays)\n\n return tree","def main():\n\n ''' Reading the training data file '''\n original_training_data = pd.read_csv(\"DT_Data_CakeVsMuffin_v012_TRAIN.csv\")\n\n ''' Storing the final decision tree '''\n final_tree = decision_tree(original_training_data,0)\n\n ''' Printing the final decision tree '''\n print(\"This is the resulting decision tree: \\n\")\n print(final_tree)\n\n ''' Iterating through the dictionary by using the key values '''\n for key in final_tree.keys():\n ''' Parent = Flour <= 5.1636'''\n parent = key\n ''' left_child = [{'Oils <= 3.1265': [{'Flour <= 2.7291': [{'Proteins <= 2.6527': ['Muffin', 'CupCake']}, 'Muffin']}, 'CupCake']}'''\n left_child = final_tree[parent][0]\n ''' right_child = {'Oils <= 7.7793': ['Muffin', {'Flour <= 8.2225': ['CupCake', 'Muffin']}]}]'''\n right_child = final_tree[parent][1]\n\n ''' Writing a file which generates code for classification '''\n file = open('HW06_Parchand_Nihal_Classifier.py','w+')\n file.write(\"'''Importing libraries''' \"\n \"\\n\\nimport pandas as pd \\n\\ndef main():\"\n \"\\n\\tdata_df = pd.read_csv('DT_Data_CakeVsMuffin_v012_TEST.csv')\"\n \"\\n\\tresult = []\"\n \"\\n\\tfor row in range(0,len(data_df)):\"\n \"\\n\\t\\tFlour = data_df.loc[row][0]\"\n \"\\n\\t\\tSugar = data_df.loc[row][1]\"\n \"\\n\\t\\tOils = data_df.loc[row][2]\"\n \"\\n\\t\\tProteins = data_df.loc[row][3]\"\n \"\\n\\t\\tif {}:\\n\".format(parent))\n\n ''' Iterating through the left_tree '''\n for key in left_child.keys():\n file.write(\"\\t\\t\\tif {}:\\n\".format(key))\n\n ''' Iterating through the inner left_tree '''\n for inner_key in left_child[key][0].keys():\n file.write(\"\\t\\t\\t\\tif {}:\\n\".format(inner_key))\n\n for inner_inner_key in ((left_child[key][0])[inner_key])[0]:\n file.write(\"\\t\\t\\t\\t\\tif {}:\\n\".format(inner_inner_key))\n file.write(\"\\t\\t\\t\\t\\t\\tresult.append(0)\\n\")\n file.write(\"\\t\\t\\t\\t\\telse:\\n\".format(inner_inner_key))\n file.write(\"\\t\\t\\t\\t\\t\\tresult.append(1)\\n\")\n\n file.write(\"\\t\\t\\t\\telse:\\n\")\n file.write(\"\\t\\t\\t\\t\\tresult.append(0)\\n\")\n file.write(\"\\t\\t\\telse:\\n\")\n file.write(\"\\t\\t\\t\\tresult.append(1)\\n\")\n file.write(\"\\t\\telse:\\n\")\n\n ''' Iterating through the right_tree '''\n for key in right_child.keys():\n file.write(\"\\t\\t\\tif {}:\\n\".format(key))\n file.write(\"\\t\\t\\t\\tresult.append(0)\\n\")\n for inner_key in right_child[key][1].keys():\n file.write(\"\\t\\t\\telif {}:\\n\".format(inner_key))\n file.write(\"\\t\\t\\t\\tresult.append(1)\\n\")\n file.write(\"\\t\\t\\telse:\\n\")\n file.write(\"\\t\\t\\t\\tresult.append(0)\\n\\n\")\n\n ''' Writing the results of classifier to a csv file '''\n file.write(\n \"\\twith open('HW06_Parchand_Nihal_MyClassifications.csv', 'w+') as file2:\\n\"\n \"\\t\\tfor value in result:\\n\"\n \"\\t\\t\\tfile2.write(str(value))\\n\"\n \"\\t\\t\\tfile2.write('\\\\n')\\n\\n\"\n \"main()\")","def build_tree(self, df=None, tree=None, depth=0):\n if df is None:\n df = self.df\n target = self.target\n\n node = self.get_lowest_entropy_feature(df)\n if not node:\n print(\"Pure solution not possible in current branch...\")\n return tree\n variables = df[node].unique()\n\n if tree is None: \n tree = {}\n tree[node] = {}\n\n for value in variables:\n subtable = df[df[node] == value].reset_index(drop=True)\n inner_variables, counts = np.unique(subtable[target], return_counts=True) \n \n if len(counts) == 1:\n tree[node][value] = inner_variables[0] \n elif depth >= self.max_depth:\n return tree \n else:\n depth += 1 \n tree[node][value] = self.build_tree(df=subtable, depth=depth)\n \n return tree","def classify_treeInsert(self, query_name, cluster_naming_function):\n classes = Set(self.class_map.values())\n\n full_dist_matrix = self.orig_dist_matrix.drop(query_name)\n full_dist_matrix = full_dist_matrix.drop(query_name, axis=1)\n\n if PROGRESS: print '\\nStarting treeInsert!'\n \n #1] Build a tree for each class\n class_trees = {}\n all_elements = full_dist_matrix.columns.values.tolist()\n classes_done = 0\n num_of_classes = len(classes)\n for c in classes:\n\n #1a. Construct a mini distance matrix for the current class\n nonclass_members = [i for i in all_elements if self.class_map[i] != c]\n class_dist_matrix = full_dist_matrix.drop(nonclass_members)\n class_dist_matrix = class_dist_matrix.drop(nonclass_members, axis=1)\n\n #1b. Build class tree\n if PROGRESS: print 'Building class tree for ' + c\n\n class_njt = NJTree()\n class_njt.build(class_dist_matrix, self.class_map, myClusterNaming)\n class_trees[c] = class_njt\n classes_done = classes_done + 1\n\n if PROGRESS:\n print str(classes_done) + \" classes down, \" + str(num_of_classes - classes_done) \n + \" to go...\"\n\n #2] Determine the insertion cost of each tree\n class_insert_costs = {}\n for c,class_tree in class_trees.iteritems():\n\n #2a. Find insertion cost of each leaf in the tree\n leaves = [i for i in class_tree.tree.nodes() if class_tree.isLeaf(i)] \n leaf_insert_costs = {}\n for leaf_i in leaves:\n\n parent_i = class_tree.tree.neighbors(leaf_i)[0] \n cons = ({'type': 'eq',\n 'fun': lambda x: x[0] + x[1] - nx.shortest_path_length(class_tree.tree, \n source=parent_i, target=leaf_i, weight='length')})\n optimum_leaf_insert_cost = optimize.minimize(_leaf_insertion_cost, [0,0,0], \n args=(class_tree.orig_dist_matrix, leaf_i, leaves, query_name, self), method='SLSQP', \n constraints=cons)\n\n if DEBUG:\n print \"Optimum cost for \", leaf_i, \" : \", optimum_leaf_insert_cost.x[0]\n\n leaf_insert_costs[leaf_i] = optimum_leaf_insert_cost.x[0]\n \n class_insert_costs[c] = min(list(leaf_insert_costs.values()))\n\n #3] Output the class name of tree with minimum insertion cost\n min_insert_cost = min(list(class_insert_costs.values()))\n for c,cost in class_insert_costs.iteritems():\n if cost==min_insert_cost:\n return c\n break","def ndcg(self, gt, pred, k):\n k = min(k, len(pred))\n idcg = self.idcg(k)\n dcg_k = sum([int(pred[i] in gt) / math.log(i + 2, 2) for i in range(k)])\n return dcg_k / idcg","def KruskalD(self):\n g = Graph(attr={DIRECTED: False})\n # Create set for each v of V[G]\n parent = []\n rank = []\n for v in self.get_vertices():\n parent.append(v)\n rank.append(0)\n\n # Sort edges by weight\n q = sorted(self.edges.items(), key=lambda e: e[1].attr[\"WEIGHT\"])\n for e in q:\n (u, v) = e[0]\n v1 = self.find(parent, u)\n v2 = self.find(parent, v)\n if v1 != v2:\n g.add_vertex(vertex.Vertex(u))\n g.add_vertex(vertex.Vertex(v))\n g.add_edge(edge.Edge(u, v, {\"WEIGHT\": e[1].attr[\"WEIGHT\"]}))\n if rank[v1] < rank[v2]:\n parent[v1] = v2\n rank[v2] += 1\n else:\n parent[v2] = v1\n rank[v1] += 1\n return g","def buildTree(self):\n self.initClusters()\n while len(self.centroids) > 1:\n print \"Iteration %d\" % self.labelcount\n for i in self.centroids:\n \tprint self.centroids[i], i\n \n l1, l2 = self.closestClusters()\n n1, n2 = self.getNumPoints(l1, l2)\n\n self.centroids[('Cl_%d' % self.labelcount, l1, l2)] = \\\n (n1*self.centroids[l1] + n2*self.centroids[l2])/(n1 + n2)\n \n del self.centroids[l1]\n del self.centroids[l2]\n self.labelcount += 1\n\n\n return ('Cl_%d' % (self.labelcount-1), l1, l2)","def test_tdsp_dijsktra_with_timesteps_4(self):\n input_file = \"/home/andy/code/python/om_task/data/test_data3.txt\"\n graph_data = process_input_file(input_file)\n\n graphs = {}\n\n today = datetime.today()\n t = time(0, 0) # input time value\n start_time = datetime(today.year, today.month, today.day, t.hour, t.minute)\n\n for timestep in graph_data:\n # print(\"Timestep=\", timestep)\n # Create a graph for each timestep\n graphs[timestep] = Graph()\n for edge in graph_data.get(timestep):\n graphs[timestep].add_edge(*edge)\n\n for graph in graphs:\n print(graphs[graph].edges)\n print(graphs[graph].weights)\n\n path = tdsp_dijsktra(graphs, start_time, 'A0', 'A1')\n\n print(path)\n\n self.assertEqual(['A0', 'B0', 'B1', 'A1'], path)","def test_dijsktra(self):\n input_file = \"/home/andy/code/python/om_task/data/test_data.txt\"\n graph_data = process_input_file(input_file)\n\n graphs = {}\n\n for timestep in graph_data:\n # print(\"Timestep=\", timestep)\n # Create a graph for each timestep\n graphs[timestep] = Graph()\n for edge in graph_data.get(timestep):\n graphs[timestep].add_edge(*edge)\n\n path = dijsktra(graphs[0], 'A0', 'B2')\n\n print(path)\n\n self.assertEqual(['A0', 'A1', 'A2', 'B2'], path)","def decision_tree(original_training_data,call_depth):\n\n ''' Checking the stopping criterion. If yes then it returns the majority class (Muffin or CupCake) '''\n if check_if_stopping_criterion_is_met(original_training_data.values) or call_depth > 10:\n majority = classification(original_training_data)\n return majority\n\n else:\n ''' Each time we split the data and go deeper, we increment the depth of the tree '''\n call_depth += 1\n\n ''' Finding the best attribute, best threshold to split data, best minimum entropy '''\n best_split_index, best_attribute, best_threshold, best_minimum_entropy = find_best_attribute_threshold_entropy(original_training_data)\n original_training_data_values = original_training_data.values\n\n best_split_values = original_training_data_values[:,best_split_index]\n\n less_than_threshold = original_training_data[best_split_values <= best_threshold]\n more_than_threshold = original_training_data[best_split_values > best_threshold]\n\n ''' Initializing a variable called as condition which stores the format of the key for the resulting decision tree dictionary '''\n condition = original_training_data.columns[best_split_index] + \" <= \" + str(best_threshold)\n\n ''' Initializing a dictionary where key is condition and value is a list. This is the basic data structure in which the\n resulting decision tree is stored '''\n sub_tree = {condition: []}\n\n ''' Calling the decision tree recursively '''\n left_tree = decision_tree(less_than_threshold, call_depth)\n right_tree = decision_tree(more_than_threshold, call_depth)\n\n ''' For removing edge cases where on either split, the resulting decision tree gives the same result '''\n if left_tree == right_tree:\n sub_tree = left_tree\n else:\n ''' Appending the smaller trees in the final decision tree '''\n sub_tree[condition].append(left_tree)\n sub_tree[condition].append(right_tree)\n\n return sub_tree","def __init__(self, dataset: List[SEMData], file_name):\n\n tree = Tree(file_name)\n\n self.nodes = dict()\n self.n_nodes = 0\n\n\n self.nodes['N0'] = SEMTreeNode('node')\n self.nodes['N0'].add_dist('N1', 20.8)\n self.nodes['N0'].add_dist('N3', 20.8)\n\n self.nodes['N1'] = SEMTreeNode('node')\n self.nodes['N1'].add_dist('N0', 20.8)\n self.nodes['N1'].add_dist('N2', 33.7)\n self.nodes['N1'].add_dist('NKL', 112.3)\n self.nodes['N1'].add_dist('B19', 112.3)\n\n self.nodes['NKL'] = SEMTreeNode('leaf')\n self.nodes['NKL'].add_dist('N1', 112.3)\n\n self.nodes['B19'] = SEMTreeNode('leaf')\n self.nodes['B19'].add_dist('N1', 112.3)\n\n self.nodes['N2'] = SEMTreeNode('node')\n self.nodes['N2'].add_dist('N1', 33.7)\n self.nodes['N2'].add_dist('CD4', 78.6)\n self.nodes['N2'].add_dist('CD8', 78.6)\n\n self.nodes['CD4'] = SEMTreeNode('leaf')\n self.nodes['CD4'].add_dist('N2', 78.6)\n\n self.nodes['CD8'] = SEMTreeNode('leaf')\n self.nodes['CD8'].add_dist('N2', 78.6)\n\n self.nodes['N3'] = SEMTreeNode('node')\n self.nodes['N3'].add_dist('N0', 20.8)\n self.nodes['N3'].add_dist('MON', 41.8)\n self.nodes['N3'].add_dist('NEU', 112.3)\n\n self.nodes['NEU'] = SEMTreeNode('leaf')\n self.nodes['NEU'].add_dist('N3', 112.3)\n\n self.nodes['MON'] = SEMTreeNode('leaf')\n self.nodes['MON'].add_dist('N3', 41.8)\n self.nodes['MON'].add_dist('DEN', 70.5)\n self.nodes['MON'].add_dist('MRF', 70.5)\n\n self.nodes['MRF'] = SEMTreeNode('leaf')\n self.nodes['MRF'].add_dist('MON', 70.5)\n\n self.nodes['DEN'] = SEMTreeNode('leaf')\n self.nodes['DEN'].add_dist('MON', 70.5)\n\n\n # self.get_nodes(tree)\n print(tree)\n\n # # Compare names of datasets and leaves of the tree\n # data_names = [data.name for data in dataset]\n # print(data_names)\n # for name in data_names:\n # if name not in self.nodes.keys():\n # raise ValueError('Dataset and Tree do not match')","def classify(observations, tree, dataMissing=False):\n\n def classifyWithoutMissingData(observations, tree):\n if tree.results != None: # leaf\n return tree.results\n else:\n v = observations[tree.col]\n branch = None\n #if isinstance(v, int) or isinstance(v, float):\n #if v >= tree.value: branch = tree.trueBranch\n #else: branch = tree.falseBranch\n #else:\n if v == tree.value: branch = tree.trueBranch\n else: branch = tree.falseBranch\n return classifyWithoutMissingData(observations, branch)\n\n\n def classifyWithMissingData(observations, tree):\n if tree.results != None: # leaf\n return tree.results\n else:\n v = observations[tree.col]\n if v == None:\n tr = classifyWithMissingData(observations, tree.trueBranch)\n fr = classifyWithMissingData(observations, tree.falseBranch)\n tcount = sum(tr.values())\n fcount = sum(fr.values())\n tw = float(tcount)/(tcount + fcount)\n fw = float(fcount)/(tcount + fcount)\n result = collections.defaultdict(int) # Problem description: http://blog.ludovf.net/python-collections-defaultdict/\n for k, v in tr.items(): result[k] += v*tw\n for k, v in fr.items(): result[k] += v*fw\n return dict(result)\n else:\n branch = None\n #if isinstance(v, int) or isinstance(v, float):\n # if v >= tree.value: branch = tree.trueBranch\n # else: branch = tree.falseBranch\n #else:\n if v == tree.value: branch = tree.trueBranch\n else: branch = tree.falseBranch\n return classifyWithMissingData(observations, branch)\n\n # function body\n if dataMissing:\n return classifyWithMissingData(observations, tree)\n else:\n return classifyWithoutMissingData(observations, tree)","def compare_trees(tree1, tree2):\n \tresponse = {}\n \tstart_time = time.time()\n \ttry:\t\n \t\ttns = dendropy.TaxonNamespace() \t\n \t\n \t\ttree_obj1 = dendropy.Tree.get(data=tree1, schema=\"newick\",taxon_namespace=tns)\n \t\ttree_obj2 = dendropy.Tree.get(data=tree2, schema=\"newick\",taxon_namespace=tns)\n\n \t\ttree_obj1.encode_bipartitions()\n \t\ttree_obj2.encode_bipartitions()\n\n \t\t#-----------------------------------------------------------\n \t\t#This method returns the symmetric distance between two trees. \n \t\t#The symmetric distance between two trees is the sum of the number of splits found in one of the trees but not the other. \n \t\t#It is common to see this statistic called the Robinson-Foulds distance\n\n \t\tareSame = True if treecompare.symmetric_difference(tree_obj1, tree_obj2) == 0 else False\n \t\tstatus = 200\n \t\tmessage = \"Success\"\n \t\tresponse['are_same_tree'] = areSame\n \n \texcept Exception, e:\n \t\tif \"Incomplete or improperly-terminated tree statement\" in str(e): #invalid: \"((A,B),C,D));\" valid: ((A,B),(C,D)); \n \t\t\tmessage = \"NewickReaderIncompleteTreeStatementError: \" + str(e)\n \t \t\tstatus = 400\n \t\telif \"Unbalanced parentheses at tree statement\" in str(e): #invalid: \"((A,B),(C,D);\" valid: ((A,B),(C,D)); \n \t\t\tmessage = \"NewickReaderMalformedStatementError: \"+str(e) \n \t \t\tstatus = 400\n \t\telif \"Multiple occurrences of the same taxa\" in str(e): #invalid: \"((A,B),(C,C));\" valid: ((A,B),(C,D));\n \t\t\tmessage = \"NewickReaderDuplicateTaxonError: \"+str(e)\n \t \t\tstatus = 400\n \t\telif \"Unexpected end of stream\" in str(e): # invalid: \"((A,B),(C,D))\" valid: ((A,B),(C,D));\n \t\t\tmessage = \"UnexpectedEndOfStreamError: \"+str(e)\n \t \t\tstatus = 400\n \t\telse:\n \t\t\tmessage = \"Error: Failed to compare trees. \"+str(e)\n \t \t\tstatus = 500\n \t \t\n \tresponse['status_code'] = status\n \tresponse['message'] = message\n\n \tend_time = time.time()\n \texecution_time = end_time-start_time\n #service result creation time\n \tcreation_time = datetime.datetime.now().isoformat()\n \tmeta_data = {'creation_time': creation_time, 'execution_time': float('{:4.2f}'.format(execution_time)), 'source_urls':[\"http://dendropy.org/library/treecompare.html#module-dendropy.calculate.treecompare\"] }\n\n \tresponse['meta_data'] = meta_data\n \tprint response\n \treturn response","def implement_SQLtree(X, y, labels, k, alpha_range, criterion, d, cur):\r\n\r\n kf = KFold(n_splits=k)\r\n min_avg_error = math.inf\r\n best_alpha = None\r\n avg_time_build_tree = 0\r\n for alpha in alpha_range:\r\n total_error = 0\r\n for train_idx, test_idx in kf.split(X):\r\n X_train, X_test = X[train_idx], X[test_idx]\r\n y_train, y_test = y[train_idx], y[test_idx]\r\n SQL_train = SQLTree(criterion, X_train, y_train, labels, \"articles\", cur)\r\n SQL_test = SQLTree(criterion, X_test, y_test, labels, \"articles\", cur)\r\n start = time.time()\r\n SQL_train_tree = SQL_train.build_tree(SQL_train.root, 0, d, SQL_train.labels, None, \"\")\r\n test_all(SQL_train)\r\n end = time.time()\r\n SQL_train.is_valid(SQL_train_tree)\r\n period = end - start\r\n pruned_SQLtree, pruned_alpha, error = SQL_train.prune_tree(SQL_train_tree, alpha, SQL_test.X, SQL_test.y,\r\n SQL_test.labels)\r\n total_error += error\r\n avg_time_build_tree += period\r\n avg_error = total_error / k\r\n if avg_error < min_avg_error:\r\n min_avg_error = avg_error\r\n best_alpha = alpha\r\n avg_time_build_tree = avg_time_build_tree / (k * len(alpha_range))\r\n\r\n return min_avg_error, best_alpha, avg_time_build_tree","def tree2pwdist(tree):\n if isinstance(tree, type(Phylo.BaseTree.Tree())):\n N = len(tree.get_terminals())\n names = [node.name for node in tree.get_terminals()]\n pwdist = np.zeros((N, N))\n for i, node1 in enumerate(tree.get_terminals()):\n for j, node2 in enumerate(tree.get_terminals()):\n \"\"\"Compute half of these and assume symmetry\"\"\"\n if i==j:\n pwdist[i, j] = 0\n elif i<j:\n pwdist[i, j] = tree.distance(node1, node2)\n pwdist[j, i] = pwdist[i, j]\n elif isinstance(tree, type(dendropy.Tree())):\n pdm = dendropy.treecalc.PatristicDistanceMatrix(tree)\n taxon_set = [n.taxon for n in tree.leaf_nodes()]\n N = len(taxon_set)\n names = [taxa.label for taxa in taxon_set]\n pwdist = np.zeros((N, N))\n for i, t1 in enumerate(taxon_set):\n for j, t2 in enumerate(taxon_set):\n \"\"\"Compute half of these and assume symmetry\"\"\"\n if i==j:\n pwdist[i, j] = 0\n elif i<j:\n pwdist[i, j] = pdm(t1, t2)\n pwdist[j, i] = pwdist[i, j]\n else:\n print('Tree type does not match Phylo.BaseTree.Tree or dendropy.Tree')\n return\n return pd.DataFrame(pwdist, index = names, columns = names)","def make_tree(self):\n\n # list [station_name]\n visited = []\n\n # creates empty station object for each station and adds coordinates\n for station in self.stations:\n new_station = Station(station)\n coordinates = self.stations[station].get_coordinates()\n new_station.add_coordinates(coordinates[0], coordinates[1])\n\n # saves station in prims_tree dictionary\n self.prims_tree[station] = new_station\n\n # choose random beginning station\n random_station = random.choice(list(self.stations.values()))\n\n # sort station connections and retrieve shortest\n station_connections = random_station.get_connections()\n station_connections = sorted(station_connections.items(), key=operator.itemgetter(1))\n new_connection = station_connections.pop(0)\n new_station = new_connection[0]\n new_time = new_connection[1]\n\n # retrieve empty stations from prims_tree dictionary\n first_station = self.prims_tree[random_station.name]\n new_station = self.prims_tree[new_station.name]\n\n # add shortest connection to stations\n first_station.add_connection(new_station, new_time)\n new_station.add_connection(first_station, new_time)\n\n # add stations to visited\n visited.append(first_station.name)\n visited.append(new_station.name)\n\n # runs until all stations are visited\n while len(visited) is not len(self.prims_tree):\n # starts as arbitrarily high number\n min_connection_time = 9999\n\n # get connections of visited stations\n for station in visited:\n connections = self.stations[station].get_connections()\n\n # get time of connections\n for connection in connections:\n connection_time = connections[connection]\n\n # save smallest connection if time is smallest and station is not visited\n if connection.name not in visited and connection_time < min_connection_time:\n smallest_connection = self.prims_tree[connection.name]\n smallest_connection_station = self.prims_tree[station]\n min_connection_time = connection_time\n else:\n continue\n\n # add smallest connection to station in prims_tree dictionary\n smallest_connection_station.add_connection(smallest_connection, min_connection_time)\n smallest_connection.add_connection(smallest_connection_station, min_connection_time)\n\n # add new connection to visited list\n visited.append(smallest_connection.name)\n\n return self.prims_tree","def test_tdsp_dijsktra_with_timesteps_key_error(self):\n input_file = \"/home/andy/code/python/om_task/data/input_data2.txt\"\n graph_data = process_input_file(input_file)\n\n graphs = {}\n\n today = datetime.today()\n t = time(0, 0) # input time value\n start_time = datetime(today.year, today.month, today.day, t.hour, t.minute)\n\n #print(\"graph_data = \", graph_data)\n print(\"graph_data[4] = \", graph_data[4])\n\n for timestep in graph_data:\n # print(\"Timestep=\", timestep)\n # Create a graph for each timestep\n graphs[timestep] = Graph()\n for edge in graph_data.get(timestep):\n graphs[timestep].add_edge(*edge)\n\n for graph in graphs:\n print(graphs[graph].edges)\n print(graphs[graph].weights)\n\n print(\"graph[4].weights = \", graphs[4].weights)\n\n print(\"Start Test 1\")\n path = tdsp_dijsktra(graphs, start_time, 'A0', 'E1')\n print(\"Path = \", path)\n self.assertEqual(['A0', 'B9', 'C0', 'D0', 'E1'], path)","def improve_tree(tree, freq_dict):\n # todo","def kruskal(self):\n travelled_distance = self.ordered_distance[0]\n travelled_distance_list = [self.ordered_distance[0]]\n travelled_edges = [self.ordered_links[0]]\n travelled_nodes = [self.ordered_links[0][0], self.ordered_links[0][1]]\n for i, j in zip(self.ordered_links[1:], self.ordered_distance[1:]):\n G = (Graph(travelled_edges, travelled_distance_list, # noqa N806\n directed=False))\n if not (G.add_edge(i, j).cyclic()):\n travelled_nodes.append(i[0])\n travelled_nodes.append(i[1])\n travelled_edges.append(i)\n travelled_distance_list.append(j)\n travelled_distance += j\n if (G.connected() and\n len(set(travelled_nodes)) == self.number_nodes):\n return (set(travelled_nodes), travelled_edges,\n travelled_distance_list, travelled_distance)","def linkage(self):\n self.tree = {}\n un_linked = []\n for i in range(len(self.leaves)):\n leaf = self.leaves[i]\n un_linked.append({\n 'id': i,\n 'x': 0,\n 'y': 0,\n 'value': 0,\n 'set': leaf,\n 'children': []\n })\n pass\n while len(un_linked) > 1:\n # for i in tqdm(range(len(un_linked))):\n # print(\"Linking... {} nodes left\".format(len(un_linked)))\n for node in un_linked:\n for d in node['set']:\n node['x'] += d['x']\n node['y'] += d['y']\n node['value'] += d['value']\n pass\n node['x'] /= len(node['set'])\n node['y'] /= len(node['set'])\n node['value'] /= len(node['set'])\n pass\n # min_dif = ((un_linked[1]['x'] - un_linked[0]['x']) ** 2 + (un_linked[1]['y'] - un_linked[0]['y']) ** 2) \\\n # * self._alpha + (un_linked[1]['value'] - un_linked[0]['value']) * (1 - self._alpha)\n min_dif = ((un_linked[1]['x'] - un_linked[0]['x']) ** 2 + (un_linked[1]['y'] - un_linked[0]['y']) ** 2)\n min_cp = [0, 1]\n for i in range(len(un_linked) - 1):\n for j in range(i + 1, len(un_linked)):\n # dif = self._alpha * ((un_linked[j]['x'] - un_linked[i]['x']) ** 2\n # + (un_linked[j]['x'] - un_linked[i]['x']) ** 2) \\\n # + (1 - self._alpha) * (un_linked[j]['value'] - un_linked[i]['value'])\n dif = ((un_linked[j]['x'] - un_linked[i]['x']) ** 2\n + (un_linked[j]['x'] - un_linked[i]['x']) ** 2)\n if dif < min_dif:\n min_dif = dif\n min_cp = [i, j]\n pass\n pass\n pass\n set_a = []\n for each in un_linked[min_cp[0]]['set']:\n set_a.append(each)\n pass\n for each in un_linked[min_cp[1]]['set']:\n set_a.append(each)\n pass\n next_un_linked = []\n new_children = []\n if len(un_linked[min_cp[0]]['children']) != 0:\n new_children.append({'children': un_linked[min_cp[0]]['children'],\n 'value': len(un_linked[min_cp[0]]['set'])})\n pass\n else:\n new_children.append({'id': un_linked[min_cp[0]]['id'],\n 'value': len(un_linked[min_cp[0]]['set'])})\n if len(un_linked[min_cp[1]]['children']) != 0:\n new_children.append({'children': un_linked[min_cp[1]]['children'],\n 'value': len(un_linked[min_cp[1]]['set'])})\n pass\n else:\n new_children.append({'id': un_linked[min_cp[1]]['id'],\n 'value': len(un_linked[min_cp[1]]['set'])})\n pass\n next_un_linked.append({\n 'x': 0,\n 'y': 0,\n 'value': 0,\n 'set': set_a,\n 'children': new_children\n })\n del un_linked[min_cp[0]]['set']\n del un_linked[min_cp[0]]['x']\n del un_linked[min_cp[0]]['y']\n # del un_linked[min_cp[0]]['value']\n del un_linked[min_cp[1]]['set']\n del un_linked[min_cp[1]]['x']\n del un_linked[min_cp[1]]['y']\n # del un_linked[min_cp[1]]['value']\n for s in range(len(un_linked)):\n if s not in min_cp:\n next_un_linked.append(un_linked[s])\n pass\n pass\n un_linked = next_un_linked\n pass\n del un_linked[0]['set']\n del un_linked[0]['x']\n del un_linked[0]['y']\n # del un_linked[0]['value']\n self.tree = un_linked[0]\n self._count = 0\n\n self.tree = self._resolve(self.tree)\n return","def test_tdsp_dijsktra_with_timesteps_2(self):\n input_file = \"/home/andy/code/python/om_task/data/test_data3.txt\"\n graph_data = process_input_file(input_file)\n\n graphs = {}\n\n today = datetime.today()\n t = time(0, 0) # input time value\n start_time = datetime(today.year, today.month, today.day, t.hour, t.minute)\n\n for timestep in graph_data:\n # print(\"Timestep=\", timestep)\n # Create a graph for each timestep\n graphs[timestep] = Graph()\n for edge in graph_data.get(timestep):\n graphs[timestep].add_edge(*edge)\n\n for graph in graphs:\n print(graphs[graph].edges)\n print(graphs[graph].weights)\n\n path = tdsp_dijsktra(graphs, start_time, 'A0', 'B1')\n\n print(path)\n\n self.assertEqual(['A0', 'B0', 'B1'], path)","def neighborJoining(distances):\n\n tree = {}\n\n while(len(distances.keys()) > 2):\n\n r = calcRs(distances)\n M = makeMMatrix(distances, r)\n\n smallest = 10000\n smallestKey = (\"\",\"\")\n\n #Find nearest neighbors\n for key in M.keys():\n for subkey in M[key].keys():\n if M[key][subkey] < smallest:\n smallest = M[key][subkey]\n smallestKey = (key, subkey)\n\n #Add new node and update distances to rest of tree\n newname = smallestKey[0] + \"-\" + smallestKey[1]\n distances[newname] = {}\n tree[smallestKey[0]] = {}\n tree[smallestKey[1]] = {}\n dij = distances[smallestKey[0]][smallestKey[1]]\n for key in M.keys():\n if key in smallestKey:\n continue\n distances[newname][key] = .5*(distances[smallestKey[0]][key] \\\n + distances[smallestKey[1]][key] - dij)\n distances[key][newname] = distances[newname][key]\n\n #Update distances to parents of node\n dik = (dij + r[smallestKey[0]] - r[smallestKey[1]])/2\n tree[smallestKey[0]][newname] = dik\n tree[smallestKey[1]][newname] = dij-dik\n detachDict(distances, smallestKey[0], smallestKey[1])\n\n #Connect final two nodes\n tree[distances.keys()[0]] = {}\n tree[distances.keys()[0]][distances[distances.keys()[0]].keys()[0]] =\\\n distances[distances.keys()[0]][distances[distances.keys()[0]].keys()[0]] \n return tree","def test_tdsp_dijsktra_with_timesteps_3(self):\n input_file = \"/home/andy/code/python/om_task/data/test_data3.txt\"\n graph_data = process_input_file(input_file)\n\n graphs = {}\n\n today = datetime.today()\n t = time(0, 0) # input time value\n start_time = datetime(today.year, today.month, today.day, t.hour, t.minute)\n\n for timestep in graph_data:\n # print(\"Timestep=\", timestep)\n # Create a graph for each timestep\n graphs[timestep] = Graph()\n for edge in graph_data.get(timestep):\n graphs[timestep].add_edge(*edge)\n\n for graph in graphs:\n print(graphs[graph].edges)\n print(graphs[graph].weights)\n\n path = tdsp_dijsktra(graphs, start_time, 'A0', 'A1')\n\n print(path)\n\n self.assertEqual(['A0', 'B0', 'B1', 'A1'], path)","def build_mht(eddies_data,\n\tprune_depth = 2,\n\twithin_bounds = lambda x: True,\n\tdo_lookahead = True,\n\tgate_dist = 150,\n\tprev_data = None,\n\tprune_mode = 'parent'):\n\n\troots = []\n\tdepth = 0\n\n\tif prev_data is not None:\n\t\troots = prev_data['roots']\n\t\tdepth = prev_data['start_depth']\n\t\tprune_depth = prev_data['prune_depth']\n\t\tgate_dist = prev_data['gate_dist']\n\n\tif depth >= len(eddies_data):\n\t\treturn roots\n\n\tfor dataset in eddies_data[depth:]:\n\t\tstart_time = time.mktime(time.localtime())\n\t\tprint dataset[0]\n\t\tmat = scipy.io.loadmat(dataset[1], struct_as_record=False)\n\t\teddies = mat['eddies'][0]\n\t\tpnodes = get_nodes_at_depth(roots, depth - 1)\n\t\tfor i in range(len(eddies)):\n\t\t\teddy = Eddy(eddies[i].Stats[0,0],\n\t\t\t\teddies[i].Lat[0,0],\n\t\t\t\teddies[i].Lon[0,0],\n\t\t\t\teddies[i].Amplitude[0,0],\n\t\t\t\teddies[i].ThreshFound[0,0],\n\t\t\t\teddies[i].SurfaceArea[0,0],\n\t\t\t\teddies[i].Date[0,0],\n\t\t\t\teddies[i].Cyc[0,0],\n\t\t\t\teddies[i].MeanGeoSpeed[0,0],\n\t\t\t\teddies[i].DetectedBy[0])\n\t\t\tif not within_bounds(eddy):\n\t\t\t\tcontinue\n\t\t\tmk_node_and_add(eddy, depth, pnodes, roots, gate_dist)\n\n\t\tif do_lookahead:\n\t\t\tlookahead.add_lookahead_nodes(pnodes, gate_dist)\n\n\t\tif depth >= prune_depth:\n\t\t\tif prune_mode == 'parent':\n\t\t\t\tprune_parent(roots, depth-prune_depth, gate_dist)\n\t\t\telse:\n\t\t\t\tprune(roots, depth-prune_depth, gate_dist)\n\t\tdepth += 1\n\t\tprint 'time:', time.mktime(time.localtime())-start_time\n\n\treturn roots","def test_tdsp_dijsktra_with_timesteps(self):\n input_file = \"/home/andy/code/python/om_task/data/test_data3.txt\"\n graph_data = process_input_file(input_file)\n\n graphs = {}\n\n today = datetime.today()\n t = time(0, 0) # input time value\n start_time = datetime(today.year, today.month, today.day, t.hour, t.minute)\n\n for timestep in graph_data:\n # print(\"Timestep=\", timestep)\n # Create a graph for each timestep\n graphs[timestep] = Graph()\n for edge in graph_data.get(timestep):\n graphs[timestep].add_edge(*edge)\n\n for graph in graphs:\n print(graphs[graph].edges)\n print(graphs[graph].weights)\n\n print(\"Start Test 1\")\n path = tdsp_dijsktra(graphs, start_time, 'A0', 'A1')\n print(path)\n self.assertEqual(['A0', 'B0', 'B1', 'A1'], path)\n\n print(\"Start Test 2\")\n t = time(0, 0) # input time value\n start_time = datetime(today.year, today.month, today.day, t.hour, t.minute)\n path = tdsp_dijsktra(graphs, start_time, 'B0', 'A1')\n print(path)\n self.assertEqual(['B0', 'B1', 'A1'], path)\n\n print(\"Start Test 3\")\n t = time(1, 1) # input time value\n start_time = datetime(today.year, today.month, today.day, t.hour, t.minute)\n path = tdsp_dijsktra(graphs, start_time, 'B0', 'A1')\n print(path)\n self.assertEqual(['B0', 'A1'], path)","def construct_taxonomy(termID_list, dih, dih_metric_name=\"invCL\", taxorg_method=\"NoCyc\", \n graph_init_threshold=0.01, verbose=True):\n assert dih_metric_name in [\"weeds_prec\", \"clarkeDE\", \"invCL\"]\n assert taxorg_method in [\"NoCyc\", \"DMST\"]\n\n # DIH\n weighted_edges = [] # (hyper, hypo, score)\n for termID_pair in tqdm(list(combinations(termID_list, r=2))):\n score = 2.0 * dih.predict(termID_pair[0], termID_pair[1], dih_metric_name)\n if score >= graph_init_threshold:\n weighted_edges.append([termID_pair[1], termID_pair[0], score])\n\n # reverse order testing\n score = 2.0 * dih.predict(termID_pair[1], termID_pair[0], dih_metric_name)\n if score >= graph_init_threshold:\n weighted_edges.append([termID_pair[0], termID_pair[1], score])\n\n # Taxonomy organization\n G = construct_graph(termID_list, weighted_edges)\n if verbose:\n print(\"Before Taxonomy Organization\")\n describe_graph(G)\n print(\"=\"*89)\n\n if taxorg_method == \"NoCyc\":\n T = NoCyc(G)\n elif taxorg_method == \"DMST\":\n T = DMST(G)\n\n if verbose:\n print(\"After Taxonomy Organization\")\n describe_graph(T)\n \n return T","def cross_validation(self, x, t, k=5):\n print(\"Cross validation of the Decision Tree Classifier...\")\n bestCriteria = ''\n bestMax_depth= 2\n bestError = float('inf')\n\n N = len(x)\n N_train = math.floor(0.8 * N)\n\n dicCriteria = ['gini', 'entropy']\n min_depth = 2\n max_depth = 40\n\n for crit in dicCriteria:\n for d in range(min_depth, max_depth):\n errors = np.zeros(k)\n\n for j in range(k):\n map_index = list(zip(x, t))\n random.shuffle(map_index)\n random_X, random_t = zip(*map_index)\n\n train_x = random_X[:N_train]\n valid_x = random_X[N_train:]\n train_t = random_t[:N_train]\n valid_t = random_t[N_train:]\n\n self.model = tree.DecisionTreeClassifier(max_depth=d, criterion=crit)\n self.train(train_x, train_t)\n error_valid = np.array([self.error(x_n, t_n)\n for t_n, x_n in zip(valid_t, valid_x)])\n errors[j] = error_valid.mean()\n\n mean_error = np.mean(errors)\n if mean_error < bestError:\n bestError = mean_error\n bestCriteria = crit\n bestMax_depth = d\n print(\"The new best hyper parameters are : \", bestMax_depth, bestCriteria)\n\n print(\"Best hyper parameters are : \", bestMax_depth, bestCriteria)\n print(\"Validation error : \", 100 * bestError, \"%\")\n self.model = tree.DecisionTreeClassifier(max_depth=bestMax_depth, criterion=bestCriteria)\n self.train(x, t)","def buildSweepTree(self):\n minXPos, minXNodeID = self._findMinX()\n cellDistanceArray = self._computeDistance(minXPos, minXNodeID) # distance of all cells to the \"min X node\"\n # sort distance array based on distances to min X cell\n cellDistanceArray = cellDistanceArray[cellDistanceArray[:, 2].argsort()]\n # cellDistanceArray = np.sort(cellDistanceArray, axis=2)\n for w, cellD in enumerate(cellDistanceArray):\n self.sweepTree.append((int(cellD[0]), int(cellD[1])))","def create_density_tree(dataset, dimensions, clusters, parentnode=None, side_label=None):\n\n # split\n\n dim_max, val_dim_max, _, _ = get_best_split(dataset, labelled=False)\n left, right, e_left, e_right = split(dataset, dim_max, val_dim_max,\n get_entropy=True) # split along best dimension\n\n treenode = DensityNode() # initial node\n\n # save tree node\n treenode.split_dimension = dim_max\n treenode.split_value = val_dim_max\n\n treenode.dataset = dataset\n treenode.left_dataset = left\n treenode.right_dataset = right\n\n treenode.dataset_len = len(dataset)\n treenode.left_dataset_len = len(left)\n treenode.right_dataset_len = len(right)\n treenode.entropy = entropy_gaussian(dataset)\n treenode.cov = np.cov(dataset.T)\n treenode.mean = np.mean(dataset, axis=0)\n treenode.left_cov = np.cov(left.T)\n treenode.left_mean = np.mean(left, axis=0)\n treenode.right_cov = np.cov(right.T)\n treenode.right_mean = np.mean(right, axis=0)\n treenode.left_entropy = e_left\n treenode.right_entropy = e_right\n\n # link parent node to new node.\n if parentnode is not None:\n treenode.parent = parentnode\n if side_label == 'left':\n treenode.parent.left = treenode\n elif side_label == 'right':\n treenode.parent.right = treenode\n\n clusters_left = clusters - 1\n if clusters_left > 1:\n # recursively continue splitting\n # continue splitting always splitting on worst side (highest entropy)\n # find node where left or right entropy is highest and left or right node is not split yet\n node_e, e, side = treenode.get_root().highest_entropy(dataset, 0, 'None')\n\n if side == 'left':\n dataset = node_e.left_dataset\n side_label = 'left'\n elif side == 'right':\n dataset = node_e.right_dataset\n side_label = 'right'\n\n create_density_tree(dataset, dimensions, clusters=clusters_left,\n parentnode=node_e, side_label=side_label) # iterate\n\n return treenode","def fit(self, X, y):\n #Initialize the tree with the given data\n tree = kd(X)\n self.tree = tree\n self.labels = y","def test_dijsktra2(self):\n input_file = \"/home/andy/code/python/om_task/data/test_data4.txt\"\n graph_data = process_input_file(input_file)\n\n graphs = {}\n\n for timestep in graph_data:\n # print(\"Timestep=\", timestep)\n # Create a graph for each timestep\n graphs[timestep] = Graph()\n for edge in graph_data.get(timestep):\n graphs[timestep].add_edge(*edge)\n\n path = dijsktra(graphs[1], 'A0', 'B2')\n\n print(path)\n\n self.assertEqual(['A0', 'B0', 'B1', 'B2'], path)","def support_tree(self):\n v = self.vertices\n h_edges = list(self.hyper_edges)\n\n h_edges.sort(key=lambda x: len(x))\n support_tree = Graph()\n\n for x in v:\n support_tree.add(x)\n\n for h_edge in h_edges:\n kruskal = list(connected_parts(support_tree, vertex_subset=h_edge))\n if len(kruskal) == 1:\n continue\n else:\n for i in range(len(kruskal) - 1):\n support_tree.update([(next(iter(kruskal[i])), next(iter(kruskal[i + 1])))],\n node_creation=False)\n\n return support_tree","def findknn(xTr,xTe,k):\n\n # YOUR CODE HERE\n if k > len(xTr):\n k = len(xTr)\n \n D=l2distance(xTe, xTr)\n (m,n) = D.shape\n \n indices = []\n dists = []\n for i in range(m):\n smallest_indices = np.argsort(D[i])\n ind = smallest_indices[:k]\n dis = D[i,smallest_indices[:k]]\n indices.append(ind)\n dists.append(dis)\n \n indices = np.transpose(np.array(indices))\n dists = np.transpose(np.array(dists))\n return indices, dists","def test_dijsktra_2(self):\n input_file = \"/home/andy/code/python/om_task/data/test_data3.txt\"\n graph_data = process_input_file(input_file)\n\n graphs = {}\n\n for timestep in graph_data:\n # print(\"Timestep=\", timestep)\n # Create a graph for each timestep\n graphs[timestep] = Graph()\n for edge in graph_data.get(timestep):\n graphs[timestep].add_edge(*edge)\n\n path = dijsktra(graphs[0], 'A0', 'A1')\n\n print(path)\n\n self.assertEqual(['A0', 'A1'], path)","def buildTree(self, data, attributes):\n\n totalClasses = []\n data = data[:]\n assignedClass = self.assignClass()\n\n for record in data:\n totalClasses.append(record[-1])\n\n # if all attributes have been traversed or the algo runs out of data/records\n if len(attributes) < 1 or not data:\n return assignedClass\n\n countClass = totalClasses.count(totalClasses[0])\n\n # if all classes are same\n if countClass == len(totalClasses):\n return totalClasses[0]\n\n splitAttrIndex = self.getSplitAttr(data, attributes)\n decisionTree = {splitAttrIndex: {}}\n\n # Splitting the data using the attribute with the hightest info gain\n for attrVal in ['True', 'False']:\n subtreeAttributes = attributes[:]\n subtreeAttributes.pop(splitAttrIndex)\n # Get new data for children node\n subtreeData = self.getNewData(data, splitAttrIndex, attrVal)\n # generate subtree\n subtree = self.buildTree(subtreeData, subtreeAttributes)\n decisionTree[splitAttrIndex][attrVal] = subtree\n\n return decisionTree","def idw(kdtree, z, xi, yi):\n neighbors = 12\n power = 2\n distances, indicies = kdtree.query([xi, yi], k=neighbors)\n z_n = z[indicies]\n if 0 not in distances:\n weights = power / distances\n else:\n distances += 0.000000001\n weights = power / distances\n weights /= weights.sum(axis=0)\n zi = np.dot(weights.T, z_n)\n return zi","def draw_tree(self):\n nx.draw(self.diffusion_tree, with_labels=True)","def G_to_kdtree(G_, x_coord='x', y_coord='y', verbose=False):\n\n nrows = len(G_.nodes())\n ncols = 2\n kd_idx_dic = {}\n arr = np.zeros((nrows, ncols))\n # populate node array\n t1 = time.time()\n for i, n in enumerate(G_.nodes()):\n n_props = G_.node[n]\n lat, lon = n_props['lat'], n_props['lon']\n x0, y0 = n_props[x_coord], n_props[y_coord]\n\n if x_coord == 'lon':\n x, y = lon, lat\n else:\n x, y = x0, y0\n\n arr[i] = [x, y]\n kd_idx_dic[i] = n\n\n # now create kdtree from numpy array\n kdtree = scipy.spatial.KDTree(arr)\n if verbose:\n print(\"Time to create k-d tree:\", time.time() - t1, \"seconds\")\n return kd_idx_dic, kdtree, arr","def prepare_data_for_d(self):\n\n center_nodes = []\n neighbor_nodes = []\n labels = []\n for i in self.root_nodes:\n if np.random.rand() < config.update_ratio:\n # self.graph[i] = [neighbors of i]\n pos = self.graph[i]\n neg, _ = self.sample(i, self.trees[i], len(pos), for_d=True)\n # print(\"tree_i_d: \", self.trees[i])\n # print(\"neg_samples: \", neg)\n # print(\"neg is: \", neg)\n if len(pos) != 0 and neg is not None:\n # positive samples\n center_nodes.extend([i] * len(pos))\n neighbor_nodes.extend(pos)\n labels.extend([1] * len(pos))\n\n # negative samples\n center_nodes.extend([i] * len(pos))\n neighbor_nodes.extend(neg)\n labels.extend([0] * len(neg))\n # print(\"cen: \", center_nodes)\n return center_nodes, neighbor_nodes, labels","def make_nltk_tree(derivation):\n\td = defaultdict(None, ((r.lhs, r.rhs) for r in derivation))\n\t\n\tdef make_tree(lhs):\n\t\treturn Tree(lhs[1:-1], (child if child not in d else make_tree(child) for child in d[lhs]))\n\t\n\treturn make_tree(derivation[0].lhs)","def support_tree(self):\n if not self.is_atomistic():\n raise ValueError(\"lattice is not atomistic\")\n\n class_order = topological_sort(self._hase_diagram)\n objects = self.above(self.bottom)\n representatives = {element: element for element in objects}\n classes = {element: {element} for element in objects}\n tree = Graph(objects)\n n_connected_parts = len(objects)\n colors = {obj: i for i, obj in enumerate(objects)}\n\n class_order_position = 1\n while n_connected_parts > 1:\n class_order_position += 1\n current_class_index = class_order[class_order_position]\n if current_class_index not in objects:\n predecessors = list(self.under(current_class_index))\n first_class_representative = representatives[predecessors[0]]\n second_class_representative = representatives[predecessors[1]]\n representatives[current_class_index] = random.choice(\n [first_class_representative, second_class_representative])\n if colors[first_class_representative] != colors[second_class_representative]:\n tree.update(((first_class_representative, second_class_representative),))\n color_to_change = colors[second_class_representative]\n color_to_keep = colors[first_class_representative]\n for element in colors:\n if colors[element] == color_to_change:\n colors[element] = color_to_keep\n n_connected_parts -= 1\n classes[current_class_index] = classes[predecessors[0]].union(classes[predecessors[1]])\n return tree","def test_tdsp_dijsktra_with_timesteps_real_data(self):\n input_file = \"/home/andy/code/python/om_task/data/input_data1.txt\"\n graph_data = process_input_file(input_file)\n\n graphs = {}\n\n today = datetime.today()\n t = time(0, 0) # input time value\n start_time = datetime(today.year, today.month, today.day, t.hour, t.minute)\n\n for timestep in graph_data:\n # print(\"Timestep=\", timestep)\n # Create a graph for each timestep\n graphs[timestep] = Graph()\n for edge in graph_data.get(timestep):\n graphs[timestep].add_edge(*edge)\n\n for graph in graphs:\n print(graphs[graph].edges)\n print(graphs[graph].weights)\n\n print(\"Start Test 1\")\n path = tdsp_dijsktra(graphs, start_time, 'B1', 'F4')\n print(path)\n self.assertEqual(['B1', 'C2', 'D3', 'E4', 'F4'], path)","def discretize(self,dt):\n res = self.discretize_state(dt)\n n = len(res.milestones[0])//2\n return Trajectory(res.times,[m[:n] for m in res.milestones])","def buildDecisionTree(self, data):\n self.data = data\n self.decisionTree = self.buildTree(self.data, self.listAttributes)\n with open(\"decision_tree_model\", \"wb\") as f:\n pickle.dump(self.decisionTree, f, pickle.HIGHEST_PROTOCOL)\n return self.decisionTree","def graphite_diffusivity_Kim2011(sto, T, T_inf, E_D_s, R_g):\n\n D_ref = 9 * 10 ** (-14)\n arrhenius = exp(E_D_s / R_g * (1 / T_inf - 1 / T))\n\n return D_ref * arrhenius","def kdtree( data, leafsize):\n\n leaves = []\n\n ndim = data.shape[0]\n ndata = data.shape[1]\n #print ndim\n #print ndata\n\n # find bounding hyper-rectangle\n hrect = numpy.zeros((2,data.shape[0]))\n hrect[0,:] = data.min(axis=1)\n hrect[1,:] = data.max(axis=1)\n\n # create root of kd-tree\n idx = numpy.argsort(data[0,:], kind='mergesort')\n data[:,:] = data[:,idx]\n splitval = data[0,ndata/2]\n\n left_hrect = hrect.copy()\n right_hrect = hrect.copy()\n left_hrect[1, 0] = splitval\n right_hrect[0, 0] = splitval\n\n tree = [(None, None, left_hrect, right_hrect, None, None)]\n\n stack = [(data[:,:ndata/2], idx[:ndata/2], 1, 0, True),\n (data[:,ndata/2:], idx[ndata/2:], 1, 0, False)]\n\n # recursively split data in halves using hyper-rectangles:\n while stack:\n\n # pop data off stack\n data, didx, depth, parent, leftbranch = stack.pop()\n ndata = data.shape[1]\n nodeptr = len(tree)\n\n # update parent node\n\n _didx, _data, _left_hrect, _right_hrect, left, right = tree[parent]\n\n tree[parent] = (_didx, _data, _left_hrect, _right_hrect, nodeptr, right) if leftbranch \\\n else (_didx, _data, _left_hrect, _right_hrect, left, nodeptr)\n\n # insert node in kd-tree\n\n # leaf node?\n if ndata <= leafsize:\n _didx = didx.copy()\n _data = data.copy()\n leaf = (_didx, _data, None, None, 0, 0)\n #leaf = (_data)\n tree.append(leaf)\n leaves.append(_didx)\n\n # not a leaf, split the data in two \n else:\n splitdim = depth % ndim\n idx = numpy.argsort(data[splitdim,:], kind='mergesort')\n data[:,:] = data[:,idx]\n didx = didx[idx]\n nodeptr = len(tree)\n stack.append((data[:,:ndata/2], didx[:ndata/2], depth+1, nodeptr, True))\n stack.append((data[:,ndata/2:], didx[ndata/2:], depth+1, nodeptr, False))\n splitval = data[splitdim,ndata/2]\n if leftbranch:\n left_hrect = _left_hrect.copy()\n right_hrect = _left_hrect.copy()\n else:\n left_hrect = _right_hrect.copy()\n right_hrect = _right_hrect.copy()\n left_hrect[1, splitdim] = splitval\n right_hrect[0, splitdim] = splitval\n\n #print data\n # append node to tree\n tree.append((None, None, left_hrect, right_hrect, None, None))\n\n return tree,leaves","def subtree_cond_pdf(tree1, tree2, tree2_subtree_nodes, new):\n\n # if new is isolated in the underlying graph\n\n #if len(tree2_subtree_nodes) == 1 and tree2_subtree_nodes.values()[0] is None:\n if len(tree2_subtree_nodes) == 1 and list(tree2_subtree_nodes.values())[0] is None:\n sep = frozenset([])\n c = frozenset([new])\n lognu = trilearn.graph.junction_tree.log_nu(tree2, sep)\n return ({c: np.exp(-lognu)}, {c: 1.0})\n\n # Get the subtree induced by the nodes\n #tree1_subtree = tree1.subgraph([c_t1 for c_t2, c_t1 in tree2_subtree_nodes.iteritems()])\n tree1_subtree = tree1.subgraph([c_t1 for c_t2, c_t1 in tree2_subtree_nodes.items()])\n\n\n # Get the separating sets\n # S = sepsets_in_subgraph(tree2_subtree_nodes, tree1_subtree)\n\n # S = {c: set() for c_t2, c in tree2_subtree_nodes.iteritems()}\n S = {c: set() for c_t2, c in tree2_subtree_nodes.items()}\n #for c_tree2, c in tree2_subtree_nodes.iteritems():\n for c_tree2, c in tree2_subtree_nodes.items():\n for neig in tree1_subtree.neighbors(c):\n S[c] = S[c] | (c & neig)\n\n # P, N get_subset_probabilities(tree2_)\n # Get the chosen internal nodes\n M = {}\n # for c_tree2, c in tree2_subtree_nodes.iteritems():\n for c_tree2, c in tree2_subtree_nodes.items():\n M[c] = c_tree2 - {new} - S[c]\n\n # Calculate probabilities corresponding to each clique\n P = {}\n N = {}\n # for c_tree2, c in tree2_subtree_nodes.iteritems():\n for c_tree2, c in tree2_subtree_nodes.items():\n neigs = {neig for neig in tree1.neighbors(c) if\n neig & c <= c_tree2 and neig not in tree1_subtree.nodes()}\n RM = c - S[c]\n gamma = tree1_subtree.order()\n # sepCondition = len({neig for neig in nx.neighbors(tree1_subtree, c) if\n # S[c] == neig & c}) > 0 or gamma == 1\n sepCondition = len({neig for neig in tree1_subtree.neighbors(c) if\n S[c] == neig & c}) > 0 or gamma == 1\n\n N[c] = 1.0\n #N[c] = Fraction(1,1)\n if sepCondition is False:\n # Every internal node in c belongs to a separator\n P[c] = np.power(2.0, - len(RM))\n #P[c] = Fraction(1, 2 ** len(RM))\n if not len(c) + 1 == len(c_tree2):\n N[c] = np.power(2.0, -len(neigs))\n #N[c] = Fraction(1, 2**len(neigs))\n else:\n P[c] = 1.0\n if len(RM) > 1:\n P[c] = (1.0 / len(RM)) * np.power(2.0, -(len(RM) - 1.0)) * len(M[c])\n #P[c] = Fraction(1, len(RM)) * Fraction(len(M[c]), 2 ** (len(RM) - 1.0)) \n if not len(c) + 1 == len(c_tree2):\n N[c] = np.power(2.0, -len(neigs))\n #N[c] = Fraction(1, 2**len(neigs))\n return (P, N)","def test_tdsp_dijsktra_with_timesteps_5(self):\n input_file = \"/home/andy/code/python/om_task/data/test_data4.txt\"\n graph_data = process_input_file(input_file)\n\n graphs = {}\n\n today = datetime.today()\n t = time(0, 0) # input time value\n start_time = datetime(today.year, today.month, today.day, t.hour, t.minute)\n\n for timestep in graph_data:\n # print(\"Timestep=\", timestep)\n # Create a graph for each timestep\n graphs[timestep] = Graph()\n for edge in graph_data.get(timestep):\n graphs[timestep].add_edge(*edge)\n\n for graph in graphs:\n print(graphs[graph].edges)\n print(graphs[graph].weights)\n\n print(\"Start Test 1\")\n path = tdsp_dijsktra(graphs, start_time, 'A0', 'B2')\n print(\"Path = \", path)\n self.assertEqual(['A0', 'B0', 'B1', 'B2'], path)","def dist_tree(self, other, tol=1e-8):\n if self.ndim == 2:\n from pyparm import d2 as sim\n else:\n from pyparm import d3 as sim\n\n assert self.ndim == other.ndim\n\n if self.shear != 0 or other.shear != 0:\n assert np.abs(self.shear - other.shear) <= tol\n shear = (self.shear + other.shear) / 2.0\n box = sim.LeesEdwardsBox(sim.Vec(1, 1), shear)\n else:\n box = sim.OriginBox(1.0)\n\n sz1 = self.size_indices()\n assert(len(sz1) == 2)\n cutoff1 = int(np.sum(sz1[0]))\n\n sz2 = other.size_indices()\n assert(len(sz2) == 2)\n cutoff2 = int(np.sum(sz2[0]))\n\n vs1 = np.array([xy for idx in sz1 for xy in self.rs[idx]])\n vs2 = np.array([xy for idx in sz2 for xy in other.rs[idx]])\n\n tree = sim.JammingTreeBD(box, vs1, vs2, cutoff1, cutoff2)\n return tree","def brute_tree(XTRAIN,istopTRAIN,XTEST,istopTEST):\n \n ntrain=XTRAIN.shape[0]\n ntest=XTEST.shape[0]\n \n if np.sum(istopTRAIN)==0:\n return 0,[]\n\n cost0=np.zeros(Ngammas*Nreps)\n cost1=np.zeros(Ngammas*Nreps)\n cost0test=np.zeros(Ngammas*Nreps)\n cost1test=np.zeros(Ngammas*Nreps)\n \n precisionTRAIN=np.zeros(Ngammas*Nreps)\n precisionTEST=np.zeros(Ngammas*Nreps)\n recallTEST=np.zeros(Ngammas*Nreps)\n rate=np.zeros(Ngammas*Nreps)\n \n for iii in range(Ngammas):\n \n gamma=GAMMA[iii]\n \n for jjj in range(Nreps):\n \n \"\"\" train a tree using training data with random splitting \"\"\"\n \n tree_hyperparameters['class_weight']={0:1,1:gamma}\n clf=tree.DecisionTreeClassifier(**tree_hyperparameters)\n clf.fit(XTRAIN,istopTRAIN)\n \n \"\"\"\" record costs and precision on validation data \"\"\"\n \n pTRAIN=clf.predict(XTRAIN)\n precisionTRAIN[iii*Nreps+jjj]=np.divide(sum(1 for i in range(ntrain) if pTRAIN[i] == 1 and istopTRAIN[i]==1),sum(pTRAIN))\n cost0[iii*Nreps+jjj]=sum(1 for i in range(ntrain) if pTRAIN[i] == 1 and istopTRAIN[i]==0)\n cost1[iii*Nreps+jjj]=sum(1 for i in range(ntrain) if pTRAIN[i] == 0 and istopTRAIN[i]==1)\n \n \"\"\" record precision on test data \"\"\"\n \n pTEST=clf.predict(XTEST)\n precisionTEST[iii*Nreps+jjj]=np.divide(sum(1 for i in range(ntest) if pTEST[i] == 1 and istopTEST[i]==1),sum(pTEST))\n recallTEST[iii*Nreps+jjj]=sum(1 for i in range(ntest) if pTEST[i] == 1 and istopTEST[i]==1)/sum(istopTEST)\n cost0test[iii*Nreps+jjj]=sum(1 for i in range(ntest) if pTEST[i] == 1 and istopTEST[i]==0)\n cost1test[iii*Nreps+jjj]=sum(1 for i in range(ntest) if pTEST[i] == 0 and istopTEST[i]==1)\n \n \"\"\" record positive rate on full data \"\"\"\n \n rate[iii*Nreps+jjj]=(sum(pTRAIN)+sum(pTEST))/(ntrain+ntest)\n \n \"\"\" Compute Pareto front for validation data \"\"\"\n \n Pareto = Lower_Convex_Hull(np.concatenate((cost0.reshape(-1,1),cost1.reshape(-1,1)),1))\n \n \"\"\" make some nice plots for whoever is watching \"\"\"\n \n plt.figure(figsize=(10,5))\n plt.subplot(121)\n plt.plot(cost0,cost1,'.')\n plt.plot(cost0[Pareto],cost1[Pareto],'d')\n plt.xlabel('errors on class zero training data')\n plt.ylabel('errors on class one training data')\n\n plt.subplot(122)\n plt.plot(cost0test,cost1test,'.')\n plt.plot(cost0test[Pareto],cost1test[Pareto],'d')\n plt.xlabel('errors on class zero test data')\n plt.ylabel('errors on class one test data')\n plt.show()\n \n plt.figure(figsize=(15,5))\n plt.subplot(131)\n plt.semilogy(precisionTRAIN,rate,'.')\n plt.semilogy(precisionTRAIN[Pareto],rate[Pareto],'d')\n plt.xlabel('precision on training data')\n plt.ylabel('positive rate')\n\n plt.subplot(132) \n plt.semilogy(precisionTEST,rate,'.')\n plt.semilogy(precisionTEST[Pareto],rate[Pareto],'d')\n plt.xlabel('precision on test data')\n plt.ylabel('positive rate')\n\n plt.subplot(133) \n plt.plot(precisionTEST,recallTEST,'.')\n plt.plot(precisionTEST[Pareto],recallTEST[Pareto],'d')\n plt.xlabel('precision on test data')\n plt.ylabel('recall on test data')\n plt.show() \n \n return {'cost0':cost0,'cost1':cost1,'cost0test':cost0test,'cost1test':cost1test,'precisionTRAIN':precisionTRAIN,'precisionTEST':precisionTEST,'recallTEST':recallTEST,'rate':rate,'Pareto':Pareto}","def test_compare_old_to_new_method_to_create_trees(self):\n nodes = util.generate_sequence_of_points(2, 2)\n tree1 = kdtree.createNewTree(nodes)\n kdtree.visualize(tree1)\n \n sel_axis = (lambda axis: axis)\n tree2 = kdtree.createNewTree([[0.5, 0.5]],axis = 0, sel_axis= sel_axis)\n tree2.split2([0.25, 0.5], axis = 1)\n tree2.split2([0.75, 0.5], axis = 1)\n \n #left\n tree2.split2([0.25, 0.25], axis = 0, sel_axis = sel_axis)\n tree2.split2([0.25, 0.75], axis = 0, sel_axis = sel_axis)\n \n #right\n tree2.split2([0.75, 0.25], axis = 0, sel_axis = sel_axis)\n tree2.split2([0.75, 0.75], axis = 0, sel_axis = sel_axis)\n \n kdtree.visualize(tree2)\n \n for n in zip(kdtree.level_order(tree1), kdtree.level_order(tree2)):\n self.assertEqual(n[0].data, n[1].data, \"elements not equal\")\n \n if n[0].data is not None and n[1].data is not None:\n self.assertEqual(n[0].axis, n[1].axis, \"elements not equal\")","def dijsktra(graph, initial):\n # Sets initial node score to 10\n visited = {initial: 10}\n\n nodes = set(graph.nodes)\n max_weight = graph.distances[max(graph.distances, key=graph.distances.get)]\n min_weight = graph.distances[min(graph.distances, key=graph.distances.get)]\n\n # Defines the number of nodes to explore as the number of conected nodes\n nodes_to_explore = len(graph.edges[initial]) + 2\n explored = 1\n while explored < nodes_to_explore:\n # Finds nodes with maximum value that has not yet been explored\n max_node = None\n for node in nodes:\n if node in visited:\n if max_node is None:\n max_node = node\n elif visited[node] > visited[max_node]:\n max_node = node\n\n if max_node is None:\n break\n\n nodes.remove(max_node)\n current_weight = visited[max_node]\n\n # Finds score of the next node if node has already been visited\n # changes score if it is greater\n for edge in graph.edges[max_node]:\n weight = graph.distances[(max_node, edge)]\n if max_weight - min_weight == 0:\n normalised = 1\n else:\n normalised = ((weight - min_weight) / (max_weight - min_weight)) + 1\n weight = current_weight - (1 / normalised)\n if edge not in visited or weight > visited[edge]:\n visited[edge] = round(weight, 2)\n explored += 1\n\n return visited","def get_result_k(att_trees, data):\n data_back = copy.deepcopy(data)\n all_ncp = []\n all_rtime = []\n all_pollution = []\n deletion_all_ncp = []\n deletion_all_rtime = []\n # for k in range(5, 105, 5):\n for k in [2, 5, 10, 25, 50, 100]:\n if __DEBUG:\n print '#' * 30\n print \"K=%d\" % k\n print \"Enhanced Mondrian\"\n _, eval_result = mondrian(att_trees, data, k)\n data = copy.deepcopy(data_back)\n all_ncp.append(round(eval_result[0], 2))\n all_rtime.append(round(eval_result[1], 2))\n all_pollution.append(round(eval_result[2], 2))\n if __DEBUG:\n print \"NCP %0.2f\" % eval_result[0] + \"%\"\n print \"Running time %0.2f\" % eval_result[1] + \"seconds\"\n print \"Missing Pollution = %.2f %%\" % eval_result[2]\n print \"Mondrian\"\n _, eval_result = mondrian_delete_missing(att_trees, data, k)\n data = copy.deepcopy(data_back)\n if __DEBUG:\n print \"NCP %0.2f\" % eval_result[0] + \"%\"\n print \"Running time %0.2f\" % eval_result[1] + \"seconds\"\n deletion_all_ncp.append(round(eval_result[0], 2))\n deletion_all_rtime.append(round(eval_result[1], 2))\n print \"Mondrian\"\n print \"All NCP\", deletion_all_ncp\n print \"All Running time\", deletion_all_rtime\n print \"Enhanced Mondrian\"\n print \"All NCP\", all_ncp\n print \"All Running time\", all_rtime\n print \"Missing Pollution\", all_pollution","def prune(tree, testSet, res, technique):\n assert technique in [\"reduced_error\"]\n if technique == \"reduced_error\":\n tbSet = testSet[testSet[tree.col] >= tree.value] #find which test observations belong to this tree's true branch\n fbSet = testSet[testSet[tree.col] < tree.value] #find which test observations belong to this tree's false branch\n \n if tree.tb.results is None: #Check if the true branch of this sub-tree is a leaf\n ptb = prune(tree.tb, tbSet, res, technique) #If not, recursively travel down the true branch and prune it.\n else:\n ptb = tree.tb #If the true branch is a leaf, then the true branch has--in essence--already been pruned.\n if tree.fb.results is None: #Check if the false branch of this sub-tree is a leaf\n pfb = prune(tree.fb, fbSet, res, technique) #If not, recursively travel down the false branch and prune it.\n else:\n pfb = tree.fb #If the false branch is a leaf, then the false branch has--in essence--already been pruned.\n \n #Sum the number of misclassifications of the test data at each of the leaves of this node\n wrong_in_leaves = __deep_count_errors(ptb, tbSet, res) + __deep_count_errors(pfb, fbSet, res)\n \n #Count the number of misclassificationsof the test data that would occur if this node were treated as a leaf\n wrong_at_node = __count_errors(tree, testSet, res)\n \n #Assess whether or not treating the node as a leaf improves the accuracy on the test set\n if wrong_at_node <= wrong_in_leaves: \n #NOTE:The following line of code seems slightly redundant since count_errors(tree, testSet, res) had to call \n #__get_results(tree). I should set up some way to save the output of that function call instead of calling it twice.\n return decisionNode(results = __get_results(tree)) #If so, return a decisionNode where the node is a leaf\n else:\n #If not, return a decisionNode where the node splits on the same column and value as before, but the \n #true and false branches are the pruned-versions of the original true and false branches. See above for\n #definition of ptb and pfb\n return decisionNode(col = tree.col, value = tree.value, tb = ptb, fb = pfb)","def tdidt(instances, available_attributes, domain):\n\n # Select attribute, initialize tree\n split_attrib, leaves = select_attribute(instances, available_attributes, domain)\n tree = [\"Attribute\", \"att\" + str(split_attrib)]\n rem_attribs = [ x for x in available_attributes if x != split_attrib]\n\n # partition on attribute\n for value, partition in partition_instances(instances, split_attrib, domain).items():\n val_segment = [\"Value\", value]\n\n \n # CASE 1: all values are the same\n if(len(leaves) > 0 and value == leaves[0][0]):\n val_segment.append([\"Leaf\", *leaves[0][1:]])\n leaves.pop(0)\n\n # CASE 2: there are instances, but no more attributes\n elif(len(partition) > 0 and len(rem_attribs) == 0):\n most_common = max(partition_stats(partition, -1), key=lambda x: x[1])\n val_segment.append([\"Leaf\", *most_common])\n \n # CASE 3: no instances, replace attribute with leaf node\n elif(len(partition) == 0):\n most_common = max(partition_stats(instances, -1), key=lambda x: x[1])\n return [\"Leaf\", *most_common]\n \n # Default case. Recurse on partition\n else:\n val_segment.append(tdidt(partition, rem_attribs, domain))\n\n tree.append(val_segment)\n return tree","def _K_compute_eq(self):\r\n t_eq = self._t[self._index==0]\r\n if self._t2 is None:\r\n if t_eq.size==0:\r\n self._K_eq = np.zeros((0, 0))\r\n return\r\n self._dist2 = np.square(t_eq[:, None] - t_eq[None, :])\r\n else:\r\n t2_eq = self._t2[self._index2==0]\r\n if t_eq.size==0 or t2_eq.size==0:\r\n self._K_eq = np.zeros((t_eq.size, t2_eq.size))\r\n return\r\n self._dist2 = np.square(t_eq[:, None] - t2_eq[None, :])\r\n \r\n self._K_eq = np.exp(-self._dist2/(2*self.lengthscale*self.lengthscale))\r\n if self.is_normalized:\r\n self._K_eq/=(np.sqrt(2*np.pi)*self.lengthscale)","def get_t_d(conf, r_inputs, d_data):\n # to crop out unstable part for temporal discriminator, details in TecoGAN supplemental paper\n crop_size_dt = int(conf.train.crop_size * 4 * conf.gan.crop_dt)\n offset_dt = (conf.train.crop_size * 4 - crop_size_dt) // 2\n crop_size_dt = conf.train.crop_size * 4 - offset_dt*2\n paddings = (0, 0, offset_dt, offset_dt, offset_dt, offset_dt, 0, 0)\n\n with nn.parameter_scope(\"discriminator\"):\n real_warp = warp_by_flow(d_data.t_targets, d_data.t_vel)\n real_warp = space_to_depth_disc(real_warp, d_data.t_batch)\n\n # equivalent to tf.image.crop_to_bounding_box\n real_warp = real_warp[:, offset_dt:offset_dt +\n crop_size_dt, offset_dt:offset_dt+crop_size_dt, :]\n real_warp = F.pad(real_warp, paddings)\n before_warp = space_to_depth_disc(d_data.t_targets, d_data.t_batch)\n t_input = space_to_depth_disc(\n r_inputs[:, :d_data.t_size, :, :, :], d_data.t_batch)\n # resizing using bilinear interpolation\n input_hi = F.interpolate(t_input, scale=(4, 4), mode='linear',\n channel_last=True)\n real_warp = F.concatenate(before_warp, real_warp, input_hi)\n\n tdiscrim_real_output, real_layers = discriminator(real_warp)\n\n fake_warp = warp_by_flow(d_data.t_gen_output, d_data.t_vel)\n fake_warp = space_to_depth_disc(fake_warp, d_data.t_batch)\n fake_warp = fake_warp[:, offset_dt:offset_dt +\n crop_size_dt, offset_dt:offset_dt+crop_size_dt, :]\n fake_warp = F.pad(fake_warp, paddings)\n before_warp = space_to_depth_disc(\n d_data.t_gen_output, d_data.t_batch, inplace=False)\n fake_warp = F.concatenate(before_warp, fake_warp, input_hi)\n tdiscrim_fake_output, fake_layers = discriminator(fake_warp)\n\n temporal_disc = collections.namedtuple('temporal_disc', 'tdiscrim_real_output,'\n 'real_layers, tdiscrim_fake_output, fake_layers')\n return temporal_disc(\n tdiscrim_real_output=tdiscrim_real_output,\n real_layers=real_layers,\n tdiscrim_fake_output=tdiscrim_fake_output,\n fake_layers=fake_layers\n )","def decision_tree(df):\n features = df[['Temperature(F)', 'Humidity(%)', 'Visibility(mi)', 'Wind_Speed(mph)',\n 'Precipitation(in)', 'Amenity', 'Bump', 'Crossing', 'Give_Way',\n 'Junction', 'No_Exit', 'Railway', 'Roundabout', 'Station', 'Stop',\n 'Traffic_Calming', 'Traffic_Signal', 'Civil_Twilight', 'Rush Hour', 'Weekend',\n 'Side_R', 'Season_Spring', 'Season_Summer',\n 'Season_Winter', 'Weather_Condition_Clear', 'Weather_Condition_Fog',\n 'Weather_Condition_Other', 'Weather_Condition_Rain',\n 'Weather_Condition_Snow', 'Weather_Condition_Thunderstorm']]\n X= features\n y = df['Severity']\n clf = DecisionTreeClassifier(min_samples_split=6, min_samples_leaf=2, max_depth=3, \n criterion = 'gini', random_state=42)\n clf.fit(X, y)\n\n plt.figure(figsize=(25,10))\n a = plot_tree(clf, \n feature_names=X.columns.to_list(), \n filled=True, \n rounded=True, \n fontsize=14)\n plt.savefig(\"../Images/rockies_decision_tree.png\")\n plt.show()","def __dikjstra(self, start_node):\n visited = []\n unvisited = [x for x in self.__node]\n shortest_dist_from_start_node = 0\n current_node = start_node\n\n current_node.setShortestDist(shortest_dist_from_start_node)\n\n while current_node:\n #check unvisited neighbor\n for neighbor_node, distance in current_node.getNeighbors().items():\n #print(neighbor_node.getId(), distance) troubleshoot je ni\n if neighbor_node in visited:\n continue\n\n #add up shortest_dist_from_start_node with distance from neighbor distance\n calc_dist = shortest_dist_from_start_node + distance\n\n if calc_dist < neighbor_node.getShortestDist():\n neighbor_node.setShortestDist(calc_dist)\n neighbor_node.setPrevNode(current_node)\n\n # add current node to visited array\n visited.append(current_node)\n unvisited.remove(current_node)\n \n #update next node and next shortest distance\n next_shortest_dist_from_start_node = inf\n next_node = None\n\n for unvisited_node in unvisited:\n if unvisited_node.getShortestDist() < next_shortest_dist_from_start_node:\n next_shortest_dist_from_start_node = unvisited_node.getShortestDist()\n next_node = unvisited_node\n\n # update current node and shortest distance from start vertex\n if next_node:\n current_node = next_node\n shortest_dist_from_start_node = next_shortest_dist_from_start_node\n #if there are left over unvisited node\n else: \n if unvisited:\n current_node = unvisited[0]\n else:\n current_node = None","def tdidt(current_instances, available_attributes, attribute_domains, is_forest, previous_instances=None):\n\n # Select an attribute to split on\n available_dict = {}\n for key in attribute_domains:\n try:\n available_attributes.index(key)\n #available\n available_dict[key] = attribute_domains[key]\n except:\n #not available, so don't add\n pass\n\n if(previous_instances == None):\n previous_instances = len(current_instances)\n\n split_attribute = None\n if is_forest:\n F = 2\n if len(current_instances) < 2:\n F = len(current_instances)\n curr_subset = compute_random_subset(current_instances, F)\n split_attribute = select_attribute(curr_subset, available_dict)\n else:\n split_attribute = select_attribute(current_instances, available_dict)\n available_attributes.remove(split_attribute)\n # cannot split on the same attribute twice in a branch\n # recall: python is pass by object reference!!\n tree = [\"Attribute\", split_attribute]\n\n # group data by attribute domains (creates pairwise disjoint partitions)\n partitions = partition_instances(current_instances, split_attribute, attribute_domains)\n\n # for each partition, repeat unless one of the following occurs (base case)\n for attribute_value, partition in partitions.items():\n value_subtree = [\"Value\", attribute_value]\n # CASE 1: all class labels of the partition are the same => make a leaf node\n if len(partition) > 0 and all_same_class(partition):\n #print(\"CASE 1\")\n leaf = [\"Leaf\",partition[0][len(partition[0])-1],len(partition),len(current_instances)]\n value_subtree.append(leaf)\n # CASE 2: no more attributes to select (clash) => handle clash w/majority vote leaf node\n elif len(partition) > 0 and len(available_attributes) == 0:\n #print(\"CASE 2\")\n values, sums = get_frequencies(create_list(partition))\n max_index = sums.index(max(sums)) #finds index of the max count\n leaf = [\"Leaf\",values[max_index],max(sums),sum(sums)]\n value_subtree.append(leaf)\n # CASE 3: no more instances to partition (empty partition) => backtrack and replace attribute node with majority vote leaf node\n elif len(partition) == 0:\n values, sums = get_frequencies(create_list(current_instances))\n max_index = sums.index(max(sums)) #finds index of the max count\n return [\"Leaf\",values[max_index],len(current_instances),previous_instances]\n else: # all base cases are false... recurse!!\n subtree = tdidt(partition, available_attributes.copy(),attribute_domains, is_forest, len(current_instances))\n # need to append subtree to value_subtree and appropriately append value subtree\n # to tree\n value_subtree.append(subtree)\n tree.append(value_subtree)\n \n return tree","def discretize(self, dt: float) -> 'Trajectory':\n return self.discretize_state(dt)","def __build_tree__(self, features, classes, depth=0):\n\n # TODO: finish this.\n root = None\n if (len(set(classes)) <= 1) and (len(classes) != 0) :\n return DecisionNode(None,None,None,classes[0])\n elif (len(classes) == 0):\n return DecisionNode(None,None,None,2)\n elif depth == self.depth_limit:\n return DecisionNode(None,None,None,max(set(classes), key=list(classes).count))\n else:\n# if depth == 0:\n features = np.array(features)\n classes = np.array(classes).reshape(-1,1)\n feat_shape = features.shape\n sample_list = range(feat_shape[0])\n gains = np.zeros((feat_shape[1]))\n indices = np.zeros((feat_shape[1]))\n for i in range(feat_shape[1]):\n attribute = features[:,i]\n for j in range(20):\n split_indx = int(np.random.choice(sample_list, replace=False))\n idx_above = np.where(attribute > attribute[split_indx])[0]\n idx_below = np.where(attribute < attribute[split_indx])[0]\n classes_below = classes[idx_below,:].reshape(1,-1)[0]\n classes_above = classes[idx_above,:].reshape(1,-1)[0]\n gain = gini_gain(list(classes.reshape(1,-1)[0]),[list(classes_below),list(classes_above)])\n if gain > gains[i]:\n gains[i] = gain\n indices[i] = split_indx\n indx = np.argmax(gains)\n split_indx = int(indices[indx])\n attribute = features[:,indx]\n idx_above = np.where(attribute > attribute[split_indx])[0]\n idx_below = np.where(attribute < attribute[split_indx])[0] \n features_below = features[idx_below,:]\n features_above = features[idx_above,:]\n classes_below = classes[idx_below,:].reshape(1,-1)[0]\n classes_above = classes[idx_above,:].reshape(1,-1)[0]\n if (len(classes_below) != 0) and (len(classes_above) != 0):\n root = DecisionNode(None,None,lambda feat:feat[indx] > features[split_indx,indx])\n root.left = self.__build_tree__(features_above, classes_above, depth+1)\n root.right = self.__build_tree__(features_below, classes_below, depth+1)\n return root\n elif (len(classes_below) == 0) and (len(classes_above) != 0):\n return DecisionNode(None,None,None,max(set(classes_above), key=list(classes_above).count))\n elif (len(classes_above) == 0) and (len(classes_below) !=0):\n return DecisionNode(None,None,None,max(set(classes_below), key=list(classes_below).count))\n else:\n return DecisionNode(None,None,None,2)","def __init__(self):\n self.tree = nx.Graph() \n self.orig_dist_matrix = pd.DataFrame()\n self.work_dist_matrix = pd.DataFrame() \n self.cluster_map = {} \n self.class_map = {}","def _dK_ode_dtheta(self, target):\r\n t_ode = self._t[self._index>0]\r\n dL_dK_ode = self._dL_dK[self._index>0, :]\r\n index_ode = self._index[self._index>0]-1\r\n if self._t2 is None:\r\n if t_ode.size==0:\r\n return \r\n t2_ode = t_ode\r\n dL_dK_ode = dL_dK_ode[:, self._index>0]\r\n index2_ode = index_ode\r\n else:\r\n t2_ode = self._t2[self._index2>0]\r\n dL_dK_ode = dL_dK_ode[:, self._index2>0]\r\n if t_ode.size==0 or t2_ode.size==0:\r\n return\r\n index2_ode = self._index2[self._index2>0]-1\r\n\r\n h1 = self._compute_H(t_ode, index_ode, t2_ode, index2_ode, stationary=self.is_stationary, update_derivatives=True)\r\n #self._dK_ddelay = self._dh_ddelay\r\n self._dK_dsigma = self._dh_dsigma\r\n\r\n if self._t2 is None:\r\n h2 = h1\r\n else:\r\n h2 = self._compute_H(t2_ode, index2_ode, t_ode, index_ode, stationary=self.is_stationary, update_derivatives=True)\r\n\r\n #self._dK_ddelay += self._dh_ddelay.T\r\n self._dK_dsigma += self._dh_dsigma.T\r\n # C1 = self.sensitivity\r\n # C2 = self.sensitivity\r\n\r\n # K = 0.5 * (h1 + h2.T)\r\n # var2 = C1*C2\r\n # if self.is_normalized:\r\n # dk_dD1 = (sum(sum(dL_dK.*dh1_dD1)) + sum(sum(dL_dK.*dh2_dD1.T)))*0.5*var2\r\n # dk_dD2 = (sum(sum(dL_dK.*dh1_dD2)) + sum(sum(dL_dK.*dh2_dD2.T)))*0.5*var2\r\n # dk_dsigma = 0.5 * var2 * sum(sum(dL_dK.*dK_dsigma))\r\n # dk_dC1 = C2 * sum(sum(dL_dK.*K))\r\n # dk_dC2 = C1 * sum(sum(dL_dK.*K))\r\n # else:\r\n # K = np.sqrt(np.pi) * K\r\n # dk_dD1 = (sum(sum(dL_dK.*dh1_dD1)) + * sum(sum(dL_dK.*K))\r\n # dk_dC2 = self.sigma * C1 * sum(sum(dL_dK.*K))\r\n\r\n\r\n # dk_dSim1Variance = dk_dC1\r\n # Last element is the length scale.\r\n (dL_dK_ode[:, :, None]*self._dh_ddelay[:, None, :]).sum(2)\r\n\r\n target[-1] += (dL_dK_ode*self._dK_dsigma/np.sqrt(2)).sum()\r\n\r\n\r\n # # only pass the gradient with respect to the inverse width to one\r\n # # of the gradient vectors ... otherwise it is counted twice.\r\n # g1 = real([dk_dD1 dk_dinvWidth dk_dSim1Variance])\r\n # g2 = real([dk_dD2 0 dk_dSim2Variance])\r\n # return g1, g2\"\"\"\r","def result(data, taxon_names, tree, sample_names, **kwargs):\r\n\r\n envs = make_envs_dict(data, sample_names, taxon_names)\r\n unifrac_res = fast_unifrac(\r\n tree, envs, weighted=weighted, metric=metric,\r\n is_symmetric=is_symmetric, modes=[\"distance_matrix\"], **kwargs)\r\n dist_mtx = _reorder_unifrac_res(unifrac_res['distance_matrix'],\r\n sample_names)\r\n return dist_mtx","def buildGraph(pickl, his=False):\n\n dic = pickl\n\n dic1 = dic\n G = nx.Graph()\n dic3 = dict(dic)\n checked = []\n\n # Adding nodes with bios greater than 30 words.\n for key in dic:\n if((re.sub(\"[ ]+\", \"\", dic[key]) != \"\") and len(dic[key])) > 30:\n G.add_node(key)\n else:\n del dic3[key]\n\n dic1 = dic3\n\n vect = TfidfVectorizer(min_df=1)\n coefs = list()\n\n joint_dict = dict()\n # Cosine similarity measure matrix\n F = vect.fit_transform(dic3.values())\n Cosine_mat = (F*F.T).A # Symmetric matrix:\n # Traverse uper triangle for cosine similarity measures.\n for i, key in enumerate(dic3):\n for j, key1 in enumerate(dic1):\n if(i > j):\n # obtain coef for corresponding key\n tfidf = Cosine_mat[i, j]\n # Repeated nodes must be filtered\n if dic[key] == dic[key1]:\n\n continue\n else:\n coefs.append(tfidf)\n joint_dict[str(key) + str(key1)] = tfidf\n\n data = [c for c in coefs if c]\n # max(data)\n\n mu = np.mean(data)\n std = np.std(data)\n binwidth = 0.007\n if his:\n plt.subplot(1, 2, 0)\n plt.hist(data, bins=np.arange(min(data), max(data) + binwidth, binwidth))\n # PLot gaussian fit contrast\n plt.xlabel(\"$cos(\\\\theta)$\")\n plt.ylabel(\"frecuency count of $cos(\\\\theta)$ values\")\n plt.subplot(1, 2, 1)\n plt.plot(np.arange(0, max(data), 0.001),\n gaussian(np.arange(0, max(data), 0.001), mu, std),\n linewidth=2)\n plt.xlabel(\"$cos(\\\\theta)$\")\n plt.ylabel(\"fitted gaussian\")\n plt.show()\n\n # Edge creation !\n for key in dic3:\n for key1 in dic1:\n if(key != key1):\n try:\n x = joint_dict[str(key) + str(key1)]\n # If cosine similarity is an outlier with 95% change\n # Make edge between nodes that conform the similarity\n if(x - mu > 2 * std):\n G.add_edge(key, key1)\n except:\n pass\n\n # Return the conected component with largest cardinality of nodes\n # Throw away small connected components we are interested in the big one\n # For our mini project exploration purposes\n G = max(nx.connected_component_subgraphs(G), key=len)\n return G","def all_DU_paths(CG, T):\n path_to_visit = []\n path_not_visited = []\n for var in CG.var:\n vert_var_in_defv=set()\n vert_var_in_refv=set()\n for vert in CG.vertices:\n if var in vert.defv:\n vert_var_in_defv.add(vert.label)\n elif var in vert.refv:\n vert_var_in_refv.add(vert.label)\n couples = product(vert_var_in_defv, vert_var_in_refv)\n for vert_def, vert_ref in couples:\n #paths_remaining = list(CG.simple_partial_paths[(vert_def, vert_ref)])\n path_to_visit += list(CG.simple_partial_paths[(vert_def, vert_ref)])\n path_not_visited += list(CG.simple_partial_paths[(vert_def, vert_ref)])\n for t in T:\n path, variables = apply_path(CG, t)\n vert_found = False\n for label in path:\n cur_vertice=find_vertice_with_label(CG,label)\n if vert_def == label:\n vert_found = True\n\n elif vert_ref == label and vert_found:\n if path in path_not_visited:\n path_not_visited.remove(path)\n break\n\n elif vert_ref != label and var in cur_vertice.refv:\n break\n #print('Test failed')\n #print(paths_remaining, var,vert_def, vert_ref, path)\n #return False\n\n #if paths_remaining:\n # print('Test failed')\n # print(paths_remaining, var, vert_def, vert_ref, path)\n # return False\n\n Graph.coverage_criteria2(path_to_visit, \"not_visited\", path_not_visited)\n if path_not_visited:\n print('Test failed')\n \n else:\n print('Test passed')\n return True","def cluster(name, epsilon: float, delta: float, tau: float, tree_structure='automatic', threads=1,\n sequential_search_threshold=2000, return_list=False, epsilon_multiple=None, output_name=None):\n global dataListGlobal, index_inverse_global\n\n if output_name is None:\n output_name = get_filename(name)\n\n neighbour_radius = max(delta, tau)\n\n # Choose tree structure if tree structure is automatic\n if tree_structure == 'automatic':\n if neighbour_radius > 0.05:\n tree_structure = 'm'\n else:\n if neighbour_radius > 0.01:\n tree_structure = 'kd'\n else:\n tree_structure = 'r'\n\n if tree_structure not in ['r', 'm', 'kd']:\n raise Exception(\"Tree structure can only be m, r, kd\")\n\n logging.info(\"Tree structure chosen: %s\", tree_structure)\n\n # Setup\n dataList = []\n with open(name) as csvfile:\n data = csv.reader(csvfile)\n for row in data:\n dataList.append(tuple([float(s) for s in row]))\n\n\n # Only a portion of the data is taken\n portion = dataList.__len__()\n #portion = 500\n dataList = dataList[0:portion]\n\n original_data = dataList.copy()\n\n\n if tree_structure == 'm':\n stime = time.time()\n logging.info(\"Sorting using z-curve...\")\n # Sort dataList:\n dataList.sort(key=lambda x: morton_code(x))\n logging.info(\"Data set sorted. Time for sorting: %f\", (time.time() - stime))\n\n #Define constants\n n: int = len(dataList)\n d: int = len(dataList[0])\n dimension_const: float = n * delta ** d\n\n dataListGlobal = dataList\n\n mtr.dataList = dataList\n\n #Define sq_metric\n sq_metric_index = mtr.choose_sq_metric_index(d)\n sq_metric = mtr.choose_sq_metric(d)\n\n #Define metric\n metric_index = mtr.choose_metric_index(d)\n\n\n\n # Create index inverse\n index_inverse_global = {dataList[i]: i for i in range(n)}\n\n\n # Build tree\n stime = time.time()\n if tree_structure == 'm':\n logging.info(\"Filling M-Tree...\")\n tree = MTree(metric_index, 6, 20, sq_metric=sq_metric_index)\n for p in range(n):\n tree.insert(p)\n logging.info(\"Done filling M-Tree. Time for filling: %f\", (time.time()-stime))\n if tree_structure == 'r':\n logging.info(\"Filling R-Tree...\")\n #Make a copy, since bulk_load_r_tree sorts dataList\n dataListCopy = dataList.copy()\n tree = bulk_load_r_tree_str(dataListCopy, 40, 20, 20, 10, sq_metric=sq_metric)\n logging.info(\"Done filling R-Tree. Time for filling: %f\", (time.time() - stime))\n if tree_structure == 'kd':\n logging.info(\"Constructing KD-Tree...\")\n tree = create_kd_tree(dataList, 50, sq_metric=sq_metric)\n logging.info(\"KD-Tree constructed. Time for construction: %f\", (time.time() - stime))\n\n\n stime = time.time()\n logging.info(\"Precomputing neighbourhood list...\")\n number_of_cores = multiprocessing.cpu_count()\n if threads == 'automatic':\n if n > 5000:\n number_of_threads = math.ceil(number_of_cores/2)\n else:\n if n > 2500:\n number_of_threads = min(number_of_cores, 2)\n else:\n number_of_threads = 1\n else:\n number_of_threads = threads\n\n logging.info(\"Number of cores detected: %s. Using %s thread(s)\", number_of_cores, number_of_threads)\n\n\n neighbour_radius_dict = compute_neighbours(number_of_threads, n, tree, tree_structure, tau)\n\n\n logging.info(\"max(tau,delta)-neighbourhood list computed. Time for computation: %f\", (time.time()-stime))\n average_neighbour_number = sum([neighbour_radius_dict[p].__len__() for p in range(n)])/n\n logging.info(\"Average number of neighbours: %d\", average_neighbour_number)\n stime = time.time()\n if math.isclose(delta,neighbour_radius) and math.isclose(tau, neighbour_radius):\n logging.info(\"Delta = Tau, saving distance calculations\")\n neighbour_dict = neighbour_radius_dict\n density_dict = [neighbour_radius_dict[i].__len__() for i in range(n)]\n else:\n if math.isclose(delta, neighbour_radius):\n logging.info(\"Tau is smaller than delta, computing tau-neighbours...\")\n if average_neighbour_number <= sequential_search_threshold:\n #Compute tau-neighbours sequentially\n neighbour_dict = [[] for x in range(n)]\n tau_sq = tau*tau\n for p in range(n):\n for y in neighbour_radius_dict[p]:\n if y <= p and sq_metric_index(y, p) <= tau_sq:\n neighbour_dict[p].append(y)\n neighbour_dict[y].append(p)\n else:\n #Compute tau-neighbours through the tree structure\n neighbour_dict = compute_neighbours(number_of_threads, n, tree, tree_structure, tau)\n density_dict = [l.__len__() for l in neighbour_radius_dict]\n logging.info(\"Tau-neighbours computed. Time for computation: %f\", (time.time()-stime))\n else:\n logging.info(\"Delta is smaller than tau, computing density...\")\n if average_neighbour_number <= sequential_search_threshold:\n #Compute delta-neighbours sequentially\n density_dict = [0 for x in range(n)]\n delta_sq = delta * delta\n for p in range(n):\n for y in neighbour_radius_dict[p]:\n if y <= p and sq_metric_index(y, p) <= delta_sq:\n density_dict[p] += 1\n density_dict[y] += 1\n else:\n # Compute delta-neighbours through the tree structure\n d_dict = compute_neighbours(number_of_threads, n, tree, tree_structure, delta)\n density_dict = [d_dict[i].__len__() for i in range(n)]\n neighbour_dict = neighbour_radius_dict\n logging.info(\"Density computed. Time for computation: %f\", (time.time()-stime))\n\n\n #Allocate a disjoint-set (find-union) data structure\n disjoint_set_dict = [None]*n\n rank_dict = [None]*n\n\n for x in range(n):\n #Initialize all disjoint sets\n disjoint_set_dict[x] = x\n rank_dict[x] = 1\n\n\n inc_constant: float = 1 / dimension_const\n\n upper_bound_on_density = max([density_dict[p] for p in range(n)])/dimension_const\n logging.info(\"Upper bound on density: %f. Required iterations: %f\", upper_bound_on_density, upper_bound_on_density*dimension_const)\n\n if epsilon_multiple != None:\n epsilon = math.sqrt(upper_bound_on_density/dimension_const)*epsilon_multiple\n\n rho: float = upper_bound_on_density\n\n old_level_set = {}\n\n number_of_connected_components = 0\n\n #Save the last branching point\n\n saved_rho = 0\n saved_disjoint_set_dict = []\n saved_level_set_rho = {}\n saved_number_of_components = 0\n logging.info(\"Entering loop\")\n start_time = time.time()\n # The incrementation is decreased until rho = 0\n while rho >= -1.0e-9:\n logging.info(\"--- new loop ---\")\n epsrho: float = rho + 2 * epsilon\n logging.info(\"Computing level set...\")\n\n level_set_rho = {y:True for y in range(n) if density_dict[y]/dimension_const >= rho}\n if rho == upper_bound_on_density:\n number_of_connected_components = level_set_rho.__len__()\n\n logging.info(\"Level set computed\")\n\n logging.info(\"Computing connected components...\")\n\n #For each new element call union\n for y in level_set_rho:\n if y not in old_level_set:\n number_of_connected_components += 1\n for z in neighbour_dict[y]:\n if z in level_set_rho:\n if dst.union(y, z, disjoint_set_dict, rank_dict):\n number_of_connected_components -= 1\n\n logging.info(\"Connected components computed: %d\", number_of_connected_components)\n\n logging.info(\"Removing insignificant components...\")\n\n #TODO: Optimize this part, i.e. persisting_components should be an integer\n persisting_components = {}\n for y in level_set_rho:\n if density_dict[y]/dimension_const >= epsrho:\n persisting_components[dst.get_root(y, disjoint_set_dict)] = True\n\n logging.info(\"Insignificant components removed. Now there are: %d\", persisting_components.__len__())\n\n if persisting_components.__len__() > 1:\n logging.info(\"Branching point, data saved.\")\n #Save the branching point\n saved_rho = rho\n saved_disjoint_set_dict = disjoint_set_dict.copy()\n saved_level_set_rho = level_set_rho.copy()\n saved_number_of_components = persisting_components.__len__()\n\n # Decrease rho\n rho -= inc_constant\n old_level_set = level_set_rho\n logging.info(\"Decreasing rho to: %f\", rho)\n\n logging.info(\"--- %f seconds in loop ---\", (time.time()-start_time))\n\n logging.info(\"Components detected %s\", saved_number_of_components)\n logging.info(\"Writing to file...\")\n\n #Determine component dictionary\n\n component_dict = [None]*n\n counter = 1\n for y in range(n):\n if y in saved_level_set_rho:\n root = dst.get_root(y, saved_disjoint_set_dict)\n if not component_dict[root] is None:\n component_dict[y] = component_dict[root]\n else:\n component_dict[root] = counter\n component_dict[y] = counter\n counter += 1\n else:\n component_dict[y] = 0\n\n #Write to file in the original order\n failed = saved_level_set_rho.__len__() == 0\n\n with open(output_name+'.result.csv', 'w') as file:\n for p in original_data:\n if failed:\n file.write(str(1))\n else:\n file.write(str(component_dict[index_inverse_global[p]]))\n file.write(', ')\n file.write(', '.join(map(str,p)))\n file.write('\\n')\n file.close()\n\n #Create list of components\n if return_list:\n list_of_components = [[] for i in range(counter)]\n for y in range(n):\n list_of_components[component_dict[y]].append(dataList[y])\n return list_of_components\n\n logging.info(\"Done.\")","def decision_tree(data_frame, filename=0):\n\tprint \"Building decision tree...\"\n\tr = robjects.r\n\trpart = importr(\"rpart\")\n\tfit = rpart.rpart(\"category~bpm+speechiness+time_sig+key+duration+loudness+\\\n\t\t\tend_of_fade_in+start_of_fade_out+bpm_range+\\\n\t\t\tmax_bpm_spike+num_keys\", data=data_frame, method=\"class\", \n\t\t\tna_action='na.rpart', control='rpart.control(cp = .0001)')\n\trpart.printcp(fit)\n\tr.plot(fit, uniform=True, main=\"Classification Tree for Genre\")\n\tr.text(fit, use_n=True, all=True, cex=.8)\n\tif filename != 0:\n\t\trpart.post(fit, file=filename, title=\"Classification Tree for Genre\")\n\traw_input(\"> Press enter to continue.\")\n\treturn fit","def decision_tree(df, variables, test_size):\n from sklearn.model_selection import train_test_split\n from sklearn import tree\n\n # Define input\n X = encoding_df(df, variables)\n\n # Set validation\n y = df['target']\n\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)\n\n clf = tree.DecisionTreeRegressor()\n clf = clf.fit(X_train, y_train)\n\n print(compute_rmse(y_test, clf.predict(X_test)))\n return clf.predict(X_test), y_test","def build_graph(self, K, D):\r\n tf.reset_default_graph()\r\n\r\n # X -> N x D\r\n self.X = tf.placeholder(tf.float32, shape=(None, D), name='X')\r\n\r\n # Mu -> K x D\r\n self.Mu = tf.get_variable('Mu', shape=(K, D),\r\n initializer=tf.initializers.truncated_normal())\r\n\r\n # d2 -> N x K (pairwise distance matrix)\r\n d2 = self.distance(self.X, self.Mu)\r\n\r\n # min(N x K) => (N)\r\n min_d2 = tf.reduce_min(d2, axis=1)\r\n\r\n # loss = sum(N) => scalar\r\n self.loss = tf.reduce_sum(min_d2, axis=0, name='loss')\r\n self.opt_op = tf.train.AdamOptimizer(\r\n learning_rate=1e-2,\r\n beta1=0.9,\r\n beta2=0.99,\r\n epsilon=1e-5\r\n ).minimize(self.loss)\r\n\r\n self.predict_op = tf.argmin(d2, axis=1)","def tldiffusion(self, dt):\n\n # Reset erosion, depo, trans and flux_in to 0\n self.erosion[:] = 0.0\n self.depo[:] = 0.0\n self.trans[:] = 0.0\n self.flux_in[:] = 0.0\n\n # Downstream steepest slope at node:\n self.steepest = self.grid.at_node[\"topographic__steepest_slope\"]\n # On each node, node ID of downstream receiver node\n # (on node (i), ID of node that receives flow from node (i)):\n self.receiver = self.grid.at_node[\"flow__receiver_node\"]\n\n dx = self.grid.dx\n cores = self.grid.core_nodes\n\n # Calculate influx rate on node i = outflux of nodes\n # whose receiver is i\n for i in self.grid.core_nodes:\n self.flux_in[self.receiver[i]] += self.flux_out[i]\n\n # Calculate transport coefficient\n # When S ~ Scrit, d_coeff is set to \"infinity\", for stability and\n # so that there is no deposition\n if self.steepest[i] >= self.slope_crit:\n self.d_coeff[i] = 1000000000.0\n else:\n self.d_coeff[i] = 1 / (\n 1 - (np.power(((self.steepest[i]) / self.slope_crit), 2))\n )\n\n # Calculate deposition rate on node\n self.depo[cores] = self.flux_in[cores] / self.d_coeff[cores]\n\n # Calculate erosion rate on node (positive value)\n # If S > Scrit, erosion is simply set for the slope to return to Scrit\n # Otherwise, erosion is slope times erodibility coefficent\n for i in self.grid.core_nodes:\n if self.steepest[i] > self.slope_crit:\n self.erosion[i] = dx * (self.steepest[i] - self.slope_crit) / (100 * dt)\n else:\n self.erosion[i] = self.k * self.steepest[i]\n\n # Update elevation\n self.elev[i] += (-self.erosion[i] + self.depo[i]) * dt\n\n # Calculate transfer rate over node\n self.trans[cores] = self.flux_in[cores] - self.depo[cores]\n\n # Calculate outflux rate\n self.flux_out[:] = self.erosion + self.trans","def __init__(self, train_data=None, train_label=None, dimensions=None,\n axis=0, sel_axis=None):\n # As train_data is a list of samples, we use dict() to change data\n # structure of samples.\n self.train_data = train_data\n self.train_label = train_label\n self.labels = set(self.train_label)\n self.class_prb = self._calc_train_class_prb(self.train_label)\n self.kdtree = kdtree.create(\n copy.deepcopy(train_data), dimensions, axis, sel_axis)","def create(self, X, dimensions=None):\n n_samples, n_features = X.shape\n self.X = X\n if not dimensions:\n dimensions = n_features\n\n self.root = KdNode(depth=0,\n splitting_feature=0,\n splitting_value=np.median(X[:, 0]),\n idx=np.arange(n_samples),\n parent=None)\n # grow the tree by DFS\n stack = [self.root]\n while stack:\n node = stack.pop()\n # splitting samples in the node into two children\n sample_values = X[node.idx, node.splitting_feature]\n left_idx = node.idx[sample_values < node.splitting_value]\n right_idx = node.idx[sample_values > node.splitting_value]\n node.idx = node.idx[sample_values == node.splitting_value]\n # since left and right subtrees are divided by the median of their parent,\n # the sizes of the two subtrees are expected to be equal\n assert len(left_idx) == len(right_idx),\\\n 'left and right subtrees should have the same number of samples'\n # append left and right children\n if len(left_idx):\n child_depth = node.depth + 1\n child_feature = (node.depth + 1) % dimensions\n left_value = np.median(X[left_idx, child_feature])\n node.left = KdNode(depth=child_depth, splitting_feature=child_feature,\n splitting_value=left_value, idx=left_idx, parent=node)\n right_value = np.median(X[right_idx, child_feature])\n node.right = KdNode(depth=child_depth, splitting_feature=child_feature,\n splitting_value=right_value, idx=right_idx, parent=node)\n stack.append(node.left)\n stack.append(node.right)","def quantifier_lower(self):\n tree = deepcopy(self)\n all_DPs = [subtree for subtree in tree.subtree_dict.values() if (isinstance(subtree.label, FeatStructNonterminal) and (subtree.label[feature_type] == 'DP'))]\n trace_DPs = [DP for DP in all_DPs if DP.label['TRACE']]\n non_trace_DPs = diff(all_DPs, trace_DPs)\n for DP in trace_DPs:\n for other_DP in non_trace_DPs:\n if (DP.children[0].label.index == other_DP.ID):\n DP.label = deepcopy(other_DP.label)\n DP.children = deepcopy(other_DP.children)\n break\n tree = remove_QRed_DPs(tree)\n tree.label_nodes()\n tree.make_nx_tree()\n return tree","def __init__(self):\n\t\tself.clust_idx_part = -1\n\t\t\"\"\"\n\t\tthis list stores the indices of input trees having this taxon\n\t\t\"\"\"\n\t\tself.Support_Tree_List = []","def _K_compute_ode_eq(self, transpose=False):\r\n\r\n if self._t2 is not None:\r\n if transpose:\r\n t_eq = self._t[self._index==0]\r\n t_ode = self._t2[self._index2>0]\r\n index_ode = self._index2[self._index2>0]-1\r\n else:\r\n t_eq = self._t2[self._index2==0]\r\n t_ode = self._t[self._index>0]\r\n index_ode = self._index[self._index>0]-1\r\n else:\r\n t_eq = self._t[self._index==0]\r\n t_ode = self._t[self._index>0]\r\n index_ode = self._index[self._index>0]-1\r\n\r\n if t_ode.size==0 or t_eq.size==0:\r\n if transpose:\r\n self._K_eq_ode = np.zeros((t_eq.shape[0], t_ode.shape[0]))\r\n else:\r\n self._K_ode_eq = np.zeros((t_ode.shape[0], t_eq.shape[0]))\r\n return\r\n\r\n t_ode_mat = t_ode[:, None]\r\n t_eq_mat = t_eq[None, :]\r\n if self.delay is not None:\r\n t_ode_mat -= self.delay[index_ode, None]\r\n diff_t = (t_ode_mat - t_eq_mat)\r\n\r\n inv_sigma_diff_t = 1./self.sigma*diff_t\r\n decay_vals = self.decay[index_ode][:, None]\r\n half_sigma_d_i = 0.5*self.sigma*decay_vals\r\n\r\n if self.is_stationary:\r\n ln_part, signs = ln_diff_erfs(inf, half_sigma_d_i - inv_sigma_diff_t, return_sign=True)\r\n else:\r\n ln_part, signs = ln_diff_erfs(half_sigma_d_i + t_eq_mat/self.sigma, half_sigma_d_i - inv_sigma_diff_t, return_sign=True)\r\n sK = signs*np.exp(half_sigma_d_i*half_sigma_d_i - decay_vals*diff_t + ln_part)\r\n\r\n sK *= 0.5\r\n\r\n if not self.is_normalized:\r\n sK *= np.sqrt(np.pi)*self.sigma\r\n\r\n\r\n if transpose:\r\n self._K_eq_ode = sK.T\r\n else:\r\n self._K_ode_eq = sK","def discretizespace(self):\n \n # Grid\n self.xgriddim = ( self.x0_n , self.x1_n )\n \n self.xd = [ None , None ]\n self.xd[0] = np.linspace( self.DS.x_lb[0] , self.DS.x_ub[0] , self.x0_n )\n self.xd[1] = np.linspace( self.DS.x_lb[1] , self.DS.x_ub[1] , self.x1_n )\n \n self.x_grid2node = np.zeros( ( self.x0_n , self.x1_n ) , dtype = int ) # grid of corresponding index\n \n # 1-D List of nodes\n self.nodes_n = self.x0_n * self.x1_n\n self.nodes_state = np.zeros(( self.nodes_n , self.DS.n ), dtype = float ) # Number of nodes x state dimensions\n self.nodes_index = np.zeros(( self.nodes_n , self.DS.n ), dtype = int ) # Number of nodes x state dimensions","def fit_dtree(X, y):\n dtree = DecisionTreeRegressor(max_depth=2)\n dtree.fit(X, y)\n return dtree"],"string":"[\n \"def generate_kdtree(self):\\n if self.method==2:\\n coordinates = self.unassigned_data[0:3,:]\\n else:\\n coordinates = self.unassigned_data[0:2,:]\\n tree = cKDTree(coordinates.T)\\n\\n return tree\",\n \"def compute_kdtree_and_dr_tractogram(tractogram, num_prototypes=None):\\n tractogram = np.array(tractogram, dtype=np.object)\\n print(\\\"Computing dissimilarity matrices...\\\")\\n if num_prototypes is None:\\n num_prototypes = 40\\n print(\\\"Using %s prototypes as in Olivetti et al. 2012.\\\"\\n % num_prototypes)\\n print(\\\"Using %s prototypes\\\" % num_prototypes)\\n dm_tractogram, prototype_idx = compute_dissimilarity(tractogram,\\n num_prototypes=num_prototypes,\\n distance=bundles_distances_mam,\\n prototype_policy='sff',\\n n_jobs=-1,\\n verbose=False)\\n prototypes = tractogram[prototype_idx]\\n print(\\\"Building the KD-tree of tractogram.\\\")\\n kdt = KDTree(dm_tractogram)\\n return kdt, prototypes\",\n \"def CreateTree(d,list_L):\\r\\n \\r\\n d_new = defaultdict(dict)\\r\\n \\r\\n #labels corresponding to minimum evolutionary distance in Matrix\\r\\n a,b = minInDict(d)\\r\\n\\r\\n n = ['(',a,',',b,')']\\r\\n new_cluster = ''.join(n)\\r\\n \\r\\n #remove the entries in newly formed cluster from our list\\r\\n for k in list_L:\\r\\n if k == a:\\r\\n list_L.remove(k)\\r\\n break\\r\\n for p in list_L:\\r\\n if p == b:\\r\\n list_L.remove(p)\\r\\n break\\r\\n\\r\\n #to remove an existing cluster with those entries\\r\\n for j in list_L:\\r\\n if a in j or b in j:\\r\\n list_L.remove(j)\\r\\n\\r\\n #Base case of our recursive function \\r\\n if len(d) == 1:\\r\\n return new_cluster\\r\\n\\r\\n #calculating distances from our new cluster(eg: (a,b))to other labels/clusters\\r\\n for q in list_L:\\r\\n \\r\\n if b in d:\\r\\n if q in d[a] and q in d[b]:\\r\\n d_new[new_cluster][q] = (d[a][q] + d[b][q])/2\\r\\n continue\\r\\n if q in d:\\r\\n if b in d[q] and q in d[a]:\\r\\n d_new[new_cluster][q] = (d[a][q] + d[q][b])/2\\r\\n continue\\r\\n if b in d:\\r\\n if a in d[q] and q in d[b]:\\r\\n d_new[new_cluster][q] = (d[q][a] + d[b][q])/2\\r\\n continue\\r\\n if a in d[q] and b in d[q]:\\r\\n d_new[new_cluster][q] = (d[q][a] + d[q][b])/2\\r\\n \\r\\n list_L.insert(0,new_cluster)\\r\\n \\r\\n #deleting row and column corresponding to a and b\\r\\n del d[a]\\r\\n if b in d:\\r\\n del d[b]\\r\\n \\r\\n for p in d:\\r\\n for q in d[p]:\\r\\n if q != b and q != a:\\r\\n d_new[p][q] = d[p][q]\\r\\n\\r\\n return CreateTree(d_new,list_L)\",\n \"def _getKDTree(self, index=None):\\n\\n if self._n_csets:\\n if index is None:\\n index = self._acsi\\n kdtree = self._kdtrees[index]\\n if kdtree is None:\\n kdtree = KDTree(self._coords[index])\\n self._kdtrees[index] = kdtree\\n return kdtree\\n else:\\n return None\",\n \"def create_tree():\\n\\tdtree = {}\\n\\n\\tdtree['stats'] = None\\n\\tdtree['libs'] = {'fort':\\n\\t\\t\\t\\t\\t\\t\\t {'integers': None, 'floats': None, 'data': None}\\n\\t\\t\\t\\t\\t ,'cache blocking': None}\\n\\tdtree['grid'] = {'size' :\\n\\t\\t\\t\\t\\t\\t\\t {'nxgb': None, 'nygb': None, 'nzgb': None}\\n\\t\\t\\t\\t\\t ,'geom' :\\n\\t\\t\\t\\t\\t\\t\\t {'Lx' : None, 'Ly' : None, 'Lz' : None\\n\\t\\t\\t\\t\\t\\t\\t ,'dx' : None, 'dy' : None, 'dz' : None\\n\\t\\t\\t\\t\\t\\t\\t ,'x' : None, 'y' : None, 'z' : None\\n\\t\\t\\t\\t\\t\\t\\t ,'xloc': None, 'yloc': None, 'zloc': None}}\\t\\t\\t\\t\\t\\t\\t \\t\\t\\t\\t\\t \\t\\t \\n\\tdtree['eqns'] = {'qvec' : \\n\\t\\t\\t\\t\\t\\t\\t {'nvars': None, 'solved': None, 'stored': None, 'views': None}\\n\\t\\t\\t\\t\\t ,'coeff': None\\n\\t\\t\\t\\t\\t ,'time' : None\\n\\t\\t\\t\\t\\t ,'ndim' : None}\\n\\n\\tdtree['misc'] = {'verbose': None, 'working precision': None}\\n\\tdtree['mpi'] = {'split': \\n\\t\\t\\t\\t\\t\\t\\t {'nxpr': None, 'nypr': None, 'nzpr': None}\\n\\t\\t\\t\\t\\t ,'dMpi' : None}\\t\\t \\t\\n\\tdtree['num'] = {'hlo' : None\\n\\t\\t\\t\\t\\t ,'deriv': \\n\\t\\t\\t\\t\\t\\t\\t {'order': None, 'stencil': None, 'hlo': None} \\n\\t\\t\\t\\t\\t ,'filtr': \\n\\t\\t\\t\\t \\t {'order': None, 'stencil': None, 'hlo': None,'eps': None}\\n\\t\\t\\t\\t\\t ,'tint' : \\n\\t\\t\\t\\t\\t {'tstep': None, 'cfl': None, 'itn': None}}\\n\\n\\tdtree['bc'] = {'wall': \\n\\t\\t\\t\\t\\t\\t\\t {'isoT': None, 'zeroQ': None, 'slip': None}}\\t\\n\\n\\tdtree['usr'] = None\\n\\tdtree['ios'] = None\\n\\t\\n\\tfrom rhsinfo import dim, stencil, order, coefficients, varname, varsolved, varstored, varbc, wp,hlo_rhs\\n\\n\\tdtree['eqns']['qvec']['solved'] = []\\n\\tdtree['eqns']['qvec']['stored'] = []\\n\\tdtree['eqns']['qvec']['bcs'] = {'face':{'i' :[],'j' :[],'k' :[]},\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t 'edge':{'ij':[],'jk':[],'ik':[]}}\\n\\n\\tfor v in varsolved:\\n\\t\\tdtree['eqns']['qvec']['solved'].append([v,varname[v]])\\t\\n\\n\\tfor v in varstored:\\n\\t\\tdtree['eqns']['qvec']['stored'].append([v,varstored[v]['ind']])\\n\\n\\tfor v in varbc:\\n\\t\\tfor bcloc in ['face','edge']:\\n\\t\\t\\tif bcloc in varbc[v]:\\n\\t\\t\\t\\tloctype = ''.join(sorted(varbc[v][bcloc].replace('1','').replace('max','')))\\n\\t\\t\\t\\tdtree['eqns']['qvec']['bcs'][bcloc][loctype].append([v,varbc[v]['ind']])\\n\\n\\n\\tdtree['eqns']['coeff'] = []\\n\\tfor v in coefficients:\\n\\t\\tdtree['eqns']['coeff'].append([v,coefficients[v]])\\t\\n\\n\\tdtree['eqns']['qvec']['nvars'] = len(varname)#+len(dtree['eqns']['qvec']['stored'])\\t\\n\\tdtree['num']['deriv']['stencil'] = stencil\\n\\tdtree['num']['deriv']['hlo'] = hlo_rhs #int((stencil-1)/2)\\n\\tdtree['num']['deriv']['order'] = order\\n\\t\\n\\t# if dtree['num']['filtr']['hlo'] != None:\\n\\t# \\tdtree['num']['hlo'] = max(dtree['num']['deriv']['hlo'],dtree['num']['filtr']['hlo'])\\n\\t# else:\\n\\t# \\tdtree['num']['hlo'] = dtree['num']['deriv']['hlo']\\t\\n \\n\\tdtree['num']['hlo'] = hlo_rhs\\t\\t\\t\\n\\t\\n\\tdtree['eqns']['ndim'] = dim\\n\\tdtree['misc']['working precision'] = wp\\n\\tdtree['misc']['verbose'] = True\\n\\n\\t# dtree['libs']['cache blocking'] = [256,2,6] # good for 11 pts 3D, div. forme of N.S.\\n\\t\\n\\tdtree['libs']['cache blocking'] = [2560000,2,6]\\n\\n\\t# recover BCs info:\\n\\t\\n\\ttry:\\n\\t from rhsinfo import bc_info\\n\\texcept: \\n\\t\\tbc_info = [{},{}]\\n\\n\\tdtree['bc']\\t = {'allbc':bc_info[1],'mybc':[]} # OVERWRITE predefined 'bc' key.\\n\\n\\treturn dtree\",\n \"def distanceToTree_deprec(self,taxonomic_forest,update_inner_attributes=True):\\n #Thankfully the distance is symmetric by definition so order doesn't matter.\\n Jacobian = []\\n logger.warning('[FOR DEVELOPER] This is not optimized. Should be necessary to use attr_t1 and attr_t2 with name_id (integer)')\\n for i in settings.TAXONOMIC_TREE_KEYS:\\n F1 = []\\n for j in settings.TAXONOMIC_TREE_KEYS:\\n d = self.forest[i].robinson_foulds(taxonomic_forest[j],unrooted_trees=True)[0]\\n F1.append(float(d))\\n Jacobian.append(F1)\\n if update_inner_attributes:\\n self.JacobiM = np.matrix(Jacobian)\\n self.Jacobi = np.linalg.det(self.JacobiM)\\n return np.matrix(Jacobian)\",\n \"def implement_tree(X, y, labels, k, alpha_range, criterion, d):\\r\\n\\r\\n kf = KFold(n_splits=k)\\r\\n min_avg_error = math.inf\\r\\n best_alpha = None\\r\\n avg_time_build_tree = 0\\r\\n for alpha in alpha_range:\\r\\n total_error = 0\\r\\n for train_idx, test_idx in kf.split(X):\\r\\n X_train, X_test = X[train_idx], X[test_idx]\\r\\n y_train, y_test = y[train_idx], y[test_idx]\\r\\n train = Tree(criterion, X_train, y_train, labels)\\r\\n test = Tree(criterion, X_test, y_test, labels)\\r\\n start = time.time()\\r\\n train_tree = train.build_tree(train.root, train.X, train.y, 0, d, train.labels, train.root.X_idx, None)\\r\\n test_all(train)\\r\\n end = time.time()\\r\\n train.is_valid(train_tree)\\r\\n period = end - start\\r\\n avg_time_build_tree += period\\r\\n pruned_tree, pruned_alpha, error = train.prune_tree(train_tree, alpha, test.X, test.y, test.labels)\\r\\n total_error += error\\r\\n avg_error = total_error / k\\r\\n if avg_error < min_avg_error:\\r\\n min_avg_error = avg_error\\r\\n best_alpha = alpha\\r\\n avg_time_build_tree = avg_time_build_tree / (k*len(alpha_range))\\r\\n\\r\\n return min_avg_error, best_alpha, avg_time_build_tree\",\n \"def discretize(self,dt):\\n self._skip_deriv = True\\n res = self.discretize_state(dt)\\n self._skip_deriv = False\\n n = self.geodesic.extrinsicDimension()\\n return GeodesicTrajectory(self.geodesic,res.times,[m[:n] for m in res.milestones])\",\n \"def set_kd_tree(self, reset=False):\\n if reset or self.tree is None:\\n isoxy = np.column_stack((np.sum(self.meshx[self.ikle2],\\n axis=1)/3.0,\\n np.sum(self.meshy[self.ikle2],\\n axis=1)/3.0))\\n self.tree = cKDTree(isoxy)\",\n \"def problem3(self, s):\\n\\n points = 0\\n\\n # Test KDTNode.__init__ (can only hold np.ndarrays; 2 points)\\n try:\\n s.KDTNode(\\\"This is not a numpy array\\\")\\n self.feedback += \\\"\\\\n\\\\tKDTNode(x) failed to raise a TypeError \\\"\\n self.feedback += \\\"for x not a numpy array (np.ndarray)\\\"\\n except:\\n points += 2\\n\\n # Test KDTNode.__sub__ (euclidean distance; 2 points)\\n x = np.random.random(10); y = np.random.random(10)\\n A = KDTNode(x); B = KDTNode(y)\\n C = s.KDTNode(x); D = s.KDTNode(y)\\n points += 2*self.numTest(A-B, C-D, \\\"\\\\n\\\\tKDTNode.__sub__ failed\\\")\\n\\n # Test KDTNode.__eq__ (1 Point)\\n D = s.KDTNode(1.5*x)\\n if not (C == D):\\n points += 1\\n else:\\n self.feedback += \\\"\\\\n\\\\tKDTNode.__eq__ failed on nonequal\\\"\\n\\n # Test KDTNode.__lt__ and KDTNode.__gt__ (5 points)\\n x = s.KDTNode(np.array([3,1,0,5], dtype=np.int)); x.axis = 0\\n y = s.KDTNode(np.array([1,2,4,3], dtype=np.int)); y.axis = 1\\n if x < y: points += 1\\n else: self.feedback += \\\"\\\\n\\\\tKDTNode.__lt__ failed\\\"\\n if y < x: points += 1\\n else: self.feedback += \\\"\\\\n\\\\tKDTNode.__lt__ failed\\\"\\n\\n x.axis = 2; y.axis = 3\\n if x > y: points += 1\\n else: self.feedback += \\\"\\\\n\\\\tKDTNode.__gt__ failed\\\"\\n if y > x: points += 2\\n else: self.feedback += \\\"\\\\n\\\\tKDTNode.__gt__ failed\\\"\\n\\n return points\",\n \"def get_featured_tree(self):\\n\\n for t in self.tree.get_terminals():\\n t.sample_series = self.feature_table[t.name]\\n self.feature_tree = self.recursion_tree(self.tree.root)\\n for clade in self.feature_tree.find_clades(order='level'):\\n clade.depth = 1+len(self.feature_tree.get_path(clade))\\n \\n #i = 0\\n #for clade in self.feature_tree.find_clades(order='level'):\\n # clade.ID_num = i \\n #clade.abu = np.mean(clade.sample_series.values)\\n #clade.domain_otu = clade.sample_series.idxmax()\",\n \"def test_nti(self):\\r\\n # using the input of the distance matrix generated from this tree\\r\\n # '(((sp1:.06,sp2:.1)A:.031,(sp3:.001,sp4:.01)B:.2)AB:.4,((sp5:.03,sp6:.02)C:.13,(sp7:.01,sp8:.005)D:.1)CD:.3)root;'\\r\\n # using this tree and this clumping we get almost exact agreement\\r\\n # between phylocom and relatedness.py\\r\\n distmat = array(\\r\\n [[0., 0.16, 0.292, 0.301, 0.951, 0.941, 0.901, 0.896],\\r\\n [0.16,\\r\\n 0.,\\r\\n 0.332,\\r\\n 0.341,\\r\\n 0.991,\\r\\n 0.981,\\r\\n 0.941,\\r\\n 0.936],\\r\\n [0.292,\\r\\n 0.332,\\r\\n 0.,\\r\\n 0.011,\\r\\n 1.061,\\r\\n 1.051,\\r\\n 1.011,\\r\\n 1.006],\\r\\n [0.301,\\r\\n 0.341,\\r\\n 0.011,\\r\\n 0.,\\r\\n 1.07,\\r\\n 1.06,\\r\\n 1.02,\\r\\n 1.015],\\r\\n [0.951,\\r\\n 0.991,\\r\\n 1.061,\\r\\n 1.07,\\r\\n 0.,\\r\\n 0.05,\\r\\n 0.27,\\r\\n 0.265],\\r\\n [0.941,\\r\\n 0.981,\\r\\n 1.051,\\r\\n 1.06,\\r\\n 0.05,\\r\\n 0.,\\r\\n 0.26,\\r\\n 0.255],\\r\\n [0.901,\\r\\n 0.941,\\r\\n 1.011,\\r\\n 1.02,\\r\\n 0.27,\\r\\n 0.26,\\r\\n 0.,\\r\\n 0.015],\\r\\n [0.896, 0.936, 1.006, 1.015, 0.265, 0.255, 0.015, 0.]])\\r\\n\\r\\n seed(0)\\r\\n obs_nti = nti(distmat, list(arange(8)), [0, 1, 3, 6], 1000)\\r\\n assert_almost_equal(-1.2046544711672049, obs_nti)\",\n \"def copyAndCleanTree (self):\\n\\t\\t# TODO: Need to do several things here:\\n\\t\\t# - NoNames\\n\\t\\t# - copy support scores to internal branch names\\n\\n\\t\\t## Main:\\n\\t\\t# Copy the tree so as not to damage original\\n\\t\\tete_tree = deepcopy (self.data)\\n\\n\\t\\t# set root branch to zero, make change later\\n\\t\\tete_tree.dist = 0.0\\n\\n\\t\\t# find max / min branchlength for diagnostic purposes\\n\\t\\t# doesn't use negative or zero branch lengths\\n\\t\\t# Also clean names\\n\\t\\tmax_bl = None\\n\\t\\tmin_bl = None\\n\\t\\tfor n in ete_tree.traverse (\\\"postorder\\\"):\\n\\t\\t\\tif (0.0 < n.dist):\\n\\t\\t\\t\\tif (max_bl is None) or (max_bl < n.dist):\\n\\t\\t\\t\\t\\tmax_bl = n.dist\\n\\t\\t\\t\\tif (min_bl is None) or (n.dist < min_bl):\\n\\t\\t\\t\\t\\tmin_bl = n.dist\\n\\t\\t\\tclean_name = n.name.strip()\\n\\t\\t\\tif (clean_name[0] == \\\"'\\\") and (clean_name[-1] == \\\"'\\\"):\\n\\t\\t\\t\\tclean_name = clean_name[1:-1]\\n\\t\\t\\tn.name = clean_name\\n\\n\\t\\t# set all branches to be at least 1/100 of the largest or 1/10 the\\n\\t\\t# smallest, whichever is larger\\n\\t\\tdefault_bl = max (max_bl / 100, min_bl/10)\\n\\t\\tfor n in ete_tree.traverse (\\\"postorder\\\"):\\n\\t\\t\\tif (n.dist <= 0.0):\\n\\t\\t\\t\\tn.dist = default_bl\\n\\n\\t\\t# get support values on tree by setting supprt as name\\n\\t\\tfor n in ete_tree.traverse (\\\"postorder\\\"):\\n\\t\\t\\t# if an internal node\\n\\t\\t\\tif (not n.is_leaf()):\\n\\t\\t\\t\\tn.name = config.SUPPORT_FMT % n.support\\t\\n\\n\\t\\t# very hacky - calc appropriate scale bar size and stick on root\\n\\t\\tmagn = int (floor (log10 (max_bl)))\\n\\t\\tscale_size = 10**magn\\n\\t\\tete_tree.scale_size = scale_size\\n\\n\\t\\t## Postcondtions & return:int ( floor ( log10 (x)))\\n\\t\\treturn ete_tree\",\n \"def get_kd(model, X_train, Y_train, X_test, X_test_noisy, X_test_adv, dataset='mnist'):\\r\\n # Get deep feature representations\\r\\n print('Getting deep feature representations...')\\r\\n X_train_features = get_deep_representations(model, X_train,\\r\\n batch_size=batch_size)\\r\\n X_test_normal_features = get_deep_representations(model, X_test,\\r\\n batch_size=batch_size)\\r\\n X_test_noisy_features = get_deep_representations(model, X_test_noisy,\\r\\n batch_size=batch_size)\\r\\n X_test_adv_features = get_deep_representations(model, X_test_adv,\\r\\n batch_size=batch_size)\\r\\n # Train one KDE per class\\r\\n print('Training KDEs...')\\r\\n class_inds = {}\\r\\n for i in range(Y_train.shape[1]):\\r\\n class_inds[i] = np.where(Y_train.argmax(axis=1) == i)[0]\\r\\n kdes = {}\\r\\n warnings.warn(\\\"Using pre-set kernel bandwidths that were determined \\\"\\r\\n \\\"optimal for the specific CNN models of the paper. If you've \\\"\\r\\n \\\"changed your model, you'll need to re-optimize the \\\"\\r\\n \\\"bandwidth.\\\")\\r\\n print('bandwidth %.4f for %s' % (BANDWIDTHS[dataset], dataset))\\r\\n for i in range(Y_train.shape[1]):\\r\\n kdes[i] = KernelDensity(kernel='gaussian',\\r\\n bandwidth=BANDWIDTHS[dataset]) \\\\\\r\\n .fit(X_train_features[class_inds[i]])\\r\\n # Get model predictions\\r\\n print('Computing model predictions...')\\r\\n preds_test_normal = model.predict_classes(X_test, verbose=0,\\r\\n batch_size=batch_size)\\r\\n preds_test_noisy = model.predict_classes(X_test_noisy, verbose=0,\\r\\n batch_size=batch_size)\\r\\n preds_test_adv = model.predict_classes(X_test_adv, verbose=0,\\r\\n batch_size=batch_size)\\r\\n # Get density estimates\\r\\n print('computing densities...')\\r\\n densities_normal = score_samples(\\r\\n kdes,\\r\\n X_test_normal_features,\\r\\n preds_test_normal\\r\\n )\\r\\n densities_noisy = score_samples(\\r\\n kdes,\\r\\n X_test_noisy_features,\\r\\n preds_test_noisy\\r\\n )\\r\\n densities_adv = score_samples(\\r\\n kdes,\\r\\n X_test_adv_features,\\r\\n preds_test_adv\\r\\n )\\r\\n\\r\\n print(\\\"densities_normal:\\\", densities_normal.shape)\\r\\n print(\\\"densities_adv:\\\", densities_adv.shape)\\r\\n print(\\\"densities_noisy:\\\", densities_noisy.shape)\\r\\n\\r\\n ## skip the normalization, you may want to try different normalizations later\\r\\n ## so at this step, just save the raw values\\r\\n # densities_normal_z, densities_adv_z, densities_noisy_z = normalize(\\r\\n # densities_normal,\\r\\n # densities_adv,\\r\\n # densities_noisy\\r\\n # )\\r\\n\\r\\n densities_pos = densities_adv\\r\\n densities_neg = np.concatenate((densities_normal, densities_noisy))\\r\\n artifacts, labels = merge_and_generate_labels(densities_pos, densities_neg)\\r\\n\\r\\n return artifacts, labels\",\n \"def build(self, dist_matrix, class_map, cluster_naming_function):\\n # Update attributes\\n self.orig_dist_matrix = dist_matrix \\n self.class_map = class_map \\n self.work_dist_matrix = dist_matrix\\n\\n # Get number of elements\\n n = dist_matrix.shape[0]\\n\\n if PROGRESS:\\n print 'Starting tree build now!'\\n\\n # Loop through n-3 elements & add nodes in tree\\n for i in range(n - 3):\\n\\n if DEBUG:\\n print 'Distance Matrix'\\n pprint(self.work_dist_matrix)\\n print\\n\\n # Calculate q_matrix matrix from distances\\n q_matrix = _calculate_q_matrix(self.work_dist_matrix)\\n \\n if DEBUG:\\n print 'Q matrix:'\\n pprint(q_matrix)\\n print\\n\\n # Find pair of elements (i,j) where q_matrix(i,j) has the lowest value\\n (min_col, min_row) = _find_min_pair(q_matrix)\\n\\n # Add nodes i,j, and cluster node of i and j to this tree\\n # And update working distance matrix accordingly\\n new_cluster_name = cluster_naming_function(min_row, min_col, self.cluster_map)\\n self.cluster_leaves(min_row, min_col, new_cluster_name) \\n\\n if DEBUG:\\n print 'Tree:'\\n pprint(nx.clustering(self.tree))\\n pprint(self.cluster_dictionary)\\n print '\\\\n\\\\n'\\n \\n # View graph after each step for debugging\\n if VIEW_ALL:\\n labels = {i[0]: i[0]+'/'+i[1]['c'] for i in njt.tree.nodes(data=True)}\\n layout = nx.spring_layout(njt.tree)\\n nx.draw_networkx(njt.tree, pos=layout, with_labels=True, labels=labels) #class labels\\n plt.show()\\n\\n if PROGRESS:\\n print str(i + 1) + \\\" down, \\\" + str(n-i-4) + \\\" to go...\\\"\\n \\n # Add remaining branch lengths and nodes from working distance matrix to this tree \\n previous_cluster = new_cluster_name\\n mid_edge_length = 0.5 * (self.work_dist_matrix.iat[0, 1]\\n + self.work_dist_matrix.iat[0, 2]\\n - self.work_dist_matrix.iat[1, 2])\\n (node1, node2) = (self.work_dist_matrix.columns[0], self.work_dist_matrix.columns[1])\\n new_cluster = cluster_naming_function(node1, node2, self.cluster_map)\\n self.cluster_leaves(node1, node2, new_cluster)\\n # Viz only scales based on a weight attribute, so we set that as the length\\n self.tree.add_edge(previous_cluster, new_cluster, length=mid_edge_length, weight=mid_edge_length)\\n\\n if DEBUG:\\n print 'Final tree:'\\n pprint(nx.clustering(self.tree))\\n pprint(self.cluster_dictionary)\",\n \"def _CalculateDissimilarities(self):\\n print 'Calculating dissimilarities'\\n num_bins = len(self._mix_bins)\\n\\n for left_cluster_id, left_bin_collection in (self\\n ._left_bin_collection_by_cluster_id.iteritems()):\\n for right_cluster_id, right_bin_collection in (self\\n ._right_bin_collection_by_cluster_id.iteritems()):\\n # This operation can be easily parallelized via multiprocessing.\\n d = self._CalculateDissimilarityBetweenClusters(\\n left_cluster_id, left_bin_collection, right_cluster_id,\\n right_bin_collection)\\n print 'Left cluster: %s, Right cluster: %s, dissimilarity: %s' % (\\n d.left_cluster_id, d.right_cluster_id, d.dissimilarity_score)\\n self._dissimilarities.append(d)\\n\\n print 'Dissimilarities are calculated'\",\n \"def decision_tree(df, dt_dict, curr_node,\\r\\n prev_attr = None, align_dir = None,\\r\\n depth = -1, no_data = False,\\r\\n ensemble = None):\\r\\n \\r\\n class_count = get_class_count(df)\\r\\n # get the class label counts for the given dataframe\\r\\n leaf_node_bool = check_leaf_node(df)\\r\\n # this function helps to check if we have a leaf node\\r\\n if leaf_node_bool:\\r\\n # if its leaf node\\r\\n curr_node[align_dir] = df['class'].values[0]\\r\\n # assign the leaf node value\\r\\n elif no_data:\\r\\n # if we are out of data points\\r\\n class_counts = df['class'].value_counts()\\r\\n # get the class counts\\r\\n curr_node[align_dir] = np.argmax(class_counts)\\r\\n # assign the majority class of prev node\\r\\n else:\\r\\n entropy_values_series = impurity.entropy_calc(df, ensemble = ensemble)\\r\\n # calculate the entropy values for each feature\\r\\n info_gain_dict = {}\\r\\n # empty dict for information gain\\r\\n for feature in entropy_values_series.index:\\r\\n # iterate over each features\\r\\n impurity.information_gain_calc(df, feature, info_gain_dict)\\r\\n # function call for information gain calculation\\r\\n for f in entropy_values_series.index:\\r\\n # iterate over each feature\\r\\n information_gain = entropy_values_series[f] - info_gain_dict[f][1]\\r\\n # calculation of information gain\\r\\n info_gain_dict[f] = (info_gain_dict[f][0], information_gain)\\r\\n # update the information gain dict\\r\\n best_feature = sorted(info_gain_dict, key = lambda x: info_gain_dict[x][1])[-1]\\r\\n # get the best feature on which to be splitted.\\r\\n #print(best_feature)\\r\\n node_value = (best_feature, info_gain_dict[best_feature], class_count[0],\\r\\n class_count[1])\\r\\n # get the node value\\r\\n \\r\\n if not leaf_node_bool and align_dir:\\r\\n # growing the tree\\r\\n if depth == 0:\\r\\n if node_value[2] > node_value[3]:\\r\\n node_value = 0\\r\\n else:\\r\\n node_value = 1\\r\\n curr_node[align_dir] = node_value\\r\\n return 0\\r\\n else:\\r\\n curr_node[align_dir] = {node_value:{}}\\r\\n curr_node = curr_node[align_dir][node_value]\\r\\n else:\\r\\n dt_dict[node_value] = {}\\r\\n curr_node = dt_dict[node_value]\\r\\n \\r\\n data_split(df, best_feature, info_gain_dict, \\r\\n dt_dict, curr_node, depth)\\r\\n # function call for data split\\r\",\n \"def dijkstra_tree(self, s):\\n l = []\\n dist = {}\\n prev = {}\\n discovered = {}\\n g = Graph(attr={DIRECTED: True})\\n g.add_vertex(vertex.Vertex(s, {\\\"WEIGHT\\\": 0}))\\n for v in self.get_vertices():\\n dist[v] = float('inf')\\n prev[v] = None\\n discovered[v] = False\\n dist[s] = 0\\n l.append((s, dist[s]))\\n while len(l) != 0:\\n u = min(l, key=lambda x: x[1])\\n l.remove(u)\\n u = u[0]\\n discovered[u] = True\\n for v in self.get_adjacent_vertices_by_vertex(u):\\n if not discovered[v]:\\n alt = dist[u] + self.get_edge((u, v)).attr[\\\"WEIGHT\\\"]\\n if alt < dist[v]:\\n dist[v] = alt\\n prev[v] = u\\n l.append((v, dist[v]))\\n g.add_vertex(vertex.Vertex(v, {\\\"WEIGHT\\\": dist[v]}))\\n g.add_edge(edge.Edge(u, v, {\\\"WEIGHT\\\": dist[v]}))\\n\\n return g\",\n \"def compute_tree(self,\\n verbose=True):\\n\\n # Tree structure in format {leaf_id: node()}\\n self.tree = {}\\n # A path is list of integers in (-1, 0, 1) indicating the set of\\n # decisions to take through the tree (lower, null, higher)\\n # based on the specified labels and cutoff of the nodes.\\n paths = [[]]\\n path_idx = 0\\n start_time = time()\\n\\n # Each path will point to a leaf that is not yet in the tree.\\n while path_idx < len(paths):\\n if verbose:\\n string = f'{path_idx}/{len(paths)} ({time()-start_time:.0f} s)'\\n sys.stdout.write('\\\\r'+string[:40]+' '*(40-len(string)))\\n sys.stdout.flush()\\n path = paths[path_idx]\\n self.compute_path(path)\\n leaf = node(path_idx)\\n if self.sub_y_data.size == 0:\\n raise NameError('No data on the leaf error')\\n if len(path) < self.max_tree_depth or self.max_tree_depth <= 0:\\n cutoffs = []\\n for split_label in self.labels:\\n cutoff, value = self.best_cutoff(split_label)\\n cutoffs.append([split_label, cutoff, value])\\n cutoffs = sorted(cutoffs, key=lambda x: -x[2])\\n split_label, cutoff, value = cutoffs[0]\\n leaf.value = value\\n if value > self.value_threshold:\\n leaf.label = split_label\\n leaf.cutoff = cutoff\\n leaf.id_lower = len(paths)\\n paths.append(path+[-1])\\n leaf.id_higher = len(paths)\\n paths.append(path+[1])\\n if np.isnan(self.sub_split_data[split_label]).any():\\n leaf.id_null = len(paths)\\n paths.append(path+[0])\\n else:\\n leaf.is_leaf = True\\n ys_with = self.sub_y_data[self.sub_bin_data]\\n ys_without = self.sub_y_data[self.sub_bin_data]\\n leaf.n_data_with = len(ys_with)\\n leaf.n_data_without = len(ys_without)\\n if ys_with.size == 0 or ys_without.size == 0:\\n leaf.effect = 0\\n else:\\n leaf.effect = ys_with.mean() - ys_without.mean()\\n self.tree[leaf.id] = leaf\\n path_idx += 1\\n\\n if verbose:\\n string = f'{path_idx}/{len(paths)} ({time()-start_time:.0f} s)'\\n sys.stdout.write('\\\\r'+string[:40]+' '*(40-len(string)))\\n sys.stdout.flush()\\n print()\",\n \"def classify_treeNN(self, query_name):\\n # 1) Find set of closest neighbors & their class names\\n # ie. leaves with at most neighborhood_max_edges edges between itself \\n # and the query node\\n neighborhood_classes = self.getNeighborhoodClasses(query_name)\\n print \\\"neighborhood \\\" , neighborhood_classes\\n\\n # 2) Find aggregate similarity score for each class\\n # Use minimum operator for distance measure & maximum for similarity measure\\n # EQ 6.1 in Chapt 6, Busa-Fekete et al\\n R = {}\\n for c,ids in neighborhood_classes.iteritems():\\n sim_score = min([nx.shortest_path_length(self.tree, source=query_name, \\n target=i, weight='length') for i in ids])\\n if DEBUG: print \\\"\\\\tCLASS / SIM_SCORE: \\\", c, sim_score\\n R[sim_score] = c # distance measure\\n\\n min_score = min(R.keys())\\n if DEBUG: print \\\"MIN_SCORE: \\\", min_score\\n\\n return R[min_score] #class of minimum distance score\",\n \"def build_kruskal_concept_tree(csvsegstack, outputdir):\\n tree_struct = Tree()\\n nodes, slidenames = get_leaves_from_segment_stack(csvsegstack)\\n for nodeid, desc in nodes.items():\\n tree_struct.descriptor[nodeid] = desc\\n tree_struct.slides[nodeid] = set()\\n tree_struct.slides[nodeid].add(slidenames[nodeid])\\n edges = knn_edges(nodes)\\n krusk = WeightedKruskal()\\n last_cpt_id = max(nodes.keys()) + 1\\n for kedge in krusk.spanning_tree(edges):\\n last_cpt = get_root(tree_struct, last_cpt_id)\\n n1, n2, w = kedge\\n rn1 = get_root(tree_struct, n1)\\n rn2 = get_root(tree_struct, n2)\\n tree_struct.parents[rn1] = last_cpt\\n tree_struct.parents[rn2] = last_cpt\\n tree_struct.children[last_cpt] = (rn1, rn2)\\n tree_struct.weights[last_cpt] = w\\n tree_struct.population[last_cpt] = tree_struct.population[rn1] + tree_struct.population[rn2]\\n tree_struct.descriptor[last_cpt] = (tree_struct.population[rn1] * tree_struct.descriptor[rn1])\\\\\\n + (tree_struct.descriptor[rn2] * tree_struct.population[rn2])\\n tree_struct.descriptor[last_cpt] /= tree_struct.population[last_cpt]\\n tree_struct.slides[last_cpt] = tree_struct.slides[rn1] | tree_struct.slides[rn2]\\n last_cpt_id += 1\\n\\n with open(os.path.join(outputdir, \\\"full_tree.p\\\"), \\\"wb\\\") as f:\\n pickle.dump(tree_struct, f)\\n return tree_struct\",\n \"def get_kdtree(x: np.ndarray, y: np.ndarray):\\n combined_x_y_arrays = np.dstack([y.ravel(), x.ravel()])[0]\\n\\n tree = KDTree(combined_x_y_arrays)\\n\\n return tree\",\n \"def main():\\n\\n ''' Reading the training data file '''\\n original_training_data = pd.read_csv(\\\"DT_Data_CakeVsMuffin_v012_TRAIN.csv\\\")\\n\\n ''' Storing the final decision tree '''\\n final_tree = decision_tree(original_training_data,0)\\n\\n ''' Printing the final decision tree '''\\n print(\\\"This is the resulting decision tree: \\\\n\\\")\\n print(final_tree)\\n\\n ''' Iterating through the dictionary by using the key values '''\\n for key in final_tree.keys():\\n ''' Parent = Flour <= 5.1636'''\\n parent = key\\n ''' left_child = [{'Oils <= 3.1265': [{'Flour <= 2.7291': [{'Proteins <= 2.6527': ['Muffin', 'CupCake']}, 'Muffin']}, 'CupCake']}'''\\n left_child = final_tree[parent][0]\\n ''' right_child = {'Oils <= 7.7793': ['Muffin', {'Flour <= 8.2225': ['CupCake', 'Muffin']}]}]'''\\n right_child = final_tree[parent][1]\\n\\n ''' Writing a file which generates code for classification '''\\n file = open('HW06_Parchand_Nihal_Classifier.py','w+')\\n file.write(\\\"'''Importing libraries''' \\\"\\n \\\"\\\\n\\\\nimport pandas as pd \\\\n\\\\ndef main():\\\"\\n \\\"\\\\n\\\\tdata_df = pd.read_csv('DT_Data_CakeVsMuffin_v012_TEST.csv')\\\"\\n \\\"\\\\n\\\\tresult = []\\\"\\n \\\"\\\\n\\\\tfor row in range(0,len(data_df)):\\\"\\n \\\"\\\\n\\\\t\\\\tFlour = data_df.loc[row][0]\\\"\\n \\\"\\\\n\\\\t\\\\tSugar = data_df.loc[row][1]\\\"\\n \\\"\\\\n\\\\t\\\\tOils = data_df.loc[row][2]\\\"\\n \\\"\\\\n\\\\t\\\\tProteins = data_df.loc[row][3]\\\"\\n \\\"\\\\n\\\\t\\\\tif {}:\\\\n\\\".format(parent))\\n\\n ''' Iterating through the left_tree '''\\n for key in left_child.keys():\\n file.write(\\\"\\\\t\\\\t\\\\tif {}:\\\\n\\\".format(key))\\n\\n ''' Iterating through the inner left_tree '''\\n for inner_key in left_child[key][0].keys():\\n file.write(\\\"\\\\t\\\\t\\\\t\\\\tif {}:\\\\n\\\".format(inner_key))\\n\\n for inner_inner_key in ((left_child[key][0])[inner_key])[0]:\\n file.write(\\\"\\\\t\\\\t\\\\t\\\\t\\\\tif {}:\\\\n\\\".format(inner_inner_key))\\n file.write(\\\"\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tresult.append(0)\\\\n\\\")\\n file.write(\\\"\\\\t\\\\t\\\\t\\\\t\\\\telse:\\\\n\\\".format(inner_inner_key))\\n file.write(\\\"\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tresult.append(1)\\\\n\\\")\\n\\n file.write(\\\"\\\\t\\\\t\\\\t\\\\telse:\\\\n\\\")\\n file.write(\\\"\\\\t\\\\t\\\\t\\\\t\\\\tresult.append(0)\\\\n\\\")\\n file.write(\\\"\\\\t\\\\t\\\\telse:\\\\n\\\")\\n file.write(\\\"\\\\t\\\\t\\\\t\\\\tresult.append(1)\\\\n\\\")\\n file.write(\\\"\\\\t\\\\telse:\\\\n\\\")\\n\\n ''' Iterating through the right_tree '''\\n for key in right_child.keys():\\n file.write(\\\"\\\\t\\\\t\\\\tif {}:\\\\n\\\".format(key))\\n file.write(\\\"\\\\t\\\\t\\\\t\\\\tresult.append(0)\\\\n\\\")\\n for inner_key in right_child[key][1].keys():\\n file.write(\\\"\\\\t\\\\t\\\\telif {}:\\\\n\\\".format(inner_key))\\n file.write(\\\"\\\\t\\\\t\\\\t\\\\tresult.append(1)\\\\n\\\")\\n file.write(\\\"\\\\t\\\\t\\\\telse:\\\\n\\\")\\n file.write(\\\"\\\\t\\\\t\\\\t\\\\tresult.append(0)\\\\n\\\\n\\\")\\n\\n ''' Writing the results of classifier to a csv file '''\\n file.write(\\n \\\"\\\\twith open('HW06_Parchand_Nihal_MyClassifications.csv', 'w+') as file2:\\\\n\\\"\\n \\\"\\\\t\\\\tfor value in result:\\\\n\\\"\\n \\\"\\\\t\\\\t\\\\tfile2.write(str(value))\\\\n\\\"\\n \\\"\\\\t\\\\t\\\\tfile2.write('\\\\\\\\n')\\\\n\\\\n\\\"\\n \\\"main()\\\")\",\n \"def build_tree(self, df=None, tree=None, depth=0):\\n if df is None:\\n df = self.df\\n target = self.target\\n\\n node = self.get_lowest_entropy_feature(df)\\n if not node:\\n print(\\\"Pure solution not possible in current branch...\\\")\\n return tree\\n variables = df[node].unique()\\n\\n if tree is None: \\n tree = {}\\n tree[node] = {}\\n\\n for value in variables:\\n subtable = df[df[node] == value].reset_index(drop=True)\\n inner_variables, counts = np.unique(subtable[target], return_counts=True) \\n \\n if len(counts) == 1:\\n tree[node][value] = inner_variables[0] \\n elif depth >= self.max_depth:\\n return tree \\n else:\\n depth += 1 \\n tree[node][value] = self.build_tree(df=subtable, depth=depth)\\n \\n return tree\",\n \"def classify_treeInsert(self, query_name, cluster_naming_function):\\n classes = Set(self.class_map.values())\\n\\n full_dist_matrix = self.orig_dist_matrix.drop(query_name)\\n full_dist_matrix = full_dist_matrix.drop(query_name, axis=1)\\n\\n if PROGRESS: print '\\\\nStarting treeInsert!'\\n \\n #1] Build a tree for each class\\n class_trees = {}\\n all_elements = full_dist_matrix.columns.values.tolist()\\n classes_done = 0\\n num_of_classes = len(classes)\\n for c in classes:\\n\\n #1a. Construct a mini distance matrix for the current class\\n nonclass_members = [i for i in all_elements if self.class_map[i] != c]\\n class_dist_matrix = full_dist_matrix.drop(nonclass_members)\\n class_dist_matrix = class_dist_matrix.drop(nonclass_members, axis=1)\\n\\n #1b. Build class tree\\n if PROGRESS: print 'Building class tree for ' + c\\n\\n class_njt = NJTree()\\n class_njt.build(class_dist_matrix, self.class_map, myClusterNaming)\\n class_trees[c] = class_njt\\n classes_done = classes_done + 1\\n\\n if PROGRESS:\\n print str(classes_done) + \\\" classes down, \\\" + str(num_of_classes - classes_done) \\n + \\\" to go...\\\"\\n\\n #2] Determine the insertion cost of each tree\\n class_insert_costs = {}\\n for c,class_tree in class_trees.iteritems():\\n\\n #2a. Find insertion cost of each leaf in the tree\\n leaves = [i for i in class_tree.tree.nodes() if class_tree.isLeaf(i)] \\n leaf_insert_costs = {}\\n for leaf_i in leaves:\\n\\n parent_i = class_tree.tree.neighbors(leaf_i)[0] \\n cons = ({'type': 'eq',\\n 'fun': lambda x: x[0] + x[1] - nx.shortest_path_length(class_tree.tree, \\n source=parent_i, target=leaf_i, weight='length')})\\n optimum_leaf_insert_cost = optimize.minimize(_leaf_insertion_cost, [0,0,0], \\n args=(class_tree.orig_dist_matrix, leaf_i, leaves, query_name, self), method='SLSQP', \\n constraints=cons)\\n\\n if DEBUG:\\n print \\\"Optimum cost for \\\", leaf_i, \\\" : \\\", optimum_leaf_insert_cost.x[0]\\n\\n leaf_insert_costs[leaf_i] = optimum_leaf_insert_cost.x[0]\\n \\n class_insert_costs[c] = min(list(leaf_insert_costs.values()))\\n\\n #3] Output the class name of tree with minimum insertion cost\\n min_insert_cost = min(list(class_insert_costs.values()))\\n for c,cost in class_insert_costs.iteritems():\\n if cost==min_insert_cost:\\n return c\\n break\",\n \"def ndcg(self, gt, pred, k):\\n k = min(k, len(pred))\\n idcg = self.idcg(k)\\n dcg_k = sum([int(pred[i] in gt) / math.log(i + 2, 2) for i in range(k)])\\n return dcg_k / idcg\",\n \"def KruskalD(self):\\n g = Graph(attr={DIRECTED: False})\\n # Create set for each v of V[G]\\n parent = []\\n rank = []\\n for v in self.get_vertices():\\n parent.append(v)\\n rank.append(0)\\n\\n # Sort edges by weight\\n q = sorted(self.edges.items(), key=lambda e: e[1].attr[\\\"WEIGHT\\\"])\\n for e in q:\\n (u, v) = e[0]\\n v1 = self.find(parent, u)\\n v2 = self.find(parent, v)\\n if v1 != v2:\\n g.add_vertex(vertex.Vertex(u))\\n g.add_vertex(vertex.Vertex(v))\\n g.add_edge(edge.Edge(u, v, {\\\"WEIGHT\\\": e[1].attr[\\\"WEIGHT\\\"]}))\\n if rank[v1] < rank[v2]:\\n parent[v1] = v2\\n rank[v2] += 1\\n else:\\n parent[v2] = v1\\n rank[v1] += 1\\n return g\",\n \"def buildTree(self):\\n self.initClusters()\\n while len(self.centroids) > 1:\\n print \\\"Iteration %d\\\" % self.labelcount\\n for i in self.centroids:\\n \\tprint self.centroids[i], i\\n \\n l1, l2 = self.closestClusters()\\n n1, n2 = self.getNumPoints(l1, l2)\\n\\n self.centroids[('Cl_%d' % self.labelcount, l1, l2)] = \\\\\\n (n1*self.centroids[l1] + n2*self.centroids[l2])/(n1 + n2)\\n \\n del self.centroids[l1]\\n del self.centroids[l2]\\n self.labelcount += 1\\n\\n\\n return ('Cl_%d' % (self.labelcount-1), l1, l2)\",\n \"def test_tdsp_dijsktra_with_timesteps_4(self):\\n input_file = \\\"/home/andy/code/python/om_task/data/test_data3.txt\\\"\\n graph_data = process_input_file(input_file)\\n\\n graphs = {}\\n\\n today = datetime.today()\\n t = time(0, 0) # input time value\\n start_time = datetime(today.year, today.month, today.day, t.hour, t.minute)\\n\\n for timestep in graph_data:\\n # print(\\\"Timestep=\\\", timestep)\\n # Create a graph for each timestep\\n graphs[timestep] = Graph()\\n for edge in graph_data.get(timestep):\\n graphs[timestep].add_edge(*edge)\\n\\n for graph in graphs:\\n print(graphs[graph].edges)\\n print(graphs[graph].weights)\\n\\n path = tdsp_dijsktra(graphs, start_time, 'A0', 'A1')\\n\\n print(path)\\n\\n self.assertEqual(['A0', 'B0', 'B1', 'A1'], path)\",\n \"def test_dijsktra(self):\\n input_file = \\\"/home/andy/code/python/om_task/data/test_data.txt\\\"\\n graph_data = process_input_file(input_file)\\n\\n graphs = {}\\n\\n for timestep in graph_data:\\n # print(\\\"Timestep=\\\", timestep)\\n # Create a graph for each timestep\\n graphs[timestep] = Graph()\\n for edge in graph_data.get(timestep):\\n graphs[timestep].add_edge(*edge)\\n\\n path = dijsktra(graphs[0], 'A0', 'B2')\\n\\n print(path)\\n\\n self.assertEqual(['A0', 'A1', 'A2', 'B2'], path)\",\n \"def decision_tree(original_training_data,call_depth):\\n\\n ''' Checking the stopping criterion. If yes then it returns the majority class (Muffin or CupCake) '''\\n if check_if_stopping_criterion_is_met(original_training_data.values) or call_depth > 10:\\n majority = classification(original_training_data)\\n return majority\\n\\n else:\\n ''' Each time we split the data and go deeper, we increment the depth of the tree '''\\n call_depth += 1\\n\\n ''' Finding the best attribute, best threshold to split data, best minimum entropy '''\\n best_split_index, best_attribute, best_threshold, best_minimum_entropy = find_best_attribute_threshold_entropy(original_training_data)\\n original_training_data_values = original_training_data.values\\n\\n best_split_values = original_training_data_values[:,best_split_index]\\n\\n less_than_threshold = original_training_data[best_split_values <= best_threshold]\\n more_than_threshold = original_training_data[best_split_values > best_threshold]\\n\\n ''' Initializing a variable called as condition which stores the format of the key for the resulting decision tree dictionary '''\\n condition = original_training_data.columns[best_split_index] + \\\" <= \\\" + str(best_threshold)\\n\\n ''' Initializing a dictionary where key is condition and value is a list. This is the basic data structure in which the\\n resulting decision tree is stored '''\\n sub_tree = {condition: []}\\n\\n ''' Calling the decision tree recursively '''\\n left_tree = decision_tree(less_than_threshold, call_depth)\\n right_tree = decision_tree(more_than_threshold, call_depth)\\n\\n ''' For removing edge cases where on either split, the resulting decision tree gives the same result '''\\n if left_tree == right_tree:\\n sub_tree = left_tree\\n else:\\n ''' Appending the smaller trees in the final decision tree '''\\n sub_tree[condition].append(left_tree)\\n sub_tree[condition].append(right_tree)\\n\\n return sub_tree\",\n \"def __init__(self, dataset: List[SEMData], file_name):\\n\\n tree = Tree(file_name)\\n\\n self.nodes = dict()\\n self.n_nodes = 0\\n\\n\\n self.nodes['N0'] = SEMTreeNode('node')\\n self.nodes['N0'].add_dist('N1', 20.8)\\n self.nodes['N0'].add_dist('N3', 20.8)\\n\\n self.nodes['N1'] = SEMTreeNode('node')\\n self.nodes['N1'].add_dist('N0', 20.8)\\n self.nodes['N1'].add_dist('N2', 33.7)\\n self.nodes['N1'].add_dist('NKL', 112.3)\\n self.nodes['N1'].add_dist('B19', 112.3)\\n\\n self.nodes['NKL'] = SEMTreeNode('leaf')\\n self.nodes['NKL'].add_dist('N1', 112.3)\\n\\n self.nodes['B19'] = SEMTreeNode('leaf')\\n self.nodes['B19'].add_dist('N1', 112.3)\\n\\n self.nodes['N2'] = SEMTreeNode('node')\\n self.nodes['N2'].add_dist('N1', 33.7)\\n self.nodes['N2'].add_dist('CD4', 78.6)\\n self.nodes['N2'].add_dist('CD8', 78.6)\\n\\n self.nodes['CD4'] = SEMTreeNode('leaf')\\n self.nodes['CD4'].add_dist('N2', 78.6)\\n\\n self.nodes['CD8'] = SEMTreeNode('leaf')\\n self.nodes['CD8'].add_dist('N2', 78.6)\\n\\n self.nodes['N3'] = SEMTreeNode('node')\\n self.nodes['N3'].add_dist('N0', 20.8)\\n self.nodes['N3'].add_dist('MON', 41.8)\\n self.nodes['N3'].add_dist('NEU', 112.3)\\n\\n self.nodes['NEU'] = SEMTreeNode('leaf')\\n self.nodes['NEU'].add_dist('N3', 112.3)\\n\\n self.nodes['MON'] = SEMTreeNode('leaf')\\n self.nodes['MON'].add_dist('N3', 41.8)\\n self.nodes['MON'].add_dist('DEN', 70.5)\\n self.nodes['MON'].add_dist('MRF', 70.5)\\n\\n self.nodes['MRF'] = SEMTreeNode('leaf')\\n self.nodes['MRF'].add_dist('MON', 70.5)\\n\\n self.nodes['DEN'] = SEMTreeNode('leaf')\\n self.nodes['DEN'].add_dist('MON', 70.5)\\n\\n\\n # self.get_nodes(tree)\\n print(tree)\\n\\n # # Compare names of datasets and leaves of the tree\\n # data_names = [data.name for data in dataset]\\n # print(data_names)\\n # for name in data_names:\\n # if name not in self.nodes.keys():\\n # raise ValueError('Dataset and Tree do not match')\",\n \"def classify(observations, tree, dataMissing=False):\\n\\n def classifyWithoutMissingData(observations, tree):\\n if tree.results != None: # leaf\\n return tree.results\\n else:\\n v = observations[tree.col]\\n branch = None\\n #if isinstance(v, int) or isinstance(v, float):\\n #if v >= tree.value: branch = tree.trueBranch\\n #else: branch = tree.falseBranch\\n #else:\\n if v == tree.value: branch = tree.trueBranch\\n else: branch = tree.falseBranch\\n return classifyWithoutMissingData(observations, branch)\\n\\n\\n def classifyWithMissingData(observations, tree):\\n if tree.results != None: # leaf\\n return tree.results\\n else:\\n v = observations[tree.col]\\n if v == None:\\n tr = classifyWithMissingData(observations, tree.trueBranch)\\n fr = classifyWithMissingData(observations, tree.falseBranch)\\n tcount = sum(tr.values())\\n fcount = sum(fr.values())\\n tw = float(tcount)/(tcount + fcount)\\n fw = float(fcount)/(tcount + fcount)\\n result = collections.defaultdict(int) # Problem description: http://blog.ludovf.net/python-collections-defaultdict/\\n for k, v in tr.items(): result[k] += v*tw\\n for k, v in fr.items(): result[k] += v*fw\\n return dict(result)\\n else:\\n branch = None\\n #if isinstance(v, int) or isinstance(v, float):\\n # if v >= tree.value: branch = tree.trueBranch\\n # else: branch = tree.falseBranch\\n #else:\\n if v == tree.value: branch = tree.trueBranch\\n else: branch = tree.falseBranch\\n return classifyWithMissingData(observations, branch)\\n\\n # function body\\n if dataMissing:\\n return classifyWithMissingData(observations, tree)\\n else:\\n return classifyWithoutMissingData(observations, tree)\",\n \"def compare_trees(tree1, tree2):\\n \\tresponse = {}\\n \\tstart_time = time.time()\\n \\ttry:\\t\\n \\t\\ttns = dendropy.TaxonNamespace() \\t\\n \\t\\n \\t\\ttree_obj1 = dendropy.Tree.get(data=tree1, schema=\\\"newick\\\",taxon_namespace=tns)\\n \\t\\ttree_obj2 = dendropy.Tree.get(data=tree2, schema=\\\"newick\\\",taxon_namespace=tns)\\n\\n \\t\\ttree_obj1.encode_bipartitions()\\n \\t\\ttree_obj2.encode_bipartitions()\\n\\n \\t\\t#-----------------------------------------------------------\\n \\t\\t#This method returns the symmetric distance between two trees. \\n \\t\\t#The symmetric distance between two trees is the sum of the number of splits found in one of the trees but not the other. \\n \\t\\t#It is common to see this statistic called the Robinson-Foulds distance\\n\\n \\t\\tareSame = True if treecompare.symmetric_difference(tree_obj1, tree_obj2) == 0 else False\\n \\t\\tstatus = 200\\n \\t\\tmessage = \\\"Success\\\"\\n \\t\\tresponse['are_same_tree'] = areSame\\n \\n \\texcept Exception, e:\\n \\t\\tif \\\"Incomplete or improperly-terminated tree statement\\\" in str(e): #invalid: \\\"((A,B),C,D));\\\" valid: ((A,B),(C,D)); \\n \\t\\t\\tmessage = \\\"NewickReaderIncompleteTreeStatementError: \\\" + str(e)\\n \\t \\t\\tstatus = 400\\n \\t\\telif \\\"Unbalanced parentheses at tree statement\\\" in str(e): #invalid: \\\"((A,B),(C,D);\\\" valid: ((A,B),(C,D)); \\n \\t\\t\\tmessage = \\\"NewickReaderMalformedStatementError: \\\"+str(e) \\n \\t \\t\\tstatus = 400\\n \\t\\telif \\\"Multiple occurrences of the same taxa\\\" in str(e): #invalid: \\\"((A,B),(C,C));\\\" valid: ((A,B),(C,D));\\n \\t\\t\\tmessage = \\\"NewickReaderDuplicateTaxonError: \\\"+str(e)\\n \\t \\t\\tstatus = 400\\n \\t\\telif \\\"Unexpected end of stream\\\" in str(e): # invalid: \\\"((A,B),(C,D))\\\" valid: ((A,B),(C,D));\\n \\t\\t\\tmessage = \\\"UnexpectedEndOfStreamError: \\\"+str(e)\\n \\t \\t\\tstatus = 400\\n \\t\\telse:\\n \\t\\t\\tmessage = \\\"Error: Failed to compare trees. \\\"+str(e)\\n \\t \\t\\tstatus = 500\\n \\t \\t\\n \\tresponse['status_code'] = status\\n \\tresponse['message'] = message\\n\\n \\tend_time = time.time()\\n \\texecution_time = end_time-start_time\\n #service result creation time\\n \\tcreation_time = datetime.datetime.now().isoformat()\\n \\tmeta_data = {'creation_time': creation_time, 'execution_time': float('{:4.2f}'.format(execution_time)), 'source_urls':[\\\"http://dendropy.org/library/treecompare.html#module-dendropy.calculate.treecompare\\\"] }\\n\\n \\tresponse['meta_data'] = meta_data\\n \\tprint response\\n \\treturn response\",\n \"def implement_SQLtree(X, y, labels, k, alpha_range, criterion, d, cur):\\r\\n\\r\\n kf = KFold(n_splits=k)\\r\\n min_avg_error = math.inf\\r\\n best_alpha = None\\r\\n avg_time_build_tree = 0\\r\\n for alpha in alpha_range:\\r\\n total_error = 0\\r\\n for train_idx, test_idx in kf.split(X):\\r\\n X_train, X_test = X[train_idx], X[test_idx]\\r\\n y_train, y_test = y[train_idx], y[test_idx]\\r\\n SQL_train = SQLTree(criterion, X_train, y_train, labels, \\\"articles\\\", cur)\\r\\n SQL_test = SQLTree(criterion, X_test, y_test, labels, \\\"articles\\\", cur)\\r\\n start = time.time()\\r\\n SQL_train_tree = SQL_train.build_tree(SQL_train.root, 0, d, SQL_train.labels, None, \\\"\\\")\\r\\n test_all(SQL_train)\\r\\n end = time.time()\\r\\n SQL_train.is_valid(SQL_train_tree)\\r\\n period = end - start\\r\\n pruned_SQLtree, pruned_alpha, error = SQL_train.prune_tree(SQL_train_tree, alpha, SQL_test.X, SQL_test.y,\\r\\n SQL_test.labels)\\r\\n total_error += error\\r\\n avg_time_build_tree += period\\r\\n avg_error = total_error / k\\r\\n if avg_error < min_avg_error:\\r\\n min_avg_error = avg_error\\r\\n best_alpha = alpha\\r\\n avg_time_build_tree = avg_time_build_tree / (k * len(alpha_range))\\r\\n\\r\\n return min_avg_error, best_alpha, avg_time_build_tree\",\n \"def tree2pwdist(tree):\\n if isinstance(tree, type(Phylo.BaseTree.Tree())):\\n N = len(tree.get_terminals())\\n names = [node.name for node in tree.get_terminals()]\\n pwdist = np.zeros((N, N))\\n for i, node1 in enumerate(tree.get_terminals()):\\n for j, node2 in enumerate(tree.get_terminals()):\\n \\\"\\\"\\\"Compute half of these and assume symmetry\\\"\\\"\\\"\\n if i==j:\\n pwdist[i, j] = 0\\n elif i<j:\\n pwdist[i, j] = tree.distance(node1, node2)\\n pwdist[j, i] = pwdist[i, j]\\n elif isinstance(tree, type(dendropy.Tree())):\\n pdm = dendropy.treecalc.PatristicDistanceMatrix(tree)\\n taxon_set = [n.taxon for n in tree.leaf_nodes()]\\n N = len(taxon_set)\\n names = [taxa.label for taxa in taxon_set]\\n pwdist = np.zeros((N, N))\\n for i, t1 in enumerate(taxon_set):\\n for j, t2 in enumerate(taxon_set):\\n \\\"\\\"\\\"Compute half of these and assume symmetry\\\"\\\"\\\"\\n if i==j:\\n pwdist[i, j] = 0\\n elif i<j:\\n pwdist[i, j] = pdm(t1, t2)\\n pwdist[j, i] = pwdist[i, j]\\n else:\\n print('Tree type does not match Phylo.BaseTree.Tree or dendropy.Tree')\\n return\\n return pd.DataFrame(pwdist, index = names, columns = names)\",\n \"def make_tree(self):\\n\\n # list [station_name]\\n visited = []\\n\\n # creates empty station object for each station and adds coordinates\\n for station in self.stations:\\n new_station = Station(station)\\n coordinates = self.stations[station].get_coordinates()\\n new_station.add_coordinates(coordinates[0], coordinates[1])\\n\\n # saves station in prims_tree dictionary\\n self.prims_tree[station] = new_station\\n\\n # choose random beginning station\\n random_station = random.choice(list(self.stations.values()))\\n\\n # sort station connections and retrieve shortest\\n station_connections = random_station.get_connections()\\n station_connections = sorted(station_connections.items(), key=operator.itemgetter(1))\\n new_connection = station_connections.pop(0)\\n new_station = new_connection[0]\\n new_time = new_connection[1]\\n\\n # retrieve empty stations from prims_tree dictionary\\n first_station = self.prims_tree[random_station.name]\\n new_station = self.prims_tree[new_station.name]\\n\\n # add shortest connection to stations\\n first_station.add_connection(new_station, new_time)\\n new_station.add_connection(first_station, new_time)\\n\\n # add stations to visited\\n visited.append(first_station.name)\\n visited.append(new_station.name)\\n\\n # runs until all stations are visited\\n while len(visited) is not len(self.prims_tree):\\n # starts as arbitrarily high number\\n min_connection_time = 9999\\n\\n # get connections of visited stations\\n for station in visited:\\n connections = self.stations[station].get_connections()\\n\\n # get time of connections\\n for connection in connections:\\n connection_time = connections[connection]\\n\\n # save smallest connection if time is smallest and station is not visited\\n if connection.name not in visited and connection_time < min_connection_time:\\n smallest_connection = self.prims_tree[connection.name]\\n smallest_connection_station = self.prims_tree[station]\\n min_connection_time = connection_time\\n else:\\n continue\\n\\n # add smallest connection to station in prims_tree dictionary\\n smallest_connection_station.add_connection(smallest_connection, min_connection_time)\\n smallest_connection.add_connection(smallest_connection_station, min_connection_time)\\n\\n # add new connection to visited list\\n visited.append(smallest_connection.name)\\n\\n return self.prims_tree\",\n \"def test_tdsp_dijsktra_with_timesteps_key_error(self):\\n input_file = \\\"/home/andy/code/python/om_task/data/input_data2.txt\\\"\\n graph_data = process_input_file(input_file)\\n\\n graphs = {}\\n\\n today = datetime.today()\\n t = time(0, 0) # input time value\\n start_time = datetime(today.year, today.month, today.day, t.hour, t.minute)\\n\\n #print(\\\"graph_data = \\\", graph_data)\\n print(\\\"graph_data[4] = \\\", graph_data[4])\\n\\n for timestep in graph_data:\\n # print(\\\"Timestep=\\\", timestep)\\n # Create a graph for each timestep\\n graphs[timestep] = Graph()\\n for edge in graph_data.get(timestep):\\n graphs[timestep].add_edge(*edge)\\n\\n for graph in graphs:\\n print(graphs[graph].edges)\\n print(graphs[graph].weights)\\n\\n print(\\\"graph[4].weights = \\\", graphs[4].weights)\\n\\n print(\\\"Start Test 1\\\")\\n path = tdsp_dijsktra(graphs, start_time, 'A0', 'E1')\\n print(\\\"Path = \\\", path)\\n self.assertEqual(['A0', 'B9', 'C0', 'D0', 'E1'], path)\",\n \"def improve_tree(tree, freq_dict):\\n # todo\",\n \"def kruskal(self):\\n travelled_distance = self.ordered_distance[0]\\n travelled_distance_list = [self.ordered_distance[0]]\\n travelled_edges = [self.ordered_links[0]]\\n travelled_nodes = [self.ordered_links[0][0], self.ordered_links[0][1]]\\n for i, j in zip(self.ordered_links[1:], self.ordered_distance[1:]):\\n G = (Graph(travelled_edges, travelled_distance_list, # noqa N806\\n directed=False))\\n if not (G.add_edge(i, j).cyclic()):\\n travelled_nodes.append(i[0])\\n travelled_nodes.append(i[1])\\n travelled_edges.append(i)\\n travelled_distance_list.append(j)\\n travelled_distance += j\\n if (G.connected() and\\n len(set(travelled_nodes)) == self.number_nodes):\\n return (set(travelled_nodes), travelled_edges,\\n travelled_distance_list, travelled_distance)\",\n \"def linkage(self):\\n self.tree = {}\\n un_linked = []\\n for i in range(len(self.leaves)):\\n leaf = self.leaves[i]\\n un_linked.append({\\n 'id': i,\\n 'x': 0,\\n 'y': 0,\\n 'value': 0,\\n 'set': leaf,\\n 'children': []\\n })\\n pass\\n while len(un_linked) > 1:\\n # for i in tqdm(range(len(un_linked))):\\n # print(\\\"Linking... {} nodes left\\\".format(len(un_linked)))\\n for node in un_linked:\\n for d in node['set']:\\n node['x'] += d['x']\\n node['y'] += d['y']\\n node['value'] += d['value']\\n pass\\n node['x'] /= len(node['set'])\\n node['y'] /= len(node['set'])\\n node['value'] /= len(node['set'])\\n pass\\n # min_dif = ((un_linked[1]['x'] - un_linked[0]['x']) ** 2 + (un_linked[1]['y'] - un_linked[0]['y']) ** 2) \\\\\\n # * self._alpha + (un_linked[1]['value'] - un_linked[0]['value']) * (1 - self._alpha)\\n min_dif = ((un_linked[1]['x'] - un_linked[0]['x']) ** 2 + (un_linked[1]['y'] - un_linked[0]['y']) ** 2)\\n min_cp = [0, 1]\\n for i in range(len(un_linked) - 1):\\n for j in range(i + 1, len(un_linked)):\\n # dif = self._alpha * ((un_linked[j]['x'] - un_linked[i]['x']) ** 2\\n # + (un_linked[j]['x'] - un_linked[i]['x']) ** 2) \\\\\\n # + (1 - self._alpha) * (un_linked[j]['value'] - un_linked[i]['value'])\\n dif = ((un_linked[j]['x'] - un_linked[i]['x']) ** 2\\n + (un_linked[j]['x'] - un_linked[i]['x']) ** 2)\\n if dif < min_dif:\\n min_dif = dif\\n min_cp = [i, j]\\n pass\\n pass\\n pass\\n set_a = []\\n for each in un_linked[min_cp[0]]['set']:\\n set_a.append(each)\\n pass\\n for each in un_linked[min_cp[1]]['set']:\\n set_a.append(each)\\n pass\\n next_un_linked = []\\n new_children = []\\n if len(un_linked[min_cp[0]]['children']) != 0:\\n new_children.append({'children': un_linked[min_cp[0]]['children'],\\n 'value': len(un_linked[min_cp[0]]['set'])})\\n pass\\n else:\\n new_children.append({'id': un_linked[min_cp[0]]['id'],\\n 'value': len(un_linked[min_cp[0]]['set'])})\\n if len(un_linked[min_cp[1]]['children']) != 0:\\n new_children.append({'children': un_linked[min_cp[1]]['children'],\\n 'value': len(un_linked[min_cp[1]]['set'])})\\n pass\\n else:\\n new_children.append({'id': un_linked[min_cp[1]]['id'],\\n 'value': len(un_linked[min_cp[1]]['set'])})\\n pass\\n next_un_linked.append({\\n 'x': 0,\\n 'y': 0,\\n 'value': 0,\\n 'set': set_a,\\n 'children': new_children\\n })\\n del un_linked[min_cp[0]]['set']\\n del un_linked[min_cp[0]]['x']\\n del un_linked[min_cp[0]]['y']\\n # del un_linked[min_cp[0]]['value']\\n del un_linked[min_cp[1]]['set']\\n del un_linked[min_cp[1]]['x']\\n del un_linked[min_cp[1]]['y']\\n # del un_linked[min_cp[1]]['value']\\n for s in range(len(un_linked)):\\n if s not in min_cp:\\n next_un_linked.append(un_linked[s])\\n pass\\n pass\\n un_linked = next_un_linked\\n pass\\n del un_linked[0]['set']\\n del un_linked[0]['x']\\n del un_linked[0]['y']\\n # del un_linked[0]['value']\\n self.tree = un_linked[0]\\n self._count = 0\\n\\n self.tree = self._resolve(self.tree)\\n return\",\n \"def test_tdsp_dijsktra_with_timesteps_2(self):\\n input_file = \\\"/home/andy/code/python/om_task/data/test_data3.txt\\\"\\n graph_data = process_input_file(input_file)\\n\\n graphs = {}\\n\\n today = datetime.today()\\n t = time(0, 0) # input time value\\n start_time = datetime(today.year, today.month, today.day, t.hour, t.minute)\\n\\n for timestep in graph_data:\\n # print(\\\"Timestep=\\\", timestep)\\n # Create a graph for each timestep\\n graphs[timestep] = Graph()\\n for edge in graph_data.get(timestep):\\n graphs[timestep].add_edge(*edge)\\n\\n for graph in graphs:\\n print(graphs[graph].edges)\\n print(graphs[graph].weights)\\n\\n path = tdsp_dijsktra(graphs, start_time, 'A0', 'B1')\\n\\n print(path)\\n\\n self.assertEqual(['A0', 'B0', 'B1'], path)\",\n \"def neighborJoining(distances):\\n\\n tree = {}\\n\\n while(len(distances.keys()) > 2):\\n\\n r = calcRs(distances)\\n M = makeMMatrix(distances, r)\\n\\n smallest = 10000\\n smallestKey = (\\\"\\\",\\\"\\\")\\n\\n #Find nearest neighbors\\n for key in M.keys():\\n for subkey in M[key].keys():\\n if M[key][subkey] < smallest:\\n smallest = M[key][subkey]\\n smallestKey = (key, subkey)\\n\\n #Add new node and update distances to rest of tree\\n newname = smallestKey[0] + \\\"-\\\" + smallestKey[1]\\n distances[newname] = {}\\n tree[smallestKey[0]] = {}\\n tree[smallestKey[1]] = {}\\n dij = distances[smallestKey[0]][smallestKey[1]]\\n for key in M.keys():\\n if key in smallestKey:\\n continue\\n distances[newname][key] = .5*(distances[smallestKey[0]][key] \\\\\\n + distances[smallestKey[1]][key] - dij)\\n distances[key][newname] = distances[newname][key]\\n\\n #Update distances to parents of node\\n dik = (dij + r[smallestKey[0]] - r[smallestKey[1]])/2\\n tree[smallestKey[0]][newname] = dik\\n tree[smallestKey[1]][newname] = dij-dik\\n detachDict(distances, smallestKey[0], smallestKey[1])\\n\\n #Connect final two nodes\\n tree[distances.keys()[0]] = {}\\n tree[distances.keys()[0]][distances[distances.keys()[0]].keys()[0]] =\\\\\\n distances[distances.keys()[0]][distances[distances.keys()[0]].keys()[0]] \\n return tree\",\n \"def test_tdsp_dijsktra_with_timesteps_3(self):\\n input_file = \\\"/home/andy/code/python/om_task/data/test_data3.txt\\\"\\n graph_data = process_input_file(input_file)\\n\\n graphs = {}\\n\\n today = datetime.today()\\n t = time(0, 0) # input time value\\n start_time = datetime(today.year, today.month, today.day, t.hour, t.minute)\\n\\n for timestep in graph_data:\\n # print(\\\"Timestep=\\\", timestep)\\n # Create a graph for each timestep\\n graphs[timestep] = Graph()\\n for edge in graph_data.get(timestep):\\n graphs[timestep].add_edge(*edge)\\n\\n for graph in graphs:\\n print(graphs[graph].edges)\\n print(graphs[graph].weights)\\n\\n path = tdsp_dijsktra(graphs, start_time, 'A0', 'A1')\\n\\n print(path)\\n\\n self.assertEqual(['A0', 'B0', 'B1', 'A1'], path)\",\n \"def build_mht(eddies_data,\\n\\tprune_depth = 2,\\n\\twithin_bounds = lambda x: True,\\n\\tdo_lookahead = True,\\n\\tgate_dist = 150,\\n\\tprev_data = None,\\n\\tprune_mode = 'parent'):\\n\\n\\troots = []\\n\\tdepth = 0\\n\\n\\tif prev_data is not None:\\n\\t\\troots = prev_data['roots']\\n\\t\\tdepth = prev_data['start_depth']\\n\\t\\tprune_depth = prev_data['prune_depth']\\n\\t\\tgate_dist = prev_data['gate_dist']\\n\\n\\tif depth >= len(eddies_data):\\n\\t\\treturn roots\\n\\n\\tfor dataset in eddies_data[depth:]:\\n\\t\\tstart_time = time.mktime(time.localtime())\\n\\t\\tprint dataset[0]\\n\\t\\tmat = scipy.io.loadmat(dataset[1], struct_as_record=False)\\n\\t\\teddies = mat['eddies'][0]\\n\\t\\tpnodes = get_nodes_at_depth(roots, depth - 1)\\n\\t\\tfor i in range(len(eddies)):\\n\\t\\t\\teddy = Eddy(eddies[i].Stats[0,0],\\n\\t\\t\\t\\teddies[i].Lat[0,0],\\n\\t\\t\\t\\teddies[i].Lon[0,0],\\n\\t\\t\\t\\teddies[i].Amplitude[0,0],\\n\\t\\t\\t\\teddies[i].ThreshFound[0,0],\\n\\t\\t\\t\\teddies[i].SurfaceArea[0,0],\\n\\t\\t\\t\\teddies[i].Date[0,0],\\n\\t\\t\\t\\teddies[i].Cyc[0,0],\\n\\t\\t\\t\\teddies[i].MeanGeoSpeed[0,0],\\n\\t\\t\\t\\teddies[i].DetectedBy[0])\\n\\t\\t\\tif not within_bounds(eddy):\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tmk_node_and_add(eddy, depth, pnodes, roots, gate_dist)\\n\\n\\t\\tif do_lookahead:\\n\\t\\t\\tlookahead.add_lookahead_nodes(pnodes, gate_dist)\\n\\n\\t\\tif depth >= prune_depth:\\n\\t\\t\\tif prune_mode == 'parent':\\n\\t\\t\\t\\tprune_parent(roots, depth-prune_depth, gate_dist)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprune(roots, depth-prune_depth, gate_dist)\\n\\t\\tdepth += 1\\n\\t\\tprint 'time:', time.mktime(time.localtime())-start_time\\n\\n\\treturn roots\",\n \"def test_tdsp_dijsktra_with_timesteps(self):\\n input_file = \\\"/home/andy/code/python/om_task/data/test_data3.txt\\\"\\n graph_data = process_input_file(input_file)\\n\\n graphs = {}\\n\\n today = datetime.today()\\n t = time(0, 0) # input time value\\n start_time = datetime(today.year, today.month, today.day, t.hour, t.minute)\\n\\n for timestep in graph_data:\\n # print(\\\"Timestep=\\\", timestep)\\n # Create a graph for each timestep\\n graphs[timestep] = Graph()\\n for edge in graph_data.get(timestep):\\n graphs[timestep].add_edge(*edge)\\n\\n for graph in graphs:\\n print(graphs[graph].edges)\\n print(graphs[graph].weights)\\n\\n print(\\\"Start Test 1\\\")\\n path = tdsp_dijsktra(graphs, start_time, 'A0', 'A1')\\n print(path)\\n self.assertEqual(['A0', 'B0', 'B1', 'A1'], path)\\n\\n print(\\\"Start Test 2\\\")\\n t = time(0, 0) # input time value\\n start_time = datetime(today.year, today.month, today.day, t.hour, t.minute)\\n path = tdsp_dijsktra(graphs, start_time, 'B0', 'A1')\\n print(path)\\n self.assertEqual(['B0', 'B1', 'A1'], path)\\n\\n print(\\\"Start Test 3\\\")\\n t = time(1, 1) # input time value\\n start_time = datetime(today.year, today.month, today.day, t.hour, t.minute)\\n path = tdsp_dijsktra(graphs, start_time, 'B0', 'A1')\\n print(path)\\n self.assertEqual(['B0', 'A1'], path)\",\n \"def construct_taxonomy(termID_list, dih, dih_metric_name=\\\"invCL\\\", taxorg_method=\\\"NoCyc\\\", \\n graph_init_threshold=0.01, verbose=True):\\n assert dih_metric_name in [\\\"weeds_prec\\\", \\\"clarkeDE\\\", \\\"invCL\\\"]\\n assert taxorg_method in [\\\"NoCyc\\\", \\\"DMST\\\"]\\n\\n # DIH\\n weighted_edges = [] # (hyper, hypo, score)\\n for termID_pair in tqdm(list(combinations(termID_list, r=2))):\\n score = 2.0 * dih.predict(termID_pair[0], termID_pair[1], dih_metric_name)\\n if score >= graph_init_threshold:\\n weighted_edges.append([termID_pair[1], termID_pair[0], score])\\n\\n # reverse order testing\\n score = 2.0 * dih.predict(termID_pair[1], termID_pair[0], dih_metric_name)\\n if score >= graph_init_threshold:\\n weighted_edges.append([termID_pair[0], termID_pair[1], score])\\n\\n # Taxonomy organization\\n G = construct_graph(termID_list, weighted_edges)\\n if verbose:\\n print(\\\"Before Taxonomy Organization\\\")\\n describe_graph(G)\\n print(\\\"=\\\"*89)\\n\\n if taxorg_method == \\\"NoCyc\\\":\\n T = NoCyc(G)\\n elif taxorg_method == \\\"DMST\\\":\\n T = DMST(G)\\n\\n if verbose:\\n print(\\\"After Taxonomy Organization\\\")\\n describe_graph(T)\\n \\n return T\",\n \"def cross_validation(self, x, t, k=5):\\n print(\\\"Cross validation of the Decision Tree Classifier...\\\")\\n bestCriteria = ''\\n bestMax_depth= 2\\n bestError = float('inf')\\n\\n N = len(x)\\n N_train = math.floor(0.8 * N)\\n\\n dicCriteria = ['gini', 'entropy']\\n min_depth = 2\\n max_depth = 40\\n\\n for crit in dicCriteria:\\n for d in range(min_depth, max_depth):\\n errors = np.zeros(k)\\n\\n for j in range(k):\\n map_index = list(zip(x, t))\\n random.shuffle(map_index)\\n random_X, random_t = zip(*map_index)\\n\\n train_x = random_X[:N_train]\\n valid_x = random_X[N_train:]\\n train_t = random_t[:N_train]\\n valid_t = random_t[N_train:]\\n\\n self.model = tree.DecisionTreeClassifier(max_depth=d, criterion=crit)\\n self.train(train_x, train_t)\\n error_valid = np.array([self.error(x_n, t_n)\\n for t_n, x_n in zip(valid_t, valid_x)])\\n errors[j] = error_valid.mean()\\n\\n mean_error = np.mean(errors)\\n if mean_error < bestError:\\n bestError = mean_error\\n bestCriteria = crit\\n bestMax_depth = d\\n print(\\\"The new best hyper parameters are : \\\", bestMax_depth, bestCriteria)\\n\\n print(\\\"Best hyper parameters are : \\\", bestMax_depth, bestCriteria)\\n print(\\\"Validation error : \\\", 100 * bestError, \\\"%\\\")\\n self.model = tree.DecisionTreeClassifier(max_depth=bestMax_depth, criterion=bestCriteria)\\n self.train(x, t)\",\n \"def buildSweepTree(self):\\n minXPos, minXNodeID = self._findMinX()\\n cellDistanceArray = self._computeDistance(minXPos, minXNodeID) # distance of all cells to the \\\"min X node\\\"\\n # sort distance array based on distances to min X cell\\n cellDistanceArray = cellDistanceArray[cellDistanceArray[:, 2].argsort()]\\n # cellDistanceArray = np.sort(cellDistanceArray, axis=2)\\n for w, cellD in enumerate(cellDistanceArray):\\n self.sweepTree.append((int(cellD[0]), int(cellD[1])))\",\n \"def create_density_tree(dataset, dimensions, clusters, parentnode=None, side_label=None):\\n\\n # split\\n\\n dim_max, val_dim_max, _, _ = get_best_split(dataset, labelled=False)\\n left, right, e_left, e_right = split(dataset, dim_max, val_dim_max,\\n get_entropy=True) # split along best dimension\\n\\n treenode = DensityNode() # initial node\\n\\n # save tree node\\n treenode.split_dimension = dim_max\\n treenode.split_value = val_dim_max\\n\\n treenode.dataset = dataset\\n treenode.left_dataset = left\\n treenode.right_dataset = right\\n\\n treenode.dataset_len = len(dataset)\\n treenode.left_dataset_len = len(left)\\n treenode.right_dataset_len = len(right)\\n treenode.entropy = entropy_gaussian(dataset)\\n treenode.cov = np.cov(dataset.T)\\n treenode.mean = np.mean(dataset, axis=0)\\n treenode.left_cov = np.cov(left.T)\\n treenode.left_mean = np.mean(left, axis=0)\\n treenode.right_cov = np.cov(right.T)\\n treenode.right_mean = np.mean(right, axis=0)\\n treenode.left_entropy = e_left\\n treenode.right_entropy = e_right\\n\\n # link parent node to new node.\\n if parentnode is not None:\\n treenode.parent = parentnode\\n if side_label == 'left':\\n treenode.parent.left = treenode\\n elif side_label == 'right':\\n treenode.parent.right = treenode\\n\\n clusters_left = clusters - 1\\n if clusters_left > 1:\\n # recursively continue splitting\\n # continue splitting always splitting on worst side (highest entropy)\\n # find node where left or right entropy is highest and left or right node is not split yet\\n node_e, e, side = treenode.get_root().highest_entropy(dataset, 0, 'None')\\n\\n if side == 'left':\\n dataset = node_e.left_dataset\\n side_label = 'left'\\n elif side == 'right':\\n dataset = node_e.right_dataset\\n side_label = 'right'\\n\\n create_density_tree(dataset, dimensions, clusters=clusters_left,\\n parentnode=node_e, side_label=side_label) # iterate\\n\\n return treenode\",\n \"def fit(self, X, y):\\n #Initialize the tree with the given data\\n tree = kd(X)\\n self.tree = tree\\n self.labels = y\",\n \"def test_dijsktra2(self):\\n input_file = \\\"/home/andy/code/python/om_task/data/test_data4.txt\\\"\\n graph_data = process_input_file(input_file)\\n\\n graphs = {}\\n\\n for timestep in graph_data:\\n # print(\\\"Timestep=\\\", timestep)\\n # Create a graph for each timestep\\n graphs[timestep] = Graph()\\n for edge in graph_data.get(timestep):\\n graphs[timestep].add_edge(*edge)\\n\\n path = dijsktra(graphs[1], 'A0', 'B2')\\n\\n print(path)\\n\\n self.assertEqual(['A0', 'B0', 'B1', 'B2'], path)\",\n \"def support_tree(self):\\n v = self.vertices\\n h_edges = list(self.hyper_edges)\\n\\n h_edges.sort(key=lambda x: len(x))\\n support_tree = Graph()\\n\\n for x in v:\\n support_tree.add(x)\\n\\n for h_edge in h_edges:\\n kruskal = list(connected_parts(support_tree, vertex_subset=h_edge))\\n if len(kruskal) == 1:\\n continue\\n else:\\n for i in range(len(kruskal) - 1):\\n support_tree.update([(next(iter(kruskal[i])), next(iter(kruskal[i + 1])))],\\n node_creation=False)\\n\\n return support_tree\",\n \"def findknn(xTr,xTe,k):\\n\\n # YOUR CODE HERE\\n if k > len(xTr):\\n k = len(xTr)\\n \\n D=l2distance(xTe, xTr)\\n (m,n) = D.shape\\n \\n indices = []\\n dists = []\\n for i in range(m):\\n smallest_indices = np.argsort(D[i])\\n ind = smallest_indices[:k]\\n dis = D[i,smallest_indices[:k]]\\n indices.append(ind)\\n dists.append(dis)\\n \\n indices = np.transpose(np.array(indices))\\n dists = np.transpose(np.array(dists))\\n return indices, dists\",\n \"def test_dijsktra_2(self):\\n input_file = \\\"/home/andy/code/python/om_task/data/test_data3.txt\\\"\\n graph_data = process_input_file(input_file)\\n\\n graphs = {}\\n\\n for timestep in graph_data:\\n # print(\\\"Timestep=\\\", timestep)\\n # Create a graph for each timestep\\n graphs[timestep] = Graph()\\n for edge in graph_data.get(timestep):\\n graphs[timestep].add_edge(*edge)\\n\\n path = dijsktra(graphs[0], 'A0', 'A1')\\n\\n print(path)\\n\\n self.assertEqual(['A0', 'A1'], path)\",\n \"def buildTree(self, data, attributes):\\n\\n totalClasses = []\\n data = data[:]\\n assignedClass = self.assignClass()\\n\\n for record in data:\\n totalClasses.append(record[-1])\\n\\n # if all attributes have been traversed or the algo runs out of data/records\\n if len(attributes) < 1 or not data:\\n return assignedClass\\n\\n countClass = totalClasses.count(totalClasses[0])\\n\\n # if all classes are same\\n if countClass == len(totalClasses):\\n return totalClasses[0]\\n\\n splitAttrIndex = self.getSplitAttr(data, attributes)\\n decisionTree = {splitAttrIndex: {}}\\n\\n # Splitting the data using the attribute with the hightest info gain\\n for attrVal in ['True', 'False']:\\n subtreeAttributes = attributes[:]\\n subtreeAttributes.pop(splitAttrIndex)\\n # Get new data for children node\\n subtreeData = self.getNewData(data, splitAttrIndex, attrVal)\\n # generate subtree\\n subtree = self.buildTree(subtreeData, subtreeAttributes)\\n decisionTree[splitAttrIndex][attrVal] = subtree\\n\\n return decisionTree\",\n \"def idw(kdtree, z, xi, yi):\\n neighbors = 12\\n power = 2\\n distances, indicies = kdtree.query([xi, yi], k=neighbors)\\n z_n = z[indicies]\\n if 0 not in distances:\\n weights = power / distances\\n else:\\n distances += 0.000000001\\n weights = power / distances\\n weights /= weights.sum(axis=0)\\n zi = np.dot(weights.T, z_n)\\n return zi\",\n \"def draw_tree(self):\\n nx.draw(self.diffusion_tree, with_labels=True)\",\n \"def G_to_kdtree(G_, x_coord='x', y_coord='y', verbose=False):\\n\\n nrows = len(G_.nodes())\\n ncols = 2\\n kd_idx_dic = {}\\n arr = np.zeros((nrows, ncols))\\n # populate node array\\n t1 = time.time()\\n for i, n in enumerate(G_.nodes()):\\n n_props = G_.node[n]\\n lat, lon = n_props['lat'], n_props['lon']\\n x0, y0 = n_props[x_coord], n_props[y_coord]\\n\\n if x_coord == 'lon':\\n x, y = lon, lat\\n else:\\n x, y = x0, y0\\n\\n arr[i] = [x, y]\\n kd_idx_dic[i] = n\\n\\n # now create kdtree from numpy array\\n kdtree = scipy.spatial.KDTree(arr)\\n if verbose:\\n print(\\\"Time to create k-d tree:\\\", time.time() - t1, \\\"seconds\\\")\\n return kd_idx_dic, kdtree, arr\",\n \"def prepare_data_for_d(self):\\n\\n center_nodes = []\\n neighbor_nodes = []\\n labels = []\\n for i in self.root_nodes:\\n if np.random.rand() < config.update_ratio:\\n # self.graph[i] = [neighbors of i]\\n pos = self.graph[i]\\n neg, _ = self.sample(i, self.trees[i], len(pos), for_d=True)\\n # print(\\\"tree_i_d: \\\", self.trees[i])\\n # print(\\\"neg_samples: \\\", neg)\\n # print(\\\"neg is: \\\", neg)\\n if len(pos) != 0 and neg is not None:\\n # positive samples\\n center_nodes.extend([i] * len(pos))\\n neighbor_nodes.extend(pos)\\n labels.extend([1] * len(pos))\\n\\n # negative samples\\n center_nodes.extend([i] * len(pos))\\n neighbor_nodes.extend(neg)\\n labels.extend([0] * len(neg))\\n # print(\\\"cen: \\\", center_nodes)\\n return center_nodes, neighbor_nodes, labels\",\n \"def make_nltk_tree(derivation):\\n\\td = defaultdict(None, ((r.lhs, r.rhs) for r in derivation))\\n\\t\\n\\tdef make_tree(lhs):\\n\\t\\treturn Tree(lhs[1:-1], (child if child not in d else make_tree(child) for child in d[lhs]))\\n\\t\\n\\treturn make_tree(derivation[0].lhs)\",\n \"def support_tree(self):\\n if not self.is_atomistic():\\n raise ValueError(\\\"lattice is not atomistic\\\")\\n\\n class_order = topological_sort(self._hase_diagram)\\n objects = self.above(self.bottom)\\n representatives = {element: element for element in objects}\\n classes = {element: {element} for element in objects}\\n tree = Graph(objects)\\n n_connected_parts = len(objects)\\n colors = {obj: i for i, obj in enumerate(objects)}\\n\\n class_order_position = 1\\n while n_connected_parts > 1:\\n class_order_position += 1\\n current_class_index = class_order[class_order_position]\\n if current_class_index not in objects:\\n predecessors = list(self.under(current_class_index))\\n first_class_representative = representatives[predecessors[0]]\\n second_class_representative = representatives[predecessors[1]]\\n representatives[current_class_index] = random.choice(\\n [first_class_representative, second_class_representative])\\n if colors[first_class_representative] != colors[second_class_representative]:\\n tree.update(((first_class_representative, second_class_representative),))\\n color_to_change = colors[second_class_representative]\\n color_to_keep = colors[first_class_representative]\\n for element in colors:\\n if colors[element] == color_to_change:\\n colors[element] = color_to_keep\\n n_connected_parts -= 1\\n classes[current_class_index] = classes[predecessors[0]].union(classes[predecessors[1]])\\n return tree\",\n \"def test_tdsp_dijsktra_with_timesteps_real_data(self):\\n input_file = \\\"/home/andy/code/python/om_task/data/input_data1.txt\\\"\\n graph_data = process_input_file(input_file)\\n\\n graphs = {}\\n\\n today = datetime.today()\\n t = time(0, 0) # input time value\\n start_time = datetime(today.year, today.month, today.day, t.hour, t.minute)\\n\\n for timestep in graph_data:\\n # print(\\\"Timestep=\\\", timestep)\\n # Create a graph for each timestep\\n graphs[timestep] = Graph()\\n for edge in graph_data.get(timestep):\\n graphs[timestep].add_edge(*edge)\\n\\n for graph in graphs:\\n print(graphs[graph].edges)\\n print(graphs[graph].weights)\\n\\n print(\\\"Start Test 1\\\")\\n path = tdsp_dijsktra(graphs, start_time, 'B1', 'F4')\\n print(path)\\n self.assertEqual(['B1', 'C2', 'D3', 'E4', 'F4'], path)\",\n \"def discretize(self,dt):\\n res = self.discretize_state(dt)\\n n = len(res.milestones[0])//2\\n return Trajectory(res.times,[m[:n] for m in res.milestones])\",\n \"def buildDecisionTree(self, data):\\n self.data = data\\n self.decisionTree = self.buildTree(self.data, self.listAttributes)\\n with open(\\\"decision_tree_model\\\", \\\"wb\\\") as f:\\n pickle.dump(self.decisionTree, f, pickle.HIGHEST_PROTOCOL)\\n return self.decisionTree\",\n \"def graphite_diffusivity_Kim2011(sto, T, T_inf, E_D_s, R_g):\\n\\n D_ref = 9 * 10 ** (-14)\\n arrhenius = exp(E_D_s / R_g * (1 / T_inf - 1 / T))\\n\\n return D_ref * arrhenius\",\n \"def kdtree( data, leafsize):\\n\\n leaves = []\\n\\n ndim = data.shape[0]\\n ndata = data.shape[1]\\n #print ndim\\n #print ndata\\n\\n # find bounding hyper-rectangle\\n hrect = numpy.zeros((2,data.shape[0]))\\n hrect[0,:] = data.min(axis=1)\\n hrect[1,:] = data.max(axis=1)\\n\\n # create root of kd-tree\\n idx = numpy.argsort(data[0,:], kind='mergesort')\\n data[:,:] = data[:,idx]\\n splitval = data[0,ndata/2]\\n\\n left_hrect = hrect.copy()\\n right_hrect = hrect.copy()\\n left_hrect[1, 0] = splitval\\n right_hrect[0, 0] = splitval\\n\\n tree = [(None, None, left_hrect, right_hrect, None, None)]\\n\\n stack = [(data[:,:ndata/2], idx[:ndata/2], 1, 0, True),\\n (data[:,ndata/2:], idx[ndata/2:], 1, 0, False)]\\n\\n # recursively split data in halves using hyper-rectangles:\\n while stack:\\n\\n # pop data off stack\\n data, didx, depth, parent, leftbranch = stack.pop()\\n ndata = data.shape[1]\\n nodeptr = len(tree)\\n\\n # update parent node\\n\\n _didx, _data, _left_hrect, _right_hrect, left, right = tree[parent]\\n\\n tree[parent] = (_didx, _data, _left_hrect, _right_hrect, nodeptr, right) if leftbranch \\\\\\n else (_didx, _data, _left_hrect, _right_hrect, left, nodeptr)\\n\\n # insert node in kd-tree\\n\\n # leaf node?\\n if ndata <= leafsize:\\n _didx = didx.copy()\\n _data = data.copy()\\n leaf = (_didx, _data, None, None, 0, 0)\\n #leaf = (_data)\\n tree.append(leaf)\\n leaves.append(_didx)\\n\\n # not a leaf, split the data in two \\n else:\\n splitdim = depth % ndim\\n idx = numpy.argsort(data[splitdim,:], kind='mergesort')\\n data[:,:] = data[:,idx]\\n didx = didx[idx]\\n nodeptr = len(tree)\\n stack.append((data[:,:ndata/2], didx[:ndata/2], depth+1, nodeptr, True))\\n stack.append((data[:,ndata/2:], didx[ndata/2:], depth+1, nodeptr, False))\\n splitval = data[splitdim,ndata/2]\\n if leftbranch:\\n left_hrect = _left_hrect.copy()\\n right_hrect = _left_hrect.copy()\\n else:\\n left_hrect = _right_hrect.copy()\\n right_hrect = _right_hrect.copy()\\n left_hrect[1, splitdim] = splitval\\n right_hrect[0, splitdim] = splitval\\n\\n #print data\\n # append node to tree\\n tree.append((None, None, left_hrect, right_hrect, None, None))\\n\\n return tree,leaves\",\n \"def subtree_cond_pdf(tree1, tree2, tree2_subtree_nodes, new):\\n\\n # if new is isolated in the underlying graph\\n\\n #if len(tree2_subtree_nodes) == 1 and tree2_subtree_nodes.values()[0] is None:\\n if len(tree2_subtree_nodes) == 1 and list(tree2_subtree_nodes.values())[0] is None:\\n sep = frozenset([])\\n c = frozenset([new])\\n lognu = trilearn.graph.junction_tree.log_nu(tree2, sep)\\n return ({c: np.exp(-lognu)}, {c: 1.0})\\n\\n # Get the subtree induced by the nodes\\n #tree1_subtree = tree1.subgraph([c_t1 for c_t2, c_t1 in tree2_subtree_nodes.iteritems()])\\n tree1_subtree = tree1.subgraph([c_t1 for c_t2, c_t1 in tree2_subtree_nodes.items()])\\n\\n\\n # Get the separating sets\\n # S = sepsets_in_subgraph(tree2_subtree_nodes, tree1_subtree)\\n\\n # S = {c: set() for c_t2, c in tree2_subtree_nodes.iteritems()}\\n S = {c: set() for c_t2, c in tree2_subtree_nodes.items()}\\n #for c_tree2, c in tree2_subtree_nodes.iteritems():\\n for c_tree2, c in tree2_subtree_nodes.items():\\n for neig in tree1_subtree.neighbors(c):\\n S[c] = S[c] | (c & neig)\\n\\n # P, N get_subset_probabilities(tree2_)\\n # Get the chosen internal nodes\\n M = {}\\n # for c_tree2, c in tree2_subtree_nodes.iteritems():\\n for c_tree2, c in tree2_subtree_nodes.items():\\n M[c] = c_tree2 - {new} - S[c]\\n\\n # Calculate probabilities corresponding to each clique\\n P = {}\\n N = {}\\n # for c_tree2, c in tree2_subtree_nodes.iteritems():\\n for c_tree2, c in tree2_subtree_nodes.items():\\n neigs = {neig for neig in tree1.neighbors(c) if\\n neig & c <= c_tree2 and neig not in tree1_subtree.nodes()}\\n RM = c - S[c]\\n gamma = tree1_subtree.order()\\n # sepCondition = len({neig for neig in nx.neighbors(tree1_subtree, c) if\\n # S[c] == neig & c}) > 0 or gamma == 1\\n sepCondition = len({neig for neig in tree1_subtree.neighbors(c) if\\n S[c] == neig & c}) > 0 or gamma == 1\\n\\n N[c] = 1.0\\n #N[c] = Fraction(1,1)\\n if sepCondition is False:\\n # Every internal node in c belongs to a separator\\n P[c] = np.power(2.0, - len(RM))\\n #P[c] = Fraction(1, 2 ** len(RM))\\n if not len(c) + 1 == len(c_tree2):\\n N[c] = np.power(2.0, -len(neigs))\\n #N[c] = Fraction(1, 2**len(neigs))\\n else:\\n P[c] = 1.0\\n if len(RM) > 1:\\n P[c] = (1.0 / len(RM)) * np.power(2.0, -(len(RM) - 1.0)) * len(M[c])\\n #P[c] = Fraction(1, len(RM)) * Fraction(len(M[c]), 2 ** (len(RM) - 1.0)) \\n if not len(c) + 1 == len(c_tree2):\\n N[c] = np.power(2.0, -len(neigs))\\n #N[c] = Fraction(1, 2**len(neigs))\\n return (P, N)\",\n \"def test_tdsp_dijsktra_with_timesteps_5(self):\\n input_file = \\\"/home/andy/code/python/om_task/data/test_data4.txt\\\"\\n graph_data = process_input_file(input_file)\\n\\n graphs = {}\\n\\n today = datetime.today()\\n t = time(0, 0) # input time value\\n start_time = datetime(today.year, today.month, today.day, t.hour, t.minute)\\n\\n for timestep in graph_data:\\n # print(\\\"Timestep=\\\", timestep)\\n # Create a graph for each timestep\\n graphs[timestep] = Graph()\\n for edge in graph_data.get(timestep):\\n graphs[timestep].add_edge(*edge)\\n\\n for graph in graphs:\\n print(graphs[graph].edges)\\n print(graphs[graph].weights)\\n\\n print(\\\"Start Test 1\\\")\\n path = tdsp_dijsktra(graphs, start_time, 'A0', 'B2')\\n print(\\\"Path = \\\", path)\\n self.assertEqual(['A0', 'B0', 'B1', 'B2'], path)\",\n \"def dist_tree(self, other, tol=1e-8):\\n if self.ndim == 2:\\n from pyparm import d2 as sim\\n else:\\n from pyparm import d3 as sim\\n\\n assert self.ndim == other.ndim\\n\\n if self.shear != 0 or other.shear != 0:\\n assert np.abs(self.shear - other.shear) <= tol\\n shear = (self.shear + other.shear) / 2.0\\n box = sim.LeesEdwardsBox(sim.Vec(1, 1), shear)\\n else:\\n box = sim.OriginBox(1.0)\\n\\n sz1 = self.size_indices()\\n assert(len(sz1) == 2)\\n cutoff1 = int(np.sum(sz1[0]))\\n\\n sz2 = other.size_indices()\\n assert(len(sz2) == 2)\\n cutoff2 = int(np.sum(sz2[0]))\\n\\n vs1 = np.array([xy for idx in sz1 for xy in self.rs[idx]])\\n vs2 = np.array([xy for idx in sz2 for xy in other.rs[idx]])\\n\\n tree = sim.JammingTreeBD(box, vs1, vs2, cutoff1, cutoff2)\\n return tree\",\n \"def brute_tree(XTRAIN,istopTRAIN,XTEST,istopTEST):\\n \\n ntrain=XTRAIN.shape[0]\\n ntest=XTEST.shape[0]\\n \\n if np.sum(istopTRAIN)==0:\\n return 0,[]\\n\\n cost0=np.zeros(Ngammas*Nreps)\\n cost1=np.zeros(Ngammas*Nreps)\\n cost0test=np.zeros(Ngammas*Nreps)\\n cost1test=np.zeros(Ngammas*Nreps)\\n \\n precisionTRAIN=np.zeros(Ngammas*Nreps)\\n precisionTEST=np.zeros(Ngammas*Nreps)\\n recallTEST=np.zeros(Ngammas*Nreps)\\n rate=np.zeros(Ngammas*Nreps)\\n \\n for iii in range(Ngammas):\\n \\n gamma=GAMMA[iii]\\n \\n for jjj in range(Nreps):\\n \\n \\\"\\\"\\\" train a tree using training data with random splitting \\\"\\\"\\\"\\n \\n tree_hyperparameters['class_weight']={0:1,1:gamma}\\n clf=tree.DecisionTreeClassifier(**tree_hyperparameters)\\n clf.fit(XTRAIN,istopTRAIN)\\n \\n \\\"\\\"\\\"\\\" record costs and precision on validation data \\\"\\\"\\\"\\n \\n pTRAIN=clf.predict(XTRAIN)\\n precisionTRAIN[iii*Nreps+jjj]=np.divide(sum(1 for i in range(ntrain) if pTRAIN[i] == 1 and istopTRAIN[i]==1),sum(pTRAIN))\\n cost0[iii*Nreps+jjj]=sum(1 for i in range(ntrain) if pTRAIN[i] == 1 and istopTRAIN[i]==0)\\n cost1[iii*Nreps+jjj]=sum(1 for i in range(ntrain) if pTRAIN[i] == 0 and istopTRAIN[i]==1)\\n \\n \\\"\\\"\\\" record precision on test data \\\"\\\"\\\"\\n \\n pTEST=clf.predict(XTEST)\\n precisionTEST[iii*Nreps+jjj]=np.divide(sum(1 for i in range(ntest) if pTEST[i] == 1 and istopTEST[i]==1),sum(pTEST))\\n recallTEST[iii*Nreps+jjj]=sum(1 for i in range(ntest) if pTEST[i] == 1 and istopTEST[i]==1)/sum(istopTEST)\\n cost0test[iii*Nreps+jjj]=sum(1 for i in range(ntest) if pTEST[i] == 1 and istopTEST[i]==0)\\n cost1test[iii*Nreps+jjj]=sum(1 for i in range(ntest) if pTEST[i] == 0 and istopTEST[i]==1)\\n \\n \\\"\\\"\\\" record positive rate on full data \\\"\\\"\\\"\\n \\n rate[iii*Nreps+jjj]=(sum(pTRAIN)+sum(pTEST))/(ntrain+ntest)\\n \\n \\\"\\\"\\\" Compute Pareto front for validation data \\\"\\\"\\\"\\n \\n Pareto = Lower_Convex_Hull(np.concatenate((cost0.reshape(-1,1),cost1.reshape(-1,1)),1))\\n \\n \\\"\\\"\\\" make some nice plots for whoever is watching \\\"\\\"\\\"\\n \\n plt.figure(figsize=(10,5))\\n plt.subplot(121)\\n plt.plot(cost0,cost1,'.')\\n plt.plot(cost0[Pareto],cost1[Pareto],'d')\\n plt.xlabel('errors on class zero training data')\\n plt.ylabel('errors on class one training data')\\n\\n plt.subplot(122)\\n plt.plot(cost0test,cost1test,'.')\\n plt.plot(cost0test[Pareto],cost1test[Pareto],'d')\\n plt.xlabel('errors on class zero test data')\\n plt.ylabel('errors on class one test data')\\n plt.show()\\n \\n plt.figure(figsize=(15,5))\\n plt.subplot(131)\\n plt.semilogy(precisionTRAIN,rate,'.')\\n plt.semilogy(precisionTRAIN[Pareto],rate[Pareto],'d')\\n plt.xlabel('precision on training data')\\n plt.ylabel('positive rate')\\n\\n plt.subplot(132) \\n plt.semilogy(precisionTEST,rate,'.')\\n plt.semilogy(precisionTEST[Pareto],rate[Pareto],'d')\\n plt.xlabel('precision on test data')\\n plt.ylabel('positive rate')\\n\\n plt.subplot(133) \\n plt.plot(precisionTEST,recallTEST,'.')\\n plt.plot(precisionTEST[Pareto],recallTEST[Pareto],'d')\\n plt.xlabel('precision on test data')\\n plt.ylabel('recall on test data')\\n plt.show() \\n \\n return {'cost0':cost0,'cost1':cost1,'cost0test':cost0test,'cost1test':cost1test,'precisionTRAIN':precisionTRAIN,'precisionTEST':precisionTEST,'recallTEST':recallTEST,'rate':rate,'Pareto':Pareto}\",\n \"def test_compare_old_to_new_method_to_create_trees(self):\\n nodes = util.generate_sequence_of_points(2, 2)\\n tree1 = kdtree.createNewTree(nodes)\\n kdtree.visualize(tree1)\\n \\n sel_axis = (lambda axis: axis)\\n tree2 = kdtree.createNewTree([[0.5, 0.5]],axis = 0, sel_axis= sel_axis)\\n tree2.split2([0.25, 0.5], axis = 1)\\n tree2.split2([0.75, 0.5], axis = 1)\\n \\n #left\\n tree2.split2([0.25, 0.25], axis = 0, sel_axis = sel_axis)\\n tree2.split2([0.25, 0.75], axis = 0, sel_axis = sel_axis)\\n \\n #right\\n tree2.split2([0.75, 0.25], axis = 0, sel_axis = sel_axis)\\n tree2.split2([0.75, 0.75], axis = 0, sel_axis = sel_axis)\\n \\n kdtree.visualize(tree2)\\n \\n for n in zip(kdtree.level_order(tree1), kdtree.level_order(tree2)):\\n self.assertEqual(n[0].data, n[1].data, \\\"elements not equal\\\")\\n \\n if n[0].data is not None and n[1].data is not None:\\n self.assertEqual(n[0].axis, n[1].axis, \\\"elements not equal\\\")\",\n \"def dijsktra(graph, initial):\\n # Sets initial node score to 10\\n visited = {initial: 10}\\n\\n nodes = set(graph.nodes)\\n max_weight = graph.distances[max(graph.distances, key=graph.distances.get)]\\n min_weight = graph.distances[min(graph.distances, key=graph.distances.get)]\\n\\n # Defines the number of nodes to explore as the number of conected nodes\\n nodes_to_explore = len(graph.edges[initial]) + 2\\n explored = 1\\n while explored < nodes_to_explore:\\n # Finds nodes with maximum value that has not yet been explored\\n max_node = None\\n for node in nodes:\\n if node in visited:\\n if max_node is None:\\n max_node = node\\n elif visited[node] > visited[max_node]:\\n max_node = node\\n\\n if max_node is None:\\n break\\n\\n nodes.remove(max_node)\\n current_weight = visited[max_node]\\n\\n # Finds score of the next node if node has already been visited\\n # changes score if it is greater\\n for edge in graph.edges[max_node]:\\n weight = graph.distances[(max_node, edge)]\\n if max_weight - min_weight == 0:\\n normalised = 1\\n else:\\n normalised = ((weight - min_weight) / (max_weight - min_weight)) + 1\\n weight = current_weight - (1 / normalised)\\n if edge not in visited or weight > visited[edge]:\\n visited[edge] = round(weight, 2)\\n explored += 1\\n\\n return visited\",\n \"def get_result_k(att_trees, data):\\n data_back = copy.deepcopy(data)\\n all_ncp = []\\n all_rtime = []\\n all_pollution = []\\n deletion_all_ncp = []\\n deletion_all_rtime = []\\n # for k in range(5, 105, 5):\\n for k in [2, 5, 10, 25, 50, 100]:\\n if __DEBUG:\\n print '#' * 30\\n print \\\"K=%d\\\" % k\\n print \\\"Enhanced Mondrian\\\"\\n _, eval_result = mondrian(att_trees, data, k)\\n data = copy.deepcopy(data_back)\\n all_ncp.append(round(eval_result[0], 2))\\n all_rtime.append(round(eval_result[1], 2))\\n all_pollution.append(round(eval_result[2], 2))\\n if __DEBUG:\\n print \\\"NCP %0.2f\\\" % eval_result[0] + \\\"%\\\"\\n print \\\"Running time %0.2f\\\" % eval_result[1] + \\\"seconds\\\"\\n print \\\"Missing Pollution = %.2f %%\\\" % eval_result[2]\\n print \\\"Mondrian\\\"\\n _, eval_result = mondrian_delete_missing(att_trees, data, k)\\n data = copy.deepcopy(data_back)\\n if __DEBUG:\\n print \\\"NCP %0.2f\\\" % eval_result[0] + \\\"%\\\"\\n print \\\"Running time %0.2f\\\" % eval_result[1] + \\\"seconds\\\"\\n deletion_all_ncp.append(round(eval_result[0], 2))\\n deletion_all_rtime.append(round(eval_result[1], 2))\\n print \\\"Mondrian\\\"\\n print \\\"All NCP\\\", deletion_all_ncp\\n print \\\"All Running time\\\", deletion_all_rtime\\n print \\\"Enhanced Mondrian\\\"\\n print \\\"All NCP\\\", all_ncp\\n print \\\"All Running time\\\", all_rtime\\n print \\\"Missing Pollution\\\", all_pollution\",\n \"def prune(tree, testSet, res, technique):\\n assert technique in [\\\"reduced_error\\\"]\\n if technique == \\\"reduced_error\\\":\\n tbSet = testSet[testSet[tree.col] >= tree.value] #find which test observations belong to this tree's true branch\\n fbSet = testSet[testSet[tree.col] < tree.value] #find which test observations belong to this tree's false branch\\n \\n if tree.tb.results is None: #Check if the true branch of this sub-tree is a leaf\\n ptb = prune(tree.tb, tbSet, res, technique) #If not, recursively travel down the true branch and prune it.\\n else:\\n ptb = tree.tb #If the true branch is a leaf, then the true branch has--in essence--already been pruned.\\n if tree.fb.results is None: #Check if the false branch of this sub-tree is a leaf\\n pfb = prune(tree.fb, fbSet, res, technique) #If not, recursively travel down the false branch and prune it.\\n else:\\n pfb = tree.fb #If the false branch is a leaf, then the false branch has--in essence--already been pruned.\\n \\n #Sum the number of misclassifications of the test data at each of the leaves of this node\\n wrong_in_leaves = __deep_count_errors(ptb, tbSet, res) + __deep_count_errors(pfb, fbSet, res)\\n \\n #Count the number of misclassificationsof the test data that would occur if this node were treated as a leaf\\n wrong_at_node = __count_errors(tree, testSet, res)\\n \\n #Assess whether or not treating the node as a leaf improves the accuracy on the test set\\n if wrong_at_node <= wrong_in_leaves: \\n #NOTE:The following line of code seems slightly redundant since count_errors(tree, testSet, res) had to call \\n #__get_results(tree). I should set up some way to save the output of that function call instead of calling it twice.\\n return decisionNode(results = __get_results(tree)) #If so, return a decisionNode where the node is a leaf\\n else:\\n #If not, return a decisionNode where the node splits on the same column and value as before, but the \\n #true and false branches are the pruned-versions of the original true and false branches. See above for\\n #definition of ptb and pfb\\n return decisionNode(col = tree.col, value = tree.value, tb = ptb, fb = pfb)\",\n \"def tdidt(instances, available_attributes, domain):\\n\\n # Select attribute, initialize tree\\n split_attrib, leaves = select_attribute(instances, available_attributes, domain)\\n tree = [\\\"Attribute\\\", \\\"att\\\" + str(split_attrib)]\\n rem_attribs = [ x for x in available_attributes if x != split_attrib]\\n\\n # partition on attribute\\n for value, partition in partition_instances(instances, split_attrib, domain).items():\\n val_segment = [\\\"Value\\\", value]\\n\\n \\n # CASE 1: all values are the same\\n if(len(leaves) > 0 and value == leaves[0][0]):\\n val_segment.append([\\\"Leaf\\\", *leaves[0][1:]])\\n leaves.pop(0)\\n\\n # CASE 2: there are instances, but no more attributes\\n elif(len(partition) > 0 and len(rem_attribs) == 0):\\n most_common = max(partition_stats(partition, -1), key=lambda x: x[1])\\n val_segment.append([\\\"Leaf\\\", *most_common])\\n \\n # CASE 3: no instances, replace attribute with leaf node\\n elif(len(partition) == 0):\\n most_common = max(partition_stats(instances, -1), key=lambda x: x[1])\\n return [\\\"Leaf\\\", *most_common]\\n \\n # Default case. Recurse on partition\\n else:\\n val_segment.append(tdidt(partition, rem_attribs, domain))\\n\\n tree.append(val_segment)\\n return tree\",\n \"def _K_compute_eq(self):\\r\\n t_eq = self._t[self._index==0]\\r\\n if self._t2 is None:\\r\\n if t_eq.size==0:\\r\\n self._K_eq = np.zeros((0, 0))\\r\\n return\\r\\n self._dist2 = np.square(t_eq[:, None] - t_eq[None, :])\\r\\n else:\\r\\n t2_eq = self._t2[self._index2==0]\\r\\n if t_eq.size==0 or t2_eq.size==0:\\r\\n self._K_eq = np.zeros((t_eq.size, t2_eq.size))\\r\\n return\\r\\n self._dist2 = np.square(t_eq[:, None] - t2_eq[None, :])\\r\\n \\r\\n self._K_eq = np.exp(-self._dist2/(2*self.lengthscale*self.lengthscale))\\r\\n if self.is_normalized:\\r\\n self._K_eq/=(np.sqrt(2*np.pi)*self.lengthscale)\",\n \"def get_t_d(conf, r_inputs, d_data):\\n # to crop out unstable part for temporal discriminator, details in TecoGAN supplemental paper\\n crop_size_dt = int(conf.train.crop_size * 4 * conf.gan.crop_dt)\\n offset_dt = (conf.train.crop_size * 4 - crop_size_dt) // 2\\n crop_size_dt = conf.train.crop_size * 4 - offset_dt*2\\n paddings = (0, 0, offset_dt, offset_dt, offset_dt, offset_dt, 0, 0)\\n\\n with nn.parameter_scope(\\\"discriminator\\\"):\\n real_warp = warp_by_flow(d_data.t_targets, d_data.t_vel)\\n real_warp = space_to_depth_disc(real_warp, d_data.t_batch)\\n\\n # equivalent to tf.image.crop_to_bounding_box\\n real_warp = real_warp[:, offset_dt:offset_dt +\\n crop_size_dt, offset_dt:offset_dt+crop_size_dt, :]\\n real_warp = F.pad(real_warp, paddings)\\n before_warp = space_to_depth_disc(d_data.t_targets, d_data.t_batch)\\n t_input = space_to_depth_disc(\\n r_inputs[:, :d_data.t_size, :, :, :], d_data.t_batch)\\n # resizing using bilinear interpolation\\n input_hi = F.interpolate(t_input, scale=(4, 4), mode='linear',\\n channel_last=True)\\n real_warp = F.concatenate(before_warp, real_warp, input_hi)\\n\\n tdiscrim_real_output, real_layers = discriminator(real_warp)\\n\\n fake_warp = warp_by_flow(d_data.t_gen_output, d_data.t_vel)\\n fake_warp = space_to_depth_disc(fake_warp, d_data.t_batch)\\n fake_warp = fake_warp[:, offset_dt:offset_dt +\\n crop_size_dt, offset_dt:offset_dt+crop_size_dt, :]\\n fake_warp = F.pad(fake_warp, paddings)\\n before_warp = space_to_depth_disc(\\n d_data.t_gen_output, d_data.t_batch, inplace=False)\\n fake_warp = F.concatenate(before_warp, fake_warp, input_hi)\\n tdiscrim_fake_output, fake_layers = discriminator(fake_warp)\\n\\n temporal_disc = collections.namedtuple('temporal_disc', 'tdiscrim_real_output,'\\n 'real_layers, tdiscrim_fake_output, fake_layers')\\n return temporal_disc(\\n tdiscrim_real_output=tdiscrim_real_output,\\n real_layers=real_layers,\\n tdiscrim_fake_output=tdiscrim_fake_output,\\n fake_layers=fake_layers\\n )\",\n \"def decision_tree(df):\\n features = df[['Temperature(F)', 'Humidity(%)', 'Visibility(mi)', 'Wind_Speed(mph)',\\n 'Precipitation(in)', 'Amenity', 'Bump', 'Crossing', 'Give_Way',\\n 'Junction', 'No_Exit', 'Railway', 'Roundabout', 'Station', 'Stop',\\n 'Traffic_Calming', 'Traffic_Signal', 'Civil_Twilight', 'Rush Hour', 'Weekend',\\n 'Side_R', 'Season_Spring', 'Season_Summer',\\n 'Season_Winter', 'Weather_Condition_Clear', 'Weather_Condition_Fog',\\n 'Weather_Condition_Other', 'Weather_Condition_Rain',\\n 'Weather_Condition_Snow', 'Weather_Condition_Thunderstorm']]\\n X= features\\n y = df['Severity']\\n clf = DecisionTreeClassifier(min_samples_split=6, min_samples_leaf=2, max_depth=3, \\n criterion = 'gini', random_state=42)\\n clf.fit(X, y)\\n\\n plt.figure(figsize=(25,10))\\n a = plot_tree(clf, \\n feature_names=X.columns.to_list(), \\n filled=True, \\n rounded=True, \\n fontsize=14)\\n plt.savefig(\\\"../Images/rockies_decision_tree.png\\\")\\n plt.show()\",\n \"def __dikjstra(self, start_node):\\n visited = []\\n unvisited = [x for x in self.__node]\\n shortest_dist_from_start_node = 0\\n current_node = start_node\\n\\n current_node.setShortestDist(shortest_dist_from_start_node)\\n\\n while current_node:\\n #check unvisited neighbor\\n for neighbor_node, distance in current_node.getNeighbors().items():\\n #print(neighbor_node.getId(), distance) troubleshoot je ni\\n if neighbor_node in visited:\\n continue\\n\\n #add up shortest_dist_from_start_node with distance from neighbor distance\\n calc_dist = shortest_dist_from_start_node + distance\\n\\n if calc_dist < neighbor_node.getShortestDist():\\n neighbor_node.setShortestDist(calc_dist)\\n neighbor_node.setPrevNode(current_node)\\n\\n # add current node to visited array\\n visited.append(current_node)\\n unvisited.remove(current_node)\\n \\n #update next node and next shortest distance\\n next_shortest_dist_from_start_node = inf\\n next_node = None\\n\\n for unvisited_node in unvisited:\\n if unvisited_node.getShortestDist() < next_shortest_dist_from_start_node:\\n next_shortest_dist_from_start_node = unvisited_node.getShortestDist()\\n next_node = unvisited_node\\n\\n # update current node and shortest distance from start vertex\\n if next_node:\\n current_node = next_node\\n shortest_dist_from_start_node = next_shortest_dist_from_start_node\\n #if there are left over unvisited node\\n else: \\n if unvisited:\\n current_node = unvisited[0]\\n else:\\n current_node = None\",\n \"def tdidt(current_instances, available_attributes, attribute_domains, is_forest, previous_instances=None):\\n\\n # Select an attribute to split on\\n available_dict = {}\\n for key in attribute_domains:\\n try:\\n available_attributes.index(key)\\n #available\\n available_dict[key] = attribute_domains[key]\\n except:\\n #not available, so don't add\\n pass\\n\\n if(previous_instances == None):\\n previous_instances = len(current_instances)\\n\\n split_attribute = None\\n if is_forest:\\n F = 2\\n if len(current_instances) < 2:\\n F = len(current_instances)\\n curr_subset = compute_random_subset(current_instances, F)\\n split_attribute = select_attribute(curr_subset, available_dict)\\n else:\\n split_attribute = select_attribute(current_instances, available_dict)\\n available_attributes.remove(split_attribute)\\n # cannot split on the same attribute twice in a branch\\n # recall: python is pass by object reference!!\\n tree = [\\\"Attribute\\\", split_attribute]\\n\\n # group data by attribute domains (creates pairwise disjoint partitions)\\n partitions = partition_instances(current_instances, split_attribute, attribute_domains)\\n\\n # for each partition, repeat unless one of the following occurs (base case)\\n for attribute_value, partition in partitions.items():\\n value_subtree = [\\\"Value\\\", attribute_value]\\n # CASE 1: all class labels of the partition are the same => make a leaf node\\n if len(partition) > 0 and all_same_class(partition):\\n #print(\\\"CASE 1\\\")\\n leaf = [\\\"Leaf\\\",partition[0][len(partition[0])-1],len(partition),len(current_instances)]\\n value_subtree.append(leaf)\\n # CASE 2: no more attributes to select (clash) => handle clash w/majority vote leaf node\\n elif len(partition) > 0 and len(available_attributes) == 0:\\n #print(\\\"CASE 2\\\")\\n values, sums = get_frequencies(create_list(partition))\\n max_index = sums.index(max(sums)) #finds index of the max count\\n leaf = [\\\"Leaf\\\",values[max_index],max(sums),sum(sums)]\\n value_subtree.append(leaf)\\n # CASE 3: no more instances to partition (empty partition) => backtrack and replace attribute node with majority vote leaf node\\n elif len(partition) == 0:\\n values, sums = get_frequencies(create_list(current_instances))\\n max_index = sums.index(max(sums)) #finds index of the max count\\n return [\\\"Leaf\\\",values[max_index],len(current_instances),previous_instances]\\n else: # all base cases are false... recurse!!\\n subtree = tdidt(partition, available_attributes.copy(),attribute_domains, is_forest, len(current_instances))\\n # need to append subtree to value_subtree and appropriately append value subtree\\n # to tree\\n value_subtree.append(subtree)\\n tree.append(value_subtree)\\n \\n return tree\",\n \"def discretize(self, dt: float) -> 'Trajectory':\\n return self.discretize_state(dt)\",\n \"def __build_tree__(self, features, classes, depth=0):\\n\\n # TODO: finish this.\\n root = None\\n if (len(set(classes)) <= 1) and (len(classes) != 0) :\\n return DecisionNode(None,None,None,classes[0])\\n elif (len(classes) == 0):\\n return DecisionNode(None,None,None,2)\\n elif depth == self.depth_limit:\\n return DecisionNode(None,None,None,max(set(classes), key=list(classes).count))\\n else:\\n# if depth == 0:\\n features = np.array(features)\\n classes = np.array(classes).reshape(-1,1)\\n feat_shape = features.shape\\n sample_list = range(feat_shape[0])\\n gains = np.zeros((feat_shape[1]))\\n indices = np.zeros((feat_shape[1]))\\n for i in range(feat_shape[1]):\\n attribute = features[:,i]\\n for j in range(20):\\n split_indx = int(np.random.choice(sample_list, replace=False))\\n idx_above = np.where(attribute > attribute[split_indx])[0]\\n idx_below = np.where(attribute < attribute[split_indx])[0]\\n classes_below = classes[idx_below,:].reshape(1,-1)[0]\\n classes_above = classes[idx_above,:].reshape(1,-1)[0]\\n gain = gini_gain(list(classes.reshape(1,-1)[0]),[list(classes_below),list(classes_above)])\\n if gain > gains[i]:\\n gains[i] = gain\\n indices[i] = split_indx\\n indx = np.argmax(gains)\\n split_indx = int(indices[indx])\\n attribute = features[:,indx]\\n idx_above = np.where(attribute > attribute[split_indx])[0]\\n idx_below = np.where(attribute < attribute[split_indx])[0] \\n features_below = features[idx_below,:]\\n features_above = features[idx_above,:]\\n classes_below = classes[idx_below,:].reshape(1,-1)[0]\\n classes_above = classes[idx_above,:].reshape(1,-1)[0]\\n if (len(classes_below) != 0) and (len(classes_above) != 0):\\n root = DecisionNode(None,None,lambda feat:feat[indx] > features[split_indx,indx])\\n root.left = self.__build_tree__(features_above, classes_above, depth+1)\\n root.right = self.__build_tree__(features_below, classes_below, depth+1)\\n return root\\n elif (len(classes_below) == 0) and (len(classes_above) != 0):\\n return DecisionNode(None,None,None,max(set(classes_above), key=list(classes_above).count))\\n elif (len(classes_above) == 0) and (len(classes_below) !=0):\\n return DecisionNode(None,None,None,max(set(classes_below), key=list(classes_below).count))\\n else:\\n return DecisionNode(None,None,None,2)\",\n \"def __init__(self):\\n self.tree = nx.Graph() \\n self.orig_dist_matrix = pd.DataFrame()\\n self.work_dist_matrix = pd.DataFrame() \\n self.cluster_map = {} \\n self.class_map = {}\",\n \"def _dK_ode_dtheta(self, target):\\r\\n t_ode = self._t[self._index>0]\\r\\n dL_dK_ode = self._dL_dK[self._index>0, :]\\r\\n index_ode = self._index[self._index>0]-1\\r\\n if self._t2 is None:\\r\\n if t_ode.size==0:\\r\\n return \\r\\n t2_ode = t_ode\\r\\n dL_dK_ode = dL_dK_ode[:, self._index>0]\\r\\n index2_ode = index_ode\\r\\n else:\\r\\n t2_ode = self._t2[self._index2>0]\\r\\n dL_dK_ode = dL_dK_ode[:, self._index2>0]\\r\\n if t_ode.size==0 or t2_ode.size==0:\\r\\n return\\r\\n index2_ode = self._index2[self._index2>0]-1\\r\\n\\r\\n h1 = self._compute_H(t_ode, index_ode, t2_ode, index2_ode, stationary=self.is_stationary, update_derivatives=True)\\r\\n #self._dK_ddelay = self._dh_ddelay\\r\\n self._dK_dsigma = self._dh_dsigma\\r\\n\\r\\n if self._t2 is None:\\r\\n h2 = h1\\r\\n else:\\r\\n h2 = self._compute_H(t2_ode, index2_ode, t_ode, index_ode, stationary=self.is_stationary, update_derivatives=True)\\r\\n\\r\\n #self._dK_ddelay += self._dh_ddelay.T\\r\\n self._dK_dsigma += self._dh_dsigma.T\\r\\n # C1 = self.sensitivity\\r\\n # C2 = self.sensitivity\\r\\n\\r\\n # K = 0.5 * (h1 + h2.T)\\r\\n # var2 = C1*C2\\r\\n # if self.is_normalized:\\r\\n # dk_dD1 = (sum(sum(dL_dK.*dh1_dD1)) + sum(sum(dL_dK.*dh2_dD1.T)))*0.5*var2\\r\\n # dk_dD2 = (sum(sum(dL_dK.*dh1_dD2)) + sum(sum(dL_dK.*dh2_dD2.T)))*0.5*var2\\r\\n # dk_dsigma = 0.5 * var2 * sum(sum(dL_dK.*dK_dsigma))\\r\\n # dk_dC1 = C2 * sum(sum(dL_dK.*K))\\r\\n # dk_dC2 = C1 * sum(sum(dL_dK.*K))\\r\\n # else:\\r\\n # K = np.sqrt(np.pi) * K\\r\\n # dk_dD1 = (sum(sum(dL_dK.*dh1_dD1)) + * sum(sum(dL_dK.*K))\\r\\n # dk_dC2 = self.sigma * C1 * sum(sum(dL_dK.*K))\\r\\n\\r\\n\\r\\n # dk_dSim1Variance = dk_dC1\\r\\n # Last element is the length scale.\\r\\n (dL_dK_ode[:, :, None]*self._dh_ddelay[:, None, :]).sum(2)\\r\\n\\r\\n target[-1] += (dL_dK_ode*self._dK_dsigma/np.sqrt(2)).sum()\\r\\n\\r\\n\\r\\n # # only pass the gradient with respect to the inverse width to one\\r\\n # # of the gradient vectors ... otherwise it is counted twice.\\r\\n # g1 = real([dk_dD1 dk_dinvWidth dk_dSim1Variance])\\r\\n # g2 = real([dk_dD2 0 dk_dSim2Variance])\\r\\n # return g1, g2\\\"\\\"\\\"\\r\",\n \"def result(data, taxon_names, tree, sample_names, **kwargs):\\r\\n\\r\\n envs = make_envs_dict(data, sample_names, taxon_names)\\r\\n unifrac_res = fast_unifrac(\\r\\n tree, envs, weighted=weighted, metric=metric,\\r\\n is_symmetric=is_symmetric, modes=[\\\"distance_matrix\\\"], **kwargs)\\r\\n dist_mtx = _reorder_unifrac_res(unifrac_res['distance_matrix'],\\r\\n sample_names)\\r\\n return dist_mtx\",\n \"def buildGraph(pickl, his=False):\\n\\n dic = pickl\\n\\n dic1 = dic\\n G = nx.Graph()\\n dic3 = dict(dic)\\n checked = []\\n\\n # Adding nodes with bios greater than 30 words.\\n for key in dic:\\n if((re.sub(\\\"[ ]+\\\", \\\"\\\", dic[key]) != \\\"\\\") and len(dic[key])) > 30:\\n G.add_node(key)\\n else:\\n del dic3[key]\\n\\n dic1 = dic3\\n\\n vect = TfidfVectorizer(min_df=1)\\n coefs = list()\\n\\n joint_dict = dict()\\n # Cosine similarity measure matrix\\n F = vect.fit_transform(dic3.values())\\n Cosine_mat = (F*F.T).A # Symmetric matrix:\\n # Traverse uper triangle for cosine similarity measures.\\n for i, key in enumerate(dic3):\\n for j, key1 in enumerate(dic1):\\n if(i > j):\\n # obtain coef for corresponding key\\n tfidf = Cosine_mat[i, j]\\n # Repeated nodes must be filtered\\n if dic[key] == dic[key1]:\\n\\n continue\\n else:\\n coefs.append(tfidf)\\n joint_dict[str(key) + str(key1)] = tfidf\\n\\n data = [c for c in coefs if c]\\n # max(data)\\n\\n mu = np.mean(data)\\n std = np.std(data)\\n binwidth = 0.007\\n if his:\\n plt.subplot(1, 2, 0)\\n plt.hist(data, bins=np.arange(min(data), max(data) + binwidth, binwidth))\\n # PLot gaussian fit contrast\\n plt.xlabel(\\\"$cos(\\\\\\\\theta)$\\\")\\n plt.ylabel(\\\"frecuency count of $cos(\\\\\\\\theta)$ values\\\")\\n plt.subplot(1, 2, 1)\\n plt.plot(np.arange(0, max(data), 0.001),\\n gaussian(np.arange(0, max(data), 0.001), mu, std),\\n linewidth=2)\\n plt.xlabel(\\\"$cos(\\\\\\\\theta)$\\\")\\n plt.ylabel(\\\"fitted gaussian\\\")\\n plt.show()\\n\\n # Edge creation !\\n for key in dic3:\\n for key1 in dic1:\\n if(key != key1):\\n try:\\n x = joint_dict[str(key) + str(key1)]\\n # If cosine similarity is an outlier with 95% change\\n # Make edge between nodes that conform the similarity\\n if(x - mu > 2 * std):\\n G.add_edge(key, key1)\\n except:\\n pass\\n\\n # Return the conected component with largest cardinality of nodes\\n # Throw away small connected components we are interested in the big one\\n # For our mini project exploration purposes\\n G = max(nx.connected_component_subgraphs(G), key=len)\\n return G\",\n \"def all_DU_paths(CG, T):\\n path_to_visit = []\\n path_not_visited = []\\n for var in CG.var:\\n vert_var_in_defv=set()\\n vert_var_in_refv=set()\\n for vert in CG.vertices:\\n if var in vert.defv:\\n vert_var_in_defv.add(vert.label)\\n elif var in vert.refv:\\n vert_var_in_refv.add(vert.label)\\n couples = product(vert_var_in_defv, vert_var_in_refv)\\n for vert_def, vert_ref in couples:\\n #paths_remaining = list(CG.simple_partial_paths[(vert_def, vert_ref)])\\n path_to_visit += list(CG.simple_partial_paths[(vert_def, vert_ref)])\\n path_not_visited += list(CG.simple_partial_paths[(vert_def, vert_ref)])\\n for t in T:\\n path, variables = apply_path(CG, t)\\n vert_found = False\\n for label in path:\\n cur_vertice=find_vertice_with_label(CG,label)\\n if vert_def == label:\\n vert_found = True\\n\\n elif vert_ref == label and vert_found:\\n if path in path_not_visited:\\n path_not_visited.remove(path)\\n break\\n\\n elif vert_ref != label and var in cur_vertice.refv:\\n break\\n #print('Test failed')\\n #print(paths_remaining, var,vert_def, vert_ref, path)\\n #return False\\n\\n #if paths_remaining:\\n # print('Test failed')\\n # print(paths_remaining, var, vert_def, vert_ref, path)\\n # return False\\n\\n Graph.coverage_criteria2(path_to_visit, \\\"not_visited\\\", path_not_visited)\\n if path_not_visited:\\n print('Test failed')\\n \\n else:\\n print('Test passed')\\n return True\",\n \"def cluster(name, epsilon: float, delta: float, tau: float, tree_structure='automatic', threads=1,\\n sequential_search_threshold=2000, return_list=False, epsilon_multiple=None, output_name=None):\\n global dataListGlobal, index_inverse_global\\n\\n if output_name is None:\\n output_name = get_filename(name)\\n\\n neighbour_radius = max(delta, tau)\\n\\n # Choose tree structure if tree structure is automatic\\n if tree_structure == 'automatic':\\n if neighbour_radius > 0.05:\\n tree_structure = 'm'\\n else:\\n if neighbour_radius > 0.01:\\n tree_structure = 'kd'\\n else:\\n tree_structure = 'r'\\n\\n if tree_structure not in ['r', 'm', 'kd']:\\n raise Exception(\\\"Tree structure can only be m, r, kd\\\")\\n\\n logging.info(\\\"Tree structure chosen: %s\\\", tree_structure)\\n\\n # Setup\\n dataList = []\\n with open(name) as csvfile:\\n data = csv.reader(csvfile)\\n for row in data:\\n dataList.append(tuple([float(s) for s in row]))\\n\\n\\n # Only a portion of the data is taken\\n portion = dataList.__len__()\\n #portion = 500\\n dataList = dataList[0:portion]\\n\\n original_data = dataList.copy()\\n\\n\\n if tree_structure == 'm':\\n stime = time.time()\\n logging.info(\\\"Sorting using z-curve...\\\")\\n # Sort dataList:\\n dataList.sort(key=lambda x: morton_code(x))\\n logging.info(\\\"Data set sorted. Time for sorting: %f\\\", (time.time() - stime))\\n\\n #Define constants\\n n: int = len(dataList)\\n d: int = len(dataList[0])\\n dimension_const: float = n * delta ** d\\n\\n dataListGlobal = dataList\\n\\n mtr.dataList = dataList\\n\\n #Define sq_metric\\n sq_metric_index = mtr.choose_sq_metric_index(d)\\n sq_metric = mtr.choose_sq_metric(d)\\n\\n #Define metric\\n metric_index = mtr.choose_metric_index(d)\\n\\n\\n\\n # Create index inverse\\n index_inverse_global = {dataList[i]: i for i in range(n)}\\n\\n\\n # Build tree\\n stime = time.time()\\n if tree_structure == 'm':\\n logging.info(\\\"Filling M-Tree...\\\")\\n tree = MTree(metric_index, 6, 20, sq_metric=sq_metric_index)\\n for p in range(n):\\n tree.insert(p)\\n logging.info(\\\"Done filling M-Tree. Time for filling: %f\\\", (time.time()-stime))\\n if tree_structure == 'r':\\n logging.info(\\\"Filling R-Tree...\\\")\\n #Make a copy, since bulk_load_r_tree sorts dataList\\n dataListCopy = dataList.copy()\\n tree = bulk_load_r_tree_str(dataListCopy, 40, 20, 20, 10, sq_metric=sq_metric)\\n logging.info(\\\"Done filling R-Tree. Time for filling: %f\\\", (time.time() - stime))\\n if tree_structure == 'kd':\\n logging.info(\\\"Constructing KD-Tree...\\\")\\n tree = create_kd_tree(dataList, 50, sq_metric=sq_metric)\\n logging.info(\\\"KD-Tree constructed. Time for construction: %f\\\", (time.time() - stime))\\n\\n\\n stime = time.time()\\n logging.info(\\\"Precomputing neighbourhood list...\\\")\\n number_of_cores = multiprocessing.cpu_count()\\n if threads == 'automatic':\\n if n > 5000:\\n number_of_threads = math.ceil(number_of_cores/2)\\n else:\\n if n > 2500:\\n number_of_threads = min(number_of_cores, 2)\\n else:\\n number_of_threads = 1\\n else:\\n number_of_threads = threads\\n\\n logging.info(\\\"Number of cores detected: %s. Using %s thread(s)\\\", number_of_cores, number_of_threads)\\n\\n\\n neighbour_radius_dict = compute_neighbours(number_of_threads, n, tree, tree_structure, tau)\\n\\n\\n logging.info(\\\"max(tau,delta)-neighbourhood list computed. Time for computation: %f\\\", (time.time()-stime))\\n average_neighbour_number = sum([neighbour_radius_dict[p].__len__() for p in range(n)])/n\\n logging.info(\\\"Average number of neighbours: %d\\\", average_neighbour_number)\\n stime = time.time()\\n if math.isclose(delta,neighbour_radius) and math.isclose(tau, neighbour_radius):\\n logging.info(\\\"Delta = Tau, saving distance calculations\\\")\\n neighbour_dict = neighbour_radius_dict\\n density_dict = [neighbour_radius_dict[i].__len__() for i in range(n)]\\n else:\\n if math.isclose(delta, neighbour_radius):\\n logging.info(\\\"Tau is smaller than delta, computing tau-neighbours...\\\")\\n if average_neighbour_number <= sequential_search_threshold:\\n #Compute tau-neighbours sequentially\\n neighbour_dict = [[] for x in range(n)]\\n tau_sq = tau*tau\\n for p in range(n):\\n for y in neighbour_radius_dict[p]:\\n if y <= p and sq_metric_index(y, p) <= tau_sq:\\n neighbour_dict[p].append(y)\\n neighbour_dict[y].append(p)\\n else:\\n #Compute tau-neighbours through the tree structure\\n neighbour_dict = compute_neighbours(number_of_threads, n, tree, tree_structure, tau)\\n density_dict = [l.__len__() for l in neighbour_radius_dict]\\n logging.info(\\\"Tau-neighbours computed. Time for computation: %f\\\", (time.time()-stime))\\n else:\\n logging.info(\\\"Delta is smaller than tau, computing density...\\\")\\n if average_neighbour_number <= sequential_search_threshold:\\n #Compute delta-neighbours sequentially\\n density_dict = [0 for x in range(n)]\\n delta_sq = delta * delta\\n for p in range(n):\\n for y in neighbour_radius_dict[p]:\\n if y <= p and sq_metric_index(y, p) <= delta_sq:\\n density_dict[p] += 1\\n density_dict[y] += 1\\n else:\\n # Compute delta-neighbours through the tree structure\\n d_dict = compute_neighbours(number_of_threads, n, tree, tree_structure, delta)\\n density_dict = [d_dict[i].__len__() for i in range(n)]\\n neighbour_dict = neighbour_radius_dict\\n logging.info(\\\"Density computed. Time for computation: %f\\\", (time.time()-stime))\\n\\n\\n #Allocate a disjoint-set (find-union) data structure\\n disjoint_set_dict = [None]*n\\n rank_dict = [None]*n\\n\\n for x in range(n):\\n #Initialize all disjoint sets\\n disjoint_set_dict[x] = x\\n rank_dict[x] = 1\\n\\n\\n inc_constant: float = 1 / dimension_const\\n\\n upper_bound_on_density = max([density_dict[p] for p in range(n)])/dimension_const\\n logging.info(\\\"Upper bound on density: %f. Required iterations: %f\\\", upper_bound_on_density, upper_bound_on_density*dimension_const)\\n\\n if epsilon_multiple != None:\\n epsilon = math.sqrt(upper_bound_on_density/dimension_const)*epsilon_multiple\\n\\n rho: float = upper_bound_on_density\\n\\n old_level_set = {}\\n\\n number_of_connected_components = 0\\n\\n #Save the last branching point\\n\\n saved_rho = 0\\n saved_disjoint_set_dict = []\\n saved_level_set_rho = {}\\n saved_number_of_components = 0\\n logging.info(\\\"Entering loop\\\")\\n start_time = time.time()\\n # The incrementation is decreased until rho = 0\\n while rho >= -1.0e-9:\\n logging.info(\\\"--- new loop ---\\\")\\n epsrho: float = rho + 2 * epsilon\\n logging.info(\\\"Computing level set...\\\")\\n\\n level_set_rho = {y:True for y in range(n) if density_dict[y]/dimension_const >= rho}\\n if rho == upper_bound_on_density:\\n number_of_connected_components = level_set_rho.__len__()\\n\\n logging.info(\\\"Level set computed\\\")\\n\\n logging.info(\\\"Computing connected components...\\\")\\n\\n #For each new element call union\\n for y in level_set_rho:\\n if y not in old_level_set:\\n number_of_connected_components += 1\\n for z in neighbour_dict[y]:\\n if z in level_set_rho:\\n if dst.union(y, z, disjoint_set_dict, rank_dict):\\n number_of_connected_components -= 1\\n\\n logging.info(\\\"Connected components computed: %d\\\", number_of_connected_components)\\n\\n logging.info(\\\"Removing insignificant components...\\\")\\n\\n #TODO: Optimize this part, i.e. persisting_components should be an integer\\n persisting_components = {}\\n for y in level_set_rho:\\n if density_dict[y]/dimension_const >= epsrho:\\n persisting_components[dst.get_root(y, disjoint_set_dict)] = True\\n\\n logging.info(\\\"Insignificant components removed. Now there are: %d\\\", persisting_components.__len__())\\n\\n if persisting_components.__len__() > 1:\\n logging.info(\\\"Branching point, data saved.\\\")\\n #Save the branching point\\n saved_rho = rho\\n saved_disjoint_set_dict = disjoint_set_dict.copy()\\n saved_level_set_rho = level_set_rho.copy()\\n saved_number_of_components = persisting_components.__len__()\\n\\n # Decrease rho\\n rho -= inc_constant\\n old_level_set = level_set_rho\\n logging.info(\\\"Decreasing rho to: %f\\\", rho)\\n\\n logging.info(\\\"--- %f seconds in loop ---\\\", (time.time()-start_time))\\n\\n logging.info(\\\"Components detected %s\\\", saved_number_of_components)\\n logging.info(\\\"Writing to file...\\\")\\n\\n #Determine component dictionary\\n\\n component_dict = [None]*n\\n counter = 1\\n for y in range(n):\\n if y in saved_level_set_rho:\\n root = dst.get_root(y, saved_disjoint_set_dict)\\n if not component_dict[root] is None:\\n component_dict[y] = component_dict[root]\\n else:\\n component_dict[root] = counter\\n component_dict[y] = counter\\n counter += 1\\n else:\\n component_dict[y] = 0\\n\\n #Write to file in the original order\\n failed = saved_level_set_rho.__len__() == 0\\n\\n with open(output_name+'.result.csv', 'w') as file:\\n for p in original_data:\\n if failed:\\n file.write(str(1))\\n else:\\n file.write(str(component_dict[index_inverse_global[p]]))\\n file.write(', ')\\n file.write(', '.join(map(str,p)))\\n file.write('\\\\n')\\n file.close()\\n\\n #Create list of components\\n if return_list:\\n list_of_components = [[] for i in range(counter)]\\n for y in range(n):\\n list_of_components[component_dict[y]].append(dataList[y])\\n return list_of_components\\n\\n logging.info(\\\"Done.\\\")\",\n \"def decision_tree(data_frame, filename=0):\\n\\tprint \\\"Building decision tree...\\\"\\n\\tr = robjects.r\\n\\trpart = importr(\\\"rpart\\\")\\n\\tfit = rpart.rpart(\\\"category~bpm+speechiness+time_sig+key+duration+loudness+\\\\\\n\\t\\t\\tend_of_fade_in+start_of_fade_out+bpm_range+\\\\\\n\\t\\t\\tmax_bpm_spike+num_keys\\\", data=data_frame, method=\\\"class\\\", \\n\\t\\t\\tna_action='na.rpart', control='rpart.control(cp = .0001)')\\n\\trpart.printcp(fit)\\n\\tr.plot(fit, uniform=True, main=\\\"Classification Tree for Genre\\\")\\n\\tr.text(fit, use_n=True, all=True, cex=.8)\\n\\tif filename != 0:\\n\\t\\trpart.post(fit, file=filename, title=\\\"Classification Tree for Genre\\\")\\n\\traw_input(\\\"> Press enter to continue.\\\")\\n\\treturn fit\",\n \"def decision_tree(df, variables, test_size):\\n from sklearn.model_selection import train_test_split\\n from sklearn import tree\\n\\n # Define input\\n X = encoding_df(df, variables)\\n\\n # Set validation\\n y = df['target']\\n\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)\\n\\n clf = tree.DecisionTreeRegressor()\\n clf = clf.fit(X_train, y_train)\\n\\n print(compute_rmse(y_test, clf.predict(X_test)))\\n return clf.predict(X_test), y_test\",\n \"def build_graph(self, K, D):\\r\\n tf.reset_default_graph()\\r\\n\\r\\n # X -> N x D\\r\\n self.X = tf.placeholder(tf.float32, shape=(None, D), name='X')\\r\\n\\r\\n # Mu -> K x D\\r\\n self.Mu = tf.get_variable('Mu', shape=(K, D),\\r\\n initializer=tf.initializers.truncated_normal())\\r\\n\\r\\n # d2 -> N x K (pairwise distance matrix)\\r\\n d2 = self.distance(self.X, self.Mu)\\r\\n\\r\\n # min(N x K) => (N)\\r\\n min_d2 = tf.reduce_min(d2, axis=1)\\r\\n\\r\\n # loss = sum(N) => scalar\\r\\n self.loss = tf.reduce_sum(min_d2, axis=0, name='loss')\\r\\n self.opt_op = tf.train.AdamOptimizer(\\r\\n learning_rate=1e-2,\\r\\n beta1=0.9,\\r\\n beta2=0.99,\\r\\n epsilon=1e-5\\r\\n ).minimize(self.loss)\\r\\n\\r\\n self.predict_op = tf.argmin(d2, axis=1)\",\n \"def tldiffusion(self, dt):\\n\\n # Reset erosion, depo, trans and flux_in to 0\\n self.erosion[:] = 0.0\\n self.depo[:] = 0.0\\n self.trans[:] = 0.0\\n self.flux_in[:] = 0.0\\n\\n # Downstream steepest slope at node:\\n self.steepest = self.grid.at_node[\\\"topographic__steepest_slope\\\"]\\n # On each node, node ID of downstream receiver node\\n # (on node (i), ID of node that receives flow from node (i)):\\n self.receiver = self.grid.at_node[\\\"flow__receiver_node\\\"]\\n\\n dx = self.grid.dx\\n cores = self.grid.core_nodes\\n\\n # Calculate influx rate on node i = outflux of nodes\\n # whose receiver is i\\n for i in self.grid.core_nodes:\\n self.flux_in[self.receiver[i]] += self.flux_out[i]\\n\\n # Calculate transport coefficient\\n # When S ~ Scrit, d_coeff is set to \\\"infinity\\\", for stability and\\n # so that there is no deposition\\n if self.steepest[i] >= self.slope_crit:\\n self.d_coeff[i] = 1000000000.0\\n else:\\n self.d_coeff[i] = 1 / (\\n 1 - (np.power(((self.steepest[i]) / self.slope_crit), 2))\\n )\\n\\n # Calculate deposition rate on node\\n self.depo[cores] = self.flux_in[cores] / self.d_coeff[cores]\\n\\n # Calculate erosion rate on node (positive value)\\n # If S > Scrit, erosion is simply set for the slope to return to Scrit\\n # Otherwise, erosion is slope times erodibility coefficent\\n for i in self.grid.core_nodes:\\n if self.steepest[i] > self.slope_crit:\\n self.erosion[i] = dx * (self.steepest[i] - self.slope_crit) / (100 * dt)\\n else:\\n self.erosion[i] = self.k * self.steepest[i]\\n\\n # Update elevation\\n self.elev[i] += (-self.erosion[i] + self.depo[i]) * dt\\n\\n # Calculate transfer rate over node\\n self.trans[cores] = self.flux_in[cores] - self.depo[cores]\\n\\n # Calculate outflux rate\\n self.flux_out[:] = self.erosion + self.trans\",\n \"def __init__(self, train_data=None, train_label=None, dimensions=None,\\n axis=0, sel_axis=None):\\n # As train_data is a list of samples, we use dict() to change data\\n # structure of samples.\\n self.train_data = train_data\\n self.train_label = train_label\\n self.labels = set(self.train_label)\\n self.class_prb = self._calc_train_class_prb(self.train_label)\\n self.kdtree = kdtree.create(\\n copy.deepcopy(train_data), dimensions, axis, sel_axis)\",\n \"def create(self, X, dimensions=None):\\n n_samples, n_features = X.shape\\n self.X = X\\n if not dimensions:\\n dimensions = n_features\\n\\n self.root = KdNode(depth=0,\\n splitting_feature=0,\\n splitting_value=np.median(X[:, 0]),\\n idx=np.arange(n_samples),\\n parent=None)\\n # grow the tree by DFS\\n stack = [self.root]\\n while stack:\\n node = stack.pop()\\n # splitting samples in the node into two children\\n sample_values = X[node.idx, node.splitting_feature]\\n left_idx = node.idx[sample_values < node.splitting_value]\\n right_idx = node.idx[sample_values > node.splitting_value]\\n node.idx = node.idx[sample_values == node.splitting_value]\\n # since left and right subtrees are divided by the median of their parent,\\n # the sizes of the two subtrees are expected to be equal\\n assert len(left_idx) == len(right_idx),\\\\\\n 'left and right subtrees should have the same number of samples'\\n # append left and right children\\n if len(left_idx):\\n child_depth = node.depth + 1\\n child_feature = (node.depth + 1) % dimensions\\n left_value = np.median(X[left_idx, child_feature])\\n node.left = KdNode(depth=child_depth, splitting_feature=child_feature,\\n splitting_value=left_value, idx=left_idx, parent=node)\\n right_value = np.median(X[right_idx, child_feature])\\n node.right = KdNode(depth=child_depth, splitting_feature=child_feature,\\n splitting_value=right_value, idx=right_idx, parent=node)\\n stack.append(node.left)\\n stack.append(node.right)\",\n \"def quantifier_lower(self):\\n tree = deepcopy(self)\\n all_DPs = [subtree for subtree in tree.subtree_dict.values() if (isinstance(subtree.label, FeatStructNonterminal) and (subtree.label[feature_type] == 'DP'))]\\n trace_DPs = [DP for DP in all_DPs if DP.label['TRACE']]\\n non_trace_DPs = diff(all_DPs, trace_DPs)\\n for DP in trace_DPs:\\n for other_DP in non_trace_DPs:\\n if (DP.children[0].label.index == other_DP.ID):\\n DP.label = deepcopy(other_DP.label)\\n DP.children = deepcopy(other_DP.children)\\n break\\n tree = remove_QRed_DPs(tree)\\n tree.label_nodes()\\n tree.make_nx_tree()\\n return tree\",\n \"def __init__(self):\\n\\t\\tself.clust_idx_part = -1\\n\\t\\t\\\"\\\"\\\"\\n\\t\\tthis list stores the indices of input trees having this taxon\\n\\t\\t\\\"\\\"\\\"\\n\\t\\tself.Support_Tree_List = []\",\n \"def _K_compute_ode_eq(self, transpose=False):\\r\\n\\r\\n if self._t2 is not None:\\r\\n if transpose:\\r\\n t_eq = self._t[self._index==0]\\r\\n t_ode = self._t2[self._index2>0]\\r\\n index_ode = self._index2[self._index2>0]-1\\r\\n else:\\r\\n t_eq = self._t2[self._index2==0]\\r\\n t_ode = self._t[self._index>0]\\r\\n index_ode = self._index[self._index>0]-1\\r\\n else:\\r\\n t_eq = self._t[self._index==0]\\r\\n t_ode = self._t[self._index>0]\\r\\n index_ode = self._index[self._index>0]-1\\r\\n\\r\\n if t_ode.size==0 or t_eq.size==0:\\r\\n if transpose:\\r\\n self._K_eq_ode = np.zeros((t_eq.shape[0], t_ode.shape[0]))\\r\\n else:\\r\\n self._K_ode_eq = np.zeros((t_ode.shape[0], t_eq.shape[0]))\\r\\n return\\r\\n\\r\\n t_ode_mat = t_ode[:, None]\\r\\n t_eq_mat = t_eq[None, :]\\r\\n if self.delay is not None:\\r\\n t_ode_mat -= self.delay[index_ode, None]\\r\\n diff_t = (t_ode_mat - t_eq_mat)\\r\\n\\r\\n inv_sigma_diff_t = 1./self.sigma*diff_t\\r\\n decay_vals = self.decay[index_ode][:, None]\\r\\n half_sigma_d_i = 0.5*self.sigma*decay_vals\\r\\n\\r\\n if self.is_stationary:\\r\\n ln_part, signs = ln_diff_erfs(inf, half_sigma_d_i - inv_sigma_diff_t, return_sign=True)\\r\\n else:\\r\\n ln_part, signs = ln_diff_erfs(half_sigma_d_i + t_eq_mat/self.sigma, half_sigma_d_i - inv_sigma_diff_t, return_sign=True)\\r\\n sK = signs*np.exp(half_sigma_d_i*half_sigma_d_i - decay_vals*diff_t + ln_part)\\r\\n\\r\\n sK *= 0.5\\r\\n\\r\\n if not self.is_normalized:\\r\\n sK *= np.sqrt(np.pi)*self.sigma\\r\\n\\r\\n\\r\\n if transpose:\\r\\n self._K_eq_ode = sK.T\\r\\n else:\\r\\n self._K_ode_eq = sK\",\n \"def discretizespace(self):\\n \\n # Grid\\n self.xgriddim = ( self.x0_n , self.x1_n )\\n \\n self.xd = [ None , None ]\\n self.xd[0] = np.linspace( self.DS.x_lb[0] , self.DS.x_ub[0] , self.x0_n )\\n self.xd[1] = np.linspace( self.DS.x_lb[1] , self.DS.x_ub[1] , self.x1_n )\\n \\n self.x_grid2node = np.zeros( ( self.x0_n , self.x1_n ) , dtype = int ) # grid of corresponding index\\n \\n # 1-D List of nodes\\n self.nodes_n = self.x0_n * self.x1_n\\n self.nodes_state = np.zeros(( self.nodes_n , self.DS.n ), dtype = float ) # Number of nodes x state dimensions\\n self.nodes_index = np.zeros(( self.nodes_n , self.DS.n ), dtype = int ) # Number of nodes x state dimensions\",\n \"def fit_dtree(X, y):\\n dtree = DecisionTreeRegressor(max_depth=2)\\n dtree.fit(X, y)\\n return dtree\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63991743","0.627999","0.5658187","0.56529063","0.5617922","0.5457617","0.5414897","0.53491503","0.523481","0.51466924","0.5110569","0.51012075","0.5071617","0.50715053","0.5054275","0.50493664","0.5049205","0.5047612","0.50431067","0.50336504","0.5027287","0.5021362","0.5018012","0.5011014","0.50014263","0.49795678","0.4976827","0.4962611","0.4948359","0.49459708","0.49459222","0.49285018","0.49255118","0.4924109","0.49202043","0.49145007","0.49129567","0.49057007","0.49044082","0.49032333","0.48955584","0.48880583","0.48862717","0.4881423","0.48805833","0.4869601","0.4869179","0.48326543","0.48219493","0.48076293","0.48027727","0.4787094","0.47806424","0.47785488","0.47772354","0.4776477","0.4767567","0.4761639","0.47604957","0.47577372","0.47546384","0.475056","0.47434697","0.47367415","0.4736489","0.47350404","0.47342396","0.47281283","0.47241387","0.47179428","0.47135705","0.47108918","0.47098482","0.47035578","0.4701788","0.47004318","0.46973678","0.46946028","0.469191","0.4689032","0.46872076","0.4687151","0.46845293","0.46817753","0.46800333","0.46726477","0.46707863","0.46693936","0.4662625","0.4660163","0.4658857","0.4649365","0.4649114","0.4642083","0.46379152","0.4636058","0.46350563","0.46328646","0.4630957","0.4622696"],"string":"[\n \"0.63991743\",\n \"0.627999\",\n \"0.5658187\",\n \"0.56529063\",\n \"0.5617922\",\n \"0.5457617\",\n \"0.5414897\",\n \"0.53491503\",\n \"0.523481\",\n \"0.51466924\",\n \"0.5110569\",\n \"0.51012075\",\n \"0.5071617\",\n \"0.50715053\",\n \"0.5054275\",\n \"0.50493664\",\n \"0.5049205\",\n \"0.5047612\",\n \"0.50431067\",\n \"0.50336504\",\n \"0.5027287\",\n \"0.5021362\",\n \"0.5018012\",\n \"0.5011014\",\n \"0.50014263\",\n \"0.49795678\",\n \"0.4976827\",\n \"0.4962611\",\n \"0.4948359\",\n \"0.49459708\",\n \"0.49459222\",\n \"0.49285018\",\n \"0.49255118\",\n \"0.4924109\",\n \"0.49202043\",\n \"0.49145007\",\n \"0.49129567\",\n \"0.49057007\",\n \"0.49044082\",\n \"0.49032333\",\n \"0.48955584\",\n \"0.48880583\",\n \"0.48862717\",\n \"0.4881423\",\n \"0.48805833\",\n \"0.4869601\",\n \"0.4869179\",\n \"0.48326543\",\n \"0.48219493\",\n \"0.48076293\",\n \"0.48027727\",\n \"0.4787094\",\n \"0.47806424\",\n \"0.47785488\",\n \"0.47772354\",\n \"0.4776477\",\n \"0.4767567\",\n \"0.4761639\",\n \"0.47604957\",\n \"0.47577372\",\n \"0.47546384\",\n \"0.475056\",\n \"0.47434697\",\n \"0.47367415\",\n \"0.4736489\",\n \"0.47350404\",\n \"0.47342396\",\n \"0.47281283\",\n \"0.47241387\",\n \"0.47179428\",\n \"0.47135705\",\n \"0.47108918\",\n \"0.47098482\",\n \"0.47035578\",\n \"0.4701788\",\n \"0.47004318\",\n \"0.46973678\",\n \"0.46946028\",\n \"0.469191\",\n \"0.4689032\",\n \"0.46872076\",\n \"0.4687151\",\n \"0.46845293\",\n \"0.46817753\",\n \"0.46800333\",\n \"0.46726477\",\n \"0.46707863\",\n \"0.46693936\",\n \"0.4662625\",\n \"0.4660163\",\n \"0.4658857\",\n \"0.4649365\",\n \"0.4649114\",\n \"0.4642083\",\n \"0.46379152\",\n \"0.4636058\",\n \"0.46350563\",\n \"0.46328646\",\n \"0.4630957\",\n \"0.4622696\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.59463567"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":89390,"cells":{"query":{"kind":"string","value":"deletes the specified keys not atomic across shards."},"document":{"kind":"string","value":"def delete(self, *names):\n\n return [shard.delete(*keys) for shard, keys\n in self.gather_keys_by_shard(names)]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def delete_many(self, keys):\n raise NotImplementedError()","async def delete(self, *keys, **kwargs):\n\n def gen_keys(keys):\n all_keys = []\n for key in keys:\n if isinstance(key, list):\n all_keys += gen_keys(keys=key)\n else:\n all_keys.append(key)\n return all_keys\n\n all_keys = gen_keys(keys)\n for key in all_keys:\n await self._client_conn.hdel(key=self.name, field=key)","def delete_many(self, keys):\n return self.delete_many_values(keys)","def delete_many(self, keys):\n try:\n if keys:\n self._cache.delete(*map(self.prepare_key, keys))\n except Exception as err:\n return self.warn_or_error(err)","def delete(cls, keys, pipe=None):\n with cls._pipe(pipe) as pipe:\n core = cls.core(pipe)\n core.delete(*keys)","def delete_many(self, keys, version=None, client=None):\r\n\r\n if client is None:\r\n client = self.get_client(write=True)\r\n\r\n if not keys:\r\n return\r\n\r\n keys = [self.make_key(k, version=version) for k in keys]\r\n try:\r\n return client.delete(*keys)\r\n except ConnectionError:\r\n raise ConnectionInterrupted(connection=client)","def delete_many(self, *keys):\n self.collection.remove({'_id': {'$in': keys}})\n return True","def multi_del(self, keys, no_update_log=False):\n # TODO: write better documentation: why would user need the no_update_log param?\n opts = (no_update_log and TyrantProtocol.RDBMONOULOG or 0)\n if not isinstance(keys, (list, tuple)):\n keys = list(keys)\n\n wait(self.proto.misc(\"outlist\", keys, opts))","def delete(\n self, keys: Optional[Iterable[Text]] = None\n ) -> Dict[Text, StateDictInterface]:\n records = self.execute()\n assert isinstance(records, dict)\n\n self.store.delete_many(records.values(), keys=keys)\n return records","def delete_keys_tags(self,\r\n index,\r\n deletedkeys):\r\n\r\n\r\n for k_temp in deletedkeys:\r\n k_temp = k_temp.strip()\r\n if k_temp in set(self.get_keys()):\r\n self.discard_index_from_key(k_temp, index)\r\n if self.get_indexes_for_key(k_temp) == set():\r\n self.eliminate_key(k_temp)\r\n for t_temp in self.get_tags():\r\n if k_temp in self.get_keys_for_tag(t_temp):\r\n self.discard_key_from_tag(t_temp,k_temp)\r\n if not self.get_keys_for_tag(t_temp):\r\n self.delete_tag(t_temp)","def Delete(keys):\n keys, multiple = NormalizeAndTypeCheckKeys(keys)\n\n if multiple and not keys:\n return\n\n req = datastore_pb.DeleteRequest()\n req.key_list().extend([key._Key__reference for key in keys])\n\n tx = _MaybeSetupTransaction(req, keys)\n\n resp = datastore_pb.DeleteResponse()\n try:\n apiproxy_stub_map.MakeSyncCall('datastore_v3', 'Delete', req, resp)\n except apiproxy_errors.ApplicationError, err:\n raise _ToDatastoreError(err)","def delete(self, *keys: KeyT) -> ResponseT:\n return self._split_command_across_slots(\"DEL\", *keys)","def delete(cls, *keys):\n todelete = []\n namespace, kind, member = Schema.Get(cls)\n for key in keys:\n assert isinstance(key, str)\n todelete.append(Key(namespace, kind, key)) \n Lisa.delete(*todelete)","def _delete_volatile_keys(self, solr_dict):\n\n def delete(del_solr_dict, path_list):\n k = path_list[0]\n if k in del_solr_dict:\n if len(path_list) > 1:\n delete(del_solr_dict[k], path_list[1:])\n else:\n del del_solr_dict[k]\n\n delete(solr_dict, ['response', 'maxScore'])\n delete(solr_dict, ['responseHeader', 'QTime'])","def delete_many(self, keys, version=None):\r\n res = 0\r\n for key in [self.make_key(k, version=version) for k in keys]:\r\n client = self.get_server(key)\r\n res += self.delete(key, client=client)\r\n return res","def del_all(self, items):\n for item in items:\n item.key.delete()\n logger.debug(\"Deleted all the items\")","def delete(self, keys: List[K]) -> List[bool]:\n raise NotImplementedError('delete must be reimplemented in concrete implementation')","def delete(self, *names):\n if len(names) != 1:\n raise RedisClusterException(\"deleting multiple keys is not implemented in pipeline command\")\n\n return self.execute_command('DEL', names[0])","def deleteTable(*keys):\r\n\treturn getGameData().delTable(*keys)","def delete_by_key(self, key_fields):\n tmp = dict(zip(self._key_columns, key_fields))\n return self.delete_by_template(tmp)","def del_quiet(dic, *keys):\n for key in keys:\n try:\n del dic[key]\n except KeyError:\n pass","def delete_key(self,\r\n dkey):\r\n\r\n\r\n if (input(queries.DELETE_CONF_BEG\r\n +dkey+queries.DELETE_CONF_END) in YESTERMS):\r\n\r\n if dkey in self.keys():\r\n\r\n for i_temp in self.get_all_indexes():\r\n if dkey in self.get_keys_from_note(i_temp):\r\n tempnote = self.get_note(i_temp).delete_keys({dkey})\r\n self.add_note(i_temp,note=tempnote)\r\n if self.get_keys_from_note(i_temp) == set():\r\n temp = self.get_keys_from_note(i_temp)\r\n temp.add(VOIDTERM)\r\n self.add_note(i_temp,\r\n keyset_only=temp)\r\n self.add_keys_tags(i_temp,\r\n {VOIDTERM})\r\n\r\n self.delete_keys_tags(i_temp, {dkey})","def remove_keys(data: dict, keys: list[str]) -> None:\n for k in keys:\n _ = data.pop(k, None)","def delete_keys(prefix, *args):\n rc = redis.StrictRedis(host=REDIS_SINGLE_HOST, port=REDIS_PORT, db=0)\n # 如果有多个参数，将多个参数拼接成一个key\n if args:\n for i in args:\n prefix = str(prefix) + str(i)\n keys = rc.keys(\"*\" + prefix + \"*\")\n for key in keys:\n rc.delete(key)","def delete(self, key):","def del_seqs(self, keys):\n for j in range(len(keys)):\n del self._d_seqs[keys[j]]\n self._num_seqs = int(len(self._d_seqs))\n self._d_seqs = self._d_seqs\n self._seqs = list(self._d_seqs)","def delete_multi(self, keys, dead_time=0, key_prefix=''):\n\n\t\tserver_keys, _deprefix = yield self._map_keys_to_servers(keys, key_prefix)\n\n\t\t# send out all requests on each server before reading anything\n\t\tdead_servers = []\n\n\t\tif dead_time is None:\n\t\t\tdead_time = 0\n\n\t\tret = True\n\n\t\tfor server in server_keys.iterkeys():\n\t\t\tcommands = []\n\t\t\tfor prefixed_key, _original_key in server_keys[server]:\n\t\t\t\tcommands.append(\"delete %s %d\\r\\n\" % (prefixed_key, dead_time))\n\n\t\t\ttry:\n\t\t\t\tserver.send_cmds(''.join(commands))\n\t\t\texcept tcp.ConnectionClosedException:\n\t\t\t\tserver.mark_dead()\n\t\t\t\tdead_servers.append(server)\n\n\t\t# if any servers died on the way, don't expect them to respond.\n\t\tfor server in dead_servers:\n\t\t\tdel server_keys[server]\n\n\t\tfor server, keys in server_keys.iteritems():\n\t\t\ttry:\n\t\t\t\tfor _key in keys:\n\t\t\t\t\tres = yield server.read_line()\n\t\t\t\t\tif res != \"DELETED\":\n\t\t\t\t\t\tself._debuglog(\"expected 'DELETED', got %r\" % (res, ))\n\t\t\texcept tcp.ConnectionClosedException:\n\t\t\t\tserver.mark_dead()\n\t\t\t\tret = False\n\n\t\traise StopIteration(ret)","def delete(self, resource, keys, url_prefix, auth, session, send_opts):\n success = True\n exc = HTTPErrorList('At least one key-value update failed.')\n\n for key in keys:\n req = self.get_metadata_request(\n resource, 'DELETE', 'application/json', url_prefix, auth, key)\n prep = session.prepare_request(req)\n resp = session.send(prep, **send_opts)\n if resp.status_code == 204:\n continue\n err = (\n 'Delete failed for {}: {}, got HTTP response: ({}) - {}'\n .format(resource.name, key, resp.status_code, resp.text))\n exc.http_errors.append(HTTPError(err, request=req, response=resp))\n success = False\n\n if not success:\n raise exc","def invalidate_keys(self, keys):\r\n if not keys:\r\n return\r\n flush, flush_keys = self.find_flush_lists(keys)\r\n\r\n if flush:\r\n cache.delete_many(flush)\r\n if flush_keys:\r\n self.clear_flush_lists(flush_keys)","def rem(self, keys: Union[str, Iterable]):\n return(self.db.delVal(db=self.sdb, key=self._tokey(keys)))","def rem(self, keys: Union[str, Iterable]):\n return(self.db.delVal(db=self.sdb, key=self._tokey(keys)))","def rem(self, keys: Union[str, Iterable]):\n return(self.db.delVal(db=self.sdb, key=self._tokey(keys)))","def delete_compound_key(self, ids, entity_name=None):\n try:\n query_result = self.query(\n \"delete\",\n entity_name or self.__entity_name,\n {\n \"options\": ids\n }\n )\n except Exception as error:\n error_detected = error\n return False\n\n return True","def delete_key_HELPER(data_dict, key_list, key_to_delete):\n data_dict = get_key_from_dict_HELPER(data_dict, key_list[:-1])\n data_dict.pop(key_to_delete)\n return data_dict","def _del_item(dic: dict, keys: list):\n\tdic = _get_item(dic, keys[:-1])\n\tdel dic[keys[-1]]","def delete_metadata(self, keys):\n return self.manager.delete_metadata(self, keys)","def delete(self, key):\n self._cur_batch.delete(key)\n self._num_mutations += 1\n if self._num_mutations >= MAX_MUTATIONS_IN_BATCH:\n self.commit()\n self.begin()","def locked_delete(self):\n\n query = {self.key_name: self.key_value}\n entities = self.model_class.objects.filter(**query).delete()","def try_del(d, keys):\n for key in keys:\n try:\n del d[key]\n except KeyError:\n pass","def delete_from_index(self,delete_list):\n self.__mode = self.WRITE_MODE\n if not self.__storage:\n self.__load_index()\n \n try:\n for to_delete in delete_list:\n if self.__storage.has_key(to_delete):\n del self.__storage[to_delete]\n except Exception,e:\n print e\n self.__storage = None\n return False\n \n self.__close_storage()\n return True","async def delete_files(client, bucket, files: Set):\n result = await client.delete_objects(Bucket=bucket, Delete={\n 'Objects': [{'Key': file} for file in files]\n })\n print(result)","def delete_key_command():\n incident = demisto.args().get('id', get_investigation_id())\n key = demisto.args().get('key')\n # Search Collection for incident_id and key\n search = incident + '.key'\n cursor = COLLECTION.find_one({search: key})\n if cursor is not None:\n object_id = cursor.get('_id')\n COLLECTION.delete_one({'_id': object_id})\n return f'Incident \"{incident}\" - key/value collection - 1 document deleted', {}, {}\n return f'Key \"{key}\" for incident_id \"{incident}\" does not exist', {}, {}","def delete(self, key):\r\n index = self.search(key)\r\n if self.contains_key_at(key, index):\r\n del self.keys[index]","def delete(self, key):\n pass","def delete(self, key):\n pass","def clear_flush_lists(self, keys):\r\n cache.delete_many(keys)","async def delete(self, key: str):","def unset(self, fields, keys, table_name=None, match_any=False):\n return self._get_storage().unset(fields, keys, table_name=table_name, match_any=match_any)","def delete(aMap,key):\n\tbucket=get_bucket(aMap,key)\n\t\n\tfor i in xrange(len(bucket)):\n\t\tk,v=bucket[i]\n\t\tif key==k:\n\t\t\tdel bucket[i]\n\t\t\tbreak","def delete(client, indices):\n return delete_indices(client, indices)","def _delete_key(self):\n return self.connection.delete(self.key)","def delete_objects(self, bucket_name, key_list):\n result = []\n for key in key_list:\n self.delete_object(bucket_name, key)\n return result","def removeAllKeys(self) -> None:\n ...","def delete(aMap, key):\n\tbucket = get_bucket(aMap, key)\n\tfor i in range(len(bucket)):\n\t\tk, v = bucket[i]\n\t\tif key == k :\n\t\t\tdel bucket[i]\n\t\t\tbreak","def delete(self,key):\n\n pass","def deleteShards():\n os.popen('rm *_shard')","def delete_keys_from_dict(dictionary, list_keys):\n for k in list_keys:\n try:\n del dictionary[k]\n except KeyError:\n pass\n for v in dictionary.values():\n if isinstance(v, dict):\n delete_keys_from_dict(v, list_keys)\n\n return dictionary","def delete(self, keyword, key):","def remove_keys(_dict, _keys):\n if isinstance(_keys, str):\n if _keys in _dict:\n del _dict[_keys]\n else:\n for _key in _keys:\n _dict = remove_keys(_dict, _key)\n return _dict","def _delete_keys_script(key_pattern):\n return \"\"\"\n local curkey = redis.call('keys', '%(key_pattern)s')\n if next(curkey) then\n redis.call('del', unpack(curkey))\n end\n \"\"\" % dict(key_pattern = key_pattern)","def test_sftp_delete_all_keys(self):\n config = Config()\n metadata_bucket = config.config.get(\"metadata\", \"bucket\")\n data_bucket = config.config.get(\"data\", \"bucket\")\n metadata_provider = sftp.Sftp(config, metadata_bucket).connect()\n provider = sftp.Sftp(config, data_bucket).connect()\n for key, metadata in metadata_provider.list().items():\n metadata_provider.delete(key)\n for key, data in provider.list().items():\n provider.delete(key)\n metadata_provider.disconnect()\n provider.disconnect()","def delete(aMap, key):\n\tbucket = get_bucket(aMap, key)\n\n\tfor i in xrange(len(bucket)):\n\t\tk, v = bucket[i]\n\t\tif key == k:\n\t\t\tdel bucket[i]\n\t\t\tbreak","def _delete_keys(bucket, keys):\n result = {}\n status = -1\n output = None\n try:\n deleted_result = bucket.delete_keys(keys)\n for deleted in deleted_result.deleted:\n result[deleted.key] = {'status': 0, 'output': None}\n for error in deleted_result.errors:\n result[error.key] = {'status': -1, 'output': error.message}\n except:\n status = -1\n output = \"Failed to delete keys, error: %s\" % (traceback.format_exc())\n\n for key in keys:\n if key not in result:\n result[key] = {'status': status, 'output': output}\n return result","def delete(aMap, key):\n\t#get the bucket that they key is in, and sets it to bucket\n\tbucket = get_bucket(aMap, key)\n\n\tfor i in xrange(len(bucket)):\n\t\tk, v = bucket[i]\n\t\tif key == k:\n\t\t\tdel bucket[i]\n\t\t\t#we can break here, since we know there can be only one key/value pair\n\t\t\tbreak","def test_fuzz_deletions():\n key_range = 2 ** 64\n value_range = 1024\n key_set = set()\n \n d = OrderedTreeDict()\n for value in range(0, value_range):\n key = randint(0, key_range)\n d.put(key, value)\n key_set.add(key)\n \n sorted_keys = list(sorted(key_set))\n sorted_keys_slice = sorted_keys[0:len(sorted_keys) // 2]\n \n for key in sorted_keys_slice:\n d.delete(key)\n assert len(d) > 0\n assert key not in d\n assert d.depth() <= int(2 * math.log(len(d), 2)), \"Should stay as balanced as a red black tree. \"\n \n keys = list(d.keys())\n assert len(keys) == len(sorted_keys_slice), \"Length should reflect number of items inserted.\"\n assert len(keys) == len(list(keys)), \"Iteration should find all items in tree.\"","def run_delete_example():\n table = \"actors\"\n delete_conds = {\n 'name': \"Matt\",\n 'last_name': \"Smith\"}\n print querify.delete_from_dict(table, delete_conds)","def del_dict_keys(dict_in, keys):\n for key in keys:\n if key in dict_in:\n del dict_in[key]\n return dict_in","def _delete_key(self):\n return self.connection.hdel(self.key, self.name)","def Exclude(*keys):\n\n def exclude(row):\n res = dict(row)\n for k in keys:\n if k in res:\n del res[k]\n return res\n\n return \"Exclude\" >> beam.Map(exclude)","def delete_keys_from_dict(d, keys):\n if isinstance(d, dict):\n for field in d.keys():\n if field in keys:\n del d[field]\n elif isinstance(d[field], dict) or isinstance(d[field], list) or isinstance(d[field], set):\n delete_keys_from_dict(d[field], keys)\n elif isinstance(d, dict) or isinstance(d, list) or isinstance(d, set):\n for i in d:\n delete_keys_from_dict(i, keys)","async def clear_entry(self, *keys):\n\n keys = tuple(keys)\n\n async with self._lock:\n if keys in self._non_existent:\n log.info(\n \"Clearing non-existent entry %s for table %s\", keys, self.table\n )\n self._non_existent.remove(keys)\n\n removed = bool(self.entries.pop(keys, False))\n\n if removed:\n log.info(\"Clearing entry %s for table %s\", keys, self.table)\n\n return removed","def delete_keypairs(self,\n keypairs,\n check=True):\n for keypair in keypairs:\n self._client.delete(keypair.id)\n\n if check:\n self.check_keypairs_presence(keypairs, must_present=False)","def test_remove_multiple_key(self):\n\n expected = {\n \"Hello\": \"world\",\n \"World\": {\"world\": \"hello\"},\n \"pyfunceble\": [\"funilrys\"],\n }\n\n actual = Dict(self.test_subject).remove_key([\"funilrys\", \"Py\"])\n\n self.assertEqual(expected, actual)","def discard_index_from_key(self,key,index):\r\n\r\n # with shelf\r\n if self.using_shelf:\r\n\r\n if key in self.key_dict:\r\n\r\n self.key_dict[key].discard(str(index))\r\n\r\n\r\n #with database\r\n if self.using_database:\r\n value_tuple = (notebookname,key,str(index),)\r\n db_cursor.execute(\"DELETE FROM\"\r\n +\" keys_to_indexes\"\r\n +\" WHERE notebook=?\"\r\n +\" AND keyword=?\"\r\n +\" AND note_index=?;\",\r\n value_tuple)\r\n\r\n db_cursor.execute(\"SELECT * FROM\"\r\n +\" keys_to_indexes\"\r\n +\" WHERE notebook=?\"\r\n +\" and keyword=?;\",\r\n value_tuple[0:2])\r\n if db_cursor.fetchone():\r\n db_cursor.execute(\"DELETE FROM\"\r\n +\" all_keys WHERE notebook=?\"\r\n +\" AND keyword=?;\",\r\n value_tuple[0:2])","def delete_keys_from_dict(dict_del, the_keys):\n # make sure the_keys is a set to get O(1) lookups\n if type(the_keys) is not set:\n the_keys = set(the_keys)\n for k, v in dict_del.items():\n if k in the_keys:\n del dict_del[k]\n if isinstance(v, dict):\n delete_keys_from_dict(v, the_keys)\n if isinstance(v, list):\n for item in v:\n if isinstance(item, dict):\n delete_keys_from_dict(item, the_keys)\n return dict_del","def test_delete_many(self):\n\n # That one fails in r5 (because of improper handling of batches)\n\n doc_count = 10\n ids = [get_rand_string() for x in range(doc_count)]\n\n # Same data and user_id for all documents\n data = user_id = get_rand_string()\n\n for id in ids:\n self.conn.add(id=id, data=data, user_id=user_id)\n self.conn.commit()\n\n # Make sure they've been added\n for id in ids:\n results = self.conn.query(\"id:\" + id).results\n self.assertEquals(len(results), 1,\n \"Document (id:%s) should've been added to index\" % id)\n\n # Delete documents by their ID and commit changes\n self.conn.delete(ids)\n self.conn.commit()\n\n # Make sure they've been deleted\n for id in ids:\n results = self.conn.query(\"id:\" + id).results\n self.assertEquals(len(results), 0,\n \"Document (id:%s) should've been deleted from index\" % id)","def delete_metadata(self, keys=None):\n return self.manager.delete_metadata(self, keys=keys)","def keep_in_dictionary(self,dictionary,*keys):\r\n remove_keys = [k for k in dictionary if k not in keys]\r\n self.remove_from_dictionary(dictionary,*remove_keys)","def delete_docs(self, docs, empty_on_delete=False, **params):\n\n if empty_on_delete:\n for doc in docs:\n new_doc = {\"_id\": doc[\"_id\"],\n \"_rev\": doc[\"_rev\"],\n \"_deleted\": True}\n doc.clear()\n doc.update(new_doc)\n else:\n for doc in docs:\n doc['_deleted'] = True\n\n return self.bulk_save(docs, use_uuids=False, **params)","def delete_bucketlist():\n pass","def purge(self):\n keys = [k for (k, v) in self.get_range()]\n\n [self.remove(k) for k in keys]","def delete(self, key, key_type=None):\n pass","def delete(self, **kwargs):\n\n rst = self.del_sngl_pair(kwargs)\n return rst","def remove_by_keys(self, keys):\n return list(filter(lambda item: item.keyword not in set(keys), self._metadata))","def flushdb(self):\n allKeys = self.redis.keys(self.appendKeys(\"*\"))\n # for some reason deleteing with a list of keys isn't working\n p = self.redis.pipeline()\n for key in allKeys:\n p.delete(key)\n p.execute()","def remove_keys(d, keys):\n pp = deepcopy(d)\n if isinstance(keys, (list, tuple)):\n for k in keys:\n pp.pop(k, None)\n else:\n pp.pop(keys, None)\n return pp","def clear(self, keys=None, records=None, **kwargs):\n keys = keys or kwargs.get('key')\n records = records or kwargs.get('record')\n if isinstance(keys, list) and isinstance(records, list):\n return self.client.clearKeysRecords(keys, records, self.creds, self.transaction, self.environment)\n elif isinstance(records, list) and not keys:\n return self.client.clearRecords(records, self.creds, self.transaction, self.environment)\n elif isinstance(keys, list) and records:\n return self.client.clearKeysRecord(keys, records, self.creds, self.transaction, self.environment)\n elif isinstance(records, list) and keys:\n return self.client.clearKeyRecords(keys, records, self.creds, self.transaction, self.environment)\n elif keys and records:\n return self.client.clearKeyRecord(keys, records, self.creds, self.transaction, self.environment)\n elif records:\n return self.client.clearRecord(records, self.creds, self.transaction, self.environment)\n else:\n require_kwarg('record or records')","def remove_keys_from_dict(dictionary, keys):\n\n # Copy dictionary\n dictionary_updated = dictionary.copy()\n try:\n [dictionary_updated.pop(key) for key in keys]\n except:\n print(\"Error: No ratio and sampling strategy parameters\")\n return dictionary_updated","def delete_metadata(self, volume, keys, deletes=10, delete_size=3):\n if len(keys) < deletes * delete_size:\n raise exceptions.InvalidArgumentsException(\n \"Not enough metadata keys to delete: \"\n \"%(num_keys)s keys, but asked to delete %(num_deletes)s\" %\n {\"num_keys\": len(keys),\n \"num_deletes\": deletes * delete_size})\n # make a shallow copy of the list of keys so that, when we pop\n # from it later, we don't modify the original list.\n keys = list(keys)\n random.shuffle(keys)\n action_name = (\"cinder_v%s.delete_%s_metadatas_%s_times\"\n % (self.version, delete_size, deletes))\n with atomic.ActionTimer(self, action_name):\n for i in range(deletes):\n to_del = keys[i * delete_size:(i + 1) * delete_size]\n self._get_client().volumes.delete_metadata(volume, to_del)","def key_delete(self, name=None):\n raise NotImplementedError","def delete_keys_with_prefix(prefix):\n rc = redis.StrictRedis(host=REDIS_SINGLE_HOST, port=REDIS_PORT, db=0)\n keys = rc.keys(\"*\" + prefix + \"*\")\n for key in keys:\n rc.delete(key)","def remove(self, fields, pipe=None):\n if not fields:\n return\n\n if self.key_name in fields:\n raise InvalidOperation('cannot remove the redis key')\n\n with self._pipe(pipe) as pipe:\n core = self.core(pipe=pipe)\n core.hdel(self.key, *fields)\n\n def cb():\n for k in fields:\n try:\n del self._data[k]\n except KeyError:\n pass\n\n pipe.on_execute(cb)","def __delitem__(self, key):\r\n self.client.delete(id=key, ignore=[404], **self.kwargs)","def unset(keys):\n exit_code = 0\n if len(keys) == 1:\n return exit_code\n for x in keys[1:]:\n try:\n del environ[x]\n except KeyError:\n exit_code = 1\n pass\n return exit_code","def delete(self, key):\n raise NotImplementedError","def delete(self, key):\n raise NotImplementedError","def remove_from_dictionary(self,dictionary,*keys):\r\n for key in keys:\r\n if key in dictionary:\r\n value = dictionary.pop(key)\r\n logger.info(\"removed item with key '%s' and value '%s'\" %(key,value))\r\n else:\r\n logger.info(\"Key '%s' not found\" %(key))","def validate_deletions(taglist):\n preexisting_keys = list_of_keys_of(taglist.current_list)\n keys_of_tags_to_delete = unicode_decode_keys(taglist.deletions)\n\n non_existent_key_set = list(set(keys_of_tags_to_delete) - set(preexisting_keys))\n\n if non_existent_key_set:\n raise_validation_error(\n problematic_key_set=non_existent_key_set,\n problem_message=strings['tags.tag_keys_dont_exist_for_deletion'],\n exception_class=InvalidAttemptToModifyTagsError\n )","def test_merge_backup_with_purge_deleted_keys(self):\n self.log.info(\"Load 1st batch docs\")\n create_gen1 = BlobGenerator(\"ent-backup1\", \"ent-backup-\", self.value_size,\n end=self.num_items)\n self._load_all_buckets(self.master, create_gen1, \"create\", 0)\n self.log.info(\"Delete half docs of 1st batch\")\n delete_gen = BlobGenerator(\"ent-backup1\", \"ent-backup-\", self.value_size,\n end=self.num_items // 2)\n self._load_all_buckets(self.master, delete_gen, \"delete\", 0)\n self.log.info(\"Load 2nd batch docs\")\n create_gen2 = BlobGenerator(\"ent-backup2\", \"ent-backup-\", self.value_size,\n end=self.num_items // 2)\n self._load_all_buckets(self.master, create_gen2, \"create\", 0)\n self.log.info(\"Start backup\")\n self.backup_create()\n self.backup_cluster()\n nodes = []\n upto_seq = 100000\n self.log.info(\"Start compact each vbucket in bucket\")\n\n rest = RestConnection(self.master)\n cluster_nodes = rest.get_nodes()\n for bucket in RestConnection(self.master).get_buckets():\n found = self.get_info_in_database(self.backupset.cluster_host, bucket, \"deleted\")\n if found:\n shell = RemoteMachineShellConnection(self.backupset.cluster_host)\n shell.compact_vbuckets(len(bucket.vbuckets), cluster_nodes, upto_seq)\n shell.disconnect()\n found = self.get_info_in_database(self.backupset.cluster_host, bucket, \"deleted\")\n if not found:\n self.log.info(\"Load another docs to bucket %s \" % bucket.name)\n create_gen3 = BlobGenerator(\"ent-backup3\", \"ent-backup-\", self.value_size,\n end=self.num_items // 4)\n self._load_bucket(bucket, self.master, create_gen3, \"create\",\n self.expire_time)\n self.backup_cluster()\n create_gen4 = BlobGenerator(\"ent-backup3\", \"ent-backup-\", self.value_size,\n end=self.num_items // 4)\n self._load_bucket(bucket, self.master, create_gen4, \"create\",\n self.expire_time)\n self.backup_cluster()\n self.backupset.end = 3\n status, output, message = self.backup_merge()\n if not status:\n self.fail(message)\n else:\n self.fail(\"cbcompact failed to purge deleted key\")","def remove(self, table_name, keys=None, any=False, eids=None):\n table = self.db.table(table_name)\n if eids is not None:\n LOGGER.debug(\"%r: remove(eids=%r)\" % (table_name, eids))\n if isinstance(eids, list):\n return table.remove(eids=eids)\n else:\n return table.remove(eids=[eids])\n else:\n LOGGER.debug(\"%r: remove(keys=%r)\" % (table_name, keys))\n return table.remove(self._getQuery(keys, any))"],"string":"[\n \"def delete_many(self, keys):\\n raise NotImplementedError()\",\n \"async def delete(self, *keys, **kwargs):\\n\\n def gen_keys(keys):\\n all_keys = []\\n for key in keys:\\n if isinstance(key, list):\\n all_keys += gen_keys(keys=key)\\n else:\\n all_keys.append(key)\\n return all_keys\\n\\n all_keys = gen_keys(keys)\\n for key in all_keys:\\n await self._client_conn.hdel(key=self.name, field=key)\",\n \"def delete_many(self, keys):\\n return self.delete_many_values(keys)\",\n \"def delete_many(self, keys):\\n try:\\n if keys:\\n self._cache.delete(*map(self.prepare_key, keys))\\n except Exception as err:\\n return self.warn_or_error(err)\",\n \"def delete(cls, keys, pipe=None):\\n with cls._pipe(pipe) as pipe:\\n core = cls.core(pipe)\\n core.delete(*keys)\",\n \"def delete_many(self, keys, version=None, client=None):\\r\\n\\r\\n if client is None:\\r\\n client = self.get_client(write=True)\\r\\n\\r\\n if not keys:\\r\\n return\\r\\n\\r\\n keys = [self.make_key(k, version=version) for k in keys]\\r\\n try:\\r\\n return client.delete(*keys)\\r\\n except ConnectionError:\\r\\n raise ConnectionInterrupted(connection=client)\",\n \"def delete_many(self, *keys):\\n self.collection.remove({'_id': {'$in': keys}})\\n return True\",\n \"def multi_del(self, keys, no_update_log=False):\\n # TODO: write better documentation: why would user need the no_update_log param?\\n opts = (no_update_log and TyrantProtocol.RDBMONOULOG or 0)\\n if not isinstance(keys, (list, tuple)):\\n keys = list(keys)\\n\\n wait(self.proto.misc(\\\"outlist\\\", keys, opts))\",\n \"def delete(\\n self, keys: Optional[Iterable[Text]] = None\\n ) -> Dict[Text, StateDictInterface]:\\n records = self.execute()\\n assert isinstance(records, dict)\\n\\n self.store.delete_many(records.values(), keys=keys)\\n return records\",\n \"def delete_keys_tags(self,\\r\\n index,\\r\\n deletedkeys):\\r\\n\\r\\n\\r\\n for k_temp in deletedkeys:\\r\\n k_temp = k_temp.strip()\\r\\n if k_temp in set(self.get_keys()):\\r\\n self.discard_index_from_key(k_temp, index)\\r\\n if self.get_indexes_for_key(k_temp) == set():\\r\\n self.eliminate_key(k_temp)\\r\\n for t_temp in self.get_tags():\\r\\n if k_temp in self.get_keys_for_tag(t_temp):\\r\\n self.discard_key_from_tag(t_temp,k_temp)\\r\\n if not self.get_keys_for_tag(t_temp):\\r\\n self.delete_tag(t_temp)\",\n \"def Delete(keys):\\n keys, multiple = NormalizeAndTypeCheckKeys(keys)\\n\\n if multiple and not keys:\\n return\\n\\n req = datastore_pb.DeleteRequest()\\n req.key_list().extend([key._Key__reference for key in keys])\\n\\n tx = _MaybeSetupTransaction(req, keys)\\n\\n resp = datastore_pb.DeleteResponse()\\n try:\\n apiproxy_stub_map.MakeSyncCall('datastore_v3', 'Delete', req, resp)\\n except apiproxy_errors.ApplicationError, err:\\n raise _ToDatastoreError(err)\",\n \"def delete(self, *keys: KeyT) -> ResponseT:\\n return self._split_command_across_slots(\\\"DEL\\\", *keys)\",\n \"def delete(cls, *keys):\\n todelete = []\\n namespace, kind, member = Schema.Get(cls)\\n for key in keys:\\n assert isinstance(key, str)\\n todelete.append(Key(namespace, kind, key)) \\n Lisa.delete(*todelete)\",\n \"def _delete_volatile_keys(self, solr_dict):\\n\\n def delete(del_solr_dict, path_list):\\n k = path_list[0]\\n if k in del_solr_dict:\\n if len(path_list) > 1:\\n delete(del_solr_dict[k], path_list[1:])\\n else:\\n del del_solr_dict[k]\\n\\n delete(solr_dict, ['response', 'maxScore'])\\n delete(solr_dict, ['responseHeader', 'QTime'])\",\n \"def delete_many(self, keys, version=None):\\r\\n res = 0\\r\\n for key in [self.make_key(k, version=version) for k in keys]:\\r\\n client = self.get_server(key)\\r\\n res += self.delete(key, client=client)\\r\\n return res\",\n \"def del_all(self, items):\\n for item in items:\\n item.key.delete()\\n logger.debug(\\\"Deleted all the items\\\")\",\n \"def delete(self, keys: List[K]) -> List[bool]:\\n raise NotImplementedError('delete must be reimplemented in concrete implementation')\",\n \"def delete(self, *names):\\n if len(names) != 1:\\n raise RedisClusterException(\\\"deleting multiple keys is not implemented in pipeline command\\\")\\n\\n return self.execute_command('DEL', names[0])\",\n \"def deleteTable(*keys):\\r\\n\\treturn getGameData().delTable(*keys)\",\n \"def delete_by_key(self, key_fields):\\n tmp = dict(zip(self._key_columns, key_fields))\\n return self.delete_by_template(tmp)\",\n \"def del_quiet(dic, *keys):\\n for key in keys:\\n try:\\n del dic[key]\\n except KeyError:\\n pass\",\n \"def delete_key(self,\\r\\n dkey):\\r\\n\\r\\n\\r\\n if (input(queries.DELETE_CONF_BEG\\r\\n +dkey+queries.DELETE_CONF_END) in YESTERMS):\\r\\n\\r\\n if dkey in self.keys():\\r\\n\\r\\n for i_temp in self.get_all_indexes():\\r\\n if dkey in self.get_keys_from_note(i_temp):\\r\\n tempnote = self.get_note(i_temp).delete_keys({dkey})\\r\\n self.add_note(i_temp,note=tempnote)\\r\\n if self.get_keys_from_note(i_temp) == set():\\r\\n temp = self.get_keys_from_note(i_temp)\\r\\n temp.add(VOIDTERM)\\r\\n self.add_note(i_temp,\\r\\n keyset_only=temp)\\r\\n self.add_keys_tags(i_temp,\\r\\n {VOIDTERM})\\r\\n\\r\\n self.delete_keys_tags(i_temp, {dkey})\",\n \"def remove_keys(data: dict, keys: list[str]) -> None:\\n for k in keys:\\n _ = data.pop(k, None)\",\n \"def delete_keys(prefix, *args):\\n rc = redis.StrictRedis(host=REDIS_SINGLE_HOST, port=REDIS_PORT, db=0)\\n # 如果有多个参数，将多个参数拼接成一个key\\n if args:\\n for i in args:\\n prefix = str(prefix) + str(i)\\n keys = rc.keys(\\\"*\\\" + prefix + \\\"*\\\")\\n for key in keys:\\n rc.delete(key)\",\n \"def delete(self, key):\",\n \"def del_seqs(self, keys):\\n for j in range(len(keys)):\\n del self._d_seqs[keys[j]]\\n self._num_seqs = int(len(self._d_seqs))\\n self._d_seqs = self._d_seqs\\n self._seqs = list(self._d_seqs)\",\n \"def delete_multi(self, keys, dead_time=0, key_prefix=''):\\n\\n\\t\\tserver_keys, _deprefix = yield self._map_keys_to_servers(keys, key_prefix)\\n\\n\\t\\t# send out all requests on each server before reading anything\\n\\t\\tdead_servers = []\\n\\n\\t\\tif dead_time is None:\\n\\t\\t\\tdead_time = 0\\n\\n\\t\\tret = True\\n\\n\\t\\tfor server in server_keys.iterkeys():\\n\\t\\t\\tcommands = []\\n\\t\\t\\tfor prefixed_key, _original_key in server_keys[server]:\\n\\t\\t\\t\\tcommands.append(\\\"delete %s %d\\\\r\\\\n\\\" % (prefixed_key, dead_time))\\n\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tserver.send_cmds(''.join(commands))\\n\\t\\t\\texcept tcp.ConnectionClosedException:\\n\\t\\t\\t\\tserver.mark_dead()\\n\\t\\t\\t\\tdead_servers.append(server)\\n\\n\\t\\t# if any servers died on the way, don't expect them to respond.\\n\\t\\tfor server in dead_servers:\\n\\t\\t\\tdel server_keys[server]\\n\\n\\t\\tfor server, keys in server_keys.iteritems():\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tfor _key in keys:\\n\\t\\t\\t\\t\\tres = yield server.read_line()\\n\\t\\t\\t\\t\\tif res != \\\"DELETED\\\":\\n\\t\\t\\t\\t\\t\\tself._debuglog(\\\"expected 'DELETED', got %r\\\" % (res, ))\\n\\t\\t\\texcept tcp.ConnectionClosedException:\\n\\t\\t\\t\\tserver.mark_dead()\\n\\t\\t\\t\\tret = False\\n\\n\\t\\traise StopIteration(ret)\",\n \"def delete(self, resource, keys, url_prefix, auth, session, send_opts):\\n success = True\\n exc = HTTPErrorList('At least one key-value update failed.')\\n\\n for key in keys:\\n req = self.get_metadata_request(\\n resource, 'DELETE', 'application/json', url_prefix, auth, key)\\n prep = session.prepare_request(req)\\n resp = session.send(prep, **send_opts)\\n if resp.status_code == 204:\\n continue\\n err = (\\n 'Delete failed for {}: {}, got HTTP response: ({}) - {}'\\n .format(resource.name, key, resp.status_code, resp.text))\\n exc.http_errors.append(HTTPError(err, request=req, response=resp))\\n success = False\\n\\n if not success:\\n raise exc\",\n \"def invalidate_keys(self, keys):\\r\\n if not keys:\\r\\n return\\r\\n flush, flush_keys = self.find_flush_lists(keys)\\r\\n\\r\\n if flush:\\r\\n cache.delete_many(flush)\\r\\n if flush_keys:\\r\\n self.clear_flush_lists(flush_keys)\",\n \"def rem(self, keys: Union[str, Iterable]):\\n return(self.db.delVal(db=self.sdb, key=self._tokey(keys)))\",\n \"def rem(self, keys: Union[str, Iterable]):\\n return(self.db.delVal(db=self.sdb, key=self._tokey(keys)))\",\n \"def rem(self, keys: Union[str, Iterable]):\\n return(self.db.delVal(db=self.sdb, key=self._tokey(keys)))\",\n \"def delete_compound_key(self, ids, entity_name=None):\\n try:\\n query_result = self.query(\\n \\\"delete\\\",\\n entity_name or self.