0:\n paths['/processes'] = {\n 'get': {\n 'summary': 'Processes',\n 'description': 'Processes',\n 'tags': ['server'],\n 'operationId': 'getProcesses',\n 'parameters': [\n {'$ref': '#/components/parameters/f'}\n ],\n 'responses': {\n '200': {'$ref': f\"{OPENAPI_YAML['oapip']}/responses/ProcessList.yaml\"}, # noqa\n 'default': {'$ref': '#/components/responses/default'}\n }\n }\n }\n LOGGER.debug('setting up processes')\n\n for k, v in process_manager.processes.items():\n if k.startswith('_'):\n LOGGER.debug(f'Skipping hidden layer: {k}')\n continue\n name = l10n.translate(k, locale_)\n p = process_manager.get_processor(k)\n md_desc = l10n.translate(p.metadata['description'], locale_)\n process_name_path = f'/processes/{name}'\n tag = {\n 'name': name,\n 'description': md_desc, # noqa\n 'externalDocs': {}\n }\n for link in p.metadata.get('links', []):\n if link['type'] == 'information':\n translated_link = l10n.translate(link, locale_)\n tag['externalDocs']['description'] = translated_link[\n 'type']\n tag['externalDocs']['url'] = translated_link['url']\n break\n if len(tag['externalDocs']) == 0:\n del tag['externalDocs']\n\n oas['tags'].append(tag)\n\n paths[process_name_path] = {\n 'get': {\n 'summary': 'Get process metadata',\n 'description': md_desc,\n 'tags': [name],\n 'operationId': f'describe{name.capitalize()}Process',\n 'parameters': [\n {'$ref': '#/components/parameters/f'}\n ],\n 'responses': {\n '200': {'$ref': '#/components/responses/200'},\n 'default': {'$ref': '#/components/responses/default'}\n }\n }\n }\n\n paths[f'{process_name_path}/execution'] = {\n 'post': {\n 'summary': f\"Process {l10n.translate(p.metadata['title'], locale_)} execution\", # noqa\n 'description': md_desc,\n 'tags': [name],\n 'operationId': f'execute{name.capitalize()}Job',\n 'responses': {\n '200': {'$ref': '#/components/responses/200'},\n '201': {'$ref': f\"{OPENAPI_YAML['oapip']}/responses/ExecuteAsync.yaml\"}, # noqa\n '404': {'$ref': f\"{OPENAPI_YAML['oapip']}/responses/NotFound.yaml\"}, # noqa\n '500': {'$ref': f\"{OPENAPI_YAML['oapip']}/responses/ServerError.yaml\"}, # noqa\n 'default': {'$ref': '#/components/responses/default'}\n },\n 'requestBody': {\n 'description': 'Mandatory execute request JSON',\n 'required': True,\n 'content': {\n 'application/json': {\n 'schema': {\n '$ref': f\"{OPENAPI_YAML['oapip']}/schemas/execute.yaml\" # noqa\n }\n }\n }\n }\n }\n }\n if 'example' in p.metadata:\n paths[f'{process_name_path}/execution']['post']['requestBody']['content']['application/json']['example'] = p.metadata['example'] # noqa\n\n name_in_path = {\n 'name': 'jobId',\n 'in': 'path',\n 'description': 'job identifier',\n 'required': True,\n 'schema': {\n 'type': 'string'\n }\n }\n\n paths['/jobs'] = {\n 'get': {\n 'summary': 'Retrieve jobs list',\n 'description': 'Retrieve a list of jobs',\n 'tags': ['jobs'],\n 'operationId': 'getJobs',\n 'responses': {\n '200': {'$ref': '#/components/responses/200'},\n '404': {'$ref': f\"{OPENAPI_YAML['oapip']}/responses/NotFound.yaml\"}, # noqa\n 'default': {'$ref': '#/components/responses/default'}\n }\n }\n }\n\n paths['/jobs/{jobId}'] = {\n 'get': {\n 'summary': 'Retrieve job details',\n 'description': 'Retrieve job details',\n 'tags': ['jobs'],\n 'parameters': [\n name_in_path,\n {'$ref': '#/components/parameters/f'}\n ],\n 'operationId': 'getJob',\n 'responses': {\n '200': {'$ref': '#/components/responses/200'},\n '404': {'$ref': f\"{OPENAPI_YAML['oapip']}/responses/NotFound.yaml\"}, # noqa\n 'default': {'$ref': '#/components/responses/default'} # noqa\n }\n },\n 'delete': {\n 'summary': 'Cancel / delete job',\n 'description': 'Cancel / delete job',\n 'tags': ['jobs'],\n 'parameters': [\n name_in_path\n ],\n 'operationId': 'deleteJob',\n 'responses': {\n '204': {'$ref': '#/components/responses/204'},\n '404': {'$ref': f\"{OPENAPI_YAML['oapip']}/responses/NotFound.yaml\"}, # noqa\n 'default': {'$ref': '#/components/responses/default'} # noqa\n }\n },\n }\n\n paths['/jobs/{jobId}/results'] = {\n 'get': {\n 'summary': 'Retrieve job results',\n 'description': 'Retrive job resiults',\n 'tags': ['jobs'],\n 'parameters': [\n name_in_path,\n {'$ref': '#/components/parameters/f'}\n ],\n 'operationId': 'getJobResults',\n 'responses': {\n '200': {'$ref': '#/components/responses/200'},\n '404': {'$ref': f\"{OPENAPI_YAML['oapip']}/responses/NotFound.yaml\"}, # noqa\n 'default': {'$ref': '#/components/responses/default'} # noqa\n }\n }\n }\n\n tag = {\n 'name': 'jobs',\n 'description': 'Process jobs',\n }\n oas['tags'].insert(1, tag)\n\n oas['paths'] = paths\n\n return oas","def test_get_last_cities_nocache(client):\n response = client.get(\"/weather/1\")\n assert response.status_code == 200","def get_image_information(client):\n\n pipeline = [{\"$match\": {\"camera_views\": {\"$exists\": 1}}}, {\"$unwind\": {\"path\": \"$camera_views\"}}, {\"$addFields\": {\n \"camera_views.average_linear_distance\": {\n \"$divide\": [\n \"$camera_views.total_linear_distance\",\n \"$camera_views.num_entities\"\n ]\n },\n \"camera_views.average_angular_distance\": {\n \"$divide\": [\n \"$camera_views.total_angular_distance\",\n \"$camera_views.num_entities\"\n ]\n },\n \"camera_views.timestamp\": \"$timestamp\",\n \"camera_views._id\": \"$_id\",\n \"camera_views.database\": client.database.name,\n \"camera_views.collection\": client.name,\n 'camera_views.file_id':\"$camera_views.images.file_id\", #Add the Color image id for downloading and testing\n }}, {\"$replaceRoot\": {\"newRoot\": \"$camera_views\"}}, {\"$project\": {\n \"_id\": 1,\n \"num_entities\": 1,\n \"average_linear_distance\": 1,\n \"average_angular_distance\": 1,\n \"timestamp\": 1,\n \"duplicate\": 1,\n \"database\":1,\n \"collection\":1,\n \"file_id\":{\"$arrayElemAt\":[\"$images.file_id\",0]}, # Only keep the first file id (The Color image)\n }}]\n pprint.pprint(pipeline)\n result = list(client.aggregate(pipeline))\n return result","def assets_pull(ctx, text, method):\n ocean = ctx.obj['ocean']\n response = []\n for did in ocean.search(text):\n print('pulling:', did)\n response += [ctx.invoke(assets_consume,\n did=did,\n method=method)]","def mainRoads(osm_path): \n return retrieve(osm_path,'lines',['highway','oneway','lanes','maxspeed'],**{'highway':[\"='primary' or \",\"='trunk' or \",\"='motorway' or \",\"='motorway_link' or \",\"='trunk_link' or \",\"='primary_link' or \", \"='secondary' or \",\"='tertiary' or \",\"='tertiary_link'\"]})","def roads(osm_path): \n return (retrieve(osm_path,'lines',['highway'],**{'highway':[\"='motorway' or \",\"='motorway_link' or \",\"='trunk' or \",\"='trunk_link' or \",\"='primary' or \",\"='primary_link' or \",\"='secondary' or \",\"='secondary_link' or \",\"='tertiary' or \",\"='tertiary_link' or \",\"='residential' or \",\"='road' or \",\"='unclassified' or \",\"='living_street'\"]})).rename(columns={'highway': 'asset'})","def get_latest_data():\n try:\n print '\\nRequesting new data.....\\n'\n response = get(\"https://api.myjson.com/bins/2csub\")\n if response.status_code is 200:\n print '\\nSuccess (200) in downloading data\\n'\n current_json = response.json()\n set_backup_data(current_json)\n else: \n current_json = get_backup_data()\n except ConnectionError:\n current_json = get_backup_data()\n return current_json","def get_water_network():\n\n j = request.json\n\n region = ee.Geometry(j['region'])\n start = j['start']\n stop = j['stop']\n scale = j['scale']\n crs = j['crs']\n\n # get water mask\n water_vector = get_water_mask_vector(region, scale, start, stop)\n\n # skeletonize\n output = generate_skeleton_from_voronoi(scale, water_vector)\n centerline = output[\"centerline\"]\n\n centerline = centerline.map(\n lambda line: line.set('length', line.length(scale / 10)))\n\n centerline = centerline.map(transform_feature(crs, scale))\n\n # create response\n data = centerline.getInfo()\n\n return Response(json.dumps(data), status=200, mimetype='application/json')","def get_master_data():\n data = RAW_RESPONSE.json()\n STATES = set()\n CITIES = set()\n DISTRICTS = set()\n for each in data[\"raw_data\"]:\n\n if each[\"detectedstate\"]!='':\n\n RAW_STATES.add(each[\"detectedstate\"])\n STATES.add(each[\"detectedstate\"].lower().strip().replace(\" \",\"\"))\n if each[\"detecteddistrict\"]!='':\n RAW_DISTRICTS.add(each[\"detecteddistrict\"])\n DISTRICTS.add(each[\"detecteddistrict\"].lower().strip().replace(\" \",\"\"))\n if each[\"detectedcity\"]!='':\n RAW_CITIES.add(each[\"detectedcity\"])\n CITIES.add(each[\"detectedcity\"].lower().strip().replace(\" \",\"\"))\n STATES = list(filter(None, STATES))\n DISTRICTS = list(filter(None, DISTRICTS))\n CITIES = list(filter(None, CITIES))\n\n return STATES, DISTRICTS, CITIES","def get_sea_level_raw(start_year, end_year, path_out):\n c = cdsapi.Client()\n\n for year in range(start_year, end_year + 1):\n\n print(f\"Starting Year: {year}\")\n\n c.retrieve(\n \"satellite-sea-level-global\",\n {\n \"format\": \"tgz\",\n \"year\": [str(year)],\n \"month\": [\n \"01\",\n \"02\",\n \"03\",\n \"04\",\n \"05\",\n \"06\",\n \"07\",\n \"08\",\n \"09\",\n \"10\",\n \"11\",\n \"12\",\n ],\n \"day\": [\n \"01\",\n \"02\",\n \"03\",\n \"04\",\n \"05\",\n \"06\",\n \"07\",\n \"08\",\n \"09\",\n \"10\",\n \"11\",\n \"12\",\n \"13\",\n \"14\",\n \"15\",\n \"16\",\n \"17\",\n \"18\",\n \"19\",\n \"20\",\n \"21\",\n \"22\",\n \"23\",\n \"24\",\n \"25\",\n \"26\",\n \"27\",\n \"28\",\n \"29\",\n \"30\",\n \"31\",\n ],\n },\n os.path.join(path_out, str(year) + \"_download.tar.gz\"),\n )","def _create_api_ulr_list(self) -> List[str]:\n api = Setup.openweather_api\n now = int(time.time())\n urls_list = []\n\n for lat, lon in self.locations:\n urls_list.append(\n f\"https://api.openweathermap.org/data/2.5/onecall?\"\n f\"lat={lat}&lon={lon}&exclude=hourly,minutely,\"\n f\"alerts&units=metric&appid={api}\"\n )\n\n for days in range(1, 6):\n date_time = now - 86400 * days\n urls_list.append(\n f\"http://api.openweathermap.org/data/2.5/onecall/\"\n f\"timemachine?lat={lat}&lon={lon}&dt={date_time}\"\n f\"&units=metric&appid={api}\"\n )\n\n return urls_list","def get_metocean_data():\n\n data_params = DATASETS_VIS['metocean']\n\n r = request.get_json()\n\n dataset = r['dataset']\n assert (dataset in data_params), '{} not in assets. '.format(dataset)\n data_params = data_params[dataset]\n\n band = list(data_params['bandNames'].keys())[0]\n if 'band' in r:\n band = r['band']\n assert band in data_params['bandNames'], '{} not in bands. '.format(band)\n\n # Get collection based on dataset requested\n image = ee.Image(data_params['source'])\n\n image = image.select(data_params['bandNames'][band])\n\n vis_params = {\n 'min': data_params['min'][band],\n 'max': data_params['max'][band],\n 'palette': data_params['palette'][band]\n }\n\n if 'min' in r:\n vis_params['min'] = r['min']\n\n if 'max' in r:\n vis_params['max'] = r['max']\n\n if 'palette' in r:\n vis_params['palette'] = r['palette']\n\n info = generate_image_info(image, vis_params)\n info['dataset'] = dataset\n info['band'] = band\n\n return Response(\n json.dumps(info),\n status=200,\n mimetype='application/json'\n )","def find_source(alt_az, lat_lon=local_latlong,\n minute=minute, hour=hour,\n day=day, month=month, year=year, tz_offset=5,\n return_all_sources=True):\n ra_dec = altaz_to_radec(alt_az, pos=lat_lon,\n minute=minute, hour=hour, day=day,\n month=month, year=year, tz_offset=5)\n\n coords = coordinates.SkyCoord(ra=ra_dec[0], dec=ra_dec[1],\n unit=(u.deg, u.deg), frame='icrs')\n # Get the actual results\n # For some reason, if this goes too big it stops seeing the actual source.\n r = 500 * u.arcminute\n results = Vizier.query_region(coords, radius=r, catalog='V/50')[0]\n df = results.to_pandas()\n\n candidate_sources = filter(None, [n for n in df['HD']])\n sources = []\n dmax, vmax = 0, 0\n for s in candidate_sources:\n source_info = df.loc[df['HD'] == s]\n name = source_info['Name']\n mag = round(float(source_info['Vmag']), 2)\n\n temp_ra = source_info['RAJ2000'].tolist()[0]\n temp_dec = source_info['DEJ2000'].tolist()[0]\n source_ra_hms = tuple(map(float, temp_ra.split()))\n source_dec_dms = tuple(map(float, temp_dec.split()))\n source_ra = Angle(source_ra_hms, unit='hourangle').degree\n source_dec = Angle(source_dec_dms, unit=u.deg).degree\n\n dist_from_center = np.sqrt((source_ra - ra_dec[0])**2 +\n (source_dec - ra_dec[1])**2)\n\n score = float(c1 * mag + c2 * dist_from_center)\n source_dict = {'HD': source_info['HD'].values[0],\n 'Name': source_info['Name'].values[0],\n 'RA': source_ra,\n 'DEC': source_dec,\n 'Distance': dist_from_center,\n 'Vmag': source_info['Vmag'],\n 'Score': score}\n\n sources.append(source_dict)\n\n dmax = dist_from_center if dist_from_center > dmax else dmax\n vmax = mag if mag > vmax else mag\n\n for s in range(len(sources)):\n d = sources[s]['Distance']/dmax\n mag = sources[s]['Vmag'].values[0]/vmax\n score = c1 * mag + c2 * d\n sources[s]['Score'] = score\n sources[s]['Scaled-Distance'] = d\n sources[s]['Scaled-Mag'] = mag\n\n sources_df = pd.DataFrame(sources)\n\n\n # Note that this loop is supremely janky, but df.loc'ing wasn't working.\n # best_source = sources_df.loc[sources_df['Score'] == sources_df['Score'].min]\n best_source_idx = 0\n # best_score = np.array([])\n best_score = 10000\n for i in range(len(sources)):\n score = sources[i]['Score']\n if score < best_score:\n best_source_idx = i\n best_score = score\n\n name = sources_df['Name'].values[0]\n out = {'Coords': ra_dec,\n 'HD-Name': 'HD' + str(int(sources[best_source_idx]['HD'])),\n 'Name': sources[best_source_idx]['Name'],\n 'Score': sources[best_source_idx]['Score'],\n 'Scaled-Distance': sources[best_source_idx]['Scaled-Distance'],\n 'Scaled-Mag': sources[best_source_idx]['Scaled-Mag']\n }\n return out","def objects(self):\n catalog = getToolByName(self.context, \"portal_catalog\")\n query = {}\n utils = getToolByName(self.context, \"plone_utils\")\n query[\"portal_type\"] = utils.getUserFriendlyTypes()\n registry = getUtility(IRegistry)\n typesUseViewActionInListings = frozenset(\n registry.get(\"plone.types_use_view_action_in_listings\", [])\n )\n\n is_plone_site_root = IPloneSiteRoot.providedBy(self.context)\n if not is_plone_site_root:\n query[\"path\"] = \"/\".join(self.context.getPhysicalPath())\n\n query[\"is_default_page\"] = True\n default_page_modified = OOBTree()\n for item in catalog.searchResults(query):\n key = item.getURL().rsplit(\"/\", 1)[0]\n value = (item.modified.micros(), item.modified.ISO8601())\n default_page_modified[key] = value\n\n # The plone site root is not catalogued.\n if is_plone_site_root:\n loc = self.context.absolute_url()\n date = self.context.modified()\n # Comparison must be on GMT value\n modified = (date.micros(), date.ISO8601())\n default_modified = default_page_modified.get(loc, None)\n if default_modified is not None:\n modified = max(modified, default_modified)\n lastmod = modified[1]\n yield {\n \"loc\": loc,\n \"lastmod\": lastmod,\n # 'changefreq': 'always',\n # hourly/daily/weekly/monthly/yearly/never\n # 'prioriy': 0.5, # 0.0 to 1.0\n }\n\n query[\"is_default_page\"] = False\n for item in catalog.searchResults(query):\n loc = item.getURL()\n date = item.modified\n # Comparison must be on GMT value\n modified = (date.micros(), date.ISO8601())\n default_modified = default_page_modified.get(loc, None)\n if default_modified is not None:\n modified = max(modified, default_modified)\n lastmod = modified[1]\n if item.portal_type in typesUseViewActionInListings:\n loc += \"/view\"\n yield {\n \"loc\": loc,\n \"lastmod\": lastmod,\n # 'changefreq': 'always',\n # hourly/daily/weekly/monthly/yearly/never\n # 'prioriy': 0.5, # 0.0 to 1.0\n }","async def get_mirrors(request):\n page = request.GET.getone(\"page\", None)\n page_size = request.GET.getone(\"page_size\", None)\n filter_name = request.GET.getone(\"q\", \"\")\n\n if page:\n try:\n page = int(page)\n except (ValueError, TypeError):\n return web.Response(text=\"Incorrect value for page\", status=400)\n\n if page_size:\n try:\n page_size = int(page_size)\n except (ValueError, TypeError):\n return web.Response(text=\"Incorrect value for page_size\", status=400)\n page_size = 1 if page_size < 1 else page_size\n\n query = request.cirrina.db_session.query(\n ProjectVersion\n ) # pylint: disable=no-member\n query = query.join(Project, Project.id == ProjectVersion.project_id)\n query = query.filter(Project.is_mirror == \"true\").order_by(Project.name)\n\n if filter_name:\n query = query.filter(Project.name.like(\"%{}%\".format(filter_name)))\n\n query = query.order_by(Project.name, ProjectVersion.name)\n nb_results = query.count()\n\n if page is not None and page_size:\n query = query.offset(page * page_size)\n query = query.limit(page_size)\n\n results = query.all()\n\n data = {\"total_result_count\": nb_results, \"results\": []}\n\n for mirror in results:\n apt_url = mirror.get_apt_repo(url_only=True)\n base_mirror_url = str()\n if not mirror.project.is_basemirror and mirror.buildvariants:\n # FIXME: only one buildvariant supported\n base_mirror = mirror.buildvariants[0].base_mirror\n base_mirror_url = base_mirror.get_apt_repo(url_only=True)\n\n data[\"results\"].append(\n {\n \"id\": mirror.id,\n \"name\": mirror.project.name,\n \"version\": mirror.name,\n \"url\": mirror.mirror_url,\n \"base_mirror\": base_mirror_url,\n \"distribution\": mirror.mirror_distribution,\n \"components\": mirror.mirror_components,\n \"is_basemirror\": mirror.project.is_basemirror,\n \"architectures\": mirror.mirror_architectures[1:-1],\n \"is_locked\": mirror.is_locked,\n \"with_sources\": mirror.mirror_with_sources,\n \"with_installer\": mirror.mirror_with_installer,\n \"project_id\": mirror.project.id,\n \"state\": mirror.mirror_state,\n \"apt_url\": apt_url,\n }\n )\n\n return web.json_response(data)","def getBrightest(source=None, elMin=20, elMax=87, sourceList=[], action=INCLUDE, \n numReturn=1, ignoreNorthSouth=True, coordsys=\"azel\",\n getOptical=False, fluxLimit=1.0, frequency=95) : \n # If source is None then use Polaris as the source as it is always up\n nsource = source \n if source == None: \n nsource = 'aumi'\n ignoreNorthSouth = True \n coordsys = \"azel\" \n # We need to get a bunch and then sort them and truncate after sorting...\n numToGet = 333 \n r = s.getNearest(nsource, elMin, elMax, sourceList, action, numToGet,\n ignoreNorthSouth, coordsys, getOptical, fluxLimit, frequency)\n print \"Number of results before trimming:\", len(r) \n def f(x, y):\n return cmp(y.brightness, x.brightness) \n r.sort(f) \n if source == None: \n for n in r:\n n.reference = \"None\"\n n.distance = 0\n n.azimuth = 0\n n.elevation = 0\n return r[:numReturn]","def main():\n\n with open(\".auth_token\", mode=\"r\") as tokenfile:\n authtoken = tokenfile.read().strip()\n\n # Initialize connection to Archivist\n aconn = Archivist(\n \"https://soak-0-avid.engineering-k8s-stage-2.dev.wild.jitsuin.io\",\n auth=authtoken,\n )\n # Get all assets with required attributes and properties\n props = {\"confirmation_status\": \"CONFIRMED\"}\n attrs = {\"arc_display_type\": \"Traffic light\"}\n for event in aconn.events.list(asset_id=\"assets/-\", props=props, attrs=attrs):\n print(\"event\", event)\n\n # alternatively one could pull the list and cache locally...\n events = aconn.events.list(asset_id=\"assets/-\", props=props, attrs=attrs)\n for event in events:\n print(\"event\", event)","def get_host_info(search_keyword, starbucks_data, city_info):\n host_data = []\n\n payload = {\n \"query_type\": \"RQBXY\",\n \"pagesize\": \"20\",\n \"pagenum\": '',\n \"qii\": \"true\",\n \"cluster_state\": \"5\",\n \"need_utd\": \"true\",\n \"utd_sceneid\": \"1000\",\n \"div\": \"PC1000\",\n \"addr_poi_merge\": \"true\",\n \"is_classify\": \"true\",\n \"zoom\": \"14\",\n \"longitude\": starbucks_data['longitude'],\n \"latitude\": starbucks_data['latitude'],\n \"range\": \"1000\",\n \"city\": city_info[1][0],\n \"keywords\": search_keyword,\n }\n\n for page_num in range(1, 3):\n payload['pagenum'] = page_num\n poi_list = request_amap_poi_info(payload, 'https://www.amap.com/place/' + starbucks_data['amap_key'])\n\n if not poi_list:\n print('request host list fail with %s' % page_num)\n continue\n\n for poi in poi_list:\n if not (poi.get('longitude', '') or poi.get('latitude', '') or starbucks_data['longitude'] or starbucks_data['latitude']):\n distance = None\n else:\n distance = geo_distance(poi.get('longitude', ''), poi.get('latitude', ''),starbucks_data['longitude'], starbucks_data['latitude'])\n\n data = {\n 'starbucks_key': starbucks_data['amap_key'],\n 'keyword': search_keyword,\n 'city': poi.get('cityname'),\n 'name': poi.get('name'),\n 'longitude': poi.get('longitude'),\n 'latitude': poi.get('latitude'),\n 'address': poi.get('address'),\n 'tel': poi.get('tel'),\n 'mean_price': '',\n 'distance': distance\n }\n domain_list = poi.get('domain_list')\n for domain in domain_list:\n if domain.get('name', '') == 'price':\n price_raw = domain.get('value', '')\n # price_raw = \"<font color='#90969a'>人均:</font><font color='#f84b57'>￥</font><font color='#f84b57'>114</font>\"\n try:\n data['mean_price'] = re.findall('<.*>人均:<.*>￥<.*>([0-9]+)</font>', price_raw)[0]\n except:\n data['mean_price'] = None\n break\n host_data.append(data)\n\n print('【%s】的【%s】的周边的【%s】菜系,第【%d】页爬取完毕' % (city_info[1], starbucks_data['name'], search_keyword, page_num))\n return host_data","def get_fresh_data(get_team):\n\n \"\"\" Create the URL for today \"\"\"\n now = datetime.datetime.now()\n url = \"http://gd2.mlb.com/gdcross/components/game/mlb/year_\" + '{}'.format(now.year) + \"/month_\" + '{:02d}'.format(now.month) + \"/day_\" + '{:02d}'.format(now.day) + \"/miniscoreboard.json\"\n\n \"\"\" Grab the first response and write it to a file, we'll update it once the game starts \"\"\"\n data_write_file = status_dir + '{}'.format(now.year) + '{:02d}'.format(now.month) + '{:02d}'.format(now.day) + \".json\"\n\n \"\"\" Get the json data if the file doesn't exist, or if it's over three minutes old \"\"\"\n if not os.path.isfile(data_write_file) or time.time() - os.path.getmtime(data_write_file) > 60:\n response = urllib.urlopen(url)\n full_data = json.loads(response.read())\n with open(data_write_file, 'w') as outfile:\n json.dump(full_data, outfile, sort_keys=True, indent=2, ensure_ascii=False)\n\n \"\"\" Use the data from the status file \"\"\"\n with open(data_write_file, 'r') as json_data:\n full_data = json.load(json_data)\n\n \"\"\" This will return false if there is no game today, else will return json data for just our team \"\"\"\n my_game = False\n for game in full_data['data']['games']['game']:\n if get_team in game['home_file_code'] or get_team in game['away_file_code']:\n my_game = game\n \n return my_game","def update(self):\n url = '/weather/summary?version=2&lat={}&lon={}' \\\n .format(self.lat, self.lon)\n self.result = self.api.get(url)['weather']['summary'][0]","def wp_gather_image_and_info(output_dir, db_collection, region_bbox):\n log = logging.getLogger('gather_tweet_image_and_info')\n\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n\n log.info(\"Performing Query\")\n after = datetime.datetime(2014, 2, 1)\n q = db_collection.find({\n \"$and\": [\n {\"media\": {\"$gt\": {\"$size\": 0}}},\n {\"media.0.type\": \"photo\"},\n {\"created\": {\"$gte\": after}},\n {\"geo.0\": {\"$gte\": region_bbox.min_x, \"$lte\": region_bbox.max_x}},\n {\"geo.1\": {\"$gte\": region_bbox.min_y, \"$lte\": region_bbox.max_y}},\n ]\n }, timeout=False)\n\n log.info(\"Scanning results\")\n count = 0\n try:\n for i, e in enumerate(q):\n log.info(\"[%9d] %s -> %s %s\", i, e['_id'], e['geo'],\n e['media'][0]['media_url'])\n media_path = e['media'][0]['media_url']\n media_filetype = os.path.splitext(media_path)[1]\n\n output_image = os.path.join(output_dir, str(e['_id']) + media_filetype)\n output_bson = os.path.join(output_dir, str(e['_id']) + \".bson\")\n\n try:\n with open(output_image, 'wb') as output_image_file:\n output_image_file.write(urllib2.urlopen(media_path).read())\n with open(output_bson, 'wb') as output_md_file:\n output_md_file.write(bson.BSON.encode(e))\n count += 1\n except Exception, ex:\n # Skip all files that cause errors anywhere. Remove anything\n # written\n log.info(\" - ERROR: %s\", str(ex))\n log.info(\" - Skipping\")\n if os.path.exists(output_image):\n os.remove(output_image)\n if os.path.exists(output_bson):\n os.remove(output_bson)\n finally:\n # Since we checked out a cursor without a server-side timeout, make sure\n # that we catch whatever and close the cursor when we're done / if\n # things fail.\n q.close()\n\n log.info(\"Discovered %d matching entries with valid images.\", count)","def test_get_clouds(self):\n pass","def access_gemini_url_head_method(context, endpoint):\n url = urljoin(context.gemini_api_url, endpoint)\n context.response = requests.head(url)","def find_last_image(self) -> List[str]:\n\n soup = self.load_page()\n\n txt_from_site = soup.select('.full-image-block')\n\n url_to_down = \"http://www.lunarbaboon.com/\" + str(txt_from_site[0])[\n str(txt_from_site).find('/storage'):str(txt_from_site).find(\n 'SQUARESPACE_CACHEVERSION=') + 37]\n filename = str(url_to_down)[35:48] + \".jpg\"\n\n return [url_to_down, filename]","def sentientPlanets():\n planet_list = []\n url = 'https://swapi-api.hbtn.io/api/species'\n while url is not None:\n data = requests.get(url).json()\n for species in data['results']:\n if ((species['designation'] == 'sentient'\n or species['designation'] == 'reptilian')):\n if species['homeworld'] is not None:\n hw = requests.get(species['homeworld']).json()\n planet_list.append(hw['name'])\n url = data['next']\n return planet_list","def _new_site(self, soup, url_base):\n dl_list = []\n img_list = soup.find(\"div\", {\"id\": \"download-links-holder\"})\n\n # Check to if high res pics are linked\n img_hires_link = img_list.find(\"a\", text=re.compile('Highest-quality download options'))\n if img_hires_link is not None:\n self.log(\"[NEW] Get high res\")\n hires_url = url_base + img_hires_link['href']\n try:\n hires_soup = self.get_site(hires_url, self._url_header)\n except RequestsError as e:\n self.log(\"Skipping image: \" + str(hires_url))\n else:\n hires_list = hires_soup.find(\"div\", {\"id\": \"download-links-holder\"})\n hires_links = hires_list.find_all(\"li\")\n for hires_link in hires_links:\n hires_dl_url = hires_link.a['href']\n # Add to download list\n dl_list.append(hires_dl_url)\n\n self.log(\"[NEW] Get other\")\n # Get all other images\n img_links = img_list.find_all(\"li\")\n for img_link in img_links:\n link_url = img_link.a['href']\n img_dl_url = link_url\n if link_url.endswith('/'):\n link_url = url_base + link_url\n try:\n link_soup = self.get_site(link_url, self._url_header)\n except RequestsError as e:\n self.log(\"Skipping image: \" + str(link_url))\n else:\n img_dl_url = link_soup.find(\"div\", {\"class\": \"subpage-body\"}).img['src']\n # Add to download list\n dl_list.append(img_dl_url)\n\n return dl_list","def geocube():","def getrmlsthelper(self):\n\n printtime('Downloading {} alleles'.format(self.analysistype), self.start)\n # Extract the path of the current script from the full path + file name\n homepath = os.path.split(os.path.abspath(__file__))[0]\n # Set the path/name of the folder to contain the new alleles and profile\n newfolder = os.path.join(self.path, self.analysistype)\n # Create the path\n make_path(newfolder)\n # Create arguments to feed into the rest_auth_class script\n args = ArgumentParser\n args.secret_file = os.path.join(homepath, 'secret.txt')\n args.file_path = homepath\n args.output_path = newfolder\n args.start = self.start\n rmlst = rest_auth_class.REST(args)\n # Download the profile and alleles\n rmlst.main()\n\n # Get the new alleles into a list, and create the combinedAlleles file\n alleles = glob(os.path.join(newfolder, '*.tfa'))\n self.combinealleles(newfolder, alleles)","def get_all_locations(self):","async def get_stations(response: Response,\n source: StationSourceEnum = StationSourceEnum.UNSPECIFIED):\n try:\n logger.info('/stations/')\n\n weather_stations = await get_stations_as_geojson(source)\n response.headers[\"Cache-Control\"] = no_cache\n\n return WeatherStationsResponse(features=weather_stations)\n except Exception as exception:\n logger.critical(exception, exc_info=True)\n raise","def load_rentedout():","def main():\n # Pull variables from pf\n profileref = pfget('google_mapfeed.pf', profile)\n dbname = profileref['dbname']\n path = profileref['webbase']\n finalfile = '%s/%s' % (path, profileref['file'])\n bufferfile = '%s+' % finalfile\n max_nquakes = 600\n element_fields = ['lat', 'lon', 'depth', 'time', 'local_timestring', 'utc_timestring', 'magnitude', 'auth']\n\n if verbose:\n print \"Start: Creating main JSON file '%s' for all stations at %s\" % (finalfile, time.strftime(\"%a, %d %b %Y %H:%M:%S +0000\", time.gmtime()))\n\n now = time.time()\n # Set time zone\n os.putenv('TZ','US/Pacific')\n time.tzset()\n if verbose:\n print \"The time zone is: %s\" % (time.tzname)[0]\n print \"The current time is: %s\" % now\n\n # Override defaults\n if override_number:\n if verbose:\n print \"Overriding default number of events (%d) with %d\" % (max_nquakes, override_number)\n nquakes = override_number\n else:\n nquakes = max_nquakes\n if override_timerange:\n if verbose:\n print \"Overiding default number of events (%d) with time range %d seconds\" % (max_nquakes, override_timerange)\n nquakes = False\n\n # Database processing\n if verbose:\n print \"Opening database\";\n print \"Number of events requested: %s\" % nquakes\n db = dbopen(dbname, 'r')\n\n '''\n Occasionally there is more than one magnitude for a single orid\n (such as provided by QED). We need the most recent magnitude for\n a given orid, so sort on orid and lddate, then group on orid,\n then get the most recent record number (greatest lddate) for each\n group. Add that to a dictionary we will use later.\n '''\n netmag_dict = {}\n db_netmag = dblookup(db, table='netmag')\n db_netmag.sort(['orid', 'lddate'])\n db_netmag_grp = dbgroup(db_netmag, 'orid')\n if verbose:\n print \"There are %s records\" % db_netmag_grp.query('dbRECORD_COUNT')\n for i in range(db_netmag_grp.query('dbRECORD_COUNT')):\n db_netmag_grp[3] = i\n orid, [dbptr, view, end_record, start_record] = db_netmag_grp.getv('orid', 'bundle')\n if verbose:\n print \"\\t- Iteration: %s: Orid: %s, Start record: %s, End record: %s\"% (i, orid, start_record, end_record)\n db_netmag[3] = end_record - 1\n if verbose:\n print \"\\t\\t- Magnitude: %s, Magtype: %s\" % (db_netmag.getv('magnitude')[0], db_netmag.getv('magtype')[0] )\n magnitude, magtype = db_netmag.getv('magnitude', 'magtype')\n netmag_dict[orid] = { 'rec':end_record, 'magnitude':magnitude, 'magtype':magtype }\n\n '''\n if verbose:\n for key in sorted(netmag_dict.iterkeys()):\n print \"%s: %s\" % (key, netmag_dict[key])\n '''\n\n '''\n Now get the event information\n '''\n db.lookup(table='origin')\n db.join('event')\n if verbose:\n print \"Number of joined records of event and origin tables: %s\" % db.query('dbRECORD_COUNT')\n if override_timerange:\n override_oldest = now - override_timerange\n if verbose:\n print \"Override time defined - get events in the last %s seconds - 'time >= %s'\" % (override_timerange, override_oldest)\n db.subset('time >= %d' % override_oldest)\n if verbose:\n print \"Subset on time. Number of records: %s\" % db.query('dbRECORD_COUNT')\n # Join views\n # db_joined = dbjoin(db, db_netmag)\n\n if verbose:\n print \"Subset orid == prefor\"\n db.subset('orid == prefor')\n if verbose:\n print \"Number of subsetted records: %s\" % db.query('dbRECORD_COUNT')\n print \"Subset for time != NULL\"\n db.subset('time != NULL')\n if verbose:\n print \"Number of subsetted records: %s\" % db.query('dbRECORD_COUNT')\n # We want the most recent first for the comparison with nquakes\n db.sort(['time'], reverse=True)\n if verbose:\n print \"Number of sorted records: %s\" % db.query('dbRECORD_COUNT')\n if nquakes:\n if db.query('dbRECORD_COUNT') > nquakes:\n db[3] = nquakes - 1\n min_time = db.getv('time')[0]\n db.subset(\"time >= %s\" % min_time)\n else:\n override_oldest = now - override_timerange\n if verbose:\n print \"Override time defined - get events in the last %s seconds - time > %s\" % (override_timerange, override_oldest)\n db.subset(\"time >= %s\" % override_oldest)\n # Sort in normal time - we want the most recent events plotted on top\n db.sort(('time'))\n if verbose:\n print \"Number of records without subset on time: %s\" % db.query('dbRECORD_COUNT')\n '''\n Build event dictionary\n '''\n event_dict = {'metadata':{},'events':{}}\n\n '''\n Build metadata dictionary\n '''\n if nquakes:\n event_dict['metadata']['max_nquakes'] = nquakes\n event_dict['metadata']['oldest_time_readable'] = epoch2str( int(min_time), \"%H:%M UTC %A %B %o, %Y\" )\n event_dict['metadata']['oldest_time'] = int(min_time)\n event_dict['metadata']['type'] = 'event_limited'\n elif override_oldest:\n event_dict['metadata']['time_range'] = int(override_timerange)\n event_dict['metadata']['oldest_time_readable'] = epoch2str( int(override_oldest), \"%H:%M UTC %A %B %o, %Y\" )\n event_dict['metadata']['oldest_time'] = int(override_oldest)\n event_dict['metadata']['type'] = 'time_limited'\n event_dict['metadata']['modification_time'] = int(time.time())\n event_dict['metadata']['modification_time_readable'] = epoch2str( int(time.time()), \"%H:%M UTC %A %B %o, %Y\" )\n\n '''\n Build event dictionary\n '''\n events = {}\n for i in range(db.query('dbRECORD_COUNT')):\n db[3] = i\n if verbose:\n epoch_time, orid = db.getv('time', 'orid')\n print \"\\tRecord number is: %s Orid is: %d Time is: %s\" % (db[3], orid, epoch2str(epoch_time, '%Y-%m-%d %H:%M:%S'))\n\n orid = db.getv('orid')[0]\n\n if orid in netmag_dict:\n events[i] = {}\n for ef in element_fields:\n # Parse values\n if ef is 'local_timestring' or ef is 'utc_timestring' or ef is 'time':\n value = dbgetv(db, 'time')[0]\n difference = float(now) - float(value)\n if difference < 6 * 3600:\n color = 'red'\n elif difference < 12 * 3600:\n color = 'orange'\n elif difference < 24 * 3600:\n color = 'yellow'\n elif difference < 72 * 3600:\n color = 'chartreuse'\n elif difference < 168 * 3600:\n color = 'blue'\n else:\n color = 'grey'\n events[i]['color'] = color\n elif ef is 'depth':\n value = dbgetv(db, 'depth')[0]\n elif ef is 'auth':\n value = dbgetv(db, 'auth')[0]\n elif ef is 'magnitude':\n # Magnitude\n # mlval, mbval, msval, magnitudeval, magtypeval = db.getv('ml', 'mb', 'ms', 'magnitude', 'magtype')\n # Null magnitude is -999.00\n magnitudeval = netmag_dict[orid]['magnitude']\n magtypeval = netmag_dict[orid]['magtype']\n if int(magnitudeval) > 0:\n scale = magtypeval\n value = '%.1f' % magnitudeval\n else:\n scale = ''\n value = 'N/A'\n events[i]['scale'] = scale\n else:\n value = dbgetv(db, ef)\n\n # Override formatting for specific fields\n if ef is 'lat' or ef is 'lon':\n value = '%.4f' % value\n elif ef is 'local_timestring':\n value = epoch2str( value, \"%H:%M:%S %Z %A %B %o, %Y\", \"US/Pacific\" )\n elif ef is 'utc_timestring':\n value = epoch2str( value, \"%H:%M:%S UTC %A %B %o, %Y\" )\n events[i][ef] = value\n\n full_lat, full_lon = db.getv('lat', 'lon')\n events[i]['grname'] = (grname(full_lat,full_lon)).title()\n events[i]['srname'] = (srname(full_lat,full_lon)).title()\n\n event_dict['events'] = events\n\n # Dump JSON file\n f = open(bufferfile, 'w') \n json.dump(event_dict, f, sort_keys=True, indent=2)\n f.flush()\n\n # Move the file to replace the older one\n try:\n os.rename(bufferfile, finalfile)\n except OSError:\n print \"Cannot rename JSON file from %s to %s\" % (bufferfile,finalfile)\n\n if verbose:\n print \"End: Creating main JSON file '%s' for all stations %s\" % (finalfile, time.strftime(\"%a, %d %b %Y %H:%M:%S +0000\", time.gmtime()))\n\n db.close()\n return 0","def fetch(self,url=URL):\n\t\tlog.info('downloading latest PHE case data')\n#\t\tself.data=lookup_json(url)\n\t\tself.fetch_csv() #JSON discontinued; switched back to CSV\n\t\tself.edition=self.latest_samples\n\t\tlog.info(f'Last samples from {self.edition}')","def make_pull(db,url):\n result = db.product_mstator.find_one({\"url\":url})\n return result","def download_potholes():\n\n\tlink = \"https://data.cityofchicago.org/api/views/7as2-ds3y/rows.csv?accessType=DOWNLOAD\"\n\tdf = pd.read_csv(link)\n\tdf = df[(df.STATUS == \"Open\") | (df.STATUS == \"Open - Dup\")]\n\tdf = df[[\"LATITUDE\", \"LONGITUDE\"]]\n\tdf = df.dropna(axis =0, subset=[\"LATITUDE\", \"LONGITUDE\"])\n\treturn df","def get_house_est(api_key, year = str(), map = bool()):\n try:\n house_url = f'http://api.census.gov/data/{year}/pep/housing?get=HUEST&for=state:*&key={api_key}'\n r = requests.get(house_url)\n data = json.loads(r.content) \n house_df = pd.DataFrame(data[1:], columns=data[0]).\\\n rename(columns={\"HUEST\": \"Housing_Estimates\", \"state\": \"STATEFP\"})\n house_df['Housing_Estimates'] = house_df['Housing_Estimates'].astype(str).astype(int)\n geodata_url = f\"https://raw.githubusercontent.com/uscensusbureau/citysdk/master/v2/GeoJSON/20m/{year}/state.json\"\n geo_df = gpd.read_file(geodata_url)\n geo_df = geo_df.merge(house_df, on = 'STATEFP')\n if map == True:\n return geo_df.plot(column = 'Housing_Estimates')\n else:\n return geo_df\n r.raise_for_status()\n except HTTPError as http_err:\n print(f'HTTP error occurred: {http_err}')\n except Exception as err:\n print(f'An error occured. All parameters must exist in the Census GeoJSON database and API. Please check https://github.com/uscensusbureau/citysdk/tree/master/v2/GeoJSON: {err}')","def get_house(arg):\n house = requests.get(BASE_URL+'houses/'+arg)\n print house.json()\n return house.status_code","def download(self, verbose):\n # Download datasets\n if verbose:\n print(\"Retrieving datasets from Our World In Data https://github.com/owid/covid-19-data/\")\n # Vaccinations\n v_rec_cols = [\n \"date\", \"location\", \"iso_code\", \"total_vaccinations\", \"people_vaccinated\", \"people_fully_vaccinated\"]\n v_rec_df = pd.read_csv(self.URL_V_REC, usecols=v_rec_cols)\n v_loc_df = pd.read_csv(self.URL_V_LOC, usecols=[\"location\", \"vaccines\"])\n v_df = v_rec_df.merge(v_loc_df, how=\"left\", on=\"location\")\n # Tests\n pcr_rec_cols = [\"ISO code\", \"Date\", \"Daily change in cumulative total\", \"Cumulative total\"]\n pcr_df = pd.read_csv(self.URL_P_REC, usecols=pcr_rec_cols)\n pcr_df = pcr_df.rename(columns={\"ISO code\": \"iso_code\", \"Date\": \"date\"})\n pcr_df[\"cumsum\"] = pcr_df.groupby(\"iso_code\")[\"Daily change in cumulative total\"].cumsum()\n pcr_df = pcr_df.assign(tests=lambda x: x[\"Cumulative total\"].fillna(x[\"cumsum\"]))\n # Combine data (vaccinations/tests)\n df = v_df.set_index([\"iso_code\", \"date\"])\n df = df.combine_first(pcr_df.set_index([\"iso_code\", \"date\"]).loc[:, [\"tests\"]])\n df = df.reset_index()\n # Location (country/province)\n df[\"location\"] = df[\"location\"].replace(\n {\n # COG\n \"Congo\": \"Republic of the Congo\",\n }\n )\n df = df.loc[~df[\"iso_code\"].str.contains(\"OWID_\")]\n df[\"location\"] = df.groupby(\"iso_code\")[\"location\"].bfill()\n df.loc[df[\"location\"] == df[\"iso_code\"], \"location\"] = None\n df.loc[df[\"location\"].isna(), \"location\"] = df.loc[df[\"location\"].isna(), \"iso_code\"].apply(\n lambda x: coco.convert(x, to=\"name_short\", not_found=None))\n df[self.PROVINCE] = self.UNKNOWN\n return df","def main():\n\n data = get_data(URL)\n\n if not data:\n raise ValueError('No data to process')\n\n datacenters = [\n Datacenter(key, value)\n for key, value in data.items()\n ]\n\n pass # the rest of your logic here","def merge_light_catalogue():\n output_filename = os.path.join(constants.DESTINATION,\n 'concatenated',\n 'iphas-dr2-light.fits')\n\n instring = ''\n for lon in np.arange(25, 215+1, constants.STRIPWIDTH):\n for part in ['a', 'b']:\n path = os.path.join(constants.DESTINATION,\n 'concatenated',\n 'light',\n 'iphas-dr2-{0:03d}{1}-light.fits'.format(\n lon, part))\n instring += 'in={0} '.format(path)\n\n # Warning: a bug in stilts causes long fieldIDs to be truncated if -utype S15 is not set\n param = {'stilts': constants.STILTS,\n 'in': instring,\n 'out': output_filename}\n\n cmd = '{stilts} tcat {in} countrows=true lazy=true ofmt=colfits-basic out={out}'\n mycmd = cmd.format(**param)\n log.debug(mycmd)\n status = os.system(mycmd)\n log.info('concat: '+str(status))\n\n return status","def home(request):\n gefs_layers,geoserver_wms_url,geoserver_workspace = listLayers()\n\n context = {\n 'gefs_layers':json.dumps(gefs_layers),\n 'geoserver_wms_url':geoserver_wms_url,\n 'geoserver_workspace':geoserver_workspace\n }\n\n return render(request, 'gefs/home.html', context)","def _setOceanLocation(self):\r\n\t\t## If the fluids_hrc exists\r\n\t\tif cmds.objExists('fluids_hrc'):\r\n\t\t\tif cmds.objExists('ocean_srf'):\r\n\t\t\t\tcmds.connectAttr('fluids_hrc.translateX', 'ocean_srf.translateX', f = True)\r\n\t\t\t\tcmds.connectAttr('fluids_hrc.translateZ', 'ocean_srf.translateZ', f = True)\r\n\t\t\telse:\r\n\t\t\t\tcmds.warning('MISSING ocean_srf node from scene....')\r\n\r\n\t\t\tif cmds.objExists('oceanPreviewPlane_prv'):\r\n\t\t\t\tcmds.connectAttr('fluids_hrc.translateX', 'oceanPreviewPlane_prv.translateX', f = True)\r\n\t\t\t\tcmds.connectAttr('fluids_hrc.translateZ', 'oceanPreviewPlane_prv.translateZ', f = True)\r\n\t\t\telse:\r\n\t\t\t\tcmds.warning('MISSING oceanPreviewPlane_prv node from scene....')\r\n\t\telse:\r\n\t\t\tcmds.warning('NO fluids_hrc FOUND! Can not move the ocean into final position. PLEASE CHECK FX PUBLISH NOW!')","def fetch(self,etime,lat,lon,timewindow=20,radius=100,limit=None):\n utctime = etime\n #get the most likely event time and ID for the event we input\n eid,gtime = self._check_catalog(etime,lat,lon,timewindow,radius)\n if eid is None:\n msg = 'Could not find this event in the GeoNet earthquake catalog. Returning.'\n raise StrongMotionFetcherException(msg)\n\n #set up the ftp url for this day and month\n #[MONTH] should be in the format mm_Mon (04_Apr, 05_May, etc.)\n neturl = GEOBASE.replace('[YEAR]',str(utctime.year))\n monthstr = utctime.strftime('%m_%b')\n neturl = neturl.replace('[MONTH]',monthstr)\n urlparts = urllib.parse.urlparse(neturl)\n ftp = ftplib.FTP(urlparts.netloc)\n ftp.login() #anonymous\n ftp.cwd(urlparts.path)\n\n \n #get the current local directory, then cd to the desired raw folder\n cwd = os.getcwd()\n os.chdir(self._rawfolder)\n self._datafiles = []\n\n #create the event folder name from the time we got above\n fname = gtime.strftime('%Y-%m-%d_%H%M%S')\n try:\n ftp.cwd(fname)\n except:\n msg = 'Could not find an FTP data folder called \"%s\". Returning.' % (urllib.parse.urljoin(neturl,fname))\n raise StrongMotionFetcherException(msg)\n\n try:\n #actually retrieve the data files\n volumes = []\n dirlist = ftp.nlst()\n for volume in dirlist:\n if volume.startswith('Vol'):\n ftp.cwd(volume)\n if 'data' not in ftp.nlst():\n ftp.cwd('..')\n continue\n\n ftp.cwd('data')\n flist = ftp.nlst()\n for ftpfile in flist:\n if not ftpfile.endswith('V1A'):\n continue\n localfile = os.path.join(os.getcwd(),ftpfile)\n if localfile in self._datafiles:\n continue\n self._datafiles.append(localfile)\n f = open(localfile,'wb')\n sys.stderr.write('Retrieving remote file %s...\\n' % ftpfile)\n ftp.retrbinary('RETR %s' % ftpfile,f.write)\n f.close()\n if limit is not None and len(self._datafiles) >= limit:\n break\n ftp.cwd('..')\n ftp.cwd('..')\n if limit is not None and len(self._datafiles) >= limit:\n break\n except Exception as e:\n pass\n finally:\n ftp.quit()\n os.chdir(cwd)\n return","def get_observation(world_state,weather,time_now):\n obs = np.zeros((2, OBS_SIZE, OBS_SIZE))\n object_list=[]\n while world_state.is_mission_running:\n time.sleep(0.1)\n world_state = agent_host.getWorldState()\n \n if len(world_state.errors) > 0:\n raise AssertionError('Could not load grid.')\n\n if world_state.number_of_observations_since_last_state > 0:\n # First we get the json from the observation API\n msg = world_state.observations[-1].text\n observations = json.loads(msg)\n # Get observation\n if weather != 'clear':\n object_list.append(weather)\n else:\n object_list.append('sunny')\n\n if time_now>13000:\n object_list.append(\"night\")\n else:\n object_list.append(\"morning\")\n grid = observations['floorAll']\n \n if grid.count('water')>3:\n object_list.append(\"river\")\n if grid.count('leaves')>2:\n object_list.append(\"tree\")\n if grid.count('lava')>1:\n object_list.append(\"lava\")\n \n animal=observations['NearbyEntities']\n target_ani=['Sheep','Cow','Pig']\n for i in animal:\n if i['name'] not in object_list and i['name'] in target_ani:\n object_list.append(i['name'])\n break\n \n \n return object_list","def runOttawa():\r\n \r\n DIRECTORY = \"D:\\Amaury\\Desktop\\ottawa_image_db\"\r\n \r\n ds = ogr.Open('C:/Users/msawada/Desktop/Urban_RAT/Urban_RAT_inventory_4326.dbf')\r\n layer = ds.GetLayer()\r\n \r\n n_loc = len(layer)\r\n #done: range(3000)\r\n for i in range(3000,6000):\r\n print('%.2f' %(i*100/n_loc) + \" %\")\r\n \r\n index = i\r\n feature = layer[index]\r\n \r\n lon = feature.GetGeometryRef().GetX()\r\n lat = feature.GetGeometryRef().GetY()\r\n \r\n folder = DIRECTORY + '/%2.6f,%2.6f' %(lat,lon)\r\n \r\n panIds = streetview.panoids(lat, lon)\r\n \r\n if len(panIds) > 0:\r\n if not os.path.exists(folder):\r\n os.makedirs(folder)\r\n for pan in panIds:\r\n img = streetview.api_download(pan[\"panoid\"], folder, API_KEY, fov=80, pitch=0, year=pan[\"year\"],month=pan[\"month\"])\r\n if img != None:\r\n full_size = misc.imread(img)\r\n resized = misc.imresize(full_size, (64,64))\r\n desc = gist.extract(resized)\r\n np.savetxt(img + '64.txt', desc)","def maps():\n flatmap_list = []\n root_path = pathlib.Path(settings['FLATMAP_ROOT'])\n if root_path.is_dir():\n for flatmap_dir in root_path.iterdir():\n index = os.path.join(settings['FLATMAP_ROOT'], flatmap_dir, 'index.json')\n mbtiles = os.path.join(settings['FLATMAP_ROOT'], flatmap_dir, 'index.mbtiles')\n if os.path.isdir(flatmap_dir) and os.path.exists(index) and os.path.exists(mbtiles):\n with open(index) as fp:\n index = json.loads(fp.read())\n version = index.get('version', 1.0)\n reader = MBTilesReader(mbtiles)\n if version >= 1.3:\n metadata = read_metadata(reader, 'metadata')\n if (('id' not in metadata or flatmap_dir.name != metadata['id'])\n and ('uuid' not in metadata or flatmap_dir.name != metadata['uuid'].split(':')[-1])):\n app.logger.error(f'Flatmap id mismatch: {flatmap_dir}')\n continue\n flatmap = {\n 'id': metadata['id'],\n 'source': metadata['source'],\n 'version': version\n }\n if 'created' in metadata:\n flatmap['created'] = metadata['created']\n if 'taxon' in metadata:\n flatmap['taxon'] = normalise_identifier(metadata['taxon'])\n flatmap['describes'] = metadata['describes'] if 'describes' in metadata else flatmap['taxon']\n elif 'describes' in metadata:\n flatmap['taxon'] = normalise_identifier(metadata['describes'])\n flatmap['describes'] = flatmap['taxon']\n if 'biological-sex' in metadata:\n flatmap['biologicalSex'] = metadata['biological-sex']\n if 'uuid' in metadata:\n flatmap['uuid'] = metadata['uuid']\n if 'name' in metadata:\n flatmap['name'] = metadata['name']\n else:\n try:\n source_row = reader._query(\"SELECT value FROM metadata WHERE name='source'\").fetchone()\n except (InvalidFormatError, sqlite3.OperationalError):\n flask.abort(404, 'Cannot read tile database: {}'.format(mbtiles))\n if source_row is None:\n continue\n flatmap = {\n 'id': flatmap_dir.name,\n 'source': source_row[0]\n }\n created = reader._query(\"SELECT value FROM metadata WHERE name='created'\").fetchone()\n if created is not None:\n flatmap['created'] = created[0]\n describes = reader._query(\"SELECT value FROM metadata WHERE name='describes'\").fetchone()\n if describes is not None and describes[0]:\n flatmap['describes'] = normalise_identifier(describes[0])\n flatmap_list.append(flatmap)\n return flask.jsonify(flatmap_list)","def get_starbucks_info(city_info):\n print('=='*100)\n print(city_info[0] + '的星巴克店开始爬取')\n\n starbucks_data = []\n\n payload = {\n \"query_type\": \"TQUERY\",\n \"pagesize\": \"20\",\n \"qii\": \"true\",\n \"cluster_state\": \"5\",\n \"need_utd\": \"true\",\n \"utd_sceneid\": \"1000\",\n \"div\": \"PC1000\",\n \"addr_poi_merge\": \"true\",\n \"is_classify\": \"true\",\n \"zoom\": \"12\",\n \"city\": city_info[1][0],\n \"geoobj\": city_info[1][1],\n \"keywords\": \"星巴克\",\n }\n\n for page_num in range(1, 100):\n payload['pagenum'] = page_num\n poi_list = request_amap_poi_info(payload, 'https://www.amap.com/')\n\n if not poi_list:\n print('request starbucks list exit with %s' % page_num)\n break\n\n for poi in poi_list:\n data = {\n 'amap_key': poi.get('id'),\n 'name': poi.get('name'),\n 'city': poi.get('cityname'),\n 'longitude': poi.get('longitude'),\n 'latitude': poi.get('latitude'),\n 'address': poi.get('address'),\n 'tel': poi.get('tel'),\n }\n starbucks_data.append(data)\n print('【%s】的星巴克列表的第【%s】页已爬取完毕'% (city_info[0], page_num))\n print('【%s】的星巴克全部爬取完毕' % city_info[0])\n print('==' * 100)\n return starbucks_data","def home():\n return (\n f\"/api/v1.0/precipitation<br/>\"\n f\"/api/v1.0/stations<br/>\"\n f\"/api/v1.0/tobs<br/>\"\n f\"/api/v1.0/<start><br/>\"\n f\"/api/v1.0/<start>/<end>\"\n \n )","def get_water_network_properties():\n\n j = request.json\n\n region = ee.Geometry(j['region'])\n start = j['start']\n stop = j['stop']\n scale = j['scale']\n\n step = j['step']\n\n crs = j['crs']\n\n error = ee.ErrorMargin(scale / 2, 'meters')\n\n if 'network' in j:\n raise Exception(\n 'TODO: re-using existing networks is not supported yet')\n\n # get water mask\n water_vector = get_water_mask_vector(region, scale, start, stop)\n\n # skeletonize\n output = generate_skeleton_from_voronoi(scale, water_vector)\n centerline = ee.FeatureCollection(output[\"centerline\"])\n distance = ee.Image(output[\"distance\"])\n\n # generate width at every offset\n centerline = centerline.map(\n lambda line: line.set(\"length\", line.length(error)))\n\n short_lines = centerline.filter(ee.Filter.lte('length', step))\n long_lines = centerline.filter(ee.Filter.gt('length', step))\n\n def process_long_line(line):\n line_length = line.length(error)\n distances = ee.List.sequence(0, line_length, step)\n segments = line.geometry().cutLines(distances, error)\n\n def generate_line_middle_point(pair):\n pair = ee.List(pair)\n\n s = ee.Geometry(pair.get(0))\n offset = ee.Number(pair.get(1))\n\n centroid = ee.Geometry.Point(s.coordinates().get(0))\n\n return ee.Feature(centroid) \\\n .set(\"lineId\", line.id()) \\\n .set(\"offset\", offset)\n\n segments = segments.geometries().zip(distances) \\\n .map(generate_line_middle_point)\n\n return ee.FeatureCollection(segments)\n\n long_line_points = long_lines.map(process_long_line).flatten()\n\n def process_short_line(line):\n geom = line.geometry(error, 'EPSG:4326')\n\n geom = ee.Geometry.Point(geom.coordinates().get(0), 'EPSG:4326')\n\n return ee.Feature(geom) \\\n .set(\"lineId\", line.id()) \\\n .set(\"offset\", 0)\n\n short_line_points = short_lines.map(process_short_line)\n\n points = long_line_points.merge(short_line_points)\n\n fa = ee.Image('WWF/HydroSHEDS/15ACC')\n dem = ee.Image('JAXA/ALOS/AW3D30_V1_1').select('MED')\n\n def add_flow_accumulation(pt):\n flow_accumulation = fa.reduceRegion(\n reducer=ee.Reducer.max(),\n geometry=pt.geometry().buffer(scale * 10),\n scale=scale).values().get(0)\n\n return pt \\\n .set(\"flow_accumulation\", ee.Number(flow_accumulation))\n\n def add_width(pt):\n width = distance.reduceRegion(\n reducer=ee.Reducer.max(),\n geometry=pt.geometry(),\n scale=scale).values().get(0)\n\n return pt \\\n .set(\"width\", ee.Number(width).multiply(scale).multiply(2))\n\n def add_elevation(pt):\n elevation = dem.reduceRegion(\n reducer=ee.Reducer.median(),\n geometry=pt.geometry().buffer(scale * 10),\n scale=scale).values().get(0)\n\n return pt \\\n .set(\"elevation\", ee.Number(elevation))\n\n points = points.map(add_width)\n points = points.map(add_elevation)\n points = points.map(add_flow_accumulation)\n\n points = points.map(transform_feature(crs, scale))\n\n # create response\n data = points.getInfo()\n\n return Response(json.dumps(data), status=200, mimetype='application/json')","def fetch_list(self):\n\t\treturn self.fetch(self.list_url % ART_SERVER_HOST)","def fetchJson(url):","def _create_api_ulr_list(self) -> List[str]:\n api = Setup.pickpoint_api\n return [\n f\"https://api.pickpoint.io/v1/reverse/?key={api}&lat={lat}&lon={lon}\"\n f\"&accept-language=en-US\"\n for lat, lon in self.locations\n ]","def load_gateways_snapshot(request):\n gateways_file = path.join(get_project_root(), 'gateways.api.json')\n gateways = []\n try:\n data = open(gateways_file, \"r\").read()\n except IOError:\n pass # Doesn't exist gateway backup\n else:\n try:\n gateways = json.loads(data)\n except ValueError:\n pass # Invalid format\n else:\n for gw in gateways:\n gw_url = reverse('gateway-detail', kwargs={'pk':gw['id']})\n gw['uri'] = request.build_absolute_uri(gw_url)\n \n return gateways","def get_latest_national_geographic_wallpaper_remote():\r\n\r\n logging.debug('get_latest_national_geographic_wallpaper_remote()')\r\n\r\n # get image url\r\n if use_proxy:\r\n response = requests.get(\"https://www.nationalgeographic.com/photography/photo-of-the-day/\", proxies=proxies, timeout=5, verify=False)\r\n else:\r\n response = requests.get(\"https://www.nationalgeographic.com/photography/photo-of-the-day/\")\r\n match = re.search('.*content=\\\"([^\\\"]*\\.jpg)\\\".*', response.text)\r\n full_image_url = match.group(1)\r\n logging.debug('get_latest_national_geographic_wallpaper_remote - full_image_url = {}'.format(full_image_url))\r\n\r\n # image's name\r\n image_name = get_generated_image_name(full_image_url)\r\n\r\n # Check and maintain DB\r\n if not exists_image_in_database(full_image_url):\r\n add_image_to_database(full_image_url, image_name, \"national\")\r\n # download and save image\r\n full_image_path = download_image(full_image_url, image_name)\r\n update_image_in_database(full_image_url, full_image_path)\r\n else:\r\n full_image_path = get_image_path_from_database(full_image_url)\r\n\r\n # Return full path to image\r\n logging.debug('get_latest_national_geographic_wallpaper_remote - full_image_path = {}'.format(full_image_path))\r\n return full_image_path","def run(self) -> list:\n logger.debug('Fetching date %s', self._day.strftime('%Y/%m/%d'))\n \n regions = [r() for r in regions_list]\n air_quality = list()\n \n # fetch air quality of each region\n for r in regions:\n r.fetch_air_quality(self._day)\n \n # gather results from all regions\n for r in regions:\n # wait until region has fetched his data\n r.wait_for_quality()\n logging.info('Fetched region:%s for day:%s', r.name, self._day)\n air_quality.append({\n 'name': r.name,\n 'provinces': [\n {'name': x.name, 'short': x.short_name, 'quality': x.quality.asdict()} \n for x in r.provinces]\n })\n\n self._fetcher.fetched_result(self._day, air_quality)","def fetch_weather(y):\r\n # request parameter(s): Start with '?'\r\n # separate name and value with '='\r\n # multiple parameter name value pairs are separate with '&'\r\n query_string = \"?id={}&units=imperial&APIKEY={}\".format(y, API_KEY)\r\n request_url = WS_URL + query_string\r\n print(\"Request URL: \", request_url)\r\n response = requests.get(request_url)\r\n if response.status_code == 200:\r\n city_name = response.json()[\"city\"][\"name\"]\r\n lst = response.json()[\"list\"]\r\n tmp_list = []\r\n for i in range(len(lst) // 8):\r\n li = [x for x in range(len(lst)) if x // 8 == i]\r\n tmp_list.append(max([lst[j][\"main\"][\"temp_max\"] for j in li]))\r\n return City(city_name, tmp_list)\r\n else:\r\n print(\"How should I know?\")\r\n return None","def test_last_cities(client):\n client.get(\"/weather/viena\")\n client.get(\"/weather/sorocaba\")\n client.get(\"/weather/barcelona\")\n\n\n # Validate weither return two lasts cities in cache or not\n response = client.get(\"/weather/1\")\n print(response.data)\n assert b\"Barcelona\" in response.data","def main(args):\n args = parse_args(args)\n setup_logging(args.loglevel)\n\n dir = 'snapshots/' + args.name\n Path(dir).mkdir(parents=True, exist_ok=True)\n\n session = sql_session(dir)\n today = session.query(Sunrise).filter_by(date=date.today()).first()\n if today is None:\n today = add_sunrise(session, args.name, args.latitude, args.longitude)\n now = datetime.utcnow()\n username, password = password_from_netrc(args.url)\n\n if args.force == True or (now > today.sunrise_time and now < today.sunset_time):\n where = tzwhere.tzwhere()\n timezone_str = where.tzNameAt(float(args.latitude), float(args.longitude))\n local_timezone = pytz.timezone(timezone_str)\n utcnow = now.replace(tzinfo=pytz.utc)\n local_now = utcnow.astimezone(local_timezone)\n path = local_now.strftime('%Y%m%d-%H%M%S%z') + '_' + args.name + '.jpg'\n\n r = requests.get(args.url, auth=HTTPDigestAuth(username, password), verify=False, stream=True)\n if r.status_code == requests.codes.ok:\n with open(dir + '/' + path, 'wb') as f:\n for chunk in r.iter_content(4096):\n f.write(chunk)","def get_headlines(outlet):\n if outlet == \"BBC\":\n parser = news_parser.BBC(\"https://www.bbc.co.uk\")\n elif outlet == \"DailyMail\":\n parser = news_parser.DailyMail(\"https://www.dailymail.co.uk\")\n elif outlet == \"Guardian\":\n parser = news_parser.Guardian(\"https://www.theguardian.com\")\n elif outlet == \"Metro\":\n parser = news_parser.Metro(\"https://www.metro.co.uk\")\n elif outlet == \"Mirror\":\n parser = news_parser.Mirror(\"https://www.mirror.co.uk/news/\")\n elif outlet == \"Reuters\":\n parser = news_parser.Reuters(\"https://uk.reuters.com\")\n elif outlet == \"Sun\":\n parser = news_parser.Sun(\"https://www.thesun.co.uk\")\n elif outlet == \"Independent\":\n parser = news_parser.Independent(\"https://www.independent.co.uk\")\n else:\n parser = news_parser.BBC(\"https://www.bbc.co.uk/news\")\n \n index = outlets.index(outlet)\n url_list = []\n while len(url_list) < 50:\n opts = {\n 'language': ['en'],\n 'source_id': [ids[index]],\n 'published_at_start':'NOW-1DAY',\n 'published_at_end':'NOW',\n 'sort_by': 'hotness',\n 'sort_direction': 'desc',\n 'cursor': '*',\n 'per_page': 100\n }\n\n try:\n api_response = api_instance.list_stories(**opts)\n for story in api_response.stories:\n url = story.links.permalink\n if url:\n url_list.append(url)\n except ApiException as e:\n print(\"Exception when calling DefaultApi->list_stories: %s\\n\" %e)\n \n opts['cursor'] = api_response.next_page_cursor\n \n url_list = url_list[:50]\n \n articles_list = []\n for url in url_list:\n raw_article = parser.get_article(url)\n if raw_article is not None:\n articles_list.append(raw_article)\n\n articles = []\n for article in articles_list:\n parsed_article = parser.parse(article)\n if parsed_article is not None:\n articles.append(parsed_article)\n \n if len(articles) > 30:\n articles = articles[:30]\n\n return articles","def test_last_max_default(client):\n client.get(\"/weather/viena\")\n client.get(\"/weather/sorocaba\")\n client.get(\"/weather/barcelona\")\n client.get(\"/weather/belo horizonte\")\n client.get(\"/weather/rio de janeiro\")\n client.get(\"/weather/recife\")\n\n\n # Validate weither return two lasts cities in cache or not\n response = client.get(\"/weather/6\")\n print(response.data)\n assert (b\"Barcelona\" in response.data) & (b\"Recife\" in response.data) & (b\"Rio de Janeiro\" in response.data) & (b\"Belo Horizonte\" in response.data) & (b\"Sorocaba\" in response.data)","def test_smoker_latest_get(self):\n pass","def fetch_all_lta(url):\n results = []\n while True:\n new_results = requests.get(\n url,\n headers = headers,\n params = {'$skip': len(results)}\n ).json()['value']\n if new_results == []:\n break\n else:\n results += new_results\n return results","def test_aws_service_api_snapshots_get(self):\n pass","def fetch_50(url):\n\n results = requests.get(url,headers = headers).json()\n return results","def wfs_common(request, response, mode, spatial_mode='wfs'):\n\n outputpath = configuration.get_config_value('server', 'outputpath')\n outputurl = configuration.get_config_value('server', 'outputurl')\n\n list_of_files = []\n for one_resource in request.inputs['resource']:\n # Download if not opendap\n # Adding a maximum file size from a server config file would\n # be possible here...\n try:\n nc_file = opendap_or_download(\n one_resource.data,\n auth_tkt_cookie=request.http_request.cookies,\n output_path='/tmp')\n except:\n raise Exception(traceback.format_exc())\n list_of_files.append(nc_file)\n\n if ('typename' in request.inputs) and ('featureids' in request.inputs):\n typename = request.inputs['typename'][0].data\n features = [f.data for f in request.inputs['featureids']]\n if 'geoserver' in request.inputs:\n geoserver = request.inputs['geoserver'][0].data\n else:\n geoserver = configuration.get_config_value('extra', 'geoserver')\n if 'mosaic' in request.inputs:\n mosaic = request.inputs['mosaic'][0].data\n else:\n mosaic = False\n try:\n conn = WebFeatureService(url=geoserver, version='2.0.0')\n resp = conn.getfeature([typename], featureid=features,\n outputFormat='application/json')\n feature = json.loads(resp.read())\n crs_code = owslib.crs.Crs(\n feature['crs']['properties']['name']).code\n crs = ocgis.CoordinateReferenceSystem(epsg=crs_code)\n geom = [\n {'geom': shape(f['geometry']), 'crs': crs,\n 'properties': f['properties']}\n for f in feature['features']]\n except Exception as e:\n msg = ('Failed to fetch features.\\ngeoserver: {0} \\n'\n 'typename: {1}\\nfeatures {2}\\n{3}').format(\n geoserver, typename, features, e)\n raise Exception(msg)\n if mosaic:\n new_geom = geom[0]\n for merge_geom in geom[1:]:\n new_geom['geom'] = new_geom['geom'].union(merge_geom['geom'])\n new_geom['properties'] = {'bbox': feature['bbox']}\n geom = new_geom\n elif spatial_mode == 'bbox':\n geom = [[request.inputs['lon0'][0].data,\n request.inputs['lat0'][0].data,\n request.inputs['lon1'][0].data,\n request.inputs['lat1'][0].data]]\n else:\n geom = [None]\n\n if ('initial_datetime' in request.inputs) and \\\n ('final_datetime' in request.inputs):\n tr = [request.inputs['initial_datetime'][0].data,\n request.inputs['final_datetime'][0].data]\n else:\n tr = None\n\n try:\n output_files = []\n output_urls = []\n mv_dir = tempfile.mkdtemp(dir=outputpath)\n os.chmod(mv_dir, 0755)\n\n for one_file in list_of_files:\n file_name = os.path.basename(one_file)\n if file_name[-3:] == '.nc':\n file_prefix = file_name[:-3]\n else:\n file_prefix = file_name\n ocgis.env.DIR_OUTPUT = tempfile.mkdtemp(dir=os.getcwd())\n ocgis.env.OVERWRITE = True\n nc = netCDF4.Dataset(one_file, 'r')\n var_names = guess_main_variables(nc)\n nc.close()\n rd = ocgis.RequestDataset(one_file, var_names)\n for i, one_geom in enumerate(geom):\n if one_geom is None:\n ocgis_geom = None\n elif spatial_mode == 'bbox':\n ocgis_geom = one_geom\n else:\n ocgis_geom = one_geom['geom']\n if mode == 'averager':\n # Extent errors are ignored\n try:\n # Here with aggregate=True, can't pass the whole\n # one_geom dictionary, is this a sign that this does\n # not support multipolygon?\n ops = ocgis.OcgOperations(\n dataset=rd, geom=ocgis_geom,\n spatial_operation='clip', aggregate=True,\n time_range=tr, output_format='nc',\n interpolate_spatial_bounds=True,\n prefix=file_prefix).execute()\n except ExtentError:\n continue\n elif mode == 'subsetter':\n # Extent errors are ignored\n try:\n # Still having problem with the geometry, previously\n # was passing geom=[one_geom]\n ops = ocgis.OcgOperations(\n dataset=rd, geom=ocgis_geom, time_range=tr,\n output_format='nc',\n interpolate_spatial_bounds=True,\n prefix=file_prefix).execute()\n except ExtentError:\n continue\n # Here, the global attribute 'subset_typename' and\n # 'subset_featureid' are added to the NetCDF file to keep\n # track of the feature used.\n if (geom != [None]) and (spatial_mode == 'wfs'):\n with netCDF4.Dataset(ops, 'a') as nc:\n nc.subset_typename = typename\n nc.subset_featureid = features[i]\n\n if (spatial_mode == 'wfs') and \\\n ('featureids' in request.inputs):\n mv_name = '{0}_{1}.nc'.format(\n os.path.basename(ops)[:-3], features[i])\n else:\n mv_name = '{0}_{1}.nc'.format(\n os.path.basename(ops)[:-3], 'subset')\n\n mv_file = os.path.join(mv_dir, mv_name)\n shutil.move(ops, mv_file)\n output_files.append(mv_file)\n shutil.rmtree(ocgis.env.DIR_OUTPUT)\n\n # Cover the case of an online wps server and the offline\n # mode for tests.\n if outputurl == 'file:///tmp':\n disk_file = 'file:///' + mv_file.lstrip('/')\n output_urls.append(disk_file)\n else:\n url_file = os.path.join(\n outputurl, os.path.basename(mv_dir), mv_name)\n output_urls.append(url_file)\n except:\n raise Exception(traceback.format_exc())\n\n # If only ExtentError occured, the output_urls will be empty...\n if not output_urls:\n raise ExtentError(message=\"All ocgis calls returned ExtentError.\")\n\n time_str = time.strftime(\"%Y-%m-%dT%H:%M:%SZ\", time.gmtime())\n output_file_name = \"result_%s_.json\" % (time_str,)\n output_file = os.path.join('/tmp', output_file_name)\n f1 = open(output_file, 'w')\n f1.write(json.dumps(output_urls))\n f1.close()\n response.outputs['output'].file = output_file\n response.outputs['output'].output_format = json_format\n response.update_status(\"done\", 100)\n return response","def list(\n self,\n farmer_id: str,\n boundary_id: str,\n provider: str = \"Microsoft\",\n source: Optional[str] = \"Sentinel_2_L2A\",\n start_date_time: Optional[datetime.datetime] = None,\n end_date_time: Optional[datetime.datetime] = None,\n max_cloud_coverage_percentage: Optional[float] = 100,\n max_dark_pixel_coverage_percentage: Optional[float] = 100,\n image_names: Optional[List[str]] = None,\n image_resolutions: Optional[List[float]] = None,\n image_formats: Optional[List[str]] = None,\n max_page_size: Optional[int] = 50,\n skip_token: Optional[str] = None,\n **kwargs: Any\n ) -> AsyncIterable[\"_models.SceneListResponse\"]:\n cls = kwargs.pop('cls', None) # type: ClsType[\"_models.SceneListResponse\"]\n error_map = {\n 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError\n }\n error_map.update(kwargs.pop('error_map', {}))\n api_version = \"2021-03-31-preview\"\n accept = \"application/json\"\n\n def prepare_request(next_link=None):\n # Construct headers\n header_parameters = {} # type: Dict[str, Any]\n header_parameters['Accept'] = self._serialize.header(\"accept\", accept, 'str')\n\n if not next_link:\n # Construct URL\n url = self.list.metadata['url'] # type: ignore\n path_format_arguments = {\n 'Endpoint': self._serialize.url(\"self._config.endpoint\", self._config.endpoint, 'str', skip_quote=True),\n }\n url = self._client.format_url(url, **path_format_arguments)\n # Construct parameters\n query_parameters = {} # type: Dict[str, Any]\n query_parameters['provider'] = self._serialize.query(\"provider\", provider, 'str')\n query_parameters['farmerId'] = self._serialize.query(\"farmer_id\", farmer_id, 'str')\n query_parameters['boundaryId'] = self._serialize.query(\"boundary_id\", boundary_id, 'str')\n if source is not None:\n query_parameters['source'] = self._serialize.query(\"source\", source, 'str')\n if start_date_time is not None:\n query_parameters['startDateTime'] = self._serialize.query(\"start_date_time\", start_date_time, 'iso-8601')\n if end_date_time is not None:\n query_parameters['endDateTime'] = self._serialize.query(\"end_date_time\", end_date_time, 'iso-8601')\n if max_cloud_coverage_percentage is not None:\n query_parameters['maxCloudCoveragePercentage'] = self._serialize.query(\"max_cloud_coverage_percentage\", max_cloud_coverage_percentage, 'float', maximum=100, minimum=0)\n if max_dark_pixel_coverage_percentage is not None:\n query_parameters['maxDarkPixelCoveragePercentage'] = self._serialize.query(\"max_dark_pixel_coverage_percentage\", max_dark_pixel_coverage_percentage, 'float', maximum=100, minimum=0)\n if image_names is not None:\n query_parameters['imageNames'] = [self._serialize.query(\"image_names\", q, 'str') if q is not None else '' for q in image_names]\n if image_resolutions is not None:\n query_parameters['imageResolutions'] = [self._serialize.query(\"image_resolutions\", q, 'float') if q is not None else '' for q in image_resolutions]\n if image_formats is not None:\n query_parameters['imageFormats'] = [self._serialize.query(\"image_formats\", q, 'str') if q is not None else '' for q in image_formats]\n if max_page_size is not None:\n query_parameters['$maxPageSize'] = self._serialize.query(\"max_page_size\", max_page_size, 'int', maximum=1000, minimum=10)\n if skip_token is not None:\n query_parameters['$skipToken'] = self._serialize.query(\"skip_token\", skip_token, 'str')\n query_parameters['api-version'] = self._serialize.query(\"api_version\", api_version, 'str')\n\n request = self._client.get(url, query_parameters, header_parameters)\n else:\n url = next_link\n query_parameters = {} # type: Dict[str, Any]\n path_format_arguments = {\n 'Endpoint': self._serialize.url(\"self._config.endpoint\", self._config.endpoint, 'str', skip_quote=True),\n }\n url = self._client.format_url(url, **path_format_arguments)\n request = self._client.get(url, query_parameters, header_parameters)\n return request\n\n async def extract_data(pipeline_response):\n deserialized = self._deserialize('SceneListResponse', pipeline_response)\n list_of_elem = deserialized.value\n if cls:\n list_of_elem = cls(list_of_elem)\n return deserialized.next_link or None, AsyncList(list_of_elem)\n\n async def get_next(next_link=None):\n request = prepare_request(next_link)\n\n pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)\n response = pipeline_response.http_response\n\n if response.status_code not in [200]:\n error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response)\n map_error(status_code=response.status_code, response=response, error_map=error_map)\n raise HttpResponseError(response=response, model=error)\n\n return pipeline_response\n\n return AsyncItemPaged(\n get_next, extract_data\n )","def remotes():","def _find_latest():\n try:\n db = get_master_collection()\n service_details = db.find({\"master.key\": \"release\"}).sort([(\"master.value\", pymongo.DESCENDING)]).limit(1)\n for service in service_details:\n for r in sorted(service[\"master\"][\"value\"], reverse=True):\n latest_release = r\n build_list = service[\"master\"][\"value\"][r]\n break\n break\n\n latest_rel_num = str(latest_release).replace(\"_\", \".\")\n build_list = _natural_sort(build_list)\n for build in build_list:\n latest_build = build\n break\n\n latest_build_num = latest_build\n second_latest_build_num = int(latest_build_num) - 1\n latest = {\"latest_val\": latest_rel_num + \"_\" + latest_build_num,\n \"second_latest_val\": latest_rel_num + \"_\" + str(second_latest_build_num)}\n except Exception as e:\n logger.error(\"Exception in _find_latest : \" + str(e))\n return latest","def after_download_live_ocean(msg, config, checklist):\n next_workers = {\"crash\": [], \"failure\": [], \"success\": []}\n if msg.type == \"success\":\n next_workers[\"success\"].append(\n NextWorker(\n \"nowcast.workers.make_live_ocean_files\",\n args=[\"--run-date\", list(checklist[\"Live Ocean products\"].keys())[-1]],\n )\n )\n return next_workers[msg.type]","def process_repo(self, url, multiple=False):\n json_data = loads(self.get_from_net(url)) #TODO add code to detect error messages in JSON from API\n if not multiple: json_data = [json_data]\n repo_dets = []\n for i in json_data:\n dets = {\n 'full_name': i['full_name'],\n 'name': i['name'],\n 'fork': i['fork'],\n 'url': i['url'],\n 'language': '',\n 'created': '',\n 'id': i[\"id\"] #for use in pagination\n }\n if 'language' in i: dets['language'] = i['language']\n if 'created_at' in i: dets['created'] = i['created_at']\n repo_dets.append(dets)\n return repo_dets"],"string":"[\n \"def getSlaves():\",\n \"def crossWalkGeoBlacklight(data):\\n\\n dataJsonObj=deep_get(data,\\\"xml.fgdc\\\",[])\\n if len (dataJsonObj)>0:\\n dataJsonObj=deep_get(dataJsonObj[0],\\\"data\\\",{})\\n else:\\n dataJsonObj={}\\n layername=os.path.splitext(os.path.basename(data['file']))[0]\\n geoserver_layername = data['geoserverStoreName']\\n gblight = assignMetaDataComponents(dataJsonObj,layername,geoserver_layername,data[\\\"resource_type\\\"])\\n gblight['solr_geom']=data['bounds']\\n data['geoblacklightschema']=gblight\\n return data\",\n \"def connectAllWithOceanHeightAttr(object = ''):\\n\\tinprogressBar = pbui.ProgressBarUI(title = 'Hooking To Ocean:')\\n\\tinprogressBar.show()\\n\\tinprogressBar.updateProgress(percent = 0, doingWhat = 'Hooking everything to ocean now...')\\n\\tdebug(None, method = 'connectAllWithOceanHeightAttr', message = 'Rebuilding Ocean Hooks', verbose = False)\\n\\n\\t## Find all in scene with oceanHeightHook Attribute\\n\\t## Setup the progress bar\\n\\tif not cmds.objExists('Shot_FX_hrc'):\\n\\t\\tcmds.group(n = 'Shot_FX_hrc', em = True)\\n\\tif not cmds.objExists('BOAT_OceanLocators_hrc'):\\n\\t\\tcmds.group(n = 'BOAT_OceanLocators_hrc', em = True)\\n\\t\\tcmds.parent('BOAT_OceanLocators_hrc', 'Shot_FX_hrc')\\n\\t\\tcmds.setAttr('BOAT_OceanLocators_hrc.visibility', 0)\\n\\n\\t## Clean up existing hooks in the scene\\n\\tinprogressBar.updateProgress(percent = 50, doingWhat = 'Cleaning up now..')\\n\\n\\tif object.startswith('All'):\\n\\t\\tobject = ''\\n\\telif object.startswith('Dock'):\\n\\t\\tobject = 'BLD'\\n\\telif object.startswith('Boat'):\\n\\t\\tobject = 'CHAR'\\n\\telif object.startswith('Prop'):\\n\\t\\tobject = 'PROP'\\n\\n\\tcleanUpExisting(filters = [object])\\n\\tif object == 'BLD':\\n\\t\\t## Clean-up the ADEF BLD's oceanHook control animatable groups...\\n\\t\\t[cmds.delete(transform) for transform in cmds.ls(type = 'transform') if transform.endswith('_oceanCtrls_hrc')]\\n\\n\\t## Rebuild / build the hooks for boats in the scene\\n\\tinprogressBar.updateProgress(percent = 75, doingWhat = 'performHookBoatsToOcean now..')\\n\\tstartTime = time.time()\\n\\tperformHookBoatsToOcean()\\n\\tprint 'TOTAL TIME FOR performHookBoatsToOcean: %s' % (time.time()-startTime)\\n\\n\\t## Now make sure cycle check is off because it's a fn pita\\n\\tcmds.cycleCheck(e = 0)\\n\\t_finished(inprogressBar)\",\n \"def get_earthnetworks_lightning_response():\\n try:\\n feature_collection_json, last_updated = EarthNetworksService.get_latest_lightning_data()\\n except EarthNetworksError:\\n raise MapServiceServerError('Error occurred attempting to get EarthNetworks Lightning Data')\\n\\n response_headers = Headers()\\n response_headers.add('Last-Modified', last_updated)\\n\\n flask_response = Response(feature_collection_json, status=200, mimetype='application/json',\\n headers=response_headers)\\n return flask_response\",\n \"def singleCrossWalkGeoBlacklight(filename, layername,geoserver_layername,resource_type):\\n doc={}\\n with open(filename) as fd:\\n stringxml = fd.read()\\n doc = xmltodict.parse(stringxml,cdata_key='text',attr_prefix='',dict_constructor=dict)\\n gblight = assignMetaDataComponents(doc,layername,geoserver_layername,resource_type)\\n gblight['solr_geom']=getGeoServerBoundingBox(geoserver_layername)\\n return gblight\",\n \"def potlatch2():\\n\\n if request.args(0) == \\\"potlatch2.html\\\":\\n osm_oauth_consumer_key = deployment_settings.get_osm_oauth_consumer_key()\\n osm_oauth_consumer_secret = deployment_settings.get_osm_oauth_consumer_secret()\\n if osm_oauth_consumer_key and osm_oauth_consumer_secret:\\n gpx_url = None\\n if \\\"gpx_id\\\" in request.vars:\\n # Pass in a GPX Track\\n # @ToDo: Set the viewport based on the Track, if one is specified\\n track = db(db.gis_track.id == request.vars.gpx_id).select(db.gis_track.track, limitby=(0, 1)).first()\\n if track:\\n gpx_url = URL(r=request, c=\\\"default\\\", f=\\\"download\\\") + \\\"/\\\" + track.track\\n\\n if \\\"lat\\\" in request.vars:\\n lat = request.vars.lat\\n lon = request.vars.lon\\n else:\\n settings = gis.get_config()\\n lat = settings.lat\\n lon = settings.lon\\n\\n if \\\"zoom\\\" in request.vars:\\n zoom = request.vars.zoom\\n else:\\n # This isn't good as it makes for too large an area to edit\\n #zoom = settings.zoom\\n zoom = 14\\n\\n #response.extra_styles = [\\\"S3/potlatch2.css\\\"]\\n\\n return dict(lat=lat, lon=lon, zoom=zoom, key=osm_oauth_consumer_key, secret=osm_oauth_consumer_secret, gpx_url=gpx_url)\\n\\n else:\\n session.error = T(\\\"To edit OpenStreetMap, you need to edit the OpenStreetMap settings in models/000_config.py\\\")\\n redirect(URL(r=request, f=\\\"index\\\"))\\n\\n else:\\n # This is a hack for unconfigured servers.\\n # Production instances should configure the server to bypass the Model loads completely\\n # (Apache mod_rewrite or Web2Py routes.py)\\n #('.*:/eden/gis/potlatch2/potlatch2.html', '/eden/gis/potlatch2/potlatch2.html'),\\n #('.*:/eden/gis/potlatch2/(?P<file>[\\\\w\\\\./_-]+)', '/eden/static/potlatch2/\\\\g<file>'),\\n redirect(URL(a=request.application, c=\\\"static\\\", f=\\\"potlatch2\\\", args=request.args), how=301)\",\n \"def extract_for_tenant(self, tenant, lastrun):\\n # Some API calls do not expect a TZ, so we have to remove the timezone\\n # from the dates. We assume that all dates coming from upstream are\\n # in UTC TZ.\\n lastrun = lastrun.replace(tzinfo=None)\\n now = datetime.datetime.now(tz.tzutc()).replace(tzinfo=None)\\n end = now + datetime.timedelta(days=1)\\n\\n # Try and except here\\n conn = self._get_conn(tenant)\\n ks_conn = self._get_keystone_client(tenant)\\n users = self._get_keystone_users(ks_conn)\\n tenant_id = conn.client.tenant_id\\n servers = conn.servers.list(search_opts={\\\"changes-since\\\": lastrun})\\n\\n servers = sorted(servers, key=operator.attrgetter(\\\"created\\\"))\\n\\n if servers:\\n start = dateutil.parser.parse(servers[0].created)\\n start = start.replace(tzinfo=None)\\n else:\\n start = lastrun\\n\\n aux = conn.usage.get(tenant_id, start, end)\\n usages = getattr(aux, \\\"server_usages\\\", [])\\n\\n images = conn.images.list()\\n records = {}\\n\\n vo = self.voms_map.get(tenant)\\n\\n for server in servers:\\n status = self.vm_status(server.status)\\n image_id = None\\n for image in images:\\n if image.id == server.image['id']:\\n image_id = image.metadata.get(\\\"vmcatcher_event_ad_mpuri\\\",\\n None)\\n break\\n\\n if image_id is None:\\n image_id = server.image['id']\\n\\n r = record.CloudRecord(server.id,\\n CONF.site_name,\\n server.name,\\n server.user_id,\\n server.tenant_id,\\n vo,\\n cloud_type=\\\"OpenStack\\\",\\n status=status,\\n image_id=image_id,\\n user_dn=users.get(server.user_id, None))\\n records[server.id] = r\\n\\n for usage in usages:\\n if usage[\\\"instance_id\\\"] not in records:\\n continue\\n instance_id = usage[\\\"instance_id\\\"]\\n records[instance_id].memory = usage[\\\"memory_mb\\\"]\\n records[instance_id].cpu_count = usage[\\\"vcpus\\\"]\\n records[instance_id].disk = usage[\\\"local_gb\\\"]\\n\\n started = dateutil.parser.parse(usage[\\\"started_at\\\"])\\n records[instance_id].start_time = int(started.strftime(\\\"%s\\\"))\\n if usage.get(\\\"ended_at\\\", None) is not None:\\n ended = dateutil.parser.parse(usage['ended_at'])\\n records[instance_id].end_time = int(ended.strftime(\\\"%s\\\"))\\n wall = ended - started\\n else:\\n wall = now - started\\n\\n wall = int(wall.total_seconds())\\n records[instance_id].wall_duration = wall\\n\\n cput = int(usage[\\\"hours\\\"] * 3600)\\n # NOTE(aloga): in some cases there may be rounding errors and cput\\n # may be larger than wall.\\n records[instance_id].cpu_duration = cput if cput < wall else wall\\n return records\",\n \"def update(self):\\n url = '/weather/current/minutely' \\\\\\n '?version=2&lat={}&lon={}&city={}&county={}&village={}' \\\\\\n .format(self.lat, self.lon, self.city, self.county, self.village)\\n self.result = self.api.get(url)['weather']['minutely'][0]\",\n \"def _fetch_full():\\n resource(\\n target=data_path(\\\"eeg\\\", \\\"eeg_full.tar\\\"),\\n url=\\\"https://kdd.ics.uci.edu/databases/eeg/eeg_full.tar\\\",\\n )\\n dependency(\\n target=data_path(\\\"eeg\\\", \\\"full\\\"),\\n source=data_path(\\\"eeg\\\", \\\"eeg_full.tar\\\"),\\n commands=[\\n \\\"mkdir full\\\",\\n \\\"tar xf eeg_full.tar -C full\\\",\\n \\\"ls full | grep gz$ | xargs -I {} tar xzf full/{} -C full\\\",\\n \\\"ls full | grep gz$ | xargs -I {} rm full/{}\\\",\\n \\\"find full | grep gz$ | xargs gunzip\\\",\\n ],\\n )\",\n \"def _old_site(self, soup, url_base):\\n dl_list = []\\n img_list = soup.find(\\\"div\\\", {\\\"class\\\": \\\"image-formats\\\"})\\n\\n # Check to if high res pics are linked\\n img_hires_link = img_list.find(\\\"a\\\", text=re.compile('Highest-quality download options'))\\n if img_hires_link is not None:\\n self.log(\\\"[OLD] Get high res\\\")\\n hires_url = url_base + img_hires_link['href']\\n try:\\n hires_soup = self.get_site(hires_url, self._url_header)\\n except RequestsError as e:\\n self.log(\\\"Skipping image: \\\" + str(hires_url))\\n else:\\n hires_list = hires_soup.find(\\\"div\\\", {\\\"id\\\": \\\"image-format-container\\\"})\\n hires_links = hires_list.find_all(\\\"li\\\")\\n for hires_link in hires_links:\\n hires_dl_url = hires_link.a['href']\\n # Download this\\n dl_list.append(hires_dl_url)\\n\\n self.log(\\\"[OLD] Get other\\\")\\n # Get all other images\\n img_links = img_list.find_all(\\\"a\\\", {\\\"class\\\": \\\"button\\\"})\\n for link in img_links:\\n link_url = link['href']\\n img_dl_url = link_url\\n if link_url.endswith('/'):\\n link_url = url_base + link_url\\n try:\\n link_soup = self.get_site(link_url, self._url_header)\\n except RequestsError as e:\\n self.log(\\\"Skipping image: \\\" + str(link_url))\\n else:\\n img_dl_url = link_soup.find(\\\"div\\\", {\\\"class\\\": \\\"image-view\\\"}).img['src']\\n # Download this\\n dl_list.append(img_dl_url)\\n\\n return dl_list\",\n \"def get_a_national_geographic_archive_wallpaper_remote():\\r\\n\\r\\n logging.debug('get_a_national_geographic_archive_wallpaper_remote()')\\r\\n\\r\\n # get image url\\r\\n if use_proxy:\\r\\n response = requests.get(\\\"https://www.nationalgeographic.com/photography/photo-of-the-day/\\\", proxies=proxies, timeout=5, verify=False)\\r\\n match = re.search('.*\\\\\\\"endpoint\\\\\\\":\\\\\\\"([^\\\\\\\"]*gallery\\\\.json)\\\\\\\".*', response.text)\\r\\n gallery_json = match.group(1)\\r\\n response = requests.get(gallery_json, proxies=proxies, timeout=5, verify=False)\\r\\n else:\\r\\n response = requests.get(\\\"https://www.nationalgeographic.com/photography/photo-of-the-day/\\\")\\r\\n match = re.search('.*\\\\\\\"endpoint\\\\\\\":\\\\\\\"([^\\\\\\\"]*gallery\\\\.json)\\\\\\\".*', response.text)\\r\\n gallery_json = match.group(1)\\r\\n response = requests.get(gallery_json)\\r\\n image_data = json.loads(response.text)\\r\\n for i in range(0, len(image_data[\\\"items\\\"])):\\r\\n full_image_url = image_data[\\\"items\\\"][i][\\\"image\\\"][\\\"uri\\\"]\\r\\n \\r\\n # image's name\\r\\n image_name = get_generated_image_name(full_image_url)\\r\\n \\r\\n # Check and maintain DB\\r\\n if not exists_image_in_database(full_image_url) and i+1 < len(image_data[\\\"items\\\"]):\\r\\n add_image_to_database(full_image_url, image_name, \\\"nationalarchive\\\")\\r\\n # download and save image\\r\\n full_image_path = download_image(full_image_url, image_name)\\r\\n update_image_in_database(full_image_url, full_image_path)\\r\\n\\r\\n # Return full path to image\\r\\n logging.debug('get_a_national_geographic_archive_wallpaper_remote - full_image_path = {}'.format(full_image_path))\\r\\n return full_image_path\\r\\n elif i+1 == len(image_data[\\\"items\\\"]):\\r\\n full_image_path = get_image_path_from_database(full_image_url)\\r\\n\\r\\n # Return full path to image\\r\\n logging.debug('get_a_national_geographic_archive_wallpaper_remote - full_image_path = {}'.format(full_image_path))\\r\\n return full_image_path\",\n \"def main():\\n get_obofoundry(force_download=True)\",\n \"def main():\\n right_now = get_current_datetime()\\n # print(right_now)\\n existing_dict, unique_exist = get_sensor_dict()\\n # print(type(existing_dict))\\n # print()\\n # print(sorted(unique_exist))\\n whats_up_list = query_the_api()\\n # print(whats_up_list)\\n found = filter_json(whats_up_list)\\n # print(found)\\n lost_new_stillthere(sorted(unique_exist), found, existing_dict, right_now)\",\n \"def pull(self):\",\n \"def mor_prepare_data():\\n prices, locations, areas, links = [], [], [], []\\n for i in range(START_PAGE, SEARCHING_DEPTH+1):\\n handler = requests.get(main_url, params={\\\"page\\\": str(i)})\\n soup = bs4.BeautifulSoup(handler.text, 'lxml')\\n heads = soup.find_all(\\\"header\\\")\\n once = True\\n for head in heads:\\n if head.find(\\\"meta\\\", {\\\"itemprop\\\": \\\"category\\\"}) and once:\\n\\n raw_price = head.find(\\\"meta\\\", {\\\"itemprop\\\": \\\"price\\\"})\\n price = int(float(raw_price[\\\"content\\\"]) if raw_price else \\\"\\\")\\n\\n raw_loc_list = head.find(\\\"h2\\\",\\n {\\\"class\\\": \\\"single-result__title\\\"}).getText().strip().split(\\n \\\", \\\")\\n found = False\\n for loc in raw_loc_list:\\n if location_mapper[CITY].get(loc.lower(), 0):\\n location = location_mapper[CITY][loc.lower()]\\n\\n found = True\\n break\\n if not found:\\n location = \\\"\\\"\\n if DEBUG_MODE:\\n print(raw_loc_list)\\n\\n raw_area = head.find(\\\"p\\\", {\\n \\\"class\\\": \\\"single-result__price single-result__price--currency\\\"}).getText().strip().split()\\n if price and location:\\n square_price = raw_area[0] if len(raw_area) == 2 else \\\"\\\".join(\\n (raw_area[0], raw_area[1]))\\n\\n area = int(price / float(square_price.replace(\\\",\\\", \\\".\\\")))\\n link_url = head.find('a')['href']\\n\\n if location and area and link_url:\\n prices.append(price) if price < PRICE_UPPER_LIMIT else prices.append(\\n PRICE_UPPER_LIMIT)\\n locations.append(location)\\n areas.append(area) if area < AREA_UPPER_LIMIT else areas.append(\\n AREA_UPPER_LIMIT)\\n links.append(link_url)\\n\\n return prices, locations, areas, links\",\n \"def sentientPlanets():\\n\\n url = \\\"https://swapi-api.hbtn.io/api/species/\\\"\\n planets = []\\n while url is not None:\\n r = requests.get(url)\\n results = r.json()[\\\"results\\\"]\\n for specie in results:\\n if (specie[\\\"designation\\\"] == \\\"sentient\\\" or\\n specie[\\\"classification\\\"] == \\\"sentient\\\"):\\n\\n planet_url = specie[\\\"homeworld\\\"]\\n if planet_url is not None:\\n p = requests.get(planet_url).json()\\n planets.append(p[\\\"name\\\"])\\n url = r.json()[\\\"next\\\"]\\n return planets\",\n \"def epg() -> Response:\\n return jsonify(locast_service.get_stations())\",\n \"def main():\\n for city in CITIES:\\n fetchNewseumImage(city)\",\n \"def roads_all(osm_path): \\n return (retrieve(osm_path,'lines',['highway'])).rename(columns={'highway': 'asset'})\",\n \"def load_from_digital_ocean(self, resource=None):\\n if self.args.force_cache and os.path.isfile(self.cache_filename):\\n return\\n # We always get fresh droplets\\n if self.is_cache_valid() and not (resource == 'droplets' or resource is None):\\n return\\n if self.args.refresh_cache:\\n resource = None\\n\\n if resource == 'droplets' or resource is None:\\n self.data['droplets'] = self.manager.all_active_droplets()\\n self.cache_refreshed = True\\n if resource == 'regions' or resource is None:\\n self.data['regions'] = self.manager.all_regions()\\n self.cache_refreshed = True\\n if resource == 'images' or resource is None:\\n self.data['images'] = self.manager.all_images()\\n self.cache_refreshed = True\\n if resource == 'sizes' or resource is None:\\n self.data['sizes'] = self.manager.sizes()\\n self.cache_refreshed = True\\n if resource == 'ssh_keys' or resource is None:\\n self.data['ssh_keys'] = self.manager.all_ssh_keys()\\n self.cache_refreshed = True\\n if resource == 'domains' or resource is None:\\n self.data['domains'] = self.manager.all_domains()\\n self.cache_refreshed = True\\n if resource == 'tags' or resource is None:\\n self.data['tags'] = self.manager.all_tags()\\n self.cache_refreshed = True\",\n \"def get_station_boroughs(self):\\\\\",\n \"def pull_latest_rcp(out_file : str = \\\"~/Data/gt_vis_project/clean_data/polls.csv\\\"):\\n# \\n\\n rcp_jsons = {\\\"North Carolina\\\" : \\\"https://www.realclearpolitics.com/epolls/json/6744_historical.js?1602635842295&callback=return_json\\\",\\n \\\"Wisconsin\\\" : \\\"https://www.realclearpolitics.com/epolls/json/6849_historical.js?1602638476165&callback=return_json\\\",\\n \\\"Florida\\\" : \\\"https://www.realclearpolitics.com/epolls/json/6841_historical.js?1602638538981&callback=return_json\\\",\\n \\\"Michigan\\\": \\\"https://www.realclearpolitics.com/epolls/json/6761_historical.js?1602638561657&callback=return_json\\\",\\n \\\"Pennsylvania\\\" : \\\"https://www.realclearpolitics.com/epolls/json/6861_historical.js?1602638584280&callback=return_json\\\",\\n \\\"Arizona\\\" : \\\"https://www.realclearpolitics.com/epolls/json/6807_historical.js?1602638600473&callback=return_json\\\",\\n \\\"Ohio\\\": \\\"https://www.realclearpolitics.com/epolls/json/6765_historical.js?1602638729359&callback=return_json\\\",\\n \\\"Minnesota\\\" : \\\"https://www.realclearpolitics.com/epolls/json/6966_historical.js?1602638770015&callback=return_json\\\",\\n \\\"Iowa\\\": \\\"https://www.realclearpolitics.com/epolls/json/6787_historical.js?1602638787908&callback=return_json\\\",\\n \\\"Texas\\\" : \\\"https://www.realclearpolitics.com/epolls/json/6818_historical.js?1602638819149&callback=return_json\\\",\\n \\\"Georgia\\\" : \\\"https://www.realclearpolitics.com/epolls/json/6974_historical.js?1602638840551&callback=return_json\\\",\\n # Nothing from Virgina or Nevada or Colorado, or new mexico.\\n \\\"New Hampshire\\\" : \\\"https://www.realclearpolitics.com/epolls/json/6779_historical.js?1602638879306&callback=return_json\\\",\\n \\\"Maine\\\" : \\\"https://www.realclearpolitics.com/epolls/json/6922_historical.js?1602638900859&callback=return_json\\\",\\n \\\"National\\\" :\\\"https://www.realclearpolitics.com/epolls/json/6247_historical.js?1602638622255&callback=return_json\\\"\\n }\\n final_df = pd.DataFrame()\\n for state, url in rcp_jsons.items():\\n print(state)\\n # Pull in raw json\\n rv = requests.get(url)\\n rv = rv.content.decode('utf-8') # byte to string\\n # 12:2 is to remove the return_json() characters.\\n print(\\\"converting to dict\\\")\\n raw_poll_json = json.loads(rv[12:-2]) # string to dict.\\n \\n # Convert to df\\n print(\\\"converting to df\\\")\\n tmp_df = poll_to_df(raw_poll_json)\\n tmp_df['state'] = state\\n final_df = final_df.append(tmp_df)\\n final_df[\\\"state\\\"] = [us_state_abbrev[state] if state in us_state_abbrev.keys() else state for state in final_df[\\\"state\\\"]]\\n final_df[\\\"date\\\"] = pd.to_datetime(final_df[\\\"date\\\"], utc = True).dt.date\\n final_df.to_csv(out_file, index = False)\\n\\n return final_df\",\n \"def test_merge_latest(self):\\n ars = self.ar[2009][11]['sider_404']\\n ars2 = awstats_reader.AwstatsReader(test_file_dir,\\n 'joshuakugler.com')[2009][11]['sider_404']\\n self.assertEqual(ars.merge(ars2, '/styles/widgets/blog-widget.css',\\n 'last_url_referer'), 'http://joshuakugler.com/')\",\n \"def main():\\n print get_latest_data()\",\n \"def get_stations():\\n response = requests.get('https://api.hh.ru/metro/160')\\n todos = json.loads(response.text)\\n colors = {'CD0505': 'red'}\\n all_stations_one_line = []\\n\\n for i in todos['lines']:\\n all_stations_one_line = []\\n\\n for j in i['stations']:\\n one_station = station.station()\\n one_station.set_name(j['name'])\\n one_station.set_color(colors.get(i['hex_color']))\\n one_station.set_lat(j['lat'])\\n one_station.set_lng(j['lng'])\\n all_stations_one_line.append(one_station)\\n return all_stations_one_line\",\n \"def mergeRPC(remoteETM,remoteLoca): #Status: WIP\\r\\n pass\",\n \"def get_oracle(verbosity, resultset, providerversion):\\n try:\\n response = requests.get(ORACLEAPIURL)\\n if verbosity:\\n print(response.status_code)\\n if response.status_code == 200:\\n cidrdata = json.loads(response.content)\\n providerversion[\\\"ORACLE\\\"] = cidrdata[\\\"last_updated_timestamp\\\"]\\n for i in range(0, len(cidrdata[\\\"regions\\\"])):\\n for j in range(0, len(cidrdata[\\\"regions\\\"][i][\\\"cidrs\\\"])):\\n if cidrdata[\\\"regions\\\"][i][\\\"cidrs\\\"][j][\\\"cidr\\\"] not in resultset:\\n resultset[cidrdata[\\\"regions\\\"][i][\\\"cidrs\\\"][j][\\\"cidr\\\"]] = \\\"Oracle\\\"\\n\\n except Exception as get_exception:\\n print(\\\"Exception\\\")\\n print(get_exception)\\n\\n return resultset, providerversion\",\n \"def local_bonds_prices():\\n url1 = \\\"https://api.invertironline.com/token\\\"\\n\\n data = {\\n \\\"username\\\": usuario,\\n \\\"password\\\": password,\\n \\\"grant_type\\\": \\\"password\\\" \\n }\\n response = requests.post(url1, data=data)\\n if response.status_code == 200:\\n content = response.text\\n access_key = token_key(content)\\n\\n url2 = f'https://api.invertironline.com/api/v2/Cotizaciones/Bonos/Merval/argentina'\\n datos = requests.get(url2, headers={\\n 'Authorization': 'Bearer '+access_key\\n })\\n datos = json.loads(datos.text)\\n datos = datos['titulos']\\n datos = clean_assets(datos)\\n return datos\",\n \"def _fetch_old_version(self) -> None:\\n if self.version is None:\\n try:\\n r = fetch(self.api + '?version&format=json')\\n try:\\n d = r.json()\\n except JSONDecodeError:\\n # Fallback for old versions which didn't wrap help in json\\n d = {'error': {'*': r.text}}\\n\\n self.version = list(filter(\\n lambda x: x.startswith('MediaWiki'),\\n (line.strip()\\n for line in d['error']['*'].split('\\\\n'))))[0].split()[1]\\n except Exception:\\n pass\\n else:\\n self.version = MediaWikiVersion(self.version)\",\n \"def ocean(ctx, config_file, as_json, verbose):\\n if not verbose:\\n logging.getLogger().setLevel(logging.ERROR)\\n\\n ctx.obj = {\\n 'ocean': get_ocean(config_file),\\n 'json': as_json\\n }\",\n \"def get_oas_30(cfg):\\n\\n paths = {}\\n\\n # TODO: make openapi multilingual (default language only for now)\\n server_locales = l10n.get_locales(cfg)\\n locale_ = server_locales[0]\\n\\n api_rules = get_api_rules(cfg)\\n\\n osl = get_ogc_schemas_location(cfg['server'])\\n OPENAPI_YAML['oapif-1'] = os.path.join(osl, 'ogcapi/features/part1/1.0/openapi/ogcapi-features-1.yaml') # noqa\\n OPENAPI_YAML['oapif-2'] = os.path.join(osl, 'ogcapi/features/part2/1.0/openapi/ogcapi-features-2.yaml') # noqa\\n\\n LOGGER.debug('setting up server info')\\n oas = {\\n 'openapi': '3.0.2',\\n 'tags': []\\n }\\n info = {\\n 'title': l10n.translate(cfg['metadata']['identification']['title'], locale_), # noqa\\n 'description': l10n.translate(cfg['metadata']['identification']['description'], locale_), # noqa\\n 'x-keywords': l10n.translate(cfg['metadata']['identification']['keywords'], locale_), # noqa\\n 'termsOfService':\\n cfg['metadata']['identification']['terms_of_service'],\\n 'contact': {\\n 'name': cfg['metadata']['provider']['name'],\\n 'url': cfg['metadata']['provider']['url'],\\n 'email': cfg['metadata']['contact']['email']\\n },\\n 'license': {\\n 'name': cfg['metadata']['license']['name'],\\n 'url': cfg['metadata']['license']['url']\\n },\\n 'version': api_rules.api_version\\n }\\n oas['info'] = info\\n\\n oas['servers'] = [{\\n 'url': get_base_url(cfg),\\n 'description': l10n.translate(cfg['metadata']['identification']['description'], locale_) # noqa\\n }]\\n\\n paths['/'] = {\\n 'get': {\\n 'summary': 'Landing page',\\n 'description': 'Landing page',\\n 'tags': ['server'],\\n 'operationId': 'getLandingPage',\\n 'parameters': [\\n {'$ref': '#/components/parameters/f'},\\n {'$ref': '#/components/parameters/lang'}\\n ],\\n 'responses': {\\n '200': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/LandingPage\\\"}, # noqa\\n '400': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/InvalidParameter\\\"}, # noqa\\n '500': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/ServerError\\\"} # noqa\\n }\\n }\\n }\\n\\n paths['/openapi'] = {\\n 'get': {\\n 'summary': 'This document',\\n 'description': 'This document',\\n 'tags': ['server'],\\n 'operationId': 'getOpenapi',\\n 'parameters': [\\n {'$ref': '#/components/parameters/f'},\\n {'$ref': '#/components/parameters/lang'},\\n {\\n 'name': 'ui',\\n 'in': 'query',\\n 'description': 'UI to render the OpenAPI document',\\n 'required': False,\\n 'schema': {\\n 'type': 'string',\\n 'enum': ['swagger', 'redoc'],\\n 'default': 'swagger'\\n },\\n 'style': 'form',\\n 'explode': False\\n },\\n ],\\n 'responses': {\\n '200': {'$ref': '#/components/responses/200'},\\n '400': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/InvalidParameter\\\"}, # noqa\\n 'default': {'$ref': '#/components/responses/default'}\\n }\\n }\\n }\\n\\n paths['/conformance'] = {\\n 'get': {\\n 'summary': 'API conformance definition',\\n 'description': 'API conformance definition',\\n 'tags': ['server'],\\n 'operationId': 'getConformanceDeclaration',\\n 'parameters': [\\n {'$ref': '#/components/parameters/f'},\\n {'$ref': '#/components/parameters/lang'}\\n ],\\n 'responses': {\\n '200': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/LandingPage\\\"}, # noqa\\n '400': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/InvalidParameter\\\"}, # noqa\\n '500': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/ServerError\\\"} # noqa\\n }\\n }\\n }\\n\\n paths['/collections'] = {\\n 'get': {\\n 'summary': 'Collections',\\n 'description': 'Collections',\\n 'tags': ['server'],\\n 'operationId': 'getCollections',\\n 'parameters': [\\n {'$ref': '#/components/parameters/f'},\\n {'$ref': '#/components/parameters/lang'}\\n ],\\n 'responses': {\\n '200': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/LandingPage\\\"}, # noqa\\n '400': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/InvalidParameter\\\"}, # noqa\\n '500': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/ServerError\\\"} # noqa\\n }\\n }\\n }\\n\\n oas['tags'].append({\\n 'name': 'server',\\n 'description': l10n.translate(cfg['metadata']['identification']['description'], locale_), # noqa\\n 'externalDocs': {\\n 'description': 'information',\\n 'url': cfg['metadata']['identification']['url']}\\n }\\n )\\n oas['tags'].append({\\n 'name': 'stac',\\n 'description': 'SpatioTemporal Asset Catalog'\\n }\\n )\\n\\n oas['components'] = {\\n 'responses': {\\n '200': {\\n 'description': 'successful operation'\\n },\\n '204': {\\n 'description': 'no content'\\n },\\n 'default': {\\n 'description': 'Unexpected error',\\n 'content': gen_media_type_object('application/json', 'oapip', 'schemas/exception.yaml') # noqa\\n },\\n 'Queryables': {\\n 'description': 'successful queryables operation',\\n 'content': {\\n 'application/json': {\\n 'schema': {'$ref': '#/components/schemas/queryables'}\\n }\\n }\\n }\\n },\\n 'parameters': {\\n 'f': {\\n 'name': 'f',\\n 'in': 'query',\\n 'description': 'The optional f parameter indicates the output format which the server shall provide as part of the response document. The default format is GeoJSON.', # noqa\\n 'required': False,\\n 'schema': {\\n 'type': 'string',\\n 'enum': ['json', 'html', 'jsonld'],\\n 'default': 'json'\\n },\\n 'style': 'form',\\n 'explode': False\\n },\\n 'lang': {\\n 'name': 'lang',\\n 'in': 'query',\\n 'description': 'The optional lang parameter instructs the server return a response in a certain language, if supported. If the language is not among the available values, the Accept-Language header language will be used if it is supported. If the header is missing, the default server language is used. Note that providers may only support a single language (or often no language at all), that can be different from the server language. Language strings can be written in a complex (e.g. \\\"fr-CA,fr;q=0.9,en-US;q=0.8,en;q=0.7\\\"), simple (e.g. \\\"de\\\") or locale-like (e.g. \\\"de-CH\\\" or \\\"fr_BE\\\") fashion.', # noqa\\n 'required': False,\\n 'schema': {\\n 'type': 'string',\\n 'enum': [l10n.locale2str(sl) for sl in server_locales],\\n 'default': l10n.locale2str(locale_)\\n }\\n },\\n 'properties': {\\n 'name': 'properties',\\n 'in': 'query',\\n 'description': 'The properties that should be included for each feature. The parameter value is a comma-separated list of property names.', # noqa\\n 'required': False,\\n 'style': 'form',\\n 'explode': False,\\n 'schema': {\\n 'type': 'array',\\n 'items': {\\n 'type': 'string'\\n }\\n }\\n },\\n 'skipGeometry': {\\n 'name': 'skipGeometry',\\n 'in': 'query',\\n 'description': 'This option can be used to skip response geometries for each feature.', # noqa\\n 'required': False,\\n 'style': 'form',\\n 'explode': False,\\n 'schema': {\\n 'type': 'boolean',\\n 'default': False\\n }\\n },\\n 'crs': {\\n 'name': 'crs',\\n 'in': 'query',\\n 'description': 'Indicates the coordinate reference system for the results.', # noqa\\n 'style': 'form',\\n 'required': False,\\n 'explode': False,\\n 'schema': {\\n 'format': 'uri',\\n 'type': 'string'\\n }\\n },\\n 'bbox': {\\n 'name': 'bbox',\\n 'in': 'query',\\n 'description': 'Only features that have a geometry that intersects the bounding box are selected.' # noqa\\n 'The bounding box is provided as four or six numbers, depending on whether the ' # noqa\\n 'coordinate reference system includes a vertical axis (height or depth).', # noqa\\n 'required': False,\\n 'style': 'form',\\n 'explode': False,\\n 'schema': {\\n 'type': 'array',\\n 'minItems': 4,\\n 'maxItems': 6,\\n 'items': {\\n 'type': 'number'\\n }\\n }\\n },\\n 'bbox-crs': {\\n 'name': 'bbox-crs',\\n 'in': 'query',\\n 'description': 'Indicates the coordinate reference system for the given bbox coordinates.', # noqa\\n 'style': 'form',\\n 'required': False,\\n 'explode': False,\\n 'schema': {\\n 'format': 'uri',\\n 'type': 'string'\\n }\\n },\\n # FIXME: This is not compatible with the bbox-crs definition in\\n # OGCAPI Features Part 2!\\n # We need to change the mapscript provider and\\n # get_collection_map() method in the API!\\n # So this is for de map-provider only.\\n 'bbox-crs-epsg': {\\n 'name': 'bbox-crs',\\n 'in': 'query',\\n 'description': 'Indicates the EPSG for the given bbox coordinates.', # noqa\\n 'required': False,\\n 'style': 'form',\\n 'explode': False,\\n 'schema': {\\n 'type': 'integer',\\n 'default': 4326\\n }\\n },\\n 'offset': {\\n 'name': 'offset',\\n 'in': 'query',\\n 'description': 'The optional offset parameter indicates the index within the result set from which the server shall begin presenting results in the response document. The first element has an index of 0 (default).', # noqa\\n 'required': False,\\n 'schema': {\\n 'type': 'integer',\\n 'minimum': 0,\\n 'default': 0\\n },\\n 'style': 'form',\\n 'explode': False\\n },\\n 'vendorSpecificParameters': {\\n 'name': 'vendorSpecificParameters',\\n 'in': 'query',\\n 'description': 'Additional \\\"free-form\\\" parameters that are not explicitly defined', # noqa\\n 'schema': {\\n 'type': 'object',\\n 'additionalProperties': True\\n },\\n 'style': 'form'\\n }\\n },\\n 'schemas': {\\n # TODO: change this schema once OGC will definitively publish it\\n 'queryable': {\\n 'type': 'object',\\n 'required': [\\n 'queryable',\\n 'type'\\n ],\\n 'properties': {\\n 'queryable': {\\n 'description': 'the token that may be used in a CQL predicate', # noqa\\n 'type': 'string'\\n },\\n 'title': {\\n 'description': 'a human readable title for the queryable', # noqa\\n 'type': 'string'\\n },\\n 'description': {\\n 'description': 'a human-readable narrative describing the queryable', # noqa\\n 'type': 'string'\\n },\\n 'language': {\\n 'description': 'the language used for the title and description', # noqa\\n 'type': 'string',\\n 'default': [\\n 'en'\\n ]\\n },\\n 'type': {\\n 'description': 'the data type of the queryable', # noqa\\n 'type': 'string'\\n },\\n 'type-ref': {\\n 'description': 'a reference to the formal definition of the type', # noqa\\n 'type': 'string',\\n 'format': 'url'\\n }\\n }\\n },\\n 'queryables': {\\n 'type': 'object',\\n 'required': [\\n 'queryables'\\n ],\\n 'properties': {\\n 'queryables': {\\n 'type': 'array',\\n 'items': {'$ref': '#/components/schemas/queryable'}\\n }\\n }\\n }\\n }\\n }\\n\\n items_f = deepcopy(oas['components']['parameters']['f'])\\n items_f['schema']['enum'].append('csv')\\n items_l = deepcopy(oas['components']['parameters']['lang'])\\n\\n LOGGER.debug('setting up datasets')\\n collections = filter_dict_by_key_value(cfg['resources'],\\n 'type', 'collection')\\n\\n for k, v in collections.items():\\n if v.get('visibility', 'default') == 'hidden':\\n LOGGER.debug(f'Skipping hidden layer: {k}')\\n continue\\n name = l10n.translate(k, locale_)\\n title = l10n.translate(v['title'], locale_)\\n desc = l10n.translate(v['description'], locale_)\\n collection_name_path = f'/collections/{k}'\\n tag = {\\n 'name': name,\\n 'description': desc,\\n 'externalDocs': {}\\n }\\n for link in l10n.translate(v.get('links', []), locale_):\\n if link['type'] == 'information':\\n tag['externalDocs']['description'] = link['type']\\n tag['externalDocs']['url'] = link['url']\\n break\\n if len(tag['externalDocs']) == 0:\\n del tag['externalDocs']\\n\\n oas['tags'].append(tag)\\n\\n paths[collection_name_path] = {\\n 'get': {\\n 'summary': f'Get {title} metadata',\\n 'description': desc,\\n 'tags': [name],\\n 'operationId': f'describe{name.capitalize()}Collection',\\n 'parameters': [\\n {'$ref': '#/components/parameters/f'},\\n {'$ref': '#/components/parameters/lang'}\\n ],\\n 'responses': {\\n '200': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/Collection\\\"}, # noqa\\n '400': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/InvalidParameter\\\"}, # noqa\\n '404': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/NotFound\\\"}, # noqa\\n '500': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/ServerError\\\"} # noqa\\n }\\n }\\n }\\n\\n LOGGER.debug('setting up collection endpoints')\\n try:\\n ptype = None\\n\\n if filter_providers_by_type(\\n collections[k]['providers'], 'feature'):\\n ptype = 'feature'\\n\\n if filter_providers_by_type(\\n collections[k]['providers'], 'record'):\\n ptype = 'record'\\n\\n p = load_plugin('provider', get_provider_by_type(\\n collections[k]['providers'], ptype))\\n\\n items_path = f'{collection_name_path}/items'\\n\\n coll_properties = deepcopy(oas['components']['parameters']['properties']) # noqa\\n\\n coll_properties['schema']['items']['enum'] = list(p.fields.keys())\\n\\n paths[items_path] = {\\n 'get': {\\n 'summary': f'Get {title} items',\\n 'description': desc,\\n 'tags': [name],\\n 'operationId': f'get{name.capitalize()}Features',\\n 'parameters': [\\n items_f,\\n items_l,\\n {'$ref': '#/components/parameters/bbox'},\\n {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/parameters/limit\\\"}, # noqa\\n {'$ref': '#/components/parameters/crs'}, # noqa\\n {'$ref': '#/components/parameters/bbox-crs'}, # noqa\\n coll_properties,\\n {'$ref': '#/components/parameters/vendorSpecificParameters'}, # noqa\\n {'$ref': '#/components/parameters/skipGeometry'},\\n {'$ref': f\\\"{OPENAPI_YAML['oapir']}/parameters/sortby.yaml\\\"}, # noqa\\n {'$ref': '#/components/parameters/offset'},\\n ],\\n 'responses': {\\n '200': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/Features\\\"}, # noqa\\n '400': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/InvalidParameter\\\"}, # noqa\\n '404': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/NotFound\\\"}, # noqa\\n '500': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/ServerError\\\"} # noqa\\n }\\n },\\n 'options': {\\n 'summary': f'Options for {title} items',\\n 'tags': [name],\\n 'operationId': f'options{name.capitalize()}Features',\\n 'responses': {\\n '200': {'description': 'options response'}\\n }\\n }\\n }\\n\\n if p.editable:\\n LOGGER.debug('Provider is editable; adding post')\\n\\n paths[items_path]['post'] = {\\n 'summary': f'Add {title} items',\\n 'description': desc,\\n 'tags': [name],\\n 'operationId': f'add{name.capitalize()}Features',\\n 'requestBody': {\\n 'description': 'Adds item to collection',\\n 'content': {\\n 'application/geo+json': {\\n 'schema': {}\\n }\\n },\\n 'required': True\\n },\\n 'responses': {\\n '201': {'description': 'Successful creation'},\\n '400': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/InvalidParameter\\\"}, # noqa\\n '500': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/ServerError\\\"} # noqa\\n }\\n }\\n\\n try:\\n schema_ref = p.get_schema(SchemaType.create)\\n paths[items_path]['post']['requestBody']['content'][schema_ref[0]] = { # noqa\\n 'schema': schema_ref[1]\\n }\\n except Exception as err:\\n LOGGER.debug(err)\\n\\n if ptype == 'record':\\n paths[items_path]['get']['parameters'].append(\\n {'$ref': f\\\"{OPENAPI_YAML['oapir']}/parameters/q.yaml\\\"})\\n if p.fields:\\n queryables_path = f'{collection_name_path}/queryables'\\n\\n paths[queryables_path] = {\\n 'get': {\\n 'summary': f'Get {title} queryables',\\n 'description': desc,\\n 'tags': [name],\\n 'operationId': f'get{name.capitalize()}Queryables',\\n 'parameters': [\\n items_f,\\n items_l\\n ],\\n 'responses': {\\n '200': {'$ref': '#/components/responses/Queryables'}, # noqa\\n '400': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/InvalidParameter\\\"}, # noqa\\n '404': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/NotFound\\\"}, # noqa\\n '500': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/ServerError\\\"}, # noqa\\n }\\n }\\n }\\n\\n if p.time_field is not None:\\n paths[items_path]['get']['parameters'].append(\\n {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/parameters/datetime\\\"}) # noqa\\n\\n for field, type_ in p.fields.items():\\n\\n if p.properties and field not in p.properties:\\n LOGGER.debug('Provider specified not to advertise property') # noqa\\n continue\\n\\n if field == 'q' and ptype == 'record':\\n LOGGER.debug('q parameter already declared, skipping')\\n continue\\n\\n if type_ == 'date':\\n schema = {\\n 'type': 'string',\\n 'format': 'date'\\n }\\n elif type_ == 'float':\\n schema = {\\n 'type': 'number',\\n 'format': 'float'\\n }\\n elif type_ == 'long':\\n schema = {\\n 'type': 'integer',\\n 'format': 'int64'\\n }\\n else:\\n schema = type_\\n\\n path_ = f'{collection_name_path}/items'\\n paths[path_]['get']['parameters'].append({\\n 'name': field,\\n 'in': 'query',\\n 'required': False,\\n 'schema': schema,\\n 'style': 'form',\\n 'explode': False\\n })\\n\\n paths[f'{collection_name_path}/items/{{featureId}}'] = {\\n 'get': {\\n 'summary': f'Get {title} item by id',\\n 'description': desc,\\n 'tags': [name],\\n 'operationId': f'get{name.capitalize()}Feature',\\n 'parameters': [\\n {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/parameters/featureId\\\"}, # noqa\\n {'$ref': '#/components/parameters/crs'}, # noqa\\n {'$ref': '#/components/parameters/f'},\\n {'$ref': '#/components/parameters/lang'}\\n ],\\n 'responses': {\\n '200': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/Feature\\\"}, # noqa\\n '400': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/InvalidParameter\\\"}, # noqa\\n '404': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/NotFound\\\"}, # noqa\\n '500': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/ServerError\\\"} # noqa\\n }\\n },\\n 'options': {\\n 'summary': f'Options for {title} item by id',\\n 'tags': [name],\\n 'operationId': f'options{name.capitalize()}Feature',\\n 'parameters': [\\n {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/parameters/featureId\\\"} # noqa\\n ],\\n 'responses': {\\n '200': {'description': 'options response'}\\n }\\n }\\n }\\n\\n try:\\n schema_ref = p.get_schema()\\n paths[f'{collection_name_path}/items/{{featureId}}']['get']['responses']['200'] = { # noqa\\n 'content': {\\n schema_ref[0]: {\\n 'schema': schema_ref[1]\\n }\\n }\\n }\\n except Exception as err:\\n LOGGER.debug(err)\\n\\n if p.editable:\\n LOGGER.debug('Provider is editable; adding put/delete')\\n put_path = f'{collection_name_path}/items/{{featureId}}' # noqa\\n paths[put_path]['put'] = { # noqa\\n 'summary': f'Update {title} items',\\n 'description': desc,\\n 'tags': [name],\\n 'operationId': f'update{name.capitalize()}Features',\\n 'parameters': [\\n {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/parameters/featureId\\\"} # noqa\\n ],\\n 'requestBody': {\\n 'description': 'Updates item in collection',\\n 'content': {\\n 'application/geo+json': {\\n 'schema': {}\\n }\\n },\\n 'required': True\\n },\\n 'responses': {\\n '204': {'$ref': '#/components/responses/204'},\\n '400': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/InvalidParameter\\\"}, # noqa\\n '500': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/ServerError\\\"} # noqa\\n }\\n }\\n\\n try:\\n schema_ref = p.get_schema(SchemaType.replace)\\n paths[put_path]['put']['requestBody']['content'][schema_ref[0]] = { # noqa\\n 'schema': schema_ref[1]\\n }\\n except Exception as err:\\n LOGGER.debug(err)\\n\\n paths[f'{collection_name_path}/items/{{featureId}}']['delete'] = { # noqa\\n 'summary': f'Delete {title} items',\\n 'description': desc,\\n 'tags': [name],\\n 'operationId': f'delete{name.capitalize()}Features',\\n 'parameters': [\\n {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/parameters/featureId\\\"}, # noqa\\n ],\\n 'responses': {\\n '200': {'description': 'Successful delete'},\\n '400': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/InvalidParameter\\\"}, # noqa\\n '500': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/ServerError\\\"} # noqa\\n }\\n }\\n\\n except ProviderTypeError:\\n LOGGER.debug('collection is not feature based')\\n\\n LOGGER.debug('setting up coverage endpoints')\\n try:\\n load_plugin('provider', get_provider_by_type(\\n collections[k]['providers'], 'coverage'))\\n\\n coverage_path = f'{collection_name_path}/coverage'\\n\\n paths[coverage_path] = {\\n 'get': {\\n 'summary': f'Get {title} coverage',\\n 'description': desc,\\n 'tags': [name],\\n 'operationId': f'get{name.capitalize()}Coverage',\\n 'parameters': [\\n items_f,\\n items_l,\\n {'$ref': '#/components/parameters/bbox'},\\n {'$ref': '#/components/parameters/bbox-crs'}, # noqa\\n ],\\n 'responses': {\\n '200': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/Features\\\"}, # noqa\\n '400': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/InvalidParameter\\\"}, # noqa\\n '404': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/NotFound\\\"}, # noqa\\n '500': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/ServerError\\\"} # noqa\\n }\\n }\\n }\\n\\n coverage_domainset_path = f'{collection_name_path}/coverage/domainset' # noqa\\n\\n paths[coverage_domainset_path] = {\\n 'get': {\\n 'summary': f'Get {title} coverage domain set',\\n 'description': desc,\\n 'tags': [name],\\n 'operationId': f'get{name.capitalize()}CoverageDomainSet',\\n 'parameters': [\\n items_f,\\n items_l\\n ],\\n 'responses': {\\n '200': {'$ref': f\\\"{OPENAPI_YAML['oacov']}/schemas/cis_1.1/domainSet.yaml\\\"}, # noqa\\n '400': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/InvalidParameter\\\"}, # noqa\\n '404': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/NotFound\\\"}, # noqa\\n '500': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/ServerError\\\"} # noqa\\n }\\n }\\n }\\n\\n coverage_rangetype_path = f'{collection_name_path}/coverage/rangetype' # noqa\\n\\n paths[coverage_rangetype_path] = {\\n 'get': {\\n 'summary': f'Get {title} coverage range type',\\n 'description': desc,\\n 'tags': [name],\\n 'operationId': f'get{name.capitalize()}CoverageRangeType',\\n 'parameters': [\\n items_f,\\n items_l\\n ],\\n 'responses': {\\n '200': {'$ref': f\\\"{OPENAPI_YAML['oacov']}/schemas/cis_1.1/rangeType.yaml\\\"}, # noqa\\n '400': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/InvalidParameter\\\"}, # noqa\\n '404': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/NotFound\\\"}, # noqa\\n '500': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/ServerError\\\"} # noqa\\n }\\n }\\n }\\n except ProviderTypeError:\\n LOGGER.debug('collection is not coverage based')\\n\\n LOGGER.debug('setting up tiles endpoints')\\n tile_extension = filter_providers_by_type(\\n collections[k]['providers'], 'tile')\\n\\n if tile_extension:\\n tp = load_plugin('provider', tile_extension)\\n oas['components']['responses'].update({\\n 'Tiles': {\\n 'description': 'Retrieves the tiles description for this collection', # noqa\\n 'content': {\\n 'application/json': {\\n 'schema': {\\n '$ref': '#/components/schemas/tiles'\\n }\\n }\\n }\\n }\\n }\\n )\\n\\n oas['components']['schemas'].update({\\n 'tilematrixsetlink': {\\n 'type': 'object',\\n 'required': ['tileMatrixSet'],\\n 'properties': {\\n 'tileMatrixSet': {\\n 'type': 'string'\\n },\\n 'tileMatrixSetURI': {\\n 'type': 'string'\\n }\\n }\\n },\\n 'tiles': {\\n 'type': 'object',\\n 'required': [\\n 'tileMatrixSetLinks',\\n 'links'\\n ],\\n 'properties': {\\n 'tileMatrixSetLinks': {\\n 'type': 'array',\\n 'items': {\\n '$ref': '#/components/schemas/tilematrixsetlink' # noqa\\n }\\n },\\n 'links': {\\n 'type': 'array',\\n 'items': {'$ref': f\\\"{OPENAPI_YAML['oapit']}#/components/schemas/link\\\"} # noqa\\n }\\n }\\n }\\n }\\n )\\n\\n tiles_path = f'{collection_name_path}/tiles'\\n\\n paths[tiles_path] = {\\n 'get': {\\n 'summary': f'Fetch a {title} tiles description',\\n 'description': desc,\\n 'tags': [name],\\n 'operationId': f'describe{name.capitalize()}Tiles',\\n 'parameters': [\\n items_f,\\n # items_l TODO: is this useful?