__entity_name,\\n {\\n \\\"options\\\": ids\\n }\\n )\\n except Exception as error:\\n error_detected = error\\n return False\\n\\n return True\",\n \"def delete_key_HELPER(data_dict, key_list, key_to_delete):\\n data_dict = get_key_from_dict_HELPER(data_dict, key_list[:-1])\\n data_dict.pop(key_to_delete)\\n return data_dict\",\n \"def _del_item(dic: dict, keys: list):\\n\\tdic = _get_item(dic, keys[:-1])\\n\\tdel dic[keys[-1]]\",\n \"def delete_metadata(self, keys):\\n return self.manager.delete_metadata(self, keys)\",\n \"def delete(self, key):\\n self._cur_batch.delete(key)\\n self._num_mutations += 1\\n if self._num_mutations >= MAX_MUTATIONS_IN_BATCH:\\n self.commit()\\n self.begin()\",\n \"def locked_delete(self):\\n\\n query = {self.key_name: self.key_value}\\n entities = self.model_class.objects.filter(**query).delete()\",\n \"def try_del(d, keys):\\n for key in keys:\\n try:\\n del d[key]\\n except KeyError:\\n pass\",\n \"def delete_from_index(self,delete_list):\\n self.__mode = self.WRITE_MODE\\n if not self.__storage:\\n self.__load_index()\\n \\n try:\\n for to_delete in delete_list:\\n if self.__storage.has_key(to_delete):\\n del self.__storage[to_delete]\\n except Exception,e:\\n print e\\n self.__storage = None\\n return False\\n \\n self.__close_storage()\\n return True\",\n \"async def delete_files(client, bucket, files: Set):\\n result = await client.delete_objects(Bucket=bucket, Delete={\\n 'Objects': [{'Key': file} for file in files]\\n })\\n print(result)\",\n \"def delete_key_command():\\n incident = demisto.args().get('id', get_investigation_id())\\n key = demisto.args().get('key')\\n # Search Collection for incident_id and key\\n search = incident + '.key'\\n cursor = COLLECTION.find_one({search: key})\\n if cursor is not None:\\n object_id = cursor.get('_id')\\n COLLECTION.delete_one({'_id': object_id})\\n return f'Incident \\\"{incident}\\\" - key/value collection - 1 document deleted', {}, {}\\n return f'Key \\\"{key}\\\" for incident_id \\\"{incident}\\\" does not exist', {}, {}\",\n \"def delete(self, key):\\r\\n index = self.search(key)\\r\\n if self.contains_key_at(key, index):\\r\\n del self.keys[index]\",\n \"def delete(self, key):\\n pass\",\n \"def delete(self, key):\\n pass\",\n \"def clear_flush_lists(self, keys):\\r\\n cache.delete_many(keys)\",\n \"async def delete(self, key: str):\",\n \"def unset(self, fields, keys, table_name=None, match_any=False):\\n return self._get_storage().unset(fields, keys, table_name=table_name, match_any=match_any)\",\n \"def delete(aMap,key):\\n\\tbucket=get_bucket(aMap,key)\\n\\t\\n\\tfor i in xrange(len(bucket)):\\n\\t\\tk,v=bucket[i]\\n\\t\\tif key==k:\\n\\t\\t\\tdel bucket[i]\\n\\t\\t\\tbreak\",\n \"def delete(client, indices):\\n return delete_indices(client, indices)\",\n \"def _delete_key(self):\\n return self.connection.delete(self.key)\",\n \"def delete_objects(self, bucket_name, key_list):\\n result = []\\n for key in key_list:\\n self.delete_object(bucket_name, key)\\n return result\",\n \"def removeAllKeys(self) -> None:\\n ...\",\n \"def delete(aMap, key):\\n\\tbucket = get_bucket(aMap, key)\\n\\tfor i in range(len(bucket)):\\n\\t\\tk, v = bucket[i]\\n\\t\\tif key == k :\\n\\t\\t\\tdel bucket[i]\\n\\t\\t\\tbreak\",\n \"def delete(self,key):\\n\\n pass\",\n \"def deleteShards():\\n os.popen('rm *_shard')\",\n \"def delete_keys_from_dict(dictionary, list_keys):\\n for k in list_keys:\\n try:\\n del dictionary[k]\\n except KeyError:\\n pass\\n for v in dictionary.values():\\n if isinstance(v, dict):\\n delete_keys_from_dict(v, list_keys)\\n\\n return dictionary\",\n \"def delete(self, keyword, key):\",\n \"def remove_keys(_dict, _keys):\\n if isinstance(_keys, str):\\n if _keys in _dict:\\n del _dict[_keys]\\n else:\\n for _key in _keys:\\n _dict = remove_keys(_dict, _key)\\n return _dict\",\n \"def _delete_keys_script(key_pattern):\\n return \\\"\\\"\\\"\\n local curkey = redis.call('keys', '%(key_pattern)s')\\n if next(curkey) then\\n redis.call('del', unpack(curkey))\\n end\\n \\\"\\\"\\\" % dict(key_pattern = key_pattern)\",\n \"def test_sftp_delete_all_keys(self):\\n config = Config()\\n metadata_bucket = config.config.get(\\\"metadata\\\", \\\"bucket\\\")\\n data_bucket = config.config.get(\\\"data\\\", \\\"bucket\\\")\\n metadata_provider = sftp.Sftp(config, metadata_bucket).connect()\\n provider = sftp.Sftp(config, data_bucket).connect()\\n for key, metadata in metadata_provider.list().items():\\n metadata_provider.delete(key)\\n for key, data in provider.list().items():\\n provider.delete(key)\\n metadata_provider.disconnect()\\n provider.disconnect()\",\n \"def delete(aMap, key):\\n\\tbucket = get_bucket(aMap, key)\\n\\n\\tfor i in xrange(len(bucket)):\\n\\t\\tk, v = bucket[i]\\n\\t\\tif key == k:\\n\\t\\t\\tdel bucket[i]\\n\\t\\t\\tbreak\",\n \"def _delete_keys(bucket, keys):\\n result = {}\\n status = -1\\n output = None\\n try:\\n deleted_result = bucket.delete_keys(keys)\\n for deleted in deleted_result.deleted:\\n result[deleted.key] = {'status': 0, 'output': None}\\n for error in deleted_result.errors:\\n result[error.key] = {'status': -1, 'output': error.message}\\n except:\\n status = -1\\n output = \\\"Failed to delete keys, error: %s\\\" % (traceback.format_exc())\\n\\n for key in keys:\\n if key not in result:\\n result[key] = {'status': status, 'output': output}\\n return result\",\n \"def delete(aMap, key):\\n\\t#get the bucket that they key is in, and sets it to bucket\\n\\tbucket = get_bucket(aMap, key)\\n\\n\\tfor i in xrange(len(bucket)):\\n\\t\\tk, v = bucket[i]\\n\\t\\tif key == k:\\n\\t\\t\\tdel bucket[i]\\n\\t\\t\\t#we can break here, since we know there can be only one key/value pair\\n\\t\\t\\tbreak\",\n \"def test_fuzz_deletions():\\n key_range = 2 ** 64\\n value_range = 1024\\n key_set = set()\\n \\n d = OrderedTreeDict()\\n for value in range(0, value_range):\\n key = randint(0, key_range)\\n d.put(key, value)\\n key_set.add(key)\\n \\n sorted_keys = list(sorted(key_set))\\n sorted_keys_slice = sorted_keys[0:len(sorted_keys) // 2]\\n \\n for key in sorted_keys_slice:\\n d.delete(key)\\n assert len(d) > 0\\n assert key not in d\\n assert d.depth() <= int(2 * math.log(len(d), 2)), \\\"Should stay as balanced as a red black tree. \\\"\\n \\n keys = list(d.keys())\\n assert len(keys) == len(sorted_keys_slice), \\\"Length should reflect number of items inserted.\\\"\\n assert len(keys) == len(list(keys)), \\\"Iteration should find all items in tree.\\\"\",\n \"def run_delete_example():\\n table = \\\"actors\\\"\\n delete_conds = {\\n 'name': \\\"Matt\\\",\\n 'last_name': \\\"Smith\\\"}\\n print querify.delete_from_dict(table, delete_conds)\",\n \"def del_dict_keys(dict_in, keys):\\n for key in keys:\\n if key in dict_in:\\n del dict_in[key]\\n return dict_in\",\n \"def _delete_key(self):\\n return self.connection.hdel(self.key, self.name)\",\n \"def Exclude(*keys):\\n\\n def exclude(row):\\n res = dict(row)\\n for k in keys:\\n if k in res:\\n del res[k]\\n return res\\n\\n return \\\"Exclude\\\" >> beam.Map(exclude)\",\n \"def delete_keys_from_dict(d, keys):\\n if isinstance(d, dict):\\n for field in d.keys():\\n if field in keys:\\n del d[field]\\n elif isinstance(d[field], dict) or isinstance(d[field], list) or isinstance(d[field], set):\\n delete_keys_from_dict(d[field], keys)\\n elif isinstance(d, dict) or isinstance(d, list) or isinstance(d, set):\\n for i in d:\\n delete_keys_from_dict(i, keys)\",\n \"async def clear_entry(self, *keys):\\n\\n keys = tuple(keys)\\n\\n async with self._lock:\\n if keys in self._non_existent:\\n log.info(\\n \\\"Clearing non-existent entry %s for table %s\\\", keys, self.table\\n )\\n self._non_existent.remove(keys)\\n\\n removed = bool(self.entries.pop(keys, False))\\n\\n if removed:\\n log.info(\\\"Clearing entry %s for table %s\\\", keys, self.table)\\n\\n return removed\",\n \"def delete_keypairs(self,\\n keypairs,\\n check=True):\\n for keypair in keypairs:\\n self._client.delete(keypair.id)\\n\\n if check:\\n self.check_keypairs_presence(keypairs, must_present=False)\",\n \"def test_remove_multiple_key(self):\\n\\n expected = {\\n \\\"Hello\\\": \\\"world\\\",\\n \\\"World\\\": {\\\"world\\\": \\\"hello\\\"},\\n \\\"pyfunceble\\\": [\\\"funilrys\\\"],\\n }\\n\\n actual = Dict(self.test_subject).remove_key([\\\"funilrys\\\", \\\"Py\\\"])\\n\\n self.assertEqual(expected, actual)\",\n \"def discard_index_from_key(self,key,index):\\r\\n\\r\\n # with shelf\\r\\n if self.using_shelf:\\r\\n\\r\\n if key in self.key_dict:\\r\\n\\r\\n self.key_dict[key].discard(str(index))\\r\\n\\r\\n\\r\\n #with database\\r\\n if self.using_database:\\r\\n value_tuple = (notebookname,key,str(index),)\\r\\n db_cursor.execute(\\\"DELETE FROM\\\"\\r\\n +\\\" keys_to_indexes\\\"\\r\\n +\\\" WHERE notebook=?\\\"\\r\\n +\\\" AND keyword=?\\\"\\r\\n +\\\" AND note_index=?;\\\",\\r\\n value_tuple)\\r\\n\\r\\n db_cursor.execute(\\\"SELECT * FROM\\\"\\r\\n +\\\" keys_to_indexes\\\"\\r\\n +\\\" WHERE notebook=?\\\"\\r\\n +\\\" and keyword=?;\\\",\\r\\n value_tuple[0:2])\\r\\n if db_cursor.fetchone():\\r\\n db_cursor.execute(\\\"DELETE FROM\\\"\\r\\n +\\\" all_keys WHERE notebook=?\\\"\\r\\n +\\\" AND keyword=?;\\\",\\r\\n value_tuple[0:2])\",\n \"def delete_keys_from_dict(dict_del, the_keys):\\n # make sure the_keys is a set to get O(1) lookups\\n if type(the_keys) is not set:\\n the_keys = set(the_keys)\\n for k, v in dict_del.items():\\n if k in the_keys:\\n del dict_del[k]\\n if isinstance(v, dict):\\n delete_keys_from_dict(v, the_keys)\\n if isinstance(v, list):\\n for item in v:\\n if isinstance(item, dict):\\n delete_keys_from_dict(item, the_keys)\\n return dict_del\",\n \"def test_delete_many(self):\\n\\n # That one fails in r5 (because of improper handling of batches)\\n\\n doc_count = 10\\n ids = [get_rand_string() for x in range(doc_count)]\\n\\n # Same data and user_id for all documents\\n data = user_id = get_rand_string()\\n\\n for id in ids:\\n self.conn.add(id=id, data=data, user_id=user_id)\\n self.conn.commit()\\n\\n # Make sure they've been added\\n for id in ids:\\n results = self.conn.query(\\\"id:\\\" + id).results\\n self.assertEquals(len(results), 1,\\n \\\"Document (id:%s) should've been added to index\\\" % id)\\n\\n # Delete documents by their ID and commit changes\\n self.conn.delete(ids)\\n self.conn.commit()\\n\\n # Make sure they've been deleted\\n for id in ids:\\n results = self.conn.query(\\\"id:\\\" + id).results\\n self.assertEquals(len(results), 0,\\n \\\"Document (id:%s) should've been deleted from index\\\" % id)\",\n \"def delete_metadata(self, keys=None):\\n return self.manager.delete_metadata(self, keys=keys)\",\n \"def keep_in_dictionary(self,dictionary,*keys):\\r\\n remove_keys = [k for k in dictionary if k not in keys]\\r\\n self.remove_from_dictionary(dictionary,*remove_keys)\",\n \"def delete_docs(self, docs, empty_on_delete=False, **params):\\n\\n if empty_on_delete:\\n for doc in docs:\\n new_doc = {\\\"_id\\\": doc[\\\"_id\\\"],\\n \\\"_rev\\\": doc[\\\"_rev\\\"],\\n \\\"_deleted\\\": True}\\n doc.clear()\\n doc.update(new_doc)\\n else:\\n for doc in docs:\\n doc['_deleted'] = True\\n\\n return self.bulk_save(docs, use_uuids=False, **params)\",\n \"def delete_bucketlist():\\n pass\",\n \"def purge(self):\\n keys = [k for (k, v) in self.get_range()]\\n\\n [self.remove(k) for k in keys]\",\n \"def delete(self, key, key_type=None):\\n pass\",\n \"def delete(self, **kwargs):\\n\\n rst = self.del_sngl_pair(kwargs)\\n return rst\",\n \"def remove_by_keys(self, keys):\\n return list(filter(lambda item: item.keyword not in set(keys), self._metadata))\",\n \"def flushdb(self):\\n allKeys = self.redis.keys(self.appendKeys(\\\"*\\\"))\\n # for some reason deleteing with a list of keys isn't working\\n p = self.redis.pipeline()\\n for key in allKeys:\\n p.delete(key)\\n p.execute()\",\n \"def remove_keys(d, keys):\\n pp = deepcopy(d)\\n if isinstance(keys, (list, tuple)):\\n for k in keys:\\n pp.pop(k, None)\\n else:\\n pp.pop(keys, None)\\n return pp\",\n \"def clear(self, keys=None, records=None, **kwargs):\\n keys = keys or kwargs.get('key')\\n records = records or kwargs.get('record')\\n if isinstance(keys, list) and isinstance(records, list):\\n return self.client.clearKeysRecords(keys, records, self.creds, self.transaction, self.environment)\\n elif isinstance(records, list) and not keys:\\n return self.client.clearRecords(records, self.creds, self.transaction, self.environment)\\n elif isinstance(keys, list) and records:\\n return self.client.clearKeysRecord(keys, records, self.creds, self.transaction, self.environment)\\n elif isinstance(records, list) and keys:\\n return self.client.clearKeyRecords(keys, records, self.creds, self.transaction, self.environment)\\n elif keys and records:\\n return self.client.clearKeyRecord(keys, records, self.creds, self.transaction, self.environment)\\n elif records:\\n return self.client.clearRecord(records, self.creds, self.transaction, self.environment)\\n else:\\n require_kwarg('record or records')\",\n \"def remove_keys_from_dict(dictionary, keys):\\n\\n # Copy dictionary\\n dictionary_updated = dictionary.copy()\\n try:\\n [dictionary_updated.pop(key) for key in keys]\\n except:\\n print(\\\"Error: No ratio and sampling strategy parameters\\\")\\n return dictionary_updated\",\n \"def delete_metadata(self, volume, keys, deletes=10, delete_size=3):\\n if len(keys) < deletes * delete_size:\\n raise exceptions.InvalidArgumentsException(\\n \\\"Not enough metadata keys to delete: \\\"\\n \\\"%(num_keys)s keys, but asked to delete %(num_deletes)s\\\" %\\n {\\\"num_keys\\\": len(keys),\\n \\\"num_deletes\\\": deletes * delete_size})\\n # make a shallow copy of the list of keys so that, when we pop\\n # from it later, we don't modify the original list.\\n keys = list(keys)\\n random.shuffle(keys)\\n action_name = (\\\"cinder_v%s.delete_%s_metadatas_%s_times\\\"\\n % (self.version, delete_size, deletes))\\n with atomic.ActionTimer(self, action_name):\\n for i in range(deletes):\\n to_del = keys[i * delete_size:(i + 1) * delete_size]\\n self._get_client().volumes.delete_metadata(volume, to_del)\",\n \"def key_delete(self, name=None):\\n raise NotImplementedError\",\n \"def delete_keys_with_prefix(prefix):\\n rc = redis.StrictRedis(host=REDIS_SINGLE_HOST, port=REDIS_PORT, db=0)\\n keys = rc.keys(\\\"*\\\" + prefix + \\\"*\\\")\\n for key in keys:\\n rc.delete(key)\",\n \"def remove(self, fields, pipe=None):\\n if not fields:\\n return\\n\\n if self.key_name in fields:\\n raise InvalidOperation('cannot remove the redis key')\\n\\n with self._pipe(pipe) as pipe:\\n core = self.core(pipe=pipe)\\n core.hdel(self.key, *fields)\\n\\n def cb():\\n for k in fields:\\n try:\\n del self._data[k]\\n except KeyError:\\n pass\\n\\n pipe.on_execute(cb)\",\n \"def __delitem__(self, key):\\r\\n self.client.delete(id=key, ignore=[404], **self.kwargs)\",\n \"def unset(keys):\\n exit_code = 0\\n if len(keys) == 1:\\n return exit_code\\n for x in keys[1:]:\\n try:\\n del environ[x]\\n except KeyError:\\n exit_code = 1\\n pass\\n return exit_code\",\n \"def delete(self, key):\\n raise NotImplementedError\",\n \"def delete(self, key):\\n raise NotImplementedError\",\n \"def remove_from_dictionary(self,dictionary,*keys):\\r\\n for key in keys:\\r\\n if key in dictionary:\\r\\n value = dictionary.pop(key)\\r\\n logger.info(\\\"removed item with key '%s' and value '%s'\\\" %(key,value))\\r\\n else:\\r\\n logger.info(\\\"Key '%s' not found\\\" %(key))\",\n \"def validate_deletions(taglist):\\n preexisting_keys = list_of_keys_of(taglist.current_list)\\n keys_of_tags_to_delete = unicode_decode_keys(taglist.deletions)\\n\\n non_existent_key_set = list(set(keys_of_tags_to_delete) - set(preexisting_keys))\\n\\n if non_existent_key_set:\\n raise_validation_error(\\n problematic_key_set=non_existent_key_set,\\n problem_message=strings['tags.tag_keys_dont_exist_for_deletion'],\\n exception_class=InvalidAttemptToModifyTagsError\\n )\",\n \"def test_merge_backup_with_purge_deleted_keys(self):\\n self.log.info(\\\"Load 1st batch docs\\\")\\n create_gen1 = BlobGenerator(\\\"ent-backup1\\\", \\\"ent-backup-\\\", self.value_size,\\n end=self.num_items)\\n self._load_all_buckets(self.master, create_gen1, \\\"create\\\", 0)\\n self.log.info(\\\"Delete half docs of 1st batch\\\")\\n delete_gen = BlobGenerator(\\\"ent-backup1\\\", \\\"ent-backup-\\\", self.value_size,\\n end=self.num_items // 2)\\n self._load_all_buckets(self.master, delete_gen, \\\"delete\\\", 0)\\n self.log.info(\\\"Load 2nd batch docs\\\")\\n create_gen2 = BlobGenerator(\\\"ent-backup2\\\", \\\"ent-backup-\\\", self.value_size,\\n end=self.num_items // 2)\\n self._load_all_buckets(self.master, create_gen2, \\\"create\\\", 0)\\n self.log.info(\\\"Start backup\\\")\\n self.backup_create()\\n self.backup_cluster()\\n nodes = []\\n upto_seq = 100000\\n self.log.info(\\\"Start compact each vbucket in bucket\\\")\\n\\n rest = RestConnection(self.master)\\n cluster_nodes = rest.get_nodes()\\n for bucket in RestConnection(self.master).get_buckets():\\n found = self.get_info_in_database(self.backupset.cluster_host, bucket, \\\"deleted\\\")\\n if found:\\n shell = RemoteMachineShellConnection(self.backupset.cluster_host)\\n shell.compact_vbuckets(len(bucket.vbuckets), cluster_nodes, upto_seq)\\n shell.disconnect()\\n found = self.get_info_in_database(self.backupset.cluster_host, bucket, \\\"deleted\\\")\\n if not found:\\n self.log.info(\\\"Load another docs to bucket %s \\\" % bucket.name)\\n create_gen3 = BlobGenerator(\\\"ent-backup3\\\", \\\"ent-backup-\\\", self.value_size,\\n end=self.num_items // 4)\\n self._load_bucket(bucket, self.master, create_gen3, \\\"create\\\",\\n self.expire_time)\\n self.backup_cluster()\\n create_gen4 = BlobGenerator(\\\"ent-backup3\\\", \\\"ent-backup-\\\", self.value_size,\\n end=self.num_items // 4)\\n self._load_bucket(bucket, self.master, create_gen4, \\\"create\\\",\\n self.expire_time)\\n self.backup_cluster()\\n self.backupset.end = 3\\n status, output, message = self.backup_merge()\\n if not status:\\n self.fail(message)\\n else:\\n self.fail(\\\"cbcompact failed to purge deleted key\\\")\",\n \"def remove(self, table_name, keys=None, any=False, eids=None):\\n table = self.db.table(table_name)\\n if eids is not None:\\n LOGGER.debug(\\\"%r: remove(eids=%r)\\\" % (table_name, eids))\\n if isinstance(eids, list):\\n return table.remove(eids=eids)\\n else:\\n return table.remove(eids=[eids])\\n else:\\n LOGGER.debug(\\\"%r: remove(keys=%r)\\\" % (table_name, keys))\\n return table.remove(self._getQuery(keys, any))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7468936","0.7286904","0.71825546","0.7122302","0.70103353","0.70015174","0.69674885","0.688871","0.68676025","0.68633324","0.68574625","0.68523395","0.6762416","0.67384684","0.6734635","0.6674827","0.6622758","0.65964293","0.6570637","0.6519055","0.65152115","0.6448601","0.6446398","0.6417904","0.64133704","0.6395171","0.6357207","0.6351878","0.63284516","0.62954843","0.62954843","0.62954843","0.62507975","0.6220902","0.61354345","0.6129066","0.6115365","0.6100265","0.60978323","0.609064","0.6067847","0.6065405","0.6063739","0.6060271","0.6060271","0.60481685","0.6033634","0.6022272","0.60131145","0.600443","0.5987767","0.5986803","0.59646267","0.5963611","0.5955595","0.59373945","0.5920747","0.59088856","0.5901731","0.5891188","0.58907783","0.58720684","0.58482766","0.58305466","0.5828724","0.58051723","0.58042943","0.5802145","0.5793116","0.5784654","0.5779118","0.57776403","0.577678","0.57750535","0.5766548","0.57620656","0.57591546","0.57542425","0.57413715","0.57362044","0.5729877","0.57192683","0.57169396","0.5716183","0.57101065","0.5707951","0.5693461","0.5692784","0.5690142","0.5684687","0.5671867","0.5670214","0.56669724","0.56667215","0.5666408","0.5666408","0.5665248","0.56563234","0.5652164","0.56512564"],"string":"[\n \"0.7468936\",\n \"0.7286904\",\n \"0.71825546\",\n \"0.7122302\",\n \"0.70103353\",\n \"0.70015174\",\n \"0.69674885\",\n \"0.688871\",\n \"0.68676025\",\n \"0.68633324\",\n \"0.68574625\",\n \"0.68523395\",\n \"0.6762416\",\n \"0.67384684\",\n \"0.6734635\",\n \"0.6674827\",\n \"0.6622758\",\n \"0.65964293\",\n \"0.6570637\",\n \"0.6519055\",\n \"0.65152115\",\n \"0.6448601\",\n \"0.6446398\",\n \"0.6417904\",\n \"0.64133704\",\n \"0.6395171\",\n \"0.6357207\",\n \"0.6351878\",\n \"0.63284516\",\n \"0.62954843\",\n \"0.62954843\",\n \"0.62954843\",\n \"0.62507975\",\n \"0.6220902\",\n \"0.61354345\",\n \"0.6129066\",\n \"0.6115365\",\n \"0.6100265\",\n \"0.60978323\",\n \"0.609064\",\n \"0.6067847\",\n \"0.6065405\",\n \"0.6063739\",\n \"0.6060271\",\n \"0.6060271\",\n \"0.60481685\",\n \"0.6033634\",\n \"0.6022272\",\n \"0.60131145\",\n \"0.600443\",\n \"0.5987767\",\n \"0.5986803\",\n \"0.59646267\",\n \"0.5963611\",\n \"0.5955595\",\n \"0.59373945\",\n \"0.5920747\",\n \"0.59088856\",\n \"0.5901731\",\n \"0.5891188\",\n \"0.58907783\",\n \"0.58720684\",\n \"0.58482766\",\n \"0.58305466\",\n \"0.5828724\",\n \"0.58051723\",\n \"0.58042943\",\n \"0.5802145\",\n \"0.5793116\",\n \"0.5784654\",\n \"0.5779118\",\n \"0.57776403\",\n \"0.577678\",\n \"0.57750535\",\n \"0.5766548\",\n \"0.57620656\",\n \"0.57591546\",\n \"0.57542425\",\n \"0.57413715\",\n \"0.57362044\",\n \"0.5729877\",\n \"0.57192683\",\n \"0.57169396\",\n \"0.5716183\",\n \"0.57101065\",\n \"0.5707951\",\n \"0.5693461\",\n \"0.5692784\",\n \"0.5690142\",\n \"0.5684687\",\n \"0.5671867\",\n \"0.5670214\",\n \"0.56669724\",\n \"0.56667215\",\n \"0.5666408\",\n \"0.5666408\",\n \"0.5665248\",\n \"0.56563234\",\n \"0.5652164\",\n \"0.56512564\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.66377497"},"document_rank":{"kind":"string","value":"16"}}},{"rowIdx":89391,"cells":{"query":{"kind":"string","value":"Gather all keys that match pattern, by shard, then invoke shardfn once per shard, passing the shard, and the list of matching keys on that shard."},"document":{"kind":"string","value":"def with_keys_by_shard(self, shardfn, pattern='*'):\n for shard in self.shards:\n keys = shard.keys(pattern)\n shardfn(shard, keys)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def group_by_key(fileset, keyfunc, min_group_size=2, workers=16):\n pool = ThreadPool(workers)\n\n def evaluate(entry):\n return keyfunc(entry), entry\n\n keyed_fileset = pool.map(evaluate, fileset)\n files_by_key = {}\n for key, entry in keyed_fileset:\n if key is None:\n continue\n files_by_key.setdefault(key, []).append(entry)\n\n return [group for group in files_by_key.values() if\n len(group) >= min_group_size]","def find(ctx, pattern):\n config = buildConfig(ctx.obj[\"HOST\"], ctx.obj[\"PORT\"])\n clientList = getClientList(config)\n for client in clientList:\n srvFound = 0\n for id in slabIds(client.stats(\"items\")):\n for key in client.stats('cachedump', id, '0').keys():\n if re.search(pattern, key.decode('utf-8')):\n click.echo(key)\n srvFound += 1\n click.echo(f\"********** SERVER {client.server} contained {srvFound} matching results\")\n print()","def find_matches(self, match_fn):\n return\n yield # Turns this function into a generator but that is empty","def find_matches(self, match_fn):\n return\n yield # Turns this function into a generator but that is empty","def findall(self, key_list):\n for i in range(3):\n try:\n return self.redis_handler.mget(key_list) \n except:\n continue","def _to_key_ranges_by_shard(cls, app, namespaces, shard_count, query_spec):\n key_ranges_by_ns = []\n\n\n for namespace in namespaces:\n ranges = cls._split_ns_by_scatter(\n shard_count,\n namespace,\n query_spec.entity_kind,\n app)\n\n\n random.shuffle(ranges)\n key_ranges_by_ns.append(ranges)\n\n\n\n\n ranges_by_shard = [[] for _ in range(shard_count)]\n for ranges in key_ranges_by_ns:\n for i, k_range in enumerate(ranges):\n if k_range:\n ranges_by_shard[i].append(k_range)\n\n key_ranges_by_shard = []\n for ranges in ranges_by_shard:\n if ranges:\n key_ranges_by_shard.append(key_ranges.KeyRangesFactory.create_from_list(\n ranges))\n return key_ranges_by_shard","def shard(self, dataset_iter):\n return dataset_iter","def store_matches(\n self,\n parameter_handler: ParameterHandler,\n topology: \"Topology\",\n ) -> None:\n if self.key_map:\n # TODO: Should the key_map always be reset, or should we be able to partially\n # update it? Also Note the duplicated code in the child classes\n self.key_map: dict[\n Union[TopologyKey, LibraryChargeTopologyKey],\n PotentialKey,\n ] = dict()\n matches = parameter_handler.find_matches(topology)\n for key, val in matches.items():\n topology_key = TopologyKey(atom_indices=key)\n potential_key = PotentialKey(\n id=val.parameter_type.smirks,\n associated_handler=parameter_handler.TAGNAME,\n )\n self.key_map[topology_key] = potential_key\n\n if self.__class__.__name__ in [\n \"SMIRNOFFBondCollection\",\n \"SMIRNOFFAngleCollection\",\n ]:\n valence_terms = self.valence_terms(topology)\n\n _check_all_valence_terms_assigned(\n handler=parameter_handler,\n assigned_terms=matches,\n topology=topology,\n valence_terms=valence_terms,\n )","def rds_scan_keys(rds, glob):\n n = 0\n keys = []\n while(True):\n n, k = rds.scan(n, match=glob)\n keys.extend(k)\n if n == 0:\n break\n return keys","def multifilter(names, patterns):\n for name in names:\n if isinstance(name, collections.Mapping):\n for key in name.iterkeys():\n for pattern in patterns:\n if fnmatch.fnmatch(key, pattern):\n yield key\n break\n else:\n for pattern in patterns:\n if fnmatch.fnmatch(name, pattern):\n yield name\n break","def key_lookup_batch(self, batchiter):\n pass","def read_entries_(self, start_key=None, end_key=None):\n # TODO (andrin): fetch a couple of shards instead of just one based on\n # method argument\n current_key = start_key\n if current_key == None:\n current_key = (\"\", )\n limit_shard_name = RecordIOShard.key_name(\n self.name, lo=start_key, hi=end_key).split(SPLIT_CHAR)\n while True:\n shard = RecordIOShard.get_shards_for_key_values(\n self.name, [current_key], keys_only=False).next()[0]\n self.db_search_and_get += 1\n if shard == None:\n raise RecordIOShardDoesNotExistError(self.name)\n hi = shard.lo_hi()[1]\n shard_name = shard.key().name().split(SPLIT_CHAR)\n if (shard_name[6:10] >= limit_shard_name[6:10] and\n (shard_name[2:5] < limit_shard_name[2:5] or\n limit_shard_name[2] == SPLIT_CHAR_AFTER)):\n # Read the whole shard\n for entry in shard:\n yield entry\n else:\n # Read parts of the shard\n for entry in shard.read(current_key, end_key):\n yield entry\n if hi == None:\n # Was the last shard\n return\n current_key = hi\n if (end_key != None and\n RecordIORecords.entry_comperator(current_key, end_key) >= 0):\n # Next shard is after end_key\n return","def _choose_split_points(cls, sorted_keys, shard_count):\n assert len(sorted_keys) >= shard_count\n index_stride = len(sorted_keys) / float(shard_count)\n return [sorted_keys[int(round(index_stride * i))]\n for i in range(1, shard_count)]","def find_fks_well_distributed(keylist):\n global _max_k\n global _l1_bucket_max\n mlist = _get_l1_mlist(len(keylist)) # number of buckets\n for m in mlist:\n for p in _primes:\n for k in range(3, _max_k):\n hash_f = hash_fun_fks_t(k,p,m)\n if xedhash.is_well_distributed(keylist, hash_f, _l1_bucket_max):\n return hash_f\n del hash_f\n return None","def _map_and_prefix_keys(self, key_iterable, key_prefix):\r\n # Check it just once ...\r\n key_extra_len=len(key_prefix)\r\n #changed by steve\r\n #if key_prefix:\r\n #self.check_key(key_prefix)\r\n\r\n # server (_Host) -> list of unprefixed server keys in mapping\r\n server_keys = {}\r\n\r\n prefixed_to_orig_key = {}\r\n # build up a list for each server of all the keys we want.\r\n for orig_key in key_iterable:\r\n if isinstance(orig_key, tuple):\r\n # Tuple of hashvalue, key ala _get_server(). Caller is essentially telling us what server to stuff this on.\r\n # Ensure call to _get_server gets a Tuple as well.\r\n str_orig_key = str(orig_key[1])\r\n server, key = self._get_server((orig_key[0], key_prefix + str_orig_key)) # Gotta pre-mangle key before hashing to a server. Returns the mangled key.\r\n else:\r\n str_orig_key = str(orig_key) # set_multi supports int / long keys.\r\n server, key = self._get_server(key_prefix + str_orig_key)\r\n\r\n # Now check to make sure key length is proper ...\r\n #changed by steve\r\n #self.check_key(str_orig_key, key_extra_len=key_extra_len)\r\n key = self.check_key(key_prefix + str_orig_key)\r\n\r\n if not server:\r\n continue\r\n\r\n if server not in server_keys:\r\n server_keys[server] = []\r\n server_keys[server].append(key)\r\n prefixed_to_orig_key[key] = orig_key\r\n\r\n return (server_keys, prefixed_to_orig_key)","def searchcolkeys(self,fctn,cols,keylist,*args):\n goodkeys=[]\n for key in keylist:\n temp=[]\n for c in cols:\n temp.append(self.getentry(key,c))\n for i in range(len(args)):\n temp.append(args[i])\n\n if fctn(*tuple(temp)):\n goodkeys.append(key)\n return(goodkeys)","def _sharded_pre_load_state_dict_hook(\n self,\n state_dict: Dict[str, Any],\n prefix: str,\n ) -> None:\n _replace_by_prefix(state_dict, prefix, prefix + f\"{FSDP_WRAPPED_MODULE}.\")\n if not self._fsdp_wrapped_module.has_params:\n return\n\n if not self._fsdp_wrapped_module.handle.uses_sharded_strategy:\n raise RuntimeError(\n \"load_sharded_state_dict can only be called when parameters \"\n \"are flatten and sharded.\"\n )\n\n nonsharded_tensors = []\n # TODO: Reduce the communication by using only one _all_gather_base to\n # gather all the parameters in this layer. This can be achieved by\n # concatenated all the local shards and then append the padding.\n # https://github.com/pytorch/pytorch/issues/77461\n for (param_name, _, module_name) in self._fsdp_wrapped_module.handle.flat_param._param_infos:\n module_name = self._convert_to_wrapped_module_name(module_name)\n fqn = f\"{prefix}{FSDP_WRAPPED_MODULE}.{module_name}{param_name}\"\n param = state_dict.pop(fqn)\n\n # All-gather the param (ShardedTensor)\n param, shards = _ext_pre_load_state_dict_transform(param)\n assert len(shards) < 2, (\n f\"Expects 0 or 1 shard per rank but got {len(shards)} shards on rank {self.rank}\"\n )\n param_numel = param.size().numel()\n dim_0_size = param.size()[0]\n chunk_size = (\n math.ceil(dim_0_size / self.world_size) * param_numel // dim_0_size\n )\n if shards:\n local_tensor = cast(torch.Tensor, shards[0].tensor).flatten()\n if not local_tensor.is_cuda:\n local_tensor = local_tensor.cuda()\n num_padding = chunk_size - local_tensor.numel()\n if num_padding > 0:\n local_tensor = F.pad(local_tensor, [0, num_padding])\n else:\n local_tensor = torch.zeros(chunk_size, dtype=param.dtype).cuda()\n tensor = torch.empty(\n chunk_size * self.world_size, dtype=local_tensor.dtype\n ).cuda()\n dist._all_gather_base(tensor, local_tensor, group=self.process_group)\n tensor = tensor.narrow(0, 0, param_numel).reshape(param.size())\n nonsharded_tensors.append(tensor)\n\n # Create a new flat_param from the loaded, non-sharded tensors.\n flat_param = self._fsdp_wrapped_module.flat_param\n loaded_flat_param = FlatParamHandle.flatten_params(nonsharded_tensors, requires_grad=False)\n\n # Get the chunk from the loaded flat_param for the local rank.\n loaded_flat_param, num_to_pad = FlatParamHandle._get_shard(\n loaded_flat_param, self.rank, self.world_size,\n )\n loaded_flat_param.to(flat_param.device)\n assert flat_param.numel() == loaded_flat_param.numel(), (\n f\"The loaded local chunk has different numel({flat_param.numel()}) \"\n f\"from the local chunk {flat_param.numel()}.\"\n )\n assert flat_param._shard_numel_padded == num_to_pad, (\n f\"The loaded local chunk has different padding({num_to_pad}) \"\n f\"from the local chunk {flat_param._shard_numel_padded}.\"\n )\n state_dict[f\"{prefix}_fsdp_wrapped_module.flat_param\"] = loaded_flat_param","def store_matches(\n self,\n parameter_handler: ImproperTorsionHandler,\n topology: Topology,\n ) -> None:\n if self.key_map:\n self.key_map = dict()\n matches = parameter_handler.find_matches(topology)\n for key, val in matches.items():\n parameter_handler._assert_correct_connectivity(\n val,\n [\n (0, 1),\n (1, 2),\n (1, 3),\n ],\n )\n n_terms = len(val.parameter_type.k)\n for n in range(n_terms):\n smirks = val.parameter_type.smirks\n non_central_indices = [key[0], key[2], key[3]]\n\n for permuted_key in [\n (\n non_central_indices[i],\n non_central_indices[j],\n non_central_indices[k],\n )\n for (i, j, k) in [(0, 1, 2), (1, 2, 0), (2, 0, 1)]\n ]:\n topology_key = ImproperTorsionKey(\n atom_indices=(key[1], *permuted_key),\n mult=n,\n )\n potential_key = PotentialKey(\n id=smirks,\n mult=n,\n associated_handler=\"ImproperTorsions\",\n )\n self.key_map[topology_key] = potential_key","def forever(shard):\n def repeat(*args, **kwargs):\n while True:\n for delay in shard(*args, **kwargs):\n yield delay\n return repeat","def return_matches(self, hashes, batch_size: int=1000):\n # Create a dictionary of hash => offset pairs for later lookups\n mapper = {}\n for hsh, offset in hashes:\n if hsh in mapper.keys():\n mapper[hsh].append(offset)\n else:\n mapper[hsh] = [offset]\n\n values = list(mapper.keys())\n\n # in order to count each hash only once per db offset we use the dic below\n dedup_hashes = {}\n\n results = []\n\n for index in range(0, len(values), batch_size):\n # Create our IN part of the query\n # query = self.SELECT_MULTIPLE % ', '.join([self.IN_MATCH] * len(values[index: index + batch_size]))\n\n res = self.elasticClient.search(index=\"fingerprints\",\n body={\"query\": {\"terms\": {\"doc.hash\": values[index: index+batch_size]}}}, size=1000000)\n\n query_res = res['hits']['hits']\n for doc in query_res:\n hsh = doc['_source']['doc']['hash']\n sid = doc['_source']['doc']['song_id']\n offset = doc['_source']['doc']['offset']\n if sid not in dedup_hashes.keys():\n dedup_hashes[sid] = 1\n else:\n dedup_hashes[sid] += 1\n # we now evaluate all offset for each hash matched\n for song_sampled_offset in mapper[hsh]:\n results.append((sid, offset - song_sampled_offset))\n\n return results, dedup_hashes","def ShardList(list_to_shard, total_shards, shard_idx):\n length = len(list_to_shard)\n split_lists = []\n for i in range(total_shards):\n start_idx = i * length // total_shards\n end_idx = (i + 1) * length // total_shards\n split_lists.append(list_to_shard[start_idx: end_idx])\n\n return split_lists[shard_idx]","def _GetFileMappingsByPath(self, safe_key_path):\n candidate_mappings = []\n for mapping in self._REGISTRY_FILE_MAPPINGS_NT:\n if safe_key_path.startswith(mapping.key_path_prefix):\n candidate_mappings.append(mapping)\n\n # Sort the candidate mappings by longest (most specific) match first.\n candidate_mappings.sort(\n key=lambda mapping: len(mapping.key_path_prefix), reverse=True)\n for mapping in candidate_mappings:\n yield mapping","def _build_keys(self, slug, date=None, granularity='all'):\n slug = slugify(slug) # Ensure slugs have a consistent format\n if date is None:\n date = datetime.utcnow()\n patts = self._build_key_patterns(slug, date)\n if granularity == \"all\":\n return list(patts.values())\n return [patts[granularity]]","def apply(self, func, *args):\n import ray\n done_ids, undone_ids = ray.wait([shard.apply.remote(func, *args)\n for shard in self.shard_list],\n num_returns=len(self.shard_list))\n assert len(undone_ids) == 0\n return self","def fetch_s3_keys_by_regex_pattern(s3_bucket, s3_directory, pattern):\n bucket_contents = s3_bucket.list(s3_directory)\n return [key for key in bucket_contents if pattern.search(key.name)]","def get_shards(data_dir, file_list, shard_size, istraining):\n file_idxs = np.arange(0, len(file_list))\n np.random.shuffle(file_idxs) # randomly extract data from files\n\n shard_num = len(file_list) // shard_size\n\n for shard_idx in range(shard_num):\n\n start_idx = shard_idx * shard_size\n end_idx = (shard_idx + 1) * shard_size\n shard_files_idxs = file_idxs[start_idx: end_idx]\n\n all_data, all_label, all_names, all_node_img = [], [], [], []\n for fn in shard_files_idxs:\n\n if not data_dir:\n raw_data = np.load(file_list[fn])\n else:\n raw_data = np.load(os.path.join(data_dir, file_list[fn]))\n\n current_data = raw_data['vgg_features']\n node_img_path = raw_data['img_path']\n # pid = raw_data['pid']\n # time = raw_data['time']\n if len(current_data) < MIN_NUM_POINT:\n # skip WSI of too few patches\n continue\n\n # if len(current_data) > MAX_NUM_POINT:\n # continue\n\n curr_path = file_list[fn]\n\n curr_type = curr_path.split('/')[-4]\n curr_filename = curr_path.split('/')[-1]\n\n if curr_type == 'LUAD':\n # LUAD -> class 0, LUSC -> class 1\n current_label = 0\n else:\n current_label = 1\n\n # if istraining:\n \"random select at most MAX_NUM_POINT nodes for WSI\"\n list_node_idx = np.arange(0, current_data.shape[0])\n np.random.shuffle(list_node_idx)\n sel_ids = list_node_idx[0: MAX_NUM_POINT]\n\n current_data = current_data[sel_ids]\n current_data = np.expand_dims(current_data, 0)\n node_img_path = node_img_path[sel_ids]\n\n all_data.append(current_data)\n all_label.append(current_label)\n all_names.append(curr_filename)\n all_node_img.append(node_img_path)\n\n \"\"\" create numpy for all data and label\"\"\"\n all_label = np.squeeze(np.hstack(all_label))\n\n yield all_data, all_label, all_names, all_node_img","def sharding(nodes, terms, document=None):\n shards = defaultdict(lambda: defaultdict(list))\n document_hash = None\n\n if document:\n document_hash = get_document_hash(document)\n node = get_node_by_document_hash(nodes, document_hash)\n shards[node]['document'] = document\n shards[node]['document_hash'] = document_hash\n\n for term in terms:\n shards[get_node_by_term(nodes, term)]['terms'].append(term)\n shards[get_node_by_term(nodes, term)]['document_hash'] = document_hash\n\n return shards","def select_multiple_from_strategy(dist: dict, num: int) -> []:\n\n output = []\n dist_c = dist.copy()\n\n for _ in range(num):\n key = Tools.select_from_strategy(dist_c)\n output.append(key)\n\n del dist_c[key]\n dist_c = Tools.normalise(dist_c)\n\n return output","def _scan(self, keys, cursor, *args):\n cursor = int(cursor)\n (pattern, _type, count), _ = extract_args(args, (\"*match\", \"*type\", \"+count\"))\n count = 10 if count is None else count\n data = sorted(keys)\n bits_len = (len(keys) - 1).bit_length()\n cursor = bin_reverse(cursor, bits_len)\n if cursor >= len(keys):\n return [0, []]\n result_cursor = cursor + count\n result_data = []\n\n regex = compile_pattern(pattern) if pattern is not None else None\n\n def match_key(key: bytes) -> Union[bool, Match[bytes], None]:\n return regex.match(key) if regex is not None else True\n\n def match_type(key) -> bool:\n return _type is None or casematch(key_value_type(self._db[key]).value, _type)\n\n if pattern is not None or _type is not None:\n for val in itertools.islice(data, cursor, cursor + count):\n compare_val = val[0] if isinstance(val, tuple) else val\n if match_key(compare_val) and match_type(compare_val):\n result_data.append(val)\n else:\n result_data = data[cursor: cursor + count]\n\n if result_cursor >= len(data):\n result_cursor = 0\n return [str(bin_reverse(result_cursor, bits_len)).encode(), result_data]","def _collapse_exact_matches(self, seqs, prefix_length, suffix_length):\r\n cluster_map = {}\r\n for seq_id, seq in seqs:\r\n seq_id = seq_id.split()[0]\r\n seq_hash = self._build_seq_hash(seq, prefix_length, suffix_length)\r\n try:\r\n cluster_map[seq_hash].append(seq_id)\r\n except KeyError:\r\n cluster_map[seq_hash] = [seq_id]\r\n\r\n return cluster_map.values()","def parse_all_shards(shard_dir_path):\n files_to_process = utils.absolute_paths_of_files_in_dir(shard_dir_path)\n list_of_shard_results = [parse_shard(f) for f in files_to_process]\n return pd.DataFrame(\n list(itertools.chain(*list_of_shard_results)),\n columns=['sequence_name', 'predictions'])","def test_pattern_matching(self):\n\n for hashtype in [HashTypes.SHA1, HashTypes.SHA2, HashTypes.SHA3, ]:\n self.do_test_pattern_matching(hashtype)","def redis_query(self, pattern, count=False):\n def func(server):\n keys = server.inspect(pattern=pattern, debug=False)\n res = server.query(keys, ttl=False)\n if count:\n resp = []\n for k,v in res.items():\n resp.append({k:len(v)})\n return resp\n return res\n self.__run_redis_cmd(func, dbs=range(0,8))","def repair(\n self,\n start_key=None,\n end_key=None,\n shard=0,\n nshards=1,\n callback_on_progress=None,\n ):\n pass","def run_against_hosts(*, partial_func: functools.partial, hosts: list):\n with concurrent.futures.ThreadPoolExecutor(len(hosts)) as executor:\n futures = {\n executor.submit(functools.partial(partial_func, host=host))\n for host in hosts\n }\n concurrent.futures.wait(futures, return_when=FIRST_EXCEPTION)\n for future in futures:\n future.result()","def filter_keys_c(func):\n return partial(filter_keys, func)","def repair(self, start_key=None, end_key=None, shard=0, nshards=1, callback_on_progress=None):\n super(NoneCache, self).repair()","def lcs_hamming_only_matches_with_many_k(s1: str, s2: str, ks: List[int], length: int, matches_lst: List, query_name, target_name):\n file_name = './chromo_matches_' + str(length) + '.txt'\n f = open(file_name, mode='a')\n count = [0, 0, 0]\n for i in range(0, len(s1) - length + 1):\n for j in range(0, len(s2) - length + 1):\n sub1 = s1[i: i + length]\n sub2 = s2[j: j + length]\n result = hamming_distance(sub1, sub2)\n if result <= 0:\n # matches_lst.append([query_name, target_name, i, j, result, sub1, sub2])\n f.write(query_name + ',' + target_name + ',' + str(i) + ',' + str(j) + ',' + str(k)+ ',' + str(sub1) + ',' + sub2 + '\\n')\n count[0] += 1\n if result <= 1:\n f.write(query_name + ',' + target_name + ',' + str(i) + ',' + str(j) + ',' + str(k)+ ',' + str(sub1) + ',' + sub2 + '\\n')\n count[1] += 1\n if result <= 2:\n f.write(query_name + ',' + target_name + ',' + str(i) + ',' + str(j) + ',' + str(k)+ ',' + str(sub1) + ',' + sub2 + '\\n')\n count[2] += 1\n\n # print(\"total matches: \" + str(count))\n f.close()\n return count","def _map_keys_to_servers(self, key_iterable, key_prefix):\n\n\t\t# Only check the prefix once\n\t\tkey_extra_len = len(key_prefix)\n\t\tif key_prefix:\n\t\t\tcheck_key(key_prefix)\n\n\t\t# server -> list of (prefixed_key, value)\n\t\tserver_keys = {}\n\t\tdeprefix = {}\n\n\t\t# build up a list for each server of all the keys we want.\n\t\tfor orig_key in key_iterable:\n\t\t\tif type(orig_key) is tuple:\n\t\t\t\t# Tuple of hashvalue, key ala _get_server_for(). The caller is essentially\n\t\t\t\t# telling us what server to stuff this on.\n\t\t\t\tstr_orig_key = str(orig_key[1])\n\t\t\t\t# Ensure call to _get_server_for gets a Tuple as well.\n\t\t\t\t# Gotta pre-mangle key before hashing to a server. Returns the mangled key.\n\t\t\t\tserver, key = yield self._get_server_for((orig_key[0], key_prefix + str_orig_key))\n\t\t\telse:\n\t\t\t\tstr_orig_key = str(orig_key) # set_multi supports int / long keys.\n\t\t\t\tserver, key = yield self._get_server_for(key_prefix + str_orig_key)\n\n\t\t\t# Now check to make sure key length is proper ...\n\t\t\tcheck_key(str_orig_key, key_extra_len=key_extra_len)\n\n\t\t\tif not server:\n\t\t\t\tcontinue\n\n\t\t\tif server not in server_keys:\n\t\t\t\tserver_keys[server] = []\n\n\t\t\tserver_keys[server].append((key, orig_key))\n\t\t\tdeprefix[key] = orig_key\n\t\t\t\n\n\t\traise StopIteration((server_keys, deprefix))","def split_scp_dict(scp_dict: Dict[str, List[Tuple[str, str]]],\n n_parts=1, randomize=False, seed: int = None,\n batch_size=1, max_len=None, utt2len=None,\n ensure_equal_parts=True) \\\n -> List[Tuple[Dict[str, List[Tuple[str, str]]], List[int]]]:\n assert batch_size >= 1\n archives = list(scp_dict.keys())\n n_archives = len(archives)\n\n # Determine the orders in which to iterate over archives & the utterances\n # within those archives\n if randomize:\n rs = np.random.RandomState(seed=seed)\n archive_order = rs.permutation(n_archives)\n archive2utt_order = {archive: rs.permutation(len(scp_dict[archive])) for\n archive in archives}\n else:\n archive_order = np.arange(n_archives)\n archive2utt_order = {archive: np.arange(len(scp_dict[archive])) for\n archive in archives}\n\n # Get all utterances in the right order (along with their archive)\n all_utts = [(archives[i], *scp_dict[archives[i]][j]) # (path, uttid, loc)\n for i in archive_order\n for j in archive2utt_order[archives[i]]]\n\n # Compute the appropriate sequence (possibly dynamic) of batch sizes\n start, batch_sizes = 0, []\n while start < len(all_utts):\n tmp_end = min(start + batch_size, len(all_utts))\n if max_len is None or utt2len is None:\n end = tmp_end\n else:\n candidate_utts = all_utts[start:tmp_end]\n lens = [utt2len[uttid] for archive, uttid, loc in candidate_utts]\n dynamic_batch_size = (np.cumsum(lens) <= max_len * batch_size).sum()\n end = start + max(1, dynamic_batch_size)\n batch_sizes.append(end - start)\n start = end\n\n # Some batches will get 1 extra batch from the first part\n n_utts, n_batches = len(all_utts), len(batch_sizes)\n n_batches_per_part, extra = divmod(n_batches, n_parts)\n if n_batches < n_parts:\n raise RuntimeError(\n f\"Cannot partition a SCP with {n_utts} utterances into \"\n f\"{n_parts} parts with batch size {batch_size}. This causes \"\n f\"the number of batches ({n_batches}) to be less than the \"\n f\"number of parts ({n_parts}).\")\n\n # Determine the sequence of batch sizes for each part\n part_n_batches = [n_batches_per_part + int(i < extra)\n for i in range(n_parts)]\n part_j0_batch = np.cumsum([0, *part_n_batches])\n part_batch_sizes = [batch_sizes[part_j0_batch[i]:part_j0_batch[i+1]]\n for i in range(n_parts)]\n\n # Determine the sequence of utterances for each part\n part_n_utts = [sum(x) for x in part_batch_sizes]\n part_j0_utt = np.cumsum([0, *part_n_utts])\n part_utts = [all_utts[part_j0_utt[i]:part_j0_utt[i+1]]\n for i in range(n_parts)]\n\n # Ensure each part has the same number of batches (if desired)\n if ensure_equal_parts and extra > 0:\n extra_batch_sizes = batch_sizes[:n_parts - extra]\n extra_j0_utt = np.cumsum([0, *extra_batch_sizes])\n for i, j in enumerate(range(extra, n_parts)):\n part_batch_sizes[j].append(batch_sizes[i])\n part_utts[j].extend(all_utts[extra_j0_utt[i]:extra_j0_utt[i+1]])\n\n if ensure_equal_parts:\n assert all(len(part_batch_sizes[i]) == len(part_batch_sizes[0])\n for i in range(n_parts)), \\\n \"All parts should have the same number of batches.\"\n\n assert all(sum(part_batch_sizes[i]) == len(part_utts[i])\n for i in range(n_parts)),\\\n \"Sum of batch sizes for each part should match the number of \" \\\n \"utterances in the part.\"\n\n # Construct each part as an ordered dict (archive -> List[uttid, loc])\n parts = []\n for i in range(n_parts):\n current_part = OrderedDict()\n for archive, uttid, loc in part_utts[i]:\n if archive not in current_part:\n current_part[archive] = []\n current_part[archive].append((uttid, loc))\n parts.append(current_part)\n\n return list(zip(parts, part_batch_sizes))","def Exact_Match(pack):\n cache = Cache()\n if _options.single:\n list_app = []\n for k in cache:\n if pack in k.name:\n item = Pack()\n item.name = k.name\n item.ratio = 0\n list_app.append(item)\n return list_app\n else:\n _pool = Pool(processes=_MAX_PEERS)\n result = _pool.map(Thread_Exact, cache._set)\n return result","def test_wildcard(word_list):\n failure=False\n\n # test 1\n hand = {'a': 1, 'r': 1, 'e': 1, 'j': 2, 'm': 1, '*': 1}\n word = \"e*m\"\n\n if is_valid_word(word, hand, word_list):\n print(\"FAILURE: test_is_valid_word() with wildcards\")\n print(\"\\tExpected False, but got True for word: '\" + word + \"' and hand:\", hand)\n\n failure = True\n\n # test 2\n hand = {'n': 1, 'h': 1, '*': 1, 'y': 1, 'd':1, 'w':1, 'e': 2}\n word = \"honey\"\n\n if is_valid_word(word, hand, word_list):\n print(\"FAILURE: test_is_valid_word() with wildcards\")\n print(\"\\tExpected False, but got True for word: '\"+ word +\"' and hand:\", hand)\n\n failure = True\n\n # test 3\n hand = {'n': 1, 'h': 1, '*': 1, 'y': 1, 'd':1, 'w':1, 'e': 2}\n word = \"h*ney\"\n\n if not is_valid_word(word, hand, word_list):\n print(\"FAILURE: test_is_valid_word() with wildcards\")\n print(\"\\tExpected True, but got False for word: '\"+ word +\"' and hand:\", hand)\n\n failure = True\n\n # test 4\n hand = {'c': 1, 'o': 1, '*': 1, 'w': 1, 's':1, 'z':1, 'y': 2}\n word = \"c*wz\"\n\n if is_valid_word(word, hand, word_list):\n print(\"FAILURE: test_is_valid_word() with wildcards\")\n print(\"\\tExpected False, but got True for word: '\"+ word +\"' and hand:\", hand)\n\n failure = True \n\n # dictionary of words and scores WITH wildcards\n words = {(\"h*ney\", 7):290, (\"c*ws\", 6):176, (\"wa*ls\", 7):203}\n for (word, n) in words.keys():\n score = get_word_score(word, n)\n if score != words[(word, n)]:\n print(\"FAILURE: test_get_word_score() with wildcards\")\n print(\"\\tExpected\", words[(word, n)], \"points but got '\" + \\\n str(score) + \"' for word '\" + word + \"', n=\" + str(n))\n failure=True \n\n if not failure:\n print(\"SUCCESS: test_wildcard()\")","def _internal_match(self, pattern):\n compiled_re = re.compile(pattern)\n for word in self.words:\n if compiled_re.fullmatch(word) is not None:\n yield word","def keysWhichMatch(cls, *args):\n if len(cls.keys) < len(args) > 0:\n raise ValueError('Number of keys provided is too long.\\n'\n 'Len Class Keys: %s\\n'\n 'Len Provided Keys: %s\\n' % (len(cls.keys), len(args)))\n\n index = 0\n output = cls.db_key_tuples()\n\n for keyToCheck in args:\n temp = []\n for key in output:\n if key[index] == keyToCheck:\n temp.append(key)\n\n index += 1\n output = temp\n\n return output","def findTopKViaSharding(filename, k, shards):\n \n # First, we calculate the number of lines for each shard\n filelength = findFLength(filename)\n shardsize = int(np.ceil(filelength / shards))\n \n # Now we split the input file into shards\n splitFileIntoShards(filename, shardsize)\n sleep(1) #might change this to sleep(shards/100) if 1 second is too little\n \n # Some Initial Set-Up...\n filenames = [\"x{:04d}_shard\".format(i) for i in range(shards)]\n totaldict = Counter()\n #shardtimes = []\n \n # Iterate through the Shard Files\n # In each Shard, run countWords(shardfilename) to get a wordcount Counter\n # Update the overall Counter in totaldict with more wordcounts from the Shard\n # Delete the Shard File when we're done with it.\n for f in filenames:\n #print(\"Running Shard {}...\".format(f), end=' ')\n #startf = time()\n \n try:\n wordcounts = countWords(f)\n totaldict.update(wordcounts)\n deleteSingleFile(f)\n #print(\"Shard {} Complete.\".format(f))\n except OSError as e:\n print(\"Something unexpected happened in the OS. Error: {}. Moving on...\".format(e))\n \n #endf = time()\n #shardtimes.append(endf - startf)\n \n #print(\"\\nDone Sharding\")\n #deleteShards()\n #print(\"Average Shard Processing Time for K={} and Shards={} is: {}\".format(k, shards, np.mean(shardtimes)))\n #print(\"Average Shard Processing Time for K={} and Shards={} is: {}\".format(k, shards, np.std(shardtimes)))\n \n # All Shard Processing is complete. Return the Top K words from the overall Counter\n return totaldict.most_common(k)","def create_shards(\n it_shards,\n shard_dir,\n key,\n files,\n labels,\n targets,\n im_size,\n label_size,\n preprocess,\n store_z,\n normalize_im):\n all_files = files[key]\n all_labels = labels[key]\n total_data = len(all_files) / it_shards\n mask = np.arange(it_shards).reshape(1, -1).repeat(total_data).reshape(-1)\n all_files = all_files[:len(mask)]\n all_labels = all_labels[:len(mask)]\n total_shards = pad_zeros(str(it_shards), 5)\n for idx in tqdm(\n range(it_shards), total=it_shards, desc='Building %s' % key):\n it_mask = mask == idx\n shard_label = pad_zeros(str(idx), 5)\n shard_name = os.path.join(\n shard_dir,\n '%s-%s-of-%s.tfrecords' % (key, shard_label, total_shards))\n it_files = {key: all_files[it_mask]}\n it_labels = {key: all_labels[it_mask]}\n data_to_tfrecords(\n files=it_files,\n labels=it_labels,\n targets=targets,\n ds_name=shard_name,\n im_size=im_size,\n label_size=label_size,\n preprocess=preprocess,\n store_z=store_z,\n it_ds_name=shard_name,\n normalize_im=normalize_im)","def _build_key_patterns(self, slug, date):\n # we want to keep the order, from smallest to largest granularity\n patts = OrderedDict()\n for g in self._granularities():\n patts[g] = self._get_metric_key_pattern(g, slug, date)\n return patts","def dispatch_list_lengths(self, client):\n key_dict = {}\n for db, patterns in list(self.llen_keys.items()):\n client.send(\"select %d\" % db)\n try:\n resp = client.read_response()\n except RedisError as e:\n collectd.error(\"Could not select Redis db %s: %s\" % (db, e))\n continue\n\n for pattern in patterns:\n keys = []\n # If there is a glob, get every key matching it\n if \"*\" in pattern:\n client.send(\"KEYS %s\" % pattern)\n keys = client.read_response()\n else:\n keys = [pattern]\n\n for key in keys:\n self.fetch_and_dispatch_llen_for_key(client, key, db)","async def _cache_patterns(self) -> None:\n results: [asyncpg.Record] = await db_fetch(\n self.bot.db_conn,\n \"SELECT * FROM filter\"\n )\n for result in results:\n re_compiled = re.compile(result[\"filter_pattern\"])\n self._filter_cache[result[\"server_id\"]][re_compiled] = [result[\"filter_identifier\"]]","def get_all(any, shard, shard_term_features, qterms):\n tmp = 1\n for t in qterms:\n if t in shard_term_features[shard]:\n cdf = shard_term_features[shard][t].df\n else:\n cdf = 0\n tmp *= cdf/any\n all = tmp * any\n return all","def _fe_keyword_match(self, sample):\n result = OrderedDict()\n\n for item in self._keywords:\n result[item + \"_kw\"] = 1 if item in sample['fqdn'] else 0\n\n return result","def get_keywords(self, pattern=\"*\"):\n\n sql = \"\"\"SELECT collection.collection_id, collection.name,\n keyword.name, keyword.doc, keyword.args\n FROM collection_table as collection\n JOIN keyword_table as keyword\n WHERE collection.collection_id == keyword.collection_id\n AND keyword.name like ?\n ORDER by collection.name, keyword.name\n \"\"\"\n pattern = self._glob_to_sql(pattern)\n cursor = self._execute(sql, (pattern,))\n result = [(row[0], row[1], row[2], row[3], row[4])\n for row in cursor.fetchall()]\n return list(sorted(set(result), key=itemgetter(2)))","def bucket(things, key):\n ret = defaultdict(list)\n for thing in things:\n ret[key(thing)].append(thing)\n return ret","def groups_based_ks(y_pred, mask, sample_weight, groups_indices):\n assert len(y_pred) == len(sample_weight) == len(mask)\n group_weights = compute_group_weights(groups_indices, sample_weight=sample_weight)\n prepared_data, prepared_weight, prep_F = prepare_distibution(y_pred[mask], weights=sample_weight[mask])\n\n result = 0.\n for group_weight, group_indices in zip(group_weights, groups_indices):\n local_distribution = y_pred[group_indices]\n local_weights = sample_weight[group_indices]\n result += group_weight * \\\n _ks_2samp_fast(prepared_data, local_distribution, prepared_weight, local_weights, prep_F)\n return result","def match(\n self,\n pattern: str\n ) -> Iterable[AbstractPage]:\n paths: List[str] = [page.path for page in self._page_table.keys()]\n for match in sorted(fn_filter(paths, pattern)):\n yield self[match]","def store_matches(\n self,\n parameter_handler: ParameterHandler,\n topology: Topology,\n ) -> None:\n if self.key_map:\n # TODO: Should the key_map always be reset, or should we be able to partially\n # update it? Also Note the duplicated code in the child classes\n self.key_map: dict[BondKey, PotentialKey] = dict() # type: ignore[assignment]\n matches = parameter_handler.find_matches(topology)\n for key, val in matches.items():\n param = val.parameter_type\n if param.k_bondorder or param.length_bondorder:\n bond = topology.get_bond_between(*key)\n fractional_bond_order = bond.fractional_bond_order\n if not fractional_bond_order:\n assert self._get_uses_interpolation(parameter_handler)\n raise RuntimeError(\n \"Bond orders should already be assigned at this point\",\n )\n else:\n fractional_bond_order = None\n topology_key = BondKey(\n atom_indices=key,\n bond_order=fractional_bond_order,\n )\n\n potential_key = PotentialKey(\n id=val.parameter_type.smirks,\n associated_handler=parameter_handler.TAGNAME,\n bond_order=fractional_bond_order,\n )\n self.key_map[topology_key] = potential_key\n\n valence_terms = self.valence_terms(topology)\n\n _check_all_valence_terms_assigned(\n handler=parameter_handler,\n topology=topology,\n assigned_terms=matches,\n valence_terms=valence_terms,\n )","def _scan_hosts(self):\n results = []\n for item in glob.glob(self._pattern):\n results.append(item)\n return results","def groupby(f, coll):\n d = dict()\n for item in coll:\n key = f(item)\n if key not in d:\n d[key] = []\n d[key].append(item)\n return d","def _findKeyForTemplate(self, template, listener=False):\n if listener == True:\n prependStr = 'listener:'\n else:\n prependStr = 'tuple:'\n # Prepare a deferred result for this operation\n outerDf = defer.Deferred()\n if listener == True:\n subtupleKeys = self._keywordHashesFromTuple(template, listener)\n else:\n subtupleKeys = self._keywordHashesFromTemplate(template, listener)\n \n kwIndex = [-1] # using a list for this counter because Python doesn't allow binding a new value to a name in an enclosing (non-global) scope\n havePossibleMatches = [False]\n filteredResults = []\n \n listenerResults = []\n listenerSubtupleSetCounter = [0]\n \n #TODO: If all elements in the template are None, we only have the tuple length... maybe raise an exception?\n \n def filterResult(result):\n kwKey = subtupleKeys[kwIndex[0]]\n if type(result) == dict:\n # Value was found; this should be list of keys for tuples matching this criterion\n index = result[kwKey]\n if havePossibleMatches[0] == False:\n havePossibleMatches[0] = True\n filteredResults.extend(index)\n else:\n # Filter the our list of possible matching tuples with the new results\n delKeys = []\n for tupleKey in filteredResults:\n if tupleKey not in index:\n delKeys.append(tupleKey)\n for tupleKey in delKeys:\n filteredResults.remove(tupleKey)\n if len(filteredResults) == 0:\n # No matches for this template exist at this point; there is no use in searching further\n outerDf.callback(None)\n else:\n findNextSubtuple()\n else:\n # Value wasn't found; thus no matches for this template exist - stop the search\n outerDf.callback(None)\n \n def filterListenerResult(result):\n \"\"\" Same as filterResult(), except that 2 sets of subtuples keys' results are OR'ed \"\"\"\n if kwIndex[0] == -1:\n # This was the deterministic search\n if type(result) == dict:\n # An exact template match was found, callback with this\n outerDf.callback(result[mainKey])\n else:\n # The deterministic search did not find anything; start searching subtuples\n findNextSubtuple()\n return\n \n kwKey = subtupleKeys[kwIndex[0]]\n \n if type(result) == dict:\n # Value was found; this should be list of keys for tuples matching this criterion\n index = result[kwKey]\n listenerResults.extend(index)\n listenerSubtupleSetCounter[0] += 1\n \n if listenerSubtupleSetCounter[0] == 3:\n if havePossibleMatches[0] == False:\n havePossibleMatches[0] = True\n filteredResults.extend(listenerResults)\n else:\n # Filter the our list of possible matching tuples with the new results\n delKeys = []\n for tupleKey in filteredResults:\n if tupleKey not in listenerResults:\n delKeys.append(tupleKey)\n for tupleKey in delKeys:\n try:\n filteredResults.remove(tupleKey)\n except ValueError:\n pass\n\n if len(filteredResults) == 0:\n # No matches for this template exist at this point; there is no use in searching further\n outerDf.callback(None)\n else:\n # Reset the cycle\n listenerSubtupleSetCounter[0] = 0\n while len(listenerResults):\n listenerResults.pop()\n findNextSubtuple()\n else:\n findNextSubtuple()\n \n def findNextSubtuple(results=None):\n kwIndex[0] += 1\n if kwIndex[0] < len(subtupleKeys):\n kwKey = subtupleKeys[kwIndex[0]]\n #TODO: kademlia is going to replicate the un-updated inverted index; stop that from happening!!\n df = self.iterativeFindValue(kwKey)\n if listener == True:\n df.addCallback(filterListenerResult)\n else:\n df.addCallback(filterResult)\n else:\n # We're done. Let the caller of the parent method know, and return the key of the first qualifying tuple in the list of results\n outerDf.callback(filteredResults[0])\n \n if subtupleKeys == None:\n # The template is deterministic; thus we can retrieve the corresponding tuple directly\n h = hashlib.sha1()\n tupleValue = cPickle.dumps(template)\n h.update(prependStr + tupleValue)\n mainKey = h.digest()\n outerDf.callback(mainKey)\n else:\n if listener == True:\n # First look for an exact match if we are looking for listener tuples\n h = hashlib.sha1()\n tupleValue = cPickle.dumps(template)\n h.update(prependStr + tupleValue)\n mainKey = h.digest()\n df = self.iterativeFindValue(mainKey)\n df.addCallback(filterListenerResult)\n else:\n # Query the DHT for the first subtuple (this implicitly specifies the requested tuple length as well)\n findNextSubtuple()\n \n return outerDf","def sortReplicas(replicas, token):\n # if token exists, insist on using it\n\n from SiteMover import SiteMover\n _replicas_mcdisk_datadisk = []\n _replicas_hotdisk = []\n _replicas_disk = []\n _replicas_tape = []\n\n # put any tape resident replicas at the end of the list unless requested with the dispatchDBlockToken\n if token:\n # only keep replicas matching the given space token (tape should be first)\n for _replica in replicas:\n if token.lower() in _replica.sfn.lower():\n _replicas_tape.append(_replica)\n else:\n # look up the DQ2 site using the surl in case of obscured tape site\n if _replica.filetype == \"TAPE\":\n _replicas_tape.append(_replica)\n else:\n _replicas_disk.append(_replica)\n replicas = _replicas_tape + _replicas_disk\n else:\n # separate tape and mcdisk/datadisk replicas from other disk replicas\n for _replica in replicas:\n if \"tape\" in _replica.sfn.lower() or \"t1d0\" in _replica.sfn.lower():\n _replicas_tape.append(_replica)\n elif \"hotdisk\" in _replica.sfn.lower():\n _replicas_hotdisk.append(_replica)\n elif \"mcdisk\" in _replica.sfn.lower() or \"datadisk\" in _replica.sfn.lower() or \"bnlt0d1\" in _replica.sfn.lower():\n _replicas_mcdisk_datadisk.append(_replica)\n else:\n # look up the DQ2 site using the surl in case of obscured tape site\n tolog(\"Replica: %s\" % (_replica.sfn))\n if _replica.filetype == \"TAPE\":\n _replicas_tape.append(_replica)\n else:\n _replicas_disk.append(_replica)\n replicas = _replicas_hotdisk + _replicas_mcdisk_datadisk + _replicas_disk + _replicas_tape\n\n return replicas","def _GetShardedBatch() -> tf.types.experimental.distributed.PerReplica:\n per_host_batches: List[py_utils.NestedMap] = []\n # Note: `available_devices` omits the executor host; just those with TPUs.\n for host_device in py_utils.Flatten(\n cluster_factory.Current().available_devices.tolist()\n ):\n with tf.device(host_device):\n batch = self.task.input.GetPreprocessedInputBatch()\n\n # Remove bucket_keys; this relates to GenericInput pipelines.\n batch = batch.FilterKeyVal(lambda k, _: not k.endswith('bucket_keys'))\n\n # Process embedding ID features according to their specified types.\n batch = batch.TransformWithKey(\n tpu_embedding_layers_v2.TPU_EMBEDDING_MANAGER.ProcessInputFeature\n )\n\n per_host_batches.extend(Split(batch, replicas_per_host))\n\n return strategy.experimental_distribute_values_from_function(\n lambda ctx: per_host_batches[ctx.replica_id_in_sync_group]\n )","def get_matches(dst, threshold):\n print(time.asctime(), ' Filtering and sorting distances')\n start = time.time()\n dup_dict = util.find_duplicates(dst, threshold)\n n_matches = len(dup_dict)\n end = time.time()\n print(time.asctime(), ' Done Filtering distances for ', n_matches, ' matches in ', end-start, ' seconds', flush=True)\n return dup_dict","def match_filter_patterns(self, content: str, guild_id: int) -> Optional[typing.Tuple[re.Match, str]]:\n for pattern, pattern_identifier in self._filter_cache[guild_id].items():\n if search := pattern.search(content):\n return search, pattern_identifier","def group_by_hash(fileset, hashfunc=hashlib.md5, min_group_size=2, workers=16):\n return group_by_key(fileset, hash_key(hashfunc=hashfunc),\n min_group_size=min_group_size, workers=workers)","def read_all_shards(partition, data_dir, bucket_name):\n\n shards = []\n gcsfs = GCSFS(bucket_name)\n for fn in gcsfs.listdir(os.path.join(data_dir, partition)):\n with gcsfs.open(os.path.join(data_dir, partition, fn)) as f:\n shards.append(pd.read_csv(f, index_col=None))\n \n return pd.concat(shards)","def get_knitting_patterns_by_ids(self, ids_list):\n ids_string = ''\n\n for i in ids_list:\n ids_string = '{}, {}'.format(ids_string, i)\n\n ids_string = ids_string[1:] \n\n params = {\n \"ids\" : ids_string,\n \"craft\" : \"knitting\"\n }\n\n resp_from_server = self.get_api_result(self.URL_RETURNS_PATTERNS, params)\n\n\n if resp_from_server.get('status'):\n return resp_from_server\n else:\n patterns_dicts = self.get_patterns_dict(resp_from_server)\n\n return patterns_dicts","def do_keys_match_bucket(args):\n\n sanitised_group = args.group.replace('/', '-')\n expected_path = \"/etc/ugr/conf.d/{}.conf\".format(sanitised_group)\n if not os.path.exists(expected_path):\n return 1\n\n with open(expected_path, \"r\") as f:\n lines = f.read().splitlines()\n expected_public_line = \"locplugin.{}-{}.s3.pub_key: \".format(sanitised_group, args.bucket)\n expected_private_line = \"locplugin.{}-{}.s3.priv_key: \".format(sanitised_group, args.bucket)\n public_key = private_key = None\n\n for line in lines:\n if line.startswith(expected_public_line):\n public_key = line.split(expected_public_line)[1]\n elif line.startswith(expected_private_line):\n private_key = line.split(expected_private_line)[1]\n \n if public_key is not None and private_key is not None:\n if public_key == args.public_key and private_key == args.private_key:\n return 0\n break\n \n return 1","def hshingle(s, k):\n for s in shingle(s, k):\n yield hash(s)","def search_by_pattern(self, pattern, key=lambda data: data['meta']):\n result = []\n for node, data in self.traverse():\n if re.search(pattern, key(data), flags=re.VERBOSE):\n result.append([node, data])\n return result","def expand_fingerprints(fingerprints, auth, depth=0):\n fingerprints = set(fingerprints)\n while True:\n q = {\n 'size': 0,\n 'query': {\n 'bool': {\n 'must': [\n {'terms': {'fingerprints': list(fingerprints)}},\n {'terms': {'groups': auth.groups}}\n ]\n }\n },\n 'aggregations': {\n 'fingerprints': {\n 'terms': {'field': 'fingerprints', 'size': 1000}\n }\n }\n }\n result = es.search(index=es_index, doc_type=Schema.ENTITY, body=q)\n buckets = result['aggregations']['fingerprints']['buckets']\n old_count = len(fingerprints)\n for bucket in buckets:\n fingerprints.add(bucket['key'])\n if len(fingerprints) == old_count or len(fingerprints) > MAX_LEN:\n return list(fingerprints)[:MAX_LEN]","def divy_keys(self,spot):\n\n\t\tgroup_counts = [sum([i[0]=='subpattern' \n\t\t\tfor i in re.sre_parse.parse(self.spots[spot][key])]) \n\t\t\t#---apply naming convention\n\t\t\tfor key in ['top','step','part']]\n\t\tcursor = ([0]+[sum(group_counts[:i+1]) for i in range(len(group_counts))])\n\t\tslices = [slice(cursor[i],cursor[i+1]) for i in range(len(cursor)-1)]\n\t\tdivy = lambda x: [y[0] if len(y)==1 else y for y in [x[s] for s in slices]]\n\t\treturn divy","def return_matches(hashes):\n # Create a dictionary of hash => offset pairs for later lookups\n mapper = {}\n for hash, offset in hashes:\n mapper[hash.upper()] = offset\n\n # Get an iteratable of all the hashes we need\n values = mapper.keys()\n\n conn = MySQLdb.connect(**connection_string[\"Common\"])\n cur = conn.cursor()\n\n for split_values in grouper(values, 1000):\n # Create our IN part of the query\n query = SELECT_MULTIPLE\n query %= ', '.join(['UNHEX(%s)'] * len(split_values))\n\n cur.execute(query, split_values)\n\n for hash, sid, offset in cur:\n # (sid, db_offset - song_sampled_offset)\n yield (sid, offset - mapper[hash])","def scan(self, ts):\n redis = get_redis_instance()\n\n keys = []\n pattern = self.scan_pattern % ts\n for k in redis.scan_iter(pattern):\n keys.append(k)\n return keys","def pullAll(*keys):","def fuzzy_filter(\n search: str,\n schemas: List[dict],\n root_only: bool = False,\n key: str = \"name_qual\",\n fuzzy_keys: List[str] = FUZZY_SCHEMAS_KEYS,\n **kwargs,\n ) -> List[dict]:\n\n def do_skip(schema):\n is_details = schema[\"name\"].endswith(\"_details\")\n is_all = schema[\"name\"] == \"all\"\n not_select = not schema.get(\"selectable\", True)\n is_root = root_only and not schema[\"is_root\"]\n\n if any([schema in matches, is_details, is_all, not_select, is_root]):\n return True\n\n return False\n\n matches = []\n\n # try to do string matches first\n for schema in schemas:\n if not do_skip(schema) and any(\n [search.strip().lower() in x for x in [schema[x] for x in fuzzy_keys]]\n ):\n matches.append(schema)\n\n # if no string matches, try to find matches with fuzzyfinder\n if not matches:\n for schema in schemas:\n if not do_skip(schema) and list(\n fuzzyfinder(search, [schema[x] for x in fuzzy_keys])\n ):\n matches.append(schema)\n\n return [x[key] for x in matches] if key else matches","def group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):\n current_sample = None\n for filesample in data:\n assert isinstance(filesample, dict)\n fname, value = filesample[\"fname\"], filesample[\"data\"]\n prefix, suffix = keys(fname)\n if prefix is None:\n continue\n if lcase:\n suffix = suffix.lower()\n # FIXME webdataset version throws if suffix in current_sample, but we have a potential for\n # this happening in the current LAION400m dataset if a tar ends with same prefix as the next\n # begins, rare, but can happen since prefix aren't unique across tar files in that dataset\n if current_sample is None or prefix != current_sample[\"__key__\"] or suffix in current_sample:\n if valid_sample(current_sample):\n yield current_sample\n current_sample = {\"__key__\": prefix, \"__url__\": filesample[\"__url__\"]}\n if suffixes is None or suffix in suffixes:\n current_sample[suffix] = value\n if valid_sample(current_sample):\n yield current_sample","def _split_ns_by_scatter(cls,\n shard_count,\n namespace,\n raw_entity_kind,\n app):\n if shard_count == 1:\n\n return [key_range.KeyRange(namespace=namespace, _app=app)]\n\n ds_query = datastore.Query(kind=raw_entity_kind,\n namespace=namespace,\n _app=app,\n keys_only=True)\n ds_query.Order(\"__scatter__\")\n oversampling_factor = 32\n random_keys = ds_query.Get(shard_count * oversampling_factor)\n\n if not random_keys:\n\n\n return ([key_range.KeyRange(namespace=namespace, _app=app)] +\n [None] * (shard_count - 1))\n\n random_keys.sort()\n\n if len(random_keys) >= shard_count:\n\n random_keys = cls._choose_split_points(random_keys, shard_count)\n\n k_ranges = []\n\n k_ranges.append(key_range.KeyRange(\n key_start=None,\n key_end=random_keys[0],\n direction=key_range.KeyRange.ASC,\n include_start=False,\n include_end=False,\n namespace=namespace,\n _app=app))\n\n for i in range(0, len(random_keys) - 1):\n k_ranges.append(key_range.KeyRange(\n key_start=random_keys[i],\n key_end=random_keys[i+1],\n direction=key_range.KeyRange.ASC,\n include_start=True,\n include_end=False,\n namespace=namespace,\n _app=app))\n\n k_ranges.append(key_range.KeyRange(\n key_start=random_keys[-1],\n key_end=None,\n direction=key_range.KeyRange.ASC,\n include_start=True,\n include_end=False,\n namespace=namespace,\n _app=app))\n\n if len(k_ranges) < shard_count:\n\n k_ranges += [None] * (shard_count - len(k_ranges))\n return k_ranges","def find_matches(self, match_fn):\n for item in self.hook:\n if match_fn(item):\n yield item\n if not isinstance(item, text_type):\n for subitem in item.find_matches(match_fn):\n yield subitem","def group_by(s, fn):\n\n dict = {}\n for item in s:\n output = fn(item)\n if output in dict.keys():\n dict[output].extend([item])\n else:\n dict[output] = [item]\n return dict","def topic_pattern_match(pattern):\n client = AdminClient({\"bootstrap.servers\": \"PLAINTEXT://localhost:9092\"})\n topic_metadata = client.list_topics()\n topics = topic_metadata.topics\n filtered_topics = {key: value for key, value in topics.items() if contains_substring(key, pattern)}\n return len(filtered_topics) > 0","def _shard_parameters_(self, params_to_shard) -> None:\n if len(params_to_shard) > 0:\n # When freeing the full parameters, we point their internal XLATensor to this placeholder\n # (so that the XLA compiler can reuse the memory storage).\n self._dummy_data_placeholder = torch.zeros(\n 1, dtype=self.compute_dtype, device=self.xla_device)\n\n # get the module names of each full parameter to shard\n params_to_shard_set = set(params_to_shard)\n assert len(params_to_shard_set) == len(params_to_shard), \\\n \"params_to_shard should not have dups\"\n full_param_infos = []\n shared_full_param_memo = {}\n shared_full_param_infos = []\n full_params = []\n for module_name, m in self.named_modules():\n for n, p in m.named_parameters(recurse=False):\n if p.dtype != torch.float32:\n raise TypeError(\"only fp32 parameters are supported\")\n if p in params_to_shard_set:\n if p in shared_full_param_memo:\n mname, shared_m, shared_n = shared_full_param_memo[p]\n shared_full_param_infos.append(\n (module_name, mname, m, n, shared_m, shared_n))\n else:\n shared_full_param_memo[p] = (module_name, m, n)\n full_param_infos.append((module_name, m, n))\n full_params.append(p)\n assert len(full_params) == len(params_to_shard_set), \\\n f\"there are parameters in params_to_shard not belonging to this module.\"\n del shared_full_param_memo\n self.full_params = full_params\n self.full_param_infos = full_param_infos\n self.shared_full_param_infos = shared_full_param_infos\n\n # allocate and register new sharded parameters\n self.sharded_params = []\n for idx, (module_name, m, n) in enumerate(self.full_param_infos):\n p = self.full_params[idx]\n assert not hasattr(p, \"_is_sharded\")\n\n shard_data = self._get_shard(p)\n if shard_data.device != self.xla_device:\n # cast to XLA device if not already on XLA\n shard_data = shard_data.to(self.xla_device)\n p_shard = nn.Parameter(shard_data, requires_grad=p.requires_grad)\n p_shard._is_sharded = True\n p_shard._orig_size = p.size()\n p_shard._orig_name = f\"{module_name}.{n}\"\n p_shard._name = f\"_fsdp_shard.{p_shard._orig_name}\".replace(\n \".\", \"_FSDP_SHARD_SEPARATOR_\")\n self.register_parameter(p_shard._name, p_shard)\n self.sharded_params.append(p_shard)\n if p.device != self.xla_device:\n # cast to XLA device if not already on XLA\n p = p.to(self.xla_device).requires_grad_(p.requires_grad)\n # update p in full_params since id(p) changed after the casting\n self.full_params[idx] = p\n # Free the full parameter storage (here we free its internal XLATensor) but keep the tensor itself\n # for auto-grad tracing (like `torch.autograd.Variable` before the tensor-variable merge).\n if XLA_DISABLE_FUNCTIONALIZATION:\n p.data = p.new_zeros(1) # Old behavior before Functionalization.\n else:\n torch_xla._XLAC._replace_xla_tensor(p, p.new_zeros(1))\n p._sharded_param = p_shard # add a handle to the sharded parameter\n p._has_full_param = False\n # deregister the full parameter tensors from their modules (so that they won't\n # appear in the FSDP model's `parameters()` or `named_parameters()` outputs;\n # only the sharded parameters should appear in the FSDP model's `parameters()`)\n assert n in m._parameters\n m._parameters.pop(n)\n object.__setattr__(m, n, p)\n\n # also deregister the shared parameters\n for _, _, m, n, shared_m, shared_n in self.shared_full_param_infos:\n assert n in m._parameters\n m._parameters.pop(n)\n shared_p = getattr(shared_m, shared_n)\n object.__setattr__(m, n, shared_p)\n\n assert len(self.sharded_params) == len(self.full_params)","def groupby_apply_parallel(grouped_df, func, *args):\n with Pool(cpu_count()) as p:\n return_list = p.starmap(func, [(group, *args) for name, group in grouped_df])\n return pd.concat(return_list)","def filtered(self, keys, lst=None, func=\"all\"):\n lst = self if lst is None else lst\n if len(lst) == 0:\n raise ValueError(\"No rows in list\")\n return [row for row in self.filter(keys, lst, func=func)]","def match_source_key(self, match):\n raise NotImplementedError","def match_words_to_search(chunks, searchresult, compare_func, join=True):\n wordlist = [hebstrip(w)[1] for w in word_bound.split(searchresult)]\n wordset = set(wordlist)\n genlist = [\n m\n for m in [\n match_one(rlist, wordset)\n for rlist in chunks.linked_heb\n if rlist.data\n ]\n if m\n ]\n ours = [i[0] for i in genlist]\n theirs = [i[1] for i in genlist]\n if join:\n return compare_func(\" \".join(ours), \" \".join(wordlist)), theirs\n else:\n return compare_func(ours, wordlist), theirs","def keys(self, pattern=\"*\"):\n lenOfPrefix = len(self.appendKeys(\"\"))\n return [key[lenOfPrefix:] for key in\n self.redis.keys(self.appendKeys(pattern))]","def apply(self, func):\n logging.debug(\"applying function %s to db\", str(func))\n return [ y for y in [ func(x, self._db[x]) for x in self._db.keys() ] if y ]","def repair(\n self,\n start_key=None,\n end_key=None,\n shard=0,\n nshards=1,\n callback_on_progress=None,\n ):\n with self._lock:\n self._repair(start_key, end_key, shard, nshards, callback_on_progress)","def _add_matches(self):\r\n for record in self.records:\r\n match_dict={key_to_track: record.get(key_to_track)\r\n for key_to_track in self.key_matcher.keys()}\r\n self.key_matcher.add(obj=record,\r\n match_dict=match_dict)","def __call__(self, f):\n def w(*args, **kwargs):\n if not isinstance(self.keys, list):\n self.keys = [self.keys]\n\n for key in self.keys:\n clazz = resolve(key)\n args = args + (clazz,)\n\n return f(*args, **kwargs)\n\n return w","def many(keys: List[str]):\n for key in keys:\n actions.key(key)","def rh_get_match(x, y, k, d=7, hash_size=\"automatic\"):\n n = len(x)\n m = len(y)\n\n if hash_size == \"automatic\":\n q = n * m\n else:\n q = hash_size\n\n output = list()\n\n for i in range(n - k + 1):\n matches = rabin_karp_matcher(\n target=y,\n potential=x[i:i + k],\n d=d,\n q=q\n )\n\n for match in matches:\n output.append((i, match))\n\n return output","def get_s3_keys_anatomy(\n subject,\n freesurfer_outputs=('label', 'mri', 'surf'),\n meg_anatomy_outputs=('head_model', 'transforms'),\n mode='minimal',\n hcp_path_bucket='HCP_900'):\n aws_keys = list()\n for output in freesurfer_outputs:\n aws_keys.extend(\n get_file_paths(subject=subject, data_type='freesurfer',\n output=output,\n mode=mode,\n hcp_path=hcp_path_bucket))\n for output in meg_anatomy_outputs:\n aws_keys.extend(\n get_file_paths(subject=subject, data_type='meg_anatomy',\n output=output,\n hcp_path=hcp_path_bucket))\n return aws_keys","def keysAll():","def reduce_tiles(\n self,\n preserve=(\"s22\", \"ld\"),\n tries=10,\n threads=1,\n returntype=\"equiv\",\n best=1,\n key=None,\n initequiv=None,\n ):\n raise NotImplementedError\n # from fastreduceD import fastreduce\n # return fastreduce.reduce_tiles(\n # self, preserve, tries, threads, returntype, best, key, initequiv\n # )","def fisher_exact_worker(sparse_dict, overlap_count, user_count, gene_count, universe_count, set_list, combinations):\n i, j = combinations[0], combinations[1]\n table = build_fisher_contingency_table(overlap_count[i, j], user_count[j], gene_count[0, i], universe_count)\n pvalue = stats.fisher_exact(table, alternative=\"greater\")[1]\n new_pval = np.round(-1.0 * np.log10(pvalue), 12)\n row_item = [set_list[j], sparse_dict[i], new_pval, int(universe_count), \n int(user_count[j]), int(gene_count[0, i]), int(overlap_count[i, j])]\n return row_item","def fn(k):\n seen = set()\n for i in range(len(s)-k+1): \n val = (prefix[i+k] - prefix[i]*fac[k]) % MOD \n if val in seen: return True # rolling hash (ver. Monte Carlo)\n seen.add(val)\n return False","def find_sample(self, pattern, prefix):\n m = re.match(pattern, prefix).groupdict()\n _samples = []\n for s in self.samples:\n if m.items() <= s.items():\n _samples.append(s)\n return _samples","def get_matched_custom_feed(cls, fsid, topic_name): \n custom_feed_map = cls.get_custom_feed_map(fsid)\n if not custom_feed_map:\n return [], None\n if custom_feed_map.has_key(topic_name):\n return [], custom_feed_map.get(topic_name)\n wc_topic_name, required_keywords = cls.to_bracket_wild_card(topic_name)\n if wc_topic_name and custom_feed_map.has_key(wc_topic_name):\n return required_keywords, custom_feed_map.get(wc_topic_name)\n return [], None","def find_matches(words, min_match_ratio):\n couples = []\n with Pool(processes=mp.cpu_count()) as pool:\n results = pool.starmap(\n get_fuzz_ratio, itertools.combinations(words, 2))\n for result, word, paired_word in results:\n if result >= min_match_ratio:\n couples.append([word, paired_word])\n return couples"],"string":"[\n \"def group_by_key(fileset, keyfunc, min_group_size=2, workers=16):\\n pool = ThreadPool(workers)\\n\\n def evaluate(entry):\\n return keyfunc(entry), entry\\n\\n keyed_fileset = pool.map(evaluate, fileset)\\n files_by_key = {}\\n for key, entry in keyed_fileset:\\n if key is None:\\n continue\\n files_by_key.setdefault(key, []).append(entry)\\n\\n return [group for group in files_by_key.values() if\\n len(group) >= min_group_size]\",\n \"def find(ctx, pattern):\\n config = buildConfig(ctx.obj[\\\"HOST\\\"], ctx.obj[\\\"PORT\\\"])\\n clientList = getClientList(config)\\n for client in clientList:\\n srvFound = 0\\n for id in slabIds(client.stats(\\\"items\\\")):\\n for key in client.stats('cachedump', id, '0').keys():\\n if re.search(pattern, key.decode('utf-8')):\\n click.echo(key)\\n srvFound += 1\\n click.echo(f\\\"********** SERVER {client.server} contained {srvFound} matching results\\\")\\n print()\",\n \"def find_matches(self, match_fn):\\n return\\n yield # Turns this function into a generator but that is empty\",\n \"def find_matches(self, match_fn):\\n return\\n yield # Turns this function into a generator but that is empty\",\n \"def findall(self, key_list):\\n for i in range(3):\\n try:\\n return self.redis_handler.mget(key_list) \\n except:\\n continue\",\n \"def _to_key_ranges_by_shard(cls, app, namespaces, shard_count, query_spec):\\n key_ranges_by_ns = []\\n\\n\\n for namespace in namespaces:\\n ranges = cls._split_ns_by_scatter(\\n shard_count,\\n namespace,\\n query_spec.entity_kind,\\n app)\\n\\n\\n random.shuffle(ranges)\\n key_ranges_by_ns.append(ranges)\\n\\n\\n\\n\\n ranges_by_shard = [[] for _ in range(shard_count)]\\n for ranges in key_ranges_by_ns:\\n for i, k_range in enumerate(ranges):\\n if k_range:\\n ranges_by_shard[i].append(k_range)\\n\\n key_ranges_by_shard = []\\n for ranges in ranges_by_shard:\\n if ranges:\\n key_ranges_by_shard.append(key_ranges.KeyRangesFactory.create_from_list(\\n ranges))\\n return key_ranges_by_shard\",\n \"def shard(self, dataset_iter):\\n return dataset_iter\",\n \"def store_matches(\\n self,\\n parameter_handler: ParameterHandler,\\n topology: \\\"Topology\\\",\\n ) -> None:\\n if self.key_map:\\n # TODO: Should the key_map always be reset, or should we be able to partially\\n # update it? Also Note the duplicated code in the child classes\\n self.key_map: dict[\\n Union[TopologyKey, LibraryChargeTopologyKey],\\n PotentialKey,\\n ] = dict()\\n matches = parameter_handler.find_matches(topology)\\n for key, val in matches.items():\\n topology_key = TopologyKey(atom_indices=key)\\n potential_key = PotentialKey(\\n id=val.parameter_type.smirks,\\n associated_handler=parameter_handler.TAGNAME,\\n )\\n self.key_map[topology_key] = potential_key\\n\\n if self.__class__.__name__ in [\\n \\\"SMIRNOFFBondCollection\\\",\\n \\\"SMIRNOFFAngleCollection\\\",\\n ]:\\n valence_terms = self.valence_terms(topology)\\n\\n _check_all_valence_terms_assigned(\\n handler=parameter_handler,\\n assigned_terms=matches,\\n topology=topology,\\n valence_terms=valence_terms,\\n )\",\n \"def rds_scan_keys(rds, glob):\\n n = 0\\n keys = []\\n while(True):\\n n, k = rds.scan(n, match=glob)\\n keys.extend(k)\\n if n == 0:\\n break\\n return keys\",\n \"def multifilter(names, patterns):\\n for name in names:\\n if isinstance(name, collections.Mapping):\\n for key in name.iterkeys():\\n for pattern in patterns:\\n if fnmatch.fnmatch(key, pattern):\\n yield key\\n break\\n else:\\n for pattern in patterns:\\n if fnmatch.fnmatch(name, pattern):\\n yield name\\n break\",\n \"def key_lookup_batch(self, batchiter):\\n pass\",\n \"def read_entries_(self, start_key=None, end_key=None):\\n # TODO (andrin): fetch a couple of shards instead of just one based on\\n # method argument\\n current_key = start_key\\n if current_key == None:\\n current_key = (\\\"\\\", )\\n limit_shard_name = RecordIOShard.key_name(\\n self.name, lo=start_key, hi=end_key).split(SPLIT_CHAR)\\n while True:\\n shard = RecordIOShard.get_shards_for_key_values(\\n self.name, [current_key], keys_only=False).next()[0]\\n self.db_search_and_get += 1\\n if shard == None:\\n raise RecordIOShardDoesNotExistError(self.name)\\n hi = shard.lo_hi()[1]\\n shard_name = shard.key().name().split(SPLIT_CHAR)\\n if (shard_name[6:10] >= limit_shard_name[6:10] and\\n (shard_name[2:5] < limit_shard_name[2:5] or\\n limit_shard_name[2] == SPLIT_CHAR_AFTER)):\\n # Read the whole shard\\n for entry in shard:\\n yield entry\\n else:\\n # Read parts of the shard\\n for entry in shard.read(current_key, end_key):\\n yield entry\\n if hi == None:\\n # Was the last shard\\n return\\n current_key = hi\\n if (end_key != None and\\n RecordIORecords.entry_comperator(current_key, end_key) >= 0):\\n # Next shard is after end_key\\n return\",\n \"def _choose_split_points(cls, sorted_keys, shard_count):\\n assert len(sorted_keys) >= shard_count\\n index_stride = len(sorted_keys) / float(shard_count)\\n return [sorted_keys[int(round(index_stride * i))]\\n for i in range(1, shard_count)]\",\n \"def find_fks_well_distributed(keylist):\\n global _max_k\\n global _l1_bucket_max\\n mlist = _get_l1_mlist(len(keylist)) # number of buckets\\n for m in mlist:\\n for p in _primes:\\n for k in range(3, _max_k):\\n hash_f = hash_fun_fks_t(k,p,m)\\n if xedhash.is_well_distributed(keylist, hash_f, _l1_bucket_max):\\n return hash_f\\n del hash_f\\n return None\",\n \"def _map_and_prefix_keys(self, key_iterable, key_prefix):\\r\\n # Check it just once ...\\r\\n key_extra_len=len(key_prefix)\\r\\n #changed by steve\\r\\n #if key_prefix:\\r\\n #self.check_key(key_prefix)\\r\\n\\r\\n # server (_Host) -> list of unprefixed server keys in mapping\\r\\n server_keys = {}\\r\\n\\r\\n prefixed_to_orig_key = {}\\r\\n # build up a list for each server of all the keys we want.\\r\\n for orig_key in key_iterable:\\r\\n if isinstance(orig_key, tuple):\\r\\n # Tuple of hashvalue, key ala _get_server(). Caller is essentially telling us what server to stuff this on.\\r\\n # Ensure call to _get_server gets a Tuple as well.\\r\\n str_orig_key = str(orig_key[1])\\r\\n server, key = self._get_server((orig_key[0], key_prefix + str_orig_key)) # Gotta pre-mangle key before hashing to a server. Returns the mangled key.\\r\\n else:\\r\\n str_orig_key = str(orig_key) # set_multi supports int / long keys.\\r\\n server, key = self._get_server(key_prefix + str_orig_key)\\r\\n\\r\\n # Now check to make sure key length is proper ...\\r\\n #changed by steve\\r\\n #self.check_key(str_orig_key, key_extra_len=key_extra_len)\\r\\n key = self.check_key(key_prefix + str_orig_key)\\r\\n\\r\\n if not server:\\r\\n continue\\r\\n\\r\\n if server not in server_keys:\\r\\n server_keys[server] = []\\r\\n server_keys[server].append(key)\\r\\n prefixed_to_orig_key[key] = orig_key\\r\\n\\r\\n return (server_keys, prefixed_to_orig_key)\",\n \"def searchcolkeys(self,fctn,cols,keylist,*args):\\n goodkeys=[]\\n for key in keylist:\\n temp=[]\\n for c in cols:\\n temp.append(self.getentry(key,c))\\n for i in range(len(args)):\\n temp.append(args[i])\\n\\n if fctn(*tuple(temp)):\\n goodkeys.append(key)\\n return(goodkeys)\",\n \"def _sharded_pre_load_state_dict_hook(\\n self,\\n state_dict: Dict[str, Any],\\n prefix: str,\\n ) -> None:\\n _replace_by_prefix(state_dict, prefix, prefix + f\\\"{FSDP_WRAPPED_MODULE}.\\\")\\n if not self._fsdp_wrapped_module.has_params:\\n return\\n\\n if not self._fsdp_wrapped_module.handle.uses_sharded_strategy:\\n raise RuntimeError(\\n \\\"load_sharded_state_dict can only be called when parameters \\\"\\n \\\"are flatten and sharded.\\\"\\n )\\n\\n nonsharded_tensors = []\\n # TODO: Reduce the communication by using only one _all_gather_base to\\n # gather all the parameters in this layer. This can be achieved by\\n # concatenated all the local shards and then append the padding.\\n # https://github.com/pytorch/pytorch/issues/77461\\n for (param_name, _, module_name) in self._fsdp_wrapped_module.handle.flat_param._param_infos:\\n module_name = self._convert_to_wrapped_module_name(module_name)\\n fqn = f\\\"{prefix}{FSDP_WRAPPED_MODULE}.{module_name}{param_name}\\\"\\n param = state_dict.pop(fqn)\\n\\n # All-gather the param (ShardedTensor)\\n param, shards = _ext_pre_load_state_dict_transform(param)\\n assert len(shards) < 2, (\\n f\\\"Expects 0 or 1 shard per rank but got {len(shards)} shards on rank {self.rank}\\\"\\n )\\n param_numel = param.size().numel()\\n dim_0_size = param.size()[0]\\n chunk_size = (\\n math.ceil(dim_0_size / self.world_size) * param_numel // dim_0_size\\n )\\n if shards:\\n local_tensor = cast(torch.Tensor, shards[0].tensor).flatten()\\n if not local_tensor.is_cuda:\\n local_tensor = local_tensor.cuda()\\n num_padding = chunk_size - local_tensor.numel()\\n if num_padding > 0:\\n local_tensor = F.pad(local_tensor, [0, num_padding])\\n else:\\n local_tensor = torch.zeros(chunk_size, dtype=param.dtype).cuda()\\n tensor = torch.empty(\\n chunk_size * self.world_size, dtype=local_tensor.dtype\\n ).cuda()\\n dist._all_gather_base(tensor, local_tensor, group=self.process_group)\\n tensor = tensor.narrow(0, 0, param_numel).reshape(param.size())\\n nonsharded_tensors.append(tensor)\\n\\n # Create a new flat_param from the loaded, non-sharded tensors.\\n flat_param = self._fsdp_wrapped_module.flat_param\\n loaded_flat_param = FlatParamHandle.flatten_params(nonsharded_tensors, requires_grad=False)\\n\\n # Get the chunk from the loaded flat_param for the local rank.\\n loaded_flat_param, num_to_pad = FlatParamHandle._get_shard(\\n loaded_flat_param, self.rank, self.world_size,\\n )\\n loaded_flat_param.to(flat_param.device)\\n assert flat_param.numel() == loaded_flat_param.numel(), (\\n f\\\"The loaded local chunk has different numel({flat_param.numel()}) \\\"\\n f\\\"from the local chunk {flat_param.numel()}.\\\"\\n )\\n assert flat_param._shard_numel_padded == num_to_pad, (\\n f\\\"The loaded local chunk has different padding({num_to_pad}) \\\"\\n f\\\"from the local chunk {flat_param._shard_numel_padded}.\\\"\\n )\\n state_dict[f\\\"{prefix}_fsdp_wrapped_module.flat_param\\\"] = loaded_flat_param\",\n \"def store_matches(\\n self,\\n parameter_handler: ImproperTorsionHandler,\\n topology: Topology,\\n ) -> None:\\n if self.key_map:\\n self.key_map = dict()\\n matches = parameter_handler.find_matches(topology)\\n for key, val in matches.items():\\n parameter_handler._assert_correct_connectivity(\\n val,\\n [\\n (0, 1),\\n (1, 2),\\n (1, 3),\\n ],\\n )\\n n_terms = len(val.parameter_type.k)\\n for n in range(n_terms):\\n smirks = val.parameter_type.smirks\\n non_central_indices = [key[0], key[2], key[3]]\\n\\n for permuted_key in [\\n (\\n non_central_indices[i],\\n non_central_indices[j],\\n non_central_indices[k],\\n )\\n for (i, j, k) in [(0, 1, 2), (1, 2, 0), (2, 0, 1)]\\n ]:\\n topology_key = ImproperTorsionKey(\\n atom_indices=(key[1], *permuted_key),\\n mult=n,\\n )\\n potential_key = PotentialKey(\\n id=smirks,\\n mult=n,\\n associated_handler=\\\"ImproperTorsions\\\",\\n )\\n self.key_map[topology_key] = potential_key\",\n \"def forever(shard):\\n def repeat(*args, **kwargs):\\n while True:\\n for delay in shard(*args, **kwargs):\\n yield delay\\n return repeat\",\n \"def return_matches(self, hashes, batch_size: int=1000):\\n # Create a dictionary of hash => offset pairs for later lookups\\n mapper = {}\\n for hsh, offset in hashes:\\n if hsh in mapper.keys():\\n mapper[hsh].append(offset)\\n else:\\n mapper[hsh] = [offset]\\n\\n values = list(mapper.keys())\\n\\n # in order to count each hash only once per db offset we use the dic below\\n dedup_hashes = {}\\n\\n results = []\\n\\n for index in range(0, len(values), batch_size):\\n # Create our IN part of the query\\n # query = self.SELECT_MULTIPLE % ', '.join([self.IN_MATCH] * len(values[index: index + batch_size]))\\n\\n res = self.elasticClient.search(index=\\\"fingerprints\\\",\\n body={\\\"query\\\": {\\\"terms\\\": {\\\"doc.hash\\\": values[index: index+batch_size]}}}, size=1000000)\\n\\n query_res = res['hits']['hits']\\n for doc in query_res:\\n hsh = doc['_source']['doc']['hash']\\n sid = doc['_source']['doc']['song_id']\\n offset = doc['_source']['doc']['offset']\\n if sid not in dedup_hashes.keys():\\n dedup_hashes[sid] = 1\\n else:\\n dedup_hashes[sid] += 1\\n # we now evaluate all offset for each hash matched\\n for song_sampled_offset in mapper[hsh]:\\n results.append((sid, offset - song_sampled_offset))\\n\\n return results, dedup_hashes\",\n \"def ShardList(list_to_shard, total_shards, shard_idx):\\n length = len(list_to_shard)\\n split_lists = []\\n for i in range(total_shards):\\n start_idx = i * length // total_shards\\n end_idx = (i + 1) * length // total_shards\\n split_lists.append(list_to_shard[start_idx: end_idx])\\n\\n return split_lists[shard_idx]\",\n \"def _GetFileMappingsByPath(self, safe_key_path):\\n candidate_mappings = []\\n for mapping in self._REGISTRY_FILE_MAPPINGS_NT:\\n if safe_key_path.startswith(mapping.key_path_prefix):\\n candidate_mappings.append(mapping)\\n\\n # Sort the candidate mappings by longest (most specific) match first.\\n candidate_mappings.sort(\\n key=lambda mapping: len(mapping.key_path_prefix), reverse=True)\\n for mapping in candidate_mappings:\\n yield mapping\",\n \"def _build_keys(self, slug, date=None, granularity='all'):\\n slug = slugify(slug) # Ensure slugs have a consistent format\\n if date is None:\\n date = datetime.utcnow()\\n patts = self._build_key_patterns(slug, date)\\n if granularity == \\\"all\\\":\\n return list(patts.values())\\n return [patts[granularity]]\",\n \"def apply(self, func, *args):\\n import ray\\n done_ids, undone_ids = ray.wait([shard.apply.remote(func, *args)\\n for shard in self.shard_list],\\n num_returns=len(self.shard_list))\\n assert len(undone_ids) == 0\\n return self\",\n \"def fetch_s3_keys_by_regex_pattern(s3_bucket, s3_directory, pattern):\\n bucket_contents = s3_bucket.list(s3_directory)\\n return [key for key in bucket_contents if pattern.search(key.name)]\",\n \"def get_shards(data_dir, file_list, shard_size, istraining):\\n file_idxs = np.arange(0, len(file_list))\\n np.random.shuffle(file_idxs) # randomly extract data from files\\n\\n shard_num = len(file_list) // shard_size\\n\\n for shard_idx in range(shard_num):\\n\\n start_idx = shard_idx * shard_size\\n end_idx = (shard_idx + 1) * shard_size\\n shard_files_idxs = file_idxs[start_idx: end_idx]\\n\\n all_data, all_label, all_names, all_node_img = [], [], [], []\\n for fn in shard_files_idxs:\\n\\n if not data_dir:\\n raw_data = np.load(file_list[fn])\\n else:\\n raw_data = np.load(os.path.join(data_dir, file_list[fn]))\\n\\n current_data = raw_data['vgg_features']\\n node_img_path = raw_data['img_path']\\n # pid = raw_data['pid']\\n # time = raw_data['time']\\n if len(current_data) < MIN_NUM_POINT:\\n # skip WSI of too few patches\\n continue\\n\\n # if len(current_data) > MAX_NUM_POINT:\\n # continue\\n\\n curr_path = file_list[fn]\\n\\n curr_type = curr_path.split('/')[-4]\\n curr_filename = curr_path.split('/')[-1]\\n\\n if curr_type == 'LUAD':\\n # LUAD -> class 0, LUSC -> class 1\\n current_label = 0\\n else:\\n current_label = 1\\n\\n # if istraining:\\n \\\"random select at most MAX_NUM_POINT nodes for WSI\\\"\\n list_node_idx = np.arange(0, current_data.shape[0])\\n np.random.shuffle(list_node_idx)\\n sel_ids = list_node_idx[0: MAX_NUM_POINT]\\n\\n current_data = current_data[sel_ids]\\n current_data = np.expand_dims(current_data, 0)\\n node_img_path = node_img_path[sel_ids]\\n\\n all_data.append(current_data)\\n all_label.append(current_label)\\n all_names.append(curr_filename)\\n all_node_img.append(node_img_path)\\n\\n \\\"\\\"\\\" create numpy for all data and label\\\"\\\"\\\"\\n all_label = np.squeeze(np.hstack(all_label))\\n\\n yield all_data, all_label, all_names, all_node_img\",\n \"def sharding(nodes, terms, document=None):\\n shards = defaultdict(lambda: defaultdict(list))\\n document_hash = None\\n\\n if document:\\n document_hash = get_document_hash(document)\\n node = get_node_by_document_hash(nodes, document_hash)\\n shards[node]['document'] = document\\n shards[node]['document_hash'] = document_hash\\n\\n for term in terms:\\n shards[get_node_by_term(nodes, term)]['terms'].append(term)\\n shards[get_node_by_term(nodes, term)]['document_hash'] = document_hash\\n\\n return shards\",\n \"def select_multiple_from_strategy(dist: dict, num: int) -> []:\\n\\n output = []\\n dist_c = dist.copy()\\n\\n for _ in range(num):\\n key = Tools.select_from_strategy(dist_c)\\n output.append(key)\\n\\n del dist_c[key]\\n dist_c = Tools.normalise(dist_c)\\n\\n return output\",\n \"def _scan(self, keys, cursor, *args):\\n cursor = int(cursor)\\n (pattern, _type, count), _ = extract_args(args, (\\\"*match\\\", \\\"*type\\\", \\\"+count\\\"))\\n count = 10 if count is None else count\\n data = sorted(keys)\\n bits_len = (len(keys) - 1).bit_length()\\n cursor = bin_reverse(cursor, bits_len)\\n if cursor >= len(keys):\\n return [0, []]\\n result_cursor = cursor + count\\n result_data = []\\n\\n regex = compile_pattern(pattern) if pattern is not None else None\\n\\n def match_key(key: bytes) -> Union[bool, Match[bytes], None]:\\n return regex.match(key) if regex is not None else True\\n\\n def match_type(key) -> bool:\\n return _type is None or casematch(key_value_type(self._db[key]).value, _type)\\n\\n if pattern is not None or _type is not None:\\n for val in itertools.islice(data, cursor, cursor + count):\\n compare_val = val[0] if isinstance(val, tuple) else val\\n if match_key(compare_val) and match_type(compare_val):\\n result_data.append(val)\\n else:\\n result_data = data[cursor: cursor + count]\\n\\n if result_cursor >= len(data):\\n result_cursor = 0\\n return [str(bin_reverse(result_cursor, bits_len)).encode(), result_data]\",\n \"def _collapse_exact_matches(self, seqs, prefix_length, suffix_length):\\r\\n cluster_map = {}\\r\\n for seq_id, seq in seqs:\\r\\n seq_id = seq_id.split()[0]\\r\\n seq_hash = self._build_seq_hash(seq, prefix_length, suffix_length)\\r\\n try:\\r\\n cluster_map[seq_hash].append(seq_id)\\r\\n except KeyError:\\r\\n cluster_map[seq_hash] = [seq_id]\\r\\n\\r\\n return cluster_map.values()\",\n \"def parse_all_shards(shard_dir_path):\\n files_to_process = utils.absolute_paths_of_files_in_dir(shard_dir_path)\\n list_of_shard_results = [parse_shard(f) for f in files_to_process]\\n return pd.DataFrame(\\n list(itertools.chain(*list_of_shard_results)),\\n columns=['sequence_name', 'predictions'])\",\n \"def test_pattern_matching(self):\\n\\n for hashtype in [HashTypes.SHA1, HashTypes.SHA2, HashTypes.SHA3, ]:\\n self.do_test_pattern_matching(hashtype)\",\n \"def redis_query(self, pattern, count=False):\\n def func(server):\\n keys = server.inspect(pattern=pattern, debug=False)\\n res = server.query(keys, ttl=False)\\n if count:\\n resp = []\\n for k,v in res.items():\\n resp.append({k:len(v)})\\n return resp\\n return res\\n self.__run_redis_cmd(func, dbs=range(0,8))\",\n \"def repair(\\n self,\\n start_key=None,\\n end_key=None,\\n shard=0,\\n nshards=1,\\n callback_on_progress=None,\\n ):\\n pass\",\n \"def run_against_hosts(*, partial_func: functools.partial, hosts: list):\\n with concurrent.futures.ThreadPoolExecutor(len(hosts)) as executor:\\n futures = {\\n executor.submit(functools.partial(partial_func, host=host))\\n for host in hosts\\n }\\n concurrent.futures.wait(futures, return_when=FIRST_EXCEPTION)\\n for future in futures:\\n future.result()\",\n \"def filter_keys_c(func):\\n return partial(filter_keys, func)\",\n \"def repair(self, start_key=None, end_key=None, shard=0, nshards=1, callback_on_progress=None):\\n super(NoneCache, self).repair()\",\n \"def lcs_hamming_only_matches_with_many_k(s1: str, s2: str, ks: List[int], length: int, matches_lst: List, query_name, target_name):\\n file_name = './chromo_matches_' + str(length) + '.txt'\\n f = open(file_name, mode='a')\\n count = [0, 0, 0]\\n for i in range(0, len(s1) - length + 1):\\n for j in range(0, len(s2) - length + 1):\\n sub1 = s1[i: i + length]\\n sub2 = s2[j: j + length]\\n result = hamming_distance(sub1, sub2)\\n if result <= 0:\\n # matches_lst.append([query_name, target_name, i, j, result, sub1, sub2])\\n f.write(query_name + ',' + target_name + ',' + str(i) + ',' + str(j) + ',' + str(k)+ ',' + str(sub1) + ',' + sub2 + '\\\\n')\\n count[0] += 1\\n if result <= 1:\\n f.write(query_name + ',' + target_name + ',' + str(i) + ',' + str(j) + ',' + str(k)+ ',' + str(sub1) + ',' + sub2 + '\\\\n')\\n count[1] += 1\\n if result <= 2:\\n f.write(query_name + ',' + target_name + ',' + str(i) + ',' + str(j) + ',' + str(k)+ ',' + str(sub1) + ',' + sub2 + '\\\\n')\\n count[2] += 1\\n\\n # print(\\\"total matches: \\\" + str(count))\\n f.close()\\n return count\",\n \"def _map_keys_to_servers(self, key_iterable, key_prefix):\\n\\n\\t\\t# Only check the prefix once\\n\\t\\tkey_extra_len = len(key_prefix)\\n\\t\\tif key_prefix:\\n\\t\\t\\tcheck_key(key_prefix)\\n\\n\\t\\t# server -> list of (prefixed_key, value)\\n\\t\\tserver_keys = {}\\n\\t\\tdeprefix = {}\\n\\n\\t\\t# build up a list for each server of all the keys we want.\\n\\t\\tfor orig_key in key_iterable:\\n\\t\\t\\tif type(orig_key) is tuple:\\n\\t\\t\\t\\t# Tuple of hashvalue, key ala _get_server_for(). The caller is essentially\\n\\t\\t\\t\\t# telling us what server to stuff this on.\\n\\t\\t\\t\\tstr_orig_key = str(orig_key[1])\\n\\t\\t\\t\\t# Ensure call to _get_server_for gets a Tuple as well.\\n\\t\\t\\t\\t# Gotta pre-mangle key before hashing to a server. Returns the mangled key.\\n\\t\\t\\t\\tserver, key = yield self._get_server_for((orig_key[0], key_prefix + str_orig_key))\\n\\t\\t\\telse:\\n\\t\\t\\t\\tstr_orig_key = str(orig_key) # set_multi supports int / long keys.\\n\\t\\t\\t\\tserver, key = yield self._get_server_for(key_prefix + str_orig_key)\\n\\n\\t\\t\\t# Now check to make sure key length is proper ...\\n\\t\\t\\tcheck_key(str_orig_key, key_extra_len=key_extra_len)\\n\\n\\t\\t\\tif not server:\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\tif server not in server_keys:\\n\\t\\t\\t\\tserver_keys[server] = []\\n\\n\\t\\t\\tserver_keys[server].append((key, orig_key))\\n\\t\\t\\tdeprefix[key] = orig_key\\n\\t\\t\\t\\n\\n\\t\\traise StopIteration((server_keys, deprefix))\",\n \"def split_scp_dict(scp_dict: Dict[str, List[Tuple[str, str]]],\\n n_parts=1, randomize=False, seed: int = None,\\n batch_size=1, max_len=None, utt2len=None,\\n ensure_equal_parts=True) \\\\\\n -> List[Tuple[Dict[str, List[Tuple[str, str]]], List[int]]]:\\n assert batch_size >= 1\\n archives = list(scp_dict.keys())\\n n_archives = len(archives)\\n\\n # Determine the orders in which to iterate over archives & the utterances\\n # within those archives\\n if randomize:\\n rs = np.random.RandomState(seed=seed)\\n archive_order = rs.permutation(n_archives)\\n archive2utt_order = {archive: rs.permutation(len(scp_dict[archive])) for\\n archive in archives}\\n else:\\n archive_order = np.arange(n_archives)\\n archive2utt_order = {archive: np.arange(len(scp_dict[archive])) for\\n archive in archives}\\n\\n # Get all utterances in the right order (along with their archive)\\n all_utts = [(archives[i], *scp_dict[archives[i]][j]) # (path, uttid, loc)\\n for i in archive_order\\n for j in archive2utt_order[archives[i]]]\\n\\n # Compute the appropriate sequence (possibly dynamic) of batch sizes\\n start, batch_sizes = 0, []\\n while start < len(all_utts):\\n tmp_end = min(start + batch_size, len(all_utts))\\n if max_len is None or utt2len is None:\\n end = tmp_end\\n else:\\n candidate_utts = all_utts[start:tmp_end]\\n lens = [utt2len[uttid] for archive, uttid, loc in candidate_utts]\\n dynamic_batch_size = (np.cumsum(lens) <= max_len * batch_size).sum()\\n end = start + max(1, dynamic_batch_size)\\n batch_sizes.append(end - start)\\n start = end\\n\\n # Some batches will get 1 extra batch from the first part\\n n_utts, n_batches = len(all_utts), len(batch_sizes)\\n n_batches_per_part, extra = divmod(n_batches, n_parts)\\n if n_batches < n_parts:\\n raise RuntimeError(\\n f\\\"Cannot partition a SCP with {n_utts} utterances into \\\"\\n f\\\"{n_parts} parts with batch size {batch_size}. This causes \\\"\\n f\\\"the number of batches ({n_batches}) to be less than the \\\"\\n f\\\"number of parts ({n_parts}).\\\")\\n\\n # Determine the sequence of batch sizes for each part\\n part_n_batches = [n_batches_per_part + int(i < extra)\\n for i in range(n_parts)]\\n part_j0_batch = np.cumsum([0, *part_n_batches])\\n part_batch_sizes = [batch_sizes[part_j0_batch[i]:part_j0_batch[i+1]]\\n for i in range(n_parts)]\\n\\n # Determine the sequence of utterances for each part\\n part_n_utts = [sum(x) for x in part_batch_sizes]\\n part_j0_utt = np.cumsum([0, *part_n_utts])\\n part_utts = [all_utts[part_j0_utt[i]:part_j0_utt[i+1]]\\n for i in range(n_parts)]\\n\\n # Ensure each part has the same number of batches (if desired)\\n if ensure_equal_parts and extra > 0:\\n extra_batch_sizes = batch_sizes[:n_parts - extra]\\n extra_j0_utt = np.cumsum([0, *extra_batch_sizes])\\n for i, j in enumerate(range(extra, n_parts)):\\n part_batch_sizes[j].append(batch_sizes[i])\\n part_utts[j].extend(all_utts[extra_j0_utt[i]:extra_j0_utt[i+1]])\\n\\n if ensure_equal_parts:\\n assert all(len(part_batch_sizes[i]) == len(part_batch_sizes[0])\\n for i in range(n_parts)), \\\\\\n \\\"All parts should have the same number of batches.\\\"\\n\\n assert all(sum(part_batch_sizes[i]) == len(part_utts[i])\\n for i in range(n_parts)),\\\\\\n \\\"Sum of batch sizes for each part should match the number of \\\" \\\\\\n \\\"utterances in the part.\\\"\\n\\n # Construct each part as an ordered dict (archive -> List[uttid, loc])\\n parts = []\\n for i in range(n_parts):\\n current_part = OrderedDict()\\n for archive, uttid, loc in part_utts[i]:\\n if archive not in current_part:\\n current_part[archive] = []\\n current_part[archive].append((uttid, loc))\\n parts.append(current_part)\\n\\n return list(zip(parts, part_batch_sizes))\",\n \"def Exact_Match(pack):\\n cache = Cache()\\n if _options.single:\\n list_app = []\\n for k in cache:\\n if pack in k.name:\\n item = Pack()\\n item.name = k.name\\n item.ratio = 0\\n list_app.append(item)\\n return list_app\\n else:\\n _pool = Pool(processes=_MAX_PEERS)\\n result = _pool.map(Thread_Exact, cache._set)\\n return result\",\n \"def test_wildcard(word_list):\\n failure=False\\n\\n # test 1\\n hand = {'a': 1, 'r': 1, 'e': 1, 'j': 2, 'm': 1, '*': 1}\\n word = \\\"e*m\\\"\\n\\n if is_valid_word(word, hand, word_list):\\n print(\\\"FAILURE: test_is_valid_word() with wildcards\\\")\\n print(\\\"\\\\tExpected False, but got True for word: '\\\" + word + \\\"' and hand:\\\", hand)\\n\\n failure = True\\n\\n # test 2\\n hand = {'n': 1, 'h': 1, '*': 1, 'y': 1, 'd':1, 'w':1, 'e': 2}\\n word = \\\"honey\\\"\\n\\n if is_valid_word(word, hand, word_list):\\n print(\\\"FAILURE: test_is_valid_word() with wildcards\\\")\\n print(\\\"\\\\tExpected False, but got True for word: '\\\"+ word +\\\"' and hand:\\\", hand)\\n\\n failure = True\\n\\n # test 3\\n hand = {'n': 1, 'h': 1, '*': 1, 'y': 1, 'd':1, 'w':1, 'e': 2}\\n word = \\\"h*ney\\\"\\n\\n if not is_valid_word(word, hand, word_list):\\n print(\\\"FAILURE: test_is_valid_word() with wildcards\\\")\\n print(\\\"\\\\tExpected True, but got False for word: '\\\"+ word +\\\"' and hand:\\\", hand)\\n\\n failure = True\\n\\n # test 4\\n hand = {'c': 1, 'o': 1, '*': 1, 'w': 1, 's':1, 'z':1, 'y': 2}\\n word = \\\"c*wz\\\"\\n\\n if is_valid_word(word, hand, word_list):\\n print(\\\"FAILURE: test_is_valid_word() with wildcards\\\")\\n print(\\\"\\\\tExpected False, but got True for word: '\\\"+ word +\\\"' and hand:\\\", hand)\\n\\n failure = True \\n\\n # dictionary of words and scores WITH wildcards\\n words = {(\\\"h*ney\\\", 7):290, (\\\"c*ws\\\", 6):176, (\\\"wa*ls\\\", 7):203}\\n for (word, n) in words.keys():\\n score = get_word_score(word, n)\\n if score != words[(word, n)]:\\n print(\\\"FAILURE: test_get_word_score() with wildcards\\\")\\n print(\\\"\\\\tExpected\\\", words[(word, n)], \\\"points but got '\\\" + \\\\\\n str(score) + \\\"' for word '\\\" + word + \\\"', n=\\\" + str(n))\\n failure=True \\n\\n if not failure:\\n print(\\\"SUCCESS: test_wildcard()\\\")\",\n \"def _internal_match(self, pattern):\\n compiled_re = re.compile(pattern)\\n for word in self.words:\\n if compiled_re.fullmatch(word) is not None:\\n yield word\",\n \"def keysWhichMatch(cls, *args):\\n if len(cls.keys) < len(args) > 0:\\n raise ValueError('Number of keys provided is too long.\\\\n'\\n 'Len Class Keys: %s\\\\n'\\n 'Len Provided Keys: %s\\\\n' % (len(cls.keys), len(args)))\\n\\n index = 0\\n output = cls.db_key_tuples()\\n\\n for keyToCheck in args:\\n temp = []\\n for key in output:\\n if key[index] == keyToCheck:\\n temp.append(key)\\n\\n index += 1\\n output = temp\\n\\n return output\",\n \"def findTopKViaSharding(filename, k, shards):\\n \\n # First, we calculate the number of lines for each shard\\n filelength = findFLength(filename)\\n shardsize = int(np.ceil(filelength / shards))\\n \\n # Now we split the input file into shards\\n splitFileIntoShards(filename, shardsize)\\n sleep(1) #might change this to sleep(shards/100) if 1 second is too little\\n \\n # Some Initial Set-Up...\\n filenames = [\\\"x{:04d}_shard\\\".format(i) for i in range(shards)]\\n totaldict = Counter()\\n #shardtimes = []\\n \\n # Iterate through the Shard Files\\n # In each Shard, run countWords(shardfilename) to get a wordcount Counter\\n # Update the overall Counter in totaldict with more wordcounts from the Shard\\n # Delete the Shard File when we're done with it.\\n for f in filenames:\\n #print(\\\"Running Shard {}...\\\".format(f), end=' ')\\n #startf = time()\\n \\n try:\\n wordcounts = countWords(f)\\n totaldict.update(wordcounts)\\n deleteSingleFile(f)\\n #print(\\\"Shard {} Complete.\\\".format(f))\\n except OSError as e:\\n print(\\\"Something unexpected happened in the OS. Error: {}. Moving on...\\\".format(e))\\n \\n #endf = time()\\n #shardtimes.append(endf - startf)\\n \\n #print(\\\"\\\\nDone Sharding\\\")\\n #deleteShards()\\n #print(\\\"Average Shard Processing Time for K={} and Shards={} is: {}\\\".format(k, shards, np.mean(shardtimes)))\\n #print(\\\"Average Shard Processing Time for K={} and Shards={} is: {}\\\".format(k, shards, np.std(shardtimes)))\\n \\n # All Shard Processing is complete. Return the Top K words from the overall Counter\\n return totaldict.most_common(k)\",\n \"def create_shards(\\n it_shards,\\n shard_dir,\\n key,\\n files,\\n labels,\\n targets,\\n im_size,\\n label_size,\\n preprocess,\\n store_z,\\n normalize_im):\\n all_files = files[key]\\n all_labels = labels[key]\\n total_data = len(all_files) / it_shards\\n mask = np.arange(it_shards).reshape(1, -1).repeat(total_data).reshape(-1)\\n all_files = all_files[:len(mask)]\\n all_labels = all_labels[:len(mask)]\\n total_shards = pad_zeros(str(it_shards), 5)\\n for idx in tqdm(\\n range(it_shards), total=it_shards, desc='Building %s' % key):\\n it_mask = mask == idx\\n shard_label = pad_zeros(str(idx), 5)\\n shard_name = os.path.join(\\n shard_dir,\\n '%s-%s-of-%s.tfrecords' % (key, shard_label, total_shards))\\n it_files = {key: all_files[it_mask]}\\n it_labels = {key: all_labels[it_mask]}\\n data_to_tfrecords(\\n files=it_files,\\n labels=it_labels,\\n targets=targets,\\n ds_name=shard_name,\\n im_size=im_size,\\n label_size=label_size,\\n preprocess=preprocess,\\n store_z=store_z,\\n it_ds_name=shard_name,\\n normalize_im=normalize_im)\",\n \"def _build_key_patterns(self, slug, date):\\n # we want to keep the order, from smallest to largest granularity\\n patts = OrderedDict()\\n for g in self._granularities():\\n patts[g] = self._get_metric_key_pattern(g, slug, date)\\n return patts\",\n \"def dispatch_list_lengths(self, client):\\n key_dict = {}\\n for db, patterns in list(self.llen_keys.items()):\\n client.send(\\\"select %d\\\" % db)\\n try:\\n resp = client.read_response()\\n except RedisError as e:\\n collectd.error(\\\"Could not select Redis db %s: %s\\\" % (db, e))\\n continue\\n\\n for pattern in patterns:\\n keys = []\\n # If there is a glob, get every key matching it\\n if \\\"*\\\" in pattern:\\n client.send(\\\"KEYS %s\\\" % pattern)\\n keys = client.read_response()\\n else:\\n keys = [pattern]\\n\\n for key in keys:\\n self.fetch_and_dispatch_llen_for_key(client, key, db)\",\n \"async def _cache_patterns(self) -> None:\\n results: [asyncpg.Record] = await db_fetch(\\n self.bot.db_conn,\\n \\\"SELECT * FROM filter\\\"\\n )\\n for result in results:\\n re_compiled = re.compile(result[\\\"filter_pattern\\\"])\\n self._filter_cache[result[\\\"server_id\\\"]][re_compiled] = [result[\\\"filter_identifier\\\"]]\",\n \"def get_all(any, shard, shard_term_features, qterms):\\n tmp = 1\\n for t in qterms:\\n if t in shard_term_features[shard]:\\n cdf = shard_term_features[shard][t].df\\n else:\\n cdf = 0\\n tmp *= cdf/any\\n all = tmp * any\\n return all\",\n \"def _fe_keyword_match(self, sample):\\n result = OrderedDict()\\n\\n for item in self._keywords:\\n result[item + \\\"_kw\\\"] = 1 if item in sample['fqdn'] else 0\\n\\n return result\",\n \"def get_keywords(self, pattern=\\\"*\\\"):\\n\\n sql = \\\"\\\"\\\"SELECT collection.collection_id, collection.name,\\n keyword.name, keyword.doc, keyword.args\\n FROM collection_table as collection\\n JOIN keyword_table as keyword\\n WHERE collection.collection_id == keyword.collection_id\\n AND keyword.name like ?\\n ORDER by collection.name, keyword.name\\n \\\"\\\"\\\"\\n pattern = self._glob_to_sql(pattern)\\n cursor = self._execute(sql, (pattern,))\\n result = [(row[0], row[1], row[2], row[3], row[4])\\n for row in cursor.fetchall()]\\n return list(sorted(set(result), key=itemgetter(2)))\",\n \"def bucket(things, key):\\n ret = defaultdict(list)\\n for thing in things:\\n ret[key(thing)].append(thing)\\n return ret\",\n \"def groups_based_ks(y_pred, mask, sample_weight, groups_indices):\\n assert len(y_pred) == len(sample_weight) == len(mask)\\n group_weights = compute_group_weights(groups_indices, sample_weight=sample_weight)\\n prepared_data, prepared_weight, prep_F = prepare_distibution(y_pred[mask], weights=sample_weight[mask])\\n\\n result = 0.\\n for group_weight, group_indices in zip(group_weights, groups_indices):\\n local_distribution = y_pred[group_indices]\\n local_weights = sample_weight[group_indices]\\n result += group_weight * \\\\\\n _ks_2samp_fast(prepared_data, local_distribution, prepared_weight, local_weights, prep_F)\\n return result\",\n \"def match(\\n self,\\n pattern: str\\n ) -> Iterable[AbstractPage]:\\n paths: List[str] = [page.path for page in self._page_table.keys()]\\n for match in sorted(fn_filter(paths, pattern)):\\n yield self[match]\",\n \"def store_matches(\\n self,\\n parameter_handler: ParameterHandler,\\n topology: Topology,\\n ) -> None:\\n if self.key_map:\\n # TODO: Should the key_map always be reset, or should we be able to partially\\n # update it? Also Note the duplicated code in the child classes\\n self.key_map: dict[BondKey, PotentialKey] = dict() # type: ignore[assignment]\\n matches = parameter_handler.find_matches(topology)\\n for key, val in matches.items():\\n param = val.parameter_type\\n if param.k_bondorder or param.length_bondorder:\\n bond = topology.get_bond_between(*key)\\n fractional_bond_order = bond.fractional_bond_order\\n if not fractional_bond_order:\\n assert self._get_uses_interpolation(parameter_handler)\\n raise RuntimeError(\\n \\\"Bond orders should already be assigned at this point\\\",\\n )\\n else:\\n fractional_bond_order = None\\n topology_key = BondKey(\\n atom_indices=key,\\n bond_order=fractional_bond_order,\\n )\\n\\n potential_key = PotentialKey(\\n id=val.parameter_type.smirks,\\n associated_handler=parameter_handler.TAGNAME,\\n bond_order=fractional_bond_order,\\n )\\n self.key_map[topology_key] = potential_key\\n\\n valence_terms = self.valence_terms(topology)\\n\\n _check_all_valence_terms_assigned(\\n handler=parameter_handler,\\n topology=topology,\\n assigned_terms=matches,\\n valence_terms=valence_terms,\\n )\",\n \"def _scan_hosts(self):\\n results = []\\n for item in glob.glob(self._pattern):\\n results.append(item)\\n return results\",\n \"def groupby(f, coll):\\n d = dict()\\n for item in coll:\\n key = f(item)\\n if key not in d:\\n d[key] = []\\n d[key].append(item)\\n return d\",\n \"def _findKeyForTemplate(self, template, listener=False):\\n if listener == True:\\n prependStr = 'listener:'\\n else:\\n prependStr = 'tuple:'\\n # Prepare a deferred result for this operation\\n outerDf = defer.Deferred()\\n if listener == True:\\n subtupleKeys = self._keywordHashesFromTuple(template, listener)\\n else:\\n subtupleKeys = self._keywordHashesFromTemplate(template, listener)\\n \\n kwIndex = [-1] # using a list for this counter because Python doesn't allow binding a new value to a name in an enclosing (non-global) scope\\n havePossibleMatches = [False]\\n filteredResults = []\\n \\n listenerResults = []\\n listenerSubtupleSetCounter = [0]\\n \\n #TODO: If all elements in the template are None, we only have the tuple length... maybe raise an exception?\\n \\n def filterResult(result):\\n kwKey = subtupleKeys[kwIndex[0]]\\n if type(result) == dict:\\n # Value was found; this should be list of keys for tuples matching this criterion\\n index = result[kwKey]\\n if havePossibleMatches[0] == False:\\n havePossibleMatches[0] = True\\n filteredResults.extend(index)\\n else:\\n # Filter the our list of possible matching tuples with the new results\\n delKeys = []\\n for tupleKey in filteredResults:\\n if tupleKey not in index:\\n delKeys.append(tupleKey)\\n for tupleKey in delKeys:\\n filteredResults.remove(tupleKey)\\n if len(filteredResults) == 0:\\n # No matches for this template exist at this point; there is no use in searching further\\n outerDf.callback(None)\\n else:\\n findNextSubtuple()\\n else:\\n # Value wasn't found; thus no matches for this template exist - stop the search\\n outerDf.callback(None)\\n \\n def filterListenerResult(result):\\n \\\"\\\"\\\" Same as filterResult(), except that 2 sets of subtuples keys' results are OR'ed \\\"\\\"\\\"\\n if kwIndex[0] == -1:\\n # This was the deterministic search\\n if type(result) == dict:\\n # An exact template match was found, callback with this\\n outerDf.callback(result[mainKey])\\n else:\\n # The deterministic search did not find anything; start searching subtuples\\n findNextSubtuple()\\n return\\n \\n kwKey = subtupleKeys[kwIndex[0]]\\n \\n if type(result) == dict:\\n # Value was found; this should be list of keys for tuples matching this criterion\\n index = result[kwKey]\\n listenerResults.extend(index)\\n listenerSubtupleSetCounter[0] += 1\\n \\n if listenerSubtupleSetCounter[0] == 3:\\n if havePossibleMatches[0] == False:\\n havePossibleMatches[0] = True\\n filteredResults.extend(listenerResults)\\n else:\\n # Filter the our list of possible matching tuples with the new results\\n delKeys = []\\n for tupleKey in filteredResults:\\n if tupleKey not in listenerResults:\\n delKeys.append(tupleKey)\\n for tupleKey in delKeys:\\n try:\\n filteredResults.remove(tupleKey)\\n except ValueError:\\n pass\\n\\n if len(filteredResults) == 0:\\n # No matches for this template exist at this point; there is no use in searching further\\n outerDf.callback(None)\\n else:\\n # Reset the cycle\\n listenerSubtupleSetCounter[0] = 0\\n while len(listenerResults):\\n listenerResults.pop()\\n findNextSubtuple()\\n else:\\n findNextSubtuple()\\n \\n def findNextSubtuple(results=None):\\n kwIndex[0] += 1\\n if kwIndex[0] < len(subtupleKeys):\\n kwKey = subtupleKeys[kwIndex[0]]\\n #TODO: kademlia is going to replicate the un-updated inverted index; stop that from happening!!\\n df = self.iterativeFindValue(kwKey)\\n if listener == True:\\n df.addCallback(filterListenerResult)\\n else:\\n df.addCallback(filterResult)\\n else:\\n # We're done. Let the caller of the parent method know, and return the key of the first qualifying tuple in the list of results\\n outerDf.callback(filteredResults[0])\\n \\n if subtupleKeys == None:\\n # The template is deterministic; thus we can retrieve the corresponding tuple directly\\n h = hashlib.sha1()\\n tupleValue = cPickle.dumps(template)\\n h.update(prependStr + tupleValue)\\n mainKey = h.digest()\\n outerDf.callback(mainKey)\\n else:\\n if listener == True:\\n # First look for an exact match if we are looking for listener tuples\\n h = hashlib.sha1()\\n tupleValue = cPickle.dumps(template)\\n h.update(prependStr + tupleValue)\\n mainKey = h.digest()\\n df = self.iterativeFindValue(mainKey)\\n df.addCallback(filterListenerResult)\\n else:\\n # Query the DHT for the first subtuple (this implicitly specifies the requested tuple length as well)\\n findNextSubtuple()\\n \\n return outerDf\",\n \"def sortReplicas(replicas, token):\\n # if token exists, insist on using it\\n\\n from SiteMover import SiteMover\\n _replicas_mcdisk_datadisk = []\\n _replicas_hotdisk = []\\n _replicas_disk = []\\n _replicas_tape = []\\n\\n # put any tape resident replicas at the end of the list unless requested with the dispatchDBlockToken\\n if token:\\n # only keep replicas matching the given space token (tape should be first)\\n for _replica in replicas:\\n if token.lower() in _replica.sfn.lower():\\n _replicas_tape.append(_replica)\\n else:\\n # look up the DQ2 site using the surl in case of obscured tape site\\n if _replica.filetype == \\\"TAPE\\\":\\n _replicas_tape.append(_replica)\\n else:\\n _replicas_disk.append(_replica)\\n replicas = _replicas_tape + _replicas_disk\\n else:\\n # separate tape and mcdisk/datadisk replicas from other disk replicas\\n for _replica in replicas:\\n if \\\"tape\\\" in _replica.sfn.lower() or \\\"t1d0\\\" in _replica.sfn.lower():\\n _replicas_tape.append(_replica)\\n elif \\\"hotdisk\\\" in _replica.sfn.lower():\\n _replicas_hotdisk.append(_replica)\\n elif \\\"mcdisk\\\" in _replica.sfn.lower() or \\\"datadisk\\\" in _replica.sfn.lower() or \\\"bnlt0d1\\\" in _replica.sfn.lower():\\n _replicas_mcdisk_datadisk.append(_replica)\\n else:\\n # look up the DQ2 site using the surl in case of obscured tape site\\n tolog(\\\"Replica: %s\\\" % (_replica.sfn))\\n if _replica.filetype == \\\"TAPE\\\":\\n _replicas_tape.append(_replica)\\n else:\\n _replicas_disk.append(_replica)\\n replicas = _replicas_hotdisk + _replicas_mcdisk_datadisk + _replicas_disk + _replicas_tape\\n\\n return replicas\",\n \"def _GetShardedBatch() -> tf.types.experimental.distributed.PerReplica:\\n per_host_batches: List[py_utils.NestedMap] = []\\n # Note: `available_devices` omits the executor host; just those with TPUs.\\n for host_device in py_utils.Flatten(\\n cluster_factory.Current().available_devices.tolist()\\n ):\\n with tf.device(host_device):\\n batch = self.task.input.GetPreprocessedInputBatch()\\n\\n # Remove bucket_keys; this relates to GenericInput pipelines.\\n batch = batch.FilterKeyVal(lambda k, _: not k.endswith('bucket_keys'))\\n\\n # Process embedding ID features according to their specified types.\\n batch = batch.TransformWithKey(\\n tpu_embedding_layers_v2.TPU_EMBEDDING_MANAGER.ProcessInputFeature\\n )\\n\\n per_host_batches.extend(Split(batch, replicas_per_host))\\n\\n return strategy.experimental_distribute_values_from_function(\\n lambda ctx: per_host_batches[ctx.replica_id_in_sync_group]\\n )\",\n \"def get_matches(dst, threshold):\\n print(time.asctime(), ' Filtering and sorting distances')\\n start = time.time()\\n dup_dict = util.find_duplicates(dst, threshold)\\n n_matches = len(dup_dict)\\n end = time.time()\\n print(time.asctime(), ' Done Filtering distances for ', n_matches, ' matches in ', end-start, ' seconds', flush=True)\\n return dup_dict\",\n \"def match_filter_patterns(self, content: str, guild_id: int) -> Optional[typing.Tuple[re.Match, str]]:\\n for pattern, pattern_identifier in self._filter_cache[guild_id].items():\\n if search := pattern.search(content):\\n return search, pattern_identifier\",\n \"def group_by_hash(fileset, hashfunc=hashlib.md5, min_group_size=2, workers=16):\\n return group_by_key(fileset, hash_key(hashfunc=hashfunc),\\n min_group_size=min_group_size, workers=workers)\",\n \"def read_all_shards(partition, data_dir, bucket_name):\\n\\n shards = []\\n gcsfs = GCSFS(bucket_name)\\n for fn in gcsfs.listdir(os.path.join(data_dir, partition)):\\n with gcsfs.open(os.path.join(data_dir, partition, fn)) as f:\\n shards.append(pd.read_csv(f, index_col=None))\\n \\n return pd.concat(shards)\",\n \"def get_knitting_patterns_by_ids(self, ids_list):\\n ids_string = ''\\n\\n for i in ids_list:\\n ids_string = '{}, {}'.format(ids_string, i)\\n\\n ids_string = ids_string[1:] \\n\\n params = {\\n \\\"ids\\\" : ids_string,\\n \\\"craft\\\" : \\\"knitting\\\"\\n }\\n\\n resp_from_server = self.get_api_result(self.URL_RETURNS_PATTERNS, params)\\n\\n\\n if resp_from_server.get('status'):\\n return resp_from_server\\n else:\\n patterns_dicts = self.get_patterns_dict(resp_from_server)\\n\\n return patterns_dicts\",\n \"def do_keys_match_bucket(args):\\n\\n sanitised_group = args.group.replace('/', '-')\\n expected_path = \\\"/etc/ugr/conf.d/{}.conf\\\".format(sanitised_group)\\n if not os.path.exists(expected_path):\\n return 1\\n\\n with open(expected_path, \\\"r\\\") as f:\\n lines = f.read().splitlines()\\n expected_public_line = \\\"locplugin.{}-{}.s3.pub_key: \\\".format(sanitised_group, args.bucket)\\n expected_private_line = \\\"locplugin.{}-{}.s3.priv_key: \\\".format(sanitised_group, args.bucket)\\n public_key = private_key = None\\n\\n for line in lines:\\n if line.startswith(expected_public_line):\\n public_key = line.split(expected_public_line)[1]\\n elif line.startswith(expected_private_line):\\n private_key = line.split(expected_private_line)[1]\\n \\n if public_key is not None and private_key is not None:\\n if public_key == args.public_key and private_key == args.private_key:\\n return 0\\n break\\n \\n return 1\",\n \"def hshingle(s, k):\\n for s in shingle(s, k):\\n yield hash(s)\",\n \"def search_by_pattern(self, pattern, key=lambda data: data['meta']):\\n result = []\\n for node, data in self.traverse():\\n if re.search(pattern, key(data), flags=re.VERBOSE):\\n result.append([node, data])\\n return result\",\n \"def expand_fingerprints(fingerprints, auth, depth=0):\\n fingerprints = set(fingerprints)\\n while True:\\n q = {\\n 'size': 0,\\n 'query': {\\n 'bool': {\\n 'must': [\\n {'terms': {'fingerprints': list(fingerprints)}},\\n {'terms': {'groups': auth.groups}}\\n ]\\n }\\n },\\n 'aggregations': {\\n 'fingerprints': {\\n 'terms': {'field': 'fingerprints', 'size': 1000}\\n }\\n }\\n }\\n result = es.search(index=es_index, doc_type=Schema.ENTITY, body=q)\\n buckets = result['aggregations']['fingerprints']['buckets']\\n old_count = len(fingerprints)\\n for bucket in buckets:\\n fingerprints.add(bucket['key'])\\n if len(fingerprints) == old_count or len(fingerprints) > MAX_LEN:\\n return list(fingerprints)[:MAX_LEN]\",\n \"def divy_keys(self,spot):\\n\\n\\t\\tgroup_counts = [sum([i[0]=='subpattern' \\n\\t\\t\\tfor i in re.sre_parse.parse(self.spots[spot][key])]) \\n\\t\\t\\t#---apply naming convention\\n\\t\\t\\tfor key in ['top','step','part']]\\n\\t\\tcursor = ([0]+[sum(group_counts[:i+1]) for i in range(len(group_counts))])\\n\\t\\tslices = [slice(cursor[i],cursor[i+1]) for i in range(len(cursor)-1)]\\n\\t\\tdivy = lambda x: [y[0] if len(y)==1 else y for y in [x[s] for s in slices]]\\n\\t\\treturn divy\",\n \"def return_matches(hashes):\\n # Create a dictionary of hash => offset pairs for later lookups\\n mapper = {}\\n for hash, offset in hashes:\\n mapper[hash.upper()] = offset\\n\\n # Get an iteratable of all the hashes we need\\n values = mapper.keys()\\n\\n conn = MySQLdb.connect(**connection_string[\\\"Common\\\"])\\n cur = conn.cursor()\\n\\n for split_values in grouper(values, 1000):\\n # Create our IN part of the query\\n query = SELECT_MULTIPLE\\n query %= ', '.join(['UNHEX(%s)'] * len(split_values))\\n\\n cur.execute(query, split_values)\\n\\n for hash, sid, offset in cur:\\n # (sid, db_offset - song_sampled_offset)\\n yield (sid, offset - mapper[hash])\",\n \"def scan(self, ts):\\n redis = get_redis_instance()\\n\\n keys = []\\n pattern = self.scan_pattern % ts\\n for k in redis.scan_iter(pattern):\\n keys.append(k)\\n return keys\",\n \"def pullAll(*keys):\",\n \"def fuzzy_filter(\\n search: str,\\n schemas: List[dict],\\n root_only: bool = False,\\n key: str = \\\"name_qual\\\",\\n fuzzy_keys: List[str] = FUZZY_SCHEMAS_KEYS,\\n **kwargs,\\n ) -> List[dict]:\\n\\n def do_skip(schema):\\n is_details = schema[\\\"name\\\"].endswith(\\\"_details\\\")\\n is_all = schema[\\\"name\\\"] == \\\"all\\\"\\n not_select = not schema.get(\\\"selectable\\\", True)\\n is_root = root_only and not schema[\\\"is_root\\\"]\\n\\n if any([schema in matches, is_details, is_all, not_select, is_root]):\\n return True\\n\\n return False\\n\\n matches = []\\n\\n # try to do string matches first\\n for schema in schemas:\\n if not do_skip(schema) and any(\\n [search.strip().lower() in x for x in [schema[x] for x in fuzzy_keys]]\\n ):\\n matches.append(schema)\\n\\n # if no string matches, try to find matches with fuzzyfinder\\n if not matches:\\n for schema in schemas:\\n if not do_skip(schema) and list(\\n fuzzyfinder(search, [schema[x] for x in fuzzy_keys])\\n ):\\n matches.append(schema)\\n\\n return [x[key] for x in matches] if key else matches\",\n \"def group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):\\n current_sample = None\\n for filesample in data:\\n assert isinstance(filesample, dict)\\n fname, value = filesample[\\\"fname\\\"], filesample[\\\"data\\\"]\\n prefix, suffix = keys(fname)\\n if prefix is None:\\n continue\\n if lcase:\\n suffix = suffix.lower()\\n # FIXME webdataset version throws if suffix in current_sample, but we have a potential for\\n # this happening in the current LAION400m dataset if a tar ends with same prefix as the next\\n # begins, rare, but can happen since prefix aren't unique across tar files in that dataset\\n if current_sample is None or prefix != current_sample[\\\"__key__\\\"] or suffix in current_sample:\\n if valid_sample(current_sample):\\n yield current_sample\\n current_sample = {\\\"__key__\\\": prefix, \\\"__url__\\\": filesample[\\\"__url__\\\"]}\\n if suffixes is None or suffix in suffixes:\\n current_sample[suffix] = value\\n if valid_sample(current_sample):\\n yield current_sample\",\n \"def _split_ns_by_scatter(cls,\\n shard_count,\\n namespace,\\n raw_entity_kind,\\n app):\\n if shard_count == 1:\\n\\n return [key_range.KeyRange(namespace=namespace, _app=app)]\\n\\n ds_query = datastore.Query(kind=raw_entity_kind,\\n namespace=namespace,\\n _app=app,\\n keys_only=True)\\n ds_query.Order(\\\"__scatter__\\\")\\n oversampling_factor = 32\\n random_keys = ds_query.Get(shard_count * oversampling_factor)\\n\\n if not random_keys:\\n\\n\\n return ([key_range.KeyRange(namespace=namespace, _app=app)] +\\n [None] * (shard_count - 1))\\n\\n random_keys.sort()\\n\\n if len(random_keys) >= shard_count:\\n\\n random_keys = cls._choose_split_points(random_keys, shard_count)\\n\\n k_ranges = []\\n\\n k_ranges.append(key_range.KeyRange(\\n key_start=None,\\n key_end=random_keys[0],\\n direction=key_range.KeyRange.ASC,\\n include_start=False,\\n include_end=False,\\n namespace=namespace,\\n _app=app))\\n\\n for i in range(0, len(random_keys) - 1):\\n k_ranges.append(key_range.KeyRange(\\n key_start=random_keys[i],\\n key_end=random_keys[i+1],\\n direction=key_range.KeyRange.ASC,\\n include_start=True,\\n include_end=False,\\n namespace=namespace,\\n _app=app))\\n\\n k_ranges.append(key_range.KeyRange(\\n key_start=random_keys[-1],\\n key_end=None,\\n direction=key_range.KeyRange.ASC,\\n include_start=True,\\n include_end=False,\\n namespace=namespace,\\n _app=app))\\n\\n if len(k_ranges) < shard_count:\\n\\n k_ranges += [None] * (shard_count - len(k_ranges))\\n return k_ranges\",\n \"def find_matches(self, match_fn):\\n for item in self.hook:\\n if match_fn(item):\\n yield item\\n if not isinstance(item, text_type):\\n for subitem in item.find_matches(match_fn):\\n yield subitem\",\n \"def group_by(s, fn):\\n\\n dict = {}\\n for item in s:\\n output = fn(item)\\n if output in dict.keys():\\n dict[output].extend([item])\\n else:\\n dict[output] = [item]\\n return dict\",\n \"def topic_pattern_match(pattern):\\n client = AdminClient({\\\"bootstrap.servers\\\": \\\"PLAINTEXT://localhost:9092\\\"})\\n topic_metadata = client.list_topics()\\n topics = topic_metadata.topics\\n filtered_topics = {key: value for key, value in topics.items() if contains_substring(key, pattern)}\\n return len(filtered_topics) > 0\",\n \"def _shard_parameters_(self, params_to_shard) -> None:\\n if len(params_to_shard) > 0:\\n # When freeing the full parameters, we point their internal XLATensor to this placeholder\\n # (so that the XLA compiler can reuse the memory storage).\\n self._dummy_data_placeholder = torch.zeros(\\n 1, dtype=self.compute_dtype, device=self.xla_device)\\n\\n # get the module names of each full parameter to shard\\n params_to_shard_set = set(params_to_shard)\\n assert len(params_to_shard_set) == len(params_to_shard), \\\\\\n \\\"params_to_shard should not have dups\\\"\\n full_param_infos = []\\n shared_full_param_memo = {}\\n shared_full_param_infos = []\\n full_params = []\\n for module_name, m in self.named_modules():\\n for n, p in m.named_parameters(recurse=False):\\n if p.dtype != torch.float32:\\n raise TypeError(\\\"only fp32 parameters are supported\\\")\\n if p in params_to_shard_set:\\n if p in shared_full_param_memo:\\n mname, shared_m, shared_n = shared_full_param_memo[p]\\n shared_full_param_infos.append(\\n (module_name, mname, m, n, shared_m, shared_n))\\n else:\\n shared_full_param_memo[p] = (module_name, m, n)\\n full_param_infos.append((module_name, m, n))\\n full_params.append(p)\\n assert len(full_params) == len(params_to_shard_set), \\\\\\n f\\\"there are parameters in params_to_shard not belonging to this module.\\\"\\n del shared_full_param_memo\\n self.full_params = full_params\\n self.full_param_infos = full_param_infos\\n self.shared_full_param_infos = shared_full_param_infos\\n\\n # allocate and register new sharded parameters\\n self.sharded_params = []\\n for idx, (module_name, m, n) in enumerate(self.full_param_infos):\\n p = self.full_params[idx]\\n assert not hasattr(p, \\\"_is_sharded\\\")\\n\\n shard_data = self._get_shard(p)\\n if shard_data.device != self.xla_device:\\n # cast to XLA device if not already on XLA\\n shard_data = shard_data.to(self.xla_device)\\n p_shard = nn.Parameter(shard_data, requires_grad=p.requires_grad)\\n p_shard._is_sharded = True\\n p_shard._orig_size = p.size()\\n p_shard._orig_name = f\\\"{module_name}.{n}\\\"\\n p_shard._name = f\\\"_fsdp_shard.{p_shard._orig_name}\\\".replace(\\n \\\".\\\", \\\"_FSDP_SHARD_SEPARATOR_\\\")\\n self.register_parameter(p_shard._name, p_shard)\\n self.sharded_params.append(p_shard)\\n if p.device != self.xla_device:\\n # cast to XLA device if not already on XLA\\n p = p.to(self.xla_device).requires_grad_(p.requires_grad)\\n # update p in full_params since id(p) changed after the casting\\n self.full_params[idx] = p\\n # Free the full parameter storage (here we free its internal XLATensor) but keep the tensor itself\\n # for auto-grad tracing (like `torch.autograd.Variable` before the tensor-variable merge).\\n if XLA_DISABLE_FUNCTIONALIZATION:\\n p.data = p.new_zeros(1) # Old behavior before Functionalization.\\n else:\\n torch_xla._XLAC._replace_xla_tensor(p, p.new_zeros(1))\\n p._sharded_param = p_shard # add a handle to the sharded parameter\\n p._has_full_param = False\\n # deregister the full parameter tensors from their modules (so that they won't\\n # appear in the FSDP model's `parameters()` or `named_parameters()` outputs;\\n # only the sharded parameters should appear in the FSDP model's `parameters()`)\\n assert n in m._parameters\\n m._parameters.pop(n)\\n object.__setattr__(m, n, p)\\n\\n # also deregister the shared parameters\\n for _, _, m, n, shared_m, shared_n in self.shared_full_param_infos:\\n assert n in m._parameters\\n m._parameters.pop(n)\\n shared_p = getattr(shared_m, shared_n)\\n object.__setattr__(m, n, shared_p)\\n\\n assert len(self.sharded_params) == len(self.full_params)\",\n \"def groupby_apply_parallel(grouped_df, func, *args):\\n with Pool(cpu_count()) as p:\\n return_list = p.starmap(func, [(group, *args) for name, group in grouped_df])\\n return pd.concat(return_list)\",\n \"def filtered(self, keys, lst=None, func=\\\"all\\\"):\\n lst = self if lst is None else lst\\n if len(lst) == 0:\\n raise ValueError(\\\"No rows in list\\\")\\n return [row for row in self.filter(keys, lst, func=func)]\",\n \"def match_source_key(self, match):\\n raise NotImplementedError\",\n \"def match_words_to_search(chunks, searchresult, compare_func, join=True):\\n wordlist = [hebstrip(w)[1] for w in word_bound.split(searchresult)]\\n wordset = set(wordlist)\\n genlist = [\\n m\\n for m in [\\n match_one(rlist, wordset)\\n for rlist in chunks.linked_heb\\n if rlist.data\\n ]\\n if m\\n ]\\n ours = [i[0] for i in genlist]\\n theirs = [i[1] for i in genlist]\\n if join:\\n return compare_func(\\\" \\\".join(ours), \\\" \\\".join(wordlist)), theirs\\n else:\\n return compare_func(ours, wordlist), theirs\",\n \"def keys(self, pattern=\\\"*\\\"):\\n lenOfPrefix = len(self.appendKeys(\\\"\\\"))\\n return [key[lenOfPrefix:] for key in\\n self.redis.keys(self.appendKeys(pattern))]\",\n \"def apply(self, func):\\n logging.debug(\\\"applying function %s to db\\\", str(func))\\n return [ y for y in [ func(x, self._db[x]) for x in self._db.keys() ] if y ]\",\n \"def repair(\\n self,\\n start_key=None,\\n end_key=None,\\n shard=0,\\n nshards=1,\\n callback_on_progress=None,\\n ):\\n with self._lock:\\n self._repair(start_key, end_key, shard, nshards, callback_on_progress)\",\n \"def _add_matches(self):\\r\\n for record in self.records:\\r\\n match_dict={key_to_track: record.get(key_to_track)\\r\\n for key_to_track in self.key_matcher.keys()}\\r\\n self.key_matcher.add(obj=record,\\r\\n match_dict=match_dict)\",\n \"def __call__(self, f):\\n def w(*args, **kwargs):\\n if not isinstance(self.keys, list):\\n self.keys = [self.keys]\\n\\n for key in self.keys:\\n clazz = resolve(key)\\n args = args + (clazz,)\\n\\n return f(*args, **kwargs)\\n\\n return w\",\n \"def many(keys: List[str]):\\n for key in keys:\\n actions.key(key)\",\n \"def rh_get_match(x, y, k, d=7, hash_size=\\\"automatic\\\"):\\n n = len(x)\\n m = len(y)\\n\\n if hash_size == \\\"automatic\\\":\\n q = n * m\\n else:\\n q = hash_size\\n\\n output = list()\\n\\n for i in range(n - k + 1):\\n matches = rabin_karp_matcher(\\n target=y,\\n potential=x[i:i + k],\\n d=d,\\n q=q\\n )\\n\\n for match in matches:\\n output.append((i, match))\\n\\n return output\",\n \"def get_s3_keys_anatomy(\\n subject,\\n freesurfer_outputs=('label', 'mri', 'surf'),\\n meg_anatomy_outputs=('head_model', 'transforms'),\\n mode='minimal',\\n hcp_path_bucket='HCP_900'):\\n aws_keys = list()\\n for output in freesurfer_outputs:\\n aws_keys.extend(\\n get_file_paths(subject=subject, data_type='freesurfer',\\n output=output,\\n mode=mode,\\n hcp_path=hcp_path_bucket))\\n for output in meg_anatomy_outputs:\\n aws_keys.extend(\\n get_file_paths(subject=subject, data_type='meg_anatomy',\\n output=output,\\n hcp_path=hcp_path_bucket))\\n return aws_keys\",\n \"def keysAll():\",\n \"def reduce_tiles(\\n self,\\n preserve=(\\\"s22\\\", \\\"ld\\\"),\\n tries=10,\\n threads=1,\\n returntype=\\\"equiv\\\",\\n best=1,\\n key=None,\\n initequiv=None,\\n ):\\n raise NotImplementedError\\n # from fastreduceD import fastreduce\\n # return fastreduce.reduce_tiles(\\n # self, preserve, tries, threads, returntype, best, key, initequiv\\n # )\",\n \"def fisher_exact_worker(sparse_dict, overlap_count, user_count, gene_count, universe_count, set_list, combinations):\\n i, j = combinations[0], combinations[1]\\n table = build_fisher_contingency_table(overlap_count[i, j], user_count[j], gene_count[0, i], universe_count)\\n pvalue = stats.fisher_exact(table, alternative=\\\"greater\\\")[1]\\n new_pval = np.round(-1.0 * np.log10(pvalue), 12)\\n row_item = [set_list[j], sparse_dict[i], new_pval, int(universe_count), \\n int(user_count[j]), int(gene_count[0, i]), int(overlap_count[i, j])]\\n return row_item\",\n \"def fn(k):\\n seen = set()\\n for i in range(len(s)-k+1): \\n val = (prefix[i+k] - prefix[i]*fac[k]) % MOD \\n if val in seen: return True # rolling hash (ver. Monte Carlo)\\n seen.add(val)\\n return False\",\n \"def find_sample(self, pattern, prefix):\\n m = re.match(pattern, prefix).groupdict()\\n _samples = []\\n for s in self.samples:\\n if m.items() <= s.items():\\n _samples.append(s)\\n return _samples\",\n \"def get_matched_custom_feed(cls, fsid, topic_name): \\n custom_feed_map = cls.get_custom_feed_map(fsid)\\n if not custom_feed_map:\\n return [], None\\n if custom_feed_map.has_key(topic_name):\\n return [], custom_feed_map.get(topic_name)\\n wc_topic_name, required_keywords = cls.to_bracket_wild_card(topic_name)\\n if wc_topic_name and custom_feed_map.has_key(wc_topic_name):\\n return required_keywords, custom_feed_map.get(wc_topic_name)\\n return [], None\",\n \"def find_matches(words, min_match_ratio):\\n couples = []\\n with Pool(processes=mp.cpu_count()) as pool:\\n results = pool.starmap(\\n get_fuzz_ratio, itertools.combinations(words, 2))\\n for result, word, paired_word in results:\\n if result >= min_match_ratio:\\n couples.append([word, paired_word])\\n return couples\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.48097777","0.47111169","0.47066587","0.47066587","0.46778187","0.46645233","0.4651981","0.4619422","0.45933166","0.45721135","0.45453283","0.4536613","0.4532428","0.45301318","0.4529499","0.45236236","0.45110145","0.45036453","0.44981468","0.44917077","0.44750637","0.4467311","0.44656035","0.44463307","0.44333893","0.44304124","0.4421608","0.44073796","0.44069728","0.43958527","0.43705356","0.43671444","0.4348566","0.43401155","0.43309107","0.43301892","0.43287438","0.4312092","0.4310904","0.4307276","0.42981806","0.4285926","0.4280708","0.42802203","0.4274088","0.42640868","0.42583144","0.42527798","0.42522487","0.42454436","0.42423087","0.42370638","0.4236259","0.42152336","0.42086297","0.41971633","0.41950718","0.41928598","0.41914743","0.4188625","0.41860127","0.4185415","0.4183175","0.41815895","0.41742495","0.41731888","0.41563633","0.41524023","0.4152211","0.41520658","0.4145966","0.41436642","0.4140736","0.41405863","0.41339168","0.41332975","0.41301942","0.41267765","0.41255298","0.41161278","0.41123745","0.41087312","0.41080782","0.41061237","0.41056743","0.40905514","0.40902993","0.40862834","0.4083254","0.40754038","0.40632468","0.4058143","0.4052777","0.40520775","0.4046071","0.40438098","0.40432838","0.4040783","0.40392935","0.4031138"],"string":"[\n \"0.48097777\",\n \"0.47111169\",\n \"0.47066587\",\n \"0.47066587\",\n \"0.46778187\",\n \"0.46645233\",\n \"0.4651981\",\n \"0.4619422\",\n \"0.45933166\",\n \"0.45721135\",\n \"0.45453283\",\n \"0.4536613\",\n \"0.4532428\",\n \"0.45301318\",\n \"0.4529499\",\n \"0.45236236\",\n \"0.45110145\",\n \"0.45036453\",\n \"0.44981468\",\n \"0.44917077\",\n \"0.44750637\",\n \"0.4467311\",\n \"0.44656035\",\n \"0.44463307\",\n \"0.44333893\",\n \"0.44304124\",\n \"0.4421608\",\n \"0.44073796\",\n \"0.44069728\",\n \"0.43958527\",\n \"0.43705356\",\n \"0.43671444\",\n \"0.4348566\",\n \"0.43401155\",\n \"0.43309107\",\n \"0.43301892\",\n \"0.43287438\",\n \"0.4312092\",\n \"0.4310904\",\n \"0.4307276\",\n \"0.42981806\",\n \"0.4285926\",\n \"0.4280708\",\n \"0.42802203\",\n \"0.4274088\",\n \"0.42640868\",\n \"0.42583144\",\n \"0.42527798\",\n \"0.42522487\",\n \"0.42454436\",\n \"0.42423087\",\n \"0.42370638\",\n \"0.4236259\",\n \"0.42152336\",\n \"0.42086297\",\n \"0.41971633\",\n \"0.41950718\",\n \"0.41928598\",\n \"0.41914743\",\n \"0.4188625\",\n \"0.41860127\",\n \"0.4185415\",\n \"0.4183175\",\n \"0.41815895\",\n \"0.41742495\",\n \"0.41731888\",\n \"0.41563633\",\n \"0.41524023\",\n \"0.4152211\",\n \"0.41520658\",\n \"0.4145966\",\n \"0.41436642\",\n \"0.4140736\",\n \"0.41405863\",\n \"0.41339168\",\n \"0.41332975\",\n \"0.41301942\",\n \"0.41267765\",\n \"0.41255298\",\n \"0.41161278\",\n \"0.41123745\",\n \"0.41087312\",\n \"0.41080782\",\n \"0.41061237\",\n \"0.41056743\",\n \"0.40905514\",\n \"0.40902993\",\n \"0.40862834\",\n \"0.4083254\",\n \"0.40754038\",\n \"0.40632468\",\n \"0.4058143\",\n \"0.4052777\",\n \"0.40520775\",\n \"0.4046071\",\n \"0.40438098\",\n \"0.40432838\",\n \"0.4040783\",\n \"0.40392935\",\n \"0.4031138\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8449861"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":89392,"cells":{"query":{"kind":"string","value":"Generates scenario 1. Todo define scenario 1"},"document":{"kind":"string","value":"def generate_scenario_1(seed=1996, permanent_save=True, sigma_process=0.01, sigma_meas_radar=3, sigma_meas_ais=1):\n # specify seed to be able repeat example\n start_time = datetime.now()\n\n np.random.seed(seed)\n\n # combine two 1-D CV models to create a 2-D CV model\n transition_model = CombinedLinearGaussianTransitionModel([ConstantVelocity(sigma_process),\n ConstantVelocity(sigma_process)])\n\n # starting at 0,0 and moving NE\n truth = GroundTruthPath([GroundTruthState([0, 1, 0, 1], timestamp=start_time)])\n\n # generate truth using transition_model and noise\n for k in range(1, 21):\n truth.append(GroundTruthState(\n transition_model.function(truth[k - 1], noise=True, time_interval=timedelta(seconds=1)),\n timestamp=start_time + timedelta(seconds=k)))\n\n # Simulate measurements\n # Specify measurement model for radar\n measurement_model_radar = LinearGaussian(\n ndim_state=4, # number of state dimensions\n mapping=(0, 2), # mapping measurement vector index to state index\n noise_covar=np.array([[sigma_meas_radar, 0], # covariance matrix for Gaussian PDF\n [0, sigma_meas_radar]])\n )\n\n # Specify measurement model for AIS\n measurement_model_ais = LinearGaussian(\n ndim_state=4,\n mapping=(0, 2),\n noise_covar=np.array([[sigma_meas_ais, 0],\n [0, sigma_meas_ais]])\n )\n\n # generate \"radar\" measurements\n measurements_radar = []\n for state in truth:\n measurement = measurement_model_radar.function(state, noise=True)\n measurements_radar.append(Detection(measurement, timestamp=state.timestamp))\n\n # generate \"AIS\" measurements\n measurements_ais = []\n state_num = 0\n for state in truth:\n state_num += 1\n if not state_num % 2: # measurement every second time step\n measurement = measurement_model_ais.function(state, noise=True)\n measurements_ais.append(Detection(measurement, timestamp=state.timestamp))\n\n if permanent_save:\n save_folder_name = seed.__str__()\n else:\n save_folder_name = \"temp\"\n\n save_folder = \"../scenarios/scenario1/\" + save_folder_name + \"/\"\n\n # save the ground truth and the measurements for the radar and the AIS\n store_object.store_object(truth, save_folder, \"ground_truth.pk1\")\n store_object.store_object(measurements_radar, save_folder, \"measurements_radar.pk1\")\n store_object.store_object(measurements_ais, save_folder, \"measurements_ais.pk1\")\n store_object.store_object(start_time, save_folder, \"start_time.pk1\")\n store_object.store_object(measurement_model_radar, save_folder, \"measurement_model_radar.pk1\")\n store_object.store_object(measurement_model_ais, save_folder, \"measurement_model_ais.pk1\")\n store_object.store_object(transition_model, save_folder, \"transition_model.pk1\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_create_scenario1(self):\n pass","def test_create_scenario(self):\n pass","def TestOneStep(self):\n pass","def test_get_scenario(self):\n pass","def test_2nd_scenario():\n start_entered_scenario(config.MERAKI_CAMERAS[0][\"serial\"])\n return \"ok\"","def test_get_scenarios(self):\n pass","def test_pytest_bdd_scenario(self):\n self.testdir.makefile(\n \".feature\",\n simple=_SIMPLE_SCENARIO,\n )\n py_file = self.testdir.makepyfile(\n \"\"\"\n from pytest_bdd import scenario, given, then, when\n\n @scenario(\"simple.feature\", \"Simple scenario\")\n def test_simple():\n pass\n\n BAR = None\n\n @given(\"I have a bar\")\n def bar():\n global BAR\n BAR = 1\n\n @when(\"I eat it\")\n def eat():\n global BAR\n BAR -= 1\n\n @then(\"I don't have a bar\")\n def check():\n assert BAR == 0\n \"\"\"\n )\n file_name = os.path.basename(py_file.strpath)\n self.inline_run(\"--ddtrace\", file_name)\n spans = self.pop_spans()\n\n assert len(spans) == 7\n assert spans[0].get_tag(\"component\") == \"pytest\"\n assert spans[0].get_tag(\"test.name\") == \"Simple scenario\"\n assert spans[0].span_type == \"test\"\n assert spans[1].resource == \"I have a bar\"\n assert spans[1].name == \"given\"\n assert spans[2].resource == \"I eat it\"\n assert spans[2].name == \"when\"\n assert spans[3].resource == \"I don't have a bar\"\n assert spans[3].name == \"then\"","def create_scenarios(self, params, num_scenarios, random_seed):\n return None","def test_pytest_bdd_with_missing_step_implementation(self):\n self.testdir.makefile(\n \".feature\",\n simple=_SIMPLE_SCENARIO,\n )\n py_file = self.testdir.makepyfile(\n \"\"\"\n from pytest_bdd import scenario, given, then, when\n\n @scenario(\"simple.feature\", \"Simple scenario\")\n def test_simple():\n pass\n \"\"\"\n )\n file_name = os.path.basename(py_file.strpath)\n self.inline_run(\"--ddtrace\", file_name)\n spans = self.pop_spans()\n\n assert len(spans) == 4\n assert spans[0].get_tag(ERROR_MSG)","def test_generate_missing(pytester):\n pytester.makefile(\n \".feature\",\n generation=textwrap.dedent(\n \"\"\"\\\n Feature: Missing code generation\n\n Background:\n Given I have a foobar\n\n Scenario: Scenario tests which are already bound to the tests stay as is\n Given I have a bar\n\n\n Scenario: Code is generated for scenarios which are not bound to any tests\n Given I have a bar\n\n\n Scenario: Code is generated for scenario steps which are not yet defined(implemented)\n Given I have a custom bar\n \"\"\"\n ),\n )\n\n pytester.makepyfile(\n textwrap.dedent(\n \"\"\"\\\n import functools\n\n from pytest_bdd import scenario, given\n\n scenario = functools.partial(scenario, \"generation.feature\")\n\n @given(\"I have a bar\")\n def _():\n return \"bar\"\n\n @scenario(\"Scenario tests which are already bound to the tests stay as is\")\n def test_foo():\n pass\n\n @scenario(\"Code is generated for scenario steps which are not yet defined(implemented)\")\n def test_missing_steps():\n pass\n \"\"\"\n )\n )\n\n result = pytester.runpytest(\"--generate-missing\", \"--feature\", \"generation.feature\")\n result.assert_outcomes(passed=0, failed=0, errors=0)\n assert not result.stderr.str()\n assert result.ret == 0\n\n result.stdout.fnmatch_lines(\n ['Scenario \"Code is generated for scenarios which are not bound to any tests\" is not bound to any test *']\n )\n\n result.stdout.fnmatch_lines(\n [\n 'Step Given \"I have a custom bar\" is not defined in the scenario '\n '\"Code is generated for scenario steps which are not yet defined(implemented)\" *'\n ]\n )\n\n result.stdout.fnmatch_lines(\n ['Step Given \"I have a foobar\" is not defined in the background of the feature \"Missing code generation\" *']\n )\n\n result.stdout.fnmatch_lines([\"Please place the code above to the test file(s):\"])","def test_dataset_scenario_generation_full(self):\n params = ParameterServer()\n\n map_filename = os.path.join(os.path.dirname(__file__), \"data/DR_DEU_Merging_MT_v01_shifted.xodr\")\n track_filename = os.path.join(os.path.dirname(__file__), \"data/interaction_dataset_DEU_Merging_dummy_track.csv\")\n\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"MapFilename\"] = map_filename\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"TrackFilenameList\"] = [track_filename]\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"StartingOffsetMs\"] = 0\n\n scenario_generation = InteractionDatasetScenarioGenerationFull(\n params=params, num_scenarios=2)\n\n self.assertEqual(scenario_generation.get_num_scenarios(), 2)\n\n # first scenario\n scenario0 = scenario_generation.get_scenario(0)\n agent11 = scenario0.GetWorldState().agents[1]\n agent12 = scenario0.GetWorldState().agents[2]\n self.assertEqual(agent11.first_valid_timestamp, 0.0)\n self.assertEqual(agent12.first_valid_timestamp, 0.0)\n # agents are initialized with Behavior::NotValidYet\n self.assertEqual(list(scenario0.GetWorldState().agents_valid.keys()), [])\n scenario0.GetWorldState().Step(0.01)\n self.assertEqual(list(scenario0.GetWorldState().agents_valid.keys()), [1,2])\n\n # second scenario\n scenario1 = scenario_generation.get_scenario(1)\n agent21 = scenario1.GetWorldState().agents[1]\n agent22 = scenario1.GetWorldState().agents[2]\n self.assertEqual(agent21.first_valid_timestamp, 0.0)\n self.assertEqual(agent22.first_valid_timestamp, 0.0)\n self.assertEqual(list(scenario1.GetWorldState().agents_valid.keys()), [])\n scenario1.GetWorldState().Step(0.01)\n self.assertEqual(list(scenario1.GetWorldState().agents_valid.keys()), [1,2])","def test_generate(monkeypatch, capsys):\n monkeypatch.setattr(sys, \"argv\", [\"\", \"generate\", os.path.join(PATH, \"generate.feature\")])\n main()\n out, err = capsys.readouterr()\n assert out == textwrap.dedent(\n '''\n # coding=utf-8\n \"\"\"Code generation feature tests.\"\"\"\n\n from pytest_bdd import (\n given,\n scenario,\n then,\n when,\n )\n\n\n @scenario('scripts/generate.feature', 'Given and when using the same fixture should not evaluate it twice')\n def test_given_and_when_using_the_same_fixture_should_not_evaluate_it_twice():\n \"\"\"Given and when using the same fixture should not evaluate it twice.\"\"\"\n\n\n @given('1 have a fixture (appends 1 to a list) in reuse syntax')\n def have_a_fixture_appends_1_to_a_list_in_reuse_syntax():\n \"\"\"1 have a fixture (appends 1 to a list) in reuse syntax.\"\"\"\n raise NotImplementedError\n\n\n @given('I have an empty list')\n def i_have_an_empty_list():\n \"\"\"I have an empty list.\"\"\"\n raise NotImplementedError\n\n\n @when('I use this fixture')\n def i_use_this_fixture():\n \"\"\"I use this fixture.\"\"\"\n raise NotImplementedError\n\n\n @then('my list should be [1]')\n def my_list_should_be_1():\n \"\"\"my list should be [1].\"\"\"\n raise NotImplementedError\n\n '''[\n 1:\n ].replace(\n u\"'\", u\"'\"\n )\n )","def test_virtualmachineconsole_scenario1(self):\n call_scenario1(self)","def test_get_scenarios_expanded(self):\n pass","def test_pytest_bdd_scenario_with_failed_step(self):\n self.testdir.makefile(\n \".feature\",\n simple=_SIMPLE_SCENARIO,\n )\n py_file = self.testdir.makepyfile(\n \"\"\"\n from pytest_bdd import scenario, given, then, when\n\n @scenario(\"simple.feature\", \"Simple scenario\")\n def test_simple():\n pass\n\n BAR = None\n\n @given(\"I have a bar\")\n def bar():\n global BAR\n BAR = 1\n\n @when(\"I eat it\")\n def eat():\n global BAR\n BAR -= 1\n\n @then(\"I don't have a bar\")\n def check():\n assert BAR == -1\n \"\"\"\n )\n file_name = os.path.basename(py_file.strpath)\n self.inline_run(\"--ddtrace\", file_name)\n spans = self.pop_spans()\n\n assert len(spans) == 7\n assert spans[3].name == \"then\"\n assert spans[3].get_tag(ERROR_MSG)","def test_pytest_bdd_scenario_with_parameters(self):\n self.testdir.makefile(\n \".feature\",\n parameters=\"\"\"\n Feature: Parameters\n Scenario: Passing scenario\n Given I have 0 bars\n When I eat it\n Then I have -1 bars\n\n Scenario: Failing scenario\n Given I have 2 bars\n When I eat it\n Then I have 0 bar\n\n Scenario: Failing converter\n Given I have no bar\n \"\"\",\n )\n py_file = self.testdir.makepyfile(\n \"\"\"\n from pytest_bdd import scenarios, given, then, when, parsers\n\n scenarios(\"parameters.feature\")\n\n BAR = None\n\n @given(parsers.re(\"^I have (?P<bars>[^ ]+) bar$\")) # loose regex\n def have_simple(bars):\n global BAR\n BAR = bars\n\n @given(parsers.re(\"^I have (?P<bars>\\\\d+) bars$\"), converters=dict(bars=int))\n def have(bars):\n global BAR\n BAR = bars\n\n @when(\"I eat it\")\n def eat():\n global BAR\n BAR -= 1\n\n @then(parsers.parse(\"I have {bars:d} bar\"))\n def check_parse(bars):\n assert BAR == bars\n\n @then(parsers.cfparse(\"I have {bars:d} bars\"))\n def check_cfparse(bars):\n assert BAR == bars\n \"\"\"\n )\n file_name = os.path.basename(py_file.strpath)\n self.inline_run(\"--ddtrace\", file_name)\n spans = self.pop_spans()\n\n assert len(spans) == 13 # 3 scenarios + 7 steps + 1 module\n assert json.loads(spans[1].get_tag(test.PARAMETERS)) == {\"bars\": 0}\n assert json.loads(spans[3].get_tag(test.PARAMETERS)) == {\"bars\": -1}\n assert json.loads(spans[5].get_tag(test.PARAMETERS)) == {\"bars\": 2}\n assert json.loads(spans[7].get_tag(test.PARAMETERS)) == {\"bars\": 0}\n assert json.loads(spans[9].get_tag(test.PARAMETERS)) == {\"bars\": \"no\"}","def step_impl(context):\n pass","def step_impl(context):\n pass","def step_name(self):\n return \"main\"","def test_create_goal(self):\n pass","def main(self):\n root = etree.Element(\"OpenSCENARIO\")\n self.get_header(root)\n self.get_parameter_declarations(root)\n etree.SubElement(root, \"CatalogLocations\")\n self.get_road_network(root)\n self.get_entities(root)\n storyboard = etree.SubElement(root, \"Storyboard\")\n self.get_init(storyboard)\n story = etree.SubElement(storyboard, \"Story\")\n story.set(\"name\", \"OSC Generated Story\")\n act = etree.SubElement(story, \"Act\")\n act.set(\"name\", \"OSC Generated Act\")\n self.get_maneuvers(act)\n self.get_story_start_trigger(act)\n self.get_story_stop_trigger(act)\n self.get_end_eval_criteria(storyboard)\n\n generated_xml = etree.tostring(root)\n self.write_xosc(generated_xml)","def workbench_scenarios():\n\t\treturn [(\"TCXBlock\",\n\t\t\"\"\"\n\t\t<tournamentcreator/>\n\t\t\"\"\"),\n\t\t]","def test_scenario_comments(testdir):\n testdir.makefile(\n \".feature\",\n comments=textwrap.dedent(\n \"\"\"\\\n Feature: Comments\n Scenario: Comments\n # Comment\n Given I have a bar\n\n Scenario: Strings that are not comments\n Given comments should be at the start of words\n Then this is not a#comment\n And this is not \"#acomment\"\n\n \"\"\"\n ),\n )\n\n testdir.makepyfile(\n textwrap.dedent(\n \"\"\"\\\n import re\n import pytest\n from pytest_bdd import parsers, given, then, scenario\n\n @scenario(\"comments.feature\", \"Comments\")\n def test_1():\n pass\n\n @scenario(\"comments.feature\", \"Strings that are not comments\")\n def test_2():\n pass\n\n\n @given(\"I have a bar\")\n def bar():\n return \"bar\"\n\n\n @given(\"comments should be at the start of words\")\n def comments():\n pass\n\n\n @then(parsers.parse(\"this is not {acomment}\"))\n def a_comment(acomment):\n assert re.search(\"a.*comment\", acomment)\n\n \"\"\"\n )\n )\n\n result = testdir.runpytest()\n\n result.assert_outcomes(passed=2)","def scenario(self, scenario):\n # -- LAST SCENARIO: Report failures (if any).\n self.report_scenario_completed()\n\n # -- NEW SCENARIO:\n assert not self.failures\n self.current_scenario = scenario\n scenario_name = scenario.name\n prefix = self.scenario_prefix\n if self.current_rule:\n prefix += u\" \"\n if scenario_name:\n scenario_name += \" \"\n self.stream.write(u\"%s%s \" % (prefix, scenario_name))\n self.stream.flush()","def test_simple(testdir, pytest_params):\n testdir.makefile(\n \".feature\",\n simple=\"\"\"\n Feature: Simple feature\n Scenario: Simple scenario\n Given I have a bar\n \"\"\",\n )\n testdir.makepyfile(\n \"\"\"\n from pytest_bdd import scenario, given, then\n\n @scenario(\"simple.feature\", \"Simple scenario\")\n def test_simple():\n pass\n\n @given(\"I have a bar\")\n def bar():\n return \"bar\"\n\n @then(\"pass\")\n def bar():\n pass\n \"\"\"\n )\n result = testdir.runpytest_subprocess(*pytest_params)\n result.assert_outcomes(passed=1)","def _run_scenario(self, cls, method_name, context, args, config):","def test_scenario_no_strict_gherkin(pytester):\n pytester.makepyfile(\n test_gherkin=\"\"\"\n import pytest\n\n from pytest_bdd import when, scenario\n\n @scenario(\n \"no_strict_gherkin_scenario.feature\",\n \"Test scenario\",\n )\n def test_scenario():\n pass\n\n\n @pytest.fixture\n def foo():\n return {}\n\n @when('foo has a value \"bar\"')\n def _(foo):\n foo[\"bar\"] = \"bar\"\n return foo[\"bar\"]\n\n\n @when('foo is not boolean')\n def _(foo):\n assert foo is not bool\n\n\n @when('foo has not a value \"baz\"')\n def _(foo):\n assert \"baz\" not in foo\n \"\"\"\n )\n\n pytester.makefile(\n \".feature\",\n no_strict_gherkin_scenario=\"\"\"\n Feature: No strict Gherkin Scenario support\n\n Scenario: Test scenario\n When foo has a value \"bar\"\n And foo is not boolean\n And foo has not a value \"baz\"\n\n \"\"\",\n )\n\n result = pytester.runpytest()\n result.assert_outcomes(passed=1)","def test_integration1(self):\n self._test_integration(1)","def test_Demo(self):\n self._run(self._example_scenarios, \"Demo\")","def run_scenario(self, run, run_id):\n\n raise NotImplementedError","def workbench_scenarios():\n return [\n (\"simstudentXBlock\",\n \"\"\"<vertical_demo>\n <simstudent/>\n </vertical_demo>\n \"\"\"),\n ]","def start_scenario(self, _, scenario, **kwargs):\n if scenario.tags and \"skip\" in scenario.tags:\n scenario.skip(\"Marked with @skip\")\n self._scenario_id = self._rp.start_test_item(\n name=scenario.name,\n start_time=timestamp(),\n item_type=\"STEP\",\n parent_item_id=self._feature_id,\n code_ref=self._code_ref(scenario),\n attributes=self._attributes(scenario),\n parameters=self._get_parameters(scenario),\n description=self._item_description(scenario),\n test_case_id=self._test_case_id(scenario),\n **kwargs,\n )\n self._log_fixtures(scenario, \"BEFORE_TEST\", self._scenario_id)\n self._log_item_id = self._scenario_id","def workbench_scenarios():\n return [\n (\"Oppia Embedding\",\n \"\"\"<vertical_demo>\n <oppia oppiaid=\"0\" src=\"https://www.oppia.org\" width=\"700\" />\n </vertical_demo>\n \"\"\"),\n ]","def step_imp1(context,message):\n context.driver.get(context.base_url)","def run_one_step(self):\n pass","def pytest_cmdline_main(config):\n try:\n if len(config.option.scenarios) == 0:\n print(\"Available scenarios:\")\n for scenario in Scenario.scenarios.values():\n print(f\" {scenario.name} - {scenario.description}\")\n return 0\n except:\n pass\n\n return None","def test_1():","def get_scenario_template(self, target: str = 'sqlplus', login: typing.Optional[str] = None) -> str:\n env = os.environ.get\n scenario = []\n login = login if login else '/nolog'\n\n if target == 'sqlplus':\n executable = \"/bin/%s -S %s\" % (target, login)\n elif target == 'rman':\n executable = \"/bin/\" + target\n elif target == 'psql':\n executable = \"/usr/bin/\" + target\n else:\n raise Exception(\"Unknown scenario target: %s\" % target)\n\n if target in ['sqlplus', 'rman']:\n if env('PATH') and env('ORACLE_BASE') and env('ORACLE_SID') and env('ORACLE_HOME'):\n scenario.append(\"export ORACLE_BASE=\" + env('ORACLE_BASE', ''))\n scenario.append(\"export ORACLE_SID=\" + env('ORACLE_SID', ''))\n scenario.append(\"export ORACLE_HOME=\" + env('ORACLE_HOME', ''))\n scenario.append(\"export PATH=\" + env('PATH', ''))\n else:\n raise Exception(\"Underlying error: environment for Oracle Database cannot be constructed.\")\n\n scenario.append(\"cat - << EOF | \" + env('ORACLE_HOME', '') + executable)\n if target == 'sqlplus' and login.lower() == '/nolog':\n scenario.append(\"CONNECT / AS SYSDBA;\")\n elif target == 'rman':\n scenario.append(\"CONNECT TARGET /\")\n\n scenario.append(\"@scenario\")\n scenario.append(\"EXIT;\")\n scenario.append(\"EOF\")\n elif target in ['psql']:\n scenario.append((\"cat - << EOF | \" + executable + \" -t --pset footer=off \" + self.config.get('db_name', '')).strip())\n scenario.append(\"@scenario\")\n scenario.append(\"EOF\")\n\n if self.debug:\n print(\"\\n\" + (\"-\" * 40) + \"8<\" + (\"-\" * 40))\n print('\\n'.join(scenario))\n print((\"-\" * 40) + \"8<\" + (\"-\" * 40))\n\n return '\\n'.join(scenario)","def test_generate_all_testing(self):\n pass","def enter_case(self, case):\n print \" \" + case.how_to_behave + '.'","def test_dataset_scenario_generation_full_incomplete(self):\n params = ParameterServer()\n\n map_filename = os.path.join(os.path.dirname(__file__), \"data/DR_CHN_Merging_ZS_partial_v02.xodr\")\n track_filename = os.path.join(os.path.dirname(__file__), \"data/interaction_dataset_CHN_Merging_dummy_track_incomplete.csv\")\n\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"MapFilename\"] = map_filename\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"TrackFilenameList\"] = [track_filename]\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"StartingOffsetMs\"] = 0\n\n scenario_generation = InteractionDatasetScenarioGenerationFull(\n params=params, num_scenarios=3)\n self.assertEqual(scenario_generation.get_num_scenarios(), 2)\n\n # first scenario\n agent12 = scenario_generation.get_scenario(0).GetWorldState().agents[2]\n agent17 = scenario_generation.get_scenario(0).GetWorldState().agents[7]\n self.assertEqual(agent12.first_valid_timestamp, 0.0)\n self.assertEqual(agent17.first_valid_timestamp, 0.0)\n\n # second scenario\n agent22 = scenario_generation.get_scenario(1).GetWorldState().agents[2]\n agent27 = scenario_generation.get_scenario(1).GetWorldState().agents[7]\n self.assertEqual(agent22.first_valid_timestamp, 0.0)\n self.assertEqual(agent27.first_valid_timestamp, 0.0)","def test_single_test_case():\n pass","def __init__(self, scenario):\n source.__init__(self, scenario)","def set_up(self):\n for section_name, section_body in self.sections.iteritems():\n scenario = Scenario(section_name, section_body)\n self.scenarios.append(scenario)","def test_scenario(self):\n scenario_ids = list(scenarios.get_scenarios().keys())\n\n for scenario_id in scenario_ids:\n url = reverse('workbench_show_scenario', kwargs={'scenario_id': scenario_id})\n client = Client()\n response = client.get(url, follow=True)\n assert response.status_code == 200, scenario_id\n\n # Be sure we got the whole scenario. Again, we can't know what to expect\n # here, but at the very least, if there are verticals, they should not be\n # empty. That would be a sign that some data wasn't loaded properly while\n # rendering the scenario.\n html = lxml.html.fromstring(response.content)\n for vertical_tag in html.xpath('//div[@class=\"vertical\"]'):\n # No vertical tag should be empty.\n assert list(vertical_tag), u\"Scenario {}: Empty <vertical> shouldn't happen!\".format(scenario_id)","def test_generate_missing_with_step_parsers(pytester):\n pytester.makefile(\n \".feature\",\n generation=textwrap.dedent(\n \"\"\"\\\n Feature: Missing code generation with step parsers\n\n Scenario: Step parsers are correctly discovered\n Given I use the string parser without parameter\n And I use parsers.parse with parameter 1\n And I use parsers.re with parameter 2\n And I use parsers.cfparse with parameter 3\n \"\"\"\n ),\n )\n\n pytester.makepyfile(\n textwrap.dedent(\n \"\"\"\\\n import functools\n\n from pytest_bdd import scenarios, given, parsers\n\n scenarios(\"generation.feature\")\n\n @given(\"I use the string parser without parameter\")\n def _():\n return None\n\n @given(parsers.parse(\"I use parsers.parse with parameter {param}\"))\n def _(param):\n return param\n\n @given(parsers.re(r\"^I use parsers.re with parameter (?P<param>.*?)$\"))\n def _(param):\n return param\n\n @given(parsers.cfparse(\"I use parsers.cfparse with parameter {param:d}\"))\n def _(param):\n return param\n \"\"\"\n )\n )\n\n result = pytester.runpytest(\"--generate-missing\", \"--feature\", \"generation.feature\")\n result.assert_outcomes(passed=0, failed=0, errors=0)\n assert not result.stderr.str()\n assert result.ret == 0\n\n output = result.stdout.str()\n\n assert \"I use the string parser\" not in output\n assert \"I use parsers.parse\" not in output\n assert \"I use parsers.re\" not in output\n assert \"I use parsers.cfparse\" not in output","def scenarioReference_factory(i, section_type, root, this):\n \n # no loops in section, should print 0\n if i == 0:\n return ScenarioReference()\n \n if section_type == SectionType.Plant:\n name = root.plants.nameof(i)\n scen = ScenarioReference(SectionType.Plant, name, root, this)\n \n elif section_type == SectionType.Op:\n name = root.ops.nameof(i)\n scen = ScenarioReference(SectionType.Op, name, root, this)\n \n elif section_type == SectionType.Ini:\n name = root.inis.nameof(i)\n scen = ScenarioReference(SectionType.Ini, name, root, this)\n \n elif section_type == SectionType.Surf:\n name = root.surfs.nameof(i)\n scen = ScenarioReference(SectionType.Surf, name, root, this)\n \n elif section_type == SectionType.Contour:\n name = root.contours.nameof(i)\n scen = ScenarioReference(SectionType.Contour, name, root, this)\n \n elif section_type == SectionType.Drain:\n name = root.drains.nameof(i)\n scen = ScenarioReference(SectionType.Drain, name, root, this)\n \n elif section_type == SectionType.Year:\n name = root.years.nameof(i)\n scen = ScenarioReference(SectionType.Year, name, root, this)\n \n else:\n raise ValueError('Unknown SectionType')\n \n return scen","def run_scenario(self):\n self.initialize_random_map()\n self.visualize_environment('initial')\n self.get_tower_target_coverages()\n self.solve_environment()\n self.visualize_environment('solved')","def testgen(self):\n self.parse()\n self.generate()","def execute(self):\n if self._cli_arguments.get('<samplename>') == 'cfn':\n generate_sample_cfn_module(self.env_root)\n elif self._cli_arguments.get('<samplename>') == 'static-angular':\n generate_sample_static_angular(self.env_root)\n elif self._cli_arguments.get('<samplename>') == 'static-react':\n generate_sample_static_react(self.env_root)\n elif self._cli_arguments.get('<samplename>') == 'sls-py':\n generate_sample_sls_module(self.env_root, 'sls-py')\n elif self._cli_arguments.get('<samplename>') == 'sls-tsc':\n generate_sample_sls_module(self.env_root, 'sls-tsc')\n elif self._cli_arguments.get('<samplename>') == 'stacker':\n generate_sample_stacker_module(self.env_root)\n elif self._cli_arguments.get('<samplename>') == 'tf':\n generate_sample_tf_module(self.env_root)\n elif self._cli_arguments.get('<samplename>') == 'k8s-cfn-repo':\n generate_sample_k8s_cfn_repo(self.env_root)\n elif self._cli_arguments.get('<samplename>') == 'k8s-tf-repo':\n generate_sample_k8s_tf_repo(self.env_root)\n elif self._cli_arguments.get('<samplename>') == 'cdk-tsc':\n generate_sample_cdk_tsc_module(self.env_root)\n elif self._cli_arguments.get('<samplename>') == 'cdk-py':\n generate_sample_cdk_py_module(self.env_root)\n elif self._cli_arguments.get('<samplename>') == 'cdk-csharp':\n generate_sample_cdk_cs_module(self.env_root)\n else:\n LOGGER.info(\"Available samples to generate:\")\n for i in ['cfn', 'static-angular', 'static-react', 'sls-tsc',\n 'sls-py', 'tf', 'k8s-cfn-repo', 'k8s-tf-repo',\n 'stacker', 'cdk-tsc', 'cdk-py', 'cdk-csharp']:\n print(i)","def test_workbench_scenarios(self):\n result_title = 'Adaptive Numeric Input XBlock'\n basic_scenario = \"<adaptivenumericinput />\"\n test_result = self.xblock.workbench_scenarios()\n self.assertEquals(result_title, test_result[0][0])\n self.assertIn(basic_scenario, test_result[0][1])","def generate():","def on_GenerateA1_clicked(self):\n # TODO: not implemented yet\n raise NotImplementedError","def generate_code(features: list[Feature], scenarios: list[ScenarioTemplate], steps: list[Step]) -> str:\n grouped_steps = group_steps(steps)\n template = template_lookup.get_template(\"test.py.mak\")\n code = template.render(\n features=features,\n scenarios=scenarios,\n steps=grouped_steps,\n make_python_name=make_python_name,\n make_python_docstring=make_python_docstring,\n make_string_literal=make_string_literal,\n )\n return cast(str, code)","def feature(self, node=\"clickhouse1\"):\n self.context.node = self.context.cluster.node(node)\n\n for scenario in loads(current_module(), Scenario):\n scenario()","def workbench_scenarios():\n return [\n (\"SummaryXBlock\",\n \"\"\"<summary/>\n \"\"\"),\n (\"Multiple SummaryXBlock\",\n \"\"\"<vertical_demo>\n <summary/>\n <summary/>\n <summary/>\n </vertical_demo>\n \"\"\"),\n ]","def run(self,**kwargs):\n members = getmembers(self.bs)\n targets = \"scenario\"\n scenarios=[m[0] for m in members if m[0].startswith(targets)]\n entries={}\n for scenario in scenarios:\n entries[scenario[len(targets):]] = getattr(self.bs,scenario) # save scenario names in entries\n self.mm.addGenericMenu(targets,self.mm.cur_page,\"Select your desired scenario\", entries)\n self.mm.loadMenu(targets)","def load_scenario(self):\n logger.info(\"Loading scenario...\")\n for manager in self.config[\"fleets\"]:\n name = manager[\"name\"]\n password = manager[\"password\"] if \"password\" in manager else faker_factory.password()\n fleet_type = manager[\"fleet_type\"]\n strategy = manager.get(\"strategy\")\n icon = manager.get(\"icon\")\n agent = self.create_fleetmanager_agent(name, password, fleet_type=fleet_type, strategy=strategy)\n\n self.set_icon(agent, icon, default=fleet_type)\n\n while len(self.manager_agents) < self.config.num_managers:\n time.sleep(0.1)\n\n for transport in self.config[\"transports\"]:\n name = transport[\"name\"]\n password = transport[\"password\"] if \"password\" in transport else faker_factory.password()\n position = transport[\"position\"]\n fleetmanager = transport[\"fleet\"]\n fleet_type = transport[\"fleet_type\"]\n speed = transport.get(\"speed\")\n trust = transport.get(\"trust\")\n rates = transport.get(\"rates\")\n velocity_factor = transport.get(\"velocity_factor\")\n fuel = transport.get(\"fuel\")\n autonomy = transport.get(\"autonomy\")\n current_autonomy = transport.get(\"current_autonomy\")\n strategy = transport.get(\"strategy\")\n icon = transport.get(\"icon\")\n agent = self.create_transport_agent(name, password, position=position, speed=speed, fleet_type=fleet_type,\n fleetmanager=fleetmanager, strategy=strategy, autonomy=autonomy,\n current_autonomy=current_autonomy, trust=trust, rates=rates, velocity_factor=velocity_factor)\n\n if icon:\n self.set_icon(agent, icon, default=fleet_type)\n\n for customer in self.config[\"customers\"]:\n name = customer[\"name\"]\n password = customer[\"password\"] if \"password\" in customer else faker_factory.password()\n fleet_type = customer[\"fleet_type\"]\n position = customer[\"position\"]\n target = customer[\"destination\"]\n strategy = customer.get(\"strategy\")\n icon = customer.get(\"icon\")\n agent = self.create_customer_agent(name, password, fleet_type, position=position, target=target,\n strategy=strategy)\n\n self.set_icon(agent, icon, default=\"customer\")\n\n for station in self.config[\"stations\"]:\n password = station[\"password\"] if \"password\" in station else faker_factory.password()\n strategy = station.get(\"strategy\")\n icon = station.get(\"icon\")\n agent = self.create_station_agent(station[\"name\"], password, position=station[\"position\"],\n power=station[\"power\"], places=station[\"places\"], strategy=strategy)\n self.set_icon(agent, icon, default=\"electric_station\")","def generate_goal(self, factors=None, obs=None):\n pass","def feature(self, node=\"clickhouse1\"):\n node = self.context.cluster.node(node)\n\n @contextmanager\n def setup(users=0, roles=0):\n try:\n with Given(\"I have some users and roles\"):\n for i in range(users):\n node.query(f\"CREATE USER OR REPLACE user{i}\")\n for j in range(roles):\n node.query(f\"CREATE ROLE OR REPLACE role{j}\")\n yield\n finally:\n with Finally(\"I drop the users and roles\"):\n for i in range(users):\n node.query(f\"DROP USER IF EXISTS user{i}\")\n for j in range(roles):\n node.query(f\"DROP ROLE IF EXISTS role{j}\")\n\n with Scenario(\n \"I grant a role to a user\", requirements=[RQ_SRS_006_RBAC_Grant_Role(\"1.0\")]\n ):\n with setup(1, 1):\n with When(\"I grant a role\"):\n node.query(\"GRANT role0 TO user0\")\n\n with Scenario(\n \"I grant a nonexistent role to user\",\n requirements=[RQ_SRS_006_RBAC_Grant_Role(\"1.0\")],\n ):\n with setup(1, 0):\n with When(\"I grant nonexistent role to a user\"):\n exitcode, message = errors.role_not_found_in_disk(name=\"role0\")\n node.query(\"GRANT role0 TO user0\", exitcode=exitcode, message=message)\n\n # with nonexistent object name, GRANT assumes type role (treats user0 as role)\n with Scenario(\n \"I grant a role to a nonexistent user\",\n requirements=[RQ_SRS_006_RBAC_Grant_Role(\"1.0\")],\n ):\n with setup(0, 1):\n with When(\"I grant role to a nonexistent user\"):\n exitcode, message = errors.role_not_found_in_disk(name=\"user0\")\n node.query(\"GRANT role0 TO user0\", exitcode=exitcode, message=message)\n\n with Scenario(\n \"I grant a nonexistent role to a nonexistent user\",\n requirements=[RQ_SRS_006_RBAC_Grant_Role(\"1.0\")],\n ):\n with setup(0, 0):\n with When(\"I grant nonexistent role to a nonexistent user\"):\n exitcode, message = (\n errors.role_not_found_in_disk(name=\"user0\")\n if check_clickhouse_version(\">=21.09\")(self)\n else errors.role_not_found_in_disk(name=\"role0\")\n )\n node.query(\"GRANT role0 TO user0\", exitcode=exitcode, message=message)\n\n with Scenario(\n \"I grant a role to multiple users\",\n requirements=[RQ_SRS_006_RBAC_Grant_Role(\"1.0\")],\n ):\n with setup(2, 1):\n with When(\"I grant role to a multiple users\"):\n node.query(\"GRANT role0 TO user0, user1\")\n\n with Scenario(\n \"I grant multiple roles to multiple users\",\n requirements=[RQ_SRS_006_RBAC_Grant_Role(\"1.0\")],\n ):\n with setup(2, 2):\n with When(\"I grant multiple roles to multiple users\"):\n node.query(\"GRANT role0, role1 TO user0, user1\")\n\n with Scenario(\n \"I grant role to current user\",\n requirements=[RQ_SRS_006_RBAC_Grant_Role_CurrentUser(\"1.0\")],\n ):\n with setup(1, 1):\n with Given(\"I have a user with access management privilege\"):\n node.query(\"GRANT ACCESS MANAGEMENT ON *.* TO user0\")\n with When(\"I grant role to current user\"):\n node.query(\"GRANT role0 TO CURRENT_USER\", settings=[(\"user\", \"user0\")])\n\n with Scenario(\n \"I grant role to default user, throws exception\",\n requirements=[RQ_SRS_006_RBAC_Grant_Role_CurrentUser(\"1.0\")],\n ):\n with setup(1, 1):\n with When(\"I grant role to default user\"):\n exitcode, message = errors.cannot_update_default()\n node.query(\n \"GRANT role0 TO CURRENT_USER\", exitcode=exitcode, message=message\n )\n\n with Scenario(\n \"I grant role to user with admin option\",\n requirements=[RQ_SRS_006_RBAC_Grant_Role_AdminOption(\"1.0\")],\n ):\n with setup(1, 1):\n with When(\"I grant role to a user with admin option\"):\n node.query(\"GRANT role0 TO user0 WITH ADMIN OPTION\")\n\n with Scenario(\n \"I grant role to user on cluster\",\n requirements=[RQ_SRS_006_RBAC_Grant_Role_OnCluster(\"1.0\")],\n ):\n try:\n with Given(\"I have a user and a role on a cluster\"):\n node.query(\"CREATE USER OR REPLACE user0 ON CLUSTER sharded_cluster\")\n node.query(\"CREATE ROLE OR REPLACE role0 ON CLUSTER sharded_cluster\")\n with When(\"I grant the role to the user\"):\n node.query(\"GRANT ON CLUSTER sharded_cluster role0 TO user0\")\n finally:\n with Finally(\"I drop the user and role\"):\n node.query(\"DROP USER IF EXISTS user0 ON CLUSTER sharded_cluster\")\n node.query(\"DROP ROLE IF EXISTS role0 ON CLUSTER sharded_cluster\")\n\n with Scenario(\n \"I grant role to user on fake cluster, throws exception\",\n requirements=[RQ_SRS_006_RBAC_Grant_Role_OnCluster(\"1.0\")],\n ):\n with setup(1, 1):\n with When(\"I grant the role to the user\"):\n exitcode, message = errors.cluster_not_found(\"fake_cluster\")\n node.query(\n \"GRANT ON CLUSTER fake_cluster role0 TO user0\",\n exitcode=exitcode,\n message=message,\n )","def workbench_scenarios():\n return [\n (\"FeedbackXBlock\",\n \"\"\"<vertical_demo>\n <feedback/>\n </vertical_demo>\n \"\"\"),\n ]","def get_first_sample() -> Sample:\n print(get_intro_message())\n\n user_satisfied = False\n while not user_satisfied:\n sample = get_single_sample()\n\n print(\"Sample recorded : \\\"\" + sample.string + \"\\\"\")\n user_satisfied = get_binary_validation(\n \"Do you want to keep this sample ?\", True\n )\n\n sample.impostor = False\n\n return sample","def test_dataset_scenario_generation_full_late(self):\n \n params = ParameterServer()\n\n map_filename = os.path.join(os.path.dirname(__file__), \"data/DR_DEU_Merging_MT_v01_shifted.xodr\")\n track_filename = os.path.join(os.path.dirname(__file__), \"data/interaction_dataset_DEU_Merging_dummy_track_late.csv\")\n\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"MapFilename\"] = map_filename\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"TrackFilenameList\"] = [track_filename]\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"StartingOffsetMs\"] = 0\n\n scenario_generation = InteractionDatasetScenarioGenerationFull(\n params=params, num_scenarios=1)\n\n scenario = scenario_generation.get_scenario(0)\n world_state = scenario.GetWorldState()\n agent1 = world_state.GetAgent(1)\n agent2 = world_state.GetAgent(2)\n\n self.assertAlmostEqual(agent1.first_valid_timestamp, 0.0)\n self.assertAlmostEqual(agent2.first_valid_timestamp, 0.3)\n \n self.assertEqual(isinstance(agent1, Agent), True)\n self.assertEqual(agent1.IsValidAtTime(world_state.time), True)\n \n self.assertEqual(isinstance(agent2, Agent), True)\n self.assertEqual(agent2.IsValidAtTime(world_state.time), False)","def main():\n user = intro()\n paths = [scott_adventure, team_1_adv, team_2_adv, team_3_adv, team_4_adv, team_5_adv, team_6_adv, team_7_adv, team_8_adv, team_9_adv, team_10_adv, team_11_adv, team_12_adv, team_13_adv, team_14_adv, team_15_adv, team_16_adv]\n random.shuffle(paths) # Shuffles the order of paths, so each adventure is different\n for i in range(len(paths)):\n paths[i]() # Runs each function in the paths list\n end_story(user)","def test_case_3(self):\n config_generator = Genesis()\n\n data = open_json_file('r1.json')\n\n task_1 = Task.new(data=data, name=\"R1_BASE_CONFIG\")\n task_2 = Task.new(data=data, name=\"R1_BASE_CONFIG_2\")\n\n config_generator.add_task(task_1)\n config_generator.add_task(task_2)\n\n results = config_generator.generate()\n self.assertIsInstance(results, dict)\n\n for task in config_generator.tasks:\n self.assertTrue(task.is_complete)\n self.assertIsInstance(task.rendered_data, str)","def scenario3(sim):\n\n init_logging(logfile=None, debug=True)\n second = 1000.0\n duration = 10\n tau_m = 20 # ms\n cm = 1.0 # nF\n v_reset = -60\n cell_parameters = dict(\n tau_m=tau_m,\n cm=cm,\n v_rest=-70,\n e_rev_E=0,\n e_rev_I=-70,\n v_thresh=-54,\n v_reset=v_reset,\n tau_syn_E=5,\n tau_syn_I=5,\n )\n g_leak = cm / tau_m # µS\n\n w_min = 0.0 * g_leak\n w_max = 0.05 * g_leak\n\n r1 = 5.0\n r2 = 40.0\n\n sim.setup()\n pre = sim.Population(100, sim.SpikeSourcePoisson())\n post = sim.Population(10, sim.IF_cond_exp())\n\n pre.set(duration=duration * second)\n pre.set(start=0.0)\n pre[:50].set(rate=r1)\n pre[50:].set(rate=r2)\n assert_equal(pre[49].rate, r1)\n assert_equal(pre[50].rate, r2)\n post.set(**cell_parameters)\n post.initialize(v=RandomDistribution('normal', mu=v_reset, sigma=5.0))\n\n stdp = sim.STDPMechanism(\n sim.SpikePairRule(tau_plus=20.0, tau_minus=20.0,\n A_plus=0.01, A_minus=0.01),\n sim.AdditiveWeightDependence(w_min=w_min, w_max=w_max),\n #dendritic_delay_fraction=0.5))\n dendritic_delay_fraction=1)\n\n connections = sim.Projection(pre, post, sim.AllToAllConnector(),\n synapse_type=stdp,\n receptor_type='excitatory')\n\n initial_weight_distr = RandomDistribution('uniform', low=w_min, high=w_max)\n connections.randomizeWeights(initial_weight_distr)\n initial_weights = connections.get('weight', format='array', gather=False)\n # assert initial_weights.min() >= w_min\n # assert initial_weights.max() < w_max\n # assert initial_weights[0, 0] != initial_weights[1, 0]\n\n pre.record('spikes')\n post.record('spikes')\n post[0:1].record('v')\n\n sim.run(duration * second)\n\n actual_rate = pre.mean_spike_count() / duration\n expected_rate = (r1 + r2) / 2\n errmsg = \"actual rate: %g expected rate: %g\" % (actual_rate, expected_rate)\n assert abs(actual_rate - expected_rate) < 1, errmsg\n #assert abs(pre[:50].mean_spike_count()/duration - r1) < 1\n #assert abs(pre[50:].mean_spike_count()/duration- r2) < 1\n final_weights = connections.get('weight', format='array', gather=False)\n assert initial_weights[0, 0] != final_weights[0, 0]\n\n try:\n import scipy.stats\n except ImportError:\n raise SkipTest\n t, p = scipy.stats.ttest_ind(initial_weights[:50, :].flat, initial_weights[50:, :].flat)\n assert p > 0.05, p\n t, p = scipy.stats.ttest_ind(final_weights[:50, :].flat, final_weights[50:, :].flat)\n assert p < 0.01, p\n assert final_weights[:50, :].mean() < final_weights[50:, :].mean()\n sim.end()\n return initial_weights, final_weights, pre, post, connections","def test_generate_sample_sheet(self):\n pass","def main():\n\n user = start_story()\n paths = [scott_adventure, team_1_adv, team_2_adv,\n team_3_adv, team_4_adv, team_5_adv,\n team_6_adv, team_7_adv, team_8_adv,\n team_9_adv, team_10_adv, team_11_adv,\n team_12_adv, team_13_adv, team_14_adv,\n team_15_adv, team_16_adv, team_17_adv,\n team_18_adv, team_19_adv, team_20_adv]\n random.shuffle(paths) # Shuffles the order of paths, so each adventure is different\n\n for i in range(len(paths)):\n paths[i]() # Runs each function in the paths list\n\n end_story(user)","def process_result_multi_scen(path_to_scenarios, path_shapefile_input):\n # Get all folders with scenario run results (name of folder is scenario)\n scenarios = os.listdir(path_to_scenarios)\n\n for scenario in scenarios:\n single_scenario.main(\n os.path.join(path_to_scenarios, scenario),\n path_shapefile_input)\n\n return","def workbench_scenarios(): \n return [\n (\"TermsXBlock\",\n \"\"\"<terms/>\n \"\"\"),\n (\"Multiple TermsXBlock\",\n \"\"\"<vertical_demo>\n <terms/>\n <terms/>\n <terms/>\n </vertical_demo>\n \"\"\"),\n ]","def test_before(self):\n\n support.create_project(self, 'candice')\n support.add_step(self)\n support.add_step(self, position='0')\n\n project = cauldron.project.get_internal_project()\n steps = project.steps\n\n self.assertTrue(steps[0].filename.startswith('S01'))\n self.assertTrue(steps[1].filename.startswith('S02'))","def test_modes(self):\n step = self.run_step('S01-first.py')\n self.assertTrue(step.success)\n self.assertTrue(step.local.is_testing)\n self.assertFalse(step.local.is_interactive)\n self.assertFalse(step.local.is_single_run)","def test_create_run(self):\n pass","def generate_submissons_all_steps():\n\n\n data_en = read_json_file(\"Test_Data/test-en.json\")\n data_pr = read_json_file(\"Test_Data/test-pr.json\")\n data_es = read_json_file(\"Test_Data/test-es.json\")\n res_en = generate_embeddings_sentence_test_data(data_en, \"Test_Data/embd-en.pkl\")\n res_es = generate_embeddings_sentence_test_data(data_es, \"Test_Data/embd-es.pkl\")\n res_pr = generate_embeddings_sentence_test_data(data_pr, \"Test_Data/embd-pr.pkl\")\n model = load_model(\"model_doc\")\n make_submission(res_es, model, \"submission-es\")\n make_submission(res_pr, model, \"submission-pr\")\n make_submission(res_en, model, \"submission-en\")\n exit()","def worker_scenario(scenario_names, feature_files, output):\n cmd = [\"behave\"]\n if scenario_names is not None:\n for i in range(len(scenario_names)):\n cmd = cmd + [\"-n\"] + [scenario_names[i]]\n elif feature_files is not None:\n for i in range(len(feature_files)):\n cmd = cmd + [feature_files[i]]\n else:\n print(\"Running whole testsuite\\n\")\n if output is not None:\n cmd = cmd + [\"-o\"] + [output]\n sp.call(cmd)\n print(\"\\n\\n\\n\")","def test_dataset_scenario_generation_full_outside1(self):\n params = ParameterServer()\n\n map_filename = os.path.join(os.path.dirname(__file__), \"data/DR_DEU_Merging_MT_v01_shifted.xodr\")\n track_filename = os.path.join(os.path.dirname(__file__), \"data/interaction_dataset_DEU_Merging_dummy_track_outside.csv\")\n\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"MapFilename\"] = map_filename\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"TrackFilenameList\"] = [track_filename]\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"StartingOffsetMs\"] = 0\n\n scenario_generation = InteractionDatasetScenarioGenerationFull(\n params=params, num_scenarios=1)\n\n scenario = scenario_generation.get_scenario(0)\n self.assertAlmostEqual(scenario.eval_agent_ids, [1])\n\n world_state = scenario.GetWorldState()\n agent11 = world_state.GetAgent(1)\n agent12 = world_state.GetAgent(2)\n agent13 = world_state.GetAgent(3)\n\n self.assertAlmostEqual(agent11.first_valid_timestamp, 0.0)\n self.assertAlmostEqual(agent12.first_valid_timestamp, 0.0)\n self.assertNotEqual(agent13.first_valid_timestamp, 0.0)\n \n # agent13 should not be valid at the beginning, as he is outside of map\n world_state.time = 0\n self.assertEqual(isinstance(agent11, Agent), True)\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent11.InsideRoadCorridor(), True)\n \n self.assertEqual(isinstance(agent12, Agent), True)\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent12.InsideRoadCorridor(), True)\n\n self.assertEqual(isinstance(agent13, Agent), True)\n self.assertEqual(agent13.IsValidAtTime(world_state.time), False)\n # as we use only state once it's in map, this will be true, although the time step is not valid yet\n self.assertEqual(agent13.InsideRoadCorridor(), True)\n\n # agent13 should not be valid at the beginning, as he is outside of map\n world_state.Step(0.05)\n\n self.assertEqual(isinstance(agent11, Agent), True)\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent11.InsideRoadCorridor(), True)\n \n self.assertEqual(isinstance(agent12, Agent), True)\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent12.InsideRoadCorridor(), True)\n\n self.assertEqual(isinstance(agent13, Agent), True)\n self.assertEqual(agent13.IsValidAtTime(world_state.time), False)\n # as we use only state once it's in map, this will be true, although the time step is not valid yet\n self.assertEqual(agent13.InsideRoadCorridor(), True)\n\n self.assertEqual(list(world_state.agents_valid.keys()), [1,2])\n\n # agent13 should be valid at some point\n world_state.Step(agent13.first_valid_timestamp)\n world_state.Step(0.01) # agent13.IsValidAtTime() uses previous time stamp, therefore we increment it one more step\n\n self.assertEqual(isinstance(agent11, Agent), True)\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent11.InsideRoadCorridor(), True)\n \n self.assertEqual(isinstance(agent12, Agent), True)\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent12.InsideRoadCorridor(), True)\n\n self.assertEqual(isinstance(agent13, Agent), True)\n self.assertEqual(agent13.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent13.InsideRoadCorridor(), True)\n\n self.assertEqual(list(world_state.agents_valid.keys()), [1,2,3])","def _pileup_scenario(self):\n workflow = self.workflow\n campaign = self.campaign\n pileupDataset = self._pileup_dataset()\n if pileupDataset != 'None':\n [subscribedOurSite, subscribedOtherSite] = checkAcceptedSubscriptionRequest(self.url, pileupDataset, siteSE)\n if not subscribedOurSite:\n print('ERROR: pileup dataset not subscribed/approved to required Disk endpoint')\n sys.exit(0) \n \n # Determine pileup scenario\n # - Fall11_R2 & Fall11_R4 don't add pileup so extract pileup scenario from input\n pileupScenario = ''\n pileupScenario = getPileupScenario(self.winfo, self.config)\n if campaign == 'Summer12_DR53X_RD':\n pileupScenario = 'PU_RD1'\n if pileupScenario == 'Unknown' and 'MinBias' in pileupDataset and 'LowPU2010DR42' not in workflow:\n print('ERROR: unable to determine pileup scenario')\n sys.exit(0)\n elif 'Fall11_R2' in workflow or 'Fall11_R4' in workflow or 'Fall11R2' in workflow or 'Fall11R4' in workflow:\n matchObj = re.match(r\".*Fall11-(.*)_START.*\", inputDataset)\n if matchObj:\n pileupScenario = matchObj.group(1)\n else:\n pileupScenario == 'Unknown'\n elif pileupScenario == 'Unknown' and 'MinBias' not in pileupDataset:\n pileupScenario = 'NoPileUp'\n\n if pileupScenario == 'Unknown':\n pileupScenario = ''\n\n if 'LowPU2010_DR42' in workflow or 'LowPU2010DR42' in workflow:\n pileupScenario = 'PU_S0'\n if 'HiWinter13' in workflow and 'DR53X' in workflow:\n pileupScenario = '' \n if 'pAWinter13' in workflow and 'DR53X' in workflow:\n pileupScenario = 'pa' # not actually the pileup scenario of course\n if 'ppWinter13' in workflow and 'DR53X' in workflow:\n pileupScenario = 'pp' # not actually the pileup scenario of course\n return pileupScenario","def workbench_scenarios():\n return [\n (\"MyXBlock\",\n \"\"\"<myxblock/>\n \"\"\"),\n (\"Multiple MyXBlock\",\n \"\"\"<vertical_demo>\n <myxblock/>\n <myxblock/>\n <myxblock/>\n </vertical_demo>\n \"\"\"),\n ]","def CASE1( self, main ):\n import re\n import time\n import copy\n\n main.topoIndex = \"topo\" + str( main.params[ 'TEST' ][ 'topo' ] )\n\n main.log.report( \"Load Mininet topology and Balance all Mininet switches across controllers\" )\n main.log.report( \"________________________________________________________________________\" )\n main.case( \"Assign and Balance all Mininet switches across controllers\" )\n\n main.step( \"Start Mininet topology\" )\n newTopo = main.params[ 'TOPO' ][ main.topoIndex ][ 'fileName' ]\n mininetDir = main.Mininet1.home + \"/custom/\"\n topoPath = main.testDir + \"/dependencies/topologies/\" + newTopo\n main.ONOSbench.secureCopy( main.Mininet1.user_name, main.Mininet1.ip_address, topoPath, mininetDir, direction=\"to\" )\n topoPath = mininetDir + newTopo\n startStatus = main.Mininet1.startNet( topoFile=topoPath )\n main.mininetSwitches = main.Mininet1.getSwitches()\n main.mininetHosts = main.Mininet1.getHosts()\n main.mininetLinks = main.Mininet1.getLinks( timeout=60 )\n utilities.assert_equals( expect=main.TRUE,\n actual=startStatus,\n onpass=\"Start Mininet topology test PASS\",\n onfail=\"Start Mininet topology test FAIL\" )\n\n main.step( \"Assign switches to controllers\" )\n switchMastership = main.TRUE\n for switchName in main.mininetSwitches.keys():\n ips = main.Cluster.getIps()\n main.Mininet1.assignSwController( sw=switchName, ip=ips )\n response = main.Mininet1.getSwController( switchName )\n print( \"Response is \" + str( response ) )\n if re.search( \"tcp:\" + main.Cluster.active( 0 ).ipAddress, response ):\n switchMastership = switchMastership and main.TRUE\n else:\n switchMastership = main.FALSE\n utilities.assert_equals( expect=main.TRUE,\n actual=switchMastership,\n onpass=\"Assign switches to controllers test PASS\",\n onfail=\"Assign switches to controllers test FAIL\" )\n # Waiting here to make sure topology converges across all nodes\n sleep = int( main.params[ 'TEST' ][ 'loadTopoSleep' ] )\n time.sleep( sleep )\n\n main.step( \"Balance devices across controllers\" )\n balanceResult = main.Cluster.active( 0 ).CLI.balanceMasters()\n # giving some breathing time for ONOS to complete re-balance\n time.sleep( sleep )\n\n caseResult = ( startStatus and switchMastership and balanceResult )\n utilities.assert_equals( expect=main.TRUE,\n actual=caseResult,\n onpass=\"Starting new Att topology test PASS\",\n onfail=\"Starting new Att topology test FAIL\" )","def sample(self):","def test_functionality(self):\n self.templateName = \"Test Template\"\n self.browserObject = globalVars.browserObject\n\n self.preRunSetup()\n \n self.runTestCase()\n \n self.postRunCleanup()","def fixture_other_case() -> str:\n return \"angrybird\"","def step(self): \n self.reset_parameters()\n\n if np.random.uniform(0, 1) < self.model.churn_prob: self.exit_triggered = True \n if self.exit_triggered:\n self.exit()\n else:\n self.register_deposit(self.deposit_intent)\n self.register_contribution(self.contribution_intent)\n self.register_sponsorship(self.sponsor_intent)\n self.register_euro_exchange(self.euro_exchange_intent)\n self.register_teo_exchange(self.teo_exchange_intent)\n self.register_withdraw(self.withdraw_intent)","def source_test_file_name():\n return 'feature'","def task_generate_sc_figure1():\n for dept in Department.list():\n yield {\n 'name': dept.name,\n 'file_dep': [\n dept.guessed_city_path,\n dept.police_precincts_path,\n ],\n 'task_dep': ['download_place_boundaries'],\n 'targets': [dept.sc_figure1_path],\n 'actions': [dept.generate_sc_figure1],\n 'clean': True,\n }","def test_get_goal(self):\n pass","def enumeration_separation(self, stage1_vars, sample_name, limit=1,\n enumerate_all=True):\n sample = self.tsdro.samples[sample_name]\n scenarios = self.tsdro.scenarios\n lr_instance = self.tsdro.lr_instance\n curr_remaining = self.tsdro.remaining_scenario_names[sample_name]\n curr_remaining_copy = curr_remaining.copy()\n\n found_hyp = False\n count = 0\n max_violation_scenario_name = None\n max_violation = -np.inf\n for scenario_name in curr_remaining:\n scenario = scenarios[scenario_name]\n if scenario_name not in self.stage2_objvals:\n self.stage2_objvals[scenario_name] = self.get_stage2_costs(\n stage1_vars, [scenario_name])\n\n if scenario_name == sample_name or self.method == \"RO\":\n scenario_distance = 0\n rhs_val = self.stage2_objvals[scenario_name]\n else:\n scenario_distance = sc.get_scenario_distance(scenario, sample,\n lr_instance)\n rhs_val = (self.stage2_objvals[scenario_name]\n - self.wass_mult.X * scenario_distance)\n if self.epi_vars[sample_name].X < rhs_val + 1e-7:\n found_hyp = True\n if not enumerate_all:\n count += 1\n if scenario_name not in self.master_scenarios:\n tmp = lr_instance.add_stage2(self.master, self.stage1_vars,\n scenario, scenario_name)\n self.stage2_vars[scenario_name] = tmp[0]\n self.master_scenarios.append(scenario_name)\n objexpr_stage2 = lr_instance.get_objective_stage2(\n self.stage2_vars[scenario_name], scenario)\n rhs = objexpr_stage2 - self.wass_mult * scenario_distance\n self.master.addLConstr(self.epi_vars[sample_name], \">\", rhs)\n curr_remaining_copy.remove(scenario_name)\n else:\n if rhs_val - self.epi_vars[sample_name].X > max_violation:\n max_violation = rhs_val - self.epi_vars[sample_name].X\n max_violation_scenario_name = scenario_name\n if count == limit:\n break\n if enumerate_all and found_hyp:\n scenario = scenarios[max_violation_scenario_name]\n if scenario_name == sample_name or self.method == \"RO\":\n scenario_distance = 0\n else:\n scenario_distance = sc.get_scenario_distance(scenario, sample,\n lr_instance)\n if scenario_name not in self.master_scenarios:\n tmp = lr_instance.add_stage2(self.master, self.stage1_vars,\n scenario,\n max_violation_scenario_name)\n self.stage2_vars[max_violation_scenario_name] = tmp[0]\n self.master_scenarios.append(max_violation_scenario_name)\n objexpr_stage2 = lr_instance.get_objective_stage2(\n self.stage2_vars[max_violation_scenario_name], scenario)\n rhs = objexpr_stage2 - self.wass_mult * scenario_distance\n self.master.addLConstr(self.epi_vars[sample_name], \">\", rhs)\n curr_remaining_copy.remove(max_violation_scenario_name)\n self.tsdro.remaining_scenario_names[sample_name] = curr_remaining_copy\n\n if count > 0:\n print(sample_name, \"added\", count, \"scenarios.\")\n return found_hyp","def test_dataset_scenario_generation_full_outside3(self):\n params = ParameterServer()\n\n map_filename = os.path.join(os.path.dirname(__file__), \"data/DR_DEU_Merging_MT_v01_shifted.xodr\")\n track_filename = os.path.join(os.path.dirname(__file__), \"data/interaction_dataset_DEU_Merging_dummy_track_outside.csv\")\n\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"MapFilename\"] = map_filename\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"TrackFilenameList\"] = [track_filename]\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"StartingOffsetMs\"] = 0\n\n scenario_generation = InteractionDatasetScenarioGenerationFull(\n params=params, num_scenarios=3)\n\n scenario = scenario_generation.get_scenario(2)\n self.assertAlmostEqual(scenario.eval_agent_ids, [3])\n world_state = scenario.GetWorldState()\n agent31 = world_state.GetAgent(1)\n agent32 = world_state.GetAgent(2)\n agent33 = world_state.GetAgent(3)\n\n # they all should be valid at the beginning\n world_state.time = 0\n self.assertEqual(isinstance(agent31, Agent), True)\n self.assertEqual(agent31.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent31.InsideRoadCorridor(), True)\n \n self.assertEqual(isinstance(agent32, Agent), True)\n self.assertEqual(agent32.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent32.InsideRoadCorridor(), True)\n\n self.assertEqual(isinstance(agent33, Agent), True)\n self.assertEqual(agent33.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent33.InsideRoadCorridor(), True)\n\n world_state.Step(0.05)\n self.assertEqual(len(world_state.agents_valid), 3)","def test_stepregistry_should_create_one_step_decorator_per_keyword():\n # given\n registry = StepRegistry()\n context = {}\n\n # when\n registry.create_step_decorators(context)\n\n # then\n assert len(context) == 4\n assert \"given\" in context\n assert \"when\" in context\n assert \"then\" in context\n assert \"step\" in context","def test_creating_simple_feature():\n # given & when\n feature = Feature(1, \"Feature\", \"I am a feature\", \"foo.feature\", 1, tags=None)\n\n # then\n assert feature.id == 1\n assert feature.keyword == \"Feature\"\n assert feature.sentence == \"I am a feature\"\n assert feature.path == \"foo.feature\"\n assert feature.line == 1\n assert feature.tags == []","def main():\n num_of_tests = int(input())\n\n # iterate over test cases\n for test_case in range(1, num_of_tests + 1):\n result = handle_case()\n printable_result = handle_result(result)\n print(\"Case #{}: {}\".format(test_case, printable_result))","def workbench_scenarios():\n return [\n (\"Markdown\",\n \"\"\"<markdowna />\n \"\"\")\n ]","def create_sample(i):\n return Sample(**{\n 'name': f'Sample{i}',\n 'metadata': {'foobar': f'baz{i}'},\n KrakenResultModule.name(): create_kraken(),\n KrakenHLLResultModule.name(): create_krakenhll(),\n Metaphlan2ResultModule.name(): create_metaphlan2(),\n }).save()","def test_strategy(self):\n self.first_play_test(C)","def scenario(self, scenario):\n self.report_scenario_completed()\n self.current_scenario = scenario","def test_dataset_scenario_generation_full_outside1_behavior_overwritten(self):\n params = ParameterServer()\n\n map_filename = os.path.join(os.path.dirname(__file__), \"data/DR_DEU_Merging_MT_v01_shifted.xodr\")\n track_filename = os.path.join(os.path.dirname(__file__), \"data/interaction_dataset_DEU_Merging_dummy_track_outside.csv\")\n\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"MapFilename\"] = map_filename\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"TrackFilenameList\"] = [track_filename]\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"StartingOffsetMs\"] = 0\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"BehaviorModel\"] = \"BehaviorMobilRuleBased\"\n\n scenario_generation = InteractionDatasetScenarioGenerationFull(\n params=params, num_scenarios=1)\n\n scenario = scenario_generation.get_scenario(0)\n self.assertAlmostEqual(scenario.eval_agent_ids, [1])\n\n world_state = scenario.GetWorldState()\n agent11 = world_state.GetAgent(1)\n agent12 = world_state.GetAgent(2)\n agent13 = world_state.GetAgent(3)\n\n # others are defined as BehaviorMobilRuleBased\n self.assertTrue(isinstance(agent11.behavior_model, BehaviorStaticTrajectory))\n self.assertTrue(isinstance(agent12.behavior_model, BehaviorMobilRuleBased))\n self.assertTrue(isinstance(agent13.behavior_model, BehaviorMobilRuleBased))\n\n self.assertAlmostEqual(agent11.first_valid_timestamp, 0.0)\n self.assertAlmostEqual(agent12.first_valid_timestamp, 0.0)\n self.assertNotEqual(agent13.first_valid_timestamp, 0.0)\n \n # agent13 should not be valid at the beginning, as he is outside of map\n world_state.time = 0\n self.assertEqual(isinstance(agent11, Agent), True)\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent11.InsideRoadCorridor(), True)\n \n self.assertEqual(isinstance(agent12, Agent), True)\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent12.InsideRoadCorridor(), True)\n\n self.assertEqual(isinstance(agent13, Agent), True)\n self.assertEqual(agent13.IsValidAtTime(world_state.time), False)\n # as we use only state once it's in map, this will be true, although the time step is not valid yet\n self.assertEqual(agent13.InsideRoadCorridor(), True)\n\n # agent13 should not be valid at the beginning, as he is outside of map\n world_state.Step(0.05)\n\n self.assertEqual(isinstance(agent11, Agent), True)\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent11.InsideRoadCorridor(), True)\n \n self.assertEqual(isinstance(agent12, Agent), True)\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent12.InsideRoadCorridor(), True)\n\n self.assertEqual(isinstance(agent13, Agent), True)\n self.assertEqual(agent13.IsValidAtTime(world_state.time), False)\n # as we use only state once it's in map, this will be true, although the time step is not valid yet\n self.assertEqual(agent13.InsideRoadCorridor(), True)\n\n self.assertEqual(list(world_state.agents_valid.keys()), [1,2])\n\n # agent13 should be valid at some point\n world_state.Step(agent13.first_valid_timestamp)\n world_state.Step(0.01) # agent13.IsValidAtTime() uses previous time stamp, therefore we increment it one more step\n\n self.assertEqual(isinstance(agent11, Agent), True)\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent11.InsideRoadCorridor(), True)\n \n self.assertEqual(isinstance(agent12, Agent), True)\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent12.InsideRoadCorridor(), True)\n\n self.assertEqual(isinstance(agent13, Agent), True)\n self.assertEqual(agent13.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent13.InsideRoadCorridor(), True)\n\n self.assertEqual(list(world_state.agents_valid.keys()), [1,2,3])","def scenario(self):\n scenario = []\n for action in self.application_tree['scenario']:\n scenario.append(action)\n return scenario","def __init__(self, scenario):\n client.__init__(self, scenario)\n # TODO: Your initialization, if any (not likely). Oh, and remove the next line.\n raise Exception( \"DO NOT instantiate the skeleton implementation\" )","def fixture_microbial_sample_id():\n return \"microbial_sample_test\"","def test_feature_all_scenarios(mocker):\n # given\n feature = Feature(1, \"Feature\", \"I am a feature\", \"foo.feature\", 1, tags=None)\n # add regular Scenarios to Feature\n feature.scenarios.extend([mocker.MagicMock(id=1), mocker.MagicMock(id=2)])\n # add Scenario Outline to Feature\n feature.scenarios.append(\n mocker.MagicMock(\n spec=ScenarioOutline,\n id=3,\n scenarios=[mocker.MagicMock(id=4), mocker.MagicMock(id=5)],\n )\n )\n # add Scenario Loop to Feature\n feature.scenarios.append(\n mocker.MagicMock(\n spec=ScenarioLoop,\n id=6,\n scenarios=[mocker.MagicMock(id=7), mocker.MagicMock(id=8)],\n )\n )\n\n # when\n all_scenarios = feature.all_scenarios\n\n # then\n assert len(all_scenarios) == 8\n assert all_scenarios[0].id == 1\n assert all_scenarios[1].id == 2\n assert all_scenarios[2].id == 3\n assert all_scenarios[3].id == 4\n assert all_scenarios[4].id == 5\n assert all_scenarios[5].id == 6\n assert all_scenarios[6].id == 7\n assert all_scenarios[7].id == 8","def startOfTestcase(self):\n pass # nothing to do here. Hence pass statement is called."],"string":"[\n \"def test_create_scenario1(self):\\n pass\",\n \"def test_create_scenario(self):\\n pass\",\n \"def TestOneStep(self):\\n pass\",\n \"def test_get_scenario(self):\\n pass\",\n \"def test_2nd_scenario():\\n start_entered_scenario(config.MERAKI_CAMERAS[0][\\\"serial\\\"])\\n return \\\"ok\\\"\",\n \"def test_get_scenarios(self):\\n pass\",\n \"def test_pytest_bdd_scenario(self):\\n self.testdir.makefile(\\n \\\".feature\\\",\\n simple=_SIMPLE_SCENARIO,\\n )\\n py_file = self.testdir.makepyfile(\\n \\\"\\\"\\\"\\n from pytest_bdd import scenario, given, then, when\\n\\n @scenario(\\\"simple.feature\\\", \\\"Simple scenario\\\")\\n def test_simple():\\n pass\\n\\n BAR = None\\n\\n @given(\\\"I have a bar\\\")\\n def bar():\\n global BAR\\n BAR = 1\\n\\n @when(\\\"I eat it\\\")\\n def eat():\\n global BAR\\n BAR -= 1\\n\\n @then(\\\"I don't have a bar\\\")\\n def check():\\n assert BAR == 0\\n \\\"\\\"\\\"\\n )\\n file_name = os.path.basename(py_file.strpath)\\n self.inline_run(\\\"--ddtrace\\\", file_name)\\n spans = self.pop_spans()\\n\\n assert len(spans) == 7\\n assert spans[0].get_tag(\\\"component\\\") == \\\"pytest\\\"\\n assert spans[0].get_tag(\\\"test.name\\\") == \\\"Simple scenario\\\"\\n assert spans[0].span_type == \\\"test\\\"\\n assert spans[1].resource == \\\"I have a bar\\\"\\n assert spans[1].name == \\\"given\\\"\\n assert spans[2].resource == \\\"I eat it\\\"\\n assert spans[2].name == \\\"when\\\"\\n assert spans[3].resource == \\\"I don't have a bar\\\"\\n assert spans[3].name == \\\"then\\\"\",\n \"def create_scenarios(self, params, num_scenarios, random_seed):\\n return None\",\n \"def test_pytest_bdd_with_missing_step_implementation(self):\\n self.testdir.makefile(\\n \\\".feature\\\",\\n simple=_SIMPLE_SCENARIO,\\n )\\n py_file = self.testdir.makepyfile(\\n \\\"\\\"\\\"\\n from pytest_bdd import scenario, given, then, when\\n\\n @scenario(\\\"simple.feature\\\", \\\"Simple scenario\\\")\\n def test_simple():\\n pass\\n \\\"\\\"\\\"\\n )\\n file_name = os.path.basename(py_file.strpath)\\n self.inline_run(\\\"--ddtrace\\\", file_name)\\n spans = self.pop_spans()\\n\\n assert len(spans) == 4\\n assert spans[0].get_tag(ERROR_MSG)\",\n \"def test_generate_missing(pytester):\\n pytester.makefile(\\n \\\".feature\\\",\\n generation=textwrap.dedent(\\n \\\"\\\"\\\"\\\\\\n Feature: Missing code generation\\n\\n Background:\\n Given I have a foobar\\n\\n Scenario: Scenario tests which are already bound to the tests stay as is\\n Given I have a bar\\n\\n\\n Scenario: Code is generated for scenarios which are not bound to any tests\\n Given I have a bar\\n\\n\\n Scenario: Code is generated for scenario steps which are not yet defined(implemented)\\n Given I have a custom bar\\n \\\"\\\"\\\"\\n ),\\n )\\n\\n pytester.makepyfile(\\n textwrap.dedent(\\n \\\"\\\"\\\"\\\\\\n import functools\\n\\n from pytest_bdd import scenario, given\\n\\n scenario = functools.partial(scenario, \\\"generation.feature\\\")\\n\\n @given(\\\"I have a bar\\\")\\n def _():\\n return \\\"bar\\\"\\n\\n @scenario(\\\"Scenario tests which are already bound to the tests stay as is\\\")\\n def test_foo():\\n pass\\n\\n @scenario(\\\"Code is generated for scenario steps which are not yet defined(implemented)\\\")\\n def test_missing_steps():\\n pass\\n \\\"\\\"\\\"\\n )\\n )\\n\\n result = pytester.runpytest(\\\"--generate-missing\\\", \\\"--feature\\\", \\\"generation.feature\\\")\\n result.assert_outcomes(passed=0, failed=0, errors=0)\\n assert not result.stderr.str()\\n assert result.ret == 0\\n\\n result.stdout.fnmatch_lines(\\n ['Scenario \\\"Code is generated for scenarios which are not bound to any tests\\\" is not bound to any test *']\\n )\\n\\n result.stdout.fnmatch_lines(\\n [\\n 'Step Given \\\"I have a custom bar\\\" is not defined in the scenario '\\n '\\\"Code is generated for scenario steps which are not yet defined(implemented)\\\" *'\\n ]\\n )\\n\\n result.stdout.fnmatch_lines(\\n ['Step Given \\\"I have a foobar\\\" is not defined in the background of the feature \\\"Missing code generation\\\" *']\\n )\\n\\n result.stdout.fnmatch_lines([\\\"Please place the code above to the test file(s):\\\"])\",\n \"def test_dataset_scenario_generation_full(self):\\n params = ParameterServer()\\n\\n map_filename = os.path.join(os.path.dirname(__file__), \\\"data/DR_DEU_Merging_MT_v01_shifted.xodr\\\")\\n track_filename = os.path.join(os.path.dirname(__file__), \\\"data/interaction_dataset_DEU_Merging_dummy_track.csv\\\")\\n\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"MapFilename\\\"] = map_filename\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"TrackFilenameList\\\"] = [track_filename]\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"StartingOffsetMs\\\"] = 0\\n\\n scenario_generation = InteractionDatasetScenarioGenerationFull(\\n params=params, num_scenarios=2)\\n\\n self.assertEqual(scenario_generation.get_num_scenarios(), 2)\\n\\n # first scenario\\n scenario0 = scenario_generation.get_scenario(0)\\n agent11 = scenario0.GetWorldState().agents[1]\\n agent12 = scenario0.GetWorldState().agents[2]\\n self.assertEqual(agent11.first_valid_timestamp, 0.0)\\n self.assertEqual(agent12.first_valid_timestamp, 0.0)\\n # agents are initialized with Behavior::NotValidYet\\n self.assertEqual(list(scenario0.GetWorldState().agents_valid.keys()), [])\\n scenario0.GetWorldState().Step(0.01)\\n self.assertEqual(list(scenario0.GetWorldState().agents_valid.keys()), [1,2])\\n\\n # second scenario\\n scenario1 = scenario_generation.get_scenario(1)\\n agent21 = scenario1.GetWorldState().agents[1]\\n agent22 = scenario1.GetWorldState().agents[2]\\n self.assertEqual(agent21.first_valid_timestamp, 0.0)\\n self.assertEqual(agent22.first_valid_timestamp, 0.0)\\n self.assertEqual(list(scenario1.GetWorldState().agents_valid.keys()), [])\\n scenario1.GetWorldState().Step(0.01)\\n self.assertEqual(list(scenario1.GetWorldState().agents_valid.keys()), [1,2])\",\n \"def test_generate(monkeypatch, capsys):\\n monkeypatch.setattr(sys, \\\"argv\\\", [\\\"\\\", \\\"generate\\\", os.path.join(PATH, \\\"generate.feature\\\")])\\n main()\\n out, err = capsys.readouterr()\\n assert out == textwrap.dedent(\\n '''\\n # coding=utf-8\\n \\\"\\\"\\\"Code generation feature tests.\\\"\\\"\\\"\\n\\n from pytest_bdd import (\\n given,\\n scenario,\\n then,\\n when,\\n )\\n\\n\\n @scenario('scripts/generate.feature', 'Given and when using the same fixture should not evaluate it twice')\\n def test_given_and_when_using_the_same_fixture_should_not_evaluate_it_twice():\\n \\\"\\\"\\\"Given and when using the same fixture should not evaluate it twice.\\\"\\\"\\\"\\n\\n\\n @given('1 have a fixture (appends 1 to a list) in reuse syntax')\\n def have_a_fixture_appends_1_to_a_list_in_reuse_syntax():\\n \\\"\\\"\\\"1 have a fixture (appends 1 to a list) in reuse syntax.\\\"\\\"\\\"\\n raise NotImplementedError\\n\\n\\n @given('I have an empty list')\\n def i_have_an_empty_list():\\n \\\"\\\"\\\"I have an empty list.\\\"\\\"\\\"\\n raise NotImplementedError\\n\\n\\n @when('I use this fixture')\\n def i_use_this_fixture():\\n \\\"\\\"\\\"I use this fixture.\\\"\\\"\\\"\\n raise NotImplementedError\\n\\n\\n @then('my list should be [1]')\\n def my_list_should_be_1():\\n \\\"\\\"\\\"my list should be [1].\\\"\\\"\\\"\\n raise NotImplementedError\\n\\n '''[\\n 1:\\n ].replace(\\n u\\\"'\\\", u\\\"'\\\"\\n )\\n )\",\n \"def test_virtualmachineconsole_scenario1(self):\\n call_scenario1(self)\",\n \"def test_get_scenarios_expanded(self):\\n pass\",\n \"def test_pytest_bdd_scenario_with_failed_step(self):\\n self.testdir.makefile(\\n \\\".feature\\\",\\n simple=_SIMPLE_SCENARIO,\\n )\\n py_file = self.testdir.makepyfile(\\n \\\"\\\"\\\"\\n from pytest_bdd import scenario, given, then, when\\n\\n @scenario(\\\"simple.feature\\\", \\\"Simple scenario\\\")\\n def test_simple():\\n pass\\n\\n BAR = None\\n\\n @given(\\\"I have a bar\\\")\\n def bar():\\n global BAR\\n BAR = 1\\n\\n @when(\\\"I eat it\\\")\\n def eat():\\n global BAR\\n BAR -= 1\\n\\n @then(\\\"I don't have a bar\\\")\\n def check():\\n assert BAR == -1\\n \\\"\\\"\\\"\\n )\\n file_name = os.path.basename(py_file.strpath)\\n self.inline_run(\\\"--ddtrace\\\", file_name)\\n spans = self.pop_spans()\\n\\n assert len(spans) == 7\\n assert spans[3].name == \\\"then\\\"\\n assert spans[3].get_tag(ERROR_MSG)\",\n \"def test_pytest_bdd_scenario_with_parameters(self):\\n self.testdir.makefile(\\n \\\".feature\\\",\\n parameters=\\\"\\\"\\\"\\n Feature: Parameters\\n Scenario: Passing scenario\\n Given I have 0 bars\\n When I eat it\\n Then I have -1 bars\\n\\n Scenario: Failing scenario\\n Given I have 2 bars\\n When I eat it\\n Then I have 0 bar\\n\\n Scenario: Failing converter\\n Given I have no bar\\n \\\"\\\"\\\",\\n )\\n py_file = self.testdir.makepyfile(\\n \\\"\\\"\\\"\\n from pytest_bdd import scenarios, given, then, when, parsers\\n\\n scenarios(\\\"parameters.feature\\\")\\n\\n BAR = None\\n\\n @given(parsers.re(\\\"^I have (?P<bars>[^ ]+) bar$\\\")) # loose regex\\n def have_simple(bars):\\n global BAR\\n BAR = bars\\n\\n @given(parsers.re(\\\"^I have (?P<bars>\\\\\\\\d+) bars$\\\"), converters=dict(bars=int))\\n def have(bars):\\n global BAR\\n BAR = bars\\n\\n @when(\\\"I eat it\\\")\\n def eat():\\n global BAR\\n BAR -= 1\\n\\n @then(parsers.parse(\\\"I have {bars:d} bar\\\"))\\n def check_parse(bars):\\n assert BAR == bars\\n\\n @then(parsers.cfparse(\\\"I have {bars:d} bars\\\"))\\n def check_cfparse(bars):\\n assert BAR == bars\\n \\\"\\\"\\\"\\n )\\n file_name = os.path.basename(py_file.strpath)\\n self.inline_run(\\\"--ddtrace\\\", file_name)\\n spans = self.pop_spans()\\n\\n assert len(spans) == 13 # 3 scenarios + 7 steps + 1 module\\n assert json.loads(spans[1].get_tag(test.PARAMETERS)) == {\\\"bars\\\": 0}\\n assert json.loads(spans[3].get_tag(test.PARAMETERS)) == {\\\"bars\\\": -1}\\n assert json.loads(spans[5].get_tag(test.PARAMETERS)) == {\\\"bars\\\": 2}\\n assert json.loads(spans[7].get_tag(test.PARAMETERS)) == {\\\"bars\\\": 0}\\n assert json.loads(spans[9].get_tag(test.PARAMETERS)) == {\\\"bars\\\": \\\"no\\\"}\",\n \"def step_impl(context):\\n pass\",\n \"def step_impl(context):\\n pass\",\n \"def step_name(self):\\n return \\\"main\\\"\",\n \"def test_create_goal(self):\\n pass\",\n \"def main(self):\\n root = etree.Element(\\\"OpenSCENARIO\\\")\\n self.get_header(root)\\n self.get_parameter_declarations(root)\\n etree.SubElement(root, \\\"CatalogLocations\\\")\\n self.get_road_network(root)\\n self.get_entities(root)\\n storyboard = etree.SubElement(root, \\\"Storyboard\\\")\\n self.get_init(storyboard)\\n story = etree.SubElement(storyboard, \\\"Story\\\")\\n story.set(\\\"name\\\", \\\"OSC Generated Story\\\")\\n act = etree.SubElement(story, \\\"Act\\\")\\n act.set(\\\"name\\\", \\\"OSC Generated Act\\\")\\n self.get_maneuvers(act)\\n self.get_story_start_trigger(act)\\n self.get_story_stop_trigger(act)\\n self.get_end_eval_criteria(storyboard)\\n\\n generated_xml = etree.tostring(root)\\n self.write_xosc(generated_xml)\",\n \"def workbench_scenarios():\\n\\t\\treturn [(\\\"TCXBlock\\\",\\n\\t\\t\\\"\\\"\\\"\\n\\t\\t<tournamentcreator/>\\n\\t\\t\\\"\\\"\\\"),\\n\\t\\t]\",\n \"def test_scenario_comments(testdir):\\n testdir.makefile(\\n \\\".feature\\\",\\n comments=textwrap.dedent(\\n \\\"\\\"\\\"\\\\\\n Feature: Comments\\n Scenario: Comments\\n # Comment\\n Given I have a bar\\n\\n Scenario: Strings that are not comments\\n Given comments should be at the start of words\\n Then this is not a#comment\\n And this is not \\\"#acomment\\\"\\n\\n \\\"\\\"\\\"\\n ),\\n )\\n\\n testdir.makepyfile(\\n textwrap.dedent(\\n \\\"\\\"\\\"\\\\\\n import re\\n import pytest\\n from pytest_bdd import parsers, given, then, scenario\\n\\n @scenario(\\\"comments.feature\\\", \\\"Comments\\\")\\n def test_1():\\n pass\\n\\n @scenario(\\\"comments.feature\\\", \\\"Strings that are not comments\\\")\\n def test_2():\\n pass\\n\\n\\n @given(\\\"I have a bar\\\")\\n def bar():\\n return \\\"bar\\\"\\n\\n\\n @given(\\\"comments should be at the start of words\\\")\\n def comments():\\n pass\\n\\n\\n @then(parsers.parse(\\\"this is not {acomment}\\\"))\\n def a_comment(acomment):\\n assert re.search(\\\"a.*comment\\\", acomment)\\n\\n \\\"\\\"\\\"\\n )\\n )\\n\\n result = testdir.runpytest()\\n\\n result.assert_outcomes(passed=2)\",\n \"def scenario(self, scenario):\\n # -- LAST SCENARIO: Report failures (if any).\\n self.report_scenario_completed()\\n\\n # -- NEW SCENARIO:\\n assert not self.failures\\n self.current_scenario = scenario\\n scenario_name = scenario.name\\n prefix = self.scenario_prefix\\n if self.current_rule:\\n prefix += u\\\" \\\"\\n if scenario_name:\\n scenario_name += \\\" \\\"\\n self.stream.write(u\\\"%s%s \\\" % (prefix, scenario_name))\\n self.stream.flush()\",\n \"def test_simple(testdir, pytest_params):\\n testdir.makefile(\\n \\\".feature\\\",\\n simple=\\\"\\\"\\\"\\n Feature: Simple feature\\n Scenario: Simple scenario\\n Given I have a bar\\n \\\"\\\"\\\",\\n )\\n testdir.makepyfile(\\n \\\"\\\"\\\"\\n from pytest_bdd import scenario, given, then\\n\\n @scenario(\\\"simple.feature\\\", \\\"Simple scenario\\\")\\n def test_simple():\\n pass\\n\\n @given(\\\"I have a bar\\\")\\n def bar():\\n return \\\"bar\\\"\\n\\n @then(\\\"pass\\\")\\n def bar():\\n pass\\n \\\"\\\"\\\"\\n )\\n result = testdir.runpytest_subprocess(*pytest_params)\\n result.assert_outcomes(passed=1)\",\n \"def _run_scenario(self, cls, method_name, context, args, config):\",\n \"def test_scenario_no_strict_gherkin(pytester):\\n pytester.makepyfile(\\n test_gherkin=\\\"\\\"\\\"\\n import pytest\\n\\n from pytest_bdd import when, scenario\\n\\n @scenario(\\n \\\"no_strict_gherkin_scenario.feature\\\",\\n \\\"Test scenario\\\",\\n )\\n def test_scenario():\\n pass\\n\\n\\n @pytest.fixture\\n def foo():\\n return {}\\n\\n @when('foo has a value \\\"bar\\\"')\\n def _(foo):\\n foo[\\\"bar\\\"] = \\\"bar\\\"\\n return foo[\\\"bar\\\"]\\n\\n\\n @when('foo is not boolean')\\n def _(foo):\\n assert foo is not bool\\n\\n\\n @when('foo has not a value \\\"baz\\\"')\\n def _(foo):\\n assert \\\"baz\\\" not in foo\\n \\\"\\\"\\\"\\n )\\n\\n pytester.makefile(\\n \\\".feature\\\",\\n no_strict_gherkin_scenario=\\\"\\\"\\\"\\n Feature: No strict Gherkin Scenario support\\n\\n Scenario: Test scenario\\n When foo has a value \\\"bar\\\"\\n And foo is not boolean\\n And foo has not a value \\\"baz\\\"\\n\\n \\\"\\\"\\\",\\n )\\n\\n result = pytester.runpytest()\\n result.assert_outcomes(passed=1)\",\n \"def test_integration1(self):\\n self._test_integration(1)\",\n \"def test_Demo(self):\\n self._run(self._example_scenarios, \\\"Demo\\\")\",\n \"def run_scenario(self, run, run_id):\\n\\n raise NotImplementedError\",\n \"def workbench_scenarios():\\n return [\\n (\\\"simstudentXBlock\\\",\\n \\\"\\\"\\\"<vertical_demo>\\n <simstudent/>\\n </vertical_demo>\\n \\\"\\\"\\\"),\\n ]\",\n \"def start_scenario(self, _, scenario, **kwargs):\\n if scenario.tags and \\\"skip\\\" in scenario.tags:\\n scenario.skip(\\\"Marked with @skip\\\")\\n self._scenario_id = self._rp.start_test_item(\\n name=scenario.name,\\n start_time=timestamp(),\\n item_type=\\\"STEP\\\",\\n parent_item_id=self._feature_id,\\n code_ref=self._code_ref(scenario),\\n attributes=self._attributes(scenario),\\n parameters=self._get_parameters(scenario),\\n description=self._item_description(scenario),\\n test_case_id=self._test_case_id(scenario),\\n **kwargs,\\n )\\n self._log_fixtures(scenario, \\\"BEFORE_TEST\\\", self._scenario_id)\\n self._log_item_id = self._scenario_id\",\n \"def workbench_scenarios():\\n return [\\n (\\\"Oppia Embedding\\\",\\n \\\"\\\"\\\"<vertical_demo>\\n <oppia oppiaid=\\\"0\\\" src=\\\"https://www.oppia.org\\\" width=\\\"700\\\" />\\n </vertical_demo>\\n \\\"\\\"\\\"),\\n ]\",\n \"def step_imp1(context,message):\\n context.driver.get(context.base_url)\",\n \"def run_one_step(self):\\n pass\",\n \"def pytest_cmdline_main(config):\\n try:\\n if len(config.option.scenarios) == 0:\\n print(\\\"Available scenarios:\\\")\\n for scenario in Scenario.scenarios.values():\\n print(f\\\" {scenario.name} - {scenario.description}\\\")\\n return 0\\n except:\\n pass\\n\\n return None\",\n \"def test_1():\",\n \"def get_scenario_template(self, target: str = 'sqlplus', login: typing.Optional[str] = None) -> str:\\n env = os.environ.get\\n scenario = []\\n login = login if login else '/nolog'\\n\\n if target == 'sqlplus':\\n executable = \\\"/bin/%s -S %s\\\" % (target, login)\\n elif target == 'rman':\\n executable = \\\"/bin/\\\" + target\\n elif target == 'psql':\\n executable = \\\"/usr/bin/\\\" + target\\n else:\\n raise Exception(\\\"Unknown scenario target: %s\\\" % target)\\n\\n if target in ['sqlplus', 'rman']:\\n if env('PATH') and env('ORACLE_BASE') and env('ORACLE_SID') and env('ORACLE_HOME'):\\n scenario.append(\\\"export ORACLE_BASE=\\\" + env('ORACLE_BASE', ''))\\n scenario.append(\\\"export ORACLE_SID=\\\" + env('ORACLE_SID', ''))\\n scenario.append(\\\"export ORACLE_HOME=\\\" + env('ORACLE_HOME', ''))\\n scenario.append(\\\"export PATH=\\\" + env('PATH', ''))\\n else:\\n raise Exception(\\\"Underlying error: environment for Oracle Database cannot be constructed.\\\")\\n\\n scenario.append(\\\"cat - << EOF | \\\" + env('ORACLE_HOME', '') + executable)\\n if target == 'sqlplus' and login.lower() == '/nolog':\\n scenario.append(\\\"CONNECT / AS SYSDBA;\\\")\\n elif target == 'rman':\\n scenario.append(\\\"CONNECT TARGET /\\\")\\n\\n scenario.append(\\\"@scenario\\\")\\n scenario.append(\\\"EXIT;\\\")\\n scenario.append(\\\"EOF\\\")\\n elif target in ['psql']:\\n scenario.append((\\\"cat - << EOF | \\\" + executable + \\\" -t --pset footer=off \\\" + self.config.get('db_name', '')).strip())\\n scenario.append(\\\"@scenario\\\")\\n scenario.append(\\\"EOF\\\")\\n\\n if self.debug:\\n print(\\\"\\\\n\\\" + (\\\"-\\\" * 40) + \\\"8<\\\" + (\\\"-\\\" * 40))\\n print('\\\\n'.join(scenario))\\n print((\\\"-\\\" * 40) + \\\"8<\\\" + (\\\"-\\\" * 40))\\n\\n return '\\\\n'.join(scenario)\",\n \"def test_generate_all_testing(self):\\n pass\",\n \"def enter_case(self, case):\\n print \\\" \\\" + case.how_to_behave + '.'\",\n \"def test_dataset_scenario_generation_full_incomplete(self):\\n params = ParameterServer()\\n\\n map_filename = os.path.join(os.path.dirname(__file__), \\\"data/DR_CHN_Merging_ZS_partial_v02.xodr\\\")\\n track_filename = os.path.join(os.path.dirname(__file__), \\\"data/interaction_dataset_CHN_Merging_dummy_track_incomplete.csv\\\")\\n\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"MapFilename\\\"] = map_filename\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"TrackFilenameList\\\"] = [track_filename]\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"StartingOffsetMs\\\"] = 0\\n\\n scenario_generation = InteractionDatasetScenarioGenerationFull(\\n params=params, num_scenarios=3)\\n self.assertEqual(scenario_generation.get_num_scenarios(), 2)\\n\\n # first scenario\\n agent12 = scenario_generation.get_scenario(0).GetWorldState().agents[2]\\n agent17 = scenario_generation.get_scenario(0).GetWorldState().agents[7]\\n self.assertEqual(agent12.first_valid_timestamp, 0.0)\\n self.assertEqual(agent17.first_valid_timestamp, 0.0)\\n\\n # second scenario\\n agent22 = scenario_generation.get_scenario(1).GetWorldState().agents[2]\\n agent27 = scenario_generation.get_scenario(1).GetWorldState().agents[7]\\n self.assertEqual(agent22.first_valid_timestamp, 0.0)\\n self.assertEqual(agent27.first_valid_timestamp, 0.0)\",\n \"def test_single_test_case():\\n pass\",\n \"def __init__(self, scenario):\\n source.__init__(self, scenario)\",\n \"def set_up(self):\\n for section_name, section_body in self.sections.iteritems():\\n scenario = Scenario(section_name, section_body)\\n self.scenarios.append(scenario)\",\n \"def test_scenario(self):\\n scenario_ids = list(scenarios.get_scenarios().keys())\\n\\n for scenario_id in scenario_ids:\\n url = reverse('workbench_show_scenario', kwargs={'scenario_id': scenario_id})\\n client = Client()\\n response = client.get(url, follow=True)\\n assert response.status_code == 200, scenario_id\\n\\n # Be sure we got the whole scenario. Again, we can't know what to expect\\n # here, but at the very least, if there are verticals, they should not be\\n # empty. That would be a sign that some data wasn't loaded properly while\\n # rendering the scenario.\\n html = lxml.html.fromstring(response.content)\\n for vertical_tag in html.xpath('//div[@class=\\\"vertical\\\"]'):\\n # No vertical tag should be empty.\\n assert list(vertical_tag), u\\\"Scenario {}: Empty <vertical> shouldn't happen!\\\".format(scenario_id)\",\n \"def test_generate_missing_with_step_parsers(pytester):\\n pytester.makefile(\\n \\\".feature\\\",\\n generation=textwrap.dedent(\\n \\\"\\\"\\\"\\\\\\n Feature: Missing code generation with step parsers\\n\\n Scenario: Step parsers are correctly discovered\\n Given I use the string parser without parameter\\n And I use parsers.parse with parameter 1\\n And I use parsers.re with parameter 2\\n And I use parsers.cfparse with parameter 3\\n \\\"\\\"\\\"\\n ),\\n )\\n\\n pytester.makepyfile(\\n textwrap.dedent(\\n \\\"\\\"\\\"\\\\\\n import functools\\n\\n from pytest_bdd import scenarios, given, parsers\\n\\n scenarios(\\\"generation.feature\\\")\\n\\n @given(\\\"I use the string parser without parameter\\\")\\n def _():\\n return None\\n\\n @given(parsers.parse(\\\"I use parsers.parse with parameter {param}\\\"))\\n def _(param):\\n return param\\n\\n @given(parsers.re(r\\\"^I use parsers.re with parameter (?P<param>.*?)$\\\"))\\n def _(param):\\n return param\\n\\n @given(parsers.cfparse(\\\"I use parsers.cfparse with parameter {param:d}\\\"))\\n def _(param):\\n return param\\n \\\"\\\"\\\"\\n )\\n )\\n\\n result = pytester.runpytest(\\\"--generate-missing\\\", \\\"--feature\\\", \\\"generation.feature\\\")\\n result.assert_outcomes(passed=0, failed=0, errors=0)\\n assert not result.stderr.str()\\n assert result.ret == 0\\n\\n output = result.stdout.str()\\n\\n assert \\\"I use the string parser\\\" not in output\\n assert \\\"I use parsers.parse\\\" not in output\\n assert \\\"I use parsers.re\\\" not in output\\n assert \\\"I use parsers.cfparse\\\" not in output\",\n \"def scenarioReference_factory(i, section_type, root, this):\\n \\n # no loops in section, should print 0\\n if i == 0:\\n return ScenarioReference()\\n \\n if section_type == SectionType.Plant:\\n name = root.plants.nameof(i)\\n scen = ScenarioReference(SectionType.Plant, name, root, this)\\n \\n elif section_type == SectionType.Op:\\n name = root.ops.nameof(i)\\n scen = ScenarioReference(SectionType.Op, name, root, this)\\n \\n elif section_type == SectionType.Ini:\\n name = root.inis.nameof(i)\\n scen = ScenarioReference(SectionType.Ini, name, root, this)\\n \\n elif section_type == SectionType.Surf:\\n name = root.surfs.nameof(i)\\n scen = ScenarioReference(SectionType.Surf, name, root, this)\\n \\n elif section_type == SectionType.Contour:\\n name = root.contours.nameof(i)\\n scen = ScenarioReference(SectionType.Contour, name, root, this)\\n \\n elif section_type == SectionType.Drain:\\n name = root.drains.nameof(i)\\n scen = ScenarioReference(SectionType.Drain, name, root, this)\\n \\n elif section_type == SectionType.Year:\\n name = root.years.nameof(i)\\n scen = ScenarioReference(SectionType.Year, name, root, this)\\n \\n else:\\n raise ValueError('Unknown SectionType')\\n \\n return scen\",\n \"def run_scenario(self):\\n self.initialize_random_map()\\n self.visualize_environment('initial')\\n self.get_tower_target_coverages()\\n self.solve_environment()\\n self.visualize_environment('solved')\",\n \"def testgen(self):\\n self.parse()\\n self.generate()\",\n \"def execute(self):\\n if self._cli_arguments.get('<samplename>') == 'cfn':\\n generate_sample_cfn_module(self.env_root)\\n elif self._cli_arguments.get('<samplename>') == 'static-angular':\\n generate_sample_static_angular(self.env_root)\\n elif self._cli_arguments.get('<samplename>') == 'static-react':\\n generate_sample_static_react(self.env_root)\\n elif self._cli_arguments.get('<samplename>') == 'sls-py':\\n generate_sample_sls_module(self.env_root, 'sls-py')\\n elif self._cli_arguments.get('<samplename>') == 'sls-tsc':\\n generate_sample_sls_module(self.env_root, 'sls-tsc')\\n elif self._cli_arguments.get('<samplename>') == 'stacker':\\n generate_sample_stacker_module(self.env_root)\\n elif self._cli_arguments.get('<samplename>') == 'tf':\\n generate_sample_tf_module(self.env_root)\\n elif self._cli_arguments.get('<samplename>') == 'k8s-cfn-repo':\\n generate_sample_k8s_cfn_repo(self.env_root)\\n elif self._cli_arguments.get('<samplename>') == 'k8s-tf-repo':\\n generate_sample_k8s_tf_repo(self.env_root)\\n elif self._cli_arguments.get('<samplename>') == 'cdk-tsc':\\n generate_sample_cdk_tsc_module(self.env_root)\\n elif self._cli_arguments.get('<samplename>') == 'cdk-py':\\n generate_sample_cdk_py_module(self.env_root)\\n elif self._cli_arguments.get('<samplename>') == 'cdk-csharp':\\n generate_sample_cdk_cs_module(self.env_root)\\n else:\\n LOGGER.info(\\\"Available samples to generate:\\\")\\n for i in ['cfn', 'static-angular', 'static-react', 'sls-tsc',\\n 'sls-py', 'tf', 'k8s-cfn-repo', 'k8s-tf-repo',\\n 'stacker', 'cdk-tsc', 'cdk-py', 'cdk-csharp']:\\n print(i)\",\n \"def test_workbench_scenarios(self):\\n result_title = 'Adaptive Numeric Input XBlock'\\n basic_scenario = \\\"<adaptivenumericinput />\\\"\\n test_result = self.xblock.workbench_scenarios()\\n self.assertEquals(result_title, test_result[0][0])\\n self.assertIn(basic_scenario, test_result[0][1])\",\n \"def generate():\",\n \"def on_GenerateA1_clicked(self):\\n # TODO: not implemented yet\\n raise NotImplementedError\",\n \"def generate_code(features: list[Feature], scenarios: list[ScenarioTemplate], steps: list[Step]) -> str:\\n grouped_steps = group_steps(steps)\\n template = template_lookup.get_template(\\\"test.py.mak\\\")\\n code = template.render(\\n features=features,\\n scenarios=scenarios,\\n steps=grouped_steps,\\n make_python_name=make_python_name,\\n make_python_docstring=make_python_docstring,\\n make_string_literal=make_string_literal,\\n )\\n return cast(str, code)\",\n \"def feature(self, node=\\\"clickhouse1\\\"):\\n self.context.node = self.context.cluster.node(node)\\n\\n for scenario in loads(current_module(), Scenario):\\n scenario()\",\n \"def workbench_scenarios():\\n return [\\n (\\\"SummaryXBlock\\\",\\n \\\"\\\"\\\"<summary/>\\n \\\"\\\"\\\"),\\n (\\\"Multiple SummaryXBlock\\\",\\n \\\"\\\"\\\"<vertical_demo>\\n <summary/>\\n <summary/>\\n <summary/>\\n </vertical_demo>\\n \\\"\\\"\\\"),\\n ]\",\n \"def run(self,**kwargs):\\n members = getmembers(self.bs)\\n targets = \\\"scenario\\\"\\n scenarios=[m[0] for m in members if m[0].startswith(targets)]\\n entries={}\\n for scenario in scenarios:\\n entries[scenario[len(targets):]] = getattr(self.bs,scenario) # save scenario names in entries\\n self.mm.addGenericMenu(targets,self.mm.cur_page,\\\"Select your desired scenario\\\", entries)\\n self.mm.loadMenu(targets)\",\n \"def load_scenario(self):\\n logger.info(\\\"Loading scenario...\\\")\\n for manager in self.config[\\\"fleets\\\"]:\\n name = manager[\\\"name\\\"]\\n password = manager[\\\"password\\\"] if \\\"password\\\" in manager else faker_factory.password()\\n fleet_type = manager[\\\"fleet_type\\\"]\\n strategy = manager.get(\\\"strategy\\\")\\n icon = manager.get(\\\"icon\\\")\\n agent = self.create_fleetmanager_agent(name, password, fleet_type=fleet_type, strategy=strategy)\\n\\n self.set_icon(agent, icon, default=fleet_type)\\n\\n while len(self.manager_agents) < self.config.num_managers:\\n time.sleep(0.1)\\n\\n for transport in self.config[\\\"transports\\\"]:\\n name = transport[\\\"name\\\"]\\n password = transport[\\\"password\\\"] if \\\"password\\\" in transport else faker_factory.password()\\n position = transport[\\\"position\\\"]\\n fleetmanager = transport[\\\"fleet\\\"]\\n fleet_type = transport[\\\"fleet_type\\\"]\\n speed = transport.get(\\\"speed\\\")\\n trust = transport.get(\\\"trust\\\")\\n rates = transport.get(\\\"rates\\\")\\n velocity_factor = transport.get(\\\"velocity_factor\\\")\\n fuel = transport.get(\\\"fuel\\\")\\n autonomy = transport.get(\\\"autonomy\\\")\\n current_autonomy = transport.get(\\\"current_autonomy\\\")\\n strategy = transport.get(\\\"strategy\\\")\\n icon = transport.get(\\\"icon\\\")\\n agent = self.create_transport_agent(name, password, position=position, speed=speed, fleet_type=fleet_type,\\n fleetmanager=fleetmanager, strategy=strategy, autonomy=autonomy,\\n current_autonomy=current_autonomy, trust=trust, rates=rates, velocity_factor=velocity_factor)\\n\\n if icon:\\n self.set_icon(agent, icon, default=fleet_type)\\n\\n for customer in self.config[\\\"customers\\\"]:\\n name = customer[\\\"name\\\"]\\n password = customer[\\\"password\\\"] if \\\"password\\\" in customer else faker_factory.password()\\n fleet_type = customer[\\\"fleet_type\\\"]\\n position = customer[\\\"position\\\"]\\n target = customer[\\\"destination\\\"]\\n strategy = customer.get(\\\"strategy\\\")\\n icon = customer.get(\\\"icon\\\")\\n agent = self.create_customer_agent(name, password, fleet_type, position=position, target=target,\\n strategy=strategy)\\n\\n self.set_icon(agent, icon, default=\\\"customer\\\")\\n\\n for station in self.config[\\\"stations\\\"]:\\n password = station[\\\"password\\\"] if \\\"password\\\" in station else faker_factory.password()\\n strategy = station.get(\\\"strategy\\\")\\n icon = station.get(\\\"icon\\\")\\n agent = self.create_station_agent(station[\\\"name\\\"], password, position=station[\\\"position\\\"],\\n power=station[\\\"power\\\"], places=station[\\\"places\\\"], strategy=strategy)\\n self.set_icon(agent, icon, default=\\\"electric_station\\\")\",\n \"def generate_goal(self, factors=None, obs=None):\\n pass\",\n \"def feature(self, node=\\\"clickhouse1\\\"):\\n node = self.context.cluster.node(node)\\n\\n @contextmanager\\n def setup(users=0, roles=0):\\n try:\\n with Given(\\\"I have some users and roles\\\"):\\n for i in range(users):\\n node.query(f\\\"CREATE USER OR REPLACE user{i}\\\")\\n for j in range(roles):\\n node.query(f\\\"CREATE ROLE OR REPLACE role{j}\\\")\\n yield\\n finally:\\n with Finally(\\\"I drop the users and roles\\\"):\\n for i in range(users):\\n node.query(f\\\"DROP USER IF EXISTS user{i}\\\")\\n for j in range(roles):\\n node.query(f\\\"DROP ROLE IF EXISTS role{j}\\\")\\n\\n with Scenario(\\n \\\"I grant a role to a user\\\", requirements=[RQ_SRS_006_RBAC_Grant_Role(\\\"1.0\\\")]\\n ):\\n with setup(1, 1):\\n with When(\\\"I grant a role\\\"):\\n node.query(\\\"GRANT role0 TO user0\\\")\\n\\n with Scenario(\\n \\\"I grant a nonexistent role to user\\\",\\n requirements=[RQ_SRS_006_RBAC_Grant_Role(\\\"1.0\\\")],\\n ):\\n with setup(1, 0):\\n with When(\\\"I grant nonexistent role to a user\\\"):\\n exitcode, message = errors.role_not_found_in_disk(name=\\\"role0\\\")\\n node.query(\\\"GRANT role0 TO user0\\\", exitcode=exitcode, message=message)\\n\\n # with nonexistent object name, GRANT assumes type role (treats user0 as role)\\n with Scenario(\\n \\\"I grant a role to a nonexistent user\\\",\\n requirements=[RQ_SRS_006_RBAC_Grant_Role(\\\"1.0\\\")],\\n ):\\n with setup(0, 1):\\n with When(\\\"I grant role to a nonexistent user\\\"):\\n exitcode, message = errors.role_not_found_in_disk(name=\\\"user0\\\")\\n node.query(\\\"GRANT role0 TO user0\\\", exitcode=exitcode, message=message)\\n\\n with Scenario(\\n \\\"I grant a nonexistent role to a nonexistent user\\\",\\n requirements=[RQ_SRS_006_RBAC_Grant_Role(\\\"1.0\\\")],\\n ):\\n with setup(0, 0):\\n with When(\\\"I grant nonexistent role to a nonexistent user\\\"):\\n exitcode, message = (\\n errors.role_not_found_in_disk(name=\\\"user0\\\")\\n if check_clickhouse_version(\\\">=21.09\\\")(self)\\n else errors.role_not_found_in_disk(name=\\\"role0\\\")\\n )\\n node.query(\\\"GRANT role0 TO user0\\\", exitcode=exitcode, message=message)\\n\\n with Scenario(\\n \\\"I grant a role to multiple users\\\",\\n requirements=[RQ_SRS_006_RBAC_Grant_Role(\\\"1.0\\\")],\\n ):\\n with setup(2, 1):\\n with When(\\\"I grant role to a multiple users\\\"):\\n node.query(\\\"GRANT role0 TO user0, user1\\\")\\n\\n with Scenario(\\n \\\"I grant multiple roles to multiple users\\\",\\n requirements=[RQ_SRS_006_RBAC_Grant_Role(\\\"1.0\\\")],\\n ):\\n with setup(2, 2):\\n with When(\\\"I grant multiple roles to multiple users\\\"):\\n node.query(\\\"GRANT role0, role1 TO user0, user1\\\")\\n\\n with Scenario(\\n \\\"I grant role to current user\\\",\\n requirements=[RQ_SRS_006_RBAC_Grant_Role_CurrentUser(\\\"1.0\\\")],\\n ):\\n with setup(1, 1):\\n with Given(\\\"I have a user with access management privilege\\\"):\\n node.query(\\\"GRANT ACCESS MANAGEMENT ON *.* TO user0\\\")\\n with When(\\\"I grant role to current user\\\"):\\n node.query(\\\"GRANT role0 TO CURRENT_USER\\\", settings=[(\\\"user\\\", \\\"user0\\\")])\\n\\n with Scenario(\\n \\\"I grant role to default user, throws exception\\\",\\n requirements=[RQ_SRS_006_RBAC_Grant_Role_CurrentUser(\\\"1.0\\\")],\\n ):\\n with setup(1, 1):\\n with When(\\\"I grant role to default user\\\"):\\n exitcode, message = errors.cannot_update_default()\\n node.query(\\n \\\"GRANT role0 TO CURRENT_USER\\\", exitcode=exitcode, message=message\\n )\\n\\n with Scenario(\\n \\\"I grant role to user with admin option\\\",\\n requirements=[RQ_SRS_006_RBAC_Grant_Role_AdminOption(\\\"1.0\\\")],\\n ):\\n with setup(1, 1):\\n with When(\\\"I grant role to a user with admin option\\\"):\\n node.query(\\\"GRANT role0 TO user0 WITH ADMIN OPTION\\\")\\n\\n with Scenario(\\n \\\"I grant role to user on cluster\\\",\\n requirements=[RQ_SRS_006_RBAC_Grant_Role_OnCluster(\\\"1.0\\\")],\\n ):\\n try:\\n with Given(\\\"I have a user and a role on a cluster\\\"):\\n node.query(\\\"CREATE USER OR REPLACE user0 ON CLUSTER sharded_cluster\\\")\\n node.query(\\\"CREATE ROLE OR REPLACE role0 ON CLUSTER sharded_cluster\\\")\\n with When(\\\"I grant the role to the user\\\"):\\n node.query(\\\"GRANT ON CLUSTER sharded_cluster role0 TO user0\\\")\\n finally:\\n with Finally(\\\"I drop the user and role\\\"):\\n node.query(\\\"DROP USER IF EXISTS user0 ON CLUSTER sharded_cluster\\\")\\n node.query(\\\"DROP ROLE IF EXISTS role0 ON CLUSTER sharded_cluster\\\")\\n\\n with Scenario(\\n \\\"I grant role to user on fake cluster, throws exception\\\",\\n requirements=[RQ_SRS_006_RBAC_Grant_Role_OnCluster(\\\"1.0\\\")],\\n ):\\n with setup(1, 1):\\n with When(\\\"I grant the role to the user\\\"):\\n exitcode, message = errors.cluster_not_found(\\\"fake_cluster\\\")\\n node.query(\\n \\\"GRANT ON CLUSTER fake_cluster role0 TO user0\\\",\\n exitcode=exitcode,\\n message=message,\\n )\",\n \"def workbench_scenarios():\\n return [\\n (\\\"FeedbackXBlock\\\",\\n \\\"\\\"\\\"<vertical_demo>\\n <feedback/>\\n </vertical_demo>\\n \\\"\\\"\\\"),\\n ]\",\n \"def get_first_sample() -> Sample:\\n print(get_intro_message())\\n\\n user_satisfied = False\\n while not user_satisfied:\\n sample = get_single_sample()\\n\\n print(\\\"Sample recorded : \\\\\\\"\\\" + sample.string + \\\"\\\\\\\"\\\")\\n user_satisfied = get_binary_validation(\\n \\\"Do you want to keep this sample ?\\\", True\\n )\\n\\n sample.impostor = False\\n\\n return sample\",\n \"def test_dataset_scenario_generation_full_late(self):\\n \\n params = ParameterServer()\\n\\n map_filename = os.path.join(os.path.dirname(__file__), \\\"data/DR_DEU_Merging_MT_v01_shifted.xodr\\\")\\n track_filename = os.path.join(os.path.dirname(__file__), \\\"data/interaction_dataset_DEU_Merging_dummy_track_late.csv\\\")\\n\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"MapFilename\\\"] = map_filename\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"TrackFilenameList\\\"] = [track_filename]\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"StartingOffsetMs\\\"] = 0\\n\\n scenario_generation = InteractionDatasetScenarioGenerationFull(\\n params=params, num_scenarios=1)\\n\\n scenario = scenario_generation.get_scenario(0)\\n world_state = scenario.GetWorldState()\\n agent1 = world_state.GetAgent(1)\\n agent2 = world_state.GetAgent(2)\\n\\n self.assertAlmostEqual(agent1.first_valid_timestamp, 0.0)\\n self.assertAlmostEqual(agent2.first_valid_timestamp, 0.3)\\n \\n self.assertEqual(isinstance(agent1, Agent), True)\\n self.assertEqual(agent1.IsValidAtTime(world_state.time), True)\\n \\n self.assertEqual(isinstance(agent2, Agent), True)\\n self.assertEqual(agent2.IsValidAtTime(world_state.time), False)\",\n \"def main():\\n user = intro()\\n paths = [scott_adventure, team_1_adv, team_2_adv, team_3_adv, team_4_adv, team_5_adv, team_6_adv, team_7_adv, team_8_adv, team_9_adv, team_10_adv, team_11_adv, team_12_adv, team_13_adv, team_14_adv, team_15_adv, team_16_adv]\\n random.shuffle(paths) # Shuffles the order of paths, so each adventure is different\\n for i in range(len(paths)):\\n paths[i]() # Runs each function in the paths list\\n end_story(user)\",\n \"def test_case_3(self):\\n config_generator = Genesis()\\n\\n data = open_json_file('r1.json')\\n\\n task_1 = Task.new(data=data, name=\\\"R1_BASE_CONFIG\\\")\\n task_2 = Task.new(data=data, name=\\\"R1_BASE_CONFIG_2\\\")\\n\\n config_generator.add_task(task_1)\\n config_generator.add_task(task_2)\\n\\n results = config_generator.generate()\\n self.assertIsInstance(results, dict)\\n\\n for task in config_generator.tasks:\\n self.assertTrue(task.is_complete)\\n self.assertIsInstance(task.rendered_data, str)\",\n \"def scenario3(sim):\\n\\n init_logging(logfile=None, debug=True)\\n second = 1000.0\\n duration = 10\\n tau_m = 20 # ms\\n cm = 1.0 # nF\\n v_reset = -60\\n cell_parameters = dict(\\n tau_m=tau_m,\\n cm=cm,\\n v_rest=-70,\\n e_rev_E=0,\\n e_rev_I=-70,\\n v_thresh=-54,\\n v_reset=v_reset,\\n tau_syn_E=5,\\n tau_syn_I=5,\\n )\\n g_leak = cm / tau_m # µS\\n\\n w_min = 0.0 * g_leak\\n w_max = 0.05 * g_leak\\n\\n r1 = 5.0\\n r2 = 40.0\\n\\n sim.setup()\\n pre = sim.Population(100, sim.SpikeSourcePoisson())\\n post = sim.Population(10, sim.IF_cond_exp())\\n\\n pre.set(duration=duration * second)\\n pre.set(start=0.0)\\n pre[:50].set(rate=r1)\\n pre[50:].set(rate=r2)\\n assert_equal(pre[49].rate, r1)\\n assert_equal(pre[50].rate, r2)\\n post.set(**cell_parameters)\\n post.initialize(v=RandomDistribution('normal', mu=v_reset, sigma=5.0))\\n\\n stdp = sim.STDPMechanism(\\n sim.SpikePairRule(tau_plus=20.0, tau_minus=20.0,\\n A_plus=0.01, A_minus=0.01),\\n sim.AdditiveWeightDependence(w_min=w_min, w_max=w_max),\\n #dendritic_delay_fraction=0.5))\\n dendritic_delay_fraction=1)\\n\\n connections = sim.Projection(pre, post, sim.AllToAllConnector(),\\n synapse_type=stdp,\\n receptor_type='excitatory')\\n\\n initial_weight_distr = RandomDistribution('uniform', low=w_min, high=w_max)\\n connections.randomizeWeights(initial_weight_distr)\\n initial_weights = connections.get('weight', format='array', gather=False)\\n # assert initial_weights.min() >= w_min\\n # assert initial_weights.max() < w_max\\n # assert initial_weights[0, 0] != initial_weights[1, 0]\\n\\n pre.record('spikes')\\n post.record('spikes')\\n post[0:1].record('v')\\n\\n sim.run(duration * second)\\n\\n actual_rate = pre.mean_spike_count() / duration\\n expected_rate = (r1 + r2) / 2\\n errmsg = \\\"actual rate: %g expected rate: %g\\\" % (actual_rate, expected_rate)\\n assert abs(actual_rate - expected_rate) < 1, errmsg\\n #assert abs(pre[:50].mean_spike_count()/duration - r1) < 1\\n #assert abs(pre[50:].mean_spike_count()/duration- r2) < 1\\n final_weights = connections.get('weight', format='array', gather=False)\\n assert initial_weights[0, 0] != final_weights[0, 0]\\n\\n try:\\n import scipy.stats\\n except ImportError:\\n raise SkipTest\\n t, p = scipy.stats.ttest_ind(initial_weights[:50, :].flat, initial_weights[50:, :].flat)\\n assert p > 0.05, p\\n t, p = scipy.stats.ttest_ind(final_weights[:50, :].flat, final_weights[50:, :].flat)\\n assert p < 0.01, p\\n assert final_weights[:50, :].mean() < final_weights[50:, :].mean()\\n sim.end()\\n return initial_weights, final_weights, pre, post, connections\",\n \"def test_generate_sample_sheet(self):\\n pass\",\n \"def main():\\n\\n user = start_story()\\n paths = [scott_adventure, team_1_adv, team_2_adv,\\n team_3_adv, team_4_adv, team_5_adv,\\n team_6_adv, team_7_adv, team_8_adv,\\n team_9_adv, team_10_adv, team_11_adv,\\n team_12_adv, team_13_adv, team_14_adv,\\n team_15_adv, team_16_adv, team_17_adv,\\n team_18_adv, team_19_adv, team_20_adv]\\n random.shuffle(paths) # Shuffles the order of paths, so each adventure is different\\n\\n for i in range(len(paths)):\\n paths[i]() # Runs each function in the paths list\\n\\n end_story(user)\",\n \"def process_result_multi_scen(path_to_scenarios, path_shapefile_input):\\n # Get all folders with scenario run results (name of folder is scenario)\\n scenarios = os.listdir(path_to_scenarios)\\n\\n for scenario in scenarios:\\n single_scenario.main(\\n os.path.join(path_to_scenarios, scenario),\\n path_shapefile_input)\\n\\n return\",\n \"def workbench_scenarios(): \\n return [\\n (\\\"TermsXBlock\\\",\\n \\\"\\\"\\\"<terms/>\\n \\\"\\\"\\\"),\\n (\\\"Multiple TermsXBlock\\\",\\n \\\"\\\"\\\"<vertical_demo>\\n <terms/>\\n <terms/>\\n <terms/>\\n </vertical_demo>\\n \\\"\\\"\\\"),\\n ]\",\n \"def test_before(self):\\n\\n support.create_project(self, 'candice')\\n support.add_step(self)\\n support.add_step(self, position='0')\\n\\n project = cauldron.project.get_internal_project()\\n steps = project.steps\\n\\n self.assertTrue(steps[0].filename.startswith('S01'))\\n self.assertTrue(steps[1].filename.startswith('S02'))\",\n \"def test_modes(self):\\n step = self.run_step('S01-first.py')\\n self.assertTrue(step.success)\\n self.assertTrue(step.local.is_testing)\\n self.assertFalse(step.local.is_interactive)\\n self.assertFalse(step.local.is_single_run)\",\n \"def test_create_run(self):\\n pass\",\n \"def generate_submissons_all_steps():\\n\\n\\n data_en = read_json_file(\\\"Test_Data/test-en.json\\\")\\n data_pr = read_json_file(\\\"Test_Data/test-pr.json\\\")\\n data_es = read_json_file(\\\"Test_Data/test-es.json\\\")\\n res_en = generate_embeddings_sentence_test_data(data_en, \\\"Test_Data/embd-en.pkl\\\")\\n res_es = generate_embeddings_sentence_test_data(data_es, \\\"Test_Data/embd-es.pkl\\\")\\n res_pr = generate_embeddings_sentence_test_data(data_pr, \\\"Test_Data/embd-pr.pkl\\\")\\n model = load_model(\\\"model_doc\\\")\\n make_submission(res_es, model, \\\"submission-es\\\")\\n make_submission(res_pr, model, \\\"submission-pr\\\")\\n make_submission(res_en, model, \\\"submission-en\\\")\\n exit()\",\n \"def worker_scenario(scenario_names, feature_files, output):\\n cmd = [\\\"behave\\\"]\\n if scenario_names is not None:\\n for i in range(len(scenario_names)):\\n cmd = cmd + [\\\"-n\\\"] + [scenario_names[i]]\\n elif feature_files is not None:\\n for i in range(len(feature_files)):\\n cmd = cmd + [feature_files[i]]\\n else:\\n print(\\\"Running whole testsuite\\\\n\\\")\\n if output is not None:\\n cmd = cmd + [\\\"-o\\\"] + [output]\\n sp.call(cmd)\\n print(\\\"\\\\n\\\\n\\\\n\\\")\",\n \"def test_dataset_scenario_generation_full_outside1(self):\\n params = ParameterServer()\\n\\n map_filename = os.path.join(os.path.dirname(__file__), \\\"data/DR_DEU_Merging_MT_v01_shifted.xodr\\\")\\n track_filename = os.path.join(os.path.dirname(__file__), \\\"data/interaction_dataset_DEU_Merging_dummy_track_outside.csv\\\")\\n\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"MapFilename\\\"] = map_filename\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"TrackFilenameList\\\"] = [track_filename]\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"StartingOffsetMs\\\"] = 0\\n\\n scenario_generation = InteractionDatasetScenarioGenerationFull(\\n params=params, num_scenarios=1)\\n\\n scenario = scenario_generation.get_scenario(0)\\n self.assertAlmostEqual(scenario.eval_agent_ids, [1])\\n\\n world_state = scenario.GetWorldState()\\n agent11 = world_state.GetAgent(1)\\n agent12 = world_state.GetAgent(2)\\n agent13 = world_state.GetAgent(3)\\n\\n self.assertAlmostEqual(agent11.first_valid_timestamp, 0.0)\\n self.assertAlmostEqual(agent12.first_valid_timestamp, 0.0)\\n self.assertNotEqual(agent13.first_valid_timestamp, 0.0)\\n \\n # agent13 should not be valid at the beginning, as he is outside of map\\n world_state.time = 0\\n self.assertEqual(isinstance(agent11, Agent), True)\\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent11.InsideRoadCorridor(), True)\\n \\n self.assertEqual(isinstance(agent12, Agent), True)\\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent12.InsideRoadCorridor(), True)\\n\\n self.assertEqual(isinstance(agent13, Agent), True)\\n self.assertEqual(agent13.IsValidAtTime(world_state.time), False)\\n # as we use only state once it's in map, this will be true, although the time step is not valid yet\\n self.assertEqual(agent13.InsideRoadCorridor(), True)\\n\\n # agent13 should not be valid at the beginning, as he is outside of map\\n world_state.Step(0.05)\\n\\n self.assertEqual(isinstance(agent11, Agent), True)\\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent11.InsideRoadCorridor(), True)\\n \\n self.assertEqual(isinstance(agent12, Agent), True)\\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent12.InsideRoadCorridor(), True)\\n\\n self.assertEqual(isinstance(agent13, Agent), True)\\n self.assertEqual(agent13.IsValidAtTime(world_state.time), False)\\n # as we use only state once it's in map, this will be true, although the time step is not valid yet\\n self.assertEqual(agent13.InsideRoadCorridor(), True)\\n\\n self.assertEqual(list(world_state.agents_valid.keys()), [1,2])\\n\\n # agent13 should be valid at some point\\n world_state.Step(agent13.first_valid_timestamp)\\n world_state.Step(0.01) # agent13.IsValidAtTime() uses previous time stamp, therefore we increment it one more step\\n\\n self.assertEqual(isinstance(agent11, Agent), True)\\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent11.InsideRoadCorridor(), True)\\n \\n self.assertEqual(isinstance(agent12, Agent), True)\\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent12.InsideRoadCorridor(), True)\\n\\n self.assertEqual(isinstance(agent13, Agent), True)\\n self.assertEqual(agent13.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent13.InsideRoadCorridor(), True)\\n\\n self.assertEqual(list(world_state.agents_valid.keys()), [1,2,3])\",\n \"def _pileup_scenario(self):\\n workflow = self.workflow\\n campaign = self.campaign\\n pileupDataset = self._pileup_dataset()\\n if pileupDataset != 'None':\\n [subscribedOurSite, subscribedOtherSite] = checkAcceptedSubscriptionRequest(self.url, pileupDataset, siteSE)\\n if not subscribedOurSite:\\n print('ERROR: pileup dataset not subscribed/approved to required Disk endpoint')\\n sys.exit(0) \\n \\n # Determine pileup scenario\\n # - Fall11_R2 & Fall11_R4 don't add pileup so extract pileup scenario from input\\n pileupScenario = ''\\n pileupScenario = getPileupScenario(self.winfo, self.config)\\n if campaign == 'Summer12_DR53X_RD':\\n pileupScenario = 'PU_RD1'\\n if pileupScenario == 'Unknown' and 'MinBias' in pileupDataset and 'LowPU2010DR42' not in workflow:\\n print('ERROR: unable to determine pileup scenario')\\n sys.exit(0)\\n elif 'Fall11_R2' in workflow or 'Fall11_R4' in workflow or 'Fall11R2' in workflow or 'Fall11R4' in workflow:\\n matchObj = re.match(r\\\".*Fall11-(.*)_START.*\\\", inputDataset)\\n if matchObj:\\n pileupScenario = matchObj.group(1)\\n else:\\n pileupScenario == 'Unknown'\\n elif pileupScenario == 'Unknown' and 'MinBias' not in pileupDataset:\\n pileupScenario = 'NoPileUp'\\n\\n if pileupScenario == 'Unknown':\\n pileupScenario = ''\\n\\n if 'LowPU2010_DR42' in workflow or 'LowPU2010DR42' in workflow:\\n pileupScenario = 'PU_S0'\\n if 'HiWinter13' in workflow and 'DR53X' in workflow:\\n pileupScenario = '' \\n if 'pAWinter13' in workflow and 'DR53X' in workflow:\\n pileupScenario = 'pa' # not actually the pileup scenario of course\\n if 'ppWinter13' in workflow and 'DR53X' in workflow:\\n pileupScenario = 'pp' # not actually the pileup scenario of course\\n return pileupScenario\",\n \"def workbench_scenarios():\\n return [\\n (\\\"MyXBlock\\\",\\n \\\"\\\"\\\"<myxblock/>\\n \\\"\\\"\\\"),\\n (\\\"Multiple MyXBlock\\\",\\n \\\"\\\"\\\"<vertical_demo>\\n <myxblock/>\\n <myxblock/>\\n <myxblock/>\\n </vertical_demo>\\n \\\"\\\"\\\"),\\n ]\",\n \"def CASE1( self, main ):\\n import re\\n import time\\n import copy\\n\\n main.topoIndex = \\\"topo\\\" + str( main.params[ 'TEST' ][ 'topo' ] )\\n\\n main.log.report( \\\"Load Mininet topology and Balance all Mininet switches across controllers\\\" )\\n main.log.report( \\\"________________________________________________________________________\\\" )\\n main.case( \\\"Assign and Balance all Mininet switches across controllers\\\" )\\n\\n main.step( \\\"Start Mininet topology\\\" )\\n newTopo = main.params[ 'TOPO' ][ main.topoIndex ][ 'fileName' ]\\n mininetDir = main.Mininet1.home + \\\"/custom/\\\"\\n topoPath = main.testDir + \\\"/dependencies/topologies/\\\" + newTopo\\n main.ONOSbench.secureCopy( main.Mininet1.user_name, main.Mininet1.ip_address, topoPath, mininetDir, direction=\\\"to\\\" )\\n topoPath = mininetDir + newTopo\\n startStatus = main.Mininet1.startNet( topoFile=topoPath )\\n main.mininetSwitches = main.Mininet1.getSwitches()\\n main.mininetHosts = main.Mininet1.getHosts()\\n main.mininetLinks = main.Mininet1.getLinks( timeout=60 )\\n utilities.assert_equals( expect=main.TRUE,\\n actual=startStatus,\\n onpass=\\\"Start Mininet topology test PASS\\\",\\n onfail=\\\"Start Mininet topology test FAIL\\\" )\\n\\n main.step( \\\"Assign switches to controllers\\\" )\\n switchMastership = main.TRUE\\n for switchName in main.mininetSwitches.keys():\\n ips = main.Cluster.getIps()\\n main.Mininet1.assignSwController( sw=switchName, ip=ips )\\n response = main.Mininet1.getSwController( switchName )\\n print( \\\"Response is \\\" + str( response ) )\\n if re.search( \\\"tcp:\\\" + main.Cluster.active( 0 ).ipAddress, response ):\\n switchMastership = switchMastership and main.TRUE\\n else:\\n switchMastership = main.FALSE\\n utilities.assert_equals( expect=main.TRUE,\\n actual=switchMastership,\\n onpass=\\\"Assign switches to controllers test PASS\\\",\\n onfail=\\\"Assign switches to controllers test FAIL\\\" )\\n # Waiting here to make sure topology converges across all nodes\\n sleep = int( main.params[ 'TEST' ][ 'loadTopoSleep' ] )\\n time.sleep( sleep )\\n\\n main.step( \\\"Balance devices across controllers\\\" )\\n balanceResult = main.Cluster.active( 0 ).CLI.balanceMasters()\\n # giving some breathing time for ONOS to complete re-balance\\n time.sleep( sleep )\\n\\n caseResult = ( startStatus and switchMastership and balanceResult )\\n utilities.assert_equals( expect=main.TRUE,\\n actual=caseResult,\\n onpass=\\\"Starting new Att topology test PASS\\\",\\n onfail=\\\"Starting new Att topology test FAIL\\\" )\",\n \"def sample(self):\",\n \"def test_functionality(self):\\n self.templateName = \\\"Test Template\\\"\\n self.browserObject = globalVars.browserObject\\n\\n self.preRunSetup()\\n \\n self.runTestCase()\\n \\n self.postRunCleanup()\",\n \"def fixture_other_case() -> str:\\n return \\\"angrybird\\\"\",\n \"def step(self): \\n self.reset_parameters()\\n\\n if np.random.uniform(0, 1) < self.model.churn_prob: self.exit_triggered = True \\n if self.exit_triggered:\\n self.exit()\\n else:\\n self.register_deposit(self.deposit_intent)\\n self.register_contribution(self.contribution_intent)\\n self.register_sponsorship(self.sponsor_intent)\\n self.register_euro_exchange(self.euro_exchange_intent)\\n self.register_teo_exchange(self.teo_exchange_intent)\\n self.register_withdraw(self.withdraw_intent)\",\n \"def source_test_file_name():\\n return 'feature'\",\n \"def task_generate_sc_figure1():\\n for dept in Department.list():\\n yield {\\n 'name': dept.name,\\n 'file_dep': [\\n dept.guessed_city_path,\\n dept.police_precincts_path,\\n ],\\n 'task_dep': ['download_place_boundaries'],\\n 'targets': [dept.sc_figure1_path],\\n 'actions': [dept.generate_sc_figure1],\\n 'clean': True,\\n }\",\n \"def test_get_goal(self):\\n pass\",\n \"def enumeration_separation(self, stage1_vars, sample_name, limit=1,\\n enumerate_all=True):\\n sample = self.tsdro.samples[sample_name]\\n scenarios = self.tsdro.scenarios\\n lr_instance = self.tsdro.lr_instance\\n curr_remaining = self.tsdro.remaining_scenario_names[sample_name]\\n curr_remaining_copy = curr_remaining.copy()\\n\\n found_hyp = False\\n count = 0\\n max_violation_scenario_name = None\\n max_violation = -np.inf\\n for scenario_name in curr_remaining:\\n scenario = scenarios[scenario_name]\\n if scenario_name not in self.stage2_objvals:\\n self.stage2_objvals[scenario_name] = self.get_stage2_costs(\\n stage1_vars, [scenario_name])\\n\\n if scenario_name == sample_name or self.method == \\\"RO\\\":\\n scenario_distance = 0\\n rhs_val = self.stage2_objvals[scenario_name]\\n else:\\n scenario_distance = sc.get_scenario_distance(scenario, sample,\\n lr_instance)\\n rhs_val = (self.stage2_objvals[scenario_name]\\n - self.wass_mult.X * scenario_distance)\\n if self.epi_vars[sample_name].X < rhs_val + 1e-7:\\n found_hyp = True\\n if not enumerate_all:\\n count += 1\\n if scenario_name not in self.master_scenarios:\\n tmp = lr_instance.add_stage2(self.master, self.stage1_vars,\\n scenario, scenario_name)\\n self.stage2_vars[scenario_name] = tmp[0]\\n self.master_scenarios.append(scenario_name)\\n objexpr_stage2 = lr_instance.get_objective_stage2(\\n self.stage2_vars[scenario_name], scenario)\\n rhs = objexpr_stage2 - self.wass_mult * scenario_distance\\n self.master.addLConstr(self.epi_vars[sample_name], \\\">\\\", rhs)\\n curr_remaining_copy.remove(scenario_name)\\n else:\\n if rhs_val - self.epi_vars[sample_name].X > max_violation:\\n max_violation = rhs_val - self.epi_vars[sample_name].X\\n max_violation_scenario_name = scenario_name\\n if count == limit:\\n break\\n if enumerate_all and found_hyp:\\n scenario = scenarios[max_violation_scenario_name]\\n if scenario_name == sample_name or self.method == \\\"RO\\\":\\n scenario_distance = 0\\n else:\\n scenario_distance = sc.get_scenario_distance(scenario, sample,\\n lr_instance)\\n if scenario_name not in self.master_scenarios:\\n tmp = lr_instance.add_stage2(self.master, self.stage1_vars,\\n scenario,\\n max_violation_scenario_name)\\n self.stage2_vars[max_violation_scenario_name] = tmp[0]\\n self.master_scenarios.append(max_violation_scenario_name)\\n objexpr_stage2 = lr_instance.get_objective_stage2(\\n self.stage2_vars[max_violation_scenario_name], scenario)\\n rhs = objexpr_stage2 - self.wass_mult * scenario_distance\\n self.master.addLConstr(self.epi_vars[sample_name], \\\">\\\", rhs)\\n curr_remaining_copy.remove(max_violation_scenario_name)\\n self.tsdro.remaining_scenario_names[sample_name] = curr_remaining_copy\\n\\n if count > 0:\\n print(sample_name, \\\"added\\\", count, \\\"scenarios.\\\")\\n return found_hyp\",\n \"def test_dataset_scenario_generation_full_outside3(self):\\n params = ParameterServer()\\n\\n map_filename = os.path.join(os.path.dirname(__file__), \\\"data/DR_DEU_Merging_MT_v01_shifted.xodr\\\")\\n track_filename = os.path.join(os.path.dirname(__file__), \\\"data/interaction_dataset_DEU_Merging_dummy_track_outside.csv\\\")\\n\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"MapFilename\\\"] = map_filename\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"TrackFilenameList\\\"] = [track_filename]\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"StartingOffsetMs\\\"] = 0\\n\\n scenario_generation = InteractionDatasetScenarioGenerationFull(\\n params=params, num_scenarios=3)\\n\\n scenario = scenario_generation.get_scenario(2)\\n self.assertAlmostEqual(scenario.eval_agent_ids, [3])\\n world_state = scenario.GetWorldState()\\n agent31 = world_state.GetAgent(1)\\n agent32 = world_state.GetAgent(2)\\n agent33 = world_state.GetAgent(3)\\n\\n # they all should be valid at the beginning\\n world_state.time = 0\\n self.assertEqual(isinstance(agent31, Agent), True)\\n self.assertEqual(agent31.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent31.InsideRoadCorridor(), True)\\n \\n self.assertEqual(isinstance(agent32, Agent), True)\\n self.assertEqual(agent32.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent32.InsideRoadCorridor(), True)\\n\\n self.assertEqual(isinstance(agent33, Agent), True)\\n self.assertEqual(agent33.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent33.InsideRoadCorridor(), True)\\n\\n world_state.Step(0.05)\\n self.assertEqual(len(world_state.agents_valid), 3)\",\n \"def test_stepregistry_should_create_one_step_decorator_per_keyword():\\n # given\\n registry = StepRegistry()\\n context = {}\\n\\n # when\\n registry.create_step_decorators(context)\\n\\n # then\\n assert len(context) == 4\\n assert \\\"given\\\" in context\\n assert \\\"when\\\" in context\\n assert \\\"then\\\" in context\\n assert \\\"step\\\" in context\",\n \"def test_creating_simple_feature():\\n # given & when\\n feature = Feature(1, \\\"Feature\\\", \\\"I am a feature\\\", \\\"foo.feature\\\", 1, tags=None)\\n\\n # then\\n assert feature.id == 1\\n assert feature.keyword == \\\"Feature\\\"\\n assert feature.sentence == \\\"I am a feature\\\"\\n assert feature.path == \\\"foo.feature\\\"\\n assert feature.line == 1\\n assert feature.tags == []\",\n \"def main():\\n num_of_tests = int(input())\\n\\n # iterate over test cases\\n for test_case in range(1, num_of_tests + 1):\\n result = handle_case()\\n printable_result = handle_result(result)\\n print(\\\"Case #{}: {}\\\".format(test_case, printable_result))\",\n \"def workbench_scenarios():\\n return [\\n (\\\"Markdown\\\",\\n \\\"\\\"\\\"<markdowna />\\n \\\"\\\"\\\")\\n ]\",\n \"def create_sample(i):\\n return Sample(**{\\n 'name': f'Sample{i}',\\n 'metadata': {'foobar': f'baz{i}'},\\n KrakenResultModule.name(): create_kraken(),\\n KrakenHLLResultModule.name(): create_krakenhll(),\\n Metaphlan2ResultModule.name(): create_metaphlan2(),\\n }).save()\",\n \"def test_strategy(self):\\n self.first_play_test(C)\",\n \"def scenario(self, scenario):\\n self.report_scenario_completed()\\n self.current_scenario = scenario\",\n \"def test_dataset_scenario_generation_full_outside1_behavior_overwritten(self):\\n params = ParameterServer()\\n\\n map_filename = os.path.join(os.path.dirname(__file__), \\\"data/DR_DEU_Merging_MT_v01_shifted.xodr\\\")\\n track_filename = os.path.join(os.path.dirname(__file__), \\\"data/interaction_dataset_DEU_Merging_dummy_track_outside.csv\\\")\\n\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"MapFilename\\\"] = map_filename\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"TrackFilenameList\\\"] = [track_filename]\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"StartingOffsetMs\\\"] = 0\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"BehaviorModel\\\"] = \\\"BehaviorMobilRuleBased\\\"\\n\\n scenario_generation = InteractionDatasetScenarioGenerationFull(\\n params=params, num_scenarios=1)\\n\\n scenario = scenario_generation.get_scenario(0)\\n self.assertAlmostEqual(scenario.eval_agent_ids, [1])\\n\\n world_state = scenario.GetWorldState()\\n agent11 = world_state.GetAgent(1)\\n agent12 = world_state.GetAgent(2)\\n agent13 = world_state.GetAgent(3)\\n\\n # others are defined as BehaviorMobilRuleBased\\n self.assertTrue(isinstance(agent11.behavior_model, BehaviorStaticTrajectory))\\n self.assertTrue(isinstance(agent12.behavior_model, BehaviorMobilRuleBased))\\n self.assertTrue(isinstance(agent13.behavior_model, BehaviorMobilRuleBased))\\n\\n self.assertAlmostEqual(agent11.first_valid_timestamp, 0.0)\\n self.assertAlmostEqual(agent12.first_valid_timestamp, 0.0)\\n self.assertNotEqual(agent13.first_valid_timestamp, 0.0)\\n \\n # agent13 should not be valid at the beginning, as he is outside of map\\n world_state.time = 0\\n self.assertEqual(isinstance(agent11, Agent), True)\\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent11.InsideRoadCorridor(), True)\\n \\n self.assertEqual(isinstance(agent12, Agent), True)\\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent12.InsideRoadCorridor(), True)\\n\\n self.assertEqual(isinstance(agent13, Agent), True)\\n self.assertEqual(agent13.IsValidAtTime(world_state.time), False)\\n # as we use only state once it's in map, this will be true, although the time step is not valid yet\\n self.assertEqual(agent13.InsideRoadCorridor(), True)\\n\\n # agent13 should not be valid at the beginning, as he is outside of map\\n world_state.Step(0.05)\\n\\n self.assertEqual(isinstance(agent11, Agent), True)\\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent11.InsideRoadCorridor(), True)\\n \\n self.assertEqual(isinstance(agent12, Agent), True)\\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent12.InsideRoadCorridor(), True)\\n\\n self.assertEqual(isinstance(agent13, Agent), True)\\n self.assertEqual(agent13.IsValidAtTime(world_state.time), False)\\n # as we use only state once it's in map, this will be true, although the time step is not valid yet\\n self.assertEqual(agent13.InsideRoadCorridor(), True)\\n\\n self.assertEqual(list(world_state.agents_valid.keys()), [1,2])\\n\\n # agent13 should be valid at some point\\n world_state.Step(agent13.first_valid_timestamp)\\n world_state.Step(0.01) # agent13.IsValidAtTime() uses previous time stamp, therefore we increment it one more step\\n\\n self.assertEqual(isinstance(agent11, Agent), True)\\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent11.InsideRoadCorridor(), True)\\n \\n self.assertEqual(isinstance(agent12, Agent), True)\\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent12.InsideRoadCorridor(), True)\\n\\n self.assertEqual(isinstance(agent13, Agent), True)\\n self.assertEqual(agent13.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent13.InsideRoadCorridor(), True)\\n\\n self.assertEqual(list(world_state.agents_valid.keys()), [1,2,3])\",\n \"def scenario(self):\\n scenario = []\\n for action in self.application_tree['scenario']:\\n scenario.append(action)\\n return scenario\",\n \"def __init__(self, scenario):\\n client.__init__(self, scenario)\\n # TODO: Your initialization, if any (not likely). Oh, and remove the next line.\\n raise Exception( \\\"DO NOT instantiate the skeleton implementation\\\" )\",\n \"def fixture_microbial_sample_id():\\n return \\\"microbial_sample_test\\\"\",\n \"def test_feature_all_scenarios(mocker):\\n # given\\n feature = Feature(1, \\\"Feature\\\", \\\"I am a feature\\\", \\\"foo.feature\\\", 1, tags=None)\\n # add regular Scenarios to Feature\\n feature.scenarios.extend([mocker.MagicMock(id=1), mocker.MagicMock(id=2)])\\n # add Scenario Outline to Feature\\n feature.scenarios.append(\\n mocker.MagicMock(\\n spec=ScenarioOutline,\\n id=3,\\n scenarios=[mocker.MagicMock(id=4), mocker.MagicMock(id=5)],\\n )\\n )\\n # add Scenario Loop to Feature\\n feature.scenarios.append(\\n mocker.MagicMock(\\n spec=ScenarioLoop,\\n id=6,\\n scenarios=[mocker.MagicMock(id=7), mocker.MagicMock(id=8)],\\n )\\n )\\n\\n # when\\n all_scenarios = feature.all_scenarios\\n\\n # then\\n assert len(all_scenarios) == 8\\n assert all_scenarios[0].id == 1\\n assert all_scenarios[1].id == 2\\n assert all_scenarios[2].id == 3\\n assert all_scenarios[3].id == 4\\n assert all_scenarios[4].id == 5\\n assert all_scenarios[5].id == 6\\n assert all_scenarios[6].id == 7\\n assert all_scenarios[7].id == 8\",\n \"def startOfTestcase(self):\\n pass # nothing to do here. Hence pass statement is called.\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7691797","0.707937","0.67126054","0.6650521","0.65280485","0.64290357","0.6334851","0.62848485","0.62843955","0.62091523","0.6069071","0.6062225","0.599871","0.5980599","0.59706324","0.595907","0.5943288","0.5943288","0.59366554","0.593","0.5900602","0.58844084","0.585394","0.5849055","0.58469754","0.58162516","0.5810937","0.5779196","0.5731352","0.5725286","0.5695163","0.56799203","0.5678859","0.5673829","0.56727576","0.56717265","0.5644588","0.56399083","0.56369275","0.56336904","0.56218654","0.56097543","0.56084234","0.5608094","0.55952746","0.5594317","0.5579513","0.5564419","0.5537079","0.55345404","0.5531107","0.55152416","0.5511833","0.54999983","0.54925805","0.54796845","0.54760253","0.5473919","0.54648906","0.5454832","0.5439204","0.5403764","0.53894323","0.5375836","0.5375593","0.53747964","0.5357909","0.53569007","0.53357357","0.53313","0.5318187","0.5316185","0.5312391","0.5310383","0.5307372","0.5302601","0.5295092","0.5285812","0.5283662","0.52828175","0.5280688","0.52641535","0.52639604","0.52614206","0.5261196","0.52589995","0.52567714","0.5255594","0.52450407","0.52443874","0.524101","0.52392846","0.5233784","0.52312785","0.5229932","0.5229406","0.5226385","0.52248085","0.52219254","0.5215366","0.5214717"],"string":"[\n \"0.7691797\",\n \"0.707937\",\n \"0.67126054\",\n \"0.6650521\",\n \"0.65280485\",\n \"0.64290357\",\n \"0.6334851\",\n \"0.62848485\",\n \"0.62843955\",\n \"0.62091523\",\n \"0.6069071\",\n \"0.6062225\",\n \"0.599871\",\n \"0.5980599\",\n \"0.59706324\",\n \"0.595907\",\n \"0.5943288\",\n \"0.5943288\",\n \"0.59366554\",\n \"0.593\",\n \"0.5900602\",\n \"0.58844084\",\n \"0.585394\",\n \"0.5849055\",\n \"0.58469754\",\n \"0.58162516\",\n \"0.5810937\",\n \"0.5779196\",\n \"0.5731352\",\n \"0.5725286\",\n \"0.5695163\",\n \"0.56799203\",\n \"0.5678859\",\n \"0.5673829\",\n \"0.56727576\",\n \"0.56717265\",\n \"0.5644588\",\n \"0.56399083\",\n \"0.56369275\",\n \"0.56336904\",\n \"0.56218654\",\n \"0.56097543\",\n \"0.56084234\",\n \"0.5608094\",\n \"0.55952746\",\n \"0.5594317\",\n \"0.5579513\",\n \"0.5564419\",\n \"0.5537079\",\n \"0.55345404\",\n \"0.5531107\",\n \"0.55152416\",\n \"0.5511833\",\n \"0.54999983\",\n \"0.54925805\",\n \"0.54796845\",\n \"0.54760253\",\n \"0.5473919\",\n \"0.54648906\",\n \"0.5454832\",\n \"0.5439204\",\n \"0.5403764\",\n \"0.53894323\",\n \"0.5375836\",\n \"0.5375593\",\n \"0.53747964\",\n \"0.5357909\",\n \"0.53569007\",\n \"0.53357357\",\n \"0.53313\",\n \"0.5318187\",\n \"0.5316185\",\n \"0.5312391\",\n \"0.5310383\",\n \"0.5307372\",\n \"0.5302601\",\n \"0.5295092\",\n \"0.5285812\",\n \"0.5283662\",\n \"0.52828175\",\n \"0.5280688\",\n \"0.52641535\",\n \"0.52639604\",\n \"0.52614206\",\n \"0.5261196\",\n \"0.52589995\",\n \"0.52567714\",\n \"0.5255594\",\n \"0.52450407\",\n \"0.52443874\",\n \"0.524101\",\n \"0.52392846\",\n \"0.5233784\",\n \"0.52312785\",\n \"0.5229932\",\n \"0.5229406\",\n \"0.5226385\",\n \"0.52248085\",\n \"0.52219254\",\n \"0.5215366\",\n \"0.5214717\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":89393,"cells":{"query":{"kind":"string","value":"Generates scenario 2. Scenario 2 consists of radar and ais measurements with the same sampling rate."},"document":{"kind":"string","value":"def generate_scenario_2(seed=1996, permanent_save=True, sigma_process=0.01, sigma_meas_radar=3, sigma_meas_ais=1,\n timesteps=20):\n start_time = datetime.now()\n\n # specify seed to be able repeat example\n np.random.seed(seed)\n\n # combine two 1-D CV models to create a 2-D CV model\n transition_model = CombinedLinearGaussianTransitionModel(\n [ConstantVelocity(sigma_process), ConstantVelocity(sigma_process)])\n\n # starting at 0,0 and moving NE\n truth = GroundTruthPath([GroundTruthState([0, 1, 0, 1], timestamp=start_time)])\n\n # generate truth using transition_model and noise\n for k in range(1, timesteps):\n truth.append(GroundTruthState(\n transition_model.function(truth[k - 1], noise=True, time_interval=timedelta(seconds=1)),\n timestamp=start_time + timedelta(seconds=k)))\n\n # Simulate measurements\n # Specify measurement model for radar\n measurement_model_radar = LinearGaussian(\n ndim_state=4, # number of state dimensions\n mapping=(0, 2), # mapping measurement vector index to state index\n noise_covar=np.array([[sigma_meas_radar, 0], # covariance matrix for Gaussian PDF\n [0, sigma_meas_radar]])\n )\n\n # Specify measurement model for AIS (Same as for radar)\n measurement_model_ais = LinearGaussian(\n ndim_state=4,\n mapping=(0, 2),\n noise_covar=np.array([[sigma_meas_ais, 0],\n [0, sigma_meas_ais]])\n )\n\n # generate \"radar\" measurements\n measurements_radar = []\n for state in truth:\n measurement = measurement_model_radar.function(state, noise=True)\n measurements_radar.append(Detection(measurement, timestamp=state.timestamp))\n\n # generate \"AIS\" measurements\n measurements_ais = []\n for state in truth:\n measurement = measurement_model_ais.function(state, noise=True)\n measurements_ais.append(Detection(measurement, timestamp=state.timestamp))\n\n if permanent_save:\n save_folder_name = seed.__str__()\n else:\n save_folder_name = \"temp\"\n\n save_folder = \"../scenarios/scenario2/\" + save_folder_name + \"/\"\n\n # save the ground truth and the measurements for the radar and the AIS\n store_object.store_object(truth, save_folder, \"ground_truth.pk1\")\n store_object.store_object(measurements_radar, save_folder, \"measurements_radar.pk1\")\n store_object.store_object(measurements_ais, save_folder, \"measurements_ais.pk1\")\n store_object.store_object(start_time, save_folder, \"start_time.pk1\")\n store_object.store_object(measurement_model_radar, save_folder, \"measurement_model_radar.pk1\")\n store_object.store_object(measurement_model_ais, save_folder, \"measurement_model_ais.pk1\")\n store_object.store_object(transition_model, save_folder, \"transition_model.pk1\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_sampling2 () :\n delta = 2 * np.pi / 3\n r = Reward(partial(stepFunction, \n xRange=(-delta/2, delta/2), \n yRange=(-delta/2, delta/2)), \n (-1, 0))\n states = []\n xs = np.arange(-np.pi, np.pi, delta)\n ys = np.arange(-np.pi, np.pi, delta)\n for x, y in product(xs, ys) : \n states.append(\n toExternalStateRep([x + delta / 2, y + delta / 2, 0, 0]).astype(float)\n )\n agent = findOptimalAgent(r)\n vals = estimateValueFromAgent(states, agent, r)\n for s, v in zip(states, vals) : \n print(toInternalStateRep(s)[:2], v)","def test_two_unsampled_arms(self):\n self._test_two_unsampled_arms()","def generate_scenario_3(seed=1996, permanent_save=True, radar_meas_rate=1, ais_meas_rate=5, sigma_process=0.01,\n sigma_meas_radar=3, sigma_meas_ais=1, timesteps=20):\n start_time = datetime.now()\n\n # specify seed to be able to repeat the example\n np.random.seed(seed)\n\n # combine two 1-D CV models to create a 2-D CV model\n transition_model = CombinedLinearGaussianTransitionModel(\n [ConstantVelocity(sigma_process), ConstantVelocity(sigma_process)])\n\n # starting at 0,0 and moving NE\n truth = GroundTruthPath([GroundTruthState([0, 1, 0, 1], timestamp=start_time)])\n\n # generate truth using transition_model and noise\n end_time = start_time + timedelta(seconds=timesteps * max(radar_meas_rate, ais_meas_rate))\n time = start_time + timedelta(seconds=1)\n while time < end_time:\n truth.append(GroundTruthState(\n transition_model.function(truth[-1], noise=True, time_interval=timedelta(seconds=1)),\n timestamp=time))\n time += timedelta(seconds=1)\n\n # Simulate measurements\n # Specify measurement model for radar\n measurement_model_radar = LinearGaussian(\n ndim_state=4, # number of state dimensions\n mapping=(0, 2), # mapping measurement vector index to state index\n noise_covar=np.array([[sigma_meas_radar, 0], # covariance matrix for Gaussian PDF\n [0, sigma_meas_radar]])\n )\n\n # Specify measurement model for AIS (Same as for radar)\n measurement_model_ais = LinearGaussian(\n ndim_state=4,\n mapping=(0, 2),\n noise_covar=np.array([[sigma_meas_ais, 0],\n [0, sigma_meas_ais]])\n )\n\n # generate \"radar\" measurements\n measurements_radar = []\n measurements_ais = []\n next_radar_meas_time = start_time\n next_ais_meas_time = start_time\n for state in truth:\n # check whether we want to generate a measurement from this gt\n if state.timestamp == next_radar_meas_time:\n measurement = measurement_model_radar.function(state, noise=True)\n measurements_radar.append(Detection(measurement, timestamp=state.timestamp))\n next_radar_meas_time += timedelta(seconds=radar_meas_rate)\n\n if state.timestamp == next_ais_meas_time:\n measurement = measurement_model_ais.function(state, noise=True)\n measurements_ais.append(Detection(measurement, timestamp=state.timestamp))\n next_ais_meas_time += timedelta(seconds=ais_meas_rate)\n\n if permanent_save:\n save_folder_name = seed.__str__()\n else:\n save_folder_name = \"temp\"\n\n save_folder = \"../scenarios/scenario3/\" + save_folder_name + \"/\"\n\n # save the ground truth and the measurements for the radar and the AIS\n store_object.store_object(truth, save_folder, \"ground_truth.pk1\")\n store_object.store_object(measurements_radar, save_folder, \"measurements_radar.pk1\")\n store_object.store_object(measurements_ais, save_folder, \"measurements_ais.pk1\")\n store_object.store_object(start_time, save_folder, \"start_time.pk1\")\n store_object.store_object(measurement_model_radar, save_folder, \"measurement_model_radar.pk1\")\n store_object.store_object(measurement_model_ais, save_folder, \"measurement_model_ais.pk1\")\n store_object.store_object(transition_model, save_folder, \"transition_model.pk1\")","def test_2nd_scenario():\n start_entered_scenario(config.MERAKI_CAMERAS[0][\"serial\"])\n return \"ok\"","def simulationTwoDrugsVirusPopulations():\n #TODO","def test_i2c():\n\n bus0 = i2c_interface.I2CBus(0)\n bus1 = i2c_interface.I2CBus(1)\n\n bus1.init_device(0)\n bus1.init_device(1)\n bus0.init_device(1)\n\n acc0 = i2c_interface.Accelerometer(bus1.get_device(0))\n acc1 = i2c_interface.Accelerometer(bus1.get_device(1))\n acc2 = i2c_interface.Accelerometer(bus0.get_device(1))\n\n ti = time.time()\n print(\"Reading data...\")\n while time.time() - ti < 5:\n acc0.read()\n acc1.read()\n acc2.read()\n print(\"done\")\n\n acc0_sample_times = np.array(acc0.get_sample_times()) - ti\n acc0_magnitudes = acc0.get_magnitude_data()\n acc1_sample_times = np.array(acc1.get_sample_times()) - ti\n acc1_magnitudes = acc1.get_magnitude_data()\n acc2_sample_times = np.array(acc2.get_sample_times()) - ti\n acc2_magnitudes = acc2.get_magnitude_data()\n \n plt.plot(acc0_sample_times, acc0_magnitudes, label='acc0')\n plt.plot(acc1_sample_times, acc1_magnitudes, label='acc1')\n plt.plot(acc2_sample_times, acc2_magnitudes, label='acc2')\n plt.legend()\n plt.show()\n\n tdoa10 = blockmatching.TDOA(acc1_magnitudes, acc0_magnitudes, acc0_sample_times)\n tdoa20 = blockmatching.TDOA(acc2_magnitudes, acc0_magnitudes, acc0_sample_times)\n print(tdoa10, tdoa20)","def generate_scenario_1(seed=1996, permanent_save=True, sigma_process=0.01, sigma_meas_radar=3, sigma_meas_ais=1):\n # specify seed to be able repeat example\n start_time = datetime.now()\n\n np.random.seed(seed)\n\n # combine two 1-D CV models to create a 2-D CV model\n transition_model = CombinedLinearGaussianTransitionModel([ConstantVelocity(sigma_process),\n ConstantVelocity(sigma_process)])\n\n # starting at 0,0 and moving NE\n truth = GroundTruthPath([GroundTruthState([0, 1, 0, 1], timestamp=start_time)])\n\n # generate truth using transition_model and noise\n for k in range(1, 21):\n truth.append(GroundTruthState(\n transition_model.function(truth[k - 1], noise=True, time_interval=timedelta(seconds=1)),\n timestamp=start_time + timedelta(seconds=k)))\n\n # Simulate measurements\n # Specify measurement model for radar\n measurement_model_radar = LinearGaussian(\n ndim_state=4, # number of state dimensions\n mapping=(0, 2), # mapping measurement vector index to state index\n noise_covar=np.array([[sigma_meas_radar, 0], # covariance matrix for Gaussian PDF\n [0, sigma_meas_radar]])\n )\n\n # Specify measurement model for AIS\n measurement_model_ais = LinearGaussian(\n ndim_state=4,\n mapping=(0, 2),\n noise_covar=np.array([[sigma_meas_ais, 0],\n [0, sigma_meas_ais]])\n )\n\n # generate \"radar\" measurements\n measurements_radar = []\n for state in truth:\n measurement = measurement_model_radar.function(state, noise=True)\n measurements_radar.append(Detection(measurement, timestamp=state.timestamp))\n\n # generate \"AIS\" measurements\n measurements_ais = []\n state_num = 0\n for state in truth:\n state_num += 1\n if not state_num % 2: # measurement every second time step\n measurement = measurement_model_ais.function(state, noise=True)\n measurements_ais.append(Detection(measurement, timestamp=state.timestamp))\n\n if permanent_save:\n save_folder_name = seed.__str__()\n else:\n save_folder_name = \"temp\"\n\n save_folder = \"../scenarios/scenario1/\" + save_folder_name + \"/\"\n\n # save the ground truth and the measurements for the radar and the AIS\n store_object.store_object(truth, save_folder, \"ground_truth.pk1\")\n store_object.store_object(measurements_radar, save_folder, \"measurements_radar.pk1\")\n store_object.store_object(measurements_ais, save_folder, \"measurements_ais.pk1\")\n store_object.store_object(start_time, save_folder, \"start_time.pk1\")\n store_object.store_object(measurement_model_radar, save_folder, \"measurement_model_radar.pk1\")\n store_object.store_object(measurement_model_ais, save_folder, \"measurement_model_ais.pk1\")\n store_object.store_object(transition_model, save_folder, \"transition_model.pk1\")","def scenario3(sim):\n\n init_logging(logfile=None, debug=True)\n second = 1000.0\n duration = 10\n tau_m = 20 # ms\n cm = 1.0 # nF\n v_reset = -60\n cell_parameters = dict(\n tau_m=tau_m,\n cm=cm,\n v_rest=-70,\n e_rev_E=0,\n e_rev_I=-70,\n v_thresh=-54,\n v_reset=v_reset,\n tau_syn_E=5,\n tau_syn_I=5,\n )\n g_leak = cm / tau_m # µS\n\n w_min = 0.0 * g_leak\n w_max = 0.05 * g_leak\n\n r1 = 5.0\n r2 = 40.0\n\n sim.setup()\n pre = sim.Population(100, sim.SpikeSourcePoisson())\n post = sim.Population(10, sim.IF_cond_exp())\n\n pre.set(duration=duration * second)\n pre.set(start=0.0)\n pre[:50].set(rate=r1)\n pre[50:].set(rate=r2)\n assert_equal(pre[49].rate, r1)\n assert_equal(pre[50].rate, r2)\n post.set(**cell_parameters)\n post.initialize(v=RandomDistribution('normal', mu=v_reset, sigma=5.0))\n\n stdp = sim.STDPMechanism(\n sim.SpikePairRule(tau_plus=20.0, tau_minus=20.0,\n A_plus=0.01, A_minus=0.01),\n sim.AdditiveWeightDependence(w_min=w_min, w_max=w_max),\n #dendritic_delay_fraction=0.5))\n dendritic_delay_fraction=1)\n\n connections = sim.Projection(pre, post, sim.AllToAllConnector(),\n synapse_type=stdp,\n receptor_type='excitatory')\n\n initial_weight_distr = RandomDistribution('uniform', low=w_min, high=w_max)\n connections.randomizeWeights(initial_weight_distr)\n initial_weights = connections.get('weight', format='array', gather=False)\n # assert initial_weights.min() >= w_min\n # assert initial_weights.max() < w_max\n # assert initial_weights[0, 0] != initial_weights[1, 0]\n\n pre.record('spikes')\n post.record('spikes')\n post[0:1].record('v')\n\n sim.run(duration * second)\n\n actual_rate = pre.mean_spike_count() / duration\n expected_rate = (r1 + r2) / 2\n errmsg = \"actual rate: %g expected rate: %g\" % (actual_rate, expected_rate)\n assert abs(actual_rate - expected_rate) < 1, errmsg\n #assert abs(pre[:50].mean_spike_count()/duration - r1) < 1\n #assert abs(pre[50:].mean_spike_count()/duration- r2) < 1\n final_weights = connections.get('weight', format='array', gather=False)\n assert initial_weights[0, 0] != final_weights[0, 0]\n\n try:\n import scipy.stats\n except ImportError:\n raise SkipTest\n t, p = scipy.stats.ttest_ind(initial_weights[:50, :].flat, initial_weights[50:, :].flat)\n assert p > 0.05, p\n t, p = scipy.stats.ttest_ind(final_weights[:50, :].flat, final_weights[50:, :].flat)\n assert p < 0.01, p\n assert final_weights[:50, :].mean() < final_weights[50:, :].mean()\n sim.end()\n return initial_weights, final_weights, pre, post, connections","def simulationTwoDrugsDelayedTreatment():\n\n # TODO","def simulate_test_data(self):\n # simulation config\n if self.instrument == 'IMA':\n sim_config = SimConfig.makeSim(name=\"IMA Simulation\", rel_obsdate=0.0, scene=\"scene.ini\", POP='IMA',\n ConfigPath='IMA_FULL', Dither=False,StartInd=1, NDither=4,\n DitherPat=\"ima_recommended_dither.dat\", filter=\"F1130W\",\n readDetect= 'FULL', ima_mode= 'FAST', ima_exposures=1,\n ima_integrations=1, ima_frames=50, disperser= 'SHORT', detector= 'SW',\n mrs_mode= 'SLOW', mrs_exposures=5,mrs_integrations=4,mrs_frames=10)\n\n # scene config\n background = Background(level='low', gradient=5., pa=15.0, centreFOV=(0., 0.))\n\n SED1 = BBSed(Temp=300., wref=10., flux=1.e8)\n Gal1 = Galaxy(Cen=(0., 0.), n=1., re=200, q=0.99, pa=0.1)\n Gal1.set_SED(SED1)\n targets = [Gal1]\n\n scene_config = SceneConfig.makeScene(loglevel=0, background=background, targets=targets)\n\n elif self.instrument == 'MRS':\n sim_config = SimConfig.makeSim(name=\"MRS Simulation\", rel_obsdate=0.0, scene=\"scene.ini\",\n POP='MRS', ConfigPath='MRS_1SHORT', Dither=False, StartInd=1,\n NDither=4, DitherPat=\"mrs_recommended_dither.dat\", filter=\"F1130W\",\n readDetect='FULL', ima_mode='FAST', ima_exposures=1,\n ima_integrations=1, ima_frames=20, disperser='SHORT', detector='SW',\n mrs_mode='FAST', mrs_exposures=1, mrs_integrations=1, mrs_frames=50)\n\n # scene config\n background = Background(level='low',gradient=5.,pa=15.0,centreFOV=(0., 0.))\n\n SED1 = BBSed(Temp=300., wref=10., flux=5.e6)\n Gal1 = Galaxy(Cen=(0.,0.), n=1., re=2, q=0.99, pa=0.1)\n Gal1.set_SED(SED1)\n targets = [Gal1]\n\n scene_config = SceneConfig.makeScene(loglevel=0, background=background, targets=targets)\n\n # simulator config\n if self.noise:\n simulator_config = SimulatorConfig.makeSimulator(max_fsm=0.050, max_dither=20.0, mrs_ref_channel=1,\n mrs_ref_band=\"SHORT\", tau_telescope=0.88,tau_eol=0.8,\n telescope_area=25.032, telescope_pupil_diam=6.6052,\n take_webbPsf=False, include_refpix=True,\n include_poisson=True, include_readnoise=True,\n include_badpix=True, include_dark=True,\n include_flat=False, include_gain=True,\n include_nonlinearity=self.linearity, include_drifts=True,\n include_latency=False, cosmic_ray_mode='NONE')\n else:\n simulator_config = SimulatorConfig.makeSimulator(max_fsm=0.050, max_dither=20.0, mrs_ref_channel=1,\n mrs_ref_band=\"SHORT\", tau_telescope=0.88, tau_eol=0.8,\n telescope_area=25.032, telescope_pupil_diam=6.6052,\n take_webbPsf=False, include_refpix=True,\n include_poisson=False, include_readnoise=False,\n include_badpix=True, include_dark=True,\n include_flat=False, include_gain=True,\n include_nonlinearity=self.linearity, include_drifts=True,\n include_latency=False, cosmic_ray_mode='NONE')\n\n\n # run the simulation\n simulation = MiriSimulation(sim_config=sim_config, scene_config=scene_config,\n simulator_config=simulator_config, loglevel='DEBUG')\n simulation.run()\n\n # we only need the sim file so move it to output_dir and remove everthing else\n det_image_file = glob.glob(os.path.join(simulation.path_out, 'det_images', '*.fits'))[0]\n self.ramp_file = os.path.join(self.output_dir, os.path.basename(det_image_file))\n shutil.move(det_image_file, self.ramp_file)\n shutil.rmtree(simulation.path_out)","def test_f2_circuit_maker(self):\n fho = tfho.test_file_handle_object()\n W = 5\n G = 20\n fg = .9\n X = 10\n fx = .85\n gate_maker = g.TYPE_TO_GATE_GEN[g.TEST_TYPES.RANDOM]\n # family 2 files:\n t_circuit_file_name = \"circuit_file_trimming\"\n t_circuit_file = fho.get_file_object(t_circuit_file_name, 'w')\n t_input_file_name = \"input_file_trimming\"\n t_input_file = fho.get_file_object(t_input_file_name, 'w')\n t_output_file_name = \"output_file_trimming\"\n t_output_file = fho.get_file_object(t_output_file_name, 'w')\n nt_circuit_file_name = \"circuit_file_no_trimming\"\n nt_circuit_file = fho.get_file_object(nt_circuit_file_name, 'w')\n nt_input_file_name = \"input_file_no_trimming\"\n nt_input_file = fho.get_file_object(nt_input_file_name, 'w')\n nt_output_file_name = \"output_file_no_trimming\"\n nt_output_file = fho.get_file_object(nt_output_file_name, 'w')\n level_type_array = [g.LEVEL_TYPES.XOR, g.LEVEL_TYPES.RANDOM,\n g.LEVEL_TYPES.XOR]\n F = 2\n # make a family 1 circuit with trimming:\n sr.seed(self.rand_seed)\n t_gen = g.f1f2_circuit_maker_with_trimming_switch(W, G, fg,\n t_circuit_file,\n t_input_file,\n t_output_file,\n X, fx, gate_maker,\n level_type_array, True)\n t_gen.generate()\n # make a family 1 circuit without trimming, with the same randomness:\n sr.seed(self.rand_seed)\n nt_gen = g.f1f2_circuit_maker_with_trimming_switch(W, G, fg,\n nt_circuit_file,\n nt_input_file,\n nt_output_file,\n X, fx, gate_maker,\n level_type_array, False)\n nt_gen.generate()\n # obtain strings representing the contents of all the resulting files:\n t_circuit_string = fho.get_file(t_circuit_file_name).getvalue()\n t_input_string = fho.get_file(t_input_file_name).getvalue()\n t_output_string = fho.get_file(t_output_file_name).getvalue()\n nt_circuit_string = fho.get_file(nt_circuit_file_name).getvalue()\n nt_input_string = fho.get_file(nt_input_file_name).getvalue()\n nt_output_string = fho.get_file(nt_output_file_name).getvalue()\n # make sure that the inputs and outputs produced by the trimming and\n # no trimming algorithms are the same:\n self.assertEqual(t_input_string, nt_input_string)\n self.assertEqual(t_output_string, nt_output_string)\n # make sure that the input begins and ends with a bracket:\n self.assertEqual(\"[\", t_input_string[0])\n self.assertEqual(\"]\", t_input_string[-1])\n # make sure that each input element is a bit:\n for bit in t_input_string[1:-1]:\n self.assertTrue((bit == '0') or (bit == '1'))\n # make sure that the output is a bit:\n self.assertTrue((t_output_string == '0') or (t_output_string == '1'))\n # make sure that the two circuit headers are the same, and that they\n # contain the correct values:\n t_circuit_header = t_circuit_string.split(\"\\n\")[0]\n nt_circuit_header = nt_circuit_string.split(\"\\n\")[0]\n self.assertEqual(t_circuit_header, nt_circuit_header)\n (W_string, G_string, X_string, F_string) = t_circuit_header.split(\",\")\n W_value = int(W_string.split(\"=\")[-1])\n G_value = int(G_string.split(\"=\")[-1])\n X_value = int(X_string.split(\"=\")[-1])\n F_value = int(F_string.split(\"=\")[-1])\n self.assertEqual(W, W_value)\n self.assertEqual(G, G_value)\n self.assertEqual(F, F_value)\n # note that we cannot test that the circuits themselves are the same,\n # because the trimming algorithm produces a circuit with gates listed\n # in a different order.","def generate_syntetic_data( self ):\n label_list = []\n img_ground_truth = np.zeros( ( self.img_h, self.img_w ), dtype = np.float32 )\n img = np.zeros( ( self.img_h, self.img_w, 3 ), dtype = np.float32 )\n class_blue = np.zeros( ( self.img_h, self.img_w ), dtype = np.float32 )\n class_red = np.zeros( ( self.img_h, self.img_w ), dtype = np.float32 )\n\n '''line, square, grill, rectangle, cross'''\n if self.option_shape == 'line':\n sample_type = 0\n elif self.option_shape == 'circle':\n sample_type = 1\n elif self.option_shape == 'rectangle':\n sample_type = 2\n else: \n sample_type = np.random.randint( 3 ) #0,1,2\n\n if sample_type == 0:\n self.generate_line( img_ground_truth, img, class_blue, class_red )\n elif sample_type == 1:\n self.generate_circle( img_ground_truth, img, class_blue, class_red )\n else:\n self.generate_rectangle( img_ground_truth, img, class_blue, class_red )\n\n label_list.append( class_blue )\n label_list.append( class_red )\n # 2 classes generates\n label_all = np.dstack( label_list ).astype( np.float32 ) \n r, g, b = cv2.split( img )\n img_bgr = cv2.merge( [ b, g, r ] )\n\n gen_adj = GenerateAdjMatrx( type_dist = self.type_dist )\n A_gt = gen_adj.adjmatrx_groundthuth( img_ground_truth )\n\n B_in = gen_adj.adjmatrx_groundthuth(img_ground_truth * 0)\n\n return img_bgr, img_ground_truth, label_all, A_gt, B_in","def _choose_sample(self):\n\n \t #periodically generate a new reconstruction for the purposes of sampling","def punto2_2():\r\n\tprint(\"2.2R/\")\r\n\tp_small = [0.005*i for i in range(20)]\r\n\tp_large =[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1]\r\n\tp_large= p_small + p_large\t\r\n\ts = multStats2(p_small,100,500)\r\n\ty1,y2,y3,y4,y5= s[0],s[1],s[2],s[3],s[4]\r\n\tplt.plot(p_small,y1, label = \"Avrg num of ArtP\")\r\n\tplt.plot(p_small,y2, label = \"Avrg num of Brid\")\r\n\tplt.plot(p_small,y3, label = \"Avrg size per CC\")\r\n\tplt.plot(p_small,y4, label = \"Avrg node Deg\")\r\n\t#plt.plot(p_small,y5)\r\n\tplt.xlabel(\"P_Small\")\r\n\tplt.ylabel(\"Average Values\")\r\n\tplt.title(\"Random Graph Metrics\")\r\n\tplt.legend()\r\n\tplt.show()\r\n\ts = multStats3(p_large,20,100)\r\n\ty2,y3,y4 = s[0],s[1],s[2]\r\n\ty1 = multStats4(p_large,100,500)\r\n\tplt.plot(p_large,y1, label = \"Avrg num of CC\")\r\n\tplt.plot(p_large,y2, label = \"Avrg num of triangles\")\r\n\tplt.plot(p_large,y3, label = \"Avrg num of triplets\")\r\n\tplt.plot(p_large,y4, label = \"#triangles/#triplets\")\r\n\tplt.xlabel(\"P_Small & P_Large\")\r\n\tplt.ylabel(\"Average Values\")\r\n\tplt.title(\"Random Graph Metrics\")\r\n\tplt.legend()\r\n\tplt.show()\r\n\treturn","def radarScat(sp, wl, K2=0.93):\n#TODO check if K2 is for ice or liquid!\n prefactor = 2*np.pi*wl**4/(np.pi**5*K2)\n \n \n reflect_hh = prefactor*(sp.Z11+sp.Z22+sp.Z12+sp.Z21)\n reflect_vv = prefactor*(sp.Z11+sp.Z22-sp.Z12-sp.Z21)\n kdp = 1e-3*(180.0/np.pi)*wl*sp.S22r_S11r\n\n reflect_hv = prefactor*(sp.Z11 - sp.Z12 + sp.Z21 - sp.Z22)\n #reflect_vh = prefactor*(sp.Z11 + sp.Z12 - sp.Z21 - sp.Z22).values\n ldr_h = reflect_hh/reflect_hv\n \n # delta_hv np.arctan2(Z[2,3] - Z[3,2], -Z[2,2] - Z[3,3])\n #a = (Z[2,2] + Z[3,3])**2 + (Z[3,2] - Z[2,3])**2\n #b = (Z[0,0] - Z[0,1] - Z[1,0] + Z[1,1])\n #c = (Z[0,0] + Z[0,1] + Z[1,0] + Z[1,1])\n #rho_hv np.sqrt(a / (b*c))\n rho_hv = np.nan*np.ones_like(reflect_hh) # disable rho_hv for now\n #Ah = 4.343e-3 * 2 * scatterer.wavelength * sp.S22i.values # attenuation horizontal polarization\n #Av = 4.343e-3 * 2 * scatterer.wavelength * sp.S11i.values # attenuation vertical polarization\n\n #- test: calculate extinction: TODO: test Cextx that is given in DDA with this calculation.\n k = 2 * np.pi / (wl)\n cext_hh = sp.S22i*4.0*np.pi/k\n cext_vv = sp.S11i*4.0*np.pi/k\n \n return reflect_hh, reflect_vv, reflect_hv, kdp, rho_hv, cext_hh, cext_vv","def test_sample(system_generator):\n\n name, test = system_generator()\n print(name)\n\n w_F, w_R, N_k = test.sample([10, 8], mode=\"wFwR\")\n w_F, w_R, N_k = test.sample([1, 1], mode=\"wFwR\")\n w_F, w_R, N_k = test.sample([10, 0], mode=\"wFwR\")\n w_F, w_R, N_k = test.sample([0, 5], mode=\"wFwR\")","def get_next_sample(self):","def test_y_generate():\n a = Attractor()\n #say x, y, z = [0.1, 0.0, 0.0]\n\n dx = (10.0 * (0.0 - 0.1)) * (80.0-0.0)/10000 + 0.1\n dy = (0.1 * (28 - 0.0) - 0.0) * (80.0-0.0)/10000 + 0.0\n dz = ((0.1 * 0.0) - (8/3 * 0.0)) * (80.0-0.0)/10000 + 0.0\n ex_1 = np.array([dx, dy, dz])\n\n dx2 = (10.0 * (dy - dx)) * (80.0-0.0)/10000.0 + dx \n dy2 = (dx * (28.0 - dz) - dy) * (80.0-0.0)/10000.0 + dy\n dz2 = ((dx * dy) - (8/3 * dz)) * (80.0-0.0)/10000.0 + dz\n ex_2 = np.array([dx2, dy2, dz2])\n\n dx3 = (10.0 * (dy2 - dx2)) * (80.0-0.0)/10000.0 + dx2\n dy3 = (dx2 * (28.0 - dz2) - dy2) * (80.0-0.0)/10000.0 + dy2\n dz3 = ((dx2 * dy2) - (8/3 * dz2)) * (80.0-0.0)/10000.0 + dz2\n ex_3 = np.array([dx3, dy3, dz3])\n\n dx4 = (10.0 * (dy3 - dx3)) * (80.0-0.0)/10000.0 + dx3\n dy4 = (dx3 * (28 - dz3) - dy3) * (80.0-0.0)/10000.0 + dy3\n dz4 = ((dx3 * dy3) - (8/3 * dz3)) * (80.0-0.0)/10000.0 + dz3\n ex_4 = np.array([dx4, dy4, dz4])\n\n dx5 = (10.0 * (dy4 - dx4)) * (80.0-0.0)/10000.0 + dx4\n dy5 = (dx4 * (28 - dz4) - dy4) * (80.0-0.0)/10000.0 + dy4\n dz5 = ((dx4 * dy4) - (8/3 * dz4)) * (80.0-0.0)/10000.0 + dz4\n ex_5 = np.array([dx5, dy5, dz5])\n\n \n a.evolve(order = 4)\n y_list = a.solution['y'].tolist()\n \n for i in y_list[:6]:\n yy = round(i, 2)\n for j in [0.0, dy, dy2, dy3, dy4, dy5]:\n yyy = round(j, 2)\n \n print (\"Actual increments: \", yy)#str(a.solution()['x']).strip('[]'))\n print (\"Expected increments: \", yyy)\n assert yy == yyy","def generate_n2(self):\r\n obj = Random\r\n self.n_2 = int(obj.time_split[0])+int(obj.time_split[1])+int(obj.time_split[2])\r\n self.n_2decimal = int(abs(pi)*math.pow(10, self.n_2)) % 10","def test_dataset_scenario_generation_full(self):\n params = ParameterServer()\n\n map_filename = os.path.join(os.path.dirname(__file__), \"data/DR_DEU_Merging_MT_v01_shifted.xodr\")\n track_filename = os.path.join(os.path.dirname(__file__), \"data/interaction_dataset_DEU_Merging_dummy_track.csv\")\n\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"MapFilename\"] = map_filename\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"TrackFilenameList\"] = [track_filename]\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"StartingOffsetMs\"] = 0\n\n scenario_generation = InteractionDatasetScenarioGenerationFull(\n params=params, num_scenarios=2)\n\n self.assertEqual(scenario_generation.get_num_scenarios(), 2)\n\n # first scenario\n scenario0 = scenario_generation.get_scenario(0)\n agent11 = scenario0.GetWorldState().agents[1]\n agent12 = scenario0.GetWorldState().agents[2]\n self.assertEqual(agent11.first_valid_timestamp, 0.0)\n self.assertEqual(agent12.first_valid_timestamp, 0.0)\n # agents are initialized with Behavior::NotValidYet\n self.assertEqual(list(scenario0.GetWorldState().agents_valid.keys()), [])\n scenario0.GetWorldState().Step(0.01)\n self.assertEqual(list(scenario0.GetWorldState().agents_valid.keys()), [1,2])\n\n # second scenario\n scenario1 = scenario_generation.get_scenario(1)\n agent21 = scenario1.GetWorldState().agents[1]\n agent22 = scenario1.GetWorldState().agents[2]\n self.assertEqual(agent21.first_valid_timestamp, 0.0)\n self.assertEqual(agent22.first_valid_timestamp, 0.0)\n self.assertEqual(list(scenario1.GetWorldState().agents_valid.keys()), [])\n scenario1.GetWorldState().Step(0.01)\n self.assertEqual(list(scenario1.GetWorldState().agents_valid.keys()), [1,2])","def test_g2(self):\n self.setUp()\n theta = self.data.theta\n beta_0, beta_1 = theta[\"beta_0\"], theta[\"beta_1\"]\n gamma_0, gamma_1 = theta[\"gamma_0\"], theta[\"gamma_1\"]\n g2 = self.E_func.g2(self.S, gamma_0, beta_0, gamma_1, beta_1)\n # values of g2 at first group and first sample\n g2_0_1 = np.array([347/3, 326/3, 218/3, 266/3])\n # values of g2 at second group and second sample\n g2_1_3 = np.array([153 , 178.5, 151.5, 67.5])\n np.testing.assert_almost_equal(g2[0, 0], g2_0_1)\n np.testing.assert_almost_equal(g2[1, 1], g2_1_3)","def scenario2(angle, speed):\n speedHorizontal = speed * math.cos(angle)\n speedVertical = speed * math.sin(angle)\n timePeak = speedVertical / 9.81\n time = timePeak * 2\n result = speedHorizontal * time\n return result","def samplecost(app, endclasses, fxnmode, samptype='std', title=\"\"):\n associated_scens=[]\n for phase in app.phases:\n associated_scens = associated_scens + app.scenids.get((fxnmode, phase), [])\n costs = np.array([endclasses[scen]['cost'] for scen in associated_scens])\n times = np.array([time for phase, timemodes in app.sampletimes.items() if timemodes for time in timemodes if fxnmode in timemodes.get(time)] ) \n rates = np.array(list(app.rates_timeless[fxnmode].values()))\n \n tPlot, axes = plt.subplots(2, 1, sharey=False, gridspec_kw={'height_ratios': [3, 1]})\n phasetimes_start =[times[0] for phase, times in app.phases.items()]\n phasetimes_end =[times[1] for phase, times in app.phases.items()]\n ratetimes =[]\n ratesvect =[]\n phaselocs = []\n for (ind, phasetime) in enumerate(phasetimes_start):\n axes[0].axvline(phasetime, color=\"black\") \n phaselocs= phaselocs +[(phasetimes_end[ind]-phasetimes_start[ind])/2 + phasetimes_start[ind]]\n\n axes[1].axvline(phasetime, color=\"black\") \n ratetimes = ratetimes + [phasetimes_start[ind]] + [phasetimes_end[ind]]\n ratesvect = ratesvect + [rates[ind]] + [rates[ind]]\n #axes[1].text(middletime, 0.5*max(rates), list(app.phases.keys())[ind], ha='center', backgroundcolor=\"white\")\n #rate plots\n axes[1].set_xticks(phaselocs)\n axes[1].set_xticklabels(list(app.phases.keys()))\n \n axes[1].plot(ratetimes, ratesvect)\n axes[1].set_xlim(phasetimes_start[0], phasetimes_end[-1])\n axes[1].set_ylim(0, np.max(ratesvect)*1.2 )\n axes[1].set_ylabel(\"Rate\")\n axes[1].set_xlabel(\"Time (\"+str(app.units)+\")\")\n axes[1].grid()\n #cost plots\n axes[0].set_xlim(phasetimes_start[0], phasetimes_end[-1])\n axes[0].set_ylim(0, 1.2*np.max(costs))\n if samptype=='fullint':\n axes[0].plot(times, costs, label=\"cost\")\n else:\n if samptype=='quadrature' or samptype=='pruned piecewise-linear': \n sizes = 1000*np.array([weight if weight !=1/len(timeweights) else 0.0 for phase, timeweights in app.weights[fxnmode].items() for time, weight in timeweights.items() if time in times])\n axes[0].scatter(times, costs,s=sizes, label=\"cost\", alpha=0.5)\n axes[0].stem(times, costs, label=\"cost\", markerfmt=\",\", use_line_collection=True)\n \n axes[0].set_ylabel(\"Cost\")\n axes[0].grid()\n if title: axes[0].set_title(title)\n elif type(fxnmode[0])==tuple: axes[0].set_title(\"Cost function of \"+str(fxnmode)+\" over time\")\n else: axes[0].set_title(\"Cost function of \"+fxnmode[0]+\": \"+fxnmode[1]+\" over time\")\n #plt.subplot_adjust()\n plt.tight_layout()","def test_f_two_sample(self):\r\n\r\n # The expected values in this test are obtained through R.\r\n # In R the F test is var.test(x,y) different alternative hypotheses\r\n # can be specified (two sided, less, or greater).\r\n # The vectors are random samples from a particular normal distribution\r\n #(mean and sd specified).\r\n\r\n # a: 50 elem, mean=0 sd=1\r\n a = [-0.70701689, -1.24788845, -1.65516470, 0.10443876, -0.48526915,\r\n -0.71820656, -1.02603596, 0.03975982, -2.23404324, -0.21509363,\r\n 0.08438468, -0.01970062, -0.67907971, -0.89853667, 1.11137131,\r\n 0.05960496, -1.51172084, -0.79733957, -1.60040659, 0.80530639,\r\n -0.81715836, -0.69233474, 0.95750665, 0.99576429, -1.61340216,\r\n -0.43572590, -1.50862327, 0.92847551, -0.68382338, -1.12523522,\r\n -0.09147488, 0.66756023, -0.87277588, -1.36539039, -0.11748707,\r\n -1.63632578, -0.31343078, -0.28176086, 0.33854483, -0.51785630,\r\n 2.25360559, -0.80761191, 1.18983499, 0.57080342, -1.44601700,\r\n -0.53906955, -0.01975266, -1.37147915, -0.31537616, 0.26877544]\r\n\r\n # b: 50 elem, mean=0, sd=1.2\r\n b = [\r\n 0.081418743, 0.276571612, -\r\n 1.864316504, 0.675213612, -0.769202643,\r\n 0.140372825, -1.426250184, 0.058617884, -\r\n 0.819287409, -0.007701916,\r\n -0.782722020, -\r\n 0.285891593, 0.661980419, 0.383225191, 0.622444946,\r\n -0.192446150, 0.297150571, 0.408896059, -\r\n 0.167359383, -0.552381362,\r\n 0.982168338, 1.439730446, 1.967616101, -\r\n 0.579607307, 1.095590943,\r\n 0.240591302, -1.566937143, -\r\n 0.199091349, -1.232983905, 0.362378169,\r\n 1.166061081, -0.604676222, -\r\n 0.536560206, -0.303117595, 1.519222792,\r\n -0.319146503, 2.206220810, -\r\n 0.566351124, -0.720397392, -0.452001377,\r\n 0.250890097, 0.320685395, -\r\n 1.014632725, -3.010346273, -1.703955054,\r\n 0.592587381, -1.237451255, 0.172243366, -0.452641122, -0.982148581]\r\n\r\n # c: 60 elem, mean=5, sd=1\r\n c = [4.654329, 5.242129, 6.272640, 5.781779, 4.391241, 3.800752,\r\n 4.559463, 4.318922, 3.243020, 5.121280, 4.126385, 5.541131,\r\n 4.777480, 5.646913, 6.972584, 3.817172, 6.128700, 4.731467,\r\n 6.762068, 5.082983, 5.298511, 5.491125, 4.532369, 4.265552,\r\n 5.697317, 5.509730, 2.935704, 4.507456, 3.786794, 5.548383,\r\n 3.674487, 5.536556, 5.297847, 2.439642, 4.759836, 5.114649,\r\n 5.986774, 4.517485, 4.579208, 4.579374, 2.502890, 5.190955,\r\n 5.983194, 6.766645, 4.905079, 4.214273, 3.950364, 6.262393,\r\n 8.122084, 6.330007, 4.767943, 5.194029, 3.503136, 6.039079,\r\n 4.485647, 6.116235, 6.302268, 3.596693, 5.743316, 6.860152]\r\n\r\n # d: 30 elem, mean=0, sd =0.05\r\n d = [\r\n 0.104517366, 0.023039678, 0.005579091, 0.052928250, 0.020724823,\r\n -0.060823243, -0.019000890, -\r\n 0.064133996, -0.016321594, -0.008898334,\r\n -0.027626992, -0.051946186, 0.085269587, -\r\n 0.031190678, 0.065172938,\r\n -0.054628573, 0.019257306, -\r\n 0.032427056, -0.058767356, 0.030927400,\r\n 0.052247357, -\r\n 0.042954937, 0.031842104, 0.094130522, -0.024828465,\r\n 0.011320453, -0.016195062, 0.015631245, -0.050335598, -0.031658335]\r\n\r\n a, b, c, d = map(array, [a, b, c, d])\r\n self.assertEqual(map(len, [a, b, c, d]), [50, 50, 60, 30])\r\n\r\n # allowed error. This big, because results from R\r\n # are rounded at 4 decimals\r\n error = 1e-4\r\n\r\n self.assertFloatEqual(f_two_sample(a, a), (49, 49, 1, 1), eps=error)\r\n self.assertFloatEqual(f_two_sample(a, b), (49, 49, 0.8575, 0.5925),\r\n eps=error)\r\n self.assertFloatEqual(f_two_sample(b, a), (49, 49, 1.1662, 0.5925),\r\n eps=error)\r\n self.assertFloatEqual(f_two_sample(a, b, tails='low'),\r\n (49, 49, 0.8575, 0.2963), eps=error)\r\n self.assertFloatEqual(f_two_sample(a, b, tails='high'),\r\n (49, 49, 0.8575, 0.7037), eps=error)\r\n self.assertFloatEqual(f_two_sample(a, c),\r\n (49, 59, 0.6587, 0.1345), eps=error)\r\n # p value very small, so first check df's and F value\r\n self.assertFloatEqualAbs(f_two_sample(d, a, tails='low')[0:3],\r\n (29, 49, 0.0028), eps=error)\r\n assert f_two_sample(d, a, tails='low')[3] < 2.2e-16 # p value\r","def test_t_two_sample(self):\r\n I = array([7.2, 7.1, 9.1, 7.2, 7.3, 7.2, 7.5])\r\n II = array([8.8, 7.5, 7.7, 7.6, 7.4, 6.7, 7.2])\r\n self.assertFloatEqual(t_two_sample(I, II), (-0.1184, 0.45385 * 2),\r\n 0.001)","def compute_rwse(self, traffic_density):\r\n # Ensure experiment has not beem done before\r\n np.random.seed(2020)\r\n file_names = os.listdir(self.dirName)\r\n if traffic_density+'rwse' in file_names:\r\n print(\"This experiment has been done already!\")\r\n return None\r\n elif traffic_density == '':\r\n print(\"select a traffic dinsity level!\")\r\n return None\r\n rwse_dict = {'vel_m':0,\r\n 'lat_vel':1,\r\n 'vel_y':2,\r\n 'vel_f':3,\r\n 'vel_fadj':4}\r\n\r\n pred_step_n = self.pred_h*10+1\r\n splits_n = 6 # number of splits across an entire trajectory\r\n pred_arrs = [np.zeros([self.episode_n*self.traj_n*6,\r\n pred_step_n]) for i in range(5)]\r\n truth_arrs = [np.zeros([self.episode_n*self.traj_n*6,\r\n pred_step_n]) for i in range(5)]\r\n _row = 0\r\n\r\n for episode_id in self.test_data.test_episodes[:self.episode_n]:\r\n # for episode_id in [1289]:\r\n st_seq, cond_seq, st_arr, targ_arr = self.episodeSetup(episode_id)\r\n self.gen_model.max_pc = max(st_arr[:, self.gen_model.indx_m['pc']])\r\n self.gen_model.min_pc = min(st_arr[:, self.gen_model.indx_m['pc']])\r\n if len(st_seq) >= 6:\r\n splits_n = 6\r\n else:\r\n splits_n = len(st_seq)\r\n obs_n = self.test_data.data_obj.obs_n\r\n traj_splits = np.random.choice(range(19, 19+len(st_seq)), splits_n, replace=False)\r\n # leave value of 19 to ensure scenarios remain consistent\r\n for split in traj_splits:\r\n st_seq_i, cond_seq_i, bc_der_i, _, st_i, targ_i = self.sceneSetup(st_seq,\r\n cond_seq,\r\n st_arr,\r\n targ_arr,\r\n current_step=split,\r\n pred_h=self.pred_h)\r\n targ_i.shape = (1, pred_step_n, 5)\r\n st_init = np.repeat(np.reshape(st_i[0,:], [1,17]), self.traj_n, axis=0)\r\n\r\n actions, _, _ = self.policy.get_actions([st_seq_i, cond_seq_i], bc_der_i,\r\n traj_n=self.traj_n, pred_h=self.pred_h)\r\n st_pred = self.gen_model.forwardSim(st_init, actions, pred_step_n)\r\n\r\n truth_arrs[0][_row:_row+self.traj_n, :] = \\\r\n st_i[:,self.gen_model.indx_m['vel']]\r\n pred_arrs[0][_row:_row+self.traj_n, :] = \\\r\n st_pred[:,:,self.gen_model.indx_m['vel']]\r\n\r\n truth_arrs[1][_row:_row+self.traj_n, :] = \\\r\n st_i[:,self.gen_model.indx_m['act_lat_p']]\r\n pred_arrs[1][_row:_row+self.traj_n, :] = \\\r\n st_pred[:,:,self.gen_model.indx_m['act_lat_p']]\r\n\r\n truth_arrs[2][_row:_row+self.traj_n, :] = \\\r\n st_i[:,self.gen_model.indx_y['vel']]\r\n pred_arrs[2][_row:_row+self.traj_n, :] = \\\r\n st_pred[:,:,self.gen_model.indx_y['vel']]\r\n\r\n truth_arrs[3][_row:_row+self.traj_n, :] = \\\r\n st_i[:,self.gen_model.indx_f['vel']]\r\n pred_arrs[3][_row:_row+self.traj_n, :] = \\\r\n st_pred[:,:,self.gen_model.indx_f['vel']]\r\n\r\n truth_arrs[4][_row:_row+self.traj_n, :] = \\\r\n st_i[:,self.gen_model.indx_fadj['vel']]\r\n pred_arrs[4][_row:_row+self.traj_n, :] = \\\r\n st_pred[:,:,self.gen_model.indx_fadj['vel']]\r\n\r\n\r\n _row += self.traj_n\r\n # return st_pred\r\n print('Episode ', episode_id, ' has been completed!')\r\n for key in rwse_dict.keys():\r\n rwse_dict[key] = self.root_weightet_sqr(truth_arrs[rwse_dict[key]], \\\r\n pred_arrs[rwse_dict[key]])\r\n\r\n with open(self.dirName+'/'+ traffic_density + 'rwse', \"wb\") as f:\r\n pickle.dump(rwse_dict, f)\r\n return rwse_dict","def createRadarSims(self):\n import anwp.sims\n # remove old sims if any\n self.removeRadarSims()\n # create resource sims\n self.radarSims = []\n for systemID, systemDict in self.game.allSystems.iteritems():\n imageFileName = ''\n \n if systemDict['intelReport']['round'] == self.game.currentRound:\n # there is a current report from this system indicate with green radar\n imageFileName = '%sradar_green.png' % self.game.app.genImagePath\n \n if systemDict['myEmpireID'] == self.game.myEmpireID:\n if systemDict['radarStrength'] > 0:\n # players system has radar indicate with blue radar\n imageFileName = '%sradar_blue.png' % self.game.app.genImagePath\n \n if imageFileName <> '':\n # create sim\n sim = RadarEntity(self, anwp.sims.categories.StaticCategory(imageFileName, 'radar'))\n \n # add sim to world\n self.radarSims.append(sim)\n x = systemDict['x']-46\n y = systemDict['y']+65\n facing = 0\n speed = 0\n force = 1\n self.world.addToWorld(sim, x, y, facing, speed, force)","def enumeration_separation(self, stage1_vars, sample_name, limit=1,\n enumerate_all=True):\n sample = self.tsdro.samples[sample_name]\n scenarios = self.tsdro.scenarios\n lr_instance = self.tsdro.lr_instance\n curr_remaining = self.tsdro.remaining_scenario_names[sample_name]\n curr_remaining_copy = curr_remaining.copy()\n\n found_hyp = False\n count = 0\n max_violation_scenario_name = None\n max_violation = -np.inf\n for scenario_name in curr_remaining:\n scenario = scenarios[scenario_name]\n if scenario_name not in self.stage2_objvals:\n self.stage2_objvals[scenario_name] = self.get_stage2_costs(\n stage1_vars, [scenario_name])\n\n if scenario_name == sample_name or self.method == \"RO\":\n scenario_distance = 0\n rhs_val = self.stage2_objvals[scenario_name]\n else:\n scenario_distance = sc.get_scenario_distance(scenario, sample,\n lr_instance)\n rhs_val = (self.stage2_objvals[scenario_name]\n - self.wass_mult.X * scenario_distance)\n if self.epi_vars[sample_name].X < rhs_val + 1e-7:\n found_hyp = True\n if not enumerate_all:\n count += 1\n if scenario_name not in self.master_scenarios:\n tmp = lr_instance.add_stage2(self.master, self.stage1_vars,\n scenario, scenario_name)\n self.stage2_vars[scenario_name] = tmp[0]\n self.master_scenarios.append(scenario_name)\n objexpr_stage2 = lr_instance.get_objective_stage2(\n self.stage2_vars[scenario_name], scenario)\n rhs = objexpr_stage2 - self.wass_mult * scenario_distance\n self.master.addLConstr(self.epi_vars[sample_name], \">\", rhs)\n curr_remaining_copy.remove(scenario_name)\n else:\n if rhs_val - self.epi_vars[sample_name].X > max_violation:\n max_violation = rhs_val - self.epi_vars[sample_name].X\n max_violation_scenario_name = scenario_name\n if count == limit:\n break\n if enumerate_all and found_hyp:\n scenario = scenarios[max_violation_scenario_name]\n if scenario_name == sample_name or self.method == \"RO\":\n scenario_distance = 0\n else:\n scenario_distance = sc.get_scenario_distance(scenario, sample,\n lr_instance)\n if scenario_name not in self.master_scenarios:\n tmp = lr_instance.add_stage2(self.master, self.stage1_vars,\n scenario,\n max_violation_scenario_name)\n self.stage2_vars[max_violation_scenario_name] = tmp[0]\n self.master_scenarios.append(max_violation_scenario_name)\n objexpr_stage2 = lr_instance.get_objective_stage2(\n self.stage2_vars[max_violation_scenario_name], scenario)\n rhs = objexpr_stage2 - self.wass_mult * scenario_distance\n self.master.addLConstr(self.epi_vars[sample_name], \">\", rhs)\n curr_remaining_copy.remove(max_violation_scenario_name)\n self.tsdro.remaining_scenario_names[sample_name] = curr_remaining_copy\n\n if count > 0:\n print(sample_name, \"added\", count, \"scenarios.\")\n return found_hyp","def use_AR(train1, train2, test, features):\n for feature in features:\n print('----------------------------------- Feature: %s -----------------------------------' % feature)\n # train on train1 and test on train2\n model_fit = AR(train1[feature]).fit() # fit the AR model\n window = model_fit.k_ar # retrieve the p value from the fitted model\n coeff = model_fit.params # retrieve the coefficients\n history = list(train1[feature][len(train1[feature]) - window:]) # create initial history\n predictions = predict_AR(train2[feature], history, window, coeff) # make the predictions\n predictions.index = train2.index # set the index properly\n error = mean_squared_error(train2[feature], predictions) # calculate the MSE\n print('Test MSE: %.3f' % error)\n\n # plot the predicted values against the true values of the signals\n fig = plt.figure()\n ax = fig.add_subplot(111)\n ax.plot(train2[feature], label='true signal')\n ax.plot(predictions, label='predicted signal')\n ax.xaxis.set_major_locator(mdates.DayLocator([10, 20, 30]))\n ax.xaxis.set_major_formatter(mdates.DateFormatter('%d/%m'))\n plt.xlabel(\"time\")\n plt.ylabel(feature)\n plt.xticks(rotation=45)\n plt.grid()\n plt.legend()\n plt.title('AR prediction on training set 2')\n plt.savefig('plots/naive_predictions/training2_prediction_%s.png' % feature, bbox_inches='tight')\n\n # train on train1 and test on test\n model_fit = AR(train1[feature]).fit() # fit the AR model\n window = model_fit.k_ar # retrieve the p value from the fitted model\n coeff = model_fit.params # retrieve the coefficients\n history = list(train1[feature][len(train1[feature]) - window:]) # create initial history\n predictions = predict_AR(test[feature], history, window, coeff) # make the predictions\n predictions.index = test.index # set the index properly\n error = mean_squared_error(test[feature], predictions) # calculate the MSE\n print('Test MSE: %.3f' % error)\n\n # plot the predicted values against the true values of the signals\n fig = plt.figure()\n ax = fig.add_subplot(111)\n ax.plot(test[feature], label='true signal')\n ax.plot(predictions, label='predicted signal')\n ax.xaxis.set_major_locator(mdates.DayLocator([10, 20, 30]))\n ax.xaxis.set_major_formatter(mdates.DateFormatter('%d/%m'))\n plt.xlabel(\"time\")\n plt.ylabel(feature)\n plt.xticks(rotation=45)\n plt.grid()\n plt.legend()\n plt.title('AR prediction on test set')\n plt.savefig('plots/naive_predictions/test_prediction_%s.png' % feature, bbox_inches='tight')\n\n # train on train2 and test on test\n model_fit = AR(train2[feature]).fit() # fit the AR model\n window = model_fit.k_ar # retrieve the p value from the fitted model\n coeff = model_fit.params # retrieve the coefficients\n history = list(train2[feature][len(train2[feature]) - window:]) # create initial history\n predictions = predict_AR(test[feature], history, window, coeff) # make the predictions\n predictions.index = test.index # set the index properly\n error = mean_squared_error(test[feature], predictions) # calculate the MSE\n print('Test MSE: %.3f' % error)\n\n # plot the predicted values against the true values of the signals\n fig = plt.figure()\n ax = fig.add_subplot(111)\n ax.plot(test[feature], label='true signal')\n ax.plot(predictions, label='predicted signal')\n ax.xaxis.set_major_locator(mdates.DayLocator([10, 20, 30]))\n ax.xaxis.set_major_formatter(mdates.DateFormatter('%d/%m'))\n plt.xlabel(\"time\")\n plt.ylabel(feature)\n plt.xticks(rotation=45)\n plt.grid()\n plt.legend()\n plt.title('AR prediction on test set based on training set 2')\n plt.savefig('plots/naive_predictions/train2_test_prediction_%s.png' % feature, bbox_inches='tight')","def calculate_R2(self):\n\n d1 = self.T - self.Y\n d2 = self.T - self.T.mean()\n\n self.r2 = 1 - d1.dot(d1) / d2.dot(d2)\n self.r2 = format(self.r2, '.3f')\n\n print \"\"\n print \"R2:\", self.r2","def _sample_episode(self):\n\n Knovel = random.sample(self.labelIds, self.nKnovel)\n #print(Knovel)\n #exit(0)\n nKnovel = len(Knovel)\n assert((self.nTestNovel % nKnovel) == 0)\n nEvalExamplesPerClass = int(self.nTestNovel / nKnovel)\n #print(nEvalExamplesPerClass)\n #exit(0)\n Tnovel = []\n Exemplars = []\n for Knovel_idx in range(len(Knovel)):\n ids = (nEvalExamplesPerClass + self.nExemplars)\n img_ids = random.sample(self.labels2inds[Knovel[Knovel_idx]], ids) \n\n imgs_tnovel = img_ids[:nEvalExamplesPerClass]\n imgs_emeplars = img_ids[nEvalExamplesPerClass:]\n #print(imgs_tnovel)\n #exit(0)\n Tnovel += [(img_id, Knovel_idx) for img_id in imgs_tnovel]\n Exemplars += [(img_id, Knovel_idx) for img_id in imgs_emeplars]\n assert(len(Tnovel) == self.nTestNovel)\n assert(len(Exemplars) == nKnovel * self.nExemplars)\n random.shuffle(Exemplars)\n random.shuffle(Tnovel)\n\n return Tnovel, Exemplars","def run():\n step = 0\n o2r = 4 #orange to red delay time\n r2g = 2 #red to green delay time\n A_4235 = 0\n B_4235 = 1\n C_4235 = 2\n AB1_4235 = 3\n AB2_4235 = 4\n AC1_4235 = 5\n AC2_4235 = 6\n BA1_4235 = 7\n BA2_4235 = 8\n BC1_4235 = 9\n BC2_4235 = 10\n CA1_4235 = 11\n CA2_4235 = 12\n CB1_4235 = 13\n CB2_4235 = 14\n A_4219 = 0\n B_4219 = 1\n C_4219 = 2\n D_4219 = 3\n E_4219 = 4\n F_4219 = 5\n G_4219 = 6\n AB1_4219 = 7\n AB2_4219 = 8\n AC1_4219 = 9\n AC2_4219 = 10\n AD1_4219 = 11\n AD2_4219 = 12\n AE1_4219 = 13\n AE2_4219 = 14\n AF1_4219 = 16\n AF2_4219 = 17\n AG1_4219 = 18\n AG2_4219 = 19\n BA1_4219 = 20\n BA2_4219 = 21\n BC1_4219 = 22\n BC2_4219 = 23\n BD1_4219 = 24\n BD2_4219 = 25\n BE1_4219 = 26\n BE2_4219 = 27\n BF1_4219 = 28\n BF2_4219 = 29\n BG1_4219 = 30\n BG2_4219 = 31\n CA1_4219 = 32\n CA2_4219 = 33\n CB1_4219 = 34\n CB2_4219 = 35\n CD1_4219 = 36\n CD2_4219 = 37\n CE1_4219 = 38\n CE2_4219 = 39\n CF1_4219 = 40\n CF2_4219 = 41\n CG1_4219 = 42\n CG2_4219 = 43\n DA1_4219 = 44\n DA2_4219 = 45\n DB1_4219 = 46\n DB2_4219 = 47\n DC1_4219 = 48\n DC2_4219 = 49\n DE1_4219 = 50\n DE2_4219 = 51\n DF1_4219 = 52\n DF2_4219 = 53\n DG1_4219 = 54\n DG2_4219 = 55\n EA1_4219 = 56\n EA2_4219 = 57\n EB1_4219 = 58\n EB2_4219 = 59\n EC1_4219 = 60\n EC2_4219 = 61\n ED1_4219 = 62\n ED2_4219 = 63\n EF1_4219 = 64\n EF2_4219 = 65\n EG1_4219 = 66\n EG2_4219 = 67\n FA1_4219 = 68\n FA2_4219 = 69\n FB1_4219 = 70\n FB2_4219 = 71\n FC1_4219 = 72\n FC2_4219 = 73\n FD1_4219 = 74\n FD2_4219 = 75\n FE1_4219 = 76\n FE2_4219 = 77\n FG1_4219 = 78\n FG2_4219 = 79\n GA1_4219 = 80\n GA2_4219 = 81\n GB1_4219 = 82\n GB2_4219 = 83\n GC1_4219 = 84\n GC2_4219 = 85\n GD1_4219 = 86\n GD2_4219 = 87\n GE1_4219 = 88\n GE2_4219 = 89\n GF1_4219 = 90\n GF2_4219 = 91\n A_4220 = 0\n B_4220 = 1\n C_4220 = 2\n D_4220 = 3\n E_4220 = 4\n AB1_4220 = 5\n AB2_4220 = 6\n AC1_4220 = 7\n AC2_4220 = 8\n AD1_4220 = 9\n AD2_4220 = 10\n AE1_4220 = 11\n AE2_4220 = 12\n BA1_4220 = 13\n BA2_4220 = 14\n BC1_4220 = 15\n BC2_4220 = 16\n BD1_4220 = 17\n BD2_4220 = 18\n BE1_4220 = 19\n BE2_4220 = 20\n CA1_4220 = 21\n CA2_4220 = 22\n CB1_4220 = 23\n CB2_4220 = 24\n CD1_4220 = 25\n CD2_4220 = 26\n CE1_4220 = 27\n CE2_4220 = 28\n DA1_4220 = 29\n DA2_4220 = 30\n DB1_4220 = 31\n DB2_4220 = 32\n DC1_4220 = 33\n DC2_4220 = 34\n DE1_4220 = 35\n DE2_4220 = 36\n EA1_4220 = 37\n EA2_4220 = 38\n EB1_4220 = 39\n EB2_4220 = 40\n EC1_4220 = 41\n EC2_4220 = 42\n ED1_4220 = 43\n ED2_4220 = 44\n A_4221 = 0\n B_4221 = 1\n C_4221 = 2\n D_4221 = 3\n E_4221 = 4\n F_4221 = 5\n AB1_4221 = 6\n AB2_4221 = 7\n AC1_4221 = 8\n AC2_4221 = 9\n AD1_4221 = 10\n AD2_4221 = 11\n AE1_4221 = 12\n AE2_4221 = 13\n AF1_4221 = 14\n AF2_4221 = 15\n BA1_4221 = 16\n BA2_4221 = 17\n BC1_4221 = 18\n BC2_4221 = 19\n BD1_4221 = 20\n BD2_4221 = 21\n BE1_4221 = 22\n BE2_4221 = 23\n BF1_4221 = 24\n BF2_4221 = 25\n CA1_4221 = 26\n CA2_4221 = 27\n CB1_4221 = 28\n CB2_4221 = 29\n CD1_4221 = 30\n CD2_4221 = 31\n CE1_4221 = 32\n CE2_4221 = 33\n CF1_4221 = 34\n CF2_4221 = 35\n DA1_4221 = 36\n DA2_4221 = 37\n DB1_4221 = 38\n DB2_4221 = 39\n DC1_4221 = 40\n DC2_4221 = 41\n DE1_4221 = 42\n DE2_4221 = 43\n DF1_4221 = 44\n DF2_4221 = 45\n EA1_4221 = 46\n EA2_4221 = 47\n EB1_4221 = 48\n EB2_4221 = 49\n EC1_4221 = 50\n EC2_4221 = 51\n ED1_4221 = 52\n ED2_4221 = 53\n EF1_4221 = 54\n EF2_4221 = 55\n FA1_4221 = 56\n FA2_4221 = 57\n FB1_4221 = 58\n FB2_4221 = 59\n FC1_4221 = 60\n FC2_4221 = 61\n FD1_4221 = 62\n FD2_4221 = 63\n FE1_4221 = 64\n FE2_4221 = 65\n \n #while traci.simulation.getMinExpectedNumber() > 0:\n while step < 600:\n traci.simulationStep()\n if step == 0:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",6)\n if step == 6:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 10:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 12:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", A_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",75)\n if step == 87:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 91:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 93:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",15)\n if step == 108:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 112:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 114:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", C_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",12)\n if step == 126:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 130:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 132:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",32)\n if step == 164:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 168:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 170:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", A_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",50)\n if step == 220:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 224:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 226:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",15)\n if step == 241:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 245:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 247:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", C_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",14)\n if step == 261:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 265:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 267:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",13)\n if step == 280:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 284:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 286:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", A_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",74)\n if step == 360:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 364:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 366:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",15)\n if step == 381:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 385:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 387:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", C_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",10)\n if step == 397:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 401:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 403:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",16)\n if step == 419:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 423:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 425:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", A_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",74)\n if step == 499:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 503:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 505:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",15)\n if step == 520:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 524:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 526:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", C_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",8)\n if step == 534:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 538:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 540:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",16)\n if step == 556:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 560:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 562:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", A_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",38)\n if step == 0:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", A_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 6)\n if step == 6:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 10:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 12:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", D_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 20)\n if step == 32:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 36:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 38:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", E_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 5)\n if step == 43:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 47:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 49:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", F_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 31)\n if step == 80:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 84:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 86:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", G_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 12)\n if step == 98:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 102:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 104:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", A_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 29)\n if step == 133:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 137:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 139:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", D_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 20)\n if step == 159:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 163:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 165:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", E_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 5)\n if step == 170:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 174:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 176:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", F_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 38)\n if step == 214:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 218:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 220:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", G_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 13)\n if step == 233:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 237:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 239:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", A_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 34)\n if step == 273:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 277:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 279:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", D_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 22)\n if step == 301:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 305:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 307:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", E_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 5)\n if step == 312:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 316:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 318:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", F_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 31)\n if step == 349:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 353:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 355:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", G_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 18)\n if step == 373:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 377:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 379:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", A_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 36)\n if step == 415:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 419:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 421:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", D_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 19)\n if step == 440:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 444:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 446:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", E_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 5)\n if step == 451:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 455:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 457:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", F_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 29)\n if step == 486:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 490:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 492:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", G_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 18)\n if step == 510:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 514:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 516:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", A_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 41)\n if step == 557:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 561:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 563:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", D_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 19)\n if step == 582:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 586:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 588:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", E_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 8)\n if step == 596:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 0:\n traci.trafficlight.setPhase(\"gneJ41\", D_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 17)\n if step == 17:\n traci.trafficlight.setPhase(\"gneJ41\", DE1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 21:\n traci.trafficlight.setPhase(\"gneJ41\", DE2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 23:\n traci.trafficlight.setPhase(\"gneJ41\", E_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 9)\n if step == 32:\n traci.trafficlight.setPhase(\"gneJ41\", EA1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 36:\n traci.trafficlight.setPhase(\"gneJ41\", EA2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 38:\n traci.trafficlight.setPhase(\"gneJ41\", A_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 67)\n if step == 105:\n traci.trafficlight.setPhase(\"gneJ41\", AB1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 109:\n traci.trafficlight.setPhase(\"gneJ41\", AB2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 111:\n traci.trafficlight.setPhase(\"gneJ41\", B_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 19)\n if step == 130:\n traci.trafficlight.setPhase(\"gneJ41\", BD1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 134:\n traci.trafficlight.setPhase(\"gneJ41\", BD2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 136:\n traci.trafficlight.setPhase(\"gneJ41\", D_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 16)\n if step == 152:\n traci.trafficlight.setPhase(\"gneJ41\", DE1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 156:\n traci.trafficlight.setPhase(\"gneJ41\", DE2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 158:\n traci.trafficlight.setPhase(\"gneJ41\", E_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 11)\n if step == 169:\n traci.trafficlight.setPhase(\"gneJ41\", EA1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 173:\n traci.trafficlight.setPhase(\"gneJ41\", EA2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 175:\n traci.trafficlight.setPhase(\"gneJ41\", A_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 63)\n if step == 238:\n traci.trafficlight.setPhase(\"gneJ41\", AD1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 242:\n traci.trafficlight.setPhase(\"gneJ41\", AD2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 244:\n traci.trafficlight.setPhase(\"gneJ41\", D_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 13)\n if step == 257:\n traci.trafficlight.setPhase(\"gneJ41\", DE1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 261:\n traci.trafficlight.setPhase(\"gneJ41\", DE2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 263:\n traci.trafficlight.setPhase(\"gneJ41\", E_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 9)\n if step == 272:\n traci.trafficlight.setPhase(\"gneJ41\", EA1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 276:\n traci.trafficlight.setPhase(\"gneJ41\", EA2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 278:\n traci.trafficlight.setPhase(\"gneJ41\", A_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 95)\n if step == 373:\n traci.trafficlight.setPhase(\"gneJ41\", AB1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 377:\n traci.trafficlight.setPhase(\"gneJ41\", AB2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 379:\n traci.trafficlight.setPhase(\"gneJ41\", B_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 19)\n if step == 398:\n traci.trafficlight.setPhase(\"gneJ41\", BD1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 402:\n traci.trafficlight.setPhase(\"gneJ41\", BD2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 404:\n traci.trafficlight.setPhase(\"gneJ41\", D_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 24)\n if step == 428:\n traci.trafficlight.setPhase(\"gneJ41\", DE1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 432:\n traci.trafficlight.setPhase(\"gneJ41\", DE2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 434:\n traci.trafficlight.setPhase(\"gneJ41\", E_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 13)\n if step == 447:\n traci.trafficlight.setPhase(\"gneJ41\", EA1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 451:\n traci.trafficlight.setPhase(\"gneJ41\", EA2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 453:\n traci.trafficlight.setPhase(\"gneJ41\", A_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 56)\n if step == 509:\n traci.trafficlight.setPhase(\"gneJ41\", AB1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 513:\n traci.trafficlight.setPhase(\"gneJ41\", AB2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 515:\n traci.trafficlight.setPhase(\"gneJ41\", B_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 19)\n if step == 534:\n traci.trafficlight.setPhase(\"gneJ41\", BD1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 538:\n traci.trafficlight.setPhase(\"gneJ41\", BD2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 540:\n traci.trafficlight.setPhase(\"gneJ41\", D_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 22)\n if step == 562:\n traci.trafficlight.setPhase(\"gneJ41\", DE1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 566:\n traci.trafficlight.setPhase(\"gneJ41\", DE2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 568:\n traci.trafficlight.setPhase(\"gneJ41\", E_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 20)\n if step == 588:\n traci.trafficlight.setPhase(\"gneJ41\", EA1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 592:\n traci.trafficlight.setPhase(\"gneJ41\", EA2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 594:\n traci.trafficlight.setPhase(\"gneJ41\", A_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 6)\n if step == 0:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 9)\n if step == 9:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 13:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 15:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", F_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 19)\n if step == 34:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 38:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 40:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", A_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 20)\n if step == 60:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 64:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 66:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", D_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 24)\n if step == 90:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 94:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 96:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 9)\n if step == 105:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 109:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 111:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", F_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 19)\n if step == 130:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 134:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 136:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", A_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 30)\n if step == 166:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 170:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 172:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", D_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 28)\n if step == 200:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 204:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 206:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 19)\n if step == 225:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 229:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 231:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", F_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 8)\n if step == 239:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 243:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 245:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", A_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 32)\n if step == 277:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 281:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 283:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", D_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 27)\n if step == 310:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 314:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 316:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 15)\n if step == 331:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 335:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 337:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", F_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 14)\n if step == 351:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 355:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 357:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", A_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 22)\n if step == 379:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 383:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 385:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", D_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 24)\n if step == 409:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 413:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 415:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 11)\n if step == 426:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 430:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 432:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", F_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 14)\n if step == 446:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 450:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 452:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", A_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 30)\n if step == 482:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 486:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 488:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", D_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 26)\n if step == 514:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 518:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 520:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 8)\n if step == 528:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 532:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 534:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", F_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 18)\n if step == 552:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 556:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 558:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", A_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 19)\n if step == 577:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 581:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 583:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", D_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 9)\n if step == 592:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 596:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 598:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 2)\n\n step += 1\n\n traci.close()\n sys.stdout.flush()","def _test_two_unsampled_arms(self):\n bandit = self.bandit_class(self.two_unsampled_arms_test_case)\n assert bandit.allocate_arms() == {\"arm1\": 0.5, \"arm2\": 0.5}","def test_dataset_scenario_generation_full_outside1(self):\n params = ParameterServer()\n\n map_filename = os.path.join(os.path.dirname(__file__), \"data/DR_DEU_Merging_MT_v01_shifted.xodr\")\n track_filename = os.path.join(os.path.dirname(__file__), \"data/interaction_dataset_DEU_Merging_dummy_track_outside.csv\")\n\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"MapFilename\"] = map_filename\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"TrackFilenameList\"] = [track_filename]\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"StartingOffsetMs\"] = 0\n\n scenario_generation = InteractionDatasetScenarioGenerationFull(\n params=params, num_scenarios=1)\n\n scenario = scenario_generation.get_scenario(0)\n self.assertAlmostEqual(scenario.eval_agent_ids, [1])\n\n world_state = scenario.GetWorldState()\n agent11 = world_state.GetAgent(1)\n agent12 = world_state.GetAgent(2)\n agent13 = world_state.GetAgent(3)\n\n self.assertAlmostEqual(agent11.first_valid_timestamp, 0.0)\n self.assertAlmostEqual(agent12.first_valid_timestamp, 0.0)\n self.assertNotEqual(agent13.first_valid_timestamp, 0.0)\n \n # agent13 should not be valid at the beginning, as he is outside of map\n world_state.time = 0\n self.assertEqual(isinstance(agent11, Agent), True)\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent11.InsideRoadCorridor(), True)\n \n self.assertEqual(isinstance(agent12, Agent), True)\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent12.InsideRoadCorridor(), True)\n\n self.assertEqual(isinstance(agent13, Agent), True)\n self.assertEqual(agent13.IsValidAtTime(world_state.time), False)\n # as we use only state once it's in map, this will be true, although the time step is not valid yet\n self.assertEqual(agent13.InsideRoadCorridor(), True)\n\n # agent13 should not be valid at the beginning, as he is outside of map\n world_state.Step(0.05)\n\n self.assertEqual(isinstance(agent11, Agent), True)\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent11.InsideRoadCorridor(), True)\n \n self.assertEqual(isinstance(agent12, Agent), True)\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent12.InsideRoadCorridor(), True)\n\n self.assertEqual(isinstance(agent13, Agent), True)\n self.assertEqual(agent13.IsValidAtTime(world_state.time), False)\n # as we use only state once it's in map, this will be true, although the time step is not valid yet\n self.assertEqual(agent13.InsideRoadCorridor(), True)\n\n self.assertEqual(list(world_state.agents_valid.keys()), [1,2])\n\n # agent13 should be valid at some point\n world_state.Step(agent13.first_valid_timestamp)\n world_state.Step(0.01) # agent13.IsValidAtTime() uses previous time stamp, therefore we increment it one more step\n\n self.assertEqual(isinstance(agent11, Agent), True)\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent11.InsideRoadCorridor(), True)\n \n self.assertEqual(isinstance(agent12, Agent), True)\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent12.InsideRoadCorridor(), True)\n\n self.assertEqual(isinstance(agent13, Agent), True)\n self.assertEqual(agent13.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent13.InsideRoadCorridor(), True)\n\n self.assertEqual(list(world_state.agents_valid.keys()), [1,2,3])","def test_stop_start(self):\n log.info(\"CONFIG: %s\", self._agent_config())\n self.create_sample_data_set_dir('node59p1_step2.dat', TELEM_DIR, \"node59p1.dat\")\n\n self.assert_initialize(final_state=ResourceAgentState.COMMAND)\n\n # Slow down processing to 1 per second to give us time to stop\n self.dataset_agent_client.set_resource({DriverParameter.RECORDS_PER_SECOND: 1})\n self.assert_start_sampling()\n\n # Verify we get one sample\n try:\n # Read the first file and verify the data\n result = self.data_subscribers.get_samples(DataParticleType.CONTROL, 1)\n result1 = self.data_subscribers.get_samples(DataParticleType.SAMPLE, 3)\n result.extend(result1)\n log.debug(\"RESULT: %s\", result)\n\n # Verify values\n self.assert_data_values(result, 'test_data_1-2.txt.result.yml')\n self.assert_sample_queue_size(DataParticleType.CONTROL, 0)\n self.assert_sample_queue_size(DataParticleType.SAMPLE, 0)\n\n self.create_sample_data_set_dir('node59p1_step4.dat', TELEM_DIR, \"node59p1.dat\")\n # Now read the first record of the second file then stop\n result = self.data_subscribers.get_samples(DataParticleType.SAMPLE, 3)\n log.debug(\"RESULT 1: %s\", result)\n self.assert_stop_sampling()\n self.assert_sample_queue_size(DataParticleType.CONTROL, 0)\n self.assert_sample_queue_size(DataParticleType.SAMPLE, 0)\n\n # Restart sampling and ensure we get the last records of the file\n self.assert_start_sampling()\n result2 = self.data_subscribers.get_samples(DataParticleType.SAMPLE, 3)\n log.debug(\"RESULT 2: %s\", result2)\n result.extend(result2)\n log.debug(\"RESULT: %s\", result)\n self.assert_data_values(result, 'test_data_3-4.txt.result.yml')\n self.assert_sample_queue_size(DataParticleType.CONTROL, 0)\n self.assert_sample_queue_size(DataParticleType.SAMPLE, 0)\n except SampleTimeout as e:\n log.error(\"Exception trapped: %s\", e, exc_info=True)\n self.fail(\"Sample timeout.\")","def ar2_GWR(GWRMod): \r\n tss = np.sum((GWRMod.y - GWRMod.y_mean)**2)\r\n n = GWRMod.nObs # (scalar) number of observations\r\n if GWRMod.tr_S >= GWRMod.tr_STS:\t\t\r\n\tdof_res = GWRMod.nObs-2.0*GWRMod.tr_S+GWRMod.tr_STS\r\n else:\r\n\tdof_res = GWRMod.nObs-GWRMod.tr_S\r\n ar2_result = 1.0 - GWRMod.res2 / tss * ( n - 1.0) / (dof_res - 1.0)\r\n \r\n return ar2_result","def radialApproxEffect(hubdist1,hubdist2,width,length):\n #Grating coordinates\n x,y = np.meshgrid(np.linspace(-width,width,1000),\\\n np.linspace(-length,length,1000))\n y1 = y + hubdist1\n y2 = y + hubdist2\n\n #Convert to period and yaw angle\n period1 = np.sqrt(x**2+y1**2)/hubdist1*160. #nm\n period2 = np.sqrt(x**2+y2**2)/hubdist2*160. #nm\n yaw = blazeYaw(1.5*np.pi/180,2.4,3,160.)\n yaw1 = np.pi/2 - np.arctan(x/y1) + yaw\n yaw2 = np.pi/2 - np.arctan(x/y2) + yaw\n\n #Determine alpha and beta\n beta0,alpha0 = litBetaAlpha(1.5*np.pi/180,2.4,3,160.)\n alpha1 = alpha0 + 3*2.4/period1*np.sin(yaw1)\n alpha2 = alpha0 + 3*2.4/period2*np.sin(yaw2)\n beta1 = beta0 + (3*2.4/period1)*np.cos(yaw1)\n beta2 = beta0 + (3*2.4/period2)*np.cos(yaw2)\n\n #Determine spot shifts\n x1 = hubdist2*(alpha1/beta1)\n x2 = hubdist2*(alpha2/beta2)\n \n\n pdb.set_trace()\n \n return x1,x2","def runSimulation(self, R=5000, N=1,s=1000, method='RL'):\n global n_ec\n import numpy as np\n import matplotlib.pyplot as plt\n import matplotlib\n matplotlib.use('Agg')\n matplotlib.pyplot.switch_backend('agg')\n\n plt.rcParams.update({'font.size':20})\n plt.rc('xtick', labelsize=20)\n plt.rc('ytick', labelsize=20)\n # step = 2 fs\n # each round is 2 fs * 1000 = 2 ps\n\n init = 'ala2_1stFrame.pdb' #pdb name\n inits = init\n n_ec = 2 # angles\n count = 1\n newPoints_name = 'start_r_'+str(count)+'.pdb'\n \n #W_0 = [1/n_ec for i in range(n_ec)] # no direction\n #W_0 = [[0.25, 0.25], [0.25, 0.25]]\n W_0 = [[1/(2*n_ec), 1/(2*n_ec)] for i in range(n_ec)] # directional\n print(W_0)\n\n Ws = []\n Ws.append(W_0)\n \n trj1 = self.run(production_steps = s, start=inits, production='trj_R_0.pdb') # return mdtraj object\n comb_trj1 = trj1 # single trajectory\n trjs = comb_trj1\n trj1_theta = self.map_angles(trj1) # changed for angles to display\n print('trj1_theta', len(trj1_theta), len(trj1_theta[0]))\n trj1_Ps_theta, index = self.PreSamp(trj1_theta, myn_clusters = 10) # pre analysis (least count)\n trj1_Ps_w_theta, index_w = self.PreSamp(trj1_theta, myn_clusters = 100) # for updating the weights\n print('trj1_Ps_theta', len(trj1_Ps_theta), len(trj1_Ps_theta[0]))\n\n newPoints_index_orig = self.findStarting(trj1_Ps_theta, index, W_0, starting_n = N , method = 'RL') #need change\n newPoints = trj1[newPoints_index_orig[0]]\n newPoints.save_pdb(newPoints_name)\n \n \n print('trj1_theta[0]',trj1_theta[0])\n plt.scatter(trj1_theta[0], trj1_theta[1], color='dodgerblue', s=5, alpha=0.2)\n plt.xlim([-180, 180])\n plt.ylim([-180, 180])\n newPoints_theta_x = trj1_theta[0][newPoints_index_orig[0]]\n newPoints_theta_y = trj1_theta[1][newPoints_index_orig[0]]\n plt.scatter(newPoints_theta_x, newPoints_theta_y, color='red', s=50)\n plt.xlabel(r'$\\phi$')\n plt.ylabel(r'$\\psi$')\n plt.savefig('fig_'+str(count))\n plt.close()\n trjs_theta = trj1_theta\n trjs_Ps_theta = trj1_Ps_theta\n trjs_Ps_w_theta = trj1_Ps_w_theta \n for round in range(R):\n self.updateStat(trjs_theta) # based on all trajectories\n #W_1 = self.updateW(trjs_Ps_theta, W_0) \n W_1 = self.updateW(trjs_Ps_w_theta, W_0) \n W_0 = W_1\n W_1 = W_0\n Ws.append(W_0)\n s = 1000\n trj1 = self.run(production_steps = s, start=newPoints_name, production='trj_R_'+str(count)+'.pdb') # return mdtraj object\n com_trjs = trjs.join(trj1) \n trjs = com_trjs\n trjs_theta = np.array(self.map_angles(trjs)) \n trjs_Ps_theta, index = self.PreSamp(trjs_theta, myn_clusters = 100)\n myn_clusters1 = 100 #int(10*(round)+1)\n trjs_Ps_w_theta = trjs_Ps_theta\n #trjs_Ps_w_theta, index_w = self.PreSamp(trjs_theta, myn_clusters = myn_clusters1)\n newPoints_index_orig = self.findStarting(trjs_Ps_theta, index, W_1, starting_n = N , method = 'RL')\n newPoints = trjs[newPoints_index_orig[0]] \n \n count = count + 1\n newPoints_name = 'start_r_'+str(count)+'.pdb'\n newPoints.save_pdb(newPoints_name)\n\n print( myn_clusters1, W_1, self.theta_mean)\n plt.scatter(trjs_theta[0], trjs_theta[1], color='dodgerblue', s=5, alpha=0.2)\n plt.xlim([-np.pi, np.pi])\n plt.ylim([-np.pi, np.pi])\n newPoints_theta_x = trjs_theta[0][newPoints_index_orig[0]]\n newPoints_theta_y = trjs_theta[1][newPoints_index_orig[0]]\n plt.scatter(newPoints_theta_x, newPoints_theta_y, color='red', s=50)\n plt.scatter(trjs_Ps_w_theta[0], trjs_Ps_w_theta[1], color='green', s=5)\n plt.xlabel(r'$\\phi$')\n plt.ylabel(r'$\\psi$')\n plt.savefig('fig_'+str(count))\n plt.close()\n \n np.save('w_'+'r'+str(int(R))+'N'+str(N)+'s'+str(s), Ws)\n np.save('trjs_theta', trjs_theta)\n return","def __init__(self, flagTrackShape = 0):\n\n \"\"\" Nos interesa que el planner tenga una pista algo mas reducida de la real\n para conservar algo de robustez y no salirnos de la pista en el primer segundo. \"\"\"\n \n ### is HW is the half width of vehicle dimension + some saftey factor?\n ### what is slack??\n \n # HW = rospy.get_param(\"halfWidth\")+0.1\n HW = 0.4\n # print (\"HW\",HW)\n # if flagTrackShape == 0:\n # selectedTrack = rospy.get_param(\"trackShape\") # comentado para el testeo del planner\n # # selectedTrack = \"L_shape\"\n # else:\n # selectedTrack = \"oval\"\n\n selectedTrack = \"L_shape\"\n print (\"track selected\",selectedTrack)\n if selectedTrack == \"3110\":\n self.halfWidth = 0.6\n self.slack = 0.15\n spec = np.array([[60 * 0.03, 0],\n [80 * 0.03, +80 * 0.03 * 2 / np.pi],\n [20 * 0.03, 0],\n [80 * 0.03, +80 * 0.03 * 2 / np.pi],\n [40 * 0.03, -40 * 0.03 * 10 / np.pi],\n [60 * 0.03, +60 * 0.03 * 5 / np.pi],\n [40 * 0.03, -40 * 0.03 * 10 / np.pi],\n [80 * 0.03, +80 * 0.03 * 2 / np.pi],\n [20 * 0.03, 0],\n [80 * 0.03, +80 * 0.03 * 2 / np.pi],\n [80 * 0.03, 0]])\n\n elif selectedTrack == \"oval\":\n self.halfWidth = HW\n self.slack = 0.15\n spec = np.array([[1.0, 0],\n [4.5, 4.5 / np.pi],\n [2.0, 0],\n [4.5, 4.5 / np.pi],\n [1.0, 0]])\n\n # elif selectedTrack == \"L_shape\":\n # self.halfWidth = HW\n # self.slack = 0.01\n # lengthCurve = 4.5\n # spec = np.array([[1.0, 0],\n # [lengthCurve, lengthCurve / np.pi],\n # # Note s = 1 * np.pi / 2 and r = -1 ---> Angle spanned = np.pi / 2\n # [lengthCurve/2,-lengthCurve / np.pi ],\n # [lengthCurve, lengthCurve / np.pi],\n # [lengthCurve / np.pi *2, 0],\n # [lengthCurve/2, lengthCurve / np.pi]])\n\n elif selectedTrack == \"L_shape_n\":\n self.halfWidth = HW\n self.slack = 0.01\n lengthCurve = 4.5\n spec = np.array([[1.0, 0],\n [lengthCurve, lengthCurve / np.pi],\n [lengthCurve/2,-lengthCurve / np.pi ],\n [lengthCurve, lengthCurve / np.pi],\n [lengthCurve / np.pi *2, 0],\n [lengthCurve/2, lengthCurve / np.pi]])\n\n elif selectedTrack == \"L_shape_IDIADA\":\n self.halfWidth = HW\n self.slack = 6*0.45\n lengthCurve = 10*4.5\n spec = np.array([[1.0, 0],\n [lengthCurve, lengthCurve / np.pi],\n # Note s = 1 * np.pi / 2 and r = -1 ---> Angle spanned = np.pi / 2\n [lengthCurve/2,-lengthCurve / np.pi ],\n [lengthCurve, lengthCurve / np.pi],\n [lengthCurve / np.pi *2, 0],\n [lengthCurve/2, lengthCurve / np.pi]])\n\n elif selectedTrack == \"L_shape\":\n # elif selectedTrack == \"SLAM_shape1\":\n self.halfWidth = 0.4\n self.slack = 0.01\n lengthCurve = 1.5*(np.pi/2)\n spec = np.array([[2.5,0],\n [2*lengthCurve,(lengthCurve*2)/np.pi],\n [lengthCurve,-(lengthCurve*2) / np.pi],\n [1.0,0],\n [lengthCurve,lengthCurve*2/np.pi],\n [2.0,0],\n [lengthCurve,(lengthCurve*2)/np.pi],\n [4.0,0],\n [lengthCurve,(lengthCurve*2)/np.pi],\n [2.6,0]])\n\n\n elif selectedTrack == \"8_track\":\n self.halfWidth = 0.4\n self.slack = 0.15\n lengthCurve = 1.5*(np.pi/2)\n spec = np.array([[0.5,0],\n [lengthCurve,(lengthCurve*2)/np.pi],\n [1.0,0],\n [lengthCurve,-(lengthCurve*2) / np.pi],\n [lengthCurve,lengthCurve*2/np.pi],\n [lengthCurve,lengthCurve*2/np.pi],\n [1.0,0],\n [lengthCurve,(lengthCurve*2)/np.pi],\n [lengthCurve,-(lengthCurve*2)/np.pi],\n [lengthCurve,(lengthCurve*2)/np.pi],\n [1.0,0],\n [lengthCurve,lengthCurve*2/np.pi]])\n\n\n\n # Now given the above segments we compute the (x, y) points of the track and the angle of the tangent vector (psi) at\n # these points. For each segment we compute the (x, y, psi) coordinate at the last point of the segment. Furthermore,\n # we compute also the cumulative s at the starting point of the segment at signed curvature\n # PointAndTangent = [x, y, psi, cumulative s, segment length, signed curvature]\n\n ### what is cumulative s and signed curvature.?\n\n PointAndTangent = np.zeros((spec.shape[0] + 1, 6))\n for i in range(0, spec.shape[0]):\n if spec[i, 1] == 0.0: # If the current segment is a straight line\n l = spec[i, 0] # Length of the segments\n if i == 0:\n ang = 0 # Angle of the tangent vector at the starting point of the segment\n x = 0 + l * np.cos(ang) # x coordinate of the last point of the segment\n y = 0 + l * np.sin(ang) # y coordinate of the last point of the segment\n else:\n ang = PointAndTangent[i - 1, 2] # Angle of the tangent vector at the starting point of the segment\n x = PointAndTangent[i-1, 0] + l * np.cos(ang) # x coordinate of the last point of the segment\n y = PointAndTangent[i-1, 1] + l * np.sin(ang) # y coordinate of the last point of the segment\n psi = ang # Angle of the tangent vector at the last point of the segment\n\n # # With the above information create the new line\n # if i == 0:\n # NewLine = np.array([x, y, psi, PointAndTangent[i, 3], l, 0])\n # else:\n # NewLine = np.array([x, y, psi, PointAndTangent[i, 3] + PointAndTangent[i, 4], l, 0])\n #\n # PointAndTangent[i + 1, :] = NewLine # Write the new info\n\n if i == 0:\n NewLine = np.array([x, y, psi, PointAndTangent[i, 3], l, 0])\n else:\n NewLine = np.array([x, y, psi, PointAndTangent[i-1, 3] + PointAndTangent[i-1, 4], l, 0])\n\n PointAndTangent[i, :] = NewLine # Write the new info\n else:\n l = spec[i, 0] # Length of the segment\n r = spec[i, 1] # Radius of curvature\n\n\n if r >= 0:\n direction = 1\n else:\n direction = -1\n\n if i == 0:\n ang = 0 # Angle of the tangent vector at the\n # starting point of the segment\n CenterX = 0 \\\n + np.abs(r) * np.cos(ang + direction * np.pi / 2) # x coordinate center of circle\n CenterY = 0 \\\n + np.abs(r) * np.sin(ang + direction * np.pi / 2) # y coordinate center of circle\n else:\n ang = PointAndTangent[i - 1, 2] # Angle of the tangent vector at the\n # starting point of the segment\n CenterX = PointAndTangent[i-1, 0] \\\n + np.abs(r) * np.cos(ang + direction * np.pi / 2) # x coordinate center of circle\n CenterY = PointAndTangent[i-1, 1] \\\n + np.abs(r) * np.sin(ang + direction * np.pi / 2) # y coordinate center of circle\n\n spanAng = l / np.abs(r) # Angle spanned by the circle\n psi = wrap(ang + spanAng * np.sign(r)) # Angle of the tangent vector at the last point of the segment\n\n angleNormal = wrap((direction * np.pi / 2 + ang))\n angle = -(np.pi - np.abs(angleNormal)) * (sign(angleNormal))\n x = CenterX + np.abs(r) * np.cos(\n angle + direction * spanAng) # x coordinate of the last point of the segment\n y = CenterY + np.abs(r) * np.sin(\n angle + direction * spanAng) # y coordinate of the last point of the segment\n\n # With the above information create the new line\n # plt.plot(CenterX, CenterY, 'bo')\n # plt.plot(x, y, 'ro')\n\n # if i == 0:\n # NewLine = np.array([x, y, psi, PointAndTangent[i, 3], l, 1 / r])\n # else:\n # NewLine = np.array([x, y, psi, PointAndTangent[i, 3] + PointAndTangent[i, 4], l, 1 / r])\n #\n # PointAndTangent[i + 1, :] = NewLine # Write the new info\n\n if i == 0:\n NewLine = np.array([x, y, psi, PointAndTangent[i, 3], l, 1 / r])\n else:\n NewLine = np.array([x, y, psi, PointAndTangent[i-1, 3] + PointAndTangent[i-1, 4], l, 1 / r])\n\n PointAndTangent[i, :] = NewLine # Write the new info\n # plt.plot(x, y, 'or')\n\n # Now update info on last point\n # xs = PointAndTangent[PointAndTangent.shape[0] - 2, 0]\n # ys = PointAndTangent[PointAndTangent.shape[0] - 2, 1]\n # xf = PointAndTangent[0, 0]\n # yf = PointAndTangent[0, 1]\n # psif = PointAndTangent[PointAndTangent.shape[0] - 2, 2]\n #\n # # plt.plot(xf, yf, 'or')\n # # plt.show()\n # l = np.sqrt((xf - xs) ** 2 + (yf - ys) ** 2)\n #\n # NewLine = np.array([xf, yf, psif, PointAndTangent[PointAndTangent.shape[0] - 2, 3] + PointAndTangent[\n # PointAndTangent.shape[0] - 2, 4], l, 0])\n # PointAndTangent[-1, :] = NewLine\n\n\n xs = PointAndTangent[-2, 0]\n ys = PointAndTangent[-2, 1]\n xf = 0\n yf = 0\n psif = 0\n\n # plt.plot(xf, yf, 'or')\n # plt.show()\n l = np.sqrt((xf - xs) ** 2 + (yf - ys) ** 2)\n\n NewLine = np.array([xf, yf, psif, PointAndTangent[-2, 3] + PointAndTangent[-2, 4], l, 0])\n PointAndTangent[-1, :] = NewLine\n\n self.PointAndTangent = PointAndTangent\n self.TrackLength = PointAndTangent[-1, 3] + PointAndTangent[-1, 4]","def test_f1_circuit_maker(self):\n fho = tfho.test_file_handle_object()\n W = 5\n G = 20\n fg = .9\n X = 10\n fx = .85\n gate_maker = g.TYPE_TO_GATE_GEN[g.TEST_TYPES.RANDOM]\n # family 1 files:\n t_circuit_file_name = \"circuit_file_trimming\"\n t_circuit_file = fho.get_file_object(t_circuit_file_name, 'w')\n t_input_file_name = \"input_file_trimming\"\n t_input_file = fho.get_file_object(t_input_file_name, 'w')\n t_output_file_name = \"output_file_trimming\"\n t_output_file = fho.get_file_object(t_output_file_name, 'w')\n nt_circuit_file_name = \"circuit_file_no_trimming\"\n nt_circuit_file = fho.get_file_object(nt_circuit_file_name, 'w')\n nt_input_file_name = \"input_file_no_trimming\"\n nt_input_file = fho.get_file_object(nt_input_file_name, 'w')\n nt_output_file_name = \"output_file_no_trimming\"\n nt_output_file = fho.get_file_object(nt_output_file_name, 'w')\n level_type_array = [g.LEVEL_TYPES.RANDOM]\n F = 1\n # make a family 1 circuit with trimming:\n sr.seed(self.rand_seed)\n t_gen = g.f1f2_circuit_maker_with_trimming_switch(W, G, fg,\n t_circuit_file,\n t_input_file,\n t_output_file,\n X, fx, gate_maker,\n level_type_array, True)\n t_gen.generate()\n # make a family 1 circuit without trimming, with the same randomness:\n sr.seed(self.rand_seed)\n nt_gen = g.f1f2_circuit_maker_with_trimming_switch(W, G, fg,\n nt_circuit_file,\n nt_input_file,\n nt_output_file,\n X, fx, gate_maker,\n level_type_array, False)\n nt_gen.generate()\n # obtain strings representing the contents of all the resulting files:\n t_circuit_string = fho.get_file(t_circuit_file_name).getvalue()\n t_input_string = fho.get_file(t_input_file_name).getvalue()\n t_output_string = fho.get_file(t_output_file_name).getvalue()\n nt_circuit_string = fho.get_file(nt_circuit_file_name).getvalue()\n nt_input_string = fho.get_file(nt_input_file_name).getvalue()\n nt_output_string = fho.get_file(nt_output_file_name).getvalue()\n # make sure that the inputs and outputs produced by the trimming and\n # no trimming algorithms are the same:\n self.assertEqual(t_input_string, nt_input_string)\n self.assertEqual(t_output_string, nt_output_string)\n # make sure that the input begins and ends with a bracket:\n self.assertEqual(\"[\", t_input_string[0])\n self.assertEqual(\"]\", t_input_string[-1])\n # make sure that each input element is a bit:\n for bit in t_input_string[1:-1]:\n self.assertTrue((bit == '0') or (bit == '1'))\n # make sure that the output is a bit:\n self.assertTrue((t_output_string == '0') or (t_output_string == '1'))\n # make sure that the two circuit headers are the same, and that they\n # contain the correct values:\n t_circuit_header = t_circuit_string.split(\"\\n\")[0]\n nt_circuit_header = nt_circuit_string.split(\"\\n\")[0]\n self.assertEqual(t_circuit_header, nt_circuit_header)\n (W_string, G_string, F_string) = t_circuit_header.split(\",\")\n W_value = int(W_string.split(\"=\")[-1])\n G_value = int(G_string.split(\"=\")[-1])\n F_value = int(F_string.split(\"=\")[-1])\n self.assertEqual(W, W_value)\n self.assertEqual(G, G_value)\n self.assertEqual(F, F_value)\n # note that we cannot test that the circuits themselves are the same,\n # because the trimming algorithm produces a circuit with gates listed\n # in a different order.","def compare_ratios(path='/Volumes/OptiHDD/data/pylith/3d/agu2013/output',\n\t\t\t\tsteps=['step01','step02'],\n\t\t\t\t#labels='',\n\t\t\t\tshow=True,\n\t\t\t\txscale=1e3,\n\t\t\t\tyscale=1e-2):\n\tplt.figure()\n\t#path = '/Users/scott/Desktop/elastic'\n\n\t# Deep source\n\tlabels = ['no APMB', 'APMB']\n\tdeep = {}\n\tuzmax = 0.824873455364\n\t# NOT sure why hardcoded...\n\tuzmax = 1\n\tfor i,outdir in enumerate(steps):\n\t\tpointsFile = os.path.join(path, outdir, 'points.h5')\n\n\t\tx,y,z,ux,uy,uz = pu.extract_points(pointsFile)\n\n\t\tX = x / xscale\n\t\tY1 = ux / yscale\n\n\t\tx_fem = X #/ xscale #double scaling!\n\t\tur_fem = Y1 #/ yscale\n\t\tuz_fem = uz / yscale\n\n\t\t#print(pointsFile)\n\t\tprint(ur_fem.min(), ur_fem.max(), uz_fem.min(), uz_fem.max(), uz_fem.max() / ur_fem.max())\n\n\t\t#normalize\n\t\tuz_fem = uz_fem / uzmax\n\t\tur_fem = ur_fem / uzmax\n\t\tx_fem = x_fem / 30.0\n\n\t\tl, = plt.plot(x_fem,uz_fem,'o-',ms=4,lw=4,label=labels[i])\n\t\tplt.plot(x_fem,ur_fem,'o--',ms=4,lw=4,color=l.get_color()) #mfc='none' transparent\n\t\tdeep[outdir] = uz_fem/uz_fem\n\n\n\t# Shallow Source\n\tshallow = {}\n\tuzmax = 0.949652827795 # Why?\n\tfor i,outdir in enumerate(['step11','step12']):\n\t\tpointsFile = os.path.join(path, outdir, 'points.h5')\n\n\t\tx,y,z,ux,uy,uz = pu.extract_points(pointsFile)\n\n\t\tX = x / xscale\n\t\tY1 = ux / yscale\n\n\t\tx_fem = X #/ xscale #double scaling!\n\t\tur_fem = Y1 #/ yscale\n\t\tuz_fem = uz / yscale\n\n\t\t#print(pointsFile)\n\t\tprint(ur_fem.min(), ur_fem.max(), uz_fem.min(), uz_fem.max(), uz_fem.max() / ur_fem.max())\n\n\t\t#normalize\n\t\tuz_fem = uz_fem / uzmax\n\t\tur_fem = ur_fem / uzmax\n\t\tx_fem = x_fem / 20.0\n\n\t\tl, = plt.plot(x_fem,uz_fem,'.-', mfc='w', lw=4,label=labels[i])\n\t\tplt.plot(x_fem,ur_fem,'.--',lw=4, mfc='w',color=l.get_color()) #mfc='none' transparent\n\n\t\tshallow[outdir] = uz_fem/ur_fem\n\n\t# Annotate\n\tplt.axhline(color='k',lw=0.5)\n\t#plt.xlabel('Distance [{}]'.format(get_unit(xscale)))\n\t#plt.ylabel('Displacement [{}]'.format(get_unit(yscale)))\n\tplt.legend()\n\tplt.grid()\n\t#plt.ylim(-0.5, 3.5)\n\t#plt.savefig('deep.png',bbox_inches='tight')\n\t#plt.savefig('shallow.png',bbox_inches='tight')\n\n\t# normalized\n\tplt.ylim(-0.5, 4)\n\tplt.xlim(0,10)\n\tplt.xlabel('Normalized Radial Distance [R / D]')\n\tplt.ylabel('Normalized Displacement [U / Uz_max]')\n\t#plt.savefig('normalized_deep.png',bbox_inches='tight')\n\tplt.savefig('normalized_shallow.png',bbox_inches='tight')\n\n\n\t# Plot ratios of uz versus NOTE: this plot is confusing,,, just keep ratio of uz_max to ur_max\n\t'''\n\tplt.figure()\n\tplt.plot(x_fem, deep['step01'], label='Deep no APMB')\n\tplt.plot(x_fem, deep['step02'], label='Deep w/ APMB')\n\tplt.plot(x_fem, shallow['step11'], label='Shallow no APMB')\n\tplt.plot(x_fem, shallow['step12'], label='Shallow w/ APMB')\n\tplt.xlabel('Distance [km]') #NOTE: maybe plot normailzed X-axis (R-d)\n\t#plt.xlabel('Normalized Distance [R/d]')\n\tplt.ylabel('Ratio [Uz/Ur]')\n\tplt.title('Ratio of vertical to radial displacement')\n\tplt.legend()\n\tplt.show()\n\t'''","def test_case_generate(self):\n\n # initialization\n state = np.random.choice(self.init_states)\n model = rm.randint(0, self.model_num - 1)\n duration = np.random.choice(self.step_values)\n temp = rm.randint(self.min_temp, self.max_temp)\n\n self.states = [[model, duration, temp]]\n self.time = duration\n\n while self.time < self.max_time:\n if state == \"inc_tmp\":\n change = np.random.choice(\n self.transitionName[0], p=self.transitionMatrix[0]\n ) # choose the next state\n if change == \"S1S1\": # stay in the same state\n temp = self.get_temp_inc(temp)\n model = rm.randint(0, self.model_num - 1)\n diff = (\n self.max_time - self.time\n ) # this is for ensuring the maximum duration is not exceeded\n if (diff) < self.max_step and (diff) > self.min_step:\n duration = diff\n self.states.append([model, duration, temp])\n return self.states_to_dict()\n elif diff < self.min_step:\n self.states[len(self.states) - 1][1] += diff\n return self.states_to_dict()\n else:\n duration = np.random.choice(self.step_values)\n self.time += duration\n self.states.append([model, duration, temp])\n\n elif change == \"S1S2\": # change from increase to decrease\n temp = self.get_temp_dec(temp)\n model = rm.randint(0, self.model_num - 1)\n state = \"dec_tmp\"\n\n diff = self.max_time - self.time\n if (diff) < self.max_step and (diff) > self.min_step:\n duration = diff\n self.states.append([model, duration, temp])\n return self.states_to_dict()\n elif diff < self.min_step:\n self.states[len(self.states) - 1][1] += diff\n return self.states_to_dict()\n else:\n duration = np.random.choice(self.step_values)\n self.time += duration\n self.states.append([model, duration, temp])\n else:\n print(\"Error\")\n\n elif state == \"dec_tmp\":\n change = np.random.choice(\n self.transitionName[1], p=self.transitionMatrix[1]\n )\n if change == \"S2S1\":\n temp = self.get_temp_inc(temp)\n model = rm.randint(0, self.model_num - 1)\n state = \"inc_tmp\"\n diff = self.max_time - self.time\n if (diff) < self.max_step and (diff) > self.min_step:\n duration = diff\n self.states.append([model, duration, temp])\n return self.states_to_dict()\n elif diff < self.min_step:\n self.states[len(self.states) - 1][1] += diff\n return self.states_to_dict()\n else:\n duration = np.random.choice(self.step_values)\n\n self.time += duration\n self.states.append([model, duration, temp])\n\n elif change == \"S2S2\":\n temp = self.get_temp_dec(temp)\n model = rm.randint(0, self.model_num - 1)\n\n diff = self.max_time - self.time\n if (diff) < self.max_step and (diff) > self.min_step:\n duration = diff\n self.states.append([model, duration, temp])\n return self.states_to_dict()\n elif diff < self.min_step:\n self.states[len(self.states) - 1][1] += diff\n return self.states_to_dict()\n else:\n duration = np.random.choice(self.step_values)\n self.time += duration\n self.states.append([model, duration, temp])\n\n else:\n print(\"Error\")\n pass\n else:\n print(\"Error\")\n\n return self.states_to_dict()","def test_dataset_scenario_generation_full_late(self):\n \n params = ParameterServer()\n\n map_filename = os.path.join(os.path.dirname(__file__), \"data/DR_DEU_Merging_MT_v01_shifted.xodr\")\n track_filename = os.path.join(os.path.dirname(__file__), \"data/interaction_dataset_DEU_Merging_dummy_track_late.csv\")\n\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"MapFilename\"] = map_filename\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"TrackFilenameList\"] = [track_filename]\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"StartingOffsetMs\"] = 0\n\n scenario_generation = InteractionDatasetScenarioGenerationFull(\n params=params, num_scenarios=1)\n\n scenario = scenario_generation.get_scenario(0)\n world_state = scenario.GetWorldState()\n agent1 = world_state.GetAgent(1)\n agent2 = world_state.GetAgent(2)\n\n self.assertAlmostEqual(agent1.first_valid_timestamp, 0.0)\n self.assertAlmostEqual(agent2.first_valid_timestamp, 0.3)\n \n self.assertEqual(isinstance(agent1, Agent), True)\n self.assertEqual(agent1.IsValidAtTime(world_state.time), True)\n \n self.assertEqual(isinstance(agent2, Agent), True)\n self.assertEqual(agent2.IsValidAtTime(world_state.time), False)","def test_stop_start(self):\n log.info(\"CONFIG: %s\", self._agent_config())\n self.create_sample_data_set_dir(\n 'node59p1_step2.dat',\n TELEM_DIR,\n \"node59p1.dat\",\n copy_metadata=False\n )\n\n self.assert_initialize(final_state=ResourceAgentState.COMMAND)\n\n # Slow down processing to 1 per second to give us time to stop\n self.dataset_agent_client.set_resource({DriverParameter.RECORDS_PER_SECOND: 1})\n self.assert_start_sampling()\n\n # Verify we get one sample\n try:\n # Read the first file and verify the data\n result = self.data_subscribers.get_samples(DataParticleType.METADATA_TELEMETERED, 1)\n result1 = self.data_subscribers.get_samples(DataParticleType.SAMPLE_TELEMETERED, 2)\n result.extend(result1)\n log.debug(\"RESULT: %s\", result)\n\n # Verify values\n self.assert_data_values(result, 'test_data_1-2.txt.result.yml')\n self.assert_sample_queue_size(DataParticleType.METADATA_TELEMETERED, 0)\n self.assert_sample_queue_size(DataParticleType.SAMPLE_TELEMETERED, 0)\n\n self.create_sample_data_set_dir(\n 'node59p1_step4.dat',\n TELEM_DIR,\n \"node59p1.dat\",\n copy_metadata=False\n )\n # Now read the first records of the second file then stop\n result1 = self.data_subscribers.get_samples(DataParticleType.SAMPLE_TELEMETERED, 2)\n log.debug(\"RESULT 1: %s\", result1)\n self.assert_stop_sampling()\n self.assert_sample_queue_size(DataParticleType.METADATA_TELEMETERED, 0)\n self.assert_sample_queue_size(DataParticleType.SAMPLE_TELEMETERED, 0)\n\n # Restart sampling and ensure we get the last 2 records of the file\n self.assert_start_sampling()\n result2 = self.data_subscribers.get_samples(DataParticleType.SAMPLE_TELEMETERED, 2)\n log.debug(\"RESULT 2: %s\", result2)\n result = result1\n result.extend(result2)\n log.debug(\"RESULT: %s\", result)\n self.assert_sample_queue_size(DataParticleType.METADATA_TELEMETERED, 0)\n self.assert_data_values(result, 'test_data_3-4.txt.result.yml')\n self.assert_sample_queue_size(DataParticleType.METADATA_TELEMETERED, 0)\n self.assert_sample_queue_size(DataParticleType.SAMPLE_TELEMETERED, 0)\n except SampleTimeout as e:\n log.error(\"Exception trapped: %s\", e, exc_info=True)\n self.fail(\"Sample timeout.\")","def test_stop_start(self):\n log.info(\"START QUAL TEST STOP START\")\n self.create_sample_data('all_A0000003.DEC', \"A0000003.DEC\")\n self.assert_initialize(final_state=ResourceAgentState.COMMAND)\n self.dataset_agent_client.set_resource(\n {DriverParameter.RECORDS_PER_SECOND: 1})\n self.assert_start_sampling()\n\n try:\n # Read the first file (3 velocity records) and verify the data.\n result = self.get_samples(DataParticleType.VELOCITY_PARTICLE, 3)\n time_result = self.get_samples(DataParticleType.TIME_PARTICLE, 1)\n result.extend(time_result)\n\n # Verify values\n self.assert_data_values(result, 'all_A0000003.yml')\n self.verify_queue_empty()\n\n # Read the first 2 velocity records of the second file then stop.\n self.create_sample_data('valid_A0000004.DEC', \"A0000004.DEC\")\n result = self.get_samples(DataParticleType.VELOCITY_PARTICLE, 2)\n self.assert_stop_sampling()\n self.verify_queue_empty()\n\n # Restart sampling and get the last 2 records of the file\n # and combine with the previous ones we read.\n self.assert_start_sampling()\n result2 = self.get_samples(DataParticleType.VELOCITY_PARTICLE, 2)\n result.extend(result2)\n\n # Get the time record and combine with previous records.\n time_result = self.data_subscribers.get_samples(\n DataParticleType.TIME_PARTICLE, 1)\n result.extend(time_result)\n self.assert_data_values(result, 'valid_A0000004.yml')\n\n self.verify_queue_empty()\n\n except SampleTimeout as e:\n log.error(\"Exception trapped: %s\", e, exc_info=True)\n self.fail(\"Sample timeout.\")\n\n log.info(\"END QUAL TEST STOP START\")","def main(m1=1.4, m2=0.3, r1=10, a0=100, eta=110, rel=\"off\",\n max_time=1, out1_path=out1_path_default, out2_path=out2_path_default):\n\n if not (rel in (\"on\", \"off\")):\n er(\"wrong rel\")\n\n if m1 < m2:\n er('m1 must be more than m2!')\n\n if eta > 200:\n er('error: eta must be less than 200!')\n\n if eta < 60:\n er(\"error: eta must be more than 60!\")\n\n if not (m2 > 0.1 and m2 < 0.8):\n er('error: m2 must be in (0.1, 0.8)!')\n\n if m2 < 0: \n er('error: m2 must be positive!')\n\n if m2 > 1: \n er('warning, mass of m1 is more than 1 M_sol so approximation of Sotani may be not valid!')\n \n if m1 > 2.3:\n print('Warning, m1 have a mass of BH!')\n\n \n with open(out1_path, 'w') as out1:\n with open(out2_path, 'w') as out2:\n res1, res2 = run(m1=m1, m2=m2, r1=r1, a0=a0, eta=eta, rel=rel, max_time=max_time)\n write_res(res1, out1)\n write_res(res2, out2)","def rtest_power_and_reward_crossover():\n #define pilot cohort size\n npatients = 10\n\n #simulate drugX and drugY policies for long term in short term increments\n nstep = int(long_term/short_term)\n\n #init rewards per patient per time for drugx and drugy policies\n drugx_reward = np.zeros((nstep,npatients))\n drugy_reward = np.zeros((nstep,npatients))\n\n #init drugX simulation\n simulator = AbbcEnvironment(patients=npatients)\n #define action taken\n action = np.repeat(1, npatients)\n #main simulation loop\n for step in range(nstep):\n _, drugx_reward[step,:] = simulator.take_action(action=action, simtime=short_term)\n\n #init drugY simulation\n simulator = AbbcEnvironment(patients=npatients)\n #define action taken\n action = np.repeat(2, npatients)\n #main simulation loop\n for step in range(nstep):\n _, drugy_reward[step,:] = simulator.take_action(action=action, simtime=short_term)\n\n #calculate sample size required to resolve effect size for each simulation step.\n zalpha = 1.96 #critical zvalue for p=1-alpha/2\n zbeta = 0.8416 #critical zvalue for p=1-beta\n zsquared = (zalpha + zbeta)**2\n #calculate the diference in effect size\n delta = (np.mean(drugx_reward, axis=1) - np.mean(drugy_reward, axis=1))\n samplesize = np.divide((np.var(drugx_reward, axis=1) + np.var(drugx_reward, axis=1))*zsquared,delta**2)\n print(samplesize)\n print(delta)\n\n #assert max sample size is greater than 100\n assert np.max(samplesize)>100\n\n #assert max sample size at is at least 50 times the mins at short and long terms\n assert np.max(samplesize)>50*samplesize[0]\n assert np.max(samplesize)>50*samplesize[-1]\n\n #assert DrugX is better short term than DrugY\n assert delta[0]>0\n #assert DrugY is better in long term than DrugX\n assert delta[-1]<0","def make_simulations(self):\n pass","def get_param_scenario3():\n nb_carre_x = 42\n nb_carre_y = 45\n largeur_x = 0.825\n largeur_y = 0.645\n z1 = np.array([[11], [32], [17.26]])\n z2 = np.array([[12], [32], [18.43]])\n z3 = np.array([[13], [32], [23.2]])\n z4 = np.array([[15], [31], [23.4]])\n z5 = np.array([[16], [30], [23.29]])\n z6 = np.array([[18], [29], [22.28]])\n z7 = np.array([[19], [28], [35.79]])\n z8 = np.array([[20], [27], [36.87]])\n z9 = np.array([[21], [26], [33.92]])\n z10 = np.array([[23], [24], [38.11]])\n z11 = np.array([[24], [23], [37.76]])\n z12 = np.array([[25], [22], [45.6]])\n z13 = np.array([[26], [20], [56.4]])\n z14 = np.array([[27], [18], [55.2]])\n z15 = np.array([[28], [16], [57.53]])\n z16 = np.array([[29], [14], [58.64]])\n z17 = np.array([[30], [13], [66.04]])\n z18 = np.array([[30], [11], [70.02]])\n z19 = np.array([[31], [9], [69.64]])\n z20 = np.array([[31], [6], [68.82]])\n\n meas = [z1, z2, z3, z4, z5, z6, z7, z8, z9, z10, z11, z12, z13, z14, z15, z16, z17, z18, z19, z20]\n\n z = [np.array([[x[0][0] * largeur_x / nb_carre_x], [x[1][0] * largeur_y / nb_carre_y], [-x[2][0] * np.pi / 180]])\n for x in meas]\n\n # We take a measure every second\n thymio_speed_to_mms = 0.4753\n Ts = 1\n\n Thymio_speed_left = [73, 92, 94, 102, 100, 89, 92, 103, 97, 100, 100, 104, 95, 97, 103, 101, 100, 101, 92, 98]\n Thymio_speed_right = [67, 96, 102, 89, 105, -45, 102, 101, 104, 71, 100, 93, 94, 105, 96, -45, 95, 103, 88, 94]\n delta_sr_test = [x * Ts / thymio_speed_to_mms / 1000 for x in Thymio_speed_right]\n delta_sl_test = [x * Ts / thymio_speed_to_mms / 1000 for x in Thymio_speed_left]\n\n return delta_sr_test, delta_sl_test, z","def scenario1(height, speed):\n time = math.sqrt((2 * height) / 9.81)\n result = speed * time\n return result","def test_dataset_scenario_generation_full_outside1_behavior_overwritten(self):\n params = ParameterServer()\n\n map_filename = os.path.join(os.path.dirname(__file__), \"data/DR_DEU_Merging_MT_v01_shifted.xodr\")\n track_filename = os.path.join(os.path.dirname(__file__), \"data/interaction_dataset_DEU_Merging_dummy_track_outside.csv\")\n\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"MapFilename\"] = map_filename\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"TrackFilenameList\"] = [track_filename]\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"StartingOffsetMs\"] = 0\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"BehaviorModel\"] = \"BehaviorMobilRuleBased\"\n\n scenario_generation = InteractionDatasetScenarioGenerationFull(\n params=params, num_scenarios=1)\n\n scenario = scenario_generation.get_scenario(0)\n self.assertAlmostEqual(scenario.eval_agent_ids, [1])\n\n world_state = scenario.GetWorldState()\n agent11 = world_state.GetAgent(1)\n agent12 = world_state.GetAgent(2)\n agent13 = world_state.GetAgent(3)\n\n # others are defined as BehaviorMobilRuleBased\n self.assertTrue(isinstance(agent11.behavior_model, BehaviorStaticTrajectory))\n self.assertTrue(isinstance(agent12.behavior_model, BehaviorMobilRuleBased))\n self.assertTrue(isinstance(agent13.behavior_model, BehaviorMobilRuleBased))\n\n self.assertAlmostEqual(agent11.first_valid_timestamp, 0.0)\n self.assertAlmostEqual(agent12.first_valid_timestamp, 0.0)\n self.assertNotEqual(agent13.first_valid_timestamp, 0.0)\n \n # agent13 should not be valid at the beginning, as he is outside of map\n world_state.time = 0\n self.assertEqual(isinstance(agent11, Agent), True)\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent11.InsideRoadCorridor(), True)\n \n self.assertEqual(isinstance(agent12, Agent), True)\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent12.InsideRoadCorridor(), True)\n\n self.assertEqual(isinstance(agent13, Agent), True)\n self.assertEqual(agent13.IsValidAtTime(world_state.time), False)\n # as we use only state once it's in map, this will be true, although the time step is not valid yet\n self.assertEqual(agent13.InsideRoadCorridor(), True)\n\n # agent13 should not be valid at the beginning, as he is outside of map\n world_state.Step(0.05)\n\n self.assertEqual(isinstance(agent11, Agent), True)\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent11.InsideRoadCorridor(), True)\n \n self.assertEqual(isinstance(agent12, Agent), True)\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent12.InsideRoadCorridor(), True)\n\n self.assertEqual(isinstance(agent13, Agent), True)\n self.assertEqual(agent13.IsValidAtTime(world_state.time), False)\n # as we use only state once it's in map, this will be true, although the time step is not valid yet\n self.assertEqual(agent13.InsideRoadCorridor(), True)\n\n self.assertEqual(list(world_state.agents_valid.keys()), [1,2])\n\n # agent13 should be valid at some point\n world_state.Step(agent13.first_valid_timestamp)\n world_state.Step(0.01) # agent13.IsValidAtTime() uses previous time stamp, therefore we increment it one more step\n\n self.assertEqual(isinstance(agent11, Agent), True)\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent11.InsideRoadCorridor(), True)\n \n self.assertEqual(isinstance(agent12, Agent), True)\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent12.InsideRoadCorridor(), True)\n\n self.assertEqual(isinstance(agent13, Agent), True)\n self.assertEqual(agent13.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent13.InsideRoadCorridor(), True)\n\n self.assertEqual(list(world_state.agents_valid.keys()), [1,2,3])","def test_three_arms_one_unsampled_arm(self):\n self._test_three_arms_one_unsampled_arm()","def instrumentationAmplifier():\n eq = list()\n eq.append(\"Eq(vo,A_v*(v2-v1)\")\n eq.append(\"Eq(Av, 1 + 2*R / R_G\")","def run():\n trials = 100\n\n multipliers = [0.25, 0.3, 0.35, 0.5, 0.75, 1, 1.25, 1.45, 1.5, 1.55, 1.6] # Coefficients for learning rate\n\n mean_penalty = []\n median_penalty = []\n std_penalty = []\n\n mean_trial_time = []\n median_trial_time = []\n std_trial_time = []\n\n mean_success_rate = []\n median_success_rate = []\n std_success_rate = []\n\n for m in multipliers:\n all_penalties = [] # All penalties from trail sets\n all_average_trial_time = []\n all_success_rates = []\n\n for i in range(0, 20):\n # print \"Trial set:\", i\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n agent = e.create_agent(LearnerAgent) # create agent\n agent.mult = m\n e.set_primary_agent(agent, enforce_deadline=True) # specify agent to track\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.0, display=False) # create simulator (uses pygame when display=True, if available)\n\n sim.run(n_trials=trials) # run for a specified number of trials\n\n all_penalties.append(agent.all_trails_penalties)\n all_average_trial_time.append(agent.time/float(trials))\n all_success_rates.append(float(trials-agent.aborted_trials)/trials)\n\n mean_penalty.append(np.mean(all_penalties))\n median_penalty.append(np.median(all_penalties))\n std_penalty.append(np.std(all_penalties))\n\n mean_trial_time.append(np.mean(all_average_trial_time))\n median_trial_time.append(np.median(all_average_trial_time))\n std_trial_time.append(np.std(all_average_trial_time))\n\n mean_success_rate.append(np.mean(all_success_rates))\n median_success_rate.append(np.median(all_success_rates))\n std_success_rate.append(np.std(all_success_rates))\n\n for i in range(0, len(multipliers)):\n print \"\"\n print \"Multiplier:\", multipliers[i]\n print \"\"\n print \"Mean penalty per {} trials:\".format(trials), mean_penalty[i]\n print \"Median penalty per {} trials:\".format(trials), median_penalty[i]\n print \"Std.Dev. penalty per {} trials:\".format(trials), std_penalty[i]\n\n print \"\"\n print \"Mean trial time:\", mean_trial_time[i]\n print \"Median trial time:\", median_trial_time[i]\n print \"Std.Dev. trial time:\", std_trial_time[i]\n\n print \"\"\n print \"Mean success rate per {} trials:\".format(trials), mean_success_rate[i]\n print \"Median success rate per {} trials:\".format(trials), median_success_rate[i]\n print \"Std.Dev. success rate per {} trials:\".format(trials), std_success_rate[i]","def test_calc_SR2():\n tb = TradeBot(\n pair='EUR_GBP',\n timeframe='H12',\n start='2017-06-11 22:00:00',\n end='2017-06-15 22:00:00',\n settingf=\"../../data/settings.ini\"\n )\n\n harealst = tb.calc_SR(datetime.datetime(2017, 6, 13, 22, 0))\n\n # check the length of HAreaList.halist\n assert len(harealst.halist) == 4","def Transformer(robot1,robot2): \n #with a 50% change surgery will be triggered\n return Random_Actuation.surgery(\"Transformer\",robot1,robot2,50)","def _evaluate_performance__static_winners(self):\n # | - _evaluate_performance__\n\n # | - class attributes #################################################\n AL = self\n al_gen = self.al_gen\n verbose = self.verbose\n seed_ids = self.seed_ids\n acquisition_bin = self.acquisition_bin\n completed_ids = self.completed_ids\n CandidateSpace = self.CandidateSpace\n RegressionModel = self.RegressionModel\n DuplicateFinder = self.DuplicateFinder\n al_gen_dict = self.al_gen_dict\n\n stop_mode = self.stop_mode\n stop_num_generations = self.stop_num_generations\n\n index_acq_gen_dict = self.index_acq_gen_dict\n #__| #################################################################\n\n # #####################################################################\n mode = \"lowest_N\" # 'lowest_N' or 'lowest_perc'\n\n N_ids = 10\n lowest_perc = 5\n\n # Number of consecutive generations that the Nth best systems must\n # remain static\n M_gens = 3\n # #####################################################################\n\n if mode == \"lowest_perc\":\n num_candidates = CandidateSpace.FingerPrints.df_pre.shape[0]\n N_ids = int(num_candidates * (lowest_perc * 0.01))\n\n gen_keys = list(AL.al_gen_dict.keys())\n\n if len(gen_keys) > M_gens:\n latest_M_keys = gen_keys[-(M_gens + 1):]\n last_gen_key = gen_keys[-1]\n\n al_gen_dict_subset_i = dict(zip(\n latest_M_keys,\n [AL.al_gen_dict.get(i, None) for i in latest_M_keys]))\n\n indices_list = []\n iterator = enumerate(al_gen_dict_subset_i.items())\n for i_cnt, (gen_i, AL_i) in iterator:\n model_i = AL_i.model\n\n model_i = AL.add_main_Y_to_model(\n model_i, plot_dft_instead_of_pred=True)\n model_i = model_i[(model_i[\"duplicate\"] == False)]\n model_i = model_i.sort_values(\"Y_main\")\n\n indices_i = model_i.index.tolist()\n\n indices_list.append(indices_i)\n\n if i_cnt >= M_gens:\n indices_i = indices_list[i_cnt][0:N_ids]\n ids_static_list = []\n for j in range(M_gens):\n indices_j = indices_list[i_cnt - (j + 1)][0:N_ids]\n ids_static = indices_j == indices_i\n ids_static_list.append(ids_static)\n\n ids_are_static = all(ids_static_list)\n\n self.performance__static_winners[last_gen_key] = ids_are_static\n #__|","def make_test_data(self):\r\n\r\n \r\n\r\n print (\"Creating Test Sample:\")\r\n\r\n print (' Period, rate, reps, phases: ', self.period, self.framerate, self.nrepetitions, self.nPhases)\r\n\r\n nframes = int(self.period * self.framerate * self.nrepetitions)\r\n\r\n print (' nframes: ', nframes)\r\n\r\n if self.bkgdNoise > 0.:\r\n\r\n d = np.random.normal(size=(nframes,self.imageSize[0],self.imageSize[1]),\r\n\r\n loc=self.bkgdIntensity, scale=self.bkgdNoise).astype('float32')\r\n\r\n else:\r\n\r\n d = self.bkgdIntensity*np.ones((nframes,self.imageSize[0],self.imageSize[1])).astype('float32')\r\n\r\n \r\n\r\n ds = d.shape\r\n\r\n print (' data shape: ', ds)\r\n\r\n dx = int(ds[2]/4)\r\n\r\n xc = int(ds[2]/2)\r\n\r\n xo = [xc-dx, xc+dx]\r\n\r\n ywidth = int(ds[2]/(self.nPhases+2))\r\n\r\n framedelay = 4\r\n\r\n\r\n\r\n if not self.mode:\r\n\r\n self.phasex = []\r\n\r\n self.phasey = []\r\n\r\n for i in range(0,self.nPhases):\r\n\r\n dy = int((i+1)*ds[2]/(self.nPhases+2)) # each phase is assigned to a region\r\n\r\n self.resp = np.zeros((nframes,))\r\n\r\n self.resp = np.cos(\r\n\r\n np.linspace(0, 2.0*np.pi*nframes/(self.period*self.framerate), nframes-framedelay)+i*np.pi/8 - np.pi/2.0)\r\n\r\n self.resp = np.concatenate((np.zeros(framedelay), self.resp))\r\n\r\n d[:, xo[0]:xo[1], dy:dy+ywidth ] += self.resp[:, np.newaxis, np.newaxis]\r\n\r\n self.phasey.append( (2+(dy+int(ds[2]/self.nPhases))/2))\r\n\r\n self.phasex.append((6+int(ds[1]/2)/2)) # make the signal equivalent of digitized one (baseline 3000, signal at 1e-4 of baseline)\r\n\r\n else:\r\n\r\n self.nPhases = 4\r\n\r\n self.spotsize = 16\r\n\r\n nrpts = 20\r\n\r\n nsites = 4\r\n\r\n one_rep = int(self.period*self.framerate)\r\n\r\n isi = int(self.period*self.framerate/self.nPhases)\r\n\r\n print('period, isi: ', self.period, isi)\r\n\r\n r = np.arange(0, nrpts, 1.)\r\n\r\n alpha = 4.\r\n\r\n A = r/alpha *np.exp(-(r-alpha)/alpha) # scaled alpha function\r\n\r\n self.spot= self.gauss_spot(self.spotsize, 3.) # the 2d spot\r\n\r\n sigsize = np.random.normal(size=self.nPhases, loc=self.signal_size, scale=self.signal_size*2)\r\n\r\n sigsize = [np.abs(s) for s in sigsize] # restrict to positive amplitudes\r\n\r\n print ('sigsize: ', sigsize)\r\n\r\n for j in range(self.nrepetitions):\r\n\r\n for i in range(self.nPhases):\r\n\r\n self.resp = np.zeros((nrpts, self.spot.shape[0], self.spot.shape[1]))\r\n\r\n for k in range(nrpts):\r\n\r\n self.resp[k,:,:] += sigsize[i]*A[k] * self.spot # make response an alpha time course of gaussian spot\r\n\r\n start = j*one_rep + i*isi + framedelay\r\n\r\n stop = start + nrpts\r\n\r\n dy = int((i+1)*ds[2]/(self.nPhases+2)) # location for phase\r\n\r\n #dy = dy + 2*z\r\n\r\n# print ('start, stop: ', start, stop)\r\n\r\n for z in range(nsites):\r\n\r\n #self.resp = np.concatenate((np.zeros(framedelay), self.resp))\r\n\r\n xp = xo[0] + i*10 - 10*z\r\n\r\n yp = dy - i*10 + 10*z\r\n\r\n d[start:stop, xp:xp+self.spotsize, yp:yp+self.spotsize ] += self.resp\r\n\r\n self.imageData = d # reduce to a 16-bit map to match camera data type\r\n\r\n self.nFrames = self.imageData.shape[0]\r\n\r\n self.times = np.arange(0, nframes/self.framerate, 1.0/self.framerate)\r\n\r\n print( \" Test Image Created\")\r\n\r\n # imv = pg.ImageView()\r\n\r\n # imv.show()\r\n\r\n # imv.setImage(self.imageData)\r\n\r\n\r\n\r\n if self.layout is not None:\r\n\r\n self.layout.addWidget(imv, 0, 0)\r\n\r\n\r\n\r\n avgImage = np.mean(self.imageData, axis=0)\r\n\r\n ima = pg.ImageView()\r\n\r\n ima.setImage(avgImage)\r\n\r\n self.layout.addWidget(ima, 0, 1)\r\n\r\n self.adjust_image_data()\r\n\r\n self.avgimg = np.mean(self.imageData, axis=0) # get mean image for reference later: average across all time\r\n\r\n print (' Test file, original Image Info: ')\r\n\r\n self.print_image_info()\r\n\r\n self.rebin_image()\r\n\r\n #self.clean_windowerrors()\r\n\r\n # pg.image(self.imageData)\r\n\r\n # pg.show()\r\n\r\n # mpl.figure(1)\r\n\r\n # mpl.show()\r\n\r\n if not self.mode: # FFT analysis\r\n\r\n self.analysis_fourier_map(target=1, mode=0)\r\n\r\n self.plot_maps(mode=2, gfilter=self.gfilter)\r\n\r\n else:\r\n\r\n self.analysis_dFF_map()\r\n\r\n mpl.show()","def setUpClass(cls):\n cls.sim1 = Simulation(logging_level=50)\n cls.sim1.set_simulation_parameters(\n seed=1, task=47, output_directory=\"output\", min_speciation_rate=0.01, sigma=2, deme=1, sample_size=0.01\n )\n cls.sim1.set_map_files(\n \"null\", fine_file=\"sample/SA_sample_fine.tif\", reproduction_map=\"sample/SA_sample_reproduction.tif\"\n )\n cls.sim1.run()\n cls.sim2 = Simulation(logging_level=50)\n cls.sim2.set_simulation_parameters(\n seed=1, task=48, output_directory=\"output\", min_speciation_rate=0.01, sigma=2, deme=1, sample_size=0.01\n )\n cls.sim2.set_map_files(\"null\", fine_file=\"sample/SA_sample_fine.tif\")\n cls.sim2.run()\n cls.sim3 = Simulation(logging_level=50)\n cls.sim3.set_simulation_parameters(\n seed=2, task=47, output_directory=\"output\", min_speciation_rate=0.01, sigma=2, deme=1, sample_size=0.01\n )\n cls.sim3.set_map_files(\n \"null\",\n fine_file=\"sample/SA_sample_fine.tif\",\n death_map=\"sample/SA_sample_reproduction.tif\",\n reproduction_map=\"sample/SA_sample_reproduction.tif\",\n )\n cls.sim3.run()\n cls.sim4 = Simulation(logging_level=50)\n cls.sim4.set_simulation_parameters(\n seed=4, task=47, output_directory=\"output\", min_speciation_rate=0.01, sigma=2, deme=1, sample_size=0.01\n )\n cls.sim4.set_map_files(\"null\", fine_file=\"sample/SA_sample_coarse_pristine.tif\")\n cls.sim4.add_reproduction_map(reproduction_map=\"sample/SA_reproduction_coarse.tif\")\n cls.sim4.add_death_map(death_map=\"sample/SA_death.tif\")\n cls.sim4.add_dispersal_map(dispersal_map=\"sample/dispersal_fine2.tif\")\n cls.sim4.run()\n cls.coal1 = CoalescenceTree(cls.sim1)\n cls.coal2 = CoalescenceTree(cls.sim2)\n cls.coal3 = CoalescenceTree(cls.sim3)\n cls.coal4 = CoalescenceTree(cls.sim4)","def test_make_uniform_random_f1f2_gate(self):\n num_trials = 100\n fanin_frac = .5\n # test fam1 and fam2 gates:\n generate = g.TYPE_TO_GATE_GEN[g.TEST_TYPES.RANDOM]\n W = 10\n ultimate_level = [g.make_random_input_wire(\"W\" + str(gate_num))\n for gate_num in xrange(W)]\n # a dictionary mapping each gate to how many times it was used as input:\n num_uses_dict = dict(\n (wire.get_name(), 0) for wire in ultimate_level)\n # a dictionary mapping gate type to a list of the number of times\n # that gate type is generated:\n gate_type_dict = dict(\n (gate_type, 0) for gate_type in g.GATE_TYPES.values_generator())\n for trial_num in xrange(num_trials):\n # create a new gate:\n gate = generate(ultimate_level, fanin_frac, \"this_gate\")\n # add the statistics to num_uses and gate_type_dist:\n gate_type_dict[gate.get_func_name()] += 1\n for wire in gate.get_inputs():\n num_uses_dict[wire.get_name()] += 1\n # make sure that each gate type appears between .2 and .8 of the time:\n # (note that these numbers are chosen randomly, and therefor this test\n # might occasionally fail with a low probability)\n for gate_type in gate_type_dict.keys():\n self.assertTrue(float(gate_type_dict[gate_type]) / float(num_trials)\n > .2)\n self.assertTrue(float(gate_type_dict[gate_type]) / float(num_trials)\n < .8)\n # make sure that each wire is chosen as input between .2 and .8 of the\n # time: (note that these numbers are chosen randomly, and therefor this\n # test might occasionally fail with a low probability)\n for wire in num_uses_dict.keys():\n self.assertTrue(float(num_uses_dict[wire]) / float(num_trials)\n > (fanin_frac) / 2.0)\n self.assertTrue(float(num_uses_dict[wire]) / float(num_trials)\n < max(fanin_frac * 2.0, 1.0))","def test_shuffled(self):\n self.setup_flags()\n self.io_args.matches = os.path.join(\n self.io_args.output_root, \"shuffled\", \"matches.json\"\n )\n self._calibration_error_test(\"shuffled\", \"GeometricCalibration\")","def test2():\n\t\n\td = np.linspace(-15000,15000,2500)\n\t#Voigt\n\n\n\t## 700 G, 84 C, Cs, 75mm\n\n\tp_dict = {'Bfield':700,'Btheta':90*np.pi/180,'lcell':75e-3,'T':84,'Dline':'D2','Elem':'Cs'}\n\tpol = 1./np.sqrt(2)*np.array([1.0,1.0,0.0])\n\tTVx = get_spectra(d,pol,p_dict,outputs=['I_M45','I_P45','Ix','Iy','S0','Iz'])\n\t\n\tfig2 = plt.figure()\n\tax1a = fig2.add_subplot(411)\n\tax2a = fig2.add_subplot(412,sharex=ax1a)\n\tax3a = fig2.add_subplot(413,sharex=ax1a)\n\tax4a = fig2.add_subplot(414,sharex=ax1a)\n\t\n\tax1a.plot(d,TVx[0],'r',lw=2,label=r'$I_{-45}$')\n\tax2a.plot(d,TVx[1],'b',lw=2,label=r'$I_{+45}$')\n\tax3a.plot(d,TVx[2],'r',lw=2,label=r'$I_x$')\n\tax4a.plot(d,TVx[3],'b',lw=2,label=r'$I_y$')\n\tax4a.plot(d,TVx[0]+TVx[1],'r:',lw=3.5,label=r'$I_{+45}+I_{-45}$')\n\tax4a.plot(d,TVx[2]+TVx[3],'k:',lw=2.5,label=r'$I_x + I_y$')\n\tax4a.plot(d,TVx[4],'g--',lw=1.5,label='$S_0$')\n\t#\tax4a.plot(d,TVx[5],'c--',lw=2.5,label='$I_z$')\n\t\n\t\n\tax4a.set_xlabel('Detuning (MHz)')\n\tax1a.set_ylabel('I -45')\n\tax2a.set_ylabel('I +45')\n\tax3a.set_ylabel('Ix')\n\tax4a.set_ylabel('Iy')\n\t\n\tax4a.set_xlim(d[0],d[-1]+3000)\n\tax4a.legend(loc=0)\n\t\n\tplt.show()","def simulate_stage2(lr_instance, stage1_vars_vals, scenarios, weights=None,\n stage2_info=None):\n weighted_costs = []\n stage2_costs = {}\n for i, (scenario_name, scenario) in enumerate(scenarios.items()):\n if stage2_info is None and i == 0:\n stage2_info = lr_instance.construct_stage2(scenario)\n # update and solve stage 2\n stage2_info, stage2_objval = lr_instance.update_stage2(\n stage1_vars_vals, scenario, stage2_info, True)\n if stage2_objval is None:\n sys.exit(\"Stage 2 is infeasible or unbounded for scenario\",\n scenario_name)\n stage2_costs[scenario_name] = stage2_objval\n if weights is not None:\n weighted_costs.append(weights[scenario_name] * stage2_objval)\n return np.sum(weighted_costs), stage2_costs","def test_stop_start_recovered(self):\n log.info(\"CONFIG: %s\", self._agent_config())\n self.create_sample_data_set_dir(\n 'DOS15908_1st7_step1.DAT',\n RECOV_DIR,\n \"DOS15908.DAT\",\n copy_metadata=False\n )\n\n self.assert_initialize(final_state=ResourceAgentState.COMMAND)\n\n # Slow down processing to 1 per second to give us time to stop\n self.dataset_agent_client.set_resource({DriverParameter.RECORDS_PER_SECOND: 1})\n self.assert_start_sampling()\n\n # Verify we get one sample\n try:\n # Read the first file and verify the data\n result = self.data_subscribers.get_samples(DataParticleType.METADATA_RECOVERED, 1)\n result1 = self.data_subscribers.get_samples(DataParticleType.SAMPLE_RECOVERED, 1)\n result.extend(result1)\n log.debug(\"RESULT: %s\", result)\n\n # Verify values\n self.assert_data_values(result, 'test_data_1r.txt.result.yml')\n self.assert_sample_queue_size(DataParticleType.METADATA_RECOVERED, 0)\n self.assert_sample_queue_size(DataParticleType.SAMPLE_RECOVERED, 0)\n\n self.create_sample_data_set_dir(\n 'DOS15908_1st7_step4.DAT',\n RECOV_DIR,\n \"DOS15909.DAT\",\n copy_metadata=False\n )\n # Now read the first records of the second file then stop\n result1 = self.data_subscribers.get_samples(DataParticleType.SAMPLE_RECOVERED, 2)\n log.debug(\"RESULT 1: %s\", result1)\n self.assert_stop_sampling()\n \n # Restart sampling and ensure we get the last 2 records of the file\n self.assert_start_sampling()\n result2 = self.data_subscribers.get_samples(DataParticleType.SAMPLE_RECOVERED, 2)\n log.debug(\"RESULT 2: %s\", result2)\n result = result1\n result.extend(result2)\n log.debug(\"RESULT: %s\", result)\n self.assert_sample_queue_size(DataParticleType.METADATA_RECOVERED, 1)\n self.assert_data_values(result, 'test_data_4r.txt.result.yml')\n except SampleTimeout as e:\n log.error(\"Exception trapped: %s\", e, exc_info=True)\n self.fail(\"Sample timeout.\")","def test_detector1pipeline4(_bigdata):\n step = Detector1Pipeline()\n step.save_calibrated_ramp = True\n step.ipc.skip = True\n step.persistence.skip = True\n step.jump.rejection_threshold = 4.0\n step.ramp_fit.save_opt = False\n step.output_file = 'jw84600007001_02101_00001_nrs1_rate.fits'\n step.run(_bigdata+'/pipelines/jw84600007001_02101_00001_nrs1_uncal.fits')\n\n # Compare ramp product\n n_ramp = 'jw84600007001_02101_00001_nrs1_ramp.fits'\n h = fits.open( n_ramp )\n n_ref = _bigdata+'/pipelines/jw84600007001_02101_00001_nrs1_ramp_ref.fits'\n href = fits.open( n_ref )\n newh = fits.HDUList([h['primary'],h['sci'],h['err'],h['groupdq'],h['pixeldq']])\n newhref = fits.HDUList([href['primary'],href['sci'],href['err'],href['groupdq'],h['pixeldq']])\n result = fits.diff.FITSDiff(newh,\n newhref,\n ignore_keywords = ['DATE','CAL_VER','CAL_VCS','CRDS_VER','CRDS_CTX'],\n rtol = 0.00001\n )\n assert result.identical, result.report()\n\n # Compare countrate image product\n n_cr = 'jw84600007001_02101_00001_nrs1_rate.fits'\n h = fits.open( n_cr )\n n_ref = _bigdata+'/pipelines/jw84600007001_02101_00001_nrs1_rate_ref.fits'\n href = fits.open( n_ref )\n newh = fits.HDUList([h['primary'],h['sci'],h['err'],h['dq']])\n newhref = fits.HDUList([href['primary'],href['sci'],href['err'],href['dq']])\n result = fits.diff.FITSDiff(newh,\n newhref,\n ignore_keywords = ['DATE','CAL_VER','CAL_VCS','CRDS_VER','CRDS_CTX'],\n rtol = 0.00001\n )\n assert result.identical, result.report()","def generate(self):\n tempSn_1 = self.sn_1\n self.sn_1 = (self.an_2*self.sn_2 + self.an_1*self.sn_1) % self.mod\n self.sn_2 = tempSn_1\n return self.sn_1 / self.mod","def setUp(self):\n self.s = Simulation()\n self.s['Retina']=GeneratorSheet(nominal_density=4.0)\n self.s['V1']= CFSheet(nominal_density=4.0)\n self.s['V2'] = CFSheet(nominal_density=4.0)\n\n self.s.connect('Retina','V1',delay=0.5,connection_type=CFProjection,\n name='RtoV1',learning_fn=CFPLF_Hebbian())\n\n self.s.connect('Retina','V2',delay=0.5,connection_type=CFProjection,\n name='RtoV2',learning_fn=CFPLF_Hebbian())","def sample(self):","def build_oblique_foF2_observed_by_radar(rad=\"cvw\", dates=[dt.datetime(2017,8,21), dt.datetime(2017,8,21)], bmnum=11, scan_num=1,\n remove_first_range=800, remove_last_range=2500, power_drop=10., plot=True):\n csv_fname = \"data/{rad}_{start}_{end}.csv\".format(rad=rad, start=dates[0].strftime(\"%Y.%m.%d.%H.%M\"), \n end=dates[1].strftime(\"%Y.%m.%d.%H.%M\"))\n if not os.path.exists(csv_fname):\n fdata = FetchData( rad, [dates[0], dates[1]] )\n s_params = [\"noise.sky\", \"tfreq\", \"frang\", \"rsep\", \"scan\", \"bmnum\"]\n v_params = [\"slist\", \"v\", \"w_l\", \"p_l\", \"elv\"]\n _, scans = fdata.fetch_data(by=\"scan\", s_params=s_params, v_params=v_params)\n beams = []\n # Resacle tx_frequency and estimate slant range \n for scan in scans:\n for beam in scan.beams:\n if len(beam.slist) > 0:\n setattr(beam, \"slant_range\", beam.frang + np.array(beam.slist.tolist()) * beam.rsep)\n setattr(beam, \"tfreq\", np.round(beam.tfreq/1e3,1))\n beams.append(beam)\n # Extract oblique foF2 or MUF scan by scan\n print(\"\\n Data will be averaged over {} scans\".format(scan_num))\n print(\" Processing beam {}\".format(bmnum))\n print(\" Remove first range {}\".format(remove_first_range))\n print(\" Remove last range {}\".format(remove_last_range))\n print(\" Power drop {}\".format(power_drop))\n skip_distance, o_foF2, time_start, time_end = [], [], [], []\n for i in range(len(scans)-scan_num):\n rscan = scans[i:i+scan_num]\n p_l, srange, tfrq, angle = [], [], [], []\n for scan in rscan:\n for beam in scan.beams:\n if beam.bmnum == bmnum:\n if len(beam.slist) > 0:\n p_l.extend(beam.p_l.tolist())\n srange.extend(beam.slant_range.tolist())\n tfrq.append(beam.tfreq)\n if type(beam.elv) is list: angle.extend(beam.elv)\n else: angle.extend(beam.elv.tolist())\n du = pd.DataFrame()\n du[\"p_l\"], du[\"srange\"] = p_l, srange\n du = du[(du.srange>remove_first_range) & (du.srange<remove_last_range)]\n fname = \"images/{}.png\".format(rscan[0].stime.strftime(\"%Y-%m-%d-%H-%M\"))\n if len(du) > 0:\n sd = fit_lambda(du, power_drop, tfreq=np.mean(tfrq), elv=np.mean(angle).round(1), \n xlabel=\"srange\", ylabel=\"p_l\", fname=fname, plot=plot)\n if sd != np.nan:\n skip_distance.append(sd)\n o_foF2.append(np.mean(tfrq))\n time_start.append(rscan[0].stime)\n time_end.append(rscan[-1].stime)\n df = pd.DataFrame()\n df[\"skip_distance\"], df[\"o_foF2\"], df[\"time_start\"], df[\"time_end\"] = skip_distance, o_foF2, time_start, time_end\n df.to_csv(csv_fname, index=False, header=True)\n else: df = pd.read_csv(csv_fname, parse_dates=[\"time_start\", \"time_end\"])\n print(\" Header:\\n\",df.head())\n return df","def test_get_spectra_bi():\r\n\r\n methods = (None,\r\n {\"this_method\": 'welch', \"NFFT\": 256, \"Fs\": 2 * np.pi},\r\n {\"this_method\": 'welch', \"NFFT\": 1024, \"Fs\": 2 * np.pi})\r\n\r\n for method in methods:\r\n arsig1, _, _ = utils.ar_generator(N=2 ** 16)\r\n arsig2, _, _ = utils.ar_generator(N=2 ** 16)\r\n\r\n avg_pwr1 = (arsig1 ** 2).mean()\r\n avg_pwr2 = (arsig2 ** 2).mean()\r\n avg_xpwr = (arsig1 * arsig2.conjugate()).mean()\r\n\r\n tseries = np.vstack([arsig1, arsig2])\r\n\r\n f, fxx, fyy, fxy = tsa.get_spectra_bi(arsig1, arsig2, method=method)\r\n\r\n # \\sum_{\\omega} PSD(\\omega) d\\omega:\r\n est_pwr1 = np.sum(fxx * (f[1] - f[0]))\r\n est_pwr2 = np.sum(fyy * (f[1] - f[0]))\r\n est_xpwr = np.sum(fxy * (f[1] - f[0])).real\r\n\r\n # Test that we have the right order of magnitude:\r\n npt.assert_array_almost_equal(est_pwr1, avg_pwr1, decimal=-1)\r\n npt.assert_array_almost_equal(est_pwr2, avg_pwr2, decimal=-1)\r\n npt.assert_array_almost_equal(np.mean(est_xpwr),\r\n np.mean(avg_xpwr),\r\n decimal=-1)","def test_dataset_scenario_generation_full_outside3(self):\n params = ParameterServer()\n\n map_filename = os.path.join(os.path.dirname(__file__), \"data/DR_DEU_Merging_MT_v01_shifted.xodr\")\n track_filename = os.path.join(os.path.dirname(__file__), \"data/interaction_dataset_DEU_Merging_dummy_track_outside.csv\")\n\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"MapFilename\"] = map_filename\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"TrackFilenameList\"] = [track_filename]\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"StartingOffsetMs\"] = 0\n\n scenario_generation = InteractionDatasetScenarioGenerationFull(\n params=params, num_scenarios=3)\n\n scenario = scenario_generation.get_scenario(2)\n self.assertAlmostEqual(scenario.eval_agent_ids, [3])\n world_state = scenario.GetWorldState()\n agent31 = world_state.GetAgent(1)\n agent32 = world_state.GetAgent(2)\n agent33 = world_state.GetAgent(3)\n\n # they all should be valid at the beginning\n world_state.time = 0\n self.assertEqual(isinstance(agent31, Agent), True)\n self.assertEqual(agent31.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent31.InsideRoadCorridor(), True)\n \n self.assertEqual(isinstance(agent32, Agent), True)\n self.assertEqual(agent32.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent32.InsideRoadCorridor(), True)\n\n self.assertEqual(isinstance(agent33, Agent), True)\n self.assertEqual(agent33.IsValidAtTime(world_state.time), True)\n self.assertEqual(agent33.InsideRoadCorridor(), True)\n\n world_state.Step(0.05)\n self.assertEqual(len(world_state.agents_valid), 3)","def test_evidence_2(base_settings):\n filename = (\n base_settings[\"unittest_data_dir\"]\n / \"evidence-example-stroke-3-4half-alteplase-vs-no-alteplase-mRS0-2.json\"\n )\n inst = evidence.Evidence.parse_file(\n filename, content_type=\"application/json\", encoding=\"utf-8\"\n )\n assert \"Evidence\" == inst.resource_type\n\n impl_evidence_2(inst)\n\n # testing reverse by generating data from itself and create again.\n data = inst.dict()\n assert \"Evidence\" == data[\"resourceType\"]\n\n inst2 = evidence.Evidence(**data)\n impl_evidence_2(inst2)","def radiation_measurement_analysis():\n import pint\n ureg = pint.UnitRegistry()\n\n mrem_h = ureg.parse_units('mrem') / ureg.hour\n m = ureg.parse_units('meters')\n s = ureg.parse_units('seconds')\n\n # Measurements of background radiation\n bg_dist = ureg.parse_expression('10 m') # estimate of how far away we are wrt background\n background_rows = [\n dict(vid=1, distance=bg_dist, rad=0.023 * mrem_h, capture_time=0.0 * s),\n dict(vid=1, distance=bg_dist, rad=0.022 * mrem_h, capture_time=0.0 * s),\n dict(vid=1, distance=bg_dist, rad=0.023 * mrem_h, capture_time=4.0 * s),\n dict(vid=1, distance=bg_dist, rad=0.021 * mrem_h, capture_time=5.0 * s),\n dict(vid=1, distance=bg_dist, rad=0.023 * mrem_h, capture_time=11.0 * s),\n dict(vid=1, distance=bg_dist, rad=0.023 * mrem_h, capture_time=16.0 * s),\n dict(vid=1, distance=bg_dist, rad=0.024 * mrem_h, capture_time=20.0 * s),\n ]\n\n # Measurements of sample radiation\n esp_dist = ureg.parse_expression('1 inch').to(m) / 2 # estimate of how far we are from the sample when very close\n dist0_rows = [\n dict(vid=2, distance=esp_dist, rad=0.060 * mrem_h, capture_time=0.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.061 * mrem_h, capture_time=3.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.060 * mrem_h, capture_time=5.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.059 * mrem_h, capture_time=9.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.060 * mrem_h, capture_time=10.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.059 * mrem_h, capture_time=11.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.057 * mrem_h, capture_time=12.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.058 * mrem_h, capture_time=13.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.059 * mrem_h, capture_time=14.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.060 * mrem_h, capture_time=15.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.061 * mrem_h, capture_time=16.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.062 * mrem_h, capture_time=18.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.062 * mrem_h, capture_time=18.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.064 * mrem_h, capture_time=20.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.065 * mrem_h, capture_time=22.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.066 * mrem_h, capture_time=23.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.065 * mrem_h, capture_time=24.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.063 * mrem_h, capture_time=25.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.065 * mrem_h, capture_time=26.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.064 * mrem_h, capture_time=27.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.064 * mrem_h, capture_time=27.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.065 * mrem_h, capture_time=28.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.063 * mrem_h, capture_time=30.0 * s),\n ]\n\n dist0_v2_rows = [\n dict(vid=3, distance=esp_dist, rad=0.012 * mrem_h, capture_time=0.0 * s),\n dict(vid=3, distance=esp_dist, rad=0.011 * mrem_h, capture_time=1.0 * s),\n dict(vid=3, distance=esp_dist, rad=0.013 * mrem_h, capture_time=8.0 * s),\n dict(vid=3, distance=esp_dist, rad=0.013 * mrem_h, capture_time=9.0 * s),\n ]\n\n close_rows = [\n dict(vid=4, distance=0.5 * m, rad=0.013 * mrem_h, capture_time=0.0 * s),\n dict(vid=4, distance=0.5 * m, rad=0.014 * mrem_h, capture_time=5.0 * s),\n dict(vid=4, distance=0.5 * m, rad=0.012 * mrem_h, capture_time=7.0 * s),\n dict(vid=4, distance=0.5 * m, rad=0.011 * mrem_h, capture_time=15.0 * s),\n dict(vid=4, distance=0.5 * m, rad=0.012 * mrem_h, capture_time=16.0 * s),\n ]\n\n mid_rows = [\n dict(vid=5, distance=1.0 * m, rad=0.014 * mrem_h, capture_time=0.0 * s),\n dict(vid=5, distance=1.0 * m, rad=0.015 * mrem_h, capture_time=5.0 * s),\n dict(vid=5, distance=1.0 * m, rad=0.013 * mrem_h, capture_time=10.0 * s),\n ]\n\n far_rows = [\n dict(vid=6, distance=2.0 * m, rad=0.023 * mrem_h, capture_time=0.0 * s),\n dict(vid=6, distance=2.0 * m, rad=0.025 * mrem_h, capture_time=0.1 * s),\n ]\n\n # guess_dist = ureg.parse_expression('0.3 m') # estimate of how far away we are wrt background\n # guess_rows = [\n # dict(vid=9, distance=guess_dist, rad=0.030 * mrem_h, capture_time=0.0 * s),\n # dict(vid=9, distance=guess_dist, rad=0.041 * mrem_h, capture_time=2.0 * s),\n # dict(vid=9, distance=guess_dist, rad=0.051 * mrem_h, capture_time=3.0 * s),\n # ]\n\n rows = dist0_rows + background_rows + dist0_v2_rows + close_rows + mid_rows + far_rows\n # rows += guess_rows\n\n import pandas as pd\n import numpy as np\n table = pd.DataFrame(rows)\n\n # Ensure comparable units\n units = {\n 'rad': mrem_h,\n 'distance': m,\n 'capture_time': s,\n }\n for key, unit in units.items():\n table[key] = table[key].apply(lambda c: c.to(unit).m)\n table['rad'] = table['rad'].astype(float)\n table['distance'] = table['distance'].astype(float)\n\n # Weight each measurement based on the amount of time the measurement was\n # sustained in the video.\n average_rad_rows = []\n for vid, group in table.groupby('vid'):\n from statsmodels.stats.weightstats import DescrStatsW\n weights = (-1 * group['capture_time'].diff(periods=-1).fillna(0)) / group['capture_time'].iloc[-1]\n table.loc[group.index, 'weight'] = weights\n values = group['rad']\n weighted_stats = DescrStatsW(values, weights=weights, ddof=0)\n dists = group['distance'].unique()\n assert len(dists) == 1\n average_rad_rows.append({\n 'vid': vid,\n 'distance': dists[0],\n 'rad_mean': weighted_stats.mean,\n 'rad_std': weighted_stats.std,\n })\n stats_table = pd.DataFrame(average_rad_rows)\n\n bg_row = stats_table.loc[stats_table['distance'].argmax()]\n fg_row = stats_table.loc[stats_table['distance'].argmin()]\n\n # -------------------\n ADD_DUMMY_VALUES = 0\n if ADD_DUMMY_VALUES:\n # Hack: because we don't have enough samples we can fudge the value\n # knowning that the value should be the background radiation in the\n # limit.\n\n dummy_measurements = []\n extra_support = 1\n for idx in range(3, 3 + extra_support):\n dummy_row = {\n 'vid': -idx,\n 'distance': bg_row['distance'] + idx,\n 'rad_mean': bg_row['rad_mean'],\n 'rad_std': 0.01,\n }\n dummy_measurements.append(dummy_row)\n\n # also add an extra value close to the sample\n rad_bg = bg_row['rad_mean']\n rad_above_bg = fg_row['rad_mean'] - rad_bg\n dummy_row = {\n 'vid': -1,\n 'distance': fg_row['distance'] / 2,\n 'rad_mean': rad_bg + (rad_above_bg * 4),\n 'rad_std': 0.5,\n }\n dummy_measurements.append(dummy_row)\n\n # dummy_row = {\n # 'vid': -2,\n # 'distance': fg_row['distance'] / 4,\n # 'rad_mean': rad_bg + (rad_above_bg * 16),\n # }\n # dummy_measurements.append(dummy_row)\n\n dummy_stats = pd.DataFrame(dummy_measurements)\n dummy_stats['weight'] = 0.5\n stats_table['weight'] = 1.0\n stats_table2 = pd.concat([stats_table, dummy_stats]).reset_index(drop=True).sort_values('distance')\n else:\n stats_table2 = stats_table\n # -------------------\n\n import scipy\n scipy.optimize.curve_fit\n\n # Because we know the radiation should follow an inverse square law wrt to\n # distance, we can fit a polynomial of degree 2 (parabola) to interpolate /\n # extrapolate the **inverse** values.\n x = stats_table2['distance'].values\n y = stats_table2['rad_mean'].values\n s = stats_table2['rad_std'].values\n\n # Model the squared falloff directly\n def invsquare(x, a, b):\n return a * (1 / (0.01 + x ** 2)) + b\n # bg_row['rad_mean']\n # Use curve_fit to constrain the first coefficient to be zero\n try:\n coef = scipy.optimize.curve_fit(invsquare, x, y, sigma=s, method='trf')[0]\n except Exception as ex:\n coef = None\n print(f'ex={ex}')\n\n # Also fit one to the raw weighted points as a sanity check\n # inv_poly2 = Polynomial.fit(table['distance'], 1 / table['rad'], w=table['weight'], deg=2)\n\n import kwplot\n sns = kwplot.autosns()\n plt = kwplot.autoplt()\n # ax = sns.boxplot(data=table, x='distance', y='rad', width=0.1)\n\n # Add in points to show each observation\n ax = sns.relplot(x=\"distance\", y=\"rad\", data=table, size=4, color=\".3\",\n linewidth=0, alpha=0.5, palette='deep')\n\n ax = plt.gca()\n ax.set_xlabel('distance from sample ({})'.format(str(units['distance'])))\n ax.set_ylabel('radiation dosage ({})'.format(str(units['rad'])))\n\n max_meters = 10\n\n extrap_x = np.linspace(0, max_meters, 1000)\n if coef is not None:\n extrap_y1 = invsquare(extrap_x, *coef)\n # extrap_y2 = 1 / inv_poly2(extrap_x)\n ax.plot(stats_table2['distance'].values, stats_table2['rad_mean'].values, 'rx')\n ax.plot(stats_table['distance'].values, stats_table['rad_mean'].values, 'bo')\n ax.plot(extrap_x, extrap_y1, '--')\n ax.set_ylim(0.001, 0.1)\n ax.set_yscale('log')\n # ax.plot(extrap_x, extrap_y2, '--')","def sample_trajectory(self, env, animate_this_episode, is_evaluation):\n # Using current task with meta inside\n env.reset_task(is_evaluation=is_evaluation)\n stats = []\n #====================================================================================#\n # ----------PROBLEM 2----------\n #====================================================================================#\n ep_steps = 0\n steps = 0\n\n num_samples = max(self.history, self.max_path_length + 1)\n meta_obs = np.zeros((num_samples + self.history + 1, self.meta_ob_dim))\n rewards = []\n\n while True:\n if animate_this_episode:\n env.render()\n time.sleep(0.1)\n\n if ep_steps == 0:\n ob = env.reset()\n # first meta ob has only the observation\n # set a, r, d to zero, construct first meta observation in meta_obs\n # YOUR CODE HERE\n ac = np.zeros(self.ac_dim); rew = np.zeros(self.reward_dim); done = np.zeros(self.terminal_dim)\n meta_obs[steps, :] = np.concatenate((ob, ac, rew, done))\n steps += 1\n\n # index into the meta_obs array to get the window that ends with the current timestep\n # please name the windowed observation `in_` for compatibilty with the code that adds to the replay buffer (lines 418, 420)\n # YOUR CODE HERE\n # padding for input obs size\n sample_action_in_ = meta_obs[steps-self.history:steps, :] if steps>=self.history else np.squeeze(np.concatenate(([meta_obs[0,:], ] * (self.history - steps), meta_obs[:steps, :]), axis=0))\n # need to clear hidden size, in order to avoid previous hidden state as it may be generated by the other totally different task (env setting may be changed)\n hidden = np.zeros((1, self.gru_size), dtype=np.float32)\n\n # get action from the policy\n # YOUR CODE HERE\n # Tensor(\"ob:0\", shape=(?, 1, 10), dtype=float32)\n # print(self.sy_ob_no)\n # Tensor(\"hidden:0\", shape=(?, 32), dtype=float32)\n # print(self.sy_hidden)\n ac = self.sess.run(self.sy_sampled_ac, feed_dict={\n self.sy_ob_no: sample_action_in_.reshape(-1, self.history, self.meta_ob_dim),\n self.sy_hidden: hidden,\n })\n assert len(ac) == 1\n ac = ac[0]\n\n # step the environment\n # YOUR CODE HERE\n ob, rew, done, _= env.step(ac)\n\n ep_steps += 1\n\n done = bool(done) or ep_steps == self.max_path_length\n # construct the meta-observation and add it to meta_obs\n # YOUR CODE HERE\n meta_obs[steps, :] = np.concatenate((ob, ac, [rew], [done]))\n\n rewards.append(rew)\n steps += 1\n\n in_ = meta_obs[steps, :]\n # add sample to replay buffer\n if is_evaluation:\n self.val_replay_buffer.add_sample(in_, ac, rew, done, hidden, env._goal)\n else:\n self.replay_buffer.add_sample(in_, ac, rew, done, hidden, env._goal)\n\n # start new episode\n if done:\n # compute stats over trajectory\n s = dict()\n s['rewards']= rewards[-ep_steps:]\n s['ep_len'] = ep_steps\n stats.append(s)\n ep_steps = 0\n\n if steps >= num_samples:\n break\n\n return steps, stats","def rate_experiment_two(rate_x, var_rate, rate_y, var_rate2, rate_z, pepa_model):\n from itertools import product\n rate_ys = []\n rate_xs = []\n rate_zs = []\n rates = pepa_model.get_rates()\n if rate_x not in rates:\n print(\"No such rate {}\".format(rate_x))\n exit(1)\n for i,j in product(var_rate, var_rate2):\n rates[rate_x] = str(i)\n rates[rate_y] = str(j)\n rate_xs.append(float(i))\n rate_ys.append(float(j))\n pepa_model.recalculate(rates)\n pepa_model.steady_state()\n rate_zs.append( get_rate_from_actset(rate_z, pepa_model.get_throughoutput()))\n return (rate_xs, rate_ys, rate_zs)","def ssd_synthia_car_fine_tune():\n merged_annotation = '/home/public/synthia/ssd_car_fine_tune/SYNTHIA-SEQS-01-TRAIN_MERGED-shuffle.json'\n if False:\n print('we combine the training and validation here')\n annotations_url_1 = '/home/public/synthia/SYNTHIA-SEQS-01-TRAIN-shuffle.json'\n annotations_url_2 = '/home/public/synthia/SYNTHIA-SEQS-01-VALIDATE-shuffle.json'\n combine_gt(annotations_url_1, annotations_url_2, merged_annotation)\n\n if False:\n print('collect front and rear cars')\n annotations_url_1 = '/home/public/synthia/SYNTHIA-SEQS-01-TRAIN-shuffle.json'\n annotations_url_2 = '/home/public/synthia/SYNTHIA-SEQS-01-VALIDATE-shuffle.json'\n save_dir = '/home/stevenwudi/PycharmProjects/autonomous_driving/Experiments/SEQ_01_SEQ_06_cars'\n collect_front_and_rear_gt(annotations_url_1, annotations_url_2, save_dir, image_interval=50)\n\n gt_file = '/home/public/synthia/ssd_car_fine_tune/ssd_car_fine_tune_gt-shuffle.pkl'\n if False:\n print('Training annotation conversion')\n converting_gt(merged_annotation, gt_file, POR=1e-3)\n # POR: 1e-3 Finish converting, total annotated car number is 22332 in total image of 8814.\n # POR: 5e-4: Finish converting, total annotated fish number is 26800 in total image of 8814.\n\n model_checkpoint = '/home/public/synthia/ssd_car_fine_tune/weights_512.54-0.19.hdf5'\n if False:\n print('Start DDS 512 training')\n train_ssd512(gt_file, model_checkpoint=model_checkpoint, base_lr=1e-5)\n\n test_gt_file = '/home/public/synthia/ssd_car_fine_tune/ssd_car_test_gt-shuffle.pkl'\n if False:\n print('Converting testing GT')\n annotations_url = '/home/public/synthia/SYNTHIA-SEQS-01-TEST-shuffle.json'\n converting_gt(annotations_url, test_gt_file)\n if False:\n # Examine test data\n examine_ssd512(test_gt_file, model_checkpoint)\n\n test_json_file = '/home/public/synthia/ssd_car_fine_tune/ssd_car_test-shuffle_nms_'+str(nms_thresh)+'.json'\n if False:\n test_ssd512(test_gt_file, model_checkpoint, test_json_file)\n # A separate file for accepting gt file and predicted json fil\n if True:\n calculate_iou(test_gt_file, test_json_file, POR=2e-3, draw=False)\n\n test_gt_file = '/home/public/synthia/ssd_car_fine_tune/ssd_car_test_gt-shuffle.pkl'\n test_json_file = '/home/public/synthia/ssd_car_test_faster-shuffle.json'\n\n calculate_iou(test_gt_file, test_json_file, POR=2e-3, draw=False)\n \"\"\"\n<<<<<<< HEAD\n This is the network results train by SSD512 (with 0.05% POR trained)\n Conf: [ 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]\n \n ### POR=1e-3\n Total GT: 2327. \n Total prediction: [ 2016. 1986. 1951. 1918. 1888. 1852. 1815. 1774. 1711. 1641.]\n Precision: [ 0.913 0.908 0.9 0.895 0.886 0.879 0.867 0.853 0.83 0.804]\n Recall: [ 0.791 0.787 0.78 0.775 0.768 0.761 0.751 0.739 0.719 0.697]\n F score: [[ 0.847 0.843 0.837 0.825 0.816 0.79 0.748 0.666 0.449 0.106]\n [ 0.843 0.839 0.834 0.823 0.813 0.788 0.748 0.665 0.449 0.106]\n [ 0.835 0.833 0.829 0.818 0.809 0.786 0.747 0.665 0.449 0.106]\n [ 0.831 0.829 0.826 0.815 0.807 0.784 0.746 0.664 0.449 0.105]\n [ 0.823 0.822 0.82 0.81 0.802 0.781 0.744 0.663 0.447 0.105]\n [ 0.816 0.816 0.815 0.806 0.799 0.778 0.741 0.661 0.446 0.105]\n [ 0.805 0.805 0.806 0.797 0.792 0.772 0.738 0.66 0.445 0.105]\n [ 0.792 0.793 0.793 0.789 0.784 0.766 0.734 0.658 0.445 0.105]\n [ 0.77 0.773 0.775 0.771 0.768 0.753 0.725 0.652 0.441 0.104]\n [ 0.746 0.749 0.751 0.751 0.75 0.741 0.716 0.645 0.439 0.103]]\n<<<<<<< HEAD\n\n ### faster RCNN detection result POR= 1e-3\n Total GT: 2327.\n Total prediction: [ 2120. 2084. 2062. 2037. 2003. 1971. 1947. 1901. 1862. 1785.]\n Precision: [ 0.83 0.826 0.825 0.823 0.82 0.818 0.817 0.807 0.801 0.787]\n Recall: [ 0.756 0.752 0.751 0.75 0.747 0.745 0.744 0.735 0.73 0.717]\n F score: [[ 0.791 0.758 0.72 0.669 0.59 0.485 0.332 0.174 0.054 0.006]\n [ 0.787 0.756 0.719 0.669 0.59 0.485 0.332 0.174 0.054 0.006]\n [ 0.786 0.755 0.718 0.668 0.589 0.485 0.332 0.174 0.054 0.006]\n [ 0.785 0.754 0.718 0.668 0.589 0.484 0.332 0.174 0.054 0.006]\n [ 0.782 0.752 0.716 0.666 0.588 0.484 0.331 0.174 0.054 0.006]\n [ 0.78 0.75 0.715 0.666 0.588 0.484 0.331 0.174 0.054 0.006]\n [ 0.779 0.749 0.714 0.665 0.588 0.484 0.331 0.174 0.054 0.006]\n [ 0.77 0.745 0.711 0.663 0.586 0.483 0.331 0.174 0.054 0.006]\n [ 0.764 0.74 0.709 0.66 0.584 0.482 0.331 0.173 0.054 0.006]\n [ 0.75 0.73 0.703 0.657 0.582 0.481 0.33 0.173 0.054 0.006]]\n\n=======\n \n ### POR shuffle\n Total GT: 2563. \n Total prediction: [ 2185. 2137. 2081. 2047. 2010. 1976. 1937. 1893. 1831. 1713.]\n Precision: [ 0.843 0.836 0.826 0.819 0.813 0.805 0.795 0.784 0.766 0.726]\n Recall: [ 0.719 0.712 0.704 0.698 0.693 0.686 0.678 0.669 0.653 0.619]\n F score: [[ 0.776 0.698 0.646 0.619 0.597 0.571 0.525 0.447 0.31 0.106]\n [ 0.769 0.693 0.642 0.616 0.595 0.57 0.524 0.447 0.31 0.106]\n [ 0.76 0.686 0.637 0.612 0.591 0.568 0.523 0.445 0.31 0.106]\n [ 0.754 0.681 0.632 0.608 0.588 0.565 0.521 0.443 0.31 0.106]\n [ 0.749 0.677 0.629 0.605 0.586 0.563 0.52 0.442 0.309 0.106]\n [ 0.741 0.671 0.624 0.602 0.583 0.561 0.518 0.441 0.309 0.106]\n [ 0.732 0.664 0.619 0.597 0.579 0.558 0.516 0.44 0.308 0.106]\n [ 0.722 0.656 0.612 0.59 0.573 0.553 0.515 0.439 0.308 0.106]\n [ 0.705 0.642 0.6 0.58 0.564 0.545 0.508 0.435 0.306 0.105]\n [ 0.668 0.614 0.578 0.561 0.548 0.531 0.496 0.426 0.302 0.105]]\n \n POR = 2e-3\n=======\n ############################# SSD512 NMS 0.6 ###########################\n Conf: [ 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]\n>>>>>>> 419d28c4e27b82c4cfef6a3aa01425cf29929973\n Total GT: 1433. \n Total prediction: [ 1617. 1605. 1593. 1579. 1568. 1551. 1537. 1517. 1498. 1465.]\n Precision: [ 0.821 0.819 0.818 0.818 0.816 0.811 0.809 0.804 0.8 0.792]\n Recall: [ 0.927 0.925 0.923 0.923 0.92 0.915 0.913 0.907 0.902 0.894]\n F score: [[ 0.871 0.868 0.859 0.852 0.836 0.81 0.742 0.636 0.48 0.161]\n [ 0.869 0.866 0.857 0.85 0.834 0.808 0.741 0.635 0.48 0.161]\n [ 0.867 0.864 0.857 0.849 0.834 0.808 0.741 0.635 0.48 0.161]\n [ 0.867 0.864 0.857 0.849 0.834 0.808 0.741 0.635 0.48 0.161]\n [ 0.865 0.862 0.855 0.848 0.832 0.807 0.741 0.635 0.48 0.161]\n [ 0.86 0.857 0.851 0.844 0.828 0.804 0.739 0.633 0.479 0.161]\n [ 0.858 0.855 0.849 0.843 0.827 0.802 0.738 0.633 0.478 0.161]\n [ 0.852 0.85 0.844 0.839 0.823 0.8 0.736 0.631 0.477 0.16 ]\n [ 0.848 0.846 0.841 0.836 0.82 0.797 0.734 0.631 0.477 0.16 ]\n [ 0.84 0.838 0.834 0.829 0.814 0.792 0.729 0.629 0.476 0.16 ]]\n \n ############################# SSD512 NMS 0.45 ###########################\n Total GT: 1433. \n Total prediction: [ 1438. 1428. 1421. 1413. 1403. 1398. 1384. 1374. 1348. 1305.]\n Precision: [ 0.92 0.918 0.917 0.914 0.911 0.91 0.905 0.902 0.894 0.874]\n Recall: [ 0.923 0.921 0.92 0.918 0.914 0.913 0.908 0.905 0.897 0.877]\n F score: [[ 0.922 0.918 0.91 0.901 0.883 0.851 0.785 0.673 0.476 0.164]\n [ 0.92 0.916 0.908 0.9 0.882 0.851 0.785 0.673 0.476 0.164]\n [ 0.918 0.915 0.907 0.899 0.882 0.851 0.785 0.673 0.476 0.164]\n [ 0.916 0.913 0.905 0.897 0.88 0.849 0.785 0.673 0.476 0.164]\n [ 0.913 0.91 0.903 0.895 0.878 0.847 0.783 0.671 0.475 0.164]\n [ 0.911 0.909 0.901 0.894 0.877 0.846 0.782 0.671 0.475 0.164]\n [ 0.906 0.905 0.898 0.89 0.874 0.843 0.781 0.67 0.475 0.164]\n [ 0.904 0.901 0.895 0.888 0.871 0.84 0.78 0.67 0.475 0.164]\n [ 0.896 0.894 0.888 0.882 0.866 0.836 0.776 0.668 0.474 0.164]\n [ 0.876 0.876 0.872 0.869 0.855 0.829 0.77 0.663 0.471 0.164]]\n \n \n<<<<<<< HEAD\n>>>>>>> bb5caf05d4bc41e182e41686b8b5e497053f9ca5\n ### POR = None (consider all testing examples)\n Total GT: 2696. \n Total prediction: [ 2273. 2221. 2166. 2111. 2055. 2000. 1945. 1875. 1786. 1684.]\n Precision: [ 0.822 0.818 0.81 0.805 0.796 0.789 0.777 0.763 0.741 0.716]\n Recall: [ 0.693 0.69 0.683 0.678 0.671 0.665 0.655 0.643 0.625 0.604]\n F score: [[ 0.752 0.746 0.74 0.729 0.724 0.703 0.668 0.597 0.405 0.096]\n [ 0.748 0.744 0.737 0.727 0.722 0.702 0.668 0.597 0.405 0.096]\n [ 0.741 0.737 0.733 0.723 0.718 0.699 0.667 0.596 0.405 0.096]\n [ 0.736 0.733 0.729 0.72 0.716 0.698 0.666 0.596 0.404 0.095]\n [ 0.728 0.726 0.724 0.716 0.712 0.695 0.664 0.595 0.403 0.095]\n [ 0.722 0.721 0.72 0.712 0.708 0.692 0.661 0.593 0.402 0.095]\n [ 0.711 0.711 0.711 0.704 0.703 0.687 0.659 0.592 0.401 0.095]\n [ 0.698 0.7 0.7 0.696 0.696 0.682 0.655 0.59 0.401 0.095]\n [ 0.678 0.682 0.684 0.681 0.681 0.67 0.647 0.585 0.398 0.095]\n [ 0.655 0.659 0.662 0.663 0.665 0.659 0.639 0.578 0.395 0.094]]\n\n POR = 2e-3 fastercnn result\n Conf: [ 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]\n Total GT: 1433.\n Total prediction: [ 1614. 1601. 1593. 1585. 1576. 1561. 1557. 1544. 1532. 1507.]\n=======\n ############################# Faster-RCNN ###########################\n Conf: [ 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]\n Total GT: 1433. \n Total prediction: [ 1614. 1601. 1593. 1585. 1576. 1561. 1557. 1544. 1532. 1507.]\n>>>>>>> 419d28c4e27b82c4cfef6a3aa01425cf29929973\n Precision: [ 0.812 0.812 0.811 0.811 0.811 0.81 0.81 0.807 0.805 0.8 ]\n Recall: [ 0.914 0.914 0.913 0.913 0.913 0.913 0.913 0.909 0.907 0.902]\n F score: [[ 0.86 0.843 0.808 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\n [ 0.86 0.843 0.808 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\n [ 0.859 0.842 0.807 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\n [ 0.859 0.842 0.807 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\n [ 0.859 0.842 0.807 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\n [ 0.859 0.842 0.807 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\n [ 0.859 0.842 0.807 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\n [ 0.855 0.839 0.804 0.748 0.665 0.576 0.458 0.311 0.14 0.016]\n [ 0.853 0.838 0.804 0.747 0.664 0.575 0.458 0.311 0.14 0.016]\n [ 0.848 0.834 0.8 0.745 0.662 0.574 0.457 0.31 0.139 0.016]]\n \"\"\"","def second_step(self, plan, dlvl_root_img, rphase_root_img, root_sequence, sag_sequence):\n # Sagittal respiratory patterns associated with sagittal sequence analyzed\n lpatterns =\\\n [x for x in self.dataset if int(x.split('-')[3]) == self.sag_sequences[self.sag_sequences.index(sag_sequence)]]\n # print('(Step 2) Sagittal patterns: {} ({})\\n'.format(lpatterns, len(lpatterns)))\n\n # Respiratory patterns linked to the sequence containing the root image\n pattern = None\n for p in lpatterns:\n if int(p.split('-')[1]) == root_sequence:\n pattern = p\n # print(\"(Step 2) Pattern: {}\".format(pattern))\n # print(\"(Step 2) DL: {}\".format(dlvl_root_img))\n # print(\"(Step 2) DL[{}]: {}\".format(self.sag_sequences.index(sag_sequence), dlvl_root_img[self.sag_sequences.index(sag_sequence)]))\n # print(\"(Step 2) RP: {}\".format(rphase_root_img))\n # print(\"(Step 2) RP[{}]: {}\\n\".format(self.sag_sequences.index(sag_sequence), rphase_root_img[self.sag_sequences.index(sag_sequence)]))\n\n \"\"\" Get the diaphragmatic level of each image of the analyzed sagittal sequence\n that crosses the coronal root image \"\"\"\n pts_pattern = self.pattern_sagittal('{}.png'.format(pattern))\n diaph_lvl = [max(x) for x in self.diaphragmatic_level_sagittal(pts_pattern)]\n resp_phase = self.respiratory_phase_sagittal(self.diaphragmatic_level_sagittal(pts_pattern))\n # print(\"(Step 2) Diaphragmatic level: {} ({})\\n\".format(diaph_lvl, len(diaph_lvl)))\n # print(\"(Step 2) Respiratory phase: {} ({})\\n\".format(resp_phase, len(resp_phase)))\n\n \"\"\" Check register condition:\n 1) If there is same diaphragmatic level \"\"\"\n index_imgs_registered = list() # Store index of the sagittal registered images\n for index, i in enumerate(diaph_lvl):\n if i == dlvl_root_img[self.sag_sequences.index(sag_sequence)]:\n index_imgs_registered.append(index)\n # print(\"(Step 2) Index of registered images: {} ({})\\n\".format(index_imgs_registered, len(index_imgs_registered)))\n\n # TODO - Uncomment this for release\n '''\n \"\"\" Check register condition:\n 2) If the instants are in the same respiratory phase \"\"\"\n for index, i in enumerate(resp_phase):\n if index in index_imgs_registered:\n if resp_phase[index] != rphase_root_img[self.sag_sequences.index(sag_sequence)]:\n index_imgs_registered.remove(index)\n # print(\"(Step 2) Index of registered images: {} ({})\\n\".format(index_imgs_registered, len(index_imgs_registered)))\n '''\n\n # If there is no registered image\n if len(index_imgs_registered) == 0:\n \"\"\" If there are no records, there is the possibility of an error in the segmentation\n (which is done manually), then analyzing the respiratory phase is added in one unit\n the value of the diaphragmatic level (inspiration case) or subtracted one unit\n (expiration case) \"\"\"\n\n index_imgs_registered =\\\n self.second_step_second_attempt(\n diaph_lvl=diaph_lvl,\n dlvl_root_img=dlvl_root_img,\n rphase_root_img=rphase_root_img,\n sag_sequence=sag_sequence,\n option=True)\n\n if len(index_imgs_registered) == 0:\n return -1, -1, -1\n\n # Get first sagittal image that was registered with root image\n imgnum = index_imgs_registered[0]\n # print(\"(Step 2) Imagem: {}\\n\".format(imgnum))\n\n \"\"\" Represents the row in the register matrix that represents the sequence used\n It's used to populate the registration matrix correctly \"\"\"\n row = self.sag_sequences.index(sag_sequence)\n # print('(Step 2) Row: {}\\n'.format(row))\n\n for i in range(self.matcols):\n pts_pattern = self.pattern_sagittal('{}.png'.format(lpatterns[i]))\n diaph_lvl = [max(x) for x in self.diaphragmatic_level_sagittal(pts_pattern)]\n resp_phase =\\\n self.respiratory_phase_sagittal(self.diaphragmatic_level_sagittal(pts_pattern))\n\n if self.matDL[row, i] == 0.0:\n self.matDL[row, i] = diaph_lvl[imgnum]\n self.matRegistration[row, i] = self.green\n self.matRP[row, i] = resp_phase[i]\n\n # print(\"(Step 2) Diaphragmatic level matrix:\\n{}\\n\".format(self.matDL))\n # print(\"(Step 2) Registration matrix:\\n{}\\n\".format(self.matRegistration))\n # print(\"(Step 2) Respiratory phase:\\n{}\\n\".format(self.matRP))\n\n imgnum = imgnum + 1\n dlvl = [self.matDL[row, i] for i in range(len(self.cor_sequences))]\n rphase = [int(self.matRP[row, i]) for i in range(len(self.cor_sequences))]\n # print(\"(Step 2) DL: {}\\n\".format(dlvl))\n\n return (imgnum, dlvl, rphase)","def _calc_r2(self):\n sse = np.sum((self.data.y - self.predict(self.data.x))**2)\n sst = np.sum((self.data.y - self.data.y.mean())**2)\n return (1. - sse/sst)","def testAlternateMetrics(self):\n\n def result2(t, rew):\n return TrainingResult(time_total_s=t, neg_mean_loss=rew)\n\n sched = HyperBandScheduler(\n time_attr='time_total_s', reward_attr='neg_mean_loss')\n stats = self.default_statistics()\n\n for i in range(stats[\"max_trials\"]):\n t = Trial(\"__fake\")\n sched.on_trial_add(None, t)\n runner = _MockTrialRunner(sched)\n\n big_bracket = sched._hyperbands[0][-1]\n\n for trl in big_bracket.current_trials():\n runner._launch_trial(trl)\n current_length = len(big_bracket.current_trials())\n\n # Provides results from 0 to 8 in order, keeping the last one running\n for i, trl in enumerate(big_bracket.current_trials()):\n action = sched.on_trial_result(runner, trl, result2(1, i))\n runner.process_action(trl, action)\n\n new_length = len(big_bracket.current_trials())\n self.assertEqual(action, TrialScheduler.CONTINUE)\n self.assertEqual(new_length, self.downscale(current_length, sched))","def beacon_readings(actual_x, actual_y):\n d_a = math.sqrt((-100 - actual_x) ** 2 + (100 - actual_y) ** 2)\n d_b = math.sqrt((150 - actual_x) ** 2 + (90 - actual_y) ** 2)\n r_a_ = random.normalvariate(d_a, 1)\n r_b_ = random.normalvariate(d_b, 1)\n return r_a_, r_b_","def reset(self):\n p.resetSimulation() \n dir_path = os.path.dirname(os.path.realpath(__file__))\n path_to_rsc = dir_path + '/rsc/'\n p.loadURDF(path_to_rsc+\"plane.urdf\")\n\n self.wall1 = p.loadURDF(path_to_rsc+\"wall.urdf\", basePosition = [0,self.max_dist_y,1], baseOrientation = p.getQuaternionFromEuler([1.5707,0,0]), useFixedBase = True)\n self.wall2 = p.loadURDF(path_to_rsc+\"wall.urdf\", basePosition = [0,-self.max_dist_y,1], baseOrientation = p.getQuaternionFromEuler([1.5707,0,0]), useFixedBase = True)\n self.wall3 = p.loadURDF(path_to_rsc+\"wall.urdf\", basePosition = [self.max_dist_x,0,1], baseOrientation = p.getQuaternionFromEuler([1.5707,0,1.5707]), useFixedBase = True)\n self.wall4 = p.loadURDF(path_to_rsc+\"wall.urdf\", basePosition = [-self.max_dist_x,0,1], baseOrientation = p.getQuaternionFromEuler([1.5707,0,1.5707]), useFixedBase = True)\n\n p.setGravity(0, 0, -10)\n self.car1pos = [random.uniform(-self.max_dist_x,0),random.uniform(-self.max_dist_y,0), 0.03]\n self.car2pos = [random.uniform(0,self.max_dist_x),random.uniform(0,self.max_dist_y), 0.03]\n\n self.car1 = p.loadURDF(path_to_rsc+\"/car/AUTO1.urdf\", self.car1pos[0], self.car1pos[1], self.car1pos[2])\n self.car2 = p.loadURDF(path_to_rsc+\"/car/AUTO2.urdf\", self.car2pos[0], self.car2pos[1], self.car2pos[2])\n self.reduce = 1\n self.step_counter=0\n return self._get_obs()","def test_make_sampled_format(self):\n for num_inputs in [1, 3]:\n for num_outputs in [1, 2, 4]:\n for num_time_steps in [4, 10, 12]:\n # Generate data\n # P=2 format [0, 1, 2, 3, ...]\n sample_interval = 2\n dt_system = np.random.random()\n dt_sample = sample_interval * dt_system\n outputs = np.random.random(\n (num_time_steps, num_outputs, num_inputs))\n time_steps = make_time_steps(\n num_time_steps, sample_interval)\n time_values = time_steps * dt_system\n\n # Compute using modred\n my_ERA = era.ERA()\n time_steps_computed, outputs_computed =\\\n era.make_sampled_format(time_values, outputs)\n #self.assertEqual(dt_system_computed, dt_system)\n\n # Reference values\n num_time_steps_true = (num_time_steps - 1) * 2\n time_steps_true = make_time_steps(num_time_steps_true, 1)\n outputs_true = np.zeros(\n (num_time_steps_true, num_outputs, num_inputs))\n outputs_true[::2] = outputs[:-1]\n outputs_true[1::2] = outputs[1:]\n\n # Compare values\n np.testing.assert_equal(\n time_steps_computed, time_steps_true)\n np.testing.assert_equal(outputs_computed, outputs_true)\n\n # Test that if there is a wrong time value, get an error\n time_values[num_time_steps // 2] = -1\n self.assertRaises(\n ValueError, era.make_sampled_format, time_values,\n outputs)","def test_dataset_scenario_generation_full_late_behavior_overwritten(self):\n params = ParameterServer()\n\n map_filename = os.path.join(os.path.dirname(__file__), \"data/DR_DEU_Merging_MT_v01_shifted.xodr\")\n track_filename = os.path.join(os.path.dirname(__file__), \"data/interaction_dataset_DEU_Merging_dummy_track_late.csv\")\n\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"MapFilename\"] = map_filename\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"TrackFilenameList\"] = [track_filename]\n params[\"Scenario\"][\"Generation\"][\"InteractionDatasetScenarioGenerationFull\"][\"StartingOffsetMs\"] = 0\n\n scenario_generation = InteractionDatasetScenarioGenerationFull(\n params=params, num_scenarios=1)\n\n scenario = scenario_generation.get_scenario(0)\n world_state = scenario.GetWorldState()\n agent1 = world_state.GetAgent(1)\n agent2 = world_state.GetAgent(2)\n\n self.assertAlmostEqual(agent1.first_valid_timestamp, 0.0)\n self.assertAlmostEqual(agent2.first_valid_timestamp, 0.3)\n \n self.assertEqual(isinstance(agent1, Agent), True)\n self.assertEqual(agent1.IsValidAtTime(world_state.time), True)\n \n self.assertEqual(isinstance(agent2, Agent), True)\n self.assertEqual(agent2.IsValidAtTime(world_state.time), False)","def sample(self):\r\n raise NotImplementedError","def __init__(self, path: str, rmf_path: str, arf_path: str, b_path: str,\n central_coord: Quantity, inn_rad: Quantity, out_rad: Quantity, obs_id: str, instrument: str,\n grouped: bool, min_counts: int, min_sn: float, over_sample: int, stdout_str: str,\n stderr_str: str, gen_cmd: str, region: bool = False, b_rmf_path: str = '', b_arf_path: str = ''):\n super().__init__(path, obs_id, instrument, stdout_str, stderr_str, gen_cmd)\n self._prod_type = \"spectrum\"\n\n if os.path.exists(rmf_path):\n self._rmf = rmf_path\n else:\n self._rmf = ''\n self._usable = False\n self._why_unusable.append(\"RMFPathDoesNotExist\")\n\n if os.path.exists(arf_path):\n self._arf = arf_path\n else:\n self._arf = ''\n self._usable = False\n self._why_unusable.append(\"ARFPathDoesNotExist\")\n\n if os.path.exists(b_path):\n self._back_spec = b_path\n else:\n self._back_spec = ''\n self._usable = False\n self._why_unusable.append(\"BackSpecPathDoesNotExist\")\n\n if b_rmf_path != '' and os.path.exists(b_rmf_path):\n self._back_rmf = b_rmf_path\n elif b_rmf_path == '':\n self._back_rmf = None\n else:\n self._back_rmf = ''\n self._usable = False\n self._why_unusable.append(\"BackRMFPathDoesNotExist\")\n\n if b_arf_path != '' and os.path.exists(b_arf_path):\n self._back_arf = b_arf_path\n elif b_arf_path == '':\n self._back_arf = None\n else:\n self._back_arf = ''\n self._usable = False\n self._why_unusable.append(\"BackARFPathDoesNotExist\")\n\n # Storing the central coordinate of this spectrum\n self._central_coord = central_coord\n\n # Storing the region information\n self._inner_rad = inn_rad\n self._outer_rad = out_rad\n # And also the shape of the region\n if self._inner_rad.isscalar:\n self._shape = 'circular'\n else:\n self._shape = 'elliptical'\n\n # If this spectrum has just been generated by XGA then we'll set the headers, otherwise its\n # too slow and must be avoided. I am assuming here that the gen_cmd will be \"\" if the object\n # hasn't just been generated - which is true of XGA's behaviour\n if gen_cmd != \"\":\n try:\n self._update_spec_headers(\"main\")\n self._update_spec_headers(\"back\")\n except OSError as err:\n self._usable = False\n self._why_unusable.append(\"FITSIOOSError\")\n\n self._exp = None\n self._plot_data = {}\n self._luminosities = {}\n self._count_rate = {}\n\n # This is specifically for fakeit runs (for cntrate - lum conversions) on the ARF/RMF\n # associated with this Spectrum\n self._conv_factors = {}\n\n # This set of properties describe the configuration of evselect/specgroup during generation\n self._grouped = grouped\n self._min_counts = min_counts\n self._min_sn = min_sn\n if self._grouped and self._min_counts is not None:\n self._grouped_on = 'counts'\n elif self._grouped and self._min_sn is not None:\n self._grouped_on = 'signal to noise'\n else:\n self._grouped_on = None\n\n # Not to do with grouping, but this states the level of oversampling requested from evselect\n self._over_sample = over_sample\n\n # This describes whether this spectrum was generated directly from a region present in a region file\n self._region = region\n\n # Here we generate the storage key for this object, its just convenient to do it in here\n # Sets up the extra part of the storage key name depending on if grouping is enabled\n if grouped and min_counts is not None:\n extra_name = \"_mincnt{}\".format(min_counts)\n elif grouped and min_sn is not None:\n extra_name = \"_minsn{}\".format(min_sn)\n else:\n extra_name = ''\n\n # And if it was oversampled during generation then we need to include that as well\n if over_sample is not None:\n extra_name += \"_ovsamp{ov}\".format(ov=over_sample)\n\n spec_storage_name = \"ra{ra}_dec{dec}_ri{ri}_ro{ro}_grp{gr}\"\n if not self._region and self.inner_rad.isscalar:\n spec_storage_name = spec_storage_name.format(ra=self.central_coord[0].value,\n dec=self.central_coord[1].value,\n ri=self._inner_rad.value, ro=self._outer_rad.value,\n gr=grouped)\n elif not self._region and not self._inner_rad.isscalar:\n inn_rad_str = 'and'.join(self._inner_rad.value.astype(str))\n out_rad_str = 'and'.join(self._outer_rad.value.astype(str))\n spec_storage_name = spec_storage_name.format(ra=self.central_coord[0].value,\n dec=self.central_coord[1].value, ri=inn_rad_str,\n ro=out_rad_str, gr=grouped)\n else:\n spec_storage_name = \"region_grp{gr}\".format(gr=grouped)\n\n spec_storage_name += extra_name\n # And we save the completed key to an attribute\n self._storage_key = spec_storage_name\n\n # This attribute is set via the property, ONLY if this spectrum is considered to be a member of a set\n # of annular spectra. It describes which position in the set this spectrum has\n self._ann_ident = None\n # This holds a unique random identifier for the set itself, and again will only be set from outside\n self._set_ident = None","def setUp(self):\r\n self.res1 = RichnessEstimatesResults()\r\n\r\n self.res2 = RichnessEstimatesResults()\r\n self.res2.addSample('S2', 52)\r\n self.res2.addSampleEstimate('S2', 1, 3, 0.4, 2.5, 3.5)\r\n self.res2.addSample('S1', 42)\r\n self.res2.addSampleEstimate('S1', 10, 20, 2.5, 2.5, 3.5)\r\n self.res2.addSampleEstimate('S1', 20, 30, 3.5, 2.5, 3.5)\r\n self.res2.addSampleEstimate('S1', 5, 21, 1.5, 2.5, 3.5)","def generate():\n strategies = []\n\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\n limits=[50, 20, 10]\n sources = ['his', 'our', 'dna']\n for limit in limits:\n for source in sources:\n strategies.extend(\n generate_meta_strategy_pair(\n RFindStrategy,\n limit=limit,\n src=source,\n shenanigans=False,\n ))\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(TestimonoStrategy))\n\n # Add RPS Geometry\n strategies.extend(\n generate_meta_strategy_pair(GeometryV4Strategy))\n\n # Add Iocaine Powder strategy\n strategies.extend(\n generate_meta_strategy_pair(IocanePowderStrategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def generate():\n strategies = []\n\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\n limits=[50, 20, 10]\n sources = ['his', 'our', 'dna']\n for limit in limits:\n for source in sources:\n strategies.extend(\n generate_meta_strategy_pair(\n RFindStrategy,\n limit=limit,\n src=source,\n shenanigans=False,\n ))\n\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\n strategies.extend(\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeStrategy))\n\n # Add decision tree strategies\n strategies.extend(\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\n\n # Add HPS Dojo strategies\n strategies.extend(\n generate_meta_strategy_pair(HPSDojoStrategy))\n\n # Add testing please ignore strategies\n strategies.extend(\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\n\n # Add testimono strategy\n strategies.extend(\n generate_meta_strategy_pair(TestimonoStrategy))\n\n # Add RPS Geometry\n strategies.extend(\n generate_meta_strategy_pair(GeometryV4Strategy))\n\n # Add Iocaine Powder strategy\n strategies.extend(\n generate_meta_strategy_pair(IocanePowderStrategy))\n\n # By default, rotate everything\n do_rotations = [True for _ in strategies]\n\n # Anti Trivial\n strategies.extend(\n generate_meta_strategy_pair(\n AntiTrivialStrategy, mirroring=False))\n do_rotations.extend([False])\n\n return strategies, do_rotations","def test_integration2(self):\n self._test_integration(2)","def iter_fun(self):\n\n run_id = self._run_id\n etopo_dir = driver_home\n topodir = driver_home\n\n # load input info\n if self._input_info == None:\n scn_fname = os.path.join(self._run_home,'scenario_pts.txt') \n scn = np.loadtxt(scn_fname)\n scn_list = scn.tolist()\n else:\n scn_list = self._input_info\n \n # total number of runs\n M = len(scn_list)\n N = 8*M + 2 # 8*M runs plus two empty bathymetry runs\n\n if run_id == N:\n raise StopIteration()\n\n else:\n \n #=========================\n # set coarse and fine grids\n #\n t_shelf = 0. # time approaching continental slope\n t_harbor = 0. # time approaching harbor\n\n if ((run_id >= 0) and (run_id < 4*M)) or (run_id == 8*M):\n #------------------\n # setrun for coarse\n #\n grid = 'coarse'\n \n self._rundata.amrdata.amr_levels_max = 4\n # coarse grid run = 10\"\n # dx = 30', 5', 1', 10\"\n self._rundata.amrdata.refinement_ratios_x = [6, 5, 6]\n self._rundata.amrdata.refinement_ratios_y = [6, 5, 6]\n self._rundata.amrdata.refinement_ratios_t = [6, 5, 6]\n\n\n # add topography (coarse)\n topofiles = self._rundata.topo_data.topofiles\n # for topography, append lines of the form\n # [topotype, minlevel, maxlevel, t1, t2, fname]\n topofiles = []\n\n topofiles.append([3, 1, 4, 0., 1.e10, \\\n os.path.join(etopo_dir, 'etopo1_-130_-124_38_45_1min.asc')])\n topofiles.append([-3, 3, 4, 0., 1.e10, \\\n os.path.join(topodir, 'cc-1sec.asc')])\n\n # add regions\n regions = self._rundata.regiondata.regions \n # between shelf and CC \n regions = []\n regions.append(\\\n [2, 3, t_shelf, 1e9, -125, -124.05, 40.5, 43]) \n regions.append(\\\n [3, 4, t_harbor, 1e9, -124.26, -124.14, 41.67, 41.79])\n regions.append(\\\n [4, 4, t_harbor, 1e9, -124.218,-124.17, 41.7345, 41.77])\n\n # == fgmax.data values ==\n fgmax_files = self._rundata.fgmax_data.fgmax_files\n fgmax_files = []\n \n # for fixed grids append to this list names of any fgmax input files\n fgmax1_fname = os.path.join(driver_home,'fgmax1_coarse.txt')\n fgmax2_fname = os.path.join(driver_home,'fgmax2_coarse.txt')\n fgmax3_fname = os.path.join(driver_home,'fgmax3_coarse.txt')\n\n fgmax_files.append(fgmax1_fname) \n fgmax_files.append(fgmax2_fname) \n fgmax_files.append(fgmax3_fname) \n \n self._rundata.fgmax_data.num_fgmax_val = 2\n \n \n elif ((run_id >= 4*M) and (run_id < 8*M)) or (run_id == 8*M+1):\n #----------------\n # setrun for fine\n #\n grid = 'fine'\n \n self._rundata.amrdata.amr_levels_max = 6\n\n ## fine grid run = 2/3\"\n ## dx = 30', 5', 1', 10\", 2\", 2/3\"\n self._rundata.amrdata.refinement_ratios_x = [6, 5, 6, 5, 3]\n self._rundata.amrdata.refinement_ratios_y = [6, 5, 6, 5, 3]\n self._rundata.amrdata.refinement_ratios_t = [6, 5, 6, 5, 3]\n\n regions = self._rundata.regiondata.regions \n regions = []\n # between shelf and CC\n regions.append(\\\n [2, 4, t_shelf, 1e9, -125, -124.05, 40.5, 43]) \n regions.append(\\\n [4, 5, t_harbor, 1e9, -124.26, -124.14, 41.67, 41.79])\n regions.append(\\\n [6, 6, t_harbor, 1e9, -124.218,-124.17, 41.7345, 41.77])\n\n # add topography (fine)\n topofiles = self._rundata.topo_data.topofiles\n # for topography, append lines of the form\n # [topotype, minlevel, maxlevel, t1, t2, fname]\n topofiles = []\n\n topofiles.append([3, 1, 6, 0., 1.e10, \\\n os.path.join(etopo_dir, 'etopo1_-130_-124_38_45_1min.asc')])\n topofiles.append([-3, 4, 6, 0., 1.e10, \\\n os.path.join(topodir, 'cc-1sec.asc')])\n topofiles.append([3, 6, 6, 0., 1.e10, \\\n os.path.join(topodir,'cc-1_3sec-c_pierless.asc')])\n \n # == fgmax.data values ==\n fgmax_files = self._rundata.fgmax_data.fgmax_files\n fgmax_files = []\n \n # for fixed grids append to this list names of any fgmax input files\n fgmax1_fname = os.path.join(driver_home,'fgmax1_fine.txt')\n fgmax2_fname = os.path.join(driver_home,'fgmax2_fine.txt')\n fgmax3_fname = os.path.join(driver_home,'fgmax3_fine.txt')\n\n fgmax_files.append(fgmax1_fname) \n fgmax_files.append(fgmax2_fname) \n fgmax_files.append(fgmax3_fname) \n \n self._rundata.fgmax_data.num_fgmax_val = 2\n \n\n\n #\n # set desired magnitude\n #\n if ((run_id >= 0) and (run_id < M)) \\\n or ((run_id >= 4*M) and (run_id < 5*M)):\n self.KL_Mw_desired = 8.6\n elif ((run_id >= M) and (run_id < 2*M)) \\\n or ((run_id >= 5*M) and (run_id < 6*M)):\n self.KL_Mw_desired = 8.8\n elif ((run_id >= 2*M) and (run_id < 3*M)) \\\n or ((run_id >= 6*M) and (run_id < 7*M)):\n self.KL_Mw_desired = 9.0\n elif ((run_id >= 3*M) and (run_id < 4*M)) \\\n or ((run_id >= 7*M) and (run_id < 8*M)):\n self.KL_Mw_desired = 9.2\n \n #\n # set slip distribution\n #\n run_id_mod = run_id - 100*(run_id/100)\n m = scn_list[run_id_mod]\n self.set_KL_slip(m)\n \n if run_id < 8*M:\n dir_grid_Mw = '../geoclaw_output/' + str(grid) + '_' + str(self.KL_Mw_desired)\n self._rundir = os.path.join(dir_grid_Mw, 'run_' + str(run_id_mod))\n else:\n # empty runs to obtain bathymetry\n \n dir_grid_Mw = '../geoclaw_output/' + str(grid) + '_B0'\n self._rundir = dir_grid_Mw\n self.KL_Mw_desired = 0.0\n self.set_KL_slip([0.]*len(m)) # set output\n self._rundata.clawdata.output_times = [1.0, 3.0]\n \n self._run_id += 1\n \n return self","def run():\n\n Number_repetitions = 1\n Rate = np.zeros((Number_repetitions,1))\n Rate20 = np.zeros((Number_repetitions,1))\n Penalty20 = np.zeros((Number_repetitions, 1))\n\n # Loop to average\n for idx in np.arange(0,Number_repetitions,1):\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.0000001, display=True) # create simulator (uses pygame when display=True, if available)\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\n\n sim.run(n_trials=100) # run for a specified number of trials\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\n\n # I've edited the enviroment variable to do the plot creating an completions array\n completions = np.array(e.completions)\n rate = float(completions.sum())/float((len(completions)))\n rate20 = float(completions[-20:].sum())/20\n\n Rate[idx] = rate\n Rate20[idx] = rate20\n\n Wrong = np.array(a.wrong_moves_per_run[-20:]).mean()\n Penalty20[idx] = Wrong\n\n plt.scatter(np.arange(0,len(completions)),completions)\n plt.plot(Wrong)\n plt.xlabel('Trial')\n plt.ylabel('1 = Get in the destination, 0 = did not get')\n plt.title('Reiforcement learning progress')\n plt.legend(['Rate of completion: ' + str(rate) + '. Rate last 20: ' + str(rate20) + '.Mean penalty last 20: ' + str(Wrong)])\n plt.show()\n\n #print 'Accuracy: ' + str(Rate) + '. Mean: ' + str(np.mean(Rate))\n #print 'Mean 20: ' + str(np.mean(Rate20))#'Accuracy 20: ' + str(Rate20) + '. Mean 20: ' + str(np.mean(Rate20))\n #print 'Mean_penalty: ' + str(np.mean(Penalty20))\n\n # Print state table with actions\n #t = 0\n #for state in a.states:\n #print 'State ' + str(state) + '. Best action: ' + str((str(np.argmax(a.QTable[t][:]))))\n #t += 1","def combine(self, scenario2):\n prob = self.probability * scenario2.probability\n keys = list(())\n for key in self.nodeKeys:\n keys.append(key)\n for key in scenario2.nodeKeys:\n keys.append(key)\n return Scenario(prob, keys)","def make_plot_two_hospitals_arrival_split(\n lambda_2,\n lambda_1_1,\n lambda_1_2,\n mu_1,\n mu_2,\n num_of_servers_1,\n num_of_servers_2,\n threshold_1,\n threshold_2,\n measurement_type=\"b\",\n seed_num_1=None,\n seed_num_2=None,\n warm_up_time=100,\n trials=1,\n accuracy=10,\n runtime=1440,\n):\n hospital_times_1 = []\n hospital_times_2 = []\n all_arrival_rates = np.linspace(0, lambda_2, accuracy + 1)\n for arrival_rate_1 in all_arrival_rates[1:-1]:\n arrival_rate_2 = lambda_2 - arrival_rate_1\n times_1 = get_multiple_runs_results(\n arrival_rate_1,\n lambda_1_1,\n mu_1,\n num_of_servers_1,\n threshold_1,\n seed_num_1,\n warm_up_time,\n trials,\n runtime,\n )\n times_2 = get_multiple_runs_results(\n arrival_rate_2,\n lambda_1_2,\n mu_2,\n num_of_servers_2,\n threshold_2,\n seed_num_2,\n warm_up_time,\n trials,\n runtime,\n )\n hospital_times_1, hospital_times_2 = update_hospitals_lists(\n hospital_times_1, hospital_times_2, times_1, times_2, measurement_type\n )\n\n x_axis_label, y_axis_label, title = get_two_hospital_plot_labels(measurement_type)\n x_labels = all_arrival_rates[1:-1] / all_arrival_rates[-1]\n plt.figure(figsize=(23, 10))\n waiting_time_plot = plt.plot(x_labels, hospital_times_1, ls=\"solid\", lw=1.5)\n plt.plot(x_labels, hospital_times_2, ls=\"solid\", lw=1.5)\n plt.legend([\"Hospital 1\", \"Hospital 2\"], fontsize=\"x-large\")\n plt.title(title, fontsize=18)\n plt.xlabel(x_axis_label, fontsize=15, fontweight=\"bold\")\n plt.ylabel(y_axis_label, fontsize=15, fontweight=\"bold\")\n\n return waiting_time_plot","def test_coverage_2(base_settings):\n filename = base_settings[\"unittest_data_dir\"] / \"coverage-example-selfpay.json\"\n inst = coverage.Coverage.parse_file(\n filename, content_type=\"application/json\", encoding=\"utf-8\"\n )\n assert \"Coverage\" == inst.resource_type\n\n impl_coverage_2(inst)\n\n # testing reverse by generating data from itself and create again.\n data = inst.dict()\n assert \"Coverage\" == data[\"resourceType\"]\n\n inst2 = coverage.Coverage(**data)\n impl_coverage_2(inst2)","def test_mc_t_two_sample(self):\r\n # Verified against R's t.test() and Deducer::perm.t.test().\r\n\r\n # With numpy array as input.\r\n exp = (-0.11858541225631833, 0.90756579317867436)\r\n I = array([7.2, 7.1, 9.1, 7.2, 7.3, 7.2, 7.5])\r\n II = array([8.8, 7.5, 7.7, 7.6, 7.4, 6.7, 7.2])\r\n obs = mc_t_two_sample(I, II)\r\n self.assertFloatEqual(obs[:2], exp)\r\n self.assertEqual(len(obs[2]), 999)\r\n self.assertCorrectPValue(0.8, 0.9, mc_t_two_sample, [I, II],\r\n p_val_idx=3)\r\n\r\n # With python list as input.\r\n exp = (-0.11858541225631833, 0.90756579317867436)\r\n I = [7.2, 7.1, 9.1, 7.2, 7.3, 7.2, 7.5]\r\n II = [8.8, 7.5, 7.7, 7.6, 7.4, 6.7, 7.2]\r\n obs = mc_t_two_sample(I, II)\r\n self.assertFloatEqual(obs[:2], exp)\r\n self.assertEqual(len(obs[2]), 999)\r\n self.assertCorrectPValue(0.8, 0.9, mc_t_two_sample, [I, II],\r\n p_val_idx=3)\r\n\r\n exp = (-0.11858541225631833, 0.45378289658933718)\r\n obs = mc_t_two_sample(I, II, tails='low')\r\n self.assertFloatEqual(obs[:2], exp)\r\n self.assertEqual(len(obs[2]), 999)\r\n self.assertCorrectPValue(0.4, 0.47, mc_t_two_sample, [I, II],\r\n {'tails': 'low'}, p_val_idx=3)\r\n\r\n exp = (-0.11858541225631833, 0.54621710341066287)\r\n obs = mc_t_two_sample(I, II, tails='high', permutations=99)\r\n self.assertFloatEqual(obs[:2], exp)\r\n self.assertEqual(len(obs[2]), 99)\r\n self.assertCorrectPValue(0.4, 0.62, mc_t_two_sample, [I, II],\r\n {'tails': 'high', 'permutations': 99}, p_val_idx=3)\r\n\r\n exp = (-2.8855783649036986, 0.99315596652421401)\r\n obs = mc_t_two_sample(I, II, tails='high', permutations=99, exp_diff=1)\r\n self.assertFloatEqual(obs[:2], exp)\r\n self.assertEqual(len(obs[2]), 99)\r\n self.assertCorrectPValue(0.55, 0.99, mc_t_two_sample, [I, II],\r\n {'tails': 'high', 'permutations': 99, 'exp_diff': 1}, p_val_idx=3)","def test_run_sim_1():\n rnd = rand.Arrivals(36, 41)\n sim.run_sim(3, 2, 5, 6, 22, rnd)","def prepare_experiment(assumptions):\n print(\"\\nGenerate species parameters\")\n np.random.seed(assumptions['seed']) \n params = MakeParams(assumptions) \n if assumptions[\"selected_function\"] == \"f5_invader_suppression\":\n print(\"\\nDraw invader feature\")\n params = create_invader(params, assumptions)\n print(params[\"c\"])\n \n print(\"\\nDraw per-capita function and cost\")\n f1_species_smooth, f1_species_rugged, f2_species_smooth, f2_species_rugged = draw_species_function(assumptions)\n params.update({\"f1_species_smooth\": f1_species_smooth, \"f1_species_rugged\": f1_species_rugged, \"f2_species_smooth\": f2_species_smooth, \"f2_species_rugged\": f2_species_rugged})\n gi = draw_species_cost(f1_species_smooth, assumptions)\n params.update({\"g\": gi})\n \n print(\"\\nConstruct plate\")\n np.random.seed(assumptions['seed']) \n plate = make_plate(assumptions,params)\n \n print(\"\\nAdd community function to plate\")\n plate = add_community_function(plate, assumptions, params)\n \n if not pd.isnull(assumptions[\"overwrite_plate\"]) :\n print(\"\\nUpdating the initial plate composition by overwrite_plate\")\n plate = overwrite_plate(plate, assumptions)\n \n print(\"\\nPrepare Protocol\")\n #Extract Protocol from protocol database\n algorithms = make_algorithms(assumptions)\n params_algorithm = algorithms[algorithms['algorithm_name'] == assumptions['protocol']]\n \n #Params_simulation by default contains all assumptions not stored in params.\n params_simulation = dict((k, assumptions[k]) for k in assumptions.keys() if k not in params.keys())\n \n return params, params_simulation , params_algorithm, plate","def test_create_scenario1(self):\n pass","def test_autosample(self):\n \n # Start data subscribers.\n #self._start_data_subscribers(6)\n #self.addCleanup(self._stop_data_subscribers) \n \n # Set up a subscriber to collect error events.\n #self._start_event_subscriber('ResourceAgentResourceStateEvent', 7)\n #self.addCleanup(self._stop_event_subscriber) \n \n state = self._ia_client.get_agent_state()\n self.assertEqual(state, ResourceAgentState.UNINITIALIZED)\n \n cmd = AgentCommand(command=ResourceAgentEvent.INITIALIZE)\n retval = self._ia_client.execute_agent(cmd)\n state = self._ia_client.get_agent_state()\n self.assertEqual(state, ResourceAgentState.INACTIVE)\n\n cmd = AgentCommand(command=ResourceAgentEvent.GO_ACTIVE)\n retval = self._ia_client.execute_agent(cmd)\n state = self._ia_client.get_agent_state()\n self.assertEqual(state, ResourceAgentState.IDLE)\n\n cmd = AgentCommand(command=ResourceAgentEvent.RUN)\n retval = self._ia_client.execute_agent(cmd)\n state = self._ia_client.get_agent_state()\n self.assertEqual(state, ResourceAgentState.COMMAND)\n\n cmd = AgentCommand(command=SBE37ProtocolEvent.START_AUTOSAMPLE)\n retval = self._ia_client.execute_resource(cmd)\n \n gevent.sleep(15)\n \n cmd = AgentCommand(command=SBE37ProtocolEvent.STOP_AUTOSAMPLE)\n retval = self._ia_client.execute_resource(cmd)\n \n cmd = AgentCommand(command=ResourceAgentEvent.RESET)\n retval = self._ia_client.execute_agent(cmd)\n state = self._ia_client.get_agent_state()\n self.assertEqual(state, ResourceAgentState.UNINITIALIZED)\n\n #self._async_event_result.get(timeout=CFG.endpoint.receive.timeout)\n #self.assertGreaterEqual(len(self._events_received), 6)\n\n #self._async_sample_result.get(timeout=CFG.endpoint.receive.timeout)\n #self.assertGreaterEqual(len(self._samples_received), 6)","def make_second_recommendation() -> str:\r\n growth_rate_info = highest_growth_rate()\r\n rate_strings = growth_rate_info[0]\r\n most_demand_string = growth_rate_info[1]\r\n return render_template(\"make_second_recommendation.html\",\r\n display_rates=rate_strings,\r\n most_demand_display=most_demand_string)"],"string":"[\n \"def test_sampling2 () :\\n delta = 2 * np.pi / 3\\n r = Reward(partial(stepFunction, \\n xRange=(-delta/2, delta/2), \\n yRange=(-delta/2, delta/2)), \\n (-1, 0))\\n states = []\\n xs = np.arange(-np.pi, np.pi, delta)\\n ys = np.arange(-np.pi, np.pi, delta)\\n for x, y in product(xs, ys) : \\n states.append(\\n toExternalStateRep([x + delta / 2, y + delta / 2, 0, 0]).astype(float)\\n )\\n agent = findOptimalAgent(r)\\n vals = estimateValueFromAgent(states, agent, r)\\n for s, v in zip(states, vals) : \\n print(toInternalStateRep(s)[:2], v)\",\n \"def test_two_unsampled_arms(self):\\n self._test_two_unsampled_arms()\",\n \"def generate_scenario_3(seed=1996, permanent_save=True, radar_meas_rate=1, ais_meas_rate=5, sigma_process=0.01,\\n sigma_meas_radar=3, sigma_meas_ais=1, timesteps=20):\\n start_time = datetime.now()\\n\\n # specify seed to be able to repeat the example\\n np.random.seed(seed)\\n\\n # combine two 1-D CV models to create a 2-D CV model\\n transition_model = CombinedLinearGaussianTransitionModel(\\n [ConstantVelocity(sigma_process), ConstantVelocity(sigma_process)])\\n\\n # starting at 0,0 and moving NE\\n truth = GroundTruthPath([GroundTruthState([0, 1, 0, 1], timestamp=start_time)])\\n\\n # generate truth using transition_model and noise\\n end_time = start_time + timedelta(seconds=timesteps * max(radar_meas_rate, ais_meas_rate))\\n time = start_time + timedelta(seconds=1)\\n while time < end_time:\\n truth.append(GroundTruthState(\\n transition_model.function(truth[-1], noise=True, time_interval=timedelta(seconds=1)),\\n timestamp=time))\\n time += timedelta(seconds=1)\\n\\n # Simulate measurements\\n # Specify measurement model for radar\\n measurement_model_radar = LinearGaussian(\\n ndim_state=4, # number of state dimensions\\n mapping=(0, 2), # mapping measurement vector index to state index\\n noise_covar=np.array([[sigma_meas_radar, 0], # covariance matrix for Gaussian PDF\\n [0, sigma_meas_radar]])\\n )\\n\\n # Specify measurement model for AIS (Same as for radar)\\n measurement_model_ais = LinearGaussian(\\n ndim_state=4,\\n mapping=(0, 2),\\n noise_covar=np.array([[sigma_meas_ais, 0],\\n [0, sigma_meas_ais]])\\n )\\n\\n # generate \\\"radar\\\" measurements\\n measurements_radar = []\\n measurements_ais = []\\n next_radar_meas_time = start_time\\n next_ais_meas_time = start_time\\n for state in truth:\\n # check whether we want to generate a measurement from this gt\\n if state.timestamp == next_radar_meas_time:\\n measurement = measurement_model_radar.function(state, noise=True)\\n measurements_radar.append(Detection(measurement, timestamp=state.timestamp))\\n next_radar_meas_time += timedelta(seconds=radar_meas_rate)\\n\\n if state.timestamp == next_ais_meas_time:\\n measurement = measurement_model_ais.function(state, noise=True)\\n measurements_ais.append(Detection(measurement, timestamp=state.timestamp))\\n next_ais_meas_time += timedelta(seconds=ais_meas_rate)\\n\\n if permanent_save:\\n save_folder_name = seed.__str__()\\n else:\\n save_folder_name = \\\"temp\\\"\\n\\n save_folder = \\\"../scenarios/scenario3/\\\" + save_folder_name + \\\"/\\\"\\n\\n # save the ground truth and the measurements for the radar and the AIS\\n store_object.store_object(truth, save_folder, \\\"ground_truth.pk1\\\")\\n store_object.store_object(measurements_radar, save_folder, \\\"measurements_radar.pk1\\\")\\n store_object.store_object(measurements_ais, save_folder, \\\"measurements_ais.pk1\\\")\\n store_object.store_object(start_time, save_folder, \\\"start_time.pk1\\\")\\n store_object.store_object(measurement_model_radar, save_folder, \\\"measurement_model_radar.pk1\\\")\\n store_object.store_object(measurement_model_ais, save_folder, \\\"measurement_model_ais.pk1\\\")\\n store_object.store_object(transition_model, save_folder, \\\"transition_model.pk1\\\")\",\n \"def test_2nd_scenario():\\n start_entered_scenario(config.MERAKI_CAMERAS[0][\\\"serial\\\"])\\n return \\\"ok\\\"\",\n \"def simulationTwoDrugsVirusPopulations():\\n #TODO\",\n \"def test_i2c():\\n\\n bus0 = i2c_interface.I2CBus(0)\\n bus1 = i2c_interface.I2CBus(1)\\n\\n bus1.init_device(0)\\n bus1.init_device(1)\\n bus0.init_device(1)\\n\\n acc0 = i2c_interface.Accelerometer(bus1.get_device(0))\\n acc1 = i2c_interface.Accelerometer(bus1.get_device(1))\\n acc2 = i2c_interface.Accelerometer(bus0.get_device(1))\\n\\n ti = time.time()\\n print(\\\"Reading data...\\\")\\n while time.time() - ti < 5:\\n acc0.read()\\n acc1.read()\\n acc2.read()\\n print(\\\"done\\\")\\n\\n acc0_sample_times = np.array(acc0.get_sample_times()) - ti\\n acc0_magnitudes = acc0.get_magnitude_data()\\n acc1_sample_times = np.array(acc1.get_sample_times()) - ti\\n acc1_magnitudes = acc1.get_magnitude_data()\\n acc2_sample_times = np.array(acc2.get_sample_times()) - ti\\n acc2_magnitudes = acc2.get_magnitude_data()\\n \\n plt.plot(acc0_sample_times, acc0_magnitudes, label='acc0')\\n plt.plot(acc1_sample_times, acc1_magnitudes, label='acc1')\\n plt.plot(acc2_sample_times, acc2_magnitudes, label='acc2')\\n plt.legend()\\n plt.show()\\n\\n tdoa10 = blockmatching.TDOA(acc1_magnitudes, acc0_magnitudes, acc0_sample_times)\\n tdoa20 = blockmatching.TDOA(acc2_magnitudes, acc0_magnitudes, acc0_sample_times)\\n print(tdoa10, tdoa20)\",\n \"def generate_scenario_1(seed=1996, permanent_save=True, sigma_process=0.01, sigma_meas_radar=3, sigma_meas_ais=1):\\n # specify seed to be able repeat example\\n start_time = datetime.now()\\n\\n np.random.seed(seed)\\n\\n # combine two 1-D CV models to create a 2-D CV model\\n transition_model = CombinedLinearGaussianTransitionModel([ConstantVelocity(sigma_process),\\n ConstantVelocity(sigma_process)])\\n\\n # starting at 0,0 and moving NE\\n truth = GroundTruthPath([GroundTruthState([0, 1, 0, 1], timestamp=start_time)])\\n\\n # generate truth using transition_model and noise\\n for k in range(1, 21):\\n truth.append(GroundTruthState(\\n transition_model.function(truth[k - 1], noise=True, time_interval=timedelta(seconds=1)),\\n timestamp=start_time + timedelta(seconds=k)))\\n\\n # Simulate measurements\\n # Specify measurement model for radar\\n measurement_model_radar = LinearGaussian(\\n ndim_state=4, # number of state dimensions\\n mapping=(0, 2), # mapping measurement vector index to state index\\n noise_covar=np.array([[sigma_meas_radar, 0], # covariance matrix for Gaussian PDF\\n [0, sigma_meas_radar]])\\n )\\n\\n # Specify measurement model for AIS\\n measurement_model_ais = LinearGaussian(\\n ndim_state=4,\\n mapping=(0, 2),\\n noise_covar=np.array([[sigma_meas_ais, 0],\\n [0, sigma_meas_ais]])\\n )\\n\\n # generate \\\"radar\\\" measurements\\n measurements_radar = []\\n for state in truth:\\n measurement = measurement_model_radar.function(state, noise=True)\\n measurements_radar.append(Detection(measurement, timestamp=state.timestamp))\\n\\n # generate \\\"AIS\\\" measurements\\n measurements_ais = []\\n state_num = 0\\n for state in truth:\\n state_num += 1\\n if not state_num % 2: # measurement every second time step\\n measurement = measurement_model_ais.function(state, noise=True)\\n measurements_ais.append(Detection(measurement, timestamp=state.timestamp))\\n\\n if permanent_save:\\n save_folder_name = seed.__str__()\\n else:\\n save_folder_name = \\\"temp\\\"\\n\\n save_folder = \\\"../scenarios/scenario1/\\\" + save_folder_name + \\\"/\\\"\\n\\n # save the ground truth and the measurements for the radar and the AIS\\n store_object.store_object(truth, save_folder, \\\"ground_truth.pk1\\\")\\n store_object.store_object(measurements_radar, save_folder, \\\"measurements_radar.pk1\\\")\\n store_object.store_object(measurements_ais, save_folder, \\\"measurements_ais.pk1\\\")\\n store_object.store_object(start_time, save_folder, \\\"start_time.pk1\\\")\\n store_object.store_object(measurement_model_radar, save_folder, \\\"measurement_model_radar.pk1\\\")\\n store_object.store_object(measurement_model_ais, save_folder, \\\"measurement_model_ais.pk1\\\")\\n store_object.store_object(transition_model, save_folder, \\\"transition_model.pk1\\\")\",\n \"def scenario3(sim):\\n\\n init_logging(logfile=None, debug=True)\\n second = 1000.0\\n duration = 10\\n tau_m = 20 # ms\\n cm = 1.0 # nF\\n v_reset = -60\\n cell_parameters = dict(\\n tau_m=tau_m,\\n cm=cm,\\n v_rest=-70,\\n e_rev_E=0,\\n e_rev_I=-70,\\n v_thresh=-54,\\n v_reset=v_reset,\\n tau_syn_E=5,\\n tau_syn_I=5,\\n )\\n g_leak = cm / tau_m # µS\\n\\n w_min = 0.0 * g_leak\\n w_max = 0.05 * g_leak\\n\\n r1 = 5.0\\n r2 = 40.0\\n\\n sim.setup()\\n pre = sim.Population(100, sim.SpikeSourcePoisson())\\n post = sim.Population(10, sim.IF_cond_exp())\\n\\n pre.set(duration=duration * second)\\n pre.set(start=0.0)\\n pre[:50].set(rate=r1)\\n pre[50:].set(rate=r2)\\n assert_equal(pre[49].rate, r1)\\n assert_equal(pre[50].rate, r2)\\n post.set(**cell_parameters)\\n post.initialize(v=RandomDistribution('normal', mu=v_reset, sigma=5.0))\\n\\n stdp = sim.STDPMechanism(\\n sim.SpikePairRule(tau_plus=20.0, tau_minus=20.0,\\n A_plus=0.01, A_minus=0.01),\\n sim.AdditiveWeightDependence(w_min=w_min, w_max=w_max),\\n #dendritic_delay_fraction=0.5))\\n dendritic_delay_fraction=1)\\n\\n connections = sim.Projection(pre, post, sim.AllToAllConnector(),\\n synapse_type=stdp,\\n receptor_type='excitatory')\\n\\n initial_weight_distr = RandomDistribution('uniform', low=w_min, high=w_max)\\n connections.randomizeWeights(initial_weight_distr)\\n initial_weights = connections.get('weight', format='array', gather=False)\\n # assert initial_weights.min() >= w_min\\n # assert initial_weights.max() < w_max\\n # assert initial_weights[0, 0] != initial_weights[1, 0]\\n\\n pre.record('spikes')\\n post.record('spikes')\\n post[0:1].record('v')\\n\\n sim.run(duration * second)\\n\\n actual_rate = pre.mean_spike_count() / duration\\n expected_rate = (r1 + r2) / 2\\n errmsg = \\\"actual rate: %g expected rate: %g\\\" % (actual_rate, expected_rate)\\n assert abs(actual_rate - expected_rate) < 1, errmsg\\n #assert abs(pre[:50].mean_spike_count()/duration - r1) < 1\\n #assert abs(pre[50:].mean_spike_count()/duration- r2) < 1\\n final_weights = connections.get('weight', format='array', gather=False)\\n assert initial_weights[0, 0] != final_weights[0, 0]\\n\\n try:\\n import scipy.stats\\n except ImportError:\\n raise SkipTest\\n t, p = scipy.stats.ttest_ind(initial_weights[:50, :].flat, initial_weights[50:, :].flat)\\n assert p > 0.05, p\\n t, p = scipy.stats.ttest_ind(final_weights[:50, :].flat, final_weights[50:, :].flat)\\n assert p < 0.01, p\\n assert final_weights[:50, :].mean() < final_weights[50:, :].mean()\\n sim.end()\\n return initial_weights, final_weights, pre, post, connections\",\n \"def simulationTwoDrugsDelayedTreatment():\\n\\n # TODO\",\n \"def simulate_test_data(self):\\n # simulation config\\n if self.instrument == 'IMA':\\n sim_config = SimConfig.makeSim(name=\\\"IMA Simulation\\\", rel_obsdate=0.0, scene=\\\"scene.ini\\\", POP='IMA',\\n ConfigPath='IMA_FULL', Dither=False,StartInd=1, NDither=4,\\n DitherPat=\\\"ima_recommended_dither.dat\\\", filter=\\\"F1130W\\\",\\n readDetect= 'FULL', ima_mode= 'FAST', ima_exposures=1,\\n ima_integrations=1, ima_frames=50, disperser= 'SHORT', detector= 'SW',\\n mrs_mode= 'SLOW', mrs_exposures=5,mrs_integrations=4,mrs_frames=10)\\n\\n # scene config\\n background = Background(level='low', gradient=5., pa=15.0, centreFOV=(0., 0.))\\n\\n SED1 = BBSed(Temp=300., wref=10., flux=1.e8)\\n Gal1 = Galaxy(Cen=(0., 0.), n=1., re=200, q=0.99, pa=0.1)\\n Gal1.set_SED(SED1)\\n targets = [Gal1]\\n\\n scene_config = SceneConfig.makeScene(loglevel=0, background=background, targets=targets)\\n\\n elif self.instrument == 'MRS':\\n sim_config = SimConfig.makeSim(name=\\\"MRS Simulation\\\", rel_obsdate=0.0, scene=\\\"scene.ini\\\",\\n POP='MRS', ConfigPath='MRS_1SHORT', Dither=False, StartInd=1,\\n NDither=4, DitherPat=\\\"mrs_recommended_dither.dat\\\", filter=\\\"F1130W\\\",\\n readDetect='FULL', ima_mode='FAST', ima_exposures=1,\\n ima_integrations=1, ima_frames=20, disperser='SHORT', detector='SW',\\n mrs_mode='FAST', mrs_exposures=1, mrs_integrations=1, mrs_frames=50)\\n\\n # scene config\\n background = Background(level='low',gradient=5.,pa=15.0,centreFOV=(0., 0.))\\n\\n SED1 = BBSed(Temp=300., wref=10., flux=5.e6)\\n Gal1 = Galaxy(Cen=(0.,0.), n=1., re=2, q=0.99, pa=0.1)\\n Gal1.set_SED(SED1)\\n targets = [Gal1]\\n\\n scene_config = SceneConfig.makeScene(loglevel=0, background=background, targets=targets)\\n\\n # simulator config\\n if self.noise:\\n simulator_config = SimulatorConfig.makeSimulator(max_fsm=0.050, max_dither=20.0, mrs_ref_channel=1,\\n mrs_ref_band=\\\"SHORT\\\", tau_telescope=0.88,tau_eol=0.8,\\n telescope_area=25.032, telescope_pupil_diam=6.6052,\\n take_webbPsf=False, include_refpix=True,\\n include_poisson=True, include_readnoise=True,\\n include_badpix=True, include_dark=True,\\n include_flat=False, include_gain=True,\\n include_nonlinearity=self.linearity, include_drifts=True,\\n include_latency=False, cosmic_ray_mode='NONE')\\n else:\\n simulator_config = SimulatorConfig.makeSimulator(max_fsm=0.050, max_dither=20.0, mrs_ref_channel=1,\\n mrs_ref_band=\\\"SHORT\\\", tau_telescope=0.88, tau_eol=0.8,\\n telescope_area=25.032, telescope_pupil_diam=6.6052,\\n take_webbPsf=False, include_refpix=True,\\n include_poisson=False, include_readnoise=False,\\n include_badpix=True, include_dark=True,\\n include_flat=False, include_gain=True,\\n include_nonlinearity=self.linearity, include_drifts=True,\\n include_latency=False, cosmic_ray_mode='NONE')\\n\\n\\n # run the simulation\\n simulation = MiriSimulation(sim_config=sim_config, scene_config=scene_config,\\n simulator_config=simulator_config, loglevel='DEBUG')\\n simulation.run()\\n\\n # we only need the sim file so move it to output_dir and remove everthing else\\n det_image_file = glob.glob(os.path.join(simulation.path_out, 'det_images', '*.fits'))[0]\\n self.ramp_file = os.path.join(self.output_dir, os.path.basename(det_image_file))\\n shutil.move(det_image_file, self.ramp_file)\\n shutil.rmtree(simulation.path_out)\",\n \"def test_f2_circuit_maker(self):\\n fho = tfho.test_file_handle_object()\\n W = 5\\n G = 20\\n fg = .9\\n X = 10\\n fx = .85\\n gate_maker = g.TYPE_TO_GATE_GEN[g.TEST_TYPES.RANDOM]\\n # family 2 files:\\n t_circuit_file_name = \\\"circuit_file_trimming\\\"\\n t_circuit_file = fho.get_file_object(t_circuit_file_name, 'w')\\n t_input_file_name = \\\"input_file_trimming\\\"\\n t_input_file = fho.get_file_object(t_input_file_name, 'w')\\n t_output_file_name = \\\"output_file_trimming\\\"\\n t_output_file = fho.get_file_object(t_output_file_name, 'w')\\n nt_circuit_file_name = \\\"circuit_file_no_trimming\\\"\\n nt_circuit_file = fho.get_file_object(nt_circuit_file_name, 'w')\\n nt_input_file_name = \\\"input_file_no_trimming\\\"\\n nt_input_file = fho.get_file_object(nt_input_file_name, 'w')\\n nt_output_file_name = \\\"output_file_no_trimming\\\"\\n nt_output_file = fho.get_file_object(nt_output_file_name, 'w')\\n level_type_array = [g.LEVEL_TYPES.XOR, g.LEVEL_TYPES.RANDOM,\\n g.LEVEL_TYPES.XOR]\\n F = 2\\n # make a family 1 circuit with trimming:\\n sr.seed(self.rand_seed)\\n t_gen = g.f1f2_circuit_maker_with_trimming_switch(W, G, fg,\\n t_circuit_file,\\n t_input_file,\\n t_output_file,\\n X, fx, gate_maker,\\n level_type_array, True)\\n t_gen.generate()\\n # make a family 1 circuit without trimming, with the same randomness:\\n sr.seed(self.rand_seed)\\n nt_gen = g.f1f2_circuit_maker_with_trimming_switch(W, G, fg,\\n nt_circuit_file,\\n nt_input_file,\\n nt_output_file,\\n X, fx, gate_maker,\\n level_type_array, False)\\n nt_gen.generate()\\n # obtain strings representing the contents of all the resulting files:\\n t_circuit_string = fho.get_file(t_circuit_file_name).getvalue()\\n t_input_string = fho.get_file(t_input_file_name).getvalue()\\n t_output_string = fho.get_file(t_output_file_name).getvalue()\\n nt_circuit_string = fho.get_file(nt_circuit_file_name).getvalue()\\n nt_input_string = fho.get_file(nt_input_file_name).getvalue()\\n nt_output_string = fho.get_file(nt_output_file_name).getvalue()\\n # make sure that the inputs and outputs produced by the trimming and\\n # no trimming algorithms are the same:\\n self.assertEqual(t_input_string, nt_input_string)\\n self.assertEqual(t_output_string, nt_output_string)\\n # make sure that the input begins and ends with a bracket:\\n self.assertEqual(\\\"[\\\", t_input_string[0])\\n self.assertEqual(\\\"]\\\", t_input_string[-1])\\n # make sure that each input element is a bit:\\n for bit in t_input_string[1:-1]:\\n self.assertTrue((bit == '0') or (bit == '1'))\\n # make sure that the output is a bit:\\n self.assertTrue((t_output_string == '0') or (t_output_string == '1'))\\n # make sure that the two circuit headers are the same, and that they\\n # contain the correct values:\\n t_circuit_header = t_circuit_string.split(\\\"\\\\n\\\")[0]\\n nt_circuit_header = nt_circuit_string.split(\\\"\\\\n\\\")[0]\\n self.assertEqual(t_circuit_header, nt_circuit_header)\\n (W_string, G_string, X_string, F_string) = t_circuit_header.split(\\\",\\\")\\n W_value = int(W_string.split(\\\"=\\\")[-1])\\n G_value = int(G_string.split(\\\"=\\\")[-1])\\n X_value = int(X_string.split(\\\"=\\\")[-1])\\n F_value = int(F_string.split(\\\"=\\\")[-1])\\n self.assertEqual(W, W_value)\\n self.assertEqual(G, G_value)\\n self.assertEqual(F, F_value)\\n # note that we cannot test that the circuits themselves are the same,\\n # because the trimming algorithm produces a circuit with gates listed\\n # in a different order.\",\n \"def generate_syntetic_data( self ):\\n label_list = []\\n img_ground_truth = np.zeros( ( self.img_h, self.img_w ), dtype = np.float32 )\\n img = np.zeros( ( self.img_h, self.img_w, 3 ), dtype = np.float32 )\\n class_blue = np.zeros( ( self.img_h, self.img_w ), dtype = np.float32 )\\n class_red = np.zeros( ( self.img_h, self.img_w ), dtype = np.float32 )\\n\\n '''line, square, grill, rectangle, cross'''\\n if self.option_shape == 'line':\\n sample_type = 0\\n elif self.option_shape == 'circle':\\n sample_type = 1\\n elif self.option_shape == 'rectangle':\\n sample_type = 2\\n else: \\n sample_type = np.random.randint( 3 ) #0,1,2\\n\\n if sample_type == 0:\\n self.generate_line( img_ground_truth, img, class_blue, class_red )\\n elif sample_type == 1:\\n self.generate_circle( img_ground_truth, img, class_blue, class_red )\\n else:\\n self.generate_rectangle( img_ground_truth, img, class_blue, class_red )\\n\\n label_list.append( class_blue )\\n label_list.append( class_red )\\n # 2 classes generates\\n label_all = np.dstack( label_list ).astype( np.float32 ) \\n r, g, b = cv2.split( img )\\n img_bgr = cv2.merge( [ b, g, r ] )\\n\\n gen_adj = GenerateAdjMatrx( type_dist = self.type_dist )\\n A_gt = gen_adj.adjmatrx_groundthuth( img_ground_truth )\\n\\n B_in = gen_adj.adjmatrx_groundthuth(img_ground_truth * 0)\\n\\n return img_bgr, img_ground_truth, label_all, A_gt, B_in\",\n \"def _choose_sample(self):\\n\\n \\t #periodically generate a new reconstruction for the purposes of sampling\",\n \"def punto2_2():\\r\\n\\tprint(\\\"2.2R/\\\")\\r\\n\\tp_small = [0.005*i for i in range(20)]\\r\\n\\tp_large =[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1]\\r\\n\\tp_large= p_small + p_large\\t\\r\\n\\ts = multStats2(p_small,100,500)\\r\\n\\ty1,y2,y3,y4,y5= s[0],s[1],s[2],s[3],s[4]\\r\\n\\tplt.plot(p_small,y1, label = \\\"Avrg num of ArtP\\\")\\r\\n\\tplt.plot(p_small,y2, label = \\\"Avrg num of Brid\\\")\\r\\n\\tplt.plot(p_small,y3, label = \\\"Avrg size per CC\\\")\\r\\n\\tplt.plot(p_small,y4, label = \\\"Avrg node Deg\\\")\\r\\n\\t#plt.plot(p_small,y5)\\r\\n\\tplt.xlabel(\\\"P_Small\\\")\\r\\n\\tplt.ylabel(\\\"Average Values\\\")\\r\\n\\tplt.title(\\\"Random Graph Metrics\\\")\\r\\n\\tplt.legend()\\r\\n\\tplt.show()\\r\\n\\ts = multStats3(p_large,20,100)\\r\\n\\ty2,y3,y4 = s[0],s[1],s[2]\\r\\n\\ty1 = multStats4(p_large,100,500)\\r\\n\\tplt.plot(p_large,y1, label = \\\"Avrg num of CC\\\")\\r\\n\\tplt.plot(p_large,y2, label = \\\"Avrg num of triangles\\\")\\r\\n\\tplt.plot(p_large,y3, label = \\\"Avrg num of triplets\\\")\\r\\n\\tplt.plot(p_large,y4, label = \\\"#triangles/#triplets\\\")\\r\\n\\tplt.xlabel(\\\"P_Small & P_Large\\\")\\r\\n\\tplt.ylabel(\\\"Average Values\\\")\\r\\n\\tplt.title(\\\"Random Graph Metrics\\\")\\r\\n\\tplt.legend()\\r\\n\\tplt.show()\\r\\n\\treturn\",\n \"def radarScat(sp, wl, K2=0.93):\\n#TODO check if K2 is for ice or liquid!\\n prefactor = 2*np.pi*wl**4/(np.pi**5*K2)\\n \\n \\n reflect_hh = prefactor*(sp.Z11+sp.Z22+sp.Z12+sp.Z21)\\n reflect_vv = prefactor*(sp.Z11+sp.Z22-sp.Z12-sp.Z21)\\n kdp = 1e-3*(180.0/np.pi)*wl*sp.S22r_S11r\\n\\n reflect_hv = prefactor*(sp.Z11 - sp.Z12 + sp.Z21 - sp.Z22)\\n #reflect_vh = prefactor*(sp.Z11 + sp.Z12 - sp.Z21 - sp.Z22).values\\n ldr_h = reflect_hh/reflect_hv\\n \\n # delta_hv np.arctan2(Z[2,3] - Z[3,2], -Z[2,2] - Z[3,3])\\n #a = (Z[2,2] + Z[3,3])**2 + (Z[3,2] - Z[2,3])**2\\n #b = (Z[0,0] - Z[0,1] - Z[1,0] + Z[1,1])\\n #c = (Z[0,0] + Z[0,1] + Z[1,0] + Z[1,1])\\n #rho_hv np.sqrt(a / (b*c))\\n rho_hv = np.nan*np.ones_like(reflect_hh) # disable rho_hv for now\\n #Ah = 4.343e-3 * 2 * scatterer.wavelength * sp.S22i.values # attenuation horizontal polarization\\n #Av = 4.343e-3 * 2 * scatterer.wavelength * sp.S11i.values # attenuation vertical polarization\\n\\n #- test: calculate extinction: TODO: test Cextx that is given in DDA with this calculation.\\n k = 2 * np.pi / (wl)\\n cext_hh = sp.S22i*4.0*np.pi/k\\n cext_vv = sp.S11i*4.0*np.pi/k\\n \\n return reflect_hh, reflect_vv, reflect_hv, kdp, rho_hv, cext_hh, cext_vv\",\n \"def test_sample(system_generator):\\n\\n name, test = system_generator()\\n print(name)\\n\\n w_F, w_R, N_k = test.sample([10, 8], mode=\\\"wFwR\\\")\\n w_F, w_R, N_k = test.sample([1, 1], mode=\\\"wFwR\\\")\\n w_F, w_R, N_k = test.sample([10, 0], mode=\\\"wFwR\\\")\\n w_F, w_R, N_k = test.sample([0, 5], mode=\\\"wFwR\\\")\",\n \"def get_next_sample(self):\",\n \"def test_y_generate():\\n a = Attractor()\\n #say x, y, z = [0.1, 0.0, 0.0]\\n\\n dx = (10.0 * (0.0 - 0.1)) * (80.0-0.0)/10000 + 0.1\\n dy = (0.1 * (28 - 0.0) - 0.0) * (80.0-0.0)/10000 + 0.0\\n dz = ((0.1 * 0.0) - (8/3 * 0.0)) * (80.0-0.0)/10000 + 0.0\\n ex_1 = np.array([dx, dy, dz])\\n\\n dx2 = (10.0 * (dy - dx)) * (80.0-0.0)/10000.0 + dx \\n dy2 = (dx * (28.0 - dz) - dy) * (80.0-0.0)/10000.0 + dy\\n dz2 = ((dx * dy) - (8/3 * dz)) * (80.0-0.0)/10000.0 + dz\\n ex_2 = np.array([dx2, dy2, dz2])\\n\\n dx3 = (10.0 * (dy2 - dx2)) * (80.0-0.0)/10000.0 + dx2\\n dy3 = (dx2 * (28.0 - dz2) - dy2) * (80.0-0.0)/10000.0 + dy2\\n dz3 = ((dx2 * dy2) - (8/3 * dz2)) * (80.0-0.0)/10000.0 + dz2\\n ex_3 = np.array([dx3, dy3, dz3])\\n\\n dx4 = (10.0 * (dy3 - dx3)) * (80.0-0.0)/10000.0 + dx3\\n dy4 = (dx3 * (28 - dz3) - dy3) * (80.0-0.0)/10000.0 + dy3\\n dz4 = ((dx3 * dy3) - (8/3 * dz3)) * (80.0-0.0)/10000.0 + dz3\\n ex_4 = np.array([dx4, dy4, dz4])\\n\\n dx5 = (10.0 * (dy4 - dx4)) * (80.0-0.0)/10000.0 + dx4\\n dy5 = (dx4 * (28 - dz4) - dy4) * (80.0-0.0)/10000.0 + dy4\\n dz5 = ((dx4 * dy4) - (8/3 * dz4)) * (80.0-0.0)/10000.0 + dz4\\n ex_5 = np.array([dx5, dy5, dz5])\\n\\n \\n a.evolve(order = 4)\\n y_list = a.solution['y'].tolist()\\n \\n for i in y_list[:6]:\\n yy = round(i, 2)\\n for j in [0.0, dy, dy2, dy3, dy4, dy5]:\\n yyy = round(j, 2)\\n \\n print (\\\"Actual increments: \\\", yy)#str(a.solution()['x']).strip('[]'))\\n print (\\\"Expected increments: \\\", yyy)\\n assert yy == yyy\",\n \"def generate_n2(self):\\r\\n obj = Random\\r\\n self.n_2 = int(obj.time_split[0])+int(obj.time_split[1])+int(obj.time_split[2])\\r\\n self.n_2decimal = int(abs(pi)*math.pow(10, self.n_2)) % 10\",\n \"def test_dataset_scenario_generation_full(self):\\n params = ParameterServer()\\n\\n map_filename = os.path.join(os.path.dirname(__file__), \\\"data/DR_DEU_Merging_MT_v01_shifted.xodr\\\")\\n track_filename = os.path.join(os.path.dirname(__file__), \\\"data/interaction_dataset_DEU_Merging_dummy_track.csv\\\")\\n\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"MapFilename\\\"] = map_filename\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"TrackFilenameList\\\"] = [track_filename]\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"StartingOffsetMs\\\"] = 0\\n\\n scenario_generation = InteractionDatasetScenarioGenerationFull(\\n params=params, num_scenarios=2)\\n\\n self.assertEqual(scenario_generation.get_num_scenarios(), 2)\\n\\n # first scenario\\n scenario0 = scenario_generation.get_scenario(0)\\n agent11 = scenario0.GetWorldState().agents[1]\\n agent12 = scenario0.GetWorldState().agents[2]\\n self.assertEqual(agent11.first_valid_timestamp, 0.0)\\n self.assertEqual(agent12.first_valid_timestamp, 0.0)\\n # agents are initialized with Behavior::NotValidYet\\n self.assertEqual(list(scenario0.GetWorldState().agents_valid.keys()), [])\\n scenario0.GetWorldState().Step(0.01)\\n self.assertEqual(list(scenario0.GetWorldState().agents_valid.keys()), [1,2])\\n\\n # second scenario\\n scenario1 = scenario_generation.get_scenario(1)\\n agent21 = scenario1.GetWorldState().agents[1]\\n agent22 = scenario1.GetWorldState().agents[2]\\n self.assertEqual(agent21.first_valid_timestamp, 0.0)\\n self.assertEqual(agent22.first_valid_timestamp, 0.0)\\n self.assertEqual(list(scenario1.GetWorldState().agents_valid.keys()), [])\\n scenario1.GetWorldState().Step(0.01)\\n self.assertEqual(list(scenario1.GetWorldState().agents_valid.keys()), [1,2])\",\n \"def test_g2(self):\\n self.setUp()\\n theta = self.data.theta\\n beta_0, beta_1 = theta[\\\"beta_0\\\"], theta[\\\"beta_1\\\"]\\n gamma_0, gamma_1 = theta[\\\"gamma_0\\\"], theta[\\\"gamma_1\\\"]\\n g2 = self.E_func.g2(self.S, gamma_0, beta_0, gamma_1, beta_1)\\n # values of g2 at first group and first sample\\n g2_0_1 = np.array([347/3, 326/3, 218/3, 266/3])\\n # values of g2 at second group and second sample\\n g2_1_3 = np.array([153 , 178.5, 151.5, 67.5])\\n np.testing.assert_almost_equal(g2[0, 0], g2_0_1)\\n np.testing.assert_almost_equal(g2[1, 1], g2_1_3)\",\n \"def scenario2(angle, speed):\\n speedHorizontal = speed * math.cos(angle)\\n speedVertical = speed * math.sin(angle)\\n timePeak = speedVertical / 9.81\\n time = timePeak * 2\\n result = speedHorizontal * time\\n return result\",\n \"def samplecost(app, endclasses, fxnmode, samptype='std', title=\\\"\\\"):\\n associated_scens=[]\\n for phase in app.phases:\\n associated_scens = associated_scens + app.scenids.get((fxnmode, phase), [])\\n costs = np.array([endclasses[scen]['cost'] for scen in associated_scens])\\n times = np.array([time for phase, timemodes in app.sampletimes.items() if timemodes for time in timemodes if fxnmode in timemodes.get(time)] ) \\n rates = np.array(list(app.rates_timeless[fxnmode].values()))\\n \\n tPlot, axes = plt.subplots(2, 1, sharey=False, gridspec_kw={'height_ratios': [3, 1]})\\n phasetimes_start =[times[0] for phase, times in app.phases.items()]\\n phasetimes_end =[times[1] for phase, times in app.phases.items()]\\n ratetimes =[]\\n ratesvect =[]\\n phaselocs = []\\n for (ind, phasetime) in enumerate(phasetimes_start):\\n axes[0].axvline(phasetime, color=\\\"black\\\") \\n phaselocs= phaselocs +[(phasetimes_end[ind]-phasetimes_start[ind])/2 + phasetimes_start[ind]]\\n\\n axes[1].axvline(phasetime, color=\\\"black\\\") \\n ratetimes = ratetimes + [phasetimes_start[ind]] + [phasetimes_end[ind]]\\n ratesvect = ratesvect + [rates[ind]] + [rates[ind]]\\n #axes[1].text(middletime, 0.5*max(rates), list(app.phases.keys())[ind], ha='center', backgroundcolor=\\\"white\\\")\\n #rate plots\\n axes[1].set_xticks(phaselocs)\\n axes[1].set_xticklabels(list(app.phases.keys()))\\n \\n axes[1].plot(ratetimes, ratesvect)\\n axes[1].set_xlim(phasetimes_start[0], phasetimes_end[-1])\\n axes[1].set_ylim(0, np.max(ratesvect)*1.2 )\\n axes[1].set_ylabel(\\\"Rate\\\")\\n axes[1].set_xlabel(\\\"Time (\\\"+str(app.units)+\\\")\\\")\\n axes[1].grid()\\n #cost plots\\n axes[0].set_xlim(phasetimes_start[0], phasetimes_end[-1])\\n axes[0].set_ylim(0, 1.2*np.max(costs))\\n if samptype=='fullint':\\n axes[0].plot(times, costs, label=\\\"cost\\\")\\n else:\\n if samptype=='quadrature' or samptype=='pruned piecewise-linear': \\n sizes = 1000*np.array([weight if weight !=1/len(timeweights) else 0.0 for phase, timeweights in app.weights[fxnmode].items() for time, weight in timeweights.items() if time in times])\\n axes[0].scatter(times, costs,s=sizes, label=\\\"cost\\\", alpha=0.5)\\n axes[0].stem(times, costs, label=\\\"cost\\\", markerfmt=\\\",\\\", use_line_collection=True)\\n \\n axes[0].set_ylabel(\\\"Cost\\\")\\n axes[0].grid()\\n if title: axes[0].set_title(title)\\n elif type(fxnmode[0])==tuple: axes[0].set_title(\\\"Cost function of \\\"+str(fxnmode)+\\\" over time\\\")\\n else: axes[0].set_title(\\\"Cost function of \\\"+fxnmode[0]+\\\": \\\"+fxnmode[1]+\\\" over time\\\")\\n #plt.subplot_adjust()\\n plt.tight_layout()\",\n \"def test_f_two_sample(self):\\r\\n\\r\\n # The expected values in this test are obtained through R.\\r\\n # In R the F test is var.test(x,y) different alternative hypotheses\\r\\n # can be specified (two sided, less, or greater).\\r\\n # The vectors are random samples from a particular normal distribution\\r\\n #(mean and sd specified).\\r\\n\\r\\n # a: 50 elem, mean=0 sd=1\\r\\n a = [-0.70701689, -1.24788845, -1.65516470, 0.10443876, -0.48526915,\\r\\n -0.71820656, -1.02603596, 0.03975982, -2.23404324, -0.21509363,\\r\\n 0.08438468, -0.01970062, -0.67907971, -0.89853667, 1.11137131,\\r\\n 0.05960496, -1.51172084, -0.79733957, -1.60040659, 0.80530639,\\r\\n -0.81715836, -0.69233474, 0.95750665, 0.99576429, -1.61340216,\\r\\n -0.43572590, -1.50862327, 0.92847551, -0.68382338, -1.12523522,\\r\\n -0.09147488, 0.66756023, -0.87277588, -1.36539039, -0.11748707,\\r\\n -1.63632578, -0.31343078, -0.28176086, 0.33854483, -0.51785630,\\r\\n 2.25360559, -0.80761191, 1.18983499, 0.57080342, -1.44601700,\\r\\n -0.53906955, -0.01975266, -1.37147915, -0.31537616, 0.26877544]\\r\\n\\r\\n # b: 50 elem, mean=0, sd=1.2\\r\\n b = [\\r\\n 0.081418743, 0.276571612, -\\r\\n 1.864316504, 0.675213612, -0.769202643,\\r\\n 0.140372825, -1.426250184, 0.058617884, -\\r\\n 0.819287409, -0.007701916,\\r\\n -0.782722020, -\\r\\n 0.285891593, 0.661980419, 0.383225191, 0.622444946,\\r\\n -0.192446150, 0.297150571, 0.408896059, -\\r\\n 0.167359383, -0.552381362,\\r\\n 0.982168338, 1.439730446, 1.967616101, -\\r\\n 0.579607307, 1.095590943,\\r\\n 0.240591302, -1.566937143, -\\r\\n 0.199091349, -1.232983905, 0.362378169,\\r\\n 1.166061081, -0.604676222, -\\r\\n 0.536560206, -0.303117595, 1.519222792,\\r\\n -0.319146503, 2.206220810, -\\r\\n 0.566351124, -0.720397392, -0.452001377,\\r\\n 0.250890097, 0.320685395, -\\r\\n 1.014632725, -3.010346273, -1.703955054,\\r\\n 0.592587381, -1.237451255, 0.172243366, -0.452641122, -0.982148581]\\r\\n\\r\\n # c: 60 elem, mean=5, sd=1\\r\\n c = [4.654329, 5.242129, 6.272640, 5.781779, 4.391241, 3.800752,\\r\\n 4.559463, 4.318922, 3.243020, 5.121280, 4.126385, 5.541131,\\r\\n 4.777480, 5.646913, 6.972584, 3.817172, 6.128700, 4.731467,\\r\\n 6.762068, 5.082983, 5.298511, 5.491125, 4.532369, 4.265552,\\r\\n 5.697317, 5.509730, 2.935704, 4.507456, 3.786794, 5.548383,\\r\\n 3.674487, 5.536556, 5.297847, 2.439642, 4.759836, 5.114649,\\r\\n 5.986774, 4.517485, 4.579208, 4.579374, 2.502890, 5.190955,\\r\\n 5.983194, 6.766645, 4.905079, 4.214273, 3.950364, 6.262393,\\r\\n 8.122084, 6.330007, 4.767943, 5.194029, 3.503136, 6.039079,\\r\\n 4.485647, 6.116235, 6.302268, 3.596693, 5.743316, 6.860152]\\r\\n\\r\\n # d: 30 elem, mean=0, sd =0.05\\r\\n d = [\\r\\n 0.104517366, 0.023039678, 0.005579091, 0.052928250, 0.020724823,\\r\\n -0.060823243, -0.019000890, -\\r\\n 0.064133996, -0.016321594, -0.008898334,\\r\\n -0.027626992, -0.051946186, 0.085269587, -\\r\\n 0.031190678, 0.065172938,\\r\\n -0.054628573, 0.019257306, -\\r\\n 0.032427056, -0.058767356, 0.030927400,\\r\\n 0.052247357, -\\r\\n 0.042954937, 0.031842104, 0.094130522, -0.024828465,\\r\\n 0.011320453, -0.016195062, 0.015631245, -0.050335598, -0.031658335]\\r\\n\\r\\n a, b, c, d = map(array, [a, b, c, d])\\r\\n self.assertEqual(map(len, [a, b, c, d]), [50, 50, 60, 30])\\r\\n\\r\\n # allowed error. This big, because results from R\\r\\n # are rounded at 4 decimals\\r\\n error = 1e-4\\r\\n\\r\\n self.assertFloatEqual(f_two_sample(a, a), (49, 49, 1, 1), eps=error)\\r\\n self.assertFloatEqual(f_two_sample(a, b), (49, 49, 0.8575, 0.5925),\\r\\n eps=error)\\r\\n self.assertFloatEqual(f_two_sample(b, a), (49, 49, 1.1662, 0.5925),\\r\\n eps=error)\\r\\n self.assertFloatEqual(f_two_sample(a, b, tails='low'),\\r\\n (49, 49, 0.8575, 0.2963), eps=error)\\r\\n self.assertFloatEqual(f_two_sample(a, b, tails='high'),\\r\\n (49, 49, 0.8575, 0.7037), eps=error)\\r\\n self.assertFloatEqual(f_two_sample(a, c),\\r\\n (49, 59, 0.6587, 0.1345), eps=error)\\r\\n # p value very small, so first check df's and F value\\r\\n self.assertFloatEqualAbs(f_two_sample(d, a, tails='low')[0:3],\\r\\n (29, 49, 0.0028), eps=error)\\r\\n assert f_two_sample(d, a, tails='low')[3] < 2.2e-16 # p value\\r\",\n \"def test_t_two_sample(self):\\r\\n I = array([7.2, 7.1, 9.1, 7.2, 7.3, 7.2, 7.5])\\r\\n II = array([8.8, 7.5, 7.7, 7.6, 7.4, 6.7, 7.2])\\r\\n self.assertFloatEqual(t_two_sample(I, II), (-0.1184, 0.45385 * 2),\\r\\n 0.001)\",\n \"def compute_rwse(self, traffic_density):\\r\\n # Ensure experiment has not beem done before\\r\\n np.random.seed(2020)\\r\\n file_names = os.listdir(self.dirName)\\r\\n if traffic_density+'rwse' in file_names:\\r\\n print(\\\"This experiment has been done already!\\\")\\r\\n return None\\r\\n elif traffic_density == '':\\r\\n print(\\\"select a traffic dinsity level!\\\")\\r\\n return None\\r\\n rwse_dict = {'vel_m':0,\\r\\n 'lat_vel':1,\\r\\n 'vel_y':2,\\r\\n 'vel_f':3,\\r\\n 'vel_fadj':4}\\r\\n\\r\\n pred_step_n = self.pred_h*10+1\\r\\n splits_n = 6 # number of splits across an entire trajectory\\r\\n pred_arrs = [np.zeros([self.episode_n*self.traj_n*6,\\r\\n pred_step_n]) for i in range(5)]\\r\\n truth_arrs = [np.zeros([self.episode_n*self.traj_n*6,\\r\\n pred_step_n]) for i in range(5)]\\r\\n _row = 0\\r\\n\\r\\n for episode_id in self.test_data.test_episodes[:self.episode_n]:\\r\\n # for episode_id in [1289]:\\r\\n st_seq, cond_seq, st_arr, targ_arr = self.episodeSetup(episode_id)\\r\\n self.gen_model.max_pc = max(st_arr[:, self.gen_model.indx_m['pc']])\\r\\n self.gen_model.min_pc = min(st_arr[:, self.gen_model.indx_m['pc']])\\r\\n if len(st_seq) >= 6:\\r\\n splits_n = 6\\r\\n else:\\r\\n splits_n = len(st_seq)\\r\\n obs_n = self.test_data.data_obj.obs_n\\r\\n traj_splits = np.random.choice(range(19, 19+len(st_seq)), splits_n, replace=False)\\r\\n # leave value of 19 to ensure scenarios remain consistent\\r\\n for split in traj_splits:\\r\\n st_seq_i, cond_seq_i, bc_der_i, _, st_i, targ_i = self.sceneSetup(st_seq,\\r\\n cond_seq,\\r\\n st_arr,\\r\\n targ_arr,\\r\\n current_step=split,\\r\\n pred_h=self.pred_h)\\r\\n targ_i.shape = (1, pred_step_n, 5)\\r\\n st_init = np.repeat(np.reshape(st_i[0,:], [1,17]), self.traj_n, axis=0)\\r\\n\\r\\n actions, _, _ = self.policy.get_actions([st_seq_i, cond_seq_i], bc_der_i,\\r\\n traj_n=self.traj_n, pred_h=self.pred_h)\\r\\n st_pred = self.gen_model.forwardSim(st_init, actions, pred_step_n)\\r\\n\\r\\n truth_arrs[0][_row:_row+self.traj_n, :] = \\\\\\r\\n st_i[:,self.gen_model.indx_m['vel']]\\r\\n pred_arrs[0][_row:_row+self.traj_n, :] = \\\\\\r\\n st_pred[:,:,self.gen_model.indx_m['vel']]\\r\\n\\r\\n truth_arrs[1][_row:_row+self.traj_n, :] = \\\\\\r\\n st_i[:,self.gen_model.indx_m['act_lat_p']]\\r\\n pred_arrs[1][_row:_row+self.traj_n, :] = \\\\\\r\\n st_pred[:,:,self.gen_model.indx_m['act_lat_p']]\\r\\n\\r\\n truth_arrs[2][_row:_row+self.traj_n, :] = \\\\\\r\\n st_i[:,self.gen_model.indx_y['vel']]\\r\\n pred_arrs[2][_row:_row+self.traj_n, :] = \\\\\\r\\n st_pred[:,:,self.gen_model.indx_y['vel']]\\r\\n\\r\\n truth_arrs[3][_row:_row+self.traj_n, :] = \\\\\\r\\n st_i[:,self.gen_model.indx_f['vel']]\\r\\n pred_arrs[3][_row:_row+self.traj_n, :] = \\\\\\r\\n st_pred[:,:,self.gen_model.indx_f['vel']]\\r\\n\\r\\n truth_arrs[4][_row:_row+self.traj_n, :] = \\\\\\r\\n st_i[:,self.gen_model.indx_fadj['vel']]\\r\\n pred_arrs[4][_row:_row+self.traj_n, :] = \\\\\\r\\n st_pred[:,:,self.gen_model.indx_fadj['vel']]\\r\\n\\r\\n\\r\\n _row += self.traj_n\\r\\n # return st_pred\\r\\n print('Episode ', episode_id, ' has been completed!')\\r\\n for key in rwse_dict.keys():\\r\\n rwse_dict[key] = self.root_weightet_sqr(truth_arrs[rwse_dict[key]], \\\\\\r\\n pred_arrs[rwse_dict[key]])\\r\\n\\r\\n with open(self.dirName+'/'+ traffic_density + 'rwse', \\\"wb\\\") as f:\\r\\n pickle.dump(rwse_dict, f)\\r\\n return rwse_dict\",\n \"def createRadarSims(self):\\n import anwp.sims\\n # remove old sims if any\\n self.removeRadarSims()\\n # create resource sims\\n self.radarSims = []\\n for systemID, systemDict in self.game.allSystems.iteritems():\\n imageFileName = ''\\n \\n if systemDict['intelReport']['round'] == self.game.currentRound:\\n # there is a current report from this system indicate with green radar\\n imageFileName = '%sradar_green.png' % self.game.app.genImagePath\\n \\n if systemDict['myEmpireID'] == self.game.myEmpireID:\\n if systemDict['radarStrength'] > 0:\\n # players system has radar indicate with blue radar\\n imageFileName = '%sradar_blue.png' % self.game.app.genImagePath\\n \\n if imageFileName <> '':\\n # create sim\\n sim = RadarEntity(self, anwp.sims.categories.StaticCategory(imageFileName, 'radar'))\\n \\n # add sim to world\\n self.radarSims.append(sim)\\n x = systemDict['x']-46\\n y = systemDict['y']+65\\n facing = 0\\n speed = 0\\n force = 1\\n self.world.addToWorld(sim, x, y, facing, speed, force)\",\n \"def enumeration_separation(self, stage1_vars, sample_name, limit=1,\\n enumerate_all=True):\\n sample = self.tsdro.samples[sample_name]\\n scenarios = self.tsdro.scenarios\\n lr_instance = self.tsdro.lr_instance\\n curr_remaining = self.tsdro.remaining_scenario_names[sample_name]\\n curr_remaining_copy = curr_remaining.copy()\\n\\n found_hyp = False\\n count = 0\\n max_violation_scenario_name = None\\n max_violation = -np.inf\\n for scenario_name in curr_remaining:\\n scenario = scenarios[scenario_name]\\n if scenario_name not in self.stage2_objvals:\\n self.stage2_objvals[scenario_name] = self.get_stage2_costs(\\n stage1_vars, [scenario_name])\\n\\n if scenario_name == sample_name or self.method == \\\"RO\\\":\\n scenario_distance = 0\\n rhs_val = self.stage2_objvals[scenario_name]\\n else:\\n scenario_distance = sc.get_scenario_distance(scenario, sample,\\n lr_instance)\\n rhs_val = (self.stage2_objvals[scenario_name]\\n - self.wass_mult.X * scenario_distance)\\n if self.epi_vars[sample_name].X < rhs_val + 1e-7:\\n found_hyp = True\\n if not enumerate_all:\\n count += 1\\n if scenario_name not in self.master_scenarios:\\n tmp = lr_instance.add_stage2(self.master, self.stage1_vars,\\n scenario, scenario_name)\\n self.stage2_vars[scenario_name] = tmp[0]\\n self.master_scenarios.append(scenario_name)\\n objexpr_stage2 = lr_instance.get_objective_stage2(\\n self.stage2_vars[scenario_name], scenario)\\n rhs = objexpr_stage2 - self.wass_mult * scenario_distance\\n self.master.addLConstr(self.epi_vars[sample_name], \\\">\\\", rhs)\\n curr_remaining_copy.remove(scenario_name)\\n else:\\n if rhs_val - self.epi_vars[sample_name].X > max_violation:\\n max_violation = rhs_val - self.epi_vars[sample_name].X\\n max_violation_scenario_name = scenario_name\\n if count == limit:\\n break\\n if enumerate_all and found_hyp:\\n scenario = scenarios[max_violation_scenario_name]\\n if scenario_name == sample_name or self.method == \\\"RO\\\":\\n scenario_distance = 0\\n else:\\n scenario_distance = sc.get_scenario_distance(scenario, sample,\\n lr_instance)\\n if scenario_name not in self.master_scenarios:\\n tmp = lr_instance.add_stage2(self.master, self.stage1_vars,\\n scenario,\\n max_violation_scenario_name)\\n self.stage2_vars[max_violation_scenario_name] = tmp[0]\\n self.master_scenarios.append(max_violation_scenario_name)\\n objexpr_stage2 = lr_instance.get_objective_stage2(\\n self.stage2_vars[max_violation_scenario_name], scenario)\\n rhs = objexpr_stage2 - self.wass_mult * scenario_distance\\n self.master.addLConstr(self.epi_vars[sample_name], \\\">\\\", rhs)\\n curr_remaining_copy.remove(max_violation_scenario_name)\\n self.tsdro.remaining_scenario_names[sample_name] = curr_remaining_copy\\n\\n if count > 0:\\n print(sample_name, \\\"added\\\", count, \\\"scenarios.