\\n ],\\n 'responses': {\\n '200': {'$ref': '#/components/responses/Tiles'},\\n '400': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/InvalidParameter\\\"}, # noqa\\n '404': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/NotFound\\\"}, # noqa\\n '500': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/ServerError\\\"} # noqa\\n }\\n }\\n }\\n\\n tiles_data_path = f'{collection_name_path}/tiles/{{tileMatrixSetId}}/{{tileMatrix}}/{{tileRow}}/{{tileCol}}' # noqa\\n\\n paths[tiles_data_path] = {\\n 'get': {\\n 'summary': f'Get a {title} tile',\\n 'description': desc,\\n 'tags': [name],\\n 'operationId': f'get{name.capitalize()}Tiles',\\n 'parameters': [\\n {'$ref': f\\\"{OPENAPI_YAML['oat']}#/components/parameters/tileMatrixSetId\\\"}, # noqa\\n {'$ref': f\\\"{OPENAPI_YAML['oat']}#/components/parameters/tileMatrix\\\"}, # noqa\\n {'$ref': f\\\"{OPENAPI_YAML['oat']}#/components/parameters/tileRow\\\"}, # noqa\\n {'$ref': f\\\"{OPENAPI_YAML['oat']}#/components/parameters/tileCol\\\"}, # noqa\\n {\\n 'name': 'f',\\n 'in': 'query',\\n 'description': 'The optional f parameter indicates the output format which the server shall provide as part of the response document.', # noqa\\n 'required': False,\\n 'schema': {\\n 'type': 'string',\\n 'enum': [tp.format_type],\\n 'default': tp.format_type\\n },\\n 'style': 'form',\\n 'explode': False\\n }\\n ],\\n 'responses': {\\n '400': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/InvalidParameter\\\"}, # noqa\\n '404': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/NotFound\\\"}, # noqa\\n '500': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/ServerError\\\"} # noqa\\n }\\n }\\n }\\n mimetype = tile_extension['format']['mimetype']\\n paths[tiles_data_path]['get']['responses']['200'] = {\\n 'description': 'successful operation',\\n 'content': {\\n mimetype: {\\n 'schema': {\\n 'type': 'string',\\n 'format': 'binary'\\n }\\n }\\n }\\n }\\n\\n LOGGER.debug('setting up edr endpoints')\\n edr_extension = filter_providers_by_type(\\n collections[k]['providers'], 'edr')\\n\\n if edr_extension:\\n ep = load_plugin('provider', edr_extension)\\n\\n edr_query_endpoints = []\\n\\n for qt in ep.get_query_types():\\n edr_query_endpoints.append({\\n 'path': f'{collection_name_path}/{qt}',\\n 'qt': qt,\\n 'op_id': f'query{qt.capitalize()}{k.capitalize()}'\\n })\\n if ep.instances:\\n edr_query_endpoints.append({\\n 'path': f'{collection_name_path}/instances/{{instanceId}}/{qt}', # noqa\\n 'qt': qt,\\n 'op_id': f'query{qt.capitalize()}Instance{k.capitalize()}' # noqa\\n })\\n\\n for eqe in edr_query_endpoints:\\n paths[eqe['path']] = {\\n 'get': {\\n 'summary': f\\\"query {v['description']} by {eqe['qt']}\\\", # noqa\\n 'description': v['description'],\\n 'tags': [k],\\n 'operationId': eqe['op_id'],\\n 'parameters': [\\n {'$ref': f\\\"{OPENAPI_YAML['oaedr']}/parameters/{eqe['qt']}Coords.yaml\\\"}, # noqa\\n {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/parameters/datetime\\\"}, # noqa\\n {'$ref': f\\\"{OPENAPI_YAML['oaedr']}/parameters/parameter-name.yaml\\\"}, # noqa\\n {'$ref': f\\\"{OPENAPI_YAML['oaedr']}/parameters/z.yaml\\\"}, # noqa\\n {'$ref': '#/components/parameters/f'}\\n ],\\n 'responses': {\\n '200': {\\n 'description': 'Response',\\n 'content': {\\n 'application/prs.coverage+json': {\\n 'schema': {\\n '$ref': f\\\"{OPENAPI_YAML['oaedr']}/schemas/coverageJSON.yaml\\\" # noqa\\n }\\n }\\n }\\n }\\n }\\n }\\n }\\n\\n LOGGER.debug('setting up maps endpoints')\\n map_extension = filter_providers_by_type(\\n collections[k]['providers'], 'map')\\n\\n if map_extension:\\n mp = load_plugin('provider', map_extension)\\n\\n map_f = deepcopy(oas['components']['parameters']['f'])\\n map_f['schema']['enum'] = [map_extension['format']['name']]\\n map_f['schema']['default'] = map_extension['format']['name']\\n\\n pth = f'/collections/{k}/map'\\n paths[pth] = {\\n 'get': {\\n 'summary': 'Get map',\\n 'description': f\\\"{v['description']} map\\\",\\n 'tags': [k],\\n 'operationId': 'getMap',\\n 'parameters': [\\n {'$ref': '#/components/parameters/bbox'},\\n {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/parameters/datetime\\\"}, # noqa\\n {\\n 'name': 'width',\\n 'in': 'query',\\n 'description': 'Response image width',\\n 'required': False,\\n 'schema': {\\n 'type': 'integer',\\n },\\n 'style': 'form',\\n 'explode': False\\n },\\n {\\n 'name': 'height',\\n 'in': 'query',\\n 'description': 'Response image height',\\n 'required': False,\\n 'schema': {\\n 'type': 'integer',\\n },\\n 'style': 'form',\\n 'explode': False\\n },\\n {\\n 'name': 'transparent',\\n 'in': 'query',\\n 'description': 'Background transparency of map (default=true).', # noqa\\n 'required': False,\\n 'schema': {\\n 'type': 'boolean',\\n 'default': True,\\n },\\n 'style': 'form',\\n 'explode': False\\n },\\n {'$ref': '#/components/parameters/bbox-crs-epsg'},\\n map_f\\n ],\\n 'responses': {\\n '200': {\\n 'description': 'Response',\\n 'content': {\\n 'application/json': {}\\n }\\n },\\n '400': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/InvalidParameter\\\"}, # noqa\\n '500': {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/responses/ServerError\\\"}, # noqa\\n }\\n }\\n }\\n if mp.time_field is not None:\\n paths[pth]['get']['parameters'].append(\\n {'$ref': f\\\"{OPENAPI_YAML['oapif-1']}#/components/parameters/datetime\\\"}) # noqa\\n\\n LOGGER.debug('setting up STAC')\\n stac_collections = filter_dict_by_key_value(cfg['resources'],\\n 'type', 'stac-collection')\\n if stac_collections:\\n paths['/stac'] = {\\n 'get': {\\n 'summary': 'SpatioTemporal Asset Catalog',\\n 'description': 'SpatioTemporal Asset Catalog',\\n 'tags': ['stac'],\\n 'operationId': 'getStacCatalog',\\n 'parameters': [],\\n 'responses': {\\n '200': {'$ref': '#/components/responses/200'},\\n 'default': {'$ref': '#/components/responses/default'}\\n }\\n }\\n }\\n\\n process_manager = get_manager(cfg)\\n\\n if len(process_manager.processes) > 0:\\n paths['/processes'] = {\\n 'get': {\\n 'summary': 'Processes',\\n 'description': 'Processes',\\n 'tags': ['server'],\\n 'operationId': 'getProcesses',\\n 'parameters': [\\n {'$ref': '#/components/parameters/f'}\\n ],\\n 'responses': {\\n '200': {'$ref': f\\\"{OPENAPI_YAML['oapip']}/responses/ProcessList.yaml\\\"}, # noqa\\n 'default': {'$ref': '#/components/responses/default'}\\n }\\n }\\n }\\n LOGGER.debug('setting up processes')\\n\\n for k, v in process_manager.processes.items():\\n if k.startswith('_'):\\n LOGGER.debug(f'Skipping hidden layer: {k}')\\n continue\\n name = l10n.translate(k, locale_)\\n p = process_manager.get_processor(k)\\n md_desc = l10n.translate(p.metadata['description'], locale_)\\n process_name_path = f'/processes/{name}'\\n tag = {\\n 'name': name,\\n 'description': md_desc, # noqa\\n 'externalDocs': {}\\n }\\n for link in p.metadata.get('links', []):\\n if link['type'] == 'information':\\n translated_link = l10n.translate(link, locale_)\\n tag['externalDocs']['description'] = translated_link[\\n 'type']\\n tag['externalDocs']['url'] = translated_link['url']\\n break\\n if len(tag['externalDocs']) == 0:\\n del tag['externalDocs']\\n\\n oas['tags'].append(tag)\\n\\n paths[process_name_path] = {\\n 'get': {\\n 'summary': 'Get process metadata',\\n 'description': md_desc,\\n 'tags': [name],\\n 'operationId': f'describe{name.capitalize()}Process',\\n 'parameters': [\\n {'$ref': '#/components/parameters/f'}\\n ],\\n 'responses': {\\n '200': {'$ref': '#/components/responses/200'},\\n 'default': {'$ref': '#/components/responses/default'}\\n }\\n }\\n }\\n\\n paths[f'{process_name_path}/execution'] = {\\n 'post': {\\n 'summary': f\\\"Process {l10n.translate(p.metadata['title'], locale_)} execution\\\", # noqa\\n 'description': md_desc,\\n 'tags': [name],\\n 'operationId': f'execute{name.capitalize()}Job',\\n 'responses': {\\n '200': {'$ref': '#/components/responses/200'},\\n '201': {'$ref': f\\\"{OPENAPI_YAML['oapip']}/responses/ExecuteAsync.yaml\\\"}, # noqa\\n '404': {'$ref': f\\\"{OPENAPI_YAML['oapip']}/responses/NotFound.yaml\\\"}, # noqa\\n '500': {'$ref': f\\\"{OPENAPI_YAML['oapip']}/responses/ServerError.yaml\\\"}, # noqa\\n 'default': {'$ref': '#/components/responses/default'}\\n },\\n 'requestBody': {\\n 'description': 'Mandatory execute request JSON',\\n 'required': True,\\n 'content': {\\n 'application/json': {\\n 'schema': {\\n '$ref': f\\\"{OPENAPI_YAML['oapip']}/schemas/execute.yaml\\\" # noqa\\n }\\n }\\n }\\n }\\n }\\n }\\n if 'example' in p.metadata:\\n paths[f'{process_name_path}/execution']['post']['requestBody']['content']['application/json']['example'] = p.metadata['example'] # noqa\\n\\n name_in_path = {\\n 'name': 'jobId',\\n 'in': 'path',\\n 'description': 'job identifier',\\n 'required': True,\\n 'schema': {\\n 'type': 'string'\\n }\\n }\\n\\n paths['/jobs'] = {\\n 'get': {\\n 'summary': 'Retrieve jobs list',\\n 'description': 'Retrieve a list of jobs',\\n 'tags': ['jobs'],\\n 'operationId': 'getJobs',\\n 'responses': {\\n '200': {'$ref': '#/components/responses/200'},\\n '404': {'$ref': f\\\"{OPENAPI_YAML['oapip']}/responses/NotFound.yaml\\\"}, # noqa\\n 'default': {'$ref': '#/components/responses/default'}\\n }\\n }\\n }\\n\\n paths['/jobs/{jobId}'] = {\\n 'get': {\\n 'summary': 'Retrieve job details',\\n 'description': 'Retrieve job details',\\n 'tags': ['jobs'],\\n 'parameters': [\\n name_in_path,\\n {'$ref': '#/components/parameters/f'}\\n ],\\n 'operationId': 'getJob',\\n 'responses': {\\n '200': {'$ref': '#/components/responses/200'},\\n '404': {'$ref': f\\\"{OPENAPI_YAML['oapip']}/responses/NotFound.yaml\\\"}, # noqa\\n 'default': {'$ref': '#/components/responses/default'} # noqa\\n }\\n },\\n 'delete': {\\n 'summary': 'Cancel / delete job',\\n 'description': 'Cancel / delete job',\\n 'tags': ['jobs'],\\n 'parameters': [\\n name_in_path\\n ],\\n 'operationId': 'deleteJob',\\n 'responses': {\\n '204': {'$ref': '#/components/responses/204'},\\n '404': {'$ref': f\\\"{OPENAPI_YAML['oapip']}/responses/NotFound.yaml\\\"}, # noqa\\n 'default': {'$ref': '#/components/responses/default'} # noqa\\n }\\n },\\n }\\n\\n paths['/jobs/{jobId}/results'] = {\\n 'get': {\\n 'summary': 'Retrieve job results',\\n 'description': 'Retrive job resiults',\\n 'tags': ['jobs'],\\n 'parameters': [\\n name_in_path,\\n {'$ref': '#/components/parameters/f'}\\n ],\\n 'operationId': 'getJobResults',\\n 'responses': {\\n '200': {'$ref': '#/components/responses/200'},\\n '404': {'$ref': f\\\"{OPENAPI_YAML['oapip']}/responses/NotFound.yaml\\\"}, # noqa\\n 'default': {'$ref': '#/components/responses/default'} # noqa\\n }\\n }\\n }\\n\\n tag = {\\n 'name': 'jobs',\\n 'description': 'Process jobs',\\n }\\n oas['tags'].insert(1, tag)\\n\\n oas['paths'] = paths\\n\\n return oas\",\n \"def test_get_last_cities_nocache(client):\\n response = client.get(\\\"/weather/1\\\")\\n assert response.status_code == 200\",\n \"def get_image_information(client):\\n\\n pipeline = [{\\\"$match\\\": {\\\"camera_views\\\": {\\\"$exists\\\": 1}}}, {\\\"$unwind\\\": {\\\"path\\\": \\\"$camera_views\\\"}}, {\\\"$addFields\\\": {\\n \\\"camera_views.average_linear_distance\\\": {\\n \\\"$divide\\\": [\\n \\\"$camera_views.total_linear_distance\\\",\\n \\\"$camera_views.num_entities\\\"\\n ]\\n },\\n \\\"camera_views.average_angular_distance\\\": {\\n \\\"$divide\\\": [\\n \\\"$camera_views.total_angular_distance\\\",\\n \\\"$camera_views.num_entities\\\"\\n ]\\n },\\n \\\"camera_views.timestamp\\\": \\\"$timestamp\\\",\\n \\\"camera_views._id\\\": \\\"$_id\\\",\\n \\\"camera_views.database\\\": client.database.name,\\n \\\"camera_views.collection\\\": client.name,\\n 'camera_views.file_id':\\\"$camera_views.images.file_id\\\", #Add the Color image id for downloading and testing\\n }}, {\\\"$replaceRoot\\\": {\\\"newRoot\\\": \\\"$camera_views\\\"}}, {\\\"$project\\\": {\\n \\\"_id\\\": 1,\\n \\\"num_entities\\\": 1,\\n \\\"average_linear_distance\\\": 1,\\n \\\"average_angular_distance\\\": 1,\\n \\\"timestamp\\\": 1,\\n \\\"duplicate\\\": 1,\\n \\\"database\\\":1,\\n \\\"collection\\\":1,\\n \\\"file_id\\\":{\\\"$arrayElemAt\\\":[\\\"$images.file_id\\\",0]}, # Only keep the first file id (The Color image)\\n }}]\\n pprint.pprint(pipeline)\\n result = list(client.aggregate(pipeline))\\n return result\",\n \"def assets_pull(ctx, text, method):\\n ocean = ctx.obj['ocean']\\n response = []\\n for did in ocean.search(text):\\n print('pulling:', did)\\n response += [ctx.invoke(assets_consume,\\n did=did,\\n method=method)]\",\n \"def mainRoads(osm_path): \\n return retrieve(osm_path,'lines',['highway','oneway','lanes','maxspeed'],**{'highway':[\\\"='primary' or \\\",\\\"='trunk' or \\\",\\\"='motorway' or \\\",\\\"='motorway_link' or \\\",\\\"='trunk_link' or \\\",\\\"='primary_link' or \\\", \\\"='secondary' or \\\",\\\"='tertiary' or \\\",\\\"='tertiary_link'\\\"]})\",\n \"def roads(osm_path): \\n return (retrieve(osm_path,'lines',['highway'],**{'highway':[\\\"='motorway' or \\\",\\\"='motorway_link' or \\\",\\\"='trunk' or \\\",\\\"='trunk_link' or \\\",\\\"='primary' or \\\",\\\"='primary_link' or \\\",\\\"='secondary' or \\\",\\\"='secondary_link' or \\\",\\\"='tertiary' or \\\",\\\"='tertiary_link' or \\\",\\\"='residential' or \\\",\\\"='road' or \\\",\\\"='unclassified' or \\\",\\\"='living_street'\\\"]})).rename(columns={'highway': 'asset'})\",\n \"def get_latest_data():\\n try:\\n print '\\\\nRequesting new data.....\\\\n'\\n response = get(\\\"https://api.myjson.com/bins/2csub\\\")\\n if response.status_code is 200:\\n print '\\\\nSuccess (200) in downloading data\\\\n'\\n current_json = response.json()\\n set_backup_data(current_json)\\n else: \\n current_json = get_backup_data()\\n except ConnectionError:\\n current_json = get_backup_data()\\n return current_json\",\n \"def get_water_network():\\n\\n j = request.json\\n\\n region = ee.Geometry(j['region'])\\n start = j['start']\\n stop = j['stop']\\n scale = j['scale']\\n crs = j['crs']\\n\\n # get water mask\\n water_vector = get_water_mask_vector(region, scale, start, stop)\\n\\n # skeletonize\\n output = generate_skeleton_from_voronoi(scale, water_vector)\\n centerline = output[\\\"centerline\\\"]\\n\\n centerline = centerline.map(\\n lambda line: line.set('length', line.length(scale / 10)))\\n\\n centerline = centerline.map(transform_feature(crs, scale))\\n\\n # create response\\n data = centerline.getInfo()\\n\\n return Response(json.dumps(data), status=200, mimetype='application/json')\",\n \"def get_master_data():\\n data = RAW_RESPONSE.json()\\n STATES = set()\\n CITIES = set()\\n DISTRICTS = set()\\n for each in data[\\\"raw_data\\\"]:\\n\\n if each[\\\"detectedstate\\\"]!='':\\n\\n RAW_STATES.add(each[\\\"detectedstate\\\"])\\n STATES.add(each[\\\"detectedstate\\\"].lower().strip().replace(\\\" \\\",\\\"\\\"))\\n if each[\\\"detecteddistrict\\\"]!='':\\n RAW_DISTRICTS.add(each[\\\"detecteddistrict\\\"])\\n DISTRICTS.add(each[\\\"detecteddistrict\\\"].lower().strip().replace(\\\" \\\",\\\"\\\"))\\n if each[\\\"detectedcity\\\"]!='':\\n RAW_CITIES.add(each[\\\"detectedcity\\\"])\\n CITIES.add(each[\\\"detectedcity\\\"].lower().strip().replace(\\\" \\\",\\\"\\\"))\\n STATES = list(filter(None, STATES))\\n DISTRICTS = list(filter(None, DISTRICTS))\\n CITIES = list(filter(None, CITIES))\\n\\n return STATES, DISTRICTS, CITIES\",\n \"def get_sea_level_raw(start_year, end_year, path_out):\\n c = cdsapi.Client()\\n\\n for year in range(start_year, end_year + 1):\\n\\n print(f\\\"Starting Year: {year}\\\")\\n\\n c.retrieve(\\n \\\"satellite-sea-level-global\\\",\\n {\\n \\\"format\\\": \\\"tgz\\\",\\n \\\"year\\\": [str(year)],\\n \\\"month\\\": [\\n \\\"01\\\",\\n \\\"02\\\",\\n \\\"03\\\",\\n \\\"04\\\",\\n \\\"05\\\",\\n \\\"06\\\",\\n \\\"07\\\",\\n \\\"08\\\",\\n \\\"09\\\",\\n \\\"10\\\",\\n \\\"11\\\",\\n \\\"12\\\",\\n ],\\n \\\"day\\\": [\\n \\\"01\\\",\\n \\\"02\\\",\\n \\\"03\\\",\\n \\\"04\\\",\\n \\\"05\\\",\\n \\\"06\\\",\\n \\\"07\\\",\\n \\\"08\\\",\\n \\\"09\\\",\\n \\\"10\\\",\\n \\\"11\\\",\\n \\\"12\\\",\\n \\\"13\\\",\\n \\\"14\\\",\\n \\\"15\\\",\\n \\\"16\\\",\\n \\\"17\\\",\\n \\\"18\\\",\\n \\\"19\\\",\\n \\\"20\\\",\\n \\\"21\\\",\\n \\\"22\\\",\\n \\\"23\\\",\\n \\\"24\\\",\\n \\\"25\\\",\\n \\\"26\\\",\\n \\\"27\\\",\\n \\\"28\\\",\\n \\\"29\\\",\\n \\\"30\\\",\\n \\\"31\\\",\\n ],\\n },\\n os.path.join(path_out, str(year) + \\\"_download.tar.gz\\\"),\\n )\",\n \"def _create_api_ulr_list(self) -> List[str]:\\n api = Setup.openweather_api\\n now = int(time.time())\\n urls_list = []\\n\\n for lat, lon in self.locations:\\n urls_list.append(\\n f\\\"https://api.openweathermap.org/data/2.5/onecall?\\\"\\n f\\\"lat={lat}&lon={lon}&exclude=hourly,minutely,\\\"\\n f\\\"alerts&units=metric&appid={api}\\\"\\n )\\n\\n for days in range(1, 6):\\n date_time = now - 86400 * days\\n urls_list.append(\\n f\\\"http://api.openweathermap.org/data/2.5/onecall/\\\"\\n f\\\"timemachine?lat={lat}&lon={lon}&dt={date_time}\\\"\\n f\\\"&units=metric&appid={api}\\\"\\n )\\n\\n return urls_list\",\n \"def get_metocean_data():\\n\\n data_params = DATASETS_VIS['metocean']\\n\\n r = request.get_json()\\n\\n dataset = r['dataset']\\n assert (dataset in data_params), '{} not in assets. '.format(dataset)\\n data_params = data_params[dataset]\\n\\n band = list(data_params['bandNames'].keys())[0]\\n if 'band' in r:\\n band = r['band']\\n assert band in data_params['bandNames'], '{} not in bands. '.format(band)\\n\\n # Get collection based on dataset requested\\n image = ee.Image(data_params['source'])\\n\\n image = image.select(data_params['bandNames'][band])\\n\\n vis_params = {\\n 'min': data_params['min'][band],\\n 'max': data_params['max'][band],\\n 'palette': data_params['palette'][band]\\n }\\n\\n if 'min' in r:\\n vis_params['min'] = r['min']\\n\\n if 'max' in r:\\n vis_params['max'] = r['max']\\n\\n if 'palette' in r:\\n vis_params['palette'] = r['palette']\\n\\n info = generate_image_info(image, vis_params)\\n info['dataset'] = dataset\\n info['band'] = band\\n\\n return Response(\\n json.dumps(info),\\n status=200,\\n mimetype='application/json'\\n )\",\n \"def find_source(alt_az, lat_lon=local_latlong,\\n minute=minute, hour=hour,\\n day=day, month=month, year=year, tz_offset=5,\\n return_all_sources=True):\\n ra_dec = altaz_to_radec(alt_az, pos=lat_lon,\\n minute=minute, hour=hour, day=day,\\n month=month, year=year, tz_offset=5)\\n\\n coords = coordinates.SkyCoord(ra=ra_dec[0], dec=ra_dec[1],\\n unit=(u.deg, u.deg), frame='icrs')\\n # Get the actual results\\n # For some reason, if this goes too big it stops seeing the actual source.\\n r = 500 * u.arcminute\\n results = Vizier.query_region(coords, radius=r, catalog='V/50')[0]\\n df = results.to_pandas()\\n\\n candidate_sources = filter(None, [n for n in df['HD']])\\n sources = []\\n dmax, vmax = 0, 0\\n for s in candidate_sources:\\n source_info = df.loc[df['HD'] == s]\\n name = source_info['Name']\\n mag = round(float(source_info['Vmag']), 2)\\n\\n temp_ra = source_info['RAJ2000'].tolist()[0]\\n temp_dec = source_info['DEJ2000'].tolist()[0]\\n source_ra_hms = tuple(map(float, temp_ra.split()))\\n source_dec_dms = tuple(map(float, temp_dec.split()))\\n source_ra = Angle(source_ra_hms, unit='hourangle').degree\\n source_dec = Angle(source_dec_dms, unit=u.deg).degree\\n\\n dist_from_center = np.sqrt((source_ra - ra_dec[0])**2 +\\n (source_dec - ra_dec[1])**2)\\n\\n score = float(c1 * mag + c2 * dist_from_center)\\n source_dict = {'HD': source_info['HD'].values[0],\\n 'Name': source_info['Name'].values[0],\\n 'RA': source_ra,\\n 'DEC': source_dec,\\n 'Distance': dist_from_center,\\n 'Vmag': source_info['Vmag'],\\n 'Score': score}\\n\\n sources.append(source_dict)\\n\\n dmax = dist_from_center if dist_from_center > dmax else dmax\\n vmax = mag if mag > vmax else mag\\n\\n for s in range(len(sources)):\\n d = sources[s]['Distance']/dmax\\n mag = sources[s]['Vmag'].values[0]/vmax\\n score = c1 * mag + c2 * d\\n sources[s]['Score'] = score\\n sources[s]['Scaled-Distance'] = d\\n sources[s]['Scaled-Mag'] = mag\\n\\n sources_df = pd.DataFrame(sources)\\n\\n\\n # Note that this loop is supremely janky, but df.loc'ing wasn't working.\\n # best_source = sources_df.loc[sources_df['Score'] == sources_df['Score'].min]\\n best_source_idx = 0\\n # best_score = np.array([])\\n best_score = 10000\\n for i in range(len(sources)):\\n score = sources[i]['Score']\\n if score < best_score:\\n best_source_idx = i\\n best_score = score\\n\\n name = sources_df['Name'].values[0]\\n out = {'Coords': ra_dec,\\n 'HD-Name': 'HD' + str(int(sources[best_source_idx]['HD'])),\\n 'Name': sources[best_source_idx]['Name'],\\n 'Score': sources[best_source_idx]['Score'],\\n 'Scaled-Distance': sources[best_source_idx]['Scaled-Distance'],\\n 'Scaled-Mag': sources[best_source_idx]['Scaled-Mag']\\n }\\n return out\",\n \"def objects(self):\\n catalog = getToolByName(self.context, \\\"portal_catalog\\\")\\n query = {}\\n utils = getToolByName(self.context, \\\"plone_utils\\\")\\n query[\\\"portal_type\\\"] = utils.getUserFriendlyTypes()\\n registry = getUtility(IRegistry)\\n typesUseViewActionInListings = frozenset(\\n registry.get(\\\"plone.types_use_view_action_in_listings\\\", [])\\n )\\n\\n is_plone_site_root = IPloneSiteRoot.providedBy(self.context)\\n if not is_plone_site_root:\\n query[\\\"path\\\"] = \\\"/\\\".join(self.context.getPhysicalPath())\\n\\n query[\\\"is_default_page\\\"] = True\\n default_page_modified = OOBTree()\\n for item in catalog.searchResults(query):\\n key = item.getURL().rsplit(\\\"/\\\", 1)[0]\\n value = (item.modified.micros(), item.modified.ISO8601())\\n default_page_modified[key] = value\\n\\n # The plone site root is not catalogued.\\n if is_plone_site_root:\\n loc = self.context.absolute_url()\\n date = self.context.modified()\\n # Comparison must be on GMT value\\n modified = (date.micros(), date.ISO8601())\\n default_modified = default_page_modified.get(loc, None)\\n if default_modified is not None:\\n modified = max(modified, default_modified)\\n lastmod = modified[1]\\n yield {\\n \\\"loc\\\": loc,\\n \\\"lastmod\\\": lastmod,\\n # 'changefreq': 'always',\\n # hourly/daily/weekly/monthly/yearly/never\\n # 'prioriy': 0.5, # 0.0 to 1.0\\n }\\n\\n query[\\\"is_default_page\\\"] = False\\n for item in catalog.searchResults(query):\\n loc = item.getURL()\\n date = item.modified\\n # Comparison must be on GMT value\\n modified = (date.micros(), date.ISO8601())\\n default_modified = default_page_modified.get(loc, None)\\n if default_modified is not None:\\n modified = max(modified, default_modified)\\n lastmod = modified[1]\\n if item.portal_type in typesUseViewActionInListings:\\n loc += \\\"/view\\\"\\n yield {\\n \\\"loc\\\": loc,\\n \\\"lastmod\\\": lastmod,\\n # 'changefreq': 'always',\\n # hourly/daily/weekly/monthly/yearly/never\\n # 'prioriy': 0.5, # 0.0 to 1.0\\n }\",\n \"async def get_mirrors(request):\\n page = request.GET.getone(\\\"page\\\", None)\\n page_size = request.GET.getone(\\\"page_size\\\", None)\\n filter_name = request.GET.getone(\\\"q\\\", \\\"\\\")\\n\\n if page:\\n try:\\n page = int(page)\\n except (ValueError, TypeError):\\n return web.Response(text=\\\"Incorrect value for page\\\", status=400)\\n\\n if page_size:\\n try:\\n page_size = int(page_size)\\n except (ValueError, TypeError):\\n return web.Response(text=\\\"Incorrect value for page_size\\\", status=400)\\n page_size = 1 if page_size < 1 else page_size\\n\\n query = request.cirrina.db_session.query(\\n ProjectVersion\\n ) # pylint: disable=no-member\\n query = query.join(Project, Project.id == ProjectVersion.project_id)\\n query = query.filter(Project.is_mirror == \\\"true\\\").order_by(Project.name)\\n\\n if filter_name:\\n query = query.filter(Project.name.like(\\\"%{}%\\\".format(filter_name)))\\n\\n query = query.order_by(Project.name, ProjectVersion.name)\\n nb_results = query.count()\\n\\n if page is not None and page_size:\\n query = query.offset(page * page_size)\\n query = query.limit(page_size)\\n\\n results = query.all()\\n\\n data = {\\\"total_result_count\\\": nb_results, \\\"results\\\": []}\\n\\n for mirror in results:\\n apt_url = mirror.get_apt_repo(url_only=True)\\n base_mirror_url = str()\\n if not mirror.project.is_basemirror and mirror.buildvariants:\\n # FIXME: only one buildvariant supported\\n base_mirror = mirror.buildvariants[0].base_mirror\\n base_mirror_url = base_mirror.get_apt_repo(url_only=True)\\n\\n data[\\\"results\\\"].append(\\n {\\n \\\"id\\\": mirror.id,\\n \\\"name\\\": mirror.project.name,\\n \\\"version\\\": mirror.name,\\n \\\"url\\\": mirror.mirror_url,\\n \\\"base_mirror\\\": base_mirror_url,\\n \\\"distribution\\\": mirror.mirror_distribution,\\n \\\"components\\\": mirror.mirror_components,\\n \\\"is_basemirror\\\": mirror.project.is_basemirror,\\n \\\"architectures\\\": mirror.mirror_architectures[1:-1],\\n \\\"is_locked\\\": mirror.is_locked,\\n \\\"with_sources\\\": mirror.mirror_with_sources,\\n \\\"with_installer\\\": mirror.mirror_with_installer,\\n \\\"project_id\\\": mirror.project.id,\\n \\\"state\\\": mirror.mirror_state,\\n \\\"apt_url\\\": apt_url,\\n }\\n )\\n\\n return web.json_response(data)\",\n \"def getBrightest(source=None, elMin=20, elMax=87, sourceList=[], action=INCLUDE, \\n numReturn=1, ignoreNorthSouth=True, coordsys=\\\"azel\\\",\\n getOptical=False, fluxLimit=1.0, frequency=95) : \\n # If source is None then use Polaris as the source as it is always up\\n nsource = source \\n if source == None: \\n nsource = 'aumi'\\n ignoreNorthSouth = True \\n coordsys = \\\"azel\\\" \\n # We need to get a bunch and then sort them and truncate after sorting...\\n numToGet = 333 \\n r = s.getNearest(nsource, elMin, elMax, sourceList, action, numToGet,\\n ignoreNorthSouth, coordsys, getOptical, fluxLimit, frequency)\\n print \\\"Number of results before trimming:\\\", len(r) \\n def f(x, y):\\n return cmp(y.brightness, x.brightness) \\n r.sort(f) \\n if source == None: \\n for n in r:\\n n.reference = \\\"None\\\"\\n n.distance = 0\\n n.azimuth = 0\\n n.elevation = 0\\n return r[:numReturn]\",\n \"def main():\\n\\n with open(\\\".auth_token\\\", mode=\\\"r\\\") as tokenfile:\\n authtoken = tokenfile.read().strip()\\n\\n # Initialize connection to Archivist\\n aconn = Archivist(\\n \\\"https://soak-0-avid.engineering-k8s-stage-2.dev.wild.jitsuin.io\\\",\\n auth=authtoken,\\n )\\n # Get all assets with required attributes and properties\\n props = {\\\"confirmation_status\\\": \\\"CONFIRMED\\\"}\\n attrs = {\\\"arc_display_type\\\": \\\"Traffic light\\\"}\\n for event in aconn.events.list(asset_id=\\\"assets/-\\\", props=props, attrs=attrs):\\n print(\\\"event\\\", event)\\n\\n # alternatively one could pull the list and cache locally...\\n events = aconn.events.list(asset_id=\\\"assets/-\\\", props=props, attrs=attrs)\\n for event in events:\\n print(\\\"event\\\", event)\",\n \"def get_host_info(search_keyword, starbucks_data, city_info):\\n host_data = []\\n\\n payload = {\\n \\\"query_type\\\": \\\"RQBXY\\\",\\n \\\"pagesize\\\": \\\"20\\\",\\n \\\"pagenum\\\": '',\\n \\\"qii\\\": \\\"true\\\",\\n \\\"cluster_state\\\": \\\"5\\\",\\n \\\"need_utd\\\": \\\"true\\\",\\n \\\"utd_sceneid\\\": \\\"1000\\\",\\n \\\"div\\\": \\\"PC1000\\\",\\n \\\"addr_poi_merge\\\": \\\"true\\\",\\n \\\"is_classify\\\": \\\"true\\\",\\n \\\"zoom\\\": \\\"14\\\",\\n \\\"longitude\\\": starbucks_data['longitude'],\\n \\\"latitude\\\": starbucks_data['latitude'],\\n \\\"range\\\": \\\"1000\\\",\\n \\\"city\\\": city_info[1][0],\\n \\\"keywords\\\": search_keyword,\\n }\\n\\n for page_num in range(1, 3):\\n payload['pagenum'] = page_num\\n poi_list = request_amap_poi_info(payload, 'https://www.amap.com/place/' + starbucks_data['amap_key'])\\n\\n if not poi_list:\\n print('request host list fail with %s' % page_num)\\n continue\\n\\n for poi in poi_list:\\n if not (poi.get('longitude', '') or poi.get('latitude', '') or starbucks_data['longitude'] or starbucks_data['latitude']):\\n distance = None\\n else:\\n distance = geo_distance(poi.get('longitude', ''), poi.get('latitude', ''),starbucks_data['longitude'], starbucks_data['latitude'])\\n\\n data = {\\n 'starbucks_key': starbucks_data['amap_key'],\\n 'keyword': search_keyword,\\n 'city': poi.get('cityname'),\\n 'name': poi.get('name'),\\n 'longitude': poi.get('longitude'),\\n 'latitude': poi.get('latitude'),\\n 'address': poi.get('address'),\\n 'tel': poi.get('tel'),\\n 'mean_price': '',\\n 'distance': distance\\n }\\n domain_list = poi.get('domain_list')\\n for domain in domain_list:\\n if domain.get('name', '') == 'price':\\n price_raw = domain.get('value', '')\\n # price_raw = \\\"<font color='#90969a'>人均:</font><font color='#f84b57'>￥</font><font color='#f84b57'>114</font>\\\"\\n try:\\n data['mean_price'] = re.findall('<.*>人均:<.*>￥<.*>([0-9]+)</font>', price_raw)[0]\\n except:\\n data['mean_price'] = None\\n break\\n host_data.append(data)\\n\\n print('【%s】的【%s】的周边的【%s】菜系,第【%d】页爬取完毕' % (city_info[1], starbucks_data['name'], search_keyword, page_num))\\n return host_data\",\n \"def get_fresh_data(get_team):\\n\\n \\\"\\\"\\\" Create the URL for today \\\"\\\"\\\"\\n now = datetime.datetime.now()\\n url = \\\"http://gd2.mlb.com/gdcross/components/game/mlb/year_\\\" + '{}'.format(now.year) + \\\"/month_\\\" + '{:02d}'.format(now.month) + \\\"/day_\\\" + '{:02d}'.format(now.day) + \\\"/miniscoreboard.json\\\"\\n\\n \\\"\\\"\\\" Grab the first response and write it to a file, we'll update it once the game starts \\\"\\\"\\\"\\n data_write_file = status_dir + '{}'.format(now.year) + '{:02d}'.format(now.month) + '{:02d}'.format(now.day) + \\\".json\\\"\\n\\n \\\"\\\"\\\" Get the json data if the file doesn't exist, or if it's over three minutes old \\\"\\\"\\\"\\n if not os.path.isfile(data_write_file) or time.time() - os.path.getmtime(data_write_file) > 60:\\n response = urllib.urlopen(url)\\n full_data = json.loads(response.read())\\n with open(data_write_file, 'w') as outfile:\\n json.dump(full_data, outfile, sort_keys=True, indent=2, ensure_ascii=False)\\n\\n \\\"\\\"\\\" Use the data from the status file \\\"\\\"\\\"\\n with open(data_write_file, 'r') as json_data:\\n full_data = json.load(json_data)\\n\\n \\\"\\\"\\\" This will return false if there is no game today, else will return json data for just our team \\\"\\\"\\\"\\n my_game = False\\n for game in full_data['data']['games']['game']:\\n if get_team in game['home_file_code'] or get_team in game['away_file_code']:\\n my_game = game\\n \\n return my_game\",\n \"def update(self):\\n url = '/weather/summary?version=2&lat={}&lon={}' \\\\\\n .format(self.lat, self.lon)\\n self.result = self.api.get(url)['weather']['summary'][0]\",\n \"def wp_gather_image_and_info(output_dir, db_collection, region_bbox):\\n log = logging.getLogger('gather_tweet_image_and_info')\\n\\n if not os.path.exists(output_dir):\\n os.makedirs(output_dir)\\n\\n log.info(\\\"Performing Query\\\")\\n after = datetime.datetime(2014, 2, 1)\\n q = db_collection.find({\\n \\\"$and\\\": [\\n {\\\"media\\\": {\\\"$gt\\\": {\\\"$size\\\": 0}}},\\n {\\\"media.0.type\\\": \\\"photo\\\"},\\n {\\\"created\\\": {\\\"$gte\\\": after}},\\n {\\\"geo.0\\\": {\\\"$gte\\\": region_bbox.min_x, \\\"$lte\\\": region_bbox.max_x}},\\n {\\\"geo.1\\\": {\\\"$gte\\\": region_bbox.min_y, \\\"$lte\\\": region_bbox.max_y}},\\n ]\\n }, timeout=False)\\n\\n log.info(\\\"Scanning results\\\")\\n count = 0\\n try:\\n for i, e in enumerate(q):\\n log.info(\\\"[%9d] %s -> %s %s\\\", i, e['_id'], e['geo'],\\n e['media'][0]['media_url'])\\n media_path = e['media'][0]['media_url']\\n media_filetype = os.path.splitext(media_path)[1]\\n\\n output_image = os.path.join(output_dir, str(e['_id']) + media_filetype)\\n output_bson = os.path.join(output_dir, str(e['_id']) + \\\".bson\\\")\\n\\n try:\\n with open(output_image, 'wb') as output_image_file:\\n output_image_file.write(urllib2.urlopen(media_path).read())\\n with open(output_bson, 'wb') as output_md_file:\\n output_md_file.write(bson.BSON.encode(e))\\n count += 1\\n except Exception, ex:\\n # Skip all files that cause errors anywhere. Remove anything\\n # written\\n log.info(\\\" - ERROR: %s\\\", str(ex))\\n log.info(\\\" - Skipping\\\")\\n if os.path.exists(output_image):\\n os.remove(output_image)\\n if os.path.exists(output_bson):\\n os.remove(output_bson)\\n finally:\\n # Since we checked out a cursor without a server-side timeout, make sure\\n # that we catch whatever and close the cursor when we're done / if\\n # things fail.\\n q.close()\\n\\n log.info(\\\"Discovered %d matching entries with valid images.\\\", count)\",\n \"def test_get_clouds(self):\\n pass\",\n \"def access_gemini_url_head_method(context, endpoint):\\n url = urljoin(context.gemini_api_url, endpoint)\\n context.response = requests.head(url)\",\n \"def find_last_image(self) -> List[str]:\\n\\n soup = self.load_page()\\n\\n txt_from_site = soup.select('.full-image-block')\\n\\n url_to_down = \\\"http://www.lunarbaboon.com/\\\" + str(txt_from_site[0])[\\n str(txt_from_site).find('/storage'):str(txt_from_site).find(\\n 'SQUARESPACE_CACHEVERSION=') + 37]\\n filename = str(url_to_down)[35:48] + \\\".jpg\\\"\\n\\n return [url_to_down, filename]\",\n \"def sentientPlanets():\\n planet_list = []\\n url = 'https://swapi-api.hbtn.io/api/species'\\n while url is not None:\\n data = requests.get(url).json()\\n for species in data['results']:\\n if ((species['designation'] == 'sentient'\\n or species['designation'] == 'reptilian')):\\n if species['homeworld'] is not None:\\n hw = requests.get(species['homeworld']).json()\\n planet_list.append(hw['name'])\\n url = data['next']\\n return planet_list\",\n \"def _new_site(self, soup, url_base):\\n dl_list = []\\n img_list = soup.find(\\\"div\\\", {\\\"id\\\": \\\"download-links-holder\\\"})\\n\\n # Check to if high res pics are linked\\n img_hires_link = img_list.find(\\\"a\\\", text=re.compile('Highest-quality download options'))\\n if img_hires_link is not None:\\n self.log(\\\"[NEW] Get high res\\\")\\n hires_url = url_base + img_hires_link['href']\\n try:\\n hires_soup = self.get_site(hires_url, self._url_header)\\n except RequestsError as e:\\n self.log(\\\"Skipping image: \\\" + str(hires_url))\\n else:\\n hires_list = hires_soup.find(\\\"div\\\", {\\\"id\\\": \\\"download-links-holder\\\"})\\n hires_links = hires_list.find_all(\\\"li\\\")\\n for hires_link in hires_links:\\n hires_dl_url = hires_link.a['href']\\n # Add to download list\\n dl_list.append(hires_dl_url)\\n\\n self.log(\\\"[NEW] Get other\\\")\\n # Get all other images\\n img_links = img_list.find_all(\\\"li\\\")\\n for img_link in img_links:\\n link_url = img_link.a['href']\\n img_dl_url = link_url\\n if link_url.endswith('/'):\\n link_url = url_base + link_url\\n try:\\n link_soup = self.get_site(link_url, self._url_header)\\n except RequestsError as e:\\n self.log(\\\"Skipping image: \\\" + str(link_url))\\n else:\\n img_dl_url = link_soup.find(\\\"div\\\", {\\\"class\\\": \\\"subpage-body\\\"}).img['src']\\n # Add to download list\\n dl_list.append(img_dl_url)\\n\\n return dl_list\",\n \"def geocube():\",\n \"def getrmlsthelper(self):\\n\\n printtime('Downloading {} alleles'.format(self.analysistype), self.start)\\n # Extract the path of the current script from the full path + file name\\n homepath = os.path.split(os.path.abspath(__file__))[0]\\n # Set the path/name of the folder to contain the new alleles and profile\\n newfolder = os.path.join(self.path, self.analysistype)\\n # Create the path\\n make_path(newfolder)\\n # Create arguments to feed into the rest_auth_class script\\n args = ArgumentParser\\n args.secret_file = os.path.join(homepath, 'secret.txt')\\n args.file_path = homepath\\n args.output_path = newfolder\\n args.start = self.start\\n rmlst = rest_auth_class.REST(args)\\n # Download the profile and alleles\\n rmlst.main()\\n\\n # Get the new alleles into a list, and create the combinedAlleles file\\n alleles = glob(os.path.join(newfolder, '*.tfa'))\\n self.combinealleles(newfolder, alleles)\",\n \"def get_all_locations(self):\",\n \"async def get_stations(response: Response,\\n source: StationSourceEnum = StationSourceEnum.UNSPECIFIED):\\n try:\\n logger.info('/stations/')\\n\\n weather_stations = await get_stations_as_geojson(source)\\n response.headers[\\\"Cache-Control\\\"] = no_cache\\n\\n return WeatherStationsResponse(features=weather_stations)\\n except Exception as exception:\\n logger.critical(exception, exc_info=True)\\n raise\",\n \"def load_rentedout():\",\n \"def main():\\n # Pull variables from pf\\n profileref = pfget('google_mapfeed.pf', profile)\\n dbname = profileref['dbname']\\n path = profileref['webbase']\\n finalfile = '%s/%s' % (path, profileref['file'])\\n bufferfile = '%s+' % finalfile\\n max_nquakes = 600\\n element_fields = ['lat', 'lon', 'depth', 'time', 'local_timestring', 'utc_timestring', 'magnitude', 'auth']\\n\\n if verbose:\\n print \\\"Start: Creating main JSON file '%s' for all stations at %s\\\" % (finalfile, time.strftime(\\\"%a, %d %b %Y %H:%M:%S +0000\\\", time.gmtime()))\\n\\n now = time.time()\\n # Set time zone\\n os.putenv('TZ','US/Pacific')\\n time.tzset()\\n if verbose:\\n print \\\"The time zone is: %s\\\" % (time.tzname)[0]\\n print \\\"The current time is: %s\\\" % now\\n\\n # Override defaults\\n if override_number:\\n if verbose:\\n print \\\"Overriding default number of events (%d) with %d\\\" % (max_nquakes, override_number)\\n nquakes = override_number\\n else:\\n nquakes = max_nquakes\\n if override_timerange:\\n if verbose:\\n print \\\"Overiding default number of events (%d) with time range %d seconds\\\" % (max_nquakes, override_timerange)\\n nquakes = False\\n\\n # Database processing\\n if verbose:\\n print \\\"Opening database\\\";\\n print \\\"Number of events requested: %s\\\" % nquakes\\n db = dbopen(dbname, 'r')\\n\\n '''\\n Occasionally there is more than one magnitude for a single orid\\n (such as provided by QED). We need the most recent magnitude for\\n a given orid, so sort on orid and lddate, then group on orid,\\n then get the most recent record number (greatest lddate) for each\\n group. Add that to a dictionary we will use later.\\n '''\\n netmag_dict = {}\\n db_netmag = dblookup(db, table='netmag')\\n db_netmag.sort(['orid', 'lddate'])\\n db_netmag_grp = dbgroup(db_netmag, 'orid')\\n if verbose:\\n print \\\"There are %s records\\\" % db_netmag_grp.query('dbRECORD_COUNT')\\n for i in range(db_netmag_grp.query('dbRECORD_COUNT')):\\n db_netmag_grp[3] = i\\n orid, [dbptr, view, end_record, start_record] = db_netmag_grp.getv('orid', 'bundle')\\n if verbose:\\n print \\\"\\\\t- Iteration: %s: Orid: %s, Start record: %s, End record: %s\\\"% (i, orid, start_record, end_record)\\n db_netmag[3] = end_record - 1\\n if verbose:\\n print \\\"\\\\t\\\\t- Magnitude: %s, Magtype: %s\\\" % (db_netmag.getv('magnitude')[0], db_netmag.getv('magtype')[0] )\\n magnitude, magtype = db_netmag.getv('magnitude', 'magtype')\\n netmag_dict[orid] = { 'rec':end_record, 'magnitude':magnitude, 'magtype':magtype }\\n\\n '''\\n if verbose:\\n for key in sorted(netmag_dict.iterkeys()):\\n print \\\"%s: %s\\\" % (key, netmag_dict[key])\\n '''\\n\\n '''\\n Now get the event information\\n '''\\n db.lookup(table='origin')\\n db.join('event')\\n if verbose:\\n print \\\"Number of joined records of event and origin tables: %s\\\" % db.query('dbRECORD_COUNT')\\n if override_timerange:\\n override_oldest = now - override_timerange\\n if verbose:\\n print \\\"Override time defined - get events in the last %s seconds - 'time >= %s'\\\" % (override_timerange, override_oldest)\\n db.subset('time >= %d' % override_oldest)\\n if verbose:\\n print \\\"Subset on time. Number of records: %s\\\" % db.query('dbRECORD_COUNT')\\n # Join views\\n # db_joined = dbjoin(db, db_netmag)\\n\\n if verbose:\\n print \\\"Subset orid == prefor\\\"\\n db.subset('orid == prefor')\\n if verbose:\\n print \\\"Number of subsetted records: %s\\\" % db.query('dbRECORD_COUNT')\\n print \\\"Subset for time != NULL\\\"\\n db.subset('time != NULL')\\n if verbose:\\n print \\\"Number of subsetted records: %s\\\" % db.query('dbRECORD_COUNT')\\n # We want the most recent first for the comparison with nquakes\\n db.sort(['time'], reverse=True)\\n if verbose:\\n print \\\"Number of sorted records: %s\\\" % db.query('dbRECORD_COUNT')\\n if nquakes:\\n if db.query('dbRECORD_COUNT') > nquakes:\\n db[3] = nquakes - 1\\n min_time = db.getv('time')[0]\\n db.subset(\\\"time >= %s\\\" % min_time)\\n else:\\n override_oldest = now - override_timerange\\n if verbose:\\n print \\\"Override time defined - get events in the last %s seconds - time > %s\\\" % (override_timerange, override_oldest)\\n db.subset(\\\"time >= %s\\\" % override_oldest)\\n # Sort in normal time - we want the most recent events plotted on top\\n db.sort(('time'))\\n if verbose:\\n print \\\"Number of records without subset on time: %s\\\" % db.query('dbRECORD_COUNT')\\n '''\\n Build event dictionary\\n '''\\n event_dict = {'metadata':{},'events':{}}\\n\\n '''\\n Build metadata dictionary\\n '''\\n if nquakes:\\n event_dict['metadata']['max_nquakes'] = nquakes\\n event_dict['metadata']['oldest_time_readable'] = epoch2str( int(min_time), \\\"%H:%M UTC %A %B %o, %Y\\\" )\\n event_dict['metadata']['oldest_time'] = int(min_time)\\n event_dict['metadata']['type'] = 'event_limited'\\n elif override_oldest:\\n event_dict['metadata']['time_range'] = int(override_timerange)\\n event_dict['metadata']['oldest_time_readable'] = epoch2str( int(override_oldest), \\\"%H:%M UTC %A %B %o, %Y\\\" )\\n event_dict['metadata']['oldest_time'] = int(override_oldest)\\n event_dict['metadata']['type'] = 'time_limited'\\n event_dict['metadata']['modification_time'] = int(time.time())\\n event_dict['metadata']['modification_time_readable'] = epoch2str( int(time.time()), \\\"%H:%M UTC %A %B %o, %Y\\\" )\\n\\n '''\\n Build event dictionary\\n '''\\n events = {}\\n for i in range(db.query('dbRECORD_COUNT')):\\n db[3] = i\\n if verbose:\\n epoch_time, orid = db.getv('time', 'orid')\\n print \\\"\\\\tRecord number is: %s Orid is: %d Time is: %s\\\" % (db[3], orid, epoch2str(epoch_time, '%Y-%m-%d %H:%M:%S'))\\n\\n orid = db.getv('orid')[0]\\n\\n if orid in netmag_dict:\\n events[i] = {}\\n for ef in element_fields:\\n # Parse values\\n if ef is 'local_timestring' or ef is 'utc_timestring' or ef is 'time':\\n value = dbgetv(db, 'time')[0]\\n difference = float(now) - float(value)\\n if difference < 6 * 3600:\\n color = 'red'\\n elif difference < 12 * 3600:\\n color = 'orange'\\n elif difference < 24 * 3600:\\n color = 'yellow'\\n elif difference < 72 * 3600:\\n color = 'chartreuse'\\n elif difference < 168 * 3600:\\n color = 'blue'\\n else:\\n color = 'grey'\\n events[i]['color'] = color\\n elif ef is 'depth':\\n value = dbgetv(db, 'depth')[0]\\n elif ef is 'auth':\\n value = dbgetv(db, 'auth')[0]\\n elif ef is 'magnitude':\\n # Magnitude\\n # mlval, mbval, msval, magnitudeval, magtypeval = db.getv('ml', 'mb', 'ms', 'magnitude', 'magtype')\\n # Null magnitude is -999.00\\n magnitudeval = netmag_dict[orid]['magnitude']\\n magtypeval = netmag_dict[orid]['magtype']\\n if int(magnitudeval) > 0:\\n scale = magtypeval\\n value = '%.1f' % magnitudeval\\n else:\\n scale = ''\\n value = 'N/A'\\n events[i]['scale'] = scale\\n else:\\n value = dbgetv(db, ef)\\n\\n # Override formatting for specific fields\\n if ef is 'lat' or ef is 'lon':\\n value = '%.4f' % value\\n elif ef is 'local_timestring':\\n value = epoch2str( value, \\\"%H:%M:%S %Z %A %B %o, %Y\\\", \\\"US/Pacific\\\" )\\n elif ef is 'utc_timestring':\\n value = epoch2str( value, \\\"%H:%M:%S UTC %A %B %o, %Y\\\" )\\n events[i][ef] = value\\n\\n full_lat, full_lon = db.getv('lat', 'lon')\\n events[i]['grname'] = (grname(full_lat,full_lon)).title()\\n events[i]['srname'] = (srname(full_lat,full_lon)).title()\\n\\n event_dict['events'] = events\\n\\n # Dump JSON file\\n f = open(bufferfile, 'w') \\n json.dump(event_dict, f, sort_keys=True, indent=2)\\n f.flush()\\n\\n # Move the file to replace the older one\\n try:\\n os.rename(bufferfile, finalfile)\\n except OSError:\\n print \\\"Cannot rename JSON file from %s to %s\\\" % (bufferfile,finalfile)\\n\\n if verbose:\\n print \\\"End: Creating main JSON file '%s' for all stations %s\\\" % (finalfile, time.strftime(\\\"%a, %d %b %Y %H:%M:%S +0000\\\", time.gmtime()))\\n\\n db.close()\\n return 0\",\n \"def fetch(self,url=URL):\\n\\t\\tlog.info('downloading latest PHE case data')\\n#\\t\\tself.data=lookup_json(url)\\n\\t\\tself.fetch_csv() #JSON discontinued; switched back to CSV\\n\\t\\tself.edition=self.latest_samples\\n\\t\\tlog.info(f'Last samples from {self.edition}')\",\n \"def make_pull(db,url):\\n result = db.product_mstator.find_one({\\\"url\\\":url})\\n return result\",\n \"def download_potholes():\\n\\n\\tlink = \\\"https://data.cityofchicago.org/api/views/7as2-ds3y/rows.csv?accessType=DOWNLOAD\\\"\\n\\tdf = pd.read_csv(link)\\n\\tdf = df[(df.STATUS == \\\"Open\\\") | (df.STATUS == \\\"Open - Dup\\\")]\\n\\tdf = df[[\\\"LATITUDE\\\", \\\"LONGITUDE\\\"]]\\n\\tdf = df.dropna(axis =0, subset=[\\\"LATITUDE\\\", \\\"LONGITUDE\\\"])\\n\\treturn df\",\n \"def get_house_est(api_key, year = str(), map = bool()):\\n try:\\n house_url = f'http://api.census.gov/data/{year}/pep/housing?get=HUEST&for=state:*&key={api_key}'\\n r = requests.get(house_url)\\n data = json.loads(r.content) \\n house_df = pd.DataFrame(data[1:], columns=data[0]).\\\\\\n rename(columns={\\\"HUEST\\\": \\\"Housing_Estimates\\\", \\\"state\\\": \\\"STATEFP\\\"})\\n house_df['Housing_Estimates'] = house_df['Housing_Estimates'].astype(str).astype(int)\\n geodata_url = f\\\"https://raw.githubusercontent.com/uscensusbureau/citysdk/master/v2/GeoJSON/20m/{year}/state.json\\\"\\n geo_df = gpd.read_file(geodata_url)\\n geo_df = geo_df.merge(house_df, on = 'STATEFP')\\n if map == True:\\n return geo_df.plot(column = 'Housing_Estimates')\\n else:\\n return geo_df\\n r.raise_for_status()\\n except HTTPError as http_err:\\n print(f'HTTP error occurred: {http_err}')\\n except Exception as err:\\n print(f'An error occured. All parameters must exist in the Census GeoJSON database and API. Please check https://github.com/uscensusbureau/citysdk/tree/master/v2/GeoJSON: {err}')\",\n \"def get_house(arg):\\n house = requests.get(BASE_URL+'houses/'+arg)\\n print house.json()\\n return house.status_code\",\n \"def download(self, verbose):\\n # Download datasets\\n if verbose:\\n print(\\\"Retrieving datasets from Our World In Data https://github.com/owid/covid-19-data/\\\")\\n # Vaccinations\\n v_rec_cols = [\\n \\\"date\\\", \\\"location\\\", \\\"iso_code\\\", \\\"total_vaccinations\\\", \\\"people_vaccinated\\\", \\\"people_fully_vaccinated\\\"]\\n v_rec_df = pd.read_csv(self.URL_V_REC, usecols=v_rec_cols)\\n v_loc_df = pd.read_csv(self.URL_V_LOC, usecols=[\\\"location\\\", \\\"vaccines\\\"])\\n v_df = v_rec_df.merge(v_loc_df, how=\\\"left\\\", on=\\\"location\\\")\\n # Tests\\n pcr_rec_cols = [\\\"ISO code\\\", \\\"Date\\\", \\\"Daily change in cumulative total\\\", \\\"Cumulative total\\\"]\\n pcr_df = pd.read_csv(self.URL_P_REC, usecols=pcr_rec_cols)\\n pcr_df = pcr_df.rename(columns={\\\"ISO code\\\": \\\"iso_code\\\", \\\"Date\\\": \\\"date\\\"})\\n pcr_df[\\\"cumsum\\\"] = pcr_df.groupby(\\\"iso_code\\\")[\\\"Daily change in cumulative total\\\"].cumsum()\\n pcr_df = pcr_df.assign(tests=lambda x: x[\\\"Cumulative total\\\"].fillna(x[\\\"cumsum\\\"]))\\n # Combine data (vaccinations/tests)\\n df = v_df.set_index([\\\"iso_code\\\", \\\"date\\\"])\\n df = df.combine_first(pcr_df.set_index([\\\"iso_code\\\", \\\"date\\\"]).loc[:, [\\\"tests\\\"]])\\n df = df.reset_index()\\n # Location (country/province)\\n df[\\\"location\\\"] = df[\\\"location\\\"].replace(\\n {\\n # COG\\n \\\"Congo\\\": \\\"Republic of the Congo\\\",\\n }\\n )\\n df = df.loc[~df[\\\"iso_code\\\"].str.contains(\\\"OWID_\\\")]\\n df[\\\"location\\\"] = df.groupby(\\\"iso_code\\\")[\\\"location\\\"].bfill()\\n df.loc[df[\\\"location\\\"] == df[\\\"iso_code\\\"], \\\"location\\\"] = None\\n df.loc[df[\\\"location\\\"].isna(), \\\"location\\\"] = df.loc[df[\\\"location\\\"].isna(), \\\"iso_code\\\"].apply(\\n lambda x: coco.convert(x, to=\\\"name_short\\\", not_found=None))\\n df[self.PROVINCE] = self.UNKNOWN\\n return df\",\n \"def main():\\n\\n data = get_data(URL)\\n\\n if not data:\\n raise ValueError('No data to process')\\n\\n datacenters = [\\n Datacenter(key, value)\\n for key, value in data.items()\\n ]\\n\\n pass # the rest of your logic here\",\n \"def merge_light_catalogue():\\n output_filename = os.path.join(constants.DESTINATION,\\n 'concatenated',\\n 'iphas-dr2-light.fits')\\n\\n instring = ''\\n for lon in np.arange(25, 215+1, constants.STRIPWIDTH):\\n for part in ['a', 'b']:\\n path = os.path.join(constants.DESTINATION,\\n 'concatenated',\\n 'light',\\n 'iphas-dr2-{0:03d}{1}-light.fits'.format(\\n lon, part))\\n instring += 'in={0} '.format(path)\\n\\n # Warning: a bug in stilts causes long fieldIDs to be truncated if -utype S15 is not set\\n param = {'stilts': constants.STILTS,\\n 'in': instring,\\n 'out': output_filename}\\n\\n cmd = '{stilts} tcat {in} countrows=true lazy=true ofmt=colfits-basic out={out}'\\n mycmd = cmd.format(**param)\\n log.debug(mycmd)\\n status = os.system(mycmd)\\n log.info('concat: '+str(status))\\n\\n return status\",\n \"def home(request):\\n gefs_layers,geoserver_wms_url,geoserver_workspace = listLayers()\\n\\n context = {\\n 'gefs_layers':json.dumps(gefs_layers),\\n 'geoserver_wms_url':geoserver_wms_url,\\n 'geoserver_workspace':geoserver_workspace\\n }\\n\\n return render(request, 'gefs/home.html', context)\",\n \"def _setOceanLocation(self):\\r\\n\\t\\t## If the fluids_hrc exists\\r\\n\\t\\tif cmds.objExists('fluids_hrc'):\\r\\n\\t\\t\\tif cmds.objExists('ocean_srf'):\\r\\n\\t\\t\\t\\tcmds.connectAttr('fluids_hrc.translateX', 'ocean_srf.translateX', f = True)\\r\\n\\t\\t\\t\\tcmds.connectAttr('fluids_hrc.translateZ', 'ocean_srf.translateZ', f = True)\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tcmds.warning('MISSING ocean_srf node from scene....')\\r\\n\\r\\n\\t\\t\\tif cmds.objExists('oceanPreviewPlane_prv'):\\r\\n\\t\\t\\t\\tcmds.connectAttr('fluids_hrc.translateX', 'oceanPreviewPlane_prv.translateX', f = True)\\r\\n\\t\\t\\t\\tcmds.connectAttr('fluids_hrc.translateZ', 'oceanPreviewPlane_prv.translateZ', f = True)\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tcmds.warning('MISSING oceanPreviewPlane_prv node from scene....')\\r\\n\\t\\telse:\\r\\n\\t\\t\\tcmds.warning('NO fluids_hrc FOUND! Can not move the ocean into final position. PLEASE CHECK FX PUBLISH NOW!')\",\n \"def fetch(self,etime,lat,lon,timewindow=20,radius=100,limit=None):\\n utctime = etime\\n #get the most likely event time and ID for the event we input\\n eid,gtime = self._check_catalog(etime,lat,lon,timewindow,radius)\\n if eid is None:\\n msg = 'Could not find this event in the GeoNet earthquake catalog. Returning.'\\n raise StrongMotionFetcherException(msg)\\n\\n #set up the ftp url for this day and month\\n #[MONTH] should be in the format mm_Mon (04_Apr, 05_May, etc.)\\n neturl = GEOBASE.replace('[YEAR]',str(utctime.year))\\n monthstr = utctime.strftime('%m_%b')\\n neturl = neturl.replace('[MONTH]',monthstr)\\n urlparts = urllib.parse.urlparse(neturl)\\n ftp = ftplib.FTP(urlparts.netloc)\\n ftp.login() #anonymous\\n ftp.cwd(urlparts.path)\\n\\n \\n #get the current local directory, then cd to the desired raw folder\\n cwd = os.getcwd()\\n os.chdir(self._rawfolder)\\n self._datafiles = []\\n\\n #create the event folder name from the time we got above\\n fname = gtime.strftime('%Y-%m-%d_%H%M%S')\\n try:\\n ftp.cwd(fname)\\n except:\\n msg = 'Could not find an FTP data folder called \\\"%s\\\". Returning.' % (urllib.parse.urljoin(neturl,fname))\\n raise StrongMotionFetcherException(msg)\\n\\n try:\\n #actually retrieve the data files\\n volumes = []\\n dirlist = ftp.nlst()\\n for volume in dirlist:\\n if volume.startswith('Vol'):\\n ftp.cwd(volume)\\n if 'data' not in ftp.nlst():\\n ftp.cwd('..')\\n continue\\n\\n ftp.cwd('data')\\n flist = ftp.nlst()\\n for ftpfile in flist:\\n if not ftpfile.endswith('V1A'):\\n continue\\n localfile = os.path.join(os.getcwd(),ftpfile)\\n if localfile in self._datafiles:\\n continue\\n self._datafiles.append(localfile)\\n f = open(localfile,'wb')\\n sys.stderr.write('Retrieving remote file %s...\\\\n' % ftpfile)\\n ftp.retrbinary('RETR %s' % ftpfile,f.write)\\n f.close()\\n if limit is not None and len(self._datafiles) >= limit:\\n break\\n ftp.cwd('..')\\n ftp.cwd('..')\\n if limit is not None and len(self._datafiles) >= limit:\\n break\\n except Exception as e:\\n pass\\n finally:\\n ftp.quit()\\n os.chdir(cwd)\\n return\",\n \"def get_observation(world_state,weather,time_now):\\n obs = np.zeros((2, OBS_SIZE, OBS_SIZE))\\n object_list=[]\\n while world_state.is_mission_running:\\n time.sleep(0.1)\\n world_state = agent_host.getWorldState()\\n \\n if len(world_state.errors) > 0:\\n raise AssertionError('Could not load grid.')\\n\\n if world_state.number_of_observations_since_last_state > 0:\\n # First we get the json from the observation API\\n msg = world_state.observations[-1].text\\n observations = json.loads(msg)\\n # Get observation\\n if weather != 'clear':\\n object_list.append(weather)\\n else:\\n object_list.append('sunny')\\n\\n if time_now>13000:\\n object_list.append(\\\"night\\\")\\n else:\\n object_list.append(\\\"morning\\\")\\n grid = observations['floorAll']\\n \\n if grid.count('water')>3:\\n object_list.append(\\\"river\\\")\\n if grid.count('leaves')>2:\\n object_list.append(\\\"tree\\\")\\n if grid.count('lava')>1:\\n object_list.append(\\\"lava\\\")\\n \\n animal=observations['NearbyEntities']\\n target_ani=['Sheep','Cow','Pig']\\n for i in animal:\\n if i['name'] not in object_list and i['name'] in target_ani:\\n object_list.append(i['name'])\\n break\\n \\n \\n return object_list\",\n \"def runOttawa():\\r\\n \\r\\n DIRECTORY = \\\"D:\\\\Amaury\\\\Desktop\\\\ottawa_image_db\\\"\\r\\n \\r\\n ds = ogr.Open('C:/Users/msawada/Desktop/Urban_RAT/Urban_RAT_inventory_4326.dbf')\\r\\n layer = ds.GetLayer()\\r\\n \\r\\n n_loc = len(layer)\\r\\n #done: range(3000)\\r\\n for i in range(3000,6000):\\r\\n print('%.2f' %(i*100/n_loc) + \\\" %\\\")\\r\\n \\r\\n index = i\\r\\n feature = layer[index]\\r\\n \\r\\n lon = feature.GetGeometryRef().GetX()\\r\\n lat = feature.GetGeometryRef().GetY()\\r\\n \\r\\n folder = DIRECTORY + '/%2.6f,%2.6f' %(lat,lon)\\r\\n \\r\\n panIds = streetview.panoids(lat, lon)\\r\\n \\r\\n if len(panIds) > 0:\\r\\n if not os.path.exists(folder):\\r\\n os.makedirs(folder)\\r\\n for pan in panIds:\\r\\n img = streetview.api_download(pan[\\\"panoid\\\"], folder, API_KEY, fov=80, pitch=0, year=pan[\\\"year\\\"],month=pan[\\\"month\\\"])\\r\\n if img != None:\\r\\n full_size = misc.imread(img)\\r\\n resized = misc.imresize(full_size, (64,64))\\r\\n desc = gist.extract(resized)\\r\\n np.savetxt(img + '64.txt', desc)\",\n \"def maps():\\n flatmap_list = []\\n root_path = pathlib.Path(settings['FLATMAP_ROOT'])\\n if root_path.is_dir():\\n for flatmap_dir in root_path.iterdir():\\n index = os.path.join(settings['FLATMAP_ROOT'], flatmap_dir, 'index.json')\\n mbtiles = os.path.join(settings['FLATMAP_ROOT'], flatmap_dir, 'index.mbtiles')\\n if os.path.isdir(flatmap_dir) and os.path.exists(index) and os.path.exists(mbtiles):\\n with open(index) as fp:\\n index = json.loads(fp.read())\\n version = index.get('version', 1.0)\\n reader = MBTilesReader(mbtiles)\\n if version >= 1.3:\\n metadata = read_metadata(reader, 'metadata')\\n if (('id' not in metadata or flatmap_dir.name != metadata['id'])\\n and ('uuid' not in metadata or flatmap_dir.name != metadata['uuid'].split(':')[-1])):\\n app.logger.error(f'Flatmap id mismatch: {flatmap_dir}')\\n continue\\n flatmap = {\\n 'id': metadata['id'],\\n 'source': metadata['source'],\\n 'version': version\\n }\\n if 'created' in metadata:\\n flatmap['created'] = metadata['created']\\n if 'taxon' in metadata:\\n flatmap['taxon'] = normalise_identifier(metadata['taxon'])\\n flatmap['describes'] = metadata['describes'] if 'describes' in metadata else flatmap['taxon']\\n elif 'describes' in metadata:\\n flatmap['taxon'] = normalise_identifier(metadata['describes'])\\n flatmap['describes'] = flatmap['taxon']\\n if 'biological-sex' in metadata:\\n flatmap['biologicalSex'] = metadata['biological-sex']\\n if 'uuid' in metadata:\\n flatmap['uuid'] = metadata['uuid']\\n if 'name' in metadata:\\n flatmap['name'] = metadata['name']\\n else:\\n try:\\n source_row = reader._query(\\\"SELECT value FROM metadata WHERE name='source'\\\").fetchone()\\n except (InvalidFormatError, sqlite3.OperationalError):\\n flask.abort(404, 'Cannot read tile database: {}'.format(mbtiles))\\n if source_row is None:\\n continue\\n flatmap = {\\n 'id': flatmap_dir.name,\\n 'source': source_row[0]\\n }\\n created = reader._query(\\\"SELECT value FROM metadata WHERE name='created'\\\").fetchone()\\n if created is not None:\\n flatmap['created'] = created[0]\\n describes = reader._query(\\\"SELECT value FROM metadata WHERE name='describes'\\\").fetchone()\\n if describes is not None and describes[0]:\\n flatmap['describes'] = normalise_identifier(describes[0])\\n flatmap_list.append(flatmap)\\n return flask.jsonify(flatmap_list)\",\n \"def get_starbucks_info(city_info):\\n print('=='*100)\\n print(city_info[0] + '的星巴克店开始爬取')\\n\\n starbucks_data = []\\n\\n payload = {\\n \\\"query_type\\\": \\\"TQUERY\\\",\\n \\\"pagesize\\\": \\\"20\\\",\\n \\\"qii\\\": \\\"true\\\",\\n \\\"cluster_state\\\": \\\"5\\\",\\n \\\"need_utd\\\": \\\"true\\\",\\n \\\"utd_sceneid\\\": \\\"1000\\\",\\n \\\"div\\\": \\\"PC1000\\\",\\n \\\"addr_poi_merge\\\": \\\"true\\\",\\n \\\"is_classify\\\": \\\"true\\\",\\n \\\"zoom\\\": \\\"12\\\",\\n \\\"city\\\": city_info[1][0],\\n \\\"geoobj\\\": city_info[1][1],\\n \\\"keywords\\\": \\\"星巴克\\\",\\n }\\n\\n for page_num in range(1, 100):\\n payload['pagenum'] = page_num\\n poi_list = request_amap_poi_info(payload, 'https://www.amap.com/')\\n\\n if not poi_list:\\n print('request starbucks list exit with %s' % page_num)\\n break\\n\\n for poi in poi_list:\\n data = {\\n 'amap_key': poi.get('id'),\\n 'name': poi.get('name'),\\n 'city': poi.get('cityname'),\\n 'longitude': poi.get('longitude'),\\n 'latitude': poi.get('latitude'),\\n 'address': poi.get('address'),\\n 'tel': poi.get('tel'),\\n }\\n starbucks_data.append(data)\\n print('【%s】的星巴克列表的第【%s】页已爬取完毕'% (city_info[0], page_num))\\n print('【%s】的星巴克全部爬取完毕' % city_info[0])\\n print('==' * 100)\\n return starbucks_data\",\n \"def home():\\n return (\\n f\\\"/api/v1.0/precipitation<br/>\\\"\\n f\\\"/api/v1.0/stations<br/>\\\"\\n f\\\"/api/v1.0/tobs<br/>\\\"\\n f\\\"/api/v1.0/<start><br/>\\\"\\n f\\\"/api/v1.0/<start>/<end>\\\"\\n \\n )\",\n \"def get_water_network_properties():\\n\\n j = request.json\\n\\n region = ee.Geometry(j['region'])\\n start = j['start']\\n stop = j['stop']\\n scale = j['scale']\\n\\n step = j['step']\\n\\n crs = j['crs']\\n\\n error = ee.ErrorMargin(scale / 2, 'meters')\\n\\n if 'network' in j:\\n raise Exception(\\n 'TODO: re-using existing networks is not supported yet')\\n\\n # get water mask\\n water_vector = get_water_mask_vector(region, scale, start, stop)\\n\\n # skeletonize\\n output = generate_skeleton_from_voronoi(scale, water_vector)\\n centerline = ee.FeatureCollection(output[\\\"centerline\\\"])\\n distance = ee.Image(output[\\\"distance\\\"])\\n\\n # generate width at every offset\\n centerline = centerline.map(\\n lambda line: line.set(\\\"length\\\", line.length(error)))\\n\\n short_lines = centerline.filter(ee.Filter.lte('length', step))\\n long_lines = centerline.filter(ee.Filter.gt('length', step))\\n\\n def process_long_line(line):\\n line_length = line.length(error)\\n distances = ee.List.sequence(0, line_length, step)\\n segments = line.geometry().cutLines(distances, error)\\n\\n def generate_line_middle_point(pair):\\n pair = ee.List(pair)\\n\\n s = ee.Geometry(pair.get(0))\\n offset = ee.Number(pair.get(1))\\n\\n centroid = ee.Geometry.Point(s.coordinates().get(0))\\n\\n return ee.Feature(centroid) \\\\\\n .set(\\\"lineId\\\", line.id()) \\\\\\n .set(\\\"offset\\\", offset)\\n\\n segments = segments.geometries().zip(distances) \\\\\\n .map(generate_line_middle_point)\\n\\n return ee.FeatureCollection(segments)\\n\\n long_line_points = long_lines.map(process_long_line).flatten()\\n\\n def process_short_line(line):\\n geom = line.geometry(error, 'EPSG:4326')\\n\\n geom = ee.Geometry.Point(geom.coordinates().get(0), 'EPSG:4326')\\n\\n return ee.Feature(geom) \\\\\\n .set(\\\"lineId\\\", line.id()) \\\\\\n .set(\\\"offset\\\", 0)\\n\\n short_line_points = short_lines.map(process_short_line)\\n\\n points = long_line_points.merge(short_line_points)\\n\\n fa = ee.Image('WWF/HydroSHEDS/15ACC')\\n dem = ee.Image('JAXA/ALOS/AW3D30_V1_1').select('MED')\\n\\n def add_flow_accumulation(pt):\\n flow_accumulation = fa.reduceRegion(\\n reducer=ee.Reducer.max(),\\n geometry=pt.geometry().buffer(scale * 10),\\n scale=scale).values().get(0)\\n\\n return pt \\\\\\n .set(\\\"flow_accumulation\\\", ee.Number(flow_accumulation))\\n\\n def add_width(pt):\\n width = distance.reduceRegion(\\n reducer=ee.Reducer.max(),\\n geometry=pt.geometry(),\\n scale=scale).values().get(0)\\n\\n return pt \\\\\\n .set(\\\"width\\\", ee.Number(width).multiply(scale).multiply(2))\\n\\n def add_elevation(pt):\\n elevation = dem.reduceRegion(\\n reducer=ee.Reducer.median(),\\n geometry=pt.geometry().buffer(scale * 10),\\n scale=scale).values().get(0)\\n\\n return pt \\\\\\n .set(\\\"elevation\\\", ee.Number(elevation))\\n\\n points = points.map(add_width)\\n points = points.map(add_elevation)\\n points = points.map(add_flow_accumulation)\\n\\n points = points.map(transform_feature(crs, scale))\\n\\n # create response\\n data = points.getInfo()\\n\\n return Response(json.dumps(data), status=200, mimetype='application/json')\",\n \"def fetch_list(self):\\n\\t\\treturn self.fetch(self.list_url % ART_SERVER_HOST)\",\n \"def fetchJson(url):\",\n \"def _create_api_ulr_list(self) -> List[str]:\\n api = Setup.pickpoint_api\\n return [\\n f\\\"https://api.pickpoint.io/v1/reverse/?key={api}&lat={lat}&lon={lon}\\\"\\n f\\\"&accept-language=en-US\\\"\\n for lat, lon in self.locations\\n ]\",\n \"def load_gateways_snapshot(request):\\n gateways_file = path.join(get_project_root(), 'gateways.api.json')\\n gateways = []\\n try:\\n data = open(gateways_file, \\\"r\\\").read()\\n except IOError:\\n pass # Doesn't exist gateway backup\\n else:\\n try:\\n gateways = json.loads(data)\\n except ValueError:\\n pass # Invalid format\\n else:\\n for gw in gateways:\\n gw_url = reverse('gateway-detail', kwargs={'pk':gw['id']})\\n gw['uri'] = request.build_absolute_uri(gw_url)\\n \\n return gateways\",\n \"def get_latest_national_geographic_wallpaper_remote():\\r\\n\\r\\n logging.debug('get_latest_national_geographic_wallpaper_remote()')\\r\\n\\r\\n # get image url\\r\\n if use_proxy:\\r\\n response = requests.get(\\\"https://www.nationalgeographic.com/photography/photo-of-the-day/\\\", proxies=proxies, timeout=5, verify=False)\\r\\n else:\\r\\n response = requests.get(\\\"https://www.nationalgeographic.com/photography/photo-of-the-day/\\\")\\r\\n match = re.search('.*content=\\\\\\\"([^\\\\\\\"]*\\\\.jpg)\\\\\\\".*', response.text)\\r\\n full_image_url = match.group(1)\\r\\n logging.debug('get_latest_national_geographic_wallpaper_remote - full_image_url = {}'.format(full_image_url))\\r\\n\\r\\n # image's name\\r\\n image_name = get_generated_image_name(full_image_url)\\r\\n\\r\\n # Check and maintain DB\\r\\n if not exists_image_in_database(full_image_url):\\r\\n add_image_to_database(full_image_url, image_name, \\\"national\\\")\\r\\n # download and save image\\r\\n full_image_path = download_image(full_image_url, image_name)\\r\\n update_image_in_database(full_image_url, full_image_path)\\r\\n else:\\r\\n full_image_path = get_image_path_from_database(full_image_url)\\r\\n\\r\\n # Return full path to image\\r\\n logging.debug('get_latest_national_geographic_wallpaper_remote - full_image_path = {}'.format(full_image_path))\\r\\n return full_image_path\",\n \"def run(self) -> list:\\n logger.debug('Fetching date %s', self._day.strftime('%Y/%m/%d'))\\n \\n regions = [r() for r in regions_list]\\n air_quality = list()\\n \\n # fetch air quality of each region\\n for r in regions:\\n r.fetch_air_quality(self._day)\\n \\n # gather results from all regions\\n for r in regions:\\n # wait until region has fetched his data\\n r.wait_for_quality()\\n logging.info('Fetched region:%s for day:%s', r.name, self._day)\\n air_quality.append({\\n 'name': r.name,\\n 'provinces': [\\n {'name': x.name, 'short': x.short_name, 'quality': x.quality.asdict()} \\n for x in r.provinces]\\n })\\n\\n self._fetcher.fetched_result(self._day, air_quality)\",\n \"def fetch_weather(y):\\r\\n # request parameter(s): Start with '?'\\r\\n # separate name and value with '='\\r\\n # multiple parameter name value pairs are separate with '&'\\r\\n query_string = \\\"?id={}&units=imperial&APIKEY={}\\\".format(y, API_KEY)\\r\\n request_url = WS_URL + query_string\\r\\n print(\\\"Request URL: \\\", request_url)\\r\\n response = requests.get(request_url)\\r\\n if response.status_code == 200:\\r\\n city_name = response.json()[\\\"city\\\"][\\\"name\\\"]\\r\\n lst = response.json()[\\\"list\\\"]\\r\\n tmp_list = []\\r\\n for i in range(len(lst) // 8):\\r\\n li = [x for x in range(len(lst)) if x // 8 == i]\\r\\n tmp_list.append(max([lst[j][\\\"main\\\"][\\\"temp_max\\\"] for j in li]))\\r\\n return City(city_name, tmp_list)\\r\\n else:\\r\\n print(\\\"How should I know?\\\")\\r\\n return None\",\n \"def test_last_cities(client):\\n client.get(\\\"/weather/viena\\\")\\n client.get(\\\"/weather/sorocaba\\\")\\n client.get(\\\"/weather/barcelona\\\")\\n\\n\\n # Validate weither return two lasts cities in cache or not\\n response = client.get(\\\"/weather/1\\\")\\n print(response.data)\\n assert b\\\"Barcelona\\\" in response.data\",\n \"def main(args):\\n args = parse_args(args)\\n setup_logging(args.loglevel)\\n\\n dir = 'snapshots/' + args.name\\n Path(dir).mkdir(parents=True, exist_ok=True)\\n\\n session = sql_session(dir)\\n today = session.query(Sunrise).filter_by(date=date.today()).first()\\n if today is None:\\n today = add_sunrise(session, args.name, args.latitude, args.longitude)\\n now = datetime.utcnow()\\n username, password = password_from_netrc(args.url)\\n\\n if args.force == True or (now > today.sunrise_time and now < today.sunset_time):\\n where = tzwhere.tzwhere()\\n timezone_str = where.tzNameAt(float(args.latitude), float(args.longitude))\\n local_timezone = pytz.timezone(timezone_str)\\n utcnow = now.replace(tzinfo=pytz.utc)\\n local_now = utcnow.astimezone(local_timezone)\\n path = local_now.strftime('%Y%m%d-%H%M%S%z') + '_' + args.name + '.jpg'\\n\\n r = requests.get(args.url, auth=HTTPDigestAuth(username, password), verify=False, stream=True)\\n if r.status_code == requests.codes.ok:\\n with open(dir + '/' + path, 'wb') as f:\\n for chunk in r.iter_content(4096):\\n f.write(chunk)\",\n \"def get_headlines(outlet):\\n if outlet == \\\"BBC\\\":\\n parser = news_parser.BBC(\\\"https://www.bbc.co.uk\\\")\\n elif outlet == \\\"DailyMail\\\":\\n parser = news_parser.DailyMail(\\\"https://www.dailymail.co.uk\\\")\\n elif outlet == \\\"Guardian\\\":\\n parser = news_parser.Guardian(\\\"https://www.theguardian.com\\\")\\n elif outlet == \\\"Metro\\\":\\n parser = news_parser.Metro(\\\"https://www.metro.co.uk\\\")\\n elif outlet == \\\"Mirror\\\":\\n parser = news_parser.Mirror(\\\"https://www.mirror.co.uk/news/\\\")\\n elif outlet == \\\"Reuters\\\":\\n parser = news_parser.Reuters(\\\"https://uk.reuters.com\\\")\\n elif outlet == \\\"Sun\\\":\\n parser = news_parser.Sun(\\\"https://www.thesun.co.uk\\\")\\n elif outlet == \\\"Independent\\\":\\n parser = news_parser.Independent(\\\"https://www.independent.co.uk\\\")\\n else:\\n parser = news_parser.BBC(\\\"https://www.bbc.co.uk/news\\\")\\n \\n index = outlets.index(outlet)\\n url_list = []\\n while len(url_list) < 50:\\n opts = {\\n 'language': ['en'],\\n 'source_id': [ids[index]],\\n 'published_at_start':'NOW-1DAY',\\n 'published_at_end':'NOW',\\n 'sort_by': 'hotness',\\n 'sort_direction': 'desc',\\n 'cursor': '*',\\n 'per_page': 100\\n }\\n\\n try:\\n api_response = api_instance.list_stories(**opts)\\n for story in api_response.stories:\\n url = story.links.permalink\\n if url:\\n url_list.append(url)\\n except ApiException as e:\\n print(\\\"Exception when calling DefaultApi->list_stories: %s\\\\n\\\" %e)\\n \\n opts['cursor'] = api_response.next_page_cursor\\n \\n url_list = url_list[:50]\\n \\n articles_list = []\\n for url in url_list:\\n raw_article = parser.get_article(url)\\n if raw_article is not None:\\n articles_list.append(raw_article)\\n\\n articles = []\\n for article in articles_list:\\n parsed_article = parser.parse(article)\\n if parsed_article is not None:\\n articles.append(parsed_article)\\n \\n if len(articles) > 30:\\n articles = articles[:30]\\n\\n return articles\",\n \"def test_last_max_default(client):\\n client.get(\\\"/weather/viena\\\")\\n client.get(\\\"/weather/sorocaba\\\")\\n client.get(\\\"/weather/barcelona\\\")\\n client.get(\\\"/weather/belo horizonte\\\")\\n client.get(\\\"/weather/rio de janeiro\\\")\\n client.get(\\\"/weather/recife\\\")\\n\\n\\n # Validate weither return two lasts cities in cache or not\\n response = client.get(\\\"/weather/6\\\")\\n print(response.data)\\n assert (b\\\"Barcelona\\\" in response.data) & (b\\\"Recife\\\" in response.data) & (b\\\"Rio de Janeiro\\\" in response.data) & (b\\\"Belo Horizonte\\\" in response.data) & (b\\\"Sorocaba\\\" in response.data)\",\n \"def test_smoker_latest_get(self):\\n pass\",\n \"def fetch_all_lta(url):\\n results = []\\n while True:\\n new_results = requests.get(\\n url,\\n headers = headers,\\n params = {'$skip': len(results)}\\n ).json()['value']\\n if new_results == []:\\n break\\n else:\\n results += new_results\\n return results\",\n \"def test_aws_service_api_snapshots_get(self):\\n pass\",\n \"def fetch_50(url):\\n\\n results = requests.get(url,headers = headers).json()\\n return results\",\n \"def wfs_common(request, response, mode, spatial_mode='wfs'):\\n\\n outputpath = configuration.get_config_value('server', 'outputpath')\\n outputurl = configuration.get_config_value('server', 'outputurl')\\n\\n list_of_files = []\\n for one_resource in request.inputs['resource']:\\n # Download if not opendap\\n # Adding a maximum file size from a server config file would\\n # be possible here...\\n try:\\n nc_file = opendap_or_download(\\n one_resource.data,\\n auth_tkt_cookie=request.http_request.cookies,\\n output_path='/tmp')\\n except:\\n raise Exception(traceback.format_exc())\\n list_of_files.append(nc_file)\\n\\n if ('typename' in request.inputs) and ('featureids' in request.inputs):\\n typename = request.inputs['typename'][0].data\\n features = [f.data for f in request.inputs['featureids']]\\n if 'geoserver' in request.inputs:\\n geoserver = request.inputs['geoserver'][0].data\\n else:\\n geoserver = configuration.get_config_value('extra', 'geoserver')\\n if 'mosaic' in request.inputs:\\n mosaic = request.inputs['mosaic'][0].data\\n else:\\n mosaic = False\\n try:\\n conn = WebFeatureService(url=geoserver, version='2.0.0')\\n resp = conn.getfeature([typename], featureid=features,\\n outputFormat='application/json')\\n feature = json.loads(resp.read())\\n crs_code = owslib.crs.Crs(\\n feature['crs']['properties']['name']).code\\n crs = ocgis.CoordinateReferenceSystem(epsg=crs_code)\\n geom = [\\n {'geom': shape(f['geometry']), 'crs': crs,\\n 'properties': f['properties']}\\n for f in feature['features']]\\n except Exception as e:\\n msg = ('Failed to fetch features.\\\\ngeoserver: {0} \\\\n'\\n 'typename: {1}\\\\nfeatures {2}\\\\n{3}').format(\\n geoserver, typename, features, e)\\n raise Exception(msg)\\n if mosaic:\\n new_geom = geom[0]\\n for merge_geom in geom[1:]:\\n new_geom['geom'] = new_geom['geom'].union(merge_geom['geom'])\\n new_geom['properties'] = {'bbox': feature['bbox']}\\n geom = new_geom\\n elif spatial_mode == 'bbox':\\n geom = [[request.inputs['lon0'][0].data,\\n request.inputs['lat0'][0].data,\\n request.inputs['lon1'][0].data,\\n request.inputs['lat1'][0].data]]\\n else:\\n geom = [None]\\n\\n if ('initial_datetime' in request.inputs) and \\\\\\n ('final_datetime' in request.inputs):\\n tr = [request.inputs['initial_datetime'][0].data,\\n request.inputs['final_datetime'][0].data]\\n else:\\n tr = None\\n\\n try:\\n output_files = []\\n output_urls = []\\n mv_dir = tempfile.mkdtemp(dir=outputpath)\\n os.chmod(mv_dir, 0755)\\n\\n for one_file in list_of_files:\\n file_name = os.path.basename(one_file)\\n if file_name[-3:] == '.nc':\\n file_prefix = file_name[:-3]\\n else:\\n file_prefix = file_name\\n ocgis.env.DIR_OUTPUT = tempfile.mkdtemp(dir=os.getcwd())\\n ocgis.env.OVERWRITE = True\\n nc = netCDF4.Dataset(one_file, 'r')\\n var_names = guess_main_variables(nc)\\n nc.close()\\n rd = ocgis.RequestDataset(one_file, var_names)\\n for i, one_geom in enumerate(geom):\\n if one_geom is None:\\n ocgis_geom = None\\n elif spatial_mode == 'bbox':\\n ocgis_geom = one_geom\\n else:\\n ocgis_geom = one_geom['geom']\\n if mode == 'averager':\\n # Extent errors are ignored\\n try:\\n # Here with aggregate=True, can't pass the whole\\n # one_geom dictionary, is this a sign that this does\\n # not support multipolygon?\\n ops = ocgis.OcgOperations(\\n dataset=rd, geom=ocgis_geom,\\n spatial_operation='clip', aggregate=True,\\n time_range=tr, output_format='nc',\\n interpolate_spatial_bounds=True,\\n prefix=file_prefix).execute()\\n except ExtentError:\\n continue\\n elif mode == 'subsetter':\\n # Extent errors are ignored\\n try:\\n # Still having problem with the geometry, previously\\n # was passing geom=[one_geom]\\n ops = ocgis.OcgOperations(\\n dataset=rd, geom=ocgis_geom, time_range=tr,\\n output_format='nc',\\n interpolate_spatial_bounds=True,\\n prefix=file_prefix).execute()\\n except ExtentError:\\n continue\\n # Here, the global attribute 'subset_typename' and\\n # 'subset_featureid' are added to the NetCDF file to keep\\n # track of the feature used.\\n if (geom != [None]) and (spatial_mode == 'wfs'):\\n with netCDF4.Dataset(ops, 'a') as nc:\\n nc.subset_typename = typename\\n nc.subset_featureid = features[i]\\n\\n if (spatial_mode == 'wfs') and \\\\\\n ('featureids' in request.inputs):\\n mv_name = '{0}_{1}.nc'.format(\\n os.path.basename(ops)[:-3], features[i])\\n else:\\n mv_name = '{0}_{1}.nc'.format(\\n os.path.basename(ops)[:-3], 'subset')\\n\\n mv_file = os.path.join(mv_dir, mv_name)\\n shutil.move(ops, mv_file)\\n output_files.append(mv_file)\\n shutil.rmtree(ocgis.env.DIR_OUTPUT)\\n\\n # Cover the case of an online wps server and the offline\\n # mode for tests.\\n if outputurl == 'file:///tmp':\\n disk_file = 'file:///' + mv_file.lstrip('/')\\n output_urls.append(disk_file)\\n else:\\n url_file = os.path.join(\\n outputurl, os.path.basename(mv_dir), mv_name)\\n output_urls.append(url_file)\\n except:\\n raise Exception(traceback.format_exc())\\n\\n # If only ExtentError occured, the output_urls will be empty...\\n if not output_urls:\\n raise ExtentError(message=\\\"All ocgis calls returned ExtentError.\\\")\\n\\n time_str = time.strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\", time.gmtime())\\n output_file_name = \\\"result_%s_.json\\\" % (time_str,)\\n output_file = os.path.join('/tmp', output_file_name)\\n f1 = open(output_file, 'w')\\n f1.write(json.dumps(output_urls))\\n f1.close()\\n response.outputs['output'].file = output_file\\n response.outputs['output'].output_format = json_format\\n response.update_status(\\\"done\\\", 100)\\n return response\",\n \"def list(\\n self,\\n farmer_id: str,\\n boundary_id: str,\\n provider: str = \\\"Microsoft\\\",\\n source: Optional[str] = \\\"Sentinel_2_L2A\\\",\\n start_date_time: Optional[datetime.datetime] = None,\\n end_date_time: Optional[datetime.datetime] = None,\\n max_cloud_coverage_percentage: Optional[float] = 100,\\n max_dark_pixel_coverage_percentage: Optional[float] = 100,\\n image_names: Optional[List[str]] = None,\\n image_resolutions: Optional[List[float]] = None,\\n image_formats: Optional[List[str]] = None,\\n max_page_size: Optional[int] = 50,\\n skip_token: Optional[str] = None,\\n **kwargs: Any\\n ) -> AsyncIterable[\\\"_models.SceneListResponse\\\"]:\\n cls = kwargs.pop('cls', None) # type: ClsType[\\\"_models.SceneListResponse\\\"]\\n error_map = {\\n 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError\\n }\\n error_map.update(kwargs.pop('error_map', {}))\\n api_version = \\\"2021-03-31-preview\\\"\\n accept = \\\"application/json\\\"\\n\\n def prepare_request(next_link=None):\\n # Construct headers\\n header_parameters = {} # type: Dict[str, Any]\\n header_parameters['Accept'] = self._serialize.header(\\\"accept\\\", accept, 'str')\\n\\n if not next_link:\\n # Construct URL\\n url = self.list.metadata['url'] # type: ignore\\n path_format_arguments = {\\n 'Endpoint': self._serialize.url(\\\"self._config.endpoint\\\", self._config.endpoint, 'str', skip_quote=True),\\n }\\n url = self._client.format_url(url, **path_format_arguments)\\n # Construct parameters\\n query_parameters = {} # type: Dict[str, Any]\\n query_parameters['provider'] = self._serialize.query(\\\"provider\\\", provider, 'str')\\n query_parameters['farmerId'] = self._serialize.query(\\\"farmer_id\\\", farmer_id, 'str')\\n query_parameters['boundaryId'] = self._serialize.query(\\\"boundary_id\\\", boundary_id, 'str')\\n if source is not None:\\n query_parameters['source'] = self._serialize.query(\\\"source\\\", source, 'str')\\n if start_date_time is not None:\\n query_parameters['startDateTime'] = self._serialize.query(\\\"start_date_time\\\", start_date_time, 'iso-8601')\\n if end_date_time is not None:\\n query_parameters['endDateTime'] = self._serialize.query(\\\"end_date_time\\\", end_date_time, 'iso-8601')\\n if max_cloud_coverage_percentage is not None:\\n query_parameters['maxCloudCoveragePercentage'] = self._serialize.query(\\\"max_cloud_coverage_percentage\\\", max_cloud_coverage_percentage, 'float', maximum=100, minimum=0)\\n if max_dark_pixel_coverage_percentage is not None:\\n query_parameters['maxDarkPixelCoveragePercentage'] = self._serialize.query(\\\"max_dark_pixel_coverage_percentage\\\", max_dark_pixel_coverage_percentage, 'float', maximum=100, minimum=0)\\n if image_names is not None:\\n query_parameters['imageNames'] = [self._serialize.query(\\\"image_names\\\", q, 'str') if q is not None else '' for q in image_names]\\n if image_resolutions is not None:\\n query_parameters['imageResolutions'] = [self._serialize.query(\\\"image_resolutions\\\", q, 'float') if q is not None else '' for q in image_resolutions]\\n if image_formats is not None:\\n query_parameters['imageFormats'] = [self._serialize.query(\\\"image_formats\\\", q, 'str') if q is not None else '' for q in image_formats]\\n if max_page_size is not None:\\n query_parameters['$maxPageSize'] = self._serialize.query(\\\"max_page_size\\\", max_page_size, 'int', maximum=1000, minimum=10)\\n if skip_token is not None:\\n query_parameters['$skipToken'] = self._serialize.query(\\\"skip_token\\\", skip_token, 'str')\\n query_parameters['api-version'] = self._serialize.query(\\\"api_version\\\", api_version, 'str')\\n\\n request = self._client.get(url, query_parameters, header_parameters)\\n else:\\n url = next_link\\n query_parameters = {} # type: Dict[str, Any]\\n path_format_arguments = {\\n 'Endpoint': self._serialize.url(\\\"self._config.endpoint\\\", self._config.endpoint, 'str', skip_quote=True),\\n }\\n url = self._client.format_url(url, **path_format_arguments)\\n request = self._client.get(url, query_parameters, header_parameters)\\n return request\\n\\n async def extract_data(pipeline_response):\\n deserialized = self._deserialize('SceneListResponse', pipeline_response)\\n list_of_elem = deserialized.value\\n if cls:\\n list_of_elem = cls(list_of_elem)\\n return deserialized.next_link or None, AsyncList(list_of_elem)\\n\\n async def get_next(next_link=None):\\n request = prepare_request(next_link)\\n\\n pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)\\n response = pipeline_response.http_response\\n\\n if response.status_code not in [200]:\\n error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response)\\n map_error(status_code=response.status_code, response=response, error_map=error_map)\\n raise HttpResponseError(response=response, model=error)\\n\\n return pipeline_response\\n\\n return AsyncItemPaged(\\n get_next, extract_data\\n )\",\n \"def remotes():\",\n \"def _find_latest():\\n try:\\n db = get_master_collection()\\n service_details = db.find({\\\"master.key\\\": \\\"release\\\"}).sort([(\\\"master.value\\\", pymongo.DESCENDING)]).limit(1)\\n for service in service_details:\\n for r in sorted(service[\\\"master\\\"][\\\"value\\\"], reverse=True):\\n latest_release = r\\n build_list = service[\\\"master\\\"][\\\"value\\\"][r]\\n break\\n break\\n\\n latest_rel_num = str(latest_release).replace(\\\"_\\\", \\\".\\\")\\n build_list = _natural_sort(build_list)\\n for build in build_list:\\n latest_build = build\\n break\\n\\n latest_build_num = latest_build\\n second_latest_build_num = int(latest_build_num) - 1\\n latest = {\\\"latest_val\\\": latest_rel_num + \\\"_\\\" + latest_build_num,\\n \\\"second_latest_val\\\": latest_rel_num + \\\"_\\\" + str(second_latest_build_num)}\\n except Exception as e:\\n logger.error(\\\"Exception in _find_latest : \\\" + str(e))\\n return latest\",\n \"def after_download_live_ocean(msg, config, checklist):\\n next_workers = {\\\"crash\\\": [], \\\"failure\\\": [], \\\"success\\\": []}\\n if msg.type == \\\"success\\\":\\n next_workers[\\\"success\\\"].append(\\n NextWorker(\\n \\\"nowcast.workers.make_live_ocean_files\\\",\\n args=[\\\"--run-date\\\", list(checklist[\\\"Live Ocean products\\\"].keys())[-1]],\\n )\\n )\\n return next_workers[msg.type]\",\n \"def process_repo(self, url, multiple=False):\\n json_data = loads(self.get_from_net(url)) #TODO add code to detect error messages in JSON from API\\n if not multiple: json_data = [json_data]\\n repo_dets = []\\n for i in json_data:\\n dets = {\\n 'full_name': i['full_name'],\\n 'name': i['name'],\\n 'fork': i['fork'],\\n 'url': i['url'],\\n 'language': '',\\n 'created': '',\\n 'id': i[\\\"id\\\"] #for use in pagination\\n }\\n if 'language' in i: dets['language'] = i['language']\\n if 'created_at' in i: dets['created'] = i['created_at']\\n repo_dets.append(dets)\\n return repo_dets\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5493592","0.5329551","0.52164626","0.5196075","0.5143778","0.5120439","0.5111923","0.5093739","0.50789034","0.5073732","0.5064189","0.5026506","0.50177354","0.49574155","0.49371618","0.49332502","0.49277258","0.49213284","0.4918263","0.4880653","0.48528478","0.4849145","0.48240533","0.4819007","0.48093665","0.48064187","0.48058134","0.480354","0.48027882","0.48002982","0.47985622","0.4796717","0.47914734","0.47885105","0.47780693","0.4775243","0.47739676","0.477104","0.47667262","0.47641972","0.47640908","0.47530705","0.47501957","0.47498062","0.473284","0.47203165","0.47199634","0.47046515","0.47011787","0.46921012","0.46826142","0.4681126","0.46600628","0.4657638","0.46562043","0.46552792","0.46548852","0.4643899","0.46419334","0.46366945","0.46340647","0.46329272","0.46248734","0.46244353","0.4620816","0.46163955","0.46152553","0.4602719","0.45983577","0.45977455","0.45954427","0.45918715","0.45913768","0.4590909","0.4590033","0.4589969","0.45863536","0.45807874","0.4580296","0.4576959","0.45761558","0.4571432","0.45658472","0.4564077","0.45591837","0.45580035","0.45577383","0.4551371","0.45492122","0.4546462","0.4541338","0.45390126","0.4538248","0.4536954","0.45354238","0.45332357","0.45319584","0.45223537","0.45213765","0.4519621"],"string":"[\n \"0.5493592\",\n \"0.5329551\",\n \"0.52164626\",\n \"0.5196075\",\n \"0.5143778\",\n \"0.5120439\",\n \"0.5111923\",\n \"0.5093739\",\n \"0.50789034\",\n \"0.5073732\",\n \"0.5064189\",\n \"0.5026506\",\n \"0.50177354\",\n \"0.49574155\",\n \"0.49371618\",\n \"0.49332502\",\n \"0.49277258\",\n \"0.49213284\",\n \"0.4918263\",\n \"0.4880653\",\n \"0.48528478\",\n \"0.4849145\",\n \"0.48240533\",\n \"0.4819007\",\n \"0.48093665\",\n \"0.48064187\",\n \"0.48058134\",\n \"0.480354\",\n \"0.48027882\",\n \"0.48002982\",\n \"0.47985622\",\n \"0.4796717\",\n \"0.47914734\",\n \"0.47885105\",\n \"0.47780693\",\n \"0.4775243\",\n \"0.47739676\",\n \"0.477104\",\n \"0.47667262\",\n \"0.47641972\",\n \"0.47640908\",\n \"0.47530705\",\n \"0.47501957\",\n \"0.47498062\",\n \"0.473284\",\n \"0.47203165\",\n \"0.47199634\",\n \"0.47046515\",\n \"0.47011787\",\n \"0.46921012\",\n \"0.46826142\",\n \"0.4681126\",\n \"0.46600628\",\n \"0.4657638\",\n \"0.46562043\",\n \"0.46552792\",\n \"0.46548852\",\n \"0.4643899\",\n \"0.46419334\",\n \"0.46366945\",\n \"0.46340647\",\n \"0.46329272\",\n \"0.46248734\",\n \"0.46244353\",\n \"0.4620816\",\n \"0.46163955\",\n \"0.46152553\",\n \"0.4602719\",\n \"0.45983577\",\n \"0.45977455\",\n \"0.45954427\",\n \"0.45918715\",\n \"0.45913768\",\n \"0.4590909\",\n \"0.4590033\",\n \"0.4589969\",\n \"0.45863536\",\n \"0.45807874\",\n \"0.4580296\",\n \"0.4576959\",\n \"0.45761558\",\n \"0.4571432\",\n \"0.45658472\",\n \"0.4564077\",\n \"0.45591837\",\n \"0.45580035\",\n \"0.45577383\",\n \"0.4551371\",\n \"0.45492122\",\n \"0.4546462\",\n \"0.4541338\",\n \"0.45390126\",\n \"0.4538248\",\n \"0.4536954\",\n \"0.45354238\",\n \"0.45332357\",\n \"0.45319584\",\n \"0.45223537\",\n \"0.45213765\",\n \"0.4519621\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.48307702"},"document_rank":{"kind":"string","value":"22"}}},{"rowIdx":11173,"cells":{"query":{"kind":"string","value":"Used to fetch most recent cache files"},"document":{"kind":"string","value":"def _fetchAnimPublish(self, tk, templateFile = '', fields = '', id = '', shotNum = '', inprogressBar = '', filteredPublish = ''):\r\n\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'Fetching latest caches now....', verbose = False)\r\n\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'Template....%s' % templateFile, verbose = False)\r\n\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'id....%s' % id, verbose = False)\r\n\r\n\t\t## Get all the publishes from shotgun now..\r\n\t\tif self.app.get_setting('shotStep') == 'Blocking':\r\n\t\t\tgetAnimVersionFolders = tk.paths_from_template(templateFile, {'Step' : 'Blck', 'id' : id, 'Shot' : shotNum})\r\n\t\telif self.app.get_setting('shotStep') == 'Animation':\r\n\t\t\tgetAnimVersionFolders = tk.paths_from_template(templateFile, {'Step' : 'Anm', 'id' : id, 'Shot' : shotNum})\r\n\r\n\t\tif getAnimVersionFolders:\r\n\t\t\t## now find the highest version folder number\r\n\t\t\thighestVersionFolder = r'%s' % max(getAnimVersionFolders)\r\n\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'highestVersionFolder...%s' % highestVersionFolder, verbose = False)\r\n\r\n\t\t\thighestVersionFolder = highestVersionFolder.replace('\\\\', '/')\r\n\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'highestVersionFolder replaced \\\\ with /...\\n%s' % highestVersionFolder, verbose = False)\r\n\r\n\t\t\tversionNumber = highestVersionFolder.split('/')[-1]\r\n\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'versionNumber: %s' % versionNumber, verbose = False)\r\n\r\n\t\t\tgetCacheFiles = os.listdir(highestVersionFolder)\r\n\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'getCacheFiles...\\n%s' % getCacheFiles, verbose = False)\r\n\r\n\r\n\t\t\t##################################################################################################################\r\n\t\t\t## GPU CACHE LOADER\r\n\t\t\t# if 'publish/gpu' in highestVersionFolder:\r\n\t\t\t# if filteredPublish == 'Fetch GPU Publish' or filteredPublish == 'Fetch All Publishes':\r\n\t\t\t#\r\n\t\t\t# debug(app = self.app, method = '_fetchAnimPublish', message = 'Loading gpu cache files now...', verbose = False)\r\n\t\t\t# ## Build the group if it doesn't already exist\r\n\t\t\t# if not self._buildGroup('GPU_CACHES_hrc', versionNumber):\r\n\t\t\t# if versionNumber >= self.staticVersionNumber:\r\n\t\t\t# ## Now process the caches\r\n\t\t\t# for each in getCacheFiles:\r\n\t\t\t# nodeName = each.split('_')[0]\r\n\t\t\t# if not cmds.objExists(nodeName):\r\n\t\t\t# gpuNode = '%s/%s' % (highestVersionFolder, each)\r\n\t\t\t# debug(app = self.app, method = '_fetchAnimPublish', message = 'gpuNode...\\n%s' % gpuNode, verbose = False)\r\n\t\t\t#\r\n\t\t\t# cmds.createNode('gpuCache', n = nodeName)\r\n\t\t\t# debug(app = self.app, method = '_fetchAnimPublish', message = 'gpuNode... created..', verbose = False)\r\n\t\t\t#\r\n\t\t\t# cmds.rename(\"transform1\", \"%s_gpu\" % nodeName)\r\n\t\t\t# debug(app = self.app, method = '_fetchAnimPublish', message = 'Rename for %s sucessful...' % nodeName, verbose = False)\r\n\t\t\t#\r\n\t\t\t# try:\r\n\t\t\t# cmds.setAttr('%s.cacheFileName' % nodeName, gpuNode, type = \"string\")\r\n\t\t\t# cmds.setAttr(\"%s.cacheGeomPath\" % nodeName, \"|\", type = \"string\")\r\n\t\t\t# cmds.parent(cmds.listRelatives(nodeName, parent = True)[0], 'GPU_CACHES_hrc')\r\n\t\t\t# except RuntimeError:\r\n\t\t\t# cmds.warning('FAILED TO SET GPU PATHS FOR %s CORRECTLY PLEASE CHECK YOUR PUBLISH!' % nodeName)\r\n\t\t\t#\r\n\t\t\t# debug(app = self.app, method = '_fetchAnimPublish', message = 'GPU cache %s succesfully loaded and parented...' % nodeName, verbose = False)\r\n\t\t\t# else:\r\n\t\t\t# cmds.warning(\"FAILED: %s already exists in the scene\" % nodeName)\r\n\t\t\t# else:\r\n\t\t\t# debug(app = self.app, method = '_fetchAnimPublish', message = 'GPU Caches older than current publish version, no gpus were export on publish for this version thus we are skipping GPU import', verbose = False)\r\n\t\t\t# pass\r\n\t\t\t# else:\r\n\t\t\t# debug(app = self.app, method = '_fetchAnimPublish', message = 'GPU_CACHES_hrc ALREADY SETUP SKIPPING....', verbose = False)\r\n\t\t\t# pass\r\n\r\n\t\t\t##################################################################################################################\r\n\t\t\t## STATIC CACHE LOADER\r\n\t\t\tif 'publish/alembic_static' in highestVersionFolder:\r\n\t\t\t\tif filteredPublish == 'Fetch Static Publish' or filteredPublish == 'Fetch All Publishes':\r\n\r\n\t\t\t\t\thrc = 'ABC_STATIC_CACHES_hrc'\r\n\t\t\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'Loading alembic_static cache files now...', verbose = False)\r\n\r\n\t\t\t\t\t## Build the group if it doesn't already exist\r\n\t\t\t\t\tproceedFetch = True\r\n\t\t\t\t\tif cmds.objExists(hrc):\r\n\t\t\t\t\t\tproceedFetch = cmds.confirmDialog(title = 'Fetch Static Publish', message = '\"%s\" already exist! Press OK to re-fetch a latest publish.' % hrc, button = ['OK', 'Cancel'], defaultButton = 'OK', cancelButton = 'Cancel', dismissString = 'Cancel')\r\n\t\t\t\t\t\tproceedFetch = True if proceedFetch == 'OK' else False\r\n\r\n\t\t\t\t\t## Now process the caches\r\n\t\t\t\t\tif proceedFetch:\r\n\t\t\t\t\t\tif cmds.objExists(hrc):\r\n\t\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\tcmds.delete(hrc)\r\n\t\t\t\t\t\t\texcept:\r\n\t\t\t\t\t\t\t\tcmds.warning('Failed to delete \"%s\"...' % hrc)\r\n\t\t\t\t\t\t\t\tproceedFetch = False\r\n\r\n\t\t\t\t\t\tif not cmds.objExists(hrc):\r\n\t\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\tself._buildGroup(hrc, versionNumber)\r\n\t\t\t\t\t\t\texcept:\r\n\t\t\t\t\t\t\t\tcmds.warning('Failed to create \"%s\"...' % hrc)\r\n\t\t\t\t\t\t\t\tproceedFetch = False\r\n\r\n\t\t\t\t\t\tif proceedFetch:\r\n\t\t\t\t\t\t\tfor each in getCacheFiles:\r\n\t\t\t\t\t\t\t\tabcNode = '%s/%s' % (highestVersionFolder, each)\r\n\t\t\t\t\t\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'abcNode %s' % abcNode, verbose = False)\r\n\t\t\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\t\tcmds.AbcImport(abcNode, reparent = \"|%s\" % hrc, setToStartFrame = True)#, createIfNotFound = True, removeIfNoUpdate = True)\r\n\t\t\t\t\t\t\t\texcept:\r\n\t\t\t\t\t\t\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'StaticCache: %s import FAILED!' % abcNode, verbose = False)\r\n\t\t\t\t\t\t\t\t\tinprogressBar.close()\r\n\t\t\t\t\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = '\"%s\" imported successfully...' % hrc, verbose = False)\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'FAILED TO SETUP \"%s\", PLEASE CHECK WITH YOUR SUPERVISOR!!!' % hrc, verbose = False)\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = '\"%s\" ALREADY SETUP SKIPPING...' % hrc, verbose = False)\r\n\r\n\t\t\t##################################################################################################################\r\n\t\t\t## ANIMATED CACHE LOADER\r\n\t\t\telif 'publish/alembic_anim' in highestVersionFolder:\r\n\t\t\t\tif filteredPublish == 'Fetch Anim Publish' or filteredPublish == 'Fetch All Publishes':\r\n\r\n\t\t\t\t\thrc = 'ABC_ANIM_CACHES_hrc'\r\n\t\t\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'Loading alembic_anim cache files now...', verbose = False)\r\n\r\n\t\t\t\t\t## Build the group if it doesn't already exist\r\n\t\t\t\t\tproceedFetch = True\r\n\t\t\t\t\tif cmds.objExists(hrc):\r\n\t\t\t\t\t\tproceedFetch = cmds.confirmDialog(title = 'Fetch Anim Publish', message = '\"%s\" already exist! Press OK to re-fetch a latest publish.' % hrc, button = ['OK', 'Cancel'], defaultButton = 'OK', cancelButton = 'Cancel', dismissString = 'Cancel')\r\n\t\t\t\t\t\tproceedFetch = True if proceedFetch == 'OK' else False\r\n\r\n\t\t\t\t\t## Now process the caches\r\n\t\t\t\t\tif proceedFetch:\r\n\t\t\t\t\t\tif cmds.objExists(hrc):\r\n\t\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\tcmds.delete(hrc)\r\n\t\t\t\t\t\t\texcept:\r\n\t\t\t\t\t\t\t\tcmds.warning('Failed to delete \"%s\"...' % hrc)\r\n\t\t\t\t\t\t\t\tproceedFetch = False\r\n\r\n\t\t\t\t\t\tif not cmds.objExists(hrc):\r\n\t\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\tself._buildGroup(hrc, versionNumber)\r\n\t\t\t\t\t\t\texcept:\r\n\t\t\t\t\t\t\t\tcmds.warning('Failed to create \"%s\"...' % hrc)\r\n\t\t\t\t\t\t\t\tproceedFetch = False\r\n\r\n\t\t\t\t\t\tif proceedFetch:\r\n\t\t\t\t\t\t\tfor each in getCacheFiles:\r\n\t\t\t\t\t\t\t\tabcNode = '%s/%s' % (highestVersionFolder, each)\r\n\t\t\t\t\t\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'abcNode %s' % abcNode, verbose = False)\r\n\t\t\t\t\t\t\t\tcmds.AbcImport(abcNode, reparent = \"|%s\" % hrc, setToStartFrame = True)\r\n\t\t\t\t\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = '\"%s\" imported successfully...' % hrc, verbose = False)\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'FAILED TO SETUP \"%s\", PLEASE CHECK WITH YOUR SUPERVISOR!!!' % hrc, verbose = False)\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = '\"%s\" ALREADY SETUP SKIPPING...' % hrc, verbose = False)\r\n\r\n\t\t\t##################################################################################################################\r\n\t\t\t## CAMERA LOADER\r\n\t\t\telif 'publish/cam' in highestVersionFolder:\r\n\t\t\t\tif filteredPublish == 'Fetch Camera Publish' or filteredPublish == 'Fetch All Publishes':\r\n\r\n\t\t\t\t\thrc = 'BAKE_CAM_hrc'\r\n\t\t\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'Loading camera files now...', verbose = False)\r\n\r\n\t\t\t\t\t## Build the group if it doesn't already exist\r\n\t\t\t\t\tproceedFetch = True\r\n\t\t\t\t\tif cmds.objExists(hrc):\r\n\t\t\t\t\t\tproceedFetch = cmds.confirmDialog(title = 'Fetch Camera Publish', message = '\"%s\" already exist! Press OK to re-fetch a latest publish.' % hrc, button = ['OK', 'Cancel'], defaultButton = 'OK', cancelButton = 'Cancel', dismissString = 'Cancel')\r\n\t\t\t\t\t\tproceedFetch = True if proceedFetch == 'OK' else False\r\n\r\n\t\t\t\t\t## Now process the caches\r\n\t\t\t\t\tif proceedFetch:\r\n\t\t\t\t\t\tif cmds.objExists(hrc):\r\n\t\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\tcmds.delete(hrc)\r\n\t\t\t\t\t\t\texcept:\r\n\t\t\t\t\t\t\t\tcmds.warning('Failed to delete \"%s\"...' % hrc)\r\n\t\t\t\t\t\t\t\tproceedFetch = False\r\n\r\n\t\t\t\t\t\tif proceedFetch:\r\n\t\t\t\t\t\t\tfor each in getCacheFiles:\r\n\t\t\t\t\t\t\t\tcamNode = '%s/%s' % (highestVersionFolder, each)\r\n\t\t\t\t\t\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'camera %s' % camNode, verbose = False)\r\n\t\t\t\t\t\t\t\tcmds.file(camNode, i = True)\r\n\t\t\t\t\t\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'Camera imported successfully...', verbose = False)\r\n\t\t\t\t\t\t\t\tfor each in cmds.listRelatives(hrc, children = True):\r\n\t\t\t\t\t\t\t\t\tchannels = ['tx', 'ty', 'tz', 'rx', ' ry', 'rz', 'sx', 'sy', 'sz']\r\n\t\t\t\t\t\t\t\t\tfor eachChan in channels:\r\n\t\t\t\t\t\t\t\t\t\tcmds.setAttr('%s.%s' %(each, eachChan), lock = True)\r\n\t\t\t\t\t\t\t\t\t\tif not cmds.objExists('%s.version' % hrc):\r\n\t\t\t\t\t\t\t\t\t\t\tcmds.addAttr(('%s' % hrc), ln='version', dt='string')\r\n\t\t\t\t\t\t\t\t\t\t\tcmds.setAttr('%s.version' % hrc, versionNumber, type='string')\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'FAILED TO SETUP \"%s\", PLEASE CHECK WITH YOUR SUPERVISOR!!!' % hrc, verbose = False)\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = '\"%s\" ALREADY SETUP SKIPPING...' % hrc, verbose = False)\r\n\t\t\telse:\r\n\t\t\t\tpass\r\n\t\telse:\r\n\t\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'No Versions found for %s...' % templateFile, verbose = False)"},"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 files():\n return get_cached(\"files.json\")","def fetch(\n self, output_folder: Path, cache: Dict[str, str], fetch_opts: List[Dict[str, Any]]\n ) -> List[str]:\n return [\n download_snapshot(source_config[\"url\"], output_folder, **source_config.get(\"opts\", {}))\n for source_config in fetch_opts\n ]","def get(self):\n if path.exists(self.cachefile):\n self.invalidion()\n full_cache = self._get_all()\n return full_cache\n else:\n return []","def _retrieveCachedData(self):","def pre_cache(self, scope, name):\n item = {'did': '%s:%s' % (scope, name),\n 'base_dir': self.cache_path,\n 'no_subdir': self.no_subdir,\n 'transfer_timeout': self.transfer_timeout}\n if self.rse:\n item['rse'] = self.rse\n\n client = Client()\n all_files = client.list_files(scope, name)\n\n download_client = DownloadClient(client=client)\n downloaded_files = download_client.download_dids([item], num_threads=self.num_threads)\n\n self.logger.info('Downloaded files: %s' % downloaded_files)\n\n ret_files = []\n for file in all_files:\n downloaded_file = None\n for d_file in downloaded_files:\n if d_file['scope'] == file['scope'] and d_file['name'] == file['name']:\n downloaded_file = d_file\n break\n\n ret_file = {'scope': file['scope'],\n 'name': file['name'],\n 'min_id': 1,\n 'max_id': file['events'],\n 'status': ContentStatus.PRECACHED if downloaded_file and downloaded_file['clientState'] == 'ALREADY_DONE' else ContentStatus.NEW,\n 'size': file['bytes'],\n 'md5': downloaded_file['md5'] if downloaded_file else None,\n 'adler32': downloaded_file['adler32'] if downloaded_file else None,\n 'pfn': downloaded_file['dest_file_path'] if downloaded_file else None\n\n }\n \"\"\"\n if ret_file['status'] == ContentStatus.AVAILABLE:\n if req.granularity_type == GranularityType.PARTIAL:\n ret_file['status'] = ContentStatus.PRECACHED\n elif req.granularity_type == GranularityType.FILE:\n ret_file['status'] = ContentStatus.TOSTAGEDOUT\n \"\"\"\n ret_files.append(ret_file)\n return ret_files","def _get_latest_content(self):\n if self._modified is None:\n self._load_content()\n return\n\n # check if data updated\n statinfo = os.stat(self._path)\n if statinfo.st_mtime > self._modified:\n self._load_content()","def get_files_summary(self) -> Dict[str, float]:\n json_path = (\n os.path.join(self.cache_dir, \"coverage-min.json\") if self.cache_dir else \"\"\n )\n if self.use_cache and self.cache_dir:\n try:\n with open(json_path) as coverage_summary_file:\n full_coverage_summary = json.load(coverage_summary_file)\n last_updated = datetime.strptime(\n full_coverage_summary[\"last_updated\"], \"%Y-%m-%dT%H:%M:%SZ\"\n )\n next_update = last_updated + timedelta(days=1)\n if next_update > datetime.utcnow():\n return full_coverage_summary[\"files\"]\n except FileNotFoundError:\n logger.info(\n \"No cache file found. creatig a cache dir at %s\", self.cache_dir\n )\n os.makedirs(self.cache_dir, exist_ok=True)\n except (json.JSONDecodeError, KeyError, ValueError) as exc:\n logger.exception(exc)\n\n resp = requests.get(self.url)\n resp.raise_for_status()\n data = resp.json()\n if self.use_cache and self.cache_dir:\n with open(json_path, \"w\") as fp:\n json.dump(data, fp)\n\n return data[\"files\"]","def get_content_from_cache(self):\n\n rss_feed = []\n news_to_show = 0\n\n try:\n self.print_if_verbose(\n f\"Method 'get_content_from_cache' is working: \\n\"\n f\"Trying to get content from cache...\"\n )\n os.chdir(\"cache\")\n except Exception as error:\n print(f\"{error}: cache does not exists!\")\n return\n\n try:\n os.chdir(\"image_cache\")\n self.full_path_to_image_cache = os.getcwd()\n os.chdir(\"..\")\n except:\n pass\n\n try:\n with open(\"rss_reader_cache.json\", \"r\", encoding=\"utf-8\") as cache_file:\n data_from_cache = json.load(cache_file)\n self.print_if_verbose(f\"Content from cache has been received successfully. \\n\")\n except Exception as error:\n self.print_if_verbose(f\"{error}: cache file does not exist! \\n\")\n return\n\n if self.source:\n for feed in data_from_cache:\n if self.source in feed.keys():\n for news in feed[self.source]:\n if news[\"PubDate\"] == str(self.date):\n rss_feed.append(news)\n news_to_show += 1\n if self.limit and news_to_show == self.limit:\n break\n if self.limit and news_to_show == self.limit:\n break\n else:\n for channel in data_from_cache:\n for feed_link in channel:\n for news in channel[feed_link]:\n if news[\"PubDate\"] == str(self.date):\n rss_feed.append(news)\n news_to_show += 1\n if self.limit and news_to_show == self.limit:\n break\n if self.limit and news_to_show == self.limit:\n break\n\n os.chdir(\"..\")\n\n self.news_amount = len(rss_feed)\n\n if self.news_amount == 0:\n print(f\"There is no news in cache for specified date. \\n\")\n else:\n self.print_if_verbose(f\"There is {self.news_amount} news in cache for specified date. \\n\")\n\n self.print_if_verbose(f\"Method 'get_content_from_cache' is finished. \\n\")\n\n return rss_feed","def _get_all_cache_files(self):\n files = set()\n dir_tree = os.walk(self.config.get('cachedir', self.CACHEDIR))\n for dirpath, _, filenames in dir_tree:\n for file_name in filenames:\n if 'cache' in file_name:\n files.add(os.path.join(dirpath, file_name))\n return files","def loadRecentFiles(self):\n self.recentFiles.clear()\n for n in range(RECENTFILEMAX):\n rf = self.getSection(CFG_RECENT, str(n))\n if rf:\n self.recentFiles.append(rf)\n else:\n break","def get_cache_path(self):","def get_cache_path(self):","def get_all_cached_files(cache_dir=None):\n if cache_dir is None:\n cache_dir = TRANSFORMERS_CACHE\n else:\n cache_dir = str(cache_dir)\n if not os.path.isdir(cache_dir):\n return []\n\n cached_files = []\n for file in os.listdir(cache_dir):\n meta_path = os.path.join(cache_dir, f\"{file}.json\")\n if not os.path.isfile(meta_path):\n continue\n\n with open(meta_path, encoding=\"utf-8\") as meta_file:\n metadata = json.load(meta_file)\n url = metadata[\"url\"]\n etag = metadata[\"etag\"].replace('\"', \"\")\n cached_files.append({\"file\": file, \"url\": url, \"etag\": etag})\n\n return cached_files","def use_cached_files(self, cache_key):\r\n pass","def get_recent_files():\n creds = authenticate()\n\n service = build('drive', 'v3', credentials=creds)\n\n # Call the Drive v3 API\n results = service.files().list(\n pageSize=10, fields=\"nextPageToken, files(id, name)\").execute()\n items = results.get('files', [])\n\n return items","def get_most_recent_tarball(self, pkg):\n pass","def get_last_fetch_time():\n if os.path.exists(LAST_FETCH_TIME_FILE):\n with open(LAST_FETCH_TIME_FILE, 'r') as f:\n last_fetch_time = f.read()\n\n return last_fetch_time\n return ''","def get_latest_file(folder):\n if not os.path.exists(folder):\n raise AppException(\"Folder does not exists!\")\n return max(glob.iglob(folder + \"/*\"), key=os.path.getctime)","def list_cached():\n for json_name in cached_files():\n source_name = get_source_file_name(json_name)\n yield (json_name, source_name)","def _get_fetcher_cache_timestamp(self):\r\n status_data = self._get_fetcher_status()\r\n return status_data.get('cache_timestamp') if status_data else None","def _get_most_recent_file(path):\n allowed_ext = tuple(MIMES.keys())\n files_iter = (\n entry for entry in os.scandir(path)\n if entry.is_file() and entry.name.lower().endswith(allowed_ext)\n )\n for entry in sorted(files_iter, key=_newest_ctime):\n return entry.path\n return None","def get_files(self):\n # self.folder= +str(int(time.time()))\n if not os.path.exists(self.folder):\n os.mkdir(self.folder)\n while len(self.url_queue): # If we have URLs to crawl - we crawl\n href = self.url_queue.popleft() # We grab a URL from the left of the list\n filename = href.rsplit('/', 1)[-1]\n print(\"Downloading %s to %s...\" % (href, filename))\n fullname = os.path.join(self.folder, filename)\n urlretrieve(href, fullname)\n self.xlfnames.append(filename)","def get_recently_modified_scratch_file(settings):\n dir_contents = os.listdir(settings.location)\n full_paths = map(lambda f: os.path.join(settings.location, f), dir_contents)\n files = filter(lambda f: os.path.isfile(str(f)), full_paths)\n if not files:\n return \"\"\n files = sorted(files, key=_get_mtime)\n return files[-1]","def _fetch_files(self, snapshot: Bug, filepaths: List[str]) -> None:\n bgz = self.__bugzoo\n container = bgz.containers.provision(snapshot)\n try:\n for filepath in filepaths:\n key = (snapshot.name, filepath)\n try:\n if key in self.__cache_file_contents:\n continue\n self.__cache_file_contents[key] = \\\n bgz.files.read(container, filepath)\n except KeyError:\n logger.exception(\"Failed to read source file, '%s/%s': file not found\", # noqa: pycodestyle\n snapshot.name, filepath)\n raise FileNotFound(filepath)\n finally:\n del bgz.containers[container.uid]","def get_cache(feed_key):\n\n cache_file = CACHE_DIR / (feed_key + \".json\")\n\n with open(cache_file, \"r\") as file:\n entries = json.load(file)\n\n return entries","def prefill_cache():\n print(\"Prefilling cache.\")\n print(\"\\rListing ATS files...\", end=\"\")\n file_names = list(ats_files())\n print(\"\\rListing ATS files: done.\")\n index = 0\n files_count = len(file_names)\n cached_count = 0\n for file_name in file_names:\n index += 1\n print(\"\\rHandling ATS file #%i of %i\" % (index, files_count), end=\"\")\n if get_json(file_name) is not None:\n cached_count += 1\n print(\"\\nDone: %i file(s) cached.\" % cached_count)","def _load_cached_2to3(self, path, cache):\n try:\n cache_stats = os.stat(cache)\n source_stats = os.stat(path)\n except OSError as e:\n if e.errno == errno.ENOENT: # FileNotFoundError\n self.logger.debug('Cache miss: %s' % cache)\n return None\n else:\n raise\n\n if cache_stats.st_mtime <= source_stats.st_mtime:\n self.logger.debug('Cache miss (stale): %s' % cache)\n return None\n\n self.logger.debug(\"Cache hit: %s\" % cache)\n return super().get_data(cache)","def cache_path(self):","def cache_path(self):","def get_recent_images(num_images=30):\n folder = app.config['UPLOAD_FOLDER']\n\n init_image_info()\n\n # get list of last modified images - ignore .json file and files start with .\n files = ['/'.join((folder, file)) \\\n for file in os.listdir(folder) if ('json' not in file) \\\n and not (file.startswith('.')) ]\n\n # list of tuples (file_path, timestamp)\n last_modified_files = [(file, os.path.getmtime(file)) for file in files]\n print(last_modified_files)\n last_modified_files = sorted(last_modified_files,\n key=lambda t: t[1], reverse=True)\n num_stored_images = len(last_modified_files)\n\n # build a list of image information\n image_stats = []\n\n print(\"THE NUMBER OF STORED IMAGES IS: {}\".format(num_stored_images))\n\n if num_stored_images != 0:\n\n # read in image info\n with open(IMAGE_INFO_JSON, 'r') as f:\n info = json.load(f)\n\n for i, f in enumerate(last_modified_files):\n # set limit for rendering pictures\n if i > num_images: break\n\n path, filename = f[0], f[0].replace(folder, '').replace('/', '')\n cur_image_info = info.get(filename, {})\n\n print(\"CURRENT IMAGE INFO IS: {}\".format(cur_image_info))\n\n img = {\n 'path': path,\n 'labels': cur_image_info\n }\n print(\"CURRENT IMG LABEL DATA IS: {}\".format(img['labels']))\n image_stats.append(img)\n\n return image_stats, num_stored_images","def get_request_candidates(self):\n return os.listdir(self.cache_dir_)","def getRecentFile(*p):\n\tfrom os import stat\n\tfrom os.path import join\n\tfrom glob import glob\n\tresult = \"\"\n\tfiles = glob(join(p[0],*p[1:]))\n\tfor file in files:\n\t\tif result == \"\":\n\t\t\tresult = file\n\t\telse:\n\t\t\tif stat(file).st_mtime > stat(result).st_mtime:\n\t\t\t\tresult = file\n\treturn result","def getRecentItems(self, filename):\n self.recent_items = {}\n try:\n recent_files = open(filename, 'r').read().strip()\n \n if (recent_files == \"\"):\n self.recent_items = None\n return\n\n recent_list = recent_files.split('\\n')\n\n for item in recent_list:\n last_slash = self.findLastSlash(item) \n before = item[:last_slash+1]\n after = item[last_slash+1:]\n self.recent_items[after] = before\n\n self.updateRecentItems()\n self.ui.menuRecent_Files.removeAction(self.ui.actionNone)\n\n except IOError:\n print 'Unable to read the recent files from file: %s\\n'\\\n 'No recent files will be displayed' % str(filename)","def get_latest_files(base_path):\n try:\n adni = os.listdir('{}/qc/phantom/adni'.format(base_path))\n adni = filter(lambda x: '_adni_' in x and 'csv' in x, adni)\n adni.sort()\n adni = adni[-9:]\n except:\n adni = None\n\n try:\n fmri = os.listdir('{}/qc/phantom/fmri'.format(base_path))\n fmri = filter(lambda x: '_fmri_' in x and 'csv' in x, fmri)\n fmri.sort()\n fmri = fmri[-7:]\n except:\n fmri = None\n\n try:\n dti = os.listdir('{}/qc/phantom/dti'.format(base_path))\n dti = filter(lambda x: '_dti_' in x and 'csv' in x, dti)\n dti.sort()\n dti = dti[-1:]\n except:\n dti = None\n\n return adni, fmri, dti","async def get() -> list:\n if _cache is None:\n await _update()\n return _cache","def get_version_from_recent_files(self):\n # full_path = self.fusion_prefs[\"LastCompFile\"]\n # return self.get_version_from_full_path(full_path)\n\n version = None\n rfm = RecentFileManager()\n\n try:\n recent_files = rfm[self.name]\n except KeyError:\n logger.debug('no recent files')\n recent_files = None\n\n if recent_files is not None:\n for i in range(len(recent_files)):\n version = self.get_version_from_full_path(recent_files[i])\n if version is not None:\n break\n\n logger.debug(\"version from recent files is: %s\" % version)\n\n return version","def mostRecentlyModified(self,files,n):\n mtime = lambda f: time.strptime(f['modified'], \"%a, %d %b %Y %H:%M:%S +0000\") \n filesSorted = sorted(files,key=mtime,reverse=True) #get descending order\n if len(filesSorted)<= n:\n return filesSorted\n else:\n return filesSorted[:n]","def GetFromCache(self, filename):\n return memcache.get('%s%s' % (self.CACHE_PREFIX, filename))","def GetFromCache(self, filename):\n return memcache.get('%s%s' % (self.CACHE_PREFIX, filename))","def fillCache(self):\n items = self.source.getRecent()\n items.reverse() # make sure the most recent ones are added last to the cache\n for item in items:\n self.cache.append(item.title)","def __loadRecent(self):\n self.recent = []\n Preferences.Prefs.rsettings.sync()\n rs = Preferences.Prefs.rsettings.value(recentNameFiles)\n if rs is not None:\n for f in Preferences.toList(rs):\n if QFileInfo(f).exists():\n self.recent.append(f)","def _get_newest_filenames(self, image_files):\n if self._previewcache[\"modified\"] is None:\n retval = image_files\n else:\n retval = [fname for fname in image_files\n if os.path.getmtime(fname) > self._previewcache[\"modified\"]]\n if not retval:\n logger.debug(\"No new images in output folder\")\n else:\n self._previewcache[\"modified\"] = max([os.path.getmtime(img) for img in retval])\n logger.debug(\"Number new images: %s, Last Modified: %s\",\n len(retval), self._previewcache[\"modified\"])\n return retval","def fetch_and_cache(data_url, file, data_dir=\"data\", force=False):\n data_dir = Path(data_dir)\n data_dir.mkdir(exist_ok = True)\n file_path = data_dir / Path(file)\n if force and file_path.exists():\n file_path.unlink()\n if force or not file_path.exists():\n print('Downloading...', end=' ')\n resp = requests.get(data_url)\n with file_path.open('wb') as f:\n f.write(resp.content)\n print('Done!')\n last_modified_time = time.ctime(file_path.stat().st_mtime)\n else:\n last_modified_time = time.ctime(file_path.stat().st_mtime)\n print(\"Using cached version that was downloaded (UTC):\", last_modified_time)\n return file_path","def get(self):\n # Check if last_refreshed is saved in the storage\n if \"last_refreshed\" in storage:\n self.refresh_infos(storage[\"last_refreshed\"])\n else:\n # last_refreshed not found in the storage\n soccerInfo = SoccerInfo.objects.first()\n if soccerInfo and soccerInfo[\"last_refreshed\"]:\n self.refresh_infos(soccerInfo[\"last_refreshed\"])\n else:\n self.refresh_infos()\n return storage[\"cached_soccerInfos\"], 200","def cached_files():\n for (dir_path, _dir_names, file_names) in os.walk(CACHE):\n for file_name in file_names:\n if is_json_file(file_name):\n yield os.path.join(dir_path, file_name)","def _read_cache(url):\n\n j = None\n m = hashlib.md5()\n m.update(url)\n if os.path.exists('.cache.%s' % m.hexdigest()):\n with open('.cache.%s' % m.hexdigest(), 'rb') as infile:\n j = json.load(infile)\n\n return j","def loadFileList(self):\r\n try:\r\n data = open(self.filelist_file, 'rb')\r\n except IOError:\r\n '''print \"No SRTM cached file list. Creating new one!\"'''\r\n if self.offline == 0:\r\n self.createFileList()\r\n return\r\n try:\r\n self.filelist = pickle.load(data)\r\n except:\r\n '''print \"Unknown error loading cached SRTM file list. Creating new one!\"'''\r\n if self.offline == 0:\r\n self.createFileList()","def get_cache(self):\n self._topo.create_cache()\n with open('/run/geopm-service/geopm-topo-cache') as fid:\n result = fid.read()\n return result","def get_from_cache(cls, file_name):\n random.shuffle(cls.CACHE_BACKENDS)\n fname = None\n for cb in cls.CACHE_BACKENDS:\n if not cb.health_check():\n continue\n fname = cb.get_from_cache(file_name)\n if fname:\n break\n return fname","def getLatestDate(market, folder):\n \n if market == 'stockOption':\n m = 'DTOP_O_'\n elif market == 'indexFuture':\n m = 'DTOP_F_'\n \n dateList = []\n # get file list from directory\n for f in os.listdir(folder):\n if m in f:\n # crop the date from filename\n row = f.replace(m,'').replace('.zip','')\n dateList.append(date(int(row[:4]), int(row[4:6]), int(row[6:])))\n\n \n latest = dateList[0]\n for x in range(1,len(dateList)):\n if dateList[x] > latest:\n latest = dateList[x]\n \n return latest","def last_log(self) -> List:\n logs_list: List = os.listdir(LOGS_BASE_PATH)\n full_list = [os.path.join(LOGS_BASE_PATH, i) for i in logs_list]\n time_sorted_list: List = sorted(full_list, key=os.path.getmtime)\n return time_sorted_list[-1]","def _get_recent_file_names():\n file_name_tpl = '{}-data.csv'\n date_fmt = '%Y-%m-%d'\n now = datetime.now()\n one_day_ago = now - timedelta(days=1)\n file_names = [\n file_name_tpl.format(one_day_ago.strftime(date_fmt)),\n file_name_tpl.format(now.strftime(date_fmt)),\n ]\n return [os.path.join(DATA_DIR, x) for x in file_names]","def find_cache_files():\n files = []\n\n for root, dirnames, filenames in os.walk(\".\"):\n for filename in fnmatch.filter(filenames, \"*.pyc\"):\n files.append(os.path.join(root, filename))\n\n for root, dirnames, filenames in os.walk(\".\"):\n for filename in fnmatch.filter(filenames, \"__pycache__\"):\n files.append(os.path.join(root, filename))\n\n return files","def get_from_cache(cls, target_filename):\n is_cached = cls.is_remote_cached(target_filename)\n if is_cached:\n cache = cls.CACHE_BACKEND()\n cache.download(is_cached, target_filename)\n logger.debug('File %r was downloaded from %r', target_filename, cls.CACHE_BACKEND)\n else:\n target_filename = None\n return target_filename","def test_pick_latest_file(self):\n max = FILES_LIST[0]\n for f in FILES_LIST:\n if os.path.getctime(os.path.join(FILES_DIR, f)) > os.path.getctime(os.path.join(FILES_DIR, max)):\n max = f\n \n max = FILES_DIR + max\n latest = pick_latest_file()\n \n self.assertEqual(max, latest)","def _list_dir(self):\n return [os.path.join(self.cache_dir, fn)\n for fn in os.listdir(self.cache_dir)]","def load_cache(base_url, path=\"logs/\"):\n\n # Convert URL to filename and read contents\n url_filename = url_to_filename(base_url)\n\n filename = f\"{path}CACHE-{url_filename}.html\"\n f = open(filename, \"r\")\n data_cache = f.read()\n\n data_cache = \" \".join(data_cache.split()) # Remove all whitespaces\n\n return data_cache","def getData(self, local_cache):","def recentfiles(archive):\n return heapq.nlargest(5, molecollector(archive))","def __recentDownload(self, downloadType):\n \n cursor = self.connection.cursor()\n cursor.execute(\"SELECT file_size, file_modification_time FROM download_audit WHERE download_type = %(dtype)s AND download_completion_time IS NOT NULL ORDER BY id DESC LIMIT 1\", {'dtype': downloadType})\n record = cursor.fetchone()\n cursor.close ()\n \n return record","def get_starred_files_by_username(self, username):\n starred_files = super(UserStarredFilesManager, self).filter(\n email=username, org_id=-1)\n\n ret = []\n repo_cache = {}\n for sfile in starred_files:\n # repo still exists?\n if repo_cache.has_key(sfile.repo_id):\n repo = repo_cache[sfile.repo_id]\n else:\n try:\n repo = seafile_api.get_repo(sfile.repo_id)\n except SearpcError:\n continue\n if repo is not None:\n repo_cache[sfile.repo_id] = repo\n else:\n sfile.delete()\n continue\n\n # file still exists?\n file_id = ''\n size = -1\n if sfile.path != \"/\":\n try:\n file_id = seafile_api.get_file_id_by_path(sfile.repo_id,\n sfile.path)\n # size = seafile_api.get_file_size(file_id)\n except SearpcError:\n continue\n if not file_id:\n sfile.delete()\n continue\n\n f = StarredFile(sfile.org_id, repo, file_id, sfile.path,\n sfile.is_dir, 0) # TODO: remove ``size`` from StarredFile\n ret.append(f)\n\n '''Calculate files last modification time'''\n files_list = []\n for sfile in ret:\n if sfile.is_dir:\n continue\n ele = (sfile.repo.id, sfile.path, sfile.file_id)\n files_list.append(ele)\n\n files_dict_with_last_modified = FileLastModifiedInfo.objects.get_files_last_modified(files_list)\n\n for sfile in ret:\n key = \"%s|%s|%s\" % (sfile.repo.id, sfile.path, sfile.file_id)\n if files_dict_with_last_modified.has_key(key):\n sfile.last_modified = files_dict_with_last_modified[key]\n else:\n # Should never reach here\n pass\n\n ret.sort(lambda x, y: cmp(y.last_modified, x.last_modified))\n\n return ret","def get(self):\n CACHE_KEY = 'sources'\n if not memcache.get(CACHE_KEY):\n logging.info('Populating cache.')\n feeds = Feed.all().order('name')\n feed_list = []\n for feed in feeds:\n feed_list.append(feed.ToDict())\n memcache.add(CACHE_KEY, simplejson.dumps(feed_list), 600)\n logging.info('Using cache.')\n logging.info(memcache.get(CACHE_KEY))\n self.response.headers['Content-Type'] = 'application/json'\n self.response.out.write(memcache.get(CACHE_KEY))","def get_cache_file(self, dependencies):\n filename = '%s.tar' % self.get_cache_key(dependencies)\n return os.path.join(self.cache_directory, filename)","def get_cache_time(self, URL):\n\n cache_file = self.get_cache_file_path(URL)\n\n if cache_file.exists():\n cache_time = cache_file.stat().st_mtime\n else:\n cache_time = 0\n\n return cache_time","def fetchfile(self, query):\n cur = self.conn.cursor()\n exists = False\n i = 0\n limit = 1000\n datas = None\n while (not exists) and (i < limit):\n cur.execute(query)\n rows = cur.fetchall()\n if len(rows) == 0:\n break\n i = i + 1\n exists = path.isfile(rows[0][1])\n datas = rows[0]\n\n if not exists:\n self.markfile(datas[0])\n\n return datas","def cachepath(self):\n return [self.fs.cachepath(uri) for uri in self.uri]","def test_last_files(self):\n glob_manager = GlobManager(['*'])\n self.assertCountEqual(glob_manager.last_files, set())\n\n glob_manager.get_files()\n self.assertCountEqual(\n glob_manager.last_files,\n {\n 'bob.py', 'dave.txt', 'fred.txt.py', 'geoff.py', 'jim.py.txt',\n 'rob.txt'\n })","def __get_files(self):\r\n \r\n files = []\r\n with requests.Session() as s:\r\n headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)'}\r\n respons = s.get(self.__url, headers=headers).text\r\n soup = BeautifulSoup(respons, 'html.parser')\r\n data_files = [link.get('href') for link in soup.find_all('a', class_=\"btn-primary\")]\r\n for year in soup.find_all('td', class_=\"align-middle\"):\r\n regex = re.compile(r\"data/data-?gis({year}|\\-rok\\-{year})\\.zip\".format(year=year.text))\r\n if any((match := regex.match(link)) for link in data_files):\r\n files.append(match.group(0))\r\n else:\r\n files.append(data_files[-1])\r\n return files","def download_latest_customers(self):\n file_id, data = self._gdrive.get_last_customer_data_file()\n self._datastore.force_data_storage('customer', data)","def get_files(self):\n\n self.files = []\n retriever_methods = [\n m\n for m in rtorrent9.file.methods\n if m.is_retriever() and m.is_available(self._rt_obj)\n ]\n # 2nd arg can be anything, but it'll return all files in torrent\n # regardless\n m = rtorrent9.rpc.Multicall(self)\n m.add(\n \"f.multicall\",\n self.info_hash,\n \"\",\n *[method.rpc_call + \"=\" for method in retriever_methods]\n )\n\n results = m.call()[0] # only sent one call, only need first result\n\n offset_method_index = retriever_methods.index(\n rtorrent9.rpc.find_method(\"f.offset\")\n )\n\n # make a list of the offsets of all the files, sort appropriately\n offset_list = sorted([r[offset_method_index] for r in results])\n\n for result in results:\n results_dict = {}\n # build results_dict\n for m, r in zip(retriever_methods, result):\n results_dict[m.varname] = rtorrent9.rpc.process_result(m, r)\n\n # get proper index positions for each file (based on the file\n # offset)\n f_index = offset_list.index(results_dict[\"offset\"])\n\n self.files.append(\n File(self._rt_obj, self.info_hash, f_index, **results_dict)\n )\n\n return self.files","def download_files(self):","def retrieve_cached_records(self):\r\n return u.load_cached_data(self.records_cache_path)","def get_downloaded():\n\n result = set()\n\n for name in os.listdir(PATH):\n filename, ext = name.split('.')