\\\")\\n return found_hyp\",\n \"def use_AR(train1, train2, test, features):\\n for feature in features:\\n print('----------------------------------- Feature: %s -----------------------------------' % feature)\\n # train on train1 and test on train2\\n model_fit = AR(train1[feature]).fit() # fit the AR model\\n window = model_fit.k_ar # retrieve the p value from the fitted model\\n coeff = model_fit.params # retrieve the coefficients\\n history = list(train1[feature][len(train1[feature]) - window:]) # create initial history\\n predictions = predict_AR(train2[feature], history, window, coeff) # make the predictions\\n predictions.index = train2.index # set the index properly\\n error = mean_squared_error(train2[feature], predictions) # calculate the MSE\\n print('Test MSE: %.3f' % error)\\n\\n # plot the predicted values against the true values of the signals\\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n ax.plot(train2[feature], label='true signal')\\n ax.plot(predictions, label='predicted signal')\\n ax.xaxis.set_major_locator(mdates.DayLocator([10, 20, 30]))\\n ax.xaxis.set_major_formatter(mdates.DateFormatter('%d/%m'))\\n plt.xlabel(\\\"time\\\")\\n plt.ylabel(feature)\\n plt.xticks(rotation=45)\\n plt.grid()\\n plt.legend()\\n plt.title('AR prediction on training set 2')\\n plt.savefig('plots/naive_predictions/training2_prediction_%s.png' % feature, bbox_inches='tight')\\n\\n # train on train1 and test on test\\n model_fit = AR(train1[feature]).fit() # fit the AR model\\n window = model_fit.k_ar # retrieve the p value from the fitted model\\n coeff = model_fit.params # retrieve the coefficients\\n history = list(train1[feature][len(train1[feature]) - window:]) # create initial history\\n predictions = predict_AR(test[feature], history, window, coeff) # make the predictions\\n predictions.index = test.index # set the index properly\\n error = mean_squared_error(test[feature], predictions) # calculate the MSE\\n print('Test MSE: %.3f' % error)\\n\\n # plot the predicted values against the true values of the signals\\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n ax.plot(test[feature], label='true signal')\\n ax.plot(predictions, label='predicted signal')\\n ax.xaxis.set_major_locator(mdates.DayLocator([10, 20, 30]))\\n ax.xaxis.set_major_formatter(mdates.DateFormatter('%d/%m'))\\n plt.xlabel(\\\"time\\\")\\n plt.ylabel(feature)\\n plt.xticks(rotation=45)\\n plt.grid()\\n plt.legend()\\n plt.title('AR prediction on test set')\\n plt.savefig('plots/naive_predictions/test_prediction_%s.png' % feature, bbox_inches='tight')\\n\\n # train on train2 and test on test\\n model_fit = AR(train2[feature]).fit() # fit the AR model\\n window = model_fit.k_ar # retrieve the p value from the fitted model\\n coeff = model_fit.params # retrieve the coefficients\\n history = list(train2[feature][len(train2[feature]) - window:]) # create initial history\\n predictions = predict_AR(test[feature], history, window, coeff) # make the predictions\\n predictions.index = test.index # set the index properly\\n error = mean_squared_error(test[feature], predictions) # calculate the MSE\\n print('Test MSE: %.3f' % error)\\n\\n # plot the predicted values against the true values of the signals\\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n ax.plot(test[feature], label='true signal')\\n ax.plot(predictions, label='predicted signal')\\n ax.xaxis.set_major_locator(mdates.DayLocator([10, 20, 30]))\\n ax.xaxis.set_major_formatter(mdates.DateFormatter('%d/%m'))\\n plt.xlabel(\\\"time\\\")\\n plt.ylabel(feature)\\n plt.xticks(rotation=45)\\n plt.grid()\\n plt.legend()\\n plt.title('AR prediction on test set based on training set 2')\\n plt.savefig('plots/naive_predictions/train2_test_prediction_%s.png' % feature, bbox_inches='tight')\",\n \"def calculate_R2(self):\\n\\n d1 = self.T - self.Y\\n d2 = self.T - self.T.mean()\\n\\n self.r2 = 1 - d1.dot(d1) / d2.dot(d2)\\n self.r2 = format(self.r2, '.3f')\\n\\n print \\\"\\\"\\n print \\\"R2:\\\", self.r2\",\n \"def _sample_episode(self):\\n\\n Knovel = random.sample(self.labelIds, self.nKnovel)\\n #print(Knovel)\\n #exit(0)\\n nKnovel = len(Knovel)\\n assert((self.nTestNovel % nKnovel) == 0)\\n nEvalExamplesPerClass = int(self.nTestNovel / nKnovel)\\n #print(nEvalExamplesPerClass)\\n #exit(0)\\n Tnovel = []\\n Exemplars = []\\n for Knovel_idx in range(len(Knovel)):\\n ids = (nEvalExamplesPerClass + self.nExemplars)\\n img_ids = random.sample(self.labels2inds[Knovel[Knovel_idx]], ids) \\n\\n imgs_tnovel = img_ids[:nEvalExamplesPerClass]\\n imgs_emeplars = img_ids[nEvalExamplesPerClass:]\\n #print(imgs_tnovel)\\n #exit(0)\\n Tnovel += [(img_id, Knovel_idx) for img_id in imgs_tnovel]\\n Exemplars += [(img_id, Knovel_idx) for img_id in imgs_emeplars]\\n assert(len(Tnovel) == self.nTestNovel)\\n assert(len(Exemplars) == nKnovel * self.nExemplars)\\n random.shuffle(Exemplars)\\n random.shuffle(Tnovel)\\n\\n return Tnovel, Exemplars\",\n \"def run():\\n step = 0\\n o2r = 4 #orange to red delay time\\n r2g = 2 #red to green delay time\\n A_4235 = 0\\n B_4235 = 1\\n C_4235 = 2\\n AB1_4235 = 3\\n AB2_4235 = 4\\n AC1_4235 = 5\\n AC2_4235 = 6\\n BA1_4235 = 7\\n BA2_4235 = 8\\n BC1_4235 = 9\\n BC2_4235 = 10\\n CA1_4235 = 11\\n CA2_4235 = 12\\n CB1_4235 = 13\\n CB2_4235 = 14\\n A_4219 = 0\\n B_4219 = 1\\n C_4219 = 2\\n D_4219 = 3\\n E_4219 = 4\\n F_4219 = 5\\n G_4219 = 6\\n AB1_4219 = 7\\n AB2_4219 = 8\\n AC1_4219 = 9\\n AC2_4219 = 10\\n AD1_4219 = 11\\n AD2_4219 = 12\\n AE1_4219 = 13\\n AE2_4219 = 14\\n AF1_4219 = 16\\n AF2_4219 = 17\\n AG1_4219 = 18\\n AG2_4219 = 19\\n BA1_4219 = 20\\n BA2_4219 = 21\\n BC1_4219 = 22\\n BC2_4219 = 23\\n BD1_4219 = 24\\n BD2_4219 = 25\\n BE1_4219 = 26\\n BE2_4219 = 27\\n BF1_4219 = 28\\n BF2_4219 = 29\\n BG1_4219 = 30\\n BG2_4219 = 31\\n CA1_4219 = 32\\n CA2_4219 = 33\\n CB1_4219 = 34\\n CB2_4219 = 35\\n CD1_4219 = 36\\n CD2_4219 = 37\\n CE1_4219 = 38\\n CE2_4219 = 39\\n CF1_4219 = 40\\n CF2_4219 = 41\\n CG1_4219 = 42\\n CG2_4219 = 43\\n DA1_4219 = 44\\n DA2_4219 = 45\\n DB1_4219 = 46\\n DB2_4219 = 47\\n DC1_4219 = 48\\n DC2_4219 = 49\\n DE1_4219 = 50\\n DE2_4219 = 51\\n DF1_4219 = 52\\n DF2_4219 = 53\\n DG1_4219 = 54\\n DG2_4219 = 55\\n EA1_4219 = 56\\n EA2_4219 = 57\\n EB1_4219 = 58\\n EB2_4219 = 59\\n EC1_4219 = 60\\n EC2_4219 = 61\\n ED1_4219 = 62\\n ED2_4219 = 63\\n EF1_4219 = 64\\n EF2_4219 = 65\\n EG1_4219 = 66\\n EG2_4219 = 67\\n FA1_4219 = 68\\n FA2_4219 = 69\\n FB1_4219 = 70\\n FB2_4219 = 71\\n FC1_4219 = 72\\n FC2_4219 = 73\\n FD1_4219 = 74\\n FD2_4219 = 75\\n FE1_4219 = 76\\n FE2_4219 = 77\\n FG1_4219 = 78\\n FG2_4219 = 79\\n GA1_4219 = 80\\n GA2_4219 = 81\\n GB1_4219 = 82\\n GB2_4219 = 83\\n GC1_4219 = 84\\n GC2_4219 = 85\\n GD1_4219 = 86\\n GD2_4219 = 87\\n GE1_4219 = 88\\n GE2_4219 = 89\\n GF1_4219 = 90\\n GF2_4219 = 91\\n A_4220 = 0\\n B_4220 = 1\\n C_4220 = 2\\n D_4220 = 3\\n E_4220 = 4\\n AB1_4220 = 5\\n AB2_4220 = 6\\n AC1_4220 = 7\\n AC2_4220 = 8\\n AD1_4220 = 9\\n AD2_4220 = 10\\n AE1_4220 = 11\\n AE2_4220 = 12\\n BA1_4220 = 13\\n BA2_4220 = 14\\n BC1_4220 = 15\\n BC2_4220 = 16\\n BD1_4220 = 17\\n BD2_4220 = 18\\n BE1_4220 = 19\\n BE2_4220 = 20\\n CA1_4220 = 21\\n CA2_4220 = 22\\n CB1_4220 = 23\\n CB2_4220 = 24\\n CD1_4220 = 25\\n CD2_4220 = 26\\n CE1_4220 = 27\\n CE2_4220 = 28\\n DA1_4220 = 29\\n DA2_4220 = 30\\n DB1_4220 = 31\\n DB2_4220 = 32\\n DC1_4220 = 33\\n DC2_4220 = 34\\n DE1_4220 = 35\\n DE2_4220 = 36\\n EA1_4220 = 37\\n EA2_4220 = 38\\n EB1_4220 = 39\\n EB2_4220 = 40\\n EC1_4220 = 41\\n EC2_4220 = 42\\n ED1_4220 = 43\\n ED2_4220 = 44\\n A_4221 = 0\\n B_4221 = 1\\n C_4221 = 2\\n D_4221 = 3\\n E_4221 = 4\\n F_4221 = 5\\n AB1_4221 = 6\\n AB2_4221 = 7\\n AC1_4221 = 8\\n AC2_4221 = 9\\n AD1_4221 = 10\\n AD2_4221 = 11\\n AE1_4221 = 12\\n AE2_4221 = 13\\n AF1_4221 = 14\\n AF2_4221 = 15\\n BA1_4221 = 16\\n BA2_4221 = 17\\n BC1_4221 = 18\\n BC2_4221 = 19\\n BD1_4221 = 20\\n BD2_4221 = 21\\n BE1_4221 = 22\\n BE2_4221 = 23\\n BF1_4221 = 24\\n BF2_4221 = 25\\n CA1_4221 = 26\\n CA2_4221 = 27\\n CB1_4221 = 28\\n CB2_4221 = 29\\n CD1_4221 = 30\\n CD2_4221 = 31\\n CE1_4221 = 32\\n CE2_4221 = 33\\n CF1_4221 = 34\\n CF2_4221 = 35\\n DA1_4221 = 36\\n DA2_4221 = 37\\n DB1_4221 = 38\\n DB2_4221 = 39\\n DC1_4221 = 40\\n DC2_4221 = 41\\n DE1_4221 = 42\\n DE2_4221 = 43\\n DF1_4221 = 44\\n DF2_4221 = 45\\n EA1_4221 = 46\\n EA2_4221 = 47\\n EB1_4221 = 48\\n EB2_4221 = 49\\n EC1_4221 = 50\\n EC2_4221 = 51\\n ED1_4221 = 52\\n ED2_4221 = 53\\n EF1_4221 = 54\\n EF2_4221 = 55\\n FA1_4221 = 56\\n FA2_4221 = 57\\n FB1_4221 = 58\\n FB2_4221 = 59\\n FC1_4221 = 60\\n FC2_4221 = 61\\n FD1_4221 = 62\\n FD2_4221 = 63\\n FE1_4221 = 64\\n FE2_4221 = 65\\n \\n #while traci.simulation.getMinExpectedNumber() > 0:\\n while step < 600:\\n traci.simulationStep()\\n if step == 0:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",6)\\n if step == 6:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 10:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 12:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", A_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",75)\\n if step == 87:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 91:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 93:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",15)\\n if step == 108:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 112:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 114:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", C_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",12)\\n if step == 126:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 130:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 132:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",32)\\n if step == 164:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 168:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 170:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", A_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",50)\\n if step == 220:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 224:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 226:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",15)\\n if step == 241:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 245:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 247:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", C_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",14)\\n if step == 261:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 265:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 267:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",13)\\n if step == 280:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 284:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 286:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", A_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",74)\\n if step == 360:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 364:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 366:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",15)\\n if step == 381:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 385:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 387:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", C_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",10)\\n if step == 397:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 401:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 403:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",16)\\n if step == 419:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 423:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 425:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", A_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",74)\\n if step == 499:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 503:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 505:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",15)\\n if step == 520:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 524:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 526:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", C_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",8)\\n if step == 534:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 538:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 540:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",16)\\n if step == 556:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 560:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 562:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", A_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",38)\\n if step == 0:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", A_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 6)\\n if step == 6:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 10:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 12:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", D_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 20)\\n if step == 32:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 36:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 38:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", E_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 5)\\n if step == 43:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 47:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 49:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", F_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 31)\\n if step == 80:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 84:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 86:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", G_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 12)\\n if step == 98:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 102:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 104:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", A_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 29)\\n if step == 133:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 137:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 139:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", D_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 20)\\n if step == 159:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 163:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 165:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", E_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 5)\\n if step == 170:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 174:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 176:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", F_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 38)\\n if step == 214:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 218:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 220:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", G_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 13)\\n if step == 233:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 237:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 239:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", A_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 34)\\n if step == 273:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 277:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 279:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", D_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 22)\\n if step == 301:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 305:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 307:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", E_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 5)\\n if step == 312:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 316:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 318:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", F_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 31)\\n if step == 349:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 353:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 355:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", G_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 18)\\n if step == 373:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 377:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 379:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", A_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 36)\\n if step == 415:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 419:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 421:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", D_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 19)\\n if step == 440:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 444:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 446:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", E_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 5)\\n if step == 451:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 455:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 457:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", F_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 29)\\n if step == 486:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 490:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 492:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", G_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 18)\\n if step == 510:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 514:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 516:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", A_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 41)\\n if step == 557:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 561:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 563:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", D_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 19)\\n if step == 582:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 586:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 588:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", E_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 8)\\n if step == 596:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 0:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", D_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 17)\\n if step == 17:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 21:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 23:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", E_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 9)\\n if step == 32:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 36:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 38:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", A_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 67)\\n if step == 105:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AB1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 109:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AB2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 111:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", B_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 19)\\n if step == 130:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", BD1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 134:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", BD2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 136:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", D_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 16)\\n if step == 152:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 156:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 158:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", E_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 11)\\n if step == 169:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 173:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 175:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", A_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 63)\\n if step == 238:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AD1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 242:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AD2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 244:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", D_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 13)\\n if step == 257:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 261:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 263:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", E_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 9)\\n if step == 272:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 276:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 278:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", A_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 95)\\n if step == 373:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AB1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 377:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AB2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 379:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", B_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 19)\\n if step == 398:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", BD1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 402:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", BD2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 404:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", D_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 24)\\n if step == 428:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 432:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 434:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", E_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 13)\\n if step == 447:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 451:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 453:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", A_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 56)\\n if step == 509:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AB1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 513:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AB2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 515:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", B_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 19)\\n if step == 534:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", BD1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 538:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", BD2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 540:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", D_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 22)\\n if step == 562:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 566:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 568:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", E_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 20)\\n if step == 588:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 592:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 594:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", A_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 6)\\n if step == 0:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 9)\\n if step == 9:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 13:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 15:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", F_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 19)\\n if step == 34:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 38:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 40:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", A_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 20)\\n if step == 60:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 64:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 66:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", D_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 24)\\n if step == 90:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 94:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 96:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 9)\\n if step == 105:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 109:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 111:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", F_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 19)\\n if step == 130:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 134:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 136:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", A_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 30)\\n if step == 166:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 170:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 172:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", D_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 28)\\n if step == 200:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 204:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 206:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 19)\\n if step == 225:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 229:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 231:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", F_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 8)\\n if step == 239:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 243:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 245:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", A_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 32)\\n if step == 277:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 281:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 283:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", D_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 27)\\n if step == 310:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 314:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 316:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 15)\\n if step == 331:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 335:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 337:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", F_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 14)\\n if step == 351:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 355:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 357:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", A_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 22)\\n if step == 379:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 383:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 385:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", D_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 24)\\n if step == 409:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 413:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 415:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 11)\\n if step == 426:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 430:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 432:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", F_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 14)\\n if step == 446:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 450:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 452:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", A_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 30)\\n if step == 482:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 486:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 488:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", D_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 26)\\n if step == 514:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 518:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 520:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 8)\\n if step == 528:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 532:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 534:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", F_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 18)\\n if step == 552:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 556:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 558:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", A_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 19)\\n if step == 577:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 581:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 583:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", D_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 9)\\n if step == 592:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 596:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 598:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 2)\\n\\n step += 1\\n\\n traci.close()\\n sys.stdout.flush()\",\n \"def _test_two_unsampled_arms(self):\\n bandit = self.bandit_class(self.two_unsampled_arms_test_case)\\n assert bandit.allocate_arms() == {\\\"arm1\\\": 0.5, \\\"arm2\\\": 0.5}\",\n \"def test_dataset_scenario_generation_full_outside1(self):\\n params = ParameterServer()\\n\\n map_filename = os.path.join(os.path.dirname(__file__), \\\"data/DR_DEU_Merging_MT_v01_shifted.xodr\\\")\\n track_filename = os.path.join(os.path.dirname(__file__), \\\"data/interaction_dataset_DEU_Merging_dummy_track_outside.csv\\\")\\n\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"MapFilename\\\"] = map_filename\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"TrackFilenameList\\\"] = [track_filename]\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"StartingOffsetMs\\\"] = 0\\n\\n scenario_generation = InteractionDatasetScenarioGenerationFull(\\n params=params, num_scenarios=1)\\n\\n scenario = scenario_generation.get_scenario(0)\\n self.assertAlmostEqual(scenario.eval_agent_ids, [1])\\n\\n world_state = scenario.GetWorldState()\\n agent11 = world_state.GetAgent(1)\\n agent12 = world_state.GetAgent(2)\\n agent13 = world_state.GetAgent(3)\\n\\n self.assertAlmostEqual(agent11.first_valid_timestamp, 0.0)\\n self.assertAlmostEqual(agent12.first_valid_timestamp, 0.0)\\n self.assertNotEqual(agent13.first_valid_timestamp, 0.0)\\n \\n # agent13 should not be valid at the beginning, as he is outside of map\\n world_state.time = 0\\n self.assertEqual(isinstance(agent11, Agent), True)\\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent11.InsideRoadCorridor(), True)\\n \\n self.assertEqual(isinstance(agent12, Agent), True)\\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent12.InsideRoadCorridor(), True)\\n\\n self.assertEqual(isinstance(agent13, Agent), True)\\n self.assertEqual(agent13.IsValidAtTime(world_state.time), False)\\n # as we use only state once it's in map, this will be true, although the time step is not valid yet\\n self.assertEqual(agent13.InsideRoadCorridor(), True)\\n\\n # agent13 should not be valid at the beginning, as he is outside of map\\n world_state.Step(0.05)\\n\\n self.assertEqual(isinstance(agent11, Agent), True)\\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent11.InsideRoadCorridor(), True)\\n \\n self.assertEqual(isinstance(agent12, Agent), True)\\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent12.InsideRoadCorridor(), True)\\n\\n self.assertEqual(isinstance(agent13, Agent), True)\\n self.assertEqual(agent13.IsValidAtTime(world_state.time), False)\\n # as we use only state once it's in map, this will be true, although the time step is not valid yet\\n self.assertEqual(agent13.InsideRoadCorridor(), True)\\n\\n self.assertEqual(list(world_state.agents_valid.keys()), [1,2])\\n\\n # agent13 should be valid at some point\\n world_state.Step(agent13.first_valid_timestamp)\\n world_state.Step(0.01) # agent13.IsValidAtTime() uses previous time stamp, therefore we increment it one more step\\n\\n self.assertEqual(isinstance(agent11, Agent), True)\\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent11.InsideRoadCorridor(), True)\\n \\n self.assertEqual(isinstance(agent12, Agent), True)\\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent12.InsideRoadCorridor(), True)\\n\\n self.assertEqual(isinstance(agent13, Agent), True)\\n self.assertEqual(agent13.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent13.InsideRoadCorridor(), True)\\n\\n self.assertEqual(list(world_state.agents_valid.keys()), [1,2,3])\",\n \"def test_stop_start(self):\\n log.info(\\\"CONFIG: %s\\\", self._agent_config())\\n self.create_sample_data_set_dir('node59p1_step2.dat', TELEM_DIR, \\\"node59p1.dat\\\")\\n\\n self.assert_initialize(final_state=ResourceAgentState.COMMAND)\\n\\n # Slow down processing to 1 per second to give us time to stop\\n self.dataset_agent_client.set_resource({DriverParameter.RECORDS_PER_SECOND: 1})\\n self.assert_start_sampling()\\n\\n # Verify we get one sample\\n try:\\n # Read the first file and verify the data\\n result = self.data_subscribers.get_samples(DataParticleType.CONTROL, 1)\\n result1 = self.data_subscribers.get_samples(DataParticleType.SAMPLE, 3)\\n result.extend(result1)\\n log.debug(\\\"RESULT: %s\\\", result)\\n\\n # Verify values\\n self.assert_data_values(result, 'test_data_1-2.txt.result.yml')\\n self.assert_sample_queue_size(DataParticleType.CONTROL, 0)\\n self.assert_sample_queue_size(DataParticleType.SAMPLE, 0)\\n\\n self.create_sample_data_set_dir('node59p1_step4.dat', TELEM_DIR, \\\"node59p1.dat\\\")\\n # Now read the first record of the second file then stop\\n result = self.data_subscribers.get_samples(DataParticleType.SAMPLE, 3)\\n log.debug(\\\"RESULT 1: %s\\\", result)\\n self.assert_stop_sampling()\\n self.assert_sample_queue_size(DataParticleType.CONTROL, 0)\\n self.assert_sample_queue_size(DataParticleType.SAMPLE, 0)\\n\\n # Restart sampling and ensure we get the last records of the file\\n self.assert_start_sampling()\\n result2 = self.data_subscribers.get_samples(DataParticleType.SAMPLE, 3)\\n log.debug(\\\"RESULT 2: %s\\\", result2)\\n result.extend(result2)\\n log.debug(\\\"RESULT: %s\\\", result)\\n self.assert_data_values(result, 'test_data_3-4.txt.result.yml')\\n self.assert_sample_queue_size(DataParticleType.CONTROL, 0)\\n self.assert_sample_queue_size(DataParticleType.SAMPLE, 0)\\n except SampleTimeout as e:\\n log.error(\\\"Exception trapped: %s\\\", e, exc_info=True)\\n self.fail(\\\"Sample timeout.\\\")\",\n \"def ar2_GWR(GWRMod): \\r\\n tss = np.sum((GWRMod.y - GWRMod.y_mean)**2)\\r\\n n = GWRMod.nObs # (scalar) number of observations\\r\\n if GWRMod.tr_S >= GWRMod.tr_STS:\\t\\t\\r\\n\\tdof_res = GWRMod.nObs-2.0*GWRMod.tr_S+GWRMod.tr_STS\\r\\n else:\\r\\n\\tdof_res = GWRMod.nObs-GWRMod.tr_S\\r\\n ar2_result = 1.0 - GWRMod.res2 / tss * ( n - 1.0) / (dof_res - 1.0)\\r\\n \\r\\n return ar2_result\",\n \"def radialApproxEffect(hubdist1,hubdist2,width,length):\\n #Grating coordinates\\n x,y = np.meshgrid(np.linspace(-width,width,1000),\\\\\\n np.linspace(-length,length,1000))\\n y1 = y + hubdist1\\n y2 = y + hubdist2\\n\\n #Convert to period and yaw angle\\n period1 = np.sqrt(x**2+y1**2)/hubdist1*160. #nm\\n period2 = np.sqrt(x**2+y2**2)/hubdist2*160. #nm\\n yaw = blazeYaw(1.5*np.pi/180,2.4,3,160.)\\n yaw1 = np.pi/2 - np.arctan(x/y1) + yaw\\n yaw2 = np.pi/2 - np.arctan(x/y2) + yaw\\n\\n #Determine alpha and beta\\n beta0,alpha0 = litBetaAlpha(1.5*np.pi/180,2.4,3,160.)\\n alpha1 = alpha0 + 3*2.4/period1*np.sin(yaw1)\\n alpha2 = alpha0 + 3*2.4/period2*np.sin(yaw2)\\n beta1 = beta0 + (3*2.4/period1)*np.cos(yaw1)\\n beta2 = beta0 + (3*2.4/period2)*np.cos(yaw2)\\n\\n #Determine spot shifts\\n x1 = hubdist2*(alpha1/beta1)\\n x2 = hubdist2*(alpha2/beta2)\\n \\n\\n pdb.set_trace()\\n \\n return x1,x2\",\n \"def runSimulation(self, R=5000, N=1,s=1000, method='RL'):\\n global n_ec\\n import numpy as np\\n import matplotlib.pyplot as plt\\n import matplotlib\\n matplotlib.use('Agg')\\n matplotlib.pyplot.switch_backend('agg')\\n\\n plt.rcParams.update({'font.size':20})\\n plt.rc('xtick', labelsize=20)\\n plt.rc('ytick', labelsize=20)\\n # step = 2 fs\\n # each round is 2 fs * 1000 = 2 ps\\n\\n init = 'ala2_1stFrame.pdb' #pdb name\\n inits = init\\n n_ec = 2 # angles\\n count = 1\\n newPoints_name = 'start_r_'+str(count)+'.pdb'\\n \\n #W_0 = [1/n_ec for i in range(n_ec)] # no direction\\n #W_0 = [[0.25, 0.25], [0.25, 0.25]]\\n W_0 = [[1/(2*n_ec), 1/(2*n_ec)] for i in range(n_ec)] # directional\\n print(W_0)\\n\\n Ws = []\\n Ws.append(W_0)\\n \\n trj1 = self.run(production_steps = s, start=inits, production='trj_R_0.pdb') # return mdtraj object\\n comb_trj1 = trj1 # single trajectory\\n trjs = comb_trj1\\n trj1_theta = self.map_angles(trj1) # changed for angles to display\\n print('trj1_theta', len(trj1_theta), len(trj1_theta[0]))\\n trj1_Ps_theta, index = self.PreSamp(trj1_theta, myn_clusters = 10) # pre analysis (least count)\\n trj1_Ps_w_theta, index_w = self.PreSamp(trj1_theta, myn_clusters = 100) # for updating the weights\\n print('trj1_Ps_theta', len(trj1_Ps_theta), len(trj1_Ps_theta[0]))\\n\\n newPoints_index_orig = self.findStarting(trj1_Ps_theta, index, W_0, starting_n = N , method = 'RL') #need change\\n newPoints = trj1[newPoints_index_orig[0]]\\n newPoints.save_pdb(newPoints_name)\\n \\n \\n print('trj1_theta[0]',trj1_theta[0])\\n plt.scatter(trj1_theta[0], trj1_theta[1], color='dodgerblue', s=5, alpha=0.2)\\n plt.xlim([-180, 180])\\n plt.ylim([-180, 180])\\n newPoints_theta_x = trj1_theta[0][newPoints_index_orig[0]]\\n newPoints_theta_y = trj1_theta[1][newPoints_index_orig[0]]\\n plt.scatter(newPoints_theta_x, newPoints_theta_y, color='red', s=50)\\n plt.xlabel(r'$\\\\phi$')\\n plt.ylabel(r'$\\\\psi$')\\n plt.savefig('fig_'+str(count))\\n plt.close()\\n trjs_theta = trj1_theta\\n trjs_Ps_theta = trj1_Ps_theta\\n trjs_Ps_w_theta = trj1_Ps_w_theta \\n for round in range(R):\\n self.updateStat(trjs_theta) # based on all trajectories\\n #W_1 = self.updateW(trjs_Ps_theta, W_0) \\n W_1 = self.updateW(trjs_Ps_w_theta, W_0) \\n W_0 = W_1\\n W_1 = W_0\\n Ws.append(W_0)\\n s = 1000\\n trj1 = self.run(production_steps = s, start=newPoints_name, production='trj_R_'+str(count)+'.pdb') # return mdtraj object\\n com_trjs = trjs.join(trj1) \\n trjs = com_trjs\\n trjs_theta = np.array(self.map_angles(trjs)) \\n trjs_Ps_theta, index = self.PreSamp(trjs_theta, myn_clusters = 100)\\n myn_clusters1 = 100 #int(10*(round)+1)\\n trjs_Ps_w_theta = trjs_Ps_theta\\n #trjs_Ps_w_theta, index_w = self.PreSamp(trjs_theta, myn_clusters = myn_clusters1)\\n newPoints_index_orig = self.findStarting(trjs_Ps_theta, index, W_1, starting_n = N , method = 'RL')\\n newPoints = trjs[newPoints_index_orig[0]] \\n \\n count = count + 1\\n newPoints_name = 'start_r_'+str(count)+'.pdb'\\n newPoints.save_pdb(newPoints_name)\\n\\n print( myn_clusters1, W_1, self.theta_mean)\\n plt.scatter(trjs_theta[0], trjs_theta[1], color='dodgerblue', s=5, alpha=0.2)\\n plt.xlim([-np.pi, np.pi])\\n plt.ylim([-np.pi, np.pi])\\n newPoints_theta_x = trjs_theta[0][newPoints_index_orig[0]]\\n newPoints_theta_y = trjs_theta[1][newPoints_index_orig[0]]\\n plt.scatter(newPoints_theta_x, newPoints_theta_y, color='red', s=50)\\n plt.scatter(trjs_Ps_w_theta[0], trjs_Ps_w_theta[1], color='green', s=5)\\n plt.xlabel(r'$\\\\phi$')\\n plt.ylabel(r'$\\\\psi$')\\n plt.savefig('fig_'+str(count))\\n plt.close()\\n \\n np.save('w_'+'r'+str(int(R))+'N'+str(N)+'s'+str(s), Ws)\\n np.save('trjs_theta', trjs_theta)\\n return\",\n \"def __init__(self, flagTrackShape = 0):\\n\\n \\\"\\\"\\\" Nos interesa que el planner tenga una pista algo mas reducida de la real\\n para conservar algo de robustez y no salirnos de la pista en el primer segundo. \\\"\\\"\\\"\\n \\n ### is HW is the half width of vehicle dimension + some saftey factor?\\n ### what is slack??\\n \\n # HW = rospy.get_param(\\\"halfWidth\\\")+0.1\\n HW = 0.4\\n # print (\\\"HW\\\",HW)\\n # if flagTrackShape == 0:\\n # selectedTrack = rospy.get_param(\\\"trackShape\\\") # comentado para el testeo del planner\\n # # selectedTrack = \\\"L_shape\\\"\\n # else:\\n # selectedTrack = \\\"oval\\\"\\n\\n selectedTrack = \\\"L_shape\\\"\\n print (\\\"track selected\\\",selectedTrack)\\n if selectedTrack == \\\"3110\\\":\\n self.halfWidth = 0.6\\n self.slack = 0.15\\n spec = np.array([[60 * 0.03, 0],\\n [80 * 0.03, +80 * 0.03 * 2 / np.pi],\\n [20 * 0.03, 0],\\n [80 * 0.03, +80 * 0.03 * 2 / np.pi],\\n [40 * 0.03, -40 * 0.03 * 10 / np.pi],\\n [60 * 0.03, +60 * 0.03 * 5 / np.pi],\\n [40 * 0.03, -40 * 0.03 * 10 / np.pi],\\n [80 * 0.03, +80 * 0.03 * 2 / np.pi],\\n [20 * 0.03, 0],\\n [80 * 0.03, +80 * 0.03 * 2 / np.pi],\\n [80 * 0.03, 0]])\\n\\n elif selectedTrack == \\\"oval\\\":\\n self.halfWidth = HW\\n self.slack = 0.15\\n spec = np.array([[1.0, 0],\\n [4.5, 4.5 / np.pi],\\n [2.0, 0],\\n [4.5, 4.5 / np.pi],\\n [1.0, 0]])\\n\\n # elif selectedTrack == \\\"L_shape\\\":\\n # self.halfWidth = HW\\n # self.slack = 0.01\\n # lengthCurve = 4.5\\n # spec = np.array([[1.0, 0],\\n # [lengthCurve, lengthCurve / np.pi],\\n # # Note s = 1 * np.pi / 2 and r = -1 ---> Angle spanned = np.pi / 2\\n # [lengthCurve/2,-lengthCurve / np.pi ],\\n # [lengthCurve, lengthCurve / np.pi],\\n # [lengthCurve / np.pi *2, 0],\\n # [lengthCurve/2, lengthCurve / np.pi]])\\n\\n elif selectedTrack == \\\"L_shape_n\\\":\\n self.halfWidth = HW\\n self.slack = 0.01\\n lengthCurve = 4.5\\n spec = np.array([[1.0, 0],\\n [lengthCurve, lengthCurve / np.pi],\\n [lengthCurve/2,-lengthCurve / np.pi ],\\n [lengthCurve, lengthCurve / np.pi],\\n [lengthCurve / np.pi *2, 0],\\n [lengthCurve/2, lengthCurve / np.pi]])\\n\\n elif selectedTrack == \\\"L_shape_IDIADA\\\":\\n self.halfWidth = HW\\n self.slack = 6*0.45\\n lengthCurve = 10*4.5\\n spec = np.array([[1.0, 0],\\n [lengthCurve, lengthCurve / np.pi],\\n # Note s = 1 * np.pi / 2 and r = -1 ---> Angle spanned = np.pi / 2\\n [lengthCurve/2,-lengthCurve / np.pi ],\\n [lengthCurve, lengthCurve / np.pi],\\n [lengthCurve / np.pi *2, 0],\\n [lengthCurve/2, lengthCurve / np.pi]])\\n\\n elif selectedTrack == \\\"L_shape\\\":\\n # elif selectedTrack == \\\"SLAM_shape1\\\":\\n self.halfWidth = 0.4\\n self.slack = 0.01\\n lengthCurve = 1.5*(np.pi/2)\\n spec = np.array([[2.5,0],\\n [2*lengthCurve,(lengthCurve*2)/np.pi],\\n [lengthCurve,-(lengthCurve*2) / np.pi],\\n [1.0,0],\\n [lengthCurve,lengthCurve*2/np.pi],\\n [2.0,0],\\n [lengthCurve,(lengthCurve*2)/np.pi],\\n [4.0,0],\\n [lengthCurve,(lengthCurve*2)/np.pi],\\n [2.6,0]])\\n\\n\\n elif selectedTrack == \\\"8_track\\\":\\n self.halfWidth = 0.4\\n self.slack = 0.15\\n lengthCurve = 1.5*(np.pi/2)\\n spec = np.array([[0.5,0],\\n [lengthCurve,(lengthCurve*2)/np.pi],\\n [1.0,0],\\n [lengthCurve,-(lengthCurve*2) / np.pi],\\n [lengthCurve,lengthCurve*2/np.pi],\\n [lengthCurve,lengthCurve*2/np.pi],\\n [1.0,0],\\n [lengthCurve,(lengthCurve*2)/np.pi],\\n [lengthCurve,-(lengthCurve*2)/np.pi],\\n [lengthCurve,(lengthCurve*2)/np.pi],\\n [1.0,0],\\n [lengthCurve,lengthCurve*2/np.pi]])\\n\\n\\n\\n # Now given the above segments we compute the (x, y) points of the track and the angle of the tangent vector (psi) at\\n # these points. For each segment we compute the (x, y, psi) coordinate at the last point of the segment. Furthermore,\\n # we compute also the cumulative s at the starting point of the segment at signed curvature\\n # PointAndTangent = [x, y, psi, cumulative s, segment length, signed curvature]\\n\\n ### what is cumulative s and signed curvature.?\\n\\n PointAndTangent = np.zeros((spec.shape[0] + 1, 6))\\n for i in range(0, spec.shape[0]):\\n if spec[i, 1] == 0.0: # If the current segment is a straight line\\n l = spec[i, 0] # Length of the segments\\n if i == 0:\\n ang = 0 # Angle of the tangent vector at the starting point of the segment\\n x = 0 + l * np.cos(ang) # x coordinate of the last point of the segment\\n y = 0 + l * np.sin(ang) # y coordinate of the last point of the segment\\n else:\\n ang = PointAndTangent[i - 1, 2] # Angle of the tangent vector at the starting point of the segment\\n x = PointAndTangent[i-1, 0] + l * np.cos(ang) # x coordinate of the last point of the segment\\n y = PointAndTangent[i-1, 1] + l * np.sin(ang) # y coordinate of the last point of the segment\\n psi = ang # Angle of the tangent vector at the last point of the segment\\n\\n # # With the above information create the new line\\n # if i == 0:\\n # NewLine = np.array([x, y, psi, PointAndTangent[i, 3], l, 0])\\n # else:\\n # NewLine = np.array([x, y, psi, PointAndTangent[i, 3] + PointAndTangent[i, 4], l, 0])\\n #\\n # PointAndTangent[i + 1, :] = NewLine # Write the new info\\n\\n if i == 0:\\n NewLine = np.array([x, y, psi, PointAndTangent[i, 3], l, 0])\\n else:\\n NewLine = np.array([x, y, psi, PointAndTangent[i-1, 3] + PointAndTangent[i-1, 4], l, 0])\\n\\n PointAndTangent[i, :] = NewLine # Write the new info\\n else:\\n l = spec[i, 0] # Length of the segment\\n r = spec[i, 1] # Radius of curvature\\n\\n\\n if r >= 0:\\n direction = 1\\n else:\\n direction = -1\\n\\n if i == 0:\\n ang = 0 # Angle of the tangent vector at the\\n # starting point of the segment\\n CenterX = 0 \\\\\\n + np.abs(r) * np.cos(ang + direction * np.pi / 2) # x coordinate center of circle\\n CenterY = 0 \\\\\\n + np.abs(r) * np.sin(ang + direction * np.pi / 2) # y coordinate center of circle\\n else:\\n ang = PointAndTangent[i - 1, 2] # Angle of the tangent vector at the\\n # starting point of the segment\\n CenterX = PointAndTangent[i-1, 0] \\\\\\n + np.abs(r) * np.cos(ang + direction * np.pi / 2) # x coordinate center of circle\\n CenterY = PointAndTangent[i-1, 1] \\\\\\n + np.abs(r) * np.sin(ang + direction * np.pi / 2) # y coordinate center of circle\\n\\n spanAng = l / np.abs(r) # Angle spanned by the circle\\n psi = wrap(ang + spanAng * np.sign(r)) # Angle of the tangent vector at the last point of the segment\\n\\n angleNormal = wrap((direction * np.pi / 2 + ang))\\n angle = -(np.pi - np.abs(angleNormal)) * (sign(angleNormal))\\n x = CenterX + np.abs(r) * np.cos(\\n angle + direction * spanAng) # x coordinate of the last point of the segment\\n y = CenterY + np.abs(r) * np.sin(\\n angle + direction * spanAng) # y coordinate of the last point of the segment\\n\\n # With the above information create the new line\\n # plt.plot(CenterX, CenterY, 'bo')\\n # plt.plot(x, y, 'ro')\\n\\n # if i == 0:\\n # NewLine = np.array([x, y, psi, PointAndTangent[i, 3], l, 1 / r])\\n # else:\\n # NewLine = np.array([x, y, psi, PointAndTangent[i, 3] + PointAndTangent[i, 4], l, 1 / r])\\n #\\n # PointAndTangent[i + 1, :] = NewLine # Write the new info\\n\\n if i == 0:\\n NewLine = np.array([x, y, psi, PointAndTangent[i, 3], l, 1 / r])\\n else:\\n NewLine = np.array([x, y, psi, PointAndTangent[i-1, 3] + PointAndTangent[i-1, 4], l, 1 / r])\\n\\n PointAndTangent[i, :] = NewLine # Write the new info\\n # plt.plot(x, y, 'or')\\n\\n # Now update info on last point\\n # xs = PointAndTangent[PointAndTangent.shape[0] - 2, 0]\\n # ys = PointAndTangent[PointAndTangent.shape[0] - 2, 1]\\n # xf = PointAndTangent[0, 0]\\n # yf = PointAndTangent[0, 1]\\n # psif = PointAndTangent[PointAndTangent.shape[0] - 2, 2]\\n #\\n # # plt.plot(xf, yf, 'or')\\n # # plt.show()\\n # l = np.sqrt((xf - xs) ** 2 + (yf - ys) ** 2)\\n #\\n # NewLine = np.array([xf, yf, psif, PointAndTangent[PointAndTangent.shape[0] - 2, 3] + PointAndTangent[\\n # PointAndTangent.shape[0] - 2, 4], l, 0])\\n # PointAndTangent[-1, :] = NewLine\\n\\n\\n xs = PointAndTangent[-2, 0]\\n ys = PointAndTangent[-2, 1]\\n xf = 0\\n yf = 0\\n psif = 0\\n\\n # plt.plot(xf, yf, 'or')\\n # plt.show()\\n l = np.sqrt((xf - xs) ** 2 + (yf - ys) ** 2)\\n\\n NewLine = np.array([xf, yf, psif, PointAndTangent[-2, 3] + PointAndTangent[-2, 4], l, 0])\\n PointAndTangent[-1, :] = NewLine\\n\\n self.PointAndTangent = PointAndTangent\\n self.TrackLength = PointAndTangent[-1, 3] + PointAndTangent[-1, 4]\",\n \"def test_f1_circuit_maker(self):\\n fho = tfho.test_file_handle_object()\\n W = 5\\n G = 20\\n fg = .9\\n X = 10\\n fx = .85\\n gate_maker = g.TYPE_TO_GATE_GEN[g.TEST_TYPES.RANDOM]\\n # family 1 files:\\n t_circuit_file_name = \\\"circuit_file_trimming\\\"\\n t_circuit_file = fho.get_file_object(t_circuit_file_name, 'w')\\n t_input_file_name = \\\"input_file_trimming\\\"\\n t_input_file = fho.get_file_object(t_input_file_name, 'w')\\n t_output_file_name = \\\"output_file_trimming\\\"\\n t_output_file = fho.get_file_object(t_output_file_name, 'w')\\n nt_circuit_file_name = \\\"circuit_file_no_trimming\\\"\\n nt_circuit_file = fho.get_file_object(nt_circuit_file_name, 'w')\\n nt_input_file_name = \\\"input_file_no_trimming\\\"\\n nt_input_file = fho.get_file_object(nt_input_file_name, 'w')\\n nt_output_file_name = \\\"output_file_no_trimming\\\"\\n nt_output_file = fho.get_file_object(nt_output_file_name, 'w')\\n level_type_array = [g.LEVEL_TYPES.RANDOM]\\n F = 1\\n # make a family 1 circuit with trimming:\\n sr.seed(self.rand_seed)\\n t_gen = g.f1f2_circuit_maker_with_trimming_switch(W, G, fg,\\n t_circuit_file,\\n t_input_file,\\n t_output_file,\\n X, fx, gate_maker,\\n level_type_array, True)\\n t_gen.generate()\\n # make a family 1 circuit without trimming, with the same randomness:\\n sr.seed(self.rand_seed)\\n nt_gen = g.f1f2_circuit_maker_with_trimming_switch(W, G, fg,\\n nt_circuit_file,\\n nt_input_file,\\n nt_output_file,\\n X, fx, gate_maker,\\n level_type_array, False)\\n nt_gen.generate()\\n # obtain strings representing the contents of all the resulting files:\\n t_circuit_string = fho.get_file(t_circuit_file_name).getvalue()\\n t_input_string = fho.get_file(t_input_file_name).getvalue()\\n t_output_string = fho.get_file(t_output_file_name).getvalue()\\n nt_circuit_string = fho.get_file(nt_circuit_file_name).getvalue()\\n nt_input_string = fho.get_file(nt_input_file_name).getvalue()\\n nt_output_string = fho.get_file(nt_output_file_name).getvalue()\\n # make sure that the inputs and outputs produced by the trimming and\\n # no trimming algorithms are the same:\\n self.assertEqual(t_input_string, nt_input_string)\\n self.assertEqual(t_output_string, nt_output_string)\\n # make sure that the input begins and ends with a bracket:\\n self.assertEqual(\\\"[\\\", t_input_string[0])\\n self.assertEqual(\\\"]\\\", t_input_string[-1])\\n # make sure that each input element is a bit:\\n for bit in t_input_string[1:-1]:\\n self.assertTrue((bit == '0') or (bit == '1'))\\n # make sure that the output is a bit:\\n self.assertTrue((t_output_string == '0') or (t_output_string == '1'))\\n # make sure that the two circuit headers are the same, and that they\\n # contain the correct values:\\n t_circuit_header = t_circuit_string.split(\\\"\\\\n\\\")[0]\\n nt_circuit_header = nt_circuit_string.split(\\\"\\\\n\\\")[0]\\n self.assertEqual(t_circuit_header, nt_circuit_header)\\n (W_string, G_string, F_string) = t_circuit_header.split(\\\",\\\")\\n W_value = int(W_string.split(\\\"=\\\")[-1])\\n G_value = int(G_string.split(\\\"=\\\")[-1])\\n F_value = int(F_string.split(\\\"=\\\")[-1])\\n self.assertEqual(W, W_value)\\n self.assertEqual(G, G_value)\\n self.assertEqual(F, F_value)\\n # note that we cannot test that the circuits themselves are the same,\\n # because the trimming algorithm produces a circuit with gates listed\\n # in a different order.\",\n \"def compare_ratios(path='/Volumes/OptiHDD/data/pylith/3d/agu2013/output',\\n\\t\\t\\t\\tsteps=['step01','step02'],\\n\\t\\t\\t\\t#labels='',\\n\\t\\t\\t\\tshow=True,\\n\\t\\t\\t\\txscale=1e3,\\n\\t\\t\\t\\tyscale=1e-2):\\n\\tplt.figure()\\n\\t#path = '/Users/scott/Desktop/elastic'\\n\\n\\t# Deep source\\n\\tlabels = ['no APMB', 'APMB']\\n\\tdeep = {}\\n\\tuzmax = 0.824873455364\\n\\t# NOT sure why hardcoded...\\n\\tuzmax = 1\\n\\tfor i,outdir in enumerate(steps):\\n\\t\\tpointsFile = os.path.join(path, outdir, 'points.h5')\\n\\n\\t\\tx,y,z,ux,uy,uz = pu.extract_points(pointsFile)\\n\\n\\t\\tX = x / xscale\\n\\t\\tY1 = ux / yscale\\n\\n\\t\\tx_fem = X #/ xscale #double scaling!\\n\\t\\tur_fem = Y1 #/ yscale\\n\\t\\tuz_fem = uz / yscale\\n\\n\\t\\t#print(pointsFile)\\n\\t\\tprint(ur_fem.min(), ur_fem.max(), uz_fem.min(), uz_fem.max(), uz_fem.max() / ur_fem.max())\\n\\n\\t\\t#normalize\\n\\t\\tuz_fem = uz_fem / uzmax\\n\\t\\tur_fem = ur_fem / uzmax\\n\\t\\tx_fem = x_fem / 30.0\\n\\n\\t\\tl, = plt.plot(x_fem,uz_fem,'o-',ms=4,lw=4,label=labels[i])\\n\\t\\tplt.plot(x_fem,ur_fem,'o--',ms=4,lw=4,color=l.get_color()) #mfc='none' transparent\\n\\t\\tdeep[outdir] = uz_fem/uz_fem\\n\\n\\n\\t# Shallow Source\\n\\tshallow = {}\\n\\tuzmax = 0.949652827795 # Why?\\n\\tfor i,outdir in enumerate(['step11','step12']):\\n\\t\\tpointsFile = os.path.join(path, outdir, 'points.h5')\\n\\n\\t\\tx,y,z,ux,uy,uz = pu.extract_points(pointsFile)\\n\\n\\t\\tX = x / xscale\\n\\t\\tY1 = ux / yscale\\n\\n\\t\\tx_fem = X #/ xscale #double scaling!\\n\\t\\tur_fem = Y1 #/ yscale\\n\\t\\tuz_fem = uz / yscale\\n\\n\\t\\t#print(pointsFile)\\n\\t\\tprint(ur_fem.min(), ur_fem.max(), uz_fem.min(), uz_fem.max(), uz_fem.max() / ur_fem.max())\\n\\n\\t\\t#normalize\\n\\t\\tuz_fem = uz_fem / uzmax\\n\\t\\tur_fem = ur_fem / uzmax\\n\\t\\tx_fem = x_fem / 20.0\\n\\n\\t\\tl, = plt.plot(x_fem,uz_fem,'.-', mfc='w', lw=4,label=labels[i])\\n\\t\\tplt.plot(x_fem,ur_fem,'.--',lw=4, mfc='w',color=l.get_color()) #mfc='none' transparent\\n\\n\\t\\tshallow[outdir] = uz_fem/ur_fem\\n\\n\\t# Annotate\\n\\tplt.axhline(color='k',lw=0.5)\\n\\t#plt.xlabel('Distance [{}]'.format(get_unit(xscale)))\\n\\t#plt.ylabel('Displacement [{}]'.format(get_unit(yscale)))\\n\\tplt.legend()\\n\\tplt.grid()\\n\\t#plt.ylim(-0.5, 3.5)\\n\\t#plt.savefig('deep.png',bbox_inches='tight')\\n\\t#plt.savefig('shallow.png',bbox_inches='tight')\\n\\n\\t# normalized\\n\\tplt.ylim(-0.5, 4)\\n\\tplt.xlim(0,10)\\n\\tplt.xlabel('Normalized Radial Distance [R / D]')\\n\\tplt.ylabel('Normalized Displacement [U / Uz_max]')\\n\\t#plt.savefig('normalized_deep.png',bbox_inches='tight')\\n\\tplt.savefig('normalized_shallow.png',bbox_inches='tight')\\n\\n\\n\\t# Plot ratios of uz versus NOTE: this plot is confusing,,, just keep ratio of uz_max to ur_max\\n\\t'''\\n\\tplt.figure()\\n\\tplt.plot(x_fem, deep['step01'], label='Deep no APMB')\\n\\tplt.plot(x_fem, deep['step02'], label='Deep w/ APMB')\\n\\tplt.plot(x_fem, shallow['step11'], label='Shallow no APMB')\\n\\tplt.plot(x_fem, shallow['step12'], label='Shallow w/ APMB')\\n\\tplt.xlabel('Distance [km]') #NOTE: maybe plot normailzed X-axis (R-d)\\n\\t#plt.xlabel('Normalized Distance [R/d]')\\n\\tplt.ylabel('Ratio [Uz/Ur]')\\n\\tplt.title('Ratio of vertical to radial displacement')\\n\\tplt.legend()\\n\\tplt.show()\\n\\t'''\",\n \"def test_case_generate(self):\\n\\n # initialization\\n state = np.random.choice(self.init_states)\\n model = rm.randint(0, self.model_num - 1)\\n duration = np.random.choice(self.step_values)\\n temp = rm.randint(self.min_temp, self.max_temp)\\n\\n self.states = [[model, duration, temp]]\\n self.time = duration\\n\\n while self.time < self.max_time:\\n if state == \\\"inc_tmp\\\":\\n change = np.random.choice(\\n self.transitionName[0], p=self.transitionMatrix[0]\\n ) # choose the next state\\n if change == \\\"S1S1\\\": # stay in the same state\\n temp = self.get_temp_inc(temp)\\n model = rm.randint(0, self.model_num - 1)\\n diff = (\\n self.max_time - self.time\\n ) # this is for ensuring the maximum duration is not exceeded\\n if (diff) < self.max_step and (diff) > self.min_step:\\n duration = diff\\n self.states.append([model, duration, temp])\\n return self.states_to_dict()\\n elif diff < self.min_step:\\n self.states[len(self.states) - 1][1] += diff\\n return self.states_to_dict()\\n else:\\n duration = np.random.choice(self.step_values)\\n self.time += duration\\n self.states.append([model, duration, temp])\\n\\n elif change == \\\"S1S2\\\": # change from increase to decrease\\n temp = self.get_temp_dec(temp)\\n model = rm.randint(0, self.model_num - 1)\\n state = \\\"dec_tmp\\\"\\n\\n diff = self.max_time - self.time\\n if (diff) < self.max_step and (diff) > self.min_step:\\n duration = diff\\n self.states.append([model, duration, temp])\\n return self.states_to_dict()\\n elif diff < self.min_step:\\n self.states[len(self.states) - 1][1] += diff\\n return self.states_to_dict()\\n else:\\n duration = np.random.choice(self.step_values)\\n self.time += duration\\n self.states.append([model, duration, temp])\\n else:\\n print(\\\"Error\\\")\\n\\n elif state == \\\"dec_tmp\\\":\\n change = np.random.choice(\\n self.transitionName[1], p=self.transitionMatrix[1]\\n )\\n if change == \\\"S2S1\\\":\\n temp = self.get_temp_inc(temp)\\n model = rm.randint(0, self.model_num - 1)\\n state = \\\"inc_tmp\\\"\\n diff = self.max_time - self.time\\n if (diff) < self.max_step and (diff) > self.min_step:\\n duration = diff\\n self.states.append([model, duration, temp])\\n return self.states_to_dict()\\n elif diff < self.min_step:\\n self.states[len(self.states) - 1][1] += diff\\n return self.states_to_dict()\\n else:\\n duration = np.random.choice(self.step_values)\\n\\n self.time += duration\\n self.states.append([model, duration, temp])\\n\\n elif change == \\\"S2S2\\\":\\n temp = self.get_temp_dec(temp)\\n model = rm.randint(0, self.model_num - 1)\\n\\n diff = self.max_time - self.time\\n if (diff) < self.max_step and (diff) > self.min_step:\\n duration = diff\\n self.states.append([model, duration, temp])\\n return self.states_to_dict()\\n elif diff < self.min_step:\\n self.states[len(self.states) - 1][1] += diff\\n return self.states_to_dict()\\n else:\\n duration = np.random.choice(self.step_values)\\n self.time += duration\\n self.states.append([model, duration, temp])\\n\\n else:\\n print(\\\"Error\\\")\\n pass\\n else:\\n print(\\\"Error\\\")\\n\\n return self.states_to_dict()\",\n \"def test_dataset_scenario_generation_full_late(self):\\n \\n params = ParameterServer()\\n\\n map_filename = os.path.join(os.path.dirname(__file__), \\\"data/DR_DEU_Merging_MT_v01_shifted.xodr\\\")\\n track_filename = os.path.join(os.path.dirname(__file__), \\\"data/interaction_dataset_DEU_Merging_dummy_track_late.csv\\\")\\n\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"MapFilename\\\"] = map_filename\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"TrackFilenameList\\\"] = [track_filename]\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"StartingOffsetMs\\\"] = 0\\n\\n scenario_generation = InteractionDatasetScenarioGenerationFull(\\n params=params, num_scenarios=1)\\n\\n scenario = scenario_generation.get_scenario(0)\\n world_state = scenario.GetWorldState()\\n agent1 = world_state.GetAgent(1)\\n agent2 = world_state.GetAgent(2)\\n\\n self.assertAlmostEqual(agent1.first_valid_timestamp, 0.0)\\n self.assertAlmostEqual(agent2.first_valid_timestamp, 0.3)\\n \\n self.assertEqual(isinstance(agent1, Agent), True)\\n self.assertEqual(agent1.IsValidAtTime(world_state.time), True)\\n \\n self.assertEqual(isinstance(agent2, Agent), True)\\n self.assertEqual(agent2.IsValidAtTime(world_state.time), False)\",\n \"def test_stop_start(self):\\n log.info(\\\"CONFIG: %s\\\", self._agent_config())\\n self.create_sample_data_set_dir(\\n 'node59p1_step2.dat',\\n TELEM_DIR,\\n \\\"node59p1.dat\\\",\\n copy_metadata=False\\n )\\n\\n self.assert_initialize(final_state=ResourceAgentState.COMMAND)\\n\\n # Slow down processing to 1 per second to give us time to stop\\n self.dataset_agent_client.set_resource({DriverParameter.RECORDS_PER_SECOND: 1})\\n self.assert_start_sampling()\\n\\n # Verify we get one sample\\n try:\\n # Read the first file and verify the data\\n result = self.data_subscribers.get_samples(DataParticleType.METADATA_TELEMETERED, 1)\\n result1 = self.data_subscribers.get_samples(DataParticleType.SAMPLE_TELEMETERED, 2)\\n result.extend(result1)\\n log.debug(\\\"RESULT: %s\\\", result)\\n\\n # Verify values\\n self.assert_data_values(result, 'test_data_1-2.txt.result.yml')\\n self.assert_sample_queue_size(DataParticleType.METADATA_TELEMETERED, 0)\\n self.assert_sample_queue_size(DataParticleType.SAMPLE_TELEMETERED, 0)\\n\\n self.create_sample_data_set_dir(\\n 'node59p1_step4.dat',\\n TELEM_DIR,\\n \\\"node59p1.dat\\\",\\n copy_metadata=False\\n )\\n # Now read the first records of the second file then stop\\n result1 = self.data_subscribers.get_samples(DataParticleType.SAMPLE_TELEMETERED, 2)\\n log.debug(\\\"RESULT 1: %s\\\", result1)\\n self.assert_stop_sampling()\\n self.assert_sample_queue_size(DataParticleType.METADATA_TELEMETERED, 0)\\n self.assert_sample_queue_size(DataParticleType.SAMPLE_TELEMETERED, 0)\\n\\n # Restart sampling and ensure we get the last 2 records of the file\\n self.assert_start_sampling()\\n result2 = self.data_subscribers.get_samples(DataParticleType.SAMPLE_TELEMETERED, 2)\\n log.debug(\\\"RESULT 2: %s\\\", result2)\\n result = result1\\n result.extend(result2)\\n log.debug(\\\"RESULT: %s\\\", result)\\n self.assert_sample_queue_size(DataParticleType.METADATA_TELEMETERED, 0)\\n self.assert_data_values(result, 'test_data_3-4.txt.result.yml')\\n self.assert_sample_queue_size(DataParticleType.METADATA_TELEMETERED, 0)\\n self.assert_sample_queue_size(DataParticleType.SAMPLE_TELEMETERED, 0)\\n except SampleTimeout as e:\\n log.error(\\\"Exception trapped: %s\\\", e, exc_info=True)\\n self.fail(\\\"Sample timeout.\\\")\",\n \"def test_stop_start(self):\\n log.info(\\\"START QUAL TEST STOP START\\\")\\n self.create_sample_data('all_A0000003.DEC', \\\"A0000003.DEC\\\")\\n self.assert_initialize(final_state=ResourceAgentState.COMMAND)\\n self.dataset_agent_client.set_resource(\\n {DriverParameter.RECORDS_PER_SECOND: 1})\\n self.assert_start_sampling()\\n\\n try:\\n # Read the first file (3 velocity records) and verify the data.\\n result = self.get_samples(DataParticleType.VELOCITY_PARTICLE, 3)\\n time_result = self.get_samples(DataParticleType.TIME_PARTICLE, 1)\\n result.extend(time_result)\\n\\n # Verify values\\n self.assert_data_values(result, 'all_A0000003.yml')\\n self.verify_queue_empty()\\n\\n # Read the first 2 velocity records of the second file then stop.\\n self.create_sample_data('valid_A0000004.DEC', \\\"A0000004.DEC\\\")\\n result = self.get_samples(DataParticleType.VELOCITY_PARTICLE, 2)\\n self.assert_stop_sampling()\\n self.verify_queue_empty()\\n\\n # Restart sampling and get the last 2 records of the file\\n # and combine with the previous ones we read.\\n self.assert_start_sampling()\\n result2 = self.get_samples(DataParticleType.VELOCITY_PARTICLE, 2)\\n result.extend(result2)\\n\\n # Get the time record and combine with previous records.\\n time_result = self.data_subscribers.get_samples(\\n DataParticleType.TIME_PARTICLE, 1)\\n result.extend(time_result)\\n self.assert_data_values(result, 'valid_A0000004.yml')\\n\\n self.verify_queue_empty()\\n\\n except SampleTimeout as e:\\n log.error(\\\"Exception trapped: %s\\\", e, exc_info=True)\\n self.fail(\\\"Sample timeout.\\\")\\n\\n log.info(\\\"END QUAL TEST STOP START\\\")\",\n \"def main(m1=1.4, m2=0.3, r1=10, a0=100, eta=110, rel=\\\"off\\\",\\n max_time=1, out1_path=out1_path_default, out2_path=out2_path_default):\\n\\n if not (rel in (\\\"on\\\", \\\"off\\\")):\\n er(\\\"wrong rel\\\")\\n\\n if m1 < m2:\\n er('m1 must be more than m2!')\\n\\n if eta > 200:\\n er('error: eta must be less than 200!')\\n\\n if eta < 60:\\n er(\\\"error: eta must be more than 60!\\\")\\n\\n if not (m2 > 0.1 and m2 < 0.8):\\n er('error: m2 must be in (0.1, 0.8)!')\\n\\n if m2 < 0: \\n er('error: m2 must be positive!')\\n\\n if m2 > 1: \\n er('warning, mass of m1 is more than 1 M_sol so approximation of Sotani may be not valid!')\\n \\n if m1 > 2.3:\\n print('Warning, m1 have a mass of BH!')\\n\\n \\n with open(out1_path, 'w') as out1:\\n with open(out2_path, 'w') as out2:\\n res1, res2 = run(m1=m1, m2=m2, r1=r1, a0=a0, eta=eta, rel=rel, max_time=max_time)\\n write_res(res1, out1)\\n write_res(res2, out2)\",\n \"def rtest_power_and_reward_crossover():\\n #define pilot cohort size\\n npatients = 10\\n\\n #simulate drugX and drugY policies for long term in short term increments\\n nstep = int(long_term/short_term)\\n\\n #init rewards per patient per time for drugx and drugy policies\\n drugx_reward = np.zeros((nstep,npatients))\\n drugy_reward = np.zeros((nstep,npatients))\\n\\n #init drugX simulation\\n simulator = AbbcEnvironment(patients=npatients)\\n #define action taken\\n action = np.repeat(1, npatients)\\n #main simulation loop\\n for step in range(nstep):\\n _, drugx_reward[step,:] = simulator.take_action(action=action, simtime=short_term)\\n\\n #init drugY simulation\\n simulator = AbbcEnvironment(patients=npatients)\\n #define action taken\\n action = np.repeat(2, npatients)\\n #main simulation loop\\n for step in range(nstep):\\n _, drugy_reward[step,:] = simulator.take_action(action=action, simtime=short_term)\\n\\n #calculate sample size required to resolve effect size for each simulation step.\\n zalpha = 1.96 #critical zvalue for p=1-alpha/2\\n zbeta = 0.8416 #critical zvalue for p=1-beta\\n zsquared = (zalpha + zbeta)**2\\n #calculate the diference in effect size\\n delta = (np.mean(drugx_reward, axis=1) - np.mean(drugy_reward, axis=1))\\n samplesize = np.divide((np.var(drugx_reward, axis=1) + np.var(drugx_reward, axis=1))*zsquared,delta**2)\\n print(samplesize)\\n print(delta)\\n\\n #assert max sample size is greater than 100\\n assert np.max(samplesize)>100\\n\\n #assert max sample size at is at least 50 times the mins at short and long terms\\n assert np.max(samplesize)>50*samplesize[0]\\n assert np.max(samplesize)>50*samplesize[-1]\\n\\n #assert DrugX is better short term than DrugY\\n assert delta[0]>0\\n #assert DrugY is better in long term than DrugX\\n assert delta[-1]<0\",\n \"def make_simulations(self):\\n pass\",\n \"def get_param_scenario3():\\n nb_carre_x = 42\\n nb_carre_y = 45\\n largeur_x = 0.825\\n largeur_y = 0.645\\n z1 = np.array([[11], [32], [17.26]])\\n z2 = np.array([[12], [32], [18.43]])\\n z3 = np.array([[13], [32], [23.2]])\\n z4 = np.array([[15], [31], [23.4]])\\n z5 = np.array([[16], [30], [23.29]])\\n z6 = np.array([[18], [29], [22.28]])\\n z7 = np.array([[19], [28], [35.79]])\\n z8 = np.array([[20], [27], [36.87]])\\n z9 = np.array([[21], [26], [33.92]])\\n z10 = np.array([[23], [24], [38.11]])\\n z11 = np.array([[24], [23], [37.76]])\\n z12 = np.array([[25], [22], [45.6]])\\n z13 = np.array([[26], [20], [56.4]])\\n z14 = np.array([[27], [18], [55.2]])\\n z15 = np.array([[28], [16], [57.53]])\\n z16 = np.array([[29], [14], [58.64]])\\n z17 = np.array([[30], [13], [66.04]])\\n z18 = np.array([[30], [11], [70.02]])\\n z19 = np.array([[31], [9], [69.64]])\\n z20 = np.array([[31], [6], [68.82]])\\n\\n meas = [z1, z2, z3, z4, z5, z6, z7, z8, z9, z10, z11, z12, z13, z14, z15, z16, z17, z18, z19, z20]\\n\\n z = [np.array([[x[0][0] * largeur_x / nb_carre_x], [x[1][0] * largeur_y / nb_carre_y], [-x[2][0] * np.pi / 180]])\\n for x in meas]\\n\\n # We take a measure every second\\n thymio_speed_to_mms = 0.4753\\n Ts = 1\\n\\n Thymio_speed_left = [73, 92, 94, 102, 100, 89, 92, 103, 97, 100, 100, 104, 95, 97, 103, 101, 100, 101, 92, 98]\\n Thymio_speed_right = [67, 96, 102, 89, 105, -45, 102, 101, 104, 71, 100, 93, 94, 105, 96, -45, 95, 103, 88, 94]\\n delta_sr_test = [x * Ts / thymio_speed_to_mms / 1000 for x in Thymio_speed_right]\\n delta_sl_test = [x * Ts / thymio_speed_to_mms / 1000 for x in Thymio_speed_left]\\n\\n return delta_sr_test, delta_sl_test, z\",\n \"def scenario1(height, speed):\\n time = math.sqrt((2 * height) / 9.81)\\n result = speed * time\\n return result\",\n \"def test_dataset_scenario_generation_full_outside1_behavior_overwritten(self):\\n params = ParameterServer()\\n\\n map_filename = os.path.join(os.path.dirname(__file__), \\\"data/DR_DEU_Merging_MT_v01_shifted.xodr\\\")\\n track_filename = os.path.join(os.path.dirname(__file__), \\\"data/interaction_dataset_DEU_Merging_dummy_track_outside.csv\\\")\\n\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"MapFilename\\\"] = map_filename\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"TrackFilenameList\\\"] = [track_filename]\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"StartingOffsetMs\\\"] = 0\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"BehaviorModel\\\"] = \\\"BehaviorMobilRuleBased\\\"\\n\\n scenario_generation = InteractionDatasetScenarioGenerationFull(\\n params=params, num_scenarios=1)\\n\\n scenario = scenario_generation.get_scenario(0)\\n self.assertAlmostEqual(scenario.eval_agent_ids, [1])\\n\\n world_state = scenario.GetWorldState()\\n agent11 = world_state.GetAgent(1)\\n agent12 = world_state.GetAgent(2)\\n agent13 = world_state.GetAgent(3)\\n\\n # others are defined as BehaviorMobilRuleBased\\n self.assertTrue(isinstance(agent11.behavior_model, BehaviorStaticTrajectory))\\n self.assertTrue(isinstance(agent12.behavior_model, BehaviorMobilRuleBased))\\n self.assertTrue(isinstance(agent13.behavior_model, BehaviorMobilRuleBased))\\n\\n self.assertAlmostEqual(agent11.first_valid_timestamp, 0.0)\\n self.assertAlmostEqual(agent12.first_valid_timestamp, 0.0)\\n self.assertNotEqual(agent13.first_valid_timestamp, 0.0)\\n \\n # agent13 should not be valid at the beginning, as he is outside of map\\n world_state.time = 0\\n self.assertEqual(isinstance(agent11, Agent), True)\\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent11.InsideRoadCorridor(), True)\\n \\n self.assertEqual(isinstance(agent12, Agent), True)\\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent12.InsideRoadCorridor(), True)\\n\\n self.assertEqual(isinstance(agent13, Agent), True)\\n self.assertEqual(agent13.IsValidAtTime(world_state.time), False)\\n # as we use only state once it's in map, this will be true, although the time step is not valid yet\\n self.assertEqual(agent13.InsideRoadCorridor(), True)\\n\\n # agent13 should not be valid at the beginning, as he is outside of map\\n world_state.Step(0.05)\\n\\n self.assertEqual(isinstance(agent11, Agent), True)\\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent11.InsideRoadCorridor(), True)\\n \\n self.assertEqual(isinstance(agent12, Agent), True)\\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent12.InsideRoadCorridor(), True)\\n\\n self.assertEqual(isinstance(agent13, Agent), True)\\n self.assertEqual(agent13.IsValidAtTime(world_state.time), False)\\n # as we use only state once it's in map, this will be true, although the time step is not valid yet\\n self.assertEqual(agent13.InsideRoadCorridor(), True)\\n\\n self.assertEqual(list(world_state.agents_valid.keys()), [1,2])\\n\\n # agent13 should be valid at some point\\n world_state.Step(agent13.first_valid_timestamp)\\n world_state.Step(0.01) # agent13.IsValidAtTime() uses previous time stamp, therefore we increment it one more step\\n\\n self.assertEqual(isinstance(agent11, Agent), True)\\n self.assertEqual(agent11.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent11.InsideRoadCorridor(), True)\\n \\n self.assertEqual(isinstance(agent12, Agent), True)\\n self.assertEqual(agent12.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent12.InsideRoadCorridor(), True)\\n\\n self.assertEqual(isinstance(agent13, Agent), True)\\n self.assertEqual(agent13.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent13.InsideRoadCorridor(), True)\\n\\n self.assertEqual(list(world_state.agents_valid.keys()), [1,2,3])\",\n \"def test_three_arms_one_unsampled_arm(self):\\n self._test_three_arms_one_unsampled_arm()\",\n \"def instrumentationAmplifier():\\n eq = list()\\n eq.append(\\\"Eq(vo,A_v*(v2-v1)\\\")\\n eq.append(\\\"Eq(Av, 1 + 2*R / R_G\\\")\",\n \"def run():\\n trials = 100\\n\\n multipliers = [0.25, 0.3, 0.35, 0.5, 0.75, 1, 1.25, 1.45, 1.5, 1.55, 1.6] # Coefficients for learning rate\\n\\n mean_penalty = []\\n median_penalty = []\\n std_penalty = []\\n\\n mean_trial_time = []\\n median_trial_time = []\\n std_trial_time = []\\n\\n mean_success_rate = []\\n median_success_rate = []\\n std_success_rate = []\\n\\n for m in multipliers:\\n all_penalties = [] # All penalties from trail sets\\n all_average_trial_time = []\\n all_success_rates = []\\n\\n for i in range(0, 20):\\n # print \\\"Trial set:\\\", i\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n agent = e.create_agent(LearnerAgent) # create agent\\n agent.mult = m\\n e.set_primary_agent(agent, enforce_deadline=True) # specify agent to track\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.0, display=False) # create simulator (uses pygame when display=True, if available)\\n\\n sim.run(n_trials=trials) # run for a specified number of trials\\n\\n all_penalties.append(agent.all_trails_penalties)\\n all_average_trial_time.append(agent.time/float(trials))\\n all_success_rates.append(float(trials-agent.aborted_trials)/trials)\\n\\n mean_penalty.append(np.mean(all_penalties))\\n median_penalty.append(np.median(all_penalties))\\n std_penalty.append(np.std(all_penalties))\\n\\n mean_trial_time.append(np.mean(all_average_trial_time))\\n median_trial_time.append(np.median(all_average_trial_time))\\n std_trial_time.append(np.std(all_average_trial_time))\\n\\n mean_success_rate.append(np.mean(all_success_rates))\\n median_success_rate.append(np.median(all_success_rates))\\n std_success_rate.append(np.std(all_success_rates))\\n\\n for i in range(0, len(multipliers)):\\n print \\\"\\\"\\n print \\\"Multiplier:\\\", multipliers[i]\\n print \\\"\\\"\\n print \\\"Mean penalty per {} trials:\\\".format(trials), mean_penalty[i]\\n print \\\"Median penalty per {} trials:\\\".format(trials), median_penalty[i]\\n print \\\"Std.Dev. penalty per {} trials:\\\".format(trials), std_penalty[i]\\n\\n print \\\"\\\"\\n print \\\"Mean trial time:\\\", mean_trial_time[i]\\n print \\\"Median trial time:\\\", median_trial_time[i]\\n print \\\"Std.Dev. trial time:\\\", std_trial_time[i]\\n\\n print \\\"\\\"\\n print \\\"Mean success rate per {} trials:\\\".format(trials), mean_success_rate[i]\\n print \\\"Median success rate per {} trials:\\\".format(trials), median_success_rate[i]\\n print \\\"Std.Dev. success rate per {} trials:\\\".format(trials), std_success_rate[i]\",\n \"def test_calc_SR2():\\n tb = TradeBot(\\n pair='EUR_GBP',\\n timeframe='H12',\\n start='2017-06-11 22:00:00',\\n end='2017-06-15 22:00:00',\\n settingf=\\\"../../data/settings.ini\\\"\\n )\\n\\n harealst = tb.calc_SR(datetime.datetime(2017, 6, 13, 22, 0))\\n\\n # check the length of HAreaList.halist\\n assert len(harealst.halist) == 4\",\n \"def Transformer(robot1,robot2): \\n #with a 50% change surgery will be triggered\\n return Random_Actuation.surgery(\\\"Transformer\\\",robot1,robot2,50)\",\n \"def _evaluate_performance__static_winners(self):\\n # | - _evaluate_performance__\\n\\n # | - class attributes #################################################\\n AL = self\\n al_gen = self.al_gen\\n verbose = self.verbose\\n seed_ids = self.seed_ids\\n acquisition_bin = self.acquisition_bin\\n completed_ids = self.completed_ids\\n CandidateSpace = self.CandidateSpace\\n RegressionModel = self.RegressionModel\\n DuplicateFinder = self.DuplicateFinder\\n al_gen_dict = self.al_gen_dict\\n\\n stop_mode = self.stop_mode\\n stop_num_generations = self.stop_num_generations\\n\\n index_acq_gen_dict = self.index_acq_gen_dict\\n #__| #################################################################\\n\\n # #####################################################################\\n mode = \\\"lowest_N\\\" # 'lowest_N' or 'lowest_perc'\\n\\n N_ids = 10\\n lowest_perc = 5\\n\\n # Number of consecutive generations that the Nth best systems must\\n # remain static\\n M_gens = 3\\n # #####################################################################\\n\\n if mode == \\\"lowest_perc\\\":\\n num_candidates = CandidateSpace.FingerPrints.df_pre.shape[0]\\n N_ids = int(num_candidates * (lowest_perc * 0.01))\\n\\n gen_keys = list(AL.al_gen_dict.keys())\\n\\n if len(gen_keys) > M_gens:\\n latest_M_keys = gen_keys[-(M_gens + 1):]\\n last_gen_key = gen_keys[-1]\\n\\n al_gen_dict_subset_i = dict(zip(\\n latest_M_keys,\\n [AL.al_gen_dict.get(i, None) for i in latest_M_keys]))\\n\\n indices_list = []\\n iterator = enumerate(al_gen_dict_subset_i.items())\\n for i_cnt, (gen_i, AL_i) in iterator:\\n model_i = AL_i.model\\n\\n model_i = AL.add_main_Y_to_model(\\n model_i, plot_dft_instead_of_pred=True)\\n model_i = model_i[(model_i[\\\"duplicate\\\"] == False)]\\n model_i = model_i.sort_values(\\\"Y_main\\\")\\n\\n indices_i = model_i.index.tolist()\\n\\n indices_list.append(indices_i)\\n\\n if i_cnt >= M_gens:\\n indices_i = indices_list[i_cnt][0:N_ids]\\n ids_static_list = []\\n for j in range(M_gens):\\n indices_j = indices_list[i_cnt - (j + 1)][0:N_ids]\\n ids_static = indices_j == indices_i\\n ids_static_list.append(ids_static)\\n\\n ids_are_static = all(ids_static_list)\\n\\n self.performance__static_winners[last_gen_key] = ids_are_static\\n #__|\",\n \"def make_test_data(self):\\r\\n\\r\\n \\r\\n\\r\\n print (\\\"Creating Test Sample:\\\")\\r\\n\\r\\n print (' Period, rate, reps, phases: ', self.period, self.framerate, self.nrepetitions, self.nPhases)\\r\\n\\r\\n nframes = int(self.period * self.framerate * self.nrepetitions)\\r\\n\\r\\n print (' nframes: ', nframes)\\r\\n\\r\\n if self.bkgdNoise > 0.:\\r\\n\\r\\n d = np.random.normal(size=(nframes,self.imageSize[0],self.imageSize[1]),\\r\\n\\r\\n loc=self.bkgdIntensity, scale=self.bkgdNoise).astype('float32')\\r\\n\\r\\n else:\\r\\n\\r\\n d = self.bkgdIntensity*np.ones((nframes,self.imageSize[0],self.imageSize[1])).astype('float32')\\r\\n\\r\\n \\r\\n\\r\\n ds = d.shape\\r\\n\\r\\n print (' data shape: ', ds)\\r\\n\\r\\n dx = int(ds[2]/4)\\r\\n\\r\\n xc = int(ds[2]/2)\\r\\n\\r\\n xo = [xc-dx, xc+dx]\\r\\n\\r\\n ywidth = int(ds[2]/(self.nPhases+2))\\r\\n\\r\\n framedelay = 4\\r\\n\\r\\n\\r\\n\\r\\n if not self.mode:\\r\\n\\r\\n self.phasex = []\\r\\n\\r\\n self.phasey = []\\r\\n\\r\\n for i in range(0,self.nPhases):\\r\\n\\r\\n dy = int((i+1)*ds[2]/(self.nPhases+2)) # each phase is assigned to a region\\r\\n\\r\\n self.resp = np.zeros((nframes,))\\r\\n\\r\\n self.resp = np.cos(\\r\\n\\r\\n np.linspace(0, 2.0*np.pi*nframes/(self.period*self.framerate), nframes-framedelay)+i*np.pi/8 - np.pi/2.0)\\r\\n\\r\\n self.resp = np.concatenate((np.zeros(framedelay), self.resp))\\r\\n\\r\\n d[:, xo[0]:xo[1], dy:dy+ywidth ] += self.resp[:, np.newaxis, np.newaxis]\\r\\n\\r\\n self.phasey.append( (2+(dy+int(ds[2]/self.nPhases))/2))\\r\\n\\r\\n self.phasex.append((6+int(ds[1]/2)/2)) # make the signal equivalent of digitized one (baseline 3000, signal at 1e-4 of baseline)\\r\\n\\r\\n else:\\r\\n\\r\\n self.nPhases = 4\\r\\n\\r\\n self.spotsize = 16\\r\\n\\r\\n nrpts = 20\\r\\n\\r\\n nsites = 4\\r\\n\\r\\n one_rep = int(self.period*self.framerate)\\r\\n\\r\\n isi = int(self.period*self.framerate/self.nPhases)\\r\\n\\r\\n print('period, isi: ', self.period, isi)\\r\\n\\r\\n r = np.arange(0, nrpts, 1.)\\r\\n\\r\\n alpha = 4.\\r\\n\\r\\n A = r/alpha *np.exp(-(r-alpha)/alpha) # scaled alpha function\\r\\n\\r\\n self.spot= self.gauss_spot(self.spotsize, 3.) # the 2d spot\\r\\n\\r\\n sigsize = np.random.normal(size=self.nPhases, loc=self.signal_size, scale=self.signal_size*2)\\r\\n\\r\\n sigsize = [np.abs(s) for s in sigsize] # restrict to positive amplitudes\\r\\n\\r\\n print ('sigsize: ', sigsize)\\r\\n\\r\\n for j in range(self.nrepetitions):\\r\\n\\r\\n for i in range(self.nPhases):\\r\\n\\r\\n self.resp = np.zeros((nrpts, self.spot.shape[0], self.spot.shape[1]))\\r\\n\\r\\n for k in range(nrpts):\\r\\n\\r\\n self.resp[k,:,:] += sigsize[i]*A[k] * self.spot # make response an alpha time course of gaussian spot\\r\\n\\r\\n start = j*one_rep + i*isi + framedelay\\r\\n\\r\\n stop = start + nrpts\\r\\n\\r\\n dy = int((i+1)*ds[2]/(self.nPhases+2)) # location for phase\\r\\n\\r\\n #dy = dy + 2*z\\r\\n\\r\\n# print ('start, stop: ', start, stop)\\r\\n\\r\\n for z in range(nsites):\\r\\n\\r\\n #self.resp = np.concatenate((np.zeros(framedelay), self.resp))\\r\\n\\r\\n xp = xo[0] + i*10 - 10*z\\r\\n\\r\\n yp = dy - i*10 + 10*z\\r\\n\\r\\n d[start:stop, xp:xp+self.spotsize, yp:yp+self.spotsize ] += self.resp\\r\\n\\r\\n self.imageData = d # reduce to a 16-bit map to match camera data type\\r\\n\\r\\n self.nFrames = self.imageData.shape[0]\\r\\n\\r\\n self.times = np.arange(0, nframes/self.framerate, 1.0/self.framerate)\\r\\n\\r\\n print( \\\" Test Image Created\\\")\\r\\n\\r\\n # imv = pg.ImageView()\\r\\n\\r\\n # imv.show()\\r\\n\\r\\n # imv.setImage(self.imageData)\\r\\n\\r\\n\\r\\n\\r\\n if self.layout is not None:\\r\\n\\r\\n self.layout.addWidget(imv, 0, 0)\\r\\n\\r\\n\\r\\n\\r\\n avgImage = np.mean(self.imageData, axis=0)\\r\\n\\r\\n ima = pg.ImageView()\\r\\n\\r\\n ima.setImage(avgImage)\\r\\n\\r\\n self.layout.addWidget(ima, 0, 1)\\r\\n\\r\\n self.adjust_image_data()\\r\\n\\r\\n self.avgimg = np.mean(self.imageData, axis=0) # get mean image for reference later: average across all time\\r\\n\\r\\n print (' Test file, original Image Info: ')\\r\\n\\r\\n self.print_image_info()\\r\\n\\r\\n self.rebin_image()\\r\\n\\r\\n #self.clean_windowerrors()\\r\\n\\r\\n # pg.image(self.imageData)\\r\\n\\r\\n # pg.show()\\r\\n\\r\\n # mpl.figure(1)\\r\\n\\r\\n # mpl.show()\\r\\n\\r\\n if not self.mode: # FFT analysis\\r\\n\\r\\n self.analysis_fourier_map(target=1, mode=0)\\r\\n\\r\\n self.plot_maps(mode=2, gfilter=self.gfilter)\\r\\n\\r\\n else:\\r\\n\\r\\n self.analysis_dFF_map()\\r\\n\\r\\n mpl.show()\",\n \"def setUpClass(cls):\\n cls.sim1 = Simulation(logging_level=50)\\n cls.sim1.set_simulation_parameters(\\n seed=1, task=47, output_directory=\\\"output\\\", min_speciation_rate=0.01, sigma=2, deme=1, sample_size=0.01\\n )\\n cls.sim1.set_map_files(\\n \\\"null\\\", fine_file=\\\"sample/SA_sample_fine.tif\\\", reproduction_map=\\\"sample/SA_sample_reproduction.tif\\\"\\n )\\n cls.sim1.run()\\n cls.sim2 = Simulation(logging_level=50)\\n cls.sim2.set_simulation_parameters(\\n seed=1, task=48, output_directory=\\\"output\\\", min_speciation_rate=0.01, sigma=2, deme=1, sample_size=0.01\\n )\\n cls.sim2.set_map_files(\\\"null\\\", fine_file=\\\"sample/SA_sample_fine.tif\\\")\\n cls.sim2.run()\\n cls.sim3 = Simulation(logging_level=50)\\n cls.sim3.set_simulation_parameters(\\n seed=2, task=47, output_directory=\\\"output\\\", min_speciation_rate=0.01, sigma=2, deme=1, sample_size=0.01\\n )\\n cls.sim3.set_map_files(\\n \\\"null\\\",\\n fine_file=\\\"sample/SA_sample_fine.tif\\\",\\n death_map=\\\"sample/SA_sample_reproduction.tif\\\",\\n reproduction_map=\\\"sample/SA_sample_reproduction.tif\\\",\\n )\\n cls.sim3.run()\\n cls.sim4 = Simulation(logging_level=50)\\n cls.sim4.set_simulation_parameters(\\n seed=4, task=47, output_directory=\\\"output\\\", min_speciation_rate=0.01, sigma=2, deme=1, sample_size=0.01\\n )\\n cls.sim4.set_map_files(\\\"null\\\", fine_file=\\\"sample/SA_sample_coarse_pristine.tif\\\")\\n cls.sim4.add_reproduction_map(reproduction_map=\\\"sample/SA_reproduction_coarse.tif\\\")\\n cls.sim4.add_death_map(death_map=\\\"sample/SA_death.tif\\\")\\n cls.sim4.add_dispersal_map(dispersal_map=\\\"sample/dispersal_fine2.tif\\\")\\n cls.sim4.run()\\n cls.coal1 = CoalescenceTree(cls.sim1)\\n cls.coal2 = CoalescenceTree(cls.sim2)\\n cls.coal3 = CoalescenceTree(cls.sim3)\\n cls.coal4 = CoalescenceTree(cls.sim4)\",\n \"def test_make_uniform_random_f1f2_gate(self):\\n num_trials = 100\\n fanin_frac = .5\\n # test fam1 and fam2 gates:\\n generate = g.TYPE_TO_GATE_GEN[g.TEST_TYPES.RANDOM]\\n W = 10\\n ultimate_level = [g.make_random_input_wire(\\\"W\\\" + str(gate_num))\\n for gate_num in xrange(W)]\\n # a dictionary mapping each gate to how many times it was used as input:\\n num_uses_dict = dict(\\n (wire.get_name(), 0) for wire in ultimate_level)\\n # a dictionary mapping gate type to a list of the number of times\\n # that gate type is generated:\\n gate_type_dict = dict(\\n (gate_type, 0) for gate_type in g.GATE_TYPES.values_generator())\\n for trial_num in xrange(num_trials):\\n # create a new gate:\\n gate = generate(ultimate_level, fanin_frac, \\\"this_gate\\\")\\n # add the statistics to num_uses and gate_type_dist:\\n gate_type_dict[gate.get_func_name()] += 1\\n for wire in gate.get_inputs():\\n num_uses_dict[wire.get_name()] += 1\\n # make sure that each gate type appears between .2 and .8 of the time:\\n # (note that these numbers are chosen randomly, and therefor this test\\n # might occasionally fail with a low probability)\\n for gate_type in gate_type_dict.keys():\\n self.assertTrue(float(gate_type_dict[gate_type]) / float(num_trials)\\n > .2)\\n self.assertTrue(float(gate_type_dict[gate_type]) / float(num_trials)\\n < .8)\\n # make sure that each wire is chosen as input between .2 and .8 of the\\n # time: (note that these numbers are chosen randomly, and therefor this\\n # test might occasionally fail with a low probability)\\n for wire in num_uses_dict.keys():\\n self.assertTrue(float(num_uses_dict[wire]) / float(num_trials)\\n > (fanin_frac) / 2.0)\\n self.assertTrue(float(num_uses_dict[wire]) / float(num_trials)\\n < max(fanin_frac * 2.0, 1.0))\",\n \"def test_shuffled(self):\\n self.setup_flags()\\n self.io_args.matches = os.path.join(\\n self.io_args.output_root, \\\"shuffled\\\", \\\"matches.json\\\"\\n )\\n self._calibration_error_test(\\\"shuffled\\\", \\\"GeometricCalibration\\\")\",\n \"def test2():\\n\\t\\n\\td = np.linspace(-15000,15000,2500)\\n\\t#Voigt\\n\\n\\n\\t## 700 G, 84 C, Cs, 75mm\\n\\n\\tp_dict = {'Bfield':700,'Btheta':90*np.pi/180,'lcell':75e-3,'T':84,'Dline':'D2','Elem':'Cs'}\\n\\tpol = 1./np.sqrt(2)*np.array([1.0,1.0,0.0])\\n\\tTVx = get_spectra(d,pol,p_dict,outputs=['I_M45','I_P45','Ix','Iy','S0','Iz'])\\n\\t\\n\\tfig2 = plt.figure()\\n\\tax1a = fig2.add_subplot(411)\\n\\tax2a = fig2.add_subplot(412,sharex=ax1a)\\n\\tax3a = fig2.add_subplot(413,sharex=ax1a)\\n\\tax4a = fig2.add_subplot(414,sharex=ax1a)\\n\\t\\n\\tax1a.plot(d,TVx[0],'r',lw=2,label=r'$I_{-45}$')\\n\\tax2a.plot(d,TVx[1],'b',lw=2,label=r'$I_{+45}$')\\n\\tax3a.plot(d,TVx[2],'r',lw=2,label=r'$I_x$')\\n\\tax4a.plot(d,TVx[3],'b',lw=2,label=r'$I_y$')\\n\\tax4a.plot(d,TVx[0]+TVx[1],'r:',lw=3.5,label=r'$I_{+45}+I_{-45}$')\\n\\tax4a.plot(d,TVx[2]+TVx[3],'k:',lw=2.5,label=r'$I_x + I_y$')\\n\\tax4a.plot(d,TVx[4],'g--',lw=1.5,label='$S_0$')\\n\\t#\\tax4a.plot(d,TVx[5],'c--',lw=2.5,label='$I_z$')\\n\\t\\n\\t\\n\\tax4a.set_xlabel('Detuning (MHz)')\\n\\tax1a.set_ylabel('I -45')\\n\\tax2a.set_ylabel('I +45')\\n\\tax3a.set_ylabel('Ix')\\n\\tax4a.set_ylabel('Iy')\\n\\t\\n\\tax4a.set_xlim(d[0],d[-1]+3000)\\n\\tax4a.legend(loc=0)\\n\\t\\n\\tplt.show()\",\n \"def simulate_stage2(lr_instance, stage1_vars_vals, scenarios, weights=None,\\n stage2_info=None):\\n weighted_costs = []\\n stage2_costs = {}\\n for i, (scenario_name, scenario) in enumerate(scenarios.items()):\\n if stage2_info is None and i == 0:\\n stage2_info = lr_instance.construct_stage2(scenario)\\n # update and solve stage 2\\n stage2_info, stage2_objval = lr_instance.update_stage2(\\n stage1_vars_vals, scenario, stage2_info, True)\\n if stage2_objval is None:\\n sys.exit(\\\"Stage 2 is infeasible or unbounded for scenario\\\",\\n scenario_name)\\n stage2_costs[scenario_name] = stage2_objval\\n if weights is not None:\\n weighted_costs.append(weights[scenario_name] * stage2_objval)\\n return np.sum(weighted_costs), stage2_costs\",\n \"def test_stop_start_recovered(self):\\n log.info(\\\"CONFIG: %s\\\", self._agent_config())\\n self.create_sample_data_set_dir(\\n 'DOS15908_1st7_step1.DAT',\\n RECOV_DIR,\\n \\\"DOS15908.DAT\\\",\\n copy_metadata=False\\n )\\n\\n self.assert_initialize(final_state=ResourceAgentState.COMMAND)\\n\\n # Slow down processing to 1 per second to give us time to stop\\n self.dataset_agent_client.set_resource({DriverParameter.RECORDS_PER_SECOND: 1})\\n self.assert_start_sampling()\\n\\n # Verify we get one sample\\n try:\\n # Read the first file and verify the data\\n result = self.data_subscribers.get_samples(DataParticleType.METADATA_RECOVERED, 1)\\n result1 = self.data_subscribers.get_samples(DataParticleType.SAMPLE_RECOVERED, 1)\\n result.extend(result1)\\n log.debug(\\\"RESULT: %s\\\", result)\\n\\n # Verify values\\n self.assert_data_values(result, 'test_data_1r.txt.result.yml')\\n self.assert_sample_queue_size(DataParticleType.METADATA_RECOVERED, 0)\\n self.assert_sample_queue_size(DataParticleType.SAMPLE_RECOVERED, 0)\\n\\n self.create_sample_data_set_dir(\\n 'DOS15908_1st7_step4.DAT',\\n RECOV_DIR,\\n \\\"DOS15909.DAT\\\",\\n copy_metadata=False\\n )\\n # Now read the first records of the second file then stop\\n result1 = self.data_subscribers.get_samples(DataParticleType.SAMPLE_RECOVERED, 2)\\n log.debug(\\\"RESULT 1: %s\\\", result1)\\n self.assert_stop_sampling()\\n \\n # Restart sampling and ensure we get the last 2 records of the file\\n self.assert_start_sampling()\\n result2 = self.data_subscribers.get_samples(DataParticleType.SAMPLE_RECOVERED, 2)\\n log.debug(\\\"RESULT 2: %s\\\", result2)\\n result = result1\\n result.extend(result2)\\n log.debug(\\\"RESULT: %s\\\", result)\\n self.assert_sample_queue_size(DataParticleType.METADATA_RECOVERED, 1)\\n self.assert_data_values(result, 'test_data_4r.txt.result.yml')\\n except SampleTimeout as e:\\n log.error(\\\"Exception trapped: %s\\\", e, exc_info=True)\\n self.fail(\\\"Sample timeout.\\\")\",\n \"def test_detector1pipeline4(_bigdata):\\n step = Detector1Pipeline()\\n step.save_calibrated_ramp = True\\n step.ipc.skip = True\\n step.persistence.skip = True\\n step.jump.rejection_threshold = 4.0\\n step.ramp_fit.save_opt = False\\n step.output_file = 'jw84600007001_02101_00001_nrs1_rate.fits'\\n step.run(_bigdata+'/pipelines/jw84600007001_02101_00001_nrs1_uncal.fits')\\n\\n # Compare ramp product\\n n_ramp = 'jw84600007001_02101_00001_nrs1_ramp.fits'\\n h = fits.open( n_ramp )\\n n_ref = _bigdata+'/pipelines/jw84600007001_02101_00001_nrs1_ramp_ref.fits'\\n href = fits.open( n_ref )\\n newh = fits.HDUList([h['primary'],h['sci'],h['err'],h['groupdq'],h['pixeldq']])\\n newhref = fits.HDUList([href['primary'],href['sci'],href['err'],href['groupdq'],h['pixeldq']])\\n result = fits.diff.FITSDiff(newh,\\n newhref,\\n ignore_keywords = ['DATE','CAL_VER','CAL_VCS','CRDS_VER','CRDS_CTX'],\\n rtol = 0.00001\\n )\\n assert result.identical, result.report()\\n\\n # Compare countrate image product\\n n_cr = 'jw84600007001_02101_00001_nrs1_rate.fits'\\n h = fits.open( n_cr )\\n n_ref = _bigdata+'/pipelines/jw84600007001_02101_00001_nrs1_rate_ref.fits'\\n href = fits.open( n_ref )\\n newh = fits.HDUList([h['primary'],h['sci'],h['err'],h['dq']])\\n newhref = fits.HDUList([href['primary'],href['sci'],href['err'],href['dq']])\\n result = fits.diff.FITSDiff(newh,\\n newhref,\\n ignore_keywords = ['DATE','CAL_VER','CAL_VCS','CRDS_VER','CRDS_CTX'],\\n rtol = 0.00001\\n )\\n assert result.identical, result.report()\",\n \"def generate(self):\\n tempSn_1 = self.sn_1\\n self.sn_1 = (self.an_2*self.sn_2 + self.an_1*self.sn_1) % self.mod\\n self.sn_2 = tempSn_1\\n return self.sn_1 / self.mod\",\n \"def setUp(self):\\n self.s = Simulation()\\n self.s['Retina']=GeneratorSheet(nominal_density=4.0)\\n self.s['V1']= CFSheet(nominal_density=4.0)\\n self.s['V2'] = CFSheet(nominal_density=4.0)\\n\\n self.s.connect('Retina','V1',delay=0.5,connection_type=CFProjection,\\n name='RtoV1',learning_fn=CFPLF_Hebbian())\\n\\n self.s.connect('Retina','V2',delay=0.5,connection_type=CFProjection,\\n name='RtoV2',learning_fn=CFPLF_Hebbian())\",\n \"def sample(self):\",\n \"def build_oblique_foF2_observed_by_radar(rad=\\\"cvw\\\", dates=[dt.datetime(2017,8,21), dt.datetime(2017,8,21)], bmnum=11, scan_num=1,\\n remove_first_range=800, remove_last_range=2500, power_drop=10., plot=True):\\n csv_fname = \\\"data/{rad}_{start}_{end}.csv\\\".format(rad=rad, start=dates[0].strftime(\\\"%Y.%m.%d.%H.%M\\\"), \\n end=dates[1].strftime(\\\"%Y.%m.%d.%H.%M\\\"))\\n if not os.path.exists(csv_fname):\\n fdata = FetchData( rad, [dates[0], dates[1]] )\\n s_params = [\\\"noise.sky\\\", \\\"tfreq\\\", \\\"frang\\\", \\\"rsep\\\", \\\"scan\\\", \\\"bmnum\\\"]\\n v_params = [\\\"slist\\\", \\\"v\\\", \\\"w_l\\\", \\\"p_l\\\", \\\"elv\\\"]\\n _, scans = fdata.fetch_data(by=\\\"scan\\\", s_params=s_params, v_params=v_params)\\n beams = []\\n # Resacle tx_frequency and estimate slant range \\n for scan in scans:\\n for beam in scan.beams:\\n if len(beam.slist) > 0:\\n setattr(beam, \\\"slant_range\\\", beam.frang + np.array(beam.slist.tolist()) * beam.rsep)\\n setattr(beam, \\\"tfreq\\\", np.round(beam.tfreq/1e3,1))\\n beams.append(beam)\\n # Extract oblique foF2 or MUF scan by scan\\n print(\\\"\\\\n Data will be averaged over {} scans\\\".format(scan_num))\\n print(\\\" Processing beam {}\\\".format(bmnum))\\n print(\\\" Remove first range {}\\\".format(remove_first_range))\\n print(\\\" Remove last range {}\\\".format(remove_last_range))\\n print(\\\" Power drop {}\\\".format(power_drop))\\n skip_distance, o_foF2, time_start, time_end = [], [], [], []\\n for i in range(len(scans)-scan_num):\\n rscan = scans[i:i+scan_num]\\n p_l, srange, tfrq, angle = [], [], [], []\\n for scan in rscan:\\n for beam in scan.beams:\\n if beam.bmnum == bmnum:\\n if len(beam.slist) > 0:\\n p_l.extend(beam.p_l.tolist())\\n srange.extend(beam.slant_range.tolist())\\n tfrq.append(beam.tfreq)\\n if type(beam.elv) is list: angle.extend(beam.elv)\\n else: angle.extend(beam.elv.tolist())\\n du = pd.DataFrame()\\n du[\\\"p_l\\\"], du[\\\"srange\\\"] = p_l, srange\\n du = du[(du.srange>remove_first_range) & (du.srange<remove_last_range)]\\n fname = \\\"images/{}.png\\\".format(rscan[0].stime.strftime(\\\"%Y-%m-%d-%H-%M\\\"))\\n if len(du) > 0:\\n sd = fit_lambda(du, power_drop, tfreq=np.mean(tfrq), elv=np.mean(angle).round(1), \\n xlabel=\\\"srange\\\", ylabel=\\\"p_l\\\", fname=fname, plot=plot)\\n if sd != np.nan:\\n skip_distance.append(sd)\\n o_foF2.append(np.mean(tfrq))\\n time_start.append(rscan[0].stime)\\n time_end.append(rscan[-1].stime)\\n df = pd.DataFrame()\\n df[\\\"skip_distance\\\"], df[\\\"o_foF2\\\"], df[\\\"time_start\\\"], df[\\\"time_end\\\"] = skip_distance, o_foF2, time_start, time_end\\n df.to_csv(csv_fname, index=False, header=True)\\n else: df = pd.read_csv(csv_fname, parse_dates=[\\\"time_start\\\", \\\"time_end\\\"])\\n print(\\\" Header:\\\\n\\\",df.head())\\n return df\",\n \"def test_get_spectra_bi():\\r\\n\\r\\n methods = (None,\\r\\n {\\\"this_method\\\": 'welch', \\\"NFFT\\\": 256, \\\"Fs\\\": 2 * np.pi},\\r\\n {\\\"this_method\\\": 'welch', \\\"NFFT\\\": 1024, \\\"Fs\\\": 2 * np.pi})\\r\\n\\r\\n for method in methods:\\r\\n arsig1, _, _ = utils.ar_generator(N=2 ** 16)\\r\\n arsig2, _, _ = utils.ar_generator(N=2 ** 16)\\r\\n\\r\\n avg_pwr1 = (arsig1 ** 2).mean()\\r\\n avg_pwr2 = (arsig2 ** 2).mean()\\r\\n avg_xpwr = (arsig1 * arsig2.conjugate()).mean()\\r\\n\\r\\n tseries = np.vstack([arsig1, arsig2])\\r\\n\\r\\n f, fxx, fyy, fxy = tsa.get_spectra_bi(arsig1, arsig2, method=method)\\r\\n\\r\\n # \\\\sum_{\\\\omega} PSD(\\\\omega) d\\\\omega:\\r\\n est_pwr1 = np.sum(fxx * (f[1] - f[0]))\\r\\n est_pwr2 = np.sum(fyy * (f[1] - f[0]))\\r\\n est_xpwr = np.sum(fxy * (f[1] - f[0])).real\\r\\n\\r\\n # Test that we have the right order of magnitude:\\r\\n npt.assert_array_almost_equal(est_pwr1, avg_pwr1, decimal=-1)\\r\\n npt.assert_array_almost_equal(est_pwr2, avg_pwr2, decimal=-1)\\r\\n npt.assert_array_almost_equal(np.mean(est_xpwr),\\r\\n np.mean(avg_xpwr),\\r\\n decimal=-1)\",\n \"def test_dataset_scenario_generation_full_outside3(self):\\n params = ParameterServer()\\n\\n map_filename = os.path.join(os.path.dirname(__file__), \\\"data/DR_DEU_Merging_MT_v01_shifted.xodr\\\")\\n track_filename = os.path.join(os.path.dirname(__file__), \\\"data/interaction_dataset_DEU_Merging_dummy_track_outside.csv\\\")\\n\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"MapFilename\\\"] = map_filename\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"TrackFilenameList\\\"] = [track_filename]\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"StartingOffsetMs\\\"] = 0\\n\\n scenario_generation = InteractionDatasetScenarioGenerationFull(\\n params=params, num_scenarios=3)\\n\\n scenario = scenario_generation.get_scenario(2)\\n self.assertAlmostEqual(scenario.eval_agent_ids, [3])\\n world_state = scenario.GetWorldState()\\n agent31 = world_state.GetAgent(1)\\n agent32 = world_state.GetAgent(2)\\n agent33 = world_state.GetAgent(3)\\n\\n # they all should be valid at the beginning\\n world_state.time = 0\\n self.assertEqual(isinstance(agent31, Agent), True)\\n self.assertEqual(agent31.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent31.InsideRoadCorridor(), True)\\n \\n self.assertEqual(isinstance(agent32, Agent), True)\\n self.assertEqual(agent32.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent32.InsideRoadCorridor(), True)\\n\\n self.assertEqual(isinstance(agent33, Agent), True)\\n self.assertEqual(agent33.IsValidAtTime(world_state.time), True)\\n self.assertEqual(agent33.InsideRoadCorridor(), True)\\n\\n world_state.Step(0.05)\\n self.assertEqual(len(world_state.agents_valid), 3)\",\n \"def test_evidence_2(base_settings):\\n filename = (\\n base_settings[\\\"unittest_data_dir\\\"]\\n / \\\"evidence-example-stroke-3-4half-alteplase-vs-no-alteplase-mRS0-2.json\\\"\\n )\\n inst = evidence.Evidence.parse_file(\\n filename, content_type=\\\"application/json\\\", encoding=\\\"utf-8\\\"\\n )\\n assert \\\"Evidence\\\" == inst.resource_type\\n\\n impl_evidence_2(inst)\\n\\n # testing reverse by generating data from itself and create again.\\n data = inst.dict()\\n assert \\\"Evidence\\\" == data[\\\"resourceType\\\"]\\n\\n inst2 = evidence.Evidence(**data)\\n impl_evidence_2(inst2)\",\n \"def radiation_measurement_analysis():\\n import pint\\n ureg = pint.UnitRegistry()\\n\\n mrem_h = ureg.parse_units('mrem') / ureg.hour\\n m = ureg.parse_units('meters')\\n s = ureg.parse_units('seconds')\\n\\n # Measurements of background radiation\\n bg_dist = ureg.parse_expression('10 m') # estimate of how far away we are wrt background\\n background_rows = [\\n dict(vid=1, distance=bg_dist, rad=0.023 * mrem_h, capture_time=0.0 * s),\\n dict(vid=1, distance=bg_dist, rad=0.022 * mrem_h, capture_time=0.0 * s),\\n dict(vid=1, distance=bg_dist, rad=0.023 * mrem_h, capture_time=4.0 * s),\\n dict(vid=1, distance=bg_dist, rad=0.021 * mrem_h, capture_time=5.0 * s),\\n dict(vid=1, distance=bg_dist, rad=0.023 * mrem_h, capture_time=11.0 * s),\\n dict(vid=1, distance=bg_dist, rad=0.023 * mrem_h, capture_time=16.0 * s),\\n dict(vid=1, distance=bg_dist, rad=0.024 * mrem_h, capture_time=20.0 * s),\\n ]\\n\\n # Measurements of sample radiation\\n esp_dist = ureg.parse_expression('1 inch').to(m) / 2 # estimate of how far we are from the sample when very close\\n dist0_rows = [\\n dict(vid=2, distance=esp_dist, rad=0.060 * mrem_h, capture_time=0.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.061 * mrem_h, capture_time=3.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.060 * mrem_h, capture_time=5.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.059 * mrem_h, capture_time=9.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.060 * mrem_h, capture_time=10.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.059 * mrem_h, capture_time=11.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.057 * mrem_h, capture_time=12.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.058 * mrem_h, capture_time=13.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.059 * mrem_h, capture_time=14.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.060 * mrem_h, capture_time=15.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.061 * mrem_h, capture_time=16.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.062 * mrem_h, capture_time=18.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.062 * mrem_h, capture_time=18.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.064 * mrem_h, capture_time=20.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.065 * mrem_h, capture_time=22.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.066 * mrem_h, capture_time=23.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.065 * mrem_h, capture_time=24.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.063 * mrem_h, capture_time=25.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.065 * mrem_h, capture_time=26.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.064 * mrem_h, capture_time=27.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.064 * mrem_h, capture_time=27.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.065 * mrem_h, capture_time=28.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.063 * mrem_h, capture_time=30.0 * s),\\n ]\\n\\n dist0_v2_rows = [\\n dict(vid=3, distance=esp_dist, rad=0.012 * mrem_h, capture_time=0.0 * s),\\n dict(vid=3, distance=esp_dist, rad=0.011 * mrem_h, capture_time=1.0 * s),\\n dict(vid=3, distance=esp_dist, rad=0.013 * mrem_h, capture_time=8.0 * s),\\n dict(vid=3, distance=esp_dist, rad=0.013 * mrem_h, capture_time=9.0 * s),\\n ]\\n\\n close_rows = [\\n dict(vid=4, distance=0.5 * m, rad=0.013 * mrem_h, capture_time=0.0 * s),\\n dict(vid=4, distance=0.5 * m, rad=0.014 * mrem_h, capture_time=5.0 * s),\\n dict(vid=4, distance=0.5 * m, rad=0.012 * mrem_h, capture_time=7.0 * s),\\n dict(vid=4, distance=0.5 * m, rad=0.011 * mrem_h, capture_time=15.0 * s),\\n dict(vid=4, distance=0.5 * m, rad=0.012 * mrem_h, capture_time=16.0 * s),\\n ]\\n\\n mid_rows = [\\n dict(vid=5, distance=1.0 * m, rad=0.014 * mrem_h, capture_time=0.0 * s),\\n dict(vid=5, distance=1.0 * m, rad=0.015 * mrem_h, capture_time=5.0 * s),\\n dict(vid=5, distance=1.0 * m, rad=0.013 * mrem_h, capture_time=10.0 * s),\\n ]\\n\\n far_rows = [\\n dict(vid=6, distance=2.0 * m, rad=0.023 * mrem_h, capture_time=0.0 * s),\\n dict(vid=6, distance=2.0 * m, rad=0.025 * mrem_h, capture_time=0.1 * s),\\n ]\\n\\n # guess_dist = ureg.parse_expression('0.3 m') # estimate of how far away we are wrt background\\n # guess_rows = [\\n # dict(vid=9, distance=guess_dist, rad=0.030 * mrem_h, capture_time=0.0 * s),\\n # dict(vid=9, distance=guess_dist, rad=0.041 * mrem_h, capture_time=2.0 * s),\\n # dict(vid=9, distance=guess_dist, rad=0.051 * mrem_h, capture_time=3.0 * s),\\n # ]\\n\\n rows = dist0_rows + background_rows + dist0_v2_rows + close_rows + mid_rows + far_rows\\n # rows += guess_rows\\n\\n import pandas as pd\\n import numpy as np\\n table = pd.DataFrame(rows)\\n\\n # Ensure comparable units\\n units = {\\n 'rad': mrem_h,\\n 'distance': m,\\n 'capture_time': s,\\n }\\n for key, unit in units.items():\\n table[key] = table[key].apply(lambda c: c.to(unit).m)\\n table['rad'] = table['rad'].astype(float)\\n table['distance'] = table['distance'].astype(float)\\n\\n # Weight each measurement based on the amount of time the measurement was\\n # sustained in the video.\\n average_rad_rows = []\\n for vid, group in table.groupby('vid'):\\n from statsmodels.stats.weightstats import DescrStatsW\\n weights = (-1 * group['capture_time'].diff(periods=-1).fillna(0)) / group['capture_time'].iloc[-1]\\n table.loc[group.index, 'weight'] = weights\\n values = group['rad']\\n weighted_stats = DescrStatsW(values, weights=weights, ddof=0)\\n dists = group['distance'].unique()\\n assert len(dists) == 1\\n average_rad_rows.append({\\n 'vid': vid,\\n 'distance': dists[0],\\n 'rad_mean': weighted_stats.mean,\\n 'rad_std': weighted_stats.std,\\n })\\n stats_table = pd.DataFrame(average_rad_rows)\\n\\n bg_row = stats_table.loc[stats_table['distance'].argmax()]\\n fg_row = stats_table.loc[stats_table['distance'].argmin()]\\n\\n # -------------------\\n ADD_DUMMY_VALUES = 0\\n if ADD_DUMMY_VALUES:\\n # Hack: because we don't have enough samples we can fudge the value\\n # knowning that the value should be the background radiation in the\\n # limit.\\n\\n dummy_measurements = []\\n extra_support = 1\\n for idx in range(3, 3 + extra_support):\\n dummy_row = {\\n 'vid': -idx,\\n 'distance': bg_row['distance'] + idx,\\n 'rad_mean': bg_row['rad_mean'],\\n 'rad_std': 0.01,\\n }\\n dummy_measurements.append(dummy_row)\\n\\n # also add an extra value close to the sample\\n rad_bg = bg_row['rad_mean']\\n rad_above_bg = fg_row['rad_mean'] - rad_bg\\n dummy_row = {\\n 'vid': -1,\\n 'distance': fg_row['distance'] / 2,\\n 'rad_mean': rad_bg + (rad_above_bg * 4),\\n 'rad_std': 0.5,\\n }\\n dummy_measurements.append(dummy_row)\\n\\n # dummy_row = {\\n # 'vid': -2,\\n # 'distance': fg_row['distance'] / 4,\\n # 'rad_mean': rad_bg + (rad_above_bg * 16),\\n # }\\n # dummy_measurements.append(dummy_row)\\n\\n dummy_stats = pd.DataFrame(dummy_measurements)\\n dummy_stats['weight'] = 0.5\\n stats_table['weight'] = 1.0\\n stats_table2 = pd.concat([stats_table, dummy_stats]).reset_index(drop=True).sort_values('distance')\\n else:\\n stats_table2 = stats_table\\n # -------------------\\n\\n import scipy\\n scipy.optimize.curve_fit\\n\\n # Because we know the radiation should follow an inverse square law wrt to\\n # distance, we can fit a polynomial of degree 2 (parabola) to interpolate /\\n # extrapolate the **inverse** values.\\n x = stats_table2['distance'].values\\n y = stats_table2['rad_mean'].values\\n s = stats_table2['rad_std'].values\\n\\n # Model the squared falloff directly\\n def invsquare(x, a, b):\\n return a * (1 / (0.01 + x ** 2)) + b\\n # bg_row['rad_mean']\\n # Use curve_fit to constrain the first coefficient to be zero\\n try:\\n coef = scipy.optimize.curve_fit(invsquare, x, y, sigma=s, method='trf')[0]\\n except Exception as ex:\\n coef = None\\n print(f'ex={ex}')\\n\\n # Also fit one to the raw weighted points as a sanity check\\n # inv_poly2 = Polynomial.fit(table['distance'], 1 / table['rad'], w=table['weight'], deg=2)\\n\\n import kwplot\\n sns = kwplot.autosns()\\n plt = kwplot.autoplt()\\n # ax = sns.boxplot(data=table, x='distance', y='rad', width=0.1)\\n\\n # Add in points to show each observation\\n ax = sns.relplot(x=\\\"distance\\\", y=\\\"rad\\\", data=table, size=4, color=\\\".3\\\",\\n linewidth=0, alpha=0.5, palette='deep')\\n\\n ax = plt.gca()\\n ax.set_xlabel('distance from sample ({})'.format(str(units['distance'])))\\n ax.set_ylabel('radiation dosage ({})'.format(str(units['rad'])))\\n\\n max_meters = 10\\n\\n extrap_x = np.linspace(0, max_meters, 1000)\\n if coef is not None:\\n extrap_y1 = invsquare(extrap_x, *coef)\\n # extrap_y2 = 1 / inv_poly2(extrap_x)\\n ax.plot(stats_table2['distance'].values, stats_table2['rad_mean'].values, 'rx')\\n ax.plot(stats_table['distance'].values, stats_table['rad_mean'].values, 'bo')\\n ax.plot(extrap_x, extrap_y1, '--')\\n ax.set_ylim(0.001, 0.1)\\n ax.set_yscale('log')\\n # ax.plot(extrap_x, extrap_y2, '--')\",\n \"def sample_trajectory(self, env, animate_this_episode, is_evaluation):\\n # Using current task with meta inside\\n env.reset_task(is_evaluation=is_evaluation)\\n stats = []\\n #====================================================================================#\\n # ----------PROBLEM 2----------\\n #====================================================================================#\\n ep_steps = 0\\n steps = 0\\n\\n num_samples = max(self.history, self.max_path_length + 1)\\n meta_obs = np.zeros((num_samples + self.history + 1, self.meta_ob_dim))\\n rewards = []\\n\\n while True:\\n if animate_this_episode:\\n env.render()\\n time.sleep(0.1)\\n\\n if ep_steps == 0:\\n ob = env.reset()\\n # first meta ob has only the observation\\n # set a, r, d to zero, construct first meta observation in meta_obs\\n # YOUR CODE HERE\\n ac = np.zeros(self.ac_dim); rew = np.zeros(self.reward_dim); done = np.zeros(self.terminal_dim)\\n meta_obs[steps, :] = np.concatenate((ob, ac, rew, done))\\n steps += 1\\n\\n # index into the meta_obs array to get the window that ends with the current timestep\\n # please name the windowed observation `in_` for compatibilty with the code that adds to the replay buffer (lines 418, 420)\\n # YOUR CODE HERE\\n # padding for input obs size\\n sample_action_in_ = meta_obs[steps-self.history:steps, :] if steps>=self.history else np.squeeze(np.concatenate(([meta_obs[0,:], ] * (self.history - steps), meta_obs[:steps, :]), axis=0))\\n # need to clear hidden size, in order to avoid previous hidden state as it may be generated by the other totally different task (env setting may be changed)\\n hidden = np.zeros((1, self.gru_size), dtype=np.float32)\\n\\n # get action from the policy\\n # YOUR CODE HERE\\n # Tensor(\\\"ob:0\\\", shape=(?, 1, 10), dtype=float32)\\n # print(self.sy_ob_no)\\n # Tensor(\\\"hidden:0\\\", shape=(?, 32), dtype=float32)\\n # print(self.sy_hidden)\\n ac = self.sess.run(self.sy_sampled_ac, feed_dict={\\n self.sy_ob_no: sample_action_in_.reshape(-1, self.history, self.meta_ob_dim),\\n self.sy_hidden: hidden,\\n })\\n assert len(ac) == 1\\n ac = ac[0]\\n\\n # step the environment\\n # YOUR CODE HERE\\n ob, rew, done, _= env.step(ac)\\n\\n ep_steps += 1\\n\\n done = bool(done) or ep_steps == self.max_path_length\\n # construct the meta-observation and add it to meta_obs\\n # YOUR CODE HERE\\n meta_obs[steps, :] = np.concatenate((ob, ac, [rew], [done]))\\n\\n rewards.append(rew)\\n steps += 1\\n\\n in_ = meta_obs[steps, :]\\n # add sample to replay buffer\\n if is_evaluation:\\n self.val_replay_buffer.add_sample(in_, ac, rew, done, hidden, env._goal)\\n else:\\n self.replay_buffer.add_sample(in_, ac, rew, done, hidden, env._goal)\\n\\n # start new episode\\n if done:\\n # compute stats over trajectory\\n s = dict()\\n s['rewards']= rewards[-ep_steps:]\\n s['ep_len'] = ep_steps\\n stats.append(s)\\n ep_steps = 0\\n\\n if steps >= num_samples:\\n break\\n\\n return steps, stats\",\n \"def rate_experiment_two(rate_x, var_rate, rate_y, var_rate2, rate_z, pepa_model):\\n from itertools import product\\n rate_ys = []\\n rate_xs = []\\n rate_zs = []\\n rates = pepa_model.get_rates()\\n if rate_x not in rates:\\n print(\\\"No such rate {}\\\".format(rate_x))\\n exit(1)\\n for i,j in product(var_rate, var_rate2):\\n rates[rate_x] = str(i)\\n rates[rate_y] = str(j)\\n rate_xs.append(float(i))\\n rate_ys.append(float(j))\\n pepa_model.recalculate(rates)\\n pepa_model.steady_state()\\n rate_zs.append( get_rate_from_actset(rate_z, pepa_model.get_throughoutput()))\\n return (rate_xs, rate_ys, rate_zs)\",\n \"def ssd_synthia_car_fine_tune():\\n merged_annotation = '/home/public/synthia/ssd_car_fine_tune/SYNTHIA-SEQS-01-TRAIN_MERGED-shuffle.json'\\n if False:\\n print('we combine the training and validation here')\\n annotations_url_1 = '/home/public/synthia/SYNTHIA-SEQS-01-TRAIN-shuffle.json'\\n annotations_url_2 = '/home/public/synthia/SYNTHIA-SEQS-01-VALIDATE-shuffle.json'\\n combine_gt(annotations_url_1, annotations_url_2, merged_annotation)\\n\\n if False:\\n print('collect front and rear cars')\\n annotations_url_1 = '/home/public/synthia/SYNTHIA-SEQS-01-TRAIN-shuffle.json'\\n annotations_url_2 = '/home/public/synthia/SYNTHIA-SEQS-01-VALIDATE-shuffle.json'\\n save_dir = '/home/stevenwudi/PycharmProjects/autonomous_driving/Experiments/SEQ_01_SEQ_06_cars'\\n collect_front_and_rear_gt(annotations_url_1, annotations_url_2, save_dir, image_interval=50)\\n\\n gt_file = '/home/public/synthia/ssd_car_fine_tune/ssd_car_fine_tune_gt-shuffle.pkl'\\n if False:\\n print('Training annotation conversion')\\n converting_gt(merged_annotation, gt_file, POR=1e-3)\\n # POR: 1e-3 Finish converting, total annotated car number is 22332 in total image of 8814.\\n # POR: 5e-4: Finish converting, total annotated fish number is 26800 in total image of 8814.\\n\\n model_checkpoint = '/home/public/synthia/ssd_car_fine_tune/weights_512.54-0.19.hdf5'\\n if False:\\n print('Start DDS 512 training')\\n train_ssd512(gt_file, model_checkpoint=model_checkpoint, base_lr=1e-5)\\n\\n test_gt_file = '/home/public/synthia/ssd_car_fine_tune/ssd_car_test_gt-shuffle.pkl'\\n if False:\\n print('Converting testing GT')\\n annotations_url = '/home/public/synthia/SYNTHIA-SEQS-01-TEST-shuffle.json'\\n converting_gt(annotations_url, test_gt_file)\\n if False:\\n # Examine test data\\n examine_ssd512(test_gt_file, model_checkpoint)\\n\\n test_json_file = '/home/public/synthia/ssd_car_fine_tune/ssd_car_test-shuffle_nms_'+str(nms_thresh)+'.json'\\n if False:\\n test_ssd512(test_gt_file, model_checkpoint, test_json_file)\\n # A separate file for accepting gt file and predicted json fil\\n if True:\\n calculate_iou(test_gt_file, test_json_file, POR=2e-3, draw=False)\\n\\n test_gt_file = '/home/public/synthia/ssd_car_fine_tune/ssd_car_test_gt-shuffle.pkl'\\n test_json_file = '/home/public/synthia/ssd_car_test_faster-shuffle.json'\\n\\n calculate_iou(test_gt_file, test_json_file, POR=2e-3, draw=False)\\n \\\"\\\"\\\"\\n<<<<<<< HEAD\\n This is the network results train by SSD512 (with 0.05% POR trained)\\n Conf: [ 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]\\n \\n ### POR=1e-3\\n Total GT: 2327. \\n Total prediction: [ 2016. 1986. 1951. 1918. 1888. 1852. 1815. 1774. 1711. 1641.]\\n Precision: [ 0.913 0.908 0.9 0.895 0.886 0.879 0.867 0.853 0.83 0.804]\\n Recall: [ 0.791 0.787 0.78 0.775 0.768 0.761 0.751 0.739 0.719 0.697]\\n F score: [[ 0.847 0.843 0.837 0.825 0.816 0.79 0.748 0.666 0.449 0.106]\\n [ 0.843 0.839 0.834 0.823 0.813 0.788 0.748 0.665 0.449 0.106]\\n [ 0.835 0.833 0.829 0.818 0.809 0.786 0.747 0.665 0.449 0.106]\\n [ 0.831 0.829 0.826 0.815 0.807 0.784 0.746 0.664 0.449 0.105]\\n [ 0.823 0.822 0.82 0.81 0.802 0.781 0.744 0.663 0.447 0.105]\\n [ 0.816 0.816 0.815 0.806 0.799 0.778 0.741 0.661 0.446 0.105]\\n [ 0.805 0.805 0.806 0.797 0.792 0.772 0.738 0.66 0.445 0.105]\\n [ 0.792 0.793 0.793 0.789 0.784 0.766 0.734 0.658 0.445 0.105]\\n [ 0.77 0.773 0.775 0.771 0.768 0.753 0.725 0.652 0.441 0.104]\\n [ 0.746 0.749 0.751 0.751 0.75 0.741 0.716 0.645 0.439 0.103]]\\n<<<<<<< HEAD\\n\\n ### faster RCNN detection result POR= 1e-3\\n Total GT: 2327.\\n Total prediction: [ 2120. 2084. 2062. 2037. 2003. 1971. 1947. 1901. 1862. 1785.]\\n Precision: [ 0.83 0.826 0.825 0.823 0.82 0.818 0.817 0.807 0.801 0.787]\\n Recall: [ 0.756 0.752 0.751 0.75 0.747 0.745 0.744 0.735 0.73 0.717]\\n F score: [[ 0.791 0.758 0.72 0.669 0.59 0.485 0.332 0.174 0.054 0.006]\\n [ 0.787 0.756 0.719 0.669 0.59 0.485 0.332 0.174 0.054 0.006]\\n [ 0.786 0.755 0.718 0.668 0.589 0.485 0.332 0.174 0.054 0.006]\\n [ 0.785 0.754 0.718 0.668 0.589 0.484 0.332 0.174 0.054 0.006]\\n [ 0.782 0.752 0.716 0.666 0.588 0.484 0.331 0.174 0.054 0.006]\\n [ 0.78 0.75 0.715 0.666 0.588 0.484 0.331 0.174 0.054 0.006]\\n [ 0.779 0.749 0.714 0.665 0.588 0.484 0.331 0.174 0.054 0.006]\\n [ 0.77 0.745 0.711 0.663 0.586 0.483 0.331 0.174 0.054 0.006]\\n [ 0.764 0.74 0.709 0.66 0.584 0.482 0.331 0.173 0.054 0.006]\\n [ 0.75 0.73 0.703 0.657 0.582 0.481 0.33 0.173 0.054 0.006]]\\n\\n=======\\n \\n ### POR shuffle\\n Total GT: 2563. \\n Total prediction: [ 2185. 2137. 2081. 2047. 2010. 1976. 1937. 1893. 1831. 1713.]\\n Precision: [ 0.843 0.836 0.826 0.819 0.813 0.805 0.795 0.784 0.766 0.726]\\n Recall: [ 0.719 0.712 0.704 0.698 0.693 0.686 0.678 0.669 0.653 0.619]\\n F score: [[ 0.776 0.698 0.646 0.619 0.597 0.571 0.525 0.447 0.31 0.106]\\n [ 0.769 0.693 0.642 0.616 0.595 0.57 0.524 0.447 0.31 0.106]\\n [ 0.76 0.686 0.637 0.612 0.591 0.568 0.523 0.445 0.31 0.106]\\n [ 0.754 0.681 0.632 0.608 0.588 0.565 0.521 0.443 0.31 0.106]\\n [ 0.749 0.677 0.629 0.605 0.586 0.563 0.52 0.442 0.309 0.106]\\n [ 0.741 0.671 0.624 0.602 0.583 0.561 0.518 0.441 0.309 0.106]\\n [ 0.732 0.664 0.619 0.597 0.579 0.558 0.516 0.44 0.308 0.106]\\n [ 0.722 0.656 0.612 0.59 0.573 0.553 0.515 0.439 0.308 0.106]\\n [ 0.705 0.642 0.6 0.58 0.564 0.545 0.508 0.435 0.306 0.105]\\n [ 0.668 0.614 0.578 0.561 0.548 0.531 0.496 0.426 0.302 0.105]]\\n \\n POR = 2e-3\\n=======\\n ############################# SSD512 NMS 0.6 ###########################\\n Conf: [ 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]\\n>>>>>>> 419d28c4e27b82c4cfef6a3aa01425cf29929973\\n Total GT: 1433. \\n Total prediction: [ 1617. 1605. 1593. 1579. 1568. 1551. 1537. 1517. 1498. 1465.]\\n Precision: [ 0.821 0.819 0.818 0.818 0.816 0.811 0.809 0.804 0.8 0.792]\\n Recall: [ 0.927 0.925 0.923 0.923 0.92 0.915 0.913 0.907 0.902 0.894]\\n F score: [[ 0.871 0.868 0.859 0.852 0.836 0.81 0.742 0.636 0.48 0.161]\\n [ 0.869 0.866 0.857 0.85 0.834 0.808 0.741 0.635 0.48 0.161]\\n [ 0.867 0.864 0.857 0.849 0.834 0.808 0.741 0.635 0.48 0.161]\\n [ 0.867 0.864 0.857 0.849 0.834 0.808 0.741 0.635 0.48 0.161]\\n [ 0.865 0.862 0.855 0.848 0.832 0.807 0.741 0.635 0.48 0.161]\\n [ 0.86 0.857 0.851 0.844 0.828 0.804 0.739 0.633 0.479 0.161]\\n [ 0.858 0.855 0.849 0.843 0.827 0.802 0.738 0.633 0.478 0.161]\\n [ 0.852 0.85 0.844 0.839 0.823 0.8 0.736 0.631 0.477 0.16 ]\\n [ 0.848 0.846 0.841 0.836 0.82 0.797 0.734 0.631 0.477 0.16 ]\\n [ 0.84 0.838 0.834 0.829 0.814 0.792 0.729 0.629 0.476 0.16 ]]\\n \\n ############################# SSD512 NMS 0.45 ###########################\\n Total GT: 1433. \\n Total prediction: [ 1438. 1428. 1421. 1413. 1403. 1398. 1384. 1374. 1348. 1305.]\\n Precision: [ 0.92 0.918 0.917 0.914 0.911 0.91 0.905 0.902 0.894 0.874]\\n Recall: [ 0.923 0.921 0.92 0.918 0.914 0.913 0.908 0.905 0.897 0.877]\\n F score: [[ 0.922 0.918 0.91 0.901 0.883 0.851 0.785 0.673 0.476 0.164]\\n [ 0.92 0.916 0.908 0.9 0.882 0.851 0.785 0.673 0.476 0.164]\\n [ 0.918 0.915 0.907 0.899 0.882 0.851 0.785 0.673 0.476 0.164]\\n [ 0.916 0.913 0.905 0.897 0.88 0.849 0.785 0.673 0.476 0.164]\\n [ 0.913 0.91 0.903 0.895 0.878 0.847 0.783 0.671 0.475 0.164]\\n [ 0.911 0.909 0.901 0.894 0.877 0.846 0.782 0.671 0.475 0.164]\\n [ 0.906 0.905 0.898 0.89 0.874 0.843 0.781 0.67 0.475 0.164]\\n [ 0.904 0.901 0.895 0.888 0.871 0.84 0.78 0.67 0.475 0.164]\\n [ 0.896 0.894 0.888 0.882 0.866 0.836 0.776 0.668 0.474 0.164]\\n [ 0.876 0.876 0.872 0.869 0.855 0.829 0.77 0.663 0.471 0.164]]\\n \\n \\n<<<<<<< HEAD\\n>>>>>>> bb5caf05d4bc41e182e41686b8b5e497053f9ca5\\n ### POR = None (consider all testing examples)\\n Total GT: 2696. \\n Total prediction: [ 2273. 2221. 2166. 2111. 2055. 2000. 1945. 1875. 1786. 1684.]\\n Precision: [ 0.822 0.818 0.81 0.805 0.796 0.789 0.777 0.763 0.741 0.716]\\n Recall: [ 0.693 0.69 0.683 0.678 0.671 0.665 0.655 0.643 0.625 0.604]\\n F score: [[ 0.752 0.746 0.74 0.729 0.724 0.703 0.668 0.597 0.405 0.096]\\n [ 0.748 0.744 0.737 0.727 0.722 0.702 0.668 0.597 0.405 0.096]\\n [ 0.741 0.737 0.733 0.723 0.718 0.699 0.667 0.596 0.405 0.096]\\n [ 0.736 0.733 0.729 0.72 0.716 0.698 0.666 0.596 0.404 0.095]\\n [ 0.728 0.726 0.724 0.716 0.712 0.695 0.664 0.595 0.403 0.095]\\n [ 0.722 0.721 0.72 0.712 0.708 0.692 0.661 0.593 0.402 0.095]\\n [ 0.711 0.711 0.711 0.704 0.703 0.687 0.659 0.592 0.401 0.095]\\n [ 0.698 0.7 0.7 0.696 0.696 0.682 0.655 0.59 0.401 0.095]\\n [ 0.678 0.682 0.684 0.681 0.681 0.67 0.647 0.585 0.398 0.095]\\n [ 0.655 0.659 0.662 0.663 0.665 0.659 0.639 0.578 0.395 0.094]]\\n\\n POR = 2e-3 fastercnn result\\n Conf: [ 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]\\n Total GT: 1433.\\n Total prediction: [ 1614. 1601. 1593. 1585. 1576. 1561. 1557. 1544. 1532. 1507.]\\n=======\\n ############################# Faster-RCNN ###########################\\n Conf: [ 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]\\n Total GT: 1433. \\n Total prediction: [ 1614. 1601. 1593. 1585. 1576. 1561. 1557. 1544. 1532. 1507.]\\n>>>>>>> 419d28c4e27b82c4cfef6a3aa01425cf29929973\\n Precision: [ 0.812 0.812 0.811 0.811 0.811 0.81 0.81 0.807 0.805 0.8 ]\\n Recall: [ 0.914 0.914 0.913 0.913 0.913 0.913 0.913 0.909 0.907 0.902]\\n F score: [[ 0.86 0.843 0.808 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\\n [ 0.86 0.843 0.808 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\\n [ 0.859 0.842 0.807 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\\n [ 0.859 0.842 0.807 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\\n [ 0.859 0.842 0.807 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\\n [ 0.859 0.842 0.807 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\\n [ 0.859 0.842 0.807 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\\n [ 0.855 0.839 0.804 0.748 0.665 0.576 0.458 0.311 0.14 0.016]\\n [ 0.853 0.838 0.804 0.747 0.664 0.575 0.458 0.311 0.14 0.016]\\n [ 0.848 0.834 0.8 0.745 0.662 0.574 0.457 0.31 0.139 0.016]]\\n \\\"\\\"\\\"\",\n \"def second_step(self, plan, dlvl_root_img, rphase_root_img, root_sequence, sag_sequence):\\n # Sagittal respiratory patterns associated with sagittal sequence analyzed\\n lpatterns =\\\\\\n [x for x in self.dataset if int(x.split('-')[3]) == self.sag_sequences[self.sag_sequences.index(sag_sequence)]]\\n # print('(Step 2) Sagittal patterns: {} ({})\\\\n'.format(lpatterns, len(lpatterns)))\\n\\n # Respiratory patterns linked to the sequence containing the root image\\n pattern = None\\n for p in lpatterns:\\n if int(p.split('-')[1]) == root_sequence:\\n pattern = p\\n # print(\\\"(Step 2) Pattern: {}\\\".format(pattern))\\n # print(\\\"(Step 2) DL: {}\\\".format(dlvl_root_img))\\n # print(\\\"(Step 2) DL[{}]: {}\\\".format(self.sag_sequences.index(sag_sequence), dlvl_root_img[self.sag_sequences.index(sag_sequence)]))\\n # print(\\\"(Step 2) RP: {}\\\".format(rphase_root_img))\\n # print(\\\"(Step 2) RP[{}]: {}\\\\n\\\".format(self.sag_sequences.index(sag_sequence), rphase_root_img[self.sag_sequences.index(sag_sequence)]))\\n\\n \\\"\\\"\\\" Get the diaphragmatic level of each image of the analyzed sagittal sequence\\n that crosses the coronal root image \\\"\\\"\\\"\\n pts_pattern = self.pattern_sagittal('{}.png'.format(pattern))\\n diaph_lvl = [max(x) for x in self.diaphragmatic_level_sagittal(pts_pattern)]\\n resp_phase = self.respiratory_phase_sagittal(self.diaphragmatic_level_sagittal(pts_pattern))\\n # print(\\\"(Step 2) Diaphragmatic level: {} ({})\\\\n\\\".format(diaph_lvl, len(diaph_lvl)))\\n # print(\\\"(Step 2) Respiratory phase: {} ({})\\\\n\\\".format(resp_phase, len(resp_phase)))\\n\\n \\\"\\\"\\\" Check register condition:\\n 1) If there is same diaphragmatic level \\\"\\\"\\\"\\n index_imgs_registered = list() # Store index of the sagittal registered images\\n for index, i in enumerate(diaph_lvl):\\n if i == dlvl_root_img[self.sag_sequences.index(sag_sequence)]:\\n index_imgs_registered.append(index)\\n # print(\\\"(Step 2) Index of registered images: {} ({})\\\\n\\\".format(index_imgs_registered, len(index_imgs_registered)))\\n\\n # TODO - Uncomment this for release\\n '''\\n \\\"\\\"\\\" Check register condition:\\n 2) If the instants are in the same respiratory phase \\\"\\\"\\\"\\n for index, i in enumerate(resp_phase):\\n if index in index_imgs_registered:\\n if resp_phase[index] != rphase_root_img[self.sag_sequences.index(sag_sequence)]:\\n index_imgs_registered.remove(index)\\n # print(\\\"(Step 2) Index of registered images: {} ({})\\\\n\\\".format(index_imgs_registered, len(index_imgs_registered)))\\n '''\\n\\n # If there is no registered image\\n if len(index_imgs_registered) == 0:\\n \\\"\\\"\\\" If there are no records, there is the possibility of an error in the segmentation\\n (which is done manually), then analyzing the respiratory phase is added in one unit\\n the value of the diaphragmatic level (inspiration case) or subtracted one unit\\n (expiration case) \\\"\\\"\\\"\\n\\n index_imgs_registered =\\\\\\n self.second_step_second_attempt(\\n diaph_lvl=diaph_lvl,\\n dlvl_root_img=dlvl_root_img,\\n rphase_root_img=rphase_root_img,\\n sag_sequence=sag_sequence,\\n option=True)\\n\\n if len(index_imgs_registered) == 0:\\n return -1, -1, -1\\n\\n # Get first sagittal image that was registered with root image\\n imgnum = index_imgs_registered[0]\\n # print(\\\"(Step 2) Imagem: {}\\\\n\\\".format(imgnum))\\n\\n \\\"\\\"\\\" Represents the row in the register matrix that represents the sequence used\\n It's used to populate the registration matrix correctly \\\"\\\"\\\"\\n row = self.sag_sequences.index(sag_sequence)\\n # print('(Step 2) Row: {}\\\\n'.format(row))\\n\\n for i in range(self.matcols):\\n pts_pattern = self.pattern_sagittal('{}.png'.format(lpatterns[i]))\\n diaph_lvl = [max(x) for x in self.diaphragmatic_level_sagittal(pts_pattern)]\\n resp_phase =\\\\\\n self.respiratory_phase_sagittal(self.diaphragmatic_level_sagittal(pts_pattern))\\n\\n if self.matDL[row, i] == 0.0:\\n self.matDL[row, i] = diaph_lvl[imgnum]\\n self.matRegistration[row, i] = self.green\\n self.matRP[row, i] = resp_phase[i]\\n\\n # print(\\\"(Step 2) Diaphragmatic level matrix:\\\\n{}\\\\n\\\".format(self.matDL))\\n # print(\\\"(Step 2) Registration matrix:\\\\n{}\\\\n\\\".format(self.matRegistration))\\n # print(\\\"(Step 2) Respiratory phase:\\\\n{}\\\\n\\\".format(self.matRP))\\n\\n imgnum = imgnum + 1\\n dlvl = [self.matDL[row, i] for i in range(len(self.cor_sequences))]\\n rphase = [int(self.matRP[row, i]) for i in range(len(self.cor_sequences))]\\n # print(\\\"(Step 2) DL: {}\\\\n\\\".format(dlvl))\\n\\n return (imgnum, dlvl, rphase)\",\n \"def _calc_r2(self):\\n sse = np.sum((self.data.y - self.predict(self.data.x))**2)\\n sst = np.sum((self.data.y - self.data.y.mean())**2)\\n return (1. - sse/sst)\",\n \"def testAlternateMetrics(self):\\n\\n def result2(t, rew):\\n return TrainingResult(time_total_s=t, neg_mean_loss=rew)\\n\\n sched = HyperBandScheduler(\\n time_attr='time_total_s', reward_attr='neg_mean_loss')\\n stats = self.default_statistics()\\n\\n for i in range(stats[\\\"max_trials\\\"]):\\n t = Trial(\\\"__fake\\\")\\n sched.on_trial_add(None, t)\\n runner = _MockTrialRunner(sched)\\n\\n big_bracket = sched._hyperbands[0][-1]\\n\\n for trl in big_bracket.current_trials():\\n runner._launch_trial(trl)\\n current_length = len(big_bracket.current_trials())\\n\\n # Provides results from 0 to 8 in order, keeping the last one running\\n for i, trl in enumerate(big_bracket.current_trials()):\\n action = sched.on_trial_result(runner, trl, result2(1, i))\\n runner.process_action(trl, action)\\n\\n new_length = len(big_bracket.current_trials())\\n self.assertEqual(action, TrialScheduler.CONTINUE)\\n self.assertEqual(new_length, self.downscale(current_length, sched))\",\n \"def beacon_readings(actual_x, actual_y):\\n d_a = math.sqrt((-100 - actual_x) ** 2 + (100 - actual_y) ** 2)\\n d_b = math.sqrt((150 - actual_x) ** 2 + (90 - actual_y) ** 2)\\n r_a_ = random.normalvariate(d_a, 1)\\n r_b_ = random.normalvariate(d_b, 1)\\n return r_a_, r_b_\",\n \"def reset(self):\\n p.resetSimulation() \\n dir_path = os.path.dirname(os.path.realpath(__file__))\\n path_to_rsc = dir_path + '/rsc/'\\n p.loadURDF(path_to_rsc+\\\"plane.urdf\\\")\\n\\n self.wall1 = p.loadURDF(path_to_rsc+\\\"wall.urdf\\\", basePosition = [0,self.max_dist_y,1], baseOrientation = p.getQuaternionFromEuler([1.5707,0,0]), useFixedBase = True)\\n self.wall2 = p.loadURDF(path_to_rsc+\\\"wall.urdf\\\", basePosition = [0,-self.max_dist_y,1], baseOrientation = p.getQuaternionFromEuler([1.5707,0,0]), useFixedBase = True)\\n self.wall3 = p.loadURDF(path_to_rsc+\\\"wall.urdf\\\", basePosition = [self.max_dist_x,0,1], baseOrientation = p.getQuaternionFromEuler([1.5707,0,1.5707]), useFixedBase = True)\\n self.wall4 = p.loadURDF(path_to_rsc+\\\"wall.urdf\\\", basePosition = [-self.max_dist_x,0,1], baseOrientation = p.getQuaternionFromEuler([1.5707,0,1.5707]), useFixedBase = True)\\n\\n p.setGravity(0, 0, -10)\\n self.car1pos = [random.uniform(-self.max_dist_x,0),random.uniform(-self.max_dist_y,0), 0.03]\\n self.car2pos = [random.uniform(0,self.max_dist_x),random.uniform(0,self.max_dist_y), 0.03]\\n\\n self.car1 = p.loadURDF(path_to_rsc+\\\"/car/AUTO1.urdf\\\", self.car1pos[0], self.car1pos[1], self.car1pos[2])\\n self.car2 = p.loadURDF(path_to_rsc+\\\"/car/AUTO2.urdf\\\", self.car2pos[0], self.car2pos[1], self.car2pos[2])\\n self.reduce = 1\\n self.step_counter=0\\n return self._get_obs()\",\n \"def test_make_sampled_format(self):\\n for num_inputs in [1, 3]:\\n for num_outputs in [1, 2, 4]:\\n for num_time_steps in [4, 10, 12]:\\n # Generate data\\n # P=2 format [0, 1, 2, 3, ...]\\n sample_interval = 2\\n dt_system = np.random.random()\\n dt_sample = sample_interval * dt_system\\n outputs = np.random.random(\\n (num_time_steps, num_outputs, num_inputs))\\n time_steps = make_time_steps(\\n num_time_steps, sample_interval)\\n time_values = time_steps * dt_system\\n\\n # Compute using modred\\n my_ERA = era.ERA()\\n time_steps_computed, outputs_computed =\\\\\\n era.make_sampled_format(time_values, outputs)\\n #self.assertEqual(dt_system_computed, dt_system)\\n\\n # Reference values\\n num_time_steps_true = (num_time_steps - 1) * 2\\n time_steps_true = make_time_steps(num_time_steps_true, 1)\\n outputs_true = np.zeros(\\n (num_time_steps_true, num_outputs, num_inputs))\\n outputs_true[::2] = outputs[:-1]\\n outputs_true[1::2] = outputs[1:]\\n\\n # Compare values\\n np.testing.assert_equal(\\n time_steps_computed, time_steps_true)\\n np.testing.assert_equal(outputs_computed, outputs_true)\\n\\n # Test that if there is a wrong time value, get an error\\n time_values[num_time_steps // 2] = -1\\n self.assertRaises(\\n ValueError, era.make_sampled_format, time_values,\\n outputs)\",\n \"def test_dataset_scenario_generation_full_late_behavior_overwritten(self):\\n params = ParameterServer()\\n\\n map_filename = os.path.join(os.path.dirname(__file__), \\\"data/DR_DEU_Merging_MT_v01_shifted.xodr\\\")\\n track_filename = os.path.join(os.path.dirname(__file__), \\\"data/interaction_dataset_DEU_Merging_dummy_track_late.csv\\\")\\n\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"MapFilename\\\"] = map_filename\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"TrackFilenameList\\\"] = [track_filename]\\n params[\\\"Scenario\\\"][\\\"Generation\\\"][\\\"InteractionDatasetScenarioGenerationFull\\\"][\\\"StartingOffsetMs\\\"] = 0\\n\\n scenario_generation = InteractionDatasetScenarioGenerationFull(\\n params=params, num_scenarios=1)\\n\\n scenario = scenario_generation.get_scenario(0)\\n world_state = scenario.GetWorldState()\\n agent1 = world_state.GetAgent(1)\\n agent2 = world_state.GetAgent(2)\\n\\n self.assertAlmostEqual(agent1.first_valid_timestamp, 0.0)\\n self.assertAlmostEqual(agent2.first_valid_timestamp, 0.3)\\n \\n self.assertEqual(isinstance(agent1, Agent), True)\\n self.assertEqual(agent1.IsValidAtTime(world_state.time), True)\\n \\n self.assertEqual(isinstance(agent2, Agent), True)\\n self.assertEqual(agent2.IsValidAtTime(world_state.time), False)\",\n \"def sample(self):\\r\\n raise NotImplementedError\",\n \"def __init__(self, path: str, rmf_path: str, arf_path: str, b_path: str,\\n central_coord: Quantity, inn_rad: Quantity, out_rad: Quantity, obs_id: str, instrument: str,\\n grouped: bool, min_counts: int, min_sn: float, over_sample: int, stdout_str: str,\\n stderr_str: str, gen_cmd: str, region: bool = False, b_rmf_path: str = '', b_arf_path: str = ''):\\n super().__init__(path, obs_id, instrument, stdout_str, stderr_str, gen_cmd)\\n self._prod_type = \\\"spectrum\\\"\\n\\n if os.path.exists(rmf_path):\\n self._rmf = rmf_path\\n else:\\n self._rmf = ''\\n self._usable = False\\n self._why_unusable.append(\\\"RMFPathDoesNotExist\\\")\\n\\n if os.path.exists(arf_path):\\n self._arf = arf_path\\n else:\\n self._arf = ''\\n self._usable = False\\n self._why_unusable.append(\\\"ARFPathDoesNotExist\\\")\\n\\n if os.path.exists(b_path):\\n self._back_spec = b_path\\n else:\\n self._back_spec = ''\\n self._usable = False\\n self._why_unusable.append(\\\"BackSpecPathDoesNotExist\\\")\\n\\n if b_rmf_path != '' and os.path.exists(b_rmf_path):\\n self._back_rmf = b_rmf_path\\n elif b_rmf_path == '':\\n self._back_rmf = None\\n else:\\n self._back_rmf = ''\\n self._usable = False\\n self._why_unusable.append(\\\"BackRMFPathDoesNotExist\\\")\\n\\n if b_arf_path != '' and os.path.exists(b_arf_path):\\n self._back_arf = b_arf_path\\n elif b_arf_path == '':\\n self._back_arf = None\\n else:\\n self._back_arf = ''\\n self._usable = False\\n self._why_unusable.append(\\\"BackARFPathDoesNotExist\\\")\\n\\n # Storing the central coordinate of this spectrum\\n self._central_coord = central_coord\\n\\n # Storing the region information\\n self._inner_rad = inn_rad\\n self._outer_rad = out_rad\\n # And also the shape of the region\\n if self._inner_rad.isscalar:\\n self._shape = 'circular'\\n else:\\n self._shape = 'elliptical'\\n\\n # If this spectrum has just been generated by XGA then we'll set the headers, otherwise its\\n # too slow and must be avoided. I am assuming here that the gen_cmd will be \\\"\\\" if the object\\n # hasn't just been generated - which is true of XGA's behaviour\\n if gen_cmd != \\\"\\\":\\n try:\\n self._update_spec_headers(\\\"main\\\")\\n self._update_spec_headers(\\\"back\\\")\\n except OSError as err:\\n self._usable = False\\n self._why_unusable.append(\\\"FITSIOOSError\\\")\\n\\n self._exp = None\\n self._plot_data = {}\\n self._luminosities = {}\\n self._count_rate = {}\\n\\n # This is specifically for fakeit runs (for cntrate - lum conversions) on the ARF/RMF\\n # associated with this Spectrum\\n self._conv_factors = {}\\n\\n # This set of properties describe the configuration of evselect/specgroup during generation\\n self._grouped = grouped\\n self._min_counts = min_counts\\n self._min_sn = min_sn\\n if self._grouped and self._min_counts is not None:\\n self._grouped_on = 'counts'\\n elif self._grouped and self._min_sn is not None:\\n self._grouped_on = 'signal to noise'\\n else:\\n self._grouped_on = None\\n\\n # Not to do with grouping, but this states the level of oversampling requested from evselect\\n self._over_sample = over_sample\\n\\n # This describes whether this spectrum was generated directly from a region present in a region file\\n self._region = region\\n\\n # Here we generate the storage key for this object, its just convenient to do it in here\\n # Sets up the extra part of the storage key name depending on if grouping is enabled\\n if grouped and min_counts is not None:\\n extra_name = \\\"_mincnt{}\\\".format(min_counts)\\n elif grouped and min_sn is not None:\\n extra_name = \\\"_minsn{}\\\".format(min_sn)\\n else:\\n extra_name = ''\\n\\n # And if it was oversampled during generation then we need to include that as well\\n if over_sample is not None:\\n extra_name += \\\"_ovsamp{ov}\\\".format(ov=over_sample)\\n\\n spec_storage_name = \\\"ra{ra}_dec{dec}_ri{ri}_ro{ro}_grp{gr}\\\"\\n if not self._region and self.inner_rad.isscalar:\\n spec_storage_name = spec_storage_name.format(ra=self.central_coord[0].value,\\n dec=self.central_coord[1].value,\\n ri=self._inner_rad.value, ro=self._outer_rad.value,\\n gr=grouped)\\n elif not self._region and not self._inner_rad.isscalar:\\n inn_rad_str = 'and'.join(self._inner_rad.value.astype(str))\\n out_rad_str = 'and'.join(self._outer_rad.value.astype(str))\\n spec_storage_name = spec_storage_name.format(ra=self.central_coord[0].value,\\n dec=self.central_coord[1].value, ri=inn_rad_str,\\n ro=out_rad_str, gr=grouped)\\n else:\\n spec_storage_name = \\\"region_grp{gr}\\\".format(gr=grouped)\\n\\n spec_storage_name += extra_name\\n # And we save the completed key to an attribute\\n self._storage_key = spec_storage_name\\n\\n # This attribute is set via the property, ONLY if this spectrum is considered to be a member of a set\\n # of annular spectra. It describes which position in the set this spectrum has\\n self._ann_ident = None\\n # This holds a unique random identifier for the set itself, and again will only be set from outside\\n self._set_ident = None\",\n \"def setUp(self):\\r\\n self.res1 = RichnessEstimatesResults()\\r\\n\\r\\n self.res2 = RichnessEstimatesResults()\\r\\n self.res2.addSample('S2', 52)\\r\\n self.res2.addSampleEstimate('S2', 1, 3, 0.4, 2.5, 3.5)\\r\\n self.res2.addSample('S1', 42)\\r\\n self.res2.addSampleEstimate('S1', 10, 20, 2.5, 2.5, 3.5)\\r\\n self.res2.addSampleEstimate('S1', 20, 30, 3.5, 2.5, 3.5)\\r\\n self.res2.addSampleEstimate('S1', 5, 21, 1.5, 2.5, 3.5)\",\n \"def generate():\\n strategies = []\\n\\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\\n limits=[50, 20, 10]\\n sources = ['his', 'our', 'dna']\\n for limit in limits:\\n for source in sources:\\n strategies.extend(\\n generate_meta_strategy_pair(\\n RFindStrategy,\\n limit=limit,\\n src=source,\\n shenanigans=False,\\n ))\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(TestimonoStrategy))\\n\\n # Add RPS Geometry\\n strategies.extend(\\n generate_meta_strategy_pair(GeometryV4Strategy))\\n\\n # Add Iocaine Powder strategy\\n strategies.extend(\\n generate_meta_strategy_pair(IocanePowderStrategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def generate():\\n strategies = []\\n\\n # Add RFind strategies (2 meta-strategies P0 and P'0 for each)\\n limits=[50, 20, 10]\\n sources = ['his', 'our', 'dna']\\n for limit in limits:\\n for source in sources:\\n strategies.extend(\\n generate_meta_strategy_pair(\\n RFindStrategy,\\n limit=limit,\\n src=source,\\n shenanigans=False,\\n ))\\n\\n # Add Centrifugal Bumblepuppy 16+H (RFind based)\\n strategies.extend(\\n generate_meta_strategy_pair(CentrifugalBumblepuppy16h))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeStrategy))\\n\\n # Add decision tree strategies\\n strategies.extend(\\n generate_meta_strategy_pair(DecisionTreeV10Strategy))\\n\\n # Add HPS Dojo strategies\\n strategies.extend(\\n generate_meta_strategy_pair(HPSDojoStrategy))\\n\\n # Add testing please ignore strategies\\n strategies.extend(\\n generate_meta_strategy_pair(TestingPlsIgnoreStrategy))\\n\\n # Add testimono strategy\\n strategies.extend(\\n generate_meta_strategy_pair(TestimonoStrategy))\\n\\n # Add RPS Geometry\\n strategies.extend(\\n generate_meta_strategy_pair(GeometryV4Strategy))\\n\\n # Add Iocaine Powder strategy\\n strategies.extend(\\n generate_meta_strategy_pair(IocanePowderStrategy))\\n\\n # By default, rotate everything\\n do_rotations = [True for _ in strategies]\\n\\n # Anti Trivial\\n strategies.extend(\\n generate_meta_strategy_pair(\\n AntiTrivialStrategy, mirroring=False))\\n do_rotations.extend([False])\\n\\n return strategies, do_rotations\",\n \"def test_integration2(self):\\n self._test_integration(2)\",\n \"def iter_fun(self):\\n\\n run_id = self._run_id\\n etopo_dir = driver_home\\n topodir = driver_home\\n\\n # load input info\\n if self._input_info == None:\\n scn_fname = os.path.join(self._run_home,'scenario_pts.txt') \\n scn = np.loadtxt(scn_fname)\\n scn_list = scn.tolist()\\n else:\\n scn_list = self._input_info\\n \\n # total number of runs\\n M = len(scn_list)\\n N = 8*M + 2 # 8*M runs plus two empty bathymetry runs\\n\\n if run_id == N:\\n raise StopIteration()\\n\\n else:\\n \\n #=========================\\n # set coarse and fine grids\\n #\\n t_shelf = 0. # time approaching continental slope\\n t_harbor = 0. # time approaching harbor\\n\\n if ((run_id >= 0) and (run_id < 4*M)) or (run_id == 8*M):\\n #------------------\\n # setrun for coarse\\n #\\n grid = 'coarse'\\n \\n self._rundata.amrdata.amr_levels_max = 4\\n # coarse grid run = 10\\\"\\n # dx = 30', 5', 1', 10\\\"\\n self._rundata.amrdata.refinement_ratios_x = [6, 5, 6]\\n self._rundata.amrdata.refinement_ratios_y = [6, 5, 6]\\n self._rundata.amrdata.refinement_ratios_t = [6, 5, 6]\\n\\n\\n # add topography (coarse)\\n topofiles = self._rundata.topo_data.topofiles\\n # for topography, append lines of the form\\n # [topotype, minlevel, maxlevel, t1, t2, fname]\\n topofiles = []\\n\\n topofiles.append([3, 1, 4, 0., 1.e10, \\\\\\n os.path.join(etopo_dir, 'etopo1_-130_-124_38_45_1min.asc')])\\n topofiles.append([-3, 3, 4, 0., 1.e10, \\\\\\n os.path.join(topodir, 'cc-1sec.asc')])\\n\\n # add regions\\n regions = self._rundata.regiondata.regions \\n # between shelf and CC \\n regions = []\\n regions.append(\\\\\\n [2, 3, t_shelf, 1e9, -125, -124.05, 40.5, 43]) \\n regions.append(\\\\\\n [3, 4, t_harbor, 1e9, -124.26, -124.14, 41.67, 41.79])\\n regions.append(\\\\\\n [4, 4, t_harbor, 1e9, -124.218,-124.17, 41.7345, 41.77])\\n\\n # == fgmax.data values ==\\n fgmax_files = self._rundata.fgmax_data.fgmax_files\\n fgmax_files = []\\n \\n # for fixed grids append to this list names of any fgmax input files\\n fgmax1_fname = os.path.join(driver_home,'fgmax1_coarse.txt')\\n fgmax2_fname = os.path.join(driver_home,'fgmax2_coarse.txt')\\n fgmax3_fname = os.path.join(driver_home,'fgmax3_coarse.txt')\\n\\n fgmax_files.append(fgmax1_fname) \\n fgmax_files.append(fgmax2_fname) \\n fgmax_files.append(fgmax3_fname) \\n \\n self._rundata.fgmax_data.num_fgmax_val = 2\\n \\n \\n elif ((run_id >= 4*M) and (run_id < 8*M)) or (run_id == 8*M+1):\\n #----------------\\n # setrun for fine\\n #\\n grid = 'fine'\\n \\n self._rundata.amrdata.amr_levels_max = 6\\n\\n ## fine grid run = 2/3\\\"\\n ## dx = 30', 5', 1', 10\\\", 2\\\", 2/3\\\"\\n self._rundata.amrdata.refinement_ratios_x = [6, 5, 6, 5, 3]\\n self._rundata.amrdata.refinement_ratios_y = [6, 5, 6, 5, 3]\\n self._rundata.amrdata.refinement_ratios_t = [6, 5, 6, 5, 3]\\n\\n regions = self._rundata.regiondata.regions \\n regions = []\\n # between shelf and CC\\n regions.append(\\\\\\n [2, 4, t_shelf, 1e9, -125, -124.05, 40.5, 43]) \\n regions.append(\\\\\\n [4, 5, t_harbor, 1e9, -124.26, -124.14, 41.67, 41.79])\\n regions.append(\\\\\\n [6, 6, t_harbor, 1e9, -124.218,-124.17, 41.7345, 41.77])\\n\\n # add topography (fine)\\n topofiles = self._rundata.topo_data.topofiles\\n # for topography, append lines of the form\\n # [topotype, minlevel, maxlevel, t1, t2, fname]\\n topofiles = []\\n\\n topofiles.append([3, 1, 6, 0., 1.e10, \\\\\\n os.path.join(etopo_dir, 'etopo1_-130_-124_38_45_1min.asc')])\\n topofiles.append([-3, 4, 6, 0., 1.e10, \\\\\\n os.path.join(topodir, 'cc-1sec.asc')])\\n topofiles.append([3, 6, 6, 0., 1.e10, \\\\\\n os.path.join(topodir,'cc-1_3sec-c_pierless.asc')])\\n \\n # == fgmax.data values ==\\n fgmax_files = self._rundata.fgmax_data.fgmax_files\\n fgmax_files = []\\n \\n # for fixed grids append to this list names of any fgmax input files\\n fgmax1_fname = os.path.join(driver_home,'fgmax1_fine.txt')\\n fgmax2_fname = os.path.join(driver_home,'fgmax2_fine.txt')\\n fgmax3_fname = os.path.join(driver_home,'fgmax3_fine.txt')\\n\\n fgmax_files.append(fgmax1_fname) \\n fgmax_files.append(fgmax2_fname) \\n fgmax_files.append(fgmax3_fname) \\n \\n self._rundata.fgmax_data.num_fgmax_val = 2\\n \\n\\n\\n #\\n # set desired magnitude\\n #\\n if ((run_id >= 0) and (run_id < M)) \\\\\\n or ((run_id >= 4*M) and (run_id < 5*M)):\\n self.KL_Mw_desired = 8.6\\n elif ((run_id >= M) and (run_id < 2*M)) \\\\\\n or ((run_id >= 5*M) and (run_id < 6*M)):\\n self.KL_Mw_desired = 8.8\\n elif ((run_id >= 2*M) and (run_id < 3*M)) \\\\\\n or ((run_id >= 6*M) and (run_id < 7*M)):\\n self.KL_Mw_desired = 9.0\\n elif ((run_id >= 3*M) and (run_id < 4*M)) \\\\\\n or ((run_id >= 7*M) and (run_id < 8*M)):\\n self.KL_Mw_desired = 9.2\\n \\n #\\n # set slip distribution\\n #\\n run_id_mod = run_id - 100*(run_id/100)\\n m = scn_list[run_id_mod]\\n self.set_KL_slip(m)\\n \\n if run_id < 8*M:\\n dir_grid_Mw = '../geoclaw_output/' + str(grid) + '_' + str(self.KL_Mw_desired)\\n self._rundir = os.path.join(dir_grid_Mw, 'run_' + str(run_id_mod))\\n else:\\n # empty runs to obtain bathymetry\\n \\n dir_grid_Mw = '../geoclaw_output/' + str(grid) + '_B0'\\n self._rundir = dir_grid_Mw\\n self.KL_Mw_desired = 0.0\\n self.set_KL_slip([0.]*len(m)) # set output\\n self._rundata.clawdata.output_times = [1.0, 3.0]\\n \\n self._run_id += 1\\n \\n return self\",\n \"def run():\\n\\n Number_repetitions = 1\\n Rate = np.zeros((Number_repetitions,1))\\n Rate20 = np.zeros((Number_repetitions,1))\\n Penalty20 = np.zeros((Number_repetitions, 1))\\n\\n # Loop to average\\n for idx in np.arange(0,Number_repetitions,1):\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.0000001, display=True) # create simulator (uses pygame when display=True, if available)\\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\\n\\n sim.run(n_trials=100) # run for a specified number of trials\\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\\n\\n # I've edited the enviroment variable to do the plot creating an completions array\\n completions = np.array(e.completions)\\n rate = float(completions.sum())/float((len(completions)))\\n rate20 = float(completions[-20:].sum())/20\\n\\n Rate[idx] = rate\\n Rate20[idx] = rate20\\n\\n Wrong = np.array(a.wrong_moves_per_run[-20:]).mean()\\n Penalty20[idx] = Wrong\\n\\n plt.scatter(np.arange(0,len(completions)),completions)\\n plt.plot(Wrong)\\n plt.xlabel('Trial')\\n plt.ylabel('1 = Get in the destination, 0 = did not get')\\n plt.title('Reiforcement learning progress')\\n plt.legend(['Rate of completion: ' + str(rate) + '. Rate last 20: ' + str(rate20) + '.Mean penalty last 20: ' + str(Wrong)])\\n plt.show()\\n\\n #print 'Accuracy: ' + str(Rate) + '. Mean: ' + str(np.mean(Rate))\\n #print 'Mean 20: ' + str(np.mean(Rate20))#'Accuracy 20: ' + str(Rate20) + '. Mean 20: ' + str(np.mean(Rate20))\\n #print 'Mean_penalty: ' + str(np.mean(Penalty20))\\n\\n # Print state table with actions\\n #t = 0\\n #for state in a.states:\\n #print 'State ' + str(state) + '. Best action: ' + str((str(np.argmax(a.QTable[t][:]))))\\n #t += 1\",\n \"def combine(self, scenario2):\\n prob = self.probability * scenario2.probability\\n keys = list(())\\n for key in self.nodeKeys:\\n keys.append(key)\\n for key in scenario2.nodeKeys:\\n keys.append(key)\\n return Scenario(prob, keys)\",\n \"def make_plot_two_hospitals_arrival_split(\\n lambda_2,\\n lambda_1_1,\\n lambda_1_2,\\n mu_1,\\n mu_2,\\n num_of_servers_1,\\n num_of_servers_2,\\n threshold_1,\\n threshold_2,\\n measurement_type=\\\"b\\\",\\n seed_num_1=None,\\n seed_num_2=None,\\n warm_up_time=100,\\n trials=1,\\n accuracy=10,\\n runtime=1440,\\n):\\n hospital_times_1 = []\\n hospital_times_2 = []\\n all_arrival_rates = np.linspace(0, lambda_2, accuracy + 1)\\n for arrival_rate_1 in all_arrival_rates[1:-1]:\\n arrival_rate_2 = lambda_2 - arrival_rate_1\\n times_1 = get_multiple_runs_results(\\n arrival_rate_1,\\n lambda_1_1,\\n mu_1,\\n num_of_servers_1,\\n threshold_1,\\n seed_num_1,\\n warm_up_time,\\n trials,\\n runtime,\\n )\\n times_2 = get_multiple_runs_results(\\n arrival_rate_2,\\n lambda_1_2,\\n mu_2,\\n num_of_servers_2,\\n threshold_2,\\n seed_num_2,\\n warm_up_time,\\n trials,\\n runtime,\\n )\\n hospital_times_1, hospital_times_2 = update_hospitals_lists(\\n hospital_times_1, hospital_times_2, times_1, times_2, measurement_type\\n )\\n\\n x_axis_label, y_axis_label, title = get_two_hospital_plot_labels(measurement_type)\\n x_labels = all_arrival_rates[1:-1] / all_arrival_rates[-1]\\n plt.figure(figsize=(23, 10))\\n waiting_time_plot = plt.plot(x_labels, hospital_times_1, ls=\\\"solid\\\", lw=1.5)\\n plt.plot(x_labels, hospital_times_2, ls=\\\"solid\\\", lw=1.5)\\n plt.legend([\\\"Hospital 1\\\", \\\"Hospital 2\\\"], fontsize=\\\"x-large\\\")\\n plt.title(title, fontsize=18)\\n plt.xlabel(x_axis_label, fontsize=15, fontweight=\\\"bold\\\")\\n plt.ylabel(y_axis_label, fontsize=15, fontweight=\\\"bold\\\")\\n\\n return waiting_time_plot\",\n \"def test_coverage_2(base_settings):\\n filename = base_settings[\\\"unittest_data_dir\\\"] / \\\"coverage-example-selfpay.json\\\"\\n inst = coverage.Coverage.parse_file(\\n filename, content_type=\\\"application/json\\\", encoding=\\\"utf-8\\\"\\n )\\n assert \\\"Coverage\\\" == inst.resource_type\\n\\n impl_coverage_2(inst)\\n\\n # testing reverse by generating data from itself and create again.\\n data = inst.dict()\\n assert \\\"Coverage\\\" == data[\\\"resourceType\\\"]\\n\\n inst2 = coverage.Coverage(**data)\\n impl_coverage_2(inst2)\",\n \"def test_mc_t_two_sample(self):\\r\\n # Verified against R's t.test() and Deducer::perm.t.test().\\r\\n\\r\\n # With numpy array as input.\\r\\n exp = (-0.11858541225631833, 0.90756579317867436)\\r\\n I = array([7.2, 7.1, 9.1, 7.2, 7.3, 7.2, 7.5])\\r\\n II = array([8.8, 7.5, 7.7, 7.6, 7.4, 6.7, 7.2])\\r\\n obs = mc_t_two_sample(I, II)\\r\\n self.assertFloatEqual(obs[:2], exp)\\r\\n self.assertEqual(len(obs[2]), 999)\\r\\n self.assertCorrectPValue(0.8, 0.9, mc_t_two_sample, [I, II],\\r\\n p_val_idx=3)\\r\\n\\r\\n # With python list as input.\\r\\n exp = (-0.11858541225631833, 0.90756579317867436)\\r\\n I = [7.2, 7.1, 9.1, 7.2, 7.3, 7.2, 7.5]\\r\\n II = [8.8, 7.5, 7.7, 7.6, 7.4, 6.7, 7.2]\\r\\n obs = mc_t_two_sample(I, II)\\r\\n self.assertFloatEqual(obs[:2], exp)\\r\\n self.assertEqual(len(obs[2]), 999)\\r\\n self.assertCorrectPValue(0.8, 0.9, mc_t_two_sample, [I, II],\\r\\n p_val_idx=3)\\r\\n\\r\\n exp = (-0.11858541225631833, 0.45378289658933718)\\r\\n obs = mc_t_two_sample(I, II, tails='low')\\r\\n self.assertFloatEqual(obs[:2], exp)\\r\\n self.assertEqual(len(obs[2]), 999)\\r\\n self.assertCorrectPValue(0.4, 0.47, mc_t_two_sample, [I, II],\\r\\n {'tails': 'low'}, p_val_idx=3)\\r\\n\\r\\n exp = (-0.11858541225631833, 0.54621710341066287)\\r\\n obs = mc_t_two_sample(I, II, tails='high', permutations=99)\\r\\n self.assertFloatEqual(obs[:2], exp)\\r\\n self.assertEqual(len(obs[2]), 99)\\r\\n self.assertCorrectPValue(0.4, 0.62, mc_t_two_sample, [I, II],\\r\\n {'tails': 'high', 'permutations': 99}, p_val_idx=3)\\r\\n\\r\\n exp = (-2.8855783649036986, 0.99315596652421401)\\r\\n obs = mc_t_two_sample(I, II, tails='high', permutations=99, exp_diff=1)\\r\\n self.assertFloatEqual(obs[:2], exp)\\r\\n self.assertEqual(len(obs[2]), 99)\\r\\n self.assertCorrectPValue(0.55, 0.99, mc_t_two_sample, [I, II],\\r\\n {'tails': 'high', 'permutations': 99, 'exp_diff': 1}, p_val_idx=3)\",\n \"def test_run_sim_1():\\n rnd = rand.Arrivals(36, 41)\\n sim.run_sim(3, 2, 5, 6, 22, rnd)\",\n \"def prepare_experiment(assumptions):\\n print(\\\"\\\\nGenerate species parameters\\\")\\n np.random.seed(assumptions['seed']) \\n params = MakeParams(assumptions) \\n if assumptions[\\\"selected_function\\\"] == \\\"f5_invader_suppression\\\":\\n print(\\\"\\\\nDraw invader feature\\\")\\n params = create_invader(params, assumptions)\\n print(params[\\\"c\\\"])\\n \\n print(\\\"\\\\nDraw per-capita function and cost\\\")\\n f1_species_smooth, f1_species_rugged, f2_species_smooth, f2_species_rugged = draw_species_function(assumptions)\\n params.update({\\\"f1_species_smooth\\\": f1_species_smooth, \\\"f1_species_rugged\\\": f1_species_rugged, \\\"f2_species_smooth\\\": f2_species_smooth, \\\"f2_species_rugged\\\": f2_species_rugged})\\n gi = draw_species_cost(f1_species_smooth, assumptions)\\n params.update({\\\"g\\\": gi})\\n \\n print(\\\"\\\\nConstruct plate\\\")\\n np.random.seed(assumptions['seed']) \\n plate = make_plate(assumptions,params)\\n \\n print(\\\"\\\\nAdd community function to plate\\\")\\n plate = add_community_function(plate, assumptions, params)\\n \\n if not pd.isnull(assumptions[\\\"overwrite_plate\\\"]) :\\n print(\\\"\\\\nUpdating the initial plate composition by overwrite_plate\\\")\\n plate = overwrite_plate(plate, assumptions)\\n \\n print(\\\"\\\\nPrepare Protocol\\\")\\n #Extract Protocol from protocol database\\n algorithms = make_algorithms(assumptions)\\n params_algorithm = algorithms[algorithms['algorithm_name'] == assumptions['protocol']]\\n \\n #Params_simulation by default contains all assumptions not stored in params.\\n params_simulation = dict((k, assumptions[k]) for k in assumptions.keys() if k not in params.keys())\\n \\n return params, params_simulation , params_algorithm, plate\",\n \"def test_create_scenario1(self):\\n pass\",\n \"def test_autosample(self):\\n \\n # Start data subscribers.\\n #self._start_data_subscribers(6)\\n #self.addCleanup(self._stop_data_subscribers) \\n \\n # Set up a subscriber to collect error events.\\n #self._start_event_subscriber('ResourceAgentResourceStateEvent', 7)\\n #self.addCleanup(self._stop_event_subscriber) \\n \\n state = self._ia_client.get_agent_state()\\n self.assertEqual(state, ResourceAgentState.UNINITIALIZED)\\n \\n cmd = AgentCommand(command=ResourceAgentEvent.INITIALIZE)\\n retval = self._ia_client.execute_agent(cmd)\\n state = self._ia_client.get_agent_state()\\n self.assertEqual(state, ResourceAgentState.INACTIVE)\\n\\n cmd = AgentCommand(command=ResourceAgentEvent.GO_ACTIVE)\\n retval = self._ia_client.execute_agent(cmd)\\n state = self._ia_client.get_agent_state()\\n self.assertEqual(state, ResourceAgentState.IDLE)\\n\\n cmd = AgentCommand(command=ResourceAgentEvent.RUN)\\n retval = self._ia_client.execute_agent(cmd)\\n state = self._ia_client.get_agent_state()\\n self.assertEqual(state, ResourceAgentState.COMMAND)\\n\\n cmd = AgentCommand(command=SBE37ProtocolEvent.START_AUTOSAMPLE)\\n retval = self._ia_client.execute_resource(cmd)\\n \\n gevent.sleep(15)\\n \\n cmd = AgentCommand(command=SBE37ProtocolEvent.STOP_AUTOSAMPLE)\\n retval = self._ia_client.execute_resource(cmd)\\n \\n cmd = AgentCommand(command=ResourceAgentEvent.RESET)\\n retval = self._ia_client.execute_agent(cmd)\\n state = self._ia_client.get_agent_state()\\n self.assertEqual(state, ResourceAgentState.UNINITIALIZED)\\n\\n #self._async_event_result.get(timeout=CFG.endpoint.receive.timeout)\\n #self.assertGreaterEqual(len(self._events_received), 6)\\n\\n #self._async_sample_result.get(timeout=CFG.endpoint.receive.timeout)\\n #self.assertGreaterEqual(len(self._samples_received), 6)\",\n \"def make_second_recommendation() -> str:\\r\\n growth_rate_info = highest_growth_rate()\\r\\n rate_strings = growth_rate_info[0]\\r\\n most_demand_string = growth_rate_info[1]\\r\\n return render_template(\\\"make_second_recommendation.html\\\",\\r\\n display_rates=rate_strings,\\r\\n most_demand_display=most_demand_string)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6115581","0.59218746","0.591556","0.5793499","0.5779642","0.56699425","0.5625599","0.5473677","0.54238087","0.54228085","0.53844553","0.5353695","0.5347016","0.5287351","0.52831525","0.52646875","0.5255052","0.5238365","0.5234221","0.52184963","0.52145135","0.5208618","0.51944953","0.5182903","0.5182366","0.51673514","0.51546925","0.51529616","0.5151712","0.5146874","0.5136064","0.51299775","0.51287365","0.512345","0.51137334","0.51100546","0.5107502","0.50932026","0.5091189","0.5087313","0.5085986","0.5084591","0.50797385","0.5073076","0.5060918","0.5060203","0.5058137","0.5046116","0.5041145","0.5037529","0.5032541","0.5032426","0.5027325","0.5022373","0.5021132","0.50152594","0.5013703","0.50130576","0.4999674","0.49992046","0.49941537","0.49908772","0.49894246","0.4971813","0.49709666","0.49656257","0.4964066","0.49601763","0.4955881","0.49528924","0.49523544","0.4949341","0.49439994","0.49376518","0.4933533","0.49334085","0.49319607","0.49246946","0.49246678","0.4919814","0.49178442","0.4913386","0.4910964","0.49020085","0.49010956","0.48988563","0.48907816","0.48907816","0.48882908","0.48864964","0.4881088","0.48781413","0.48632398","0.48611975","0.48500758","0.48469523","0.4846557","0.48421344","0.48408177","0.48353025"],"string":"[\n \"0.6115581\",\n \"0.59218746\",\n \"0.591556\",\n \"0.5793499\",\n \"0.5779642\",\n \"0.56699425\",\n \"0.5625599\",\n \"0.5473677\",\n \"0.54238087\",\n \"0.54228085\",\n \"0.53844553\",\n \"0.5353695\",\n \"0.5347016\",\n \"0.5287351\",\n \"0.52831525\",\n \"0.52646875\",\n \"0.5255052\",\n \"0.5238365\",\n \"0.5234221\",\n \"0.52184963\",\n \"0.52145135\",\n \"0.5208618\",\n \"0.51944953\",\n \"0.5182903\",\n \"0.5182366\",\n \"0.51673514\",\n \"0.51546925\",\n \"0.51529616\",\n \"0.5151712\",\n \"0.5146874\",\n \"0.5136064\",\n \"0.51299775\",\n \"0.51287365\",\n \"0.512345\",\n \"0.51137334\",\n \"0.51100546\",\n \"0.5107502\",\n \"0.50932026\",\n \"0.5091189\",\n \"0.5087313\",\n \"0.5085986\",\n \"0.5084591\",\n \"0.50797385\",\n \"0.5073076\",\n \"0.5060918\",\n \"0.5060203\",\n \"0.5058137\",\n \"0.5046116\",\n \"0.5041145\",\n \"0.5037529\",\n \"0.5032541\",\n \"0.5032426\",\n \"0.5027325\",\n \"0.5022373\",\n \"0.5021132\",\n \"0.50152594\",\n \"0.5013703\",\n \"0.50130576\",\n \"0.4999674\",\n \"0.49992046\",\n \"0.49941537\",\n \"0.49908772\",\n \"0.49894246\",\n \"0.4971813\",\n \"0.49709666\",\n \"0.49656257\",\n \"0.4964066\",\n \"0.49601763\",\n \"0.4955881\",\n \"0.49528924\",\n \"0.49523544\",\n \"0.4949341\",\n \"0.49439994\",\n \"0.49376518\",\n \"0.4933533\",\n \"0.49334085\",\n \"0.49319607\",\n \"0.49246946\",\n \"0.49246678\",\n \"0.4919814\",\n \"0.49178442\",\n \"0.4913386\",\n \"0.4910964\",\n \"0.49020085\",\n \"0.49010956\",\n \"0.48988563\",\n \"0.48907816\",\n \"0.48907816\",\n \"0.48882908\",\n \"0.48864964\",\n \"0.4881088\",\n \"0.48781413\",\n \"0.48632398\",\n \"0.48611975\",\n \"0.48500758\",\n \"0.48469523\",\n \"0.4846557\",\n \"0.48421344\",\n \"0.48408177\",\n \"0.48353025\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6111027"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":89394,"cells":{"query":{"kind":"string","value":"Generates scenario 3. Scenario 3 consists of radar and ais measurements with different sampling rate. The sampling rate is specified in the input params. A groundtruth is generated for each second."},"document":{"kind":"string","value":"def generate_scenario_3(seed=1996, permanent_save=True, radar_meas_rate=1, ais_meas_rate=5, sigma_process=0.01,\n sigma_meas_radar=3, sigma_meas_ais=1, timesteps=20):\n start_time = datetime.now()\n\n # specify seed to be able to repeat the example\n np.random.seed(seed)\n\n # combine two 1-D CV models to create a 2-D CV model\n transition_model = CombinedLinearGaussianTransitionModel(\n [ConstantVelocity(sigma_process), ConstantVelocity(sigma_process)])\n\n # starting at 0,0 and moving NE\n truth = GroundTruthPath([GroundTruthState([0, 1, 0, 1], timestamp=start_time)])\n\n # generate truth using transition_model and noise\n end_time = start_time + timedelta(seconds=timesteps * max(radar_meas_rate, ais_meas_rate))\n time = start_time + timedelta(seconds=1)\n while time < end_time:\n truth.append(GroundTruthState(\n transition_model.function(truth[-1], noise=True, time_interval=timedelta(seconds=1)),\n timestamp=time))\n time += timedelta(seconds=1)\n\n # Simulate measurements\n # Specify measurement model for radar\n measurement_model_radar = LinearGaussian(\n ndim_state=4, # number of state dimensions\n mapping=(0, 2), # mapping measurement vector index to state index\n noise_covar=np.array([[sigma_meas_radar, 0], # covariance matrix for Gaussian PDF\n [0, sigma_meas_radar]])\n )\n\n # Specify measurement model for AIS (Same as for radar)\n measurement_model_ais = LinearGaussian(\n ndim_state=4,\n mapping=(0, 2),\n noise_covar=np.array([[sigma_meas_ais, 0],\n [0, sigma_meas_ais]])\n )\n\n # generate \"radar\" measurements\n measurements_radar = []\n measurements_ais = []\n next_radar_meas_time = start_time\n next_ais_meas_time = start_time\n for state in truth:\n # check whether we want to generate a measurement from this gt\n if state.timestamp == next_radar_meas_time:\n measurement = measurement_model_radar.function(state, noise=True)\n measurements_radar.append(Detection(measurement, timestamp=state.timestamp))\n next_radar_meas_time += timedelta(seconds=radar_meas_rate)\n\n if state.timestamp == next_ais_meas_time:\n measurement = measurement_model_ais.function(state, noise=True)\n measurements_ais.append(Detection(measurement, timestamp=state.timestamp))\n next_ais_meas_time += timedelta(seconds=ais_meas_rate)\n\n if permanent_save:\n save_folder_name = seed.__str__()\n else:\n save_folder_name = \"temp\"\n\n save_folder = \"../scenarios/scenario3/\" + save_folder_name + \"/\"\n\n # save the ground truth and the measurements for the radar and the AIS\n store_object.store_object(truth, save_folder, \"ground_truth.pk1\")\n store_object.store_object(measurements_radar, save_folder, \"measurements_radar.pk1\")\n store_object.store_object(measurements_ais, save_folder, \"measurements_ais.pk1\")\n store_object.store_object(start_time, save_folder, \"start_time.pk1\")\n store_object.store_object(measurement_model_radar, save_folder, \"measurement_model_radar.pk1\")\n store_object.store_object(measurement_model_ais, save_folder, \"measurement_model_ais.pk1\")\n store_object.store_object(transition_model, save_folder, \"transition_model.pk1\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_param_scenario3():\n nb_carre_x = 42\n nb_carre_y = 45\n largeur_x = 0.825\n largeur_y = 0.645\n z1 = np.array([[11], [32], [17.26]])\n z2 = np.array([[12], [32], [18.43]])\n z3 = np.array([[13], [32], [23.2]])\n z4 = np.array([[15], [31], [23.4]])\n z5 = np.array([[16], [30], [23.29]])\n z6 = np.array([[18], [29], [22.28]])\n z7 = np.array([[19], [28], [35.79]])\n z8 = np.array([[20], [27], [36.87]])\n z9 = np.array([[21], [26], [33.92]])\n z10 = np.array([[23], [24], [38.11]])\n z11 = np.array([[24], [23], [37.76]])\n z12 = np.array([[25], [22], [45.6]])\n z13 = np.array([[26], [20], [56.4]])\n z14 = np.array([[27], [18], [55.2]])\n z15 = np.array([[28], [16], [57.53]])\n z16 = np.array([[29], [14], [58.64]])\n z17 = np.array([[30], [13], [66.04]])\n z18 = np.array([[30], [11], [70.02]])\n z19 = np.array([[31], [9], [69.64]])\n z20 = np.array([[31], [6], [68.82]])\n\n meas = [z1, z2, z3, z4, z5, z6, z7, z8, z9, z10, z11, z12, z13, z14, z15, z16, z17, z18, z19, z20]\n\n z = [np.array([[x[0][0] * largeur_x / nb_carre_x], [x[1][0] * largeur_y / nb_carre_y], [-x[2][0] * np.pi / 180]])\n for x in meas]\n\n # We take a measure every second\n thymio_speed_to_mms = 0.4753\n Ts = 1\n\n Thymio_speed_left = [73, 92, 94, 102, 100, 89, 92, 103, 97, 100, 100, 104, 95, 97, 103, 101, 100, 101, 92, 98]\n Thymio_speed_right = [67, 96, 102, 89, 105, -45, 102, 101, 104, 71, 100, 93, 94, 105, 96, -45, 95, 103, 88, 94]\n delta_sr_test = [x * Ts / thymio_speed_to_mms / 1000 for x in Thymio_speed_right]\n delta_sl_test = [x * Ts / thymio_speed_to_mms / 1000 for x in Thymio_speed_left]\n\n return delta_sr_test, delta_sl_test, z","def generate_scenario_2(seed=1996, permanent_save=True, sigma_process=0.01, sigma_meas_radar=3, sigma_meas_ais=1,\n timesteps=20):\n start_time = datetime.now()\n\n # specify seed to be able repeat example\n np.random.seed(seed)\n\n # combine two 1-D CV models to create a 2-D CV model\n transition_model = CombinedLinearGaussianTransitionModel(\n [ConstantVelocity(sigma_process), ConstantVelocity(sigma_process)])\n\n # starting at 0,0 and moving NE\n truth = GroundTruthPath([GroundTruthState([0, 1, 0, 1], timestamp=start_time)])\n\n # generate truth using transition_model and noise\n for k in range(1, timesteps):\n truth.append(GroundTruthState(\n transition_model.function(truth[k - 1], noise=True, time_interval=timedelta(seconds=1)),\n timestamp=start_time + timedelta(seconds=k)))\n\n # Simulate measurements\n # Specify measurement model for radar\n measurement_model_radar = LinearGaussian(\n ndim_state=4, # number of state dimensions\n mapping=(0, 2), # mapping measurement vector index to state index\n noise_covar=np.array([[sigma_meas_radar, 0], # covariance matrix for Gaussian PDF\n [0, sigma_meas_radar]])\n )\n\n # Specify measurement model for AIS (Same as for radar)\n measurement_model_ais = LinearGaussian(\n ndim_state=4,\n mapping=(0, 2),\n noise_covar=np.array([[sigma_meas_ais, 0],\n [0, sigma_meas_ais]])\n )\n\n # generate \"radar\" measurements\n measurements_radar = []\n for state in truth:\n measurement = measurement_model_radar.function(state, noise=True)\n measurements_radar.append(Detection(measurement, timestamp=state.timestamp))\n\n # generate \"AIS\" measurements\n measurements_ais = []\n for state in truth:\n measurement = measurement_model_ais.function(state, noise=True)\n measurements_ais.append(Detection(measurement, timestamp=state.timestamp))\n\n if permanent_save:\n save_folder_name = seed.__str__()\n else:\n save_folder_name = \"temp\"\n\n save_folder = \"../scenarios/scenario2/\" + save_folder_name + \"/\"\n\n # save the ground truth and the measurements for the radar and the AIS\n store_object.store_object(truth, save_folder, \"ground_truth.pk1\")\n store_object.store_object(measurements_radar, save_folder, \"measurements_radar.pk1\")\n store_object.store_object(measurements_ais, save_folder, \"measurements_ais.pk1\")\n store_object.store_object(start_time, save_folder, \"start_time.pk1\")\n store_object.store_object(measurement_model_radar, save_folder, \"measurement_model_radar.pk1\")\n store_object.store_object(measurement_model_ais, save_folder, \"measurement_model_ais.pk1\")\n store_object.store_object(transition_model, save_folder, \"transition_model.pk1\")","def generate_scenario_1(seed=1996, permanent_save=True, sigma_process=0.01, sigma_meas_radar=3, sigma_meas_ais=1):\n # specify seed to be able repeat example\n start_time = datetime.now()\n\n np.random.seed(seed)\n\n # combine two 1-D CV models to create a 2-D CV model\n transition_model = CombinedLinearGaussianTransitionModel([ConstantVelocity(sigma_process),\n ConstantVelocity(sigma_process)])\n\n # starting at 0,0 and moving NE\n truth = GroundTruthPath([GroundTruthState([0, 1, 0, 1], timestamp=start_time)])\n\n # generate truth using transition_model and noise\n for k in range(1, 21):\n truth.append(GroundTruthState(\n transition_model.function(truth[k - 1], noise=True, time_interval=timedelta(seconds=1)),\n timestamp=start_time + timedelta(seconds=k)))\n\n # Simulate measurements\n # Specify measurement model for radar\n measurement_model_radar = LinearGaussian(\n ndim_state=4, # number of state dimensions\n mapping=(0, 2), # mapping measurement vector index to state index\n noise_covar=np.array([[sigma_meas_radar, 0], # covariance matrix for Gaussian PDF\n [0, sigma_meas_radar]])\n )\n\n # Specify measurement model for AIS\n measurement_model_ais = LinearGaussian(\n ndim_state=4,\n mapping=(0, 2),\n noise_covar=np.array([[sigma_meas_ais, 0],\n [0, sigma_meas_ais]])\n )\n\n # generate \"radar\" measurements\n measurements_radar = []\n for state in truth:\n measurement = measurement_model_radar.function(state, noise=True)\n measurements_radar.append(Detection(measurement, timestamp=state.timestamp))\n\n # generate \"AIS\" measurements\n measurements_ais = []\n state_num = 0\n for state in truth:\n state_num += 1\n if not state_num % 2: # measurement every second time step\n measurement = measurement_model_ais.function(state, noise=True)\n measurements_ais.append(Detection(measurement, timestamp=state.timestamp))\n\n if permanent_save:\n save_folder_name = seed.__str__()\n else:\n save_folder_name = \"temp\"\n\n save_folder = \"../scenarios/scenario1/\" + save_folder_name + \"/\"\n\n # save the ground truth and the measurements for the radar and the AIS\n store_object.store_object(truth, save_folder, \"ground_truth.pk1\")\n store_object.store_object(measurements_radar, save_folder, \"measurements_radar.pk1\")\n store_object.store_object(measurements_ais, save_folder, \"measurements_ais.pk1\")\n store_object.store_object(start_time, save_folder, \"start_time.pk1\")\n store_object.store_object(measurement_model_radar, save_folder, \"measurement_model_radar.pk1\")\n store_object.store_object(measurement_model_ais, save_folder, \"measurement_model_ais.pk1\")\n store_object.store_object(transition_model, save_folder, \"transition_model.pk1\")","def scenario3(sim):\n\n init_logging(logfile=None, debug=True)\n second = 1000.0\n duration = 10\n tau_m = 20 # ms\n cm = 1.0 # nF\n v_reset = -60\n cell_parameters = dict(\n tau_m=tau_m,\n cm=cm,\n v_rest=-70,\n e_rev_E=0,\n e_rev_I=-70,\n v_thresh=-54,\n v_reset=v_reset,\n tau_syn_E=5,\n tau_syn_I=5,\n )\n g_leak = cm / tau_m # µS\n\n w_min = 0.0 * g_leak\n w_max = 0.05 * g_leak\n\n r1 = 5.0\n r2 = 40.0\n\n sim.setup()\n pre = sim.Population(100, sim.SpikeSourcePoisson())\n post = sim.Population(10, sim.IF_cond_exp())\n\n pre.set(duration=duration * second)\n pre.set(start=0.0)\n pre[:50].set(rate=r1)\n pre[50:].set(rate=r2)\n assert_equal(pre[49].rate, r1)\n assert_equal(pre[50].rate, r2)\n post.set(**cell_parameters)\n post.initialize(v=RandomDistribution('normal', mu=v_reset, sigma=5.0))\n\n stdp = sim.STDPMechanism(\n sim.SpikePairRule(tau_plus=20.0, tau_minus=20.0,\n A_plus=0.01, A_minus=0.01),\n sim.AdditiveWeightDependence(w_min=w_min, w_max=w_max),\n #dendritic_delay_fraction=0.5))\n dendritic_delay_fraction=1)\n\n connections = sim.Projection(pre, post, sim.AllToAllConnector(),\n synapse_type=stdp,\n receptor_type='excitatory')\n\n initial_weight_distr = RandomDistribution('uniform', low=w_min, high=w_max)\n connections.randomizeWeights(initial_weight_distr)\n initial_weights = connections.get('weight', format='array', gather=False)\n # assert initial_weights.min() >= w_min\n # assert initial_weights.max() < w_max\n # assert initial_weights[0, 0] != initial_weights[1, 0]\n\n pre.record('spikes')\n post.record('spikes')\n post[0:1].record('v')\n\n sim.run(duration * second)\n\n actual_rate = pre.mean_spike_count() / duration\n expected_rate = (r1 + r2) / 2\n errmsg = \"actual rate: %g expected rate: %g\" % (actual_rate, expected_rate)\n assert abs(actual_rate - expected_rate) < 1, errmsg\n #assert abs(pre[:50].mean_spike_count()/duration - r1) < 1\n #assert abs(pre[50:].mean_spike_count()/duration- r2) < 1\n final_weights = connections.get('weight', format='array', gather=False)\n assert initial_weights[0, 0] != final_weights[0, 0]\n\n try:\n import scipy.stats\n except ImportError:\n raise SkipTest\n t, p = scipy.stats.ttest_ind(initial_weights[:50, :].flat, initial_weights[50:, :].flat)\n assert p > 0.05, p\n t, p = scipy.stats.ttest_ind(final_weights[:50, :].flat, final_weights[50:, :].flat)\n assert p < 0.01, p\n assert final_weights[:50, :].mean() < final_weights[50:, :].mean()\n sim.end()\n return initial_weights, final_weights, pre, post, connections","def sample(self, params):\r\n old_model_trace = poutine.trace(self.model)(self.args, self.kwargs)\r\n traces = []\r\n t = 0\r\n i = 0\r\n while t < self.burn + self.lag * self.samples:\r\n i += 1\r\n # q(z' | z)\r\n new_guide_trace = poutine.block(\r\n poutine.trace(self.model))(old_model_trace, self.args, self.kwargs)\r\n # p(x, z')\r\n new_model_trace = poutine.trace(\r\n poutine.replay(self.model, new_guide_trace))(self.args, self.kwargs)\r\n # q(z | z')\r\n old_guide_trace = poutine.block(\r\n poutine.trace(\r\n poutine.replay(self.guide, old_model_trace)))(new_model_trace,\r\n self.args, self.kwargs)\r\n # p(x, z') q(z' | z) / p(x, z) q(z | z')\r\n logr = new_model_trace.log_pdf() + new_guide_trace.log_pdf() - \\\r\n old_model_trace.log_pdf() - old_guide_trace.log_pdf()\r\n rnd = pyro.sample(\"mh_step_{}\".format(i),\r\n Uniform(torch.zeros(1), torch.ones(1)))\r\n\r\n if torch.log(rnd).data[0] < logr.data[0]:\r\n # accept\r\n t += 1\r\n old_model_trace = new_model_trace\r\n if t <= self.burn or (t > self.burn and t % self.lag == 0):\r\n yield (new_model_trace, new_model_trace.log_pdf())","def run():\n trials = 100\n\n multipliers = [0.25, 0.3, 0.35, 0.5, 0.75, 1, 1.25, 1.45, 1.5, 1.55, 1.6] # Coefficients for learning rate\n\n mean_penalty = []\n median_penalty = []\n std_penalty = []\n\n mean_trial_time = []\n median_trial_time = []\n std_trial_time = []\n\n mean_success_rate = []\n median_success_rate = []\n std_success_rate = []\n\n for m in multipliers:\n all_penalties = [] # All penalties from trail sets\n all_average_trial_time = []\n all_success_rates = []\n\n for i in range(0, 20):\n # print \"Trial set:\", i\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n agent = e.create_agent(LearnerAgent) # create agent\n agent.mult = m\n e.set_primary_agent(agent, enforce_deadline=True) # specify agent to track\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.0, display=False) # create simulator (uses pygame when display=True, if available)\n\n sim.run(n_trials=trials) # run for a specified number of trials\n\n all_penalties.append(agent.all_trails_penalties)\n all_average_trial_time.append(agent.time/float(trials))\n all_success_rates.append(float(trials-agent.aborted_trials)/trials)\n\n mean_penalty.append(np.mean(all_penalties))\n median_penalty.append(np.median(all_penalties))\n std_penalty.append(np.std(all_penalties))\n\n mean_trial_time.append(np.mean(all_average_trial_time))\n median_trial_time.append(np.median(all_average_trial_time))\n std_trial_time.append(np.std(all_average_trial_time))\n\n mean_success_rate.append(np.mean(all_success_rates))\n median_success_rate.append(np.median(all_success_rates))\n std_success_rate.append(np.std(all_success_rates))\n\n for i in range(0, len(multipliers)):\n print \"\"\n print \"Multiplier:\", multipliers[i]\n print \"\"\n print \"Mean penalty per {} trials:\".format(trials), mean_penalty[i]\n print \"Median penalty per {} trials:\".format(trials), median_penalty[i]\n print \"Std.Dev. penalty per {} trials:\".format(trials), std_penalty[i]\n\n print \"\"\n print \"Mean trial time:\", mean_trial_time[i]\n print \"Median trial time:\", median_trial_time[i]\n print \"Std.Dev. trial time:\", std_trial_time[i]\n\n print \"\"\n print \"Mean success rate per {} trials:\".format(trials), mean_success_rate[i]\n print \"Median success rate per {} trials:\".format(trials), median_success_rate[i]\n print \"Std.Dev. success rate per {} trials:\".format(trials), std_success_rate[i]","def run_experiments() :\n #%%\n target_size=(32,32)\n g_specs = {\n \"batch_size\" : [ 30 , 60, 100 ],\n \"learning_rate\" : [ 0.0002, 0.0003, 0.0005 ],\n \"drop_out_rate\" : [ 0.2, 0.25, 0.3 ],\n \"rescale_mode\" : [ \"max_q\" , \"max\", \"\" ]\n }\n\n model_traits = MODEL_TRAITS[\"model2\"].copy()\n tt_obj = model_traits[\"trainer_tester_class\"]( model_traits )\n del model_traits[\"trainer_tester_class\"]\n\n cnt = 0\n for batchs, lrate, do_rate, resc_mode in product( g_specs[\"batch_size\"],\n g_specs[\"learning_rate\"],\n g_specs[\"drop_out_rate\"],\n g_specs[\"rescale_mode\"] ) :\n\n tt_obj.model_traits.update( {\"batch_size\" : batchs,\n \"learning_rate\" : lrate,\n \"rescale_mode\" : resc_mode,\n \"drop_out_rate\" : do_rate } )\n\n train_4d, train_gt = tu.make_4d_arrays( images_dir=\"images/train\",\n target_size=target_size )\n\n test_4d, test_gt = tu.make_4d_arrays( images_dir=\"images/test\",\n target_size=target_size )\n\n data = {\"train_4d\" : train_4d,\n \"test_4d\" : test_4d,\n \"train_y\" : train_gt,\n \"test_y\" : test_gt}\n\n valid_accu_log, train_accu_log = tt_obj.train( model_traits, data,\n logl=100 )\n idx_v = int(np.argmax( valid_accu_log))\n idx_t = int(np.argmax( train_accu_log))\n\n model_traits.update({\"valid_accu_log\" : valid_accu_log,\n \"train_accu_log\" : train_accu_log,\n \"best_valid\" : max(valid_accu_log),\n \"best_valid_at\" : idx_v,\n \"train_at_best_valid\" : train_accu_log[idx_v],\n \"best_train\" : max(train_accu_log),\n \"best_train_at\": idx_t })\n\n #print(cnt, pformat(model_traits) )\n print( \"%d : best_train = %.4f, best_valid = %.4f\" % \\\n (cnt, max(train_accu_log), max(valid_accu_log) ))\n\n with open( \"exp_results_%d.json\" % cnt,\n \"wt\" , encoding=\"utf8\" ) as f_out :\n print( json.dumps( model_traits ), file=f_out)\n\n\n cnt += 1\n #%%","def test_f3_circuit_maker(self):\n fho = tfho.test_file_handle_object()\n W = 5\n D = 6\n gate_maker = g.TYPE_TO_FAM3_GATE_GEN[g.TEST_TYPES.RANDOM]\n # family 3 files:\n circuit_file_name = \"circuit_file\"\n circuit_file = fho.get_file_object(circuit_file_name, 'w')\n input_file_name = \"input_file\"\n input_file = fho.get_file_object(input_file_name, 'w')\n output_file_name = \"output_file\"\n output_file = fho.get_file_object(output_file_name, 'w')\n F = 3\n # make a family 3 circuit:\n sr.seed(self.rand_seed)\n gen = g.f3_circuit_maker(W, D, circuit_file, input_file, output_file,\n gate_maker)\n gen.generate()\n # obtain strings representing the contents of all the resulting files:\n circuit_string = fho.get_file(circuit_file_name).getvalue()\n input_string = fho.get_file(input_file_name).getvalue()\n output_string = fho.get_file(output_file_name).getvalue()\n # make sure that the input begins and ends with a bracket:\n self.assertEqual(\"[\", input_string[0])\n self.assertEqual(\"]\", input_string[-1])\n # make sure that each input element is a bit:\n for bit in input_string[1:-1]:\n self.assertTrue((bit == '0') or (bit == '1'))\n # make sure that the output is a bit:\n self.assertTrue((output_string == '0') or (output_string == '1'))\n # make sure that the circuit header contains the correct values:\n circuit_header = circuit_string.split(\"\\n\")[0]\n (W_string, D_string, F_string) = circuit_header.split(\",\")\n W_value = int(W_string.split(\"=\")[-1])\n D_value = int(D_string.split(\"=\")[-1])\n F_value = int(F_string.split(\"=\")[-1])\n self.assertEqual(W, W_value)\n self.assertEqual(D, D_value)\n self.assertEqual(F, F_value)","def run_evaluation(self, example):\n assert isinstance(example, dict)\n assert 'name' in example\n\n name = example['name']\n house_id, camera_id = pbrs_utils.parse_house_and_camera_ids_from_string(name)\n\n gt_bg, gt_objects = sorted(glob.glob(path.join(config.default_out_root, 'v9_gt_mesh/{}/{}/gt*.ply'.format(house_id, camera_id))))\n gt_depths = sorted(glob.glob(path.join(config.default_out_root, 'v9_gt_mesh/{}/{}/d*.ply'.format(house_id, camera_id))))\n print(gt_depths)\n assert len(gt_depths) == 5\n\n gt_overhead_fg = sorted(glob.glob(path.join(config.default_out_root, 'v9_gt_overhead_mesh/{}/{}/overhead_fg.ply'.format(house_id, camera_id))))\n assert len(gt_overhead_fg) == 1\n gt_overhead_fg = gt_overhead_fg[0]\n assert path.exists(gt_overhead_fg)\n\n pred_depths = sorted(glob.glob(path.join(config.default_out_root, 'v9_pred_depth_mesh/{}/{}/pred_*.ply'.format(house_id, camera_id))))\n assert len(pred_depths) == 5\n\n pred_overhead_fg = sorted(glob.glob(path.join(config.default_out_root, 'v9_pred_depth_mesh/{}/{}/overhead_fg_clipped.ply'.format(house_id, camera_id))))\n assert len(pred_overhead_fg) == 1\n pred_overhead_fg = pred_overhead_fg[0]\n\n # pred_files_list = sorted(glob.glob(path.join(config.default_out_root, 'v9_pred_depth_mesh/{}/{}/*.ply'.format(house_id, camera_id))))\n # pred_files = {path.basename(item).split('.')[0]: item for item in pred_files_list}\n # f3d_pred = path.join(config.default_out_root, 'factored3d_pred/{}/{}/codes_transformed_clipped.ply'.format(house_id, camera_id))\n\n # assert path.isfile(f3d_pred), f3d_pred\n\n # if 'overhead_fg_clipped' not in pred_files:\n # print('Overhead file is not available. Skipping for now. {}'.format(name))\n # return\n\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['overhead_fg']), [gt_objects], [pred_files['overhead_fg_clipped']])\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['0']), [gt_objects], [pred_files['pred_0']])\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['1']), [gt_objects], [pred_files['pred_1']])\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['2']), [gt_objects], [pred_files['pred_2']])\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['0', 'overhead_fg']), [gt_objects], [pred_files['pred_0'], pred_files['overhead_fg_clipped']])\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['1', 'overhead_fg']), [gt_objects], [pred_files['pred_1'], pred_files['overhead_fg_clipped']])\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['2', 'overhead_fg']), [gt_objects], [pred_files['pred_2'], pred_files['overhead_fg_clipped']])\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['0', '1']), [gt_objects], [pred_files['pred_0'], pred_files['pred_1']])\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['0', '2']), [gt_objects], [pred_files['pred_0'], pred_files['pred_2']])\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['1', '2']), [gt_objects], [pred_files['pred_1'], pred_files['pred_2']])\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['0', '1', '2']), [gt_objects], [pred_files['pred_0'], pred_files['pred_1'], pred_files['pred_2']])\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['0', '1', '2', 'overhead_fg']), [gt_objects], [pred_files['pred_0'], pred_files['pred_1'], pred_files['pred_2'], pred_files['overhead_fg_clipped']])\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['0', '1', '2', 'overhead_fg', 'f3d']), [gt_objects], [pred_files['pred_0'], pred_files['pred_1'], pred_files['pred_2'], pred_files['overhead_fg_clipped'], f3d_pred])\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['f3d']), [gt_objects], [f3d_pred])\n\n # self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['1', '2', '3']), [gt_objects], [gt_depths[1], gt_depths[2], gt_depths[3]])\n # self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['2', '3']), [gt_objects], [gt_depths[2], gt_depths[3]])\n # self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['3']), [gt_objects], [gt_depths[3]])\n\n self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['0']), [gt_objects], [gt_depths[0]])\n self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['1']), [gt_objects], [gt_depths[1]])\n self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['2']), [gt_objects], [gt_depths[2]])\n self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['3']), [gt_objects], [gt_depths[3]])\n self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['overhead_fg']), [gt_objects], [gt_overhead_fg])\n self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['0', '1']), [gt_objects], [gt_depths[0], gt_depths[1]])\n self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['0', '1', '2']), [gt_objects], [gt_depths[0], gt_depths[1], gt_depths[2]])\n self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['0', '1', '2', '3']), [gt_objects], [gt_depths[0], gt_depths[1], gt_depths[2], gt_depths[3]])\n self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['0', '1', '2', '3', 'overhead_fg']), [gt_objects], [gt_depths[0], gt_depths[1], gt_depths[2], gt_depths[3], gt_overhead_fg])\n\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0']), [gt_bg, gt_objects], [gt_depths[0]])\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['1']), [gt_bg, gt_objects], [gt_depths[1]])\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['2']), [gt_bg, gt_objects], [gt_depths[2]])\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['3']), [gt_bg, gt_objects], [gt_depths[3]])\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['4']), [gt_bg, gt_objects], [gt_depths[4]])\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['overhead_fg']), [gt_bg, gt_objects], [gt_overhead_fg])\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['4', '0']), [gt_bg, gt_objects], [gt_depths[4], gt_depths[0]])\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['4', '0', '1']), [gt_bg, gt_objects], [gt_depths[4], gt_depths[0], gt_depths[1]])\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['4', '0', '1', '2']), [gt_bg, gt_objects], [gt_depths[4], gt_depths[0], gt_depths[1], gt_depths[2]])\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '1']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[1]])\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '1', '2']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[1], gt_depths[2]])\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '1', '2', '3']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[1], gt_depths[2], gt_depths[3]])\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '1', '2', '3', '4']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[1], gt_depths[2], gt_depths[3], gt_depths[4]])\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '1', '2', '3', '4', 'overhead_fg']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[1], gt_depths[2], gt_depths[3], gt_depths[4], gt_overhead_fg])\n\n # LDI\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '2', '4']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[2], gt_depths[4]])\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '2']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[2]])\n\n self.run_if_not_exists(name, self.key('pred', 'obj', ['0']), [gt_objects], [pred_depths[0]])\n self.run_if_not_exists(name, self.key('pred', 'obj', ['1']), [gt_objects], [pred_depths[1]])\n self.run_if_not_exists(name, self.key('pred', 'obj', ['2']), [gt_objects], [pred_depths[2]])\n self.run_if_not_exists(name, self.key('pred', 'obj', ['3']), [gt_objects], [pred_depths[3]])\n self.run_if_not_exists(name, self.key('pred', 'obj', ['overhead_fg']), [gt_objects], [pred_overhead_fg], force=True)\n self.run_if_not_exists(name, self.key('pred', 'obj', ['0', 'overhead_fg']), [gt_objects], [pred_depths[0], pred_overhead_fg])\n self.run_if_not_exists(name, self.key('pred', 'obj', ['0', '1']), [gt_objects], [pred_depths[0], pred_depths[1]])\n self.run_if_not_exists(name, self.key('pred', 'obj', ['0', '1', '2']), [gt_objects], [pred_depths[0], pred_depths[1], pred_depths[2]])\n self.run_if_not_exists(name, self.key('pred', 'obj', ['0', '1', '2', '3']), [gt_objects], [pred_depths[0], pred_depths[1], pred_depths[2], pred_depths[3]])\n self.run_if_not_exists(name, self.key('pred', 'obj', ['0', '1', '2', '3', 'overhead_fg']), [gt_objects], [pred_depths[0], pred_depths[1], pred_depths[2], pred_depths[3], pred_overhead_fg], force=True)\n\n self.run_if_not_exists(name, self.key('pred', 'both', ['0']), [gt_bg, gt_objects], [pred_depths[0]])\n self.run_if_not_exists(name, self.key('pred', 'both', ['1']), [gt_bg, gt_objects], [pred_depths[1]])\n self.run_if_not_exists(name, self.key('pred', 'both', ['2']), [gt_bg, gt_objects], [pred_depths[2]])\n self.run_if_not_exists(name, self.key('pred', 'both', ['3']), [gt_bg, gt_objects], [pred_depths[3]])\n self.run_if_not_exists(name, self.key('pred', 'both', ['4']), [gt_bg, gt_objects], [pred_depths[4]])\n self.run_if_not_exists(name, self.key('pred', 'both', ['overhead_fg']), [gt_bg, gt_objects], [pred_overhead_fg], force=True)\n self.run_if_not_exists(name, self.key('pred', 'both', ['4', '0']), [gt_bg, gt_objects], [pred_depths[4], pred_depths[0]])\n self.run_if_not_exists(name, self.key('pred', 'both', ['4', '0', '1']), [gt_bg, gt_objects], [pred_depths[4], pred_depths[0], pred_depths[1]])\n self.run_if_not_exists(name, self.key('pred', 'both', ['4', '0', '1', '2']), [gt_bg, gt_objects], [pred_depths[4], pred_depths[0], pred_depths[1], pred_depths[2]])\n self.run_if_not_exists(name, self.key('pred', 'both', ['4', '0', '1', '2', '3']), [gt_bg, gt_objects], [pred_depths[4], pred_depths[0], pred_depths[1], pred_depths[2], pred_depths[3]])\n self.run_if_not_exists(name, self.key('pred', 'both', ['4', '0', '1', '2', '3', 'overhead_fg']), [gt_bg, gt_objects], [pred_depths[4], pred_depths[0], pred_depths[1], pred_depths[2], pred_depths[3], pred_overhead_fg], force=True)\n\n # self.run_if_not_exists(name, self.key('pred', 'both', ['overhead_fg']), [gt_bg, gt_objects], [pred_files['overhead_fg_clipped']])\n # self.run_if_not_exists(name, self.key('pred', 'both', ['0']), [gt_bg, gt_objects], [pred_files['pred_0']])\n # self.run_if_not_exists(name, self.key('pred', 'both', ['1']), [gt_bg, gt_objects], [pred_files['pred_1']])\n # self.run_if_not_exists(name, self.key('pred', 'both', ['2']), [gt_bg, gt_objects], [pred_files['pred_2']])\n # self.run_if_not_exists(name, self.key('pred', 'both', ['3']), [gt_bg, gt_objects], [pred_files['pred_3']])\n # self.run_if_not_exists(name, self.key('pred', 'both', ['0', '3']), [gt_bg, gt_objects], [pred_files['pred_0'], pred_files['pred_3']])\n # self.run_if_not_exists(name, self.key('pred', 'both', ['0', '1', '3']), [gt_bg, gt_objects], [pred_files['pred_0'], pred_files['pred_1'], pred_files['pred_3']])\n # self.run_if_not_exists(name, self.key('pred', 'both', ['0', '1', '2', '3']), [gt_bg, gt_objects], [pred_files['pred_0'], pred_files['pred_1'], pred_files['pred_2'], pred_files['pred_3']])\n # self.run_if_not_exists(name, self.key('pred', 'both', ['0', '1', '2', '3', 'overhead_fg']), [gt_bg, gt_objects], [pred_files['pred_0'], pred_files['pred_1'], pred_files['pred_2'], pred_files['pred_3'], pred_files['overhead_fg_clipped']])\n #\n # # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['overhead_fg']), [gt_bg, gt_objects], [pred_files['overhead_fg_clipped']])\n # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0']), [gt_bg, gt_objects], [gt_depths[0]])\n # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['1']), [gt_bg, gt_objects], [gt_depths[1]])\n # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['2']), [gt_bg, gt_objects], [gt_depths[2]])\n # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['3']), [gt_bg, gt_objects], [gt_depths[3]])\n # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '3']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[3], ])\n # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '1', '3']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[1], gt_depths[3], ])\n # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '1', '2', '3']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[1], gt_depths[2], gt_depths[3], ])\n # # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '1', '2', '3', 'overhead_fg']), [gt_bg, gt_objects], [])\n #\n # # self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['2', '3']), [gt_objects], [gt_depths[2], gt_depths[3]])\n # # self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['1', '2', '3']), [gt_objects], [gt_depths[1], gt_depths[2], gt_depths[3]])\n # # self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['0', '1', '2', '3']), [gt_objects], [gt_depths[0], gt_depths[1], gt_depths[2], gt_depths[3]])\n #\n # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '1']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[1], ])\n # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '1', '2']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[1], gt_depths[2], ])","def refugia_adj_5_simsplit_3epochs(params, ns):\n #24 parameters \n nu1a, nu2a, nu3a, nu1b, nu2b, nu3b, nu1c, nu2c, nu3c, m1_12, m1_13, m1_21, m1_23, m1_31, m1_32, m3_12, m3_13, m3_21, m3_23, m3_31, m3_32, T1, T2, T3 = params\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1] + ns[2])\n fs = moments.Spectrum(sts)\n fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1] + ns[2]) \n fs = moments.Manips.split_2D_to_3D_2(fs, ns[1], ns[2])\n ## Population function and migration matrix for T2\n nu_T1 = [nu1a, nu2a, nu3a]\n mig1 = numpy.array([[0, m1_12, m1_13],[m1_21, 0, m1_23], [m1_31, m1_32, 0]]) \n fs.integrate(nu_T1, T1, m=mig1)\n ## Population function and migration matrix for T2\n nu_T2 = [nu1b, nu2b, nu3b]\n fs.integrate(nu_T2, T2)\n ## Population function and migration matrix for T3\n nu_T3 = [nu1c, nu2c, nu3c]\n mig3 = numpy.array([[0, m3_12, m3_13],[m3_21, 0, m3_23], [m3_31, m3_32, 0]]) \n fs.integrate(nu_T3, T3, m=mig3) \n return fs","def create_scenarios(self, params, num_scenarios, random_seed):\n return None","def refugia_adj_5_simsplit_4epochs_iter3 (params, ns):\n #28 parameters \n nu1a, nu2a, nu3a, nu1b, nu2b, nu3b, nu1c, nu2c, nu3c, nu1d, nu2d, nu3d, m1_12, m1_13, m1_21, m1_23, m1_31, m1_32, m3_12, m3_13, m3_21, m3_23, m3_31, m3_32, T1, T2, T3, T4 = params\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1] + ns[2])\n fs = moments.Spectrum(sts)\n fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1] + ns[2]) \n fs = moments.Manips.split_2D_to_3D_2(fs, ns[1], ns[2])\n ## Population function and migration matrix for T1 (to reflect sum effect of all previous glacial-interglacial cycles)\n nu_T1 = [nu1a, nu2a, nu3a]\n mig1 = numpy.array([[0, m1_12, m1_13],[m1_21, 0, m1_23], [m1_31, m1_32, 0]]) \n fs.integrate(nu_T1, T1, m=mig1)\n ## Population function and migration matrix for T2 (to reflect period of isolation during last glacial)\n nu_T2 = [nu1b, nu2b, nu3b]\n fs.integrate(nu_T2, T2)\n ## Population function and migration matrix for T3 (to reflect inter-glacial expansion)\n nu_T3 = [nu1c, nu2c, nu3c]\n mig3 = numpy.array([[0, m3_12, m3_13],[m3_21, 0, m3_23], [m3_31, m3_32, 0]]) \n fs.integrate(nu_T3, T3, m=mig3)\n ## Population function and migration matrix for T3 (bottleneck to capture single population representation of lineage)\n nu_T4 = [nu1d, nu2d, nu3d]\n fs.integrate(nu_T4, T4) \n return fs","def test_case_generate(self):\n\n # initialization\n state = np.random.choice(self.init_states)\n model = rm.randint(0, self.model_num - 1)\n duration = np.random.choice(self.step_values)\n temp = rm.randint(self.min_temp, self.max_temp)\n\n self.states = [[model, duration, temp]]\n self.time = duration\n\n while self.time < self.max_time:\n if state == \"inc_tmp\":\n change = np.random.choice(\n self.transitionName[0], p=self.transitionMatrix[0]\n ) # choose the next state\n if change == \"S1S1\": # stay in the same state\n temp = self.get_temp_inc(temp)\n model = rm.randint(0, self.model_num - 1)\n diff = (\n self.max_time - self.time\n ) # this is for ensuring the maximum duration is not exceeded\n if (diff) < self.max_step and (diff) > self.min_step:\n duration = diff\n self.states.append([model, duration, temp])\n return self.states_to_dict()\n elif diff < self.min_step:\n self.states[len(self.states) - 1][1] += diff\n return self.states_to_dict()\n else:\n duration = np.random.choice(self.step_values)\n self.time += duration\n self.states.append([model, duration, temp])\n\n elif change == \"S1S2\": # change from increase to decrease\n temp = self.get_temp_dec(temp)\n model = rm.randint(0, self.model_num - 1)\n state = \"dec_tmp\"\n\n diff = self.max_time - self.time\n if (diff) < self.max_step and (diff) > self.min_step:\n duration = diff\n self.states.append([model, duration, temp])\n return self.states_to_dict()\n elif diff < self.min_step:\n self.states[len(self.states) - 1][1] += diff\n return self.states_to_dict()\n else:\n duration = np.random.choice(self.step_values)\n self.time += duration\n self.states.append([model, duration, temp])\n else:\n print(\"Error\")\n\n elif state == \"dec_tmp\":\n change = np.random.choice(\n self.transitionName[1], p=self.transitionMatrix[1]\n )\n if change == \"S2S1\":\n temp = self.get_temp_inc(temp)\n model = rm.randint(0, self.model_num - 1)\n state = \"inc_tmp\"\n diff = self.max_time - self.time\n if (diff) < self.max_step and (diff) > self.min_step:\n duration = diff\n self.states.append([model, duration, temp])\n return self.states_to_dict()\n elif diff < self.min_step:\n self.states[len(self.states) - 1][1] += diff\n return self.states_to_dict()\n else:\n duration = np.random.choice(self.step_values)\n\n self.time += duration\n self.states.append([model, duration, temp])\n\n elif change == \"S2S2\":\n temp = self.get_temp_dec(temp)\n model = rm.randint(0, self.model_num - 1)\n\n diff = self.max_time - self.time\n if (diff) < self.max_step and (diff) > self.min_step:\n duration = diff\n self.states.append([model, duration, temp])\n return self.states_to_dict()\n elif diff < self.min_step:\n self.states[len(self.states) - 1][1] += diff\n return self.states_to_dict()\n else:\n duration = np.random.choice(self.step_values)\n self.time += duration\n self.states.append([model, duration, temp])\n\n else:\n print(\"Error\")\n pass\n else:\n print(\"Error\")\n\n return self.states_to_dict()","def start_training(params):\n\n\n\n # CREATE A FOLDER TO HOLD RESULTS\n\n\n exp_pref = \"../results/\" + params.EXPERIMENT_PREFIX\n time_str = time.strftime(\"_%m-%d-%H-%M_\", time.gmtime())\n exp_dir = exp_pref + time_str + \\\n \"{}\".format(params.LEARNING_RATE).replace(\".\", \"p\") + \"_\" \\\n + \"{}\".format(params.DISCOUNT).replace(\".\", \"p\")\n\n try:\n os.stat(exp_dir)\n except OSError:\n os.makedirs(exp_dir)\n\n logger = logging.getLogger(\"DeepLogger\")\n logger.setLevel(logging.INFO)\n\n # Logging filehandler\n #fh = logging.FileHandler(exp_dir + \"/log.log\")\n # Rotate file when filesize is 5 mb\n fh = RotatingFileHandler(exp_dir + \"/log.log\", maxBytes=5000000, backupCount=100)\n\n fh.setLevel(logging.INFO)\n\n # Console filehandler\n ch = logging.StreamHandler()\n ch.setLevel(logging.INFO)\n\n formatter = logging.Formatter('%(message)s')\n fh.setFormatter(formatter)\n ch.setFormatter(formatter)\n\n logger.addHandler(fh)\n\n # Prevent nohup from producing large log file, logging to file is handled internally\n # logger.addHandler(ch)\n\n log_params(logger, params)\n\n #logging.basicConfig(level=logging.INFO, filename=exp_dir + \"/log.log\")\n\n\n if params.DETERMINISTIC:\n rng = np.random.RandomState(12345)\n else:\n rng = np.random.RandomState()\n\n if params.CUDNN_DETERMINISTIC:\n theano.config.dnn.conv.algo_bwd = 'deterministic'\n\n # Init ale\n ale = ale_python_interface.ALEInterface()\n ale.setInt('random_seed', 123)\n ale.setBool('display_screen', params.DISPLAY_SCREEN)\n ale.setFloat('repeat_action_probability', params.REPEAT_ACTION_PROBABILITY)\n full_rom_path = os.path.join(params.ROM_PATH, params.ROM_NAME)\n ale.loadROM(full_rom_path)\n num_actions = len(ale.getMinimalActionSet())\n\n print \"Legal actions: \", num_actions\n print ale.getMinimalActionSet()\n\n # Instantiate network\n logger.info(\"Setting up network...\")\n network = None # Be able to continue training from a network or watch a network play\n if (params.NETWORK_PICKLE_FILE is None):\n logger.info(\"Initializing a new random network...\")\n network = q_network.DeepQLearner(params.RESIZED_WIDTH,\n params.RESIZED_HEIGHT,\n num_actions,\n params.PHI_LENGTH,\n params.DISCOUNT,\n params.LEARNING_RATE,\n params.RMS_DECAY,\n params.RMS_EPSILON,\n params.MOMENTUM,\n params.CLIP_DELTA,\n params.FREEZE_INTERVAL,\n params.BATCH_SIZE,\n params.NETWORK_TYPE,\n params.UPDATE_RULE,\n params.BATCH_ACCUMULATOR,\n rng)\n else:\n logger.info(\"Loading network instance from file...\")\n handle = open(params.NETWORK_PICKLE_FILE, 'r')\n network = cPickle.load(handle)\n\n\n # Only used when getting a random network\n if params.RANDOM_NETWORK_PICKLE:\n import sys\n sys.setrecursionlimit(10000)\n result_net_file = open(params.EXPERIMENT_PREFIX + '.pkl', 'w')\n print \"File opened\"\n cPickle.dump(network, result_net_file, -1)\n print \"Pickle dumped\"\n result_net_file.close()\n sys.exit(0)\n\n\n # Instatiate agent\n logger.info(\"Setting up agent...\")\n agent = ale_agent.NeuralAgent(network,\n params.EPSILON_START,\n params.EPSILON_MIN,\n params.EPSILON_DECAY,\n params.REPLAY_MEMORY_SIZE,\n exp_dir,\n params.REPLAY_START_SIZE,\n params.UPDATE_FREQUENCY,\n rng)\n\n # Instantiate experient\n logger.info(\"Setting up experiment...\")\n experiment = ale_experiment.ALEExperiment(ale, agent,\n params.RESIZED_WIDTH,\n params.RESIZED_HEIGHT,\n params.RESIZE_METHOD,\n params.EPOCHS,\n params.STEPS_PER_EPOCH,\n params.STEPS_PER_TEST,\n params.FRAME_SKIP,\n params.DEATH_ENDS_EPISODE,\n params.MAX_START_NULLOPS,\n rng)\n\n\n # Run experiment\n logger.info(\"Running experiment...\")\n experiment.run()","def simulate_trajectories(kav):\n print \"Simulating \"+str(kav)\n wt_trajectories = []\n avp_trajectories = []\n vip_trajectories = []\n for tn in range(100):\n # get random initial condition\n # initial phases\n init_conditions_AV = [single_osc.lc(wt_T*np.random.rand()) \n for i in range(AVPcells+VIPcells)]\n init_conditions_NAV = [single_osc.lc(wt_T*np.random.rand())[:-1]\n for i in range(NAVcells)]\n y0_random = np.hstack(init_conditions_AV+init_conditions_NAV)\n\n # do the simulation\n model = GonzeModelManyCells(param, kav=kav, \n initial_values=y0_random)\n wt_trajectories.append(model.run(show_labels=False, seed=0))\n\n # avp bmalko\n avp_model = GonzeModelManyCells(param, bmalko='AVP', kav=kav, \n initial_values=y0_random)\n avp_trajectories.append(avp_model.run(show_labels=False, seed=0))\n\n # vip bmalko\n vip_model = GonzeModelManyCells(param, bmalko='VIP', kav=kav, \n initial_values=y0_random)\n vip_trajectories.append(vip_model.run(show_labels=False, seed=0))\n\n # save results\n with open(\"Data/params/wt_\"+str(kav)+\".pickle\", \"wb\") as output_file:\n pickle.dump(wt_trajectories, output_file)\n with open(\"Data/params/avp_\"+str(kav)+\".pickle\", \"wb\") as output_file:\n pickle.dump(avp_trajectories, output_file)\n with open(\"Data/params/vip_\"+str(kav)+\".pickle\", \"wb\") as output_file:\n pickle.dump(vip_trajectories, output_file)\n\n return {'wt': wt_trajectories,\n 'avp': avp_trajectories,\n 'vip': vip_trajectories}","def __init__(self, tconst=2, tau1=5, tau2=10, lambda1=20, lambda2=50, lambda3=100): # only default arguments here\n gr.sync_block.__init__(\n self,\n name='Scenario classification', # will show up in GRC\n in_sig=[np.float32]*6,\n out_sig=None\n )\n # if an attribute with the same name as a parameter is found,\n # a callback is registered (properties work, too).\n self.tconst = tconst\n self.tau1 = tau1\n self.tau2 = tau2\n self.lambda1 = lambda1\n self.lambda2 = lambda2\n self.lambda3 = lambda3\n self.nchan = 4\n self.scenario_tau = np.array([self.tau1, self.tau2, 2*self.tau1, 4*self.tau2, self.tau1, self.tau1, self.tconst, self.lambda1, self.lambda2, self.lambda3, 0]) # scenario 10 is \"unsure, don't send\"\n self.scenario_send_window = self.scenario_tau.copy() # the send window for the hopping and Poisson scenarios differs from the deterministic ones\n self.scenario_send_window[2] = self.tau1\n self.scenario_send_window[3] = self.tau2\n for i in range(7, 10):\n self.scenario_send_window[i] = st.poisson.ppf(0.05, self.scenario_tau[i])\n print \"expected mean inter frame times: \", self.scenario_tau\n print \"send windows: \", self.scenario_send_window\n \n self.max_confidence = 3\n self.confidence = 0\n \n self.scenario = 10\n self.chan_occupied = [False] * self.nchan # list of true/false values\n self.tau = None\n \n self.message_port_register_out(pmt.intern(\"scenario\"))","def rtest_predictoutcome():\n\n #define cohort size\n npatients = 2\n\n #init healthy patients\n simulator = AbbcEnvironment(patients=npatients)\n\n #simulate healthy patients for long term in short term increments\n nstep = int(long_term/short_term)\n\n #define action taken : -1 means patients will be simulated as healthy\n action = np.repeat(-1, npatients)\n\n #init episode list\n episode = [simulator.state]\n\n #main simulation loop to generate episodes\n for step in range(nstep):\n episode += simulator.take_action(action=action, simtime=short_term)\n\n #episode length is 1+2*nstep consisting of intit state (5xnpat) followed by\n # next state and reward (1xnpat) repeating each time step.\n #print(episode)\n #print(len(episode))\n\n #---semi gradient temporal difference (0) algorithm ---\n #init hyperparameters\n alpha = .1 #learning rate\n #init Value function model\n agent = AbbcAgent(discount=1.0)\n #loop over episodes\n for patient in range(npatients):\n #state = [nstep]\n #state += episode[0][:,patient] #get inital state\n state = np.append(episode[0][:,patient],nstep).reshape((6,1)) #get inital state\n\n print(state)\n #loop over time steps in episode\n for k in range(1,nstep+1):\n #get next state and reward\n #nextstate = [nstep-k]\n #nextstate = episode[k*2-1][:,patient]\n nextstate = np.append(episode[k*2-1][:,patient],nstep-k).reshape((6,1))\n\n reward = episode[k*2][patient]\n\n #get magnitude for forces\n magnitude = alpha * (reward + agent.discount * agent.get_value(nextstate)\n - agent.get_value(state))\n #compute forces\n forces = computeforces(agent.prednet, state, 0, \"iden\")\n\n #update model\n for layer in forces:\n index = layer[\"layer\"]\n agent.prednet[index][\"weight\"] += magnitude * layer[\"fweight\"]\n agent.prednet[index][\"bias\"] += magnitude * layer[\"fbias\"]\n\n state = np.copy(nextstate)\n\n\n #make predictions\n state = np.append(episode[0][:,patient],nstep).reshape((6,1)) #get inital state\n print(agent.get_value(state))\n\n #Value function approximates outcome return at time horizon.\n assert(False)\n\n ##define action taken\n #action = np.repeat(2, npatients)\n ##main simulation loop\n #for step in range(nstep):\n # _, drugy_reward[step,:] = simulator.take_action(action=action, simtime=short_term)","def run_shape_experiment(**kwargs):\n try:\n input_file = kwargs['input_file']\n results_folder = kwargs['results_folder']\n data_folder = kwargs['data_folder']\n sampler = kwargs['sampler']\n max_part_count = None\n if 'max_part_count' in kwargs:\n max_part_count = kwargs['max_part_count']\n ll_variance = kwargs['ll_variance']\n max_pixel_value = kwargs['max_pixel_value']\n change_size_variance = kwargs['change_size_variance']\n change_viewpoint_variance = kwargs['change_viewpoint_variance']\n move_part_variance = kwargs['move_part_variance']\n burn_in = kwargs['burn_in']\n sample_count = kwargs['sample_count']\n best_sample_count = kwargs['best_sample_count']\n thinning_period = kwargs['thinning_period']\n report_period = kwargs['report_period']\n temperatures = None\n if 'temperatures' in kwargs:\n temperatures = kwargs['temperatures']\n except KeyError as e:\n raise ValueError(\"All experiment parameters should be provided. Missing parameter {0:s}\".format(e.message))\n\n import numpy as np\n\n import mcmclib.proposal as proposal\n import i3d_proposal\n import vision_forward_model as vfm\n\n # read the data file\n data = np.load(\"{0:s}/{1:s}_single_view.npy\".format(data_folder, input_file))\n render_size = data.shape[1:]\n\n fwm = vfm.VisionForwardModel(render_size=render_size)\n\n shape_params = {'LL_VARIANCE': ll_variance, 'MAX_PIXEL_VALUE': max_pixel_value}\n\n kernel_params = {'CHANGE_SIZE_VARIANCE': change_size_variance, 'MOVE_PART_VARIANCE': move_part_variance,\n 'CHANGE_VIEWPOINT_VARIANCE': change_viewpoint_variance}\n\n import shape\n\n moves = {'change_viewpoint': i3d_proposal.change_viewpoint_z,\n 'shape_add_remove_part': shape.shape_add_remove_part,\n 'shape_move_part_local': shape.shape_move_part_local,\n 'shape_change_part_size_local': shape.shape_change_part_size_local}\n\n viewpoint = [(np.sqrt(8.0), -45.0, 45.0)]\n hypothesis_class = 'Shape'\n if max_part_count is None:\n h = shape.Shape(forward_model=fwm, viewpoint=viewpoint, params=shape_params)\n else:\n import shape_maxn\n hypothesis_class = 'ShapeMaxD'\n h = shape_maxn.ShapeMaxN(forward_model=fwm, maxn=max_part_count, viewpoint=viewpoint, params=shape_params)\n kernel_params['MAX_PART_COUNT'] = max_part_count\n\n # form the proposal\n kernel = proposal.RandomMixtureProposal(moves=moves, params=kernel_params)\n\n results = run_chain(name=input_file, sampler=sampler, initial_h=h, data=data, kernel=kernel, burn_in=burn_in,\n thinning_period=thinning_period, sample_count=sample_count, best_sample_count=best_sample_count,\n report_period=report_period,\n results_folder=\"{0:s}/{1:s}\".format(results_folder, hypothesis_class),\n temperatures=temperatures)\n\n return results","def test_y_generate():\n a = Attractor()\n #say x, y, z = [0.1, 0.0, 0.0]\n\n dx = (10.0 * (0.0 - 0.1)) * (80.0-0.0)/10000 + 0.1\n dy = (0.1 * (28 - 0.0) - 0.0) * (80.0-0.0)/10000 + 0.0\n dz = ((0.1 * 0.0) - (8/3 * 0.0)) * (80.0-0.0)/10000 + 0.0\n ex_1 = np.array([dx, dy, dz])\n\n dx2 = (10.0 * (dy - dx)) * (80.0-0.0)/10000.0 + dx \n dy2 = (dx * (28.0 - dz) - dy) * (80.0-0.0)/10000.0 + dy\n dz2 = ((dx * dy) - (8/3 * dz)) * (80.0-0.0)/10000.0 + dz\n ex_2 = np.array([dx2, dy2, dz2])\n\n dx3 = (10.0 * (dy2 - dx2)) * (80.0-0.0)/10000.0 + dx2\n dy3 = (dx2 * (28.0 - dz2) - dy2) * (80.0-0.0)/10000.0 + dy2\n dz3 = ((dx2 * dy2) - (8/3 * dz2)) * (80.0-0.0)/10000.0 + dz2\n ex_3 = np.array([dx3, dy3, dz3])\n\n dx4 = (10.0 * (dy3 - dx3)) * (80.0-0.0)/10000.0 + dx3\n dy4 = (dx3 * (28 - dz3) - dy3) * (80.0-0.0)/10000.0 + dy3\n dz4 = ((dx3 * dy3) - (8/3 * dz3)) * (80.0-0.0)/10000.0 + dz3\n ex_4 = np.array([dx4, dy4, dz4])\n\n dx5 = (10.0 * (dy4 - dx4)) * (80.0-0.0)/10000.0 + dx4\n dy5 = (dx4 * (28 - dz4) - dy4) * (80.0-0.0)/10000.0 + dy4\n dz5 = ((dx4 * dy4) - (8/3 * dz4)) * (80.0-0.0)/10000.0 + dz4\n ex_5 = np.array([dx5, dy5, dz5])\n\n \n a.evolve(order = 4)\n y_list = a.solution['y'].tolist()\n \n for i in y_list[:6]:\n yy = round(i, 2)\n for j in [0.0, dy, dy2, dy3, dy4, dy5]:\n yyy = round(j, 2)\n \n print (\"Actual increments: \", yy)#str(a.solution()['x']).strip('[]'))\n print (\"Expected increments: \", yyy)\n assert yy == yyy","def apx_ratio_eval():\n print 'starting random eval'\n\n n_pl = randint(2, MAX_PL)\n n_target = randint(n_pl + 1, MAX_TARGETS)\n\n target_values = []\n for t in range(0,n_target):\n target_values.append(random())\n target_values = np.array(target_values)\n\n attacker_strategy = get_random_attacker_strategy(n_target)\n\n #I = get_random_covering_routes(n_pl, n_target)\n I = get_pathologic_covering_routes(n_pl, n_target, attacker_strategy, target_values)\n\n start_opt = time()\n br_opt = ilp.generate_row(I, attacker_strategy, target_values)\n opt_time = time() - start_opt\n\n start_apx = time()\n br_apx = greedy.generate_row(I, attacker_strategy, target_values)\n apx_time = time() - start_apx\n\n print 'opt time: ', opt_time, 'apx time: ', apx_time\n\n apx_val = get_value(br_apx, I, target_values, attacker_strategy)\n opt_val = get_value(br_opt, I, target_values, attacker_strategy)\n\n logging.info('ratio = %f - target = %d - pl = %f - max_r = %d - max_cov_t = %d',apx_val/opt_val, n_target, n_pl, MAX_ROUTES, MAX_ROUTE_LEN)\n\n return apx_val/opt_val, opt_time, apx_time","def variational_circuit(params):\n non_parametrized_layer()\n qml.RX(params[0], wires=0)\n qml.RY(params[1], wires=1)\n qml.RZ(params[2], wires=2)\n non_parametrized_layer()\n qml.RX(params[3], wires=0)\n qml.RY(params[4], wires=1)\n qml.RZ(params[5], wires=2)","def run_simulation(conf):\r\n\r\n\r\n # Validate cofiguration\r\n conf = validate_conf(conf)\r\n\r\n # Define function to run and storage path\r\n newpath = create_path(conf)\r\n\r\n # Define trajectory and time per state\r\n nr_states = 4\r\n traj = [list(range(nr_states))]\r\n T_list = [[conf.time_per_state for _ in range(nr_states)]]\r\n\r\n # Set parameters\r\n params, gamma, eta, lambda_var = parameters_linear_track_value(nr_states,\r\n conf.time_per_state,\r\n conf.alpha,\r\n conf.A_pre,\r\n )\r\n\r\n params['trajectories'] = traj\r\n params = update_params(conf.mode, params, T_list, gamma, eta)\r\n\r\n # Define the initial weights from CA3 to CA1\r\n T_mat = np.array([\r\n [0,1,0,0,0],\r\n [0,0,1,0,0],\r\n [0,0,0,1,0],\r\n [0,0,0,0,1],\r\n [0,0,0,0,1],\r\n ])\r\n M_theor = np.linalg.inv(np.eye(nr_states)-gamma*T_mat[:-1, :-1])\r\n wini = np.tile(np.expand_dims(np.expand_dims(M_theor,2),3),[1,1,params['N_pre_tot'],params['N_post']])\r\n params['wini'] = wini\r\n\r\n # Define the reward vector\r\n reward_vec = np.array([0,0,0,1])\r\n params['reward_vec'] = reward_vec\r\n\r\n # calculate theoretical value\r\n v_theor = np.matmul(M_theor, reward_vec)\r\n print(f'Theoretical Values: {list(v_theor)}')\r\n\r\n # Run spiking network\r\n params['step'] = params['step']*10\r\n reward_neuron_activity = run_value_function(**params)\r\n\r\n\r\n #############################\r\n # STORING AND PLOTTING\r\n #############################\r\n\r\n if conf.store_data:\r\n pickle.dump(reward_neuron_activity, open(newpath + '/rebuttal_value_function.pkl','wb'))\r\n\r\n rates = np.mean(np.sum(reward_neuron_activity, axis=2)/conf.time_per_state*1000, axis=1)\r\n\r\n # fs = 16\r\n\r\n fig = plt.figure()\r\n plt.bar(list(range(1,nr_states+1)), rates)\r\n if conf.store_data:\r\n fig.savefig(newpath+'/fig_value_function.eps',bbox_inches='tight',format='eps')\r\n\r\n plt.figure()\r\n for i in range(4):\r\n x = reward_neuron_activity[i]\r\n plt.plot(np.max(x,axis=0)*(i+1), '.', markersize=15)","def __init__(self, downsampling=False, wl_range = False, spectra_path = '/Users/kyleturner/Dropbox/My_Box/Code/M_Files/process_svc_v2p1/rrs_model_3C-master/spectra/'):\n\n\t\t\ta_ph = pd.read_csv(spectra_path + 'phyto.A', skiprows=11, sep='\\t', index_col=0).iloc[:,0]\n\t\t\n\t\t\ta_w = pd.read_csv(spectra_path + 'water.A', skiprows=10, sep='\\t', index_col=0).iloc[:,0]\n\t\t\tdaw_dT = pd.read_csv(spectra_path + 'daWdT.txt', skiprows=10, sep='\\t', index_col=0).iloc[:,0]\n\t\t\tastar_y = pd.read_csv(spectra_path + 'Y.A', skiprows=11, delimiter=' ', index_col=0).loc[350:].iloc[:,0]\n\n\t\t\td = pd.DataFrame(index = a_ph.index)\n\t\t\td.index.name = 'Wavelength, [nm]'\n\t\t\td['astar_ph'] = a_ph\n\t\t\td['astar_y'] = astar_y \n\t\t\td['a_w'] = a_w \n\t\t\td['daw_dT'] = daw_dT\n\n\t\t\tif downsampling == False:\n\t\t\t\tself.spectra = d\n\t\t\telse: \n\t\t\t\tself.spectra = d.apply(lambda x: pd.rolling_mean(x, downsampling, center=True))\n\n\t\t\tif wl_range != False:\n\t\t\t\tself.spectra = self.spectra.loc[wl_range[0]:wl_range[1]].dropna()\n\n\t\t\tself.wl = np.array(self.spectra.index)\n\t\t\t\t\n\t\t\tself.model = self._compile_model()","def t_test(dataType):\n\n\t# read the data\n\tparser = ExperimentUtils()\n\tdata = parser.parse_data(dataType)\n\n\tN = len(data.keys()) # number participants\n\n\t# - for trial 1 and trial 2:\n\t# \tL2 norm over each timestep, then sum all the values together\n\t# - average over two trials for each participant \n\ttask_avgs = {}\n\n\t# participant ID can take values 0 - 9\n\tfor ID in data.keys():\n\t\tfor task in data[ID]:\n\t\t\t# dont include the familiarization task (task can take values 1,2,3)\n\t\t\tif task != 0:\n\t\t\t\tif task not in task_avgs:\n\t\t\t\t\ttask_avgs[task] = {}\n\t\t\t\t\ttask_avgs[task][\"A\"] = np.array([0.0]*N)\n\t\t\t\t\ttask_avgs[task][\"B\"] = np.array([0.0]*N)\n\n\t\t\t\ttrialAsum = [0.0,0.0]\n\t\t\t\ttrialBsum = [0.0,0.0]\n\t\t\t\t# trial can take values 1 or 2\n\t\t\t\tfor trial in data[ID][task]:\n\t\t\t\t\t# only compute metrics over data, not timestamps\n\t\t\t\t\tAdata = data[ID][task][trial]['A'][1:8]\n\t\t\t\t\tBdata = data[ID][task][trial]['B'][1:8]\n\t\t\t\n\t\t\t\t\t#print str(ID)+str(task)+str(trial)+\"A\"\n\t\t\t\t\t#print \"Adata: \" + str(Adata)\n\t\t\t\t\t#print str(ID)+str(task)+str(trial)+\"B\"\n\t\t\t\t\t#print \"Bdata: \" + str(Bdata)\n\n\t\t\t\t\t(h, w) = np.shape(Adata)\n\t\t\t\t\tfor i in range(w):\n\t\t\t\t\t\ttrialAsum[trial-1] += np.linalg.norm(Adata[:,i])\n\t\t\t\t\t(h, w) = np.shape(Bdata)\n\t\t\t\t\tfor i in range(w):\n\t\t\t\t\t\ttrialBsum[trial-1] += np.linalg.norm(Bdata[:,i])\n\t\t\t\tavg_methodA = (trialAsum[0]+trialAsum[1])/2.0\n\t\t\t\tavg_methodB = (trialBsum[0]+trialBsum[1])/2.0\n\n\t\t\t\ttask_avgs[task][\"A\"][ID] = avg_methodA\n\t\t\t\ttask_avgs[task][\"B\"][ID] = avg_methodB\n\n\t# comput independent two-sample t-test \n\t# NOTE: we can assume that the two sample sizes are the same, and \n\t#\t\tthat the two distributions have the same variance\n\tfor task in range(1,4):\n\t\ttaskA = task_avgs[task][\"A\"]\n\t\ttaskB = task_avgs[task][\"B\"]\n\n\t\tmeanA = np.mean(taskA)\n\t\tmeanB = np.mean(taskB)\n\t\tprint \"meanA: \" + str(meanA)\n\t\tprint \"meanB: \" + str(meanB)\n\t\tdiff = meanA - meanB\n\t\tprint \"diff: \" + str(diff)\n\n\t\t(statistic, pvalue) = stats.ttest_ind(a=taskA, b=taskB, equal_var=True)\n\n\t\tprint \"\\n\"\n\t\tprint \"task\"+str(task)+\" statistic: \" + str(statistic)\n\t\tprint \"task\"+str(task)+\" pvalue: \" + str(pvalue)","def test_run_sim_1():\n rnd = rand.Arrivals(36, 41)\n sim.run_sim(3, 2, 5, 6, 22, rnd)","def test_make_uniform_random_f3_gate(self):\n num_trials = 100\n fanin_frac = .5\n # test fam1 and fam2 gates:\n generate = g.TYPE_TO_FAM3_GATE_GEN[g.TEST_TYPES.RANDOM]\n W = 10\n num_levels = 2\n levels = [[g.make_random_input_wire(\n \"W\" + str((W * level_num) + gate_num))\n for gate_num in xrange(W)]\n for level_num in xrange(num_levels)]\n # a dictionary mapping each gate to how many times it was used as input:\n num_uses_dict = {}\n for level in levels:\n for wire in level:\n num_uses_dict[wire.get_name()] = 0\n # a dictionary mapping gate type to a list of the number of times\n # that gate type is generated:\n gate_type_dict = dict(\n (gate_type, 0) for gate_type in g.GATE_TYPES.values_generator())\n for trial_num in xrange(num_trials):\n # create a new gate:\n gate = generate(levels, \"this_gate\")\n # add the statistics to num_uses and gate_type_dist:\n gate_type_dict[gate.get_func_name()] += 1\n for wire in gate.get_inputs():\n num_uses_dict[wire.get_name()] += 1\n # make sure that each gate type appears between .2 and .8 of the time:\n # (note that these numbers are chosen randomly, and therefor this test\n # might occasionally fail with a low probability)\n for gate_type in gate_type_dict.keys():\n self.assertTrue(float(gate_type_dict[gate_type]) / float(num_trials)\n > .2)\n self.assertTrue(float(gate_type_dict[gate_type]) / float(num_trials)\n < .8)\n # make sure that each wire in the ultimate level is chosen as input\n # between 1/(2*W) and (W-1)/W of the time:\n # (note that these numbers are chosen randomly, and therefor this test\n # might occasionally fail with a low probability)\n for wire in levels[-1]:\n self.assertTrue(float(num_uses_dict[wire.get_name()])\n / float(num_trials)\n > 1.0 / float(2.0 * W))\n self.assertTrue(float(num_uses_dict[wire.get_name()])\n / float(num_trials)\n < (float(W) - 1.0) / float(2.0 * W))\n # make sure that each wire in the other levels is chosen as input\n # between 1/(2*W*num_levels) and (W-1)/(2*W*num_levels) of the time:\n # (note that these numbers are chosen randomly, and therefor this test\n # might occasionally fail with a low probability)\n for level in levels[:-1]:\n for gate in level:\n self.assertTrue(float(num_uses_dict[wire.get_name()])\n / float(num_levels)\n > 1.0 / float(2 * W * num_levels))\n self.assertTrue(float(num_uses_dict[wire.get_name()])\n / float(num_trials)\n < (float(W) - 1.0) / float(2 * W * num_levels))","def generate_data(self):\n\n # cfg.d_sour_num = 20 # number of source domains\n self.d_sour_a = [np.random.uniform(0.1, 5.0) for _ in range(cfg.d_sour_num)]\n self.d_sour_b = [np.random.uniform(0, np.pi) for _ in range(cfg.d_sour_num)]\n # cfg.d_targ_num = 1 # number of target domain\n self.d_targ_a = [np.random.uniform(0.1, 5.0) for _ in range(cfg.d_targ_num)]\n self.d_targ_b = [np.random.uniform(0, np.pi) for _ in range(cfg.d_targ_num)]\n\n\n # cfg.train_num = 100 # number of training point in each domain\n self.train_x = np.array([np.random.uniform(-5.0, 5.0) for _ in range(cfg.train_num)], dtype=np.float32).reshape(-1,1)\n self.train_y = np.array([[self.d_sour_a[j] * np.sin(i + self.d_sour_b[j]) for i in self.train_x] for j in range(cfg.d_sour_num)], dtype=np.float32).reshape(cfg.d_sour_num, cfg.train_num, 1)\n\n # cfg.val_num = 100\n self.val_x = np.array([np.random.uniform(-5.0, 5.0) for _ in range(cfg.val_num)], dtype=np.float32).reshape(-1,1)\n self.val_y = np.array([[self.d_sour_a[j] * np.sin(i + self.d_sour_b[j]) for i in self.val_x] for j in range(cfg.d_sour_num)], dtype=np.float32).reshape(cfg.d_sour_num, cfg.val_num, 1)\n\n # cfg.support_num = 10\n self.support_x = np.array([np.random.uniform(-5.0, 5.0) for _ in range(cfg.support_num)], dtype=np.float32).reshape(-1,1)\n self.support_y = np.array([[self.d_targ_a[j] * np.sin(i + self.d_targ_b[j]) for i in self.support_x] for j in range(cfg.d_targ_num)], dtype=np.float32).reshape(cfg.d_targ_num, cfg.support_num, 1)\n\n # cfg.test_num = 100\n self.test_x = np.array([np.random.uniform(-5.0, 5.0) for _ in range(cfg.test_num)], dtype=np.float32).reshape(-1,1)\n self.test_y = np.array([[self.d_targ_a[j] * np.sin(i + self.d_targ_b[j]) for i in self.test_x] for j in range(cfg.d_targ_num)], dtype=np.float32).reshape(cfg.d_targ_num, cfg.test_num, 1)\n\n self.test_x_old = np.array([np.random.uniform(-5.0, 5.0) for _ in range(cfg.test_num)], dtype=np.float32).reshape(-1,1)\n self.test_y_old = np.array([[self.d_sour_a[j] * np.sin(i + self.d_sour_b[j]) for i in self.test_x_old] for j in range(cfg.d_sour_num)], dtype=np.float32).reshape(cfg.d_sour_num, cfg.test_num, 1)","def simulate_test_data(self):\n # simulation config\n if self.instrument == 'IMA':\n sim_config = SimConfig.makeSim(name=\"IMA Simulation\", rel_obsdate=0.0, scene=\"scene.ini\", POP='IMA',\n ConfigPath='IMA_FULL', Dither=False,StartInd=1, NDither=4,\n DitherPat=\"ima_recommended_dither.dat\", filter=\"F1130W\",\n readDetect= 'FULL', ima_mode= 'FAST', ima_exposures=1,\n ima_integrations=1, ima_frames=50, disperser= 'SHORT', detector= 'SW',\n mrs_mode= 'SLOW', mrs_exposures=5,mrs_integrations=4,mrs_frames=10)\n\n # scene config\n background = Background(level='low', gradient=5., pa=15.0, centreFOV=(0., 0.))\n\n SED1 = BBSed(Temp=300., wref=10., flux=1.e8)\n Gal1 = Galaxy(Cen=(0., 0.), n=1., re=200, q=0.99, pa=0.1)\n Gal1.set_SED(SED1)\n targets = [Gal1]\n\n scene_config = SceneConfig.makeScene(loglevel=0, background=background, targets=targets)\n\n elif self.instrument == 'MRS':\n sim_config = SimConfig.makeSim(name=\"MRS Simulation\", rel_obsdate=0.0, scene=\"scene.ini\",\n POP='MRS', ConfigPath='MRS_1SHORT', Dither=False, StartInd=1,\n NDither=4, DitherPat=\"mrs_recommended_dither.dat\", filter=\"F1130W\",\n readDetect='FULL', ima_mode='FAST', ima_exposures=1,\n ima_integrations=1, ima_frames=20, disperser='SHORT', detector='SW',\n mrs_mode='FAST', mrs_exposures=1, mrs_integrations=1, mrs_frames=50)\n\n # scene config\n background = Background(level='low',gradient=5.,pa=15.0,centreFOV=(0., 0.))\n\n SED1 = BBSed(Temp=300., wref=10., flux=5.e6)\n Gal1 = Galaxy(Cen=(0.,0.), n=1., re=2, q=0.99, pa=0.1)\n Gal1.set_SED(SED1)\n targets = [Gal1]\n\n scene_config = SceneConfig.makeScene(loglevel=0, background=background, targets=targets)\n\n # simulator config\n if self.noise:\n simulator_config = SimulatorConfig.makeSimulator(max_fsm=0.050, max_dither=20.0, mrs_ref_channel=1,\n mrs_ref_band=\"SHORT\", tau_telescope=0.88,tau_eol=0.8,\n telescope_area=25.032, telescope_pupil_diam=6.6052,\n take_webbPsf=False, include_refpix=True,\n include_poisson=True, include_readnoise=True,\n include_badpix=True, include_dark=True,\n include_flat=False, include_gain=True,\n include_nonlinearity=self.linearity, include_drifts=True,\n include_latency=False, cosmic_ray_mode='NONE')\n else:\n simulator_config = SimulatorConfig.makeSimulator(max_fsm=0.050, max_dither=20.0, mrs_ref_channel=1,\n mrs_ref_band=\"SHORT\", tau_telescope=0.88, tau_eol=0.8,\n telescope_area=25.032, telescope_pupil_diam=6.6052,\n take_webbPsf=False, include_refpix=True,\n include_poisson=False, include_readnoise=False,\n include_badpix=True, include_dark=True,\n include_flat=False, include_gain=True,\n include_nonlinearity=self.linearity, include_drifts=True,\n include_latency=False, cosmic_ray_mode='NONE')\n\n\n # run the simulation\n simulation = MiriSimulation(sim_config=sim_config, scene_config=scene_config,\n simulator_config=simulator_config, loglevel='DEBUG')\n simulation.run()\n\n # we only need the sim file so move it to output_dir and remove everthing else\n det_image_file = glob.glob(os.path.join(simulation.path_out, 'det_images', '*.fits'))[0]\n self.ramp_file = os.path.join(self.output_dir, os.path.basename(det_image_file))\n shutil.move(det_image_file, self.ramp_file)\n shutil.rmtree(simulation.path_out)","def main():\n run_simulation(spectral=False, ml=False, num_procs=1)\n run_simulation(spectral=True, ml=False, num_procs=1)\n run_simulation(spectral=False, ml=True, num_procs=1)\n run_simulation(spectral=True, ml=True, num_procs=1)\n run_simulation(spectral=False, ml=True, num_procs=10)\n run_simulation(spectral=True, ml=True, num_procs=10)","def setUpClass(cls):\n cls.sim1 = Simulation(logging_level=50)\n cls.sim1.set_simulation_parameters(\n seed=1, task=47, output_directory=\"output\", min_speciation_rate=0.01, sigma=2, deme=1, sample_size=0.01\n )\n cls.sim1.set_map_files(\n \"null\", fine_file=\"sample/SA_sample_fine.tif\", reproduction_map=\"sample/SA_sample_reproduction.tif\"\n )\n cls.sim1.run()\n cls.sim2 = Simulation(logging_level=50)\n cls.sim2.set_simulation_parameters(\n seed=1, task=48, output_directory=\"output\", min_speciation_rate=0.01, sigma=2, deme=1, sample_size=0.01\n )\n cls.sim2.set_map_files(\"null\", fine_file=\"sample/SA_sample_fine.tif\")\n cls.sim2.run()\n cls.sim3 = Simulation(logging_level=50)\n cls.sim3.set_simulation_parameters(\n seed=2, task=47, output_directory=\"output\", min_speciation_rate=0.01, sigma=2, deme=1, sample_size=0.01\n )\n cls.sim3.set_map_files(\n \"null\",\n fine_file=\"sample/SA_sample_fine.tif\",\n death_map=\"sample/SA_sample_reproduction.tif\",\n reproduction_map=\"sample/SA_sample_reproduction.tif\",\n )\n cls.sim3.run()\n cls.sim4 = Simulation(logging_level=50)\n cls.sim4.set_simulation_parameters(\n seed=4, task=47, output_directory=\"output\", min_speciation_rate=0.01, sigma=2, deme=1, sample_size=0.01\n )\n cls.sim4.set_map_files(\"null\", fine_file=\"sample/SA_sample_coarse_pristine.tif\")\n cls.sim4.add_reproduction_map(reproduction_map=\"sample/SA_reproduction_coarse.tif\")\n cls.sim4.add_death_map(death_map=\"sample/SA_death.tif\")\n cls.sim4.add_dispersal_map(dispersal_map=\"sample/dispersal_fine2.tif\")\n cls.sim4.run()\n cls.coal1 = CoalescenceTree(cls.sim1)\n cls.coal2 = CoalescenceTree(cls.sim2)\n cls.coal3 = CoalescenceTree(cls.sim3)\n cls.coal4 = CoalescenceTree(cls.sim4)","def main(input_scenario):\n\n print(\"Program running for demand scenario \" + str(input_scenario))\n\n policy_f = open(\"./Policy/policy_result\" + str(input_scenario) + \".csv\", \"w\", newline='')\n policy_writer = csv.writer(policy_f)\n\n # Read case data\n ds, orig_fl = get_data_case(input_scenario, htime, zone)\n\n # Initialize operator location\n location = initialize_location(zone, vec)\n\n # Initialize cluster\n assignment, centre = initialize_cluster(cluster, zone, mindis)\n\n # Run\n # Timestep 0\n ds = update_ds(zone, cp, ds, mindis, orig_fl[0])\n\n # Timestep 1:\n for timestep in range(1, htime):\n\n print(\"Running for timestep \" + str(timestep))\n\n # Extract demand flows in current timestep\n train_fl = [fl[ts][timestep] for ts in range(trainsample)]\n\n # Compute total demand flows in and out of each station\n train_fl_out = [[0 for s in range(zone)] for ts in range(trainsample)]\n train_fl_in = [[0 for s in range(zone)] for ts in range(trainsample)]\n\n for ts in range(trainsample):\n for s in range(zone):\n train_fl_out[ts][s] = sum(train_fl[ts][s])\n for z in range(zone):\n train_fl_in[ts][s] += train_fl[ts][z][s]\n\n # Modify cluster based on demand flows\n assignment = modify_cluster(cluster, assignment, centre, zone, cp, ds, mindis, trainsample, train_fl)\n print(\"Clusters generated.\")\n\n # Initialize global intra-cluster rebalancing result\n yp_total = [0.0 for s in range(zone)]\n yn_total = [0.0 for s in range(zone)]\n bn = [[0 for s in range(zone)] for v in range(vec)]\n\n # Intra-cluster rebalancing\n for c in range(cluster):\n\n # Extract local data\n local_list = [s for s in range(zone) if assignment[s] == c]\n\n local_zone = len(local_list)\n\n local_cp = [cp[s] for s in local_list]\n local_ds = [ds[s] for s in local_list]\n\n local_vec_list = []\n local_vec = 0\n local_cap = []\n local_location = []\n\n for v in range(vec):\n if location[v].index(1) in local_list:\n local_vec_list.append(v)\n local_vec += 1\n local_cap.append(cap[v])\n local_location.append([1 if (s == location[v].index(1)) else 0 for s in local_list])\n \n local_dis = [[dis[s][z] for z in local_list] for s in local_list]\n\n local_train_fl_out = [[train_fl_out[ts][s] for s in local_list] for ts in range(trainsample)]\n local_train_fl_in = [[train_fl_in[ts][s] for s in local_list] for ts in range(trainsample)]\n\n # Apply local rebalance\n status, local_yp_total, local_yn_total, local_bn = intra_redeployment(local_zone, local_cp, local_ds, local_vec, local_cap, local_location, local_dis, trainsample, local_train_fl_out, local_train_fl_in, dismaxpick, dismaxdrop)\n\n # Make error report if no solution\n if status == 1:\n error_f = open(\"./error_report.csv\", \"a\", newline='')\n error_writer = csv.writer(error_f)\n error_writer.writerow([str(cluster), str(input_scenario), str(timestep), str(c)])\n error_f.close()\n\n # Translate to global indexing\n for s in range(local_zone):\n yp_total[local_list[s]] = local_yp_total[s]\n yn_total[local_list[s]] = local_yn_total[s]\n \n for v in range(local_vec):\n for s in range(local_zone):\n bn[local_vec_list[v]][local_list[s]] = local_bn[v][s]\n \n print(\"Cluster done is: \" + str(c))\n \n for s in range(zone):\n policy_writer.writerow([str(timestep), str(s), str(yp_total[s]), str(yn_total[s])])\n \n # Update ds\n update_ds(zone, cp, ds, mindis, orig_fl[timestep], yp_total, yn_total)\n\n # Update operator location\n for v in range(vec):\n for s in range(zone):\n if round(bn[v][s]) == 1:\n location[v][s] = 1\n else:\n location[v][s] = 0\n \n print(\"Timestep done is: \" + str(timestep))\n \n policy_f.close()","def example_OC3():\n example = sparAssemblyCalculation()\n example.tower_base_outer_diameter = 6.5\n example.tower_top_outer_diameter = 3.87\n example.tower_length = 77.6\n example.example_turbine_size = '3MW' #not sure if this is correct\n example.RNA_center_of_gravity_y = 1.75\n example.wall_thickness = [.057, .056, .042, .046, .052]\n example.rotor_diameter = 126.\n # example.cut_out_speed\n example.air_density = 1.198\n example.wind_reference_speed = 11.\n example.wind_reference_height = 89.350\n example.gust_factor = 1.0\n example.alpha = .11\n example.RNA_center_of_gravity_x = 1.9\n example.tower_mass = 249718.0\n example.RNA_mass = 347460.\n example.stiffener_index = 259\n example.number_of_sections = 5\n example.bulk_head = ['N', 'T', 'N', 'B', 'B']\n example.number_of_rings = [3, 2, 10, 19, 32]\n example.neutral_axis = .21 #not sure if this number is correct\n # example.straight_col_cost\n # example.tapered_col_cost\n # example.outfitting_cost\n # example.ballast_cost\n example.gravity = 9.806\n example.load_condition = 'N'\n example.significant_wave_height = 8.\n example.significant_wave_period = 10.\n example.material_density = 7850\n example.E = 200.\n example.nu = .3\n example.yield_stress = 345.\n example.shell_mass_factor = 1\n example.bulkhead_mass_factor = 1.25\n # example.ring_mass_factor\n example.outfitting_factor = .06\n example.spar_mass_factor = 1.04\n example.permanent_ballast_height = 0.\n example.fixed_ballast_height = 10.\n example.permanent_ballast_density = 4000.\n example.fixed_ballast_density = 4492.48\n # example.offset_amplification_factor\n example.water_density = 1025.\n example.spar_elevations = [10.0, -4.0, -12.0, -42., -71., -120.]\n example.spar_outer_diameter = [6.5, 6.5, 9.4, 9.4, 9.4]\n example.water_depth = 320.\n example.fairlead_depth = 70.\n example.scope_ratio = 3.609\n example.pretension_percent = 11.173 #map doesnt use\n example.mooring_diameter = .09\n example.number_of_mooring_lines = 3\n example.mooring_type = 'CHAIN'\n example.anchor_type = 'PILE'\n example.fairlead_offset_from_shell = .5\n example.user_MBL = 8158000.\n example.user_WML = 71.186\n example.user_AE_storm = 384243000/.006\n example.user_MCPL = 0.\n example.user_anchor_cost = 0.\n example.misc_cost_factor = 10\n example.number_of_discretizations = 20 #map doesnt use\n example.spar.stiffener_curve_fit = False #not sure if this is correct\n example.run()\n print '-------------OC3---------------'\n sys_print(example)","def three_experiments_with_trials(family_with_trials, single_with_trials):","def step6_run_all(flow_dataset_npz=\"flow_dataset.npz\"):\n global objs, predictions, ticks, timestamps, is_slider, is_spinner, is_note_end, sv, slider_ticks, dist_multiplier, divisor, note_distance_basis, slider_length_base, slider_types, slider_type_rotation, slider_cos, slider_sin, slider_cos_each, slider_sin_each, slider_type_length, slider_lengths, tick_diff, note_distances, maps, labels, special_train_data, special_train_labels, early_stop, loss_ma, extvar, plot_noise\n\n objs, predictions, ticks, timestamps, is_slider, is_spinner, is_note_end, sv, slider_ticks, dist_multiplier = read_map_predictions(\n \"temp/rhythm_data.npz\")\n\n # get divisor from GAN_PARAMS\n divisor = GAN_PARAMS[\"divisor\"]\n\n # get basis\n note_distance_basis = GAN_PARAMS[\"note_distance_basis\"]\n\n # get next_from_slider_end\n next_from_slider_end = GAN_PARAMS[\"next_from_slider_end\"]\n\n # should be slider length each tick, which is usually SV * SMP * 100 / 4\n # e.g. SV 1.6, timing section x1.00, 1/4 divisor, then slider_length_base = 40\n slider_length_base = sv / divisor\n\n # weight for each type of sliders\n slider_type_probs = [0.25, 0.25, 0.25, 0.05, 0.05, 0.03, 0.03, 0.01,\n 0.01, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.015, 0.015, 0.01]\n slider_types = np.random.choice(\n len(slider_type_probs), is_slider.shape, p=slider_type_probs).astype(int)\n\n # these data must be kept consistent with the sliderTypes in load_map.js\n slider_type_rotation = np.array([0, -0.40703540572409336, 0.40703540572409336, -0.20131710837464062, 0.20131710837464062,\n -0.46457807316944644, 0.46457807316944644, 1.5542036732051032, -\n 1.5542036732051032, 0, 0, 0.23783592745745077, -0.23783592745745077,\n 0.5191461142465229, -0.5191461142465229, -0.16514867741462683, 0.16514867741462683, 3.141592653589793])\n\n # this is vector length! I should change the variable name probably...\n slider_type_length = np.array([1.0, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97,\n 0.64, 0.64, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.96, 0.96, 0])\n\n slider_cos = np.cos(slider_type_rotation)\n slider_sin = np.sin(slider_type_rotation)\n\n slider_cos_each = slider_cos[slider_types]\n slider_sin_each = slider_sin[slider_types]\n\n slider_lengths = np.array([slider_type_length[int(\n k)] * slider_length_base[i] for i, k in enumerate(slider_types)]) * slider_ticks\n\n tick_diff = np.concatenate([[100], ticks[1:] - ticks[:-1]])\n\n if next_from_slider_end:\n tick_diff = np.concatenate(\n [[100], tick_diff[1:] - np.floor(slider_ticks * is_slider)[:-1]])\n\n # Timing section reset == tick_diff < 0\n # Use 1 as default value\n tick_diff = np.where(tick_diff < 0, 1, tick_diff)\n\n note_distances = np.clip(tick_diff, 1, divisor * 2) * \\\n (note_distance_basis / divisor)\n\n # Load the flow dataset saved in part 4\n with np.load(flow_dataset_npz) as flow_dataset:\n maps = flow_dataset[\"maps\"]\n labels = np.ones(maps.shape[0])\n\n order2 = np.argsort(np.random.random(maps.shape[0]))\n special_train_data = maps[order2]\n special_train_labels = labels[order2]\n\n early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=20)\n\n # Start model training\n\n loss_ma = [90, 90, 90]\n extvar = {\"begin\": 10}\n\n plot_noise = np.random.random((1, GAN_PARAMS[\"g_input_size\"]))\n\n if GAN_PARAMS[\"max_epoch\"] == 0:\n osu_a = put_everything_in_the_center()\n else:\n osu_a = generate_map()\n\n data = objs, predictions, ticks, timestamps, is_slider, is_spinner, is_note_end, sv, slider_ticks, dist_multiplier, slider_types, slider_length_base\n return osu_a, data","def make_test_data(self):\r\n\r\n \r\n\r\n print (\"Creating Test Sample:\")\r\n\r\n print (' Period, rate, reps, phases: ', self.period, self.framerate, self.nrepetitions, self.nPhases)\r\n\r\n nframes = int(self.period * self.framerate * self.nrepetitions)\r\n\r\n print (' nframes: ', nframes)\r\n\r\n if self.bkgdNoise > 0.:\r\n\r\n d = np.random.normal(size=(nframes,self.imageSize[0],self.imageSize[1]),\r\n\r\n loc=self.bkgdIntensity, scale=self.bkgdNoise).astype('float32')\r\n\r\n else:\r\n\r\n d = self.bkgdIntensity*np.ones((nframes,self.imageSize[0],self.imageSize[1])).astype('float32')\r\n\r\n \r\n\r\n ds = d.shape\r\n\r\n print (' data shape: ', ds)\r\n\r\n dx = int(ds[2]/4)\r\n\r\n xc = int(ds[2]/2)\r\n\r\n xo = [xc-dx, xc+dx]\r\n\r\n ywidth = int(ds[2]/(self.nPhases+2))\r\n\r\n framedelay = 4\r\n\r\n\r\n\r\n if not self.mode:\r\n\r\n self.phasex = []\r\n\r\n self.phasey = []\r\n\r\n for i in range(0,self.nPhases):\r\n\r\n dy = int((i+1)*ds[2]/(self.nPhases+2)) # each phase is assigned to a region\r\n\r\n self.resp = np.zeros((nframes,))\r\n\r\n self.resp = np.cos(\r\n\r\n np.linspace(0, 2.0*np.pi*nframes/(self.period*self.framerate), nframes-framedelay)+i*np.pi/8 - np.pi/2.0)\r\n\r\n self.resp = np.concatenate((np.zeros(framedelay), self.resp))\r\n\r\n d[:, xo[0]:xo[1], dy:dy+ywidth ] += self.resp[:, np.newaxis, np.newaxis]\r\n\r\n self.phasey.append( (2+(dy+int(ds[2]/self.nPhases))/2))\r\n\r\n self.phasex.append((6+int(ds[1]/2)/2)) # make the signal equivalent of digitized one (baseline 3000, signal at 1e-4 of baseline)\r\n\r\n else:\r\n\r\n self.nPhases = 4\r\n\r\n self.spotsize = 16\r\n\r\n nrpts = 20\r\n\r\n nsites = 4\r\n\r\n one_rep = int(self.period*self.framerate)\r\n\r\n isi = int(self.period*self.framerate/self.nPhases)\r\n\r\n print('period, isi: ', self.period, isi)\r\n\r\n r = np.arange(0, nrpts, 1.)\r\n\r\n alpha = 4.\r\n\r\n A = r/alpha *np.exp(-(r-alpha)/alpha) # scaled alpha function\r\n\r\n self.spot= self.gauss_spot(self.spotsize, 3.) # the 2d spot\r\n\r\n sigsize = np.random.normal(size=self.nPhases, loc=self.signal_size, scale=self.signal_size*2)\r\n\r\n sigsize = [np.abs(s) for s in sigsize] # restrict to positive amplitudes\r\n\r\n print ('sigsize: ', sigsize)\r\n\r\n for j in range(self.nrepetitions):\r\n\r\n for i in range(self.nPhases):\r\n\r\n self.resp = np.zeros((nrpts, self.spot.shape[0], self.spot.shape[1]))\r\n\r\n for k in range(nrpts):\r\n\r\n self.resp[k,:,:] += sigsize[i]*A[k] * self.spot # make response an alpha time course of gaussian spot\r\n\r\n start = j*one_rep + i*isi + framedelay\r\n\r\n stop = start + nrpts\r\n\r\n dy = int((i+1)*ds[2]/(self.nPhases+2)) # location for phase\r\n\r\n #dy = dy + 2*z\r\n\r\n# print ('start, stop: ', start, stop)\r\n\r\n for z in range(nsites):\r\n\r\n #self.resp = np.concatenate((np.zeros(framedelay), self.resp))\r\n\r\n xp = xo[0] + i*10 - 10*z\r\n\r\n yp = dy - i*10 + 10*z\r\n\r\n d[start:stop, xp:xp+self.spotsize, yp:yp+self.spotsize ] += self.resp\r\n\r\n self.imageData = d # reduce to a 16-bit map to match camera data type\r\n\r\n self.nFrames = self.imageData.shape[0]\r\n\r\n self.times = np.arange(0, nframes/self.framerate, 1.0/self.framerate)\r\n\r\n print( \" Test Image Created\")\r\n\r\n # imv = pg.ImageView()\r\n\r\n # imv.show()\r\n\r\n # imv.setImage(self.imageData)\r\n\r\n\r\n\r\n if self.layout is not None:\r\n\r\n self.layout.addWidget(imv, 0, 0)\r\n\r\n\r\n\r\n avgImage = np.mean(self.imageData, axis=0)\r\n\r\n ima = pg.ImageView()\r\n\r\n ima.setImage(avgImage)\r\n\r\n self.layout.addWidget(ima, 0, 1)\r\n\r\n self.adjust_image_data()\r\n\r\n self.avgimg = np.mean(self.imageData, axis=0) # get mean image for reference later: average across all time\r\n\r\n print (' Test file, original Image Info: ')\r\n\r\n self.print_image_info()\r\n\r\n self.rebin_image()\r\n\r\n #self.clean_windowerrors()\r\n\r\n # pg.image(self.imageData)\r\n\r\n # pg.show()\r\n\r\n # mpl.figure(1)\r\n\r\n # mpl.show()\r\n\r\n if not self.mode: # FFT analysis\r\n\r\n self.analysis_fourier_map(target=1, mode=0)\r\n\r\n self.plot_maps(mode=2, gfilter=self.gfilter)\r\n\r\n else:\r\n\r\n self.analysis_dFF_map()\r\n\r\n mpl.show()","def _choose_sample(self):\n\n \t #periodically generate a new reconstruction for the purposes of sampling","def ssd_synthia_car_fine_tune():\n merged_annotation = '/home/public/synthia/ssd_car_fine_tune/SYNTHIA-SEQS-01-TRAIN_MERGED-shuffle.json'\n if False:\n print('we combine the training and validation here')\n annotations_url_1 = '/home/public/synthia/SYNTHIA-SEQS-01-TRAIN-shuffle.json'\n annotations_url_2 = '/home/public/synthia/SYNTHIA-SEQS-01-VALIDATE-shuffle.json'\n combine_gt(annotations_url_1, annotations_url_2, merged_annotation)\n\n if False:\n print('collect front and rear cars')\n annotations_url_1 = '/home/public/synthia/SYNTHIA-SEQS-01-TRAIN-shuffle.json'\n annotations_url_2 = '/home/public/synthia/SYNTHIA-SEQS-01-VALIDATE-shuffle.json'\n save_dir = '/home/stevenwudi/PycharmProjects/autonomous_driving/Experiments/SEQ_01_SEQ_06_cars'\n collect_front_and_rear_gt(annotations_url_1, annotations_url_2, save_dir, image_interval=50)\n\n gt_file = '/home/public/synthia/ssd_car_fine_tune/ssd_car_fine_tune_gt-shuffle.pkl'\n if False:\n print('Training annotation conversion')\n converting_gt(merged_annotation, gt_file, POR=1e-3)\n # POR: 1e-3 Finish converting, total annotated car number is 22332 in total image of 8814.\n # POR: 5e-4: Finish converting, total annotated fish number is 26800 in total image of 8814.\n\n model_checkpoint = '/home/public/synthia/ssd_car_fine_tune/weights_512.54-0.19.hdf5'\n if False:\n print('Start DDS 512 training')\n train_ssd512(gt_file, model_checkpoint=model_checkpoint, base_lr=1e-5)\n\n test_gt_file = '/home/public/synthia/ssd_car_fine_tune/ssd_car_test_gt-shuffle.pkl'\n if False:\n print('Converting testing GT')\n annotations_url = '/home/public/synthia/SYNTHIA-SEQS-01-TEST-shuffle.json'\n converting_gt(annotations_url, test_gt_file)\n if False:\n # Examine test data\n examine_ssd512(test_gt_file, model_checkpoint)\n\n test_json_file = '/home/public/synthia/ssd_car_fine_tune/ssd_car_test-shuffle_nms_'+str(nms_thresh)+'.json'\n if False:\n test_ssd512(test_gt_file, model_checkpoint, test_json_file)\n # A separate file for accepting gt file and predicted json fil\n if True:\n calculate_iou(test_gt_file, test_json_file, POR=2e-3, draw=False)\n\n test_gt_file = '/home/public/synthia/ssd_car_fine_tune/ssd_car_test_gt-shuffle.pkl'\n test_json_file = '/home/public/synthia/ssd_car_test_faster-shuffle.json'\n\n calculate_iou(test_gt_file, test_json_file, POR=2e-3, draw=False)\n \"\"\"\n<<<<<<< HEAD\n This is the network results train by SSD512 (with 0.05% POR trained)\n Conf: [ 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]\n \n ### POR=1e-3\n Total GT: 2327. \n Total prediction: [ 2016. 1986. 1951. 1918. 1888. 1852. 1815. 1774. 1711. 1641.]\n Precision: [ 0.913 0.908 0.9 0.895 0.886 0.879 0.867 0.853 0.83 0.804]\n Recall: [ 0.791 0.787 0.78 0.775 0.768 0.761 0.751 0.739 0.719 0.697]\n F score: [[ 0.847 0.843 0.837 0.825 0.816 0.79 0.748 0.666 0.449 0.106]\n [ 0.843 0.839 0.834 0.823 0.813 0.788 0.748 0.665 0.449 0.106]\n [ 0.835 0.833 0.829 0.818 0.809 0.786 0.747 0.665 0.449 0.106]\n [ 0.831 0.829 0.826 0.815 0.807 0.784 0.746 0.664 0.449 0.105]\n [ 0.823 0.822 0.82 0.81 0.802 0.781 0.744 0.663 0.447 0.105]\n [ 0.816 0.816 0.815 0.806 0.799 0.778 0.741 0.661 0.446 0.105]\n [ 0.805 0.805 0.806 0.797 0.792 0.772 0.738 0.66 0.445 0.105]\n [ 0.792 0.793 0.793 0.789 0.784 0.766 0.734 0.658 0.445 0.105]\n [ 0.77 0.773 0.775 0.771 0.768 0.753 0.725 0.652 0.441 0.104]\n [ 0.746 0.749 0.751 0.751 0.75 0.741 0.716 0.645 0.439 0.103]]\n<<<<<<< HEAD\n\n ### faster RCNN detection result POR= 1e-3\n Total GT: 2327.\n Total prediction: [ 2120. 2084. 2062. 2037. 2003. 1971. 1947. 1901. 1862. 1785.]\n Precision: [ 0.83 0.826 0.825 0.823 0.82 0.818 0.817 0.807 0.801 0.787]\n Recall: [ 0.756 0.752 0.751 0.75 0.747 0.745 0.744 0.735 0.73 0.717]\n F score: [[ 0.791 0.758 0.72 0.669 0.59 0.485 0.332 0.174 0.054 0.006]\n [ 0.787 0.756 0.719 0.669 0.59 0.485 0.332 0.174 0.054 0.006]\n [ 0.786 0.755 0.718 0.668 0.589 0.485 0.332 0.174 0.054 0.006]\n [ 0.785 0.754 0.718 0.668 0.589 0.484 0.332 0.174 0.054 0.006]\n [ 0.782 0.752 0.716 0.666 0.588 0.484 0.331 0.174 0.054 0.006]\n [ 0.78 0.75 0.715 0.666 0.588 0.484 0.331 0.174 0.054 0.006]\n [ 0.779 0.749 0.714 0.665 0.588 0.484 0.331 0.174 0.054 0.006]\n [ 0.77 0.745 0.711 0.663 0.586 0.483 0.331 0.174 0.054 0.006]\n [ 0.764 0.74 0.709 0.66 0.584 0.482 0.331 0.173 0.054 0.006]\n [ 0.75 0.73 0.703 0.657 0.582 0.481 0.33 0.173 0.054 0.006]]\n\n=======\n \n ### POR shuffle\n Total GT: 2563. \n Total prediction: [ 2185. 2137. 2081. 2047. 2010. 1976. 1937. 1893. 1831. 1713.]\n Precision: [ 0.843 0.836 0.826 0.819 0.813 0.805 0.795 0.784 0.766 0.726]\n Recall: [ 0.719 0.712 0.704 0.698 0.693 0.686 0.678 0.669 0.653 0.619]\n F score: [[ 0.776 0.698 0.646 0.619 0.597 0.571 0.525 0.447 0.31 0.106]\n [ 0.769 0.693 0.642 0.616 0.595 0.57 0.524 0.447 0.31 0.106]\n [ 0.76 0.686 0.637 0.612 0.591 0.568 0.523 0.445 0.31 0.106]\n [ 0.754 0.681 0.632 0.608 0.588 0.565 0.521 0.443 0.31 0.106]\n [ 0.749 0.677 0.629 0.605 0.586 0.563 0.52 0.442 0.309 0.106]\n [ 0.741 0.671 0.624 0.602 0.583 0.561 0.518 0.441 0.309 0.106]\n [ 0.732 0.664 0.619 0.597 0.579 0.558 0.516 0.44 0.308 0.106]\n [ 0.722 0.656 0.612 0.59 0.573 0.553 0.515 0.439 0.308 0.106]\n [ 0.705 0.642 0.6 0.58 0.564 0.545 0.508 0.435 0.306 0.105]\n [ 0.668 0.614 0.578 0.561 0.548 0.531 0.496 0.426 0.302 0.105]]\n \n POR = 2e-3\n=======\n ############################# SSD512 NMS 0.6 ###########################\n Conf: [ 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]\n>>>>>>> 419d28c4e27b82c4cfef6a3aa01425cf29929973\n Total GT: 1433. \n Total prediction: [ 1617. 1605. 1593. 1579. 1568. 1551. 1537. 1517. 1498. 1465.]\n Precision: [ 0.821 0.819 0.818 0.818 0.816 0.811 0.809 0.804 0.8 0.792]\n Recall: [ 0.927 0.925 0.923 0.923 0.92 0.915 0.913 0.907 0.902 0.894]\n F score: [[ 0.871 0.868 0.859 0.852 0.836 0.81 0.742 0.636 0.48 0.161]\n [ 0.869 0.866 0.857 0.85 0.834 0.808 0.741 0.635 0.48 0.161]\n [ 0.867 0.864 0.857 0.849 0.834 0.808 0.741 0.635 0.48 0.161]\n [ 0.867 0.864 0.857 0.849 0.834 0.808 0.741 0.635 0.48 0.161]\n [ 0.865 0.862 0.855 0.848 0.832 0.807 0.741 0.635 0.48 0.161]\n [ 0.86 0.857 0.851 0.844 0.828 0.804 0.739 0.633 0.479 0.161]\n [ 0.858 0.855 0.849 0.843 0.827 0.802 0.738 0.633 0.478 0.161]\n [ 0.852 0.85 0.844 0.839 0.823 0.8 0.736 0.631 0.477 0.16 ]\n [ 0.848 0.846 0.841 0.836 0.82 0.797 0.734 0.631 0.477 0.16 ]\n [ 0.84 0.838 0.834 0.829 0.814 0.792 0.729 0.629 0.476 0.16 ]]\n \n ############################# SSD512 NMS 0.45 ###########################\n Total GT: 1433. \n Total prediction: [ 1438. 1428. 1421. 1413. 1403. 1398. 1384. 1374. 1348. 1305.]\n Precision: [ 0.92 0.918 0.917 0.914 0.911 0.91 0.905 0.902 0.894 0.874]\n Recall: [ 0.923 0.921 0.92 0.918 0.914 0.913 0.908 0.905 0.897 0.877]\n F score: [[ 0.922 0.918 0.91 0.901 0.883 0.851 0.785 0.673 0.476 0.164]\n [ 0.92 0.916 0.908 0.9 0.882 0.851 0.785 0.673 0.476 0.164]\n [ 0.918 0.915 0.907 0.899 0.882 0.851 0.785 0.673 0.476 0.164]\n [ 0.916 0.913 0.905 0.897 0.88 0.849 0.785 0.673 0.476 0.164]\n [ 0.913 0.91 0.903 0.895 0.878 0.847 0.783 0.671 0.475 0.164]\n [ 0.911 0.909 0.901 0.894 0.877 0.846 0.782 0.671 0.475 0.164]\n [ 0.906 0.905 0.898 0.89 0.874 0.843 0.781 0.67 0.475 0.164]\n [ 0.904 0.901 0.895 0.888 0.871 0.84 0.78 0.67 0.475 0.164]\n [ 0.896 0.894 0.888 0.882 0.866 0.836 0.776 0.668 0.474 0.164]\n [ 0.876 0.876 0.872 0.869 0.855 0.829 0.77 0.663 0.471 0.164]]\n \n \n<<<<<<< HEAD\n>>>>>>> bb5caf05d4bc41e182e41686b8b5e497053f9ca5\n ### POR = None (consider all testing examples)\n Total GT: 2696. \n Total prediction: [ 2273. 2221. 2166. 2111. 2055. 2000. 1945. 1875. 1786. 1684.]\n Precision: [ 0.822 0.818 0.81 0.805 0.796 0.789 0.777 0.763 0.741 0.716]\n Recall: [ 0.693 0.69 0.683 0.678 0.671 0.665 0.655 0.643 0.625 0.604]\n F score: [[ 0.752 0.746 0.74 0.729 0.724 0.703 0.668 0.597 0.405 0.096]\n [ 0.748 0.744 0.737 0.727 0.722 0.702 0.668 0.597 0.405 0.096]\n [ 0.741 0.737 0.733 0.723 0.718 0.699 0.667 0.596 0.405 0.096]\n [ 0.736 0.733 0.729 0.72 0.716 0.698 0.666 0.596 0.404 0.095]\n [ 0.728 0.726 0.724 0.716 0.712 0.695 0.664 0.595 0.403 0.095]\n [ 0.722 0.721 0.72 0.712 0.708 0.692 0.661 0.593 0.402 0.095]\n [ 0.711 0.711 0.711 0.704 0.703 0.687 0.659 0.592 0.401 0.095]\n [ 0.698 0.7 0.7 0.696 0.696 0.682 0.655 0.59 0.401 0.095]\n [ 0.678 0.682 0.684 0.681 0.681 0.67 0.647 0.585 0.398 0.095]\n [ 0.655 0.659 0.662 0.663 0.665 0.659 0.639 0.578 0.395 0.094]]\n\n POR = 2e-3 fastercnn result\n Conf: [ 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]\n Total GT: 1433.\n Total prediction: [ 1614. 1601. 1593. 1585. 1576. 1561. 1557. 1544. 1532. 1507.]\n=======\n ############################# Faster-RCNN ###########################\n Conf: [ 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]\n Total GT: 1433. \n Total prediction: [ 1614. 1601. 1593. 1585. 1576. 1561. 1557. 1544. 1532. 1507.]\n>>>>>>> 419d28c4e27b82c4cfef6a3aa01425cf29929973\n Precision: [ 0.812 0.812 0.811 0.811 0.811 0.81 0.81 0.807 0.805 0.8 ]\n Recall: [ 0.914 0.914 0.913 0.913 0.913 0.913 0.913 0.909 0.907 0.902]\n F score: [[ 0.86 0.843 0.808 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\n [ 0.86 0.843 0.808 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\n [ 0.859 0.842 0.807 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\n [ 0.859 0.842 0.807 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\n [ 0.859 0.842 0.807 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\n [ 0.859 0.842 0.807 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\n [ 0.859 0.842 0.807 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\n [ 0.855 0.839 0.804 0.748 0.665 0.576 0.458 0.311 0.14 0.016]\n [ 0.853 0.838 0.804 0.747 0.664 0.575 0.458 0.311 0.14 0.016]\n [ 0.848 0.834 0.8 0.745 0.662 0.574 0.457 0.31 0.139 0.016]]\n \"\"\"","def initial_sampling(self, params):\n i = params\n theta_star = self.priors_sample()\n model = self.simz( theta_star )\n rho = test_dist(self.data, model)\n while rho > self.eps0: \n theta_star = self.priors_sample()\n model = self.simz( theta_star )\n rho = test_dist(self.data, model)\n data_list = [np.int(i)]\n\n for i_param in xrange(self.n_params): \n data_list.append(theta_star[i_param])\n data_list.append(1./np.float(self.N))\n data_list.append(rho)\n\treturn np.array(data_list)","def getActpolSim(iterationNum = 0, patch = 'deep5', \n season = 's13', \\\n array = 'pa1', \\\n psa = None,\\\n noiseDictFile = 'templateInputsMr3c.dict', \\\n noiseDictFilePath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'../inputParams/'), \\\n signalDictFile = 'signal.dict',\\\n signalDictFilePath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'../inputParams/'),\\\n verbose = True,\\\n simType = 'noise',\n cmbSet = 0,\n doBeam = True, applyWindow = True, noiseDiagsOnly = False, cmbMaptype = 'LensedCMB', splitWanted = None,\n applyModulation = True):\n #update the last one to True if possible\n\n\n#FIXME: get rid of absolute pathnames\n\n nDict = flipperDict.flipperDict()\n nDict.read_from_file(noiseDictFilePath + '/' + noiseDictFile)\n\n sDict = flipperDict.flipperDict()\n sDict.read_from_file(signalDictFilePath + '/' + signalDictFile)\n \n if psa == None: #psa stands for patch, season, \n psa = '%s_%s_%s' %(patch, season, array)\n\n\n #Figure out what frequencies correspond to this array, using the function defined above.\n psaFreqs = freqsInPsas(psa, nDict['freqsInArrays'])\n\n\n #unroll psa names (normally stored as a nested list of lists)\n psaList = [item for sublist in nDict['psaList'] for item in sublist]\n\n\n if psa not in psaList:\n raise ValueError('psa %s not found in psaList; options are ' % (psa ), psaList)\n\n\n noiseSeed = (cmbSet, psaList.index(psa), noiseSeedInd * 4 + (0 if splitWanted is None else splitWanted), iterationNum)\n\n #load up one sample map, just to get the shape and wcs info. Do this for \"I\" at one frequency\n sampleMap = enmap.read_map(os.path.join(os.path.dirname(os.path.abspath(__file__)))+\"/\"+nDict['dataMapDir'] + 'totalWeightMap' \\\n + 'I' + '_' + psa + '_' + psaFreqs[0] + '_fromenlib.fits') \n\n\n #Note! Foreground seed is the same for every sky patch, season, and frequency!\n #This is because they are used to generate fullsky alm's\n foregroundSeed = (cmbSet, 0, fgSeedInd, iterationNum)\n\n\n\n if simType == 'noise':\n\n return getActpolNoiseSim(noiseSeed = noiseSeed, \\\n psa = psa, \\\n noisePsdDir = os.path.dirname(os.path.abspath(__file__))+\"/\"+nDict['dataMapDir'],\n freqs = psaFreqs, \n verbose = verbose,\n noiseDiagsOnly = noiseDiagsOnly,\n splitWanted = splitWanted)\n\n elif simType == 'cmb' or simType == 'foregrounds':\n \n\n return getActpolCmbFgSim(beamfileDict = sDict['beamNames'],\n shape = sampleMap.shape, wcs = sampleMap.wcs,\n iterationNum = iterationNum,\n cmbDir = os.path.dirname(os.path.abspath(__file__))+\"/\"+sDict['cmbDir'],\n freqs = psaFreqs,\n psa = psa,\n cmbSet = cmbSet, \n doBeam = doBeam, applyWindow = applyWindow,\n verbose = verbose, cmbMaptype = cmbMaptype, foregroundSeed = foregroundSeed,\n simType = simType, foregroundPowerFile = sDict['foregroundPowerFile'],\n applyModulation = applyModulation)\n\n\n else:\n raise ValueError(\"bad input\")","def split_simsplit_3epochs_iter3(params, ns):\n #24 parameters \n nu1a, nu2a, nu3a, nu1b, nu2b, nu3b, nu1c, nu2c, nu3c, m1_12, m1_13, m1_21, m1_23, m1_31, m1_32, m2_12, m2_13, m2_21, m2_23, m2_31, m2_32, T1, T2, T3 = params\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1] + ns[2])\n fs = moments.Spectrum(sts)\n fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1] + ns[2]) \n fs = moments.Manips.split_2D_to_3D_2(fs, ns[1], ns[2])\n ## Population function and migration matrix for T1\n nu_T1 = [nu1a, nu2a, nu3a]\n mig1 = numpy.array([[0, m1_12, m1_13],[m1_21, 0, m1_23], [m1_31, m1_32, 0]]) \n fs.integrate(nu_T1, T1, m=mig1)\n ## Population function and migration matrix for T2\n nu_T2 = [nu1b, nu2b, nu3b]\n mig2 = numpy.array([[0, m2_12, m2_13],[m2_21, 0, m2_23], [m2_31, m2_32, 0]]) \n fs.integrate(nu_T2, T2, m=mig2)\n ## Population function and migration matrix for T3 (bottleneck to capture single population representation of lineage)\n nu_T3 = [nu1c, nu2c, nu3c]\n fs.integrate(nu_T3, T3) \n return fs","def main():\n\tif len(sys.argv) < 12 or len(sys.argv) > 13:\n\t\tprint(\"Input parameters must be: 'filename lambda mu C c0 Q theta L H simulation_time is_debug repeats(optionally)'\")\n\telse:\n\t\tstart_time = time.time()\n\n\t\tfile_name = sys.argv[1]\n\t\tlambd = float(sys.argv[2])\n\t\tmu = float(sys.argv[3])\n\t\tC = int(sys.argv[4])\n\t\tc0 = int(sys.argv[5])\n\t\tQ = int(sys.argv[6])\n\t\ttheta = float(sys.argv[7])\n\t\tL = int(sys.argv[8])\n\t\tH = int(sys.argv[9])\n\t\tsimulation_time = float(sys.argv[10]);\n\t\tis_debug = True if sys.argv[11] == \"True\" else False;\n\t\trepeats = int(sys.argv[12]) if len(sys.argv) == 13 else 1;\n\n\t\tprint(\"Simulation started for params: lambda =\", lambd,\n\t\t\t \", mu =\", mu,\n\t\t\t \", C =\", C,\n\t\t\t \", c0 =\", c0,\n\t\t\t \", Q =\", Q,\n\t\t\t \", theta =\", theta,\n\t\t\t \", L =\", L,\n\t\t\t \", H =\", H,\n\t\t\t \", repeats =\", repeats)\n\n\t\tblocked = 0\n\t\tserved = 0\n\t\tgenerated = 0\n\t\tB = 0\n\t\tN = 0\n\n\t\tsimulation = Simulation(\"m/m/c[c0]/r[l,h]\", lambd, mu, theta, C, c0, L, H, simulation_time, Q, is_debug)\n\t\tfor i in range(0, repeats):\n\t\t\tsimulation = Simulation(\"m/m/c[c0]/r[l,h]\", lambd, mu, theta, C, c0, L, H, simulation_time, Q, is_debug)\n\t\t\tsimulation.start()\n\t\t\tblocked += simulation.queue.blocked\n\t\t\tserved += simulation.served_count\n\t\t\tgenerated += simulation.flow.generated_count\n\t\t\tB += simulation.queue.blocked/(simulation.served_count+simulation.queue.blocked)\n\t\t\tN += simulation.served_count/simulation_time\n\t\tend_time = time.time()\n\n\t\tblocked = blocked/repeats\n\t\tserved = served/repeats\n\t\tgenerated = generated/repeats\n\t\tB = B/repeats\n\t\tN = N/repeats\n\n\t\tprint( \"\")\n\t\tprint( \"Summary results:\")\n\t\tprint( \"blocked=\", blocked, \" served=\", served, \", generated=\", generated)\n\t\tprint(\"B = \", B)\n\t\tprint(\"N = \", N)\n\t\tprint(\"Execution time = %s seconds\" % (end_time - start_time))\n\t\tprint( \"... to be implemented more summary ...\")\n\n\t\t# write stats to file\n\t\tabs_path = os.path.abspath(__file__)\n\t\tpath = os.path.relpath('stats', abs_path)\n\t\tpath = os.path.join(path, file_name + '-(%s,%s,%s,%s,%s,%s,%s,%s).csv' % (lambd,mu,theta,C,c0,L,H,simulation_time))\n\n\t\toutfile=open(path,'w')\n\t\toutput = csv.writer(outfile, delimiter=';')\n\t\toutput.writerow(['Request ID','Queue', 'Arrival_Time','Queue_Arrival_time','Server_Arrival_time','alpha','beta'])\n\n\t\ti=0\n\t\tfor request in simulation.served_requests:\n\t\t\ti=i+1\n\t\t\toutrow=[]\n\t\t\toutrow.append(request.ID)\n\t\t\toutrow.append(request.queue_size_at_serving)\n\t\t\toutrow.append(request.arrival_time)\n\t\t\toutrow.append(request.queue_arrival_time)\n\t\t\toutrow.append(request.server_arrival_time)\n\t\t\toutrow.append(request.alpha)\n\t\t\toutrow.append(request.beta)\n\t\t\toutput.writerow(outrow)\n\t\toutfile.close()\n\n\t\treturn simulation","def generate_syntetic_data( self ):\n label_list = []\n img_ground_truth = np.zeros( ( self.img_h, self.img_w ), dtype = np.float32 )\n img = np.zeros( ( self.img_h, self.img_w, 3 ), dtype = np.float32 )\n class_blue = np.zeros( ( self.img_h, self.img_w ), dtype = np.float32 )\n class_red = np.zeros( ( self.img_h, self.img_w ), dtype = np.float32 )\n\n '''line, square, grill, rectangle, cross'''\n if self.option_shape == 'line':\n sample_type = 0\n elif self.option_shape == 'circle':\n sample_type = 1\n elif self.option_shape == 'rectangle':\n sample_type = 2\n else: \n sample_type = np.random.randint( 3 ) #0,1,2\n\n if sample_type == 0:\n self.generate_line( img_ground_truth, img, class_blue, class_red )\n elif sample_type == 1:\n self.generate_circle( img_ground_truth, img, class_blue, class_red )\n else:\n self.generate_rectangle( img_ground_truth, img, class_blue, class_red )\n\n label_list.append( class_blue )\n label_list.append( class_red )\n # 2 classes generates\n label_all = np.dstack( label_list ).astype( np.float32 ) \n r, g, b = cv2.split( img )\n img_bgr = cv2.merge( [ b, g, r ] )\n\n gen_adj = GenerateAdjMatrx( type_dist = self.type_dist )\n A_gt = gen_adj.adjmatrx_groundthuth( img_ground_truth )\n\n B_in = gen_adj.adjmatrx_groundthuth(img_ground_truth * 0)\n\n return img_bgr, img_ground_truth, label_all, A_gt, B_in","def generate_base(params, out):\n\n os.makedirs(out)\n experiment_path = os.path.join(out, BASE)\n shutil.copy(\"evaluation/ablation/run_all.sh\", out)\n\n print(f\"Generating {out}/{BASE}\")\n subprocess.run(\n [\n \"racket\",\n \"src/example-gen.rkt\",\n \"-b\", \"mat-mul\",\n \"-p\", params,\n \"-o\", experiment_path\n ],\n check=True,\n stderr=subprocess.PIPE)\n\n # Add all required file for the harness.\n shutil.copy(\"evaluation/ablation/Makefile\", experiment_path)\n shutil.copy(\"evaluation/ablation/harness.c\", experiment_path)\n shutil.copy(\"evaluation/src/utils.h\", experiment_path)","def randomParam(trajA):\n lenCBFs = len(trajA[\"CBFCons\"][-1])\n CBFs = CBFSets[CBFlens.index(lenCBFs)]\n Balpha, Kp, Kd = trajA[\"param\"]\n Balpha = Balpha + 0.02*(0.5-np.random.random(size = Balpha.shape))\n Kp = Kp + 50*(0.5-np.random.random())\n Kd = Kd + 10*(0.5-np.random.random())\n return SimuTest(CBFs,Balpha,Kp,Kd)","def samplecost(app, endclasses, fxnmode, samptype='std', title=\"\"):\n associated_scens=[]\n for phase in app.phases:\n associated_scens = associated_scens + app.scenids.get((fxnmode, phase), [])\n costs = np.array([endclasses[scen]['cost'] for scen in associated_scens])\n times = np.array([time for phase, timemodes in app.sampletimes.items() if timemodes for time in timemodes if fxnmode in timemodes.get(time)] ) \n rates = np.array(list(app.rates_timeless[fxnmode].values()))\n \n tPlot, axes = plt.subplots(2, 1, sharey=False, gridspec_kw={'height_ratios': [3, 1]})\n phasetimes_start =[times[0] for phase, times in app.phases.items()]\n phasetimes_end =[times[1] for phase, times in app.phases.items()]\n ratetimes =[]\n ratesvect =[]\n phaselocs = []\n for (ind, phasetime) in enumerate(phasetimes_start):\n axes[0].axvline(phasetime, color=\"black\") \n phaselocs= phaselocs +[(phasetimes_end[ind]-phasetimes_start[ind])/2 + phasetimes_start[ind]]\n\n axes[1].axvline(phasetime, color=\"black\") \n ratetimes = ratetimes + [phasetimes_start[ind]] + [phasetimes_end[ind]]\n ratesvect = ratesvect + [rates[ind]] + [rates[ind]]\n #axes[1].text(middletime, 0.5*max(rates), list(app.phases.keys())[ind], ha='center', backgroundcolor=\"white\")\n #rate plots\n axes[1].set_xticks(phaselocs)\n axes[1].set_xticklabels(list(app.phases.keys()))\n \n axes[1].plot(ratetimes, ratesvect)\n axes[1].set_xlim(phasetimes_start[0], phasetimes_end[-1])\n axes[1].set_ylim(0, np.max(ratesvect)*1.2 )\n axes[1].set_ylabel(\"Rate\")\n axes[1].set_xlabel(\"Time (\"+str(app.units)+\")\")\n axes[1].grid()\n #cost plots\n axes[0].set_xlim(phasetimes_start[0], phasetimes_end[-1])\n axes[0].set_ylim(0, 1.2*np.max(costs))\n if samptype=='fullint':\n axes[0].plot(times, costs, label=\"cost\")\n else:\n if samptype=='quadrature' or samptype=='pruned piecewise-linear': \n sizes = 1000*np.array([weight if weight !=1/len(timeweights) else 0.0 for phase, timeweights in app.weights[fxnmode].items() for time, weight in timeweights.items() if time in times])\n axes[0].scatter(times, costs,s=sizes, label=\"cost\", alpha=0.5)\n axes[0].stem(times, costs, label=\"cost\", markerfmt=\",\", use_line_collection=True)\n \n axes[0].set_ylabel(\"Cost\")\n axes[0].grid()\n if title: axes[0].set_title(title)\n elif type(fxnmode[0])==tuple: axes[0].set_title(\"Cost function of \"+str(fxnmode)+\" over time\")\n else: axes[0].set_title(\"Cost function of \"+fxnmode[0]+\": \"+fxnmode[1]+\" over time\")\n #plt.subplot_adjust()\n plt.tight_layout()","def simulate(self):\n n_samples = self.n_samples\n n_features = self.n_features\n nb_active_features = self.nb_active_features\n K = self.K\n pi_0 = self.pi_0\n gap = self.gap\n p0 = self.p0\n p1 = self.p1\n r_c = self.r_c\n r_cf = self.r_cf\n rho = self.rho\n\n coeffs = np.zeros(n_features)\n coeffs[0:nb_active_features] = K\n\n features = features_normal_cov_toeplitz(n_samples, n_features, rho)\n\n # Add class relative information on the design matrix \n A = np.random.choice(range(n_samples), size=int((1 - pi_0) * n_samples),\n replace=False)\n A_ = np.delete(range(n_samples), A)\n\n index_plus_gap = nb_active_features + int(\n (n_features - nb_active_features) * r_cf)\n features[A, :index_plus_gap] += gap\n features[A_, :index_plus_gap] -= gap\n\n self.features = features\n xc = features.dot(coeffs)\n\n # Simulation of latent variables\n pi = self.logistic_grad(-xc)\n u = np.random.rand(n_samples)\n Z = (u <= 1 - pi)\n self.Z = Z\n\n # Simulation of true times\n n_samples_class_1 = np.sum(Z)\n n_samples_class_0 = n_samples - n_samples_class_1\n T = np.empty(n_samples)\n pi_0_est = 1 - Z.mean()\n T[Z == 0] = np.random.geometric(p0, size=n_samples_class_0)\n\n # Compute p_c to obtain censoring rate r_c\n r_c_ = 1 - r_c\n p0_ = 1 - p0\n p1_ = 1 - p1\n pi_0_ = 1 - pi_0_est\n a = r_c_ * p0_ * p1_\n b = p0 * pi_0_est * p1_ + p1 * pi_0_ * p0_ - r_c_ * (p1_ + p0_)\n c = r_c_ - p0 * pi_0_est - p1 * pi_0_\n res = self.poldeg2_solver(a=a, b=b, c=c)\n if isinstance(res, list):\n if res[0] > 0:\n pc = 1 - res[0]\n else:\n pc = 1 - res[1]\n else:\n pc = 1 - res\n T[Z == 1] = np.random.geometric(p1, size=n_samples_class_1)\n\n # Simulation of the censoring\n C = np.random.geometric(pc, size=n_samples)\n\n # Censoring indicator: 1 if it is a time of failure, 0 if it's \n # censoring.\n delta = (T <= C).astype(int)\n\n # Observed time\n Y = np.minimum(T, C).astype(int)\n if np.sum(Y == 0) > 0:\n Y += 1\n self.delta = delta\n self.Y = Y\n return features, Y, delta","def test_init_out_with_rate(self):\n n = 3\n rates = [10, 50, 90]\n\n G = RandomLayer(n, ini_rate=rates)\n G.prepare(10.0, 0.1)\n\n self.assertLess(np.max(np.abs(G.out - rates)), 1e-9)","def run(inputs, parameters = None):\n def thickness(x, t, chord):\n y = af.Naca00XX(chord, t, [x], return_dict = 'y')\n thickness_at_x = y['u'] - y['l']\n return thickness_at_x \n\n if parameters != None:\n eng = parameters[0]\n import_matlab = False\n else:\n eng = None\n import_matlab = True\n \n sma = inputs['sma']\n linear = inputs['linear']\n R = inputs['R']\n\n sigma_o = 100e6\n\n \n airfoil = \"naca0012\"\n chord = 1.#0.6175\n\n J = {'x':0.75, 'y':0.}\n \n #Adding the area key to the dictionaries\n sma['area'] = math.pi*(0.000381/2.)**2\n linear['area'] = 0.001\n \n # Design constants \n #arm length to center of gravity\n r_w = 0.10\n \n #Aicraft weight (mass times gravity)\n W = 0.0523*9.8 #0.06*9.8\n alpha = 0.\n V = 10 #m/s\n altitude = 10000. #feet\n \n # Temperature\n T_0 = 273.15 + 30.\n T_final = 273.15 + 140.\n \n #Initial martensitic volume fraction\n MVF_init = 1.\n \n # Number of steps and cycles\n n = 200\n n_cycles = 0\n #~~~~~~~~~~~~~~~~~~~~~bb~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n #Parameters to select how to output stuff\n all_outputs = True\n save_data = True\n #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n if all_outputs:\n eps_s, eps_l, theta, sigma, MVF, T, eps_t, theta, F_l, k, L_s = flap(airfoil, \n chord, J, sma, linear, sigma_o, \n W, r_w, V, altitude, alpha, T_0, \n T_final, MVF_init, n, R, all_outputs = True,\n import_matlab = import_matlab, eng=eng,\n n_cycles = n_cycles)\n\n import matplotlib.pyplot as plt\n plt.figure()\n plt.plot(np.rad2deg(theta), eps_s, lw=2., label = \"$\\epsilon_s$\")\n plt.plot(np.rad2deg(theta), eps_l, 'b--',lw=2, label = \"$\\epsilon_l$\")\n# plt.scatter(theta, eps_s, c = 'b')\n# plt.scatter(theta, eps_l, c = 'b')\n plt.ylabel('$\\epsilon$', fontsize=24)\n plt.xlabel(r'$\\theta ({}^{\\circ})$', fontsize=20)\n plt.legend(loc = 'best', fontsize = 'x-large')\n plt.grid()\n \n print len(T), len(eps_s), len(eps_l), len(theta), len(eps_t)\n plt.figure()\n plt.plot(np.rad2deg(theta), eps_t, lw=2.)\n# plt.scatter(theta, eps_t, c = 'b')\n plt.ylabel('$\\epsilon_t$', fontsize=24)\n plt.xlabel(r'$\\theta ({}^{\\circ})$', fontsize=20)\n plt.legend(loc = 'best', fontsize = 'x-large')\n plt.grid()\n \n plt.figure()\n plt.plot(np.rad2deg(theta), MVF, lw=2.)\n# plt.scatter(theta, MVF, c = 'b')\n plt.ylabel('$MVF$', fontsize=24)\n plt.xlabel(r'$\\theta ({}^{\\circ})$', fontsize=20)\n plt.legend(loc = 'best', fontsize = 'x-large')\n plt.grid()\n\n plt.figure()\n plt.plot(T, MVF, lw=2.)\n# plt.scatter(T, MVF, c = 'b')\n plt.ylabel('$MVF$', fontsize=24)\n plt.xlabel('$T (K)$', fontsize=20)\n plt.legend(loc = 'best', fontsize = 'x-large')\n plt.grid()\n\n plt.figure()\n plt.plot(T, sigma, lw=2.)\n# plt.scatter(T, sigma, c = 'b')\n plt.ylabel('$\\sigma$', fontsize=24)\n plt.xlabel('$T (K)$', fontsize=20)\n plt.legend(loc = 'best', fontsize = 'x-large')\n plt.grid()\n \n plt.figure()\n plt.plot(T, eps_s, 'b', lw=2., label = \"$\\epsilon_s$\")\n plt.plot(T, eps_l, 'b--',lw=2, label = \"$\\epsilon_l$\")\n# plt.scatter(T, eps_s, c = 'b')\n# plt.scatter(T, eps_l, c = 'b')\n plt.xlabel('$T (K)$', fontsize=20)\n plt.ylabel('$\\epsilon$', fontsize=24)\n plt.legend(loc = 'best', fontsize = 'x-large')\n plt.grid()\n \n plt.figure()\n plt.plot(T, np.rad2deg(theta), lw=2.)\n# plt.scatter(T, theta, c = 'b')\n plt.xlabel('$T (K)$', fontsize=20)\n plt.ylabel(r'$\\theta ({}^{\\circ})$', fontsize=20)\n plt.grid()\n \n F_s = []\n for i in range(len(sigma)):\n F_s.append(sigma[i]*sma['area'])\n# sigma_MPa = []\n# for sigma_i in sigma:\n# sigma_MPa.append(sigma_i/1e6)\n plt.figure()\n plt.plot(theta, F_s, 'b', lw=2., label = \"$F_s$\")\n plt.plot(theta, F_l, 'b--', lw=2., label = \"$F_l$\")\n# plt.scatter(theta, F_s, c = 'b')\n# plt.scatter(theta, F_l, c = 'b')\n plt.ylabel('$F (N)$', fontsize=20)\n plt.xlabel(r'$\\theta ({}^{\\circ})$', fontsize=20)\n plt.legend(loc = 'best', fontsize = 'x-large')\n plt.grid() \n else:\n theta, k= flap(airfoil, chord, J, sma, linear, sigma_o, \n W, r_w, V, altitude, alpha, T_0, \n T_final, MVF_init, n, R, all_outputs = False,\n import_matlab = import_matlab, eng=eng,\n n_cycles = n_cycles)\n \n if save_data == True:\n Data = {'theta': theta, 'eps_s': eps_s, 'eps_l': eps_l, \n 'sigma': sigma, 'xi': MVF, 'T': T, 'eps_t': eps_t,\n 'F_l': F_l, 'k': k, 'L_s':L_s}\n pickle.dump(Data, open( \"data.p\", \"wb\" ) )\n \n return {'theta': theta, 'k': k}","def init_params():\n p = {}\n \n # p['rootFolder'] = 'C:/Users/Umberto Gostoli/SPHSU/Social Care Model II'\n # p['rootFolder'] = 'N:/Social Care Model Paper III'\n \n p['noPolicySim'] = False\n p['multiprocessing'] = True\n p['numberProcessors'] = 9\n p['numRepeats'] = 3\n \n p['startYear'] = 1860\n p['endYear'] = 2040\n p['thePresent'] = 2012\n p['statsCollectFrom'] = 1990\n p['regressionCollectFrom'] = 1960 \n p['implementPoliciesFromYear'] = 2020\n p['yearOutcome'] = 2015\n \n p['favouriteSeed'] = 123\n p['loadFromFile'] = False\n p['verboseDebugging'] = False\n p['singleRunGraphs'] = False\n p['saveChecks'] = True\n p['getCheckVariablesAtYear'] = 2015\n # To change through command-line arguments\n\n p['numberPolicyParameters'] = 2\n p['valuesPerParam'] = 1\n p['numberScenarios'] = 3\n \n ############ Policy Parameters #######################\n p['incomeCareParam'] = 0.0005 #[0.00025 - 0.001]\n p['taxBreakRate'] = 0.0\n p['ageOfRetirement'] = 65\n p['socialSupportLevel'] = 5\n # p['educationCosts']\n #############################################################\n p['socialCareCreditShare'] = 0.0\n p['maxWtWChildAge'] = 5\n # The basics: starting population and year, etc.\n \n p['discountingFactor'] = 0.03\n \n \n p['initialPop'] = 600 \n \n p['minStartAge'] = 24\n p['maxStartAge'] = 45\n p['numberClasses'] = 5\n p['socialClasses'] = ['unskilled', 'skilled', 'lower', 'middle', 'upper']\n p['initialClassShares'] = [0.2, 0.25, 0.3, 0.2, 0.05]\n p['initialUnemployment'] = [0.25, 0.2, 0.15, 0.1, 0.1]\n p['unemploymentAgeBandParam'] = 0.3\n \n # doDeath function parameters\n p['mortalityBias'] = 0.85 # After 1950\n p['careNeedBias'] = 0.9\n p['unmetCareNeedBias'] = 0.5\n p['baseDieProb'] = 0.0001\n p['babyDieProb'] = 0.005\n p['maleAgeScaling'] = 14.0\n p['maleAgeDieProb'] = 0.00021\n p['femaleAgeScaling'] = 15.5\n p['femaleAgeDieProb'] = 0.00019\n \n p['orphansRelocationParam'] = 0.5\n \n # doBirths function parameters\n p['minPregnancyAge'] = 17\n p['maxPregnancyAge'] = 42\n p['growingPopBirthProb'] = 0.215\n p['fertilityCorrector'] = 1.0\n p['fertilityBias'] = 0.9\n \n # careTransitions function parameters\n p['zeroYearCare'] = 80.0\n p['childcareDecreaseRate'] = 0.25\n p['personCareProb'] = 0.0008\n p['maleAgeCareScaling'] = 18.0 # p['maleAgeCareProb'] = 0.0008\n p['femaleAgeCareScaling'] = 19.0 # p['femaleAgeCareProb'] = 0.0008\n p['baseCareProb'] = 0.0002\n p['careBias'] = 0.9\n p['careTransitionRate'] = 0.7\n\n p['unmetNeedExponent'] = 1.0 # 0.005 #[0.005 - 0.02]\n \n p['numCareLevels'] = 5\n p['careLevelNames'] = ['none','low','moderate','substantial','critical']\n p['careDemandInHours'] = [ 0.0, 8.0, 16.0, 32.0, 80.0 ]\n p['quantumCare'] = 4.0\n \n # careSupplies getCare and probSuppliers function parameters\n \n ######## Key parameter 1 ##############\n \n \n p['weeklyHours'] = 40.0\n \n \n p['priceChildCare'] = 0.76 # 6 \n p['schoolAge'] = 5\n p['maxFormalChildcareHours'] = 48\n p['schoolHours'] = 30\n p['freeChildcareHours'] = 15\n p['workingParentsFreeChildcareHours'] = 30\n p['minAgeStartChildCareSupport'] = 3\n p['minAgeStartChildCareSupportByIncome'] = 2\n p['maxHouseholdIncomeChildCareSupport'] = 40 # 320\n \n ######## Key parameter 2 ##############\n # 5: No public supply \n \n p['retiredHours'] = [48.0, 36.0, 20.0, 10.0] # 60.0\n p['studentHours'] = [24.0, 16.0, 8.0, 4.0]\n p['teenAgersHours'] = [16.0, 0.0, 0.0, 0.0]\n p['unemployedHours'] = [32.0, 24.0, 16.0, 8.0]\n p['employedHours'] = [28.0, 20.0, 12.0, 8.0]\n p['formalCareDiscountFactor'] = 0.5\n \n p['socialNetworkDistances'] = [0.0, 1.0, 2.0, 1.0, 2.0, 2.0, 3.0, 3.0]\n p['networkDistanceParam'] = 2.0\n p['socialCareWeightBias'] = 1.0\n p['unmetCareNeedDiscountParam'] = 0.5\n p['shareUnmetNeedDiscountParam'] = 0.5\n # p['pastShareUnmetNeedWeight'] = 0.5\n \n \n \n p['networkSizeParam'] = 10.0 # 1.0\n \n p['careSupplyBias'] = 0.5\n p['careIncomeParam'] = 0.001\n \n # Hospitalization Costs\n p['qalyBeta'] = 0.18\n p['qalyAlpha'] = 1.5\n p['qalyDiscountRate'] = 0.035\n p['qalyIndexes'] = [1.0, 0.8, 0.6, 0.4, 0.2]\n p['unmetCareHealthParam'] = 0.1\n p['hospitalizationParam'] = 0.5\n p['needLevelParam'] = 2.0\n p['unmetSocialCareParam'] = 2.0\n p['costHospitalizationPerDay'] = 400\n \n # ageTransitions, enterWorkForce and marketWage functions parameters\n p['ageTeenagers'] = 12\n p['minWorkingAge'] = 16\n \n ######## Key parameter 3 ##############\n \n p['careBankingSchemeOn'] = False\n p['socialCareBankingAge'] = 65\n \n p['absoluteCreditQuantity'] = False\n p['quantityYearlyIncrease'] = 0.0\n p['socialCareCreditQuantity'] = 0\n p['kinshipNetworkCarePropension'] = 0.5\n p['volunteersCarePropensionCoefficient'] = 0.01\n p['pensionContributionRate'] = 0.05\n \n p['hillHealthLevelThreshold'] = 3\n p['seriouslyHillSupportRate'] = 0.5\n \n ### Prices ####\n p['pricePublicSocialCare'] = 20.0 # [2.55] # 20\n p['priceSocialCare'] = 17.0 # [2.29] # 18\n p['taxBrackets'] = [663, 228, 0] # [28.16, 110.23] # [221, 865]\n p['taxBandsNumber'] = 3\n p['bandsTaxationRates'] = [0.4, 0.2, 0.0] # [0.0, 0.2, 0.4]\n # Tax Break Policy\n\n \n p['pensionWage'] = [5.0, 7.0, 10.0, 13.0, 18.0] # [0.64, 0.89, 1.27, 1.66, 2.29] # \n p['incomeInitialLevels'] = [5.0, 7.0, 9.0, 11.0, 14.0] #[0.64, 0.89, 1.15, 1.40, 1.78] # \n p['incomeFinalLevels'] = [10.0, 15.0, 22.0, 33.0, 50.0] #[1.27, 1.91, 2.80, 4.21, 6.37] # \n p['educationCosts'] = [0.0, 100.0, 150.0, 200.0] #[0.0, 12.74, 19.12, 25.49] # \n \n # Priced growth #####\n p['wageGrowthRate'] = 1.0 # 1.01338 # \n\n p['incomeGrowthRate'] = [0.4, 0.35, 0.35, 0.3, 0.25]\n \n # SES inter-generational mobility parameters\n p['leaveHomeStudentsProb'] = 0.5\n \n p['eduWageSensitivity'] = 0.2 # 0.5\n p['eduRankSensitivity'] = 3.0 # 5.0\n p['costantIncomeParam'] = 80.0 # 20.0\n p['costantEduParam'] = 10.0 # 10.0\n p['careEducationParam'] = 0.005 # 0.04\n \n \n \n # p['incEduExp'] = 0.25\n p['educationLevels'] = ['GCSE', 'A-Level', 'HND', 'Degree', 'Higher Degree']\n p['workingAge'] = [16, 18, 20, 22, 24]\n \n # doDivorce function parameters\n p['basicDivorceRate'] = 0.06\n p['variableDivorce'] = 0.06\n p['divorceModifierByDecade'] = [ 0.0, 1.0, 0.9, 0.5, 0.4, 0.2, 0.1, 0.03, 0.01, 0.001, 0.001, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0 ]\n p['divorceBias'] = 1.0\n \n # doMarriages function parameters\n p['deltageProb'] = [0.0, 0.1, 0.25, 0.4, 0.2, 0.05]\n p['incomeMarriageParam'] = 0.025\n p['studentFactorParam'] = 0.5\n ######## Key parameter 4 ##############\n p['betaGeoExp'] = 2.0 #[1.0 - 4.0]\n \n p['betaSocExp'] = 2.0\n p['rankGenderBias'] = 0.5\n p['basicMaleMarriageProb'] = 0.9\n p['maleMarriageModifierByDecade'] = [ 0.0, 0.16, 0.5, 1.0, 0.8, 0.7, 0.66, 0.5, 0.4, 0.2, 0.1, 0.05, 0.01, 0.0, 0.0, 0.0, 0.0 ]\n \n # jobMarket, updateWork and unemploymentRate functions parameters\n p['unemploymentClassBias'] = 0.75\n p['unemploymentAgeBias'] = [1.0, 0.55, 0.35, 0.25, 0.2, 0.2]\n p['numberAgeBands'] = 6\n p['jobMobilitySlope'] = 0.004\n p['jobMobilityIntercept'] = 0.05\n p['ageBiasParam'] = [7.0, 3.0, 1.0, 0.5, 0.35, 0.15]\n p['deltaIncomeExp'] = 0.05\n p['unemployedCareBurdernParam'] = 0.025\n # Potential key parameter\n p['relocationCareLossExp'] = 1.0 # 40.0 # \n p['incomeSocialCostRelativeWeight'] = 0.5\n \n p['firingParam'] = 0.2\n p['wageVar'] = 0.06\n p['workDiscountingTime'] = 0.75 # 0.8\n p['sizeWeightParam'] = 0.7\n p['minClassWeightParam'] = 1.0\n p['incomeDiscountingExponent'] = 4.0\n p['discountingMultiplier'] = 2.0\n #p['incomeDiscountingParam'] = 2.0\n \n # relocationPensioners function parameters\n p['agingParentsMoveInWithKids'] = 0.1\n p['variableMoveBack'] = 0.1\n p['retiredRelocationParam'] = 0.001 # 0.005\n \n # houseMap function parameters\n p['geoDistanceSensitivityParam'] = 2.0\n p['socDistanceSensitivityParam'] = 2.0\n p['classAffinityWeight'] = 4.0\n p['distanceSensitivityParam'] = 0.5\n \n # relocationProb function parameters\n p['baseRelocatingProb'] = 0.05\n p['relocationParameter'] = 1.0 \n p['apprenticesRelocationProb'] = 0.5\n #p['expReloc'] = 1.0\n \n # computeRelocationCost and relocation Propensity functions parameters\n p['yearsInTownSensitivityParam'] = 0.5\n \n ######## Key parameter 5 ##############\n p['relocationCostParam'] = 0.5 # 1.0 \n \n ######## Key parameter 6 ##############\n p['propensityRelocationParam'] = 2.0 # 2.0 \n p['denRelocationWeight'] = 0.5\n \n \n ## Description of the map, towns, and houses\n p['mapGridXDimension'] = 8\n p['mapGridYDimension'] = 12 \n p['townGridDimension'] = 70\n p['cdfHouseClasses'] = [ 0.6, 0.9, 5.0 ]\n p['ukMap'] = [[ 0.0, 0.1, 0.2, 0.1, 0.0, 0.0, 0.0, 0.0 ],\n [ 0.1, 0.1, 0.2, 0.2, 0.3, 0.0, 0.0, 0.0 ],\n [ 0.0, 0.2, 0.2, 0.3, 0.0, 0.0, 0.0, 0.0 ],\n [ 0.0, 0.2, 1.0, 0.5, 0.0, 0.0, 0.0, 0.0 ],\n [ 0.4, 0.0, 0.2, 0.2, 0.4, 0.0, 0.0, 0.0 ],\n [ 0.6, 0.0, 0.0, 0.3, 0.8, 0.2, 0.0, 0.0 ],\n [ 0.0, 0.0, 0.0, 0.6, 0.8, 0.4, 0.0, 0.0 ],\n [ 0.0, 0.0, 0.2, 1.0, 0.8, 0.6, 0.1, 0.0 ],\n [ 0.0, 0.0, 0.1, 0.2, 1.0, 0.6, 0.3, 0.4 ],\n [ 0.0, 0.0, 0.5, 0.7, 0.5, 1.0, 1.0, 0.0 ],\n [ 0.0, 0.0, 0.2, 0.4, 0.6, 1.0, 1.0, 0.0 ],\n [ 0.0, 0.2, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0 ]]\n p['ukClassBias'] = [[ 0.0, -0.05, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],\n [ -0.05, -0.05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ],\n [ 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0, 0.0 ],\n [ 0.0, -0.05, -0.05, 0.05, 0.0, 0.0, 0.0, 0.0 ],\n [ -0.05, 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],\n [ -0.05, 0.0, 0.0, -0.05, -0.05, -0.05, 0.0, 0.0 ],\n [ 0.0, 0.0, 0.0, -0.05, -0.05, -0.05, 0.0, 0.0 ],\n [ 0.0, 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],\n [ 0.0, 0.0, -0.05, 0.0, -0.05, 0.0, 0.0, 0.0 ],\n [ 0.0, 0.0, 0.0, -0.05, 0.0, 0.2, 0.15, 0.0 ],\n [ 0.0, 0.0, 0.0, 0.0, 0.1, 0.2, 0.15, 0.0 ],\n [ 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0 ] ]\n p['mapDensityModifier'] = 0.6\n # p['numHouseClasses'] = 3\n # p['houseClasses'] = ['small','medium','large']\n \n ## Graphical interface details\n p['interactiveGraphics'] = False #True\n p['delayTime'] = 0.0\n p['screenWidth'] = 1300\n p['screenHeight'] = 700\n p['bgColour'] = 'black'\n p['mainFont'] = 'Helvetica 18'\n p['fontColour'] = 'white'\n p['dateX'] = 70\n p['dateY'] = 20\n p['popX'] = 70\n p['popY'] = 50\n p['pixelsInPopPyramid'] = 2000\n p['num5YearAgeClasses'] = 28\n p['careLevelColour'] = ['blue','green','yellow','orange','red']\n p['houseSizeColour'] = ['brown','purple','yellow']\n p['pixelsPerTown'] = 56\n p['maxTextUpdateList'] = 22\n \n # p['eduEduSensitivity'] = 0.5\n # p['mortalityBias'] = [1.0, 0.92, 0.84, 0.76, 0.68]\n # p['fertilityBias'] = [1.0, 0.92, 0.84, 0.76, 0.68]\n # p['divorceBias'] = [2.0, 1.5, 1.0, 0.75, 0.5]\n\n ## Transitions to care statistics\n \n ## Availability of care statistics\n \n #p['childHours'] = 5.0\n # p['employedHours'] = 12.0\n #p['homeAdultHours'] = 30.0\n #p['workingAdultHours'] = 25.0\n #p['maxEmployedHours'] = 60.0\n \n #p['lowCareHandicap'] = 0.5\n #p['hourlyCostOfCare'] = 20.0\n \n ## Fertility statistics\n \n # p['steadyPopBirthProb'] = 0.13\n # p['transitionYear'] = 1965\n \n ## Class and employment statistics\n # p['numClasses'] = 5\n # p['occupationClasses'] = ['lower','intermediate','higher']\n # p['cdfOccupationClasses'] = [ 0.6, 0.9, 1.0 ]\n\n ## Age transition statistics\n # p['ageOfAdulthood'] = 17\n \n ## Marriage function parameters\n \n # p['basicFemaleMarriageProb'] = 0.25\n # p['femaleMarriageModifierByDecade'] = [ 0.0, 0.5, 1.0, 1.0, 1.0, 0.6, 0.5, 0.4, 0.1, 0.01, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0 ]\n # p['femaleMarriageProb'] = [0.01, 0.15, 0.3, 0.2, 0.1, 0.1, 0.06, 0.05, 0.02, 0.01, 0.01, 0.005]\n # p['maleMarriageProb'] = [0.005, 0.08, 0.25, 0.25, 0.15, 0.1, 0.07, 0.05, 0.03, 0.02, 0.01, 0.005]\n \n ## Leaving home and moving around statistics\n # p['probApartWillMoveTogether'] = 0.3\n # p['coupleMovesToExistingHousehold'] = 0.3\n # p['basicProbAdultMoveOut'] = 0.22\n # p['probAdultMoveOutModifierByDecade'] = [ 0.0, 0.2, 1.0, 0.6, 0.3, 0.15, 0.03, 0.03, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]\n # p['basicProbSingleMove'] = 0.05\n # p['probSingleMoveModifierByDecade'] = [ 0.0, 1.0, 1.0, 0.8, 0.4, 0.06, 0.04, 0.02, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]\n # p['basicProbFamilyMove'] = 0.03\n # p['probFamilyMoveModifierByDecade'] = [ 0.0, 0.5, 0.8, 0.5, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1 ]\n\n \n return p","def time(is_train):\n context = composition.Context()\n start_minutes = random.randint(1, 24*60 - 1)\n while True:\n duration_minutes = random.randint(1, 12*60 - 1)\n if train_test_split.is_train(duration_minutes) == is_train:\n break\n end_minutes = start_minutes + duration_minutes\n\n def format_12hr(minutes):\n \"\"\"Format minutes from midnight in 12 hr format.\"\"\"\n hours = (minutes // 60) % 24\n minutes %= 60\n #am_pm = 'AM' if hours < 12 else 'PM'\n #hours = (hours - 1) % 12 + 1\n return '{}:{:02}'.format(hours, minutes)\n #return '{}:{:02} {}'.format(hours, minutes, am_pm)\n\n start = format_12hr(start_minutes)\n end = format_12hr(end_minutes)\n\n which_question = random.randint(0, 3)\n if which_question == 0:\n # Question: What is start = end - duration?\n template = random.choice([\n 'Che ore sono se mancano {duration} minuti alle {end}?',\n ])\n return example.Problem(\n question=example.question(\n context, template, duration=duration_minutes, end=end),\n answer=start)\n elif which_question == 1:\n # Question: What is end = start + duration?\n template = random.choice([\n 'Che ore sono se sono passati {duration} minuti dalle {start}?',\n ])\n return example.Problem(\n question=example.question(\n context, template, duration=duration_minutes, start=start),\n answer=end)\n else:\n # Question: What is duration = end - start?\n template = random.choice([\n 'Quanti minuti ci sono tra le {start} e le {end}?',\n ])\n return example.Problem(\n question=example.question(context, template, start=start, end=end),\n answer=duration_minutes)","def __init__(self, time_constant: float, sampling_time: float):\n self.alpha = sampling_time / (time_constant + sampling_time)\n self.state = None","def refugia_adj_5_simsplit_4epochs_iter1 (params, ns):\n #28 parameters \n nu1a, nu2a, nu3a, nu1b, nu2b, nu3b, nu1c, nu2c, nu3c, nu1d, nu2d, nu3d, m1_12, m1_13, m1_21, m1_23, m1_31, m1_32, m3_12, m3_13, m3_21, m3_23, m3_31, m3_32, T1, T2, T3, T4 = params\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1] + ns[2])\n fs = moments.Spectrum(sts)\n fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1] + ns[2]) \n fs = moments.Manips.split_2D_to_3D_2(fs, ns[1], ns[2])\n ## Population function and migration matrix for T1 (to reflect sum effect of all previous glacial-interglacial cycles)\n nu_T1 = [nu1a, nu2a, nu3a]\n mig1 = numpy.array([[0, m1_12, m1_13],[m1_21, 0, m1_23], [m1_31, m1_32, 0]]) \n fs.integrate(nu_T1, T1, m=mig1)\n ## Population function and migration matrix for T2 (to reflect period of isolation during last glacial)\n nu_T2 = [nu1b, nu2b, nu3b]\n fs.integrate(nu_T2, T2)\n ## Population function and migration matrix for T3 (to reflect inter-glacial expansion)\n nu_T3 = [nu1c, nu2c, nu3c]\n mig3 = numpy.array([[0, m3_12, m3_13],[m3_21, 0, m3_23], [m3_31, m3_32, 0]]) \n fs.integrate(nu_T3, T3, m=mig3)\n ## Population function and migration matrix for T3 (bottleneck to capture single population representation of lineage)\n nu_T4 = [nu1d, nu2d, nu3d]\n fs.integrate(nu_T4, T4) \n return fs","def run_evaluation(self, example):\n assert isinstance(example, dict)\n assert 'name' in example\n\n name = example['name']\n house_id, camera_id = pbrs_utils.parse_house_and_camera_ids_from_string(name)\n\n gt_bg, gt_objects = sorted(glob.glob(path.join(config.default_out_root, 'v9_gt_mesh/{}/{}/gt*.ply'.format(house_id, camera_id))))\n gt_depths = sorted(glob.glob(path.join(config.default_out_root, 'v9_gt_mesh/{}/{}/d*.ply'.format(house_id, camera_id))))\n assert len(gt_depths) == 5\n\n gt_overhead_fg = sorted(glob.glob(path.join(config.default_out_root, 'v9_gt_overhead_mesh/{}/{}/overhead_fg.ply'.format(house_id, camera_id))))\n assert len(gt_overhead_fg) == 1\n gt_overhead_fg = gt_overhead_fg[0]\n assert path.exists(gt_overhead_fg)\n\n pred_depths = sorted(glob.glob(path.join(config.default_out_root, 'v9_pred_depth_mesh/{}/{}/pred_*.ply'.format(house_id, camera_id))))\n assert len(pred_depths) == 5\n\n pred_overhead_fg = sorted(glob.glob(path.join(config.default_out_root, 'v9_pred_depth_mesh/{}/{}/overhead_fg_clipped.ply'.format(house_id, camera_id))))\n assert len(pred_overhead_fg) == 1\n pred_overhead_fg = pred_overhead_fg[0]\n\n print('gt_depths', gt_depths)\n print('gt_objects', gt_objects)\n print('pred_depths', pred_depths)\n\n voxel_related_data_out_basedir = '/mnt/ramdisk/voxels_data/tmp_{}'.format(name.replace('/', '_'))\n for fname in glob.glob(path.join(voxel_related_data_out_basedir, '*')):\n os.remove(fname)\n\n camera_filename = example['camera_filename']\n fv = convert_mesh_to_camcoord(gt_objects, camera_filename)\n gt_objects_camcoord = path.join(voxel_related_data_out_basedir, 'gt_objects_camcoord.off')\n io_utils.save_off(fv, gt_objects_camcoord)\n\n # vox_res = 64\n vox_res = 400\n vox_method_flags = '-pb'\n\n self.run_command('/home/daeyun/usr/bin/binvox {} -d {} -bb -5 -5 -10 5 5 0 {}'.format(vox_method_flags, vox_res, gt_objects_camcoord))\n gt_object_voxel_file = gt_objects_camcoord.replace('.off', '.binvox')\n convert_binvox_to_pcl(gt_object_voxel_file)\n # convert_binvox_to_mesh(gt_object_voxel_file)\n\n ret = {}\n\n # f3d voxelization\n f3d_pred = path.join(config.default_out_root_v8, 'factored3d_pred/{}/{}/codes_transformed_clipped.ply'.format(house_id, camera_id))\n assert path.isfile(f3d_pred), f3d_pred\n fv = convert_mesh_to_camcoord(f3d_pred, camera_filename)\n f3d_objects_camcoord = path.join(voxel_related_data_out_basedir, 'f3d_objects_camcoord.off')\n io_utils.save_off(fv, f3d_objects_camcoord)\n\n self.run_command('/home/daeyun/usr/bin/binvox {} -d {} -bb -5 -5 -10 5 5 0 {}'.format(vox_method_flags, vox_res, f3d_objects_camcoord))\n f3d_objects_voxel_file = f3d_objects_camcoord.replace('.off', '.binvox')\n convert_binvox_to_pcl(f3d_objects_voxel_file)\n # convert_binvox_to_mesh(f3d_objects_voxel_file)\n\n iou = binvox_iou(f3d_objects_voxel_file, gt_object_voxel_file)\n print('IoU_f3d', iou)\n ret['IoU_f3d'] = iou\n\n '''\n # depth overhead only\n depth_meshes_fv_list = [io_utils.read_mesh_assimp(pred_d) for pred_d in [pred_overhead_fg]]\n # depth_meshes_fv_list = [io_utils.read_mesh_assimp(pred_d) for pred_d in pred_depths[:4]]\n\n depth_meshes_merged = io_utils.merge_meshes(*depth_meshes_fv_list)\n fv = convert_mesh_to_camcoord(depth_meshes_merged, camera_filename)\n depth_objects_camcoord = path.join(voxel_related_data_out_basedir, 'depth_meshes_ovh_camcoord.off')\n io_utils.save_off(fv, depth_objects_camcoord)\n self.run_command('/home/daeyun/usr/bin/binvox {} -d {} -bb -5 -5 -10 5 5 0 {}'.format(vox_method_flags, vox_res, depth_objects_camcoord))\n depth_ovh_voxel_file = depth_objects_camcoord.replace('.off', '.binvox')\n convert_binvox_to_pcl(depth_ovh_voxel_file)\n convert_binvox_to_mesh(depth_ovh_voxel_file)\n iou = binvox_iou(depth_ovh_voxel_file, gt_object_voxel_file)\n print('IoU_overheadonly', iou)\n ret['IoU_overheadonly'] = iou\n '''\n\n # depth voxelization no overhead\n depth_meshes_fv_list = [io_utils.read_mesh_assimp(pred_d) for pred_d in pred_depths[:4]]\n # depth_meshes_fv_list = [io_utils.read_mesh_assimp(pred_d) for pred_d in pred_depths[:4]]\n\n depth_meshes_merged = io_utils.merge_meshes(*depth_meshes_fv_list)\n fv = convert_mesh_to_camcoord(depth_meshes_merged, camera_filename)\n depth_objects_camcoord = path.join(voxel_related_data_out_basedir, 'depth_meshes_frontal4_camcoord.off')\n io_utils.save_off(fv, depth_objects_camcoord)\n self.run_command('/home/daeyun/usr/bin/binvox {} -d {} -bb -5 -5 -10 5 5 0 {}'.format(vox_method_flags, vox_res, depth_objects_camcoord))\n depth_objects_voxel_file = depth_objects_camcoord.replace('.off', '.binvox')\n convert_binvox_to_pcl(depth_objects_voxel_file)\n # convert_binvox_to_mesh(depth_objects_voxel_file)\n\n iou = binvox_iou(depth_objects_voxel_file, gt_object_voxel_file)\n print('IoU_frontaldepthonly', iou)\n ret['IoU_frontaldepthonly'] = iou\n\n # depth voxelization\n depth_meshes_fv_list = [io_utils.read_mesh_assimp(pred_d) for pred_d in pred_depths[:4] + [pred_overhead_fg]]\n # depth_meshes_fv_list = [io_utils.read_mesh_assimp(pred_d) for pred_d in pred_depths[:4]]\n\n depth_meshes_merged = io_utils.merge_meshes(*depth_meshes_fv_list)\n fv = convert_mesh_to_camcoord(depth_meshes_merged, camera_filename)\n depth_objects_camcoord = path.join(voxel_related_data_out_basedir, 'depth_meshes_frontal4_and_ovh_camcoord.off')\n io_utils.save_off(fv, depth_objects_camcoord)\n self.run_command('/home/daeyun/usr/bin/binvox {} -d {} -bb -5 -5 -10 5 5 0 {}'.format(vox_method_flags, vox_res, depth_objects_camcoord))\n depth_objects_voxel_file = depth_objects_camcoord.replace('.off', '.binvox')\n convert_binvox_to_pcl(depth_objects_voxel_file)\n # convert_binvox_to_mesh(depth_objects_voxel_file)\n\n iou = binvox_iou(depth_objects_voxel_file, gt_object_voxel_file)\n print('IoU_alldepths', iou)\n ret['IoU_alldepths'] = iou\n\n return voxel_related_data_out_basedir, ret","def run():\n step = 0\n o2r = 4 #orange to red delay time\n r2g = 2 #red to green delay time\n A_4235 = 0\n B_4235 = 1\n C_4235 = 2\n AB1_4235 = 3\n AB2_4235 = 4\n AC1_4235 = 5\n AC2_4235 = 6\n BA1_4235 = 7\n BA2_4235 = 8\n BC1_4235 = 9\n BC2_4235 = 10\n CA1_4235 = 11\n CA2_4235 = 12\n CB1_4235 = 13\n CB2_4235 = 14\n A_4219 = 0\n B_4219 = 1\n C_4219 = 2\n D_4219 = 3\n E_4219 = 4\n F_4219 = 5\n G_4219 = 6\n AB1_4219 = 7\n AB2_4219 = 8\n AC1_4219 = 9\n AC2_4219 = 10\n AD1_4219 = 11\n AD2_4219 = 12\n AE1_4219 = 13\n AE2_4219 = 14\n AF1_4219 = 16\n AF2_4219 = 17\n AG1_4219 = 18\n AG2_4219 = 19\n BA1_4219 = 20\n BA2_4219 = 21\n BC1_4219 = 22\n BC2_4219 = 23\n BD1_4219 = 24\n BD2_4219 = 25\n BE1_4219 = 26\n BE2_4219 = 27\n BF1_4219 = 28\n BF2_4219 = 29\n BG1_4219 = 30\n BG2_4219 = 31\n CA1_4219 = 32\n CA2_4219 = 33\n CB1_4219 = 34\n CB2_4219 = 35\n CD1_4219 = 36\n CD2_4219 = 37\n CE1_4219 = 38\n CE2_4219 = 39\n CF1_4219 = 40\n CF2_4219 = 41\n CG1_4219 = 42\n CG2_4219 = 43\n DA1_4219 = 44\n DA2_4219 = 45\n DB1_4219 = 46\n DB2_4219 = 47\n DC1_4219 = 48\n DC2_4219 = 49\n DE1_4219 = 50\n DE2_4219 = 51\n DF1_4219 = 52\n DF2_4219 = 53\n DG1_4219 = 54\n DG2_4219 = 55\n EA1_4219 = 56\n EA2_4219 = 57\n EB1_4219 = 58\n EB2_4219 = 59\n EC1_4219 = 60\n EC2_4219 = 61\n ED1_4219 = 62\n ED2_4219 = 63\n EF1_4219 = 64\n EF2_4219 = 65\n EG1_4219 = 66\n EG2_4219 = 67\n FA1_4219 = 68\n FA2_4219 = 69\n FB1_4219 = 70\n FB2_4219 = 71\n FC1_4219 = 72\n FC2_4219 = 73\n FD1_4219 = 74\n FD2_4219 = 75\n FE1_4219 = 76\n FE2_4219 = 77\n FG1_4219 = 78\n FG2_4219 = 79\n GA1_4219 = 80\n GA2_4219 = 81\n GB1_4219 = 82\n GB2_4219 = 83\n GC1_4219 = 84\n GC2_4219 = 85\n GD1_4219 = 86\n GD2_4219 = 87\n GE1_4219 = 88\n GE2_4219 = 89\n GF1_4219 = 90\n GF2_4219 = 91\n A_4220 = 0\n B_4220 = 1\n C_4220 = 2\n D_4220 = 3\n E_4220 = 4\n AB1_4220 = 5\n AB2_4220 = 6\n AC1_4220 = 7\n AC2_4220 = 8\n AD1_4220 = 9\n AD2_4220 = 10\n AE1_4220 = 11\n AE2_4220 = 12\n BA1_4220 = 13\n BA2_4220 = 14\n BC1_4220 = 15\n BC2_4220 = 16\n BD1_4220 = 17\n BD2_4220 = 18\n BE1_4220 = 19\n BE2_4220 = 20\n CA1_4220 = 21\n CA2_4220 = 22\n CB1_4220 = 23\n CB2_4220 = 24\n CD1_4220 = 25\n CD2_4220 = 26\n CE1_4220 = 27\n CE2_4220 = 28\n DA1_4220 = 29\n DA2_4220 = 30\n DB1_4220 = 31\n DB2_4220 = 32\n DC1_4220 = 33\n DC2_4220 = 34\n DE1_4220 = 35\n DE2_4220 = 36\n EA1_4220 = 37\n EA2_4220 = 38\n EB1_4220 = 39\n EB2_4220 = 40\n EC1_4220 = 41\n EC2_4220 = 42\n ED1_4220 = 43\n ED2_4220 = 44\n A_4221 = 0\n B_4221 = 1\n C_4221 = 2\n D_4221 = 3\n E_4221 = 4\n F_4221 = 5\n AB1_4221 = 6\n AB2_4221 = 7\n AC1_4221 = 8\n AC2_4221 = 9\n AD1_4221 = 10\n AD2_4221 = 11\n AE1_4221 = 12\n AE2_4221 = 13\n AF1_4221 = 14\n AF2_4221 = 15\n BA1_4221 = 16\n BA2_4221 = 17\n BC1_4221 = 18\n BC2_4221 = 19\n BD1_4221 = 20\n BD2_4221 = 21\n BE1_4221 = 22\n BE2_4221 = 23\n BF1_4221 = 24\n BF2_4221 = 25\n CA1_4221 = 26\n CA2_4221 = 27\n CB1_4221 = 28\n CB2_4221 = 29\n CD1_4221 = 30\n CD2_4221 = 31\n CE1_4221 = 32\n CE2_4221 = 33\n CF1_4221 = 34\n CF2_4221 = 35\n DA1_4221 = 36\n DA2_4221 = 37\n DB1_4221 = 38\n DB2_4221 = 39\n DC1_4221 = 40\n DC2_4221 = 41\n DE1_4221 = 42\n DE2_4221 = 43\n DF1_4221 = 44\n DF2_4221 = 45\n EA1_4221 = 46\n EA2_4221 = 47\n EB1_4221 = 48\n EB2_4221 = 49\n EC1_4221 = 50\n EC2_4221 = 51\n ED1_4221 = 52\n ED2_4221 = 53\n EF1_4221 = 54\n EF2_4221 = 55\n FA1_4221 = 56\n FA2_4221 = 57\n FB1_4221 = 58\n FB2_4221 = 59\n FC1_4221 = 60\n FC2_4221 = 61\n FD1_4221 = 62\n FD2_4221 = 63\n FE1_4221 = 64\n FE2_4221 = 65\n \n #while traci.simulation.getMinExpectedNumber() > 0:\n while step < 600:\n traci.simulationStep()\n if step == 0:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",6)\n if step == 6:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 10:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 12:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", A_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",75)\n if step == 87:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 91:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 93:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",15)\n if step == 108:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 112:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 114:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", C_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",12)\n if step == 126:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 130:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 132:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",32)\n if step == 164:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 168:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 170:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", A_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",50)\n if step == 220:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 224:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 226:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",15)\n if step == 241:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 245:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 247:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", C_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",14)\n if step == 261:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 265:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 267:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",13)\n if step == 280:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 284:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 286:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", A_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",74)\n if step == 360:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 364:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 366:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",15)\n if step == 381:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 385:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 387:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", C_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",10)\n if step == 397:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 401:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 403:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",16)\n if step == 419:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 423:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 425:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", A_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",74)\n if step == 499:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 503:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 505:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",15)\n if step == 520:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 524:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 526:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", C_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",8)\n if step == 534:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 538:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 540:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",16)\n if step == 556:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 560:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 562:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", A_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",38)\n if step == 0:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", A_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 6)\n if step == 6:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 10:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 12:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", D_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 20)\n if step == 32:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 36:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 38:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", E_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 5)\n if step == 43:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 47:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 49:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", F_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 31)\n if step == 80:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 84:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 86:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", G_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 12)\n if step == 98:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 102:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 104:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", A_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 29)\n if step == 133:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 137:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 139:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", D_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 20)\n if step == 159:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 163:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 165:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", E_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 5)\n if step == 170:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 174:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 176:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", F_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 38)\n if step == 214:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 218:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 220:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", G_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 13)\n if step == 233:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 237:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 239:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", A_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 34)\n if step == 273:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 277:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 279:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", D_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 22)\n if step == 301:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 305:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 307:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", E_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 5)\n if step == 312:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 316:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 318:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", F_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 31)\n if step == 349:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 353:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 355:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", G_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 18)\n if step == 373:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 377:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 379:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", A_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 36)\n if step == 415:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 419:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 421:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", D_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 19)\n if step == 440:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 444:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 446:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", E_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 5)\n if step == 451:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 455:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 457:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", F_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 29)\n if step == 486:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 490:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 492:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", G_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 18)\n if step == 510:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 514:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 516:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", A_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 41)\n if step == 557:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 561:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 563:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", D_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 19)\n if step == 582:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 586:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 588:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", E_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 8)\n if step == 596:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 0:\n traci.trafficlight.setPhase(\"gneJ41\", D_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 17)\n if step == 17:\n traci.trafficlight.setPhase(\"gneJ41\", DE1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 21:\n traci.trafficlight.setPhase(\"gneJ41\", DE2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 23:\n traci.trafficlight.setPhase(\"gneJ41\", E_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 9)\n if step == 32:\n traci.trafficlight.setPhase(\"gneJ41\", EA1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 36:\n traci.trafficlight.setPhase(\"gneJ41\", EA2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 38:\n traci.trafficlight.setPhase(\"gneJ41\", A_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 67)\n if step == 105:\n traci.trafficlight.setPhase(\"gneJ41\", AB1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 109:\n traci.trafficlight.setPhase(\"gneJ41\", AB2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 111:\n traci.trafficlight.setPhase(\"gneJ41\", B_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 19)\n if step == 130:\n traci.trafficlight.setPhase(\"gneJ41\", BD1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 134:\n traci.trafficlight.setPhase(\"gneJ41\", BD2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 136:\n traci.trafficlight.setPhase(\"gneJ41\", D_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 16)\n if step == 152:\n traci.trafficlight.setPhase(\"gneJ41\", DE1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 156:\n traci.trafficlight.setPhase(\"gneJ41\", DE2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 158:\n traci.trafficlight.setPhase(\"gneJ41\", E_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 11)\n if step == 169:\n traci.trafficlight.setPhase(\"gneJ41\", EA1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 173:\n traci.trafficlight.setPhase(\"gneJ41\", EA2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 175:\n traci.trafficlight.setPhase(\"gneJ41\", A_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 63)\n if step == 238:\n traci.trafficlight.setPhase(\"gneJ41\", AD1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 242:\n traci.trafficlight.setPhase(\"gneJ41\", AD2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 244:\n traci.trafficlight.setPhase(\"gneJ41\", D_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 13)\n if step == 257:\n traci.trafficlight.setPhase(\"gneJ41\", DE1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 261:\n traci.trafficlight.setPhase(\"gneJ41\", DE2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 263:\n traci.trafficlight.setPhase(\"gneJ41\", E_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 9)\n if step == 272:\n traci.trafficlight.setPhase(\"gneJ41\", EA1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 276:\n traci.trafficlight.setPhase(\"gneJ41\", EA2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 278:\n traci.trafficlight.setPhase(\"gneJ41\", A_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 95)\n if step == 373:\n traci.trafficlight.setPhase(\"gneJ41\", AB1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 377:\n traci.trafficlight.setPhase(\"gneJ41\", AB2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 379:\n traci.trafficlight.setPhase(\"gneJ41\", B_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 19)\n if step == 398:\n traci.trafficlight.setPhase(\"gneJ41\", BD1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 402:\n traci.trafficlight.setPhase(\"gneJ41\", BD2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 404:\n traci.trafficlight.setPhase(\"gneJ41\", D_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 24)\n if step == 428:\n traci.trafficlight.setPhase(\"gneJ41\", DE1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 432:\n traci.trafficlight.setPhase(\"gneJ41\", DE2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 434:\n traci.trafficlight.setPhase(\"gneJ41\", E_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 13)\n if step == 447:\n traci.trafficlight.setPhase(\"gneJ41\", EA1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 451:\n traci.trafficlight.setPhase(\"gneJ41\", EA2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 453:\n traci.trafficlight.setPhase(\"gneJ41\", A_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 56)\n if step == 509:\n traci.trafficlight.setPhase(\"gneJ41\", AB1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 513:\n traci.trafficlight.setPhase(\"gneJ41\", AB2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 515:\n traci.trafficlight.setPhase(\"gneJ41\", B_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 19)\n if step == 534:\n traci.trafficlight.setPhase(\"gneJ41\", BD1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 538:\n traci.trafficlight.setPhase(\"gneJ41\", BD2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 540:\n traci.trafficlight.setPhase(\"gneJ41\", D_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 22)\n if step == 562:\n traci.trafficlight.setPhase(\"gneJ41\", DE1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 566:\n traci.trafficlight.setPhase(\"gneJ41\", DE2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 568:\n traci.trafficlight.setPhase(\"gneJ41\", E_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 20)\n if step == 588:\n traci.trafficlight.setPhase(\"gneJ41\", EA1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 592:\n traci.trafficlight.setPhase(\"gneJ41\", EA2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 594:\n traci.trafficlight.setPhase(\"gneJ41\", A_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 6)\n if step == 0:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 9)\n if step == 9:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 13:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 15:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", F_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 19)\n if step == 34:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 38:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 40:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", A_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 20)\n if step == 60:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 64:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 66:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", D_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 24)\n if step == 90:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 94:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 96:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 9)\n if step == 105:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 109:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 111:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", F_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 19)\n if step == 130:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 134:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 136:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", A_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 30)\n if step == 166:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 170:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 172:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", D_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 28)\n if step == 200:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 204:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 206:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 19)\n if step == 225:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 229:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 231:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", F_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 8)\n if step == 239:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 243:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 245:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", A_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 32)\n if step == 277:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 281:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 283:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", D_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 27)\n if step == 310:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 314:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 316:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 15)\n if step == 331:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 335:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 337:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", F_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 14)\n if step == 351:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 355:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 357:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", A_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 22)\n if step == 379:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 383:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 385:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", D_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 24)\n if step == 409:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 413:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 415:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 11)\n if step == 426:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 430:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 432:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", F_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 14)\n if step == 446:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 450:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 452:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", A_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 30)\n if step == 482:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 486:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 488:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", D_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 26)\n if step == 514:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 518:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 520:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 8)\n if step == 528:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 532:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 534:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", F_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 18)\n if step == 552:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 556:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 558:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", A_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 19)\n if step == 577:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 581:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 583:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", D_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 9)\n if step == 592:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 596:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 598:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 2)\n\n step += 1\n\n traci.close()\n sys.stdout.flush()","def lr_experiment():\n\n print(\"LR_EXPERIMENT\\n\")\n\n # set the name of the experiment\n now = datetime.datetime.now()\n experiment_id = str(now.day) + \"_\" + str(now.month) + \"_\" + str(now.hour) + \".\" + str(now.minute)\n experiment_name = 'lr_' + str(experiment_id)\n\n # define if you want to use preprocessed data from file\n use_prep_data = False\n if use_prep_data:\n set_params(preproc_data_id='16_5_10.16.47')\n else:\n set_params(use_preproc_data=False)\n\n # define the changing parameter and its value\n changing_param_name = 'lr'\n changing_param_value = [0.00001, 0.00003, 0.0001, 0.0003, 0.001, 0.003, 0.01, 0.03, 0.1]\n\n # set constant parameters\n set_params(use_word_emb=1)\n set_params(epochs=20)\n\n # save constant parameters to a new \"experiment_..\" file\n save_constant_parameters(experiment_name, changing_param_name)\n\n # run experiment for every parameter value\n for value in changing_param_value:\n # update the changing parameter value\n set_params(lr = value)\n\n # update the model_id for this new model\n now = datetime.datetime.now()\n new_model_id = str(now.day) + \"_\" + str(now.month) + \"_\" + str(now.hour) + \".\" + str(now.minute) + \".\" + str(now.second)\n set_params(model_id = new_model_id)\n\n # evaluate the new model and save the results in the experiment file\n oneExperiment = Process(target=run_experiment, args=(experiment_name, new_model_id, changing_param_name, value,))\n oneExperiment.start()\n oneExperiment.join()\n\n if value == changing_param_value[0]:\n set_params(preproc_data_id=new_model_id)","def simulate_data(parameters=None, **kwargs):\n\n # merge default parameters with input parameters\n default_parameters = {\n\n # simulation parameters\n 'seed': 0, # seed for random number generator\n 'num_sites': 20, # number of binding sites\n 'binding_eff': .7, # binding efficiency\n\n # constants\n 'dt': 1, # time step\n 'gain': 20, # gain\n\n # variables\n 'states': None, # photostates\n 'num_flor': None, # number of fluorophores present\n 'mu_flor': 100, # brightness of fluorophore\n 'mu_back': 100, # brightness of background\n 'transitions': None, # photostate transition probability matrix\n\n # numbers\n 'num_rois': 1, # number of ROIs\n 'num_data': 2000, # number of time levels\n 'num_states': 3, # number of states\n\n }\n if parameters is None:\n parameters = {**default_parameters, **kwargs}\n else:\n parameters = {**default_parameters, **parameters, **kwargs}\n\n # set RNG seed\n np.random.seed(parameters['seed'])\n\n # extract parameters\n gain = parameters['gain']\n eff = parameters['binding_eff']\n mu_flor = parameters['mu_flor']\n mu_back = parameters['mu_back']\n pi = parameters['transitions']\n num_rois = parameters['num_rois']\n num_data = parameters['num_data']\n num_sites = parameters['num_sites']\n num_states = parameters['num_states']\n if pi is None:\n pi = np.ones((num_states + 1, num_states))\n # increase self transition\n pi[:-1, :] += 100 * np.eye(num_states)\n # photobleached only self transitions\n pi[-2, :-1] = 0\n # fluorophores cannot start photobleached\n pi[-1, -1] = 0\n # no blink to bleached transitions\n if num_states > 2:\n pi[0, -1] = 0\n for k in range(num_states + 1):\n pi[k, :] /= np.sum(pi[k, :])\n # create effective pi by allowing nonbinding flurorophores to start bleached\n pi[-1, :] = np.array([*((1 - eff) * pi[-1, :-1]), eff])\n parameters['transitions'] = pi\n if np.isscalar(mu_flor):\n if num_states == 2:\n mu_flor = np.array([1, 0]) * mu_flor\n else:\n mu_flor = np.array([0, 1, *np.random.rand(num_states - 3), 0]) * mu_flor\n parameters['mu_flor'] = mu_flor\n if np.isscalar(mu_back):\n mu_back = np.ones(num_rois) * mu_back\n parameters['mu_back'] = mu_back\n # make effective transition matrix\n pi_eff = np.zeros((num_states + 1, num_states))\n pi_eff[:-1, :] = pi[:-1, :]\n pi_eff[-1, :] = pi[-1, :] * eff + (1 - eff) * np.array([0] * (num_states - 1) + [1])\n\n # simulate data\n \"\"\"\n We wrap the HMM sampler in a numba jit wrapper for faster\n sampling of states.\n \"\"\"\n @nb.njit(cache=True)\n def sample_HMM_nb(transition_matrix, num_data):\n\n # set variables\n num_states = transition_matrix.shape[1]\n states = np.zeros(num_data, dtype=np.int64)\n\n # normalize the transition matrix\n wp = transition_matrix.copy()\n for k in range(num_states + 1):\n wp[k, :] = np.cumsum(wp[k, :])\n\n # sample initial state\n states[0] = np.searchsorted(wp[-1, :], np.random.rand())\n for n in range(1, num_data):\n # sample each following state from the row of the transition matrix corresponding to the previous state\n states[n] = np.searchsorted(wp[states[n - 1], :], np.random.rand())\n\n return states\n\n data = np.zeros((num_rois, num_data))\n states = np.zeros((num_rois, num_sites, num_data), dtype=int)\n num_flor = np.zeros(num_rois, dtype=int)\n for r in range(num_rois):\n # find the state of each fluorophore at each time\n for l in range(num_sites):\n # sample whether or not the fluorophore is bound\n while True:\n # sample states within a while loop to ensure that they all end up photobleached\n states[r, l, :] = sample_HMM_nb(pi_eff, num_data)\n if states[r, l, -1] == num_states - 1:\n break\n\n # total number of fluorophores is the number that do not start photobleached\n num_flor[r] = np.sum(states[r, :, 0] < num_states - 1)\n\n # sample the brightness data\n brightness = mu_flor @ states_to_pops(states[r, :, :], num_states) + mu_back[r]\n data[r, :] = stats.gamma.rvs(a=brightness, scale=gain)\n\n parameters['states'] = states\n parameters['num_flor'] = num_flor\n\n return data, parameters","def run(n_trials, params):\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\n a.set_Qtable(params['epsilon'], params['gamma'], params['learn_rate'], params['lrconst'],\n params['learn_rate_decay'], params['epsconst'], params['epsilon_decay'])\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.000001, display=False) # create simulator (uses pygame when display=True, if available)\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\n\n sim.run(n_trials=n_trials) # run for a specified number of trials\n\n return a.reached_destination","def setUp(self):\n np.random.seed(1337)\n self.w0_val = np.random.rand(2, 3)\n self.w1_val = np.random.rand(3, 5)\n self.w2_val = np.random.rand(10, 3)\n self.w3_val = np.random.rand(10, 5)\n self.w4_val = np.random.rand(10, 5)\n self.w5_val = np.linspace(-7, 7, 200)\n # self.w5_val = np.random.rand(15)\n self.w6_val = np.random.rand(2, 3, 5, 7)\n\n self.w3_val /= np.expand_dims(np.sum(self.w3_val, axis=1), axis=1) # w3 is a probability distribution\n\n self.tf_w0 = tf.constant(self.w0_val, dtype=tf.float64)\n self.tf_w1 = tf.constant(self.w1_val, dtype=tf.float64)\n self.tf_w2 = tf.constant(self.w2_val, dtype=tf.float64)\n self.tf_w3 = tf.constant(self.w3_val, dtype=tf.float64)\n self.tf_w4 = tf.constant(self.w4_val, dtype=tf.float64)\n self.tf_w5 = tf.constant(self.w5_val, dtype=tf.float64)\n self.tf_w6 = tf.constant(self.w6_val, dtype=tf.float64)\n\n self.my_w0 = ad.Variable(self.w0_val, name=\"w0_val\")\n self.my_w1 = ad.Variable(self.w1_val, name=\"w1_val\")\n self.my_w2 = ad.Variable(self.w2_val, name=\"w2_val\")\n self.my_w3 = ad.Variable(self.w3_val, name=\"w3_val\")\n self.my_w4 = ad.Variable(self.w4_val, name=\"w4_val\")\n self.my_w5 = ad.Variable(self.w5_val, name=\"w5_val\")\n self.my_w6 = ad.Variable(self.w6_val, name=\"w6_val\")","def SC_generation(hourly_radiation, prop_observers, number_groups, weather_data, g, Sz, Az, ha, Tin_C, height,\n panel_properties, latitude):\n\n\n n0 = panel_properties['n0']\n c1 = panel_properties['c1']\n c2 = panel_properties['c2']\n mB0_r = panel_properties['mB0_r']\n mB_max_r = panel_properties['mB_max_r']\n mB_min_r = panel_properties['mB_min_r']\n C_eff = panel_properties['C_eff']\n t_max = panel_properties['t_max']\n IAM_d = panel_properties['IAM_d']\n Aratio = panel_properties['aperture_area_ratio']\n Apanel = panel_properties['module_area']\n dP1 = panel_properties['dP1']\n dP2 = panel_properties['dP2']\n dP3 = panel_properties['dP3']\n dP4 = panel_properties['dP4']\n Cp_fluid_JperkgK = panel_properties['Cp_fluid'] # J/kgK\n\n # create lists to store results\n list_results = [None] * number_groups\n list_areas_groups = [None] * number_groups\n Sum_mcp_kWperC = np.zeros(8760)\n Sum_qout_kWh = np.zeros(8760)\n Sum_Eaux_kWh = np.zeros(8760)\n Sum_qloss = np.zeros(8760)\n Sum_radiation_kWh = np.zeros(8760)\n\n Tin_array_C = np.zeros(8760) + Tin_C\n aperature_area_per_module = Aratio * Apanel\n total_area_module = prop_observers['total_area_module'].sum() # total area for panel installation\n\n # calculate equivalent length of pipes\n lv = panel_properties['module_length'] # module length\n number_modules = round(total_area_module/Apanel) # this is an estimation\n l_ext_mperm2 = (2 * lv * number_modules/ (total_area_module * Aratio)) # pipe length within the collectors\n l_int_mperm2 = 2 * height / (total_area_module * Aratio) # pipe length from building substation to roof top collectors\n Leq_mperm2 = l_int_mperm2 + l_ext_mperm2 # in m/m2 aperture\n\n if panel_properties['type'] == 'ET': # for evacuated tubes\n Nseg = 100 # default number of subsdivisions for the calculation\n else:\n Nseg = 10 # default number of subsdivisions for the calculation\n\n for group in range(number_groups):\n # load panel angles from group\n teta_z = prop_observers.loc[group, 'surface_azimuth'] # azimuth of panels of group\n area_per_group = prop_observers.loc[group, 'total_area_module']\n tilt_angle_deg = prop_observers.loc[group, 'tilt'] # tilt angle of panels\n\n # create dataframe with irradiation from group\n\n radiation_Wh = pd.DataFrame({'I_sol': hourly_radiation[group]})\n radiation_Wh['I_diffuse'] = weather_data.ratio_diffhout * radiation_Wh.I_sol # calculate diffuse radiation\n radiation_Wh['I_direct'] = radiation_Wh['I_sol'] - radiation_Wh['I_diffuse'] # calculate direct radiation\n radiation_Wh.fillna(0, inplace=True) # set nan to zero\n\n # calculate incidence angle modifier for beam radiation\n IAM_b = calc_IAM_beam_SC(Az, g, ha, teta_z, tilt_angle_deg, panel_properties['type'], Sz, latitude)\n\n # calculate heat production from a solar collector of each group\n list_results[group] = calc_SC_module(tilt_angle_deg, IAM_b, IAM_d, radiation_Wh.I_direct,\n radiation_Wh.I_diffuse, weather_data.drybulb_C, n0,\n c1, c2, mB0_r, mB_max_r, mB_min_r, C_eff, t_max,\n aperature_area_per_module, dP1, dP2, dP3, dP4,\n Cp_fluid_JperkgK, Tin_C, Leq_mperm2, l_ext_mperm2,\n l_int_mperm2, Nseg)\n\n\n # multiplying the results with the number of panels in each group and write to list\n number_modules_per_group = area_per_group / Apanel\n list_areas_groups[group] = area_per_group\n radiation_array = hourly_radiation[group] * list_areas_groups[group] / 1000 # kWh\n Sum_qout_kWh = Sum_qout_kWh + list_results[group][1] * number_modules_per_group\n Sum_Eaux_kWh = Sum_Eaux_kWh + list_results[group][2] * number_modules_per_group\n Sum_qloss = Sum_qloss + list_results[group][0] * number_modules_per_group\n Sum_mcp_kWperC = Sum_mcp_kWperC + list_results[group][5] * number_modules_per_group\n Sum_radiation_kWh = Sum_radiation_kWh + radiation_Wh['I_sol']*area_per_group/1000\n\n Tout_group_C = (Sum_qout_kWh / Sum_mcp_kWperC) + Tin_C # in C assuming all collectors are connected in parallel\n\n Final = pd.DataFrame(\n {'Q_SC_gen_kWh': Sum_qout_kWh, 'T_SC_sup_C': Tin_array_C, 'T_SC_re_C': Tout_group_C, 'mcp_SC_kWperC': Sum_mcp_kWperC, 'Eaux_SC_kWh': Sum_Eaux_kWh,\n 'Q_SC_l_kWh': Sum_qloss, 'Area_SC_m2': sum(list_areas_groups), 'radiation_kWh': Sum_radiation_kWh}, index=range(8760))\n\n return list_results, Final","def SampleConditions(testcase, delta):\n if testcase == 0:\n t0 = delta + 1\n t1 = 0\n elif testcase == 1:\n t0 = 0\n t1 = delta + 1\n else:\n t0 = delta + 1\n t1 = delta + 1\n\n # Sample a test case\n passed = False\n while not passed:\n x0 = _xm * rng.uniform(-1, 1)\n x1 = _xm * rng.uniform(-1, 1)\n vm = _vm * rng.uniform(0, 1)\n am = _am * rng.uniform(0, 1)\n v0 = vm * rng.uniform(-1, 1)\n v1 = vm * rng.uniform(-1, 1)\n\n curve1 = interpolator.Compute1DTrajectory(x0, x1, v0, v1, vm, am)\n if not (len(curve1) == 2):\n continue\n t0 = curve1[0].duration\n t1 = curve1[1].duration\n if testcase == 0:\n passed = t0 < delta and t1 >= delta\n elif testcase == 1:\n passed = t0 >= delta and t1 < delta\n else:\n passed = t0 < delta and t1 < delta\n \n return x0, x1, v0, v1, vm, am","def test_three_arms_one_unsampled_arm(self):\n self._test_three_arms_one_unsampled_arm()","def run_example():\n\n print(\"\\nRun a example with parameters of the coding challenge from get in it and BWI.\\n\")\n print(\"Predefined parameters:\")\n print(\"Truck count: 2\")\n print(\"Truck 1 weight: 1100kg\")\n print(\"Truck 2 weight: 1100kg\")\n print(\"Driver 1 weight: 72.4kg\")\n print(\"Driver 2 weight: 85.7kg\")\n print(\"Items from file ./items.csv\")\n print(\"---------------------------------------------------------------\")\n\n # trucks count and max loading weight\n trucks_max_loadings = [1100, 1100]\n # can be 'kg' or 'gram'\n trucks_measuring_unit = 'kg'\n # drivers weight\n drivers_weights = [72.4, 85.7]\n # can be 'kg' or 'gram'\n drivers_measuring_unit = 'kg'\n\n # path to the csv file with all items\n items_csv_file = './items.csv'\n # if False, the column names in the csv must be name, max_count, weight, utility\n # if True, the column names from csv will be changed by columns_mapping below\n column_mapping_required = True\n # the csv column names mapped to name, max_count, weight and utility\n # \"<required column>\" : \"\"<mapping csv column>\"\"\n columns_mapping = {\n 'name': 'name',\n 'max_count': 'required count',\n 'weight': 'weight in gram',\n 'utility': 'utility'\n }\n # delimiter of the csv columns\n csv_delimiter = ';'\n\n # start example run\n delivery_optimization(trucks_max_loadings,\n drivers_weights,\n items_csv_file,\n trucks_measuring_unit,\n drivers_measuring_unit,\n csv_delimiter,\n column_mapping_required,\n columns_mapping)","def setUpClass(cls):\n cls.c = Simulation(logging_level=logging.ERROR)\n cls.c.set_simulation_parameters(\n seed=1, task=39, output_directory=\"output\", min_speciation_rate=1, deme=100, spatial=False\n )\n cls.c.run()\n cls.c2 = Simulation(logging_level=logging.ERROR)\n cls.c2.set_simulation_parameters(\n seed=1, task=40, output_directory=\"output\", min_speciation_rate=0.5, deme=100, spatial=False\n )\n cls.c2.run()\n cls.c3 = Simulation(logging_level=logging.ERROR)\n cls.c3.set_simulation_parameters(\n seed=1,\n task=41,\n output_directory=\"output\",\n min_speciation_rate=0.5,\n deme=100,\n spatial=False,\n protracted=True,\n min_speciation_gen=0,\n max_speciation_gen=1,\n )\n cls.c3.run()\n cls.c4 = Simulation(logging_level=logging.ERROR)\n cls.c4.set_simulation_parameters(\n seed=1,\n task=42,\n output_directory=\"output\",\n min_speciation_rate=0.5,\n deme=100,\n spatial=False,\n protracted=True,\n min_speciation_gen=100,\n max_speciation_gen=1000,\n )\n cls.c4.run()","def __init__(self,\n debug=False,\n urdf_version=None,\n control_time_step=0.005,\n action_repeat=5,\n control_latency=0,\n pd_latency=0,\n on_rack=False,\n motor_kp=1.0,\n motor_kd=0.02,\n render=False,\n num_steps_to_log=2000,\n env_randomizer=None,\n log_path=None,\n signal_type='ik',\n target_position=None,\n backwards=None,\n gait_type='trot',\n terrain_type='plane',\n terrain_id='plane',\n mark='base',\n ):\n self.phase = 0\n\n self._gait_type = gait_type \n # for observation space bounding \n self.max_speed = 1.0\n self.min_speed = 0.5 # change back to 0.2 for OLD TD3 model evaluation\n \n self.min_side_speed = 0.0\n self.max_side_speed = 0.0\n\n self.speed = np.random.uniform(self.min_speed, self.max_speed)\n self.side_speed = np.random.uniform(self.min_side_speed, self.max_side_speed)\n self.speed_des = [self.speed, self.side_speed]\n\n # Initialization variables for periodic reward sum composition\n self.theta_FL = phase_constants.PHASE_VALS[self._gait_type]['front_left']\n self.theta_FR = phase_constants.PHASE_VALS[self._gait_type]['front_right']\n self.theta_RL = phase_constants.PHASE_VALS[self._gait_type]['rear_left']\n self.theta_RR = phase_constants.PHASE_VALS[self._gait_type]['rear_right']\n\n self.min_swing_ratio = 0.6\n self.max_swing_ratio = 0.8\n self.ratio = np.random.uniform(self.min_swing_ratio, self.max_swing_ratio)\n\n super(rexPeriodicRewardEnv,\n self).__init__(urdf_version=urdf_version,\n accurate_motor_model_enabled=True,\n motor_overheat_protection=False,\n motor_kp=motor_kp,\n motor_kd=motor_kd,\n remove_default_joint_damping=False,\n control_latency=control_latency,\n pd_latency=pd_latency,\n on_rack=on_rack,\n render=render,\n num_steps_to_log=num_steps_to_log,\n env_randomizer=env_randomizer,\n log_path=log_path,\n control_time_step=control_time_step,\n action_repeat=action_repeat,\n target_position=target_position,\n signal_type=signal_type,\n backwards=backwards,\n debug=debug,\n terrain_id=terrain_id,\n terrain_type=terrain_type,\n mark=mark,\n ratio=self.ratio,\n forward_reward_cap=5\n )\n\n self.height_des = 0.206 # this is init standing height for rex\n\n self.cycle_complete = 0\n self.cycle_len = 1000 # this is L\n \n # vonmises variables\n self.kappa = phase_constants.VON_MISES_KAPPA\n\n rex_joints = p.getNumJoints(bodyUniqueId=self.rex.quadruped)\n link_name_to_ID = {}\n for i in range(rex_joints):\n name = p.getJointInfo(self.rex.quadruped, i)[12].decode('UTF-8')\n link_name_to_ID[name] = i\n\n self.link_name_to_ID = link_name_to_ID\n self.toe_pos_last = { 'front_left_toe_pos' : p.getLinkState(self.rex.quadruped, self.link_name_to_ID['front_left_toe_link'])[0],\n 'front_right_toe_pos' : p.getLinkState(self.rex.quadruped, self.link_name_to_ID['front_right_toe_link'])[0],\n 'rear_left_toe_pos' : p.getLinkState(self.rex.quadruped, self.link_name_to_ID['rear_left_toe_link'])[0],\n 'rear_right_toe_pos' : p.getLinkState(self.rex.quadruped, self.link_name_to_ID['rear_right_toe_link'])[0]\n\n } \n\n print('Using Periodic Reward Composition Rex Environment')","def sampling(args):\n t_mean, t_log_var = args\n # YOUR CODE HERE\n epsilon = K.random_normal(t_mean.shape)\n z = epsilon * K.exp(0.5 * t_log_var) + t_mean\n return z","def test_sample(system_generator):\n\n name, test = system_generator()\n print(name)\n\n w_F, w_R, N_k = test.sample([10, 8], mode=\"wFwR\")\n w_F, w_R, N_k = test.sample([1, 1], mode=\"wFwR\")\n w_F, w_R, N_k = test.sample([10, 0], mode=\"wFwR\")\n w_F, w_R, N_k = test.sample([0, 5], mode=\"wFwR\")","def predictions(files, species, files2, species2, nbC=1000, nbC2=None, gating='line', showgat=False,\r\n predAn='prediction',\r\n predtype='neur', ratio=1 / 7.0, repeat=1, average=True,\r\n doubt=0, random_state=None, save='save', fc='Accuri',\r\n param=None,channels = [], dicChannels = {},cwd='Results/' ):\r\n # todo default channel name and dicChannels\r\n if param is None:\r\n param = ['FL3-A', 'FL1-A', 'FSC-A']\r\n # Create result directory\r\n if not os.path.exists(cwd):\r\n os. mkdir('Results/')\r\n cwd = 'Results/'\r\n now = datetime.now()\r\n dirName = now.strftime(\"%Y%m%d-%H_%M_%S/\")\r\n os.mkdir(cwd + dirName)\r\n cwd = cwd + dirName\r\n # Create file option with all parameter given in the input\r\n f.fileOption(cwd, files, species, files2, species2, nbC, nbC2, gating, predAn, predtype, ratio, repeat, average,\r\n doubt,channels=channels,dicChannels=dicChannels,fc=fc)\r\n\r\n if showgat and save is not None:\r\n showgat = save\r\n else:\r\n showgat = None\r\n # Gate Data: according to method\r\n Data = []\r\n target2 = []\r\n refArrays = f.importFile(files, gating=gating, save=showgat, fc=fc, cwd=cwd,channels=channels,dicChannels=dicChannels)\r\n if refArrays == [] :\r\n return 'None'\r\n predArrays = f.importFile(files2, gating=gating, save=showgat, fc=fc, cwd=cwd,channels=channels,dicChannels=dicChannels)\r\n if predArrays == []:\r\n return 'None'\r\n\r\n blankArrays = []\r\n blk =['BLANK', 'Blank', 'blank']\r\n for i in range(len(species)):\r\n if species[i] in blk:\r\n blankArrays.append(refArrays[i])\r\n if gating == 'machine':\r\n refArrays,species = f.machineGating(refArrays, species, refArrays, species,cwd=cwd,show=showgat,name='ref', predType=predtype, param=param)\r\n if predAn == 'analysis':\r\n predArrays, species2 = f.machineGating(refArrays+blankArrays, species+['blank']*len(blankArrays), predArrays, species2,cwd = cwd,show = showgat, name='pred',param=param,predType=predtype)\r\n # todo check if 10 and 5000 is not too much!! here we add the former blank from the new gating output\r\n else :\r\n refArrays = refArrays + blankArrays\r\n species = species+['blank']*len(blankArrays)\r\n # Selection of data for prediction and analysis\r\n if predAn == 'prediction':\r\n for anArray in predArrays:\r\n data2, atarget2, species2 = f.treat([anArray], species2, None, mode=predAn)\r\n Data.append(data2)\r\n target2.append(atarget2)\r\n else:\r\n data2, target2, species2 = f.treat(predArrays, species2, nbC2, mode=predAn)\r\n Data.append(data2)#TODO replace by data2 after testing feature impotance\r\n target2 = [target2]\r\n\r\n # treat data\r\n statisticsLearn = []\r\n statisticsPred = []\r\n predict_lbls = []\r\n confusionM = []\r\n confusionM2 = []\r\n acc1 = []\r\n F1 = []\r\n oldSpecies = species.copy()\r\n for i in range(repeat):\r\n stat, stat2, predict_lbl, target, conf, conf2, species, \\\r\n data2 = predictionMultiple(files, refArrays, oldSpecies, files2, Data, target2, nbC, repeat=i,\r\n param=param, predAn=predAn, predType=predtype, ratio=ratio,\r\n random_state=random_state, save=save, cwd=cwd, average=average)\r\n statisticsLearn.append(stat)\r\n statisticsPred.append(stat2)\r\n #f.save_prediction(predict_lbl,i,cwd=cwd)\r\n predict_lbls.append(predict_lbl)\r\n confusionM.append(conf)\r\n confusionM2.append(conf2)\r\n acc1.append(stat.loc['MEAN', 'ACC'])\r\n F1.append(stat.loc['MEAN', 'F1'])\r\n\r\n f.assessmentValue(statisticsLearn, species, cwd, [], 'align')\r\n confM = f.averageConfM(confusionM)\r\n confM2 = f.averageConfM(confusionM2)\r\n f.cmFile(confM, species, cwd, 'Reference CM')\r\n if save is not None:\r\n f.plotConfusionMatrix(confM, species, save, cwd, normalize=True, name=' average CM for reference',\r\n predAn='training')\r\n target = np.array(target)\r\n predict_lbls = np.array(predict_lbls)\r\n conf2 = []\r\n acc2 = []\r\n f12 = []\r\n for i in range(len(statisticsPred[0])):\r\n PredStat = []\r\n for j in range(repeat):\r\n PredStat.append(statisticsPred[j][i])\r\n if average:\r\n predict_lbl = f.bestPred(list(predict_lbls[:, i]), doubt=doubt) # average of 10\r\n if len(predict_lbl) > 0:\r\n conf2.append(confusion_matrix(target[i], predict_lbl, labels=species + ['unknown']))\r\n statistics2 = f.statAnalysis(list(predict_lbl), list(target[i]), species)\r\n acc2.append(statistics2.loc['MEAN', 'ACC'])\r\n f12.append(statistics2.loc['MEAN', 'F1'])\r\n\r\n if predAn == 'prediction':\r\n if save is not None and save != 'None':\r\n f.graph3d(data2[i], predict_lbl, list(target[i]), species + ['unknown'], param, statistics2, save,\r\n cwd, i + 1, name=files2[i].split('/')[-1][:-4], predtype=predAn)\r\n f.exportPrediction(list(predict_lbl), [files2[i].split('/')[-1][:-4]], cwd, 'predict',\r\n i + 1, 'AVERAGE', repeat)\r\n else:\r\n f.exportStatistics(statistics2, [n.split('/')[-1][:-4] for n in files2], cwd,\r\n 'predict')\r\n f.assessmentValue([statistics2], species, cwd, [files2[i].split('/')[-1][:-4]], 'predict')\r\n f.exportPrediction(list(predict_lbl), [files2[i].split('/')[-1][:-4]], cwd, 'predict',\r\n i + 1, 'AVERAGE', repeat)\r\n\r\n f.cmFile(conf2[i], species+['unknown'], cwd, 'Prediction CM')\r\n if save is not None:\r\n f.graph3d(data2[i], predict_lbl, target[i], species + ['unknown'], param, statistics2, save,\r\n cwd, i + 1, name=str(len(species)) + ' species', predtype=predAn)\r\n f.plotConfusionMatrix(conf2[i], species + ['unknown'], save, cwd, normalize=True,\r\n name='CM with ' + str(len(species)) + ' species', predAn='analysis')\r\n\r\n else:\r\n conf2 = []\r\n else:\r\n for j in range(repeat):\r\n if predAn == 'prediction':\r\n f.exportPrediction(list(predict_lbls[j][i]), [files2[i].split('/')[-1][:-4]], cwd,\r\n 'predict', i, 'SINGLE')\r\n else:\r\n f.exportStatistics(PredStat[j], [files2[i].split('/')[-1][:-4]], cwd, 'predict')\r\n f.exportPrediction(list(predict_lbls[j][i]), [files2[i].split('/')[-1][:-4]], cwd,\r\n 'predict', i, 'SINGLE')\r\n if predAn == 'analysis':\r\n f.assessmentValue(PredStat, species, cwd, [files2[i].split('/')[-1][:-4]], 'predict')\r\n f.cmFile(confM2, species+['unknown'], cwd, 'Prediction CM')\r\n if save is not None:\r\n f.plotConfusionMatrix(confM2, species + ['unknown'], save, cwd, normalize=True,\r\n name='average confusion matrix for Tool analysis', predAn='analysis')\r\n\r\n return os.getcwd().replace('\\\\', '/') + '/' + cwd","def vibratingAssessStrategyGlobal(km, dur_train, duration_val_matches, delta, xgb_params, nb_players, nb_tournaments,\r\n xtrain, data):\r\n confTest1 = assessStrategyGlobal(km, dur_train, duration_val_matches, delta, xgb_params, nb_players, nb_tournaments,\r\n xtrain, data, \"1\")\r\n confTest2 = assessStrategyGlobal(km, dur_train - 10, duration_val_matches, delta, xgb_params, nb_players,\r\n nb_tournaments, xtrain, data, \"2\")\r\n confTest3 = assessStrategyGlobal(km, dur_train + 10, duration_val_matches, delta, xgb_params, nb_players,\r\n nb_tournaments, xtrain, data, \"3\")\r\n confTest4 = assessStrategyGlobal(km, dur_train - 30, duration_val_matches, delta, xgb_params, nb_players,\r\n nb_tournaments, xtrain, data, \"4\")\r\n confTest5 = assessStrategyGlobal(km, dur_train + 30, duration_val_matches, delta, xgb_params, nb_players,\r\n nb_tournaments, xtrain, data, \"5\")\r\n confTest6 = assessStrategyGlobal(km, dur_train - 45, duration_val_matches, delta, xgb_params, nb_players,\r\n nb_tournaments, xtrain, data, \"6\")\r\n confTest7 = assessStrategyGlobal(km, dur_train + 45, duration_val_matches, delta, xgb_params, nb_players,\r\n nb_tournaments, xtrain, data, \"7\")\r\n if (type(confTest1) != int) & (type(confTest2) != int) & (type(confTest3) != int) & (type(confTest4) != int) & (\r\n type(confTest5) != int):\r\n c = confTest1.merge(confTest2, on=[\"match\", \"PSW\"])\r\n c = c.merge(confTest3, on=[\"match\", \"PSW\"])\r\n c = c.merge(confTest4, on=[\"match\", \"PSW\"])\r\n c = c.merge(confTest5, on=[\"match\", \"PSW\"])\r\n c = c.merge(confTest6, on=[\"match\", \"PSW\"])\r\n c = c.merge(confTest7, on=[\"match\", \"PSW\"])\r\n c = pd.Series(list(zip(c.win1, c.win2, c.win3, c.win4, c.win5,\r\n c.win6, c.win7,\r\n c.confidence1, c.confidence2, c.confidence3,\r\n c.confidence4, c.confidence5,\r\n c.confidence6, c.confidence7)))\r\n c = pd.DataFrame.from_records(list(c.apply(mer)))\r\n conf = pd.concat([confTest1[[\"match\", \"PSW\"]], c], 1)\r\n conf.columns = [\"match\", \"PSW\", \"win0\", \"confidence0\"]\r\n else:\r\n conf = 0\r\n return conf","def refugia_adj_5_simsplit_4epochs_iter2 (params, ns):\n #28 parameters \n nu1a, nu2a, nu3a, nu1b, nu2b, nu3b, nu1c, nu2c, nu3c, nu1d, nu2d, nu3d, m1_12, m1_13, m1_21, m1_23, m1_31, m1_32, m3_12, m3_13, m3_21, m3_23, m3_31, m3_32, T1, T2, T3, T4 = params\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1] + ns[2])\n fs = moments.Spectrum(sts)\n fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1] + ns[2]) \n fs = moments.Manips.split_2D_to_3D_2(fs, ns[1], ns[2])\n ## Population function and migration matrix for T1 (to reflect sum effect of all previous glacial-interglacial cycles)\n nu_T1 = [nu1a, nu2a, nu3a]\n mig1 = numpy.array([[0, m1_12, m1_13],[m1_21, 0, m1_23], [m1_31, m1_32, 0]]) \n fs.integrate(nu_T1, T1, m=mig1)\n ## Population function and migration matrix for T2 (to reflect period of isolation during last glacial)\n nu_T2 = [nu1b, nu2b, nu3b]\n fs.integrate(nu_T2, T2)\n ## Population function and migration matrix for T3 (to reflect inter-glacial expansion)\n nu_T3 = [nu1c, nu2c, nu3c]\n mig3 = numpy.array([[0, m3_12, m3_13],[m3_21, 0, m3_23], [m3_31, m3_32, 0]]) \n fs.integrate(nu_T3, T3, m=mig3)\n ## Population function and migration matrix for T3 (bottleneck to capture single population representation of lineage)\n nu_T4 = [nu1d, nu2d, nu3d]\n fs.integrate(nu_T4, T4) \n return fs","def split_full_3epochs_iter3 (params, ns):\n #19 parameters \n nu1a, nuA, nu1b, nu2b, nu3b, nu1c, nu2c, nu3c, m1_12, m1_21, m2_12, m2_13, m2_21, m2_23, m2_31, m2_32, T1, T2, T3 = params\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1] + ns[2])\n fs = moments.Spectrum(sts)\n fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1] + ns[2])\n ## Population function and migration matrix for T1\n nu_T1 = [nu1a, nuA]\n mig1 = numpy.array([[0, m1_12],[m1_21, 0]])\n fs.integrate(nu_T1, T1, m=mig1)\n fs = moments.Manips.split_2D_to_3D_2(fs, ns[1], ns[2])\n ## Population function and migration matrix for T2\n nu_T2 = [nu1b, nu2b, nu3b]\n mig2 = numpy.array([[0, m2_12, m2_13],[m2_21, 0, m2_23], [m2_31, m2_32, 0]]) \n fs.integrate(nu_T2, T2, m=mig2)\n ## Population function and migration matrix for T3\n nu_T3 = [nu1c, nu2c, nu3c]\n fs.integrate(nu_T3, T3) \n return fs","def tgt_samples(self, params):\r\n def save_json(save_path, file_id, samples):\r\n init_logger()\r\n for i, sample in enumerate(samples):\r\n save_ = os.path.join(save_path, \"{:s}_{:d}.json\".format(file_id, i))\r\n with open(save_, 'w') as file:\r\n json.dump(sample, file)\r\n logger.info(\"{:s} saved at {:s}\".format(save_, save_path))\r\n\r\n\r\n json_file, save_path = params\r\n init_logger()\r\n _, tgt = self.load_json(json_file)\r\n\r\n file_id = json_file.split(\"/\")[-1].split(\".\")[0]\r\n if len(tgt) >= self.args.min_sents_num and len(tgt) <= self.args.max_sents_num:\r\n tgt_ = list(tgt)\r\n random.seed(66)\r\n random.shuffle(tgt_)\r\n\r\n # make sentence pair and write in a single file\r\n positive_sents = tgt\r\n positive_pairs = [(positive_sents[i], positive_sents[i+1]) for i in range(len(positive_sents)-1)]\r\n\r\n negative_sents = tgt_\r\n negative_pairs = [(negative_sents[i], negative_sents[i+1]) for i in range(len(negative_sents)-1)]\r\n\r\n positive_samples = [{\"tgt\": pair, \"coherence\": 0} for pair in positive_pairs] # 0 represents coherent\r\n negative_samples = [{\"tgt\": pair, \"coherence\": 1} for pair in negative_pairs] # 1 represents incoherent\r\n\r\n save_json(save_path, file_id, positive_samples)\r\n save_json(save_path, file_id+\"_r\", negative_samples)","def gen_params(self) -> Dict:\n param_dict: Dict = {}\n\n gX_name: List[str] = ['g_leak', 'g_nav', 'g_kvhh', 'g_kva', 'g_kvsi', \n 'g_cav', 'g_kca', 'g_nap', 'g_kir']\n gX_name: List[str] = list(itertools.compress(gX_name, list(self.channel_bool.values())[:9]))\n gX_log: np.ndarray = 4 * np.random.rand(len(gX_name)) - 2 # from -2 to 2\n gX: np.ndarray = (10 * np.ones(len(gX_name))) ** gX_log # 0.01 ~ 100\n gX_itr: Iterator = zip(gX_name, gX)\n\n gR_name: List[str] = ['g_ampar', 'g_nmdar', 'g_gabar']\n gR_name: List[str] = list(itertools.compress(gR_name, list(self.channel_bool.values())[9:12]))\n gR_log: np.ndarray = 4 * np.random.rand(len(gR_name)) - 3 # from -3 to 1\n gR: np.ndarray = (10 * np.ones(len(gR_name))) ** gR_log # 0.001 ~ 10\n gR_itr: Iterator = zip(gR_name, gR)\n\n param_dict.update(gX_itr)\n param_dict.update(gR_itr)\n\n if self.channel_bool['ca']:\n tCa_log: float = 2 * np.random.rand(1) + 1 # from 1 to 3\n tCa: float = 10 ** tCa_log # 10 ~ 1000\n tCa_dict: Dict = {'t_ca': tCa}\n param_dict.update(tCa_dict)\n\n return param_dict","def one_experiment():\n\n # set the name of the experiment\n now = datetime.datetime.now()\n experiment_id = str(now.day) + \"_\" + str(now.month) + \"_\" + str(now.hour) + \".\" + str(now.minute)\n experiment_name = 'overfit_' + str(experiment_id)\n\n # define if you want to use preprocessed data from file\n use_prep_data = False\n if use_prep_data:\n set_params(preproc_data_id='16_5_10.16.47')\n\n # define the changing parameter and its value\n changing_param_name = 'class_weights'\n changing_param_value = [{0: 15, 1: 85}]\n # {0:15, 1:85}]#, {0:4, 1:100}, {0:3, 1:100}, {0:2, 1:100}, {0:1, 1:100}] #[{0:1, 1:1}, {0:15, 1:85}]#\n\n features_to_use = ['user', 'countries', 'session', 'format', 'token']\n # set constant parameters\n set_params(use_word_emb=1)\n set_params(epochs=40)\n set_params(features_to_use=features_to_use)\n\n # save constant parameters to a new \"experiment_..\" filgithx+P@2ub\n save_constant_parameters(experiment_name, changing_param_name)\n\n # run experiment for every parameter value\n for value in changing_param_value:\n process = psutil.Process(os.getpid())\n print(\"-----MEMORY before starting experiment ------\", int(process.memory_info().rss/(8*10**3)), \"KB\")\n\n # update the parameter value\n set_params(class_weights_1=value)\n\n # update the model_id for this new model\n now = datetime.datetime.now()\n new_model_id = str(now.day) + \"_\" + str(now.month) + \"_\" + str(now.hour) + \".\" + str(now.minute) + \".\" + str(now.second)\n\n set_params(model_id=new_model_id)\n\n # evaluate the new model and save the results in the experiment file\n oneExperiment = Process(target=run_experiment, args=(experiment_name,\n new_model_id, changing_param_name, value,))\n oneExperiment.start()\n oneExperiment.join()","def part_3(l, m, n):\n # initilize variables\n size = 50000\n samples, loops, R = size / 10, 20, None\n lrs = [1.5, 0.25, 0.03, 0.005, 0.001]\n alphas = [1.1, 1.01, 1.005, 1.0005, 1.0001]\n gammas = [2.0, 0.3, 0.04, 0.006, 0.001]\n \n # divide dataset: 10% - training, 10% - test.\n # y, x = [], []\n (y, x) = gen(l, m, n, 10, False)\n (y_train, x_train) = gen(l, m, n, 50000, True)\n (y_test, x_test) = gen(l, m, n, 10000, False)\n \n # build online learning algorithms\n perceptron = Perceptron(R, x_train, x_test, y_train, y_test, n, size, size)\n perceptron_margin = Perceptron_Margin(R, x, x_train, x_test, y, y_train, y_test, n, samples, size, loops, lrs)\n winnon = Winnon(R, x, x_train, x_test, y, y_train, y_test, n, samples, size, loops, alphas)\n winnon_margin = Winnon_Margin(R, x, x_train, x_test, y, y_train, y_test, n, samples, size, loops, alphas, gammas)\n adagrad = AdaGrad(R, x, x_train, x_test, y, y_train, y_test, n, samples, size, loops, lrs)","def main(_):\n configs.DEFINE_string(\"train_datafile\", None,\"Training file\")\n configs.DEFINE_string(\"optimizer\", 'gd', 'Optimizer to use gd, adam, adagrad, momentum')\n configs.DEFINE_float(\"lr_decay\",0.9, \"Learning rate decay\")\n configs.DEFINE_float(\"initial_learning_rate\",1.0,\"Initial learning rate\")\n configs.DEFINE_float(\"validation_size\",0.0,\"Size of validation set as %\")\n configs.DEFINE_float(\"passes\",1.0,\"Passes through day per epoch\")\n configs.DEFINE_float(\"rnn_loss_weight\",None,\"How much moret to weight kth example\")\n configs.DEFINE_integer(\"max_epoch\",0,\"Stop after max_epochs\")\n configs.DEFINE_integer(\"early_stop\",None,\"Early stop parameter\")\n configs.DEFINE_integer(\"seed\",None,\"Seed for deterministic training\")\n\n config = configs.get_configs()\n\n if config.train_datafile is None:\n config.train_datafile = config.datafile\n\n train_path = model_utils.get_data_path(config.data_dir,config.train_datafile)\n\n print(\"Loading training data ...\")\n\n train_data = BatchGenerator(train_path, config,\n\t\t\t config.batch_size,config.num_unrollings,\n\t\t\t validation_size=config.validation_size,\n\t\t\t randomly_sample=True)\n\n tf_config = tf.ConfigProto( allow_soft_placement=True ,\n log_device_placement=False )\n\n with tf.Graph().as_default(), tf.Session(config=tf_config) as session:\n\n if config.seed is not None:\n tf.set_random_seed(config.seed)\n\n print(\"Constructing model ...\")\n\n model = model_utils.get_training_model(session, config, verbose=True)\n\n\n if config.early_stop is not None:\n print(\"Training will early stop without \"\n \"improvement after %d epochs.\"%config.early_stop)\n \n train_history = list()\n valid_history = list()\n # This sets the initial learning rate tensor\n lr = model.assign_lr(session,config.initial_learning_rate)\n\n for i in range(config.max_epoch):\n\n trc, tre, vdc, vde = run_epoch(session, model, train_data,\n keep_prob=config.keep_prob,\n passes=config.passes,\n verbose=True)\n\n trc = 999.0 if trc > 999.0 else trc\n vdc = 999.0 if vdc > 999.0 else vdc\n\n print( ('Epoch: %d loss: %.6f %.6f'\n ' error: %.6f %.6f Learning rate: %.4f') %\n (i + 1, trc, vdc, tre, vde, lr) )\n sys.stdout.flush()\n\n train_history.append( trc )\n valid_history.append( vdc )\n\n # update learning rate \n if config.optimizer == 'gd' or config.optimizer == 'momentum':\n lr = model_utils.adjust_learning_rate(session, model, \n lr, config.lr_decay, train_history )\n\n if not os.path.exists(config.model_dir):\n print(\"Creating directory %s\" % config.model_dir)\n os.mkdir(config.model_dir)\n\n chkpt_file_prefix = \"training.ckpt\"\n if model_utils.stop_training(config,valid_history,chkpt_file_prefix):\n print(\"Training stopped.\")\n quit()\n else:\n checkpoint_path = os.path.join(config.model_dir, chkpt_file_prefix)\n tf.train.Saver().save(session, checkpoint_path, global_step=i)","def test(output_seq,M1,M2,M3,obs_file):\n predicted = []\n mus_c = 0\n mod_c = 0\n arn_c = 0\n next = 0\n t = 0.000\n d=dict()\n d[\"other\"]=0\n d[\"modi\"]=0\n d[\"arnab\"]=0\n print(obs_file+\":\")\n for obs in output_seq:\n p1 = M1.forward(obs)\n p2 = M2.forward(obs)\n p3 = M3.forward(obs)\n\n\n if(p1 > p2 and p1 > p3):\n predicted.append(\"Other\")\n mus_c+=1\n d[\"other\"]+=1\n elif(p2 > p3 and p2 > p1):\n predicted.append(\"Modi\")\n mod_c+=1\n d[\"modi\"]+=1\n else:\n predicted.append(\"Arnab\")\n arn_c+=1\n d[\"arnab\"]+=1\n t+=0.05\n next+=1\n\n if(next % 20 == 0):\n p_other = d[\"other\"]/20.0\n p_modi = d[\"modi\"]/20.0\n p_arnab = d[\"arnab\"]/20.0\n #print(\"{0} : Modi = {1}, Arnab = {2}, Other = {3}\".format(t,p_modi,p_arnab,p_other))\n if p_other>p_modi and p_other>p_arnab:\n print \"{0} : Speech : Other\".format(t)\n elif p_modi>p_other and p_modi>p_arnab:\n print \"{0} : Speech : Modi\".format(t)\n else:\n print \"{0} : Speech : Arnab\".format(t)\n d[\"other\"]=0\n d[\"modi\"]=0\n d[\"arnab\"]=0\n\n time = 0.000\n with open(\"op_\"+obs_file,\"w\") as g:\n for val in predicted:\n g.write(str(time)+\" :\\t\"+val+\"\\n\")\n time+=0.005\n\n return predicted","def test_run_sim():\n rnd = rand.Arrivals(31, 40)\n sim.run_sim(2, 1, 3, 4, 24, rnd)","def _evaluate_performance__static_winners(self):\n # | - _evaluate_performance__\n\n # | - class attributes #################################################\n AL = self\n al_gen = self.al_gen\n verbose = self.verbose\n seed_ids = self.seed_ids\n acquisition_bin = self.acquisition_bin\n completed_ids = self.completed_ids\n CandidateSpace = self.CandidateSpace\n RegressionModel = self.RegressionModel\n DuplicateFinder = self.DuplicateFinder\n al_gen_dict = self.al_gen_dict\n\n stop_mode = self.stop_mode\n stop_num_generations = self.stop_num_generations\n\n index_acq_gen_dict = self.index_acq_gen_dict\n #__| #################################################################\n\n # #####################################################################\n mode = \"lowest_N\" # 'lowest_N' or 'lowest_perc'\n\n N_ids = 10\n lowest_perc = 5\n\n # Number of consecutive generations that the Nth best systems must\n # remain static\n M_gens = 3\n # #####################################################################\n\n if mode == \"lowest_perc\":\n num_candidates = CandidateSpace.FingerPrints.df_pre.shape[0]\n N_ids = int(num_candidates * (lowest_perc * 0.01))\n\n gen_keys = list(AL.al_gen_dict.keys())\n\n if len(gen_keys) > M_gens:\n latest_M_keys = gen_keys[-(M_gens + 1):]\n last_gen_key = gen_keys[-1]\n\n al_gen_dict_subset_i = dict(zip(\n latest_M_keys,\n [AL.al_gen_dict.get(i, None) for i in latest_M_keys]))\n\n indices_list = []\n iterator = enumerate(al_gen_dict_subset_i.items())\n for i_cnt, (gen_i, AL_i) in iterator:\n model_i = AL_i.model\n\n model_i = AL.add_main_Y_to_model(\n model_i, plot_dft_instead_of_pred=True)\n model_i = model_i[(model_i[\"duplicate\"] == False)]\n model_i = model_i.sort_values(\"Y_main\")\n\n indices_i = model_i.index.tolist()\n\n indices_list.append(indices_i)\n\n if i_cnt >= M_gens:\n indices_i = indices_list[i_cnt][0:N_ids]\n ids_static_list = []\n for j in range(M_gens):\n indices_j = indices_list[i_cnt - (j + 1)][0:N_ids]\n ids_static = indices_j == indices_i\n ids_static_list.append(ids_static)\n\n ids_are_static = all(ids_static_list)\n\n self.performance__static_winners[last_gen_key] = ids_are_static\n #__|","def top_conditioned_sample():\n global vqvae\n assert vqvae is not None\n global DEVICE\n assert DEVICE is not None\n global transformer_bottom\n assert transformer_bottom is not None\n global label_encoders_per_modality\n assert label_encoders_per_modality is not None\n global spectrograms_helper\n assert spectrograms_helper is not None\n\n BYPASS = False\n\n top_code, bottom_code = parse_codes(request)\n global_instrument_family_str = str(\n request.args.get('instrument_family_str'))\n min_pitch = int(request.args.get('min_pitch'))\n max_pitch = int(request.args.get('max_pitch'))\n\n if not BYPASS:\n temperature = float(request.args.get('temperature'))\n top_p = float(request.args.get('top_p') or 0.0)\n top_k = int(request.args.get('top_k') or 0)\n\n class_conditioning_tensors_bottom = make_conditioning_tensors(\n {'pitch': (min_pitch, max_pitch),\n 'instrument_family_str': global_instrument_family_str},\n label_encoders_per_modality\n )\n\n # repeat the top codemap for all bottom samples\n num_samples = max_pitch - min_pitch\n top_code = top_code.expand(num_samples, -1, -1)\n\n bottom_code = sample_model(\n transformer_bottom, DEVICE, num_samples,\n transformer_bottom.shape,\n temperature, condition=top_code,\n class_conditioning=class_conditioning_tensors_bottom,\n top_p_sampling_p=top_p,\n top_k_sampling_k=top_k\n )\n else:\n import time\n num_samples = 1\n top_code = top_code.expand(num_samples, -1, -1)\n bottom_code = bottom_code.expand(num_samples, -1, -1)\n time.sleep(2)\n\n logmelspectrogram_and_IF = vqvae.decode_code(top_code,\n bottom_code)\n\n zip_path = upload_directory + 'samples.zip'\n with ZipFile(zip_path, 'w') as zf:\n for pitch, sample in zip(range(min_pitch, max_pitch),\n logmelspectrogram_and_IF):\n # convert to audio and write to file\n audio = spectrograms_helper.to_audio(sample.unsqueeze(0))[0]\n audio_path = write_audio_to_file(\n audio,\n f'-{global_instrument_family_str}-{pitch}')\n zf.write(audio_path,\n arcname=f'{global_instrument_family_str}-{pitch}.wav')\n\n return flask.send_file(zip_path, mimetype=\"application/zip\",\n cache_timeout=-1 # disable cache\n )","def test_scenario(timestep_per_pi, int_method):\n\n #determine BC and IC\n x0 = 0.0 #init pos\n v0 = 1.0 #init vel\n t0 = 0.0 #start-time\n tn = 4.0*np.pi #end-time\n tau = timestep_per_pi*np.pi #timesteps\n n = (tn-t0)/tau + 1 #number of timesteps\n \n time = np.linspace(t0, tn, n) #time-array\n\n #acceleration of point particle with k=m=1\n acc1 = lambda x,v,t: -1.0*x #function must take three arguments!\n\n pos, vel, time = integrate_time(func=acc1,\n init=(x0,v0),\n timearray=time,\n method=int_method)\n\n #analytical solutions\n pos_an = np.sin(time)\n vel_an = np.cos(time)\n\n return time, pos, pos_an, vel, vel_an","def gen_spec(model_params):\n xarr11 = SpectroscopicAxis(np.linspace(-40,40,1000)*u.km/u.s,\n velocity_convention='radio',\n refX=freq_dict['oneone']).as_unit(u.GHz)\n xarr22 = SpectroscopicAxis(np.linspace(-40,40,1000)*u.km/u.s,\n velocity_convention='radio',\n refX=freq_dict['twotwo']).as_unit(u.GHz)\n xarr = SpectroscopicAxes([xarr11,xarr22])\n tkin = model_params[0]\n tex = model_params[1]\n ntot = model_params[2]\n width = model_params[3]\n fortho = 0.0\n synthspec = pyspeckit.spectrum.models.ammonia.cold_ammonia(xarr,tkin=tkin,tex=tex,ntot=ntot,\n width=width,fortho=fortho)\n spectrum = pyspeckit.Spectrum(xarr=xarr,data=synthspec)\n return spectrum","def _tcrsampler_svgs(\n tcrrep,\n default_background = None, \n default_background_if_missing = None,\n cdr3_name = 'cdr3_b_aa',\n pwmat_str = 'pw_cdr3_b_aa', \n chain = 'beta', \n gene_names = ['v_b_gene','j_b_gene'],\n combine_olga = False,\n verbose = True): \n from tcrsampler.sampler import TCRsampler\n from palmotif import compute_pal_motif, svg_logo\n import pandas as pd\n from tcrdist.summarize import _select\n \n if chain == 'alpha' and tcrrep.organism == \"mouse\":\n # Here we enforce the rule that alpha-mouse cannot use an olga-sampler\n # TODO: This should be removed as soon as TCRsampler can be updated with a valid \n # mouse-alpha simulated background.\n combine_olga = False\n \n # _default_sampler returns a TCRSampler based on organism and chain\n if verbose: print(f\"INITIALIZING A TCRSAMPLER\")\n print(tcrrep.organism, chain)\n t = _default_sampler(organism = tcrrep.organism, chain = chain)(\n default_background = default_background, \n default_background_if_missing = default_background_if_missing)\n\n build_kargs = { 'max_rows' : 100,\n 'stratify_by_subject' : True,\n 'use_frequency' : True,\n 'make_singleton' : False}\n\n build_kargs_olga = { 'max_rows' : 1000,\n 'stratify_by_subject' : False,\n 'use_frequency' : True,\n 'make_singleton' : False}\n \n if verbose: print(f\"BUILDING A DEEPER BACKGROUND {report_kwargs(build_kargs)}\")\n\n t.build_background(**build_kargs)\n # Olga Sampler\n \n if combine_olga:\n t_olga = _default_sampler_olga(chain = chain, organism = tcrrep.organism)()\n if verbose: print(f\"BUILDING A DEEPER BACKGROUND {report_kwargs(build_kargs_olga)}\")\n t.build_background(**build_kargs_olga )\n\n olga_model = {\n ('beta','human') : OlgaModel(recomb_type=\"VDJ\", chain_folder = \"human_T_beta\"),\n ('alpha','human') : OlgaModel(recomb_type=\"VJ\", chain_folder = \"human_T_alpha\"),\n ('beta','mouse') : OlgaModel(recomb_type=\"VDJ\", chain_folder = \"mouse_T_beta\")\n }[(chain, tcrrep.organism)]\n\n\n\n if 'prune' not in tcrrep.hcluster_df.columns:\n if verbose: print(\"NO PRUNE COLUMNS USED ALL SET TO 0\")\n tcrrep.hcluster_df['prune'] = 0\n \n print(\"ITERATE THROUGH CLUSTERS\")\n svgs = list()\n svgs_raw = list()\n reference_unique = list()\n reference_unique_olga= list()\n reference_size = list()\n reference_size_olga = list()\n percent_missing_sampler = list()\n percent_missing_sampler_olga = list()\n n_rows = tcrrep.hcluster_df.shape[0]\n \n \n bar = IncrementalBar(f'Make {chain} SVGs :', max = n_rows, suffix='%(percent)d%%')\n for i,r in tcrrep.hcluster_df.iterrows():\n bar.next()\n if r['prune'] == 0:\n # <dfnode> is dataframe with all the clones at a given tree node\n dfnode = tcrrep.clone_df.iloc[r['neighbors_i'],].copy()\n # <pwnode> Pairwise Matrix for node sequences\n pwnode = getattr(tcrrep, pwmat_str)[r['neighbors_i'],:][:,r['neighbors_i']].copy()\n iloc_idx = pwnode.sum(axis = 0).argmin()\n centroid = dfnode[cdr3_name].to_list()[iloc_idx]\n \n # Compute gene usage at the node \n # Convert to allele_01\n for gene_name in gene_names:\n dfnode[gene_name] = dfnode[gene_name].apply(lambda x : allele_01(x)) \n \n gene_usage = dfnode.groupby(gene_names).size() # e.g., ['v_b_gene','j_b_gene']\n gene_usage_tuples = gene_usage.reset_index().to_dict('split')['data']\n # Given gene usage use the <t> a TCRsampler instance to get background seqs\n \n # Adjust depth for small nodes\n adjust_depth = 10*round(10 / dfnode.shape[0])\n if adjust_depth < 10:\n adjust_depth = 10\n\n sampled_rep = t.sample( gene_usage_tuples,\n flatten = True, depth = adjust_depth * 10)\n \n # Only keep the non-none sequences\n sampled_rep = [x for x in sampled_rep if x is not None]\n # < missing_gene > Count the percentage missing, sampler returns none when no v,j pair is present\n expected_depth = dfnode.shape[0] * adjust_depth * 10\n recovered_depth = len(sampled_rep)\n percent_missing = round(100* (1- (recovered_depth / expected_depth)), 1)\n \n percent_missing_sampler.append(f\"{percent_missing}%\") \n reference_unique.append(str(pd.Series(sampled_rep).nunique()))\n reference_size.append(str(pd.Series(sampled_rep).count()))\n\n if combine_olga:\n # We modified Olga source code slightly, such that we simulated sequences \n # with a given V,J gene usage\n # OLD METHOD WHERE WE ACTUALLY SAMPLED, slower but can go much deeper. I don't think one rare sequence however, really make a big difference.\n #flatten = lambda l: [item for sublist in l for item in sublist]\n #sampled_rep_olga = [olga_model.gen_cdr3s(allele_01(v),allele_01(j),n*adjust_depth*10) for v,j,n in gene_usage_tuples]\n #sampled_rep_olga = [x for x in flatten(sampled_rep_olga) if x is not None]\n sampled_rep_olga = t_olga.sample( gene_usage_tuples,\n flatten = True, depth = adjust_depth * 10)\n\n sampled_rep_olga = [x for x in sampled_rep_olga if x is not None]\n \n expected_depth = dfnode.shape[0] * adjust_depth * 10\n recovered_depth = len(sampled_rep_olga)\n percent_missing_olga = round(100* (1- (recovered_depth / expected_depth)), 1)\n \n percent_missing_sampler_olga.append(f\"{percent_missing_olga}%\") \n reference_unique_olga.append(str(pd.Series(sampled_rep_olga).nunique()))\n reference_size_olga.append(str(pd.Series(sampled_rep_olga).count()))\n \n # HERE WE COMBINE INTO A SINGLE BACKGROUND:\n sampled_rep = sampled_rep + sampled_rep_olga\n\n\n # Get motif matrix and motif stats\n motif, stat = compute_pal_motif(\n seqs = _select(df = tcrrep.clone_df, \n iloc_rows = r['neighbors_i'], \n col = cdr3_name),\n refs = sampled_rep, \n centroid = centroid)\n\n svgs.append(svg_logo(motif, return_str= True))\n\n # repeaat without references\n raw_motif, raw_stat = compute_pal_motif(\n seqs = _select(df = tcrrep.clone_df, \n iloc_rows = r['neighbors_i'], \n col = cdr3_name),\n centroid = centroid)\n # Convert the motif matrix into an svg_logo, append to list\n svgs_raw.append(svg_logo(raw_motif, return_str= True))\n else: \n # If prune column is 1 don't go to the trouble of sampling and generating seqs\n svgs.append(\"PRUNE\")\n svgs_raw.append(\"PRUNE\")\n reference_size.append(\"PRUNE\")\n reference_unique.append(\"PRUNE\")\n percent_missing_sampler.append(\"PRUNE\")\n percent_missing_sampler_olga.append(\"PRUNE\") \n reference_unique_olga.append(\"PRUNE\") \n reference_size_olga.append(\"PRUNE\") \n\n bar.next(); bar.finish()\n\n # The standard svg_ includes background, whereas raw has no background \n tcrrep.hcluster_df_detailed[f'svg_{chain}'] \t\t= svgs\n tcrrep.hcluster_df_detailed[f'svg_raw_{chain}'] \t= svgs_raw\n tcrrep.hcluster_df_detailed[f'ref_size_{chain}'] \t= reference_size\n tcrrep.hcluster_df_detailed[f'ref_unique_{chain}'] \t= reference_unique\n tcrrep.hcluster_df_detailed[f'percent_missing_{chain}'] = percent_missing_sampler \n if combine_olga:\n tcrrep.hcluster_df_detailed[f'ref_size_olga_{chain}'] \t = reference_size_olga\n tcrrep.hcluster_df_detailed[f'ref_unique_olga_{chain}'] \t = reference_unique_olga\n tcrrep.hcluster_df_detailed[f'percent_missing_olga_{chain}'] = percent_missing_sampler_olga \n \n return True","def generate():\n global transformer_top\n assert transformer_top is not None\n global transformer_bottom\n assert transformer_bottom is not None\n global label_encoders_per_modality\n assert label_encoders_per_modality is not None\n global DEVICE\n assert DEVICE is not None\n global partial_sample_model\n assert partial_sample_model is not None\n\n temperature = float(request.args.get('temperature'))\n pitch = int(request.args.get('pitch'))\n instrument_family_str = str(request.args.get('instrument_family_str'))\n\n class_conditioning_top = class_conditioning_bottom = {\n 'pitch': pitch,\n 'instrument_family_str': instrument_family_str\n }\n class_conditioning_tensors_top = make_conditioning_tensors(\n class_conditioning_top,\n label_encoders_per_modality)\n class_conditioning_tensors_bottom = make_conditioning_tensors(\n class_conditioning_bottom,\n label_encoders_per_modality)\n\n batch_size = 1\n top_code = partial_sample_model(\n model=transformer_top,\n batch_size=batch_size,\n codemap_size=transformer_top.shape,\n temperature=temperature,\n class_conditioning=class_conditioning_tensors_top\n )\n bottom_code = partial_sample_model(\n model=transformer_bottom,\n condition=top_code,\n batch_size=batch_size,\n codemap_size=transformer_bottom.shape,\n temperature=temperature,\n class_conditioning=class_conditioning_tensors_bottom,\n )\n\n class_conditioning_top_map = {\n modality: make_matrix(transformer_top.shape,\n value)\n for modality, value in class_conditioning_top.items()\n }\n class_conditioning_bottom_map = {\n modality: make_matrix(transformer_bottom.shape,\n value)\n for modality, value in class_conditioning_bottom.items()\n }\n\n response = make_response(top_code, bottom_code,\n class_conditioning_top_map,\n class_conditioning_bottom_map)\n return response","def refugia_adj_5_simsplit_4epochs_iter4 (params, ns):\n #28 parameters \n nu1a, nu2a, nu3a, nu1b, nu2b, nu3b, nu1c, nu2c, nu3c, nu1d, nu2d, nu3d, m1_12, m1_13, m1_21, m1_23, m1_31, m1_32, m3_12, m3_13, m3_21, m3_23, m3_31, m3_32, T1, T2, T3, T4 = params\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1] + ns[2])\n fs = moments.Spectrum(sts)\n fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1] + ns[2]) \n fs = moments.Manips.split_2D_to_3D_2(fs, ns[1], ns[2])\n ## Population function and migration matrix for T1 (to reflect sum effect of all previous glacial-interglacial cycles)\n nu_T1 = [nu1a, nu2a, nu3a]\n mig1 = numpy.array([[0, m1_12, m1_13],[m1_21, 0, m1_23], [m1_31, m1_32, 0]]) \n fs.integrate(nu_T1, T1, m=mig1)\n ## Population function and migration matrix for T2 (to reflect period of isolation during last glacial)\n nu_T2 = [nu1b, nu2b, nu3b]\n fs.integrate(nu_T2, T2)\n ## Population function and migration matrix for T3 (to reflect inter-glacial expansion)\n nu_T3 = [nu1c, nu2c, nu3c]\n mig3 = numpy.array([[0, m3_12, m3_13],[m3_21, 0, m3_23], [m3_31, m3_32, 0]]) \n fs.integrate(nu_T3, T3, m=mig3)\n ## Population function and migration matrix for T3 (bottleneck to capture single population representation of lineage)\n nu_T4 = [nu1d, nu2d, nu3d]\n fs.integrate(nu_T4, T4) \n return fs","def split_simsplit_3epochs_iter4(params, ns):\n #24 parameters \n nu1a, nu2a, nu3a, nu1b, nu2b, nu3b, nu1c, nu2c, nu3c, m1_12, m1_13, m1_21, m1_23, m1_31, m1_32, m2_12, m2_13, m2_21, m2_23, m2_31, m2_32, T1, T2, T3 = params\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1] + ns[2])\n fs = moments.Spectrum(sts)\n fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1] + ns[2]) \n fs = moments.Manips.split_2D_to_3D_2(fs, ns[1], ns[2])\n ## Population function and migration matrix for T1\n nu_T1 = [nu1a, nu2a, nu3a]\n mig1 = numpy.array([[0, m1_12, m1_13],[m1_21, 0, m1_23], [m1_31, m1_32, 0]]) \n fs.integrate(nu_T1, T1, m=mig1)\n ## Population function and migration matrix for T2\n nu_T2 = [nu1b, nu2b, nu3b]\n mig2 = numpy.array([[0, m2_12, m2_13],[m2_21, 0, m2_23], [m2_31, m2_32, 0]]) \n fs.integrate(nu_T2, T2, m=mig2)\n ## Population function and migration matrix for T3 (bottleneck to capture single population representation of lineage)\n nu_T3 = [nu1c, nu2c, nu3c]\n fs.integrate(nu_T3, T3) \n return fs","def doParametersOfInterest(self):\n \n self.modelBuilder.doVar('expr::cosW(\"0.87681811112\",)')\n self.modelBuilder.doVar('expr::sinW(\"0.48082221247\",)')\n self.modelBuilder.doVar('expr::mZ(\"91.2\",)')\n self.modelBuilder.doVar('expr::Lambda1(\"100.0\",)')\n self.modelBuilder.doVar('expr::e2(\"0.0917\",)')\n self.modelBuilder.doVar('expr::gs2(\"1.533\",)')\n\n # EFT Higgs basis couplings\n\n self.modelBuilder.doVar('cZ[0,-1,1]') \n self.modelBuilder.doVar(\"cZZ[0,-2,2]\") \n self.modelBuilder.doVar(\"cZZt[0,-2,2]\") \n self.modelBuilder.doVar(\"cZB[0,-6,6]\") \n\n poi='cZ,cZZ,cZZt,cZB'\n\n # Amplitude couplings from EFT couplings \n\n self.modelBuilder.doVar('expr::a1(\"@0+1\",cZ)') # (\"2*(@0+1)\",cZ) in AN/Paper but a1 = 1 for signal model and width calculation\n self.modelBuilder.doVar('expr::a2(\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\",cZZ,e2,sinW,cosW)')\n self.modelBuilder.doVar('expr::a3(\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\",cZZt,e2,sinW,cosW)')\n self.modelBuilder.doVar('expr::k1(\"@0*(@1*pow(@2,2)/(pow(@3,2)*pow(@4,2)))\",cZB,e2,Lambda1,sinW,mZ)')\n self.modelBuilder.doVar('expr::k1L1(\"@0/pow(@1,2)\",k1,Lambda1)')\n\n ###### gamma_H ########\n\n # SMEFT relationships for VV couplings (Expressed using amplitude couplings)\n\n self.modelBuilder.doVar('expr::kappa(\"1.0\",)')\n self.modelBuilder.doVar('expr::kappa_tilde(\"0.0\",)') \n\n self.modelBuilder.doVar('expr::a1_WW(\"@0\",a1)')\n self.modelBuilder.doVar('expr::a2_WW(\"@0*@0*@1\",cosW,a2)')\n self.modelBuilder.doVar('expr::a3_WW(\"@0*@0*@1\",cosW,a3)')\n self.modelBuilder.doVar('expr::k1_WW(\"(@2 / (@0*@0 - @1*@1) - 2*@1*@1*@3*@4*@4 /(@5*@5*(@0*@0 - @1*@1)))\",cosW,sinW,k1,a2,Lambda1,mZ)')\n self.modelBuilder.doVar('expr::k2_k1(\"2*@0*@1*@2/(@0*@0 - @1*@1)\",cosW,sinW,k1)')\n self.modelBuilder.doVar('expr::k2_a2(\"-2*@0*@1*@3*@4*@4/((@2*@2)*(@0*@0 - @1*@1))\",cosW,sinW,mZ,a2,Lambda1)')\n self.modelBuilder.doVar('expr::k2(\"@0 + @1\",k2_k1,k2_a2)')\n\n # Determine gamma_H from VV couplings\n\n zz_expr = '\"4*(@0*@0/4. + 0.1695*@3*@3 + 0.09076*@1*@1 + 0.03809*@2*@2 + 0.8095*@0*@3/2. + 0.5046*@0*@1/2. + 0.2092*@1*@3 + 0.1023*@4*@4 + 0.1901*@0*@4/2. + 0.07429*@3*@4 + 0.04710*@1*@4) \",a1,a2,a3,k1,k2'\n ww_expr = '\"4*(@0*@0/4. + 0.1320*@3*@3 + 0.1944*@1*@1 + 0.08075*@2*@2 + 0.7204*@0*@3/2. + 0.7437*@0*@1/2. + 0.2774*@3*@1) \",a1_WW,a2_WW,a3_WW,k1_WW'\n zgamma_expr = '\"4*(1.118600*@0*@0/4. +0.0035*@1*@1 - 0.125010*@0*@1/2. + 0.000003*@1*@1 - 0.00018*@1*@1 + 0.003100*@0*@1/2. +0.00126*@2*@2 + 0.000005*@2*@2 -0.00047*@2*@2)\",a1_WW,kappa,kappa_tilde'\n gg_expr = '\"(1.1068*@0*@0 + 0.0082*@0*@0 - 0.1150*@0*@0 + 2.5717*@1*@1 + 0.0091*@1*@1 - 0.1982*@1*@1)\",kappa,kappa_tilde'\n bb_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n cc_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n tautau_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n mumu_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n gmgm_expr = '\"4*(1.6054*@0*@0/4. + 0.07312*@1*@1 - 0.6854*@0*@1/2. + 0.00002*@1*@1 - 0.0018*@1*@1 + 0.0085*@0*@1/2. + 0.1699*@2*@2 + 0.00002*@2*@2 - 0.0031*@2*@2)\",a1_WW,kappa,kappa_tilde'\n \n self.modelBuilder.doVar('expr::R_WW('+str(ww_expr)+')')\n self.modelBuilder.doVar('expr::R_ZZ('+str(zz_expr)+')')\n self.modelBuilder.doVar('expr::R_Zgamma('+str(zgamma_expr)+')')\n self.modelBuilder.doVar('expr::R_gg('+str(gg_expr)+')')\n self.modelBuilder.doVar('expr::R_bb('+str(bb_expr)+')')\n self.modelBuilder.doVar('expr::R_cc('+str(cc_expr)+')')\n self.modelBuilder.doVar('expr::R_tautau('+str(tautau_expr)+')')\n self.modelBuilder.doVar('expr::R_mumu('+str(mumu_expr)+')')\n self.modelBuilder.doVar('expr:R_gammagamma('+str(gmgm_expr)+')')\n\n self.modelBuilder.doVar('expr::gammaH(\"(0.5824*@0 + 0.2137*@1 + 0.08187*@2 + 0.06272*@3 + 0.02891*@4 + 0.02619*@5 + 0.002270*@6 + 0.001533*@7 + 0.0002176*@8 )/0.9998\",R_bb,R_WW,R_gg,R_tautau,R_cc,R_ZZ,R_gammagamma,R_Zgamma,R_mumu)') \n\n ###########################\n\n self.g1V = GetCoupTerms(1,1,1,-0.0001,\"1V\") # Compensate for scaling of k1 templates \n self.g2V = GetCoupTerms(1,1,1,-0.0001,\"2V\") \n \n self.modelBuilder.doVar(\"expr::g2V_1(\\\"\"+str(self.g2V[0])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T1(\\\"((pow(@0,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T1_Neg(\\\"-1*((pow(@0,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\") \n self.modelBuilder.doVar(\"expr::g2V_2(\\\"\"+str(self.g2V[1])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T2(\\\"((pow(@0,3)*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T2_Neg(\\\"-1*((pow(@0,3)*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\") \n self.modelBuilder.doVar(\"expr::g2V_3(\\\"\"+str(self.g2V[2])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T3(\\\"((pow(@0,2)*pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T3_Neg(\\\"-1*((pow(@0,2)*pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\") \n self.modelBuilder.doVar(\"expr::g2V_4(\\\"\"+str(self.g2V[3])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T4(\\\"((@0*pow(@1,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T4_Neg(\\\"-1*((@0*pow(@1,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\") \n self.modelBuilder.doVar(\"expr::g2V_5(\\\"\"+str(self.g2V[4])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T5(\\\"((pow(@1,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T5_Neg(\\\"-1*((pow(@1,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\") \n self.modelBuilder.doVar(\"expr::g2V_6(\\\"\"+str(self.g2V[5])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T6(\\\"((pow(@0,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T6_Neg(\\\"-1*((pow(@0,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\") \n self.modelBuilder.doVar(\"expr::g2V_7(\\\"\"+str(self.g2V[6])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T7(\\\"((pow(@0,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T7_Neg(\\\"-1*((pow(@0,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\") \n self.modelBuilder.doVar(\"expr::g2V_8(\\\"\"+str(self.g2V[7])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T8(\\\"((@0*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T8_Neg(\\\"-1*((@0*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\") \n self.modelBuilder.doVar(\"expr::g2V_9(\\\"\"+str(self.g2V[8])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T9(\\\"((pow(@2,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T9_Neg(\\\"-1*((pow(@2,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\") \n self.modelBuilder.doVar(\"expr::g2V_10(\\\"\"+str(self.g2V[9])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T10(\\\"((pow(@0,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T10_Neg(\\\"-1*((pow(@0,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\") \n self.modelBuilder.doVar(\"expr::g2V_11(\\\"\"+str(self.g2V[10])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T11(\\\"((pow(@0,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T11_Neg(\\\"-1*((pow(@0,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\") \n self.modelBuilder.doVar(\"expr::g2V_12(\\\"\"+str(self.g2V[11])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T12(\\\"((@0*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T12_Neg(\\\"-1*((@0*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\") \n self.modelBuilder.doVar(\"expr::g2V_13(\\\"\"+str(self.g2V[12])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T13(\\\"((pow(@3,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T13_Neg(\\\"-1*((pow(@3,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\") \n self.modelBuilder.doVar(\"expr::g2V_14(\\\"\"+str(self.g2V[13])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T14(\\\"((pow(@1,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T14_Neg(\\\"-1*((pow(@1,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\") \n self.modelBuilder.doVar(\"expr::g2V_15(\\\"\"+str(self.g2V[14])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T15(\\\"((pow(@1,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T15_Neg(\\\"-1*((pow(@1,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\") \n self.modelBuilder.doVar(\"expr::g2V_16(\\\"\"+str(self.g2V[15])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T16(\\\"((@1*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T16_Neg(\\\"-1*((@1*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\") \n self.modelBuilder.doVar(\"expr::g2V_17(\\\"\"+str(self.g2V[16])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T17(\\\"((pow(@1,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T17_Neg(\\\"-1*((pow(@1,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\") \n self.modelBuilder.doVar(\"expr::g2V_18(\\\"\"+str(self.g2V[17])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T18(\\\"((pow(@1,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T18_Neg(\\\"-1*((pow(@1,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\") \n self.modelBuilder.doVar(\"expr::g2V_19(\\\"\"+str(self.g2V[18])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T19(\\\"((@1*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T19_Neg(\\\"-1*((@1*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\") \n self.modelBuilder.doVar(\"expr::g2V_20(\\\"\"+str(self.g2V[19])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T20(\\\"((pow(@2,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T20_Neg(\\\"-1*((pow(@2,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\") \n self.modelBuilder.doVar(\"expr::g2V_21(\\\"\"+str(self.g2V[20])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T21(\\\"((pow(@2,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T21_Neg(\\\"-1*((pow(@2,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\") \n self.modelBuilder.doVar(\"expr::g2V_22(\\\"\"+str(self.g2V[21])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T22(\\\"((@2*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T22_Neg(\\\"-1*((@2*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\") \n self.modelBuilder.doVar(\"expr::g2V_23(\\\"\"+str(self.g2V[22])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T23(\\\"((@0*@1*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T23_Neg(\\\"-1*((@0*@1*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\") \n self.modelBuilder.doVar(\"expr::g2V_24(\\\"\"+str(self.g2V[23])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T24(\\\"((@0*pow(@1,2)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T24_Neg(\\\"-1*((@0*pow(@1,2)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\") \n self.modelBuilder.doVar(\"expr::g2V_25(\\\"\"+str(self.g2V[24])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T25(\\\"((pow(@0,2)*@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T25_Neg(\\\"-1*((pow(@0,2)*@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\") \n self.modelBuilder.doVar(\"expr::g2V_26(\\\"\"+str(self.g2V[25])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T26(\\\"((@0*@1*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T26_Neg(\\\"-1*((@0*@1*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\") \n self.modelBuilder.doVar(\"expr::g2V_27(\\\"\"+str(self.g2V[26])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T27(\\\"((@0*pow(@1,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T27_Neg(\\\"-1*((@0*pow(@1,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\") \n self.modelBuilder.doVar(\"expr::g2V_28(\\\"\"+str(self.g2V[27])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T28(\\\"((pow(@0,2)*@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T28_Neg(\\\"-1*((pow(@0,2)*@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\") \n self.modelBuilder.doVar(\"expr::g2V_29(\\\"\"+str(self.g2V[28])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T29(\\\"((@0*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T29_Neg(\\\"-1*((@0*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\") \n self.modelBuilder.doVar(\"expr::g2V_30(\\\"\"+str(self.g2V[29])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T30(\\\"((@0*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T30_Neg(\\\"-1*((@0*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\") \n self.modelBuilder.doVar(\"expr::g2V_31(\\\"\"+str(self.g2V[30])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T31(\\\"((pow(@0,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T31_Neg(\\\"-1*((pow(@0,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\") \n self.modelBuilder.doVar(\"expr::g2V_32(\\\"\"+str(self.g2V[31])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T32(\\\"((@1*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T32_Neg(\\\"-1*((@1*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\") \n self.modelBuilder.doVar(\"expr::g2V_33(\\\"\"+str(self.g2V[32])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T33(\\\"((@1*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T33_Neg(\\\"-1*((@1*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\") \n self.modelBuilder.doVar(\"expr::g2V_34(\\\"\"+str(self.g2V[33])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T34(\\\"((pow(@1,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T34_Neg(\\\"-1*((pow(@1,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\") \n self.modelBuilder.doVar(\"expr::g2V_35(\\\"\"+str(self.g2V[34])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T35(\\\"((@0*@1*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T35_Neg(\\\"-1*((@0*@1*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\") \n \n self.modelBuilder.doVar(\"expr::g1V_1(\\\"\"+str(self.g1V[0])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T1(\\\"((pow(@0,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T1_Neg(\\\"-1*((pow(@0,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\") \n self.modelBuilder.doVar(\"expr::g1V_2(\\\"\"+str(self.g1V[1])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T2(\\\"((@0*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T2_Neg(\\\"-1*((@0*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\") \n self.modelBuilder.doVar(\"expr::g1V_3(\\\"\"+str(self.g1V[2])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T3(\\\"((pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T3_Neg(\\\"-1*((pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\") \n self.modelBuilder.doVar(\"expr::g1V_4(\\\"\"+str(self.g1V[3])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T4(\\\"((@0*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T4_Neg(\\\"-1*((@0*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\") \n self.modelBuilder.doVar(\"expr::g1V_5(\\\"\"+str(self.g1V[4])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T5(\\\"((pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T5_Neg(\\\"-1*((pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\") \n self.modelBuilder.doVar(\"expr::g1V_6(\\\"\"+str(self.g1V[5])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T6(\\\"((@0*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T6_Neg(\\\"-1*((@0*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\") \n self.modelBuilder.doVar(\"expr::g1V_7(\\\"\"+str(self.g1V[6])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T7(\\\"((pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T7_Neg(\\\"-1*((pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\") \n self.modelBuilder.doVar(\"expr::g1V_8(\\\"\"+str(self.g1V[7])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T8(\\\"((@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T8_Neg(\\\"-1*((@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\") \n self.modelBuilder.doVar(\"expr::g1V_9(\\\"\"+str(self.g1V[8])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T9(\\\"((@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T9_Neg(\\\"-1*((@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\") \n self.modelBuilder.doVar(\"expr::g1V_10(\\\"\"+str(self.g1V[9])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T10(\\\"((@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T10_Neg(\\\"-1*((@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\") \n \n self.modelBuilder.doSet(\"POI\",poi)","def sample_td3_params(trial: optuna.Trial,\n octree_observations: bool = True,\n octree_depth: int = 4,\n octree_full_depth: int = 2,\n octree_channels_in: int = 7,\n octree_fast_conv: bool = True,\n octree_batch_norm: bool = True) -> Dict[str, Any]:\n\n buffer_size = 150000\n # learning_starts = trial.suggest_categorical(\n # \"learning_starts\", [5000, 10000, 20000])\n learning_starts = 5000\n\n batch_size = trial.suggest_categorical(\"batch_size\", [32, 64, 128])\n learning_rate = trial.suggest_float(\n \"learning_rate\", low=0.000001, high=0.001, log=True)\n\n gamma = trial.suggest_float(\"gamma\", low=0.98, high=1.0, log=True)\n tau = trial.suggest_float(\"tau\", low=0.001, high=0.025, log=True)\n\n target_policy_noise = trial.suggest_float(\n \"target_policy_noise\", low=0.0, high=0.5, log=True)\n target_noise_clip = 0.5\n\n noise_std = trial.suggest_float(\"noise_std\", low=0.025, high=0.5, log=True)\n action_noise = NormalActionNoise(mean=np.zeros(trial.n_actions),\n sigma=np.ones(trial.n_actions)*noise_std)\n\n train_freq = 1\n gradient_steps = trial.suggest_categorical(\"gradient_steps\", [1, 2])\n\n policy_kwargs = dict()\n net_arch = trial.suggest_categorical(\"net_arch\", [\"small [256, 128]\",\n \"medium [384, 256]\",\n \"big [512, 384]\"])\n policy_kwargs[\"net_arch\"] = {\"small [256, 128]\": [256, 128],\n \"medium [384, 256]\": [384, 256],\n \"big [512, 384]\": [512, 384]}[net_arch]\n if octree_observations:\n features_extractor_kwargs = dict()\n\n features_extractor_kwargs[\"depth\"] = octree_depth\n features_extractor_kwargs[\"full_depth\"] = octree_full_depth\n features_extractor_kwargs[\"channels_in\"] = octree_channels_in\n\n features_extractor_kwargs[\"channel_multiplier\"] = \\\n trial.suggest_categorical(\"channel_multiplier\", [8, 16, 32, 64])\n\n features_extractor_kwargs[\"features_dim\"] = \\\n trial.suggest_categorical(\"features_dim\", [256, 512, 768])\n\n features_extractor_kwargs[\"fast_conv\"] = octree_fast_conv\n features_extractor_kwargs[\"batch_normalization\"] = octree_batch_norm\n\n policy_kwargs[\"features_extractor_kwargs\"] = features_extractor_kwargs\n\n return {\n \"buffer_size\": buffer_size,\n \"learning_starts\": learning_starts,\n \"batch_size\": batch_size,\n \"learning_rate\": learning_rate,\n \"gamma\": gamma,\n \"tau\": tau,\n \"target_policy_noise\": target_policy_noise,\n \"target_noise_clip\": target_noise_clip,\n \"action_noise\": action_noise,\n \"train_freq\": train_freq,\n \"gradient_steps\": gradient_steps,\n \"policy_kwargs\": policy_kwargs,\n }","def test013(testDir, dirDict, pflag):\n shutil.copy(osp.join(dirDict[\"baseConfig\"], \"params_system.cfg\"), testDir)\n shutil.copy(osp.join(dirDict[\"baseConfig\"], \"params_c.cfg\"), testDir)\n shutil.copy(osp.join(dirDict[\"baseConfig\"], \"params_a.cfg\"), testDir)\n psys = osp.join(testDir, \"params_system.cfg\")\n ptrodec = osp.join(testDir, \"params_c.cfg\")\n ptrodea = osp.join(testDir, \"params_a.cfg\")\n P_s = IO.get_config(psys)\n P_s.set(\"Sim Params\", \"profileType\", \"CV\")\n P_s.set(\"Sim Params\", \"Vset\", \"3.8\")\n P_s.set(\"Sim Params\", \"tsteps\", \"25\")\n P_s.set(\"Sim Params\", \"Nvol_c\", \"2\")\n P_s.set(\"Sim Params\", \"Nvol_s\", \"2\")\n P_s.set(\"Sim Params\", \"Nvol_a\", \"2\")\n P_s.set(\"Particles\", \"cs0_c\", \"0.2\")\n P_s.set(\"Particles\", \"cs0_a\", \"0.95\")\n P_s.set(\"Electrolyte\", \"elyteModelType\", \"dilute\")\n IO.write_config_file(P_s, psys)\n P = IO.get_config(ptrodec)\n P.set(\"Particles\", \"type\", \"homog\")\n P.set(\"Particles\", \"shape\", \"sphere\")\n P.set(\"Material\", \"muRfunc\", \"LiMn2O4_ss2\")\n P.set(\"Reactions\", \"rxnType\", \"Marcus\")\n IO.write_config_file(P, ptrodec)\n P = IO.get_config(ptrodea)\n P.set(\"Particles\", \"discretization\", \"2.5e-9\")\n P.set(\"Particles\", \"shape\", \"cylinder\")\n P.set(\"Material\", \"muRfunc\", \"testIS_ss\")\n P.set(\"Reactions\", \"rxnType\", \"BV_raw\")\n IO.write_config_file(P, ptrodea)\n main.main(psys, keepArchive=False)\n shutil.move(dirDict[\"simOut\"], testDir)\n if pflag:\n corePlots(testDir, dirDict)\n elytePlots(testDir, dirDict)\n electrodePlots(testDir, dirDict, \"c\")\n cmpr.bulkpf(testDir, dirDict, \"c\")\n electrodePlots(testDir, dirDict, \"a\")\n cmpr.bulkpf(testDir, dirDict, \"a\")","def generate_sample(amplitude, t0, width, n_samples = 6):\n if n_samples == 6:\n times = np.linspace(-apv_dt-t0, 4*apv_dt-t0, 6, endpoint = True)\n elif n_samples == 3:\n times = np.linspace(-t0, 2*apv_dt-t0, 3, endpoint = True)\n elif n_samples == 1:\n times = np.asarray([-t0])\n else:\n print(\"Unsupported number of APV samples in generate_sample!\")\n times = np.zeros(1)\n return amplitude * betaprime_wave(times, width)","def timesteps_experiment():\n\n print(\"TIMESTEPS EXPERIMENT\")\n\n # set the name of the experiment\n now = datetime.datetime.now()\n experiment_id = str(now.day) + \"_\" + str(now.month) + \"_\" + str(now.hour) + \".\" + str(now.minute)\n experiment_name = 'timestep_' + str(experiment_id)\n\n # define if you want to use preprocessed data from file\n use_prep_data = False\n if use_prep_data:\n set_params(preproc_data_id='16_5_10.16.47')\n else:\n set_params(use_preproc_data=False)\n\n # define the changing parameter and its value\n changing_param_name = 'time_steps'\n changing_param_value = [1, 2, 4, 8, 16, 32, 64, 128, 256]\n # {0:4, 1:100}, {0:3, 1:100}, {0:2, 1:100}, {0:1, 1:100}] #[{0:1, 1:1}, {0:15, 1:85}]#\n\n # set constant parameters\n set_params(epochs=20)\n set_params(dropout=0.3)\n set_params(use_word_emb=1)\n\n # save constant parameters to a new \"experiment_..\" file\n save_constant_parameters(experiment_name, changing_param_name)\n\n # run experiment for every parameter value\n for value in changing_param_value:\n process = psutil.Process(os.getpid())\n print(\"-----MEMORY before starting experiment ------\", int(process.memory_info().rss/(8*10**(3))), \"KB\")\n\n # update the parameter value\n set_params(use_word_emb = value)\n\n # update the model_id for this new model\n now = datetime.datetime.now()\n new_model_id = str(now.day) + \"_\" + str(now.month) + \"_\" + str(now.hour) + \".\" + str(now.minute) + \".\" + str(now.second)\n set_params(model_id = new_model_id)\n\n # evaluate the new model and save the results in the experiment file\n oneExperiment = Process(target=run_experiment, args=(experiment_name, new_model_id, changing_param_name, value,))\n oneExperiment.start()\n oneExperiment.join()\n\n if value == changing_param_value[0]:\n set_params(preproc_data_id=new_model_id)","def main():\n\n # Experiment Start\n start_time = datetime.now()\n logger.info(\n '################ Bergson Team Experiment Start #################')\n logger.info(\n f'Starting Bergson Astro Pi team experiment at {start_time.strftime(\"%Y-%m-%d %H:%M:%S\")}')\n\n '''\n # Load simple Conv2D AI Model\n logger.info(\"Loading AI Convolutional Model\")\n conv2D_model = load_model(\"Conv2D_TF114\")\n '''\n\n # Load TFLite Model\n logger.info(\"Loading TFLite Mobilenetv2 Model\")\n mobilenetv2_interpreter = load_tflite_model(\"./Mobilenetv2_TF114.tflite\")\n\n # Create Log File\n logger.info(f'Creating Log file at {str(data_file)}')\n with open(data_file, 'w') as f:\n writer = csv.writer(f)\n header = (\"Date/time\", \"Location\", \"Picture Name\", \"Predicted NO2\")\n writer.writerow(header)\n\n # Start Loop over 3 hours\n\n now_time = datetime.now()\n i = 0\n # run a loop for 2 minutes\n while (now_time < start_time + timedelta(minutes=175)):\n\n # Take Earth Picture\n timestamp = datetime.now().strftime(\"%Y-%m-%d_%H:%M:%S\")\n pic_name = f'bergson_img_{timestamp}.jpg'\n capture(rpi_cam, str(dir_path/pic_name))\n logger.info(f'Experiment Pipeline {i} on picture {pic_name}')\n\n # NDVI Preprocessing\n ndvi_image = get_ndvi(str(dir_path/pic_name))\n ndvi_image = np.expand_dims(ndvi_image, axis=2)\n\n # RGB Prepprocessing for expected shape by Mobilenetv2 - comment below line when using simple Conv2D model\n ndvi_rgb_image = get_ndvi_rgb(ndvi_image)\n\n '''\n # Do Inference with simple Conv2D AI Model\n prediction = make_inference(ndvi_image,conv2D_model)\n '''\n \n # Do Inference with TFLite Model\n ndvi_rgb_image = ndvi_rgb_image.astype('float32')\n prediction = make_tflite_inference(\n ndvi_rgb_image, mobilenetv2_interpreter)\n\n # Get Decoded Inference results\n decoded_prediction = decode_prediction(prediction)\n\n # Write Prediction as CSV to disk\n logger.info(\n f'Logging NO2 prediction \\\"{decoded_prediction}\\\" for {pic_name}')\n exif_data = get_img_exif(pic_name, iss, decoded_prediction)\n row = (exif_data['Date/Time'], exif_data['Location'],\n pic_name, exif_data['NO2'])\n with open(data_file, mode='a') as f:\n writer = csv.writer(f)\n writer.writerow(row)\n\n # update the current time\n now_time = datetime.now()\n i = i+1\n\n # End Loop over 3 hours\n\n # Experiment End\n end_time = datetime.now()\n logger.info(\n f'Finishing Bergson Astro Pi team experiment at {end_time.strftime(\"%Y-%m-%d %H:%M:%S\")}')\n experiment_time = end_time - start_time\n logger.info(f'Bergson Astro Pi team experiment run time {experiment_time}')\n logger.info('################ Bergson Team Experiment End #################')","def importance_sampling(self, params):\n i_part = params\n\n theta_star = weighted_sampling( self.theta_t_1, self.w_t_1 ) \n np.random.seed()\n\n theta_starstar = multivariate_normal( theta_star, self.sig_t_1 ).rvs(size=1)\n model_starstar = self.simz( theta_starstar )\n\n rho = test_dist(self.data, model_starstar) \n\n while rho > self.eps_t: \n theta_star = weighted_sampling( self.theta_t_1, self.w_t_1 )\n theta_starstar = multivariate_normal(theta_star, self.sig_t_1).rvs(size=1)\n\n model_starstar = self.simz( theta_starstar )\n\n rho = test_dist(self.data, model_starstar) \n\n p_theta = self.prior_of_priors(theta_starstar)\n\n pos_t = np.dstack(self.theta_t_1)\n\n tmp_w_t = p_theta / np.sum(self.w_t_1 * multivariate_normal(self.theta_t[:,i_part], self.sig_t_1).pdf(pos_t))\n\n data_list = [np.int(i_part)]\n\n for i_param in xrange(self.n_params): \n\n data_list.append(theta_starstar[i_param])\n\n data_list.append(tmp_w_t)\n data_list.append(rho)\n\n return np.array(data_list)","def generate(self): \r\n \r\n self.pfn={} # phase joint functions \r\n self.afn={} # anti phase joint functions\r\n\r\n ## Foot and hip -> Lateral motion\r\n foot_func=SinusoidFunction()\r\n foot_func.angular_frequency= self.parameters[\"step_frequency\"]\r\n foot_func.amplitude= self.parameters[\"foot_amplitude\"]\r\n foot_func.amplitude_offset= self.parameters[\"foot_amplitude_offset\"]\r\n foot_func.phase_offset= self.parameters[\"foot_phase_offset\"]\r\n self.pfn[\"l_foot_joint\"]=foot_func \r\n foot_func_af=foot_func.mirror()\r\n self.afn[\"l_foot_joint\"]=foot_func_af\r\n \r\n hip_func=SinusoidFunction()\r\n hip_func.angular_frequency= self.parameters[\"step_frequency\"]\r\n hip_func.amplitude= self.parameters[\"hip_amplitude\"]\r\n hip_func.amplitude_offset= self.parameters[\"hip_amplitude_offset\"]\r\n hip_func.phase_offset= self.parameters[\"hip_phase_offset\"]\r\n self.pfn[\"l_hip_joint\"]=hip_func\r\n hip_func_af=hip_func.mirror()\r\n self.afn[\"l_hip_joint\"]=hip_func_af\r\n \r\n ## Thigh, ankle and knee -> Frontal motion\r\n thigh_func=SinusoidFunction()\r\n thigh_func.angular_frequency= self.parameters[\"step_frequency\"]\r\n thigh_func.amplitude= self.parameters[\"thigh_amplitude\"]\r\n thigh_func.amplitude_offset= self.parameters[\"thigh_amplitude_offset\"]\r\n thigh_func.phase_offset= self.parameters[\"thigh_phase_offset\"]\r\n self.pfn[\"l_thigh_joint\"]=thigh_func\r\n thigh_func_af=thigh_func.mirror_keep_amplitude_offset()\r\n self.afn[\"l_thigh_joint\"]=thigh_func_af\r\n \r\n ankle_func=SinusoidFunction()\r\n ankle_func.angular_frequency= self.parameters[\"step_frequency\"]\r\n ankle_func.amplitude= self.parameters[\"ankle_amplitude\"]\r\n ankle_func.amplitude_offset= self.parameters[\"ankle_amplitude_offset\"]\r\n ankle_func.phase_offset= self.parameters[\"ankle_phase_offset\"]\r\n self.pfn[\"l_ankle_joint\"]=ankle_func\r\n ankle_func_af=ankle_func.mirror_keep_amplitude_offset()\r\n self.afn[\"l_ankle_joint\"]=ankle_func_af\r\n \r\n knee_func=SinusoidFunction()\r\n knee_func.angular_frequency= self.parameters[\"step_frequency\"]\r\n knee_func.amplitude= self.parameters[\"knee_amplitude\"]\r\n knee_func.amplitude_offset= self.parameters[\"knee_amplitude_offset\"]\r\n knee_func.phase_offset= self.parameters[\"knee_phase_offset\"]\r\n self.pfn[\"l_knee_joint\"]=knee_func\r\n knee_func_af=knee_func.mirror_keep_amplitude_offset()\r\n self.afn[\"l_knee_joint\"]=knee_func_af\r\n \r\n #f3=SinusoidFunction()\r\n #f3.angular_frequency=self.parameters[\"step_frequency\"]\r\n #f3.amplitude=self.parameters[\"step_amplitude\"]\r\n #f3.amplitude_offset=self.parameters[\"step_amplitude_offset\"]\r\n #self.pfn[\"l_thigh_joint\"]= f3\r\n #f33=f3.clone()\r\n #f33.amplitude_offset = self.parameters[\"ankle_amplitude_offset\"]\r\n #f33.amplitude = self.parameters[\"ankle_amplitude\"]\r\n #self.pfn[\"l_ankle_joint\"]=f33\r\n #f4=f3.mirror()\r\n ##f4.amplitude_offset -= 0.4\r\n #self.pfn[\"l_knee_joint\"]=f4\r\n \r\n #f5=f3.mirror_keep_amplitude_offset()\r\n #self.afn[\"l_thigh_joint\"]=f5\r\n \r\n #f6=f33.mirror_keep_amplitude_offset()\r\n #self.afn[\"l_ankle_joint\"]=f6\r\n #f7=f5.mirror()\r\n ##f7.amplitude_offset -= 0.4\r\n #self.afn[\"l_knee_joint\"]=f7\r\n \r\n self.generate_right()\r\n \r\n self.show()","def run(config, tim=None):\n import common_lib\n import dr_lib\n import DST\n \n if tim is not None:\n tim.getTime(False)\n old_time = tim.getOldTime()\n\n if config.data is None:\n raise RuntimeError(\"Need to pass a data filename to the driver \"\\\n +\"script.\")\n\n # Read in geometry if one is provided\n if config.inst_geom is not None:\n if config.verbose:\n print \"Reading in instrument geometry file\"\n \n inst_geom_dst = DST.getInstance(\"application/x-NxsGeom\",\n config.inst_geom)\n else:\n inst_geom_dst = None\n\n config.so_axis = \"time_of_flight\"\n\n # Steps 1-3: Produce a scaled summed dark current dataset\n dc_som = dr_lib.scaled_summed_data(config.dkcur, config,\n dataset_type=\"dark_current\",\n timer=tim)\n\n # Perform Steps 3-6 on black can data\n if config.bcan is not None:\n b_som1 = dr_lib.calibrate_dgs_data(config.bcan, config, dc_som,\n dataset_type=\"black_can\",\n inst_geom_dst=inst_geom_dst,\n tib_const=config.tib_const,\n cwp=config.cwp_bcan,\n timer=tim)\n else:\n b_som1 = None\n\n # Perform Steps 3-6 on empty can data \n if config.ecan is not None:\n e_som1 = dr_lib.calibrate_dgs_data(config.ecan, config, dc_som,\n dataset_type=\"empty_can\",\n inst_geom_dst=inst_geom_dst,\n tib_const=config.tib_const,\n cwp=config.cwp_ecan,\n timer=tim)\n else:\n e_som1 = None\n\n # Perform Steps 3-6 on normalization data\n n_som1 = dr_lib.calibrate_dgs_data(config.data, config, dc_som,\n dataset_type=\"normalization\",\n inst_geom_dst=inst_geom_dst,\n tib_const=config.tib_const,\n cwp=config.cwp_data,\n timer=tim)\n\n # Perform Steps 7-16 on normalization data\n if config.norm_trans_coeff is None:\n norm_trans_coeff = None\n else:\n norm_trans_coeff = config.norm_trans_coeff.toValErrTuple()\n\n # Determine if we need to rebin the empty or black can data\n if config.ecan is not None and e_som1 is not None:\n ecan_cwp = True\n else:\n ecan_cwp = False\n\n if config.bcan is not None and b_som1 is not None:\n bcan_cwp = True\n else:\n bcan_cwp = False \n\n cwp_used = ecan_cwp or bcan_cwp\n\n n_som2 = dr_lib.process_dgs_data(n_som1, config, b_som1, e_som1,\n norm_trans_coeff,\n dataset_type=\"normalization\",\n cwp_used=cwp_used,\n timer=tim)\n \n del n_som1, b_som1, e_som1\n\n # Step 17: Integrate normalization spectra\n if config.verbose:\n print \"Integrating normalization spectra\"\n\n if tim is not None:\n tim.getTime(False)\n\n if config.norm_int_range is None:\n start_val = float(\"inf\")\n end_val = float(\"inf\")\n else:\n if not config.wb_norm:\n # Translate energy transfer to final energy\n ef_start = config.initial_energy.getValue() - \\\n config.norm_int_range[0]\n ef_end = config.initial_energy.getValue() - \\\n config.norm_int_range[1]\n # Convert final energy to final wavelength\n start_val = common_lib.energy_to_wavelength((ef_start, 0.0))[0]\n end_val = common_lib.energy_to_wavelength((ef_end, 0.0))[0]\n else:\n start_val = config.norm_int_range[0]\n end_val = config.norm_int_range[1]\n \n n_som3 = dr_lib.integrate_spectra(n_som2, start=start_val,\n end=end_val, width=True)\n\n del n_som2\n \n if tim is not None:\n tim.getTime(msg=\"After integrating normalization spectra \")\n\n file_comment = \"Normalization Integration range: %0.3fA, %0.3fA\" \\\n % (start_val, end_val)\n \n hlr_utils.write_file(config.output, \"text/num-info\", n_som3,\n output_ext=\"norm\",\n data_ext=config.ext_replacement,\n path_replacement=config.path_replacement,\n verbose=config.verbose,\n message=\"normalization values\",\n comments=[file_comment],\n tag=\"Integral\", units=\"counts\") \n \n if tim is not None:\n tim.getTime(False)\n\n if config.verbose:\n print \"Making mask file\"\n\n # Make mask file from threshold\n dr_lib.filter_normalization(n_som3, config.lo_threshold,\n config.hi_threshold, config)\n\n if tim is not None:\n tim.getTime(msg=\"After making mask file \")\n\n # Write out RMD file\n n_som3.attr_list[\"config\"] = config\n\n hlr_utils.write_file(config.output, \"text/rmd\", n_som3,\n output_ext=\"rmd\",\n data_ext=config.ext_replacement, \n path_replacement=config.path_replacement,\n verbose=config.verbose,\n message=\"metadata\")\n \n if tim is not None:\n tim.setOldTime(old_time)\n tim.getTime(msg=\"Total Running Time\")","def generate(self):\n self.training_data.gen_x(self.x_func)\n self.training_data.gen_a(self.a_func)\n self.training_data.gen_y(self.y_func)\n \n self.testing_data.gen_x(self.x_func)\n self.testing_data.gen_ys(self.y_func)\n self.testing_data.gen_azero(self.ytotal_func)","def split_full_4epochs_iter3 (params, ns):\n #29 parameters \n nu1x, nuA, nu1a, nu2a, nu3a, nu1b, nu2b, nu3b, nu1c, nu2c, nu3c, m0_12, m0_21, m1_12, m1_13, m1_21, m1_23, m1_31, m1_32, m2_12, m2_13, m2_21, m2_23, m2_31, m2_32, T0, T1, T2, T3 = params\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1] + ns[2])\n fs = moments.Spectrum(sts)\n fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1] + ns[2])\n ## Population function and migration matrix for T0 (initial split; the definition of this time epoch differentiates this model from refugia_adj_5_simsplit_4epochs)\n nu_T0 = [nu1x, nuA]\n mig0 = numpy.array([[0, m0_12],[m0_21, 0]])\n fs.integrate(nu_T0, T0, m=mig0)\n fs = moments.Manips.split_2D_to_3D_2(fs, ns[1], ns[2])\n ## Population function and migration matrix for T1\n nu_T1 = [nu1a, nu2a, nu3a]\n mig1 = numpy.array([[0, m1_12, m1_13],[m1_21, 0, m1_23], [m1_31, m1_32, 0]]) \n fs.integrate(nu_T1, T1, m=mig1)\n ## Population function and migration matrix for T2\n nu_T2 = [nu1b, nu2b, nu3b]\n mig2 = numpy.array([[0, m2_12, m2_13],[m2_21, 0, m2_23], [m2_31, m2_32, 0]]) \n fs.integrate(nu_T2, T2, m=mig2)\n ## Population function and migration matrix for T3 (bottleneck to capture single population representation of lineage)\n nu_T3 = [nu1c, nu2c, nu3c]\n fs.integrate(nu_T3, T3) \n return fs","def prepare_experiment(assumptions):\n print(\"\\nGenerate species parameters\")\n np.random.seed(assumptions['seed']) \n params = MakeParams(assumptions) \n if assumptions[\"selected_function\"] == \"f5_invader_suppression\":\n print(\"\\nDraw invader feature\")\n params = create_invader(params, assumptions)\n print(params[\"c\"])\n \n print(\"\\nDraw per-capita function and cost\")\n f1_species_smooth, f1_species_rugged, f2_species_smooth, f2_species_rugged = draw_species_function(assumptions)\n params.update({\"f1_species_smooth\": f1_species_smooth, \"f1_species_rugged\": f1_species_rugged, \"f2_species_smooth\": f2_species_smooth, \"f2_species_rugged\": f2_species_rugged})\n gi = draw_species_cost(f1_species_smooth, assumptions)\n params.update({\"g\": gi})\n \n print(\"\\nConstruct plate\")\n np.random.seed(assumptions['seed']) \n plate = make_plate(assumptions,params)\n \n print(\"\\nAdd community function to plate\")\n plate = add_community_function(plate, assumptions, params)\n \n if not pd.isnull(assumptions[\"overwrite_plate\"]) :\n print(\"\\nUpdating the initial plate composition by overwrite_plate\")\n plate = overwrite_plate(plate, assumptions)\n \n print(\"\\nPrepare Protocol\")\n #Extract Protocol from protocol database\n algorithms = make_algorithms(assumptions)\n params_algorithm = algorithms[algorithms['algorithm_name'] == assumptions['protocol']]\n \n #Params_simulation by default contains all assumptions not stored in params.\n params_simulation = dict((k, assumptions[k]) for k in assumptions.keys() if k not in params.keys())\n \n return params, params_simulation , params_algorithm, plate","def train():\n\n # Load camera parameters\n rcams = cameras.load_cameras()\n\n # Load 3d data and 2d projections\n full_train_set_3d, full_test_set_3d, data_mean_3d, data_std_3d, dim_to_ignore_3d, dim_to_use_3d =\\\n data_utils.read_3d_data( FLAGS.camera_frame, rcams, FLAGS.origin_bc, FLAGS.augment_data,\n FLAGS.procrustes, FLAGS.lowpass )\n \n # Read stacked hourglass 2D predictions\n full_train_set_2d, full_test_set_2d, data_mean_2d, data_std_2d, dim_to_ignore_2d, dim_to_use_2d = \\\n data_utils.read_2d_predictions( FLAGS.origin_bc, FLAGS.augment_data )\n \n print(\"\\n[+] done reading and normalizing data\")\n # Getting the number of training and test subjects\n tr_subj = 0\n for v in full_train_set_3d.values():\n tr_subj += v.shape[0]\n te_subj = 0\n for v in full_test_set_3d.values():\n te_subj += v.shape[0]\n print(\"{0} training subjects, {1} test subjects\".format(tr_subj, te_subj))\n print(dim_to_use_2d)\n print(dim_to_use_3d)\n # Un-normalizing data for visualizations\n unNorm_ftrs2d = data_utils.unNormalize_dic(full_train_set_2d, data_mean_2d, data_std_2d, dim_to_use_2d)\n unNorm_ftrs3d = data_utils.unNormalize_dic(full_train_set_3d, data_mean_3d, data_std_3d, dim_to_use_3d)\n unNorm_ftes3d = data_utils.unNormalize_dic(full_test_set_3d, data_mean_3d, data_std_3d, dim_to_use_3d)\n # Visualize the data\n viz.visualize_train_sample(unNorm_ftrs2d, unNorm_ftrs3d, FLAGS.camera_frame)\n viz.visualize_files_oneatatime(unNorm_ftrs3d, unNorm_ftes3d)\n\n # Getting only the dimensions to use (get rid of body coxas, other limb, antennas, abdomen\n train_set_3d, train_set_2d, test_set_3d, test_set_2d = {}, {}, {}, {}\n for k in full_train_set_3d:\n (f, c) = k\n train_set_3d[k] = full_train_set_3d[k][:, dim_to_use_3d]\n train_set_2d[(f, data_utils.CAMERA_TO_USE)] =\\\n full_train_set_2d[(f, data_utils.CAMERA_TO_USE)][:, dim_to_use_2d]\n for k in full_test_set_3d:\n (f, c) = k\n test_set_3d[k] = full_test_set_3d[k][:, dim_to_use_3d]\n test_set_2d[(f, data_utils.CAMERA_TO_USE)] =\\\n full_test_set_2d[(f, data_utils.CAMERA_TO_USE)][:, dim_to_use_2d]\n \n print(\"3D data mean:\")\n print(data_mean_3d)\n print(\"3D data std:\")\n print(data_std_3d)\n\n print(\"2D data mean:\")\n print(data_mean_2d)\n print(\"2D data std:\")\n print(data_std_2d)\n \n input(\"Press Enter to continue...\")\n\n # Avoid using the GPU if requested\n device_count = {\"GPU\": 0} if FLAGS.use_cpu else {\"GPU\": 1}\n with tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(\n device_count=device_count,\n allow_soft_placement=True )) as sess:\n\n # === Create the model ===\n print(\"[*] creating %d bi-layers of %d units.\" % (FLAGS.num_layers, FLAGS.linear_size))\n model = create_model( sess, FLAGS.batch_size )\n model.train_writer.add_graph( sess.graph )\n print(\"[+] model created\")\n \n #=== This is the training loop ===\n step_time, loss, val_loss = 0.0, 0.0, 0.0\n current_step = 0 if FLAGS.load <= 0 else FLAGS.load + 1\n previous_losses = []\n\n step_time, loss = 0, 0\n current_epoch = 0\n log_every_n_batches = 100\n losses, errors, joint_errors = [], [], []\n for _ in range( FLAGS.epochs ):\n current_epoch = current_epoch + 1\n\n # === Load training batches for one epoch ===\n encoder_inputs, decoder_outputs =\\\n model.get_all_batches( train_set_2d, train_set_3d, FLAGS.camera_frame, training=True )\n nbatches = len( encoder_inputs )\n print(\"[*] there are {0} train batches\".format( nbatches ))\n start_time, loss = time.time(), 0.\n # === Loop through all the training batches ===\n for i in range( nbatches ):\n\n if (i+1) % log_every_n_batches == 0:\n # Print progress every log_every_n_batches batches\n print(\"Working on epoch {0}, batch {1} / {2}...\".format( current_epoch, i+1, nbatches),end=\"\" )\n\n enc_in, dec_out = encoder_inputs[i], decoder_outputs[i]\n step_loss, loss_summary, lr_summary, _ =\\\n model.step( sess, enc_in, dec_out, FLAGS.dropout, isTraining=True )\n\n if (i+1) % log_every_n_batches == 0:\n # Log and print progress every log_every_n_batches batchespixels = pixels / pixels[2,:]\n model.train_writer.add_summary( loss_summary, current_step )\n model.train_writer.add_summary( lr_summary, current_step )\n step_time = (time.time() - start_time)\n start_time = time.time()\n print(\"done in {0:.2f} ms\".format( 1000*step_time / log_every_n_batches ) )\n\n loss += step_loss\n current_step += 1\n # === end looping through training batches ===\n\n loss = loss / nbatches\n losses.append(loss)\n print(\"=============================\\n\"\n \"Global step: %d\\n\"\n \"Learning rate: %.2e\\n\"\n \"Train loss avg: %.4f\\n\"\n \"=============================\" % (model.global_step.eval(),\n model.learning_rate.eval(), loss) )\n # === End training for an epoch ===\n\n # === Testing after this epoch ===\n isTraining = False\n \n n_joints = len(data_utils.DIMENSIONS_TO_USE)\n if FLAGS.origin_bc:\n n_joints -= len(data_utils.ROOT_POSITIONS)\n\n encoder_inputs, decoder_outputs =\\\n model.get_all_batches( test_set_2d, test_set_3d, FLAGS.camera_frame, training=False)\n\n total_err, coordwise_err, joint_err, step_time, loss = evaluate_batches( sess, model,\n data_mean_3d, data_std_3d, dim_to_use_3d, dim_to_ignore_3d,\n data_mean_2d, data_std_2d, dim_to_use_2d, dim_to_ignore_2d,\n current_step, encoder_inputs, decoder_outputs, current_epoch )\n\n print(\"=============================\\n\"\n \"Step-time (ms): %.4f\\n\"\n \"Val loss avg: %.4f\\n\"\n \"Val error avg (mm): %.2f (%.2f, %.2f, %.2f)\\n\"\n \"=============================\" % ( 1000*step_time, loss, total_err,\n coordwise_err[0], coordwise_err[1], coordwise_err[2] ))\n\n for i in range(n_joints):\n # 6 spaces, right-aligned, 5 decimal places\n print(\"Error in joint {0:02d} (mm): {1:>5.2f}\".format(i+1, joint_err[i]))\n print(\"=============================\")\n errors.append(coordwise_err)\n joint_errors.append(joint_err)\n # Log the error to tensorboard\n summaries = sess.run( model.err_mm_summary, {model.err_mm: total_err} )\n model.test_writer.add_summary( summaries, current_step )\n\n # Save the model\n print( \"Saving the model... \", end=\"\" )\n start_time = time.time()\n model.saver.save(sess, os.path.join(train_dir, 'checkpoint'), global_step=current_step )\n print( \"done in {0:.2f} ms\".format(1000*(time.time() - start_time)) )\n\n # Reset global time and loss\n step_time, loss = 0, 0\n\n sys.stdout.flush()\n # Save losses for future plots\n def print_list_tofile(l, filename):\n with open(filename, 'wb') as f:\n pickle.dump(l, f)\n print_list_tofile(losses, train_dir+\"/losses.pkl\")\n print_list_tofile(errors, train_dir+\"/errors.pkl\")\n print_list_tofile(joint_errors, train_dir+\"/joint_errors.pkl\")","def sample_random_architecture(self, dataset_api):\n while True:\n matrix = np.random.choice(\n [0, 1], size=(NUM_VERTICES, NUM_VERTICES))\n matrix = np.triu(matrix, 1)\n ops = np.random.choice(OPS, size=NUM_VERTICES).tolist()\n ops[0] = INPUT\n ops[-1] = OUTPUT\n spec = dataset_api['api'].ModelSpec(matrix=matrix, ops=ops)\n if dataset_api['nb101_data'].is_valid(spec):\n break\n \n self.set_spec({'matrix':matrix, 'ops':ops})","def runSimulation(num_robots, speed, width, height, min_coverage, num_trials,\n robot_type, visualize):\n #initialization of variables\n list_of_results = []\n \n #trial loop\n for i in range(num_trials):\n list_of_results.append(singleSimulation(num_robots, speed, width, height, min_coverage, robot_type, visualize))\n return list_of_results"],"string":"[\n \"def get_param_scenario3():\\n nb_carre_x = 42\\n nb_carre_y = 45\\n largeur_x = 0.825\\n largeur_y = 0.645\\n z1 = np.array([[11], [32], [17.26]])\\n z2 = np.array([[12], [32], [18.43]])\\n z3 = np.array([[13], [32], [23.2]])\\n z4 = np.array([[15], [31], [23.4]])\\n z5 = np.array([[16], [30], [23.29]])\\n z6 = np.array([[18], [29], [22.28]])\\n z7 = np.array([[19], [28], [35.79]])\\n z8 = np.array([[20], [27], [36.87]])\\n z9 = np.array([[21], [26], [33.92]])\\n z10 = np.array([[23], [24], [38.11]])\\n z11 = np.array([[24], [23], [37.76]])\\n z12 = np.array([[25], [22], [45.6]])\\n z13 = np.array([[26], [20], [56.4]])\\n z14 = np.array([[27], [18], [55.2]])\\n z15 = np.array([[28], [16], [57.53]])\\n z16 = np.array([[29], [14], [58.64]])\\n z17 = np.array([[30], [13], [66.04]])\\n z18 = np.array([[30], [11], [70.02]])\\n z19 = np.array([[31], [9], [69.64]])\\n z20 = np.array([[31], [6], [68.82]])\\n\\n meas = [z1, z2, z3, z4, z5, z6, z7, z8, z9, z10, z11, z12, z13, z14, z15, z16, z17, z18, z19, z20]\\n\\n z = [np.array([[x[0][0] * largeur_x / nb_carre_x], [x[1][0] * largeur_y / nb_carre_y], [-x[2][0] * np.pi / 180]])\\n for x in meas]\\n\\n # We take a measure every second\\n thymio_speed_to_mms = 0.4753\\n Ts = 1\\n\\n Thymio_speed_left = [73, 92, 94, 102, 100, 89, 92, 103, 97, 100, 100, 104, 95, 97, 103, 101, 100, 101, 92, 98]\\n Thymio_speed_right = [67, 96, 102, 89, 105, -45, 102, 101, 104, 71, 100, 93, 94, 105, 96, -45, 95, 103, 88, 94]\\n delta_sr_test = [x * Ts / thymio_speed_to_mms / 1000 for x in Thymio_speed_right]\\n delta_sl_test = [x * Ts / thymio_speed_to_mms / 1000 for x in Thymio_speed_left]\\n\\n return delta_sr_test, delta_sl_test, z\",\n \"def generate_scenario_2(seed=1996, permanent_save=True, sigma_process=0.01, sigma_meas_radar=3, sigma_meas_ais=1,\\n timesteps=20):\\n start_time = datetime.now()\\n\\n # specify seed to be able repeat example\\n np.random.seed(seed)\\n\\n # combine two 1-D CV models to create a 2-D CV model\\n transition_model = CombinedLinearGaussianTransitionModel(\\n [ConstantVelocity(sigma_process), ConstantVelocity(sigma_process)])\\n\\n # starting at 0,0 and moving NE\\n truth = GroundTruthPath([GroundTruthState([0, 1, 0, 1], timestamp=start_time)])\\n\\n # generate truth using transition_model and noise\\n for k in range(1, timesteps):\\n truth.append(GroundTruthState(\\n transition_model.function(truth[k - 1], noise=True, time_interval=timedelta(seconds=1)),\\n timestamp=start_time + timedelta(seconds=k)))\\n\\n # Simulate measurements\\n # Specify measurement model for radar\\n measurement_model_radar = LinearGaussian(\\n ndim_state=4, # number of state dimensions\\n mapping=(0, 2), # mapping measurement vector index to state index\\n noise_covar=np.array([[sigma_meas_radar, 0], # covariance matrix for Gaussian PDF\\n [0, sigma_meas_radar]])\\n )\\n\\n # Specify measurement model for AIS (Same as for radar)\\n measurement_model_ais = LinearGaussian(\\n ndim_state=4,\\n mapping=(0, 2),\\n noise_covar=np.array([[sigma_meas_ais, 0],\\n [0, sigma_meas_ais]])\\n )\\n\\n # generate \\\"radar\\\" measurements\\n measurements_radar = []\\n for state in truth:\\n measurement = measurement_model_radar.function(state, noise=True)\\n measurements_radar.append(Detection(measurement, timestamp=state.timestamp))\\n\\n # generate \\\"AIS\\\" measurements\\n measurements_ais = []\\n for state in truth:\\n measurement = measurement_model_ais.function(state, noise=True)\\n measurements_ais.append(Detection(measurement, timestamp=state.timestamp))\\n\\n if permanent_save:\\n save_folder_name = seed.__str__()\\n else:\\n save_folder_name = \\\"temp\\\"\\n\\n save_folder = \\\"../scenarios/scenario2/\\\" + save_folder_name + \\\"/\\\"\\n\\n # save the ground truth and the measurements for the radar and the AIS\\n store_object.store_object(truth, save_folder, \\\"ground_truth.pk1\\\")\\n store_object.store_object(measurements_radar, save_folder, \\\"measurements_radar.pk1\\\")\\n store_object.store_object(measurements_ais, save_folder, \\\"measurements_ais.pk1\\\")\\n store_object.store_object(start_time, save_folder, \\\"start_time.pk1\\\")\\n store_object.store_object(measurement_model_radar, save_folder, \\\"measurement_model_radar.pk1\\\")\\n store_object.store_object(measurement_model_ais, save_folder, \\\"measurement_model_ais.pk1\\\")\\n store_object.store_object(transition_model, save_folder, \\\"transition_model.pk1\\\")\",\n \"def generate_scenario_1(seed=1996, permanent_save=True, sigma_process=0.01, sigma_meas_radar=3, sigma_meas_ais=1):\\n # specify seed to be able repeat example\\n start_time = datetime.now()\\n\\n np.random.seed(seed)\\n\\n # combine two 1-D CV models to create a 2-D CV model\\n transition_model = CombinedLinearGaussianTransitionModel([ConstantVelocity(sigma_process),\\n ConstantVelocity(sigma_process)])\\n\\n # starting at 0,0 and moving NE\\n truth = GroundTruthPath([GroundTruthState([0, 1, 0, 1], timestamp=start_time)])\\n\\n # generate truth using transition_model and noise\\n for k in range(1, 21):\\n truth.append(GroundTruthState(\\n transition_model.function(truth[k - 1], noise=True, time_interval=timedelta(seconds=1)),\\n timestamp=start_time + timedelta(seconds=k)))\\n\\n # Simulate measurements\\n # Specify measurement model for radar\\n measurement_model_radar = LinearGaussian(\\n ndim_state=4, # number of state dimensions\\n mapping=(0, 2), # mapping measurement vector index to state index\\n noise_covar=np.array([[sigma_meas_radar, 0], # covariance matrix for Gaussian PDF\\n [0, sigma_meas_radar]])\\n )\\n\\n # Specify measurement model for AIS\\n measurement_model_ais = LinearGaussian(\\n ndim_state=4,\\n mapping=(0, 2),\\n noise_covar=np.array([[sigma_meas_ais, 0],\\n [0, sigma_meas_ais]])\\n )\\n\\n # generate \\\"radar\\\" measurements\\n measurements_radar = []\\n for state in truth:\\n measurement = measurement_model_radar.function(state, noise=True)\\n measurements_radar.append(Detection(measurement, timestamp=state.timestamp))\\n\\n # generate \\\"AIS\\\" measurements\\n measurements_ais = []\\n state_num = 0\\n for state in truth:\\n state_num += 1\\n if not state_num % 2: # measurement every second time step\\n measurement = measurement_model_ais.function(state, noise=True)\\n measurements_ais.append(Detection(measurement, timestamp=state.timestamp))\\n\\n if permanent_save:\\n save_folder_name = seed.__str__()\\n else:\\n save_folder_name = \\\"temp\\\"\\n\\n save_folder = \\\"../scenarios/scenario1/\\\" + save_folder_name + \\\"/\\\"\\n\\n # save the ground truth and the measurements for the radar and the AIS\\n store_object.store_object(truth, save_folder, \\\"ground_truth.pk1\\\")\\n store_object.store_object(measurements_radar, save_folder, \\\"measurements_radar.pk1\\\")\\n store_object.store_object(measurements_ais, save_folder, \\\"measurements_ais.pk1\\\")\\n store_object.store_object(start_time, save_folder, \\\"start_time.pk1\\\")\\n store_object.store_object(measurement_model_radar, save_folder, \\\"measurement_model_radar.pk1\\\")\\n store_object.store_object(measurement_model_ais, save_folder, \\\"measurement_model_ais.pk1\\\")\\n store_object.store_object(transition_model, save_folder, \\\"transition_model.pk1\\\")\",\n \"def scenario3(sim):\\n\\n init_logging(logfile=None, debug=True)\\n second = 1000.0\\n duration = 10\\n tau_m = 20 # ms\\n cm = 1.0 # nF\\n v_reset = -60\\n cell_parameters = dict(\\n tau_m=tau_m,\\n cm=cm,\\n v_rest=-70,\\n e_rev_E=0,\\n e_rev_I=-70,\\n v_thresh=-54,\\n v_reset=v_reset,\\n tau_syn_E=5,\\n tau_syn_I=5,\\n )\\n g_leak = cm / tau_m # µS\\n\\n w_min = 0.0 * g_leak\\n w_max = 0.05 * g_leak\\n\\n r1 = 5.0\\n r2 = 40.0\\n\\n sim.setup()\\n pre = sim.Population(100, sim.SpikeSourcePoisson())\\n post = sim.Population(10, sim.IF_cond_exp())\\n\\n pre.set(duration=duration * second)\\n pre.set(start=0.0)\\n pre[:50].set(rate=r1)\\n pre[50:].set(rate=r2)\\n assert_equal(pre[49].rate, r1)\\n assert_equal(pre[50].rate, r2)\\n post.set(**cell_parameters)\\n post.initialize(v=RandomDistribution('normal', mu=v_reset, sigma=5.0))\\n\\n stdp = sim.STDPMechanism(\\n sim.SpikePairRule(tau_plus=20.0, tau_minus=20.0,\\n A_plus=0.01, A_minus=0.01),\\n sim.AdditiveWeightDependence(w_min=w_min, w_max=w_max),\\n #dendritic_delay_fraction=0.5))\\n dendritic_delay_fraction=1)\\n\\n connections = sim.Projection(pre, post, sim.AllToAllConnector(),\\n synapse_type=stdp,\\n receptor_type='excitatory')\\n\\n initial_weight_distr = RandomDistribution('uniform', low=w_min, high=w_max)\\n connections.randomizeWeights(initial_weight_distr)\\n initial_weights = connections.get('weight', format='array', gather=False)\\n # assert initial_weights.min() >= w_min\\n # assert initial_weights.max() < w_max\\n # assert initial_weights[0, 0] != initial_weights[1, 0]\\n\\n pre.record('spikes')\\n post.record('spikes')\\n post[0:1].record('v')\\n\\n sim.run(duration * second)\\n\\n actual_rate = pre.mean_spike_count() / duration\\n expected_rate = (r1 + r2) / 2\\n errmsg = \\\"actual rate: %g expected rate: %g\\\" % (actual_rate, expected_rate)\\n assert abs(actual_rate - expected_rate) < 1, errmsg\\n #assert abs(pre[:50].mean_spike_count()/duration - r1) < 1\\n #assert abs(pre[50:].mean_spike_count()/duration- r2) < 1\\n final_weights = connections.get('weight', format='array', gather=False)\\n assert initial_weights[0, 0] != final_weights[0, 0]\\n\\n try:\\n import scipy.stats\\n except ImportError:\\n raise SkipTest\\n t, p = scipy.stats.ttest_ind(initial_weights[:50, :].flat, initial_weights[50:, :].flat)\\n assert p > 0.05, p\\n t, p = scipy.stats.ttest_ind(final_weights[:50, :].flat, final_weights[50:, :].flat)\\n assert p < 0.01, p\\n assert final_weights[:50, :].mean() < final_weights[50:, :].mean()\\n sim.end()\\n return initial_weights, final_weights, pre, post, connections\",\n \"def sample(self, params):\\r\\n old_model_trace = poutine.trace(self.model)(self.args, self.kwargs)\\r\\n traces = []\\r\\n t = 0\\r\\n i = 0\\r\\n while t < self.burn + self.lag * self.samples:\\r\\n i += 1\\r\\n # q(z' | z)\\r\\n new_guide_trace = poutine.block(\\r\\n poutine.trace(self.model))(old_model_trace, self.args, self.kwargs)\\r\\n # p(x, z')\\r\\n new_model_trace = poutine.trace(\\r\\n poutine.replay(self.model, new_guide_trace))(self.args, self.kwargs)\\r\\n # q(z | z')\\r\\n old_guide_trace = poutine.block(\\r\\n poutine.trace(\\r\\n poutine.replay(self.guide, old_model_trace)))(new_model_trace,\\r\\n self.args, self.kwargs)\\r\\n # p(x, z') q(z' | z) / p(x, z) q(z | z')\\r\\n logr = new_model_trace.log_pdf() + new_guide_trace.log_pdf() - \\\\\\r\\n old_model_trace.log_pdf() - old_guide_trace.log_pdf()\\r\\n rnd = pyro.sample(\\\"mh_step_{}\\\".format(i),\\r\\n Uniform(torch.zeros(1), torch.ones(1)))\\r\\n\\r\\n if torch.log(rnd).data[0] < logr.data[0]:\\r\\n # accept\\r\\n t += 1\\r\\n old_model_trace = new_model_trace\\r\\n if t <= self.burn or (t > self.burn and t % self.lag == 0):\\r\\n yield (new_model_trace, new_model_trace.log_pdf())\",\n \"def run():\\n trials = 100\\n\\n multipliers = [0.25, 0.3, 0.35, 0.5, 0.75, 1, 1.25, 1.45, 1.5, 1.55, 1.6] # Coefficients for learning rate\\n\\n mean_penalty = []\\n median_penalty = []\\n std_penalty = []\\n\\n mean_trial_time = []\\n median_trial_time = []\\n std_trial_time = []\\n\\n mean_success_rate = []\\n median_success_rate = []\\n std_success_rate = []\\n\\n for m in multipliers:\\n all_penalties = [] # All penalties from trail sets\\n all_average_trial_time = []\\n all_success_rates = []\\n\\n for i in range(0, 20):\\n # print \\\"Trial set:\\\", i\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n agent = e.create_agent(LearnerAgent) # create agent\\n agent.mult = m\\n e.set_primary_agent(agent, enforce_deadline=True) # specify agent to track\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.0, display=False) # create simulator (uses pygame when display=True, if available)\\n\\n sim.run(n_trials=trials) # run for a specified number of trials\\n\\n all_penalties.append(agent.all_trails_penalties)\\n all_average_trial_time.append(agent.time/float(trials))\\n all_success_rates.append(float(trials-agent.aborted_trials)/trials)\\n\\n mean_penalty.append(np.mean(all_penalties))\\n median_penalty.append(np.median(all_penalties))\\n std_penalty.append(np.std(all_penalties))\\n\\n mean_trial_time.append(np.mean(all_average_trial_time))\\n median_trial_time.append(np.median(all_average_trial_time))\\n std_trial_time.append(np.std(all_average_trial_time))\\n\\n mean_success_rate.append(np.mean(all_success_rates))\\n median_success_rate.append(np.median(all_success_rates))\\n std_success_rate.append(np.std(all_success_rates))\\n\\n for i in range(0, len(multipliers)):\\n print \\\"\\\"\\n print \\\"Multiplier:\\\", multipliers[i]\\n print \\\"\\\"\\n print \\\"Mean penalty per {} trials:\\\".format(trials), mean_penalty[i]\\n print \\\"Median penalty per {} trials:\\\".format(trials), median_penalty[i]\\n print \\\"Std.Dev. penalty per {} trials:\\\".format(trials), std_penalty[i]\\n\\n print \\\"\\\"\\n print \\\"Mean trial time:\\\", mean_trial_time[i]\\n print \\\"Median trial time:\\\", median_trial_time[i]\\n print \\\"Std.Dev. trial time:\\\", std_trial_time[i]\\n\\n print \\\"\\\"\\n print \\\"Mean success rate per {} trials:\\\".format(trials), mean_success_rate[i]\\n print \\\"Median success rate per {} trials:\\\".format(trials), median_success_rate[i]\\n print \\\"Std.Dev. success rate per {} trials:\\\".format(trials), std_success_rate[i]\",\n \"def run_experiments() :\\n #%%\\n target_size=(32,32)\\n g_specs = {\\n \\\"batch_size\\\" : [ 30 , 60, 100 ],\\n \\\"learning_rate\\\" : [ 0.0002, 0.0003, 0.0005 ],\\n \\\"drop_out_rate\\\" : [ 0.2, 0.25, 0.3 ],\\n \\\"rescale_mode\\\" : [ \\\"max_q\\\" , \\\"max\\\", \\\"\\\" ]\\n }\\n\\n model_traits = MODEL_TRAITS[\\\"model2\\\"].copy()\\n tt_obj = model_traits[\\\"trainer_tester_class\\\"]( model_traits )\\n del model_traits[\\\"trainer_tester_class\\\"]\\n\\n cnt = 0\\n for batchs, lrate, do_rate, resc_mode in product( g_specs[\\\"batch_size\\\"],\\n g_specs[\\\"learning_rate\\\"],\\n g_specs[\\\"drop_out_rate\\\"],\\n g_specs[\\\"rescale_mode\\\"] ) :\\n\\n tt_obj.model_traits.update( {\\\"batch_size\\\" : batchs,\\n \\\"learning_rate\\\" : lrate,\\n \\\"rescale_mode\\\" : resc_mode,\\n \\\"drop_out_rate\\\" : do_rate } )\\n\\n train_4d, train_gt = tu.make_4d_arrays( images_dir=\\\"images/train\\\",\\n target_size=target_size )\\n\\n test_4d, test_gt = tu.make_4d_arrays( images_dir=\\\"images/test\\\",\\n target_size=target_size )\\n\\n data = {\\\"train_4d\\\" : train_4d,\\n \\\"test_4d\\\" : test_4d,\\n \\\"train_y\\\" : train_gt,\\n \\\"test_y\\\" : test_gt}\\n\\n valid_accu_log, train_accu_log = tt_obj.train( model_traits, data,\\n logl=100 )\\n idx_v = int(np.argmax( valid_accu_log))\\n idx_t = int(np.argmax( train_accu_log))\\n\\n model_traits.update({\\\"valid_accu_log\\\" : valid_accu_log,\\n \\\"train_accu_log\\\" : train_accu_log,\\n \\\"best_valid\\\" : max(valid_accu_log),\\n \\\"best_valid_at\\\" : idx_v,\\n \\\"train_at_best_valid\\\" : train_accu_log[idx_v],\\n \\\"best_train\\\" : max(train_accu_log),\\n \\\"best_train_at\\\": idx_t })\\n\\n #print(cnt, pformat(model_traits) )\\n print( \\\"%d : best_train = %.4f, best_valid = %.4f\\\" % \\\\\\n (cnt, max(train_accu_log), max(valid_accu_log) ))\\n\\n with open( \\\"exp_results_%d.json\\\" % cnt,\\n \\\"wt\\\" , encoding=\\\"utf8\\\" ) as f_out :\\n print( json.dumps( model_traits ), file=f_out)\\n\\n\\n cnt += 1\\n #%%\",\n \"def test_f3_circuit_maker(self):\\n fho = tfho.test_file_handle_object()\\n W = 5\\n D = 6\\n gate_maker = g.TYPE_TO_FAM3_GATE_GEN[g.TEST_TYPES.RANDOM]\\n # family 3 files:\\n circuit_file_name = \\\"circuit_file\\\"\\n circuit_file = fho.get_file_object(circuit_file_name, 'w')\\n input_file_name = \\\"input_file\\\"\\n input_file = fho.get_file_object(input_file_name, 'w')\\n output_file_name = \\\"output_file\\\"\\n output_file = fho.get_file_object(output_file_name, 'w')\\n F = 3\\n # make a family 3 circuit:\\n sr.seed(self.rand_seed)\\n gen = g.f3_circuit_maker(W, D, circuit_file, input_file, output_file,\\n gate_maker)\\n gen.generate()\\n # obtain strings representing the contents of all the resulting files:\\n circuit_string = fho.get_file(circuit_file_name).getvalue()\\n input_string = fho.get_file(input_file_name).getvalue()\\n output_string = fho.get_file(output_file_name).getvalue()\\n # make sure that the input begins and ends with a bracket:\\n self.assertEqual(\\\"[\\\", input_string[0])\\n self.assertEqual(\\\"]\\\", input_string[-1])\\n # make sure that each input element is a bit:\\n for bit in input_string[1:-1]:\\n self.assertTrue((bit == '0') or (bit == '1'))\\n # make sure that the output is a bit:\\n self.assertTrue((output_string == '0') or (output_string == '1'))\\n # make sure that the circuit header contains the correct values:\\n circuit_header = circuit_string.split(\\\"\\\\n\\\")[0]\\n (W_string, D_string, F_string) = circuit_header.split(\\\",\\\")\\n W_value = int(W_string.split(\\\"=\\\")[-1])\\n D_value = int(D_string.split(\\\"=\\\")[-1])\\n F_value = int(F_string.split(\\\"=\\\")[-1])\\n self.assertEqual(W, W_value)\\n self.assertEqual(D, D_value)\\n self.assertEqual(F, F_value)\",\n \"def run_evaluation(self, example):\\n assert isinstance(example, dict)\\n assert 'name' in example\\n\\n name = example['name']\\n house_id, camera_id = pbrs_utils.parse_house_and_camera_ids_from_string(name)\\n\\n gt_bg, gt_objects = sorted(glob.glob(path.join(config.default_out_root, 'v9_gt_mesh/{}/{}/gt*.ply'.format(house_id, camera_id))))\\n gt_depths = sorted(glob.glob(path.join(config.default_out_root, 'v9_gt_mesh/{}/{}/d*.ply'.format(house_id, camera_id))))\\n print(gt_depths)\\n assert len(gt_depths) == 5\\n\\n gt_overhead_fg = sorted(glob.glob(path.join(config.default_out_root, 'v9_gt_overhead_mesh/{}/{}/overhead_fg.ply'.format(house_id, camera_id))))\\n assert len(gt_overhead_fg) == 1\\n gt_overhead_fg = gt_overhead_fg[0]\\n assert path.exists(gt_overhead_fg)\\n\\n pred_depths = sorted(glob.glob(path.join(config.default_out_root, 'v9_pred_depth_mesh/{}/{}/pred_*.ply'.format(house_id, camera_id))))\\n assert len(pred_depths) == 5\\n\\n pred_overhead_fg = sorted(glob.glob(path.join(config.default_out_root, 'v9_pred_depth_mesh/{}/{}/overhead_fg_clipped.ply'.format(house_id, camera_id))))\\n assert len(pred_overhead_fg) == 1\\n pred_overhead_fg = pred_overhead_fg[0]\\n\\n # pred_files_list = sorted(glob.glob(path.join(config.default_out_root, 'v9_pred_depth_mesh/{}/{}/*.ply'.format(house_id, camera_id))))\\n # pred_files = {path.basename(item).split('.')[0]: item for item in pred_files_list}\\n # f3d_pred = path.join(config.default_out_root, 'factored3d_pred/{}/{}/codes_transformed_clipped.ply'.format(house_id, camera_id))\\n\\n # assert path.isfile(f3d_pred), f3d_pred\\n\\n # if 'overhead_fg_clipped' not in pred_files:\\n # print('Overhead file is not available. Skipping for now. {}'.format(name))\\n # return\\n\\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['overhead_fg']), [gt_objects], [pred_files['overhead_fg_clipped']])\\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['0']), [gt_objects], [pred_files['pred_0']])\\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['1']), [gt_objects], [pred_files['pred_1']])\\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['2']), [gt_objects], [pred_files['pred_2']])\\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['0', 'overhead_fg']), [gt_objects], [pred_files['pred_0'], pred_files['overhead_fg_clipped']])\\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['1', 'overhead_fg']), [gt_objects], [pred_files['pred_1'], pred_files['overhead_fg_clipped']])\\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['2', 'overhead_fg']), [gt_objects], [pred_files['pred_2'], pred_files['overhead_fg_clipped']])\\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['0', '1']), [gt_objects], [pred_files['pred_0'], pred_files['pred_1']])\\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['0', '2']), [gt_objects], [pred_files['pred_0'], pred_files['pred_2']])\\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['1', '2']), [gt_objects], [pred_files['pred_1'], pred_files['pred_2']])\\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['0', '1', '2']), [gt_objects], [pred_files['pred_0'], pred_files['pred_1'], pred_files['pred_2']])\\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['0', '1', '2', 'overhead_fg']), [gt_objects], [pred_files['pred_0'], pred_files['pred_1'], pred_files['pred_2'], pred_files['overhead_fg_clipped']])\\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['0', '1', '2', 'overhead_fg', 'f3d']), [gt_objects], [pred_files['pred_0'], pred_files['pred_1'], pred_files['pred_2'], pred_files['overhead_fg_clipped'], f3d_pred])\\n # self.run_if_not_exists(name, self.key('pred', 'obj', ['f3d']), [gt_objects], [f3d_pred])\\n\\n # self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['1', '2', '3']), [gt_objects], [gt_depths[1], gt_depths[2], gt_depths[3]])\\n # self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['2', '3']), [gt_objects], [gt_depths[2], gt_depths[3]])\\n # self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['3']), [gt_objects], [gt_depths[3]])\\n\\n self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['0']), [gt_objects], [gt_depths[0]])\\n self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['1']), [gt_objects], [gt_depths[1]])\\n self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['2']), [gt_objects], [gt_depths[2]])\\n self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['3']), [gt_objects], [gt_depths[3]])\\n self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['overhead_fg']), [gt_objects], [gt_overhead_fg])\\n self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['0', '1']), [gt_objects], [gt_depths[0], gt_depths[1]])\\n self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['0', '1', '2']), [gt_objects], [gt_depths[0], gt_depths[1], gt_depths[2]])\\n self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['0', '1', '2', '3']), [gt_objects], [gt_depths[0], gt_depths[1], gt_depths[2], gt_depths[3]])\\n self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['0', '1', '2', '3', 'overhead_fg']), [gt_objects], [gt_depths[0], gt_depths[1], gt_depths[2], gt_depths[3], gt_overhead_fg])\\n\\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0']), [gt_bg, gt_objects], [gt_depths[0]])\\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['1']), [gt_bg, gt_objects], [gt_depths[1]])\\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['2']), [gt_bg, gt_objects], [gt_depths[2]])\\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['3']), [gt_bg, gt_objects], [gt_depths[3]])\\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['4']), [gt_bg, gt_objects], [gt_depths[4]])\\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['overhead_fg']), [gt_bg, gt_objects], [gt_overhead_fg])\\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['4', '0']), [gt_bg, gt_objects], [gt_depths[4], gt_depths[0]])\\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['4', '0', '1']), [gt_bg, gt_objects], [gt_depths[4], gt_depths[0], gt_depths[1]])\\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['4', '0', '1', '2']), [gt_bg, gt_objects], [gt_depths[4], gt_depths[0], gt_depths[1], gt_depths[2]])\\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '1']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[1]])\\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '1', '2']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[1], gt_depths[2]])\\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '1', '2', '3']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[1], gt_depths[2], gt_depths[3]])\\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '1', '2', '3', '4']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[1], gt_depths[2], gt_depths[3], gt_depths[4]])\\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '1', '2', '3', '4', 'overhead_fg']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[1], gt_depths[2], gt_depths[3], gt_depths[4], gt_overhead_fg])\\n\\n # LDI\\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '2', '4']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[2], gt_depths[4]])\\n self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '2']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[2]])\\n\\n self.run_if_not_exists(name, self.key('pred', 'obj', ['0']), [gt_objects], [pred_depths[0]])\\n self.run_if_not_exists(name, self.key('pred', 'obj', ['1']), [gt_objects], [pred_depths[1]])\\n self.run_if_not_exists(name, self.key('pred', 'obj', ['2']), [gt_objects], [pred_depths[2]])\\n self.run_if_not_exists(name, self.key('pred', 'obj', ['3']), [gt_objects], [pred_depths[3]])\\n self.run_if_not_exists(name, self.key('pred', 'obj', ['overhead_fg']), [gt_objects], [pred_overhead_fg], force=True)\\n self.run_if_not_exists(name, self.key('pred', 'obj', ['0', 'overhead_fg']), [gt_objects], [pred_depths[0], pred_overhead_fg])\\n self.run_if_not_exists(name, self.key('pred', 'obj', ['0', '1']), [gt_objects], [pred_depths[0], pred_depths[1]])\\n self.run_if_not_exists(name, self.key('pred', 'obj', ['0', '1', '2']), [gt_objects], [pred_depths[0], pred_depths[1], pred_depths[2]])\\n self.run_if_not_exists(name, self.key('pred', 'obj', ['0', '1', '2', '3']), [gt_objects], [pred_depths[0], pred_depths[1], pred_depths[2], pred_depths[3]])\\n self.run_if_not_exists(name, self.key('pred', 'obj', ['0', '1', '2', '3', 'overhead_fg']), [gt_objects], [pred_depths[0], pred_depths[1], pred_depths[2], pred_depths[3], pred_overhead_fg], force=True)\\n\\n self.run_if_not_exists(name, self.key('pred', 'both', ['0']), [gt_bg, gt_objects], [pred_depths[0]])\\n self.run_if_not_exists(name, self.key('pred', 'both', ['1']), [gt_bg, gt_objects], [pred_depths[1]])\\n self.run_if_not_exists(name, self.key('pred', 'both', ['2']), [gt_bg, gt_objects], [pred_depths[2]])\\n self.run_if_not_exists(name, self.key('pred', 'both', ['3']), [gt_bg, gt_objects], [pred_depths[3]])\\n self.run_if_not_exists(name, self.key('pred', 'both', ['4']), [gt_bg, gt_objects], [pred_depths[4]])\\n self.run_if_not_exists(name, self.key('pred', 'both', ['overhead_fg']), [gt_bg, gt_objects], [pred_overhead_fg], force=True)\\n self.run_if_not_exists(name, self.key('pred', 'both', ['4', '0']), [gt_bg, gt_objects], [pred_depths[4], pred_depths[0]])\\n self.run_if_not_exists(name, self.key('pred', 'both', ['4', '0', '1']), [gt_bg, gt_objects], [pred_depths[4], pred_depths[0], pred_depths[1]])\\n self.run_if_not_exists(name, self.key('pred', 'both', ['4', '0', '1', '2']), [gt_bg, gt_objects], [pred_depths[4], pred_depths[0], pred_depths[1], pred_depths[2]])\\n self.run_if_not_exists(name, self.key('pred', 'both', ['4', '0', '1', '2', '3']), [gt_bg, gt_objects], [pred_depths[4], pred_depths[0], pred_depths[1], pred_depths[2], pred_depths[3]])\\n self.run_if_not_exists(name, self.key('pred', 'both', ['4', '0', '1', '2', '3', 'overhead_fg']), [gt_bg, gt_objects], [pred_depths[4], pred_depths[0], pred_depths[1], pred_depths[2], pred_depths[3], pred_overhead_fg], force=True)\\n\\n # self.run_if_not_exists(name, self.key('pred', 'both', ['overhead_fg']), [gt_bg, gt_objects], [pred_files['overhead_fg_clipped']])\\n # self.run_if_not_exists(name, self.key('pred', 'both', ['0']), [gt_bg, gt_objects], [pred_files['pred_0']])\\n # self.run_if_not_exists(name, self.key('pred', 'both', ['1']), [gt_bg, gt_objects], [pred_files['pred_1']])\\n # self.run_if_not_exists(name, self.key('pred', 'both', ['2']), [gt_bg, gt_objects], [pred_files['pred_2']])\\n # self.run_if_not_exists(name, self.key('pred', 'both', ['3']), [gt_bg, gt_objects], [pred_files['pred_3']])\\n # self.run_if_not_exists(name, self.key('pred', 'both', ['0', '3']), [gt_bg, gt_objects], [pred_files['pred_0'], pred_files['pred_3']])\\n # self.run_if_not_exists(name, self.key('pred', 'both', ['0', '1', '3']), [gt_bg, gt_objects], [pred_files['pred_0'], pred_files['pred_1'], pred_files['pred_3']])\\n # self.run_if_not_exists(name, self.key('pred', 'both', ['0', '1', '2', '3']), [gt_bg, gt_objects], [pred_files['pred_0'], pred_files['pred_1'], pred_files['pred_2'], pred_files['pred_3']])\\n # self.run_if_not_exists(name, self.key('pred', 'both', ['0', '1', '2', '3', 'overhead_fg']), [gt_bg, gt_objects], [pred_files['pred_0'], pred_files['pred_1'], pred_files['pred_2'], pred_files['pred_3'], pred_files['overhead_fg_clipped']])\\n #\\n # # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['overhead_fg']), [gt_bg, gt_objects], [pred_files['overhead_fg_clipped']])\\n # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0']), [gt_bg, gt_objects], [gt_depths[0]])\\n # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['1']), [gt_bg, gt_objects], [gt_depths[1]])\\n # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['2']), [gt_bg, gt_objects], [gt_depths[2]])\\n # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['3']), [gt_bg, gt_objects], [gt_depths[3]])\\n # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '3']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[3], ])\\n # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '1', '3']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[1], gt_depths[3], ])\\n # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '1', '2', '3']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[1], gt_depths[2], gt_depths[3], ])\\n # # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '1', '2', '3', 'overhead_fg']), [gt_bg, gt_objects], [])\\n #\\n # # self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['2', '3']), [gt_objects], [gt_depths[2], gt_depths[3]])\\n # # self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['1', '2', '3']), [gt_objects], [gt_depths[1], gt_depths[2], gt_depths[3]])\\n # # self.run_if_not_exists(name, self.key('gt_depth', 'obj', ['0', '1', '2', '3']), [gt_objects], [gt_depths[0], gt_depths[1], gt_depths[2], gt_depths[3]])\\n #\\n # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '1']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[1], ])\\n # self.run_if_not_exists(name, self.key('gt_depth', 'both', ['0', '1', '2']), [gt_bg, gt_objects], [gt_depths[0], gt_depths[1], gt_depths[2], ])\",\n \"def refugia_adj_5_simsplit_3epochs(params, ns):\\n #24 parameters \\n nu1a, nu2a, nu3a, nu1b, nu2b, nu3b, nu1c, nu2c, nu3c, m1_12, m1_13, m1_21, m1_23, m1_31, m1_32, m3_12, m3_13, m3_21, m3_23, m3_31, m3_32, T1, T2, T3 = params\\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1] + ns[2])\\n fs = moments.Spectrum(sts)\\n fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1] + ns[2]) \\n fs = moments.Manips.split_2D_to_3D_2(fs, ns[1], ns[2])\\n ## Population function and migration matrix for T2\\n nu_T1 = [nu1a, nu2a, nu3a]\\n mig1 = numpy.array([[0, m1_12, m1_13],[m1_21, 0, m1_23], [m1_31, m1_32, 0]]) \\n fs.integrate(nu_T1, T1, m=mig1)\\n ## Population function and migration matrix for T2\\n nu_T2 = [nu1b, nu2b, nu3b]\\n fs.integrate(nu_T2, T2)\\n ## Population function and migration matrix for T3\\n nu_T3 = [nu1c, nu2c, nu3c]\\n mig3 = numpy.array([[0, m3_12, m3_13],[m3_21, 0, m3_23], [m3_31, m3_32, 0]]) \\n fs.integrate(nu_T3, T3, m=mig3) \\n return fs\",\n \"def create_scenarios(self, params, num_scenarios, random_seed):\\n return None\",\n \"def refugia_adj_5_simsplit_4epochs_iter3 (params, ns):\\n #28 parameters \\n nu1a, nu2a, nu3a, nu1b, nu2b, nu3b, nu1c, nu2c, nu3c, nu1d, nu2d, nu3d, m1_12, m1_13, m1_21, m1_23, m1_31, m1_32, m3_12, m3_13, m3_21, m3_23, m3_31, m3_32, T1, T2, T3, T4 = params\\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1] + ns[2])\\n fs = moments.Spectrum(sts)\\n fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1] + ns[2]) \\n fs = moments.Manips.split_2D_to_3D_2(fs, ns[1], ns[2])\\n ## Population function and migration matrix for T1 (to reflect sum effect of all previous glacial-interglacial cycles)\\n nu_T1 = [nu1a, nu2a, nu3a]\\n mig1 = numpy.array([[0, m1_12, m1_13],[m1_21, 0, m1_23], [m1_31, m1_32, 0]]) \\n fs.integrate(nu_T1, T1, m=mig1)\\n ## Population function and migration matrix for T2 (to reflect period of isolation during last glacial)\\n nu_T2 = [nu1b, nu2b, nu3b]\\n fs.integrate(nu_T2, T2)\\n ## Population function and migration matrix for T3 (to reflect inter-glacial expansion)\\n nu_T3 = [nu1c, nu2c, nu3c]\\n mig3 = numpy.array([[0, m3_12, m3_13],[m3_21, 0, m3_23], [m3_31, m3_32, 0]]) \\n fs.integrate(nu_T3, T3, m=mig3)\\n ## Population function and migration matrix for T3 (bottleneck to capture single population representation of lineage)\\n nu_T4 = [nu1d, nu2d, nu3d]\\n fs.integrate(nu_T4, T4) \\n return fs\",\n \"def test_case_generate(self):\\n\\n # initialization\\n state = np.random.choice(self.init_states)\\n model = rm.randint(0, self.model_num - 1)\\n duration = np.random.choice(self.step_values)\\n temp = rm.randint(self.min_temp, self.max_temp)\\n\\n self.states = [[model, duration, temp]]\\n self.time = duration\\n\\n while self.time < self.max_time:\\n if state == \\\"inc_tmp\\\":\\n change = np.random.choice(\\n self.transitionName[0], p=self.transitionMatrix[0]\\n ) # choose the next state\\n if change == \\\"S1S1\\\": # stay in the same state\\n temp = self.get_temp_inc(temp)\\n model = rm.randint(0, self.model_num - 1)\\n diff = (\\n self.max_time - self.time\\n ) # this is for ensuring the maximum duration is not exceeded\\n if (diff) < self.max_step and (diff) > self.min_step:\\n duration = diff\\n self.states.append([model, duration, temp])\\n return self.states_to_dict()\\n elif diff < self.min_step:\\n self.states[len(self.states) - 1][1] += diff\\n return self.states_to_dict()\\n else:\\n duration = np.random.choice(self.step_values)\\n self.time += duration\\n self.states.append([model, duration, temp])\\n\\n elif change == \\\"S1S2\\\": # change from increase to decrease\\n temp = self.get_temp_dec(temp)\\n model = rm.randint(0, self.model_num - 1)\\n state = \\\"dec_tmp\\\"\\n\\n diff = self.max_time - self.time\\n if (diff) < self.max_step and (diff) > self.min_step:\\n duration = diff\\n self.states.append([model, duration, temp])\\n return self.states_to_dict()\\n elif diff < self.min_step:\\n self.states[len(self.states) - 1][1] += diff\\n return self.states_to_dict()\\n else:\\n duration = np.random.choice(self.step_values)\\n self.time += duration\\n self.states.append([model, duration, temp])\\n else:\\n print(\\\"Error\\\")\\n\\n elif state == \\\"dec_tmp\\\":\\n change = np.random.choice(\\n self.transitionName[1], p=self.transitionMatrix[1]\\n )\\n if change == \\\"S2S1\\\":\\n temp = self.get_temp_inc(temp)\\n model = rm.randint(0, self.model_num - 1)\\n state = \\\"inc_tmp\\\"\\n diff = self.max_time - self.time\\n if (diff) < self.max_step and (diff) > self.min_step:\\n duration = diff\\n self.states.append([model, duration, temp])\\n return self.states_to_dict()\\n elif diff < self.min_step:\\n self.states[len(self.states) - 1][1] += diff\\n return self.states_to_dict()\\n else:\\n duration = np.random.choice(self.step_values)\\n\\n self.time += duration\\n self.states.append([model, duration, temp])\\n\\n elif change == \\\"S2S2\\\":\\n temp = self.get_temp_dec(temp)\\n model = rm.randint(0, self.model_num - 1)\\n\\n diff = self.max_time - self.time\\n if (diff) < self.max_step and (diff) > self.min_step:\\n duration = diff\\n self.states.append([model, duration, temp])\\n return self.states_to_dict()\\n elif diff < self.min_step:\\n self.states[len(self.states) - 1][1] += diff\\n return self.states_to_dict()\\n else:\\n duration = np.random.choice(self.step_values)\\n self.time += duration\\n self.states.append([model, duration, temp])\\n\\n else:\\n print(\\\"Error\\\")\\n pass\\n else:\\n print(\\\"Error\\\")\\n\\n return self.states_to_dict()\",\n \"def start_training(params):\\n\\n\\n\\n # CREATE A FOLDER TO HOLD RESULTS\\n\\n\\n exp_pref = \\\"../results/\\\" + params.EXPERIMENT_PREFIX\\n time_str = time.strftime(\\\"_%m-%d-%H-%M_\\\", time.gmtime())\\n exp_dir = exp_pref + time_str + \\\\\\n \\\"{}\\\".format(params.LEARNING_RATE).replace(\\\".\\\", \\\"p\\\") + \\\"_\\\" \\\\\\n + \\\"{}\\\".format(params.DISCOUNT).replace(\\\".\\\", \\\"p\\\")\\n\\n try:\\n os.stat(exp_dir)\\n except OSError:\\n os.makedirs(exp_dir)\\n\\n logger = logging.getLogger(\\\"DeepLogger\\\")\\n logger.setLevel(logging.INFO)\\n\\n # Logging filehandler\\n #fh = logging.FileHandler(exp_dir + \\\"/log.log\\\")\\n # Rotate file when filesize is 5 mb\\n fh = RotatingFileHandler(exp_dir + \\\"/log.log\\\", maxBytes=5000000, backupCount=100)\\n\\n fh.setLevel(logging.INFO)\\n\\n # Console filehandler\\n ch = logging.StreamHandler()\\n ch.setLevel(logging.INFO)\\n\\n formatter = logging.Formatter('%(message)s')\\n fh.setFormatter(formatter)\\n ch.setFormatter(formatter)\\n\\n logger.addHandler(fh)\\n\\n # Prevent nohup from producing large log file, logging to file is handled internally\\n # logger.addHandler(ch)\\n\\n log_params(logger, params)\\n\\n #logging.basicConfig(level=logging.INFO, filename=exp_dir + \\\"/log.log\\\")\\n\\n\\n if params.DETERMINISTIC:\\n rng = np.random.RandomState(12345)\\n else:\\n rng = np.random.RandomState()\\n\\n if params.CUDNN_DETERMINISTIC:\\n theano.config.dnn.conv.algo_bwd = 'deterministic'\\n\\n # Init ale\\n ale = ale_python_interface.ALEInterface()\\n ale.setInt('random_seed', 123)\\n ale.setBool('display_screen', params.DISPLAY_SCREEN)\\n ale.setFloat('repeat_action_probability', params.REPEAT_ACTION_PROBABILITY)\\n full_rom_path = os.path.join(params.ROM_PATH, params.ROM_NAME)\\n ale.loadROM(full_rom_path)\\n num_actions = len(ale.getMinimalActionSet())\\n\\n print \\\"Legal actions: \\\", num_actions\\n print ale.getMinimalActionSet()\\n\\n # Instantiate network\\n logger.info(\\\"Setting up network...\\\")\\n network = None # Be able to continue training from a network or watch a network play\\n if (params.NETWORK_PICKLE_FILE is None):\\n logger.info(\\\"Initializing a new random network...\\\")\\n network = q_network.DeepQLearner(params.RESIZED_WIDTH,\\n params.RESIZED_HEIGHT,\\n num_actions,\\n params.PHI_LENGTH,\\n params.DISCOUNT,\\n params.LEARNING_RATE,\\n params.RMS_DECAY,\\n params.RMS_EPSILON,\\n params.MOMENTUM,\\n params.CLIP_DELTA,\\n params.FREEZE_INTERVAL,\\n params.BATCH_SIZE,\\n params.NETWORK_TYPE,\\n params.UPDATE_RULE,\\n params.BATCH_ACCUMULATOR,\\n rng)\\n else:\\n logger.info(\\\"Loading network instance from file...\\\")\\n handle = open(params.NETWORK_PICKLE_FILE, 'r')\\n network = cPickle.load(handle)\\n\\n\\n # Only used when getting a random network\\n if params.RANDOM_NETWORK_PICKLE:\\n import sys\\n sys.setrecursionlimit(10000)\\n result_net_file = open(params.EXPERIMENT_PREFIX + '.pkl', 'w')\\n print \\\"File opened\\\"\\n cPickle.dump(network, result_net_file, -1)\\n print \\\"Pickle dumped\\\"\\n result_net_file.close()\\n sys.exit(0)\\n\\n\\n # Instatiate agent\\n logger.info(\\\"Setting up agent...\\\")\\n agent = ale_agent.NeuralAgent(network,\\n params.EPSILON_START,\\n params.EPSILON_MIN,\\n params.EPSILON_DECAY,\\n params.REPLAY_MEMORY_SIZE,\\n exp_dir,\\n params.REPLAY_START_SIZE,\\n params.UPDATE_FREQUENCY,\\n rng)\\n\\n # Instantiate experient\\n logger.info(\\\"Setting up experiment...\\\")\\n experiment = ale_experiment.ALEExperiment(ale, agent,\\n params.RESIZED_WIDTH,\\n params.RESIZED_HEIGHT,\\n params.RESIZE_METHOD,\\n params.EPOCHS,\\n params.STEPS_PER_EPOCH,\\n params.STEPS_PER_TEST,\\n params.FRAME_SKIP,\\n params.DEATH_ENDS_EPISODE,\\n params.MAX_START_NULLOPS,\\n rng)\\n\\n\\n # Run experiment\\n logger.info(\\\"Running experiment...\\\")\\n experiment.run()\",\n \"def simulate_trajectories(kav):\\n print \\\"Simulating \\\"+str(kav)\\n wt_trajectories = []\\n avp_trajectories = []\\n vip_trajectories = []\\n for tn in range(100):\\n # get random initial condition\\n # initial phases\\n init_conditions_AV = [single_osc.lc(wt_T*np.random.rand()) \\n for i in range(AVPcells+VIPcells)]\\n init_conditions_NAV = [single_osc.lc(wt_T*np.random.rand())[:-1]\\n for i in range(NAVcells)]\\n y0_random = np.hstack(init_conditions_AV+init_conditions_NAV)\\n\\n # do the simulation\\n model = GonzeModelManyCells(param, kav=kav, \\n initial_values=y0_random)\\n wt_trajectories.append(model.run(show_labels=False, seed=0))\\n\\n # avp bmalko\\n avp_model = GonzeModelManyCells(param, bmalko='AVP', kav=kav, \\n initial_values=y0_random)\\n avp_trajectories.append(avp_model.run(show_labels=False, seed=0))\\n\\n # vip bmalko\\n vip_model = GonzeModelManyCells(param, bmalko='VIP', kav=kav, \\n initial_values=y0_random)\\n vip_trajectories.append(vip_model.run(show_labels=False, seed=0))\\n\\n # save results\\n with open(\\\"Data/params/wt_\\\"+str(kav)+\\\".pickle\\\", \\\"wb\\\") as output_file:\\n pickle.dump(wt_trajectories, output_file)\\n with open(\\\"Data/params/avp_\\\"+str(kav)+\\\".pickle\\\", \\\"wb\\\") as output_file:\\n pickle.dump(avp_trajectories, output_file)\\n with open(\\\"Data/params/vip_\\\"+str(kav)+\\\".pickle\\\", \\\"wb\\\") as output_file:\\n pickle.dump(vip_trajectories, output_file)\\n\\n return {'wt': wt_trajectories,\\n 'avp': avp_trajectories,\\n 'vip': vip_trajectories}\",\n \"def __init__(self, tconst=2, tau1=5, tau2=10, lambda1=20, lambda2=50, lambda3=100): # only default arguments here\\n gr.sync_block.__init__(\\n self,\\n name='Scenario classification', # will show up in GRC\\n in_sig=[np.float32]*6,\\n out_sig=None\\n )\\n # if an attribute with the same name as a parameter is found,\\n # a callback is registered (properties work, too).\\n self.tconst = tconst\\n self.tau1 = tau1\\n self.tau2 = tau2\\n self.lambda1 = lambda1\\n self.lambda2 = lambda2\\n self.lambda3 = lambda3\\n self.nchan = 4\\n self.scenario_tau = np.array([self.tau1, self.tau2, 2*self.tau1, 4*self.tau2, self.tau1, self.tau1, self.tconst, self.lambda1, self.lambda2, self.lambda3, 0]) # scenario 10 is \\\"unsure, don't send\\\"\\n self.scenario_send_window = self.scenario_tau.copy() # the send window for the hopping and Poisson scenarios differs from the deterministic ones\\n self.scenario_send_window[2] = self.tau1\\n self.scenario_send_window[3] = self.tau2\\n for i in range(7, 10):\\n self.scenario_send_window[i] = st.poisson.ppf(0.05, self.scenario_tau[i])\\n print \\\"expected mean inter frame times: \\\", self.scenario_tau\\n print \\\"send windows: \\\", self.scenario_send_window\\n \\n self.max_confidence = 3\\n self.confidence = 0\\n \\n self.scenario = 10\\n self.chan_occupied = [False] * self.nchan # list of true/false values\\n self.tau = None\\n \\n self.message_port_register_out(pmt.intern(\\\"scenario\\\"))\",\n \"def rtest_predictoutcome():\\n\\n #define cohort size\\n npatients = 2\\n\\n #init healthy patients\\n simulator = AbbcEnvironment(patients=npatients)\\n\\n #simulate healthy patients for long term in short term increments\\n nstep = int(long_term/short_term)\\n\\n #define action taken : -1 means patients will be simulated as healthy\\n action = np.repeat(-1, npatients)\\n\\n #init episode list\\n episode = [simulator.state]\\n\\n #main simulation loop to generate episodes\\n for step in range(nstep):\\n episode += simulator.take_action(action=action, simtime=short_term)\\n\\n #episode length is 1+2*nstep consisting of intit state (5xnpat) followed by\\n # next state and reward (1xnpat) repeating each time step.\\n #print(episode)\\n #print(len(episode))\\n\\n #---semi gradient temporal difference (0) algorithm ---\\n #init hyperparameters\\n alpha = .1 #learning rate\\n #init Value function model\\n agent = AbbcAgent(discount=1.0)\\n #loop over episodes\\n for patient in range(npatients):\\n #state = [nstep]\\n #state += episode[0][:,patient] #get inital state\\n state = np.append(episode[0][:,patient],nstep).reshape((6,1)) #get inital state\\n\\n print(state)\\n #loop over time steps in episode\\n for k in range(1,nstep+1):\\n #get next state and reward\\n #nextstate = [nstep-k]\\n #nextstate = episode[k*2-1][:,patient]\\n nextstate = np.append(episode[k*2-1][:,patient],nstep-k).reshape((6,1))\\n\\n reward = episode[k*2][patient]\\n\\n #get magnitude for forces\\n magnitude = alpha * (reward + agent.discount * agent.get_value(nextstate)\\n - agent.get_value(state))\\n #compute forces\\n forces = computeforces(agent.prednet, state, 0, \\\"iden\\\")\\n\\n #update model\\n for layer in forces:\\n index = layer[\\\"layer\\\"]\\n agent.prednet[index][\\\"weight\\\"] += magnitude * layer[\\\"fweight\\\"]\\n agent.prednet[index][\\\"bias\\\"] += magnitude * layer[\\\"fbias\\\"]\\n\\n state = np.copy(nextstate)\\n\\n\\n #make predictions\\n state = np.append(episode[0][:,patient],nstep).reshape((6,1)) #get inital state\\n print(agent.get_value(state))\\n\\n #Value function approximates outcome return at time horizon.\\n assert(False)\\n\\n ##define action taken\\n #action = np.repeat(2, npatients)\\n ##main simulation loop\\n #for step in range(nstep):\\n # _, drugy_reward[step,:] = simulator.take_action(action=action, simtime=short_term)\",\n \"def run_shape_experiment(**kwargs):\\n try:\\n input_file = kwargs['input_file']\\n results_folder = kwargs['results_folder']\\n data_folder = kwargs['data_folder']\\n sampler = kwargs['sampler']\\n max_part_count = None\\n if 'max_part_count' in kwargs:\\n max_part_count = kwargs['max_part_count']\\n ll_variance = kwargs['ll_variance']\\n max_pixel_value = kwargs['max_pixel_value']\\n change_size_variance = kwargs['change_size_variance']\\n change_viewpoint_variance = kwargs['change_viewpoint_variance']\\n move_part_variance = kwargs['move_part_variance']\\n burn_in = kwargs['burn_in']\\n sample_count = kwargs['sample_count']\\n best_sample_count = kwargs['best_sample_count']\\n thinning_period = kwargs['thinning_period']\\n report_period = kwargs['report_period']\\n temperatures = None\\n if 'temperatures' in kwargs:\\n temperatures = kwargs['temperatures']\\n except KeyError as e:\\n raise ValueError(\\\"All experiment parameters should be provided. Missing parameter {0:s}\\\".format(e.message))\\n\\n import numpy as np\\n\\n import mcmclib.proposal as proposal\\n import i3d_proposal\\n import vision_forward_model as vfm\\n\\n # read the data file\\n data = np.load(\\\"{0:s}/{1:s}_single_view.npy\\\".format(data_folder, input_file))\\n render_size = data.shape[1:]\\n\\n fwm = vfm.VisionForwardModel(render_size=render_size)\\n\\n shape_params = {'LL_VARIANCE': ll_variance, 'MAX_PIXEL_VALUE': max_pixel_value}\\n\\n kernel_params = {'CHANGE_SIZE_VARIANCE': change_size_variance, 'MOVE_PART_VARIANCE': move_part_variance,\\n 'CHANGE_VIEWPOINT_VARIANCE': change_viewpoint_variance}\\n\\n import shape\\n\\n moves = {'change_viewpoint': i3d_proposal.change_viewpoint_z,\\n 'shape_add_remove_part': shape.shape_add_remove_part,\\n 'shape_move_part_local': shape.shape_move_part_local,\\n 'shape_change_part_size_local': shape.shape_change_part_size_local}\\n\\n viewpoint = [(np.sqrt(8.0), -45.0, 45.0)]\\n hypothesis_class = 'Shape'\\n if max_part_count is None:\\n h = shape.Shape(forward_model=fwm, viewpoint=viewpoint, params=shape_params)\\n else:\\n import shape_maxn\\n hypothesis_class = 'ShapeMaxD'\\n h = shape_maxn.ShapeMaxN(forward_model=fwm, maxn=max_part_count, viewpoint=viewpoint, params=shape_params)\\n kernel_params['MAX_PART_COUNT'] = max_part_count\\n\\n # form the proposal\\n kernel = proposal.RandomMixtureProposal(moves=moves, params=kernel_params)\\n\\n results = run_chain(name=input_file, sampler=sampler, initial_h=h, data=data, kernel=kernel, burn_in=burn_in,\\n thinning_period=thinning_period, sample_count=sample_count, best_sample_count=best_sample_count,\\n report_period=report_period,\\n results_folder=\\\"{0:s}/{1:s}\\\".format(results_folder, hypothesis_class),\\n temperatures=temperatures)\\n\\n return results\",\n \"def test_y_generate():\\n a = Attractor()\\n #say x, y, z = [0.1, 0.0, 0.0]\\n\\n dx = (10.0 * (0.0 - 0.1)) * (80.0-0.0)/10000 + 0.1\\n dy = (0.1 * (28 - 0.0) - 0.0) * (80.0-0.0)/10000 + 0.0\\n dz = ((0.1 * 0.0) - (8/3 * 0.0)) * (80.0-0.0)/10000 + 0.0\\n ex_1 = np.array([dx, dy, dz])\\n\\n dx2 = (10.0 * (dy - dx)) * (80.0-0.0)/10000.0 + dx \\n dy2 = (dx * (28.0 - dz) - dy) * (80.0-0.0)/10000.0 + dy\\n dz2 = ((dx * dy) - (8/3 * dz)) * (80.0-0.0)/10000.0 + dz\\n ex_2 = np.array([dx2, dy2, dz2])\\n\\n dx3 = (10.0 * (dy2 - dx2)) * (80.0-0.0)/10000.0 + dx2\\n dy3 = (dx2 * (28.0 - dz2) - dy2) * (80.0-0.0)/10000.0 + dy2\\n dz3 = ((dx2 * dy2) - (8/3 * dz2)) * (80.0-0.0)/10000.0 + dz2\\n ex_3 = np.array([dx3, dy3, dz3])\\n\\n dx4 = (10.0 * (dy3 - dx3)) * (80.0-0.0)/10000.0 + dx3\\n dy4 = (dx3 * (28 - dz3) - dy3) * (80.0-0.0)/10000.0 + dy3\\n dz4 = ((dx3 * dy3) - (8/3 * dz3)) * (80.0-0.0)/10000.0 + dz3\\n ex_4 = np.array([dx4, dy4, dz4])\\n\\n dx5 = (10.0 * (dy4 - dx4)) * (80.0-0.0)/10000.0 + dx4\\n dy5 = (dx4 * (28 - dz4) - dy4) * (80.0-0.0)/10000.0 + dy4\\n dz5 = ((dx4 * dy4) - (8/3 * dz4)) * (80.0-0.0)/10000.0 + dz4\\n ex_5 = np.array([dx5, dy5, dz5])\\n\\n \\n a.evolve(order = 4)\\n y_list = a.solution['y'].tolist()\\n \\n for i in y_list[:6]:\\n yy = round(i, 2)\\n for j in [0.0, dy, dy2, dy3, dy4, dy5]:\\n yyy = round(j, 2)\\n \\n print (\\\"Actual increments: \\\", yy)#str(a.solution()['x']).strip('[]'))\\n print (\\\"Expected increments: \\\", yyy)\\n assert yy == yyy\",\n \"def apx_ratio_eval():\\n print 'starting random eval'\\n\\n n_pl = randint(2, MAX_PL)\\n n_target = randint(n_pl + 1, MAX_TARGETS)\\n\\n target_values = []\\n for t in range(0,n_target):\\n target_values.append(random())\\n target_values = np.array(target_values)\\n\\n attacker_strategy = get_random_attacker_strategy(n_target)\\n\\n #I = get_random_covering_routes(n_pl, n_target)\\n I = get_pathologic_covering_routes(n_pl, n_target, attacker_strategy, target_values)\\n\\n start_opt = time()\\n br_opt = ilp.generate_row(I, attacker_strategy, target_values)\\n opt_time = time() - start_opt\\n\\n start_apx = time()\\n br_apx = greedy.generate_row(I, attacker_strategy, target_values)\\n apx_time = time() - start_apx\\n\\n print 'opt time: ', opt_time, 'apx time: ', apx_time\\n\\n apx_val = get_value(br_apx, I, target_values, attacker_strategy)\\n opt_val = get_value(br_opt, I, target_values, attacker_strategy)\\n\\n logging.info('ratio = %f - target = %d - pl = %f - max_r = %d - max_cov_t = %d',apx_val/opt_val, n_target, n_pl, MAX_ROUTES, MAX_ROUTE_LEN)\\n\\n return apx_val/opt_val, opt_time, apx_time\",\n \"def variational_circuit(params):\\n non_parametrized_layer()\\n qml.RX(params[0], wires=0)\\n qml.RY(params[1], wires=1)\\n qml.RZ(params[2], wires=2)\\n non_parametrized_layer()\\n qml.RX(params[3], wires=0)\\n qml.RY(params[4], wires=1)\\n qml.RZ(params[5], wires=2)\",\n \"def run_simulation(conf):\\r\\n\\r\\n\\r\\n # Validate cofiguration\\r\\n conf = validate_conf(conf)\\r\\n\\r\\n # Define function to run and storage path\\r\\n newpath = create_path(conf)\\r\\n\\r\\n # Define trajectory and time per state\\r\\n nr_states = 4\\r\\n traj = [list(range(nr_states))]\\r\\n T_list = [[conf.time_per_state for _ in range(nr_states)]]\\r\\n\\r\\n # Set parameters\\r\\n params, gamma, eta, lambda_var = parameters_linear_track_value(nr_states,\\r\\n conf.time_per_state,\\r\\n conf.alpha,\\r\\n conf.A_pre,\\r\\n )\\r\\n\\r\\n params['trajectories'] = traj\\r\\n params = update_params(conf.mode, params, T_list, gamma, eta)\\r\\n\\r\\n # Define the initial weights from CA3 to CA1\\r\\n T_mat = np.array([\\r\\n [0,1,0,0,0],\\r\\n [0,0,1,0,0],\\r\\n [0,0,0,1,0],\\r\\n [0,0,0,0,1],\\r\\n [0,0,0,0,1],\\r\\n ])\\r\\n M_theor = np.linalg.inv(np.eye(nr_states)-gamma*T_mat[:-1, :-1])\\r\\n wini = np.tile(np.expand_dims(np.expand_dims(M_theor,2),3),[1,1,params['N_pre_tot'],params['N_post']])\\r\\n params['wini'] = wini\\r\\n\\r\\n # Define the reward vector\\r\\n reward_vec = np.array([0,0,0,1])\\r\\n params['reward_vec'] = reward_vec\\r\\n\\r\\n # calculate theoretical value\\r\\n v_theor = np.matmul(M_theor, reward_vec)\\r\\n print(f'Theoretical Values: {list(v_theor)}')\\r\\n\\r\\n # Run spiking network\\r\\n params['step'] = params['step']*10\\r\\n reward_neuron_activity = run_value_function(**params)\\r\\n\\r\\n\\r\\n #############################\\r\\n # STORING AND PLOTTING\\r\\n #############################\\r\\n\\r\\n if conf.store_data:\\r\\n pickle.dump(reward_neuron_activity, open(newpath + '/rebuttal_value_function.pkl','wb'))\\r\\n\\r\\n rates = np.mean(np.sum(reward_neuron_activity, axis=2)/conf.time_per_state*1000, axis=1)\\r\\n\\r\\n # fs = 16\\r\\n\\r\\n fig = plt.figure()\\r\\n plt.bar(list(range(1,nr_states+1)), rates)\\r\\n if conf.store_data:\\r\\n fig.savefig(newpath+'/fig_value_function.eps',bbox_inches='tight',format='eps')\\r\\n\\r\\n plt.figure()\\r\\n for i in range(4):\\r\\n x = reward_neuron_activity[i]\\r\\n plt.plot(np.max(x,axis=0)*(i+1), '.', markersize=15)\",\n \"def __init__(self, downsampling=False, wl_range = False, spectra_path = '/Users/kyleturner/Dropbox/My_Box/Code/M_Files/process_svc_v2p1/rrs_model_3C-master/spectra/'):\\n\\n\\t\\t\\ta_ph = pd.read_csv(spectra_path + 'phyto.A', skiprows=11, sep='\\\\t', index_col=0).iloc[:,0]\\n\\t\\t\\n\\t\\t\\ta_w = pd.read_csv(spectra_path + 'water.A', skiprows=10, sep='\\\\t', index_col=0).iloc[:,0]\\n\\t\\t\\tdaw_dT = pd.read_csv(spectra_path + 'daWdT.txt', skiprows=10, sep='\\\\t', index_col=0).iloc[:,0]\\n\\t\\t\\tastar_y = pd.read_csv(spectra_path + 'Y.A', skiprows=11, delimiter=' ', index_col=0).loc[350:].iloc[:,0]\\n\\n\\t\\t\\td = pd.DataFrame(index = a_ph.index)\\n\\t\\t\\td.index.name = 'Wavelength, [nm]'\\n\\t\\t\\td['astar_ph'] = a_ph\\n\\t\\t\\td['astar_y'] = astar_y \\n\\t\\t\\td['a_w'] = a_w \\n\\t\\t\\td['daw_dT'] = daw_dT\\n\\n\\t\\t\\tif downsampling == False:\\n\\t\\t\\t\\tself.spectra = d\\n\\t\\t\\telse: \\n\\t\\t\\t\\tself.spectra = d.apply(lambda x: pd.rolling_mean(x, downsampling, center=True))\\n\\n\\t\\t\\tif wl_range != False:\\n\\t\\t\\t\\tself.spectra = self.spectra.loc[wl_range[0]:wl_range[1]].dropna()\\n\\n\\t\\t\\tself.wl = np.array(self.spectra.index)\\n\\t\\t\\t\\t\\n\\t\\t\\tself.model = self._compile_model()\",\n \"def t_test(dataType):\\n\\n\\t# read the data\\n\\tparser = ExperimentUtils()\\n\\tdata = parser.parse_data(dataType)\\n\\n\\tN = len(data.keys()) # number participants\\n\\n\\t# - for trial 1 and trial 2:\\n\\t# \\tL2 norm over each timestep, then sum all the values together\\n\\t# - average over two trials for each participant \\n\\ttask_avgs = {}\\n\\n\\t# participant ID can take values 0 - 9\\n\\tfor ID in data.keys():\\n\\t\\tfor task in data[ID]:\\n\\t\\t\\t# dont include the familiarization task (task can take values 1,2,3)\\n\\t\\t\\tif task != 0:\\n\\t\\t\\t\\tif task not in task_avgs:\\n\\t\\t\\t\\t\\ttask_avgs[task] = {}\\n\\t\\t\\t\\t\\ttask_avgs[task][\\\"A\\\"] = np.array([0.0]*N)\\n\\t\\t\\t\\t\\ttask_avgs[task][\\\"B\\\"] = np.array([0.0]*N)\\n\\n\\t\\t\\t\\ttrialAsum = [0.0,0.0]\\n\\t\\t\\t\\ttrialBsum = [0.0,0.0]\\n\\t\\t\\t\\t# trial can take values 1 or 2\\n\\t\\t\\t\\tfor trial in data[ID][task]:\\n\\t\\t\\t\\t\\t# only compute metrics over data, not timestamps\\n\\t\\t\\t\\t\\tAdata = data[ID][task][trial]['A'][1:8]\\n\\t\\t\\t\\t\\tBdata = data[ID][task][trial]['B'][1:8]\\n\\t\\t\\t\\n\\t\\t\\t\\t\\t#print str(ID)+str(task)+str(trial)+\\\"A\\\"\\n\\t\\t\\t\\t\\t#print \\\"Adata: \\\" + str(Adata)\\n\\t\\t\\t\\t\\t#print str(ID)+str(task)+str(trial)+\\\"B\\\"\\n\\t\\t\\t\\t\\t#print \\\"Bdata: \\\" + str(Bdata)\\n\\n\\t\\t\\t\\t\\t(h, w) = np.shape(Adata)\\n\\t\\t\\t\\t\\tfor i in range(w):\\n\\t\\t\\t\\t\\t\\ttrialAsum[trial-1] += np.linalg.norm(Adata[:,i])\\n\\t\\t\\t\\t\\t(h, w) = np.shape(Bdata)\\n\\t\\t\\t\\t\\tfor i in range(w):\\n\\t\\t\\t\\t\\t\\ttrialBsum[trial-1] += np.linalg.norm(Bdata[:,i])\\n\\t\\t\\t\\tavg_methodA = (trialAsum[0]+trialAsum[1])/2.0\\n\\t\\t\\t\\tavg_methodB = (trialBsum[0]+trialBsum[1])/2.0\\n\\n\\t\\t\\t\\ttask_avgs[task][\\\"A\\\"][ID] = avg_methodA\\n\\t\\t\\t\\ttask_avgs[task][\\\"B\\\"][ID] = avg_methodB\\n\\n\\t# comput independent two-sample t-test \\n\\t# NOTE: we can assume that the two sample sizes are the same, and \\n\\t#\\t\\tthat the two distributions have the same variance\\n\\tfor task in range(1,4):\\n\\t\\ttaskA = task_avgs[task][\\\"A\\\"]\\n\\t\\ttaskB = task_avgs[task][\\\"B\\\"]\\n\\n\\t\\tmeanA = np.mean(taskA)\\n\\t\\tmeanB = np.mean(taskB)\\n\\t\\tprint \\\"meanA: \\\" + str(meanA)\\n\\t\\tprint \\\"meanB: \\\" + str(meanB)\\n\\t\\tdiff = meanA - meanB\\n\\t\\tprint \\\"diff: \\\" + str(diff)\\n\\n\\t\\t(statistic, pvalue) = stats.ttest_ind(a=taskA, b=taskB, equal_var=True)\\n\\n\\t\\tprint \\\"\\\\n\\\"\\n\\t\\tprint \\\"task\\\"+str(task)+\\\" statistic: \\\" + str(statistic)\\n\\t\\tprint \\\"task\\\"+str(task)+\\\" pvalue: \\\" + str(pvalue)\",\n \"def test_run_sim_1():\\n rnd = rand.Arrivals(36, 41)\\n sim.run_sim(3, 2, 5, 6, 22, rnd)\",\n \"def test_make_uniform_random_f3_gate(self):\\n num_trials = 100\\n fanin_frac = .5\\n # test fam1 and fam2 gates:\\n generate = g.TYPE_TO_FAM3_GATE_GEN[g.TEST_TYPES.RANDOM]\\n W = 10\\n num_levels = 2\\n levels = [[g.make_random_input_wire(\\n \\\"W\\\" + str((W * level_num) + gate_num))\\n for gate_num in xrange(W)]\\n for level_num in xrange(num_levels)]\\n # a dictionary mapping each gate to how many times it was used as input:\\n num_uses_dict = {}\\n for level in levels:\\n for wire in level:\\n num_uses_dict[wire.get_name()] = 0\\n # a dictionary mapping gate type to a list of the number of times\\n # that gate type is generated:\\n gate_type_dict = dict(\\n (gate_type, 0) for gate_type in g.GATE_TYPES.values_generator())\\n for trial_num in xrange(num_trials):\\n # create a new gate:\\n gate = generate(levels, \\\"this_gate\\\")\\n # add the statistics to num_uses and gate_type_dist:\\n gate_type_dict[gate.get_func_name()] += 1\\n for wire in gate.get_inputs():\\n num_uses_dict[wire.get_name()] += 1\\n # make sure that each gate type appears between .2 and .8 of the time:\\n # (note that these numbers are chosen randomly, and therefor this test\\n # might occasionally fail with a low probability)\\n for gate_type in gate_type_dict.keys():\\n self.assertTrue(float(gate_type_dict[gate_type]) / float(num_trials)\\n > .2)\\n self.assertTrue(float(gate_type_dict[gate_type]) / float(num_trials)\\n < .8)\\n # make sure that each wire in the ultimate level is chosen as input\\n # between 1/(2*W) and (W-1)/W of the time:\\n # (note that these numbers are chosen randomly, and therefor this test\\n # might occasionally fail with a low probability)\\n for wire in levels[-1]:\\n self.assertTrue(float(num_uses_dict[wire.get_name()])\\n / float(num_trials)\\n > 1.0 / float(2.0 * W))\\n self.assertTrue(float(num_uses_dict[wire.get_name()])\\n / float(num_trials)\\n < (float(W) - 1.0) / float(2.0 * W))\\n # make sure that each wire in the other levels is chosen as input\\n # between 1/(2*W*num_levels) and (W-1)/(2*W*num_levels) of the time:\\n # (note that these numbers are chosen randomly, and therefor this test\\n # might occasionally fail with a low probability)\\n for level in levels[:-1]:\\n for gate in level:\\n self.assertTrue(float(num_uses_dict[wire.get_name()])\\n / float(num_levels)\\n > 1.0 / float(2 * W * num_levels))\\n self.assertTrue(float(num_uses_dict[wire.get_name()])\\n / float(num_trials)\\n < (float(W) - 1.0) / float(2 * W * num_levels))\",\n \"def generate_data(self):\\n\\n # cfg.d_sour_num = 20 # number of source domains\\n self.d_sour_a = [np.random.uniform(0.1, 5.0) for _ in range(cfg.d_sour_num)]\\n self.d_sour_b = [np.random.uniform(0, np.pi) for _ in range(cfg.d_sour_num)]\\n # cfg.d_targ_num = 1 # number of target domain\\n self.d_targ_a = [np.random.uniform(0.1, 5.0) for _ in range(cfg.d_targ_num)]\\n self.d_targ_b = [np.random.uniform(0, np.pi) for _ in range(cfg.d_targ_num)]\\n\\n\\n # cfg.train_num = 100 # number of training point in each domain\\n self.train_x = np.array([np.random.uniform(-5.0, 5.0) for _ in range(cfg.train_num)], dtype=np.float32).reshape(-1,1)\\n self.train_y = np.array([[self.d_sour_a[j] * np.sin(i + self.d_sour_b[j]) for i in self.train_x] for j in range(cfg.d_sour_num)], dtype=np.float32).reshape(cfg.d_sour_num, cfg.train_num, 1)\\n\\n # cfg.val_num = 100\\n self.val_x = np.array([np.random.uniform(-5.0, 5.0) for _ in range(cfg.val_num)], dtype=np.float32).reshape(-1,1)\\n self.val_y = np.array([[self.d_sour_a[j] * np.sin(i + self.d_sour_b[j]) for i in self.val_x] for j in range(cfg.d_sour_num)], dtype=np.float32).reshape(cfg.d_sour_num, cfg.val_num, 1)\\n\\n # cfg.support_num = 10\\n self.support_x = np.array([np.random.uniform(-5.0, 5.0) for _ in range(cfg.support_num)], dtype=np.float32).reshape(-1,1)\\n self.support_y = np.array([[self.d_targ_a[j] * np.sin(i + self.d_targ_b[j]) for i in self.support_x] for j in range(cfg.d_targ_num)], dtype=np.float32).reshape(cfg.d_targ_num, cfg.support_num, 1)\\n\\n # cfg.test_num = 100\\n self.test_x = np.array([np.random.uniform(-5.0, 5.0) for _ in range(cfg.test_num)], dtype=np.float32).reshape(-1,1)\\n self.test_y = np.array([[self.d_targ_a[j] * np.sin(i + self.d_targ_b[j]) for i in self.test_x] for j in range(cfg.d_targ_num)], dtype=np.float32).reshape(cfg.d_targ_num, cfg.test_num, 1)\\n\\n self.test_x_old = np.array([np.random.uniform(-5.0, 5.0) for _ in range(cfg.test_num)], dtype=np.float32).reshape(-1,1)\\n self.test_y_old = np.array([[self.d_sour_a[j] * np.sin(i + self.d_sour_b[j]) for i in self.test_x_old] for j in range(cfg.d_sour_num)], dtype=np.float32).reshape(cfg.d_sour_num, cfg.test_num, 1)\",\n \"def simulate_test_data(self):\\n # simulation config\\n if self.instrument == 'IMA':\\n sim_config = SimConfig.makeSim(name=\\\"IMA Simulation\\\", rel_obsdate=0.0, scene=\\\"scene.ini\\\", POP='IMA',\\n ConfigPath='IMA_FULL', Dither=False,StartInd=1, NDither=4,\\n DitherPat=\\\"ima_recommended_dither.dat\\\", filter=\\\"F1130W\\\",\\n readDetect= 'FULL', ima_mode= 'FAST', ima_exposures=1,\\n ima_integrations=1, ima_frames=50, disperser= 'SHORT', detector= 'SW',\\n mrs_mode= 'SLOW', mrs_exposures=5,mrs_integrations=4,mrs_frames=10)\\n\\n # scene config\\n background = Background(level='low', gradient=5., pa=15.0, centreFOV=(0., 0.))\\n\\n SED1 = BBSed(Temp=300., wref=10., flux=1.e8)\\n Gal1 = Galaxy(Cen=(0., 0.), n=1., re=200, q=0.99, pa=0.1)\\n Gal1.set_SED(SED1)\\n targets = [Gal1]\\n\\n scene_config = SceneConfig.makeScene(loglevel=0, background=background, targets=targets)\\n\\n elif self.instrument == 'MRS':\\n sim_config = SimConfig.makeSim(name=\\\"MRS Simulation\\\", rel_obsdate=0.0, scene=\\\"scene.ini\\\",\\n POP='MRS', ConfigPath='MRS_1SHORT', Dither=False, StartInd=1,\\n NDither=4, DitherPat=\\\"mrs_recommended_dither.dat\\\", filter=\\\"F1130W\\\",\\n readDetect='FULL', ima_mode='FAST', ima_exposures=1,\\n ima_integrations=1, ima_frames=20, disperser='SHORT', detector='SW',\\n mrs_mode='FAST', mrs_exposures=1, mrs_integrations=1, mrs_frames=50)\\n\\n # scene config\\n background = Background(level='low',gradient=5.,pa=15.0,centreFOV=(0., 0.))\\n\\n SED1 = BBSed(Temp=300., wref=10., flux=5.e6)\\n Gal1 = Galaxy(Cen=(0.,0.), n=1., re=2, q=0.99, pa=0.1)\\n Gal1.set_SED(SED1)\\n targets = [Gal1]\\n\\n scene_config = SceneConfig.makeScene(loglevel=0, background=background, targets=targets)\\n\\n # simulator config\\n if self.noise:\\n simulator_config = SimulatorConfig.makeSimulator(max_fsm=0.050, max_dither=20.0, mrs_ref_channel=1,\\n mrs_ref_band=\\\"SHORT\\\", tau_telescope=0.88,tau_eol=0.8,\\n telescope_area=25.032, telescope_pupil_diam=6.6052,\\n take_webbPsf=False, include_refpix=True,\\n include_poisson=True, include_readnoise=True,\\n include_badpix=True, include_dark=True,\\n include_flat=False, include_gain=True,\\n include_nonlinearity=self.linearity, include_drifts=True,\\n include_latency=False, cosmic_ray_mode='NONE')\\n else:\\n simulator_config = SimulatorConfig.makeSimulator(max_fsm=0.050, max_dither=20.0, mrs_ref_channel=1,\\n mrs_ref_band=\\\"SHORT\\\", tau_telescope=0.88, tau_eol=0.8,\\n telescope_area=25.032, telescope_pupil_diam=6.6052,\\n take_webbPsf=False, include_refpix=True,\\n include_poisson=False, include_readnoise=False,\\n include_badpix=True, include_dark=True,\\n include_flat=False, include_gain=True,\\n include_nonlinearity=self.linearity, include_drifts=True,\\n include_latency=False, cosmic_ray_mode='NONE')\\n\\n\\n # run the simulation\\n simulation = MiriSimulation(sim_config=sim_config, scene_config=scene_config,\\n simulator_config=simulator_config, loglevel='DEBUG')\\n simulation.run()\\n\\n # we only need the sim file so move it to output_dir and remove everthing else\\n det_image_file = glob.glob(os.path.join(simulation.path_out, 'det_images', '*.fits'))[0]\\n self.ramp_file = os.path.join(self.output_dir, os.path.basename(det_image_file))\\n shutil.move(det_image_file, self.ramp_file)\\n shutil.rmtree(simulation.path_out)\",\n \"def main():\\n run_simulation(spectral=False, ml=False, num_procs=1)\\n run_simulation(spectral=True, ml=False, num_procs=1)\\n run_simulation(spectral=False, ml=True, num_procs=1)\\n run_simulation(spectral=True, ml=True, num_procs=1)\\n run_simulation(spectral=False, ml=True, num_procs=10)\\n run_simulation(spectral=True, ml=True, num_procs=10)\",\n \"def setUpClass(cls):\\n cls.sim1 = Simulation(logging_level=50)\\n cls.sim1.set_simulation_parameters(\\n seed=1, task=47, output_directory=\\\"output\\\", min_speciation_rate=0.01, sigma=2, deme=1, sample_size=0.01\\n )\\n cls.sim1.set_map_files(\\n \\\"null\\\", fine_file=\\\"sample/SA_sample_fine.tif\\\", reproduction_map=\\\"sample/SA_sample_reproduction.tif\\\"\\n )\\n cls.sim1.run()\\n cls.sim2 = Simulation(logging_level=50)\\n cls.sim2.set_simulation_parameters(\\n seed=1, task=48, output_directory=\\\"output\\\", min_speciation_rate=0.01, sigma=2, deme=1, sample_size=0.01\\n )\\n cls.sim2.set_map_files(\\\"null\\\", fine_file=\\\"sample/SA_sample_fine.tif\\\")\\n cls.sim2.run()\\n cls.sim3 = Simulation(logging_level=50)\\n cls.sim3.set_simulation_parameters(\\n seed=2, task=47, output_directory=\\\"output\\\", min_speciation_rate=0.01, sigma=2, deme=1, sample_size=0.01\\n )\\n cls.sim3.set_map_files(\\n \\\"null\\\",\\n fine_file=\\\"sample/SA_sample_fine.tif\\\",\\n death_map=\\\"sample/SA_sample_reproduction.tif\\\",\\n reproduction_map=\\\"sample/SA_sample_reproduction.tif\\\",\\n )\\n cls.sim3.run()\\n cls.sim4 = Simulation(logging_level=50)\\n cls.sim4.set_simulation_parameters(\\n seed=4, task=47, output_directory=\\\"output\\\", min_speciation_rate=0.01, sigma=2, deme=1, sample_size=0.01\\n )\\n cls.sim4.set_map_files(\\\"null\\\", fine_file=\\\"sample/SA_sample_coarse_pristine.tif\\\")\\n cls.sim4.add_reproduction_map(reproduction_map=\\\"sample/SA_reproduction_coarse.tif\\\")\\n cls.sim4.add_death_map(death_map=\\\"sample/SA_death.tif\\\")\\n cls.sim4.add_dispersal_map(dispersal_map=\\\"sample/dispersal_fine2.tif\\\")\\n cls.sim4.run()\\n cls.coal1 = CoalescenceTree(cls.sim1)\\n cls.coal2 = CoalescenceTree(cls.sim2)\\n cls.coal3 = CoalescenceTree(cls.sim3)\\n cls.coal4 = CoalescenceTree(cls.sim4)\",\n \"def main(input_scenario):\\n\\n print(\\\"Program running for demand scenario \\\" + str(input_scenario))\\n\\n policy_f = open(\\\"./Policy/policy_result\\\" + str(input_scenario) + \\\".csv\\\", \\\"w\\\", newline='')\\n policy_writer = csv.writer(policy_f)\\n\\n # Read case data\\n ds, orig_fl = get_data_case(input_scenario, htime, zone)\\n\\n # Initialize operator location\\n location = initialize_location(zone, vec)\\n\\n # Initialize cluster\\n assignment, centre = initialize_cluster(cluster, zone, mindis)\\n\\n # Run\\n # Timestep 0\\n ds = update_ds(zone, cp, ds, mindis, orig_fl[0])\\n\\n # Timestep 1:\\n for timestep in range(1, htime):\\n\\n print(\\\"Running for timestep \\\" + str(timestep))\\n\\n # Extract demand flows in current timestep\\n train_fl = [fl[ts][timestep] for ts in range(trainsample)]\\n\\n # Compute total demand flows in and out of each station\\n train_fl_out = [[0 for s in range(zone)] for ts in range(trainsample)]\\n train_fl_in = [[0 for s in range(zone)] for ts in range(trainsample)]\\n\\n for ts in range(trainsample):\\n for s in range(zone):\\n train_fl_out[ts][s] = sum(train_fl[ts][s])\\n for z in range(zone):\\n train_fl_in[ts][s] += train_fl[ts][z][s]\\n\\n # Modify cluster based on demand flows\\n assignment = modify_cluster(cluster, assignment, centre, zone, cp, ds, mindis, trainsample, train_fl)\\n print(\\\"Clusters generated.\\\")\\n\\n # Initialize global intra-cluster rebalancing result\\n yp_total = [0.0 for s in range(zone)]\\n yn_total = [0.0 for s in range(zone)]\\n bn = [[0 for s in range(zone)] for v in range(vec)]\\n\\n # Intra-cluster rebalancing\\n for c in range(cluster):\\n\\n # Extract local data\\n local_list = [s for s in range(zone) if assignment[s] == c]\\n\\n local_zone = len(local_list)\\n\\n local_cp = [cp[s] for s in local_list]\\n local_ds = [ds[s] for s in local_list]\\n\\n local_vec_list = []\\n local_vec = 0\\n local_cap = []\\n local_location = []\\n\\n for v in range(vec):\\n if location[v].index(1) in local_list:\\n local_vec_list.append(v)\\n local_vec += 1\\n local_cap.append(cap[v])\\n local_location.append([1 if (s == location[v].index(1)) else 0 for s in local_list])\\n \\n local_dis = [[dis[s][z] for z in local_list] for s in local_list]\\n\\n local_train_fl_out = [[train_fl_out[ts][s] for s in local_list] for ts in range(trainsample)]\\n local_train_fl_in = [[train_fl_in[ts][s] for s in local_list] for ts in range(trainsample)]\\n\\n # Apply local rebalance\\n status, local_yp_total, local_yn_total, local_bn = intra_redeployment(local_zone, local_cp, local_ds, local_vec, local_cap, local_location, local_dis, trainsample, local_train_fl_out, local_train_fl_in, dismaxpick, dismaxdrop)\\n\\n # Make error report if no solution\\n if status == 1:\\n error_f = open(\\\"./error_report.csv\\\", \\\"a\\\", newline='')\\n error_writer = csv.writer(error_f)\\n error_writer.writerow([str(cluster), str(input_scenario), str(timestep), str(c)])\\n error_f.close()\\n\\n # Translate to global indexing\\n for s in range(local_zone):\\n yp_total[local_list[s]] = local_yp_total[s]\\n yn_total[local_list[s]] = local_yn_total[s]\\n \\n for v in range(local_vec):\\n for s in range(local_zone):\\n bn[local_vec_list[v]][local_list[s]] = local_bn[v][s]\\n \\n print(\\\"Cluster done is: \\\" + str(c))\\n \\n for s in range(zone):\\n policy_writer.writerow([str(timestep), str(s), str(yp_total[s]), str(yn_total[s])])\\n \\n # Update ds\\n update_ds(zone, cp, ds, mindis, orig_fl[timestep], yp_total, yn_total)\\n\\n # Update operator location\\n for v in range(vec):\\n for s in range(zone):\\n if round(bn[v][s]) == 1:\\n location[v][s] = 1\\n else:\\n location[v][s] = 0\\n \\n print(\\\"Timestep done is: \\\" + str(timestep))\\n \\n policy_f.close()\",\n \"def example_OC3():\\n example = sparAssemblyCalculation()\\n example.tower_base_outer_diameter = 6.5\\n example.tower_top_outer_diameter = 3.87\\n example.tower_length = 77.6\\n example.example_turbine_size = '3MW' #not sure if this is correct\\n example.RNA_center_of_gravity_y = 1.75\\n example.wall_thickness = [.057, .056, .042, .046, .052]\\n example.rotor_diameter = 126.\\n # example.cut_out_speed\\n example.air_density = 1.198\\n example.wind_reference_speed = 11.\\n example.wind_reference_height = 89.350\\n example.gust_factor = 1.0\\n example.alpha = .11\\n example.RNA_center_of_gravity_x = 1.9\\n example.tower_mass = 249718.0\\n example.RNA_mass = 347460.\\n example.stiffener_index = 259\\n example.number_of_sections = 5\\n example.bulk_head = ['N', 'T', 'N', 'B', 'B']\\n example.number_of_rings = [3, 2, 10, 19, 32]\\n example.neutral_axis = .21 #not sure if this number is correct\\n # example.straight_col_cost\\n # example.tapered_col_cost\\n # example.outfitting_cost\\n # example.ballast_cost\\n example.gravity = 9.806\\n example.load_condition = 'N'\\n example.significant_wave_height = 8.\\n example.significant_wave_period = 10.\\n example.material_density = 7850\\n example.E = 200.\\n example.nu = .3\\n example.yield_stress = 345.\\n example.shell_mass_factor = 1\\n example.bulkhead_mass_factor = 1.25\\n # example.ring_mass_factor\\n example.outfitting_factor = .06\\n example.spar_mass_factor = 1.04\\n example.permanent_ballast_height = 0.\\n example.fixed_ballast_height = 10.\\n example.permanent_ballast_density = 4000.\\n example.fixed_ballast_density = 4492.48\\n # example.offset_amplification_factor\\n example.water_density = 1025.\\n example.spar_elevations = [10.0, -4.0, -12.0, -42., -71., -120.]\\n example.spar_outer_diameter = [6.5, 6.5, 9.4, 9.4, 9.4]\\n example.water_depth = 320.\\n example.fairlead_depth = 70.\\n example.scope_ratio = 3.609\\n example.pretension_percent = 11.173 #map doesnt use\\n example.mooring_diameter = .09\\n example.number_of_mooring_lines = 3\\n example.mooring_type = 'CHAIN'\\n example.anchor_type = 'PILE'\\n example.fairlead_offset_from_shell = .5\\n example.user_MBL = 8158000.\\n example.user_WML = 71.186\\n example.user_AE_storm = 384243000/.006\\n example.user_MCPL = 0.\\n example.user_anchor_cost = 0.\\n example.misc_cost_factor = 10\\n example.number_of_discretizations = 20 #map doesnt use\\n example.spar.stiffener_curve_fit = False #not sure if this is correct\\n example.run()\\n print '-------------OC3---------------'\\n sys_print(example)\",\n \"def three_experiments_with_trials(family_with_trials, single_with_trials):\",\n \"def step6_run_all(flow_dataset_npz=\\\"flow_dataset.npz\\\"):\\n global objs, predictions, ticks, timestamps, is_slider, is_spinner, is_note_end, sv, slider_ticks, dist_multiplier, divisor, note_distance_basis, slider_length_base, slider_types, slider_type_rotation, slider_cos, slider_sin, slider_cos_each, slider_sin_each, slider_type_length, slider_lengths, tick_diff, note_distances, maps, labels, special_train_data, special_train_labels, early_stop, loss_ma, extvar, plot_noise\\n\\n objs, predictions, ticks, timestamps, is_slider, is_spinner, is_note_end, sv, slider_ticks, dist_multiplier = read_map_predictions(\\n \\\"temp/rhythm_data.npz\\\")\\n\\n # get divisor from GAN_PARAMS\\n divisor = GAN_PARAMS[\\\"divisor\\\"]\\n\\n # get basis\\n note_distance_basis = GAN_PARAMS[\\\"note_distance_basis\\\"]\\n\\n # get next_from_slider_end\\n next_from_slider_end = GAN_PARAMS[\\\"next_from_slider_end\\\"]\\n\\n # should be slider length each tick, which is usually SV * SMP * 100 / 4\\n # e.g. SV 1.6, timing section x1.00, 1/4 divisor, then slider_length_base = 40\\n slider_length_base = sv / divisor\\n\\n # weight for each type of sliders\\n slider_type_probs = [0.25, 0.25, 0.25, 0.05, 0.05, 0.03, 0.03, 0.01,\\n 0.01, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.015, 0.015, 0.01]\\n slider_types = np.random.choice(\\n len(slider_type_probs), is_slider.shape, p=slider_type_probs).astype(int)\\n\\n # these data must be kept consistent with the sliderTypes in load_map.js\\n slider_type_rotation = np.array([0, -0.40703540572409336, 0.40703540572409336, -0.20131710837464062, 0.20131710837464062,\\n -0.46457807316944644, 0.46457807316944644, 1.5542036732051032, -\\n 1.5542036732051032, 0, 0, 0.23783592745745077, -0.23783592745745077,\\n 0.5191461142465229, -0.5191461142465229, -0.16514867741462683, 0.16514867741462683, 3.141592653589793])\\n\\n # this is vector length! I should change the variable name probably...\\n slider_type_length = np.array([1.0, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97,\\n 0.64, 0.64, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.96, 0.96, 0])\\n\\n slider_cos = np.cos(slider_type_rotation)\\n slider_sin = np.sin(slider_type_rotation)\\n\\n slider_cos_each = slider_cos[slider_types]\\n slider_sin_each = slider_sin[slider_types]\\n\\n slider_lengths = np.array([slider_type_length[int(\\n k)] * slider_length_base[i] for i, k in enumerate(slider_types)]) * slider_ticks\\n\\n tick_diff = np.concatenate([[100], ticks[1:] - ticks[:-1]])\\n\\n if next_from_slider_end:\\n tick_diff = np.concatenate(\\n [[100], tick_diff[1:] - np.floor(slider_ticks * is_slider)[:-1]])\\n\\n # Timing section reset == tick_diff < 0\\n # Use 1 as default value\\n tick_diff = np.where(tick_diff < 0, 1, tick_diff)\\n\\n note_distances = np.clip(tick_diff, 1, divisor * 2) * \\\\\\n (note_distance_basis / divisor)\\n\\n # Load the flow dataset saved in part 4\\n with np.load(flow_dataset_npz) as flow_dataset:\\n maps = flow_dataset[\\\"maps\\\"]\\n labels = np.ones(maps.shape[0])\\n\\n order2 = np.argsort(np.random.random(maps.shape[0]))\\n special_train_data = maps[order2]\\n special_train_labels = labels[order2]\\n\\n early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=20)\\n\\n # Start model training\\n\\n loss_ma = [90, 90, 90]\\n extvar = {\\\"begin\\\": 10}\\n\\n plot_noise = np.random.random((1, GAN_PARAMS[\\\"g_input_size\\\"]))\\n\\n if GAN_PARAMS[\\\"max_epoch\\\"] == 0:\\n osu_a = put_everything_in_the_center()\\n else:\\n osu_a = generate_map()\\n\\n data = objs, predictions, ticks, timestamps, is_slider, is_spinner, is_note_end, sv, slider_ticks, dist_multiplier, slider_types, slider_length_base\\n return osu_a, data\",\n \"def make_test_data(self):\\r\\n\\r\\n \\r\\n\\r\\n print (\\\"Creating Test Sample:\\\")\\r\\n\\r\\n print (' Period, rate, reps, phases: ', self.period, self.framerate, self.nrepetitions, self.nPhases)\\r\\n\\r\\n nframes = int(self.period * self.framerate * self.nrepetitions)\\r\\n\\r\\n print (' nframes: ', nframes)\\r\\n\\r\\n if self.bkgdNoise > 0.:\\r\\n\\r\\n d = np.random.normal(size=(nframes,self.imageSize[0],self.imageSize[1]),\\r\\n\\r\\n loc=self.bkgdIntensity, scale=self.bkgdNoise).astype('float32')\\r\\n\\r\\n else:\\r\\n\\r\\n d = self.bkgdIntensity*np.ones((nframes,self.imageSize[0],self.imageSize[1])).astype('float32')\\r\\n\\r\\n \\r\\n\\r\\n ds = d.shape\\r\\n\\r\\n print (' data shape: ', ds)\\r\\n\\r\\n dx = int(ds[2]/4)\\r\\n\\r\\n xc = int(ds[2]/2)\\r\\n\\r\\n xo = [xc-dx, xc+dx]\\r\\n\\r\\n ywidth = int(ds[2]/(self.nPhases+2))\\r\\n\\r\\n framedelay = 4\\r\\n\\r\\n\\r\\n\\r\\n if not self.mode:\\r\\n\\r\\n self.phasex = []\\r\\n\\r\\n self.phasey = []\\r\\n\\r\\n for i in range(0,self.nPhases):\\r\\n\\r\\n dy = int((i+1)*ds[2]/(self.nPhases+2)) # each phase is assigned to a region\\r\\n\\r\\n self.resp = np.zeros((nframes,))\\r\\n\\r\\n self.resp = np.cos(\\r\\n\\r\\n np.linspace(0, 2.0*np.pi*nframes/(self.period*self.framerate), nframes-framedelay)+i*np.pi/8 - np.pi/2.0)\\r\\n\\r\\n self.resp = np.concatenate((np.zeros(framedelay), self.resp))\\r\\n\\r\\n d[:, xo[0]:xo[1], dy:dy+ywidth ] += self.resp[:, np.newaxis, np.newaxis]\\r\\n\\r\\n self.phasey.append( (2+(dy+int(ds[2]/self.nPhases))/2))\\r\\n\\r\\n self.phasex.append((6+int(ds[1]/2)/2)) # make the signal equivalent of digitized one (baseline 3000, signal at 1e-4 of baseline)\\r\\n\\r\\n else:\\r\\n\\r\\n self.nPhases = 4\\r\\n\\r\\n self.spotsize = 16\\r\\n\\r\\n nrpts = 20\\r\\n\\r\\n nsites = 4\\r\\n\\r\\n one_rep = int(self.period*self.framerate)\\r\\n\\r\\n isi = int(self.period*self.framerate/self.nPhases)\\r\\n\\r\\n print('period, isi: ', self.period, isi)\\r\\n\\r\\n r = np.arange(0, nrpts, 1.)\\r\\n\\r\\n alpha = 4.\\r\\n\\r\\n A = r/alpha *np.exp(-(r-alpha)/alpha) # scaled alpha function\\r\\n\\r\\n self.spot= self.gauss_spot(self.spotsize, 3.) # the 2d spot\\r\\n\\r\\n sigsize = np.random.normal(size=self.nPhases, loc=self.signal_size, scale=self.signal_size*2)\\r\\n\\r\\n sigsize = [np.abs(s) for s in sigsize] # restrict to positive amplitudes\\r\\n\\r\\n print ('sigsize: ', sigsize)\\r\\n\\r\\n for j in range(self.nrepetitions):\\r\\n\\r\\n for i in range(self.nPhases):\\r\\n\\r\\n self.resp = np.zeros((nrpts, self.spot.shape[0], self.spot.shape[1]))\\r\\n\\r\\n for k in range(nrpts):\\r\\n\\r\\n self.resp[k,:,:] += sigsize[i]*A[k] * self.spot # make response an alpha time course of gaussian spot\\r\\n\\r\\n start = j*one_rep + i*isi + framedelay\\r\\n\\r\\n stop = start + nrpts\\r\\n\\r\\n dy = int((i+1)*ds[2]/(self.nPhases+2)) # location for phase\\r\\n\\r\\n #dy = dy + 2*z\\r\\n\\r\\n# print ('start, stop: ', start, stop)\\r\\n\\r\\n for z in range(nsites):\\r\\n\\r\\n #self.resp = np.concatenate((np.zeros(framedelay), self.resp))\\r\\n\\r\\n xp = xo[0] + i*10 - 10*z\\r\\n\\r\\n yp = dy - i*10 + 10*z\\r\\n\\r\\n d[start:stop, xp:xp+self.spotsize, yp:yp+self.spotsize ] += self.resp\\r\\n\\r\\n self.imageData = d # reduce to a 16-bit map to match camera data type\\r\\n\\r\\n self.nFrames = self.imageData.shape[0]\\r\\n\\r\\n self.times = np.arange(0, nframes/self.framerate, 1.0/self.framerate)\\r\\n\\r\\n print( \\\" Test Image Created\\\")\\r\\n\\r\\n # imv = pg.ImageView()\\r\\n\\r\\n # imv.show()\\r\\n\\r\\n # imv.setImage(self.imageData)\\r\\n\\r\\n\\r\\n\\r\\n if self.layout is not None:\\r\\n\\r\\n self.layout.addWidget(imv, 0, 0)\\r\\n\\r\\n\\r\\n\\r\\n avgImage = np.mean(self.imageData, axis=0)\\r\\n\\r\\n ima = pg.ImageView()\\r\\n\\r\\n ima.setImage(avgImage)\\r\\n\\r\\n self.layout.addWidget(ima, 0, 1)\\r\\n\\r\\n self.adjust_image_data()\\r\\n\\r\\n self.avgimg = np.mean(self.imageData, axis=0) # get mean image for reference later: average across all time\\r\\n\\r\\n print (' Test file, original Image Info: ')\\r\\n\\r\\n self.print_image_info()\\r\\n\\r\\n self.rebin_image()\\r\\n\\r\\n #self.clean_windowerrors()\\r\\n\\r\\n # pg.image(self.imageData)\\r\\n\\r\\n # pg.show()\\r\\n\\r\\n # mpl.figure(1)\\r\\n\\r\\n # mpl.show()\\r\\n\\r\\n if not self.mode: # FFT analysis\\r\\n\\r\\n self.analysis_fourier_map(target=1, mode=0)\\r\\n\\r\\n self.plot_maps(mode=2, gfilter=self.gfilter)\\r\\n\\r\\n else:\\r\\n\\r\\n self.analysis_dFF_map()\\r\\n\\r\\n mpl.show()\",\n \"def _choose_sample(self):\\n\\n \\t #periodically generate a new reconstruction for the purposes of sampling\",\n \"def ssd_synthia_car_fine_tune():\\n merged_annotation = '/home/public/synthia/ssd_car_fine_tune/SYNTHIA-SEQS-01-TRAIN_MERGED-shuffle.json'\\n if False:\\n print('we combine the training and validation here')\\n annotations_url_1 = '/home/public/synthia/SYNTHIA-SEQS-01-TRAIN-shuffle.json'\\n annotations_url_2 = '/home/public/synthia/SYNTHIA-SEQS-01-VALIDATE-shuffle.json'\\n combine_gt(annotations_url_1, annotations_url_2, merged_annotation)\\n\\n if False:\\n print('collect front and rear cars')\\n annotations_url_1 = '/home/public/synthia/SYNTHIA-SEQS-01-TRAIN-shuffle.json'\\n annotations_url_2 = '/home/public/synthia/SYNTHIA-SEQS-01-VALIDATE-shuffle.json'\\n save_dir = '/home/stevenwudi/PycharmProjects/autonomous_driving/Experiments/SEQ_01_SEQ_06_cars'\\n collect_front_and_rear_gt(annotations_url_1, annotations_url_2, save_dir, image_interval=50)\\n\\n gt_file = '/home/public/synthia/ssd_car_fine_tune/ssd_car_fine_tune_gt-shuffle.pkl'\\n if False:\\n print('Training annotation conversion')\\n converting_gt(merged_annotation, gt_file, POR=1e-3)\\n # POR: 1e-3 Finish converting, total annotated car number is 22332 in total image of 8814.\\n # POR: 5e-4: Finish converting, total annotated fish number is 26800 in total image of 8814.\\n\\n model_checkpoint = '/home/public/synthia/ssd_car_fine_tune/weights_512.54-0.19.hdf5'\\n if False:\\n print('Start DDS 512 training')\\n train_ssd512(gt_file, model_checkpoint=model_checkpoint, base_lr=1e-5)\\n\\n test_gt_file = '/home/public/synthia/ssd_car_fine_tune/ssd_car_test_gt-shuffle.pkl'\\n if False:\\n print('Converting testing GT')\\n annotations_url = '/home/public/synthia/SYNTHIA-SEQS-01-TEST-shuffle.json'\\n converting_gt(annotations_url, test_gt_file)\\n if False:\\n # Examine test data\\n examine_ssd512(test_gt_file, model_checkpoint)\\n\\n test_json_file = '/home/public/synthia/ssd_car_fine_tune/ssd_car_test-shuffle_nms_'+str(nms_thresh)+'.json'\\n if False:\\n test_ssd512(test_gt_file, model_checkpoint, test_json_file)\\n # A separate file for accepting gt file and predicted json fil\\n if True:\\n calculate_iou(test_gt_file, test_json_file, POR=2e-3, draw=False)\\n\\n test_gt_file = '/home/public/synthia/ssd_car_fine_tune/ssd_car_test_gt-shuffle.pkl'\\n test_json_file = '/home/public/synthia/ssd_car_test_faster-shuffle.json'\\n\\n calculate_iou(test_gt_file, test_json_file, POR=2e-3, draw=False)\\n \\\"\\\"\\\"\\n<<<<<<< HEAD\\n This is the network results train by SSD512 (with 0.05% POR trained)\\n Conf: [ 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]\\n \\n ### POR=1e-3\\n Total GT: 2327. \\n Total prediction: [ 2016. 1986. 1951. 1918. 1888. 1852. 1815. 1774. 1711. 1641.]\\n Precision: [ 0.913 0.908 0.9 0.895 0.886 0.879 0.867 0.853 0.83 0.804]\\n Recall: [ 0.791 0.787 0.78 0.775 0.768 0.761 0.751 0.739 0.719 0.697]\\n F score: [[ 0.847 0.843 0.837 0.825 0.816 0.79 0.748 0.666 0.449 0.106]\\n [ 0.843 0.839 0.834 0.823 0.813 0.788 0.748 0.665 0.449 0.106]\\n [ 0.835 0.833 0.829 0.818 0.809 0.786 0.747 0.665 0.449 0.106]\\n [ 0.831 0.829 0.826 0.815 0.807 0.784 0.746 0.664 0.449 0.105]\\n [ 0.823 0.822 0.82 0.81 0.802 0.781 0.744 0.663 0.447 0.105]\\n [ 0.816 0.816 0.815 0.806 0.799 0.778 0.741 0.661 0.446 0.105]\\n [ 0.805 0.805 0.806 0.797 0.792 0.772 0.738 0.66 0.445 0.105]\\n [ 0.792 0.793 0.793 0.789 0.784 0.766 0.734 0.658 0.445 0.105]\\n [ 0.77 0.773 0.775 0.771 0.768 0.753 0.725 0.652 0.441 0.104]\\n [ 0.746 0.749 0.751 0.751 0.75 0.741 0.716 0.645 0.439 0.103]]\\n<<<<<<< HEAD\\n\\n ### faster RCNN detection result POR= 1e-3\\n Total GT: 2327.\\n Total prediction: [ 2120. 2084. 2062. 2037. 2003. 1971. 1947. 1901. 1862. 1785.]\\n Precision: [ 0.83 0.826 0.825 0.823 0.82 0.818 0.817 0.807 0.801 0.787]\\n Recall: [ 0.756 0.752 0.751 0.75 0.747 0.745 0.744 0.735 0.73 0.717]\\n F score: [[ 0.791 0.758 0.72 0.669 0.59 0.485 0.332 0.174 0.054 0.006]\\n [ 0.787 0.756 0.719 0.669 0.59 0.485 0.332 0.174 0.054 0.006]\\n [ 0.786 0.755 0.718 0.668 0.589 0.485 0.332 0.174 0.054 0.006]\\n [ 0.785 0.754 0.718 0.668 0.589 0.484 0.332 0.174 0.054 0.006]\\n [ 0.782 0.752 0.716 0.666 0.588 0.484 0.331 0.174 0.054 0.006]\\n [ 0.78 0.75 0.715 0.666 0.588 0.484 0.331 0.174 0.054 0.006]\\n [ 0.779 0.749 0.714 0.665 0.588 0.484 0.331 0.174 0.054 0.006]\\n [ 0.77 0.745 0.711 0.663 0.586 0.483 0.331 0.174 0.054 0.006]\\n [ 0.764 0.74 0.709 0.66 0.584 0.482 0.331 0.173 0.054 0.006]\\n [ 0.75 0.73 0.703 0.657 0.582 0.481 0.33 0.173 0.054 0.006]]\\n\\n=======\\n \\n ### POR shuffle\\n Total GT: 2563. \\n Total prediction: [ 2185. 2137. 2081. 2047. 2010. 1976. 1937. 1893. 1831. 1713.]\\n Precision: [ 0.843 0.836 0.826 0.819 0.813 0.805 0.795 0.784 0.766 0.726]\\n Recall: [ 0.719 0.712 0.704 0.698 0.693 0.686 0.678 0.669 0.653 0.619]\\n F score: [[ 0.776 0.698 0.646 0.619 0.597 0.571 0.525 0.447 0.31 0.106]\\n [ 0.769 0.693 0.642 0.616 0.595 0.57 0.524 0.447 0.31 0.106]\\n [ 0.76 0.686 0.637 0.612 0.591 0.568 0.523 0.445 0.31 0.106]\\n [ 0.754 0.681 0.632 0.608 0.588 0.565 0.521 0.443 0.31 0.106]\\n [ 0.749 0.677 0.629 0.605 0.586 0.563 0.52 0.442 0.309 0.106]\\n [ 0.741 0.671 0.624 0.602 0.583 0.561 0.518 0.441 0.309 0.106]\\n [ 0.732 0.664 0.619 0.597 0.579 0.558 0.516 0.44 0.308 0.106]\\n [ 0.722 0.656 0.612 0.59 0.573 0.553 0.515 0.439 0.308 0.106]\\n [ 0.705 0.642 0.6 0.58 0.564 0.545 0.508 0.435 0.306 0.105]\\n [ 0.668 0.614 0.578 0.561 0.548 0.531 0.496 0.426 0.302 0.105]]\\n \\n POR = 2e-3\\n=======\\n ############################# SSD512 NMS 0.6 ###########################\\n Conf: [ 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]\\n>>>>>>> 419d28c4e27b82c4cfef6a3aa01425cf29929973\\n Total GT: 1433. \\n Total prediction: [ 1617. 1605. 1593. 1579. 1568. 1551. 1537. 1517. 1498. 1465.]\\n Precision: [ 0.821 0.819 0.818 0.818 0.816 0.811 0.809 0.804 0.8 0.792]\\n Recall: [ 0.927 0.925 0.923 0.923 0.92 0.915 0.913 0.907 0.902 0.894]\\n F score: [[ 0.871 0.868 0.859 0.852 0.836 0.81 0.742 0.636 0.48 0.161]\\n [ 0.869 0.866 0.857 0.85 0.834 0.808 0.741 0.635 0.48 0.161]\\n [ 0.867 0.864 0.857 0.849 0.834 0.808 0.741 0.635 0.48 0.161]\\n [ 0.867 0.864 0.857 0.849 0.834 0.808 0.741 0.635 0.48 0.161]\\n [ 0.865 0.862 0.855 0.848 0.832 0.807 0.741 0.635 0.48 0.161]\\n [ 0.86 0.857 0.851 0.844 0.828 0.804 0.739 0.633 0.479 0.161]\\n [ 0.858 0.855 0.849 0.843 0.827 0.802 0.738 0.633 0.478 0.161]\\n [ 0.852 0.85 0.844 0.839 0.823 0.8 0.736 0.631 0.477 0.16 ]\\n [ 0.848 0.846 0.841 0.836 0.82 0.797 0.734 0.631 0.477 0.16 ]\\n [ 0.84 0.838 0.834 0.829 0.814 0.792 0.729 0.629 0.476 0.16 ]]\\n \\n ############################# SSD512 NMS 0.45 ###########################\\n Total GT: 1433. \\n Total prediction: [ 1438. 1428. 1421. 1413. 1403. 1398. 1384. 1374. 1348. 1305.]\\n Precision: [ 0.92 0.918 0.917 0.914 0.911 0.91 0.905 0.902 0.894 0.874]\\n Recall: [ 0.923 0.921 0.92 0.918 0.914 0.913 0.908 0.905 0.897 0.877]\\n F score: [[ 0.922 0.918 0.91 0.901 0.883 0.851 0.785 0.673 0.476 0.164]\\n [ 0.92 0.916 0.908 0.9 0.882 0.851 0.785 0.673 0.476 0.164]\\n [ 0.918 0.915 0.907 0.899 0.882 0.851 0.785 0.673 0.476 0.164]\\n [ 0.916 0.913 0.905 0.897 0.88 0.849 0.785 0.673 0.476 0.164]\\n [ 0.913 0.91 0.903 0.895 0.878 0.847 0.783 0.671 0.475 0.164]\\n [ 0.911 0.909 0.901 0.894 0.877 0.846 0.782 0.671 0.475 0.164]\\n [ 0.906 0.905 0.898 0.89 0.874 0.843 0.781 0.67 0.475 0.164]\\n [ 0.904 0.901 0.895 0.888 0.871 0.84 0.78 0.67 0.475 0.164]\\n [ 0.896 0.894 0.888 0.882 0.866 0.836 0.776 0.668 0.474 0.164]\\n [ 0.876 0.876 0.872 0.869 0.855 0.829 0.77 0.663 0.471 0.164]]\\n \\n \\n<<<<<<< HEAD\\n>>>>>>> bb5caf05d4bc41e182e41686b8b5e497053f9ca5\\n ### POR = None (consider all testing examples)\\n Total GT: 2696. \\n Total prediction: [ 2273. 2221. 2166. 2111. 2055. 2000. 1945. 1875. 1786. 1684.]\\n Precision: [ 0.822 0.818 0.81 0.805 0.796 0.789 0.777 0.763 0.741 0.716]\\n Recall: [ 0.693 0.69 0.683 0.678 0.671 0.665 0.655 0.643 0.625 0.604]\\n F score: [[ 0.752 0.746 0.74 0.729 0.724 0.703 0.668 0.597 0.405 0.096]\\n [ 0.748 0.744 0.737 0.727 0.722 0.702 0.668 0.597 0.405 0.096]\\n [ 0.741 0.737 0.733 0.723 0.718 0.699 0.667 0.596 0.405 0.096]\\n [ 0.736 0.733 0.729 0.72 0.716 0.698 0.666 0.596 0.404 0.095]\\n [ 0.728 0.726 0.724 0.716 0.712 0.695 0.664 0.595 0.403 0.095]\\n [ 0.722 0.721 0.72 0.712 0.708 0.692 0.661 0.593 0.402 0.095]\\n [ 0.711 0.711 0.711 0.704 0.703 0.687 0.659 0.592 0.401 0.095]\\n [ 0.698 0.7 0.7 0.696 0.696 0.682 0.655 0.59 0.401 0.095]\\n [ 0.678 0.682 0.684 0.681 0.681 0.67 0.647 0.585 0.398 0.095]\\n [ 0.655 0.659 0.662 0.663 0.665 0.659 0.639 0.578 0.395 0.094]]\\n\\n POR = 2e-3 fastercnn result\\n Conf: [ 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]\\n Total GT: 1433.\\n Total prediction: [ 1614. 1601. 1593. 1585. 1576. 1561. 1557. 1544. 1532. 1507.]\\n=======\\n ############################# Faster-RCNN ###########################\\n Conf: [ 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]\\n Total GT: 1433. \\n Total prediction: [ 1614. 1601. 1593. 1585. 1576. 1561. 1557. 1544. 1532. 1507.]\\n>>>>>>> 419d28c4e27b82c4cfef6a3aa01425cf29929973\\n Precision: [ 0.812 0.812 0.811 0.811 0.811 0.81 0.81 0.807 0.805 0.8 ]\\n Recall: [ 0.914 0.914 0.913 0.913 0.913 0.913 0.913 0.909 0.907 0.902]\\n F score: [[ 0.86 0.843 0.808 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\\n [ 0.86 0.843 0.808 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\\n [ 0.859 0.842 0.807 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\\n [ 0.859 0.842 0.807 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\\n [ 0.859 0.842 0.807 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\\n [ 0.859 0.842 0.807 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\\n [ 0.859 0.842 0.807 0.75 0.666 0.577 0.459 0.311 0.14 0.016]\\n [ 0.855 0.839 0.804 0.748 0.665 0.576 0.458 0.311 0.14 0.016]\\n [ 0.853 0.838 0.804 0.747 0.664 0.575 0.458 0.311 0.14 0.016]\\n [ 0.848 0.834 0.8 0.745 0.662 0.574 0.457 0.31 0.139 0.016]]\\n \\\"\\\"\\\"\",\n \"def initial_sampling(self, params):\\n i = params\\n theta_star = self.priors_sample()\\n model = self.simz( theta_star )\\n rho = test_dist(self.data, model)\\n while rho > self.eps0: \\n theta_star = self.priors_sample()\\n model = self.simz( theta_star )\\n rho = test_dist(self.data, model)\\n data_list = [np.int(i)]\\n\\n for i_param in xrange(self.n_params): \\n data_list.append(theta_star[i_param])\\n data_list.append(1./np.float(self.N))\\n data_list.append(rho)\\n\\treturn np.array(data_list)\",\n \"def getActpolSim(iterationNum = 0, patch = 'deep5', \\n season = 's13', \\\\\\n array = 'pa1', \\\\\\n psa = None,\\\\\\n noiseDictFile = 'templateInputsMr3c.dict', \\\\\\n noiseDictFilePath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'../inputParams/'), \\\\\\n signalDictFile = 'signal.dict',\\\\\\n signalDictFilePath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'../inputParams/'),\\\\\\n verbose = True,\\\\\\n simType = 'noise',\\n cmbSet = 0,\\n doBeam = True, applyWindow = True, noiseDiagsOnly = False, cmbMaptype = 'LensedCMB', splitWanted = None,\\n applyModulation = True):\\n #update the last one to True if possible\\n\\n\\n#FIXME: get rid of absolute pathnames\\n\\n nDict = flipperDict.flipperDict()\\n nDict.read_from_file(noiseDictFilePath + '/' + noiseDictFile)\\n\\n sDict = flipperDict.flipperDict()\\n sDict.read_from_file(signalDictFilePath + '/' + signalDictFile)\\n \\n if psa == None: #psa stands for patch, season, \\n psa = '%s_%s_%s' %(patch, season, array)\\n\\n\\n #Figure out what frequencies correspond to this array, using the function defined above.\\n psaFreqs = freqsInPsas(psa, nDict['freqsInArrays'])\\n\\n\\n #unroll psa names (normally stored as a nested list of lists)\\n psaList = [item for sublist in nDict['psaList'] for item in sublist]\\n\\n\\n if psa not in psaList:\\n raise ValueError('psa %s not found in psaList; options are ' % (psa ), psaList)\\n\\n\\n noiseSeed = (cmbSet, psaList.index(psa), noiseSeedInd * 4 + (0 if splitWanted is None else splitWanted), iterationNum)\\n\\n #load up one sample map, just to get the shape and wcs info. Do this for \\\"I\\\" at one frequency\\n sampleMap = enmap.read_map(os.path.join(os.path.dirname(os.path.abspath(__file__)))+\\\"/\\\"+nDict['dataMapDir'] + 'totalWeightMap' \\\\\\n + 'I' + '_' + psa + '_' + psaFreqs[0] + '_fromenlib.fits') \\n\\n\\n #Note! Foreground seed is the same for every sky patch, season, and frequency!\\n #This is because they are used to generate fullsky alm's\\n foregroundSeed = (cmbSet, 0, fgSeedInd, iterationNum)\\n\\n\\n\\n if simType == 'noise':\\n\\n return getActpolNoiseSim(noiseSeed = noiseSeed, \\\\\\n psa = psa, \\\\\\n noisePsdDir = os.path.dirname(os.path.abspath(__file__))+\\\"/\\\"+nDict['dataMapDir'],\\n freqs = psaFreqs, \\n verbose = verbose,\\n noiseDiagsOnly = noiseDiagsOnly,\\n splitWanted = splitWanted)\\n\\n elif simType == 'cmb' or simType == 'foregrounds':\\n \\n\\n return getActpolCmbFgSim(beamfileDict = sDict['beamNames'],\\n shape = sampleMap.shape, wcs = sampleMap.wcs,\\n iterationNum = iterationNum,\\n cmbDir = os.path.dirname(os.path.abspath(__file__))+\\\"/\\\"+sDict['cmbDir'],\\n freqs = psaFreqs,\\n psa = psa,\\n cmbSet = cmbSet, \\n doBeam = doBeam, applyWindow = applyWindow,\\n verbose = verbose, cmbMaptype = cmbMaptype, foregroundSeed = foregroundSeed,\\n simType = simType, foregroundPowerFile = sDict['foregroundPowerFile'],\\n applyModulation = applyModulation)\\n\\n\\n else:\\n raise ValueError(\\\"bad input\\\")\",\n \"def split_simsplit_3epochs_iter3(params, ns):\\n #24 parameters \\n nu1a, nu2a, nu3a, nu1b, nu2b, nu3b, nu1c, nu2c, nu3c, m1_12, m1_13, m1_21, m1_23, m1_31, m1_32, m2_12, m2_13, m2_21, m2_23, m2_31, m2_32, T1, T2, T3 = params\\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1] + ns[2])\\n fs = moments.Spectrum(sts)\\n fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1] + ns[2]) \\n fs = moments.Manips.split_2D_to_3D_2(fs, ns[1], ns[2])\\n ## Population function and migration matrix for T1\\n nu_T1 = [nu1a, nu2a, nu3a]\\n mig1 = numpy.array([[0, m1_12, m1_13],[m1_21, 0, m1_23], [m1_31, m1_32, 0]]) \\n fs.integrate(nu_T1, T1, m=mig1)\\n ## Population function and migration matrix for T2\\n nu_T2 = [nu1b, nu2b, nu3b]\\n mig2 = numpy.array([[0, m2_12, m2_13],[m2_21, 0, m2_23], [m2_31, m2_32, 0]]) \\n fs.integrate(nu_T2, T2, m=mig2)\\n ## Population function and migration matrix for T3 (bottleneck to capture single population representation of lineage)\\n nu_T3 = [nu1c, nu2c, nu3c]\\n fs.integrate(nu_T3, T3) \\n return fs\",\n \"def main():\\n\\tif len(sys.argv) < 12 or len(sys.argv) > 13:\\n\\t\\tprint(\\\"Input parameters must be: 'filename lambda mu C c0 Q theta L H simulation_time is_debug repeats(optionally)'\\\")\\n\\telse:\\n\\t\\tstart_time = time.time()\\n\\n\\t\\tfile_name = sys.argv[1]\\n\\t\\tlambd = float(sys.argv[2])\\n\\t\\tmu = float(sys.argv[3])\\n\\t\\tC = int(sys.argv[4])\\n\\t\\tc0 = int(sys.argv[5])\\n\\t\\tQ = int(sys.argv[6])\\n\\t\\ttheta = float(sys.argv[7])\\n\\t\\tL = int(sys.argv[8])\\n\\t\\tH = int(sys.argv[9])\\n\\t\\tsimulation_time = float(sys.argv[10]);\\n\\t\\tis_debug = True if sys.argv[11] == \\\"True\\\" else False;\\n\\t\\trepeats = int(sys.argv[12]) if len(sys.argv) == 13 else 1;\\n\\n\\t\\tprint(\\\"Simulation started for params: lambda =\\\", lambd,\\n\\t\\t\\t \\\", mu =\\\", mu,\\n\\t\\t\\t \\\", C =\\\", C,\\n\\t\\t\\t \\\", c0 =\\\", c0,\\n\\t\\t\\t \\\", Q =\\\", Q,\\n\\t\\t\\t \\\", theta =\\\", theta,\\n\\t\\t\\t \\\", L =\\\", L,\\n\\t\\t\\t \\\", H =\\\", H,\\n\\t\\t\\t \\\", repeats =\\\", repeats)\\n\\n\\t\\tblocked = 0\\n\\t\\tserved = 0\\n\\t\\tgenerated = 0\\n\\t\\tB = 0\\n\\t\\tN = 0\\n\\n\\t\\tsimulation = Simulation(\\\"m/m/c[c0]/r[l,h]\\\", lambd, mu, theta, C, c0, L, H, simulation_time, Q, is_debug)\\n\\t\\tfor i in range(0, repeats):\\n\\t\\t\\tsimulation = Simulation(\\\"m/m/c[c0]/r[l,h]\\\", lambd, mu, theta, C, c0, L, H, simulation_time, Q, is_debug)\\n\\t\\t\\tsimulation.start()\\n\\t\\t\\tblocked += simulation.queue.blocked\\n\\t\\t\\tserved += simulation.served_count\\n\\t\\t\\tgenerated += simulation.flow.generated_count\\n\\t\\t\\tB += simulation.queue.blocked/(simulation.served_count+simulation.queue.blocked)\\n\\t\\t\\tN += simulation.served_count/simulation_time\\n\\t\\tend_time = time.time()\\n\\n\\t\\tblocked = blocked/repeats\\n\\t\\tserved = served/repeats\\n\\t\\tgenerated = generated/repeats\\n\\t\\tB = B/repeats\\n\\t\\tN = N/repeats\\n\\n\\t\\tprint( \\\"\\\")\\n\\t\\tprint( \\\"Summary results:\\\")\\n\\t\\tprint( \\\"blocked=\\\", blocked, \\\" served=\\\", served, \\\", generated=\\\", generated)\\n\\t\\tprint(\\\"B = \\\", B)\\n\\t\\tprint(\\\"N = \\\", N)\\n\\t\\tprint(\\\"Execution time = %s seconds\\\" % (end_time - start_time))\\n\\t\\tprint( \\\"... to be implemented more summary ...