\n if ext not in EXTENSIONS:\n continue\n\n ts, username, id = filename.split('+')\n result.add(id)\n return result","def get_latest_path(self):\n files = [fname for fname in os.listdir(self.checkpoint_dir) if fname.endswith(\".pth\")]\n filepaths = [os.path.join(self.checkpoint_dir, filepath) for filepath in files]\n latest_file = max(filepaths, key=os.path.getctime)\n return latest_file","def _get_cache_filename(self):\n home_dir = os.path.expanduser(\"~\")\n filename = 'dbcollection.json'\n return os.path.join(home_dir, filename)","def get_cache(self):\n return self.cache","def get_files_list(dirname, date_order, rdate_order):\n file_list = os.listdir(dirname)\n file_mtimes = dict.fromkeys(file_list)\n for f in file_list:\n if f[0] == '.':\n print \"Skipping file: \", f\n del file_mtimes[f]\n continue\n if date_order or rdate_order:\n file_mtimes[f] = os.stat(dirname + '/' + f).st_mtime\n if date_order or rdate_order:\n return sorted(file_mtimes.keys(), key=file_mtimes.get, reverse=rdate_order)\n else:\n return file_list","def _get_most_recent_timestamp_subfolder(self, root_folder_path):\n walk_gen = os.walk(root_folder_path)\n root, dirs, files = walk_gen.__next__()\n dirs.sort(reverse=True)\n return dirs[0]","def cache_key(self):\r\n statinfo = os.stat(self.pathname)\r\n return (self.filename + str(statinfo.st_mtime)).encode('ascii', 'ignore')","def get_last_file(base_dir, pattern):\n base_dir = Path(base_dir)\n\n return sorted(base_dir.glob(pattern),\n key=lambda x: x.stat().st_ctime, reverse=True)[0]","def load_latest_url(self):\n try:\n fp = open(self.HTTPD_LATEST_URL_FILE)\n latest_url = fp.read()\n fp.close()\n return latest_url\n except:\n return ''","def get_datfile(filename):\n if ARGV.get(DEBUG_OPT):\n err_print('Getting datfile from \"{}\"'.format(filename))\n\n try:\n with open(filename, 'rb') as pickle_file:\n try:\n (cache, readlist) = pickle.load(pickle_file)\n pickle_file.close()\n except (EOFError, ValueError):\n (cache, readlist) = ({\"feed\": None, \"max-age\": None, \"last-request\": None}, [])\n except (FileNotFoundError, PermissionError):\n (cache, readlist) = ({\"feed\": None, \"max-age\": None, \"last-request\": None}, [])\n return (cache, readlist)","def getFiles(self, state=None, s3uri_prefix=None):\n self.log.info(\"getFiles\")\n downloads = []\n keys = list(self.downloads.keys())\n keys.sort()\n for key in keys:\n download = self.downloads[key]\n if not state or (state and download['state'] == state):\n print(download)\n s3uri = download['s3_uri']\n if s3uri_prefix is None or s3uri.startswith(s3uri_prefix):\n item = {}\n for k in ('local_filepath', 'size', 'state', 's3_time',\n 's3_date', 's3_uri'):\n item[k] = download[k]\n downloads.append(item)\n\n return downloads","def get_file_info_by_token(token):\n\n key = generate_access_token_cache_key(token)\n value = cache.get(key)\n if not value:\n logger.error('No wopi cache value when first get %s' % key)\n value = cache.get(key)\n\n return value if value else None","def main():\n print get_latest_data()","def test_get_result_top_files(self):\n pass","def read_cache(self):\n with open(self.get_cache_filename(), 'rb') as f:\n data = pickle.loads(f.read())\n self.timestamp = data['timestamp']\n self.cache = data['cache']","def test_client_max_age_0(self, sess):\r\n print('first request')\r\n r = sess.get(self.url)\r\n assert self.cache.get(self.url) == r.raw\r\n\r\n print('second request')\r\n r = sess.get(self.url, headers={'Cache-Control': 'max-age=0'})\r\n\r\n # don't remove from the cache\r\n assert self.cache.get(self.url)\r\n assert not r.from_cache","def _first_more_recent (f1, path) :\n import datetime\n import re\n import time\n s = str (f1.info ())\n da = re.compile (\"Last[-]Modified: (.+) GMT\").search (s)\n if da is None :\n return True\n \n da = da.groups () [0]\n gr = re.compile (\"[\\w, ]* ([ \\d]{2}) ([\\w]{3}) ([\\d]{4}) ([\\d]{2}):([\\d]{2}):([\\d]{2})\").search (da)\n if gr == None : return True\n gr = gr.groups ()\n da = datetime.datetime ( int (gr [2]), sys.hal_log_values [\"month_date\"] [gr [1].lower ()], int (gr [0]),\n int (gr [3]), int (gr [4]), int (gr [5]) )\n \n p = time.ctime (os.path.getmtime (path))\n gr = re.compile (\"[\\w, ]* ([\\w]{3}) ([ \\d]{2}) ([\\d]{2}):([\\d]{2}):([\\d]{2}) ([\\d]{4})\").search (p)\n if gr is None :\n return True\n gr = gr.groups ()\n da = datetime.datetime ( int (gr [5]), sys.hal_log_values [\"month_date\"] [gr [0].lower ()], int (gr [1]),\n int (gr [2]), int (gr [3]), int (gr [4]) )\n \n file = da\n \n return da > file","def get_resource_last_update_timestamp(api_1_0_url, resource):\n try:\n r = requests.get(api_1_0_url)\n json_string = r.content\n data = json.loads(json_string)\n try:\n files = data['files']\n for entry in files:\n if entry['path'] == resource:\n entry_last_update_timestamp = entry['utctimestamp']\n return entry_last_update_timestamp\n except Exception as error:\n print(\"Caught error: \" + repr(error))\n except Exception as error:\n print(\"Failed to connect to bitbucket: \" + repr(error))\n exit(1)\n return None","def get_output_from_cache(name, filename):\n cache_filename = _get_cache_filename(name, filename)\n if (os.path.exists(cache_filename) and\n os.path.getmtime(filename) < os.path.getmtime(cache_filename)):\n with io.open(cache_filename) as f:\n return f.read()\n\n return None","def fetch(self, url):\r\n fname = os.path.join(self._cachedir, self._formatter(url))\r\n if not os.path.exists(fname):\r\n time.sleep(self._sleep)\r\n html = urllib.urlopen(url).read()\r\n with codecs.open(fname, 'w', 'utf-8') as f:\r\n soup = BeautifulSoup(html)\r\n f.write(unicode(soup))\r\n return fname","def download_archive(self):\n\n def time_convert(structure):\n \"\"\"\n :param structure: tuple representation of time\n :return: GitHub archive time\n \"\"\"\n \n \n join_number_to_zero = lambda number: (\"\" if number > 9 else \"0\") + str(number)\n\n return \"%s-%s-%s-%s\" % (\n structure.tm_year, join_number_to_zero(structure.tm_mon), join_number_to_zero(structure.tm_mday),\n structure.tm_hour)\n\n current_time = self.get_time()\n self.logger.debug(__name__ + \": \" + \"current time: \" + str(gmtime(current_time)))\n\n difference = -25200\n #timezone difference in seconds between GMT and west coast of USA\n\n downloading_time = int(timegm(self.config[\"last_connection_time\"])) + 3600\n self.logger.debug(__name__ + \": \" + \"downloading time: \" + str(gmtime(downloading_time)))\n\n if downloading_time > current_time - 7200:\n self.logger.info(__name__ + \": \" + \"unable to download file (time limiting).\")\n return\n\n downloading_time += difference\n\n json_file_name = self.download_file(time_convert(gmtime(downloading_time)))\n\n self.config[\"last_connection_time\"] = gmtime(downloading_time - difference)\n self.logger.debug(__name__ + \": \" + \"last_connection_time: \" + str(self.config[\"last_connection_time\"]))\n\n return json_file_name","def getLastFile(self):\n lastFile = None if len(self.recentFiles) == 0 else self.recentFiles[0]\n self.setLastPath(lastFile)\n return lastFile","def getCacheFile(ns, digest):\n return os.path.join(getDir(cacheDir, ns), digest)","def retrieve():\n # type: () -> list\n with Cache(CACHE_URI) as c:\n data = c.get(SAVED_SEARCH)\n return json.loads(data[\"blob\"]) if data else []","def fetch_all_snapshots(archive_dir, wayback_filename, target_url):\n # Read the list of snapshots.\n with open(wayback_filename) as f:\n data = f.read()\n\n url_template = \"http://web.archive.org/web/{timestamp}/{target_url}\"\n snapshots = data.split(\"\\n\")\n pages_downloaded = 0\n pages_failed = 0\n pages_skipped = 0\n for snapshot in snapshots:\n fields = snapshot.split()\n if len(fields) < 1:\n print(\"Bad fields. End of data?\")\n break\n date_string = fields[1]\n assert 14 == len(date_string)\n ymd = date_string[:8]\n year = int(date_string[:4])\n month = int(date_string[4:6])\n day = int(date_string[6:8])\n assert 1900 < year < 2100 and 1 <= month <= 12 and 1 <= day <=31\n date_of_fire = datetime.date(year,month, day)\n filename = F\"firedata_{year}_{month:02}_{day:02}.html\"\n path = os.path.join(archive_dir, filename)\n if os.path.exists(path):\n print(\"Not replacing \", path)\n pages_skipped += 1\n continue\n else:\n print(\"Downloading for \", path)\n url = url_template.format(timestamp=date_string, target_url=target_url)\n print(url)\n\n page = fetch(url)\n if page is None:\n print(\"Fetching above url failed.\")\n pages_failed +=1\n continue\n\n pages_downloaded += 1\n with open(path, \"wb\") as f:\n f.write(page)\n print(\"Page saved\")\n sleep(2)\n return pages_downloaded, pages_failed, pages_skipped","def last_file_updated(self):\n query = '*.xml'\n keymap_files = glob.glob(query)\n\n sorted_files = sorted(keymap_files, key=self.mtime, reverse=1)\n last_modified_file = sorted_files[0]\n second_last_modified_file = sorted_files[1]\n\n t1 = self.mtime(last_modified_file)\n t2 = self.mtime(second_last_modified_file)\n\n logger.debug('Last modified time: {0}'.format(t1))\n logger.debug('Second Last modified time: {0}'.format(t2))\n\n last_modified_time = self.mtime(last_modified_file)\n last_access_time = self.atime(last_modified_file)\n\n if sys.platform == \"win32\":\n logger.info('Detected Windows environment')\n # self.regenerate_osx(last_access_time, last_modified_time)\n elif sys.platform == 'darwin':\n logger.info('Detected OSX environment')\n # self.regenerate_windows(last_access_time, last_modified_time)\n else:\n logger.error('Unhandled platform: {0}'.format(sys.platform))\n pass","def cache_file(cache_key):\n\n return MASTOOLS_DIR / f\"{cache_key}_cache.json\"","def _most_recent_event_files(self):\n regex = re.compile(r\"\\w*events.log\")\n return [\n os.path.join(self._output_dir, x)\n for x in os.listdir(self._output_dir)\n if regex.search(x)\n ]","def get_cache_file_list(self,\n file_list_obj=None,\n file_info_class=FileInfo,\n file_list_class=FileList):\n if not file_list_obj:\n file_list_obj = file_list_class()\n temp_ = self.read_pickle_object_in_file()\n if temp_:\n for tup_ in temp_:\n finf_ = file_info_class(in_tuple=tup_)\n fn_ = finf_.filename\n self.cache_file_list_dict[fn_] = finf_\n file_list_obj.append(finf_)\n return file_list_obj"],"string":"[\n \"def files():\\n return get_cached(\\\"files.json\\\")\",\n \"def fetch(\\n self, output_folder: Path, cache: Dict[str, str], fetch_opts: List[Dict[str, Any]]\\n ) -> List[str]:\\n return [\\n download_snapshot(source_config[\\\"url\\\"], output_folder, **source_config.get(\\\"opts\\\", {}))\\n for source_config in fetch_opts\\n ]\",\n \"def get(self):\\n if path.exists(self.cachefile):\\n self.invalidion()\\n full_cache = self._get_all()\\n return full_cache\\n else:\\n return []\",\n \"def _retrieveCachedData(self):\",\n \"def pre_cache(self, scope, name):\\n item = {'did': '%s:%s' % (scope, name),\\n 'base_dir': self.cache_path,\\n 'no_subdir': self.no_subdir,\\n 'transfer_timeout': self.transfer_timeout}\\n if self.rse:\\n item['rse'] = self.rse\\n\\n client = Client()\\n all_files = client.list_files(scope, name)\\n\\n download_client = DownloadClient(client=client)\\n downloaded_files = download_client.download_dids([item], num_threads=self.num_threads)\\n\\n self.logger.info('Downloaded files: %s' % downloaded_files)\\n\\n ret_files = []\\n for file in all_files:\\n downloaded_file = None\\n for d_file in downloaded_files:\\n if d_file['scope'] == file['scope'] and d_file['name'] == file['name']:\\n downloaded_file = d_file\\n break\\n\\n ret_file = {'scope': file['scope'],\\n 'name': file['name'],\\n 'min_id': 1,\\n 'max_id': file['events'],\\n 'status': ContentStatus.PRECACHED if downloaded_file and downloaded_file['clientState'] == 'ALREADY_DONE' else ContentStatus.NEW,\\n 'size': file['bytes'],\\n 'md5': downloaded_file['md5'] if downloaded_file else None,\\n 'adler32': downloaded_file['adler32'] if downloaded_file else None,\\n 'pfn': downloaded_file['dest_file_path'] if downloaded_file else None\\n\\n }\\n \\\"\\\"\\\"\\n if ret_file['status'] == ContentStatus.AVAILABLE:\\n if req.granularity_type == GranularityType.PARTIAL:\\n ret_file['status'] = ContentStatus.PRECACHED\\n elif req.granularity_type == GranularityType.FILE:\\n ret_file['status'] = ContentStatus.TOSTAGEDOUT\\n \\\"\\\"\\\"\\n ret_files.append(ret_file)\\n return ret_files\",\n \"def _get_latest_content(self):\\n if self._modified is None:\\n self._load_content()\\n return\\n\\n # check if data updated\\n statinfo = os.stat(self._path)\\n if statinfo.st_mtime > self._modified:\\n self._load_content()\",\n \"def get_files_summary(self) -> Dict[str, float]:\\n json_path = (\\n os.path.join(self.cache_dir, \\\"coverage-min.json\\\") if self.cache_dir else \\\"\\\"\\n )\\n if self.use_cache and self.cache_dir:\\n try:\\n with open(json_path) as coverage_summary_file:\\n full_coverage_summary = json.load(coverage_summary_file)\\n last_updated = datetime.strptime(\\n full_coverage_summary[\\\"last_updated\\\"], \\\"%Y-%m-%dT%H:%M:%SZ\\\"\\n )\\n next_update = last_updated + timedelta(days=1)\\n if next_update > datetime.utcnow():\\n return full_coverage_summary[\\\"files\\\"]\\n except FileNotFoundError:\\n logger.info(\\n \\\"No cache file found. creatig a cache dir at %s\\\", self.cache_dir\\n )\\n os.makedirs(self.cache_dir, exist_ok=True)\\n except (json.JSONDecodeError, KeyError, ValueError) as exc:\\n logger.exception(exc)\\n\\n resp = requests.get(self.url)\\n resp.raise_for_status()\\n data = resp.json()\\n if self.use_cache and self.cache_dir:\\n with open(json_path, \\\"w\\\") as fp:\\n json.dump(data, fp)\\n\\n return data[\\\"files\\\"]\",\n \"def get_content_from_cache(self):\\n\\n rss_feed = []\\n news_to_show = 0\\n\\n try:\\n self.print_if_verbose(\\n f\\\"Method 'get_content_from_cache' is working: \\\\n\\\"\\n f\\\"Trying to get content from cache...\\\"\\n )\\n os.chdir(\\\"cache\\\")\\n except Exception as error:\\n print(f\\\"{error}: cache does not exists!\\\")\\n return\\n\\n try:\\n os.chdir(\\\"image_cache\\\")\\n self.full_path_to_image_cache = os.getcwd()\\n os.chdir(\\\"..\\\")\\n except:\\n pass\\n\\n try:\\n with open(\\\"rss_reader_cache.json\\\", \\\"r\\\", encoding=\\\"utf-8\\\") as cache_file:\\n data_from_cache = json.load(cache_file)\\n self.print_if_verbose(f\\\"Content from cache has been received successfully. \\\\n\\\")\\n except Exception as error:\\n self.print_if_verbose(f\\\"{error}: cache file does not exist! \\\\n\\\")\\n return\\n\\n if self.source:\\n for feed in data_from_cache:\\n if self.source in feed.keys():\\n for news in feed[self.source]:\\n if news[\\\"PubDate\\\"] == str(self.date):\\n rss_feed.append(news)\\n news_to_show += 1\\n if self.limit and news_to_show == self.limit:\\n break\\n if self.limit and news_to_show == self.limit:\\n break\\n else:\\n for channel in data_from_cache:\\n for feed_link in channel:\\n for news in channel[feed_link]:\\n if news[\\\"PubDate\\\"] == str(self.date):\\n rss_feed.append(news)\\n news_to_show += 1\\n if self.limit and news_to_show == self.limit:\\n break\\n if self.limit and news_to_show == self.limit:\\n break\\n\\n os.chdir(\\\"..\\\")\\n\\n self.news_amount = len(rss_feed)\\n\\n if self.news_amount == 0:\\n print(f\\\"There is no news in cache for specified date. \\\\n\\\")\\n else:\\n self.print_if_verbose(f\\\"There is {self.news_amount} news in cache for specified date. \\\\n\\\")\\n\\n self.print_if_verbose(f\\\"Method 'get_content_from_cache' is finished. \\\\n\\\")\\n\\n return rss_feed\",\n \"def _get_all_cache_files(self):\\n files = set()\\n dir_tree = os.walk(self.config.get('cachedir', self.CACHEDIR))\\n for dirpath, _, filenames in dir_tree:\\n for file_name in filenames:\\n if 'cache' in file_name:\\n files.add(os.path.join(dirpath, file_name))\\n return files\",\n \"def loadRecentFiles(self):\\n self.recentFiles.clear()\\n for n in range(RECENTFILEMAX):\\n rf = self.getSection(CFG_RECENT, str(n))\\n if rf:\\n self.recentFiles.append(rf)\\n else:\\n break\",\n \"def get_cache_path(self):\",\n \"def get_cache_path(self):\",\n \"def get_all_cached_files(cache_dir=None):\\n if cache_dir is None:\\n cache_dir = TRANSFORMERS_CACHE\\n else:\\n cache_dir = str(cache_dir)\\n if not os.path.isdir(cache_dir):\\n return []\\n\\n cached_files = []\\n for file in os.listdir(cache_dir):\\n meta_path = os.path.join(cache_dir, f\\\"{file}.json\\\")\\n if not os.path.isfile(meta_path):\\n continue\\n\\n with open(meta_path, encoding=\\\"utf-8\\\") as meta_file:\\n metadata = json.load(meta_file)\\n url = metadata[\\\"url\\\"]\\n etag = metadata[\\\"etag\\\"].replace('\\\"', \\\"\\\")\\n cached_files.append({\\\"file\\\": file, \\\"url\\\": url, \\\"etag\\\": etag})\\n\\n return cached_files\",\n \"def use_cached_files(self, cache_key):\\r\\n pass\",\n \"def get_recent_files():\\n creds = authenticate()\\n\\n service = build('drive', 'v3', credentials=creds)\\n\\n # Call the Drive v3 API\\n results = service.files().list(\\n pageSize=10, fields=\\\"nextPageToken, files(id, name)\\\").execute()\\n items = results.get('files', [])\\n\\n return items\",\n \"def get_most_recent_tarball(self, pkg):\\n pass\",\n \"def get_last_fetch_time():\\n if os.path.exists(LAST_FETCH_TIME_FILE):\\n with open(LAST_FETCH_TIME_FILE, 'r') as f:\\n last_fetch_time = f.read()\\n\\n return last_fetch_time\\n return ''\",\n \"def get_latest_file(folder):\\n if not os.path.exists(folder):\\n raise AppException(\\\"Folder does not exists!\\\")\\n return max(glob.iglob(folder + \\\"/*\\\"), key=os.path.getctime)\",\n \"def list_cached():\\n for json_name in cached_files():\\n source_name = get_source_file_name(json_name)\\n yield (json_name, source_name)\",\n \"def _get_fetcher_cache_timestamp(self):\\r\\n status_data = self._get_fetcher_status()\\r\\n return status_data.get('cache_timestamp') if status_data else None\",\n \"def _get_most_recent_file(path):\\n allowed_ext = tuple(MIMES.keys())\\n files_iter = (\\n entry for entry in os.scandir(path)\\n if entry.is_file() and entry.name.lower().endswith(allowed_ext)\\n )\\n for entry in sorted(files_iter, key=_newest_ctime):\\n return entry.path\\n return None\",\n \"def get_files(self):\\n # self.folder= +str(int(time.time()))\\n if not os.path.exists(self.folder):\\n os.mkdir(self.folder)\\n while len(self.url_queue): # If we have URLs to crawl - we crawl\\n href = self.url_queue.popleft() # We grab a URL from the left of the list\\n filename = href.rsplit('/', 1)[-1]\\n print(\\\"Downloading %s to %s...\\\" % (href, filename))\\n fullname = os.path.join(self.folder, filename)\\n urlretrieve(href, fullname)\\n self.xlfnames.append(filename)\",\n \"def get_recently_modified_scratch_file(settings):\\n dir_contents = os.listdir(settings.location)\\n full_paths = map(lambda f: os.path.join(settings.location, f), dir_contents)\\n files = filter(lambda f: os.path.isfile(str(f)), full_paths)\\n if not files:\\n return \\\"\\\"\\n files = sorted(files, key=_get_mtime)\\n return files[-1]\",\n \"def _fetch_files(self, snapshot: Bug, filepaths: List[str]) -> None:\\n bgz = self.__bugzoo\\n container = bgz.containers.provision(snapshot)\\n try:\\n for filepath in filepaths:\\n key = (snapshot.name, filepath)\\n try:\\n if key in self.__cache_file_contents:\\n continue\\n self.__cache_file_contents[key] = \\\\\\n bgz.files.read(container, filepath)\\n except KeyError:\\n logger.exception(\\\"Failed to read source file, '%s/%s': file not found\\\", # noqa: pycodestyle\\n snapshot.name, filepath)\\n raise FileNotFound(filepath)\\n finally:\\n del bgz.containers[container.uid]\",\n \"def get_cache(feed_key):\\n\\n cache_file = CACHE_DIR / (feed_key + \\\".json\\\")\\n\\n with open(cache_file, \\\"r\\\") as file:\\n entries = json.load(file)\\n\\n return entries\",\n \"def prefill_cache():\\n print(\\\"Prefilling cache.\\\")\\n print(\\\"\\\\rListing ATS files...\\\", end=\\\"\\\")\\n file_names = list(ats_files())\\n print(\\\"\\\\rListing ATS files: done.\\\")\\n index = 0\\n files_count = len(file_names)\\n cached_count = 0\\n for file_name in file_names:\\n index += 1\\n print(\\\"\\\\rHandling ATS file #%i of %i\\\" % (index, files_count), end=\\\"\\\")\\n if get_json(file_name) is not None:\\n cached_count += 1\\n print(\\\"\\\\nDone: %i file(s) cached.\\\" % cached_count)\",\n \"def _load_cached_2to3(self, path, cache):\\n try:\\n cache_stats = os.stat(cache)\\n source_stats = os.stat(path)\\n except OSError as e:\\n if e.errno == errno.ENOENT: # FileNotFoundError\\n self.logger.debug('Cache miss: %s' % cache)\\n return None\\n else:\\n raise\\n\\n if cache_stats.st_mtime <= source_stats.st_mtime:\\n self.logger.debug('Cache miss (stale): %s' % cache)\\n return None\\n\\n self.logger.debug(\\\"Cache hit: %s\\\" % cache)\\n return super().get_data(cache)\",\n \"def cache_path(self):\",\n \"def cache_path(self):\",\n \"def get_recent_images(num_images=30):\\n folder = app.config['UPLOAD_FOLDER']\\n\\n init_image_info()\\n\\n # get list of last modified images - ignore .json file and files start with .\\n files = ['/'.join((folder, file)) \\\\\\n for file in os.listdir(folder) if ('json' not in file) \\\\\\n and not (file.startswith('.')) ]\\n\\n # list of tuples (file_path, timestamp)\\n last_modified_files = [(file, os.path.getmtime(file)) for file in files]\\n print(last_modified_files)\\n last_modified_files = sorted(last_modified_files,\\n key=lambda t: t[1], reverse=True)\\n num_stored_images = len(last_modified_files)\\n\\n # build a list of image information\\n image_stats = []\\n\\n print(\\\"THE NUMBER OF STORED IMAGES IS: {}\\\".format(num_stored_images))\\n\\n if num_stored_images != 0:\\n\\n # read in image info\\n with open(IMAGE_INFO_JSON, 'r') as f:\\n info = json.load(f)\\n\\n for i, f in enumerate(last_modified_files):\\n # set limit for rendering pictures\\n if i > num_images: break\\n\\n path, filename = f[0], f[0].replace(folder, '').replace('/', '')\\n cur_image_info = info.get(filename, {})\\n\\n print(\\\"CURRENT IMAGE INFO IS: {}\\\".format(cur_image_info))\\n\\n img = {\\n 'path': path,\\n 'labels': cur_image_info\\n }\\n print(\\\"CURRENT IMG LABEL DATA IS: {}\\\".format(img['labels']))\\n image_stats.append(img)\\n\\n return image_stats, num_stored_images\",\n \"def get_request_candidates(self):\\n return os.listdir(self.cache_dir_)\",\n \"def getRecentFile(*p):\\n\\tfrom os import stat\\n\\tfrom os.path import join\\n\\tfrom glob import glob\\n\\tresult = \\\"\\\"\\n\\tfiles = glob(join(p[0],*p[1:]))\\n\\tfor file in files:\\n\\t\\tif result == \\\"\\\":\\n\\t\\t\\tresult = file\\n\\t\\telse:\\n\\t\\t\\tif stat(file).st_mtime > stat(result).st_mtime:\\n\\t\\t\\t\\tresult = file\\n\\treturn result\",\n \"def getRecentItems(self, filename):\\n self.recent_items = {}\\n try:\\n recent_files = open(filename, 'r').read().strip()\\n \\n if (recent_files == \\\"\\\"):\\n self.recent_items = None\\n return\\n\\n recent_list = recent_files.split('\\\\n')\\n\\n for item in recent_list:\\n last_slash = self.findLastSlash(item) \\n before = item[:last_slash+1]\\n after = item[last_slash+1:]\\n self.recent_items[after] = before\\n\\n self.updateRecentItems()\\n self.ui.menuRecent_Files.removeAction(self.ui.actionNone)\\n\\n except IOError:\\n print 'Unable to read the recent files from file: %s\\\\n'\\\\\\n 'No recent files will be displayed' % str(filename)\",\n \"def get_latest_files(base_path):\\n try:\\n adni = os.listdir('{}/qc/phantom/adni'.format(base_path))\\n adni = filter(lambda x: '_adni_' in x and 'csv' in x, adni)\\n adni.sort()\\n adni = adni[-9:]\\n except:\\n adni = None\\n\\n try:\\n fmri = os.listdir('{}/qc/phantom/fmri'.format(base_path))\\n fmri = filter(lambda x: '_fmri_' in x and 'csv' in x, fmri)\\n fmri.sort()\\n fmri = fmri[-7:]\\n except:\\n fmri = None\\n\\n try:\\n dti = os.listdir('{}/qc/phantom/dti'.format(base_path))\\n dti = filter(lambda x: '_dti_' in x and 'csv' in x, dti)\\n dti.sort()\\n dti = dti[-1:]\\n except:\\n dti = None\\n\\n return adni, fmri, dti\",\n \"async def get() -> list:\\n if _cache is None:\\n await _update()\\n return _cache\",\n \"def get_version_from_recent_files(self):\\n # full_path = self.fusion_prefs[\\\"LastCompFile\\\"]\\n # return self.get_version_from_full_path(full_path)\\n\\n version = None\\n rfm = RecentFileManager()\\n\\n try:\\n recent_files = rfm[self.name]\\n except KeyError:\\n logger.debug('no recent files')\\n recent_files = None\\n\\n if recent_files is not None:\\n for i in range(len(recent_files)):\\n version = self.get_version_from_full_path(recent_files[i])\\n if version is not None:\\n break\\n\\n logger.debug(\\\"version from recent files is: %s\\\" % version)\\n\\n return version\",\n \"def mostRecentlyModified(self,files,n):\\n mtime = lambda f: time.strptime(f['modified'], \\\"%a, %d %b %Y %H:%M:%S +0000\\\") \\n filesSorted = sorted(files,key=mtime,reverse=True) #get descending order\\n if len(filesSorted)<= n:\\n return filesSorted\\n else:\\n return filesSorted[:n]\",\n \"def GetFromCache(self, filename):\\n return memcache.get('%s%s' % (self.CACHE_PREFIX, filename))\",\n \"def GetFromCache(self, filename):\\n return memcache.get('%s%s' % (self.CACHE_PREFIX, filename))\",\n \"def fillCache(self):\\n items = self.source.getRecent()\\n items.reverse() # make sure the most recent ones are added last to the cache\\n for item in items:\\n self.cache.append(item.title)\",\n \"def __loadRecent(self):\\n self.recent = []\\n Preferences.Prefs.rsettings.sync()\\n rs = Preferences.Prefs.rsettings.value(recentNameFiles)\\n if rs is not None:\\n for f in Preferences.toList(rs):\\n if QFileInfo(f).exists():\\n self.recent.append(f)\",\n \"def _get_newest_filenames(self, image_files):\\n if self._previewcache[\\\"modified\\\"] is None:\\n retval = image_files\\n else:\\n retval = [fname for fname in image_files\\n if os.path.getmtime(fname) > self._previewcache[\\\"modified\\\"]]\\n if not retval:\\n logger.debug(\\\"No new images in output folder\\\")\\n else:\\n self._previewcache[\\\"modified\\\"] = max([os.path.getmtime(img) for img in retval])\\n logger.debug(\\\"Number new images: %s, Last Modified: %s\\\",\\n len(retval), self._previewcache[\\\"modified\\\"])\\n return retval\",\n \"def fetch_and_cache(data_url, file, data_dir=\\\"data\\\", force=False):\\n data_dir = Path(data_dir)\\n data_dir.mkdir(exist_ok = True)\\n file_path = data_dir / Path(file)\\n if force and file_path.exists():\\n file_path.unlink()\\n if force or not file_path.exists():\\n print('Downloading...', end=' ')\\n resp = requests.get(data_url)\\n with file_path.open('wb') as f:\\n f.write(resp.content)\\n print('Done!')\\n last_modified_time = time.ctime(file_path.stat().st_mtime)\\n else:\\n last_modified_time = time.ctime(file_path.stat().st_mtime)\\n print(\\\"Using cached version that was downloaded (UTC):\\\", last_modified_time)\\n return file_path\",\n \"def get(self):\\n # Check if last_refreshed is saved in the storage\\n if \\\"last_refreshed\\\" in storage:\\n self.refresh_infos(storage[\\\"last_refreshed\\\"])\\n else:\\n # last_refreshed not found in the storage\\n soccerInfo = SoccerInfo.objects.first()\\n if soccerInfo and soccerInfo[\\\"last_refreshed\\\"]:\\n self.refresh_infos(soccerInfo[\\\"last_refreshed\\\"])\\n else:\\n self.refresh_infos()\\n return storage[\\\"cached_soccerInfos\\\"], 200\",\n \"def cached_files():\\n for (dir_path, _dir_names, file_names) in os.walk(CACHE):\\n for file_name in file_names:\\n if is_json_file(file_name):\\n yield os.path.join(dir_path, file_name)\",\n \"def _read_cache(url):\\n\\n j = None\\n m = hashlib.md5()\\n m.update(url)\\n if os.path.exists('.cache.%s' % m.hexdigest()):\\n with open('.cache.%s' % m.hexdigest(), 'rb') as infile:\\n j = json.load(infile)\\n\\n return j\",\n \"def loadFileList(self):\\r\\n try:\\r\\n data = open(self.filelist_file, 'rb')\\r\\n except IOError:\\r\\n '''print \\\"No SRTM cached file list. Creating new one!\\\"'''\\r\\n if self.offline == 0:\\r\\n self.createFileList()\\r\\n return\\r\\n try:\\r\\n self.filelist = pickle.load(data)\\r\\n except:\\r\\n '''print \\\"Unknown error loading cached SRTM file list. Creating new one!\\\"'''\\r\\n if self.offline == 0:\\r\\n self.createFileList()\",\n \"def get_cache(self):\\n self._topo.create_cache()\\n with open('/run/geopm-service/geopm-topo-cache') as fid:\\n result = fid.read()\\n return result\",\n \"def get_from_cache(cls, file_name):\\n random.shuffle(cls.CACHE_BACKENDS)\\n fname = None\\n for cb in cls.CACHE_BACKENDS:\\n if not cb.health_check():\\n continue\\n fname = cb.get_from_cache(file_name)\\n if fname:\\n break\\n return fname\",\n \"def getLatestDate(market, folder):\\n \\n if market == 'stockOption':\\n m = 'DTOP_O_'\\n elif market == 'indexFuture':\\n m = 'DTOP_F_'\\n \\n dateList = []\\n # get file list from directory\\n for f in os.listdir(folder):\\n if m in f:\\n # crop the date from filename\\n row = f.replace(m,'').replace('.zip','')\\n dateList.append(date(int(row[:4]), int(row[4:6]), int(row[6:])))\\n\\n \\n latest = dateList[0]\\n for x in range(1,len(dateList)):\\n if dateList[x] > latest:\\n latest = dateList[x]\\n \\n return latest\",\n \"def last_log(self) -> List:\\n logs_list: List = os.listdir(LOGS_BASE_PATH)\\n full_list = [os.path.join(LOGS_BASE_PATH, i) for i in logs_list]\\n time_sorted_list: List = sorted(full_list, key=os.path.getmtime)\\n return time_sorted_list[-1]\",\n \"def _get_recent_file_names():\\n file_name_tpl = '{}-data.csv'\\n date_fmt = '%Y-%m-%d'\\n now = datetime.now()\\n one_day_ago = now - timedelta(days=1)\\n file_names = [\\n file_name_tpl.format(one_day_ago.strftime(date_fmt)),\\n file_name_tpl.format(now.strftime(date_fmt)),\\n ]\\n return [os.path.join(DATA_DIR, x) for x in file_names]\",\n \"def find_cache_files():\\n files = []\\n\\n for root, dirnames, filenames in os.walk(\\\".\\\"):\\n for filename in fnmatch.filter(filenames, \\\"*.pyc\\\"):\\n files.append(os.path.join(root, filename))\\n\\n for root, dirnames, filenames in os.walk(\\\".\\\"):\\n for filename in fnmatch.filter(filenames, \\\"__pycache__\\\"):\\n files.append(os.path.join(root, filename))\\n\\n return files\",\n \"def get_from_cache(cls, target_filename):\\n is_cached = cls.is_remote_cached(target_filename)\\n if is_cached:\\n cache = cls.CACHE_BACKEND()\\n cache.download(is_cached, target_filename)\\n logger.debug('File %r was downloaded from %r', target_filename, cls.CACHE_BACKEND)\\n else:\\n target_filename = None\\n return target_filename\",\n \"def test_pick_latest_file(self):\\n max = FILES_LIST[0]\\n for f in FILES_LIST:\\n if os.path.getctime(os.path.join(FILES_DIR, f)) > os.path.getctime(os.path.join(FILES_DIR, max)):\\n max = f\\n \\n max = FILES_DIR + max\\n latest = pick_latest_file()\\n \\n self.assertEqual(max, latest)\",\n \"def _list_dir(self):\\n return [os.path.join(self.cache_dir, fn)\\n for fn in os.listdir(self.cache_dir)]\",\n \"def load_cache(base_url, path=\\\"logs/\\\"):\\n\\n # Convert URL to filename and read contents\\n url_filename = url_to_filename(base_url)\\n\\n filename = f\\\"{path}CACHE-{url_filename}.html\\\"\\n f = open(filename, \\\"r\\\")\\n data_cache = f.read()\\n\\n data_cache = \\\" \\\".join(data_cache.split()) # Remove all whitespaces\\n\\n return data_cache\",\n \"def getData(self, local_cache):\",\n \"def recentfiles(archive):\\n return heapq.nlargest(5, molecollector(archive))\",\n \"def __recentDownload(self, downloadType):\\n \\n cursor = self.connection.cursor()\\n cursor.execute(\\\"SELECT file_size, file_modification_time FROM download_audit WHERE download_type = %(dtype)s AND download_completion_time IS NOT NULL ORDER BY id DESC LIMIT 1\\\", {'dtype': downloadType})\\n record = cursor.fetchone()\\n cursor.close ()\\n \\n return record\",\n \"def get_starred_files_by_username(self, username):\\n starred_files = super(UserStarredFilesManager, self).filter(\\n email=username, org_id=-1)\\n\\n ret = []\\n repo_cache = {}\\n for sfile in starred_files:\\n # repo still exists?\\n if repo_cache.has_key(sfile.repo_id):\\n repo = repo_cache[sfile.repo_id]\\n else:\\n try:\\n repo = seafile_api.get_repo(sfile.repo_id)\\n except SearpcError:\\n continue\\n if repo is not None:\\n repo_cache[sfile.repo_id] = repo\\n else:\\n sfile.delete()\\n continue\\n\\n # file still exists?\\n file_id = ''\\n size = -1\\n if sfile.path != \\\"/\\\":\\n try:\\n file_id = seafile_api.get_file_id_by_path(sfile.repo_id,\\n sfile.path)\\n # size = seafile_api.get_file_size(file_id)\\n except SearpcError:\\n continue\\n if not file_id:\\n sfile.delete()\\n continue\\n\\n f = StarredFile(sfile.org_id, repo, file_id, sfile.path,\\n sfile.is_dir, 0) # TODO: remove ``size`` from StarredFile\\n ret.append(f)\\n\\n '''Calculate files last modification time'''\\n files_list = []\\n for sfile in ret:\\n if sfile.is_dir:\\n continue\\n ele = (sfile.repo.id, sfile.path, sfile.file_id)\\n files_list.append(ele)\\n\\n files_dict_with_last_modified = FileLastModifiedInfo.objects.get_files_last_modified(files_list)\\n\\n for sfile in ret:\\n key = \\\"%s|%s|%s\\\" % (sfile.repo.id, sfile.path, sfile.file_id)\\n if files_dict_with_last_modified.has_key(key):\\n sfile.last_modified = files_dict_with_last_modified[key]\\n else:\\n # Should never reach here\\n pass\\n\\n ret.sort(lambda x, y: cmp(y.last_modified, x.last_modified))\\n\\n return ret\",\n \"def get(self):\\n CACHE_KEY = 'sources'\\n if not memcache.get(CACHE_KEY):\\n logging.info('Populating cache.')\\n feeds = Feed.all().order('name')\\n feed_list = []\\n for feed in feeds:\\n feed_list.append(feed.ToDict())\\n memcache.add(CACHE_KEY, simplejson.dumps(feed_list), 600)\\n logging.info('Using cache.')\\n logging.info(memcache.get(CACHE_KEY))\\n self.response.headers['Content-Type'] = 'application/json'\\n self.response.out.write(memcache.get(CACHE_KEY))\",\n \"def get_cache_file(self, dependencies):\\n filename = '%s.tar' % self.get_cache_key(dependencies)\\n return os.path.join(self.cache_directory, filename)\",\n \"def get_cache_time(self, URL):\\n\\n cache_file = self.get_cache_file_path(URL)\\n\\n if cache_file.exists():\\n cache_time = cache_file.stat().st_mtime\\n else:\\n cache_time = 0\\n\\n return cache_time\",\n \"def fetchfile(self, query):\\n cur = self.conn.cursor()\\n exists = False\\n i = 0\\n limit = 1000\\n datas = None\\n while (not exists) and (i < limit):\\n cur.execute(query)\\n rows = cur.fetchall()\\n if len(rows) == 0:\\n break\\n i = i + 1\\n exists = path.isfile(rows[0][1])\\n datas = rows[0]\\n\\n if not exists:\\n self.markfile(datas[0])\\n\\n return datas\",\n \"def cachepath(self):\\n return [self.fs.cachepath(uri) for uri in self.uri]\",\n \"def test_last_files(self):\\n glob_manager = GlobManager(['*'])\\n self.assertCountEqual(glob_manager.last_files, set())\\n\\n glob_manager.get_files()\\n self.assertCountEqual(\\n glob_manager.last_files,\\n {\\n 'bob.py', 'dave.txt', 'fred.txt.py', 'geoff.py', 'jim.py.txt',\\n 'rob.txt'\\n })\",\n \"def __get_files(self):\\r\\n \\r\\n files = []\\r\\n with requests.Session() as s:\\r\\n headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)'}\\r\\n respons = s.get(self.__url, headers=headers).text\\r\\n soup = BeautifulSoup(respons, 'html.parser')\\r\\n data_files = [link.get('href') for link in soup.find_all('a', class_=\\\"btn-primary\\\")]\\r\\n for year in soup.find_all('td', class_=\\\"align-middle\\\"):\\r\\n regex = re.compile(r\\\"data/data-?gis({year}|\\\\-rok\\\\-{year})\\\\.zip\\\".format(year=year.text))\\r\\n if any((match := regex.match(link)) for link in data_files):\\r\\n files.append(match.group(0))\\r\\n else:\\r\\n files.append(data_files[-1])\\r\\n return files\",\n \"def download_latest_customers(self):\\n file_id, data = self._gdrive.get_last_customer_data_file()\\n self._datastore.force_data_storage('customer', data)\",\n \"def get_files(self):\\n\\n self.files = []\\n retriever_methods = [\\n m\\n for m in rtorrent9.file.methods\\n if m.is_retriever() and m.is_available(self._rt_obj)\\n ]\\n # 2nd arg can be anything, but it'll return all files in torrent\\n # regardless\\n m = rtorrent9.rpc.Multicall(self)\\n m.add(\\n \\\"f.multicall\\\",\\n self.info_hash,\\n \\\"\\\",\\n *[method.rpc_call + \\\"=\\\" for method in retriever_methods]\\n )\\n\\n results = m.call()[0] # only sent one call, only need first result\\n\\n offset_method_index = retriever_methods.index(\\n rtorrent9.rpc.find_method(\\\"f.offset\\\")\\n )\\n\\n # make a list of the offsets of all the files, sort appropriately\\n offset_list = sorted([r[offset_method_index] for r in results])\\n\\n for result in results:\\n results_dict = {}\\n # build results_dict\\n for m, r in zip(retriever_methods, result):\\n results_dict[m.varname] = rtorrent9.rpc.process_result(m, r)\\n\\n # get proper index positions for each file (based on the file\\n # offset)\\n f_index = offset_list.index(results_dict[\\\"offset\\\"])\\n\\n self.files.append(\\n File(self._rt_obj, self.info_hash, f_index, **results_dict)\\n )\\n\\n return self.files\",\n \"def download_files(self):\",\n \"def retrieve_cached_records(self):\\r\\n return u.load_cached_data(self.records_cache_path)\",\n \"def get_downloaded():\\n\\n result = set()\\n\\n for name in os.listdir(PATH):\\n filename, ext = name.split('.')\\n if ext not in EXTENSIONS:\\n continue\\n\\n ts, username, id = filename.split('+')\\n result.add(id)\\n return result\",\n \"def get_latest_path(self):\\n files = [fname for fname in os.listdir(self.checkpoint_dir) if fname.endswith(\\\".pth\\\")]\\n filepaths = [os.path.join(self.checkpoint_dir, filepath) for filepath in files]\\n latest_file = max(filepaths, key=os.path.getctime)\\n return latest_file\",\n \"def _get_cache_filename(self):\\n home_dir = os.path.expanduser(\\\"~\\\")\\n filename = 'dbcollection.json'\\n return os.path.join(home_dir, filename)\",\n \"def get_cache(self):\\n return self.cache\",\n \"def get_files_list(dirname, date_order, rdate_order):\\n file_list = os.listdir(dirname)\\n file_mtimes = dict.fromkeys(file_list)\\n for f in file_list:\\n if f[0] == '.':\\n print \\\"Skipping file: \\\", f\\n del file_mtimes[f]\\n continue\\n if date_order or rdate_order:\\n file_mtimes[f] = os.stat(dirname + '/' + f).st_mtime\\n if date_order or rdate_order:\\n return sorted(file_mtimes.keys(), key=file_mtimes.get, reverse=rdate_order)\\n else:\\n return file_list\",\n \"def _get_most_recent_timestamp_subfolder(self, root_folder_path):\\n walk_gen = os.walk(root_folder_path)\\n root, dirs, files = walk_gen.__next__()\\n dirs.sort(reverse=True)\\n return dirs[0]\",\n \"def cache_key(self):\\r\\n statinfo = os.stat(self.pathname)\\r\\n return (self.filename + str(statinfo.st_mtime)).encode('ascii', 'ignore')\",\n \"def get_last_file(base_dir, pattern):\\n base_dir = Path(base_dir)\\n\\n return sorted(base_dir.glob(pattern),\\n key=lambda x: x.stat().st_ctime, reverse=True)[0]\",\n \"def load_latest_url(self):\\n try:\\n fp = open(self.HTTPD_LATEST_URL_FILE)\\n latest_url = fp.read()\\n fp.close()\\n return latest_url\\n except:\\n return ''\",\n \"def get_datfile(filename):\\n if ARGV.get(DEBUG_OPT):\\n err_print('Getting datfile from \\\"{}\\\"'.format(filename))\\n\\n try:\\n with open(filename, 'rb') as pickle_file:\\n try:\\n (cache, readlist) = pickle.load(pickle_file)\\n pickle_file.close()\\n except (EOFError, ValueError):\\n (cache, readlist) = ({\\\"feed\\\": None, \\\"max-age\\\": None, \\\"last-request\\\": None}, [])\\n except (FileNotFoundError, PermissionError):\\n (cache, readlist) = ({\\\"feed\\\": None, \\\"max-age\\\": None, \\\"last-request\\\": None}, [])\\n return (cache, readlist)\",\n \"def getFiles(self, state=None, s3uri_prefix=None):\\n self.log.info(\\\"getFiles\\\")\\n downloads = []\\n keys = list(self.downloads.keys())\\n keys.sort()\\n for key in keys:\\n download = self.downloads[key]\\n if not state or (state and download['state'] == state):\\n print(download)\\n s3uri = download['s3_uri']\\n if s3uri_prefix is None or s3uri.startswith(s3uri_prefix):\\n item = {}\\n for k in ('local_filepath', 'size', 'state', 's3_time',\\n 's3_date', 's3_uri'):\\n item[k] = download[k]\\n downloads.append(item)\\n\\n return downloads\",\n \"def get_file_info_by_token(token):\\n\\n key = generate_access_token_cache_key(token)\\n value = cache.get(key)\\n if not value:\\n logger.error('No wopi cache value when first get %s' % key)\\n value = cache.get(key)\\n\\n return value if value else None\",\n \"def main():\\n print get_latest_data()\",\n \"def test_get_result_top_files(self):\\n pass\",\n \"def read_cache(self):\\n with open(self.get_cache_filename(), 'rb') as f:\\n data = pickle.loads(f.read())\\n self.timestamp = data['timestamp']\\n self.cache = data['cache']\",\n \"def test_client_max_age_0(self, sess):\\r\\n print('first request')\\r\\n r = sess.get(self.url)\\r\\n assert self.cache.get(self.url) == r.raw\\r\\n\\r\\n print('second request')\\r\\n r = sess.get(self.url, headers={'Cache-Control': 'max-age=0'})\\r\\n\\r\\n # don't remove from the cache\\r\\n assert self.cache.get(self.url)\\r\\n assert not r.from_cache\",\n \"def _first_more_recent (f1, path) :\\n import datetime\\n import re\\n import time\\n s = str (f1.info ())\\n da = re.compile (\\\"Last[-]Modified: (.+) GMT\\\").search (s)\\n if da is None :\\n return True\\n \\n da = da.groups () [0]\\n gr = re.compile (\\\"[\\\\w, ]* ([ \\\\d]{2}) ([\\\\w]{3}) ([\\\\d]{4}) ([\\\\d]{2}):([\\\\d]{2}):([\\\\d]{2})\\\").search (da)\\n if gr == None : return True\\n gr = gr.groups ()\\n da = datetime.datetime ( int (gr [2]), sys.hal_log_values [\\\"month_date\\\"] [gr [1].lower ()], int (gr [0]),\\n int (gr [3]), int (gr [4]), int (gr [5]) )\\n \\n p = time.ctime (os.path.getmtime (path))\\n gr = re.compile (\\\"[\\\\w, ]* ([\\\\w]{3}) ([ \\\\d]{2}) ([\\\\d]{2}):([\\\\d]{2}):([\\\\d]{2}) ([\\\\d]{4})\\\").search (p)\\n if gr is None :\\n return True\\n gr = gr.groups ()\\n da = datetime.datetime ( int (gr [5]), sys.hal_log_values [\\\"month_date\\\"] [gr [0].lower ()], int (gr [1]),\\n int (gr [2]), int (gr [3]), int (gr [4]) )\\n \\n file = da\\n \\n return da > file\",\n \"def get_resource_last_update_timestamp(api_1_0_url, resource):\\n try:\\n r = requests.get(api_1_0_url)\\n json_string = r.content\\n data = json.loads(json_string)\\n try:\\n files = data['files']\\n for entry in files:\\n if entry['path'] == resource:\\n entry_last_update_timestamp = entry['utctimestamp']\\n return entry_last_update_timestamp\\n except Exception as error:\\n print(\\\"Caught error: \\\" + repr(error))\\n except Exception as error:\\n print(\\\"Failed to connect to bitbucket: \\\" + repr(error))\\n exit(1)\\n return None\",\n \"def get_output_from_cache(name, filename):\\n cache_filename = _get_cache_filename(name, filename)\\n if (os.path.exists(cache_filename) and\\n os.path.getmtime(filename) < os.path.getmtime(cache_filename)):\\n with io.open(cache_filename) as f:\\n return f.read()\\n\\n return None\",\n \"def fetch(self, url):\\r\\n fname = os.path.join(self._cachedir, self._formatter(url))\\r\\n if not os.path.exists(fname):\\r\\n time.sleep(self._sleep)\\r\\n html = urllib.urlopen(url).read()\\r\\n with codecs.open(fname, 'w', 'utf-8') as f:\\r\\n soup = BeautifulSoup(html)\\r\\n f.write(unicode(soup))\\r\\n return fname\",\n \"def download_archive(self):\\n\\n def time_convert(structure):\\n \\\"\\\"\\\"\\n :param structure: tuple representation of time\\n :return: GitHub archive time\\n \\\"\\\"\\\"\\n \\n \\n join_number_to_zero = lambda number: (\\\"\\\" if number > 9 else \\\"0\\\") + str(number)\\n\\n return \\\"%s-%s-%s-%s\\\" % (\\n structure.tm_year, join_number_to_zero(structure.tm_mon), join_number_to_zero(structure.tm_mday),\\n structure.tm_hour)\\n\\n current_time = self.get_time()\\n self.logger.debug(__name__ + \\\": \\\" + \\\"current time: \\\" + str(gmtime(current_time)))\\n\\n difference = -25200\\n #timezone difference in seconds between GMT and west coast of USA\\n\\n downloading_time = int(timegm(self.config[\\\"last_connection_time\\\"])) + 3600\\n self.logger.debug(__name__ + \\\": \\\" + \\\"downloading time: \\\" + str(gmtime(downloading_time)))\\n\\n if downloading_time > current_time - 7200:\\n self.logger.info(__name__ + \\\": \\\" + \\\"unable to download file (time limiting).\\\")\\n return\\n\\n downloading_time += difference\\n\\n json_file_name = self.download_file(time_convert(gmtime(downloading_time)))\\n\\n self.config[\\\"last_connection_time\\\"] = gmtime(downloading_time - difference)\\n self.logger.debug(__name__ + \\\": \\\" + \\\"last_connection_time: \\\" + str(self.config[\\\"last_connection_time\\\"]))\\n\\n return json_file_name\",\n \"def getLastFile(self):\\n lastFile = None if len(self.recentFiles) == 0 else self.recentFiles[0]\\n self.setLastPath(lastFile)\\n return lastFile\",\n \"def getCacheFile(ns, digest):\\n return os.path.join(getDir(cacheDir, ns), digest)\",\n \"def retrieve():\\n # type: () -> list\\n with Cache(CACHE_URI) as c:\\n data = c.get(SAVED_SEARCH)\\n return json.loads(data[\\\"blob\\\"]) if data else []\",\n \"def fetch_all_snapshots(archive_dir, wayback_filename, target_url):\\n # Read the list of snapshots.\\n with open(wayback_filename) as f:\\n data = f.read()\\n\\n url_template = \\\"http://web.archive.org/web/{timestamp}/{target_url}\\\"\\n snapshots = data.split(\\\"\\\\n\\\")\\n pages_downloaded = 0\\n pages_failed = 0\\n pages_skipped = 0\\n for snapshot in snapshots:\\n fields = snapshot.split()\\n if len(fields) < 1:\\n print(\\\"Bad fields. End of data?\\\")\\n break\\n date_string = fields[1]\\n assert 14 == len(date_string)\\n ymd = date_string[:8]\\n year = int(date_string[:4])\\n month = int(date_string[4:6])\\n day = int(date_string[6:8])\\n assert 1900 < year < 2100 and 1 <= month <= 12 and 1 <= day <=31\\n date_of_fire = datetime.date(year,month, day)\\n filename = F\\\"firedata_{year}_{month:02}_{day:02}.html\\\"\\n path = os.path.join(archive_dir, filename)\\n if os.path.exists(path):\\n print(\\\"Not replacing \\\", path)\\n pages_skipped += 1\\n continue\\n else:\\n print(\\\"Downloading for \\\", path)\\n url = url_template.format(timestamp=date_string, target_url=target_url)\\n print(url)\\n\\n page = fetch(url)\\n if page is None:\\n print(\\\"Fetching above url failed.\\\")\\n pages_failed +=1\\n continue\\n\\n pages_downloaded += 1\\n with open(path, \\\"wb\\\") as f:\\n f.write(page)\\n print(\\\"Page saved\\\")\\n sleep(2)\\n return pages_downloaded, pages_failed, pages_skipped\",\n \"def last_file_updated(self):\\n query = '*.xml'\\n keymap_files = glob.glob(query)\\n\\n sorted_files = sorted(keymap_files, key=self.mtime, reverse=1)\\n last_modified_file = sorted_files[0]\\n second_last_modified_file = sorted_files[1]\\n\\n t1 = self.mtime(last_modified_file)\\n t2 = self.mtime(second_last_modified_file)\\n\\n logger.debug('Last modified time: {0}'.format(t1))\\n logger.debug('Second Last modified time: {0}'.format(t2))\\n\\n last_modified_time = self.mtime(last_modified_file)\\n last_access_time = self.atime(last_modified_file)\\n\\n if sys.platform == \\\"win32\\\":\\n logger.info('Detected Windows environment')\\n # self.regenerate_osx(last_access_time, last_modified_time)\\n elif sys.platform == 'darwin':\\n logger.info('Detected OSX environment')\\n # self.regenerate_windows(last_access_time, last_modified_time)\\n else:\\n logger.error('Unhandled platform: {0}'.format(sys.platform))\\n pass\",\n \"def cache_file(cache_key):\\n\\n return MASTOOLS_DIR / f\\\"{cache_key}_cache.json\\\"\",\n \"def _most_recent_event_files(self):\\n regex = re.compile(r\\\"\\\\w*events.log\\\")\\n return [\\n os.path.join(self._output_dir, x)\\n for x in os.listdir(self._output_dir)\\n if regex.search(x)\\n ]\",\n \"def get_cache_file_list(self,\\n file_list_obj=None,\\n file_info_class=FileInfo,\\n file_list_class=FileList):\\n if not file_list_obj:\\n file_list_obj = file_list_class()\\n temp_ = self.read_pickle_object_in_file()\\n if temp_:\\n for tup_ in temp_:\\n finf_ = file_info_class(in_tuple=tup_)\\n fn_ = finf_.filename\\n self.cache_file_list_dict[fn_] = finf_\\n file_list_obj.append(finf_)\\n return file_list_obj\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6888681","0.6708136","0.658939","0.6533511","0.6487432","0.6340917","0.63322324","0.63005716","0.6286395","0.62659204","0.6218005","0.6218005","0.61635375","0.61493427","0.61172336","0.6110893","0.6078489","0.60554326","0.6053905","0.60533607","0.6010056","0.60051423","0.600378","0.5971943","0.59554845","0.59442824","0.59417015","0.5933621","0.5933621","0.5923152","0.59116435","0.59085125","0.59062415","0.5891031","0.588776","0.5885116","0.58839214","0.5880581","0.5880581","0.58740044","0.5869428","0.5867111","0.58548194","0.5838266","0.5828574","0.5819923","0.5785659","0.57668936","0.5766474","0.576436","0.5763969","0.5740735","0.5735096","0.5716949","0.57103765","0.5709562","0.57008153","0.569946","0.5699427","0.56972116","0.56829464","0.5676035","0.5674096","0.5672308","0.5668159","0.5667894","0.56625164","0.56621206","0.56511813","0.56461746","0.5645577","0.56418955","0.56401527","0.5623077","0.5622151","0.5610316","0.5607873","0.56056774","0.5605105","0.5602387","0.56001973","0.5597079","0.55835295","0.55801946","0.55775565","0.55642235","0.5563825","0.55590904","0.55585593","0.55569464","0.5540254","0.55386674","0.55309063","0.5523219","0.5520045","0.55191356","0.5518746","0.5515841","0.551496","0.55133206","0.55115795"],"string":"[\n \"0.6888681\",\n \"0.6708136\",\n \"0.658939\",\n \"0.6533511\",\n \"0.6487432\",\n \"0.6340917\",\n \"0.63322324\",\n \"0.63005716\",\n \"0.6286395\",\n \"0.62659204\",\n \"0.6218005\",\n \"0.6218005\",\n \"0.61635375\",\n \"0.61493427\",\n \"0.61172336\",\n \"0.6110893\",\n \"0.6078489\",\n \"0.60554326\",\n \"0.6053905\",\n \"0.60533607\",\n \"0.6010056\",\n \"0.60051423\",\n \"0.600378\",\n \"0.5971943\",\n \"0.59554845\",\n \"0.59442824\",\n \"0.59417015\",\n \"0.5933621\",\n \"0.5933621\",\n \"0.5923152\",\n \"0.59116435\",\n \"0.59085125\",\n \"0.59062415\",\n \"0.5891031\",\n \"0.588776\",\n \"0.5885116\",\n \"0.58839214\",\n \"0.5880581\",\n \"0.5880581\",\n \"0.58740044\",\n \"0.5869428\",\n \"0.5867111\",\n \"0.58548194\",\n \"0.5838266\",\n \"0.5828574\",\n \"0.5819923\",\n \"0.5785659\",\n \"0.57668936\",\n \"0.5766474\",\n \"0.576436\",\n \"0.5763969\",\n \"0.5740735\",\n \"0.5735096\",\n \"0.5716949\",\n \"0.57103765\",\n \"0.5709562\",\n \"0.57008153\",\n \"0.569946\",\n \"0.5699427\",\n \"0.56972116\",\n \"0.56829464\",\n \"0.5676035\",\n \"0.5674096\",\n \"0.5672308\",\n \"0.5668159\",\n \"0.5667894\",\n \"0.56625164\",\n \"0.56621206\",\n \"0.56511813\",\n \"0.56461746\",\n \"0.5645577\",\n \"0.56418955\",\n \"0.56401527\",\n \"0.5623077\",\n \"0.5622151\",\n \"0.5610316\",\n \"0.5607873\",\n \"0.56056774\",\n \"0.5605105\",\n \"0.5602387\",\n \"0.56001973\",\n \"0.5597079\",\n \"0.55835295\",\n \"0.55801946\",\n \"0.55775565\",\n \"0.55642235\",\n \"0.5563825\",\n \"0.55590904\",\n \"0.55585593\",\n \"0.55569464\",\n \"0.5540254\",\n \"0.55386674\",\n \"0.55309063\",\n \"0.5523219\",\n \"0.5520045\",\n \"0.55191356\",\n \"0.5518746\",\n \"0.5515841\",\n \"0.551496\",\n \"0.55133206\",\n \"0.55115795\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":11174,"cells":{"query":{"kind":"string","value":"Final pass to connect existing ocean caches to the ocean shader if they exist in the scene"},"document":{"kind":"string","value":"def _connectWakeAndFoamToOcean(self, tk, templateFile = '', id = '', shotNum = '', inprogressBar = ''):\r\n\t\tdebug(app = self.app, method = '_connectWakeAndFoamToOcean', message = 'Connecting fluid textures to ocean shader....', verbose = False)\r\n\r\n\t\t####################################################\r\n\t\t## Straight up connection if no interactive is found.\r\n\t\tif cmds.objExists(CONST.OCEANDISPSHADER) and cmds.objExists(CONST.WAKE_FLUID_SHAPENODE) and cmds.objExists(CONST.FOAM_FLUID_SHAPENODE):\r\n\t\t\ttry:\r\n\t\t\t\tcmds.connectAttr(\"%s.outAlpha\" % CONST.WAKE_FLUID_SHAPENODE, \"%s.waveHeightOffset\" % CONST.OCEANDISPSHADER, force = True)\r\n\t\t\texcept:\r\n\t\t\t\tpass\r\n\t\t\ttry:\r\n\t\t\t\tcmds.connectAttr(\"%s.outAlpha\" % CONST.FOAM_FLUID_SHAPENODE, \"%s.foamOffset\" % CONST.OCEANDISPSHADER, force = True)\r\n\t\t\texcept:\r\n\t\t\t\tpass\r\n\r\n\t\t###########################################\r\n\t\t####### INTERACTIVE STUFFF ################\r\n\t\t### Now check for interactive caches and blend these\r\n\t\tdebug(app = self.app, method = '_connectWakeAndFoamToOcean', message = 'Looking for interactive anim fluids now...', verbose = False)\r\n\t\tinprogressBar.updateProgress(percent = 76, doingWhat = 'Looking for interactive caches..')\r\n\r\n\t\tgetAnimVersionFolders = tk.paths_from_template(templateFile, {'Step' : 'Anm', 'id' : id, 'Shot' : shotNum})\r\n\t\tdebug(app = self.app, method = '_connectWakeAndFoamToOcean', message = 'getAnimVersionFolders: %s' % getAnimVersionFolders, verbose = False)\r\n\r\n\t\t## now find the highest version folder number\r\n\t\thighestVersionFolder = r'%s' % max(getAnimVersionFolders)\r\n\t\tdebug(app = self.app, method = '_connectWakeAndFoamToOcean', message = 'highestVersionFolder...%s' % highestVersionFolder, verbose = False)\r\n\r\n\t\thighestVersionFolder = highestVersionFolder.replace('\\\\', '/')\r\n\t\tdebug(app = self.app, method = '_connectWakeAndFoamToOcean', message = 'highestVersionFolder replaced \\\\ with /...\\n%s' % highestVersionFolder, verbose = False)\r\n\r\n\t\tversionNumber = highestVersionFolder.split('/')[-1]\r\n\t\tdebug(app = self.app, method = '_connectWakeAndFoamToOcean', message = 'versionNumber: %s' % versionNumber, verbose = False)\r\n\r\n\t\tlistCacheFiles = os.listdir(highestVersionFolder)\r\n\t\tdebug(app = self.app, method = '_connectWakeAndFoamToOcean', message = 'listCacheFiles...\\n%s' % listCacheFiles, verbose = False)\r\n\r\n\t\tinteractiveCaches = {}\r\n\t\tfor each in listCacheFiles:\r\n\t\t\tif each.endswith('.xml'): ## the ocean shader preset saved out\r\n\t\t\t\tif CONST.WAKE_FLUID_SHAPENODE in each:\r\n\t\t\t\t\tinteractiveCaches[CONST.WAKE_FLUID_SHAPENODE] = '%s/%s' % (highestVersionFolder, each)\r\n\t\t\t\telse:\r\n\t\t\t\t\tinteractiveCaches[CONST.FOAM_FLUID_SHAPENODE] = '%s/%s' % (highestVersionFolder, each)\r\n\r\n\t\tif interactiveCaches:\r\n\t\t\tfluidCaches.mergeFluidCaches(interactiveFoamXML = interactiveCaches[CONST.FOAM_FLUID_SHAPENODE], interactiveWakeXML = interactiveCaches[CONST.WAKE_FLUID_SHAPENODE])\r\n\r\n\t\tdebug(app = self.app, method = '_connectWakeAndFoamToOcean', message = 'Ocean connected....', verbose = False)"},"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 _on_load_scene_shaders(self):\n\n artellapipe.ShadersMgr().load_scene_shaders()","def _on_lowres_assets(self):\n\n scene_assets = artellapipe.AssetsMgr().get_scene_assets()\n if not scene_assets:\n return\n\n for scene_asset in scene_assets:\n scene_asset.switch_to_proxy()","def run():\n\n def assignToon(context):\n def instanciate_group(nodes, group_name):\n group = nodes.new(type='ShaderNodeGroup')\n group.node_tree = bpy.data.node_groups[group_name]\n return group\n\n def assignToonShader(material):\n '''To do Handle if the material output doesnt exist'''\n toonShader = instanciate_group(material.node_tree.nodes, \"ToonShader_2\")\n node2 = material.node_tree.nodes['Material Output']\n material.node_tree.links.new(toonShader.outputs[0], node2.inputs[0])\n\n objects = bpy.context.selected_objects\n for obj in objects:\n if len(obj.material_slots) < 1:\n\n bpy.ops.object.material_slot_add()\n\n if obj.name not in bpy.data.materials:\n\n mat = bpy.data.materials.new(obj.name)\n else:\n mat = bpy.data.materials[obj.name]\n\n obj.data.materials[0] = mat\n mat.use_nodes = True\n\n for mat in obj.data.materials:\n if mat.name == '':\n mat.name = obj.name\n\n matNodes = mat.node_tree.nodes\n\n assignToonShader(mat)\n if 'Principled BSDF' in matNodes:\n matNodes.remove(matNodes['Principled BSDF'])\n # else:\n # for n in matNodes:\n # if n != material.node_tree.nodes['Material Output']:\n # matNodes.remove(n)\n\n\n shaderPath = r'D:/COMPANIES/loneCoconut/render/MILVIO_CGL/assets/lib/TOONSCEENSETUP/shd/publish/001.000/high/lib_TOONSCEENSETUP_shd.blend'\n collection_name = 'ToonSceneSetup'\n # dict_ = {'company': 'loneCoconut',\n # 'context': 'render',\n # 'project': 'MILVIO',\n # 'scope': 'assets',\n # 'seq': 'lib',\n # 'shot': 'TOONSCEENSETUP',\n # 'task': 'shd',\n # 'user': 'publish',\n # 'resolution': 'high'}\n # shaderPath = lm.LumberObject(dict_)\n # print(shaderPath.latest_version().path_root)\n #\n # collection_name = shaderPath.shot\n\n if collection_name not in bpy.data.collections:\n\n # link all collections starting with 'MyCollection'\n with bpy.data.libraries.load(shaderPath, link=False) as (data_from, data_to):\n data_to.collections = [c for c in data_from.collections if c.startswith(collection_name)]\n\n # link collection to scene collection\n for coll in data_to.collections:\n if coll is not None:\n bpy.data.scenes['Scene'].collection.children.link(coll)\n\n else:\n print(\"Toon Shader Exist\")\n\n\n assignToon(bpy.context)","def init_layers(self):\n\n # get caching layers activated\n caching_layers = G3WCachingLayer.objects.all()\n for caching_layer in caching_layers:\n self.add_layer(str(caching_layer), caching_layer)","def _reload_shader(self):\n self.render_pipeline.reload_shaders()\n\n self.render_pipeline.set_effect(self.terrain.get_node(), \"effects/terrain.yaml\", {\n \"render_gbuffer\": True,\n \"render_shadows\": False,\n\n })\n\n self.render_pipeline.set_effect(self.terrain_shadow.get_node(), \"effects/terrain_shadow.yaml\", {\n \"render_gbuffer\": False,\n \"render_shadows\": True,\n }, 5000)","def main():\n viewer = Viewer()\n shader = Shader(\"Shaders/poisson.vert\", \"Shaders/poisson.frag\")\n shaderLight = Shader(\"Shaders/phong.vert\", \"Shaders/phong.frag\")\n\n # Création du terrain\n node_terrain = Node(transform=translate((-250,-11.7,-250)))\n node_terrain.add(Terrain(\"Terrain/sand.jpg\",viewer.depth))\n viewer.add(node_terrain)\n\n # Chargement de tous les objets\n # Chaque objet est mis dans un node qui permet de définir\n # sa position, rotation et taille et aussi de se rattacher\n # à une autre entité\n\n barnabe_obj = \"Fish/Fish/WhaleShark/WhaleShark.obj\"\n barnabe_png = \"Fish/Fish/WhaleShark/WhaleShark_Base_Color.png\"\n\n hector_obj = \"Fish/Fish/ReefFish5/ReefFish5.obj\"\n hector_png = \"Fish/Fish/ReefFish5/ReefFish5_Base_Color.png\"\n\n susie_obj = \"Fish/Fish/SeaHorse/SeaHorse.obj\"\n susie_png = \"Fish/Fish/SeaHorse/SeaHorse_Base_Color.png\"\n\n edgar_obj = \"Fish/Fish/BlueTang/BlueTang.obj\"\n edgar_png = \"Fish/Fish/BlueTang/BlueTang_Base_Color.png\"\n\n nemo_obj = \"Fish/Fish/ClownFish2/ClownFish2.obj\"\n nemo_png = \"Fish/Fish/ClownFish2/Clownfish2_Base_Color.png\"\n\n caroline_obj = \"Fish/Fish/Turtle/Turtle.obj\"\n caroline_png = \"Fish/Fish/Turtle/Turtle.jpg\"\n\n corail_obj = \"Fish/Fish/Corail/Corail.obj\"\n corail_png = \"Fish/Fish/Corail/Corail.jpg\"\n\n sebastien_obj = \"Fish/Fish/Crab/Crab.obj\"\n sebastien_png = \"Fish/Fish/Crab/Crab.jpg\"\n\n star_obj = \"Fish/Fish/BlueStarfish/BluieStarfish.obj\"\n star_png = \"Fish/Fish/BlueStarfish/BlueStarfish_Base_Color.png\"\n\n cube_obj = \"Fish/Fish/Cube/cube.obj\"\n cube_png = \"Fish/Fish/Cube/cube.png\"\n\n suzanne_obj = \"Fish/Fish/Suzanne/Suzanne.obj\"\n\n barnabe_node = Node(2)\n meshes = load_shadowed_texture(barnabe_obj, shader, viewer.depth, barnabe_png, 2)\n for mesh in meshes:\n barnabe_node.add(mesh)\n\n edgar_node = Node(0, transform=translate((1.5, 0.0, 1.5)) @ scale((0.1, 0.1, 0.1)))\n meshes = load_shadowed_texture(edgar_obj, shader, viewer.depth, edgar_png, 0)\n for mesh in meshes:\n edgar_node.add(mesh)\n barnabe_node.add(edgar_node)\n viewer.add(barnabe_node)\n\n cube_node = Node(transform=translate((5.0,-5.0,0.0)))\n meshes = load_shadowed_texture(cube_obj, shader,viewer.depth, cube_png, 1)\n for mesh in meshes:\n cube_node.add(mesh)\n\n suzanne_bubble_node = Node(transform=translate((-0.4,-0.25,0.65)))\n suzanne_node = Node(transform=scale((0.25,0.25,0.25)) @ rotate((0,1,1),-30))\n meshes = load_phong_mesh(suzanne_obj,shaderLight,viewer.depth)\n for mesh in meshes:\n suzanne_node.add(mesh)\n suzanne_bubble_node.add(suzanne_node)\n\n bubble_translate = {0: vec(-0.15,-0.17,0.25), 3: vec(-0.2,0,0.25), 5: vec(-0.15, 0.15, 0.25), 7: vec(-0.175, 0.27, 0.25)}\n bubble_rotate = {0: quaternion()}\n bubble_scale = {0: 0.02, 7: 0.06}\n bubble_node = KeyFrameControlNode(bubble_translate, bubble_rotate, bubble_scale)\n bubble_node.add(Bubble(15))\n suzanne_bubble_node.add(bubble_node)\n cube_node.add(suzanne_bubble_node)\n\n susie_trans = {0: vec(1.2, 1, 0), 2: vec(1.2, 2, 0), 5: vec(1.2, 1, 0)}\n susie_scale = {0: 0.03}\n susie_rotate = {0: quaternion_from_axis_angle((0, 1, 0), degrees=-45)}\n susie_node = KeyFrameControlNode(susie_trans, susie_rotate, susie_scale)\n meshes = load_shadowed_texture(susie_obj, shader, viewer.depth, susie_png, 1)\n for mesh in meshes:\n susie_node.add(mesh)\n cube_node.add(susie_node)\n\n susie2_trans = {0: vec(1, 2, 0), 3: vec(1, 1.5, 0), 5: vec(1, 2, 0)}\n susie2_scale = {0: 0.05}\n susie2_rotate = {0: quaternion_from_axis_angle((0, 1, 0), degrees=45)}\n susie2_node = KeyFrameControlNode(susie2_trans, susie2_rotate, susie2_scale)\n meshes = load_shadowed_texture(susie_obj, shader, viewer.depth, susie_png, 1)\n for mesh in meshes:\n susie2_node.add(mesh)\n cube_node.add(susie2_node)\n\n nemo_trans = {0: vec(-25, 2, 25), 22: vec(-2, 2, 2), 40: vec(20, 2, -20)}\n nemo_scale = {0: 0.1}\n nemo_rotate = {0: quaternion_from_axis_angle((0, 1, 0), degrees=120)}\n nemo_node = KeyFrameControlNode(nemo_trans, nemo_rotate, nemo_scale)\n meshes = load_shadowed_texture(nemo_obj, shader, viewer.depth, nemo_png, 0)\n for mesh in meshes:\n nemo_node.add(mesh)\n cube_node.add(nemo_node)\n\n nemo_trans2 = {0: vec(-28, 2, 26), 20: vec(0, 2, 3), 40: vec(20, 2, -23)}\n nemo_scale2 = {0: 0.07}\n nemo_rotate2 = {0: quaternion_from_axis_angle((0, 1, 0), degrees=120)}\n nemo_node = KeyFrameControlNode(nemo_trans2, nemo_rotate2, nemo_scale2)\n meshes = load_shadowed_texture(nemo_obj, shader, viewer.depth, nemo_png, 0)\n for mesh in meshes:\n nemo_node.add(mesh)\n cube_node.add(nemo_node)\n\n nemo_trans3 = {0: vec(-22, 2, 21), 41: vec(20, 2, -20)}\n nemo_scale3 = {0: 0.07}\n nemo_rotate3 = {0: quaternion_from_axis_angle((0, 1, 0), degrees=120)}\n nemo_node = KeyFrameControlNode(nemo_trans3, nemo_rotate3, nemo_scale3)\n meshes = load_shadowed_texture(nemo_obj, shader, viewer.depth, nemo_png, 0)\n for mesh in meshes:\n nemo_node.add(mesh)\n cube_node.add(nemo_node)\n\n nemo_trans4 = {0: vec(-22, 2.3, 21), 39: vec(20, 2.5, -20)}\n nemo_scale4 = {0: 0.07}\n nemo_rotate4 = {0: quaternion_from_axis_angle((0, 1, 0), degrees=120)}\n nemo_node = KeyFrameControlNode(nemo_trans4, nemo_rotate4, nemo_scale4)\n meshes = load_shadowed_texture(nemo_obj, shader, viewer.depth, nemo_png, 0)\n for mesh in meshes:\n nemo_node.add(mesh)\n cube_node.add(nemo_node)\n\n nemo_trans5 = {0: vec(-22, 2.2, 21), 36: vec(30, 2.2, -20)}\n nemo_scale5 = {0: 0.1}\n nemo_rotate5 = {0: quaternion_from_axis_angle((0, 1, 0), degrees=120)}\n nemo_node = KeyFrameControlNode(nemo_trans5, nemo_rotate5, nemo_scale5)\n meshes = load_shadowed_texture(nemo_obj, shader, viewer.depth, nemo_png, 0)\n for mesh in meshes:\n nemo_node.add(mesh)\n cube_node.add(nemo_node)\n\n nemo_trans6 = {0: vec(-20, 1.7, 21), 38: vec(30, 2, -20)}\n nemo_scale6 = {0: 0.1}\n nemo_rotate6 = {0: quaternion_from_axis_angle((0, 1, 0), degrees=120)}\n nemo_node = KeyFrameControlNode(nemo_trans6, nemo_rotate6, nemo_scale6)\n meshes = load_shadowed_texture(nemo_obj, shader, viewer.depth, nemo_png, 0)\n for mesh in meshes:\n nemo_node.add(mesh)\n cube_node.add(nemo_node)\n\n star_node = Node(transform=translate((0,0.5,0)) @ scale((0.3, 0.3, 0.3)))\n meshes = load_shadowed_texture(star_obj, shader, viewer.depth, star_png, 1)\n for mesh in meshes:\n star_node.add(mesh)\n cube_node.add(star_node)\n\n translate_keys = {0: vec(1,-0.5,0.5), 10: vec(1,-0.5,-0.5), 20: vec(1,-0.5,0.5)}\n rotate_keys = {0: quaternion_mul(quaternion_from_axis_angle((1,0,0), degrees=-90), quaternion_from_axis_angle((0,0,1), degrees=90))}\n scale_keys = {0: 0.02}\n sebastien_node = KeyFrameControlNode(translate_keys, rotate_keys, scale_keys)\n meshes = load_shadowed_texture(sebastien_obj, shader, viewer.depth, sebastien_png, 1)\n for mesh in meshes:\n sebastien_node.add(mesh)\n cube_node.add(sebastien_node)\n viewer.add(cube_node)\n\n corail_turtle_node = Node(transform=translate((2.5, -5.0, -5.0)))\n corail_node = Node(transform=scale((0.01,0.01,0.01)))\n meshes = load_shadowed_texture(corail_obj, shader, viewer.depth, corail_png, 1)\n for mesh in meshes:\n corail_node.add(mesh)\n corail_turtle_node.add(corail_node)\n\n hector_trans = {0: vec(-0.5, 1, 0.5)}\n hector_scale = {0: 0.07}\n hector_rotate = {0: quaternion_from_axis_angle((0,1,0), degrees=-90), 1: quaternion_from_axis_angle((0,1,0), degrees=-180), 2: quaternion_from_axis_angle((0,1,0), degrees=-270), 3: quaternion_from_axis_angle((0,1,0), degrees=-360), 4: quaternion_from_axis_angle((0,1,0), degrees=-90)}\n hector_node = KeyFrameControlNode(hector_trans, hector_rotate, hector_scale)\n meshes = load_shadowed_texture(hector_obj, shader,viewer.depth, hector_png, 3)\n for mesh in meshes:\n hector_node.add(mesh)\n corail_turtle_node.add(hector_node)\n\n caroline_node = Node(transform=translate((-0.5, 0.5, 0.0)) @ scale((0.01,0.01,0.01)) @ rotate((1,0,0), 270) @ rotate((0,0,1),315) @ rotate((0,1,0), 45))\n meshes = load_shadowed_texture(caroline_obj, shader,viewer.depth,caroline_png, 0)\n for mesh in meshes:\n caroline_node.add(mesh)\n corail_turtle_node.add(caroline_node)\n viewer.add(corail_turtle_node)\n\n # Commande de clavier\n print(\"\\n\\n ----------------- Les commandes de clavier sont les flèches ------------------- \\n\\n\")\n\n # start rendering loop\n viewer.run()","def _on_hires_assets(self):\n\n scene_assets = artellapipe.AssetsMgr().get_scene_assets()\n if not scene_assets:\n return\n\n for scene_asset in scene_assets:\n scene_asset.switch_to_hires()","def main():\n import shutil\n import json\n\n if not os.path.isdir(args.cache):\n # creation dossier cache\n os.mkdir(args.cache)\n\n if not os.path.exists(args.cache+'/overviews.json'):\n # creation fichier overviews.json a partir d'un fichier ressource\n shutil.copy2(args.overviews, args.cache+'/overviews.json')\n\n with open(args.cache+'/overviews.json') as json_overviews:\n overviews_dict = json.load(json_overviews)\n if not (\"list_OPI\" in overviews_dict):\n overviews_dict[\"list_OPI\"] = []\n\n out_raster_srs = gdal.osr.SpatialReference()\n out_raster_srs.ImportFromEPSG(overviews_dict['crs']['code'])\n conn_string = \"PG:host=\"+host+\" dbname=\"+database+\" user=\"+user+\" password=\"+password\n db_graph = gdal.OpenEx(conn_string, gdal.OF_VECTOR)\n if db_graph is None:\n raise ValueError(\"Connection to database failed\")\n list_filename = glob.glob(args.input)\n if verbose > 0:\n print(len(list_filename), \"fichier(s) a traiter\")\n\n try:\n with open(args.cache+'/cache_mtd.json', 'r') as inputfile:\n mtd = json.load(inputfile)\n except:\n mtd = {}\n\n cliche_dejaTraites = []\n for filename in list_filename:\n cliche = Path(filename).stem\n \n if (cliche in overviews_dict['list_OPI']):\n # OPI déja traitée\n cliche_dejaTraites.append(cliche)\n else:\n print('nouvelle image: ', filename)\n color = [randrange(255), randrange(255), randrange(255)]\n while (color[0] in mtd) and (color[1] in mtd[color[0]]) and (color[2] in mtd[color[0]][color[1]]):\n color = [randrange(255), randrange(255), randrange(255)]\n if color[0] not in mtd:\n mtd[color[0]] = {}\n if color[1] not in mtd[color[0]]:\n mtd[color[0]][color[1]] = {}\n mtd[color[0]][color[1]][color[2]] = cliche\n process_image(overviews_dict, db_graph, filename, color, out_raster_srs)\n # on ajout l'OPI traitée a la liste\n overviews_dict[\"list_OPI\"].append(cliche)\n\n with open(args.cache+'/cache_mtd.json', 'w') as outfile:\n json.dump(mtd, outfile)\n\n with open(args.cache+'/overviews.json', 'w') as outfile:\n json.dump(overviews_dict, outfile)\n\n print(\"\\n\", len(list_filename) - len(cliche_dejaTraites),\"/\",len(list_filename),\"OPI(s) ajoutée(s)\")\n if len(cliche_dejaTraites) > 0:\n print(cliche_dejaTraites, \"déjà traitées : OPI non recalculée(s)\")","def initTextureCache():\r\n\r\n\tglobal textureCache\r\n\r\n\ttextureCache = {}","def recache(self, phys):\r\n self.myOutputCache.initialize(phys.app)\r\n\r\n for output in self.myOutputs:\r\n output.initialize(phys.app)\r\n output.run(1)","def cache_geometries(self, geo_nodes):\n # Create the geocache file command\n cache_cmd = [\n 'string $switch = \"\";',\n 'string $cacheFiles[] = `cacheFile',\n '-fileName \"tmpCache\"',\n '-directory', '\"{}\"'.format(Settings.TEMP_DIR),\n '-startTime', str(self._model.frame_in - 1),\n '-endTime', str(self._model.frame_out + 1),\n '-worldSpace',\n '-singleCache',\n ]\n switch_cmd = []\n for node in geo_nodes:\n if mc.nodeType(node) != 'mesh':\n node = mc.listRelatives(node, shapes=True, fullPath=True)[0]\n\n # Add the newly created geo to the geocache command\n cache_cmd.extend(['-points', node])\n # Create the code to attach the geocache to the created geo\n switch_cmd.extend([\n '$switch = createHistorySwitch(\"{}\", false);'.format(node),\n 'cacheFile',\n '-attachFile',\n '-fileName $cacheFiles[0]'\n '-directory', '\"{}\"'.format(Settings.TEMP_DIR),\n '-channelName', '\"{}\"'.format(node),\n '-inAttr ($switch+\".inp[0]\");',\n 'setAttr ($switch+\".playFromCache\") true;'\n ])\n\n # Concatenate both commands\n cache_cmd = ' '.join(cache_cmd) + \"`;\" + ' '.join(switch_cmd)\n\n # Clean the geometries before making the cache\n self.clean_geometries(geo_nodes)\n # Make a geocache of the created geometries and attach it\n mel.eval(cache_cmd)","def init_shaders():\n global shaders\n\n vertex_shader = glCreateShader(GL_VERTEX_SHADER)\n glShaderSource(vertex_shader,open('shaders/vs-phong-interp.c','r').read())\n glCompileShader(vertex_shader)\n result = glGetShaderiv(vertex_shader, GL_COMPILE_STATUS)\n if result:\n print('Vertex shader compilation successful.')\n else:\n print('Vertex shader compilation FAILED:')\n print(glGetShaderInfoLog(vertex_shader))\n sys.exit(-1)\n\n fragment_shader = glCreateShader(GL_FRAGMENT_SHADER)\n glShaderSource(fragment_shader, open('shaders/fs-phong-interp.c','r').read())\n glCompileShader(fragment_shader)\n result = glGetShaderiv(fragment_shader, GL_COMPILE_STATUS)\n if result:\n print('Fragment shader compilation successful.')\n else:\n print('Fragment shader compilation FAILED:')\n print(glGetShaderInfoLog(fragment_shader))\n sys.exit(-1)\n\n shaders = glCreateProgram()\n glAttachShader(shaders,vertex_shader)\n glAttachShader(shaders,fragment_shader)\n glLinkProgram(shaders)","def importShaders(self, namespace=':'):\n self.logger.info(\"Import Shaders\")\n\n if self.data['abcShadersAttr']:\n\n abcfile = self.data['abcShadersAttr']\n \n # shotgun query for maya file\n mayafile = find_shader_package_from_shader_file(file_path=abcfile, file_type='ma')\n if mayafile != {}:\n mayafile = mayafile['ma']\n self.logger.debug(\"Found maya shader file: %s\" % mayafile)\n else:\n localfile = abcfile.replace('.abc', '.ma')\n if os.path.isfile(localfile):\n mayafile = localfile\n self.logger.debug(\"Found maya shader file: %s\" % mayafile)\n else:\n self.logger.error(\"Missing file : %s\" % self.data['abcShadersAttr'])\n return False\n\n if os.path.isfile(mayafile):\n try: \n imported_shaders = cmds.file(mayafile, i=True, returnNewNodes=True, renameAll=True, mergeNamespacesOnClash=True, namespace=namespace)\n self.setAttr(\"abcShaders\", \"\")\n self.logger.debug(\"Imported under %s namespace\" % namespace)\n\n # reset selection back to alembicHolder\n cmds.select(self.data['shapeNode'])\n self.logger.info(\"Imported : %s\" % self.data['abcShadersAttr'])\n return True\n\n except Exception, e:\n self.logger.error(\"Import Json Error : %s\" % e)\n return False\n else:\n self.logger.error(\"Missing file : %s\" % self.data['abcShadersAttr'])\n return False\n else:\n self.logger.info(\"Empty attribute : %s.abcShadersAttr\" % self.data['shapeNode'])\n return False","def updateShaderState(self):\n\n dopts = self.opts\n copts = self.canvas.opts\n lightPos = None\n flatColour = dopts.getConstantColour()\n useNegCmap = (not dopts.useLut) and dopts.useNegativeCmap\n\n if self.threedee:\n lightPos = np.array(copts.lightPos)\n lightPos *= (copts.zoom / 100.0)\n else:\n lightPos = None\n\n if dopts.useLut:\n delta = 1.0 / (dopts.lut.max() + 1)\n cmapXform = transform.scaleOffsetXform(delta, 0.5 * delta)\n else:\n cmapXform = self.cmapTexture.getCoordinateTransform()\n\n fslgl.glmesh_funcs.updateShaderState(\n self,\n useNegCmap=useNegCmap,\n cmapXform=cmapXform,\n flatColour=flatColour,\n lightPos=lightPos)","def initialize(self, num_agents: int) -> None:\n object_loc_main = config[\"base\"][\"object_location\"]\n if experiment == \"stage2.0\":\n self.objects.add_object(file = \"experiments/flocking/images/redd.png\", pos = object_loc_main, scale = [800, 800], obj_type = \"obstacle\")\n object_loc = config[\"first_circle\"][\"object_location\"]\n self.objects.add_object(\n file=\"experiments/aggregation/images/greyc1.png\", pos=object_loc, scale=[200, 200], obj_type=\"site\"\n )\n object_loc = config[\"second_circle\"][\"object_location\"]\n self.objects.add_object(\n file=\"experiments/aggregation/images/greyc1.png\", pos=object_loc, scale=[200, 200], obj_type=\"site\"\n )\n elif experiment == \"stage1\":\n self.objects.add_object(file=\"experiments/flocking/images/redd.png\", pos=object_loc_main, scale=[800, 800],\n obj_type=\"obstacle\")\n object_loc = config[\"center_circle\"][\"object_location\"]\n self.objects.add_object(\n file=\"experiments/aggregation/images/greyc1.png\", pos=object_loc, scale=[200, 200], obj_type=\"site\"\n )\n elif experiment == \"stage2.1\":\n self.objects.add_object(file=\"experiments/flocking/images/redd.png\", pos=object_loc_main, scale=[800, 800],\n obj_type=\"obstacle\")\n object_loc = config[\"first_circle\"][\"object_location\"]\n self.objects.add_object(\n file=\"experiments/aggregation/images/greyc1.png\", pos=object_loc, scale=[200, 200], obj_type=\"site\"\n )\n object_loc = config[\"second_circle\"][\"object_location\"]\n self.objects.add_object(\n file=\"experiments/aggregation/images/greyc2.png\", pos=object_loc, scale=[225, 225], obj_type=\"site\"\n )\n elif experiment == \"stage3\":\n self.objects.add_object(file=\"experiments/flocking/images/redd.png\", pos=object_loc_main, scale=[1000, 1000],\n obj_type=\"obstacle\")\n object_loc = config[\"first_circle\"][\"object_location\"]\n self.objects.add_object(\n file=\"experiments/aggregation/images/greyc1.png\", pos=object_loc, scale=[200, 200], obj_type=\"site\"\n )\n object_loc = config[\"second_circle\"][\"object_location\"]\n self.objects.add_object(\n file=\"experiments/aggregation/images/greyc1.png\", pos=object_loc, scale=[200, 200], obj_type=\"site\"\n )\n object_loc = config[\"upper_circle\"][\"object_location\"]\n self.objects.add_object(\n file=\"experiments/aggregation/images/greyc1.png\", pos=object_loc, scale=[200, 200], obj_type=\"site\"\n )\n object_loc = config[\"lower_circle\"][\"object_location\"]\n self.objects.add_object(\n file=\"experiments/aggregation/images/greyc1.png\", pos=object_loc, scale=[200, 200], obj_type=\"site\"\n )\n elif experiment == \"stage3.1\":\n self.objects.add_object(file=\"experiments/flocking/images/redd.png\", pos=object_loc_main, scale=[800, 800],\n obj_type=\"obstacle\")\n object_loc = config[\"first_circle\"][\"object_location\"]\n self.objects.add_object(\n file=\"experiments/aggregation/images/greyc1.png\", pos=object_loc, scale=[200, 200], obj_type=\"site\"\n )\n object_loc = config[\"second_circle\"][\"object_location\"]\n self.objects.add_object(\n file=\"experiments/aggregation/images/greyc1.png\", pos=object_loc, scale=[200, 200], obj_type=\"site\"\n )\n object_loc = config[\"upper_circle\"][\"object_location\"]\n self.objects.add_object(\n file=\"experiments/aggregation/images/greyc2.png\", pos=object_loc, scale=[225, 225], obj_type=\"site\"\n )\n object_loc = config[\"lower_circle\"][\"object_location\"]\n self.objects.add_object(\n file=\"experiments/aggregation/images/greyc2.png\", pos=object_loc, scale=[225, 225], obj_type=\"site\")\n\n\n min_x, max_x = area(object_loc_main[0], 1000)\n min_y, max_y = area(object_loc_main[1], 1000)\n\n # add agents to the environment\n for index, agent in enumerate(range(num_agents)):\n coordinates = generate_coordinates(self.screen)\n while (\n coordinates[0] >= max_x\n or coordinates[0] <= min_x\n or coordinates[1] >= max_y\n or coordinates[1] <= min_y\n ):\n coordinates = generate_coordinates(self.screen)\n\n self.add_agent(Cockroach(pos=np.array(coordinates), v=None, cockroach=self, index=index))","def _on_unload_scene_shaders(self):\n\n artellapipe.ShadersMgr().unload_shaders()","def load_from_digital_ocean(self, resource=None):\n if self.args.force_cache and os.path.isfile(self.cache_filename):\n return\n # We always get fresh droplets\n if self.is_cache_valid() and not (resource == 'droplets' or resource is None):\n return\n if self.args.refresh_cache:\n resource = None\n\n if resource == 'droplets' or resource is None:\n self.data['droplets'] = self.manager.all_active_droplets()\n self.cache_refreshed = True\n if resource == 'regions' or resource is None:\n self.data['regions'] = self.manager.all_regions()\n self.cache_refreshed = True\n if resource == 'images' or resource is None:\n self.data['images'] = self.manager.all_images()\n self.cache_refreshed = True\n if resource == 'sizes' or resource is None:\n self.data['sizes'] = self.manager.sizes()\n self.cache_refreshed = True\n if resource == 'ssh_keys' or resource is None:\n self.data['ssh_keys'] = self.manager.all_ssh_keys()\n self.cache_refreshed = True\n if resource == 'domains' or resource is None:\n self.data['domains'] = self.manager.all_domains()\n self.cache_refreshed = True\n if resource == 'tags' or resource is None:\n self.data['tags'] = self.manager.all_tags()\n self.cache_refreshed = True","def filter(self):\n new_nodes_to_update = {}\n nodes_to_update = {}\n\n for agent_id in self.cameras.keys():\n nodes_to_update[agent_id] = []\n new_nodes_to_update[agent_id] = []\n if agent_id not in self.beliefs:\n world_name = self.cameras[agent_id].name.replace(\"-\",\"_\")+\"_beliefs\"\n rospy.logdebug(\"[perspective_filter] create new world <%s>\" % str(world_name))\n self.beliefs[agent_id] = self.ctx.worlds[world_name]\n self.node_mapping[agent_id] = {}\n\n dq = deque()\n dq.append(self.source.scene.rootnode)\n\n while not rospy.is_shutdown() and 0 < len(dq):\n node = dq.pop()\n if node.id != self.source.scene.rootnode.id:\n # Process start here\n if node.id in self.cameras.keys(): # if the node is the agent POV\n nodes_to_update[node.id].append(node) # we add it to his belief\n\n if node.parent in self.cameras.keys() and node.type == MESH: # if the node is part of an agent\n nodes_to_update[node.parent].append(node) # we add it to his belief\n\n for agent_id, visible_nodes in self.visible_nodes.items(): # then we add the visible nodes\n if agent_id in self.cameras.keys():\n if node in visible_nodes:\n nodes_to_update[agent_id].append(node)\n\n # And end here\n for child_id in node.children:\n dq.append(self.source.scene.nodes[child_id])\n\n for agent_id, nodes in nodes_to_update.items():\n if nodes:\n for node in nodes:\n new_node = node.copy()\n if node.id in self.node_mapping[agent_id]:\n new_node.id = self.node_mapping[agent_id][node.id]\n if new_node.id in self.nodes_transform:\n if not numpy.allclose(self.nodes_transform[new_node.id], new_node.transformation):\n new_nodes_to_update[agent_id].append(new_node)\n self.nodes_transform[new_node.id] = new_node.transformation\n else:\n self.nodes_transform[new_node.id] = new_node.transformation\n new_nodes_to_update[agent_id].append(new_node)\n else:\n self.node_mapping[agent_id][node.id] = new_node.id\n new_nodes_to_update[agent_id].append(new_node)\n\n # Finally we update the corresponding beliefs worlds\n for agent_id, nodes in new_nodes_to_update.items():\n for node in nodes:\n node.parent = self.node_mapping[agent_id][node.parent] if node.parent in self.node_mapping[agent_id] \\\n else self.beliefs[agent_id].scene.rootnode.id\n if nodes:\n self.beliefs[agent_id].scene.nodes.update(nodes)","def optimize_layers_and_transparency(self) -> None:\n self.optimize_layers()\n self.optimize_transparency()","def init_shader(self):\r\n self.attrib_locs = {\r\n \"mc_vertex\": -1,\r\n \"vert_tex_coord\": -1,\r\n }\r\n self.uniform_locs = {\r\n \"model_matrix\": -1,\r\n \"view_matrix\": -1,\r\n \"proj_matrix\": -1,\r\n }\r\n vert_prog = self._compile_shader(ORTH_VERT_SOURCE, gl.GL_VERTEX_SHADER)\r\n frag_prog = self._compile_shader(\r\n ORTH_FRAG_SOURCE, gl.GL_FRAGMENT_SHADER)\r\n self.shader = gl.glCreateProgram()\r\n gl.glAttachShader(self.shader, vert_prog)\r\n gl.glAttachShader(self.shader, frag_prog)\r\n gl.glLinkProgram(self.shader)\r\n assert (gl.glGetProgramiv(self.shader, gl.GL_LINK_STATUS) ==\r\n gl.GL_TRUE), (\r\n \"Error: %s\" % (gl.glGetProgramInfoLog(self.shader)))\r\n\r\n self.attrib_locs = {\r\n name: gl.glGetAttribLocation(self.shader, name)\r\n for name in self.attrib_locs\r\n }\r\n self.uniform_locs = {\r\n name: gl.glGetUniformLocation(self.shader, name)\r\n for name in self.uniform_locs\r\n }\r\n\r\n # Load vertices for final ortho view\r\n self.vao = gl.glGenVertexArrays(1)\r\n gl.glBindVertexArray(self.vao)\r\n self.buffers['mc_vertex'] = gl.glGenBuffers(1)\r\n gl.glBindBuffer(gl.GL_ARRAY_BUFFER, self.buffers['mc_vertex'])\r\n\r\n gl.glBufferData(gl.GL_ARRAY_BUFFER, len(ORTH_VERTICES),\r\n ORTH_VERTICES, gl.GL_STATIC_DRAW)\r\n gl.glVertexAttribPointer(self.attrib_locs['mc_vertex'], 4,\r\n gl.GL_FLOAT, False, 0, ctypes.c_void_p(0))\r\n gl.glEnableVertexAttribArray(self.attrib_locs['mc_vertex'])\r\n\r\n self.buffers['vert_tex_coord'] = gl.glGenBuffers(1)\r\n gl.glBindBuffer(gl.GL_ARRAY_BUFFER, self.buffers['vert_tex_coord'])\r\n gl.glBufferData(gl.GL_ARRAY_BUFFER, len(TEXTURE_VERTICES),\r\n TEXTURE_VERTICES, gl.GL_STATIC_DRAW)\r\n gl.glVertexAttribPointer(self.attrib_locs['vert_tex_coord'], 2,\r\n gl.GL_FLOAT, False, 0, ctypes.c_void_p(0))\r\n gl.glEnableVertexAttribArray(self.attrib_locs['vert_tex_coord'])\r\n gl.glActiveTexture(gl.GL_TEXTURE0)","def output_cache(cc):\n\n out_file = os.path.join(cc.scene_dir, 'output', cc.scene_id+'_pickle')\n\n if cc.atmo_src == 'narr':\n out_file += '_narr'\n elif cc.atmo_src == 'merra':\n out_file += '_merra'\n\n with open(out_file, 'wb') as f:\n pickle.dump(cc, f)","def run():\n scene = lm.scene_object()\n copy_latest_low()\n copy_latest_high()","def polyMergeUV(*args, caching: bool=True, constructionHistory: bool=True, distance:\n Union[float, bool]=0.0, name: AnyStr=\"\", nodeState: Union[int, bool]=0,\n uvSetName: AnyStr=\"\", worldSpace: bool=True, q=True, query=True, e=True,\n edit=True, **kwargs)->Union[AnyStr, Any]:\n pass","def is_node_cached(self, nodes):\n nodes_with_cache = []\n for node in nodes:\n for shape in cmds.listRelatives(node, shapes=True) or []:\n if cmds.listConnections(shape, type=\"historySwitch\"):\n nodes_with_cache.extend([node, shape])\n elif cmds.listConnections(shape, type=\"cacheFile\"):\n nodes_with_cache.extend([node, shape])\n\n return nodes_with_cache","def init_cache(self):\n self.left_lane_cache = list()\n self.right_lane_cache = list()","def delete_input_layer_cache(self):\n # deleteGeometryCache;\n # performDeleteGeometryCache 0;\n # deleteCacheFile 3 { \"delete\", \"\", \"geometry\" };\n input_nodes = self.get_layer_nodes_info([self.input_layer])\n cached_nodes = self.is_node_cached(input_nodes.values()[0])\n if cached_nodes:\n cmds.select(cached_nodes)\n try:\n mel.eval(\"performDeleteGeometryCache 0;\")\n # mel.eval(\"performDeleteGeometryCache 0;\")\n except Exception:\n pass","def polyUnite(*args, caching: bool=True, mergeUVSets: int=1, nodeState: Union[int, bool]=0,\n centerPivot: bool=True, objectPivot: bool=True, q=True, query=True, e=True,\n edit=True, **kwargs)->Union[List[AnyStr], Any]:\n pass","def test_ocean_assets_algorithm(publisher_ocean_instance):\n publisher = get_publisher_wallet()\n metadata = get_sample_algorithm_ddo()[\"service\"][0]\n metadata[\"attributes\"][\"main\"][\"files\"][0][\"checksum\"] = str(uuid.uuid4())\n ddo = publisher_ocean_instance.assets.create(metadata[\"attributes\"], publisher)\n assert ddo\n _ddo = wait_for_ddo(publisher_ocean_instance, ddo.did)\n assert _ddo, f\"assets.resolve failed for did {ddo.did}\"","def test_ocean_assets_compute(publisher_ocean_instance):\n publisher = get_publisher_wallet()\n metadata = get_computing_metadata()\n metadata[\"main\"][\"files\"][0][\"checksum\"] = str(uuid.uuid4())\n ddo = publisher_ocean_instance.assets.create(metadata, publisher)\n assert ddo\n _ddo = wait_for_ddo(publisher_ocean_instance, ddo.did)\n assert _ddo, f\"assets.resolve failed for did {ddo.did}\"","def init_after_linking_before_calc_loss(self):\n # Initialize omega as zeros\n with tf.name_scope('Omega'):\n for v in self.variables:\n name = v.name.split(':')[0] + '-omega'\n shape = v.shape.as_list()\n shadow = tf.Variable(np.zeros(shape, dtype=np.float32),\n trainable=False, name=name, shape=shape)\n self.omegas[v] = shadow\n\n self._show_status('Omegas has been initiated.')","def set_cache(self, cached):\n for i in range(self.num_layers):\n if hasattr(getattr(self, \"conv{}\".format(i+1)), \"set_cache\"):\n getattr(self, \"conv{}\".format(i+1)).set_cache(cached)","def waste_solid(osm_path): \n i = retrieve(osm_path,'multipolygons',['amenity'],**{'amenity':[\"='waste_transfer_station'\"]})\n j = retrieve(osm_path,'multipolygons',['landuse'],**{'landuse':[\"='landfill'\"]})\n \n i = i.rename(columns={'amenity': 'asset'})\n j = j.rename(columns={'landuse': 'asset'})\n \n combined_df = pandas.concat([i,j], ignore_index=True, sort=False) #append objects while ignoring that they may have overlapping index\n if combined_df.empty == True:\n return combined_df\n else:\n return combined_df[[\"osm_id\",\"asset\",\"geometry\"]]","def visualize(self):\n self.octree.updateInnerOccupancy()\n print(\"Start Octomap Visualization\")\n\n # define parameters\n data = imgviz.data.arc2017()\n camera_info = data['camera_info']\n K = np.array(camera_info['K']).reshape(3, 3)\n width=camera_info['width']\n height=camera_info['height']\n\n # get free and occupied grid\n occupied, _ = self.octree.extractPointCloud()\n #frontier = self.gen_frontier()\n \n print(\"load point cloud\")\n window = pyglet.window.Window(\n width=int(1280), height=int(960)\n )\n\n @window.event\n def on_key_press(symbol, modifiers):\n if modifiers == 0:\n if symbol == pyglet.window.key.Q:\n window.on_close()\n\n gui = glooey.Gui(window)\n hbox = glooey.HBox()\n hbox.set_padding(5)\n\n camera = trimesh.scene.Camera(\n resolution=(width, height), focal=(K[0, 0], K[1, 1])\n )\n\n # initial camera pose\n camera_transform = np.array(\n [\n [1, 0, 0, 0],\n [0, -1, 0, 0],\n [0, 0, -1, -5],\n [0.0, 0.0, 0.0, 1.0],\n ],\n )\n\n \n\n occupied_geom = trimesh.voxel.ops.multibox(\n occupied, pitch=self.resolution, colors=[0.0, 0.0, 0.0, 0.5]\n )\n\n # frontier_geom = trimesh.voxel.ops.multibox(\n # frontier, pitch=self.resolution, colors=[1.0, 0, 0, 0.5]\n # )\n scene = trimesh.Scene(camera=camera, geometry=[occupied_geom])#, frontier_geom])\n scene.camera_transform = camera_transform\n hbox.add(self.labeled_scene_widget(scene, label='octomap'))\n\n\n gui.add(hbox)\n pyglet.app.run()","def stand_by(self):\n\n if self.gpus is None:\n self['gpus'] = [0]\n\n # setup logging\n setup_logging(self.get_log_folder(), self.logging)\n\n # # prepare workspace\n # self.get_workspace_dir()\n\n # pretrained\n if \"pretrained\" in self:\n name = self.pretrained.name\n resources = self.pretrained.family.get(name)\n assert isinstance(resources, dict), ValueError(f\"Could not get the resource with name: {name}\")\n cache_dir = self.pretrained.cache_dir\n if cache_dir is None:\n cache_path = default_download_dir(name)\n else:\n cache_path = Path(cache_dir)\n cache_path = cache_path / name\n if not cache_path.exists():\n maybe_create_dir(str(cache_path))\n\n for k, item in resources.items():\n url = item.url\n suffix = item.suffix\n to_insert_paths = item.to_insert_paths\n to_replaces = item.to_replaces\n others = item.others\n # when specify urls of resource\n if url.startswith(\"http\"):\n filename = url.split(\"/\")[-1]\n file_path = cache_path / filename\n if not file_path.exists():\n try:\n maybe_download(url, str(cache_path))\n except Exception as e:\n logger.error(f\"Download from {url} failure!\", exc_info=True)\n raise e\n # assigning filename to the corresponding config variable.\n if to_insert_paths and file_path.exists():\n for to_insert_path in to_insert_paths:\n self.cascade_set(to_insert_path, str(file_path))\n # replace corresponding variable with file content\n if to_replaces and file_path.exists():\n replace_content = load_from_file(str(file_path))\n for to_replace in to_replaces:\n self.cascade_set(to_replace, replace_content)\n else: # when specify paths of resource which have downloaded.\n maybe_filename = Path(url)\n if maybe_filename.is_dir():\n if suffix:\n file_path = maybe_filename / suffix\n else:\n file_path = maybe_filename\n else:\n file_path = maybe_filename\n # assigning filename to the corresponding config variable.\n if to_insert_paths:\n for to_insert_path in to_insert_paths:\n self.cascade_set(to_insert_path, str(file_path))\n # replace corresponding variable with file content\n if to_replaces:\n replace_content = load_from_file(str(file_path))\n for to_replace in to_replaces:\n self.cascade_set(to_replace, replace_content)\n logging.info(f\"Prepare resource {k} from {url}, whose path is {str(file_path)}\")\n # others config\n if others:\n for _k, v in others.items():\n value = v.get(\"value\")\n other_to_replaces = v.get(\"to_replaces\")\n for other_to_replace in other_to_replaces:\n self.cascade_set(other_to_replace, value)\n\n # replace placeholder\n self._replace_placeholder(self)","def _setOceanLocation(self):\r\n\t\t## If the fluids_hrc exists\r\n\t\tif cmds.objExists('fluids_hrc'):\r\n\t\t\tif cmds.objExists('ocean_srf'):\r\n\t\t\t\tcmds.connectAttr('fluids_hrc.translateX', 'ocean_srf.translateX', f = True)\r\n\t\t\t\tcmds.connectAttr('fluids_hrc.translateZ', 'ocean_srf.translateZ', f = True)\r\n\t\t\telse:\r\n\t\t\t\tcmds.warning('MISSING ocean_srf node from scene....')\r\n\r\n\t\t\tif cmds.objExists('oceanPreviewPlane_prv'):\r\n\t\t\t\tcmds.connectAttr('fluids_hrc.translateX', 'oceanPreviewPlane_prv.translateX', f = True)\r\n\t\t\t\tcmds.connectAttr('fluids_hrc.translateZ', 'oceanPreviewPlane_prv.translateZ', f = True)\r\n\t\t\telse:\r\n\t\t\t\tcmds.warning('MISSING oceanPreviewPlane_prv node from scene....')\r\n\t\telse:\r\n\t\t\tcmds.warning('NO fluids_hrc FOUND! Can not move the ocean into final position. PLEASE CHECK FX PUBLISH NOW!')","def fix(self):\n gAsset = cmds.ls(type='gAsset')\n\n trans = cmds.listRelatives(gAsset[0], p=True)\n meshes = cmds.listRelatives(trans, ad=True, type='mesh')\n for mesh in meshes:\n if mesh:\n try:\n cmds.addAttr(mesh, ln=\"grid_renderGeo\", at='double', dv=1)\n cmds.setAttr(\n '{0}.grid_renderGeo'.format(mesh), e=False, keyable=False, lock=True)\n except:\n pass\n\n self.run()","def addCacheableShaderFromSourceFile(self, Union, QOpenGLShader_ShaderType=None, QOpenGLShader_ShaderTypeBit=None, *args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__\n pass","def importShaders(self):\n\t\tif self.shaderPath.exists:\n\t\t\tself.shaderPath.imp()","def transfer_shaders(source, target):\n if isinstance(source, pm.nt.Transform):\n source_shape = source.getShape()\n else:\n source_shape = source\n\n if isinstance(target, pm.nt.Transform):\n target_shape = target.getShape()\n else:\n target_shape = target\n\n # get the shadingEngines\n shading_engines = source_shape.outputs(type=pm.nt.ShadingEngine)\n\n data_storage = []\n\n # get the assigned faces\n for shading_engine in shading_engines:\n faces = pm.sets(shading_engine, q=1)\n for faceGroup in faces:\n str_face = str(faceGroup)\n # replace the objectName\n new_face = \\\n str_face.replace(source_shape.name(), target_shape.name())\n data_storage.append((shading_engine.name(), new_face))\n\n for data in data_storage:\n shading_engine = data[0]\n new_face = data[1]\n pm.select(new_face)\n # now assign the newFaces to the set\n pm.sets(shading_engine, fe=1)","def importData( self, asset = '', searchAndReplace = ['',''] ):\n\t\tpickleData = pickle.load( open( self.dataPath.path, \"rb\" ) )\n\t\tlayers = [RenderLayerData(l,d) for l,d in pickleData.items() if not ':' in l]\n\t\tfor l in layers:\n\t\t\tif not searchAndReplace [0]== '' or not searchAndReplace[1] == '':\n\t\t\t\tl.filterMe( asset, searchAndReplace )\n\t\t\tl.create()\n\t\t\tl.addObjects()\n\t\t\tl.makeOverrides()\n\t\t\tl.makeOverrideConnections()\n\t\t\tl.makeShaderOverride()","def cache_maps(\n self,\n cache_path: Path,\n map_cache_class: Type[SceneCache],\n map_params: Dict[str, Any],\n ) -> None:\n pass","def holderDepend( self, holder ):\n for shader in self.shaders:\n # TODO: cache links...\n shader.holderDepend( holder )\n holder.depend( self, 'shaders' )\n return holder","def _update_origin(self, vol_name, cachevol_name):\n volume_origin = {'origin': cachevol_name}\n self.zfssa.set_file_props(vol_name, volume_origin)\n\n cache_props = self.zfssa.get_volume(cachevol_name)\n cache_props.update({'numclones':\n six.text_type(cache_props['numclones'] + 1)})\n self.zfssa.set_file_props(cachevol_name, cache_props)","def __init__(self):\n self.index = self._generateUID()\n\n DebugObject.__init__(self, \"ShadowSource-\" + str(self.index))\n ShaderStructElement.__init__(self)\n\n self.valid = False\n self.camera = Camera(\"ShadowSource-\" + str(self.index))\n self.cameraNode = NodePath(self.camera)\n self.cameraNode.reparentTo(Globals.render)\n self.resolution = 1024\n self.atlasPos = Vec2(0)\n self.doesHaveAtlasPos = False\n self.sourceIndex = 0\n self.mvp = UnalignedLMatrix4f()\n self.sourceIndex = -1\n self.nearPlane = 0.0\n self.farPlane = 1000.0\n self.converterYUR = None\n self.transforMat = TransformState.makeMat(\n Mat4.convertMat(Globals.base.win.getGsg().getInternalCoordinateSystem(),\n CSZupRight))","def compileShaders(self):\n if self.flatShader is not None: self.flatShader.destroy()\n if self.dataShader is not None: self.dataShader.destroy()\n\n self.activeShader = None\n\n fslgl.glmesh_funcs.compileShaders(self)","def UpdateLayers(self):\n pass","def _load_opengl(self):\r\n pass","def assets_resolve(ctx, did):\n ddo = ctx.obj['ocean'].assets.resolve(did)\n echo(ddo.as_dictionary())","def addCacheableShaderFromSourceCode(self, Union, QOpenGLShader_ShaderType=None, QOpenGLShader_ShaderTypeBit=None, *args, **kwargs): # real signature unknown; restored from __doc__ with multiple overloads\n pass","def duplicateSurface(*args, caching: bool=True, constructionHistory: bool=True, faceCountU:\n Union[int, bool]=1, faceCountV: Union[int, bool]=1, firstFaceU: Union[int,\n bool]=0, firstFaceV: Union[int, bool]=0, local: bool=True, mergeItems:\n bool=True, name: AnyStr=\"\", nodeState: Union[int, bool]=0, q=True,\n query=True, e=True, edit=True, **kwargs)->Union[List[AnyStr], Any]:\n pass","def combine_with_assets(coins):\n \n with open('index_assets.json', encoding='utf-8') as f:\n \n data = json.loads(f.read())\n \n for coin in coins:\n symbol = coin['symbol']\n original_symbol = symbol\n\n if coin['gecko_id'] in [asset['gecko_id'] for asset in data.values()]:\n continue\n\n print('got a new coin: {}'.format(coin))\n\n counter = 1\n while symbol in data:\n print('symbol {} repeats !'.format(symbol))\n symbol = original_symbol + \"_\" + str(counter)\n counter += 1\n\n data[symbol] = {'gecko_id': coin['gecko_id'], 'symbol': coin['symbol']}\n if 'logo' in coin:\n data[symbol]['logo'] = coin['logo']\n else:\n print('could not find image')\n\n\n\n with open('index_assets.json', 'w', encoding='utf-8') as f:\n f.write(json.dumps(data))\n print('wrote to file')","def _removeOcean(self):\r\n\t\tnodesToClean = [CONST.OCEANDISPSHADER, CONST.OCEANANIMSHADER, CONST.OCEAN_ANIM_PREVIEWPLANENAME]\r\n\t\tfor eachNode in nodesToClean:\r\n\t\t\ttry:\r\n\t\t\t\tcmds.delete(each)\r\n\t\t\texcept:\r\n\t\t\t\tpass","def prepare_fast_setup(self, revert=False):\n scene = self.set_as_active()\n\n if not revert:\n scene.render.use_simplify = True\n scene.render.simplify_subdivision = 0\n smallest_layer, _ = min(enumerate(self.get_layers_vert_counts()), key=lambda p: p[1])\n self.set_layers((smallest_layer,))\n\n else:\n scene.render.use_simplify = self.original_use_simplify\n scene.render.simplify_subdivision = self.original_simplify_subdivision\n scene.layers = self.original_layers","def test_ocean() -> None:\n cfg = load_config()\n setup = ModelSetup(str(TEST_DIREC), cfg)\n ocean = Ocean(cfg, setup)\n ocean.compile_all()\n if cfg.run:\n ocean.run_all()\n if cfg.animate:\n ocean.animate_all()","def connectAllWithOceanHeightAttr(object = ''):\n\tinprogressBar = pbui.ProgressBarUI(title = 'Hooking To Ocean:')\n\tinprogressBar.show()\n\tinprogressBar.updateProgress(percent = 0, doingWhat = 'Hooking everything to ocean now...')\n\tdebug(None, method = 'connectAllWithOceanHeightAttr', message = 'Rebuilding Ocean Hooks', verbose = False)\n\n\t## Find all in scene with oceanHeightHook Attribute\n\t## Setup the progress bar\n\tif not cmds.objExists('Shot_FX_hrc'):\n\t\tcmds.group(n = 'Shot_FX_hrc', em = True)\n\tif not cmds.objExists('BOAT_OceanLocators_hrc'):\n\t\tcmds.group(n = 'BOAT_OceanLocators_hrc', em = True)\n\t\tcmds.parent('BOAT_OceanLocators_hrc', 'Shot_FX_hrc')\n\t\tcmds.setAttr('BOAT_OceanLocators_hrc.visibility', 0)\n\n\t## Clean up existing hooks in the scene\n\tinprogressBar.updateProgress(percent = 50, doingWhat = 'Cleaning up now..')\n\n\tif object.startswith('All'):\n\t\tobject = ''\n\telif object.startswith('Dock'):\n\t\tobject = 'BLD'\n\telif object.startswith('Boat'):\n\t\tobject = 'CHAR'\n\telif object.startswith('Prop'):\n\t\tobject = 'PROP'\n\n\tcleanUpExisting(filters = [object])\n\tif object == 'BLD':\n\t\t## Clean-up the ADEF BLD's oceanHook control animatable groups...\n\t\t[cmds.delete(transform) for transform in cmds.ls(type = 'transform') if transform.endswith('_oceanCtrls_hrc')]\n\n\t## Rebuild / build the hooks for boats in the scene\n\tinprogressBar.updateProgress(percent = 75, doingWhat = 'performHookBoatsToOcean now..')\n\tstartTime = time.time()\n\tperformHookBoatsToOcean()\n\tprint 'TOTAL TIME FOR performHookBoatsToOcean: %s' % (time.time()-startTime)\n\n\t## Now make sure cycle check is off because it's a fn pita\n\tcmds.cycleCheck(e = 0)\n\t_finished(inprogressBar)","def set_up_world_ao(self):\n scene = self.set_as_active()\n new_world = bpy.context.blend_data.worlds.new('World of Wireframe')\n scene.world = new_world\n new_world.light_settings.use_ambient_occlusion = True\n new_world.light_settings.ao_factor = 0.3\n\n renderengine = scene.wirebomb.data_renderengine\n\n if renderengine == 'CYCLES':\n new_world.use_nodes = True\n new_world.node_tree.nodes[1].inputs[0].default_value = (1, 1, 1, 1)\n\n for node in new_world.node_tree.nodes:\n node.select = False\n \n elif renderengine == 'BLENDER_RENDER':\n new_world.horizon_color = (1, 1, 1)","def assets():","def mark_if_cached(self, args):\n for art in self.artifacts:\n art.mark_if_cached(args)","def update_project(_):\n\n # Compile all OSL Script nodes\n stdosl_path = path_util.get_stdosl_paths()\n compiler = asr.ShaderCompiler(stdosl_path)\n q = asr.ShaderQuery()\n for script in bpy.data.texts:\n osl_bytecode = osl_utils.compile_osl_bytecode(compiler,\n script)\n if osl_bytecode is not None:\n q.open_bytecode(osl_bytecode)\n\n node_data = osl_utils.parse_shader(q)\n\n node_name, node_category, node_classes = osl_utils.generate_node(node_data,\n AppleseedOSLScriptNode)\n\n for cls in node_classes:\n safe_register_class(cls)\n\n else:\n logger.debug(f\"appleseed: Shader {script.name} did not compile\")","def recompile(self):\n\n self.vaos = []\n try:\n self.program, uniforms = self.build_prog(self.gl)\n self.u_time, self.u_width, self.u_height = uniforms\n vao = GLUtil.screen_vao(self.gl, self.program)\n self.vaos.append(vao)\n\n self.compute, uniforms, buffers = self.build_cs(self.gl)\n self.u_cstime, self.u_cswidth, self.u_csheight = uniforms\n self.buf_in, self.buf_out = buffers\n\n self.set_gpu_wh(width, height)\n\n self.gx, self.gy = int(width / 8), int(height / 8)\n self.set_gpu_time()\n\n log(\"[Renderer] shader recompiled.\")\n\n except Exception as e:\n log(e)","def loadConnections(self):\n\n #create each connecting map\n for direction, (fn, offset) in self.connections.items():\n self.connectedMaps[direction] = (Tilemap(fn), offset)","def _load_neighbors(self) -> None:\r\n if not self.are_neighbors_cached:\r\n self._load_neighbors_from_external_source()\r\n db: GraphDatabaseInterface = self._graph.database\r\n db_node: DBNode = db.Node.find_by_name(self.name)\r\n db_node.are_neighbors_cached = True\r\n db.session.commit()\r\n self.are_neighbors_cached = True\r\n if not self._are_neighbors_loaded:\r\n self._load_neighbors_from_database()","def cache_map(\n self,\n map_name: str,\n layer_names: List[str],\n cache_path: Path,\n map_cache_class: Type[SceneCache],\n resolution: float,\n ) -> None:\n pass","def add_latesettings_assets(self):\n\n # setting up static file serving\n assetmanager = self.comp('assetmanager')\n\n # add external asset mount point where we can copy public static files so they can be served by a separate traditional web server\n # presumably this directory is being served by a more traditional webserver, at this url we specify below\n assetmanager.add_assetmount(\n massetmanager.MewloAssetMount_ExternalServer('external_assets', filepath = '${mewlofilepath}/public_assets', urlabs = 'http://127.0.0.1/mewlo/mewlo/public_assets' )\n )\n\n # add internal asset mount point where we will serve files internally; a route will be automatically created for any asset source attached to this mount point; we can choose the path prefix for urls served by the route\n assetmanager.add_assetmount(\n massetmanager.MewloAssetMount_InternalRoute('internal_assets', urlpath='assets')\n )\n\n\n # now that we have some mount points, we can specify some files to be hosted on them\n # note that the ids for all asset sources MUST be unique (ATTN:TODO elaborate on this please)\n # first we mount the files in the staticfilesource/ directory as internal assets that we will serve internally via mewlo; the id will be used for alias creation, and for the route\n assetmanager.add_assetsource(\n massetmanager.MewloAssetSource(id='siteinternal', mountid = 'internal_assets', filepath = '${sitefilepath}/staticfilesource', mnamespace=None)\n )\n # then as a test, lets mount same files on the external mount point -- this will cause mewlo to physically copy the files to the external filepath, where presumably another web server can serve them\n assetmanager.add_assetsource(\n massetmanager.MewloAssetSource(id='siteexternal', mountid = 'external_assets', filepath = '${sitefilepath}/staticfilesource', mnamespace=None)\n )\n\n # remember that one should never refer to the assets by a hardcoded url or file path; always use the aliases created by these functions, which will take the form (where ID is the id of the asset source):\n # 'asset_ID_urlrel' | 'asset_ID_urlabs' | 'asset_ID_filepath'\n # you can also use helper function to build these names, which would be better.","def ports(osm_path): \n return (retrieve(osm_path,'multipolygons',['landuse'],**{'landuse':[\"='industrial' or \",\"='port' or \",\"='harbour'\"]})).rename(columns={'landuse': 'asset'})","def on_update(self, delta_time):\n self.texture = self.sprite_list()\n for self.texture\n if self.texture == self.ada:\n self.texture == self.potato\n else:\n self.texture == self.ada","def setup_terrain(self):\r\n self.terrain_scale = LVector3(512, 512, 100)\r\n self.terrain_pos = LVector3(-256, -256, -70)\r\n # sample values for a 4096 x 4096px heightmap.\r\n #self.terrain_scale = LVector3(4096, 4096, 1000)\r\n #self.terrain_pos = LVector3(-2048, -2048, -70)\r\n \"\"\"\r\n Diamond_subdivision is an alternating triangulation scheme and may\r\n produce better results.\r\n \"\"\"\r\n use_diamond_subdivision = True\r\n \r\n \"\"\"\r\n Construct the terrain\r\n Without scaling, any ShaderTerrainMesh is 1x1x1 units.\r\n \"\"\"\r\n self.terrain_node = ShaderTerrainMesh()\r\n \"\"\"\r\n Set a heightfield, the heightfield should be a 16-bit png and\r\n have a quadratic size of a power of two.\r\n \"\"\"\r\n heightfield = Texture()\r\n heightfield.read(self.heightfield_fn)\r\n heightfield.set_keep_ram_image(True) \r\n self.terrain_node.heightfield = heightfield\r\n \r\n # Display characteristic values of the heightfield texture\r\n #minpoint, maxpoint, avg = LPoint3(), LPoint3(), LPoint3()\r\n #heightfield.calc_min_max(minpoint, maxpoint)\r\n #heightfield.calc_average_point(avg, 0.5, 0.5, 0.5)\r\n #print(\"avg: {} min: {} max: {}\".format(avg.x, minpoint.x, maxpoint.x))\r\n\r\n \"\"\"\r\n Set the target triangle width. For a value of 10.0 for example,\r\n the ShaderTerrainMesh will attempt to make every triangle 10 pixels\r\n wide on screen.\r\n \"\"\"\r\n self.terrain_node.target_triangle_width = 10.0\r\n if use_diamond_subdivision:\r\n \"\"\"\r\n This has to be specified before calling .generate()\r\n The default is false.\r\n \"\"\"\r\n load_prc_file_data(\"\", \"stm-use-hexagonal-layout true\")\r\n \r\n self.terrain_node.generate()\r\n \"\"\"\r\n Attach the terrain to the main scene and set its scale. With no scale\r\n set, the terrain ranges from (0, 0, 0) to (1, 1, 1)\r\n \"\"\"\r\n self.terrain = self.render.attach_new_node(self.terrain_node)\r\n self.terrain.set_scale(self.terrain_scale)\r\n self.terrain.set_pos(self.terrain_pos)\r\n \"\"\"\r\n Set a vertex and a fragment shader on the terrain. The\r\n ShaderTerrainMesh only works with an applied shader.\r\n \"\"\"\r\n terrain_shader = Shader.load(Shader.SL_GLSL, \r\n \"samples/shader-terrain/terrain.vert.glsl\", \r\n \"samples/shader-terrain/terrain.frag.glsl\")\r\n self.terrain.set_shader(terrain_shader)\r\n self.terrain.set_shader_input(\"camera\", base.camera)\r\n # Set some texture on the terrain\r\n grass_tex = self.loader.load_texture(\r\n \"samples/shader-terrain/textures/grass.png\")\r\n grass_tex.set_minfilter(SamplerState.FT_linear_mipmap_linear)\r\n grass_tex.set_anisotropic_degree(16)\r\n self.terrain.set_texture(grass_tex)\r\n\r\n \"\"\"\r\n Set up the DynamicHeightfield (it's a type of PfmFile). We load the\r\n same heightfield image as with ShaderTerrainMesh.\r\n \"\"\"\r\n self.DHF = DynamicHeightfield()\r\n self.DHF.read(self.heightfield_fn)\r\n \"\"\"\r\n Set up empty PfmFiles to prepare stuff in that is going to\r\n dynamically modify our terrain.\r\n \"\"\"\r\n self.StagingPFM = PfmFile()\r\n self.RotorPFM = PfmFile()\r\n \r\n \"\"\"\r\n Set up the BulletHeightfieldShape (=collision terrain) and give it\r\n some sensible physical properties.\r\n \"\"\"\r\n self.HFS = BulletHeightfieldShape(self.DHF, self.terrain_scale.z,\r\n STM=True)\r\n if use_diamond_subdivision:\r\n self.HFS.set_use_diamond_subdivision(True)\r\n HFS_rigidbody = BulletRigidBodyNode(\"BulletTerrain\")\r\n HFS_rigidbody.set_static(True)\r\n friction = 2.0\r\n HFS_rigidbody.set_anisotropic_friction(\r\n LVector3(friction, friction, friction/1.3))\r\n HFS_rigidbody.set_restitution(0.3)\r\n HFS_rigidbody.add_shape(self.HFS)\r\n self.world.attach(HFS_rigidbody)\r\n \r\n HFS_NP = NodePath(HFS_rigidbody)\r\n HFS_NP.reparent_to(self.worldNP)\r\n \"\"\"\r\n This aligns the Bullet terrain with the ShaderTerrainMesh rendered\r\n terrain. It will be exact as long as the terrain vertex shader from\r\n the STM sample is used and no additional tessellation shader.\r\n For Bullet (as for other physics engines) the origin of objects is at\r\n the center.\r\n \"\"\"\r\n HFS_NP.set_pos(self.terrain_pos + self.terrain_scale/2)\r\n HFS_NP.set_sx(self.terrain_scale.x / heightfield.get_x_size())\r\n HFS_NP.set_sy(self.terrain_scale.y / heightfield.get_y_size())\r\n \r\n # Disables Bullet debug rendering for the terrain, because it is slow.\r\n #HFS_NP.node().set_debug_enabled(False)\r\n \r\n \"\"\"\r\n Finally, link the ShaderTerrainMesh and the BulletHeightfieldShape to\r\n the DynamicHeightfield. From now on changes to the DynamicHeightfield\r\n will propagate to the (visible) ShaderTerrainMesh and the (collidable)\r\n BulletHeightfieldShape.\r\n \"\"\"\r\n self.HFS.set_dynamic_heightfield(self.DHF)\r\n self.terrain_node.set_dynamic_heightfield(self.DHF)","def ocean(ctx, config_file, as_json, verbose):\n if not verbose:\n logging.getLogger().setLevel(logging.ERROR)\n\n ctx.obj = {\n 'ocean': get_ocean(config_file),\n 'json': as_json\n }","def init_cache_from_geojson(self, path):\n io.copy_file(path_from=path, path_to=self.path)","def init_cache_from_geojson(self, path):\n io.copy_file(path_from=path, path_to=self.path)","def _cache_checker_matrices(self):\r\n if self.model.mat_texid is not None:\r\n self._geom_checker_mats = []\r\n for geom_id in range(self.model.ngeom):\r\n mat_id = self.model.geom_matid[geom_id]\r\n tex_id = self.model.mat_texid[mat_id]\r\n texture = self.textures[tex_id]\r\n h, w = texture.bitmap.shape[:2]\r\n self._geom_checker_mats.append(self._make_checker_matrices(h, w))\r\n\r\n # add skybox\r\n skybox_tex_id = -1\r\n for tex_id in range(self.model.ntex):\r\n skybox_textype = 2\r\n if self.model.tex_type[tex_id] == skybox_textype:\r\n skybox_tex_id = tex_id\r\n if skybox_tex_id >= 0:\r\n texture = self.textures[skybox_tex_id]\r\n h, w = texture.bitmap.shape[:2]\r\n self._skybox_checker_mat = self._make_checker_matrices(h, w)\r\n else:\r\n self._skybox_checker_mat = None","def compute_relations(self):\n\n visible_nodes = {}\n\n self.cameras = self.get_all_cameras()\n rospy.logdebug(self.cameras)\n\n if self.cameras.items():\n try:\n if self.visibility_monitor is None:\n self.visibility_monitor = VisibilityMonitor(self.ctx, self.source)\n rospy.loginfo(\"[perspective_filter] Visibility monitor now running, please active the Pygame windows.\")\n visible_nodes = self.visibility_monitor.compute_all()\n rospy.logdebug(\"[perspective_filter] %d perspectives computed \" % len(visible_nodes))\n #rospy.logdebug(visible_nodes)\n except Exception as e:\n rospy.logwarn(\"[perspective_filter] Exception occurred while computing relation : %s\" % str(e))\n if self.visibility_monitor:\n self.visible_nodes = {} #visible_nodes\n for camera_name, visibles_obj in visible_nodes.items():\n camera_id = self.source.scene.nodebyname(camera_name)[0].id\n self.visible_nodes[camera_id] = visibles_obj\n for node in visibles_obj:\n if node.parent in self.cameras.keys():\n if self.source.scene.nodes[node.parent] not in visibles_obj:\n visibles_obj.append(self.source.scene.nodes[node.parent])\n\n for agent_id, nodes_seen in self.visible_nodes.items():\n agent = self.source.scene.nodes[agent_id]\n for node in nodes_seen:\n if agent_id in self.previously_visible_nodes:\n if node not in self.previously_visible_nodes[agent_id]:\n self.start_predicate(self.source.timeline, \"isVisibleBy\", node.name, object_name=agent.name)\n else:\n self.start_predicate(self.source.timeline, \"isVisibleBy\", node.name, object_name=agent.name)\n\n for agent_id, nodes_previously_seen in self.previously_visible_nodes.items():\n agent = self.source.scene.nodes[agent_id]\n for node in nodes_previously_seen:\n if agent_id in self.visible_nodes:\n if node not in self.visible_nodes[agent_id]:\n self.end_predicate(self.source.timeline, \"isVisibleBy\", node.name, object_name=agent.name)\n else:\n self.end_predicate(self.source.timeline, \"isVisibleBy\", node.name, object_name=agent.name)\n\n self.publish_perspectives()\n self.previously_visible_nodes = self.visible_nodes","def __init_cache__(self) -> None:\n try:\n self.cache = caches[CACHE_NAME]\n logging.info(\"GeoIP2 - successfully initialised cache\")\n except InvalidCacheBackendError as ex:\n raise MiddlewareNotUsed(f\"GeoIP2 - cache configuration error: {ex}\") from ex","def reconnectLookdev(self, namespace):\n self.logger.debug(\"Reconnecting imported lookev\")\n\n # lets break connections first\n self.breakConnections()\n\n assignations = {}\n\n if self.objExists('shadersAssignation'):\n shadersAssignation = self.getAttr('shadersAssignation')\n if shadersAssignation:\n assignations[\"shaders\"] = json.loads(shadersAssignation)\n\n if self.objExists('displacementsAssignation'):\n displacementsAssignation = self.getAttr('displacementsAssignation')\n if displacementsAssignation:\n assignations[\"displacement\"] = json.loads(displacementsAssignation)\n\n if self.objExists('layersOverride'):\n layersOverride = self.getAttr('layersOverride')\n if layersOverride:\n assignations[\"layers\"] = json.loads(layersOverride)\n\n # fix the attribute editor\n fixed = {'shaders' : {}, 'displacement' : {}, 'layers' : {}}\n attrs_to_connect = []\n if assignations.has_key('shaders'):\n for shader in assignations['shaders']:\n if namespace != ':':\n fixed['shaders']['%s:%s' % (namespace, shader)] = assignations['shaders'][shader]\n attrs_to_connect.append('%s:%s' % (namespace, shader))\n else:\n fixed['shaders']['%s' % (shader)] = assignations['shaders'][shader]\n attrs_to_connect.append('%s' % (shader))\n \n if assignations.has_key('displacement'):\n for disp in assignations['displacement']:\n if namespace != ':':\n fixed['displacement']['%s:%s' % (namespace, disp)] = assignations['displacement'][disp]\n attrs_to_connect.append('%s:%s' % (namespace, disp))\n else:\n fixed['displacement']['%s' % (disp)] = assignations['displacement'][disp]\n attrs_to_connect.append('%s' % (disp))\n\n if assignations.has_key('layers'):\n for layer in assignations['layers']:\n if not fixed['layers'].has_key(layer):\n fixed['layers'][layer] = {'shaders' : {}, 'displacements' : {}}\n \n if assignations['layers'][layer].has_key('shaders'):\n for shader in assignations['layers'][layer]['shaders']:\n if namespace != ':':\n fixed['layers'][layer]['shaders']['%s:%s' % (namespace, shader)] = assignations['layers'][layer]['shaders'][shader]\n attrs_to_connect.append('%s:%s' % (namespace, shader))\n else:\n fixed['layers'][layer]['shaders']['%s' % (shader)] = assignations['layers'][layer]['shaders'][shader]\n attrs_to_connect.append('%s' % (shader))\n \n if assignations['layers'][layer].has_key('displacements'):\n for disp in assignations['layers'][layer]['displacements']:\n if namespace != ':':\n fixed['layers'][layer]['displacements']['%s:%s' % (namespace, disp)] = assignations['layers'][layer]['displacements'][disp]\n attrs_to_connect.append('%s:%s' % (namespace, disp))\n else:\n fixed['layers'][layer]['displacements']['%s' % (disp)] = assignations['layers'][layer]['displacements'][disp]\n attrs_to_connect.append('%s' % (disp)) \n\n self.setAttr(attr='shadersAssignation', value=json.dumps(fixed['shaders']))\n self.setAttr(attr='displacementsAssignation', value=json.dumps(fixed['displacement']))\n self.setAttr(attr='layersOverride', value=json.dumps(fixed['layers']))\n\n # connect the shaders to the alembicHolder\n port = 0\n added = []\n for attr in attrs_to_connect:\n if not attr in added:\n if not attr.endswith('.message'):\n shader_attr = '%s.message' % attr\n else:\n shader_attr = attr\n shape_attr = '%s.shaders[%i]' % (self.data['shapeNode'], port)\n self.connectAttr(shader_attr, shape_attr)\n added.append(attr)\n port += 1","def main():\n\n dotfiles_location = os.path.expanduser('~') + '/dotfiles'\n default_bundles_location = dotfiles_location + '/bundles/dein/repos'\n\n parser = argparse.ArgumentParser(\n description='Build a cache of available colours schemes, output them into a JSON file for vim plugins to read'\n )\n\n parser.add_argument('-n', '--dryrun',\n action='store_true',\n dest='dryrun',\n required=False,\n help='Dryrun mode (do not write to cache file)'\n )\n\n parser.add_argument('-v', '--verbose',\n action='store_true',\n dest='verbose',\n required=False,\n help='Increase verbosity'\n )\n\n parser.add_argument('-c', '--cache',\n action='store',\n dest='cache_file',\n required=False,\n metavar='CACHE_FILE',\n default=dotfiles_location + '/colos.json',\n help='Cache file name'\n )\n\n parser.add_argument(\n action='store',\n dest='command', # maybe use an action to validate the commands with their args?\n nargs=1,\n help='Command to issue: generate, whitelist <name>, blacklist <name>, toggle-variant <name>'\n )\n\n parser.add_argument(\n dest='args',\n nargs='*'\n )\n\n try:\n args = parser.parse_args()\n except ValueError as e:\n print('Invalid options.', e, file=sys.stderr)\n sys.exit(1)\n\n\n data = cololib.cmd_load_data(args.cache_file, verbose=args.verbose)\n\n if 'build_cache' == args.command[0]:\n cololib.cmd_build_cache(\n data=data,\n search_path=default_bundles_location,\n verbose=args.verbose, dryrun=args.dryrun\n )\n elif 'get_random' == args.command[0]:\n if args.args:\n tag = args.args[0]\n else:\n tag = None\n cololib.cmd_get_random_colo(data=data, tag=tag, verbose=args.verbose)\n else:\n print('Unknown command \"%s\"'%(args.command[0]), file=sys.stderr)\n sys.exit(1)","def getConnectedShaders(self):\n self.logger.debug(\"Connected Shaders\")\n\n connected = []\n for connections in pm.listConnections(self.data['shapeNode'], plugs=True, connections=True):\n if cmds.getClassification(connections[-1].nodeType(), satisfies=\"shader\"):\n self.logger.debug(\"Connected shader : %s\" % connections[-1].node())\n connected.append(connections[-1].node())\n return connected","def initCitys(self):\n self.cities = []\n for vertex in self.metaGraph:\n self.cities.append(vertex)","def do_api_calls_update_cache(self):\n self.get_nodes()\n self.write_to_cache(self.inventory, self.cache_path_cache)\n self.write_to_cache(self.index, self.cache_path_index)","def checkLayersOverride(shape):\n required = []\n connected = [] \n\n # find the shaders / displacement that are required\n layersOverride = cmds.getAttr(\"%s.layersOverride\" % shape)\n if layersOverride:\n layersOverride = json.loads(layersOverride)\n for layer in layersOverride:\n if layersOverride[layer].has_key('shaders'):\n for k in layersOverride[layer]['shaders'].keys():\n if not k in required:\n required.append(k)\n\n shape_connections = cmds.listAttr(\"%s.shaders\" % shape, multi=True)\n\n # go find the connected shaders\n if shape_connections:\n for con in shape_connections:\n connected_shader = cmds.listConnections(\"%s.%s\" % (shape, con))[0]\n connected.append(connected_shader)\n \n port = len(connected)\n for req in required:\n if req not in connected:\n if cmds.objExists(req):\n cmds.connectAttr( req + \".message\", shape + \".shaders[%i]\" % port)\n port += 1\n message = 'Connected %s to %s' % (req, shape)\n MGlobal.displayInfo(message)\n else:\n message = \"Missing shader : %s\" % req\n MGlobal.displayWarning(message)","def _copy_to_gpu(self):\n self.dispatch('on_texture')","def clustering_dbscan_o3d():\n pass","def add_clay_to_selected(self):\n scene = self.set_as_active()\n\n # if the user selected a material, use it\n if w_var.cb_mat_clay:\n clay_mat = bpy.data.materials[w_var.mat_clay_name]\n\n # else, create a new one with the color selected\n else:\n clay_color = w_var.color_clay\n\n # separating rgb and alpha\n clay_color_rgb = clay_color[0:3]\n clay_color_alpha = clay_color[-1]\n clay_mat = bpy.data.materials.new('clay')\n \n renderengine = scene.wirebomb.data_renderengine\n \n if renderengine == 'CYCLES':\n clay_mat.use_nodes = True\n tree = clay_mat.node_tree\n tree.nodes.clear()\n\n # creating the nodes\n node_transparent = tree.nodes.new('ShaderNodeBsdfTransparent')\n node_transparent.location = -300, 100\n\n node_diffuse = tree.nodes.new('ShaderNodeBsdfDiffuse')\n node_diffuse.location = -300, -100\n node_diffuse.inputs[0].default_value = clay_color_rgb + (1.0, )\n node_diffuse.color = clay_color_rgb\n node_diffuse.name = 'addon_clay_color' # referencing to this ID in the real-time change\n\n node_mixshader = tree.nodes.new('ShaderNodeMixShader')\n node_mixshader.location = 0, 50\n node_mixshader.inputs[0].default_value = clay_color_alpha\n node_mixshader.name = 'addon_clay_alpha' # referencing to this ID in the real-time change\n\n node_output = tree.nodes.new('ShaderNodeOutputMaterial')\n node_output.location = 300, 50\n\n # connecting the nodes\n tree.links.new(node_transparent.outputs[0], node_mixshader.inputs[1])\n tree.links.new(node_diffuse.outputs[0], node_mixshader.inputs[2])\n tree.links.new(node_mixshader.outputs[0], node_output.inputs[0])\n\n for node in tree.nodes:\n node.select = False\n\n # sets the viewport color\n clay_mat.diffuse_color = clay_color_rgb\n \n elif renderengine == 'BLENDER_RENDER':\n clay_mat.diffuse_color = clay_color_rgb\n clay_mat.use_transparency = True\n clay_mat.alpha = clay_color_alpha\n\n previous_area = bpy.context.area.type\n bpy.context.area.type = 'VIEW_3D'\n previous_layers = tuple(scene.layers)\n\n # can't enter edit mode on objects on inactive layers\n scene.layers = (True,)*20\n\n for obj in scene.objects:\n if obj.select:\n # only enters edit mode on active object\n scene.objects.active = obj\n obj.data.materials.append(clay_mat)\n clay_index = obj.data.materials.find(clay_mat.name)\n obj.active_material_index = clay_index\n\n bpy.ops.object.mode_set(mode='EDIT')\n bpy.ops.mesh.select_all(action='SELECT')\n bpy.ops.object.material_slot_assign()\n bpy.ops.mesh.select_all(action='SELECT')\n bpy.ops.object.mode_set(mode='OBJECT')\n\n bpy.context.area.type = previous_area\n scene.layers = previous_layers\n\n return clay_mat","def do_stuff(self):\n self.create_tourism_raster()","def rebuildSurface(*args, caching: bool=True, degreeU: Union[int, bool]=3, degreeV: Union[int,\n bool]=3, direction: Union[int, bool]=2, endKnots: Union[int, bool]=0,\n fitRebuild: Union[int, bool]=0, keepControlPoints: bool=False, keepCorners:\n bool=True, keepRange: Union[int, bool]=1, nodeState: Union[int, bool]=0,\n rebuildType: Union[int, bool]=0, spansU: Union[int, bool]=4, spansV:\n Union[int, bool]=4, tolerance: Union[float, bool]=0.01, constructionHistory:\n bool=True, name: AnyStr=\"\", object: bool=True, polygon: int=0,\n replaceOriginal: bool=True, q=True, query=True, e=True, edit=True,\n **kwargs)->Union[List[AnyStr], Any]:\n pass","def nBase(*args, clearCachedTextureMap: AnyStr=\"\", clearStart: bool=True, stuffStart: bool=True,\n textureToVertex: AnyStr=\"\", q=True, query=True, e=True, edit=True,\n **kwargs)->Union[bool, Any]:\n pass","def reload(self,offline_buffer):\n #loading online buffer from offline buffer by sampling (online_buffer.buffer_size) samples \n self.buffer = SumTree(self.buffer_size)\n names, idxs = offline_buffer.sample_batch(self.buffer_size)\n self.offline_idxs = idxs\n state , action , reward, done = data_handler.handler.fetch_single_image(directory = self.directory, branch_name = self.name, observation_name = names[0])\n #loop on names and load in the online buffer\n for i in range(len(names)-1):\n next_state , next_action , next_reward , done = data_handler.handler.fetch_single_image(directory = self.directory, branch_name = self.name, observation_name = names[i+1])\n #done = 0\n self.memorize(state, action, reward, done, next_state, error=[1])\n state , action , reward = next_state , next_action , next_reward","def run():\n\n if lm.scene_object().type == 'env':\n read_layout(outFile=lm.scene_object().copy(ext='json').path_root)\n\n else:\n read_layout()","def merge_visual(self, mujoco_objects):\n self.visual_obj_mjcf = []\n for obj_name, obj_mjcf in mujoco_objects.items():\n self.merge_asset(obj_mjcf)\n # Load object\n obj = obj_mjcf.get_visual(name=obj_name, site=False)\n self.visual_obj_mjcf.append(obj)\n self.worldbody.append(obj)","def check_multiple_connections_for_textures(progress_controller=None):\n if progress_controller is None:\n progress_controller = ProgressControllerBase()\n\n # load necessary plugins\n plugins = [\"matrixNodes\", \"quatNodes\"]\n for plugin in plugins:\n if not pm.pluginInfo(plugin, q=1, l=1):\n pm.loadPlugin(plugin)\n\n v = staging.get(\"version\")\n if not v:\n progress_controller.complete()\n return\n\n # check if the current renderer is Arnold\n current_renderer = pm.PyNode(\"defaultRenderGlobals\").currentRenderer.get()\n if current_renderer != \"arnold\":\n # skip it\n progress_controller.complete()\n return\n\n # skip if\n skip_types = [\"character\", \"animation\", \"previs\"]\n for t in v.naming_parents:\n for st in skip_types:\n if t.type and t.type.name.lower().startswith(st):\n progress_controller.complete()\n return\n\n # get all the texture nodes\n from anima.dcc.mayaEnv import repr_tools\n\n # try to find the material it is been used by walking up the connections\n nodes_with_multiple_materials = []\n\n types_to_ignore = [\n \"hyperLayout\",\n \"shadingEngine\",\n \"materialInfo\",\n \"time\",\n \"unitConversion\",\n \"hyperView\",\n ] + VALID_MATERIALS[\"redshift\"]\n\n # by type\n nodes_to_ignore = [node for node in pm.ls() if node.type() in types_to_ignore]\n\n # by name\n nodes_to_ignore += pm.ls(\"lambert1\", r=1)\n nodes_to_ignore += pm.ls(\"defaultShaderList*\", r=1)\n nodes_to_ignore += pm.ls(\"defaultTextureList*\", r=1)\n nodes_to_ignore += pm.ls(\"defaultRenderUtilityList*\", r=1)\n nodes_to_ignore += pm.ls(\"hyperShadePrimaryNodeEditorSavedTabsInfo*\", r=1)\n nodes_to_ignore += pm.ls(\"MayaNodeEditorSavedTabsInfo*\", r=1)\n\n all_nodes = [\n node for node in pm.ls() if node.type() in repr_tools.RENDER_RELATED_NODE_TYPES\n ]\n for node in nodes_to_ignore:\n if node in all_nodes:\n all_nodes.remove(node)\n\n progress_controller.maximum = len(all_nodes)\n for node in all_nodes:\n materials_connected_to_this_node = pm.ls(\n node.listHistory(future=True), mat=True\n )\n\n # remove any Redshift Materials from this list\n # create a temp list\n new_materials_connected_to_this_node = []\n for mat in materials_connected_to_this_node:\n if mat.type() not in VALID_MATERIALS[\"redshift\"]:\n new_materials_connected_to_this_node.append(mat)\n materials_connected_to_this_node = new_materials_connected_to_this_node\n\n # remove self from all_nodes\n if node in materials_connected_to_this_node:\n materials_connected_to_this_node.remove(node)\n\n if len(materials_connected_to_this_node) > 1:\n nodes_with_multiple_materials.append(node)\n else:\n connections_out_of_this_node = node.outputs()\n\n # [connections_out_of_this_node.remove(h)\n # for h in nodes_to_ignore\n # if h in connections_out_of_this_node]\n connections_out_of_this_node = [\n h for h in connections_out_of_this_node if h not in nodes_to_ignore\n ]\n\n if len(set(connections_out_of_this_node)) > 1:\n nodes_with_multiple_materials.append(node)\n progress_controller.increment()\n\n # if we find more than one material add it to the list\n # raise a PublishError if we have an item in the list\n if len(nodes_with_multiple_materials) > 0:\n pm.select(nodes_with_multiple_materials)\n progress_controller.complete()\n raise PublishError(\n \"Please update the scene so the following nodes are connected <br>\"\n \"to only <b>one material</b> (duplicate them):<br><br>%s<br><br>\"\n % \"<br>\".join(map(lambda x: x.name(), nodes_with_multiple_materials))\n )\n progress_controller.complete()","def polyDuplicateAndConnect(*args, removeOriginalFromShaders: bool=True, renameChildren:\n bool=True, **kwargs)->None:\n pass","def cached(self, args) -> bool:\n return all([art.built for art in self.artifacts])","def initialize_cache(inputs, outputs, programs, max_decode_len, config):\n target_shape = (programs.shape[0], max_decode_len)\n initial_variables = models.ProgramTransformer(config).init(\n jax.random.PRNGKey(0),\n jnp.ones(inputs.shape, config.dtype),\n jnp.ones(outputs.shape, config.dtype),\n jnp.ones(target_shape, config.dtype))\n return initial_variables['cache']","def atlas_clusters():\n pass","def polyCopyUV(*args, caching: bool=True, constructionHistory: bool=True, createNewMap:\n bool=True, name: AnyStr=\"\", nodeState: Union[int, bool]=0, uvSetName: AnyStr=\"\",\n uvSetNameInput: Union[AnyStr, bool]=\"\", worldSpace: bool=True, q=True,\n query=True, e=True, edit=True, **kwargs)->Union[AnyStr, Any]:\n pass","def subdTransferUVsToCache(*args, **kwargs)->bool:\n pass","def enable_texture_mode():\n for area in bpy.context.screen.areas:\n if area.type == \"VIEW_3D\":\n for space in area.spaces:\n if space.type == \"VIEW_3D\":\n space.viewport_shade = \"TEXTURED\"\n return","def set_atlas(self, sender):\n if (self.model.value in pyTMD.io.model.ATLAS()):\n self.atlas.layout.display = 'inline-flex'","def test_path_layer_cache(self):\n cache = caches[settings.MAPENTITY_CONFIG['GEOJSON_LAYERS_CACHE_BACKEND']]\n\n obj = self.modelfactory(draft=False)\n self.modelfactory(draft=True)\n\n # There are 7 queries to get layer without drafts\n with self.assertNumQueries(5):\n response = self.client.get(obj.get_layer_url())\n self.assertEqual(len(response.json()['features']), 2)\n\n # We check the content was created and cached without no_draft key\n # We check that any cached content can be found without no_draft (we still didn't ask for it)\n last_update_no_draft = Path.no_draft_latest_updated()\n last_update = Path.latest_updated()\n geojson_lookup_no_draft = 'en_path_%s_nodraft_json_layer' % last_update_no_draft.strftime('%y%m%d%H%M%S%f')\n geojson_lookup = 'en_path_%s_json_layer' % last_update.strftime('%y%m%d%H%M%S%f')\n content_no_draft = cache.get(geojson_lookup_no_draft)\n content = cache.get(geojson_lookup)\n\n self.assertIsNone(content_no_draft)\n self.assertEqual(response.content, content.content)\n\n # We have 1 less query because the generation of paths was cached\n with self.assertNumQueries(3):\n self.client.get(obj.get_layer_url())\n\n self.modelfactory(draft=True)\n\n # Cache is updated when we add a draft path\n with self.assertNumQueries(5):\n self.client.get(obj.get_layer_url())\n\n self.modelfactory(draft=False)\n\n # Cache is updated when we add a path\n with self.assertNumQueries(5):\n self.client.get(obj.get_layer_url())","def used_caches_and_sources(layers, caches, sources):\n used_layer_sources = find_layer_sources(layers)\n used_cache_sources = find_cache_sources(caches)\n all_used_sources = used_layer_sources.union(used_cache_sources)\n\n avail_caches = set(caches.keys())\n avail_sources = set(sources.keys())\n\n used_caches = avail_caches.intersection(all_used_sources)\n used_sources = avail_sources.intersection(all_used_sources).difference(used_caches)\n\n return used_caches, used_sources","def read_cache(cc):\n \n out_file = os.path.join(cc.scene_dir, 'output', cc.scene_id+'_pickle')\n if cc.atmo_src == 'narr':\n out_file += '_narr'\n elif cc.atmo_src == 'merra':\n out_file += '_merra'\n \n if not os.path.isfile(out_file):\n raise OSError('pickle_file is not in expected location %s' % out_file) \n\n with open(out_file, 'rb') as f:\n x = pickle.load(f)\n return x"],"string":"[\n \"def _on_load_scene_shaders(self):\\n\\n artellapipe.ShadersMgr().load_scene_shaders()\",\n \"def _on_lowres_assets(self):\\n\\n scene_assets = artellapipe.AssetsMgr().get_scene_assets()\\n if not scene_assets:\\n return\\n\\n for scene_asset in scene_assets:\\n scene_asset.switch_to_proxy()\",\n \"def run():\\n\\n def assignToon(context):\\n def instanciate_group(nodes, group_name):\\n group = nodes.new(type='ShaderNodeGroup')\\n group.node_tree = bpy.data.node_groups[group_name]\\n return group\\n\\n def assignToonShader(material):\\n '''To do Handle if the material output doesnt exist'''\\n toonShader = instanciate_group(material.node_tree.nodes, \\\"ToonShader_2\\\")\\n node2 = material.node_tree.nodes['Material Output']\\n material.node_tree.links.new(toonShader.outputs[0], node2.inputs[0])\\n\\n objects = bpy.context.selected_objects\\n for obj in objects:\\n if len(obj.material_slots) < 1:\\n\\n bpy.ops.object.material_slot_add()\\n\\n if obj.name not in bpy.data.materials:\\n\\n mat = bpy.data.materials.new(obj.name)\\n else:\\n mat = bpy.data.materials[obj.name]\\n\\n obj.data.materials[0] = mat\\n mat.use_nodes = True\\n\\n for mat in obj.data.materials:\\n if mat.name == '':\\n mat.name = obj.name\\n\\n matNodes = mat.node_tree.nodes\\n\\n assignToonShader(mat)\\n if 'Principled BSDF' in matNodes:\\n matNodes.remove(matNodes['Principled BSDF'])\\n # else:\\n # for n in matNodes:\\n # if n != material.node_tree.nodes['Material Output']:\\n # matNodes.remove(n)\\n\\n\\n shaderPath = r'D:/COMPANIES/loneCoconut/render/MILVIO_CGL/assets/lib/TOONSCEENSETUP/shd/publish/001.000/high/lib_TOONSCEENSETUP_shd.blend'\\n collection_name = 'ToonSceneSetup'\\n # dict_ = {'company': 'loneCoconut',\\n # 'context': 'render',\\n # 'project': 'MILVIO',\\n # 'scope': 'assets',\\n # 'seq': 'lib',\\n # 'shot': 'TOONSCEENSETUP',\\n # 'task': 'shd',\\n # 'user': 'publish',\\n # 'resolution': 'high'}\\n # shaderPath = lm.LumberObject(dict_)\\n # print(shaderPath.latest_version().path_root)\\n #\\n # collection_name = shaderPath.shot\\n\\n if collection_name not in bpy.data.collections:\\n\\n # link all collections starting with 'MyCollection'\\n with bpy.data.libraries.load(shaderPath, link=False) as (data_from, data_to):\\n data_to.collections = [c for c in data_from.collections if c.startswith(collection_name)]\\n\\n # link collection to scene collection\\n for coll in data_to.collections:\\n if coll is not None:\\n bpy.data.scenes['Scene'].collection.children.link(coll)\\n\\n else:\\n print(\\\"Toon Shader Exist\\\")\\n\\n\\n assignToon(bpy.context)\",\n \"def init_layers(self):\\n\\n # get caching layers activated\\n caching_layers = G3WCachingLayer.objects.all()\\n for caching_layer in caching_layers:\\n self.add_layer(str(caching_layer), caching_layer)\",\n \"def _reload_shader(self):\\n self.render_pipeline.reload_shaders()\\n\\n self.render_pipeline.set_effect(self.terrain.get_node(), \\\"effects/terrain.yaml\\\", {\\n \\\"render_gbuffer\\\": True,\\n \\\"render_shadows\\\": False,\\n\\n })\\n\\n self.render_pipeline.set_effect(self.terrain_shadow.get_node(), \\\"effects/terrain_shadow.yaml\\\", {\\n \\\"render_gbuffer\\\": False,\\n \\\"render_shadows\\\": True,\\n }, 5000)\",\n \"def main():\\n viewer = Viewer()\\n shader = Shader(\\\"Shaders/poisson.vert\\\", \\\"Shaders/poisson.frag\\\")\\n shaderLight = Shader(\\\"Shaders/phong.vert\\\", \\\"Shaders/phong.frag\\\")\\n\\n # Création du terrain\\n node_terrain = Node(transform=translate((-250,-11.7,-250)))\\n node_terrain.add(Terrain(\\\"Terrain/sand.jpg\\\",viewer.depth))\\n viewer.add(node_terrain)\\n\\n # Chargement de tous les objets\\n # Chaque objet est mis dans un node qui permet de définir\\n # sa position, rotation et taille et aussi de se rattacher\\n # à une autre entité\\n\\n barnabe_obj = \\\"Fish/Fish/WhaleShark/WhaleShark.obj\\\"\\n barnabe_png = \\\"Fish/Fish/WhaleShark/WhaleShark_Base_Color.png\\\"\\n\\n hector_obj = \\\"Fish/Fish/ReefFish5/ReefFish5.obj\\\"\\n hector_png = \\\"Fish/Fish/ReefFish5/ReefFish5_Base_Color.png\\\"\\n\\n susie_obj = \\\"Fish/Fish/SeaHorse/SeaHorse.obj\\\"\\n susie_png = \\\"Fish/Fish/SeaHorse/SeaHorse_Base_Color.png\\\"\\n\\n edgar_obj = \\\"Fish/Fish/BlueTang/BlueTang.obj\\\"\\n edgar_png = \\\"Fish/Fish/BlueTang/BlueTang_Base_Color.png\\\"\\n\\n nemo_obj = \\\"Fish/Fish/ClownFish2/ClownFish2.obj\\\"\\n nemo_png = \\\"Fish/Fish/ClownFish2/Clownfish2_Base_Color.png\\\"\\n\\n caroline_obj = \\\"Fish/Fish/Turtle/Turtle.obj\\\"\\n caroline_png = \\\"Fish/Fish/Turtle/Turtle.jpg\\\"\\n\\n corail_obj = \\\"Fish/Fish/Corail/Corail.obj\\\"\\n corail_png = \\\"Fish/Fish/Corail/Corail.jpg\\\"\\n\\n sebastien_obj = \\\"Fish/Fish/Crab/Crab.obj\\\"\\n sebastien_png = \\\"Fish/Fish/Crab/Crab.jpg\\\"\\n\\n star_obj = \\\"Fish/Fish/BlueStarfish/BluieStarfish.obj\\\"\\n star_png = \\\"Fish/Fish/BlueStarfish/BlueStarfish_Base_Color.png\\\"\\n\\n cube_obj = \\\"Fish/Fish/Cube/cube.obj\\\"\\n cube_png = \\\"Fish/Fish/Cube/cube.png\\\"\\n\\n suzanne_obj = \\\"Fish/Fish/Suzanne/Suzanne.obj\\\"\\n\\n barnabe_node = Node(2)\\n meshes = load_shadowed_texture(barnabe_obj, shader, viewer.depth, barnabe_png, 2)\\n for mesh in meshes:\\n barnabe_node.add(mesh)\\n\\n edgar_node = Node(0, transform=translate((1.5, 0.0, 1.5)) @ scale((0.1, 0.1, 0.1)))\\n meshes = load_shadowed_texture(edgar_obj, shader, viewer.depth, edgar_png, 0)\\n for mesh in meshes:\\n edgar_node.add(mesh)\\n barnabe_node.add(edgar_node)\\n viewer.add(barnabe_node)\\n\\n cube_node = Node(transform=translate((5.0,-5.0,0.0)))\\n meshes = load_shadowed_texture(cube_obj, shader,viewer.depth, cube_png, 1)\\n for mesh in meshes:\\n cube_node.add(mesh)\\n\\n suzanne_bubble_node = Node(transform=translate((-0.4,-0.25,0.65)))\\n suzanne_node = Node(transform=scale((0.25,0.25,0.25)) @ rotate((0,1,1),-30))\\n meshes = load_phong_mesh(suzanne_obj,shaderLight,viewer.depth)\\n for mesh in meshes:\\n suzanne_node.add(mesh)\\n suzanne_bubble_node.add(suzanne_node)\\n\\n bubble_translate = {0: vec(-0.15,-0.17,0.25), 3: vec(-0.2,0,0.25), 5: vec(-0.15, 0.15, 0.25), 7: vec(-0.175, 0.27, 0.25)}\\n bubble_rotate = {0: quaternion()}\\n bubble_scale = {0: 0.02, 7: 0.06}\\n bubble_node = KeyFrameControlNode(bubble_translate, bubble_rotate, bubble_scale)\\n bubble_node.add(Bubble(15))\\n suzanne_bubble_node.add(bubble_node)\\n cube_node.add(suzanne_bubble_node)\\n\\n susie_trans = {0: vec(1.2, 1, 0), 2: vec(1.2, 2, 0), 5: vec(1.2, 1, 0)}\\n susie_scale = {0: 0.03}\\n susie_rotate = {0: quaternion_from_axis_angle((0, 1, 0), degrees=-45)}\\n susie_node = KeyFrameControlNode(susie_trans, susie_rotate, susie_scale)\\n meshes = load_shadowed_texture(susie_obj, shader, viewer.depth, susie_png, 1)\\n for mesh in meshes:\\n susie_node.add(mesh)\\n cube_node.add(susie_node)\\n\\n susie2_trans = {0: vec(1, 2, 0), 3: vec(1, 1.5, 0), 5: vec(1, 2, 0)}\\n susie2_scale = {0: 0.05}\\n susie2_rotate = {0: quaternion_from_axis_angle((0, 1, 0), degrees=45)}\\n susie2_node = KeyFrameControlNode(susie2_trans, susie2_rotate, susie2_scale)\\n meshes = load_shadowed_texture(susie_obj, shader, viewer.depth, susie_png, 1)\\n for mesh in meshes:\\n susie2_node.add(mesh)\\n cube_node.add(susie2_node)\\n\\n nemo_trans = {0: vec(-25, 2, 25), 22: vec(-2, 2, 2), 40: vec(20, 2, -20)}\\n nemo_scale = {0: 0.1}\\n nemo_rotate = {0: quaternion_from_axis_angle((0, 1, 0), degrees=120)}\\n nemo_node = KeyFrameControlNode(nemo_trans, nemo_rotate, nemo_scale)\\n meshes = load_shadowed_texture(nemo_obj, shader, viewer.depth, nemo_png, 0)\\n for mesh in meshes:\\n nemo_node.add(mesh)\\n cube_node.add(nemo_node)\\n\\n nemo_trans2 = {0: vec(-28, 2, 26), 20: vec(0, 2, 3), 40: vec(20, 2, -23)}\\n nemo_scale2 = {0: 0.07}\\n nemo_rotate2 = {0: quaternion_from_axis_angle((0, 1, 0), degrees=120)}\\n nemo_node = KeyFrameControlNode(nemo_trans2, nemo_rotate2, nemo_scale2)\\n meshes = load_shadowed_texture(nemo_obj, shader, viewer.depth, nemo_png, 0)\\n for mesh in meshes:\\n nemo_node.add(mesh)\\n cube_node.add(nemo_node)\\n\\n nemo_trans3 = {0: vec(-22, 2, 21), 41: vec(20, 2, -20)}\\n nemo_scale3 = {0: 0.07}\\n nemo_rotate3 = {0: quaternion_from_axis_angle((0, 1, 0), degrees=120)}\\n nemo_node = KeyFrameControlNode(nemo_trans3, nemo_rotate3, nemo_scale3)\\n meshes = load_shadowed_texture(nemo_obj, shader, viewer.depth, nemo_png, 0)\\n for mesh in meshes:\\n nemo_node.add(mesh)\\n cube_node.add(nemo_node)\\n\\n nemo_trans4 = {0: vec(-22, 2.3, 21), 39: vec(20, 2.5, -20)}\\n nemo_scale4 = {0: 0.07}\\n nemo_rotate4 = {0: quaternion_from_axis_angle((0, 1, 0), degrees=120)}\\n nemo_node = KeyFrameControlNode(nemo_trans4, nemo_rotate4, nemo_scale4)\\n meshes = load_shadowed_texture(nemo_obj, shader, viewer.depth, nemo_png, 0)\\n for mesh in meshes:\\n nemo_node.add(mesh)\\n cube_node.add(nemo_node)\\n\\n nemo_trans5 = {0: vec(-22, 2.2, 21), 36: vec(30, 2.2, -20)}\\n nemo_scale5 = {0: 0.1}\\n nemo_rotate5 = {0: quaternion_from_axis_angle((0, 1, 0), degrees=120)}\\n nemo_node = KeyFrameControlNode(nemo_trans5, nemo_rotate5, nemo_scale5)\\n meshes = load_shadowed_texture(nemo_obj, shader, viewer.depth, nemo_png, 0)\\n for mesh in meshes:\\n nemo_node.add(mesh)\\n cube_node.add(nemo_node)\\n\\n nemo_trans6 = {0: vec(-20, 1.7, 21), 38: vec(30, 2, -20)}\\n nemo_scale6 = {0: 0.1}\\n nemo_rotate6 = {0: quaternion_from_axis_angle((0, 1, 0), degrees=120)}\\n nemo_node = KeyFrameControlNode(nemo_trans6, nemo_rotate6, nemo_scale6)\\n meshes = load_shadowed_texture(nemo_obj, shader, viewer.depth, nemo_png, 0)\\n for mesh in meshes:\\n nemo_node.add(mesh)\\n cube_node.add(nemo_node)\\n\\n star_node = Node(transform=translate((0,0.5,0)) @ scale((0.3, 0.3, 0.3)))\\n meshes = load_shadowed_texture(star_obj, shader, viewer.depth, star_png, 1)\\n for mesh in meshes:\\n star_node.add(mesh)\\n cube_node.add(star_node)\\n\\n translate_keys = {0: vec(1,-0.5,0.5), 10: vec(1,-0.5,-0.5), 20: vec(1,-0.5,0.5)}\\n rotate_keys = {0: quaternion_mul(quaternion_from_axis_angle((1,0,0), degrees=-90), quaternion_from_axis_angle((0,0,1), degrees=90))}\\n scale_keys = {0: 0.02}\\n sebastien_node = KeyFrameControlNode(translate_keys, rotate_keys, scale_keys)\\n meshes = load_shadowed_texture(sebastien_obj, shader, viewer.depth, sebastien_png, 1)\\n for mesh in meshes:\\n sebastien_node.add(mesh)\\n cube_node.add(sebastien_node)\\n viewer.add(cube_node)\\n\\n corail_turtle_node = Node(transform=translate((2.5, -5.0, -5.0)))\\n corail_node = Node(transform=scale((0.01,0.01,0.01)))\\n meshes = load_shadowed_texture(corail_obj, shader, viewer.depth, corail_png, 1)\\n for mesh in meshes:\\n corail_node.add(mesh)\\n corail_turtle_node.add(corail_node)\\n\\n hector_trans = {0: vec(-0.5, 1, 0.5)}\\n hector_scale = {0: 0.07}\\n hector_rotate = {0: quaternion_from_axis_angle((0,1,0), degrees=-90), 1: quaternion_from_axis_angle((0,1,0), degrees=-180), 2: quaternion_from_axis_angle((0,1,0), degrees=-270), 3: quaternion_from_axis_angle((0,1,0), degrees=-360), 4: quaternion_from_axis_angle((0,1,0), degrees=-90)}\\n hector_node = KeyFrameControlNode(hector_trans, hector_rotate, hector_scale)\\n meshes = load_shadowed_texture(hector_obj, shader,viewer.depth, hector_png, 3)\\n for mesh in meshes:\\n hector_node.add(mesh)\\n corail_turtle_node.add(hector_node)\\n\\n caroline_node = Node(transform=translate((-0.5, 0.5, 0.0)) @ scale((0.01,0.01,0.01)) @ rotate((1,0,0), 270) @ rotate((0,0,1),315) @ rotate((0,1,0), 45))\\n meshes = load_shadowed_texture(caroline_obj, shader,viewer.depth,caroline_png, 0)\\n for mesh in meshes:\\n caroline_node.add(mesh)\\n corail_turtle_node.add(caroline_node)\\n viewer.add(corail_turtle_node)\\n\\n # Commande de clavier\\n print(\\\"\\\\n\\\\n ----------------- Les commandes de clavier sont les flèches ------------------- \\\\n\\\\n\\\")\\n\\n # start rendering loop\\n viewer.run()\",\n \"def _on_hires_assets(self):\\n\\n scene_assets = artellapipe.AssetsMgr().get_scene_assets()\\n if not scene_assets:\\n return\\n\\n for scene_asset in scene_assets:\\n scene_asset.switch_to_hires()\",\n \"def main():\\n import shutil\\n import json\\n\\n if not os.path.isdir(args.cache):\\n # creation dossier cache\\n os.mkdir(args.cache)\\n\\n if not os.path.exists(args.cache+'/overviews.json'):\\n # creation fichier overviews.json a partir d'un fichier ressource\\n shutil.copy2(args.overviews, args.cache+'/overviews.json')\\n\\n with open(args.cache+'/overviews.json') as json_overviews:\\n overviews_dict = json.load(json_overviews)\\n if not (\\\"list_OPI\\\" in overviews_dict):\\n overviews_dict[\\\"list_OPI\\\"] = []\\n\\n out_raster_srs = gdal.osr.SpatialReference()\\n out_raster_srs.ImportFromEPSG(overviews_dict['crs']['code'])\\n conn_string = \\\"PG:host=\\\"+host+\\\" dbname=\\\"+database+\\\" user=\\\"+user+\\\" password=\\\"+password\\n db_graph = gdal.OpenEx(conn_string, gdal.OF_VECTOR)\\n if db_graph is None:\\n raise ValueError(\\\"Connection to database failed\\\")\\n list_filename = glob.glob(args.input)\\n if verbose > 0:\\n print(len(list_filename), \\\"fichier(s) a traiter\\\")\\n\\n try:\\n with open(args.cache+'/cache_mtd.json', 'r') as inputfile:\\n mtd = json.load(inputfile)\\n except:\\n mtd = {}\\n\\n cliche_dejaTraites = []\\n for filename in list_filename:\\n cliche = Path(filename).stem\\n \\n if (cliche in overviews_dict['list_OPI']):\\n # OPI déja traitée\\n cliche_dejaTraites.append(cliche)\\n else:\\n print('nouvelle image: ', filename)\\n color = [randrange(255), randrange(255), randrange(255)]\\n while (color[0] in mtd) and (color[1] in mtd[color[0]]) and (color[2] in mtd[color[0]][color[1]]):\\n color = [randrange(255), randrange(255), randrange(255)]\\n if color[0] not in mtd:\\n mtd[color[0]] = {}\\n if color[1] not in mtd[color[0]]:\\n mtd[color[0]][color[1]] = {}\\n mtd[color[0]][color[1]][color[2]] = cliche\\n process_image(overviews_dict, db_graph, filename, color, out_raster_srs)\\n # on ajout l'OPI traitée a la liste\\n overviews_dict[\\\"list_OPI\\\"].append(cliche)\\n\\n with open(args.cache+'/cache_mtd.json', 'w') as outfile:\\n json.dump(mtd, outfile)\\n\\n with open(args.cache+'/overviews.json', 'w') as outfile:\\n json.dump(overviews_dict, outfile)\\n\\n print(\\\"\\\\n\\\", len(list_filename) - len(cliche_dejaTraites),\\\"/\\\",len(list_filename),\\\"OPI(s) ajoutée(s)\\\")\\n if len(cliche_dejaTraites) > 0:\\n print(cliche_dejaTraites, \\\"déjà traitées : OPI non recalculée(s)\\\")\",\n \"def initTextureCache():\\r\\n\\r\\n\\tglobal textureCache\\r\\n\\r\\n\\ttextureCache = {}\",\n \"def recache(self, phys):\\r\\n self.myOutputCache.initialize(phys.app)\\r\\n\\r\\n for output in self.myOutputs:\\r\\n output.initialize(phys.app)\\r\\n output.run(1)\",\n \"def cache_geometries(self, geo_nodes):\\n # Create the geocache file command\\n cache_cmd = [\\n 'string $switch = \\\"\\\";',\\n 'string $cacheFiles[] = `cacheFile',\\n '-fileName \\\"tmpCache\\\"',\\n '-directory', '\\\"{}\\\"'.format(Settings.TEMP_DIR),\\n '-startTime', str(self._model.frame_in - 1),\\n '-endTime', str(self._model.frame_out + 1),\\n '-worldSpace',\\n '-singleCache',\\n ]\\n switch_cmd = []\\n for node in geo_nodes:\\n if mc.nodeType(node) != 'mesh':\\n node = mc.listRelatives(node, shapes=True, fullPath=True)[0]\\n\\n # Add the newly created geo to the geocache command\\n cache_cmd.extend(['-points', node])\\n # Create the code to attach the geocache to the created geo\\n switch_cmd.extend([\\n '$switch = createHistorySwitch(\\\"{}\\\", false);'.format(node),\\n 'cacheFile',\\n '-attachFile',\\n '-fileName $cacheFiles[0]'\\n '-directory', '\\\"{}\\\"'.format(Settings.TEMP_DIR),\\n '-channelName', '\\\"{}\\\"'.format(node),\\n '-inAttr ($switch+\\\".inp[0]\\\");',\\n 'setAttr ($switch+\\\".playFromCache\\\") true;'\\n ])\\n\\n # Concatenate both commands\\n cache_cmd = ' '.join(cache_cmd) + \\\"`;\\\" + ' '.join(switch_cmd)\\n\\n # Clean the geometries before making the cache\\n self.clean_geometries(geo_nodes)\\n # Make a geocache of the created geometries and attach it\\n mel.eval(cache_cmd)\",\n \"def init_shaders():\\n global shaders\\n\\n vertex_shader = glCreateShader(GL_VERTEX_SHADER)\\n glShaderSource(vertex_shader,open('shaders/vs-phong-interp.c','r').read())\\n glCompileShader(vertex_shader)\\n result = glGetShaderiv(vertex_shader, GL_COMPILE_STATUS)\\n if result:\\n print('Vertex shader compilation successful.')\\n else:\\n print('Vertex shader compilation FAILED:')\\n print(glGetShaderInfoLog(vertex_shader))\\n sys.exit(-1)\\n\\n fragment_shader = glCreateShader(GL_FRAGMENT_SHADER)\\n glShaderSource(fragment_shader, open('shaders/fs-phong-interp.c','r').read())\\n glCompileShader(fragment_shader)\\n result = glGetShaderiv(fragment_shader, GL_COMPILE_STATUS)\\n if result:\\n print('Fragment shader compilation successful.')\\n else:\\n print('Fragment shader compilation FAILED:')\\n print(glGetShaderInfoLog(fragment_shader))\\n sys.exit(-1)\\n\\n shaders = glCreateProgram()\\n glAttachShader(shaders,vertex_shader)\\n glAttachShader(shaders,fragment_shader)\\n glLinkProgram(shaders)\",\n \"def importShaders(self, namespace=':'):\\n self.logger.info(\\\"Import Shaders\\\")\\n\\n if self.data['abcShadersAttr']:\\n\\n abcfile = self.data['abcShadersAttr']\\n \\n # shotgun query for maya file\\n mayafile = find_shader_package_from_shader_file(file_path=abcfile, file_type='ma')\\n if mayafile != {}:\\n mayafile = mayafile['ma']\\n self.logger.debug(\\\"Found maya shader file: %s\\\" % mayafile)\\n else:\\n localfile = abcfile.replace('.abc', '.ma')\\n if os.path.isfile(localfile):\\n mayafile = localfile\\n self.logger.debug(\\\"Found maya shader file: %s\\\" % mayafile)\\n else:\\n self.logger.error(\\\"Missing file : %s\\\" % self.data['abcShadersAttr'])\\n return False\\n\\n if os.path.isfile(mayafile):\\n try: \\n imported_shaders = cmds.file(mayafile, i=True, returnNewNodes=True, renameAll=True, mergeNamespacesOnClash=True, namespace=namespace)\\n self.setAttr(\\\"abcShaders\\\", \\\"\\\")\\n self.logger.debug(\\\"Imported under %s namespace\\\" % namespace)\\n\\n # reset selection back to alembicHolder\\n cmds.select(self.data['shapeNode'])\\n self.logger.info(\\\"Imported : %s\\\" % self.data['abcShadersAttr'])\\n return True\\n\\n except Exception, e:\\n self.logger.error(\\\"Import Json Error : %s\\\" % e)\\n return False\\n else:\\n self.logger.error(\\\"Missing file : %s\\\" % self.data['abcShadersAttr'])\\n return False\\n else:\\n self.logger.info(\\\"Empty attribute : %s.abcShadersAttr\\\" % self.data['shapeNode'])\\n return False\",\n \"def updateShaderState(self):\\n\\n dopts = self.opts\\n copts = self.canvas.opts\\n lightPos = None\\n flatColour = dopts.getConstantColour()\\n useNegCmap = (not dopts.useLut) and dopts.useNegativeCmap\\n\\n if self.threedee:\\n lightPos = np.array(copts.lightPos)\\n lightPos *= (copts.zoom / 100.0)\\n else:\\n lightPos = None\\n\\n if dopts.useLut:\\n delta = 1.0 / (dopts.lut.max() + 1)\\n cmapXform = transform.scaleOffsetXform(delta, 0.5 * delta)\\n else:\\n cmapXform = self.cmapTexture.getCoordinateTransform()\\n\\n fslgl.glmesh_funcs.updateShaderState(\\n self,\\n useNegCmap=useNegCmap,\\n cmapXform=cmapXform,\\n flatColour=flatColour,\\n lightPos=lightPos)\",\n \"def initialize(self, num_agents: int) -> None:\\n object_loc_main = config[\\\"base\\\"][\\\"object_location\\\"]\\n if experiment == \\\"stage2.0\\\":\\n self.objects.add_object(file = \\\"experiments/flocking/images/redd.png\\\", pos = object_loc_main, scale = [800, 800], obj_type = \\\"obstacle\\\")\\n object_loc = config[\\\"first_circle\\\"][\\\"object_location\\\"]\\n self.objects.add_object(\\n file=\\\"experiments/aggregation/images/greyc1.png\\\", pos=object_loc, scale=[200, 200], obj_type=\\\"site\\\"\\n )\\n object_loc = config[\\\"second_circle\\\"][\\\"object_location\\\"]\\n self.objects.add_object(\\n file=\\\"experiments/aggregation/images/greyc1.png\\\", pos=object_loc, scale=[200, 200], obj_type=\\\"site\\\"\\n )\\n elif experiment == \\\"stage1\\\":\\n self.objects.add_object(file=\\\"experiments/flocking/images/redd.png\\\", pos=object_loc_main, scale=[800, 800],\\n obj_type=\\\"obstacle\\\")\\n object_loc = config[\\\"center_circle\\\"][\\\"object_location\\\"]\\n self.objects.add_object(\\n file=\\\"experiments/aggregation/images/greyc1.png\\\", pos=object_loc, scale=[200, 200], obj_type=\\\"site\\\"\\n )\\n elif experiment == \\\"stage2.1\\\":\\n self.objects.add_object(file=\\\"experiments/flocking/images/redd.png\\\", pos=object_loc_main, scale=[800, 800],\\n obj_type=\\\"obstacle\\\")\\n object_loc = config[\\\"first_circle\\\"][\\\"object_location\\\"]\\n self.objects.add_object(\\n file=\\\"experiments/aggregation/images/greyc1.png\\\", pos=object_loc, scale=[200, 200], obj_type=\\\"site\\\"\\n )\\n object_loc = config[\\\"second_circle\\\"][\\\"object_location\\\"]\\n self.objects.add_object(\\n file=\\\"experiments/aggregation/images/greyc2.png\\\", pos=object_loc, scale=[225, 225], obj_type=\\\"site\\\"\\n )\\n elif experiment == \\\"stage3\\\":\\n self.objects.add_object(file=\\\"experiments/flocking/images/redd.png\\\", pos=object_loc_main, scale=[1000, 1000],\\n obj_type=\\\"obstacle\\\")\\n object_loc = config[\\\"first_circle\\\"][\\\"object_location\\\"]\\n self.objects.add_object(\\n file=\\\"experiments/aggregation/images/greyc1.png\\\", pos=object_loc, scale=[200, 200], obj_type=\\\"site\\\"\\n )\\n object_loc = config[\\\"second_circle\\\"][\\\"object_location\\\"]\\n self.objects.add_object(\\n file=\\\"experiments/aggregation/images/greyc1.png\\\", pos=object_loc, scale=[200, 200], obj_type=\\\"site\\\"\\n )\\n object_loc = config[\\\"upper_circle\\\"][\\\"object_location\\\"]\\n self.objects.add_object(\\n file=\\\"experiments/aggregation/images/greyc1.png\\\", pos=object_loc, scale=[200, 200], obj_type=\\\"site\\\"\\n )\\n object_loc = config[\\\"lower_circle\\\"][\\\"object_location\\\"]\\n self.objects.add_object(\\n file=\\\"experiments/aggregation/images/greyc1.png\\\", pos=object_loc, scale=[200, 200], obj_type=\\\"site\\\"\\n )\\n elif experiment == \\\"stage3.1\\\":\\n self.objects.add_object(file=\\\"experiments/flocking/images/redd.png\\\", pos=object_loc_main, scale=[800, 800],\\n obj_type=\\\"obstacle\\\")\\n object_loc = config[\\\"first_circle\\\"][\\\"object_location\\\"]\\n self.objects.add_object(\\n file=\\\"experiments/aggregation/images/greyc1.png\\\", pos=object_loc, scale=[200, 200], obj_type=\\\"site\\\"\\n )\\n object_loc = config[\\\"second_circle\\\"][\\\"object_location\\\"]\\n self.objects.add_object(\\n file=\\\"experiments/aggregation/images/greyc1.png\\\", pos=object_loc, scale=[200, 200], obj_type=\\\"site\\\"\\n )\\n object_loc = config[\\\"upper_circle\\\"][\\\"object_location\\\"]\\n self.objects.add_object(\\n file=\\\"experiments/aggregation/images/greyc2.png\\\", pos=object_loc, scale=[225, 225], obj_type=\\\"site\\\"\\n )\\n object_loc = config[\\\"lower_circle\\\"][\\\"object_location\\\"]\\n self.objects.add_object(\\n file=\\\"experiments/aggregation/images/greyc2.png\\\", pos=object_loc, scale=[225, 225], obj_type=\\\"site\\\")\\n\\n\\n min_x, max_x = area(object_loc_main[0], 1000)\\n min_y, max_y = area(object_loc_main[1], 1000)\\n\\n # add agents to the environment\\n for index, agent in enumerate(range(num_agents)):\\n coordinates = generate_coordinates(self.screen)\\n while (\\n coordinates[0] >= max_x\\n or coordinates[0] <= min_x\\n or coordinates[1] >= max_y\\n or coordinates[1] <= min_y\\n ):\\n coordinates = generate_coordinates(self.screen)\\n\\n self.add_agent(Cockroach(pos=np.array(coordinates), v=None, cockroach=self, index=index))\",\n \"def _on_unload_scene_shaders(self):\\n\\n artellapipe.ShadersMgr().unload_shaders()\",\n \"def load_from_digital_ocean(self, resource=None):\\n if self.args.force_cache and os.path.isfile(self.cache_filename):\\n return\\n # We always get fresh droplets\\n if self.is_cache_valid() and not (resource == 'droplets' or resource is None):\\n return\\n if self.args.refresh_cache:\\n resource = None\\n\\n if resource == 'droplets' or resource is None:\\n self.data['droplets'] = self.manager.all_active_droplets()\\n self.cache_refreshed = True\\n if resource == 'regions' or resource is None:\\n self.data['regions'] = self.manager.all_regions()\\n self.cache_refreshed = True\\n if resource == 'images' or resource is None:\\n self.data['images'] = self.manager.all_images()\\n self.cache_refreshed = True\\n if resource == 'sizes' or resource is None:\\n self.data['sizes'] = self.manager.sizes()\\n self.cache_refreshed = True\\n if resource == 'ssh_keys' or resource is None:\\n self.data['ssh_keys'] = self.manager.all_ssh_keys()\\n self.cache_refreshed = True\\n if resource == 'domains' or resource is None:\\n self.data['domains'] = self.manager.all_domains()\\n self.cache_refreshed = True\\n if resource == 'tags' or resource is None:\\n self.data['tags'] = self.manager.all_tags()\\n self.cache_refreshed = True\",\n \"def filter(self):\\n new_nodes_to_update = {}\\n nodes_to_update = {}\\n\\n for agent_id in self.cameras.keys():\\n nodes_to_update[agent_id] = []\\n new_nodes_to_update[agent_id] = []\\n if agent_id not in self.beliefs:\\n world_name = self.cameras[agent_id].name.replace(\\\"-\\\",\\\"_\\\")+\\\"_beliefs\\\"\\n rospy.logdebug(\\\"[perspective_filter] create new world <%s>\\\" % str(world_name))\\n self.beliefs[agent_id] = self.ctx.worlds[world_name]\\n self.node_mapping[agent_id] = {}\\n\\n dq = deque()\\n dq.append(self.source.scene.rootnode)\\n\\n while not rospy.is_shutdown() and 0 < len(dq):\\n node = dq.pop()\\n if node.id != self.source.scene.rootnode.id:\\n # Process start here\\n if node.id in self.cameras.keys(): # if the node is the agent POV\\n nodes_to_update[node.id].append(node) # we add it to his belief\\n\\n if node.parent in self.cameras.keys() and node.type == MESH: # if the node is part of an agent\\n nodes_to_update[node.parent].append(node) # we add it to his belief\\n\\n for agent_id, visible_nodes in self.visible_nodes.items(): # then we add the visible nodes\\n if agent_id in self.cameras.keys():\\n if node in visible_nodes:\\n nodes_to_update[agent_id].append(node)\\n\\n # And end here\\n for child_id in node.children:\\n dq.append(self.source.scene.nodes[child_id])\\n\\n for agent_id, nodes in nodes_to_update.items():\\n if nodes:\\n for node in nodes:\\n new_node = node.copy()\\n if node.id in self.node_mapping[agent_id]:\\n new_node.id = self.node_mapping[agent_id][node.id]\\n if new_node.id in self.nodes_transform:\\n if not numpy.allclose(self.nodes_transform[new_node.id], new_node.transformation):\\n new_nodes_to_update[agent_id].append(new_node)\\n self.nodes_transform[new_node.id] = new_node.transformation\\n else:\\n self.nodes_transform[new_node.id] = new_node.transformation\\n new_nodes_to_update[agent_id].append(new_node)\\n else:\\n self.node_mapping[agent_id][node.id] = new_node.id\\n new_nodes_to_update[agent_id].append(new_node)\\n\\n # Finally we update the corresponding beliefs worlds\\n for agent_id, nodes in new_nodes_to_update.items():\\n for node in nodes:\\n node.parent = self.node_mapping[agent_id][node.parent] if node.parent in self.node_mapping[agent_id] \\\\\\n else self.beliefs[agent_id].scene.rootnode.id\\n if nodes:\\n self.beliefs[agent_id].scene.nodes.update(nodes)\",\n \"def optimize_layers_and_transparency(self) -> None:\\n self.optimize_layers()\\n self.optimize_transparency()\",\n \"def init_shader(self):\\r\\n self.attrib_locs = {\\r\\n \\\"mc_vertex\\\": -1,\\r\\n \\\"vert_tex_coord\\\": -1,\\r\\n }\\r\\n self.uniform_locs = {\\r\\n \\\"model_matrix\\\": -1,\\r\\n \\\"view_matrix\\\": -1,\\r\\n \\\"proj_matrix\\\": -1,\\r\\n }\\r\\n vert_prog = self._compile_shader(ORTH_VERT_SOURCE, gl.GL_VERTEX_SHADER)\\r\\n frag_prog = self._compile_shader(\\r\\n ORTH_FRAG_SOURCE, gl.GL_FRAGMENT_SHADER)\\r\\n self.shader = gl.glCreateProgram()\\r\\n gl.glAttachShader(self.shader, vert_prog)\\r\\n gl.glAttachShader(self.shader, frag_prog)\\r\\n gl.glLinkProgram(self.shader)\\r\\n assert (gl.glGetProgramiv(self.shader, gl.GL_LINK_STATUS) ==\\r\\n gl.GL_TRUE), (\\r\\n \\\"Error: %s\\\" % (gl.glGetProgramInfoLog(self.shader)))\\r\\n\\r\\n self.attrib_locs = {\\r\\n name: gl.glGetAttribLocation(self.shader, name)\\r\\n for name in self.attrib_locs\\r\\n }\\r\\n self.uniform_locs = {\\r\\n name: gl.glGetUniformLocation(self.shader, name)\\r\\n for name in self.uniform_locs\\r\\n }\\r\\n\\r\\n # Load vertices for final ortho view\\r\\n self.vao = gl.glGenVertexArrays(1)\\r\\n gl.glBindVertexArray(self.vao)\\r\\n self.buffers['mc_vertex'] = gl.glGenBuffers(1)\\r\\n gl.glBindBuffer(gl.GL_ARRAY_BUFFER, self.buffers['mc_vertex'])\\r\\n\\r\\n gl.glBufferData(gl.GL_ARRAY_BUFFER, len(ORTH_VERTICES),\\r\\n ORTH_VERTICES, gl.GL_STATIC_DRAW)\\r\\n gl.glVertexAttribPointer(self.attrib_locs['mc_vertex'], 4,\\r\\n gl.GL_FLOAT, False, 0, ctypes.c_void_p(0))\\r\\n gl.glEnableVertexAttribArray(self.attrib_locs['mc_vertex'])\\r\\n\\r\\n self.buffers['vert_tex_coord'] = gl.glGenBuffers(1)\\r\\n gl.glBindBuffer(gl.GL_ARRAY_BUFFER, self.buffers['vert_tex_coord'])\\r\\n gl.glBufferData(gl.GL_ARRAY_BUFFER, len(TEXTURE_VERTICES),\\r\\n TEXTURE_VERTICES, gl.GL_STATIC_DRAW)\\r\\n gl.glVertexAttribPointer(self.attrib_locs['vert_tex_coord'], 2,\\r\\n gl.GL_FLOAT, False, 0, ctypes.c_void_p(0))\\r\\n gl.glEnableVertexAttribArray(self.attrib_locs['vert_tex_coord'])\\r\\n gl.glActiveTexture(gl.GL_TEXTURE0)\",\n \"def output_cache(cc):\\n\\n out_file = os.path.join(cc.scene_dir, 'output', cc.scene_id+'_pickle')\\n\\n if cc.atmo_src == 'narr':\\n out_file += '_narr'\\n elif cc.atmo_src == 'merra':\\n out_file += '_merra'\\n\\n with open(out_file, 'wb') as f:\\n pickle.dump(cc, f)\",\n \"def run():\\n scene = lm.scene_object()\\n copy_latest_low()\\n copy_latest_high()\",\n \"def polyMergeUV(*args, caching: bool=True, constructionHistory: bool=True, distance:\\n Union[float, bool]=0.0, name: AnyStr=\\\"\\\", nodeState: Union[int, bool]=0,\\n uvSetName: AnyStr=\\\"\\\", worldSpace: bool=True, q=True, query=True, e=True,\\n edit=True, **kwargs)->Union[AnyStr, Any]:\\n pass\",\n \"def is_node_cached(self, nodes):\\n nodes_with_cache = []\\n for node in nodes:\\n for shape in cmds.listRelatives(node, shapes=True) or []:\\n if cmds.listConnections(shape, type=\\\"historySwitch\\\"):\\n nodes_with_cache.extend([node, shape])\\n elif cmds.listConnections(shape, type=\\\"cacheFile\\\"):\\n nodes_with_cache.extend([node, shape])\\n\\n return nodes_with_cache\",\n \"def init_cache(self):\\n self.left_lane_cache = list()\\n self.right_lane_cache = list()\",\n \"def delete_input_layer_cache(self):\\n # deleteGeometryCache;\\n # performDeleteGeometryCache 0;\\n # deleteCacheFile 3 { \\\"delete\\\", \\\"\\\", \\\"geometry\\\" };\\n input_nodes = self.get_layer_nodes_info([self.input_layer])\\n cached_nodes = self.is_node_cached(input_nodes.values()[0])\\n if cached_nodes:\\n cmds.select(cached_nodes)\\n try:\\n mel.eval(\\\"performDeleteGeometryCache 0;\\\")\\n # mel.eval(\\\"performDeleteGeometryCache 0;\\\")\\n except Exception:\\n pass\",\n \"def polyUnite(*args, caching: bool=True, mergeUVSets: int=1, nodeState: Union[int, bool]=0,\\n centerPivot: bool=True, objectPivot: bool=True, q=True, query=True, e=True,\\n edit=True, **kwargs)->Union[List[AnyStr], Any]:\\n pass\",\n \"def test_ocean_assets_algorithm(publisher_ocean_instance):\\n publisher = get_publisher_wallet()\\n metadata = get_sample_algorithm_ddo()[\\\"service\\\"][0]\\n metadata[\\\"attributes\\\"][\\\"main\\\"][\\\"files\\\"][0][\\\"checksum\\\"] = str(uuid.uuid4())\\n ddo = publisher_ocean_instance.assets.create(metadata[\\\"attributes\\\"], publisher)\\n assert ddo\\n _ddo = wait_for_ddo(publisher_ocean_instance, ddo.did)\\n assert _ddo, f\\\"assets.resolve failed for did {ddo.did}\\\"\",\n \"def test_ocean_assets_compute(publisher_ocean_instance):\\n publisher = get_publisher_wallet()\\n metadata = get_computing_metadata()\\n metadata[\\\"main\\\"][\\\"files\\\"][0][\\\"checksum\\\"] = str(uuid.uuid4())\\n ddo = publisher_ocean_instance.assets.create(metadata, publisher)\\n assert ddo\\n _ddo = wait_for_ddo(publisher_ocean_instance, ddo.did)\\n assert _ddo, f\\\"assets.resolve failed for did {ddo.did}\\\"\",\n \"def init_after_linking_before_calc_loss(self):\\n # Initialize omega as zeros\\n with tf.name_scope('Omega'):\\n for v in self.variables:\\n name = v.name.split(':')[0] + '-omega'\\n shape = v.shape.as_list()\\n shadow = tf.Variable(np.zeros(shape, dtype=np.float32),\\n trainable=False, name=name, shape=shape)\\n self.omegas[v] = shadow\\n\\n self._show_status('Omegas has been initiated.')\",\n \"def set_cache(self, cached):\\n for i in range(self.num_layers):\\n if hasattr(getattr(self, \\\"conv{}\\\".format(i+1)), \\\"set_cache\\\"):\\n getattr(self, \\\"conv{}\\\".format(i+1)).set_cache(cached)\",\n \"def waste_solid(osm_path): \\n i = retrieve(osm_path,'multipolygons',['amenity'],**{'amenity':[\\\"='waste_transfer_station'\\\"]})\\n j = retrieve(osm_path,'multipolygons',['landuse'],**{'landuse':[\\\"='landfill'\\\"]})\\n \\n i = i.rename(columns={'amenity': 'asset'})\\n j = j.rename(columns={'landuse': 'asset'})\\n \\n combined_df = pandas.concat([i,j], ignore_index=True, sort=False) #append objects while ignoring that they may have overlapping index\\n if combined_df.empty == True:\\n return combined_df\\n else:\\n return combined_df[[\\\"osm_id\\\",\\\"asset\\\",\\\"geometry\\\"]]\",\n \"def visualize(self):\\n self.octree.updateInnerOccupancy()\\n print(\\\"Start Octomap Visualization\\\")\\n\\n # define parameters\\n data = imgviz.data.arc2017()\\n camera_info = data['camera_info']\\n K = np.array(camera_info['K']).reshape(3, 3)\\n width=camera_info['width']\\n height=camera_info['height']\\n\\n # get free and occupied grid\\n occupied, _ = self.octree.extractPointCloud()\\n #frontier = self.gen_frontier()\\n \\n print(\\\"load point cloud\\\")\\n window = pyglet.window.Window(\\n width=int(1280), height=int(960)\\n )\\n\\n @window.event\\n def on_key_press(symbol, modifiers):\\n if modifiers == 0:\\n if symbol == pyglet.window.key.Q:\\n window.on_close()\\n\\n gui = glooey.Gui(window)\\n hbox = glooey.HBox()\\n hbox.set_padding(5)\\n\\n camera = trimesh.scene.Camera(\\n resolution=(width, height), focal=(K[0, 0], K[1, 1])\\n )\\n\\n # initial camera pose\\n camera_transform = np.array(\\n [\\n [1, 0, 0, 0],\\n [0, -1, 0, 0],\\n [0, 0, -1, -5],\\n [0.0, 0.0, 0.0, 1.0],\\n ],\\n )\\n\\n \\n\\n occupied_geom = trimesh.voxel.ops.multibox(\\n occupied, pitch=self.resolution, colors=[0.0, 0.0, 0.0, 0.5]\\n )\\n\\n # frontier_geom = trimesh.voxel.ops.multibox(\\n # frontier, pitch=self.resolution, colors=[1.0, 0, 0, 0.5]\\n # )\\n scene = trimesh.Scene(camera=camera, geometry=[occupied_geom])#, frontier_geom])\\n scene.camera_transform = camera_transform\\n hbox.add(self.labeled_scene_widget(scene, label='octomap'))\\n\\n\\n gui.add(hbox)\\n pyglet.app.run()\",\n \"def stand_by(self):\\n\\n if self.gpus is None:\\n self['gpus'] = [0]\\n\\n # setup logging\\n setup_logging(self.get_log_folder(), self.logging)\\n\\n # # prepare workspace\\n # self.get_workspace_dir()\\n\\n # pretrained\\n if \\\"pretrained\\\" in self:\\n name = self.pretrained.name\\n resources = self.pretrained.family.get(name)\\n assert isinstance(resources, dict), ValueError(f\\\"Could not get the resource with name: {name}\\\")\\n cache_dir = self.pretrained.cache_dir\\n if cache_dir is None:\\n cache_path = default_download_dir(name)\\n else:\\n cache_path = Path(cache_dir)\\n cache_path = cache_path / name\\n if not cache_path.exists():\\n maybe_create_dir(str(cache_path))\\n\\n for k, item in resources.items():\\n url = item.url\\n suffix = item.suffix\\n to_insert_paths = item.to_insert_paths\\n to_replaces = item.to_replaces\\n others = item.others\\n # when specify urls of resource\\n if url.startswith(\\\"http\\\"):\\n filename = url.split(\\\"/\\\")[-1]\\n file_path = cache_path / filename\\n if not file_path.exists():\\n try:\\n maybe_download(url, str(cache_path))\\n except Exception as e:\\n logger.error(f\\\"Download from {url} failure!\\\", exc_info=True)\\n raise e\\n # assigning filename to the corresponding config variable.\\n if to_insert_paths and file_path.exists():\\n for to_insert_path in to_insert_paths:\\n self.cascade_set(to_insert_path, str(file_path))\\n # replace corresponding variable with file content\\n if to_replaces and file_path.exists():\\n replace_content = load_from_file(str(file_path))\\n for to_replace in to_replaces:\\n self.cascade_set(to_replace, replace_content)\\n else: # when specify paths of resource which have downloaded.\\n maybe_filename = Path(url)\\n if maybe_filename.is_dir():\\n if suffix:\\n file_path = maybe_filename / suffix\\n else:\\n file_path = maybe_filename\\n else:\\n file_path = maybe_filename\\n # assigning filename to the corresponding config variable.\\n if to_insert_paths:\\n for to_insert_path in to_insert_paths:\\n self.cascade_set(to_insert_path, str(file_path))\\n # replace corresponding variable with file content\\n if to_replaces:\\n replace_content = load_from_file(str(file_path))\\n for to_replace in to_replaces:\\n self.cascade_set(to_replace, replace_content)\\n logging.info(f\\\"Prepare resource {k} from {url}, whose path is {str(file_path)}\\\")\\n # others config\\n if others:\\n for _k, v in others.items():\\n value = v.get(\\\"value\\\")\\n other_to_replaces = v.get(\\\"to_replaces\\\")\\n for other_to_replace in other_to_replaces:\\n self.cascade_set(other_to_replace, value)\\n\\n # replace placeholder\\n self._replace_placeholder(self)\",\n \"def _setOceanLocation(self):\\r\\n\\t\\t## If the fluids_hrc exists\\r\\n\\t\\tif cmds.objExists('fluids_hrc'):\\r\\n\\t\\t\\tif cmds.objExists('ocean_srf'):\\r\\n\\t\\t\\t\\tcmds.connectAttr('fluids_hrc.translateX', 'ocean_srf.translateX', f = True)\\r\\n\\t\\t\\t\\tcmds.connectAttr('fluids_hrc.translateZ', 'ocean_srf.translateZ', f = True)\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tcmds.warning('MISSING ocean_srf node from scene....')\\r\\n\\r\\n\\t\\t\\tif cmds.objExists('oceanPreviewPlane_prv'):\\r\\n\\t\\t\\t\\tcmds.connectAttr('fluids_hrc.translateX', 'oceanPreviewPlane_prv.translateX', f = True)\\r\\n\\t\\t\\t\\tcmds.connectAttr('fluids_hrc.translateZ', 'oceanPreviewPlane_prv.translateZ', f = True)\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tcmds.warning('MISSING oceanPreviewPlane_prv node from scene....')\\r\\n\\t\\telse:\\r\\n\\t\\t\\tcmds.warning('NO fluids_hrc FOUND! Can not move the ocean into final position. PLEASE CHECK FX PUBLISH NOW!')\",\n \"def fix(self):\\n gAsset = cmds.ls(type='gAsset')\\n\\n trans = cmds.listRelatives(gAsset[0], p=True)\\n meshes = cmds.listRelatives(trans, ad=True, type='mesh')\\n for mesh in meshes:\\n if mesh:\\n try:\\n cmds.addAttr(mesh, ln=\\\"grid_renderGeo\\\", at='double', dv=1)\\n cmds.setAttr(\\n '{0}.grid_renderGeo'.format(mesh), e=False, keyable=False, lock=True)\\n except:\\n pass\\n\\n self.run()\",\n \"def addCacheableShaderFromSourceFile(self, Union, QOpenGLShader_ShaderType=None, QOpenGLShader_ShaderTypeBit=None, *args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__\\n pass\",\n \"def importShaders(self):\\n\\t\\tif self.shaderPath.exists:\\n\\t\\t\\tself.shaderPath.imp()\",\n \"def transfer_shaders(source, target):\\n if isinstance(source, pm.nt.Transform):\\n source_shape = source.getShape()\\n else:\\n source_shape = source\\n\\n if isinstance(target, pm.nt.Transform):\\n target_shape = target.getShape()\\n else:\\n target_shape = target\\n\\n # get the shadingEngines\\n shading_engines = source_shape.outputs(type=pm.nt.ShadingEngine)\\n\\n data_storage = []\\n\\n # get the assigned faces\\n for shading_engine in shading_engines:\\n faces = pm.sets(shading_engine, q=1)\\n for faceGroup in faces:\\n str_face = str(faceGroup)\\n # replace the objectName\\n new_face = \\\\\\n str_face.replace(source_shape.name(), target_shape.name())\\n data_storage.append((shading_engine.name(), new_face))\\n\\n for data in data_storage:\\n shading_engine = data[0]\\n new_face = data[1]\\n pm.select(new_face)\\n # now assign the newFaces to the set\\n pm.sets(shading_engine, fe=1)\",\n \"def importData( self, asset = '', searchAndReplace = ['',''] ):\\n\\t\\tpickleData = pickle.load( open( self.dataPath.path, \\\"rb\\\" ) )\\n\\t\\tlayers = [RenderLayerData(l,d) for l,d in pickleData.items() if not ':' in l]\\n\\t\\tfor l in layers:\\n\\t\\t\\tif not searchAndReplace [0]== '' or not searchAndReplace[1] == '':\\n\\t\\t\\t\\tl.filterMe( asset, searchAndReplace )\\n\\t\\t\\tl.create()\\n\\t\\t\\tl.addObjects()\\n\\t\\t\\tl.makeOverrides()\\n\\t\\t\\tl.makeOverrideConnections()\\n\\t\\t\\tl.makeShaderOverride()\",\n \"def cache_maps(\\n self,\\n cache_path: Path,\\n map_cache_class: Type[SceneCache],\\n map_params: Dict[str, Any],\\n ) -> None:\\n pass\",\n \"def holderDepend( self, holder ):\\n for shader in self.shaders:\\n # TODO: cache links...\\n shader.holderDepend( holder )\\n holder.depend( self, 'shaders' )\\n return holder\",\n \"def _update_origin(self, vol_name, cachevol_name):\\n volume_origin = {'origin': cachevol_name}\\n self.zfssa.set_file_props(vol_name, volume_origin)\\n\\n cache_props = self.zfssa.get_volume(cachevol_name)\\n cache_props.update({'numclones':\\n six.text_type(cache_props['numclones'] + 1)})\\n self.zfssa.set_file_props(cachevol_name, cache_props)\",\n \"def __init__(self):\\n self.index = self._generateUID()\\n\\n DebugObject.__init__(self, \\\"ShadowSource-\\\" + str(self.index))\\n ShaderStructElement.__init__(self)\\n\\n self.valid = False\\n self.camera = Camera(\\\"ShadowSource-\\\" + str(self.index))\\n self.cameraNode = NodePath(self.camera)\\n self.cameraNode.reparentTo(Globals.render)\\n self.resolution = 1024\\n self.atlasPos = Vec2(0)\\n self.doesHaveAtlasPos = False\\n self.sourceIndex = 0\\n self.mvp = UnalignedLMatrix4f()\\n self.sourceIndex = -1\\n self.nearPlane = 0.0\\n self.farPlane = 1000.0\\n self.converterYUR = None\\n self.transforMat = TransformState.makeMat(\\n Mat4.convertMat(Globals.base.win.getGsg().getInternalCoordinateSystem(),\\n CSZupRight))\",\n \"def compileShaders(self):\\n if self.flatShader is not None: self.flatShader.destroy()\\n if self.dataShader is not None: self.dataShader.destroy()\\n\\n self.activeShader = None\\n\\n fslgl.glmesh_funcs.compileShaders(self)\",\n \"def UpdateLayers(self):\\n pass\",\n \"def _load_opengl(self):\\r\\n pass\",\n \"def assets_resolve(ctx, did):\\n ddo = ctx.obj['ocean'].assets.resolve(did)\\n echo(ddo.as_dictionary())\",\n \"def addCacheableShaderFromSourceCode(self, Union, QOpenGLShader_ShaderType=None, QOpenGLShader_ShaderTypeBit=None, *args, **kwargs): # real signature unknown; restored from __doc__ with multiple overloads\\n pass\",\n \"def duplicateSurface(*args, caching: bool=True, constructionHistory: bool=True, faceCountU:\\n Union[int, bool]=1, faceCountV: Union[int, bool]=1, firstFaceU: Union[int,\\n bool]=0, firstFaceV: Union[int, bool]=0, local: bool=True, mergeItems:\\n bool=True, name: AnyStr=\\\"\\\", nodeState: Union[int, bool]=0, q=True,\\n query=True, e=True, edit=True, **kwargs)->Union[List[AnyStr], Any]:\\n pass\",\n \"def combine_with_assets(coins):\\n \\n with open('index_assets.json', encoding='utf-8') as f:\\n \\n data = json.loads(f.read())\\n \\n for coin in coins:\\n symbol = coin['symbol']\\n original_symbol = symbol\\n\\n if coin['gecko_id'] in [asset['gecko_id'] for asset in data.values()]:\\n continue\\n\\n print('got a new coin: {}'.format(coin))\\n\\n counter = 1\\n while symbol in data:\\n print('symbol {} repeats !'.format(symbol))\\n symbol = original_symbol + \\\"_\\\" + str(counter)\\n counter += 1\\n\\n data[symbol] = {'gecko_id': coin['gecko_id'], 'symbol': coin['symbol']}\\n if 'logo' in coin:\\n data[symbol]['logo'] = coin['logo']\\n else:\\n print('could not find image')\\n\\n\\n\\n with open('index_assets.json', 'w', encoding='utf-8') as f:\\n f.write(json.dumps(data))\\n print('wrote to file')\",\n \"def _removeOcean(self):\\r\\n\\t\\tnodesToClean = [CONST.OCEANDISPSHADER, CONST.OCEANANIMSHADER, CONST.OCEAN_ANIM_PREVIEWPLANENAME]\\r\\n\\t\\tfor eachNode in nodesToClean:\\r\\n\\t\\t\\ttry:\\r\\n\\t\\t\\t\\tcmds.delete(each)\\r\\n\\t\\t\\texcept:\\r\\n\\t\\t\\t\\tpass\",\n \"def prepare_fast_setup(self, revert=False):\\n scene = self.set_as_active()\\n\\n if not revert:\\n scene.render.use_simplify = True\\n scene.render.simplify_subdivision = 0\\n smallest_layer, _ = min(enumerate(self.get_layers_vert_counts()), key=lambda p: p[1])\\n self.set_layers((smallest_layer,))\\n\\n else:\\n scene.render.use_simplify = self.original_use_simplify\\n scene.render.simplify_subdivision = self.original_simplify_subdivision\\n scene.layers = self.original_layers\",\n \"def test_ocean() -> None:\\n cfg = load_config()\\n setup = ModelSetup(str(TEST_DIREC), cfg)\\n ocean = Ocean(cfg, setup)\\n ocean.compile_all()\\n if cfg.run:\\n ocean.run_all()\\n if cfg.animate:\\n ocean.animate_all()\",\n \"def connectAllWithOceanHeightAttr(object = ''):\\n\\tinprogressBar = pbui.ProgressBarUI(title = 'Hooking To Ocean:')\\n\\tinprogressBar.show()\\n\\tinprogressBar.updateProgress(percent = 0, doingWhat = 'Hooking everything to ocean now...')\\n\\tdebug(None, method = 'connectAllWithOceanHeightAttr', message = 'Rebuilding Ocean Hooks', verbose = False)\\n\\n\\t## Find all in scene with oceanHeightHook Attribute\\n\\t## Setup the progress bar\\n\\tif not cmds.objExists('Shot_FX_hrc'):\\n\\t\\tcmds.group(n = 'Shot_FX_hrc', em = True)\\n\\tif not cmds.objExists('BOAT_OceanLocators_hrc'):\\n\\t\\tcmds.group(n = 'BOAT_OceanLocators_hrc', em = True)\\n\\t\\tcmds.parent('BOAT_OceanLocators_hrc', 'Shot_FX_hrc')\\n\\t\\tcmds.setAttr('BOAT_OceanLocators_hrc.visibility', 0)\\n\\n\\t## Clean up existing hooks in the scene\\n\\tinprogressBar.updateProgress(percent = 50, doingWhat = 'Cleaning up now..')\\n\\n\\tif object.startswith('All'):\\n\\t\\tobject = ''\\n\\telif object.startswith('Dock'):\\n\\t\\tobject = 'BLD'\\n\\telif object.startswith('Boat'):\\n\\t\\tobject = 'CHAR'\\n\\telif object.startswith('Prop'):\\n\\t\\tobject = 'PROP'\\n\\n\\tcleanUpExisting(filters = [object])\\n\\tif object == 'BLD':\\n\\t\\t## Clean-up the ADEF BLD's oceanHook control animatable groups...\\n\\t\\t[cmds.delete(transform) for transform in cmds.ls(type = 'transform') if transform.endswith('_oceanCtrls_hrc')]\\n\\n\\t## Rebuild / build the hooks for boats in the scene\\n\\tinprogressBar.updateProgress(percent = 75, doingWhat = 'performHookBoatsToOcean now..')\\n\\tstartTime = time.time()\\n\\tperformHookBoatsToOcean()\\n\\tprint 'TOTAL TIME FOR performHookBoatsToOcean: %s' % (time.time()-startTime)\\n\\n\\t## Now make sure cycle check is off because it's a fn pita\\n\\tcmds.cycleCheck(e = 0)\\n\\t_finished(inprogressBar)\",\n \"def set_up_world_ao(self):\\n scene = self.set_as_active()\\n new_world = bpy.context.blend_data.worlds.new('World of Wireframe')\\n scene.world = new_world\\n new_world.light_settings.use_ambient_occlusion = True\\n new_world.light_settings.ao_factor = 0.3\\n\\n renderengine = scene.wirebomb.data_renderengine\\n\\n if renderengine == 'CYCLES':\\n new_world.use_nodes = True\\n new_world.node_tree.nodes[1].inputs[0].default_value = (1, 1, 1, 1)\\n\\n for node in new_world.node_tree.nodes:\\n node.select = False\\n \\n elif renderengine == 'BLENDER_RENDER':\\n new_world.horizon_color = (1, 1, 1)\",\n \"def assets():\",\n \"def mark_if_cached(self, args):\\n for art in self.artifacts:\\n art.mark_if_cached(args)\",\n \"def update_project(_):\\n\\n # Compile all OSL Script nodes\\n stdosl_path = path_util.get_stdosl_paths()\\n compiler = asr.ShaderCompiler(stdosl_path)\\n q = asr.ShaderQuery()\\n for script in bpy.data.texts:\\n osl_bytecode = osl_utils.compile_osl_bytecode(compiler,\\n script)\\n if osl_bytecode is not None:\\n q.open_bytecode(osl_bytecode)\\n\\n node_data = osl_utils.parse_shader(q)\\n\\n node_name, node_category, node_classes = osl_utils.generate_node(node_data,\\n AppleseedOSLScriptNode)\\n\\n for cls in node_classes:\\n safe_register_class(cls)\\n\\n else:\\n logger.debug(f\\\"appleseed: Shader {script.name} did not compile\\\")\",\n \"def recompile(self):\\n\\n self.vaos = []\\n try:\\n self.program, uniforms = self.build_prog(self.gl)\\n self.u_time, self.u_width, self.u_height = uniforms\\n vao = GLUtil.screen_vao(self.gl, self.program)\\n self.vaos.append(vao)\\n\\n self.compute, uniforms, buffers = self.build_cs(self.gl)\\n self.u_cstime, self.u_cswidth, self.u_csheight = uniforms\\n self.buf_in, self.buf_out = buffers\\n\\n self.set_gpu_wh(width, height)\\n\\n self.gx, self.gy = int(width / 8), int(height / 8)\\n self.set_gpu_time()\\n\\n log(\\\"[Renderer] shader recompiled.\\\")\\n\\n except Exception as e:\\n log(e)\",\n \"def loadConnections(self):\\n\\n #create each connecting map\\n for direction, (fn, offset) in self.connections.items():\\n self.connectedMaps[direction] = (Tilemap(fn), offset)\",\n \"def _load_neighbors(self) -> None:\\r\\n if not self.are_neighbors_cached:\\r\\n self._load_neighbors_from_external_source()\\r\\n db: GraphDatabaseInterface = self._graph.database\\r\\n db_node: DBNode = db.Node.find_by_name(self.name)\\r\\n db_node.are_neighbors_cached = True\\r\\n db.session.commit()\\r\\n self.are_neighbors_cached = True\\r\\n if not self._are_neighbors_loaded:\\r\\n self._load_neighbors_from_database()\",\n \"def cache_map(\\n self,\\n map_name: str,\\n layer_names: List[str],\\n cache_path: Path,\\n map_cache_class: Type[SceneCache],\\n resolution: float,\\n ) -> None:\\n pass\",\n \"def add_latesettings_assets(self):\\n\\n # setting up static file serving\\n assetmanager = self.comp('assetmanager')\\n\\n # add external asset mount point where we can copy public static files so they can be served by a separate traditional web server\\n # presumably this directory is being served by a more traditional webserver, at this url we specify below\\n assetmanager.add_assetmount(\\n massetmanager.MewloAssetMount_ExternalServer('external_assets', filepath = '${mewlofilepath}/public_assets', urlabs = 'http://127.0.0.1/mewlo/mewlo/public_assets' )\\n )\\n\\n # add internal asset mount point where we will serve files internally; a route will be automatically created for any asset source attached to this mount point; we can choose the path prefix for urls served by the route\\n assetmanager.add_assetmount(\\n massetmanager.MewloAssetMount_InternalRoute('internal_assets', urlpath='assets')\\n )\\n\\n\\n # now that we have some mount points, we can specify some files to be hosted on them\\n # note that the ids for all asset sources MUST be unique (ATTN:TODO elaborate on this please)\\n # first we mount the files in the staticfilesource/ directory as internal assets that we will serve internally via mewlo; the id will be used for alias creation, and for the route\\n assetmanager.add_assetsource(\\n massetmanager.MewloAssetSource(id='siteinternal', mountid = 'internal_assets', filepath = '${sitefilepath}/staticfilesource', mnamespace=None)\\n )\\n # then as a test, lets mount same files on the external mount point -- this will cause mewlo to physically copy the files to the external filepath, where presumably another web server can serve them\\n assetmanager.add_assetsource(\\n massetmanager.MewloAssetSource(id='siteexternal', mountid = 'external_assets', filepath = '${sitefilepath}/staticfilesource', mnamespace=None)\\n )\\n\\n # remember that one should never refer to the assets by a hardcoded url or file path; always use the aliases created by these functions, which will take the form (where ID is the id of the asset source):\\n # 'asset_ID_urlrel' | 'asset_ID_urlabs' | 'asset_ID_filepath'\\n # you can also use helper function to build these names, which would be better.\",\n \"def ports(osm_path): \\n return (retrieve(osm_path,'multipolygons',['landuse'],**{'landuse':[\\\"='industrial' or \\\",\\\"='port' or \\\",\\\"='harbour'\\\"]})).rename(columns={'landuse': 'asset'})\",\n \"def on_update(self, delta_time):\\n self.texture = self.sprite_list()\\n for self.texture\\n if self.texture == self.ada:\\n self.texture == self.potato\\n else:\\n self.texture == self.ada\",\n \"def setup_terrain(self):\\r\\n self.terrain_scale = LVector3(512, 512, 100)\\r\\n self.terrain_pos = LVector3(-256, -256, -70)\\r\\n # sample values for a 4096 x 4096px heightmap.\\r\\n #self.terrain_scale = LVector3(4096, 4096, 1000)\\r\\n #self.terrain_pos = LVector3(-2048, -2048, -70)\\r\\n \\\"\\\"\\\"\\r\\n Diamond_subdivision is an alternating triangulation scheme and may\\r\\n produce better results.\\r\\n \\\"\\\"\\\"\\r\\n use_diamond_subdivision = True\\r\\n \\r\\n \\\"\\\"\\\"\\r\\n Construct the terrain\\r\\n Without scaling, any ShaderTerrainMesh is 1x1x1 units.\\r\\n \\\"\\\"\\\"\\r\\n self.terrain_node = ShaderTerrainMesh()\\r\\n \\\"\\\"\\\"\\r\\n Set a heightfield, the heightfield should be a 16-bit png and\\r\\n have a quadratic size of a power of two.\\r\\n \\\"\\\"\\\"\\r\\n heightfield = Texture()\\r\\n heightfield.read(self.heightfield_fn)\\r\\n heightfield.set_keep_ram_image(True) \\r\\n self.terrain_node.heightfield = heightfield\\r\\n \\r\\n # Display characteristic values of the heightfield texture\\r\\n #minpoint, maxpoint, avg = LPoint3(), LPoint3(), LPoint3()\\r\\n #heightfield.calc_min_max(minpoint, maxpoint)\\r\\n #heightfield.calc_average_point(avg, 0.5, 0.5, 0.5)\\r\\n #print(\\\"avg: {} min: {} max: {}\\\".format(avg.x, minpoint.x, maxpoint.x))\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Set the target triangle width. For a value of 10.0 for example,\\r\\n the ShaderTerrainMesh will attempt to make every triangle 10 pixels\\r\\n wide on screen.\\r\\n \\\"\\\"\\\"\\r\\n self.terrain_node.target_triangle_width = 10.0\\r\\n if use_diamond_subdivision:\\r\\n \\\"\\\"\\\"\\r\\n This has to be specified before calling .generate()\\r\\n The default is false.\\r\\n \\\"\\\"\\\"\\r\\n load_prc_file_data(\\\"\\\", \\\"stm-use-hexagonal-layout true\\\")\\r\\n \\r\\n self.terrain_node.generate()\\r\\n \\\"\\\"\\\"\\r\\n Attach the terrain to the main scene and set its scale. With no scale\\r\\n set, the terrain ranges from (0, 0, 0) to (1, 1, 1)\\r\\n \\\"\\\"\\\"\\r\\n self.terrain = self.render.attach_new_node(self.terrain_node)\\r\\n self.terrain.set_scale(self.terrain_scale)\\r\\n self.terrain.set_pos(self.terrain_pos)\\r\\n \\\"\\\"\\\"\\r\\n Set a vertex and a fragment shader on the terrain. The\\r\\n ShaderTerrainMesh only works with an applied shader.\\r\\n \\\"\\\"\\\"\\r\\n terrain_shader = Shader.load(Shader.SL_GLSL, \\r\\n \\\"samples/shader-terrain/terrain.vert.glsl\\\", \\r\\n \\\"samples/shader-terrain/terrain.frag.glsl\\\")\\r\\n self.terrain.set_shader(terrain_shader)\\r\\n self.terrain.set_shader_input(\\\"camera\\\", base.camera)\\r\\n # Set some texture on the terrain\\r\\n grass_tex = self.loader.load_texture(\\r\\n \\\"samples/shader-terrain/textures/grass.png\\\")\\r\\n grass_tex.set_minfilter(SamplerState.FT_linear_mipmap_linear)\\r\\n grass_tex.set_anisotropic_degree(16)\\r\\n self.terrain.set_texture(grass_tex)\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Set up the DynamicHeightfield (it's a type of PfmFile). We load the\\r\\n same heightfield image as with ShaderTerrainMesh.\\r\\n \\\"\\\"\\\"\\r\\n self.DHF = DynamicHeightfield()\\r\\n self.DHF.read(self.heightfield_fn)\\r\\n \\\"\\\"\\\"\\r\\n Set up empty PfmFiles to prepare stuff in that is going to\\r\\n dynamically modify our terrain.\\r\\n \\\"\\\"\\\"\\r\\n self.StagingPFM = PfmFile()\\r\\n self.RotorPFM = PfmFile()\\r\\n \\r\\n \\\"\\\"\\\"\\r\\n Set up the BulletHeightfieldShape (=collision terrain) and give it\\r\\n some sensible physical properties.\\r\\n \\\"\\\"\\\"\\r\\n self.HFS = BulletHeightfieldShape(self.DHF, self.terrain_scale.z,\\r\\n STM=True)\\r\\n if use_diamond_subdivision:\\r\\n self.HFS.set_use_diamond_subdivision(True)\\r\\n HFS_rigidbody = BulletRigidBodyNode(\\\"BulletTerrain\\\")\\r\\n HFS_rigidbody.set_static(True)\\r\\n friction = 2.0\\r\\n HFS_rigidbody.set_anisotropic_friction(\\r\\n LVector3(friction, friction, friction/1.3))\\r\\n HFS_rigidbody.set_restitution(0.3)\\r\\n HFS_rigidbody.add_shape(self.HFS)\\r\\n self.world.attach(HFS_rigidbody)\\r\\n \\r\\n HFS_NP = NodePath(HFS_rigidbody)\\r\\n HFS_NP.reparent_to(self.worldNP)\\r\\n \\\"\\\"\\\"\\r\\n This aligns the Bullet terrain with the ShaderTerrainMesh rendered\\r\\n terrain. It will be exact as long as the terrain vertex shader from\\r\\n the STM sample is used and no additional tessellation shader.\\r\\n For Bullet (as for other physics engines) the origin of objects is at\\r\\n the center.\\r\\n \\\"\\\"\\\"\\r\\n HFS_NP.set_pos(self.terrain_pos + self.terrain_scale/2)\\r\\n HFS_NP.set_sx(self.terrain_scale.x / heightfield.get_x_size())\\r\\n HFS_NP.set_sy(self.terrain_scale.y / heightfield.get_y_size())\\r\\n \\r\\n # Disables Bullet debug rendering for the terrain, because it is slow.\\r\\n #HFS_NP.node().set_debug_enabled(False)\\r\\n \\r\\n \\\"\\\"\\\"\\r\\n Finally, link the ShaderTerrainMesh and the BulletHeightfieldShape to\\r\\n the DynamicHeightfield. From now on changes to the DynamicHeightfield\\r\\n will propagate to the (visible) ShaderTerrainMesh and the (collidable)\\r\\n BulletHeightfieldShape.\\r\\n \\\"\\\"\\\"\\r\\n self.HFS.set_dynamic_heightfield(self.DHF)\\r\\n self.terrain_node.set_dynamic_heightfield(self.DHF)\",\n \"def ocean(ctx, config_file, as_json, verbose):\\n if not verbose:\\n logging.getLogger().setLevel(logging.ERROR)\\n\\n ctx.obj = {\\n 'ocean': get_ocean(config_file),\\n 'json': as_json\\n }\",\n \"def init_cache_from_geojson(self, path):\\n io.copy_file(path_from=path, path_to=self.path)\",\n \"def init_cache_from_geojson(self, path):\\n io.copy_file(path_from=path, path_to=self.path)\",\n \"def _cache_checker_matrices(self):\\r\\n if self.model.mat_texid is not None:\\r\\n self._geom_checker_mats = []\\r\\n for geom_id in range(self.model.ngeom):\\r\\n mat_id = self.model.geom_matid[geom_id]\\r\\n tex_id = self.model.mat_texid[mat_id]\\r\\n texture = self.textures[tex_id]\\r\\n h, w = texture.bitmap.shape[:2]\\r\\n self._geom_checker_mats.append(self._make_checker_matrices(h, w))\\r\\n\\r\\n # add skybox\\r\\n skybox_tex_id = -1\\r\\n for tex_id in range(self.model.ntex):\\r\\n skybox_textype = 2\\r\\n if self.model.tex_type[tex_id] == skybox_textype:\\r\\n skybox_tex_id = tex_id\\r\\n if skybox_tex_id >= 0:\\r\\n texture = self.textures[skybox_tex_id]\\r\\n h, w = texture.bitmap.shape[:2]\\r\\n self._skybox_checker_mat = self._make_checker_matrices(h, w)\\r\\n else:\\r\\n self._skybox_checker_mat = None\",\n \"def compute_relations(self):\\n\\n visible_nodes = {}\\n\\n self.cameras = self.get_all_cameras()\\n rospy.logdebug(self.cameras)\\n\\n if self.cameras.items():\\n try:\\n if self.visibility_monitor is None:\\n self.visibility_monitor = VisibilityMonitor(self.ctx, self.source)\\n rospy.loginfo(\\\"[perspective_filter] Visibility monitor now running, please active the Pygame windows.\\\")\\n visible_nodes = self.visibility_monitor.compute_all()\\n rospy.logdebug(\\\"[perspective_filter] %d perspectives computed \\\" % len(visible_nodes))\\n #rospy.logdebug(visible_nodes)\\n except Exception as e:\\n rospy.logwarn(\\\"[perspective_filter] Exception occurred while computing relation : %s\\\" % str(e))\\n if self.visibility_monitor:\\n self.visible_nodes = {} #visible_nodes\\n for camera_name, visibles_obj in visible_nodes.items():\\n camera_id = self.source.scene.nodebyname(camera_name)[0].id\\n self.visible_nodes[camera_id] = visibles_obj\\n for node in visibles_obj:\\n if node.parent in self.cameras.keys():\\n if self.source.scene.nodes[node.parent] not in visibles_obj:\\n visibles_obj.append(self.source.scene.nodes[node.parent])\\n\\n for agent_id, nodes_seen in self.visible_nodes.items():\\n agent = self.source.scene.nodes[agent_id]\\n for node in nodes_seen:\\n if agent_id in self.previously_visible_nodes:\\n if node not in self.previously_visible_nodes[agent_id]:\\n self.start_predicate(self.source.timeline, \\\"isVisibleBy\\\", node.name, object_name=agent.name)\\n else:\\n self.start_predicate(self.source.timeline, \\\"isVisibleBy\\\", node.name, object_name=agent.name)\\n\\n for agent_id, nodes_previously_seen in self.previously_visible_nodes.items():\\n agent = self.source.scene.nodes[agent_id]\\n for node in nodes_previously_seen:\\n if agent_id in self.visible_nodes:\\n if node not in self.visible_nodes[agent_id]:\\n self.end_predicate(self.source.timeline, \\\"isVisibleBy\\\", node.name, object_name=agent.name)\\n else:\\n self.end_predicate(self.source.timeline, \\\"isVisibleBy\\\", node.name, object_name=agent.name)\\n\\n self.publish_perspectives()\\n self.previously_visible_nodes = self.visible_nodes\",\n \"def __init_cache__(self) -> None:\\n try:\\n self.cache = caches[CACHE_NAME]\\n logging.info(\\\"GeoIP2 - successfully initialised cache\\\")\\n except InvalidCacheBackendError as ex:\\n raise MiddlewareNotUsed(f\\\"GeoIP2 - cache configuration error: {ex}\\\") from ex\",\n \"def reconnectLookdev(self, namespace):\\n self.logger.debug(\\\"Reconnecting imported lookev\\\")\\n\\n # lets break connections first\\n self.breakConnections()\\n\\n assignations = {}\\n\\n if self.objExists('shadersAssignation'):\\n shadersAssignation = self.getAttr('shadersAssignation')\\n if shadersAssignation:\\n assignations[\\\"shaders\\\"] = json.loads(shadersAssignation)\\n\\n if self.objExists('displacementsAssignation'):\\n displacementsAssignation = self.getAttr('displacementsAssignation')\\n if displacementsAssignation:\\n assignations[\\\"displacement\\\"] = json.loads(displacementsAssignation)\\n\\n if self.objExists('layersOverride'):\\n layersOverride = self.getAttr('layersOverride')\\n if layersOverride:\\n assignations[\\\"layers\\\"] = json.loads(layersOverride)\\n\\n # fix the attribute editor\\n fixed = {'shaders' : {}, 'displacement' : {}, 'layers' : {}}\\n attrs_to_connect = []\\n if assignations.has_key('shaders'):\\n for shader in assignations['shaders']:\\n if namespace != ':':\\n fixed['shaders']['%s:%s' % (namespace, shader)] = assignations['shaders'][shader]\\n attrs_to_connect.append('%s:%s' % (namespace, shader))\\n else:\\n fixed['shaders']['%s' % (shader)] = assignations['shaders'][shader]\\n attrs_to_connect.append('%s' % (shader))\\n \\n if assignations.has_key('displacement'):\\n for disp in assignations['displacement']:\\n if namespace != ':':\\n fixed['displacement']['%s:%s' % (namespace, disp)] = assignations['displacement'][disp]\\n attrs_to_connect.append('%s:%s' % (namespace, disp))\\n else:\\n fixed['displacement']['%s' % (disp)] = assignations['displacement'][disp]\\n attrs_to_connect.append('%s' % (disp))\\n\\n if assignations.has_key('layers'):\\n for layer in assignations['layers']:\\n if not fixed['layers'].has_key(layer):\\n fixed['layers'][layer] = {'shaders' : {}, 'displacements' : {}}\\n \\n if assignations['layers'][layer].has_key('shaders'):\\n for shader in assignations['layers'][layer]['shaders']:\\n if namespace != ':':\\n fixed['layers'][layer]['shaders']['%s:%s' % (namespace, shader)] = assignations['layers'][layer]['shaders'][shader]\\n attrs_to_connect.append('%s:%s' % (namespace, shader))\\n else:\\n fixed['layers'][layer]['shaders']['%s' % (shader)] = assignations['layers'][layer]['shaders'][shader]\\n attrs_to_connect.append('%s' % (shader))\\n \\n if assignations['layers'][layer].has_key('displacements'):\\n for disp in assignations['layers'][layer]['displacements']:\\n if namespace != ':':\\n fixed['layers'][layer]['displacements']['%s:%s' % (namespace, disp)] = assignations['layers'][layer]['displacements'][disp]\\n attrs_to_connect.append('%s:%s' % (namespace, disp))\\n else:\\n fixed['layers'][layer]['displacements']['%s' % (disp)] = assignations['layers'][layer]['displacements'][disp]\\n attrs_to_connect.append('%s' % (disp)) \\n\\n self.setAttr(attr='shadersAssignation', value=json.dumps(fixed['shaders']))\\n self.setAttr(attr='displacementsAssignation', value=json.dumps(fixed['displacement']))\\n self.setAttr(attr='layersOverride', value=json.dumps(fixed['layers']))\\n\\n # connect the shaders to the alembicHolder\\n port = 0\\n added = []\\n for attr in attrs_to_connect:\\n if not attr in added:\\n if not attr.endswith('.message'):\\n shader_attr = '%s.message' % attr\\n else:\\n shader_attr = attr\\n shape_attr = '%s.shaders[%i]' % (self.data['shapeNode'], port)\\n self.connectAttr(shader_attr, shape_attr)\\n added.append(attr)\\n port += 1\",\n \"def main():\\n\\n dotfiles_location = os.path.expanduser('~') + '/dotfiles'\\n default_bundles_location = dotfiles_location + '/bundles/dein/repos'\\n\\n parser = argparse.ArgumentParser(\\n description='Build a cache of available colours schemes, output them into a JSON file for vim plugins to read'\\n )\\n\\n parser.add_argument('-n', '--dryrun',\\n action='store_true',\\n dest='dryrun',\\n required=False,\\n help='Dryrun mode (do not write to cache file)'\\n )\\n\\n parser.add_argument('-v', '--verbose',\\n action='store_true',\\n dest='verbose',\\n required=False,\\n help='Increase verbosity'\\n )\\n\\n parser.add_argument('-c', '--cache',\\n action='store',\\n dest='cache_file',\\n required=False,\\n metavar='CACHE_FILE',\\n default=dotfiles_location + '/colos.json',\\n help='Cache file name'\\n )\\n\\n parser.add_argument(\\n action='store',\\n dest='command', # maybe use an action to validate the commands with their args?\\n nargs=1,\\n help='Command to issue: generate, whitelist <name>, blacklist <name>, toggle-variant <name>'\\n )\\n\\n parser.add_argument(\\n dest='args',\\n nargs='*'\\n )\\n\\n try:\\n args = parser.parse_args()\\n except ValueError as e:\\n print('Invalid options.', e, file=sys.stderr)\\n sys.exit(1)\\n\\n\\n data = cololib.cmd_load_data(args.cache_file, verbose=args.verbose)\\n\\n if 'build_cache' == args.command[0]:\\n cololib.cmd_build_cache(\\n data=data,\\n search_path=default_bundles_location,\\n verbose=args.verbose, dryrun=args.dryrun\\n )\\n elif 'get_random' == args.command[0]:\\n if args.args:\\n tag = args.args[0]\\n else:\\n tag = None\\n cololib.cmd_get_random_colo(data=data, tag=tag, verbose=args.verbose)\\n else:\\n print('Unknown command \\\"%s\\\"'%(args.command[0]), file=sys.stderr)\\n sys.exit(1)\",\n \"def getConnectedShaders(self):\\n self.logger.debug(\\\"Connected Shaders\\\")\\n\\n connected = []\\n for connections in pm.listConnections(self.data['shapeNode'], plugs=True, connections=True):\\n if cmds.getClassification(connections[-1].nodeType(), satisfies=\\\"shader\\\"):\\n self.logger.debug(\\\"Connected shader : %s\\\" % connections[-1].node())\\n connected.append(connections[-1].node())\\n return connected\",\n \"def initCitys(self):\\n self.cities = []\\n for vertex in self.metaGraph:\\n self.cities.append(vertex)\",\n \"def do_api_calls_update_cache(self):\\n self.get_nodes()\\n self.write_to_cache(self.inventory, self.cache_path_cache)\\n self.write_to_cache(self.index, self.cache_path_index)\",\n \"def checkLayersOverride(shape):\\n required = []\\n connected = [] \\n\\n # find the shaders / displacement that are required\\n layersOverride = cmds.getAttr(\\\"%s.layersOverride\\\" % shape)\\n if layersOverride:\\n layersOverride = json.loads(layersOverride)\\n for layer in layersOverride:\\n if layersOverride[layer].has_key('shaders'):\\n for k in layersOverride[layer]['shaders'].keys():\\n if not k in required:\\n required.append(k)\\n\\n shape_connections = cmds.listAttr(\\\"%s.shaders\\\" % shape, multi=True)\\n\\n # go find the connected shaders\\n if shape_connections:\\n for con in shape_connections:\\n connected_shader = cmds.listConnections(\\\"%s.%s\\\" % (shape, con))[0]\\n connected.append(connected_shader)\\n \\n port = len(connected)\\n for req in required:\\n if req not in connected:\\n if cmds.objExists(req):\\n cmds.connectAttr( req + \\\".message\\\", shape + \\\".shaders[%i]\\\" % port)\\n port += 1\\n message = 'Connected %s to %s' % (req, shape)\\n MGlobal.displayInfo(message)\\n else:\\n message = \\\"Missing shader : %s\\\" % req\\n MGlobal.displayWarning(message)\",\n \"def _copy_to_gpu(self):\\n self.dispatch('on_texture')\",\n \"def clustering_dbscan_o3d():\\n pass\",\n \"def add_clay_to_selected(self):\\n scene = self.set_as_active()\\n\\n # if the user selected a material, use it\\n if w_var.cb_mat_clay:\\n clay_mat = bpy.data.materials[w_var.mat_clay_name]\\n\\n # else, create a new one with the color selected\\n else:\\n clay_color = w_var.color_clay\\n\\n # separating rgb and alpha\\n clay_color_rgb = clay_color[0:3]\\n clay_color_alpha = clay_color[-1]\\n clay_mat = bpy.data.materials.new('clay')\\n \\n renderengine = scene.wirebomb.data_renderengine\\n \\n if renderengine == 'CYCLES':\\n clay_mat.use_nodes = True\\n tree = clay_mat.node_tree\\n tree.nodes.clear()\\n\\n # creating the nodes\\n node_transparent = tree.nodes.new('ShaderNodeBsdfTransparent')\\n node_transparent.location = -300, 100\\n\\n node_diffuse = tree.nodes.new('ShaderNodeBsdfDiffuse')\\n node_diffuse.location = -300, -100\\n node_diffuse.inputs[0].default_value = clay_color_rgb + (1.0, )\\n node_diffuse.color = clay_color_rgb\\n node_diffuse.name = 'addon_clay_color' # referencing to this ID in the real-time change\\n\\n node_mixshader = tree.nodes.new('ShaderNodeMixShader')\\n node_mixshader.location = 0, 50\\n node_mixshader.inputs[0].default_value = clay_color_alpha\\n node_mixshader.name = 'addon_clay_alpha' # referencing to this ID in the real-time change\\n\\n node_output = tree.nodes.new('ShaderNodeOutputMaterial')\\n node_output.location = 300, 50\\n\\n # connecting the nodes\\n tree.links.new(node_transparent.outputs[0], node_mixshader.inputs[1])\\n tree.links.new(node_diffuse.outputs[0], node_mixshader.inputs[2])\\n tree.links.new(node_mixshader.outputs[0], node_output.inputs[0])\\n\\n for node in tree.nodes:\\n node.select = False\\n\\n # sets the viewport color\\n clay_mat.diffuse_color = clay_color_rgb\\n \\n elif renderengine == 'BLENDER_RENDER':\\n clay_mat.diffuse_color = clay_color_rgb\\n clay_mat.use_transparency = True\\n clay_mat.alpha = clay_color_alpha\\n\\n previous_area = bpy.context.area.type\\n bpy.context.area.type = 'VIEW_3D'\\n previous_layers = tuple(scene.layers)\\n\\n # can't enter edit mode on objects on inactive layers\\n scene.layers = (True,)*20\\n\\n for obj in scene.objects:\\n if obj.select:\\n # only enters edit mode on active object\\n scene.objects.active = obj\\n obj.data.materials.append(clay_mat)\\n clay_index = obj.data.materials.find(clay_mat.name)\\n obj.active_material_index = clay_index\\n\\n bpy.ops.object.mode_set(mode='EDIT')\\n bpy.ops.mesh.select_all(action='SELECT')\\n bpy.ops.object.material_slot_assign()\\n bpy.ops.mesh.select_all(action='SELECT')\\n bpy.ops.object.mode_set(mode='OBJECT')\\n\\n bpy.context.area.type = previous_area\\n scene.layers = previous_layers\\n\\n return clay_mat\",\n \"def do_stuff(self):\\n self.create_tourism_raster()\",\n \"def rebuildSurface(*args, caching: bool=True, degreeU: Union[int, bool]=3, degreeV: Union[int,\\n bool]=3, direction: Union[int, bool]=2, endKnots: Union[int, bool]=0,\\n fitRebuild: Union[int, bool]=0, keepControlPoints: bool=False, keepCorners:\\n bool=True, keepRange: Union[int, bool]=1, nodeState: Union[int, bool]=0,\\n rebuildType: Union[int, bool]=0, spansU: Union[int, bool]=4, spansV:\\n Union[int, bool]=4, tolerance: Union[float, bool]=0.01, constructionHistory:\\n bool=True, name: AnyStr=\\\"\\\", object: bool=True, polygon: int=0,\\n replaceOriginal: bool=True, q=True, query=True, e=True, edit=True,\\n **kwargs)->Union[List[AnyStr], Any]:\\n pass\",\n \"def nBase(*args, clearCachedTextureMap: AnyStr=\\\"\\\", clearStart: bool=True, stuffStart: bool=True,\\n textureToVertex: AnyStr=\\\"\\\", q=True, query=True, e=True, edit=True,\\n **kwargs)->Union[bool, Any]:\\n pass\",\n \"def reload(self,offline_buffer):\\n #loading online buffer from offline buffer by sampling (online_buffer.buffer_size) samples \\n self.buffer = SumTree(self.buffer_size)\\n names, idxs = offline_buffer.sample_batch(self.buffer_size)\\n self.offline_idxs = idxs\\n state , action , reward, done = data_handler.handler.fetch_single_image(directory = self.directory, branch_name = self.name, observation_name = names[0])\\n #loop on names and load in the online buffer\\n for i in range(len(names)-1):\\n next_state , next_action , next_reward , done = data_handler.handler.fetch_single_image(directory = self.directory, branch_name = self.name, observation_name = names[i+1])\\n #done = 0\\n self.memorize(state, action, reward, done, next_state, error=[1])\\n state , action , reward = next_state , next_action , next_reward\",\n \"def run():\\n\\n if lm.scene_object().type == 'env':\\n read_layout(outFile=lm.scene_object().copy(ext='json').path_root)\\n\\n else:\\n read_layout()\",\n \"def merge_visual(self, mujoco_objects):\\n self.visual_obj_mjcf = []\\n for obj_name, obj_mjcf in mujoco_objects.items():\\n self.merge_asset(obj_mjcf)\\n # Load object\\n obj = obj_mjcf.get_visual(name=obj_name, site=False)\\n self.visual_obj_mjcf.append(obj)\\n self.worldbody.append(obj)\",\n \"def check_multiple_connections_for_textures(progress_controller=None):\\n if progress_controller is None:\\n progress_controller = ProgressControllerBase()\\n\\n # load necessary plugins\\n plugins = [\\\"matrixNodes\\\", \\\"quatNodes\\\"]\\n for plugin in plugins:\\n if not pm.pluginInfo(plugin, q=1, l=1):\\n pm.loadPlugin(plugin)\\n\\n v = staging.get(\\\"version\\\")\\n if not v:\\n progress_controller.complete()\\n return\\n\\n # check if the current renderer is Arnold\\n current_renderer = pm.PyNode(\\\"defaultRenderGlobals\\\").currentRenderer.get()\\n if current_renderer != \\\"arnold\\\":\\n # skip it\\n progress_controller.complete()\\n return\\n\\n # skip if\\n skip_types = [\\\"character\\\", \\\"animation\\\", \\\"previs\\\"]\\n for t in v.naming_parents:\\n for st in skip_types:\\n if t.type and t.type.name.lower().startswith(st):\\n progress_controller.complete()\\n return\\n\\n # get all the texture nodes\\n from anima.dcc.mayaEnv import repr_tools\\n\\n # try to find the material it is been used by walking up the connections\\n nodes_with_multiple_materials = []\\n\\n types_to_ignore = [\\n \\\"hyperLayout\\\",\\n \\\"shadingEngine\\\",\\n \\\"materialInfo\\\",\\n \\\"time\\\",\\n \\\"unitConversion\\\",\\n \\\"hyperView\\\",\\n ] + VALID_MATERIALS[\\\"redshift\\\"]\\n\\n # by type\\n nodes_to_ignore = [node for node in pm.ls() if node.type() in types_to_ignore]\\n\\n # by name\\n nodes_to_ignore += pm.ls(\\\"lambert1\\\", r=1)\\n nodes_to_ignore += pm.ls(\\\"defaultShaderList*\\\", r=1)\\n nodes_to_ignore += pm.ls(\\\"defaultTextureList*\\\", r=1)\\n nodes_to_ignore += pm.ls(\\\"defaultRenderUtilityList*\\\", r=1)\\n nodes_to_ignore += pm.ls(\\\"hyperShadePrimaryNodeEditorSavedTabsInfo*\\\", r=1)\\n nodes_to_ignore += pm.ls(\\\"MayaNodeEditorSavedTabsInfo*\\\", r=1)\\n\\n all_nodes = [\\n node for node in pm.ls() if node.type() in repr_tools.RENDER_RELATED_NODE_TYPES\\n ]\\n for node in nodes_to_ignore:\\n if node in all_nodes:\\n all_nodes.remove(node)\\n\\n progress_controller.maximum = len(all_nodes)\\n for node in all_nodes:\\n materials_connected_to_this_node = pm.ls(\\n node.listHistory(future=True), mat=True\\n )\\n\\n # remove any Redshift Materials from this list\\n # create a temp list\\n new_materials_connected_to_this_node = []\\n for mat in materials_connected_to_this_node:\\n if mat.type() not in VALID_MATERIALS[\\\"redshift\\\"]:\\n new_materials_connected_to_this_node.append(mat)\\n materials_connected_to_this_node = new_materials_connected_to_this_node\\n\\n # remove self from all_nodes\\n if node in materials_connected_to_this_node:\\n materials_connected_to_this_node.remove(node)\\n\\n if len(materials_connected_to_this_node) > 1:\\n nodes_with_multiple_materials.append(node)\\n else:\\n connections_out_of_this_node = node.outputs()\\n\\n # [connections_out_of_this_node.remove(h)\\n # for h in nodes_to_ignore\\n # if h in connections_out_of_this_node]\\n connections_out_of_this_node = [\\n h for h in connections_out_of_this_node if h not in nodes_to_ignore\\n ]\\n\\n if len(set(connections_out_of_this_node)) > 1:\\n nodes_with_multiple_materials.append(node)\\n progress_controller.increment()\\n\\n # if we find more than one material add it to the list\\n # raise a PublishError if we have an item in the list\\n if len(nodes_with_multiple_materials) > 0:\\n pm.select(nodes_with_multiple_materials)\\n progress_controller.complete()\\n raise PublishError(\\n \\\"Please update the scene so the following nodes are connected <br>\\\"\\n \\\"to only <b>one material</b> (duplicate them):<br><br>%s<br><br>\\\"\\n % \\\"<br>\\\".join(map(lambda x: x.name(), nodes_with_multiple_materials))\\n )\\n progress_controller.complete()\",\n \"def polyDuplicateAndConnect(*args, removeOriginalFromShaders: bool=True, renameChildren:\\n bool=True, **kwargs)->None:\\n pass\",\n \"def cached(self, args) -> bool:\\n return all([art.built for art in self.artifacts])\",\n \"def initialize_cache(inputs, outputs, programs, max_decode_len, config):\\n target_shape = (programs.shape[0], max_decode_len)\\n initial_variables = models.ProgramTransformer(config).init(\\n jax.random.PRNGKey(0),\\n jnp.ones(inputs.shape, config.dtype),\\n jnp.ones(outputs.shape, config.dtype),\\n jnp.ones(target_shape, config.dtype))\\n return initial_variables['cache']\",\n \"def atlas_clusters():\\n pass\",\n \"def polyCopyUV(*args, caching: bool=True, constructionHistory: bool=True, createNewMap:\\n bool=True, name: AnyStr=\\\"\\\", nodeState: Union[int, bool]=0, uvSetName: AnyStr=\\\"\\\",\\n uvSetNameInput: Union[AnyStr, bool]=\\\"\\\", worldSpace: bool=True, q=True,\\n query=True, e=True, edit=True, **kwargs)->Union[AnyStr, Any]:\\n pass\",\n \"def subdTransferUVsToCache(*args, **kwargs)->bool:\\n pass\",\n \"def enable_texture_mode():\\n for area in bpy.context.screen.areas:\\n if area.type == \\\"VIEW_3D\\\":\\n for space in area.spaces:\\n if space.type == \\\"VIEW_3D\\\":\\n space.viewport_shade = \\\"TEXTURED\\\"\\n return\",\n \"def set_atlas(self, sender):\\n if (self.model.value in pyTMD.io.model.ATLAS()):\\n self.atlas.layout.display = 'inline-flex'\",\n \"def test_path_layer_cache(self):\\n cache = caches[settings.MAPENTITY_CONFIG['GEOJSON_LAYERS_CACHE_BACKEND']]\\n\\n obj = self.modelfactory(draft=False)\\n self.modelfactory(draft=True)\\n\\n # There are 7 queries to get layer without drafts\\n with self.assertNumQueries(5):\\n response = self.client.get(obj.get_layer_url())\\n self.assertEqual(len(response.json()['features']), 2)\\n\\n # We check the content was created and cached without no_draft key\\n # We check that any cached content can be found without no_draft (we still didn't ask for it)\\n last_update_no_draft = Path.no_draft_latest_updated()\\n last_update = Path.latest_updated()\\n geojson_lookup_no_draft = 'en_path_%s_nodraft_json_layer' % last_update_no_draft.strftime('%y%m%d%H%M%S%f')\\n geojson_lookup = 'en_path_%s_json_layer' % last_update.strftime('%y%m%d%H%M%S%f')\\n content_no_draft = cache.get(geojson_lookup_no_draft)\\n content = cache.get(geojson_lookup)\\n\\n self.assertIsNone(content_no_draft)\\n self.assertEqual(response.content, content.content)\\n\\n # We have 1 less query because the generation of paths was cached\\n with self.assertNumQueries(3):\\n self.client.get(obj.get_layer_url())\\n\\n self.modelfactory(draft=True)\\n\\n # Cache is updated when we add a draft path\\n with self.assertNumQueries(5):\\n self.client.get(obj.get_layer_url())\\n\\n self.modelfactory(draft=False)\\n\\n # Cache is updated when we add a path\\n with self.assertNumQueries(5):\\n self.client.get(obj.get_layer_url())\",\n \"def used_caches_and_sources(layers, caches, sources):\\n used_layer_sources = find_layer_sources(layers)\\n used_cache_sources = find_cache_sources(caches)\\n all_used_sources = used_layer_sources.union(used_cache_sources)\\n\\n avail_caches = set(caches.keys())\\n avail_sources = set(sources.keys())\\n\\n used_caches = avail_caches.intersection(all_used_sources)\\n used_sources = avail_sources.intersection(all_used_sources).difference(used_caches)\\n\\n return used_caches, used_sources\",\n \"def read_cache(cc):\\n \\n out_file = os.path.join(cc.scene_dir, 'output', cc.scene_id+'_pickle')\\n if cc.atmo_src == 'narr':\\n out_file += '_narr'\\n elif cc.atmo_src == 'merra':\\n out_file += '_merra'\\n \\n if not os.path.isfile(out_file):\\n raise OSError('pickle_file is not in expected location %s' % out_file) \\n\\n with open(out_file, 'rb') as f:\\n x = pickle.load(f)\\n return x\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5968839","0.5813282","0.57067865","0.56659585","0.5622924","0.5525419","0.5465","0.5386124","0.53504324","0.53005147","0.5141424","0.511689","0.50960606","0.50883526","0.5087522","0.5083337","0.50820434","0.50608236","0.5042474","0.50115144","0.49761882","0.4958528","0.49491546","0.4943185","0.49204975","0.49054354","0.48922956","0.4884875","0.48758695","0.4875266","0.48742402","0.4874163","0.4867788","0.48623532","0.4858879","0.48566884","0.48566473","0.48425364","0.4841264","0.4820426","0.48172972","0.48137528","0.48061427","0.48046306","0.48024958","0.4796984","0.47911322","0.47834206","0.478316","0.4780334","0.47802073","0.4777939","0.47701567","0.47664568","0.47601318","0.4758407","0.47555047","0.47498053","0.47417185","0.4737192","0.47350946","0.47308773","0.47191775","0.47099754","0.46993786","0.46908537","0.46808636","0.46785298","0.46744636","0.46744636","0.4673736","0.46713328","0.4664817","0.46440253","0.46393254","0.46379787","0.4636981","0.4636327","0.4618275","0.46144667","0.4613045","0.4612454","0.461094","0.46058518","0.46018386","0.45983565","0.45966882","0.4593796","0.4587555","0.4579392","0.4571373","0.45665228","0.45606795","0.4559421","0.45565304","0.45564654","0.4554808","0.45526275","0.45501772","0.4548666"],"string":"[\n \"0.5968839\",\n \"0.5813282\",\n \"0.57067865\",\n \"0.56659585\",\n \"0.5622924\",\n \"0.5525419\",\n \"0.5465\",\n \"0.5386124\",\n \"0.53504324\",\n \"0.53005147\",\n \"0.5141424\",\n \"0.511689\",\n \"0.50960606\",\n \"0.50883526\",\n \"0.5087522\",\n \"0.5083337\",\n \"0.50820434\",\n \"0.50608236\",\n \"0.5042474\",\n \"0.50115144\",\n \"0.49761882\",\n \"0.4958528\",\n \"0.49491546\",\n \"0.4943185\",\n \"0.49204975\",\n \"0.49054354\",\n \"0.48922956\",\n \"0.4884875\",\n \"0.48758695\",\n \"0.4875266\",\n \"0.48742402\",\n \"0.4874163\",\n \"0.4867788\",\n \"0.48623532\",\n \"0.4858879\",\n \"0.48566884\",\n \"0.48566473\",\n \"0.48425364\",\n \"0.4841264\",\n \"0.4820426\",\n \"0.48172972\",\n \"0.48137528\",\n \"0.48061427\",\n \"0.48046306\",\n \"0.48024958\",\n \"0.4796984\",\n \"0.47911322\",\n \"0.47834206\",\n \"0.478316\",\n \"0.4780334\",\n \"0.47802073\",\n \"0.4777939\",\n \"0.47701567\",\n \"0.47664568\",\n \"0.47601318\",\n \"0.4758407\",\n \"0.47555047\",\n \"0.47498053\",\n \"0.47417185\",\n \"0.4737192\",\n \"0.47350946\",\n \"0.47308773\",\n \"0.47191775\",\n \"0.47099754\",\n \"0.46993786\",\n \"0.46908537\",\n \"0.46808636\",\n \"0.46785298\",\n \"0.46744636\",\n \"0.46744636\",\n \"0.4673736\",\n \"0.46713328\",\n \"0.4664817\",\n \"0.46440253\",\n \"0.46393254\",\n \"0.46379787\",\n \"0.4636981\",\n \"0.4636327\",\n \"0.4618275\",\n \"0.46144667\",\n \"0.4613045\",\n \"0.4612454\",\n \"0.461094\",\n \"0.46058518\",\n \"0.46018386\",\n \"0.45983565\",\n \"0.45966882\",\n \"0.4593796\",\n \"0.4587555\",\n \"0.4579392\",\n \"0.4571373\",\n \"0.45665228\",\n \"0.45606795\",\n \"0.4559421\",\n \"0.45565304\",\n \"0.45564654\",\n \"0.4554808\",\n \"0.45526275\",\n \"0.45501772\",\n \"0.4548666\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.49265927"},"document_rank":{"kind":"string","value":"24"}}},{"rowIdx":11175,"cells":{"query":{"kind":"string","value":"Return if the selected digits from start in the number are a palindrome"},"document":{"kind":"string","value":"def is_number_palindrome(number, digits, start):\n number = str((number // 10**start) % 10**digits).zfill(digits)\n return is_palindrome(number)"},"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 is_palindrome(n):\n d = digits(n)\n r = int(\"\".join([str(i) for i in d]))\n return n == r","def isPalindrome(Number):\r\n ListOfDigit=[int(d) for d in str(Number)]\r\n n=len(ListOfDigit)\r\n for i in range(n//2):\r\n if ListOfDigit[i]!=ListOfDigit[-(i+1)]:\r\n return(False)\r\n return(True)","def is_palindrome(num):\n digitList = int2list(num)\n \n i = 0\n while i <= round(len(digitList)/2):\n if digitList[i] != digitList[-(i+1)]:\n return False\n i += 1\n return True","def is_palindrome(number_):\n temp = number_\n reverse = 0\n while number_ > 0:\n digit = number_ % 10\n reverse = reverse * 10 + digit\n number_ = number_ // 10\n if temp == reverse:\n return True\n else:\n return False","def palindrome_check(num):\n num= str(num)\n len_num= len(num)\n for i in range(len_num/2):\n if num[i] == num[len_num-i-1]:\n ans= True\n else:\n ans= False\n break\n return ans","def is_palindrome(a):\n\tmax = a\n\tmin = 0\n\twhile max > 0:\n\t\tmin = (min * 10 + max % 10)\n\t\tmax /= 10\n\treturn min == a","def check_palindrome():","def is_palindrome(number):\r\n str_input = str(number)\r\n return str_input == reversed(str_input)","def is_palindrome(n):\n v = []\n while n > 0:\n v.append(n % 10)\n n //= 10\n for i in range(len(v)//2):\n if v[i] != v[len(v)-i-1]:\n return False\n return True","def is_palindrome(n):\n # store locally\n temp = n\n rev = 0\n while n > 0:\n # get digit one by one\n digit = n % 10\n # find reverse number\n rev = rev * 10 + digit\n # divide the number\n n = n // 10\n return temp == rev","def is_palindrome(n):\n x, y = n, 0\n f = lambda: y * 10 + x % 10\n while x > 0:\n x, y = x // 10, f()\n return y == n","def isPalendrome(number):\n\t\n\tnum = str(number)\n\ti \t= 0\n\tj \t= len(num) - 1\n\tmid = len(num) // 2\n\n\t#print(mid)\n\t\n\t# While i and j are not in the middle\n\twhile( i != mid):\n\t\t#print(i,j,sep=\"\\t\")\n\t\t#print(num[i],num[j], sep=\"\\t\")\n\t\tif(num[i] != num[j]):\n\t\t\treturn(False)\n\t\telse:\n\t\t\ti = i + 1\n\t\t\tj = j - 1\n\n\treturn(True)","def is_palindrome(n):\n x, y = n, 0\n f = lambda: 10 * y + x % 10\n while x > 0:\n x, y = x // 10, f()\n return y == n","def is_palindrome(x):\n \n # Assume negative number is not a palindromic number.\n if x < 0:\n return False\n\n ten_base = 1\n \n # Determine num size with a base of 10\n while x / ten_base >= 10:\n ten_base *= 10\n\n while x > 0:\n left_num, right_num = x / ten_base, x % 10\n if left_num != right_num:\n return False\n \n # Update and prep for next iteration.\n x = (x % ten_base) / 10\n ten_base /= 100\n\n return True","def isPalindromic(n: int):\n return str(n) == str(n)[::-1]","def is_number_palindrome(n):\n digits = list() # list of all digits in n (reversed order)\n while n > 0:\n last_digit = n % 10\n digits.append(last_digit)\n n = n // 10\n if is_list_palindrome(digits):\n return True\n else:\n return False","def is_palindrome(n):\n return(n == reverse(n))","def check_pal(num):\r\n \r\n num = str(num) #Convert number to string.\r\n \r\n #If a number is a palindrome, rreturn True \r\n if num[0] == num[len(num)-1] and len(num) <= 3:\r\n return True\r\n \r\n #If the first and last digits of a number are equal when its length is > 3,\r\n #strip the end digits away analyse the resulting number.\r\n elif num[0] == num[len(num)-1]:\r\n return check_pal(num[1:len(num)-1])\r\n \r\n #If a number is not a palindrome, return False\r\n else:\r\n return False","def is_palindrome(num):\n str_num = str(num)\n\n if len(str_num) == 1:\n return True\n elif len(str_num) == 2:\n return str_num[0] == str_num[1]\n\n if str_num[0] == str_num[len(str_num)-1]:\n return is_palindrome(str_num[1:len(str_num)-1])\n else:\n return False","def is_palindrome(n):\n ns = str(n)\n for i in range(0, len(ns) // 2):\n if ns[i] != ns[len(ns) - 1 - i]: return False\n return True","def is_palindrome(x):\n strx = str(x)\n return strx == strx[::-1]\n # chars = [c for c in x] if not is_number(x) else digits(x)\n # for i in range(len(chars) // 2):\n # if chars[i] != chars[len(chars) - i - 1]:\n # return False\n # return True","def is_palindrome(num_in):\n if str(num_in) == str(num_in)[::-1]:\n return True\n\n return False","def has_palindrome(i, start, length):\r\n s = str(i)[start:start+length]\r\n return s[::-1] == s","def is_palindrome(num):\n\treversed_num = str(num)[::-1]\n\tif reversed_num == str(num): return True\n\telse: return False","def is_antipalindrome(n):\n v = []\n while n > 0:\n v.append(n % 10)\n n //= 10\n for i in range(len(v)//2):\n if v[i] == v[len(v)-i-1]:\n return False\n return True","def check_palindrome(number):\r\n \r\n number = str(number) #Converting a number to a string.\r\n \r\n #If the number is a palindrome then it will return True \r\n if number[0] ==number[len(number)-1] and len(number) <= 3:\r\n return True\r\n #If the first and last digits of a number are equal when its length is > 3,\r\n #strip the end digits away analyse the resulting number.\r\n elif number[0] == number[len(number)-1]:\r\n return check_palindrome(number[1:len(number)-1])\r\n \r\n #If a number is not a palindrome, return False\r\n else:\r\n return False","def is_palindromic(n: int) -> bool:\n str_n = str(n)\n if str_n == str_n[::-1]:\n return True\n return False","def is_palindrome(element):\n if int(element) < 0:\n return False\n if element == element[::-1]:\n return True\n else:\n return False","def isPalindrome(x):\n # Write the functionality:\n\n if x == str(x)[::-1]:\n return True\n elif x==121:\n return True\n else:\n return False","def palindrome_itertive(a):\n # TODO make this less crappy\n start = 0 \n end = len(a) - 1\n while start != end:\n # print(end)\n # print('start: ', start, ' a: ', a[start])\n # print('end: ', end, ' a: ', a[end])\n if not a[start] == a[end]:\n return False\n else:\n start += 1\n end -= 1\n return True","def ispalind(num):\n num = list(str(num))\n if num == num[::-1]:\n return True","def is_palindrome(n):\n num = list(str(n))\n h1 = num[:int(len(num)/2)] # first half of palindrome\n if len(num) % 2 == 0:\n h2 = num[int(len(num)/2):] # second half of palindrome\n else:\n h2 = num[int(len(num)/2) + 1:]\n return h1 == list(reversed(h2))","def palindrome_integer(string):\n integers = string.split(\" \")\n for integer in integers:\n reverse = str(int)[::-1]\n '''Check if palindrome not then return False'''\n if integer != reverse:\n return False\n return True","def obtener_palindromo(valor):\n \n #Luego de convertirlo a str, los vamos a insertar en una lista para luego verificar\n palindromo = list(str(valor))\n #lo insertamos en una nueva lista\n palindromo_final = palindromo\n \n #Luego aplicaremos la verificacion para comprobar si es un palindromo\n if palindromo [:: -1] == palindromo_final:\n return True\n\n\n \n #print('El numero es un palindromo')","def isPalindrome(n):\n str_n = str(n)\n return list(str_n) == list(reversed(str_n))","def check_palindrome(s):\n return s[::-1] == s","def isPalindrome(s):\r\n return isPal(toChars(s))","def is_antipalindrome(n):\n digits = [int(num) for num in str(n)]\n reverseDigits = digits[::-1]\n return all([reverseDigits[i]==(9-digits[i]) for i in range(len(digits))])","def isPalindrome(string):\n for i in range(len(string)//2):\n if string[i] != string[(i*-1)-1]:\n return False\n return True","def is_palindrome(string):\n k, mid = len(string), len(string) // 2\n # checking even palindromes\n if k % 2 == 0:\n return string[:mid] == get_reverse_complement(string[mid:])\n # checking odd palindromes\n else:\n return string[:mid] == get_reverse_complement(string[mid + 1:])","def is_palindrome_v2(s):\n n = len(s)\n\n return s[:n/2] == reverse(s[n-n/2:])","def is_palindrome_v3(s):\n i = 0\n j = len(s)-1\n\n while i < j and s[i] == s[j]:\n i = i + 1\n j = j -1\n\n return j <= i","def isLychrel(Number):\r\n Number+=reverse(Number)\r\n n=0\r\n while n<50:\r\n if isPalindrome(Number):\r\n return False\r\n else:\r\n Number+=reverse(Number)\r\n n+=1\r\n return True","def is_palindrome(s):\n i, end = 0, len(s) // 2\n while i < end:\n if s[i] != s[len(s) - i - 1]:\n return False\n i += 1\n return True","def is_palindrome(sub):\n for i in range(len(sub)):\n if sub[i] != sub[len(sub) - i - 1]:\n return False\n return True","def part3(string):\n palindrome = True\n for i in range(0, int(len(string)/2) + 1):\n if(string[i] != string[int(len(string))-i-1]):\n palindrome = False\n print(palindrome)","def check_is_palindrome(mer1, mer2):\n return mer1.find(mer2[::-1]) == 0","def is_palindrome(s):\n return s == s[::-1]","def isReversible(n): \n if n % 10 == 0:\n return False\n s = n + reverseNum(n)\n while s > 0:\n digit = s % 10\n if not digit in [1,3,5,7,9]:\n return False\n s //= 10\n return True","def is_palindrome(s):\n rev_str = \"\"\n for let in range(1, len(s) + 1):\n rev_str += s[-let]\n return rev_str == s","def palindrome(word):\n reverse = word[::-1]\n return word == reverse","def _is_palindrome(input_str, start, end):\n if start >= end:\n return True\n if input_str[start] != input_str[end]:\n return False\n else:\n return _is_palindrome(input_str, start + 1, end - 1)","def main():\n for l in range(999,890,-1):\n for r in range(999,890,-1):\n num= l*r\n ans= palindrome_check(num)\n if ans:\n print l,r,num\n return\n print l,r,num\n print \"No palindrome found.\"\n return","def is_palindrome_v1(s):\n return reverse(s) == s","def palindrome(x):\n pass","def is_palindrome(str):\n n = len(str)\n for i in range(n//2): # loop to middle of string str\n if str[i] != str[-1-i]:\n return False # find a character doesn't match with its mirror-positioned character\n return True # reach this, then str is palindrome","def is_palindrome(string):\n return string[::-1] == string","def is_palindromic(lst):\n return all( lst[i] == lst[-(i+1)] for i in range(len(lst)) )","def is_palindrome(word):\n if len(word)>2 and not is_palindrome(middle(word)):\n return False\n else:\n return first(word) == last(word)","def checkPalindrome(self, s: str) -> str:\n # return s[:len(s) // 2] == s[(len(s) + 1) // 2::-1]\n return s == s[::-1]","def is_palindrome(word):\n if word == word[::-1]:\n return True\n else:\n return False","def check(i):\r\n return (has_palindrome(i, 2, 4) and\r\n has_palindrome(i+1, 1, 5) and\r\n has_palindrome(i+2, 1, 4) and\r\n has_palindrome(i+3, 0, 6))","def is_pal(n):\r\n # Change the number into a string and then a list.\r\n as_list_of_chars = list(str(n))\r\n # Copy the list and reverse it.\r\n reversed_list_of_chars = list(as_list_of_chars)\r\n reversed_list_of_chars.reverse()\r\n # True if the list of chars is palindromic.\r\n return as_list_of_chars == reversed_list_of_chars","def palindromes(n: int) -> int:\n # 1 -> 2 -> 3 ... 9 -> 11 -> 22 -> 33 -> 44 .. 99 -> 101\n # 101 -> 111 -> 121 -> 131 -> ... -> 191 -> 202 -> 212\n # 989 -> 999 -> 1001 -> 1111 -> 1221\n # 9889 -> 9999 -> 10001 -> 10101 -> 10201\n prev = n\n s = str(n)\n even = len(s) % 2 == 0\n s = s[:ceil(len(s) / 2)]\n n = int(s)\n while True:\n if even:\n pal = int(''.join([s, s[-1::-1]])) # join '12' with '21'\n else:\n pal = int(''.join([s, s[-2::-1]])) # join '12' with '1'\n if prev <= pal:\n yield pal\n \n n += 1\n if all(digit == '9' for digit in s):\n even = not even\n if even: n //= 10\n s = str(n)","def is_palindrome(word):\n if first(word) != last(word):\n print 'not a palindrome'\n return False\n else:\n return is_palindrome(middle(word))","def closest_palindrome_number(number):\n\n def check_all_9(number):\n\n for n in number:\n if n != 9:\n return False\n return True\n\n num_list = [int(i) for i in str(number)]\n\n num_size = len(num_list)\n\n if check_all_9(num_list):\n return number + 2\n\n mid_point = int(num_size/2)\n\n def list_to_int(nums): return int(''.join(str(i) for i in nums))\n\n def check_palindromes(all_palindromes, number):\n min_found = sys.maxsize\n pal_found = 0\n multiple_pals = []\n\n for i in all_palindromes:\n pal = list_to_int(i)\n distance = abs(number - pal)\n if distance <= min_found and distance != 0:\n if distance == min_found:\n multiple_pals.append(i)\n else:\n multiple_pals = []\n min_found = distance\n pal_found = i\n multiple_pals.append(i)\n\n if len(multiple_pals) == 1:\n return list_to_int(pal_found)\n else:\n numbers = []\n for i in multiple_pals:\n number = list_to_int(i)\n numbers.append(number)\n return numbers\n\n if num_size % 2 == 0:\n\n # Even number\n splitted = num_list[0: mid_point]\n mirrored = splitted + splitted[::-1]\n\n all_palindromes = []\n all_palindromes.append(mirrored)\n\n if splitted[-1] != 9:\n split_add_one = list(splitted)\n split_add_one[-1] += 1\n split_add_one = all_palindromes.append(\n split_add_one + split_add_one[::-1])\n\n if splitted[-1] != 0:\n split_sub_one = list(splitted)\n split_sub_one[-1] -= 1\n split_sub_one = all_palindromes.append(\n split_sub_one + split_sub_one[::-1])\n\n else:\n # Odd number\n splitted = num_list[0: mid_point]\n middle_num = num_list[mid_point]\n\n all_palindromes = []\n all_palindromes.append(\n splitted + [middle_num] + splitted[::-1])\n\n if middle_num != 9:\n all_palindromes.append(\n splitted + [middle_num + 1] + splitted[::-1])\n\n if middle_num != 0:\n all_palindromes.append(\n splitted + [middle_num - 1] + splitted[::-1])\n\n return check_palindromes(all_palindromes, number)","def is_palindrome(string):\n return","def is_palindrome_2(n):\n number_as_list = list(str(n))\n for n in number_as_list:\n # check if current item is not equal to last item in the list\n print(\"List at each iteration...\", number_as_list)\n if n != number_as_list.pop():\n return False\n return True","def num_palindrome():\n nums = map(str, range(1000000))\n odo = []\n for i in range(len(nums)):\n if len(nums[i]) < 6:\n odo.append('0'*(6-len(nums[i])) + nums[i])\n elif len(nums[i]) == 6:\n odo.append(nums[i])\n \n for i in range(len(odo)-3): \n first = odo[i][2:] == odo[i][:1:-1]\n second = odo[i+1][1:] == odo[i+1][:0:-1]\n third = odo[i+2][1:5] == odo[i+2][4:0:-1]\n fourth = odo[i+3][:] == odo[i+3][::-1]\n if first & second & third & fourth:\n print 'A possible odometer reading is '+odo[i]","def palindrom_permutation(string: str):\n string = re.sub(r'\\W+', '', string.lower())\n\n chars = dict()\n for c in string:\n chars[c] = chars[c] + 1 if c in chars else 1\n\n almost_not_okey = False\n for val in chars.values():\n if val % 2 == 1:\n if not almost_not_okey:\n almost_not_okey = True\n else:\n return False\n\n if almost_not_okey:\n return len(string) % 2 == 1\n return True","def is_palindrome(string):\r\n r_string = string[::-1]\r\n cnt = 0\r\n while cnt < len(string):\r\n if string[cnt] == r_string[cnt]:\r\n cnt += 1\r\n continue\r\n else:\r\n return False\r\n #cnt += 1\r\n return True","def is_palindrome2(some_string):\n return reverse_string(some_string) == some_string","def string_palidrome(word):\n if word == string_reverse(word):\n return True\n else:\n return False","def palindrome():\n c = 0\n d = ''\n e = 0\n f = 0\n g = 0\n for a in range(100, 1000):\n for b in range(100, 1000):\n c = a * b\n d = str(c)\n if d == d[::-1] and c > e:\n e = c\n f = a\n g = b\n return e","def exactly_two_same_digits(num):\n output = False\n digits = [int(i) for i in str(num)]\n for i, dig in enumerate(digits[:-2]):\n if i == 0:\n if dig == digits[i + 1] and dig != digits[i + 2]:\n output = True\n else:\n if (dig != digits[i - 1] \n and dig == digits[i + 1] \n and dig != digits[i + 2]):\n output = True\n if i == len(digits) - 3:\n if dig != digits[i + 1] and digits[i + 1] == digits[i + 2]:\n output = True\n return output","def is_palindrome(string):\n if string == string[::-1]:\n return True\n return False","def isPal(x):\n temp = x\n temp.reverse()\n if temp == x:\n print('true')\n return True\n else:\n print('false')\n return False","def palindrome(string):\r\n if len(string) <= 1:\r\n return True\r\n else:","def is_Lychrel(n):\n count = 1\n current = n\n while count < 50:\n current = reverse_and_add(current)\n if is_palindrome(current):\n return False\n count += 1\n return True","def is_palindromic(phrase):\n\n val = str(phrase).lower().replace(\" \", \"\")\n if val == val[::-1]: # Reverse order\n return True\n else:\n return False","def check_palindrome(inp_string):\n if len(inp_string) <= 2:\n return False\n elif inp_string == inp_string[::-1]:\n return True\n else:\n return False","def part3():\n Input = raw_input('please enter a string:')\n for i in range(len(Input)):\n if Input[i] != Input[len(Input)-i-1]:\n print('It is not a palidrome')\n break\n else:\n i = i + 1","def isPalindromes(s):\n\n def toChar(s):\n s= s.lower()\n letters=''\n for c in s:\n if c in \"abcdefgh\":\n letters= letters+c\n return letters\n\n def isPal(s):\n if len(s) <=1:\n return True\n else:\n return s[0]==s[-1] and isPal(s[1:-1])\n return isPal(toChar(s))","def isPalindrome(S):\n if len(S) == 0:\n return True\n \n if S[0] != S[-1]:\n return False\n \n return isPalindrome(S[1:len(S) - 1])","def has_palindrome_permutation(given_string):\n\n unpaired_characters = set()\n\n for char in given_string:\n if char in unpaired_characters:\n unpaired_characters.remove(char)\n else:\n unpaired_characters.add(char) \n\n return len(unpaired_characters) <= 1","def palindrom():\r\n pal = []\r\n\r\n sub_str = gen_substring(\"abaabbaab\")\r\n\r\n for i in range(len(sub_str)):\r\n\r\n rev = reverse_string(sub_str[i])\r\n\r\n if rev == sub_str[i]:\r\n\r\n pal.append(rev)\r\n\r\n return pal","def check_palindrome():\n # Your code goes here...","def palindrome_recursive(a):\n # Base case\n if len(a) <= 1:\n return True\n else:\n if a[0] == a[len(a)-1]:\n return palindrome_recursive(a[1:-1])\n else:\n return False","def validPalindrome(self, s: str) -> bool:\n n = len(s)\n i = 0\n j = n - 1\n while i < j:\n if s[i] == s[j]:\n i += 1\n j -= 1\n else:\n # error, for -1, start > end. Indexing is like range\n # return s[i:j] == s[i:j:-1] or s[i+1:j+1] == s[i+1:j+1:-1]\n return self.is_palindrome(s[i:j]) or self.is_palindrome(s[i+1:j+1])\n\n return True","def is_square(number): \n s = number * number\n return is_palindrome(s)","def is_palindrome(word):\n word = validator(word)\n\n if word == \"\":\n return True\n\n elif word[0] == word[-1]:\n return is_palindrome(word[1:-1])\n\n return False","def palindromeString(str, first = 0, end = -1):\n assert len(str) > 0, \"String Must be hava one character at least.\"\n\n # first bigger or equal then length's of string then yes\n if first >= len(str):\n return True\n # Compare first with end character recursively.\n if str[first] == str[end]:\n return palindromeString(str, first + 1, end - 1)\n else :\n return False","def check_number(number):\n digits = str(number)\n if len(digits) != 6:\n return False\n\n double = False\n last = '0'\n for digit in digits:\n if digit < last:\n return False\n\n if digit == last:\n double = True\n\n last = digit\n\n return double","def is_palindrome(word: str) -> bool:\n\n # Todo\n return False","def is_palindrome(word):\n\t# The degenerate case is 1 letter or none. We consider these palindromes\n\tif len(word) <= 1:\n\t\treturn True\n\n\t# For something to be a palindrome, the first and last letters much match\n\t# and the string between them must also be a palindrome\n\tif first(word) == last(word) and is_palindrome(middle(word)):\n\t\treturn True\n\telse:\n\t\treturn False","def is_permutation_palindrome(str):\n for s in permutations(str): # loop through all permutations of str\n if is_palindrome(s):\n return True # successfully find a palindrome permutation\n return False # reach this, then no possible permutation is palindrome","def palindromes():\n for n in count(1):\n if str(n) == str(n)[::-1]:\n yield n","def isPalindrome(word):\n\n input_str = IGNORE_NON_ALPHA_CHARACTER.sub(\"\", str(word)).casefold()\n return input_str == input_str[::-1]","def ispalindrome(string):\n if isinstance(string, (str, int, float)):\n string = str(string).replace(\" \", \"\").lower()\n if len(string) in [0,1]:\n return True\n half_index = len(string) // 2\n first = string[:half_index]\n second = string[-half_index:]\n if first == second[::-1]:\n return True\n else:\n return False\n else:\n print(\"ERROR: ispalindrome received invalid input.\\nREASON: input\",\n \"must be convertible to string format.\")","def palCheck(input_string):\n\n # ADD NECESSARY LINES OF CODE SO THAT ALL UNITTESTS PASS\n\n d = Deque()\n for char in input_string:\n d.addFront(char)\n\n while d.size() > 1:\n firstChar = d.removeRear()\n lastChar = d.removeFront()\n if firstChar != lastChar:\n print(\"No, '\" + input_string + \"', is not a palindrom\")\n return False\n\n print(\"Yes, '\" + input_string + \"', is a palindrom!!\")\n return True"],"string":"[\n \"def is_palindrome(n):\\n d = digits(n)\\n r = int(\\\"\\\".join([str(i) for i in d]))\\n return n == r\",\n \"def isPalindrome(Number):\\r\\n ListOfDigit=[int(d) for d in str(Number)]\\r\\n n=len(ListOfDigit)\\r\\n for i in range(n//2):\\r\\n if ListOfDigit[i]!=ListOfDigit[-(i+1)]:\\r\\n return(False)\\r\\n return(True)\",\n \"def is_palindrome(num):\\n digitList = int2list(num)\\n \\n i = 0\\n while i <= round(len(digitList)/2):\\n if digitList[i] != digitList[-(i+1)]:\\n return False\\n i += 1\\n return True\",\n \"def is_palindrome(number_):\\n temp = number_\\n reverse = 0\\n while number_ > 0:\\n digit = number_ % 10\\n reverse = reverse * 10 + digit\\n number_ = number_ // 10\\n if temp == reverse:\\n return True\\n else:\\n return False\",\n \"def palindrome_check(num):\\n num= str(num)\\n len_num= len(num)\\n for i in range(len_num/2):\\n if num[i] == num[len_num-i-1]:\\n ans= True\\n else:\\n ans= False\\n break\\n return ans\",\n \"def is_palindrome(a):\\n\\tmax = a\\n\\tmin = 0\\n\\twhile max > 0:\\n\\t\\tmin = (min * 10 + max % 10)\\n\\t\\tmax /= 10\\n\\treturn min == a\",\n \"def check_palindrome():\",\n \"def is_palindrome(number):\\r\\n str_input = str(number)\\r\\n return str_input == reversed(str_input)\",\n \"def is_palindrome(n):\\n v = []\\n while n > 0:\\n v.append(n % 10)\\n n //= 10\\n for i in range(len(v)//2):\\n if v[i] != v[len(v)-i-1]:\\n return False\\n return True\",\n \"def is_palindrome(n):\\n # store locally\\n temp = n\\n rev = 0\\n while n > 0:\\n # get digit one by one\\n digit = n % 10\\n # find reverse number\\n rev = rev * 10 + digit\\n # divide the number\\n n = n // 10\\n return temp == rev\",\n \"def is_palindrome(n):\\n x, y = n, 0\\n f = lambda: y * 10 + x % 10\\n while x > 0:\\n x, y = x // 10, f()\\n return y == n\",\n \"def isPalendrome(number):\\n\\t\\n\\tnum = str(number)\\n\\ti \\t= 0\\n\\tj \\t= len(num) - 1\\n\\tmid = len(num) // 2\\n\\n\\t#print(mid)\\n\\t\\n\\t# While i and j are not in the middle\\n\\twhile( i != mid):\\n\\t\\t#print(i,j,sep=\\\"\\\\t\\\")\\n\\t\\t#print(num[i],num[j], sep=\\\"\\\\t\\\")\\n\\t\\tif(num[i] != num[j]):\\n\\t\\t\\treturn(False)\\n\\t\\telse:\\n\\t\\t\\ti = i + 1\\n\\t\\t\\tj = j - 1\\n\\n\\treturn(True)\",\n \"def is_palindrome(n):\\n x, y = n, 0\\n f = lambda: 10 * y + x % 10\\n while x > 0:\\n x, y = x // 10, f()\\n return y == n\",\n \"def is_palindrome(x):\\n \\n # Assume negative number is not a palindromic number.\\n if x < 0:\\n return False\\n\\n ten_base = 1\\n \\n # Determine num size with a base of 10\\n while x / ten_base >= 10:\\n ten_base *= 10\\n\\n while x > 0:\\n left_num, right_num = x / ten_base, x % 10\\n if left_num != right_num:\\n return False\\n \\n # Update and prep for next iteration.\\n x = (x % ten_base) / 10\\n ten_base /= 100\\n\\n return True\",\n \"def isPalindromic(n: int):\\n return str(n) == str(n)[::-1]\",\n \"def is_number_palindrome(n):\\n digits = list() # list of all digits in n (reversed order)\\n while n > 0:\\n last_digit = n % 10\\n digits.append(last_digit)\\n n = n // 10\\n if is_list_palindrome(digits):\\n return True\\n else:\\n return False\",\n \"def is_palindrome(n):\\n return(n == reverse(n))\",\n \"def check_pal(num):\\r\\n \\r\\n num = str(num) #Convert number to string.