\\\")\\n\\n\\t\\t# write stats to file\\n\\t\\tabs_path = os.path.abspath(__file__)\\n\\t\\tpath = os.path.relpath('stats', abs_path)\\n\\t\\tpath = os.path.join(path, file_name + '-(%s,%s,%s,%s,%s,%s,%s,%s).csv' % (lambd,mu,theta,C,c0,L,H,simulation_time))\\n\\n\\t\\toutfile=open(path,'w')\\n\\t\\toutput = csv.writer(outfile, delimiter=';')\\n\\t\\toutput.writerow(['Request ID','Queue', 'Arrival_Time','Queue_Arrival_time','Server_Arrival_time','alpha','beta'])\\n\\n\\t\\ti=0\\n\\t\\tfor request in simulation.served_requests:\\n\\t\\t\\ti=i+1\\n\\t\\t\\toutrow=[]\\n\\t\\t\\toutrow.append(request.ID)\\n\\t\\t\\toutrow.append(request.queue_size_at_serving)\\n\\t\\t\\toutrow.append(request.arrival_time)\\n\\t\\t\\toutrow.append(request.queue_arrival_time)\\n\\t\\t\\toutrow.append(request.server_arrival_time)\\n\\t\\t\\toutrow.append(request.alpha)\\n\\t\\t\\toutrow.append(request.beta)\\n\\t\\t\\toutput.writerow(outrow)\\n\\t\\toutfile.close()\\n\\n\\t\\treturn simulation\",\n \"def generate_syntetic_data( self ):\\n label_list = []\\n img_ground_truth = np.zeros( ( self.img_h, self.img_w ), dtype = np.float32 )\\n img = np.zeros( ( self.img_h, self.img_w, 3 ), dtype = np.float32 )\\n class_blue = np.zeros( ( self.img_h, self.img_w ), dtype = np.float32 )\\n class_red = np.zeros( ( self.img_h, self.img_w ), dtype = np.float32 )\\n\\n '''line, square, grill, rectangle, cross'''\\n if self.option_shape == 'line':\\n sample_type = 0\\n elif self.option_shape == 'circle':\\n sample_type = 1\\n elif self.option_shape == 'rectangle':\\n sample_type = 2\\n else: \\n sample_type = np.random.randint( 3 ) #0,1,2\\n\\n if sample_type == 0:\\n self.generate_line( img_ground_truth, img, class_blue, class_red )\\n elif sample_type == 1:\\n self.generate_circle( img_ground_truth, img, class_blue, class_red )\\n else:\\n self.generate_rectangle( img_ground_truth, img, class_blue, class_red )\\n\\n label_list.append( class_blue )\\n label_list.append( class_red )\\n # 2 classes generates\\n label_all = np.dstack( label_list ).astype( np.float32 ) \\n r, g, b = cv2.split( img )\\n img_bgr = cv2.merge( [ b, g, r ] )\\n\\n gen_adj = GenerateAdjMatrx( type_dist = self.type_dist )\\n A_gt = gen_adj.adjmatrx_groundthuth( img_ground_truth )\\n\\n B_in = gen_adj.adjmatrx_groundthuth(img_ground_truth * 0)\\n\\n return img_bgr, img_ground_truth, label_all, A_gt, B_in\",\n \"def generate_base(params, out):\\n\\n os.makedirs(out)\\n experiment_path = os.path.join(out, BASE)\\n shutil.copy(\\\"evaluation/ablation/run_all.sh\\\", out)\\n\\n print(f\\\"Generating {out}/{BASE}\\\")\\n subprocess.run(\\n [\\n \\\"racket\\\",\\n \\\"src/example-gen.rkt\\\",\\n \\\"-b\\\", \\\"mat-mul\\\",\\n \\\"-p\\\", params,\\n \\\"-o\\\", experiment_path\\n ],\\n check=True,\\n stderr=subprocess.PIPE)\\n\\n # Add all required file for the harness.\\n shutil.copy(\\\"evaluation/ablation/Makefile\\\", experiment_path)\\n shutil.copy(\\\"evaluation/ablation/harness.c\\\", experiment_path)\\n shutil.copy(\\\"evaluation/src/utils.h\\\", experiment_path)\",\n \"def randomParam(trajA):\\n lenCBFs = len(trajA[\\\"CBFCons\\\"][-1])\\n CBFs = CBFSets[CBFlens.index(lenCBFs)]\\n Balpha, Kp, Kd = trajA[\\\"param\\\"]\\n Balpha = Balpha + 0.02*(0.5-np.random.random(size = Balpha.shape))\\n Kp = Kp + 50*(0.5-np.random.random())\\n Kd = Kd + 10*(0.5-np.random.random())\\n return SimuTest(CBFs,Balpha,Kp,Kd)\",\n \"def samplecost(app, endclasses, fxnmode, samptype='std', title=\\\"\\\"):\\n associated_scens=[]\\n for phase in app.phases:\\n associated_scens = associated_scens + app.scenids.get((fxnmode, phase), [])\\n costs = np.array([endclasses[scen]['cost'] for scen in associated_scens])\\n times = np.array([time for phase, timemodes in app.sampletimes.items() if timemodes for time in timemodes if fxnmode in timemodes.get(time)] ) \\n rates = np.array(list(app.rates_timeless[fxnmode].values()))\\n \\n tPlot, axes = plt.subplots(2, 1, sharey=False, gridspec_kw={'height_ratios': [3, 1]})\\n phasetimes_start =[times[0] for phase, times in app.phases.items()]\\n phasetimes_end =[times[1] for phase, times in app.phases.items()]\\n ratetimes =[]\\n ratesvect =[]\\n phaselocs = []\\n for (ind, phasetime) in enumerate(phasetimes_start):\\n axes[0].axvline(phasetime, color=\\\"black\\\") \\n phaselocs= phaselocs +[(phasetimes_end[ind]-phasetimes_start[ind])/2 + phasetimes_start[ind]]\\n\\n axes[1].axvline(phasetime, color=\\\"black\\\") \\n ratetimes = ratetimes + [phasetimes_start[ind]] + [phasetimes_end[ind]]\\n ratesvect = ratesvect + [rates[ind]] + [rates[ind]]\\n #axes[1].text(middletime, 0.5*max(rates), list(app.phases.keys())[ind], ha='center', backgroundcolor=\\\"white\\\")\\n #rate plots\\n axes[1].set_xticks(phaselocs)\\n axes[1].set_xticklabels(list(app.phases.keys()))\\n \\n axes[1].plot(ratetimes, ratesvect)\\n axes[1].set_xlim(phasetimes_start[0], phasetimes_end[-1])\\n axes[1].set_ylim(0, np.max(ratesvect)*1.2 )\\n axes[1].set_ylabel(\\\"Rate\\\")\\n axes[1].set_xlabel(\\\"Time (\\\"+str(app.units)+\\\")\\\")\\n axes[1].grid()\\n #cost plots\\n axes[0].set_xlim(phasetimes_start[0], phasetimes_end[-1])\\n axes[0].set_ylim(0, 1.2*np.max(costs))\\n if samptype=='fullint':\\n axes[0].plot(times, costs, label=\\\"cost\\\")\\n else:\\n if samptype=='quadrature' or samptype=='pruned piecewise-linear': \\n sizes = 1000*np.array([weight if weight !=1/len(timeweights) else 0.0 for phase, timeweights in app.weights[fxnmode].items() for time, weight in timeweights.items() if time in times])\\n axes[0].scatter(times, costs,s=sizes, label=\\\"cost\\\", alpha=0.5)\\n axes[0].stem(times, costs, label=\\\"cost\\\", markerfmt=\\\",\\\", use_line_collection=True)\\n \\n axes[0].set_ylabel(\\\"Cost\\\")\\n axes[0].grid()\\n if title: axes[0].set_title(title)\\n elif type(fxnmode[0])==tuple: axes[0].set_title(\\\"Cost function of \\\"+str(fxnmode)+\\\" over time\\\")\\n else: axes[0].set_title(\\\"Cost function of \\\"+fxnmode[0]+\\\": \\\"+fxnmode[1]+\\\" over time\\\")\\n #plt.subplot_adjust()\\n plt.tight_layout()\",\n \"def simulate(self):\\n n_samples = self.n_samples\\n n_features = self.n_features\\n nb_active_features = self.nb_active_features\\n K = self.K\\n pi_0 = self.pi_0\\n gap = self.gap\\n p0 = self.p0\\n p1 = self.p1\\n r_c = self.r_c\\n r_cf = self.r_cf\\n rho = self.rho\\n\\n coeffs = np.zeros(n_features)\\n coeffs[0:nb_active_features] = K\\n\\n features = features_normal_cov_toeplitz(n_samples, n_features, rho)\\n\\n # Add class relative information on the design matrix \\n A = np.random.choice(range(n_samples), size=int((1 - pi_0) * n_samples),\\n replace=False)\\n A_ = np.delete(range(n_samples), A)\\n\\n index_plus_gap = nb_active_features + int(\\n (n_features - nb_active_features) * r_cf)\\n features[A, :index_plus_gap] += gap\\n features[A_, :index_plus_gap] -= gap\\n\\n self.features = features\\n xc = features.dot(coeffs)\\n\\n # Simulation of latent variables\\n pi = self.logistic_grad(-xc)\\n u = np.random.rand(n_samples)\\n Z = (u <= 1 - pi)\\n self.Z = Z\\n\\n # Simulation of true times\\n n_samples_class_1 = np.sum(Z)\\n n_samples_class_0 = n_samples - n_samples_class_1\\n T = np.empty(n_samples)\\n pi_0_est = 1 - Z.mean()\\n T[Z == 0] = np.random.geometric(p0, size=n_samples_class_0)\\n\\n # Compute p_c to obtain censoring rate r_c\\n r_c_ = 1 - r_c\\n p0_ = 1 - p0\\n p1_ = 1 - p1\\n pi_0_ = 1 - pi_0_est\\n a = r_c_ * p0_ * p1_\\n b = p0 * pi_0_est * p1_ + p1 * pi_0_ * p0_ - r_c_ * (p1_ + p0_)\\n c = r_c_ - p0 * pi_0_est - p1 * pi_0_\\n res = self.poldeg2_solver(a=a, b=b, c=c)\\n if isinstance(res, list):\\n if res[0] > 0:\\n pc = 1 - res[0]\\n else:\\n pc = 1 - res[1]\\n else:\\n pc = 1 - res\\n T[Z == 1] = np.random.geometric(p1, size=n_samples_class_1)\\n\\n # Simulation of the censoring\\n C = np.random.geometric(pc, size=n_samples)\\n\\n # Censoring indicator: 1 if it is a time of failure, 0 if it's \\n # censoring.\\n delta = (T <= C).astype(int)\\n\\n # Observed time\\n Y = np.minimum(T, C).astype(int)\\n if np.sum(Y == 0) > 0:\\n Y += 1\\n self.delta = delta\\n self.Y = Y\\n return features, Y, delta\",\n \"def test_init_out_with_rate(self):\\n n = 3\\n rates = [10, 50, 90]\\n\\n G = RandomLayer(n, ini_rate=rates)\\n G.prepare(10.0, 0.1)\\n\\n self.assertLess(np.max(np.abs(G.out - rates)), 1e-9)\",\n \"def run(inputs, parameters = None):\\n def thickness(x, t, chord):\\n y = af.Naca00XX(chord, t, [x], return_dict = 'y')\\n thickness_at_x = y['u'] - y['l']\\n return thickness_at_x \\n\\n if parameters != None:\\n eng = parameters[0]\\n import_matlab = False\\n else:\\n eng = None\\n import_matlab = True\\n \\n sma = inputs['sma']\\n linear = inputs['linear']\\n R = inputs['R']\\n\\n sigma_o = 100e6\\n\\n \\n airfoil = \\\"naca0012\\\"\\n chord = 1.#0.6175\\n\\n J = {'x':0.75, 'y':0.}\\n \\n #Adding the area key to the dictionaries\\n sma['area'] = math.pi*(0.000381/2.)**2\\n linear['area'] = 0.001\\n \\n # Design constants \\n #arm length to center of gravity\\n r_w = 0.10\\n \\n #Aicraft weight (mass times gravity)\\n W = 0.0523*9.8 #0.06*9.8\\n alpha = 0.\\n V = 10 #m/s\\n altitude = 10000. #feet\\n \\n # Temperature\\n T_0 = 273.15 + 30.\\n T_final = 273.15 + 140.\\n \\n #Initial martensitic volume fraction\\n MVF_init = 1.\\n \\n # Number of steps and cycles\\n n = 200\\n n_cycles = 0\\n #~~~~~~~~~~~~~~~~~~~~~bb~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\n #Parameters to select how to output stuff\\n all_outputs = True\\n save_data = True\\n #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\n if all_outputs:\\n eps_s, eps_l, theta, sigma, MVF, T, eps_t, theta, F_l, k, L_s = flap(airfoil, \\n chord, J, sma, linear, sigma_o, \\n W, r_w, V, altitude, alpha, T_0, \\n T_final, MVF_init, n, R, all_outputs = True,\\n import_matlab = import_matlab, eng=eng,\\n n_cycles = n_cycles)\\n\\n import matplotlib.pyplot as plt\\n plt.figure()\\n plt.plot(np.rad2deg(theta), eps_s, lw=2., label = \\\"$\\\\epsilon_s$\\\")\\n plt.plot(np.rad2deg(theta), eps_l, 'b--',lw=2, label = \\\"$\\\\epsilon_l$\\\")\\n# plt.scatter(theta, eps_s, c = 'b')\\n# plt.scatter(theta, eps_l, c = 'b')\\n plt.ylabel('$\\\\epsilon$', fontsize=24)\\n plt.xlabel(r'$\\\\theta ({}^{\\\\circ})$', fontsize=20)\\n plt.legend(loc = 'best', fontsize = 'x-large')\\n plt.grid()\\n \\n print len(T), len(eps_s), len(eps_l), len(theta), len(eps_t)\\n plt.figure()\\n plt.plot(np.rad2deg(theta), eps_t, lw=2.)\\n# plt.scatter(theta, eps_t, c = 'b')\\n plt.ylabel('$\\\\epsilon_t$', fontsize=24)\\n plt.xlabel(r'$\\\\theta ({}^{\\\\circ})$', fontsize=20)\\n plt.legend(loc = 'best', fontsize = 'x-large')\\n plt.grid()\\n \\n plt.figure()\\n plt.plot(np.rad2deg(theta), MVF, lw=2.)\\n# plt.scatter(theta, MVF, c = 'b')\\n plt.ylabel('$MVF$', fontsize=24)\\n plt.xlabel(r'$\\\\theta ({}^{\\\\circ})$', fontsize=20)\\n plt.legend(loc = 'best', fontsize = 'x-large')\\n plt.grid()\\n\\n plt.figure()\\n plt.plot(T, MVF, lw=2.)\\n# plt.scatter(T, MVF, c = 'b')\\n plt.ylabel('$MVF$', fontsize=24)\\n plt.xlabel('$T (K)$', fontsize=20)\\n plt.legend(loc = 'best', fontsize = 'x-large')\\n plt.grid()\\n\\n plt.figure()\\n plt.plot(T, sigma, lw=2.)\\n# plt.scatter(T, sigma, c = 'b')\\n plt.ylabel('$\\\\sigma$', fontsize=24)\\n plt.xlabel('$T (K)$', fontsize=20)\\n plt.legend(loc = 'best', fontsize = 'x-large')\\n plt.grid()\\n \\n plt.figure()\\n plt.plot(T, eps_s, 'b', lw=2., label = \\\"$\\\\epsilon_s$\\\")\\n plt.plot(T, eps_l, 'b--',lw=2, label = \\\"$\\\\epsilon_l$\\\")\\n# plt.scatter(T, eps_s, c = 'b')\\n# plt.scatter(T, eps_l, c = 'b')\\n plt.xlabel('$T (K)$', fontsize=20)\\n plt.ylabel('$\\\\epsilon$', fontsize=24)\\n plt.legend(loc = 'best', fontsize = 'x-large')\\n plt.grid()\\n \\n plt.figure()\\n plt.plot(T, np.rad2deg(theta), lw=2.)\\n# plt.scatter(T, theta, c = 'b')\\n plt.xlabel('$T (K)$', fontsize=20)\\n plt.ylabel(r'$\\\\theta ({}^{\\\\circ})$', fontsize=20)\\n plt.grid()\\n \\n F_s = []\\n for i in range(len(sigma)):\\n F_s.append(sigma[i]*sma['area'])\\n# sigma_MPa = []\\n# for sigma_i in sigma:\\n# sigma_MPa.append(sigma_i/1e6)\\n plt.figure()\\n plt.plot(theta, F_s, 'b', lw=2., label = \\\"$F_s$\\\")\\n plt.plot(theta, F_l, 'b--', lw=2., label = \\\"$F_l$\\\")\\n# plt.scatter(theta, F_s, c = 'b')\\n# plt.scatter(theta, F_l, c = 'b')\\n plt.ylabel('$F (N)$', fontsize=20)\\n plt.xlabel(r'$\\\\theta ({}^{\\\\circ})$', fontsize=20)\\n plt.legend(loc = 'best', fontsize = 'x-large')\\n plt.grid() \\n else:\\n theta, k= flap(airfoil, chord, J, sma, linear, sigma_o, \\n W, r_w, V, altitude, alpha, T_0, \\n T_final, MVF_init, n, R, all_outputs = False,\\n import_matlab = import_matlab, eng=eng,\\n n_cycles = n_cycles)\\n \\n if save_data == True:\\n Data = {'theta': theta, 'eps_s': eps_s, 'eps_l': eps_l, \\n 'sigma': sigma, 'xi': MVF, 'T': T, 'eps_t': eps_t,\\n 'F_l': F_l, 'k': k, 'L_s':L_s}\\n pickle.dump(Data, open( \\\"data.p\\\", \\\"wb\\\" ) )\\n \\n return {'theta': theta, 'k': k}\",\n \"def init_params():\\n p = {}\\n \\n # p['rootFolder'] = 'C:/Users/Umberto Gostoli/SPHSU/Social Care Model II'\\n # p['rootFolder'] = 'N:/Social Care Model Paper III'\\n \\n p['noPolicySim'] = False\\n p['multiprocessing'] = True\\n p['numberProcessors'] = 9\\n p['numRepeats'] = 3\\n \\n p['startYear'] = 1860\\n p['endYear'] = 2040\\n p['thePresent'] = 2012\\n p['statsCollectFrom'] = 1990\\n p['regressionCollectFrom'] = 1960 \\n p['implementPoliciesFromYear'] = 2020\\n p['yearOutcome'] = 2015\\n \\n p['favouriteSeed'] = 123\\n p['loadFromFile'] = False\\n p['verboseDebugging'] = False\\n p['singleRunGraphs'] = False\\n p['saveChecks'] = True\\n p['getCheckVariablesAtYear'] = 2015\\n # To change through command-line arguments\\n\\n p['numberPolicyParameters'] = 2\\n p['valuesPerParam'] = 1\\n p['numberScenarios'] = 3\\n \\n ############ Policy Parameters #######################\\n p['incomeCareParam'] = 0.0005 #[0.00025 - 0.001]\\n p['taxBreakRate'] = 0.0\\n p['ageOfRetirement'] = 65\\n p['socialSupportLevel'] = 5\\n # p['educationCosts']\\n #############################################################\\n p['socialCareCreditShare'] = 0.0\\n p['maxWtWChildAge'] = 5\\n # The basics: starting population and year, etc.\\n \\n p['discountingFactor'] = 0.03\\n \\n \\n p['initialPop'] = 600 \\n \\n p['minStartAge'] = 24\\n p['maxStartAge'] = 45\\n p['numberClasses'] = 5\\n p['socialClasses'] = ['unskilled', 'skilled', 'lower', 'middle', 'upper']\\n p['initialClassShares'] = [0.2, 0.25, 0.3, 0.2, 0.05]\\n p['initialUnemployment'] = [0.25, 0.2, 0.15, 0.1, 0.1]\\n p['unemploymentAgeBandParam'] = 0.3\\n \\n # doDeath function parameters\\n p['mortalityBias'] = 0.85 # After 1950\\n p['careNeedBias'] = 0.9\\n p['unmetCareNeedBias'] = 0.5\\n p['baseDieProb'] = 0.0001\\n p['babyDieProb'] = 0.005\\n p['maleAgeScaling'] = 14.0\\n p['maleAgeDieProb'] = 0.00021\\n p['femaleAgeScaling'] = 15.5\\n p['femaleAgeDieProb'] = 0.00019\\n \\n p['orphansRelocationParam'] = 0.5\\n \\n # doBirths function parameters\\n p['minPregnancyAge'] = 17\\n p['maxPregnancyAge'] = 42\\n p['growingPopBirthProb'] = 0.215\\n p['fertilityCorrector'] = 1.0\\n p['fertilityBias'] = 0.9\\n \\n # careTransitions function parameters\\n p['zeroYearCare'] = 80.0\\n p['childcareDecreaseRate'] = 0.25\\n p['personCareProb'] = 0.0008\\n p['maleAgeCareScaling'] = 18.0 # p['maleAgeCareProb'] = 0.0008\\n p['femaleAgeCareScaling'] = 19.0 # p['femaleAgeCareProb'] = 0.0008\\n p['baseCareProb'] = 0.0002\\n p['careBias'] = 0.9\\n p['careTransitionRate'] = 0.7\\n\\n p['unmetNeedExponent'] = 1.0 # 0.005 #[0.005 - 0.02]\\n \\n p['numCareLevels'] = 5\\n p['careLevelNames'] = ['none','low','moderate','substantial','critical']\\n p['careDemandInHours'] = [ 0.0, 8.0, 16.0, 32.0, 80.0 ]\\n p['quantumCare'] = 4.0\\n \\n # careSupplies getCare and probSuppliers function parameters\\n \\n ######## Key parameter 1 ##############\\n \\n \\n p['weeklyHours'] = 40.0\\n \\n \\n p['priceChildCare'] = 0.76 # 6 \\n p['schoolAge'] = 5\\n p['maxFormalChildcareHours'] = 48\\n p['schoolHours'] = 30\\n p['freeChildcareHours'] = 15\\n p['workingParentsFreeChildcareHours'] = 30\\n p['minAgeStartChildCareSupport'] = 3\\n p['minAgeStartChildCareSupportByIncome'] = 2\\n p['maxHouseholdIncomeChildCareSupport'] = 40 # 320\\n \\n ######## Key parameter 2 ##############\\n # 5: No public supply \\n \\n p['retiredHours'] = [48.0, 36.0, 20.0, 10.0] # 60.0\\n p['studentHours'] = [24.0, 16.0, 8.0, 4.0]\\n p['teenAgersHours'] = [16.0, 0.0, 0.0, 0.0]\\n p['unemployedHours'] = [32.0, 24.0, 16.0, 8.0]\\n p['employedHours'] = [28.0, 20.0, 12.0, 8.0]\\n p['formalCareDiscountFactor'] = 0.5\\n \\n p['socialNetworkDistances'] = [0.0, 1.0, 2.0, 1.0, 2.0, 2.0, 3.0, 3.0]\\n p['networkDistanceParam'] = 2.0\\n p['socialCareWeightBias'] = 1.0\\n p['unmetCareNeedDiscountParam'] = 0.5\\n p['shareUnmetNeedDiscountParam'] = 0.5\\n # p['pastShareUnmetNeedWeight'] = 0.5\\n \\n \\n \\n p['networkSizeParam'] = 10.0 # 1.0\\n \\n p['careSupplyBias'] = 0.5\\n p['careIncomeParam'] = 0.001\\n \\n # Hospitalization Costs\\n p['qalyBeta'] = 0.18\\n p['qalyAlpha'] = 1.5\\n p['qalyDiscountRate'] = 0.035\\n p['qalyIndexes'] = [1.0, 0.8, 0.6, 0.4, 0.2]\\n p['unmetCareHealthParam'] = 0.1\\n p['hospitalizationParam'] = 0.5\\n p['needLevelParam'] = 2.0\\n p['unmetSocialCareParam'] = 2.0\\n p['costHospitalizationPerDay'] = 400\\n \\n # ageTransitions, enterWorkForce and marketWage functions parameters\\n p['ageTeenagers'] = 12\\n p['minWorkingAge'] = 16\\n \\n ######## Key parameter 3 ##############\\n \\n p['careBankingSchemeOn'] = False\\n p['socialCareBankingAge'] = 65\\n \\n p['absoluteCreditQuantity'] = False\\n p['quantityYearlyIncrease'] = 0.0\\n p['socialCareCreditQuantity'] = 0\\n p['kinshipNetworkCarePropension'] = 0.5\\n p['volunteersCarePropensionCoefficient'] = 0.01\\n p['pensionContributionRate'] = 0.05\\n \\n p['hillHealthLevelThreshold'] = 3\\n p['seriouslyHillSupportRate'] = 0.5\\n \\n ### Prices ####\\n p['pricePublicSocialCare'] = 20.0 # [2.55] # 20\\n p['priceSocialCare'] = 17.0 # [2.29] # 18\\n p['taxBrackets'] = [663, 228, 0] # [28.16, 110.23] # [221, 865]\\n p['taxBandsNumber'] = 3\\n p['bandsTaxationRates'] = [0.4, 0.2, 0.0] # [0.0, 0.2, 0.4]\\n # Tax Break Policy\\n\\n \\n p['pensionWage'] = [5.0, 7.0, 10.0, 13.0, 18.0] # [0.64, 0.89, 1.27, 1.66, 2.29] # \\n p['incomeInitialLevels'] = [5.0, 7.0, 9.0, 11.0, 14.0] #[0.64, 0.89, 1.15, 1.40, 1.78] # \\n p['incomeFinalLevels'] = [10.0, 15.0, 22.0, 33.0, 50.0] #[1.27, 1.91, 2.80, 4.21, 6.37] # \\n p['educationCosts'] = [0.0, 100.0, 150.0, 200.0] #[0.0, 12.74, 19.12, 25.49] # \\n \\n # Priced growth #####\\n p['wageGrowthRate'] = 1.0 # 1.01338 # \\n\\n p['incomeGrowthRate'] = [0.4, 0.35, 0.35, 0.3, 0.25]\\n \\n # SES inter-generational mobility parameters\\n p['leaveHomeStudentsProb'] = 0.5\\n \\n p['eduWageSensitivity'] = 0.2 # 0.5\\n p['eduRankSensitivity'] = 3.0 # 5.0\\n p['costantIncomeParam'] = 80.0 # 20.0\\n p['costantEduParam'] = 10.0 # 10.0\\n p['careEducationParam'] = 0.005 # 0.04\\n \\n \\n \\n # p['incEduExp'] = 0.25\\n p['educationLevels'] = ['GCSE', 'A-Level', 'HND', 'Degree', 'Higher Degree']\\n p['workingAge'] = [16, 18, 20, 22, 24]\\n \\n # doDivorce function parameters\\n p['basicDivorceRate'] = 0.06\\n p['variableDivorce'] = 0.06\\n p['divorceModifierByDecade'] = [ 0.0, 1.0, 0.9, 0.5, 0.4, 0.2, 0.1, 0.03, 0.01, 0.001, 0.001, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0 ]\\n p['divorceBias'] = 1.0\\n \\n # doMarriages function parameters\\n p['deltageProb'] = [0.0, 0.1, 0.25, 0.4, 0.2, 0.05]\\n p['incomeMarriageParam'] = 0.025\\n p['studentFactorParam'] = 0.5\\n ######## Key parameter 4 ##############\\n p['betaGeoExp'] = 2.0 #[1.0 - 4.0]\\n \\n p['betaSocExp'] = 2.0\\n p['rankGenderBias'] = 0.5\\n p['basicMaleMarriageProb'] = 0.9\\n p['maleMarriageModifierByDecade'] = [ 0.0, 0.16, 0.5, 1.0, 0.8, 0.7, 0.66, 0.5, 0.4, 0.2, 0.1, 0.05, 0.01, 0.0, 0.0, 0.0, 0.0 ]\\n \\n # jobMarket, updateWork and unemploymentRate functions parameters\\n p['unemploymentClassBias'] = 0.75\\n p['unemploymentAgeBias'] = [1.0, 0.55, 0.35, 0.25, 0.2, 0.2]\\n p['numberAgeBands'] = 6\\n p['jobMobilitySlope'] = 0.004\\n p['jobMobilityIntercept'] = 0.05\\n p['ageBiasParam'] = [7.0, 3.0, 1.0, 0.5, 0.35, 0.15]\\n p['deltaIncomeExp'] = 0.05\\n p['unemployedCareBurdernParam'] = 0.025\\n # Potential key parameter\\n p['relocationCareLossExp'] = 1.0 # 40.0 # \\n p['incomeSocialCostRelativeWeight'] = 0.5\\n \\n p['firingParam'] = 0.2\\n p['wageVar'] = 0.06\\n p['workDiscountingTime'] = 0.75 # 0.8\\n p['sizeWeightParam'] = 0.7\\n p['minClassWeightParam'] = 1.0\\n p['incomeDiscountingExponent'] = 4.0\\n p['discountingMultiplier'] = 2.0\\n #p['incomeDiscountingParam'] = 2.0\\n \\n # relocationPensioners function parameters\\n p['agingParentsMoveInWithKids'] = 0.1\\n p['variableMoveBack'] = 0.1\\n p['retiredRelocationParam'] = 0.001 # 0.005\\n \\n # houseMap function parameters\\n p['geoDistanceSensitivityParam'] = 2.0\\n p['socDistanceSensitivityParam'] = 2.0\\n p['classAffinityWeight'] = 4.0\\n p['distanceSensitivityParam'] = 0.5\\n \\n # relocationProb function parameters\\n p['baseRelocatingProb'] = 0.05\\n p['relocationParameter'] = 1.0 \\n p['apprenticesRelocationProb'] = 0.5\\n #p['expReloc'] = 1.0\\n \\n # computeRelocationCost and relocation Propensity functions parameters\\n p['yearsInTownSensitivityParam'] = 0.5\\n \\n ######## Key parameter 5 ##############\\n p['relocationCostParam'] = 0.5 # 1.0 \\n \\n ######## Key parameter 6 ##############\\n p['propensityRelocationParam'] = 2.0 # 2.0 \\n p['denRelocationWeight'] = 0.5\\n \\n \\n ## Description of the map, towns, and houses\\n p['mapGridXDimension'] = 8\\n p['mapGridYDimension'] = 12 \\n p['townGridDimension'] = 70\\n p['cdfHouseClasses'] = [ 0.6, 0.9, 5.0 ]\\n p['ukMap'] = [[ 0.0, 0.1, 0.2, 0.1, 0.0, 0.0, 0.0, 0.0 ],\\n [ 0.1, 0.1, 0.2, 0.2, 0.3, 0.0, 0.0, 0.0 ],\\n [ 0.0, 0.2, 0.2, 0.3, 0.0, 0.0, 0.0, 0.0 ],\\n [ 0.0, 0.2, 1.0, 0.5, 0.0, 0.0, 0.0, 0.0 ],\\n [ 0.4, 0.0, 0.2, 0.2, 0.4, 0.0, 0.0, 0.0 ],\\n [ 0.6, 0.0, 0.0, 0.3, 0.8, 0.2, 0.0, 0.0 ],\\n [ 0.0, 0.0, 0.0, 0.6, 0.8, 0.4, 0.0, 0.0 ],\\n [ 0.0, 0.0, 0.2, 1.0, 0.8, 0.6, 0.1, 0.0 ],\\n [ 0.0, 0.0, 0.1, 0.2, 1.0, 0.6, 0.3, 0.4 ],\\n [ 0.0, 0.0, 0.5, 0.7, 0.5, 1.0, 1.0, 0.0 ],\\n [ 0.0, 0.0, 0.2, 0.4, 0.6, 1.0, 1.0, 0.0 ],\\n [ 0.0, 0.2, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0 ]]\\n p['ukClassBias'] = [[ 0.0, -0.05, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],\\n [ -0.05, -0.05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ],\\n [ 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0, 0.0 ],\\n [ 0.0, -0.05, -0.05, 0.05, 0.0, 0.0, 0.0, 0.0 ],\\n [ -0.05, 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],\\n [ -0.05, 0.0, 0.0, -0.05, -0.05, -0.05, 0.0, 0.0 ],\\n [ 0.0, 0.0, 0.0, -0.05, -0.05, -0.05, 0.0, 0.0 ],\\n [ 0.0, 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],\\n [ 0.0, 0.0, -0.05, 0.0, -0.05, 0.0, 0.0, 0.0 ],\\n [ 0.0, 0.0, 0.0, -0.05, 0.0, 0.2, 0.15, 0.0 ],\\n [ 0.0, 0.0, 0.0, 0.0, 0.1, 0.2, 0.15, 0.0 ],\\n [ 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0 ] ]\\n p['mapDensityModifier'] = 0.6\\n # p['numHouseClasses'] = 3\\n # p['houseClasses'] = ['small','medium','large']\\n \\n ## Graphical interface details\\n p['interactiveGraphics'] = False #True\\n p['delayTime'] = 0.0\\n p['screenWidth'] = 1300\\n p['screenHeight'] = 700\\n p['bgColour'] = 'black'\\n p['mainFont'] = 'Helvetica 18'\\n p['fontColour'] = 'white'\\n p['dateX'] = 70\\n p['dateY'] = 20\\n p['popX'] = 70\\n p['popY'] = 50\\n p['pixelsInPopPyramid'] = 2000\\n p['num5YearAgeClasses'] = 28\\n p['careLevelColour'] = ['blue','green','yellow','orange','red']\\n p['houseSizeColour'] = ['brown','purple','yellow']\\n p['pixelsPerTown'] = 56\\n p['maxTextUpdateList'] = 22\\n \\n # p['eduEduSensitivity'] = 0.5\\n # p['mortalityBias'] = [1.0, 0.92, 0.84, 0.76, 0.68]\\n # p['fertilityBias'] = [1.0, 0.92, 0.84, 0.76, 0.68]\\n # p['divorceBias'] = [2.0, 1.5, 1.0, 0.75, 0.5]\\n\\n ## Transitions to care statistics\\n \\n ## Availability of care statistics\\n \\n #p['childHours'] = 5.0\\n # p['employedHours'] = 12.0\\n #p['homeAdultHours'] = 30.0\\n #p['workingAdultHours'] = 25.0\\n #p['maxEmployedHours'] = 60.0\\n \\n #p['lowCareHandicap'] = 0.5\\n #p['hourlyCostOfCare'] = 20.0\\n \\n ## Fertility statistics\\n \\n # p['steadyPopBirthProb'] = 0.13\\n # p['transitionYear'] = 1965\\n \\n ## Class and employment statistics\\n # p['numClasses'] = 5\\n # p['occupationClasses'] = ['lower','intermediate','higher']\\n # p['cdfOccupationClasses'] = [ 0.6, 0.9, 1.0 ]\\n\\n ## Age transition statistics\\n # p['ageOfAdulthood'] = 17\\n \\n ## Marriage function parameters\\n \\n # p['basicFemaleMarriageProb'] = 0.25\\n # p['femaleMarriageModifierByDecade'] = [ 0.0, 0.5, 1.0, 1.0, 1.0, 0.6, 0.5, 0.4, 0.1, 0.01, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0 ]\\n # p['femaleMarriageProb'] = [0.01, 0.15, 0.3, 0.2, 0.1, 0.1, 0.06, 0.05, 0.02, 0.01, 0.01, 0.005]\\n # p['maleMarriageProb'] = [0.005, 0.08, 0.25, 0.25, 0.15, 0.1, 0.07, 0.05, 0.03, 0.02, 0.01, 0.005]\\n \\n ## Leaving home and moving around statistics\\n # p['probApartWillMoveTogether'] = 0.3\\n # p['coupleMovesToExistingHousehold'] = 0.3\\n # p['basicProbAdultMoveOut'] = 0.22\\n # p['probAdultMoveOutModifierByDecade'] = [ 0.0, 0.2, 1.0, 0.6, 0.3, 0.15, 0.03, 0.03, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]\\n # p['basicProbSingleMove'] = 0.05\\n # p['probSingleMoveModifierByDecade'] = [ 0.0, 1.0, 1.0, 0.8, 0.4, 0.06, 0.04, 0.02, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]\\n # p['basicProbFamilyMove'] = 0.03\\n # p['probFamilyMoveModifierByDecade'] = [ 0.0, 0.5, 0.8, 0.5, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1 ]\\n\\n \\n return p\",\n \"def time(is_train):\\n context = composition.Context()\\n start_minutes = random.randint(1, 24*60 - 1)\\n while True:\\n duration_minutes = random.randint(1, 12*60 - 1)\\n if train_test_split.is_train(duration_minutes) == is_train:\\n break\\n end_minutes = start_minutes + duration_minutes\\n\\n def format_12hr(minutes):\\n \\\"\\\"\\\"Format minutes from midnight in 12 hr format.\\\"\\\"\\\"\\n hours = (minutes // 60) % 24\\n minutes %= 60\\n #am_pm = 'AM' if hours < 12 else 'PM'\\n #hours = (hours - 1) % 12 + 1\\n return '{}:{:02}'.format(hours, minutes)\\n #return '{}:{:02} {}'.format(hours, minutes, am_pm)\\n\\n start = format_12hr(start_minutes)\\n end = format_12hr(end_minutes)\\n\\n which_question = random.randint(0, 3)\\n if which_question == 0:\\n # Question: What is start = end - duration?\\n template = random.choice([\\n 'Che ore sono se mancano {duration} minuti alle {end}?',\\n ])\\n return example.Problem(\\n question=example.question(\\n context, template, duration=duration_minutes, end=end),\\n answer=start)\\n elif which_question == 1:\\n # Question: What is end = start + duration?\\n template = random.choice([\\n 'Che ore sono se sono passati {duration} minuti dalle {start}?',\\n ])\\n return example.Problem(\\n question=example.question(\\n context, template, duration=duration_minutes, start=start),\\n answer=end)\\n else:\\n # Question: What is duration = end - start?\\n template = random.choice([\\n 'Quanti minuti ci sono tra le {start} e le {end}?',\\n ])\\n return example.Problem(\\n question=example.question(context, template, start=start, end=end),\\n answer=duration_minutes)\",\n \"def __init__(self, time_constant: float, sampling_time: float):\\n self.alpha = sampling_time / (time_constant + sampling_time)\\n self.state = None\",\n \"def refugia_adj_5_simsplit_4epochs_iter1 (params, ns):\\n #28 parameters \\n nu1a, nu2a, nu3a, nu1b, nu2b, nu3b, nu1c, nu2c, nu3c, nu1d, nu2d, nu3d, m1_12, m1_13, m1_21, m1_23, m1_31, m1_32, m3_12, m3_13, m3_21, m3_23, m3_31, m3_32, T1, T2, T3, T4 = params\\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1] + ns[2])\\n fs = moments.Spectrum(sts)\\n fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1] + ns[2]) \\n fs = moments.Manips.split_2D_to_3D_2(fs, ns[1], ns[2])\\n ## Population function and migration matrix for T1 (to reflect sum effect of all previous glacial-interglacial cycles)\\n nu_T1 = [nu1a, nu2a, nu3a]\\n mig1 = numpy.array([[0, m1_12, m1_13],[m1_21, 0, m1_23], [m1_31, m1_32, 0]]) \\n fs.integrate(nu_T1, T1, m=mig1)\\n ## Population function and migration matrix for T2 (to reflect period of isolation during last glacial)\\n nu_T2 = [nu1b, nu2b, nu3b]\\n fs.integrate(nu_T2, T2)\\n ## Population function and migration matrix for T3 (to reflect inter-glacial expansion)\\n nu_T3 = [nu1c, nu2c, nu3c]\\n mig3 = numpy.array([[0, m3_12, m3_13],[m3_21, 0, m3_23], [m3_31, m3_32, 0]]) \\n fs.integrate(nu_T3, T3, m=mig3)\\n ## Population function and migration matrix for T3 (bottleneck to capture single population representation of lineage)\\n nu_T4 = [nu1d, nu2d, nu3d]\\n fs.integrate(nu_T4, T4) \\n return fs\",\n \"def run_evaluation(self, example):\\n assert isinstance(example, dict)\\n assert 'name' in example\\n\\n name = example['name']\\n house_id, camera_id = pbrs_utils.parse_house_and_camera_ids_from_string(name)\\n\\n gt_bg, gt_objects = sorted(glob.glob(path.join(config.default_out_root, 'v9_gt_mesh/{}/{}/gt*.ply'.format(house_id, camera_id))))\\n gt_depths = sorted(glob.glob(path.join(config.default_out_root, 'v9_gt_mesh/{}/{}/d*.ply'.format(house_id, camera_id))))\\n assert len(gt_depths) == 5\\n\\n gt_overhead_fg = sorted(glob.glob(path.join(config.default_out_root, 'v9_gt_overhead_mesh/{}/{}/overhead_fg.ply'.format(house_id, camera_id))))\\n assert len(gt_overhead_fg) == 1\\n gt_overhead_fg = gt_overhead_fg[0]\\n assert path.exists(gt_overhead_fg)\\n\\n pred_depths = sorted(glob.glob(path.join(config.default_out_root, 'v9_pred_depth_mesh/{}/{}/pred_*.ply'.format(house_id, camera_id))))\\n assert len(pred_depths) == 5\\n\\n pred_overhead_fg = sorted(glob.glob(path.join(config.default_out_root, 'v9_pred_depth_mesh/{}/{}/overhead_fg_clipped.ply'.format(house_id, camera_id))))\\n assert len(pred_overhead_fg) == 1\\n pred_overhead_fg = pred_overhead_fg[0]\\n\\n print('gt_depths', gt_depths)\\n print('gt_objects', gt_objects)\\n print('pred_depths', pred_depths)\\n\\n voxel_related_data_out_basedir = '/mnt/ramdisk/voxels_data/tmp_{}'.format(name.replace('/', '_'))\\n for fname in glob.glob(path.join(voxel_related_data_out_basedir, '*')):\\n os.remove(fname)\\n\\n camera_filename = example['camera_filename']\\n fv = convert_mesh_to_camcoord(gt_objects, camera_filename)\\n gt_objects_camcoord = path.join(voxel_related_data_out_basedir, 'gt_objects_camcoord.off')\\n io_utils.save_off(fv, gt_objects_camcoord)\\n\\n # vox_res = 64\\n vox_res = 400\\n vox_method_flags = '-pb'\\n\\n self.run_command('/home/daeyun/usr/bin/binvox {} -d {} -bb -5 -5 -10 5 5 0 {}'.format(vox_method_flags, vox_res, gt_objects_camcoord))\\n gt_object_voxel_file = gt_objects_camcoord.replace('.off', '.binvox')\\n convert_binvox_to_pcl(gt_object_voxel_file)\\n # convert_binvox_to_mesh(gt_object_voxel_file)\\n\\n ret = {}\\n\\n # f3d voxelization\\n f3d_pred = path.join(config.default_out_root_v8, 'factored3d_pred/{}/{}/codes_transformed_clipped.ply'.format(house_id, camera_id))\\n assert path.isfile(f3d_pred), f3d_pred\\n fv = convert_mesh_to_camcoord(f3d_pred, camera_filename)\\n f3d_objects_camcoord = path.join(voxel_related_data_out_basedir, 'f3d_objects_camcoord.off')\\n io_utils.save_off(fv, f3d_objects_camcoord)\\n\\n self.run_command('/home/daeyun/usr/bin/binvox {} -d {} -bb -5 -5 -10 5 5 0 {}'.format(vox_method_flags, vox_res, f3d_objects_camcoord))\\n f3d_objects_voxel_file = f3d_objects_camcoord.replace('.off', '.binvox')\\n convert_binvox_to_pcl(f3d_objects_voxel_file)\\n # convert_binvox_to_mesh(f3d_objects_voxel_file)\\n\\n iou = binvox_iou(f3d_objects_voxel_file, gt_object_voxel_file)\\n print('IoU_f3d', iou)\\n ret['IoU_f3d'] = iou\\n\\n '''\\n # depth overhead only\\n depth_meshes_fv_list = [io_utils.read_mesh_assimp(pred_d) for pred_d in [pred_overhead_fg]]\\n # depth_meshes_fv_list = [io_utils.read_mesh_assimp(pred_d) for pred_d in pred_depths[:4]]\\n\\n depth_meshes_merged = io_utils.merge_meshes(*depth_meshes_fv_list)\\n fv = convert_mesh_to_camcoord(depth_meshes_merged, camera_filename)\\n depth_objects_camcoord = path.join(voxel_related_data_out_basedir, 'depth_meshes_ovh_camcoord.off')\\n io_utils.save_off(fv, depth_objects_camcoord)\\n self.run_command('/home/daeyun/usr/bin/binvox {} -d {} -bb -5 -5 -10 5 5 0 {}'.format(vox_method_flags, vox_res, depth_objects_camcoord))\\n depth_ovh_voxel_file = depth_objects_camcoord.replace('.off', '.binvox')\\n convert_binvox_to_pcl(depth_ovh_voxel_file)\\n convert_binvox_to_mesh(depth_ovh_voxel_file)\\n iou = binvox_iou(depth_ovh_voxel_file, gt_object_voxel_file)\\n print('IoU_overheadonly', iou)\\n ret['IoU_overheadonly'] = iou\\n '''\\n\\n # depth voxelization no overhead\\n depth_meshes_fv_list = [io_utils.read_mesh_assimp(pred_d) for pred_d in pred_depths[:4]]\\n # depth_meshes_fv_list = [io_utils.read_mesh_assimp(pred_d) for pred_d in pred_depths[:4]]\\n\\n depth_meshes_merged = io_utils.merge_meshes(*depth_meshes_fv_list)\\n fv = convert_mesh_to_camcoord(depth_meshes_merged, camera_filename)\\n depth_objects_camcoord = path.join(voxel_related_data_out_basedir, 'depth_meshes_frontal4_camcoord.off')\\n io_utils.save_off(fv, depth_objects_camcoord)\\n self.run_command('/home/daeyun/usr/bin/binvox {} -d {} -bb -5 -5 -10 5 5 0 {}'.format(vox_method_flags, vox_res, depth_objects_camcoord))\\n depth_objects_voxel_file = depth_objects_camcoord.replace('.off', '.binvox')\\n convert_binvox_to_pcl(depth_objects_voxel_file)\\n # convert_binvox_to_mesh(depth_objects_voxel_file)\\n\\n iou = binvox_iou(depth_objects_voxel_file, gt_object_voxel_file)\\n print('IoU_frontaldepthonly', iou)\\n ret['IoU_frontaldepthonly'] = iou\\n\\n # depth voxelization\\n depth_meshes_fv_list = [io_utils.read_mesh_assimp(pred_d) for pred_d in pred_depths[:4] + [pred_overhead_fg]]\\n # depth_meshes_fv_list = [io_utils.read_mesh_assimp(pred_d) for pred_d in pred_depths[:4]]\\n\\n depth_meshes_merged = io_utils.merge_meshes(*depth_meshes_fv_list)\\n fv = convert_mesh_to_camcoord(depth_meshes_merged, camera_filename)\\n depth_objects_camcoord = path.join(voxel_related_data_out_basedir, 'depth_meshes_frontal4_and_ovh_camcoord.off')\\n io_utils.save_off(fv, depth_objects_camcoord)\\n self.run_command('/home/daeyun/usr/bin/binvox {} -d {} -bb -5 -5 -10 5 5 0 {}'.format(vox_method_flags, vox_res, depth_objects_camcoord))\\n depth_objects_voxel_file = depth_objects_camcoord.replace('.off', '.binvox')\\n convert_binvox_to_pcl(depth_objects_voxel_file)\\n # convert_binvox_to_mesh(depth_objects_voxel_file)\\n\\n iou = binvox_iou(depth_objects_voxel_file, gt_object_voxel_file)\\n print('IoU_alldepths', iou)\\n ret['IoU_alldepths'] = iou\\n\\n return voxel_related_data_out_basedir, ret\",\n \"def run():\\n step = 0\\n o2r = 4 #orange to red delay time\\n r2g = 2 #red to green delay time\\n A_4235 = 0\\n B_4235 = 1\\n C_4235 = 2\\n AB1_4235 = 3\\n AB2_4235 = 4\\n AC1_4235 = 5\\n AC2_4235 = 6\\n BA1_4235 = 7\\n BA2_4235 = 8\\n BC1_4235 = 9\\n BC2_4235 = 10\\n CA1_4235 = 11\\n CA2_4235 = 12\\n CB1_4235 = 13\\n CB2_4235 = 14\\n A_4219 = 0\\n B_4219 = 1\\n C_4219 = 2\\n D_4219 = 3\\n E_4219 = 4\\n F_4219 = 5\\n G_4219 = 6\\n AB1_4219 = 7\\n AB2_4219 = 8\\n AC1_4219 = 9\\n AC2_4219 = 10\\n AD1_4219 = 11\\n AD2_4219 = 12\\n AE1_4219 = 13\\n AE2_4219 = 14\\n AF1_4219 = 16\\n AF2_4219 = 17\\n AG1_4219 = 18\\n AG2_4219 = 19\\n BA1_4219 = 20\\n BA2_4219 = 21\\n BC1_4219 = 22\\n BC2_4219 = 23\\n BD1_4219 = 24\\n BD2_4219 = 25\\n BE1_4219 = 26\\n BE2_4219 = 27\\n BF1_4219 = 28\\n BF2_4219 = 29\\n BG1_4219 = 30\\n BG2_4219 = 31\\n CA1_4219 = 32\\n CA2_4219 = 33\\n CB1_4219 = 34\\n CB2_4219 = 35\\n CD1_4219 = 36\\n CD2_4219 = 37\\n CE1_4219 = 38\\n CE2_4219 = 39\\n CF1_4219 = 40\\n CF2_4219 = 41\\n CG1_4219 = 42\\n CG2_4219 = 43\\n DA1_4219 = 44\\n DA2_4219 = 45\\n DB1_4219 = 46\\n DB2_4219 = 47\\n DC1_4219 = 48\\n DC2_4219 = 49\\n DE1_4219 = 50\\n DE2_4219 = 51\\n DF1_4219 = 52\\n DF2_4219 = 53\\n DG1_4219 = 54\\n DG2_4219 = 55\\n EA1_4219 = 56\\n EA2_4219 = 57\\n EB1_4219 = 58\\n EB2_4219 = 59\\n EC1_4219 = 60\\n EC2_4219 = 61\\n ED1_4219 = 62\\n ED2_4219 = 63\\n EF1_4219 = 64\\n EF2_4219 = 65\\n EG1_4219 = 66\\n EG2_4219 = 67\\n FA1_4219 = 68\\n FA2_4219 = 69\\n FB1_4219 = 70\\n FB2_4219 = 71\\n FC1_4219 = 72\\n FC2_4219 = 73\\n FD1_4219 = 74\\n FD2_4219 = 75\\n FE1_4219 = 76\\n FE2_4219 = 77\\n FG1_4219 = 78\\n FG2_4219 = 79\\n GA1_4219 = 80\\n GA2_4219 = 81\\n GB1_4219 = 82\\n GB2_4219 = 83\\n GC1_4219 = 84\\n GC2_4219 = 85\\n GD1_4219 = 86\\n GD2_4219 = 87\\n GE1_4219 = 88\\n GE2_4219 = 89\\n GF1_4219 = 90\\n GF2_4219 = 91\\n A_4220 = 0\\n B_4220 = 1\\n C_4220 = 2\\n D_4220 = 3\\n E_4220 = 4\\n AB1_4220 = 5\\n AB2_4220 = 6\\n AC1_4220 = 7\\n AC2_4220 = 8\\n AD1_4220 = 9\\n AD2_4220 = 10\\n AE1_4220 = 11\\n AE2_4220 = 12\\n BA1_4220 = 13\\n BA2_4220 = 14\\n BC1_4220 = 15\\n BC2_4220 = 16\\n BD1_4220 = 17\\n BD2_4220 = 18\\n BE1_4220 = 19\\n BE2_4220 = 20\\n CA1_4220 = 21\\n CA2_4220 = 22\\n CB1_4220 = 23\\n CB2_4220 = 24\\n CD1_4220 = 25\\n CD2_4220 = 26\\n CE1_4220 = 27\\n CE2_4220 = 28\\n DA1_4220 = 29\\n DA2_4220 = 30\\n DB1_4220 = 31\\n DB2_4220 = 32\\n DC1_4220 = 33\\n DC2_4220 = 34\\n DE1_4220 = 35\\n DE2_4220 = 36\\n EA1_4220 = 37\\n EA2_4220 = 38\\n EB1_4220 = 39\\n EB2_4220 = 40\\n EC1_4220 = 41\\n EC2_4220 = 42\\n ED1_4220 = 43\\n ED2_4220 = 44\\n A_4221 = 0\\n B_4221 = 1\\n C_4221 = 2\\n D_4221 = 3\\n E_4221 = 4\\n F_4221 = 5\\n AB1_4221 = 6\\n AB2_4221 = 7\\n AC1_4221 = 8\\n AC2_4221 = 9\\n AD1_4221 = 10\\n AD2_4221 = 11\\n AE1_4221 = 12\\n AE2_4221 = 13\\n AF1_4221 = 14\\n AF2_4221 = 15\\n BA1_4221 = 16\\n BA2_4221 = 17\\n BC1_4221 = 18\\n BC2_4221 = 19\\n BD1_4221 = 20\\n BD2_4221 = 21\\n BE1_4221 = 22\\n BE2_4221 = 23\\n BF1_4221 = 24\\n BF2_4221 = 25\\n CA1_4221 = 26\\n CA2_4221 = 27\\n CB1_4221 = 28\\n CB2_4221 = 29\\n CD1_4221 = 30\\n CD2_4221 = 31\\n CE1_4221 = 32\\n CE2_4221 = 33\\n CF1_4221 = 34\\n CF2_4221 = 35\\n DA1_4221 = 36\\n DA2_4221 = 37\\n DB1_4221 = 38\\n DB2_4221 = 39\\n DC1_4221 = 40\\n DC2_4221 = 41\\n DE1_4221 = 42\\n DE2_4221 = 43\\n DF1_4221 = 44\\n DF2_4221 = 45\\n EA1_4221 = 46\\n EA2_4221 = 47\\n EB1_4221 = 48\\n EB2_4221 = 49\\n EC1_4221 = 50\\n EC2_4221 = 51\\n ED1_4221 = 52\\n ED2_4221 = 53\\n EF1_4221 = 54\\n EF2_4221 = 55\\n FA1_4221 = 56\\n FA2_4221 = 57\\n FB1_4221 = 58\\n FB2_4221 = 59\\n FC1_4221 = 60\\n FC2_4221 = 61\\n FD1_4221 = 62\\n FD2_4221 = 63\\n FE1_4221 = 64\\n FE2_4221 = 65\\n \\n #while traci.simulation.getMinExpectedNumber() > 0:\\n while step < 600:\\n traci.simulationStep()\\n if step == 0:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",6)\\n if step == 6:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 10:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 12:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", A_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",75)\\n if step == 87:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 91:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 93:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",15)\\n if step == 108:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 112:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 114:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", C_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",12)\\n if step == 126:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 130:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 132:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",32)\\n if step == 164:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 168:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 170:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", A_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",50)\\n if step == 220:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 224:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 226:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",15)\\n if step == 241:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 245:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 247:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", C_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",14)\\n if step == 261:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 265:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 267:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",13)\\n if step == 280:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 284:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 286:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", A_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",74)\\n if step == 360:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 364:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 366:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",15)\\n if step == 381:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 385:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 387:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", C_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",10)\\n if step == 397:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 401:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 403:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",16)\\n if step == 419:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 423:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 425:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", A_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",74)\\n if step == 499:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 503:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 505:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",15)\\n if step == 520:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 524:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 526:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", C_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",8)\\n if step == 534:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 538:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 540:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",16)\\n if step == 556:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 560:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 562:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", A_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",38)\\n if step == 0:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", A_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 6)\\n if step == 6:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 10:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 12:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", D_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 20)\\n if step == 32:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 36:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 38:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", E_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 5)\\n if step == 43:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 47:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 49:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", F_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 31)\\n if step == 80:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 84:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 86:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", G_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 12)\\n if step == 98:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 102:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 104:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", A_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 29)\\n if step == 133:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 137:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 139:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", D_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 20)\\n if step == 159:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 163:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 165:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", E_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 5)\\n if step == 170:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 174:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 176:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", F_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 38)\\n if step == 214:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 218:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 220:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", G_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 13)\\n if step == 233:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 237:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 239:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", A_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 34)\\n if step == 273:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 277:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 279:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", D_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 22)\\n if step == 301:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 305:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 307:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", E_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 5)\\n if step == 312:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 316:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 318:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", F_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 31)\\n if step == 349:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 353:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 355:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", G_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 18)\\n if step == 373:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 377:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 379:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", A_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 36)\\n if step == 415:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 419:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 421:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", D_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 19)\\n if step == 440:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 444:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 446:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", E_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 5)\\n if step == 451:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 455:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 457:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", F_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 29)\\n if step == 486:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 490:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 492:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", G_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 18)\\n if step == 510:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 514:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 516:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", A_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 41)\\n if step == 557:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 561:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 563:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", D_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 19)\\n if step == 582:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 586:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 588:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", E_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 8)\\n if step == 596:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 0:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", D_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 17)\\n if step == 17:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 21:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 23:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", E_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 9)\\n if step == 32:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 36:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 38:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", A_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 67)\\n if step == 105:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AB1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 109:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AB2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 111:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", B_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 19)\\n if step == 130:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", BD1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 134:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", BD2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 136:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", D_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 16)\\n if step == 152:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 156:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 158:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", E_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 11)\\n if step == 169:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 173:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 175:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", A_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 63)\\n if step == 238:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AD1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 242:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AD2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 244:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", D_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 13)\\n if step == 257:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 261:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 263:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", E_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 9)\\n if step == 272:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 276:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 278:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", A_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 95)\\n if step == 373:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AB1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 377:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AB2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 379:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", B_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 19)\\n if step == 398:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", BD1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 402:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", BD2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 404:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", D_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 24)\\n if step == 428:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 432:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 434:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", E_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 13)\\n if step == 447:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 451:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 453:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", A_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 56)\\n if step == 509:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AB1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 513:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AB2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 515:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", B_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 19)\\n if step == 534:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", BD1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 538:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", BD2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 540:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", D_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 22)\\n if step == 562:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 566:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 568:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", E_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 20)\\n if step == 588:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 592:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 594:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", A_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 6)\\n if step == 0:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 9)\\n if step == 9:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 13:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 15:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", F_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 19)\\n if step == 34:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 38:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 40:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", A_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 20)\\n if step == 60:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 64:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 66:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", D_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 24)\\n if step == 90:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 94:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 96:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 9)\\n if step == 105:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 109:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 111:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", F_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 19)\\n if step == 130:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 134:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 136:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", A_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 30)\\n if step == 166:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 170:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 172:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", D_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 28)\\n if step == 200:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 204:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 206:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 19)\\n if step == 225:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 229:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 231:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", F_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 8)\\n if step == 239:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 243:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 245:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", A_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 32)\\n if step == 277:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 281:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 283:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", D_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 27)\\n if step == 310:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 314:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 316:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 15)\\n if step == 331:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 335:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 337:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", F_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 14)\\n if step == 351:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 355:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 357:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", A_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 22)\\n if step == 379:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 383:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 385:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", D_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 24)\\n if step == 409:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 413:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 415:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 11)\\n if step == 426:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 430:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 432:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", F_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 14)\\n if step == 446:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 450:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 452:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", A_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 30)\\n if step == 482:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 486:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 488:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", D_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 26)\\n if step == 514:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 518:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 520:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 8)\\n if step == 528:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 532:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 534:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", F_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 18)\\n if step == 552:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 556:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 558:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", A_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 19)\\n if step == 577:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 581:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 583:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", D_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 9)\\n if step == 592:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 596:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 598:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 2)\\n\\n step += 1\\n\\n traci.close()\\n sys.stdout.flush()\",\n \"def lr_experiment():\\n\\n print(\\\"LR_EXPERIMENT\\\\n\\\")\\n\\n # set the name of the experiment\\n now = datetime.datetime.now()\\n experiment_id = str(now.day) + \\\"_\\\" + str(now.month) + \\\"_\\\" + str(now.hour) + \\\".\\\" + str(now.minute)\\n experiment_name = 'lr_' + str(experiment_id)\\n\\n # define if you want to use preprocessed data from file\\n use_prep_data = False\\n if use_prep_data:\\n set_params(preproc_data_id='16_5_10.16.47')\\n else:\\n set_params(use_preproc_data=False)\\n\\n # define the changing parameter and its value\\n changing_param_name = 'lr'\\n changing_param_value = [0.00001, 0.00003, 0.0001, 0.0003, 0.001, 0.003, 0.01, 0.03, 0.1]\\n\\n # set constant parameters\\n set_params(use_word_emb=1)\\n set_params(epochs=20)\\n\\n # save constant parameters to a new \\\"experiment_..\\\" file\\n save_constant_parameters(experiment_name, changing_param_name)\\n\\n # run experiment for every parameter value\\n for value in changing_param_value:\\n # update the changing parameter value\\n set_params(lr = value)\\n\\n # update the model_id for this new model\\n now = datetime.datetime.now()\\n new_model_id = str(now.day) + \\\"_\\\" + str(now.month) + \\\"_\\\" + str(now.hour) + \\\".\\\" + str(now.minute) + \\\".\\\" + str(now.second)\\n set_params(model_id = new_model_id)\\n\\n # evaluate the new model and save the results in the experiment file\\n oneExperiment = Process(target=run_experiment, args=(experiment_name, new_model_id, changing_param_name, value,))\\n oneExperiment.start()\\n oneExperiment.join()\\n\\n if value == changing_param_value[0]:\\n set_params(preproc_data_id=new_model_id)\",\n \"def simulate_data(parameters=None, **kwargs):\\n\\n # merge default parameters with input parameters\\n default_parameters = {\\n\\n # simulation parameters\\n 'seed': 0, # seed for random number generator\\n 'num_sites': 20, # number of binding sites\\n 'binding_eff': .7, # binding efficiency\\n\\n # constants\\n 'dt': 1, # time step\\n 'gain': 20, # gain\\n\\n # variables\\n 'states': None, # photostates\\n 'num_flor': None, # number of fluorophores present\\n 'mu_flor': 100, # brightness of fluorophore\\n 'mu_back': 100, # brightness of background\\n 'transitions': None, # photostate transition probability matrix\\n\\n # numbers\\n 'num_rois': 1, # number of ROIs\\n 'num_data': 2000, # number of time levels\\n 'num_states': 3, # number of states\\n\\n }\\n if parameters is None:\\n parameters = {**default_parameters, **kwargs}\\n else:\\n parameters = {**default_parameters, **parameters, **kwargs}\\n\\n # set RNG seed\\n np.random.seed(parameters['seed'])\\n\\n # extract parameters\\n gain = parameters['gain']\\n eff = parameters['binding_eff']\\n mu_flor = parameters['mu_flor']\\n mu_back = parameters['mu_back']\\n pi = parameters['transitions']\\n num_rois = parameters['num_rois']\\n num_data = parameters['num_data']\\n num_sites = parameters['num_sites']\\n num_states = parameters['num_states']\\n if pi is None:\\n pi = np.ones((num_states + 1, num_states))\\n # increase self transition\\n pi[:-1, :] += 100 * np.eye(num_states)\\n # photobleached only self transitions\\n pi[-2, :-1] = 0\\n # fluorophores cannot start photobleached\\n pi[-1, -1] = 0\\n # no blink to bleached transitions\\n if num_states > 2:\\n pi[0, -1] = 0\\n for k in range(num_states + 1):\\n pi[k, :] /= np.sum(pi[k, :])\\n # create effective pi by allowing nonbinding flurorophores to start bleached\\n pi[-1, :] = np.array([*((1 - eff) * pi[-1, :-1]), eff])\\n parameters['transitions'] = pi\\n if np.isscalar(mu_flor):\\n if num_states == 2:\\n mu_flor = np.array([1, 0]) * mu_flor\\n else:\\n mu_flor = np.array([0, 1, *np.random.rand(num_states - 3), 0]) * mu_flor\\n parameters['mu_flor'] = mu_flor\\n if np.isscalar(mu_back):\\n mu_back = np.ones(num_rois) * mu_back\\n parameters['mu_back'] = mu_back\\n # make effective transition matrix\\n pi_eff = np.zeros((num_states + 1, num_states))\\n pi_eff[:-1, :] = pi[:-1, :]\\n pi_eff[-1, :] = pi[-1, :] * eff + (1 - eff) * np.array([0] * (num_states - 1) + [1])\\n\\n # simulate data\\n \\\"\\\"\\\"\\n We wrap the HMM sampler in a numba jit wrapper for faster\\n sampling of states.\\n \\\"\\\"\\\"\\n @nb.njit(cache=True)\\n def sample_HMM_nb(transition_matrix, num_data):\\n\\n # set variables\\n num_states = transition_matrix.shape[1]\\n states = np.zeros(num_data, dtype=np.int64)\\n\\n # normalize the transition matrix\\n wp = transition_matrix.copy()\\n for k in range(num_states + 1):\\n wp[k, :] = np.cumsum(wp[k, :])\\n\\n # sample initial state\\n states[0] = np.searchsorted(wp[-1, :], np.random.rand())\\n for n in range(1, num_data):\\n # sample each following state from the row of the transition matrix corresponding to the previous state\\n states[n] = np.searchsorted(wp[states[n - 1], :], np.random.rand())\\n\\n return states\\n\\n data = np.zeros((num_rois, num_data))\\n states = np.zeros((num_rois, num_sites, num_data), dtype=int)\\n num_flor = np.zeros(num_rois, dtype=int)\\n for r in range(num_rois):\\n # find the state of each fluorophore at each time\\n for l in range(num_sites):\\n # sample whether or not the fluorophore is bound\\n while True:\\n # sample states within a while loop to ensure that they all end up photobleached\\n states[r, l, :] = sample_HMM_nb(pi_eff, num_data)\\n if states[r, l, -1] == num_states - 1:\\n break\\n\\n # total number of fluorophores is the number that do not start photobleached\\n num_flor[r] = np.sum(states[r, :, 0] < num_states - 1)\\n\\n # sample the brightness data\\n brightness = mu_flor @ states_to_pops(states[r, :, :], num_states) + mu_back[r]\\n data[r, :] = stats.gamma.rvs(a=brightness, scale=gain)\\n\\n parameters['states'] = states\\n parameters['num_flor'] = num_flor\\n\\n return data, parameters\",\n \"def run(n_trials, params):\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\\n a.set_Qtable(params['epsilon'], params['gamma'], params['learn_rate'], params['lrconst'],\\n params['learn_rate_decay'], params['epsconst'], params['epsilon_decay'])\\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.000001, display=False) # create simulator (uses pygame when display=True, if available)\\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\\n\\n sim.run(n_trials=n_trials) # run for a specified number of trials\\n\\n return a.reached_destination\",\n \"def setUp(self):\\n np.random.seed(1337)\\n self.w0_val = np.random.rand(2, 3)\\n self.w1_val = np.random.rand(3, 5)\\n self.w2_val = np.random.rand(10, 3)\\n self.w3_val = np.random.rand(10, 5)\\n self.w4_val = np.random.rand(10, 5)\\n self.w5_val = np.linspace(-7, 7, 200)\\n # self.w5_val = np.random.rand(15)\\n self.w6_val = np.random.rand(2, 3, 5, 7)\\n\\n self.w3_val /= np.expand_dims(np.sum(self.w3_val, axis=1), axis=1) # w3 is a probability distribution\\n\\n self.tf_w0 = tf.constant(self.w0_val, dtype=tf.float64)\\n self.tf_w1 = tf.constant(self.w1_val, dtype=tf.float64)\\n self.tf_w2 = tf.constant(self.w2_val, dtype=tf.float64)\\n self.tf_w3 = tf.constant(self.w3_val, dtype=tf.float64)\\n self.tf_w4 = tf.constant(self.w4_val, dtype=tf.float64)\\n self.tf_w5 = tf.constant(self.w5_val, dtype=tf.float64)\\n self.tf_w6 = tf.constant(self.w6_val, dtype=tf.float64)\\n\\n self.my_w0 = ad.Variable(self.w0_val, name=\\\"w0_val\\\")\\n self.my_w1 = ad.Variable(self.w1_val, name=\\\"w1_val\\\")\\n self.my_w2 = ad.Variable(self.w2_val, name=\\\"w2_val\\\")\\n self.my_w3 = ad.Variable(self.w3_val, name=\\\"w3_val\\\")\\n self.my_w4 = ad.Variable(self.w4_val, name=\\\"w4_val\\\")\\n self.my_w5 = ad.Variable(self.w5_val, name=\\\"w5_val\\\")\\n self.my_w6 = ad.Variable(self.w6_val, name=\\\"w6_val\\\")\",\n \"def SC_generation(hourly_radiation, prop_observers, number_groups, weather_data, g, Sz, Az, ha, Tin_C, height,\\n panel_properties, latitude):\\n\\n\\n n0 = panel_properties['n0']\\n c1 = panel_properties['c1']\\n c2 = panel_properties['c2']\\n mB0_r = panel_properties['mB0_r']\\n mB_max_r = panel_properties['mB_max_r']\\n mB_min_r = panel_properties['mB_min_r']\\n C_eff = panel_properties['C_eff']\\n t_max = panel_properties['t_max']\\n IAM_d = panel_properties['IAM_d']\\n Aratio = panel_properties['aperture_area_ratio']\\n Apanel = panel_properties['module_area']\\n dP1 = panel_properties['dP1']\\n dP2 = panel_properties['dP2']\\n dP3 = panel_properties['dP3']\\n dP4 = panel_properties['dP4']\\n Cp_fluid_JperkgK = panel_properties['Cp_fluid'] # J/kgK\\n\\n # create lists to store results\\n list_results = [None] * number_groups\\n list_areas_groups = [None] * number_groups\\n Sum_mcp_kWperC = np.zeros(8760)\\n Sum_qout_kWh = np.zeros(8760)\\n Sum_Eaux_kWh = np.zeros(8760)\\n Sum_qloss = np.zeros(8760)\\n Sum_radiation_kWh = np.zeros(8760)\\n\\n Tin_array_C = np.zeros(8760) + Tin_C\\n aperature_area_per_module = Aratio * Apanel\\n total_area_module = prop_observers['total_area_module'].sum() # total area for panel installation\\n\\n # calculate equivalent length of pipes\\n lv = panel_properties['module_length'] # module length\\n number_modules = round(total_area_module/Apanel) # this is an estimation\\n l_ext_mperm2 = (2 * lv * number_modules/ (total_area_module * Aratio)) # pipe length within the collectors\\n l_int_mperm2 = 2 * height / (total_area_module * Aratio) # pipe length from building substation to roof top collectors\\n Leq_mperm2 = l_int_mperm2 + l_ext_mperm2 # in m/m2 aperture\\n\\n if panel_properties['type'] == 'ET': # for evacuated tubes\\n Nseg = 100 # default number of subsdivisions for the calculation\\n else:\\n Nseg = 10 # default number of subsdivisions for the calculation\\n\\n for group in range(number_groups):\\n # load panel angles from group\\n teta_z = prop_observers.loc[group, 'surface_azimuth'] # azimuth of panels of group\\n area_per_group = prop_observers.loc[group, 'total_area_module']\\n tilt_angle_deg = prop_observers.loc[group, 'tilt'] # tilt angle of panels\\n\\n # create dataframe with irradiation from group\\n\\n radiation_Wh = pd.DataFrame({'I_sol': hourly_radiation[group]})\\n radiation_Wh['I_diffuse'] = weather_data.ratio_diffhout * radiation_Wh.I_sol # calculate diffuse radiation\\n radiation_Wh['I_direct'] = radiation_Wh['I_sol'] - radiation_Wh['I_diffuse'] # calculate direct radiation\\n radiation_Wh.fillna(0, inplace=True) # set nan to zero\\n\\n # calculate incidence angle modifier for beam radiation\\n IAM_b = calc_IAM_beam_SC(Az, g, ha, teta_z, tilt_angle_deg, panel_properties['type'], Sz, latitude)\\n\\n # calculate heat production from a solar collector of each group\\n list_results[group] = calc_SC_module(tilt_angle_deg, IAM_b, IAM_d, radiation_Wh.I_direct,\\n radiation_Wh.I_diffuse, weather_data.drybulb_C, n0,\\n c1, c2, mB0_r, mB_max_r, mB_min_r, C_eff, t_max,\\n aperature_area_per_module, dP1, dP2, dP3, dP4,\\n Cp_fluid_JperkgK, Tin_C, Leq_mperm2, l_ext_mperm2,\\n l_int_mperm2, Nseg)\\n\\n\\n # multiplying the results with the number of panels in each group and write to list\\n number_modules_per_group = area_per_group / Apanel\\n list_areas_groups[group] = area_per_group\\n radiation_array = hourly_radiation[group] * list_areas_groups[group] / 1000 # kWh\\n Sum_qout_kWh = Sum_qout_kWh + list_results[group][1] * number_modules_per_group\\n Sum_Eaux_kWh = Sum_Eaux_kWh + list_results[group][2] * number_modules_per_group\\n Sum_qloss = Sum_qloss + list_results[group][0] * number_modules_per_group\\n Sum_mcp_kWperC = Sum_mcp_kWperC + list_results[group][5] * number_modules_per_group\\n Sum_radiation_kWh = Sum_radiation_kWh + radiation_Wh['I_sol']*area_per_group/1000\\n\\n Tout_group_C = (Sum_qout_kWh / Sum_mcp_kWperC) + Tin_C # in C assuming all collectors are connected in parallel\\n\\n Final = pd.DataFrame(\\n {'Q_SC_gen_kWh': Sum_qout_kWh, 'T_SC_sup_C': Tin_array_C, 'T_SC_re_C': Tout_group_C, 'mcp_SC_kWperC': Sum_mcp_kWperC, 'Eaux_SC_kWh': Sum_Eaux_kWh,\\n 'Q_SC_l_kWh': Sum_qloss, 'Area_SC_m2': sum(list_areas_groups), 'radiation_kWh': Sum_radiation_kWh}, index=range(8760))\\n\\n return list_results, Final\",\n \"def SampleConditions(testcase, delta):\\n if testcase == 0:\\n t0 = delta + 1\\n t1 = 0\\n elif testcase == 1:\\n t0 = 0\\n t1 = delta + 1\\n else:\\n t0 = delta + 1\\n t1 = delta + 1\\n\\n # Sample a test case\\n passed = False\\n while not passed:\\n x0 = _xm * rng.uniform(-1, 1)\\n x1 = _xm * rng.uniform(-1, 1)\\n vm = _vm * rng.uniform(0, 1)\\n am = _am * rng.uniform(0, 1)\\n v0 = vm * rng.uniform(-1, 1)\\n v1 = vm * rng.uniform(-1, 1)\\n\\n curve1 = interpolator.Compute1DTrajectory(x0, x1, v0, v1, vm, am)\\n if not (len(curve1) == 2):\\n continue\\n t0 = curve1[0].duration\\n t1 = curve1[1].duration\\n if testcase == 0:\\n passed = t0 < delta and t1 >= delta\\n elif testcase == 1:\\n passed = t0 >= delta and t1 < delta\\n else:\\n passed = t0 < delta and t1 < delta\\n \\n return x0, x1, v0, v1, vm, am\",\n \"def test_three_arms_one_unsampled_arm(self):\\n self._test_three_arms_one_unsampled_arm()\",\n \"def run_example():\\n\\n print(\\\"\\\\nRun a example with parameters of the coding challenge from get in it and BWI.\\\\n\\\")\\n print(\\\"Predefined parameters:\\\")\\n print(\\\"Truck count: 2\\\")\\n print(\\\"Truck 1 weight: 1100kg\\\")\\n print(\\\"Truck 2 weight: 1100kg\\\")\\n print(\\\"Driver 1 weight: 72.4kg\\\")\\n print(\\\"Driver 2 weight: 85.7kg\\\")\\n print(\\\"Items from file ./items.csv\\\")\\n print(\\\"---------------------------------------------------------------\\\")\\n\\n # trucks count and max loading weight\\n trucks_max_loadings = [1100, 1100]\\n # can be 'kg' or 'gram'\\n trucks_measuring_unit = 'kg'\\n # drivers weight\\n drivers_weights = [72.4, 85.7]\\n # can be 'kg' or 'gram'\\n drivers_measuring_unit = 'kg'\\n\\n # path to the csv file with all items\\n items_csv_file = './items.csv'\\n # if False, the column names in the csv must be name, max_count, weight, utility\\n # if True, the column names from csv will be changed by columns_mapping below\\n column_mapping_required = True\\n # the csv column names mapped to name, max_count, weight and utility\\n # \\\"<required column>\\\" : \\\"\\\"<mapping csv column>\\\"\\\"\\n columns_mapping = {\\n 'name': 'name',\\n 'max_count': 'required count',\\n 'weight': 'weight in gram',\\n 'utility': 'utility'\\n }\\n # delimiter of the csv columns\\n csv_delimiter = ';'\\n\\n # start example run\\n delivery_optimization(trucks_max_loadings,\\n drivers_weights,\\n items_csv_file,\\n trucks_measuring_unit,\\n drivers_measuring_unit,\\n csv_delimiter,\\n column_mapping_required,\\n columns_mapping)\",\n \"def setUpClass(cls):\\n cls.c = Simulation(logging_level=logging.ERROR)\\n cls.c.set_simulation_parameters(\\n seed=1, task=39, output_directory=\\\"output\\\", min_speciation_rate=1, deme=100, spatial=False\\n )\\n cls.c.run()\\n cls.c2 = Simulation(logging_level=logging.ERROR)\\n cls.c2.set_simulation_parameters(\\n seed=1, task=40, output_directory=\\\"output\\\", min_speciation_rate=0.5, deme=100, spatial=False\\n )\\n cls.c2.run()\\n cls.c3 = Simulation(logging_level=logging.ERROR)\\n cls.c3.set_simulation_parameters(\\n seed=1,\\n task=41,\\n output_directory=\\\"output\\\",\\n min_speciation_rate=0.5,\\n deme=100,\\n spatial=False,\\n protracted=True,\\n min_speciation_gen=0,\\n max_speciation_gen=1,\\n )\\n cls.c3.run()\\n cls.c4 = Simulation(logging_level=logging.ERROR)\\n cls.c4.set_simulation_parameters(\\n seed=1,\\n task=42,\\n output_directory=\\\"output\\\",\\n min_speciation_rate=0.5,\\n deme=100,\\n spatial=False,\\n protracted=True,\\n min_speciation_gen=100,\\n max_speciation_gen=1000,\\n )\\n cls.c4.run()\",\n \"def __init__(self,\\n debug=False,\\n urdf_version=None,\\n control_time_step=0.005,\\n action_repeat=5,\\n control_latency=0,\\n pd_latency=0,\\n on_rack=False,\\n motor_kp=1.0,\\n motor_kd=0.02,\\n render=False,\\n num_steps_to_log=2000,\\n env_randomizer=None,\\n log_path=None,\\n signal_type='ik',\\n target_position=None,\\n backwards=None,\\n gait_type='trot',\\n terrain_type='plane',\\n terrain_id='plane',\\n mark='base',\\n ):\\n self.phase = 0\\n\\n self._gait_type = gait_type \\n # for observation space bounding \\n self.max_speed = 1.0\\n self.min_speed = 0.5 # change back to 0.2 for OLD TD3 model evaluation\\n \\n self.min_side_speed = 0.0\\n self.max_side_speed = 0.0\\n\\n self.speed = np.random.uniform(self.min_speed, self.max_speed)\\n self.side_speed = np.random.uniform(self.min_side_speed, self.max_side_speed)\\n self.speed_des = [self.speed, self.side_speed]\\n\\n # Initialization variables for periodic reward sum composition\\n self.theta_FL = phase_constants.PHASE_VALS[self._gait_type]['front_left']\\n self.theta_FR = phase_constants.PHASE_VALS[self._gait_type]['front_right']\\n self.theta_RL = phase_constants.PHASE_VALS[self._gait_type]['rear_left']\\n self.theta_RR = phase_constants.PHASE_VALS[self._gait_type]['rear_right']\\n\\n self.min_swing_ratio = 0.6\\n self.max_swing_ratio = 0.8\\n self.ratio = np.random.uniform(self.min_swing_ratio, self.max_swing_ratio)\\n\\n super(rexPeriodicRewardEnv,\\n self).__init__(urdf_version=urdf_version,\\n accurate_motor_model_enabled=True,\\n motor_overheat_protection=False,\\n motor_kp=motor_kp,\\n motor_kd=motor_kd,\\n remove_default_joint_damping=False,\\n control_latency=control_latency,\\n pd_latency=pd_latency,\\n on_rack=on_rack,\\n render=render,\\n num_steps_to_log=num_steps_to_log,\\n env_randomizer=env_randomizer,\\n log_path=log_path,\\n control_time_step=control_time_step,\\n action_repeat=action_repeat,\\n target_position=target_position,\\n signal_type=signal_type,\\n backwards=backwards,\\n debug=debug,\\n terrain_id=terrain_id,\\n terrain_type=terrain_type,\\n mark=mark,\\n ratio=self.ratio,\\n forward_reward_cap=5\\n )\\n\\n self.height_des = 0.206 # this is init standing height for rex\\n\\n self.cycle_complete = 0\\n self.cycle_len = 1000 # this is L\\n \\n # vonmises variables\\n self.kappa = phase_constants.VON_MISES_KAPPA\\n\\n rex_joints = p.getNumJoints(bodyUniqueId=self.rex.quadruped)\\n link_name_to_ID = {}\\n for i in range(rex_joints):\\n name = p.getJointInfo(self.rex.quadruped, i)[12].decode('UTF-8')\\n link_name_to_ID[name] = i\\n\\n self.link_name_to_ID = link_name_to_ID\\n self.toe_pos_last = { 'front_left_toe_pos' : p.getLinkState(self.rex.quadruped, self.link_name_to_ID['front_left_toe_link'])[0],\\n 'front_right_toe_pos' : p.getLinkState(self.rex.quadruped, self.link_name_to_ID['front_right_toe_link'])[0],\\n 'rear_left_toe_pos' : p.getLinkState(self.rex.quadruped, self.link_name_to_ID['rear_left_toe_link'])[0],\\n 'rear_right_toe_pos' : p.getLinkState(self.rex.quadruped, self.link_name_to_ID['rear_right_toe_link'])[0]\\n\\n } \\n\\n print('Using Periodic Reward Composition Rex Environment')\",\n \"def sampling(args):\\n t_mean, t_log_var = args\\n # YOUR CODE HERE\\n epsilon = K.random_normal(t_mean.shape)\\n z = epsilon * K.exp(0.5 * t_log_var) + t_mean\\n return z\",\n \"def test_sample(system_generator):\\n\\n name, test = system_generator()\\n print(name)\\n\\n w_F, w_R, N_k = test.sample([10, 8], mode=\\\"wFwR\\\")\\n w_F, w_R, N_k = test.sample([1, 1], mode=\\\"wFwR\\\")\\n w_F, w_R, N_k = test.sample([10, 0], mode=\\\"wFwR\\\")\\n w_F, w_R, N_k = test.sample([0, 5], mode=\\\"wFwR\\\")\",\n \"def predictions(files, species, files2, species2, nbC=1000, nbC2=None, gating='line', showgat=False,\\r\\n predAn='prediction',\\r\\n predtype='neur', ratio=1 / 7.0, repeat=1, average=True,\\r\\n doubt=0, random_state=None, save='save', fc='Accuri',\\r\\n param=None,channels = [], dicChannels = {},cwd='Results/' ):\\r\\n # todo default channel name and dicChannels\\r\\n if param is None:\\r\\n param = ['FL3-A', 'FL1-A', 'FSC-A']\\r\\n # Create result directory\\r\\n if not os.path.exists(cwd):\\r\\n os. mkdir('Results/')\\r\\n cwd = 'Results/'\\r\\n now = datetime.now()\\r\\n dirName = now.strftime(\\\"%Y%m%d-%H_%M_%S/\\\")\\r\\n os.mkdir(cwd + dirName)\\r\\n cwd = cwd + dirName\\r\\n # Create file option with all parameter given in the input\\r\\n f.fileOption(cwd, files, species, files2, species2, nbC, nbC2, gating, predAn, predtype, ratio, repeat, average,\\r\\n doubt,channels=channels,dicChannels=dicChannels,fc=fc)\\r\\n\\r\\n if showgat and save is not None:\\r\\n showgat = save\\r\\n else:\\r\\n showgat = None\\r\\n # Gate Data: according to method\\r\\n Data = []\\r\\n target2 = []\\r\\n refArrays = f.importFile(files, gating=gating, save=showgat, fc=fc, cwd=cwd,channels=channels,dicChannels=dicChannels)\\r\\n if refArrays == [] :\\r\\n return 'None'\\r\\n predArrays = f.importFile(files2, gating=gating, save=showgat, fc=fc, cwd=cwd,channels=channels,dicChannels=dicChannels)\\r\\n if predArrays == []:\\r\\n return 'None'\\r\\n\\r\\n blankArrays = []\\r\\n blk =['BLANK', 'Blank', 'blank']\\r\\n for i in range(len(species)):\\r\\n if species[i] in blk:\\r\\n blankArrays.append(refArrays[i])\\r\\n if gating == 'machine':\\r\\n refArrays,species = f.machineGating(refArrays, species, refArrays, species,cwd=cwd,show=showgat,name='ref', predType=predtype, param=param)\\r\\n if predAn == 'analysis':\\r\\n predArrays, species2 = f.machineGating(refArrays+blankArrays, species+['blank']*len(blankArrays), predArrays, species2,cwd = cwd,show = showgat, name='pred',param=param,predType=predtype)\\r\\n # todo check if 10 and 5000 is not too much!! here we add the former blank from the new gating output\\r\\n else :\\r\\n refArrays = refArrays + blankArrays\\r\\n species = species+['blank']*len(blankArrays)\\r\\n # Selection of data for prediction and analysis\\r\\n if predAn == 'prediction':\\r\\n for anArray in predArrays:\\r\\n data2, atarget2, species2 = f.treat([anArray], species2, None, mode=predAn)\\r\\n Data.append(data2)\\r\\n target2.append(atarget2)\\r\\n else:\\r\\n data2, target2, species2 = f.treat(predArrays, species2, nbC2, mode=predAn)\\r\\n Data.append(data2)#TODO replace by data2 after testing feature impotance\\r\\n target2 = [target2]\\r\\n\\r\\n # treat data\\r\\n statisticsLearn = []\\r\\n statisticsPred = []\\r\\n predict_lbls = []\\r\\n confusionM = []\\r\\n confusionM2 = []\\r\\n acc1 = []\\r\\n F1 = []\\r\\n oldSpecies = species.copy()\\r\\n for i in range(repeat):\\r\\n stat, stat2, predict_lbl, target, conf, conf2, species, \\\\\\r\\n data2 = predictionMultiple(files, refArrays, oldSpecies, files2, Data, target2, nbC, repeat=i,\\r\\n param=param, predAn=predAn, predType=predtype, ratio=ratio,\\r\\n random_state=random_state, save=save, cwd=cwd, average=average)\\r\\n statisticsLearn.append(stat)\\r\\n statisticsPred.append(stat2)\\r\\n #f.save_prediction(predict_lbl,i,cwd=cwd)\\r\\n predict_lbls.append(predict_lbl)\\r\\n confusionM.append(conf)\\r\\n confusionM2.append(conf2)\\r\\n acc1.append(stat.loc['MEAN', 'ACC'])\\r\\n F1.append(stat.loc['MEAN', 'F1'])\\r\\n\\r\\n f.assessmentValue(statisticsLearn, species, cwd, [], 'align')\\r\\n confM = f.averageConfM(confusionM)\\r\\n confM2 = f.averageConfM(confusionM2)\\r\\n f.cmFile(confM, species, cwd, 'Reference CM')\\r\\n if save is not None:\\r\\n f.plotConfusionMatrix(confM, species, save, cwd, normalize=True, name=' average CM for reference',\\r\\n predAn='training')\\r\\n target = np.array(target)\\r\\n predict_lbls = np.array(predict_lbls)\\r\\n conf2 = []\\r\\n acc2 = []\\r\\n f12 = []\\r\\n for i in range(len(statisticsPred[0])):\\r\\n PredStat = []\\r\\n for j in range(repeat):\\r\\n PredStat.append(statisticsPred[j][i])\\r\\n if average:\\r\\n predict_lbl = f.bestPred(list(predict_lbls[:, i]), doubt=doubt) # average of 10\\r\\n if len(predict_lbl) > 0:\\r\\n conf2.append(confusion_matrix(target[i], predict_lbl, labels=species + ['unknown']))\\r\\n statistics2 = f.statAnalysis(list(predict_lbl), list(target[i]), species)\\r\\n acc2.append(statistics2.loc['MEAN', 'ACC'])\\r\\n f12.append(statistics2.loc['MEAN', 'F1'])\\r\\n\\r\\n if predAn == 'prediction':\\r\\n if save is not None and save != 'None':\\r\\n f.graph3d(data2[i], predict_lbl, list(target[i]), species + ['unknown'], param, statistics2, save,\\r\\n cwd, i + 1, name=files2[i].split('/')[-1][:-4], predtype=predAn)\\r\\n f.exportPrediction(list(predict_lbl), [files2[i].split('/')[-1][:-4]], cwd, 'predict',\\r\\n i + 1, 'AVERAGE', repeat)\\r\\n else:\\r\\n f.exportStatistics(statistics2, [n.split('/')[-1][:-4] for n in files2], cwd,\\r\\n 'predict')\\r\\n f.assessmentValue([statistics2], species, cwd, [files2[i].split('/')[-1][:-4]], 'predict')\\r\\n f.exportPrediction(list(predict_lbl), [files2[i].split('/')[-1][:-4]], cwd, 'predict',\\r\\n i + 1, 'AVERAGE', repeat)\\r\\n\\r\\n f.cmFile(conf2[i], species+['unknown'], cwd, 'Prediction CM')\\r\\n if save is not None:\\r\\n f.graph3d(data2[i], predict_lbl, target[i], species + ['unknown'], param, statistics2, save,\\r\\n cwd, i + 1, name=str(len(species)) + ' species', predtype=predAn)\\r\\n f.plotConfusionMatrix(conf2[i], species + ['unknown'], save, cwd, normalize=True,\\r\\n name='CM with ' + str(len(species)) + ' species', predAn='analysis')\\r\\n\\r\\n else:\\r\\n conf2 = []\\r\\n else:\\r\\n for j in range(repeat):\\r\\n if predAn == 'prediction':\\r\\n f.exportPrediction(list(predict_lbls[j][i]), [files2[i].split('/')[-1][:-4]], cwd,\\r\\n 'predict', i, 'SINGLE')\\r\\n else:\\r\\n f.exportStatistics(PredStat[j], [files2[i].split('/')[-1][:-4]], cwd, 'predict')\\r\\n f.exportPrediction(list(predict_lbls[j][i]), [files2[i].split('/')[-1][:-4]], cwd,\\r\\n 'predict', i, 'SINGLE')\\r\\n if predAn == 'analysis':\\r\\n f.assessmentValue(PredStat, species, cwd, [files2[i].split('/')[-1][:-4]], 'predict')\\r\\n f.cmFile(confM2, species+['unknown'], cwd, 'Prediction CM')\\r\\n if save is not None:\\r\\n f.plotConfusionMatrix(confM2, species + ['unknown'], save, cwd, normalize=True,\\r\\n name='average confusion matrix for Tool analysis', predAn='analysis')\\r\\n\\r\\n return os.getcwd().replace('\\\\\\\\', '/') + '/' + cwd\",\n \"def vibratingAssessStrategyGlobal(km, dur_train, duration_val_matches, delta, xgb_params, nb_players, nb_tournaments,\\r\\n xtrain, data):\\r\\n confTest1 = assessStrategyGlobal(km, dur_train, duration_val_matches, delta, xgb_params, nb_players, nb_tournaments,\\r\\n xtrain, data, \\\"1\\\")\\r\\n confTest2 = assessStrategyGlobal(km, dur_train - 10, duration_val_matches, delta, xgb_params, nb_players,\\r\\n nb_tournaments, xtrain, data, \\\"2\\\")\\r\\n confTest3 = assessStrategyGlobal(km, dur_train + 10, duration_val_matches, delta, xgb_params, nb_players,\\r\\n nb_tournaments, xtrain, data, \\\"3\\\")\\r\\n confTest4 = assessStrategyGlobal(km, dur_train - 30, duration_val_matches, delta, xgb_params, nb_players,\\r\\n nb_tournaments, xtrain, data, \\\"4\\\")\\r\\n confTest5 = assessStrategyGlobal(km, dur_train + 30, duration_val_matches, delta, xgb_params, nb_players,\\r\\n nb_tournaments, xtrain, data, \\\"5\\\")\\r\\n confTest6 = assessStrategyGlobal(km, dur_train - 45, duration_val_matches, delta, xgb_params, nb_players,\\r\\n nb_tournaments, xtrain, data, \\\"6\\\")\\r\\n confTest7 = assessStrategyGlobal(km, dur_train + 45, duration_val_matches, delta, xgb_params, nb_players,\\r\\n nb_tournaments, xtrain, data, \\\"7\\\")\\r\\n if (type(confTest1) != int) & (type(confTest2) != int) & (type(confTest3) != int) & (type(confTest4) != int) & (\\r\\n type(confTest5) != int):\\r\\n c = confTest1.merge(confTest2, on=[\\\"match\\\", \\\"PSW\\\"])\\r\\n c = c.merge(confTest3, on=[\\\"match\\\", \\\"PSW\\\"])\\r\\n c = c.merge(confTest4, on=[\\\"match\\\", \\\"PSW\\\"])\\r\\n c = c.merge(confTest5, on=[\\\"match\\\", \\\"PSW\\\"])\\r\\n c = c.merge(confTest6, on=[\\\"match\\\", \\\"PSW\\\"])\\r\\n c = c.merge(confTest7, on=[\\\"match\\\", \\\"PSW\\\"])\\r\\n c = pd.Series(list(zip(c.win1, c.win2, c.win3, c.win4, c.win5,\\r\\n c.win6, c.win7,\\r\\n c.confidence1, c.confidence2, c.confidence3,\\r\\n c.confidence4, c.confidence5,\\r\\n c.confidence6, c.confidence7)))\\r\\n c = pd.DataFrame.from_records(list(c.apply(mer)))\\r\\n conf = pd.concat([confTest1[[\\\"match\\\", \\\"PSW\\\"]], c], 1)\\r\\n conf.columns = [\\\"match\\\", \\\"PSW\\\", \\\"win0\\\", \\\"confidence0\\\"]\\r\\n else:\\r\\n conf = 0\\r\\n return conf\",\n \"def refugia_adj_5_simsplit_4epochs_iter2 (params, ns):\\n #28 parameters \\n nu1a, nu2a, nu3a, nu1b, nu2b, nu3b, nu1c, nu2c, nu3c, nu1d, nu2d, nu3d, m1_12, m1_13, m1_21, m1_23, m1_31, m1_32, m3_12, m3_13, m3_21, m3_23, m3_31, m3_32, T1, T2, T3, T4 = params\\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1] + ns[2])\\n fs = moments.Spectrum(sts)\\n fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1] + ns[2]) \\n fs = moments.Manips.split_2D_to_3D_2(fs, ns[1], ns[2])\\n ## Population function and migration matrix for T1 (to reflect sum effect of all previous glacial-interglacial cycles)\\n nu_T1 = [nu1a, nu2a, nu3a]\\n mig1 = numpy.array([[0, m1_12, m1_13],[m1_21, 0, m1_23], [m1_31, m1_32, 0]]) \\n fs.integrate(nu_T1, T1, m=mig1)\\n ## Population function and migration matrix for T2 (to reflect period of isolation during last glacial)\\n nu_T2 = [nu1b, nu2b, nu3b]\\n fs.integrate(nu_T2, T2)\\n ## Population function and migration matrix for T3 (to reflect inter-glacial expansion)\\n nu_T3 = [nu1c, nu2c, nu3c]\\n mig3 = numpy.array([[0, m3_12, m3_13],[m3_21, 0, m3_23], [m3_31, m3_32, 0]]) \\n fs.integrate(nu_T3, T3, m=mig3)\\n ## Population function and migration matrix for T3 (bottleneck to capture single population representation of lineage)\\n nu_T4 = [nu1d, nu2d, nu3d]\\n fs.integrate(nu_T4, T4) \\n return fs\",\n \"def split_full_3epochs_iter3 (params, ns):\\n #19 parameters \\n nu1a, nuA, nu1b, nu2b, nu3b, nu1c, nu2c, nu3c, m1_12, m1_21, m2_12, m2_13, m2_21, m2_23, m2_31, m2_32, T1, T2, T3 = params\\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1] + ns[2])\\n fs = moments.Spectrum(sts)\\n fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1] + ns[2])\\n ## Population function and migration matrix for T1\\n nu_T1 = [nu1a, nuA]\\n mig1 = numpy.array([[0, m1_12],[m1_21, 0]])\\n fs.integrate(nu_T1, T1, m=mig1)\\n fs = moments.Manips.split_2D_to_3D_2(fs, ns[1], ns[2])\\n ## Population function and migration matrix for T2\\n nu_T2 = [nu1b, nu2b, nu3b]\\n mig2 = numpy.array([[0, m2_12, m2_13],[m2_21, 0, m2_23], [m2_31, m2_32, 0]]) \\n fs.integrate(nu_T2, T2, m=mig2)\\n ## Population function and migration matrix for T3\\n nu_T3 = [nu1c, nu2c, nu3c]\\n fs.integrate(nu_T3, T3) \\n return fs\",\n \"def tgt_samples(self, params):\\r\\n def save_json(save_path, file_id, samples):\\r\\n init_logger()\\r\\n for i, sample in enumerate(samples):\\r\\n save_ = os.path.join(save_path, \\\"{:s}_{:d}.json\\\".format(file_id, i))\\r\\n with open(save_, 'w') as file:\\r\\n json.dump(sample, file)\\r\\n logger.info(\\\"{:s} saved at {:s}\\\".format(save_, save_path))\\r\\n\\r\\n\\r\\n json_file, save_path = params\\r\\n init_logger()\\r\\n _, tgt = self.load_json(json_file)\\r\\n\\r\\n file_id = json_file.split(\\\"/\\\")[-1].split(\\\".\\\")[0]\\r\\n if len(tgt) >= self.args.min_sents_num and len(tgt) <= self.args.max_sents_num:\\r\\n tgt_ = list(tgt)\\r\\n random.seed(66)\\r\\n random.shuffle(tgt_)\\r\\n\\r\\n # make sentence pair and write in a single file\\r\\n positive_sents = tgt\\r\\n positive_pairs = [(positive_sents[i], positive_sents[i+1]) for i in range(len(positive_sents)-1)]\\r\\n\\r\\n negative_sents = tgt_\\r\\n negative_pairs = [(negative_sents[i], negative_sents[i+1]) for i in range(len(negative_sents)-1)]\\r\\n\\r\\n positive_samples = [{\\\"tgt\\\": pair, \\\"coherence\\\": 0} for pair in positive_pairs] # 0 represents coherent\\r\\n negative_samples = [{\\\"tgt\\\": pair, \\\"coherence\\\": 1} for pair in negative_pairs] # 1 represents incoherent\\r\\n\\r\\n save_json(save_path, file_id, positive_samples)\\r\\n save_json(save_path, file_id+\\\"_r\\\", negative_samples)\",\n \"def gen_params(self) -> Dict:\\n param_dict: Dict = {}\\n\\n gX_name: List[str] = ['g_leak', 'g_nav', 'g_kvhh', 'g_kva', 'g_kvsi', \\n 'g_cav', 'g_kca', 'g_nap', 'g_kir']\\n gX_name: List[str] = list(itertools.compress(gX_name, list(self.channel_bool.values())[:9]))\\n gX_log: np.ndarray = 4 * np.random.rand(len(gX_name)) - 2 # from -2 to 2\\n gX: np.ndarray = (10 * np.ones(len(gX_name))) ** gX_log # 0.01 ~ 100\\n gX_itr: Iterator = zip(gX_name, gX)\\n\\n gR_name: List[str] = ['g_ampar', 'g_nmdar', 'g_gabar']\\n gR_name: List[str] = list(itertools.compress(gR_name, list(self.channel_bool.values())[9:12]))\\n gR_log: np.ndarray = 4 * np.random.rand(len(gR_name)) - 3 # from -3 to 1\\n gR: np.ndarray = (10 * np.ones(len(gR_name))) ** gR_log # 0.001 ~ 10\\n gR_itr: Iterator = zip(gR_name, gR)\\n\\n param_dict.update(gX_itr)\\n param_dict.update(gR_itr)\\n\\n if self.channel_bool['ca']:\\n tCa_log: float = 2 * np.random.rand(1) + 1 # from 1 to 3\\n tCa: float = 10 ** tCa_log # 10 ~ 1000\\n tCa_dict: Dict = {'t_ca': tCa}\\n param_dict.update(tCa_dict)\\n\\n return param_dict\",\n \"def one_experiment():\\n\\n # set the name of the experiment\\n now = datetime.datetime.now()\\n experiment_id = str(now.day) + \\\"_\\\" + str(now.month) + \\\"_\\\" + str(now.hour) + \\\".\\\" + str(now.minute)\\n experiment_name = 'overfit_' + str(experiment_id)\\n\\n # define if you want to use preprocessed data from file\\n use_prep_data = False\\n if use_prep_data:\\n set_params(preproc_data_id='16_5_10.16.47')\\n\\n # define the changing parameter and its value\\n changing_param_name = 'class_weights'\\n changing_param_value = [{0: 15, 1: 85}]\\n # {0:15, 1:85}]#, {0:4, 1:100}, {0:3, 1:100}, {0:2, 1:100}, {0:1, 1:100}] #[{0:1, 1:1}, {0:15, 1:85}]#\\n\\n features_to_use = ['user', 'countries', 'session', 'format', 'token']\\n # set constant parameters\\n set_params(use_word_emb=1)\\n set_params(epochs=40)\\n set_params(features_to_use=features_to_use)\\n\\n # save constant parameters to a new \\\"experiment_..\\\" filgithx+P@2ub\\n save_constant_parameters(experiment_name, changing_param_name)\\n\\n # run experiment for every parameter value\\n for value in changing_param_value:\\n process = psutil.Process(os.getpid())\\n print(\\\"-----MEMORY before starting experiment ------\\\", int(process.memory_info().rss/(8*10**3)), \\\"KB\\\")\\n\\n # update the parameter value\\n set_params(class_weights_1=value)\\n\\n # update the model_id for this new model\\n now = datetime.datetime.now()\\n new_model_id = str(now.day) + \\\"_\\\" + str(now.month) + \\\"_\\\" + str(now.hour) + \\\".\\\" + str(now.minute) + \\\".\\\" + str(now.second)\\n\\n set_params(model_id=new_model_id)\\n\\n # evaluate the new model and save the results in the experiment file\\n oneExperiment = Process(target=run_experiment, args=(experiment_name,\\n new_model_id, changing_param_name, value,))\\n oneExperiment.start()\\n oneExperiment.join()\",\n \"def part_3(l, m, n):\\n # initilize variables\\n size = 50000\\n samples, loops, R = size / 10, 20, None\\n lrs = [1.5, 0.25, 0.03, 0.005, 0.001]\\n alphas = [1.1, 1.01, 1.005, 1.0005, 1.0001]\\n gammas = [2.0, 0.3, 0.04, 0.006, 0.001]\\n \\n # divide dataset: 10% - training, 10% - test.\\n # y, x = [], []\\n (y, x) = gen(l, m, n, 10, False)\\n (y_train, x_train) = gen(l, m, n, 50000, True)\\n (y_test, x_test) = gen(l, m, n, 10000, False)\\n \\n # build online learning algorithms\\n perceptron = Perceptron(R, x_train, x_test, y_train, y_test, n, size, size)\\n perceptron_margin = Perceptron_Margin(R, x, x_train, x_test, y, y_train, y_test, n, samples, size, loops, lrs)\\n winnon = Winnon(R, x, x_train, x_test, y, y_train, y_test, n, samples, size, loops, alphas)\\n winnon_margin = Winnon_Margin(R, x, x_train, x_test, y, y_train, y_test, n, samples, size, loops, alphas, gammas)\\n adagrad = AdaGrad(R, x, x_train, x_test, y, y_train, y_test, n, samples, size, loops, lrs)\",\n \"def main(_):\\n configs.DEFINE_string(\\\"train_datafile\\\", None,\\\"Training file\\\")\\n configs.DEFINE_string(\\\"optimizer\\\", 'gd', 'Optimizer to use gd, adam, adagrad, momentum')\\n configs.DEFINE_float(\\\"lr_decay\\\",0.9, \\\"Learning rate decay\\\")\\n configs.DEFINE_float(\\\"initial_learning_rate\\\",1.0,\\\"Initial learning rate\\\")\\n configs.DEFINE_float(\\\"validation_size\\\",0.0,\\\"Size of validation set as %\\\")\\n configs.DEFINE_float(\\\"passes\\\",1.0,\\\"Passes through day per epoch\\\")\\n configs.DEFINE_float(\\\"rnn_loss_weight\\\",None,\\\"How much moret to weight kth example\\\")\\n configs.DEFINE_integer(\\\"max_epoch\\\",0,\\\"Stop after max_epochs\\\")\\n configs.DEFINE_integer(\\\"early_stop\\\",None,\\\"Early stop parameter\\\")\\n configs.DEFINE_integer(\\\"seed\\\",None,\\\"Seed for deterministic training\\\")\\n\\n config = configs.get_configs()\\n\\n if config.train_datafile is None:\\n config.train_datafile = config.datafile\\n\\n train_path = model_utils.get_data_path(config.data_dir,config.train_datafile)\\n\\n print(\\\"Loading training data ...\\\")\\n\\n train_data = BatchGenerator(train_path, config,\\n\\t\\t\\t config.batch_size,config.num_unrollings,\\n\\t\\t\\t validation_size=config.validation_size,\\n\\t\\t\\t randomly_sample=True)\\n\\n tf_config = tf.ConfigProto( allow_soft_placement=True ,\\n log_device_placement=False )\\n\\n with tf.Graph().as_default(), tf.Session(config=tf_config) as session:\\n\\n if config.seed is not None:\\n tf.set_random_seed(config.seed)\\n\\n print(\\\"Constructing model ...\\\")\\n\\n model = model_utils.get_training_model(session, config, verbose=True)\\n\\n\\n if config.early_stop is not None:\\n print(\\\"Training will early stop without \\\"\\n \\\"improvement after %d epochs.\\\"%config.early_stop)\\n \\n train_history = list()\\n valid_history = list()\\n # This sets the initial learning rate tensor\\n lr = model.assign_lr(session,config.initial_learning_rate)\\n\\n for i in range(config.max_epoch):\\n\\n trc, tre, vdc, vde = run_epoch(session, model, train_data,\\n keep_prob=config.keep_prob,\\n passes=config.passes,\\n verbose=True)\\n\\n trc = 999.0 if trc > 999.0 else trc\\n vdc = 999.0 if vdc > 999.0 else vdc\\n\\n print( ('Epoch: %d loss: %.6f %.6f'\\n ' error: %.6f %.6f Learning rate: %.4f') %\\n (i + 1, trc, vdc, tre, vde, lr) )\\n sys.stdout.flush()\\n\\n train_history.append( trc )\\n valid_history.append( vdc )\\n\\n # update learning rate \\n if config.optimizer == 'gd' or config.optimizer == 'momentum':\\n lr = model_utils.adjust_learning_rate(session, model, \\n lr, config.lr_decay, train_history )\\n\\n if not os.path.exists(config.model_dir):\\n print(\\\"Creating directory %s\\\" % config.model_dir)\\n os.mkdir(config.model_dir)\\n\\n chkpt_file_prefix = \\\"training.ckpt\\\"\\n if model_utils.stop_training(config,valid_history,chkpt_file_prefix):\\n print(\\\"Training stopped.\\\")\\n quit()\\n else:\\n checkpoint_path = os.path.join(config.model_dir, chkpt_file_prefix)\\n tf.train.Saver().save(session, checkpoint_path, global_step=i)\",\n \"def test(output_seq,M1,M2,M3,obs_file):\\n predicted = []\\n mus_c = 0\\n mod_c = 0\\n arn_c = 0\\n next = 0\\n t = 0.000\\n d=dict()\\n d[\\\"other\\\"]=0\\n d[\\\"modi\\\"]=0\\n d[\\\"arnab\\\"]=0\\n print(obs_file+\\\":\\\")\\n for obs in output_seq:\\n p1 = M1.forward(obs)\\n p2 = M2.forward(obs)\\n p3 = M3.forward(obs)\\n\\n\\n if(p1 > p2 and p1 > p3):\\n predicted.append(\\\"Other\\\")\\n mus_c+=1\\n d[\\\"other\\\"]+=1\\n elif(p2 > p3 and p2 > p1):\\n predicted.append(\\\"Modi\\\")\\n mod_c+=1\\n d[\\\"modi\\\"]+=1\\n else:\\n predicted.append(\\\"Arnab\\\")\\n arn_c+=1\\n d[\\\"arnab\\\"]+=1\\n t+=0.05\\n next+=1\\n\\n if(next % 20 == 0):\\n p_other = d[\\\"other\\\"]/20.0\\n p_modi = d[\\\"modi\\\"]/20.0\\n p_arnab = d[\\\"arnab\\\"]/20.0\\n #print(\\\"{0} : Modi = {1}, Arnab = {2}, Other = {3}\\\".format(t,p_modi,p_arnab,p_other))\\n if p_other>p_modi and p_other>p_arnab:\\n print \\\"{0} : Speech : Other\\\".format(t)\\n elif p_modi>p_other and p_modi>p_arnab:\\n print \\\"{0} : Speech : Modi\\\".format(t)\\n else:\\n print \\\"{0} : Speech : Arnab\\\".format(t)\\n d[\\\"other\\\"]=0\\n d[\\\"modi\\\"]=0\\n d[\\\"arnab\\\"]=0\\n\\n time = 0.000\\n with open(\\\"op_\\\"+obs_file,\\\"w\\\") as g:\\n for val in predicted:\\n g.write(str(time)+\\\" :\\\\t\\\"+val+\\\"\\\\n\\\")\\n time+=0.005\\n\\n return predicted\",\n \"def test_run_sim():\\n rnd = rand.Arrivals(31, 40)\\n sim.run_sim(2, 1, 3, 4, 24, rnd)\",\n \"def _evaluate_performance__static_winners(self):\\n # | - _evaluate_performance__\\n\\n # | - class attributes #################################################\\n AL = self\\n al_gen = self.al_gen\\n verbose = self.verbose\\n seed_ids = self.seed_ids\\n acquisition_bin = self.acquisition_bin\\n completed_ids = self.completed_ids\\n CandidateSpace = self.CandidateSpace\\n RegressionModel = self.RegressionModel\\n DuplicateFinder = self.DuplicateFinder\\n al_gen_dict = self.al_gen_dict\\n\\n stop_mode = self.stop_mode\\n stop_num_generations = self.stop_num_generations\\n\\n index_acq_gen_dict = self.index_acq_gen_dict\\n #__| #################################################################\\n\\n # #####################################################################\\n mode = \\\"lowest_N\\\" # 'lowest_N' or 'lowest_perc'\\n\\n N_ids = 10\\n lowest_perc = 5\\n\\n # Number of consecutive generations that the Nth best systems must\\n # remain static\\n M_gens = 3\\n # #####################################################################\\n\\n if mode == \\\"lowest_perc\\\":\\n num_candidates = CandidateSpace.FingerPrints.df_pre.shape[0]\\n N_ids = int(num_candidates * (lowest_perc * 0.01))\\n\\n gen_keys = list(AL.al_gen_dict.keys())\\n\\n if len(gen_keys) > M_gens:\\n latest_M_keys = gen_keys[-(M_gens + 1):]\\n last_gen_key = gen_keys[-1]\\n\\n al_gen_dict_subset_i = dict(zip(\\n latest_M_keys,\\n [AL.al_gen_dict.get(i, None) for i in latest_M_keys]))\\n\\n indices_list = []\\n iterator = enumerate(al_gen_dict_subset_i.items())\\n for i_cnt, (gen_i, AL_i) in iterator:\\n model_i = AL_i.model\\n\\n model_i = AL.add_main_Y_to_model(\\n model_i, plot_dft_instead_of_pred=True)\\n model_i = model_i[(model_i[\\\"duplicate\\\"] == False)]\\n model_i = model_i.sort_values(\\\"Y_main\\\")\\n\\n indices_i = model_i.index.tolist()\\n\\n indices_list.append(indices_i)\\n\\n if i_cnt >= M_gens:\\n indices_i = indices_list[i_cnt][0:N_ids]\\n ids_static_list = []\\n for j in range(M_gens):\\n indices_j = indices_list[i_cnt - (j + 1)][0:N_ids]\\n ids_static = indices_j == indices_i\\n ids_static_list.append(ids_static)\\n\\n ids_are_static = all(ids_static_list)\\n\\n self.performance__static_winners[last_gen_key] = ids_are_static\\n #__|\",\n \"def top_conditioned_sample():\\n global vqvae\\n assert vqvae is not None\\n global DEVICE\\n assert DEVICE is not None\\n global transformer_bottom\\n assert transformer_bottom is not None\\n global label_encoders_per_modality\\n assert label_encoders_per_modality is not None\\n global spectrograms_helper\\n assert spectrograms_helper is not None\\n\\n BYPASS = False\\n\\n top_code, bottom_code = parse_codes(request)\\n global_instrument_family_str = str(\\n request.args.get('instrument_family_str'))\\n min_pitch = int(request.args.get('min_pitch'))\\n max_pitch = int(request.args.get('max_pitch'))\\n\\n if not BYPASS:\\n temperature = float(request.args.get('temperature'))\\n top_p = float(request.args.get('top_p') or 0.0)\\n top_k = int(request.args.get('top_k') or 0)\\n\\n class_conditioning_tensors_bottom = make_conditioning_tensors(\\n {'pitch': (min_pitch, max_pitch),\\n 'instrument_family_str': global_instrument_family_str},\\n label_encoders_per_modality\\n )\\n\\n # repeat the top codemap for all bottom samples\\n num_samples = max_pitch - min_pitch\\n top_code = top_code.expand(num_samples, -1, -1)\\n\\n bottom_code = sample_model(\\n transformer_bottom, DEVICE, num_samples,\\n transformer_bottom.shape,\\n temperature, condition=top_code,\\n class_conditioning=class_conditioning_tensors_bottom,\\n top_p_sampling_p=top_p,\\n top_k_sampling_k=top_k\\n )\\n else:\\n import time\\n num_samples = 1\\n top_code = top_code.expand(num_samples, -1, -1)\\n bottom_code = bottom_code.expand(num_samples, -1, -1)\\n time.sleep(2)\\n\\n logmelspectrogram_and_IF = vqvae.decode_code(top_code,\\n bottom_code)\\n\\n zip_path = upload_directory + 'samples.zip'\\n with ZipFile(zip_path, 'w') as zf:\\n for pitch, sample in zip(range(min_pitch, max_pitch),\\n logmelspectrogram_and_IF):\\n # convert to audio and write to file\\n audio = spectrograms_helper.to_audio(sample.unsqueeze(0))[0]\\n audio_path = write_audio_to_file(\\n audio,\\n f'-{global_instrument_family_str}-{pitch}')\\n zf.write(audio_path,\\n arcname=f'{global_instrument_family_str}-{pitch}.wav')\\n\\n return flask.send_file(zip_path, mimetype=\\\"application/zip\\\",\\n cache_timeout=-1 # disable cache\\n )\",\n \"def test_scenario(timestep_per_pi, int_method):\\n\\n #determine BC and IC\\n x0 = 0.0 #init pos\\n v0 = 1.0 #init vel\\n t0 = 0.0 #start-time\\n tn = 4.0*np.pi #end-time\\n tau = timestep_per_pi*np.pi #timesteps\\n n = (tn-t0)/tau + 1 #number of timesteps\\n \\n time = np.linspace(t0, tn, n) #time-array\\n\\n #acceleration of point particle with k=m=1\\n acc1 = lambda x,v,t: -1.0*x #function must take three arguments!\\n\\n pos, vel, time = integrate_time(func=acc1,\\n init=(x0,v0),\\n timearray=time,\\n method=int_method)\\n\\n #analytical solutions\\n pos_an = np.sin(time)\\n vel_an = np.cos(time)\\n\\n return time, pos, pos_an, vel, vel_an\",\n \"def gen_spec(model_params):\\n xarr11 = SpectroscopicAxis(np.linspace(-40,40,1000)*u.km/u.s,\\n velocity_convention='radio',\\n refX=freq_dict['oneone']).as_unit(u.GHz)\\n xarr22 = SpectroscopicAxis(np.linspace(-40,40,1000)*u.km/u.s,\\n velocity_convention='radio',\\n refX=freq_dict['twotwo']).as_unit(u.GHz)\\n xarr = SpectroscopicAxes([xarr11,xarr22])\\n tkin = model_params[0]\\n tex = model_params[1]\\n ntot = model_params[2]\\n width = model_params[3]\\n fortho = 0.0\\n synthspec = pyspeckit.spectrum.models.ammonia.cold_ammonia(xarr,tkin=tkin,tex=tex,ntot=ntot,\\n width=width,fortho=fortho)\\n spectrum = pyspeckit.Spectrum(xarr=xarr,data=synthspec)\\n return spectrum\",\n \"def _tcrsampler_svgs(\\n tcrrep,\\n default_background = None, \\n default_background_if_missing = None,\\n cdr3_name = 'cdr3_b_aa',\\n pwmat_str = 'pw_cdr3_b_aa', \\n chain = 'beta', \\n gene_names = ['v_b_gene','j_b_gene'],\\n combine_olga = False,\\n verbose = True): \\n from tcrsampler.sampler import TCRsampler\\n from palmotif import compute_pal_motif, svg_logo\\n import pandas as pd\\n from tcrdist.summarize import _select\\n \\n if chain == 'alpha' and tcrrep.organism == \\\"mouse\\\":\\n # Here we enforce the rule that alpha-mouse cannot use an olga-sampler\\n # TODO: This should be removed as soon as TCRsampler can be updated with a valid \\n # mouse-alpha simulated background.\\n combine_olga = False\\n \\n # _default_sampler returns a TCRSampler based on organism and chain\\n if verbose: print(f\\\"INITIALIZING A TCRSAMPLER\\\")\\n print(tcrrep.organism, chain)\\n t = _default_sampler(organism = tcrrep.organism, chain = chain)(\\n default_background = default_background, \\n default_background_if_missing = default_background_if_missing)\\n\\n build_kargs = { 'max_rows' : 100,\\n 'stratify_by_subject' : True,\\n 'use_frequency' : True,\\n 'make_singleton' : False}\\n\\n build_kargs_olga = { 'max_rows' : 1000,\\n 'stratify_by_subject' : False,\\n 'use_frequency' : True,\\n 'make_singleton' : False}\\n \\n if verbose: print(f\\\"BUILDING A DEEPER BACKGROUND {report_kwargs(build_kargs)}\\\")\\n\\n t.build_background(**build_kargs)\\n # Olga Sampler\\n \\n if combine_olga:\\n t_olga = _default_sampler_olga(chain = chain, organism = tcrrep.organism)()\\n if verbose: print(f\\\"BUILDING A DEEPER BACKGROUND {report_kwargs(build_kargs_olga)}\\\")\\n t.build_background(**build_kargs_olga )\\n\\n olga_model = {\\n ('beta','human') : OlgaModel(recomb_type=\\\"VDJ\\\", chain_folder = \\\"human_T_beta\\\"),\\n ('alpha','human') : OlgaModel(recomb_type=\\\"VJ\\\", chain_folder = \\\"human_T_alpha\\\"),\\n ('beta','mouse') : OlgaModel(recomb_type=\\\"VDJ\\\", chain_folder = \\\"mouse_T_beta\\\")\\n }[(chain, tcrrep.organism)]\\n\\n\\n\\n if 'prune' not in tcrrep.hcluster_df.columns:\\n if verbose: print(\\\"NO PRUNE COLUMNS USED ALL SET TO 0\\\")\\n tcrrep.hcluster_df['prune'] = 0\\n \\n print(\\\"ITERATE THROUGH CLUSTERS\\\")\\n svgs = list()\\n svgs_raw = list()\\n reference_unique = list()\\n reference_unique_olga= list()\\n reference_size = list()\\n reference_size_olga = list()\\n percent_missing_sampler = list()\\n percent_missing_sampler_olga = list()\\n n_rows = tcrrep.hcluster_df.shape[0]\\n \\n \\n bar = IncrementalBar(f'Make {chain} SVGs :', max = n_rows, suffix='%(percent)d%%')\\n for i,r in tcrrep.hcluster_df.iterrows():\\n bar.next()\\n if r['prune'] == 0:\\n # <dfnode> is dataframe with all the clones at a given tree node\\n dfnode = tcrrep.clone_df.iloc[r['neighbors_i'],].copy()\\n # <pwnode> Pairwise Matrix for node sequences\\n pwnode = getattr(tcrrep, pwmat_str)[r['neighbors_i'],:][:,r['neighbors_i']].copy()\\n iloc_idx = pwnode.sum(axis = 0).argmin()\\n centroid = dfnode[cdr3_name].to_list()[iloc_idx]\\n \\n # Compute gene usage at the node \\n # Convert to allele_01\\n for gene_name in gene_names:\\n dfnode[gene_name] = dfnode[gene_name].apply(lambda x : allele_01(x)) \\n \\n gene_usage = dfnode.groupby(gene_names).size() # e.g., ['v_b_gene','j_b_gene']\\n gene_usage_tuples = gene_usage.reset_index().to_dict('split')['data']\\n # Given gene usage use the <t> a TCRsampler instance to get background seqs\\n \\n # Adjust depth for small nodes\\n adjust_depth = 10*round(10 / dfnode.shape[0])\\n if adjust_depth < 10:\\n adjust_depth = 10\\n\\n sampled_rep = t.sample( gene_usage_tuples,\\n flatten = True, depth = adjust_depth * 10)\\n \\n # Only keep the non-none sequences\\n sampled_rep = [x for x in sampled_rep if x is not None]\\n # < missing_gene > Count the percentage missing, sampler returns none when no v,j pair is present\\n expected_depth = dfnode.shape[0] * adjust_depth * 10\\n recovered_depth = len(sampled_rep)\\n percent_missing = round(100* (1- (recovered_depth / expected_depth)), 1)\\n \\n percent_missing_sampler.append(f\\\"{percent_missing}%\\\") \\n reference_unique.append(str(pd.Series(sampled_rep).nunique()))\\n reference_size.append(str(pd.Series(sampled_rep).count()))\\n\\n if combine_olga:\\n # We modified Olga source code slightly, such that we simulated sequences \\n # with a given V,J gene usage\\n # OLD METHOD WHERE WE ACTUALLY SAMPLED, slower but can go much deeper. I don't think one rare sequence however, really make a big difference.\\n #flatten = lambda l: [item for sublist in l for item in sublist]\\n #sampled_rep_olga = [olga_model.gen_cdr3s(allele_01(v),allele_01(j),n*adjust_depth*10) for v,j,n in gene_usage_tuples]\\n #sampled_rep_olga = [x for x in flatten(sampled_rep_olga) if x is not None]\\n sampled_rep_olga = t_olga.sample( gene_usage_tuples,\\n flatten = True, depth = adjust_depth * 10)\\n\\n sampled_rep_olga = [x for x in sampled_rep_olga if x is not None]\\n \\n expected_depth = dfnode.shape[0] * adjust_depth * 10\\n recovered_depth = len(sampled_rep_olga)\\n percent_missing_olga = round(100* (1- (recovered_depth / expected_depth)), 1)\\n \\n percent_missing_sampler_olga.append(f\\\"{percent_missing_olga}%\\\") \\n reference_unique_olga.append(str(pd.Series(sampled_rep_olga).nunique()))\\n reference_size_olga.append(str(pd.Series(sampled_rep_olga).count()))\\n \\n # HERE WE COMBINE INTO A SINGLE BACKGROUND:\\n sampled_rep = sampled_rep + sampled_rep_olga\\n\\n\\n # Get motif matrix and motif stats\\n motif, stat = compute_pal_motif(\\n seqs = _select(df = tcrrep.clone_df, \\n iloc_rows = r['neighbors_i'], \\n col = cdr3_name),\\n refs = sampled_rep, \\n centroid = centroid)\\n\\n svgs.append(svg_logo(motif, return_str= True))\\n\\n # repeaat without references\\n raw_motif, raw_stat = compute_pal_motif(\\n seqs = _select(df = tcrrep.clone_df, \\n iloc_rows = r['neighbors_i'], \\n col = cdr3_name),\\n centroid = centroid)\\n # Convert the motif matrix into an svg_logo, append to list\\n svgs_raw.append(svg_logo(raw_motif, return_str= True))\\n else: \\n # If prune column is 1 don't go to the trouble of sampling and generating seqs\\n svgs.append(\\\"PRUNE\\\")\\n svgs_raw.append(\\\"PRUNE\\\")\\n reference_size.append(\\\"PRUNE\\\")\\n reference_unique.append(\\\"PRUNE\\\")\\n percent_missing_sampler.append(\\\"PRUNE\\\")\\n percent_missing_sampler_olga.append(\\\"PRUNE\\\") \\n reference_unique_olga.append(\\\"PRUNE\\\") \\n reference_size_olga.append(\\\"PRUNE\\\") \\n\\n bar.next(); bar.finish()\\n\\n # The standard svg_ includes background, whereas raw has no background \\n tcrrep.hcluster_df_detailed[f'svg_{chain}'] \\t\\t= svgs\\n tcrrep.hcluster_df_detailed[f'svg_raw_{chain}'] \\t= svgs_raw\\n tcrrep.hcluster_df_detailed[f'ref_size_{chain}'] \\t= reference_size\\n tcrrep.hcluster_df_detailed[f'ref_unique_{chain}'] \\t= reference_unique\\n tcrrep.hcluster_df_detailed[f'percent_missing_{chain}'] = percent_missing_sampler \\n if combine_olga:\\n tcrrep.hcluster_df_detailed[f'ref_size_olga_{chain}'] \\t = reference_size_olga\\n tcrrep.hcluster_df_detailed[f'ref_unique_olga_{chain}'] \\t = reference_unique_olga\\n tcrrep.hcluster_df_detailed[f'percent_missing_olga_{chain}'] = percent_missing_sampler_olga \\n \\n return True\",\n \"def generate():\\n global transformer_top\\n assert transformer_top is not None\\n global transformer_bottom\\n assert transformer_bottom is not None\\n global label_encoders_per_modality\\n assert label_encoders_per_modality is not None\\n global DEVICE\\n assert DEVICE is not None\\n global partial_sample_model\\n assert partial_sample_model is not None\\n\\n temperature = float(request.args.get('temperature'))\\n pitch = int(request.args.get('pitch'))\\n instrument_family_str = str(request.args.get('instrument_family_str'))\\n\\n class_conditioning_top = class_conditioning_bottom = {\\n 'pitch': pitch,\\n 'instrument_family_str': instrument_family_str\\n }\\n class_conditioning_tensors_top = make_conditioning_tensors(\\n class_conditioning_top,\\n label_encoders_per_modality)\\n class_conditioning_tensors_bottom = make_conditioning_tensors(\\n class_conditioning_bottom,\\n label_encoders_per_modality)\\n\\n batch_size = 1\\n top_code = partial_sample_model(\\n model=transformer_top,\\n batch_size=batch_size,\\n codemap_size=transformer_top.shape,\\n temperature=temperature,\\n class_conditioning=class_conditioning_tensors_top\\n )\\n bottom_code = partial_sample_model(\\n model=transformer_bottom,\\n condition=top_code,\\n batch_size=batch_size,\\n codemap_size=transformer_bottom.shape,\\n temperature=temperature,\\n class_conditioning=class_conditioning_tensors_bottom,\\n )\\n\\n class_conditioning_top_map = {\\n modality: make_matrix(transformer_top.shape,\\n value)\\n for modality, value in class_conditioning_top.items()\\n }\\n class_conditioning_bottom_map = {\\n modality: make_matrix(transformer_bottom.shape,\\n value)\\n for modality, value in class_conditioning_bottom.items()\\n }\\n\\n response = make_response(top_code, bottom_code,\\n class_conditioning_top_map,\\n class_conditioning_bottom_map)\\n return response\",\n \"def refugia_adj_5_simsplit_4epochs_iter4 (params, ns):\\n #28 parameters \\n nu1a, nu2a, nu3a, nu1b, nu2b, nu3b, nu1c, nu2c, nu3c, nu1d, nu2d, nu3d, m1_12, m1_13, m1_21, m1_23, m1_31, m1_32, m3_12, m3_13, m3_21, m3_23, m3_31, m3_32, T1, T2, T3, T4 = params\\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1] + ns[2])\\n fs = moments.Spectrum(sts)\\n fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1] + ns[2]) \\n fs = moments.Manips.split_2D_to_3D_2(fs, ns[1], ns[2])\\n ## Population function and migration matrix for T1 (to reflect sum effect of all previous glacial-interglacial cycles)\\n nu_T1 = [nu1a, nu2a, nu3a]\\n mig1 = numpy.array([[0, m1_12, m1_13],[m1_21, 0, m1_23], [m1_31, m1_32, 0]]) \\n fs.integrate(nu_T1, T1, m=mig1)\\n ## Population function and migration matrix for T2 (to reflect period of isolation during last glacial)\\n nu_T2 = [nu1b, nu2b, nu3b]\\n fs.integrate(nu_T2, T2)\\n ## Population function and migration matrix for T3 (to reflect inter-glacial expansion)\\n nu_T3 = [nu1c, nu2c, nu3c]\\n mig3 = numpy.array([[0, m3_12, m3_13],[m3_21, 0, m3_23], [m3_31, m3_32, 0]]) \\n fs.integrate(nu_T3, T3, m=mig3)\\n ## Population function and migration matrix for T3 (bottleneck to capture single population representation of lineage)\\n nu_T4 = [nu1d, nu2d, nu3d]\\n fs.integrate(nu_T4, T4) \\n return fs\",\n \"def split_simsplit_3epochs_iter4(params, ns):\\n #24 parameters \\n nu1a, nu2a, nu3a, nu1b, nu2b, nu3b, nu1c, nu2c, nu3c, m1_12, m1_13, m1_21, m1_23, m1_31, m1_32, m2_12, m2_13, m2_21, m2_23, m2_31, m2_32, T1, T2, T3 = params\\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1] + ns[2])\\n fs = moments.Spectrum(sts)\\n fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1] + ns[2]) \\n fs = moments.Manips.split_2D_to_3D_2(fs, ns[1], ns[2])\\n ## Population function and migration matrix for T1\\n nu_T1 = [nu1a, nu2a, nu3a]\\n mig1 = numpy.array([[0, m1_12, m1_13],[m1_21, 0, m1_23], [m1_31, m1_32, 0]]) \\n fs.integrate(nu_T1, T1, m=mig1)\\n ## Population function and migration matrix for T2\\n nu_T2 = [nu1b, nu2b, nu3b]\\n mig2 = numpy.array([[0, m2_12, m2_13],[m2_21, 0, m2_23], [m2_31, m2_32, 0]]) \\n fs.integrate(nu_T2, T2, m=mig2)\\n ## Population function and migration matrix for T3 (bottleneck to capture single population representation of lineage)\\n nu_T3 = [nu1c, nu2c, nu3c]\\n fs.integrate(nu_T3, T3) \\n return fs\",\n \"def doParametersOfInterest(self):\\n \\n self.modelBuilder.doVar('expr::cosW(\\\"0.87681811112\\\",)')\\n self.modelBuilder.doVar('expr::sinW(\\\"0.48082221247\\\",)')\\n self.modelBuilder.doVar('expr::mZ(\\\"91.2\\\",)')\\n self.modelBuilder.doVar('expr::Lambda1(\\\"100.0\\\",)')\\n self.modelBuilder.doVar('expr::e2(\\\"0.0917\\\",)')\\n self.modelBuilder.doVar('expr::gs2(\\\"1.533\\\",)')\\n\\n # EFT Higgs basis couplings\\n\\n self.modelBuilder.doVar('cZ[0,-1,1]') \\n self.modelBuilder.doVar(\\\"cZZ[0,-2,2]\\\") \\n self.modelBuilder.doVar(\\\"cZZt[0,-2,2]\\\") \\n self.modelBuilder.doVar(\\\"cZB[0,-6,6]\\\") \\n\\n poi='cZ,cZZ,cZZt,cZB'\\n\\n # Amplitude couplings from EFT couplings \\n\\n self.modelBuilder.doVar('expr::a1(\\\"@0+1\\\",cZ)') # (\\\"2*(@0+1)\\\",cZ) in AN/Paper but a1 = 1 for signal model and width calculation\\n self.modelBuilder.doVar('expr::a2(\\\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\\\",cZZ,e2,sinW,cosW)')\\n self.modelBuilder.doVar('expr::a3(\\\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\\\",cZZt,e2,sinW,cosW)')\\n self.modelBuilder.doVar('expr::k1(\\\"@0*(@1*pow(@2,2)/(pow(@3,2)*pow(@4,2)))\\\",cZB,e2,Lambda1,sinW,mZ)')\\n self.modelBuilder.doVar('expr::k1L1(\\\"@0/pow(@1,2)\\\",k1,Lambda1)')\\n\\n ###### gamma_H ########\\n\\n # SMEFT relationships for VV couplings (Expressed using amplitude couplings)\\n\\n self.modelBuilder.doVar('expr::kappa(\\\"1.0\\\",)')\\n self.modelBuilder.doVar('expr::kappa_tilde(\\\"0.0\\\",)') \\n\\n self.modelBuilder.doVar('expr::a1_WW(\\\"@0\\\",a1)')\\n self.modelBuilder.doVar('expr::a2_WW(\\\"@0*@0*@1\\\",cosW,a2)')\\n self.modelBuilder.doVar('expr::a3_WW(\\\"@0*@0*@1\\\",cosW,a3)')\\n self.modelBuilder.doVar('expr::k1_WW(\\\"(@2 / (@0*@0 - @1*@1) - 2*@1*@1*@3*@4*@4 /(@5*@5*(@0*@0 - @1*@1)))\\\",cosW,sinW,k1,a2,Lambda1,mZ)')\\n self.modelBuilder.doVar('expr::k2_k1(\\\"2*@0*@1*@2/(@0*@0 - @1*@1)\\\",cosW,sinW,k1)')\\n self.modelBuilder.doVar('expr::k2_a2(\\\"-2*@0*@1*@3*@4*@4/((@2*@2)*(@0*@0 - @1*@1))\\\",cosW,sinW,mZ,a2,Lambda1)')\\n self.modelBuilder.doVar('expr::k2(\\\"@0 + @1\\\",k2_k1,k2_a2)')\\n\\n # Determine gamma_H from VV couplings\\n\\n zz_expr = '\\\"4*(@0*@0/4. + 0.1695*@3*@3 + 0.09076*@1*@1 + 0.03809*@2*@2 + 0.8095*@0*@3/2. + 0.5046*@0*@1/2. + 0.2092*@1*@3 + 0.1023*@4*@4 + 0.1901*@0*@4/2. + 0.07429*@3*@4 + 0.04710*@1*@4) \\\",a1,a2,a3,k1,k2'\\n ww_expr = '\\\"4*(@0*@0/4. + 0.1320*@3*@3 + 0.1944*@1*@1 + 0.08075*@2*@2 + 0.7204*@0*@3/2. + 0.7437*@0*@1/2. + 0.2774*@3*@1) \\\",a1_WW,a2_WW,a3_WW,k1_WW'\\n zgamma_expr = '\\\"4*(1.118600*@0*@0/4. +0.0035*@1*@1 - 0.125010*@0*@1/2. + 0.000003*@1*@1 - 0.00018*@1*@1 + 0.003100*@0*@1/2. +0.00126*@2*@2 + 0.000005*@2*@2 -0.00047*@2*@2)\\\",a1_WW,kappa,kappa_tilde'\\n gg_expr = '\\\"(1.1068*@0*@0 + 0.0082*@0*@0 - 0.1150*@0*@0 + 2.5717*@1*@1 + 0.0091*@1*@1 - 0.1982*@1*@1)\\\",kappa,kappa_tilde'\\n bb_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n cc_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n tautau_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n mumu_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n gmgm_expr = '\\\"4*(1.6054*@0*@0/4. + 0.07312*@1*@1 - 0.6854*@0*@1/2. + 0.00002*@1*@1 - 0.0018*@1*@1 + 0.0085*@0*@1/2. + 0.1699*@2*@2 + 0.00002*@2*@2 - 0.0031*@2*@2)\\\",a1_WW,kappa,kappa_tilde'\\n \\n self.modelBuilder.doVar('expr::R_WW('+str(ww_expr)+')')\\n self.modelBuilder.doVar('expr::R_ZZ('+str(zz_expr)+')')\\n self.modelBuilder.doVar('expr::R_Zgamma('+str(zgamma_expr)+')')\\n self.modelBuilder.doVar('expr::R_gg('+str(gg_expr)+')')\\n self.modelBuilder.doVar('expr::R_bb('+str(bb_expr)+')')\\n self.modelBuilder.doVar('expr::R_cc('+str(cc_expr)+')')\\n self.modelBuilder.doVar('expr::R_tautau('+str(tautau_expr)+')')\\n self.modelBuilder.doVar('expr::R_mumu('+str(mumu_expr)+')')\\n self.modelBuilder.doVar('expr:R_gammagamma('+str(gmgm_expr)+')')\\n\\n self.modelBuilder.doVar('expr::gammaH(\\\"(0.5824*@0 + 0.2137*@1 + 0.08187*@2 + 0.06272*@3 + 0.02891*@4 + 0.02619*@5 + 0.002270*@6 + 0.001533*@7 + 0.0002176*@8 )/0.9998\\\",R_bb,R_WW,R_gg,R_tautau,R_cc,R_ZZ,R_gammagamma,R_Zgamma,R_mumu)') \\n\\n ###########################\\n\\n self.g1V = GetCoupTerms(1,1,1,-0.0001,\\\"1V\\\") # Compensate for scaling of k1 templates \\n self.g2V = GetCoupTerms(1,1,1,-0.0001,\\\"2V\\\") \\n \\n self.modelBuilder.doVar(\\\"expr::g2V_1(\\\\\\\"\\\"+str(self.g2V[0])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T1(\\\\\\\"((pow(@0,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T1_Neg(\\\\\\\"-1*((pow(@0,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_2(\\\\\\\"\\\"+str(self.g2V[1])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T2(\\\\\\\"((pow(@0,3)*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T2_Neg(\\\\\\\"-1*((pow(@0,3)*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_3(\\\\\\\"\\\"+str(self.g2V[2])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T3(\\\\\\\"((pow(@0,2)*pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T3_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_4(\\\\\\\"\\\"+str(self.g2V[3])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T4(\\\\\\\"((@0*pow(@1,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T4_Neg(\\\\\\\"-1*((@0*pow(@1,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_5(\\\\\\\"\\\"+str(self.g2V[4])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T5(\\\\\\\"((pow(@1,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T5_Neg(\\\\\\\"-1*((pow(@1,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_6(\\\\\\\"\\\"+str(self.g2V[5])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T6(\\\\\\\"((pow(@0,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T6_Neg(\\\\\\\"-1*((pow(@0,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_7(\\\\\\\"\\\"+str(self.g2V[6])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T7(\\\\\\\"((pow(@0,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T7_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_8(\\\\\\\"\\\"+str(self.g2V[7])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T8(\\\\\\\"((@0*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T8_Neg(\\\\\\\"-1*((@0*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_9(\\\\\\\"\\\"+str(self.g2V[8])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T9(\\\\\\\"((pow(@2,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T9_Neg(\\\\\\\"-1*((pow(@2,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_10(\\\\\\\"\\\"+str(self.g2V[9])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T10(\\\\\\\"((pow(@0,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T10_Neg(\\\\\\\"-1*((pow(@0,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_11(\\\\\\\"\\\"+str(self.g2V[10])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T11(\\\\\\\"((pow(@0,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T11_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_12(\\\\\\\"\\\"+str(self.g2V[11])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T12(\\\\\\\"((@0*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T12_Neg(\\\\\\\"-1*((@0*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_13(\\\\\\\"\\\"+str(self.g2V[12])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T13(\\\\\\\"((pow(@3,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T13_Neg(\\\\\\\"-1*((pow(@3,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_14(\\\\\\\"\\\"+str(self.g2V[13])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T14(\\\\\\\"((pow(@1,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T14_Neg(\\\\\\\"-1*((pow(@1,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_15(\\\\\\\"\\\"+str(self.g2V[14])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T15(\\\\\\\"((pow(@1,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T15_Neg(\\\\\\\"-1*((pow(@1,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_16(\\\\\\\"\\\"+str(self.g2V[15])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T16(\\\\\\\"((@1*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T16_Neg(\\\\\\\"-1*((@1*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_17(\\\\\\\"\\\"+str(self.g2V[16])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T17(\\\\\\\"((pow(@1,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T17_Neg(\\\\\\\"-1*((pow(@1,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_18(\\\\\\\"\\\"+str(self.g2V[17])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T18(\\\\\\\"((pow(@1,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T18_Neg(\\\\\\\"-1*((pow(@1,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_19(\\\\\\\"\\\"+str(self.g2V[18])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T19(\\\\\\\"((@1*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T19_Neg(\\\\\\\"-1*((@1*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_20(\\\\\\\"\\\"+str(self.g2V[19])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T20(\\\\\\\"((pow(@2,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T20_Neg(\\\\\\\"-1*((pow(@2,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_21(\\\\\\\"\\\"+str(self.g2V[20])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T21(\\\\\\\"((pow(@2,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T21_Neg(\\\\\\\"-1*((pow(@2,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_22(\\\\\\\"\\\"+str(self.g2V[21])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T22(\\\\\\\"((@2*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T22_Neg(\\\\\\\"-1*((@2*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_23(\\\\\\\"\\\"+str(self.g2V[22])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T23(\\\\\\\"((@0*@1*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T23_Neg(\\\\\\\"-1*((@0*@1*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_24(\\\\\\\"\\\"+str(self.g2V[23])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T24(\\\\\\\"((@0*pow(@1,2)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T24_Neg(\\\\\\\"-1*((@0*pow(@1,2)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_25(\\\\\\\"\\\"+str(self.g2V[24])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T25(\\\\\\\"((pow(@0,2)*@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T25_Neg(\\\\\\\"-1*((pow(@0,2)*@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_26(\\\\\\\"\\\"+str(self.g2V[25])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T26(\\\\\\\"((@0*@1*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T26_Neg(\\\\\\\"-1*((@0*@1*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_27(\\\\\\\"\\\"+str(self.g2V[26])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T27(\\\\\\\"((@0*pow(@1,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T27_Neg(\\\\\\\"-1*((@0*pow(@1,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_28(\\\\\\\"\\\"+str(self.g2V[27])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T28(\\\\\\\"((pow(@0,2)*@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T28_Neg(\\\\\\\"-1*((pow(@0,2)*@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_29(\\\\\\\"\\\"+str(self.g2V[28])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T29(\\\\\\\"((@0*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T29_Neg(\\\\\\\"-1*((@0*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_30(\\\\\\\"\\\"+str(self.g2V[29])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T30(\\\\\\\"((@0*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T30_Neg(\\\\\\\"-1*((@0*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_31(\\\\\\\"\\\"+str(self.g2V[30])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T31(\\\\\\\"((pow(@0,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T31_Neg(\\\\\\\"-1*((pow(@0,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_32(\\\\\\\"\\\"+str(self.g2V[31])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T32(\\\\\\\"((@1*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T32_Neg(\\\\\\\"-1*((@1*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_33(\\\\\\\"\\\"+str(self.g2V[32])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T33(\\\\\\\"((@1*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T33_Neg(\\\\\\\"-1*((@1*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_34(\\\\\\\"\\\"+str(self.g2V[33])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T34(\\\\\\\"((pow(@1,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T34_Neg(\\\\\\\"-1*((pow(@1,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_35(\\\\\\\"\\\"+str(self.g2V[34])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T35(\\\\\\\"((@0*@1*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T35_Neg(\\\\\\\"-1*((@0*@1*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\\\") \\n \\n self.modelBuilder.doVar(\\\"expr::g1V_1(\\\\\\\"\\\"+str(self.g1V[0])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T1(\\\\\\\"((pow(@0,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T1_Neg(\\\\\\\"-1*((pow(@0,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_2(\\\\\\\"\\\"+str(self.g1V[1])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T2(\\\\\\\"((@0*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T2_Neg(\\\\\\\"-1*((@0*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_3(\\\\\\\"\\\"+str(self.g1V[2])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T3(\\\\\\\"((pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T3_Neg(\\\\\\\"-1*((pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_4(\\\\\\\"\\\"+str(self.g1V[3])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T4(\\\\\\\"((@0*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T4_Neg(\\\\\\\"-1*((@0*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_5(\\\\\\\"\\\"+str(self.g1V[4])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T5(\\\\\\\"((pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T5_Neg(\\\\\\\"-1*((pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_6(\\\\\\\"\\\"+str(self.g1V[5])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T6(\\\\\\\"((@0*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T6_Neg(\\\\\\\"-1*((@0*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_7(\\\\\\\"\\\"+str(self.g1V[6])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T7(\\\\\\\"((pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T7_Neg(\\\\\\\"-1*((pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_8(\\\\\\\"\\\"+str(self.g1V[7])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T8(\\\\\\\"((@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T8_Neg(\\\\\\\"-1*((@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_9(\\\\\\\"\\\"+str(self.g1V[8])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T9(\\\\\\\"((@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T9_Neg(\\\\\\\"-1*((@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_10(\\\\\\\"\\\"+str(self.g1V[9])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T10(\\\\\\\"((@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T10_Neg(\\\\\\\"-1*((@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\\\") \\n \\n self.modelBuilder.doSet(\\\"POI\\\",poi)\",\n \"def sample_td3_params(trial: optuna.Trial,\\n octree_observations: bool = True,\\n octree_depth: int = 4,\\n octree_full_depth: int = 2,\\n octree_channels_in: int = 7,\\n octree_fast_conv: bool = True,\\n octree_batch_norm: bool = True) -> Dict[str, Any]:\\n\\n buffer_size = 150000\\n # learning_starts = trial.suggest_categorical(\\n # \\\"learning_starts\\\", [5000, 10000, 20000])\\n learning_starts = 5000\\n\\n batch_size = trial.suggest_categorical(\\\"batch_size\\\", [32, 64, 128])\\n learning_rate = trial.suggest_float(\\n \\\"learning_rate\\\", low=0.000001, high=0.001, log=True)\\n\\n gamma = trial.suggest_float(\\\"gamma\\\", low=0.98, high=1.0, log=True)\\n tau = trial.suggest_float(\\\"tau\\\", low=0.001, high=0.025, log=True)\\n\\n target_policy_noise = trial.suggest_float(\\n \\\"target_policy_noise\\\", low=0.0, high=0.5, log=True)\\n target_noise_clip = 0.5\\n\\n noise_std = trial.suggest_float(\\\"noise_std\\\", low=0.025, high=0.5, log=True)\\n action_noise = NormalActionNoise(mean=np.zeros(trial.n_actions),\\n sigma=np.ones(trial.n_actions)*noise_std)\\n\\n train_freq = 1\\n gradient_steps = trial.suggest_categorical(\\\"gradient_steps\\\", [1, 2])\\n\\n policy_kwargs = dict()\\n net_arch = trial.suggest_categorical(\\\"net_arch\\\", [\\\"small [256, 128]\\\",\\n \\\"medium [384, 256]\\\",\\n \\\"big [512, 384]\\\"])\\n policy_kwargs[\\\"net_arch\\\"] = {\\\"small [256, 128]\\\": [256, 128],\\n \\\"medium [384, 256]\\\": [384, 256],\\n \\\"big [512, 384]\\\": [512, 384]}[net_arch]\\n if octree_observations:\\n features_extractor_kwargs = dict()\\n\\n features_extractor_kwargs[\\\"depth\\\"] = octree_depth\\n features_extractor_kwargs[\\\"full_depth\\\"] = octree_full_depth\\n features_extractor_kwargs[\\\"channels_in\\\"] = octree_channels_in\\n\\n features_extractor_kwargs[\\\"channel_multiplier\\\"] = \\\\\\n trial.suggest_categorical(\\\"channel_multiplier\\\", [8, 16, 32, 64])\\n\\n features_extractor_kwargs[\\\"features_dim\\\"] = \\\\\\n trial.suggest_categorical(\\\"features_dim\\\", [256, 512, 768])\\n\\n features_extractor_kwargs[\\\"fast_conv\\\"] = octree_fast_conv\\n features_extractor_kwargs[\\\"batch_normalization\\\"] = octree_batch_norm\\n\\n policy_kwargs[\\\"features_extractor_kwargs\\\"] = features_extractor_kwargs\\n\\n return {\\n \\\"buffer_size\\\": buffer_size,\\n \\\"learning_starts\\\": learning_starts,\\n \\\"batch_size\\\": batch_size,\\n \\\"learning_rate\\\": learning_rate,\\n \\\"gamma\\\": gamma,\\n \\\"tau\\\": tau,\\n \\\"target_policy_noise\\\": target_policy_noise,\\n \\\"target_noise_clip\\\": target_noise_clip,\\n \\\"action_noise\\\": action_noise,\\n \\\"train_freq\\\": train_freq,\\n \\\"gradient_steps\\\": gradient_steps,\\n \\\"policy_kwargs\\\": policy_kwargs,\\n }\",\n \"def test013(testDir, dirDict, pflag):\\n shutil.copy(osp.join(dirDict[\\\"baseConfig\\\"], \\\"params_system.cfg\\\"), testDir)\\n shutil.copy(osp.join(dirDict[\\\"baseConfig\\\"], \\\"params_c.cfg\\\"), testDir)\\n shutil.copy(osp.join(dirDict[\\\"baseConfig\\\"], \\\"params_a.cfg\\\"), testDir)\\n psys = osp.join(testDir, \\\"params_system.cfg\\\")\\n ptrodec = osp.join(testDir, \\\"params_c.cfg\\\")\\n ptrodea = osp.join(testDir, \\\"params_a.cfg\\\")\\n P_s = IO.get_config(psys)\\n P_s.set(\\\"Sim Params\\\", \\\"profileType\\\", \\\"CV\\\")\\n P_s.set(\\\"Sim Params\\\", \\\"Vset\\\", \\\"3.8\\\")\\n P_s.set(\\\"Sim Params\\\", \\\"tsteps\\\", \\\"25\\\")\\n P_s.set(\\\"Sim Params\\\", \\\"Nvol_c\\\", \\\"2\\\")\\n P_s.set(\\\"Sim Params\\\", \\\"Nvol_s\\\", \\\"2\\\")\\n P_s.set(\\\"Sim Params\\\", \\\"Nvol_a\\\", \\\"2\\\")\\n P_s.set(\\\"Particles\\\", \\\"cs0_c\\\", \\\"0.2\\\")\\n P_s.set(\\\"Particles\\\", \\\"cs0_a\\\", \\\"0.95\\\")\\n P_s.set(\\\"Electrolyte\\\", \\\"elyteModelType\\\", \\\"dilute\\\")\\n IO.write_config_file(P_s, psys)\\n P = IO.get_config(ptrodec)\\n P.set(\\\"Particles\\\", \\\"type\\\", \\\"homog\\\")\\n P.set(\\\"Particles\\\", \\\"shape\\\", \\\"sphere\\\")\\n P.set(\\\"Material\\\", \\\"muRfunc\\\", \\\"LiMn2O4_ss2\\\")\\n P.set(\\\"Reactions\\\", \\\"rxnType\\\", \\\"Marcus\\\")\\n IO.write_config_file(P, ptrodec)\\n P = IO.get_config(ptrodea)\\n P.set(\\\"Particles\\\", \\\"discretization\\\", \\\"2.5e-9\\\")\\n P.set(\\\"Particles\\\", \\\"shape\\\", \\\"cylinder\\\")\\n P.set(\\\"Material\\\", \\\"muRfunc\\\", \\\"testIS_ss\\\")\\n P.set(\\\"Reactions\\\", \\\"rxnType\\\", \\\"BV_raw\\\")\\n IO.write_config_file(P, ptrodea)\\n main.main(psys, keepArchive=False)\\n shutil.move(dirDict[\\\"simOut\\\"], testDir)\\n if pflag:\\n corePlots(testDir, dirDict)\\n elytePlots(testDir, dirDict)\\n electrodePlots(testDir, dirDict, \\\"c\\\")\\n cmpr.bulkpf(testDir, dirDict, \\\"c\\\")\\n electrodePlots(testDir, dirDict, \\\"a\\\")\\n cmpr.bulkpf(testDir, dirDict, \\\"a\\\")\",\n \"def generate_sample(amplitude, t0, width, n_samples = 6):\\n if n_samples == 6:\\n times = np.linspace(-apv_dt-t0, 4*apv_dt-t0, 6, endpoint = True)\\n elif n_samples == 3:\\n times = np.linspace(-t0, 2*apv_dt-t0, 3, endpoint = True)\\n elif n_samples == 1:\\n times = np.asarray([-t0])\\n else:\\n print(\\\"Unsupported number of APV samples in generate_sample!\\\")\\n times = np.zeros(1)\\n return amplitude * betaprime_wave(times, width)\",\n \"def timesteps_experiment():\\n\\n print(\\\"TIMESTEPS EXPERIMENT\\\")\\n\\n # set the name of the experiment\\n now = datetime.datetime.now()\\n experiment_id = str(now.day) + \\\"_\\\" + str(now.month) + \\\"_\\\" + str(now.hour) + \\\".\\\" + str(now.minute)\\n experiment_name = 'timestep_' + str(experiment_id)\\n\\n # define if you want to use preprocessed data from file\\n use_prep_data = False\\n if use_prep_data:\\n set_params(preproc_data_id='16_5_10.16.47')\\n else:\\n set_params(use_preproc_data=False)\\n\\n # define the changing parameter and its value\\n changing_param_name = 'time_steps'\\n changing_param_value = [1, 2, 4, 8, 16, 32, 64, 128, 256]\\n # {0:4, 1:100}, {0:3, 1:100}, {0:2, 1:100}, {0:1, 1:100}] #[{0:1, 1:1}, {0:15, 1:85}]#\\n\\n # set constant parameters\\n set_params(epochs=20)\\n set_params(dropout=0.3)\\n set_params(use_word_emb=1)\\n\\n # save constant parameters to a new \\\"experiment_..\\\" file\\n save_constant_parameters(experiment_name, changing_param_name)\\n\\n # run experiment for every parameter value\\n for value in changing_param_value:\\n process = psutil.Process(os.getpid())\\n print(\\\"-----MEMORY before starting experiment ------\\\", int(process.memory_info().rss/(8*10**(3))), \\\"KB\\\")\\n\\n # update the parameter value\\n set_params(use_word_emb = value)\\n\\n # update the model_id for this new model\\n now = datetime.datetime.now()\\n new_model_id = str(now.day) + \\\"_\\\" + str(now.month) + \\\"_\\\" + str(now.hour) + \\\".\\\" + str(now.minute) + \\\".\\\" + str(now.second)\\n set_params(model_id = new_model_id)\\n\\n # evaluate the new model and save the results in the experiment file\\n oneExperiment = Process(target=run_experiment, args=(experiment_name, new_model_id, changing_param_name, value,))\\n oneExperiment.start()\\n oneExperiment.join()\\n\\n if value == changing_param_value[0]:\\n set_params(preproc_data_id=new_model_id)\",\n \"def main():\\n\\n # Experiment Start\\n start_time = datetime.now()\\n logger.info(\\n '################ Bergson Team Experiment Start #################')\\n logger.info(\\n f'Starting Bergson Astro Pi team experiment at {start_time.strftime(\\\"%Y-%m-%d %H:%M:%S\\\")}')\\n\\n '''\\n # Load simple Conv2D AI Model\\n logger.info(\\\"Loading AI Convolutional Model\\\")\\n conv2D_model = load_model(\\\"Conv2D_TF114\\\")\\n '''\\n\\n # Load TFLite Model\\n logger.info(\\\"Loading TFLite Mobilenetv2 Model\\\")\\n mobilenetv2_interpreter = load_tflite_model(\\\"./Mobilenetv2_TF114.tflite\\\")\\n\\n # Create Log File\\n logger.info(f'Creating Log file at {str(data_file)}')\\n with open(data_file, 'w') as f:\\n writer = csv.writer(f)\\n header = (\\\"Date/time\\\", \\\"Location\\\", \\\"Picture Name\\\", \\\"Predicted NO2\\\")\\n writer.writerow(header)\\n\\n # Start Loop over 3 hours\\n\\n now_time = datetime.now()\\n i = 0\\n # run a loop for 2 minutes\\n while (now_time < start_time + timedelta(minutes=175)):\\n\\n # Take Earth Picture\\n timestamp = datetime.now().strftime(\\\"%Y-%m-%d_%H:%M:%S\\\")\\n pic_name = f'bergson_img_{timestamp}.jpg'\\n capture(rpi_cam, str(dir_path/pic_name))\\n logger.info(f'Experiment Pipeline {i} on picture {pic_name}')\\n\\n # NDVI Preprocessing\\n ndvi_image = get_ndvi(str(dir_path/pic_name))\\n ndvi_image = np.expand_dims(ndvi_image, axis=2)\\n\\n # RGB Prepprocessing for expected shape by Mobilenetv2 - comment below line when using simple Conv2D model\\n ndvi_rgb_image = get_ndvi_rgb(ndvi_image)\\n\\n '''\\n # Do Inference with simple Conv2D AI Model\\n prediction = make_inference(ndvi_image,conv2D_model)\\n '''\\n \\n # Do Inference with TFLite Model\\n ndvi_rgb_image = ndvi_rgb_image.astype('float32')\\n prediction = make_tflite_inference(\\n ndvi_rgb_image, mobilenetv2_interpreter)\\n\\n # Get Decoded Inference results\\n decoded_prediction = decode_prediction(prediction)\\n\\n # Write Prediction as CSV to disk\\n logger.info(\\n f'Logging NO2 prediction \\\\\\\"{decoded_prediction}\\\\\\\" for {pic_name}')\\n exif_data = get_img_exif(pic_name, iss, decoded_prediction)\\n row = (exif_data['Date/Time'], exif_data['Location'],\\n pic_name, exif_data['NO2'])\\n with open(data_file, mode='a') as f:\\n writer = csv.writer(f)\\n writer.writerow(row)\\n\\n # update the current time\\n now_time = datetime.now()\\n i = i+1\\n\\n # End Loop over 3 hours\\n\\n # Experiment End\\n end_time = datetime.now()\\n logger.info(\\n f'Finishing Bergson Astro Pi team experiment at {end_time.strftime(\\\"%Y-%m-%d %H:%M:%S\\\")}')\\n experiment_time = end_time - start_time\\n logger.info(f'Bergson Astro Pi team experiment run time {experiment_time}')\\n logger.info('################ Bergson Team Experiment End #################')\",\n \"def importance_sampling(self, params):\\n i_part = params\\n\\n theta_star = weighted_sampling( self.theta_t_1, self.w_t_1 ) \\n np.random.seed()\\n\\n theta_starstar = multivariate_normal( theta_star, self.sig_t_1 ).rvs(size=1)\\n model_starstar = self.simz( theta_starstar )\\n\\n rho = test_dist(self.data, model_starstar) \\n\\n while rho > self.eps_t: \\n theta_star = weighted_sampling( self.theta_t_1, self.w_t_1 )\\n theta_starstar = multivariate_normal(theta_star, self.sig_t_1).rvs(size=1)\\n\\n model_starstar = self.simz( theta_starstar )\\n\\n rho = test_dist(self.data, model_starstar) \\n\\n p_theta = self.prior_of_priors(theta_starstar)\\n\\n pos_t = np.dstack(self.theta_t_1)\\n\\n tmp_w_t = p_theta / np.sum(self.w_t_1 * multivariate_normal(self.theta_t[:,i_part], self.sig_t_1).pdf(pos_t))\\n\\n data_list = [np.int(i_part)]\\n\\n for i_param in xrange(self.n_params): \\n\\n data_list.append(theta_starstar[i_param])\\n\\n data_list.append(tmp_w_t)\\n data_list.append(rho)\\n\\n return np.array(data_list)\",\n \"def generate(self): \\r\\n \\r\\n self.pfn={} # phase joint functions \\r\\n self.afn={} # anti phase joint functions\\r\\n\\r\\n ## Foot and hip -> Lateral motion\\r\\n foot_func=SinusoidFunction()\\r\\n foot_func.angular_frequency= self.parameters[\\\"step_frequency\\\"]\\r\\n foot_func.amplitude= self.parameters[\\\"foot_amplitude\\\"]\\r\\n foot_func.amplitude_offset= self.parameters[\\\"foot_amplitude_offset\\\"]\\r\\n foot_func.phase_offset= self.parameters[\\\"foot_phase_offset\\\"]\\r\\n self.pfn[\\\"l_foot_joint\\\"]=foot_func \\r\\n foot_func_af=foot_func.mirror()\\r\\n self.afn[\\\"l_foot_joint\\\"]=foot_func_af\\r\\n \\r\\n hip_func=SinusoidFunction()\\r\\n hip_func.angular_frequency= self.parameters[\\\"step_frequency\\\"]\\r\\n hip_func.amplitude= self.parameters[\\\"hip_amplitude\\\"]\\r\\n hip_func.amplitude_offset= self.parameters[\\\"hip_amplitude_offset\\\"]\\r\\n hip_func.phase_offset= self.parameters[\\\"hip_phase_offset\\\"]\\r\\n self.pfn[\\\"l_hip_joint\\\"]=hip_func\\r\\n hip_func_af=hip_func.mirror()\\r\\n self.afn[\\\"l_hip_joint\\\"]=hip_func_af\\r\\n \\r\\n ## Thigh, ankle and knee -> Frontal motion\\r\\n thigh_func=SinusoidFunction()\\r\\n thigh_func.angular_frequency= self.parameters[\\\"step_frequency\\\"]\\r\\n thigh_func.amplitude= self.parameters[\\\"thigh_amplitude\\\"]\\r\\n thigh_func.amplitude_offset= self.parameters[\\\"thigh_amplitude_offset\\\"]\\r\\n thigh_func.phase_offset= self.parameters[\\\"thigh_phase_offset\\\"]\\r\\n self.pfn[\\\"l_thigh_joint\\\"]=thigh_func\\r\\n thigh_func_af=thigh_func.mirror_keep_amplitude_offset()\\r\\n self.afn[\\\"l_thigh_joint\\\"]=thigh_func_af\\r\\n \\r\\n ankle_func=SinusoidFunction()\\r\\n ankle_func.angular_frequency= self.parameters[\\\"step_frequency\\\"]\\r\\n ankle_func.amplitude= self.parameters[\\\"ankle_amplitude\\\"]\\r\\n ankle_func.amplitude_offset= self.parameters[\\\"ankle_amplitude_offset\\\"]\\r\\n ankle_func.phase_offset= self.parameters[\\\"ankle_phase_offset\\\"]\\r\\n self.pfn[\\\"l_ankle_joint\\\"]=ankle_func\\r\\n ankle_func_af=ankle_func.mirror_keep_amplitude_offset()\\r\\n self.afn[\\\"l_ankle_joint\\\"]=ankle_func_af\\r\\n \\r\\n knee_func=SinusoidFunction()\\r\\n knee_func.angular_frequency= self.parameters[\\\"step_frequency\\\"]\\r\\n knee_func.amplitude= self.parameters[\\\"knee_amplitude\\\"]\\r\\n knee_func.amplitude_offset= self.parameters[\\\"knee_amplitude_offset\\\"]\\r\\n knee_func.phase_offset= self.parameters[\\\"knee_phase_offset\\\"]\\r\\n self.pfn[\\\"l_knee_joint\\\"]=knee_func\\r\\n knee_func_af=knee_func.mirror_keep_amplitude_offset()\\r\\n self.afn[\\\"l_knee_joint\\\"]=knee_func_af\\r\\n \\r\\n #f3=SinusoidFunction()\\r\\n #f3.angular_frequency=self.parameters[\\\"step_frequency\\\"]\\r\\n #f3.amplitude=self.parameters[\\\"step_amplitude\\\"]\\r\\n #f3.amplitude_offset=self.parameters[\\\"step_amplitude_offset\\\"]\\r\\n #self.pfn[\\\"l_thigh_joint\\\"]= f3\\r\\n #f33=f3.clone()\\r\\n #f33.amplitude_offset = self.parameters[\\\"ankle_amplitude_offset\\\"]\\r\\n #f33.amplitude = self.parameters[\\\"ankle_amplitude\\\"]\\r\\n #self.pfn[\\\"l_ankle_joint\\\"]=f33\\r\\n #f4=f3.mirror()\\r\\n ##f4.amplitude_offset -= 0.4\\r\\n #self.pfn[\\\"l_knee_joint\\\"]=f4\\r\\n \\r\\n #f5=f3.mirror_keep_amplitude_offset()\\r\\n #self.afn[\\\"l_thigh_joint\\\"]=f5\\r\\n \\r\\n #f6=f33.mirror_keep_amplitude_offset()\\r\\n #self.afn[\\\"l_ankle_joint\\\"]=f6\\r\\n #f7=f5.mirror()\\r\\n ##f7.amplitude_offset -= 0.4\\r\\n #self.afn[\\\"l_knee_joint\\\"]=f7\\r\\n \\r\\n self.generate_right()\\r\\n \\r\\n self.show()\",\n \"def run(config, tim=None):\\n import common_lib\\n import dr_lib\\n import DST\\n \\n if tim is not None:\\n tim.getTime(False)\\n old_time = tim.getOldTime()\\n\\n if config.data is None:\\n raise RuntimeError(\\\"Need to pass a data filename to the driver \\\"\\\\\\n +\\\"script.\\\")\\n\\n # Read in geometry if one is provided\\n if config.inst_geom is not None:\\n if config.verbose:\\n print \\\"Reading in instrument geometry file\\\"\\n \\n inst_geom_dst = DST.getInstance(\\\"application/x-NxsGeom\\\",\\n config.inst_geom)\\n else:\\n inst_geom_dst = None\\n\\n config.so_axis = \\\"time_of_flight\\\"\\n\\n # Steps 1-3: Produce a scaled summed dark current dataset\\n dc_som = dr_lib.scaled_summed_data(config.dkcur, config,\\n dataset_type=\\\"dark_current\\\",\\n timer=tim)\\n\\n # Perform Steps 3-6 on black can data\\n if config.bcan is not None:\\n b_som1 = dr_lib.calibrate_dgs_data(config.bcan, config, dc_som,\\n dataset_type=\\\"black_can\\\",\\n inst_geom_dst=inst_geom_dst,\\n tib_const=config.tib_const,\\n cwp=config.cwp_bcan,\\n timer=tim)\\n else:\\n b_som1 = None\\n\\n # Perform Steps 3-6 on empty can data \\n if config.ecan is not None:\\n e_som1 = dr_lib.calibrate_dgs_data(config.ecan, config, dc_som,\\n dataset_type=\\\"empty_can\\\",\\n inst_geom_dst=inst_geom_dst,\\n tib_const=config.tib_const,\\n cwp=config.cwp_ecan,\\n timer=tim)\\n else:\\n e_som1 = None\\n\\n # Perform Steps 3-6 on normalization data\\n n_som1 = dr_lib.calibrate_dgs_data(config.data, config, dc_som,\\n dataset_type=\\\"normalization\\\",\\n inst_geom_dst=inst_geom_dst,\\n tib_const=config.tib_const,\\n cwp=config.cwp_data,\\n timer=tim)\\n\\n # Perform Steps 7-16 on normalization data\\n if config.norm_trans_coeff is None:\\n norm_trans_coeff = None\\n else:\\n norm_trans_coeff = config.norm_trans_coeff.toValErrTuple()\\n\\n # Determine if we need to rebin the empty or black can data\\n if config.ecan is not None and e_som1 is not None:\\n ecan_cwp = True\\n else:\\n ecan_cwp = False\\n\\n if config.bcan is not None and b_som1 is not None:\\n bcan_cwp = True\\n else:\\n bcan_cwp = False \\n\\n cwp_used = ecan_cwp or bcan_cwp\\n\\n n_som2 = dr_lib.process_dgs_data(n_som1, config, b_som1, e_som1,\\n norm_trans_coeff,\\n dataset_type=\\\"normalization\\\",\\n cwp_used=cwp_used,\\n timer=tim)\\n \\n del n_som1, b_som1, e_som1\\n\\n # Step 17: Integrate normalization spectra\\n if config.verbose:\\n print \\\"Integrating normalization spectra\\\"\\n\\n if tim is not None:\\n tim.getTime(False)\\n\\n if config.norm_int_range is None:\\n start_val = float(\\\"inf\\\")\\n end_val = float(\\\"inf\\\")\\n else:\\n if not config.wb_norm:\\n # Translate energy transfer to final energy\\n ef_start = config.initial_energy.getValue() - \\\\\\n config.norm_int_range[0]\\n ef_end = config.initial_energy.getValue() - \\\\\\n config.norm_int_range[1]\\n # Convert final energy to final wavelength\\n start_val = common_lib.energy_to_wavelength((ef_start, 0.0))[0]\\n end_val = common_lib.energy_to_wavelength((ef_end, 0.0))[0]\\n else:\\n start_val = config.norm_int_range[0]\\n end_val = config.norm_int_range[1]\\n \\n n_som3 = dr_lib.integrate_spectra(n_som2, start=start_val,\\n end=end_val, width=True)\\n\\n del n_som2\\n \\n if tim is not None:\\n tim.getTime(msg=\\\"After integrating normalization spectra \\\")\\n\\n file_comment = \\\"Normalization Integration range: %0.3fA, %0.3fA\\\" \\\\\\n % (start_val, end_val)\\n \\n hlr_utils.write_file(config.output, \\\"text/num-info\\\", n_som3,\\n output_ext=\\\"norm\\\",\\n data_ext=config.ext_replacement,\\n path_replacement=config.path_replacement,\\n verbose=config.verbose,\\n message=\\\"normalization values\\\",\\n comments=[file_comment],\\n tag=\\\"Integral\\\", units=\\\"counts\\\") \\n \\n if tim is not None:\\n tim.getTime(False)\\n\\n if config.verbose:\\n print \\\"Making mask file\\\"\\n\\n # Make mask file from threshold\\n dr_lib.filter_normalization(n_som3, config.lo_threshold,\\n config.hi_threshold, config)\\n\\n if tim is not None:\\n tim.getTime(msg=\\\"After making mask file \\\")\\n\\n # Write out RMD file\\n n_som3.attr_list[\\\"config\\\"] = config\\n\\n hlr_utils.write_file(config.output, \\\"text/rmd\\\", n_som3,\\n output_ext=\\\"rmd\\\",\\n data_ext=config.ext_replacement, \\n path_replacement=config.path_replacement,\\n verbose=config.verbose,\\n message=\\\"metadata\\\")\\n \\n if tim is not None:\\n tim.setOldTime(old_time)\\n tim.getTime(msg=\\\"Total Running Time\\\")\",\n \"def generate(self):\\n self.training_data.gen_x(self.x_func)\\n self.training_data.gen_a(self.a_func)\\n self.training_data.gen_y(self.y_func)\\n \\n self.testing_data.gen_x(self.x_func)\\n self.testing_data.gen_ys(self.y_func)\\n self.testing_data.gen_azero(self.ytotal_func)\",\n \"def split_full_4epochs_iter3 (params, ns):\\n #29 parameters \\n nu1x, nuA, nu1a, nu2a, nu3a, nu1b, nu2b, nu3b, nu1c, nu2c, nu3c, m0_12, m0_21, m1_12, m1_13, m1_21, m1_23, m1_31, m1_32, m2_12, m2_13, m2_21, m2_23, m2_31, m2_32, T0, T1, T2, T3 = params\\n sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1] + ns[2])\\n fs = moments.Spectrum(sts)\\n fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1] + ns[2])\\n ## Population function and migration matrix for T0 (initial split; the definition of this time epoch differentiates this model from refugia_adj_5_simsplit_4epochs)\\n nu_T0 = [nu1x, nuA]\\n mig0 = numpy.array([[0, m0_12],[m0_21, 0]])\\n fs.integrate(nu_T0, T0, m=mig0)\\n fs = moments.Manips.split_2D_to_3D_2(fs, ns[1], ns[2])\\n ## Population function and migration matrix for T1\\n nu_T1 = [nu1a, nu2a, nu3a]\\n mig1 = numpy.array([[0, m1_12, m1_13],[m1_21, 0, m1_23], [m1_31, m1_32, 0]]) \\n fs.integrate(nu_T1, T1, m=mig1)\\n ## Population function and migration matrix for T2\\n nu_T2 = [nu1b, nu2b, nu3b]\\n mig2 = numpy.array([[0, m2_12, m2_13],[m2_21, 0, m2_23], [m2_31, m2_32, 0]]) \\n fs.integrate(nu_T2, T2, m=mig2)\\n ## Population function and migration matrix for T3 (bottleneck to capture single population representation of lineage)\\n nu_T3 = [nu1c, nu2c, nu3c]\\n fs.integrate(nu_T3, T3) \\n return fs\",\n \"def prepare_experiment(assumptions):\\n print(\\\"\\\\nGenerate species parameters\\\")\\n np.random.seed(assumptions['seed']) \\n params = MakeParams(assumptions) \\n if assumptions[\\\"selected_function\\\"] == \\\"f5_invader_suppression\\\":\\n print(\\\"\\\\nDraw invader feature\\\")\\n params = create_invader(params, assumptions)\\n print(params[\\\"c\\\"])\\n \\n print(\\\"\\\\nDraw per-capita function and cost\\\")\\n f1_species_smooth, f1_species_rugged, f2_species_smooth, f2_species_rugged = draw_species_function(assumptions)\\n params.update({\\\"f1_species_smooth\\\": f1_species_smooth, \\\"f1_species_rugged\\\": f1_species_rugged, \\\"f2_species_smooth\\\": f2_species_smooth, \\\"f2_species_rugged\\\": f2_species_rugged})\\n gi = draw_species_cost(f1_species_smooth, assumptions)\\n params.update({\\\"g\\\": gi})\\n \\n print(\\\"\\\\nConstruct plate\\\")\\n np.random.seed(assumptions['seed']) \\n plate = make_plate(assumptions,params)\\n \\n print(\\\"\\\\nAdd community function to plate\\\")\\n plate = add_community_function(plate, assumptions, params)\\n \\n if not pd.isnull(assumptions[\\\"overwrite_plate\\\"]) :\\n print(\\\"\\\\nUpdating the initial plate composition by overwrite_plate\\\")\\n plate = overwrite_plate(plate, assumptions)\\n \\n print(\\\"\\\\nPrepare Protocol\\\")\\n #Extract Protocol from protocol database\\n algorithms = make_algorithms(assumptions)\\n params_algorithm = algorithms[algorithms['algorithm_name'] == assumptions['protocol']]\\n \\n #Params_simulation by default contains all assumptions not stored in params.\\n params_simulation = dict((k, assumptions[k]) for k in assumptions.keys() if k not in params.keys())\\n \\n return params, params_simulation , params_algorithm, plate\",\n \"def train():\\n\\n # Load camera parameters\\n rcams = cameras.load_cameras()\\n\\n # Load 3d data and 2d projections\\n full_train_set_3d, full_test_set_3d, data_mean_3d, data_std_3d, dim_to_ignore_3d, dim_to_use_3d =\\\\\\n data_utils.read_3d_data( FLAGS.camera_frame, rcams, FLAGS.origin_bc, FLAGS.augment_data,\\n FLAGS.procrustes, FLAGS.lowpass )\\n \\n # Read stacked hourglass 2D predictions\\n full_train_set_2d, full_test_set_2d, data_mean_2d, data_std_2d, dim_to_ignore_2d, dim_to_use_2d = \\\\\\n data_utils.read_2d_predictions( FLAGS.origin_bc, FLAGS.augment_data )\\n \\n print(\\\"\\\\n[+] done reading and normalizing data\\\")\\n # Getting the number of training and test subjects\\n tr_subj = 0\\n for v in full_train_set_3d.values():\\n tr_subj += v.shape[0]\\n te_subj = 0\\n for v in full_test_set_3d.values():\\n te_subj += v.shape[0]\\n print(\\\"{0} training subjects, {1} test subjects\\\".format(tr_subj, te_subj))\\n print(dim_to_use_2d)\\n print(dim_to_use_3d)\\n # Un-normalizing data for visualizations\\n unNorm_ftrs2d = data_utils.unNormalize_dic(full_train_set_2d, data_mean_2d, data_std_2d, dim_to_use_2d)\\n unNorm_ftrs3d = data_utils.unNormalize_dic(full_train_set_3d, data_mean_3d, data_std_3d, dim_to_use_3d)\\n unNorm_ftes3d = data_utils.unNormalize_dic(full_test_set_3d, data_mean_3d, data_std_3d, dim_to_use_3d)\\n # Visualize the data\\n viz.visualize_train_sample(unNorm_ftrs2d, unNorm_ftrs3d, FLAGS.camera_frame)\\n viz.visualize_files_oneatatime(unNorm_ftrs3d, unNorm_ftes3d)\\n\\n # Getting only the dimensions to use (get rid of body coxas, other limb, antennas, abdomen\\n train_set_3d, train_set_2d, test_set_3d, test_set_2d = {}, {}, {}, {}\\n for k in full_train_set_3d:\\n (f, c) = k\\n train_set_3d[k] = full_train_set_3d[k][:, dim_to_use_3d]\\n train_set_2d[(f, data_utils.CAMERA_TO_USE)] =\\\\\\n full_train_set_2d[(f, data_utils.CAMERA_TO_USE)][:, dim_to_use_2d]\\n for k in full_test_set_3d:\\n (f, c) = k\\n test_set_3d[k] = full_test_set_3d[k][:, dim_to_use_3d]\\n test_set_2d[(f, data_utils.CAMERA_TO_USE)] =\\\\\\n full_test_set_2d[(f, data_utils.CAMERA_TO_USE)][:, dim_to_use_2d]\\n \\n print(\\\"3D data mean:\\\")\\n print(data_mean_3d)\\n print(\\\"3D data std:\\\")\\n print(data_std_3d)\\n\\n print(\\\"2D data mean:\\\")\\n print(data_mean_2d)\\n print(\\\"2D data std:\\\")\\n print(data_std_2d)\\n \\n input(\\\"Press Enter to continue...\\\")\\n\\n # Avoid using the GPU if requested\\n device_count = {\\\"GPU\\\": 0} if FLAGS.use_cpu else {\\\"GPU\\\": 1}\\n with tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(\\n device_count=device_count,\\n allow_soft_placement=True )) as sess:\\n\\n # === Create the model ===\\n print(\\\"[*] creating %d bi-layers of %d units.\\\" % (FLAGS.num_layers, FLAGS.linear_size))\\n model = create_model( sess, FLAGS.batch_size )\\n model.train_writer.add_graph( sess.graph )\\n print(\\\"[+] model created\\\")\\n \\n #=== This is the training loop ===\\n step_time, loss, val_loss = 0.0, 0.0, 0.0\\n current_step = 0 if FLAGS.load <= 0 else FLAGS.load + 1\\n previous_losses = []\\n\\n step_time, loss = 0, 0\\n current_epoch = 0\\n log_every_n_batches = 100\\n losses, errors, joint_errors = [], [], []\\n for _ in range( FLAGS.epochs ):\\n current_epoch = current_epoch + 1\\n\\n # === Load training batches for one epoch ===\\n encoder_inputs, decoder_outputs =\\\\\\n model.get_all_batches( train_set_2d, train_set_3d, FLAGS.camera_frame, training=True )\\n nbatches = len( encoder_inputs )\\n print(\\\"[*] there are {0} train batches\\\".format( nbatches ))\\n start_time, loss = time.time(), 0.\\n # === Loop through all the training batches ===\\n for i in range( nbatches ):\\n\\n if (i+1) % log_every_n_batches == 0:\\n # Print progress every log_every_n_batches batches\\n print(\\\"Working on epoch {0}, batch {1} / {2}...\\\".format( current_epoch, i+1, nbatches),end=\\\"\\\" )\\n\\n enc_in, dec_out = encoder_inputs[i], decoder_outputs[i]\\n step_loss, loss_summary, lr_summary, _ =\\\\\\n model.step( sess, enc_in, dec_out, FLAGS.dropout, isTraining=True )\\n\\n if (i+1) % log_every_n_batches == 0:\\n # Log and print progress every log_every_n_batches batchespixels = pixels / pixels[2,:]\\n model.train_writer.add_summary( loss_summary, current_step )\\n model.train_writer.add_summary( lr_summary, current_step )\\n step_time = (time.time() - start_time)\\n start_time = time.time()\\n print(\\\"done in {0:.2f} ms\\\".format( 1000*step_time / log_every_n_batches ) )\\n\\n loss += step_loss\\n current_step += 1\\n # === end looping through training batches ===\\n\\n loss = loss / nbatches\\n losses.append(loss)\\n print(\\\"=============================\\\\n\\\"\\n \\\"Global step: %d\\\\n\\\"\\n \\\"Learning rate: %.2e\\\\n\\\"\\n \\\"Train loss avg: %.4f\\\\n\\\"\\n \\\"=============================\\\" % (model.global_step.eval(),\\n model.learning_rate.eval(), loss) )\\n # === End training for an epoch ===\\n\\n # === Testing after this epoch ===\\n isTraining = False\\n \\n n_joints = len(data_utils.DIMENSIONS_TO_USE)\\n if FLAGS.origin_bc:\\n n_joints -= len(data_utils.ROOT_POSITIONS)\\n\\n encoder_inputs, decoder_outputs =\\\\\\n model.get_all_batches( test_set_2d, test_set_3d, FLAGS.camera_frame, training=False)\\n\\n total_err, coordwise_err, joint_err, step_time, loss = evaluate_batches( sess, model,\\n data_mean_3d, data_std_3d, dim_to_use_3d, dim_to_ignore_3d,\\n data_mean_2d, data_std_2d, dim_to_use_2d, dim_to_ignore_2d,\\n current_step, encoder_inputs, decoder_outputs, current_epoch )\\n\\n print(\\\"=============================\\\\n\\\"\\n \\\"Step-time (ms): %.4f\\\\n\\\"\\n \\\"Val loss avg: %.4f\\\\n\\\"\\n \\\"Val error avg (mm): %.2f (%.2f, %.2f, %.2f)\\\\n\\\"\\n \\\"=============================\\\" % ( 1000*step_time, loss, total_err,\\n coordwise_err[0], coordwise_err[1], coordwise_err[2] ))\\n\\n for i in range(n_joints):\\n # 6 spaces, right-aligned, 5 decimal places\\n print(\\\"Error in joint {0:02d} (mm): {1:>5.2f}\\\".format(i+1, joint_err[i]))\\n print(\\\"=============================\\\")\\n errors.append(coordwise_err)\\n joint_errors.append(joint_err)\\n # Log the error to tensorboard\\n summaries = sess.run( model.err_mm_summary, {model.err_mm: total_err} )\\n model.test_writer.add_summary( summaries, current_step )\\n\\n # Save the model\\n print( \\\"Saving the model... \\\", end=\\\"\\\" )\\n start_time = time.time()\\n model.saver.save(sess, os.path.join(train_dir, 'checkpoint'), global_step=current_step )\\n print( \\\"done in {0:.2f} ms\\\".format(1000*(time.time() - start_time)) )\\n\\n # Reset global time and loss\\n step_time, loss = 0, 0\\n\\n sys.stdout.flush()\\n # Save losses for future plots\\n def print_list_tofile(l, filename):\\n with open(filename, 'wb') as f:\\n pickle.dump(l, f)\\n print_list_tofile(losses, train_dir+\\\"/losses.pkl\\\")\\n print_list_tofile(errors, train_dir+\\\"/errors.pkl\\\")\\n print_list_tofile(joint_errors, train_dir+\\\"/joint_errors.pkl\\\")\",\n \"def sample_random_architecture(self, dataset_api):\\n while True:\\n matrix = np.random.choice(\\n [0, 1], size=(NUM_VERTICES, NUM_VERTICES))\\n matrix = np.triu(matrix, 1)\\n ops = np.random.choice(OPS, size=NUM_VERTICES).tolist()\\n ops[0] = INPUT\\n ops[-1] = OUTPUT\\n spec = dataset_api['api'].ModelSpec(matrix=matrix, ops=ops)\\n if dataset_api['nb101_data'].is_valid(spec):\\n break\\n \\n self.set_spec({'matrix':matrix, 'ops':ops})\",\n \"def runSimulation(num_robots, speed, width, height, min_coverage, num_trials,\\n robot_type, visualize):\\n #initialization of variables\\n list_of_results = []\\n \\n #trial loop\\n for i in range(num_trials):\\n list_of_results.append(singleSimulation(num_robots, speed, width, height, min_coverage, robot_type, visualize))\\n return list_of_results\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61304635","0.6005348","0.59966356","0.5971647","0.5719661","0.5554706","0.5553684","0.54804504","0.5467986","0.54541695","0.5442281","0.53921455","0.5386651","0.537223","0.535773","0.5339545","0.5323583","0.53214395","0.52994394","0.52926755","0.5286437","0.52755606","0.5256391","0.5237262","0.5236887","0.5235495","0.52308065","0.52098036","0.52053314","0.519061","0.51869327","0.51839256","0.51741725","0.51713204","0.51691663","0.51687413","0.5147549","0.5147274","0.51394147","0.51330763","0.5123556","0.5122486","0.510986","0.510766","0.50963324","0.5091154","0.50840014","0.50806403","0.5076128","0.5068288","0.506764","0.50575036","0.50568444","0.5056342","0.50557166","0.50549823","0.5051745","0.50464433","0.50463533","0.504011","0.50386846","0.50350404","0.5032976","0.5022392","0.5013624","0.50087905","0.5004907","0.5003494","0.50027716","0.49989286","0.4994535","0.49934173","0.49900445","0.498713","0.49845654","0.498446","0.4977519","0.49769548","0.4976546","0.49729988","0.49657384","0.4964349","0.49629784","0.4961429","0.4959824","0.49571398","0.49564812","0.49529064","0.49508402","0.49494645","0.4949069","0.49474362","0.4944475","0.4943577","0.49408203","0.493698","0.49369428","0.49357516","0.49335104","0.49331415"],"string":"[\n \"0.61304635\",\n \"0.6005348\",\n \"0.59966356\",\n \"0.5971647\",\n \"0.5719661\",\n \"0.5554706\",\n \"0.5553684\",\n \"0.54804504\",\n \"0.5467986\",\n \"0.54541695\",\n \"0.5442281\",\n \"0.53921455\",\n \"0.5386651\",\n \"0.537223\",\n \"0.535773\",\n \"0.5339545\",\n \"0.5323583\",\n \"0.53214395\",\n \"0.52994394\",\n \"0.52926755\",\n \"0.5286437\",\n \"0.52755606\",\n \"0.5256391\",\n \"0.5237262\",\n \"0.5236887\",\n \"0.5235495\",\n \"0.52308065\",\n \"0.52098036\",\n \"0.52053314\",\n \"0.519061\",\n \"0.51869327\",\n \"0.51839256\",\n \"0.51741725\",\n \"0.51713204\",\n \"0.51691663\",\n \"0.51687413\",\n \"0.5147549\",\n \"0.5147274\",\n \"0.51394147\",\n \"0.51330763\",\n \"0.5123556\",\n \"0.5122486\",\n \"0.510986\",\n \"0.510766\",\n \"0.50963324\",\n \"0.5091154\",\n \"0.50840014\",\n \"0.50806403\",\n \"0.5076128\",\n \"0.5068288\",\n \"0.506764\",\n \"0.50575036\",\n \"0.50568444\",\n \"0.5056342\",\n \"0.50557166\",\n \"0.50549823\",\n \"0.5051745\",\n \"0.50464433\",\n \"0.50463533\",\n \"0.504011\",\n \"0.50386846\",\n \"0.50350404\",\n \"0.5032976\",\n \"0.5022392\",\n \"0.5013624\",\n \"0.50087905\",\n \"0.5004907\",\n \"0.5003494\",\n \"0.50027716\",\n \"0.49989286\",\n \"0.4994535\",\n \"0.49934173\",\n \"0.49900445\",\n \"0.498713\",\n \"0.49845654\",\n \"0.498446\",\n \"0.4977519\",\n \"0.49769548\",\n \"0.4976546\",\n \"0.49729988\",\n \"0.49657384\",\n \"0.4964349\",\n \"0.49629784\",\n \"0.4961429\",\n \"0.4959824\",\n \"0.49571398\",\n \"0.49564812\",\n \"0.49529064\",\n \"0.49508402\",\n \"0.49494645\",\n \"0.4949069\",\n \"0.49474362\",\n \"0.4944475\",\n \"0.4943577\",\n \"0.49408203\",\n \"0.493698\",\n \"0.49369428\",\n \"0.49357516\",\n \"0.49335104\",\n \"0.49331415\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69548637"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":89395,"cells":{"query":{"kind":"string","value":"Create a Simple Good Turing estimator for the given N_r values (as specified in the 'N' kwarg). N[r] == N_r, so N[0] should be blank."},"document":{"kind":"string","value":"def __init__(self, N, *args, **kwargs):\n self.N = copy.copy(N)\n if isinstance(self.N, list):\n self.max_r = len(self.N)\n elif isinstance(self.N, dict) or isinstance(self.N, defaultdict):\n self.max_r = max(self.N.keys())\n super(Estimator, self).__init__(*args, **kwargs)\n assert not self.N[0]\n self._precompute()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _uniform_number(self, N=None):\n num_frames = self.data.shape[0]\n if not N:\n N = int(np.floor(\n Constants.DEFAULT_SAMPLE_PERCENTAGE * num_frames\n ))\n k = int(np.floor(num_frames / N))\n if k == 0:\n k = 1\n elif N > num_frames:\n k = 1\n else:\n assert type(N) is IntType, 'N is not an integer: {}'.format(N)\n k = int(np.floor(num_frames / N))\n\n # ensure that at most N samples are taken if N does not divide nicely\n return self.data[::k][:N], self.labels[::k][:N]","def tirageRnp1CondRn(self, rn):\n\n proba = np.zeros(shape=(3))\n if rn == 0.:\n proba[0] = self.__alpha0/self.__D0\n proba[1] = self.__beta /self.__D0\n \n elif rn == 1.:\n proba[0] = self.__beta /self.__D1\n proba[1] = self.__alpha1/self.__D1\n else:\n Dr1 = 1.5 + rn - rn*rn\n proba[0] = (1.-rn) / Dr1\n proba[1] = rn / Dr1\n proba[2] = 1. - (proba[0]+proba[1])\n \n typeSample = random.choices(population=['0.', '1.', 'F'], weights=proba)[0]\n \n if typeSample != 'F':\n rnp1 = float(typeSample)\n else:\n if rn == 0.:\n rnp1 = self.__rv_pente0.rvs()\n elif rn == 1.:\n rnp1 = self.__rv_pente1.rvs()\n else:\n rnp1 = self.__rv_triangle.rvs(self.__alpha0, self.__alpha1, self.__beta, rn)\n\n return rnp1","def _construct_r_grid(n, dr=None, r=None):\n if dr is None and r is None:\n # default value, we don't care about the scaling since the mesh size was not provided\n dr = 1.0\n\n if dr is not None and r is not None:\n raise ValueError('Both r and dr input parameters cannot be specified at the same time')\n elif dr is None and r is not None:\n if r.ndim != 1 or r.shape[0] != n:\n raise ValueError('The input parameter r should be a 1D array'\n 'of shape = ({},), got shape = {}'.format(n, r.shape))\n # not so sure about this, needs verification\n dr = np.gradient(r) \n\n else:\n if isinstance(dr, np.ndarray):\n raise NotImplementedError\n r = (np.arange(n) + 0.5)*dr\n return r, dr","def _random_pr_init(self, r, n, _random_prec_n=None):\n deg = self.degree\n random_gens = [x._array_form for x in self.generators]\n k = len(random_gens)\n if k < r:\n for i in range(k, r):\n random_gens.append(random_gens[i - k])\n acc = list(range(deg))\n random_gens.append(acc)\n self._random_gens = random_gens\n\n # handle randomized input for testing purposes\n if _random_prec_n is None:\n for i in range(n):\n self.random_pr()\n else:\n for i in range(n):\n self.random_pr(_random_prec=_random_prec_n[i])","def __init__(self, n: int) -> None:\n\n assert n > 1, \"for n = 1 use Bernoulli distribution.\"\n\n self.n = n","def __call__(self, ngood, nbad, nsample, size=None, **kwargs):\n return super().__call__(ngood, nbad, nsample, size=size, **kwargs)","def __init__(self, n, o, r):\n self.N = array(n)\n self.o = array(o)\n self.r = r","def rand_rate_uni( N, tmin, tmax, **kwargs):\n return np.random.uniform( tmin, tmax, size = N)","def __init__(self, dim, rn, gammak=1.0, sine=False):\n\t\tself.dim = dim\n\t\tself.rn = rn\n\t\tself.gammak = gammak\n\t\tself.sine = sine\n\n\t\tself.generateCoefficients()","def drum_like_env(N, sr):\n ## TODO: Fill this in\n return np.zeros(N)","def preprocess(N, sigma2, R, f_def, params):\n\n #\n if 'scale' in params:\n if params['scale']:\n\n #\n N0 = np.sum(R * f_def, axis=1)\n\n #\n f_def *= np.average(N / N0)\n\n return N, sigma2, R, f_def, params","def __init__(self, N):\n self.data = list(range(N))\n self.weights = [1]*N","def optionNPV(kind, s0, k, t, sigma, r=BASE_RATE):\n\n S_0 = torch.tensor([float(s0)], requires_grad=True)\n K = torch.tensor([float(k)], requires_grad=False) # don't care\n T = torch.tensor([float(t)], requires_grad=True)\n Sigma = torch.tensor([float(sigma)], requires_grad=True)\n R = torch.tensor([float(r)], requires_grad=False) # don't care\n\n return blackScholes_pyTorch(kind, S_0, K, T, Sigma, R)","def __init__(self,\n topn=None,\n gain_fn=lambda label: label,\n rank_discount_fn=lambda rank: 1. / rank,\n normalized=False,\n smooth_fraction=0.):\n super().__init__(topn, gain_fn, rank_discount_fn, normalized)\n if not 0. <= smooth_fraction <= 1.:\n raise ValueError('smooth_fraction %s should be in range [0, 1].' %\n smooth_fraction)\n self._smooth_fraction = smooth_fraction","def TuranGraph(n,r):\n\n if n<1 or n<r or r<1:\n raise ValueError('Input parameters must satisfy \"1 < r < n\".')\n\n from sage.graphs.generators.basic import CompleteMultipartiteGraph\n\n vertex_sets = [n//r]*(r-(n%r))+[n//r+1]*(n%r)\n\n g = CompleteMultipartiteGraph(vertex_sets)\n g.name('Turan Graph with n: {}, r: {}'.format(n,r))\n\n return g","def __init__(self, r: float, i: float = 0):\n self.r = r\n self.i = i","def __init__(self, n, g_bases, g_len, m_prob):\n self.n = n\n self.g_bases = g_bases\n self.g_len = g_len\n self.m_prob = m_prob","def get_n1(r,N):\n n1 = N - np.sum(r)\n return n1","def run(self,n=10):\n return self.transduce([None] * n)","def calc_a_run(run):\n if run.get('N_dependance',False): ## light version's size is independent of N.\n # if all N are equal, we should use N, otherwise use largest.\n N = np.max(run['args']['number_of_points'])\n r = Factory_thouless_psi(run['npz_fname'].format(**run['args']), N= N)\n else:\n r = Factory_Transmission_g(run['npz_fname'].format(**run['args']))\n \n return r.create_if_missing(run['args'])","def generate(self, approx_n: int) -> Tuple[np.ndarray, float]:\n # number of samples per tree\n n_per_tree = approx_n // self._rf.n_estimators\n n = self._rf.n_estimators * n_per_tree # actual number of samples\n\n # default values (some features won't be set by the below algorithm)\n stds = np.sqrt(self._scaler.var_)\n X = np.random.normal(size=(n, self._dim)) * stds + self._scaler.mean_\n\n # generate n_per_tree samples from each tree\n for i_tree, estimator in enumerate(self._rf.estimators_):\n tree = estimator.tree_\n for i in range(n_per_tree):\n row_index = i_tree * n_per_tree + i\n node_index = 0\n right_bound = np.ones(self._dim) * np.inf\n left_bound = -right_bound\n\n # randomly pick one path in the tree\n while node_index != TREE_LEAF and \\\n tree.children_left[node_index] != tree.children_right[node_index]:\n threshold = tree.threshold[node_index]\n feature_i = tree.feature[node_index]\n\n # probability of branching left or right\n left_prob = self._counting_trees[i_tree].left_probability(node_index)\n\n # we pick a value close to the threshold...\n shift = 0.05 * np.abs(np.random.normal()) * stds[feature_i]\n if random.random() <= left_prob:\n value = threshold - shift\n else:\n value = threshold + shift\n # ... but still within the known bounds\n value = min(right_bound[feature_i], max(left_bound[feature_i], value))\n # alternatively, we could keep the value already set, but I believe\n # the chosen method restricts the value to be even closer to the\n # decision boundary\n X[row_index, feature_i] = value\n\n # branching\n if value <= threshold:\n node_index = tree.children_left[node_index]\n right_bound[feature_i] = min(right_bound[feature_i], threshold)\n else:\n node_index = tree.children_right[node_index]\n left_bound[feature_i] = max(left_bound[feature_i], threshold)\n\n return X, self._total_samples / X.shape[0]","def __init__(self, n=0, e=0):\r\n raise NotImplementedError()","def make_grid(n, gl=False):\n\n if gl:\n low = 0.0 # Lower Range\n high = 1.0\n p = 0.5\n\n # The method here uses 2*n points so halve it\n n, r, wt = GridGenerator.gaussp(low, high, n//2)\n r = np.concatenate((r, np.zeros((n))))\n wt = np.concatenate((wt, np.zeros((n))))\n for i in range(n):\n r[2*n-(i+1)] = (1.0/r[i])**2\n wt[2*n-(i+1)] = (wt[i]/p)*r[2*n - (i+1)]**1.5\n else:\n n, r, wt = GridGenerator.radial_chebyshev(n)\n\n return n, r, wt","def _TP_estimator(DD, DR, RR, ND1, ND2, NR1, NR2, estimator):\n\n ND1 = convert_to_ndarray(ND1)\n ND2 = convert_to_ndarray(ND2)\n NR1 = convert_to_ndarray(NR1)\n NR2 = convert_to_ndarray(NR2)\n Ns = np.array([len(ND1), len(ND2), len(NR1), len(NR2)])\n\n if np.any(Ns>1):\n #used for the jackknife calculations\n #the outer dimension is the number of samples.\n #the N arrays are the number of points in each dimension.\n #so, what we want to do is multiple each row of e.g. DD by the number of 1/N\n mult = lambda x, y: (x*y.T).T # annoying and ugly, but works.\n else:\n mult = lambda x, y: x*y # used for all else\n\n if estimator == 'Natural':\n factor = ND1*ND2/(NR1*NR2)\n #DD/RR-1\n xi = mult(1.0/factor, DD/RR) - 1.0\n elif estimator == 'Davis-Peebles':\n factor = ND1*ND2/(ND1*NR2)\n #DD/DR-1\n xi = mult(1.0/factor, DD/DR) - 1.0\n elif estimator == 'Hewett':\n factor1 = ND1*ND2/(NR1*NR2)\n factor2 = ND1*NR2/(NR1*NR2)\n #(DD-DR)/RR\n xi = mult(1.0/factor1, DD/RR) - mult(1.0/factor2, DR/RR)\n elif estimator == 'Hamilton':\n #DDRR/DRDR-1\n xi = (DD*RR)/(DR*DR) - 1.0\n elif estimator == 'Landy-Szalay':\n factor1 = ND1*ND2/(NR1*NR2)\n factor2 = ND1*NR2/(NR1*NR2)\n #(DD - 2.0*DR + RR)/RR\n xi = mult(1.0/factor1, DD/RR) - mult(1.0/factor2, 2.0*DR/RR) + 1.0\n else:\n raise ValueError(\"unsupported estimator!\")\n\n if np.shape(xi)[0]==1: return xi[0]\n else: return xi # for jackknife","def __init__(self, n_upper, n, activation=sigmoid):\n self.w = wb_width * np.random.randn(n_upper, n)\n self.b = wb_width * np.random.random(n)","def G_SNR(T2s, TEs):\r\n N = len(TEs)\r\n TEs = TEs-TEs[0]\r\n \r\n return N / np.sqrt(np.sum(np.exp(TEs / T2s)**2))","def __init__(self, nb):\n\n # Approximation parameters\n self.o_min = -.1\n self.o_max = .1\n self.o_nb = nb\n self.o_bw = (self.o_max - self.o_min) / self.o_nb\n self.o_e = np.linspace(self.o_min, self.o_max, self.o_nb+1)\n self.o = self.o_e[:-1] + (self.o_bw/2)\n self.r_e = [0, 1]\n self.r_nb = len(self.r_e)\n self.mu_min = 0\n self.mu_max = 1\n self.mu_nb = nb\n self.mu_bw = (self.mu_max - self.mu_min) / self.mu_nb\n self.mu_e = np.linspace(self.mu_min, self.mu_max, self.mu_nb+1)\n self.mu = self.mu_e[:-1] + (self.mu_bw/2)\n self.p_s_giv_o_hat = np.nan\n self.S = np.nan\n self.p_mu_giv_r_o_hat = np.nan","def rastrigin_nd_randnone(*xi):\n # Return None 20% of the time\n if np.random.rand() > .8:\n return None\n else:\n return rastrigin_nd(*xi)","def __init__(self, n=5, factors=50, learning_rate=0.001, bias_regularization=0.001, user_regularization=0.001,\n positive_item_regularization=0.001, negative_item_regularization=0.001,iter = 50):\n print 'bpr begin'\n self.n = n\n self.factors = factors\n self.learning_rate = learning_rate\n self.bias_regularization = bias_regularization\n self.user_regularization = user_regularization\n self.positive_item_regularization = positive_item_regularization\n self.negative_item_regularization = negative_item_regularization\n self.iter = iter","def main(n_samples):\n uso = usolib.uso.uar(N)\n lst = [usolib.randomfacet.randomfacet_sample(uso, N) for i in range(n_samples)]\n return sum(lst) / float(n_samples)","def __init__(self, n_per_sample, title, unit):\n self.min_sample = 0xffffffffffffffff\n self.max_sample = 0\n self.n_per_sample = n_per_sample\n self.title = title\n self.unit = unit\n self.sum_ = 0\n self.count = 0\n self.sample_lock = threading.Lock()","def __init__(\n self,\n n = 2,\n learning_rate = 0.0001,\n early_stop = 1e-4,\n steps = 100000,\n ):\n self.n = n\n self.learning_rate = learning_rate\n self.early_stop = early_stop\n self.steps = steps\n self.coefficients = self.random_coefficients(n)\n self.loss = np.array([])\n self.x_values = np.array([])\n self.y_values = np.array([])\n self.old_loss = 0","def simplestRandom(n):\n # do something \"perlin noise like\" - with various frequency scales\n level1 = numpy.random.randint(0,4,size=4)\n level2 = numpy.random.randn(10)\n level3 = numpy.random.randn(50) * .5\n # make splines for each\n u1 = INTERP.UnivariateSpline(numpy.linspace(0,1,4) ,level1,s=0)\n u2 = INTERP.UnivariateSpline(numpy.linspace(0,1,10),level2,s=0)\n u3 = INTERP.UnivariateSpline(numpy.linspace(0,1,50),level3,s=0)\n # build the signal on the range 0..1 - then use linspace to sample it\n samples = numpy.linspace(0,1,n)\n return numpy.array([u1(u)+u2(u)+u3(u) for u in samples])","def __init__(self, n=5, name=None):\n super().__init__(name=name)\n self.n = n\n self._images = []\n self._sigmas = []","def G(self, n, zeta, r):\n\n # Principal quantum number factors for STO normalization\n FACTORS = np.array([factorial(2*nn) for nn in range(1,max(n)+1)])**(-0.5)\n n_facs = FACTORS[n - 1]\n try:\n c = n_facs*(2.0*zeta)**(n + 0.5)\n except ValueError:\n print(\"Exponents and principal number factors are different shapes.\")\n print(\"Did you typo a ',' for a decimal point? e.g. '1,23456' for '1.23456'\")\n raise ValueError(\"exponent or principal number error\")\n rn = np.power.outer(r, (n - 1))\n es = np.einsum('j,ij->ji', c, rn)\n pw = np.exp(-np.outer(zeta, r))\n return es*pw","def __init__(self, n):\n self.n = n","def __init__(self, n):\n self.n = n","def __init__(self, ratio = 0.63, N = 20, max_depth = 10, candidate_splits = 500):\n assert ratio <= 1.0, \"Cannot have ratio greater than one\"\n self.ratio = ratio\n self.N = N \n self.learners = []\n self.candidate_splits = candidate_splits\n self.max_depth = max_depth","def sample(self, n=1):\n raise NotImplementedError","def sample(self, n):\n raise NotImplementedError","def construct_Q(self, N, r, t0, tf):\n q = np.zeros((N, N))\n for i in xrange(N): \n for l in xrange(N):\n if i>= r and l>=r: \n q[i,l] = 2*self.prodoftwoterms(i,l,r)*(tf**(i+l-2*r+1) - t0**(i+l-2*r+1))/(i+l-2*r+1)\n return q","def __init__(self, n, m = None, t = 0.4):\n self.n = n\n self.m = n if m is None else m\n self.array = np.random.random_sample(size= (self.n, self.m))\n self.t = t\n # self.kernel = np.array([[-.125,-.125,-.125],\n # [-.125, 1,-.125],\n # [-.125,-.125,-.125]])\n # self.options = dict(mode='same', boundary='wrap')","def wood_drum_env(N, sr):\n ## TODO: Fill this in\n return np.zeros(N)","def __init__(self, dim, rn, gammak, sine=False, feature_generator=None):\n\n\t\tself.dim = dim\n\t\tself.rn = rn\n\t\tself.gammak = gammak\n\n\t\tif feature_generator is None:\n\t\t\tself.feature_generator = GaussianRandomFeatures(self.dim, self.rn, self.gammak, sine=sine)\n\t\telse: self.feature_generator = feature_generator","def construct_A(self, N, r, t0, tf):\n a = np.zeros((2*r,N))\n \n for i in range(N): \n for j in range(r): \n if i >= j : \n a[j,i] = self.prod(xrange(i-(j-1), i+1))*t0**(i-j)\n a[r+j,i] = self.prod(xrange(i-(j-1), i+1))*tf**(i-j)\n return a","def random(cls, n=random.randint(5, 10), d=2, borns=[-1, 1], **kwargs):\n points = [Point.random(d=d, borns=borns) for i in range(n)]\n form = cls(points, **kwargs)\n form.makeSparse()\n return form","def _sersic_frac_mass_enclosed(r, n_s=2.2, R_e=4.):\n a_n = 2 * n_s\n b_n = 2. * n_s - 1./3. + 0.0098765/n_s + 0.0018/n_s**2 # approximation from Ciotti ...something\n\n # rmax = r_max.to(u.kpc).value\n # argc = b_n * (rmax/R_e) ** (1./n_s)\n # gamnorm = gammainc(a_n, argc)\n gamnorm = 1. # r_max = infinity\n\n arg = b_n * (r/R_e)**(1./n_s)\n return gammainc(a_n, arg) / gamnorm","def brass_env(N, sr):\n ## TODO: Fill this in\n return np.zeros(N)","def __init__(self, n, prey_cnt=0, predator_cnt=0):\n # print n, prey_cnt, predator_cnt\n self.grid_size = n\n self.grid = []\n for i in range(n):\n row = [0]*n # row is a list of n zeros\n self.grid.append(row)\n self.init_animals(prey_cnt, predator_cnt)","def generate_samples(self, n_samples):","def generate_samples(self, n_samples):","def model(r, p0, n=1):\n# print \"oi\"\n Pt = zeros(n, float) # initialize the output vector\n P = p0\n for i in xrange(n):\n Pt[i] = r*P\n P = Pt[i]\n \n return Pt","def ER_Theory(N,Kappa) :\n\tMu2 = Kappa - ( 2*Kappa*(1.0 - (Kappa/N))*math.log(N) )**0.5 + (( (Kappa*(1.0 - (Kappa/N)))/math.log(N) )**0.5)*( math.log( (2*math.pi*math.log((N**2)/(2*math.pi))) ) - 0.5772)\n\treturn Mu2","def new( self, nSamplingRate = 44100, nNbrChannel = 1, nNbrBitsPerSample = 16 ):\n self.reset()\n self.nNbrChannel = nNbrChannel\n self.nSamplingRate = nSamplingRate\n self.nNbrBitsPerSample = nNbrBitsPerSample \n self.updateHeaderComputedValues()","def __init__(self, N):\n pass","def ComputeNrb(self):\r\n pass","def define_pseudo_ising_helper_functions(N):\n\n @njit\n def get_multipliers_r(r, multipliers, N=N):\n \"\"\"Return r's field and all couplings to spin r.\n\n Parameters\n ----------\n r : int\n multipliers : ndarray\n All fields and couplings concatenated together.\n\n Returns\n -------\n ndarray\n Relevant multipliers.\n list\n Index of where multipliers appear in full multipliers array.\n \"\"\"\n \n ix = [r] \n multipliersr = np.zeros(N)\n multipliersr[0] = multipliers[r] # first entry is the biasing field\n\n # fill in the couplings\n ixcounter = 1\n for i in range(N):\n if i!=r:\n if i<r:\n ix.append( sub_to_ind(N, i, r) + N )\n multipliersr[ixcounter] = multipliers[ix[ixcounter]]\n else:\n ix.append( sub_to_ind(N, r, i) + N )\n multipliersr[ixcounter] = multipliers[ix[ixcounter]]\n ixcounter += 1\n return multipliersr, ix\n\n @njit\n def calc_observables_r(r, X, N=N):\n \"\"\"Return the observables relevant for calculating the conditional probability of\n spin r.\n\n Parameters\n ----------\n r : int\n Spin index.\n X : ndarray\n Data samples of dimensions (n_samples, n_dim).\n\n Returns\n -------\n ndarray\n observables\n \"\"\"\n\n obs = np.zeros((X.shape[0],N))\n \n for rowix in range(X.shape[0]):\n ixcounter = 1\n obs[rowix,0] = X[rowix,r]\n \n for i in range(N-1):\n for j in range(i+1,N):\n if i==r or j==r:\n obs[rowix,ixcounter] = X[rowix,i]*X[rowix,j]\n ixcounter += 1\n return obs\n\n return get_multipliers_r, calc_observables_r","def DG(self, n, e, r, f):\n\n pre = -e[:, None] + np.divide.outer((n - 1), r)\n return pre*f","def calculateSNR(self):\n pass","def easy_sample(self, num, **kwargs):\n return self.preprocess(self.sample(num, **kwargs), **kwargs)","def sample_utility(n, model, alpha, beta, bmax):\n A, b = matrix(0.0, (n,n)), matrix(ra.uniform(0,bmax,(n,1)))\n \n if model == 1: A = matrix(ra.uniform(0,beta,(n,n)))\n \n if model == 2:\n for i in range(n):\n for j in range(n/2):\n A[i, int(np.mod(i+j+1,n))] = beta**(j+1)\n A[i, int(np.mod(i-(j+1),n))] = beta**(j+1)\n \n if model == 3: A = 0.5*matrix(ra.binomial(1,beta,(n,n)))\n \n for i in range(n): A[i,i] = 1.0\n \n return Utility((alpha*A,b), 'sqrt')","def generate_real_samples(n):\n\n # Generate the population\n population = 1000000\n x1, x2 = real_distribution(population)\n X = np.c_[x1, x2]\n\n # Pick a subsample\n i = np.random.randint(0, population, n)\n X = X[i]\n\n # Generate class labels\n y = np.ones((n, 1))\n\n return X, y","def generate_good(self, m, n, rank, mu=2, ka=2):\n sr = random.random()\n s = []\n s.append(sr)\n for r in range(rank-1):\n newele = s[-1] * (1 + ka * random.random() / (rank-1))\n s.append(newele)\n s.reverse()\n \n # best_u = None\n # best_mu0 = 0\n # while best_mu0 == 0:\n # for _ in range(10):\n # A = np.random.rand(m,m)\n # A = scipy.linalg.orth(A)\n # u = A[:, :rank]\n # mu0 = self.compute_mu(u, m, rank)\n # print(\"mu0 : \", mu0)\n # if mu0 <= mu and mu0 >= best_mu0:\n # best_mu0 = mu0\n # best_u = u\n # print(\"mu0 for u:\", best_mu0)\n # # print(u.T @ u)\n \n # best_v = None\n # best_mu0 = 0\n # while best_mu0 == 0:\n # for _ in range(10):\n # B = np.random.rand(n,n)\n # B = scipy.linalg.orth(B)\n # v = B[:, :rank]\n # mu0 = self.compute_mu(v, n, rank)\n # print(\"mu0 : \", mu0)\n # if mu0 <= mu and mu0 >= best_mu0:\n # best_mu0 = mu0\n # best_v = v\n # print(\"mu0 for v:\", best_mu0)\n # u = best_u\n # v = best_v\n\n for _ in range(100):\n A = np.random.rand(m,m)\n A = scipy.linalg.orth(A)\n u = A[:, :rank]\n mu0 = self.compute_mu(u, m, rank)\n print(\"mu0 : \", mu0)\n if mu0 <= mu:\n break\n print(\"mu0 for u:\", mu0) \n\n for _ in range(10):\n B = np.random.rand(n,n)\n B = scipy.linalg.orth(B)\n v = B[:, :rank]\n mu0 = self.compute_mu(v, n, rank)\n print(\"mu0 : \", mu0)\n if mu0 <= mu:\n break\n print(\"mu0 for both:\", mu0)\n\n matrix = np.dot(u*s, v.T)\n \n kappa = s[0] / s[-1]\n print(\"kappa=\", kappa)\n \n ss = np.copy(s)\n for k in range(rank):\n ss[k] = s[k] / s[0]\n \n max_entry = np.max(np.abs(np.outer(u[:,:rank], v.T[:rank,:])))\n mu1 = max_entry * math.sqrt(m * n / rank)\n print(\"mu1=\", mu1)\n \n return matrix","def snr(v_est, v_true):\n assert(v_est.shape == v_true.shape)\n\n return 10 * np.log10(norm2(v_true) ** 2 / norm2(v_est - v_true) ** 2)","def __init__(self, N, maxevals=10, aggregate=np.median, reevals=None, epsilon=1e-7, parallel=False):\r\n self.lam_reeval = reevals # 2 + popsize/20, see method indices(), originally 2 + popsize/10\r\n self.epsilon = epsilon\r\n self.parallel = parallel\r\n self.theta = 0.5 # originally 0.2\r\n self.cum = 0.3 # originally 1, 0.3 allows one disagreement of current point with resulting noiseS\r\n self.alphasigma = 1 + 2 / (N+10)\r\n self.alphaevals = 1 + 2 / (N+10) # originally 1.5\r\n self.alphaevalsdown = self.alphaevals**-0.25 # originally 1/1.5\r\n self.evaluations = 1 # to aggregate for a single f-evaluation\r\n self.minevals = 1\r\n self.maxevals = int(np.max(maxevals))\r\n if hasattr(maxevals, '__contains__'): # i.e. can deal with ``in``\r\n if len(maxevals) > 1:\r\n self.minevals = min(maxevals)\r\n self.evaluations = self.minevals\r\n if len(maxevals) > 2:\r\n self.evaluations = np.median(maxevals)\r\n self.f_aggregate = aggregate\r\n self.evaluations_just_done = 0 # actually conducted evals, only for documentation\r\n self.noiseS = 0","def nspin_defaults(n):\n v, J = getWINDNMRdefault(n)\n v_ppm = v / 300\n return v_ppm, J","def __init__(self,\n topn=None,\n gain_fn=lambda label: label,\n rank_discount_fn=lambda rank: 1. / rank,\n normalized=False):\n self._topn = topn\n self._gain_fn = gain_fn\n self._rank_discount_fn = rank_discount_fn\n self._normalized = normalized","def model_gutenberg(r):\n\n\timport numpy as np\n\n\t#- initialisations ----------------------------------------------------------------------------\n\n\t#- depth intervals\n\td = np.array([0.0, 19.0, 38.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 125.0, 150.0, 175.0, 200.0, 225.0, 250.0, 300.0, 350.0, 400.0, 450.0, 500.0, 600.0, 700.0, 800.0, 900.0, 1000.0])\n\td = 6371000.0 - 1000.0 * d\n\n\t#- density\n\trho_array = np.array([2.74, 3.00, 3.32, 3.34, 3.35, 3.36, 3.37, 3.38, 3.39, 3.41, 3.53, 3.46, 3.48, 3.50, 3.53, 3.58, 3.62, 3.69, 3.82, 4.01, 4.21, 4.40, 4.56, 4.63])\n\n\t#- vp\n\tvp_array = np.array([6.14, 6.58, 8.20, 8.17, 8.14, 8.10, 8.07, 8.02, 7.93, 7.85, 7.89, 7.98, 8.10, 8.21, 8.38, 8.62, 8.87, 9.15, 9.45, 9.88, 10.30, 10.71, 11.10, 11.35])\n\n\t#- vs\n\tvs_array = np.array([3.55, 3.80, 4.65, 4.62, 4.57, 4.51, 4.46, 4.41, 4.37, 4.35, 4.36, 4.38, 4.42, 4.46, 4.54, 4.68, 4.85, 5.04, 5.21, 5.45, 5.76, 6.03, 6.23, 6.32])\n\n\t#- determine vp, vs and rho for the relevant layer --------------------------------------------\n\n\tfor k in np.arange(len(rho_array)):\n\t\tif (r <= d[k]) & (r > d[k+1]):\n\t\t\trho = rho_array[k]\n\t\t\tvpv = vp_array[k]\n\t\t\tvph = vpv\n\t\t\tvsv = vs_array[k]\n\t\t\tvsh = vsv\n\t\t\teta = 1.0\n\t\t\tcontinue\n\t\telif r <= 5371000.0:\n\t\t\trho = 4.63\n\t\t\tvpv = 11.35\n\t\t\tvph = vpv\n\t\t\tvsv = 6.32\n\t\t\tvsh = vsv\n\t\t\teta = 1.0\n\n\t#- convert to elastic parameters --------------------------------------------------------------\n\n\trho = 1000.0 * rho\n\tvpv = 1000.0 * vpv\n\tvph = 1000.0 * vph\n\tvsv = 1000.0 * vsv\n\tvsh = 1000.0 * vsh\n\n\tA = rho * vph**2\n\tC = rho * vpv**2\n\tN = rho * vsh**2\n\tL = rho * vsv**2\n\tF = eta * (A - 2 * L)\n\n\treturn rho, A, C, F, L, N","def __init__(self, nrows: int, ncols: int, rank: int):\n try:\n nrows = int(nrows)\n except ValueError:\n print(\"Invalid value of nrows. Must be an int.\")\n self.nrows = nrows\n try:\n ncols = int(ncols)\n except ValueError:\n print(\"Invalid value of ncols. Must be an int.\")\n self.ncols = ncols\n if rank is None:\n rank = np.minimum(nrows, ncols)\n try:\n rank = int(rank)\n except ValueError:\n print(\"Invalid value of rank. Must be an int.\")\n self.rank = rank\n super(GaussianRandomSampling, self).__init__(mat=self.build_sampling_matrix())","def __init__(self, n, sents):\n # call superclass to compute counts\n super(AddOneNGram, self).__init__(n, sents)","def gen_normal(self,n_step=100):\n if n_step < 30:\n print(\"WARNING! The number of steps is small. It may not generate a good stochastic process sequence!\")\n \n w = np.ones(n_step)*self.x0\n \n for i in range(1,n_step):\n # Sampling from the Normal distribution\n yi = np.random.normal()\n # Weiner process\n w[i] = yi\n \n return w","def initGD( X, N ):\n seq = np.ndarray(len(X), dtype=np.object)\n for i in range( len(X) ):\n a = np.floor(np.linspace(0,N-.00000001,len(X[i])))\n seq[i] = a\n return seq","def _u_naught_simple(self):\n # Random is better to give different multipliers in the subgradient phase\n return np.random.rand(self.mrows)*1.","def __init__(self, n_points, kind='cubic'):\n self.n_points = n_points\n self.kind = kind","def rand(self, nData):\n \n #*** Insert your own code here and remove the following error message \n # = np.random.choice(self.probMass,size=[1,nData])\n #return RR\n\n R = np.random.choice(a=np.arange(0, len(self.probMass),dtype=int),\n size=[1,nData], p=self.probMass)\n return R","def genresnet50(**kwargs):\n return EresNet(resnetblocks.EresNetBottleneck, [3, 4, 6, 3], **kwargs)","def stableSpeci(n,grow=True):\n unstable=True\n while unstable:\n G,base = randomGraph(n)\n sG = specializeGraph(G,base)\n rhoG = spectralRad(laplacian(G))\n rhoSG = spectralRad(laplacian(sG))\n if sG.shape[0] > n:\n if np.isclose(rhoG,rhoSG):\n unstable=False\n\n return G,base","def test_rr_noid(model):\n results = model.fit(rc_range=np.asarray([0.0, np.inf]))\n assert np.isneginf(results.params[3])\n assert np.isposinf(results.params[4])\n msk = np.full((5, 5), True)\n msk[0:3, 0:3] = False\n assert all(results.cov_params[msk] == 0.)\n assert all(results.params_se()[3:] == 0)\n assert np.isneginf(results.params_z()[3])\n assert np.isposinf(results.params_z()[4])\n assert all(results.params_pvalue()[3:] == 0.0)\n assert all(np.isneginf(results.params_ci()[:, 3]))\n assert all(np.isposinf(results.params_ci()[:, 4]))\n assert np.isneginf(results.effect_ci_conservative()[0])\n assert np.isposinf(results.effect_ci_conservative()[1])\n assert np.isneginf(results.effect_ci_upper()[0])\n assert np.isposinf(results.effect_ci_upper()[1])\n assert np.isneginf(results.effect_ci_lower()[0])\n assert np.isposinf(results.effect_ci_lower()[1])\n assert np.isneginf(results.effect_ci_imbensmanski()[0])\n assert np.isposinf(results.effect_ci_imbensmanski()[1])","def get_random_approx_discrete(m,n):\n\n return np.random.choice([-0.99,0.99], size=(m,n))","def gen(self, n=0):\n if n != 0:\n raise IndexError(\"n must be 0\")\n return ComplexNumber(self, 0, 1)","def tirageR1(self):\n\n proba = np.zeros(shape=(3))\n proba[0] = self.__alpha0 + self.__beta + self.__eta/2.\n proba[1] = self.__alpha1 + self.__beta + self.__eta/2.\n proba[2] = 1. - (proba[0]+proba[1])\n # typeSample = np.random.choice(a=['0.', '1.', 'F'], size=1, p=proba)[0]\n typeSample = random.choices(population=['0.', '1.', 'F'], weights=proba)[0]\n if typeSample != 'F':\n r1 = float(typeSample)\n else:\n r1 = self.__rv_parab.rvs()\n\n return r1","def idensity(n):\n I = zeroes(n, n)\n for i in range(n):\n I.g[i][i] = 1.0\n return I","def __init__(self, N, tokens):\n\n self.N = N\n ngrams = self.create_ngrams(tokens)\n\n self.ngrams = ngrams\n self.mdl = self.train(ngrams)\n\n if N < 2:\n raise Exception('N must be greater than 1')\n elif N == 2:\n self.prev_mdl = UnigramLM(tokens)\n else:\n mdl = NGramLM(N-1, tokens)\n self.prev_mdl = mdl","def __init__(self, k: int) -> None:\n\n assert k > 2, \"for k = 2 use Bernoulli distribution.\"\n\n self.k = k","def get_N(mu0, T, omega_rho, AR, density_model='hartree_fock', split=False, *args, **kwargs):\n Rmax = kwargs.get('Rmax', 3e-3)\n dr = kwargs.get('dr', 3e-7)\n r = np.arange(0, Rmax, dr)\n V = 0.5 * mass * omega_rho**2 * r**2\n fun = getattr(thismodule, f\"n_{density_model}\")\n kw = {'omega_ho': omega_rho, 'dr': dr, 'Rmax': Rmax}\n kw.update(kwargs)\n if split:\n n0, nt = fun(mu0, T, V, split=True, solver_kwargs=kw)\n N0 = integrate_density(r, n0, AR)\n Nt = integrate_density(r, nt, AR)\n return N0, Nt\n else:\n n = fun(mu0, T, V, solver_kwargs=kw)\n return integrate_density(r, n, AR)","def create_random_grid(N):\n return np.random.choice(values, N*N, p=[0.2, 0.8]).reshape(N, N)","def SNR(X, k, n):\n signal, NSA = X # signal = mean signal intensity / SD\n return k * signal * (NSA ** n)","def __init__(self, model, n_pix=1, strategy='rand1bin', max_iter=100, popsize=400,\n init='normal', target=None, args=None, **kwargs):\n self.model = model\n self.n_pix = n_pix\n self.strategy = strategy\n self.max_iter = max_iter\n self.popsize = popsize\n self.init = init\n self.target = target\n self.args = args\n self.kwargs = kwargs\n\n self.step_meter = AverageMeter()","def eg1(r_train, r_test, N_train=1000, N_test=500):\n\n def eg1_kernel(r, N):\n X1 = np.random.randn(N)\n X2_1 = np.exp(X1)\n X2_2 = np.random.randn(N)\n X2_prob = np.random.uniform(0, 1, N)\n X2 = np.where(X2_prob < r, X2_1, X2_2)\n X3 = np.random.randn(N)\n X4 = np.random.randn(N)\n Y = 210 + 27.4 * X1 + 13.7 * X2 + 13.7 * X3 + 13.7 * X4 + np.random.randn(N)\n\n data = {}\n data['X1'] = X1\n data['X2'] = X2\n data['X3'] = X3\n data['X4'] = X4\n data['Y'] = Y\n return data\n\n data_train = eg1_kernel(r_train, N_train)\n data_test = eg1_kernel(r_test, N_test)\n\n return data_train, data_test","def prob1(n):\n#raise NotImplementedError(\"Problem 1 Incomplete\")\n if n == 0 :\n raise ValueError(\"Sampling 0 points is not defined.\")\n total = 0\n for i in xrange(n) :\n if np.random.normal() > 3 :\n total += 1\n return float(total)/n","def uns(cls, n):\n return cls._createVetor([1] * n)","def qr_step_factorization(q, r, iter, n):\n v = column_convertor(r[iter:, iter])\n Hbar, reflect = householder_transformation(v)\n H = np.identity(n)\n H[iter:, iter:] = Hbar\n r = np.matmul(H, r)\n q = np.matmul(q, H)\n return q, r,reflect","def run_step(self, N=None):\n if N is None:\n N = self.N\n t = len(N)\n r = self.r\n c = self.c\n b = self.b\n \n return N[t-2] * math.exp(r - (b * N[t-2]) - (c * b * N[t-1]))","def test_random_forest_n_estimators_parameter(params, X_train, X_test, y_train, y_test):","def random(n: int = 1):\n if not isinstance(n, int):\n n = 1\n\n return QuantumState(states=[QuantumState.normalized_random_array() for _ in list(range(n))])","def __init__(self, eta, mu, n_feature):\r\n self.eta = 0.09\r\n self.weight = [0.0] * n_feature\r\n self.temp = [0.0] * n_feature\r\n self.mu = 0.0\r\n self.size= n_feature","def GenerateNormalExpression(n):\n if int(n) != n or n < 1:\n return None\n return '+'.join(['a[%d]' % ii for ii in range(n)])","def deriveGreeks1(kind, s0, k, t, sigma, r=BASE_RATE):\n\n S_0 = torch.tensor([float(s0)], requires_grad=True)\n K = torch.tensor([float(k)], requires_grad=False) # don't care\n T = torch.tensor([float(t)], requires_grad=True)\n Sigma = torch.tensor([float(sigma)], requires_grad=True)\n R = torch.tensor([float(r)], requires_grad=False) # don't care\n\n npv = blackScholes_pyTorch(kind, S_0, K, T, Sigma, R)\n npv.backward() # retain_graph=True)\n\n # dNPV / dT (change in time remaining, based on percentage of year)\n theta = -T.grad / 365.25\n\n # dNPV / dS_0 (change in underlying price)\n delta = S_0.grad\n\n # dNPV / dSigma (change in underlying iv)\n vega = Sigma.grad / 100\n\n # dNPV / dR (change in risk-free interest rate)\n # rho = R.grad / 100\n\n # Not relevant unless options have adjustable strike prices:\n # http://sfb649.wiwi.hu-berlin.de/fedc_homepage/xplore/tutorials/xlghtmlnode64.html#MDBOOK:200\n # digital = -K.grad # ?\n\n return npv, theta, delta, vega","def take_nth(n):\n def _take_nth_xducer(step):\n outer = {\"idx\": 0}\n def _take_nth_step(r=Missing, x=Missing):\n if r is Missing: return step()\n if x is Missing:\n return step(r)\n if outer[\"idx\"] % n:\n outer[\"idx\"] += 1\n return r\n else:\n outer[\"idx\"] += 1\n return step(r, x)\n return _take_nth_step\n return _take_nth_xducer","def __init__(self, k):\n self.k = k\n self.N = 2**self.k"],"string":"[\n \"def _uniform_number(self, N=None):\\n num_frames = self.data.shape[0]\\n if not N:\\n N = int(np.floor(\\n Constants.DEFAULT_SAMPLE_PERCENTAGE * num_frames\\n ))\\n k = int(np.floor(num_frames / N))\\n if k == 0:\\n k = 1\\n elif N > num_frames:\\n k = 1\\n else:\\n assert type(N) is IntType, 'N is not an integer: {}'.format(N)\\n k = int(np.floor(num_frames / N))\\n\\n # ensure that at most N samples are taken if N does not divide nicely\\n return self.data[::k][:N], self.labels[::k][:N]\",\n \"def tirageRnp1CondRn(self, rn):\\n\\n proba = np.zeros(shape=(3))\\n if rn == 0.:\\n proba[0] = self.__alpha0/self.__D0\\n proba[1] = self.__beta /self.__D0\\n \\n elif rn == 1.:\\n proba[0] = self.__beta /self.__D1\\n proba[1] = self.__alpha1/self.__D1\\n else:\\n Dr1 = 1.5 + rn - rn*rn\\n proba[0] = (1.-rn) / Dr1\\n proba[1] = rn / Dr1\\n proba[2] = 1. - (proba[0]+proba[1])\\n \\n typeSample = random.choices(population=['0.', '1.', 'F'], weights=proba)[0]\\n \\n if typeSample != 'F':\\n rnp1 = float(typeSample)\\n else:\\n if rn == 0.:\\n rnp1 = self.__rv_pente0.rvs()\\n elif rn == 1.:\\n rnp1 = self.__rv_pente1.rvs()\\n else:\\n rnp1 = self.__rv_triangle.rvs(self.__alpha0, self.__alpha1, self.__beta, rn)\\n\\n return rnp1\",\n \"def _construct_r_grid(n, dr=None, r=None):\\n if dr is None and r is None:\\n # default value, we don't care about the scaling since the mesh size was not provided\\n dr = 1.0\\n\\n if dr is not None and r is not None:\\n raise ValueError('Both r and dr input parameters cannot be specified at the same time')\\n elif dr is None and r is not None:\\n if r.ndim != 1 or r.shape[0] != n:\\n raise ValueError('The input parameter r should be a 1D array'\\n 'of shape = ({},), got shape = {}'.format(n, r.shape))\\n # not so sure about this, needs verification\\n dr = np.gradient(r) \\n\\n else:\\n if isinstance(dr, np.ndarray):\\n raise NotImplementedError\\n r = (np.arange(n) + 0.5)*dr\\n return r, dr\",\n \"def _random_pr_init(self, r, n, _random_prec_n=None):\\n deg = self.degree\\n random_gens = [x._array_form for x in self.generators]\\n k = len(random_gens)\\n if k < r:\\n for i in range(k, r):\\n random_gens.append(random_gens[i - k])\\n acc = list(range(deg))\\n random_gens.append(acc)\\n self._random_gens = random_gens\\n\\n # handle randomized input for testing purposes\\n if _random_prec_n is None:\\n for i in range(n):\\n self.random_pr()\\n else:\\n for i in range(n):\\n self.random_pr(_random_prec=_random_prec_n[i])\",\n \"def __init__(self, n: int) -> None:\\n\\n assert n > 1, \\\"for n = 1 use Bernoulli distribution.\\\"\\n\\n self.n = n\",\n \"def __call__(self, ngood, nbad, nsample, size=None, **kwargs):\\n return super().__call__(ngood, nbad, nsample, size=size, **kwargs)\",\n \"def __init__(self, n, o, r):\\n self.N = array(n)\\n self.o = array(o)\\n self.r = r\",\n \"def rand_rate_uni( N, tmin, tmax, **kwargs):\\n return np.random.uniform( tmin, tmax, size = N)\",\n \"def __init__(self, dim, rn, gammak=1.0, sine=False):\\n\\t\\tself.dim = dim\\n\\t\\tself.rn = rn\\n\\t\\tself.gammak = gammak\\n\\t\\tself.sine = sine\\n\\n\\t\\tself.generateCoefficients()\",\n \"def drum_like_env(N, sr):\\n ## TODO: Fill this in\\n return np.zeros(N)\",\n \"def preprocess(N, sigma2, R, f_def, params):\\n\\n #\\n if 'scale' in params:\\n if params['scale']:\\n\\n #\\n N0 = np.sum(R * f_def, axis=1)\\n\\n #\\n f_def *= np.average(N / N0)\\n\\n return N, sigma2, R, f_def, params\",\n \"def __init__(self, N):\\n self.data = list(range(N))\\n self.weights = [1]*N\",\n \"def optionNPV(kind, s0, k, t, sigma, r=BASE_RATE):\\n\\n S_0 = torch.tensor([float(s0)], requires_grad=True)\\n K = torch.tensor([float(k)], requires_grad=False) # don't care\\n T = torch.tensor([float(t)], requires_grad=True)\\n Sigma = torch.tensor([float(sigma)], requires_grad=True)\\n R = torch.tensor([float(r)], requires_grad=False) # don't care\\n\\n return blackScholes_pyTorch(kind, S_0, K, T, Sigma, R)\",\n \"def __init__(self,\\n topn=None,\\n gain_fn=lambda label: label,\\n rank_discount_fn=lambda rank: 1. / rank,\\n normalized=False,\\n smooth_fraction=0.):\\n super().__init__(topn, gain_fn, rank_discount_fn, normalized)\\n if not 0. <= smooth_fraction <= 1.:\\n raise ValueError('smooth_fraction %s should be in range [0, 1].' %\\n smooth_fraction)\\n self._smooth_fraction = smooth_fraction\",\n \"def TuranGraph(n,r):\\n\\n if n<1 or n<r or r<1:\\n raise ValueError('Input parameters must satisfy \\\"1 < r < n\\\".')\\n\\n from sage.graphs.generators.basic import CompleteMultipartiteGraph\\n\\n vertex_sets = [n//r]*(r-(n%r))+[n//r+1]*(n%r)\\n\\n g = CompleteMultipartiteGraph(vertex_sets)\\n g.name('Turan Graph with n: {}, r: {}'.format(n,r))\\n\\n return g\",\n \"def __init__(self, r: float, i: float = 0):\\n self.r = r\\n self.i = i\",\n \"def __init__(self, n, g_bases, g_len, m_prob):\\n self.n = n\\n self.g_bases = g_bases\\n self.g_len = g_len\\n self.m_prob = m_prob\",\n \"def get_n1(r,N):\\n n1 = N - np.sum(r)\\n return n1\",\n \"def run(self,n=10):\\n return self.transduce([None] * n)\",\n \"def calc_a_run(run):\\n if run.get('N_dependance',False): ## light version's size is independent of N.\\n # if all N are equal, we should use N, otherwise use largest.\\n N = np.max(run['args']['number_of_points'])\\n r = Factory_thouless_psi(run['npz_fname'].format(**run['args']), N= N)\\n else:\\n r = Factory_Transmission_g(run['npz_fname'].format(**run['args']))\\n \\n return r.create_if_missing(run['args'])\",\n \"def generate(self, approx_n: int) -> Tuple[np.ndarray, float]:\\n # number of samples per tree\\n n_per_tree = approx_n // self._rf.n_estimators\\n n = self._rf.n_estimators * n_per_tree # actual number of samples\\n\\n # default values (some features won't be set by the below algorithm)\\n stds = np.sqrt(self._scaler.var_)\\n X = np.random.normal(size=(n, self._dim)) * stds + self._scaler.mean_\\n\\n # generate n_per_tree samples from each tree\\n for i_tree, estimator in enumerate(self._rf.estimators_):\\n tree = estimator.tree_\\n for i in range(n_per_tree):\\n row_index = i_tree * n_per_tree + i\\n node_index = 0\\n right_bound = np.ones(self._dim) * np.inf\\n left_bound = -right_bound\\n\\n # randomly pick one path in the tree\\n while node_index != TREE_LEAF and \\\\\\n tree.children_left[node_index] != tree.children_right[node_index]:\\n threshold = tree.threshold[node_index]\\n feature_i = tree.feature[node_index]\\n\\n # probability of branching left or right\\n left_prob = self._counting_trees[i_tree].left_probability(node_index)\\n\\n # we pick a value close to the threshold...\\n shift = 0.05 * np.abs(np.random.normal()) * stds[feature_i]\\n if random.random() <= left_prob:\\n value = threshold - shift\\n else:\\n value = threshold + shift\\n # ... but still within the known bounds\\n value = min(right_bound[feature_i], max(left_bound[feature_i], value))\\n # alternatively, we could keep the value already set, but I believe\\n # the chosen method restricts the value to be even closer to the\\n # decision boundary\\n X[row_index, feature_i] = value\\n\\n # branching\\n if value <= threshold:\\n node_index = tree.children_left[node_index]\\n right_bound[feature_i] = min(right_bound[feature_i], threshold)\\n else:\\n node_index = tree.children_right[node_index]\\n left_bound[feature_i] = max(left_bound[feature_i], threshold)\\n\\n return X, self._total_samples / X.shape[0]\",\n \"def __init__(self, n=0, e=0):\\r\\n raise NotImplementedError()\",\n \"def make_grid(n, gl=False):\\n\\n if gl:\\n low = 0.0 # Lower Range\\n high = 1.0\\n p = 0.5\\n\\n # The method here uses 2*n points so halve it\\n n, r, wt = GridGenerator.gaussp(low, high, n//2)\\n r = np.concatenate((r, np.zeros((n))))\\n wt = np.concatenate((wt, np.zeros((n))))\\n for i in range(n):\\n r[2*n-(i+1)] = (1.0/r[i])**2\\n wt[2*n-(i+1)] = (wt[i]/p)*r[2*n - (i+1)]**1.5\\n else:\\n n, r, wt = GridGenerator.radial_chebyshev(n)\\n\\n return n, r, wt\",\n \"def _TP_estimator(DD, DR, RR, ND1, ND2, NR1, NR2, estimator):\\n\\n ND1 = convert_to_ndarray(ND1)\\n ND2 = convert_to_ndarray(ND2)\\n NR1 = convert_to_ndarray(NR1)\\n NR2 = convert_to_ndarray(NR2)\\n Ns = np.array([len(ND1), len(ND2), len(NR1), len(NR2)])\\n\\n if np.any(Ns>1):\\n #used for the jackknife calculations\\n #the outer dimension is the number of samples.\\n #the N arrays are the number of points in each dimension.\\n #so, what we want to do is multiple each row of e.g. DD by the number of 1/N\\n mult = lambda x, y: (x*y.T).T # annoying and ugly, but works.\\n else:\\n mult = lambda x, y: x*y # used for all else\\n\\n if estimator == 'Natural':\\n factor = ND1*ND2/(NR1*NR2)\\n #DD/RR-1\\n xi = mult(1.0/factor, DD/RR) - 1.0\\n elif estimator == 'Davis-Peebles':\\n factor = ND1*ND2/(ND1*NR2)\\n #DD/DR-1\\n xi = mult(1.0/factor, DD/DR) - 1.0\\n elif estimator == 'Hewett':\\n factor1 = ND1*ND2/(NR1*NR2)\\n factor2 = ND1*NR2/(NR1*NR2)\\n #(DD-DR)/RR\\n xi = mult(1.0/factor1, DD/RR) - mult(1.0/factor2, DR/RR)\\n elif estimator == 'Hamilton':\\n #DDRR/DRDR-1\\n xi = (DD*RR)/(DR*DR) - 1.0\\n elif estimator == 'Landy-Szalay':\\n factor1 = ND1*ND2/(NR1*NR2)\\n factor2 = ND1*NR2/(NR1*NR2)\\n #(DD - 2.0*DR + RR)/RR\\n xi = mult(1.0/factor1, DD/RR) - mult(1.0/factor2, 2.0*DR/RR) + 1.0\\n else:\\n raise ValueError(\\\"unsupported estimator!\\\")\\n\\n if np.shape(xi)[0]==1: return xi[0]\\n else: return xi # for jackknife\",\n \"def __init__(self, n_upper, n, activation=sigmoid):\\n self.w = wb_width * np.random.randn(n_upper, n)\\n self.b = wb_width * np.random.random(n)\",\n \"def G_SNR(T2s, TEs):\\r\\n N = len(TEs)\\r\\n TEs = TEs-TEs[0]\\r\\n \\r\\n return N / np.sqrt(np.sum(np.exp(TEs / T2s)**2))\",\n \"def __init__(self, nb):\\n\\n # Approximation parameters\\n self.o_min = -.1\\n self.o_max = .1\\n self.o_nb = nb\\n self.o_bw = (self.o_max - self.o_min) / self.o_nb\\n self.o_e = np.linspace(self.o_min, self.o_max, self.o_nb+1)\\n self.o = self.o_e[:-1] + (self.o_bw/2)\\n self.r_e = [0, 1]\\n self.r_nb = len(self.r_e)\\n self.mu_min = 0\\n self.mu_max = 1\\n self.mu_nb = nb\\n self.mu_bw = (self.mu_max - self.mu_min) / self.mu_nb\\n self.mu_e = np.linspace(self.mu_min, self.mu_max, self.mu_nb+1)\\n self.mu = self.mu_e[:-1] + (self.mu_bw/2)\\n self.p_s_giv_o_hat = np.nan\\n self.S = np.nan\\n self.p_mu_giv_r_o_hat = np.nan\",\n \"def rastrigin_nd_randnone(*xi):\\n # Return None 20% of the time\\n if np.random.rand() > .8:\\n return None\\n else:\\n return rastrigin_nd(*xi)\",\n \"def __init__(self, n=5, factors=50, learning_rate=0.001, bias_regularization=0.001, user_regularization=0.001,\\n positive_item_regularization=0.001, negative_item_regularization=0.001,iter = 50):\\n print 'bpr begin'\\n self.n = n\\n self.factors = factors\\n self.learning_rate = learning_rate\\n self.bias_regularization = bias_regularization\\n self.user_regularization = user_regularization\\n self.positive_item_regularization = positive_item_regularization\\n self.negative_item_regularization = negative_item_regularization\\n self.iter = iter\",\n \"def main(n_samples):\\n uso = usolib.uso.uar(N)\\n lst = [usolib.randomfacet.randomfacet_sample(uso, N) for i in range(n_samples)]\\n return sum(lst) / float(n_samples)\",\n \"def __init__(self, n_per_sample, title, unit):\\n self.min_sample = 0xffffffffffffffff\\n self.max_sample = 0\\n self.n_per_sample = n_per_sample\\n self.title = title\\n self.unit = unit\\n self.sum_ = 0\\n self.count = 0\\n self.sample_lock = threading.Lock()\",\n \"def __init__(\\n self,\\n n = 2,\\n learning_rate = 0.0001,\\n early_stop = 1e-4,\\n steps = 100000,\\n ):\\n self.n = n\\n self.learning_rate = learning_rate\\n self.early_stop = early_stop\\n self.steps = steps\\n self.coefficients = self.random_coefficients(n)\\n self.loss = np.array([])\\n self.x_values = np.array([])\\n self.y_values = np.array([])\\n self.old_loss = 0\",\n \"def simplestRandom(n):\\n # do something \\\"perlin noise like\\\" - with various frequency scales\\n level1 = numpy.random.randint(0,4,size=4)\\n level2 = numpy.random.randn(10)\\n level3 = numpy.random.randn(50) * .5\\n # make splines for each\\n u1 = INTERP.UnivariateSpline(numpy.linspace(0,1,4) ,level1,s=0)\\n u2 = INTERP.UnivariateSpline(numpy.linspace(0,1,10),level2,s=0)\\n u3 = INTERP.UnivariateSpline(numpy.linspace(0,1,50),level3,s=0)\\n # build the signal on the range 0..1 - then use linspace to sample it\\n samples = numpy.linspace(0,1,n)\\n return numpy.array([u1(u)+u2(u)+u3(u) for u in samples])\",\n \"def __init__(self, n=5, name=None):\\n super().__init__(name=name)\\n self.n = n\\n self._images = []\\n self._sigmas = []\",\n \"def G(self, n, zeta, r):\\n\\n # Principal quantum number factors for STO normalization\\n FACTORS = np.array([factorial(2*nn) for nn in range(1,max(n)+1)])**(-0.5)\\n n_facs = FACTORS[n - 1]\\n try:\\n c = n_facs*(2.0*zeta)**(n + 0.5)\\n except ValueError:\\n print(\\\"Exponents and principal number factors are different shapes.\\\")\\n print(\\\"Did you typo a ',' for a decimal point? e.g. '1,23456' for '1.23456'\\\")\\n raise ValueError(\\\"exponent or principal number error\\\")\\n rn = np.power.outer(r, (n - 1))\\n es = np.einsum('j,ij->ji', c, rn)\\n pw = np.exp(-np.outer(zeta, r))\\n return es*pw\",\n \"def __init__(self, n):\\n self.n = n\",\n \"def __init__(self, n):\\n self.n = n\",\n \"def __init__(self, ratio = 0.63, N = 20, max_depth = 10, candidate_splits = 500):\\n assert ratio <= 1.0, \\\"Cannot have ratio greater than one\\\"\\n self.ratio = ratio\\n self.N = N \\n self.learners = []\\n self.candidate_splits = candidate_splits\\n self.max_depth = max_depth\",\n \"def sample(self, n=1):\\n raise NotImplementedError\",\n \"def sample(self, n):\\n raise NotImplementedError\",\n \"def construct_Q(self, N, r, t0, tf):\\n q = np.zeros((N, N))\\n for i in xrange(N): \\n for l in xrange(N):\\n if i>= r and l>=r: \\n q[i,l] = 2*self.prodoftwoterms(i,l,r)*(tf**(i+l-2*r+1) - t0**(i+l-2*r+1))/(i+l-2*r+1)\\n return q\",\n \"def __init__(self, n, m = None, t = 0.4):\\n self.n = n\\n self.m = n if m is None else m\\n self.array = np.random.random_sample(size= (self.n, self.m))\\n self.t = t\\n # self.kernel = np.array([[-.125,-.125,-.125],\\n # [-.125, 1,-.125],\\n # [-.125,-.125,-.125]])\\n # self.options = dict(mode='same', boundary='wrap')\",\n \"def wood_drum_env(N, sr):\\n ## TODO: Fill this in\\n return np.zeros(N)\",\n \"def __init__(self, dim, rn, gammak, sine=False, feature_generator=None):\\n\\n\\t\\tself.dim = dim\\n\\t\\tself.rn = rn\\n\\t\\tself.gammak = gammak\\n\\n\\t\\tif feature_generator is None:\\n\\t\\t\\tself.feature_generator = GaussianRandomFeatures(self.dim, self.rn, self.gammak, sine=sine)\\n\\t\\telse: self.feature_generator = feature_generator\",\n \"def construct_A(self, N, r, t0, tf):\\n a = np.zeros((2*r,N))\\n \\n for i in range(N): \\n for j in range(r): \\n if i >= j : \\n a[j,i] = self.prod(xrange(i-(j-1), i+1))*t0**(i-j)\\n a[r+j,i] = self.prod(xrange(i-(j-1), i+1))*tf**(i-j)\\n return a\",\n \"def random(cls, n=random.randint(5, 10), d=2, borns=[-1, 1], **kwargs):\\n points = [Point.random(d=d, borns=borns) for i in range(n)]\\n form = cls(points, **kwargs)\\n form.makeSparse()\\n return form\",\n \"def _sersic_frac_mass_enclosed(r, n_s=2.2, R_e=4.):\\n a_n = 2 * n_s\\n b_n = 2. * n_s - 1./3. + 0.0098765/n_s + 0.0018/n_s**2 # approximation from Ciotti ...something\\n\\n # rmax = r_max.to(u.kpc).value\\n # argc = b_n * (rmax/R_e) ** (1./n_s)\\n # gamnorm = gammainc(a_n, argc)\\n gamnorm = 1. # r_max = infinity\\n\\n arg = b_n * (r/R_e)**(1./n_s)\\n return gammainc(a_n, arg) / gamnorm\",\n \"def brass_env(N, sr):\\n ## TODO: Fill this in\\n return np.zeros(N)\",\n \"def __init__(self, n, prey_cnt=0, predator_cnt=0):\\n # print n, prey_cnt, predator_cnt\\n self.grid_size = n\\n self.grid = []\\n for i in range(n):\\n row = [0]*n # row is a list of n zeros\\n self.grid.append(row)\\n self.init_animals(prey_cnt, predator_cnt)\",\n \"def generate_samples(self, n_samples):\",\n \"def generate_samples(self, n_samples):\",\n \"def model(r, p0, n=1):\\n# print \\\"oi\\\"\\n Pt = zeros(n, float) # initialize the output vector\\n P = p0\\n for i in xrange(n):\\n Pt[i] = r*P\\n P = Pt[i]\\n \\n return Pt\",\n \"def ER_Theory(N,Kappa) :\\n\\tMu2 = Kappa - ( 2*Kappa*(1.0 - (Kappa/N))*math.log(N) )**0.5 + (( (Kappa*(1.0 - (Kappa/N)))/math.log(N) )**0.5)*( math.log( (2*math.pi*math.log((N**2)/(2*math.pi))) ) - 0.5772)\\n\\treturn Mu2\",\n \"def new( self, nSamplingRate = 44100, nNbrChannel = 1, nNbrBitsPerSample = 16 ):\\n self.reset()\\n self.nNbrChannel = nNbrChannel\\n self.nSamplingRate = nSamplingRate\\n self.nNbrBitsPerSample = nNbrBitsPerSample \\n self.updateHeaderComputedValues()\",\n \"def __init__(self, N):\\n pass\",\n \"def ComputeNrb(self):\\r\\n pass\",\n \"def define_pseudo_ising_helper_functions(N):\\n\\n @njit\\n def get_multipliers_r(r, multipliers, N=N):\\n \\\"\\\"\\\"Return r's field and all couplings to spin r.\\n\\n Parameters\\n ----------\\n r : int\\n multipliers : ndarray\\n All fields and couplings concatenated together.\\n\\n Returns\\n -------\\n ndarray\\n Relevant multipliers.\\n list\\n Index of where multipliers appear in full multipliers array.\\n \\\"\\\"\\\"\\n \\n ix = [r] \\n multipliersr = np.zeros(N)\\n multipliersr[0] = multipliers[r] # first entry is the biasing field\\n\\n # fill in the couplings\\n ixcounter = 1\\n for i in range(N):\\n if i!=r:\\n if i<r:\\n ix.append( sub_to_ind(N, i, r) + N )\\n multipliersr[ixcounter] = multipliers[ix[ixcounter]]\\n else:\\n ix.append( sub_to_ind(N, r, i) + N )\\n multipliersr[ixcounter] = multipliers[ix[ixcounter]]\\n ixcounter += 1\\n return multipliersr, ix\\n\\n @njit\\n def calc_observables_r(r, X, N=N):\\n \\\"\\\"\\\"Return the observables relevant for calculating the conditional probability of\\n spin r.\\n\\n Parameters\\n ----------\\n r : int\\n Spin index.\\n X : ndarray\\n Data samples of dimensions (n_samples, n_dim).\\n\\n Returns\\n -------\\n ndarray\\n observables\\n \\\"\\\"\\\"\\n\\n obs = np.zeros((X.shape[0],N))\\n \\n for rowix in range(X.shape[0]):\\n ixcounter = 1\\n obs[rowix,0] = X[rowix,r]\\n \\n for i in range(N-1):\\n for j in range(i+1,N):\\n if i==r or j==r:\\n obs[rowix,ixcounter] = X[rowix,i]*X[rowix,j]\\n ixcounter += 1\\n return obs\\n\\n return get_multipliers_r, calc_observables_r\",\n \"def DG(self, n, e, r, f):\\n\\n pre = -e[:, None] + np.divide.outer((n - 1), r)\\n return pre*f\",\n \"def calculateSNR(self):\\n pass\",\n \"def easy_sample(self, num, **kwargs):\\n return self.preprocess(self.sample(num, **kwargs), **kwargs)\",\n \"def sample_utility(n, model, alpha, beta, bmax):\\n A, b = matrix(0.0, (n,n)), matrix(ra.uniform(0,bmax,(n,1)))\\n \\n if model == 1: A = matrix(ra.uniform(0,beta,(n,n)))\\n \\n if model == 2:\\n for i in range(n):\\n for j in range(n/2):\\n A[i, int(np.mod(i+j+1,n))] = beta**(j+1)\\n A[i, int(np.mod(i-(j+1),n))] = beta**(j+1)\\n \\n if model == 3: A = 0.5*matrix(ra.binomial(1,beta,(n,n)))\\n \\n for i in range(n): A[i,i] = 1.0\\n \\n return Utility((alpha*A,b), 'sqrt')\",\n \"def generate_real_samples(n):\\n\\n # Generate the population\\n population = 1000000\\n x1, x2 = real_distribution(population)\\n X = np.c_[x1, x2]\\n\\n # Pick a subsample\\n i = np.random.randint(0, population, n)\\n X = X[i]\\n\\n # Generate class labels\\n y = np.ones((n, 1))\\n\\n return X, y\",\n \"def generate_good(self, m, n, rank, mu=2, ka=2):\\n sr = random.random()\\n s = []\\n s.append(sr)\\n for r in range(rank-1):\\n newele = s[-1] * (1 + ka * random.random() / (rank-1))\\n s.append(newele)\\n s.reverse()\\n \\n # best_u = None\\n # best_mu0 = 0\\n # while best_mu0 == 0:\\n # for _ in range(10):\\n # A = np.random.rand(m,m)\\n # A = scipy.linalg.orth(A)\\n # u = A[:, :rank]\\n # mu0 = self.compute_mu(u, m, rank)\\n # print(\\\"mu0 : \\\", mu0)\\n # if mu0 <= mu and mu0 >= best_mu0:\\n # best_mu0 = mu0\\n # best_u = u\\n # print(\\\"mu0 for u:\\\", best_mu0)\\n # # print(u.T @ u)\\n \\n # best_v = None\\n # best_mu0 = 0\\n # while best_mu0 == 0:\\n # for _ in range(10):\\n # B = np.random.rand(n,n)\\n # B = scipy.linalg.orth(B)\\n # v = B[:, :rank]\\n # mu0 = self.compute_mu(v, n, rank)\\n # print(\\\"mu0 : \\\", mu0)\\n # if mu0 <= mu and mu0 >= best_mu0:\\n # best_mu0 = mu0\\n # best_v = v\\n # print(\\\"mu0 for v:\\\", best_mu0)\\n # u = best_u\\n # v = best_v\\n\\n for _ in range(100):\\n A = np.random.rand(m,m)\\n A = scipy.linalg.orth(A)\\n u = A[:, :rank]\\n mu0 = self.compute_mu(u, m, rank)\\n print(\\\"mu0 : \\\", mu0)\\n if mu0 <= mu:\\n break\\n print(\\\"mu0 for u:\\\", mu0) \\n\\n for _ in range(10):\\n B = np.random.rand(n,n)\\n B = scipy.linalg.orth(B)\\n v = B[:, :rank]\\n mu0 = self.compute_mu(v, n, rank)\\n print(\\\"mu0 : \\\", mu0)\\n if mu0 <= mu:\\n break\\n print(\\\"mu0 for both:\\\", mu0)\\n\\n matrix = np.dot(u*s, v.T)\\n \\n kappa = s[0] / s[-1]\\n print(\\\"kappa=\\\", kappa)\\n \\n ss = np.copy(s)\\n for k in range(rank):\\n ss[k] = s[k] / s[0]\\n \\n max_entry = np.max(np.abs(np.outer(u[:,:rank], v.T[:rank,:])))\\n mu1 = max_entry * math.sqrt(m * n / rank)\\n print(\\\"mu1=\\\", mu1)\\n \\n return matrix\",\n \"def snr(v_est, v_true):\\n assert(v_est.shape == v_true.shape)\\n\\n return 10 * np.log10(norm2(v_true) ** 2 / norm2(v_est - v_true) ** 2)\",\n \"def __init__(self, N, maxevals=10, aggregate=np.median, reevals=None, epsilon=1e-7, parallel=False):\\r\\n self.lam_reeval = reevals # 2 + popsize/20, see method indices(), originally 2 + popsize/10\\r\\n self.epsilon = epsilon\\r\\n self.parallel = parallel\\r\\n self.theta = 0.5 # originally 0.2\\r\\n self.cum = 0.3 # originally 1, 0.3 allows one disagreement of current point with resulting noiseS\\r\\n self.alphasigma = 1 + 2 / (N+10)\\r\\n self.alphaevals = 1 + 2 / (N+10) # originally 1.5\\r\\n self.alphaevalsdown = self.alphaevals**-0.25 # originally 1/1.5\\r\\n self.evaluations = 1 # to aggregate for a single f-evaluation\\r\\n self.minevals = 1\\r\\n self.maxevals = int(np.max(maxevals))\\r\\n if hasattr(maxevals, '__contains__'): # i.e. can deal with ``in``\\r\\n if len(maxevals) > 1:\\r\\n self.minevals = min(maxevals)\\r\\n self.evaluations = self.minevals\\r\\n if len(maxevals) > 2:\\r\\n self.evaluations = np.median(maxevals)\\r\\n self.f_aggregate = aggregate\\r\\n self.evaluations_just_done = 0 # actually conducted evals, only for documentation\\r\\n self.noiseS = 0\",\n \"def nspin_defaults(n):\\n v, J = getWINDNMRdefault(n)\\n v_ppm = v / 300\\n return v_ppm, J\",\n \"def __init__(self,\\n topn=None,\\n gain_fn=lambda label: label,\\n rank_discount_fn=lambda rank: 1. / rank,\\n normalized=False):\\n self._topn = topn\\n self._gain_fn = gain_fn\\n self._rank_discount_fn = rank_discount_fn\\n self._normalized = normalized\",\n \"def model_gutenberg(r):\\n\\n\\timport numpy as np\\n\\n\\t#- initialisations ----------------------------------------------------------------------------\\n\\n\\t#- depth intervals\\n\\td = np.array([0.0, 19.0, 38.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 125.0, 150.0, 175.0, 200.0, 225.0, 250.0, 300.0, 350.0, 400.0, 450.0, 500.0, 600.0, 700.0, 800.0, 900.0, 1000.0])\\n\\td = 6371000.0 - 1000.0 * d\\n\\n\\t#- density\\n\\trho_array = np.array([2.74, 3.00, 3.32, 3.34, 3.35, 3.36, 3.37, 3.38, 3.39, 3.41, 3.53, 3.46, 3.48, 3.50, 3.53, 3.58, 3.62, 3.69, 3.82, 4.01, 4.21, 4.40, 4.56, 4.63])\\n\\n\\t#- vp\\n\\tvp_array = np.array([6.14, 6.58, 8.20, 8.17, 8.14, 8.10, 8.07, 8.02, 7.93, 7.85, 7.89, 7.98, 8.10, 8.21, 8.38, 8.62, 8.87, 9.15, 9.45, 9.88, 10.30, 10.71, 11.10, 11.35])\\n\\n\\t#- vs\\n\\tvs_array = np.array([3.55, 3.80, 4.65, 4.62, 4.57, 4.51, 4.46, 4.41, 4.37, 4.35, 4.36, 4.38, 4.42, 4.46, 4.54, 4.68, 4.85, 5.04, 5.21, 5.45, 5.76, 6.03, 6.23, 6.32])\\n\\n\\t#- determine vp, vs and rho for the relevant layer --------------------------------------------\\n\\n\\tfor k in np.arange(len(rho_array)):\\n\\t\\tif (r <= d[k]) & (r > d[k+1]):\\n\\t\\t\\trho = rho_array[k]\\n\\t\\t\\tvpv = vp_array[k]\\n\\t\\t\\tvph = vpv\\n\\t\\t\\tvsv = vs_array[k]\\n\\t\\t\\tvsh = vsv\\n\\t\\t\\teta = 1.0\\n\\t\\t\\tcontinue\\n\\t\\telif r <= 5371000.0:\\n\\t\\t\\trho = 4.63\\n\\t\\t\\tvpv = 11.35\\n\\t\\t\\tvph = vpv\\n\\t\\t\\tvsv = 6.32\\n\\t\\t\\tvsh = vsv\\n\\t\\t\\teta = 1.0\\n\\n\\t#- convert to elastic parameters --------------------------------------------------------------\\n\\n\\trho = 1000.0 * rho\\n\\tvpv = 1000.0 * vpv\\n\\tvph = 1000.0 * vph\\n\\tvsv = 1000.0 * vsv\\n\\tvsh = 1000.0 * vsh\\n\\n\\tA = rho * vph**2\\n\\tC = rho * vpv**2\\n\\tN = rho * vsh**2\\n\\tL = rho * vsv**2\\n\\tF = eta * (A - 2 * L)\\n\\n\\treturn rho, A, C, F, L, N\",\n \"def __init__(self, nrows: int, ncols: int, rank: int):\\n try:\\n nrows = int(nrows)\\n except ValueError:\\n print(\\\"Invalid value of nrows. Must be an int.\\\")\\n self.nrows = nrows\\n try:\\n ncols = int(ncols)\\n except ValueError:\\n print(\\\"Invalid value of ncols. Must be an int.\\\")\\n self.ncols = ncols\\n if rank is None:\\n rank = np.minimum(nrows, ncols)\\n try:\\n rank = int(rank)\\n except ValueError:\\n print(\\\"Invalid value of rank. Must be an int.\\\")\\n self.rank = rank\\n super(GaussianRandomSampling, self).__init__(mat=self.build_sampling_matrix())\",\n \"def __init__(self, n, sents):\\n # call superclass to compute counts\\n super(AddOneNGram, self).__init__(n, sents)\",\n \"def gen_normal(self,n_step=100):\\n if n_step < 30:\\n print(\\\"WARNING! The number of steps is small. It may not generate a good stochastic process sequence!\\\")\\n \\n w = np.ones(n_step)*self.x0\\n \\n for i in range(1,n_step):\\n # Sampling from the Normal distribution\\n yi = np.random.normal()\\n # Weiner process\\n w[i] = yi\\n \\n return w\",\n \"def initGD( X, N ):\\n seq = np.ndarray(len(X), dtype=np.object)\\n for i in range( len(X) ):\\n a = np.floor(np.linspace(0,N-.00000001,len(X[i])))\\n seq[i] = a\\n return seq\",\n \"def _u_naught_simple(self):\\n # Random is better to give different multipliers in the subgradient phase\\n return np.random.rand(self.mrows)*1.\",\n \"def __init__(self, n_points, kind='cubic'):\\n self.n_points = n_points\\n self.kind = kind\",\n \"def rand(self, nData):\\n \\n #*** Insert your own code here and remove the following error message \\n # = np.random.choice(self.probMass,size=[1,nData])\\n #return RR\\n\\n R = np.random.choice(a=np.arange(0, len(self.probMass),dtype=int),\\n size=[1,nData], p=self.probMass)\\n return R\",\n \"def genresnet50(**kwargs):\\n return EresNet(resnetblocks.EresNetBottleneck, [3, 4, 6, 3], **kwargs)\",\n \"def stableSpeci(n,grow=True):\\n unstable=True\\n while unstable:\\n G,base = randomGraph(n)\\n sG = specializeGraph(G,base)\\n rhoG = spectralRad(laplacian(G))\\n rhoSG = spectralRad(laplacian(sG))\\n if sG.shape[0] > n:\\n if np.isclose(rhoG,rhoSG):\\n unstable=False\\n\\n return G,base\",\n \"def test_rr_noid(model):\\n results = model.fit(rc_range=np.asarray([0.0, np.inf]))\\n assert np.isneginf(results.params[3])\\n assert np.isposinf(results.params[4])\\n msk = np.full((5, 5), True)\\n msk[0:3, 0:3] = False\\n assert all(results.cov_params[msk] == 0.)\\n assert all(results.params_se()[3:] == 0)\\n assert np.isneginf(results.params_z()[3])\\n assert np.isposinf(results.params_z()[4])\\n assert all(results.params_pvalue()[3:] == 0.0)\\n assert all(np.isneginf(results.params_ci()[:, 3]))\\n assert all(np.isposinf(results.params_ci()[:, 4]))\\n assert np.isneginf(results.effect_ci_conservative()[0])\\n assert np.isposinf(results.effect_ci_conservative()[1])\\n assert np.isneginf(results.effect_ci_upper()[0])\\n assert np.isposinf(results.effect_ci_upper()[1])\\n assert np.isneginf(results.effect_ci_lower()[0])\\n assert np.isposinf(results.effect_ci_lower()[1])\\n assert np.isneginf(results.effect_ci_imbensmanski()[0])\\n assert np.isposinf(results.effect_ci_imbensmanski()[1])\",\n \"def get_random_approx_discrete(m,n):\\n\\n return np.random.choice([-0.99,0.99], size=(m,n))\",\n \"def gen(self, n=0):\\n if n != 0:\\n raise IndexError(\\\"n must be 0\\\")\\n return ComplexNumber(self, 0, 1)\",\n \"def tirageR1(self):\\n\\n proba = np.zeros(shape=(3))\\n proba[0] = self.__alpha0 + self.__beta + self.__eta/2.\\n proba[1] = self.__alpha1 + self.__beta + self.__eta/2.\\n proba[2] = 1. - (proba[0]+proba[1])\\n # typeSample = np.random.choice(a=['0.', '1.', 'F'], size=1, p=proba)[0]\\n typeSample = random.choices(population=['0.', '1.', 'F'], weights=proba)[0]\\n if typeSample != 'F':\\n r1 = float(typeSample)\\n else:\\n r1 = self.__rv_parab.rvs()\\n\\n return r1\",\n \"def idensity(n):\\n I = zeroes(n, n)\\n for i in range(n):\\n I.g[i][i] = 1.0\\n return I\",\n \"def __init__(self, N, tokens):\\n\\n self.N = N\\n ngrams = self.create_ngrams(tokens)\\n\\n self.ngrams = ngrams\\n self.mdl = self.train(ngrams)\\n\\n if N < 2:\\n raise Exception('N must be greater than 1')\\n elif N == 2:\\n self.prev_mdl = UnigramLM(tokens)\\n else:\\n mdl = NGramLM(N-1, tokens)\\n self.prev_mdl = mdl\",\n \"def __init__(self, k: int) -> None:\\n\\n assert k > 2, \\\"for k = 2 use Bernoulli distribution.\\\"\\n\\n self.k = k\",\n \"def get_N(mu0, T, omega_rho, AR, density_model='hartree_fock', split=False, *args, **kwargs):\\n Rmax = kwargs.get('Rmax', 3e-3)\\n dr = kwargs.get('dr', 3e-7)\\n r = np.arange(0, Rmax, dr)\\n V = 0.5 * mass * omega_rho**2 * r**2\\n fun = getattr(thismodule, f\\\"n_{density_model}\\\")\\n kw = {'omega_ho': omega_rho, 'dr': dr, 'Rmax': Rmax}\\n kw.update(kwargs)\\n if split:\\n n0, nt = fun(mu0, T, V, split=True, solver_kwargs=kw)\\n N0 = integrate_density(r, n0, AR)\\n Nt = integrate_density(r, nt, AR)\\n return N0, Nt\\n else:\\n n = fun(mu0, T, V, solver_kwargs=kw)\\n return integrate_density(r, n, AR)\",\n \"def create_random_grid(N):\\n return np.random.choice(values, N*N, p=[0.2, 0.8]).reshape(N, N)\",\n \"def SNR(X, k, n):\\n signal, NSA = X # signal = mean signal intensity / SD\\n return k * signal * (NSA ** n)\",\n \"def __init__(self, model, n_pix=1, strategy='rand1bin', max_iter=100, popsize=400,\\n init='normal', target=None, args=None, **kwargs):\\n self.model = model\\n self.n_pix = n_pix\\n self.strategy = strategy\\n self.max_iter = max_iter\\n self.popsize = popsize\\n self.init = init\\n self.target = target\\n self.args = args\\n self.kwargs = kwargs\\n\\n self.step_meter = AverageMeter()\",\n \"def eg1(r_train, r_test, N_train=1000, N_test=500):\\n\\n def eg1_kernel(r, N):\\n X1 = np.random.randn(N)\\n X2_1 = np.exp(X1)\\n X2_2 = np.random.randn(N)\\n X2_prob = np.random.uniform(0, 1, N)\\n X2 = np.where(X2_prob < r, X2_1, X2_2)\\n X3 = np.random.randn(N)\\n X4 = np.random.randn(N)\\n Y = 210 + 27.4 * X1 + 13.7 * X2 + 13.7 * X3 + 13.7 * X4 + np.random.randn(N)\\n\\n data = {}\\n data['X1'] = X1\\n data['X2'] = X2\\n data['X3'] = X3\\n data['X4'] = X4\\n data['Y'] = Y\\n return data\\n\\n data_train = eg1_kernel(r_train, N_train)\\n data_test = eg1_kernel(r_test, N_test)\\n\\n return data_train, data_test\",\n \"def prob1(n):\\n#raise NotImplementedError(\\\"Problem 1 Incomplete\\\")\\n if n == 0 :\\n raise ValueError(\\\"Sampling 0 points is not defined.\\\")\\n total = 0\\n for i in xrange(n) :\\n if np.random.normal() > 3 :\\n total += 1\\n return float(total)/n\",\n \"def uns(cls, n):\\n return cls._createVetor([1] * n)\",\n \"def qr_step_factorization(q, r, iter, n):\\n v = column_convertor(r[iter:, iter])\\n Hbar, reflect = householder_transformation(v)\\n H = np.identity(n)\\n H[iter:, iter:] = Hbar\\n r = np.matmul(H, r)\\n q = np.matmul(q, H)\\n return q, r,reflect\",\n \"def run_step(self, N=None):\\n if N is None:\\n N = self.N\\n t = len(N)\\n r = self.r\\n c = self.c\\n b = self.b\\n \\n return N[t-2] * math.exp(r - (b * N[t-2]) - (c * b * N[t-1]))\",\n \"def test_random_forest_n_estimators_parameter(params, X_train, X_test, y_train, y_test):\",\n \"def random(n: int = 1):\\n if not isinstance(n, int):\\n n = 1\\n\\n return QuantumState(states=[QuantumState.normalized_random_array() for _ in list(range(n))])\",\n \"def __init__(self, eta, mu, n_feature):\\r\\n self.eta = 0.09\\r\\n self.weight = [0.0] * n_feature\\r\\n self.temp = [0.0] * n_feature\\r\\n self.mu = 0.0\\r\\n self.size= n_feature\",\n \"def GenerateNormalExpression(n):\\n if int(n) != n or n < 1:\\n return None\\n return '+'.join(['a[%d]' % ii for ii in range(n)])\",\n \"def deriveGreeks1(kind, s0, k, t, sigma, r=BASE_RATE):\\n\\n S_0 = torch.tensor([float(s0)], requires_grad=True)\\n K = torch.tensor([float(k)], requires_grad=False) # don't care\\n T = torch.tensor([float(t)], requires_grad=True)\\n Sigma = torch.tensor([float(sigma)], requires_grad=True)\\n R = torch.tensor([float(r)], requires_grad=False) # don't care\\n\\n npv = blackScholes_pyTorch(kind, S_0, K, T, Sigma, R)\\n npv.backward() # retain_graph=True)\\n\\n # dNPV / dT (change in time remaining, based on percentage of year)\\n theta = -T.grad / 365.25\\n\\n # dNPV / dS_0 (change in underlying price)\\n delta = S_0.grad\\n\\n # dNPV / dSigma (change in underlying iv)\\n vega = Sigma.grad / 100\\n\\n # dNPV / dR (change in risk-free interest rate)\\n # rho = R.grad / 100\\n\\n # Not relevant unless options have adjustable strike prices:\\n # http://sfb649.wiwi.hu-berlin.de/fedc_homepage/xplore/tutorials/xlghtmlnode64.html#MDBOOK:200\\n # digital = -K.grad # ?\\n\\n return npv, theta, delta, vega\",\n \"def take_nth(n):\\n def _take_nth_xducer(step):\\n outer = {\\\"idx\\\": 0}\\n def _take_nth_step(r=Missing, x=Missing):\\n if r is Missing: return step()\\n if x is Missing:\\n return step(r)\\n if outer[\\\"idx\\\"] % n:\\n outer[\\\"idx\\\"] += 1\\n return r\\n else:\\n outer[\\\"idx\\\"] += 1\\n return step(r, x)\\n return _take_nth_step\\n return _take_nth_xducer\",\n \"def __init__(self, k):\\n self.k = k\\n self.N = 2**self.k\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.559078","0.55552906","0.5441996","0.5357272","0.53438485","0.5331821","0.5251055","0.5232737","0.52025616","0.5200852","0.5196297","0.51273173","0.51019603","0.5092297","0.5092026","0.5085834","0.5084632","0.5060103","0.504482","0.50389826","0.5034161","0.4994188","0.49935523","0.49767894","0.49756488","0.49616048","0.49598184","0.49567747","0.49549973","0.49490088","0.49452785","0.49382088","0.49349552","0.49273488","0.49214676","0.49209702","0.49209702","0.4920968","0.4917631","0.49085283","0.49060872","0.48999655","0.48980713","0.48938757","0.48938218","0.4891838","0.48881575","0.4882861","0.4880247","0.48779038","0.48779038","0.4873974","0.48637962","0.4859376","0.48567146","0.48462132","0.48366073","0.48353672","0.48307422","0.4830075","0.48281386","0.4825884","0.48232672","0.48222652","0.4819502","0.48135075","0.48046485","0.47979316","0.4794274","0.47921973","0.4790829","0.4788133","0.47849303","0.47843626","0.47838327","0.47764757","0.47751775","0.4772771","0.47710058","0.47689292","0.47599658","0.47598568","0.47584006","0.475305","0.47525057","0.47490954","0.47466072","0.4742167","0.47349024","0.47347653","0.47315255","0.47230726","0.4721567","0.47128087","0.47120073","0.47095102","0.47092003","0.47065327","0.47044936","0.47041354"],"string":"[\n \"0.559078\",\n \"0.55552906\",\n \"0.5441996\",\n \"0.5357272\",\n \"0.53438485\",\n \"0.5331821\",\n \"0.5251055\",\n \"0.5232737\",\n \"0.52025616\",\n \"0.5200852\",\n \"0.5196297\",\n \"0.51273173\",\n \"0.51019603\",\n \"0.5092297\",\n \"0.5092026\",\n \"0.5085834\",\n \"0.5084632\",\n \"0.5060103\",\n \"0.504482\",\n \"0.50389826\",\n \"0.5034161\",\n \"0.4994188\",\n \"0.49935523\",\n \"0.49767894\",\n \"0.49756488\",\n \"0.49616048\",\n \"0.49598184\",\n \"0.49567747\",\n \"0.49549973\",\n \"0.49490088\",\n \"0.49452785\",\n \"0.49382088\",\n \"0.49349552\",\n \"0.49273488\",\n \"0.49214676\",\n \"0.49209702\",\n \"0.49209702\",\n \"0.4920968\",\n \"0.4917631\",\n \"0.49085283\",\n \"0.49060872\",\n \"0.48999655\",\n \"0.48980713\",\n \"0.48938757\",\n \"0.48938218\",\n \"0.4891838\",\n \"0.48881575\",\n \"0.4882861\",\n \"0.4880247\",\n \"0.48779038\",\n \"0.48779038\",\n \"0.4873974\",\n \"0.48637962\",\n \"0.4859376\",\n \"0.48567146\",\n \"0.48462132\",\n \"0.48366073\",\n \"0.48353672\",\n \"0.48307422\",\n \"0.4830075\",\n \"0.48281386\",\n \"0.4825884\",\n \"0.48232672\",\n \"0.48222652\",\n \"0.4819502\",\n \"0.48135075\",\n \"0.48046485\",\n \"0.47979316\",\n \"0.4794274\",\n \"0.47921973\",\n \"0.4790829\",\n \"0.4788133\",\n \"0.47849303\",\n \"0.47843626\",\n \"0.47838327\",\n \"0.47764757\",\n \"0.47751775\",\n \"0.4772771\",\n \"0.47710058\",\n \"0.47689292\",\n \"0.47599658\",\n \"0.47598568\",\n \"0.47584006\",\n \"0.475305\",\n \"0.47525057\",\n \"0.47490954\",\n \"0.47466072\",\n \"0.4742167\",\n \"0.47349024\",\n \"0.47347653\",\n \"0.47315255\",\n \"0.47230726\",\n \"0.4721567\",\n \"0.47128087\",\n \"0.47120073\",\n \"0.47095102\",\n \"0.47092003\",\n \"0.47065327\",\n \"0.47044936\",\n \"0.47041354\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.56046593"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":89396,"cells":{"query":{"kind":"string","value":"Do the necessary precomputation to compute estimates"},"document":{"kind":"string","value":"def _precompute(self):\n N = self.N\n # Store 'N' as used by Gale in N[0] --- this is the total # of\n # occurrences.\n N[0] = sum(N[r] * r for r in range(1, self.max_r + 1))\n self.Z = Z = averaging_transform.transform(N, self.max_r) # Z[r] = Z_r\n self.b, self.a = self._regress(Z) # 'a' and 'b' as used in (Gale); a\n # is intercept and b is slope.\n assert self.b < -1, (\"Log-linear slope > -1 (%f); SGT not applicable\" %\n self.b)\n # Find the transition point between linear Good-Turing estimate and the\n # Turing estimate.\n self.linear_cutoff = self._find_cutoff()\n self.norm_constant = self._find_norm_constant()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def precalculate():\n pass","def precalculate():\n pass","def E_step_precompute(self, model_params, my_suff_stat, my_data):","def run():\n return estimate(0,1,0)","def _conventional_precalculation(self):\n # Run all modules\n for module in self._conventional_modules:\n module.compute()","def precompute(self, features, mode, params):\n return None","def pre_compute(self, e_list):\n\t\tpass","def _pre_fit(self):\n pass","def optimize(self):\n self.vbe_step()\n self.compute_responsibilities()\n self.compute_sufficient_stats()\n self.vbmstep()","def precompute_scoring():\n global volume_void_inclusion\n global attract_point_distances\n global perlin_values\n \n volume_void_inclusion = []\n for i,void in enumerate(volumes_void):\n inclusion = gh.PointInBrep(void,points_input,False)\n volume_void_inclusion.append(inclusion)\n \n attract_point_distances = []\n for i,point in enumerate(points_attractor):\n distances = gh.Division(gh.Distance(point,points_input),max_dist)\n attract_point_distances.append(distances)","def _precompute(self, A):\n return ()","def _compute_results(self):\n self.Y_best = best_value(self.Y)\n self.x_opt = self.X[np.argmin(self.Y),:]\n self.fx_opt = np.min(self.Y)\n self.distance = self._compute_distance_betw_consecutive_x()","def _optimise(self):\n pass","def pretrain(self, X):\n\t\tv_X = share_data(X)\n\t\tdA_layers = self.formula_.dA_layers\n\t\tfor dA_layer in dA_layers:\n\t\t\tdA_layer_infor = build_batch_fixed_iter_model_infor(dA_layer, v_X, \n\t\t\t\tbatch_size = self.pretrain_batch_size, \n\t\t\t\tlearning_rate = self.pretrain_learning_rate, \n\t\t\t\tmodel_X = self.formula_.X) # it is formula x instead of daLayer.X\n\t\t\tbest_params = batch_fixed_iter_optimize(dA_layer_infor, \n\t\t\t\t\tn_epochs = self.pretrain_epochs)\n\t\t\tfor i in xrange(len(best_params)):\n\t\t\t\tdA_layer.params[i].set_value(best_params[i], borrow=True)\n\t\treturn self","def main():\n df = prepro_last()\n X, y = train_build(df)\n fit_store(X, y)","def pre_analyse():\n t = transform()\n model = modified_resnet50()\n model.load_state_dict(\n torch.load(\n \"model.pth.tar\",\n map_location=torch.device(\"cpu\"),\n )[\"state_dict\"]\n )\n model.eval()\n\n def get_preds(img_path):\n \"\"\"\n Gives labelds and probabilities for a single image\n This is were we preprocess the image, using a function defined in the model class\n \"\"\"\n # load image\n img = Image.open(img_path).convert(\"RGB\")\n # process it\n x = t(img)\n # get in in the right format\n x = Variable(x).unsqueeze(0)\n # predictions\n output = model(x)\n # decode\n output = decode(output.cpu().data.numpy()[0])\n\n # filter\n # return pred, proba\n return output\n\n return get_preds(\"image.jpg\")","def estimate(self) -> None:\n\n if self.model is None:\n raise ValueError(\"Model must not be None before a call to estimate\")\n if self.measurements is None:\n raise ValueError(\"measurements must not be None before a call to estimate\")\n if self.camera_frame_directions is None:\n raise ValueError(\"camera_frame_directions must not be None before a call to estimate\")\n if self.weighted_estimation and (self.measurement_covariance is None):\n raise ValueError(\"measurement_covariance must not be None before a call to estimate \"\n \"if weighed_estimation is True\")\n if self.model.use_a_priori and (self.a_priori_state_covariance is None):\n raise ValueError(\"a_priori_state_covariance must not be None before a call to estimate \"\n \"if model.use_a_priori is True\")\n\n # get the size of the state vector\n a_priori_state = np.array(self.model.state_vector)\n state_size = len(a_priori_state)\n\n # get the number of measurements\n num_meas = self.measurements.size\n\n # get the weight matrix\n weight_matrix = self._compute_weight_matrix(state_size, num_meas)\n\n # calculate the prefit residuals\n prefit_residuals = self.compute_residuals()\n pre_ss = prefit_residuals.ravel()@prefit_residuals.ravel()\n\n for _ in range(self.max_iter):\n\n jacobian = self.model.compute_jacobian(self.camera_frame_directions, temperature=self.temperatures)\n\n if self.model.use_a_priori:\n residuals_vec = np.concatenate([prefit_residuals.reshape((-1, 1), order='F'),\n np.zeros((state_size, 1))], axis=0)\n\n else:\n residuals_vec = prefit_residuals.reshape((-1, 1), order='F')\n\n if np.isscalar(weight_matrix):\n lhs = np.sqrt(weight_matrix)*jacobian.T@jacobian\n rhs = np.sqrt(weight_matrix)*jacobian.T@residuals_vec\n else:\n lhs = jacobian.T@weight_matrix@jacobian\n rhs = jacobian.T@weight_matrix@residuals_vec\n\n update_vec = np.linalg.solve(lhs, rhs)\n\n model_copy = self.model.copy()\n\n model_copy.apply_update(update_vec)\n\n postfit_residuals = self.compute_residuals(model=model_copy)\n post_ss = postfit_residuals.ravel()@postfit_residuals.ravel()\n resid_change = abs(pre_ss-post_ss)\n\n # check for convergence\n if resid_change <= (self.residual_atol+self.residual_rtol*pre_ss):\n self._successful = True\n self._postfit_residuals = postfit_residuals\n self.model = model_copy\n self._jacobian = self.model.compute_jacobian(self.camera_frame_directions, temperature=self.temperatures)\n return\n\n elif (np.abs(update_vec) <= (self.state_atol+self.state_rtol*a_priori_state)).all():\n self._successful = True\n self._postfit_residuals = postfit_residuals\n self.model = model_copy\n self._jacobian = self.model.compute_jacobian(self.camera_frame_directions, temperature=self.temperatures)\n return\n\n elif pre_ss < post_ss: # check for divergence\n warnings.warn('Solution is diverging. Stopping iteration.'\n '\\n\\tpre-update residuals {}'\n '\\n\\tpost-update residuals {}'.format(pre_ss, post_ss))\n self._successful = False\n self._postfit_residuals = None\n self._jacobian = None\n return\n\n else: # converging\n self.model = model_copy\n prefit_residuals = postfit_residuals\n pre_ss = post_ss\n a_priori_state = np.array(self.model.state_vector)\n\n warnings.warn(\"Solution didn't converge in the requested number of iterations\")\n self._successful = False\n self._postfit_residuals = prefit_residuals\n self.model\n self._jacobian = self.model.compute_jacobian(self.camera_frame_directions, temperature=self.temperatures)","def computeIntercepts():\n pass","def compute_all(self) -> None:\n self.compute_j_matrix()\n self.compute_outter_distribution()\n self.compute_max_prior()\n self.compute_max_poutter()","def pre_compute_evolution(self):\n logger.info(\"Precomputing evolution parameters\")\n self.lineFreqs = []\n self.linePhis = []\n self.lineh0s = []\n self.mid_times = []\n\n linePhi = 0\n lineFreq_old = 0\n\n for i in trange(self.nsfts):\n mid_time = self.tstart + (i + 0.5) * self.Tsft\n lineFreq = self.get_frequency(mid_time)\n\n self.mid_times.append(mid_time)\n self.lineFreqs.append(lineFreq)\n self.linePhis.append(\n linePhi + np.pi * self.Tsft * (lineFreq_old + lineFreq)\n )\n self.lineh0s.append(self.get_h0(mid_time))\n\n lineFreq_old = lineFreq","def estimate(self) -> None:\n pass","def compute(self): \n Ex=np.zeros((self.nx,self.ny+1))\n Ey=np.zeros((self.nx+1,self.ny))\n Hz=np.zeros((self.nx,self.ny))\n Hzx=np.zeros((self.nx,self.ny))\n Hzy=np.zeros((self.nx,self.ny))\n \n imx = []\n #eps, mu = self.makeenv()\n mu=np.ones((self.nx,self.ny))*const.mu_0\n eps = self.luneberg(int(self.nx/2), int(self.ny*2/3), self.R)\n eps[:20,:] *= self.q #adself.ds a space of higher permittivity \n eps[-20:,:] *= self.q #adself.ds a space of higher permittivity \n eps[:,:20] *= self.q #adself.ds a space of higher permittivity \n eps[:,-20:] *= self.q #adself.ds a space of higher permittivity \n\n c = self.dt/(eps*self.ds)\n d = self.dt/(mu* self.ds)\n \n sigma = self.pml(eps, mu, 20)\n cax = 1 - (sigma[0] * self.dt / eps)\n cay = 1 - (sigma[1] * self.dt / eps)\n dax = 1 - (sigma[2] * self.dt / mu) \n day = 1 - (sigma[3] * self.dt / mu)\n \n bar = progressbar.ProgressBar()\n for n in bar(range(self.nt+1)):\n Ex[:,1:-1] = (cay[:,1:]+cay[:,:-1])/2*Ex[:,1:-1] + (c[:,1:]+c[:,:-1])/2*(Hz[:,1:]-Hz[:,:-1])\n Ey[1:-1,:] = (cax[1:,:]+cax[:-1,:])/2*Ey[1:-1,:] - (c[1:,:]+c[:-1,:])/2*(Hz[1:,:]-Hz[:-1,:])\n \n Hzx = dax*Hzx - d*(Ey[1:,:] - Ey[:-1,:])\n Hzy = day*Hzy + d*(Ex[:,1:] - Ex[:,:-1]) \n Hz = Hzx + Hzy + self.actualsource(self.source, self.f, n, self.dt) \n \n if(n%self.interval == 0): imx.append(Ex[:self.nx,:self.ny]**2 + Ey[:self.nx, :self.ny]**2)\n\n return imx","def precompute(self):\n self.cov_inv = np.linalg.inv(self.cov)\n self.root_2pi_d_det = math.sqrt((2.0*math.pi)**self.raw_data.shape[1] *\n np.linalg.det(self.cov))","def estimate(self) -> None:\n if self.model is None:\n raise ValueError(\"Model must not be None before a call to estimate\")\n if self.measurements is None:\n raise ValueError(\"measurements must not be None before a call to estimate\")\n if self.camera_frame_directions is None:\n raise ValueError(\"camera_frame_directions must not be None before a call to estimate\")\n if self.weighted_estimation and (self.measurement_covariance is None):\n raise ValueError(\"measurement_covariance must not be None before a call to estimate \"\n \"if weighed_estimation is True\")\n if self.model.use_a_priori and (self.a_priori_state_covariance is None):\n raise ValueError(\"a_priori_state_covariance must not be None before a call to estimate \"\n \"if model.use_a_priori is True\")\n\n # get the size of the state vector\n a_priori_state = np.array(self.model.state_vector)\n state_size = len(a_priori_state)\n\n # get the number of measurements\n num_meas = self.measurements.size\n\n # get the weight matrix\n weight_matrix = self._compute_weight_matrix(state_size, num_meas)\n\n # calculate the prefit residuals\n prefit_residuals = self.compute_residuals()\n pre_ss = prefit_residuals.ravel() @ prefit_residuals.ravel()\n\n # a flag specifying this is the first time through so we need to initialize the lma_coefficient\n first = True\n lma_coefficient = 0\n n_diverge = 0\n\n # iterate to convergence\n for _ in range(self.max_iter):\n\n # get the jacobian matrix\n jacobian = self.model.compute_jacobian(self.camera_frame_directions, temperature=self.temperatures)\n\n if first:\n # initialize the lma_coefficient\n lma_coefficient = 0.001 * np.trace(jacobian.T @ jacobian) / jacobian.shape[1]\n\n if self.model.use_a_priori:\n residuals_vec = np.concatenate([prefit_residuals.reshape((-1, 1), order='F'),\n np.zeros((state_size, 1))], axis=0)\n\n else:\n residuals_vec = prefit_residuals.reshape((-1, 1), order='F')\n\n if np.isscalar(weight_matrix):\n lhs = np.sqrt(weight_matrix) * jacobian.T @ jacobian\n rhs = np.sqrt(weight_matrix) * jacobian.T @ residuals_vec\n else:\n lhs = jacobian.T @ weight_matrix @ jacobian\n rhs = jacobian.T @ weight_matrix @ residuals_vec\n\n # get the update vector using LMA\n update_vec = np.linalg.solve(lhs + lma_coefficient*np.diag(np.diag(lhs)), rhs)\n\n model_copy = self.model.copy()\n\n model_copy.apply_update(update_vec)\n\n postfit_residuals = self.compute_residuals(model=model_copy)\n post_ss = postfit_residuals.ravel() @ postfit_residuals.ravel()\n resid_change = abs(pre_ss - post_ss)\n\n # check for convergence\n if resid_change <= (self.residual_atol + self.residual_rtol * pre_ss):\n self._successful = True\n self._postfit_residuals = postfit_residuals\n self.model = model_copy\n self._jacobian = self.model.compute_jacobian(self.camera_frame_directions, temperature=self.temperatures)\n return\n\n elif (np.abs(update_vec) <= (self.state_atol + self.state_rtol * a_priori_state)).all():\n self._successful = True\n self._postfit_residuals = postfit_residuals\n self.model = model_copy\n self._jacobian = self.model.compute_jacobian(self.camera_frame_directions, temperature=self.temperatures)\n return\n\n elif pre_ss < post_ss: # check for divergence\n\n n_diverge += 1\n\n if n_diverge > self.max_divergence_steps:\n warnings.warn('Solution is diverging. Stopping iteration.'\n '\\n\\tpre-update residuals {}'\n '\\n\\tpost-update residuals {}'\n '\\n\\tdiverged for {} iterations'.format(pre_ss, post_ss, n_diverge))\n self._successful = False\n self._postfit_residuals = None\n self._jacobian = None\n return\n\n # update the lma coefficient\n lma_coefficient *= 10\n\n else: # converging\n # reset the divergence counter\n n_diverge = 0\n # update the lma coefficient\n lma_coefficient /= 10\n # prepare for the next iteration\n self.model = model_copy\n prefit_residuals = postfit_residuals\n pre_ss = post_ss\n a_priori_state = np.array(self.model.state_vector)\n\n warnings.warn(\"Solution didn't converge in the requested number of iterations\")\n self._successful = False\n self._postfit_residuals = prefit_residuals\n self.model\n self._jacobian = self.model.compute_jacobian(self.camera_frame_directions, temperature=self.temperatures)","def on_iteration_start(self):\n\n self.Xprv = self.X.copy()\n if (not self.opt['FastSolve'] or isinstance(self.backtrack,\n BacktrackRobust)):\n self.Yprv = self.Y.copy()\n\n if self.opt['Monotone']:\n if self.k == 0:\n self.objfn = self.eval_objfn()\n self.objfn_prev = self.objfn","def preprocess_train_data(self):\r\n print(\"* Preprocessing training data.\", flush=True)\r\n prep.create_HDF_file(self.C.training_set, is_training_set=True)\r\n\r\n self.print_time_elapsed()","def prep(self):\n \n # create a dict with prior probabilities\n self.row_priors = [0.0]*len(self.rows)\n self.feature_priors = dict()\n \n # denominator is given by reference priors\n denominator = sum(self.column_priors)\n # null_feature_prior is used when feature is not observed at all\n # this is set up to scale with features, i.e. arbitrarily adding\n # child features into an ontology should not skew sums over repr.\n null_feature_prior = 1/max(denominator, float(len(self.rows)))\n \n for rowname, rowindex in self.rows.items(): \n numerator = 0\n for colname, colindex in self.columns.items(): \n colprior = self.column_priors[colindex]\n numerator += self.data[colindex][rowindex]*colprior\n if numerator == 0:\n numerator = null_feature_prior \n self.row_priors[rowindex] = float(numerator)/denominator\n self.feature_priors[rowname] = self.row_priors[rowindex]\n\n return self","def on_iteration_start(self):\n\n self.Xfprv = self.Xf.copy()\n if (not self.opt['FastSolve'] or isinstance(self.backtrack,\n BacktrackRobust)):\n self.Yfprv = self.Yf.copy()\n\n if self.opt['Monotone']:\n if self.k == 0:\n self.objfn = self.eval_objfn()\n self.objfn_prev = self.objfn","def _preprocess(self):\n if self.gt_matrix_PCs is None:\n start = time.time()\n gt_matrix = self._process_vcf().get('gt_matrix')\n print('gt_matrix took {} secs'.format(time.time() - start))\n \n # Normalize gt_matrix by site\n gt_matrix_norm = gt_matrix - np.mean(gt_matrix, axis=1)[:, np.newaxis]\n \n # PCA\n start = time.time()\n u, s, vh = np.linalg.svd(gt_matrix_norm.T, full_matrices=False)\n print('SVD took {} secs'.format(time.time() - start))\n self.gt_matrix_PCs = -u @ np.diag(s)\n \n # Get relevant objects from result of ripser\n if self.ripser_result is None:\n start = time.time()\n print(\"Getting ripser object\")\n self.ripser_result = ripser(self.gt_matrix_PCs, coeff=2, maxdim=1, do_cocycles=True)\n print('Ripser took {} secs'.format(time.time() - start))","def compute(self) -> None:\n \n self.model.eval()\n \n with torch.no_grad():\n for (input, target, _) in self.loader:\n\n # self.model = self.model.train(False) # TEST @lacoupe\n output, _ = self.model(input)\n \n output = (output >= 0.5)\n \n for out, tar in zip(output, target):\n \n tar = bool(tar)\n \n if out and tar:\n self.confusion['true_positive'] += 1\n elif not out and not tar:\n self.confusion['true_negative'] += 1\n elif out and not tar:\n self.confusion['false_positive'] += 1\n elif not out and tar:\n self.confusion['false_negative'] += 1\n \n self.accuracy = (self.confusion['true_positive'] + self.confusion['true_negative']) \\\n / sum(list(self.confusion.values()))\n \n if (self.confusion['true_positive'] + self.confusion['false_positive']) == 0.:\n self.precision = 0.\n else:\n self.precision = self.confusion['true_positive'] \\\n / (self.confusion['true_positive'] + self.confusion['false_positive'])\n \n if (self.confusion['true_positive'] + self.confusion['false_negative']) == 0.:\n self.recall = 0.\n else:\n self.recall = self.confusion['true_positive'] \\\n / (self.confusion['true_positive'] + self.confusion['false_negative'])\n \n if (self.precision + self.recall) == 0.:\n self.f1_score = 0.\n else:\n self.f1_score = 2 * self.precision * self.recall / (self.precision + self.recall)","def __estimatedata(data, payout=500, randomsample=0):\n if randomsample > 0:\n data = data.sort('prob')\n data = data.head(randomsample)\n\n data['premium'] = 0.0\n data['payout'] = payout\n\n # get the model parameters\n \n # estimate\n estimator = EtheriscEstimator(data)\n estimator.estimate()\n return estimator","def _computations(self,do_Kmm=True, do_Kmm_grad=True):\r\n\r\n if do_Kmm:\r\n self.Lm = jitchol(self.Kmm)\r\n\r\n # The rather complex computations of self.A\r\n if self.has_uncertain_inputs:\r\n if self.likelihood.is_heteroscedastic:\r\n psi2_beta = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.batchsize, 1, 1))).sum(0)\r\n else:\r\n psi2_beta = self.psi2.sum(0) * self.likelihood.precision\r\n evals, evecs = np.linalg.eigh(psi2_beta)\r\n clipped_evals = np.clip(evals, 0., 1e6) # TODO: make clipping configurable\r\n tmp = evecs * np.sqrt(clipped_evals)\r\n else:\r\n if self.likelihood.is_heteroscedastic:\r\n tmp = self.psi1.T * (np.sqrt(self.likelihood.precision.flatten().reshape(1, self.batchsize)))\r\n else:\r\n tmp = self.psi1.T * (np.sqrt(self.likelihood.precision))\r\n tmp, _ = dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)\r\n self.A = tdot(tmp)\r\n\r\n self.V = self.likelihood.precision*self.likelihood.Y\r\n self.VmT = np.dot(self.V,self.q_u_expectation[0].T)\r\n self.psi1V = np.dot(self.psi1.T, self.V)\r\n\r\n self.B = np.eye(self.num_inducing)*self.data_prop + self.A\r\n self.Lambda = backsub_both_sides(self.Lm, self.B.T)\r\n self.LQL = backsub_both_sides(self.Lm,self.q_u_expectation[1].T,transpose='right')\r\n\r\n self.trace_K = self.psi0.sum() - np.trace(self.A)/self.likelihood.precision\r\n self.Kmmi_m, _ = dpotrs(self.Lm, self.q_u_expectation[0], lower=1)\r\n self.projected_mean = np.dot(self.psi1,self.Kmmi_m)\r\n\r\n # Compute dL_dpsi\r\n self.dL_dpsi0 = - 0.5 * self.output_dim * self.likelihood.precision * np.ones(self.batchsize)\r\n self.dL_dpsi1, _ = dpotrs(self.Lm,np.asfortranarray(self.VmT.T),lower=1)\r\n self.dL_dpsi1 = self.dL_dpsi1.T\r\n\r\n dL_dpsi2 = -0.5 * self.likelihood.precision * backsub_both_sides(self.Lm, self.LQL - self.output_dim * np.eye(self.num_inducing))\r\n if self.has_uncertain_inputs:\r\n self.dL_dpsi2 = np.repeat(dL_dpsi2[None,:,:],self.batchsize,axis=0)\r\n else:\r\n self.dL_dpsi1 += 2.