\\r\\n \\r\\n #If a number is a palindrome, rreturn True \\r\\n if num[0] == num[len(num)-1] and len(num) <= 3:\\r\\n return True\\r\\n \\r\\n #If the first and last digits of a number are equal when its length is > 3,\\r\\n #strip the end digits away analyse the resulting number.\\r\\n elif num[0] == num[len(num)-1]:\\r\\n return check_pal(num[1:len(num)-1])\\r\\n \\r\\n #If a number is not a palindrome, return False\\r\\n else:\\r\\n return False\",\n \"def is_palindrome(num):\\n str_num = str(num)\\n\\n if len(str_num) == 1:\\n return True\\n elif len(str_num) == 2:\\n return str_num[0] == str_num[1]\\n\\n if str_num[0] == str_num[len(str_num)-1]:\\n return is_palindrome(str_num[1:len(str_num)-1])\\n else:\\n return False\",\n \"def is_palindrome(n):\\n ns = str(n)\\n for i in range(0, len(ns) // 2):\\n if ns[i] != ns[len(ns) - 1 - i]: return False\\n return True\",\n \"def is_palindrome(x):\\n strx = str(x)\\n return strx == strx[::-1]\\n # chars = [c for c in x] if not is_number(x) else digits(x)\\n # for i in range(len(chars) // 2):\\n # if chars[i] != chars[len(chars) - i - 1]:\\n # return False\\n # return True\",\n \"def is_palindrome(num_in):\\n if str(num_in) == str(num_in)[::-1]:\\n return True\\n\\n return False\",\n \"def has_palindrome(i, start, length):\\r\\n s = str(i)[start:start+length]\\r\\n return s[::-1] == s\",\n \"def is_palindrome(num):\\n\\treversed_num = str(num)[::-1]\\n\\tif reversed_num == str(num): return True\\n\\telse: return False\",\n \"def is_antipalindrome(n):\\n v = []\\n while n > 0:\\n v.append(n % 10)\\n n //= 10\\n for i in range(len(v)//2):\\n if v[i] == v[len(v)-i-1]:\\n return False\\n return True\",\n \"def check_palindrome(number):\\r\\n \\r\\n number = str(number) #Converting a number to a string.\\r\\n \\r\\n #If the number is a palindrome then it will return True \\r\\n if number[0] ==number[len(number)-1] and len(number) <= 3:\\r\\n return True\\r\\n #If the first and last digits of a number are equal when its length is > 3,\\r\\n #strip the end digits away analyse the resulting number.\\r\\n elif number[0] == number[len(number)-1]:\\r\\n return check_palindrome(number[1:len(number)-1])\\r\\n \\r\\n #If a number is not a palindrome, return False\\r\\n else:\\r\\n return False\",\n \"def is_palindromic(n: int) -> bool:\\n str_n = str(n)\\n if str_n == str_n[::-1]:\\n return True\\n return False\",\n \"def is_palindrome(element):\\n if int(element) < 0:\\n return False\\n if element == element[::-1]:\\n return True\\n else:\\n return False\",\n \"def isPalindrome(x):\\n # Write the functionality:\\n\\n if x == str(x)[::-1]:\\n return True\\n elif x==121:\\n return True\\n else:\\n return False\",\n \"def palindrome_itertive(a):\\n # TODO make this less crappy\\n start = 0 \\n end = len(a) - 1\\n while start != end:\\n # print(end)\\n # print('start: ', start, ' a: ', a[start])\\n # print('end: ', end, ' a: ', a[end])\\n if not a[start] == a[end]:\\n return False\\n else:\\n start += 1\\n end -= 1\\n return True\",\n \"def ispalind(num):\\n num = list(str(num))\\n if num == num[::-1]:\\n return True\",\n \"def is_palindrome(n):\\n num = list(str(n))\\n h1 = num[:int(len(num)/2)] # first half of palindrome\\n if len(num) % 2 == 0:\\n h2 = num[int(len(num)/2):] # second half of palindrome\\n else:\\n h2 = num[int(len(num)/2) + 1:]\\n return h1 == list(reversed(h2))\",\n \"def palindrome_integer(string):\\n integers = string.split(\\\" \\\")\\n for integer in integers:\\n reverse = str(int)[::-1]\\n '''Check if palindrome not then return False'''\\n if integer != reverse:\\n return False\\n return True\",\n \"def obtener_palindromo(valor):\\n \\n #Luego de convertirlo a str, los vamos a insertar en una lista para luego verificar\\n palindromo = list(str(valor))\\n #lo insertamos en una nueva lista\\n palindromo_final = palindromo\\n \\n #Luego aplicaremos la verificacion para comprobar si es un palindromo\\n if palindromo [:: -1] == palindromo_final:\\n return True\\n\\n\\n \\n #print('El numero es un palindromo')\",\n \"def isPalindrome(n):\\n str_n = str(n)\\n return list(str_n) == list(reversed(str_n))\",\n \"def check_palindrome(s):\\n return s[::-1] == s\",\n \"def isPalindrome(s):\\r\\n return isPal(toChars(s))\",\n \"def is_antipalindrome(n):\\n digits = [int(num) for num in str(n)]\\n reverseDigits = digits[::-1]\\n return all([reverseDigits[i]==(9-digits[i]) for i in range(len(digits))])\",\n \"def isPalindrome(string):\\n for i in range(len(string)//2):\\n if string[i] != string[(i*-1)-1]:\\n return False\\n return True\",\n \"def is_palindrome(string):\\n k, mid = len(string), len(string) // 2\\n # checking even palindromes\\n if k % 2 == 0:\\n return string[:mid] == get_reverse_complement(string[mid:])\\n # checking odd palindromes\\n else:\\n return string[:mid] == get_reverse_complement(string[mid + 1:])\",\n \"def is_palindrome_v2(s):\\n n = len(s)\\n\\n return s[:n/2] == reverse(s[n-n/2:])\",\n \"def is_palindrome_v3(s):\\n i = 0\\n j = len(s)-1\\n\\n while i < j and s[i] == s[j]:\\n i = i + 1\\n j = j -1\\n\\n return j <= i\",\n \"def isLychrel(Number):\\r\\n Number+=reverse(Number)\\r\\n n=0\\r\\n while n<50:\\r\\n if isPalindrome(Number):\\r\\n return False\\r\\n else:\\r\\n Number+=reverse(Number)\\r\\n n+=1\\r\\n return True\",\n \"def is_palindrome(s):\\n i, end = 0, len(s) // 2\\n while i < end:\\n if s[i] != s[len(s) - i - 1]:\\n return False\\n i += 1\\n return True\",\n \"def is_palindrome(sub):\\n for i in range(len(sub)):\\n if sub[i] != sub[len(sub) - i - 1]:\\n return False\\n return True\",\n \"def part3(string):\\n palindrome = True\\n for i in range(0, int(len(string)/2) + 1):\\n if(string[i] != string[int(len(string))-i-1]):\\n palindrome = False\\n print(palindrome)\",\n \"def check_is_palindrome(mer1, mer2):\\n return mer1.find(mer2[::-1]) == 0\",\n \"def is_palindrome(s):\\n return s == s[::-1]\",\n \"def isReversible(n): \\n if n % 10 == 0:\\n return False\\n s = n + reverseNum(n)\\n while s > 0:\\n digit = s % 10\\n if not digit in [1,3,5,7,9]:\\n return False\\n s //= 10\\n return True\",\n \"def is_palindrome(s):\\n rev_str = \\\"\\\"\\n for let in range(1, len(s) + 1):\\n rev_str += s[-let]\\n return rev_str == s\",\n \"def palindrome(word):\\n reverse = word[::-1]\\n return word == reverse\",\n \"def _is_palindrome(input_str, start, end):\\n if start >= end:\\n return True\\n if input_str[start] != input_str[end]:\\n return False\\n else:\\n return _is_palindrome(input_str, start + 1, end - 1)\",\n \"def main():\\n for l in range(999,890,-1):\\n for r in range(999,890,-1):\\n num= l*r\\n ans= palindrome_check(num)\\n if ans:\\n print l,r,num\\n return\\n print l,r,num\\n print \\\"No palindrome found.\\\"\\n return\",\n \"def is_palindrome_v1(s):\\n return reverse(s) == s\",\n \"def palindrome(x):\\n pass\",\n \"def is_palindrome(str):\\n n = len(str)\\n for i in range(n//2): # loop to middle of string str\\n if str[i] != str[-1-i]:\\n return False # find a character doesn't match with its mirror-positioned character\\n return True # reach this, then str is palindrome\",\n \"def is_palindrome(string):\\n return string[::-1] == string\",\n \"def is_palindromic(lst):\\n return all( lst[i] == lst[-(i+1)] for i in range(len(lst)) )\",\n \"def is_palindrome(word):\\n if len(word)>2 and not is_palindrome(middle(word)):\\n return False\\n else:\\n return first(word) == last(word)\",\n \"def checkPalindrome(self, s: str) -> str:\\n # return s[:len(s) // 2] == s[(len(s) + 1) // 2::-1]\\n return s == s[::-1]\",\n \"def is_palindrome(word):\\n if word == word[::-1]:\\n return True\\n else:\\n return False\",\n \"def check(i):\\r\\n return (has_palindrome(i, 2, 4) and\\r\\n has_palindrome(i+1, 1, 5) and\\r\\n has_palindrome(i+2, 1, 4) and\\r\\n has_palindrome(i+3, 0, 6))\",\n \"def is_pal(n):\\r\\n # Change the number into a string and then a list.\\r\\n as_list_of_chars = list(str(n))\\r\\n # Copy the list and reverse it.\\r\\n reversed_list_of_chars = list(as_list_of_chars)\\r\\n reversed_list_of_chars.reverse()\\r\\n # True if the list of chars is palindromic.\\r\\n return as_list_of_chars == reversed_list_of_chars\",\n \"def palindromes(n: int) -> int:\\n # 1 -> 2 -> 3 ... 9 -> 11 -> 22 -> 33 -> 44 .. 99 -> 101\\n # 101 -> 111 -> 121 -> 131 -> ... -> 191 -> 202 -> 212\\n # 989 -> 999 -> 1001 -> 1111 -> 1221\\n # 9889 -> 9999 -> 10001 -> 10101 -> 10201\\n prev = n\\n s = str(n)\\n even = len(s) % 2 == 0\\n s = s[:ceil(len(s) / 2)]\\n n = int(s)\\n while True:\\n if even:\\n pal = int(''.join([s, s[-1::-1]])) # join '12' with '21'\\n else:\\n pal = int(''.join([s, s[-2::-1]])) # join '12' with '1'\\n if prev <= pal:\\n yield pal\\n \\n n += 1\\n if all(digit == '9' for digit in s):\\n even = not even\\n if even: n //= 10\\n s = str(n)\",\n \"def is_palindrome(word):\\n if first(word) != last(word):\\n print 'not a palindrome'\\n return False\\n else:\\n return is_palindrome(middle(word))\",\n \"def closest_palindrome_number(number):\\n\\n def check_all_9(number):\\n\\n for n in number:\\n if n != 9:\\n return False\\n return True\\n\\n num_list = [int(i) for i in str(number)]\\n\\n num_size = len(num_list)\\n\\n if check_all_9(num_list):\\n return number + 2\\n\\n mid_point = int(num_size/2)\\n\\n def list_to_int(nums): return int(''.join(str(i) for i in nums))\\n\\n def check_palindromes(all_palindromes, number):\\n min_found = sys.maxsize\\n pal_found = 0\\n multiple_pals = []\\n\\n for i in all_palindromes:\\n pal = list_to_int(i)\\n distance = abs(number - pal)\\n if distance <= min_found and distance != 0:\\n if distance == min_found:\\n multiple_pals.append(i)\\n else:\\n multiple_pals = []\\n min_found = distance\\n pal_found = i\\n multiple_pals.append(i)\\n\\n if len(multiple_pals) == 1:\\n return list_to_int(pal_found)\\n else:\\n numbers = []\\n for i in multiple_pals:\\n number = list_to_int(i)\\n numbers.append(number)\\n return numbers\\n\\n if num_size % 2 == 0:\\n\\n # Even number\\n splitted = num_list[0: mid_point]\\n mirrored = splitted + splitted[::-1]\\n\\n all_palindromes = []\\n all_palindromes.append(mirrored)\\n\\n if splitted[-1] != 9:\\n split_add_one = list(splitted)\\n split_add_one[-1] += 1\\n split_add_one = all_palindromes.append(\\n split_add_one + split_add_one[::-1])\\n\\n if splitted[-1] != 0:\\n split_sub_one = list(splitted)\\n split_sub_one[-1] -= 1\\n split_sub_one = all_palindromes.append(\\n split_sub_one + split_sub_one[::-1])\\n\\n else:\\n # Odd number\\n splitted = num_list[0: mid_point]\\n middle_num = num_list[mid_point]\\n\\n all_palindromes = []\\n all_palindromes.append(\\n splitted + [middle_num] + splitted[::-1])\\n\\n if middle_num != 9:\\n all_palindromes.append(\\n splitted + [middle_num + 1] + splitted[::-1])\\n\\n if middle_num != 0:\\n all_palindromes.append(\\n splitted + [middle_num - 1] + splitted[::-1])\\n\\n return check_palindromes(all_palindromes, number)\",\n \"def is_palindrome(string):\\n return\",\n \"def is_palindrome_2(n):\\n number_as_list = list(str(n))\\n for n in number_as_list:\\n # check if current item is not equal to last item in the list\\n print(\\\"List at each iteration...\\\", number_as_list)\\n if n != number_as_list.pop():\\n return False\\n return True\",\n \"def num_palindrome():\\n nums = map(str, range(1000000))\\n odo = []\\n for i in range(len(nums)):\\n if len(nums[i]) < 6:\\n odo.append('0'*(6-len(nums[i])) + nums[i])\\n elif len(nums[i]) == 6:\\n odo.append(nums[i])\\n \\n for i in range(len(odo)-3): \\n first = odo[i][2:] == odo[i][:1:-1]\\n second = odo[i+1][1:] == odo[i+1][:0:-1]\\n third = odo[i+2][1:5] == odo[i+2][4:0:-1]\\n fourth = odo[i+3][:] == odo[i+3][::-1]\\n if first & second & third & fourth:\\n print 'A possible odometer reading is '+odo[i]\",\n \"def palindrom_permutation(string: str):\\n string = re.sub(r'\\\\W+', '', string.lower())\\n\\n chars = dict()\\n for c in string:\\n chars[c] = chars[c] + 1 if c in chars else 1\\n\\n almost_not_okey = False\\n for val in chars.values():\\n if val % 2 == 1:\\n if not almost_not_okey:\\n almost_not_okey = True\\n else:\\n return False\\n\\n if almost_not_okey:\\n return len(string) % 2 == 1\\n return True\",\n \"def is_palindrome(string):\\r\\n r_string = string[::-1]\\r\\n cnt = 0\\r\\n while cnt < len(string):\\r\\n if string[cnt] == r_string[cnt]:\\r\\n cnt += 1\\r\\n continue\\r\\n else:\\r\\n return False\\r\\n #cnt += 1\\r\\n return True\",\n \"def is_palindrome2(some_string):\\n return reverse_string(some_string) == some_string\",\n \"def string_palidrome(word):\\n if word == string_reverse(word):\\n return True\\n else:\\n return False\",\n \"def palindrome():\\n c = 0\\n d = ''\\n e = 0\\n f = 0\\n g = 0\\n for a in range(100, 1000):\\n for b in range(100, 1000):\\n c = a * b\\n d = str(c)\\n if d == d[::-1] and c > e:\\n e = c\\n f = a\\n g = b\\n return e\",\n \"def exactly_two_same_digits(num):\\n output = False\\n digits = [int(i) for i in str(num)]\\n for i, dig in enumerate(digits[:-2]):\\n if i == 0:\\n if dig == digits[i + 1] and dig != digits[i + 2]:\\n output = True\\n else:\\n if (dig != digits[i - 1] \\n and dig == digits[i + 1] \\n and dig != digits[i + 2]):\\n output = True\\n if i == len(digits) - 3:\\n if dig != digits[i + 1] and digits[i + 1] == digits[i + 2]:\\n output = True\\n return output\",\n \"def is_palindrome(string):\\n if string == string[::-1]:\\n return True\\n return False\",\n \"def isPal(x):\\n temp = x\\n temp.reverse()\\n if temp == x:\\n print('true')\\n return True\\n else:\\n print('false')\\n return False\",\n \"def palindrome(string):\\r\\n if len(string) <= 1:\\r\\n return True\\r\\n else:\",\n \"def is_Lychrel(n):\\n count = 1\\n current = n\\n while count < 50:\\n current = reverse_and_add(current)\\n if is_palindrome(current):\\n return False\\n count += 1\\n return True\",\n \"def is_palindromic(phrase):\\n\\n val = str(phrase).lower().replace(\\\" \\\", \\\"\\\")\\n if val == val[::-1]: # Reverse order\\n return True\\n else:\\n return False\",\n \"def check_palindrome(inp_string):\\n if len(inp_string) <= 2:\\n return False\\n elif inp_string == inp_string[::-1]:\\n return True\\n else:\\n return False\",\n \"def part3():\\n Input = raw_input('please enter a string:')\\n for i in range(len(Input)):\\n if Input[i] != Input[len(Input)-i-1]:\\n print('It is not a palidrome')\\n break\\n else:\\n i = i + 1\",\n \"def isPalindromes(s):\\n\\n def toChar(s):\\n s= s.lower()\\n letters=''\\n for c in s:\\n if c in \\\"abcdefgh\\\":\\n letters= letters+c\\n return letters\\n\\n def isPal(s):\\n if len(s) <=1:\\n return True\\n else:\\n return s[0]==s[-1] and isPal(s[1:-1])\\n return isPal(toChar(s))\",\n \"def isPalindrome(S):\\n if len(S) == 0:\\n return True\\n \\n if S[0] != S[-1]:\\n return False\\n \\n return isPalindrome(S[1:len(S) - 1])\",\n \"def has_palindrome_permutation(given_string):\\n\\n unpaired_characters = set()\\n\\n for char in given_string:\\n if char in unpaired_characters:\\n unpaired_characters.remove(char)\\n else:\\n unpaired_characters.add(char) \\n\\n return len(unpaired_characters) <= 1\",\n \"def palindrom():\\r\\n pal = []\\r\\n\\r\\n sub_str = gen_substring(\\\"abaabbaab\\\")\\r\\n\\r\\n for i in range(len(sub_str)):\\r\\n\\r\\n rev = reverse_string(sub_str[i])\\r\\n\\r\\n if rev == sub_str[i]:\\r\\n\\r\\n pal.append(rev)\\r\\n\\r\\n return pal\",\n \"def check_palindrome():\\n # Your code goes here...\",\n \"def palindrome_recursive(a):\\n # Base case\\n if len(a) <= 1:\\n return True\\n else:\\n if a[0] == a[len(a)-1]:\\n return palindrome_recursive(a[1:-1])\\n else:\\n return False\",\n \"def validPalindrome(self, s: str) -> bool:\\n n = len(s)\\n i = 0\\n j = n - 1\\n while i < j:\\n if s[i] == s[j]:\\n i += 1\\n j -= 1\\n else:\\n # error, for -1, start > end. Indexing is like range\\n # return s[i:j] == s[i:j:-1] or s[i+1:j+1] == s[i+1:j+1:-1]\\n return self.is_palindrome(s[i:j]) or self.is_palindrome(s[i+1:j+1])\\n\\n return True\",\n \"def is_square(number): \\n s = number * number\\n return is_palindrome(s)\",\n \"def is_palindrome(word):\\n word = validator(word)\\n\\n if word == \\\"\\\":\\n return True\\n\\n elif word[0] == word[-1]:\\n return is_palindrome(word[1:-1])\\n\\n return False\",\n \"def palindromeString(str, first = 0, end = -1):\\n assert len(str) > 0, \\\"String Must be hava one character at least.\\\"\\n\\n # first bigger or equal then length's of string then yes\\n if first >= len(str):\\n return True\\n # Compare first with end character recursively.\\n if str[first] == str[end]:\\n return palindromeString(str, first + 1, end - 1)\\n else :\\n return False\",\n \"def check_number(number):\\n digits = str(number)\\n if len(digits) != 6:\\n return False\\n\\n double = False\\n last = '0'\\n for digit in digits:\\n if digit < last:\\n return False\\n\\n if digit == last:\\n double = True\\n\\n last = digit\\n\\n return double\",\n \"def is_palindrome(word: str) -> bool:\\n\\n # Todo\\n return False\",\n \"def is_palindrome(word):\\n\\t# The degenerate case is 1 letter or none. We consider these palindromes\\n\\tif len(word) <= 1:\\n\\t\\treturn True\\n\\n\\t# For something to be a palindrome, the first and last letters much match\\n\\t# and the string between them must also be a palindrome\\n\\tif first(word) == last(word) and is_palindrome(middle(word)):\\n\\t\\treturn True\\n\\telse:\\n\\t\\treturn False\",\n \"def is_permutation_palindrome(str):\\n for s in permutations(str): # loop through all permutations of str\\n if is_palindrome(s):\\n return True # successfully find a palindrome permutation\\n return False # reach this, then no possible permutation is palindrome\",\n \"def palindromes():\\n for n in count(1):\\n if str(n) == str(n)[::-1]:\\n yield n\",\n \"def isPalindrome(word):\\n\\n input_str = IGNORE_NON_ALPHA_CHARACTER.sub(\\\"\\\", str(word)).casefold()\\n return input_str == input_str[::-1]\",\n \"def ispalindrome(string):\\n if isinstance(string, (str, int, float)):\\n string = str(string).replace(\\\" \\\", \\\"\\\").lower()\\n if len(string) in [0,1]:\\n return True\\n half_index = len(string) // 2\\n first = string[:half_index]\\n second = string[-half_index:]\\n if first == second[::-1]:\\n return True\\n else:\\n return False\\n else:\\n print(\\\"ERROR: ispalindrome received invalid input.\\\\nREASON: input\\\",\\n \\\"must be convertible to string format.\\\")\",\n \"def palCheck(input_string):\\n\\n # ADD NECESSARY LINES OF CODE SO THAT ALL UNITTESTS PASS\\n\\n d = Deque()\\n for char in input_string:\\n d.addFront(char)\\n\\n while d.size() > 1:\\n firstChar = d.removeRear()\\n lastChar = d.removeFront()\\n if firstChar != lastChar:\\n print(\\\"No, '\\\" + input_string + \\\"', is not a palindrom\\\")\\n return False\\n\\n print(\\\"Yes, '\\\" + input_string + \\\"', is a palindrom!!\\\")\\n return True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7902744","0.78919506","0.7875554","0.78522855","0.780591","0.77704966","0.7650538","0.7627759","0.7580284","0.75686455","0.7507331","0.75047344","0.7498053","0.7495534","0.74926513","0.74589795","0.74185145","0.73735946","0.7350664","0.73400944","0.7291305","0.7287503","0.72614264","0.72503674","0.7219647","0.7216595","0.7204814","0.71877784","0.71868503","0.7126142","0.71242213","0.7107482","0.7107035","0.709845","0.7085804","0.7084489","0.7065525","0.704224","0.6994443","0.69629836","0.6960248","0.695643","0.69471127","0.69448704","0.6941993","0.6941662","0.6927269","0.69179255","0.6874089","0.68714863","0.68618953","0.68591464","0.68583345","0.6857278","0.6856612","0.6838304","0.6803825","0.68013686","0.67999876","0.6797803","0.6767285","0.67548937","0.6748201","0.6736628","0.672822","0.6713474","0.67123586","0.6706399","0.6704945","0.6632572","0.6627174","0.66248935","0.6616289","0.66070205","0.65951204","0.65812236","0.6577163","0.6575936","0.6509182","0.649922","0.6482897","0.6477252","0.646891","0.64462876","0.64436424","0.644273","0.64200914","0.64166623","0.6389406","0.63881534","0.63801664","0.63771725","0.63458407","0.6343372","0.6337181","0.6336484","0.63351315","0.6333286","0.6331941","0.6322186"],"string":"[\n \"0.7902744\",\n \"0.78919506\",\n \"0.7875554\",\n \"0.78522855\",\n \"0.780591\",\n \"0.77704966\",\n \"0.7650538\",\n \"0.7627759\",\n \"0.7580284\",\n \"0.75686455\",\n \"0.7507331\",\n \"0.75047344\",\n \"0.7498053\",\n \"0.7495534\",\n \"0.74926513\",\n \"0.74589795\",\n \"0.74185145\",\n \"0.73735946\",\n \"0.7350664\",\n \"0.73400944\",\n \"0.7291305\",\n \"0.7287503\",\n \"0.72614264\",\n \"0.72503674\",\n \"0.7219647\",\n \"0.7216595\",\n \"0.7204814\",\n \"0.71877784\",\n \"0.71868503\",\n \"0.7126142\",\n \"0.71242213\",\n \"0.7107482\",\n \"0.7107035\",\n \"0.709845\",\n \"0.7085804\",\n \"0.7084489\",\n \"0.7065525\",\n \"0.704224\",\n \"0.6994443\",\n \"0.69629836\",\n \"0.6960248\",\n \"0.695643\",\n \"0.69471127\",\n \"0.69448704\",\n \"0.6941993\",\n \"0.6941662\",\n \"0.6927269\",\n \"0.69179255\",\n \"0.6874089\",\n \"0.68714863\",\n \"0.68618953\",\n \"0.68591464\",\n \"0.68583345\",\n \"0.6857278\",\n \"0.6856612\",\n \"0.6838304\",\n \"0.6803825\",\n \"0.68013686\",\n \"0.67999876\",\n \"0.6797803\",\n \"0.6767285\",\n \"0.67548937\",\n \"0.6748201\",\n \"0.6736628\",\n \"0.672822\",\n \"0.6713474\",\n \"0.67123586\",\n \"0.6706399\",\n \"0.6704945\",\n \"0.6632572\",\n \"0.6627174\",\n \"0.66248935\",\n \"0.6616289\",\n \"0.66070205\",\n \"0.65951204\",\n \"0.65812236\",\n \"0.6577163\",\n \"0.6575936\",\n \"0.6509182\",\n \"0.649922\",\n \"0.6482897\",\n \"0.6477252\",\n \"0.646891\",\n \"0.64462876\",\n \"0.64436424\",\n \"0.644273\",\n \"0.64200914\",\n \"0.64166623\",\n \"0.6389406\",\n \"0.63881534\",\n \"0.63801664\",\n \"0.63771725\",\n \"0.63458407\",\n \"0.6343372\",\n \"0.6337181\",\n \"0.6336484\",\n \"0.63351315\",\n \"0.6333286\",\n \"0.6331941\",\n \"0.6322186\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8182947"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11176,"cells":{"query":{"kind":"string","value":"Model is initialized with tag model m"},"document":{"kind":"string","value":"def __init__(self, tags):\n self.tags = tags"},"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 init_model(self):\n pass","def initialize_model(self):\n pass","def initialize(self, model):\n pass","def __init__(self, model):\n\t\tself.model = model","def __init__(self, model):\n self._model = model","def __init__(self, model):\n self.model = model","def __init__(self, model):\n self.model = model","def __init__(self, model):\n self.model = model","def __init__(self, model):\n self.model = model","def __init__(self):\n self.model = None","def __init__(self):\n self.model = None","def __init__(self, model):\n\n self.model = model\n self.paths = PathOperations()\n self.tags = Tags()","def __init__(self, ml):\n # Save a reference to the model.\n self.ml = ml","def __init__(self, model: object):\n self.model = model","def __init__(self, model: Model1D):\n self._model = model","def _add_init(self, p_model):\r\n\r\n raise NotImplementedError","def __init__(self, model: MT):\n self.model: Final[MT] = model","def initialize(self) -> None:\n self.model = load(self.path)","def __init__(self):\n self.model = self.load_model()\n self.form_html = self.create_form_html()","def __init__(self, model: Type[ModelType]):\n self.model = model","def __init__(self, model: Type[ModelType]):\n self.model = model","def initialize_model(self):\n model = self.model_class()\n return model","def __init__(self, model: str, **kwargs):\n\n super().__init__(model=model, **kwargs)\n logger.info('load model done')","def __init__(self, model: Optional[Model] = None) -> None:\n self.model = model","def __init__(self,model,device):\n self.model = model\n self.device = device","def __init__(self, model: Model1D):\n super().__init__(model=model)","def __init__(self, model: Model1D):\n super().__init__(model=model)","def __init__(self, model: Model1D):\n super().__init__(model=model)","def __init__(self, model: Model1D):\n super().__init__(model=model)","def __init__(self, model: Model1D):\n super().__init__(model=model)","def model(self, model):\n \n self._model = model","def initialize_model(self, initial_data):\n # EDIT THIS METHOD TO RETURN A MINIMAX MODEL ###\n return None","def _attach_to_model(self, model):\n self._model = model","def __init__(self, model, **kwargs):\n self.model = model\n\n super(RecordView, self).__init__(**kwargs)","def set_model(self):\n self.model = self.get_model()","def __init__(self, model):\n TreeLikelihoodBase.__init__(self, model)","def load_model(self):\n pass","def init_model(self, model_name, config=None):\n ModelDirectory.init_model(model_name, pipeline=self, config=config)\n return self\n #self._action_list.append({'name': INIT_MODEL_ID, 'model_name': model_name, 'config': config})\n #return self.append_action()","def __init__(self, tag):\n self.tag = tag","def __init__(self, **kwargs):\n super(Model, self).__init__(**kwargs)\n self._params = self.find_params()","def __init__(self, **kwargs):\n super(Model, self).__init__(**kwargs)","def build_model():","def init_model(self):\n cxnlib.CXNNetInitModel(self.handle)","def create_model(self):\n self.model = None\n pass","def model(self):","def model(self):","def model(self):","def model(self):","def model(self):","def __init__(self, model_pool):\n super(Template, self).__init__(model_pool)\n self.nlp_model = None","def model(self, model):\n\n self._model = model","def model(self, model):\n\n self._model = model","def model(self, model):\n\n self._model = model","def model(self, model):\n\n self._model = model","def model(self, model):\n\n self._model = model","def __init__(self):\n self.tag = None","def create_model(self):\n pass","def create_model(self):\n pass","def prepare_model(self, **kwargs):\n pass","def __init__(self, model: str, **kwargs):\n super().__init__(model=model)","def set_model(self, likelihood_model_instance):\n pass","def set_model(self, likelihood_model_instance):\n pass","def build_model(self):\n pass","def build_model(self):\n pass","def __init__(self, model_name):\n self.name = model_name\n self.words = {}\n self.word_lengths = {}\n self.stems = {}\n self.sentence_lengths = {}\n self.punctuation = {}","def initialize(self):\n for key in self.parameter_dict:\n self.models[key] = self._create_model(key)","def __init__ (self):\n self.tag = 0\n self.mDict = {}","def __init__(self,m):\n # initialize model parameters\n \n # w is the m x 1 vector of weights.\n # m: num of features\n self.w = np.random.rand(m)","def load_model(self) -> Any:","def _init_model(self):\r\n\r\n self.model = ResNet152V2(weights='imagenet')","def model_initialise_tags(model, report=None):\n if hasattr(model._meta, 'get_fields'):\n # Django 1.8 uses new meta API\n fields = model._meta.get_fields()\n else:\n fields = model._meta.fields + model._meta.many_to_many\n \n for field in fields:\n if isinstance(\n field,\n (SingleTagField, TagField)\n ):\n field_initialise_tags(model, field, report)","def set_model(self, model):\n self.model = model","def set_model(self, model):\n self.model = model","def basic_model_init(model_args, task_infos, tokenizer):\n config = AutoConfig.from_pretrained(\n model_args.model_name_or_path,\n num_labels=task_infos.num_labels,\n cache_dir=model_args.model_cache_dir,\n id2label=task_infos.id2label,\n label2id=task_infos.label2id,\n )\n model_cls = getattr(mod, model_args.architectures,\n AutoModelForSequenceClassification)\n model = model_cls.from_pretrained(\n model_args.model_name_or_path,\n config=config,\n cache_dir=model_args.model_cache_dir,\n )\n if model.config.vocab_size < len(tokenizer):\n print(\"resize...\")\n model.resize_token_embeddings(len(tokenizer))\n return model","def __init__(self):\n #MdvData. __init__(self,model.target_fragments)\n #self.mdv = {}\n self.mdvtc ={}\n self.mode = \"timecourse\"","def init(self):\n # IMPORTANT: create a new gob database model entry for this object\n self.gobify()","def create_models( self ):","def __init__(self, modeled, viewed):\n self.modeled = modeled\n self.viewed = viewed","def __init__(self, model_name):\n self.name = model_name\n self.words = {}\n self.word_lengths = {}\n self.sentence_lengths = {}\n self.stems = {}\n self.commas_per_sentence = {}","def __init__(self, model_name):\n\n self.name = model_name\n self.words = {}\n self.word_lengths = {}\n self.stems={}\n self.sentence_lengths={}\n self.endings={}\n self.total = 0","def __load_model(self):\n loaded = load(self.__file_name)\n self.__model = loaded['model']\n self.__meta_data = loaded['metadata']\n self.__is_ready = True","def __init__(self, model):\n self.model = model\n self.n = 0\n self._cards = []\n self._comments = []","def __init__(self, model_name):\r\n self.name = model_name\r\n self.words = ({})\r\n self.word_lengths = ({})\r\n self.stems = ({})\r\n self.sentence_lengths = ({})\r\n self.punctuation = ({})","def __init__(self, model, screen):\n self.model = model\n self.screen = screen","def __init__(self, word2vec_model):\n self._model = word2vec_model","def MakeModel(self):\n pass","def initialize(self):\n LOG.info(\"Initializing Model.\")\n self.model = self.convert(df=self.training_df)\n if self.bootstraps is not None:\n LOG.info(\"Bootstrapping Data.\")\n self.bootstrap_data()","def setModel(self):\n if self.model != \"\":\n # Setup the visual model\n # Animated stuff should be added soon\n print MODEL_DIR\n print self.model\n model = loader.loadModel(MODEL_DIR + self.model)\n model.reparentTo(self.bulletBody)","def _connectModel(self):\n pass","def __init__(self, view, model):\n self.view = view\n self.view.set_controller(self)\n self.model = model","def __init__(self, tokens):\n self.mdl = self.train(tokens)","def __init__(self, tokens):\n self.mdl = self.train(tokens)","def __init__(self):\n self.model = self._get_model()\n\n # NOTE: The order of this list hardcoded here, and needs to be changed when re-training the model!\n # When exporting the model in tflite format, the model_spec is lost, so we cannot do it like that:\n # classes = ['???'] * model.model_spec.config.num_classes\n # label_map = model.model_spec.config.label_map\n # for label_id, label_name in label_map.as_dict().items():\n # classes[label_id-1] = label_name\n self.classes = ['Baked Goods', 'Salad', 'Cheese', 'Seafood', 'Tomato']","def __init__(self, tags=''):\n self.tags = tags","def _set_attributes(self, model):\n\n if model:\n self._get_dict(model)","def model_setup(self):\n self.DNN = SganMLP(self.settings.number_of_bins)\n self.D = SganMLP(self.settings.number_of_bins)\n self.G = Generator()","def initialize(self, context):\n self.initialized = True\n properties = context.system_properties\n # Contains the url parameter passed to the load request\n model_dir = properties.get(\"model_dir\") \n gpu_id = properties.get(\"gpu_id\")\n\n # Load Gluonts Model\n self.mx_model = self.load_model(model_dir)","def __init__(self):\n self.emotion_map = {0: \"anger\", 1: \"fear\", 2 : \"joy\", 3: \"sadness\"}\n self.tokenizer = AutoTokenizer.from_pretrained(\"MilaNLProc/feel-it-italian-emotion\")\n self.model = AutoModelForSequenceClassification.from_pretrained(\"MilaNLProc/feel-it-italian-emotion\")\n self.model.eval()\n self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')","def __init__(self, model):\r\n self._tensorflow_session = model._tensorflow_session\r\n self._model = model","def __init__(self, hparams: Namespace):\n super(TransformerTagger, self).__init__()\n self.hparams = hparams\n self.labels = ['O', 'I']\n config_class, model_class, tokenizer_class = MODEL_CLASSES[self.hparams.model_type]\n\n config = config_class.from_pretrained(\n self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path,\n num_labels=self.hparams.num_labels,\n cache_dir=self.hparams.cache_dir if self.hparams.cache_dir else None,\n )\n tokenizer = tokenizer_class.from_pretrained(\n self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,\n do_lower_case=self.hparams.do_lower_case,\n cache_dir=self.hparams.cache_dir if self.hparams.cache_dir else None,\n )\n model = model_class.from_pretrained(\n self.hparams.model_name_or_path,\n from_tf=bool('.ckpt' in self.hparams.model_name_or_path),\n config=config,\n cache_dir=self.hparams.cache_dir if self.hparams.cache_dir else None,\n )\n\n self.config, self.tokenizer, self.model = config, tokenizer, model\n self.pad_token_label_id = nn.CrossEntropyLoss().ignore_index\n\n self.prepare_data()\n\n # get total train steps - for lr scheduler, idk how to do better for now without double loading\n self.total_train_steps = self.get_total_train_steps()\n logger.info('Total training steps: %s', self.total_train_steps)\n\n # init predictions\n self.preds = {\n 'val': defaultdict(dict),\n 'test': defaultdict(dict),\n }\n\n self.model_id_name = (f'{hparams.model_name_or_path}_bs-{hparams.train_batch_size}'\n f'_accum-{hparams.accumulate_grad_batches}'\n f'_lr-{hparams.lr}_labmode-{hparams.label_mode}'\n f'_maxep-{hparams.max_epochs}')"],"string":"[\n \"def init_model(self):\\n pass\",\n \"def initialize_model(self):\\n pass\",\n \"def initialize(self, model):\\n pass\",\n \"def __init__(self, model):\\n\\t\\tself.model = model\",\n \"def __init__(self, model):\\n self._model = model\",\n \"def __init__(self, model):\\n self.model = model\",\n \"def __init__(self, model):\\n self.model = model\",\n \"def __init__(self, model):\\n self.model = model\",\n \"def __init__(self, model):\\n self.model = model\",\n \"def __init__(self):\\n self.model = None\",\n \"def __init__(self):\\n self.model = None\",\n \"def __init__(self, model):\\n\\n self.model = model\\n self.paths = PathOperations()\\n self.tags = Tags()\",\n \"def __init__(self, ml):\\n # Save a reference to the model.\\n self.ml = ml\",\n \"def __init__(self, model: object):\\n self.model = model\",\n \"def __init__(self, model: Model1D):\\n self._model = model\",\n \"def _add_init(self, p_model):\\r\\n\\r\\n raise NotImplementedError\",\n \"def __init__(self, model: MT):\\n self.model: Final[MT] = model\",\n \"def initialize(self) -> None:\\n self.model = load(self.path)\",\n \"def __init__(self):\\n self.model = self.load_model()\\n self.form_html = self.create_form_html()\",\n \"def __init__(self, model: Type[ModelType]):\\n self.model = model\",\n \"def __init__(self, model: Type[ModelType]):\\n self.model = model\",\n \"def initialize_model(self):\\n model = self.model_class()\\n return model\",\n \"def __init__(self, model: str, **kwargs):\\n\\n super().__init__(model=model, **kwargs)\\n logger.info('load model done')\",\n \"def __init__(self, model: Optional[Model] = None) -> None:\\n self.model = model\",\n \"def __init__(self,model,device):\\n self.model = model\\n self.device = device\",\n \"def __init__(self, model: Model1D):\\n super().__init__(model=model)\",\n \"def __init__(self, model: Model1D):\\n super().__init__(model=model)\",\n \"def __init__(self, model: Model1D):\\n super().__init__(model=model)\",\n \"def __init__(self, model: Model1D):\\n super().__init__(model=model)\",\n \"def __init__(self, model: Model1D):\\n super().__init__(model=model)\",\n \"def model(self, model):\\n \\n self._model = model\",\n \"def initialize_model(self, initial_data):\\n # EDIT THIS METHOD TO RETURN A MINIMAX MODEL ###\\n return None\",\n \"def _attach_to_model(self, model):\\n self._model = model\",\n \"def __init__(self, model, **kwargs):\\n self.model = model\\n\\n super(RecordView, self).__init__(**kwargs)\",\n \"def set_model(self):\\n self.model = self.get_model()\",\n \"def __init__(self, model):\\n TreeLikelihoodBase.__init__(self, model)\",\n \"def load_model(self):\\n pass\",\n \"def init_model(self, model_name, config=None):\\n ModelDirectory.init_model(model_name, pipeline=self, config=config)\\n return self\\n #self._action_list.append({'name': INIT_MODEL_ID, 'model_name': model_name, 'config': config})\\n #return self.append_action()\",\n \"def __init__(self, tag):\\n self.tag = tag\",\n \"def __init__(self, **kwargs):\\n super(Model, self).__init__(**kwargs)\\n self._params = self.find_params()\",\n \"def __init__(self, **kwargs):\\n super(Model, self).__init__(**kwargs)\",\n \"def build_model():\",\n \"def init_model(self):\\n cxnlib.CXNNetInitModel(self.handle)\",\n \"def create_model(self):\\n self.model = None\\n pass\",\n \"def model(self):\",\n \"def model(self):\",\n \"def model(self):\",\n \"def model(self):\",\n \"def model(self):\",\n \"def __init__(self, model_pool):\\n super(Template, self).__init__(model_pool)\\n self.nlp_model = None\",\n \"def model(self, model):\\n\\n self._model = model\",\n \"def model(self, model):\\n\\n self._model = model\",\n \"def model(self, model):\\n\\n self._model = model\",\n \"def model(self, model):\\n\\n self._model = model\",\n \"def model(self, model):\\n\\n self._model = model\",\n \"def __init__(self):\\n self.tag = None\",\n \"def create_model(self):\\n pass\",\n \"def create_model(self):\\n pass\",\n \"def prepare_model(self, **kwargs):\\n pass\",\n \"def __init__(self, model: str, **kwargs):\\n super().__init__(model=model)\",\n \"def set_model(self, likelihood_model_instance):\\n pass\",\n \"def set_model(self, likelihood_model_instance):\\n pass\",\n \"def build_model(self):\\n pass\",\n \"def build_model(self):\\n pass\",\n \"def __init__(self, model_name):\\n self.name = model_name\\n self.words = {}\\n self.word_lengths = {}\\n self.stems = {}\\n self.sentence_lengths = {}\\n self.punctuation = {}\",\n \"def initialize(self):\\n for key in self.parameter_dict:\\n self.models[key] = self._create_model(key)\",\n \"def __init__ (self):\\n self.tag = 0\\n self.mDict = {}\",\n \"def __init__(self,m):\\n # initialize model parameters\\n \\n # w is the m x 1 vector of weights.\\n # m: num of features\\n self.w = np.random.rand(m)\",\n \"def load_model(self) -> Any:\",\n \"def _init_model(self):\\r\\n\\r\\n self.model = ResNet152V2(weights='imagenet')\",\n \"def model_initialise_tags(model, report=None):\\n if hasattr(model._meta, 'get_fields'):\\n # Django 1.8 uses new meta API\\n fields = model._meta.get_fields()\\n else:\\n fields = model._meta.fields + model._meta.many_to_many\\n \\n for field in fields:\\n if isinstance(\\n field,\\n (SingleTagField, TagField)\\n ):\\n field_initialise_tags(model, field, report)\",\n \"def set_model(self, model):\\n self.model = model\",\n \"def set_model(self, model):\\n self.model = model\",\n \"def basic_model_init(model_args, task_infos, tokenizer):\\n config = AutoConfig.from_pretrained(\\n model_args.model_name_or_path,\\n num_labels=task_infos.num_labels,\\n cache_dir=model_args.model_cache_dir,\\n id2label=task_infos.id2label,\\n label2id=task_infos.label2id,\\n )\\n model_cls = getattr(mod, model_args.architectures,\\n AutoModelForSequenceClassification)\\n model = model_cls.from_pretrained(\\n model_args.model_name_or_path,\\n config=config,\\n cache_dir=model_args.model_cache_dir,\\n )\\n if model.config.vocab_size < len(tokenizer):\\n print(\\\"resize...\\\")\\n model.resize_token_embeddings(len(tokenizer))\\n return model\",\n \"def __init__(self):\\n #MdvData. __init__(self,model.target_fragments)\\n #self.mdv = {}\\n self.mdvtc ={}\\n self.mode = \\\"timecourse\\\"\",\n \"def init(self):\\n # IMPORTANT: create a new gob database model entry for this object\\n self.gobify()\",\n \"def create_models( self ):\",\n \"def __init__(self, modeled, viewed):\\n self.modeled = modeled\\n self.viewed = viewed\",\n \"def __init__(self, model_name):\\n self.name = model_name\\n self.words = {}\\n self.word_lengths = {}\\n self.sentence_lengths = {}\\n self.stems = {}\\n self.commas_per_sentence = {}\",\n \"def __init__(self, model_name):\\n\\n self.name = model_name\\n self.words = {}\\n self.word_lengths = {}\\n self.stems={}\\n self.sentence_lengths={}\\n self.endings={}\\n self.total = 0\",\n \"def __load_model(self):\\n loaded = load(self.__file_name)\\n self.__model = loaded['model']\\n self.__meta_data = loaded['metadata']\\n self.__is_ready = True\",\n \"def __init__(self, model):\\n self.model = model\\n self.n = 0\\n self._cards = []\\n self._comments = []\",\n \"def __init__(self, model_name):\\r\\n self.name = model_name\\r\\n self.words = ({})\\r\\n self.word_lengths = ({})\\r\\n self.stems = ({})\\r\\n self.sentence_lengths = ({})\\r\\n self.punctuation = ({})\",\n \"def __init__(self, model, screen):\\n self.model = model\\n self.screen = screen\",\n \"def __init__(self, word2vec_model):\\n self._model = word2vec_model\",\n \"def MakeModel(self):\\n pass\",\n \"def initialize(self):\\n LOG.info(\\\"Initializing Model.\\\")\\n self.model = self.convert(df=self.training_df)\\n if self.bootstraps is not None:\\n LOG.info(\\\"Bootstrapping Data.\\\")\\n self.bootstrap_data()\",\n \"def setModel(self):\\n if self.model != \\\"\\\":\\n # Setup the visual model\\n # Animated stuff should be added soon\\n print MODEL_DIR\\n print self.model\\n model = loader.loadModel(MODEL_DIR + self.model)\\n model.reparentTo(self.bulletBody)\",\n \"def _connectModel(self):\\n pass\",\n \"def __init__(self, view, model):\\n self.view = view\\n self.view.set_controller(self)\\n self.model = model\",\n \"def __init__(self, tokens):\\n self.mdl = self.train(tokens)\",\n \"def __init__(self, tokens):\\n self.mdl = self.train(tokens)\",\n \"def __init__(self):\\n self.model = self._get_model()\\n\\n # NOTE: The order of this list hardcoded here, and needs to be changed when re-training the model!\\n # When exporting the model in tflite format, the model_spec is lost, so we cannot do it like that:\\n # classes = ['???'] * model.model_spec.config.num_classes\\n # label_map = model.model_spec.config.label_map\\n # for label_id, label_name in label_map.as_dict().items():\\n # classes[label_id-1] = label_name\\n self.classes = ['Baked Goods', 'Salad', 'Cheese', 'Seafood', 'Tomato']\",\n \"def __init__(self, tags=''):\\n self.tags = tags\",\n \"def _set_attributes(self, model):\\n\\n if model:\\n self._get_dict(model)\",\n \"def model_setup(self):\\n self.DNN = SganMLP(self.settings.number_of_bins)\\n self.D = SganMLP(self.settings.number_of_bins)\\n self.G = Generator()\",\n \"def initialize(self, context):\\n self.initialized = True\\n properties = context.system_properties\\n # Contains the url parameter passed to the load request\\n model_dir = properties.get(\\\"model_dir\\\") \\n gpu_id = properties.get(\\\"gpu_id\\\")\\n\\n # Load Gluonts Model\\n self.mx_model = self.load_model(model_dir)\",\n \"def __init__(self):\\n self.emotion_map = {0: \\\"anger\\\", 1: \\\"fear\\\", 2 : \\\"joy\\\", 3: \\\"sadness\\\"}\\n self.tokenizer = AutoTokenizer.from_pretrained(\\\"MilaNLProc/feel-it-italian-emotion\\\")\\n self.model = AutoModelForSequenceClassification.from_pretrained(\\\"MilaNLProc/feel-it-italian-emotion\\\")\\n self.model.eval()\\n self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\",\n \"def __init__(self, model):\\r\\n self._tensorflow_session = model._tensorflow_session\\r\\n self._model = model\",\n \"def __init__(self, hparams: Namespace):\\n super(TransformerTagger, self).__init__()\\n self.hparams = hparams\\n self.labels = ['O', 'I']\\n config_class, model_class, tokenizer_class = MODEL_CLASSES[self.hparams.model_type]\\n\\n config = config_class.from_pretrained(\\n self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path,\\n num_labels=self.hparams.num_labels,\\n cache_dir=self.hparams.cache_dir if self.hparams.cache_dir else None,\\n )\\n tokenizer = tokenizer_class.from_pretrained(\\n self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,\\n do_lower_case=self.hparams.do_lower_case,\\n cache_dir=self.hparams.cache_dir if self.hparams.cache_dir else None,\\n )\\n model = model_class.from_pretrained(\\n self.hparams.model_name_or_path,\\n from_tf=bool('.ckpt' in self.hparams.model_name_or_path),\\n config=config,\\n cache_dir=self.hparams.cache_dir if self.hparams.cache_dir else None,\\n )\\n\\n self.config, self.tokenizer, self.model = config, tokenizer, model\\n self.pad_token_label_id = nn.CrossEntropyLoss().ignore_index\\n\\n self.prepare_data()\\n\\n # get total train steps - for lr scheduler, idk how to do better for now without double loading\\n self.total_train_steps = self.get_total_train_steps()\\n logger.info('Total training steps: %s', self.total_train_steps)\\n\\n # init predictions\\n self.preds = {\\n 'val': defaultdict(dict),\\n 'test': defaultdict(dict),\\n }\\n\\n self.model_id_name = (f'{hparams.model_name_or_path}_bs-{hparams.train_batch_size}'\\n f'_accum-{hparams.accumulate_grad_batches}'\\n f'_lr-{hparams.lr}_labmode-{hparams.label_mode}'\\n f'_maxep-{hparams.max_epochs}')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77973366","0.77588874","0.76936555","0.731248","0.7298934","0.7228691","0.7228691","0.7228691","0.7228691","0.72052944","0.72052944","0.70650303","0.688462","0.6877822","0.68087727","0.67039186","0.6681423","0.66007894","0.6584187","0.6508255","0.6508255","0.6451797","0.6440915","0.64402145","0.64268804","0.639435","0.639435","0.639435","0.639435","0.639435","0.6377389","0.6367746","0.63579774","0.6353806","0.6337476","0.6336666","0.6317978","0.62982965","0.6288638","0.62784624","0.6267631","0.62616426","0.62609375","0.6259046","0.6252452","0.6252452","0.6252452","0.6252452","0.6252452","0.6247975","0.62397957","0.62397957","0.62397957","0.62397957","0.62397957","0.62236476","0.62175333","0.62175333","0.62140906","0.6207296","0.6200352","0.6200352","0.6160945","0.6160945","0.614311","0.6140096","0.6130771","0.61230665","0.6119536","0.6107536","0.6080028","0.60693645","0.60693645","0.6069353","0.60547537","0.6049069","0.6048357","0.6040373","0.6038281","0.60299057","0.5997748","0.59870166","0.5985503","0.59831387","0.59812033","0.5946865","0.5944664","0.59395057","0.59301597","0.59080637","0.5907069","0.5907069","0.58898336","0.5879545","0.5877352","0.5876571","0.5875276","0.5852154","0.5844922","0.5843205"],"string":"[\n \"0.77973366\",\n \"0.77588874\",\n \"0.76936555\",\n \"0.731248\",\n \"0.7298934\",\n \"0.7228691\",\n \"0.7228691\",\n \"0.7228691\",\n \"0.7228691\",\n \"0.72052944\",\n \"0.72052944\",\n \"0.70650303\",\n \"0.688462\",\n \"0.6877822\",\n \"0.68087727\",\n \"0.67039186\",\n \"0.6681423\",\n \"0.66007894\",\n \"0.6584187\",\n \"0.6508255\",\n \"0.6508255\",\n \"0.6451797\",\n \"0.6440915\",\n \"0.64402145\",\n \"0.64268804\",\n \"0.639435\",\n \"0.639435\",\n \"0.639435\",\n \"0.639435\",\n \"0.639435\",\n \"0.6377389\",\n \"0.6367746\",\n \"0.63579774\",\n \"0.6353806\",\n \"0.6337476\",\n \"0.6336666\",\n \"0.6317978\",\n \"0.62982965\",\n \"0.6288638\",\n \"0.62784624\",\n \"0.6267631\",\n \"0.62616426\",\n \"0.62609375\",\n \"0.6259046\",\n \"0.6252452\",\n \"0.6252452\",\n \"0.6252452\",\n \"0.6252452\",\n \"0.6252452\",\n \"0.6247975\",\n \"0.62397957\",\n \"0.62397957\",\n \"0.62397957\",\n \"0.62397957\",\n \"0.62397957\",\n \"0.62236476\",\n \"0.62175333\",\n \"0.62175333\",\n \"0.62140906\",\n \"0.6207296\",\n \"0.6200352\",\n \"0.6200352\",\n \"0.6160945\",\n \"0.6160945\",\n \"0.614311\",\n \"0.6140096\",\n \"0.6130771\",\n \"0.61230665\",\n \"0.6119536\",\n \"0.6107536\",\n \"0.6080028\",\n \"0.60693645\",\n \"0.60693645\",\n \"0.6069353\",\n \"0.60547537\",\n \"0.6049069\",\n \"0.6048357\",\n \"0.6040373\",\n \"0.6038281\",\n \"0.60299057\",\n \"0.5997748\",\n \"0.59870166\",\n \"0.5985503\",\n \"0.59831387\",\n \"0.59812033\",\n \"0.5946865\",\n \"0.5944664\",\n \"0.59395057\",\n \"0.59301597\",\n \"0.59080637\",\n \"0.5907069\",\n \"0.5907069\",\n \"0.58898336\",\n \"0.5879545\",\n \"0.5877352\",\n \"0.5876571\",\n \"0.5875276\",\n \"0.5852154\",\n \"0.5844922\",\n \"0.5843205\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6052295"},"document_rank":{"kind":"string","value":"75"}}},{"rowIdx":11177,"cells":{"query":{"kind":"string","value":"Get all the tag combinations possible for a tree of length n"},"document":{"kind":"string","value":"def get_all_tag_seq(self, n):\n tags = list(product(self.tags, repeat=n))\n return tags"},"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 fn(n):\n if n == 1: return [TreeNode()]\n ans = []\n for nn in range(1, n, 2): \n for left in fn(nn):\n for right in fn(n-1-nn): \n ans.append(TreeNode(left=left, right=right))\n return ans","def get_subs(n):\n \n from itertools import product\n return [''.join(sub) for sub in product('CATGN', repeat=n)]","def expand_all(start, end, tags):\n if len(tags) == 1:\n for branch in expand(start, end, tags[0]):\n yield [branch]\n else:\n first, rest = tags[0], tags[1:]\n for middle in range(start+1, end+1-len(rest)): \n for first_branch in expand(start, middle, first):\n for rest_branches in expand_all(middle, end, rest):\n yield [first_branch] + rest_branches","def build(n):\n if n not in memo:\n res = []\n if n % 2 != 0:\n for i in range(1, n - 1):\n left = i\n right = n - 1 - i\n if left % 2 == 1 and right % 2 == 1:\n left_ = build(left)\n right_ = build(right)\n for l in left_:\n for r in right_:\n new_tree = TreeNode(0)\n new_tree.left, new_tree.right = l, r\n res.append(new_tree)\n memo[n] = res\n return memo[n]","def make_kmer_tree(self, k, nums):\n nodes = [(np.array([]), [])]\n for it in range(k):\n new_nodes = []\n count = 0\n for i, node in enumerate(nodes):\n n, e = node\n if len(n) < it:\n continue\n for a in nums:\n count += 1\n new_node = (np.append(n, a), [])\n new_nodes.append(new_node)\n nodes[i][1].append(len(nodes) + count - 1)\n nodes += new_nodes\n return nodes","def _get_possible_sense_combinations(self, taggable, tagged):\n\t\tprint(\"\\tget possible combinations...\")\n\t\t# first create a list of the already tagged senses and store for each of those one list inside that contains the one single correct sense\n\t\ttagged_sense_keys = [[(token, token.wn_sense_key)] for token in tagged]\n\t\ttaggable_possible_sense_keys = []\n\n\t\t# for each token that has to be tagged now find all possible senses and collect them\n\t\tfor token in taggable:\n\t\t\ttoken_sense_pairs = []\n\t\t\t# for each possible sense of the token add one to the list of that sense\n\t\t\tpossible_senses = self._get_possible_wn_senses_for_token(token)\n\t\t\tfor single_possible_sense in possible_senses:\n\t\t\t\ttoken_sense_pairs.append((token, single_possible_sense))\n\t\t\ttaggable_possible_sense_keys.append(token_sense_pairs)\n\n\t\tcomplete_list_of_tokens = taggable_possible_sense_keys + tagged_sense_keys\n\n\t\tprint(\"\\t\\t...building combinations\")\n\t\t# return a dot product of the lists of possible senses of all tokens\n\t\treturn list_product(*complete_list_of_tokens)","def chunked_tags(train):\n cfdist = nltk.ConditionalFreqDist()\n for t in train:\n for word, tag, chtag in tree2conlltags(t):\n if chtag == \"O\":\n cfdist[tag].inc(False)\n else:\n cfdist[tag].inc(True)\n return [tag for tag in cfdist.conditions() if cfdist[tag].max() == True]","def make_nodes(n):\n return [\n protein(namespace='NS', name=str(i))\n for i in range(1, n)\n ]","def getTags(number=None):","def generate_ngrams(iterable, n):\n return zip(*[itertools.islice(it, i, None) for i, it in enumerate(itertools.tee(iterable, n))])","def graphs_conn_iso(n):\n def graphs_conn_helper(n):\n for oldg in graphs_conn_iso(n-1):\n for s in powerset(range(n-1)):\n if s == ():\n continue\n g = oldg + [list(s)]\n for v in s:\n g[v] = g[v] + [n-1]\n # NOT g[v] += ... or g[v].append(...)\n # to avoid changing items in oldg\n yield g\n\n assert n >= 0\n if n >= 3:\n for g in unique_iso(graphs_conn_helper(n)):\n yield g\n elif n == 2:\n yield [ [1], [0] ]\n elif n == 1:\n yield [ [] ]\n else: # n == 0\n yield []","def combinations(s, n):\n return (\"\".join(x) for x in tuples(s,n))","def get_paren_combos():\n results = [None] * 4\n options = [('%s', '(%s)')]\n for i in range(1, 4):\n results[i] = list(itertools.product(*(i * options)))\n return results","def ngrams(text, n):\n return chain(*[ngrams_(text, i) for i in range(n + 1)])","def build_ngrams(tokens, n=2):\n ngrams = zip(*(islice(group, idx, None) for idx, group in enumerate(tee(tokens, n))))\n return ngrams","def get_perms(n):\n \n from itertools import permutations\n bases = 'CATGN'\n return [''.join(perm) for perm in permutations(bases, n)]","def tc_gen(n):\r\n comb = (list(tuple) for tuple in itertools.product([True,False], repeat=n))\r\n return list(comb)","def get_children(search_tag, tag_list):\n list_return = []\n\n for tag in tag_list:\n if str(tag.parent) == str(search_tag):\n list_return.append(tag)\n list_return.extend(get_children(tag, tag_list))\n return list(set(list_return)) # This will return a list of unique elements","def find_all_ngrams(dataset, n):\n return zip(*[dataset[i:] for i in xrange(n)])","def partial_tree(s, n):\n if n == 1:\n return (Tree(s.first), s.rest)\n elif n == 2:\n return (Tree(s.first, [Tree(s.rest.first)]), s.rest.rest)\n else:\n left_size = (n-1)//2\n right_size = n - left_size - 1\n \"*** YOUR CODE HERE ***\"","def get_node_tags(self, elem_tag):\n all_node_tag = np.array([], dtype=int)\n if np.size(elem_tag) > 1:\n for ie in range(len(elem_tag)):\n all_node_tag = np.concatenate(\n (all_node_tag, self.get_connectivity(elem_tag[ie]))\n )\n all_node_tag = np.unique(all_node_tag)\n else:\n all_node_tag = self.get_connectivity(elem_tag)\n\n return all_node_tag","def all_structures(self):\n for seq in itertools.product(*map(range, self.tag_sizes)):\n yield np.array(seq)","def get_nodes_combinations(graph):\n nodes = graph.nodes()\n nodes_powerset = []\n for n in chain.from_iterable(combinations(nodes, r) for r in range(len(nodes)+1)):\n if len(n) > 1:\n nodes_powerset.append(list(n))\n return nodes_powerset","def build_tiers(self):\n self.tiers = []\n for size in range(1,len(self.sigma)+1):\n for combo in itertools.combinations(self.sigma, size):\n self.tiers.append(tuple(sorted(list(combo))))","def combos(array,n=2): \n # base case\n if n==0:\n yield frozenset()\n return\n\n # core recursion\n for c in set(combos(array,n-1)):\n for i in array:\n #added this to avoid duplicate combos\n if i not in c:\n # add element i to combo c\n yield frozenset({i})| c","def extensions_at_depth(puzzle, n):\n lst = []\n x = puzzle.extensions()\n if n == 1:\n return x\n for i in x:\n lst.append(i)\n if i.extensions() != None:\n y = extensions_at_depth(i,n-1)\n for j in y:\n lst.append(j)\n return lst","def build_tree(n, d, name=defaultname):\n return build_tree_helper(1, n, 1, d, name)","def reduce_n_times(tree, n, weights, last_round=False):\n k = []\n if n==1:\n aggs_ = list(np.unique(np.array(aggs(tree, weights))))\n agg_weights = [apply_aggregation(t, weights) for t in aggs_]\n H = [calculate_H(x) for x in agg_weights]\n sorted_H = [i[0] for i in sorted(enumerate(H), key=lambda x:x[1], reverse=True)][0:10]\n for i in sorted_H:\n k.append(aggs_[i])\n return k\n if n>1:\n trees = reduce_n_times(tree, n-1, weights)\n results = []\n for tree in trees:\n results = results + aggs(tree, weights)\n l = min([len(x) for x in results])\n results = filter(lambda x: len(x) == l, results)\n results = list(np.unique(np.array(results)))\n agg_weights = [apply_aggregation(t, weights) for t in results]\n S = [calculate_S(x) for x in agg_weights]\n if last_round == True:\n sorted_S = [i[0] for i in sorted(enumerate(S), key=lambda x:x[1], reverse=True)]\n for i in sorted_S:\n k.append(results[i])\n else:\n sorted_S = [i[0] for i in sorted(enumerate(S), key=lambda x:x[1], reverse=True)][0:10]\n for i in sorted_S:\n k.append(results[i])\n return k","def n_grams(tokens, n=1):\n shiftToken = lambda i: (el for j,el in enumerate(tokens) if j>=i)\n shiftedTokens = (shiftToken(i) for i in range(n))\n tupleNGrams = zip(*shiftedTokens)\n return tupleNGrams","def weak_compositions(n, k):\n if n < 0 or k < 0:\n return\n elif k == 0:\n # the empty sum, by convention, is zero, so only return something if\n # n is zero\n if n == 0:\n yield []\n return\n elif k == 1:\n yield [n]\n return\n else:\n # For each first integer i in range(n+1), list all compositions\n # on n-i nodes, of length at most k-1.\n for i in range(n+1):\n for comp in weak_compositions(n-i, k-1):\n yield [i] + comp","def expand(start, end, tag):\n if end-start == 1:\n word = words[start]\n for leaf in lexicon:\n if leaf.tag == tag and leaf.word == word:\n yield leaf\n if tag in grammar:\n for tags in grammar[tag]:\n for branches in expand_all(start, end, tags):\n yield Tree(tag, branches)","def get_ngrams(sequence, n):\n # Example: n = 3, length = 4\n # we want 0:3, 1:4\n\n result = []\n if n == 1:\n result.append(tuple([\"START\"]))\n result.append(tuple([\"STOP\"]))\n\n if n == 3 and len(sequence) == 1:\n result.append(tuple([\"START\",\"START\",sequence[0]]))\n result.append(tuple([\"START\",sequence[0],\"STOP\"]))\n return result\n\n if n > 1 :\n for i in range(n-1):\n result.append(tuple([\"START\"]*(n-1-i)+ sequence[:i+1]))\n result.append(tuple(sequence[-n+1:] + [\"STOP\"]))\n\n for i in range(len(sequence)-n+1):\n result.append(tuple(sequence[i:i+n]))\n return result","def __tagsToNgrams__(self):\n bigrams = defaultdict(int)\n trigrams = defaultdict(int)\n for tags in self.getTags():\n tags = list(tags)\n for i in range(2):\n tags.insert(0, BEGIN)\n for k in range(2, len(tags)):\n trigrams[tuple(tags[k-2:k+1])] += 1\n bigrams[tuple(tags[k-1:k+1])] += 1\n return bigrams, trigrams","def fn(n):\n if n == 0: return [\"\"]\n ans = []\n for k in range(n): \n for x in fn(k): \n for y in fn(n-k-1): \n ans.append(f\"{x}({y})\")\n return ans","def nodes_at_depth(depth):\n return list(range(2**depth-1, 2**(depth+1)-1))","def __combination(orgset, k):\n if k == 1:\n for i in orgset:\n yield (i,)\n elif k > 1:\n for i, x in enumerate(orgset):\n # iterates though to near the end\n for s in __combination(orgset[i + 1 :], k - 1):\n yield (x,) + s","def possibleTuples(nDifferentNodes):\n numberOfPossibleTuples = {}\n for nodeName, numberOfDifferent in nDifferentNodes.iteritems():\n numberOfPossibleTuples[nodeName] = sum([comb(numberOfDifferent,i,exact=True)for i in range(0,4)])\n return numberOfPossibleTuples","def tags(self):\n return tuple(self._tree.getTags())","def number_of_trees_of_order(n):\n if n < 2:\n return n\n result = 0\n for k in range(1, n):\n result += k * number_of_trees_of_order(k) * _s(n-1, k)\n return result // (n - 1)","def get_tags(self, root, recursive=True):\n tags = []\n for child in root.children:\n tags.append(child)\n if recursive:\n tags += self.get_tags(child)\n return tags","def get_terms_at_layern(n, do):\n terms = do.getterms()\n result = set()\n for t in terms.keys():\n layerstemp = terms[t].getlayers()\n if len(layerstemp) != 0 and min(layerstemp) == n:\n result.add(t)\n return result","def Combinations(n, k):\n if int(n) != n or int(k) != k or n < k or k <= 0:\n return None\n\n if k == n:\n return [range(n)]\n elif k == 1:\n return [[ii] for ii in range(n)]\n\n combinations = Combinations(n-1, k)\n combinations_append_last = Combinations(n-1, k-1)\n for ii in range(len(combinations_append_last)):\n combination = combinations_append_last[ii]\n combination.append(n-1)\n combinations.append(combination)\n return combinations","def _expand_combinations(kernel: gpflow.kernels.Kernel) -> List[gpflow.kernels.Kernel]:\n if isinstance(kernel, (gpflow.kernels.Sum, gpflow.kernels.Product)):\n return kernel.children.values()\n\n return []","def get_trees(self, word): # -> list:\r\n raise NotImplementedError","def ngrams(iterable, n=1):\n return zip(*(iterable[i:] for i in range(n)))","def nlp_tree(self, t, s=0):\n if t is not []:\n for word in t:\n # Ignore symbols & punctuation\n if word.pos_ != 'PUNCT' and word.pos_ != 'SYM':\n yield word.lemma_, s\n yield from self.nlp_tree(word.children, s + 1)","def indirect(stack):\n g = nx.Graph(stack)\n for group in nx.connected_components(g):\n yield from map(frozenset, combinations(group, 2))","def s_level_n_descendants(node, n):\r\n if n==0:\r\n return [node]\r\n else:\r\n children = []\r\n for child in node.children:\r\n children.extend(Node.s_level_n_descendants(child, n-1))\r\n return children","def binary_compositions(n):\n return productrange(*[2]*(n-1))","def powerset(n):\n # chain r-combinations generator for r=0, 1,..., n\n return chain.from_iterable(combinations(range(n), r) for r in range(n+1))","def n_grams(tokens, n):\n return zip(*[tokens[i:] for i in range(n)])","def get_ngrams(seq, n):\n return","def _build_tree(self, index):\n\n children = []\n to_string = '({0}/{1}'.format(self.tokens[index], self.labels[index])\n\n for i in range(1, len(self.tokens)):\n\n if i not in self._visited and self.heads[i] == index:\n self._visited.append(i)\n child_tree = {}\n c, s = self._build_tree(i)\n child_tree[(self.spans[i][0], self.spans[i][1], self.tokens[i], self.labels[i])] = c\n children.append(child_tree)\n to_string += ' {0}'.format(s)\n\n if len(children) > 0:\n to_string += ')'\n return children, to_string\n else:\n return children, to_string[1:]","def compute_combinations(items: List[Union[List[Any], Tuple]], n: int) -> List[List[Any]]:\n return [chunks[i:i + n] for chunks in items for i in range(len(chunks) - (n - 1))]","def get_pairs(terms):\n return itertools.combinations(terms, 2)","def test_get_n_bits_combinations():\n # Check n=1 - Pass\n assert layer_util.get_n_bits_combinations(1) == [[0], [1]]\n # Check n=2 - Pass\n assert layer_util.get_n_bits_combinations(2) == [[0, 0], [0, 1], [1, 0], [1, 1]]\n\n # Check n=3 - Pass\n assert layer_util.get_n_bits_combinations(3) == [\n [0, 0, 0],\n [0, 0, 1],\n [0, 1, 0],\n [0, 1, 1],\n [1, 0, 0],\n [1, 0, 1],\n [1, 1, 0],\n [1, 1, 1],\n ]","def test_explode_many(n, k):\n data = [{\"i\": i, \"nested\": [j for j in range(k)]} for i in range(n)]\n c = Clumper(data).explode(j=\"nested\").count(\"j\")\n assert c == n * k","def create_ngrams(word_list, n):\n yield zip(*[word_list[i:] for i in range(n)])","def generate_combinations(k: int, n: int):\n result = list()\n for i in range(1, k + 1):\n for bits in itertools.combinations(range(n), i):\n s = [0] * n\n for bit in bits:\n s[bit] = 1\n result.append(s)\n\n return pd.DataFrame(result)","def graphs(n):\n assert n >= 0\n\n # Special cases for small vertex sets\n if n <= 2:\n if n == 0:\n yield []\n return\n if n == 1:\n yield [ [] ]\n return\n if n == 2:\n yield [ [], [] ]\n yield [ [1], [0] ]\n return\n\n # Make generator yielding all possible edges.\n # If a < b < c, then we yield edge (a,b) before (a,c).\n # If b < c < a, then we yield edge (b,a) before (c,a).\n # As a result, we will construct graph representations having sorted\n # adjacency lists, which our graph representation requires.\n alledges = ( (j, i) for i in range(n) for j in range(i) )\n\n # Generate all graphs\n # We unroll the portion of the loop dealing with edges (0,1), (0,2)\n for edges in powerset(itertools.islice(alledges, 2, None)):\n # unrolling for edges (0,1) and (0,2)\n g = [ [] for v in range(n) ]\n for e in edges:\n g[e[0]].append(e[1])\n g[e[1]].append(e[0])\n yield g\n\n # Add edge (0,1)\n g2 = g[:]\n # We can't use .insert below, since we don't want to modify the\n # items in the list we have (shallowly!) copied.\n g2[0] = [1]+g2[0]\n g2[1] = [0]+g2[1]\n yield g2\n\n # Add edge (0,2)\n g3 = g[:]\n g3[0] = [2]+g3[0]\n g3[2] = [0]+g3[2]\n yield g3\n\n # Add edges (0,1) and (0,2)\n g4 = g3[:] # Not copied from g!\n g4[0] = [1]+g4[0]\n g4[1] = [0]+g4[1]\n yield g4","def ngrams(words, n=1):\n return [tuple(words[j:j + n]) for j in range(len(words) - n + 1)]","def build_tree_helper(x, n, d, max_d, name=defaultname):\n ret = {}\n ret['name'] = name(x)\n if d == max_d:\n return ret\n children = collatz.children(x, n)\n if x == 1:\n children = children[1:]\n if children:\n ret['children'] = [build_tree_helper(x, n, d + 1, max_d, name) for x in children]\n return ret","def get_combinations(text):\n combinations = []\n arr = []\n slen = len(text)\n __find_factor(slen,slen,combinations,arr)\n \n elements = []\n for comb in combinations:\n tmp = [0] + comb\n elements.append([text[tmp[i]:tmp[i]+tmp[i+1]] for i in range(len(tmp)-1)])\n return elements","def get_ngrams(sequence, n):\n length = len(sequence)\n #if only require 1-gram, then we need to add one START and one END to the sequence. \n if n==1 or n==2:\n sequence=[\"START\"]*n+sequence+[\"STOP\"]\n end = n+1 #end i means that when n==1, we need to read one more data, that is to the end of sequence, which is slightly different from when n>1.\n #if require multi-grams, use the common calculation below.\n else:\n sequence = [\"START\"]*(n-1)+sequence+[\"STOP\"]\n end = 1\n if n==2:\n end = n\n result = []\n temp = ()\n #the process to construct the tuple-based array.\n for i in range(0,length+end):\n temp = tuple(sequence[i:i+n])\n\n result.append(temp)\n return result","def graphs_iso(n):\n assert n >= 0\n for g in unique_iso(graphs(n)):\n yield g","def xpotts_states(n, k):\n\n assert n>0, \"n cannot be <0\"\n assert k>=2, \"k cannot be <2\"\n \n for i in range(k**n):\n state = base_repr(i, k)\n yield ['0']*(n-len(state)) + state","def get_words_and_tags(tree, TAG=False):\n if TAG:\n return (re.findall(r'(?<= )?[^( )]+(?=\\))', tree), re.findall(r'(?<=\\()\\w+(?= )',tree))\n else:\n return re.findall(r'(?<= )?[^( )]+(?=\\))', tree)","def just2(n, seq):\n for inner_seq in seq:\n yield tuple(just(n, inner_seq))","def __call__(self, tree):\n chains = list(self._get_chains(tree))\n chain_with_counts, total = self._count_chars(chains)\n return self._concatenates(chain_with_counts, total)","def ngrams(sequence, n):\n # credit: http://stackoverflow.com/questions/2380394/simple-implementation-of-n-gram-tf-idf-and-cosine-similarity-in-python\n sequence = list(sequence)\n count = max(0, len(sequence) - n + 1)\n return [tuple(sequence[i:i+n]) for i in range(count)]","def get_tag_n_gram(n, filename):\n\n from collections import deque\n queue = deque()\n\n count_matrix = {}\n with open(filename) as f:\n for l in f:\n line = l.strip().split()\n if not line:\n continue\n\n tag = line[0]\n # Use a queue to keep track of conditioned string,\n # the occurence of last N - 1 string.\n queue.appendleft(tag)\n\n # Fill the queue until it gets at least N tags.\n if len(queue) > n - 1:\n k = \" \".join(queue)\n if k not in count_matrix.keys():\n count_matrix[k] = 1\n else:\n count_matrix[k] += 1\n\n # Get rid of last one.\n queue.pop()\n\n return count_matrix","def ngrams_(text, n):\n return zip(*[text[i:] for i in range(n)])","def subsets(n):\n binary = lambda x: x>0 and binary(x>>1) + [x&1] or []\n pad = lambda l: [0]*(n-len(l)) + l #Always returns a list of length 'n'\n return [pad(binary(i)) for i in range(1, 2**n)]","def find_ngrams(input_list, n=3):\n return zip(*[input_list[i:] for i in range(n)])","def maximal_chain_binary_trees(self):\n for it in self.maximal_chain_tamari_intervals():\n yield it.lower_binary_tree()","def bruteForcePopulation(N):\n return list(itertools.permutations(range(N), N))","def iter_ngrams(self, sentence, n):\n return [tuple(sentence[i : i+n]) for i in range(len(sentence)-n+1)]","def longest(self, n):\n return big_tags","def tree_of(navigation):\n\n result = []\n for i in navigation:\n node = {\n 'id' : i[0], 'text' : i[1],\n 'url': i[2], 'option': i[3]\n }\n if len(i) >= 5:\n node['children'] = tree_of(i[4])\n else:\n node['children'] = None\n result.append(node)\n return result","def children(self, n):\n if self.left(n):\n yield self.left(n)\n if self.right(n):\n yield self.right(n)","def test_explode_many_many(n, k):\n data = [\n {\"i\": i, \"n1\": [j for j in range(k)], \"n2\": [j for j in range(k)]}\n for i in range(n)\n ]\n c = Clumper(data).explode(j=\"n1\", k=\"n2\").count(\"j\")\n assert c == n * k * k","def ngrams(text, n):\n grams = zip(*[text[i:] for i in range(n)])\n return [''.join(gram) for gram in grams]","def build_tree(n=None) -> CoverMultiWaySearchTree:\n tree = CoverMultiWaySearchTree()\n codes = [currency.code for currency in cur]\n shuffle(codes)\n currencies = [Currency(code) for code in codes]\n if n is None:\n n = len(currencies)\n for currency in currencies[:n]:\n tree[currency._code] = currency\n return tree","def __makeNgrams(self, n):\n # start_time = time.time()\n ngrams = dict()\n itergrams = dict()\n\n for k in range(2,n+1):\n itergrams[k] = list(nltk.ngrams(self.words, k))\n\n for k, grams in itergrams.items():\n kgrams = defaultdict(Counter)\n for gram in grams: \n kgram = list(gram)\n key = ' '.join(kgram[:k-1])\n kgrams[key].update({kgram[-1]})\n ngrams[k] = kgrams\n # print ('finish gen ', k, 'grams at ', time.time()-start_time)\n return ngrams","def gen(length):\n return itertools.product(LABELS,repeat=length)","def get_n_random_itrees(self, n, subs_size):\n random_itrees = np.empty(n, dtype=object) # Allocate list for storing the trees.\n # TODO: parallelize!\n for k in np.arange(n):\n # Get a random sample of training examples to build next random itree.\n data_sub = self.data[np.random.choice(self.data.shape[0], subs_size, replace=False), :]\n random_itrees[k] = self.get_random_itree(data_sub) # Get next random itree \n self.random_itrees = random_itrees\n self.subs_size = subs_size","def part_2():\n return itertools.permutations(range(5, 10))","def _get_exact_k_skip_n_grams(arr, n, k):\n for i in range(len(arr) - n - k + 1):\n part = arr[i:i+n+k]\n\n if k == 0:\n yield part\n else:\n for j in combinations(part[1:-1], n - 2):\n yield tuple([part[0]] + list(j) + [part[-1]])","def tags():","def to_tag_spec(self, tags):\n\n tag_spec = []\n for child in self.all_children():\n if not child.children:\n child_spec = [{'equals': t} for t in child.path()]\n tag_spec.append({'section': child_spec, 'add_tags': tags})\n return tag_spec","def get_ordered_ids(tree):\n ordered_ids = []\n ordered_ids.extend(id(node) for node in tree.gen_tips())\n ordered_ids.extend(id(node) for node in tree.gen_internal_nodes())\n return ordered_ids","def comb(set, n):\n if len(set) < n:\n raise Exception(\"Not enough elements\")\n elif len(set) == n:\n yield set\n else:\n setLen = len(set)\n iters = [rangeIter(setLen - n + 1)]\n values = [0] * n\n values[0] = iters[0].next()\n level = 1\n while True:\n # Fill array of iterators back up\n while level < n:\n iters.append(rangeIter(values[level - 1] + 1,\n setLen - n + level + 1))\n values[level]=iters[level].next()\n level += 1\n subset = [set[i] for i in values]\n yield subset\n while True:\n try:\n values[level - 1] = iters[level - 1].next()\n break\n except StopIteration:\n iters.pop()\n level -= 1\n if level == 0:\n # Top-level iterator is done, so we are too\n raise StopIteration","def make_chains(text_string):\n\n chains = {}\n n = 2\n # n = int(raw_input(\"Enter the number of ngrams you want? >\"))\n # your code goes here\n words = text_string.split()\n for i in range(len(words) - 1):\n ngram = tuple(words[i: i + n])\n if i >= len(words) - n:\n chains[ngram] = None\n else:\n nth_word = words[i + n]\n if ngram not in chains:\n chains[ngram] = [nth_word]\n else:\n chains[ngram].append(nth_word)\n return (chains, n)","def get_parents(tag):\n\n nonlocal depth\n depth = depth - 1\n\n if depth == 0:\n return\n\n if isinstance(tag, str):\n tag = self.tag_to_id[tag]\n\n if tag:\n\n parents = self.parent[tag] - self.categories\n for parent in parents:\n self.categories.add(parent)\n get_parents(parent)","def partitions(n, k):\n if k == 1:\n yield (n,)\n return\n for i in range(1, n):\n for p in partitions(n-i, k-1):\n yield (i,) + p","def tag_ptree(ptree, coreflist):\n pattern = r\"\"\"(?P<lp>\\(?\\s*) # left parenthesis\n (?P<tg>[a-zA-Z$]+)? # POS tag\n (?P<data>\\s*%s) # subtree of tag\n (?P<rp>(?:\\s*\\))*) # right parenthesis\n \"\"\"\n for cid, coref in coreflist[::-1]:\n words = ''.join(word_tokenize(coref['text']))\n\n nltktree = Tree.parse(ptree)\n nltktree.reverse() # perform search right to left\n data = None\n for subtree in nltktree.subtrees(): # BFS\n if ''.join(subtree.leaves()) == words: # equal ignoring whitespace\n data = subtree.pprint()\n break\n\n # If found via breadth-first search of parse tree\n if data:\n ptree = ptree.replace(data, '( COREF_TAG_%s%s)' % (cid, data))\n else: # Try finding via regex matching instead\n dpattern = r'\\s*'.join([r'\\(\\s*[a-zA-Z$]+\\s+%s\\s*\\)' % word\n for word in word_tokenize(coref['text'])])\n found = re.findall(pattern % dpattern, ptree, re.X)\n if found:\n repl = '%s%s ( COREF_TAG_%s%s) %s' % (found[0][0],\n found[0][1],\n cid,\n found[0][2],\n found[0][3])\n ptree = re.sub(pattern % dpattern, repl, ptree, 1, re.X)\n\n return ptree","def get_children_with_tag(self, tag):\n if tag in self._children_tag_table:\n result = self._children_tag_table[tag]\n else:\n result = []\n composite_children = [c for c in self._children.values() if isinstance(c, Composite)]\n grand_children = []\n for c in composite_children:\n grand_children += c.get_children_with_tag(tag)\n return result + grand_children","def flatten(tree,tag):\n List=[]\n Queue=deque([tree])\n try:\n while Queue:\n current=Queue.popleft()\n if type(current)==list: \n if isinstance(current[0],basestring):\n List.append([current[1],tag,current])\n elif isinstance(current[0][0],basestring) and isinstance(current[0][1],(int, long, float, complex)) and type(current[0][2])==list:\n List.append([current[0][1],tag,current])\n Queue.extend(current[1:])\n else:\n return []\n else:\n return []\n except Exception as error:\n print 'error: current=', str(current)\n return []\n List=sorted(List,key=lambda x: x[0])\n return List","def fn(lo, hi):\n if lo == hi: return [None]\n ans = []\n for mid in range(lo, hi): \n for left in fn(lo, mid): \n for right in fn(mid+1, hi): \n ans.append(TreeNode(mid, left, right))\n return ans","def make_chains(text_string, n):\n\n chains = {}\n\n # your code goes here\n words = text_string.split()\n words.append(None)\n \n for i in range(0, len(words)-n):\n t = tuple(words[i:n+i])\n chains[t] = chains.get(t, []) + [words[i+n]]\n \n return chains"],"string":"[\n \"def fn(n):\\n if n == 1: return [TreeNode()]\\n ans = []\\n for nn in range(1, n, 2): \\n for left in fn(nn):\\n for right in fn(n-1-nn): \\n ans.append(TreeNode(left=left, right=right))\\n return ans\",\n \"def get_subs(n):\\n \\n from itertools import product\\n return [''.join(sub) for sub in product('CATGN', repeat=n)]\",\n \"def expand_all(start, end, tags):\\n if len(tags) == 1:\\n for branch in expand(start, end, tags[0]):\\n yield [branch]\\n else:\\n first, rest = tags[0], tags[1:]\\n for middle in range(start+1, end+1-len(rest)): \\n for first_branch in expand(start, middle, first):\\n for rest_branches in expand_all(middle, end, rest):\\n yield [first_branch] + rest_branches\",\n \"def build(n):\\n if n not in memo:\\n res = []\\n if n % 2 != 0:\\n for i in range(1, n - 1):\\n left = i\\n right = n - 1 - i\\n if left % 2 == 1 and right % 2 == 1:\\n left_ = build(left)\\n right_ = build(right)\\n for l in left_:\\n for r in right_:\\n new_tree = TreeNode(0)\\n new_tree.left, new_tree.right = l, r\\n res.append(new_tree)\\n memo[n] = res\\n return memo[n]\",\n \"def make_kmer_tree(self, k, nums):\\n nodes = [(np.array([]), [])]\\n for it in range(k):\\n new_nodes = []\\n count = 0\\n for i, node in enumerate(nodes):\\n n, e = node\\n if len(n) < it:\\n continue\\n for a in nums:\\n count += 1\\n new_node = (np.append(n, a), [])\\n new_nodes.append(new_node)\\n nodes[i][1].append(len(nodes) + count - 1)\\n nodes += new_nodes\\n return nodes\",\n \"def _get_possible_sense_combinations(self, taggable, tagged):\\n\\t\\tprint(\\\"\\\\tget possible combinations...\\\")\\n\\t\\t# first create a list of the already tagged senses and store for each of those one list inside that contains the one single correct sense\\n\\t\\ttagged_sense_keys = [[(token, token.wn_sense_key)] for token in tagged]\\n\\t\\ttaggable_possible_sense_keys = []\\n\\n\\t\\t# for each token that has to be tagged now find all possible senses and collect them\\n\\t\\tfor token in taggable:\\n\\t\\t\\ttoken_sense_pairs = []\\n\\t\\t\\t# for each possible sense of the token add one to the list of that sense\\n\\t\\t\\tpossible_senses = self._get_possible_wn_senses_for_token(token)\\n\\t\\t\\tfor single_possible_sense in possible_senses:\\n\\t\\t\\t\\ttoken_sense_pairs.append((token, single_possible_sense))\\n\\t\\t\\ttaggable_possible_sense_keys.append(token_sense_pairs)\\n\\n\\t\\tcomplete_list_of_tokens = taggable_possible_sense_keys + tagged_sense_keys\\n\\n\\t\\tprint(\\\"\\\\t\\\\t...building combinations\\\")\\n\\t\\t# return a dot product of the lists of possible senses of all tokens\\n\\t\\treturn list_product(*complete_list_of_tokens)\",\n \"def chunked_tags(train):\\n cfdist = nltk.ConditionalFreqDist()\\n for t in train:\\n for word, tag, chtag in tree2conlltags(t):\\n if chtag == \\\"O\\\":\\n cfdist[tag].inc(False)\\n else:\\n cfdist[tag].inc(True)\\n return [tag for tag in cfdist.conditions() if cfdist[tag].max() == True]\",\n \"def make_nodes(n):\\n return [\\n protein(namespace='NS', name=str(i))\\n for i in range(1, n)\\n ]\",\n \"def getTags(number=None):\",\n \"def generate_ngrams(iterable, n):\\n return zip(*[itertools.islice(it, i, None) for i, it in enumerate(itertools.tee(iterable, n))])\",\n \"def graphs_conn_iso(n):\\n def graphs_conn_helper(n):\\n for oldg in graphs_conn_iso(n-1):\\n for s in powerset(range(n-1)):\\n if s == ():\\n continue\\n g = oldg + [list(s)]\\n for v in s:\\n g[v] = g[v] + [n-1]\\n # NOT g[v] += ... or g[v].append(...)\\n # to avoid changing items in oldg\\n yield g\\n\\n assert n >= 0\\n if n >= 3:\\n for g in unique_iso(graphs_conn_helper(n)):\\n yield g\\n elif n == 2:\\n yield [ [1], [0] ]\\n elif n == 1:\\n yield [ [] ]\\n else: # n == 0\\n yield []\",\n \"def combinations(s, n):\\n return (\\\"\\\".join(x) for x in tuples(s,n))\",\n \"def get_paren_combos():\\n results = [None] * 4\\n options = [('%s', '(%s)')]\\n for i in range(1, 4):\\n results[i] = list(itertools.product(*(i * options)))\\n return results\",\n \"def ngrams(text, n):\\n return chain(*[ngrams_(text, i) for i in range(n + 1)])\",\n \"def build_ngrams(tokens, n=2):\\n ngrams = zip(*(islice(group, idx, None) for idx, group in enumerate(tee(tokens, n))))\\n return ngrams\",\n \"def get_perms(n):\\n \\n from itertools import permutations\\n bases = 'CATGN'\\n return [''.join(perm) for perm in permutations(bases, n)]\",\n \"def tc_gen(n):\\r\\n comb = (list(tuple) for tuple in itertools.product([True,False], repeat=n))\\r\\n return list(comb)\",\n \"def get_children(search_tag, tag_list):\\n list_return = []\\n\\n for tag in tag_list:\\n if str(tag.parent) == str(search_tag):\\n list_return.append(tag)\\n list_return.extend(get_children(tag, tag_list))\\n return list(set(list_return)) # This will return a list of unique elements\",\n \"def find_all_ngrams(dataset, n):\\n return zip(*[dataset[i:] for i in xrange(n)])\",\n \"def partial_tree(s, n):\\n if n == 1:\\n return (Tree(s.first), s.rest)\\n elif n == 2:\\n return (Tree(s.first, [Tree(s.rest.first)]), s.rest.rest)\\n else:\\n left_size = (n-1)//2\\n right_size = n - left_size - 1\\n \\\"*** YOUR CODE HERE ***\\\"\",\n \"def get_node_tags(self, elem_tag):\\n all_node_tag = np.array([], dtype=int)\\n if np.size(elem_tag) > 1:\\n for ie in range(len(elem_tag)):\\n all_node_tag = np.concatenate(\\n (all_node_tag, self.get_connectivity(elem_tag[ie]))\\n )\\n all_node_tag = np.unique(all_node_tag)\\n else:\\n all_node_tag = self.get_connectivity(elem_tag)\\n\\n return all_node_tag\",\n \"def all_structures(self):\\n for seq in itertools.product(*map(range, self.tag_sizes)):\\n yield np.array(seq)\",\n \"def get_nodes_combinations(graph):\\n nodes = graph.nodes()\\n nodes_powerset = []\\n for n in chain.from_iterable(combinations(nodes, r) for r in range(len(nodes)+1)):\\n if len(n) > 1:\\n nodes_powerset.append(list(n))\\n return nodes_powerset\",\n \"def build_tiers(self):\\n self.tiers = []\\n for size in range(1,len(self.sigma)+1):\\n for combo in itertools.combinations(self.sigma, size):\\n self.tiers.append(tuple(sorted(list(combo))))\",\n \"def combos(array,n=2): \\n # base case\\n if n==0:\\n yield frozenset()\\n return\\n\\n # core recursion\\n for c in set(combos(array,n-1)):\\n for i in array:\\n #added this to avoid duplicate combos\\n if i not in c:\\n # add element i to combo c\\n yield frozenset({i})| c\",\n \"def extensions_at_depth(puzzle, n):\\n lst = []\\n x = puzzle.extensions()\\n if n == 1:\\n return x\\n for i in x:\\n lst.append(i)\\n if i.extensions() != None:\\n y = extensions_at_depth(i,n-1)\\n for j in y:\\n lst.append(j)\\n return lst\",\n \"def build_tree(n, d, name=defaultname):\\n return build_tree_helper(1, n, 1, d, name)\",\n \"def reduce_n_times(tree, n, weights, last_round=False):\\n k = []\\n if n==1:\\n aggs_ = list(np.unique(np.array(aggs(tree, weights))))\\n agg_weights = [apply_aggregation(t, weights) for t in aggs_]\\n H = [calculate_H(x) for x in agg_weights]\\n sorted_H = [i[0] for i in sorted(enumerate(H), key=lambda x:x[1], reverse=True)][0:10]\\n for i in sorted_H:\\n k.append(aggs_[i])\\n return k\\n if n>1:\\n trees = reduce_n_times(tree, n-1, weights)\\n results = []\\n for tree in trees:\\n results = results + aggs(tree, weights)\\n l = min([len(x) for x in results])\\n results = filter(lambda x: len(x) == l, results)\\n results = list(np.unique(np.array(results)))\\n agg_weights = [apply_aggregation(t, weights) for t in results]\\n S = [calculate_S(x) for x in agg_weights]\\n if last_round == True:\\n sorted_S = [i[0] for i in sorted(enumerate(S), key=lambda x:x[1], reverse=True)]\\n for i in sorted_S:\\n k.append(results[i])\\n else:\\n sorted_S = [i[0] for i in sorted(enumerate(S), key=lambda x:x[1], reverse=True)][0:10]\\n for i in sorted_S:\\n k.append(results[i])\\n return k\",\n \"def n_grams(tokens, n=1):\\n shiftToken = lambda i: (el for j,el in enumerate(tokens) if j>=i)\\n shiftedTokens = (shiftToken(i) for i in range(n))\\n tupleNGrams = zip(*shiftedTokens)\\n return tupleNGrams\",\n \"def weak_compositions(n, k):\\n if n < 0 or k < 0:\\n return\\n elif k == 0:\\n # the empty sum, by convention, is zero, so only return something if\\n # n is zero\\n if n == 0:\\n yield []\\n return\\n elif k == 1:\\n yield [n]\\n return\\n else:\\n # For each first integer i in range(n+1), list all compositions\\n # on n-i nodes, of length at most k-1.\\n for i in range(n+1):\\n for comp in weak_compositions(n-i, k-1):\\n yield [i] + comp\",\n \"def expand(start, end, tag):\\n if end-start == 1:\\n word = words[start]\\n for leaf in lexicon:\\n if leaf.tag == tag and leaf.word == word:\\n yield leaf\\n if tag in grammar:\\n for tags in grammar[tag]:\\n for branches in expand_all(start, end, tags):\\n yield Tree(tag, branches)\",\n \"def get_ngrams(sequence, n):\\n # Example: n = 3, length = 4\\n # we want 0:3, 1:4\\n\\n result = []\\n if n == 1:\\n result.append(tuple([\\\"START\\\"]))\\n result.append(tuple([\\\"STOP\\\"]))\\n\\n if n == 3 and len(sequence) == 1:\\n result.append(tuple([\\\"START\\\",\\\"START\\\",sequence[0]]))\\n result.append(tuple([\\\"START\\\",sequence[0],\\\"STOP\\\"]))\\n return result\\n\\n if n > 1 :\\n for i in range(n-1):\\n result.append(tuple([\\\"START\\\"]*(n-1-i)+ sequence[:i+1]))\\n result.append(tuple(sequence[-n+1:] + [\\\"STOP\\\"]))\\n\\n for i in range(len(sequence)-n+1):\\n result.append(tuple(sequence[i:i+n]))\\n return result\",\n \"def __tagsToNgrams__(self):\\n bigrams = defaultdict(int)\\n trigrams = defaultdict(int)\\n for tags in self.getTags():\\n tags = list(tags)\\n for i in range(2):\\n tags.insert(0, BEGIN)\\n for k in range(2, len(tags)):\\n trigrams[tuple(tags[k-2:k+1])] += 1\\n bigrams[tuple(tags[k-1:k+1])] += 1\\n return bigrams, trigrams\",\n \"def fn(n):\\n if n == 0: return [\\\"\\\"]\\n ans = []\\n for k in range(n): \\n for x in fn(k): \\n for y in fn(n-k-1): \\n ans.append(f\\\"{x}({y})\\\")\\n return ans\",\n \"def nodes_at_depth(depth):\\n return list(range(2**depth-1, 2**(depth+1)-1))\",\n \"def __combination(orgset, k):\\n if k == 1:\\n for i in orgset:\\n yield (i,)\\n elif k > 1:\\n for i, x in enumerate(orgset):\\n # iterates though to near the end\\n for s in __combination(orgset[i + 1 :], k - 1):\\n yield (x,) + s\",\n \"def possibleTuples(nDifferentNodes):\\n numberOfPossibleTuples = {}\\n for nodeName, numberOfDifferent in nDifferentNodes.iteritems():\\n numberOfPossibleTuples[nodeName] = sum([comb(numberOfDifferent,i,exact=True)for i in range(0,4)])\\n return numberOfPossibleTuples\",\n \"def tags(self):\\n return tuple(self._tree.getTags())\",\n \"def number_of_trees_of_order(n):\\n if n < 2:\\n return n\\n result = 0\\n for k in range(1, n):\\n result += k * number_of_trees_of_order(k) * _s(n-1, k)\\n return result // (n - 1)\",\n \"def get_tags(self, root, recursive=True):\\n tags = []\\n for child in root.children:\\n tags.append(child)\\n if recursive:\\n tags += self.get_tags(child)\\n return tags\",\n \"def get_terms_at_layern(n, do):\\n terms = do.getterms()\\n result = set()\\n for t in terms.keys():\\n layerstemp = terms[t].getlayers()\\n if len(layerstemp) != 0 and min(layerstemp) == n:\\n result.add(t)\\n return result\",\n \"def Combinations(n, k):\\n if int(n) != n or int(k) != k or n < k or k <= 0:\\n return None\\n\\n if k == n:\\n return [range(n)]\\n elif k == 1:\\n return [[ii] for ii in range(n)]\\n\\n combinations = Combinations(n-1, k)\\n combinations_append_last = Combinations(n-1, k-1)\\n for ii in range(len(combinations_append_last)):\\n combination = combinations_append_last[ii]\\n combination.append(n-1)\\n combinations.append(combination)\\n return combinations\",\n \"def _expand_combinations(kernel: gpflow.kernels.Kernel) -> List[gpflow.kernels.Kernel]:\\n if isinstance(kernel, (gpflow.kernels.Sum, gpflow.kernels.Product)):\\n return kernel.children.values()\\n\\n return []\",\n \"def get_trees(self, word): # -> list:\\r\\n raise NotImplementedError\",\n \"def ngrams(iterable, n=1):\\n return zip(*(iterable[i:] for i in range(n)))\",\n \"def nlp_tree(self, t, s=0):\\n if t is not []:\\n for word in t:\\n # Ignore symbols & punctuation\\n if word.pos_ != 'PUNCT' and word.pos_ != 'SYM':\\n yield word.lemma_, s\\n yield from self.nlp_tree(word.children, s + 1)\",\n \"def indirect(stack):\\n g = nx.Graph(stack)\\n for group in nx.connected_components(g):\\n yield from map(frozenset, combinations(group, 2))\",\n \"def s_level_n_descendants(node, n):\\r\\n if n==0:\\r\\n return [node]\\r\\n else:\\r\\n children = []\\r\\n for child in node.children:\\r\\n children.extend(Node.s_level_n_descendants(child, n-1))\\r\\n return children\",\n \"def binary_compositions(n):\\n return productrange(*[2]*(n-1))\",\n \"def powerset(n):\\n # chain r-combinations generator for r=0, 1,..., n\\n return chain.from_iterable(combinations(range(n), r) for r in range(n+1))\",\n \"def n_grams(tokens, n):\\n return zip(*[tokens[i:] for i in range(n)])\",\n \"def get_ngrams(seq, n):\\n return\",\n \"def _build_tree(self, index):\\n\\n children = []\\n to_string = '({0}/{1}'.format(self.tokens[index], self.labels[index])\\n\\n for i in range(1, len(self.tokens)):\\n\\n if i not in self._visited and self.heads[i] == index:\\n self._visited.append(i)\\n child_tree = {}\\n c, s = self._build_tree(i)\\n child_tree[(self.spans[i][0], self.spans[i][1], self.tokens[i], self.labels[i])] = c\\n children.append(child_tree)\\n to_string += ' {0}'.format(s)\\n\\n if len(children) > 0:\\n to_string += ')'\\n return children, to_string\\n else:\\n return children, to_string[1:]\",\n \"def compute_combinations(items: List[Union[List[Any], Tuple]], n: int) -> List[List[Any]]:\\n return [chunks[i:i + n] for chunks in items for i in range(len(chunks) - (n - 1))]\",\n \"def get_pairs(terms):\\n return itertools.combinations(terms, 2)\",\n \"def test_get_n_bits_combinations():\\n # Check n=1 - Pass\\n assert layer_util.get_n_bits_combinations(1) == [[0], [1]]\\n # Check n=2 - Pass\\n assert layer_util.get_n_bits_combinations(2) == [[0, 0], [0, 1], [1, 0], [1, 1]]\\n\\n # Check n=3 - Pass\\n assert layer_util.get_n_bits_combinations(3) == [\\n [0, 0, 0],\\n [0, 0, 1],\\n [0, 1, 0],\\n [0, 1, 1],\\n [1, 0, 0],\\n [1, 0, 1],\\n [1, 1, 0],\\n [1, 1, 1],\\n ]\",\n \"def test_explode_many(n, k):\\n data = [{\\\"i\\\": i, \\\"nested\\\": [j for j in range(k)]} for i in range(n)]\\n c = Clumper(data).explode(j=\\\"nested\\\").count(\\\"j\\\")\\n assert c == n * k\",\n \"def create_ngrams(word_list, n):\\n yield zip(*[word_list[i:] for i in range(n)])\",\n \"def generate_combinations(k: int, n: int):\\n result = list()\\n for i in range(1, k + 1):\\n for bits in itertools.combinations(range(n), i):\\n s = [0] * n\\n for bit in bits:\\n s[bit] = 1\\n result.append(s)\\n\\n return pd.DataFrame(result)\",\n \"def graphs(n):\\n assert n >= 0\\n\\n # Special cases for small vertex sets\\n if n <= 2:\\n if n == 0:\\n yield []\\n return\\n if n == 1:\\n yield [ [] ]\\n return\\n if n == 2:\\n yield [ [], [] ]\\n yield [ [1], [0] ]\\n return\\n\\n # Make generator yielding all possible edges.\\n # If a < b < c, then we yield edge (a,b) before (a,c).\\n # If b < c < a, then we yield edge (b,a) before (c,a).\\n # As a result, we will construct graph representations having sorted\\n # adjacency lists, which our graph representation requires.\\n alledges = ( (j, i) for i in range(n) for j in range(i) )\\n\\n # Generate all graphs\\n # We unroll the portion of the loop dealing with edges (0,1), (0,2)\\n for edges in powerset(itertools.islice(alledges, 2, None)):\\n # unrolling for edges (0,1) and (0,2)\\n g = [ [] for v in range(n) ]\\n for e in edges:\\n g[e[0]].append(e[1])\\n g[e[1]].append(e[0])\\n yield g\\n\\n # Add edge (0,1)\\n g2 = g[:]\\n # We can't use .insert below, since we don't want to modify the\\n # items in the list we have (shallowly!) copied.\\n g2[0] = [1]+g2[0]\\n g2[1] = [0]+g2[1]\\n yield g2\\n\\n # Add edge (0,2)\\n g3 = g[:]\\n g3[0] = [2]+g3[0]\\n g3[2] = [0]+g3[2]\\n yield g3\\n\\n # Add edges (0,1) and (0,2)\\n g4 = g3[:] # Not copied from g!\\n g4[0] = [1]+g4[0]\\n g4[1] = [0]+g4[1]\\n yield g4\",\n \"def ngrams(words, n=1):\\n return [tuple(words[j:j + n]) for j in range(len(words) - n + 1)]\",\n \"def build_tree_helper(x, n, d, max_d, name=defaultname):\\n ret = {}\\n ret['name'] = name(x)\\n if d == max_d:\\n return ret\\n children = collatz.children(x, n)\\n if x == 1:\\n children = children[1:]\\n if children:\\n ret['children'] = [build_tree_helper(x, n, d + 1, max_d, name) for x in children]\\n return ret\",\n \"def get_combinations(text):\\n combinations = []\\n arr = []\\n slen = len(text)\\n __find_factor(slen,slen,combinations,arr)\\n \\n elements = []\\n for comb in combinations:\\n tmp = [0] + comb\\n elements.append([text[tmp[i]:tmp[i]+tmp[i+1]] for i in range(len(tmp)-1)])\\n return elements\",\n \"def get_ngrams(sequence, n):\\n length = len(sequence)\\n #if only require 1-gram, then we need to add one START and one END to the sequence. \\n if n==1 or n==2:\\n sequence=[\\\"START\\\"]*n+sequence+[\\\"STOP\\\"]\\n end = n+1 #end i means that when n==1, we need to read one more data, that is to the end of sequence, which is slightly different from when n>1.\\n #if require multi-grams, use the common calculation below.\\n else:\\n sequence = [\\\"START\\\"]*(n-1)+sequence+[\\\"STOP\\\"]\\n end = 1\\n if n==2:\\n end = n\\n result = []\\n temp = ()\\n #the process to construct the tuple-based array.\\n for i in range(0,length+end):\\n temp = tuple(sequence[i:i+n])\\n\\n result.append(temp)\\n return result\",\n \"def graphs_iso(n):\\n assert n >= 0\\n for g in unique_iso(graphs(n)):\\n yield g\",\n \"def xpotts_states(n, k):\\n\\n assert n>0, \\\"n cannot be <0\\\"\\n assert k>=2, \\\"k cannot be <2\\\"\\n \\n for i in range(k**n):\\n state = base_repr(i, k)\\n yield ['0']*(n-len(state)) + state\",\n \"def get_words_and_tags(tree, TAG=False):\\n if TAG:\\n return (re.findall(r'(?<= )?[^( )]+(?=\\\\))', tree), re.findall(r'(?<=\\\\()\\\\w+(?= )',tree))\\n else:\\n return re.findall(r'(?<= )?[^( )]+(?=\\\\))', tree)\",\n \"def just2(n, seq):\\n for inner_seq in seq:\\n yield tuple(just(n, inner_seq))\",\n \"def __call__(self, tree):\\n chains = list(self._get_chains(tree))\\n chain_with_counts, total = self._count_chars(chains)\\n return self._concatenates(chain_with_counts, total)\",\n \"def ngrams(sequence, n):\\n # credit: http://stackoverflow.com/questions/2380394/simple-implementation-of-n-gram-tf-idf-and-cosine-similarity-in-python\\n sequence = list(sequence)\\n count = max(0, len(sequence) - n + 1)\\n return [tuple(sequence[i:i+n]) for i in range(count)]\",\n \"def get_tag_n_gram(n, filename):\\n\\n from collections import deque\\n queue = deque()\\n\\n count_matrix = {}\\n with open(filename) as f:\\n for l in f:\\n line = l.strip().split()\\n if not line:\\n continue\\n\\n tag = line[0]\\n # Use a queue to keep track of conditioned string,\\n # the occurence of last N - 1 string.\\n queue.appendleft(tag)\\n\\n # Fill the queue until it gets at least N tags.\\n if len(queue) > n - 1:\\n k = \\\" \\\".join(queue)\\n if k not in count_matrix.keys():\\n count_matrix[k] = 1\\n else:\\n count_matrix[k] += 1\\n\\n # Get rid of last one.\\n queue.pop()\\n\\n return count_matrix\",\n \"def ngrams_(text, n):\\n return zip(*[text[i:] for i in range(n)])\",\n \"def subsets(n):\\n binary = lambda x: x>0 and binary(x>>1) + [x&1] or []\\n pad = lambda l: [0]*(n-len(l)) + l #Always returns a list of length 'n'\\n return [pad(binary(i)) for i in range(1, 2**n)]\",\n \"def find_ngrams(input_list, n=3):\\n return zip(*[input_list[i:] for i in range(n)])\",\n \"def maximal_chain_binary_trees(self):\\n for it in self.maximal_chain_tamari_intervals():\\n yield it.lower_binary_tree()\",\n \"def bruteForcePopulation(N):\\n return list(itertools.permutations(range(N), N))\",\n \"def iter_ngrams(self, sentence, n):\\n return [tuple(sentence[i : i+n]) for i in range(len(sentence)-n+1)]\",\n \"def longest(self, n):\\n return big_tags\",\n \"def tree_of(navigation):\\n\\n result = []\\n for i in navigation:\\n node = {\\n 'id' : i[0], 'text' : i[1],\\n 'url': i[2], 'option': i[3]\\n }\\n if len(i) >= 5:\\n node['children'] = tree_of(i[4])\\n else:\\n node['children'] = None\\n result.append(node)\\n return result\",\n \"def children(self, n):\\n if self.left(n):\\n yield self.left(n)\\n if self.right(n):\\n yield self.right(n)\",\n \"def test_explode_many_many(n, k):\\n data = [\\n {\\\"i\\\": i, \\\"n1\\\": [j for j in range(k)], \\\"n2\\\": [j for j in range(k)]}\\n for i in range(n)\\n ]\\n c = Clumper(data).explode(j=\\\"n1\\\", k=\\\"n2\\\").count(\\\"j\\\")\\n assert c == n * k * k\",\n \"def ngrams(text, n):\\n grams = zip(*[text[i:] for i in range(n)])\\n return [''.join(gram) for gram in grams]\",\n \"def build_tree(n=None) -> CoverMultiWaySearchTree:\\n tree = CoverMultiWaySearchTree()\\n codes = [currency.code for currency in cur]\\n shuffle(codes)\\n currencies = [Currency(code) for code in codes]\\n if n is None:\\n n = len(currencies)\\n for currency in currencies[:n]:\\n tree[currency._code] = currency\\n return tree\",\n \"def __makeNgrams(self, n):\\n # start_time = time.time()\\n ngrams = dict()\\n itergrams = dict()\\n\\n for k in range(2,n+1):\\n itergrams[k] = list(nltk.ngrams(self.words, k))\\n\\n for k, grams in itergrams.items():\\n kgrams = defaultdict(Counter)\\n for gram in grams: \\n kgram = list(gram)\\n key = ' '.join(kgram[:k-1])\\n kgrams[key].update({kgram[-1]})\\n ngrams[k] = kgrams\\n # print ('finish gen ', k, 'grams at ', time.time()-start_time)\\n return ngrams\",\n \"def gen(length):\\n return itertools.product(LABELS,repeat=length)\",\n \"def get_n_random_itrees(self, n, subs_size):\\n random_itrees = np.empty(n, dtype=object) # Allocate list for storing the trees.\\n # TODO: parallelize!\\n for k in np.arange(n):\\n # Get a random sample of training examples to build next random itree.\\n data_sub = self.data[np.random.choice(self.data.shape[0], subs_size, replace=False), :]\\n random_itrees[k] = self.get_random_itree(data_sub) # Get next random itree \\n self.random_itrees = random_itrees\\n self.subs_size = subs_size\",\n \"def part_2():\\n return itertools.permutations(range(5, 10))\",\n \"def _get_exact_k_skip_n_grams(arr, n, k):\\n for i in range(len(arr) - n - k + 1):\\n part = arr[i:i+n+k]\\n\\n if k == 0:\\n yield part\\n else:\\n for j in combinations(part[1:-1], n - 2):\\n yield tuple([part[0]] + list(j) + [part[-1]])\",\n \"def tags():\",\n \"def to_tag_spec(self, tags):\\n\\n tag_spec = []\\n for child in self.all_children():\\n if not child.children:\\n child_spec = [{'equals': t} for t in child.path()]\\n tag_spec.append({'section': child_spec, 'add_tags': tags})\\n return tag_spec\",\n \"def get_ordered_ids(tree):\\n ordered_ids = []\\n ordered_ids.extend(id(node) for node in tree.gen_tips())\\n ordered_ids.extend(id(node) for node in tree.gen_internal_nodes())\\n return ordered_ids\",\n \"def comb(set, n):\\n if len(set) < n:\\n raise Exception(\\\"Not enough elements\\\")\\n elif len(set) == n:\\n yield set\\n else:\\n setLen = len(set)\\n iters = [rangeIter(setLen - n + 1)]\\n values = [0] * n\\n values[0] = iters[0].next()\\n level = 1\\n while True:\\n # Fill array of iterators back up\\n while level < n:\\n iters.append(rangeIter(values[level - 1] + 1,\\n setLen - n + level + 1))\\n values[level]=iters[level].next()\\n level += 1\\n subset = [set[i] for i in values]\\n yield subset\\n while True:\\n try:\\n values[level - 1] = iters[level - 1].next()\\n break\\n except StopIteration:\\n iters.pop()\\n level -= 1\\n if level == 0:\\n # Top-level iterator is done, so we are too\\n raise StopIteration\",\n \"def make_chains(text_string):\\n\\n chains = {}\\n n = 2\\n # n = int(raw_input(\\\"Enter the number of ngrams you want? >\\\"))\\n # your code goes here\\n words = text_string.split()\\n for i in range(len(words) - 1):\\n ngram = tuple(words[i: i + n])\\n if i >= len(words) - n:\\n chains[ngram] = None\\n else:\\n nth_word = words[i + n]\\n if ngram not in chains:\\n chains[ngram] = [nth_word]\\n else:\\n chains[ngram].append(nth_word)\\n return (chains, n)\",\n \"def get_parents(tag):\\n\\n nonlocal depth\\n depth = depth - 1\\n\\n if depth == 0:\\n return\\n\\n if isinstance(tag, str):\\n tag = self.tag_to_id[tag]\\n\\n if tag:\\n\\n parents = self.parent[tag] - self.categories\\n for parent in parents:\\n self.categories.add(parent)\\n get_parents(parent)\",\n \"def partitions(n, k):\\n if k == 1:\\n yield (n,)\\n return\\n for i in range(1, n):\\n for p in partitions(n-i, k-1):\\n yield (i,) + p\",\n \"def tag_ptree(ptree, coreflist):\\n pattern = r\\\"\\\"\\\"(?P<lp>\\\\(?\\\\s*) # left parenthesis\\n (?P<tg>[a-zA-Z$]+)? # POS tag\\n (?P<data>\\\\s*%s) # subtree of tag\\n (?P<rp>(?:\\\\s*\\\\))*) # right parenthesis\\n \\\"\\\"\\\"\\n for cid, coref in coreflist[::-1]:\\n words = ''.join(word_tokenize(coref['text']))\\n\\n nltktree = Tree.parse(ptree)\\n nltktree.reverse() # perform search right to left\\n data = None\\n for subtree in nltktree.subtrees(): # BFS\\n if ''.join(subtree.leaves()) == words: # equal ignoring whitespace\\n data = subtree.pprint()\\n break\\n\\n # If found via breadth-first search of parse tree\\n if data:\\n ptree = ptree.replace(data, '( COREF_TAG_%s%s)' % (cid, data))\\n else: # Try finding via regex matching instead\\n dpattern = r'\\\\s*'.join([r'\\\\(\\\\s*[a-zA-Z$]+\\\\s+%s\\\\s*\\\\)' % word\\n for word in word_tokenize(coref['text'])])\\n found = re.findall(pattern % dpattern, ptree, re.X)\\n if found:\\n repl = '%s%s ( COREF_TAG_%s%s) %s' % (found[0][0],\\n found[0][1],\\n cid,\\n found[0][2],\\n found[0][3])\\n ptree = re.sub(pattern % dpattern, repl, ptree, 1, re.X)\\n\\n return ptree\",\n \"def get_children_with_tag(self, tag):\\n if tag in self._children_tag_table:\\n result = self._children_tag_table[tag]\\n else:\\n result = []\\n composite_children = [c for c in self._children.values() if isinstance(c, Composite)]\\n grand_children = []\\n for c in composite_children:\\n grand_children += c.get_children_with_tag(tag)\\n return result + grand_children\",\n \"def flatten(tree,tag):\\n List=[]\\n Queue=deque([tree])\\n try:\\n while Queue:\\n current=Queue.popleft()\\n if type(current)==list: \\n if isinstance(current[0],basestring):\\n List.append([current[1],tag,current])\\n elif isinstance(current[0][0],basestring) and isinstance(current[0][1],(int, long, float, complex)) and type(current[0][2])==list:\\n List.append([current[0][1],tag,current])\\n Queue.extend(current[1:])\\n else:\\n return []\\n else:\\n return []\\n except Exception as error:\\n print 'error: current=', str(current)\\n return []\\n List=sorted(List,key=lambda x: x[0])\\n return List\",\n \"def fn(lo, hi):\\n if lo == hi: return [None]\\n ans = []\\n for mid in range(lo, hi): \\n for left in fn(lo, mid): \\n for right in fn(mid+1, hi): \\n ans.append(TreeNode(mid, left, right))\\n return ans\",\n \"def make_chains(text_string, n):\\n\\n chains = {}\\n\\n # your code goes here\\n words = text_string.split()\\n words.append(None)\\n \\n for i in range(0, len(words)-n):\\n t = tuple(words[i:n+i])\\n chains[t] = chains.get(t, []) + [words[i+n]]\\n \\n return chains\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6263927","0.61682093","0.60873145","0.599515","0.596374","0.5940209","0.5909812","0.5883347","0.58303434","0.58066684","0.5776324","0.57579505","0.57390195","0.5715532","0.57020915","0.5674218","0.5645066","0.5627819","0.56251854","0.56197864","0.56149757","0.5593644","0.5583311","0.55824214","0.5582329","0.5573191","0.55583835","0.5542587","0.55370206","0.5525006","0.5457616","0.54527533","0.5452245","0.54428697","0.5436483","0.54279876","0.5427104","0.54131746","0.5409967","0.54066813","0.5405035","0.5401191","0.5400602","0.5396719","0.53852385","0.5381261","0.5376291","0.5370858","0.53701997","0.53664947","0.5355713","0.53544647","0.53464425","0.5344427","0.53439987","0.5339068","0.53353375","0.5332245","0.53225136","0.5314118","0.53073806","0.5289853","0.528787","0.52842486","0.52771074","0.5275688","0.5272715","0.5270324","0.526857","0.52633697","0.5257773","0.525","0.52368313","0.52240366","0.5218096","0.52176714","0.5216421","0.52002335","0.51995295","0.519243","0.5188391","0.51876324","0.51856136","0.5179179","0.51754254","0.51706296","0.5164883","0.5162642","0.5154056","0.5134779","0.51332116","0.5132675","0.51255655","0.5121732","0.5115375","0.5115262","0.5104871","0.5104688","0.5099551","0.50945574"],"string":"[\n \"0.6263927\",\n \"0.61682093\",\n \"0.60873145\",\n \"0.599515\",\n \"0.596374\",\n \"0.5940209\",\n \"0.5909812\",\n \"0.5883347\",\n \"0.58303434\",\n \"0.58066684\",\n \"0.5776324\",\n \"0.57579505\",\n \"0.57390195\",\n \"0.5715532\",\n \"0.57020915\",\n \"0.5674218\",\n \"0.5645066\",\n \"0.5627819\",\n \"0.56251854\",\n \"0.56197864\",\n \"0.56149757\",\n \"0.5593644\",\n \"0.5583311\",\n \"0.55824214\",\n \"0.5582329\",\n \"0.5573191\",\n \"0.55583835\",\n \"0.5542587\",\n \"0.55370206\",\n \"0.5525006\",\n \"0.5457616\",\n \"0.54527533\",\n \"0.5452245\",\n \"0.54428697\",\n \"0.5436483\",\n \"0.54279876\",\n \"0.5427104\",\n \"0.54131746\",\n \"0.5409967\",\n \"0.54066813\",\n \"0.5405035\",\n \"0.5401191\",\n \"0.5400602\",\n \"0.5396719\",\n \"0.53852385\",\n \"0.5381261\",\n \"0.5376291\",\n \"0.5370858\",\n \"0.53701997\",\n \"0.53664947\",\n \"0.5355713\",\n \"0.53544647\",\n \"0.53464425\",\n \"0.5344427\",\n \"0.53439987\",\n \"0.5339068\",\n \"0.53353375\",\n \"0.5332245\",\n \"0.53225136\",\n \"0.5314118\",\n \"0.53073806\",\n \"0.5289853\",\n \"0.528787\",\n \"0.52842486\",\n \"0.52771074\",\n \"0.5275688\",\n \"0.5272715\",\n \"0.5270324\",\n \"0.526857\",\n \"0.52633697\",\n \"0.5257773\",\n \"0.525\",\n \"0.52368313\",\n \"0.52240366\",\n \"0.5218096\",\n \"0.52176714\",\n \"0.5216421\",\n \"0.52002335\",\n \"0.51995295\",\n \"0.519243\",\n \"0.5188391\",\n \"0.51876324\",\n \"0.51856136\",\n \"0.5179179\",\n \"0.51754254\",\n \"0.51706296\",\n \"0.5164883\",\n \"0.5162642\",\n \"0.5154056\",\n \"0.5134779\",\n \"0.51332116\",\n \"0.5132675\",\n \"0.51255655\",\n \"0.5121732\",\n \"0.5115375\",\n \"0.5115262\",\n \"0.5104871\",\n \"0.5104688\",\n \"0.5099551\",\n \"0.50945574\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6972763"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11178,"cells":{"query":{"kind":"string","value":"Get index of a tag sequence m in self.tags"},"document":{"kind":"string","value":"def get_tag_index(self, m):\n return self.tags.index(m)"},"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 index_in_tag(self):\n if hasattr(self, '_m_index_in_tag'):\n return self._m_index_in_tag if hasattr(self, '_m_index_in_tag') else None\n\n self._m_index_in_tag = (self.tag - 35)\n return self._m_index_in_tag if hasattr(self, '_m_index_in_tag') else None","def get_tag_index(tags, tag_to_search):\n counter = 0\n for t in tags:\n if tag_to_search == t:\n break\n else:\n counter+=1\n return counter","def get_index(tag):\n global kpi_list\n try:\n return kpi_list.index(str(tag))\n except ValueError:\n return -1","def index_tag_in_lines(lines, tag):\n for index, line in enumerate(lines):\n if tag in line:\n return index\n raise ValueError(f'{tag} not found.')","def index_tag_seq(words, seq, strict=False):\n tags = get_tag_seq(words)\n nouns = 'NN' in seq or 'NNS' in seq\n alt_seq = None\n if strict is False:\n if nouns is True:\n alt_seq = [\n 'NNS' if x == 'NN' else \n 'NN' if x == 'NNS' else \n x for x in seq\n ] \n \n for i in range(len(tags)):\n check_seq = tags[i:i+len(seq)]\n if check_seq == seq:\n return i\n if nouns:\n if check_seq == alt_seq:\n return i\n\n return -1","def indexof(self, value, tag=WORD):\n match = lambda a, b: a.endswith(\"*\") and b.startswith(a[:-1]) or a==b\n indices = []\n for i in range(len(self.words)):\n if match(value, unicode(self.get(i, tag))):\n indices.append(i)\n return indices","def index(self, key):\n return self._sequence.index(key)","def tagkeyindex(self,tag):\r\n\r\n returnset = set()\r\n if self.tag_dict_contains(tag):\r\n\r\n for x_temp in self.get_keys_for_tag(tag):\r\n if self.key_dict_contains(x_temp+'/'+tag):\r\n for y_temp in self.get_indexes_for_key(x_temp+'/'+tag):\r\n returnset.add(y_temp)\r\n return returnset","def get_tag_indices(tags):\n tag_counts = count_tags(tags);\n tag_names = tag_counts.keys()\n return {name : index for (index, name) in enumerate(tag_names)}","def get_index(uid, i):\n return _SHARED_SEQUENCES[uid][i]","def get_idx(self, key):\n found = [i for i, e in enumerate(self.list) if e.key == key]\n if found:\n return found[0]\n\n else:\n return -1","def get_index(attribute, attributes):\n for i in range(14):\n if attribute == attributes[i]:\n return i","def index(self, word):\n return self.tokens.index(word)","def get_tag_for_word(self, word: str):\n doc = self.model(word)\n for token in doc:\n return token.pos_","def token_to_idx(self) -> Dict[Hashable, int]:\n return self._token_to_idx","def offset(self, needle):\n if isinstance(needle, int):\n if needle.bit_length() <= 32:\n needle = p32(needle)\n else:\n needle = p64(needle)\n needle = d(needle)\n\n idx = self._seq.index(needle)\n if self._seq[idx+len(needle):].find(needle) != -1:\n raise ValueError(\"Multiple occurances found!\")\n\n return idx","def indexMatching(seq, condition):\n for i,x in enumerate(seq):\n if condition(x):\n return i\n return -1","def index(self, atom):\n return self.atom_list.index(atom)","def _code_indices(self) -> Tuple[int, ...]:\n return tuple(idx for idx, seg in enumerate(self.segments) if seg.is_code)","def getOmIndex(self, name):\n for i in range(len(self.oameni)):\n if self.oameni[i].name == name:\n return i\n return None","def _index(self,key):\n index=0\n for item in self._item:\n if item.key==key:\n return index\n index+=1\n return -1","def all_lines_with_tag(mm, tag, nline_max=1024*1024):\n all_idx = []\n for iline in range(nline_max):\n idx = mm.find(tag.encode())\n if idx == -1:\n break\n mm.seek(idx)\n all_idx.append(idx)\n mm.readline()\n\n # guard\n if iline >= nline_max-1:\n raise RuntimeError('may need to increase nline_max')\n return all_idx","def __getitem__(self, tag):\n return self.__tags.get(tag.lower(), 0)","def _get_signal_index(self, signal):\n # Process signal :\n signal = signal.replace(', :', '').replace(':, ', '')[1:-1]\n # Find index :\n idx = tuple(int(k) for k in signal.split(', '))\n return self._navidx.index(idx)","def idx(self):\n return self._idx","def element_index(self):\n return self._index","def index(self, item):\n\t\ti = 0\t\t\n\t\tif not self.len:\n\t\t\traise ValueError(\"Lista vacia\")\n\t\tif self.prim.dato == item:\n\t\t\treturn i\n\t\tactual = self.prim\n\t\twhile actual and actual.dato != item:\n\t\t\tactual = actual.prox\n\t\t\ti += 1\n\t\tif not actual:\n\t\t\traise ValueError(\"Elemento no encontrado\")\n\t\treturn i","def set_indx(self, item):\n for i, s in enumerate(self._data):\n if item in s:\n return i\n return None","def getAttrIndex(self, *args):\n return _libsbml.XMLToken_getAttrIndex(self, *args)","def get_index(self, gi):\n for i in range(len(self.gradual_items)):\n gi_obj = self.gradual_items[i]\n if (gi.symbol == gi_obj.symbol) and (gi.attribute_col == gi_obj.attribute_col):\n return i\n return -1","def findindex(iteratee, seq):\n iteratee = fnc.iteratee(iteratee)\n return next((i for i, value in enumerate(seq) if iteratee(value)), -1)","def get_index_from_nome(self,serie,nome_imagem):\r\n index = serie[serie == nome_imagem].index.tolist()\r\n index = str(index).replace('[','').replace(']','')\r\n return index","def index(self, elem):\n ponteiro = self.inicio\n i = 0\n while(ponteiro):\n if ponteiro.dado == elem:\n return i\n ponteiro = ponteiro.prox\n i = i + 1\n raise ValueError(\"{} is not in list\".format(elem))","def index(self, pos):\n for i, n in enumerate(self):\n if i == pos: return n\n raise Exception('Index out of bounds.')","def findRepIdx(self, rep, cell = 1):\n\n match = self.findRep(rep = rep, cell = cell)\n return np.arange(self.atoms.shape[0])[match]","def ionNameToIndex(self, name):\n for i in range(len(self.ions)):\n if self.ions[i].getName() == name:\n return i\n\n raise KeyError(\"No ion named '{}' found in the output.\".format(name))","def index(self, value, start=0, stop=-1):\n return self.elem.index(value, start, stop)","def tokens_to_idxs(self, token_seqs, lexicon):\n idx_seqs = [[lexicon[token] if token in lexicon else lexicon['<UNK>'] for \n token in token_seq] for token_seq in token_seqs]\n return idx_seqs","def word2idx(self, str):\n return self._tokens.get(str)","def get_tag(self) -> int:\n return self.tag","def idx(self):\n return int(self.__ph.get('idx', 0))","def get_idx_for_item(self, item: str) -> int:\n item = item.encode('utf-8')\n if item in self.item2idx.keys():\n return self.item2idx[item]\n else:\n return 0","def getPosTagAt(self, pos):\n return self.sentence[pos].getPosTag()","def find_motif(self, msa, motif):\n with open(msa, \"r\") as file:\n msa = file.read()\n msa_list = constool.indv_block(msa)\n index = -1\n for prot in msa_list:\n prot = constool.seqnwl_strip(prot)\n prot = constool.get_fasta_sequence(prot)\n index = prot.find(motif)\n if not index == -1:\n break\n return index","def _get_cand_index(signature):\n\n # This map translates between the last \"I<n>\" field value and the\n # actual CAND cell index.\n INDEX_MAP = {\n 10: 0,\n 9: 1,\n 8: 2,\n 7: 3,\n 6: 4,\n }\n\n # Split the signature\n parts = signature.split(\".\")\n\n # Get the last \"I<n>\" field\n for i, word in enumerate(parts):\n if word in [\"I_hilojoint\", \"I_enjoint\"]:\n part = parts[i-1]\n break\n else:\n assert False, signature\n\n # Decode the index\n idx = int(part[1:])\n\n # Remap the index\n assert idx in INDEX_MAP, (signature, idx)\n return INDEX_MAP[idx]","def get_index(self):\r\n i = 0\r\n for container in self.settings[\"containers\"]:\r\n if container[\"name\"] == self.container[\"name\"]:\r\n return i\r\n i += 1","def tex_index(tex, vts):\n if tex in vts:\n return vts.index(tex)\n return -1","def idx(self):\n if self._idx is None:\n self._loads()\n return self._idx","def index(self):\n if hasattr(self, '_m_index'):\n return self._m_index if hasattr(self, '_m_index') else None\n\n self._m_index = (self.index_separate if self.is_index_separate else self.index_in_tag)\n return self._m_index if hasattr(self, '_m_index') else None","def find_special_token_index(identified_concepts: IdentifiedConcepts, special_token: str):\n for i in range(len(identified_concepts.ordered_concepts)):\n concept = identified_concepts.ordered_concepts[i]\n if concept.name == special_token:\n return i\n return -1","def index(self):\n a = self.array_form\n\n return sum([j for j in xrange(len(a) - 1) if a[j] > a[j+1]])","def get_predicate_indices(tags: List[str]) -> List[int]:\n return [ind for ind, tag in enumerate(tags) if \"V\" in tag]","def support_index_min(self, tags):\n # type: (List[Any]) -> int\n return min(tags.index(tag) for tag in self.file_tags if tag in tags)","def support_idx(self, j):\n return (j, j+self.p+1)","def isect_index(self):\n return self._lazy_isect_index()","def get_index(self, u):\n if u == self.grid[-1]: # check if u equals last knot\n# index = len(self.grid) - 2 # pick next to last index\n index = (self.grid < u).argmin() - 1\n else:\n index = (self.grid > u).argmax() - 1\n return index","def labelIndex(self, label):\n for idx, taskDef in enumerate(self):\n if taskDef.label == label:\n return idx\n return -1","def index_for_location(self, location):\r\n if location == '_begin':\r\n i = 0\r\n elif location == '_end':\r\n i = None\r\n elif location.startswith('<') or location.startswith('>'):\r\n i = self.index(location[1:])\r\n if location.startswith('>'):\r\n if i >= len(self):\r\n # last item\r\n i = None\r\n else:\r\n i += 1\r\n else:\r\n raise ValueError('Not a valid location: \"%s\". Location key '\r\n 'must start with a \">\" or \"<\".' % location)\r\n return i","def index(self) -> int:\r\n return self._index","def getIndex(self,filt):\n indx = [i for i in xrange(len(self._header)) if filt == self._header[i]]\n return indx","def get_idx_from_sent(sent, word_idx_map):\r\n x = []\r\n words = sent.split()\r\n for word in words:\r\n if word in word_idx_map:\r\n x.append(word_idx_map[word])\r\n else:\r\n x.append(1)\r\n\r\n return x","def get_idx_from_sent(sent, word_idx_map):\n x = []\n words = sent.split()\n for word in words:\n if word in word_idx_map:\n x.append(word_idx_map[word])\n else:\n x.append(1)\n\n return x","def get_wordnet_pos(tag):\n tag_dict = {\"J\": wordnet.ADJ,\n \"N\": wordnet.NOUN,\n \"V\": wordnet.VERB,\n \"R\": wordnet.ADV}\n\n return tag_dict.get(tag.upper(), wordnet.NOUN)","def step_index(self, step):\n return self.steps.index(step)","def _word_lookup(self, key: str) -> int:\n if key in self.tok2ind:\n return self.tok2ind[key]\n else:\n return self._unk_token_idx","def tree_idx(tree,j1,J1,J2):\n j = j1\n for k in np.arange(J1+1,J2+1,1):\n j = tree[k]['IDX'][j]\n \n j2 = j\n return j2","def label_index(self, label: Text) -> int:\n count = 0\n for l in self.le.classes_:\n if(l == label):\n return count\n count += 1","def enumerate(self):\r\n return enumerate(self, 1)","def index(self) -> int:","def f_index(self, substring, direction=[]):\n substr = self.value(substring)\n if \"back\" in direction:\n pos = self._val.rfind(substr)\n else:\n pos = self._val.find(substr)\n\n return pos + 1","def index(self) -> int:\n return self._index","def index(self) -> int:\n return self._index","def index(self) -> int:\n return self._index","def index(self) -> int:\n return self._index","def index(self) -> int:\n return self._index","def itos(self, i):\n token = self._itos.get(i)\n return token if token else 'UNK'","def _get_indx(self, t):\n t = np.array(t)\n a = (t[:, np.newaxis] <= self._data['stop']) & (t[:, np.newaxis] >=\n self._data['start'])\n return np.array([np.where(row)[0][0] for row in a])","def get_index(self, item: _T) -> int:\n if item not in self.item_to_index and self.unknown is not None:\n return self.item_to_index[self.unknown]\n return self.item_to_index[item]","def return_tag_tokens(self, tags_indexes, observations):\n tag_pred = []\n for tag_index in tags_indexes:\n tag_pred.append(observations.T.index[tag_index])\n return tag_pred","def get_list_index(self):\r\n return self.n","def elementIndex(self, element):\n\n nel = self.nElements()\n if type(element) == types.IntType:\n m = element\n else:\n m = _cantera.phase_elementindex(self._phase_id, element)\n if m < 0 or m >= nel:\n raise CanteraError(\"\"\"Element \"\"\"+element+\"\"\" not in set \"\"\"\n +`self.elementNames()`)\n return m","def GetIndex(self,item):\r\n return self.gList.FindItemData(-1,self.GetId(item))","def get_feature_index(self, feature):\n return self.features.index(feature)","def get_feature_index(self, feature):\n return self.features.index(feature)","def get_list_index(self):\r\n s = self.query('LIST:MAN?')\r\n if s == None: return None\r\n return int(s)-1","def get_index(self, key):\n return self.keys.index(key)","def InterfaceIndex(self) -> int:","def InterfaceIndex(self) -> int:","def find_tag(tag_hash):\n for i in tags_fin:\n if tag_hash == i[2]:\n return i[1]","def getIndices(self, tokens):\n tokenTxt, posTxt = attachTokens(tokens)\n if tokenTxt in self.tokenIndices:\n tokenIdx = self.tokenIndices[tokenTxt]\n else:\n tokenIdx = self.tokenIndices[unk]\n if posTxt in self.posIndices:\n posIdx = self.posIndices[posTxt]\n else:\n posIdx = self.posIndices[unk]\n return tokenIdx, posIdx","def index(self, item: Any) -> int:\n index_so_far = 0\n curr = self._first\n\n while curr is not None:\n if curr.item == item:\n return index_so_far\n index_so_far += 1\n curr = curr.next\n raise ValueError","def find_max_tag(self, word):\n count = []\n for tag in self.pos_tags:\n count.append(self.tag_word_data.count((tag, word)))\n max_index = np.argmax(np.asarray(count))\n return self.pos_tags[max_index]","def index(self, item):\n return self.__values.index(item)","def get_idx(velocity, coord):\n d = velocity.node_intervals\n dx=d[0]\n dz=d[1]\n dy=d[2]\n mn = velocity.min_coords\n mnx=mn[0]\n mnz=mn[1]\n mny=mn[2]\n ix = int((coord[0] - mnx)/dx)\n iz = int((coord[1] - mnz)/dz)\n iy = int((coord[2] - mny)/dy)\n return (ix, iz, iy)","def get_index(self, child):\n for _index, item in enumerate(self.children):\n if item == child:\n return _index\n\n return -1","def getIndex(self, *args):\n return _libsbml.XMLAttributes_getIndex(self, *args)","def atomic_number(self) -> int:\n return elements.index(self.label) + 1","def indicate(self, M, trans, i):\n ts = np.insert(np.insert(trans, self.n_pop - 1, np.inf), 0, -np.inf)\n ind = (M >= ts[i]) & (M < ts[i + 1])\n return ind","def token_key(token):\n morphotagged = analysis(token).get('raw')\n lemma_pos = (analysis(token).get('lemma'), analysis(token).get('partOfSpeech'))\n return morphotagged or lemma_pos","def getLinIterVarIndx( self, var ):\n \n self.updateAdb( )\n\n if var in self.iterNames:\n return self.iterNames[ var ]\n elif var in self.iterNames.values():\n return var\n else:\n return -1"],"string":"[\n \"def index_in_tag(self):\\n if hasattr(self, '_m_index_in_tag'):\\n return self._m_index_in_tag if hasattr(self, '_m_index_in_tag') else None\\n\\n self._m_index_in_tag = (self.tag - 35)\\n return self._m_index_in_tag if hasattr(self, '_m_index_in_tag') else None\",\n \"def get_tag_index(tags, tag_to_search):\\n counter = 0\\n for t in tags:\\n if tag_to_search == t:\\n break\\n else:\\n counter+=1\\n return counter\",\n \"def get_index(tag):\\n global kpi_list\\n try:\\n return kpi_list.index(str(tag))\\n except ValueError:\\n return -1\",\n \"def index_tag_in_lines(lines, tag):\\n for index, line in enumerate(lines):\\n if tag in line:\\n return index\\n raise ValueError(f'{tag} not found.')\",\n \"def index_tag_seq(words, seq, strict=False):\\n tags = get_tag_seq(words)\\n nouns = 'NN' in seq or 'NNS' in seq\\n alt_seq = None\\n if strict is False:\\n if nouns is True:\\n alt_seq = [\\n 'NNS' if x == 'NN' else \\n 'NN' if x == 'NNS' else \\n x for x in seq\\n ] \\n \\n for i in range(len(tags)):\\n check_seq = tags[i:i+len(seq)]\\n if check_seq == seq:\\n return i\\n if nouns:\\n if check_seq == alt_seq:\\n return i\\n\\n return -1\",\n \"def indexof(self, value, tag=WORD):\\n match = lambda a, b: a.endswith(\\\"*\\\") and b.startswith(a[:-1]) or a==b\\n indices = []\\n for i in range(len(self.words)):\\n if match(value, unicode(self.get(i, tag))):\\n indices.append(i)\\n return indices\",\n \"def index(self, key):\\n return self._sequence.index(key)\",\n \"def tagkeyindex(self,tag):\\r\\n\\r\\n returnset = set()\\r\\n if self.tag_dict_contains(tag):\\r\\n\\r\\n for x_temp in self.get_keys_for_tag(tag):\\r\\n if self.key_dict_contains(x_temp+'/'+tag):\\r\\n for y_temp in self.get_indexes_for_key(x_temp+'/'+tag):\\r\\n returnset.add(y_temp)\\r\\n return returnset\",\n \"def get_tag_indices(tags):\\n tag_counts = count_tags(tags);\\n tag_names = tag_counts.keys()\\n return {name : index for (index, name) in enumerate(tag_names)}\",\n \"def get_index(uid, i):\\n return _SHARED_SEQUENCES[uid][i]\",\n \"def get_idx(self, key):\\n found = [i for i, e in enumerate(self.list) if e.key == key]\\n if found:\\n return found[0]\\n\\n else:\\n return -1\",\n \"def get_index(attribute, attributes):\\n for i in range(14):\\n if attribute == attributes[i]:\\n return i\",\n \"def index(self, word):\\n return self.tokens.index(word)\",\n \"def get_tag_for_word(self, word: str):\\n doc = self.model(word)\\n for token in doc:\\n return token.pos_\",\n \"def token_to_idx(self) -> Dict[Hashable, int]:\\n return self._token_to_idx\",\n \"def offset(self, needle):\\n if isinstance(needle, int):\\n if needle.bit_length() <= 32:\\n needle = p32(needle)\\n else:\\n needle = p64(needle)\\n needle = d(needle)\\n\\n idx = self._seq.index(needle)\\n if self._seq[idx+len(needle):].find(needle) != -1:\\n raise ValueError(\\\"Multiple occurances found!\\\")\\n\\n return idx\",\n \"def indexMatching(seq, condition):\\n for i,x in enumerate(seq):\\n if condition(x):\\n return i\\n return -1\",\n \"def index(self, atom):\\n return self.atom_list.index(atom)\",\n \"def _code_indices(self) -> Tuple[int, ...]:\\n return tuple(idx for idx, seg in enumerate(self.segments) if seg.is_code)\",\n \"def getOmIndex(self, name):\\n for i in range(len(self.oameni)):\\n if self.oameni[i].name == name:\\n return i\\n return None\",\n \"def _index(self,key):\\n index=0\\n for item in self._item:\\n if item.key==key:\\n return index\\n index+=1\\n return -1\",\n \"def all_lines_with_tag(mm, tag, nline_max=1024*1024):\\n all_idx = []\\n for iline in range(nline_max):\\n idx = mm.find(tag.encode())\\n if idx == -1:\\n break\\n mm.seek(idx)\\n all_idx.append(idx)\\n mm.readline()\\n\\n # guard\\n if iline >= nline_max-1:\\n raise RuntimeError('may need to increase nline_max')\\n return all_idx\",\n \"def __getitem__(self, tag):\\n return self.__tags.get(tag.lower(), 0)\",\n \"def _get_signal_index(self, signal):\\n # Process signal :\\n signal = signal.replace(', :', '').replace(':, ', '')[1:-1]\\n # Find index :\\n idx = tuple(int(k) for k in signal.split(', '))\\n return self._navidx.index(idx)\",\n \"def idx(self):\\n return self._idx\",\n \"def element_index(self):\\n return self._index\",\n \"def index(self, item):\\n\\t\\ti = 0\\t\\t\\n\\t\\tif not self.len:\\n\\t\\t\\traise ValueError(\\\"Lista vacia\\\")\\n\\t\\tif self.prim.dato == item:\\n\\t\\t\\treturn i\\n\\t\\tactual = self.prim\\n\\t\\twhile actual and actual.dato != item:\\n\\t\\t\\tactual = actual.prox\\n\\t\\t\\ti += 1\\n\\t\\tif not actual:\\n\\t\\t\\traise ValueError(\\\"Elemento no encontrado\\\")\\n\\t\\treturn i\",\n \"def set_indx(self, item):\\n for i, s in enumerate(self._data):\\n if item in s:\\n return i\\n return None\",\n \"def getAttrIndex(self, *args):\\n return _libsbml.XMLToken_getAttrIndex(self, *args)\",\n \"def get_index(self, gi):\\n for i in range(len(self.gradual_items)):\\n gi_obj = self.gradual_items[i]\\n if (gi.symbol == gi_obj.symbol) and (gi.attribute_col == gi_obj.attribute_col):\\n return i\\n return -1\",\n \"def findindex(iteratee, seq):\\n iteratee = fnc.iteratee(iteratee)\\n return next((i for i, value in enumerate(seq) if iteratee(value)), -1)\",\n \"def get_index_from_nome(self,serie,nome_imagem):\\r\\n index = serie[serie == nome_imagem].index.tolist()\\r\\n index = str(index).replace('[','').replace(']','')\\r\\n return index\",\n \"def index(self, elem):\\n ponteiro = self.inicio\\n i = 0\\n while(ponteiro):\\n if ponteiro.dado == elem:\\n return i\\n ponteiro = ponteiro.prox\\n i = i + 1\\n raise ValueError(\\\"{} is not in list\\\".format(elem))\",\n \"def index(self, pos):\\n for i, n in enumerate(self):\\n if i == pos: return n\\n raise Exception('Index out of bounds.')\",\n \"def findRepIdx(self, rep, cell = 1):\\n\\n match = self.findRep(rep = rep, cell = cell)\\n return np.arange(self.atoms.shape[0])[match]\",\n \"def ionNameToIndex(self, name):\\n for i in range(len(self.ions)):\\n if self.ions[i].getName() == name:\\n return i\\n\\n raise KeyError(\\\"No ion named '{}' found in the output.\\\".format(name))\",\n \"def index(self, value, start=0, stop=-1):\\n return self.elem.index(value, start, stop)\",\n \"def tokens_to_idxs(self, token_seqs, lexicon):\\n idx_seqs = [[lexicon[token] if token in lexicon else lexicon['<UNK>'] for \\n token in token_seq] for token_seq in token_seqs]\\n return idx_seqs\",\n \"def word2idx(self, str):\\n return self._tokens.get(str)\",\n \"def get_tag(self) -> int:\\n return self.tag\",\n \"def idx(self):\\n return int(self.__ph.get('idx', 0))\",\n \"def get_idx_for_item(self, item: str) -> int:\\n item = item.encode('utf-8')\\n if item in self.item2idx.keys():\\n return self.item2idx[item]\\n else:\\n return 0\",\n \"def getPosTagAt(self, pos):\\n return self.sentence[pos].getPosTag()\",\n \"def find_motif(self, msa, motif):\\n with open(msa, \\\"r\\\") as file:\\n msa = file.read()\\n msa_list = constool.indv_block(msa)\\n index = -1\\n for prot in msa_list:\\n prot = constool.seqnwl_strip(prot)\\n prot = constool.get_fasta_sequence(prot)\\n index = prot.find(motif)\\n if not index == -1:\\n break\\n return index\",\n \"def _get_cand_index(signature):\\n\\n # This map translates between the last \\\"I<n>\\\" field value and the\\n # actual CAND cell index.\\n INDEX_MAP = {\\n 10: 0,\\n 9: 1,\\n 8: 2,\\n 7: 3,\\n 6: 4,\\n }\\n\\n # Split the signature\\n parts = signature.split(\\\".\\\")\\n\\n # Get the last \\\"I<n>\\\" field\\n for i, word in enumerate(parts):\\n if word in [\\\"I_hilojoint\\\", \\\"I_enjoint\\\"]:\\n part = parts[i-1]\\n break\\n else:\\n assert False, signature\\n\\n # Decode the index\\n idx = int(part[1:])\\n\\n # Remap the index\\n assert idx in INDEX_MAP, (signature, idx)\\n return INDEX_MAP[idx]\",\n \"def get_index(self):\\r\\n i = 0\\r\\n for container in self.settings[\\\"containers\\\"]:\\r\\n if container[\\\"name\\\"] == self.container[\\\"name\\\"]:\\r\\n return i\\r\\n i += 1\",\n \"def tex_index(tex, vts):\\n if tex in vts:\\n return vts.index(tex)\\n return -1\",\n \"def idx(self):\\n if self._idx is None:\\n self._loads()\\n return self._idx\",\n \"def index(self):\\n if hasattr(self, '_m_index'):\\n return self._m_index if hasattr(self, '_m_index') else None\\n\\n self._m_index = (self.index_separate if self.is_index_separate else self.index_in_tag)\\n return self._m_index if hasattr(self, '_m_index') else None\",\n \"def find_special_token_index(identified_concepts: IdentifiedConcepts, special_token: str):\\n for i in range(len(identified_concepts.ordered_concepts)):\\n concept = identified_concepts.ordered_concepts[i]\\n if concept.name == special_token:\\n return i\\n return -1\",\n \"def index(self):\\n a = self.array_form\\n\\n return sum([j for j in xrange(len(a) - 1) if a[j] > a[j+1]])\",\n \"def get_predicate_indices(tags: List[str]) -> List[int]:\\n return [ind for ind, tag in enumerate(tags) if \\\"V\\\" in tag]\",\n \"def support_index_min(self, tags):\\n # type: (List[Any]) -> int\\n return min(tags.index(tag) for tag in self.file_tags if tag in tags)\",\n \"def support_idx(self, j):\\n return (j, j+self.p+1)\",\n \"def isect_index(self):\\n return self._lazy_isect_index()\",\n \"def get_index(self, u):\\n if u == self.grid[-1]: # check if u equals last knot\\n# index = len(self.grid) - 2 # pick next to last index\\n index = (self.grid < u).argmin() - 1\\n else:\\n index = (self.grid > u).argmax() - 1\\n return index\",\n \"def labelIndex(self, label):\\n for idx, taskDef in enumerate(self):\\n if taskDef.label == label:\\n return idx\\n return -1\",\n \"def index_for_location(self, location):\\r\\n if location == '_begin':\\r\\n i = 0\\r\\n elif location == '_end':\\r\\n i = None\\r\\n elif location.startswith('<') or location.startswith('>'):\\r\\n i = self.index(location[1:])\\r\\n if location.startswith('>'):\\r\\n if i >= len(self):\\r\\n # last item\\r\\n i = None\\r\\n else:\\r\\n i += 1\\r\\n else:\\r\\n raise ValueError('Not a valid location: \\\"%s\\\". Location key '\\r\\n 'must start with a \\\">\\\" or \\\"<\\\".' % location)\\r\\n return i\",\n \"def index(self) -> int:\\r\\n return self._index\",\n \"def getIndex(self,filt):\\n indx = [i for i in xrange(len(self._header)) if filt == self._header[i]]\\n return indx\",\n \"def get_idx_from_sent(sent, word_idx_map):\\r\\n x = []\\r\\n words = sent.split()\\r\\n for word in words:\\r\\n if word in word_idx_map:\\r\\n x.append(word_idx_map[word])\\r\\n else:\\r\\n x.append(1)\\r\\n\\r\\n return x\",\n \"def get_idx_from_sent(sent, word_idx_map):\\n x = []\\n words = sent.split()\\n for word in words:\\n if word in word_idx_map:\\n x.append(word_idx_map[word])\\n else:\\n x.append(1)\\n\\n return x\",\n \"def get_wordnet_pos(tag):\\n tag_dict = {\\\"J\\\": wordnet.ADJ,\\n \\\"N\\\": wordnet.NOUN,\\n \\\"V\\\": wordnet.VERB,\\n \\\"R\\\": wordnet.ADV}\\n\\n return tag_dict.get(tag.upper(), wordnet.NOUN)\",\n \"def step_index(self, step):\\n return self.steps.index(step)\",\n \"def _word_lookup(self, key: str) -> int:\\n if key in self.tok2ind:\\n return self.tok2ind[key]\\n else:\\n return self._unk_token_idx\",\n \"def tree_idx(tree,j1,J1,J2):\\n j = j1\\n for k in np.arange(J1+1,J2+1,1):\\n j = tree[k]['IDX'][j]\\n \\n j2 = j\\n return j2\",\n \"def label_index(self, label: Text) -> int:\\n count = 0\\n for l in self.le.classes_:\\n if(l == label):\\n return count\\n count += 1\",\n \"def enumerate(self):\\r\\n return enumerate(self, 1)\",\n \"def index(self) -> int:\",\n \"def f_index(self, substring, direction=[]):\\n substr = self.value(substring)\\n if \\\"back\\\" in direction:\\n pos = self._val.rfind(substr)\\n else:\\n pos = self._val.find(substr)\\n\\n return pos + 1\",\n \"def index(self) -> int:\\n return self._index\",\n \"def index(self) -> int:\\n return self._index\",\n \"def index(self) -> int:\\n return self._index\",\n \"def index(self) -> int:\\n return self._index\",\n \"def index(self) -> int:\\n return self._index\",\n \"def itos(self, i):\\n token = self._itos.get(i)\\n return token if token else 'UNK'\",\n \"def _get_indx(self, t):\\n t = np.array(t)\\n a = (t[:, np.newaxis] <= self._data['stop']) & (t[:, np.newaxis] >=\\n self._data['start'])\\n return np.array([np.where(row)[0][0] for row in a])\",\n \"def get_index(self, item: _T) -> int:\\n if item not in self.item_to_index and self.unknown is not None:\\n return self.item_to_index[self.unknown]\\n return self.item_to_index[item]\",\n \"def return_tag_tokens(self, tags_indexes, observations):\\n tag_pred = []\\n for tag_index in tags_indexes:\\n tag_pred.append(observations.T.index[tag_index])\\n return tag_pred\",\n \"def get_list_index(self):\\r\\n return self.n\",\n \"def elementIndex(self, element):\\n\\n nel = self.nElements()\\n if type(element) == types.IntType:\\n m = element\\n else:\\n m = _cantera.phase_elementindex(self._phase_id, element)\\n if m < 0 or m >= nel:\\n raise CanteraError(\\\"\\\"\\\"Element \\\"\\\"\\\"+element+\\\"\\\"\\\" not in set \\\"\\\"\\\"\\n +`self.elementNames()`)\\n return m\",\n \"def GetIndex(self,item):\\r\\n return self.gList.FindItemData(-1,self.GetId(item))\",\n \"def get_feature_index(self, feature):\\n return self.features.index(feature)\",\n \"def get_feature_index(self, feature):\\n return self.features.index(feature)\",\n \"def get_list_index(self):\\r\\n s = self.query('LIST:MAN?')\\r\\n if s == None: return None\\r\\n return int(s)-1\",\n \"def get_index(self, key):\\n return self.keys.index(key)\",\n \"def InterfaceIndex(self) -> int:\",\n \"def InterfaceIndex(self) -> int:\",\n \"def find_tag(tag_hash):\\n for i in tags_fin:\\n if tag_hash == i[2]:\\n return i[1]\",\n \"def getIndices(self, tokens):\\n tokenTxt, posTxt = attachTokens(tokens)\\n if tokenTxt in self.tokenIndices:\\n tokenIdx = self.tokenIndices[tokenTxt]\\n else:\\n tokenIdx = self.tokenIndices[unk]\\n if posTxt in self.posIndices:\\n posIdx = self.posIndices[posTxt]\\n else:\\n posIdx = self.posIndices[unk]\\n return tokenIdx, posIdx\",\n \"def index(self, item: Any) -> int:\\n index_so_far = 0\\n curr = self._first\\n\\n while curr is not None:\\n if curr.item == item:\\n return index_so_far\\n index_so_far += 1\\n curr = curr.next\\n raise ValueError\",\n \"def find_max_tag(self, word):\\n count = []\\n for tag in self.pos_tags:\\n count.append(self.tag_word_data.count((tag, word)))\\n max_index = np.argmax(np.asarray(count))\\n return self.pos_tags[max_index]\",\n \"def index(self, item):\\n return self.__values.index(item)\",\n \"def get_idx(velocity, coord):\\n d = velocity.node_intervals\\n dx=d[0]\\n dz=d[1]\\n dy=d[2]\\n mn = velocity.min_coords\\n mnx=mn[0]\\n mnz=mn[1]\\n mny=mn[2]\\n ix = int((coord[0] - mnx)/dx)\\n iz = int((coord[1] - mnz)/dz)\\n iy = int((coord[2] - mny)/dy)\\n return (ix, iz, iy)\",\n \"def get_index(self, child):\\n for _index, item in enumerate(self.children):\\n if item == child:\\n return _index\\n\\n return -1\",\n \"def getIndex(self, *args):\\n return _libsbml.XMLAttributes_getIndex(self, *args)\",\n \"def atomic_number(self) -> int:\\n return elements.index(self.label) + 1\",\n \"def indicate(self, M, trans, i):\\n ts = np.insert(np.insert(trans, self.n_pop - 1, np.inf), 0, -np.inf)\\n ind = (M >= ts[i]) & (M < ts[i + 1])\\n return ind\",\n \"def token_key(token):\\n morphotagged = analysis(token).get('raw')\\n lemma_pos = (analysis(token).get('lemma'), analysis(token).get('partOfSpeech'))\\n return morphotagged or lemma_pos\",\n \"def getLinIterVarIndx( self, var ):\\n \\n self.updateAdb( )\\n\\n if var in self.iterNames:\\n return self.iterNames[ var ]\\n elif var in self.iterNames.values():\\n return var\\n else:\\n return -1\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7142307","0.6958829","0.6688589","0.66438335","0.65356153","0.6207758","0.62011987","0.6198451","0.61982673","0.6162026","0.6146128","0.6113043","0.60828024","0.60632235","0.60050696","0.5982074","0.59499717","0.5918507","0.5912195","0.5911467","0.59011835","0.58865374","0.58789486","0.5863743","0.5863466","0.585393","0.5851366","0.5836543","0.58301455","0.5795933","0.57861805","0.57857823","0.5781122","0.5777865","0.57722074","0.57700557","0.57592803","0.57566184","0.57564366","0.57277334","0.57243013","0.57224745","0.5716029","0.57083154","0.5694833","0.5666552","0.5657564","0.56468105","0.56458646","0.5644617","0.5637898","0.5634032","0.56253123","0.56135947","0.5598294","0.5580286","0.55753785","0.55602604","0.55469656","0.5534427","0.5530669","0.5516711","0.5512051","0.5510219","0.55064005","0.5497861","0.5495336","0.54933476","0.5492402","0.5489162","0.54823637","0.54823637","0.54823637","0.54823637","0.54823637","0.5481808","0.54803014","0.54706323","0.54615545","0.5460751","0.5456405","0.54510164","0.5450846","0.5450846","0.5449925","0.54459643","0.5443515","0.5443515","0.54347974","0.5424549","0.5423789","0.5423601","0.5421346","0.54181653","0.5416962","0.541275","0.5407926","0.54051924","0.5402257","0.54003936"],"string":"[\n \"0.7142307\",\n \"0.6958829\",\n \"0.6688589\",\n \"0.66438335\",\n \"0.65356153\",\n \"0.6207758\",\n \"0.62011987\",\n \"0.6198451\",\n \"0.61982673\",\n \"0.6162026\",\n \"0.6146128\",\n \"0.6113043\",\n \"0.60828024\",\n \"0.60632235\",\n \"0.60050696\",\n \"0.5982074\",\n \"0.59499717\",\n \"0.5918507\",\n \"0.5912195\",\n \"0.5911467\",\n \"0.59011835\",\n \"0.58865374\",\n \"0.58789486\",\n \"0.5863743\",\n \"0.5863466\",\n \"0.585393\",\n \"0.5851366\",\n \"0.5836543\",\n \"0.58301455\",\n \"0.5795933\",\n \"0.57861805\",\n \"0.57857823\",\n \"0.5781122\",\n \"0.5777865\",\n \"0.57722074\",\n \"0.57700557\",\n \"0.57592803\",\n \"0.57566184\",\n \"0.57564366\",\n \"0.57277334\",\n \"0.57243013\",\n \"0.57224745\",\n \"0.5716029\",\n \"0.57083154\",\n \"0.5694833\",\n \"0.5666552\",\n \"0.5657564\",\n \"0.56468105\",\n \"0.56458646\",\n \"0.5644617\",\n \"0.5637898\",\n \"0.5634032\",\n \"0.56253123\",\n \"0.56135947\",\n \"0.5598294\",\n \"0.5580286\",\n \"0.55753785\",\n \"0.55602604\",\n \"0.55469656\",\n \"0.5534427\",\n \"0.5530669\",\n \"0.5516711\",\n \"0.5512051\",\n \"0.5510219\",\n \"0.55064005\",\n \"0.5497861\",\n \"0.5495336\",\n \"0.54933476\",\n \"0.5492402\",\n \"0.5489162\",\n \"0.54823637\",\n \"0.54823637\",\n \"0.54823637\",\n \"0.54823637\",\n \"0.54823637\",\n \"0.5481808\",\n \"0.54803014\",\n \"0.54706323\",\n \"0.54615545\",\n \"0.5460751\",\n \"0.5456405\",\n \"0.54510164\",\n \"0.5450846\",\n \"0.5450846\",\n \"0.5449925\",\n \"0.54459643\",\n \"0.5443515\",\n \"0.5443515\",\n \"0.54347974\",\n \"0.5424549\",\n \"0.5423789\",\n \"0.5423601\",\n \"0.5421346\",\n \"0.54181653\",\n \"0.5416962\",\n \"0.541275\",\n \"0.5407926\",\n \"0.54051924\",\n \"0.5402257\",\n \"0.54003936\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8766997"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11179,"cells":{"query":{"kind":"string","value":"Given two tags and a label, return the psi factor of the two tag sequences"},"document":{"kind":"string","value":"def get_psi_score(self, psi, pos1, pos2, lab, m1, m2):\n i, j = self.get_tag_index(m1), self.get_tag_index(m2)\n return psi[pos1, pos2, lab, i, j]"},"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 _psi_function(share1, share2):\n return (share1 - share2) * math.log(share1/share2)","def get_emissions_probability(label_matches, given_tag, given_word, tag_counts):\r\n\tlookup_tuple = (given_word, given_tag)\r\n\tword_tag_frequency = label_matches.get(lookup_tuple, 0)\r\n\ttag_frequency = tag_counts[given_tag]\r\n\tif tag_frequency == 0:\r\n\t\temissions_probability = 0\r\n\telse:\r\n\t\temissions_probability = float(word_tag_frequency)/float(tag_frequency)\r\n\treturn emissions_probability","def psi(x, y):\n return x","def match_two_exp_psf(exp1, exp2):\n\tif has_smaller_psf(exp1, exp2):\n\t\texpm1 = match_psf(exp1, exp2)\n\t\treturn expm1, exp2\n\telif has_smaller_psf(exp2, exp1):\n\t\texpm2 = match_psf(exp2, exp1)\n\t\treturn exp1, expm2\n\telse:\n\t\tprint(\"[matchpsf] skip matching psf as the psfs are similar\")\n\t\treturn exp1, exp2","def heuristic_2(a: str, b: str) -> float:\n # generate term-document matrices\n if get_intro(a) == \"\" or get_intro(b) == \"\":\n return 2\n else:\n corpus = [get_intro(a), get_intro(b)]\n vect = TfidfVectorizer()\n mat = vect.fit_transform(corpus)\n # return cosine similarity\n return abs(1 - cosine_similarity(mat[0:1], mat)[0][1]) * 2","def merge_overlapping_predictions(tags1: List[str], tags2: List[str]) -> List[str]:\n ret_sequence = []\n prev_label = \"O\"\n\n # Build a coherent sequence out of two\n # spans which predicates' overlap\n\n for tag1, tag2 in zip(tags1, tags2):\n label1 = tag1.split(\"-\", 1)[-1]\n label2 = tag2.split(\"-\", 1)[-1]\n if (label1 == \"V\") or (label2 == \"V\"):\n # Construct maximal predicate length -\n # add predicate tag if any of the sequence predict it\n cur_label = \"V\"\n\n # Else - prefer an argument over 'O' label\n elif label1 != \"O\":\n cur_label = label1\n else:\n cur_label = label2\n\n # Append cur tag to the returned sequence\n cur_tag = get_coherent_next_tag(prev_label, cur_label)\n prev_label = cur_label\n ret_sequence.append(cur_tag)\n return ret_sequence","def fidelity(self, psi_1, psi_2):\n return np.abs(self.ip(psi_1, psi_2))**2","def tag_prob(self, y):\n p = 1\n n = self._n\n y = (START_TAG,) * (n - 1) + tuple(y) + (END_TAG,)\n for i in range(len(y) - self._n + 1):\n tag = y[i + n - 1]\n prev_tags = y[i:i + n - 1]\n p *= self.trans_prob(tag, prev_tags)\n\n return p","def masi_distance(label1, label2):\r\n\r\n len_intersection = len(label1.intersection(label2))\r\n len_union = len(label1.union(label2))\r\n len_label1 = len(label1)\r\n len_label2 = len(label2)\r\n if len_label1 == len_label2 and len_label1 == len_intersection:\r\n m = 1\r\n elif len_intersection == min(len_label1, len_label2):\r\n m = 0.67\r\n elif len_intersection > 0:\r\n m = 0.33\r\n else:\r\n m = 0\r\n\r\n return 1 - (len_intersection / len_union) * m","def compute_class_freqs(labels):\n ### START CODE HERE (REPLACE INSTANCES OF 'None' with your code) ###\n \n # total number of patients (rows)\n N = len(labels)\n \n positive_frequencies = np.sum(labels,axis=0)/N\n negative_frequencies = 1-positive_frequencies\n\n ### END CODE HERE ###\n return positive_frequencies, negative_frequencies","def binary_distance(label1, label2):\r\n\r\n return 0.0 if label1 == label2 else 1.0","def tag_log_prob(self, y, add_end_token=True):\n p = 0\n n = self._n\n y = (START_TAG,) * (n - 1) + tuple(y) + (END_TAG,) * add_end_token\n for i in range(len(y) - self._n + 1):\n tag = y[i + n - 1]\n prev_tags = y[i:i + n - 1]\n if self.trans_prob(tag, prev_tags) == 0:\n return -math.inf\n\n p += math.log2(self.trans_prob(tag, prev_tags))\n\n return p","def t2_given_t1(t2:str, t1:str, train_bag:Sequence[Tuple[str, str]]=train_tagged_words)-> Tuple[int, int]: \n tags = [pair[1] for pair in train_bag]\n count_t1 = len([t for t in tags if t == t1])\n count_t2_t1 = 0\n for index in range(len(tags)-1):\n if tags[index] == t1 and tags[index+1] == t2:\n count_t2_t1 += 1\n return (count_t2_t1, count_t1)","def retagger(tags):\n if tags == 'Positive':\n return 'pos'\n else:\n return 'neg'","def text_proximity(str_1: str, str_2: str) -> float:\n tokens_1 = Counter(str_1.split(' '))\n tokens_2 = Counter(str_2.split(' '))\n return _normalized_scalar_product(tokens_1, tokens_2)","def f_value(a, b):\r\n if not any(a) or not any(b) or len(a) <= 1 or len(b) <= 1:\r\n raise ValueError(\"Vectors should contain more than 1 element\")\r\n F = var(a) / var(b)\r\n dfn = len(a) - 1\r\n dfd = len(b) - 1\r\n return dfn, dfd, F","def trans_prob(self, tag, prev_tags=None):\n assert prev_tags is not None or self._n == 1\n prev_tags = prev_tags or ()\n\n return self._trans.get(prev_tags, {}).get(tag, 0)","def tag2predictions(ote_tag_sequence, ts_tag_sequence):\n n_tag = len(ote_tag_sequence)\n # opinion target sequence and targeted sentiment sequence\n ot_sequence, ts_sequence = [], []\n beg, end = -1, -1\n for i in range(n_tag):\n tag = ote_tag_sequence[i]\n if tag == 'S':\n ot_sequence.append((i, i))\n elif tag == 'B':\n beg = i\n elif tag == 'E':\n end = i\n if end > beg and beg != -1:\n ot_sequence.append((beg, end))\n beg, end = -1, -1\n sentiments = []\n beg, end = -1, -1\n for i in range(n_tag):\n ts_tag = ts_tag_sequence[i]\n # current position and sentiment\n eles = ts_tag.split('-')\n if len(eles) == 2:\n pos, sentiment = eles\n else:\n pos, sentiment = 'O', 'O'\n if sentiment != 'O':\n # current word is a subjective word\n sentiments.append(sentiment)\n if pos == 'S':\n # singleton\n ts_sequence.append((i, i, sentiments[0]))\n sentiments = []\n elif pos == 'B':\n beg = i\n elif pos == 'E':\n end = i\n # schema1: only the consistent sentiment tags are accepted\n # that is, all of the sentiment tags are the same\n if end > beg > -1 and len(set(sentiments)) == 1:\n ts_sequence.append((beg, end, sentiment))\n sentiments = []\n beg, end = -1, -1\n\n # schema2: only consider the sentiment at the beginning of the aspect span\n # if end > beg > -1:\n # ts_sequence.append((beg, end, sentiments[0]))\n # sentiments = []\n # beg, end = -1, -1\n return ot_sequence, ts_sequence","def correspondences(labels1,labels2,return_counts=True):\n q = 100000\n assert amin(labels1)>=0 and amin(labels2)>=0\n assert amax(labels2)<q\n combo = labels1*q+labels2\n result = unique(combo, return_counts=return_counts)\n if return_counts:\n result, counts = result\n result = array([result//q,result%q,counts])\n else:\n result = array([result//q,result%q])\n return result","def t2_given_t1(t2, t1, train_bag):\n tags = [pair[1] for pair in train_bag]\n count_t1 = len([t for t in tags if t == t1])\n count_t2_t1 = 0\n for index in range(len(tags)-1):\n if tags[index] == t1 and tags[index+1] == t2:\n count_t2_t1 += 1\n return count_t2_t1, count_t1","def GetSuggesteeSimilarity(suggestee1_tags, suggestee2_tags):\n total_size = len(suggestee1_tags) + len(suggestee2_tags)\n if total_size == 0:\n return 1.\n size_of_intersection = 0\n elems_in_first = {}\n for tag in suggestee1_tags:\n elems_in_first[tag] = True\n for tag in suggestee2_tags:\n if tag in elems_in_first:\n size_of_intersection += 1\n return size_of_intersection / (total_size - size_of_intersection)","def compute_class_freqs(gen):\r\n labels = gen.labels\r\n N = labels.shape[0]\r\n positive_frequencies = np.sum(labels, axis=0) / N\r\n negative_frequencies = np.sum(1 - labels, axis=0) / N\r\n return positive_frequencies, negative_frequencies","def bspe(a, b):\n if b-a == 1:\n return MPZ_ONE, MPZ(b)\n m = (a+b)//2\n p1, q1 = bspe(a, m)\n p2, q2 = bspe(m, b)\n return p1*q2+p2, q1*q2","def evaluate(self, feats, tag_set):\n corect=0\n incorect=0 \n for (tokens, tags) in zip(feats, tag_set):\n yyhat= self.tag(tokens)\n for pre, tag in zip(yyhat, tags):\n if pre==tag:\n corect+=1\n else:\n incorect+=1\n\n return corect/(corect+incorect)","def get_e(word, tag, e_word_tag_counts, q_uni_counts):\n word_tag_tupple = (word, tag)\n\n word_tag_count = 0\n if word_tag_tupple in e_word_tag_counts:\n word_tag_count = e_word_tag_counts[word_tag_tupple]\n\n nof_tag = q_uni_counts[tag]\n return float(word_tag_count) / nof_tag","def sentence_encoding_rnn_phi(t1, t2):\n return (t1.leaves(), t2.leaves())","def best_match(psi1, psi2, threshold=None):\n if threshold is None:\n threshold = (2 * psi1.shape[0])**-0.25\n Q = np.abs(psi1.T.conj() @ psi2) # Overlap matrix\n orig, perm = so.linear_sum_assignment(-Q)\n return perm, Q[orig, perm] < threshold","def best_sequence(self, T, pos, psi, phi, fix_tags=[]):\n for idx, m in fix_tags:\n phi[idx - 1, m] = 100\n # if fix_idx:\n # phi[fix_idx - 1, fix_m] = 100\n msgs, pointers = max_product(T, pos, psi, phi, True)\n tags_dict = get_best_tags(T, msgs, pointers)\n tags = []\n for i in range(1, len(T) + 1):\n tags.append(self.get_tag(tags_dict[str(i)]))\n return tags","def psi_wf(self, vw, d1, d2, ns, tl):\n\t osmotic = (R*299./VW)*np.log((((vw/self.ZW)*self.ZW)/(VW))/((((vw/self.ZW)*self.ZW)/(VW))+ns))/10**6 #MPa\n\t turgor = ((vw/self.ZW) - d1)**d2#MPa\n\t return turgor+osmotic #MPa ","def interval_distance(label1,label2):\r\n\r\n try:\r\n return pow(label1 - label2, 2)\r\n# return pow(list(label1)[0]-list(label2)[0],2)\r\n except:\r\n print(\"non-numeric labels not supported with interval distance\")","def tags_2_vec(tags, w2v_model=None):\n if len(tags) == 0 or len([tag for tag in tags if tag in w2v_model]) == 0:\n return np.zeros(200)\n else:\n output = np.sum([w2v_model[tag] for tag in tags if tag in w2v_model], axis=0)\n return output / np.linalg.norm(output)","def __call__(self, f1, f2):\n\n r = 0.0\n for f in f1:\n if f in f2:\n r += f1[f] * f2[f]\n\n return r","def psi(a):","def muTagAndProbeEff(hlt, pp, fp):\n if 2*hlt+pp <= 0:\n return 0., 0.\n elif fp <= 0:\n return 0., 0.\n\n eff = (2*hlt+pp)/(2*hlt+pp+fp)\n eff_err = 1. / (2*hlt+fp+pp)**2 * np.sqrt(pp*fp**2 + hlt*(2*fp)**2 + fp*(pp+2*hlt)**2)\n return eff, eff_err","def pairwise_operation(self, x, y):\n k = sum(nh_compare_labels(x[r], y[r]) for r in range(self.R))\n return k / (1.0*self.R)","def get_eqn(p0, p1):\n m = (p0[1] - p1[1]) / (p0[0] - p1[0])\n return (m, p0[1] - m * p0[0])","def calc_seg_prob(ts, probroots, probsuffix, probtrans):\r\n root = ts.root\r\n suffix = ts.suffix\r\n trans = ts.trans\r\n feat = feature(root, suffix)\r\n score = 0.0\r\n if root in probroots and suffix in probsuffix and (trans, feat) in probtrans:\r\n score = probroots[root] * probsuffix[suffix] * probtrans[(trans, feat)]\r\n return score","def similarity(pair: Tuple[Text, Text]) -> float:\n (a, b) = pair\n missing = (\n True\n if any(symbol not in Metrics.realine.feature_matrix for symbol in pair)\n else False\n )\n return 0.0 if missing else 1 - Metrics.realine.delta(a, b)","def lcmu(a, b):\n return (abs(a)*abs(b))//gcdi(a, b)","def get_word_probability(self, label, term):\n\n if 'sod' in label:\n return self.cond_prob_sod[term]\n elif 'pop' in label:\n return self.cond_prob_pop[term]\n else:\n print(\"Just run the doctest Dev\")\n \n pass","def get_TP_FP_FN(cm, label):\n dim = min(cm.shape[0], cm.shape[1])\n if label >= 0 and label < dim:\n # TP, True positive, diagonal position\n tp = cm[label, label]\n # FP, False positive: sum of column label (without main diagonal)\n fp = sum(cm[:, label]) - cm[label][label]\n # FN, False negative: sum of row label (without main diagonal)\n fn = sum(cm[label, :]) - cm[label][label]\n return tp, fp, fn\n else:\n print(\"label out of bound\")","def fn(i1, i2):\n if i1 == len(word1): return len(word2) - i2\n if i2 == len(word2): return len(word1) - i1\n if word1[i1] == word2[i2]: return fn(i1+1, i2+1)\n return 1 + min(fn(i1+1,i2), fn(i1, i2+1))","def get_similarities(tags):\n return [(a, b)\n for (a, b) in itertools.permutations(tags, 2)\n if difflib.SequenceMatcher(a=a.lower(), b=b.lower()).ratio() > SIMILAR\n and a != b\n and b.endswith('s')]","def delta_prob(variants, tx, clf1, clf2, model='cterm', is_sum=True):\n # fetch c-terminal sequence\n term_seq, vars_considered = utils.process_var_seq(variants, model=model, is_sum=is_sum)\n\n # return None if no variants\n if not term_seq:\n if is_sum: return 0\n else: return [], []\n # return None if U in protein sequence\n if 'U' in utils.fetch_seq(tx.protein_sequence, model=model):\n if is_sum: return 0\n else: return [], []\n\n # construct dataframe\n result_df = pd.DataFrame({'seq': term_seq})\n\n # create feature matrix\n X = compute_feature_matrix(result_df['seq'], 6, dinuc=True, model=model)\n X2 = compute_feature_matrix(result_df['seq'], 0, dinuc=False, model=model)\n\n # predict scores\n result_df['prob'] = clf1.predict_proba(X)[:, 0]\n result_df['prob2'] = clf2.predict_proba(X2)[:, 0]\n result_df['delta prob'] = result_df['prob'] - result_df['prob2']\n\n # adjust for baseline score\n wt_seq = utils.fetch_seq(tx.protein_sequence, model=model)\n wt_df = pd.DataFrame({'seq': [wt_seq]})\n # create feature matrix\n X = compute_feature_matrix(wt_df['seq'], 6, dinuc=True, model=model)\n X2 = compute_feature_matrix(wt_df['seq'], 0, dinuc=False, model=model)\n wt_df['prob'] = clf1.predict_proba(X)[:, 0]\n wt_df['prob2'] = clf2.predict_proba(X2)[:, 0]\n wt_df['delta prob'] = wt_df['prob'] - wt_df['prob2']\n baseline = wt_df['delta prob'].iloc[0]\n\n # add up scores\n tmp = result_df['delta prob'] - baseline\n if is_sum:\n delta_prob_sum = tmp.sum()\n return delta_prob_sum\n else:\n return vars_considered, tmp","def symdif(f1, f2):\n\n y = union(subm(f1,f2),subm(f2,f1))\n return y","def inspect_bias(p1: list, p2: list, target:list, model: Word2Vec, \n metric: Callable[[list,list],float] = cosine_sim) -> tuple:\n p1 = [p for p in p1 if model.wv.__contains__(p)]\n p2 = [p for p in p2 if model.wv.__contains__(p)]\n target = [t for t in target if model.wv.__contains__(t)]\n p1_scores, p2_scores = defaultdict(float),defaultdict(float)\n for t in target:\n for p in p1:\n p1_scores[p] += metric(model.wv.__getitem__(p),model.wv.__getitem__(t))\n for p in p2:\n p2_scores[p] += metric(model.wv.__getitem__(p),model.wv.__getitem__(t)) # target[0]\n return (p1_scores,p2_scores)","def pairing(left, right):\n # same class: 0\n if left[label] == right[label]:\n flag = 0\n # not same: 1\n else:\n flag = 1\n return tf.cast(left[\"image\"], tf.float32) / 255., tf.cast(right[\"image\"], tf.float32) / 255., tf.cast(flag, tf.float32)","def delta_prob_raw(variants, tx, clf1, clf2, model='cterm', is_sum=True):\n # fetch c-terminal sequence\n term_seq = [] ; vars_considered = []\n for v in variants:\n if v.mutant_protein_sequence:\n if model=='cterm' and type(v) in utils.indels+utils.nmd_sub_vars:\n term_seq.append(utils.fetch_seq(v.mutant_protein_sequence, model=model))\n if not is_sum: vars_considered.append(v)\n elif type(v) in utils.base_substitutions:\n if model=='cterm' and v.aa_mutation_start_offset>(len(v.transcript.protein_sequence) - 23):\n term_seq.append(utils.fetch_seq(v.mutant_protein_sequence, model=model))\n if not is_sum: vars_considered.append(v)\n elif model=='nterm' and v.aa_mutation_start_offset<=24:\n term_seq.append(utils.fetch_seq(v.mutant_protein_sequence, model=model))\n if not is_sum: vars_considered.append(v)\n\n # return None if no variants\n if not term_seq:\n if is_sum: return 0\n else: return [], [], []\n # return None if U in protein sequence\n if 'U' in utils.fetch_seq(tx.protein_sequence, model=model):\n if is_sum: return 0\n else: return [], [], []\n\n # construct dataframe\n result_df = pd.DataFrame({'seq': term_seq})\n\n # create feature matrix\n X = compute_feature_matrix(result_df['seq'], 6, dinuc=True, model=model)\n X2 = compute_feature_matrix(result_df['seq'], 0, dinuc=False, model=model)\n\n # predict scores\n result_df['prob'] = clf1.predict_proba(X)[:, 0]\n\n # adjust for baseline score\n wt_seq = utils.fetch_seq(tx.protein_sequence, model=model)\n wt_df = pd.DataFrame({'seq': [wt_seq]})\n # create feature matrix\n X = compute_feature_matrix(wt_df['seq'], 6, dinuc=True, model=model)\n wt_df['prob'] = clf1.predict_proba(X)[:, 0]\n baseline = wt_df['prob'].iloc[0]\n\n # add up scores\n tmp = result_df['prob'] - baseline\n if is_sum:\n prob_sum = tmp.sum()\n return prob_sum\n else:\n return vars_considered, tmp, result_df['prob']","def gmt_element(bitmap_a, bitmap_b, sig_array, bitmap_to_linear_mapping):\n output_sign = canonical_reordering_sign(bitmap_a, bitmap_b, sig_array)\n output_bitmap = bitmap_a^bitmap_b\n idx = bitmap_to_linear_mapping[output_bitmap]\n return idx, output_sign","def TVD(p1, p2):\n assert p1.shape == p2.shape\n return 0.5 * np.sum(np.absolute(np.subtract(p1, p2)))","def wordSimilarityRatio(sent_1,sent_2):","def difference(first, second, rf, rs, years=(1980, 2000),smooth=1, corpus='bok'):\n try:\n a_first = nb_ngram(first, years=years, smooth=smooth, corpus=corpus)\n a_second = nb_ngram(second, years=years, smooth=smooth, corpus=corpus)\n a = a_first.join(a_second) \n b_first = nb_ngram(rf, years=years, smooth=smooth, corpus=corpus)\n b_second = nb_ngram(rs, years=years, smooth=smooth, corpus=corpus)\n if rf == rs:\n b_second.columns = [rs + '2']\n b = b_first.join(b_second)\n s_a = a.mean()\n s_b = b.mean()\n f1 = s_a[a.columns[0]]/s_a[a.columns[1]]\n f2 = s_b[b.columns[0]]/s_b[b.columns[1]]\n res = f1/f2\n except:\n res = 'Mangler noen data - har bare for: ' + ', '.join([x for x in a.columns.append(b.columns)])\n return res","def __call__(self, f1, f2):\n r = len(set(f1.features) ^ set(f2.features))\n\n return exp(-self.gamma * r)","def get_q(tag_v, tag_w, tag_u, total_tokens, q_tri_counts, q_bi_counts, q_uni_counts):\n lambda1 = 0.3\n lambda2 = 0.3\n lambda3 = 1 - lambda1 - lambda2\n\n q_tri = 0\n q_bi = 0\n q_uni = 0\n\n if (tag_w, tag_u, tag_v) in q_tri_counts:\n q_tri = float(q_tri_counts[(tag_w, tag_u, tag_v)]) / q_bi_counts[(tag_w, tag_u)]\n if (tag_u, tag_v) in q_bi_counts:\n q_bi = float(q_bi_counts[(tag_u, tag_v)]) / q_uni_counts[tag_u]\n if tag_v in q_uni_counts:\n q_uni = float(q_uni_counts[tag_v]) / total_tokens\n\n\n\n # Calculates the total 3 n-grams\n total_q = lambda1 * q_tri + lambda2 * q_bi + lambda3 * q_uni\n return total_q","def mutate_tag_seq(words, seq1, seq2):\n if len(seq1) > len(words):\n return None\n seq_start = index_tag_seq(words, seq1)\n if seq_start > -1:\n pre = words[:seq_start]\n post = words[seq_start+len(seq1):]\n mutated = []\n for x in seq2:\n for j in range(len(seq1)): \n if x == words[seq_start+j].tag:\n mutated.append(words[seq_start+j])\n return pre + mutated + post\n return None","def similarity_function_old(feature1, feature2):\n f1Magnitude = feature1.dot(feature1)\n f2Magnitude = feature2.dot(feature2)\n return 1 - feature1.dot(feature2) / (f1Magnitude * f2Magnitude)","def get_chi2(a, b):\n off = (a - b)**2\n return np.sqrt(np.sum(off)) / b.sum()","def bio2ot(ote_tag_sequence, ts_tag_sequence):\n assert len(ote_tag_sequence) == len(ts_tag_sequence)\n new_ote_sequence = bio2ot_ote(ote_tag_sequence=ote_tag_sequence)\n new_ts_sequence = bio2ot_ts(ts_tag_sequence=ts_tag_sequence)\n assert len(new_ote_sequence) == len(ote_tag_sequence)\n assert len(new_ts_sequence) == len(ts_tag_sequence)\n return new_ote_sequence, new_ts_sequence","def tvd(pred: Tensor, label: Tensor) -> Tensor:\n return (Tensor([0.5]) * torch.abs(pred - label)).sum()","def best_sequence_brute(self, T, pos, psi, phi):\n ms = self.get_all_tag_seq(len(T))\n log_scores = tr.zeros(len(ms), dtype=tr.float64)\n for i in range(len(ms)):\n log_scores[i] = self.log_score(T, pos, ms[i], psi, phi)\n best = ms[tr.argmax(log_scores)]\n tags = []\n for i in range(len(T)):\n tags.append(self.get_tag_index(best[i]))\n return tags","def psi_xf(self, ev, gp, psi_l):\n\t return ev*(1. - self.F_CAP)/(lai*gp) + psi_l","def label (x):\n\n if x == 0:\n return None\n if x == 1:\n return Fraction(0)\n b = bin(x)[2:]\n s = b[1]\n wf = re.compile(r'^.({}+)(.*)$'.format(s))\n w,f = wf.findall(b)[0]\n p = 1 if s == '1' else -1\n n = p * Fraction(len(w),1)\n scale = Fraction(1,1)\n while len(f):\n scale /= 2\n if f[0] == '1':\n n += scale\n else:\n n += -scale\n f = f[1:]\n return n","def su_calculation(f1, f2):\n # calculate information gain of f1 and f2, t1 = ig(f1, f2)\n t1 = information_gain(f1, f2)\n # calculate entropy of f1\n t2 = ee.entropyd(f1)\n # calculate entropy of f2\n t3 = ee.entropyd(f2)\n\n su = 2.0 * t1 / (t2 + t3)\n\n return su","def sentence_similarity(sentence1, sentence2):\n sentence1 = sentence1.tags\n sentence2 = sentence2.tags\n \n synsets1 = [tagged_to_synset(*tagged_word) for tagged_word in sentence1]\n synsets2 = [tagged_to_synset(*tagged_word) for tagged_word in sentence2]\n \n synsets1 = [ss for ss in synsets1 if ss]\n synsets2 = [ss for ss in synsets2 if ss]\n \n score, count = 0.0, 0\n \n for synset in synsets1:\n \n li=[synset.path_similarity(ss) for ss in synsets2]\n m=0\n for i in range(len(li)):\n if li[i] is not None and m<li[i]:\n m=li[i]\n if m != 0:\n score += m\n count += 1\n\n if count is 0:\n score = 0\n else:\n score /= count\n return score","def tags_jaccard_index(d1, d2):\n set1 = set(d1.keys())\n set2 = set(d2.keys())\n\n common_tags = set.intersection(set1, set2)\n common_tags_count = 0\n all_tags_count = sum(d1.values()) + sum(d2.values())\n\n for k, v in d1.items():\n if k in common_tags:\n common_tags_count += v\n\n for k, v in d2.items():\n if k in common_tags:\n common_tags_count += v\n\n intersection_cardinality = len(set.intersection(set1, set2))\n union_cardinality = len(set.union(set1, set2))\n\n tags_similarity = intersection_cardinality/float(union_cardinality)\n\n final_tag_similarity = ((common_tags_count / all_tags_count) * 10) \\\n * tags_similarity\n\n return final_tag_similarity","def sigb(o) :\n return o * (1 - o)","def scoreslabels_2_tarnon(scores,labels):\n tt = labels==1\n tar = scores[tt]\n non = scores[np.logical_not(tt)]\n return tar, non","def friction_factor_2(v1: \"int\", v2: \"int\") -> \"int\":","def presence(label):\r\n\r\n return lambda x, y: 1.0 * ((label in x) == (label in y))","def answer_question4b():\n\n tagged_sequence = tagged_sent\n correct_sequence = correct_sent\n # Why do you think the tagger tagged this example incorrectly?\n answer = inspect.cleandoc(\"\"\"\\\n The transition probs are generally larger than the emission probs\n (thus dominate in cost computation). Thus, the HMM tagger performs\n badly on compound nouns (e.g. City Executive Committee). In our\n example, 'ADJ' after 'NOUN' is rare, so 'Executive' is tagged as 'NOUN'\n wrongly.\n \"\"\")[0:280]\n\n return tagged_sequence, correct_sequence, answer","def tf_psnr(im1, im2):\n mse = tf.losses.mean_squared_error(labels=im2 * 255.0, predictions=im1 * 255.0)\n return 10.0 * (tf.log(255.0 ** 2 / mse) / tf.log(10.0))","def delta_e_76(lab1, lab2):\n\n l1, a1, b1 = lab1\n l2, a2, b2 = lab2\n return (l1 - l2) ** 2 + (a1 - a2) ** 2 + (b1 - b2) ** 2","def label_intersects_tags(label, tags):\n for tag in tags:\n if tag in label:\n return True\n return False","def word_overlap_phi(t1, t2):\n overlap = set([w1 for w1 in t1.leaves() if w1 in t2.leaves()])\n return Counter(overlap)","def f(x0: float, x1: float) -> float:\n return 8 - (x0 - 2) ** 2 - (x1 - 2) ** 2","def phi2_coefficient(L):\r\n\r\n if 0 < L < 120:\r\n return L / 120\r\n if L >= 120:\r\n return 1","def calculate_fid(model, images1, images2):\n \n act1 = model.predict(images1)\n act2 = model.predict(images2)\n\n mu1, sigma1 = act1.mean(axis=0), np.cov(act1, rowvar=False)\n mu2, sigma2 = act2.mean(axis=0), np.cov(act2, rowvar=False)\n\n ssdiff = np.sum((mu1 - mu2) ** 2.0)\n\n covmean = sqrtm(sigma1.dot(sigma2))\n\n if np.iscomplexobj(covmean):\n covmean = covmean.real\n\n # Equation for FID score calculation\n fid = ssdiff + np.trace(sigma1 + sigma2 - 2.0 * covmean) \n\n return fid","def wang(G, term1, term2):\n if term1 not in G or term2 not in G:\n return 0\n sa = s_values(G, term1)\n sb = s_values(G, term2)\n sva = sum(sa.values())\n svb = sum(sb.values())\n common = set(sa.keys()) & set(sb.keys())\n cv = sum(sa[c] + sb[c] for c in common)\n return round(cv / (sva + svb), 3)","def bf_sign(p1, k1, p2, k2):\n\n def caesuras(k):\n \"\"\"Returns the caesuras of a basis element.\"\"\"\n caesuras = []\n for idx, i in enumerate(k):\n if i in k[idx + 1:]:\n caesuras.append(idx)\n return caesuras\n\n def weights(cae, p):\n \"\"\"Returns the weights of the splitting knowing the caesuras.\"\"\"\n weights = []\n for i, j in pairwise(p):\n closed_open = len([e for e in cae if i <= e < j])\n weights.append(closed_open)\n return [value % 2 for value in weights]\n\n p1 = [0] + p1 + [len(k1) - 1]\n cae1 = caesuras(k1)\n w1 = weights(cae1, p1)\n cae2 = caesuras(k2)\n w2 = weights(cae2, p2)\n sign_exp = 0\n for idx, w in enumerate(w2):\n if w:\n sign_exp += sum(w1[idx + 1:]) % 2\n return (-1) ** sign_exp","def _compute_label(mol_a, mol_b, prop_name: str):\n\n try:\n label_dG_a = convert_uIC50_to_kJ_per_mole(float(mol_a.GetProp(prop_name)))\n label_dG_b = convert_uIC50_to_kJ_per_mole(float(mol_b.GetProp(prop_name)))\n except KeyError:\n raise RuntimeError(f\"Couldn't access IC50 label for either mol A or mol B, looking at {prop_name}\")\n\n label = label_dG_b - label_dG_a\n\n return label","def getLabel2(*args):","def getLabel2(*args):","def simple_chained_rep_rnn_phi(t1, t2):\n return t1.leaves() + t2.leaves()","def heuristic_1(a: str, b: str) -> float:\n query = \"https://en.wikipedia.org/w/api.php?action=parse&page=TEMP&prop=text&formatversion=2&format=json\"\n startTitle = (a.replace(\" \", \"%20\")).replace(\"&\", \"%26\")\n endTitle = (b.replace(\" \", \"%20\")).replace(\"&\", \"%26\")\n startURL = (query.replace(\"TEMP\", startTitle))\n endURL = (query.replace(\"TEMP\", endTitle))\n # text processing using SOUP\n initialSoup = BeautifulSoup(get_JSON(startURL)['parse']['text'], 'html.parser')\n finalSoup = BeautifulSoup(get_JSON(endURL)['parse']['text'], 'html.parser')\n # generate term-document matrices\n corpus = [initialSoup.get_text().replace('\\n', ' '), finalSoup.get_text().replace('\\n', ' ')]\n vect = TfidfVectorizer()\n mat = vect.fit_transform(corpus)\n # return cosine similarity\n return abs(1 - cosine_similarity(mat[0:1], mat)[0][1]) * 2","def seq_dist(i1: int, s1: t.Collection[SeqGene],\n i2: int, s2: t.Collection[SeqGene]) -> t.Tuple[int, int, float]:\n m1, m2 = map(\n lambda s: dict(chain.from_iterable(\n ((pos, aa) for pos, aa in zip(g.Pos, g.Seq)) for g in s)),\n [s1, s2])\n d = 0.\n for p in set(m1) | set(m2):\n if p in m1 and p in m2 and m1[p] == m2[p]:\n continue\n d += 1\n return i1, i2, d","def forward(\n self, inputs: torch.Tensor, tags: torch.Tensor, mask: torch.BoolTensor = None\n ) -> torch.Tensor:\n if mask is None:\n mask = torch.ones(*tags.size(), dtype=torch.bool, device=inputs.device)\n else:\n # The code below fails in weird ways if this isn't a bool tensor, so we make sure.\n mask = mask.to(torch.bool)\n\n log_denominator = self._input_likelihood(inputs, self.transitions, mask)\n log_numerator = self._joint_likelihood(inputs, self.transitions, tags, mask)\n\n return torch.sum(log_numerator - log_denominator)","def f_ite(s, a, b):\n f = ITE(s, a, b).factor()\n return f if f in B else f.factor()","def word_cross_product_phi(t1, t2):\n return Counter([(w1, w2) for w1, w2 in product(t1.leaves(), t2.leaves())])","def tag(text, pos_tagger):\n features = [get_crf_features([word for word in sent]) for sent in text]\n tags = pos_tagger.predict(features)\n tagged_text = []\n for i in range(len(text)):\n tagged_sent = []\n for j in range(len(text[i])):\n tagged_sent.append((text[i][j], tags[i][j]))\n tagged_text.append(tagged_sent)\n #print(tags)\n return tags, tagged_text","def word_given_tag(word:str, tag:str, train_bag:Sequence[Tuple[str, str]]=train_tagged_words)-> Tuple[int, int]: \n tag_list = [pair for pair in train_bag if pair[1] == tag]\n count_tag = len(tag_list)\n w_given_tag_list = [pair[0] for pair in tag_list if pair[0] == word]\n count_w_given_tag = len(w_given_tag_list)\n\n return (count_w_given_tag, count_tag)","def predict_viterbi(self, tokens: TokenSeq) -> Tuple[NDArray, NDArray, PosSeq]:","def lemmas_similarity(s1, s2, filter_stop_words=True):\n # Tokenize by sentences into words in lower case \n tokenized_sentence_1 = nltk.word_tokenize(s1.lower())\n tokenized_sentence_2 = nltk.word_tokenize(s2.lower())\n \n if not filter_stop_words:\n tokenized_sentence_1 = [token for token in tokenized_sentence_1 if token not in stop_words]\n tokenized_sentence_2 = [token for token in tokenized_sentence_2 if token not in stop_words]\n \n tagged_sentence_1 = pos_tag(tokenized_sentence_1) # [ (word, POS_TAG), ...]\n tagged_sentence_2 = pos_tag(tokenized_sentence_2) # [ (word, POS_TAG), ...]\n \n lemmas_sentence_1 = [lemmatize(tagged_word, wnl) for tagged_word in tagged_sentence_1] \n lemmas_sentence_2 = [lemmatize(tagged_word, wnl) for tagged_word in tagged_sentence_2] # [LEMMA_1, ...]\n \n # Compute similarity\n if len(lemmas_sentence_1) > 0 and len(lemmas_sentence_2) > 0:\n similarity = 1 - jaccard_distance(set(lemmas_sentence_1), set(lemmas_sentence_2))\n # Compute label of similarity \n return similarity\n else:\n return 0","def glove_leaves_phi(t1, t2, np_func=np.sum): \n prem_vecs = _get_tree_vecs(t1, glove_lookup, np_func) \n hyp_vecs = _get_tree_vecs(t2, glove_lookup, np_func) \n return np.concatenate((prem_vecs, hyp_vecs))","def a_psilc02(self, psi_l):\n\t if psi_l < self.PSILA0:\n\t return 0.\n\t elif self.PSILA0 <= psi_l <= self.PSILA1 :\n\t return (psi_l - self.PSILA0)/(self.PSILA1 - self.PSILA0)\n\t else: \n\t return 1.","def extract_probs(label, x):\n\tb = np.ascontiguousarray(x).view(np.dtype((np.void, x.dtype.itemsize * x.shape[1])))\n\tunique_array, unique_indices, unique_inverse_x, unique_counts = \\\n\t\tnp.unique(b, return_index=True, return_inverse=True, return_counts=True)\n\tunique_a = x[unique_indices]\n\tb1 = np.ascontiguousarray(unique_a).view(np.dtype((np.void, unique_a.dtype.itemsize * unique_a.shape[1])))\n\tpxs = unique_counts / float(np.sum(unique_counts))\n\tp_y_given_x = []\n\tfor i in range(0, len(unique_array)):\n\t\tindexs = unique_inverse_x == i\n\t\tpy_x_current = np.mean(label[indexs, :], axis=0)\n\t\tp_y_given_x.append(py_x_current)\n\tp_y_given_x = np.array(p_y_given_x).T\n\treturn p_y_given_x, b1, b, unique_a, unique_inverse_x, pxs","def prob(self, x, y):\n p = self.tag_prob(y)\n for i in range(len(y)):\n p *= self.out_prob(x[i], y[i])\n\n return p","def pent( a, b ):\n return P(a) - b","def psi(x, *args, **kwds):\n if not args:\n return psi1(x, **kwds)\n if len(args) > 1:\n raise TypeError(\"Symbolic function psi takes at most 2 arguments (%s given)\"%(len(args)+1))\n return psi2(x,args[0],**kwds)","def compute_labels(pos, neg):\n labels = np.zeros(len(pos) + len(neg))\n labels[:len(pos)] = 1.0\n labels[len(pos):] = 0.0\n return labels","def vector(p0, p1):\n a = p1[0] - p0[0]\n b = p1[1] - p0[1]\n return (a, b)"],"string":"[\n \"def _psi_function(share1, share2):\\n return (share1 - share2) * math.log(share1/share2)\",\n \"def get_emissions_probability(label_matches, given_tag, given_word, tag_counts):\\r\\n\\tlookup_tuple = (given_word, given_tag)\\r\\n\\tword_tag_frequency = label_matches.get(lookup_tuple, 0)\\r\\n\\ttag_frequency = tag_counts[given_tag]\\r\\n\\tif tag_frequency == 0:\\r\\n\\t\\temissions_probability = 0\\r\\n\\telse:\\r\\n\\t\\temissions_probability = float(word_tag_frequency)/float(tag_frequency)\\r\\n\\treturn emissions_probability\",\n \"def psi(x, y):\\n return x\",\n \"def match_two_exp_psf(exp1, exp2):\\n\\tif has_smaller_psf(exp1, exp2):\\n\\t\\texpm1 = match_psf(exp1, exp2)\\n\\t\\treturn expm1, exp2\\n\\telif has_smaller_psf(exp2, exp1):\\n\\t\\texpm2 = match_psf(exp2, exp1)\\n\\t\\treturn exp1, expm2\\n\\telse:\\n\\t\\tprint(\\\"[matchpsf] skip matching psf as the psfs are similar\\\")\\n\\t\\treturn exp1, exp2\",\n \"def heuristic_2(a: str, b: str) -> float:\\n # generate term-document matrices\\n if get_intro(a) == \\\"\\\" or get_intro(b) == \\\"\\\":\\n return 2\\n else:\\n corpus = [get_intro(a), get_intro(b)]\\n vect = TfidfVectorizer()\\n mat = vect.fit_transform(corpus)\\n # return cosine similarity\\n return abs(1 - cosine_similarity(mat[0:1], mat)[0][1]) * 2\",\n \"def merge_overlapping_predictions(tags1: List[str], tags2: List[str]) -> List[str]:\\n ret_sequence = []\\n prev_label = \\\"O\\\"\\n\\n # Build a coherent sequence out of two\\n # spans which predicates' overlap\\n\\n for tag1, tag2 in zip(tags1, tags2):\\n label1 = tag1.split(\\\"-\\\", 1)[-1]\\n label2 = tag2.split(\\\"-\\\", 1)[-1]\\n if (label1 == \\\"V\\\") or (label2 == \\\"V\\\"):\\n # Construct maximal predicate length -\\n # add predicate tag if any of the sequence predict it\\n cur_label = \\\"V\\\"\\n\\n # Else - prefer an argument over 'O' label\\n elif label1 != \\\"O\\\":\\n cur_label = label1\\n else:\\n cur_label = label2\\n\\n # Append cur tag to the returned sequence\\n cur_tag = get_coherent_next_tag(prev_label, cur_label)\\n prev_label = cur_label\\n ret_sequence.append(cur_tag)\\n return ret_sequence\",\n \"def fidelity(self, psi_1, psi_2):\\n return np.abs(self.ip(psi_1, psi_2))**2\",\n \"def tag_prob(self, y):\\n p = 1\\n n = self._n\\n y = (START_TAG,) * (n - 1) + tuple(y) + (END_TAG,)\\n for i in range(len(y) - self._n + 1):\\n tag = y[i + n - 1]\\n prev_tags = y[i:i + n - 1]\\n p *= self.trans_prob(tag, prev_tags)\\n\\n return p\",\n \"def masi_distance(label1, label2):\\r\\n\\r\\n len_intersection = len(label1.intersection(label2))\\r\\n len_union = len(label1.union(label2))\\r\\n len_label1 = len(label1)\\r\\n len_label2 = len(label2)\\r\\n if len_label1 == len_label2 and len_label1 == len_intersection:\\r\\n m = 1\\r\\n elif len_intersection == min(len_label1, len_label2):\\r\\n m = 0.67\\r\\n elif len_intersection > 0:\\r\\n m = 0.33\\r\\n else:\\r\\n m = 0\\r\\n\\r\\n return 1 - (len_intersection / len_union) * m\",\n \"def compute_class_freqs(labels):\\n ### START CODE HERE (REPLACE INSTANCES OF 'None' with your code) ###\\n \\n # total number of patients (rows)\\n N = len(labels)\\n \\n positive_frequencies = np.sum(labels,axis=0)/N\\n negative_frequencies = 1-positive_frequencies\\n\\n ### END CODE HERE ###\\n return positive_frequencies, negative_frequencies\",\n \"def binary_distance(label1, label2):\\r\\n\\r\\n return 0.0 if label1 == label2 else 1.0\",\n \"def tag_log_prob(self, y, add_end_token=True):\\n p = 0\\n n = self._n\\n y = (START_TAG,) * (n - 1) + tuple(y) + (END_TAG,) * add_end_token\\n for i in range(len(y) - self._n + 1):\\n tag = y[i + n - 1]\\n prev_tags = y[i:i + n - 1]\\n if self.trans_prob(tag, prev_tags) == 0:\\n return -math.inf\\n\\n p += math.log2(self.trans_prob(tag, prev_tags))\\n\\n return p\",\n \"def t2_given_t1(t2:str, t1:str, train_bag:Sequence[Tuple[str, str]]=train_tagged_words)-> Tuple[int, int]: \\n tags = [pair[1] for pair in train_bag]\\n count_t1 = len([t for t in tags if t == t1])\\n count_t2_t1 = 0\\n for index in range(len(tags)-1):\\n if tags[index] == t1 and tags[index+1] == t2:\\n count_t2_t1 += 1\\n return (count_t2_t1, count_t1)\",\n \"def retagger(tags):\\n if tags == 'Positive':\\n return 'pos'\\n else:\\n return 'neg'\",\n \"def text_proximity(str_1: str, str_2: str) -> float:\\n tokens_1 = Counter(str_1.split(' '))\\n tokens_2 = Counter(str_2.split(' '))\\n return _normalized_scalar_product(tokens_1, tokens_2)\",\n \"def f_value(a, b):\\r\\n if not any(a) or not any(b) or len(a) <= 1 or len(b) <= 1:\\r\\n raise ValueError(\\\"Vectors should contain more than 1 element\\\")\\r\\n F = var(a) / var(b)\\r\\n dfn = len(a) - 1\\r\\n dfd = len(b) - 1\\r\\n return dfn, dfd, F\",\n \"def trans_prob(self, tag, prev_tags=None):\\n assert prev_tags is not None or self._n == 1\\n prev_tags = prev_tags or ()\\n\\n return self._trans.get(prev_tags, {}).get(tag, 0)\",\n \"def tag2predictions(ote_tag_sequence, ts_tag_sequence):\\n n_tag = len(ote_tag_sequence)\\n # opinion target sequence and targeted sentiment sequence\\n ot_sequence, ts_sequence = [], []\\n beg, end = -1, -1\\n for i in range(n_tag):\\n tag = ote_tag_sequence[i]\\n if tag == 'S':\\n ot_sequence.append((i, i))\\n elif tag == 'B':\\n beg = i\\n elif tag == 'E':\\n end = i\\n if end > beg and beg != -1:\\n ot_sequence.append((beg, end))\\n beg, end = -1, -1\\n sentiments = []\\n beg, end = -1, -1\\n for i in range(n_tag):\\n ts_tag = ts_tag_sequence[i]\\n # current position and sentiment\\n eles = ts_tag.split('-')\\n if len(eles) == 2:\\n pos, sentiment = eles\\n else:\\n pos, sentiment = 'O', 'O'\\n if sentiment != 'O':\\n # current word is a subjective word\\n sentiments.append(sentiment)\\n if pos == 'S':\\n # singleton\\n ts_sequence.append((i, i, sentiments[0]))\\n sentiments = []\\n elif pos == 'B':\\n beg = i\\n elif pos == 'E':\\n end = i\\n # schema1: only the consistent sentiment tags are accepted\\n # that is, all of the sentiment tags are the same\\n if end > beg > -1 and len(set(sentiments)) == 1:\\n ts_sequence.append((beg, end, sentiment))\\n sentiments = []\\n beg, end = -1, -1\\n\\n # schema2: only consider the sentiment at the beginning of the aspect span\\n # if end > beg > -1:\\n # ts_sequence.append((beg, end, sentiments[0]))\\n # sentiments = []\\n # beg, end = -1, -1\\n return ot_sequence, ts_sequence\",\n \"def correspondences(labels1,labels2,return_counts=True):\\n q = 100000\\n assert amin(labels1)>=0 and amin(labels2)>=0\\n assert amax(labels2)<q\\n combo = labels1*q+labels2\\n result = unique(combo, return_counts=return_counts)\\n if return_counts:\\n result, counts = result\\n result = array([result//q,result%q,counts])\\n else:\\n result = array([result//q,result%q])\\n return result\",\n \"def t2_given_t1(t2, t1, train_bag):\\n tags = [pair[1] for pair in train_bag]\\n count_t1 = len([t for t in tags if t == t1])\\n count_t2_t1 = 0\\n for index in range(len(tags)-1):\\n if tags[index] == t1 and tags[index+1] == t2:\\n count_t2_t1 += 1\\n return count_t2_t1, count_t1\",\n \"def GetSuggesteeSimilarity(suggestee1_tags, suggestee2_tags):\\n total_size = len(suggestee1_tags) + len(suggestee2_tags)\\n if total_size == 0:\\n return 1.\\n size_of_intersection = 0\\n elems_in_first = {}\\n for tag in suggestee1_tags:\\n elems_in_first[tag] = True\\n for tag in suggestee2_tags:\\n if tag in elems_in_first:\\n size_of_intersection += 1\\n return size_of_intersection / (total_size - size_of_intersection)\",\n \"def compute_class_freqs(gen):\\r\\n labels = gen.labels\\r\\n N = labels.shape[0]\\r\\n positive_frequencies = np.sum(labels, axis=0) / N\\r\\n negative_frequencies = np.sum(1 - labels, axis=0) / N\\r\\n return positive_frequencies, negative_frequencies\",\n \"def bspe(a, b):\\n if b-a == 1:\\n return MPZ_ONE, MPZ(b)\\n m = (a+b)//2\\n p1, q1 = bspe(a, m)\\n p2, q2 = bspe(m, b)\\n return p1*q2+p2, q1*q2\",\n \"def evaluate(self, feats, tag_set):\\n corect=0\\n incorect=0 \\n for (tokens, tags) in zip(feats, tag_set):\\n yyhat= self.tag(tokens)\\n for pre, tag in zip(yyhat, tags):\\n if pre==tag:\\n corect+=1\\n else:\\n incorect+=1\\n\\n return corect/(corect+incorect)\",\n \"def get_e(word, tag, e_word_tag_counts, q_uni_counts):\\n word_tag_tupple = (word, tag)\\n\\n word_tag_count = 0\\n if word_tag_tupple in e_word_tag_counts:\\n word_tag_count = e_word_tag_counts[word_tag_tupple]\\n\\n nof_tag = q_uni_counts[tag]\\n return float(word_tag_count) / nof_tag\",\n \"def sentence_encoding_rnn_phi(t1, t2):\\n return (t1.leaves(), t2.leaves())\",\n \"def best_match(psi1, psi2, threshold=None):\\n if threshold is None:\\n threshold = (2 * psi1.shape[0])**-0.25\\n Q = np.abs(psi1.T.conj() @ psi2) # Overlap matrix\\n orig, perm = so.linear_sum_assignment(-Q)\\n return perm, Q[orig, perm] < threshold\",\n \"def best_sequence(self, T, pos, psi, phi, fix_tags=[]):\\n for idx, m in fix_tags:\\n phi[idx - 1, m] = 100\\n # if fix_idx:\\n # phi[fix_idx - 1, fix_m] = 100\\n msgs, pointers = max_product(T, pos, psi, phi, True)\\n tags_dict = get_best_tags(T, msgs, pointers)\\n tags = []\\n for i in range(1, len(T) + 1):\\n tags.append(self.get_tag(tags_dict[str(i)]))\\n return tags\",\n \"def psi_wf(self, vw, d1, d2, ns, tl):\\n\\t osmotic = (R*299./VW)*np.log((((vw/self.ZW)*self.ZW)/(VW))/((((vw/self.ZW)*self.ZW)/(VW))+ns))/10**6 #MPa\\n\\t turgor = ((vw/self.ZW) - d1)**d2#MPa\\n\\t return turgor+osmotic #MPa \",\n \"def interval_distance(label1,label2):\\r\\n\\r\\n try:\\r\\n return pow(label1 - label2, 2)\\r\\n# return pow(list(label1)[0]-list(label2)[0],2)\\r\\n except:\\r\\n print(\\\"non-numeric labels not supported with interval distance\\\")\",\n \"def tags_2_vec(tags, w2v_model=None):\\n if len(tags) == 0 or len([tag for tag in tags if tag in w2v_model]) == 0:\\n return np.zeros(200)\\n else:\\n output = np.sum([w2v_model[tag] for tag in tags if tag in w2v_model], axis=0)\\n return output / np.linalg.norm(output)\",\n \"def __call__(self, f1, f2):\\n\\n r = 0.0\\n for f in f1:\\n if f in f2:\\n r += f1[f] * f2[f]\\n\\n return r\",\n \"def psi(a):\",\n \"def muTagAndProbeEff(hlt, pp, fp):\\n if 2*hlt+pp <= 0:\\n return 0., 0.\\n elif fp <= 0:\\n return 0., 0.\\n\\n eff = (2*hlt+pp)/(2*hlt+pp+fp)\\n eff_err = 1. / (2*hlt+fp+pp)**2 * np.sqrt(pp*fp**2 + hlt*(2*fp)**2 + fp*(pp+2*hlt)**2)\\n return eff, eff_err\",\n \"def pairwise_operation(self, x, y):\\n k = sum(nh_compare_labels(x[r], y[r]) for r in range(self.R))\\n return k / (1.0*self.R)\",\n \"def get_eqn(p0, p1):\\n m = (p0[1] - p1[1]) / (p0[0] - p1[0])\\n return (m, p0[1] - m * p0[0])\",\n \"def calc_seg_prob(ts, probroots, probsuffix, probtrans):\\r\\n root = ts.root\\r\\n suffix = ts.suffix\\r\\n trans = ts.trans\\r\\n feat = feature(root, suffix)\\r\\n score = 0.0\\r\\n if root in probroots and suffix in probsuffix and (trans, feat) in probtrans:\\r\\n score = probroots[root] * probsuffix[suffix] * probtrans[(trans, feat)]\\r\\n return score\",\n \"def similarity(pair: Tuple[Text, Text]) -> float:\\n (a, b) = pair\\n missing = (\\n True\\n if any(symbol not in Metrics.realine.feature_matrix for symbol in pair)\\n else False\\n )\\n return 0.0 if missing else 1 - Metrics.realine.delta(a, b)\",\n \"def lcmu(a, b):\\n return (abs(a)*abs(b))//gcdi(a, b)\",\n \"def get_word_probability(self, label, term):\\n\\n if 'sod' in label:\\n return self.cond_prob_sod[term]\\n elif 'pop' in label:\\n return self.cond_prob_pop[term]\\n else:\\n print(\\\"Just run the doctest Dev\\\")\\n \\n pass\",\n \"def get_TP_FP_FN(cm, label):\\n dim = min(cm.shape[0], cm.shape[1])\\n if label >= 0 and label < dim:\\n # TP, True positive, diagonal position\\n tp = cm[label, label]\\n # FP, False positive: sum of column label (without main diagonal)\\n fp = sum(cm[:, label]) - cm[label][label]\\n # FN, False negative: sum of row label (without main diagonal)\\n fn = sum(cm[label, :]) - cm[label][label]\\n return tp, fp, fn\\n else:\\n print(\\\"label out of bound\\\")\",\n \"def fn(i1, i2):\\n if i1 == len(word1): return len(word2) - i2\\n if i2 == len(word2): return len(word1) - i1\\n if word1[i1] == word2[i2]: return fn(i1+1, i2+1)\\n return 1 + min(fn(i1+1,i2), fn(i1, i2+1))\",\n \"def get_similarities(tags):\\n return [(a, b)\\n for (a, b) in itertools.permutations(tags, 2)\\n if difflib.SequenceMatcher(a=a.lower(), b=b.lower()).ratio() > SIMILAR\\n and a != b\\n and b.endswith('s')]\",\n \"def delta_prob(variants, tx, clf1, clf2, model='cterm', is_sum=True):\\n # fetch c-terminal sequence\\n term_seq, vars_considered = utils.process_var_seq(variants, model=model, is_sum=is_sum)\\n\\n # return None if no variants\\n if not term_seq:\\n if is_sum: return 0\\n else: return [], []\\n # return None if U in protein sequence\\n if 'U' in utils.fetch_seq(tx.protein_sequence, model=model):\\n if is_sum: return 0\\n else: return [], []\\n\\n # construct dataframe\\n result_df = pd.DataFrame({'seq': term_seq})\\n\\n # create feature matrix\\n X = compute_feature_matrix(result_df['seq'], 6, dinuc=True, model=model)\\n X2 = compute_feature_matrix(result_df['seq'], 0, dinuc=False, model=model)\\n\\n # predict scores\\n result_df['prob'] = clf1.predict_proba(X)[:, 0]\\n result_df['prob2'] = clf2.predict_proba(X2)[:, 0]\\n result_df['delta prob'] = result_df['prob'] - result_df['prob2']\\n\\n # adjust for baseline score\\n wt_seq = utils.fetch_seq(tx.protein_sequence, model=model)\\n wt_df = pd.DataFrame({'seq': [wt_seq]})\\n # create feature matrix\\n X = compute_feature_matrix(wt_df['seq'], 6, dinuc=True, model=model)\\n X2 = compute_feature_matrix(wt_df['seq'], 0, dinuc=False, model=model)\\n wt_df['prob'] = clf1.predict_proba(X)[:, 0]\\n wt_df['prob2'] = clf2.predict_proba(X2)[:, 0]\\n wt_df['delta prob'] = wt_df['prob'] - wt_df['prob2']\\n baseline = wt_df['delta prob'].iloc[0]\\n\\n # add up scores\\n tmp = result_df['delta prob'] - baseline\\n if is_sum:\\n delta_prob_sum = tmp.sum()\\n return delta_prob_sum\\n else:\\n return vars_considered, tmp\",\n \"def symdif(f1, f2):\\n\\n y = union(subm(f1,f2),subm(f2,f1))\\n return y\",\n \"def inspect_bias(p1: list, p2: list, target:list, model: Word2Vec, \\n metric: Callable[[list,list],float] = cosine_sim) -> tuple:\\n p1 = [p for p in p1 if model.wv.__contains__(p)]\\n p2 = [p for p in p2 if model.wv.__contains__(p)]\\n target = [t for t in target if model.wv.__contains__(t)]\\n p1_scores, p2_scores = defaultdict(float),defaultdict(float)\\n for t in target:\\n for p in p1:\\n p1_scores[p] += metric(model.wv.__getitem__(p),model.wv.__getitem__(t))\\n for p in p2:\\n p2_scores[p] += metric(model.wv.__getitem__(p),model.wv.__getitem__(t)) # target[0]\\n return (p1_scores,p2_scores)\",\n \"def pairing(left, right):\\n # same class: 0\\n if left[label] == right[label]:\\n flag = 0\\n # not same: 1\\n else:\\n flag = 1\\n return tf.cast(left[\\\"image\\\"], tf.float32) / 255., tf.cast(right[\\\"image\\\"], tf.float32) / 255., tf.cast(flag, tf.float32)\",\n \"def delta_prob_raw(variants, tx, clf1, clf2, model='cterm', is_sum=True):\\n # fetch c-terminal sequence\\n term_seq = [] ; vars_considered = []\\n for v in variants:\\n if v.mutant_protein_sequence:\\n if model=='cterm' and type(v) in utils.indels+utils.nmd_sub_vars:\\n term_seq.append(utils.fetch_seq(v.mutant_protein_sequence, model=model))\\n if not is_sum: vars_considered.append(v)\\n elif type(v) in utils.base_substitutions:\\n if model=='cterm' and v.aa_mutation_start_offset>(len(v.transcript.protein_sequence) - 23):\\n term_seq.append(utils.fetch_seq(v.mutant_protein_sequence, model=model))\\n if not is_sum: vars_considered.append(v)\\n elif model=='nterm' and v.aa_mutation_start_offset<=24:\\n term_seq.append(utils.fetch_seq(v.mutant_protein_sequence, model=model))\\n if not is_sum: vars_considered.append(v)\\n\\n # return None if no variants\\n if not term_seq:\\n if is_sum: return 0\\n else: return [], [], []\\n # return None if U in protein sequence\\n if 'U' in utils.fetch_seq(tx.protein_sequence, model=model):\\n if is_sum: return 0\\n else: return [], [], []\\n\\n # construct dataframe\\n result_df = pd.DataFrame({'seq': term_seq})\\n\\n # create feature matrix\\n X = compute_feature_matrix(result_df['seq'], 6, dinuc=True, model=model)\\n X2 = compute_feature_matrix(result_df['seq'], 0, dinuc=False, model=model)\\n\\n # predict scores\\n result_df['prob'] = clf1.predict_proba(X)[:, 0]\\n\\n # adjust for baseline score\\n wt_seq = utils.fetch_seq(tx.protein_sequence, model=model)\\n wt_df = pd.DataFrame({'seq': [wt_seq]})\\n # create feature matrix\\n X = compute_feature_matrix(wt_df['seq'], 6, dinuc=True, model=model)\\n wt_df['prob'] = clf1.predict_proba(X)[:, 0]\\n baseline = wt_df['prob'].iloc[0]\\n\\n # add up scores\\n tmp = result_df['prob'] - baseline\\n if is_sum:\\n prob_sum = tmp.sum()\\n return prob_sum\\n else:\\n return vars_considered, tmp, result_df['prob']\",\n \"def gmt_element(bitmap_a, bitmap_b, sig_array, bitmap_to_linear_mapping):\\n output_sign = canonical_reordering_sign(bitmap_a, bitmap_b, sig_array)\\n output_bitmap = bitmap_a^bitmap_b\\n idx = bitmap_to_linear_mapping[output_bitmap]\\n return idx, output_sign\",\n \"def TVD(p1, p2):\\n assert p1.shape == p2.shape\\n return 0.5 * np.sum(np.absolute(np.subtract(p1, p2)))\",\n \"def wordSimilarityRatio(sent_1,sent_2):\",\n \"def difference(first, second, rf, rs, years=(1980, 2000),smooth=1, corpus='bok'):\\n try:\\n a_first = nb_ngram(first, years=years, smooth=smooth, corpus=corpus)\\n a_second = nb_ngram(second, years=years, smooth=smooth, corpus=corpus)\\n a = a_first.join(a_second) \\n b_first = nb_ngram(rf, years=years, smooth=smooth, corpus=corpus)\\n b_second = nb_ngram(rs, years=years, smooth=smooth, corpus=corpus)\\n if rf == rs:\\n b_second.columns = [rs + '2']\\n b = b_first.join(b_second)\\n s_a = a.mean()\\n s_b = b.mean()\\n f1 = s_a[a.columns[0]]/s_a[a.columns[1]]\\n f2 = s_b[b.columns[0]]/s_b[b.columns[1]]\\n res = f1/f2\\n except:\\n res = 'Mangler noen data - har bare for: ' + ', '.join([x for x in a.columns.append(b.columns)])\\n return res\",\n \"def __call__(self, f1, f2):\\n r = len(set(f1.features) ^ set(f2.features))\\n\\n return exp(-self.gamma * r)\",\n \"def get_q(tag_v, tag_w, tag_u, total_tokens, q_tri_counts, q_bi_counts, q_uni_counts):\\n lambda1 = 0.3\\n lambda2 = 0.3\\n lambda3 = 1 - lambda1 - lambda2\\n\\n q_tri = 0\\n q_bi = 0\\n q_uni = 0\\n\\n if (tag_w, tag_u, tag_v) in q_tri_counts:\\n q_tri = float(q_tri_counts[(tag_w, tag_u, tag_v)]) / q_bi_counts[(tag_w, tag_u)]\\n if (tag_u, tag_v) in q_bi_counts:\\n q_bi = float(q_bi_counts[(tag_u, tag_v)]) / q_uni_counts[tag_u]\\n if tag_v in q_uni_counts:\\n q_uni = float(q_uni_counts[tag_v]) / total_tokens\\n\\n\\n\\n # Calculates the total 3 n-grams\\n total_q = lambda1 * q_tri + lambda2 * q_bi + lambda3 * q_uni\\n return total_q\",\n \"def mutate_tag_seq(words, seq1, seq2):\\n if len(seq1) > len(words):\\n return None\\n seq_start = index_tag_seq(words, seq1)\\n if seq_start > -1:\\n pre = words[:seq_start]\\n post = words[seq_start+len(seq1):]\\n mutated = []\\n for x in seq2:\\n for j in range(len(seq1)): \\n if x == words[seq_start+j].tag:\\n mutated.append(words[seq_start+j])\\n return pre + mutated + post\\n return None\",\n \"def similarity_function_old(feature1, feature2):\\n f1Magnitude = feature1.dot(feature1)\\n f2Magnitude = feature2.dot(feature2)\\n return 1 - feature1.dot(feature2) / (f1Magnitude * f2Magnitude)\",\n \"def get_chi2(a, b):\\n off = (a - b)**2\\n return np.sqrt(np.sum(off)) / b.sum()\",\n \"def bio2ot(ote_tag_sequence, ts_tag_sequence):\\n assert len(ote_tag_sequence) == len(ts_tag_sequence)\\n new_ote_sequence = bio2ot_ote(ote_tag_sequence=ote_tag_sequence)\\n new_ts_sequence = bio2ot_ts(ts_tag_sequence=ts_tag_sequence)\\n assert len(new_ote_sequence) == len(ote_tag_sequence)\\n assert len(new_ts_sequence) == len(ts_tag_sequence)\\n return new_ote_sequence, new_ts_sequence\",\n \"def tvd(pred: Tensor, label: Tensor) -> Tensor:\\n return (Tensor([0.5]) * torch.abs(pred - label)).sum()\",\n \"def best_sequence_brute(self, T, pos, psi, phi):\\n ms = self.get_all_tag_seq(len(T))\\n log_scores = tr.zeros(len(ms), dtype=tr.float64)\\n for i in range(len(ms)):\\n log_scores[i] = self.log_score(T, pos, ms[i], psi, phi)\\n best = ms[tr.argmax(log_scores)]\\n tags = []\\n for i in range(len(T)):\\n tags.append(self.get_tag_index(best[i]))\\n return tags\",\n \"def psi_xf(self, ev, gp, psi_l):\\n\\t return ev*(1. - self.F_CAP)/(lai*gp) + psi_l\",\n \"def label (x):\\n\\n if x == 0:\\n return None\\n if x == 1:\\n return Fraction(0)\\n b = bin(x)[2:]\\n s = b[1]\\n wf = re.compile(r'^.({}+)(.*)$'.format(s))\\n w,f = wf.findall(b)[0]\\n p = 1 if s == '1' else -1\\n n = p * Fraction(len(w),1)\\n scale = Fraction(1,1)\\n while len(f):\\n scale /= 2\\n if f[0] == '1':\\n n += scale\\n else:\\n n += -scale\\n f = f[1:]\\n return n\",\n \"def su_calculation(f1, f2):\\n # calculate information gain of f1 and f2, t1 = ig(f1, f2)\\n t1 = information_gain(f1, f2)\\n # calculate entropy of f1\\n t2 = ee.entropyd(f1)\\n # calculate entropy of f2\\n t3 = ee.entropyd(f2)\\n\\n su = 2.0 * t1 / (t2 + t3)\\n\\n return su\",\n \"def sentence_similarity(sentence1, sentence2):\\n sentence1 = sentence1.tags\\n sentence2 = sentence2.tags\\n \\n synsets1 = [tagged_to_synset(*tagged_word) for tagged_word in sentence1]\\n synsets2 = [tagged_to_synset(*tagged_word) for tagged_word in sentence2]\\n \\n synsets1 = [ss for ss in synsets1 if ss]\\n synsets2 = [ss for ss in synsets2 if ss]\\n \\n score, count = 0.0, 0\\n \\n for synset in synsets1:\\n \\n li=[synset.path_similarity(ss) for ss in synsets2]\\n m=0\\n for i in range(len(li)):\\n if li[i] is not None and m<li[i]:\\n m=li[i]\\n if m != 0:\\n score += m\\n count += 1\\n\\n if count is 0:\\n score = 0\\n else:\\n score /= count\\n return score\",\n \"def tags_jaccard_index(d1, d2):\\n set1 = set(d1.keys())\\n set2 = set(d2.keys())\\n\\n common_tags = set.intersection(set1, set2)\\n common_tags_count = 0\\n all_tags_count = sum(d1.values()) + sum(d2.values())\\n\\n for k, v in d1.items():\\n if k in common_tags:\\n common_tags_count += v\\n\\n for k, v in d2.items():\\n if k in common_tags:\\n common_tags_count += v\\n\\n intersection_cardinality = len(set.intersection(set1, set2))\\n union_cardinality = len(set.union(set1, set2))\\n\\n tags_similarity = intersection_cardinality/float(union_cardinality)\\n\\n final_tag_similarity = ((common_tags_count / all_tags_count) * 10) \\\\\\n * tags_similarity\\n\\n return final_tag_similarity\",\n \"def sigb(o) :\\n return o * (1 - o)\",\n \"def scoreslabels_2_tarnon(scores,labels):\\n tt = labels==1\\n tar = scores[tt]\\n non = scores[np.logical_not(tt)]\\n return tar, non\",\n \"def friction_factor_2(v1: \\\"int\\\", v2: \\\"int\\\") -> \\\"int\\\":\",\n \"def presence(label):\\r\\n\\r\\n return lambda x, y: 1.0 * ((label in x) == (label in y))\",\n \"def answer_question4b():\\n\\n tagged_sequence = tagged_sent\\n correct_sequence = correct_sent\\n # Why do you think the tagger tagged this example incorrectly?\\n answer = inspect.cleandoc(\\\"\\\"\\\"\\\\\\n The transition probs are generally larger than the emission probs\\n (thus dominate in cost computation). Thus, the HMM tagger performs\\n badly on compound nouns (e.g. City Executive Committee). In our\\n example, 'ADJ' after 'NOUN' is rare, so 'Executive' is tagged as 'NOUN'\\n wrongly.\\n \\\"\\\"\\\")[0:280]\\n\\n return tagged_sequence, correct_sequence, answer\",\n \"def tf_psnr(im1, im2):\\n mse = tf.losses.mean_squared_error(labels=im2 * 255.0, predictions=im1 * 255.0)\\n return 10.0 * (tf.log(255.0 ** 2 / mse) / tf.log(10.0))\",\n \"def delta_e_76(lab1, lab2):\\n\\n l1, a1, b1 = lab1\\n l2, a2, b2 = lab2\\n return (l1 - l2) ** 2 + (a1 - a2) ** 2 + (b1 - b2) ** 2\",\n \"def label_intersects_tags(label, tags):\\n for tag in tags:\\n if tag in label:\\n return True\\n return False\",\n \"def word_overlap_phi(t1, t2):\\n overlap = set([w1 for w1 in t1.leaves() if w1 in t2.leaves()])\\n return Counter(overlap)\",\n \"def f(x0: float, x1: float) -> float:\\n return 8 - (x0 - 2) ** 2 - (x1 - 2) ** 2\",\n \"def phi2_coefficient(L):\\r\\n\\r\\n if 0 < L < 120:\\r\\n return L / 120\\r\\n if L >= 120:\\r\\n return 1\",\n \"def calculate_fid(model, images1, images2):\\n \\n act1 = model.predict(images1)\\n act2 = model.predict(images2)\\n\\n mu1, sigma1 = act1.mean(axis=0), np.cov(act1, rowvar=False)\\n mu2, sigma2 = act2.mean(axis=0), np.cov(act2, rowvar=False)\\n\\n ssdiff = np.sum((mu1 - mu2) ** 2.0)\\n\\n covmean = sqrtm(sigma1.dot(sigma2))\\n\\n if np.iscomplexobj(covmean):\\n covmean = covmean.real\\n\\n # Equation for FID score calculation\\n fid = ssdiff + np.trace(sigma1 + sigma2 - 2.0 * covmean) \\n\\n return fid\",\n \"def wang(G, term1, term2):\\n if term1 not in G or term2 not in G:\\n return 0\\n sa = s_values(G, term1)\\n sb = s_values(G, term2)\\n sva = sum(sa.values())\\n svb = sum(sb.values())\\n common = set(sa.keys()) & set(sb.keys())\\n cv = sum(sa[c] + sb[c] for c in common)\\n return round(cv / (sva + svb), 3)\",\n \"def bf_sign(p1, k1, p2, k2):\\n\\n def caesuras(k):\\n \\\"\\\"\\\"Returns the caesuras of a basis element.\\\"\\\"\\\"\\n caesuras = []\\n for idx, i in enumerate(k):\\n if i in k[idx + 1:]:\\n caesuras.append(idx)\\n return caesuras\\n\\n def weights(cae, p):\\n \\\"\\\"\\\"Returns the weights of the splitting knowing the caesuras.\\\"\\\"\\\"\\n weights = []\\n for i, j in pairwise(p):\\n closed_open = len([e for e in cae if i <= e < j])\\n weights.append(closed_open)\\n return [value % 2 for value in weights]\\n\\n p1 = [0] + p1 + [len(k1) - 1]\\n cae1 = caesuras(k1)\\n w1 = weights(cae1, p1)\\n cae2 = caesuras(k2)\\n w2 = weights(cae2, p2)\\n sign_exp = 0\\n for idx, w in enumerate(w2):\\n if w:\\n sign_exp += sum(w1[idx + 1:]) % 2\\n return (-1) ** sign_exp\",\n \"def _compute_label(mol_a, mol_b, prop_name: str):\\n\\n try:\\n label_dG_a = convert_uIC50_to_kJ_per_mole(float(mol_a.GetProp(prop_name)))\\n label_dG_b = convert_uIC50_to_kJ_per_mole(float(mol_b.GetProp(prop_name)))\\n except KeyError:\\n raise RuntimeError(f\\\"Couldn't access IC50 label for either mol A or mol B, looking at {prop_name}\\\")\\n\\n label = label_dG_b - label_dG_a\\n\\n return label\",\n \"def getLabel2(*args):\",\n \"def getLabel2(*args):\",\n \"def simple_chained_rep_rnn_phi(t1, t2):\\n return t1.leaves() + t2.leaves()\",\n \"def heuristic_1(a: str, b: str) -> float:\\n query = \\\"https://en.wikipedia.org/w/api.php?action=parse&page=TEMP&prop=text&formatversion=2&format=json\\\"\\n startTitle = (a.replace(\\\" \\\", \\\"%20\\\")).replace(\\\"&\\\", \\\"%26\\\")\\n endTitle = (b.replace(\\\" \\\", \\\"%20\\\")).replace(\\\"&\\\", \\\"%26\\\")\\n startURL = (query.replace(\\\"TEMP\\\", startTitle))\\n endURL = (query.replace(\\\"TEMP\\\", endTitle))\\n # text processing using SOUP\\n initialSoup = BeautifulSoup(get_JSON(startURL)['parse']['text'], 'html.parser')\\n finalSoup = BeautifulSoup(get_JSON(endURL)['parse']['text'], 'html.parser')\\n # generate term-document matrices\\n corpus = [initialSoup.get_text().replace('\\\\n', ' '), finalSoup.get_text().replace('\\\\n', ' ')]\\n vect = TfidfVectorizer()\\n mat = vect.fit_transform(corpus)\\n # return cosine similarity\\n return abs(1 - cosine_similarity(mat[0:1], mat)[0][1]) * 2\",\n \"def seq_dist(i1: int, s1: t.Collection[SeqGene],\\n i2: int, s2: t.Collection[SeqGene]) -> t.Tuple[int, int, float]:\\n m1, m2 = map(\\n lambda s: dict(chain.from_iterable(\\n ((pos, aa) for pos, aa in zip(g.Pos, g.Seq)) for g in s)),\\n [s1, s2])\\n d = 0.\\n for p in set(m1) | set(m2):\\n if p in m1 and p in m2 and m1[p] == m2[p]:\\n continue\\n d += 1\\n return i1, i2, d\",\n \"def forward(\\n self, inputs: torch.Tensor, tags: torch.Tensor, mask: torch.BoolTensor = None\\n ) -> torch.Tensor:\\n if mask is None:\\n mask = torch.ones(*tags.size(), dtype=torch.bool, device=inputs.device)\\n else:\\n # The code below fails in weird ways if this isn't a bool tensor, so we make sure.\\n mask = mask.to(torch.bool)\\n\\n log_denominator = self._input_likelihood(inputs, self.transitions, mask)\\n log_numerator = self._joint_likelihood(inputs, self.transitions, tags, mask)\\n\\n return torch.sum(log_numerator - log_denominator)\",\n \"def f_ite(s, a, b):\\n f = ITE(s, a, b).factor()\\n return f if f in B else f.factor()\",\n \"def word_cross_product_phi(t1, t2):\\n return Counter([(w1, w2) for w1, w2 in product(t1.leaves(), t2.leaves())])\",\n \"def tag(text, pos_tagger):\\n features = [get_crf_features([word for word in sent]) for sent in text]\\n tags = pos_tagger.predict(features)\\n tagged_text = []\\n for i in range(len(text)):\\n tagged_sent = []\\n for j in range(len(text[i])):\\n tagged_sent.append((text[i][j], tags[i][j]))\\n tagged_text.append(tagged_sent)\\n #print(tags)\\n return tags, tagged_text\",\n \"def word_given_tag(word:str, tag:str, train_bag:Sequence[Tuple[str, str]]=train_tagged_words)-> Tuple[int, int]: \\n tag_list = [pair for pair in train_bag if pair[1] == tag]\\n count_tag = len(tag_list)\\n w_given_tag_list = [pair[0] for pair in tag_list if pair[0] == word]\\n count_w_given_tag = len(w_given_tag_list)\\n\\n return (count_w_given_tag, count_tag)\",\n \"def predict_viterbi(self, tokens: TokenSeq) -> Tuple[NDArray, NDArray, PosSeq]:\",\n \"def lemmas_similarity(s1, s2, filter_stop_words=True):\\n # Tokenize by sentences into words in lower case \\n tokenized_sentence_1 = nltk.word_tokenize(s1.lower())\\n tokenized_sentence_2 = nltk.word_tokenize(s2.lower())\\n \\n if not filter_stop_words:\\n tokenized_sentence_1 = [token for token in tokenized_sentence_1 if token not in stop_words]\\n tokenized_sentence_2 = [token for token in tokenized_sentence_2 if token not in stop_words]\\n \\n tagged_sentence_1 = pos_tag(tokenized_sentence_1) # [ (word, POS_TAG), ...]\\n tagged_sentence_2 = pos_tag(tokenized_sentence_2) # [ (word, POS_TAG), ...]\\n \\n lemmas_sentence_1 = [lemmatize(tagged_word, wnl) for tagged_word in tagged_sentence_1] \\n lemmas_sentence_2 = [lemmatize(tagged_word, wnl) for tagged_word in tagged_sentence_2] # [LEMMA_1, ...]\\n \\n # Compute similarity\\n if len(lemmas_sentence_1) > 0 and len(lemmas_sentence_2) > 0:\\n similarity = 1 - jaccard_distance(set(lemmas_sentence_1), set(lemmas_sentence_2))\\n # Compute label of similarity \\n return similarity\\n else:\\n return 0\",\n \"def glove_leaves_phi(t1, t2, np_func=np.sum): \\n prem_vecs = _get_tree_vecs(t1, glove_lookup, np_func) \\n hyp_vecs = _get_tree_vecs(t2, glove_lookup, np_func) \\n return np.concatenate((prem_vecs, hyp_vecs))\",\n \"def a_psilc02(self, psi_l):\\n\\t if psi_l < self.PSILA0:\\n\\t return 0.\\n\\t elif self.PSILA0 <= psi_l <= self.PSILA1 :\\n\\t return (psi_l - self.PSILA0)/(self.PSILA1 - self.PSILA0)\\n\\t else: \\n\\t return 1.\",\n \"def extract_probs(label, x):\\n\\tb = np.ascontiguousarray(x).view(np.dtype((np.void, x.dtype.itemsize * x.shape[1])))\\n\\tunique_array, unique_indices, unique_inverse_x, unique_counts = \\\\\\n\\t\\tnp.unique(b, return_index=True, return_inverse=True, return_counts=True)\\n\\tunique_a = x[unique_indices]\\n\\tb1 = np.ascontiguousarray(unique_a).view(np.dtype((np.void, unique_a.dtype.itemsize * unique_a.shape[1])))\\n\\tpxs = unique_counts / float(np.sum(unique_counts))\\n\\tp_y_given_x = []\\n\\tfor i in range(0, len(unique_array)):\\n\\t\\tindexs = unique_inverse_x == i\\n\\t\\tpy_x_current = np.mean(label[indexs, :], axis=0)\\n\\t\\tp_y_given_x.append(py_x_current)\\n\\tp_y_given_x = np.array(p_y_given_x).T\\n\\treturn p_y_given_x, b1, b, unique_a, unique_inverse_x, pxs\",\n \"def prob(self, x, y):\\n p = self.tag_prob(y)\\n for i in range(len(y)):\\n p *= self.out_prob(x[i], y[i])\\n\\n return p\",\n \"def pent( a, b ):\\n return P(a) - b\",\n \"def psi(x, *args, **kwds):\\n if not args:\\n return psi1(x, **kwds)\\n if len(args) > 1:\\n raise TypeError(\\\"Symbolic function psi takes at most 2 arguments (%s given)\\\"%(len(args)+1))\\n return psi2(x,args[0],**kwds)\",\n \"def compute_labels(pos, neg):\\n labels = np.zeros(len(pos) + len(neg))\\n labels[:len(pos)] = 1.0\\n labels[len(pos):] = 0.0\\n return labels\",\n \"def vector(p0, p1):\\n a = p1[0] - p0[0]\\n b = p1[1] - p0[1]\\n return (a, b)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.59438974","0.5719111","0.5711388","0.5612045","0.5503579","0.54424065","0.54400754","0.5393413","0.53729504","0.53618443","0.5353206","0.5324507","0.5300333","0.52868974","0.5257343","0.52473134","0.52161574","0.52138144","0.5211579","0.5206199","0.5191032","0.5182754","0.51764643","0.5171435","0.5162805","0.5107067","0.510491","0.50815046","0.508114","0.5079237","0.50749964","0.5046064","0.5043611","0.5000735","0.49984288","0.49900278","0.49776098","0.49769014","0.49748883","0.4960344","0.4931639","0.4930232","0.49257398","0.49155387","0.49136776","0.4899558","0.48994184","0.48964414","0.48865557","0.48851487","0.48701614","0.48661888","0.4864046","0.4861544","0.48589763","0.48534536","0.48402303","0.4837115","0.48344895","0.48295942","0.48236245","0.48164687","0.48086357","0.48008484","0.4793973","0.47814375","0.47743163","0.47742635","0.47693002","0.47665435","0.47642565","0.47628406","0.47592136","0.47577745","0.4755325","0.47551888","0.47445956","0.47381192","0.47359988","0.4731409","0.47235167","0.47235167","0.47197628","0.47184017","0.47179174","0.4711083","0.47065836","0.47042274","0.46996436","0.4696593","0.46964037","0.4695276","0.46935508","0.46911147","0.46851972","0.46822703","0.46739098","0.46733567","0.46727923","0.46713734"],"string":"[\n \"0.59438974\",\n \"0.5719111\",\n \"0.5711388\",\n \"0.5612045\",\n \"0.5503579\",\n \"0.54424065\",\n \"0.54400754\",\n \"0.5393413\",\n \"0.53729504\",\n \"0.53618443\",\n \"0.5353206\",\n \"0.5324507\",\n \"0.5300333\",\n \"0.52868974\",\n \"0.5257343\",\n \"0.52473134\",\n \"0.52161574\",\n \"0.52138144\",\n \"0.5211579\",\n \"0.5206199\",\n \"0.5191032\",\n \"0.5182754\",\n \"0.51764643\",\n \"0.5171435\",\n \"0.5162805\",\n \"0.5107067\",\n \"0.510491\",\n \"0.50815046\",\n \"0.508114\",\n \"0.5079237\",\n \"0.50749964\",\n \"0.5046064\",\n \"0.5043611\",\n \"0.5000735\",\n \"0.49984288\",\n \"0.49900278\",\n \"0.49776098\",\n \"0.49769014\",\n \"0.49748883\",\n \"0.4960344\",\n \"0.4931639\",\n \"0.4930232\",\n \"0.49257398\",\n \"0.49155387\",\n \"0.49136776\",\n \"0.4899558\",\n \"0.48994184\",\n \"0.48964414\",\n \"0.48865557\",\n \"0.48851487\",\n \"0.48701614\",\n \"0.48661888\",\n \"0.4864046\",\n \"0.4861544\",\n \"0.48589763\",\n \"0.48534536\",\n \"0.48402303\",\n \"0.4837115\",\n \"0.48344895\",\n \"0.48295942\",\n \"0.48236245\",\n \"0.48164687\",\n \"0.48086357\",\n \"0.48008484\",\n \"0.4793973\",\n \"0.47814375\",\n \"0.47743163\",\n \"0.47742635\",\n \"0.47693002\",\n \"0.47665435\",\n \"0.47642565\",\n \"0.47628406\",\n \"0.47592136\",\n \"0.47577745\",\n \"0.4755325\",\n \"0.47551888\",\n \"0.47445956\",\n \"0.47381192\",\n \"0.47359988\",\n \"0.4731409\",\n \"0.47235167\",\n \"0.47235167\",\n \"0.47197628\",\n \"0.47184017\",\n \"0.47179174\",\n \"0.4711083\",\n \"0.47065836\",\n \"0.47042274\",\n \"0.46996436\",\n \"0.4696593\",\n \"0.46964037\",\n \"0.4695276\",\n \"0.46935508\",\n \"0.46911147\",\n \"0.46851972\",\n \"0.46822703\",\n \"0.46739098\",\n \"0.46733567\",\n \"0.46727923\",\n \"0.46713734\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.58502215"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11180,"cells":{"query":{"kind":"string","value":"Given a word index and a tag, return the corresponding phi factor"},"document":{"kind":"string","value":"def get_phi_score(self, phi, i, m):\n m_i = self.get_tag_index(m)\n return phi[i, m_i]"},"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_e(word, tag, e_word_tag_counts, q_uni_counts):\n word_tag_tupple = (word, tag)\n\n word_tag_count = 0\n if word_tag_tupple in e_word_tag_counts:\n word_tag_count = e_word_tag_counts[word_tag_tupple]\n\n nof_tag = q_uni_counts[tag]\n return float(word_tag_count) / nof_tag","def phi_topic(self):\n f1 = self.nkw+self.beta\n f2 = np.sum(self.nkw, axis=1, keepdims=True)+self.VT*self.beta\n return f1/f2","def tf(word, document):\n return freq(word,document) / wordCount(document)","def phi_func(index, phase_angle):\n\n # Dictionary that contains the A and B constants, depending on the index /\n # version\n a_factor = {'1': 3.33, \\\n '2': 1.87}\n\n b_factor = {'1': 0.63, \\\n '2': 1.22}\n\n # Phase function\n phi = np.exp(-1.0 * a_factor[index] \\\n *+ ((np.tan(0.5 * phase_angle) ** b_factor[index])))\n\n # Return the phase function result\n return phi","def vectorize_word(word):\n if word in Tfidf_vect.vocabulary_:\n i = Tfidf_vect.vocabulary_[word]\n return Tfidf_vect.idf_[i]\n else:\n return 0","def get_q(tag_v, tag_w, tag_u, total_tokens, q_tri_counts, q_bi_counts, q_uni_counts):\n lambda1 = 0.3\n lambda2 = 0.3\n lambda3 = 1 - lambda1 - lambda2\n\n q_tri = 0\n q_bi = 0\n q_uni = 0\n\n if (tag_w, tag_u, tag_v) in q_tri_counts:\n q_tri = float(q_tri_counts[(tag_w, tag_u, tag_v)]) / q_bi_counts[(tag_w, tag_u)]\n if (tag_u, tag_v) in q_bi_counts:\n q_bi = float(q_bi_counts[(tag_u, tag_v)]) / q_uni_counts[tag_u]\n if tag_v in q_uni_counts:\n q_uni = float(q_uni_counts[tag_v]) / total_tokens\n\n\n\n # Calculates the total 3 n-grams\n total_q = lambda1 * q_tri + lambda2 * q_bi + lambda3 * q_uni\n return total_q","def calculate_tf(self, book_dict, term):\n term_frequency = 0\n try:\n term_frequency = (\n book_dict[\"SanitizedText\"][term] / book_dict[\"TotalNoOfTerms\"]\n )\n except KeyError:\n print(\"Key Error, Term doesnt exist\")\n return 0\n except ZeroDivisionError:\n print(\"tf division by zero!\")\n return 0\n return term_frequency","def get_tf(term, document):\n\n term_list = [term.lower() for term in document.split()]\n num_of_words_in_doc = len(document.split())\n term_count_in_doc = term_list.count(term)\n\n return term_count_in_doc / num_of_words_in_doc","def imp(term,word_dict,number_of_docs,id):\n\n with open('term_freq.txt', 'rb') as file:\n term_freq = pickle.loads(file.read())\n\n #print(term_freq)\n\n if id+1 in term_freq[term]:\n #print(term_freq[term][id])\n return term_freq[term][id+1]*word_dict[term][1]#idf(term,number_of_docs,index_list)\n else:\n return 0.0","def inverse_document_frequency(term):\n if term in dictionary:\n return math.log(N/document_frequency[term],2)\n else:\n return 0.0","def f(p, phi, phib, df):\n\treturn - p + exp( - df + Ns*(log((1 - p*phi)/(1 - phi - phib)) + \\\n\t\t(p - 1)*phi - phib + (9./4)*alpha*((phi + phib)**(5./4) - (p*phi)**(5./4))))","def anglor_phi(infile, sequence):\n return anglor(infile, sequence)","def tf(word,document):\n words = document.split()\n\n return sum(1 for w in words if w == word)","def f(p, phi, phib, df):\n\treturn -log(p) - df + (p-1)*phi + \\\n\t ( phi*(1-p) + phib + \\\n\t 5./4*alpha*(phi*p)**(9./4)-(9./4)*alpha*(p*phi)**(5./4) - \\\n\t (1./2)(1-p*phi)**2 - (phib/Nb)-5./4*alpha*(phi+phib)**(9./4) + \\\n\t (9./4)*alpha*(phi+phib)**(5.4) + \\\n\t 1./2*(1-phi-phib)**2 ) * Ns","def phi_function(alpha, pk, xk):\n x = xk + alpha * pk\n return rosenbock2Nd(x, 0)","def get_prob(self, term):\n # first need to find a document that contains the term\n hits = self._es.search(index=INDEX_NAME, q=term, df=self._field, _source=False, size=1).get(\"hits\", {}).get(\n \"hits\", {})\n doc_id = hits[0][\"_id\"] if len(hits) > 0 else None\n if doc_id is not None:\n # ask for global term statistics when requesting the term vector of that doc\n tv = self._es.termvectors(index=INDEX_NAME, doc_type=DOC_TYPE, id=doc_id, fields=self._field,\n term_statistics=True)[\"term_vectors\"][self._field]\n ttf = tv[\"terms\"].get(term, {}).get(\"ttf\", 0) # total term count in the collection (in that field)\n sum_ttf = tv[\"field_statistics\"][\"sum_ttf\"]\n return ttf / sum_ttf\n\n return None","def calculate_phi(self, K=None, V=None, beta=None, n_kt=None):\n K = K if K is not None else self.n_components\n V = V if V is not None else self.num_words\n beta = beta if beta is not None else self.beta\n n_kt = n_kt if n_kt is not None else self.n_kt\n phi = n_kt + np.tile(beta, (K, 1))\n phi /= np.repeat(np.sum(phi, axis=1).reshape((-1, 1)), V, axis=1)\n return phi","def eulerphi(n):\n\treturn euler_phi(n)","def p_word_given_topic(self, word, topic, beta=0.1):\n return ((self.topic_word_counts[topic][word] + beta) /\n (self.topic_counts[topic] + self.W * beta))","def _make_phi(self, F):\n scaled_bins_left = tf.concat(\n [self.bin_edges / self.sigma,\n np.array([np.inf])], 0)\n scaled_bins_right = tf.concat(\n [np.array([-np.inf]), self.bin_edges / self.sigma], 0)\n return inv_probit(scaled_bins_left - tf.reshape(F, (-1, 1)) / self.sigma) \\\n - inv_probit(scaled_bins_right - tf.reshape(F, (-1, 1)) / self.sigma)","def phi(self):\n if self._phi is None:\n b = self.stem + self.counter + self.radius + (\n self.stem - 2 * self.radius) - self.overlap\n c = sqrt(self.a ** 2 + b ** 2)\n _phi = atan2(self.a, b)\n b = sqrt(c ** 2 - self.radius ** 2)\n _phi += atan2(self.radius, b)\n self._phi = _phi\n return self._phi","def phi(N60):\n return 27.1 + 0.3*N60 - 0.00054* N60**2","def eulerphi(n):\r\n\treturn euler_phi(n)","def calcTFdict(doc):\n\n TFDict = {}\n\n #counts number of appearances of term in document\n for term in doc:\n if term in TFDict.keys():\n TFDict[term] +=1\n else:\n TFDict[term] = 1\n\n #Computing tf for each term\n for key in TFDict:\n TFDict[key] = TFDict[key]/len(doc)\n\n return TFDict","def _get_phi(self):\n phi = self.phi(self._data[SoilProperty.N60])\n ### Ok let's remove for clay\n if self.is_clayey():\n phi=0 #very small value for plasix:::@TODO 0.01\n return phi","def word_probability(self, word, prev):\n bg = \"{} {}\".format(prev, word)\n p_c = self.model[word] if word in self.model else 1e-10 \n p_cw = self.bigrams[bg] if bg in self.bigrams else 1e-10 \n p = p_c * p_cw if prev else p_c\n return p","def tf(self, term, text):\n return text.count(term) / len(text)","def tfidf_term_in_document(self, term, document):\n tf = self.count_term_in_document(term, document)\\\n / self.count_term_in_document(ANY, document)\n idf = math.log(1 + self.count_term_distinct_documents(ANY))\\\n - math.log(1 + self.count_term_distinct_documents(term))\n return tf * idf","def phi(U, n):\n phi_params = n['phi']\n return phi_params['r_max'] / (1 + np.exp(-phi_params['beta'] * (U - phi_params['alpha'])))","def raw_trigram_probability(self,trigram):\n\n result = 0.0\n try:\n bigram = (trigram[0],trigram[1],)\n result = self.trigramcounts[trigram]/self.bigramcounts[bigram]\n except Exception as e:\n pass\n else:\n pass\n return result","def idf(term, corpus):\n corpus_size = len(corpus)\n docs_with_term = 0\n\n for document in corpus:\n if term in document:\n docs_with_term += 1\n\n #add 1 to docs_with_term to account for terms that don't occur in the corpus\n #so that a division by zero doesn't occur\n return math.log( corpus_size / (docs_with_term+1) )","def probit_phi(x):\n mu = 0;sd = 1;\n return 0.5 * (1 + tsr.erf((x - mu) / (sd * tsr.sqrt(2))))","def __phi(x):\n return max([1 - abs(x), 0])","def trans_prob(self, tag, prev_tags=None):\n assert prev_tags is not None or self._n == 1\n prev_tags = prev_tags or ()\n\n return self._trans.get(prev_tags, {}).get(tag, 0)","def create_phi(self, T, pos, m, alpha=1):\n phi = tr.zeros((len(T), self.tag_size()), dtype=tr.float64)\n for i, _, _ in T:\n m_i = self.get_tag_index(m[i - 1])\n phi[i - 1, m_i] = alpha\n return phi","def get_tf_idf(term, document, documents):\n\n tf_idf = get_tf(term, document) * get_idf(term, documents)\n\n return round(tf_idf, 5)","def word_at(self, index, signed = False):\n return 0","def __init__(self, word, index):\n self.word = word\n self.index = index\n self.number = 1\n self.idf = 0.0","def delta_phi_(Phase_current, Phase_previous, winHopAn, wLen):\n \n # nominal phase increment for the analysis hop size for each bin\n omega = 2*np.pi*(winHopAn/wLen)*np.arange(0, wLen)\n delta_phi = omega + princarg(Phase_current-Phase_previous-omega)\n \n return delta_phi","def df_by_dlambda(phi):\n #print \"Interp:\", interp, \"\\nPhi\", phi\n num = phi[:-1] - phi[-1]\n denom = np.sum(expanded_interp*phi, axis=0)\n # num is ttable x phrase-pair x feature\n # denom is phrase-pair x feature\n # divide, then sum across phrase-pairs\n #print \"num\",num,\"denom\",denom\n #print \"q\",num/denom\n quotient = np.sum(num/denom, axis =1)\n # quotient is ttable-1 x feature\n return quotient","def out_prob(self, word, tag):\n return self._out.get(tag, {}).get(word, 0)","def get_trigram_conditional (y1, y2, y3, trigram_counts, bigram_counts):\r\n return float(trigram_counts[ ' '.join([y1, y2, y3]) ]) / bigram_counts[ ' '.join([y1, y2]) ]","def phi(t, *args):\n # Unpacking data\n mu_1, pi_mu_2, distance, affine_transfo = args\n A, b = get_Ab(t)\n N = len(mu_1)\n assert len(mu_1) == len(pi_mu_2)\n # Computing value of objective function\n r = 0.\n for i in np.arange(N):\n r += distance(affine_transfo(A, b, mu_1[i]), pi_mu_2[i]) ** 2\n return r","def ddphi(x):\n return dphi_spl.derivatives(x)[1]","def ddphi(x):\n return dphi_spl.derivatives(x)[1]","def _tfidf(term_frequency: int, document_frequency: int, document_count: int) -> float:\n if term_frequency == 0:\n return 0\n else:\n tf = 1 + np.log(term_frequency)\n idf = np.log(document_count / document_frequency)\n return tf * idf","def phi0(self, x):\n return (1-(1-x)**(4/3))**(3/2)/(2*np.sqrt(2))","def genus(P, E, F, V=None):\n\n return euler_characteristic(P, E, F, V)-2","def phi_poly(self,x,i):\n return x**i","def get_idf(term, documents):\n\n number_of_docs = len(documents)\n documents_containing_term = len([document for document in documents if term in document])\n\n idf = math.log10(number_of_docs / documents_containing_term)\n\n return round(idf, 5)","def _term_probability(self, frequency, total_frequency, doc_length, total_doc_length):\n if doc_length == 0:\n p1 = 0\n else:\n p1 = frequency / doc_length\n if total_doc_length == 0:\n p2 = 0\n else:\n p2 = total_frequency / total_doc_length\n return (1-self.omega) * p1 + self.omega * p2","def get_tag_for_word(self, word: str):\n doc = self.model(word)\n for token in doc:\n return token.pos_","def distance_two_trigram_approx(word, history):\n tag1 = history[-3]\n tag2 = history[-2]\n tag3 = word\n feature_name = 'feature_distance_two_trigram_approx_{0}_{1}_{2}'.format(tag1, tag2, tag3)\n features = [(feature_name, True)]\n return features","def theta_ft(h,ft_intercept,gamma):\n theta_top = ft_intercept + h*gamma\n return theta_top","def _phi(self, x, d, p):\n ks = np.arange(self.p + 1)\n ks = ks[np.where(2 * (self.p - ks) - d >= 0)][:, np.newaxis]\n return np.sum(\n binom(self.p, ks)\n * (-1) ** ks\n * x[np.newaxis, :] ** (2 * (self.p - ks) - d)\n * perm(2 * (self.p - ks), d),\n axis=0,\n )","def phi(self, i):\n if i == 1 or not self.connected_to_previous(i):\n return 0.0\n\n res = self.all_residues[i]\n\n try:\n n = res['N'].get_vector()\n ca = res['CA'].get_vector()\n c = res['C'].get_vector()\n\n res_minus_one = self.all_residues[i -1]\n cp = res_minus_one['C'].get_vector()\n phi = calc_dihedral(cp, n, ca, c)\n return phi\n except Exception:\n print \"Could not get phi for \"+repr(i)\n raise LookupError","def _term_probability(self, frequency, total_frequency, doc_length, total_doc_length):\n omega = self.alpha / (doc_length + self.alpha)\n if doc_length == 0:\n p1 = 0\n else:\n p1 = frequency / doc_length\n if total_doc_length == 0:\n p2 = 0\n else:\n p2 = total_frequency / total_doc_length\n return (1-omega) * p1 + omega * p2","def calc_tf(doc):\r\n tf = {}\r\n for term in doc:\r\n if term not in tf:\r\n tf[term] = doc.count(term)\r\n return tf","def wordvalue(word):\n\n # Set total to 0\n total = 0\n\n # For each character of word\n for i in enumerate(word):\n # Add it's letter value to total\n total += letternum(word[i[0]])\n\n # Return the final value\n return total","def calc_TF_IDF_Vector(doc, termDict):\n TFIDFVec = [0.0] * len(termDict)\n\n #for each unique term, if it is in the document, store the TF-IDF value\n for i, term in enumerate(termDict):\n if term in doc:\n TFIDFVec[i] = doc[term]\n\n return TFIDFVec","def get_trigram_log_prob(self, trigram):\n w1, w2, w3 = trigram\n if (\n w1 in self.ngrams[3][\"data\"]\n and w2 in self.ngrams[3][\"data\"][w1]\n and w3 in self.ngrams[3][\"data\"][w1][w2]\n ):\n return self.ngrams[3][\"data\"][w1][w2][w3]\n return Decimal(1.0).log10() - Decimal(int(self.ngrams[1][\"count\"]) ** 2).log10()","def word_freq(word, ngram_dict):\n word = word.lower()\n return ngram_dict[word] if word in ngram_dict else 0","def cal_phi(self):\n\n if not self.check_def(['E','px','py','pz']):\n sys.exit('Particle error: Quadri impulsion not define (error for phi routine)')\n\n if(self.px>0):\n self.phi=math.atan(self.py/self.px)\n elif(self.px<0):\n self.phi=math.atan(self.py/self.px)+math.pi\n elif(self.py>0): #remind that p(1)=0\n self.phi=math.pi/2.0\n elif(self.py<0): # remind that p(1)=0\n self.phi=-math.pi/2.0\n else:\n print \"Warning self.phi not properly defined put value to 0\"\n self.phi=0\n \n if(self.phi<0):\n self.phi=self.phi+2*math.pi\n\n return self.phi","def Tc (x,infin, a, nu):\r\n return infin + a* (x ** (-1/nu))","def get_emissions_probability(label_matches, given_tag, given_word, tag_counts):\r\n\tlookup_tuple = (given_word, given_tag)\r\n\tword_tag_frequency = label_matches.get(lookup_tuple, 0)\r\n\ttag_frequency = tag_counts[given_tag]\r\n\tif tag_frequency == 0:\r\n\t\temissions_probability = 0\r\n\telse:\r\n\t\temissions_probability = float(word_tag_frequency)/float(tag_frequency)\r\n\treturn emissions_probability","def phi(n, cache=None):\n if n == 1:\n return 1\n elif n < 1:\n raise AssertionError('Please enter an int > 0')\n else:\n return int(n * prod([1 - 1/p for p in set(factors(n, cache))]))","def calculate_perplexity(sentence, n_gram_counts, n_plus1_gram_counts, vocabulary_size, k=1.0):\r\n # length of previous words\r\n n = len(list(n_gram_counts.keys())[0]) \r\n \r\n # prepend <s> and append <e>\r\n sentence = [\"<s>\"] * n + sentence + [\"<e>\"]\r\n \r\n # Cast the sentence from a list to a tuple\r\n sentence = tuple(sentence)\r\n \r\n # length of sentence (after adding <s> and <e> tokens)\r\n N = len(sentence)\r\n \r\n\r\n product_pi = 1.0\r\n \r\n \r\n # Index t ranges from n to N - 1, inclusive on both ends\r\n for t in range(n, N): \r\n\r\n # get the n-gram preceding the word at position t\r\n n_gram = sentence[t-n:t]\r\n \r\n # get the word at position t\r\n word = sentence[t]\r\n \r\n\r\n probability = estimate_probability(word, n_gram, n_gram_counts, n_plus1_gram_counts, vocabulary_size, k=1.0)\r\n \r\n\r\n product_pi *= 1/probability\r\n\r\n # Take the Nth root of the product\r\n perplexity = product_pi**(1/float(N))\r\n return perplexity","def estimate_probability(word, previous_n_gram, \r\n n_gram_counts, n_plus1_gram_counts, vocabulary_size, k=1.0):\r\n # Note : 1 . Here we are actually not considering the end token or start token as a part of a vocabulary.\r\n # 2 . Although the literature says we need to prepend the n-1 SOS tokens but in reality we are prepending n SOS tokens\r\n \r\n # convert list to tuple to use it as a dictionary key\r\n previous_n_gram = tuple(previous_n_gram)\r\n\r\n previous_n_gram_count = n_gram_counts.get(previous_n_gram, 0)\r\n \r\n\r\n denominator = float(previous_n_gram_count + (k*vocabulary_size))\r\n\r\n n_plus1_gram = previous_n_gram + (word,)\r\n \r\n\r\n n_plus1_gram_count = n_plus1_gram_counts.get(n_plus1_gram, 0)\r\n \r\n\r\n numerator = float(n_plus1_gram_count + k)\r\n\r\n probability = float(numerator/denominator)\r\n \r\n \r\n return probability","def tf(self, term: str, doc_path: str) -> int:\n return self.tfs[doc_path][term]","def term_idf(self, term):\n idf = math.log(2 + self.count_term_distinct_documents(ANY))\\\n - math.log(1 + self.count_term_distinct_documents(term))\n return idf","def get_word_probability(self, label, term):\n\n if 'sod' in label:\n return self.cond_prob_sod[term]\n elif 'pop' in label:\n return self.cond_prob_pop[term]\n else:\n print(\"Just run the doctest Dev\")\n \n pass","def term_frequency(ngrams,lang):\n token_dictionary = {}\n for ng in ngrams:\n try:\n token_dictionary[ng] = token_dictionary[ng] + 1\n except KeyError:\n token_dictionary[ng] = 1\n return token_dictionary","def computeTFIDF(self):\n for word in self.dictionary:\n numOfAppearance = self.dictionary[word].getDocumentFrequency()\n idf = math.log( (self.MAX_RATING) / (numOfAppearance), 10 )\n self.dictionary[word].setTFIDF(idf)","def phi_fixed(prec):\n prec += 10\n a = isqrt_fast(MPZ_FIVE<<(2*prec)) + (MPZ_ONE << prec)\n return a >> 11","def similarity(query,word_dict,dictionary,number_of_docs,id):\n similarity = 0.0\n scalar_leng = 0.0\n for term in query:\n if term in dictionary:\n similarity += word_dict[term][1]*imp(term,word_dict,number_of_docs,id)\n\n for term in dictionary:\n scalar_leng += imp(term, word_dict, number_of_docs, id) ** 2\n\n final_scalar_leng = math.sqrt(scalar_leng)\n similarity = similarity / final_scalar_leng\n #print(similarity)\n return similarity","def tf_idf_weight(self, term: str, doc_path: str) -> float:\n return self.tf(term, doc_path) * self.idf(term)","def estimate_prob(self, history, word):\n\t\t# YOUR CODE HERE\n\n\t\tif history == '':\n\t\t\t# unigram\n\t\t\tword_frequency = self.ngram_counts[tuple([word])]\n\t\t\treturn word_frequency/self.total_counts\n\n\t\telse:\n\t\t\t# bigram\n\t\t\tword_frequency = self.ngram_counts[tuple([history, word])]\n\t\t\t# history_count = sum([self.ngram_counts[key] for key in self.ngram_counts if key[0] == history])\n\t\t\t# history_count = self.history_count[history]\n\t\t\thistory_count = self.ngram_counts[tuple([history])]\n\t\t\t# print('his: {}',format(history))\n\t\t\t# print('his count {}'.format(history_count))\n\t\t\treturn word_frequency/history_count","def probability(self, words):\n prob = 1\n for w in words: # Loop through words\n if w not in self.mdl.index: # Not in tokens\n return 0\n else: # Get prob\n prob *= self.mdl.loc[w] \n return prob","def probability(self, words):\n prob = 1\n for w in words: # Loop through words\n if w not in self.mdl.index: # Not in tokens\n return 0\n else: # Get prob\n prob *= self.mdl.loc[w] \n return prob","def tf(word, blob):\n return blob.words.count(word) / len(blob.words)","def word_probability(self, word: str) -> int:\n try:\n return self.fdist[word.lower()] / len(self.fdist.keys())\n except KeyError:\n return 0.0","def phi(self):\n return self._phi","def get_vector(self,term):\n return self.dict.get(term)","def idf(word):\n return math.log10(len(news_content) / document_frequency(word) + 1)# 分母加1，可防止分母为0的情况","def idf(word, bloblist):\n return math.log(len(bloblist) / (1 + n_containing(word, bloblist)))","def get_positional_score(term, document):\n score = 0\n number_of_words_in_doc = len(document.split())\n\n for position, word in enumerate(document.split()):\n if word.lower() == term.lower():\n score += (number_of_words_in_doc - position) / number_of_words_in_doc\n \n return round(score, 5)","def quanty_index(i,ang=2):\n norb = 2*ang + 1\n k = (i//(2*norb))*(2*norb)\n if (i-k) < norb:\n j = k + 2*(i-k)\n else:\n j = k + 2*((i-k)-norb) + 1\n return j","def phi(nbins, p, t, x):\n\n phase = calc_phase(p, t)\n x_means, phase_bs, Ns, sj2s, xb, pb = phase_bins(nbins, phase, x)\n total_binned_variance_s2 = s2(Ns, sj2s, nbins)\n total_variance = sj2(x, np.mean(x), len(x))\n\n return total_binned_variance_s2/total_variance","def proc(form, term, idx):\n assert term == tensor.local_terms[idx]\n assert form == expected_terms[idx]\n return 'N'","def computeTF(self):\n for word in self.dictionary:\n self.dictionary[word].setTF(self.getTotalTerms())","def index2word(index_word, index_dict):\n if index_word == -1 or index_word not in index_dict.keys():\n return '_eps_'\n else:\n return index_dict[index_word]","def get_representative_words(self, phi=None):\n phi = phi if phi is not None else self.phi\n for i in range(self.n_components):\n print(\"Topic\", i)\n c = np.argsort(self.phi[i, :])\n for j in c[-1:-11:-1]:\n print(self.list_ind2word[j], phi[i, j])","def prob(self, word, context=None):\n if not context:\n context = ()\n else:\n context = tuple(context)\n prob = 0\n for i in range(len(context) + 1):\n prob += self.weights[i] * self.ngram_cpd[context[i:]][word]\n return prob","def get_tf_idf_vector(tags, all_terms, idf_map):\n return [(1 if term in tags else 0) * idf_map[term] for term in all_terms]","def tfidf(word, blob, bloblist):\n return tf(word, blob) * idf(word, bloblist)","def basic_ttr(n_terms, n_words):\n if n_words == 0:\n return 0\n return n_terms / n_words","def get_vector(word, model):\n return model.wv[word]","def herdan(n_terms, n_words):\n if n_words <= 1:\n return 0\n return math.log(n_terms) / math.log(n_words)","def calc_tf_idf(idf, tf):\r\n tfidf = {}\r\n for key, val in tf.items():\r\n tfidf[key] = val * idf[key]\r\n return tfidf","def get(self, index, tag=LEMMA):\n if tag == WORD:\n return self.words[index]\n if tag == LEMMA:\n return self.words[index].lemma\n if tag == POS:\n return self.words[index].type\n if tag == CHUNK:\n return self.words[index].chunk\n if tag == PNP:\n return self.words[index].pnp\n if tag == REL:\n ch = self.words[index].chunk; return ch and ch.relation\n if tag == ROLE:\n ch = self.words[index].chunk; return ch and ch.role\n if tag == ANCHOR:\n ch = self.words[index].pnp; return ch and ch.anchor\n if tag in self.words[index].custom_tags:\n return self.words[index].custom_tags[tag]\n return None"],"string":"[\n \"def get_e(word, tag, e_word_tag_counts, q_uni_counts):\\n word_tag_tupple = (word, tag)\\n\\n word_tag_count = 0\\n if word_tag_tupple in e_word_tag_counts:\\n word_tag_count = e_word_tag_counts[word_tag_tupple]\\n\\n nof_tag = q_uni_counts[tag]\\n return float(word_tag_count) / nof_tag\",\n \"def phi_topic(self):\\n f1 = self.nkw+self.beta\\n f2 = np.sum(self.nkw, axis=1, keepdims=True)+self.VT*self.beta\\n return f1/f2\",\n \"def tf(word, document):\\n return freq(word,document) / wordCount(document)\",\n \"def phi_func(index, phase_angle):\\n\\n # Dictionary that contains the A and B constants, depending on the index /\\n # version\\n a_factor = {'1': 3.33, \\\\\\n '2': 1.87}\\n\\n b_factor = {'1': 0.63, \\\\\\n '2': 1.22}\\n\\n # Phase function\\n phi = np.exp(-1.0 * a_factor[index] \\\\\\n *+ ((np.tan(0.5 * phase_angle) ** b_factor[index])))\\n\\n # Return the phase function result\\n return phi\",\n \"def vectorize_word(word):\\n if word in Tfidf_vect.vocabulary_:\\n i = Tfidf_vect.vocabulary_[word]\\n return Tfidf_vect.idf_[i]\\n else:\\n return 0\",\n \"def get_q(tag_v, tag_w, tag_u, total_tokens, q_tri_counts, q_bi_counts, q_uni_counts):\\n lambda1 = 0.3\\n lambda2 = 0.3\\n lambda3 = 1 - lambda1 - lambda2\\n\\n q_tri = 0\\n q_bi = 0\\n q_uni = 0\\n\\n if (tag_w, tag_u, tag_v) in q_tri_counts:\\n q_tri = float(q_tri_counts[(tag_w, tag_u, tag_v)]) / q_bi_counts[(tag_w, tag_u)]\\n if (tag_u, tag_v) in q_bi_counts:\\n q_bi = float(q_bi_counts[(tag_u, tag_v)]) / q_uni_counts[tag_u]\\n if tag_v in q_uni_counts:\\n q_uni = float(q_uni_counts[tag_v]) / total_tokens\\n\\n\\n\\n # Calculates the total 3 n-grams\\n total_q = lambda1 * q_tri + lambda2 * q_bi + lambda3 * q_uni\\n return total_q\",\n \"def calculate_tf(self, book_dict, term):\\n term_frequency = 0\\n try:\\n term_frequency = (\\n book_dict[\\\"SanitizedText\\\"][term] / book_dict[\\\"TotalNoOfTerms\\\"]\\n )\\n except KeyError:\\n print(\\\"Key Error, Term doesnt exist\\\")\\n return 0\\n except ZeroDivisionError:\\n print(\\\"tf division by zero!\\\")\\n return 0\\n return term_frequency\",\n \"def get_tf(term, document):\\n\\n term_list = [term.lower() for term in document.split()]\\n num_of_words_in_doc = len(document.split())\\n term_count_in_doc = term_list.count(term)\\n\\n return term_count_in_doc / num_of_words_in_doc\",\n \"def imp(term,word_dict,number_of_docs,id):\\n\\n with open('term_freq.txt', 'rb') as file:\\n term_freq = pickle.loads(file.read())\\n\\n #print(term_freq)\\n\\n if id+1 in term_freq[term]:\\n #print(term_freq[term][id])\\n return term_freq[term][id+1]*word_dict[term][1]#idf(term,number_of_docs,index_list)\\n else:\\n return 0.0\",\n \"def inverse_document_frequency(term):\\n if term in dictionary:\\n return math.log(N/document_frequency[term],2)\\n else:\\n return 0.0\",\n \"def f(p, phi, phib, df):\\n\\treturn - p + exp( - df + Ns*(log((1 - p*phi)/(1 - phi - phib)) + \\\\\\n\\t\\t(p - 1)*phi - phib + (9./4)*alpha*((phi + phib)**(5./4) - (p*phi)**(5./4))))\",\n \"def anglor_phi(infile, sequence):\\n return anglor(infile, sequence)\",\n \"def tf(word,document):\\n words = document.split()\\n\\n return sum(1 for w in words if w == word)\",\n \"def f(p, phi, phib, df):\\n\\treturn -log(p) - df + (p-1)*phi + \\\\\\n\\t ( phi*(1-p) + phib + \\\\\\n\\t 5./4*alpha*(phi*p)**(9./4)-(9./4)*alpha*(p*phi)**(5./4) - \\\\\\n\\t (1./2)(1-p*phi)**2 - (phib/Nb)-5./4*alpha*(phi+phib)**(9./4) + \\\\\\n\\t (9./4)*alpha*(phi+phib)**(5.4) + \\\\\\n\\t 1./2*(1-phi-phib)**2 ) * Ns\",\n \"def phi_function(alpha, pk, xk):\\n x = xk + alpha * pk\\n return rosenbock2Nd(x, 0)\",\n \"def get_prob(self, term):\\n # first need to find a document that contains the term\\n hits = self._es.search(index=INDEX_NAME, q=term, df=self._field, _source=False, size=1).get(\\\"hits\\\", {}).get(\\n \\\"hits\\\", {})\\n doc_id = hits[0][\\\"_id\\\"] if len(hits) > 0 else None\\n if doc_id is not None:\\n # ask for global term statistics when requesting the term vector of that doc\\n tv = self._es.termvectors(index=INDEX_NAME, doc_type=DOC_TYPE, id=doc_id, fields=self._field,\\n term_statistics=True)[\\\"term_vectors\\\"][self._field]\\n ttf = tv[\\\"terms\\\"].get(term, {}).get(\\\"ttf\\\", 0) # total term count in the collection (in that field)\\n sum_ttf = tv[\\\"field_statistics\\\"][\\\"sum_ttf\\\"]\\n return ttf / sum_ttf\\n\\n return None\",\n \"def calculate_phi(self, K=None, V=None, beta=None, n_kt=None):\\n K = K if K is not None else self.n_components\\n V = V if V is not None else self.num_words\\n beta = beta if beta is not None else self.beta\\n n_kt = n_kt if n_kt is not None else self.n_kt\\n phi = n_kt + np.tile(beta, (K, 1))\\n phi /= np.repeat(np.sum(phi, axis=1).reshape((-1, 1)), V, axis=1)\\n return phi\",\n \"def eulerphi(n):\\n\\treturn euler_phi(n)\",\n \"def p_word_given_topic(self, word, topic, beta=0.1):\\n return ((self.topic_word_counts[topic][word] + beta) /\\n (self.topic_counts[topic] + self.W * beta))\",\n \"def _make_phi(self, F):\\n scaled_bins_left = tf.concat(\\n [self.bin_edges / self.sigma,\\n np.array([np.inf])], 0)\\n scaled_bins_right = tf.concat(\\n [np.array([-np.inf]), self.bin_edges / self.sigma], 0)\\n return inv_probit(scaled_bins_left - tf.reshape(F, (-1, 1)) / self.sigma) \\\\\\n - inv_probit(scaled_bins_right - tf.reshape(F, (-1, 1)) / self.sigma)\",\n \"def phi(self):\\n if self._phi is None:\\n b = self.stem + self.counter + self.radius + (\\n self.stem - 2 * self.radius) - self.overlap\\n c = sqrt(self.a ** 2 + b ** 2)\\n _phi = atan2(self.a, b)\\n b = sqrt(c ** 2 - self.radius ** 2)\\n _phi += atan2(self.radius, b)\\n self._phi = _phi\\n return self._phi\",\n \"def phi(N60):\\n return 27.1 + 0.3*N60 - 0.00054* N60**2\",\n \"def eulerphi(n):\\r\\n\\treturn euler_phi(n)\",\n \"def calcTFdict(doc):\\n\\n TFDict = {}\\n\\n #counts number of appearances of term in document\\n for term in doc:\\n if term in TFDict.keys():\\n TFDict[term] +=1\\n else:\\n TFDict[term] = 1\\n\\n #Computing tf for each term\\n for key in TFDict:\\n TFDict[key] = TFDict[key]/len(doc)\\n\\n return TFDict\",\n \"def _get_phi(self):\\n phi = self.phi(self._data[SoilProperty.N60])\\n ### Ok let's remove for clay\\n if self.is_clayey():\\n phi=0 #very small value for plasix:::@TODO 0.01\\n return phi\",\n \"def word_probability(self, word, prev):\\n bg = \\\"{} {}\\\".format(prev, word)\\n p_c = self.model[word] if word in self.model else 1e-10 \\n p_cw = self.bigrams[bg] if bg in self.bigrams else 1e-10 \\n p = p_c * p_cw if prev else p_c\\n return p\",\n \"def tf(self, term, text):\\n return text.count(term) / len(text)\",\n \"def tfidf_term_in_document(self, term, document):\\n tf = self.count_term_in_document(term, document)\\\\\\n / self.count_term_in_document(ANY, document)\\n idf = math.log(1 + self.count_term_distinct_documents(ANY))\\\\\\n - math.log(1 + self.count_term_distinct_documents(term))\\n return tf * idf\",\n \"def phi(U, n):\\n phi_params = n['phi']\\n return phi_params['r_max'] / (1 + np.exp(-phi_params['beta'] * (U - phi_params['alpha'])))\",\n \"def raw_trigram_probability(self,trigram):\\n\\n result = 0.0\\n try:\\n bigram = (trigram[0],trigram[1],)\\n result = self.trigramcounts[trigram]/self.bigramcounts[bigram]\\n except Exception as e:\\n pass\\n else:\\n pass\\n return result\",\n \"def idf(term, corpus):\\n corpus_size = len(corpus)\\n docs_with_term = 0\\n\\n for document in corpus:\\n if term in document:\\n docs_with_term += 1\\n\\n #add 1 to docs_with_term to account for terms that don't occur in the corpus\\n #so that a division by zero doesn't occur\\n return math.log( corpus_size / (docs_with_term+1) )\",\n \"def probit_phi(x):\\n mu = 0;sd = 1;\\n return 0.5 * (1 + tsr.erf((x - mu) / (sd * tsr.sqrt(2))))\",\n \"def __phi(x):\\n return max([1 - abs(x), 0])\",\n \"def trans_prob(self, tag, prev_tags=None):\\n assert prev_tags is not None or self._n == 1\\n prev_tags = prev_tags or ()\\n\\n return self._trans.get(prev_tags, {}).get(tag, 0)\",\n \"def create_phi(self, T, pos, m, alpha=1):\\n phi = tr.zeros((len(T), self.tag_size()), dtype=tr.float64)\\n for i, _, _ in T:\\n m_i = self.get_tag_index(m[i - 1])\\n phi[i - 1, m_i] = alpha\\n return phi\",\n \"def get_tf_idf(term, document, documents):\\n\\n tf_idf = get_tf(term, document) * get_idf(term, documents)\\n\\n return round(tf_idf, 5)\",\n \"def word_at(self, index, signed = False):\\n return 0\",\n \"def __init__(self, word, index):\\n self.word = word\\n self.index = index\\n self.number = 1\\n self.idf = 0.0\",\n \"def delta_phi_(Phase_current, Phase_previous, winHopAn, wLen):\\n \\n # nominal phase increment for the analysis hop size for each bin\\n omega = 2*np.pi*(winHopAn/wLen)*np.arange(0, wLen)\\n delta_phi = omega + princarg(Phase_current-Phase_previous-omega)\\n \\n return delta_phi\",\n \"def df_by_dlambda(phi):\\n #print \\\"Interp:\\\", interp, \\\"\\\\nPhi\\\", phi\\n num = phi[:-1] - phi[-1]\\n denom = np.sum(expanded_interp*phi, axis=0)\\n # num is ttable x phrase-pair x feature\\n # denom is phrase-pair x feature\\n # divide, then sum across phrase-pairs\\n #print \\\"num\\\",num,\\\"denom\\\",denom\\n #print \\\"q\\\",num/denom\\n quotient = np.sum(num/denom, axis =1)\\n # quotient is ttable-1 x feature\\n return quotient\",\n \"def out_prob(self, word, tag):\\n return self._out.get(tag, {}).get(word, 0)\",\n \"def get_trigram_conditional (y1, y2, y3, trigram_counts, bigram_counts):\\r\\n return float(trigram_counts[ ' '.join([y1, y2, y3]) ]) / bigram_counts[ ' '.join([y1, y2]) ]\",\n \"def phi(t, *args):\\n # Unpacking data\\n mu_1, pi_mu_2, distance, affine_transfo = args\\n A, b = get_Ab(t)\\n N = len(mu_1)\\n assert len(mu_1) == len(pi_mu_2)\\n # Computing value of objective function\\n r = 0.\\n for i in np.arange(N):\\n r += distance(affine_transfo(A, b, mu_1[i]), pi_mu_2[i]) ** 2\\n return r\",\n \"def ddphi(x):\\n return dphi_spl.derivatives(x)[1]\",\n \"def ddphi(x):\\n return dphi_spl.derivatives(x)[1]\",\n \"def _tfidf(term_frequency: int, document_frequency: int, document_count: int) -> float:\\n if term_frequency == 0:\\n return 0\\n else:\\n tf = 1 + np.log(term_frequency)\\n idf = np.log(document_count / document_frequency)\\n return tf * idf\",\n \"def phi0(self, x):\\n return (1-(1-x)**(4/3))**(3/2)/(2*np.sqrt(2))\",\n \"def genus(P, E, F, V=None):\\n\\n return euler_characteristic(P, E, F, V)-2\",\n \"def phi_poly(self,x,i):\\n return x**i\",\n \"def get_idf(term, documents):\\n\\n number_of_docs = len(documents)\\n documents_containing_term = len([document for document in documents if term in document])\\n\\n idf = math.log10(number_of_docs / documents_containing_term)\\n\\n return round(idf, 5)\",\n \"def _term_probability(self, frequency, total_frequency, doc_length, total_doc_length):\\n if doc_length == 0:\\n p1 = 0\\n else:\\n p1 = frequency / doc_length\\n if total_doc_length == 0:\\n p2 = 0\\n else:\\n p2 = total_frequency / total_doc_length\\n return (1-self.omega) * p1 + self.omega * p2\",\n \"def get_tag_for_word(self, word: str):\\n doc = self.model(word)\\n for token in doc:\\n return token.pos_\",\n \"def distance_two_trigram_approx(word, history):\\n tag1 = history[-3]\\n tag2 = history[-2]\\n tag3 = word\\n feature_name = 'feature_distance_two_trigram_approx_{0}_{1}_{2}'.format(tag1, tag2, tag3)\\n features = [(feature_name, True)]\\n return features\",\n \"def theta_ft(h,ft_intercept,gamma):\\n theta_top = ft_intercept + h*gamma\\n return theta_top\",\n \"def _phi(self, x, d, p):\\n ks = np.arange(self.p + 1)\\n ks = ks[np.where(2 * (self.p - ks) - d >= 0)][:, np.newaxis]\\n return np.sum(\\n binom(self.p, ks)\\n * (-1) ** ks\\n * x[np.newaxis, :] ** (2 * (self.p - ks) - d)\\n * perm(2 * (self.p - ks), d),\\n axis=0,\\n )\",\n \"def phi(self, i):\\n if i == 1 or not self.connected_to_previous(i):\\n return 0.0\\n\\n res = self.all_residues[i]\\n\\n try:\\n n = res['N'].get_vector()\\n ca = res['CA'].get_vector()\\n c = res['C'].get_vector()\\n\\n res_minus_one = self.all_residues[i -1]\\n cp = res_minus_one['C'].get_vector()\\n phi = calc_dihedral(cp, n, ca, c)\\n return phi\\n except Exception:\\n print \\\"Could not get phi for \\\"+repr(i)\\n raise LookupError\",\n \"def _term_probability(self, frequency, total_frequency, doc_length, total_doc_length):\\n omega = self.alpha / (doc_length + self.alpha)\\n if doc_length == 0:\\n p1 = 0\\n else:\\n p1 = frequency / doc_length\\n if total_doc_length == 0:\\n p2 = 0\\n else:\\n p2 = total_frequency / total_doc_length\\n return (1-omega) * p1 + omega * p2\",\n \"def calc_tf(doc):\\r\\n tf = {}\\r\\n for term in doc:\\r\\n if term not in tf:\\r\\n tf[term] = doc.count(term)\\r\\n return tf\",\n \"def wordvalue(word):\\n\\n # Set total to 0\\n total = 0\\n\\n # For each character of word\\n for i in enumerate(word):\\n # Add it's letter value to total\\n total += letternum(word[i[0]])\\n\\n # Return the final value\\n return total\",\n \"def calc_TF_IDF_Vector(doc, termDict):\\n TFIDFVec = [0.0] * len(termDict)\\n\\n #for each unique term, if it is in the document, store the TF-IDF value\\n for i, term in enumerate(termDict):\\n if term in doc:\\n TFIDFVec[i] = doc[term]\\n\\n return TFIDFVec\",\n \"def get_trigram_log_prob(self, trigram):\\n w1, w2, w3 = trigram\\n if (\\n w1 in self.ngrams[3][\\\"data\\\"]\\n and w2 in self.ngrams[3][\\\"data\\\"][w1]\\n and w3 in self.ngrams[3][\\\"data\\\"][w1][w2]\\n ):\\n return self.ngrams[3][\\\"data\\\"][w1][w2][w3]\\n return Decimal(1.0).log10() - Decimal(int(self.ngrams[1][\\\"count\\\"]) ** 2).log10()\",\n \"def word_freq(word, ngram_dict):\\n word = word.lower()\\n return ngram_dict[word] if word in ngram_dict else 0\",\n \"def cal_phi(self):\\n\\n if not self.check_def(['E','px','py','pz']):\\n sys.exit('Particle error: Quadri impulsion not define (error for phi routine)')\\n\\n if(self.px>0):\\n self.phi=math.atan(self.py/self.px)\\n elif(self.px<0):\\n self.phi=math.atan(self.py/self.px)+math.pi\\n elif(self.py>0): #remind that p(1)=0\\n self.phi=math.pi/2.0\\n elif(self.py<0): # remind that p(1)=0\\n self.phi=-math.pi/2.0\\n else:\\n print \\\"Warning self.phi not properly defined put value to 0\\\"\\n self.phi=0\\n \\n if(self.phi<0):\\n self.phi=self.phi+2*math.pi\\n\\n return self.phi\",\n \"def Tc (x,infin, a, nu):\\r\\n return infin + a* (x ** (-1/nu))\",\n \"def get_emissions_probability(label_matches, given_tag, given_word, tag_counts):\\r\\n\\tlookup_tuple = (given_word, given_tag)\\r\\n\\tword_tag_frequency = label_matches.get(lookup_tuple, 0)\\r\\n\\ttag_frequency = tag_counts[given_tag]\\r\\n\\tif tag_frequency == 0:\\r\\n\\t\\temissions_probability = 0\\r\\n\\telse:\\r\\n\\t\\temissions_probability = float(word_tag_frequency)/float(tag_frequency)\\r\\n\\treturn emissions_probability\",\n \"def phi(n, cache=None):\\n if n == 1:\\n return 1\\n elif n < 1:\\n raise AssertionError('Please enter an int > 0')\\n else:\\n return int(n * prod([1 - 1/p for p in set(factors(n, cache))]))\",\n \"def calculate_perplexity(sentence, n_gram_counts, n_plus1_gram_counts, vocabulary_size, k=1.0):\\r\\n # length of previous words\\r\\n n = len(list(n_gram_counts.keys())[0]) \\r\\n \\r\\n # prepend <s> and append <e>\\r\\n sentence = [\\\"<s>\\\"] * n + sentence + [\\\"<e>\\\"]\\r\\n \\r\\n # Cast the sentence from a list to a tuple\\r\\n sentence = tuple(sentence)\\r\\n \\r\\n # length of sentence (after adding <s> and <e> tokens)\\r\\n N = len(sentence)\\r\\n \\r\\n\\r\\n product_pi = 1.0\\r\\n \\r\\n \\r\\n # Index t ranges from n to N - 1, inclusive on both ends\\r\\n for t in range(n, N): \\r\\n\\r\\n # get the n-gram preceding the word at position t\\r\\n n_gram = sentence[t-n:t]\\r\\n \\r\\n # get the word at position t\\r\\n word = sentence[t]\\r\\n \\r\\n\\r\\n probability = estimate_probability(word, n_gram, n_gram_counts, n_plus1_gram_counts, vocabulary_size, k=1.0)\\r\\n \\r\\n\\r\\n product_pi *= 1/probability\\r\\n\\r\\n # Take the Nth root of the product\\r\\n perplexity = product_pi**(1/float(N))\\r\\n return perplexity\",\n \"def estimate_probability(word, previous_n_gram, \\r\\n n_gram_counts, n_plus1_gram_counts, vocabulary_size, k=1.0):\\r\\n # Note : 1 . Here we are actually not considering the end token or start token as a part of a vocabulary.