*np.dot(dL_dpsi2,self.psi1.T).T\r\n self.dL_dpsi2 = None\r\n\r\n # Compute dL_dKmm\r\n if do_Kmm_grad:\r\n tmp = np.dot(self.LQL,self.A) - backsub_both_sides(self.Lm,np.dot(self.q_u_expectation[0],self.psi1V.T),transpose='right')\r\n tmp += tmp.T\r\n tmp += -self.output_dim*self.B\r\n tmp += self.data_prop*self.LQL\r\n self.dL_dKmm = 0.5*backsub_both_sides(self.Lm,tmp)\r\n\r\n #Compute the gradient of the log likelihood wrt noise variance\r\n self.partial_for_likelihood = -0.5*(self.batchsize*self.output_dim - np.sum(self.A*self.LQL))*self.likelihood.precision\r\n self.partial_for_likelihood += (0.5*self.output_dim*self.trace_K + 0.5 * self.likelihood.trYYT - np.sum(self.likelihood.Y*self.projected_mean))*self.likelihood.precision**2","def _evaluate_during_fit(self, test_loader, epoch):","def __calculated_initial_fitness(self) -> None:\n\n logger.info('Start calculating initial fitness')\n\n __calculated_values = (self._commands_holder_train.execute_commands(\n self.__initial_parameters))\n\n self.__initial_fitness = (self._evaluate_fitness(\n __calculated_values, self._training_dataset))\n\n logger.info('Initial fitness calculation finishes')","def compute_statistics(self):","def calculations(self):\n self.prerequisite_set = get_prerequisites(self.major, self.major2)\n self.check_prerequisites()\n self.get_requirements_for_majors()","def request_estimation(self):\n if self.can_estimate():\n res = self.get_estimation()\n if res is not None:\n self.callback.plp_estimation(res)\n else:\n self.callback.plp_no_preconditions()\n else:\n self.callback.plp_missing_data() # plp_cannot_estimate(msg)","def calc_pre_intertie_generation (self):\n\n self.pre_intertie_generation = \\\n self.forecast.get_generation(self.start_year,self.end_year)\n\n gen_eff = self.cd[\"diesel generation efficiency\"]\n self.pre_intertie_generation_fuel_used = \\\n self.pre_intertie_generation / gen_eff\n\n #~ print 'self.baseline_generatio',self.baseline_generation","def evaluate(self):\n # define start index test set\n start_test_set = int(len(self.X) * 2 / 3)\n\n # Different methods for cummulativa vs day-ahead forecasting\n if self.forecast_horizon == 1:\n # In sample\n lin_residuals_in_sample = self.y - (self.betas[0] + np.dot(self.X, self.betas[1]))\n self.rmse_in_sample = np.mean(lin_residuals_in_sample ** 2) ** 0.5\n self.var_in_sample = np.var(self.y)\n\n # Out of sample\n # Calculate MSE of wls-ev prediction\n self.mse_wlsev = np.mean((self.y[start_test_set:] - self.ols_predict()) ** 2)\n # Calculate MSE of benchmark prediction\n self.mse_benchmark = np.mean((self.y[start_test_set:] - self.benchmark_predict()) ** 2)\n else:\n # In Sample with betas estimated on full time series\n lin_residuals_in_sample = rolling_sum(self.y, self.forecast_horizon) - (\n self.betas[0] + np.dot(self.X[:-(self.forecast_horizon-1)], self.betas[1]))\n self.rmse_in_sample = np.mean(lin_residuals_in_sample ** 2) ** 0.5\n self.var_in_sample = np.var(rolling_sum(self.y, self.forecast_horizon))\n\n # Out of sample\n # calculate realized cummulative returns over forecast horizon sequences\n cum_rets_realized = rolling_sum(self.y[start_test_set:], self.forecast_horizon)\n # Calculate MSE of wls-ev prediction, only where realized values are available\n self.mse_wlsev = np.mean((cum_rets_realized - self.ols_predict()[:-(self.forecast_horizon-1)]) ** 2)\n # Calculate MSE of benchmark prediction, only where realized values are available\n self.mse_benchmark = np.mean(\n (cum_rets_realized - self.benchmark_predict()[:-(self.forecast_horizon-1)]) ** 2)\n\n # Calculate out of sample r-squared\n self.oos_r_squared = 1 - (self.mse_wlsev / self.mse_benchmark)\n # Calculate in sample r-squared\n self.in_sample_r_squared = 1.0 - (self.rmse_in_sample ** 2) / self.var_in_sample","def add_is_approximation(self):\n \n if (self.mf_steps == 0 or self.alpha ==0) and (not self.mixture)\\\n and (self.gibbs_steps ==0):\n print(\"Importance distribution is uniform\")\n weight_term = T.log(self.num_samples) + self.num_vars*T.log(0.5)\n \n elif (self.mf_steps > 0 and self.alpha > 0) and (not self.mixture)\\\n and (self.gibbs_steps ==0):\n print(\"Importance distribution is not uniform\")\n weight_term = T.log(self.num_samples)+\\\n self.get_importance_evals(T.transpose(self.x_tilda), \n T.transpose(self.sampler_theta))\n \n if self.resample:\n weight_term = T.reshape(weight_term, \n [self.batch_size, self.num_samples])\n \n elif self.mixture:\n \n if self.resample:\n n_iters = self.num_samples*self.batch_size \n else:\n n_iters = self.num_samples \n \n weight_term, _ =\\\n theano.scan(lambda j: \n self.get_mixture_evals(T.transpose(self.x_tilda[j,:])),\n sequences = [T.arange(n_iters)])\n \n if self.resample:\n weight_term = T.reshape(weight_term, \n [self.batch_size, self.num_samples]) \n \n weight_term = T.log(self.num_samples) + weight_term \n \n elif self.gibbs_steps > 0:\n print(\"Importance distribution is gibbs sampler\") \n weight_term = T.log(self.num_samples)+\\\n self.get_importance_evals(T.transpose(self.x_tilda), \n T.transpose(self.sampler_theta))\n \n if self.resample:\n weight_term = T.reshape(weight_term, \n [self.batch_size, self.num_samples]) \n \n \n if self.resample and self.num_hidden ==0:\n \n approx_Z = -self.compute_energy(self.x_tilda, \n self.num_samples*self.batch_size)\n \n elif (not self.resample) and self.num_hidden ==0:\n \n approx_Z = -self.compute_energy(self.x_tilda, \n self.num_samples)\n \n elif self.resample and self.num_hidden > 0:\n \n approx_Z = -self.compute_free_energy(self.x_tilda)\n \n elif (not self.resample) and self.num_hidden >0:\n \n approx_Z = -self.compute_free_energy(self.x_tilda)\n \n if self.resample:\n \n approx_Z = T.reshape(approx_Z, \n [self.batch_size, self.num_samples])\n \n approx_Z = approx_Z - weight_term\n \n return approx_Z","def eval_obs_pre(self, X, y):\n \n X, y = check_X_y(\n X, y, accept_sparse=False, dtype=np.float64, order=\"C\",\n ensure_min_features=2, estimator=self)\n\n n_dimensions = X.shape[1]\n self.classes_, n_samples = np.unique(y, return_counts=True)\n n_times = self.classes_.size\n\n # n_samples = np.array([x.shape[0] for x in X])\n if self.assume_centered:\n self.location_ = np.zeros((n_times, n_dimensions))\n else:\n self.location_ = np.array(\n [X[y == cl].mean(0) for cl in self.classes_])\n\n precisions = self.get_observed_precision()\n\n emp_pre_score = []\n sam_pre_score = []\n slack = []\n\n for i in range(precisions.shape[0]):\n emp_cov = empirical_covariance(X[y == i] - self.location_[i], assume_centered=True)\n precision = precisions[i, :, :]\n # slack.append(-self.constrained_to[i] + logl(emp_cov, precision))\n _, log_det = np.linalg.slogdet(precision)\n emp_pre_score.append(log_det - np.trace(emp_cov @ precision))\n sam_pre_score.append(log_det - np.array([np.trace((X[y == i][[j], :].T @ X[y == i][[j], :]) @ precision) for j in range(int(n_samples))]))\n\n return self.emp_inv_score - np.array(emp_pre_score), self.sam_inv_score - np.array(sam_pre_score), precision","def propose_optimize():\n pass","def solve(self):\n \n # Check if cost is available for both estimators\n if not self.est0.cost_avail or not self.est1.cost_avail:\n self.comp_cost = False\n \n # Initial estimate from the input node\n if self.comp_cost:\n z0, zvar0, cost0 = self.est0.est_init(return_cost=True)\n else:\n z0, zvar0 = self.est0.est_init(return_cost=False)\n cost0 = 0\n self.z0 = z0\n self.zvar0 = zvar0\n self.cost0 = cost0\n \n # Initialize other variables\n self.var_cost0 = 0\n self.var_cost1 = 0\n self.cost = 0\n self.s = np.zeros(self.shape1)\n \n for it in range(self.nit):\n \n # Forward transform to est1\n t0 = time.time()\n rvar1_new = self.A.var_dot(self.zvar0)\n rvar1_rep = common.repeat_axes(rvar1_new,self.shape1,\\\n self.var_axes1,rep=False)\n z1_mult = self.A.dot(self.z0)\n r1_new = z1_mult - rvar1_rep*self.s\n \n # Damping\n if it > 0: \n self.r1 = (1-self.step)*self.r1 + self.step*r1_new\n self.rvar1 = (1-self.step)*self.rvar1 + self.step*rvar1_new\n else:\n self.r1 = r1_new\n self.rvar1 = rvar1_new\n\n # Estimator 1 \n if self.comp_cost: \n z1, zvar1, cost1 = self.est1.est(self.r1, self.rvar1, return_cost=True) \n if not self.map_est:\n cost1 -= self.cost_adjust(self.r1,z1,self.rvar1,zvar1,\\\n self.shape1,self.var_axes1)\n else:\n z1, zvar1 = self.est1.est(self.r1, self.rvar1, return_cost=False) \n cost1 = 0\n self.z1 = z1\n self.zvar1 = zvar1\n self.cost1 = cost1 \n con_new = np.mean(np.abs(z1-z1_mult)**2) \n \n # Reverse nonlinear transform to est 0\n self.s = (self.z1-self.r1)/rvar1_rep\n self.sprec = 1/self.rvar1*(1-self.zvar1/self.rvar1)\n t1 = time.time()\n self.time_est1 = t1-t0\n \n # Reverse linear transform to est 0 \n rvar0_new = 1/self.A.var_dotH(self.sprec)\n rvar0_rep = common.repeat_axes(rvar0_new,self.shape0,\\\n self.var_axes0,rep=False)\n r0_new = self.z0 + rvar0_rep*self.A.dotH(self.s)\n \n # Damping\n if it > 0:\n self.r0 = (1-self.step)*self.r0 + self.step*r0_new\n self.rvar0 = (1-self.step)*self.rvar0 + self.step*rvar0_new\n else:\n self.r0 = r0_new\n self.rvar0 = rvar0_new\n \n \n # Estimator 0\n if self.comp_cost:\n z0, zvar0, cost0 = self.est0.est(self.r0, self.rvar0, return_cost=True)\n if not self.map_est:\n cost0 -= self.cost_adjust(self.r0,z0,self.rvar0,zvar0,\\\n self.shape0,self.var_axes0)\n \n else:\n z0, zvar0 = self.est0.est(self.r0, self.rvar0, return_cost=False)\n cost0 = 0\n self.z0 = z0\n self.zvar0 = zvar0\n self.cost0 = cost0 \n\n \n # Compute total cost and constraint \n cost_new = self.cost0 + self.cost1 \n if not self.map_est:\n cost_new += self.cost_gauss()\n \n # Step size adaptation\n if (self.step_adapt) and (it > 0):\n if (con_new < self.con):\n self.step = np.minimum(1,self.step_inc*self.step)\n else:\n self.step = np.maximum(self.step_min, self.step_dec*self.step)\n self.cost=cost_new\n self.con=con_new\n \n t2 = time.time()\n self.time_est0 = t2-t1\n self.time_iter = t2-t0\n \n # Print progress\n if self.prt_period > 0:\n if (it % self.prt_period == 0):\n if self.comp_cost:\n print(\"it={0:4d} cost={1:12.4e} con={2:12.4e} step={3:12.4e}\".format(\\\n it, self.cost, self.con, self.step))\n else:\n print(\"it={0:4d} con={1:12.4e}\".format(\\\n it, self.con))\n \n # Save history\n self.save_hist()","def compute(self):\n # update parameters with the values of widgets\n self.parmVal[0] = float(str(self.p1text.text()))\n if len(self.parmName) > 1:\n self.parmVal[1] = float(str(self.p2text.text()))\n if len(self.parmName) > 2:\n self.parmVal[2] = float(str(self.p3text.text()))\n if len(self.parmName) > 3:\n self.parmVal[3] = float(str(self.p4text.text()))\n\n if self.model == 0:\n # Polynomial fit\n order = int(self.parmVal[0])\n pcoef = np.polyfit(self.data[0], self.data[1], order)\n Yfit = np.polyval(pcoef, self.data[0])\n # compute the root mean square error (RMSE)\n err = np.sqrt(sum((Yfit-self.data[1])**2) / len(self.data[1]))\n msg = 'Least squares polynomial fit - '\n msg += 'Polynom order = {}\\n'.format(order)\n msg += 'c{0} = {1}\\n'.format(order, pcoef[0])\n for i in range(order):\n msg += 'c{0} = {1}\\n'.format(order-1, pcoef[i+1])\n msg += 'RMSE = {0:g}\\n'.format(err)\n # compute 1st derivative\n pfit = np.poly1d(pcoef)\n pdercoef = np.polyder(pcoef, 1)\n pder = np.poly1d(pdercoef)\n # print roots\n st = \"Root(s) of the 1st derivative = {0}\\n\"\n msg += st.format(np.array2string(pder.r))\n\n elif self.model == 1:\n # Fitting with exponential equation A*exp(B*x)\n # initial guesses\n p0 = [self.parmVal[0], self.parmVal[1]]\n pbest1 = leastsq(self.residuals_Exp, p0, args=(self.data[1], self.data[0]))\n Yfit = self.Exp(self.data[0], pbest1[0])\n Res = self.residuals_Exp(pbest1[0], self.data[1], self.data[0])\n # compute the root mean square error (RMSE)\n err = np.sqrt(np.sum(np.square(Res)) / len(self.data[1]))\n msg = 'Fitting with exponential equation: A * exp(B * x)\\n'\n msg += '[A, B] = {0}\\n'.format(str(pbest1[0]))\n msg += 'RMSE = {0:g}\\n\\n'.format(err)\n self.parmVal = pbest1[0]\n\n elif self.model == 2:\n # Fitting with logarithm equation A*Ln(x)+B\n # initial guesses\n p0 = [self.parmVal[0], self.parmVal[1]]\n pbest1 = leastsq(self.residuals_Log, p0, args=(self.data[1], self.data[0]))\n Yfit = self.Log(self.data[0], pbest1[0])\n Res = self.residuals_Log(pbest1[0], self.data[1], self.data[0])\n # compute the root mean square error (RMSE)\n err = np.sqrt(np.sum(np.square(Res)) / len(self.data[1]))\n msg = 'Fitting with logarithm equation: A * Ln(x) + B\\n'\n msg += '[A, B] = {0}\\n'.format(str(pbest1[0]))\n msg += 'RMSE = {0:g}\\n\\n'.format(err)\n self.parmVal = pbest1[0]\n\n elif self.model == 3:\n # Fitting with Arrhenius equation A*exp(-(E/(R*(x+273))))\n # initial guesses\n p0 = [self.parmVal[0], self.parmVal[1]]\n pbest1 = leastsq(self.residuals_Arr, p0, args=(self.data[1], self.data[0]))\n Yfit = self.Arr(self.data[0], pbest1[0])\n Res = self.residuals_Arr(pbest1[0], self.data[1], self.data[0])\n # compute the root mean square error (RMSE)\n err = np.sqrt(np.sum(np.square(Res)) / len(self.data[1]))\n msg = 'Fitting with Arrhenius equation: A * exp(-(E/(R * (x + 273))))\\n'\n msg += '[A, E] = {0}\\n'.format(str(pbest1[0]))\n msg += 'RMSE = {0:g}\\n\\n'.format(err)\n self.parmVal = pbest1[0]\n\n elif self.model == 4:\n # Fitting data with Power model\n # initial guesses\n p0 = [self.parmVal[0], self.parmVal[1]]\n pbest1 = leastsq(self.residuals_PL, p0, args=(self.data[1], self.data[0]))\n Yfit = self.PowerLaw(self.data[0], pbest1[0])\n Res = self.residuals_PL(pbest1[0], self.data[1], self.data[0])\n # compute the root mean square error (RMSE)\n err = np.sqrt(np.sum(np.square(Res)) / len(self.data[1]))\n msg = 'Fitting with Power model: A * x^B\\n'\n msg += '[A, B] = {0}\\n'.format(str(pbest1[0]))\n msg += 'RMSE = {0:g}\\n\\n'.format(err)\n self.parmVal = pbest1[0]\n\n elif self.model == 5:\n # Fitting Rheo data with Herschel-Bulkley model\n # initial guesses\n p0 = [self.parmVal[0], self.parmVal[1], self.parmVal[2]]\n pbest1 = leastsq(self.residuals_HB, p0, args=(self.data[1], self.data[0]))\n Yfit = self.HerschelBulkley(self.data[0], pbest1[0])\n Res = self.residuals_HB(pbest1[0], self.data[1], self.data[0])\n # compute the root mean square error (RMSE)\n err = np.sqrt(np.sum(np.square(Res)) / len(self.data[1]))\n msg = 'Fitting with Herschel-Bulkley model: A + B * x^C\\n'\n msg += '[A, B, C] = {0}\\n'.format(str(pbest1[0]))\n msg += 'RMSE = {0:g}\\n\\n'.format(err)\n self.parmVal = pbest1[0]\n\n elif self.model == 6:\n # Fitting with sigmoid S+H/(1+exp(-lambda*x)\n # initial guesses\n p0 = [self.parmVal[0], self.parmVal[1], self.parmVal[2], self.parmVal[3]]\n pbest1 = leastsq(self.residuals_Sigm, p0, args=(self.data[1], self.data[0]))\n Yfit = self.Sigm(self.data[0], pbest1[0])\n Res = self.residuals_Sigm(pbest1[0], self.data[1], self.data[0])\n # compute the root mean square error (RMSE)\n err = np.sqrt(np.sum(np.square(Res)) / len(self.data[1]))\n msg = 'Fitting with sigmoid equation: yo + H /(1 + exp(S * (xo - x))\\n'\n msg += '[xo, yo, H, S] = {0}\\n'.format(str(pbest1[0]))\n msg += 'RMSE = {0:g}\\n\\n'.format(err)\n self.parmVal = pbest1[0]\n\n else:\n msg = 'Unknown model'\n Yfit = self.data[1]\n\n # Show results in log windows\n self.log.setText(msg)\n self.saveToLogFile(msg)\n self.data[2] = Yfit\n self.data[3] = self.data[2] - self.data[1] - self.diffshift\n\n self.updatePlot()\n self.updateParms()","def preprocess_test_data(self):\r\n print(\"* Preprocessing test data.\", flush=True)\r\n prep.create_HDF_file(self.C.test_set)\r\n\r\n self.print_time_elapsed()","def prepare(self) -> None:\n\n \"\"\"\n Objective function\n Coefficient -2 means that we solve maximization problem (multiple all \n value to -1) and also there are left coverage area and right coverage \n area for each station (2* cov)\n \"\"\"\n\n f = [-2 * self.cov[i] for i in range(self.get_column_num)]\n self._f = np.array(f)\n\n \"\"\" Inequality Constraints\"\"\"\n ineq_cost = [self.cost[i] for i in range(self.get_column_num)]\n self._ineq_constraints = np.array(ineq_cost)\n self._b = np.array(self.cost_limit)\n\n \"\"\" \n There is no equality constraints. \n self._eq_constraints is empty\n self._beq is empty\n \"\"\"","def setup(self):\n self.model.monitor = myMonitor.get_monitor(self.model)\n self.model.monitor.time_budget_exceeded = False\n if self.algorithm is not None:\n self.algorithm.setup(model=self.model, dataset=self.dataset)\n self.setup_extensions()\n\n # Model.modify_updates is used by the training algorithm to\n # enforce constraints after each step of learning. Here we\n # make sure the constraints are enforced from the start.\n self.model.enforce_constraints()","def preprocess(self):\n\n mm_magcoord.add_aacgm_coordinates(self)\n mm_magcoord.add_quasi_dipole_coordinates(self)\n mm_sc.calculate_ecef_velocity(self)\n mm_sc.add_ram_pointing_sc_attitude_vectors(self)\n\n return","def _calculate(self):\n raise NotYetImplemented()","def before_fit(self):\n self.run = not hasattr(self.learn, 'lr_finder') and not hasattr(self, \"gather_preds\")\n if not self.run:\n return\n\n # Prepare ground truth container, set here as y_true's always stay the same\n self.y_true = []","def _build_precomputed_data(self):\n if self.num_sampled == 0:\n self._K_chol = numpy.array([])\n self._K_inv_y = numpy.array([])\n else:\n covariance_matrix = python_utils.build_covariance_matrix(\n self._covariance,\n self._points_sampled,\n noise_variance=self._points_sampled_noise_variance,\n )\n\n C = self._build_integrated_term_maxtrix(self._covariance, self._points_sampled)\n self._K_Inv = numpy.linalg.inv(covariance_matrix)\n self._K_C = numpy.empty((covariance_matrix.shape[0],covariance_matrix.shape[0]))\n self._K_C = numpy.multiply(C, self._K_Inv)\n self._K_chol = scipy.linalg.cho_factor(covariance_matrix, lower=True, overwrite_a=True)\n self._K_inv_y = scipy.linalg.cho_solve(self._K_chol, self._points_sampled_value)\n self._marginal_mean_mat = self._build_marginal_matrix_mean()\n self._marginal_mean_mat_gradient = self._build_marginal_matrix_mean_gradient()","def _prepare_fit(self):\n self.lower_conf_int, self.upper_conf_int, self.upper_whisker_res = None, None, None\n self.model_fit = None\n self.residuals, self.residuals_forecast, self.fittedvalues = None, None, None","def basic_experiments(k, p, num_iter, transforms, train_path, test_path):\n start_time = time.time()\n print(\"Generating advice\")\n with open(train_path, \"r\") as f:\n train_data = f.readlines()\n advice = generate_advice(train_data, transforms)\n print(\"Done: \" + str(time.time() - start_time) + \"\\n\")\n\n print(\"Generating real count for test data\")\n with open(test_path, \"r\") as f:\n test_data = f.readlines()\n real_count = generate_advice(test_data, transforms)\n print(\"Done: \" + str(time.time() - start_time))\n actual = [x.moment(p) for x in real_count]\n print(\"Actual moments:\")\n print(actual)\n\n print(\"Estimating using noisy advice:\")\n est_with_advice = []\n for i in range(num_iter):\n seed = str(random.randint(1, 2**64 + 1)) + str(random.randint(1, 2**64 + 1))\n this_hash_exp = lambda x: hash_exp(x, seed)\n est_with_advice.append(\n estimate_using_advice(test_data, transforms, k, p, this_hash_exp,\n advice))\n print(\"Done: \" + str(time.time() - start_time))\n print(est_with_advice)\n\n print(\"Estimating using perfect advice:\")\n est_perfect_advice = []\n for i in range(num_iter):\n seed = str(random.randint(1, 2**64 + 1)) + str(random.randint(1, 2**64 + 1))\n this_hash_exp = lambda x: hash_exp(x, seed)\n est_perfect_advice.append(\n estimate_using_advice(test_data, transforms, k, p, this_hash_exp,\n real_count))\n print(\"Done: \" + str(time.time() - start_time))\n print(est_perfect_advice)\n\n print(\"Estimating using PPSWOR:\")\n est_ppswor = []\n for i in range(num_iter):\n cur = []\n for x in real_count:\n cur.append(ppswor_estimate_moment(x.counts.items(), k, p))\n est_ppswor.append(cur)\n print(\"Done: \" + str(time.time() - start_time))\n print(est_ppswor)\n\n print(\"Estimating using ell_2 PPSWOR:\")\n est_ppswor_l2 = []\n for i in range(num_iter):\n cur = []\n for x in real_count:\n cur.append(ppswor_estimate_moment(x.counts.items(), k, p, 2))\n est_ppswor_l2.append(cur)\n print(\"Done: \" + str(time.time() - start_time))\n print(est_ppswor_l2)","def _compute(self):\n\n super(RegressionStatistics, self)._compute()\n sets = self.sets\n Nsets = len(sets)\n\n stats = {}\n\n funcs = {\n 'RMP_t': lambda p,t:rootMeanPowerFx(t),\n 'STD_t': lambda p,t:N.std(t),\n 'RMP_p': lambda p,t:rootMeanPowerFx(p),\n 'STD_p': lambda p,t:N.std(p),\n 'CCe': CorrErrorFx(),\n 'CCp': CorrErrorPFx(),\n 'RMSE': RMSErrorFx(),\n 'RMSE/RMP_t': RelativeRMSErrorFx()\n }\n\n for funcname, func in funcs.iteritems():\n funcname_all = funcname + '_all'\n stats[funcname_all] = []\n for i, (targets, predictions, values) in enumerate(sets):\n stats[funcname_all] += [func(predictions, targets)]\n stats[funcname_all] = N.array(stats[funcname_all])\n stats[funcname] = N.mean(stats[funcname_all])\n stats[funcname+'_std'] = N.std(stats[funcname_all])\n stats[funcname+'_max'] = N.max(stats[funcname_all])\n stats[funcname+'_min'] = N.min(stats[funcname_all])\n\n # create ``summary`` statistics, since some per-set statistics\n # might be uncomputable if a set contains just a single number\n # (like in the case of correlation coefficient)\n targets, predictions = [], []\n for i, (targets_, predictions_, values_) in enumerate(sets):\n targets += list(targets_)\n predictions += list(predictions_)\n\n for funcname, func in funcs.iteritems():\n funcname_all = 'Summary ' + funcname\n stats[funcname_all] = func(predictions, targets)\n\n self._stats.update(stats)","def _compute_reward(self, observations, done):\n raise NotImplementedError()","def _compute_reward(self, observations, done):\n raise NotImplementedError()","def _compute_reward(self, observations, done):\n raise NotImplementedError()","def _compute_reward(self, observations, done):\n raise NotImplementedError()","def _compute_reward(self, observations, done):\n raise NotImplementedError()","def _compute_reward(self, observations, done):\n raise NotImplementedError()","def run(self):\n self.evaluate()\n self.accumulate()\n self.summarize()","def _setup_training(self, state_ph, next_state_ph, next_state_pred):\n ### PROBLEM 1\n ### YOUR CODE HERE\n # raise NotImplementedError\n delta_state_ph = next_state_ph - state_ph\n delta_state_pred = next_state_pred - state_ph\n norm_delta_state_ph = utils.normalize(delta_state_ph,\n self._init_dataset.delta_state_mean,\n self._init_dataset.delta_state_std)\n norm_delta_state_pred = utils.normalize(delta_state_pred,\n self._init_dataset.delta_state_mean,\n self._init_dataset.delta_state_std)\n loss = tf.reduce_mean(tf.square(norm_delta_state_pred - norm_delta_state_ph))\n optimizer = tf.train.AdamOptimizer(self._learning_rate).minimize(loss)\n\n return loss, optimizer","def do_parallel_inference(args):\n from treehmm import random_params, do_inference, plot_params, plot_energy, load_params\n from treehmm.vb_mf import normalize_trans\n from treehmm.static import float_type\n\n _x = sp.load(args.observe_matrix[0])\n args.continuous_observations = _x.dtype != sp.int8\n args.I, _, args.L = _x.shape\n I = args.I\n K = args.K\n L = args.L\n args.T = 'all'\n args.free_energy = []\n args.observe = 'all.npy'\n args.last_free_energy = 0\n args.emit_sum = sp.zeros((K, L), dtype=float_type)\n\n args.out_dir = args.out_dir.format(timestamp=time.strftime('%x_%X').replace('/', '-'), **args.__dict__)\n try:\n print 'making', args.out_dir\n os.makedirs(args.out_dir)\n except OSError:\n pass\n\n if args.warm_start:\n # args.last_free_energy, args.theta, args.alpha, args.beta, args.gamma, args.emit_probs, args.emit_sum = load_params(args)\n # args.warm_start = False\n print '# loading previous params for warm start from %s' % args.warm_start\n tmpargs = copy.deepcopy(args)\n tmpargs.out_dir = args.warm_start\n tmpargs.observe = 'all.npy'\n args.free_energy, args.theta, args.alpha, args.beta, args.gamma, args.emit_probs, args.emit_sum = load_params(tmpargs)\n\n try:\n args.free_energy = list(args.free_energy)\n except TypeError: # no previous free energy\n args.free_energy = []\n print 'done'\n args.warm_start = False\n else:\n (args.theta, args.alpha, args.beta, args.gamma, args.emit_probs) = \\\n random_params(args.I, args.K, args.L, args.separate_theta)\n for p in ['free_energy', 'theta', 'alpha', 'beta', 'gamma', 'emit_probs', 'last_free_energy', 'emit_sum']:\n sp.save(os.path.join(args.out_dir, args.out_params.format(param=p, **args.__dict__)),\n args.__dict__[p])\n\n \n args.iteration = 0\n plot_params(args)\n print '# setting up job arguments'\n # set up new versions of args for other jobs\n job_args = [copy.copy(args) for i in range(len(args.observe_matrix))]\n for j, a in enumerate(job_args):\n a.observe_matrix = args.observe_matrix[j]\n a.observe = os.path.split(args.observe_matrix[j])[1]\n a.subtask = True\n a.func = None\n a.iteration = 0\n a.max_iterations = 1\n a.quiet_mode = True\n if j % 1000 == 0:\n print j\n\n if args.run_local:\n pool = multiprocessing.Pool()\n else:\n pool = sge.SGEPool()\n #job_handle = pool.imap_unordered(do_inference, job_args)\n\n converged = False\n for args.iteration in range(args.max_iterations):\n # import ipdb; ipdb.set_trace()\n # fresh parameters-- to be aggregated after jobs are run\n print 'iteration', args.iteration\n total_free = 0\n if args.separate_theta:\n args.theta = sp.zeros((I - 1, K, K, K), dtype=float_type)\n else:\n args.theta = sp.zeros((K, K, K), dtype=float_type)\n\n args.alpha = sp.zeros((K, K), dtype=float_type)\n args.beta = sp.zeros((K, K), dtype=float_type)\n args.gamma = sp.zeros((K), dtype=float_type)\n args.emit_probs = sp.zeros((K, L), dtype=float_type)\n if True: # args.approx == 'clique':\n args.emit_sum = sp.zeros_like(args.emit_probs, dtype=float_type)\n else:\n args.emit_sum = sp.zeros((K, L), dtype=float_type)\n\n if args.run_local:\n iterator = pool.imap_unordered(do_inference, job_args, chunksize=args.chunksize)\n # wait for jobs to finish\n for result in iterator:\n pass\n else:\n jobs_handle = pool.map_async(do_inference, job_args, chunksize=args.chunksize)\n # wait for all jobs to finish\n for j in jobs_handle:\n j.wait()\n\n # sum free energies and parameters from jobs\n for a in job_args:\n # print '# loading from %s' % a.observe\n free_energy, theta, alpha, beta, gamma, emit_probs, emit_sum = load_params(a)\n # print 'free energy for this part:', free_energy\n if len(free_energy) > 0:\n last_free_energy = free_energy[-1]\n else:\n last_free_energy = 0\n total_free += last_free_energy\n args.theta += theta\n args.alpha += alpha\n args.beta += beta\n args.gamma += gamma\n args.emit_probs += emit_probs\n args.emit_sum += emit_sum\n\n # renormalize and plot\n print 'normalize aggregation... total free energy is:', total_free\n args.free_energy.append(total_free)\n if len(args.free_energy) > 1 and args.free_energy[-1] != 0 and args.free_energy[-2] != 0 \\\n and abs((args.free_energy[-2] - args.free_energy[-1]) / args.free_energy[-2]) < args.epsilon:\n print 'converged. free energy diff:', args.free_energy, abs(args.free_energy[-2] - args.free_energy[-1]) / args.free_energy[-2]\n converged = True\n normalize_trans(args.theta, args.alpha, args.beta, args.gamma)\n # if True: #args.approx == 'clique':\n # #print 'clique emit renorm'\n # args.emit_probs[:] = args.emit_probs / args.emit_sum\n # else:\n # args.emit_probs[:] = sp.dot(sp.diag(1./args.emit_sum), args.emit_probs)\n args.emit_probs[:] = sp.dot(sp.diag(1. / args.emit_sum), args.emit_probs)\n for a in job_args:\n a.theta, a.alpha, a.beta, a.gamma, a.emit_probs, a.emit_sum = args.theta, args.alpha, args.beta, args.gamma, args.emit_probs, args.emit_sum\n\n for p in ['free_energy', 'theta', 'alpha', 'beta', 'gamma', 'emit_probs', 'lmd', 'tau']:\n try:\n sp.save(os.path.join(args.out_dir, args.out_params.format(param=p, **args.__dict__)),\n args.__dict__[p])\n except KeyError:\n pass\n plot_params(args)\n plot_energy(args)\n\n if args.save_Q >= 3:\n print '# reconstructing chromosomes from *chunk*',\n in_order = {}\n # Q_chr16_all.trimmed.chunk*.npy => Q_chr16_all.trimmed.npy\n all_chunks = glob.glob(os.path.join(args.out_dir, '*_Q_*chunk*.npy'))\n for chunk in all_chunks:\n print chunk\n chunk_num = int(re.search(r'chunk(\\d+)', chunk).groups()[0])\n chrom_out = re.sub('chunk(\\d+)\\.', '', chunk)\n if chrom_out not in in_order:\n in_order[chrom_out] = {}\n in_order[chrom_out][chunk_num] = sp.load(chunk)\n for chrom_out in in_order:\n print 'reconstructing chromosomes from', in_order[chrom_out]\n if len(in_order[chrom_out]) > 1:\n final_array = sp.concatenate((in_order[chrom_out][0], in_order[chrom_out][1]), axis=1)\n for i in range(2, max(in_order[chrom_out])):\n final_array = sp.concatenate((final_array, in_order[chrom_out][i]), axis=1)\n else:\n final_array = in_order[chrom_out][0]\n sp.save(chrom_out, final_array)\n\n if converged:\n break","def _prepare_evaluate(self):\n labels = list()\n labels += ['num_procs', 'num_periods', 'is_debug', 'seed_emax', 'seed_sim']\n labels += ['num_draws_emax', 'num_agents_sim', 'num_types', 'edu_spec', 'version']\n labels += ['num_draws_prob', 'seed_prob']\n num_procs, num_periods, is_debug, seed_emax, seed_sim, num_draws_emax, num_agents_sim, \\\n num_types, edu_spec, version, num_draws_prob, seed_prob = \\\n dist_class_attributes(self.respy_base, *labels)\n\n periods_draws_emax = create_draws(num_periods, num_draws_emax, seed_emax, is_debug)\n periods_draws_sims = create_draws(num_periods, num_agents_sim, seed_sim, is_debug)\n\n disturbances = (periods_draws_emax, periods_draws_sims)\n\n # We want to maintain a pure PYTHON version for testing purposes.\n args = list()\n args += [num_periods, num_types, edu_spec['start'], edu_spec['max'], edu_spec['max'] + 1]\n state_space_info = respy_f2py.wrapper_create_state_space(*args)\n if self.mpi_setup == MISSING_INT:\n slavecomm = self.mpi_setup\n else:\n slavecomm = self.mpi_setup.py2f()\n self.set_up_baseline(periods_draws_emax, None)\n\n initial_conditions = get_initial_conditions(self.respy_base)\n\n args = (smm_sample_f2py, state_space_info, initial_conditions, disturbances, slavecomm)\n self.simulate_sample = partial(*args)","def pre_process(self, x0, params=()):\n # Should be used by all methods matching \"solve_*\"\n if self.x_by_name and isinstance(x0, dict):\n x0 = [x0[k] for k in self.names]\n if self.par_by_name and isinstance(params, dict):\n params = [params[k] for k in self.param_names]\n for pre_processor in self.pre_processors:\n x0, params = pre_processor(x0, params)\n return x0, np.atleast_1d(params)","def test_fit_prep_proba():\n args = get_layer('fit', 'manual', 'temporal', True, True, window=2, step_size=3)\n run_layer(*args)","def test_predict_prep_proba():\n args = get_layer('predict', 'manual', 'temporal', True, True, window=2, step_size=3)\n run_layer(*args)","def proba(c_pred,m_pred,f_pred, dataset):\n p = np.zeros(10)\n if dataset == 'cifar10':\n for i in range(10):\n if i <4:\n if i <2:\n p[i] = c_pred[0]*(m_pred[0]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[0:2]))\n elif i <4:\n p[i] = c_pred[0]*(m_pred[1]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[2:4]))\n if i >=4:\n if i <6:\n p[i] = c_pred[1]*(m_pred[2]/(m_pred[2]+m_pred[3]+m_pred[4]))*(f_pred[i]/np.sum(f_pred[4:6]))\n elif i <8:\n p[i] = c_pred[1]*(m_pred[3]/(m_pred[2]+m_pred[3]+m_pred[4]))*(f_pred[i]/np.sum(f_pred[6:8]))\n elif i <10:\n p[i] = c_pred[1]*(m_pred[4]/(m_pred[2]+m_pred[3]+m_pred[4]))*(f_pred[i]/np.sum(f_pred[8:10]))\n else :\n for i in range(10):\n if i <5:\n if i <3:\n p[i] = c_pred[0]*(m_pred[0]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[0:3]))\n elif i <5:\n p[i] = c_pred[0]*(m_pred[1]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[3:5]))\n if i >=5:\n if i <8:\n p[i] = c_pred[1]*(m_pred[2]/(m_pred[2]+m_pred[3]))*(f_pred[i]/np.sum(f_pred[5:8]))\n elif i <10:\n p[i] = c_pred[1]*(m_pred[3]/(m_pred[2]+m_pred[3]))*(f_pred[i]/np.sum(f_pred[8:]))\n return(p)","def impress(self):\n raise NotImplementedError","def test_predictor():","def run_and_store(self):\n # Initialization assumptions\n z = self.draw_normal_initial()\n gradient = self.cv_gradient_initial(z)\n gradient[np.isnan(gradient)] = 0\n variance = np.power(gradient,2) \n final_parameters = self.current_parameters()\n final_samples = 1\n\n # Create optimizer\n if self.optimizer == 'ADAM':\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\n elif self.optimizer == 'RMSProp':\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\n\n # Stored updates\n stored_means = np.zeros((self.iterations,len(final_parameters)/2))\n stored_predictive_likelihood = np.zeros(self.iterations)\n\n # Record elbo\n if self.record_elbo is True:\n elbo_records = np.zeros(self.iterations)\n else:\n elbo_records = None\n\n for i in range(self.iterations):\n gradient = self.cv_gradient(self.draw_normal())\n gradient[np.isnan(gradient)] = 0\n new_parameters = self.optim.update(gradient)\n self.change_parameters(new_parameters)\n\n stored_means[i] = self.optim.parameters[::2]\n stored_predictive_likelihood[i] = self.neg_posterior(stored_means[i])\n\n if self.printer is True:\n self.print_progress(i,self.optim.parameters[::2])\n\n # Construct final parameters using final 10% of samples\n if i > self.iterations-round(self.iterations/10):\n final_samples += 1\n final_parameters = final_parameters+self.optim.parameters\n\n if self.record_elbo is True:\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\n\n final_parameters = final_parameters/float(final_samples)\n self.change_parameters(final_parameters)\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\n\n if not self.quiet_progress:\n print(\"\")\n print(\"Final model ELBO is \" + str(-self.neg_posterior(final_means)-self.create_normal_logq(final_means)))\n return self.q, final_means, final_ses, stored_means, stored_predictive_likelihood, elbo_records","def run_and_store(self):\n # Initialization assumptions\n z = self.draw_normal_initial()\n gradient = self.cv_gradient_initial(z)\n gradient[np.isnan(gradient)] = 0\n variance = np.power(gradient,2) \n final_parameters = self.current_parameters()\n final_samples = 1\n\n # Create optimizer\n if self.optimizer == 'ADAM':\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\n elif self.optimizer == 'RMSProp':\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\n\n # Stored updates\n stored_means = np.zeros((self.iterations,len(final_parameters)/2))\n stored_predictive_likelihood = np.zeros(self.iterations)\n\n # Record elbo\n if self.record_elbo is True:\n elbo_records = np.zeros(self.iterations)\n else:\n elbo_records = None\n\n for i in range(self.iterations):\n gradient = self.cv_gradient(self.draw_normal())\n gradient[np.isnan(gradient)] = 0\n new_parameters = self.optim.update(gradient)\n self.change_parameters(new_parameters)\n\n stored_means[i] = self.optim.parameters[::2]\n stored_predictive_likelihood[i] = self.neg_posterior(stored_means[i])\n\n if self.printer is True:\n self.print_progress(i,self.optim.parameters[::2])\n\n # Construct final parameters using final 10% of samples\n if i > self.iterations-round(self.iterations/10):\n final_samples += 1\n final_parameters = final_parameters+self.optim.parameters\n\n if self.record_elbo is True:\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\n\n final_parameters = final_parameters/float(final_samples)\n self.change_parameters(final_parameters)\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\n\n if not self.quiet_progress:\n print(\"\")\n print(\"Final model ELBO is \" + str(-self.full_neg_posterior(final_means)-self.create_normal_logq(final_means)))\n return self.q, final_means, final_ses, stored_means, stored_predictive_likelihood, elbo_records","def calculate(self):","def optimize_parameters(self):\n self.loss_total.backward() # calculate gradients\n self.optimizer.step()\n self.optimizer.zero_grad()\n torch.cuda.empty_cache()","def preprocessing(x_tra, y_tra, x_tst):\n\n selector = SelectKBest(chi2, 190)\n selector.fit(x_tra, y_tra)\n x_tra_new = selector.transform(x_tra)\n x_tst_new = selector.transform(x_tst)\n\n return x_tra_new, x_tst_new","def partial_compute(self):\n if self.partially_computed:\n return\n\n self.transformed_lines = self.network.exactlines(\n self.lines, compute_preimages=self.preimages, include_post=True)\n self.partially_computed = True","def precalc_all(REPS):\n for sigma in [0.25, 1.5]:\n print('-'*60)\n\n N_RANGE = arange(5,105,5)\n\n filename = f'categorical_K2_C{C}_sigma{sigma:.2f}_M{M}'.replace('.','_')\n with Timer(f'{filename} ({REPS} repetitions)'):\n run_precalc(filename, [(n,n,n) for n in N_RANGE], C, sigma, M, REPS)\n\n filename = f'categorical_LOO_C{C}_sigma{sigma:.2f}_M{M}'.replace('.','_')\n with Timer(f'{filename} ({REPS} repetitions)'):\n run_precalc(filename, [(n,1,n) for n in N_RANGE], C, sigma, M, REPS)","def eval_cov_pre(self):\n \n precisions = self.precision_\n\n fit_score = []\n\n for i in range(precisions.shape[0]):\n precision = precisions[i]\n if self.loss == 'LL':\n fit_score.append(neg_logl(self.emp_cov[i], precision))\n else:\n fit_score.append(dtrace(self.emp_cov[i], precision))\n\n return self.emp_inv_score, self.C, fit_score, precisions","def estimate_incumbent(self, startpoints):\n\n pass","def prepare_vars_and_flows(self):\n\n # clear previously populated vars dictionary\n self.vars.clear()\n\n # calculate vars and flows sequentially\n self.calculate_scaleup_vars()\n self.calculate_vars()\n self.calculate_flows()","def _updateInitialProbabilities(self): \n N = self.N\n K = self.K\n\n for i in range(1,self.K+1):\n s = 0\n updated_prob = 0\n for n in range(1,self.N+1):\n s = s+1\n updated_prob = updated_prob + self.posterior_state_trellis[n][(1,i)]\n self.state_initial_prob[i] = (updated_prob/s)","def pre_process(self):\n pass","def pre_process(self):\n pass","def pre_process(self):\n pass","def pre_process(self):\n pass","def pre_process(self):\n pass","def par_fit(init_file):\n check_presence_init(init_file)\n\n dict_ = read(init_file)\n np.random.seed(dict_[\"SIMULATION\"][\"seed\"])\n\n # We perform some basic consistency checks regarding the user's request.\n check_presence_estimation_dataset(dict_)\n # check_initialization_dict2(dict_)\n # check_init_file(dict_)\n\n # Distribute initialization information.\n data = read_data(dict_[\"ESTIMATION\"][\"file\"])\n num_treated = dict_[\"AUX\"][\"num_covars_treated\"]\n num_untreated = num_treated + dict_[\"AUX\"][\"num_covars_untreated\"]\n\n _, X1, X0, Z1, Z0, Y1, Y0 = process_data(data, dict_)\n\n if dict_[\"ESTIMATION\"][\"maxiter\"] == 0:\n option = \"init\"\n else:\n option = dict_[\"ESTIMATION\"][\"start\"]\n\n # Read data frame\n\n # define starting values\n x0 = start_values(dict_, data, option)\n opts, method = optimizer_options(dict_)\n dict_[\"AUX\"][\"criteria\"] = calculate_criteria(dict_, X1, X0, Z1, Z0, Y1, Y0, x0)\n dict_[\"AUX\"][\"starting_values\"] = backward_transformation(x0)\n rslt_dict = bfgs_dict()\n if opts[\"maxiter\"] == 0:\n rslt = adjust_output(None, dict_, x0, X1, X0, Z1, Z0, Y1, Y0, rslt_dict)\n else:\n opt_rslt = minimize(\n minimizing_interface,\n x0,\n args=(dict_, X1, X0, Z1, Z0, Y1, Y0, num_treated, num_untreated, rslt_dict),\n method=method,\n options=opts,\n )\n rslt = adjust_output(\n opt_rslt, dict_, opt_rslt[\"x\"], X1, X0, Z1, Z0, Y1, Y0, rslt_dict\n )\n # Print Output files\n print_logfile(dict_, rslt)\n\n if \"comparison\" in dict_[\"ESTIMATION\"].keys():\n if dict_[\"ESTIMATION\"][\"comparison\"] == 0:\n pass\n else:\n write_comparison(data, rslt)\n else:\n write_comparison(data, rslt)\n\n return rslt","def preprocess(self):","def calculatePrediction(self, a, X_train,x, t_train):\n pass","def preprocess_valid_data(self):\r\n print(\"* Preprocessing validation data.\", flush=True)\r\n prep.create_HDF_file(self.C.validation_set)\r\n\r\n self.print_time_elapsed()","def _prep_attributes(self):\n self.coeff_ = self._coeff_to_df()\n self.results_ = self._results()\n self.cv_results_ = self._cv_results()\n self.intercept_ = self.model_.intercept_\n self.params_ = self.model_.get_params()\n\n return None","def compute(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n precisions = (self.TPs + METRIC_EPS) / (self.TPs + self.FPs + METRIC_EPS)\n recalls = self.TPs / (self.TPs + self.FNs + METRIC_EPS)\n\n return precisions, recalls, self.thresholds","def _do_estimate(recommendation_type: str, pipfile: Pipfile) -> None:","def calc_statistics(self):\n\n self._ydata = np.zeros([4, len(self._depths)], dtype=float)\n\n exp_vals = np.zeros(self._ntrials, dtype=float)\n ideal_vals = np.zeros(self._ntrials, dtype=float)\n\n for depthidx, depth in enumerate(self._depths):\n\n exp_shots = 0\n\n for trialidx in range(self._ntrials):\n cname = 'qv_depth_%d_trial_%d' % (depth, trialidx)\n exp_vals[trialidx] = self._heavy_output_counts[cname]\n exp_shots += self._circ_shots[cname]\n ideal_vals[trialidx] = self._heavy_output_prob_ideal[cname]\n\n # Calculate mean and error for experimental data\n self._ydata[0][depthidx] = np.sum(exp_vals)/np.sum(exp_shots)\n self._ydata[1][depthidx] = (self._ydata[0][depthidx] *\n (1.0-self._ydata[0][depthidx])\n / self._ntrials)**0.5\n\n # Calculate mean and error for ideal data\n self._ydata[2][depthidx] = np.mean(ideal_vals)\n self._ydata[3][depthidx] = (self._ydata[2][depthidx] *\n (1.0-self._ydata[2][depthidx])\n / self._ntrials)**0.5","def main():\n housing = pd.read_csv(\"Data/train_original.csv\")\n housing[\"TotalSF\"] = (\n housing[\"TotalBsmtSF\"] + housing[\"1stFlrSF\"] + housing[\"2ndFlrSF\"]\n )\n training_features, testing_features, training_target, testing_target = impute_dummify_and_split(\n housing, drop_target=False\n )\n\n p_values = [\n (c, pearsonr(training_features[\"SalePrice\"], training_features[c])[1])\n for c in training_features.columns\n ]\n\n p_value_limits = [0.05]\n\n result = []\n ps_and_cols = {}\n\n for p_value_limit in p_value_limits:\n\n high_ps = list(\n map(lambda t: t[0], sorted(p_values, key=lambda t1: t1[1])[:15])\n )\n\n print(training_features[high_ps].corr())\n\n columns = [p[0] for p in p_values if p[1] < p_value_limit]\n\n training_features_restricted = training_features[columns].drop(\n \"SalePrice\", axis=\"columns\"\n )\n\n testing_features_restricted = testing_features[columns].drop(\n \"SalePrice\", axis=\"columns\"\n )\n\n for model in (\n linear_model.Lasso(alpha=2.1),\n linear_model.Ridge(alpha=2.1),\n ):\n\n model.fit(training_features_restricted, training_target)\n\n train_score = model.score(\n training_features_restricted, training_target\n )\n\n test_score = model.score(\n testing_features_restricted, testing_target\n )\n\n name = str(model).split(\"(\")[0]\n\n result = result + [\n (\n \"_2_restrict_features\",\n name,\n \"p value limit: {:.3f}, alpha: 2.1\".format(p_value_limit),\n train_score,\n test_score,\n )\n ]\n\n print(ps_and_cols)\n return training_features[high_ps].corr()","def prepare_estimation(model_params_init_file_name, lower, upper):\n\n # Read in data and init file sources\n model_params_df = pd.read_pickle(model_params_init_file_name)\n model_params_df[\"lower\"] = lower\n model_params_df[\"upper\"] = upper\n\n return model_params_df","def _do_compute(self, var_map):\n raise Exception(\"Not implemented. Subclass responsibility\")","def _build_preprocessing(self):\n\n # For now, do nothing\n pass","def calculate_metrics(self, caller=\"external\", ordered_data=False):\n \n # If the function call was made externally, process the request\n if caller == \"external\":\n # Open an existing model and get the training & test dataset and targets based on the request\n self.X_test, self.y_test = self._get_model_and_data(target=True, ordered_data=ordered_data) \n\n # Keep a copy of the y_test before any transformations\n y_test_copy = self.y_test.copy()\n\n # Scale the targets and increase stationarity if required\n if self.model.scale_target or self.model.make_stationary:\n # If using differencing, we assume sufficient lag values for inversing the transformation later\n y_orig = self.y_test.values.ravel() if self.model.make_stationary=='difference' else None\n # Apply the transformer to the test targets\n self.y_test = self.model.target_transformer.transform(self.y_test) \n # Drop samples where self.y_test cannot be transformed due to insufficient lags\n self.X_test = self.X_test.iloc[len(self.X_test)-len(self.y_test):]\n\n # Refresh the keras model to avoid tensorflow errors\n if self.model.using_keras:\n self._keras_refresh()\n \n # Get predictions based on the samples\n if ordered_data:\n self.y_pred = self.sequence_predict(variant=\"internal\")\n\n # Handle possible null values where a prediction could not be generated\n self.y_pred = self.y_pred[self.rows_per_pred - self.first_pred_modifier:]\n self.y_test = y_test_copy.iloc[-1*len(self.y_pred):]\n\n # Inverse transformations predictions if required \n if self.model.scale_target or self.model.make_stationary:\n # Set up indices for differencing\n end = self.placeholders\n start = end - self.diff_lags\n\n # Add lags for inversing differencing\n self.y_pred = self.y_pred if y_orig is None else np.append(y_orig[start : end], self.y_pred)\n\n # Apply the transformer to the test targets\n self.y_pred = self.model.target_transformer.inverse_transform(self.y_pred) \n\n # Remove lags used for making the series stationary in case of differencing\n if self.model.make_stationary == 'difference':\n self.y_pred = self.y_pred[self.diff_lags:]\n else:\n self.y_pred = self.model.pipe.predict(self.X_test)\n\n # Inverse transformations on the predictions if required\n if self.model.scale_target or self.model.make_stationary:\n # Apply the transformer to the predictions\n self.y_pred = self.model.target_transformer.inverse_transform(self.y_pred)\n # Reset y_test to orginal values\n self.y_test = y_test_copy\n \n # Flatten the y_test DataFrame\n self.y_test = self.y_test.values.ravel()\n\n # Try getting the metric_args from the model\n try:\n metric_args = self.model.metric_args\n except AttributeError: \n metric_args = {}\n \n if self.model.estimator_type == \"classifier\":\n labels = self.model.pipe.named_steps['estimator'].classes_\n \n # Check if the average parameter is specified\n if len(metric_args) > 0 and \"average\" in metric_args:\n # Metrics are returned as an overall average\n metric_rows = [\"overall\"]\n else:\n # Get the class labels to be used as rows for the result DataFrame\n metric_rows = labels\n \n # Get key classifier metrics\n metrics_df = pd.DataFrame([x for x in metrics.precision_recall_fscore_support\\\n (self.y_test, self.y_pred, **metric_args)],\\\n index=[\"precision\", \"recall\", \"fscore\", \"support\"], columns=metric_rows).transpose()\n # Add accuracy\n self.model.score = metrics.accuracy_score(self.y_test, self.y_pred)\n metrics_df.loc[\"overall\", \"accuracy\"] = self.model.score\n # Finalize the structure of the result DataFrame\n metrics_df.loc[:,\"model_name\"] = self.model.name\n metrics_df.loc[:,\"class\"] = metrics_df.index\n metrics_df = metrics_df.loc[:,[\"model_name\", \"class\", \"accuracy\", \"precision\", \"recall\", \"fscore\", \"support\"]]\n \n # Prepare the confusion matrix and add it to the model\n self._prep_confusion_matrix(self.y_test, self.y_pred, labels)\n \n elif self.model.estimator_type == \"regressor\":\n # Get the r2 score\n self.model.score = metrics.r2_score(self.y_test, self.y_pred, **metric_args)\n metrics_df = pd.DataFrame([[self.model.score]], columns=[\"r2_score\"])\n \n # Get the mean squared error\n metrics_df.loc[:,\"mean_squared_error\"] = metrics.mean_squared_error(self.y_test, self.y_pred, **metric_args)\n \n # Get the mean absolute error\n metrics_df.loc[:,\"mean_absolute_error\"] = metrics.mean_absolute_error(self.y_test, self.y_pred, **metric_args)\n \n # Get the median absolute error\n metrics_df.loc[:,\"median_absolute_error\"] = metrics.median_absolute_error(self.y_test, self.y_pred)\n \n # Get the explained variance score\n metrics_df.loc[:,\"explained_variance_score\"] = metrics.explained_variance_score(self.y_test, self.y_pred, **metric_args)\n\n # If the target was scaled we need to inverse transform certain metrics to the original scale\n # However, if we used the sequence prediction function, the inverse transform has already been performed\n if not ordered_data and (self.model.scale_target or self.model.make_stationary):\n for m in [\"mean_squared_error\", \"mean_absolute_error\", \"median_absolute_error\"]:\n metrics_df.loc[:, m] = self.model.target_transformer.inverse_transform(metrics_df.loc[:, [m]], array_like=False).values.ravel()\n \n # Finalize the structure of the result DataFrame\n metrics_df.loc[:,\"model_name\"] = self.model.name\n metrics_df = metrics_df.loc[:,[\"model_name\", \"r2_score\", \"mean_squared_error\", \"mean_absolute_error\",\\\n \"median_absolute_error\", \"explained_variance_score\"]]\n \n if caller == \"external\":\n \n if self.model.calc_feature_importances:\n # Calculate model agnostic feature importances\n self._calc_importances(X = self.X_test, y = self.y_test)\n\n self.response = metrics_df\n\n # Send the reponse table description to Qlik\n if self.model.estimator_type == \"classifier\":\n self._send_table_description(\"metrics_clf\")\n elif self.model.estimator_type == \"regressor\":\n self._send_table_description(\"metrics_reg\")\n\n # Debug information is printed to the terminal and logs if the paramater debug = true\n if self.model.debug:\n self._print_log(4)\n\n # Finally send the response\n return self.response\n else:\n # Save the metrics_df to the model\n self.model.metrics_df = metrics_df","def _precompute_probabilities(self):\n\n d_graph = self.d_graph\n first_travel_done = set()\n\n nodes_generator = self.graph.nodes() if self.quiet \\\n else tqdm(self.graph.nodes(), desc='Computing transition probabilities')\n\n for source in nodes_generator:\n\n # Init probabilities dict for first travel\n if self.PROBABILITIES_KEY not in d_graph[source]:\n d_graph[source][self.PROBABILITIES_KEY] = dict()\n\n for current_node in self.graph.neighbors(source):\n\n # Init probabilities dict\n if self.PROBABILITIES_KEY not in d_graph[current_node]:\n d_graph[current_node][self.PROBABILITIES_KEY] = dict()\n\n unnormalized_weights = list()\n first_travel_weights = list()\n d_neighbors = list()\n\n # Calculate unnormalized weights\n for destination in self.graph.neighbors(current_node):\n\n p = self.sampling_strategy[current_node].get(self.P_KEY,\n self.p) if current_node in self.sampling_strategy else self.p\n q = self.sampling_strategy[current_node].get(self.Q_KEY,\n self.q) if current_node in self.sampling_strategy else self.q\n\n if destination == source: # Backwards probability\n ss_weight = self.graph[current_node][destination].get(self.weight_key, 1) * 1 / p\n elif destination in self.graph[source]: # If the neighbor is connected to the source\n ss_weight = self.graph[current_node][destination].get(self.weight_key, 1)\n else:\n ss_weight = self.graph[current_node][destination].get(self.weight_key, 1) * 1 / q\n\n # Assign the unnormalized sampling strategy weight, normalize during random walk\n unnormalized_weights.append(ss_weight)\n if current_node not in first_travel_done:\n first_travel_weights.append(self.graph[current_node][destination].get(self.weight_key, 1))\n d_neighbors.append(destination)\n\n # Normalize\n unnormalized_weights = np.array(unnormalized_weights)\n d_graph[current_node][self.PROBABILITIES_KEY][\n source] = unnormalized_weights / unnormalized_weights.sum()\n\n if current_node not in first_travel_done:\n unnormalized_weights = np.array(first_travel_weights)\n d_graph[current_node][self.FIRST_TRAVEL_KEY] = unnormalized_weights / unnormalized_weights.sum()\n first_travel_done.add(current_node)\n\n # Save neighbors\n d_graph[current_node][self.NEIGHBORS_KEY] = d_neighbors"],"string":"[\n \"def precalculate():\\n pass\",\n \"def precalculate():\\n pass\",\n \"def E_step_precompute(self, model_params, my_suff_stat, my_data):\",\n \"def run():\\n return estimate(0,1,0)\",\n \"def _conventional_precalculation(self):\\n # Run all modules\\n for module in self._conventional_modules:\\n module.compute()\",\n \"def precompute(self, features, mode, params):\\n return None\",\n \"def pre_compute(self, e_list):\\n\\t\\tpass\",\n \"def _pre_fit(self):\\n pass\",\n \"def optimize(self):\\n self.vbe_step()\\n self.compute_responsibilities()\\n self.compute_sufficient_stats()\\n self.vbmstep()\",\n \"def precompute_scoring():\\n global volume_void_inclusion\\n global attract_point_distances\\n global perlin_values\\n \\n volume_void_inclusion = []\\n for i,void in enumerate(volumes_void):\\n inclusion = gh.PointInBrep(void,points_input,False)\\n volume_void_inclusion.append(inclusion)\\n \\n attract_point_distances = []\\n for i,point in enumerate(points_attractor):\\n distances = gh.Division(gh.Distance(point,points_input),max_dist)\\n attract_point_distances.append(distances)\",\n \"def _precompute(self, A):\\n return ()\",\n \"def _compute_results(self):\\n self.Y_best = best_value(self.Y)\\n self.x_opt = self.X[np.argmin(self.Y),:]\\n self.fx_opt = np.min(self.Y)\\n self.distance = self._compute_distance_betw_consecutive_x()\",\n \"def _optimise(self):\\n pass\",\n \"def pretrain(self, X):\\n\\t\\tv_X = share_data(X)\\n\\t\\tdA_layers = self.formula_.dA_layers\\n\\t\\tfor dA_layer in dA_layers:\\n\\t\\t\\tdA_layer_infor = build_batch_fixed_iter_model_infor(dA_layer, v_X, \\n\\t\\t\\t\\tbatch_size = self.pretrain_batch_size, \\n\\t\\t\\t\\tlearning_rate = self.pretrain_learning_rate, \\n\\t\\t\\t\\tmodel_X = self.formula_.X) # it is formula x instead of daLayer.X\\n\\t\\t\\tbest_params = batch_fixed_iter_optimize(dA_layer_infor, \\n\\t\\t\\t\\t\\tn_epochs = self.pretrain_epochs)\\n\\t\\t\\tfor i in xrange(len(best_params)):\\n\\t\\t\\t\\tdA_layer.params[i].set_value(best_params[i], borrow=True)\\n\\t\\treturn self\",\n \"def main():\\n df = prepro_last()\\n X, y = train_build(df)\\n fit_store(X, y)\",\n \"def pre_analyse():\\n t = transform()\\n model = modified_resnet50()\\n model.load_state_dict(\\n torch.load(\\n \\\"model.pth.tar\\\",\\n map_location=torch.device(\\\"cpu\\\"),\\n )[\\\"state_dict\\\"]\\n )\\n model.eval()\\n\\n def get_preds(img_path):\\n \\\"\\\"\\\"\\n Gives labelds and probabilities for a single image\\n This is were we preprocess the image, using a function defined in the model class\\n \\\"\\\"\\\"\\n # load image\\n img = Image.open(img_path).convert(\\\"RGB\\\")\\n # process it\\n x = t(img)\\n # get in in the right format\\n x = Variable(x).unsqueeze(0)\\n # predictions\\n output = model(x)\\n # decode\\n output = decode(output.cpu().data.numpy()[0])\\n\\n # filter\\n # return pred, proba\\n return output\\n\\n return get_preds(\\\"image.jpg\\\")\",\n \"def estimate(self) -> None:\\n\\n if self.model is None:\\n raise ValueError(\\\"Model must not be None before a call to estimate\\\")\\n if self.measurements is None:\\n raise ValueError(\\\"measurements must not be None before a call to estimate\\\")\\n if self.camera_frame_directions is None:\\n raise ValueError(\\\"camera_frame_directions must not be None before a call to estimate\\\")\\n if self.weighted_estimation and (self.measurement_covariance is None):\\n raise ValueError(\\\"measurement_covariance must not be None before a call to estimate \\\"\\n \\\"if weighed_estimation is True\\\")\\n if self.model.use_a_priori and (self.a_priori_state_covariance is None):\\n raise ValueError(\\\"a_priori_state_covariance must not be None before a call to estimate \\\"\\n \\\"if model.use_a_priori is True\\\")\\n\\n # get the size of the state vector\\n a_priori_state = np.array(self.model.state_vector)\\n state_size = len(a_priori_state)\\n\\n # get the number of measurements\\n num_meas = self.measurements.size\\n\\n # get the weight matrix\\n weight_matrix = self._compute_weight_matrix(state_size, num_meas)\\n\\n # calculate the prefit residuals\\n prefit_residuals = self.compute_residuals()\\n pre_ss = prefit_residuals.ravel()@prefit_residuals.ravel()\\n\\n for _ in range(self.max_iter):\\n\\n jacobian = self.model.compute_jacobian(self.camera_frame_directions, temperature=self.temperatures)\\n\\n if self.model.use_a_priori:\\n residuals_vec = np.concatenate([prefit_residuals.reshape((-1, 1), order='F'),\\n np.zeros((state_size, 1))], axis=0)\\n\\n else:\\n residuals_vec = prefit_residuals.reshape((-1, 1), order='F')\\n\\n if np.isscalar(weight_matrix):\\n lhs = np.sqrt(weight_matrix)*jacobian.T@jacobian\\n rhs = np.sqrt(weight_matrix)*jacobian.T@residuals_vec\\n else:\\n lhs = jacobian.T@weight_matrix@jacobian\\n rhs = jacobian.T@weight_matrix@residuals_vec\\n\\n update_vec = np.linalg.solve(lhs, rhs)\\n\\n model_copy = self.model.copy()\\n\\n model_copy.apply_update(update_vec)\\n\\n postfit_residuals = self.compute_residuals(model=model_copy)\\n post_ss = postfit_residuals.ravel()@postfit_residuals.ravel()\\n resid_change = abs(pre_ss-post_ss)\\n\\n # check for convergence\\n if resid_change <= (self.residual_atol+self.residual_rtol*pre_ss):\\n self._successful = True\\n self._postfit_residuals = postfit_residuals\\n self.model = model_copy\\n self._jacobian = self.model.compute_jacobian(self.camera_frame_directions, temperature=self.temperatures)\\n return\\n\\n elif (np.abs(update_vec) <= (self.state_atol+self.state_rtol*a_priori_state)).all():\\n self._successful = True\\n self._postfit_residuals = postfit_residuals\\n self.model = model_copy\\n self._jacobian = self.model.compute_jacobian(self.camera_frame_directions, temperature=self.temperatures)\\n return\\n\\n elif pre_ss < post_ss: # check for divergence\\n warnings.warn('Solution is diverging. Stopping iteration.'\\n '\\\\n\\\\tpre-update residuals {}'\\n '\\\\n\\\\tpost-update residuals {}'.format(pre_ss, post_ss))\\n self._successful = False\\n self._postfit_residuals = None\\n self._jacobian = None\\n return\\n\\n else: # converging\\n self.model = model_copy\\n prefit_residuals = postfit_residuals\\n pre_ss = post_ss\\n a_priori_state = np.array(self.model.state_vector)\\n\\n warnings.warn(\\\"Solution didn't converge in the requested number of iterations\\\")\\n self._successful = False\\n self._postfit_residuals = prefit_residuals\\n self.model\\n self._jacobian = self.model.compute_jacobian(self.camera_frame_directions, temperature=self.temperatures)\",\n \"def computeIntercepts():\\n pass\",\n \"def compute_all(self) -> None:\\n self.compute_j_matrix()\\n self.compute_outter_distribution()\\n self.compute_max_prior()\\n self.compute_max_poutter()\",\n \"def pre_compute_evolution(self):\\n logger.info(\\\"Precomputing evolution parameters\\\")\\n self.lineFreqs = []\\n self.linePhis = []\\n self.lineh0s = []\\n self.mid_times = []\\n\\n linePhi = 0\\n lineFreq_old = 0\\n\\n for i in trange(self.nsfts):\\n mid_time = self.tstart + (i + 0.5) * self.Tsft\\n lineFreq = self.get_frequency(mid_time)\\n\\n self.mid_times.append(mid_time)\\n self.lineFreqs.append(lineFreq)\\n self.linePhis.append(\\n linePhi + np.pi * self.Tsft * (lineFreq_old + lineFreq)\\n )\\n self.lineh0s.append(self.get_h0(mid_time))\\n\\n lineFreq_old = lineFreq\",\n \"def estimate(self) -> None:\\n pass\",\n \"def compute(self): \\n Ex=np.zeros((self.nx,self.ny+1))\\n Ey=np.zeros((self.nx+1,self.ny))\\n Hz=np.zeros((self.nx,self.ny))\\n Hzx=np.zeros((self.nx,self.ny))\\n Hzy=np.zeros((self.nx,self.ny))\\n \\n imx = []\\n #eps, mu = self.makeenv()\\n mu=np.ones((self.nx,self.ny))*const.mu_0\\n eps = self.luneberg(int(self.nx/2), int(self.ny*2/3), self.R)\\n eps[:20,:] *= self.q #adself.ds a space of higher permittivity \\n eps[-20:,:] *= self.q #adself.ds a space of higher permittivity \\n eps[:,:20] *= self.q #adself.ds a space of higher permittivity \\n eps[:,-20:] *= self.q #adself.ds a space of higher permittivity \\n\\n c = self.dt/(eps*self.ds)\\n d = self.dt/(mu* self.ds)\\n \\n sigma = self.pml(eps, mu, 20)\\n cax = 1 - (sigma[0] * self.dt / eps)\\n cay = 1 - (sigma[1] * self.dt / eps)\\n dax = 1 - (sigma[2] * self.dt / mu) \\n day = 1 - (sigma[3] * self.dt / mu)\\n \\n bar = progressbar.ProgressBar()\\n for n in bar(range(self.nt+1)):\\n Ex[:,1:-1] = (cay[:,1:]+cay[:,:-1])/2*Ex[:,1:-1] + (c[:,1:]+c[:,:-1])/2*(Hz[:,1:]-Hz[:,:-1])\\n Ey[1:-1,:] = (cax[1:,:]+cax[:-1,:])/2*Ey[1:-1,:] - (c[1:,:]+c[:-1,:])/2*(Hz[1:,:]-Hz[:-1,:])\\n \\n Hzx = dax*Hzx - d*(Ey[1:,:] - Ey[:-1,:])\\n Hzy = day*Hzy + d*(Ex[:,1:] - Ex[:,:-1]) \\n Hz = Hzx + Hzy + self.actualsource(self.source, self.f, n, self.dt) \\n \\n if(n%self.interval == 0): imx.append(Ex[:self.nx,:self.ny]**2 + Ey[:self.nx, :self.ny]**2)\\n\\n return imx\",\n \"def precompute(self):\\n self.cov_inv = np.linalg.inv(self.cov)\\n self.root_2pi_d_det = math.sqrt((2.0*math.pi)**self.raw_data.shape[1] *\\n np.linalg.det(self.cov))\",\n \"def estimate(self) -> None:\\n if self.model is None:\\n raise ValueError(\\\"Model must not be None before a call to estimate\\\")\\n if self.measurements is None:\\n raise ValueError(\\\"measurements must not be None before a call to estimate\\\")\\n if self.camera_frame_directions is None:\\n raise ValueError(\\\"camera_frame_directions must not be None before a call to estimate\\\")\\n if self.weighted_estimation and (self.measurement_covariance is None):\\n raise ValueError(\\\"measurement_covariance must not be None before a call to estimate \\\"\\n \\\"if weighed_estimation is True\\\")\\n if self.model.use_a_priori and (self.a_priori_state_covariance is None):\\n raise ValueError(\\\"a_priori_state_covariance must not be None before a call to estimate \\\"\\n \\\"if model.use_a_priori is True\\\")\\n\\n # get the size of the state vector\\n a_priori_state = np.array(self.model.state_vector)\\n state_size = len(a_priori_state)\\n\\n # get the number of measurements\\n num_meas = self.measurements.size\\n\\n # get the weight matrix\\n weight_matrix = self._compute_weight_matrix(state_size, num_meas)\\n\\n # calculate the prefit residuals\\n prefit_residuals = self.compute_residuals()\\n pre_ss = prefit_residuals.ravel() @ prefit_residuals.ravel()\\n\\n # a flag specifying this is the first time through so we need to initialize the lma_coefficient\\n first = True\\n lma_coefficient = 0\\n n_diverge = 0\\n\\n # iterate to convergence\\n for _ in range(self.max_iter):\\n\\n # get the jacobian matrix\\n jacobian = self.model.compute_jacobian(self.camera_frame_directions, temperature=self.temperatures)\\n\\n if first:\\n # initialize the lma_coefficient\\n lma_coefficient = 0.001 * np.trace(jacobian.T @ jacobian) / jacobian.shape[1]\\n\\n if self.model.use_a_priori:\\n residuals_vec = np.concatenate([prefit_residuals.reshape((-1, 1), order='F'),\\n np.zeros((state_size, 1))], axis=0)\\n\\n else:\\n residuals_vec = prefit_residuals.reshape((-1, 1), order='F')\\n\\n if np.isscalar(weight_matrix):\\n lhs = np.sqrt(weight_matrix) * jacobian.T @ jacobian\\n rhs = np.sqrt(weight_matrix) * jacobian.T @ residuals_vec\\n else:\\n lhs = jacobian.T @ weight_matrix @ jacobian\\n rhs = jacobian.T @ weight_matrix @ residuals_vec\\n\\n # get the update vector using LMA\\n update_vec = np.linalg.solve(lhs + lma_coefficient*np.diag(np.diag(lhs)), rhs)\\n\\n model_copy = self.model.copy()\\n\\n model_copy.apply_update(update_vec)\\n\\n postfit_residuals = self.compute_residuals(model=model_copy)\\n post_ss = postfit_residuals.ravel() @ postfit_residuals.ravel()\\n resid_change = abs(pre_ss - post_ss)\\n\\n # check for convergence\\n if resid_change <= (self.residual_atol + self.residual_rtol * pre_ss):\\n self._successful = True\\n self._postfit_residuals = postfit_residuals\\n self.model = model_copy\\n self._jacobian = self.model.compute_jacobian(self.camera_frame_directions, temperature=self.temperatures)\\n return\\n\\n elif (np.abs(update_vec) <= (self.state_atol + self.state_rtol * a_priori_state)).all():\\n self._successful = True\\n self._postfit_residuals = postfit_residuals\\n self.model = model_copy\\n self._jacobian = self.model.compute_jacobian(self.camera_frame_directions, temperature=self.temperatures)\\n return\\n\\n elif pre_ss < post_ss: # check for divergence\\n\\n n_diverge += 1\\n\\n if n_diverge > self.max_divergence_steps:\\n warnings.warn('Solution is diverging. Stopping iteration.'\\n '\\\\n\\\\tpre-update residuals {}'\\n '\\\\n\\\\tpost-update residuals {}'\\n '\\\\n\\\\tdiverged for {} iterations'.format(pre_ss, post_ss, n_diverge))\\n self._successful = False\\n self._postfit_residuals = None\\n self._jacobian = None\\n return\\n\\n # update the lma coefficient\\n lma_coefficient *= 10\\n\\n else: # converging\\n # reset the divergence counter\\n n_diverge = 0\\n # update the lma coefficient\\n lma_coefficient /= 10\\n # prepare for the next iteration\\n self.model = model_copy\\n prefit_residuals = postfit_residuals\\n pre_ss = post_ss\\n a_priori_state = np.array(self.model.state_vector)\\n\\n warnings.warn(\\\"Solution didn't converge in the requested number of iterations\\\")\\n self._successful = False\\n self._postfit_residuals = prefit_residuals\\n self.model\\n self._jacobian = self.model.compute_jacobian(self.camera_frame_directions, temperature=self.temperatures)\",\n \"def on_iteration_start(self):\\n\\n self.Xprv = self.X.copy()\\n if (not self.opt['FastSolve'] or isinstance(self.backtrack,\\n BacktrackRobust)):\\n self.Yprv = self.Y.copy()\\n\\n if self.opt['Monotone']:\\n if self.k == 0:\\n self.objfn = self.eval_objfn()\\n self.objfn_prev = self.objfn\",\n \"def preprocess_train_data(self):\\r\\n print(\\\"* Preprocessing training data.\\\", flush=True)\\r\\n prep.create_HDF_file(self.C.training_set, is_training_set=True)\\r\\n\\r\\n self.print_time_elapsed()\",\n \"def prep(self):\\n \\n # create a dict with prior probabilities\\n self.row_priors = [0.0]*len(self.rows)\\n self.feature_priors = dict()\\n \\n # denominator is given by reference priors\\n denominator = sum(self.column_priors)\\n # null_feature_prior is used when feature is not observed at all\\n # this is set up to scale with features, i.e. arbitrarily adding\\n # child features into an ontology should not skew sums over repr.\\n null_feature_prior = 1/max(denominator, float(len(self.rows)))\\n \\n for rowname, rowindex in self.rows.items(): \\n numerator = 0\\n for colname, colindex in self.columns.items(): \\n colprior = self.column_priors[colindex]\\n numerator += self.data[colindex][rowindex]*colprior\\n if numerator == 0:\\n numerator = null_feature_prior \\n self.row_priors[rowindex] = float(numerator)/denominator\\n self.feature_priors[rowname] = self.row_priors[rowindex]\\n\\n return self\",\n \"def on_iteration_start(self):\\n\\n self.Xfprv = self.Xf.copy()\\n if (not self.opt['FastSolve'] or isinstance(self.backtrack,\\n BacktrackRobust)):\\n self.Yfprv = self.Yf.copy()\\n\\n if self.opt['Monotone']:\\n if self.k == 0:\\n self.objfn = self.eval_objfn()\\n self.objfn_prev = self.objfn\",\n \"def _preprocess(self):\\n if self.gt_matrix_PCs is None:\\n start = time.time()\\n gt_matrix = self._process_vcf().get('gt_matrix')\\n print('gt_matrix took {} secs'.format(time.time() - start))\\n \\n # Normalize gt_matrix by site\\n gt_matrix_norm = gt_matrix - np.mean(gt_matrix, axis=1)[:, np.newaxis]\\n \\n # PCA\\n start = time.time()\\n u, s, vh = np.linalg.svd(gt_matrix_norm.T, full_matrices=False)\\n print('SVD took {} secs'.format(time.time() - start))\\n self.gt_matrix_PCs = -u @ np.diag(s)\\n \\n # Get relevant objects from result of ripser\\n if self.ripser_result is None:\\n start = time.time()\\n print(\\\"Getting ripser object\\\")\\n self.ripser_result = ripser(self.gt_matrix_PCs, coeff=2, maxdim=1, do_cocycles=True)\\n print('Ripser took {} secs'.format(time.time() - start))\",\n \"def compute(self) -> None:\\n \\n self.model.eval()\\n \\n with torch.no_grad():\\n for (input, target, _) in self.loader:\\n\\n # self.model = self.model.train(False) # TEST @lacoupe\\n output, _ = self.model(input)\\n \\n output = (output >= 0.5)\\n \\n for out, tar in zip(output, target):\\n \\n tar = bool(tar)\\n \\n if out and tar:\\n self.confusion['true_positive'] += 1\\n elif not out and not tar:\\n self.confusion['true_negative'] += 1\\n elif out and not tar:\\n self.confusion['false_positive'] += 1\\n elif not out and tar:\\n self.confusion['false_negative'] += 1\\n \\n self.accuracy = (self.confusion['true_positive'] + self.confusion['true_negative']) \\\\\\n / sum(list(self.confusion.values()))\\n \\n if (self.confusion['true_positive'] + self.confusion['false_positive']) == 0.:\\n self.precision = 0.\\n else:\\n self.precision = self.confusion['true_positive'] \\\\\\n / (self.confusion['true_positive'] + self.confusion['false_positive'])\\n \\n if (self.confusion['true_positive'] + self.confusion['false_negative']) == 0.:\\n self.recall = 0.\\n else:\\n self.recall = self.confusion['true_positive'] \\\\\\n / (self.confusion['true_positive'] + self.confusion['false_negative'])\\n \\n if (self.precision + self.recall) == 0.:\\n self.f1_score = 0.\\n else:\\n self.f1_score = 2 * self.precision * self.recall / (self.precision + self.recall)\",\n \"def __estimatedata(data, payout=500, randomsample=0):\\n if randomsample > 0:\\n data = data.sort('prob')\\n data = data.head(randomsample)\\n\\n data['premium'] = 0.0\\n data['payout'] = payout\\n\\n # get the model parameters\\n \\n # estimate\\n estimator = EtheriscEstimator(data)\\n estimator.estimate()\\n return estimator\",\n \"def _computations(self,do_Kmm=True, do_Kmm_grad=True):\\r\\n\\r\\n if do_Kmm:\\r\\n self.Lm = jitchol(self.Kmm)\\r\\n\\r\\n # The rather complex computations of self.A\\r\\n if self.has_uncertain_inputs:\\r\\n if self.likelihood.is_heteroscedastic:\\r\\n psi2_beta = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.batchsize, 1, 1))).sum(0)\\r\\n else:\\r\\n psi2_beta = self.psi2.sum(0) * self.likelihood.precision\\r\\n evals, evecs = np.linalg.eigh(psi2_beta)\\r\\n clipped_evals = np.clip(evals, 0., 1e6) # TODO: make clipping configurable\\r\\n tmp = evecs * np.sqrt(clipped_evals)\\r\\n else:\\r\\n if self.likelihood.is_heteroscedastic:\\r\\n tmp = self.psi1.T * (np.sqrt(self.likelihood.precision.flatten().reshape(1, self.batchsize)))\\r\\n else:\\r\\n tmp = self.psi1.T * (np.sqrt(self.likelihood.precision))\\r\\n tmp, _ = dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)\\r\\n self.A = tdot(tmp)\\r\\n\\r\\n self.V = self.likelihood.precision*self.likelihood.Y\\r\\n self.VmT = np.dot(self.V,self.q_u_expectation[0].T)\\r\\n self.psi1V = np.dot(self.psi1.T, self.V)\\r\\n\\r\\n self.B = np.eye(self.num_inducing)*self.data_prop + self.A\\r\\n self.Lambda = backsub_both_sides(self.Lm, self.B.T)\\r\\n self.LQL = backsub_both_sides(self.Lm,self.q_u_expectation[1].T,transpose='right')\\r\\n\\r\\n self.trace_K = self.psi0.sum() - np.trace(self.A)/self.likelihood.precision\\r\\n self.Kmmi_m, _ = dpotrs(self.Lm, self.q_u_expectation[0], lower=1)\\r\\n self.projected_mean = np.dot(self.psi1,self.Kmmi_m)\\r\\n\\r\\n # Compute dL_dpsi\\r\\n self.dL_dpsi0 = - 0.5 * self.output_dim * self.likelihood.precision * np.ones(self.batchsize)\\r\\n self.dL_dpsi1, _ = dpotrs(self.Lm,np.asfortranarray(self.VmT.T),lower=1)\\r\\n self.dL_dpsi1 = self.dL_dpsi1.T\\r\\n\\r\\n dL_dpsi2 = -0.5 * self.likelihood.precision * backsub_both_sides(self.Lm, self.LQL - self.output_dim * np.eye(self.num_inducing))\\r\\n if self.has_uncertain_inputs:\\r\\n self.dL_dpsi2 = np.repeat(dL_dpsi2[None,:,:],self.batchsize,axis=0)\\r\\n else:\\r\\n self.dL_dpsi1 += 2.*np.dot(dL_dpsi2,self.psi1.T).T\\r\\n self.dL_dpsi2 = None\\r\\n\\r\\n # Compute dL_dKmm\\r\\n if do_Kmm_grad:\\r\\n tmp = np.dot(self.LQL,self.A) - backsub_both_sides(self.Lm,np.dot(self.q_u_expectation[0],self.psi1V.T),transpose='right')\\r\\n tmp += tmp.T\\r\\n tmp += -self.output_dim*self.B\\r\\n tmp += self.data_prop*self.LQL\\r\\n self.dL_dKmm = 0.5*backsub_both_sides(self.Lm,tmp)\\r\\n\\r\\n #Compute the gradient of the log likelihood wrt noise variance\\r\\n self.partial_for_likelihood = -0.5*(self.batchsize*self.output_dim - np.sum(self.A*self.LQL))*self.likelihood.precision\\r\\n self.partial_for_likelihood += (0.5*self.output_dim*self.trace_K + 0.5 * self.likelihood.trYYT - np.sum(self.likelihood.Y*self.projected_mean))*self.likelihood.precision**2\",\n \"def _evaluate_during_fit(self, test_loader, epoch):\",\n \"def __calculated_initial_fitness(self) -> None:\\n\\n logger.info('Start calculating initial fitness')\\n\\n __calculated_values = (self._commands_holder_train.execute_commands(\\n self.__initial_parameters))\\n\\n self.__initial_fitness = (self._evaluate_fitness(\\n __calculated_values, self._training_dataset))\\n\\n logger.info('Initial fitness calculation finishes')\",\n \"def compute_statistics(self):\",\n \"def calculations(self):\\n self.prerequisite_set = get_prerequisites(self.major, self.major2)\\n self.check_prerequisites()\\n self.get_requirements_for_majors()\",\n \"def request_estimation(self):\\n if self.can_estimate():\\n res = self.get_estimation()\\n if res is not None:\\n self.callback.plp_estimation(res)\\n else:\\n self.callback.plp_no_preconditions()\\n else:\\n self.callback.plp_missing_data() # plp_cannot_estimate(msg)\",\n \"def calc_pre_intertie_generation (self):\\n\\n self.pre_intertie_generation = \\\\\\n self.forecast.get_generation(self.start_year,self.end_year)\\n\\n gen_eff = self.cd[\\\"diesel generation efficiency\\\"]\\n self.pre_intertie_generation_fuel_used = \\\\\\n self.pre_intertie_generation / gen_eff\\n\\n #~ print 'self.baseline_generatio',self.baseline_generation\",\n \"def evaluate(self):\\n # define start index test set\\n start_test_set = int(len(self.X) * 2 / 3)\\n\\n # Different methods for cummulativa vs day-ahead forecasting\\n if self.forecast_horizon == 1:\\n # In sample\\n lin_residuals_in_sample = self.y - (self.betas[0] + np.dot(self.X, self.betas[1]))\\n self.rmse_in_sample = np.mean(lin_residuals_in_sample ** 2) ** 0.5\\n self.var_in_sample = np.var(self.y)\\n\\n # Out of sample\\n # Calculate MSE of wls-ev prediction\\n self.mse_wlsev = np.mean((self.y[start_test_set:] - self.ols_predict()) ** 2)\\n # Calculate MSE of benchmark prediction\\n self.mse_benchmark = np.mean((self.y[start_test_set:] - self.benchmark_predict()) ** 2)\\n else:\\n # In Sample with betas estimated on full time series\\n lin_residuals_in_sample = rolling_sum(self.y, self.forecast_horizon) - (\\n self.betas[0] + np.dot(self.X[:-(self.forecast_horizon-1)], self.betas[1]))\\n self.rmse_in_sample = np.mean(lin_residuals_in_sample ** 2) ** 0.5\\n self.var_in_sample = np.var(rolling_sum(self.y, self.forecast_horizon))\\n\\n # Out of sample\\n # calculate realized cummulative returns over forecast horizon sequences\\n cum_rets_realized = rolling_sum(self.y[start_test_set:], self.forecast_horizon)\\n # Calculate MSE of wls-ev prediction, only where realized values are available\\n self.mse_wlsev = np.mean((cum_rets_realized - self.ols_predict()[:-(self.forecast_horizon-1)]) ** 2)\\n # Calculate MSE of benchmark prediction, only where realized values are available\\n self.mse_benchmark = np.mean(\\n (cum_rets_realized - self.benchmark_predict()[:-(self.forecast_horizon-1)]) ** 2)\\n\\n # Calculate out of sample r-squared\\n self.oos_r_squared = 1 - (self.mse_wlsev / self.mse_benchmark)\\n # Calculate in sample r-squared\\n self.in_sample_r_squared = 1.0 - (self.rmse_in_sample ** 2) / self.var_in_sample\",\n \"def add_is_approximation(self):\\n \\n if (self.mf_steps == 0 or self.alpha ==0) and (not self.mixture)\\\\\\n and (self.gibbs_steps ==0):\\n print(\\\"Importance distribution is uniform\\\")\\n weight_term = T.log(self.num_samples) + self.num_vars*T.log(0.5)\\n \\n elif (self.mf_steps > 0 and self.alpha > 0) and (not self.mixture)\\\\\\n and (self.gibbs_steps ==0):\\n print(\\\"Importance distribution is not uniform\\\")\\n weight_term = T.log(self.num_samples)+\\\\\\n self.get_importance_evals(T.transpose(self.x_tilda), \\n T.transpose(self.sampler_theta))\\n \\n if self.resample:\\n weight_term = T.reshape(weight_term, \\n [self.batch_size, self.num_samples])\\n \\n elif self.mixture:\\n \\n if self.resample:\\n n_iters = self.num_samples*self.batch_size \\n else:\\n n_iters = self.num_samples \\n \\n weight_term, _ =\\\\\\n theano.scan(lambda j: \\n self.get_mixture_evals(T.transpose(self.x_tilda[j,:])),\\n sequences = [T.arange(n_iters)])\\n \\n if self.resample:\\n weight_term = T.reshape(weight_term, \\n [self.batch_size, self.num_samples]) \\n \\n weight_term = T.log(self.num_samples) + weight_term \\n \\n elif self.gibbs_steps > 0:\\n print(\\\"Importance distribution is gibbs sampler\\\") \\n weight_term = T.log(self.num_samples)+\\\\\\n self.get_importance_evals(T.transpose(self.x_tilda), \\n T.transpose(self.sampler_theta))\\n \\n if self.resample:\\n weight_term = T.reshape(weight_term, \\n [self.batch_size, self.num_samples]) \\n \\n \\n if self.resample and self.num_hidden ==0:\\n \\n approx_Z = -self.compute_energy(self.x_tilda, \\n self.num_samples*self.batch_size)\\n \\n elif (not self.resample) and self.num_hidden ==0:\\n \\n approx_Z = -self.compute_energy(self.x_tilda, \\n self.num_samples)\\n \\n elif self.resample and self.num_hidden > 0:\\n \\n approx_Z = -self.compute_free_energy(self.x_tilda)\\n \\n elif (not self.resample) and self.num_hidden >0:\\n \\n approx_Z = -self.compute_free_energy(self.x_tilda)\\n \\n if self.resample:\\n \\n approx_Z = T.reshape(approx_Z, \\n [self.batch_size, self.num_samples])\\n \\n approx_Z = approx_Z - weight_term\\n \\n return approx_Z\",\n \"def eval_obs_pre(self, X, y):\\n \\n X, y = check_X_y(\\n X, y, accept_sparse=False, dtype=np.float64, order=\\\"C\\\",\\n ensure_min_features=2, estimator=self)\\n\\n n_dimensions = X.shape[1]\\n self.classes_, n_samples = np.unique(y, return_counts=True)\\n n_times = self.classes_.size\\n\\n # n_samples = np.array([x.shape[0] for x in X])\\n if self.assume_centered:\\n self.location_ = np.zeros((n_times, n_dimensions))\\n else:\\n self.location_ = np.array(\\n [X[y == cl].mean(0) for cl in self.classes_])\\n\\n precisions = self.get_observed_precision()\\n\\n emp_pre_score = []\\n sam_pre_score = []\\n slack = []\\n\\n for i in range(precisions.shape[0]):\\n emp_cov = empirical_covariance(X[y == i] - self.location_[i], assume_centered=True)\\n precision = precisions[i, :, :]\\n # slack.append(-self.constrained_to[i] + logl(emp_cov, precision))\\n _, log_det = np.linalg.slogdet(precision)\\n emp_pre_score.append(log_det - np.trace(emp_cov @ precision))\\n sam_pre_score.append(log_det - np.array([np.trace((X[y == i][[j], :].T @ X[y == i][[j], :]) @ precision) for j in range(int(n_samples))]))\\n\\n return self.emp_inv_score - np.array(emp_pre_score), self.sam_inv_score - np.array(sam_pre_score), precision\",\n \"def propose_optimize():\\n pass\",\n \"def solve(self):\\n \\n # Check if cost is available for both estimators\\n if not self.est0.cost_avail or not self.est1.cost_avail:\\n self.comp_cost = False\\n \\n # Initial estimate from the input node\\n if self.comp_cost:\\n z0, zvar0, cost0 = self.est0.est_init(return_cost=True)\\n else:\\n z0, zvar0 = self.est0.est_init(return_cost=False)\\n cost0 = 0\\n self.z0 = z0\\n self.zvar0 = zvar0\\n self.cost0 = cost0\\n \\n # Initialize other variables\\n self.var_cost0 = 0\\n self.var_cost1 = 0\\n self.cost = 0\\n self.s = np.zeros(self.shape1)\\n \\n for it in range(self.nit):\\n \\n # Forward transform to est1\\n t0 = time.time()\\n rvar1_new = self.A.var_dot(self.zvar0)\\n rvar1_rep = common.repeat_axes(rvar1_new,self.shape1,\\\\\\n self.var_axes1,rep=False)\\n z1_mult = self.A.dot(self.z0)\\n r1_new = z1_mult - rvar1_rep*self.s\\n \\n # Damping\\n if it > 0: \\n self.r1 = (1-self.step)*self.r1 + self.step*r1_new\\n self.rvar1 = (1-self.step)*self.rvar1 + self.step*rvar1_new\\n else:\\n self.r1 = r1_new\\n self.rvar1 = rvar1_new\\n\\n # Estimator 1 \\n if self.comp_cost: \\n z1, zvar1, cost1 = self.est1.est(self.r1, self.rvar1, return_cost=True) \\n if not self.map_est:\\n cost1 -= self.cost_adjust(self.r1,z1,self.rvar1,zvar1,\\\\\\n self.shape1,self.var_axes1)\\n else:\\n z1, zvar1 = self.est1.est(self.r1, self.rvar1, return_cost=False) \\n cost1 = 0\\n self.z1 = z1\\n self.zvar1 = zvar1\\n self.cost1 = cost1 \\n con_new = np.mean(np.abs(z1-z1_mult)**2) \\n \\n # Reverse nonlinear transform to est 0\\n self.s = (self.z1-self.r1)/rvar1_rep\\n self.sprec = 1/self.rvar1*(1-self.zvar1/self.rvar1)\\n t1 = time.time()\\n self.time_est1 = t1-t0\\n \\n # Reverse linear transform to est 0 \\n rvar0_new = 1/self.A.var_dotH(self.sprec)\\n rvar0_rep = common.repeat_axes(rvar0_new,self.shape0,\\\\\\n self.var_axes0,rep=False)\\n r0_new = self.z0 + rvar0_rep*self.A.dotH(self.s)\\n \\n # Damping\\n if it > 0:\\n self.r0 = (1-self.step)*self.r0 + self.step*r0_new\\n self.rvar0 = (1-self.step)*self.rvar0 + self.step*rvar0_new\\n else:\\n self.r0 = r0_new\\n self.rvar0 = rvar0_new\\n \\n \\n # Estimator 0\\n if self.comp_cost:\\n z0, zvar0, cost0 = self.est0.est(self.r0, self.rvar0, return_cost=True)\\n if not self.map_est:\\n cost0 -= self.cost_adjust(self.r0,z0,self.rvar0,zvar0,\\\\\\n self.shape0,self.var_axes0)\\n \\n else:\\n z0, zvar0 = self.est0.est(self.r0, self.rvar0, return_cost=False)\\n cost0 = 0\\n self.z0 = z0\\n self.zvar0 = zvar0\\n self.cost0 = cost0 \\n\\n \\n # Compute total cost and constraint \\n cost_new = self.cost0 + self.cost1 \\n if not self.map_est:\\n cost_new += self.cost_gauss()\\n \\n # Step size adaptation\\n if (self.step_adapt) and (it > 0):\\n if (con_new < self.con):\\n self.step = np.minimum(1,self.step_inc*self.step)\\n else:\\n self.step = np.maximum(self.step_min, self.step_dec*self.step)\\n self.cost=cost_new\\n self.con=con_new\\n \\n t2 = time.time()\\n self.time_est0 = t2-t1\\n self.time_iter = t2-t0\\n \\n # Print progress\\n if self.prt_period > 0:\\n if (it % self.prt_period == 0):\\n if self.comp_cost:\\n print(\\\"it={0:4d} cost={1:12.4e} con={2:12.4e} step={3:12.4e}\\\".format(\\\\\\n it, self.cost, self.con, self.step))\\n else:\\n print(\\\"it={0:4d} con={1:12.4e}\\\".format(\\\\\\n it, self.con))\\n \\n # Save history\\n self.save_hist()\",\n \"def compute(self):\\n # update parameters with the values of widgets\\n self.parmVal[0] = float(str(self.p1text.text()))\\n if len(self.parmName) > 1:\\n self.parmVal[1] = float(str(self.p2text.text()))\\n if len(self.parmName) > 2:\\n self.parmVal[2] = float(str(self.p3text.text()))\\n if len(self.parmName) > 3:\\n self.parmVal[3] = float(str(self.p4text.text()))\\n\\n if self.model == 0:\\n # Polynomial fit\\n order = int(self.parmVal[0])\\n pcoef = np.polyfit(self.data[0], self.data[1], order)\\n Yfit = np.polyval(pcoef, self.data[0])\\n # compute the root mean square error (RMSE)\\n err = np.sqrt(sum((Yfit-self.data[1])**2) / len(self.data[1]))\\n msg = 'Least squares polynomial fit - '\\n msg += 'Polynom order = {}\\\\n'.format(order)\\n msg += 'c{0} = {1}\\\\n'.format(order, pcoef[0])\\n for i in range(order):\\n msg += 'c{0} = {1}\\\\n'.format(order-1, pcoef[i+1])\\n msg += 'RMSE = {0:g}\\\\n'.format(err)\\n # compute 1st derivative\\n pfit = np.poly1d(pcoef)\\n pdercoef = np.polyder(pcoef, 1)\\n pder = np.poly1d(pdercoef)\\n # print roots\\n st = \\\"Root(s) of the 1st derivative = {0}\\\\n\\\"\\n msg += st.format(np.array2string(pder.r))\\n\\n elif self.model == 1:\\n # Fitting with exponential equation A*exp(B*x)\\n # initial guesses\\n p0 = [self.parmVal[0], self.parmVal[1]]\\n pbest1 = leastsq(self.residuals_Exp, p0, args=(self.data[1], self.data[0]))\\n Yfit = self.Exp(self.data[0], pbest1[0])\\n Res = self.residuals_Exp(pbest1[0], self.data[1], self.data[0])\\n # compute the root mean square error (RMSE)\\n err = np.sqrt(np.sum(np.square(Res)) / len(self.data[1]))\\n msg = 'Fitting with exponential equation: A * exp(B * x)\\\\n'\\n msg += '[A, B] = {0}\\\\n'.format(str(pbest1[0]))\\n msg += 'RMSE = {0:g}\\\\n\\\\n'.format(err)\\n self.parmVal = pbest1[0]\\n\\n elif self.model == 2:\\n # Fitting with logarithm equation A*Ln(x)+B\\n # initial guesses\\n p0 = [self.parmVal[0], self.parmVal[1]]\\n pbest1 = leastsq(self.residuals_Log, p0, args=(self.data[1], self.data[0]))\\n Yfit = self.Log(self.data[0], pbest1[0])\\n Res = self.residuals_Log(pbest1[0], self.data[1], self.data[0])\\n # compute the root mean square error (RMSE)\\n err = np.sqrt(np.sum(np.square(Res)) / len(self.data[1]))\\n msg = 'Fitting with logarithm equation: A * Ln(x) + B\\\\n'\\n msg += '[A, B] = {0}\\\\n'.format(str(pbest1[0]))\\n msg += 'RMSE = {0:g}\\\\n\\\\n'.format(err)\\n self.parmVal = pbest1[0]\\n\\n elif self.model == 3:\\n # Fitting with Arrhenius equation A*exp(-(E/(R*(x+273))))\\n # initial guesses\\n p0 = [self.parmVal[0], self.parmVal[1]]\\n pbest1 = leastsq(self.residuals_Arr, p0, args=(self.data[1], self.data[0]))\\n Yfit = self.Arr(self.data[0], pbest1[0])\\n Res = self.residuals_Arr(pbest1[0], self.data[1], self.data[0])\\n # compute the root mean square error (RMSE)\\n err = np.sqrt(np.sum(np.square(Res)) / len(self.data[1]))\\n msg = 'Fitting with Arrhenius equation: A * exp(-(E/(R * (x + 273))))\\\\n'\\n msg += '[A, E] = {0}\\\\n'.format(str(pbest1[0]))\\n msg += 'RMSE = {0:g}\\\\n\\\\n'.format(err)\\n self.parmVal = pbest1[0]\\n\\n elif self.model == 4:\\n # Fitting data with Power model\\n # initial guesses\\n p0 = [self.parmVal[0], self.parmVal[1]]\\n pbest1 = leastsq(self.residuals_PL, p0, args=(self.data[1], self.data[0]))\\n Yfit = self.PowerLaw(self.data[0], pbest1[0])\\n Res = self.residuals_PL(pbest1[0], self.data[1], self.data[0])\\n # compute the root mean square error (RMSE)\\n err = np.sqrt(np.sum(np.square(Res)) / len(self.data[1]))\\n msg = 'Fitting with Power model: A * x^B\\\\n'\\n msg += '[A, B] = {0}\\\\n'.format(str(pbest1[0]))\\n msg += 'RMSE = {0:g}\\\\n\\\\n'.format(err)\\n self.parmVal = pbest1[0]\\n\\n elif self.model == 5:\\n # Fitting Rheo data with Herschel-Bulkley model\\n # initial guesses\\n p0 = [self.parmVal[0], self.parmVal[1], self.parmVal[2]]\\n pbest1 = leastsq(self.residuals_HB, p0, args=(self.data[1], self.data[0]))\\n Yfit = self.HerschelBulkley(self.data[0], pbest1[0])\\n Res = self.residuals_HB(pbest1[0], self.data[1], self.data[0])\\n # compute the root mean square error (RMSE)\\n err = np.sqrt(np.sum(np.square(Res)) / len(self.data[1]))\\n msg = 'Fitting with Herschel-Bulkley model: A + B * x^C\\\\n'\\n msg += '[A, B, C] = {0}\\\\n'.format(str(pbest1[0]))\\n msg += 'RMSE = {0:g}\\\\n\\\\n'.format(err)\\n self.parmVal = pbest1[0]\\n\\n elif self.model == 6:\\n # Fitting with sigmoid S+H/(1+exp(-lambda*x)\\n # initial guesses\\n p0 = [self.parmVal[0], self.parmVal[1], self.parmVal[2], self.parmVal[3]]\\n pbest1 = leastsq(self.residuals_Sigm, p0, args=(self.data[1], self.data[0]))\\n Yfit = self.Sigm(self.data[0], pbest1[0])\\n Res = self.residuals_Sigm(pbest1[0], self.data[1], self.data[0])\\n # compute the root mean square error (RMSE)\\n err = np.sqrt(np.sum(np.square(Res)) / len(self.data[1]))\\n msg = 'Fitting with sigmoid equation: yo + H /(1 + exp(S * (xo - x))\\\\n'\\n msg += '[xo, yo, H, S] = {0}\\\\n'.format(str(pbest1[0]))\\n msg += 'RMSE = {0:g}\\\\n\\\\n'.format(err)\\n self.parmVal = pbest1[0]\\n\\n else:\\n msg = 'Unknown model'\\n Yfit = self.data[1]\\n\\n # Show results in log windows\\n self.log.setText(msg)\\n self.saveToLogFile(msg)\\n self.data[2] = Yfit\\n self.data[3] = self.data[2] - self.data[1] - self.diffshift\\n\\n self.updatePlot()\\n self.updateParms()\",\n \"def preprocess_test_data(self):\\r\\n print(\\\"* Preprocessing test data.\\\", flush=True)\\r\\n prep.create_HDF_file(self.C.test_set)\\r\\n\\r\\n self.print_time_elapsed()\",\n \"def prepare(self) -> None:\\n\\n \\\"\\\"\\\"\\n Objective function\\n Coefficient -2 means that we solve maximization problem (multiple all \\n value to -1) and also there are left coverage area and right coverage \\n area for each station (2* cov)\\n \\\"\\\"\\\"\\n\\n f = [-2 * self.cov[i] for i in range(self.get_column_num)]\\n self._f = np.array(f)\\n\\n \\\"\\\"\\\" Inequality Constraints\\\"\\\"\\\"\\n ineq_cost = [self.cost[i] for i in range(self.get_column_num)]\\n self._ineq_constraints = np.array(ineq_cost)\\n self._b = np.array(self.cost_limit)\\n\\n \\\"\\\"\\\" \\n There is no equality constraints. \\n self._eq_constraints is empty\\n self._beq is empty\\n \\\"\\\"\\\"\",\n \"def setup(self):\\n self.model.monitor = myMonitor.get_monitor(self.model)\\n self.model.monitor.time_budget_exceeded = False\\n if self.algorithm is not None:\\n self.algorithm.setup(model=self.model, dataset=self.dataset)\\n self.setup_extensions()\\n\\n # Model.modify_updates is used by the training algorithm to\\n # enforce constraints after each step of learning. Here we\\n # make sure the constraints are enforced from the start.\\n self.model.enforce_constraints()\",\n \"def preprocess(self):\\n\\n mm_magcoord.add_aacgm_coordinates(self)\\n mm_magcoord.add_quasi_dipole_coordinates(self)\\n mm_sc.calculate_ecef_velocity(self)\\n mm_sc.add_ram_pointing_sc_attitude_vectors(self)\\n\\n return\",\n \"def _calculate(self):\\n raise NotYetImplemented()\",\n \"def before_fit(self):\\n self.run = not hasattr(self.learn, 'lr_finder') and not hasattr(self, \\\"gather_preds\\\")\\n if not self.run:\\n return\\n\\n # Prepare ground truth container, set here as y_true's always stay the same\\n self.y_true = []\",\n \"def _build_precomputed_data(self):\\n if self.num_sampled == 0:\\n self._K_chol = numpy.array([])\\n self._K_inv_y = numpy.array([])\\n else:\\n covariance_matrix = python_utils.build_covariance_matrix(\\n self._covariance,\\n self._points_sampled,\\n noise_variance=self._points_sampled_noise_variance,\\n )\\n\\n C = self._build_integrated_term_maxtrix(self._covariance, self._points_sampled)\\n self._K_Inv = numpy.linalg.inv(covariance_matrix)\\n self._K_C = numpy.empty((covariance_matrix.shape[0],covariance_matrix.shape[0]))\\n self._K_C = numpy.multiply(C, self._K_Inv)\\n self._K_chol = scipy.linalg.cho_factor(covariance_matrix, lower=True, overwrite_a=True)\\n self._K_inv_y = scipy.linalg.cho_solve(self._K_chol, self._points_sampled_value)\\n self._marginal_mean_mat = self._build_marginal_matrix_mean()\\n self._marginal_mean_mat_gradient = self._build_marginal_matrix_mean_gradient()\",\n \"def _prepare_fit(self):\\n self.lower_conf_int, self.upper_conf_int, self.upper_whisker_res = None, None, None\\n self.model_fit = None\\n self.residuals, self.residuals_forecast, self.fittedvalues = None, None, None\",\n \"def basic_experiments(k, p, num_iter, transforms, train_path, test_path):\\n start_time = time.time()\\n print(\\\"Generating advice\\\")\\n with open(train_path, \\\"r\\\") as f:\\n train_data = f.readlines()\\n advice = generate_advice(train_data, transforms)\\n print(\\\"Done: \\\" + str(time.time() - start_time) + \\\"\\\\n\\\")\\n\\n print(\\\"Generating real count for test data\\\")\\n with open(test_path, \\\"r\\\") as f:\\n test_data = f.readlines()\\n real_count = generate_advice(test_data, transforms)\\n print(\\\"Done: \\\" + str(time.time() - start_time))\\n actual = [x.moment(p) for x in real_count]\\n print(\\\"Actual moments:\\\")\\n print(actual)\\n\\n print(\\\"Estimating using noisy advice:\\\")\\n est_with_advice = []\\n for i in range(num_iter):\\n seed = str(random.randint(1, 2**64 + 1)) + str(random.randint(1, 2**64 + 1))\\n this_hash_exp = lambda x: hash_exp(x, seed)\\n est_with_advice.append(\\n estimate_using_advice(test_data, transforms, k, p, this_hash_exp,\\n advice))\\n print(\\\"Done: \\\" + str(time.time() - start_time))\\n print(est_with_advice)\\n\\n print(\\\"Estimating using perfect advice:\\\")\\n est_perfect_advice = []\\n for i in range(num_iter):\\n seed = str(random.randint(1, 2**64 + 1)) + str(random.randint(1, 2**64 + 1))\\n this_hash_exp = lambda x: hash_exp(x, seed)\\n est_perfect_advice.append(\\n estimate_using_advice(test_data, transforms, k, p, this_hash_exp,\\n real_count))\\n print(\\\"Done: \\\" + str(time.time() - start_time))\\n print(est_perfect_advice)\\n\\n print(\\\"Estimating using PPSWOR:\\\")\\n est_ppswor = []\\n for i in range(num_iter):\\n cur = []\\n for x in real_count:\\n cur.append(ppswor_estimate_moment(x.counts.items(), k, p))\\n est_ppswor.append(cur)\\n print(\\\"Done: \\\" + str(time.time() - start_time))\\n print(est_ppswor)\\n\\n print(\\\"Estimating using ell_2 PPSWOR:\\\")\\n est_ppswor_l2 = []\\n for i in range(num_iter):\\n cur = []\\n for x in real_count:\\n cur.append(ppswor_estimate_moment(x.counts.items(), k, p, 2))\\n est_ppswor_l2.append(cur)\\n print(\\\"Done: \\\" + str(time.time() - start_time))\\n print(est_ppswor_l2)\",\n \"def _compute(self):\\n\\n super(RegressionStatistics, self)._compute()\\n sets = self.sets\\n Nsets = len(sets)\\n\\n stats = {}\\n\\n funcs = {\\n 'RMP_t': lambda p,t:rootMeanPowerFx(t),\\n 'STD_t': lambda p,t:N.std(t),\\n 'RMP_p': lambda p,t:rootMeanPowerFx(p),\\n 'STD_p': lambda p,t:N.std(p),\\n 'CCe': CorrErrorFx(),\\n 'CCp': CorrErrorPFx(),\\n 'RMSE': RMSErrorFx(),\\n 'RMSE/RMP_t': RelativeRMSErrorFx()\\n }\\n\\n for funcname, func in funcs.iteritems():\\n funcname_all = funcname + '_all'\\n stats[funcname_all] = []\\n for i, (targets, predictions, values) in enumerate(sets):\\n stats[funcname_all] += [func(predictions, targets)]\\n stats[funcname_all] = N.array(stats[funcname_all])\\n stats[funcname] = N.mean(stats[funcname_all])\\n stats[funcname+'_std'] = N.std(stats[funcname_all])\\n stats[funcname+'_max'] = N.max(stats[funcname_all])\\n stats[funcname+'_min'] = N.min(stats[funcname_all])\\n\\n # create ``summary`` statistics, since some per-set statistics\\n # might be uncomputable if a set contains just a single number\\n # (like in the case of correlation coefficient)\\n targets, predictions = [], []\\n for i, (targets_, predictions_, values_) in enumerate(sets):\\n targets += list(targets_)\\n predictions += list(predictions_)\\n\\n for funcname, func in funcs.iteritems():\\n funcname_all = 'Summary ' + funcname\\n stats[funcname_all] = func(predictions, targets)\\n\\n self._stats.update(stats)\",\n \"def _compute_reward(self, observations, done):\\n raise NotImplementedError()\",\n \"def _compute_reward(self, observations, done):\\n raise NotImplementedError()\",\n \"def _compute_reward(self, observations, done):\\n raise NotImplementedError()\",\n \"def _compute_reward(self, observations, done):\\n raise NotImplementedError()\",\n \"def _compute_reward(self, observations, done):\\n raise NotImplementedError()\",\n \"def _compute_reward(self, observations, done):\\n raise NotImplementedError()\",\n \"def run(self):\\n self.evaluate()\\n self.accumulate()\\n self.summarize()\",\n \"def _setup_training(self, state_ph, next_state_ph, next_state_pred):\\n ### PROBLEM 1\\n ### YOUR CODE HERE\\n # raise NotImplementedError\\n delta_state_ph = next_state_ph - state_ph\\n delta_state_pred = next_state_pred - state_ph\\n norm_delta_state_ph = utils.normalize(delta_state_ph,\\n self._init_dataset.delta_state_mean,\\n self._init_dataset.delta_state_std)\\n norm_delta_state_pred = utils.normalize(delta_state_pred,\\n self._init_dataset.delta_state_mean,\\n self._init_dataset.delta_state_std)\\n loss = tf.reduce_mean(tf.square(norm_delta_state_pred - norm_delta_state_ph))\\n optimizer = tf.train.AdamOptimizer(self._learning_rate).minimize(loss)\\n\\n return loss, optimizer\",\n \"def do_parallel_inference(args):\\n from treehmm import random_params, do_inference, plot_params, plot_energy, load_params\\n from treehmm.vb_mf import normalize_trans\\n from treehmm.static import float_type\\n\\n _x = sp.load(args.observe_matrix[0])\\n args.continuous_observations = _x.dtype != sp.int8\\n args.I, _, args.L = _x.shape\\n I = args.I\\n K = args.K\\n L = args.L\\n args.T = 'all'\\n args.free_energy = []\\n args.observe = 'all.npy'\\n args.last_free_energy = 0\\n args.emit_sum = sp.zeros((K, L), dtype=float_type)\\n\\n args.out_dir = args.out_dir.format(timestamp=time.strftime('%x_%X').replace('/', '-'), **args.__dict__)\\n try:\\n print 'making', args.out_dir\\n os.makedirs(args.out_dir)\\n except OSError:\\n pass\\n\\n if args.warm_start:\\n # args.last_free_energy, args.theta, args.alpha, args.beta, args.gamma, args.emit_probs, args.emit_sum = load_params(args)\\n # args.warm_start = False\\n print '# loading previous params for warm start from %s' % args.warm_start\\n tmpargs = copy.deepcopy(args)\\n tmpargs.out_dir = args.warm_start\\n tmpargs.observe = 'all.npy'\\n args.free_energy, args.theta, args.alpha, args.beta, args.gamma, args.emit_probs, args.emit_sum = load_params(tmpargs)\\n\\n try:\\n args.free_energy = list(args.free_energy)\\n except TypeError: # no previous free energy\\n args.free_energy = []\\n print 'done'\\n args.warm_start = False\\n else:\\n (args.theta, args.alpha, args.beta, args.gamma, args.emit_probs) = \\\\\\n random_params(args.I, args.K, args.L, args.separate_theta)\\n for p in ['free_energy', 'theta', 'alpha', 'beta', 'gamma', 'emit_probs', 'last_free_energy', 'emit_sum']:\\n sp.save(os.path.join(args.out_dir, args.out_params.format(param=p, **args.__dict__)),\\n args.__dict__[p])\\n\\n \\n args.iteration = 0\\n plot_params(args)\\n print '# setting up job arguments'\\n # set up new versions of args for other jobs\\n job_args = [copy.copy(args) for i in range(len(args.observe_matrix))]\\n for j, a in enumerate(job_args):\\n a.observe_matrix = args.observe_matrix[j]\\n a.observe = os.path.split(args.observe_matrix[j])[1]\\n a.subtask = True\\n a.func = None\\n a.iteration = 0\\n a.max_iterations = 1\\n a.quiet_mode = True\\n if j % 1000 == 0:\\n print j\\n\\n if args.run_local:\\n pool = multiprocessing.Pool()\\n else:\\n pool = sge.SGEPool()\\n #job_handle = pool.imap_unordered(do_inference, job_args)\\n\\n converged = False\\n for args.iteration in range(args.max_iterations):\\n # import ipdb; ipdb.set_trace()\\n # fresh parameters-- to be aggregated after jobs are run\\n print 'iteration', args.iteration\\n total_free = 0\\n if args.separate_theta:\\n args.theta = sp.zeros((I - 1, K, K, K), dtype=float_type)\\n else:\\n args.theta = sp.zeros((K, K, K), dtype=float_type)\\n\\n args.alpha = sp.zeros((K, K), dtype=float_type)\\n args.beta = sp.zeros((K, K), dtype=float_type)\\n args.gamma = sp.zeros((K), dtype=float_type)\\n args.emit_probs = sp.zeros((K, L), dtype=float_type)\\n if True: # args.approx == 'clique':\\n args.emit_sum = sp.zeros_like(args.emit_probs, dtype=float_type)\\n else:\\n args.emit_sum = sp.zeros((K, L), dtype=float_type)\\n\\n if args.run_local:\\n iterator = pool.imap_unordered(do_inference, job_args, chunksize=args.chunksize)\\n # wait for jobs to finish\\n for result in iterator:\\n pass\\n else:\\n jobs_handle = pool.map_async(do_inference, job_args, chunksize=args.chunksize)\\n # wait for all jobs to finish\\n for j in jobs_handle:\\n j.wait()\\n\\n # sum free energies and parameters from jobs\\n for a in job_args:\\n # print '# loading from %s' % a.observe\\n free_energy, theta, alpha, beta, gamma, emit_probs, emit_sum = load_params(a)\\n # print 'free energy for this part:', free_energy\\n if len(free_energy) > 0:\\n last_free_energy = free_energy[-1]\\n else:\\n last_free_energy = 0\\n total_free += last_free_energy\\n args.theta += theta\\n args.alpha += alpha\\n args.beta += beta\\n args.gamma += gamma\\n args.emit_probs += emit_probs\\n args.emit_sum += emit_sum\\n\\n # renormalize and plot\\n print 'normalize aggregation... total free energy is:', total_free\\n args.free_energy.append(total_free)\\n if len(args.free_energy) > 1 and args.free_energy[-1] != 0 and args.free_energy[-2] != 0 \\\\\\n and abs((args.free_energy[-2] - args.free_energy[-1]) / args.free_energy[-2]) < args.epsilon:\\n print 'converged. free energy diff:', args.free_energy, abs(args.free_energy[-2] - args.free_energy[-1]) / args.free_energy[-2]\\n converged = True\\n normalize_trans(args.theta, args.alpha, args.beta, args.gamma)\\n # if True: #args.approx == 'clique':\\n # #print 'clique emit renorm'\\n # args.emit_probs[:] = args.emit_probs / args.emit_sum\\n # else:\\n # args.emit_probs[:] = sp.dot(sp.diag(1./args.emit_sum), args.emit_probs)\\n args.emit_probs[:] = sp.dot(sp.diag(1. / args.emit_sum), args.emit_probs)\\n for a in job_args:\\n a.theta, a.alpha, a.beta, a.gamma, a.emit_probs, a.emit_sum = args.theta, args.alpha, args.beta, args.gamma, args.emit_probs, args.emit_sum\\n\\n for p in ['free_energy', 'theta', 'alpha', 'beta', 'gamma', 'emit_probs', 'lmd', 'tau']:\\n try:\\n sp.save(os.path.join(args.out_dir, args.out_params.format(param=p, **args.__dict__)),\\n args.__dict__[p])\\n except KeyError:\\n pass\\n plot_params(args)\\n plot_energy(args)\\n\\n if args.save_Q >= 3:\\n print '# reconstructing chromosomes from *chunk*',\\n in_order = {}\\n # Q_chr16_all.trimmed.chunk*.npy => Q_chr16_all.trimmed.npy\\n all_chunks = glob.glob(os.path.join(args.out_dir, '*_Q_*chunk*.npy'))\\n for chunk in all_chunks:\\n print chunk\\n chunk_num = int(re.search(r'chunk(\\\\d+)', chunk).groups()[0])\\n chrom_out = re.sub('chunk(\\\\d+)\\\\.', '', chunk)\\n if chrom_out not in in_order:\\n in_order[chrom_out] = {}\\n in_order[chrom_out][chunk_num] = sp.load(chunk)\\n for chrom_out in in_order:\\n print 'reconstructing chromosomes from', in_order[chrom_out]\\n if len(in_order[chrom_out]) > 1:\\n final_array = sp.concatenate((in_order[chrom_out][0], in_order[chrom_out][1]), axis=1)\\n for i in range(2, max(in_order[chrom_out])):\\n final_array = sp.concatenate((final_array, in_order[chrom_out][i]), axis=1)\\n else:\\n final_array = in_order[chrom_out][0]\\n sp.save(chrom_out, final_array)\\n\\n if converged:\\n break\",\n \"def _prepare_evaluate(self):\\n labels = list()\\n labels += ['num_procs', 'num_periods', 'is_debug', 'seed_emax', 'seed_sim']\\n labels += ['num_draws_emax', 'num_agents_sim', 'num_types', 'edu_spec', 'version']\\n labels += ['num_draws_prob', 'seed_prob']\\n num_procs, num_periods, is_debug, seed_emax, seed_sim, num_draws_emax, num_agents_sim, \\\\\\n num_types, edu_spec, version, num_draws_prob, seed_prob = \\\\\\n dist_class_attributes(self.respy_base, *labels)\\n\\n periods_draws_emax = create_draws(num_periods, num_draws_emax, seed_emax, is_debug)\\n periods_draws_sims = create_draws(num_periods, num_agents_sim, seed_sim, is_debug)\\n\\n disturbances = (periods_draws_emax, periods_draws_sims)\\n\\n # We want to maintain a pure PYTHON version for testing purposes.\\n args = list()\\n args += [num_periods, num_types, edu_spec['start'], edu_spec['max'], edu_spec['max'] + 1]\\n state_space_info = respy_f2py.wrapper_create_state_space(*args)\\n if self.mpi_setup == MISSING_INT:\\n slavecomm = self.mpi_setup\\n else:\\n slavecomm = self.mpi_setup.py2f()\\n self.set_up_baseline(periods_draws_emax, None)\\n\\n initial_conditions = get_initial_conditions(self.respy_base)\\n\\n args = (smm_sample_f2py, state_space_info, initial_conditions, disturbances, slavecomm)\\n self.simulate_sample = partial(*args)\",\n \"def pre_process(self, x0, params=()):\\n # Should be used by all methods matching \\\"solve_*\\\"\\n if self.x_by_name and isinstance(x0, dict):\\n x0 = [x0[k] for k in self.names]\\n if self.par_by_name and isinstance(params, dict):\\n params = [params[k] for k in self.param_names]\\n for pre_processor in self.pre_processors:\\n x0, params = pre_processor(x0, params)\\n return x0, np.atleast_1d(params)\",\n \"def test_fit_prep_proba():\\n args = get_layer('fit', 'manual', 'temporal', True, True, window=2, step_size=3)\\n run_layer(*args)\",\n \"def test_predict_prep_proba():\\n args = get_layer('predict', 'manual', 'temporal', True, True, window=2, step_size=3)\\n run_layer(*args)\",\n \"def proba(c_pred,m_pred,f_pred, dataset):\\n p = np.zeros(10)\\n if dataset == 'cifar10':\\n for i in range(10):\\n if i <4:\\n if i <2:\\n p[i] = c_pred[0]*(m_pred[0]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[0:2]))\\n elif i <4:\\n p[i] = c_pred[0]*(m_pred[1]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[2:4]))\\n if i >=4:\\n if i <6:\\n p[i] = c_pred[1]*(m_pred[2]/(m_pred[2]+m_pred[3]+m_pred[4]))*(f_pred[i]/np.sum(f_pred[4:6]))\\n elif i <8:\\n p[i] = c_pred[1]*(m_pred[3]/(m_pred[2]+m_pred[3]+m_pred[4]))*(f_pred[i]/np.sum(f_pred[6:8]))\\n elif i <10:\\n p[i] = c_pred[1]*(m_pred[4]/(m_pred[2]+m_pred[3]+m_pred[4]))*(f_pred[i]/np.sum(f_pred[8:10]))\\n else :\\n for i in range(10):\\n if i <5:\\n if i <3:\\n p[i] = c_pred[0]*(m_pred[0]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[0:3]))\\n elif i <5:\\n p[i] = c_pred[0]*(m_pred[1]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[3:5]))\\n if i >=5:\\n if i <8:\\n p[i] = c_pred[1]*(m_pred[2]/(m_pred[2]+m_pred[3]))*(f_pred[i]/np.sum(f_pred[5:8]))\\n elif i <10:\\n p[i] = c_pred[1]*(m_pred[3]/(m_pred[2]+m_pred[3]))*(f_pred[i]/np.sum(f_pred[8:]))\\n return(p)\",\n \"def impress(self):\\n raise NotImplementedError\",\n \"def test_predictor():\",\n \"def run_and_store(self):\\n # Initialization assumptions\\n z = self.draw_normal_initial()\\n gradient = self.cv_gradient_initial(z)\\n gradient[np.isnan(gradient)] = 0\\n variance = np.power(gradient,2) \\n final_parameters = self.current_parameters()\\n final_samples = 1\\n\\n # Create optimizer\\n if self.optimizer == 'ADAM':\\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\\n elif self.optimizer == 'RMSProp':\\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\\n\\n # Stored updates\\n stored_means = np.zeros((self.iterations,len(final_parameters)/2))\\n stored_predictive_likelihood = np.zeros(self.iterations)\\n\\n # Record elbo\\n if self.record_elbo is True:\\n elbo_records = np.zeros(self.iterations)\\n else:\\n elbo_records = None\\n\\n for i in range(self.iterations):\\n gradient = self.cv_gradient(self.draw_normal())\\n gradient[np.isnan(gradient)] = 0\\n new_parameters = self.optim.update(gradient)\\n self.change_parameters(new_parameters)\\n\\n stored_means[i] = self.optim.parameters[::2]\\n stored_predictive_likelihood[i] = self.neg_posterior(stored_means[i])\\n\\n if self.printer is True:\\n self.print_progress(i,self.optim.parameters[::2])\\n\\n # Construct final parameters using final 10% of samples\\n if i > self.iterations-round(self.iterations/10):\\n final_samples += 1\\n final_parameters = final_parameters+self.optim.parameters\\n\\n if self.record_elbo is True:\\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\\n\\n final_parameters = final_parameters/float(final_samples)\\n self.change_parameters(final_parameters)\\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\\n\\n if not self.quiet_progress:\\n print(\\\"\\\")\\n print(\\\"Final model ELBO is \\\" + str(-self.neg_posterior(final_means)-self.create_normal_logq(final_means)))\\n return self.q, final_means, final_ses, stored_means, stored_predictive_likelihood, elbo_records\",\n \"def run_and_store(self):\\n # Initialization assumptions\\n z = self.draw_normal_initial()\\n gradient = self.cv_gradient_initial(z)\\n gradient[np.isnan(gradient)] = 0\\n variance = np.power(gradient,2) \\n final_parameters = self.current_parameters()\\n final_samples = 1\\n\\n # Create optimizer\\n if self.optimizer == 'ADAM':\\n self.optim = ADAM(final_parameters, variance, self.learning_rate, 0.9, 0.999)\\n elif self.optimizer == 'RMSProp':\\n self.optim = RMSProp(final_parameters, variance, self.learning_rate, 0.99)\\n\\n # Stored updates\\n stored_means = np.zeros((self.iterations,len(final_parameters)/2))\\n stored_predictive_likelihood = np.zeros(self.iterations)\\n\\n # Record elbo\\n if self.record_elbo is True:\\n elbo_records = np.zeros(self.iterations)\\n else:\\n elbo_records = None\\n\\n for i in range(self.iterations):\\n gradient = self.cv_gradient(self.draw_normal())\\n gradient[np.isnan(gradient)] = 0\\n new_parameters = self.optim.update(gradient)\\n self.change_parameters(new_parameters)\\n\\n stored_means[i] = self.optim.parameters[::2]\\n stored_predictive_likelihood[i] = self.neg_posterior(stored_means[i])\\n\\n if self.printer is True:\\n self.print_progress(i,self.optim.parameters[::2])\\n\\n # Construct final parameters using final 10% of samples\\n if i > self.iterations-round(self.iterations/10):\\n final_samples += 1\\n final_parameters = final_parameters+self.optim.parameters\\n\\n if self.record_elbo is True:\\n elbo_records[i] = self.get_elbo(self.optim.parameters[::2])\\n\\n final_parameters = final_parameters/float(final_samples)\\n self.change_parameters(final_parameters)\\n final_means = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2==0])\\n final_ses = np.array([final_parameters[el] for el in range(len(final_parameters)) if el%2!=0])\\n\\n if not self.quiet_progress:\\n print(\\\"\\\")\\n print(\\\"Final model ELBO is \\\" + str(-self.full_neg_posterior(final_means)-self.create_normal_logq(final_means)))\\n return self.q, final_means, final_ses, stored_means, stored_predictive_likelihood, elbo_records\",\n \"def calculate(self):\",\n \"def optimize_parameters(self):\\n self.loss_total.backward() # calculate gradients\\n self.optimizer.step()\\n self.optimizer.zero_grad()\\n torch.cuda.empty_cache()\",\n \"def preprocessing(x_tra, y_tra, x_tst):\\n\\n selector = SelectKBest(chi2, 190)\\n selector.fit(x_tra, y_tra)\\n x_tra_new = selector.transform(x_tra)\\n x_tst_new = selector.transform(x_tst)\\n\\n return x_tra_new, x_tst_new\",\n \"def partial_compute(self):\\n if self.partially_computed:\\n return\\n\\n self.transformed_lines = self.network.exactlines(\\n self.lines, compute_preimages=self.preimages, include_post=True)\\n self.partially_computed = True\",\n \"def precalc_all(REPS):\\n for sigma in [0.25, 1.5]:\\n print('-'*60)\\n\\n N_RANGE = arange(5,105,5)\\n\\n filename = f'categorical_K2_C{C}_sigma{sigma:.2f}_M{M}'.replace('.','_')\\n with Timer(f'{filename} ({REPS} repetitions)'):\\n run_precalc(filename, [(n,n,n) for n in N_RANGE], C, sigma, M, REPS)\\n\\n filename = f'categorical_LOO_C{C}_sigma{sigma:.2f}_M{M}'.replace('.','_')\\n with Timer(f'{filename} ({REPS} repetitions)'):\\n run_precalc(filename, [(n,1,n) for n in N_RANGE], C, sigma, M, REPS)\",\n \"def eval_cov_pre(self):\\n \\n precisions = self.precision_\\n\\n fit_score = []\\n\\n for i in range(precisions.shape[0]):\\n precision = precisions[i]\\n if self.loss == 'LL':\\n fit_score.append(neg_logl(self.emp_cov[i], precision))\\n else:\\n fit_score.append(dtrace(self.emp_cov[i], precision))\\n\\n return self.emp_inv_score, self.C, fit_score, precisions\",\n \"def estimate_incumbent(self, startpoints):\\n\\n pass\",\n \"def prepare_vars_and_flows(self):\\n\\n # clear previously populated vars dictionary\\n self.vars.clear()\\n\\n # calculate vars and flows sequentially\\n self.calculate_scaleup_vars()\\n self.calculate_vars()\\n self.calculate_flows()\",\n \"def _updateInitialProbabilities(self): \\n N = self.N\\n K = self.K\\n\\n for i in range(1,self.K+1):\\n s = 0\\n updated_prob = 0\\n for n in range(1,self.N+1):\\n s = s+1\\n updated_prob = updated_prob + self.posterior_state_trellis[n][(1,i)]\\n self.state_initial_prob[i] = (updated_prob/s)\",\n \"def pre_process(self):\\n pass\",\n \"def pre_process(self):\\n pass\",\n \"def pre_process(self):\\n pass\",\n \"def pre_process(self):\\n pass\",\n \"def pre_process(self):\\n pass\",\n \"def par_fit(init_file):\\n check_presence_init(init_file)\\n\\n dict_ = read(init_file)\\n np.random.seed(dict_[\\\"SIMULATION\\\"][\\\"seed\\\"])\\n\\n # We perform some basic consistency checks regarding the user's request.\\n check_presence_estimation_dataset(dict_)\\n # check_initialization_dict2(dict_)\\n # check_init_file(dict_)\\n\\n # Distribute initialization information.\\n data = read_data(dict_[\\\"ESTIMATION\\\"][\\\"file\\\"])\\n num_treated = dict_[\\\"AUX\\\"][\\\"num_covars_treated\\\"]\\n num_untreated = num_treated + dict_[\\\"AUX\\\"][\\\"num_covars_untreated\\\"]\\n\\n _, X1, X0, Z1, Z0, Y1, Y0 = process_data(data, dict_)\\n\\n if dict_[\\\"ESTIMATION\\\"][\\\"maxiter\\\"] == 0:\\n option = \\\"init\\\"\\n else:\\n option = dict_[\\\"ESTIMATION\\\"][\\\"start\\\"]\\n\\n # Read data frame\\n\\n # define starting values\\n x0 = start_values(dict_, data, option)\\n opts, method = optimizer_options(dict_)\\n dict_[\\\"AUX\\\"][\\\"criteria\\\"] = calculate_criteria(dict_, X1, X0, Z1, Z0, Y1, Y0, x0)\\n dict_[\\\"AUX\\\"][\\\"starting_values\\\"] = backward_transformation(x0)\\n rslt_dict = bfgs_dict()\\n if opts[\\\"maxiter\\\"] == 0:\\n rslt = adjust_output(None, dict_, x0, X1, X0, Z1, Z0, Y1, Y0, rslt_dict)\\n else:\\n opt_rslt = minimize(\\n minimizing_interface,\\n x0,\\n args=(dict_, X1, X0, Z1, Z0, Y1, Y0, num_treated, num_untreated, rslt_dict),\\n method=method,\\n options=opts,\\n )\\n rslt = adjust_output(\\n opt_rslt, dict_, opt_rslt[\\\"x\\\"], X1, X0, Z1, Z0, Y1, Y0, rslt_dict\\n )\\n # Print Output files\\n print_logfile(dict_, rslt)\\n\\n if \\\"comparison\\\" in dict_[\\\"ESTIMATION\\\"].keys():\\n if dict_[\\\"ESTIMATION\\\"][\\\"comparison\\\"] == 0:\\n pass\\n else:\\n write_comparison(data, rslt)\\n else:\\n write_comparison(data, rslt)\\n\\n return rslt\",\n \"def preprocess(self):\",\n \"def calculatePrediction(self, a, X_train,x, t_train):\\n pass\",\n \"def preprocess_valid_data(self):\\r\\n print(\\\"* Preprocessing validation data.\\\", flush=True)\\r\\n prep.create_HDF_file(self.C.validation_set)\\r\\n\\r\\n self.print_time_elapsed()\",\n \"def _prep_attributes(self):\\n self.coeff_ = self._coeff_to_df()\\n self.results_ = self._results()\\n self.cv_results_ = self._cv_results()\\n self.intercept_ = self.model_.intercept_\\n self.params_ = self.model_.get_params()\\n\\n return None\",\n \"def compute(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\\n precisions = (self.TPs + METRIC_EPS) / (self.TPs + self.FPs + METRIC_EPS)\\n recalls = self.TPs / (self.TPs + self.FNs + METRIC_EPS)\\n\\n return precisions, recalls, self.thresholds\",\n \"def _do_estimate(recommendation_type: str, pipfile: Pipfile) -> None:\",\n \"def calc_statistics(self):\\n\\n self._ydata = np.zeros([4, len(self._depths)], dtype=float)\\n\\n exp_vals = np.zeros(self._ntrials, dtype=float)\\n ideal_vals = np.zeros(self._ntrials, dtype=float)\\n\\n for depthidx, depth in enumerate(self._depths):\\n\\n exp_shots = 0\\n\\n for trialidx in range(self._ntrials):\\n cname = 'qv_depth_%d_trial_%d' % (depth, trialidx)\\n exp_vals[trialidx] = self._heavy_output_counts[cname]\\n exp_shots += self._circ_shots[cname]\\n ideal_vals[trialidx] = self._heavy_output_prob_ideal[cname]\\n\\n # Calculate mean and error for experimental data\\n self._ydata[0][depthidx] = np.sum(exp_vals)/np.sum(exp_shots)\\n self._ydata[1][depthidx] = (self._ydata[0][depthidx] *\\n (1.0-self._ydata[0][depthidx])\\n / self._ntrials)**0.5\\n\\n # Calculate mean and error for ideal data\\n self._ydata[2][depthidx] = np.mean(ideal_vals)\\n self._ydata[3][depthidx] = (self._ydata[2][depthidx] *\\n (1.0-self._ydata[2][depthidx])\\n / self._ntrials)**0.5\",\n \"def main():\\n housing = pd.read_csv(\\\"Data/train_original.csv\\\")\\n housing[\\\"TotalSF\\\"] = (\\n housing[\\\"TotalBsmtSF\\\"] + housing[\\\"1stFlrSF\\\"] + housing[\\\"2ndFlrSF\\\"]\\n )\\n training_features, testing_features, training_target, testing_target = impute_dummify_and_split(\\n housing, drop_target=False\\n )\\n\\n p_values = [\\n (c, pearsonr(training_features[\\\"SalePrice\\\"], training_features[c])[1])\\n for c in training_features.columns\\n ]\\n\\n p_value_limits = [0.05]\\n\\n result = []\\n ps_and_cols = {}\\n\\n for p_value_limit in p_value_limits:\\n\\n high_ps = list(\\n map(lambda t: t[0], sorted(p_values, key=lambda t1: t1[1])[:15])\\n )\\n\\n print(training_features[high_ps].corr())\\n\\n columns = [p[0] for p in p_values if p[1] < p_value_limit]\\n\\n training_features_restricted = training_features[columns].drop(\\n \\\"SalePrice\\\", axis=\\\"columns\\\"\\n )\\n\\n testing_features_restricted = testing_features[columns].drop(\\n \\\"SalePrice\\\", axis=\\\"columns\\\"\\n )\\n\\n for model in (\\n linear_model.Lasso(alpha=2.1),\\n linear_model.Ridge(alpha=2.1),\\n ):\\n\\n model.fit(training_features_restricted, training_target)\\n\\n train_score = model.score(\\n training_features_restricted, training_target\\n )\\n\\n test_score = model.score(\\n testing_features_restricted, testing_target\\n )\\n\\n name = str(model).split(\\\"(\\\")[0]\\n\\n result = result + [\\n (\\n \\\"_2_restrict_features\\\",\\n name,\\n \\\"p value limit: {:.3f}, alpha: 2.1\\\".format(p_value_limit),\\n train_score,\\n test_score,\\n )\\n ]\\n\\n print(ps_and_cols)\\n return training_features[high_ps].corr()\",\n \"def prepare_estimation(model_params_init_file_name, lower, upper):\\n\\n # Read in data and init file sources\\n model_params_df = pd.read_pickle(model_params_init_file_name)\\n model_params_df[\\\"lower\\\"] = lower\\n model_params_df[\\\"upper\\\"] = upper\\n\\n return model_params_df\",\n \"def _do_compute(self, var_map):\\n raise Exception(\\\"Not implemented. Subclass responsibility\\\")\",\n \"def _build_preprocessing(self):\\n\\n # For now, do nothing\\n pass\",\n \"def calculate_metrics(self, caller=\\\"external\\\", ordered_data=False):\\n \\n # If the function call was made externally, process the request\\n if caller == \\\"external\\\":\\n # Open an existing model and get the training & test dataset and targets based on the request\\n self.X_test, self.y_test = self._get_model_and_data(target=True, ordered_data=ordered_data) \\n\\n # Keep a copy of the y_test before any transformations\\n y_test_copy = self.y_test.copy()\\n\\n # Scale the targets and increase stationarity if required\\n if self.model.scale_target or self.model.make_stationary:\\n # If using differencing, we assume sufficient lag values for inversing the transformation later\\n y_orig = self.y_test.values.ravel() if self.model.make_stationary=='difference' else None\\n # Apply the transformer to the test targets\\n self.y_test = self.model.target_transformer.transform(self.y_test) \\n # Drop samples where self.y_test cannot be transformed due to insufficient lags\\n self.X_test = self.X_test.iloc[len(self.X_test)-len(self.y_test):]\\n\\n # Refresh the keras model to avoid tensorflow errors\\n if self.model.using_keras:\\n self._keras_refresh()\\n \\n # Get predictions based on the samples\\n if ordered_data:\\n self.y_pred = self.sequence_predict(variant=\\\"internal\\\")\\n\\n # Handle possible null values where a prediction could not be generated\\n self.y_pred = self.y_pred[self.rows_per_pred - self.first_pred_modifier:]\\n self.y_test = y_test_copy.iloc[-1*len(self.y_pred):]\\n\\n # Inverse transformations predictions if required \\n if self.model.scale_target or self.model.make_stationary:\\n # Set up indices for differencing\\n end = self.placeholders\\n start = end - self.diff_lags\\n\\n # Add lags for inversing differencing\\n self.y_pred = self.y_pred if y_orig is None else np.append(y_orig[start : end], self.y_pred)\\n\\n # Apply the transformer to the test targets\\n self.y_pred = self.model.target_transformer.inverse_transform(self.y_pred) \\n\\n # Remove lags used for making the series stationary in case of differencing\\n if self.model.make_stationary == 'difference':\\n self.y_pred = self.y_pred[self.diff_lags:]\\n else:\\n self.y_pred = self.model.pipe.predict(self.X_test)\\n\\n # Inverse transformations on the predictions if required\\n if self.model.scale_target or self.model.make_stationary:\\n # Apply the transformer to the predictions\\n self.y_pred = self.model.target_transformer.inverse_transform(self.y_pred)\\n # Reset y_test to orginal values\\n self.y_test = y_test_copy\\n \\n # Flatten the y_test DataFrame\\n self.y_test = self.y_test.values.ravel()\\n\\n # Try getting the metric_args from the model\\n try:\\n metric_args = self.model.metric_args\\n except AttributeError: \\n metric_args = {}\\n \\n if self.model.estimator_type == \\\"classifier\\\":\\n labels = self.model.pipe.named_steps['estimator'].classes_\\n \\n # Check if the average parameter is specified\\n if len(metric_args) > 0 and \\\"average\\\" in metric_args:\\n # Metrics are returned as an overall average\\n metric_rows = [\\\"overall\\\"]\\n else:\\n # Get the class labels to be used as rows for the result DataFrame\\n metric_rows = labels\\n \\n # Get key classifier metrics\\n metrics_df = pd.DataFrame([x for x in metrics.precision_recall_fscore_support\\\\\\n (self.y_test, self.y_pred, **metric_args)],\\\\\\n index=[\\\"precision\\\", \\\"recall\\\", \\\"fscore\\\", \\\"support\\\"], columns=metric_rows).transpose()\\n # Add accuracy\\n self.model.score = metrics.accuracy_score(self.y_test, self.y_pred)\\n metrics_df.loc[\\\"overall\\\", \\\"accuracy\\\"] = self.model.score\\n # Finalize the structure of the result DataFrame\\n metrics_df.loc[:,\\\"model_name\\\"] = self.model.name\\n metrics_df.loc[:,\\\"class\\\"] = metrics_df.index\\n metrics_df = metrics_df.loc[:,[\\\"model_name\\\", \\\"class\\\", \\\"accuracy\\\", \\\"precision\\\", \\\"recall\\\", \\\"fscore\\\", \\\"support\\\"]]\\n \\n # Prepare the confusion matrix and add it to the model\\n self._prep_confusion_matrix(self.y_test, self.y_pred, labels)\\n \\n elif self.model.estimator_type == \\\"regressor\\\":\\n # Get the r2 score\\n self.model.score = metrics.r2_score(self.y_test, self.y_pred, **metric_args)\\n metrics_df = pd.DataFrame([[self.model.score]], columns=[\\\"r2_score\\\"])\\n \\n # Get the mean squared error\\n metrics_df.loc[:,\\\"mean_squared_error\\\"] = metrics.mean_squared_error(self.y_test, self.y_pred, **metric_args)\\n \\n # Get the mean absolute error\\n metrics_df.loc[:,\\\"mean_absolute_error\\\"] = metrics.mean_absolute_error(self.y_test, self.y_pred, **metric_args)\\n \\n # Get the median absolute error\\n metrics_df.loc[:,\\\"median_absolute_error\\\"] = metrics.median_absolute_error(self.y_test, self.y_pred)\\n \\n # Get the explained variance score\\n metrics_df.loc[:,\\\"explained_variance_score\\\"] = metrics.explained_variance_score(self.y_test, self.y_pred, **metric_args)\\n\\n # If the target was scaled we need to inverse transform certain metrics to the original scale\\n # However, if we used the sequence prediction function, the inverse transform has already been performed\\n if not ordered_data and (self.model.scale_target or self.model.make_stationary):\\n for m in [\\\"mean_squared_error\\\", \\\"mean_absolute_error\\\", \\\"median_absolute_error\\\"]:\\n metrics_df.loc[:, m] = self.model.target_transformer.inverse_transform(metrics_df.loc[:, [m]], array_like=False).values.ravel()\\n \\n # Finalize the structure of the result DataFrame\\n metrics_df.loc[:,\\\"model_name\\\"] = self.model.name\\n metrics_df = metrics_df.loc[:,[\\\"model_name\\\", \\\"r2_score\\\", \\\"mean_squared_error\\\", \\\"mean_absolute_error\\\",\\\\\\n \\\"median_absolute_error\\\", \\\"explained_variance_score\\\"]]\\n \\n if caller == \\\"external\\\":\\n \\n if self.model.calc_feature_importances:\\n # Calculate model agnostic feature importances\\n self._calc_importances(X = self.X_test, y = self.y_test)\\n\\n self.response = metrics_df\\n\\n # Send the reponse table description to Qlik\\n if self.model.estimator_type == \\\"classifier\\\":\\n self._send_table_description(\\\"metrics_clf\\\")\\n elif self.model.estimator_type == \\\"regressor\\\":\\n self._send_table_description(\\\"metrics_reg\\\")\\n\\n # Debug information is printed to the terminal and logs if the paramater debug = true\\n if self.model.debug:\\n self._print_log(4)\\n\\n # Finally send the response\\n return self.response\\n else:\\n # Save the metrics_df to the model\\n self.model.metrics_df = metrics_df\",\n \"def _precompute_probabilities(self):\\n\\n d_graph = self.d_graph\\n first_travel_done = set()\\n\\n nodes_generator = self.graph.nodes() if self.quiet \\\\\\n else tqdm(self.graph.nodes(), desc='Computing transition probabilities')\\n\\n for source in nodes_generator:\\n\\n # Init probabilities dict for first travel\\n if self.PROBABILITIES_KEY not in d_graph[source]:\\n d_graph[source][self.PROBABILITIES_KEY] = dict()\\n\\n for current_node in self.graph.neighbors(source):\\n\\n # Init probabilities dict\\n if self.PROBABILITIES_KEY not in d_graph[current_node]:\\n d_graph[current_node][self.PROBABILITIES_KEY] = dict()\\n\\n unnormalized_weights = list()\\n first_travel_weights = list()\\n d_neighbors = list()\\n\\n # Calculate unnormalized weights\\n for destination in self.graph.neighbors(current_node):\\n\\n p = self.sampling_strategy[current_node].get(self.P_KEY,\\n self.p) if current_node in self.sampling_strategy else self.p\\n q = self.sampling_strategy[current_node].get(self.Q_KEY,\\n self.q) if current_node in self.sampling_strategy else self.q\\n\\n if destination == source: # Backwards probability\\n ss_weight = self.graph[current_node][destination].get(self.weight_key, 1) * 1 / p\\n elif destination in self.graph[source]: # If the neighbor is connected to the source\\n ss_weight = self.graph[current_node][destination].get(self.weight_key, 1)\\n else:\\n ss_weight = self.graph[current_node][destination].get(self.weight_key, 1) * 1 / q\\n\\n # Assign the unnormalized sampling strategy weight, normalize during random walk\\n unnormalized_weights.append(ss_weight)\\n if current_node not in first_travel_done:\\n first_travel_weights.append(self.graph[current_node][destination].get(self.weight_key, 1))\\n d_neighbors.append(destination)\\n\\n # Normalize\\n unnormalized_weights = np.array(unnormalized_weights)\\n d_graph[current_node][self.PROBABILITIES_KEY][\\n source] = unnormalized_weights / unnormalized_weights.sum()\\n\\n if current_node not in first_travel_done:\\n unnormalized_weights = np.array(first_travel_weights)\\n d_graph[current_node][self.FIRST_TRAVEL_KEY] = unnormalized_weights / unnormalized_weights.sum()\\n first_travel_done.add(current_node)\\n\\n # Save neighbors\\n d_graph[current_node][self.NEIGHBORS_KEY] = d_neighbors\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.737464","0.737464","0.73522526","0.6641263","0.6628966","0.6564368","0.6496165","0.6365002","0.63627005","0.60393345","0.5983021","0.5978751","0.58568126","0.58324635","0.5828639","0.58230853","0.58050996","0.5781569","0.5775128","0.5764755","0.5760743","0.5753608","0.5734949","0.569234","0.56385505","0.55993235","0.5578003","0.5576102","0.5562371","0.5559104","0.55561095","0.55554605","0.5552434","0.55448353","0.553394","0.55138266","0.54732656","0.5455899","0.54439527","0.5434263","0.54085994","0.54083204","0.54008174","0.53957194","0.5390928","0.5387629","0.53861755","0.5378685","0.5373368","0.535771","0.53527087","0.53520703","0.5336953","0.5336319","0.53222275","0.53222275","0.53222275","0.53222275","0.53222275","0.53222275","0.5320182","0.53013355","0.52929354","0.5287375","0.5284822","0.52729595","0.5272138","0.52713597","0.52712804","0.5265015","0.526413","0.526413","0.5263322","0.525867","0.52394205","0.52332723","0.522337","0.521912","0.5216221","0.52136517","0.52125776","0.52071965","0.52071965","0.52071965","0.52071965","0.52071965","0.5202082","0.5201932","0.5200707","0.51981336","0.51980597","0.51960444","0.519228","0.51894915","0.51856273","0.5183629","0.51800686","0.5179022","0.5169047","0.5168288"],"string":"[\n \"0.737464\",\n \"0.737464\",\n \"0.73522526\",\n \"0.6641263\",\n \"0.6628966\",\n \"0.6564368\",\n \"0.6496165\",\n \"0.6365002\",\n \"0.63627005\",\n \"0.60393345\",\n \"0.5983021\",\n \"0.5978751\",\n \"0.58568126\",\n \"0.58324635\",\n \"0.5828639\",\n \"0.58230853\",\n \"0.58050996\",\n \"0.5781569\",\n \"0.5775128\",\n \"0.5764755\",\n \"0.5760743\",\n \"0.5753608\",\n \"0.5734949\",\n \"0.569234\",\n \"0.56385505\",\n \"0.55993235\",\n \"0.5578003\",\n \"0.5576102\",\n \"0.5562371\",\n \"0.5559104\",\n \"0.55561095\",\n \"0.55554605\",\n \"0.5552434\",\n \"0.55448353\",\n \"0.553394\",\n \"0.55138266\",\n \"0.54732656\",\n \"0.5455899\",\n \"0.54439527\",\n \"0.5434263\",\n \"0.54085994\",\n \"0.54083204\",\n \"0.54008174\",\n \"0.53957194\",\n \"0.5390928\",\n \"0.5387629\",\n \"0.53861755\",\n \"0.5378685\",\n \"0.5373368\",\n \"0.535771\",\n \"0.53527087\",\n \"0.53520703\",\n \"0.5336953\",\n \"0.5336319\",\n \"0.53222275\",\n \"0.53222275\",\n \"0.53222275\",\n \"0.53222275\",\n \"0.53222275\",\n \"0.53222275\",\n \"0.5320182\",\n \"0.53013355\",\n \"0.52929354\",\n \"0.5287375\",\n \"0.5284822\",\n \"0.52729595\",\n \"0.5272138\",\n \"0.52713597\",\n \"0.52712804\",\n \"0.5265015\",\n \"0.526413\",\n \"0.526413\",\n \"0.5263322\",\n \"0.525867\",\n \"0.52394205\",\n \"0.52332723\",\n \"0.522337\",\n \"0.521912\",\n \"0.5216221\",\n \"0.52136517\",\n \"0.52125776\",\n \"0.52071965\",\n \"0.52071965\",\n \"0.52071965\",\n \"0.52071965\",\n \"0.52071965\",\n \"0.5202082\",\n \"0.5201932\",\n \"0.5200707\",\n \"0.51981336\",\n \"0.51980597\",\n \"0.51960444\",\n \"0.519228\",\n \"0.51894915\",\n \"0.51856273\",\n \"0.5183629\",\n \"0.51800686\",\n \"0.5179022\",\n \"0.5169047\",\n \"0.5168288\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7158729"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":89397,"cells":{"query":{"kind":"string","value":"Perform linear regression on the given points in loglog space, return result"},"document":{"kind":"string","value":"def _regress(self, Z):\n # Make a set of the nonempty points in log scale\n x, y = zip(*[(log(r), log(Z[r]))\n for r in range(1, self.max_r + 1) if Z[r]])\n self.x, self.y = x, y\n matrix = numpy.array((x, numpy.ones(len(x)))).T\n return numpy.linalg.lstsq(matrix, y, rcond=None)[0]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def log_reg(x_train, y_train):\n\n log_reg_classifier = LogisticRegression(max_iter=1000, solver='lbfgs')\n log_reg_classifier.fit(x_train, y_train)\n return log_reg_classifier\n\n # log_reg_classifier.fit(x_train, y_train)","def loglf2py(store):\n loglike=0.0\n return loglinear.logl(store['xb'],store['xmatf'], store['beta'],store['yvec'],loglike)","def _log_linear_interpolation(predictions):\n log_probs = utils.average_arrays([mx.nd.log(p) for p in predictions])\n return -mx.nd.log(mx.nd.softmax(log_probs))","def log_reg(x, y, s):\n usx = np.array(x)\n usy = np.array(y)\n\n # split data into train and validation set\n x_train, x_test, y_train, y_test = train_test_split(usx, usy, test_size=s)\n cls_log = LogisticRegression()\n cls_log.fit(x_train, y_train)\n y_predict = cls_log.predict(x_test)\n\n # select only the probabilities of being fraud\n y_pred_prob = cls_log.predict_proba(x_test)[:, 1]\n return y_predict, y_test, y_pred_prob","def fit_exp_data(x_vals, y_vals):\n log_vals = []\n for y in y_vals:\n log_vals.append(math.log(y, 2)) #get log base 2\n fit = np.polyfit(x_vals, log_vals, 1)\n return fit, 2","def logp(self, xs, ys, **kwargs):\n ind = np.isclose(self.predict(xs, **kwargs),ys)\n axis = tuple(range(1,len(xs.shape)))\n return np.log(np.prod(ind, axis=axis)) # default behavior","def my_loglike(theta, x, data, sigma):\n\n model = my_model(theta, x)\n\n return -0.5*len(x)*np.log(2*math.pi*sigma**2) - (0.5/sigma**2) * np.sum((data-model)**2)","def loglnumpy(store):\n xbeta = dot(store['xmat'], store['beta'])\n lamb = exp(xbeta)\n return sum(store['yvec'] * xbeta - lamb)","def linear_regression(features, values):\n x_values = sm.add_constant(features)\n model = sm.OLS(values, x_values, hasconst=True)\n results = model.fit()\n all_params = results.params\n intercept = all_params[0]\n params = all_params[1:]\n return intercept, params","def get_loglikelis(\n self, points: numpy.ndarray | Sequence[numpy.ndarray]\n ) -> numpy.ndarray:\n return numpy.log(numpy.asarray(self.weights)[points])","def linear_regression(points):\n latitudes = [[i[0]] for i in points]\n longitudes = [[i[1]] for i in points]\n\n # regression logic\n regr = linear_model.LinearRegression()\n regr.fit(latitudes,longitudes)\n return [[x[0],y[0]] for x,y in zip(latitudes,regr.predict(latitudes))]","def log(self, X, Y):\n raise NotImplementedError","def loglog(self, **kwargs):\n return self.plot(plot=pylab.loglog, **kwargs)","def logistic_loss(x, y):\n N = x.shape[0]\n x_flat = np.squeeze(x)\n ex = np.exp(x_flat)\n loss = np.sum(-y*x_flat+np.log(1+ex))/N\n dx = (-y+ex/(1+ex))/N\n # dx = np.reshape(dx,(len(dx),1))\n return loss, dx","def do_stats_model(x, y):\n Xx = sm.add_constant(x)\n sm_logit = sm.Logit(y, Xx)\n result = sm_logit.fit()\n print result.summary()\n result.pred_table()\n # linear model\n print \"linear regression model:\\n\"\n sm_linear = sm.OLS(y, Xx)\n result = sm_linear.fit()\n print result.summary()","def logrels(rets):\n return np.log(rets + 1)","def nnRegression(data):","def fit_data(self, data):\n d = log(-log(data))\n return dot(d, self._fit_matrix.T)","def llinregress(x,y):\r\n TINY = 1.0e-20\r\n if len(x) <> len(y):\r\n raise ValueError, 'Input values not paired in linregress. Aborting.'\r\n n = len(x)\r\n x = map(float,x)\r\n y = map(float,y)\r\n xmean = mean(x)\r\n ymean = mean(y)\r\n r_num = float(n*(summult(x,y)) - sum(x)*sum(y))\r\n r_den = math.sqrt((n*ss(x) - square_of_sums(x))*(n*ss(y)-square_of_sums(y)))\r\n r = r_num / r_den\r\n z = 0.5*math.log((1.0+r+TINY)/(1.0-r+TINY))\r\n df = n-2\r\n t = r*math.sqrt(df/((1.0-r+TINY)*(1.0+r+TINY)))\r\n prob = betai(0.5*df,0.5,df/(df+t*t))\r\n slope = r_num / float(n*ss(x) - square_of_sums(x))\r\n intercept = ymean - slope*xmean\r\n sterrest = math.sqrt(1-r*r)*samplestdev(y)\r\n return slope, intercept, r, prob, sterrest","def linear_regression(features, values):\n clf = SGDRegressor(n_iter=100)\n clf.fit(features,values)\n print(clf.score(features,values))\n intercept = clf.intercept_ \n params = clf.coef_\n \n return intercept, params","def linear_regression(sign):\n t = np.linspace(0, 5, len(sign))\n\n return np.polyfit(t, sign, 1)[0]","def linear_regression(x, y):\n #print(\"Fitting\", y, \"\\nagainst\", x)\n matrix = np.vstack( [x, np.ones_like(x)] ).T\n slope, intercept = np.linalg.lstsq(matrix,y)[0]\n #print(\"gives slope=\", slope, \"intercept=\", intercept)\n return (slope, intercept)","def fit_lin_reg(da: xr.DataArray, calc_SSE: bool=False):\n\n series = da.to_series().dropna()\n X = np.array(series.index.tolist())[:,:2] # remove rep_idx (3rd column)\n y = np.log10(series.values)\n reg = LinearRegression().fit(X, y)\n\n if not calc_SSE:\n return reg\n\n pred_y = reg.predict(X)\n SSE = np.sum((pred_y - y)**2)\n return reg, SSE","def compute_loss_logreg(y, tx, w):\n assert len(set(y).difference({0., 1.})) == 0, \"Class labels must be encoded as {0, 1}\"\n\n z = tx.dot(w)\n\n return np.sum(np.log(1 + np.exp(z)) - y * z)","def linear_regression(data):\n x_values = [x for x, y in data] #Get x values\n y_values = [y for x, y in data] #Get y values\n x_mean = sum(x_values) / len(x_values) #Compute mean value of x\n y_mean = sum(y_values) / len(y_values) #Compute mean value of y\n # Compute\n coefficient = sum([(x - x_mean) * (y-y_mean) for x,y in data]) / sum([(x - x_mean) ** 2 for x in x_values])\n intercept = y_mean - coefficient * x_mean # Compute Intercept\n return((coefficient,intercept))","def linearRegression(x,y,f1,l1,f2,l2):\n n = len(x)\n sumx = 0.0 # sum_n(x_i)\n sumy = 0.0 # sum_n(y_i)\n sumxx = 0.0 # sum_n(x_i*x_i)\n sumxy = 0.0 # sum_n(x_i*y_i)\n count = 0\n for i2 in range(f2,l2+1):\n for i1 in range(f1,l1+1):\n xi = x[i2][i1]\n yi = y[i2][i1]\n sumx += xi\n sumy += yi\n sumxx += xi*xi\n sumxy += xi*yi\n count += 1\n beta = (sumxy-sumx*sumy/count)/(sumxx-sumx*sumx/count)\n alpha = (sumy-beta*sumx)/count\n #z = zerofloat(n)\n #for i in range(n):\n # if null is None or x[i]!=null:\n # z[i] = alpha+beta*x[i]\n #print 'slope =',beta\n #return z\n return beta","def residuals_Log(self, p, data, x):\n err = data - self.Log(x,p)\n return err","def lin_reg_pred(R, method='lin'):\n\n R_arr = np.array(R).reshape(-1, 1)\n\n if method == 'lin':\n return np.squeeze(reg_lin.predict(R_arr))\n elif method == 'log':\n return np.squeeze(10.**reg_log.predict(log10(R_arr)))\n else:\n raise ValueError(\n \"method={} cannot be used. Use 'lin' or 'log'\".format(method))","def _simple_logistic_regression(x,y,beta_start=None,verbose=False,\n CONV_THRESH=1.e-3,MAXIT=500):\n if len(x) != len(y):\n raise ValueError, \"x and y should be the same length!\"\n if beta_start is None:\n beta_start = NA.zeros(2,x.dtype.char)\n iter = 0; diff = 1.; beta = beta_start # initial values\n if verbose:\n print 'iteration beta log-likliehood |beta-beta_old|' \n while iter < MAXIT:\n beta_old = beta \n p = NA.exp(beta[0]+beta[1]*x)/(1.+NA.exp(beta[0]+beta[1]*x))\n l = NA.sum(y*NA.log(p) + (1.-y)*NA.log(1.-p)) # log-likliehood\n s = NA.array([NA.sum(y-p), NA.sum((y-p)*x)]) # scoring function\n # information matrix\n J_bar = NA.array([[NA.sum(p*(1-p)),NA.sum(p*(1-p)*x)],\n [NA.sum(p*(1-p)*x),NA.sum(p*(1-p)*x*x)]])\n beta = beta_old + NA.dot(LA.inverse(J_bar),s) # new value of beta\n diff = NA.sum(NA.fabs(beta-beta_old)) # sum of absolute differences\n if verbose:\n print iter+1, beta, l, diff\n if diff <= CONV_THRESH: break\n iter = iter + 1\n return beta, J_bar, l","def calculate_slope(cnt):\n y = OrderedDict(cnt.most_common())\n v=np.log(list(y.values()))\n k=np.log(np.arange(1,len(v)+1,1))\n return linregress(k,v)","def get_loglikelis(\n self, points: numpy.ndarray | list[numpy.ndarray] | Sequence[Sequence[float]]\n ) -> numpy.ndarray:\n points = numpy.array(points)\n weight_likelis_list = [\n numpy.log(self.weights[i] * pdf.pdf(points))\n for i, pdf in enumerate(self.pdfs)\n ]\n weight_likelis = numpy.array(weight_likelis_list)\n # (num_weights, num_points) => (num_points, num_weights)\n weight_likelis = weight_likelis.transpose()\n\n # log-sum-exp trick\n max_likeli = numpy.nanmax(weight_likelis, axis=1)\n point_likeli = max_likeli + numpy.log(\n numpy.nansum(numpy.exp(weight_likelis - max_likeli[:, None]), axis=1)\n )\n\n return point_likeli","def train_logisticRegression(data: np.array, labels: np.array)->None:\n\n n_examples = np.size(data, 0)\n n_features = np.size(data, 1)\n n_categories = np.size(labels, 1)\n\n data = np.hstack((np.ones((n_examples, 1)), data))\n\n print(data[0:5, :])\n\n X_train, X_test, y_train, y_test, idx_test = split_data(data, labels, 0.7)\n\n convergence_goal = 1e-3\n learning_rate = 0.01\n\n theta = np.random.uniform(size=((n_features+1, n_categories)))\n\n for i in range(n_categories):\n\n cost_var = 1\n\n previous_cost = 1e6\n iterations = 0\n cost_to_plot = []\n\n while cost_var > convergence_goal:\n iterations += 1\n cost, grad = costFunction(X_train, y_train[:, i], theta[:, i])\n theta[:, i] = update_theta(theta[:, i], grad, learning_rate)\n cost_var = previous_cost - cost\n previous_cost = cost\n if iterations == 1: cost_var = 1\n cost_to_plot.append(cost)\n # print(cost)\n\n plt.plot(range(iterations), cost_to_plot, 'g-', label = 'cost')\n plt.xlabel('iterations')\n plt.ylabel('cost')\n # plt.show()\n\n predictions = lrPredict(theta, X_test)\n\n print(predictions[0:5, :])\n print(y_test[0:5, :])\n\n accuracy = np.mean([p == l for p, l in zip(predictions, y_test)])\n print(\"Accuracy = {}\".format(accuracy))\n\n pass","def logistic_regression_vec(theta, trainX, trainY):\n # Add column of ones for bias\n trainX = np.hstack((np.ones((trainX.shape[0], 1)), trainX))\n h = sigmoid(np.inner(trainX, theta))\n # np.log(1-h) can lead to problems for h = 1.0\n h = np.where(h == 1.0, 1 - 1e-12, h)\n fval = -(trainY * np.log(h) + (1 - trainY) * np.log(1 - h)).sum()\n error = h - trainY\n # Negative gradient for a minimization, must be flattened for np.minimize\n grad = np.dot(trainX.T, error).flatten()\n return fval, grad","def log_error(X, y, w):\r\n N = X.shape[0]\r\n J = np.sum(np.log(1.0 + np.exp(-y * (X @ w)))) / N\r\n return J","def linear(self, X):\n return X","def predict_logit(self, x):\n with torch.no_grad():\n y_ = self.tr_model(x)\n return y_","def logistic_regression(x,y,beta_start=None,verbose=False,CONV_THRESH=1.e-3,\n MAXIT=500):\n if x.shape[-1] != len(y):\n raise ValueError, \"x.shape[-1] and y should be the same length!\"\n try:\n N, npreds = x.shape[1], x.shape[0]\n except: # single predictor, use simple logistic regression routine.\n return _simple_logistic_regression(x,y,beta_start=beta_start,\n CONV_THRESH=CONV_THRESH,MAXIT=MAXIT,verbose=verbose)\n if beta_start is None:\n beta_start = NA.zeros(npreds+1,x.dtype.char)\n X = NA.ones((npreds+1,N), x.dtype.char)\n X[1:, :] = x\n Xt = NA.transpose(X)\n iter = 0; diff = 1.; beta = beta_start # initial values\n if verbose:\n print 'iteration beta log-likliehood |beta-beta_old|' \n while iter < MAXIT:\n beta_old = beta \n ebx = NA.exp(NA.dot(beta, X))\n p = ebx/(1.+ebx)\n l = NA.sum(y*NA.log(p) + (1.-y)*NA.log(1.-p)) # log-likeliehood\n s = NA.dot(X, y-p) # scoring function\n J_bar = NA.dot(X*p,Xt) # information matrix\n beta = beta_old + NA.dot(LA.inverse(J_bar),s) # new value of beta\n diff = NA.sum(NA.fabs(beta-beta_old)) # sum of absolute differences\n if verbose:\n print iter+1, beta, l, diff\n if diff <= CONV_THRESH: break\n iter = iter + 1\n if iter == MAXIT and diff > CONV_THRESH: \n print 'warning: convergence not achieved with threshold of %s in %s iterations' % (CONV_THRESH,MAXIT)\n return beta, J_bar, l","def ln(x):\n return log(x, const.e)","def forward(self, x):\n\n embeds = self.dvector(x)\n logits = self.linear(embeds)\n\n return logits","def manual_log_like_normal(x, data):\n return np.sum(-np.log(x[1] * np.sqrt(2 * np.pi))-((data-x[0])**2) / (2*x[1]**2))","def computeRegression(Xin, Yin, Xtest, noise, ls, sv):\n\n # compute kernels\n K = kernel_sqExp(Xin, Xin, ls=ls, sv=sv)\n Kstar = kernel_sqExp(Xin, Xtest, ls=ls, sv=sv)\n Kstarstar = kernel_sqExp(Xtest, Xtest, ls=ls, sv=sv)\n\n # compute mean based on training input points\n n1 = np.shape(K)[0]\n offSet = 1e-3\n L = np.linalg.cholesky(K + noise*np.eye(n1) + offSet*np.eye(n1))\n alpha = np.linalg.solve(L.T, np.linalg.solve(L,Yin))\n f_mean = np.dot(Kstar.T,alpha) # mean of points\n \n # compute resulting covariance of predictive distribution\n v = np.linalg.solve(L, Kstar)\n cov = Kstarstar - np.dot(v.T,v)\n \n # compute log of marginal likelihood\n #lML = -0.5*np.dot(Yin.T,alpha) - np.sum(np.log(L)) - (n1/2*np.log(2*math.pi))\n lML_dataFit = -0.5*np.dot(Yin.T,alpha)\n lML_complexity = -np.sum(np.log(L))\n lML_normalize = -(n1/2*np.log(2*math.pi))\n lML_details = [lML_dataFit, lML_complexity, lML_normalize]\n lML = lML_dataFit[0] + lML_complexity + lML_normalize \n \n return f_mean, cov, lML , lML_details","def _convert_normlogprice(self, series):\n try:\n return np.log(series.div(series[0]))\n except:\n raise TypeError('ERROR: Could not transform prices to log function. Check price history data.')","def train_logistic_regression(train_x, train_y):\n\n logistic_regression_model = LogisticRegression(penalty='l2', C=1.0)\n logistic_regression_model.fit(train_x, train_y)\n return logistic_regression_model","def feature_mapped_logistic_regression(power, l):\n df = pd.read_csv('ex2data2.txt', names=['test1', 'test2', 'accepted'])\n x1 = np.array(df.test1)\n x2 = np.array(df.test2)\n y = general.get_y(df)\n\n X = feature_mapping(x1, x2, power, as_ndarray=True)\n theta = np.zeros(X.shape[1])\n\n res = opt.minimize(fun=regularized_cost,\n x0=theta,\n args=(X, y, l),\n method='TNC',\n jac=regularized_gradient)\n final_theta = res.x\n\n return final_theta","def linreg_scikit(X, y):\n lr = linear_model.LinearRegression()\n lr.fit(X, y)\n theta = lr.coef_.tolist()[1:]\n theta.insert(0, lr.intercept_)\n return theta","def logistic_loss(x, y):\n N = x.shape[0]\n x = np.squeeze(x)\n y_prime = (y + 1)/2\n h = 1 /(1 + np.exp(-x))\n loss = np.sum(-np.log( (h**y_prime) * ((1-h)**(1-y_prime)) ))/N\n dx = np.exp(-y*x)*(-y)/(1+np.exp(-y*x))/N\n return loss, dx","def linear_regression(x: pd.Series, y: pd.Series) -> LinearRegression:\n\n lr_model = LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)\n lr_model.fit(np.array(x).reshape(-1,1), y)\n\n return lr_model","def fit_logregL1(data, C=.01):\n from sklearn.linear_model import LogisticRegression\n n,m = data.shape\n \n # TODO: just build (sparse) L directly & construct with it\n # for each Xi, estimate the neighborhood of Xi using L1-reg logistic regression:\n nbrs,th_ij,th_i = [None]*n, [None]*n, np.zeros((n,))\n Xtr, Xtmp = toPM(data), toPM(data) # make two copies so we can modify\n for i in range(n): \n Xtmp[i,:] = 0. # remove ourselves\n lr = LogisticRegression(penalty='l1',C=C,solver='liblinear').fit(Xtmp.T,Xtr[i,:])\n nbrs[i] = np.where(np.abs(lr.coef_) > 1e-6)[1]\n th_ij[i]= lr.coef_[0,nbrs[i]]/2.\n th_i[i] = lr.intercept_/2.\n Xtmp[i,:] = Xtr[i,:]; # & restore after\n \n # Collect single-variable factors\n factors = [Factor(Var(i,2),[-t,t]).exp() for i,t in enumerate(th_i)]\n\n # Collect non-zero pairwise factors\n for i in range(n):\n for jj,j in enumerate(nbrs[i]):\n # TODO: FIX: double counts edges? use different combination methods?\n scope = [Var(i,2),Var(int(j),2)]\n t = th_ij[i][jj]\n factors.append( Factor(scope, [[t,-t],[-t,t]]).exp() )\n \n # Build a model from the factors\n return Ising(factors)","def logsum_pair(logx, logy):\n if logx == logzero():\n return logy\n elif logx > logy:\n return logx + np.log1p(np.exp(logy-logx))\n else:\n return logy + np.log1p(np.exp(logx-logy))","def simpleLinearRegression(values):\n # A list of x values (unixtime values)\n x = [value[0] for value in values]\n # A list of y values (light values)\n y = [value[1] for value in values]\n # Use scipy.stats to calculate linear regression values\n slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)\n # Tomorrow's unixtime\n tomorrow = x[0] + MILLISECONDS_IN_ONE_DAY\n return slope * tomorrow + intercept","def eval(self, xy, log=True):\n\n # compile theano function, if haven't already done so\n if self.eval_lprob_f is None:\n self.eval_lprob_f = theano.function(\n inputs=[self.input, self.y],\n outputs=self.L,\n givens=[(bn.m, bn.bm) for bn in self.bns] + [(bn.v, bn.bv) for bn in self.bns]\n )\n\n x, y = xy\n lprob = self.eval_lprob_f(x.astype(dtype), y.astype(dtype))\n\n return lprob if log else np.exp(lprob)","def loglike(self, params, *args, **kwargs):\n return np.sum(self.loglikeobs(params, *args, **kwargs))","def fit(self, x, y):\n # Note Logistic Regression Runtime\n start_time = time.time()\n\n # Converting Pandas DataFrame to Numpy arrays\n if not type(x).__module__ == np.__name__:\n x = x.to_numpy()\n if not type(y).__module__ == np.__name__:\n y = y.to_numpy()\n\n # Insert a column of 1 in the feature vector X for the bias term in the weights\n x = np.insert(x,0,1,axis=1)\n \n # Verify dimension of input\n if len(x) != len(y):\n print(\"The number of input features vector must be to be the same as the number of target variables\")\n else:\n losses = self.gradient_descent(x,y)\n\n # Note end time\n end_time = time.time()\n\n # Log runtime\n print(\"Logistic Regression training time: {0:.2f}s\".format(end_time - start_time))\n \n return losses","def get_y_logl(self, y_list):","def lognormalize(x, temp = 1):\n if type(x) is list: x = np.array(x)\n\n x = x - np.max(x)\n # anneal\n xp = np.power(np.exp(x), temp)\n return xp / xp.sum()","def testLogRegres(weights, dev_x, dev_y):\n predict_y = []\n numSamples, numFeatures = np.shape(dev_x)\n for i in range(numSamples):\n if sigmoid(dev_x[i, :] * weights) > 0.5:\n label = 1\n else:\n label = 0\n predict_y.append(label)\n print('Congratulations, testing complete!')\n return predict_y","def logit_transform(params, bounds):\n with np.errstate(divide=\"ignore\", invalid=\"ignore\"):\n ret_array = np.ma.array(\n [np.ma.log(np.true_divide((x - a), (b - x))) for x, (a, b) in zip(params, bounds)])\n ret_array.set_fill_value(0)\n return np.ma.filled(ret_array)","def train_logistic_regression(x_train, y_train, learning_rate, fit_intercept=False, max_iter=500):\r\n if fit_intercept:\r\n intercept = np.ones(x_train.shape[0], 1)\r\n x_train = np.hstack((intercept, x_train)) # hstacks merges 2 arrays column wise\r\n weights = np.zeros(x_train.shape[1])\r\n for iteration in range(max_iter):\r\n weights = update_weights(x_train, y_train, weights, learning_rate)\r\n # printing cost for every 100 iterations\r\n if iteration % 100 == 0:\r\n print(calculate_cost(x_train, y_train, weights))\r\n return weights","def loglike(self, params):\n\n if type(params) is not MixedLMParams:\n params = MixedLMParams.from_packed(params, self.k_fe,\n self.use_sqrt)\n\n fe_params = params.get_fe_params()\n cov_re = params.get_cov_re()\n try:\n cov_re_inv = np.linalg.inv(cov_re)\n except np.linalg.LinAlgError:\n cov_re_inv = None\n _, cov_re_logdet = np.linalg.slogdet(cov_re)\n\n # The residuals\n expval = np.dot(self.exog, fe_params)\n resid_all = self.endog - expval\n\n likeval = 0.\n\n # Handle the covariance penalty\n if self.cov_pen is not None:\n likeval -= self.cov_pen.func(cov_re, cov_re_inv)\n\n # Handle the fixed effects penalty\n if self.fe_pen is not None:\n likeval -= self.fe_pen.func(fe_params)\n\n xvx, qf = 0., 0.\n for k, lab in enumerate(self.group_labels):\n\n exog = self.exog_li[k]\n ex_r = self.exog_re_li[k]\n ex2_r = self.exog_re2_li[k]\n resid = resid_all[self.row_indices[lab]]\n\n # Part 1 of the log likelihood (for both ML and REML)\n ld = _smw_logdet(1., ex_r, ex2_r, cov_re, cov_re_inv,\n cov_re_logdet)\n likeval -= ld / 2.\n\n # Part 2 of the log likelihood (for both ML and REML)\n u = _smw_solve(1., ex_r, ex2_r, cov_re, cov_re_inv, resid)\n qf += np.dot(resid, u)\n\n # Adjustment for REML\n if self.reml:\n mat = _smw_solve(1., ex_r, ex2_r, cov_re, cov_re_inv,\n exog)\n xvx += np.dot(exog.T, mat)\n\n if self.reml:\n likeval -= (self.n_totobs - self.k_fe) * np.log(qf) / 2.\n _,ld = np.linalg.slogdet(xvx)\n likeval -= ld / 2.\n likeval -= (self.n_totobs - self.k_fe) * np.log(2 * np.pi) / 2.\n likeval += ((self.n_totobs - self.k_fe) *\n np.log(self.n_totobs - self.k_fe) / 2.)\n likeval -= (self.n_totobs - self.k_fe) / 2.\n else:\n likeval -= self.n_totobs * np.log(qf) / 2.\n likeval -= self.n_totobs * np.log(2 * np.pi) / 2.\n likeval += self.n_totobs * np.log(self.n_totobs) / 2.\n likeval -= self.n_totobs / 2.\n\n return likeval","def logm(self, x):\n\n if K.backend() == 'theano':\n # construct theano tensor operation\n from theano.tensor.nlinalg import svd, diag\n from theano.tensor.elemwise import Elemwise\n from theano.scalar import log\n import theano.tensor as T\n # This implementation would be extremely slow. but efficient?\n u, d, v = svd(x)\n d += self.eps\n inner = diag(T.log(d))\n res = T.dot(u, T.dot(inner, v))\n return res\n else:\n from kyu.tensorflow.ops.svd_gradients import batch_matrix_log\n return batch_matrix_log(x, self.eps)","def logreg_train(model, X, Y, batch=32, epochs=50, valid_split=0.2):\r\n\thistory = model.fit(\r\n\t\tX, Y,\t\t\t\t# inputs & labels\r\n\t\tbatch_size=batch,\r\n\t\tepochs=epochs,\r\n\t\tvalidation_split=0.2\r\n\t)\r\n\r\n\treturn history","def llf(self):\n return self.model.loglike(self.params)","def lnlike(theta, model, x, y, yerr):\n return -np.nansum(0.5 * np.log([2 * np.pi] * len(y)))\\\n -np.nansum(np.log(yerr))\\\n -0.5*np.nansum(((y-model(x, *theta))/yerr)**2)","def logit_cost(self, theta, X, y):\n\n cost = 0.0\n\n ### YOUR CODE HERE\n sig = utils.sigmoid(theta)\n \n for i in range(0, X.shape[0]):\n cost += (y[i]-1)*theta[i] + np.log(sig[i])\n ### END YOUR CODE\n cost = cost #+ 0.01 * self.regularizer[0](self.weights)\n return cost","def lognormal(mu, hw, x): \n return np.exp(-( np.log(x/mu) / (0.01*hw) )**2)","def linear(self, verbose=0):\n\n # Output linear regression summary with coefficients and p-values\n # if desired\n if verbose != 0:\n model = sm.OLS(self.y_train, sm.add_constant(self.X_train)).fit()\n print(model.summary())\n\n linear_regressor = LinearRegression(fit_intercept=True, normalize=False,\n copy_X=True)\n linear_score = np.mean(cross_val_score(\n estimator=linear_regressor, X=self.X_train, y=self.y_train,\n cv=5, scoring=self.scorer))\n print('Linear score: ' + str(linear_score))\n return linear_regressor","def simple_linreg(n,x,y):\n x_bar = sum(x)/float(n) # average of x\n y_bar = sum(y)/float(n) # average of y\n\n m = sum((x-x_bar)*(y-y_bar))/ float(sum((x-x_bar)**2) ) # slope\n b = y_bar - m*x_bar # y intercept\n\n print(\"The linear regression has resulted in ...\")\n print(\"m(slope) : \",m )\n print(\"b(y intercept) : \",b )\n return m,b","def spark_LogisticRegression(*args, **kwargs): \n return LogisticRegression(*args, **kwargs)","def logistic_regression(y, tx, initial_w, max_iters, gamma):\n return least_squares_SGD(y, tx, initial_w, max_iters, gamma, loss_function=logistic_loss, gradient=logistic_grad)","def test_train_logist(x_train_variable, y_train_dep):\n # Ensure the function works\n try:\n lrc = cls.train_logistic(x_train_variable, y_train_dep)\n logging.info(\"Successful Logistic Model\")\n except Exception as err:\n logging.error(\"Errors in Fitting the Logistic Regression\")\n raise err\n return lrc","def analysis(houses:pd.DataFrame) -> None:\n \n \"\"\"\n #Me just trying to fit the data without any outside influences\n f= f'SELLER_HOUSE ~ SQFT_PER + PRICE + C(LOCATION)' \n result= smf.logit(formula= str(f), data= houses).fit()\n print(result.summary2())\n y= ['SELLER_HOUSE']\n x= ['SQFT_PER', 'PRICE', 'LOC_699 - Not Defined', 'LOC_AA - Airport Area', 'LOC_CG - Columbus Grove',\n 'LOC_CV - Cypress Village', 'LOC_EASTW - Eastwood', 'LOC_EC - El Camino Real', 'LOC_GP - Great Park',\n 'LOC_IRSP - Irvine Spectrum', 'LOC_LGA - Laguna Altura', 'LOC_NK - Northpark', 'LOC_NW - Northwood', \n 'LOC_OC - Oak Creek', 'LOC_OH - Orchard Hills', 'LOC_OT - Orangetree', 'LOC_PS - Portola Springs', \n 'LOC_QH - Quail Hill', 'LOC_SH - Shady Canyon', 'LOC_SJ - Rancho San Joaquin', 'LOC_STG - Stonegate', \n 'LOC_Stonegate', 'LOC_TR - Turtle Rock', 'LOC_TRG - Turtle Ridge', 'LOC_UP - University Park',\n 'LOC_UT - University Town Center', 'LOC_WB - Woodbridge', 'LOC_WD - Woodbury', \n 'LOC_WI - West Irvine', 'LOC_WN - Walnut (Irvine)', 'LOC_WP - Westpark']\n x_train, x_test, y_train, y_test= train_test_split(houses[x], houses[y], test_size= 0.3, random_state= 500)\n logreg = LogisticRegression()\n logreg.fit(x_train, y_train.values.ravel())\n y_pred= logreg.predict(x_test)\n print('Accuracy of logistic regression classifier on test set:', round(logreg.score(x_test, y_test), 3))\n # This model is really bad\n \n \"\"\"\n \n \"\"\n houses= houses.drop(['DAYS_ON_MARKET', 'ADDRESS', 'LOCATION',\n 'STATUS', 'PROPERTY_TYPE', 'ZIP_CODE'], axis= 1)\n columns= houses.columns.values.tolist()\n y= ['SELLER_HOUSE']\n x= [i for i in columns if i not in y]\n \n # Over Sampling Using SMOTE \n x_train, _, y_train, _= train_test_split(houses[x], houses[y], test_size= 0.3, random_state= 500)\n x_columns= x_train.columns\n \n os= SMOTE(random_state= 0)\n os_x, os_y= os.fit_sample(x_train, y_train)\n os_x= pd.DataFrame(data= os_x, columns= x_columns)\n os_y= pd.DataFrame(data= os_y, columns= y)\n \n \n #Recursive Feature Elimination\n logreg= LogisticRegression(max_iter= 600)\n rfe= RFE(logreg, 20)\n rfe= rfe.fit(os_x, os_y.values.ravel())\n \n lst= [i for count, i in enumerate(x) if rfe.support_[count] == True]\n X= os_x[lst]\n Y= os_y['SELLER_HOUSE']\n \n \n #logit_model= sm.Logit(Y, X)\n #result= logit_model.fit()\n #print(result.summary2()) # Model choosen by RCE\n \n #These are features have a p-value less than 0.05\n final_x= ['BATHS', 'ZIP_92602.0', 'ZIP_92618.0', 'LOC_699 - Not Defined', 'LOC_TR - Turtle Rock', 'LOC_WD - Woodbury']\n #final_x= ['ZIP_92602.0', 'LOC_699 - Not Defined', 'LOC_TR - Turtle Rock', 'LOC_WD - Woodbury']\n X2= os_x[final_x]\n \n logit_model2= sm.Logit(Y, X2)\n result2= logit_model2.fit()\n print(result2.summary2()) # Final Model\n \n x_train2, x_test2, y_train2, y_test2= train_test_split(X2, Y, test_size= 0.3, random_state= 500)\n logreg = LogisticRegression()\n logreg.fit(x_train2, y_train2)\n \n y_pred= logreg.predict(x_test2)\n print('Accuracy of logistic regression classifier on test set:', round(logreg.score(x_test2, y_test2), 2))\n \n conf_matrix= confusion_matrix(y_test2, y_pred)\n print(conf_matrix)\n # So 22+61 correct predictions and 13+44 wrong predictions\n \n logit_roc_auc = roc_auc_score(y_test2, logreg.predict(x_test2))\n fpr, tpr, _ = roc_curve(y_test2, logreg.predict_proba(x_test2)[:,1])\n plt.figure()\n plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n plt.plot([0, 1], [0, 1],'r--')\n plt.xlim([0.0, 1.0])\n plt.ylim([0.0, 1.05])\n plt.xlabel('False Positive Rate')\n plt.ylabel('True Positive Rate')\n plt.title('Receiver operating characteristic')\n plt.legend(loc=\"lower right\")\n plt.show()\n \"\"","def linear_regression(self, x, y, x_tomorrow, x_incl_tomorrow, dates):\n regr = linear_model.LinearRegression()\n regr.fit(x, y)\n\n # Get Coefficients\n m = regr.coef_[0]\n b = regr.intercept_\n\n tomorrow_normalized = regr.predict([x_tomorrow])[0][0]\n tomorrow = round((tomorrow_normalized * self.daily['Adj Close'][0]), 2)\n today = self.daily['Adj Close'][-1]\n percent_gain = round(((tomorrow / today) - 1) * 100, 2)\n percent_gain_int = abs(int(round(percent_gain, 0)))\n\n # plot Lin Reg\n if self.will_plot:\n self.ax.plot(dates, regr.predict(x_incl_tomorrow), color='k', label='Linear Regression', dashes=([2, 2, 10, 2]))\n self.ax.legend(loc='best')\n\n # update points\n if m > 0:\n # trend is upwards\n self.debug += '\\nPositive Regression Trend: buys + 3'\n self.buys += 3\n else:\n self.debug += '\\nNegative Regression Trend: sells + 3'\n self.sells += 3\n\n if percent_gain > 0:\n self.debug += '\\nRegression: Price will be up {} %, predicted close is {}'.format(percent_gain, tomorrow)\n else:\n self.debug += '\\nRegression: Price will be down {} %, predicted close is {}'.format(percent_gain, tomorrow)\n\n return tomorrow_normalized","def logxy(islog=None, islogy=Ellipsis):\n if islog is not None:\n plt.xscale('log' if islog else 'linear')\n if islogy is Ellipsis:\n islogy = islog\n if islogy is not None:\n plt.yscale('log' if islogy else 'linear')","def log_sample(self, phi: np.ndarray, x: np.ndarray = None):\n\n # Sanity checks on phi\n if phi.ndim == 1:\n phi = np.asarray(phi).reshape(1, -1)\n assert phi.shape[1] == self.dimensions, 'The length of the parameter vector does not match the model dimensions!, ' \\\n 'Model dimension:'+str(self.dimensions)+\" but given data is of \"+str(phi.shape[1]) \\\n + str(phi)\n assert phi.ndim == 2, \"Phi needs to be a 2-dimensional array!\"\n if np.any(phi < 0.):\n raise ValueError(\"Negative values of hyperparameter value encountered! Hyperparameter requested\", phi)\n\n n, d = phi.shape\n log_lik = np.empty((n, ))\n\n # Sanity checks on x (if any)\n if x is not None:\n if x.ndim == 1:\n x = x.reshape(1, -1)\n assert x.shape[1] == self.dimensions, 'The length of the data matrix does not match the model dimensions'\n\n pred = np.empty((n, ))\n var = np.empty((n, ))\n\n for i in range(n):\n # Change the parameters of the model\n self.model.rbf.lengthscale = phi[i, :]\n # Compute the log likelihood\n log_lik[i] = self.model.log_likelihood()\n # if x is supplied, now compute the prediction from the model as well\n if x is not None:\n pred[i], var[i] = self.model.predict(x)\n\n if x is not None:\n return log_lik, pred, var\n return log_lik, None, None","def log_likelihood(self, points):\n\t\tpoint_set = list(points)\n\t\tlog_probabilities = [np.log(self.density(point)) for point in point_set]\n\t\treturn sum(log_probabilities)","def regr_ols_sklearn(Y, X, **param):\r\n clf = linear_model.LinearRegression(fit_intercept=True)\r\n clf.fit()\r\n # clf.coef_ now contains coef\r","def linear_regression(X, Y, Xs_test, Ys_test):\n\n ## YOUR CODE HERE\n #################\n return 0","def logistic_regression(X, Y):\n m, n = X.shape\n theta = np.zeros(n)\n learning_rate = 10\n\n i = 0\n while True:\n i += 1\n prev_theta = theta\n grad = calc_grad(X, Y, theta)\n theta = theta - learning_rate * grad\n if i % 10000 == 0:\n print('Finished %d iterations' % i)\n # plot decision boundary for the ith iteration listed in i_lst\n i_lst = [1, 2, 3, 10, 100, 200, 500, 1000, 10000, 30370, 40000, 50000]\n if i in i_lst:\n save_path = \"output/p01_b_a\" + str(i) + \".png\"\n plot(X, Y, theta, save_path)\n if np.linalg.norm(prev_theta - theta) < 1e-15:\n print('Converged in %d iterations' % i)\n break\n return","def logarithm(requestContext, seriesList, base=10):\n results = []\n for series in seriesList:\n newValues = []\n for val in series:\n if val is None:\n newValues.append(None)\n elif val <= 0:\n newValues.append(None)\n else:\n newValues.append(math.log(val, base))\n newName = \"log(%s, %s)\" % (series.name, base)\n newSeries = TimeSeries(newName, series.start, series.end, series.step, newValues)\n newSeries.pathExpression = newName\n results.append(newSeries)\n return results","def stability_logistic(x, y, **kwargs):\n rlr = RandomizedLogisticRegression(n_jobs=kwargs.get('n_jobs', 4))\n if 'param' in kwargs:\n rlr.set_params(**kwargs['param'])\n rlr.fit(x, y)\n return rlr.get_support()","def logfit(N, err):\n import numpy as np\n\n if len(N) != len(err):\n raise ValueError('Convergence order args do not have same length')\n\n A = np.ones([len(err), 2])\n B = np.ones([len(err), 1])\n # ERR = A*N + B\n for i in range( len(N) ) :\n A[i,0] = np.log10(N[i])\n B[i] = np.log10(err[i])\n\n x, residuals, rank, singval = np.linalg.lstsq(A, B, rcond=None)\n\n return x","def linear_regression(X, Y, Xs_test, Ys_test):\n\n X_n = (X - np.mean(X, axis = 0)) / np.std(X, axis = 0)\n XL = np.concatenate((X_n, np.ones((len(X),1))), axis = 1)\n w = np.linalg.solve(XL.T.dot(XL),XL.T.dot(Y))\n mses = []\n for i, X_test in enumerate(Xs_test):\n Y_test = Ys_test[i]\n XL_test = np.concatenate(((X_test - np.mean(X, axis = 0)) / np.std(X, axis = 0), \n np.ones((len(X_test),1))), axis = 1)\n Y_pred = XL_test.dot(w)\n mse = np.mean(np.sqrt(np.sum((Y_pred - Y_test) ** 2, axis = 1))) \n mses.append(mse) \n return mses","def test_loglike(dlm,Cl,noise,beam):\n lmax = Cl.shape[0]\n tt_exp = -1./2 * np.real(np.vdot(dlm.T,hp.almxfl(dlm,1/(beam[:lmax]**2*Cl[:,1]+noise[:lmax]))))\n #plt.plot(Cl[:,1])\n tt_det = - 1./2 *(np.arange(1,lmax+1)*np.log((noise[:lmax]+Cl[:,1]*beam[:lmax]**2))).sum() \n tt_f = tt_exp + tt_det\n return tt_exp,tt_det,tt_f#,Cl[:,1]","def log_mae(labels, predictions):\n\n result = abs(labels - predictions)\n result = reduce_mean(result)\n result = log(result)\n\n return result","def loglog_interp(x,xp,fp):\n lx = np.log(x)\n lxp = np.log(xp)\n lfp = np.log(fp)\n return np.exp(np.interp(lx,lxp,lfp))","def log1p(x):\n return Log1p().apply((x,))[0]","def logpdf(self, X) -> np.ndarray:\n if not isinstance(X, np.ndarray):\n X = np.array(X)\n H = 0.5 * ((X - self.means) / self.scales) ** 2\n Z = sqrt(2 * pi) * self.scales\n return -np.sum(H + np.log(Z), axis=1)","def reg_logistic_regression(y, tx, l, initial_w, max_iters, gamma):\r\n y_resize = (1+y)/2 #rescales target so that -1 values are changed to 0 \r\n w_list = [initial_w]\r\n loss_list = []\r\n w = initial_w\r\n\r\n for n_iter in range(max_iters):\r\n grad = calculate_gradient_LR(y_resize, tx, w) + 2*l*w\r\n w = w - gamma*grad\r\n loss = compute_loss_LG(y_resize, tx, w)+ l*np.linalg.norm(w)\r\n w_list.append(w)\r\n loss_list.append(loss)\r\n if (n_iter > 1) and (np.abs(loss_list[-1] - loss_list[-2]) <= 1e-8):\r\n break\r\n return w_list[-1],loss_list[-1]","def loglloop(store):\n suml=0.0\n for i in xrange(store['yvec'].shape[0]):\n xbeta=dot(store['xmat'][i,:],store['beta'])\n suml=suml+store['yvec'][i] * xbeta - exp(xbeta)\n return suml","def loglike(store):\n nobs = store['yvec'].shape[0]\n calcweighted(store)\n store['regsampler'].update_yvec(store['yvectil'])\n store['regsampler'].update_xmat(store['xmattil'])\n return store['regsampler'].loglike(store['sigma'], store['beta'])","def linear_reg(df, variables, test_size):\n from sklearn.model_selection import train_test_split\n from sklearn.linear_model import LinearRegression\n\n # Define input\n X = encoding_df(df, variables)\n\n # Set validation\n y = df['target']\n\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)\n\n clf = LinearRegression().fit(X_train, y_train)\n\n print('Linear Regression RMSE',compute_rmse(y_test, clf.predict(X_test)))\n return clf.predict(X_test), y_test","def grad_reglog(w, X, y, **kwargs):\n p = np.exp(-y * (np.dot(X, w)))\n P = p / (1. + p)\n return -1 * np.dot(X.T, P * y) / X.shape[0]","def fit_linear_trend(series):\n X = make_design_matrix(np.arange(len(series)) + 1)\n linear_trend_ols = sm.OLS(series.values, X).fit()\n linear_trend = linear_trend_ols.predict(X)\n return linear_trend","def __init__(self, log=True, normalize=False):\r\n self.model = LinearRegression(normalize=normalize)\r\n self.log = log","def train_logistic_regression(X_train_input, y_train_input, C=1):\r\n from sklearn.linear_model import LogisticRegression\r\n logr_clf = LogisticRegression(C=C)\r\n logr_clf.fit(X_train_input, y_train_input)\r\n return logr_clf","def predict_log_proba(self, X):\n proba = self.predict_proba(X)\n return np.log(proba)","def log_sum_exp(x):\n # TF ordering\n axis = len(x.shape) - 1\n m = paddle.max(x, axis=axis)\n m2 = paddle.max(x, axis=axis, keepdim=True)\n return m + paddle.log(paddle.sum(paddle.exp(x - m2), axis=axis))","def log_loss(actual, predicted):\n\n predicted, actual = np.array(predicted), np.array(actual)\n\n small_value = 1e-15\n predicted[predicted < small_value] = small_value\n predicted[predicted > 1 - small_value] = 1. - small_value\n return (-1. / len(actual)) * np.sum(\n actual * np.log(predicted) + (1. - actual) * np.log(1. - predicted))","def grad_log(self, X):\n # \"\"\"\n # Evaluate the gradients (with respect to the input) of the log density at\n # each of the n points in X. This is the score function.\n\n # X: n x d numpy array.\n XB = np.dot(X, self.B)\n Y = 0.5*XB + self.c\n E2y = np.exp(2*Y)\n # n x dh\n Phi = old_div((E2y-1.0),(E2y+1))\n # n x dx\n T = np.dot(Phi, 0.5*self.B.T)\n S = self.b - X + T\n return S","def RMSLE(prediction, real):\n logarithmic_error = np.log1p(prediction) - np.log1p(real)\n score = np.sqrt(1/len(real) *np.sum(logarithmic_error**2))\n return score"],"string":"[\n \"def log_reg(x_train, y_train):\\n\\n log_reg_classifier = LogisticRegression(max_iter=1000, solver='lbfgs')\\n log_reg_classifier.fit(x_train, y_train)\\n return log_reg_classifier\\n\\n # log_reg_classifier.fit(x_train, y_train)\",\n \"def loglf2py(store):\\n loglike=0.0\\n return loglinear.logl(store['xb'],store['xmatf'], store['beta'],store['yvec'],loglike)\",\n \"def _log_linear_interpolation(predictions):\\n log_probs = utils.average_arrays([mx.nd.log(p) for p in predictions])\\n return -mx.nd.log(mx.nd.softmax(log_probs))\",\n \"def log_reg(x, y, s):\\n usx = np.array(x)\\n usy = np.array(y)\\n\\n # split data into train and validation set\\n x_train, x_test, y_train, y_test = train_test_split(usx, usy, test_size=s)\\n cls_log = LogisticRegression()\\n cls_log.fit(x_train, y_train)\\n y_predict = cls_log.predict(x_test)\\n\\n # select only the probabilities of being fraud\\n y_pred_prob = cls_log.predict_proba(x_test)[:, 1]\\n return y_predict, y_test, y_pred_prob\",\n \"def fit_exp_data(x_vals, y_vals):\\n log_vals = []\\n for y in y_vals:\\n log_vals.append(math.log(y, 2)) #get log base 2\\n fit = np.polyfit(x_vals, log_vals, 1)\\n return fit, 2\",\n \"def logp(self, xs, ys, **kwargs):\\n ind = np.isclose(self.predict(xs, **kwargs),ys)\\n axis = tuple(range(1,len(xs.shape)))\\n return np.log(np.prod(ind, axis=axis)) # default behavior\",\n \"def my_loglike(theta, x, data, sigma):\\n\\n model = my_model(theta, x)\\n\\n return -0.5*len(x)*np.log(2*math.pi*sigma**2) - (0.5/sigma**2) * np.sum((data-model)**2)\",\n \"def loglnumpy(store):\\n xbeta = dot(store['xmat'], store['beta'])\\n lamb = exp(xbeta)\\n return sum(store['yvec'] * xbeta - lamb)\",\n \"def linear_regression(features, values):\\n x_values = sm.add_constant(features)\\n model = sm.OLS(values, x_values, hasconst=True)\\n results = model.fit()\\n all_params = results.params\\n intercept = all_params[0]\\n params = all_params[1:]\\n return intercept, params\",\n \"def get_loglikelis(\\n self, points: numpy.ndarray | Sequence[numpy.ndarray]\\n ) -> numpy.ndarray:\\n return numpy.log(numpy.asarray(self.weights)[points])\",\n \"def linear_regression(points):\\n latitudes = [[i[0]] for i in points]\\n longitudes = [[i[1]] for i in points]\\n\\n # regression logic\\n regr = linear_model.LinearRegression()\\n regr.fit(latitudes,longitudes)\\n return [[x[0],y[0]] for x,y in zip(latitudes,regr.predict(latitudes))]\",\n \"def log(self, X, Y):\\n raise NotImplementedError\",\n \"def loglog(self, **kwargs):\\n return self.plot(plot=pylab.loglog, **kwargs)\",\n \"def logistic_loss(x, y):\\n N = x.shape[0]\\n x_flat = np.squeeze(x)\\n ex = np.exp(x_flat)\\n loss = np.sum(-y*x_flat+np.log(1+ex))/N\\n dx = (-y+ex/(1+ex))/N\\n # dx = np.reshape(dx,(len(dx),1))\\n return loss, dx\",\n \"def do_stats_model(x, y):\\n Xx = sm.add_constant(x)\\n sm_logit = sm.Logit(y, Xx)\\n result = sm_logit.fit()\\n print result.summary()\\n result.pred_table()\\n # linear model\\n print \\\"linear regression model:\\\\n\\\"\\n sm_linear = sm.OLS(y, Xx)\\n result = sm_linear.fit()\\n print result.summary()\",\n \"def logrels(rets):\\n return np.log(rets + 1)\",\n \"def nnRegression(data):\",\n \"def fit_data(self, data):\\n d = log(-log(data))\\n return dot(d, self._fit_matrix.T)\",\n \"def llinregress(x,y):\\r\\n TINY = 1.0e-20\\r\\n if len(x) <> len(y):\\r\\n raise ValueError, 'Input values not paired in linregress. Aborting.'\\r\\n n = len(x)\\r\\n x = map(float,x)\\r\\n y = map(float,y)\\r\\n xmean = mean(x)\\r\\n ymean = mean(y)\\r\\n r_num = float(n*(summult(x,y)) - sum(x)*sum(y))\\r\\n r_den = math.sqrt((n*ss(x) - square_of_sums(x))*(n*ss(y)-square_of_sums(y)))\\r\\n r = r_num / r_den\\r\\n z = 0.5*math.log((1.0+r+TINY)/(1.0-r+TINY))\\r\\n df = n-2\\r\\n t = r*math.sqrt(df/((1.0-r+TINY)*(1.0+r+TINY)))\\r\\n prob = betai(0.5*df,0.5,df/(df+t*t))\\r\\n slope = r_num / float(n*ss(x) - square_of_sums(x))\\r\\n intercept = ymean - slope*xmean\\r\\n sterrest = math.sqrt(1-r*r)*samplestdev(y)\\r\\n return slope, intercept, r, prob, sterrest\",\n \"def linear_regression(features, values):\\n clf = SGDRegressor(n_iter=100)\\n clf.fit(features,values)\\n print(clf.score(features,values))\\n intercept = clf.intercept_ \\n params = clf.coef_\\n \\n return intercept, params\",\n \"def linear_regression(sign):\\n t = np.linspace(0, 5, len(sign))\\n\\n return np.polyfit(t, sign, 1)[0]\",\n \"def linear_regression(x, y):\\n #print(\\\"Fitting\\\", y, \\\"\\\\nagainst\\\", x)\\n matrix = np.vstack( [x, np.ones_like(x)] ).T\\n slope, intercept = np.linalg.lstsq(matrix,y)[0]\\n #print(\\\"gives slope=\\\", slope, \\\"intercept=\\\", intercept)\\n return (slope, intercept)\",\n \"def fit_lin_reg(da: xr.DataArray, calc_SSE: bool=False):\\n\\n series = da.to_series().dropna()\\n X = np.array(series.index.tolist())[:,:2] # remove rep_idx (3rd column)\\n y = np.log10(series.values)\\n reg = LinearRegression().fit(X, y)\\n\\n if not calc_SSE:\\n return reg\\n\\n pred_y = reg.predict(X)\\n SSE = np.sum((pred_y - y)**2)\\n return reg, SSE\",\n \"def compute_loss_logreg(y, tx, w):\\n assert len(set(y).difference({0., 1.})) == 0, \\\"Class labels must be encoded as {0, 1}\\\"\\n\\n z = tx.dot(w)\\n\\n return np.sum(np.log(1 + np.exp(z)) - y * z)\",\n \"def linear_regression(data):\\n x_values = [x for x, y in data] #Get x values\\n y_values = [y for x, y in data] #Get y values\\n x_mean = sum(x_values) / len(x_values) #Compute mean value of x\\n y_mean = sum(y_values) / len(y_values) #Compute mean value of y\\n # Compute\\n coefficient = sum([(x - x_mean) * (y-y_mean) for x,y in data]) / sum([(x - x_mean) ** 2 for x in x_values])\\n intercept = y_mean - coefficient * x_mean # Compute Intercept\\n return((coefficient,intercept))\",\n \"def linearRegression(x,y,f1,l1,f2,l2):\\n n = len(x)\\n sumx = 0.0 # sum_n(x_i)\\n sumy = 0.0 # sum_n(y_i)\\n sumxx = 0.0 # sum_n(x_i*x_i)\\n sumxy = 0.0 # sum_n(x_i*y_i)\\n count = 0\\n for i2 in range(f2,l2+1):\\n for i1 in range(f1,l1+1):\\n xi = x[i2][i1]\\n yi = y[i2][i1]\\n sumx += xi\\n sumy += yi\\n sumxx += xi*xi\\n sumxy += xi*yi\\n count += 1\\n beta = (sumxy-sumx*sumy/count)/(sumxx-sumx*sumx/count)\\n alpha = (sumy-beta*sumx)/count\\n #z = zerofloat(n)\\n #for i in range(n):\\n # if null is None or x[i]!=null:\\n # z[i] = alpha+beta*x[i]\\n #print 'slope =',beta\\n #return z\\n return beta\",\n \"def residuals_Log(self, p, data, x):\\n err = data - self.Log(x,p)\\n return err\",\n \"def lin_reg_pred(R, method='lin'):\\n\\n R_arr = np.array(R).reshape(-1, 1)\\n\\n if method == 'lin':\\n return np.squeeze(reg_lin.predict(R_arr))\\n elif method == 'log':\\n return np.squeeze(10.**reg_log.predict(log10(R_arr)))\\n else:\\n raise ValueError(\\n \\\"method={} cannot be used. Use 'lin' or 'log'\\\".format(method))\",\n \"def _simple_logistic_regression(x,y,beta_start=None,verbose=False,\\n CONV_THRESH=1.e-3,MAXIT=500):\\n if len(x) != len(y):\\n raise ValueError, \\\"x and y should be the same length!\\\"\\n if beta_start is None:\\n beta_start = NA.zeros(2,x.dtype.char)\\n iter = 0; diff = 1.; beta = beta_start # initial values\\n if verbose:\\n print 'iteration beta log-likliehood |beta-beta_old|' \\n while iter < MAXIT:\\n beta_old = beta \\n p = NA.exp(beta[0]+beta[1]*x)/(1.+NA.exp(beta[0]+beta[1]*x))\\n l = NA.sum(y*NA.log(p) + (1.-y)*NA.log(1.-p)) # log-likliehood\\n s = NA.array([NA.sum(y-p), NA.sum((y-p)*x)]) # scoring function\\n # information matrix\\n J_bar = NA.array([[NA.sum(p*(1-p)),NA.sum(p*(1-p)*x)],\\n [NA.sum(p*(1-p)*x),NA.sum(p*(1-p)*x*x)]])\\n beta = beta_old + NA.dot(LA.inverse(J_bar),s) # new value of beta\\n diff = NA.sum(NA.fabs(beta-beta_old)) # sum of absolute differences\\n if verbose:\\n print iter+1, beta, l, diff\\n if diff <= CONV_THRESH: break\\n iter = iter + 1\\n return beta, J_bar, l\",\n \"def calculate_slope(cnt):\\n y = OrderedDict(cnt.most_common())\\n v=np.log(list(y.values()))\\n k=np.log(np.arange(1,len(v)+1,1))\\n return linregress(k,v)\",\n \"def get_loglikelis(\\n self, points: numpy.ndarray | list[numpy.ndarray] | Sequence[Sequence[float]]\\n ) -> numpy.ndarray:\\n points = numpy.array(points)\\n weight_likelis_list = [\\n numpy.log(self.weights[i] * pdf.pdf(points))\\n for i, pdf in enumerate(self.pdfs)\\n ]\\n weight_likelis = numpy.array(weight_likelis_list)\\n # (num_weights, num_points) => (num_points, num_weights)\\n weight_likelis = weight_likelis.transpose()\\n\\n # log-sum-exp trick\\n max_likeli = numpy.nanmax(weight_likelis, axis=1)\\n point_likeli = max_likeli + numpy.log(\\n numpy.nansum(numpy.exp(weight_likelis - max_likeli[:, None]), axis=1)\\n )\\n\\n return point_likeli\",\n \"def train_logisticRegression(data: np.array, labels: np.array)->None:\\n\\n n_examples = np.size(data, 0)\\n n_features = np.size(data, 1)\\n n_categories = np.size(labels, 1)\\n\\n data = np.hstack((np.ones((n_examples, 1)), data))\\n\\n print(data[0:5, :])\\n\\n X_train, X_test, y_train, y_test, idx_test = split_data(data, labels, 0.7)\\n\\n convergence_goal = 1e-3\\n learning_rate = 0.01\\n\\n theta = np.random.uniform(size=((n_features+1, n_categories)))\\n\\n for i in range(n_categories):\\n\\n cost_var = 1\\n\\n previous_cost = 1e6\\n iterations = 0\\n cost_to_plot = []\\n\\n while cost_var > convergence_goal:\\n iterations += 1\\n cost, grad = costFunction(X_train, y_train[:, i], theta[:, i])\\n theta[:, i] = update_theta(theta[:, i], grad, learning_rate)\\n cost_var = previous_cost - cost\\n previous_cost = cost\\n if iterations == 1: cost_var = 1\\n cost_to_plot.append(cost)\\n # print(cost)\\n\\n plt.plot(range(iterations), cost_to_plot, 'g-', label = 'cost')\\n plt.xlabel('iterations')\\n plt.ylabel('cost')\\n # plt.show()\\n\\n predictions = lrPredict(theta, X_test)\\n\\n print(predictions[0:5, :])\\n print(y_test[0:5, :])\\n\\n accuracy = np.mean([p == l for p, l in zip(predictions, y_test)])\\n print(\\\"Accuracy = {}\\\".format(accuracy))\\n\\n pass\",\n \"def logistic_regression_vec(theta, trainX, trainY):\\n # Add column of ones for bias\\n trainX = np.hstack((np.ones((trainX.shape[0], 1)), trainX))\\n h = sigmoid(np.inner(trainX, theta))\\n # np.log(1-h) can lead to problems for h = 1.0\\n h = np.where(h == 1.0, 1 - 1e-12, h)\\n fval = -(trainY * np.log(h) + (1 - trainY) * np.log(1 - h)).sum()\\n error = h - trainY\\n # Negative gradient for a minimization, must be flattened for np.minimize\\n grad = np.dot(trainX.T, error).flatten()\\n return fval, grad\",\n \"def log_error(X, y, w):\\r\\n N = X.shape[0]\\r\\n J = np.sum(np.log(1.0 + np.exp(-y * (X @ w)))) / N\\r\\n return J\",\n \"def linear(self, X):\\n return X\",\n \"def predict_logit(self, x):\\n with torch.no_grad():\\n y_ = self.tr_model(x)\\n return y_\",\n \"def logistic_regression(x,y,beta_start=None,verbose=False,CONV_THRESH=1.e-3,\\n MAXIT=500):\\n if x.shape[-1] != len(y):\\n raise ValueError, \\\"x.shape[-1] and y should be the same length!\\\"\\n try:\\n N, npreds = x.shape[1], x.shape[0]\\n except: # single predictor, use simple logistic regression routine.\\n return _simple_logistic_regression(x,y,beta_start=beta_start,\\n CONV_THRESH=CONV_THRESH,MAXIT=MAXIT,verbose=verbose)\\n if beta_start is None:\\n beta_start = NA.zeros(npreds+1,x.dtype.char)\\n X = NA.ones((npreds+1,N), x.dtype.char)\\n X[1:, :] = x\\n Xt = NA.transpose(X)\\n iter = 0; diff = 1.; beta = beta_start # initial values\\n if verbose:\\n print 'iteration beta log-likliehood |beta-beta_old|' \\n while iter < MAXIT:\\n beta_old = beta \\n ebx = NA.exp(NA.dot(beta, X))\\n p = ebx/(1.+ebx)\\n l = NA.sum(y*NA.log(p) + (1.-y)*NA.log(1.-p)) # log-likeliehood\\n s = NA.dot(X, y-p) # scoring function\\n J_bar = NA.dot(X*p,Xt) # information matrix\\n beta = beta_old + NA.dot(LA.inverse(J_bar),s) # new value of beta\\n diff = NA.sum(NA.fabs(beta-beta_old)) # sum of absolute differences\\n if verbose:\\n print iter+1, beta, l, diff\\n if diff <= CONV_THRESH: break\\n iter = iter + 1\\n if iter == MAXIT and diff > CONV_THRESH: \\n print 'warning: convergence not achieved with threshold of %s in %s iterations' % (CONV_THRESH,MAXIT)\\n return beta, J_bar, l\",\n \"def ln(x):\\n return log(x, const.e)\",\n \"def forward(self, x):\\n\\n embeds = self.dvector(x)\\n logits = self.linear(embeds)\\n\\n return logits\",\n \"def manual_log_like_normal(x, data):\\n return np.sum(-np.log(x[1] * np.sqrt(2 * np.pi))-((data-x[0])**2) / (2*x[1]**2))\",\n \"def computeRegression(Xin, Yin, Xtest, noise, ls, sv):\\n\\n # compute kernels\\n K = kernel_sqExp(Xin, Xin, ls=ls, sv=sv)\\n Kstar = kernel_sqExp(Xin, Xtest, ls=ls, sv=sv)\\n Kstarstar = kernel_sqExp(Xtest, Xtest, ls=ls, sv=sv)\\n\\n # compute mean based on training input points\\n n1 = np.shape(K)[0]\\n offSet = 1e-3\\n L = np.linalg.cholesky(K + noise*np.eye(n1) + offSet*np.eye(n1))\\n alpha = np.linalg.solve(L.T, np.linalg.solve(L,Yin))\\n f_mean = np.dot(Kstar.T,alpha) # mean of points\\n \\n # compute resulting covariance of predictive distribution\\n v = np.linalg.solve(L, Kstar)\\n cov = Kstarstar - np.dot(v.T,v)\\n \\n # compute log of marginal likelihood\\n #lML = -0.5*np.dot(Yin.T,alpha) - np.sum(np.log(L)) - (n1/2*np.log(2*math.pi))\\n lML_dataFit = -0.5*np.dot(Yin.T,alpha)\\n lML_complexity = -np.sum(np.log(L))\\n lML_normalize = -(n1/2*np.log(2*math.pi))\\n lML_details = [lML_dataFit, lML_complexity, lML_normalize]\\n lML = lML_dataFit[0] + lML_complexity + lML_normalize \\n \\n return f_mean, cov, lML , lML_details\",\n \"def _convert_normlogprice(self, series):\\n try:\\n return np.log(series.div(series[0]))\\n except:\\n raise TypeError('ERROR: Could not transform prices to log function. Check price history data.')\",\n \"def train_logistic_regression(train_x, train_y):\\n\\n logistic_regression_model = LogisticRegression(penalty='l2', C=1.0)\\n logistic_regression_model.fit(train_x, train_y)\\n return logistic_regression_model\",\n \"def feature_mapped_logistic_regression(power, l):\\n df = pd.read_csv('ex2data2.txt', names=['test1', 'test2', 'accepted'])\\n x1 = np.array(df.test1)\\n x2 = np.array(df.test2)\\n y = general.get_y(df)\\n\\n X = feature_mapping(x1, x2, power, as_ndarray=True)\\n theta = np.zeros(X.shape[1])\\n\\n res = opt.minimize(fun=regularized_cost,\\n x0=theta,\\n args=(X, y, l),\\n method='TNC',\\n jac=regularized_gradient)\\n final_theta = res.x\\n\\n return final_theta\",\n \"def linreg_scikit(X, y):\\n lr = linear_model.LinearRegression()\\n lr.fit(X, y)\\n theta = lr.coef_.tolist()[1:]\\n theta.insert(0, lr.intercept_)\\n return theta\",\n \"def logistic_loss(x, y):\\n N = x.shape[0]\\n x = np.squeeze(x)\\n y_prime = (y + 1)/2\\n h = 1 /(1 + np.exp(-x))\\n loss = np.sum(-np.log( (h**y_prime) * ((1-h)**(1-y_prime)) ))/N\\n dx = np.exp(-y*x)*(-y)/(1+np.exp(-y*x))/N\\n return loss, dx\",\n \"def linear_regression(x: pd.Series, y: pd.Series) -> LinearRegression:\\n\\n lr_model = LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)\\n lr_model.fit(np.array(x).reshape(-1,1), y)\\n\\n return lr_model\",\n \"def fit_logregL1(data, C=.01):\\n from sklearn.linear_model import LogisticRegression\\n n,m = data.shape\\n \\n # TODO: just build (sparse) L directly & construct with it\\n # for each Xi, estimate the neighborhood of Xi using L1-reg logistic regression:\\n nbrs,th_ij,th_i = [None]*n, [None]*n, np.zeros((n,))\\n Xtr, Xtmp = toPM(data), toPM(data) # make two copies so we can modify\\n for i in range(n): \\n Xtmp[i,:] = 0. # remove ourselves\\n lr = LogisticRegression(penalty='l1',C=C,solver='liblinear').fit(Xtmp.T,Xtr[i,:])\\n nbrs[i] = np.where(np.abs(lr.coef_) > 1e-6)[1]\\n th_ij[i]= lr.coef_[0,nbrs[i]]/2.\\n th_i[i] = lr.intercept_/2.\\n Xtmp[i,:] = Xtr[i,:]; # & restore after\\n \\n # Collect single-variable factors\\n factors = [Factor(Var(i,2),[-t,t]).exp() for i,t in enumerate(th_i)]\\n\\n # Collect non-zero pairwise factors\\n for i in range(n):\\n for jj,j in enumerate(nbrs[i]):\\n # TODO: FIX: double counts edges? use different combination methods?\\n scope = [Var(i,2),Var(int(j),2)]\\n t = th_ij[i][jj]\\n factors.append( Factor(scope, [[t,-t],[-t,t]]).exp() )\\n \\n # Build a model from the factors\\n return Ising(factors)\",\n \"def logsum_pair(logx, logy):\\n if logx == logzero():\\n return logy\\n elif logx > logy:\\n return logx + np.log1p(np.exp(logy-logx))\\n else:\\n return logy + np.log1p(np.exp(logx-logy))\",\n \"def simpleLinearRegression(values):\\n # A list of x values (unixtime values)\\n x = [value[0] for value in values]\\n # A list of y values (light values)\\n y = [value[1] for value in values]\\n # Use scipy.stats to calculate linear regression values\\n slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)\\n # Tomorrow's unixtime\\n tomorrow = x[0] + MILLISECONDS_IN_ONE_DAY\\n return slope * tomorrow + intercept\",\n \"def eval(self, xy, log=True):\\n\\n # compile theano function, if haven't already done so\\n if self.eval_lprob_f is None:\\n self.eval_lprob_f = theano.function(\\n inputs=[self.input, self.y],\\n outputs=self.L,\\n givens=[(bn.m, bn.bm) for bn in self.bns] + [(bn.v, bn.bv) for bn in self.bns]\\n )\\n\\n x, y = xy\\n lprob = self.eval_lprob_f(x.astype(dtype), y.astype(dtype))\\n\\n return lprob if log else np.exp(lprob)\",\n \"def loglike(self, params, *args, **kwargs):\\n return np.sum(self.loglikeobs(params, *args, **kwargs))\",\n \"def fit(self, x, y):\\n # Note Logistic Regression Runtime\\n start_time = time.time()\\n\\n # Converting Pandas DataFrame to Numpy arrays\\n if not type(x).__module__ == np.__name__:\\n x = x.to_numpy()\\n if not type(y).__module__ == np.__name__:\\n y = y.to_numpy()\\n\\n # Insert a column of 1 in the feature vector X for the bias term in the weights\\n x = np.insert(x,0,1,axis=1)\\n \\n # Verify dimension of input\\n if len(x) != len(y):\\n print(\\\"The number of input features vector must be to be the same as the number of target variables\\\")\\n else:\\n losses = self.gradient_descent(x,y)\\n\\n # Note end time\\n end_time = time.time()\\n\\n # Log runtime\\n print(\\\"Logistic Regression training time: {0:.2f}s\\\".format(end_time - start_time))\\n \\n return losses\",\n \"def get_y_logl(self, y_list):\",\n \"def lognormalize(x, temp = 1):\\n if type(x) is list: x = np.array(x)\\n\\n x = x - np.max(x)\\n # anneal\\n xp = np.power(np.exp(x), temp)\\n return xp / xp.sum()\",\n \"def testLogRegres(weights, dev_x, dev_y):\\n predict_y = []\\n numSamples, numFeatures = np.shape(dev_x)\\n for i in range(numSamples):\\n if sigmoid(dev_x[i, :] * weights) > 0.5:\\n label = 1\\n else:\\n label = 0\\n predict_y.append(label)\\n print('Congratulations, testing complete!')\\n return predict_y\",\n \"def logit_transform(params, bounds):\\n with np.errstate(divide=\\\"ignore\\\", invalid=\\\"ignore\\\"):\\n ret_array = np.ma.array(\\n [np.ma.log(np.true_divide((x - a), (b - x))) for x, (a, b) in zip(params, bounds)])\\n ret_array.set_fill_value(0)\\n return np.ma.filled(ret_array)\",\n \"def train_logistic_regression(x_train, y_train, learning_rate, fit_intercept=False, max_iter=500):\\r\\n if fit_intercept:\\r\\n intercept = np.ones(x_train.shape[0], 1)\\r\\n x_train = np.hstack((intercept, x_train)) # hstacks merges 2 arrays column wise\\r\\n weights = np.zeros(x_train.shape[1])\\r\\n for iteration in range(max_iter):\\r\\n weights = update_weights(x_train, y_train, weights, learning_rate)\\r\\n # printing cost for every 100 iterations\\r\\n if iteration % 100 == 0:\\r\\n print(calculate_cost(x_train, y_train, weights))\\r\\n return weights\",\n \"def loglike(self, params):\\n\\n if type(params) is not MixedLMParams:\\n params = MixedLMParams.from_packed(params, self.k_fe,\\n self.use_sqrt)\\n\\n fe_params = params.get_fe_params()\\n cov_re = params.get_cov_re()\\n try:\\n cov_re_inv = np.linalg.inv(cov_re)\\n except np.linalg.LinAlgError:\\n cov_re_inv = None\\n _, cov_re_logdet = np.linalg.slogdet(cov_re)\\n\\n # The residuals\\n expval = np.dot(self.exog, fe_params)\\n resid_all = self.endog - expval\\n\\n likeval = 0.\\n\\n # Handle the covariance penalty\\n if self.cov_pen is not None:\\n likeval -= self.cov_pen.func(cov_re, cov_re_inv)\\n\\n # Handle the fixed effects penalty\\n if self.fe_pen is not None:\\n likeval -= self.fe_pen.func(fe_params)\\n\\n xvx, qf = 0., 0.\\n for k, lab in enumerate(self.group_labels):\\n\\n exog = self.exog_li[k]\\n ex_r = self.exog_re_li[k]\\n ex2_r = self.exog_re2_li[k]\\n resid = resid_all[self.row_indices[lab]]\\n\\n # Part 1 of the log likelihood (for both ML and REML)\\n ld = _smw_logdet(1., ex_r, ex2_r, cov_re, cov_re_inv,\\n cov_re_logdet)\\n likeval -= ld / 2.\\n\\n # Part 2 of the log likelihood (for both ML and REML)\\n u = _smw_solve(1., ex_r, ex2_r, cov_re, cov_re_inv, resid)\\n qf += np.dot(resid, u)\\n\\n # Adjustment for REML\\n if self.reml:\\n mat = _smw_solve(1., ex_r, ex2_r, cov_re, cov_re_inv,\\n exog)\\n xvx += np.dot(exog.T, mat)\\n\\n if self.reml:\\n likeval -= (self.n_totobs - self.k_fe) * np.log(qf) / 2.\\n _,ld = np.linalg.slogdet(xvx)\\n likeval -= ld / 2.\\n likeval -= (self.n_totobs - self.k_fe) * np.log(2 * np.pi) / 2.\\n likeval += ((self.n_totobs - self.k_fe) *\\n np.log(self.n_totobs - self.k_fe) / 2.)\\n likeval -= (self.n_totobs - self.k_fe) / 2.\\n else:\\n likeval -= self.n_totobs * np.log(qf) / 2.\\n likeval -= self.n_totobs * np.log(2 * np.pi) / 2.\\n likeval += self.n_totobs * np.log(self.n_totobs) / 2.\\n likeval -= self.n_totobs / 2.\\n\\n return likeval\",\n \"def logm(self, x):\\n\\n if K.backend() == 'theano':\\n # construct theano tensor operation\\n from theano.tensor.nlinalg import svd, diag\\n from theano.tensor.elemwise import Elemwise\\n from theano.scalar import log\\n import theano.tensor as T\\n # This implementation would be extremely slow. but efficient?\\n u, d, v = svd(x)\\n d += self.eps\\n inner = diag(T.log(d))\\n res = T.dot(u, T.dot(inner, v))\\n return res\\n else:\\n from kyu.tensorflow.ops.svd_gradients import batch_matrix_log\\n return batch_matrix_log(x, self.eps)\",\n \"def logreg_train(model, X, Y, batch=32, epochs=50, valid_split=0.2):\\r\\n\\thistory = model.fit(\\r\\n\\t\\tX, Y,\\t\\t\\t\\t# inputs & labels\\r\\n\\t\\tbatch_size=batch,\\r\\n\\t\\tepochs=epochs,\\r\\n\\t\\tvalidation_split=0.2\\r\\n\\t)\\r\\n\\r\\n\\treturn history\",\n \"def llf(self):\\n return self.model.loglike(self.params)\",\n \"def lnlike(theta, model, x, y, yerr):\\n return -np.nansum(0.5 * np.log([2 * np.pi] * len(y)))\\\\\\n -np.nansum(np.log(yerr))\\\\\\n -0.5*np.nansum(((y-model(x, *theta))/yerr)**2)\",\n \"def logit_cost(self, theta, X, y):\\n\\n cost = 0.0\\n\\n ### YOUR CODE HERE\\n sig = utils.sigmoid(theta)\\n \\n for i in range(0, X.shape[0]):\\n cost += (y[i]-1)*theta[i] + np.log(sig[i])\\n ### END YOUR CODE\\n cost = cost #+ 0.01 * self.regularizer[0](self.weights)\\n return cost\",\n \"def lognormal(mu, hw, x): \\n return np.exp(-( np.log(x/mu) / (0.01*hw) )**2)\",\n \"def linear(self, verbose=0):\\n\\n # Output linear regression summary with coefficients and p-values\\n # if desired\\n if verbose != 0:\\n model = sm.OLS(self.y_train, sm.add_constant(self.X_train)).fit()\\n print(model.summary())\\n\\n linear_regressor = LinearRegression(fit_intercept=True, normalize=False,\\n copy_X=True)\\n linear_score = np.mean(cross_val_score(\\n estimator=linear_regressor, X=self.X_train, y=self.y_train,\\n cv=5, scoring=self.scorer))\\n print('Linear score: ' + str(linear_score))\\n return linear_regressor\",\n \"def simple_linreg(n,x,y):\\n x_bar = sum(x)/float(n) # average of x\\n y_bar = sum(y)/float(n) # average of y\\n\\n m = sum((x-x_bar)*(y-y_bar))/ float(sum((x-x_bar)**2) ) # slope\\n b = y_bar - m*x_bar # y intercept\\n\\n print(\\\"The linear regression has resulted in ...\\\")\\n print(\\\"m(slope) : \\\",m )\\n print(\\\"b(y intercept) : \\\",b )\\n return m,b\",\n \"def spark_LogisticRegression(*args, **kwargs): \\n return LogisticRegression(*args, **kwargs)\",\n \"def logistic_regression(y, tx, initial_w, max_iters, gamma):\\n return least_squares_SGD(y, tx, initial_w, max_iters, gamma, loss_function=logistic_loss, gradient=logistic_grad)\",\n \"def test_train_logist(x_train_variable, y_train_dep):\\n # Ensure the function works\\n try:\\n lrc = cls.train_logistic(x_train_variable, y_train_dep)\\n logging.info(\\\"Successful Logistic Model\\\")\\n except Exception as err:\\n logging.error(\\\"Errors in Fitting the Logistic Regression\\\")\\n raise err\\n return lrc\",\n \"def analysis(houses:pd.DataFrame) -> None:\\n \\n \\\"\\\"\\\"\\n #Me just trying to fit the data without any outside influences\\n f= f'SELLER_HOUSE ~ SQFT_PER + PRICE + C(LOCATION)' \\n result= smf.logit(formula= str(f), data= houses).fit()\\n print(result.summary2())\\n y= ['SELLER_HOUSE']\\n x= ['SQFT_PER', 'PRICE', 'LOC_699 - Not Defined', 'LOC_AA - Airport Area', 'LOC_CG - Columbus Grove',\\n 'LOC_CV - Cypress Village', 'LOC_EASTW - Eastwood', 'LOC_EC - El Camino Real', 'LOC_GP - Great Park',\\n 'LOC_IRSP - Irvine Spectrum', 'LOC_LGA - Laguna Altura', 'LOC_NK - Northpark', 'LOC_NW - Northwood', \\n 'LOC_OC - Oak Creek', 'LOC_OH - Orchard Hills', 'LOC_OT - Orangetree', 'LOC_PS - Portola Springs', \\n 'LOC_QH - Quail Hill', 'LOC_SH - Shady Canyon', 'LOC_SJ - Rancho San Joaquin', 'LOC_STG - Stonegate', \\n 'LOC_Stonegate', 'LOC_TR - Turtle Rock', 'LOC_TRG - Turtle Ridge', 'LOC_UP - University Park',\\n 'LOC_UT - University Town Center', 'LOC_WB - Woodbridge', 'LOC_WD - Woodbury', \\n 'LOC_WI - West Irvine', 'LOC_WN - Walnut (Irvine)', 'LOC_WP - Westpark']\\n x_train, x_test, y_train, y_test= train_test_split(houses[x], houses[y], test_size= 0.3, random_state= 500)\\n logreg = LogisticRegression()\\n logreg.fit(x_train, y_train.values.ravel())\\n y_pred= logreg.predict(x_test)\\n print('Accuracy of logistic regression classifier on test set:', round(logreg.score(x_test, y_test), 3))\\n # This model is really bad\\n \\n \\\"\\\"\\\"\\n \\n \\\"\\\"\\n houses= houses.drop(['DAYS_ON_MARKET', 'ADDRESS', 'LOCATION',\\n 'STATUS', 'PROPERTY_TYPE', 'ZIP_CODE'], axis= 1)\\n columns= houses.columns.values.tolist()\\n y= ['SELLER_HOUSE']\\n x= [i for i in columns if i not in y]\\n \\n # Over Sampling Using SMOTE \\n x_train, _, y_train, _= train_test_split(houses[x], houses[y], test_size= 0.3, random_state= 500)\\n x_columns= x_train.columns\\n \\n os= SMOTE(random_state= 0)\\n os_x, os_y= os.fit_sample(x_train, y_train)\\n os_x= pd.DataFrame(data= os_x, columns= x_columns)\\n os_y= pd.DataFrame(data= os_y, columns= y)\\n \\n \\n #Recursive Feature Elimination\\n logreg= LogisticRegression(max_iter= 600)\\n rfe= RFE(logreg, 20)\\n rfe= rfe.fit(os_x, os_y.values.ravel())\\n \\n lst= [i for count, i in enumerate(x) if rfe.support_[count] == True]\\n X= os_x[lst]\\n Y= os_y['SELLER_HOUSE']\\n \\n \\n #logit_model= sm.Logit(Y, X)\\n #result= logit_model.fit()\\n #print(result.summary2()) # Model choosen by RCE\\n \\n #These are features have a p-value less than 0.05\\n final_x= ['BATHS', 'ZIP_92602.0', 'ZIP_92618.0', 'LOC_699 - Not Defined', 'LOC_TR - Turtle Rock', 'LOC_WD - Woodbury']\\n #final_x= ['ZIP_92602.0', 'LOC_699 - Not Defined', 'LOC_TR - Turtle Rock', 'LOC_WD - Woodbury']\\n X2= os_x[final_x]\\n \\n logit_model2= sm.Logit(Y, X2)\\n result2= logit_model2.fit()\\n print(result2.summary2()) # Final Model\\n \\n x_train2, x_test2, y_train2, y_test2= train_test_split(X2, Y, test_size= 0.3, random_state= 500)\\n logreg = LogisticRegression()\\n logreg.fit(x_train2, y_train2)\\n \\n y_pred= logreg.predict(x_test2)\\n print('Accuracy of logistic regression classifier on test set:', round(logreg.score(x_test2, y_test2), 2))\\n \\n conf_matrix= confusion_matrix(y_test2, y_pred)\\n print(conf_matrix)\\n # So 22+61 correct predictions and 13+44 wrong predictions\\n \\n logit_roc_auc = roc_auc_score(y_test2, logreg.predict(x_test2))\\n fpr, tpr, _ = roc_curve(y_test2, logreg.predict_proba(x_test2)[:,1])\\n plt.figure()\\n plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\\n plt.plot([0, 1], [0, 1],'r--')\\n plt.xlim([0.0, 1.0])\\n plt.ylim([0.0, 1.05])\\n plt.xlabel('False Positive Rate')\\n plt.ylabel('True Positive Rate')\\n plt.title('Receiver operating characteristic')\\n plt.legend(loc=\\\"lower right\\\")\\n plt.show()\\n \\\"\\\"\",\n \"def linear_regression(self, x, y, x_tomorrow, x_incl_tomorrow, dates):\\n regr = linear_model.LinearRegression()\\n regr.fit(x, y)\\n\\n # Get Coefficients\\n m = regr.coef_[0]\\n b = regr.intercept_\\n\\n tomorrow_normalized = regr.predict([x_tomorrow])[0][0]\\n tomorrow = round((tomorrow_normalized * self.daily['Adj Close'][0]), 2)\\n today = self.daily['Adj Close'][-1]\\n percent_gain = round(((tomorrow / today) - 1) * 100, 2)\\n percent_gain_int = abs(int(round(percent_gain, 0)))\\n\\n # plot Lin Reg\\n if self.will_plot:\\n self.ax.plot(dates, regr.predict(x_incl_tomorrow), color='k', label='Linear Regression', dashes=([2, 2, 10, 2]))\\n self.ax.legend(loc='best')\\n\\n # update points\\n if m > 0:\\n # trend is upwards\\n self.debug += '\\\\nPositive Regression Trend: buys + 3'\\n self.buys += 3\\n else:\\n self.debug += '\\\\nNegative Regression Trend: sells + 3'\\n self.sells += 3\\n\\n if percent_gain > 0:\\n self.debug += '\\\\nRegression: Price will be up {} %, predicted close is {}'.format(percent_gain, tomorrow)\\n else:\\n self.debug += '\\\\nRegression: Price will be down {} %, predicted close is {}'.format(percent_gain, tomorrow)\\n\\n return tomorrow_normalized\",\n \"def logxy(islog=None, islogy=Ellipsis):\\n if islog is not None:\\n plt.xscale('log' if islog else 'linear')\\n if islogy is Ellipsis:\\n islogy = islog\\n if islogy is not None:\\n plt.yscale('log' if islogy else 'linear')\",\n \"def log_sample(self, phi: np.ndarray, x: np.ndarray = None):\\n\\n # Sanity checks on phi\\n if phi.ndim == 1:\\n phi = np.asarray(phi).reshape(1, -1)\\n assert phi.shape[1] == self.dimensions, 'The length of the parameter vector does not match the model dimensions!, ' \\\\\\n 'Model dimension:'+str(self.dimensions)+\\\" but given data is of \\\"+str(phi.shape[1]) \\\\\\n + str(phi)\\n assert phi.ndim == 2, \\\"Phi needs to be a 2-dimensional array!\\\"\\n if np.any(phi < 0.):\\n raise ValueError(\\\"Negative values of hyperparameter value encountered! Hyperparameter requested\\\", phi)\\n\\n n, d = phi.shape\\n log_lik = np.empty((n, ))\\n\\n # Sanity checks on x (if any)\\n if x is not None:\\n if x.ndim == 1:\\n x = x.reshape(1, -1)\\n assert x.shape[1] == self.dimensions, 'The length of the data matrix does not match the model dimensions'\\n\\n pred = np.empty((n, ))\\n var = np.empty((n, ))\\n\\n for i in range(n):\\n # Change the parameters of the model\\n self.model.rbf.lengthscale = phi[i, :]\\n # Compute the log likelihood\\n log_lik[i] = self.model.log_likelihood()\\n # if x is supplied, now compute the prediction from the model as well\\n if x is not None:\\n pred[i], var[i] = self.model.predict(x)\\n\\n if x is not None:\\n return log_lik, pred, var\\n return log_lik, None, None\",\n \"def log_likelihood(self, points):\\n\\t\\tpoint_set = list(points)\\n\\t\\tlog_probabilities = [np.log(self.density(point)) for point in point_set]\\n\\t\\treturn sum(log_probabilities)\",\n \"def regr_ols_sklearn(Y, X, **param):\\r\\n clf = linear_model.LinearRegression(fit_intercept=True)\\r\\n clf.fit()\\r\\n # clf.coef_ now contains coef\\r\",\n \"def linear_regression(X, Y, Xs_test, Ys_test):\\n\\n ## YOUR CODE HERE\\n #################\\n return 0\",\n \"def logistic_regression(X, Y):\\n m, n = X.shape\\n theta = np.zeros(n)\\n learning_rate = 10\\n\\n i = 0\\n while True:\\n i += 1\\n prev_theta = theta\\n grad = calc_grad(X, Y, theta)\\n theta = theta - learning_rate * grad\\n if i % 10000 == 0:\\n print('Finished %d iterations' % i)\\n # plot decision boundary for the ith iteration listed in i_lst\\n i_lst = [1, 2, 3, 10, 100, 200, 500, 1000, 10000, 30370, 40000, 50000]\\n if i in i_lst:\\n save_path = \\\"output/p01_b_a\\\" + str(i) + \\\".png\\\"\\n plot(X, Y, theta, save_path)\\n if np.linalg.norm(prev_theta - theta) < 1e-15:\\n print('Converged in %d iterations' % i)\\n break\\n return\",\n \"def logarithm(requestContext, seriesList, base=10):\\n results = []\\n for series in seriesList:\\n newValues = []\\n for val in series:\\n if val is None:\\n newValues.append(None)\\n elif val <= 0:\\n newValues.append(None)\\n else:\\n newValues.append(math.log(val, base))\\n newName = \\\"log(%s, %s)\\\" % (series.name, base)\\n newSeries = TimeSeries(newName, series.start, series.end, series.step, newValues)\\n newSeries.pathExpression = newName\\n results.append(newSeries)\\n return results\",\n \"def stability_logistic(x, y, **kwargs):\\n rlr = RandomizedLogisticRegression(n_jobs=kwargs.get('n_jobs', 4))\\n if 'param' in kwargs:\\n rlr.set_params(**kwargs['param'])\\n rlr.fit(x, y)\\n return rlr.get_support()\",\n \"def logfit(N, err):\\n import numpy as np\\n\\n if len(N) != len(err):\\n raise ValueError('Convergence order args do not have same length')\\n\\n A = np.ones([len(err), 2])\\n B = np.ones([len(err), 1])\\n # ERR = A*N + B\\n for i in range( len(N) ) :\\n A[i,0] = np.log10(N[i])\\n B[i] = np.log10(err[i])\\n\\n x, residuals, rank, singval = np.linalg.lstsq(A, B, rcond=None)\\n\\n return x\",\n \"def linear_regression(X, Y, Xs_test, Ys_test):\\n\\n X_n = (X - np.mean(X, axis = 0)) / np.std(X, axis = 0)\\n XL = np.concatenate((X_n, np.ones((len(X),1))), axis = 1)\\n w = np.linalg.solve(XL.T.dot(XL),XL.T.dot(Y))\\n mses = []\\n for i, X_test in enumerate(Xs_test):\\n Y_test = Ys_test[i]\\n XL_test = np.concatenate(((X_test - np.mean(X, axis = 0)) / np.std(X, axis = 0), \\n np.ones((len(X_test),1))), axis = 1)\\n Y_pred = XL_test.dot(w)\\n mse = np.mean(np.sqrt(np.sum((Y_pred - Y_test) ** 2, axis = 1))) \\n mses.append(mse) \\n return mses\",\n \"def test_loglike(dlm,Cl,noise,beam):\\n lmax = Cl.shape[0]\\n tt_exp = -1./2 * np.real(np.vdot(dlm.T,hp.almxfl(dlm,1/(beam[:lmax]**2*Cl[:,1]+noise[:lmax]))))\\n #plt.plot(Cl[:,1])\\n tt_det = - 1./2 *(np.arange(1,lmax+1)*np.log((noise[:lmax]+Cl[:,1]*beam[:lmax]**2))).sum() \\n tt_f = tt_exp + tt_det\\n return tt_exp,tt_det,tt_f#,Cl[:,1]\",\n \"def log_mae(labels, predictions):\\n\\n result = abs(labels - predictions)\\n result = reduce_mean(result)\\n result = log(result)\\n\\n return result\",\n \"def loglog_interp(x,xp,fp):\\n lx = np.log(x)\\n lxp = np.log(xp)\\n lfp = np.log(fp)\\n return np.exp(np.interp(lx,lxp,lfp))\",\n \"def log1p(x):\\n return Log1p().apply((x,))[0]\",\n \"def logpdf(self, X) -> np.ndarray:\\n if not isinstance(X, np.ndarray):\\n X = np.array(X)\\n H = 0.5 * ((X - self.means) / self.scales) ** 2\\n Z = sqrt(2 * pi) * self.scales\\n return -np.sum(H + np.log(Z), axis=1)\",\n \"def reg_logistic_regression(y, tx, l, initial_w, max_iters, gamma):\\r\\n y_resize = (1+y)/2 #rescales target so that -1 values are changed to 0 \\r\\n w_list = [initial_w]\\r\\n loss_list = []\\r\\n w = initial_w\\r\\n\\r\\n for n_iter in range(max_iters):\\r\\n grad = calculate_gradient_LR(y_resize, tx, w) + 2*l*w\\r\\n w = w - gamma*grad\\r\\n loss = compute_loss_LG(y_resize, tx, w)+ l*np.linalg.norm(w)\\r\\n w_list.append(w)\\r\\n loss_list.append(loss)\\r\\n if (n_iter > 1) and (np.abs(loss_list[-1] - loss_list[-2]) <= 1e-8):\\r\\n break\\r\\n return w_list[-1],loss_list[-1]\",\n \"def loglloop(store):\\n suml=0.0\\n for i in xrange(store['yvec'].shape[0]):\\n xbeta=dot(store['xmat'][i,:],store['beta'])\\n suml=suml+store['yvec'][i] * xbeta - exp(xbeta)\\n return suml\",\n \"def loglike(store):\\n nobs = store['yvec'].shape[0]\\n calcweighted(store)\\n store['regsampler'].update_yvec(store['yvectil'])\\n store['regsampler'].update_xmat(store['xmattil'])\\n return store['regsampler'].loglike(store['sigma'], store['beta'])\",\n \"def linear_reg(df, variables, test_size):\\n from sklearn.model_selection import train_test_split\\n from sklearn.linear_model import LinearRegression\\n\\n # Define input\\n X = encoding_df(df, variables)\\n\\n # Set validation\\n y = df['target']\\n\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)\\n\\n clf = LinearRegression().fit(X_train, y_train)\\n\\n print('Linear Regression RMSE',compute_rmse(y_test, clf.predict(X_test)))\\n return clf.predict(X_test), y_test\",\n \"def grad_reglog(w, X, y, **kwargs):\\n p = np.exp(-y * (np.dot(X, w)))\\n P = p / (1. + p)\\n return -1 * np.dot(X.T, P * y) / X.shape[0]\",\n \"def fit_linear_trend(series):\\n X = make_design_matrix(np.arange(len(series)) + 1)\\n linear_trend_ols = sm.OLS(series.values, X).fit()\\n linear_trend = linear_trend_ols.predict(X)\\n return linear_trend\",\n \"def __init__(self, log=True, normalize=False):\\r\\n self.model = LinearRegression(normalize=normalize)\\r\\n self.log = log\",\n \"def train_logistic_regression(X_train_input, y_train_input, C=1):\\r\\n from sklearn.linear_model import LogisticRegression\\r\\n logr_clf = LogisticRegression(C=C)\\r\\n logr_clf.fit(X_train_input, y_train_input)\\r\\n return logr_clf\",\n \"def predict_log_proba(self, X):\\n proba = self.predict_proba(X)\\n return np.log(proba)\",\n \"def log_sum_exp(x):\\n # TF ordering\\n axis = len(x.shape) - 1\\n m = paddle.max(x, axis=axis)\\n m2 = paddle.max(x, axis=axis, keepdim=True)\\n return m + paddle.log(paddle.sum(paddle.exp(x - m2), axis=axis))\",\n \"def log_loss(actual, predicted):\\n\\n predicted, actual = np.array(predicted), np.array(actual)\\n\\n small_value = 1e-15\\n predicted[predicted < small_value] = small_value\\n predicted[predicted > 1 - small_value] = 1. - small_value\\n return (-1. / len(actual)) * np.sum(\\n actual * np.log(predicted) + (1. - actual) * np.log(1. - predicted))\",\n \"def grad_log(self, X):\\n # \\\"\\\"\\\"\\n # Evaluate the gradients (with respect to the input) of the log density at\\n # each of the n points in X. This is the score function.\\n\\n # X: n x d numpy array.\\n XB = np.dot(X, self.B)\\n Y = 0.5*XB + self.c\\n E2y = np.exp(2*Y)\\n # n x dh\\n Phi = old_div((E2y-1.0),(E2y+1))\\n # n x dx\\n T = np.dot(Phi, 0.5*self.B.T)\\n S = self.b - X + T\\n return S\",\n \"def RMSLE(prediction, real):\\n logarithmic_error = np.log1p(prediction) - np.log1p(real)\\n score = np.sqrt(1/len(real) *np.sum(logarithmic_error**2))\\n return score\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6908991","0.67901516","0.6748823","0.6555421","0.6537084","0.65136236","0.6442761","0.64340353","0.6420213","0.64125615","0.63817775","0.6373047","0.63200843","0.6316748","0.62856615","0.6277802","0.6261285","0.6257053","0.62509704","0.62499064","0.6237254","0.6232735","0.6228399","0.62219334","0.6204598","0.62020284","0.6192544","0.6182285","0.61745405","0.6108861","0.6104075","0.61036974","0.6103115","0.60929984","0.60883003","0.60871","0.60818845","0.6073839","0.6067304","0.60605645","0.6056603","0.60565585","0.6052796","0.6049626","0.6042581","0.6031839","0.60250384","0.60207456","0.59783113","0.5976536","0.59762114","0.59748316","0.59737843","0.59709054","0.5961232","0.59603316","0.5954177","0.59465384","0.5942815","0.5918251","0.5916212","0.59090024","0.5905431","0.5904437","0.5904333","0.5902128","0.5897275","0.5890207","0.5885337","0.5879148","0.5875484","0.58751553","0.5874314","0.5866364","0.5866063","0.5865542","0.58574146","0.58551884","0.58550507","0.5854721","0.5849959","0.5848697","0.5847868","0.5842766","0.5841173","0.5838312","0.58374184","0.5826036","0.5825406","0.58247215","0.5823488","0.5823482","0.58199394","0.5818524","0.58180237","0.5815033","0.5805862","0.5805433","0.5802154","0.57968694"],"string":"[\n \"0.6908991\",\n \"0.67901516\",\n \"0.6748823\",\n \"0.6555421\",\n \"0.6537084\",\n \"0.65136236\",\n \"0.6442761\",\n \"0.64340353\",\n \"0.6420213\",\n \"0.64125615\",\n \"0.63817775\",\n \"0.6373047\",\n \"0.63200843\",\n \"0.6316748\",\n \"0.62856615\",\n \"0.6277802\",\n \"0.6261285\",\n \"0.6257053\",\n \"0.62509704\",\n \"0.62499064\",\n \"0.6237254\",\n \"0.6232735\",\n \"0.6228399\",\n \"0.62219334\",\n \"0.6204598\",\n \"0.62020284\",\n \"0.6192544\",\n \"0.6182285\",\n \"0.61745405\",\n \"0.6108861\",\n \"0.6104075\",\n \"0.61036974\",\n \"0.6103115\",\n \"0.60929984\",\n \"0.60883003\",\n \"0.60871\",\n \"0.60818845\",\n \"0.6073839\",\n \"0.6067304\",\n \"0.60605645\",\n \"0.6056603\",\n \"0.60565585\",\n \"0.6052796\",\n \"0.6049626\",\n \"0.6042581\",\n \"0.6031839\",\n \"0.60250384\",\n \"0.60207456\",\n \"0.59783113\",\n \"0.5976536\",\n \"0.59762114\",\n \"0.59748316\",\n \"0.59737843\",\n \"0.59709054\",\n \"0.5961232\",\n \"0.59603316\",\n \"0.5954177\",\n \"0.59465384\",\n \"0.5942815\",\n \"0.5918251\",\n \"0.5916212\",\n \"0.59090024\",\n \"0.5905431\",\n \"0.5904437\",\n \"0.5904333\",\n \"0.5902128\",\n \"0.5897275\",\n \"0.5890207\",\n \"0.5885337\",\n \"0.5879148\",\n \"0.5875484\",\n \"0.58751553\",\n \"0.5874314\",\n \"0.5866364\",\n \"0.5866063\",\n \"0.5865542\",\n \"0.58574146\",\n \"0.58551884\",\n \"0.58550507\",\n \"0.5854721\",\n \"0.5849959\",\n \"0.5848697\",\n \"0.5847868\",\n \"0.5842766\",\n \"0.5841173\",\n \"0.5838312\",\n \"0.58374184\",\n \"0.5826036\",\n \"0.5825406\",\n \"0.58247215\",\n \"0.5823488\",\n \"0.5823482\",\n \"0.58199394\",\n \"0.5818524\",\n \"0.58180237\",\n \"0.5815033\",\n \"0.5805862\",\n \"0.5805433\",\n \"0.5802154\",\n \"0.57968694\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6528573"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":89398,"cells":{"query":{"kind":"string","value":"Find first r value s.t. the linear and turing estimates of r are not significantly different."},"document":{"kind":"string","value":"def _find_cutoff(self):\n cutoff = 1\n while ((self.linear_rstar_unnorm(cutoff) -\n self.turing_rstar_unnorm(cutoff))**2\n > self.approx_turing_variance(cutoff)):\n cutoff += 1\n return cutoff"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def find_rs(v, j):\r\n v_s, j_s = np.sort([v, j], axis=1)\r\n m = np.polyfit(v_s[-10:], j_s[-10:], 1)\r\n return 1/abs(m[0]) * 1000 #[Ohm cm^2]\r","def b_vs_r(r, t=.97):\n min_b = (np.log(1-t))/(np.log(1-.95**r))\n return min_b","def __find_r_corr_in_points(arr):\n n = len(arr)\n th = arr[n // 2] * exp(-1.0)\n for i in range(n // 2, n, 1):\n if arr[i] < th:\n return i - n // 2","def r(self, t):\n from scipy.optimize import fsolve\n t = YEAR_TO_SEC * t\n t_coll = fsolve(lambda x: self.r_free(x) - self.snr.r_reverse(x), 4e3)\n # 4e3 years is a typical value that works for fsolve\n # Radius at time of collision\n r_coll = self.r_free(t_coll)\n return np.where(t < t_coll, self.r_free(t), r_coll)","def get_S_r(self):\n\n S_r = np.sum((self.eta_model - self.eta_exp) ** 2.)\n\n return S_r","def calculateR(sapienses: list) -> float:\n r = 0\n for i in sapienses:\n r = r + i.numberInfected\n r=r/I0\n r = r*S/(S+R+D)\n return r","def find_dark_object_value(arr):\n preval = None\n step = arr.max() #/ 255.0\n for val in np.unique(arr)[:100]:\n if val == 0:\n continue\n if preval is not None and (val - preval) < step:\n break\n else:\n preval = val\n return preval","def first_value(self):\n return 0","def get_min_rm(distance_file, rm_range, step_size, potential_function):\n return min((i[1],i[0]) for i in vary_across_rm(distance_file, rm_range, step_size, potential_function))[1]","def find_local_minima(r, g_r, r_guess):\n\n all_minima = find_all_minima(g_r)\n nearest_minima, _ = find_nearest(r[all_minima], r_guess)\n return r[all_minima[nearest_minima]], g_r[all_minima[nearest_minima]]","def find_rsh(v, j):\r\n\r\n zp = sp.where(v[:-1] * v[1:] <= 0)[0][0] #make a list of A[x] * A[x -1] without usinf \"for\" loop in original python.\r\n m = np.polyfit(v[(zp - 5):(zp + 5)], j[(zp -5):(zp + 5)], 1)\r\n return 1/abs(m[0]) * 1000 #[Ohm cm^2]\r","def r(self, position: Vector) -> float:\n return self.local_coordinates(position)[1]","def wR(r, rc):\n nr = norm_numba(r)\n return (1 - nr / rc) if nr / rc < 1.0 else 0.0","def rvs(self):\n return float(self.interp(random.rand()))","def __R1(x: float) -> np.float64:\n if np.abs(x) < end:\n return np.float64((1 / np.pi) * np.sqrt(2 * N - x * x))\n return np.float64(0.0)","def r0(self):\n return self.p[0] / self.p[1]","def find_single_fit_range(lorentz_params):\n f0 = lorentz_params[1]\n FWHM = lorentz_params[2]\n return (f0 - 4 * FWHM, f0 - 2 * FWHM, f0 + 2 * FWHM, f0 + 4 * FWHM)","def Ni_find(t):\r\n return ep(t) - 1","def beta_r(r):\n return 0.","def get_R(R_e,R_h):\n R = R_e + R_h\n return R","def get_n1(r,N):\n n1 = N - np.sum(r)\n return n1","def _calc_r2(self):\n sse = np.sum((self.data.y - self.predict(self.data.x))**2)\n sst = np.sum((self.data.y - self.data.y.mean())**2)\n return (1. - sse/sst)","def get_R(self):\n return self.R_min * tf.exp(self.R_ * self.log_R_range)","def start_exact(self, hL, hR, cL, cR, uL, uR):\n dMin = min(hL, hR)\n # Use Two-Rarefaction (TRRS) solution as starting value\n hS = (1/9.81)*(0.5*(cL + cR) - 0.25 * ( uR - uL ) )**2\n if hS > dMin:\n # Use two-shock (TSRS) solution as starting value\n # with DS as computed from TRRS as estimate\n gel = math.sqrt(0.5*9.81 * (hS + hL)/(hS * hL))\n ger = math.sqrt(0.5*9.81 * (hS + hR)/(hS * hR))\n return (gel*hL+ger*hR-(uR-uL))/(gel+ger);\n return hS","def r_near(self):\n # type: () -> None\n n = len(self.nodes)\n return min(self._eta, np.sqrt(self.gamma/np.pi * np.log(n)/n))","def beta_r(r):\n return 1.","def test_find_most_rational_surfaces():\n # simple test, linear iota going from 1 to 3\n iota = PowerSeriesProfile([1, 2])\n rho, io = find_most_rational_surfaces(iota, 5)\n np.testing.assert_allclose(rho, np.linspace(0, 1, 5), atol=1e-14, rtol=0)\n np.testing.assert_allclose(io, np.linspace(1, 3, 5), atol=1e-14, rtol=0)","def RScore(x,p,d):\n \n if x <= d[p][0.20]:\n return 5\n elif x <= d[p][0.4]:\n return 4\n elif x <= d[p][0.6]: \n return 3\n elif x <= d[p][0.8]:\n return 2\n else:\n return 1","def RScore(x,p,d):\n \n if x <= d[p][0.20]:\n return 5\n elif x <= d[p][0.4]:\n return 4\n elif x <= d[p][0.6]: \n return 3\n elif x <= d[p][0.8]:\n return 2\n else:\n return 1","def find_zero(self, t, y):\n return y[0]","def getF_and_R(self):\n R = self.getR()\n # Treat the laminar case: f = 64/R\n if R < 2000: # R < 2000 implies flow is laminar\n if R > 0:\n f = 64/R # Darcy's equation for friction factor\n return {\"R\": R, \"f\": f}\n elif R == 0:\n return {\"R\": R, \"f\": 0}\n\n # Treat the Turbulent case: use Colebrook's equation.\n elif R > 4000: # R > 4000 implies flow is turbulent.\n # store the final value of \"f\" after few iterations.\n f_brooks = self.compute_f_ColeBrook(R, self.e, self.D)\n f_chen = self.compute_f_CHEN(R, self.e, self.D)\n f_barr = self.compute_f_BARR(R, self.e, self.D)\n return {\"R\": R, \"f\": f_brooks, \"f_barr\": f_barr, \"f_chen\": f_chen}\n # Treat the Critical case: interpolate between laminar and turbulent\n # boundaries.\n else: # R is between 2000 and 4000 implies flow is critical.\n # interpolate between the two reynolds number boundaries\n # that is, the laminar and turbulent.\n R_upper_limit, R_middle, R_lower_limit = 4000, R, 2000\n f = 0.025\n f_upper_limit = self.compute_f_ColeBrook(R_upper_limit, self.e, self.D)\n f_lower_limit = 64/R_lower_limit\n f_middle = self.interpolate([f_lower_limit, f_upper_limit],\n [R_lower_limit, R_upper_limit], R_middle)\n \n return {\"R\": R, \"f\": f_middle}","def do_dr(r, t):\n return 1/t**0.5","def eeg_findnearest(x,X):\t\n\t#x array or vector and X a scalar\n\tabsdif = np.abs(x-X)\n\tval = np.min(absdif)\n\tidx = absdif.argmin()\n\treturn val,idx","def rsr(self) -> float:\n return float(self.rmse() / np.std(self.true))","def find_square_kolo(r):\n s = math.pi * r **2\n return s","def test_get_r():\n\n assert get_r(100, 143, 255) == 100\n assert get_r(100, 143, 255) != 143\n assert get_r(100, 143, 255) != 255","def getR(self):\n # Reynolds number uses the absolute value of the velocity\n V = abs(self.V)\n return (V * self.D) / self.v # formula for Reynolds number","def first(self):\n return self._reduce_for_stat_function(F.first, only_numeric=False)","def test_r():\n y, x = np.indices((10,20))\n\n default_use_numexpr = accel_math._USE_NUMEXPR\n\n accel_math._USE_NUMEXPR = True\n r1 = accel_math._r(x,y)\n\n accel_math._USE_NUMEXPR = False\n r2 = accel_math._r(x,y)\n\n np.testing.assert_almost_equal(r1,r2)\n\n accel_math._USE_NUMEXPR = default_use_numexpr","def findFirstTrueValue( data ):\n # for long sound with a lot of silence and noise, it's faster to recode it having a return well placed. (8sec => 0.052sec)\n n = len(data);\n i = 0; \n while( i < n ):\n if( data[i] ):\n return i\n i += 1\n return -1","def t(o, r):\n return (r/o)**2","def search_alpha_min(self):\n alpha_min = 0\n\n for alpha in range(0, 180, 4):\n r = self.solver(alpha)[0]\n if r[-1] > 1.1*self.Rs:\n break\n\n if (alpha-4) > 0:\n alpha_min = alpha - 4\n # print(\"alpha_min :\",alpha_min,\"(-4)\")\n i = 1\n\n while alpha_min == 0 or round(alpha_min*self.img_res) != round((alpha_min+i*10)*self.img_res): #increase precision\n\n for alpha in range(int(alpha_min/i), int(180/i), 1):\n alpha = alpha*i\n r = self.solver(alpha)[0]\n\n if r[-1] > 1.1*self.Rs:\n break\n\n if (alpha-i) > 0:\n alpha_min = alpha - i\n # print(\"alpha_min : \",alpha_min,\" (-\",i,\")\",sep=\"\")\n\n i = i/10\n i = 10*i\n alpha_min += i\n print(\"alpha_min: %s [%s, %s]\" % (alpha_min, alpha_min-i, alpha_min))\n\n return alpha_min","def compute_CRR(self, FAR):\r\n print('Computing CRR')\r\n return (np.ones((1, len(FAR))) - FAR)[0]","def minimum_spanning_arborescence(sol):","def RNull(R):\n return -S*R*(r1*(K1**B1/(K1**B1 + (A/R)**B1)) - r2*(K2**B2/(K2**B2 + (A)**B2)) \\\n *(R*M)/(K3 + R*M))/((-gwt*A + R*gc)*(R+1)) + S/(R+1)","def lowest_rank_approx(A,e):","def find_intensity_at_half_radius_sersic(n=1,\n r_e=10,\n l_total=5e10):\n if n != 1:\n raise ValueError('values other than n = 1 are not acceptable yet')\n l_e = (1.678 ** 2) * l_total / (2 * (r_e ** 2) * (np.exp(1.678)) * np.pi)\n return l_e","def anisotropy_solution(r):\n return r**2","def _choose_position(self):\n \n \n # function used to find R given M\n def _root_function(r, func, uval, m_tot):\n \n return uval * m_tot - func(r)\n \n # optimization switches\n if self.optimize:\n mass_func = self._interpolate_cumulative_mass\n \n umin = self._mass_umin\n umax = self._mass_umax\n else:\n # use exact profile\n mass_func = self.DF.dprof.cumulative_mass\n \n umin = mass_func(self.DF.dprof.small_r) / self.DF.dprof.M_sys\n umax = mass_func(self.DF.dprof.large_r) / self.DF.dprof.M_sys\n \n \n failed = True\n # root finder may fail occasionally if r is too close to zero\n # keep drawing random number until it works.\n # alternate soln would be to draw from M(small_r)/M_tot to\n # M(large_r) / M_tot instead of 0 to 1... \n #while failed:\n i = 0\n while (failed and i < 100):\n \n try:\n u = np.random.rand()*(umax - umin) + umin\n\n r = opt.brentq(_root_function, self.DF.dprof.small_r, self.DF.dprof.large_r, \n args=(mass_func ,u, self.DF.dprof.M_sys,))\n failed = False\n \n except:\n failed = True\n _my_print('Root finder for position failing for the %004i time. Re-rolling.'%(i))\n \n \n i = i + 1\n # except: \n # failed = True\n\n \n \n return r","def _get_lip_best(self) -> float:\n pass","def get_rr(pred, y_truth):\n rr = 0.\n for i in range(len(y_truth)):\n for value in y_truth[i]:\n hit_indexes = np.where(pred[i] == value)[0]\n for hit_index in hit_indexes:\n rr += 1 / (hit_index + 1)\n return rr","def get_first(self):\n return self.A[1][0] if self.n > 0 else None","def rvs(self):\n rvsValue = self.ppf(random())\n return rvsValue","def rvs(self):\n rvsValue = self.ppf(random())\n return rvsValue","def rvs(self):\n rvsValue = self.ppf(random())\n return rvsValue","def best_step(self):\r\n return self._best_value_step","def _get_R(self, net_r_amp):\n return np.abs(net_r_amp)**2","def find_local_maxima(r, g_r, r_guess):\n\n all_maxima = find_all_maxima(g_r)\n nearest_maxima, _ = find_nearest(r[all_maxima], r_guess)\n return r[all_maxima[nearest_maxima]], g_r[all_maxima[nearest_maxima]]","def s2profile(r,r0,A,B):\n x = r/r0\n res = A*4./(np.exp(x)+np.exp(-x))**2 + B\n return res","def compute_cost(self, r):\n self.r_max = 1\n return np.exp(1 / (np.power(r, 2) - np.power(self.r_max, 2))) if r < self.r_max else 0","def fR(a, b, c, N, S, I, R, vital=False, vaccine=False, combined=False):\n\n if vital:\n temp = b*I - c*R - d*R\n elif vaccine:\n R = N - S - I\n temp = b*I - c*R + f*S\n elif combined:\n temp = b*I - c*R - d*R + f*S\n else:\n temp = 0\n\n return temp","def get_rzero(self):\n return self.get_resistance() * math.pow((self.ATMOCO2/self.PARA), (1./self.PARB))","def get_rzero(self):\n return self.get_resistance() * math.pow((self.ATMOCO2/self.PARA), (1./self.PARB))","def attraction_h(next_r, a):\n if a == 0:\n if next_r == 9:\n return 0.8\n elif next_r == 14:\n return 0.1\n else:\n return 0.1\n\n elif a == 1:\n if next_r == 9:\n return 0.1\n elif next_r == 14:\n return 0.1\n else:\n return 0.8\n\n elif a == 2:\n if next_r == 9:\n return 0.1\n elif next_r == 14:\n return 0.3\n else:\n return 0.6\n\n elif a == 3:\n if next_r == 9:\n return 0.1\n elif next_r == 14:\n return 0.2\n else:\n return 0.7\n\n else:\n if next_r == 9:\n return 0.1\n elif next_r == 14:\n return 0.4\n else:\n return 0.5","def get_reward(state, resolution, grid_x, grid_y):\n a,b = single_index_to_index(state, resolution)\n position = index_to_obs(a, b, grid_x, grid_y )[0]\n if position >= 0.5:\n return 0\n return -1","def paf_o_r(rr_o_r, alpha):\n return ((rr_o_r - 1) * (1 - alpha)) / ((rr_o_r - 1) * (1 - alpha) + 1)","def stirling(k, r) :\n\n return sum((-1)**(r-i)*binomial(r, i)*i**k for i in range(r+1)) / math.factorial(r)","def snr(v_est, v_true):\n assert(v_est.shape == v_true.shape)\n\n return 10 * np.log10(norm2(v_true) ** 2 / norm2(v_est - v_true) ** 2)","def solve_R(R, b):\n n = b.size\n assert R.shape == (n,n)\n x = zeros(n, dtype=R.dtype)\n for i in range(n-1,-1,-1):\n x[i] = (b[i] - dot(x[i+1:], R[i,i+1:])) / R[i,i]\n if not numpy.isfinite(x[i]):\n x[i] = 0.0\n return x","def findval(self,val,x0,method='fmin',**kwargs):\n kwargs['valtofind'] = val\n return self._optimize(x0,'val',method,**kwargs)","def estimate(values, target):\n\n # next time\n # diff(values)\n\n\n return 1.","def tirageR1(self):\n\n proba = np.zeros(shape=(3))\n proba[0] = self.__alpha0 + self.__beta + self.__eta/2.\n proba[1] = self.__alpha1 + self.__beta + self.__eta/2.\n proba[2] = 1. - (proba[0]+proba[1])\n # typeSample = np.random.choice(a=['0.', '1.', 'F'], size=1, p=proba)[0]\n typeSample = random.choices(population=['0.', '1.', 'F'], weights=proba)[0]\n if typeSample != 'F':\n r1 = float(typeSample)\n else:\n r1 = self.__rv_parab.rvs()\n\n return r1","def r(self) -> float:\n return self._ohms.real","def first_point(self) -> np.ndarray:\n return self.evaluate_at(arc_lengths=0)","def r_precision(r):\n r = np.asarray(r) != 0\n z = r.nonzero()[0]\n if not z.size:\n return 0.\n return np.mean(r[:z[-1] + 1])","def find_f_equals_1():\n f1 = scipy.optimize.brentq(f2, 0, -10)\n return f1","def findzero(x,y):\n\t\targs = np.where(y > 0)[0]\n\t\tif len(args) == 0:\n\t\t\tprint \"Unable to find zero! Using maximum T.\"\n\t\t\ti = len(y) - 1\n\t\telse:\n\t\t\ti = min(args)\n\n\t\t#Do a fit using the surrounding points, and perform interpolation based on that\n\t\tp = np.polyfit(x[i-1:i+1],y[i-1:i+1],1)\n\t\tyfit = p[0]*x[i-1:i+1] + p[1]\n\t\tf = interp.interp1d(yfit, x[i-1:i+1])\n\t\treturn float(f(0))","def simple_interest(p,r,t):\n \n try:\n p, r, t = float(p), float(r), float(t)\n i = (p*r*t)/100\n A = p + i\n A = round(A, 2)\n return i, A\n except Exception as e:\n return e","def _interpolate_relative_potential(self, r):\n \n # interpolate\n #spline = interpolate.UnivariateSpline(self._relative_potential_r,\n # self._relative_potential_psi, k = 1)\n \n # linear interpolation is more reliable assuming number of points\n # is large enough\n spline = interpolate.interp1d(self._relative_potential_r, self._relative_potential_psi)\n \n return 10.0**spline(np.log10(r))","def mrr(ground_truth, prediction):\n rr = 0.\n for rank, item in enumerate(prediction):\n if item in ground_truth:\n rr = 1. / (rank + 1)\n break\n return rr","def fun_lorentzian(p,r):\n return p[1] / ((r/p[0])**2 + 1)","def get_prediction(self, x):\n \"*** YOUR CODE HERE ***\"\n if (nn.as_scalar(self.run(x)) >= 0.0):\n return 1\n return -1","def nearest_in_n_sphere(self, value, r):\n return self.nearest_in_bounding_box(value, r)\n \n # This seems right\n # return self.binary_search_find_nearest_neighbors_in_radius(value, r)\n \n # This seems wrong\n # return self.recur_find_nearest_n_neighbor(value, r)","def best_value(self):\r\n return self._best_value","def exactsolution(x, t, u):\n if 0 <= (x - u*t) and (x - u*t) <= 0.2:\n temp = 1 - (10 * (x - u*t) -1)**2\n else:\n temp = 0\n return temp","def s(self, r, mu):\n\n assert(mu <= 1)\n\n if r < self.R:\n return r * mu + self.R * self.g(r, mu)\n else:\n if self.mu_star(r) <= mu:\n return 2. * self.R * self.g(r, mu)\n else:\n return 0","def get_r_score(self):\n return self.r_score","def r_d(self, tl):\n\t return self.RD0*exp(self.HKR/(R*self.TO)*(1. - self.TO/tl))","def run():\n return estimate(0,1,0)","def calc_R2(Fasit, Model):\n # Calculating\n R2 = 1 - sum(np.power(Fasit - Model, 2)) / sum(np.power(Fasit - np.mean(Fasit), 2))\n return R2","def optimiseOne(paramOption, row, extractdata, start, stop, step):\n stockname = row[0]\n exclAttribs = row[1]\n bestNu = row[2]\n\n tryParam = start\n bestParam = tryParam\n (trialError, trialCorrelation, trailAttrCsv) = common.lsCalcModel(\n stockname, exclAttribs, common.cv, extractdata, bestNu, \"{0} {1}\".format(paramOption, tryParam))\n\n print \"Starting correlation={0}, with param={1}\".format(trialCorrelation, bestParam)\n bestCorrelation = trialCorrelation\n\n tryParam = start + step\n while tryParam < stop:\n print \"Trial {0}={1}\".format(paramOption, tryParam)\n (trialError, trialCorrelation, trailAttrCsv) = common.lsCalcModel(\n stockname, exclAttribs, common.cv, extractdata, tryParam)\n print \"Result: correlation={0} for trial Nu={1} with current bestCorrelation={2}\".\\\n format(trialCorrelation, tryParam, bestCorrelation)\n if trialCorrelation >= bestCorrelation:\n bestCorrelation = trialCorrelation\n bestParam = tryParam\n print \"Found better or same correlation=\" + str(bestCorrelation) + \" with Nu={0}\".format(bestParam)\n else:\n print \"Not better. Best parameter value = {0} with correlation={1}\".format(bestParam, bestCorrelation)\n break\n\n tryParam = tryParam + step\n\n return (bestParam, bestCorrelation)","def r2(self) -> float:\n zx = (self.true - np.mean(self.true)) / np.std(self.true, ddof=1)\n zy = (self.predicted - np.mean(self.predicted)) / np.std(self.predicted, ddof=1)\n r = np.sum(zx * zy) / (len(self.true) - 1)\n return float(r ** 2)","def get_best_vector(results, f_x, target):\n index_min = -1\n cur_min = 10**10\n\n with Session() as sess:\n for index, individual in enumerate(results):\n f_x_res = sess.run(f_x, feed_dict={\n target: individual\n })\n if f_x_res < cur_min:\n cur_min = f_x_res\n index_min = index\n\n best = results[index_min]\n\n return best","def calculate_1rm_epley(w: float, r: int):\n\n if r == 1:\n return w\n if r == 0:\n return 0\n\n if w == 0:\n w = 1\n\n return round(w*(1 + r/30), 2)","def rmin(self) -> float:\n return self.options.rmin","def getFirstVallay(hist):\n hist = smooth(hist)\n safe_range = (len(hist)//100, len(hist)//4)\n start = max(int(round(np.argmax(hist[:int(len(hist)/4)]))), 10) # Start searching from maximum peak\n start = min(start, safe_range[1]) # start point should be smaller than 50 pixel\n start = max(safe_range[0], start) # start point should be bigger than 20\n limit = min(int(start * 1.5), 100) # Set limit of the first valley as 150% of maximum peak location\n max_val = 0\n for i in range(start, limit):\n if hist[i] > max_val:\n max_val = hist[i]\n if hist[i] <= max_val*0.5:\n return i\n return limit","def test_find_least_rational_surfaces():\n # simple test, linear iota going from 1 to 3\n iota = PowerSeriesProfile([1, 2])\n rhor, ior = find_most_rational_surfaces(iota, 10)\n rho, io = find_least_rational_surfaces(iota, 10)\n # to compare how rational/irrational things are, we use the length of the\n # continued fraction. Not a great test due to rounding errors, but seems to work\n lio = [len(dec_to_cf(i)) for i in io]\n lior = [len(dec_to_cf(i)) for i in ior]\n max_rational = max(lior)\n\n assert np.all(np.array(lio) > max_rational)","def residual(S):\n rho = seawater.density(T, S, Pa)\n return (rho_1 - rho)","def pseudo_r2(self):\n y_reg = self.time_series(len(self.data))\n SSres = ((self.data - y_reg)**2).sum()\n SStot = ((self.data - self.data.mean())**2).sum()\n return 1 - SSres/SStot","def FindClosestPointWithinRadius(self, p_float, , p_float_4):\n ...","def scoreRsrc( self, rr ):\r\n result = 0.0\r\n for tt in self.getSched( )[rr.getid( )]:\r\n for se in tt:\r\n result += 1\r\n print( \"INFO: Value for %s: %s \" % ( rr, result ) )\r\n return( result )"],"string":"[\n \"def find_rs(v, j):\\r\\n v_s, j_s = np.sort([v, j], axis=1)\\r\\n m = np.polyfit(v_s[-10:], j_s[-10:], 1)\\r\\n return 1/abs(m[0]) * 1000 #[Ohm cm^2]\\r\",\n \"def b_vs_r(r, t=.97):\\n min_b = (np.log(1-t))/(np.log(1-.95**r))\\n return min_b\",\n \"def __find_r_corr_in_points(arr):\\n n = len(arr)\\n th = arr[n // 2] * exp(-1.0)\\n for i in range(n // 2, n, 1):\\n if arr[i] < th:\\n return i - n // 2\",\n \"def r(self, t):\\n from scipy.optimize import fsolve\\n t = YEAR_TO_SEC * t\\n t_coll = fsolve(lambda x: self.r_free(x) - self.snr.r_reverse(x), 4e3)\\n # 4e3 years is a typical value that works for fsolve\\n # Radius at time of collision\\n r_coll = self.r_free(t_coll)\\n return np.where(t < t_coll, self.r_free(t), r_coll)\",\n \"def get_S_r(self):\\n\\n S_r = np.sum((self.eta_model - self.eta_exp) ** 2.)\\n\\n return S_r\",\n \"def calculateR(sapienses: list) -> float:\\n r = 0\\n for i in sapienses:\\n r = r + i.numberInfected\\n r=r/I0\\n r = r*S/(S+R+D)\\n return r\",\n \"def find_dark_object_value(arr):\\n preval = None\\n step = arr.max() #/ 255.0\\n for val in np.unique(arr)[:100]:\\n if val == 0:\\n continue\\n if preval is not None and (val - preval) < step:\\n break\\n else:\\n preval = val\\n return preval\",\n \"def first_value(self):\\n return 0\",\n \"def get_min_rm(distance_file, rm_range, step_size, potential_function):\\n return min((i[1],i[0]) for i in vary_across_rm(distance_file, rm_range, step_size, potential_function))[1]\",\n \"def find_local_minima(r, g_r, r_guess):\\n\\n all_minima = find_all_minima(g_r)\\n nearest_minima, _ = find_nearest(r[all_minima], r_guess)\\n return r[all_minima[nearest_minima]], g_r[all_minima[nearest_minima]]\",\n \"def find_rsh(v, j):\\r\\n\\r\\n zp = sp.where(v[:-1] * v[1:] <= 0)[0][0] #make a list of A[x] * A[x -1] without usinf \\\"for\\\" loop in original python.\\r\\n m = np.polyfit(v[(zp - 5):(zp + 5)], j[(zp -5):(zp + 5)], 1)\\r\\n return 1/abs(m[0]) * 1000 #[Ohm cm^2]\\r\",\n \"def r(self, position: Vector) -> float:\\n return self.local_coordinates(position)[1]\",\n \"def wR(r, rc):\\n nr = norm_numba(r)\\n return (1 - nr / rc) if nr / rc < 1.0 else 0.0\",\n \"def rvs(self):\\n return float(self.interp(random.rand()))\",\n \"def __R1(x: float) -> np.float64:\\n if np.abs(x) < end:\\n return np.float64((1 / np.pi) * np.sqrt(2 * N - x * x))\\n return np.float64(0.0)\",\n \"def r0(self):\\n return self.p[0] / self.p[1]\",\n \"def find_single_fit_range(lorentz_params):\\n f0 = lorentz_params[1]\\n FWHM = lorentz_params[2]\\n return (f0 - 4 * FWHM, f0 - 2 * FWHM, f0 + 2 * FWHM, f0 + 4 * FWHM)\",\n \"def Ni_find(t):\\r\\n return ep(t) - 1\",\n \"def beta_r(r):\\n return 0.\",\n \"def get_R(R_e,R_h):\\n R = R_e + R_h\\n return R\",\n \"def get_n1(r,N):\\n n1 = N - np.sum(r)\\n return n1\",\n \"def _calc_r2(self):\\n sse = np.sum((self.data.y - self.predict(self.data.x))**2)\\n sst = np.sum((self.data.y - self.data.y.mean())**2)\\n return (1. - sse/sst)\",\n \"def get_R(self):\\n return self.R_min * tf.exp(self.R_ * self.log_R_range)\",\n \"def start_exact(self, hL, hR, cL, cR, uL, uR):\\n dMin = min(hL, hR)\\n # Use Two-Rarefaction (TRRS) solution as starting value\\n hS = (1/9.81)*(0.5*(cL + cR) - 0.25 * ( uR - uL ) )**2\\n if hS > dMin:\\n # Use two-shock (TSRS) solution as starting value\\n # with DS as computed from TRRS as estimate\\n gel = math.sqrt(0.5*9.81 * (hS + hL)/(hS * hL))\\n ger = math.sqrt(0.5*9.81 * (hS + hR)/(hS * hR))\\n return (gel*hL+ger*hR-(uR-uL))/(gel+ger);\\n return hS\",\n \"def r_near(self):\\n # type: () -> None\\n n = len(self.nodes)\\n return min(self._eta, np.sqrt(self.gamma/np.pi * np.log(n)/n))\",\n \"def beta_r(r):\\n return 1.\",\n \"def test_find_most_rational_surfaces():\\n # simple test, linear iota going from 1 to 3\\n iota = PowerSeriesProfile([1, 2])\\n rho, io = find_most_rational_surfaces(iota, 5)\\n np.testing.assert_allclose(rho, np.linspace(0, 1, 5), atol=1e-14, rtol=0)\\n np.testing.assert_allclose(io, np.linspace(1, 3, 5), atol=1e-14, rtol=0)\",\n \"def RScore(x,p,d):\\n \\n if x <= d[p][0.20]:\\n return 5\\n elif x <= d[p][0.4]:\\n return 4\\n elif x <= d[p][0.6]: \\n return 3\\n elif x <= d[p][0.8]:\\n return 2\\n else:\\n return 1\",\n \"def RScore(x,p,d):\\n \\n if x <= d[p][0.20]:\\n return 5\\n elif x <= d[p][0.4]:\\n return 4\\n elif x <= d[p][0.6]: \\n return 3\\n elif x <= d[p][0.8]:\\n return 2\\n else:\\n return 1\",\n \"def find_zero(self, t, y):\\n return y[0]\",\n \"def getF_and_R(self):\\n R = self.getR()\\n # Treat the laminar case: f = 64/R\\n if R < 2000: # R < 2000 implies flow is laminar\\n if R > 0:\\n f = 64/R # Darcy's equation for friction factor\\n return {\\\"R\\\": R, \\\"f\\\": f}\\n elif R == 0:\\n return {\\\"R\\\": R, \\\"f\\\": 0}\\n\\n # Treat the Turbulent case: use Colebrook's equation.\\n elif R > 4000: # R > 4000 implies flow is turbulent.\\n # store the final value of \\\"f\\\" after few iterations.\\n f_brooks = self.compute_f_ColeBrook(R, self.e, self.D)\\n f_chen = self.compute_f_CHEN(R, self.e, self.D)\\n f_barr = self.compute_f_BARR(R, self.e, self.D)\\n return {\\\"R\\\": R, \\\"f\\\": f_brooks, \\\"f_barr\\\": f_barr, \\\"f_chen\\\": f_chen}\\n # Treat the Critical case: interpolate between laminar and turbulent\\n # boundaries.\\n else: # R is between 2000 and 4000 implies flow is critical.\\n # interpolate between the two reynolds number boundaries\\n # that is, the laminar and turbulent.\\n R_upper_limit, R_middle, R_lower_limit = 4000, R, 2000\\n f = 0.025\\n f_upper_limit = self.compute_f_ColeBrook(R_upper_limit, self.e, self.D)\\n f_lower_limit = 64/R_lower_limit\\n f_middle = self.interpolate([f_lower_limit, f_upper_limit],\\n [R_lower_limit, R_upper_limit], R_middle)\\n \\n return {\\\"R\\\": R, \\\"f\\\": f_middle}\",\n \"def do_dr(r, t):\\n return 1/t**0.5\",\n \"def eeg_findnearest(x,X):\\t\\n\\t#x array or vector and X a scalar\\n\\tabsdif = np.abs(x-X)\\n\\tval = np.min(absdif)\\n\\tidx = absdif.argmin()\\n\\treturn val,idx\",\n \"def rsr(self) -> float:\\n return float(self.rmse() / np.std(self.true))\",\n \"def find_square_kolo(r):\\n s = math.pi * r **2\\n return s\",\n \"def test_get_r():\\n\\n assert get_r(100, 143, 255) == 100\\n assert get_r(100, 143, 255) != 143\\n assert get_r(100, 143, 255) != 255\",\n \"def getR(self):\\n # Reynolds number uses the absolute value of the velocity\\n V = abs(self.V)\\n return (V * self.D) / self.v # formula for Reynolds number\",\n \"def first(self):\\n return self._reduce_for_stat_function(F.first, only_numeric=False)\",\n \"def test_r():\\n y, x = np.indices((10,20))\\n\\n default_use_numexpr = accel_math._USE_NUMEXPR\\n\\n accel_math._USE_NUMEXPR = True\\n r1 = accel_math._r(x,y)\\n\\n accel_math._USE_NUMEXPR = False\\n r2 = accel_math._r(x,y)\\n\\n np.testing.assert_almost_equal(r1,r2)\\n\\n accel_math._USE_NUMEXPR = default_use_numexpr\",\n \"def findFirstTrueValue( data ):\\n # for long sound with a lot of silence and noise, it's faster to recode it having a return well placed. (8sec => 0.052sec)\\n n = len(data);\\n i = 0; \\n while( i < n ):\\n if( data[i] ):\\n return i\\n i += 1\\n return -1\",\n \"def t(o, r):\\n return (r/o)**2\",\n \"def search_alpha_min(self):\\n alpha_min = 0\\n\\n for alpha in range(0, 180, 4):\\n r = self.solver(alpha)[0]\\n if r[-1] > 1.1*self.Rs:\\n break\\n\\n if (alpha-4) > 0:\\n alpha_min = alpha - 4\\n # print(\\\"alpha_min :\\\",alpha_min,\\\"(-4)\\\")\\n i = 1\\n\\n while alpha_min == 0 or round(alpha_min*self.img_res) != round((alpha_min+i*10)*self.img_res): #increase precision\\n\\n for alpha in range(int(alpha_min/i), int(180/i), 1):\\n alpha = alpha*i\\n r = self.solver(alpha)[0]\\n\\n if r[-1] > 1.1*self.Rs:\\n break\\n\\n if (alpha-i) > 0:\\n alpha_min = alpha - i\\n # print(\\\"alpha_min : \\\",alpha_min,\\\" (-\\\",i,\\\")\\\",sep=\\\"\\\")\\n\\n i = i/10\\n i = 10*i\\n alpha_min += i\\n print(\\\"alpha_min: %s [%s, %s]\\\" % (alpha_min, alpha_min-i, alpha_min))\\n\\n return alpha_min\",\n \"def compute_CRR(self, FAR):\\r\\n print('Computing CRR')\\r\\n return (np.ones((1, len(FAR))) - FAR)[0]\",\n \"def minimum_spanning_arborescence(sol):\",\n \"def RNull(R):\\n return -S*R*(r1*(K1**B1/(K1**B1 + (A/R)**B1)) - r2*(K2**B2/(K2**B2 + (A)**B2)) \\\\\\n *(R*M)/(K3 + R*M))/((-gwt*A + R*gc)*(R+1)) + S/(R+1)\",\n \"def lowest_rank_approx(A,e):\",\n \"def find_intensity_at_half_radius_sersic(n=1,\\n r_e=10,\\n l_total=5e10):\\n if n != 1:\\n raise ValueError('values other than n = 1 are not acceptable yet')\\n l_e = (1.678 ** 2) * l_total / (2 * (r_e ** 2) * (np.exp(1.678)) * np.pi)\\n return l_e\",\n \"def anisotropy_solution(r):\\n return r**2\",\n \"def _choose_position(self):\\n \\n \\n # function used to find R given M\\n def _root_function(r, func, uval, m_tot):\\n \\n return uval * m_tot - func(r)\\n \\n # optimization switches\\n if self.optimize:\\n mass_func = self._interpolate_cumulative_mass\\n \\n umin = self._mass_umin\\n umax = self._mass_umax\\n else:\\n # use exact profile\\n mass_func = self.DF.dprof.cumulative_mass\\n \\n umin = mass_func(self.DF.dprof.small_r) / self.DF.dprof.M_sys\\n umax = mass_func(self.DF.dprof.large_r) / self.DF.dprof.M_sys\\n \\n \\n failed = True\\n # root finder may fail occasionally if r is too close to zero\\n # keep drawing random number until it works.\\n # alternate soln would be to draw from M(small_r)/M_tot to\\n # M(large_r) / M_tot instead of 0 to 1... \\n #while failed:\\n i = 0\\n while (failed and i < 100):\\n \\n try:\\n u = np.random.rand()*(umax - umin) + umin\\n\\n r = opt.brentq(_root_function, self.DF.dprof.small_r, self.DF.dprof.large_r, \\n args=(mass_func ,u, self.DF.dprof.M_sys,))\\n failed = False\\n \\n except:\\n failed = True\\n _my_print('Root finder for position failing for the %004i time. Re-rolling.'%(i))\\n \\n \\n i = i + 1\\n # except: \\n # failed = True\\n\\n \\n \\n return r\",\n \"def _get_lip_best(self) -> float:\\n pass\",\n \"def get_rr(pred, y_truth):\\n rr = 0.\\n for i in range(len(y_truth)):\\n for value in y_truth[i]:\\n hit_indexes = np.where(pred[i] == value)[0]\\n for hit_index in hit_indexes:\\n rr += 1 / (hit_index + 1)\\n return rr\",\n \"def get_first(self):\\n return self.A[1][0] if self.n > 0 else None\",\n \"def rvs(self):\\n rvsValue = self.ppf(random())\\n return rvsValue\",\n \"def rvs(self):\\n rvsValue = self.ppf(random())\\n return rvsValue\",\n \"def rvs(self):\\n rvsValue = self.ppf(random())\\n return rvsValue\",\n \"def best_step(self):\\r\\n return self._best_value_step\",\n \"def _get_R(self, net_r_amp):\\n return np.abs(net_r_amp)**2\",\n \"def find_local_maxima(r, g_r, r_guess):\\n\\n all_maxima = find_all_maxima(g_r)\\n nearest_maxima, _ = find_nearest(r[all_maxima], r_guess)\\n return r[all_maxima[nearest_maxima]], g_r[all_maxima[nearest_maxima]]\",\n \"def s2profile(r,r0,A,B):\\n x = r/r0\\n res = A*4./(np.exp(x)+np.exp(-x))**2 + B\\n return res\",\n \"def compute_cost(self, r):\\n self.r_max = 1\\n return np.exp(1 / (np.power(r, 2) - np.power(self.r_max, 2))) if r < self.r_max else 0\",\n \"def fR(a, b, c, N, S, I, R, vital=False, vaccine=False, combined=False):\\n\\n if vital:\\n temp = b*I - c*R - d*R\\n elif vaccine:\\n R = N - S - I\\n temp = b*I - c*R + f*S\\n elif combined:\\n temp = b*I - c*R - d*R + f*S\\n else:\\n temp = 0\\n\\n return temp\",\n \"def get_rzero(self):\\n return self.get_resistance() * math.pow((self.ATMOCO2/self.PARA), (1./self.PARB))\",\n \"def get_rzero(self):\\n return self.get_resistance() * math.pow((self.ATMOCO2/self.PARA), (1./self.PARB))\",\n \"def attraction_h(next_r, a):\\n if a == 0:\\n if next_r == 9:\\n return 0.8\\n elif next_r == 14:\\n return 0.1\\n else:\\n return 0.1\\n\\n elif a == 1:\\n if next_r == 9:\\n return 0.1\\n elif next_r == 14:\\n return 0.1\\n else:\\n return 0.8\\n\\n elif a == 2:\\n if next_r == 9:\\n return 0.1\\n elif next_r == 14:\\n return 0.3\\n else:\\n return 0.6\\n\\n elif a == 3:\\n if next_r == 9:\\n return 0.1\\n elif next_r == 14:\\n return 0.2\\n else:\\n return 0.7\\n\\n else:\\n if next_r == 9:\\n return 0.1\\n elif next_r == 14:\\n return 0.4\\n else:\\n return 0.5\",\n \"def get_reward(state, resolution, grid_x, grid_y):\\n a,b = single_index_to_index(state, resolution)\\n position = index_to_obs(a, b, grid_x, grid_y )[0]\\n if position >= 0.5:\\n return 0\\n return -1\",\n \"def paf_o_r(rr_o_r, alpha):\\n return ((rr_o_r - 1) * (1 - alpha)) / ((rr_o_r - 1) * (1 - alpha) + 1)\",\n \"def stirling(k, r) :\\n\\n return sum((-1)**(r-i)*binomial(r, i)*i**k for i in range(r+1)) / math.factorial(r)\",\n \"def snr(v_est, v_true):\\n assert(v_est.shape == v_true.shape)\\n\\n return 10 * np.log10(norm2(v_true) ** 2 / norm2(v_est - v_true) ** 2)\",\n \"def solve_R(R, b):\\n n = b.size\\n assert R.shape == (n,n)\\n x = zeros(n, dtype=R.dtype)\\n for i in range(n-1,-1,-1):\\n x[i] = (b[i] - dot(x[i+1:], R[i,i+1:])) / R[i,i]\\n if not numpy.isfinite(x[i]):\\n x[i] = 0.0\\n return x\",\n \"def findval(self,val,x0,method='fmin',**kwargs):\\n kwargs['valtofind'] = val\\n return self._optimize(x0,'val',method,**kwargs)\",\n \"def estimate(values, target):\\n\\n # next time\\n # diff(values)\\n\\n\\n return 1.\",\n \"def tirageR1(self):\\n\\n proba = np.zeros(shape=(3))\\n proba[0] = self.__alpha0 + self.__beta + self.__eta/2.\\n proba[1] = self.__alpha1 + self.__beta + self.__eta/2.\\n proba[2] = 1. - (proba[0]+proba[1])\\n # typeSample = np.random.choice(a=['0.', '1.', 'F'], size=1, p=proba)[0]\\n typeSample = random.choices(population=['0.', '1.', 'F'], weights=proba)[0]\\n if typeSample != 'F':\\n r1 = float(typeSample)\\n else:\\n r1 = self.__rv_parab.rvs()\\n\\n return r1\",\n \"def r(self) -> float:\\n return self._ohms.real\",\n \"def first_point(self) -> np.ndarray:\\n return self.evaluate_at(arc_lengths=0)\",\n \"def r_precision(r):\\n r = np.asarray(r) != 0\\n z = r.nonzero()[0]\\n if not z.size:\\n return 0.\\n return np.mean(r[:z[-1] + 1])\",\n \"def find_f_equals_1():\\n f1 = scipy.optimize.brentq(f2, 0, -10)\\n return f1\",\n \"def findzero(x,y):\\n\\t\\targs = np.where(y > 0)[0]\\n\\t\\tif len(args) == 0:\\n\\t\\t\\tprint \\\"Unable to find zero! Using maximum T.\\\"\\n\\t\\t\\ti = len(y) - 1\\n\\t\\telse:\\n\\t\\t\\ti = min(args)\\n\\n\\t\\t#Do a fit using the surrounding points, and perform interpolation based on that\\n\\t\\tp = np.polyfit(x[i-1:i+1],y[i-1:i+1],1)\\n\\t\\tyfit = p[0]*x[i-1:i+1] + p[1]\\n\\t\\tf = interp.interp1d(yfit, x[i-1:i+1])\\n\\t\\treturn float(f(0))\",\n \"def simple_interest(p,r,t):\\n \\n try:\\n p, r, t = float(p), float(r), float(t)\\n i = (p*r*t)/100\\n A = p + i\\n A = round(A, 2)\\n return i, A\\n except Exception as e:\\n return e\",\n \"def _interpolate_relative_potential(self, r):\\n \\n # interpolate\\n #spline = interpolate.UnivariateSpline(self._relative_potential_r,\\n # self._relative_potential_psi, k = 1)\\n \\n # linear interpolation is more reliable assuming number of points\\n # is large enough\\n spline = interpolate.interp1d(self._relative_potential_r, self._relative_potential_psi)\\n \\n return 10.0**spline(np.log10(r))\",\n \"def mrr(ground_truth, prediction):\\n rr = 0.\\n for rank, item in enumerate(prediction):\\n if item in ground_truth:\\n rr = 1. / (rank + 1)\\n break\\n return rr\",\n \"def fun_lorentzian(p,r):\\n return p[1] / ((r/p[0])**2 + 1)\",\n \"def get_prediction(self, x):\\n \\\"*** YOUR CODE HERE ***\\\"\\n if (nn.as_scalar(self.run(x)) >= 0.0):\\n return 1\\n return -1\",\n \"def nearest_in_n_sphere(self, value, r):\\n return self.nearest_in_bounding_box(value, r)\\n \\n # This seems right\\n # return self.binary_search_find_nearest_neighbors_in_radius(value, r)\\n \\n # This seems wrong\\n # return self.recur_find_nearest_n_neighbor(value, r)\",\n \"def best_value(self):\\r\\n return self._best_value\",\n \"def exactsolution(x, t, u):\\n if 0 <= (x - u*t) and (x - u*t) <= 0.2:\\n temp = 1 - (10 * (x - u*t) -1)**2\\n else:\\n temp = 0\\n return temp\",\n \"def s(self, r, mu):\\n\\n assert(mu <= 1)\\n\\n if r < self.R:\\n return r * mu + self.R * self.g(r, mu)\\n else:\\n if self.mu_star(r) <= mu:\\n return 2. * self.R * self.g(r, mu)\\n else:\\n return 0\",\n \"def get_r_score(self):\\n return self.r_score\",\n \"def r_d(self, tl):\\n\\t return self.RD0*exp(self.HKR/(R*self.TO)*(1. - self.TO/tl))\",\n \"def run():\\n return estimate(0,1,0)\",\n \"def calc_R2(Fasit, Model):\\n # Calculating\\n R2 = 1 - sum(np.power(Fasit - Model, 2)) / sum(np.power(Fasit - np.mean(Fasit), 2))\\n return R2\",\n \"def optimiseOne(paramOption, row, extractdata, start, stop, step):\\n stockname = row[0]\\n exclAttribs = row[1]\\n bestNu = row[2]\\n\\n tryParam = start\\n bestParam = tryParam\\n (trialError, trialCorrelation, trailAttrCsv) = common.lsCalcModel(\\n stockname, exclAttribs, common.cv, extractdata, bestNu, \\\"{0} {1}\\\".format(paramOption, tryParam))\\n\\n print \\\"Starting correlation={0}, with param={1}\\\".format(trialCorrelation, bestParam)\\n bestCorrelation = trialCorrelation\\n\\n tryParam = start + step\\n while tryParam < stop:\\n print \\\"Trial {0}={1}\\\".format(paramOption, tryParam)\\n (trialError, trialCorrelation, trailAttrCsv) = common.lsCalcModel(\\n stockname, exclAttribs, common.cv, extractdata, tryParam)\\n print \\\"Result: correlation={0} for trial Nu={1} with current bestCorrelation={2}\\\".\\\\\\n format(trialCorrelation, tryParam, bestCorrelation)\\n if trialCorrelation >= bestCorrelation:\\n bestCorrelation = trialCorrelation\\n bestParam = tryParam\\n print \\\"Found better or same correlation=\\\" + str(bestCorrelation) + \\\" with Nu={0}\\\".format(bestParam)\\n else:\\n print \\\"Not better. Best parameter value = {0} with correlation={1}\\\".format(bestParam, bestCorrelation)\\n break\\n\\n tryParam = tryParam + step\\n\\n return (bestParam, bestCorrelation)\",\n \"def r2(self) -> float:\\n zx = (self.true - np.mean(self.true)) / np.std(self.true, ddof=1)\\n zy = (self.predicted - np.mean(self.predicted)) / np.std(self.predicted, ddof=1)\\n r = np.sum(zx * zy) / (len(self.true) - 1)\\n return float(r ** 2)\",\n \"def get_best_vector(results, f_x, target):\\n index_min = -1\\n cur_min = 10**10\\n\\n with Session() as sess:\\n for index, individual in enumerate(results):\\n f_x_res = sess.run(f_x, feed_dict={\\n target: individual\\n })\\n if f_x_res < cur_min:\\n cur_min = f_x_res\\n index_min = index\\n\\n best = results[index_min]\\n\\n return best\",\n \"def calculate_1rm_epley(w: float, r: int):\\n\\n if r == 1:\\n return w\\n if r == 0:\\n return 0\\n\\n if w == 0:\\n w = 1\\n\\n return round(w*(1 + r/30), 2)\",\n \"def rmin(self) -> float:\\n return self.options.rmin\",\n \"def getFirstVallay(hist):\\n hist = smooth(hist)\\n safe_range = (len(hist)//100, len(hist)//4)\\n start = max(int(round(np.argmax(hist[:int(len(hist)/4)]))), 10) # Start searching from maximum peak\\n start = min(start, safe_range[1]) # start point should be smaller than 50 pixel\\n start = max(safe_range[0], start) # start point should be bigger than 20\\n limit = min(int(start * 1.5), 100) # Set limit of the first valley as 150% of maximum peak location\\n max_val = 0\\n for i in range(start, limit):\\n if hist[i] > max_val:\\n max_val = hist[i]\\n if hist[i] <= max_val*0.5:\\n return i\\n return limit\",\n \"def test_find_least_rational_surfaces():\\n # simple test, linear iota going from 1 to 3\\n iota = PowerSeriesProfile([1, 2])\\n rhor, ior = find_most_rational_surfaces(iota, 10)\\n rho, io = find_least_rational_surfaces(iota, 10)\\n # to compare how rational/irrational things are, we use the length of the\\n # continued fraction. Not a great test due to rounding errors, but seems to work\\n lio = [len(dec_to_cf(i)) for i in io]\\n lior = [len(dec_to_cf(i)) for i in ior]\\n max_rational = max(lior)\\n\\n assert np.all(np.array(lio) > max_rational)\",\n \"def residual(S):\\n rho = seawater.density(T, S, Pa)\\n return (rho_1 - rho)\",\n \"def pseudo_r2(self):\\n y_reg = self.time_series(len(self.data))\\n SSres = ((self.data - y_reg)**2).sum()\\n SStot = ((self.data - self.data.mean())**2).sum()\\n return 1 - SSres/SStot\",\n \"def FindClosestPointWithinRadius(self, p_float, , p_float_4):\\n ...\",\n \"def scoreRsrc( self, rr ):\\r\\n result = 0.0\\r\\n for tt in self.getSched( )[rr.getid( )]:\\r\\n for se in tt:\\r\\n result += 1\\r\\n print( \\\"INFO: Value for %s: %s \\\" % ( rr, result ) )\\r\\n return( result )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60582757","0.5931805","0.59098005","0.5888322","0.5841924","0.5819919","0.5704931","0.5644729","0.564107","0.56170464","0.5602746","0.5596635","0.55957025","0.5592062","0.55595565","0.5538356","0.5533621","0.55293804","0.5513773","0.55079275","0.55023295","0.5499537","0.5483633","0.54784286","0.5476481","0.5473337","0.54609257","0.5451725","0.5451725","0.5449836","0.54236495","0.5419534","0.5418996","0.54161084","0.5413524","0.541133","0.5410618","0.5408117","0.54001963","0.5392693","0.53860295","0.5385586","0.53834915","0.5379473","0.537714","0.5355694","0.5353393","0.535322","0.53472155","0.5333254","0.5318737","0.5307165","0.53051853","0.53051853","0.53051853","0.5303752","0.5300862","0.5299048","0.5291741","0.5288302","0.52880394","0.52774847","0.52774847","0.526638","0.525815","0.52565557","0.5250497","0.5249468","0.52465636","0.5243623","0.52434456","0.5242","0.5240806","0.524066","0.52363193","0.52361995","0.5234194","0.5232907","0.52269745","0.5223647","0.52089995","0.5206073","0.5203706","0.52034223","0.5193856","0.519169","0.51861954","0.5183627","0.51742274","0.51681846","0.51625973","0.51539123","0.5152842","0.5150655","0.5147059","0.51462144","0.51353365","0.5134441","0.5126708","0.51238555","0.5121147"],"string":"[\n \"0.60582757\",\n \"0.5931805\",\n \"0.59098005\",\n \"0.5888322\",\n \"0.5841924\",\n \"0.5819919\",\n \"0.5704931\",\n \"0.5644729\",\n \"0.564107\",\n \"0.56170464\",\n \"0.5602746\",\n \"0.5596635\",\n \"0.55957025\",\n \"0.5592062\",\n \"0.55595565\",\n \"0.5538356\",\n \"0.5533621\",\n \"0.55293804\",\n \"0.5513773\",\n \"0.55079275\",\n \"0.55023295\",\n \"0.5499537\",\n \"0.5483633\",\n \"0.54784286\",\n \"0.5476481\",\n \"0.5473337\",\n \"0.54609257\",\n \"0.5451725\",\n \"0.5451725\",\n \"0.5449836\",\n \"0.54236495\",\n \"0.5419534\",\n \"0.5418996\",\n \"0.54161084\",\n \"0.5413524\",\n \"0.541133\",\n \"0.5410618\",\n \"0.5408117\",\n \"0.54001963\",\n \"0.5392693\",\n \"0.53860295\",\n \"0.5385586\",\n \"0.53834915\",\n \"0.5379473\",\n \"0.537714\",\n \"0.5355694\",\n \"0.5353393\",\n \"0.535322\",\n \"0.53472155\",\n \"0.5333254\",\n \"0.5318737\",\n \"0.5307165\",\n \"0.53051853\",\n \"0.53051853\",\n \"0.53051853\",\n \"0.5303752\",\n \"0.5300862\",\n \"0.5299048\",\n \"0.5291741\",\n \"0.5288302\",\n \"0.52880394\",\n \"0.52774847\",\n \"0.52774847\",\n \"0.526638\",\n \"0.525815\",\n \"0.52565557\",\n \"0.5250497\",\n \"0.5249468\",\n \"0.52465636\",\n \"0.5243623\",\n \"0.52434456\",\n \"0.5242\",\n \"0.5240806\",\n \"0.524066\",\n \"0.52363193\",\n \"0.52361995\",\n \"0.5234194\",\n \"0.5232907\",\n \"0.52269745\",\n \"0.5223647\",\n \"0.52089995\",\n \"0.5206073\",\n \"0.5203706\",\n \"0.52034223\",\n \"0.5193856\",\n \"0.519169\",\n \"0.51861954\",\n \"0.5183627\",\n \"0.51742274\",\n \"0.51681846\",\n \"0.51625973\",\n \"0.51539123\",\n \"0.5152842\",\n \"0.5150655\",\n \"0.5147059\",\n \"0.51462144\",\n \"0.51353365\",\n \"0.5134441\",\n \"0.5126708\",\n \"0.51238555\",\n \"0.5121147\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":89399,"cells":{"query":{"kind":"string","value":"Parse features from byteencoding to the correct type and shape."},"document":{"kind":"string","value":"def parse_train(self, proto, height, width):\n _, sequence_parsed = tf.io.parse_single_sequence_example(\n proto,\n context_features=self._context_features,\n sequence_features=self._sequence_features)\n\n # Deserialize images to float32 tensors.\n images = tf.map_fn(\n _deserialize_png, sequence_parsed['images'], dtype=tf.float32)\n\n # Resize images.\n if height is not None and width is not None:\n images = smurf_utils.resize(images, height, width, is_flow=False)\n\n return {'images': images}"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _read_and_decode(example_proto,data_shape,dtypes):\n features = {}\n for name in data_shape:\n features[name] = tf.FixedLenFeature([], tf.string)\n parsed_features = tf.parse_single_example(example_proto, features)\n count = 0\n res = {}\n for name in data_shape:\n res[name] = parsed_features[name]\n if dtypes[count]!=str:\n res[name]=tf.decode_raw(res[name],dtypes[count])\n if dtypes[count]==tf.float32 or dtypes[count]==tf.float64:\n res[name]=tf.convert_to_tensor(res[name],dtype=dtypes[count])\n if data_shape[name]:\n res[name]=tf.reshape(res[name],shape=data_shape[name])\n count += 1\n return res","def _parser(serialized_example):\n\n features = tf.compat.v1.parse_single_example(\n serialized_example,\n features={\n 'img_raw': tf.compat.v1.FixedLenFeature([], tf.string),\n 'label': tf.compat.v1.FixedLenFeature([], tf.int64),\n 'category': tf.compat.v1.FixedLenFeature([], tf.int64),\n 'elevation': tf.compat.v1.FixedLenFeature([], tf.int64),\n 'azimuth': tf.compat.v1.FixedLenFeature([], tf.int64),\n 'lighting': tf.compat.v1.FixedLenFeature([], tf.int64),\n })\n\n img = tf.compat.v1.decode_raw(features['img_raw'], tf.float64)\n img = tf.reshape(img, [96, 96, 1])\n img = tf.cast(img, tf.float32) # * (1. / 255) # left unnormalized\n\n lab = tf.cast(features['label'], tf.int32)\n cat = tf.cast(features['category'], tf.int32)\n elv = tf.cast(features['elevation'], tf.int32)\n azi = tf.cast(features['azimuth'], tf.int32)\n lit = tf.cast(features['lighting'], tf.int32)\n\n return img, lab, cat, elv, azi, lit","def _parse_el_example(array_feats, array_feat_types, quant_feats):\n out_example = []\n d_keys = sorted(array_feats.keys())\n for k in d_keys:\n n_feat = quant_feats[k]\n point_feat = tf.decode_raw(array_feats[k], array_feat_types[k])\n point_feat = tf.reshape(point_feat, [quant_feats[k]])\n out_example.append(point_feat)\n return tuple(out_example)","def _parse(serialized_example):\n\n feature_map = {\n 'dayofweek': tf.io.FixedLenFeature([], tf.int64),\n 'dropofflat': tf.io.FixedLenFeature([], tf.float32),\n 'dropofflon': tf.io.FixedLenFeature([], tf.float32),\n 'fare_amount': tf.io.FixedLenFeature([], tf.float32),\n 'hourofday': tf.io.FixedLenFeature([], tf.int64),\n 'passengers': tf.io.FixedLenFeature([], tf.float32),\n 'pickuplat': tf.io.FixedLenFeature([], tf.float32),\n 'pickuplon': tf.io.FixedLenFeature([], tf.float32)\n }\n\n # Parse the serialized data into a dictionary.