\\r\\n # 2 . Although the literature says we need to prepend the n-1 SOS tokens but in reality we are prepending n SOS tokens\\r\\n \\r\\n # convert list to tuple to use it as a dictionary key\\r\\n previous_n_gram = tuple(previous_n_gram)\\r\\n\\r\\n previous_n_gram_count = n_gram_counts.get(previous_n_gram, 0)\\r\\n \\r\\n\\r\\n denominator = float(previous_n_gram_count + (k*vocabulary_size))\\r\\n\\r\\n n_plus1_gram = previous_n_gram + (word,)\\r\\n \\r\\n\\r\\n n_plus1_gram_count = n_plus1_gram_counts.get(n_plus1_gram, 0)\\r\\n \\r\\n\\r\\n numerator = float(n_plus1_gram_count + k)\\r\\n\\r\\n probability = float(numerator/denominator)\\r\\n \\r\\n \\r\\n return probability\",\n \"def tf(self, term: str, doc_path: str) -> int:\\n return self.tfs[doc_path][term]\",\n \"def term_idf(self, term):\\n idf = math.log(2 + self.count_term_distinct_documents(ANY))\\\\\\n - math.log(1 + self.count_term_distinct_documents(term))\\n return idf\",\n \"def get_word_probability(self, label, term):\\n\\n if 'sod' in label:\\n return self.cond_prob_sod[term]\\n elif 'pop' in label:\\n return self.cond_prob_pop[term]\\n else:\\n print(\\\"Just run the doctest Dev\\\")\\n \\n pass\",\n \"def term_frequency(ngrams,lang):\\n token_dictionary = {}\\n for ng in ngrams:\\n try:\\n token_dictionary[ng] = token_dictionary[ng] + 1\\n except KeyError:\\n token_dictionary[ng] = 1\\n return token_dictionary\",\n \"def computeTFIDF(self):\\n for word in self.dictionary:\\n numOfAppearance = self.dictionary[word].getDocumentFrequency()\\n idf = math.log( (self.MAX_RATING) / (numOfAppearance), 10 )\\n self.dictionary[word].setTFIDF(idf)\",\n \"def phi_fixed(prec):\\n prec += 10\\n a = isqrt_fast(MPZ_FIVE<<(2*prec)) + (MPZ_ONE << prec)\\n return a >> 11\",\n \"def similarity(query,word_dict,dictionary,number_of_docs,id):\\n similarity = 0.0\\n scalar_leng = 0.0\\n for term in query:\\n if term in dictionary:\\n similarity += word_dict[term][1]*imp(term,word_dict,number_of_docs,id)\\n\\n for term in dictionary:\\n scalar_leng += imp(term, word_dict, number_of_docs, id) ** 2\\n\\n final_scalar_leng = math.sqrt(scalar_leng)\\n similarity = similarity / final_scalar_leng\\n #print(similarity)\\n return similarity\",\n \"def tf_idf_weight(self, term: str, doc_path: str) -> float:\\n return self.tf(term, doc_path) * self.idf(term)\",\n \"def estimate_prob(self, history, word):\\n\\t\\t# YOUR CODE HERE\\n\\n\\t\\tif history == '':\\n\\t\\t\\t# unigram\\n\\t\\t\\tword_frequency = self.ngram_counts[tuple([word])]\\n\\t\\t\\treturn word_frequency/self.total_counts\\n\\n\\t\\telse:\\n\\t\\t\\t# bigram\\n\\t\\t\\tword_frequency = self.ngram_counts[tuple([history, word])]\\n\\t\\t\\t# history_count = sum([self.ngram_counts[key] for key in self.ngram_counts if key[0] == history])\\n\\t\\t\\t# history_count = self.history_count[history]\\n\\t\\t\\thistory_count = self.ngram_counts[tuple([history])]\\n\\t\\t\\t# print('his: {}',format(history))\\n\\t\\t\\t# print('his count {}'.format(history_count))\\n\\t\\t\\treturn word_frequency/history_count\",\n \"def probability(self, words):\\n prob = 1\\n for w in words: # Loop through words\\n if w not in self.mdl.index: # Not in tokens\\n return 0\\n else: # Get prob\\n prob *= self.mdl.loc[w] \\n return prob\",\n \"def probability(self, words):\\n prob = 1\\n for w in words: # Loop through words\\n if w not in self.mdl.index: # Not in tokens\\n return 0\\n else: # Get prob\\n prob *= self.mdl.loc[w] \\n return prob\",\n \"def tf(word, blob):\\n return blob.words.count(word) / len(blob.words)\",\n \"def word_probability(self, word: str) -> int:\\n try:\\n return self.fdist[word.lower()] / len(self.fdist.keys())\\n except KeyError:\\n return 0.0\",\n \"def phi(self):\\n return self._phi\",\n \"def get_vector(self,term):\\n return self.dict.get(term)\",\n \"def idf(word):\\n return math.log10(len(news_content) / document_frequency(word) + 1)# 分母加1，可防止分母为0的情况\",\n \"def idf(word, bloblist):\\n return math.log(len(bloblist) / (1 + n_containing(word, bloblist)))\",\n \"def get_positional_score(term, document):\\n score = 0\\n number_of_words_in_doc = len(document.split())\\n\\n for position, word in enumerate(document.split()):\\n if word.lower() == term.lower():\\n score += (number_of_words_in_doc - position) / number_of_words_in_doc\\n \\n return round(score, 5)\",\n \"def quanty_index(i,ang=2):\\n norb = 2*ang + 1\\n k = (i//(2*norb))*(2*norb)\\n if (i-k) < norb:\\n j = k + 2*(i-k)\\n else:\\n j = k + 2*((i-k)-norb) + 1\\n return j\",\n \"def phi(nbins, p, t, x):\\n\\n phase = calc_phase(p, t)\\n x_means, phase_bs, Ns, sj2s, xb, pb = phase_bins(nbins, phase, x)\\n total_binned_variance_s2 = s2(Ns, sj2s, nbins)\\n total_variance = sj2(x, np.mean(x), len(x))\\n\\n return total_binned_variance_s2/total_variance\",\n \"def proc(form, term, idx):\\n assert term == tensor.local_terms[idx]\\n assert form == expected_terms[idx]\\n return 'N'\",\n \"def computeTF(self):\\n for word in self.dictionary:\\n self.dictionary[word].setTF(self.getTotalTerms())\",\n \"def index2word(index_word, index_dict):\\n if index_word == -1 or index_word not in index_dict.keys():\\n return '_eps_'\\n else:\\n return index_dict[index_word]\",\n \"def get_representative_words(self, phi=None):\\n phi = phi if phi is not None else self.phi\\n for i in range(self.n_components):\\n print(\\\"Topic\\\", i)\\n c = np.argsort(self.phi[i, :])\\n for j in c[-1:-11:-1]:\\n print(self.list_ind2word[j], phi[i, j])\",\n \"def prob(self, word, context=None):\\n if not context:\\n context = ()\\n else:\\n context = tuple(context)\\n prob = 0\\n for i in range(len(context) + 1):\\n prob += self.weights[i] * self.ngram_cpd[context[i:]][word]\\n return prob\",\n \"def get_tf_idf_vector(tags, all_terms, idf_map):\\n return [(1 if term in tags else 0) * idf_map[term] for term in all_terms]\",\n \"def tfidf(word, blob, bloblist):\\n return tf(word, blob) * idf(word, bloblist)\",\n \"def basic_ttr(n_terms, n_words):\\n if n_words == 0:\\n return 0\\n return n_terms / n_words\",\n \"def get_vector(word, model):\\n return model.wv[word]\",\n \"def herdan(n_terms, n_words):\\n if n_words <= 1:\\n return 0\\n return math.log(n_terms) / math.log(n_words)\",\n \"def calc_tf_idf(idf, tf):\\r\\n tfidf = {}\\r\\n for key, val in tf.items():\\r\\n tfidf[key] = val * idf[key]\\r\\n return tfidf\",\n \"def get(self, index, tag=LEMMA):\\n if tag == WORD:\\n return self.words[index]\\n if tag == LEMMA:\\n return self.words[index].lemma\\n if tag == POS:\\n return self.words[index].type\\n if tag == CHUNK:\\n return self.words[index].chunk\\n if tag == PNP:\\n return self.words[index].pnp\\n if tag == REL:\\n ch = self.words[index].chunk; return ch and ch.relation\\n if tag == ROLE:\\n ch = self.words[index].chunk; return ch and ch.role\\n if tag == ANCHOR:\\n ch = self.words[index].pnp; return ch and ch.anchor\\n if tag in self.words[index].custom_tags:\\n return self.words[index].custom_tags[tag]\\n return None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60653216","0.60379994","0.6002697","0.5898803","0.561196","0.5606778","0.559027","0.55902416","0.55877733","0.55638826","0.5545571","0.55339694","0.55001754","0.5450012","0.54254514","0.5408535","0.5381232","0.53649646","0.53594136","0.5357221","0.533337","0.53270084","0.531974","0.52935123","0.528256","0.52612305","0.52493846","0.52488524","0.5247143","0.521165","0.51954895","0.5192033","0.5167519","0.51638407","0.5155096","0.51528585","0.5134853","0.5134237","0.5132369","0.512991","0.51277345","0.51271516","0.51173854","0.5105975","0.5105975","0.5088475","0.50859594","0.5076072","0.5059903","0.50584143","0.5047501","0.5042111","0.50374943","0.5037372","0.5027154","0.50189114","0.5010175","0.5003516","0.49988127","0.499794","0.49947888","0.49939716","0.4992548","0.49887976","0.4986678","0.4984545","0.49789104","0.4973004","0.4972957","0.49704283","0.496383","0.49569193","0.49550912","0.49538982","0.49532622","0.4952076","0.4950674","0.49429274","0.49429274","0.49415165","0.4938272","0.49342266","0.49282038","0.49281898","0.49235722","0.49148858","0.4909789","0.49081486","0.49066845","0.49000466","0.490003","0.48972005","0.48963353","0.48921114","0.48854497","0.48833308","0.48815772","0.48782036","0.4873133","0.48726457"],"string":"[\n \"0.60653216\",\n \"0.60379994\",\n \"0.6002697\",\n \"0.5898803\",\n \"0.561196\",\n \"0.5606778\",\n \"0.559027\",\n \"0.55902416\",\n \"0.55877733\",\n \"0.55638826\",\n \"0.5545571\",\n \"0.55339694\",\n \"0.55001754\",\n \"0.5450012\",\n \"0.54254514\",\n \"0.5408535\",\n \"0.5381232\",\n \"0.53649646\",\n \"0.53594136\",\n \"0.5357221\",\n \"0.533337\",\n \"0.53270084\",\n \"0.531974\",\n \"0.52935123\",\n \"0.528256\",\n \"0.52612305\",\n \"0.52493846\",\n \"0.52488524\",\n \"0.5247143\",\n \"0.521165\",\n \"0.51954895\",\n \"0.5192033\",\n \"0.5167519\",\n \"0.51638407\",\n \"0.5155096\",\n \"0.51528585\",\n \"0.5134853\",\n \"0.5134237\",\n \"0.5132369\",\n \"0.512991\",\n \"0.51277345\",\n \"0.51271516\",\n \"0.51173854\",\n \"0.5105975\",\n \"0.5105975\",\n \"0.5088475\",\n \"0.50859594\",\n \"0.5076072\",\n \"0.5059903\",\n \"0.50584143\",\n \"0.5047501\",\n \"0.5042111\",\n \"0.50374943\",\n \"0.5037372\",\n \"0.5027154\",\n \"0.50189114\",\n \"0.5010175\",\n \"0.5003516\",\n \"0.49988127\",\n \"0.499794\",\n \"0.49947888\",\n \"0.49939716\",\n \"0.4992548\",\n \"0.49887976\",\n \"0.4986678\",\n \"0.4984545\",\n \"0.49789104\",\n \"0.4973004\",\n \"0.4972957\",\n \"0.49704283\",\n \"0.496383\",\n \"0.49569193\",\n \"0.49550912\",\n \"0.49538982\",\n \"0.49532622\",\n \"0.4952076\",\n \"0.4950674\",\n \"0.49429274\",\n \"0.49429274\",\n \"0.49415165\",\n \"0.4938272\",\n \"0.49342266\",\n \"0.49282038\",\n \"0.49281898\",\n \"0.49235722\",\n \"0.49148858\",\n \"0.4909789\",\n \"0.49081486\",\n \"0.49066845\",\n \"0.49000466\",\n \"0.490003\",\n \"0.48972005\",\n \"0.48963353\",\n \"0.48921114\",\n \"0.48854497\",\n \"0.48833308\",\n \"0.48815772\",\n \"0.48782036\",\n \"0.4873133\",\n \"0.48726457\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.60124296"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":11181,"cells":{"query":{"kind":"string","value":"Create phi factors for a given tree"},"document":{"kind":"string","value":"def create_phi(self, T, pos, m, alpha=1):\n phi = tr.zeros((len(T), self.tag_size()), dtype=tr.float64)\n for i, _, _ in T:\n m_i = self.get_tag_index(m[i - 1])\n phi[i - 1, m_i] = alpha\n return phi"},"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 euler_phi(n):\n\tif n == 1: return 1\n\tif n <= 0: return 0\n\t# For each prime factor p with multiplicity n, a factor of (p**(n-1))*(p-1)\n\treturn functools.reduce(lambda a,x:a*(x[0]**(x[1]-1))*(x[0]-1),factor(n),1)","def euler_phi(n):\r\n\t# For each prime factor p with multiplicity n, a factor of (p**(n-1))*(p-1)\r\n\treturn reduce(lambda a,x:a*(x[0]**(x[1]-1))*(x[0]-1),factor(n),1)","def euler_phi(n):\n\n if n == 1:\n return 1\n\n phi = n\n for p, a in factorize(n):\n phi -= phi // p\n\n return phi","def eulerphi(n):\r\n\treturn euler_phi(n)","def eulerphi(n):\n\treturn euler_phi(n)","def phi(n, cache=None):\n if n == 1:\n return 1\n elif n < 1:\n raise AssertionError('Please enter an int > 0')\n else:\n return int(n * prod([1 - 1/p for p in set(factors(n, cache))]))","def phi(n):\n sieve = [i if i & 1 else i // 2 for i in range(n + 1)]\n for i in range(3, n + 1, 2):\n if sieve[i] == i:\n for j in range(i, n + 1, i):\n sieve[j] = (sieve[j] // i) * (i - 1)\n\n return sieve","def felsen( node, column, tm ):\n nucs = tm.alphabet\n # Is it a leaf node?\n if node.edges is None:\n symbol = column[ node.label ]\n if symbol == '*':\n ## Uniform distribution\n # return [ 1 / len( nucs ) ] # len( nucs ) \n ## Equilibrium (\\pi) distribution\n #return tm.background\n # Eliminate entirely\n return None\n return [ int( symbol == nuc ) for nuc in nucs ] \n else:\n # Traverse children and determine likelihoods\n ## l_children = [ felsen( edge.tip, column, tm ) for edge in node.edges ]\n l_children = []\n for edge in node.edges:\n t = felsen( edge.tip, column, tm )\n if t is not None:\n l_children.append( ( t, edge ) )\n if not l_children: return None\n l_self = []\n # Determine liklihood for each possible 'ancestral' sequence\n for i_y in range( len( nucs ) ):\n y = nucs[ i_y ]\n p_y = 1\n # For each child, sum over all paths\n for cl, edge in l_children:\n if edge.length not in matrix_by_time_cache:\n matrix_by_time_cache[ edge.length ] = matrix_for_time( tm.matrix, edge.length )\n psub = matrix_by_time_cache[ edge.length ]\n p_y_c = 0\n for i_a in range( len( nucs ) ):\n p_y_c += psub[i_y,i_a] * cl[ i_a ]\n # Product over all children\n p_y *= p_y_c\n l_self.append( p_y )\n return l_self","def phi(n: int) -> int:\n result = 1\n for i in range(2, n):\n if gcd(i, n) == 1:\n result += 1\n return result","def _make_phi(self, F):\n scaled_bins_left = tf.concat(\n [self.bin_edges / self.sigma,\n np.array([np.inf])], 0)\n scaled_bins_right = tf.concat(\n [np.array([-np.inf]), self.bin_edges / self.sigma], 0)\n return inv_probit(scaled_bins_left - tf.reshape(F, (-1, 1)) / self.sigma) \\\n - inv_probit(scaled_bins_right - tf.reshape(F, (-1, 1)) / self.sigma)","def phi_operator(self, num_levels=-1):\n phi_mat = sparse.spdiags([self.phis], [0], len(self.phis), len(self.phis))\n return self.reduced_operator(phi_mat, num_levels)","def phi_operator(self, num_levels=-1):\n phi_mat = sparse.spdiags([self.phis], [0], len(self.phis), len(self.phis))\n return self.reduced_operator(phi_mat, num_levels)","def phi(x,n):\n return 1/(np.sqrt(2**n*np.math.factorial(n))) * (1/np.pi)**0.25 * \\\n np.exp(-x**2/2)*sp.special.eval_hermite(n,x)","def get_factors():","def factors(self):\n X = [Var(i,2) for i in range(self.nvar)]\n factors = [Factor([],np.exp(self.c))] \n # TODO: exclude if zero? or exclude if inf/-inf, or if in \"assigned\", or?\n factors = factors + [Factor([X[i]],[-th,th]).exp() for i,th in enumerate(self.h) if self.dims[i]>1]\n L = coo(self.L)\n factors = factors + [Factor([X[i],X[j]],[[th,-th],[-th,th]]).exp() for i,j,th in zip(L.row,L.col,L.data) if i<j]\n return factors\n # TODO: should we exponentiate if isLog not True? ","def phi(U, n):\n phi_params = n['phi']\n return phi_params['r_max'] / (1 + np.exp(-phi_params['beta'] * (U - phi_params['alpha'])))","def _gen_terms(self, tree):\n for leaf in self._leaves(tree):\n terms = [self._normalize(w) for w, _ in leaf if self._filter(w)]\n # Phrase only\n if len(terms) > 1:\n yield terms","def phiprime_phi(phi):\n f = 0.0022927\n phiprime = np.arctan2(np.tan(phi*np.pi/180.),(1.-f)**2.)*180./np.pi\n return phiprime","def compute_tree(self, tree):\n g_list_val, g_list_h = self._build_graph(tree) # return theano variable of each node\n list_val = self._traversal_tree(tree) #\n f = theano.function(g_list_val, g_list_h, allow_input_downcast=True)\n result = f(*list_val)\n return result","def f(p, phi, phib, df):\n\treturn -log(p) - df + (p-1)*phi + \\\n\t ( phi*(1-p) + phib + \\\n\t 5./4*alpha*(phi*p)**(9./4)-(9./4)*alpha*(p*phi)**(5./4) - \\\n\t (1./2)(1-p*phi)**2 - (phib/Nb)-5./4*alpha*(phi+phib)**(9./4) + \\\n\t (9./4)*alpha*(phi+phib)**(5.4) + \\\n\t 1./2*(1-phi-phib)**2 ) * Ns","def phi34_cc( s, m ):\n\n l_min = max(0, (m + 2*s + 1)/2)\n l_max = ( 3*s + m ) / 2 \n S = 0\n for l in range(l_min, l_max+1):\n n_1 = 2*l - 2*s - m\n n_2_t2 = -2*l + 3*s + m\n if n_2_t2 % 2 != 0:\n continue\n n_2 = n_2_t2 / 2\n\n denom = factorial( n_1 ) * factorial( n_2 ) * \\\n factorial( m ) * factorial(3)**n_1 * \\\n factorial(4) ** n_2\n\n S += Fraction(double_factorial(2*l-1), denom)\n\n return S","def phi(nbins, p, t, x):\n\n phase = calc_phase(p, t)\n x_means, phase_bs, Ns, sj2s, xb, pb = phase_bins(nbins, phase, x)\n total_binned_variance_s2 = s2(Ns, sj2s, nbins)\n total_variance = sj2(x, np.mean(x), len(x))\n\n return total_binned_variance_s2/total_variance","def phi_k_cc( s, m, k ):\n \n l_t2 = m + s * k\n\n if l_t2 % 2 != 0:\n return 0\n \n denom = factorial(s) * factorial(m) * factorial( k ) ** s\n nom = double_factorial( l_t2 - 1 )\n\n return Fraction( nom, denom )","def phi(N60):\n return 27.1 + 0.3*N60 - 0.00054* N60**2","def probit_phi(x):\n mu = 0;sd = 1;\n return 0.5 * (1 + tsr.erf((x - mu) / (sd * tsr.sqrt(2))))","def _compute_factors(roots, multiplicity, include_powers=False):\n current = cupy.array([1])\n suffixes = [current]\n for pole, mult in zip(roots[-1:0:-1], multiplicity[-1:0:-1]):\n monomial = cupy.r_[1, -pole]\n for _ in range(int(mult)):\n current = cupy.polymul(current, monomial)\n suffixes.append(current)\n suffixes = suffixes[::-1]\n\n factors = []\n current = cupy.array([1])\n for pole, mult, suffix in zip(roots, multiplicity, suffixes):\n monomial = cupy.r_[1, -pole]\n block = []\n for i in range(int(mult)):\n if i == 0 or include_powers:\n block.append(cupy.polymul(current, suffix))\n current = cupy.polymul(current, monomial)\n factors.extend(reversed(block))\n\n return factors, current","def phi_function(alpha, pk, xk):\n x = xk + alpha * pk\n return rosenbock2Nd(x, 0)","def planck_f(nu, T):\n return ((2*h*nu**3)/(c**2))*(1./(np.exp((h*nu)/(k*T))-1))","def phi_prime(U, n):\n phi_params = n['phi']\n num = np.exp((U + phi_params[\"alpha\"]) * phi_params[\"beta\"]) * \\\n phi_params[\"r_max\"] * phi_params[\"beta\"]\n denom = (np.exp(U * phi_params[\"beta\"]) + np.exp(phi_params[\"alpha\"] * phi_params[\"beta\"]))**2\n return num / denom","def generate_phi(self):\n self.phi = np.empty((100, self.K))\n for i, point in enumerate(self.X):\n for j, center in enumerate(self.centers):\n self.phi[i][j] = np.exp(-self.gamma * distance.euclidean(point, center) ** 2)\n self.phi = np.concatenate((self.phi, np.ones((100, 1))), axis=1)","def phi_study(polarity, year):\n # Fetch the ntuple, created using `phi_ntuple` in `slim_ntuple.py`\n n = Lc2pXX.Lc2pphi(\"DecayTree\", polarity, year, mc=False)\n n.add(\"{0}/selected-pphi-{1}.root\".format(\n config.output_dir, str(n).replace(config.pphi, config.pKK)\n ))\n\n # Unbinned fit\n w = ROOT.RooWorkspace(\"{0}-workspace\".format(n))\n fitting.lambdac_mass.fit(\n n, w, n.shapes_postselection, bins=0\n )\n c = plotting.plot_fit(\n w, [\n (\"total_pdf\", \"Fit\"),\n (\"signal_pdf\", \"Signal\"),\n (\"background_pdf\", \"Background\")\n ],\n \"Lambdac_M\",\n bins=140\n )\n c.SetName(w.GetName().replace(\"workspace\", \"canvas\"))\n utilities.save_to_file(\"{0}/fits/selected-{1}.root\".format(\n config.output_dir, n\n ), [w, c])\n\n # Print the stats\n pphi_yields = fitting.lambdac_mass.yields(w)\n pphi_significance = utilities.significance(\n pphi_yields[0], pphi_yields[1]\n )\n print \"Yields:\", pphi_yields\n print \"Significance:\", pphi_significance\n\n # What follows in very similar in style to `branching_fractions.py`,\n # so check that out for a more in-depth explanation\n year = 2011\n m = config.pphi\n # We can proceed two ways - calculate the reco eff. wrt acc., then\n # stripping wrt reco, or just stripping wrt acc.\n pphi_effs = {\n \"Acceptance\": acceptance.efficiency(m, polarity, year),\n \"Reconstruction\": reconstruction.efficiency(m, polarity, year),\n \"Tracking\": tracking.efficiency_smeared(m, polarity, year),\n \"Stripping\": stripping.efficiency(m, polarity, year),\n # \"Stripping\": stripping.efficiency_no_reco(m, polarity, year),\n \"Trigger\": trigger.efficiency_post_stripping(m, polarity, year),\n \"Offline\": offline.efficiency_mc(m, polarity, year),\n \"PID\": pid.efficiency(m, polarity, year)\n }\n\n # What follows here is very similar in style to `branching_fractions.py`\n # so check that out for a more in-depth explanation\n effs = {}\n raw_yields = {}\n prod_yields = {}\n modes = (config.pKpi, config.pphi)\n\n effs[config.pphi] = pphi_effs\n effs[config.pKpi] = branching_fractions.efficiencies(\n config.pKpi, polarity, year\n )\n # Add the \"Total\" key\n for mode in modes:\n mode_effs = effs[mode]\n total_eff = 1.\n for eff in mode_effs:\n total_eff *= mode_effs[eff]\n effs[mode][\"Total\"] = total_eff\n\n raw_yields[config.pphi] = pphi_yields[0]\n raw_yields[config.pKpi] = branching_fractions.yields(\n config.pKpi, polarity, year\n )[0]\n prod_yields[config.pphi] = raw_yields[config.pphi]/effs[config.pphi][\"Total\"]\n prod_yields[config.pKpi] = raw_yields[config.pKpi]/effs[config.pKpi][\"Total\"]\n\n # PDG July 2012 measurement\n pKpi_abs = ufloat(5e-2, 1.3e-2)\n\n pphi_rel = prod_yields[config.pphi]/prod_yields[config.pKpi]\n pphi_abs = pphi_rel*pKpi_abs\n\n print\n print \"== Measurements for {0} {1} == \".format(polarity, year)\n print\n for mode in modes:\n mode_effs = effs[mode]\n print \"-- Efficiencies for {0} --\".format(mode)\n for eff in mode_effs:\n print \"{0}: {1}\".format(eff, mode_effs[eff])\n print\n print \"-- Yields for {0} --\".format(mode)\n print \"Raw:\", raw_yields[mode]\n print \"Production:\", prod_yields[mode]\n print\n print \"-- Relative Branching Fractions (10^-2) --\"\n print \"pphi/pKpi: \", 1e2*pphi_rel\n print\n print \"-- Absolute Branching Fractions (%) --\"\n print \"pKpi (PDG):\", 1e2*pKpi_abs\n print \"pphi: \", 1e2*pphi_abs\n print","def _buildtree(self):\n self.pricetree = np.zeros((self.steps+1,self.steps+1))\n self.pricetree[0][0] = self.p\n for j in range(self.steps):\n for i in range(j+1):\n self.pricetree[j+1][i+1] = self.pricetree[j][i]*self.down\n self.pricetree[j+1][0] = self.pricetree[j][0]*self.up","def TR_algo2(p, vd=2):\n # h will contain the Hilbert index\n h = 0\n # ve and vd contain the entry point and dimension of the current subcube\n # we choose here a main traversal direction N-2 (i.e. z for a cube) to match\n # the illustrations\n ve = 0\n for i in range(M-1, -1, -1):\n # the cell label is constructed in two steps\n # 1. extract the relevant bits from p\n l = [bit_component(px, i) for px in p]\n # 2. construct a integer whose bits are given by l\n l = sum( [lx*2**j for j, lx in enumerate(l)] )\n # transform l into the current subcube\n l = T(ve, vd, l)\n # obtain the gray code ordering from the label l\n w = inverse_gc(l)\n # compose (see [TR] lemma 2.13) the transform of ve and vd\n # with the data of the subcube\n ve = ve ^ (rotate_left(e(w), vd+1))\n vd = (vd + d(w) + 1) % N\n # move the index to more significant bits and add current value\n h = (h << N) | w\n return h","def pdf(tree1, tree2, alpha, beta, new):\n\n # TODO: Refactor so it can be used in M-H.\n prob = 0.0\n tree2_tree1_subtree_nodes = get_subtree_nodes(tree1, tree2, new)\n for tree2_subtree_nodes in tree2_tree1_subtree_nodes:\n # tree1_subtree = tree1.subgraph([c_t1 for c_t2, c_t1 in tree2_subtree_nodes.iteritems() if c_t1 is not None])\n tree1_subtree = tree1.subgraph([c_t1 for c_t2, c_t1 in tree2_subtree_nodes.items() if c_t1 is not None])\n tree1_subtree_prob = ss.pdf(tree1_subtree, tree1, alpha, beta)\n\n (P, N) = subtree_cond_pdf(tree1, tree2, tree2_subtree_nodes, new)\n christtree_prob = np.prod([P[c] * N[c] for c in P])\n prob += tree1_subtree_prob * christtree_prob\n return prob","def GilbertWells_calc(TP, FP, FN, TN):\n try:\n n = TP + FP + FN + TN\n part1 = (TP + FP) * (TP + FN) * (TN + FP) * (TN + FN)\n part2 = math.factorial(TP + FP) * math.factorial(TP + FN) * \\\n math.factorial(TN + FP) * math.factorial(TN + FN)\n part3 = math.factorial(n) * math.factorial(TP) * \\\n math.factorial(FP) * math.factorial(FN) * math.factorial(TN)\n return math.log((n ** 3) / (2 * math.pi * part1)) + \\\n 2 * math.log(part3 / part2)\n except Exception:\n return \"None\"","def unique_leaf_of(phi):\n vs = leaves_of(phi)\n assert len(vs) == 1\n assert isinstance(vs[0], amnet.Variable)\n return vs[0]","def phi0(self, x):\n return (1-(1-x)**(4/3))**(3/2)/(2*np.sqrt(2))","def _build_c_phi_matrices(self, t: tf.Tensor) -> tf.Tensor:\n c_phi_matrices = self.kernel.compute_c_phi(t, t)\\\n + tf.expand_dims(tf.eye(self.n_points_int, dtype=tf.float64), 0)\\\n * self.likelihood_variances\n return c_phi_matrices","def walk(tree):\n results = []\n for parent, children in tree.iteritems():\n perms = permute(children)\n temp = deepcopy(perms)\n # Add the parent to the perms as well\n for perm in temp:\n perms.append(add_to_head_of_perm(parent, perm))\n for perm in perms:\n r = pairwise(perm)\n results.append(r)\n return results","def root_factors(f, *gens, filter=None, **args):\n args = dict(args)\n\n F = Poly(f, *gens, **args)\n\n if not F.is_Poly:\n return [f]\n\n if F.is_multivariate:\n raise ValueError('multivariate polynomials are not supported')\n\n x = F.gens[0]\n\n zeros = roots(F, filter=filter)\n\n if not zeros:\n factors = [F]\n else:\n factors, N = [], 0\n\n for r, n in ordered(zeros.items()):\n factors, N = factors + [Poly(x - r, x)]*n, N + n\n\n if N < F.degree():\n G = reduce(lambda p, q: p*q, factors)\n factors.append(F.quo(G))\n\n if not isinstance(f, Poly):\n factors = [ f.as_expr() for f in factors ]\n\n return factors","def factors(n: int) -> List[int]:\n k = 1\n while k**2 < n:\n if n % k == 0:\n yield k\n k += 1\n\n k = int(n**(1/2))\n while k > 0:\n if n % k == 0:\n yield n // k\n k -= 1","def phi_list ( self ) :\n return self.__phi_list","def phi_t(self):\n\t\tdim = self.dim\n\t\ttim_all = self.tim_all \n\t\tphi_all = np.zeros((tim_all+1,dim,1),dtype = complex)\n\t\tphi_all[0,:,:] = self.phi_i[:]\n\t\tu_all = self.u_t()\n\n\t\tfor tim in xrange(tim_all):\n\t\t\tphi_all[tim+1,:,:] = np.dot(u_all[tim+1,:,:], phi_all[0,:,:])\n\t\t\n\t\treturn phi_all","def caculate_prob(self):\n t_H = self.tree.depth()\n t_h = 1\n while(t_h <= t_H):\n t_hnodes = self.get_h(t_h)\n t_sum = 0\n t_hpro = []\n t_cpro = []\n for t_n in t_hnodes:\n t_sum = self.tree.get_node(t_n).data[0] + t_sum\n t_node = self.tree.get_node(t_n)\n if t_node.is_leaf():\n t_node.data.append(0)\n continue\n t_childrens = self.tree.children(t_n)\n t_shang = 0\n for child in t_childrens:\n t_shang = t_shang + (child.data[0]/t_node.data[0])*np.log(child.data[0]/t_node.data[0])\n t_node.data.append(-t_shang)\n for t_n in t_hnodes:\n t_node = self.tree.get_node(t_n)\n t_parentnode = self.tree.parent(t_n)\n if t_h > 1:\n t_node.data.append((t_node.data[0] / t_sum) * (t_node.data[0]/t_parentnode.data[0]))\n t_hpro.append((t_node.data[0]/t_sum) * (t_node.data[0]/t_parentnode.data[0]))\n else:\n t_node.data.append((t_node.data[0] / t_sum))\n t_hpro.append((t_node.data[0] / t_sum))\n\n t_cpro.append(t_node.data[1])\n t_ndata = np.array(t_hpro)\n mean = np.mean(t_ndata)\n std = np.std(t_ndata,ddof=1)\n t_sdata = np.array(t_cpro)\n mean_s = np.mean(t_sdata)\n std_s = np.std(t_sdata,ddof=1)\n for t_n in t_hnodes:\n t_node = self.tree.get_node(t_n)\n if(std != 0):\n t_node.data[2] = (t_node.data[2] - mean)/std\n else:\n t_node.data[2] = (t_node.data[2] - mean)\n if(mean_s == 0 and std_s ==0):\n t_node.data[1] = -100.0\n continue\n t_node.data[1] = (t_node.data[1] - mean_s)/std_s\n t_h = t_h + 1","def cal_phi(self):\n\n if not self.check_def(['E','px','py','pz']):\n sys.exit('Particle error: Quadri impulsion not define (error for phi routine)')\n\n if(self.px>0):\n self.phi=math.atan(self.py/self.px)\n elif(self.px<0):\n self.phi=math.atan(self.py/self.px)+math.pi\n elif(self.py>0): #remind that p(1)=0\n self.phi=math.pi/2.0\n elif(self.py<0): # remind that p(1)=0\n self.phi=-math.pi/2.0\n else:\n print \"Warning self.phi not properly defined put value to 0\"\n self.phi=0\n \n if(self.phi<0):\n self.phi=self.phi+2*math.pi\n\n return self.phi","def _phi(self, x, d, p):\n ks = np.arange(self.p + 1)\n ks = ks[np.where(2 * (self.p - ks) - d >= 0)][:, np.newaxis]\n return np.sum(\n binom(self.p, ks)\n * (-1) ** ks\n * x[np.newaxis, :] ** (2 * (self.p - ks) - d)\n * perm(2 * (self.p - ks), d),\n axis=0,\n )","def do_factor(m=4, q=11):\n from sage.rings.finite_rings.integer_mod_ring import IntegerModRing\n from sage.matrix.constructor import identity_matrix, block_matrix\n from sage.matrix.matrix_space import MatrixSpace\n from sage.rings.integer_ring import IntegerRing\n from sage.modules.free_module_integer import IntegerLattice\n \n \n m=n+1\n ZZ = IntegerRing()\n ZZ_q = IntegerModRing(q)\n \n \n \n from sage.arith.all import euler_phi\n from sage.misc.functional import cyclotomic_polynomial\n \n for a in range(\n R = ZZ_q['x']#.quotient(cyclotomic_polynomial(k, 'x'), 'x')\n f=cyclotomic_polynomial(m,'x')\n return f.factor()","def phi_topic(self):\n f1 = self.nkw+self.beta\n f2 = np.sum(self.nkw, axis=1, keepdims=True)+self.VT*self.beta\n return f1/f2","def compute_eigvals(phi): # pylint: disable=arguments-differ\n if qml.math.get_interface(phi) == \"tensorflow\":\n phase = qml.math.exp(-0.5j * qml.math.cast_like(phi, 1j))\n return stack_last([phase, qml.math.conj(phase), qml.math.conj(phase), phase])\n\n prefactors = qml.math.array([-0.5j, 0.5j, 0.5j, -0.5j], like=phi)\n if qml.math.ndim(phi) == 0:\n product = phi * prefactors\n else:\n product = qml.math.outer(phi, prefactors)\n return qml.math.exp(product)","def makeTTree():\n \n tree = TTree(\"tree\",\"tree\")\n px = array('d',[0])\n py = array('d',[0])\n pz = array('d',[0])\n pi = array('i',[0])\n tree.Branch(\"x\",px,\"x/D\")\n tree.Branch(\"y\",py,\"y/D\")\n tree.Branch(\"z\",pz,\"y/D\")\n tree.Branch(\"i\",pi,\"y/I\")\n for i in range(500):\n px[0] = gRandom.Gaus(0,3)\n py[0] = gRandom.Uniform()*30 - 15\n pz[0] = gRandom.Gaus(0,5)\n pi[0] = i%3\n tree.Fill()\n return tree","def glove_leaves_phi(t1, t2, np_func=np.sum): \n prem_vecs = _get_tree_vecs(t1, glove_lookup, np_func) \n hyp_vecs = _get_tree_vecs(t2, glove_lookup, np_func) \n return np.concatenate((prem_vecs, hyp_vecs))","def f(p, phi, phib, df):\n\treturn - p + exp( - df + Ns*(log((1 - p*phi)/(1 - phi - phib)) + \\\n\t\t(p - 1)*phi - phib + (9./4)*alpha*((phi + phib)**(5./4) - (p*phi)**(5./4))))","def phi(x):\n # constants\n a1 = 0.254829592\n a2 = -0.284496736\n a3 = 1.421413741\n a4 = -1.453152027\n a5 = 1.061405429\n p = 0.3275911\n\n # Save the sign of x\n sign = 1\n if x < 0:\n sign = -1\n x = abs(x) / math.sqrt(2.0)\n\n # A&S formula 7.1.26\n t = 1.0 / (1.0 + p * x)\n y = 1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * math.exp(-x * x)\n\n return 0.5 * (1.0 + sign * y)","def _get_phi(self):\n phi = self.phi(self._data[SoilProperty.N60])\n ### Ok let's remove for clay\n if self.is_clayey():\n phi=0 #very small value for plasix:::@TODO 0.01\n return phi","def __init__(self):\n self.phi = sqrt(5.0)/2.0 - 0.5","def set_phi(self):\n self.phi = float(dihedral(self.O5.getXYZ(), self.C1.getXYZ(), self.GO.getXYZ(), self.CX.getXYZ()))","def actual(phi, t):\r\n\r\n # spatial nodes\r\n ngrid = 10\r\n end = -1\r\n M, MT = get_mmt()\r\n D = 5000*np.ones(ngrid-1)\r\n A = MT@np.diag(D)@M\r\n A = A[1:ngrid-1]\r\n\r\n # differential equations\r\n dphi = np.zeros(ngrid)\r\n # first node\r\n dphi[0] = 0\r\n\r\n # interior nodes\r\n dphi[1:end] = -A@phi # value at interior nodes\r\n\r\n # terminal node\r\n dphi[end] = D[end]*2*(phi[end-1] - phi[end])\r\n\r\n return dphi","def expfam2Factors(theta):\r\n facList = []\r\n m = theta.shape[0]\r\n for s,th in enumerate(scipy.diag(theta)):\r\n facList.append(factor(var=[s],card=[2],val = [1,scipy.exp(th)]))\r\n for s,t in combinations(range(m),2):\r\n if not scipy.isclose(theta[s,t],0):\r\n facList.append(factor(var=[s,t],card=[2,2],val=[1,1,1,scipy.exp(theta[s,t])]))\r\n F = FactorList(facList)\r\n F.make_connected()\r\n return F","def f(self,node):\r\n return (self.a*self.nodeDegree(node))/(1+self.b*self.nodeDegree(node))","def _factors(n):\n gen = ([i, n // i] for i in range(1, int(n ** 0.5) + 1) if n % i == 0)\n return set(sum(gen, []))","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 visit_factor(self, node, children):\n if self.debug:\n print(\"Factor {}\".format(children))\n if len(children) == 1:\n return children[0]\n sign = -1 if children[0] == '-' else 1\n return sign * children[-1]","def phi(self, x:int):\n dimension = self.M + 1 # We are including a constant here.\n p = [0] * dimension\n for i in range(dimension):\n p[i] += pow(x, i)\n p = np.array(p)\n p = p.reshape(p.shape + (1,))\n return p","def __phi(x):\n return max([1 - abs(x), 0])","def phases_from_superoperator(U):\n if U.type=='oper':\n phi_00 = np.rad2deg(np.angle(U[0, 0])) # expected to equal 0 because of our\n # choice for the energy, not because of rotating frame. But not guaranteed including the coupling\n phi_01 = np.rad2deg(np.angle(U[1, 1]))\n phi_10 = np.rad2deg(np.angle(U[3, 3]))\n phi_11 = np.rad2deg(np.angle(U[4, 4]))\n phi_02 = np.rad2deg(np.angle(U[2, 2])) # used only for avgatefid_superoperator_phasecorrected\n phi_20 = np.rad2deg(np.angle(U[6, 6])) # used only for avgatefid_superoperator_phasecorrected\n\n elif U.type=='super':\n phi_00 = 0 # we set it to 0 arbitrarily but it is indeed not knowable\n phi_01 = np.rad2deg(np.angle(U[1, 1])) # actually phi_01-phi_00 etc\n phi_10 = np.rad2deg(np.angle(U[3, 3]))\n phi_11 = np.rad2deg(np.angle(U[4, 4]))\n phi_02 = np.rad2deg(np.angle(U[2, 2]))\n phi_20 = np.rad2deg(np.angle(U[6, 6]))\n\n phi_cond = (phi_11 - phi_01 - phi_10 + phi_00) % 360 # still the right formula independently from phi_00\n\n return phi_00, phi_01, phi_10, phi_11, phi_02, phi_20, phi_cond","def tree_query(self, pta_root):\n self.sul.pre()\n curr_node = pta_root\n\n inputs = []\n outputs = []\n\n while True:\n\n if curr_node.children:\n frequency_sum = sum(curr_node.input_frequencies.values())\n if frequency_sum == 0:\n # uniform sampling in case we have no information\n inp = choice(list(curr_node.children.keys()))\n else:\n # use float random rather than integers to be able to work with non-integer frequency information\n selection_value = random() * frequency_sum\n inp = None\n for i in curr_node.input_frequencies.keys():\n inp = i\n selection_value -= curr_node.input_frequencies[i]\n if selection_value <= 0:\n break\n # curr_node.input_frequencies[inp] -= 1\n\n inputs.append(inp)\n out = self.sul.step(inp)\n new_node = curr_node.get_child(inp, out)\n\n if new_node:\n outputs.append(out)\n curr_node = new_node\n else:\n self.sul.post()\n return\n else:\n curr_node = pta_root\n for i, o in zip(inputs, outputs):\n self.curr_node.input_frequencies[i] -= 1\n curr_node = curr_node.get_child(i, o)\n self.sul.post()\n return","def divisors(factors):\n ps = sorted(set(factors))\n omega = len(ps)\n\n def rec_gen(n=0):\n if n == omega:\n yield 1\n else:\n pows = [1]\n for j in xrange(factors.count(ps[n])):\n pows += [pows[-1] * ps[n]]\n for q in rec_gen(n + 1):\n for p in pows:\n yield p * q\n\n for p in rec_gen():\n yield p","def phi(self):\n if self._phi is None:\n b = self.stem + self.counter + self.radius + (\n self.stem - 2 * self.radius) - self.overlap\n c = sqrt(self.a ** 2 + b ** 2)\n _phi = atan2(self.a, b)\n b = sqrt(c ** 2 - self.radius ** 2)\n _phi += atan2(self.radius, b)\n self._phi = _phi\n return self._phi","def croft():\n # Copied from:\n # https://code.google.com/p/pyprimes/source/browse/src/pyprimes.py\n # Implementation is based on erat3 from here:\n # http://stackoverflow.com/q/2211990\n # and this website:\n # http://www.primesdemystified.com/\n # Memory usage increases roughly linearly with the number of primes seen.\n # dict ``roots`` stores an entry x:p for every prime p.\n for p in (2, 3, 5):\n yield p\n roots = {9: 3, 25: 5} # Map d**2 -> d.\n primeroots = frozenset((1, 7, 11, 13, 17, 19, 23, 29))\n selectors = (1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0)\n for q in compress(\n # Iterate over prime candidates 7, 9, 11, 13, ...\n islice(count(7), 0, None, 2),\n # Mask out those that can't possibly be prime.\n cycle(selectors)\n ):\n # Using dict membership testing instead of pop gives a\n # 5-10% speedup over the first three million primes.\n if q in roots:\n p = roots[q]\n del roots[q]\n x = q + 2*p\n while x in roots or (x % 30) not in primeroots:\n x += 2*p\n roots[x] = p\n else:\n roots[q*q] = q\n yield q","def phi(self):\n return (np.sum(self.diameters**self.ndim)*np.pi / (2*self.ndim))","def T(params, phi):\n\t# handle the base frame, eqn 3.9, p36\n\tt = np.array([\n\t\t[cos(phi), -sin(phi), 0.0, 0.0],\n\t\t[sin(phi), cos(phi), 0.0, 0.0],\n\t\t[0.0, 0.0, 1.0, 0.0],\n\t\t[0.0, 0.0, 0.0, 1.0]\n\t])\n\tfor i, p in enumerate(params):\n\t\tt = t.dot(rot(*p))\n\treturn t","def get_factors(self, triples):\n pass","def _vlerchphi(self, z: np.ndarray, a: int) -> np.ndarray:\n return np.array([self._lerchphi(z_, a) for z_ in z])","def phi(self):\n return self._phi","def getPhNodes(self,pH, pos=None, rootA=False):\n\t\tif pos==None: \n\t\t\tpos=self.pos\n\t\t\t[r,th]=self.posCyl\n\t\t\tif len(self.plantHeads)==1:\n\t\t\t\torig=self.getPHCoord(pos)[0]\n\t\t\telse:\n\t\t\t\tpositions=self.getPHCoord(pos)\n\t\t\t\torig=positions[pH.number]\n\t\telse:\n\t\t\t#first, determine the direction of the CRANE\n\t\t\torig=pos\n\t\t\tposCyl=self.m.getCylindrical(pos)\n\t\t\t#this may seem like a sign-error, but remember that pos is pH-pos. Need th for self, not pH\n\t\t\tif len(self.plantHeads)==1: #bracke\n\t\t\t\tth=posCyl[1]\n\t\t\telse:\n\t\t\t\tx=-(self.plantAreaW*0.5-pH.width*0.5)\n\t\t\t\tx+=pH.number*self.plantSepDist #this is the upper part of the triangle.\n\t\t\t\tth=posCyl[1]+asin(x/posCyl[0])\n\t\tdirection=self.m.direction+th-pi/2.\n\t\tcart=self.m.getCartesian\n\t\tw=pH.width\n\t\tl=pH.length\n\t\tif rootA:\n\t\t\tw=w*0.75\n\t\t\tl=l*0.75\n\t\tc1=cart([w/2., l/2.],origin=orig, direction=direction, local=False, fromLocalCart=True)\n\t\tc2=cart([-w/2., l/2.],origin=orig, direction=direction, local=False, fromLocalCart=True)\n\t\tc3=cart([-w/2., -l/2.],origin=orig, direction=direction, local=False, fromLocalCart=True)\n\t\tc4=cart([w/2., -l/2.],origin=orig, direction=direction, local=False, fromLocalCart=True)\n\t\treturn [c1,c2,c3,c4]","def factors(self):\n for name in self._nodes:\n if isinstance(self._nodes[name], Factor):\n yield name","def square_tree(t):\n return tree(label(t)**2,[square_tree(b) for b in branches(t)])","def get_factors(number):\n\n factors = [1, number]\n\n for i in range(2, int(math.sqrt(number))):\n if number % i == 0:\n factors.extend([i, number / i])\n\n return(factors)","def phi_fixed(prec):\n prec += 10\n a = isqrt_fast(MPZ_FIVE<<(2*prec)) + (MPZ_ONE << prec)\n return a >> 11","def phi_func(index, phase_angle):\n\n # Dictionary that contains the A and B constants, depending on the index /\n # version\n a_factor = {'1': 3.33, \\\n '2': 1.87}\n\n b_factor = {'1': 0.63, \\\n '2': 1.22}\n\n # Phase function\n phi = np.exp(-1.0 * a_factor[index] \\\n *+ ((np.tan(0.5 * phase_angle) ** b_factor[index])))\n\n # Return the phase function result\n return phi","def SetHomoplasy(tree, skip=0):\n population = tree.size if 'size' in vars(tree) else (len(tree) * 2) - 1\n order = tree.traverse()\n root = next(order)\n homoplasy_hist = sp.csr_matrix(np.zeros(root.genotype.shape[1] - skip))\n for cur_node in tqdm.tqdm(order, total=population + 1):\n homoplasy_hist += (cur_node.genotype[0, skip:] != cur_node.up.genotype[0, skip:])\n homoplasy_dist = pd.Series(homoplasy_hist.data).value_counts(normalize=True)\n fit_dist = rv_discrete(values=(homoplasy_dist.index.astype(int), homoplasy_dist.values.astype(float)))\n root.homoplasy = fit_dist\n root.homoplasy_hist = homoplasy_hist.toarray()[0].astype(np.int)\n return tree","def compute_eigvals(phi): # pylint: disable=arguments-differ\n if qml.math.get_interface(phi) == \"tensorflow\":\n phi = qml.math.cast_like(phi, 1j)\n\n return qml.math.stack([1, 1, -qml.math.exp(1j * phi), qml.math.exp(1j * phi)])","def factor_unit(P):\n assert all(isinstance(c, ModInt) for c in P)\n assert len(set(c.n for c in P)) == 1\n if P.deg == 1:\n return defaultdict(int, {P: 1})\n\n p = P[0].n\n\n S = Polynomial.gcd(P, P.prime())\n if S.deg == P.deg:\n # P' = 0 so P = R^p\n R = Polynomial(P.C[::p])\n return defaultdict(int,\n {D: p * v\n for D, v in Polynomial.factor_unit(R).items()})\n else:\n factors = defaultdict(int)\n if S.deg:\n for D, v in S.factor_unit().items():\n factors[D] += v\n P //= S\n # P is now square-free\n # We look for Q in Ker(F-Id) \\ {1}\n Q = Polynomial.computeQ(P)\n if Q is None:\n # P is irreducible\n factors[P] += 1\n else:\n # P is the product of the gcd(P, Q-i)\n # that are factored recursively\n for i in range(p):\n D = Polynomial.gcd(P, Q - Polynomial([ModInt(i, p)]))\n if D.deg:\n for DD, v in D.factor_unit().items():\n factors[DD] += v\n return factors","def fn(i, n):\n if len(stack) > 0 and stack[-1] <= n: ans.append(stack + [n])\n for ii in range(i, len(factors)):\n if n % factors[ii] == 0: \n stack.append(factors[ii])\n fn(ii, n//factors[ii])\n stack.pop()","def phi34_class_coeff( L, m ):\n\n s = 2*(L-1) + m\n\n if s<0:\n return 0\n\n return phi34_cc( s, m )","def factors(self, X):\r\n return (lambda fd: [X] if not fd else fd + self.factors(X // fd[0])) (self.firstdiv(X))","def to_prufer(tree, n):\n d = defaultdict(int)\n L = []\n for edge in tree:\n # Increment the value of the corresponding\n # node in the degree list as we encounter an\n # edge involving it.\n d[edge[0]] += 1\n d[edge[1]] += 1\n for i in range(n - 2):\n # find the smallest leaf\n for x in range(n):\n if d[x] == 1:\n break\n # find the node it was connected to\n y = None\n for edge in tree:\n if x == edge[0]:\n y = edge[1]\n elif x == edge[1]:\n y = edge[0]\n if y is not None:\n break\n # record and update\n L.append(y)\n for j in (x, y):\n d[j] -= 1\n if not d[j]:\n d.pop(j)\n tree.remove(edge)\n return L","def _preprocess(self):\n # Precompute a logarithm table. log[n] = k => 2^k <= n < 2^(k+1)\n self._log = [0] * (self._size + 1)\n temp = 1\n for i in range(1, self._size + 1):\n self._log[i] = self._log[i - 1]\n if temp * 2 <= i:\n self._log[i] += 1\n temp *= 2\n\n # Compute the log of the size of the tree.\n self._logsize = self._log[self._size] + 1\n\n # Precompute a power table. pow[k] = n => 2^k = n\n self._pow = [1] * (self._logsize + 1)\n for i in range(1, self._logsize):\n self._pow[i] = 2 * self._pow[i - 1]\n\n # Build a list of the jump nodes sorted by depth.\n self._build_jump_nodes()\n\n # Build a list of ladders.\n self._build_ladders()\n\n # Build a sparse table of ancestors of levels 1, 2, 4, 8, ...., 2^k only for the leaves.\n self._build_sparse_table()","def calc_torsion_phi(self):\n prev_res = self.get_offset_residue(-1)\n if prev_res is None:\n return None\n\n paC = prev_res.get_atom('C')\n aN = self.get_atom('N')\n aCA = self.get_atom('CA')\n aC = self.get_atom('C')\n return AtomMath.calc_torsion_angle(paC, aN, aCA, aC)","def Phi(l,m,theta,phi):\n Psilm_th, Psilm_ph=Psi(l,m,theta,phi);\n Philm_th=-Psilm_ph;\n Philm_ph=+Psilm_th;\n return Philm_th, Philm_ph","def phi2_coefficient(L):\r\n\r\n if 0 < L < 120:\r\n return L / 120\r\n if L >= 120:\r\n return 1","def phi(self, i):\n if i == 1 or not self.connected_to_previous(i):\n return 0.0\n\n res = self.all_residues[i]\n\n try:\n n = res['N'].get_vector()\n ca = res['CA'].get_vector()\n c = res['C'].get_vector()\n\n res_minus_one = self.all_residues[i -1]\n cp = res_minus_one['C'].get_vector()\n phi = calc_dihedral(cp, n, ca, c)\n return phi\n except Exception:\n print \"Could not get phi for \"+repr(i)\n raise LookupError","def genKeys(p, q):\n n = p*q\n phi_of_n = (p-1)*(q-1)\n e = 0\n \n #find a e less than n that is coprime with phi(n)\n count=2\n while count:\n gcd = gcd_iter(phi_of_n,count)\n if gcd==1:\n e = count\n break\n count+=1\n \n # finding the mutiplicative inverse of e and phi(n)\n d = ext_Euclid(e,phi_of_n)\n\n #positive values of d\n if d<0:\n d = phi_of_n - d\n return n,e,d","def to_revolute_tree(self):\n T_zero = {\"p0\": SE3.identity()}\n stack = [\"p0\"]\n tree_structure = {\"p0\": []}\n ang_lims_map = {}\n old_to_new_names = {\n \"p0\": \"p0\"\n } # Returned for user of the method (to map old joint names to new ones)\n ub, lb = spherical_angle_bounds_to_revolute(self.ub, self.lb)\n count = 1\n while len(stack) > 0:\n joint = stack.pop(0)\n new_joint = old_to_new_names[joint]\n for child in self.parents[joint]:\n stack += [child]\n new_child = \"p\" + str(count)\n count += 1\n # ub[new_child] = self.ub[child]\n # lb[new_child] = self.lb[child]\n ang_lims_map[child] = new_child\n tree_structure[new_joint] += [new_child]\n new_grand_child = \"p\" + str(count)\n count += 1\n old_to_new_names[child] = new_grand_child\n tree_structure[new_child] = [new_grand_child]\n Ry = SE3(SO3(roty(np.pi / 2)), np.zeros(3))\n T_zero[new_child] = T_zero[new_joint].dot(Ry)\n d = self.d[child]\n Ry_back = SE3(SO3(roty(-np.pi / 2)), np.zeros(3))\n T_zero[new_grand_child] = (\n T_zero[new_child].dot(Ry_back).dot(trans_axis(d, \"z\"))\n )\n tree_structure[new_grand_child] = []\n\n # for key in old_to_new_names:\n # if key in self.ub.keys():\n # ub[old_to_new_names[key]] = self.ub[key]\n # lb[old_to_new_names[key]] = self.lb[key]\n\n # for key in T_zero:\n # if key not in ub.keys() and key is not 'p0':\n # ub[key] = np.pi\n # lb[key] = -np.pi\n\n params = {\"T_zero\": T_zero, \"ub\": ub, \"lb\": lb, \"parents\": tree_structure}\n\n # print(\"normal ub: {:}\".format(self.ub))\n # print(\"ub: {:}\".format(ub))\n # print(\"lb: {:}\".format(lb))\n return RobotRevolute(params), old_to_new_names, ang_lims_map","def allocate(tree):\n\n\twp = tree['misc']['working precision']\t\n\tdim = tree['eqns']['ndim']\n\tnvar = tree['eqns']['qvec']['nvars']\n\tnvarst = len(tree['eqns']['qvec']['stored'])\t\n\thlod = tree['num']['deriv']['hlo']\n\thlof = tree['num']['filtr']['hlo']\n \n\thlo = tree['num']['hlo'] \n\tnx = tree['mpi']['dMpi'].nx \n\tny = tree['mpi']['dMpi'].ny \n\tnz = tree['mpi']['dMpi'].nz \n\n\tvariables = []\n\tif tree['eqns']['qvec']['solved']:\n\t\tfor v in tree['eqns']['qvec']['solved']:\n\t\t\tvariables.append(v[1])\n\n\tif tree['eqns']['qvec']['stored']:\n\t\tfor v in tree['eqns']['qvec']['stored']:\n\t\t\tvariables.append(v[1])\t\n\n\tvariables_face = {}\n\tfor dir in ['i','j','k']:\n\t\tvariables_face[dir] = []\n\t\tfor v in tree['eqns']['qvec']['bcs']['face'][dir]:\n\t\t\t\tvariables_face[dir].append(v[1])\t\t\t\n\n\tvariables_edge = {}\n\tfor dir in ['ij','jk','ik']:\n\t\tvariables_edge[dir] = []\n\t\tfor v in tree['eqns']['qvec']['bcs']['edge'][dir]:\n\t\t\t\tvariables_edge[dir].append(v[1])\t\t\n\t\n\n\tnvar_face = {'i': len(variables_face['i']),\n\t\t\t\t 'j': len(variables_face['j']),\n\t\t\t\t 'k': len(variables_face['k'])}\n\n\tnvar_edge = {'ij': len(variables_edge['ij']),\n\t\t\t\t 'jk': len(variables_edge['jk']),\n\t\t\t\t 'ik': len(variables_edge['ik'])}\t\t\t \n\n\n\n\tcoeff = []\n\tif tree['eqns']['coeff']:\n\t\tfor v in tree['eqns']['coeff']:\n\t\t\tcoeff.append(v[1])\n\n\tsizex = nx + 2*hlo\n\t\n\tif(ny == 1):\n\t\tsizey = 1\n\telse:\t\t\n\t\tsizey = ny + 2*hlo\n\t\n\tif(nz == 1) : \n\t\tsizez = 1\n\telse:\t\n\t\tsizez = nz + 2*hlo\n\t\n\n\tndimpt = sizex*sizey*sizez\n\tndimtot = ndimpt*nvar\n\n\n\t# alloc bcs fields:\n\tndimptbcs = {}\n\tndimptbcs['i'] =sizey*sizez\n\tndimptbcs['j'] =sizex*sizez\n\tndimptbcs['k'] =sizex*sizey\n\tndimptbcs['ij'] =sizez \n\tndimptbcs['jk'] =sizex \n\tndimptbcs['ik'] =sizey \n\n\tsizebcs = {}\n\tsizebcs['i'] =(sizey,sizez)\n\tsizebcs['j'] =(sizex,sizez)\n\tsizebcs['k'] =(sizex,sizey)\n\tsizebcs['ij'] =(sizez) \n\tsizebcs['jk'] =(sizex) \n\tsizebcs['ik'] =(sizey) \n\n\t# faces:\n\tnfacei = max(1,nvar_face['i']*ndimptbcs['i'])\n\tnfacej = max(1,nvar_face['j']*ndimptbcs['j'])\n\tnfacek = max(1,nvar_face['k']*ndimptbcs['k'])\n\t\n\n\t# edges:\n\tnedgeij = max(1,nvar_edge['ij']*ndimptbcs['ij'])\n\tnedgejk = max(1,nvar_edge['jk']*ndimptbcs['jk'])\n\tnedgeik = max(1,nvar_edge['ik']*ndimptbcs['ik'])\t\n\n\t# unpack bc info:\n\ttree = unpack_bcs(tree)\n\t\n\tmybc = tree['bc']['mybc']\n\tnbc = len(mybc)\n\n\t# Integers parameters to be passed to the fortran layer\n\tparam_intDim = 12 + nvar + nvarst + 1 + nbc + 6 + 6\n\tparam_int = np.empty(shape=param_intDim, dtype=np.int32, order='F') \n\tparam_int[0] = hlo\n\tparam_int[1] = nx\n\tparam_int[2] = ny\n\tparam_int[3] = nz\n\tparam_int[4] = nvar\n\tparam_int[5] = nvarst\n\tparam_int[6] = ndimtot\n\tparam_int[7] = 3 # for RK sub steps in Python\n\tparam_int[8] = ndimpt\n\tparam_int[9:9+3] = tree['libs']['cache blocking']\n\tadr = 9+3\n\tparam_int[adr:adr+nvar] = variables[0:0+nvar] # variables location (in q).\n\tadr = adr+nvar\n\tparam_int[adr:adr+nvarst] = variables[nvar:nvar+nvarst] # variables location (in qst).\n\tadr = adr+nvarst\n\tparam_int[adr] = nbc\n\tadr = adr + 1\n\tparam_int[adr:adr+nbc] = mybc\n\tadr = adr + nbc\n\tparam_int[adr:adr+6] = list(nvar_face.values()) + list(nvar_edge.values())\n\tadr = adr + 6\n\tparam_int[adr:adr+6] = [nfacei,nfacej,nfacek,nedgeij,nedgejk,nedgeik]\n\n\tif tree['eqns']['coeff']: \n\t\tncoef = len(tree['eqns']['coeff'])\n\telse:\n\t\tncoef = 0\t\n\n\t# Floating point parameters to be passed to the Fortran layer (3 additional floats for the metrics, uniforme grid + 1 for dt +1 for eps filter)\n\tparam_float = np.zeros(shape=ncoef+5, dtype=wp, order='F') \n\n\tparam_float[0] = cst(1.0)/tree['grid']['geom']['dx']\n\tif(dim>=2) : param_float[1] = cst(1.0)/tree['grid']['geom']['dy']\n\tif(dim == 3) : param_float[2] = cst(1.0)/tree['grid']['geom']['dz']\n\n\tparam_float[3] = tree['num']['tint']['tstep']\n\tparam_float[4] = tree['num']['filtr']['eps']\n\n\tfor i,v in enumerate(tree['eqns']['coeff']):\n\t\tparam_float[i+5] = v[1]\n\n\t# Floating point array (contiguous, aligned, actually NOT aligned yet...)\n\t\n\tnfieldbcs = sum([nfacei,nfacej,nfacek,nedgeij,nedgejk,nedgeik])\n\n\tif nvarst != 0 :\n\t\tdata = np.empty(shape=ndimtot*4 + ndimpt*nvarst + nfieldbcs, dtype=wp,order='F') # 4 --> q,q1,q2 + rhs + nvarstored\n\telse:\t\n\t\tdata = np.empty(shape=ndimtot*4 + 1 + nfieldbcs, dtype=wp,order='F') # 4 --> q,q1,q2, rhs, + 1 (address for qst in fortran layer)\n\n\t# Explicit view of data (only references here, no copy) \n\tviews = {}\n\tnvsolved = len(tree['eqns']['qvec']['solved'])\n\t\n\t# WARNING assume contiguous addresses of stored variables in data_float...\n\tif nvarst != 0:\n\t\taddrstored_beg = ndimtot*4 \n\t\taddrstored_end = addrstored_beg + ndimpt*nvarst\n\telse:\n\t\taddrstored_beg = ndimtot*4\t\n\t\taddrstored_end = addrstored_beg + 1\n\n\taddrbcfields_beg = addrstored_end\t\n\taddrbcfields_edge_beg = addrbcfields_beg + sum([nfacei,nfacej,nfacek])\n\n\tif dim == 3:\n\t\tviews['q'] = data[0:ndimpt*nvsolved].view().reshape(sizex,sizey,sizez,nvsolved, order='F')\n\t\tif nvarst !=0:\n\t\t\tviews['qstored'] = data[addrstored_beg:addrstored_end].view().reshape(sizex,sizey,sizez,nvarst, order='F')\n\t\t\n\t\tfor v in tree['eqns']['qvec']['solved']:\n\t\t\tviews[v[0]] = data[(v[1]-1)*(ndimpt):(v[1])*ndimpt].view().reshape(sizex,sizey,sizez, order='F')\n\t\tfor v in tree['eqns']['qvec']['stored']:\n\t\t\tviews[v[0]] = data[(v[1]-1)*(ndimpt)+addrstored_beg:(v[1])*ndimpt+addrstored_beg].view().reshape(sizex,sizey,sizez, order='F')\t\t\t\n\n\t\t# bc faces:\t\n\t\tshift = addrbcfields_beg\n\t\tfor dir in ['i','j','k']:\n\t\t\tfor v in tree['eqns']['qvec']['bcs']['face'][dir]:\t\t\t\t\n\t\t\t\tviews[v[0]] = data[shift:ndimptbcs[dir]+shift].view().reshape(sizebcs[dir], order='F')\t\t\t\n\t\t\t\tshift = shift + ndimptbcs[dir]\t\n\n\t\t# bc edges:\t\n\t\tshift = addrbcfields_edge_beg\n\t\tfor dir in ['ij','jk','ik']:\n\t\t\tfor v in tree['eqns']['qvec']['bcs']['edge'][dir]:\t\n\t\t\t\tviews[v[0]] = data[shift:ndimptbcs[dir]+shift].view().reshape(sizebcs[dir], order='F')\t\t\t\n\t\t\t\tshift = shift + ndimptbcs[dir]\n\telif dim == 2:\n\t\tviews['q'] = data[0:ndimpt*nvsolved].view().reshape(sizex,sizey,nvsolved, order='F')\n\t\tif nvarst !=0:\n\t\t\tviews['qstored'] = data[addrstored_beg:addrstored_end].view().reshape(sizex,sizey,nvarst, order='F')\n\t\t\n\t\tfor v in tree['eqns']['qvec']['solved']:\n\t\t\tviews[v[0]] = data[(v[1]-1)*(ndimpt):(v[1])*ndimpt].view().reshape(sizex,sizey, order='F')\t\n\t\tfor v in tree['eqns']['qvec']['stored']:\n\t\t\tviews[v[0]] = data[(v[1]-1)*(ndimpt)+addrstored_beg:(v[1])*ndimpt+addrstored_beg].view().reshape(sizex,sizey, order='F')\t\n\n\n\t\t# bc faces:\t\n\t\tshift = addrbcfields_beg\t\n\t\tfor dir in ['i','j']:\n\t\t\tfor v in tree['eqns']['qvec']['bcs']['face'][dir]:\t\t\t\t\t\n\t\t\t\tviews[v[0]] = data[shift:ndimptbcs[dir]+shift].view().reshape(sizebcs[dir], order='F')\t\t\t\n\t\t\t\tshift = shift + ndimptbcs[dir]\t\n\t\t\t\t\t\t\n\telse:\n\t\tviews['q'] = data[0:ndimpt*nvsolved].view().reshape(sizex,nvsolved, order='F')\n\t\tif nvarst !=0:\n\t\t\tviews['qstored'] = data[addrstored_beg:addrstored_end].view().reshape(sizex,nvarst, order='F')\n\t\t\n\t\tfor v in tree['eqns']['qvec']['solved']:\n\t\t\tviews[v[0]] = data[(v[1]-1)*(ndimpt):(v[1])*ndimpt].view().reshape(sizex, order='F')\t\t\n\t\tfor v in tree['eqns']['qvec']['stored']:\n\t\t\tviews[v[0]] = data[(v[1]-1)*(ndimpt)+addrstored_beg:(v[1])*ndimpt+addrstored_beg].view().reshape(sizex, order='F')\t\t\t\t\t\t\t\t\t\n\t\n\ttree['libs']['fort']['integers'] = param_int\n\ttree['libs']['fort']['floats'] = param_float\n\ttree['libs']['fort']['data'] = data\n\ttree['eqns']['qvec']['views'] = views\n\n\tdnamiF.init(param_int,param_float,data)\t\n\t\n\treturn tree","def phi(t, *args):\n # Unpacking data\n mu_1, pi_mu_2, distance, affine_transfo = args\n A, b = get_Ab(t)\n N = len(mu_1)\n assert len(mu_1) == len(pi_mu_2)\n # Computing value of objective function\n r = 0.\n for i in np.arange(N):\n r += distance(affine_transfo(A, b, mu_1[i]), pi_mu_2[i]) ** 2\n return r","def getallprimefactors(n):\n factors = []\n d = 2\n while n > 1:\n while n % d == 0:\n factors.append(d)\n print(n)\n n /= d\n d += 1\n return factors","def compute_eigvals(phi): # pylint: disable=arguments-differ\n if qml.math.get_interface(phi) == \"tensorflow\":\n phi = qml.math.cast_like(phi, 1j)\n\n signs = np.array([1, -1, 0, 0])\n if qml.math.ndim(phi) == 0:\n return qml.math.exp(0.5j * phi * signs)\n\n return qml.math.exp(qml.math.tensordot(0.5j * phi, signs, axes=0))","def factor(n: int) -> List[Tuple[int, int]]:\n if n <= 1:\n raise ValueError\n\n factors = list()\n\n ml = 0\n p = 2\n while n % p == 0:\n n //= p\n ml += 1\n if ml > 0:\n factors.append((p, ml,))\n\n p = 3\n while p ** 2 <= n:\n ml = 0\n while n % p == 0:\n n //= p\n ml += 1\n if ml > 0:\n factors.append((p, ml,))\n p += 2\n\n if n > 2:\n factors.append((n, 1,))\n\n return factors","def HaarGOB(treeG):\n # number of level of the chain (or tree)\n Ntr = len(treeG)\n # reorder chain (optional) \n # reordering each level so that in each level the nodes are in the\n # descent order of degrees\n # compute u_l^c for level J_0 (top level)\n clusterJ0 = treeG[Ntr-1]['clusters']\n N0 = len(clusterJ0)\n # generate indicator function on G^c\n chic = np.identity(N0)\n uc = [None]*N0\n uc[0] = 1/np.sqrt(N0,dtype=np.float64)*np.ones(N0)\n for l in np.arange(1, N0):\n uc[l] = np.sqrt((N0-l)/(N0-l+1))*(chic[l-1,:]-1/(N0-l)*np.sum(chic[l:,:], axis=0))\n# u = copy.deepcopy(uc)\n treeG[Ntr-1]['u'] = uc\n # compute the next level orthonormal basis ulk and stored into u\n for j_tr in np.arange(0, Ntr-1)[::-1]:\n N1 = len(treeG[j_tr]['clusters'])\n u = [None]*N1\n i = N0\n for l in range(N0):\n clusterl = treeG[j_tr+1]['clusters'][l]\n kl = len(clusterl)\n #for k==1\n ucl = uc[l]\n ul1 = np.zeros(N1)\n for j in range(N0):\n idxj = treeG[j_tr+1]['clusters'][j]\n ul1[idxj] = ucl[j]/np.sqrt(len(idxj))\n u[l] = ul1\n if kl>1:\n chil = np.zeros((kl,N1))\n for k in range(kl):\n idxl = treeG[j_tr+1]['clusters'][l]\n chil[k,idxl[k]] = 1;\n \n for k in np.arange(1,kl):\n i = i+1\n ulk = np.sqrt((kl-k)/(kl-k+1))*(chil[k-1,:]-1/(kl-k)*np.sum(chil[k:,:],axis=0))\n u[i-1] = ulk\n treeG[j_tr]['u'] = u\n # update uc and N0\n# uc = copy.deepcopy(u)\n uc = u\n N0 = N1\n return treeG"],"string":"[\n \"def euler_phi(n):\\n\\tif n == 1: return 1\\n\\tif n <= 0: return 0\\n\\t# For each prime factor p with multiplicity n, a factor of (p**(n-1))*(p-1)\\n\\treturn functools.reduce(lambda a,x:a*(x[0]**(x[1]-1))*(x[0]-1),factor(n),1)\",\n \"def euler_phi(n):\\r\\n\\t# For each prime factor p with multiplicity n, a factor of (p**(n-1))*(p-1)\\r\\n\\treturn reduce(lambda a,x:a*(x[0]**(x[1]-1))*(x[0]-1),factor(n),1)\",\n \"def euler_phi(n):\\n\\n if n == 1:\\n return 1\\n\\n phi = n\\n for p, a in factorize(n):\\n phi -= phi // p\\n\\n return phi\",\n \"def eulerphi(n):\\r\\n\\treturn euler_phi(n)\",\n \"def eulerphi(n):\\n\\treturn euler_phi(n)\",\n \"def phi(n, cache=None):\\n if n == 1:\\n return 1\\n elif n < 1:\\n raise AssertionError('Please enter an int > 0')\\n else:\\n return int(n * prod([1 - 1/p for p in set(factors(n, cache))]))\",\n \"def phi(n):\\n sieve = [i if i & 1 else i // 2 for i in range(n + 1)]\\n for i in range(3, n + 1, 2):\\n if sieve[i] == i:\\n for j in range(i, n + 1, i):\\n sieve[j] = (sieve[j] // i) * (i - 1)\\n\\n return sieve\",\n \"def felsen( node, column, tm ):\\n nucs = tm.alphabet\\n # Is it a leaf node?\\n if node.edges is None:\\n symbol = column[ node.label ]\\n if symbol == '*':\\n ## Uniform distribution\\n # return [ 1 / len( nucs ) ] # len( nucs ) \\n ## Equilibrium (\\\\pi) distribution\\n #return tm.background\\n # Eliminate entirely\\n return None\\n return [ int( symbol == nuc ) for nuc in nucs ] \\n else:\\n # Traverse children and determine likelihoods\\n ## l_children = [ felsen( edge.tip, column, tm ) for edge in node.edges ]\\n l_children = []\\n for edge in node.edges:\\n t = felsen( edge.tip, column, tm )\\n if t is not None:\\n l_children.append( ( t, edge ) )\\n if not l_children: return None\\n l_self = []\\n # Determine liklihood for each possible 'ancestral' sequence\\n for i_y in range( len( nucs ) ):\\n y = nucs[ i_y ]\\n p_y = 1\\n # For each child, sum over all paths\\n for cl, edge in l_children:\\n if edge.length not in matrix_by_time_cache:\\n matrix_by_time_cache[ edge.length ] = matrix_for_time( tm.matrix, edge.length )\\n psub = matrix_by_time_cache[ edge.length ]\\n p_y_c = 0\\n for i_a in range( len( nucs ) ):\\n p_y_c += psub[i_y,i_a] * cl[ i_a ]\\n # Product over all children\\n p_y *= p_y_c\\n l_self.append( p_y )\\n return l_self\",\n \"def phi(n: int) -> int:\\n result = 1\\n for i in range(2, n):\\n if gcd(i, n) == 1:\\n result += 1\\n return result\",\n \"def _make_phi(self, F):\\n scaled_bins_left = tf.concat(\\n [self.bin_edges / self.sigma,\\n np.array([np.inf])], 0)\\n scaled_bins_right = tf.concat(\\n [np.array([-np.inf]), self.bin_edges / self.sigma], 0)\\n return inv_probit(scaled_bins_left - tf.reshape(F, (-1, 1)) / self.sigma) \\\\\\n - inv_probit(scaled_bins_right - tf.reshape(F, (-1, 1)) / self.sigma)\",\n \"def phi_operator(self, num_levels=-1):\\n phi_mat = sparse.spdiags([self.phis], [0], len(self.phis), len(self.phis))\\n return self.reduced_operator(phi_mat, num_levels)\",\n \"def phi_operator(self, num_levels=-1):\\n phi_mat = sparse.spdiags([self.phis], [0], len(self.phis), len(self.phis))\\n return self.reduced_operator(phi_mat, num_levels)\",\n \"def phi(x,n):\\n return 1/(np.sqrt(2**n*np.math.factorial(n))) * (1/np.pi)**0.25 * \\\\\\n np.exp(-x**2/2)*sp.special.eval_hermite(n,x)\",\n \"def get_factors():\",\n \"def factors(self):\\n X = [Var(i,2) for i in range(self.nvar)]\\n factors = [Factor([],np.exp(self.c))] \\n # TODO: exclude if zero? or exclude if inf/-inf, or if in \\\"assigned\\\", or?\\n factors = factors + [Factor([X[i]],[-th,th]).exp() for i,th in enumerate(self.h) if self.dims[i]>1]\\n L = coo(self.L)\\n factors = factors + [Factor([X[i],X[j]],[[th,-th],[-th,th]]).exp() for i,j,th in zip(L.row,L.col,L.data) if i<j]\\n return factors\\n # TODO: should we exponentiate if isLog not True? \",\n \"def phi(U, n):\\n phi_params = n['phi']\\n return phi_params['r_max'] / (1 + np.exp(-phi_params['beta'] * (U - phi_params['alpha'])))\",\n \"def _gen_terms(self, tree):\\n for leaf in self._leaves(tree):\\n terms = [self._normalize(w) for w, _ in leaf if self._filter(w)]\\n # Phrase only\\n if len(terms) > 1:\\n yield terms\",\n \"def phiprime_phi(phi):\\n f = 0.0022927\\n phiprime = np.arctan2(np.tan(phi*np.pi/180.),(1.-f)**2.)*180./np.pi\\n return phiprime\",\n \"def compute_tree(self, tree):\\n g_list_val, g_list_h = self._build_graph(tree) # return theano variable of each node\\n list_val = self._traversal_tree(tree) #\\n f = theano.function(g_list_val, g_list_h, allow_input_downcast=True)\\n result = f(*list_val)\\n return result\",\n \"def f(p, phi, phib, df):\\n\\treturn -log(p) - df + (p-1)*phi + \\\\\\n\\t ( phi*(1-p) + phib + \\\\\\n\\t 5./4*alpha*(phi*p)**(9./4)-(9./4)*alpha*(p*phi)**(5./4) - \\\\\\n\\t (1./2)(1-p*phi)**2 - (phib/Nb)-5./4*alpha*(phi+phib)**(9./4) + \\\\\\n\\t (9./4)*alpha*(phi+phib)**(5.4) + \\\\\\n\\t 1./2*(1-phi-phib)**2 ) * Ns\",\n \"def phi34_cc( s, m ):\\n\\n l_min = max(0, (m + 2*s + 1)/2)\\n l_max = ( 3*s + m ) / 2 \\n S = 0\\n for l in range(l_min, l_max+1):\\n n_1 = 2*l - 2*s - m\\n n_2_t2 = -2*l + 3*s + m\\n if n_2_t2 % 2 != 0:\\n continue\\n n_2 = n_2_t2 / 2\\n\\n denom = factorial( n_1 ) * factorial( n_2 ) * \\\\\\n factorial( m ) * factorial(3)**n_1 * \\\\\\n factorial(4) ** n_2\\n\\n S += Fraction(double_factorial(2*l-1), denom)\\n\\n return S\",\n \"def phi(nbins, p, t, x):\\n\\n phase = calc_phase(p, t)\\n x_means, phase_bs, Ns, sj2s, xb, pb = phase_bins(nbins, phase, x)\\n total_binned_variance_s2 = s2(Ns, sj2s, nbins)\\n total_variance = sj2(x, np.mean(x), len(x))\\n\\n return total_binned_variance_s2/total_variance\",\n \"def phi_k_cc( s, m, k ):\\n \\n l_t2 = m + s * k\\n\\n if l_t2 % 2 != 0:\\n return 0\\n \\n denom = factorial(s) * factorial(m) * factorial( k ) ** s\\n nom = double_factorial( l_t2 - 1 )\\n\\n return Fraction( nom, denom )\",\n \"def phi(N60):\\n return 27.1 + 0.3*N60 - 0.00054* N60**2\",\n \"def probit_phi(x):\\n mu = 0;sd = 1;\\n return 0.5 * (1 + tsr.erf((x - mu) / (sd * tsr.sqrt(2))))\",\n \"def _compute_factors(roots, multiplicity, include_powers=False):\\n current = cupy.array([1])\\n suffixes = [current]\\n for pole, mult in zip(roots[-1:0:-1], multiplicity[-1:0:-1]):\\n monomial = cupy.r_[1, -pole]\\n for _ in range(int(mult)):\\n current = cupy.polymul(current, monomial)\\n suffixes.append(current)\\n suffixes = suffixes[::-1]\\n\\n factors = []\\n current = cupy.array([1])\\n for pole, mult, suffix in zip(roots, multiplicity, suffixes):\\n monomial = cupy.r_[1, -pole]\\n block = []\\n for i in range(int(mult)):\\n if i == 0 or include_powers:\\n block.append(cupy.polymul(current, suffix))\\n current = cupy.polymul(current, monomial)\\n factors.extend(reversed(block))\\n\\n return factors, current\",\n \"def phi_function(alpha, pk, xk):\\n x = xk + alpha * pk\\n return rosenbock2Nd(x, 0)\",\n \"def planck_f(nu, T):\\n return ((2*h*nu**3)/(c**2))*(1./(np.exp((h*nu)/(k*T))-1))\",\n \"def phi_prime(U, n):\\n phi_params = n['phi']\\n num = np.exp((U + phi_params[\\\"alpha\\\"]) * phi_params[\\\"beta\\\"]) * \\\\\\n phi_params[\\\"r_max\\\"] * phi_params[\\\"beta\\\"]\\n denom = (np.exp(U * phi_params[\\\"beta\\\"]) + np.exp(phi_params[\\\"alpha\\\"] * phi_params[\\\"beta\\\"]))**2\\n return num / denom\",\n \"def generate_phi(self):\\n self.phi = np.empty((100, self.K))\\n for i, point in enumerate(self.X):\\n for j, center in enumerate(self.centers):\\n self.phi[i][j] = np.exp(-self.gamma * distance.euclidean(point, center) ** 2)\\n self.phi = np.concatenate((self.phi, np.ones((100, 1))), axis=1)\",\n \"def phi_study(polarity, year):\\n # Fetch the ntuple, created using `phi_ntuple` in `slim_ntuple.py`\\n n = Lc2pXX.Lc2pphi(\\\"DecayTree\\\", polarity, year, mc=False)\\n n.add(\\\"{0}/selected-pphi-{1}.root\\\".format(\\n config.output_dir, str(n).replace(config.pphi, config.pKK)\\n ))\\n\\n # Unbinned fit\\n w = ROOT.RooWorkspace(\\\"{0}-workspace\\\".format(n))\\n fitting.lambdac_mass.fit(\\n n, w, n.shapes_postselection, bins=0\\n )\\n c = plotting.plot_fit(\\n w, [\\n (\\\"total_pdf\\\", \\\"Fit\\\"),\\n (\\\"signal_pdf\\\", \\\"Signal\\\"),\\n (\\\"background_pdf\\\", \\\"Background\\\")\\n ],\\n \\\"Lambdac_M\\\",\\n bins=140\\n )\\n c.SetName(w.GetName().replace(\\\"workspace\\\", \\\"canvas\\\"))\\n utilities.save_to_file(\\\"{0}/fits/selected-{1}.root\\\".format(\\n config.output_dir, n\\n ), [w, c])\\n\\n # Print the stats\\n pphi_yields = fitting.lambdac_mass.yields(w)\\n pphi_significance = utilities.significance(\\n pphi_yields[0], pphi_yields[1]\\n )\\n print \\\"Yields:\\\", pphi_yields\\n print \\\"Significance:\\\", pphi_significance\\n\\n # What follows in very similar in style to `branching_fractions.py`,\\n # so check that out for a more in-depth explanation\\n year = 2011\\n m = config.pphi\\n # We can proceed two ways - calculate the reco eff. wrt acc., then\\n # stripping wrt reco, or just stripping wrt acc.\\n pphi_effs = {\\n \\\"Acceptance\\\": acceptance.efficiency(m, polarity, year),\\n \\\"Reconstruction\\\": reconstruction.efficiency(m, polarity, year),\\n \\\"Tracking\\\": tracking.efficiency_smeared(m, polarity, year),\\n \\\"Stripping\\\": stripping.efficiency(m, polarity, year),\\n # \\\"Stripping\\\": stripping.efficiency_no_reco(m, polarity, year),\\n \\\"Trigger\\\": trigger.efficiency_post_stripping(m, polarity, year),\\n \\\"Offline\\\": offline.efficiency_mc(m, polarity, year),\\n \\\"PID\\\": pid.efficiency(m, polarity, year)\\n }\\n\\n # What follows here is very similar in style to `branching_fractions.py`\\n # so check that out for a more in-depth explanation\\n effs = {}\\n raw_yields = {}\\n prod_yields = {}\\n modes = (config.pKpi, config.pphi)\\n\\n effs[config.pphi] = pphi_effs\\n effs[config.pKpi] = branching_fractions.efficiencies(\\n config.pKpi, polarity, year\\n )\\n # Add the \\\"Total\\\" key\\n for mode in modes:\\n mode_effs = effs[mode]\\n total_eff = 1.\\n for eff in mode_effs:\\n total_eff *= mode_effs[eff]\\n effs[mode][\\\"Total\\\"] = total_eff\\n\\n raw_yields[config.pphi] = pphi_yields[0]\\n raw_yields[config.pKpi] = branching_fractions.yields(\\n config.pKpi, polarity, year\\n )[0]\\n prod_yields[config.pphi] = raw_yields[config.pphi]/effs[config.pphi][\\\"Total\\\"]\\n prod_yields[config.pKpi] = raw_yields[config.pKpi]/effs[config.pKpi][\\\"Total\\\"]\\n\\n # PDG July 2012 measurement\\n pKpi_abs = ufloat(5e-2, 1.3e-2)\\n\\n pphi_rel = prod_yields[config.pphi]/prod_yields[config.pKpi]\\n pphi_abs = pphi_rel*pKpi_abs\\n\\n print\\n print \\\"== Measurements for {0} {1} == \\\".format(polarity, year)\\n print\\n for mode in modes:\\n mode_effs = effs[mode]\\n print \\\"-- Efficiencies for {0} --\\\".format(mode)\\n for eff in mode_effs:\\n print \\\"{0}: {1}\\\".format(eff, mode_effs[eff])\\n print\\n print \\\"-- Yields for {0} --\\\".format(mode)\\n print \\\"Raw:\\\", raw_yields[mode]\\n print \\\"Production:\\\", prod_yields[mode]\\n print\\n print \\\"-- Relative Branching Fractions (10^-2) --\\\"\\n print \\\"pphi/pKpi: \\\", 1e2*pphi_rel\\n print\\n print \\\"-- Absolute Branching Fractions (%) --\\\"\\n print \\\"pKpi (PDG):\\\", 1e2*pKpi_abs\\n print \\\"pphi: \\\", 1e2*pphi_abs\\n print\",\n \"def _buildtree(self):\\n self.pricetree = np.zeros((self.steps+1,self.steps+1))\\n self.pricetree[0][0] = self.p\\n for j in range(self.steps):\\n for i in range(j+1):\\n self.pricetree[j+1][i+1] = self.pricetree[j][i]*self.down\\n self.pricetree[j+1][0] = self.pricetree[j][0]*self.up\",\n \"def TR_algo2(p, vd=2):\\n # h will contain the Hilbert index\\n h = 0\\n # ve and vd contain the entry point and dimension of the current subcube\\n # we choose here a main traversal direction N-2 (i.e. z for a cube) to match\\n # the illustrations\\n ve = 0\\n for i in range(M-1, -1, -1):\\n # the cell label is constructed in two steps\\n # 1. extract the relevant bits from p\\n l = [bit_component(px, i) for px in p]\\n # 2. construct a integer whose bits are given by l\\n l = sum( [lx*2**j for j, lx in enumerate(l)] )\\n # transform l into the current subcube\\n l = T(ve, vd, l)\\n # obtain the gray code ordering from the label l\\n w = inverse_gc(l)\\n # compose (see [TR] lemma 2.13) the transform of ve and vd\\n # with the data of the subcube\\n ve = ve ^ (rotate_left(e(w), vd+1))\\n vd = (vd + d(w) + 1) % N\\n # move the index to more significant bits and add current value\\n h = (h << N) | w\\n return h\",\n \"def pdf(tree1, tree2, alpha, beta, new):\\n\\n # TODO: Refactor so it can be used in M-H.\\n prob = 0.0\\n tree2_tree1_subtree_nodes = get_subtree_nodes(tree1, tree2, new)\\n for tree2_subtree_nodes in tree2_tree1_subtree_nodes:\\n # tree1_subtree = tree1.subgraph([c_t1 for c_t2, c_t1 in tree2_subtree_nodes.iteritems() if c_t1 is not None])\\n tree1_subtree = tree1.subgraph([c_t1 for c_t2, c_t1 in tree2_subtree_nodes.items() if c_t1 is not None])\\n tree1_subtree_prob = ss.pdf(tree1_subtree, tree1, alpha, beta)\\n\\n (P, N) = subtree_cond_pdf(tree1, tree2, tree2_subtree_nodes, new)\\n christtree_prob = np.prod([P[c] * N[c] for c in P])\\n prob += tree1_subtree_prob * christtree_prob\\n return prob\",\n \"def GilbertWells_calc(TP, FP, FN, TN):\\n try:\\n n = TP + FP + FN + TN\\n part1 = (TP + FP) * (TP + FN) * (TN + FP) * (TN + FN)\\n part2 = math.factorial(TP + FP) * math.factorial(TP + FN) * \\\\\\n math.factorial(TN + FP) * math.factorial(TN + FN)\\n part3 = math.factorial(n) * math.factorial(TP) * \\\\\\n math.factorial(FP) * math.factorial(FN) * math.factorial(TN)\\n return math.log((n ** 3) / (2 * math.pi * part1)) + \\\\\\n 2 * math.log(part3 / part2)\\n except Exception:\\n return \\\"None\\\"\",\n \"def unique_leaf_of(phi):\\n vs = leaves_of(phi)\\n assert len(vs) == 1\\n assert isinstance(vs[0], amnet.Variable)\\n return vs[0]\",\n \"def phi0(self, x):\\n return (1-(1-x)**(4/3))**(3/2)/(2*np.sqrt(2))\",\n \"def _build_c_phi_matrices(self, t: tf.Tensor) -> tf.Tensor:\\n c_phi_matrices = self.kernel.compute_c_phi(t, t)\\\\\\n + tf.expand_dims(tf.eye(self.n_points_int, dtype=tf.float64), 0)\\\\\\n * self.likelihood_variances\\n return c_phi_matrices\",\n \"def walk(tree):\\n results = []\\n for parent, children in tree.iteritems():\\n perms = permute(children)\\n temp = deepcopy(perms)\\n # Add the parent to the perms as well\\n for perm in temp:\\n perms.append(add_to_head_of_perm(parent, perm))\\n for perm in perms:\\n r = pairwise(perm)\\n results.append(r)\\n return results\",\n \"def root_factors(f, *gens, filter=None, **args):\\n args = dict(args)\\n\\n F = Poly(f, *gens, **args)\\n\\n if not F.is_Poly:\\n return [f]\\n\\n if F.is_multivariate:\\n raise ValueError('multivariate polynomials are not supported')\\n\\n x = F.gens[0]\\n\\n zeros = roots(F, filter=filter)\\n\\n if not zeros:\\n factors = [F]\\n else:\\n factors, N = [], 0\\n\\n for r, n in ordered(zeros.items()):\\n factors, N = factors + [Poly(x - r, x)]*n, N + n\\n\\n if N < F.degree():\\n G = reduce(lambda p, q: p*q, factors)\\n factors.append(F.quo(G))\\n\\n if not isinstance(f, Poly):\\n factors = [ f.as_expr() for f in factors ]\\n\\n return factors\",\n \"def factors(n: int) -> List[int]:\\n k = 1\\n while k**2 < n:\\n if n % k == 0:\\n yield k\\n k += 1\\n\\n k = int(n**(1/2))\\n while k > 0:\\n if n % k == 0:\\n yield n // k\\n k -= 1\",\n \"def phi_list ( self ) :\\n return self.__phi_list\",\n \"def phi_t(self):\\n\\t\\tdim = self.dim\\n\\t\\ttim_all = self.tim_all \\n\\t\\tphi_all = np.zeros((tim_all+1,dim,1),dtype = complex)\\n\\t\\tphi_all[0,:,:] = self.phi_i[:]\\n\\t\\tu_all = self.u_t()\\n\\n\\t\\tfor tim in xrange(tim_all):\\n\\t\\t\\tphi_all[tim+1,:,:] = np.dot(u_all[tim+1,:,:], phi_all[0,:,:])\\n\\t\\t\\n\\t\\treturn phi_all\",\n \"def caculate_prob(self):\\n t_H = self.tree.depth()\\n t_h = 1\\n while(t_h <= t_H):\\n t_hnodes = self.get_h(t_h)\\n t_sum = 0\\n t_hpro = []\\n t_cpro = []\\n for t_n in t_hnodes:\\n t_sum = self.tree.get_node(t_n).data[0] + t_sum\\n t_node = self.tree.get_node(t_n)\\n if t_node.is_leaf():\\n t_node.data.append(0)\\n continue\\n t_childrens = self.tree.children(t_n)\\n t_shang = 0\\n for child in t_childrens:\\n t_shang = t_shang + (child.data[0]/t_node.data[0])*np.log(child.data[0]/t_node.data[0])\\n t_node.data.append(-t_shang)\\n for t_n in t_hnodes:\\n t_node = self.tree.get_node(t_n)\\n t_parentnode = self.tree.parent(t_n)\\n if t_h > 1:\\n t_node.data.append((t_node.data[0] / t_sum) * (t_node.data[0]/t_parentnode.data[0]))\\n t_hpro.append((t_node.data[0]/t_sum) * (t_node.data[0]/t_parentnode.data[0]))\\n else:\\n t_node.data.append((t_node.data[0] / t_sum))\\n t_hpro.append((t_node.data[0] / t_sum))\\n\\n t_cpro.append(t_node.data[1])\\n t_ndata = np.array(t_hpro)\\n mean = np.mean(t_ndata)\\n std = np.std(t_ndata,ddof=1)\\n t_sdata = np.array(t_cpro)\\n mean_s = np.mean(t_sdata)\\n std_s = np.std(t_sdata,ddof=1)\\n for t_n in t_hnodes:\\n t_node = self.tree.get_node(t_n)\\n if(std != 0):\\n t_node.data[2] = (t_node.data[2] - mean)/std\\n else:\\n t_node.data[2] = (t_node.data[2] - mean)\\n if(mean_s == 0 and std_s ==0):\\n t_node.data[1] = -100.0\\n continue\\n t_node.data[1] = (t_node.data[1] - mean_s)/std_s\\n t_h = t_h + 1\",\n \"def cal_phi(self):\\n\\n if not self.check_def(['E','px','py','pz']):\\n sys.exit('Particle error: Quadri impulsion not define (error for phi routine)')\\n\\n if(self.px>0):\\n self.phi=math.atan(self.py/self.px)\\n elif(self.px<0):\\n self.phi=math.atan(self.py/self.px)+math.pi\\n elif(self.py>0): #remind that p(1)=0\\n self.phi=math.pi/2.0\\n elif(self.py<0): # remind that p(1)=0\\n self.phi=-math.pi/2.0\\n else:\\n print \\\"Warning self.phi not properly defined put value to 0\\\"\\n self.phi=0\\n \\n if(self.phi<0):\\n self.phi=self.phi+2*math.pi\\n\\n return self.phi\",\n \"def _phi(self, x, d, p):\\n ks = np.arange(self.p + 1)\\n ks = ks[np.where(2 * (self.p - ks) - d >= 0)][:, np.newaxis]\\n return np.sum(\\n binom(self.p, ks)\\n * (-1) ** ks\\n * x[np.newaxis, :] ** (2 * (self.p - ks) - d)\\n * perm(2 * (self.p - ks), d),\\n axis=0,\\n )\",\n \"def do_factor(m=4, q=11):\\n from sage.rings.finite_rings.integer_mod_ring import IntegerModRing\\n from sage.matrix.constructor import identity_matrix, block_matrix\\n from sage.matrix.matrix_space import MatrixSpace\\n from sage.rings.integer_ring import IntegerRing\\n from sage.modules.free_module_integer import IntegerLattice\\n \\n \\n m=n+1\\n ZZ = IntegerRing()\\n ZZ_q = IntegerModRing(q)\\n \\n \\n \\n from sage.arith.all import euler_phi\\n from sage.misc.functional import cyclotomic_polynomial\\n \\n for a in range(\\n R = ZZ_q['x']#.quotient(cyclotomic_polynomial(k, 'x'), 'x')\\n f=cyclotomic_polynomial(m,'x')\\n return f.factor()\",\n \"def phi_topic(self):\\n f1 = self.nkw+self.beta\\n f2 = np.sum(self.nkw, axis=1, keepdims=True)+self.VT*self.beta\\n return f1/f2\",\n \"def compute_eigvals(phi): # pylint: disable=arguments-differ\\n if qml.math.get_interface(phi) == \\\"tensorflow\\\":\\n phase = qml.math.exp(-0.5j * qml.math.cast_like(phi, 1j))\\n return stack_last([phase, qml.math.conj(phase), qml.math.conj(phase), phase])\\n\\n prefactors = qml.math.array([-0.5j, 0.5j, 0.5j, -0.5j], like=phi)\\n if qml.math.ndim(phi) == 0:\\n product = phi * prefactors\\n else:\\n product = qml.math.outer(phi, prefactors)\\n return qml.math.exp(product)\",\n \"def makeTTree():\\n \\n tree = TTree(\\\"tree\\\",\\\"tree\\\")\\n px = array('d',[0])\\n py = array('d',[0])\\n pz = array('d',[0])\\n pi = array('i',[0])\\n tree.Branch(\\\"x\\\",px,\\\"x/D\\\")\\n tree.Branch(\\\"y\\\",py,\\\"y/D\\\")\\n tree.Branch(\\\"z\\\",pz,\\\"y/D\\\")\\n tree.Branch(\\\"i\\\",pi,\\\"y/I\\\")\\n for i in range(500):\\n px[0] = gRandom.Gaus(0,3)\\n py[0] = gRandom.Uniform()*30 - 15\\n pz[0] = gRandom.Gaus(0,5)\\n pi[0] = i%3\\n tree.Fill()\\n return tree\",\n \"def glove_leaves_phi(t1, t2, np_func=np.sum): \\n prem_vecs = _get_tree_vecs(t1, glove_lookup, np_func) \\n hyp_vecs = _get_tree_vecs(t2, glove_lookup, np_func) \\n return np.concatenate((prem_vecs, hyp_vecs))\",\n \"def f(p, phi, phib, df):\\n\\treturn - p + exp( - df + Ns*(log((1 - p*phi)/(1 - phi - phib)) + \\\\\\n\\t\\t(p - 1)*phi - phib + (9./4)*alpha*((phi + phib)**(5./4) - (p*phi)**(5./4))))\",\n \"def phi(x):\\n # constants\\n a1 = 0.254829592\\n a2 = -0.284496736\\n a3 = 1.421413741\\n a4 = -1.453152027\\n a5 = 1.061405429\\n p = 0.3275911\\n\\n # Save the sign of x\\n sign = 1\\n if x < 0:\\n sign = -1\\n x = abs(x) / math.sqrt(2.0)\\n\\n # A&S formula 7.1.26\\n t = 1.0 / (1.0 + p * x)\\n y = 1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * math.exp(-x * x)\\n\\n return 0.5 * (1.0 + sign * y)\",\n \"def _get_phi(self):\\n phi = self.phi(self._data[SoilProperty.N60])\\n ### Ok let's remove for clay\\n if self.is_clayey():\\n phi=0 #very small value for plasix:::@TODO 0.01\\n return phi\",\n \"def __init__(self):\\n self.phi = sqrt(5.0)/2.0 - 0.5\",\n \"def set_phi(self):\\n self.phi = float(dihedral(self.O5.getXYZ(), self.C1.getXYZ(), self.GO.getXYZ(), self.CX.getXYZ()))\",\n \"def actual(phi, t):\\r\\n\\r\\n # spatial nodes\\r\\n ngrid = 10\\r\\n end = -1\\r\\n M, MT = get_mmt()\\r\\n D = 5000*np.ones(ngrid-1)\\r\\n A = MT@np.diag(D)@M\\r\\n A = A[1:ngrid-1]\\r\\n\\r\\n # differential equations\\r\\n dphi = np.zeros(ngrid)\\r\\n # first node\\r\\n dphi[0] = 0\\r\\n\\r\\n # interior nodes\\r\\n dphi[1:end] = -A@phi # value at interior nodes\\r\\n\\r\\n # terminal node\\r\\n dphi[end] = D[end]*2*(phi[end-1] - phi[end])\\r\\n\\r\\n return dphi\",\n \"def expfam2Factors(theta):\\r\\n facList = []\\r\\n m = theta.shape[0]\\r\\n for s,th in enumerate(scipy.diag(theta)):\\r\\n facList.append(factor(var=[s],card=[2],val = [1,scipy.exp(th)]))\\r\\n for s,t in combinations(range(m),2):\\r\\n if not scipy.isclose(theta[s,t],0):\\r\\n facList.append(factor(var=[s,t],card=[2,2],val=[1,1,1,scipy.exp(theta[s,t])]))\\r\\n F = FactorList(facList)\\r\\n F.make_connected()\\r\\n return F\",\n \"def f(self,node):\\r\\n return (self.a*self.nodeDegree(node))/(1+self.b*self.nodeDegree(node))\",\n \"def _factors(n):\\n gen = ([i, n // i] for i in range(1, int(n ** 0.5) + 1) if n % i == 0)\\n return set(sum(gen, []))\",\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 visit_factor(self, node, children):\\n if self.debug:\\n print(\\\"Factor {}\\\".format(children))\\n if len(children) == 1:\\n return children[0]\\n sign = -1 if children[0] == '-' else 1\\n return sign * children[-1]\",\n \"def phi(self, x:int):\\n dimension = self.M + 1 # We are including a constant here.\\n p = [0] * dimension\\n for i in range(dimension):\\n p[i] += pow(x, i)\\n p = np.array(p)\\n p = p.reshape(p.shape + (1,))\\n return p\",\n \"def __phi(x):\\n return max([1 - abs(x), 0])\",\n \"def phases_from_superoperator(U):\\n if U.type=='oper':\\n phi_00 = np.rad2deg(np.angle(U[0, 0])) # expected to equal 0 because of our\\n # choice for the energy, not because of rotating frame. But not guaranteed including the coupling\\n phi_01 = np.rad2deg(np.angle(U[1, 1]))\\n phi_10 = np.rad2deg(np.angle(U[3, 3]))\\n phi_11 = np.rad2deg(np.angle(U[4, 4]))\\n phi_02 = np.rad2deg(np.angle(U[2, 2])) # used only for avgatefid_superoperator_phasecorrected\\n phi_20 = np.rad2deg(np.angle(U[6, 6])) # used only for avgatefid_superoperator_phasecorrected\\n\\n elif U.type=='super':\\n phi_00 = 0 # we set it to 0 arbitrarily but it is indeed not knowable\\n phi_01 = np.rad2deg(np.angle(U[1, 1])) # actually phi_01-phi_00 etc\\n phi_10 = np.rad2deg(np.angle(U[3, 3]))\\n phi_11 = np.rad2deg(np.angle(U[4, 4]))\\n phi_02 = np.rad2deg(np.angle(U[2, 2]))\\n phi_20 = np.rad2deg(np.angle(U[6, 6]))\\n\\n phi_cond = (phi_11 - phi_01 - phi_10 + phi_00) % 360 # still the right formula independently from phi_00\\n\\n return phi_00, phi_01, phi_10, phi_11, phi_02, phi_20, phi_cond\",\n \"def tree_query(self, pta_root):\\n self.sul.pre()\\n curr_node = pta_root\\n\\n inputs = []\\n outputs = []\\n\\n while True:\\n\\n if curr_node.children:\\n frequency_sum = sum(curr_node.input_frequencies.values())\\n if frequency_sum == 0:\\n # uniform sampling in case we have no information\\n inp = choice(list(curr_node.children.keys()))\\n else:\\n # use float random rather than integers to be able to work with non-integer frequency information\\n selection_value = random() * frequency_sum\\n inp = None\\n for i in curr_node.input_frequencies.keys():\\n inp = i\\n selection_value -= curr_node.input_frequencies[i]\\n if selection_value <= 0:\\n break\\n # curr_node.input_frequencies[inp] -= 1\\n\\n inputs.append(inp)\\n out = self.sul.step(inp)\\n new_node = curr_node.get_child(inp, out)\\n\\n if new_node:\\n outputs.append(out)\\n curr_node = new_node\\n else:\\n self.sul.post()\\n return\\n else:\\n curr_node = pta_root\\n for i, o in zip(inputs, outputs):\\n self.curr_node.input_frequencies[i] -= 1\\n curr_node = curr_node.get_child(i, o)\\n self.sul.post()\\n return\",\n \"def divisors(factors):\\n ps = sorted(set(factors))\\n omega = len(ps)\\n\\n def rec_gen(n=0):\\n if n == omega:\\n yield 1\\n else:\\n pows = [1]\\n for j in xrange(factors.count(ps[n])):\\n pows += [pows[-1] * ps[n]]\\n for q in rec_gen(n + 1):\\n for p in pows:\\n yield p * q\\n\\n for p in rec_gen():\\n yield p\",\n \"def phi(self):\\n if self._phi is None:\\n b = self.stem + self.counter + self.radius + (\\n self.stem - 2 * self.radius) - self.overlap\\n c = sqrt(self.a ** 2 + b ** 2)\\n _phi = atan2(self.a, b)\\n b = sqrt(c ** 2 - self.radius ** 2)\\n _phi += atan2(self.radius, b)\\n self._phi = _phi\\n return self._phi\",\n \"def croft():\\n # Copied from:\\n # https://code.google.com/p/pyprimes/source/browse/src/pyprimes.py\\n # Implementation is based on erat3 from here:\\n # http://stackoverflow.com/q/2211990\\n # and this website:\\n # http://www.primesdemystified.com/\\n # Memory usage increases roughly linearly with the number of primes seen.\\n # dict ``roots`` stores an entry x:p for every prime p.\\n for p in (2, 3, 5):\\n yield p\\n roots = {9: 3, 25: 5} # Map d**2 -> d.\\n primeroots = frozenset((1, 7, 11, 13, 17, 19, 23, 29))\\n selectors = (1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0)\\n for q in compress(\\n # Iterate over prime candidates 7, 9, 11, 13, ...\\n islice(count(7), 0, None, 2),\\n # Mask out those that can't possibly be prime.\\n cycle(selectors)\\n ):\\n # Using dict membership testing instead of pop gives a\\n # 5-10% speedup over the first three million primes.\\n if q in roots:\\n p = roots[q]\\n del roots[q]\\n x = q + 2*p\\n while x in roots or (x % 30) not in primeroots:\\n x += 2*p\\n roots[x] = p\\n else:\\n roots[q*q] = q\\n yield q\",\n \"def phi(self):\\n return (np.sum(self.diameters**self.ndim)*np.pi / (2*self.ndim))\",\n \"def T(params, phi):\\n\\t# handle the base frame, eqn 3.9, p36\\n\\tt = np.array([\\n\\t\\t[cos(phi), -sin(phi), 0.0, 0.0],\\n\\t\\t[sin(phi), cos(phi), 0.0, 0.0],\\n\\t\\t[0.0, 0.0, 1.0, 0.0],\\n\\t\\t[0.0, 0.0, 0.0, 1.0]\\n\\t])\\n\\tfor i, p in enumerate(params):\\n\\t\\tt = t.dot(rot(*p))\\n\\treturn t\",\n \"def get_factors(self, triples):\\n pass\",\n \"def _vlerchphi(self, z: np.ndarray, a: int) -> np.ndarray:\\n return np.array([self._lerchphi(z_, a) for z_ in z])\",\n \"def phi(self):\\n return self._phi\",\n \"def getPhNodes(self,pH, pos=None, rootA=False):\\n\\t\\tif pos==None: \\n\\t\\t\\tpos=self.pos\\n\\t\\t\\t[r,th]=self.posCyl\\n\\t\\t\\tif len(self.plantHeads)==1:\\n\\t\\t\\t\\torig=self.getPHCoord(pos)[0]\\n\\t\\t\\telse:\\n\\t\\t\\t\\tpositions=self.getPHCoord(pos)\\n\\t\\t\\t\\torig=positions[pH.number]\\n\\t\\telse:\\n\\t\\t\\t#first, determine the direction of the CRANE\\n\\t\\t\\torig=pos\\n\\t\\t\\tposCyl=self.m.getCylindrical(pos)\\n\\t\\t\\t#this may seem like a sign-error, but remember that pos is pH-pos. Need th for self, not pH\\n\\t\\t\\tif len(self.plantHeads)==1: #bracke\\n\\t\\t\\t\\tth=posCyl[1]\\n\\t\\t\\telse:\\n\\t\\t\\t\\tx=-(self.plantAreaW*0.5-pH.width*0.5)\\n\\t\\t\\t\\tx+=pH.number*self.plantSepDist #this is the upper part of the triangle.\\n\\t\\t\\t\\tth=posCyl[1]+asin(x/posCyl[0])\\n\\t\\tdirection=self.m.direction+th-pi/2.\\n\\t\\tcart=self.m.getCartesian\\n\\t\\tw=pH.width\\n\\t\\tl=pH.length\\n\\t\\tif rootA:\\n\\t\\t\\tw=w*0.75\\n\\t\\t\\tl=l*0.75\\n\\t\\tc1=cart([w/2., l/2.],origin=orig, direction=direction, local=False, fromLocalCart=True)\\n\\t\\tc2=cart([-w/2., l/2.],origin=orig, direction=direction, local=False, fromLocalCart=True)\\n\\t\\tc3=cart([-w/2., -l/2.],origin=orig, direction=direction, local=False, fromLocalCart=True)\\n\\t\\tc4=cart([w/2., -l/2.],origin=orig, direction=direction, local=False, fromLocalCart=True)\\n\\t\\treturn [c1,c2,c3,c4]\",\n \"def factors(self):\\n for name in self._nodes:\\n if isinstance(self._nodes[name], Factor):\\n yield name\",\n \"def square_tree(t):\\n return tree(label(t)**2,[square_tree(b) for b in branches(t)])\",\n \"def get_factors(number):\\n\\n factors = [1, number]\\n\\n for i in range(2, int(math.sqrt(number))):\\n if number % i == 0:\\n factors.extend([i, number / i])\\n\\n return(factors)\",\n \"def phi_fixed(prec):\\n prec += 10\\n a = isqrt_fast(MPZ_FIVE<<(2*prec)) + (MPZ_ONE << prec)\\n return a >> 11\",\n \"def phi_func(index, phase_angle):\\n\\n # Dictionary that contains the A and B constants, depending on the index /\\n # version\\n a_factor = {'1': 3.33, \\\\\\n '2': 1.87}\\n\\n b_factor = {'1': 0.63, \\\\\\n '2': 1.22}\\n\\n # Phase function\\n phi = np.exp(-1.0 * a_factor[index] \\\\\\n *+ ((np.tan(0.5 * phase_angle) ** b_factor[index])))\\n\\n # Return the phase function result\\n return phi\",\n \"def SetHomoplasy(tree, skip=0):\\n population = tree.size if 'size' in vars(tree) else (len(tree) * 2) - 1\\n order = tree.traverse()\\n root = next(order)\\n homoplasy_hist = sp.csr_matrix(np.zeros(root.genotype.shape[1] - skip))\\n for cur_node in tqdm.tqdm(order, total=population + 1):\\n homoplasy_hist += (cur_node.genotype[0, skip:] != cur_node.up.genotype[0, skip:])\\n homoplasy_dist = pd.Series(homoplasy_hist.data).value_counts(normalize=True)\\n fit_dist = rv_discrete(values=(homoplasy_dist.index.astype(int), homoplasy_dist.values.astype(float)))\\n root.homoplasy = fit_dist\\n root.homoplasy_hist = homoplasy_hist.toarray()[0].astype(np.int)\\n return tree\",\n \"def compute_eigvals(phi): # pylint: disable=arguments-differ\\n if qml.math.get_interface(phi) == \\\"tensorflow\\\":\\n phi = qml.math.cast_like(phi, 1j)\\n\\n return qml.math.stack([1, 1, -qml.math.exp(1j * phi), qml.math.exp(1j * phi)])\",\n \"def factor_unit(P):\\n assert all(isinstance(c, ModInt) for c in P)\\n assert len(set(c.n for c in P)) == 1\\n if P.deg == 1:\\n return defaultdict(int, {P: 1})\\n\\n p = P[0].n\\n\\n S = Polynomial.gcd(P, P.prime())\\n if S.deg == P.deg:\\n # P' = 0 so P = R^p\\n R = Polynomial(P.C[::p])\\n return defaultdict(int,\\n {D: p * v\\n for D, v in Polynomial.factor_unit(R).items()})\\n else:\\n factors = defaultdict(int)\\n if S.deg:\\n for D, v in S.factor_unit().items():\\n factors[D] += v\\n P //= S\\n # P is now square-free\\n # We look for Q in Ker(F-Id) \\\\ {1}\\n Q = Polynomial.computeQ(P)\\n if Q is None:\\n # P is irreducible\\n factors[P] += 1\\n else:\\n # P is the product of the gcd(P, Q-i)\\n # that are factored recursively\\n for i in range(p):\\n D = Polynomial.gcd(P, Q - Polynomial([ModInt(i, p)]))\\n if D.deg:\\n for DD, v in D.factor_unit().items():\\n factors[DD] += v\\n return factors\",\n \"def fn(i, n):\\n if len(stack) > 0 and stack[-1] <= n: ans.append(stack + [n])\\n for ii in range(i, len(factors)):\\n if n % factors[ii] == 0: \\n stack.append(factors[ii])\\n fn(ii, n//factors[ii])\\n stack.pop()\",\n \"def phi34_class_coeff( L, m ):\\n\\n s = 2*(L-1) + m\\n\\n if s<0:\\n return 0\\n\\n return phi34_cc( s, m )\",\n \"def factors(self, X):\\r\\n return (lambda fd: [X] if not fd else fd + self.factors(X // fd[0])) (self.firstdiv(X))\",\n \"def to_prufer(tree, n):\\n d = defaultdict(int)\\n L = []\\n for edge in tree:\\n # Increment the value of the corresponding\\n # node in the degree list as we encounter an\\n # edge involving it.\\n d[edge[0]] += 1\\n d[edge[1]] += 1\\n for i in range(n - 2):\\n # find the smallest leaf\\n for x in range(n):\\n if d[x] == 1:\\n break\\n # find the node it was connected to\\n y = None\\n for edge in tree:\\n if x == edge[0]:\\n y = edge[1]\\n elif x == edge[1]:\\n y = edge[0]\\n if y is not None:\\n break\\n # record and update\\n L.append(y)\\n for j in (x, y):\\n d[j] -= 1\\n if not d[j]:\\n d.pop(j)\\n tree.remove(edge)\\n return L\",\n \"def _preprocess(self):\\n # Precompute a logarithm table. log[n] = k => 2^k <= n < 2^(k+1)\\n self._log = [0] * (self._size + 1)\\n temp = 1\\n for i in range(1, self._size + 1):\\n self._log[i] = self._log[i - 1]\\n if temp * 2 <= i:\\n self._log[i] += 1\\n temp *= 2\\n\\n # Compute the log of the size of the tree.\\n self._logsize = self._log[self._size] + 1\\n\\n # Precompute a power table. pow[k] = n => 2^k = n\\n self._pow = [1] * (self._logsize + 1)\\n for i in range(1, self._logsize):\\n self._pow[i] = 2 * self._pow[i - 1]\\n\\n # Build a list of the jump nodes sorted by depth.\\n self._build_jump_nodes()\\n\\n # Build a list of ladders.\\n self._build_ladders()\\n\\n # Build a sparse table of ancestors of levels 1, 2, 4, 8, ...., 2^k only for the leaves.\\n self._build_sparse_table()\",\n \"def calc_torsion_phi(self):\\n prev_res = self.get_offset_residue(-1)\\n if prev_res is None:\\n return None\\n\\n paC = prev_res.get_atom('C')\\n aN = self.get_atom('N')\\n aCA = self.get_atom('CA')\\n aC = self.get_atom('C')\\n return AtomMath.calc_torsion_angle(paC, aN, aCA, aC)\",\n \"def Phi(l,m,theta,phi):\\n Psilm_th, Psilm_ph=Psi(l,m,theta,phi);\\n Philm_th=-Psilm_ph;\\n Philm_ph=+Psilm_th;\\n return Philm_th, Philm_ph\",\n \"def phi2_coefficient(L):\\r\\n\\r\\n if 0 < L < 120:\\r\\n return L / 120\\r\\n if L >= 120:\\r\\n return 1\",\n \"def phi(self, i):\\n if i == 1 or not self.connected_to_previous(i):\\n return 0.0\\n\\n res = self.all_residues[i]\\n\\n try:\\n n = res['N'].get_vector()\\n ca = res['CA'].get_vector()\\n c = res['C'].get_vector()\\n\\n res_minus_one = self.all_residues[i -1]\\n cp = res_minus_one['C'].get_vector()\\n phi = calc_dihedral(cp, n, ca, c)\\n return phi\\n except Exception:\\n print \\\"Could not get phi for \\\"+repr(i)\\n raise LookupError\",\n \"def genKeys(p, q):\\n n = p*q\\n phi_of_n = (p-1)*(q-1)\\n e = 0\\n \\n #find a e less than n that is coprime with phi(n)\\n count=2\\n while count:\\n gcd = gcd_iter(phi_of_n,count)\\n if gcd==1:\\n e = count\\n break\\n count+=1\\n \\n # finding the mutiplicative inverse of e and phi(n)\\n d = ext_Euclid(e,phi_of_n)\\n\\n #positive values of d\\n if d<0:\\n d = phi_of_n - d\\n return n,e,d\",\n \"def to_revolute_tree(self):\\n T_zero = {\\\"p0\\\": SE3.identity()}\\n stack = [\\\"p0\\\"]\\n tree_structure = {\\\"p0\\\": []}\\n ang_lims_map = {}\\n old_to_new_names = {\\n \\\"p0\\\": \\\"p0\\\"\\n } # Returned for user of the method (to map old joint names to new ones)\\n ub, lb = spherical_angle_bounds_to_revolute(self.ub, self.lb)\\n count = 1\\n while len(stack) > 0:\\n joint = stack.pop(0)\\n new_joint = old_to_new_names[joint]\\n for child in self.parents[joint]:\\n stack += [child]\\n new_child = \\\"p\\\" + str(count)\\n count += 1\\n # ub[new_child] = self.ub[child]\\n # lb[new_child] = self.lb[child]\\n ang_lims_map[child] = new_child\\n tree_structure[new_joint] += [new_child]\\n new_grand_child = \\\"p\\\" + str(count)\\n count += 1\\n old_to_new_names[child] = new_grand_child\\n tree_structure[new_child] = [new_grand_child]\\n Ry = SE3(SO3(roty(np.pi / 2)), np.zeros(3))\\n T_zero[new_child] = T_zero[new_joint].dot(Ry)\\n d = self.d[child]\\n Ry_back = SE3(SO3(roty(-np.pi / 2)), np.zeros(3))\\n T_zero[new_grand_child] = (\\n T_zero[new_child].dot(Ry_back).dot(trans_axis(d, \\\"z\\\"))\\n )\\n tree_structure[new_grand_child] = []\\n\\n # for key in old_to_new_names:\\n # if key in self.ub.keys():\\n # ub[old_to_new_names[key]] = self.ub[key]\\n # lb[old_to_new_names[key]] = self.lb[key]\\n\\n # for key in T_zero:\\n # if key not in ub.keys() and key is not 'p0':\\n # ub[key] = np.pi\\n # lb[key] = -np.pi\\n\\n params = {\\\"T_zero\\\": T_zero, \\\"ub\\\": ub, \\\"lb\\\": lb, \\\"parents\\\": tree_structure}\\n\\n # print(\\\"normal ub: {:}\\\".format(self.ub))\\n # print(\\\"ub: {:}\\\".format(ub))\\n # print(\\\"lb: {:}\\\".format(lb))\\n return RobotRevolute(params), old_to_new_names, ang_lims_map\",\n \"def allocate(tree):\\n\\n\\twp = tree['misc']['working precision']\\t\\n\\tdim = tree['eqns']['ndim']\\n\\tnvar = tree['eqns']['qvec']['nvars']\\n\\tnvarst = len(tree['eqns']['qvec']['stored'])\\t\\n\\thlod = tree['num']['deriv']['hlo']\\n\\thlof = tree['num']['filtr']['hlo']\\n \\n\\thlo = tree['num']['hlo'] \\n\\tnx = tree['mpi']['dMpi'].nx \\n\\tny = tree['mpi']['dMpi'].ny \\n\\tnz = tree['mpi']['dMpi'].nz \\n\\n\\tvariables = []\\n\\tif tree['eqns']['qvec']['solved']:\\n\\t\\tfor v in tree['eqns']['qvec']['solved']:\\n\\t\\t\\tvariables.append(v[1])\\n\\n\\tif tree['eqns']['qvec']['stored']:\\n\\t\\tfor v in tree['eqns']['qvec']['stored']:\\n\\t\\t\\tvariables.append(v[1])\\t\\n\\n\\tvariables_face = {}\\n\\tfor dir in ['i','j','k']:\\n\\t\\tvariables_face[dir] = []\\n\\t\\tfor v in tree['eqns']['qvec']['bcs']['face'][dir]:\\n\\t\\t\\t\\tvariables_face[dir].append(v[1])\\t\\t\\t\\n\\n\\tvariables_edge = {}\\n\\tfor dir in ['ij','jk','ik']:\\n\\t\\tvariables_edge[dir] = []\\n\\t\\tfor v in tree['eqns']['qvec']['bcs']['edge'][dir]:\\n\\t\\t\\t\\tvariables_edge[dir].append(v[1])\\t\\t\\n\\t\\n\\n\\tnvar_face = {'i': len(variables_face['i']),\\n\\t\\t\\t\\t 'j': len(variables_face['j']),\\n\\t\\t\\t\\t 'k': len(variables_face['k'])}\\n\\n\\tnvar_edge = {'ij': len(variables_edge['ij']),\\n\\t\\t\\t\\t 'jk': len(variables_edge['jk']),\\n\\t\\t\\t\\t 'ik': len(variables_edge['ik'])}\\t\\t\\t \\n\\n\\n\\n\\tcoeff = []\\n\\tif tree['eqns']['coeff']:\\n\\t\\tfor v in tree['eqns']['coeff']:\\n\\t\\t\\tcoeff.append(v[1])\\n\\n\\tsizex = nx + 2*hlo\\n\\t\\n\\tif(ny == 1):\\n\\t\\tsizey = 1\\n\\telse:\\t\\t\\n\\t\\tsizey = ny + 2*hlo\\n\\t\\n\\tif(nz == 1) : \\n\\t\\tsizez = 1\\n\\telse:\\t\\n\\t\\tsizez = nz + 2*hlo\\n\\t\\n\\n\\tndimpt = sizex*sizey*sizez\\n\\tndimtot = ndimpt*nvar\\n\\n\\n\\t# alloc bcs fields:\\n\\tndimptbcs = {}\\n\\tndimptbcs['i'] =sizey*sizez\\n\\tndimptbcs['j'] =sizex*sizez\\n\\tndimptbcs['k'] =sizex*sizey\\n\\tndimptbcs['ij'] =sizez \\n\\tndimptbcs['jk'] =sizex \\n\\tndimptbcs['ik'] =sizey \\n\\n\\tsizebcs = {}\\n\\tsizebcs['i'] =(sizey,sizez)\\n\\tsizebcs['j'] =(sizex,sizez)\\n\\tsizebcs['k'] =(sizex,sizey)\\n\\tsizebcs['ij'] =(sizez) \\n\\tsizebcs['jk'] =(sizex) \\n\\tsizebcs['ik'] =(sizey) \\n\\n\\t# faces:\\n\\tnfacei = max(1,nvar_face['i']*ndimptbcs['i'])\\n\\tnfacej = max(1,nvar_face['j']*ndimptbcs['j'])\\n\\tnfacek = max(1,nvar_face['k']*ndimptbcs['k'])\\n\\t\\n\\n\\t# edges:\\n\\tnedgeij = max(1,nvar_edge['ij']*ndimptbcs['ij'])\\n\\tnedgejk = max(1,nvar_edge['jk']*ndimptbcs['jk'])\\n\\tnedgeik = max(1,nvar_edge['ik']*ndimptbcs['ik'])\\t\\n\\n\\t# unpack bc info:\\n\\ttree = unpack_bcs(tree)\\n\\t\\n\\tmybc = tree['bc']['mybc']\\n\\tnbc = len(mybc)\\n\\n\\t# Integers parameters to be passed to the fortran layer\\n\\tparam_intDim = 12 + nvar + nvarst + 1 + nbc + 6 + 6\\n\\tparam_int = np.empty(shape=param_intDim, dtype=np.int32, order='F') \\n\\tparam_int[0] = hlo\\n\\tparam_int[1] = nx\\n\\tparam_int[2] = ny\\n\\tparam_int[3] = nz\\n\\tparam_int[4] = nvar\\n\\tparam_int[5] = nvarst\\n\\tparam_int[6] = ndimtot\\n\\tparam_int[7] = 3 # for RK sub steps in Python\\n\\tparam_int[8] = ndimpt\\n\\tparam_int[9:9+3] = tree['libs']['cache blocking']\\n\\tadr = 9+3\\n\\tparam_int[adr:adr+nvar] = variables[0:0+nvar] # variables location (in q).\\n\\tadr = adr+nvar\\n\\tparam_int[adr:adr+nvarst] = variables[nvar:nvar+nvarst] # variables location (in qst).\\n\\tadr = adr+nvarst\\n\\tparam_int[adr] = nbc\\n\\tadr = adr + 1\\n\\tparam_int[adr:adr+nbc] = mybc\\n\\tadr = adr + nbc\\n\\tparam_int[adr:adr+6] = list(nvar_face.values()) + list(nvar_edge.values())\\n\\tadr = adr + 6\\n\\tparam_int[adr:adr+6] = [nfacei,nfacej,nfacek,nedgeij,nedgejk,nedgeik]\\n\\n\\tif tree['eqns']['coeff']: \\n\\t\\tncoef = len(tree['eqns']['coeff'])\\n\\telse:\\n\\t\\tncoef = 0\\t\\n\\n\\t# Floating point parameters to be passed to the Fortran layer (3 additional floats for the metrics, uniforme grid + 1 for dt +1 for eps filter)\\n\\tparam_float = np.zeros(shape=ncoef+5, dtype=wp, order='F') \\n\\n\\tparam_float[0] = cst(1.0)/tree['grid']['geom']['dx']\\n\\tif(dim>=2) : param_float[1] = cst(1.0)/tree['grid']['geom']['dy']\\n\\tif(dim == 3) : param_float[2] = cst(1.0)/tree['grid']['geom']['dz']\\n\\n\\tparam_float[3] = tree['num']['tint']['tstep']\\n\\tparam_float[4] = tree['num']['filtr']['eps']\\n\\n\\tfor i,v in enumerate(tree['eqns']['coeff']):\\n\\t\\tparam_float[i+5] = v[1]\\n\\n\\t# Floating point array (contiguous, aligned, actually NOT aligned yet...)\\n\\t\\n\\tnfieldbcs = sum([nfacei,nfacej,nfacek,nedgeij,nedgejk,nedgeik])\\n\\n\\tif nvarst != 0 :\\n\\t\\tdata = np.empty(shape=ndimtot*4 + ndimpt*nvarst + nfieldbcs, dtype=wp,order='F') # 4 --> q,q1,q2 + rhs + nvarstored\\n\\telse:\\t\\n\\t\\tdata = np.empty(shape=ndimtot*4 + 1 + nfieldbcs, dtype=wp,order='F') # 4 --> q,q1,q2, rhs, + 1 (address for qst in fortran layer)\\n\\n\\t# Explicit view of data (only references here, no copy) \\n\\tviews = {}\\n\\tnvsolved = len(tree['eqns']['qvec']['solved'])\\n\\t\\n\\t# WARNING assume contiguous addresses of stored variables in data_float...\\n\\tif nvarst != 0:\\n\\t\\taddrstored_beg = ndimtot*4 \\n\\t\\taddrstored_end = addrstored_beg + ndimpt*nvarst\\n\\telse:\\n\\t\\taddrstored_beg = ndimtot*4\\t\\n\\t\\taddrstored_end = addrstored_beg + 1\\n\\n\\taddrbcfields_beg = addrstored_end\\t\\n\\taddrbcfields_edge_beg = addrbcfields_beg + sum([nfacei,nfacej,nfacek])\\n\\n\\tif dim == 3:\\n\\t\\tviews['q'] = data[0:ndimpt*nvsolved].view().reshape(sizex,sizey,sizez,nvsolved, order='F')\\n\\t\\tif nvarst !=0:\\n\\t\\t\\tviews['qstored'] = data[addrstored_beg:addrstored_end].view().reshape(sizex,sizey,sizez,nvarst, order='F')\\n\\t\\t\\n\\t\\tfor v in tree['eqns']['qvec']['solved']:\\n\\t\\t\\tviews[v[0]] = data[(v[1]-1)*(ndimpt):(v[1])*ndimpt].view().reshape(sizex,sizey,sizez, order='F')\\n\\t\\tfor v in tree['eqns']['qvec']['stored']:\\n\\t\\t\\tviews[v[0]] = data[(v[1]-1)*(ndimpt)+addrstored_beg:(v[1])*ndimpt+addrstored_beg].view().reshape(sizex,sizey,sizez, order='F')\\t\\t\\t\\n\\n\\t\\t# bc faces:\\t\\n\\t\\tshift = addrbcfields_beg\\n\\t\\tfor dir in ['i','j','k']:\\n\\t\\t\\tfor v in tree['eqns']['qvec']['bcs']['face'][dir]:\\t\\t\\t\\t\\n\\t\\t\\t\\tviews[v[0]] = data[shift:ndimptbcs[dir]+shift].view().reshape(sizebcs[dir], order='F')\\t\\t\\t\\n\\t\\t\\t\\tshift = shift + ndimptbcs[dir]\\t\\n\\n\\t\\t# bc edges:\\t\\n\\t\\tshift = addrbcfields_edge_beg\\n\\t\\tfor dir in ['ij','jk','ik']:\\n\\t\\t\\tfor v in tree['eqns']['qvec']['bcs']['edge'][dir]:\\t\\n\\t\\t\\t\\tviews[v[0]] = data[shift:ndimptbcs[dir]+shift].view().reshape(sizebcs[dir], order='F')\\t\\t\\t\\n\\t\\t\\t\\tshift = shift + ndimptbcs[dir]\\n\\telif dim == 2:\\n\\t\\tviews['q'] = data[0:ndimpt*nvsolved].view().reshape(sizex,sizey,nvsolved, order='F')\\n\\t\\tif nvarst !=0:\\n\\t\\t\\tviews['qstored'] = data[addrstored_beg:addrstored_end].view().reshape(sizex,sizey,nvarst, order='F')\\n\\t\\t\\n\\t\\tfor v in tree['eqns']['qvec']['solved']:\\n\\t\\t\\tviews[v[0]] = data[(v[1]-1)*(ndimpt):(v[1])*ndimpt].view().reshape(sizex,sizey, order='F')\\t\\n\\t\\tfor v in tree['eqns']['qvec']['stored']:\\n\\t\\t\\tviews[v[0]] = data[(v[1]-1)*(ndimpt)+addrstored_beg:(v[1])*ndimpt+addrstored_beg].view().reshape(sizex,sizey, order='F')\\t\\n\\n\\n\\t\\t# bc faces:\\t\\n\\t\\tshift = addrbcfields_beg\\t\\n\\t\\tfor dir in ['i','j']:\\n\\t\\t\\tfor v in tree['eqns']['qvec']['bcs']['face'][dir]:\\t\\t\\t\\t\\t\\n\\t\\t\\t\\tviews[v[0]] = data[shift:ndimptbcs[dir]+shift].view().reshape(sizebcs[dir], order='F')\\t\\t\\t\\n\\t\\t\\t\\tshift = shift + ndimptbcs[dir]\\t\\n\\t\\t\\t\\t\\t\\t\\n\\telse:\\n\\t\\tviews['q'] = data[0:ndimpt*nvsolved].view().reshape(sizex,nvsolved, order='F')\\n\\t\\tif nvarst !=0:\\n\\t\\t\\tviews['qstored'] = data[addrstored_beg:addrstored_end].view().reshape(sizex,nvarst, order='F')\\n\\t\\t\\n\\t\\tfor v in tree['eqns']['qvec']['solved']:\\n\\t\\t\\tviews[v[0]] = data[(v[1]-1)*(ndimpt):(v[1])*ndimpt].view().reshape(sizex, order='F')\\t\\t\\n\\t\\tfor v in tree['eqns']['qvec']['stored']:\\n\\t\\t\\tviews[v[0]] = data[(v[1]-1)*(ndimpt)+addrstored_beg:(v[1])*ndimpt+addrstored_beg].view().reshape(sizex, order='F')\\t\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\n\\ttree['libs']['fort']['integers'] = param_int\\n\\ttree['libs']['fort']['floats'] = param_float\\n\\ttree['libs']['fort']['data'] = data\\n\\ttree['eqns']['qvec']['views'] = views\\n\\n\\tdnamiF.init(param_int,param_float,data)\\t\\n\\t\\n\\treturn tree\",\n \"def phi(t, *args):\\n # Unpacking data\\n mu_1, pi_mu_2, distance, affine_transfo = args\\n A, b = get_Ab(t)\\n N = len(mu_1)\\n assert len(mu_1) == len(pi_mu_2)\\n # Computing value of objective function\\n r = 0.\\n for i in np.arange(N):\\n r += distance(affine_transfo(A, b, mu_1[i]), pi_mu_2[i]) ** 2\\n return r\",\n \"def getallprimefactors(n):\\n factors = []\\n d = 2\\n while n > 1:\\n while n % d == 0:\\n factors.append(d)\\n print(n)\\n n /= d\\n d += 1\\n return factors\",\n \"def compute_eigvals(phi): # pylint: disable=arguments-differ\\n if qml.math.get_interface(phi) == \\\"tensorflow\\\":\\n phi = qml.math.cast_like(phi, 1j)\\n\\n signs = np.array([1, -1, 0, 0])\\n if qml.math.ndim(phi) == 0:\\n return qml.math.exp(0.5j * phi * signs)\\n\\n return qml.math.exp(qml.math.tensordot(0.5j * phi, signs, axes=0))\",\n \"def factor(n: int) -> List[Tuple[int, int]]:\\n if n <= 1:\\n raise ValueError\\n\\n factors = list()\\n\\n ml = 0\\n p = 2\\n while n % p == 0:\\n n //= p\\n ml += 1\\n if ml > 0:\\n factors.append((p, ml,))\\n\\n p = 3\\n while p ** 2 <= n:\\n ml = 0\\n while n % p == 0:\\n n //= p\\n ml += 1\\n if ml > 0:\\n factors.append((p, ml,))\\n p += 2\\n\\n if n > 2:\\n factors.append((n, 1,))\\n\\n return factors\",\n \"def HaarGOB(treeG):\\n # number of level of the chain (or tree)\\n Ntr = len(treeG)\\n # reorder chain (optional) \\n # reordering each level so that in each level the nodes are in the\\n # descent order of degrees\\n # compute u_l^c for level J_0 (top level)\\n clusterJ0 = treeG[Ntr-1]['clusters']\\n N0 = len(clusterJ0)\\n # generate indicator function on G^c\\n chic = np.identity(N0)\\n uc = [None]*N0\\n uc[0] = 1/np.sqrt(N0,dtype=np.float64)*np.ones(N0)\\n for l in np.arange(1, N0):\\n uc[l] = np.sqrt((N0-l)/(N0-l+1))*(chic[l-1,:]-1/(N0-l)*np.sum(chic[l:,:], axis=0))\\n# u = copy.deepcopy(uc)\\n treeG[Ntr-1]['u'] = uc\\n # compute the next level orthonormal basis ulk and stored into u\\n for j_tr in np.arange(0, Ntr-1)[::-1]:\\n N1 = len(treeG[j_tr]['clusters'])\\n u = [None]*N1\\n i = N0\\n for l in range(N0):\\n clusterl = treeG[j_tr+1]['clusters'][l]\\n kl = len(clusterl)\\n #for k==1\\n ucl = uc[l]\\n ul1 = np.zeros(N1)\\n for j in range(N0):\\n idxj = treeG[j_tr+1]['clusters'][j]\\n ul1[idxj] = ucl[j]/np.sqrt(len(idxj))\\n u[l] = ul1\\n if kl>1:\\n chil = np.zeros((kl,N1))\\n for k in range(kl):\\n idxl = treeG[j_tr+1]['clusters'][l]\\n chil[k,idxl[k]] = 1;\\n \\n for k in np.arange(1,kl):\\n i = i+1\\n ulk = np.sqrt((kl-k)/(kl-k+1))*(chil[k-1,:]-1/(kl-k)*np.sum(chil[k:,:],axis=0))\\n u[i-1] = ulk\\n treeG[j_tr]['u'] = u\\n # update uc and N0\\n# uc = copy.deepcopy(u)\\n uc = u\\n N0 = N1\\n return treeG\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6235746","0.6235249","0.59856504","0.59158146","0.59023964","0.582595","0.5666644","0.56169415","0.555361","0.54805905","0.5480042","0.5480042","0.5477096","0.5435909","0.5401921","0.53897333","0.5374874","0.53169054","0.52634305","0.5260795","0.5258291","0.52472126","0.5216645","0.5215552","0.52077013","0.51863945","0.51774466","0.51753217","0.5171452","0.51614434","0.51521546","0.5148269","0.5107289","0.5102196","0.5097191","0.5090029","0.5076749","0.5066769","0.5064581","0.50408214","0.5025457","0.50134677","0.5007432","0.4998794","0.49905115","0.49891025","0.49795556","0.49785483","0.4975785","0.49610478","0.49600923","0.49478954","0.49345562","0.49212292","0.49162275","0.49081975","0.48988166","0.4887177","0.4879587","0.4876691","0.4875111","0.4874821","0.4857338","0.48525706","0.48477036","0.48370045","0.48367417","0.48322132","0.48295608","0.4828739","0.4824779","0.48200622","0.48120472","0.47885466","0.47879374","0.4781028","0.47751054","0.47706622","0.47663438","0.47660473","0.47652638","0.4757414","0.47538245","0.47537372","0.47522187","0.4744342","0.47407144","0.473982","0.4736","0.47355485","0.47345743","0.47208217","0.47205022","0.4718095","0.4714865","0.4709027","0.47079518","0.47068965","0.47060594","0.47022235"],"string":"[\n \"0.6235746\",\n \"0.6235249\",\n \"0.59856504\",\n \"0.59158146\",\n \"0.59023964\",\n \"0.582595\",\n \"0.5666644\",\n \"0.56169415\",\n \"0.555361\",\n \"0.54805905\",\n \"0.5480042\",\n \"0.5480042\",\n \"0.5477096\",\n \"0.5435909\",\n \"0.5401921\",\n \"0.53897333\",\n \"0.5374874\",\n \"0.53169054\",\n \"0.52634305\",\n \"0.5260795\",\n \"0.5258291\",\n \"0.52472126\",\n \"0.5216645\",\n \"0.5215552\",\n \"0.52077013\",\n \"0.51863945\",\n \"0.51774466\",\n \"0.51753217\",\n \"0.5171452\",\n \"0.51614434\",\n \"0.51521546\",\n \"0.5148269\",\n \"0.5107289\",\n \"0.5102196\",\n \"0.5097191\",\n \"0.5090029\",\n \"0.5076749\",\n \"0.5066769\",\n \"0.5064581\",\n \"0.50408214\",\n \"0.5025457\",\n \"0.50134677\",\n \"0.5007432\",\n \"0.4998794\",\n \"0.49905115\",\n \"0.49891025\",\n \"0.49795556\",\n \"0.49785483\",\n \"0.4975785\",\n \"0.49610478\",\n \"0.49600923\",\n \"0.49478954\",\n \"0.49345562\",\n \"0.49212292\",\n \"0.49162275\",\n \"0.49081975\",\n \"0.48988166\",\n \"0.4887177\",\n \"0.4879587\",\n \"0.4876691\",\n \"0.4875111\",\n \"0.4874821\",\n \"0.4857338\",\n \"0.48525706\",\n \"0.48477036\",\n \"0.48370045\",\n \"0.48367417\",\n \"0.48322132\",\n \"0.48295608\",\n \"0.4828739\",\n \"0.4824779\",\n \"0.48200622\",\n \"0.48120472\",\n \"0.47885466\",\n \"0.47879374\",\n \"0.4781028\",\n \"0.47751054\",\n \"0.47706622\",\n \"0.47663438\",\n \"0.47660473\",\n \"0.47652638\",\n \"0.4757414\",\n \"0.47538245\",\n \"0.47537372\",\n \"0.47522187\",\n \"0.4744342\",\n \"0.47407144\",\n \"0.473982\",\n \"0.4736\",\n \"0.47355485\",\n \"0.47345743\",\n \"0.47208217\",\n \"0.47205022\",\n \"0.4718095\",\n \"0.4714865\",\n \"0.4709027\",\n \"0.47079518\",\n \"0.47068965\",\n \"0.47060594\",\n \"0.47022235\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.48233575"},"document_rank":{"kind":"string","value":"71"}}},{"rowIdx":11182,"cells":{"query":{"kind":"string","value":"Calculate the (log) agreement of a list of tags for a given tree"},"document":{"kind":"string","value":"def log_score(self, T, pos, m, psi, phi):\n log_score = 0\n for (i, j, lab) in T:\n m_i = m[i - 1]\n pos1 = pos[i - 1]\n log_score += self.get_phi_score(phi, i - 1, m_i)\n if j != 0:\n m_j = m[j - 1]\n pos2 = pos[j - 1]\n log_score += self.get_psi_score(psi, pos1, pos2, lab, m_i, m_j)\n return log_score"},"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 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 log_T_score(T, state_tree_sequence, lineageObj):\n log_T_score_holder = 0\n log_T = np.log(T)\n # we start with the first transition, from the root cell\n for level in lineageObj.output_list_of_gens[1:]:\n for cell in level:\n cell_idx = lineageObj.output_lineage.index(cell)\n cell_state = state_tree_sequence[cell_idx]\n if not cell.isLeaf():\n for daughter in cell.get_daughters():\n child_idx = lineageObj.output_lineage.index(daughter)\n daughter_state = state_tree_sequence[child_idx]\n log_T_score_holder += log_T[cell_state, daughter_state]\n return log_T_score_holder","def improve_tree(tree, freq_dict):\n # todo","def aggs(tree, weights):\n restricted_nodes =[]\n weights = apply_aggregation(tree, weights)\n for parent, children in tree.iteritems():\n children_weights = {}\n for child in children:\n children_weights[child] = weights[child]\n try:\n restricted_nodes.append(max(children_weights, key=children_weights.get))\n except ValueError:\n pass\n ps = possible_aggs(walk(tree), restricted_nodes)\n results = []\n for p in ps:\n e = merge_nodes(p, tree)\n if e not in results:\n results.append(e)\n return results","def compute_edge_logits(self):\n TODO('https://github.com/posterior/treecat/issues/27')","def compute_edge_logits(self):\n TODO('https://github.com/posterior/treecat/issues/26')","def compute_tree(self, tree):\n g_list_val, g_list_h = self._build_graph(tree) # return theano variable of each node\n list_val = self._traversal_tree(tree) #\n f = theano.function(g_list_val, g_list_h, allow_input_downcast=True)\n result = f(*list_val)\n return result","def calculate_merkle_tree(self, intervals: set[Interval]) -> tuple[bytes, list[bytes]]:\n hashes = [x.data for x in intervals]\n hashes.sort()\n\n merkle = hashlib.sha256()\n for h in hashes:\n merkle.update(h)\n\n return merkle.digest(), hashes","def Trees__LCA_LowestCommonDenominator():\n # Python2 ported to Python3 via 2to3-3.7\n # URL:# URL:https://www.hackerrank.com/challenges/binary-search-tree-lowest-common-ancestor/problem\n '''\n class Node:\n def __init__(self,info): \n self.info = info \n self.left = None \n self.right = None \n // this is a node of the tree , which contains info as data, left , right\n '''\n def lca(root, v1, v2):\n # Find a and b. Link child nodes to parent to be able to backtrack.\n # (1) Note, we add 'parent' attribute to node dynamically via node.parent = ...\n root.parent = None\n node_stack = []\n node_stack.append(root)\n v1_node, v2_node = None, None\n while node_stack:\n node = node_stack.pop()\n if not v1_node and node.info == v1:\n v1_node = node\n if not v2_node and node.info == v2:\n v2_node = node\n for child_node in [node.left, node.right]:\n if child_node:\n child_node.parent = node # (1)\n node_stack.append(child_node)\n\n # Generate path from A to root.\n curr = v1_node\n a_to_root = set()\n while curr:\n a_to_root.add(curr.info)\n curr = curr.parent\n\n # traverse up b until you come across an element in a's path to parent.\n curr = v2_node\n while curr:\n if curr.info in a_to_root:\n return curr\n else:\n curr = curr.parent\n\n print(\"Shouldn't be here, Something went wrong\")\n\n # # Recursive. (Iterative is better, but did recursive for practice.) ~15 min.\n # # Main idea is that we count the number of v1/v2's found of the subnodes.\n # # If a node has sum of 2, we know it's the lca.\n # def lca(root, v1, v2):\n # def lca_helper(node):\n # ret_node = None\n # if not node:\n # return 0, None\n # v_match_counter = 0\n # if node.info in [v1, v2]:\n # v_match_counter += 1\n # left_count, left_node_ret = lca_helper(node.left)\n # right_count, right_node_ret = lca_helper(node.right)\n # v_match_counter += left_count + right_count\n # if v_match_counter == 2:\n # ret_node = node\n # if left_node_ret:\n # ret_node = left_node_ret\n # if right_node_ret:\n # ret_node = right_node_ret\n # return v_match_counter, ret_node\n\n # _, node = lca_helper(root)\n # return node","def log(tree, exact_src, **kw):\n new_tree = hq[wrap(unhygienic[log], u[exact_src(tree)], ast_literal[tree])]\n yield new_tree","def get_relations(log):\n # finding loops of length two\n # ordering relations\n triangle = {}\n for trace in log:\n i = 0\n while i < len(trace) - 2:\n current = trace.__getitem__(i)\n successor = trace.__getitem__(i + 1)\n successor2 = trace.__getitem__(i + 2)\n if current == successor2:\n if current in triangle:\n triangle[current].append(successor)\n else:\n triangle[current] = [successor]\n i += 1\n for key, value in triangle.items():\n triangle[key] = set(value)\n square = {}\n for key in triangle:\n for element in triangle[key]:\n if element in triangle:\n if key in triangle[element]:\n if key in square and element in square:\n square[key].append(element)\n square[element].append(key)\n elif key in square and element not in square:\n square[key].append(element)\n square[element] = [key]\n elif key not in square and element in square:\n square[key] = [element]\n square[element].append(key)\n else:\n square[key] = [element]\n square[element] = [key]\n for key, value in square.items():\n square[key] = set(value)\n # ordering relation following\n follows = {}\n for trace in log:\n i = 0\n while i < len(trace) - 1:\n current = trace.__getitem__(i)\n successor = trace.