\n parsed_example = tf.io.parse_single_example(\n serialized=serialized_example,\n features=feature_map)\n\n features = add_engineered(parsed_example)\n label = features.pop(\"fare_amount\")\n\n return features, label","def parser(serialized_example):\n features = tf.parse_single_example(\n serialized_example,\n features={\n \"image\": tf.FixedLenFeature([], tf.string),\n \"label\": tf.FixedLenFeature([], tf.int64),\n })\n image = tf.decode_raw(features[\"image\"], tf.uint8)\n image.set_shape([CHANNELS * HEIGHT * WIDTH])\n # Reshape from [depth * height * width] to [depth, height, width].\n image = tf.cast(\n tf.transpose(tf.reshape(image, [CHANNELS, HEIGHT, WIDTH]), [1, 2, 0]),\n tf.float32) * (2. / 255) - 1\n\n label = tf.cast(features['label'], tf.int32)\n\n random_noise = tf.random_normal([noise_dim])\n features = {\n 'real_images': image,\n 'random_noise': random_noise}\n\n return features, label","def parse_feature(self, feature_key, lines):\n ...","def parser(serialized_example):\n features = tf.parse_single_example(\n serialized_example,\n features={\n 'env': tf.FixedLenFeature([1, 4], tf.int64),\n # 'env_segment_number': tf.FixedLenFeature([], tf.int64),\n # 'env_segment_cpu': tf.FixedLenFeature([], tf.int64),\n # 'env_segment_mem': tf.FixedLenFeature([], tf.int64),\n # 'query_plan_ops': tf.VarLenFeature(tf.string),\n # 'query_table_size': tf.VarLenFeature(tf.float32),\n # 'segment_cpu_usage': tf.VarLenFeature(tf.float32),\n 'label': tf.FixedLenFeature([], tf.float32)\n })\n env = tf.cast(features['env'], tf.float32)\n # image.set_shape([DEPTH * HEIGHT * WIDTH])\n\n # # Reshape from [depth * height * width] to [depth, height, width].\n # image = tf.cast(\n # tf.transpose(tf.reshape(image, [DEPTH, HEIGHT, WIDTH]), [1, 2, 0]),\n # tf.float32)\n label = tf.cast(features['label'], tf.float32)\n reshape_label = tf.reshape(features['label'], (1,1))\n return env, reshape_label","def decode_data(features, reader_settings):\n if features.dtype == tf.string:\n return tf.decode_raw(\n features,\n reader_settings)\n else:\n return tf.cast(\n features,\n reader_settings)","def parser(_, serialized_example):\n features = {}\n\n for i in frame_nums:\n image_name = 'image_' + str(i)\n if flags.dataset_type == 'robot':\n pose_name = 'state_' + str(i)\n action_name = 'action_' + str(i)\n joint_pos_name = 'joint_positions_' + str(i)\n\n features[pose_name] = tf.FixedLenFeature([flags.pose_dim], tf.float32)\n features[image_name] = tf.FixedLenFeature([1], tf.string)\n features[action_name] = tf.FixedLenFeature([flags.pose_dim],\n tf.float32)\n features[joint_pos_name] = tf.FixedLenFeature([flags.joint_pos_dim],\n tf.float32)\n else:\n features[image_name] = tf.FixedLenFeature([1], tf.string)\n\n parsed_input = tf.parse_single_example(serialized_example, features)\n\n for i in frame_nums:\n image_name = 'image_' + str(i)\n pose_name = 'state_' + str(i)\n action_name = 'action_' + str(i)\n joint_pos_name = 'joint_positions_' + str(i)\n\n # Process image\n image_buffer = tf.reshape(parsed_input[image_name], shape=[])\n image = tf.image.decode_jpeg(image_buffer, channels=COLOR_CHAN)\n image = tf.image.resize_images(\n image, (IMG_HEIGHT, IMG_WIDTH),\n method=tf.image.ResizeMethod.BICUBIC)\n image = tf.cast(tf.expand_dims(image, 0), tf.float32) / 255.0\n\n if flags.dataset_type == 'robot':\n pose = tf.reshape(parsed_input[pose_name], shape=[flags.pose_dim])\n pose = tf.expand_dims(pose, 0)\n action = tf.reshape(parsed_input[action_name], shape=[flags.pose_dim])\n action = tf.expand_dims(action, 0)\n joint_pos = tf.reshape(\n parsed_input[joint_pos_name], shape=[flags.joint_pos_dim])\n joint_pos = tf.expand_dims(joint_pos, 0)\n else:\n pose = tf.zeros([1, flags.pose_dim])\n action = tf.zeros([1, flags.pose_dim])\n joint_pos = tf.zeros([1, flags.joint_pos_dim])\n\n if i == 0:\n image_seq = image\n action_seq, pose_seq, joint_pos_seq = action, pose, joint_pos\n else:\n image_seq = tf.concat([image_seq, image], 0)\n action_seq = tf.concat([action_seq, action], 0)\n pose_seq = tf.concat([pose_seq, pose], 0)\n joint_pos_seq = tf.concat([joint_pos_seq, joint_pos], 0)\n\n return image_seq, action_seq, action_seq, joint_pos_seq","def parser(record):\n # keys_to_features = {\n # \"image_data\": tf.FixedLenFeature((), tf.string, default_value=\"\"),\n # \"date_time\": tf.FixedLenFeature((), tf.int64, default_value=\"\"),\n # \"label\": tf.FixedLenFeature((), tf.int64,\n # default_value=tf.zeros([], dtype=tf.int64)),\n # }\n\n keys_to_features = {\n \"image_data\": tf.FixedLenFeature((), tf.float, default_value=\"\"),\n \"label\": tf.FixedLenFeature((), tf.int32,\n default_value=tf.zeros([], dtype=tf.int64)),\n }\n parsed = tf.parse_single_example(record, keys_to_features)\n\n # Perform additional preprocessing on the parsed data.\n image = tf.image.decode_jpeg(parsed[\"image_data\"])\n image = tf.reshape(image, [299, 299, 1])\n label = tf.cast(parsed[\"label\"], tf.int32)\n\n return {\"image_data\": image, \"date_time\": parsed[\"date_time\"]}, label","def _decode_record(record):\n name_to_features = {\n \"input_ids\": tf.FixedLenFeature([self.max_sen_len], tf.int64),\n \"input_mask\": tf.FixedLenFeature([self.max_sen_len], tf.int64),\n \"segment_ids\": tf.FixedLenFeature([self.max_sen_len], tf.int64),\n \"stroke_ids\": tf.FixedLenFeature([self.max_sen_len], tf.int64),\n \"lmask\": tf.FixedLenFeature([self.max_sen_len], tf.int64),\n \"label_ids\": tf.FixedLenFeature([self.max_sen_len], tf.int64),\n }\n\n\n example = tf.parse_single_example(record, name_to_features)\n\n #int64 to int32\n for name in list(example.keys()):\n t = example[name]\n if t.dtype == tf.int64:\n t = tf.to_int32(t)\n example[name] = t\n input_ids = example['input_ids']\n input_mask = example['input_mask']\n segment_ids = example['segment_ids']\n stroke_ids = example['stroke_ids']\n label_ids = example['label_ids']\n lmask = example['lmask']\n py_labels = tf.py_func(_get_py_seq, [label_ids], [tf.int32])\n\n return input_ids, input_mask, segment_ids, stroke_ids, lmask, label_ids, py_labels","def _extract_feature(element):\n features = tf.parse_single_example(\n element,\n # Defaults are not specified since both keys are required.\n features={\n 'image/encoded': tf.FixedLenFeature([], tf.string),\n 'label/x': tf.FixedLenFeature([], tf.int64),\n 'label/y': tf.FixedLenFeature([], tf.int64)\n })\n return features","def parser(self, serialized_example):\n features = {\n 'image/height': tf.FixedLenFeature([], tf.int64),\n 'image/width': tf.FixedLenFeature([], tf.int64),\n 'image/colorspace': tf.FixedLenFeature([], tf.string),\n 'image/channels': tf.FixedLenFeature([], tf.int64),\n 'image/class/label': tf.FixedLenFeature([], tf.int64),\n 'image/class/synset': tf.FixedLenFeature([], tf.string),\n 'image/class/text': tf.FixedLenFeature([], tf.string),\n 'image/object/bbox/xmin': tf.VarLenFeature(tf.float32),\n 'image/object/bbox/ymin': tf.VarLenFeature(tf.float32),\n 'image/object/bbox/xmax': tf.VarLenFeature(tf.float32),\n 'image/object/bbox/ymax': tf.VarLenFeature(tf.float32),\n 'image/object/bbox/label': tf.VarLenFeature(tf.int64),\n 'image/format': tf.FixedLenFeature([], tf.string),\n 'image/encoded': tf.FixedLenFeature([], tf.string)}\n parsed_features = tf.parse_single_example(serialized_example, features)\n\n # Get label as a Tensor.\n label = parsed_features['image/class/label']\n\n # Decode the image JPEG string into a Tensor.\n image = tf.image.decode_jpeg(parsed_features['image/encoded'],\n channels=self.DEPTH)\n\n # VGG preprocessing borrowed from slim; includes data augmentation so train_with_distortion should be set to True.\n if self.mode == tf.estimator.ModeKeys.TRAIN:\n assert self.params['train_with_distortion'] == True\n is_training = True\n else:\n is_training = False\n image = vgg_preprocess_image(image, 224, 224, is_training=is_training)\n\n return image, label","def dataset_parser(self, value):\n keys_to_features = {\n 'image/encoded':\n tf.io.FixedLenFeature((), tf.string, ''),\n 'image/format':\n tf.io.FixedLenFeature((), tf.string, 'jpeg'),\n 'image/class/label':\n tf.io.FixedLenFeature([], tf.int64, -1),\n 'image/class/text':\n tf.io.FixedLenFeature([], tf.string, ''),\n 'image/object/bbox/xmin':\n tf.io.VarLenFeature(dtype=tf.float32),\n 'image/object/bbox/ymin':\n tf.io.VarLenFeature(dtype=tf.float32),\n 'image/object/bbox/xmax':\n tf.io.VarLenFeature(dtype=tf.float32),\n 'image/object/bbox/ymax':\n tf.io.VarLenFeature(dtype=tf.float32),\n 'image/object/class/label':\n tf.io.VarLenFeature(dtype=tf.int64),\n }\n\n parsed = tf.io.parse_single_example(value, keys_to_features)\n image_bytes = tf.reshape(parsed['image/encoded'], shape=[])\n\n tensors_dict = preprocess_image(\n image_bytes=image_bytes,\n is_training=self.is_training,\n augmentation=self.augmentation,\n use_bfloat16=self.use_bfloat16,\n saturate_uint8=self.saturate_uint8,\n scale_and_center=self.scale_and_center,\n use_default_augment=self.use_default_augment)\n\n # Subtract one so that labels are in [0, 1000).\n label = tf.cast(tf.reshape(parsed['image/class/label'], shape=()) - 1,\n dtype=tf.int32)\n tensors_dict['label'] = label\n\n return tensors_dict","def parse_record(raw_record):\n keys_to_features = {\n 'image/height':\n tf.FixedLenFeature((), tf.int64),\n 'image/width':\n tf.FixedLenFeature((), tf.int64),\n 'image/encoded':\n tf.FixedLenFeature((), tf.string, default_value=''),\n 'image/format':\n tf.FixedLenFeature((), tf.string, default_value='jpeg'),\n 'label/encoded':\n tf.FixedLenFeature((), tf.string, default_value=''),\n 'label/format':\n tf.FixedLenFeature((), tf.string, default_value='png'),\n }\n\n parsed = tf.parse_single_example(raw_record, keys_to_features)\n\n # height = tf.cast(parsed['image/height'], tf.int32)\n # width = tf.cast(parsed['image/width'], tf.int32)\n\n image = tf.image.decode_image(\n tf.reshape(parsed['image/encoded'], shape=[]), _DEPTH)\n image = tf.to_float(tf.image.convert_image_dtype(image, dtype=tf.uint8))\n image.set_shape([None, None, 3])\n\n label = tf.image.decode_image(\n tf.reshape(parsed['label/encoded'], shape=[]), 1)\n label = tf.to_int32(tf.image.convert_image_dtype(label, dtype=tf.uint8))\n label.set_shape([None, None, 1])\n\n\n return image, label","def __parser__(self, example_proto):\n # configure feature and label length\n # It is crucial that for tf.string, the length is not specified, as the data is stored as a single string!\n x_config = tf.FixedLenFeature([], tf.string) \\\n if self.x_dtype == tf.string else tf.FixedLenFeature([self.num_features], self.x_dtype)\n if self.num_labels == 0:\n proto_config = {'x': x_config}\n else:\n y_config = tf.FixedLenFeature([], tf.string) \\\n if self.y_dtype == tf.string else tf.FixedLenFeature([self.num_labels], self.y_dtype)\n proto_config = {'x': x_config, 'y': y_config}\n\n # decode examples\n datum = tf.parse_single_example(example_proto, features=proto_config)\n if self.x_dtype == tf.string: # if input is string / bytes, decode it to float32\n if self.decode_jpeg:\n # first decode compressed image string to uint8, as data is stored in this way\n # datum['x'] = tf.image.decode_image(datum['x'], channels=3)\n datum['x'] = tf.image.decode_jpeg(datum['x'], channels=3)\n else:\n # first decode data to uint8, as data is stored in this way\n datum['x'] = tf.decode_raw(datum['x'], tf.uint8)\n # then cast data to tf.float32 or tf.float16\n datum['x'] = tf.cast(datum['x'], tf.float32)\n # cannot use string_to_number as there is only one string for a whole sample\n # datum['x'] = tf.strings.to_number(datum['x'], tf.float32) # this results in possibly a large number\n\n # return data\n if 'y' in datum:\n # y can be present in many ways:\n # 1. a single integer, which requires y to be int32 or int64 (e.g, used in tf.gather in cbn)\n # 2. num-class bool/integer/float variables. This form is more flexible as it allows multiple classes and\n # prior probabilities as targets\n # 3. float variables in regression problem.\n # but...\n # y is stored as int (for case 1), string (for other int cases), or float (for float cases)\n # in the case of tf.string and tf.int64, convert to to int32\n if self.y_dtype == tf.string:\n # avoid using string labels like 'cat', 'dog', use integers instead\n datum['y'] = tf.decode_raw(datum['y'], tf.uint8)\n datum['y'] = tf.cast(datum['y'], tf.int32)\n else:\n datum['y'] = tf.cast(datum['y'], self.y_dtype)\n if self.use_one_hot_label:\n datum['y'] = tf.reshape(tf.one_hot(datum['y'], self.num_classes), (-1, ))\n if self.use_smooth_label: # label smoothing\n datum['y'] = 0.9 * datum['y'] + 0.1 / self.num_classes\n return datum['x'], datum['y']\n else:\n return datum['x']","def _parse_function(proto_string):\n _, parsed_features = tf.parse_single_sequence_example(\n proto_string, sequence_features=_parsing_spec(input_size))\n lengths = tf.shape(parsed_features['inputs'])[0]\n # Type int32 is not supported by parsing spec, but int64 is not supported by\n # XLA. Here cast to int32. The parsing functions are executed by the C++\n # threads. So, casting op will not slow down the session.run call later.\n labels = tf.to_int32(parsed_features['labels'])\n return parsed_features['inputs'], lengths, labels","def _decode_record(record,name_to_features):\n example = tf.parse_single_example(record,name_to_features)\n\n return example","def parse_features(self, skip=...):\n ...","def parse_features(self, skip=...):\n ...","def decode(value):\n keys_to_features = {\n 'image/encoded':\n tf.FixedLenFeature((), tf.string, default_value=''),\n 'image/height':\n tf.FixedLenFeature((), tf.int64, default_value=0),\n 'image/width':\n tf.FixedLenFeature((), tf.int64, default_value=0),\n 'image/segmentation/class/encoded':\n tf.FixedLenFeature((), tf.string, default_value='')\n }\n data = tf.parse_single_example(value, keys_to_features)\n return data","def parse_record(raw_record, is_training):\n keys_to_features = {\n 'image/encoded':\n tf.FixedLenFeature((), tf.string, default_value=''),\n 'image/format':\n tf.FixedLenFeature((), tf.string, default_value='jpeg'),\n 'image/class/label':\n tf.FixedLenFeature([], dtype=tf.int64, default_value=-1),\n 'image/class/text':\n tf.FixedLenFeature([], dtype=tf.string, default_value=''),\n }\n\n parsed = tf.parse_single_example(raw_record, keys_to_features)\n\n image = tf.image.decode_image(\n tf.reshape(parsed['image/encoded'], shape=[]),\n _NUM_CHANNELS)\n\n # Note that tf.image.convert_image_dtype scales the image data to [0, 1).\n image = tf.image.convert_image_dtype(image, dtype=tf.float32)\n\n label = tf.cast(\n tf.reshape(parsed['image/class/label'], shape=[]),\n dtype=tf.int32)\n\n return {\"image\": image}, label","def _decode_record(record, name_to_features):\n example = tf.parse_single_example(record, name_to_features)\n return example","def extract_features(raw_data):\n width = len(raw_data[0])\n num_features = len(raw_data) * width\n features = np.zeros((num_features, 3), dtype=bool)\n for row, line in enumerate(raw_data):\n for col, char in enumerate(line):\n if char == ' ':\n features[col + row * width][0] = True\n elif char == '+':\n features[col + row * width][1] = True\n elif char == '#':\n features[col + row * width][2] = True\n return features","def parse_fn(self, example_serialized):\n feature_description = {\n 'image_raw': tf.io.FixedLenFeature([], tf.string),\n 'label': tf.io.FixedLenFeature([], tf.int64)\n }\n features = tf.io.parse_single_example(example_serialized, feature_description)\n image = tf.io.decode_raw(features['image_raw'], tf.uint8)\n image = tf.cast(image, dtype='float32') / 255.0\n label = tf.cast(features['label'], dtype=tf.int32)\n image = tf.reshape(image, [32, 32, 3])\n if self.is_training:\n image = tf.image.resize_with_crop_or_pad(image, 32 + 8, 32 + 8)\n image = tf.image.random_crop(image, [32, 32, 3])\n image = tf.image.random_flip_left_right(image)\n return image, label","def _convert_to_features(self, img: np.ndarray) -> np.ndarray:","def parser(self, serialized_example):\n features = {\n 'image/height': tf.FixedLenFeature([], tf.int64),\n 'image/width': tf.FixedLenFeature([], tf.int64),\n 'image/colorspace': tf.FixedLenFeature([], tf.string),\n 'image/channels': tf.FixedLenFeature([], tf.int64),\n 'image/class/label': tf.FixedLenFeature([], tf.int64),\n 'image/format': tf.FixedLenFeature([], tf.string),\n 'image/encoded': tf.FixedLenFeature([], tf.string),\n 'image/fixation_pt': tf.FixedLenFeature([2], tf.float32)}\n parsed_features = tf.parse_single_example(serialized_example, features)\n\n # Get label as a Tensor.\n label = parsed_features['image/class/label']\n\n # Decode the image JPEG string into a Tensor.\n image = tf.image.decode_jpeg(parsed_features['image/encoded'],\n channels=self.DEPTH)\n\n # Convert from uint8 -> float32 and map onto range [0, 1].\n image = tf.cast(image, tf.float32) * (1. / 255)\n\n # Standardize image.\n image = tf.image.per_image_standardization(image)\n\n # Apply data augmentation.\n if (self.mode == tf.estimator.ModeKeys.TRAIN\n and self.params['train_with_distortion']):\n # Randomly flip the image, zero-pad with four pixels along\n # each edge, and take a random 32 x 32 crop.\n image = tf.image.random_flip_left_right(image)\n image = tf.image.resize_image_with_crop_or_pad(image, 40, 40)\n image = tf.image.crop_to_bounding_box(image,\n tf.random_uniform([], minval=0, maxval=8, dtype=tf.int32),\n tf.random_uniform([], minval=0, maxval=8, dtype=tf.int32),\n 32, 32)\n\n return image, label","def _parse_tfexample(example):\n\n ## parse\n features = tf.parse_single_example(example, KEYS2FEATURES)\n\n image = tf.image.decode_png(features['image/encoded'])\n label = tf.image.decode_png(features['label/encoded'])\n # label is decoded as a 3-D png image\n label = label[..., 0]\n im_path = features['image/path']\n la_path = features['label/path']\n\n return image, label, im_path, la_path","def imagenet_parser(value, image_size, is_training):\n keys_to_features = {\n 'image/encoded':\n tf.FixedLenFeature((), tf.string, ''),\n 'image/format':\n tf.FixedLenFeature((), tf.string, 'jpeg'),\n 'image/class/label':\n tf.FixedLenFeature([], tf.int64, -1),\n 'image/class/text':\n tf.FixedLenFeature([], tf.string, ''),\n 'image/object/bbox/xmin':\n tf.VarLenFeature(dtype=tf.float32),\n 'image/object/bbox/ymin':\n tf.VarLenFeature(dtype=tf.float32),\n 'image/object/bbox/xmax':\n tf.VarLenFeature(dtype=tf.float32),\n 'image/object/bbox/ymax':\n tf.VarLenFeature(dtype=tf.float32),\n 'image/object/class/label':\n tf.VarLenFeature(dtype=tf.int64),\n }\n\n parsed = tf.parse_single_example(value, keys_to_features)\n\n image_buffer = tf.reshape(parsed['image/encoded'], shape=[])\n\n xmin = tf.expand_dims(parsed['image/object/bbox/xmin'].values, 0)\n ymin = tf.expand_dims(parsed['image/object/bbox/ymin'].values, 0)\n xmax = tf.expand_dims(parsed['image/object/bbox/xmax'].values, 0)\n ymax = tf.expand_dims(parsed['image/object/bbox/ymax'].values, 0)\n # Note that ordering is (y, x)\n bbox = tf.concat([ymin, xmin, ymax, xmax], 0)\n # Force the variable number of bounding boxes into the shape\n # [1, num_boxes, coords].\n bbox = tf.expand_dims(bbox, 0)\n bbox = tf.transpose(bbox, [0, 2, 1])\n\n image = image_preprocessing(\n image_buffer=image_buffer,\n bbox=bbox,\n image_size=image_size,\n is_training=is_training\n )\n\n # Labels are in [1, 1000] range\n label = tf.cast(\n tf.reshape(parsed['image/class/label'], shape=[]), dtype=tf.int32)\n\n return image, label","def decode(self, serialized_example):\n parsed_tensors = tf.io.parse_single_example(\n serialized_example, self.KEYS_TO_FEATURES\n )\n for k in parsed_tensors:\n if isinstance(parsed_tensors[k], tf.SparseTensor):\n if parsed_tensors[k].dtype == tf.string:\n parsed_tensors[k] = tf.sparse.to_dense(\n parsed_tensors[k], default_value=\"\"\n )\n else:\n parsed_tensors[k] = tf.sparse.to_dense(\n parsed_tensors[k], default_value=0\n )\n\n image = self._decode_image(parsed_tensors)\n boxes = self._decode_boxes(parsed_tensors)\n decode_image_shape = tf.logical_or(\n tf.equal(parsed_tensors[\"image/height\"], -1),\n tf.equal(parsed_tensors[\"image/width\"], -1),\n )\n image_shape = tf.cast(tf.shape(image), dtype=tf.int64)\n\n parsed_tensors[\"image/height\"] = tf.where(\n decode_image_shape, image_shape[0], parsed_tensors[\"image/height\"]\n )\n parsed_tensors[\"image/width\"] = tf.where(\n decode_image_shape, image_shape[1], parsed_tensors[\"image/width\"]\n )\n\n decoded_tensors = {\n \"image\": image,\n \"height\": parsed_tensors[\"image/height\"],\n \"width\": parsed_tensors[\"image/width\"],\n \"groundtruth_classes\": parsed_tensors[\"image/object/class/label\"],\n \"groundtruth_boxes\": boxes,\n }\n return decoded_tensors","def parse_func(record):\n keys_to_features = {\n 'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),\n 'image/format': tf.FixedLenFeature((), tf.string, default_value='png'),\n 'image/path': tf.FixedLenFeature((), tf.string, default_value=''),\n 'label/encoded': tf.FixedLenFeature((), tf.string, default_value=''),\n 'label/format': tf.FixedLenFeature((), tf.string, default_value='png'),\n 'label/path': tf.FixedLenFeature((), tf.string, default_value=''),\n 'height': tf.FixedLenFeature((), tf.int64),\n 'width': tf.FixedLenFeature((), tf.int64)\n }\n\n features = tf.parse_single_example(record, keys_to_features)\n\n image = tf.image.decode_png(features['image/encoded'], channels=3)\n label_dtype = tf.uint8\n label = tf.image.decode_png(features['label/encoded'], channels=1, dtype=label_dtype)\n label = tf.reshape(label, tf.convert_to_tensor([features['height'], features['width'], 1]))\n label = tf.squeeze(label)\n\n paths = (features['image/path'], features['label/path'])\n return image, label, paths","def parser(record):\n record_spec = {\n \"input\": tf.FixedLenFeature([seq_len], tf.int64),\n \"labels\": tf.FixedLenFeature([tgt_len], tf.int64),\n \"input_mask\": tf.FixedLenFeature([seq_len],tf.float32),\n \"target_mask\": tf.FixedLenFeature([tgt_len],tf.float32)\n }\n\n # retrieve serialized example\n example = tf.parse_single_example(\n serialized=record,\n features=record_spec)\n\n _convert_example(example, use_bfloat16)\n\n for k, v in example.items():\n tf.logging.info(\"%s: %s\", k, v)\n\n return example","def _parse_example(self, example, scale_to_0_1: bool = False):\n\n features = {\n 'image': tf.FixedLenFeature([], tf.string),\n 'mask': tf.FixedLenFeature([], tf.string),\n }\n parsed_example = tf.parse_single_example(example, features)\n\n image = tf.decode_raw(parsed_example['image'], self.serialized_image_raw_dtype)\n image = tf.reshape(image, (self.image_width, self.image_width, self.image_channels))\n image = tf.cast(image, tf.float32)\n if scale_to_0_1:\n image /= 255.\n\n mask = tf.decode_raw(parsed_example['mask'], self.serialized_mask_raw_dtype)\n mask = tf.reshape(mask, (self.image_width, self.image_width, self.mask_channels))\n mask = tf.cast(mask, tf.float32) / 255.\n return image, mask","def _features_to_numpy(\n features: tf_feature_pb2.Features,\n flat_example_specs: Mapping[str, feature_lib.TensorInfo],\n) -> type_utils.NpArrayOrScalarDict:\n parsed_example = {}\n feature_map = features.feature\n for key in feature_map:\n if key not in flat_example_specs:\n raise KeyError(\n f\"Malformed input: {key} is found in the feature, but not in\"\n f\" {flat_example_specs}\"\n )\n with utils.try_reraise(f\"Error wile parsing feature {key}: \"):\n parsed_example[key] = _feature_to_numpy(\n feature_map[key], flat_example_specs[key], key\n )\n return parsed_example","def single_example_parser(serialized_example):\n # Dimensions of the images in the CIFAR-10 dataset.\n # See http://www.cs.toronto.edu/~kriz/cifar.html for a description of the\n # input format.\n features = tf.parse_single_example(\n serialized_example,\n features={\n 'image': tf.FixedLenFeature([], tf.string),\n 'label': tf.FixedLenFeature([], tf.int64),\n })\n image = tf.decode_raw(features['image'], tf.uint8)\n image.set_shape([DEPTH * HEIGHT * WIDTH])\n\n # Reshape from [depth * height * width] to [depth, height, width].\n image = tf.cast(\n tf.transpose(tf.reshape(image, [DEPTH, HEIGHT, WIDTH]), [1, 2, 0]),\n tf.float32)\n label = tf.cast(features['label'], tf.int32)\n \n image = train_preprocess_fn(image)\n label = tf.one_hot(label, NUM_CLASSES)\n \n return image, label","def _decode_record(record, name_to_features):\n example = tf.parse_single_example(record, name_to_features)\n\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\n # So cast all int64 to int32.\n for name in list(example.keys()):\n t = example[name]\n if t.dtype == tf.int64:\n t = tf.to_int32(t)\n example[name] = t\n\n return example","def _decode_record(record, name_to_features):\n example = tf.parse_single_example(record, name_to_features)\n\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\n # So cast all int64 to int32.\n for name in list(example.keys()):\n t = example[name]\n if t.dtype == tf.int64:\n t = tf.to_int32(t)\n example[name] = t\n\n return example","def _decode_record(record, name_to_features):\n example = tf.parse_single_example(record, name_to_features)\n\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\n # So cast all int64 to int32.\n for name in list(example.keys()):\n t = example[name]\n if t.dtype == tf.int64:\n t = tf.to_int32(t)\n example[name] = t\n\n return example","def parse_single_example(serialized_example):\n feature_description = {\n \"immrf/data\": tf.io.FixedLenFeature([], tf.string),\n \"immrf/shape\": tf.io.VarLenFeature(tf.int64),\n \"immrf/path\": tf.io.FixedLenFeature([], tf.string),\n \"tmap/data\": tf.io.FixedLenFeature([], tf.string),\n \"tmap/shape\": tf.io.VarLenFeature(tf.int64),\n \"tmap/path\": tf.io.FixedLenFeature([], tf.string),\n \"mask/data\": tf.io.FixedLenFeature([], tf.string),\n \"mask/shape\": tf.io.VarLenFeature(tf.int64),\n \"mask/path\": tf.io.FixedLenFeature([], tf.string),\n }\n slice = tf.io.parse_single_example(serialized_example, feature_description)\n for key in [\"immrf\", \"tmap\", \"mask\"]:\n slice[key + \"/data\"] = tf.io.decode_raw(slice[key + \"/data\"], out_type=tf.float32)\n slice[key + \"/data\"] = utils.reshape_back(slice, key)\n return slice","def _decode_record(record, name_to_features):\n\t\t\texample = tf.parse_single_example(record, name_to_features)\n\n\t\t\t# tf.Example only supports tf.int64, but the TPU only supports tf.int32.\n\t\t\t# So cast all int64 to int32.\n\t\t\tfor name in list(example.keys()):\n\t\t\t\tt = example[name]\n\t\t\t\tif t.dtype == tf.int64:\n\t\t\t\t\tt = tf.to_int32(t)\n\t\t\t\texample[name] = t\n\n\t\t\treturn example","def read_input_bytes_feature(feature_map, key, shape):\n if shape is None:\n (dim_z, dim_y, dim_x) = feature_map.feature[key + '/dim'].int64_list.value\n else:\n (dim_z, dim_y, dim_x) = shape\n tensor = np.fromstring(\n feature_map.feature[key].bytes_list.value[0], dtype=np.uint8).reshape(\n dim_z, dim_y, dim_x)\n return tensor","def _decode_record(self, record, name_to_features):\n example = tf.parse_single_example(record, name_to_features)\n\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\n # So cast all int64 to int32.\n for name in list(example.keys()):\n t = example[name]\n if t.dtype == tf.int64:\n t = tf.to_int32(t)\n # tf.logging.info(t)\n # t = tf.sparse.to_dense(t)\n # tf.logging.info(t.get_shape().as_list())\n # assert t.get_shape().as_list()[0] is not None\n example[name] = t\n \n del example[\"source_sos_ids\"]\n del example[\"source_sos_mask\"]\n\n return example","def _decode_record(record, name_to_features):\n example = tf.parse_single_example(record, name_to_features)\n\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\n # So cast all int64 to int32.\n for name in list(example.keys()):\n print(name)\n t = example[name]\n if t.dtype == tf.int64:\n t = tf.to_int32(t)\n example[name] = t\n\n return example","def _parse_single_example(example, options):\n # Initialize `keys_to_features`.\n keys_to_features = {\n TFExampleFields.img_id: tf.io.FixedLenFeature([], tf.string),\n TFExampleFields.annot_id: tf.io.FixedLenFeature([], tf.string),\n TFExampleFields.answer_label: tf.io.FixedLenFeature([], tf.int64),\n TFExampleFields.img_bbox_label: tf.io.VarLenFeature(tf.string),\n TFExampleFields.img_bbox_score: tf.io.VarLenFeature(tf.float32),\n TFExampleFields.img_bbox_feature: tf.io.VarLenFeature(tf.float32),\n TFExampleFields.question: tf.io.VarLenFeature(tf.string),\n TFExampleFields.question_tag: tf.io.VarLenFeature(tf.int64),\n }\n for bbox_key in TFExampleFields.img_bbox_field_keys:\n bbox_field = os.path.join(TFExampleFields.img_bbox_scope, bbox_key)\n keys_to_features[bbox_field] = tf.io.VarLenFeature(tf.float32)\n for i in range(1, 1 + NUM_CHOICES):\n keys_to_features.update({\n TFExampleFields.cls_bert + '_%i' % i:\n tf.io.VarLenFeature(tf.float32),\n TFExampleFields.question_bert + '_%i' % i:\n tf.io.VarLenFeature(tf.float32),\n TFExampleFields.answer_choice + '_%i' % i:\n tf.io.VarLenFeature(tf.string),\n TFExampleFields.answer_choice_tag + '_%i' % i:\n tf.io.VarLenFeature(tf.int64),\n TFExampleFields.answer_choice_bert + '_%i' % i:\n tf.io.VarLenFeature(tf.float32)\n })\n\n # Initialize `items_to_handlers`.\n items_to_handlers = {\n InputFields.img_id:\n tfexample_decoder.Tensor(tensor_key=TFExampleFields.img_id,\n default_value=''),\n InputFields.annot_id:\n tfexample_decoder.Tensor(tensor_key=TFExampleFields.annot_id,\n default_value=''),\n InputFields.answer_label:\n tfexample_decoder.Tensor(tensor_key=TFExampleFields.answer_label,\n default_value=-1),\n InputFields.object_bboxes:\n tfexample_decoder.BoundingBox(\n keys=TFExampleFields.img_bbox_field_keys,\n prefix=TFExampleFields.img_bbox_scope),\n InputFields.object_labels:\n tfexample_decoder.Tensor(tensor_key=TFExampleFields.img_bbox_label,\n default_value=''),\n InputFields.object_scores:\n tfexample_decoder.Tensor(tensor_key=TFExampleFields.img_bbox_score,\n default_value=0),\n InputFields.question:\n tfexample_decoder.Tensor(tensor_key=TFExampleFields.question,\n default_value=PAD),\n InputFields.question_tag:\n tfexample_decoder.Tensor(tensor_key=TFExampleFields.question_tag,\n default_value=-1),\n TFExampleFields.img_bbox_feature:\n tfexample_decoder.Tensor(tensor_key=TFExampleFields.img_bbox_feature,\n default_value=0),\n }\n\n for i in range(1, 1 + NUM_CHOICES):\n tensor_key = TFExampleFields.cls_bert + '_%i' % i\n items_to_handlers[tensor_key] = tfexample_decoder.Tensor(\n tensor_key=tensor_key, default_value=0)\n tensor_key = TFExampleFields.question_bert + '_%i' % i\n items_to_handlers[tensor_key] = tfexample_decoder.Tensor(\n tensor_key=tensor_key, default_value=0)\n tensor_key = TFExampleFields.answer_choice + '_%i' % i\n items_to_handlers[tensor_key] = tfexample_decoder.Tensor(\n tensor_key=tensor_key, default_value=PAD)\n tensor_key = TFExampleFields.answer_choice_tag + '_%i' % i\n items_to_handlers[tensor_key] = tfexample_decoder.Tensor(\n tensor_key=tensor_key, default_value=-1)\n tensor_key = TFExampleFields.answer_choice_bert + '_%i' % i\n items_to_handlers[tensor_key] = tfexample_decoder.Tensor(\n tensor_key=tensor_key, default_value=0)\n if options.decode_jpeg:\n keys_to_features.update({\n TFExampleFields.img_encoded: tf.io.FixedLenFeature([], tf.string),\n TFExampleFields.img_format: tf.io.FixedLenFeature([], tf.string),\n })\n items_to_handlers.update({\n InputFields.img_data:\n tfexample_decoder.Image(image_key=TFExampleFields.img_encoded,\n format_key=TFExampleFields.img_format,\n shape=None)\n })\n\n # Decode example.\n example_decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,\n items_to_handlers)\n\n output_keys = example_decoder.list_items()\n output_tensors = example_decoder.decode(example)\n output_tensors = [\n x if x.dtype != tf.int64 else tf.cast(x, tf.int32) for x in output_tensors\n ]\n decoded_example = dict(zip(output_keys, output_tensors))\n return _update_decoded_example(decoded_example, options)","def _decode_record(record, name_to_features):\n example = tf.parse_single_example(record, name_to_features)\n\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\n # So cast all int64 to int32.\n for name in list(example.keys()):\n t = example[name]\n if t.dtype == tf.int64:\n t = tf.to_int32(t)\n example[name] = t\n\n return example","def _decode_record(record, name_to_features):\n example = tf.parse_single_example(record, name_to_features)\n\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\n # So cast all int64 to int32.\n for name in list(example.keys()):\n t = example[name]\n if t.dtype == tf.int64:\n t = tf.to_int32(t)\n example[name] = t\n\n return example","def parser(record):\n\n record_spec = {\n \"inputs\": tf.VarLenFeature(tf.int64),\n \"type_id\": tf.FixedLenFeature([1], tf.int64),\n }\n\n # retrieve serialized example\n example = tf.parse_single_example(\n serialized=record,\n features=record_spec)\n\n inputs = example[\"inputs\"]\n inp_len = tf.shape(inputs)[0]\n\n # expand type id to full length\n example[\"type_id\"] = tf.broadcast_to(example[\"type_id\"], [inp_len])\n\n # convert all sparse example to dense\n example = sparse_to_dense(example)\n\n return example","def parse_example(self, serialized_example):\n # Because of RaggedTensor specs, feature_specs can be a 2-level nested dict,\n # so have to wrap `tf.io.parse_single_example` between\n # `flatten_nest_dict`/`pack_as_nest_dict`.\n # {\n # 'video/image': tf.io.FixedLenSequenceFeature(...),\n # 'video/object/bbox': {\n # 'ragged_flat_values': tf.io.FixedLenSequenceFeature(...),\n # 'ragged_row_lengths_0', tf.io.FixedLenSequenceFeature(...),\n # },\n # }\n example = tf.io.parse_single_example(\n serialized=serialized_example,\n features=self.flat_feature_specs,\n )\n example = utils.pack_as_nest_dict(example, self._nested_feature_specs)\n\n example = { # pylint:disable=g-complex-comprehension\n k: _deserialize_single_field(example_data, tensor_info)\n for k, (example_data, tensor_info) in utils.zip_dict(\n example, self._flat_example_specs\n )\n }\n # Reconstruct all nesting\n example = utils.pack_as_nest_dict(example, self._example_specs)\n return example","def _decode_record(record, name_to_features):\n example = tf.parse_single_example(record, name_to_features)\n\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\n # So cast all int64 to int32.\n for name in list(example.keys()):\n t = example[name]\n if t.dtype == tf.int64:\n t = tf.to_int32(t)\n example[name] = t\n\n return example","def parse_sequence_example(serialized, image_feature, caption_feature):\n\tcontext, sequence = tf.parse_single_sequence_example(\n\t\t\tserialized,\n\t\t\tcontext_features={\n\t\t\t\t\timage_feature: tf.FixedLenFeature([], dtype=tf.string)\n\t\t\t},\n\t\t\tsequence_features={\n\t\t\t\t\tcaption_feature: tf.FixedLenSequenceFeature([], dtype=tf.int64),\n\t\t\t})\n\n\tencoded_image = context[image_feature]\n\tcaption = sequence[caption_feature]\n\treturn encoded_image, caption","def parse_observation(self, ob):\n print('shape of ob:', ob)\n print(list(self.feature_size_map.keys()))\n print(ob, list(self.feature_size_map.values()))\n print(tf.split(ob, list(self.feature_size_map.values()), 1))\n feature_map = {\n k : v\n for k, v in zip(\n list(self.feature_size_map.keys()), \n tf.split(ob, list(self.feature_size_map.values()), 1))\n }\n \n for k in {'board', 'bomb_blast_strength', 'bomb_life'}:\n feature_map[k] = tf.reshape(feature_map[k], (-1, 11, 11, 1))\n return feature_map","def _decode_record(record, name_to_features):\n example = tf.parse_single_example(record, name_to_features)\n\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\n # So cast all int64 to int32.\n for name in list(example.keys()):\n t = example[name]\n if t.dtype == tf.int64:\n t = tf.to_int32(t)\n example[name] = t\n\n return example[\"src_ids\"].values, example[\"tgt_ids\"].values, example[\"label\"][0]","def extract_features(self, preprocessed_inputs):\n preprocessed_inputs.get_shape().assert_has_rank(4)\n shape_assert = tf.Assert(\n tf.logical_and(tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),\n tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),\n ['image size must at least be 33 in both height and width.'])\n\n feature_map_layout = {\n 'from_layer': ['conv4', '', '', '', '', '', ''],\n 'layer_depth': [-1, 1024, 1024, 512, 256, 256, 256],\n }\n\n with tf.control_dependencies([shape_assert]):\n with slim.arg_scope(self._conv_hyperparams):\n with tf.variable_scope('vgg_16',\n reuse=self._reuse_weights) as scope:\n net, image_features = vgg.vgg_16_base(\n preprocessed_inputs,\n final_endpoint='pool5',\n trainable=False,\n scope=scope)\n feature_maps = feature_map_generators.multi_resolution_feature_maps(\n feature_map_layout=feature_map_layout,\n depth_multiplier=self._depth_multiplier,\n min_depth=self._min_depth,\n insert_1x1_conv=True,\n image_features=image_features)\n\n return feature_maps.values()","def parse_cds_features(self, handle, alphabet=..., tags2id=...):\n ...","def data_parser(data):\n\n with open(data, 'r') as inp:\n\n # take every sample\n # the last line in the text file is empty, so reading until -1\n samples = inp.read().split('\\n')[:-1]\n\n vec = []\n labels = []\n for sample in samples:\n # file is tab delimited\n split_samples = sample.split('\\t')\n # last column contains the label\n labels.append(int(split_samples[-1]))\n\n features = []\n for feature in split_samples[:-1]:\n features.append(float(feature))\n vec.append(features)\n\n # make the features and labels as a numpy array\n vec = np.array(vec)\n labels = np.array(labels)\n return vec, labels","def _read_tf_example(self,\n record: tf.Tensor,\n feature_preprocessor: Callable[[str], List[str]]\n ) -> types.FeatureAndLabelTensors:\n\n keys_to_features = {}\n keys_to_features[self._text_feature] = tf.FixedLenFeature([], tf.string)\n for label, dtype in self._labels.items():\n keys_to_features[label] = tf.FixedLenFeature([], dtype)\n parsed = tf.parse_single_example(\n record, keys_to_features) # type: Dict[str, types.Tensor]\n\n text = parsed[self._text_feature]\n # I think this could be a feature column, but feature columns seem so beta.\n preprocessed_text = feature_preprocessor(text)\n features = {self._text_feature: preprocessed_text}\n if self._round_labels:\n labels = {label: tf.round(parsed[label]) for label in self._labels}\n else:\n labels = {label: parsed[label] for label in self._labels}\n\n return features, labels","def parse_from_tf_example(serialized,\n context_feature_spec=None,\n example_feature_spec=None,\n size_feature_name=None,\n mask_feature_name=None):\n batch_size = tf.shape(serialized)[0]\n features = tf.io.parse_example(\n serialized, features={\n **context_feature_spec,\n **example_feature_spec\n })\n for feature_key, feature_type in example_feature_spec.items():\n if isinstance(feature_type, tf.io.RaggedFeature):\n features[feature_key] = tf.expand_dims(features[feature_key], axis=1)\n else:\n # feature is either a Tensor or SparseTensor.\n features[feature_key] = utils.reshape_first_ndims(features[feature_key],\n 1, [batch_size, 1])\n if size_feature_name is not None:\n # Populate features with a size feature of value 1, corresponding to only\n # one example per list.\n features[size_feature_name] = tf.ones(shape=[batch_size])\n if mask_feature_name:\n features[mask_feature_name] = tf.sequence_mask(tf.ones(shape=[batch_size]))\n return features","def _parse_csv(rows_string_tensor):\n # Last dim is the label.\n num_features = len(FEATURE_KEYS)\n num_columns = num_features + 1\n columns = tf.decode_csv(rows_string_tensor,\n record_defaults=[[]] * num_columns)\n features = dict(zip(FEATURE_KEYS, columns[:num_features]))\n labels = tf.cast(columns[num_features], tf.int32)\n return features, labels","def parse_function(example_proto):\r\n\r\n\t# Parse through features and extract byte string\r\n\tparsed_features = tf.parse_single_example(example_proto,features ={\r\n\t\t'image': tf.FixedLenFeature([],tf.string),\r\n\t\t'joint': tf.FixedLenFeature([],tf.string),\r\n\t\t'offset': tf.FixedLenFeature([],tf.string),\r\n\t\t'handScale': tf.FixedLenFeature([],tf.string)\r\n\t\t},name='features')\r\n\r\n\t# Decode content into correct types\r\n\timage_dec = tf.decode_raw(parsed_features['image'],tf.float32)\r\n\tjoint_dec = tf.decode_raw(parsed_features['joint'],tf.float32)\r\n\toffset_dec = tf.decode_raw(parsed_features['offset'],tf.float32)\r\n\thandScale_dec = tf.decode_raw(parsed_features['handScale'],tf.float32)\r\n\r\n\t# Reshape image to 176x176\r\n\timage_reshaped = tf.reshape(image_dec,[176,176,1])\r\n\r\n\t# Crop 128x128 image around COM\r\n\timage_com_cropped = tf.image.crop_to_bounding_box(image_reshaped,24,24,128,128)\r\n\r\n\treturn image_com_cropped, joint_dec, offset_dec, handScale_dec","def _parse_fn(serialized):\n features = parse_example(serialized)\n label = features.pop('clicked')\n return features, label","def parse_tfrecord(raw_example, features_name, labels_name):\n feature_spec = {\n name: tf.io.FixedLenSequenceFeature((), tf.int64, True)\n for name in [features_name, labels_name]}\n parsed_example = tf.io.parse_single_example(\n serialized=raw_example,\n features=feature_spec)\n labels = parsed_example.pop(labels_name)\n features = parsed_example.pop(features_name)\n with tf.control_dependencies([\n tf.compat.v1.assert_equal(tf.shape(input=features), tf.shape(input=labels))\n ]):\n return features, labels","def read_metadata_record(raw_features_string):\n full_metadata = json.loads(raw_features_string)\n return {\"sha256\": full_metadata[\"sha256\"], \"appeared\": full_metadata[\"appeared\"], \"label\": full_metadata[\"label\"]}","def _preprocess(self, features, labels):\n with tf.variable_scope('preprocess'):\n with tf.variable_scope('image'):\n features['image_orig'] = features['image']\n image = tf.image.convert_image_dtype(features['image_orig'],\n dtype=tf.float32)\n if self.mode == ModeKeys.TRAIN:\n images = tf.unstack(image)\n images = [augment_image(img) for img in images]\n image = tf.stack(images)\n image = tf.subtract(image, 0.5)\n image = tf.multiply(image, 2.0)\n features['image'] = image\n\n if labels is None:\n return features, None\n\n with tf.variable_scope('label'):\n # TODO(Shancheng): use start token and end token rather constant 0\n # labels for decoder input\n labels['label_input'] = tf.concat([labels['label'][:, -1:],\n labels['label'][:, 0:-1]], axis=1)\n # from text length to training label length\n labels['length'] = tf.reshape(labels['length'], [-1])\n labels['length'] = labels['length'] + 1\n\n return features, labels","def _decode_record(record):\r\n example = tf.io.parse_single_example(serialized=record, features=feature_description)\r\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\r\n # So cast all int64 to int32.\r\n for key in [k for k in example.keys() if k not in ['example_id', 'unique_ids']]:\r\n example[key] = tf.cast(example[key], dtype=tf.int32)\r\n if is_training:\r\n features = {\r\n 'input_ids': example['input_ids'],\r\n 'input_mask': example['input_mask'],\r\n 'segment_ids': example['segment_ids']\r\n }\r\n labels = {\r\n 'start_logits_or_probs': tf.one_hot(example['start_positions'],\r\n depth=seq_length, dtype=tf.float32),\r\n 'end_logits_or_probs': tf.one_hot(example['end_positions'],\r\n depth=seq_length, dtype=tf.float32),\r\n 'ans_type': tf.one_hot(example['answer_types'],\r\n depth=len(ANSWER_TYPE_ORDER), dtype=tf.float32)\r\n }\r\n return (features, labels)\r\n else:\r\n return example","def process_features(\n config,\n raw_features: Union[tf.train.Example, features.FeatureDict],\n random_seed: int) -> features.FeatureDict:\n if isinstance(raw_features, dict):\n return features.np_example_to_features(\n np_example=raw_features,\n config=config,\n random_seed=random_seed)\n else:\n return features.tf_example_to_features(\n tf_example=raw_features,\n config=config,\n random_seed=random_seed)","def _decode_record(record, name_to_features):\n example = tf.io.parse_single_example(serialized=record, features=name_to_features)\n\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\n # So cast all int64 to int32.\n for name in list(example.keys()):\n t = example[name]\n if t.dtype == tf.int64:\n t = tf.cast(t, dtype=tf.int32)\n example[name] = t\n\n return example","def read_metadata_record(raw_features_string):\n all_data = json.loads(raw_features_string)\n metadata_keys = {\"sha256\", \"appeared\", \"label\", \"avclass\"}\n return {k: all_data[k] for k in all_data.keys() & metadata_keys}","def _as_bytes_feature(in_string):\n return tf.train.Feature(bytes_list=tf.train.BytesList(value=[in_string]))","def _handle_feature(fea):\n if len(fea.shape) == 1:\n fea = np.array([fea]).T\n\n return fea","def parse_sequence_example(serialized, image_id, image_feature, caption_feature):\r\n context, sequence = tf.parse_single_sequence_example(\r\n serialized,\r\n context_features={\r\n image_id : tf.FixedLenFeature([], dtype=tf.int64),\r\n image_feature: tf.FixedLenFeature([], dtype=tf.string)\r\n },\r\n sequence_features={\r\n caption_feature: tf.FixedLenSequenceFeature([], dtype=tf.int64),\r\n })\r\n\r\n encoded_image_id = context[image_id]\r\n encoded_image = context[image_feature]\r\n caption = sequence[caption_feature]\r\n return encoded_image_id, encoded_image, caption","def extract_features(self, inputs):\n pass","def parse_example_proto(example_serialized, semantic_level_settings):\n # Dense features in Example proto.\n feature_map = {\n 'image/encoded': tf.FixedLenFeature([], dtype=tf.string, default_value=''),\n 'image/filename': tf.FixedLenFeature([], dtype=tf.string, default_value=''),\n }\n for level, num in semantic_level_settings:\n feature_map.update({'image/label/%s' % level:\n tf.FixedLenFeature([num], dtype=tf.int64, default_value=[-1] * num)})\n features = tf.parse_single_example(example_serialized, feature_map)\n labels = [tf.cast(features['image/label/%s' % level], dtype=tf.int32)\n for level, _ in semantic_level_settings]\n return features['image/encoded'], labels, features['image/filename']","def convert_full_features_to_input_features(raw_features):\n data_features = mx.gluon.data.SimpleDataset(list(itertools.chain.from_iterable(raw_features)))\n data_features = data_features.transform(lambda *example: (\n example[0], # example_id\n example[7], # inputs_id\n example[9], # segment_ids\n example[2], # valid_length,\n example[8], # p_mask\n example[10], # start_position,\n example[11], # end_position\n example[14])) # is_impossible\n return data_features","def parser(record):\n parsed = tf.parse_single_example(record, transformed_feature_spec)\n label = parsed.pop(LABEL_KEY)\n return parsed, label","def load_features(self, features):\n pass\n # self.features = features","def parse_feature_config(feature_config, feature_map):\n feature_name = feature_config[0]\n feature_args = feature_config[1]\n featurizer = feature_map[feature_name]\n\n return feature_name, feature_args, featurizer","def convert_example_to_features_for_test(example, max_seq_length, tokenizer):\r\n features = []\r\n tokens = []\r\n labels_temp = []\r\n for i, word in enumerate(example.text_a.split(' ')):\r\n token_wordpiece = tokenizer.tokenize(word) #04/26/1882 ['04', '/', '26', '/', '1882']\r\n tokens.extend(token_wordpiece)\r\n for m in range(len(token_wordpiece)):\r\n if m == 0:\r\n labels_temp.append(0)\r\n else:\r\n labels_temp.append('X')\r\n # max_seq_length-1\r\n if len(tokens) >= max_seq_length - 1:\r\n tokens = tokens[0:(max_seq_length - 2)]\r\n labels_temp = labels_temp[0:(max_seq_length - 2)]\r\n\r\n ntokens = []\r\n new_labels_temp = []\r\n segment_ids = []\r\n\r\n ntokens.append('[CLS]')\r\n new_labels_temp.append('[CLS]')\r\n segment_ids.append(0)\r\n for i, token in enumerate(tokens):\r\n ntokens.append(token)\r\n new_labels_temp.append(labels_temp[i])\r\n segment_ids.append(0)\r\n ntokens.append('[SEP]')\r\n new_labels_temp.append('[SEP]')\r\n segment_ids.append(0)\r\n input_ids = tokenizer.convert_tokens_to_ids(ntokens)\r\n input_mask = [1] * len(input_ids)\r\n\r\n #if the length is short, tianbu 0\r\n while len(input_ids) < max_seq_length:\r\n input_ids.append(0)\r\n input_mask.append(0)\r\n segment_ids.append(0)\r\n #we do not concerned about it\r\n ntokens.append('NULL')\r\n\r\n assert len(input_ids) == max_seq_length\r\n assert len(input_mask) == max_seq_length\r\n assert len(segment_ids) == max_seq_length\r\n\r\n features.append(InputFeatures(input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, label_ids=None))\r\n return features, new_labels_temp","def _decode_csv(line):\n column_header = column_names if with_target else column_names[:4]\n record_defaults = [[0.] for _ in xrange(len(column_names) - 1)]\n # Pass label as integer.\n if with_target:\n record_defaults.append([0])\n columns = tf.decode_csv(line, record_defaults=record_defaults)\n features = dict(zip(column_header, columns))\n target = features.pop(column_names[4]) if with_target else None\n return features, target","def image_to_features(image):\n image = tf.keras.preprocessing.image.img_to_array(image)\n image = tf.keras.applications.mobilenet_v2.preprocess_input(image)\n image = np.expand_dims(image, axis=0)\n return image","def convert_sentences_to_features(sentences, seq_length, tokenizer):\n \n num_sent = len(sentences)\n input_type_ids = np.zeros((num_sent, seq_length), dtype=np.int32)\n input_ids = np.zeros((num_sent, seq_length), dtype=np.int32)\n input_mask = np.zeros((num_sent, seq_length), dtype=np.int32)\n \n for idx, sent in enumerate(sentences):\n tokens = tokenizer.tokenize(sent)\n tokens = tokens[0:min((seq_length - 2), len(tokens))] # truncate tokens longer than seq_length\n tokens.insert(0, \"[CLS]\")\n tokens.append(\"[SEP]\")\n \n input_ids[idx,:len(tokens)] = np.array(tokenizer.convert_tokens_to_ids(tokens), dtype=np.int32)\n input_mask[idx,:len(tokens)] = np.ones(len(tokens), dtype=np.int32)\n\n assert len(input_ids[idx]) == seq_length\n assert len(input_mask[idx]) == seq_length\n assert len(input_type_ids[idx]) == seq_length\n\n return input_ids, input_type_ids, input_mask","def extractFeatures(self, datum):\n abstract","def _parse_bin(self, byte):\r\n bin_line = bin(parse_int([byte], 'big'))[2:].rjust(8, \"0\")\r\n self.ct_flag = bool(int(bin_line[0], 2))\r\n self.ct_len = 3 * 2**(int(bin_line[-3:], 2) + 1) if self.ct_flag else 0\r\n self.colors_count = 2**(3 * (int(bin_line[1:4], 2) + 1))","def _process_features(self, limit):\n\n if self.testMode:\n g = self.testgraph\n else:\n g = self.graph\n model = Model(g)\n raw = '/'.join((self.rawdir, 'feature'))\n logger.info(\"building labels for features\")\n\n line_counter = 0\n with open(raw, 'r') as f:\n filereader = csv.reader(f, delimiter='\\t', quotechar='\\\"')\n f.readline() # read the header row; skip\n for line in filereader:\n (feature_id, dbxref_id, organism_id, name, uniquename,\n residues, seqlen, md5checksum, type_id, is_analysis,\n timeaccessioned, timelastmodified) = line\n\n feature_key = feature_id\n if re.search(r'[\\|\\s\\[\\]\\{\\}\\\\<\\>]', uniquename):\n # some uniquenames have pipes or other nasty chars!\n # for example: FB||||FBrf0133242|Hugh-u1\n feature_id = self._makeInternalIdentifier(\n 'feature', feature_key)\n else:\n feature_id = 'FlyBase:'+uniquename\n self.idhash['feature'][feature_key] = feature_id\n self.feature_types[feature_key] = type_id\n self.label_hash[feature_id] = name\n\n if feature_key not in self.feature_to_organism_hash:\n self.feature_to_organism_hash[feature_key] = set()\n self.feature_to_organism_hash[feature_key].add(organism_id)\n\n # HACK - FBgn are genes, and therefore classes,\n # all else be individuals\n is_gene = False\n if re.search(r'(FBgn|FBog)', feature_id):\n self.idhash['gene'][feature_key] = feature_id\n is_gene = True\n elif re.search(r'FBa[lb]', feature_id):\n self.idhash['allele'][feature_key] = feature_id\n elif re.search(r'FBt[ip]', feature_id):\n self.idhash['feature'][feature_key] = feature_id\n\n if self.testMode and \\\n int(feature_key) not in self.test_keys['gene'] + \\\n self.test_keys['allele'] + self.test_keys['feature']:\n continue\n\n # now do something with it!\n # switch on type_id\n if name.strip() == '':\n name = uniquename\n\n type_key = type_id\n type_id = self.idhash['cvterm'][type_key]\n\n # skip some features by type\n types_to_skip = [\n 'SO:0000316', # CDS\n 'SO:0000696', # oligos\n 'SO:0000358', # polypeptide\n 'SO:0000234', # transcripts\n ]\n\n type_keys_to_skip = [\n 596, # pcr_product\n 57096, # mature peptide\n 57097, # signal_peptide\n 57270, # repeat masker\n 58210, # alignment\n 59643, # cDNA_clone\n 60006, # uncharacterized_change_in_nucleotide_sequence\n 61351, # oligo\n 61467, # polypeptide_domain\n 257, # exon\n 286, # intron\n ]\n\n organisms_to_skip = [\n 2 # computational result\n ]\n\n if type_id in types_to_skip \\\n or int(type_key) in type_keys_to_skip\\\n or int(organism_id) in organisms_to_skip:\n continue\n\n line_counter += 1\n\n if int(type_key) == 604: # RNAi_reagent\n # TODO add other reagents?\n self.idhash['reagent'][feature_key] = feature_id\n\n # deal with the taxonomy\n # only get taxa for features that are actually used in our set\n tax_internal_id = self._makeInternalIdentifier(\n 'organism', organism_id)\n if organism_id not in self.checked_organisms:\n # will get the NCBITax if necessary\n tax_id = self._get_organism_id(organism_id)\n self.checked_organisms.add(organism_id)\n else:\n tax_id = self.idhash['organism'][organism_id]\n\n tax_label = self.label_hash.get(tax_id)\n if not re.search(r'FBog', feature_id) \\\n and re.search(r'Drosophila', tax_label):\n # make only fly things leaders\n model.makeLeader(feature_id)\n\n if not self.testMode \\\n and limit is not None and line_counter > limit:\n pass\n else:\n if is_gene:\n model.addClassToGraph(\n feature_id, name, type_id)\n g.addTriple(\n feature_id, model.object_properties['in_taxon'],\n tax_id)\n else:\n if re.search('FBa[lb]', feature_id):\n type_id = Genotype.genoparts['allele']\n model.addIndividualToGraph(feature_id, name, type_id)\n\n # stop adding what we do not appreciate\n # if is_obsolete == 't':\n # if is_gene:\n # model.addDeprecatedClass(feature_id)\n # else:\n # model.addDeprecatedIndividual(feature_id)\n # self.deprecated_features.add(feature_key)\n\n model.addClassToGraph(tax_id)\n if tax_id != tax_internal_id:\n model.addEquivalentClass(tax_id, tax_internal_id)\n\n model.addComment(\n feature_id,\n self._makeInternalIdentifier('feature', feature_key))\n\n # TODO save checked_organisms fbid to ncbitax mapping to\n # a local file to speed up subsequent searches\n\n return","def read_features(self):\r\n def unpack_keypoint(data):\r\n try:\r\n kpts = data['keypoints']\r\n desc = data['descriptors']\r\n keypoints = [cv.KeyPoint(x, y, _size, _angle, _response, int(_octave), int(_class_id))\r\n for x, y, _size, _angle, _response, _octave, _class_id in list(kpts)]\r\n return keypoints, np.array(desc)\r\n except(IndexError):\r\n return np.array([]), np.array([])\r\n try:\r\n data = np.load(self.features_path + self.id + \".npz\")\r\n self.keypoints, self.descriptors = unpack_keypoint(data)\r\n logging.info(f\"Existing features for {self.name} found in features directory.\")\r\n except FileNotFoundError:\r\n logging.info(f\"Features for {self.name} not found in {self.features_path}.\")","def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer):\n\n label_map = {} # label\n for (i, label) in enumerate(label_list): # ['0', '1']\n label_map[label] = i\n\n features = [] # feature\n for (ex_index, example) in enumerate(examples):\n text_a_id = int(example.text_a_id)\n text_b_id = int(example.text_b_id)\n\n text_a_fields = example.text_a.split(\" _eop_ \")\n \n tokens_a = []\n text_a_subtype = []\n for text_a_field_idx, text_a_field in enumerate(text_a_fields):\n text_a_field_token = tokenizer.tokenize(text_a_field)\n tokens_a.extend(text_a_field_token)\n text_a_subtype.extend([text_a_field_idx]*len(text_a_field_token))\n assert len(tokens_a) == len(text_a_subtype)\n\n tokens_b = None\n if example.text_b:\n tokens_b = tokenizer.tokenize(example.text_b) # text_b tokenize\n\n if tokens_b: # if has b\n # Modifies `tokens_a` and `tokens_b` in place so that the total\n # length is less than the specified length.\n # Account for [CLS], [SEP], [SEP] with \"- 3\"\n _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3) # truncate\n else:\n # Account for [CLS] and [SEP] with \"- 2\"\n if len(tokens_a) > max_seq_length - 2:\n tokens_a = tokens_a[0:(max_seq_length - 2)]\n\n # The convention in BERT is:\n # (a) For sequence pairs:\n # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]\n # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1\n # (b) For single sequences:\n # tokens: [CLS] the dog is hairy . [SEP]\n # type_ids: 0 0 0 0 0 0 0\n #\n # Where \"type_ids\" are used to indicate whether this is the first\n # sequence or the second sequence. The embedding vectors for `type=0` and\n # `type=1` were learned during pre-training and are added to the wordpiece\n # embedding vector (and position vector). This is not *strictly* necessary\n # since the [SEP] token unambiguously separates the sequences, but it makes\n # it easier for the model to learn the concept of sequences.\n #\n # For classification tasks, the first vector (corresponding to [CLS]) is\n # used as as the \"sentence vector\". Note that this only makes sense because # (?)\n # the entire model is fine-tuned.\n tokens = []\n segment_ids = []\n subtype_ids = []\n tokens.append(\"[CLS]\")\n segment_ids.append(0)\n subtype_ids.append(0)\n for token_idx, token in enumerate(tokens_a):\n tokens.append(token)\n segment_ids.append(0)\n subtype_ids.append(text_a_subtype[token_idx])\n tokens.append(\"[SEP]\")\n segment_ids.append(0)\n subtype_ids.append(1)\n\n if tokens_b:\n for token_idx, token in enumerate(tokens_b):\n tokens.append(token)\n segment_ids.append(1)\n subtype_ids.append(2)\n tokens.append(\"[SEP]\")\n segment_ids.append(1)\n subtype_ids.append(2)\n\n input_sents = tokenizer.convert_tokens_to_ids(tokens)\n\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\n # tokens are attended to.\n input_mask = [1] * len(input_sents) # mask\n\n # Zero-pad up to the sequence length.\n while len(input_sents) < max_seq_length:\n input_sents.append(0)\n input_mask.append(0)\n segment_ids.append(0)\n subtype_ids.append(0)\n\n assert len(input_sents) == max_seq_length\n assert len(input_mask) == max_seq_length\n assert len(segment_ids) == max_seq_length\n assert len(subtype_ids) == max_seq_length\n\n label_id = label_map[example.label]\n\n if ex_index%2000 == 0:\n print('convert_{}_examples_to_features'.format(ex_index))\n\n features.append(\n InputFeatures( # object\n text_a_id=text_a_id,\n text_b_id=text_b_id,\n input_sents=input_sents,\n input_mask=input_mask,\n segment_ids=segment_ids,\n subtype_ids=subtype_ids,\n label_id=label_id))\n\n return features","def parse_type(fobj, data_type):\n if data_type == \"Boolean\": ## False if 0x00 else True\n return bool(fobj.read(1))\n elif data_type == \"Byte\": ## 1 byte int\n return fobj.read(1)[0]\n elif data_type == \"DateTime\": ## 8 bytes signed int\n return struct.unpack(\"<q\", fobj.read(8))[0]\n elif data_type == \"Double\": ## 8 bytes floating point\n return struct.unpack(\"<d\", fobj.read(8))[0]\n elif data_type == \"Int\": ## 4 bytes unsigned int\n return struct.unpack(\"<I\", fobj.read(4))[0]\n elif data_type == \"Int-Double pair\": ## 0x08-Int-0x0d-Double with AssertionError\n bb = fobj.read(1)[0]\n if bb != 0x08:\n raise AssertionError('parse_type(fobj, data_type): '\n '1st byte(%s) of \"Int-Double pair\" != 0x08' % bb)\n first_int = parse_type(fobj, \"Int\")\n bb = fobj.read(1)[0]\n if bb != 0x0d:\n raise AssertionError('parse_type(fobj, data_type): '\n '6th byte(%s) of \"Int-Double pair\" != 0x0d' % bb)\n return [first_int, parse_type(fobj, \"Double\")]\n elif data_type == \"Int-Double pair*\": ## int(n) - \"Int-Double pair\"*n\n return [parse_type(fobj, \"Int-Double pair\") for i in range(parse_type(fobj, \"Int\"))]\n elif data_type == \"Long\": ## 8 bytes unsigned int\n return struct.unpack(\"<Q\", fobj.read(8))[0]\n elif data_type == \"Short\": ## 2 bytes unsigned int\n return struct.unpack(\"<H\", fobj.read(2))[0]\n elif data_type == \"Single\": ## 4 bytes floating point\n return struct.unpack(\"<f\", fobj.read(4))[0]\n elif data_type == \"String\": ## 0x00 or 0x0b - ULE128(n) - UTF-8(length=n)\n bb = fobj.read(1)[0]\n if bb == 0x00:\n return None\n elif bb != 0x0b:\n ## TODO: show integers in assertion error in hexadecimal and decimal\n ## to make debug more convenient (cause I may inspect the file in a byte reader.\n raise AssertionError('parse_type(fobj, data_type): '\n '1st byte(%s) of \"String\" not in {0x00, 0x0b}' % bb)\n strlen = parse_type(fobj, \"ULEB128\")\n return fobj.read(strlen).decode(\"utf-8\")\n elif data_type == \"ULEB128\": ## https://en.wikipedia.org/wiki/LEB128#Decode_unsigned_integer\n i = 0 ## derived from the wiki psuedo code\n res = 0\n shift = 0\n while True:\n bb = fobj.read(1)[0]\n i += 1\n res |= ((bb & 0b1111111) << shift)\n if (bb & 0b10000000) == 0:\n break\n shift += 7\n return res\n elif data_type == \"Timing point\": ## Double - Double - Boolean\n return parse_types(fobj, [\"Double\", \"Double\", \"Boolean\"])\n elif data_type == \"Timing point+\": ## int(n) - \"Timing point\"*n\n return [parse_type(fobj, \"Timing point\") for i in range(parse_type(fobj, \"Int\"))]\n else:\n raise NotImplementedError('parse_type(fobj, data_type): Unknown data type: \"%s\".' % data_type)","def read_images(idx_filename, feature_type='ip-above', only_images=True):\n with gzip.open(idx_filename, 'rb') as f:\n magic_numbers = f.read(4)\n # print('magic_number', magic_numbers)\n assert magic_numbers[0] == 0 and magic_numbers[1] == 0\n if magic_numbers[2] != 8:\n raise AssertionError('Only support for unsigned char')\n shape = magic_numbers[3]\n # print('shape', shape)\n num_examples = int.from_bytes(f.read(4), byteorder='big')\n # print('number of examples',num_examples)\n dimensions = []\n for _ in range(shape - 1):\n dimensions.append(int.from_bytes(f.read(4), byteorder='big'))\n # print('dimensions', dimensions)\n data_list = []\n for _ in range(num_examples):\n each_data_point = _read(dimensions, f)\n session_info = _extract_session_info(each_data_point[:SESSION_BYTE_LEN])\n if feature_type == 'payload-len':\n actual_pkt_count = int.from_bytes(each_data_point[SESSION_BYTE_LEN:SESSION_BYTE_LEN + 4],\n byteorder='big')\n actual_byte_count = int.from_bytes(each_data_point[SESSION_BYTE_LEN + 4:SESSION_BYTE_LEN + 6],\n byteorder='big')\n other_data = each_data_point[SESSION_BYTE_LEN + 6:]\n elif feature_type == 'ip-above':\n actual_pkt_count = each_data_point[SESSION_BYTE_LEN]\n other_data = each_data_point[SESSION_BYTE_LEN + 1:]\n else:\n raise AssertionError('feature_type could only be \"payload-len\" or \"ip-above\"')\n if only_images is True:\n data_list.append(other_data)\n elif only_images is False:\n if feature_type == 'ip-above':\n data_list.append((session_info, actual_pkt_count, other_data))\n elif feature_type == 'payload-len':\n data_list.append((session_info, actual_pkt_count, actual_byte_count, other_data))\n else:\n raise AssertionError('feature_type could only be \"payload-len\" or \"ip-above\"')\n else:\n raise AssertionError('only_images could only be True or False')\n\n assert f.read() == b''\n\n data_list = np.array(data_list)\n return data_list","def parse_record(raw_record, is_training, dtype):\n\n templar_buffer, search_buffer, templar_bbox, search_bbox = parse_example_proto(raw_record)\n templar_img, search_img, score, score_weight, tight_temp_bbox, tight_search_bbox = preprocess_pair(\n templar_buffer=templar_buffer, search_buffer=search_buffer, templar_bbox=templar_bbox,\n search_bbox=search_bbox, num_channels=_NUM_CHANNELS, is_training=is_training)\n\n templar_img = tf.cast(templar_img, dtype)\n search_img = tf.cast(search_img, dtype)\n score = tf.cast(score, tf.int32)\n score_weight = tf.cast(score_weight, dtype)\n tight_temp_bbox = tf.cast(tight_temp_bbox, tf.int32)\n #tight_search_bbox = tf.cast(tight_search_bbox, tf.int32)\n\n dict = {'templar': templar_img, 'search': search_img, 'score': score, 'score_weight': score_weight,\n 'tight_temp_bbox': tight_temp_bbox, 'tight_search_bbox': tight_search_bbox}\n\n return dict","def read_temp(temp):\n type_dict = {\"string\": str, \"unknown\": str, \"numeric\": float}\n with open(temp, 'r') as topen:\n feature_lines = topen.readlines()\n feature_labels = []\n feature_types = []\n for i, row in enumerate(feature_lines):\n if row.startswith(\"@attribute\"):\n flabel, ftype = row[11:-1].split(' ')\n feature_labels.append(flabel)\n feature_types.append(type_dict[ftype])\n elif row.startswith(\"@data\"):\n feature_values = feature_lines[i+1].split(\",\")\n if len(feature_values) < len(feature_labels):\n feature_values = feature_lines[i+2].split(\",\")\n for i, item in enumerate(feature_values):\n try:\n feature_values[i] = (feature_types[i](item))\n except:\n feature_values[i] = item\n return(dict(zip(feature_labels, feature_values)))","def _parse_function(self, example_proto):\n\n # Currently only supports jpeg and png.\n # Need to use this logic because the shape is not known for\n # tf.image.decode_image and we rely on this info to\n # extend label if necessary.\n def _decode_image(content, channels):\n return tf.cond(\n tf.image.is_jpeg(content),\n lambda: tf.image.decode_jpeg(content, channels),\n lambda: tf.image.decode_png(content, channels))\n\n features = {\n 'image/encoded':\n tf.FixedLenFeature((), tf.string, default_value=''),\n 'image/filename':\n tf.FixedLenFeature((), tf.string, default_value=''),\n 'image/format':\n tf.FixedLenFeature((), tf.string, default_value='jpeg'),\n 'image/height':\n tf.FixedLenFeature((), tf.int64, default_value=0),\n 'image/width':\n tf.FixedLenFeature((), tf.int64, default_value=0),\n 'image/segmentation/class/encoded':\n tf.FixedLenFeature((), tf.string, default_value=''),\n 'image/segmentation/class/format':\n tf.FixedLenFeature((), tf.string, default_value='png'),\n }\n\n parsed_features = tf.parse_single_example(example_proto, features)\n\n image = _decode_image(parsed_features['image/encoded'], channels=3)\n\n label = None\n if self.split_name != common.TEST_SET:\n label = _decode_image(\n parsed_features['image/segmentation/class/encoded'], channels=1)\n\n image_name = parsed_features['image/filename']\n if image_name is None:\n image_name = tf.constant('')\n\n sample = {\n common.IMAGE: image,\n common.IMAGE_NAME: image_name,\n common.HEIGHT: parsed_features['image/height'],\n common.WIDTH: parsed_features['image/width'],\n }\n\n if label is not None:\n if label.get_shape().ndims == 2:\n label = tf.expand_dims(label, 2)\n elif label.get_shape().ndims == 3 and label.shape.dims[2] == 1:\n pass\n else:\n raise ValueError('Input label shape must be [height, width], or '\n '[height, width, 1].')\n\n label.set_shape([None, None, 1])\n\n sample[common.LABELS_CLASS] = label\n\n return sample","def read_data(feature_file, label_file):","def _parse_tensor_or_dict(features):\n if isinstance(features, dict):\n keys = sorted(features.keys())\n with ops.colocate_with(features[keys[0]]):\n features = array_ops.concat([features[k] for k in keys], 1)\n return features","def featurize(self, prev_tag, prev_word, token, next_word):\n features = np.array([])\n # one of our features will be the previous tag\n if None or prev_tag == 'O':\n features = np.append(features, [0])\n else:\n features = np.append(features, [1])\n # another set of features will be our word shape\n # condensed word shape form with the truncation thing\n\n # another feature will be our pos tags\n # use nltk pos tagging\n\n # another feature will be our word embeddings\n features = np.append(features, self.model[token])\n # and the final feature will be bias\n features = np.append(features, [1])\n return features","def preprocess_feature(df):","def parse(all_blobs, all_angles):","def convert_example(example, tokenizer):\n\n feature = tokenizer(\n text=example['question'],\n text_pair=example['answer'],\n max_seq_len=args.max_seq_length)\n feature['labels'] = example['labels']\n feature['id'] = example['id']\n\n return feature","def convert_examples_to_features(\n instruction,\n example,\n max_seq_length,\n tokenizer,\n cls_token_at_end=False,\n cls_token=\"[CLS]\",\n cls_token_segment_id=1,\n sep_token=\"[SEP]\",\n sep_token_extra=False,\n pad_on_left=False,\n pad_token=0,\n cls_token_box=[0, 0, 0, 0],\n sep_token_box=[1000, 1000, 1000, 1000],\n pad_token_box=[0, 0, 0, 0],\n pad_token_segment_id=0,\n pad_token_label_id=-1,\n sequence_a_segment_id=0,\n mask_padding_with_zero=True,\n ):\n\n features = dict()\n\n tokens = []\n token_boxes = []\n box = [\n int(example[\"x0\"]),\n int(example[\"y0\"]),\n int(example[\"x1\"]),\n int(example[\"y1\"]),\n ]\n if instruction:\n instruction_tokens = tokenizer.tokenize(instruction)\n tokens.extend(instruction_tokens)\n token_boxes.extend([box] * len(tokens))\n tokens.append(\"[SEP]\")\n token_boxes.append(sep_token_box)\n\n segment_ids_first = [0] * len(tokens)\n word_tokens = tokenizer.tokenize(example[\"text\"])\n tokens.extend(word_tokens)\n token_boxes.extend([box] * len(word_tokens))\n segment_ids_second = [1] * len(word_tokens)\n segment_ids = segment_ids_first + segment_ids_second\n\n # Account for [CLS] and [SEP] with \"- 2\" and with \"- 3\" for RoBERTa.\n special_tokens_count = 3 if sep_token_extra else 2\n if len(tokens) > max_seq_length - special_tokens_count:\n tokens = tokens[: (max_seq_length - special_tokens_count)]\n token_boxes = token_boxes[: (max_seq_length - special_tokens_count)]\n segment_ids = segment_ids[: (max_seq_length - special_tokens_count)]\n\n tokens += [sep_token]\n token_boxes += [sep_token_box]\n segment_ids += [1]\n\n if sep_token_extra:\n # roberta uses an extra separator b/w pairs of sentences\n tokens += [sep_token]\n token_boxes += [sep_token_box]\n segment_ids += [1]\n # segment_ids = [sequence_a_segment_id] * len(tokens)\n\n if cls_token_at_end:\n tokens += [cls_token]\n token_boxes += [cls_token_box]\n segment_ids += [cls_token_segment_id]\n else:\n tokens = [cls_token] + tokens\n token_boxes = [cls_token_box] + token_boxes\n segment_ids = [cls_token_segment_id] + segment_ids\n\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\n\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\n # tokens are attended to.\n input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)\n\n # Zero-pad up to the sequence length.\n padding_length = max_seq_length - len(input_ids)\n if pad_on_left:\n input_ids = ([pad_token] * padding_length) + input_ids\n input_mask = (\n [0 if mask_padding_with_zero else 1] * padding_length\n ) + input_mask\n segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids\n label_ids = ([pad_token_label_id] * padding_length) + label_ids\n token_boxes = ([pad_token_box] * padding_length) + token_boxes\n else:\n input_ids += [pad_token] * padding_length\n input_mask += [0 if mask_padding_with_zero else 1] * padding_length\n segment_ids += [pad_token_segment_id] * padding_length\n token_boxes += [pad_token_box] * padding_length\n\n assert len(input_ids) == max_seq_length\n assert len(input_mask) == max_seq_length\n assert len(segment_ids) == max_seq_length\n assert len(token_boxes) == max_seq_length\n\n features[\"ui_input_ids\"] = input_ids\n features[\"ui_input_mask\"] = input_mask\n features[\"ui_segment_ids\"] = segment_ids\n features[\"ui_boxes\"] = token_boxes\n\n return features","def convert_examples_to_features(\n instruction,\n example,\n max_seq_length,\n tokenizer,\n cls_token_at_end=False,\n cls_token=\"[CLS]\",\n cls_token_segment_id=1,\n sep_token=\"[SEP]\",\n sep_token_extra=False,\n pad_on_left=False,\n pad_token=0,\n cls_token_box=[0, 0, 0, 0],\n sep_token_box=[1000, 1000, 1000, 1000],\n pad_token_box=[0, 0, 0, 0],\n pad_token_segment_id=0,\n pad_token_label_id=-1,\n sequence_a_segment_id=0,\n mask_padding_with_zero=True,\n ):\n\n features = dict()\n\n tokens = []\n token_boxes = []\n box = [\n int(example[\"x0\"]),\n int(example[\"y0\"]),\n int(example[\"x1\"]),\n int(example[\"y1\"]),\n ]\n if instruction:\n instruction_tokens = tokenizer.tokenize(instruction)\n tokens.extend(instruction_tokens)\n token_boxes.extend([box] * len(tokens))\n tokens.append(\"[SEP]\")\n token_boxes.append(sep_token_box)\n\n segment_ids_first = [0] * len(tokens)\n word_tokens = tokenizer.tokenize(example[\"text\"])\n tokens.extend(word_tokens)\n token_boxes.extend([box] * len(word_tokens))\n segment_ids_second = [1] * len(word_tokens)\n segment_ids = segment_ids_first + segment_ids_second\n\n # Account for [CLS] and [SEP] with \"- 2\" and with \"- 3\" for RoBERTa.\n special_tokens_count = 3 if sep_token_extra else 2\n if len(tokens) > max_seq_length - special_tokens_count:\n tokens = tokens[: (max_seq_length - special_tokens_count)]\n token_boxes = token_boxes[: (max_seq_length - special_tokens_count)]\n segment_ids = segment_ids[: (max_seq_length - special_tokens_count)]\n\n tokens += [sep_token]\n token_boxes += [sep_token_box]\n segment_ids += [1]\n\n if sep_token_extra:\n # roberta uses an extra separator b/w pairs of sentences\n tokens += [sep_token]\n token_boxes += [sep_token_box]\n segment_ids += [1]\n # segment_ids = [sequence_a_segment_id] * len(tokens)\n\n if cls_token_at_end:\n tokens += [cls_token]\n token_boxes += [cls_token_box]\n segment_ids += [cls_token_segment_id]\n else:\n tokens = [cls_token] + tokens\n token_boxes = [cls_token_box] + token_boxes\n segment_ids = [cls_token_segment_id] + segment_ids\n\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\n\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\n # tokens are attended to.\n input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)\n\n # Zero-pad up to the sequence length.\n padding_length = max_seq_length - len(input_ids)\n if pad_on_left:\n input_ids = ([pad_token] * padding_length) + input_ids\n input_mask = (\n [0 if mask_padding_with_zero else 1] * padding_length\n ) + input_mask\n segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids\n label_ids = ([pad_token_label_id] * padding_length) + label_ids\n token_boxes = ([pad_token_box] * padding_length) + token_boxes\n else:\n input_ids += [pad_token] * padding_length\n input_mask += [0 if mask_padding_with_zero else 1] * padding_length\n segment_ids += [pad_token_segment_id] * padding_length\n token_boxes += [pad_token_box] * padding_length\n\n assert len(input_ids) == max_seq_length\n assert len(input_mask) == max_seq_length\n assert len(segment_ids) == max_seq_length\n assert len(token_boxes) == max_seq_length\n\n features[\"ui_input_ids\"] = input_ids\n features[\"ui_input_mask\"] = input_mask\n features[\"ui_segment_ids\"] = segment_ids\n features[\"ui_boxes\"] = token_boxes\n\n return features","def _read_libffm_file(self, filename):\n\n X_true = np.zeros((self.num_rows, self.num_features))\n y_true = np.zeros((self.num_rows, 1))\n field_true = np.zeros((self.num_features, 1))\n with open(filename, 'r') as f:\n i = 0\n for line in f:\n tmp_row = line.replace('\\n', '').split(' ')\n\n # extract label\n y_true[i] = int(tmp_row[0])\n\n # extract data and fields\n for k in range(1, len(tmp_row)):\n if len(tmp_row[k]) > 0:\n tmp_str = tmp_row[k].split(':')\n j = int(tmp_str[1])\n field_true[j] = int(tmp_str[0])\n tmp_data = float(tmp_str[2])\n X_true[i, j] = tmp_data\n i = i + 1\n\n return X_true, y_true, field_true","def _parse_function(example_proto):\n # Parse the tf.example according to the features_spec definition\n parsed_features = tf.parse_single_example(example_proto, features_spec)\n sequence = parsed_features[\"sequence\"]\n # Convert the sparse sequence tensor to dense.\n sequence_d = tf.sparse_to_dense(sequence.indices, sequence.dense_shape, sequence.values)\n # Return all the elements\n return parsed_features[\"sequence_length\"], parsed_features[\"label\"], sequence_d","def _preprocess(self, feature_dict):\n return feature_dict"],"string":"[\n \"def _read_and_decode(example_proto,data_shape,dtypes):\\n features = {}\\n for name in data_shape:\\n features[name] = tf.FixedLenFeature([], tf.string)\\n parsed_features = tf.parse_single_example(example_proto, features)\\n count = 0\\n res = {}\\n for name in data_shape:\\n res[name] = parsed_features[name]\\n if dtypes[count]!=str:\\n res[name]=tf.decode_raw(res[name],dtypes[count])\\n if dtypes[count]==tf.float32 or dtypes[count]==tf.float64:\\n res[name]=tf.convert_to_tensor(res[name],dtype=dtypes[count])\\n if data_shape[name]:\\n res[name]=tf.reshape(res[name],shape=data_shape[name])\\n count += 1\\n return res\",\n \"def _parser(serialized_example):\\n\\n features = tf.compat.v1.parse_single_example(\\n serialized_example,\\n features={\\n 'img_raw': tf.compat.v1.FixedLenFeature([], tf.string),\\n 'label': tf.compat.v1.FixedLenFeature([], tf.int64),\\n 'category': tf.compat.v1.FixedLenFeature([], tf.int64),\\n 'elevation': tf.compat.v1.FixedLenFeature([], tf.int64),\\n 'azimuth': tf.compat.v1.FixedLenFeature([], tf.int64),\\n 'lighting': tf.compat.v1.FixedLenFeature([], tf.int64),\\n })\\n\\n img = tf.compat.v1.decode_raw(features['img_raw'], tf.float64)\\n img = tf.reshape(img, [96, 96, 1])\\n img = tf.cast(img, tf.float32) # * (1. / 255) # left unnormalized\\n\\n lab = tf.cast(features['label'], tf.int32)\\n cat = tf.cast(features['category'], tf.int32)\\n elv = tf.cast(features['elevation'], tf.int32)\\n azi = tf.cast(features['azimuth'], tf.int32)\\n lit = tf.cast(features['lighting'], tf.int32)\\n\\n return img, lab, cat, elv, azi, lit\",\n \"def _parse_el_example(array_feats, array_feat_types, quant_feats):\\n out_example = []\\n d_keys = sorted(array_feats.keys())\\n for k in d_keys:\\n n_feat = quant_feats[k]\\n point_feat = tf.decode_raw(array_feats[k], array_feat_types[k])\\n point_feat = tf.reshape(point_feat, [quant_feats[k]])\\n out_example.append(point_feat)\\n return tuple(out_example)\",\n \"def _parse(serialized_example):\\n\\n feature_map = {\\n 'dayofweek': tf.io.FixedLenFeature([], tf.int64),\\n 'dropofflat': tf.io.FixedLenFeature([], tf.float32),\\n 'dropofflon': tf.io.FixedLenFeature([], tf.float32),\\n 'fare_amount': tf.io.FixedLenFeature([], tf.float32),\\n 'hourofday': tf.io.FixedLenFeature([], tf.int64),\\n 'passengers': tf.io.FixedLenFeature([], tf.float32),\\n 'pickuplat': tf.io.FixedLenFeature([], tf.float32),\\n 'pickuplon': tf.io.FixedLenFeature([], tf.float32)\\n }\\n\\n # Parse the serialized data into a dictionary.\\n parsed_example = tf.io.parse_single_example(\\n serialized=serialized_example,\\n features=feature_map)\\n\\n features = add_engineered(parsed_example)\\n label = features.pop(\\\"fare_amount\\\")\\n\\n return features, label\",\n \"def parser(serialized_example):\\n features = tf.parse_single_example(\\n serialized_example,\\n features={\\n \\\"image\\\": tf.FixedLenFeature([], tf.string),\\n \\\"label\\\": tf.FixedLenFeature([], tf.int64),\\n })\\n image = tf.decode_raw(features[\\\"image\\\"], tf.uint8)\\n image.set_shape([CHANNELS * HEIGHT * WIDTH])\\n # Reshape from [depth * height * width] to [depth, height, width].\\n image = tf.cast(\\n tf.transpose(tf.reshape(image, [CHANNELS, HEIGHT, WIDTH]), [1, 2, 0]),\\n tf.float32) * (2. / 255) - 1\\n\\n label = tf.cast(features['label'], tf.int32)\\n\\n random_noise = tf.random_normal([noise_dim])\\n features = {\\n 'real_images': image,\\n 'random_noise': random_noise}\\n\\n return features, label\",\n \"def parse_feature(self, feature_key, lines):\\n ...\",\n \"def parser(serialized_example):\\n features = tf.parse_single_example(\\n serialized_example,\\n features={\\n 'env': tf.FixedLenFeature([1, 4], tf.int64),\\n # 'env_segment_number': tf.FixedLenFeature([], tf.int64),\\n # 'env_segment_cpu': tf.FixedLenFeature([], tf.int64),\\n # 'env_segment_mem': tf.FixedLenFeature([], tf.int64),\\n # 'query_plan_ops': tf.VarLenFeature(tf.string),\\n # 'query_table_size': tf.VarLenFeature(tf.float32),\\n # 'segment_cpu_usage': tf.VarLenFeature(tf.float32),\\n 'label': tf.FixedLenFeature([], tf.float32)\\n })\\n env = tf.cast(features['env'], tf.float32)\\n # image.set_shape([DEPTH * HEIGHT * WIDTH])\\n\\n # # Reshape from [depth * height * width] to [depth, height, width].\\n # image = tf.cast(\\n # tf.transpose(tf.reshape(image, [DEPTH, HEIGHT, WIDTH]), [1, 2, 0]),\\n # tf.float32)\\n label = tf.cast(features['label'], tf.float32)\\n reshape_label = tf.reshape(features['label'], (1,1))\\n return env, reshape_label\",\n \"def decode_data(features, reader_settings):\\n if features.dtype == tf.string:\\n return tf.decode_raw(\\n features,\\n reader_settings)\\n else:\\n return tf.cast(\\n features,\\n reader_settings)\",\n \"def parser(_, serialized_example):\\n features = {}\\n\\n for i in frame_nums:\\n image_name = 'image_' + str(i)\\n if flags.dataset_type == 'robot':\\n pose_name = 'state_' + str(i)\\n action_name = 'action_' + str(i)\\n joint_pos_name = 'joint_positions_' + str(i)\\n\\n features[pose_name] = tf.FixedLenFeature([flags.pose_dim], tf.float32)\\n features[image_name] = tf.FixedLenFeature([1], tf.string)\\n features[action_name] = tf.FixedLenFeature([flags.pose_dim],\\n tf.float32)\\n features[joint_pos_name] = tf.FixedLenFeature([flags.joint_pos_dim],\\n tf.float32)\\n else:\\n features[image_name] = tf.FixedLenFeature([1], tf.string)\\n\\n parsed_input = tf.parse_single_example(serialized_example, features)\\n\\n for i in frame_nums:\\n image_name = 'image_' + str(i)\\n pose_name = 'state_' + str(i)\\n action_name = 'action_' + str(i)\\n joint_pos_name = 'joint_positions_' + str(i)\\n\\n # Process image\\n image_buffer = tf.reshape(parsed_input[image_name], shape=[])\\n image = tf.image.decode_jpeg(image_buffer, channels=COLOR_CHAN)\\n image = tf.image.resize_images(\\n image, (IMG_HEIGHT, IMG_WIDTH),\\n method=tf.image.ResizeMethod.BICUBIC)\\n image = tf.cast(tf.expand_dims(image, 0), tf.float32) / 255.0\\n\\n if flags.dataset_type == 'robot':\\n pose = tf.reshape(parsed_input[pose_name], shape=[flags.pose_dim])\\n pose = tf.expand_dims(pose, 0)\\n action = tf.reshape(parsed_input[action_name], shape=[flags.pose_dim])\\n action = tf.expand_dims(action, 0)\\n joint_pos = tf.reshape(\\n parsed_input[joint_pos_name], shape=[flags.joint_pos_dim])\\n joint_pos = tf.expand_dims(joint_pos, 0)\\n else:\\n pose = tf.zeros([1, flags.pose_dim])\\n action = tf.zeros([1, flags.pose_dim])\\n joint_pos = tf.zeros([1, flags.joint_pos_dim])\\n\\n if i == 0:\\n image_seq = image\\n action_seq, pose_seq, joint_pos_seq = action, pose, joint_pos\\n else:\\n image_seq = tf.concat([image_seq, image], 0)\\n action_seq = tf.concat([action_seq, action], 0)\\n pose_seq = tf.concat([pose_seq, pose], 0)\\n joint_pos_seq = tf.concat([joint_pos_seq, joint_pos], 0)\\n\\n return image_seq, action_seq, action_seq, joint_pos_seq\",\n \"def parser(record):\\n # keys_to_features = {\\n # \\\"image_data\\\": tf.FixedLenFeature((), tf.string, default_value=\\\"\\\"),\\n # \\\"date_time\\\": tf.FixedLenFeature((), tf.int64, default_value=\\\"\\\"),\\n # \\\"label\\\": tf.FixedLenFeature((), tf.int64,\\n # default_value=tf.zeros([], dtype=tf.int64)),\\n # }\\n\\n keys_to_features = {\\n \\\"image_data\\\": tf.FixedLenFeature((), tf.float, default_value=\\\"\\\"),\\n \\\"label\\\": tf.FixedLenFeature((), tf.int32,\\n default_value=tf.zeros([], dtype=tf.int64)),\\n }\\n parsed = tf.parse_single_example(record, keys_to_features)\\n\\n # Perform additional preprocessing on the parsed data.\\n image = tf.image.decode_jpeg(parsed[\\\"image_data\\\"])\\n image = tf.reshape(image, [299, 299, 1])\\n label = tf.cast(parsed[\\\"label\\\"], tf.int32)\\n\\n return {\\\"image_data\\\": image, \\\"date_time\\\": parsed[\\\"date_time\\\"]}, label\",\n \"def _decode_record(record):\\n name_to_features = {\\n \\\"input_ids\\\": tf.FixedLenFeature([self.max_sen_len], tf.int64),\\n \\\"input_mask\\\": tf.FixedLenFeature([self.max_sen_len], tf.int64),\\n \\\"segment_ids\\\": tf.FixedLenFeature([self.max_sen_len], tf.int64),\\n \\\"stroke_ids\\\": tf.FixedLenFeature([self.max_sen_len], tf.int64),\\n \\\"lmask\\\": tf.FixedLenFeature([self.max_sen_len], tf.int64),\\n \\\"label_ids\\\": tf.FixedLenFeature([self.max_sen_len], tf.int64),\\n }\\n\\n\\n example = tf.parse_single_example(record, name_to_features)\\n\\n #int64 to int32\\n for name in list(example.keys()):\\n t = example[name]\\n if t.dtype == tf.int64:\\n t = tf.to_int32(t)\\n example[name] = t\\n input_ids = example['input_ids']\\n input_mask = example['input_mask']\\n segment_ids = example['segment_ids']\\n stroke_ids = example['stroke_ids']\\n label_ids = example['label_ids']\\n lmask = example['lmask']\\n py_labels = tf.py_func(_get_py_seq, [label_ids], [tf.int32])\\n\\n return input_ids, input_mask, segment_ids, stroke_ids, lmask, label_ids, py_labels\",\n \"def _extract_feature(element):\\n features = tf.parse_single_example(\\n element,\\n # Defaults are not specified since both keys are required.\\n features={\\n 'image/encoded': tf.FixedLenFeature([], tf.string),\\n 'label/x': tf.FixedLenFeature([], tf.int64),\\n 'label/y': tf.FixedLenFeature([], tf.int64)\\n })\\n return features\",\n \"def parser(self, serialized_example):\\n features = {\\n 'image/height': tf.FixedLenFeature([], tf.int64),\\n 'image/width': tf.FixedLenFeature([], tf.int64),\\n 'image/colorspace': tf.FixedLenFeature([], tf.string),\\n 'image/channels': tf.FixedLenFeature([], tf.int64),\\n 'image/class/label': tf.FixedLenFeature([], tf.int64),\\n 'image/class/synset': tf.FixedLenFeature([], tf.string),\\n 'image/class/text': tf.FixedLenFeature([], tf.string),\\n 'image/object/bbox/xmin': tf.VarLenFeature(tf.float32),\\n 'image/object/bbox/ymin': tf.VarLenFeature(tf.float32),\\n 'image/object/bbox/xmax': tf.VarLenFeature(tf.float32),\\n 'image/object/bbox/ymax': tf.VarLenFeature(tf.float32),\\n 'image/object/bbox/label': tf.VarLenFeature(tf.int64),\\n 'image/format': tf.FixedLenFeature([], tf.string),\\n 'image/encoded': tf.FixedLenFeature([], tf.string)}\\n parsed_features = tf.parse_single_example(serialized_example, features)\\n\\n # Get label as a Tensor.\\n label = parsed_features['image/class/label']\\n\\n # Decode the image JPEG string into a Tensor.\\n image = tf.image.decode_jpeg(parsed_features['image/encoded'],\\n channels=self.DEPTH)\\n\\n # VGG preprocessing borrowed from slim; includes data augmentation so train_with_distortion should be set to True.\\n if self.mode == tf.estimator.ModeKeys.TRAIN:\\n assert self.params['train_with_distortion'] == True\\n is_training = True\\n else:\\n is_training = False\\n image = vgg_preprocess_image(image, 224, 224, is_training=is_training)\\n\\n return image, label\",\n \"def dataset_parser(self, value):\\n keys_to_features = {\\n 'image/encoded':\\n tf.io.FixedLenFeature((), tf.string, ''),\\n 'image/format':\\n tf.io.FixedLenFeature((), tf.string, 'jpeg'),\\n 'image/class/label':\\n tf.io.FixedLenFeature([], tf.int64, -1),\\n 'image/class/text':\\n tf.io.FixedLenFeature([], tf.string, ''),\\n 'image/object/bbox/xmin':\\n tf.io.VarLenFeature(dtype=tf.float32),\\n 'image/object/bbox/ymin':\\n tf.io.VarLenFeature(dtype=tf.float32),\\n 'image/object/bbox/xmax':\\n tf.io.VarLenFeature(dtype=tf.float32),\\n 'image/object/bbox/ymax':\\n tf.io.VarLenFeature(dtype=tf.float32),\\n 'image/object/class/label':\\n tf.io.VarLenFeature(dtype=tf.int64),\\n }\\n\\n parsed = tf.io.parse_single_example(value, keys_to_features)\\n image_bytes = tf.reshape(parsed['image/encoded'], shape=[])\\n\\n tensors_dict = preprocess_image(\\n image_bytes=image_bytes,\\n is_training=self.is_training,\\n augmentation=self.augmentation,\\n use_bfloat16=self.use_bfloat16,\\n saturate_uint8=self.saturate_uint8,\\n scale_and_center=self.scale_and_center,\\n use_default_augment=self.use_default_augment)\\n\\n # Subtract one so that labels are in [0, 1000).\\n label = tf.cast(tf.reshape(parsed['image/class/label'], shape=()) - 1,\\n dtype=tf.int32)\\n tensors_dict['label'] = label\\n\\n return tensors_dict\",\n \"def parse_record(raw_record):\\n keys_to_features = {\\n 'image/height':\\n tf.FixedLenFeature((), tf.int64),\\n 'image/width':\\n tf.FixedLenFeature((), tf.int64),\\n 'image/encoded':\\n tf.FixedLenFeature((), tf.string, default_value=''),\\n 'image/format':\\n tf.FixedLenFeature((), tf.string, default_value='jpeg'),\\n 'label/encoded':\\n tf.FixedLenFeature((), tf.string, default_value=''),\\n 'label/format':\\n tf.FixedLenFeature((), tf.string, default_value='png'),\\n }\\n\\n parsed = tf.parse_single_example(raw_record, keys_to_features)\\n\\n # height = tf.cast(parsed['image/height'], tf.int32)\\n # width = tf.cast(parsed['image/width'], tf.int32)\\n\\n image = tf.image.decode_image(\\n tf.reshape(parsed['image/encoded'], shape=[]), _DEPTH)\\n image = tf.to_float(tf.image.convert_image_dtype(image, dtype=tf.uint8))\\n image.set_shape([None, None, 3])\\n\\n label = tf.image.decode_image(\\n tf.reshape(parsed['label/encoded'], shape=[]), 1)\\n label = tf.to_int32(tf.image.convert_image_dtype(label, dtype=tf.uint8))\\n label.set_shape([None, None, 1])\\n\\n\\n return image, label\",\n \"def __parser__(self, example_proto):\\n # configure feature and label length\\n # It is crucial that for tf.string, the length is not specified, as the data is stored as a single string!\\n x_config = tf.FixedLenFeature([], tf.string) \\\\\\n if self.x_dtype == tf.string else tf.FixedLenFeature([self.num_features], self.x_dtype)\\n if self.num_labels == 0:\\n proto_config = {'x': x_config}\\n else:\\n y_config = tf.FixedLenFeature([], tf.string) \\\\\\n if self.y_dtype == tf.string else tf.FixedLenFeature([self.num_labels], self.y_dtype)\\n proto_config = {'x': x_config, 'y': y_config}\\n\\n # decode examples\\n datum = tf.parse_single_example(example_proto, features=proto_config)\\n if self.x_dtype == tf.string: # if input is string / bytes, decode it to float32\\n if self.decode_jpeg:\\n # first decode compressed image string to uint8, as data is stored in this way\\n # datum['x'] = tf.image.decode_image(datum['x'], channels=3)\\n datum['x'] = tf.image.decode_jpeg(datum['x'], channels=3)\\n else:\\n # first decode data to uint8, as data is stored in this way\\n datum['x'] = tf.decode_raw(datum['x'], tf.uint8)\\n # then cast data to tf.float32 or tf.float16\\n datum['x'] = tf.cast(datum['x'], tf.float32)\\n # cannot use string_to_number as there is only one string for a whole sample\\n # datum['x'] = tf.strings.to_number(datum['x'], tf.float32) # this results in possibly a large number\\n\\n # return data\\n if 'y' in datum:\\n # y can be present in many ways:\\n # 1. a single integer, which requires y to be int32 or int64 (e.g, used in tf.gather in cbn)\\n # 2. num-class bool/integer/float variables. This form is more flexible as it allows multiple classes and\\n # prior probabilities as targets\\n # 3. float variables in regression problem.\\n # but...\\n # y is stored as int (for case 1), string (for other int cases), or float (for float cases)\\n # in the case of tf.string and tf.int64, convert to to int32\\n if self.y_dtype == tf.string:\\n # avoid using string labels like 'cat', 'dog', use integers instead\\n datum['y'] = tf.decode_raw(datum['y'], tf.uint8)\\n datum['y'] = tf.cast(datum['y'], tf.int32)\\n else:\\n datum['y'] = tf.cast(datum['y'], self.y_dtype)\\n if self.use_one_hot_label:\\n datum['y'] = tf.reshape(tf.one_hot(datum['y'], self.num_classes), (-1, ))\\n if self.use_smooth_label: # label smoothing\\n datum['y'] = 0.9 * datum['y'] + 0.1 / self.num_classes\\n return datum['x'], datum['y']\\n else:\\n return datum['x']\",\n \"def _parse_function(proto_string):\\n _, parsed_features = tf.parse_single_sequence_example(\\n proto_string, sequence_features=_parsing_spec(input_size))\\n lengths = tf.shape(parsed_features['inputs'])[0]\\n # Type int32 is not supported by parsing spec, but int64 is not supported by\\n # XLA. Here cast to int32. The parsing functions are executed by the C++\\n # threads. So, casting op will not slow down the session.run call later.\\n labels = tf.to_int32(parsed_features['labels'])\\n return parsed_features['inputs'], lengths, labels\",\n \"def _decode_record(record,name_to_features):\\n example = tf.parse_single_example(record,name_to_features)\\n\\n return example\",\n \"def parse_features(self, skip=...):\\n ...\",\n \"def parse_features(self, skip=...):\\n ...\",\n \"def decode(value):\\n keys_to_features = {\\n 'image/encoded':\\n tf.FixedLenFeature((), tf.string, default_value=''),\\n 'image/height':\\n tf.FixedLenFeature((), tf.int64, default_value=0),\\n 'image/width':\\n tf.FixedLenFeature((), tf.int64, default_value=0),\\n 'image/segmentation/class/encoded':\\n tf.FixedLenFeature((), tf.string, default_value='')\\n }\\n data = tf.parse_single_example(value, keys_to_features)\\n return data\",\n \"def parse_record(raw_record, is_training):\\n keys_to_features = {\\n 'image/encoded':\\n tf.FixedLenFeature((), tf.string, default_value=''),\\n 'image/format':\\n tf.FixedLenFeature((), tf.string, default_value='jpeg'),\\n 'image/class/label':\\n tf.FixedLenFeature([], dtype=tf.int64, default_value=-1),\\n 'image/class/text':\\n tf.FixedLenFeature([], dtype=tf.string, default_value=''),\\n }\\n\\n parsed = tf.parse_single_example(raw_record, keys_to_features)\\n\\n image = tf.image.decode_image(\\n tf.reshape(parsed['image/encoded'], shape=[]),\\n _NUM_CHANNELS)\\n\\n # Note that tf.image.convert_image_dtype scales the image data to [0, 1).\\n image = tf.image.convert_image_dtype(image, dtype=tf.float32)\\n\\n label = tf.cast(\\n tf.reshape(parsed['image/class/label'], shape=[]),\\n dtype=tf.int32)\\n\\n return {\\\"image\\\": image}, label\",\n \"def _decode_record(record, name_to_features):\\n example = tf.parse_single_example(record, name_to_features)\\n return example\",\n \"def extract_features(raw_data):\\n width = len(raw_data[0])\\n num_features = len(raw_data) * width\\n features = np.zeros((num_features, 3), dtype=bool)\\n for row, line in enumerate(raw_data):\\n for col, char in enumerate(line):\\n if char == ' ':\\n features[col + row * width][0] = True\\n elif char == '+':\\n features[col + row * width][1] = True\\n elif char == '#':\\n features[col + row * width][2] = True\\n return features\",\n \"def parse_fn(self, example_serialized):\\n feature_description = {\\n 'image_raw': tf.io.FixedLenFeature([], tf.string),\\n 'label': tf.io.FixedLenFeature([], tf.int64)\\n }\\n features = tf.io.parse_single_example(example_serialized, feature_description)\\n image = tf.io.decode_raw(features['image_raw'], tf.uint8)\\n image = tf.cast(image, dtype='float32') / 255.0\\n label = tf.cast(features['label'], dtype=tf.int32)\\n image = tf.reshape(image, [32, 32, 3])\\n if self.is_training:\\n image = tf.image.resize_with_crop_or_pad(image, 32 + 8, 32 + 8)\\n image = tf.image.random_crop(image, [32, 32, 3])\\n image = tf.image.random_flip_left_right(image)\\n return image, label\",\n \"def _convert_to_features(self, img: np.ndarray) -> np.ndarray:\",\n \"def parser(self, serialized_example):\\n features = {\\n 'image/height': tf.FixedLenFeature([], tf.int64),\\n 'image/width': tf.FixedLenFeature([], tf.int64),\\n 'image/colorspace': tf.FixedLenFeature([], tf.string),\\n 'image/channels': tf.FixedLenFeature([], tf.int64),\\n 'image/class/label': tf.FixedLenFeature([], tf.int64),\\n 'image/format': tf.FixedLenFeature([], tf.string),\\n 'image/encoded': tf.FixedLenFeature([], tf.string),\\n 'image/fixation_pt': tf.FixedLenFeature([2], tf.float32)}\\n parsed_features = tf.parse_single_example(serialized_example, features)\\n\\n # Get label as a Tensor.\\n label = parsed_features['image/class/label']\\n\\n # Decode the image JPEG string into a Tensor.\\n image = tf.image.decode_jpeg(parsed_features['image/encoded'],\\n channels=self.DEPTH)\\n\\n # Convert from uint8 -> float32 and map onto range [0, 1].\\n image = tf.cast(image, tf.float32) * (1. / 255)\\n\\n # Standardize image.\\n image = tf.image.per_image_standardization(image)\\n\\n # Apply data augmentation.\\n if (self.mode == tf.estimator.ModeKeys.TRAIN\\n and self.params['train_with_distortion']):\\n # Randomly flip the image, zero-pad with four pixels along\\n # each edge, and take a random 32 x 32 crop.\\n image = tf.image.random_flip_left_right(image)\\n image = tf.image.resize_image_with_crop_or_pad(image, 40, 40)\\n image = tf.image.crop_to_bounding_box(image,\\n tf.random_uniform([], minval=0, maxval=8, dtype=tf.int32),\\n tf.random_uniform([], minval=0, maxval=8, dtype=tf.int32),\\n 32, 32)\\n\\n return image, label\",\n \"def _parse_tfexample(example):\\n\\n ## parse\\n features = tf.parse_single_example(example, KEYS2FEATURES)\\n\\n image = tf.image.decode_png(features['image/encoded'])\\n label = tf.image.decode_png(features['label/encoded'])\\n # label is decoded as a 3-D png image\\n label = label[..., 0]\\n im_path = features['image/path']\\n la_path = features['label/path']\\n\\n return image, label, im_path, la_path\",\n \"def imagenet_parser(value, image_size, is_training):\\n keys_to_features = {\\n 'image/encoded':\\n tf.FixedLenFeature((), tf.string, ''),\\n 'image/format':\\n tf.FixedLenFeature((), tf.string, 'jpeg'),\\n 'image/class/label':\\n tf.FixedLenFeature([], tf.int64, -1),\\n 'image/class/text':\\n tf.FixedLenFeature([], tf.string, ''),\\n 'image/object/bbox/xmin':\\n tf.VarLenFeature(dtype=tf.float32),\\n 'image/object/bbox/ymin':\\n tf.VarLenFeature(dtype=tf.float32),\\n 'image/object/bbox/xmax':\\n tf.VarLenFeature(dtype=tf.float32),\\n 'image/object/bbox/ymax':\\n tf.VarLenFeature(dtype=tf.float32),\\n 'image/object/class/label':\\n tf.VarLenFeature(dtype=tf.int64),\\n }\\n\\n parsed = tf.parse_single_example(value, keys_to_features)\\n\\n image_buffer = tf.reshape(parsed['image/encoded'], shape=[])\\n\\n xmin = tf.expand_dims(parsed['image/object/bbox/xmin'].values, 0)\\n ymin = tf.expand_dims(parsed['image/object/bbox/ymin'].values, 0)\\n xmax = tf.expand_dims(parsed['image/object/bbox/xmax'].values, 0)\\n ymax = tf.expand_dims(parsed['image/object/bbox/ymax'].values, 0)\\n # Note that ordering is (y, x)\\n bbox = tf.concat([ymin, xmin, ymax, xmax], 0)\\n # Force the variable number of bounding boxes into the shape\\n # [1, num_boxes, coords].\\n bbox = tf.expand_dims(bbox, 0)\\n bbox = tf.transpose(bbox, [0, 2, 1])\\n\\n image = image_preprocessing(\\n image_buffer=image_buffer,\\n bbox=bbox,\\n image_size=image_size,\\n is_training=is_training\\n )\\n\\n # Labels are in [1, 1000] range\\n label = tf.cast(\\n tf.reshape(parsed['image/class/label'], shape=[]), dtype=tf.int32)\\n\\n return image, label\",\n \"def decode(self, serialized_example):\\n parsed_tensors = tf.io.parse_single_example(\\n serialized_example, self.KEYS_TO_FEATURES\\n )\\n for k in parsed_tensors:\\n if isinstance(parsed_tensors[k], tf.SparseTensor):\\n if parsed_tensors[k].dtype == tf.string:\\n parsed_tensors[k] = tf.sparse.to_dense(\\n parsed_tensors[k], default_value=\\\"\\\"\\n )\\n else:\\n parsed_tensors[k] = tf.sparse.to_dense(\\n parsed_tensors[k], default_value=0\\n )\\n\\n image = self._decode_image(parsed_tensors)\\n boxes = self._decode_boxes(parsed_tensors)\\n decode_image_shape = tf.logical_or(\\n tf.equal(parsed_tensors[\\\"image/height\\\"], -1),\\n tf.equal(parsed_tensors[\\\"image/width\\\"], -1),\\n )\\n image_shape = tf.cast(tf.shape(image), dtype=tf.int64)\\n\\n parsed_tensors[\\\"image/height\\\"] = tf.where(\\n decode_image_shape, image_shape[0], parsed_tensors[\\\"image/height\\\"]\\n )\\n parsed_tensors[\\\"image/width\\\"] = tf.where(\\n decode_image_shape, image_shape[1], parsed_tensors[\\\"image/width\\\"]\\n )\\n\\n decoded_tensors = {\\n \\\"image\\\": image,\\n \\\"height\\\": parsed_tensors[\\\"image/height\\\"],\\n \\\"width\\\": parsed_tensors[\\\"image/width\\\"],\\n \\\"groundtruth_classes\\\": parsed_tensors[\\\"image/object/class/label\\\"],\\n \\\"groundtruth_boxes\\\": boxes,\\n }\\n return decoded_tensors\",\n \"def parse_func(record):\\n keys_to_features = {\\n 'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),\\n 'image/format': tf.FixedLenFeature((), tf.string, default_value='png'),\\n 'image/path': tf.FixedLenFeature((), tf.string, default_value=''),\\n 'label/encoded': tf.FixedLenFeature((), tf.string, default_value=''),\\n 'label/format': tf.FixedLenFeature((), tf.string, default_value='png'),\\n 'label/path': tf.FixedLenFeature((), tf.string, default_value=''),\\n 'height': tf.FixedLenFeature((), tf.int64),\\n 'width': tf.FixedLenFeature((), tf.int64)\\n }\\n\\n features = tf.parse_single_example(record, keys_to_features)\\n\\n image = tf.image.decode_png(features['image/encoded'], channels=3)\\n label_dtype = tf.uint8\\n label = tf.image.decode_png(features['label/encoded'], channels=1, dtype=label_dtype)\\n label = tf.reshape(label, tf.convert_to_tensor([features['height'], features['width'], 1]))\\n label = tf.squeeze(label)\\n\\n paths = (features['image/path'], features['label/path'])\\n return image, label, paths\",\n \"def parser(record):\\n record_spec = {\\n \\\"input\\\": tf.FixedLenFeature([seq_len], tf.int64),\\n \\\"labels\\\": tf.FixedLenFeature([tgt_len], tf.int64),\\n \\\"input_mask\\\": tf.FixedLenFeature([seq_len],tf.float32),\\n \\\"target_mask\\\": tf.FixedLenFeature([tgt_len],tf.float32)\\n }\\n\\n # retrieve serialized example\\n example = tf.parse_single_example(\\n serialized=record,\\n features=record_spec)\\n\\n _convert_example(example, use_bfloat16)\\n\\n for k, v in example.items():\\n tf.logging.info(\\\"%s: %s\\\", k, v)\\n\\n return example\",\n \"def _parse_example(self, example, scale_to_0_1: bool = False):\\n\\n features = {\\n 'image': tf.FixedLenFeature([], tf.string),\\n 'mask': tf.FixedLenFeature([], tf.string),\\n }\\n parsed_example = tf.parse_single_example(example, features)\\n\\n image = tf.decode_raw(parsed_example['image'], self.serialized_image_raw_dtype)\\n image = tf.reshape(image, (self.image_width, self.image_width, self.image_channels))\\n image = tf.cast(image, tf.float32)\\n if scale_to_0_1:\\n image /= 255.\\n\\n mask = tf.decode_raw(parsed_example['mask'], self.serialized_mask_raw_dtype)\\n mask = tf.reshape(mask, (self.image_width, self.image_width, self.mask_channels))\\n mask = tf.cast(mask, tf.float32) / 255.\\n return image, mask\",\n \"def _features_to_numpy(\\n features: tf_feature_pb2.Features,\\n flat_example_specs: Mapping[str, feature_lib.TensorInfo],\\n) -> type_utils.NpArrayOrScalarDict:\\n parsed_example = {}\\n feature_map = features.feature\\n for key in feature_map:\\n if key not in flat_example_specs:\\n raise KeyError(\\n f\\\"Malformed input: {key} is found in the feature, but not in\\\"\\n f\\\" {flat_example_specs}\\\"\\n )\\n with utils.try_reraise(f\\\"Error wile parsing feature {key}: \\\"):\\n parsed_example[key] = _feature_to_numpy(\\n feature_map[key], flat_example_specs[key], key\\n )\\n return parsed_example\",\n \"def single_example_parser(serialized_example):\\n # Dimensions of the images in the CIFAR-10 dataset.\\n # See http://www.cs.toronto.edu/~kriz/cifar.html for a description of the\\n # input format.\\n features = tf.parse_single_example(\\n serialized_example,\\n features={\\n 'image': tf.FixedLenFeature([], tf.string),\\n 'label': tf.FixedLenFeature([], tf.int64),\\n })\\n image = tf.decode_raw(features['image'], tf.uint8)\\n image.set_shape([DEPTH * HEIGHT * WIDTH])\\n\\n # Reshape from [depth * height * width] to [depth, height, width].\\n image = tf.cast(\\n tf.transpose(tf.reshape(image, [DEPTH, HEIGHT, WIDTH]), [1, 2, 0]),\\n tf.float32)\\n label = tf.cast(features['label'], tf.int32)\\n \\n image = train_preprocess_fn(image)\\n label = tf.one_hot(label, NUM_CLASSES)\\n \\n return image, label\",\n \"def _decode_record(record, name_to_features):\\n example = tf.parse_single_example(record, name_to_features)\\n\\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\\n # So cast all int64 to int32.\\n for name in list(example.keys()):\\n t = example[name]\\n if t.dtype == tf.int64:\\n t = tf.to_int32(t)\\n example[name] = t\\n\\n return example\",\n \"def _decode_record(record, name_to_features):\\n example = tf.parse_single_example(record, name_to_features)\\n\\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\\n # So cast all int64 to int32.\\n for name in list(example.keys()):\\n t = example[name]\\n if t.dtype == tf.int64:\\n t = tf.to_int32(t)\\n example[name] = t\\n\\n return example\",\n \"def _decode_record(record, name_to_features):\\n example = tf.parse_single_example(record, name_to_features)\\n\\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\\n # So cast all int64 to int32.\\n for name in list(example.keys()):\\n t = example[name]\\n if t.dtype == tf.int64:\\n t = tf.to_int32(t)\\n example[name] = t\\n\\n return example\",\n \"def parse_single_example(serialized_example):\\n feature_description = {\\n \\\"immrf/data\\\": tf.io.FixedLenFeature([], tf.string),\\n \\\"immrf/shape\\\": tf.io.VarLenFeature(tf.int64),\\n \\\"immrf/path\\\": tf.io.FixedLenFeature([], tf.string),\\n \\\"tmap/data\\\": tf.io.FixedLenFeature([], tf.string),\\n \\\"tmap/shape\\\": tf.io.VarLenFeature(tf.int64),\\n \\\"tmap/path\\\": tf.io.FixedLenFeature([], tf.string),\\n \\\"mask/data\\\": tf.io.FixedLenFeature([], tf.string),\\n \\\"mask/shape\\\": tf.io.VarLenFeature(tf.int64),\\n \\\"mask/path\\\": tf.io.FixedLenFeature([], tf.string),\\n }\\n slice = tf.io.parse_single_example(serialized_example, feature_description)\\n for key in [\\\"immrf\\\", \\\"tmap\\\", \\\"mask\\\"]:\\n slice[key + \\\"/data\\\"] = tf.io.decode_raw(slice[key + \\\"/data\\\"], out_type=tf.float32)\\n slice[key + \\\"/data\\\"] = utils.reshape_back(slice, key)\\n return slice\",\n \"def _decode_record(record, name_to_features):\\n\\t\\t\\texample = tf.parse_single_example(record, name_to_features)\\n\\n\\t\\t\\t# tf.Example only supports tf.int64, but the TPU only supports tf.int32.\\n\\t\\t\\t# So cast all int64 to int32.\\n\\t\\t\\tfor name in list(example.keys()):\\n\\t\\t\\t\\tt = example[name]\\n\\t\\t\\t\\tif t.dtype == tf.int64:\\n\\t\\t\\t\\t\\tt = tf.to_int32(t)\\n\\t\\t\\t\\texample[name] = t\\n\\n\\t\\t\\treturn example\",\n \"def read_input_bytes_feature(feature_map, key, shape):\\n if shape is None:\\n (dim_z, dim_y, dim_x) = feature_map.feature[key + '/dim'].int64_list.value\\n else:\\n (dim_z, dim_y, dim_x) = shape\\n tensor = np.fromstring(\\n feature_map.feature[key].bytes_list.value[0], dtype=np.uint8).reshape(\\n dim_z, dim_y, dim_x)\\n return tensor\",\n \"def _decode_record(self, record, name_to_features):\\n example = tf.parse_single_example(record, name_to_features)\\n\\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\\n # So cast all int64 to int32.\\n for name in list(example.keys()):\\n t = example[name]\\n if t.dtype == tf.int64:\\n t = tf.to_int32(t)\\n # tf.logging.info(t)\\n # t = tf.sparse.to_dense(t)\\n # tf.logging.info(t.get_shape().as_list())\\n # assert t.get_shape().as_list()[0] is not None\\n example[name] = t\\n \\n del example[\\\"source_sos_ids\\\"]\\n del example[\\\"source_sos_mask\\\"]\\n\\n return example\",\n \"def _decode_record(record, name_to_features):\\n example = tf.parse_single_example(record, name_to_features)\\n\\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\\n # So cast all int64 to int32.\\n for name in list(example.keys()):\\n print(name)\\n t = example[name]\\n if t.dtype == tf.int64:\\n t = tf.to_int32(t)\\n example[name] = t\\n\\n return example\",\n \"def _parse_single_example(example, options):\\n # Initialize `keys_to_features`.\\n keys_to_features = {\\n TFExampleFields.img_id: tf.io.FixedLenFeature([], tf.string),\\n TFExampleFields.annot_id: tf.io.FixedLenFeature([], tf.string),\\n TFExampleFields.answer_label: tf.io.FixedLenFeature([], tf.int64),\\n TFExampleFields.img_bbox_label: tf.io.VarLenFeature(tf.string),\\n TFExampleFields.img_bbox_score: tf.io.VarLenFeature(tf.float32),\\n TFExampleFields.img_bbox_feature: tf.io.VarLenFeature(tf.float32),\\n TFExampleFields.question: tf.io.VarLenFeature(tf.string),\\n TFExampleFields.question_tag: tf.io.VarLenFeature(tf.int64),\\n }\\n for bbox_key in TFExampleFields.img_bbox_field_keys:\\n bbox_field = os.path.join(TFExampleFields.img_bbox_scope, bbox_key)\\n keys_to_features[bbox_field] = tf.io.VarLenFeature(tf.float32)\\n for i in range(1, 1 + NUM_CHOICES):\\n keys_to_features.update({\\n TFExampleFields.cls_bert + '_%i' % i:\\n tf.io.VarLenFeature(tf.float32),\\n TFExampleFields.question_bert + '_%i' % i:\\n tf.io.VarLenFeature(tf.float32),\\n TFExampleFields.answer_choice + '_%i' % i:\\n tf.io.VarLenFeature(tf.string),\\n TFExampleFields.answer_choice_tag + '_%i' % i:\\n tf.io.VarLenFeature(tf.int64),\\n TFExampleFields.answer_choice_bert + '_%i' % i:\\n tf.io.VarLenFeature(tf.float32)\\n })\\n\\n # Initialize `items_to_handlers`.\\n items_to_handlers = {\\n InputFields.img_id:\\n tfexample_decoder.Tensor(tensor_key=TFExampleFields.img_id,\\n default_value=''),\\n InputFields.annot_id:\\n tfexample_decoder.Tensor(tensor_key=TFExampleFields.annot_id,\\n default_value=''),\\n InputFields.answer_label:\\n tfexample_decoder.Tensor(tensor_key=TFExampleFields.answer_label,\\n default_value=-1),\\n InputFields.object_bboxes:\\n tfexample_decoder.BoundingBox(\\n keys=TFExampleFields.img_bbox_field_keys,\\n prefix=TFExampleFields.img_bbox_scope),\\n InputFields.object_labels:\\n tfexample_decoder.Tensor(tensor_key=TFExampleFields.img_bbox_label,\\n default_value=''),\\n InputFields.object_scores:\\n tfexample_decoder.Tensor(tensor_key=TFExampleFields.img_bbox_score,\\n default_value=0),\\n InputFields.question:\\n tfexample_decoder.Tensor(tensor_key=TFExampleFields.question,\\n default_value=PAD),\\n InputFields.question_tag:\\n tfexample_decoder.Tensor(tensor_key=TFExampleFields.question_tag,\\n default_value=-1),\\n TFExampleFields.img_bbox_feature:\\n tfexample_decoder.Tensor(tensor_key=TFExampleFields.img_bbox_feature,\\n default_value=0),\\n }\\n\\n for i in range(1, 1 + NUM_CHOICES):\\n tensor_key = TFExampleFields.cls_bert + '_%i' % i\\n items_to_handlers[tensor_key] = tfexample_decoder.Tensor(\\n tensor_key=tensor_key, default_value=0)\\n tensor_key = TFExampleFields.question_bert + '_%i' % i\\n items_to_handlers[tensor_key] = tfexample_decoder.Tensor(\\n tensor_key=tensor_key, default_value=0)\\n tensor_key = TFExampleFields.answer_choice + '_%i' % i\\n items_to_handlers[tensor_key] = tfexample_decoder.Tensor(\\n tensor_key=tensor_key, default_value=PAD)\\n tensor_key = TFExampleFields.answer_choice_tag + '_%i' % i\\n items_to_handlers[tensor_key] = tfexample_decoder.Tensor(\\n tensor_key=tensor_key, default_value=-1)\\n tensor_key = TFExampleFields.answer_choice_bert + '_%i' % i\\n items_to_handlers[tensor_key] = tfexample_decoder.Tensor(\\n tensor_key=tensor_key, default_value=0)\\n if options.decode_jpeg:\\n keys_to_features.update({\\n TFExampleFields.img_encoded: tf.io.FixedLenFeature([], tf.string),\\n TFExampleFields.img_format: tf.io.FixedLenFeature([], tf.string),\\n })\\n items_to_handlers.update({\\n InputFields.img_data:\\n tfexample_decoder.Image(image_key=TFExampleFields.img_encoded,\\n format_key=TFExampleFields.img_format,\\n shape=None)\\n })\\n\\n # Decode example.\\n example_decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,\\n items_to_handlers)\\n\\n output_keys = example_decoder.list_items()\\n output_tensors = example_decoder.decode(example)\\n output_tensors = [\\n x if x.dtype != tf.int64 else tf.cast(x, tf.int32) for x in output_tensors\\n ]\\n decoded_example = dict(zip(output_keys, output_tensors))\\n return _update_decoded_example(decoded_example, options)\",\n \"def _decode_record(record, name_to_features):\\n example = tf.parse_single_example(record, name_to_features)\\n\\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\\n # So cast all int64 to int32.\\n for name in list(example.keys()):\\n t = example[name]\\n if t.dtype == tf.int64:\\n t = tf.to_int32(t)\\n example[name] = t\\n\\n return example\",\n \"def _decode_record(record, name_to_features):\\n example = tf.parse_single_example(record, name_to_features)\\n\\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\\n # So cast all int64 to int32.\\n for name in list(example.keys()):\\n t = example[name]\\n if t.dtype == tf.int64:\\n t = tf.to_int32(t)\\n example[name] = t\\n\\n return example\",\n \"def parser(record):\\n\\n record_spec = {\\n \\\"inputs\\\": tf.VarLenFeature(tf.int64),\\n \\\"type_id\\\": tf.FixedLenFeature([1], tf.int64),\\n }\\n\\n # retrieve serialized example\\n example = tf.parse_single_example(\\n serialized=record,\\n features=record_spec)\\n\\n inputs = example[\\\"inputs\\\"]\\n inp_len = tf.shape(inputs)[0]\\n\\n # expand type id to full length\\n example[\\\"type_id\\\"] = tf.broadcast_to(example[\\\"type_id\\\"], [inp_len])\\n\\n # convert all sparse example to dense\\n example = sparse_to_dense(example)\\n\\n return example\",\n \"def parse_example(self, serialized_example):\\n # Because of RaggedTensor specs, feature_specs can be a 2-level nested dict,\\n # so have to wrap `tf.io.parse_single_example` between\\n # `flatten_nest_dict`/`pack_as_nest_dict`.\\n # {\\n # 'video/image': tf.io.FixedLenSequenceFeature(...),\\n # 'video/object/bbox': {\\n # 'ragged_flat_values': tf.io.FixedLenSequenceFeature(...),\\n # 'ragged_row_lengths_0', tf.io.FixedLenSequenceFeature(...),\\n # },\\n # }\\n example = tf.io.parse_single_example(\\n serialized=serialized_example,\\n features=self.flat_feature_specs,\\n )\\n example = utils.pack_as_nest_dict(example, self._nested_feature_specs)\\n\\n example = { # pylint:disable=g-complex-comprehension\\n k: _deserialize_single_field(example_data, tensor_info)\\n for k, (example_data, tensor_info) in utils.zip_dict(\\n example, self._flat_example_specs\\n )\\n }\\n # Reconstruct all nesting\\n example = utils.pack_as_nest_dict(example, self._example_specs)\\n return example\",\n \"def _decode_record(record, name_to_features):\\n example = tf.parse_single_example(record, name_to_features)\\n\\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\\n # So cast all int64 to int32.\\n for name in list(example.keys()):\\n t = example[name]\\n if t.dtype == tf.int64:\\n t = tf.to_int32(t)\\n example[name] = t\\n\\n return example\",\n \"def parse_sequence_example(serialized, image_feature, caption_feature):\\n\\tcontext, sequence = tf.parse_single_sequence_example(\\n\\t\\t\\tserialized,\\n\\t\\t\\tcontext_features={\\n\\t\\t\\t\\t\\timage_feature: tf.FixedLenFeature([], dtype=tf.string)\\n\\t\\t\\t},\\n\\t\\t\\tsequence_features={\\n\\t\\t\\t\\t\\tcaption_feature: tf.FixedLenSequenceFeature([], dtype=tf.int64),\\n\\t\\t\\t})\\n\\n\\tencoded_image = context[image_feature]\\n\\tcaption = sequence[caption_feature]\\n\\treturn encoded_image, caption\",\n \"def parse_observation(self, ob):\\n print('shape of ob:', ob)\\n print(list(self.feature_size_map.keys()))\\n print(ob, list(self.feature_size_map.values()))\\n print(tf.split(ob, list(self.feature_size_map.values()), 1))\\n feature_map = {\\n k : v\\n for k, v in zip(\\n list(self.feature_size_map.keys()), \\n tf.split(ob, list(self.feature_size_map.values()), 1))\\n }\\n \\n for k in {'board', 'bomb_blast_strength', 'bomb_life'}:\\n feature_map[k] = tf.reshape(feature_map[k], (-1, 11, 11, 1))\\n return feature_map\",\n \"def _decode_record(record, name_to_features):\\n example = tf.parse_single_example(record, name_to_features)\\n\\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\\n # So cast all int64 to int32.\\n for name in list(example.keys()):\\n t = example[name]\\n if t.dtype == tf.int64:\\n t = tf.to_int32(t)\\n example[name] = t\\n\\n return example[\\\"src_ids\\\"].values, example[\\\"tgt_ids\\\"].values, example[\\\"label\\\"][0]\",\n \"def extract_features(self, preprocessed_inputs):\\n preprocessed_inputs.get_shape().assert_has_rank(4)\\n shape_assert = tf.Assert(\\n tf.logical_and(tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),\\n tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),\\n ['image size must at least be 33 in both height and width.'])\\n\\n feature_map_layout = {\\n 'from_layer': ['conv4', '', '', '', '', '', ''],\\n 'layer_depth': [-1, 1024, 1024, 512, 256, 256, 256],\\n }\\n\\n with tf.control_dependencies([shape_assert]):\\n with slim.arg_scope(self._conv_hyperparams):\\n with tf.variable_scope('vgg_16',\\n reuse=self._reuse_weights) as scope:\\n net, image_features = vgg.vgg_16_base(\\n preprocessed_inputs,\\n final_endpoint='pool5',\\n trainable=False,\\n scope=scope)\\n feature_maps = feature_map_generators.multi_resolution_feature_maps(\\n feature_map_layout=feature_map_layout,\\n depth_multiplier=self._depth_multiplier,\\n min_depth=self._min_depth,\\n insert_1x1_conv=True,\\n image_features=image_features)\\n\\n return feature_maps.values()\",\n \"def parse_cds_features(self, handle, alphabet=..., tags2id=...):\\n ...\",\n \"def data_parser(data):\\n\\n with open(data, 'r') as inp:\\n\\n # take every sample\\n # the last line in the text file is empty, so reading until -1\\n samples = inp.read().split('\\\\n')[:-1]\\n\\n vec = []\\n labels = []\\n for sample in samples:\\n # file is tab delimited\\n split_samples = sample.split('\\\\t')\\n # last column contains the label\\n labels.append(int(split_samples[-1]))\\n\\n features = []\\n for feature in split_samples[:-1]:\\n features.append(float(feature))\\n vec.append(features)\\n\\n # make the features and labels as a numpy array\\n vec = np.array(vec)\\n labels = np.array(labels)\\n return vec, labels\",\n \"def _read_tf_example(self,\\n record: tf.Tensor,\\n feature_preprocessor: Callable[[str], List[str]]\\n ) -> types.FeatureAndLabelTensors:\\n\\n keys_to_features = {}\\n keys_to_features[self._text_feature] = tf.FixedLenFeature([], tf.string)\\n for label, dtype in self._labels.items():\\n keys_to_features[label] = tf.FixedLenFeature([], dtype)\\n parsed = tf.parse_single_example(\\n record, keys_to_features) # type: Dict[str, types.Tensor]\\n\\n text = parsed[self._text_feature]\\n # I think this could be a feature column, but feature columns seem so beta.\\n preprocessed_text = feature_preprocessor(text)\\n features = {self._text_feature: preprocessed_text}\\n if self._round_labels:\\n labels = {label: tf.round(parsed[label]) for label in self._labels}\\n else:\\n labels = {label: parsed[label] for label in self._labels}\\n\\n return features, labels\",\n \"def parse_from_tf_example(serialized,\\n context_feature_spec=None,\\n example_feature_spec=None,\\n size_feature_name=None,\\n mask_feature_name=None):\\n batch_size = tf.shape(serialized)[0]\\n features = tf.io.parse_example(\\n serialized, features={\\n **context_feature_spec,\\n **example_feature_spec\\n })\\n for feature_key, feature_type in example_feature_spec.items():\\n if isinstance(feature_type, tf.io.RaggedFeature):\\n features[feature_key] = tf.expand_dims(features[feature_key], axis=1)\\n else:\\n # feature is either a Tensor or SparseTensor.\\n features[feature_key] = utils.reshape_first_ndims(features[feature_key],\\n 1, [batch_size, 1])\\n if size_feature_name is not None:\\n # Populate features with a size feature of value 1, corresponding to only\\n # one example per list.\\n features[size_feature_name] = tf.ones(shape=[batch_size])\\n if mask_feature_name:\\n features[mask_feature_name] = tf.sequence_mask(tf.ones(shape=[batch_size]))\\n return features\",\n \"def _parse_csv(rows_string_tensor):\\n # Last dim is the label.\\n num_features = len(FEATURE_KEYS)\\n num_columns = num_features + 1\\n columns = tf.decode_csv(rows_string_tensor,\\n record_defaults=[[]] * num_columns)\\n features = dict(zip(FEATURE_KEYS, columns[:num_features]))\\n labels = tf.cast(columns[num_features], tf.int32)\\n return features, labels\",\n \"def parse_function(example_proto):\\r\\n\\r\\n\\t# Parse through features and extract byte string\\r\\n\\tparsed_features = tf.parse_single_example(example_proto,features ={\\r\\n\\t\\t'image': tf.FixedLenFeature([],tf.string),\\r\\n\\t\\t'joint': tf.FixedLenFeature([],tf.string),\\r\\n\\t\\t'offset': tf.FixedLenFeature([],tf.string),\\r\\n\\t\\t'handScale': tf.FixedLenFeature([],tf.string)\\r\\n\\t\\t},name='features')\\r\\n\\r\\n\\t# Decode content into correct types\\r\\n\\timage_dec = tf.decode_raw(parsed_features['image'],tf.float32)\\r\\n\\tjoint_dec = tf.decode_raw(parsed_features['joint'],tf.float32)\\r\\n\\toffset_dec = tf.decode_raw(parsed_features['offset'],tf.float32)\\r\\n\\thandScale_dec = tf.decode_raw(parsed_features['handScale'],tf.float32)\\r\\n\\r\\n\\t# Reshape image to 176x176\\r\\n\\timage_reshaped = tf.reshape(image_dec,[176,176,1])\\r\\n\\r\\n\\t# Crop 128x128 image around COM\\r\\n\\timage_com_cropped = tf.image.crop_to_bounding_box(image_reshaped,24,24,128,128)\\r\\n\\r\\n\\treturn image_com_cropped, joint_dec, offset_dec, handScale_dec\",\n \"def _parse_fn(serialized):\\n features = parse_example(serialized)\\n label = features.pop('clicked')\\n return features, label\",\n \"def parse_tfrecord(raw_example, features_name, labels_name):\\n feature_spec = {\\n name: tf.io.FixedLenSequenceFeature((), tf.int64, True)\\n for name in [features_name, labels_name]}\\n parsed_example = tf.io.parse_single_example(\\n serialized=raw_example,\\n features=feature_spec)\\n labels = parsed_example.pop(labels_name)\\n features = parsed_example.pop(features_name)\\n with tf.control_dependencies([\\n tf.compat.v1.assert_equal(tf.shape(input=features), tf.shape(input=labels))\\n ]):\\n return features, labels\",\n \"def read_metadata_record(raw_features_string):\\n full_metadata = json.loads(raw_features_string)\\n return {\\\"sha256\\\": full_metadata[\\\"sha256\\\"], \\\"appeared\\\": full_metadata[\\\"appeared\\\"], \\\"label\\\": full_metadata[\\\"label\\\"]}\",\n \"def _preprocess(self, features, labels):\\n with tf.variable_scope('preprocess'):\\n with tf.variable_scope('image'):\\n features['image_orig'] = features['image']\\n image = tf.image.convert_image_dtype(features['image_orig'],\\n dtype=tf.float32)\\n if self.mode == ModeKeys.TRAIN:\\n images = tf.unstack(image)\\n images = [augment_image(img) for img in images]\\n image = tf.stack(images)\\n image = tf.subtract(image, 0.5)\\n image = tf.multiply(image, 2.0)\\n features['image'] = image\\n\\n if labels is None:\\n return features, None\\n\\n with tf.variable_scope('label'):\\n # TODO(Shancheng): use start token and end token rather constant 0\\n # labels for decoder input\\n labels['label_input'] = tf.concat([labels['label'][:, -1:],\\n labels['label'][:, 0:-1]], axis=1)\\n # from text length to training label length\\n labels['length'] = tf.reshape(labels['length'], [-1])\\n labels['length'] = labels['length'] + 1\\n\\n return features, labels\",\n \"def _decode_record(record):\\r\\n example = tf.io.parse_single_example(serialized=record, features=feature_description)\\r\\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\\r\\n # So cast all int64 to int32.\\r\\n for key in [k for k in example.keys() if k not in ['example_id', 'unique_ids']]:\\r\\n example[key] = tf.cast(example[key], dtype=tf.int32)\\r\\n if is_training:\\r\\n features = {\\r\\n 'input_ids': example['input_ids'],\\r\\n 'input_mask': example['input_mask'],\\r\\n 'segment_ids': example['segment_ids']\\r\\n }\\r\\n labels = {\\r\\n 'start_logits_or_probs': tf.one_hot(example['start_positions'],\\r\\n depth=seq_length, dtype=tf.float32),\\r\\n 'end_logits_or_probs': tf.one_hot(example['end_positions'],\\r\\n depth=seq_length, dtype=tf.float32),\\r\\n 'ans_type': tf.one_hot(example['answer_types'],\\r\\n depth=len(ANSWER_TYPE_ORDER), dtype=tf.float32)\\r\\n }\\r\\n return (features, labels)\\r\\n else:\\r\\n return example\",\n \"def process_features(\\n config,\\n raw_features: Union[tf.train.Example, features.FeatureDict],\\n random_seed: int) -> features.FeatureDict:\\n if isinstance(raw_features, dict):\\n return features.np_example_to_features(\\n np_example=raw_features,\\n config=config,\\n random_seed=random_seed)\\n else:\\n return features.tf_example_to_features(\\n tf_example=raw_features,\\n config=config,\\n random_seed=random_seed)\",\n \"def _decode_record(record, name_to_features):\\n example = tf.io.parse_single_example(serialized=record, features=name_to_features)\\n\\n # tf.Example only supports tf.int64, but the TPU only supports tf.int32.\\n # So cast all int64 to int32.\\n for name in list(example.keys()):\\n t = example[name]\\n if t.dtype == tf.int64:\\n t = tf.cast(t, dtype=tf.int32)\\n example[name] = t\\n\\n return example\",\n \"def read_metadata_record(raw_features_string):\\n all_data = json.loads(raw_features_string)\\n metadata_keys = {\\\"sha256\\\", \\\"appeared\\\", \\\"label\\\", \\\"avclass\\\"}\\n return {k: all_data[k] for k in all_data.keys() & metadata_keys}\",\n \"def _as_bytes_feature(in_string):\\n return tf.train.Feature(bytes_list=tf.train.BytesList(value=[in_string]))\",\n \"def _handle_feature(fea):\\n if len(fea.shape) == 1:\\n fea = np.array([fea]).T\\n\\n return fea\",\n \"def parse_sequence_example(serialized, image_id, image_feature, caption_feature):\\r\\n context, sequence = tf.parse_single_sequence_example(\\r\\n serialized,\\r\\n context_features={\\r\\n image_id : tf.FixedLenFeature([], dtype=tf.int64),\\r\\n image_feature: tf.FixedLenFeature([], dtype=tf.string)\\r\\n },\\r\\n sequence_features={\\r\\n caption_feature: tf.FixedLenSequenceFeature([], dtype=tf.int64),\\r\\n })\\r\\n\\r\\n encoded_image_id = context[image_id]\\r\\n encoded_image = context[image_feature]\\r\\n caption = sequence[caption_feature]\\r\\n return encoded_image_id, encoded_image, caption\",\n \"def extract_features(self, inputs):\\n pass\",\n \"def parse_example_proto(example_serialized, semantic_level_settings):\\n # Dense features in Example proto.\\n feature_map = {\\n 'image/encoded': tf.FixedLenFeature([], dtype=tf.string, default_value=''),\\n 'image/filename': tf.FixedLenFeature([], dtype=tf.string, default_value=''),\\n }\\n for level, num in semantic_level_settings:\\n feature_map.update({'image/label/%s' % level:\\n tf.FixedLenFeature([num], dtype=tf.int64, default_value=[-1] * num)})\\n features = tf.parse_single_example(example_serialized, feature_map)\\n labels = [tf.cast(features['image/label/%s' % level], dtype=tf.int32)\\n for level, _ in semantic_level_settings]\\n return features['image/encoded'], labels, features['image/filename']\",\n \"def convert_full_features_to_input_features(raw_features):\\n data_features = mx.gluon.data.SimpleDataset(list(itertools.chain.from_iterable(raw_features)))\\n data_features = data_features.transform(lambda *example: (\\n example[0], # example_id\\n example[7], # inputs_id\\n example[9], # segment_ids\\n example[2], # valid_length,\\n example[8], # p_mask\\n example[10], # start_position,\\n example[11], # end_position\\n example[14])) # is_impossible\\n return data_features\",\n \"def parser(record):\\n parsed = tf.parse_single_example(record, transformed_feature_spec)\\n label = parsed.pop(LABEL_KEY)\\n return parsed, label\",\n \"def load_features(self, features):\\n pass\\n # self.features = features\",\n \"def parse_feature_config(feature_config, feature_map):\\n feature_name = feature_config[0]\\n feature_args = feature_config[1]\\n featurizer = feature_map[feature_name]\\n\\n return feature_name, feature_args, featurizer\",\n \"def convert_example_to_features_for_test(example, max_seq_length, tokenizer):\\r\\n features = []\\r\\n tokens = []\\r\\n labels_temp = []\\r\\n for i, word in enumerate(example.text_a.split(' ')):\\r\\n token_wordpiece = tokenizer.tokenize(word) #04/26/1882 ['04', '/', '26', '/', '1882']\\r\\n tokens.extend(token_wordpiece)\\r\\n for m in range(len(token_wordpiece)):\\r\\n if m == 0:\\r\\n labels_temp.append(0)\\r\\n else:\\r\\n labels_temp.append('X')\\r\\n # max_seq_length-1\\r\\n if len(tokens) >= max_seq_length - 1:\\r\\n tokens = tokens[0:(max_seq_length - 2)]\\r\\n labels_temp = labels_temp[0:(max_seq_length - 2)]\\r\\n\\r\\n ntokens = []\\r\\n new_labels_temp = []\\r\\n segment_ids = []\\r\\n\\r\\n ntokens.append('[CLS]')\\r\\n new_labels_temp.append('[CLS]')\\r\\n segment_ids.append(0)\\r\\n for i, token in enumerate(tokens):\\r\\n ntokens.append(token)\\r\\n new_labels_temp.append(labels_temp[i])\\r\\n segment_ids.append(0)\\r\\n ntokens.append('[SEP]')\\r\\n new_labels_temp.append('[SEP]')\\r\\n segment_ids.append(0)\\r\\n input_ids = tokenizer.convert_tokens_to_ids(ntokens)\\r\\n input_mask = [1] * len(input_ids)\\r\\n\\r\\n #if the length is short, tianbu 0\\r\\n while len(input_ids) < max_seq_length:\\r\\n input_ids.append(0)\\r\\n input_mask.append(0)\\r\\n segment_ids.append(0)\\r\\n #we do not concerned about it\\r\\n ntokens.append('NULL')\\r\\n\\r\\n assert len(input_ids) == max_seq_length\\r\\n assert len(input_mask) == max_seq_length\\r\\n assert len(segment_ids) == max_seq_length\\r\\n\\r\\n features.append(InputFeatures(input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, label_ids=None))\\r\\n return features, new_labels_temp\",\n \"def _decode_csv(line):\\n column_header = column_names if with_target else column_names[:4]\\n record_defaults = [[0.] for _ in xrange(len(column_names) - 1)]\\n # Pass label as integer.\\n if with_target:\\n record_defaults.append([0])\\n columns = tf.decode_csv(line, record_defaults=record_defaults)\\n features = dict(zip(column_header, columns))\\n target = features.pop(column_names[4]) if with_target else None\\n return features, target\",\n \"def image_to_features(image):\\n image = tf.keras.preprocessing.image.img_to_array(image)\\n image = tf.keras.applications.mobilenet_v2.preprocess_input(image)\\n image = np.expand_dims(image, axis=0)\\n return image\",\n \"def convert_sentences_to_features(sentences, seq_length, tokenizer):\\n \\n num_sent = len(sentences)\\n input_type_ids = np.zeros((num_sent, seq_length), dtype=np.int32)\\n input_ids = np.zeros((num_sent, seq_length), dtype=np.int32)\\n input_mask = np.zeros((num_sent, seq_length), dtype=np.int32)\\n \\n for idx, sent in enumerate(sentences):\\n tokens = tokenizer.tokenize(sent)\\n tokens = tokens[0:min((seq_length - 2), len(tokens))] # truncate tokens longer than seq_length\\n tokens.insert(0, \\\"[CLS]\\\")\\n tokens.append(\\\"[SEP]\\\")\\n \\n input_ids[idx,:len(tokens)] = np.array(tokenizer.convert_tokens_to_ids(tokens), dtype=np.int32)\\n input_mask[idx,:len(tokens)] = np.ones(len(tokens), dtype=np.int32)\\n\\n assert len(input_ids[idx]) == seq_length\\n assert len(input_mask[idx]) == seq_length\\n assert len(input_type_ids[idx]) == seq_length\\n\\n return input_ids, input_type_ids, input_mask\",\n \"def extractFeatures(self, datum):\\n abstract\",\n \"def _parse_bin(self, byte):\\r\\n bin_line = bin(parse_int([byte], 'big'))[2:].rjust(8, \\\"0\\\")\\r\\n self.ct_flag = bool(int(bin_line[0], 2))\\r\\n self.ct_len = 3 * 2**(int(bin_line[-3:], 2) + 1) if self.ct_flag else 0\\r\\n self.colors_count = 2**(3 * (int(bin_line[1:4], 2) + 1))\",\n \"def _process_features(self, limit):\\n\\n if self.testMode:\\n g = self.testgraph\\n else:\\n g = self.graph\\n model = Model(g)\\n raw = '/'.join((self.rawdir, 'feature'))\\n logger.info(\\\"building labels for features\\\")\\n\\n line_counter = 0\\n with open(raw, 'r') as f:\\n filereader = csv.reader(f, delimiter='\\\\t', quotechar='\\\\\\\"')\\n f.readline() # read the header row; skip\\n for line in filereader:\\n (feature_id, dbxref_id, organism_id, name, uniquename,\\n residues, seqlen, md5checksum, type_id, is_analysis,\\n timeaccessioned, timelastmodified) = line\\n\\n feature_key = feature_id\\n if re.search(r'[\\\\|\\\\s\\\\[\\\\]\\\\{\\\\}\\\\\\\\<\\\\>]', uniquename):\\n # some uniquenames have pipes or other nasty chars!\\n # for example: FB||||FBrf0133242|Hugh-u1\\n feature_id = self._makeInternalIdentifier(\\n 'feature', feature_key)\\n else:\\n feature_id = 'FlyBase:'+uniquename\\n self.idhash['feature'][feature_key] = feature_id\\n self.feature_types[feature_key] = type_id\\n self.label_hash[feature_id] = name\\n\\n if feature_key not in self.feature_to_organism_hash:\\n self.feature_to_organism_hash[feature_key] = set()\\n self.feature_to_organism_hash[feature_key].add(organism_id)\\n\\n # HACK - FBgn are genes, and therefore classes,\\n # all else be individuals\\n is_gene = False\\n if re.search(r'(FBgn|FBog)', feature_id):\\n self.idhash['gene'][feature_key] = feature_id\\n is_gene = True\\n elif re.search(r'FBa[lb]', feature_id):\\n self.idhash['allele'][feature_key] = feature_id\\n elif re.search(r'FBt[ip]', feature_id):\\n self.idhash['feature'][feature_key] = feature_id\\n\\n if self.testMode and \\\\\\n int(feature_key) not in self.test_keys['gene'] + \\\\\\n self.test_keys['allele'] + self.test_keys['feature']:\\n continue\\n\\n # now do something with it!\\n # switch on type_id\\n if name.strip() == '':\\n name = uniquename\\n\\n type_key = type_id\\n type_id = self.idhash['cvterm'][type_key]\\n\\n # skip some features by type\\n types_to_skip = [\\n 'SO:0000316', # CDS\\n 'SO:0000696', # oligos\\n 'SO:0000358', # polypeptide\\n 'SO:0000234', # transcripts\\n ]\\n\\n type_keys_to_skip = [\\n 596, # pcr_product\\n 57096, # mature peptide\\n 57097, # signal_peptide\\n 57270, # repeat masker\\n 58210, # alignment\\n 59643, # cDNA_clone\\n 60006, # uncharacterized_change_in_nucleotide_sequence\\n 61351, # oligo\\n 61467, # polypeptide_domain\\n 257, # exon\\n 286, # intron\\n ]\\n\\n organisms_to_skip = [\\n 2 # computational result\\n ]\\n\\n if type_id in types_to_skip \\\\\\n or int(type_key) in type_keys_to_skip\\\\\\n or int(organism_id) in organisms_to_skip:\\n continue\\n\\n line_counter += 1\\n\\n if int(type_key) == 604: # RNAi_reagent\\n # TODO add other reagents?\\n self.idhash['reagent'][feature_key] = feature_id\\n\\n # deal with the taxonomy\\n # only get taxa for features that are actually used in our set\\n tax_internal_id = self._makeInternalIdentifier(\\n 'organism', organism_id)\\n if organism_id not in self.checked_organisms:\\n # will get the NCBITax if necessary\\n tax_id = self._get_organism_id(organism_id)\\n self.checked_organisms.add(organism_id)\\n else:\\n tax_id = self.idhash['organism'][organism_id]\\n\\n tax_label = self.label_hash.get(tax_id)\\n if not re.search(r'FBog', feature_id) \\\\\\n and re.search(r'Drosophila', tax_label):\\n # make only fly things leaders\\n model.makeLeader(feature_id)\\n\\n if not self.testMode \\\\\\n and limit is not None and line_counter > limit:\\n pass\\n else:\\n if is_gene:\\n model.addClassToGraph(\\n feature_id, name, type_id)\\n g.addTriple(\\n feature_id, model.object_properties['in_taxon'],\\n tax_id)\\n else:\\n if re.search('FBa[lb]', feature_id):\\n type_id = Genotype.genoparts['allele']\\n model.addIndividualToGraph(feature_id, name, type_id)\\n\\n # stop adding what we do not appreciate\\n # if is_obsolete == 't':\\n # if is_gene:\\n # model.addDeprecatedClass(feature_id)\\n # else:\\n # model.addDeprecatedIndividual(feature_id)\\n # self.deprecated_features.add(feature_key)\\n\\n model.addClassToGraph(tax_id)\\n if tax_id != tax_internal_id:\\n model.addEquivalentClass(tax_id, tax_internal_id)\\n\\n model.addComment(\\n feature_id,\\n self._makeInternalIdentifier('feature', feature_key))\\n\\n # TODO save checked_organisms fbid to ncbitax mapping to\\n # a local file to speed up subsequent searches\\n\\n return\",\n \"def read_features(self):\\r\\n def unpack_keypoint(data):\\r\\n try:\\r\\n kpts = data['keypoints']\\r\\n desc = data['descriptors']\\r\\n keypoints = [cv.KeyPoint(x, y, _size, _angle, _response, int(_octave), int(_class_id))\\r\\n for x, y, _size, _angle, _response, _octave, _class_id in list(kpts)]\\r\\n return keypoints, np.array(desc)\\r\\n except(IndexError):\\r\\n return np.array([]), np.array([])\\r\\n try:\\r\\n data = np.load(self.features_path + self.id + \\\".npz\\\")\\r\\n self.keypoints, self.descriptors = unpack_keypoint(data)\\r\\n logging.info(f\\\"Existing features for {self.name} found in features directory.\\\")\\r\\n except FileNotFoundError:\\r\\n logging.info(f\\\"Features for {self.name} not found in {self.features_path}.\\\")\",\n \"def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer):\\n\\n label_map = {} # label\\n for (i, label) in enumerate(label_list): # ['0', '1']\\n label_map[label] = i\\n\\n features = [] # feature\\n for (ex_index, example) in enumerate(examples):\\n text_a_id = int(example.text_a_id)\\n text_b_id = int(example.text_b_id)\\n\\n text_a_fields = example.text_a.split(\\\" _eop_ \\\")\\n \\n tokens_a = []\\n text_a_subtype = []\\n for text_a_field_idx, text_a_field in enumerate(text_a_fields):\\n text_a_field_token = tokenizer.tokenize(text_a_field)\\n tokens_a.extend(text_a_field_token)\\n text_a_subtype.extend([text_a_field_idx]*len(text_a_field_token))\\n assert len(tokens_a) == len(text_a_subtype)\\n\\n tokens_b = None\\n if example.text_b:\\n tokens_b = tokenizer.tokenize(example.text_b) # text_b tokenize\\n\\n if tokens_b: # if has b\\n # Modifies `tokens_a` and `tokens_b` in place so that the total\\n # length is less than the specified length.\\n # Account for [CLS], [SEP], [SEP] with \\\"- 3\\\"\\n _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3) # truncate\\n else:\\n # Account for [CLS] and [SEP] with \\\"- 2\\\"\\n if len(tokens_a) > max_seq_length - 2:\\n tokens_a = tokens_a[0:(max_seq_length - 2)]\\n\\n # The convention in BERT is:\\n # (a) For sequence pairs:\\n # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]\\n # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1\\n # (b) For single sequences:\\n # tokens: [CLS] the dog is hairy . [SEP]\\n # type_ids: 0 0 0 0 0 0 0\\n #\\n # Where \\\"type_ids\\\" are used to indicate whether this is the first\\n # sequence or the second sequence. The embedding vectors for `type=0` and\\n # `type=1` were learned during pre-training and are added to the wordpiece\\n # embedding vector (and position vector). This is not *strictly* necessary\\n # since the [SEP] token unambiguously separates the sequences, but it makes\\n # it easier for the model to learn the concept of sequences.\\n #\\n # For classification tasks, the first vector (corresponding to [CLS]) is\\n # used as as the \\\"sentence vector\\\". Note that this only makes sense because # (?)\\n # the entire model is fine-tuned.\\n tokens = []\\n segment_ids = []\\n subtype_ids = []\\n tokens.append(\\\"[CLS]\\\")\\n segment_ids.append(0)\\n subtype_ids.append(0)\\n for token_idx, token in enumerate(tokens_a):\\n tokens.append(token)\\n segment_ids.append(0)\\n subtype_ids.append(text_a_subtype[token_idx])\\n tokens.append(\\\"[SEP]\\\")\\n segment_ids.append(0)\\n subtype_ids.append(1)\\n\\n if tokens_b:\\n for token_idx, token in enumerate(tokens_b):\\n tokens.append(token)\\n segment_ids.append(1)\\n subtype_ids.append(2)\\n tokens.append(\\\"[SEP]\\\")\\n segment_ids.append(1)\\n subtype_ids.append(2)\\n\\n input_sents = tokenizer.convert_tokens_to_ids(tokens)\\n\\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\\n # tokens are attended to.\\n input_mask = [1] * len(input_sents) # mask\\n\\n # Zero-pad up to the sequence length.\\n while len(input_sents) < max_seq_length:\\n input_sents.append(0)\\n input_mask.append(0)\\n segment_ids.append(0)\\n subtype_ids.append(0)\\n\\n assert len(input_sents) == max_seq_length\\n assert len(input_mask) == max_seq_length\\n assert len(segment_ids) == max_seq_length\\n assert len(subtype_ids) == max_seq_length\\n\\n label_id = label_map[example.label]\\n\\n if ex_index%2000 == 0:\\n print('convert_{}_examples_to_features'.format(ex_index))\\n\\n features.append(\\n InputFeatures( # object\\n text_a_id=text_a_id,\\n text_b_id=text_b_id,\\n input_sents=input_sents,\\n input_mask=input_mask,\\n segment_ids=segment_ids,\\n subtype_ids=subtype_ids,\\n label_id=label_id))\\n\\n return features\",\n \"def parse_type(fobj, data_type):\\n if data_type == \\\"Boolean\\\": ## False if 0x00 else True\\n return bool(fobj.read(1))\\n elif data_type == \\\"Byte\\\": ## 1 byte int\\n return fobj.read(1)[0]\\n elif data_type == \\\"DateTime\\\": ## 8 bytes signed int\\n return struct.unpack(\\\"<q\\\", fobj.read(8))[0]\\n elif data_type == \\\"Double\\\": ## 8 bytes floating point\\n return struct.unpack(\\\"<d\\\", fobj.read(8))[0]\\n elif data_type == \\\"Int\\\": ## 4 bytes unsigned int\\n return struct.unpack(\\\"<I\\\", fobj.read(4))[0]\\n elif data_type == \\\"Int-Double pair\\\": ## 0x08-Int-0x0d-Double with AssertionError\\n bb = fobj.read(1)[0]\\n if bb != 0x08:\\n raise AssertionError('parse_type(fobj, data_type): '\\n '1st byte(%s) of \\\"Int-Double pair\\\" != 0x08' % bb)\\n first_int = parse_type(fobj, \\\"Int\\\")\\n bb = fobj.read(1)[0]\\n if bb != 0x0d:\\n raise AssertionError('parse_type(fobj, data_type): '\\n '6th byte(%s) of \\\"Int-Double pair\\\" != 0x0d' % bb)\\n return [first_int, parse_type(fobj, \\\"Double\\\")]\\n elif data_type == \\\"Int-Double pair*\\\": ## int(n) - \\\"Int-Double pair\\\"*n\\n return [parse_type(fobj, \\\"Int-Double pair\\\") for i in range(parse_type(fobj, \\\"Int\\\"))]\\n elif data_type == \\\"Long\\\": ## 8 bytes unsigned int\\n return struct.unpack(\\\"<Q\\\", fobj.read(8))[0]\\n elif data_type == \\\"Short\\\": ## 2 bytes unsigned int\\n return struct.unpack(\\\"<H\\\", fobj.read(2))[0]\\n elif data_type == \\\"Single\\\": ## 4 bytes floating point\\n return struct.unpack(\\\"<f\\\", fobj.read(4))[0]\\n elif data_type == \\\"String\\\": ## 0x00 or 0x0b - ULE128(n) - UTF-8(length=n)\\n bb = fobj.read(1)[0]\\n if bb == 0x00:\\n return None\\n elif bb != 0x0b:\\n ## TODO: show integers in assertion error in hexadecimal and decimal\\n ## to make debug more convenient (cause I may inspect the file in a byte reader.\\n raise AssertionError('parse_type(fobj, data_type): '\\n '1st byte(%s) of \\\"String\\\" not in {0x00, 0x0b}' % bb)\\n strlen = parse_type(fobj, \\\"ULEB128\\\")\\n return fobj.read(strlen).decode(\\\"utf-8\\\")\\n elif data_type == \\\"ULEB128\\\": ## https://en.wikipedia.org/wiki/LEB128#Decode_unsigned_integer\\n i = 0 ## derived from the wiki psuedo code\\n res = 0\\n shift = 0\\n while True:\\n bb = fobj.read(1)[0]\\n i += 1\\n res |= ((bb & 0b1111111) << shift)\\n if (bb & 0b10000000) == 0:\\n break\\n shift += 7\\n return res\\n elif data_type == \\\"Timing point\\\": ## Double - Double - Boolean\\n return parse_types(fobj, [\\\"Double\\\", \\\"Double\\\", \\\"Boolean\\\"])\\n elif data_type == \\\"Timing point+\\\": ## int(n) - \\\"Timing point\\\"*n\\n return [parse_type(fobj, \\\"Timing point\\\") for i in range(parse_type(fobj, \\\"Int\\\"))]\\n else:\\n raise NotImplementedError('parse_type(fobj, data_type): Unknown data type: \\\"%s\\\".' % data_type)\",\n \"def read_images(idx_filename, feature_type='ip-above', only_images=True):\\n with gzip.open(idx_filename, 'rb') as f:\\n magic_numbers = f.read(4)\\n # print('magic_number', magic_numbers)\\n assert magic_numbers[0] == 0 and magic_numbers[1] == 0\\n if magic_numbers[2] != 8:\\n raise AssertionError('Only support for unsigned char')\\n shape = magic_numbers[3]\\n # print('shape', shape)\\n num_examples = int.from_bytes(f.read(4), byteorder='big')\\n # print('number of examples',num_examples)\\n dimensions = []\\n for _ in range(shape - 1):\\n dimensions.append(int.from_bytes(f.read(4), byteorder='big'))\\n # print('dimensions', dimensions)\\n data_list = []\\n for _ in range(num_examples):\\n each_data_point = _read(dimensions, f)\\n session_info = _extract_session_info(each_data_point[:SESSION_BYTE_LEN])\\n if feature_type == 'payload-len':\\n actual_pkt_count = int.from_bytes(each_data_point[SESSION_BYTE_LEN:SESSION_BYTE_LEN + 4],\\n byteorder='big')\\n actual_byte_count = int.from_bytes(each_data_point[SESSION_BYTE_LEN + 4:SESSION_BYTE_LEN + 6],\\n byteorder='big')\\n other_data = each_data_point[SESSION_BYTE_LEN + 6:]\\n elif feature_type == 'ip-above':\\n actual_pkt_count = each_data_point[SESSION_BYTE_LEN]\\n other_data = each_data_point[SESSION_BYTE_LEN + 1:]\\n else:\\n raise AssertionError('feature_type could only be \\\"payload-len\\\" or \\\"ip-above\\\"')\\n if only_images is True:\\n data_list.append(other_data)\\n elif only_images is False:\\n if feature_type == 'ip-above':\\n data_list.append((session_info, actual_pkt_count, other_data))\\n elif feature_type == 'payload-len':\\n data_list.append((session_info, actual_pkt_count, actual_byte_count, other_data))\\n else:\\n raise AssertionError('feature_type could only be \\\"payload-len\\\" or \\\"ip-above\\\"')\\n else:\\n raise AssertionError('only_images could only be True or False')\\n\\n assert f.read() == b''\\n\\n data_list = np.array(data_list)\\n return data_list\",\n \"def parse_record(raw_record, is_training, dtype):\\n\\n templar_buffer, search_buffer, templar_bbox, search_bbox = parse_example_proto(raw_record)\\n templar_img, search_img, score, score_weight, tight_temp_bbox, tight_search_bbox = preprocess_pair(\\n templar_buffer=templar_buffer, search_buffer=search_buffer, templar_bbox=templar_bbox,\\n search_bbox=search_bbox, num_channels=_NUM_CHANNELS, is_training=is_training)\\n\\n templar_img = tf.cast(templar_img, dtype)\\n search_img = tf.cast(search_img, dtype)\\n score = tf.cast(score, tf.int32)\\n score_weight = tf.cast(score_weight, dtype)\\n tight_temp_bbox = tf.cast(tight_temp_bbox, tf.int32)\\n #tight_search_bbox = tf.cast(tight_search_bbox, tf.int32)\\n\\n dict = {'templar': templar_img, 'search': search_img, 'score': score, 'score_weight': score_weight,\\n 'tight_temp_bbox': tight_temp_bbox, 'tight_search_bbox': tight_search_bbox}\\n\\n return dict\",\n \"def read_temp(temp):\\n type_dict = {\\\"string\\\": str, \\\"unknown\\\": str, \\\"numeric\\\": float}\\n with open(temp, 'r') as topen:\\n feature_lines = topen.readlines()\\n feature_labels = []\\n feature_types = []\\n for i, row in enumerate(feature_lines):\\n if row.startswith(\\\"@attribute\\\"):\\n flabel, ftype = row[11:-1].split(' ')\\n feature_labels.append(flabel)\\n feature_types.append(type_dict[ftype])\\n elif row.startswith(\\\"@data\\\"):\\n feature_values = feature_lines[i+1].split(\\\",\\\")\\n if len(feature_values) < len(feature_labels):\\n feature_values = feature_lines[i+2].split(\\\",\\\")\\n for i, item in enumerate(feature_values):\\n try:\\n feature_values[i] = (feature_types[i](item))\\n except:\\n feature_values[i] = item\\n return(dict(zip(feature_labels, feature_values)))\",\n \"def _parse_function(self, example_proto):\\n\\n # Currently only supports jpeg and png.\\n # Need to use this logic because the shape is not known for\\n # tf.image.decode_image and we rely on this info to\\n # extend label if necessary.\\n def _decode_image(content, channels):\\n return tf.cond(\\n tf.image.is_jpeg(content),\\n lambda: tf.image.decode_jpeg(content, channels),\\n lambda: tf.image.decode_png(content, channels))\\n\\n features = {\\n 'image/encoded':\\n tf.FixedLenFeature((), tf.string, default_value=''),\\n 'image/filename':\\n tf.FixedLenFeature((), tf.string, default_value=''),\\n 'image/format':\\n tf.FixedLenFeature((), tf.string, default_value='jpeg'),\\n 'image/height':\\n tf.FixedLenFeature((), tf.int64, default_value=0),\\n 'image/width':\\n tf.FixedLenFeature((), tf.int64, default_value=0),\\n 'image/segmentation/class/encoded':\\n tf.FixedLenFeature((), tf.string, default_value=''),\\n 'image/segmentation/class/format':\\n tf.FixedLenFeature((), tf.string, default_value='png'),\\n }\\n\\n parsed_features = tf.parse_single_example(example_proto, features)\\n\\n image = _decode_image(parsed_features['image/encoded'], channels=3)\\n\\n label = None\\n if self.split_name != common.TEST_SET:\\n label = _decode_image(\\n parsed_features['image/segmentation/class/encoded'], channels=1)\\n\\n image_name = parsed_features['image/filename']\\n if image_name is None:\\n image_name = tf.constant('')\\n\\n sample = {\\n common.IMAGE: image,\\n common.IMAGE_NAME: image_name,\\n common.HEIGHT: parsed_features['image/height'],\\n common.WIDTH: parsed_features['image/width'],\\n }\\n\\n if label is not None:\\n if label.get_shape().ndims == 2:\\n label = tf.expand_dims(label, 2)\\n elif label.get_shape().ndims == 3 and label.shape.dims[2] == 1:\\n pass\\n else:\\n raise ValueError('Input label shape must be [height, width], or '\\n '[height, width, 1].')\\n\\n label.set_shape([None, None, 1])\\n\\n sample[common.LABELS_CLASS] = label\\n\\n return sample\",\n \"def read_data(feature_file, label_file):\",\n \"def _parse_tensor_or_dict(features):\\n if isinstance(features, dict):\\n keys = sorted(features.keys())\\n with ops.colocate_with(features[keys[0]]):\\n features = array_ops.concat([features[k] for k in keys], 1)\\n return features\",\n \"def featurize(self, prev_tag, prev_word, token, next_word):\\n features = np.array([])\\n # one of our features will be the previous tag\\n if None or prev_tag == 'O':\\n features = np.append(features, [0])\\n else:\\n features = np.append(features, [1])\\n # another set of features will be our word shape\\n # condensed word shape form with the truncation thing\\n\\n # another feature will be our pos tags\\n # use nltk pos tagging\\n\\n # another feature will be our word embeddings\\n features = np.append(features, self.model[token])\\n # and the final feature will be bias\\n features = np.append(features, [1])\\n return features\",\n \"def preprocess_feature(df):\",\n \"def parse(all_blobs, all_angles):\",\n \"def convert_example(example, tokenizer):\\n\\n feature = tokenizer(\\n text=example['question'],\\n text_pair=example['answer'],\\n max_seq_len=args.max_seq_length)\\n feature['labels'] = example['labels']\\n feature['id'] = example['id']\\n\\n return feature\",\n \"def convert_examples_to_features(\\n instruction,\\n example,\\n max_seq_length,\\n tokenizer,\\n cls_token_at_end=False,\\n cls_token=\\\"[CLS]\\\",\\n cls_token_segment_id=1,\\n sep_token=\\\"[SEP]\\\",\\n sep_token_extra=False,\\n pad_on_left=False,\\n pad_token=0,\\n cls_token_box=[0, 0, 0, 0],\\n sep_token_box=[1000, 1000, 1000, 1000],\\n pad_token_box=[0, 0, 0, 0],\\n pad_token_segment_id=0,\\n pad_token_label_id=-1,\\n sequence_a_segment_id=0,\\n mask_padding_with_zero=True,\\n ):\\n\\n features = dict()\\n\\n tokens = []\\n token_boxes = []\\n box = [\\n int(example[\\\"x0\\\"]),\\n int(example[\\\"y0\\\"]),\\n int(example[\\\"x1\\\"]),\\n int(example[\\\"y1\\\"]),\\n ]\\n if instruction:\\n instruction_tokens = tokenizer.tokenize(instruction)\\n tokens.extend(instruction_tokens)\\n token_boxes.extend([box] * len(tokens))\\n tokens.append(\\\"[SEP]\\\")\\n token_boxes.append(sep_token_box)\\n\\n segment_ids_first = [0] * len(tokens)\\n word_tokens = tokenizer.tokenize(example[\\\"text\\\"])\\n tokens.extend(word_tokens)\\n token_boxes.extend([box] * len(word_tokens))\\n segment_ids_second = [1] * len(word_tokens)\\n segment_ids = segment_ids_first + segment_ids_second\\n\\n # Account for [CLS] and [SEP] with \\\"- 2\\\" and with \\\"- 3\\\" for RoBERTa.\\n special_tokens_count = 3 if sep_token_extra else 2\\n if len(tokens) > max_seq_length - special_tokens_count:\\n tokens = tokens[: (max_seq_length - special_tokens_count)]\\n token_boxes = token_boxes[: (max_seq_length - special_tokens_count)]\\n segment_ids = segment_ids[: (max_seq_length - special_tokens_count)]\\n\\n tokens += [sep_token]\\n token_boxes += [sep_token_box]\\n segment_ids += [1]\\n\\n if sep_token_extra:\\n # roberta uses an extra separator b/w pairs of sentences\\n tokens += [sep_token]\\n token_boxes += [sep_token_box]\\n segment_ids += [1]\\n # segment_ids = [sequence_a_segment_id] * len(tokens)\\n\\n if cls_token_at_end:\\n tokens += [cls_token]\\n token_boxes += [cls_token_box]\\n segment_ids += [cls_token_segment_id]\\n else:\\n tokens = [cls_token] + tokens\\n token_boxes = [cls_token_box] + token_boxes\\n segment_ids = [cls_token_segment_id] + segment_ids\\n\\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\\n\\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\\n # tokens are attended to.\\n input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)\\n\\n # Zero-pad up to the sequence length.\\n padding_length = max_seq_length - len(input_ids)\\n if pad_on_left:\\n input_ids = ([pad_token] * padding_length) + input_ids\\n input_mask = (\\n [0 if mask_padding_with_zero else 1] * padding_length\\n ) + input_mask\\n segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids\\n label_ids = ([pad_token_label_id] * padding_length) + label_ids\\n token_boxes = ([pad_token_box] * padding_length) + token_boxes\\n else:\\n input_ids += [pad_token] * padding_length\\n input_mask += [0 if mask_padding_with_zero else 1] * padding_length\\n segment_ids += [pad_token_segment_id] * padding_length\\n token_boxes += [pad_token_box] * padding_length\\n\\n assert len(input_ids) == max_seq_length\\n assert len(input_mask) == max_seq_length\\n assert len(segment_ids) == max_seq_length\\n assert len(token_boxes) == max_seq_length\\n\\n features[\\\"ui_input_ids\\\"] = input_ids\\n features[\\\"ui_input_mask\\\"] = input_mask\\n features[\\\"ui_segment_ids\\\"] = segment_ids\\n features[\\\"ui_boxes\\\"] = token_boxes\\n\\n return features\",\n \"def convert_examples_to_features(\\n instruction,\\n example,\\n max_seq_length,\\n tokenizer,\\n cls_token_at_end=False,\\n cls_token=\\\"[CLS]\\\",\\n cls_token_segment_id=1,\\n sep_token=\\\"[SEP]\\\",\\n sep_token_extra=False,\\n pad_on_left=False,\\n pad_token=0,\\n cls_token_box=[0, 0, 0, 0],\\n sep_token_box=[1000, 1000, 1000, 1000],\\n pad_token_box=[0, 0, 0, 0],\\n pad_token_segment_id=0,\\n pad_token_label_id=-1,\\n sequence_a_segment_id=0,\\n mask_padding_with_zero=True,\\n ):\\n\\n features = dict()\\n\\n tokens = []\\n token_boxes = []\\n box = [\\n int(example[\\\"x0\\\"]),\\n int(example[\\\"y0\\\"]),\\n int(example[\\\"x1\\\"]),\\n int(example[\\\"y1\\\"]),\\n ]\\n if instruction:\\n instruction_tokens = tokenizer.tokenize(instruction)\\n tokens.extend(instruction_tokens)\\n token_boxes.extend([box] * len(tokens))\\n tokens.append(\\\"[SEP]\\\")\\n token_boxes.append(sep_token_box)\\n\\n segment_ids_first = [0] * len(tokens)\\n word_tokens = tokenizer.tokenize(example[\\\"text\\\"])\\n tokens.extend(word_tokens)\\n token_boxes.extend([box] * len(word_tokens))\\n segment_ids_second = [1] * len(word_tokens)\\n segment_ids = segment_ids_first + segment_ids_second\\n\\n # Account for [CLS] and [SEP] with \\\"- 2\\\" and with \\\"- 3\\\" for RoBERTa.\\n special_tokens_count = 3 if sep_token_extra else 2\\n if len(tokens) > max_seq_length - special_tokens_count:\\n tokens = tokens[: (max_seq_length - special_tokens_count)]\\n token_boxes = token_boxes[: (max_seq_length - special_tokens_count)]\\n segment_ids = segment_ids[: (max_seq_length - special_tokens_count)]\\n\\n tokens += [sep_token]\\n token_boxes += [sep_token_box]\\n segment_ids += [1]\\n\\n if sep_token_extra:\\n # roberta uses an extra separator b/w pairs of sentences\\n tokens += [sep_token]\\n token_boxes += [sep_token_box]\\n segment_ids += [1]\\n # segment_ids = [sequence_a_segment_id] * len(tokens)\\n\\n if cls_token_at_end:\\n tokens += [cls_token]\\n token_boxes += [cls_token_box]\\n segment_ids += [cls_token_segment_id]\\n else:\\n tokens = [cls_token] + tokens\\n token_boxes = [cls_token_box] + token_boxes\\n segment_ids = [cls_token_segment_id] + segment_ids\\n\\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\\n\\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\\n # tokens are attended to.\\n input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)\\n\\n # Zero-pad up to the sequence length.\\n padding_length = max_seq_length - len(input_ids)\\n if pad_on_left:\\n input_ids = ([pad_token] * padding_length) + input_ids\\n input_mask = (\\n [0 if mask_padding_with_zero else 1] * padding_length\\n ) + input_mask\\n segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids\\n label_ids = ([pad_token_label_id] * padding_length) + label_ids\\n token_boxes = ([pad_token_box] * padding_length) + token_boxes\\n else:\\n input_ids += [pad_token] * padding_length\\n input_mask += [0 if mask_padding_with_zero else 1] * padding_length\\n segment_ids += [pad_token_segment_id] * padding_length\\n token_boxes += [pad_token_box] * padding_length\\n\\n assert len(input_ids) == max_seq_length\\n assert len(input_mask) == max_seq_length\\n assert len(segment_ids) == max_seq_length\\n assert len(token_boxes) == max_seq_length\\n\\n features[\\\"ui_input_ids\\\"] = input_ids\\n features[\\\"ui_input_mask\\\"] = input_mask\\n features[\\\"ui_segment_ids\\\"] = segment_ids\\n features[\\\"ui_boxes\\\"] = token_boxes\\n\\n return features\",\n \"def _read_libffm_file(self, filename):\\n\\n X_true = np.zeros((self.num_rows, self.num_features))\\n y_true = np.zeros((self.num_rows, 1))\\n field_true = np.zeros((self.num_features, 1))\\n with open(filename, 'r') as f:\\n i = 0\\n for line in f:\\n tmp_row = line.replace('\\\\n', '').split(' ')\\n\\n # extract label\\n y_true[i] = int(tmp_row[0])\\n\\n # extract data and fields\\n for k in range(1, len(tmp_row)):\\n if len(tmp_row[k]) > 0:\\n tmp_str = tmp_row[k].split(':')\\n j = int(tmp_str[1])\\n field_true[j] = int(tmp_str[0])\\n tmp_data = float(tmp_str[2])\\n X_true[i, j] = tmp_data\\n i = i + 1\\n\\n return X_true, y_true, field_true\",\n \"def _parse_function(example_proto):\\n # Parse the tf.example according to the features_spec definition\\n parsed_features = tf.parse_single_example(example_proto, features_spec)\\n sequence = parsed_features[\\\"sequence\\\"]\\n # Convert the sparse sequence tensor to dense.\\n sequence_d = tf.sparse_to_dense(sequence.indices, sequence.dense_shape, sequence.values)\\n # Return all the elements\\n return parsed_features[\\\"sequence_length\\\"], parsed_features[\\\"label\\\"], sequence_d\",\n \"def _preprocess(self, feature_dict):\\n return feature_dict\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68898326","0.67393595","0.6622843","0.6522131","0.6400133","0.635168","0.63479924","0.62762094","0.6270513","0.6163678","0.61197406","0.60895497","0.6085604","0.60802996","0.6062965","0.600735","0.5974631","0.5960345","0.5915046","0.5915046","0.5887663","0.5876848","0.58653706","0.5819515","0.5808876","0.5806503","0.57954687","0.577566","0.5769848","0.57567745","0.57491744","0.5718019","0.5697154","0.5668258","0.56646353","0.566179","0.566179","0.566179","0.56583333","0.5658188","0.56533635","0.5641277","0.5638258","0.5628091","0.5619702","0.56170946","0.56044865","0.55943984","0.55844325","0.5575594","0.5575239","0.55681604","0.5543952","0.55335885","0.5521142","0.5519551","0.5517334","0.55145216","0.55102915","0.5488572","0.54673135","0.5464931","0.5461372","0.5461096","0.54593956","0.5431262","0.5426217","0.542032","0.54072225","0.5401989","0.5382928","0.5381682","0.5369257","0.5355653","0.5345768","0.53428316","0.53370047","0.53348184","0.5332513","0.5331705","0.53221524","0.52968293","0.5287496","0.52837825","0.5270814","0.52695453","0.52658516","0.52618766","0.52557296","0.5254211","0.5254008","0.52382696","0.52352774","0.5220614","0.5206771","0.52022123","0.51953036","0.51953036","0.5192547","0.5192048","0.51897246"],"string":"[\n \"0.68898326\",\n \"0.67393595\",\n \"0.6622843\",\n \"0.6522131\",\n \"0.6400133\",\n \"0.635168\",\n \"0.63479924\",\n \"0.62762094\",\n \"0.6270513\",\n \"0.6163678\",\n \"0.61197406\",\n \"0.60895497\",\n \"0.6085604\",\n \"0.60802996\",\n \"0.6062965\",\n \"0.600735\",\n \"0.5974631\",\n \"0.5960345\",\n \"0.5915046\",\n \"0.5915046\",\n \"0.5887663\",\n \"0.5876848\",\n \"0.58653706\",\n \"0.5819515\",\n \"0.5808876\",\n \"0.5806503\",\n \"0.57954687\",\n \"0.577566\",\n \"0.5769848\",\n \"0.57567745\",\n \"0.57491744\",\n \"0.5718019\",\n \"0.5697154\",\n \"0.5668258\",\n \"0.56646353\",\n \"0.566179\",\n \"0.566179\",\n \"0.566179\",\n \"0.56583333\",\n \"0.5658188\",\n \"0.56533635\",\n \"0.5641277\",\n \"0.5638258\",\n \"0.5628091\",\n \"0.5619702\",\n \"0.56170946\",\n \"0.56044865\",\n \"0.55943984\",\n \"0.55844325\",\n \"0.5575594\",\n \"0.5575239\",\n \"0.55681604\",\n \"0.5543952\",\n \"0.55335885\",\n \"0.5521142\",\n \"0.5519551\",\n \"0.5517334\",\n \"0.55145216\",\n \"0.55102915\",\n \"0.5488572\",\n \"0.54673135\",\n \"0.5464931\",\n \"0.5461372\",\n \"0.5461096\",\n \"0.54593956\",\n \"0.5431262\",\n \"0.5426217\",\n \"0.542032\",\n \"0.54072225\",\n \"0.5401989\",\n \"0.5382928\",\n \"0.5381682\",\n \"0.5369257\",\n \"0.5355653\",\n \"0.5345768\",\n \"0.53428316\",\n \"0.53370047\",\n \"0.53348184\",\n \"0.5332513\",\n \"0.5331705\",\n \"0.53221524\",\n \"0.52968293\",\n \"0.5287496\",\n \"0.52837825\",\n \"0.5270814\",\n \"0.52695453\",\n \"0.52658516\",\n \"0.52618766\",\n \"0.52557296\",\n \"0.5254211\",\n \"0.5254008\",\n \"0.52382696\",\n \"0.52352774\",\n \"0.5220614\",\n \"0.5206771\",\n \"0.52022123\",\n \"0.51953036\",\n \"0.51953036\",\n \"0.5192547\",\n \"0.5192048\",\n \"0.51897246\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":893,"numItemsPerPage":100,"numTotalItems":95772,"offset":89300,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NTc4MjE5MSwianRpIjoiMTZlNzc3ODYtMGUzMy00NWNjLWJkMDUtNWU5MzliMmFjZDI4Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODU3ODU3OTEsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.2mxnQxAVwMisx1tLA1Qm9Acd5CCH0KHSLBEMdApSMv1F_MfQFUygpkjvrnGmFaoBte1_YegyD4Bu54nP-fVZAg","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Renders lambda2 contours of droplet data from Michael or Pablo's simulations, given a case config file and render config file. | def lambda2(case_config_filepath, render_config_filepath):
# Load config file with all common directory names
dirname_config = configparser.ConfigParser()
dirname_config.read("dirname.cfg")
# Get information from config files
cconfd = load_config.get_config_params(case_config_filepath)
rconfd ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main(config):\n file_paths_info = [('GLOFRIS','WATCH','ARG_inunriver_historical_000000000WATCH_1980_rp01000.tif'),\n \t\t\t\t('GLOFRIS','RCP45','ARG_inunriver_rcp4p5_0000GFDL-ESM2M_2030_rp01000.tif'),\n \t\t\t\t('GLOFRIS','RCP85','ARG_inunriver_rcp8p5_0000GFDL-ESM2M_2030_rp01000.tif'),\n \t\t\t\t('... | [
"0.6074889",
"0.574469",
"0.5545353",
"0.52796954",
"0.52744776",
"0.518162",
"0.51216215",
"0.5116855",
"0.51122236",
"0.50734866",
"0.50516486",
"0.5032492",
"0.50160444",
"0.49768373",
"0.49333197",
"0.4913025",
"0.4899455",
"0.4876432",
"0.48720288",
"0.48589456",
"0.4857... | 0.7535855 | 0 |
Calculates the distance between to points in the circular time. | def norm(self, XA, XB):
d1 = abs(XA - XB)
d2 = abs(XA - XB - 7*24*60)
return np.vstack([d1, d2]).min(axis = 0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def distance_to(self, circle):\n diff = tuple(map(sub, self.pos, circle.pos))\n return math.hypot(*diff)",
"def _calculate_distance(self):\n xy = list(zip(self.x, self.y))\n\n dist = [0]\n for i in range(1, len(xy)):\n dist.append(self.distance_between_two_points(xy[... | [
"0.6774109",
"0.6655058",
"0.62920964",
"0.62672186",
"0.6257815",
"0.6186058",
"0.6183667",
"0.61678165",
"0.6142931",
"0.6087921",
"0.6072777",
"0.6030249",
"0.6014272",
"0.60124606",
"0.60053617",
"0.59909576",
"0.59702396",
"0.5964829",
"0.5959583",
"0.5924536",
"0.590831... | 0.0 | -1 |
Map time into an integer interval [0; 7 24 60[ representing the time of day and day of week with 1 minute granuality. | def transform_ix(self, ix):
return (ix.dayofweek.values * self.timesteps_per_day + ix.map(lambda x: pd.to_timedelta(x.time().isoformat())).values / self.freq) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_min_to_day(time):\n return str(round(int(time) / (60 * 8), 1))",
"def scaledTime():\n #return (time.gmtime().tm_wday, time.gmtime().tm_hour)\n epoch = time.strptime(\"2013-02-21 11:30:00\", \"%Y-%m-%d %H:%M:%S\")\n timeInSec = time.mktime(time.gmtime()) - time.mktime(epoch)\n hourSince = ... | [
"0.6587727",
"0.6093808",
"0.60330206",
"0.5678907",
"0.56553",
"0.56367296",
"0.55319023",
"0.5520177",
"0.5512654",
"0.54910684",
"0.5483596",
"0.54657775",
"0.53240174",
"0.53161484",
"0.52447605",
"0.52369124",
"0.51902425",
"0.5186119",
"0.51574695",
"0.5155435",
"0.5128... | 0.4983597 | 32 |
remove white space & comments & unnecessary fields | def clean_data(self):
for line in self.file:
if line.startswith('//') or line.isspace():
continue
if '//' in line:
line = line.split('//')[0]
line = line.replace('\n', '')
line = line.replace(' ','')
self.commands.append... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def strip(self):\n self.document_type = self.document_type.strip()\n self.document_reg_id = self.document_reg_id.strip()\n self.owner_cross_reference = self.owner_cross_reference.strip()\n self.routing_slip_number = self.routing_slip_number.strip()\n self.bcol_account = self.bcol... | [
"0.70774215",
"0.6626586",
"0.6582071",
"0.64799535",
"0.64024925",
"0.63783985",
"0.62964535",
"0.6291502",
"0.62791693",
"0.62556684",
"0.61780673",
"0.61647797",
"0.61475337",
"0.61393124",
"0.611319",
"0.6105898",
"0.6082494",
"0.60794055",
"0.60245603",
"0.6024032",
"0.6... | 0.5920418 | 33 |
Unpack each command into it's underlying fields | def parse(self, command):
split_command = command.split('=')
semi = 1
dest = None
if len(split_command) > 1:
dest = split_command[0]
else:
semi = 0
c_or_j = split_command[semi].split(';')
comp = c_or_j[0]
jump = c_or_j[1] if len(c_o... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _extract_command(fields: List[bytes]) -> Tuple[Any, List[Any]]:\n cmd = encode_command(fields[0])\n if cmd in COMMANDS_WITH_SUB and len(fields) >= 2:\n cmd += \" \" + encode_command(fields[1])\n cmd_arguments = fields[2:]\n else:\n cmd_arguments = fields[1:]\n return cmd, cmd_a... | [
"0.6795791",
"0.63592345",
"0.6357638",
"0.61161965",
"0.59982604",
"0.5759801",
"0.57257724",
"0.5696698",
"0.56799316",
"0.56750417",
"0.5649418",
"0.56269705",
"0.5588096",
"0.55799854",
"0.5574093",
"0.55649465",
"0.5564419",
"0.55429655",
"0.55273384",
"0.5524686",
"0.55... | 0.0 | -1 |
Convert any decimal number into 16bit binary number | def convert_A_instruction(self, instruction):
number = int(instruction)
binary = bin(number).replace("0b", "")
return binary.zfill(16) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dec2bin(num):\n bin = ''\n\n while num != 0:\n quotient = num // 2\n remainder = num % 2\n if remainder == 1:\n bin = '1' + bin\n else:\n bin = '0' + bin\n num = quotient\n\n # Pad with 0's to match 16 bits\n bin = '0' + (15 - len(bin)) * '0'... | [
"0.74449545",
"0.71901727",
"0.7109376",
"0.7017403",
"0.6926526",
"0.6862779",
"0.68603647",
"0.6841977",
"0.68113583",
"0.67758965",
"0.6762503",
"0.6728445",
"0.6727819",
"0.66603327",
"0.66486925",
"0.6604718",
"0.65684354",
"0.6564815",
"0.6509841",
"0.64841473",
"0.6461... | 0.57144743 | 93 |
Translate any C instruction | def convert_C_instruction(self, instruction):
comp, dest, jump = self.parse(instruction)
return f"111{convert_comp(comp)}{convert_dest(dest)}" \
f"{convert_jump(jump)}" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compileInstruction(self, ins):\n pass",
"def process(opcode):\n opcode.process()",
"def gen_c_code(self, comp, dest, jump):\r\n return '111' + self.comp(comp) + self.dest(dest) + self.jump(jump)",
"def c_instruction_parser(c_instruction):\n binary_res = '111'\n dest, comp, jmp = ''... | [
"0.6561874",
"0.61943746",
"0.60784996",
"0.58676004",
"0.58119315",
"0.56691813",
"0.5659387",
"0.5633192",
"0.5589402",
"0.5568986",
"0.55381376",
"0.5535437",
"0.5509358",
"0.55061597",
"0.549903",
"0.5427867",
"0.5420783",
"0.54128903",
"0.5401546",
"0.5383139",
"0.538116... | 0.67511356 | 0 |
Translates line by line | def translate_line_by_line(self):
for command in self.commands:
if command.startswith('@'): # is A instruction
number = command.split('@')[-1]
instruction = self.convert_A_instruction(number)
else: # is C instruction
instruction = self.c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def translate(self):\n self.clean_data()\n self.clean_symbols()\n self.translate_line_by_line()",
"def translate_direct(\n self,\n line: str,\n from_relative: bool = True,\n unmask: bool = True,\n ) -> str:\n\n # Try ITL translation\n translated_l... | [
"0.6953802",
"0.6668016",
"0.65168977",
"0.63958037",
"0.6371038",
"0.6291207",
"0.6279356",
"0.6183269",
"0.6148969",
"0.6125871",
"0.612013",
"0.61089617",
"0.6097882",
"0.60840136",
"0.6024646",
"0.6024646",
"0.59851706",
"0.5943913",
"0.5907185",
"0.58994555",
"0.5879098"... | 0.6114247 | 11 |
Finds labels inside commands and fills the symbol table with labels | def add_labels(self):
counter = 0
labels_list = []
for i in range(len(self.commands)):
command = self.commands[i]
if command.startswith('('):
raw_value = command.replace('(', '').replace(')', '')
self.symbol_table[raw_value] = str(counter)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean_symbols(self):\n self.add_labels()\n variable_counter = 16\n for i in range(len(self.commands)):\n command = self.commands[i]\n if command.startswith('@'): # symbols always reside in A instructions\n value = command.split('@')[1]\n ... | [
"0.6857181",
"0.59875345",
"0.5967218",
"0.59576654",
"0.59051675",
"0.5721235",
"0.5716886",
"0.5536359",
"0.5536359",
"0.5535116",
"0.5508334",
"0.5488973",
"0.54202247",
"0.5391605",
"0.53611904",
"0.53439146",
"0.5304387",
"0.5279485",
"0.5262439",
"0.5260879",
"0.5241737... | 0.7957542 | 0 |
Cleans and replace symbol commands with corresponding numerical values | def clean_symbols(self):
self.add_labels()
variable_counter = 16
for i in range(len(self.commands)):
command = self.commands[i]
if command.startswith('@'): # symbols always reside in A instructions
value = command.split('@')[1]
if not valu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean_num(quote):\n for char in ROMAN:\n quote = quote.replace(*char)\n return quote",
"def _hill_normalize_symbol(symb):\n symb = ptab.to_symbol(symb)\n if symb == 'C':\n symb = ''\n if symb == 'H':\n symb = '1'\n return symb",
"def clean_inpu... | [
"0.6367131",
"0.5781873",
"0.57104903",
"0.569682",
"0.5692085",
"0.5543413",
"0.55233794",
"0.5514841",
"0.55030894",
"0.55004317",
"0.54772264",
"0.53819984",
"0.5351693",
"0.5292913",
"0.52664244",
"0.52580327",
"0.5227113",
"0.5208533",
"0.5203274",
"0.5201695",
"0.519784... | 0.6928754 | 0 |
Translate the whole file into language machine | def translate(self):
self.clean_data()
self.clean_symbols()
self.translate_line_by_line() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def translate(self, filepath):\n pass",
"def translate(self, language=None):",
"def translate():\n pass",
"def load_language(self, file: str):\n from App import App\n\n try:\n qm_file = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'translate', '{0}.qm'.form... | [
"0.7801424",
"0.69807875",
"0.6772875",
"0.66117966",
"0.6548898",
"0.6513693",
"0.635359",
"0.63453436",
"0.63216937",
"0.63002735",
"0.62734467",
"0.62678146",
"0.62483066",
"0.62475544",
"0.623506",
"0.6229862",
"0.6215358",
"0.6189174",
"0.61711746",
"0.6167787",
"0.61544... | 0.5770561 | 48 |
Recursively convert a class instance into a dict | def instance_to_dict(obj):
if isinstance(obj, (int, float, complex, bool, str)):
return obj
if isinstance(obj, dict):
new = {}
for k in obj:
new[k] = instance_to_dict(obj[k])
return new
if isinstance(obj, (list, tuple)):
new = []
for val ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def classToDict(obj=None):\n\tif obj == None:\n\t\treturn {}\n\n\t_obj = {}\n\t_obj.update(obj.__dict__)\n\n\treturn _obj",
"def _reconstruct_object(deserialized_data):\n for key, value in deserialized_data.items():\n key = key.strip('__')\n if key in registry:\n # Gat... | [
"0.72499406",
"0.66734415",
"0.6611861",
"0.6565489",
"0.65465987",
"0.650528",
"0.650528",
"0.6496662",
"0.6496662",
"0.6496662",
"0.6496662",
"0.6496662",
"0.6496662",
"0.6496662",
"0.6447857",
"0.641881",
"0.6401291",
"0.6376264",
"0.6236578",
"0.6230756",
"0.6205352",
"... | 0.63586015 | 18 |
Select the top directory of a local repository | def _cdrepo(self, cmdp):
topdir = cmdp.get("P1")
if cmdp.missing_options(): # syntax error
return False
if not FileUtil(topdir).isdir():
Msg().out("Warning: localrepo directory is invalid: ",
topdir, l=Msg.WAR)
return False... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def topdir(self):\n if self._topdir:\n return self._topdir\n\n topleveldata = self.git(\"rev-parse\", \"--show-toplevel\")\n self._topdir = topleveldata[0]\n return self._topdir",
"def download_and_expand(self):\n top_dir_name = None\n if self.git_branch:\n ... | [
"0.6676338",
"0.63332224",
"0.6175567",
"0.5963609",
"0.59066826",
"0.5878462",
"0.58573663",
"0.5803019",
"0.57918125",
"0.5761793",
"0.5724602",
"0.567481",
"0.56393355",
"0.5614459",
"0.556418",
"0.5514977",
"0.54896814",
"0.5479407",
"0.54178846",
"0.5404872",
"0.53940415... | 0.51087016 | 49 |
Check image repository syntax | def _check_imagerepo(self, imagerepo, def_imagerepo=None):
if (not imagerepo) and def_imagerepo:
imagerepo = def_imagerepo
if not (imagerepo and
self.dockerioapi.is_repo_name(imagerepo)):
Msg().err("Error: enter image or repository/image without tag")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_is_image(self):\n os.chdir(\"testimages/\")\n self.assertTrue(fileactions.is_image(\"arch_001.jpg\"))\n self.assertFalse(fileactions.is_image(\"not_an_image.jpg\"))",
"def test_retag_valid_image(self):\n alpine = self.docker.images.get(constant.ALPINE)\n self.assertTru... | [
"0.6336709",
"0.6169546",
"0.6166247",
"0.6060373",
"0.60160655",
"0.5999812",
"0.59383214",
"0.59234333",
"0.59208727",
"0.5914319",
"0.58909905",
"0.58080614",
"0.5795945",
"0.5780402",
"0.5769773",
"0.57520026",
"0.5743581",
"0.57263833",
"0.57217544",
"0.5716045",
"0.5688... | 0.5792978 | 13 |
Split image repo into hostname, repo, tag | def _split_imagespec(self, imagerepo):
if not imagerepo:
return ("", "", "", "")
transport = ""
hostname = ""
image = imagerepo
tag = ""
try:
if "://" in imagerepo:
(transport, dummy, hostname, image) = imagerepo.split('/', 4)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def split_tag(image_name):\n image = image_name.split(\":\", maxsplit=1)\n if len(image) > 1:\n image_repo = image[0]\n image_tag = image[1]\n else:\n image_repo = image[0]\n image_tag = None\n return image_repo, image_tag",
"def parse_repository_tag(repo_path):\n tag_s... | [
"0.7415347",
"0.6681896",
"0.5815166",
"0.57984143",
"0.57817656",
"0.5764575",
"0.56510293",
"0.56285614",
"0.56158316",
"0.55907804",
"0.5500821",
"0.5430561",
"0.5380617",
"0.5327849",
"0.5274233",
"0.52718174",
"0.5269566",
"0.52640927",
"0.52619106",
"0.5259118",
"0.5233... | 0.6290939 | 2 |
Print search results from v1 or v2 API | def _search_print_lines(self, repo_list, lines, fmt):
for repo in repo_list["results"]:
if "is_official" in repo and repo["is_official"]:
is_official = "[OK]"
else:
is_official = "----"
description = ""
for dfield in ("description",... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def printSearchResults(results):\n Log.Debug('Search produced %d results:' % len(results))\n index = 0\n for result in results:\n Log.Debug(' ... %d: id=\"%s\", name=\"%s\", year=\"%s\", score=\"%d\".' %\n (index, result.id, result.name, str(result.year), result.score))\n index += 1",
"def ... | [
"0.6891991",
"0.6512108",
"0.64644086",
"0.64285165",
"0.6408339",
"0.6385779",
"0.6382697",
"0.63571036",
"0.63463515",
"0.632945",
"0.6305969",
"0.62940735",
"0.62740153",
"0.62194717",
"0.6185647",
"0.6170451",
"0.6154677",
"0.61358446",
"0.6106197",
"0.6106097",
"0.607557... | 0.0 | -1 |
Print search header and loop over search results | def _search_repositories(self, expression, pause="", no_trunc=""):
term_lines, dummy = HostInfo().termsize()
term_lines -= 2
fmt = "%-55.80s %8.8s %-70.70s %5.5s"
if no_trunc:
fmt = "%-55s %8s %-70s %5s"
while True:
lines = term_lines
while lin... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def search_results(self, results):\n for index, item in enumerate(results):\n print '[%s] %s (%s) {%s}' % (\n index, \n self._color(item.title), \n self._color(item.year, 'RED'), \n self._color(item.imdbid, 'GREEN'))",
... | [
"0.6988774",
"0.6842301",
"0.6819385",
"0.6422996",
"0.6367422",
"0.6351076",
"0.63353205",
"0.6321667",
"0.62416255",
"0.6154546",
"0.6148038",
"0.61424947",
"0.61287236",
"0.61008203",
"0.60572964",
"0.60315955",
"0.59376514",
"0.59365815",
"0.5913392",
"0.5906492",
"0.5887... | 0.0 | -1 |
List tags for repository | def _list_tags(self, expression):
try:
for tag in self.dockerioapi.get_tags(expression):
Msg().out(tag)
return self.STATUS_OK
except (KeyError, TypeError, ValueError):
return self.STATUS_ERROR | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_list_tags(cs, args):\n resp, tags = cs.repositories.list_tags(args.repository)\n tags = [{\"Tag\": t} for t in tags]\n utils.print_list(tags, [\"Tag\"], sortby=\"Tag\")",
"def list_tags():\r\n tags = Tag.query.order_by(Tag.name).all()\r\n return render_template('tags.html', tags=tags)",
"... | [
"0.8235992",
"0.7776253",
"0.7739009",
"0.77201414",
"0.7666957",
"0.7369898",
"0.7295655",
"0.727383",
"0.72511375",
"0.7148935",
"0.7112638",
"0.70888156",
"0.7034062",
"0.7034062",
"0.70196223",
"0.7003448",
"0.6994803",
"0.69832987",
"0.6953225",
"0.691466",
"0.68806005",... | 0.6908422 | 20 |
Auxiliary to create(), performs the creation | def _create(self, imagespec):
if not self.dockerioapi.is_repo_name(imagespec):
Msg().err("Error: must specify image:tag or repository/image:tag")
return False
(imagerepo, tag) = self._check_imagespec(imagespec)
if imagerepo:
return ContainerStructure(self.loca... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create():",
"def create():",
"def create(self):",
"def create(self):\n ...",
"def create(self):\n pass",
"def create(self):\n pass",
"def create(self):\n pass",
"def create(self):\n\n pass",
"def create(self):\n\t\tself.creating += 1\n\t\ttry:\n\t\t\tself.adop... | [
"0.8645303",
"0.8645303",
"0.8227992",
"0.8027747",
"0.80269367",
"0.80269367",
"0.80269367",
"0.7952623",
"0.78722525",
"0.7861714",
"0.77917415",
"0.760546",
"0.74487746",
"0.7309258",
"0.720753",
"0.7173948",
"0.7096899",
"0.69178706",
"0.6909517",
"0.69019216",
"0.6900514... | 0.0 | -1 |
Read command line options into variables | def _get_run_options(self, cmdp, exec_engine=None):
cmdp.declare_options("-v= -e= -w= -u= -p= -i -t -a -P")
cmd_options = {
"netcoop": {
"fl": ("-P", "--publish-all", "--netcoop",), "act": "R",
"p2": "CMD_OPT", "p3": False
},
"portsmap"... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ReadOptions(self, args):\n (opts, args) = getopt.getopt(args, 'vxi:p:h:', ('help',))\n for (key, val) in opts:\n if key == '-h': self.hash = val\n elif key == '-i': self.input = val\n elif key == '-v':\n self.verbose = True\n util.verbose = True\n elif key == '-x':\n ... | [
"0.76137817",
"0.7174512",
"0.717139",
"0.71648765",
"0.71123534",
"0.70884395",
"0.7085598",
"0.70027703",
"0.69947946",
"0.69752973",
"0.69248885",
"0.6923879",
"0.6922594",
"0.68928343",
"0.68559855",
"0.68326515",
"0.68276066",
"0.682651",
"0.68139195",
"0.6798384",
"0.67... | 0.0 | -1 |
Build process trees from the process events. | def extract_process_tree(
procs: pd.DataFrame,
schema: "ProcSchema", # type: ignore # noqa: F821
debug: bool = False,
) -> pd.DataFrame:
# Clean data
procs_cln, schema = _clean_proc_data(procs, schema)
# Merge parent-child
merged_procs = _merge_parent_by_time(procs_cln, schema)
if deb... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pslist(self) -> Generator[dict, None, None]:\n\n # Function to switch fields to represent a parent\n def _convert_to_parent_fields(process: dict) -> dict:\n output = {}\n for left, right in [\n (FieldNames.PROCESS_IMAGE, FieldNames.PARENT_PROCESS_IMAGE),\n ... | [
"0.5943494",
"0.55186284",
"0.5474236",
"0.5396079",
"0.5336486",
"0.52977246",
"0.5260086",
"0.5252163",
"0.5249161",
"0.5246989",
"0.5233202",
"0.5203509",
"0.5188407",
"0.513476",
"0.5126268",
"0.51151633",
"0.51142776",
"0.5088811",
"0.50620705",
"0.49990997",
"0.49866286... | 0.56409377 | 1 |
Return cleaned process data. | def _clean_proc_data(
procs: pd.DataFrame,
schema: "ProcSchema", # type: ignore # noqa: F821
) -> pd.DataFrame:
procs = ensure_df_datetimes(procs, columns=schema.time_stamp)
procs_cln = (
procs.drop_duplicates().sort_values(schema.time_stamp, ascending=True).copy()
)
# Filter out any ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _process(proc_data: List[Dict]) -> List[Dict]:\n return proc_data",
"def process(proc_data):\n\n # No further processing\n return proc_data",
"def post_process(cls, data):\n return data",
"def clean_data(self):\n return self.instance.data",
"def cleanData(self):\n clean_da... | [
"0.6538192",
"0.64839023",
"0.6480206",
"0.63939846",
"0.63180095",
"0.6111915",
"0.6008082",
"0.60056484",
"0.59588325",
"0.59334844",
"0.5918431",
"0.59002125",
"0.5883267",
"0.5865411",
"0.5735515",
"0.5679415",
"0.56767935",
"0.56424737",
"0.56317866",
"0.56111044",
"0.56... | 0.0 | -1 |
Change any numeric columns in our core schema to strings. Some columns like PID arrive as float or int types. We need to convert the floats to ints (since we want to get rid of the ".0") and then convert the int columns to str. We need to do this since we build parent/child keys by concatenating some of these value int... | def _num_cols_to_str(
procs_cln: pd.DataFrame,
schema: "ProcSchema", # type: ignore # noqa: F821
) -> pd.DataFrame:
# Change float/int cols in our core schema to force int
schema_cols = [
col for col in attr.asdict(schema).values() if col and col in procs_cln.columns
]
force_int_cols =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_int_to_str(df):",
"def test__convert_to_str_dtype(self):\n new_column_types = process_mutation._convert_to_str_dtype(\n self.column_types, [\"foo\"]\n )\n assert new_column_types == {\"foo\": \"object\", \"bar\": \"object\"}",
"def create_dtypes_str(df, max_size_stri... | [
"0.6355142",
"0.62572265",
"0.5918403",
"0.58653873",
"0.5677377",
"0.55409217",
"0.5471425",
"0.54174095",
"0.5401915",
"0.538344",
"0.5363697",
"0.53505415",
"0.5312164",
"0.531032",
"0.5267433",
"0.52460843",
"0.524159",
"0.5227334",
"0.521012",
"0.5177431",
"0.5173282",
... | 0.6362964 | 0 |
Merge procs with parents using merge_asof. | def _merge_parent_by_time(
procs: pd.DataFrame,
schema: "ProcSchema", # type: ignore # noqa: F821
) -> pd.DataFrame:
parent_procs = (
procs[
[
schema.process_id,
Col.EffectiveLogonId,
Col.new_process_lc,
Col.source_index,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def example_staypoints_merge():\n p1 = Point(8.5067847, 47.4)\n\n t1 = pd.Timestamp(\"1971-01-01 00:00:00\", tz=\"utc\")\n t2 = pd.Timestamp(\"1971-01-02 05:00:00\", tz=\"utc\")\n t3 = pd.Timestamp(\"1971-01-02 06:45:00\", tz=\"utc\")\n t4 = pd.Timestamp(\"1971-01-02 08:55:00\", tz=\"utc\")\n t45... | [
"0.53150934",
"0.51900065",
"0.5058164",
"0.5057662",
"0.4970098",
"0.48208147",
"0.4743613",
"0.4735923",
"0.47224164",
"0.4721508",
"0.47203505",
"0.47181934",
"0.471794",
"0.47002944",
"0.4695418",
"0.46933174",
"0.46843734",
"0.4682874",
"0.46639788",
"0.46583503",
"0.465... | 0.6610868 | 0 |
Find any inferred parents and creates rows for them. | def _extract_inferred_parents(
merged_procs: pd.DataFrame, schema: "ProcSchema" # type: ignore # noqa: F821
) -> pd.DataFrame:
tz_aware = merged_procs.iloc[0][schema.time_stamp].tz
time_zero = pd.Timestamp(0) if tz_aware is None else pd.Timestamp(0, tz=0)
# Fill in missing values for root processes
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_parents_table(self) -> None:\n self.parents[\"A\"] = \"start\"\n self.parents[\"B\"] = \"start\"\n self.parents[\"fin\"] = None",
"def add_parent(session, df):\n try:\n for _, row in df.iterrows():\n parent = Parent()\n parent.name = row['parent_name']... | [
"0.6303411",
"0.6012777",
"0.5910273",
"0.58215433",
"0.5800772",
"0.5713827",
"0.57043797",
"0.56871605",
"0.5639912",
"0.559866",
"0.5598546",
"0.55693436",
"0.55462885",
"0.55434126",
"0.551806",
"0.54864657",
"0.5474503",
"0.5440947",
"0.5439221",
"0.5435651",
"0.54315716... | 0.5918984 | 2 |
Create process and parent keys for unambiguous parchild relation. | def _assign_proc_key(
proc_data: pd.DataFrame,
key_name: str,
proc_name_col: str,
proc_id_col: str,
timestamp_col: str,
):
proc_data[key_name] = (
proc_data[proc_name_col]
+ "|"
+ proc_data[proc_id_col].astype(str)
+ "|"
+ proc_data[timestamp_col].dt.round... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def child_to_parent_dict(self,):\n\n self.ParentMap = dict((c, p) for p in self.tree.iter() for c in p)\n self.ParentMap[self.tree.getroot()] = None\n return self.ParentMap",
"def create_parent_dic(self):\n d = {}\n for tier_ID in self.tier_hierarchy:\n for child in self.tier_hi... | [
"0.57572216",
"0.5712835",
"0.5690991",
"0.55551386",
"0.5526629",
"0.54811114",
"0.5480439",
"0.54562485",
"0.5440117",
"0.5391533",
"0.5368373",
"0.531684",
"0.53044134",
"0.5304233",
"0.5288312",
"0.5284614",
"0.52710897",
"0.52686024",
"0.52510065",
"0.52493143",
"0.52471... | 0.48914105 | 53 |
Returns the top 5 most similar winners from our pokemon league data, given a single winning team. myTeam list of 6 pokemon names. league league to be considering pokedex dictionary with pokemon names as keys to pokemon instances as values | def get_sim_winners(teamNames,league,pokedex):
bData, wHtml = loadBattleData(league)
similarities = loadSims()
results = []
myTeam = [getSimPokemon(pkm,similarities) for pkm in teamNames]
for d in bData:
winner, loser = determineWinner(d)
score = 0
for pkm in myTeam:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scoreTeams(curTeams, oppTeam, pokedex, league, minDistWanted):\n battleData, htmlData = loadBattleData(league)\n similarities = loadSims() \n \n #If not given an opponent team then simply randomly choose losers from the dataset to compare to.\n if len(oppTeam) == 0:\n picks = set([])\n ... | [
"0.68629056",
"0.6324324",
"0.6290573",
"0.59425783",
"0.57627666",
"0.5678293",
"0.5615375",
"0.5570595",
"0.55454636",
"0.55168325",
"0.55126613",
"0.54787564",
"0.54584044",
"0.54436314",
"0.54137397",
"0.53918636",
"0.53845763",
"0.5351121",
"0.5300444",
"0.52985793",
"0.... | 0.64874274 | 1 |
Returns loaded data associated with the league levels, or none if associated with no league data and also returns the associated winhtml file. league one of the 11 supported leagues | def loadBattleData(league):
path = os.path.dirname(os.path.realpath(__file__))
fString = None
try:
idx = LEAGUERANKS.index(league)
fString = path+RANKSTOWINLOSEFILES[idx]
except:
#No file associated with the league.
return None, None
try:
with open(fString, "... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_leagues(self):\n here = os.path.dirname(os.getcwd())\n with open(os.path.join(here, self.LEAGUES_FILENAME)) as json_file:\n data = json.load(json_file)\n return data['leagues']",
"def league(request,league_id=1):\n\n\t#define variables\n\targs = {}\n\tnews = []\n\n\t#gett... | [
"0.62932193",
"0.61747855",
"0.59221417",
"0.58471715",
"0.573756",
"0.5722305",
"0.5631495",
"0.5613551",
"0.55308914",
"0.54132277",
"0.5383598",
"0.53657496",
"0.5340374",
"0.5316855",
"0.5304499",
"0.5278375",
"0.52435386",
"0.5204638",
"0.5202595",
"0.5198321",
"0.512471... | 0.74904084 | 0 |
Returns NUMTEAMSRETURN number of teams from curTeams as 'top' teams based on social data, along with a list of their scores. curTeams list of pokemon teams to consider oppTeam team of pokemon representing opponent team pokedex dictionary with pokemon names as keys to pokemon instances as values league one of the 11 sup... | def scoreTeams(curTeams, oppTeam, pokedex, league, minDistWanted):
battleData, htmlData = loadBattleData(league)
similarities = loadSims()
#If not given an opponent team then simply randomly choose losers from the dataset to compare to.
if len(oppTeam) == 0:
picks = set([])
while (le... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_current_teams_score(self):\n for game in self._get_live_games():\n teams_playing = [x['abbreviation'] for index, x in game['teams'].items()]\n if self.team in teams_playing:\n # Our team is playing in this game, get the score \n ret... | [
"0.64131546",
"0.6392766",
"0.6295676",
"0.6139448",
"0.6011329",
"0.5956912",
"0.5847488",
"0.5829533",
"0.5760881",
"0.57603544",
"0.5741986",
"0.57035077",
"0.56517524",
"0.56435174",
"0.55979204",
"0.55692106",
"0.5568594",
"0.55622953",
"0.5561629",
"0.555497",
"0.555348... | 0.74281716 | 0 |
Flip [to_swap] case each time it appears in phrase. >>> flip_case('Aaaahhh', 'a') 'aAAAhhh' >>> flip_case('Aaaahhh', 'A') 'aAAAhhh' >>> flip_case('Aaaahhh', 'h') 'AaaaHHH' | def flip_case(phrase, to_swap):
swapped_phrase = ""
uppercase = to_swap.upper()
lowercased = to_swap.lower()
for char in phrase:
if char == uppercase:
swapped_phrase = swapped_phrase + lowercased
elif char == lowercased:
swapped_phrase = swapped_phrase + uppercase... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def flip_case(phrase, to_swap):\n list_a_to_z = list(map(chr, range(ord('a'), ord('z')+1)))\n list_A_to_Z = list(map(chr, range(ord('A'), ord('Z')+1)))\n lam_down = lambda x: x.lower() if x.lower() == to_swap.lower() else x\n lam_up = lambda x: x.upper() if x.lower() == to_swap.lower() else x\n ltes... | [
"0.84883106",
"0.82165617",
"0.82154304",
"0.6197163",
"0.6135013",
"0.60620505",
"0.58573896",
"0.58465344",
"0.5793862",
"0.5756991",
"0.57033134",
"0.5691282",
"0.5594358",
"0.5591244",
"0.55755985",
"0.5574078",
"0.551618",
"0.5500948",
"0.5500948",
"0.5396453",
"0.538270... | 0.8178259 | 3 |
Return the current visualization. Default is no visualization | def get_visualization():
if Visualization.__current_visualization is None:
Visualization.set_visualization(NoVisualization())
return Visualization.__current_visualization | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def current_graph(self):\n if self._linear_viewer.isVisible():\n return self._linear_viewer\n else:\n return self._flow_graph",
"def get_default_plot(self):\n return self.default_plot",
"def vis(self):\n \treturn self._vis",
"def visual_reference(self) -> pulumi.... | [
"0.7015167",
"0.66728127",
"0.6522938",
"0.6354091",
"0.6350514",
"0.62987596",
"0.6281821",
"0.6218688",
"0.62015843",
"0.6179625",
"0.61453456",
"0.61453456",
"0.61198264",
"0.60377395",
"0.60220164",
"0.5964381",
"0.5920324",
"0.5883091",
"0.5869931",
"0.58525586",
"0.5811... | 0.85380006 | 0 |
Sets a new visualization. | def set_visualization(visualization):
Visualization.__current_visualization = visualization | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_visualization(self, vis):\n # Destroy the old visualization to avoid leaking memory\n try:\n self.sizer.Remove(self.vis)\n self.vis.Destroy()\n except AttributeError:\n # Don't crash if there was no old visualization\n pass\n # Set the... | [
"0.75664",
"0.67613727",
"0.67532706",
"0.67054766",
"0.6348466",
"0.6195793",
"0.5890382",
"0.58264846",
"0.581631",
"0.5799407",
"0.5764887",
"0.5696465",
"0.5681003",
"0.56570977",
"0.565017",
"0.56282824",
"0.56247157",
"0.56245434",
"0.55731523",
"0.5568358",
"0.55621314... | 0.775578 | 0 |
Sets a new node size | def set_node_size(self, new_node_size: float):
self.node_size = new_node_size | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setsize(self, size):\n self.__size = size",
"def onSize(self, event): \n\t\tw, h = self.GetClientSizeTuple()\n\t\tself.tree.SetDimensions(0, 0, w, h)",
"def set_size(self, new_size: int):\n self.__tab_size = new_size\n self.__check_interpreter()\n self.__vals = [0... | [
"0.75852895",
"0.7240643",
"0.72272944",
"0.7107579",
"0.7069425",
"0.6965684",
"0.69520694",
"0.69520694",
"0.69520694",
"0.69520694",
"0.6937632",
"0.69327074",
"0.69160384",
"0.6872866",
"0.68569905",
"0.684477",
"0.6836281",
"0.68328637",
"0.6781417",
"0.6777022",
"0.6776... | 0.89625835 | 0 |
Sets a new output directory | def set_output_directory(self, new_output_directory: str):
self.output_directory = new_output_directory | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setOutputDir(self, outputdir):\n self.outputdir = outputdir\n if not os.path.isdir(outputdir):\n os.makedirs(outputdir)",
"def setOutputDir(self,outdir):\n\n logger = self.logger\n runstring = self.runstring\n force = self.force\n\n logger.info(runstring + \"Output... | [
"0.82821107",
"0.79771626",
"0.79613644",
"0.79485244",
"0.7578691",
"0.7565269",
"0.75264466",
"0.749439",
"0.7429396",
"0.732117",
"0.731421",
"0.72510153",
"0.72462237",
"0.72454363",
"0.7220839",
"0.7162419",
"0.7140022",
"0.6970236",
"0.6948216",
"0.6944635",
"0.68645376... | 0.8459192 | 0 |
Gives the visualization the oppertunity to prepare a node | def prepare_node(self, node):
# Every change at the position of node will be recognized
aexpr(lambda: node.position, globals(), locals())\
.on_change(lambda obs, oldv, newv: self.set_node_position(node, *newv)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_nodes(self):",
"def draw_nodes(self):\n pass",
"def draw(self):\n\n self.transform()\n\n node_object_age, proj_object_age = self._create_nodes_and_projections(nodetype='age')\n node_object_depth, proj_object_depth = self._create_nodes_and_projections(nodetype='depth')\n ... | [
"0.6763387",
"0.65652966",
"0.6554879",
"0.60727996",
"0.60079736",
"0.6000999",
"0.59870166",
"0.58894426",
"0.58782125",
"0.58391124",
"0.58299655",
"0.58040935",
"0.5766652",
"0.57188344",
"0.57129437",
"0.5675168",
"0.5668845",
"0.566694",
"0.56625974",
"0.5605432",
"0.56... | 0.0 | -1 |
Set the position of the node in the visualization. | def set_node_position(self, node, x, y, z=0):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_node_positions(self):",
"def setPosition(self):\n self.data['pos-x'] = \"%s\" % self.x()\n self.data['pos-y'] = \"%s\" % self.y()",
"def setPos(self, pos):\n self.cameraNode.setPos(pos)",
"def set_node(self, index, node):\r\n self.loc.coord[index] = node",
"def set_node_... | [
"0.80316544",
"0.76723444",
"0.7639938",
"0.7504539",
"0.7441997",
"0.7374458",
"0.7365325",
"0.73483866",
"0.73460287",
"0.7272101",
"0.72263426",
"0.7222932",
"0.7220022",
"0.7213865",
"0.721017",
"0.71766853",
"0.7170008",
"0.71616125",
"0.7080263",
"0.7057288",
"0.7053259... | 0.85538656 | 0 |
Takes vtkRenderer instance and returns an IPython Image with the rendering. | def vtk_show(renderer, width = 400, height = 300):
renderWindow = vtk.vtkRenderWindow()
renderWindow.SetOffScreenRendering(1)
renderWindow.AddRenderer(renderer)
renderWindow.SetSize(width, height)
renderWindow.Render()
windowToImageFilter = vtk.vtkWindowToImageFilter()
windowToImageFilter.Se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def vtk_show(renderer, width=400, height=300):\n\t renderWindow = vtk.vtkRenderWindow()\n\t renderWindow.SetOffScreenRendering(1)\n\t renderWindow.AddRenderer(renderer)\n\t renderWindow.SetSize(width, height)\n\t renderWindow.Render()\n\t \n\t windowToImageFilter = vtk.vtkWindowToImageFilter(... | [
"0.66868466",
"0.62471",
"0.61158806",
"0.6034977",
"0.6034977",
"0.5885496",
"0.58395505",
"0.579548",
"0.57799315",
"0.57737356",
"0.5725774",
"0.5700969",
"0.56834227",
"0.56787825",
"0.5672762",
"0.5659876",
"0.56589764",
"0.5652964",
"0.5641683",
"0.5605039",
"0.56031024... | 0.5572259 | 22 |
Method for populating a SimObject from a dictionary. | def from_dict(cls, dictionary):
instance = cls()
for key, value in dictionary.items():
instance.__dict__[key] = value
return instance | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_dict(dict):\n return SIO_SDC(**dict)",
"def from_dict(self, dict_=None):\n for key in dict_:\n if hasattr(self, key):\n setattr(self, key, dict_[key])",
"def from_dict(cls, obj):\r\n raise NotImplementedError",
"def _from_dict(self, data=None):\n ... | [
"0.6614978",
"0.650077",
"0.64925647",
"0.6438353",
"0.6405005",
"0.6405005",
"0.63604647",
"0.63278633",
"0.6316172",
"0.62846863",
"0.6272756",
"0.62303245",
"0.6150155",
"0.61341256",
"0.61329454",
"0.61329454",
"0.6121385",
"0.6121385",
"0.6121385",
"0.6121385",
"0.612138... | 0.6175366 | 12 |
Return a dictionary version of the SimObject. | def to_dict(self):
return self.__dict__ | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_json(self) -> Dict[str, Any]:\n return json.loads(self.__sim.to_json())",
"def to_obj(self):\n return dict()",
"def get_dict(self):\n return",
"def serial(self) -> dict:\n return self.__dict__",
"def dict(self):\n return objToDict(self)",
"def asdict():\n ... | [
"0.7300974",
"0.71566254",
"0.7147086",
"0.71343255",
"0.7122973",
"0.7105217",
"0.71000385",
"0.70494294",
"0.6987932",
"0.6983818",
"0.69807756",
"0.69807756",
"0.69807756",
"0.69807756",
"0.69807756",
"0.69807756",
"0.69807756",
"0.6973431",
"0.6933753",
"0.69021446",
"0.6... | 0.68923783 | 24 |
Finds SNPs with respect to the reference genome and writes the SNPs to the answer file. | def find_and_write_snps(answer_file, ref_genome, reads, lookup_table, thresh=0.6):
# Use the consensus algorithm to determine SNPs relative to the reference genome
# Write the SNPs to the answer file
# TODO use the thresh param
print "Finding SNPs..."
read_map = __map_reads__(ref_genome, reads, loo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_ssearch36_df(name, ssearch_df, fasta_inputs, out_dir):\n\n fasta_in = [x for x in fasta_inputs if name.split(\".fasta.ss\")[0] == re.sub(\".fasta$\",\"\",os.path.basename(x))][0]\n fasta_file = pyfasta.Fasta(fasta_in)\n print(name) \n try:\n os.mkdir(out_dir)\n except:\n pa... | [
"0.5883539",
"0.5657296",
"0.5609451",
"0.558543",
"0.55291706",
"0.5528156",
"0.55074143",
"0.5490762",
"0.544737",
"0.54255885",
"0.53950757",
"0.5362691",
"0.534965",
"0.5343809",
"0.53275824",
"0.5321692",
"0.53178936",
"0.5309606",
"0.5297632",
"0.5287152",
"0.52773654",... | 0.80086577 | 0 |
Maps the given read to the best position in the reference genome, if possible. Returns the best matching position in the reference genome, or None if no match is found | def get_best_read_position(ref_genome, read, positions, thresh):
least = 100
best_pos = None
for p in positions:
num_mismatches = get_num_mismatches(read, ref_genome, p)
if num_mismatches < thresh and num_mismatches < least:
least = num_mismatches
best_pos = p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_read(self, ref, read, ref_offset=0):\n\n if read.alignment.mapping_quality < self.config.min_mapq:\n return\n\n ref_pos = read.alignment.position.position - ref_offset\n read_pos = 0\n # Use set(), as some cigar operations might generate duplicated positions,\n # E.g. for insertions... | [
"0.5876357",
"0.5854108",
"0.57206434",
"0.5676287",
"0.5448705",
"0.53857327",
"0.5371495",
"0.534903",
"0.5319156",
"0.52762634",
"0.5226709",
"0.5220328",
"0.51804584",
"0.5161435",
"0.5159765",
"0.51551884",
"0.5149118",
"0.51190424",
"0.50989544",
"0.50494",
"0.502948",
... | 0.72140175 | 0 |
Returns the number of mismatches between the reference genome starting at the specified position and the given sequence | def get_num_mismatches(sequence, ref_genome, position):
characters = list(sequence)
num_mismatches = 0
for i in range(0, len(characters)):
if position + i >= len(ref_genome):
break
if characters[i] != ref_genome[position + i]:
num_mismatches += 1
return num_misma... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def count_sequence_mismatches(seq):\n trans_table = str.maketrans('ACGT', 'TGCA')\n half_len = len(seq) // 2\n second_half = seq[-half_len:].translate(trans_table)\n mismatches = 0\n for i in range(half_len):\n if seq[i] != second_half[-i - 1]:\n mismatches += 1\n return mismatc... | [
"0.7436655",
"0.6384281",
"0.63605845",
"0.6251416",
"0.6141242",
"0.61273175",
"0.60552704",
"0.59271526",
"0.58729815",
"0.5872581",
"0.5860833",
"0.5816268",
"0.5784733",
"0.5768097",
"0.5741856",
"0.5740558",
"0.5737593",
"0.5729271",
"0.5711081",
"0.5703184",
"0.56574935... | 0.86014587 | 0 |
Returns the allele with the highest count. | def get_consensus_allele(alleles):
allele_counts = {}
for a in alleles:
assert a in __allele_alphabet__
try:
allele_counts[a] += 1
except KeyError:
allele_counts[a] = 1
keys = allele_counts.keys()
win_count = 0
winner = None
for k in keys:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def most_visited_child(self):\n return max(self.children, key=lambda c: c.explore_count)",
"def max(self):\n max = 0\n a = self.array_form\n for i in xrange(len(a)):\n if a[i] != i and a[i] > max:\n max = a[i]\n return max",
"def count_max(alon):\n re... | [
"0.6655936",
"0.66463107",
"0.66287166",
"0.65923226",
"0.6573795",
"0.6566374",
"0.6550086",
"0.6542098",
"0.6534818",
"0.6492612",
"0.6476472",
"0.6468543",
"0.646583",
"0.643348",
"0.6418689",
"0.64110184",
"0.6385331",
"0.6378105",
"0.6372417",
"0.6361048",
"0.6359925",
... | 0.0 | -1 |
Execute shell command, piping output by default to base64, return output. | def runOpensslCmd( cmd1Args, cmd2Args ):
p1 = subprocess.Popen( cmd1Args, stdout=subprocess.PIPE )
p2 = subprocess.Popen( cmd2Args, stdin=p1.stdout, stdout=subprocess.PIPE )
# Close p1.stdout so only process(p2) attached to the pipe; if p2 exits,
# p1 will receive a SIGPIPE.
p1.stdout.close()
output =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def shell(cmd):\n return subprocess.check_output(cmd, shell=True).decode(\"utf-8\")",
"def native_cmd(cmd, whitespace=False):\n result = subprocess.check_output(cmd, shell=True).decode()\n\n result = subprocess.run(cmd, stdout=subprocess.PIPE).stdout.decode('utf-8')\n\n return result",
"def run_cmd... | [
"0.69443566",
"0.6795026",
"0.67916363",
"0.6679746",
"0.6538609",
"0.6487913",
"0.64708656",
"0.6440487",
"0.6401089",
"0.63661236",
"0.6365907",
"0.6349508",
"0.63177407",
"0.6306536",
"0.62950313",
"0.6276265",
"0.62589514",
"0.6241605",
"0.6207926",
"0.6176467",
"0.613766... | 0.0 | -1 |
Create a DER encoded CMS data type given a conveyedinfo file and base64encode the response. | def opensslCmsDataCreate( conveyedInfoFile ):
opensslCmdArgs = [ "openssl", "cms", "-data_create", "-in", conveyedInfoFile,
"-outform", "der" ]
conveyedInfoCmsDerBase64 = runOpensslCmd( opensslCmdArgs, [ "base64" ] )
return conveyedInfoCmsDerBase64 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_internal_value(self, data):\n try: # ToDo penetrate in order test if it has any security flaws\n decoded = base64.b64decode(data)\n mime_type = magic.from_buffer(decoded, mime=True)\n file_ext = mimetypes.guess_extension(mime_type)\n except TypeError:\n ... | [
"0.6028859",
"0.5847619",
"0.5839393",
"0.5480351",
"0.53368914",
"0.5330984",
"0.5247696",
"0.5235479",
"0.5232634",
"0.5227278",
"0.5222667",
"0.52152556",
"0.521113",
"0.5203105",
"0.5159984",
"0.5137093",
"0.51293385",
"0.5050312",
"0.5014207",
"0.50107014",
"0.49795356",... | 0.6957099 | 0 |
Create a signed CMS encoded object given a conveyedinfo file and base64 encode the response. | def opensslCmsSignedDataCreate( conveyedInfoFile, cert, privateKey ):
opensslCmdArgs = [ "openssl", "cms", "-sign", "-in", conveyedInfoFile,
"-signer", cert,
"-inkey", privateKey,
"-outform", "der", "-nodetach" ]
conveyedInfoCmsSignedDerBase64 = ru... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def opensslCmsDataCreate( conveyedInfoFile ):\n opensslCmdArgs = [ \"openssl\", \"cms\", \"-data_create\", \"-in\", conveyedInfoFile,\n \"-outform\", \"der\" ]\n conveyedInfoCmsDerBase64 = runOpensslCmd( opensslCmdArgs, [ \"base64\" ] )\n return conveyedInfoCmsDerBase64",
"def open_and... | [
"0.68802476",
"0.57600963",
"0.56639785",
"0.56213754",
"0.55112994",
"0.54730743",
"0.5465122",
"0.5425926",
"0.5361701",
"0.5321274",
"0.5312067",
"0.5278479",
"0.52773",
"0.52584666",
"0.52542394",
"0.5243983",
"0.52368605",
"0.5193875",
"0.51854706",
"0.5185009",
"0.51680... | 0.6407271 | 1 |
Create a degenerate CMS encoded object given a cert file and base64encode the response. | def opensslCmsCertCreate( ownerCertFile ):
opensslCmdArgs = [ "openssl", "crl2pkcs7", "-certfile", ownerCertFile,
"-nocrl", "-outform", "der" ]
ownerCertCmsDerBase64 = runOpensslCmd( opensslCmdArgs, [ "base64" ] )
return ownerCertCmsDerBase64 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def opensslCmsDataCreate( conveyedInfoFile ):\n opensslCmdArgs = [ \"openssl\", \"cms\", \"-data_create\", \"-in\", conveyedInfoFile,\n \"-outform\", \"der\" ]\n conveyedInfoCmsDerBase64 = runOpensslCmd( opensslCmdArgs, [ \"base64\" ] )\n return conveyedInfoCmsDerBase64",
"def opensslC... | [
"0.62944597",
"0.6059549",
"0.5580201",
"0.5461812",
"0.54521674",
"0.54492855",
"0.54234695",
"0.53850746",
"0.53842735",
"0.53842735",
"0.53802806",
"0.5374783",
"0.5361761",
"0.53443146",
"0.5234495",
"0.5207088",
"0.518509",
"0.51816165",
"0.5171256",
"0.51675254",
"0.516... | 0.58668345 | 2 |
Extract base64 value from pinneddomaincert file; i.e. given a PEM encoded cert, strip header and footer. | def getBase64PinnedDomainCert( pinnedDomainCertFile ):
tailCmdArgs = [ "tail", "-n", "+2", pinnedDomainCertFile ]
headCmdArgs = [ "head", "-n", "-1" ]
pinnedDomainCertBase64 = runOpensslCmd( tailCmdArgs, headCmdArgs )
return pinnedDomainCertBase64 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract_ca_crt_bytes_from_pem(pem_content):\n begin_search = pem_content.find(constants.BEGIN_CERTIFICATE_MARKER)\n if begin_search < 0:\n raise exception.InvalidKubernetesCA\n\n end_search = pem_content.find(constants.END_CERTIFICATE_MARKER)\n if end_search < 0:\n raise exception.Inv... | [
"0.606301",
"0.5976179",
"0.58105767",
"0.5476757",
"0.54603827",
"0.5449754",
"0.5434406",
"0.5392469",
"0.53901625",
"0.53467846",
"0.52828676",
"0.52781343",
"0.52742517",
"0.5256527",
"0.522737",
"0.5226231",
"0.52005994",
"0.5152916",
"0.5138789",
"0.513565",
"0.5134552"... | 0.714601 | 0 |
Create a file within httpd directory containing response for uri | def createHttpdResponseFile( responseXml, restconfDir="/restconf/operations" ):
if not os.path.exists( restconfDir ):
os.makedirs( restconfDir )
responseXmlFile = "{}/ietf-sztp-bootstrap-server:get-bootstrapping-data".\
format( restconfDir )
with open( responseXmlFile, "w" ) as respF... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_file(self, response):\r\n # Extract filename from response url\r\n filename = re.search('[^/]+(?=/$|$)', response.url).group(0)\r\n\r\n # Prepend download folder name to the filename\r\n filename = self.config[\"folder\"] + filename\r\n os.makedirs(os.path.dirname(filena... | [
"0.6425665",
"0.6304316",
"0.62894547",
"0.627631",
"0.61327916",
"0.6088824",
"0.6078078",
"0.5965769",
"0.5963274",
"0.5880248",
"0.587777",
"0.58741266",
"0.5837021",
"0.576541",
"0.5713638",
"0.57001615",
"0.5658703",
"0.56253684",
"0.5589897",
"0.55822724",
"0.5550171",
... | 0.69076777 | 0 |
Verify dict value types correspond to template | def verifyDictTypes( template, dictToCheck ):
for key in dictToCheck:
if not ( ( isinstance( dictToCheck[ key ], list ) and
isinstance( template[ key ], list ) ) or
( isinstance( dictToCheck[ key ], dict ) and
isinstance( template[ key ], dict ) ) or
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_dictionary_coerce():\n\n @type_checked\n def _run_test(something:{int: str}):\n for key, value in something.items():\n assert isinstance(key, int)\n assert isinstance(value, str)\n\n _run_test(something={123: \"abc\", 2314: 12312, \"123\": \"abc\"})",
"def check_for... | [
"0.66782385",
"0.66468674",
"0.6595941",
"0.6493282",
"0.6471941",
"0.6394708",
"0.62859756",
"0.62481934",
"0.61028117",
"0.6082978",
"0.60659516",
"0.60586894",
"0.60274726",
"0.6018177",
"0.60154957",
"0.6013911",
"0.59892005",
"0.5982993",
"0.59721565",
"0.596949",
"0.596... | 0.7521832 | 0 |
Generate file containing bootstrap redirect information (json). sztpRedirectServers is a list of dictionaries, of the template below. | def genConvRedirectInfoJsonFile( sztpRedirectServers, redirectFileJson ):
template = { "address": str,
"port": int,
"trust-anchor": str
}
mandatory = [ "address" ]
# verify redirect-server list is correctly constructed
assert isinstance( sztpRedirectServers, list ), "Expec... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def genSztpBootstrapResponseFile( conveyedInfoFileJson, reportingLevel ):\n\n # CMS wrap this data (unsigned)\n conveyedInfoCms = opensslCmsDataCreate( conveyedInfoFileJson )\n\n # XML encode the artifacts (conveyedInfo only)\n return genBootstrappingDataResponseFile( conveyedInfo=conveyedInfoCms,\n ... | [
"0.5130368",
"0.50381947",
"0.49691543",
"0.48931608",
"0.48542204",
"0.48228222",
"0.4813354",
"0.48039177",
"0.47937784",
"0.47869673",
"0.4624361",
"0.45916578",
"0.45715594",
"0.45654586",
"0.45513126",
"0.45467854",
"0.45035234",
"0.4503066",
"0.45007342",
"0.44736442",
... | 0.7551597 | 0 |
Generate file containing bootstrap onboarding information (json). | def genConvOnboardingInfoJsonFile( sztpOnboardingInfo, onboardingFileJson ):
template = {
"boot-image": {
"os-name": str,
"os-version": str,
"download-uri": list, # of uri strings
"image-verification": [ {
"hash-algorithm": str,
"hash-value": str } ],... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def api():\n return send_file('templates/bootstrapper.swagger.json')",
"def write_bootstrap(self):\n\n for line in Bootstrap.BOOTSTRAP:\n self.write_line(line)\n\n self.write_call(\"Sys.init\", 0)",
"def create_newfile():\n date = datetime.today().strftime('%d_%m_%Y').replace(\" ... | [
"0.59714407",
"0.5859425",
"0.5583089",
"0.5551649",
"0.5481499",
"0.54219496",
"0.5345955",
"0.53165185",
"0.5276273",
"0.5172367",
"0.50922686",
"0.5084571",
"0.50319684",
"0.50302076",
"0.50239533",
"0.5008993",
"0.50066954",
"0.50027114",
"0.49786752",
"0.4978495",
"0.497... | 0.60188127 | 0 |
Verify boot image is correct | def verifyBootImage( template, sztpBootImage ):
def verifyImageVerification( imageVerification ):
"""Verify instance of image-verification is correct"""
if "hash-algorithm" in imageVerification:
assert imageVerification[ "hash-algorithm" ] == \
"ietf-sztp-conveyed-info... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_exist(self):\n helper.RbdImageOperator._check_rbd_image(self.real_path)",
"def testCheckAvailable(self):\n img = self.img\n img.inspect()\n with converter.RootMounted(img.converter._h,\n '/dev/VolGroup00/LogVol00'):\n c = img.conv... | [
"0.6479312",
"0.64290893",
"0.6369703",
"0.635404",
"0.6286041",
"0.6180226",
"0.61096185",
"0.608363",
"0.60541505",
"0.60480213",
"0.6047562",
"0.5977092",
"0.5966454",
"0.5957976",
"0.58941513",
"0.58904773",
"0.58685666",
"0.58676755",
"0.5859996",
"0.5856276",
"0.5854957... | 0.7287546 | 0 |
Verify instance of imageverification is correct | def verifyImageVerification( imageVerification ):
if "hash-algorithm" in imageVerification:
assert imageVerification[ "hash-algorithm" ] == \
"ietf-sztp-conveyed-info:sha-256",\
"Unsupported hash-algorithm"
assert "hash-value" in imageVerification, \
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def verify_binary_image(self, image_path):\n raise NotImplementedError",
"def verify_image(self, image_location):\n checksum = self._hash_algo.hexdigest()\n LOG.debug('Verifying image at %(image_location)s against '\n '%(algo_name)s checksum %(checksum)s',\n ... | [
"0.69870156",
"0.69740856",
"0.69246584",
"0.6673187",
"0.6650353",
"0.66449857",
"0.66413474",
"0.6540088",
"0.6524494",
"0.6524494",
"0.6514677",
"0.64813495",
"0.6438443",
"0.6420936",
"0.6389883",
"0.6361156",
"0.63330185",
"0.63297325",
"0.631254",
"0.6260492",
"0.617737... | 0.7009416 | 0 |
Verify imageverification list is correct | def verifyImageVerificationList( template, sztpImageVerification ):
assert isinstance( sztpImageVerification, list ), \
"Expected list"
for imageVer in sztpImageVerification:
assert verifyDictTypes( template, imageVer ), "Unexpected value types"
assert set( imageVer... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def verifyImageVerification( imageVerification ):\n if \"hash-algorithm\" in imageVerification:\n assert imageVerification[ \"hash-algorithm\" ] == \\\n \"ietf-sztp-conveyed-info:sha-256\",\\\n \"Unsupported hash-algorithm\"\n assert \"hash-value\" in imageVer... | [
"0.6735049",
"0.672671",
"0.67036074",
"0.6464152",
"0.6445585",
"0.6409907",
"0.6409425",
"0.632909",
"0.62680525",
"0.6239925",
"0.6215872",
"0.620192",
"0.6181599",
"0.6175062",
"0.6162376",
"0.60837007",
"0.60530126",
"0.60342985",
"0.60265696",
"0.60118407",
"0.6010814",... | 0.7673073 | 0 |
Convert ownershipvoucher dictionary to json and write to file. ownershipVoucher is a dictionary of the template below with values as per RFC8366. | def genOwnershipVoucherJsonFile( ownershipVoucher, voucherFileJson ):
template = { "created-on": str,
"expires-on": str,
"assertion": str,
"serial-number": str,
"pinned-domain-cert": str,
"domain-cert-revocation-checks": bool,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _write(self):\n raw_data = {'file_version': 1}\n raw_creds = []\n raw_data['data'] = raw_creds\n for (cred_key, cred) in self._data.items():\n raw_key = dict(cred_key)\n raw_cred = json.loads(cred.to_json())\n raw_creds.append({'key': raw_key, 'crede... | [
"0.57161313",
"0.55950993",
"0.55210066",
"0.5401349",
"0.5320288",
"0.52819663",
"0.5272004",
"0.5208323",
"0.517391",
"0.51711",
"0.5164001",
"0.5157927",
"0.5157889",
"0.5153851",
"0.5132664",
"0.5128681",
"0.51168865",
"0.51157135",
"0.5086597",
"0.50822693",
"0.50820655"... | 0.79461914 | 0 |
Verify a dateTime string conforms to RFC3339 format (only Z TZ accepted) | def validDateTime( dateTime ):
try:
datetime.strptime( dateTime, "%Y-%m-%dT%H:%M:%S.%fZ" )
return True
except ValueError:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_valid(string: str, format_: str) -> bool:\n try:\n datetime.strptime(string, format_)\n return True\n except ValueError:\n return False",
"def is_time_in_given_format(time_string, time_format):\n try:\n datetime.strptime(time_string, time_format)\n ... | [
"0.697065",
"0.6781034",
"0.6582872",
"0.654285",
"0.65413713",
"0.65014714",
"0.64805967",
"0.6473195",
"0.6417286",
"0.6412882",
"0.6391962",
"0.6389369",
"0.6366332",
"0.63501763",
"0.6320752",
"0.6294031",
"0.62899727",
"0.6254448",
"0.62397885",
"0.6235958",
"0.6208644",... | 0.77382153 | 0 |
Generate SZTP redirect response for response to following https uri | def genBootstrappingDataResponseFile( conveyedInfo,
ownerCertificate=None,
ownershipVoucher=None,
reportingLevel="verbose" ):
# enclose the base64 encoded data in XML response
if not conveyedInfo:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def redirect(self) -> bytes:\n self.logger.debug(\"--- In SSO Redirect ---\")\n _info = self.unpack_redirect()\n self.logger.debug(\"Unpacked redirect :\\n{!s}\".format(pprint.pformat(_info)))\n\n ticket = _get_ticket(self.context, _info, BINDING_HTTP_REDIRECT)\n return self._red... | [
"0.64003",
"0.61148995",
"0.6106962",
"0.6028019",
"0.5923971",
"0.5665826",
"0.56172425",
"0.5564584",
"0.5510682",
"0.5488643",
"0.54605424",
"0.5430665",
"0.54139304",
"0.53932416",
"0.5389347",
"0.5357595",
"0.5305892",
"0.5294571",
"0.5279046",
"0.5268421",
"0.52389425",... | 0.0 | -1 |
Generate unsigned bootstrap restconf response that will only contain cmsunsigned(conveyedinformation(json)). | def genSztpBootstrapResponseFile( conveyedInfoFileJson, reportingLevel ):
# CMS wrap this data (unsigned)
conveyedInfoCms = opensslCmsDataCreate( conveyedInfoFileJson )
# XML encode the artifacts (conveyedInfo only)
return genBootstrappingDataResponseFile( conveyedInfo=conveyedInfoCms,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def json_bytes_signed_canonical(request) -> bytes:\n return get_test_data(request, __name__, \"config_signed_canonical.json\")",
"def output_config() -> Response:\n c = dict(config)\n c['password'] = \"*********\"\n return jsonify(c)",
"def json_bytes_canonical(request) -> bytes:\n r... | [
"0.5601099",
"0.5574756",
"0.5386519",
"0.53467643",
"0.5309439",
"0.52807665",
"0.5100658",
"0.507131",
"0.505884",
"0.50188506",
"0.50153613",
"0.5012313",
"0.49823737",
"0.49588287",
"0.4944935",
"0.4926129",
"0.49111423",
"0.4890859",
"0.48762876",
"0.4869981",
"0.4867255... | 0.55271125 | 2 |
generate signed bootstrap restconf file that will contain cmssigned(conveyedinformation(json), ownercert) cms(ownercertificate) cmssigned(ownershipvoucher, manufacturercert). | def genSztpSignedBootstrapResponseFile( conveyedInfoFileJson, ownerCert, ownerKey,
ownershipVoucher, manufacturerCert,
manufacturerKey,
reportingLevel ):
# CMS sign and wrap the conveyed-info usin... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def genSztpBootstrapResponseFile( conveyedInfoFileJson, reportingLevel ):\n\n # CMS wrap this data (unsigned)\n conveyedInfoCms = opensslCmsDataCreate( conveyedInfoFileJson )\n\n # XML encode the artifacts (conveyedInfo only)\n return genBootstrappingDataResponseFile( conveyedInfo=conveyedInfoCms,\n ... | [
"0.5879794",
"0.5550124",
"0.55329037",
"0.55057335",
"0.54887927",
"0.5484706",
"0.5392402",
"0.53486747",
"0.52842027",
"0.52580327",
"0.52540106",
"0.5248883",
"0.52446467",
"0.5234645",
"0.5156566",
"0.5097495",
"0.50847185",
"0.50532186",
"0.5051961",
"0.5005859",
"0.500... | 0.6441284 | 0 |
Support batch or single str. | def encode(self, text):
if isinstance(text, str):
text = [
self.dict[char.lower() if self._ignore_case else char]
for char in text
]
length = [len(text)]
elif isinstance(text, collections.Iterable):
length = [len(s) for s in... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_string(item: Any) -> bool:\n if isinstance(item, (bytes, bytearray, str)):\n return True\n elif (isinstance(item, (tuple, list)) and all(is_string(x) for x in item)):\n return True\n elif (isinstance(item, np.ndarray) and # binary or unicode\n (item.dtype.kind in (\"U\", \"S\") or item.dtype... | [
"0.59293324",
"0.57749236",
"0.5725571",
"0.5672212",
"0.5647407",
"0.5600418",
"0.5586601",
"0.5541914",
"0.5517008",
"0.5511494",
"0.54992914",
"0.5480194",
"0.5423163",
"0.5420587",
"0.538898",
"0.5375193",
"0.5359677",
"0.535159",
"0.53473085",
"0.53355134",
"0.53303206",... | 0.0 | -1 |
Decode encoded texts back into strs. | def decode(self, t, length, raw=False):
if length.numel() == 1:
length = length[0]
assert t.numel() == length, "text with length: {} does not match declared length: {}".format(t.numel(), length)
if raw:
return ''.join([self.alphabet[i - 1] for i in t])
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decode(text: str) -> str:\n # Reverse of reverse is original text.\n return encode(text)",
"def decode(self, s):",
"def decode(self, s):",
"def decode(self, text):\r\n\r\n decoded = \"\".join([self.chars[int(x)] for x in text if x > -1])\r\n decoded = self.remove_tokens(decoded)\r\n ... | [
"0.69890255",
"0.6748986",
"0.6748986",
"0.66816735",
"0.66223574",
"0.6482883",
"0.6418105",
"0.635815",
"0.6332993",
"0.6277069",
"0.62237376",
"0.6220144",
"0.6183788",
"0.611471",
"0.60138935",
"0.5979148",
"0.5967565",
"0.5965492",
"0.5949346",
"0.5940196",
"0.5926245",
... | 0.0 | -1 |
Ask for a number within a range. | def ask_number(question, low, high):
response = None
while response not in range(low, high, 1):
response = input(question)
return response | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _ask_user_range(question, first, last, default):\n\n while True:\n answer = input(question)\n if answer == \"\":\n answer = default\n break\n if re.findall(r\"[0-9+]\", answer):\n if int(answer) in range(first, last + 1):\n ... | [
"0.7841275",
"0.7828361",
"0.78230804",
"0.78229994",
"0.78229994",
"0.74295723",
"0.7367835",
"0.72900355",
"0.7220681",
"0.71590334",
"0.7132747",
"0.70865697",
"0.7029022",
"0.69903183",
"0.686251",
"0.6826732",
"0.6756153",
"0.6675132",
"0.6604543",
"0.6598367",
"0.657736... | 0.77285206 | 5 |
Loads an image file (.jpg, .png, etc) into a numpy array | def load_image_file(file):
file = cv2.imread(file)
return cv2.cvtColor(file, cv2.COLOR_BGR2RGB) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_img(path: str) -> np.ndarray:\n \n return np.array(Image.open(path))",
"def load_image_into_numpy_array(path):\n return np.array(Image.open(path))",
"def load_image_into_numpy_array(path):\n return np.array(Image.open(path))",
"def load_image_as_array(filename):\n im = Image.open(file... | [
"0.85898346",
"0.84998584",
"0.84998584",
"0.8495261",
"0.8432109",
"0.81946313",
"0.81913364",
"0.8181416",
"0.81582314",
"0.8153489",
"0.8080318",
"0.8075615",
"0.8068236",
"0.800013",
"0.79243386",
"0.7916516",
"0.7852631",
"0.7842063",
"0.77801836",
"0.7757637",
"0.771590... | 0.0 | -1 |
Test case for dev_fetch_api_key Fetch an API key (development only) | async def test_dev_fetch_api_key(client):
params = [('username', 'iago@zulip.com')]
headers = {
'Accept': 'application/json',
}
response = await client.request(
method='POST',
path='/api/v1/dev_fetch_api_key',
headers=headers,
params=params,
)
assert ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_api_key(api_key):\n api.get(api_key)",
"async def test_fetch_api_key(client):\n params = [('username', 'iago@zulip.com'),\n ('password', 'abcd1234')]\n headers = { \n 'Accept': 'application/json',\n }\n response = await client.request(\n method='POST',\n ... | [
"0.7381364",
"0.71456134",
"0.7035455",
"0.70299584",
"0.7007611",
"0.6980134",
"0.6950013",
"0.6817723",
"0.6753935",
"0.67477185",
"0.67321575",
"0.6708487",
"0.66948825",
"0.66835815",
"0.667592",
"0.66686666",
"0.66462886",
"0.6633064",
"0.6630445",
"0.6614227",
"0.660011... | 0.7871407 | 0 |
Test case for fetch_api_key Fetch an API key (production) | async def test_fetch_api_key(client):
params = [('username', 'iago@zulip.com'),
('password', 'abcd1234')]
headers = {
'Accept': 'application/json',
}
response = await client.request(
method='POST',
path='/api/v1/fetch_api_key',
headers=headers,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def test_dev_fetch_api_key(client):\n params = [('username', 'iago@zulip.com')]\n headers = { \n 'Accept': 'application/json',\n }\n response = await client.request(\n method='POST',\n path='/api/v1/dev_fetch_api_key',\n headers=headers,\n params=params,\n ... | [
"0.77039844",
"0.75367063",
"0.7186696",
"0.71464",
"0.7127396",
"0.6907098",
"0.68854934",
"0.6865004",
"0.68365717",
"0.68285173",
"0.6815571",
"0.68041676",
"0.67992896",
"0.6788098",
"0.67636013",
"0.6748777",
"0.6725473",
"0.67237955",
"0.6715165",
"0.66871226",
"0.66794... | 0.7483697 | 2 |
Starting point of the program execution. | def start(key, value, p):
graph = nx.read_edgelist(value, comments='%', create_using=nx.Graph(), nodetype=int)
columns = ['Percentage', 'Degree', 'EigenVector']
df = pd.DataFrame(columns=columns)
methods = {'Degree': SampledGraphDegree, 'EigenVector': SampledGraphEigenVector, 'Katz': SampledGraphK... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def start(self):\n self.start_time = dt.datetime.now()\n self.call = ' '.join(sys.argv)\n self.commands = []",
"def start():",
"def start():",
"def start():",
"def start():",
"def main():\n run_program()",
"def main():\n print(\"is Running!\")",
"def _start(self):\n ... | [
"0.75052106",
"0.7434513",
"0.7434513",
"0.7434513",
"0.7434513",
"0.73392045",
"0.7312847",
"0.7280139",
"0.7224378",
"0.7224378",
"0.72091603",
"0.72091603",
"0.72091603",
"0.71912843",
"0.7174693",
"0.71579653",
"0.71579653",
"0.71579653",
"0.71579653",
"0.71579653",
"0.71... | 0.0 | -1 |
Register a class as a custom Keras object. Taken from | def register_keras_custom_object(cls):
tf.keras.utils.get_custom_objects()[cls.__name__] = cls
return cls | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __create_custom_objects():\n # make some preparation to properly load objects from keras_contribute\n instance_holder = {\"instance\": None}\n\n class ClassWrapper(CRF):\n def __init__(self, *args, **kwargs):\n instance_holder[\"instance\"] = self\n super(ClassWrapper, sel... | [
"0.62000954",
"0.6195566",
"0.6027426",
"0.58367825",
"0.58193207",
"0.5803586",
"0.57035977",
"0.56791914",
"0.5632764",
"0.5585904",
"0.55756676",
"0.550057",
"0.5462705",
"0.5437095",
"0.54250425",
"0.5399139",
"0.5376054",
"0.537521",
"0.53704894",
"0.53660065",
"0.533990... | 0.8505611 | 0 |
Get the MySQL password from the config file or an interactive prompt. | def get_mysql_pwd(config):
# Two ways to get the password are supported.
#
# 1. Read clear-text password from config file. (least secure)
# 2. Read password as entered manually from a console prompt. (most secure)
# First try the config file. This is the least secure method. Protect the file.
m... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_auth_password():\n password = AUTH_PASSWORD_SCRIPT.get()\n if password:\n return password\n return DEFAULT_AUTH_PASSWORD.get()",
"def _password(self):\n if 'password' in self._config:\n return self._config['password']\n else:\n while True:\n passwo... | [
"0.67284405",
"0.66505927",
"0.6536618",
"0.64365983",
"0.6373743",
"0.6365045",
"0.6323383",
"0.6244692",
"0.6244692",
"0.62446654",
"0.622668",
"0.6200899",
"0.6192647",
"0.61393917",
"0.61063206",
"0.6070788",
"0.60662985",
"0.60662985",
"0.60662985",
"0.6052463",
"0.60440... | 0.8176631 | 0 |
Configure the MySQL database connection. | def get_mysql_conn(config):
mysql_conn_type = config.get('mysql', 'mysql_conn_type', 0)
mysql_user = config.get('mysql', 'mysql_user', 0)
mysql_pwd = config.get('mysql', 'mysql_pwd', 0)
if mysql_user == '':
mysql_user = getpass.getuser()
if mysql_conn_type == 'pyodbc':
mysql_pwd = g... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def configure(self):\n # Defaults\n self.db_type = DB_TYPE.POSTGRES\n self.db_name = \"ambari\"\n self.db_user = \"ambari\"\n self.db_password = \"bigdata\"\n self.db_host = \"localhost\"\n self.db_url = None\n\n if os.path.exists(AMBARI_PROPERTIES_LOCATION):\n self.ambari_props = self... | [
"0.72382593",
"0.7163564",
"0.6989661",
"0.69011605",
"0.68877184",
"0.6850185",
"0.67999387",
"0.678691",
"0.6682894",
"0.66508764",
"0.6618729",
"0.66107994",
"0.6609478",
"0.6585064",
"0.6578558",
"0.65282506",
"0.6515145",
"0.6494794",
"0.6466084",
"0.6464563",
"0.6457128... | 0.0 | -1 |
Get REDCap data as a CSV file with an API key, URL and content type. | def get_data(csv_file, redcap_key, redcap_url, content):
with open(csv_file, 'wb') as f:
c = pycurl.Curl()
c.setopt(pycurl.CAINFO, certifi.where())
c.setopt(c.URL, redcap_url)
c.setopt(c.FOLLOWLOCATION, True)
post_data = {'token': redcap_key, 'content': content, \
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _csv(content):\n response = _data_frame(content).to_csv(index=False)\n return response",
"def outputapidata_csv(filename, data, headers=None):\n with open(filename,'w',encoding='utf-8',newline = \"\", ) as f:\n if headers:\n writer = csv.DictWriter(f,fieldnames = headers)\n ... | [
"0.65286213",
"0.629478",
"0.6248273",
"0.6128851",
"0.60974103",
"0.60966414",
"0.607682",
"0.5992442",
"0.5988515",
"0.59847116",
"0.5964392",
"0.58780026",
"0.5857897",
"0.5815782",
"0.58133936",
"0.58087337",
"0.57530344",
"0.5727235",
"0.5678315",
"0.5674862",
"0.5665332... | 0.6629378 | 0 |
Get the sha1 hash of the previously uploaded data for a table. | def get_prev_hash(project, mysql_table, log_table, conn = conn):
# See if the database contains the log_table (REDCap transfer log) table.
rs = sql.execute('SHOW TABLES LIKE "' + log_table + '";', conn)
row0 = rs.fetchone()
res = ''
if (row0 is not None) and (len(row0) != 0):
res = row0[0]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _sha1(self):\n return hashlib.sha1(self._blob).hexdigest()",
"def sha1(self) -> str:\n return self.data.sha1",
"def get_hash(self):\n source = \"\"\n for cell in self.original_cells:\n source += \"\\n\".join(get_source(cell))\n return hashlib.sha256(source.encode(\... | [
"0.73520637",
"0.73342705",
"0.7033737",
"0.6831397",
"0.68296325",
"0.67296165",
"0.66682285",
"0.66600823",
"0.66417396",
"0.6601814",
"0.6600231",
"0.6562733",
"0.6552531",
"0.6552531",
"0.65496504",
"0.6544282",
"0.6515358",
"0.64558566",
"0.6447111",
"0.6425509",
"0.6413... | 0.6402452 | 22 |
Parse a CSV file with Pandas, with basic checks and error handling. | def parse_csv(csv_file):
if os.path.isfile(csv_file) == True:
num_lines = sum(1 for line in open(csv_file))
if num_lines > 1:
try:
data = pd.read_csv(csv_file, index_col=False)
data.insert(0, 'id', range(1, 1 + len(data)))
return(data)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parse_csv(csv_file: str) -> pd.DataFrame:\n return pd.read_csv(csv_file, header=0)",
"def parse_csv_file(self, csv_file: str):\n try:\n df = pd.read_csv(csv_file)\n\n if not set(['Question', 'Answer']).issubset(df.columns):\n raise BadCSVFile(\n ... | [
"0.76834667",
"0.7188155",
"0.6934689",
"0.68426764",
"0.680124",
"0.67912734",
"0.66539997",
"0.66152054",
"0.6598606",
"0.65964824",
"0.65925705",
"0.65395904",
"0.6534839",
"0.65285635",
"0.6503657",
"0.6473891",
"0.6422277",
"0.641079",
"0.63820785",
"0.63384444",
"0.6332... | 0.6573268 | 11 |
Create a hash of a file. | def hash_file(file_name):
BLOCKSIZE = 65536
hasher = hashlib.sha1()
with open(file_name, 'rb') as afile:
buf = afile.read(BLOCKSIZE)
while len(buf) > 0:
hasher.update(buf)
buf = afile.read(BLOCKSIZE)
return(hasher.hexdigest()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hashfile(file):\n\n hasher = hashlib.sha256()\n\n with open(file, 'rb') as afile:\n buf = afile.read(BLOCKSIZE)\n hasher.update(buf)\n\n return(hasher.hexdigest())",
"def hash_file(filename):\n # make a hash object\n h = hashlib.sha1()\n \n # open file for reading in binary mod... | [
"0.78154737",
"0.77822936",
"0.77822936",
"0.7758422",
"0.7737324",
"0.7732539",
"0.77142304",
"0.76906806",
"0.76843214",
"0.7680886",
"0.76533586",
"0.75843215",
"0.75759465",
"0.74643785",
"0.7459393",
"0.74388117",
"0.74151087",
"0.73923045",
"0.73651123",
"0.735946",
"0.... | 0.7568438 | 13 |
Send data from REDCap to a MySQL (or MariaDB) database. | def send_to_db(data_path, project, csv_file, dataset, mysql_table, log_table,
rcform = '', redcap_key = redcap_key, redcap_url = redcap_url,
conn = conn, mysql_user = mysql_user,
redcap_event_name_maxlen = redcap_event_name_maxlen):
if project != '':
# Prepend p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_to_rds(data, conn):\n if (len(data) < 9):\n return\n cursor = conn.cursor()\n header_data = [\"date_mutation\", \"code_postal\", \"valeur_fonciere\", \"code_type_local\", \"surface_reelle_bati\", \"nombre_pieces_principales\",\"surface_terrain\",\"longitude\",\"latitude\",\"message\"]\n ... | [
"0.6284455",
"0.5762473",
"0.5693748",
"0.5663926",
"0.56158596",
"0.5567874",
"0.555843",
"0.54980505",
"0.54530215",
"0.543758",
"0.5424383",
"0.53975487",
"0.5354361",
"0.53487647",
"0.5336851",
"0.5290581",
"0.5286743",
"0.52733654",
"0.5267045",
"0.5251107",
"0.5250329",... | 0.54948854 | 8 |
Track changes to transferred data files in local git repository. | def commit_changes(repo, project = ''):
cmd = repo.git
cmd.add(all=True)
try:
cmd.commit(m="redcap2mysql.py data sync for project " + project)
except git.exc.GitCommandError, err:
logging.info([traceback.format_exc(limit=1).splitlines()[-1]]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sync_git_repo():\n # get the current dir of this script\n current_dir = os.path.dirname(os.path.realpath(sys.argv[0]))\n repo_path = os.path.join(current_dir,REPO_NAME)\n logging.info(\"Repository path is: \"+repo_path)\n # check to see if a repo has been init already\n try: \n repo = ... | [
"0.604152",
"0.58758867",
"0.5846359",
"0.5827327",
"0.57683486",
"0.5729317",
"0.569714",
"0.5673685",
"0.56440496",
"0.56263894",
"0.5590547",
"0.55631435",
"0.5532549",
"0.55281574",
"0.55133027",
"0.55071926",
"0.5496068",
"0.5489203",
"0.54795736",
"0.5460182",
"0.545487... | 0.5350369 | 33 |
Get REDCap data and send to MySQL. | def send_data(data_path, project = ''):
# Send metadata
send_to_db(data_path, project, 'rcmeta.csv', 'metadata', 'rcmeta', 'rcxfer', 'rcform')
# Send events
#send_to_db(data_path, project, 'rcevent.csv', 'event', 'rcevent', 'rcxfer')
# Send arms
#send_to_db(data_path, project, 'rcarm.csv', 'a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def zkDBRrq(self):\n\tattendance = []\n\tcommand = CMD_DB_RRQ\t\n\tcommand_string = '\\x01'\n\tchksum = 0\n\tsession_id = self.session_id\n\n\treply_id = unpack('HHHH', self.data_recv[:8])[3]\n\n\tbuf = self.createHeader(command,chksum, session_id,reply_id, command_string)\n\tself.zkclient.sendto(buf,self.address)... | [
"0.60449487",
"0.5786203",
"0.57803607",
"0.5699076",
"0.5666099",
"0.5502018",
"0.54632294",
"0.5455676",
"0.5343841",
"0.530779",
"0.5262251",
"0.5247411",
"0.5186783",
"0.5174497",
"0.51127726",
"0.5084188",
"0.5075033",
"0.5058062",
"0.5055873",
"0.5050829",
"0.50432366",... | 0.47858062 | 55 |
Sort tree contents when a column is clicked on. | def sortby(self, col, descending):
# grab values to sort
tree = self.albumList
data = [(tree.set(child, col), child) for child in tree.get_children('')]
# reorder data
data.sort(reverse=descending)
for indx, item in enumerate(data):
tree.move(item[1], '', indx)
# switch the heading so that it will... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def OnColumnClick(self, event):\r\n columns = self.data.getParam('columns')\r\n self.SortItems(columns[event.GetColumn()],'INVERT')",
"def SortBy(self,tree, col, descending):\n \n # grab values to sort\n data = [(tree.set(child, col), child) for child in tree.get_children('')]\... | [
"0.7671702",
"0.7512669",
"0.73402053",
"0.7283163",
"0.7257272",
"0.7168303",
"0.7089924",
"0.7055548",
"0.6968464",
"0.68744236",
"0.66391414",
"0.65222603",
"0.64561796",
"0.6448045",
"0.6439114",
"0.64182967",
"0.6410943",
"0.64092404",
"0.6358155",
"0.6345279",
"0.634142... | 0.6909063 | 9 |
Validate data from user signup and login forms | def validation_form(username=None, password=None,
email=None, verify=None):
params = dict()
if not valid_username(username):
params['error_username'] = 'Login too short. Min 3 char, max 20 char'
if not valid_password(password):
params['error_password'] = 'Password too ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean(self):\n\n # Fetch cleaned email and username data.\n email = self.cleaned_data.get('email')\n username = self.cleaned_data.get('username')\n\n # Fetch possible user objects from the database\n # based on provided and email and password.\n user_email = User.objec... | [
"0.7158038",
"0.7136447",
"0.70500565",
"0.69671035",
"0.69375116",
"0.69036317",
"0.6692755",
"0.66822165",
"0.6665879",
"0.6650788",
"0.66077256",
"0.65853673",
"0.6573391",
"0.6573136",
"0.65564835",
"0.65473014",
"0.6545641",
"0.65413994",
"0.64977664",
"0.6493529",
"0.64... | 0.0 | -1 |
Return user if user is valid None otherwise | def valid_username(u):
return u and USER_RE.match(u) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_user():\n try:\n userId = request.args.get('login_as')\n return users[int(userId)]\n except Exception:\n return None",
"def get_one_user():",
"def user(self):\n u = self.user_info\n return self.user_model.get_by_id(u['user_id']) if u else None",
"def get_user(self):\n... | [
"0.7758432",
"0.75366116",
"0.73947096",
"0.7326417",
"0.72544",
"0.72544",
"0.7202691",
"0.7201735",
"0.71991974",
"0.7096658",
"0.70897675",
"0.706437",
"0.7033647",
"0.7019137",
"0.6991952",
"0.6981977",
"0.6954479",
"0.695263",
"0.69280744",
"0.69052994",
"0.6899382",
"... | 0.0 | -1 |
Construct filename and create directories as necessary. | def _get_filename(self, type_: str, name: str) -> str:
if not os.path.isdir(self._datadir):
os.mkdir(self._datadir, mode=0o700)
type_dir = os.path.join(self._datadir, type_)
if not os.path.isdir(type_dir):
os.mkdir(type_dir, mode=0o700)
fn = os.path.join(type_di... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def makefilename(self):\n fp= (pathlib.Path(self.vr_folder).expanduser()/(time.strftime(self.vr_filename))).with_suffix('')\n fp.parent.mkdir(parents=True, exist_ok=True)\n print('files setup', str(fp))\n return fp",
"def _create_dir(filename):\n head = os.path.dirname(filename)\n ... | [
"0.73006874",
"0.72687113",
"0.693885",
"0.6914036",
"0.68972456",
"0.6837264",
"0.68120617",
"0.6750221",
"0.6749781",
"0.67243695",
"0.6702448",
"0.6655797",
"0.662857",
"0.65972126",
"0.6564863",
"0.6562969",
"0.65181285",
"0.6508705",
"0.6491542",
"0.6468973",
"0.644841",... | 0.0 | -1 |
Remove an order and all it's resources. | def purge_order(self, order_id):
order = self.load_order(order_id)
for authz_id in order.authorization_ids:
authz = self.load_authorization(authz_id)
for chall_id in authz.challenge_ids:
self.delete('challenge', chall_id)
self.delete('authorization', a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_order():",
"def clear(self):\r\n self.orderitem_set.all().delete()",
"def _delete_all_orders(self):\n for order_ref in self.created_entities['order']:\n order = self.barbicanclient.orders.get(order_ref)\n if order.secret_ref:\n self.barbicanclient.s... | [
"0.69970745",
"0.67937535",
"0.67173916",
"0.66631436",
"0.6454474",
"0.64174116",
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"0.63377964",
"0.6304071",
"0.61717874",
"0.61426693",
"0.609751",
"0.60466385",
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"0.58851284",
"0.5855012",
"0.5821319",
"0.57647246",
"0.57... | 0.726116 | 0 |
Gets a node tree from the given string. | def getNodeTreeFromStr(string):
document = publish_doctree(string)
return list([node for node in document.traverse()[1:] if node.parent == document]) # we've to return the chidren of the document, as returning the document itself, seems to duplicate the content of the current file being processed. | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_tree(s):\n return parser.parse(lexer=lexer, input=s)",
"def read_tree(cls, string):\n \n # first, remove the brackets ()\n string = string.strip()\n if len(string) == 0:\n raise ValueError(\"empty string cannot be a Synstactic Tree\")\n if string[0] == \... | [
"0.73093045",
"0.70739913",
"0.6900079",
"0.66125304",
"0.6608044",
"0.65639174",
"0.65057653",
"0.6496888",
"0.64140177",
"0.6338099",
"0.62794083",
"0.6224039",
"0.6153235",
"0.6035289",
"0.601913",
"0.5981887",
"0.59077924",
"0.58890414",
"0.58856773",
"0.5878001",
"0.5825... | 0.795937 | 0 |
Restore to original value. | def _clear( self ):
self.value = ( '', ) # *Not* '()', which won't do at all!
self.operator = None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def restore_data(self):\n self.R = self._Ro\n del self._Ro",
"def restore(self):\n raise NotImplementedError",
"def restore(self, restore):\n self._restore = restore",
"def restore(self):\n self.weight = self._backup_weight",
"def _restore_default(self):\n self._da... | [
"0.77998513",
"0.77732843",
"0.74923337",
"0.74721766",
"0.74181354",
"0.731303",
"0.724172",
"0.723836",
"0.71995175",
"0.7137183",
"0.71307516",
"0.71219724",
"0.70887434",
"0.70396805",
"0.699821",
"0.6943587",
"0.6934261",
"0.68882245",
"0.6881654",
"0.6796656",
"0.678008... | 0.0 | -1 |
Return the name of skin method which renders the form used to edit this kind of criterion. | def getEditForm( self ):
return "listc_edit" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def name(self):\n # type: () -> Text\n\n return \"advice_form\"",
"def form_name(self):\n \n # Unpack\n request = self.request\n context = request.context\n \n # Prepare\n first_btn = len(self.buttons) and self.buttons[0]\n btn_is_create = fir... | [
"0.6674236",
"0.65364265",
"0.6175527",
"0.59753966",
"0.5915975",
"0.58678293",
"0.5806205",
"0.5771903",
"0.5759645",
"0.57106274",
"0.5694327",
"0.5591224",
"0.55854917",
"0.5552111",
"0.5548712",
"0.5472779",
"0.54639256",
"0.54545367",
"0.5444614",
"0.54379505",
"0.54134... | 0.5890521 | 5 |
Update the value we match against. | def edit( self, value=None, operator=None ):
if value is None:
self._clear()
else:
if type( value ) == type( '' ):
value = value.split('\n')
self.value = tuple( value )
if not operator:
operator = None
self.operator = oper... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def updated(self, value):\n pass",
"def _update_value(self, value):\n old_hash = get_hash(self._value)\n new_hash = get_hash(value)\n self._value = value\n if old_hash is None or new_hash is None or (old_hash != new_hash):\n self.is_dirty = True",
"def update... | [
"0.6900959",
"0.66881514",
"0.662792",
"0.6566491",
"0.6534293",
"0.639026",
"0.62795675",
"0.6251601",
"0.6240275",
"0.6180104",
"0.61638635",
"0.6101243",
"0.60290325",
"0.60176915",
"0.60130274",
"0.6004048",
"0.59539163",
"0.59539163",
"0.59539163",
"0.59539163",
"0.59494... | 0.0 | -1 |
Return a tuple of query elements to be passed to the catalog (used by 'Topic.buildQuery()'). | def getCriteriaItems( self ):
# filter out empty strings
result = []
value = tuple( filter( None, self.value ) )
if not value:
return ()
result.append( ( self.field, self.value ), )
if self.operator is not None:
result.append( ( '%s_operator' % s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def query(self):\n if bool(self._query_parameters): # ApiGateway set key value None in case of absence params, not an empty dict\n payload = {inflection.underscore(k): v for k, v, in self._query_parameters.items()}\n else:\n payload = dict()\n QueryTuple = namedtuple('Qu... | [
"0.6507253",
"0.642685",
"0.59497595",
"0.590914",
"0.5836972",
"0.5767123",
"0.57669634",
"0.57419294",
"0.5726026",
"0.56799763",
"0.55797607",
"0.55152404",
"0.54958004",
"0.54945856",
"0.5494529",
"0.5469922",
"0.5443926",
"0.54248416",
"0.54075605",
"0.53540444",
"0.5350... | 0.5601633 | 10 |
Compute a superset of the true target tract with kNN. | def compute_superset_with_k(true_bundle, kdt, prototypes, k=1000, distance_func=bundles_distances_mam):
true_bundle = np.array(true_bundle, dtype=np.object)
dm_true_bundle = distance_func(true_bundle, prototypes)
D, I = kdt.query(dm_true_bundle, k=k)
superset_idx = np.unique(I.flat)
# recompute the ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_superset(true_tract, kdt, prototypes, k=1000, distance_func=bundles_distances_mam):\n true_tract = np.array(true_tract, dtype=np.object)\n dm_true_tract = distance_func(true_tract, prototypes)\n D, I = kdt.query(dm_true_tract, k=k)\n superset_idx = np.unique(I.flat)\n return superset_idx... | [
"0.6441935",
"0.61719203",
"0.58573407",
"0.57999724",
"0.5726697",
"0.57163185",
"0.5672652",
"0.56291544",
"0.5529054",
"0.55143523",
"0.55143523",
"0.5510025",
"0.5475415",
"0.54626054",
"0.5443",
"0.54321223",
"0.5406091",
"0.53836495",
"0.53636736",
"0.5355484",
"0.53139... | 0.670607 | 0 |
Compute a superset of the true target tract with kNN. | def compute_superset_with_r(true_bundle, kdt, prototypes, r=1, distance_func=bundles_distances_mam):
true_bundle = np.array(true_bundle, dtype=np.object)
dm_true_bundle = distance_func(true_bundle, prototypes)
I = kdt.query_radius(dm_true_bundle, r)
superset_idx = []
for arr in I:
for e in a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_superset_with_k(true_bundle, kdt, prototypes, k=1000, distance_func=bundles_distances_mam):\n true_bundle = np.array(true_bundle, dtype=np.object)\n dm_true_bundle = distance_func(true_bundle, prototypes)\n D, I = kdt.query(dm_true_bundle, k=k)\n superset_idx = np.unique(I.flat)\n # reco... | [
"0.670574",
"0.6441951",
"0.61715454",
"0.58573383",
"0.58003443",
"0.5726443",
"0.5716722",
"0.56723654",
"0.56273323",
"0.5529329",
"0.5513812",
"0.5513812",
"0.5510223",
"0.5474325",
"0.5462067",
"0.5443153",
"0.54321855",
"0.54066193",
"0.5383252",
"0.5364684",
"0.535411"... | 0.5006581 | 68 |
Compute the dissimilarity representation of the tract and build the kdtree. | def compute_kdt_and_dr(tract, num_prototypes=None):
tract = np.array(tract, dtype=np.object)
print("Computing dissimilarity matrices...")
if num_prototypes is None:
num_prototypes = 40
print("Using %s prototypes as in Olivetti et al. 2012."
% num_prototypes)
else:
p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_kdtree(self):\n if self.method==2:\n coordinates = self.unassigned_data[0:3,:]\n else:\n coordinates = self.unassigned_data[0:2,:]\n tree = cKDTree(coordinates.T)\n\n return tree",
"def compute_kdtree_and_dr_tractogram(tractogram, num_prototypes=None):\n tractogram = np.... | [
"0.63991743",
"0.627999",
"0.5658187",
"0.56529063",
"0.5617922",
"0.5457617",
"0.5414897",
"0.53491503",
"0.523481",
"0.51466924",
"0.5110569",
"0.51012075",
"0.5071617",
"0.50715053",
"0.5054275",
"0.50493664",
"0.5049205",
"0.5047612",
"0.50431067",
"0.50336504",
"0.502728... | 0.59463567 | 2 |
deletes the specified keys not atomic across shards. | def delete(self, *names):
return [shard.delete(*keys) for shard, keys
in self.gather_keys_by_shard(names)] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_many(self, keys):\n raise NotImplementedError()",
"async def delete(self, *keys, **kwargs):\n\n def gen_keys(keys):\n all_keys = []\n for key in keys:\n if isinstance(key, list):\n all_keys += gen_keys(keys=key)\n els... | [
"0.7468936",
"0.7286904",
"0.71825546",
"0.7122302",
"0.70103353",
"0.70015174",
"0.69674885",
"0.688871",
"0.68676025",
"0.68633324",
"0.68574625",
"0.68523395",
"0.6762416",
"0.67384684",
"0.6734635",
"0.6674827",
"0.6622758",
"0.65964293",
"0.6570637",
"0.6519055",
"0.6515... | 0.66377497 | 16 |
Gather all keys that match pattern, by shard, then invoke shardfn once per shard, passing the shard, and the list of matching keys on that shard. | def with_keys_by_shard(self, shardfn, pattern='*'):
for shard in self.shards:
keys = shard.keys(pattern)
shardfn(shard, keys) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def group_by_key(fileset, keyfunc, min_group_size=2, workers=16):\n pool = ThreadPool(workers)\n\n def evaluate(entry):\n return keyfunc(entry), entry\n\n keyed_fileset = pool.map(evaluate, fileset)\n files_by_key = {}\n for key, entry in keyed_fileset:\n if key is None:\n c... | [
"0.48097777",
"0.47111169",
"0.47066587",
"0.47066587",
"0.46778187",
"0.46645233",
"0.4651981",
"0.4619422",
"0.45933166",
"0.45721135",
"0.45453283",
"0.4536613",
"0.4532428",
"0.45301318",
"0.4529499",
"0.45236236",
"0.45110145",
"0.45036453",
"0.44981468",
"0.44917077",
"... | 0.8449861 | 0 |
Generates scenario 1. Todo define scenario 1 | def generate_scenario_1(seed=1996, permanent_save=True, sigma_process=0.01, sigma_meas_radar=3, sigma_meas_ais=1):
# specify seed to be able repeat example
start_time = datetime.now()
np.random.seed(seed)
# combine two 1-D CV models to create a 2-D CV model
transition_model = CombinedLinearGaussia... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_create_scenario1(self):\n pass",
"def test_create_scenario(self):\n pass",
"def TestOneStep(self):\n pass",
"def test_get_scenario(self):\n pass",
"def test_2nd_scenario():\n start_entered_scenario(config.MERAKI_CAMERAS[0][\"serial\"])\n return \"ok\"",
"def tes... | [
"0.7691797",
"0.707937",
"0.67126054",
"0.6650521",
"0.65280485",
"0.64290357",
"0.6334851",
"0.62848485",
"0.62843955",
"0.62091523",
"0.6069071",
"0.6062225",
"0.599871",
"0.5980599",
"0.59706324",
"0.595907",
"0.5943288",
"0.5943288",
"0.59366554",
"0.593",
"0.5900602",
... | 0.0 | -1 |
Generates scenario 2. Scenario 2 consists of radar and ais measurements with the same sampling rate. | def generate_scenario_2(seed=1996, permanent_save=True, sigma_process=0.01, sigma_meas_radar=3, sigma_meas_ais=1,
timesteps=20):
start_time = datetime.now()
# specify seed to be able repeat example
np.random.seed(seed)
# combine two 1-D CV models to create a 2-D CV model
tr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_sampling2 () :\n delta = 2 * np.pi / 3\n r = Reward(partial(stepFunction, \n xRange=(-delta/2, delta/2), \n yRange=(-delta/2, delta/2)), \n (-1, 0))\n states = []\n xs = np.arange(-np.pi, np.pi, delta)\n ys = np.arange(-np.pi, np.pi,... | [
"0.6115581",
"0.59218746",
"0.591556",
"0.5793499",
"0.5779642",
"0.56699425",
"0.5625599",
"0.5473677",
"0.54238087",
"0.54228085",
"0.53844553",
"0.5353695",
"0.5347016",
"0.5287351",
"0.52831525",
"0.52646875",
"0.5255052",
"0.5238365",
"0.5234221",
"0.52184963",
"0.521451... | 0.6111027 | 1 |
Generates scenario 3. Scenario 3 consists of radar and ais measurements with different sampling rate. The sampling rate is specified in the input params. A groundtruth is generated for each second. | def generate_scenario_3(seed=1996, permanent_save=True, radar_meas_rate=1, ais_meas_rate=5, sigma_process=0.01,
sigma_meas_radar=3, sigma_meas_ais=1, timesteps=20):
start_time = datetime.now()
# specify seed to be able to repeat the example
np.random.seed(seed)
# combine two 1-... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_param_scenario3():\n nb_carre_x = 42\n nb_carre_y = 45\n largeur_x = 0.825\n largeur_y = 0.645\n z1 = np.array([[11], [32], [17.26]])\n z2 = np.array([[12], [32], [18.43]])\n z3 = np.array([[13], [32], [23.2]])\n z4 = np.array([[15], [31], [23.4]])\n z5 = np.array([[16], [30], [2... | [
"0.61304635",
"0.6005348",
"0.59966356",
"0.5971647",
"0.5719661",
"0.5554706",
"0.5553684",
"0.54804504",
"0.5467986",
"0.54541695",
"0.5442281",
"0.53921455",
"0.5386651",
"0.537223",
"0.535773",
"0.5339545",
"0.5323583",
"0.53214395",
"0.52994394",
"0.52926755",
"0.5286437... | 0.69548637 | 0 |
Create a Simple Good Turing estimator for the given N_r values (as specified in the 'N' kwarg). N[r] == N_r, so N[0] should be blank. | def __init__(self, N, *args, **kwargs):
self.N = copy.copy(N)
if isinstance(self.N, list):
self.max_r = len(self.N)
elif isinstance(self.N, dict) or isinstance(self.N, defaultdict):
self.max_r = max(self.N.keys())
super(Estimator, self).__init__(*args, **kwargs)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _uniform_number(self, N=None):\n num_frames = self.data.shape[0]\n if not N:\n N = int(np.floor(\n Constants.DEFAULT_SAMPLE_PERCENTAGE * num_frames\n ))\n k = int(np.floor(num_frames / N))\n if k == 0:\n k = 1\n elif... | [
"0.559078",
"0.55552906",
"0.5441996",
"0.5357272",
"0.53438485",
"0.5331821",
"0.5251055",
"0.5232737",
"0.52025616",
"0.5200852",
"0.5196297",
"0.51273173",
"0.51019603",
"0.5092297",
"0.5092026",
"0.5085834",
"0.5084632",
"0.5060103",
"0.504482",
"0.50389826",
"0.5034161",... | 0.56046593 | 0 |
Do the necessary precomputation to compute estimates | def _precompute(self):
N = self.N
# Store 'N' as used by Gale in N[0] --- this is the total # of
# occurrences.
N[0] = sum(N[r] * r for r in range(1, self.max_r + 1))
self.Z = Z = averaging_transform.transform(N, self.max_r) # Z[r] = Z_r
self.b, self.a = self._regress(Z)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def precalculate():\n pass",
"def precalculate():\n pass",
"def E_step_precompute(self, model_params, my_suff_stat, my_data):",
"def run():\n return estimate(0,1,0)",
"def _conventional_precalculation(self):\n # Run all modules\n for module in self._conventional_modules:\n ... | [
"0.737464",
"0.737464",
"0.73522526",
"0.6641263",
"0.6628966",
"0.6564368",
"0.6496165",
"0.6365002",
"0.63627005",
"0.60393345",
"0.5983021",
"0.5978751",
"0.58568126",
"0.58324635",
"0.5828639",
"0.58230853",
"0.58050996",
"0.5781569",
"0.5775128",
"0.5764755",
"0.5760743"... | 0.7158729 | 3 |
Perform linear regression on the given points in loglog space, return result | def _regress(self, Z):
# Make a set of the nonempty points in log scale
x, y = zip(*[(log(r), log(Z[r]))
for r in range(1, self.max_r + 1) if Z[r]])
self.x, self.y = x, y
matrix = numpy.array((x, numpy.ones(len(x)))).T
return numpy.linalg.lstsq(matr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log_reg(x_train, y_train):\n\n log_reg_classifier = LogisticRegression(max_iter=1000, solver='lbfgs')\n log_reg_classifier.fit(x_train, y_train)\n return log_reg_classifier\n\n # log_reg_classifier.fit(x_train, y_train)",
"def loglf2py(store):\n loglike=0.0\n return loglinear.logl(store['xb... | [
"0.6908991",
"0.67901516",
"0.6748823",
"0.6555421",
"0.6537084",
"0.65136236",
"0.6442761",
"0.64340353",
"0.6420213",
"0.64125615",
"0.63817775",
"0.6373047",
"0.63200843",
"0.6316748",
"0.62856615",
"0.6277802",
"0.6261285",
"0.6257053",
"0.62509704",
"0.62499064",
"0.6237... | 0.6528573 | 5 |
Find first r value s.t. the linear and turing estimates of r are not significantly different. | def _find_cutoff(self):
cutoff = 1
while ((self.linear_rstar_unnorm(cutoff) -
self.turing_rstar_unnorm(cutoff))**2
> self.approx_turing_variance(cutoff)):
cutoff += 1
return cutoff | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_rs(v, j):\r\n v_s, j_s = np.sort([v, j], axis=1)\r\n m = np.polyfit(v_s[-10:], j_s[-10:], 1)\r\n return 1/abs(m[0]) * 1000 #[Ohm cm^2]\r",
"def b_vs_r(r, t=.97):\n min_b = (np.log(1-t))/(np.log(1-.95**r))\n return min_b",
"def __find_r_corr_in_points(arr):\n n = len(arr)\n ... | [
"0.60582757",
"0.5931805",
"0.59098005",
"0.5888322",
"0.5841924",
"0.5819919",
"0.5704931",
"0.5644729",
"0.564107",
"0.56170464",
"0.5602746",
"0.5596635",
"0.55957025",
"0.5592062",
"0.55595565",
"0.5538356",
"0.5533621",
"0.55293804",
"0.5513773",
"0.55079275",
"0.5502329... | 0.0 | -1 |
Parse features from byteencoding to the correct type and shape. | def parse_train(self, proto, height, width):
_, sequence_parsed = tf.io.parse_single_sequence_example(
proto,
context_features=self._context_features,
sequence_features=self._sequence_features)
# Deserialize images to float32 tensors.
images = tf.map_fn(
_deserialize_png, se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _read_and_decode(example_proto,data_shape,dtypes):\n features = {}\n for name in data_shape:\n features[name] = tf.FixedLenFeature([], tf.string)\n parsed_features = tf.parse_single_example(example_proto, features)\n count = 0\n res = {}\n for name in data_s... | [
"0.68898326",
"0.67393595",
"0.6622843",
"0.6522131",
"0.6400133",
"0.635168",
"0.63479924",
"0.62762094",
"0.6270513",
"0.6163678",
"0.61197406",
"0.60895497",
"0.6085604",
"0.60802996",
"0.6062965",
"0.600735",
"0.5974631",
"0.5960345",
"0.5915046",
"0.5915046",
"0.5887663"... | 0.0 | -1 |
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