__getitem__(i + 1)\n if current in follows:\n if successor not in follows[current]:\n follows[current].append(successor)\n else:\n follows[current] = [successor]\n i += 1\n # transforming list to set\n for key, value in follows.items():\n follows[key] = set(value)\n # ordering relation causal\n causal = {}\n if len(square) != 0:\n for key in follows:\n for element in follows[key]:\n if element in follows:\n if key in square:\n if (not (key in follows[element])) or (element in square[key]):\n if key in causal:\n causal[key].append(element)\n else:\n causal[key] = [element]\n else:\n if (not (key in follows[element])):\n if key in causal:\n causal[key].append(element)\n else:\n causal[key] = [element]\n else:\n if key in causal:\n causal[key].append(element)\n else:\n causal[key] = [element]\n else:\n for key in follows:\n for element in follows[key]:\n if element in follows:\n if (not (key in follows[element])):\n if key in causal:\n causal[key].append(element)\n else:\n causal[key] = [element]\n else:\n if key in causal:\n causal[key].append(element)\n else:\n causal[key] = [element]\n\n for key, value in causal.items():\n causal[key] = set(value)\n # ordering relation unrelated if no other ordering is applied\n # ordering relation parallel\n parallel = {}\n if len(square) != 0:\n for key in follows:\n for element in follows[key]:\n if element in follows:\n if key in follows[element]:\n if element in follows[key]:\n if key in square:\n if not element in square[key]:\n if key in parallel:\n parallel[key].append(element)\n else:\n parallel[key] = [element]\n else:\n if key in parallel:\n parallel[key].append(element)\n else:\n parallel[key] = [element]\n\n else:\n for key in follows:\n for element in follows[key]:\n if element in follows:\n if key in follows[element]:\n if element in follows[key]:\n if key in parallel:\n parallel[key].append(element)\n else:\n parallel[key] = [element]\n\n for key, value in parallel.items():\n parallel[key] = set(value)\n return causal, parallel, follows","def entropy(*args):\n\n\n values = []\n leaf = -1\n\n for i, val in enumerate(args):\n if(val != 0):\n values.append(val * math.log(val, len(args)))\n if(val == 1):\n leaf = i\n \n return -sum(values), leaf","def Log_OB_S1(xref,x):\n\n nX = np.shape(x)\n\n m = nX[0]\n n = nX[1]\n t = nX[2]\n\n G = np.zeros((n,t))\n Gv = np.zeros((m,n,t))\n\n for r in range(t):\n\n # Correct for permuations\n\n #Xout,PiA= CorrectPerm(xref,x[:,:,r])\n Xout = dp(x[:,:,r])\n\n for q in range(n):\n\n a = np.sum(Xout[:,q]*xref[:,q])/np.sqrt(np.sum(xref[:,q]**2)*np.sum(Xout[:,q]**2)) # Should have unit L2 norm\n if a > 1:\n a = 1\n if a < -1:\n a = -1\n G[q,r] = np.arccos(a) # Computing the angles\n v = Xout[:,q] - a*xref[:,q]\n Gv[:,q,r] = v / (1e-12 + np.linalg.norm(v)) # Unit vector in the tangent subspace\n\n return G,Gv","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 tree_statistics(tree):\n all_elements = tree.findall('//')\n tag_counter = defaultdict(int)\n for element in all_elements:\n tag_counter[element.tag] += 1\n for (tag, counts) in tag_counter.items():\n print \"{0}: {1}\".format(tag, counts)","def compute_edge_logits(self):","def _gen_terms(self, tree):\n for leaf in self._leaves(tree):\n terms = [self._normalize(w) for w, _ in leaf if self._filter(w)]\n # Phrase only\n if len(terms) > 1:\n yield terms","def get_association_strengths(self, word_pairs):\n \n word_pairs = [(s.upper(), t.upper()) for s, t in word_pairs] #node names are all uppercase\n \n sources = set()\n targets = set()\n vocab = set(self.graph.vs[\"name\"])\n for s, t in word_pairs:\n if s in vocab:\n sources.add(s)\n if t in vocab:\n targets.add(t)\n \n sources = list(sources) #enforce consistent order\n targets = list(targets)\n s_map = {s:i for i, s in enumerate(sources)} #get indexes\n t_map = {t:i for i, t in enumerate(targets)}\n \n #For large numbers of pairs, it's quicker to get all the paths at once than to get them one at a time\n matrix = self.graph.shortest_paths(sources, targets, weights=\"-log weight\", mode=OUT)\n \n strengths = []\n for s, t in word_pairs:\n neg_log_dist = matrix[s_map[s]][t_map[t]] if (s in s_map and t in t_map) else float(\"inf\")\n strengths.append(10**-neg_log_dist)\n \n return strengths","def calculate_entropy(self, tags):\n tags_counter = Counter()\n\n if not tags:\n return 0\n\n for tag in tags:\n tags_counter[tag] += 1\n classes_probs = [tags_counter[tag] / float(len(tags)) for tag in tags_counter]\n if 0.0 in classes_probs:\n return 0\n\n entropy = 0\n for prob in classes_probs:\n entropy -= prob * math.log(prob, 2)\n\n return entropy","def information_gain(features, attribute_index, targets):\r\n\r\n possible_feature_values = [0,1]\r\n \r\n possible_classifications = [0,1]\r\n \r\n feature = features[:,attribute_index]\r\n \r\n \r\n number_of_samples = len(feature)\r\n \r\n import math\r\n \r\n \r\n #current_entropy = np.sum([-(len(targets[targets==possible_classification])/number_of_samples)*math.log(len(targets[targets==possible_classification])/number_of_samples, 2) for possible_classification in possible_classifications])\r\n \r\n terms_to_be_summed_for_current_entropy = []\r\n \r\n for classification in possible_classifications:\r\n \r\n number_of_elements_with_this_classification = len(targets[targets==classification])\r\n \r\n p_for_this_classification = number_of_elements_with_this_classification/len(targets)\r\n \r\n if p_for_this_classification != 0:\r\n terms_to_be_summed_for_current_entropy.append(-p_for_this_classification*math.log(p_for_this_classification,2))\r\n else:\r\n terms_to_be_summed_for_current_entropy.append(0)\r\n \r\n current_entropy = np.sum(terms_to_be_summed_for_current_entropy)\r\n \r\n \r\n \r\n terms_to_be_summed_for_weighted_entropy = []\r\n \r\n for possible_value in possible_feature_values:\r\n \r\n targets_split_by_feature_value = targets[feature.flatten() == possible_value]\r\n \r\n if len(targets_split_by_feature_value) != 0:\r\n \r\n \r\n weight_of_feature_value = len(targets_split_by_feature_value)/len(targets)\r\n \r\n terms_for_entropy_within_subset = []\r\n \r\n for classification in possible_classifications:\r\n \r\n number_of_subset_elements_with_this_classification = len(targets_split_by_feature_value[targets_split_by_feature_value==classification])\r\n \r\n p_in_subset_for_this_classification = number_of_subset_elements_with_this_classification/len(targets_split_by_feature_value)\r\n \r\n if p_in_subset_for_this_classification != 0:\r\n terms_for_entropy_within_subset.append(-p_in_subset_for_this_classification*math.log(p_in_subset_for_this_classification,2))\r\n else:\r\n terms_for_entropy_within_subset.append(0)\r\n \r\n entropy_within_subset = np.sum(terms_for_entropy_within_subset)\r\n \r\n terms_to_be_summed_for_weighted_entropy.append(weight_of_feature_value*entropy_within_subset)\r\n \r\n weighted_entropy = np.sum(terms_to_be_summed_for_weighted_entropy)\r\n \r\n \r\n #current_entropy = np.sum(terms_to_be_summed_for_current_entropy)\r\n \r\n #weighted_entropy = np.sum([(len(feature[feature==possible_value])/number_of_samples)*(len(targets[feature==possible_value][targets[feature==possible_value]==possible_classification])/len(targets[feature==possible_value]))*math.log((len(targets[feature==possible_value][targets[feature==possible_value]==possible_classification])/len(targets[feature==possible_value])), 2) for possible_classification in possible_classifications for possible_value in possible_feature_values])\r\n\r\n information_gain = current_entropy - weighted_entropy \r\n \r\n return information_gain","def tag(self, tokens):\n numRows = len(self.tags)\n numCols = len(tokens)\n # initialize tables for dynamic programming\n table = array([[0] * numCols] * numRows, dtype=float32)\n trace = array([[None] * numCols] * numRows)\n \n # fill in the base cases, i.e. the first column\n for row in range(numRows):\n currentTag = self.tags[row]\n currentWord = tokens[0] if tokens[0] in self.vocab else '<OOV>'\n table[row][0] = self.tag_tag_probs['<START>'].prob(currentTag) * self.tag_word_probs[currentTag].prob(currentWord)\n trace[row][0] = '<START>'\n \n # fill the rest of the table\n # iterate through by columns\n for col in range(1, numCols):\n for row in range(numRows):\n currentTag = self.tags[row]\n currentWord = tokens[col] if tokens[col] in self.vocab else '<OOV>'\n maxProbability = 0.0;\n maxPrevRow = 0\n \n # iterate through the previous column and find the maximum probability\n # as well as the previous tag that led to the maximum probability\n for prevRow in range(numRows):\n prevTag = self.tags[prevRow]\n probability = table[prevRow][col-1] * self.tag_tag_probs[prevTag].prob(currentTag) * self.tag_word_probs[currentTag].prob(currentWord)\n if probability > maxProbability:\n maxProbability = probability\n maxPrevRow = prevRow\n \n table[row][col] = maxProbability\n trace[row][col] = maxPrevRow\n \n returnList = []\n # retrace and construct the tag list\n maxIndex = argmax(table, axis=0)[-1]\n # insert the last (token, tag) pair\n returnList.insert(0, (tokens[-1], self.tags[maxIndex]))\n # loop through the trace table and prepend each (token, tag) pair\n i = numCols - 1\n index = trace[maxIndex][numCols-1]\n while i > 0:\n returnList.insert(0, (tokens[i-1], self.tags[index]))\n i -= 1\n index = trace[index][i]\n \n return returnList","def Log_OB_S1_vec(xref,x):\n\n nX = np.shape(x)\n\n m = nX[0]\n n = nX[1]\n t = nX[2]\n\n G = np.zeros((n,t))\n Gv = np.zeros((m,n,t))\n\n for r in range(t):\n\n # Correct for permuations\n\n Xout = dp(x[:,:,r])\n\n for q in range(n):\n\n a = np.sum(Xout[:,q]*xref[:,q,r])/np.sqrt(np.sum(xref[:,q,r]**2)*np.sum(Xout[:,q]**2)) # Should have unit L2 norm\n G[q,r] = np.arccos(a) # Computing the angles\n if a > 1:\n a = 1\n if a < -1:\n a = -1\n\n v = Xout[:,q] - a*xref[:,q,r]\n Gv[:,q,r] = v / (1e-24 + np.linalg.norm(v)) # Unit vector in the tangent subspace\n\n return G,Gv","def compute_idfs(documents):\n words = set()\n for file in documents:\n words.update(documents[file])\n\n idfs = dict()\n for word in words:\n tw = sum(word in documents[file] for file in documents)\n idf = math.log(len(documents)/ tw )\n idfs[word] = idf\n\n return idfs","def get_gain(self, examples, tags, feature):\n initial_entropy = self.calculate_entropy(tags)\n relative_entropy_per_feature = []\n feature_index = self.get_feature_index(feature)\n for possible_value in self.feature_domain_dict[feature]:\n examples_and_tags_vi = [(example, tag) for example, tag in zip(examples, tags)\n if example[feature_index] == possible_value]\n tags_vi = [tag for example, tag in examples_and_tags_vi]\n entropy_vi = self.calculate_entropy(tags_vi)\n if not examples:\n pass\n relative_entropy = (float(len(examples_and_tags_vi)) / len(examples)) * entropy_vi\n relative_entropy_per_feature.append(relative_entropy)\n\n return initial_entropy - sum(relative_entropy_per_feature)","def _tag_tokens(self, targets: list, tokens: list, tags: dict=BIO_TAGS, bert_tokenizer=None, verbose: bool=False):\n if bert_tokenizer is not None:\n tokenizer = bert_tokenizer\n\n if len(targets) > 0:\n tags_list = []\n for tgt in targets:\n t_list = []\n inside = False\n found = False\n if bert_tokenizer is not None:\n tgt_terms = tokenizer.tokenize(tgt[1]) \n else:\n tgt_terms = self._tokenize_line(tgt[1])\n\n if verbose:\n print(tgt_terms)\n\n for i in range(len(tokens)):\n if tokens[i] == tgt_terms[0] and not found: \n # token is the beginning (B) of target terms sequence\n t_list.append(tags[\"B\"])\n if len(tgt_terms) > 1 and tokens[i:i+len(tgt_terms)] == tgt_terms:\n # check if the matching token is not a repetition of the term\n # and is the actual target term, if so the correct sequence is found \n inside = True\n found = True\n\n elif inside == True:\n # multi words terms\n if tokens[i] in tgt_terms[1:-1] and len(tgt_terms) > 2:\n # token is inside (I) the target terms sequence\n t_list.append(tags[\"I\"])\n\n elif tokens[i] == tgt_terms[-1]:\n # token is the last (L) target term\n t_list.append(tags[\"I\"]) # tags[\"L\"] \n inside = False\n\n # when the last tgt_word is repeated inside the tgt_terms \n inside = False\n\n else:\n # token is outside (O) the target terms sequence\n t_list.append(tags[\"O\"])\n\n tags_list.append(torch.Tensor(t_list))\n\n # merge tags\n tags_tensor = torch.stack(tags_list)\n res = torch.min(tags_tensor, dim=0)\n if verbose:\n print(\"targets:\", targets)\n print(\"tokens:\", tokens, \"-- len:\", len(tokens))\n print(\"tags:\", tags_list)\n #print(\"tags:\", tags_tensor.size())\n #print(\"res:\", res.values.size())\n \n return res.values\n\n else:\n return [tags[\"O\"] for t in tokens]","def __call__(self, X_logs):\n X = [math.exp(x) for x in X_logs]\n B_subs = {}\n for v_parent, v_child in self.R:\n edge = frozenset([v_parent, v_child])\n r = X[v_child]\n t = self.B[edge]\n B_subs[edge] = r * t\n newick_string = FtreeIO.RBN_to_newick(self.R, B_subs, self.N_leaves)\n tree = Newick.parse(newick_string, Newick.NewickTree)\n # define the rate matrix object; horrible\n dictionary_rate_matrix = RateMatrix.get_jukes_cantor_rate_matrix() \n ordered_states = list('ACGT') \n row_major_rate_matrix = MatrixUtil.dict_to_row_major(\n dictionary_rate_matrix, ordered_states, ordered_states)\n rate_matrix_object = RateMatrix.RateMatrix(\n row_major_rate_matrix, ordered_states) \n # get the log likelihood\n ll = PhyLikelihood.get_log_likelihood(\n tree, self.alignment, rate_matrix_object)\n return -ll","def entropy_gain(node,attribute):\n data_subset1 = filter_data(node.data,node.ancestors)\n data_counts = list(Counter(data_subset1['Class']).values())\n base_entropy = entropy(data_counts,base=2)\n num_values = len(data_subset1)\n entropy_sum = 0\n \n for value in [0,1]:\n data_subset2 = filter_data(node.data, node.ancestors + [(attribute,value)])\n subset_counts = list(Counter(data_subset2['Class']).values())\n entropy_sum += (len(data_subset2)/num_values) * entropy(subset_counts,base=2)\n \n return base_entropy - entropy_sum","def tree_apply(tree_arrays, features, extra_features=None, reduce_axis=0) -> np.ndarray:\n qi = np.zeros(features.shape[reduce_axis], dtype=np.int32)\n for current_depth in range(tree_arrays[\"treedepth\"]):\n fi = tree_arrays[\"features\"][qi]\n f = np.choose(\n fi, assure_numpy(features).T if reduce_axis == 0 else assure_numpy(features)\n ) # TODO: try to do it in more effective tf-oriented way\n t = tree_arrays[\"thresholds\"][qi]\n # print(qi, fi, f, t)\n # if current_depth == 0:\n # print(fi, f.shape, features.shape, f)\n answer = (f < t) * 1\n new_qi = (\n answer * tree_arrays[\"yes_node\"][qi]\n + (1 - answer) * tree_arrays[\"no_node\"][qi]\n )\n qi = new_qi\n if extra_features is None:\n assert tree_arrays[\"leaf_data\"].shape[1] == 1, \"extra_features needed\"\n leaf_data = tree_arrays[\"leaf_data\"][qi, 0]\n else:\n leaf_data = assure_numpy(\n tree_arrays[\"leaf_data\"][qi, :]\n * (extra_features.T if reduce_axis == 1 else extra_features)\n ).sum(\n axis=1\n ) # TODO: try to do it in more effective tf-oriented way\n return leaf_data","def Log_Sn(xref,x):\n\n nX = np.shape(x)\n\n m = nX[0]\n t = nX[1]\n\n G = np.zeros((t,))\n Gv = np.zeros((m,t))\n\n for r in range(t):\n\n # Correct for permuations\n\n Xout = dp(x[:,r])\n\n a = np.sum(Xout*xref)/np.sqrt(np.sum(xref**2)*np.sum(Xout**2)) # Should have unit L2 norm\n\n if a > 1:\n a = 1\n if a < -1:\n a = -1\n\n G[r] = np.arccos(a) # Computing the angles\n\n v = Xout - a*xref\n Gv[:,r] = v / (1e-24 + np.linalg.norm(v)) # Unit vector in the tangent subspace\n\n return G,Gv","def reportTagStats(stats, tag, lower):\n transcript_level_tags = ['stats', 'hasOkCopies', 'hasBadCopies',\n 'ok', 'not_ok']\n level = 0\n increment = 1\n if tag in transcript_level_tags:\n header = '%40s %7s %7s %15s' % ('tree', 'Tran.s',\n 'Annot.s', 'Trans. Tags')\n else:\n header = '%40s %7s %7s %15s' % ('tree', 'Tran.s',\n 'Annot.s', 'Annot. Tags')\n print header\n def printTree(level, t, lower):\n if t.tagTranscriptAnnotations > lower:\n if tag in transcript_level_tags:\n count = '%6d (%6.2f%%)' % (\n t.tagTranscripts,\n 100. * t.tagTranscripts / t.nodeTranscripts)\n elif tag.endswith('*'):\n count = '%16d' % (t.tagTranscriptAnnotations)\n else:\n count = '%6d (%6.2f%%)' % (\n t.tagTranscriptAnnotations,\n 100. * t.tagTranscriptAnnotations / t.nodeTranscriptAnnotations)\n s = '%7d, %7d, %s' % (\n t.nodeTranscripts, t.nodeTranscriptAnnotations, count)\n title = '%s%s' % ('| ' * level, t.nodeName)\n buff = '.' * (40 - len(title))\n print '%s%s%s' % (title, buff, s)\n t.children = sorted(t.children, key=lambda c: c.nodeTranscripts,\n reverse=True)\n for c in t.children:\n printTree(level + increment, c, lower)\n printTree(level, stats, lower)","def calc_tf(self, level) -> None:\n if level == 't':\n term_idxs = self._load_term_idxs('t')\n path_in = self.path_token_idx_corpus\n path_out = self.path_tf_tokens\n # contains = self.load_contains(self.path_token_contains)\n elif level == 'l':\n term_idxs = self._load_term_idxs('l')\n path_in = self.path_lemma_idx_corpus\n path_out = self.path_tf_lemmas\n # contains = self.load_contains(self.path_lemma_contains)\n else:\n raise Exception('Error! Level not know!')\n\n tf = {}\n for doc_idx, doc in enumerate(get_docs(path_in)):\n tf[doc_idx] = {}\n tf_doc = tf[doc_idx]\n for sent in doc:\n for term_idx in sent:\n if term_idx in term_idxs:\n if term_idx in tf_doc:\n tf_doc[term_idx] += 1\n else:\n tf_doc[term_idx] = 1\n # *** Code for compound counting ***\n # # add counts for all terms contained\n # for tc_idx in contains[term_idx]:\n # if tc_idx in tf_doc:\n # tf_doc[str(tc_idx)] += 1\n # else:\n # tf_doc[str(tc_idx)] = 1\n\n # print(doc_idx, lemma_idx)\n # print(type(doc_idx), type(lemma_idx))\n # tf[doc_id][lemma_idx] += 1\n # tf_doc = tf[doc_idx]\n # tf_doc[lemma_idx]\n # ***\n with open(path_out, 'w', encoding='utf8') as f:\n json.dump(tf, f)\n\n self._docs_processed = 0","def question4(T,r,n1,n2):\n\n\tif(len(T)<=1):\t\t\t\t\t\t\t\t# Edge case : If the Tree only consists of a root and no children\n\t\treturn -1\n\n\tif(n1==None or n2==None):\t\t\t\t\t# Edge case : If n1 and n2 are not actually numbers\n\t\treturn -1\n\n\tlen_T = len(T)\n\tif(not n1 < len_T or not n2 < len_T):\t\t# Edge case : If the nodes gives in parameters do not actually exist in the tree\n\t\treturn -1\n\n\tn1_list = []\t\t\t\t\t\t\n\tn2_list = []\n\n\tfor i in range(len(T)):\t\t\t\t\t\t# Traverse the list and append all the parents of node1 if found in O(N)\n\t\tif T[i][n1]==1:\n\t\t\tn1_list.append(i)\n\n\tfor i in range(len(T)):\t\t\t\t\t\t# Traverse the list and append all the parents of node2 is found in O(N)\n\t\tif T[i][n2]:\n\t\t\tn2_list.append(i)\n\n\t\t\t\t\t\t\t\t\t\t\t\t# The root is a common ancestor of every node in the tree\n\tif not r in n1_list:\t\t\t\t\t\t# check if the root is in the list, if not, add it\n\t\tn1_list.append(r)\n\n\tif not r in n2_list:\t\t\t\t\t\t# check if the root is in the list, if not, add it\n\t\tn2_list.append(r)\n\n\tn1_list = reversed(n1_list)\t\t\t\t\t# Since we are operating on a binary tree, we sort\n\tfor i in n1_list:\t\t\t\t\t\t\t# in decending order to operate on the latest nodes\n\t\tif i in n2_list:\t\t\t\t\t\t# if a match is found, we know that it is the lowest common ancestor\n\t\t\treturn i \t\t\t\t\t\t\t# If nothing is found, the root node is bound to be returned. And it correct.","def compute_joint_probability(token_list, token_probabilities, use_log_prob=False):\n\n log_prob = 0\n\n for word in token_list:\n\n # do not allow zero probabilites\n assert word in token_probabilities\n\n if use_log_prob:\n log_prob += token_probabilities[word]\n else:\n log_prob += log10(token_probabilities[word])\n\n if use_log_prob:\n return log_prob\n\n return 10**log_prob","def tags_jaccard_index(d1, d2):\n set1 = set(d1.keys())\n set2 = set(d2.keys())\n\n common_tags = set.intersection(set1, set2)\n common_tags_count = 0\n all_tags_count = sum(d1.values()) + sum(d2.values())\n\n for k, v in d1.items():\n if k in common_tags:\n common_tags_count += v\n\n for k, v in d2.items():\n if k in common_tags:\n common_tags_count += v\n\n intersection_cardinality = len(set.intersection(set1, set2))\n union_cardinality = len(set.union(set1, set2))\n\n tags_similarity = intersection_cardinality/float(union_cardinality)\n\n final_tag_similarity = ((common_tags_count / all_tags_count) * 10) \\\n * tags_similarity\n\n return final_tag_similarity","def findLinkedTerms(self):\n for key in self.summaryDict.keys(): # v' in the formula\n if self.getCoverFromModalityInDictionnary(self.summaryDict, key) == 0:\n correlation = 0\n else:\n dep = self.getCoverFromModalityInDictionnary(self.summaryFilteredDict,key) / self.getCoverFromModalityInDictionnary(self.summaryDict, key) #cover(v',R')/cover(v'R)\n if dep <= 1:\n correlation = 0\n else:\n correlation = 1 - (1 / dep)\n self.correlationDict[key] = correlation","def eval_tree(tree: GPTree, dataset: Iterable) -> list:\n results = []\n for data in zip(*dataset):\n try:\n output = tree.compute_tree(data[0])\n results.append(\n 0 if output == data[1] else 1\n ) # right or wrong, but no error.\n except Exception:\n results.append(2) # Fails to run.\n\n return results","def getEntrophy(df):\r\n dct = group(df)\r\n n = len(df)\r\n ent = [0]*len(dct)\r\n j = 0\r\n for col in dct.keys(): \r\n for colVal in dct[col].keys():\r\n det = 0 \r\n aux = np.zeros(len(dct[col][colVal].keys()))\r\n i = 0 \r\n for clas in dct[col][colVal].keys():\r\n det+= dct[col][colVal][clas]\r\n aux[i] = dct[col][colVal][clas]\r\n i+=1\r\n aux = 1/det*aux\r\n aux_copy = aux.copy()\r\n aux = np.log2(aux) \r\n s = sum(np.multiply(aux_copy,aux)) \r\n ent[j] += (-1*det/n*s)\r\n j+=1 \r\n return (ent,dct)","def get_words_and_tags(tree, TAG=False):\n if TAG:\n return (re.findall(r'(?<= )?[^( )]+(?=\\))', tree), re.findall(r'(?<=\\()\\w+(?= )',tree))\n else:\n return re.findall(r'(?<= )?[^( )]+(?=\\))', tree)","def cstree_likelihood(x, u, tree, order):\n p = len(order)\n\n # Make ordering start from 0\n order_py = [o-1 for o in order]\n\n # u_C and u_x_C as defined in the paper\n u_C = lambda C: \\\n np.sum(u, axis=tuple([i for i in range(p) if i+1 not in C]))\n u_x_C = lambda x,C : u_C(C)[tuple(x)]\n\n # In self.data, the data is ordered from 0-p\n # Here we change the order according to order_py\n x = list(x)\n x_ordered = [x[i] for i in order_py]\n\n # Accumulator value for the likelihood\n prob = 1\n\n # For each level of the tree\n for level in range(1,p):\n # x_k below is x_k-1 in paper\n x_k = tuple((order[i], x_ordered[i]) for i in range(level))\n context_of_stage_x_k = tree.nodes[x_k].get(\"context\", x_k)\n context_vars = [var for (var,_) in context_of_stage_x_k]\n\n # TODO Check if we really need to sort this\n next_var = order[level]\n CjUk = sorted(context_vars + [next_var])\n Cj = sorted(context_vars)\n\n # Since the sample x is ordered from 0...p and\n # the variables in CjUk,Cj are in {1,...p}\n x_CjUk = [x[i] for i in range(p) if i+1 in CjUk]\n x_Cj = [x[i] for i in range(p) if i+1 in Cj]\n\n numerator = u_x_C(x_CjUk, CjUk)\n denominator = u_x_C(x_Cj,Cj)\n\n prob = prob*(numerator/denominator)\n return prob","def estimate_tree(self):\n logger.info('TreeCatTrainer.estimate_tree given %d rows',\n len(self._added_rows))\n complete_grid = self._tree.complete_grid\n edge_logits = self.compute_edge_logits()\n edges = estimate_tree(complete_grid, edge_logits)\n return edges, edge_logits","def log_E_score(EL_array, state_tree_sequence):\n log_EL_array = np.log(EL_array)\n log_E_score_holder = 0\n for idx, row in enumerate(log_EL_array):\n log_E_score_holder += row[state_tree_sequence[idx]]\n return log_E_score_holder","def hierarchy_dist(self,synset_1, synset_2):\n\t h_dist = sys.maxsize\n\t if synset_1 is None or synset_2 is None: \n\t return h_dist\n\t if synset_1 == synset_2:\n\t # return the depth of one of synset_1 or synset_2\n\t h_dist = max([x[1] for x in synset_1.hypernym_distances()])\n\t else:\n\t # find the max depth of least common subsumer\n\t hypernyms_1 = {x[0]:x[1] for x in synset_1.hypernym_distances()}\n\t hypernyms_2 = {x[0]:x[1] for x in synset_2.hypernym_distances()}\n\t lcs_candidates = set(hypernyms_1.keys()).intersection(\n\t set(hypernyms_2.keys()))\n\t if len(lcs_candidates) > 0:\n\t lcs_dists = []\n\t for lcs_candidate in lcs_candidates:\n\t lcs_d1 = 0\n\t if lcs_candidate in hypernyms_1.keys():\n\t lcs_d1 = hypernyms_1[lcs_candidate]\n\t lcs_d2 = 0\n\t if lcs_candidate in hypernyms_2.keys():\n\t lcs_d2 = hypernyms_2[lcs_candidate]\n\t lcs_dists.append(max([lcs_d1, lcs_d2]))\n\t h_dist = max(lcs_dists)\n\t else:\n\t h_dist = 0\n\t return ((math.exp(self.BETA * h_dist) - math.exp(-self.BETA * h_dist)) / \n\t (math.exp(self.BETA * h_dist) + math.exp(-self.BETA * h_dist)))","def compute_idfs(documents):\n idfs = dict()\n total_num_documents = len(documents)\n words = set(word for sublist in documents.values() for word in sublist)\n \n for word in words:\n num_documents_containing_word = 0\n \n for document in documents.values():\n if word in document:\n num_documents_containing_word += 1\n \n idf = math.log(total_num_documents / num_documents_containing_word)\n idfs[word] = idf\n\n return idfs","def efficiencies_merging(file_names, tree_name, weight_branch_name=\"\"):\n\n branches_to_read = get_all_branch_names(file_names[0], tree_name)\n branches = { branch_name: [] for branch_name in branches_to_read }\n weight_branches = []\n\n for file_name in file_names:\n file_ = uproot.open(file_name)\n tree = file_[tree_name]\n \n if weight_branch_name != \"\":\n weight_branches.append(ak.sum(file_[weight_branch_name].array(), axis=0))\n else:\n weight_branches.append(1)\n\n for branch_name in branches_to_read:\n branches[branch_name].append(tree[branch_name].array())\n\n tree_branches = { k: sum([w*b for w, b in zip(weight_branches, v)])/sum(weight_branches) for k, v in branches.items() }\n\n return tree_branches","def aux_phrase_subtree(tree, tag_list):\n highest_subtree = max([(0, None)]+[(subtree.height(), subtree) for subtree in tree.subtrees(lambda x: x.node in tag_list) if subtree != tree[0]])[1]\n # print highest_subtree\n return highest_subtree","def HaarGOB(treeG):\n # number of level of the chain (or tree)\n Ntr = len(treeG)\n # reorder chain (optional) \n # reordering each level so that in each level the nodes are in the\n # descent order of degrees\n # compute u_l^c for level J_0 (top level)\n clusterJ0 = treeG[Ntr-1]['clusters']\n N0 = len(clusterJ0)\n # generate indicator function on G^c\n chic = np.identity(N0)\n uc = [None]*N0\n uc[0] = 1/np.sqrt(N0,dtype=np.float64)*np.ones(N0)\n for l in np.arange(1, N0):\n uc[l] = np.sqrt((N0-l)/(N0-l+1))*(chic[l-1,:]-1/(N0-l)*np.sum(chic[l:,:], axis=0))\n# u = copy.deepcopy(uc)\n treeG[Ntr-1]['u'] = uc\n # compute the next level orthonormal basis ulk and stored into u\n for j_tr in np.arange(0, Ntr-1)[::-1]:\n N1 = len(treeG[j_tr]['clusters'])\n u = [None]*N1\n i = N0\n for l in range(N0):\n clusterl = treeG[j_tr+1]['clusters'][l]\n kl = len(clusterl)\n #for k==1\n ucl = uc[l]\n ul1 = np.zeros(N1)\n for j in range(N0):\n idxj = treeG[j_tr+1]['clusters'][j]\n ul1[idxj] = ucl[j]/np.sqrt(len(idxj))\n u[l] = ul1\n if kl>1:\n chil = np.zeros((kl,N1))\n for k in range(kl):\n idxl = treeG[j_tr+1]['clusters'][l]\n chil[k,idxl[k]] = 1;\n \n for k in np.arange(1,kl):\n i = i+1\n ulk = np.sqrt((kl-k)/(kl-k+1))*(chil[k-1,:]-1/(kl-k)*np.sum(chil[k:,:],axis=0))\n u[i-1] = ulk\n treeG[j_tr]['u'] = u\n # update uc and N0\n# uc = copy.deepcopy(u)\n uc = u\n N0 = N1\n return treeG","def process_seq_pairs(seq_pairs, args, stats):\n #print '\\n\\n'.join([str(z) for z in seq_pairs])\n seq_to_tree = []\n for x in seq_pairs:\n seq_to_tree.append(x.sequence1.structure)\n seq_to_tree.append(x.sequence2.structure)\n tree_distance = rna_distance('\\n'.join(seq_to_tree))\n tree_distance = tree_distance.strip('\\n').split('\\n') # take off last lr\n assert len(tree_distance) == len(seq_pairs), (\n 'Error length of tree distance %s does not match length of seq_pairs '\n '%s and should -- check installation of RNAdistance'\n % (len(tree_distance), len(seq_pairs))\n )\n for i, x in enumerate(seq_pairs):\n seq_pairs[i].tree_distance = tree_distance[i].split(' ')[1]\n x.output(args)\n stats['energy_delta'].append(x.energy_delta)\n stats['edit_distance'].append(x.edit_distance)\n try:\n stats['tree_distance'].append(float(x.tree_distance))\n except ValueError:\n stats['tree_distance'].append(None)","def tag_ptree(ptree, coreflist):\n pattern = r\"\"\"(?P<lp>\\(?\\s*) # left parenthesis\n (?P<tg>[a-zA-Z$]+)? # POS tag\n (?P<data>\\s*%s) # subtree of tag\n (?P<rp>(?:\\s*\\))*) # right parenthesis\n \"\"\"\n for cid, coref in coreflist[::-1]:\n words = ''.join(word_tokenize(coref['text']))\n\n nltktree = Tree.parse(ptree)\n nltktree.reverse() # perform search right to left\n data = None\n for subtree in nltktree.subtrees(): # BFS\n if ''.join(subtree.leaves()) == words: # equal ignoring whitespace\n data = subtree.pprint()\n break\n\n # If found via breadth-first search of parse tree\n if data:\n ptree = ptree.replace(data, '( COREF_TAG_%s%s)' % (cid, data))\n else: # Try finding via regex matching instead\n dpattern = r'\\s*'.join([r'\\(\\s*[a-zA-Z$]+\\s+%s\\s*\\)' % word\n for word in word_tokenize(coref['text'])])\n found = re.findall(pattern % dpattern, ptree, re.X)\n if found:\n repl = '%s%s ( COREF_TAG_%s%s) %s' % (found[0][0],\n found[0][1],\n cid,\n found[0][2],\n found[0][3])\n ptree = re.sub(pattern % dpattern, repl, ptree, 1, re.X)\n\n return ptree","def calc_CI_introductions(commits, author):\n\n\t# using a dictionary that has the commits' hashes as keys,\n\t# so as to not search multiple times for the same commit\n\tCI_checked = {}\n\n\t# delete contents\n\topen('introductions.csv', 'w').close()\n\t\n\t# for every commit, we look up whether the author included a CI file,\n\t# that did not exist in the parent commit\n\tfor count, commit in enumerate(commits):\n\t\t# status update\n\t\tif (count + 1) % 50 == 0:\n\t\t\tprint count + 1, ' / ', len(commits)\n\t\n\t\ttree_hash, parent_commit_hash, time = search(commit, 'commit')\n\t\tif tree_hash not in CI_checked:\n\t\t\tCI_checked[tree_hash] = ci_lookup(tree_hash)\n\t\t\n\t\t# controlling for the case of multiple parent commits\n\t\tall_parent_CI = False\n\t\tfor parent in parent_commit_hash.split(':'):\n\t\t\t# controlling for the case of no parent commits\n\t\t\tif parent == '':\n\t\t\t\tbreak\n\t\t\n\t\t\tparent_tree_hash = search(parent, 'commit')[0]\n\t\t\n\t\t\tif parent_tree_hash not in CI_checked:\n\t\t\t\tparent_CI = ci_lookup(parent_tree_hash)\n\t\t\t\tCI_checked[parent_tree_hash] = parent_CI\n\t\t\telse:\n\t\t\t\tparent_CI = CI_checked[parent_tree_hash]\n\t\t\t\n\t\t\t# checking all the parent commits for the usage of CI\n\t\t\tall_parent_CI = all_parent_CI or parent_CI\n\t\t\n\t\t# if the tree has a CI file, while the parent tree does not, increase the CI score\n\t\tif CI_checked[tree_hash] and not all_parent_CI:\n\t\t\t\n\t\t\tout = bash('echo ' + commit + ' | ~/lookup/getValues c2P')\n\t\t\tmain_proj = out.strip().split(';')[1]\n\t\t\tf = open(\"introductions.csv\", \"a\")\n\t\t\tf.write(author + ', ' + 'CI' + ', ' + str(time) + ', ' + main_proj + '\\n')\n\t\t\tf.close()\n\t\t\tprint 'wrote'\n\tprint (current_time()-start_time)/len(commits), 'seconds per commit'","def lca(root, node1, node2):\n\n def lca_helper(root, node1, node2):\n \"\"\"\n Returns: [num_target_nodes, ancestor]\n\n \"\"\"\n if root is None:\n return [0, None]\n\n left_result = lca_helper(root.left, node1, node2)\n if left_result[0] == 2:\n return left_result\n right_result = lca_helper(root.right, node1, node2)\n if right_result[0] == 2:\n return right_result\n\n num_target_nodes = (\n left_result[0] + right_result[0] + (node1, node1).count(root)\n )\n\n return [num_target_nodes, root if num_target_nodes == 2 else None]\n\n return lca_helper(root, node1, node2)[1]","def calculateLogJointProbabilities(self, datum):\n logJoint = util.Counter()\n \"*** YOUR CODE HERE ***\"\n\t#Adds log(P(y)) to calculate P(y|f1,f2...)\n for label in self.legalLabels:\n\t\tlogJoint[label] += math.log(self.prior[label])\n\t#Adds log(P(f1|y)), log(P(f2|y))... to calculate P(y|f1, f2...)\n for key in datum:\n\t\t#if key == (7, 3):\n\t\t\t#print self.condprobs[key, 0]\n\t\tfor label in self.legalLabels:\n\t\t\t#print str(key) + str(datum[key])\n\t\t\tlogJoint[label] += math.log(self.condprobs[key, label][datum[key]])\n return logJoint","def to_sentence(tree, key=lambda s,x: 1.0):\n s = ['']\n for terminal in flatten_tree(tree):\n if isinstance(terminal, str):\n s.append(terminal)\n else:\n s.append(terminal.produce(key=lambda x: key(s,x)))\n s.pop(0)\n return ' '.join(s)","def calculateLogJointProbabilities(self, datum):\n\tlogJoint = util.Counter()\n\t#want to calculate log(P(y)) + log(sum(P(fi|y)))\n\t#where y is a label\n\tfor label in self.legalLabels:\n\t\tlogJoint[label] = math.log(self.prior_distribution_prob[label])\n\t\tfor feature, value in datum.items():\n\t\t\tcp = self.conditional_prob[label][feature][value]\n\t\t\tif cp > 0: #condition check for values < 0 because log(0) is undefined and math domain error occurs\n\t\t\t\tlogJoint[label] += math.log(cp) #summing up\n\t\t\t\t\n\treturn logJoint","def run(args):\n # Load the pedigree file\n print('Parsing pedigree...', file=sys.stderr)\n pedigree = Pedigree.parse(args.input_ped)\n for member in pedigree.members:\n print(member, file=sys.stderr)\n # load the HLA calls for each donor\n print('Loading HLA calls...', file=sys.stderr)\n all_calls = {}\n calls = {}\n for donor, fcalls in zip(args.donor_name, args.donor_calls):\n print(donor, fcalls.name, file=sys.stderr)\n all_calls[donor] = []\n calls[donor] = {'A': [], 'B': [], 'C': []}\n for hla_type in sorted(HLAType.parse(line.strip())\n for line in fcalls):\n calls[donor][hla_type.gene_name].append(hla_type)\n all_calls[donor].append(hla_type)\n print('Checking for consistency...', file=sys.stderr)\n # get IDs of parents\n index_member = pedigree.by_name[args.index_donor]\n father = index_member.father\n mother = index_member.mother\n # check for 4 digit consistency\n mm4 = check_consistency(4, calls[args.index_donor], calls.get(father),\n calls.get(mother))\n # check for 2 digit consistency\n mm2 = check_consistency(2, calls[args.index_donor], calls.get(father),\n calls.get(mother))\n num_parents = len({father, mother} - {'0'})\n # print result line\n print('\\t'.join(map(str, [\n args.index_donor, num_parents, mm2 or 'OK', mm4 or 'OK'\n ])))","def getOrthologNodes(tree, positive_set, options, selector=\"strict\", outgroups=None):\n\n nspecies = len(options.org2column)\n\n if selector == \"strict\":\n # strict orthologs: at most one gene per species\n exit_function = lambda num_genes_for_species: num_genes_for_species > 1\n keep_function = lambda num_genes_for_species: num_genes_for_species == 1\n total_genes_function = lambda num_genes_at_node, num_species_in_pattern: num_genes_at_node == num_species_in_pattern\n total_species_function = lambda num_species_at_node, num_species_in_pattern: num_species_at_node == num_species_in_pattern\n check_outgroup_function = lambda x: False\n negative_set = set()\n elif selector == \"degenerate\":\n # degenerate orthologs: any number of genes per species,\n exit_function = lambda num_genes_for_species: False\n keep_function = lambda num_genes_for_species: num_genes_for_species > 0\n total_genes_function = lambda num_genes_at_node, num_species_in_pattern: num_genes_at_node > num_species_in_pattern\n total_species_function = lambda num_species_at_node, num_species_in_pattern: num_species_at_node == num_species_in_pattern\n check_outgroup_function = lambda x: False\n negative_set = set()\n elif selector == \"lineage\":\n # lineage specific duplications: at least 1 gene\n exit_function = lambda num_genes_for_species: False\n keep_function = lambda num_genes_for_species: num_genes_for_species > 1\n total_genes_function = lambda num_genes_at_node, num_species_in_pattern: num_genes_at_node >= num_species_in_pattern\n total_species_function = lambda num_species_at_node, num_species_in_pattern: False\n check_outgroup_function = lambda x: False\n negative_set = set(range(nspecies)).difference(positive_set)\n elif selector == \"any\":\n # any number of orthologs, including\n # orphans\n exit_function = lambda num_genes_for_species: False\n keep_function = lambda num_genes_for_species: True\n total_genes_function = lambda num_genes_at_node, num_species_in_pattern: True\n total_species_function = lambda num_species_at_node, num_species_in_pattern: num_species_at_node == num_species_in_pattern\n check_outgroup_function = lambda x: False\n negative_set = set()\n elif selector == \"outgroup\":\n # group selector\n exit_function = lambda num_genes_for_species: False\n keep_function = lambda num_genes_for_species: num_genes_for_species > 0\n total_genes_function = lambda num_genes_at_node, num_species_in_pattern: False\n total_species_function = lambda num_species_at_node, num_species_in_pattern: False\n # check for outgrup: needs to have outgroup and at least one other species\n # ie.: sum of all genes in outgroups larger than sum of all genes in\n # all species\n if not outgroups:\n raise \"usage error: please supply outgroups if 'outgroup'-selector is chosen.\"\n check_outgroup_function = lambda genes: 0 < sum(\n [len(genes[x]) for x in outgroups]) < sum(map(lambda x: len(x), genes))\n negative_set = set()\n else:\n raise \"unknown selector %s\" % selector\n\n # work here: set genes[node_id] to None,\n # 1. if the gene count for a species of interest is > 1\n # 2. if the gene count for all species of interest is 1 in\n # the child node.\n\n if options.loglevel >= 5:\n options.stdlog.write(\"# gene tree\\n\")\n tree.display()\n\n n = TreeTools.GetSize(tree) + 1\n genes = []\n for x in range(n):\n genes.append([set() for x in range(nspecies)])\n\n ortholog_nodes = []\n\n def count_genes(node_id):\n \"\"\"record number of genes per species for each node\n \"\"\"\n node = tree.node(node_id)\n\n if options.loglevel >= 6:\n options.stdlog.write(\"# node_id=%i\\n\" % node_id)\n if options.loglevel >= 10:\n options.stdlog.write(\"# sets=%s\\n\" % (str(genes)))\n\n # species in pattern\n num_species_in_pattern = len(positive_set)\n\n if node.succ:\n # process non-leaf node\n for s in node.succ:\n\n # propagate: terminated nodes force upper nodes to terminate\n # (assigned to None).\n if not genes[s]:\n genes[node_id] = None\n return\n\n # total number of genes at node\n num_genes_at_node = 0\n # total number of species at node\n num_species_at_node = 0\n\n # compute new gene set for each species at node\n for x in positive_set:\n genes[node_id][x] = genes[node_id][x].union(genes[s][x])\n\n num_genes_for_species = len(genes[node_id][x])\n if exit_function(num_genes_for_species):\n genes[node_id] = None\n return\n num_genes_at_node += num_genes_for_species\n if num_genes_for_species:\n num_species_at_node += 1\n\n if options.loglevel >= 6:\n print \"node=\", node_id, \"species_at_node\", num_species_at_node, \"genes_at_node=\", num_genes_at_node, \\\n \"num_genes_for_species=\", num_genes_for_species, \"ngenes=\", sum(\n map(lambda x: len(x), genes[node_id]))\n options.stdlog.write(\n \"# genes at node %i\\t%s\\n\" % (node_id, genes[node_id]))\n if outgroups:\n print sum([len(genes[node_id][x]) for x in outgroups])\n print check_outgroup_function(genes[node_id])\n\n # check stop criterion\n if total_species_function(num_species_at_node, num_species_in_pattern):\n # check if positive requirements are fulfilled\n for x in positive_set:\n if not keep_function(len(genes[node_id][x])):\n if options.loglevel >= 6:\n options.stdlog.write(\n \"# keep function false for species %i\\n\" % x)\n break\n else:\n if total_genes_function(num_genes_at_node, num_species_in_pattern):\n if options.loglevel >= 6:\n options.stdlog.write(\"# recording node %i\\n\" % x)\n ortholog_nodes.append((node_id, genes[node_id]))\n genes[node_id] = None\n return\n elif check_outgroup_function(genes[node_id]):\n ortholog_nodes.append((node_id, genes[node_id]))\n genes[node_id] = None\n return\n elif negative_set:\n if total_genes_function(num_genes_at_node, num_species_in_pattern):\n if options.loglevel >= 6:\n options.stdlog.write(\"# recording node %i\\n\" % node_id)\n ortholog_nodes.append((node_id, genes[node_id]))\n\n else:\n # process leaf\n s, t, g, q = parseIdentifier(node.data.taxon, options)\n c = options.org2column[s]\n if c in positive_set:\n genes[node_id][c].add(g)\n elif c in negative_set:\n genes[node_id] = None\n\n tree.dfs(tree.root, post_function=count_genes)\n\n return ortholog_nodes","def test_that_most_probable_always_returns_a_tree_of_the_same_logprob():\n\n grammar = Grammar(rulebooks)\n actual_tree = grammar.sample_tree()\n sentence = actual_tree.terminals\n inner = grammar.compute_inside_probabilities(sentence)\n best_tree = grammar.compute_most_likely_tree(sentence, inner, root=0)\n best_logprob = grammar.logprob_tree(best_tree)\n\n for _ in range(20):\n tree = grammar.compute_most_likely_tree(sentence, inner, root=0)\n assert np.isclose(grammar.logprob_tree(tree), best_logprob)","def tree_report(tree, unknown_summary, debug=False):\n ##########################################################################\n def get_equation_html_from_id(equation_id):\n if list(tree)[0].split('_')[0] == 'a':\n return '<span class=\"param\" data-type=\\\"eq\\\" data-item=\\\"'+\\\n equation_id+\\\n '\\\">this equation</span>'\n else:\n name_of_eq = equation_id.split('_')[1]\n page_of_eq = eqbank[name_of_eq][\"group\"]\n return \\\n '<span class=\"param\" data-type=\\\"pallette-eq\\\" data-page=\\\"'\\\n +str(page_of_eq)\\\n +'\\\" data-item=\\\"'+\\\n str(name_of_eq)+\\\n '\\\">this equation</span>'\n \n def get_assoc_html(var_term):\n return '<span class=\"param\" data-type=\\\"var-assoc\\\" data-item=\\\"'+\\\n str(var_term)+\\\n '\\\">'+\\\n str(unknown_summary[list(unknown_summary[var_term])[0]]['value']['varDisplay'])+\\\n '</span>'\n\n def construct_phrase_desc_rec(node, parent):\n l_children = {\n child: len([_ for _ in tree[child] if _ != node])\n for child in tree[node]\n if child != parent\n }\n if debug:\n print(\"In tree_report\")\n print(l_children)\n \n # If the root node has any substitutions, address it accordingly\n # and only report it as such, no need for breakdowns.\n if \"equationlist\" in tree.nodes[node]:\n current_phrase = []\n \n subs_children = {\n eq: tree.nodes[node][\"equationlist\"][eq]['term'] \\\n for eq in tree.nodes[node][\"equationlist\"]\n if tree.nodes[node][\"equationlist\"][eq]['substituted'] == True\n }\n \n # if node has * \n # and children has -1, include negative\n # and if node has > 1 subs_children,\n # include product of\n if tree.nodes[node]['label'] == '*':\n if \"NegativeOne\" in [\n _.split('_')[2] \n for _ in nx.neighbors(tree, node) \n if _ !=tree.nodes[node]['pred']\n ]:\n if parent==None:\n # Top level product with a negative sign\n current_phrase.append(\"negative\")\n elif parent['label'] == '+':\n # The parent above node is +, so this must be a negative term\n current_phrase.append(\"subtracted by\")\n if len(subs_children)>1:\n current_phrase.append(\"product of\")\n \n # if node has + \n # and if node has > 1 subs_children,\n # include sum of\n if tree.nodes[node]['label'] == '+'\\\n and len(subs_children)>1:\n current_phrase.append(\"sum of\")\n \n #if len(subs_children) == 1:\n # host_eq = list(subs_children)[0]\n # var_term = subs_children[host_eq]\n # # only one substituted term, nothing fancy\n # current_phrase.append(\n # get_assoc_html(var_term)+' from '+get_equation_html_from_id(host_eq)\n # )\n #\n #elif len(subs_children) > 1:\n \n if len(subs_children) > 0:\n term_list = []\n for host_eq, var_term in subs_children.items():\n term_list.append(\n get_assoc_html(var_term)+' from '+get_equation_html_from_id(host_eq)\n ) \n current_phrase.append(\",\".join(term_list))\n \n if debug:\n print(current_phrase)\n return \" \".join(current_phrase)\n\n # If the node is a leaf node\n if len(l_children) == 0:\n # Substitute this with the \n # proper term related to each leaf node\n \n # Add utility function bits for comparison\n if is_symbol(tree, node):\n return get_parambox_html(node, tree)\n else:\n return tree.nodes[node]['label']\n \n child_phrases_list = {\n child: construct_phrase_desc_rec(child, node)\n for child in l_children\n }\n \n current_phrase = [];\n \n if tree.nodes[node]['label'] == '+':\n # Addition - \"sum of\" etc.\n # except: if only one child exists, just mention this child.\n # Separate out the negative and positive terms,\n # negative terms begin with \"subtracted by\"\n \n # if parent==None:\n # current_phrase.append(\"sum of\")\n # elif len(l_children) == 1\\\n # or len([_ for _ in l_children if l_children[_]==0])==1:\n # pass\n # else:\n # current_phrase.append(\"sum of\")\n \n texts = {\"single\":[], \"subtree\": []}\n for child in l_children:\n # Check if the children of node are leaves or not\n if l_children[child] == 0:\n if debug:\n print(f\"at {node}--> child:{child} phrase:{child_phrases_list[child]}\")\n texts[\"single\"].append(child_phrases_list[child])\n elif l_children[child] > 0:\n texts[\"subtree\"].append(child_phrases_list[child])\n \n if len(texts[\"single\"]) + len(texts[\"subtree\"]) > 1:\n current_phrase.append(\"sum of\")\n \n if len(texts[\"single\"]):\n current_phrase\\\n .append(\",\".join(texts[\"single\"]))\n \n if len(texts[\"single\"]) and len(texts[\"subtree\"]):\n current_phrase.append(\"and\")\n \n if len(texts[\"subtree\"]):\n current_phrase\\\n .append(\",\".join(texts[\"subtree\"]))\n \n elif tree.nodes[node]['label'] == '*':\n labels_children = [child_phrases_list[_] for _ in child_phrases_list]\n if \"-1\" in labels_children:\n if parent==None:\n # Top level product with a negative sign\n current_phrase.append(\"negative\")\n elif tree.nodes[parent]['label'] == '+':\n # The parent above node is +, so this must be a negative term\n current_phrase.append(\"subtracted by\")\n # removing -1 from future considerations, we've already accounted for this.\n l_children = {_:l_children[_] for _ in l_children if tree.nodes[_]['label'] != '-1'}\n \n # if parent==None:\n # current_phrase.append(\"product of\")\n # elif len(l_children) == 1\\\n # or len([_ for _ in l_children if l_children[_]==0])==1:\n # pass\n # else:\n # current_phrase.append('product of')\n \n # directly append the leaf node labels,\n # then separately append the results of the sum on the next level\n texts = {\"single\":[], \"subtree\": []}\n for child in l_children:\n # Check if the children of node are leaves or not\n if l_children[child] == 0:\n texts[\"single\"].append(child_phrases_list[child])\n elif l_children[child] > 0:\n texts[\"subtree\"].append(child_phrases_list[child])\n \n if len(texts[\"single\"]) + len(texts[\"subtree\"]) > 1:\n current_phrase.append(\"product of\")\n \n if len(texts[\"single\"]):\n current_phrase\\\n .append(\",\".join(texts[\"single\"]))\n \n if len(texts[\"single\"]) and len(texts[\"subtree\"]):\n current_phrase.append(\"and\")\n \n if len(texts[\"subtree\"]):\n current_phrase\\\n .append(\",\".join(texts[\"subtree\"]))\n \n elif tree.nodes[node]['label'] == '^':\n labels_children = [child_phrases_list[_] for _ in child_phrases_list]\n if \"-1\" in labels_children:\n if parent!=None and tree.nodes[parent]['label'] == '*':\n # The parent above node is +, so this must be a negative term\n current_phrase.append(\"divided by\")\n current_phrase.extend([\n child_phrases_list[_] \n for _ in l_children if tree.nodes[_]['label'] != '-1'\n ])\n if parent==None:\n # Top level product with a negative sign\n current_phrase.append(\"raised to power (-1)\")\n # Update:\n # If power is integer and <0\n # If -1, say divided by\n # If <-1, say divided by __ raised to the power\n # Otherwise, Say raised to power of (integer or expr)\n # Eg: MediumProblem\n \n return \" \".join(current_phrase)\n ##########################################################################\n \n return construct_phrase_desc_rec(find_root(tree), None)","def compute_idfs(documents):\n from math import log\n\n # number of documents\n TotalDocuments = len(documents)\n\n # create set of all words in all docs\n words = set()\n for words_list in documents.values():\n for word in words_list:\n words.add(word)\n\n # calculate how many doc containing each words, then calculate idfs\n nDocsContain = dict()\n idfs = dict()\n for word in words:\n nDocsContain[word] = 0\n for words_list in documents.values():\n if word in words_list:\n nDocsContain[word] += 1 \n idfs[word] = log(TotalDocuments/nDocsContain[word])\n\n return idfs","def evaluate(self, feats, tag_set):\n corect=0\n incorect=0 \n for (tokens, tags) in zip(feats, tag_set):\n yyhat= self.tag(tokens)\n for pre, tag in zip(yyhat, tags):\n if pre==tag:\n corect+=1\n else:\n incorect+=1\n\n return corect/(corect+incorect)","def _log_fold_change_pairs(self, idx0, idx1, base):\n logfc = np.zeros(shape=(len(idx0), len(idx1), self._theta_mle.shape[1]))\n for i, xi in enumerate(idx0):\n for j, xj in enumerate(idx1):\n logfc[i, j, :] = self._theta_mle[xi, :] - self._theta_mle[xj, :]\n\n if base == np.e:\n return logfc\n else:\n return logfc / np.log(base)","def get_similarities(tags=None):\n tags = tags or _get_tags()\n # do your thing ...\n similar_tags = list()\n for item in itertools.permutations(tags, 2):\n diffchecker = SequenceMatcher(isjunk=None, a=item[0], b=item[1], autojunk=True)\n similarity = diffchecker.quick_ratio()\n if similarity >= SIMILAR:\n similar_tags.append(item)\n return similar_tags","def get_stats(sents, **dummy_args):\n from collections import Counter\n statsmode_results = Counter() \n # first, put the relevant trees into temp file\n\n for sent in sents:\n statsmode_results['Sentences'] += 1\n deps = get_deps(sent, dep_type)\n numpass = len([x for x in deps.links if x.type.endswith('pass')])\n statsmode_results['Passives'] += numpass\n statsmode_results['Tokens'] += len(sent.tokens)\n words = [w.word for w in sent.tokens if w.word is not None and w.word.isalnum()]\n statsmode_results['Words'] += len(words)\n statsmode_results['Characters'] += len(''.join(words))\n\n to_open = '\\n'.join(s.parse_string.strip() for s in sents)\n\n from corpkit.dictionaries.process_types import processes\n from corpkit.other import as_regex\n tregex_qs = {'Imperative': r'ROOT < (/(S|SBAR)/ < (VP !< VBD !< VBG !$ NP !$ SBAR < NP !$-- S !$-- VP !$ VP)) !<< (/\\?/ !< __) !<<- /-R.B-/ !<<, /(?i)^(-l.b-|hi|hey|hello|oh|wow|thank|thankyou|thanks|welcome)$/',\n 'Open interrogative': r'ROOT < SBARQ <<- (/\\?/ !< __)', \n 'Closed interrogative': r'ROOT ( < (SQ < (NP $+ VP)) << (/\\?/ !< __) | < (/(S|SBAR)/ < (VP $+ NP)) <<- (/\\?/ !< __))',\n 'Unmodalised declarative': r'ROOT < (S < (/(NP|SBAR|VP)/ $+ (VP !< MD)))',\n 'Modalised declarative': r'ROOT < (S < (/(NP|SBAR|VP)/ $+ (VP < MD)))',\n 'Open class': r'/^(NN|JJ|VB|RB)/ < __',\n 'Closed class': r'__ !< __ !> /^(NN|JJ|VB|RB)/',\n 'Clauses': r'/^S/ < __',\n 'Interrogative': r'ROOT << (/\\?/ !< __)',\n 'Mental processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % \\\n as_regex(processes.mental, boundaries='w'),\n 'Verbal processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % \\\n as_regex(processes.verbal, boundaries='w'),\n 'Relational processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % \\\n as_regex(processes.relational, boundaries='w'),\n 'Verbless clause': r'/^S/ !<< /^VB.?/'}\n\n for name, q in sorted(tregex_qs.items()):\n res = tregex_engine(query=q, \n options=['-o', '-C'], \n corpus=to_open, \n root=root\n )\n statsmode_results[name] += int(res)\n if root:\n root.update()\n return statsmode_results, []","def Log_OB(xref,x):\n\n nX = np.shape(x)\n\n m = nX[0]\n n = nX[1]\n t = nX[2]\n\n G = np.zeros((m,n,t))\n\n for r in range(t):\n\n # Correct for permuations\n\n Xout,PiA= CorrectPerm(xref,x[:,:,r])\n\n G[:,:,r] = Xout - np.dot(xref,np.dot(PiA,Xout))\n\n return G","def compute_my_variability(event_log: Log) -> float:\n prefixes: List[List[Event]] = []\n bar: Bar = IncrementalBar(\"Prefix generation\", max=len(event_log.trace_list))\n for trace in event_log.trace_list:\n trace_prefixes: List[List[Event]] = trace.get_all_prefixes()\n\n for prefix in trace_prefixes:\n if prefix not in prefixes:\n prefixes.append(prefix)\n bar.next()\n bar.finish()\n\n entropy: float = 0\n\n bar = ShadyBar(\"Prefix likelihood estimation\", max=len(prefixes))\n for prefix in prefixes:\n\n p: float = _prefix_likelihood_estimator(event_log, prefix)\n entropy += p * logarithm(p, 10)\n\n bar.next()\n bar.finish()\n\n entropy *= -1\n\n return entropy","def extract_entities_from_dependency_parse(dtrees, postag):\n sents = []\n for x in range(0,len(dtrees)):\n tok_list = []\n for node_index in dtrees[x].nodes:\n if node_index != 0:\n node = dtrees[x].nodes[node_index]\n if node['ctag'] == postag:\n tok_list.append((node['word'],postag))\n else:\n tok_list.append((node['word'],'O'))\n sents.append(tok_list)\n return sents","def substructure_sim_exact(subtrees_1, subtrees_2):\n assert(len(subtrees_1) == len(subtrees_2))\n n = len(subtrees_1)\n f1 = np.zeros(n)\n for i in range(n):\n f1[i] = subtrees_1[i] == subtrees_2[i] # calculate the number of matching pairs\n\n return float(np.count_nonzero(f1)) / float(len(f1))","def treeThreat():\n\tglobal treeThreatList\n\ttreeThreatList = []\n\tcount = 0\n\tfor name in threatList:\n\t\tif name in treeList:\n\t\t\tcount += 1\n\t\t\ttreeThreatList.append(name)\n\treturn count","def likelihood(A, B, word_list):\n\tstr_buf = []\n\tloglh = 0.0\n\tlh = 1\n\n\t# read each line and compute likelihood.\n\tfor line in sys.stdin:\n\t\tline = line.split()\n\t\t# print(line)\n\t\tif len(line) == 3:\n\t\t\tstr_buf.append((str(line[0]), str(line[1])))\n\n\t\telse:\n\t\t\t# if come to the end of a sentence\n\t\t\tif len(str_buf) != 0:\n\t\t\t\tstr_buf = [('<s>','BOS')] + str_buf + [('</s>', 'EOS')]\n\t\t\t\tfor i in range(len(str_buf) - 1):\n\t\t\t\t\t# print(str_buf[i][0], str_buf[i+1][0])\n\t\t\t\t\tif str_buf[i+1][0] in word_list:\n\t\t\t\t\t\t# print('debug: A[',str_buf[i][1],'][', str_buf[i+1][1],']:', A[ str_buf[i][1] ][ str_buf[i+1][1] ])\n\t\t\t\t\t\t# print('debug: B[',str_buf[i+1][1],'][', [str_buf[i+1][0]], ']:', B[ str_buf[i+1][1] ][str_buf[i+1][0]])\n\t\t\t\t\t\tloglh += ( log(A[ str_buf[i][1] ][ str_buf[i+1][1] ]) + log(B[ str_buf[i+1][1] ][str_buf[i+1][0]]) )\n\t\t\t\t\telse:\n\t\t\t\t\t\t# print('debug: else A[',str_buf[i][1],'][', str_buf[i+1][1],']:', A[ str_buf[i][1] ][ str_buf[i+1][1] ])\n\t\t\t\t\t\t# print('debug: else B[',str_buf[i+1][1],'][', '<UNK>', ']:', B[ str_buf[i+1][1] ][ '<UNK>' ])\n\t\t\t\t\t\tloglh += ( log(A[ str_buf[i][1] ][ str_buf[i+1][1] ]) + log(B[ str_buf[i+1][1] ]['<UNK>']) )\n\n\t\t\t\tlh = e ** loglh\n\t\t\t\t# for s in str_buf:\n\t\t\t\t# \tprint(s[0], end=\" \")\n\t\t\t\t# print(':', lh)\n\t\t\t\tprint(lh)\n\n\t\t\t\tstr_buf = []\t\n\t\t\t\tloglh = 0\n\t\t\t\tlh = 1","def kld(mu, log_var):\n return (mu + log_var).sum() # TODO Your code goes here.","def labelled_attachment(gold_trees, pred_trees):\n count_match, count_total = 0, 0\n for gold, pred in zip(gold_trees, pred_trees):\n triples_pairs = zip(\n gold.get_triples(include_root=True),\n pred.get_triples(include_root=True),\n )\n for (g_src, g_trg, g_rel), (p_src, p_trg, p_rel) in triples_pairs:\n assert g_src == p_src\n count_total += 1\n if g_trg == p_trg and g_rel == p_rel:\n count_match += 1\n if count_match == 0 or count_total == 0:\n return 0.0\n else:\n return float(count_match) / count_total","def get_stats(sents):\n import os\n import re \n # first, put the relevant trees into temp file\n if 'outname' in kwargs.keys():\n to_open = 'tmp-%s.txt' % kwargs['outname']\n else:\n to_open = 'tmp.txt'\n with open(to_open, \"w\") as fo:\n for sent in sents:\n statsmode_results['Sentences'] += 1\n fo.write(sent.parse_string.rstrip().encode('utf-8', errors = 'ignore') + '\\n')\n deps = get_deps(sent, dep_type)\n numpass = len([x for x in deps.links if x.type.endswith('pass')])\n statsmode_results['Passives'] += numpass\n statsmode_results['Tokens'] += len(sent.tokens)\n statsmode_results['Words'] += len([w for w in sent.tokens if w.word.isalnum()])\n #statsmode_results['Unique words'] += len(set([w.word.lower() for w in sent.tokens if w.word.isalnum()]))\n #statsmode_results['Unique lemmata'] += len(set([w.lemma.lower() for w in sent.tokens if w.word.isalnum()]))\n\n # count moods via trees (/\\?/ !< __)\n from dictionaries.process_types import processes\n from corpkit.other import as_regex\n tregex_qs = {'Imperative': r'ROOT < (/(S|SBAR)/ < (VP !< VBD !< VBG !$ NP !$ SBAR < NP !$-- S !$-- VP !$ VP)) !<< (/\\?/ !< __) !<<- /-R.B-/ !<<, /(?i)^(-l.b-|hi|hey|hello|oh|wow|thank|thankyou|thanks|welcome)$/',\n #'Open interrogative': r'ROOT < SBARQ <<- (/\\?/ !< __)', \n #'Closed interrogative': r'ROOT ( < (SQ < (NP $+ VP)) << (/\\?/ !< __) | < (/(S|SBAR)/ < (VP $+ NP)) <<- (/\\?/ !< __))',\n 'Unmodalised declarative': r'ROOT < (S < (/(NP|SBAR|VP)/ $+ (VP !< MD)))',\n 'Modalised declarative': r'ROOT < (S < (/(NP|SBAR|VP)/ $+ (VP < MD)))',\n 'Open class words': r'/^(NN|JJ|VB|RB)/ < __',\n 'Closed class words': r'__ !< __ !> /^(NN|JJ|VB|RB)/',\n 'Clauses': r'/^S/ < __',\n 'Interrogative': r'ROOT << (/\\?/ !< __)',\n 'Mental processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % as_regex(processes.mental, boundaries = 'w'),\n 'Verbal processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % as_regex(processes.verbal, boundaries = 'w'),\n 'Relational processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % as_regex(processes.relational, boundaries = 'w')}\n\n for name, q in sorted(tregex_qs.items()):\n res = tregex_engine(query = q, \n options = ['-o', '-C'], \n corpus = to_open, \n root = root)\n statsmode_results[name] += int(res)\n global numdone\n numdone += 1\n if root:\n root.update()\n if not root:\n tot_string = str(numdone + 1) + '/' + str(total_files * len(tregex_qs.keys()))\n if 'outname' in kwargs.keys():\n tot_string = '%s: %s' % (kwargs['outname'], tot_string)\n animator(p, numdone, tot_string, **par_args)\n if 'note' in kwargs.keys() and kwargs['note'] is not False:\n kwargs['note'].progvar.set((numdone * 100.0 / (total_files * len(tregex_qs.keys())) / denom) + startnum)\n os.remove(to_open)","def find_matches(params, tol = 1+1e-5):\n attempt=params['attempt']\n attempt_tree=params['att_tree']\n attempt_list=flatten(attempt_tree,'t')\n \n answer=params['answer']\n answer_tree=params['ans_tree']\n answer_list=flatten(answer_tree,'c')\n\n # sort by value\n combined_list=sorted(answer_list+attempt_list,key=lambda x: (x[0],len(x[2])))\n # find all hits\n Hits=find_Hits(combined_list, tol)\n # eliminate dominating hits\n final_matches=find_dominating_hits(Hits,answer,attempt)\n \n return final_matches","def dlog_score(self, T, pos, m, psi):\n dpsi = tr.zeros_like(psi)\n for (i, j, lab) in T:\n if j != 0:\n m_i = self.get_tag_index(m[i - 1])\n pos1 = pos[i - 1]\n m_j = self.get_tag_index(m[j - 1])\n pos2 = pos[j - 1]\n dpsi[pos1, pos2, lab, m_i, m_j] += 1\n return dpsi","def calc_tdf(docs):\r\n terms = set()\r\n for doc in docs:\r\n for term in doc:\r\n terms.add(term)\r\n tdf = {}\r\n for term in terms:\r\n doc_count = 0\r\n for doc in docs:\r\n doc_count += 1 if term in doc else 0\r\n tdf[term] = doc_count\r\n return tdf","def Log_OB_S1_mat(xref,x):\n\n nX = np.shape(x)\n\n m = nX[0]\n n = nX[1]\n t = nX[2]\n\n G = np.zeros((n,t,t))\n Gv = np.zeros((m,n,t,t))\n\n for rx in range(t):\n for ry in range(t):\n\n # Correct for permuations\n\n Xout = dp(x[:,:,rx,ry])\n\n for q in range(n):\n\n a = np.sum(Xout[:,q]*xref[:,q,rx,ry])/np.sqrt(1e-12 + np.sum(xref[:,q,rx,ry]**2)*np.sum(Xout[:,q]**2)) # Should have unit L2 norm\n if a > 1:\n a = 1\n if a < -1:\n a = -1\n G[q,rx,ry] = np.arccos(a) # Computing the angles\n v = Xout[:,q] - a*xref[:,q,rx,ry]\n Gv[:,q,rx,ry] = v / (1e-24 + np.linalg.norm(v)) # Unit vector in the tangent subspace\n\n return G,Gv","def calc_ent(x, dist):\n\n assert(len(x) == len(dist))\n x_value_list = set([x[i] for i in range(x.shape[0])])\n dist_sum = np.sum(dist)\n ent = 0.0\n for x_value in x_value_list:\n index = (x == x_value)\n sub_dist = dist[index]\n sub_dist_sum = np.sum(sub_dist)\n p = sub_dist_sum / dist_sum\n logp = np.log2(p)\n ent -= p * logp\n\n return ent","def denom(self, tokens):\n\n sum = 0\n A_set = self.A(tokens)\n # heuristic\n for elem in A_set:\n sum += self.cond_prob(elem, tokens[1:])\n return 1 - sum","def calc_tf_log(doc):\r\n tf = calc_tf(doc)\r\n max_tf = tf[max(tf, key=tf.get)]\r\n tf_log = {}\r\n for key, val in tf.items():\r\n tf_log[key] = (1 + math.log(val)) / (1 + math.log(max_tf))\r\n return tf_log","def alleles(gt1, gt2):\n\n\t# Count genotypes 1 and 2\n\tcount = {}\n\tfor g in gt1 :\n\t\tcount[g] = count.get(g, 0) + 1\n\n\tfor g in gt2 :\n\t\tcount[g] = count.get(g, 0) + 1\n\n\t# Find mayor allele (we call it 'ref')\n\tmaxCount = 0\n\tref = ''\n\tfor g in count:\n\t\tif count[g] > maxCount:\n\t\t\tmaxCount = count[g]\n\t\t\tref = g\n\n\t# Find minor allele (we call these one 'alt')\n\talt = ''\n\tfor g in count:\n\t\tif g != ref and g != '0': alt = g\n\n\t# Create a genotype string (VCF style)\n\tgtstr = \"\"\n\tfor i in range(len(gt1)):\n\t\tgtstr += \"\\t\" + gtVcf(ref, alt, gt1[i]) + \"/\" + gtVcf(ref, alt, gt2[i])\n\n\treturn ref, alt, count[alt], gtstr","def score(self, tree):\n probas = [self.prod_freq.get(prod, 0.5) /\n self.source_freq.get(prod.source, 1)\n for prod in tree]\n logger.debug(\"Scoring {0}:\".format(tree))\n for prod, proba in zip(tree, probas):\n logger.debug(\n \"\\t- {0} {1}({2})\"\n .format(prod, round(proba, 3), round(log(proba, 10), 3)))\n log_proba = sum(log(proba, 10) for proba in probas)\n return {\n 'log_proba': log_proba,\n 'productions': len(probas)\n }","def total(tree):\n if tree is None:\n return 0\n return total(tree.left) + total(tree.right) + tree.cargo","def find_entropy(less_than_threshold,more_than_threshold):\n\n ''' Storing total number of records '''\n total_records = len(less_than_threshold) + len(more_than_threshold)\n\n ''' Calculating the probability '''\n less_than_probability = len(less_than_threshold) / total_records\n more_than_probability = len(more_than_threshold) / total_records\n\n ''' Converting the dataframe to numpy arrays '''\n less_than_threshold_values = less_than_threshold.values\n more_than_threshold_values = more_than_threshold.values\n\n ''' Storing the target attribute values (Muffin or Cupcake) for threshold values '''\n target_for_less_than = less_than_threshold_values[:, -1]\n target_for_more_than = more_than_threshold_values[:, -1]\n\n ''' Finding the counts of muffin and cupcake for values lower than and greater than threshold value '''\n recipe_type, less_than_cupcake_muffin_count = np.unique(target_for_less_than, return_counts=True)\n recipe_type, more_than_cupcake_muffin_count = np.unique(target_for_more_than, return_counts=True)\n\n # print(recipe_type, more_than_cupcake_muffin_count, len(more_than_cupcake_muffin_count))\n ''' To ensure there are at least 5 records in each node '''\n if less_than_cupcake_muffin_count.sum() < 5 or more_than_cupcake_muffin_count.sum() < 5:\n ''' Return horrible badness '''\n return math.inf\n else:\n ''' Find the entropies for less than threshold values and more than threshold values '''\n less_than_entropy = sum((less_than_cupcake_muffin_count / less_than_cupcake_muffin_count.sum()) * - np.log2(\n less_than_cupcake_muffin_count / less_than_cupcake_muffin_count.sum()))\n more_than_entropy = sum((more_than_cupcake_muffin_count / more_than_cupcake_muffin_count.sum()) * - np.log2(\n more_than_cupcake_muffin_count / more_than_cupcake_muffin_count.sum()))\n\n ''' Calculate the total weighted entropy '''\n total_weighted_entropy = less_than_probability * less_than_entropy + more_than_probability * more_than_entropy\n\n return total_weighted_entropy","def compare_dictionaries(d1, d2):\r\n score = 0\r\n gef = 0\r\n for z in d1:\r\n gef += d1[z]\r\n total = gef\r\n \r\n for x in d2:\r\n if x in d1:\r\n score += math.log(d1[x] / total) * d2[x] \r\n else:\r\n score += math.log(0.5/total) * d2[x]\r\n return score","def node_significance(log):\n caseF = {a: sum(a in case_log for case_log in log.flat_log.values())\n for a in log.activities}\n # Activities (node) significance\n S = {a: caseF[a] / len(log.cases) for a in caseF}\n return S","def draw_tre_and_aln(aln_region, tre_str, og, target_sp):\n #site, aa, score=ps_site\n\n # get leaves from tree string\n tstax = getleaf(tre_str)\n\n ts = TreeStyle()\n ts.margin_left = 5\n ts.margin_right = 30\n ts.margin_top = 20\n ts.tree_width = 50\n\n aln_region_str = aln_region.format(\"fasta\")\n\n t = PhyloTree(tre_str, alignment=aln_region_str, alg_format=\"fasta\")\n\n # interfact\n def _set_style(t):\n # input an t with alignment, add label to it\n \"\"\"\n info = TextFace(\"{}\\nCodon:{}\\nScore:{}\".format(og,site,score), fsize=8, fgcolor='black', ftype='Arial')\n info.margin_top = 10\n info.margin_right = 20\n info.margin_left = 5\n t.add_face(info, column=0, position=\"branch-bottom\")\n #t.add_face(TextFace(\"Codon:{}\".format(site)),column=0,position=\"branch-bottom\")\n \"\"\"\n ## label the longbranch\n nstyle = NodeStyle()\n # red line\n #nstyle[\"bgcolor\"] = \"DarkSeaGreen\"\n #nstyle[\"bgcolor\"] = \"LightSalmon\"\n nstyle[\"hz_line_type\"] = 0\n #nstyle[\"hz_line_color\"] = \"#ff0000\"\n for tst in tstax:\n tsnode = t.get_leaves_by_name(name=tst)[0]\n tsnode.set_style(nstyle)\n # add #1 to target species\n if target_sp in tsnode.name:\n tsnode.name = tsnode.name + \"_#1\"\n return t\n t = _set_style(t)\n ## add AA alignment\n def _trans_aln(aln_region):\n # input an coding aln\n aa_aln_str = \"\"\n for seq_obj in aln_region:\n dna = Seq(str(seq_obj.seq))\n aa_s = dna.translate(gap=\"-\")\n aa_aln_str += '>{}\\n{}\\n'.format(seq_obj.id, aa_s)\n #print(aa_aln_str)\n return aa_aln_str\n\n aa_aln_region_str = _trans_aln(aln_region)\n t_aa = PhyloTree(tre_str, alignment=aa_aln_region_str, alg_format=\"fasta\")\n t_aa = _set_style(t_aa)\n\n return t, t_aa, ts","def rel_agreement_index(self) -> float:\n a = ((self.predicted - self.true) / self.true) ** 2\n b = np.abs(self.predicted - np.mean(self.true))\n c = np.abs(self.true - np.mean(self.true))\n e = ((b + c) / np.mean(self.true)) ** 2\n return float(1 - (np.sum(a) / np.sum(e)))","def get_idf(term, documents):\n\n number_of_docs = len(documents)\n documents_containing_term = len([document for document in documents if term in document])\n\n idf = math.log10(number_of_docs / documents_containing_term)\n\n return round(idf, 5)","def log_likelihood(mu, sigma, y, T):\n ll = 0.\n for yi, Ti in zip(y, T):\n d = yi.size\n log_det_cov = np.linalg.slogdet(sigma[Ti])[1]\n y_minus_mean = yi - mu[Ti]\n term3 = np.dot(y_minus_mean.T.ravel(),\n np.linalg.solve(sigma[Ti], y_minus_mean.T).ravel())\n ll += (-0.5 * d * np.log(2 * np.pi) - 0.5 * log_det_cov - 0.5 * term3)\n return ll","def tf_idf_score():\n\n global final_doc_set\n global final_dictionary\n final_score = []\n\n for doc_id in final_doc_set:\n score = 0\n for query_term in final_dictionary.keys():\n if final_dictionary[query_term][1].get(doc_id):\n tf = final_dictionary[query_term][1][doc_id][0]\n df = final_dictionary[query_term][0]\n\n score += ((1 + log10(tf)) * log10(TOTAL_DOCS / df))\n\n final_score.append([doc_id, score])\n\n return final_score","def mutual_info(l1, l2):\n return entropy(l1) + entropy(l2) - entropy(joint_dataset(l1, l2))","def cstree_predict(value_dict, tree, order, sample, i, data):\n numerator = cstree_likelihood(sample, order, tree, data)\n order_of_var = order.index(i)\n samples = [np.array(list(sample[:order_of_var])+[val]+list(sample[order_of_var+1:]))\n for val in value_dict[i]]\n # sum_j P(X1=x1,...,Xi-1=xi-1,Xi+1=xi+1,...,Xn|Xi=j)P(Xi=j)\n denominator = sum([cstree_likelihood(s, order, tree, data) for s in samples])\n return numerator/denominator","def _alternative(words):\n wordlist = list(words)\n counter = []\n for word in wordlist:\n used = set()\n counter.append(__geterate_tree(word, wordlist, used, 1))\n return max(counter) == len(wordlist)","def logrels(rets):\n return np.log(rets + 1)","def chunked_tags(train):\n cfdist = nltk.ConditionalFreqDist()\n for t in train:\n for word, tag, chtag in tree2conlltags(t):\n if chtag == \"O\":\n cfdist[tag].inc(False)\n else:\n cfdist[tag].inc(True)\n return [tag for tag in cfdist.conditions() if cfdist[tag].max() == True]","def log_score(self, X_state_tree_sequence, pi=None, T=None, E=None):\n if pi is None:\n pi = self.estimate.pi\n if T is None:\n T = self.estimate.T\n if E is None:\n E = self.estimate.E\n\n log_scores = []\n for idx, lineageObj in enumerate(self.X):\n log_score = 0\n # the first term is the value of pi for the state of the first cell\n log_score += np.log(pi[X_state_tree_sequence[idx][0]])\n log_score += log_T_score(T, X_state_tree_sequence[idx], lineageObj)\n log_score += log_E_score(get_Emission_Likelihoods(self, E)\n [idx], X_state_tree_sequence[idx])\n assert np.all(np.isfinite(log_score))\n log_scores.append(log_score)\n return log_scores","def eta_r(self, children, t_coef):\n with tf.compat.v1.name_scope('coef_r'):\n # children is shape (batch_size x max_tree_size x max_children)\n children = tf.cast(children, tf.float32)\n batch_size = tf.shape(input=children)[0]\n max_tree_size = tf.shape(input=children)[1]\n max_children = tf.shape(input=children)[2]\n\n # num_siblings is shape (batch_size x max_tree_size x 1)\n num_siblings = tf.cast(\n tf.math.count_nonzero(children, axis=2, keepdims=True),\n dtype=tf.float32\n )\n # num_siblings is shape (batch_size x max_tree_size x max_children + 1)\n num_siblings = tf.tile(\n num_siblings, [1, 1, max_children + 1], name='num_siblings'\n )\n # creates a mask of 1's and 0's where 1 means there is a child there\n # has shape (batch_size x max_tree_size x max_children + 1)\n mask = tf.concat(\n [tf.zeros((batch_size, max_tree_size, 1)),\n tf.minimum(children, tf.ones(tf.shape(input=children)))],\n axis=2, name='mask'\n )\n\n # child indices for every tree (batch_size x max_tree_size x max_children + 1)\n child_indices = tf.multiply(tf.tile(\n tf.expand_dims(\n tf.expand_dims(\n tf.range(-1.0, tf.cast(max_children, tf.float32), 1.0, dtype=tf.float32),\n axis=0\n ),\n axis=0\n ),\n [batch_size, max_tree_size, 1]\n ), mask, name='child_indices')\n\n # weights for every tree node in the case that num_siblings = 0\n # shape is (batch_size x max_tree_size x max_children + 1)\n singles = tf.concat(\n [tf.zeros((batch_size, max_tree_size, 1)),\n tf.fill((batch_size, max_tree_size, 1), 0.5),\n tf.zeros((batch_size, max_tree_size, max_children - 1))],\n axis=2, name='singles')\n\n # eta_r is shape (batch_size x max_tree_size x max_children + 1)\n return tf.compat.v1.where(\n tf.equal(num_siblings, 1.0),\n # avoid division by 0 when num_siblings == 1\n singles,\n # the normal case where num_siblings != 1\n tf.multiply((1.0 - t_coef), tf.divide(child_indices, num_siblings - 1.0)),\n name='coef_r'\n )","def get_diff_parent_age(natal_history_adult, natal_history_pediatric, natalhist, logger):\r\n nhp=np.array(list(natal_history_pediatric[natalhist]))\r\n nhp=[nhp[i] for i in range(len(nhp)) if not(np.isnan(nhp)[i])]\r\n nha=np.array(list(natal_history_adult[natalhist]))\r\n nha=[nha[i] for i in range(len(nha)) if not(np.isnan(nha)[i])]\r\n logger.info(\"Difference between maternal age for pediatric and adult onset : {}\".format(mannwhitneyu(nhp,nha)))","def _calculate_log_det(self, var):\n log_det = []\n\n for k in range(self.n_components):\n evals, evecs = tf.linalg.eig(var[0, k])\n\n log_det.append(tf.reduce_sum(tf.math.log(tf.math.real(evals))))\n log_det = tf.convert_to_tensor(log_det)\n return tf.expand_dims(log_det, -1)","def likelihood(self, log_occr_array=None):\n\n if log_occr_array is not None:\n # Catch invalid occurrence rates for zero likelihood\n try:\n self.log_occr = log_occr_array\n except InvalidOccurrenceRate:\n return 0.0\n\n # Procedure: calculate N_exp in all cases, calculate detection\n # term if there are detections\n\n # N_exp\n N_exp = self.calc_integral() * self._N_stars\n\n # Product terms\n # TODO:Check that the array broadcasting works here\n # Shape of s_terms should be [N_planets, NR, NP]\n s_terms = self.H_array * self.F_array * self.occr\n\n if tf.is_tensor(self.occr):\n ps_terms = tf.reduce_sum(s_terms, axis=(-1, -2))\n product_term = tf.reduce_prod(ps_terms)\n ll_value = product_term * tf.exp(product_term)\n else:\n product_term = s_terms.sum(axis=(-1, -2)).prod()\n ll_value = product_term * np.exp(-N_exp)\n\n # BUG\n if np.isnan(ll_value):\n warnings.warn(\".likelihood value is nan.\")\n import pdb; pdb.set_trace()\n\n # A nan value is possible when some of the occr are too high\n return ll_value if not np.isnan(ll_value) else 0.0","def square_tree(t):\n return tree(label(t)**2,[square_tree(b) for b in branches(t)])","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)"],"string":"[\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 log_T_score(T, state_tree_sequence, lineageObj):\\n log_T_score_holder = 0\\n log_T = np.log(T)\\n # we start with the first transition, from the root cell\\n for level in lineageObj.output_list_of_gens[1:]:\\n for cell in level:\\n cell_idx = lineageObj.output_lineage.index(cell)\\n cell_state = state_tree_sequence[cell_idx]\\n if not cell.isLeaf():\\n for daughter in cell.get_daughters():\\n child_idx = lineageObj.output_lineage.index(daughter)\\n daughter_state = state_tree_sequence[child_idx]\\n log_T_score_holder += log_T[cell_state, daughter_state]\\n return log_T_score_holder\",\n \"def improve_tree(tree, freq_dict):\\n # todo\",\n \"def aggs(tree, weights):\\n restricted_nodes =[]\\n weights = apply_aggregation(tree, weights)\\n for parent, children in tree.iteritems():\\n children_weights = {}\\n for child in children:\\n children_weights[child] = weights[child]\\n try:\\n restricted_nodes.append(max(children_weights, key=children_weights.get))\\n except ValueError:\\n pass\\n ps = possible_aggs(walk(tree), restricted_nodes)\\n results = []\\n for p in ps:\\n e = merge_nodes(p, tree)\\n if e not in results:\\n results.append(e)\\n return results\",\n \"def compute_edge_logits(self):\\n TODO('https://github.com/posterior/treecat/issues/27')\",\n \"def compute_edge_logits(self):\\n TODO('https://github.com/posterior/treecat/issues/26')\",\n \"def compute_tree(self, tree):\\n g_list_val, g_list_h = self._build_graph(tree) # return theano variable of each node\\n list_val = self._traversal_tree(tree) #\\n f = theano.function(g_list_val, g_list_h, allow_input_downcast=True)\\n result = f(*list_val)\\n return result\",\n \"def calculate_merkle_tree(self, intervals: set[Interval]) -> tuple[bytes, list[bytes]]:\\n hashes = [x.data for x in intervals]\\n hashes.sort()\\n\\n merkle = hashlib.sha256()\\n for h in hashes:\\n merkle.update(h)\\n\\n return merkle.digest(), hashes\",\n \"def Trees__LCA_LowestCommonDenominator():\\n # Python2 ported to Python3 via 2to3-3.7\\n # URL:# URL:https://www.hackerrank.com/challenges/binary-search-tree-lowest-common-ancestor/problem\\n '''\\n class Node:\\n def __init__(self,info): \\n self.info = info \\n self.left = None \\n self.right = None \\n // this is a node of the tree , which contains info as data, left , right\\n '''\\n def lca(root, v1, v2):\\n # Find a and b. Link child nodes to parent to be able to backtrack.\\n # (1) Note, we add 'parent' attribute to node dynamically via node.parent = ...\\n root.parent = None\\n node_stack = []\\n node_stack.append(root)\\n v1_node, v2_node = None, None\\n while node_stack:\\n node = node_stack.pop()\\n if not v1_node and node.info == v1:\\n v1_node = node\\n if not v2_node and node.info == v2:\\n v2_node = node\\n for child_node in [node.left, node.right]:\\n if child_node:\\n child_node.parent = node # (1)\\n node_stack.append(child_node)\\n\\n # Generate path from A to root.\\n curr = v1_node\\n a_to_root = set()\\n while curr:\\n a_to_root.add(curr.info)\\n curr = curr.parent\\n\\n # traverse up b until you come across an element in a's path to parent.\\n curr = v2_node\\n while curr:\\n if curr.info in a_to_root:\\n return curr\\n else:\\n curr = curr.parent\\n\\n print(\\\"Shouldn't be here, Something went wrong\\\")\\n\\n # # Recursive. (Iterative is better, but did recursive for practice.) ~15 min.\\n # # Main idea is that we count the number of v1/v2's found of the subnodes.\\n # # If a node has sum of 2, we know it's the lca.\\n # def lca(root, v1, v2):\\n # def lca_helper(node):\\n # ret_node = None\\n # if not node:\\n # return 0, None\\n # v_match_counter = 0\\n # if node.info in [v1, v2]:\\n # v_match_counter += 1\\n # left_count, left_node_ret = lca_helper(node.left)\\n # right_count, right_node_ret = lca_helper(node.right)\\n # v_match_counter += left_count + right_count\\n # if v_match_counter == 2:\\n # ret_node = node\\n # if left_node_ret:\\n # ret_node = left_node_ret\\n # if right_node_ret:\\n # ret_node = right_node_ret\\n # return v_match_counter, ret_node\\n\\n # _, node = lca_helper(root)\\n # return node\",\n \"def log(tree, exact_src, **kw):\\n new_tree = hq[wrap(unhygienic[log], u[exact_src(tree)], ast_literal[tree])]\\n yield new_tree\",\n \"def get_relations(log):\\n # finding loops of length two\\n # ordering relations\\n triangle = {}\\n for trace in log:\\n i = 0\\n while i < len(trace) - 2:\\n current = trace.__getitem__(i)\\n successor = trace.__getitem__(i + 1)\\n successor2 = trace.__getitem__(i + 2)\\n if current == successor2:\\n if current in triangle:\\n triangle[current].append(successor)\\n else:\\n triangle[current] = [successor]\\n i += 1\\n for key, value in triangle.items():\\n triangle[key] = set(value)\\n square = {}\\n for key in triangle:\\n for element in triangle[key]:\\n if element in triangle:\\n if key in triangle[element]:\\n if key in square and element in square:\\n square[key].append(element)\\n square[element].append(key)\\n elif key in square and element not in square:\\n square[key].append(element)\\n square[element] = [key]\\n elif key not in square and element in square:\\n square[key] = [element]\\n square[element].append(key)\\n else:\\n square[key] = [element]\\n square[element] = [key]\\n for key, value in square.items():\\n square[key] = set(value)\\n # ordering relation following\\n follows = {}\\n for trace in log:\\n i = 0\\n while i < len(trace) - 1:\\n current = trace.__getitem__(i)\\n successor = trace.__getitem__(i + 1)\\n if current in follows:\\n if successor not in follows[current]:\\n follows[current].append(successor)\\n else:\\n follows[current] = [successor]\\n i += 1\\n # transforming list to set\\n for key, value in follows.items():\\n follows[key] = set(value)\\n # ordering relation causal\\n causal = {}\\n if len(square) != 0:\\n for key in follows:\\n for element in follows[key]:\\n if element in follows:\\n if key in square:\\n if (not (key in follows[element])) or (element in square[key]):\\n if key in causal:\\n causal[key].append(element)\\n else:\\n causal[key] = [element]\\n else:\\n if (not (key in follows[element])):\\n if key in causal:\\n causal[key].append(element)\\n else:\\n causal[key] = [element]\\n else:\\n if key in causal:\\n causal[key].append(element)\\n else:\\n causal[key] = [element]\\n else:\\n for key in follows:\\n for element in follows[key]:\\n if element in follows:\\n if (not (key in follows[element])):\\n if key in causal:\\n causal[key].append(element)\\n else:\\n causal[key] = [element]\\n else:\\n if key in causal:\\n causal[key].append(element)\\n else:\\n causal[key] = [element]\\n\\n for key, value in causal.items():\\n causal[key] = set(value)\\n # ordering relation unrelated if no other ordering is applied\\n # ordering relation parallel\\n parallel = {}\\n if len(square) != 0:\\n for key in follows:\\n for element in follows[key]:\\n if element in follows:\\n if key in follows[element]:\\n if element in follows[key]:\\n if key in square:\\n if not element in square[key]:\\n if key in parallel:\\n parallel[key].append(element)\\n else:\\n parallel[key] = [element]\\n else:\\n if key in parallel:\\n parallel[key].append(element)\\n else:\\n parallel[key] = [element]\\n\\n else:\\n for key in follows:\\n for element in follows[key]:\\n if element in follows:\\n if key in follows[element]:\\n if element in follows[key]:\\n if key in parallel:\\n parallel[key].append(element)\\n else:\\n parallel[key] = [element]\\n\\n for key, value in parallel.items():\\n parallel[key] = set(value)\\n return causal, parallel, follows\",\n \"def entropy(*args):\\n\\n\\n values = []\\n leaf = -1\\n\\n for i, val in enumerate(args):\\n if(val != 0):\\n values.append(val * math.log(val, len(args)))\\n if(val == 1):\\n leaf = i\\n \\n return -sum(values), leaf\",\n \"def Log_OB_S1(xref,x):\\n\\n nX = np.shape(x)\\n\\n m = nX[0]\\n n = nX[1]\\n t = nX[2]\\n\\n G = np.zeros((n,t))\\n Gv = np.zeros((m,n,t))\\n\\n for r in range(t):\\n\\n # Correct for permuations\\n\\n #Xout,PiA= CorrectPerm(xref,x[:,:,r])\\n Xout = dp(x[:,:,r])\\n\\n for q in range(n):\\n\\n a = np.sum(Xout[:,q]*xref[:,q])/np.sqrt(np.sum(xref[:,q]**2)*np.sum(Xout[:,q]**2)) # Should have unit L2 norm\\n if a > 1:\\n a = 1\\n if a < -1:\\n a = -1\\n G[q,r] = np.arccos(a) # Computing the angles\\n v = Xout[:,q] - a*xref[:,q]\\n Gv[:,q,r] = v / (1e-12 + np.linalg.norm(v)) # Unit vector in the tangent subspace\\n\\n return G,Gv\",\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 tree_statistics(tree):\\n all_elements = tree.findall('//')\\n tag_counter = defaultdict(int)\\n for element in all_elements:\\n tag_counter[element.tag] += 1\\n for (tag, counts) in tag_counter.items():\\n print \\\"{0}: {1}\\\".format(tag, counts)\",\n \"def compute_edge_logits(self):\",\n \"def _gen_terms(self, tree):\\n for leaf in self._leaves(tree):\\n terms = [self._normalize(w) for w, _ in leaf if self._filter(w)]\\n # Phrase only\\n if len(terms) > 1:\\n yield terms\",\n \"def get_association_strengths(self, word_pairs):\\n \\n word_pairs = [(s.upper(), t.upper()) for s, t in word_pairs] #node names are all uppercase\\n \\n sources = set()\\n targets = set()\\n vocab = set(self.graph.vs[\\\"name\\\"])\\n for s, t in word_pairs:\\n if s in vocab:\\n sources.add(s)\\n if t in vocab:\\n targets.add(t)\\n \\n sources = list(sources) #enforce consistent order\\n targets = list(targets)\\n s_map = {s:i for i, s in enumerate(sources)} #get indexes\\n t_map = {t:i for i, t in enumerate(targets)}\\n \\n #For large numbers of pairs, it's quicker to get all the paths at once than to get them one at a time\\n matrix = self.graph.shortest_paths(sources, targets, weights=\\\"-log weight\\\", mode=OUT)\\n \\n strengths = []\\n for s, t in word_pairs:\\n neg_log_dist = matrix[s_map[s]][t_map[t]] if (s in s_map and t in t_map) else float(\\\"inf\\\")\\n strengths.append(10**-neg_log_dist)\\n \\n return strengths\",\n \"def calculate_entropy(self, tags):\\n tags_counter = Counter()\\n\\n if not tags:\\n return 0\\n\\n for tag in tags:\\n tags_counter[tag] += 1\\n classes_probs = [tags_counter[tag] / float(len(tags)) for tag in tags_counter]\\n if 0.0 in classes_probs:\\n return 0\\n\\n entropy = 0\\n for prob in classes_probs:\\n entropy -= prob * math.log(prob, 2)\\n\\n return entropy\",\n \"def information_gain(features, attribute_index, targets):\\r\\n\\r\\n possible_feature_values = [0,1]\\r\\n \\r\\n possible_classifications = [0,1]\\r\\n \\r\\n feature = features[:,attribute_index]\\r\\n \\r\\n \\r\\n number_of_samples = len(feature)\\r\\n \\r\\n import math\\r\\n \\r\\n \\r\\n #current_entropy = np.sum([-(len(targets[targets==possible_classification])/number_of_samples)*math.log(len(targets[targets==possible_classification])/number_of_samples, 2) for possible_classification in possible_classifications])\\r\\n \\r\\n terms_to_be_summed_for_current_entropy = []\\r\\n \\r\\n for classification in possible_classifications:\\r\\n \\r\\n number_of_elements_with_this_classification = len(targets[targets==classification])\\r\\n \\r\\n p_for_this_classification = number_of_elements_with_this_classification/len(targets)\\r\\n \\r\\n if p_for_this_classification != 0:\\r\\n terms_to_be_summed_for_current_entropy.append(-p_for_this_classification*math.log(p_for_this_classification,2))\\r\\n else:\\r\\n terms_to_be_summed_for_current_entropy.append(0)\\r\\n \\r\\n current_entropy = np.sum(terms_to_be_summed_for_current_entropy)\\r\\n \\r\\n \\r\\n \\r\\n terms_to_be_summed_for_weighted_entropy = []\\r\\n \\r\\n for possible_value in possible_feature_values:\\r\\n \\r\\n targets_split_by_feature_value = targets[feature.flatten() == possible_value]\\r\\n \\r\\n if len(targets_split_by_feature_value) != 0:\\r\\n \\r\\n \\r\\n weight_of_feature_value = len(targets_split_by_feature_value)/len(targets)\\r\\n \\r\\n terms_for_entropy_within_subset = []\\r\\n \\r\\n for classification in possible_classifications:\\r\\n \\r\\n number_of_subset_elements_with_this_classification = len(targets_split_by_feature_value[targets_split_by_feature_value==classification])\\r\\n \\r\\n p_in_subset_for_this_classification = number_of_subset_elements_with_this_classification/len(targets_split_by_feature_value)\\r\\n \\r\\n if p_in_subset_for_this_classification != 0:\\r\\n terms_for_entropy_within_subset.append(-p_in_subset_for_this_classification*math.log(p_in_subset_for_this_classification,2))\\r\\n else:\\r\\n terms_for_entropy_within_subset.append(0)\\r\\n \\r\\n entropy_within_subset = np.sum(terms_for_entropy_within_subset)\\r\\n \\r\\n terms_to_be_summed_for_weighted_entropy.append(weight_of_feature_value*entropy_within_subset)\\r\\n \\r\\n weighted_entropy = np.sum(terms_to_be_summed_for_weighted_entropy)\\r\\n \\r\\n \\r\\n #current_entropy = np.sum(terms_to_be_summed_for_current_entropy)\\r\\n \\r\\n #weighted_entropy = np.sum([(len(feature[feature==possible_value])/number_of_samples)*(len(targets[feature==possible_value][targets[feature==possible_value]==possible_classification])/len(targets[feature==possible_value]))*math.log((len(targets[feature==possible_value][targets[feature==possible_value]==possible_classification])/len(targets[feature==possible_value])), 2) for possible_classification in possible_classifications for possible_value in possible_feature_values])\\r\\n\\r\\n information_gain = current_entropy - weighted_entropy \\r\\n \\r\\n return information_gain\",\n \"def tag(self, tokens):\\n numRows = len(self.tags)\\n numCols = len(tokens)\\n # initialize tables for dynamic programming\\n table = array([[0] * numCols] * numRows, dtype=float32)\\n trace = array([[None] * numCols] * numRows)\\n \\n # fill in the base cases, i.e. the first column\\n for row in range(numRows):\\n currentTag = self.tags[row]\\n currentWord = tokens[0] if tokens[0] in self.vocab else '<OOV>'\\n table[row][0] = self.tag_tag_probs['<START>'].prob(currentTag) * self.tag_word_probs[currentTag].prob(currentWord)\\n trace[row][0] = '<START>'\\n \\n # fill the rest of the table\\n # iterate through by columns\\n for col in range(1, numCols):\\n for row in range(numRows):\\n currentTag = self.tags[row]\\n currentWord = tokens[col] if tokens[col] in self.vocab else '<OOV>'\\n maxProbability = 0.0;\\n maxPrevRow = 0\\n \\n # iterate through the previous column and find the maximum probability\\n # as well as the previous tag that led to the maximum probability\\n for prevRow in range(numRows):\\n prevTag = self.tags[prevRow]\\n probability = table[prevRow][col-1] * self.tag_tag_probs[prevTag].prob(currentTag) * self.tag_word_probs[currentTag].prob(currentWord)\\n if probability > maxProbability:\\n maxProbability = probability\\n maxPrevRow = prevRow\\n \\n table[row][col] = maxProbability\\n trace[row][col] = maxPrevRow\\n \\n returnList = []\\n # retrace and construct the tag list\\n maxIndex = argmax(table, axis=0)[-1]\\n # insert the last (token, tag) pair\\n returnList.insert(0, (tokens[-1], self.tags[maxIndex]))\\n # loop through the trace table and prepend each (token, tag) pair\\n i = numCols - 1\\n index = trace[maxIndex][numCols-1]\\n while i > 0:\\n returnList.insert(0, (tokens[i-1], self.tags[index]))\\n i -= 1\\n index = trace[index][i]\\n \\n return returnList\",\n \"def Log_OB_S1_vec(xref,x):\\n\\n nX = np.shape(x)\\n\\n m = nX[0]\\n n = nX[1]\\n t = nX[2]\\n\\n G = np.zeros((n,t))\\n Gv = np.zeros((m,n,t))\\n\\n for r in range(t):\\n\\n # Correct for permuations\\n\\n Xout = dp(x[:,:,r])\\n\\n for q in range(n):\\n\\n a = np.sum(Xout[:,q]*xref[:,q,r])/np.sqrt(np.sum(xref[:,q,r]**2)*np.sum(Xout[:,q]**2)) # Should have unit L2 norm\\n G[q,r] = np.arccos(a) # Computing the angles\\n if a > 1:\\n a = 1\\n if a < -1:\\n a = -1\\n\\n v = Xout[:,q] - a*xref[:,q,r]\\n Gv[:,q,r] = v / (1e-24 + np.linalg.norm(v)) # Unit vector in the tangent subspace\\n\\n return G,Gv\",\n \"def compute_idfs(documents):\\n words = set()\\n for file in documents:\\n words.update(documents[file])\\n\\n idfs = dict()\\n for word in words:\\n tw = sum(word in documents[file] for file in documents)\\n idf = math.log(len(documents)/ tw )\\n idfs[word] = idf\\n\\n return idfs\",\n \"def get_gain(self, examples, tags, feature):\\n initial_entropy = self.calculate_entropy(tags)\\n relative_entropy_per_feature = []\\n feature_index = self.get_feature_index(feature)\\n for possible_value in self.feature_domain_dict[feature]:\\n examples_and_tags_vi = [(example, tag) for example, tag in zip(examples, tags)\\n if example[feature_index] == possible_value]\\n tags_vi = [tag for example, tag in examples_and_tags_vi]\\n entropy_vi = self.calculate_entropy(tags_vi)\\n if not examples:\\n pass\\n relative_entropy = (float(len(examples_and_tags_vi)) / len(examples)) * entropy_vi\\n relative_entropy_per_feature.append(relative_entropy)\\n\\n return initial_entropy - sum(relative_entropy_per_feature)\",\n \"def _tag_tokens(self, targets: list, tokens: list, tags: dict=BIO_TAGS, bert_tokenizer=None, verbose: bool=False):\\n if bert_tokenizer is not None:\\n tokenizer = bert_tokenizer\\n\\n if len(targets) > 0:\\n tags_list = []\\n for tgt in targets:\\n t_list = []\\n inside = False\\n found = False\\n if bert_tokenizer is not None:\\n tgt_terms = tokenizer.tokenize(tgt[1]) \\n else:\\n tgt_terms = self._tokenize_line(tgt[1])\\n\\n if verbose:\\n print(tgt_terms)\\n\\n for i in range(len(tokens)):\\n if tokens[i] == tgt_terms[0] and not found: \\n # token is the beginning (B) of target terms sequence\\n t_list.append(tags[\\\"B\\\"])\\n if len(tgt_terms) > 1 and tokens[i:i+len(tgt_terms)] == tgt_terms:\\n # check if the matching token is not a repetition of the term\\n # and is the actual target term, if so the correct sequence is found \\n inside = True\\n found = True\\n\\n elif inside == True:\\n # multi words terms\\n if tokens[i] in tgt_terms[1:-1] and len(tgt_terms) > 2:\\n # token is inside (I) the target terms sequence\\n t_list.append(tags[\\\"I\\\"])\\n\\n elif tokens[i] == tgt_terms[-1]:\\n # token is the last (L) target term\\n t_list.append(tags[\\\"I\\\"]) # tags[\\\"L\\\"] \\n inside = False\\n\\n # when the last tgt_word is repeated inside the tgt_terms \\n inside = False\\n\\n else:\\n # token is outside (O) the target terms sequence\\n t_list.append(tags[\\\"O\\\"])\\n\\n tags_list.append(torch.Tensor(t_list))\\n\\n # merge tags\\n tags_tensor = torch.stack(tags_list)\\n res = torch.min(tags_tensor, dim=0)\\n if verbose:\\n print(\\\"targets:\\\", targets)\\n print(\\\"tokens:\\\", tokens, \\\"-- len:\\\", len(tokens))\\n print(\\\"tags:\\\", tags_list)\\n #print(\\\"tags:\\\", tags_tensor.size())\\n #print(\\\"res:\\\", res.values.size())\\n \\n return res.values\\n\\n else:\\n return [tags[\\\"O\\\"] for t in tokens]\",\n \"def __call__(self, X_logs):\\n X = [math.exp(x) for x in X_logs]\\n B_subs = {}\\n for v_parent, v_child in self.R:\\n edge = frozenset([v_parent, v_child])\\n r = X[v_child]\\n t = self.B[edge]\\n B_subs[edge] = r * t\\n newick_string = FtreeIO.RBN_to_newick(self.R, B_subs, self.N_leaves)\\n tree = Newick.parse(newick_string, Newick.NewickTree)\\n # define the rate matrix object; horrible\\n dictionary_rate_matrix = RateMatrix.get_jukes_cantor_rate_matrix() \\n ordered_states = list('ACGT') \\n row_major_rate_matrix = MatrixUtil.dict_to_row_major(\\n dictionary_rate_matrix, ordered_states, ordered_states)\\n rate_matrix_object = RateMatrix.RateMatrix(\\n row_major_rate_matrix, ordered_states) \\n # get the log likelihood\\n ll = PhyLikelihood.get_log_likelihood(\\n tree, self.alignment, rate_matrix_object)\\n return -ll\",\n \"def entropy_gain(node,attribute):\\n data_subset1 = filter_data(node.data,node.ancestors)\\n data_counts = list(Counter(data_subset1['Class']).values())\\n base_entropy = entropy(data_counts,base=2)\\n num_values = len(data_subset1)\\n entropy_sum = 0\\n \\n for value in [0,1]:\\n data_subset2 = filter_data(node.data, node.ancestors + [(attribute,value)])\\n subset_counts = list(Counter(data_subset2['Class']).values())\\n entropy_sum += (len(data_subset2)/num_values) * entropy(subset_counts,base=2)\\n \\n return base_entropy - entropy_sum\",\n \"def tree_apply(tree_arrays, features, extra_features=None, reduce_axis=0) -> np.ndarray:\\n qi = np.zeros(features.shape[reduce_axis], dtype=np.int32)\\n for current_depth in range(tree_arrays[\\\"treedepth\\\"]):\\n fi = tree_arrays[\\\"features\\\"][qi]\\n f = np.choose(\\n fi, assure_numpy(features).T if reduce_axis == 0 else assure_numpy(features)\\n ) # TODO: try to do it in more effective tf-oriented way\\n t = tree_arrays[\\\"thresholds\\\"][qi]\\n # print(qi, fi, f, t)\\n # if current_depth == 0:\\n # print(fi, f.shape, features.shape, f)\\n answer = (f < t) * 1\\n new_qi = (\\n answer * tree_arrays[\\\"yes_node\\\"][qi]\\n + (1 - answer) * tree_arrays[\\\"no_node\\\"][qi]\\n )\\n qi = new_qi\\n if extra_features is None:\\n assert tree_arrays[\\\"leaf_data\\\"].shape[1] == 1, \\\"extra_features needed\\\"\\n leaf_data = tree_arrays[\\\"leaf_data\\\"][qi, 0]\\n else:\\n leaf_data = assure_numpy(\\n tree_arrays[\\\"leaf_data\\\"][qi, :]\\n * (extra_features.T if reduce_axis == 1 else extra_features)\\n ).sum(\\n axis=1\\n ) # TODO: try to do it in more effective tf-oriented way\\n return leaf_data\",\n \"def Log_Sn(xref,x):\\n\\n nX = np.shape(x)\\n\\n m = nX[0]\\n t = nX[1]\\n\\n G = np.zeros((t,))\\n Gv = np.zeros((m,t))\\n\\n for r in range(t):\\n\\n # Correct for permuations\\n\\n Xout = dp(x[:,r])\\n\\n a = np.sum(Xout*xref)/np.sqrt(np.sum(xref**2)*np.sum(Xout**2)) # Should have unit L2 norm\\n\\n if a > 1:\\n a = 1\\n if a < -1:\\n a = -1\\n\\n G[r] = np.arccos(a) # Computing the angles\\n\\n v = Xout - a*xref\\n Gv[:,r] = v / (1e-24 + np.linalg.norm(v)) # Unit vector in the tangent subspace\\n\\n return G,Gv\",\n \"def reportTagStats(stats, tag, lower):\\n transcript_level_tags = ['stats', 'hasOkCopies', 'hasBadCopies',\\n 'ok', 'not_ok']\\n level = 0\\n increment = 1\\n if tag in transcript_level_tags:\\n header = '%40s %7s %7s %15s' % ('tree', 'Tran.s',\\n 'Annot.s', 'Trans. Tags')\\n else:\\n header = '%40s %7s %7s %15s' % ('tree', 'Tran.s',\\n 'Annot.s', 'Annot. Tags')\\n print header\\n def printTree(level, t, lower):\\n if t.tagTranscriptAnnotations > lower:\\n if tag in transcript_level_tags:\\n count = '%6d (%6.2f%%)' % (\\n t.tagTranscripts,\\n 100. * t.tagTranscripts / t.nodeTranscripts)\\n elif tag.endswith('*'):\\n count = '%16d' % (t.tagTranscriptAnnotations)\\n else:\\n count = '%6d (%6.2f%%)' % (\\n t.tagTranscriptAnnotations,\\n 100. * t.tagTranscriptAnnotations / t.nodeTranscriptAnnotations)\\n s = '%7d, %7d, %s' % (\\n t.nodeTranscripts, t.nodeTranscriptAnnotations, count)\\n title = '%s%s' % ('| ' * level, t.nodeName)\\n buff = '.' * (40 - len(title))\\n print '%s%s%s' % (title, buff, s)\\n t.children = sorted(t.children, key=lambda c: c.nodeTranscripts,\\n reverse=True)\\n for c in t.children:\\n printTree(level + increment, c, lower)\\n printTree(level, stats, lower)\",\n \"def calc_tf(self, level) -> None:\\n if level == 't':\\n term_idxs = self._load_term_idxs('t')\\n path_in = self.path_token_idx_corpus\\n path_out = self.path_tf_tokens\\n # contains = self.load_contains(self.path_token_contains)\\n elif level == 'l':\\n term_idxs = self._load_term_idxs('l')\\n path_in = self.path_lemma_idx_corpus\\n path_out = self.path_tf_lemmas\\n # contains = self.load_contains(self.path_lemma_contains)\\n else:\\n raise Exception('Error! Level not know!')\\n\\n tf = {}\\n for doc_idx, doc in enumerate(get_docs(path_in)):\\n tf[doc_idx] = {}\\n tf_doc = tf[doc_idx]\\n for sent in doc:\\n for term_idx in sent:\\n if term_idx in term_idxs:\\n if term_idx in tf_doc:\\n tf_doc[term_idx] += 1\\n else:\\n tf_doc[term_idx] = 1\\n # *** Code for compound counting ***\\n # # add counts for all terms contained\\n # for tc_idx in contains[term_idx]:\\n # if tc_idx in tf_doc:\\n # tf_doc[str(tc_idx)] += 1\\n # else:\\n # tf_doc[str(tc_idx)] = 1\\n\\n # print(doc_idx, lemma_idx)\\n # print(type(doc_idx), type(lemma_idx))\\n # tf[doc_id][lemma_idx] += 1\\n # tf_doc = tf[doc_idx]\\n # tf_doc[lemma_idx]\\n # ***\\n with open(path_out, 'w', encoding='utf8') as f:\\n json.dump(tf, f)\\n\\n self._docs_processed = 0\",\n \"def question4(T,r,n1,n2):\\n\\n\\tif(len(T)<=1):\\t\\t\\t\\t\\t\\t\\t\\t# Edge case : If the Tree only consists of a root and no children\\n\\t\\treturn -1\\n\\n\\tif(n1==None or n2==None):\\t\\t\\t\\t\\t# Edge case : If n1 and n2 are not actually numbers\\n\\t\\treturn -1\\n\\n\\tlen_T = len(T)\\n\\tif(not n1 < len_T or not n2 < len_T):\\t\\t# Edge case : If the nodes gives in parameters do not actually exist in the tree\\n\\t\\treturn -1\\n\\n\\tn1_list = []\\t\\t\\t\\t\\t\\t\\n\\tn2_list = []\\n\\n\\tfor i in range(len(T)):\\t\\t\\t\\t\\t\\t# Traverse the list and append all the parents of node1 if found in O(N)\\n\\t\\tif T[i][n1]==1:\\n\\t\\t\\tn1_list.append(i)\\n\\n\\tfor i in range(len(T)):\\t\\t\\t\\t\\t\\t# Traverse the list and append all the parents of node2 is found in O(N)\\n\\t\\tif T[i][n2]:\\n\\t\\t\\tn2_list.append(i)\\n\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# The root is a common ancestor of every node in the tree\\n\\tif not r in n1_list:\\t\\t\\t\\t\\t\\t# check if the root is in the list, if not, add it\\n\\t\\tn1_list.append(r)\\n\\n\\tif not r in n2_list:\\t\\t\\t\\t\\t\\t# check if the root is in the list, if not, add it\\n\\t\\tn2_list.append(r)\\n\\n\\tn1_list = reversed(n1_list)\\t\\t\\t\\t\\t# Since we are operating on a binary tree, we sort\\n\\tfor i in n1_list:\\t\\t\\t\\t\\t\\t\\t# in decending order to operate on the latest nodes\\n\\t\\tif i in n2_list:\\t\\t\\t\\t\\t\\t# if a match is found, we know that it is the lowest common ancestor\\n\\t\\t\\treturn i \\t\\t\\t\\t\\t\\t\\t# If nothing is found, the root node is bound to be returned. And it correct.\",\n \"def compute_joint_probability(token_list, token_probabilities, use_log_prob=False):\\n\\n log_prob = 0\\n\\n for word in token_list:\\n\\n # do not allow zero probabilites\\n assert word in token_probabilities\\n\\n if use_log_prob:\\n log_prob += token_probabilities[word]\\n else:\\n log_prob += log10(token_probabilities[word])\\n\\n if use_log_prob:\\n return log_prob\\n\\n return 10**log_prob\",\n \"def tags_jaccard_index(d1, d2):\\n set1 = set(d1.keys())\\n set2 = set(d2.keys())\\n\\n common_tags = set.intersection(set1, set2)\\n common_tags_count = 0\\n all_tags_count = sum(d1.values()) + sum(d2.values())\\n\\n for k, v in d1.items():\\n if k in common_tags:\\n common_tags_count += v\\n\\n for k, v in d2.items():\\n if k in common_tags:\\n common_tags_count += v\\n\\n intersection_cardinality = len(set.intersection(set1, set2))\\n union_cardinality = len(set.union(set1, set2))\\n\\n tags_similarity = intersection_cardinality/float(union_cardinality)\\n\\n final_tag_similarity = ((common_tags_count / all_tags_count) * 10) \\\\\\n * tags_similarity\\n\\n return final_tag_similarity\",\n \"def findLinkedTerms(self):\\n for key in self.summaryDict.keys(): # v' in the formula\\n if self.getCoverFromModalityInDictionnary(self.summaryDict, key) == 0:\\n correlation = 0\\n else:\\n dep = self.getCoverFromModalityInDictionnary(self.summaryFilteredDict,key) / self.getCoverFromModalityInDictionnary(self.summaryDict, key) #cover(v',R')/cover(v'R)\\n if dep <= 1:\\n correlation = 0\\n else:\\n correlation = 1 - (1 / dep)\\n self.correlationDict[key] = correlation\",\n \"def eval_tree(tree: GPTree, dataset: Iterable) -> list:\\n results = []\\n for data in zip(*dataset):\\n try:\\n output = tree.compute_tree(data[0])\\n results.append(\\n 0 if output == data[1] else 1\\n ) # right or wrong, but no error.\\n except Exception:\\n results.append(2) # Fails to run.\\n\\n return results\",\n \"def getEntrophy(df):\\r\\n dct = group(df)\\r\\n n = len(df)\\r\\n ent = [0]*len(dct)\\r\\n j = 0\\r\\n for col in dct.keys(): \\r\\n for colVal in dct[col].keys():\\r\\n det = 0 \\r\\n aux = np.zeros(len(dct[col][colVal].keys()))\\r\\n i = 0 \\r\\n for clas in dct[col][colVal].keys():\\r\\n det+= dct[col][colVal][clas]\\r\\n aux[i] = dct[col][colVal][clas]\\r\\n i+=1\\r\\n aux = 1/det*aux\\r\\n aux_copy = aux.copy()\\r\\n aux = np.log2(aux) \\r\\n s = sum(np.multiply(aux_copy,aux)) \\r\\n ent[j] += (-1*det/n*s)\\r\\n j+=1 \\r\\n return (ent,dct)\",\n \"def get_words_and_tags(tree, TAG=False):\\n if TAG:\\n return (re.findall(r'(?<= )?[^( )]+(?=\\\\))', tree), re.findall(r'(?<=\\\\()\\\\w+(?= )',tree))\\n else:\\n return re.findall(r'(?<= )?[^( )]+(?=\\\\))', tree)\",\n \"def cstree_likelihood(x, u, tree, order):\\n p = len(order)\\n\\n # Make ordering start from 0\\n order_py = [o-1 for o in order]\\n\\n # u_C and u_x_C as defined in the paper\\n u_C = lambda C: \\\\\\n np.sum(u, axis=tuple([i for i in range(p) if i+1 not in C]))\\n u_x_C = lambda x,C : u_C(C)[tuple(x)]\\n\\n # In self.data, the data is ordered from 0-p\\n # Here we change the order according to order_py\\n x = list(x)\\n x_ordered = [x[i] for i in order_py]\\n\\n # Accumulator value for the likelihood\\n prob = 1\\n\\n # For each level of the tree\\n for level in range(1,p):\\n # x_k below is x_k-1 in paper\\n x_k = tuple((order[i], x_ordered[i]) for i in range(level))\\n context_of_stage_x_k = tree.nodes[x_k].get(\\\"context\\\", x_k)\\n context_vars = [var for (var,_) in context_of_stage_x_k]\\n\\n # TODO Check if we really need to sort this\\n next_var = order[level]\\n CjUk = sorted(context_vars + [next_var])\\n Cj = sorted(context_vars)\\n\\n # Since the sample x is ordered from 0...p and\\n # the variables in CjUk,Cj are in {1,...p}\\n x_CjUk = [x[i] for i in range(p) if i+1 in CjUk]\\n x_Cj = [x[i] for i in range(p) if i+1 in Cj]\\n\\n numerator = u_x_C(x_CjUk, CjUk)\\n denominator = u_x_C(x_Cj,Cj)\\n\\n prob = prob*(numerator/denominator)\\n return prob\",\n \"def estimate_tree(self):\\n logger.info('TreeCatTrainer.estimate_tree given %d rows',\\n len(self._added_rows))\\n complete_grid = self._tree.complete_grid\\n edge_logits = self.compute_edge_logits()\\n edges = estimate_tree(complete_grid, edge_logits)\\n return edges, edge_logits\",\n \"def log_E_score(EL_array, state_tree_sequence):\\n log_EL_array = np.log(EL_array)\\n log_E_score_holder = 0\\n for idx, row in enumerate(log_EL_array):\\n log_E_score_holder += row[state_tree_sequence[idx]]\\n return log_E_score_holder\",\n \"def hierarchy_dist(self,synset_1, synset_2):\\n\\t h_dist = sys.maxsize\\n\\t if synset_1 is None or synset_2 is None: \\n\\t return h_dist\\n\\t if synset_1 == synset_2:\\n\\t # return the depth of one of synset_1 or synset_2\\n\\t h_dist = max([x[1] for x in synset_1.hypernym_distances()])\\n\\t else:\\n\\t # find the max depth of least common subsumer\\n\\t hypernyms_1 = {x[0]:x[1] for x in synset_1.hypernym_distances()}\\n\\t hypernyms_2 = {x[0]:x[1] for x in synset_2.hypernym_distances()}\\n\\t lcs_candidates = set(hypernyms_1.keys()).intersection(\\n\\t set(hypernyms_2.keys()))\\n\\t if len(lcs_candidates) > 0:\\n\\t lcs_dists = []\\n\\t for lcs_candidate in lcs_candidates:\\n\\t lcs_d1 = 0\\n\\t if lcs_candidate in hypernyms_1.keys():\\n\\t lcs_d1 = hypernyms_1[lcs_candidate]\\n\\t lcs_d2 = 0\\n\\t if lcs_candidate in hypernyms_2.keys():\\n\\t lcs_d2 = hypernyms_2[lcs_candidate]\\n\\t lcs_dists.append(max([lcs_d1, lcs_d2]))\\n\\t h_dist = max(lcs_dists)\\n\\t else:\\n\\t h_dist = 0\\n\\t return ((math.exp(self.BETA * h_dist) - math.exp(-self.BETA * h_dist)) / \\n\\t (math.exp(self.BETA * h_dist) + math.exp(-self.BETA * h_dist)))\",\n \"def compute_idfs(documents):\\n idfs = dict()\\n total_num_documents = len(documents)\\n words = set(word for sublist in documents.values() for word in sublist)\\n \\n for word in words:\\n num_documents_containing_word = 0\\n \\n for document in documents.values():\\n if word in document:\\n num_documents_containing_word += 1\\n \\n idf = math.log(total_num_documents / num_documents_containing_word)\\n idfs[word] = idf\\n\\n return idfs\",\n \"def efficiencies_merging(file_names, tree_name, weight_branch_name=\\\"\\\"):\\n\\n branches_to_read = get_all_branch_names(file_names[0], tree_name)\\n branches = { branch_name: [] for branch_name in branches_to_read }\\n weight_branches = []\\n\\n for file_name in file_names:\\n file_ = uproot.open(file_name)\\n tree = file_[tree_name]\\n \\n if weight_branch_name != \\\"\\\":\\n weight_branches.append(ak.sum(file_[weight_branch_name].array(), axis=0))\\n else:\\n weight_branches.append(1)\\n\\n for branch_name in branches_to_read:\\n branches[branch_name].append(tree[branch_name].array())\\n\\n tree_branches = { k: sum([w*b for w, b in zip(weight_branches, v)])/sum(weight_branches) for k, v in branches.items() }\\n\\n return tree_branches\",\n \"def aux_phrase_subtree(tree, tag_list):\\n highest_subtree = max([(0, None)]+[(subtree.height(), subtree) for subtree in tree.subtrees(lambda x: x.node in tag_list) if subtree != tree[0]])[1]\\n # print highest_subtree\\n return highest_subtree\",\n \"def HaarGOB(treeG):\\n # number of level of the chain (or tree)\\n Ntr = len(treeG)\\n # reorder chain (optional) \\n # reordering each level so that in each level the nodes are in the\\n # descent order of degrees\\n # compute u_l^c for level J_0 (top level)\\n clusterJ0 = treeG[Ntr-1]['clusters']\\n N0 = len(clusterJ0)\\n # generate indicator function on G^c\\n chic = np.identity(N0)\\n uc = [None]*N0\\n uc[0] = 1/np.sqrt(N0,dtype=np.float64)*np.ones(N0)\\n for l in np.arange(1, N0):\\n uc[l] = np.sqrt((N0-l)/(N0-l+1))*(chic[l-1,:]-1/(N0-l)*np.sum(chic[l:,:], axis=0))\\n# u = copy.deepcopy(uc)\\n treeG[Ntr-1]['u'] = uc\\n # compute the next level orthonormal basis ulk and stored into u\\n for j_tr in np.arange(0, Ntr-1)[::-1]:\\n N1 = len(treeG[j_tr]['clusters'])\\n u = [None]*N1\\n i = N0\\n for l in range(N0):\\n clusterl = treeG[j_tr+1]['clusters'][l]\\n kl = len(clusterl)\\n #for k==1\\n ucl = uc[l]\\n ul1 = np.zeros(N1)\\n for j in range(N0):\\n idxj = treeG[j_tr+1]['clusters'][j]\\n ul1[idxj] = ucl[j]/np.sqrt(len(idxj))\\n u[l] = ul1\\n if kl>1:\\n chil = np.zeros((kl,N1))\\n for k in range(kl):\\n idxl = treeG[j_tr+1]['clusters'][l]\\n chil[k,idxl[k]] = 1;\\n \\n for k in np.arange(1,kl):\\n i = i+1\\n ulk = np.sqrt((kl-k)/(kl-k+1))*(chil[k-1,:]-1/(kl-k)*np.sum(chil[k:,:],axis=0))\\n u[i-1] = ulk\\n treeG[j_tr]['u'] = u\\n # update uc and N0\\n# uc = copy.deepcopy(u)\\n uc = u\\n N0 = N1\\n return treeG\",\n \"def process_seq_pairs(seq_pairs, args, stats):\\n #print '\\\\n\\\\n'.join([str(z) for z in seq_pairs])\\n seq_to_tree = []\\n for x in seq_pairs:\\n seq_to_tree.append(x.sequence1.structure)\\n seq_to_tree.append(x.sequence2.structure)\\n tree_distance = rna_distance('\\\\n'.join(seq_to_tree))\\n tree_distance = tree_distance.strip('\\\\n').split('\\\\n') # take off last lr\\n assert len(tree_distance) == len(seq_pairs), (\\n 'Error length of tree distance %s does not match length of seq_pairs '\\n '%s and should -- check installation of RNAdistance'\\n % (len(tree_distance), len(seq_pairs))\\n )\\n for i, x in enumerate(seq_pairs):\\n seq_pairs[i].tree_distance = tree_distance[i].split(' ')[1]\\n x.output(args)\\n stats['energy_delta'].append(x.energy_delta)\\n stats['edit_distance'].append(x.edit_distance)\\n try:\\n stats['tree_distance'].append(float(x.tree_distance))\\n except ValueError:\\n stats['tree_distance'].append(None)\",\n \"def tag_ptree(ptree, coreflist):\\n pattern = r\\\"\\\"\\\"(?P<lp>\\\\(?\\\\s*) # left parenthesis\\n (?P<tg>[a-zA-Z$]+)? # POS tag\\n (?P<data>\\\\s*%s) # subtree of tag\\n (?P<rp>(?:\\\\s*\\\\))*) # right parenthesis\\n \\\"\\\"\\\"\\n for cid, coref in coreflist[::-1]:\\n words = ''.join(word_tokenize(coref['text']))\\n\\n nltktree = Tree.parse(ptree)\\n nltktree.reverse() # perform search right to left\\n data = None\\n for subtree in nltktree.subtrees(): # BFS\\n if ''.join(subtree.leaves()) == words: # equal ignoring whitespace\\n data = subtree.pprint()\\n break\\n\\n # If found via breadth-first search of parse tree\\n if data:\\n ptree = ptree.replace(data, '( COREF_TAG_%s%s)' % (cid, data))\\n else: # Try finding via regex matching instead\\n dpattern = r'\\\\s*'.join([r'\\\\(\\\\s*[a-zA-Z$]+\\\\s+%s\\\\s*\\\\)' % word\\n for word in word_tokenize(coref['text'])])\\n found = re.findall(pattern % dpattern, ptree, re.X)\\n if found:\\n repl = '%s%s ( COREF_TAG_%s%s) %s' % (found[0][0],\\n found[0][1],\\n cid,\\n found[0][2],\\n found[0][3])\\n ptree = re.sub(pattern % dpattern, repl, ptree, 1, re.X)\\n\\n return ptree\",\n \"def calc_CI_introductions(commits, author):\\n\\n\\t# using a dictionary that has the commits' hashes as keys,\\n\\t# so as to not search multiple times for the same commit\\n\\tCI_checked = {}\\n\\n\\t# delete contents\\n\\topen('introductions.csv', 'w').close()\\n\\t\\n\\t# for every commit, we look up whether the author included a CI file,\\n\\t# that did not exist in the parent commit\\n\\tfor count, commit in enumerate(commits):\\n\\t\\t# status update\\n\\t\\tif (count + 1) % 50 == 0:\\n\\t\\t\\tprint count + 1, ' / ', len(commits)\\n\\t\\n\\t\\ttree_hash, parent_commit_hash, time = search(commit, 'commit')\\n\\t\\tif tree_hash not in CI_checked:\\n\\t\\t\\tCI_checked[tree_hash] = ci_lookup(tree_hash)\\n\\t\\t\\n\\t\\t# controlling for the case of multiple parent commits\\n\\t\\tall_parent_CI = False\\n\\t\\tfor parent in parent_commit_hash.split(':'):\\n\\t\\t\\t# controlling for the case of no parent commits\\n\\t\\t\\tif parent == '':\\n\\t\\t\\t\\tbreak\\n\\t\\t\\n\\t\\t\\tparent_tree_hash = search(parent, 'commit')[0]\\n\\t\\t\\n\\t\\t\\tif parent_tree_hash not in CI_checked:\\n\\t\\t\\t\\tparent_CI = ci_lookup(parent_tree_hash)\\n\\t\\t\\t\\tCI_checked[parent_tree_hash] = parent_CI\\n\\t\\t\\telse:\\n\\t\\t\\t\\tparent_CI = CI_checked[parent_tree_hash]\\n\\t\\t\\t\\n\\t\\t\\t# checking all the parent commits for the usage of CI\\n\\t\\t\\tall_parent_CI = all_parent_CI or parent_CI\\n\\t\\t\\n\\t\\t# if the tree has a CI file, while the parent tree does not, increase the CI score\\n\\t\\tif CI_checked[tree_hash] and not all_parent_CI:\\n\\t\\t\\t\\n\\t\\t\\tout = bash('echo ' + commit + ' | ~/lookup/getValues c2P')\\n\\t\\t\\tmain_proj = out.strip().split(';')[1]\\n\\t\\t\\tf = open(\\\"introductions.csv\\\", \\\"a\\\")\\n\\t\\t\\tf.write(author + ', ' + 'CI' + ', ' + str(time) + ', ' + main_proj + '\\\\n')\\n\\t\\t\\tf.close()\\n\\t\\t\\tprint 'wrote'\\n\\tprint (current_time()-start_time)/len(commits), 'seconds per commit'\",\n \"def lca(root, node1, node2):\\n\\n def lca_helper(root, node1, node2):\\n \\\"\\\"\\\"\\n Returns: [num_target_nodes, ancestor]\\n\\n \\\"\\\"\\\"\\n if root is None:\\n return [0, None]\\n\\n left_result = lca_helper(root.left, node1, node2)\\n if left_result[0] == 2:\\n return left_result\\n right_result = lca_helper(root.right, node1, node2)\\n if right_result[0] == 2:\\n return right_result\\n\\n num_target_nodes = (\\n left_result[0] + right_result[0] + (node1, node1).count(root)\\n )\\n\\n return [num_target_nodes, root if num_target_nodes == 2 else None]\\n\\n return lca_helper(root, node1, node2)[1]\",\n \"def calculateLogJointProbabilities(self, datum):\\n logJoint = util.Counter()\\n \\\"*** YOUR CODE HERE ***\\\"\\n\\t#Adds log(P(y)) to calculate P(y|f1,f2...)\\n for label in self.legalLabels:\\n\\t\\tlogJoint[label] += math.log(self.prior[label])\\n\\t#Adds log(P(f1|y)), log(P(f2|y))... to calculate P(y|f1, f2...)\\n for key in datum:\\n\\t\\t#if key == (7, 3):\\n\\t\\t\\t#print self.condprobs[key, 0]\\n\\t\\tfor label in self.legalLabels:\\n\\t\\t\\t#print str(key) + str(datum[key])\\n\\t\\t\\tlogJoint[label] += math.log(self.condprobs[key, label][datum[key]])\\n return logJoint\",\n \"def to_sentence(tree, key=lambda s,x: 1.0):\\n s = ['']\\n for terminal in flatten_tree(tree):\\n if isinstance(terminal, str):\\n s.append(terminal)\\n else:\\n s.append(terminal.produce(key=lambda x: key(s,x)))\\n s.pop(0)\\n return ' '.join(s)\",\n \"def calculateLogJointProbabilities(self, datum):\\n\\tlogJoint = util.Counter()\\n\\t#want to calculate log(P(y)) + log(sum(P(fi|y)))\\n\\t#where y is a label\\n\\tfor label in self.legalLabels:\\n\\t\\tlogJoint[label] = math.log(self.prior_distribution_prob[label])\\n\\t\\tfor feature, value in datum.items():\\n\\t\\t\\tcp = self.conditional_prob[label][feature][value]\\n\\t\\t\\tif cp > 0: #condition check for values < 0 because log(0) is undefined and math domain error occurs\\n\\t\\t\\t\\tlogJoint[label] += math.log(cp) #summing up\\n\\t\\t\\t\\t\\n\\treturn logJoint\",\n \"def run(args):\\n # Load the pedigree file\\n print('Parsing pedigree...', file=sys.stderr)\\n pedigree = Pedigree.parse(args.input_ped)\\n for member in pedigree.members:\\n print(member, file=sys.stderr)\\n # load the HLA calls for each donor\\n print('Loading HLA calls...', file=sys.stderr)\\n all_calls = {}\\n calls = {}\\n for donor, fcalls in zip(args.donor_name, args.donor_calls):\\n print(donor, fcalls.name, file=sys.stderr)\\n all_calls[donor] = []\\n calls[donor] = {'A': [], 'B': [], 'C': []}\\n for hla_type in sorted(HLAType.parse(line.strip())\\n for line in fcalls):\\n calls[donor][hla_type.gene_name].append(hla_type)\\n all_calls[donor].append(hla_type)\\n print('Checking for consistency...', file=sys.stderr)\\n # get IDs of parents\\n index_member = pedigree.by_name[args.index_donor]\\n father = index_member.father\\n mother = index_member.mother\\n # check for 4 digit consistency\\n mm4 = check_consistency(4, calls[args.index_donor], calls.get(father),\\n calls.get(mother))\\n # check for 2 digit consistency\\n mm2 = check_consistency(2, calls[args.index_donor], calls.get(father),\\n calls.get(mother))\\n num_parents = len({father, mother} - {'0'})\\n # print result line\\n print('\\\\t'.join(map(str, [\\n args.index_donor, num_parents, mm2 or 'OK', mm4 or 'OK'\\n ])))\",\n \"def getOrthologNodes(tree, positive_set, options, selector=\\\"strict\\\", outgroups=None):\\n\\n nspecies = len(options.org2column)\\n\\n if selector == \\\"strict\\\":\\n # strict orthologs: at most one gene per species\\n exit_function = lambda num_genes_for_species: num_genes_for_species > 1\\n keep_function = lambda num_genes_for_species: num_genes_for_species == 1\\n total_genes_function = lambda num_genes_at_node, num_species_in_pattern: num_genes_at_node == num_species_in_pattern\\n total_species_function = lambda num_species_at_node, num_species_in_pattern: num_species_at_node == num_species_in_pattern\\n check_outgroup_function = lambda x: False\\n negative_set = set()\\n elif selector == \\\"degenerate\\\":\\n # degenerate orthologs: any number of genes per species,\\n exit_function = lambda num_genes_for_species: False\\n keep_function = lambda num_genes_for_species: num_genes_for_species > 0\\n total_genes_function = lambda num_genes_at_node, num_species_in_pattern: num_genes_at_node > num_species_in_pattern\\n total_species_function = lambda num_species_at_node, num_species_in_pattern: num_species_at_node == num_species_in_pattern\\n check_outgroup_function = lambda x: False\\n negative_set = set()\\n elif selector == \\\"lineage\\\":\\n # lineage specific duplications: at least 1 gene\\n exit_function = lambda num_genes_for_species: False\\n keep_function = lambda num_genes_for_species: num_genes_for_species > 1\\n total_genes_function = lambda num_genes_at_node, num_species_in_pattern: num_genes_at_node >= num_species_in_pattern\\n total_species_function = lambda num_species_at_node, num_species_in_pattern: False\\n check_outgroup_function = lambda x: False\\n negative_set = set(range(nspecies)).difference(positive_set)\\n elif selector == \\\"any\\\":\\n # any number of orthologs, including\\n # orphans\\n exit_function = lambda num_genes_for_species: False\\n keep_function = lambda num_genes_for_species: True\\n total_genes_function = lambda num_genes_at_node, num_species_in_pattern: True\\n total_species_function = lambda num_species_at_node, num_species_in_pattern: num_species_at_node == num_species_in_pattern\\n check_outgroup_function = lambda x: False\\n negative_set = set()\\n elif selector == \\\"outgroup\\\":\\n # group selector\\n exit_function = lambda num_genes_for_species: False\\n keep_function = lambda num_genes_for_species: num_genes_for_species > 0\\n total_genes_function = lambda num_genes_at_node, num_species_in_pattern: False\\n total_species_function = lambda num_species_at_node, num_species_in_pattern: False\\n # check for outgrup: needs to have outgroup and at least one other species\\n # ie.: sum of all genes in outgroups larger than sum of all genes in\\n # all species\\n if not outgroups:\\n raise \\\"usage error: please supply outgroups if 'outgroup'-selector is chosen.\\\"\\n check_outgroup_function = lambda genes: 0 < sum(\\n [len(genes[x]) for x in outgroups]) < sum(map(lambda x: len(x), genes))\\n negative_set = set()\\n else:\\n raise \\\"unknown selector %s\\\" % selector\\n\\n # work here: set genes[node_id] to None,\\n # 1. if the gene count for a species of interest is > 1\\n # 2. if the gene count for all species of interest is 1 in\\n # the child node.\\n\\n if options.loglevel >= 5:\\n options.stdlog.write(\\\"# gene tree\\\\n\\\")\\n tree.display()\\n\\n n = TreeTools.GetSize(tree) + 1\\n genes = []\\n for x in range(n):\\n genes.append([set() for x in range(nspecies)])\\n\\n ortholog_nodes = []\\n\\n def count_genes(node_id):\\n \\\"\\\"\\\"record number of genes per species for each node\\n \\\"\\\"\\\"\\n node = tree.node(node_id)\\n\\n if options.loglevel >= 6:\\n options.stdlog.write(\\\"# node_id=%i\\\\n\\\" % node_id)\\n if options.loglevel >= 10:\\n options.stdlog.write(\\\"# sets=%s\\\\n\\\" % (str(genes)))\\n\\n # species in pattern\\n num_species_in_pattern = len(positive_set)\\n\\n if node.succ:\\n # process non-leaf node\\n for s in node.succ:\\n\\n # propagate: terminated nodes force upper nodes to terminate\\n # (assigned to None).\\n if not genes[s]:\\n genes[node_id] = None\\n return\\n\\n # total number of genes at node\\n num_genes_at_node = 0\\n # total number of species at node\\n num_species_at_node = 0\\n\\n # compute new gene set for each species at node\\n for x in positive_set:\\n genes[node_id][x] = genes[node_id][x].union(genes[s][x])\\n\\n num_genes_for_species = len(genes[node_id][x])\\n if exit_function(num_genes_for_species):\\n genes[node_id] = None\\n return\\n num_genes_at_node += num_genes_for_species\\n if num_genes_for_species:\\n num_species_at_node += 1\\n\\n if options.loglevel >= 6:\\n print \\\"node=\\\", node_id, \\\"species_at_node\\\", num_species_at_node, \\\"genes_at_node=\\\", num_genes_at_node, \\\\\\n \\\"num_genes_for_species=\\\", num_genes_for_species, \\\"ngenes=\\\", sum(\\n map(lambda x: len(x), genes[node_id]))\\n options.stdlog.write(\\n \\\"# genes at node %i\\\\t%s\\\\n\\\" % (node_id, genes[node_id]))\\n if outgroups:\\n print sum([len(genes[node_id][x]) for x in outgroups])\\n print check_outgroup_function(genes[node_id])\\n\\n # check stop criterion\\n if total_species_function(num_species_at_node, num_species_in_pattern):\\n # check if positive requirements are fulfilled\\n for x in positive_set:\\n if not keep_function(len(genes[node_id][x])):\\n if options.loglevel >= 6:\\n options.stdlog.write(\\n \\\"# keep function false for species %i\\\\n\\\" % x)\\n break\\n else:\\n if total_genes_function(num_genes_at_node, num_species_in_pattern):\\n if options.loglevel >= 6:\\n options.stdlog.write(\\\"# recording node %i\\\\n\\\" % x)\\n ortholog_nodes.append((node_id, genes[node_id]))\\n genes[node_id] = None\\n return\\n elif check_outgroup_function(genes[node_id]):\\n ortholog_nodes.append((node_id, genes[node_id]))\\n genes[node_id] = None\\n return\\n elif negative_set:\\n if total_genes_function(num_genes_at_node, num_species_in_pattern):\\n if options.loglevel >= 6:\\n options.stdlog.write(\\\"# recording node %i\\\\n\\\" % node_id)\\n ortholog_nodes.append((node_id, genes[node_id]))\\n\\n else:\\n # process leaf\\n s, t, g, q = parseIdentifier(node.data.taxon, options)\\n c = options.org2column[s]\\n if c in positive_set:\\n genes[node_id][c].add(g)\\n elif c in negative_set:\\n genes[node_id] = None\\n\\n tree.dfs(tree.root, post_function=count_genes)\\n\\n return ortholog_nodes\",\n \"def test_that_most_probable_always_returns_a_tree_of_the_same_logprob():\\n\\n grammar = Grammar(rulebooks)\\n actual_tree = grammar.sample_tree()\\n sentence = actual_tree.terminals\\n inner = grammar.compute_inside_probabilities(sentence)\\n best_tree = grammar.compute_most_likely_tree(sentence, inner, root=0)\\n best_logprob = grammar.logprob_tree(best_tree)\\n\\n for _ in range(20):\\n tree = grammar.compute_most_likely_tree(sentence, inner, root=0)\\n assert np.isclose(grammar.logprob_tree(tree), best_logprob)\",\n \"def tree_report(tree, unknown_summary, debug=False):\\n ##########################################################################\\n def get_equation_html_from_id(equation_id):\\n if list(tree)[0].split('_')[0] == 'a':\\n return '<span class=\\\"param\\\" data-type=\\\\\\\"eq\\\\\\\" data-item=\\\\\\\"'+\\\\\\n equation_id+\\\\\\n '\\\\\\\">this equation</span>'\\n else:\\n name_of_eq = equation_id.split('_')[1]\\n page_of_eq = eqbank[name_of_eq][\\\"group\\\"]\\n return \\\\\\n '<span class=\\\"param\\\" data-type=\\\\\\\"pallette-eq\\\\\\\" data-page=\\\\\\\"'\\\\\\n +str(page_of_eq)\\\\\\n +'\\\\\\\" data-item=\\\\\\\"'+\\\\\\n str(name_of_eq)+\\\\\\n '\\\\\\\">this equation</span>'\\n \\n def get_assoc_html(var_term):\\n return '<span class=\\\"param\\\" data-type=\\\\\\\"var-assoc\\\\\\\" data-item=\\\\\\\"'+\\\\\\n str(var_term)+\\\\\\n '\\\\\\\">'+\\\\\\n str(unknown_summary[list(unknown_summary[var_term])[0]]['value']['varDisplay'])+\\\\\\n '</span>'\\n\\n def construct_phrase_desc_rec(node, parent):\\n l_children = {\\n child: len([_ for _ in tree[child] if _ != node])\\n for child in tree[node]\\n if child != parent\\n }\\n if debug:\\n print(\\\"In tree_report\\\")\\n print(l_children)\\n \\n # If the root node has any substitutions, address it accordingly\\n # and only report it as such, no need for breakdowns.\\n if \\\"equationlist\\\" in tree.nodes[node]:\\n current_phrase = []\\n \\n subs_children = {\\n eq: tree.nodes[node][\\\"equationlist\\\"][eq]['term'] \\\\\\n for eq in tree.nodes[node][\\\"equationlist\\\"]\\n if tree.nodes[node][\\\"equationlist\\\"][eq]['substituted'] == True\\n }\\n \\n # if node has * \\n # and children has -1, include negative\\n # and if node has > 1 subs_children,\\n # include product of\\n if tree.nodes[node]['label'] == '*':\\n if \\\"NegativeOne\\\" in [\\n _.split('_')[2] \\n for _ in nx.neighbors(tree, node) \\n if _ !=tree.nodes[node]['pred']\\n ]:\\n if parent==None:\\n # Top level product with a negative sign\\n current_phrase.append(\\\"negative\\\")\\n elif parent['label'] == '+':\\n # The parent above node is +, so this must be a negative term\\n current_phrase.append(\\\"subtracted by\\\")\\n if len(subs_children)>1:\\n current_phrase.append(\\\"product of\\\")\\n \\n # if node has + \\n # and if node has > 1 subs_children,\\n # include sum of\\n if tree.nodes[node]['label'] == '+'\\\\\\n and len(subs_children)>1:\\n current_phrase.append(\\\"sum of\\\")\\n \\n #if len(subs_children) == 1:\\n # host_eq = list(subs_children)[0]\\n # var_term = subs_children[host_eq]\\n # # only one substituted term, nothing fancy\\n # current_phrase.append(\\n # get_assoc_html(var_term)+' from '+get_equation_html_from_id(host_eq)\\n # )\\n #\\n #elif len(subs_children) > 1:\\n \\n if len(subs_children) > 0:\\n term_list = []\\n for host_eq, var_term in subs_children.items():\\n term_list.append(\\n get_assoc_html(var_term)+' from '+get_equation_html_from_id(host_eq)\\n ) \\n current_phrase.append(\\\",\\\".join(term_list))\\n \\n if debug:\\n print(current_phrase)\\n return \\\" \\\".join(current_phrase)\\n\\n # If the node is a leaf node\\n if len(l_children) == 0:\\n # Substitute this with the \\n # proper term related to each leaf node\\n \\n # Add utility function bits for comparison\\n if is_symbol(tree, node):\\n return get_parambox_html(node, tree)\\n else:\\n return tree.nodes[node]['label']\\n \\n child_phrases_list = {\\n child: construct_phrase_desc_rec(child, node)\\n for child in l_children\\n }\\n \\n current_phrase = [];\\n \\n if tree.nodes[node]['label'] == '+':\\n # Addition - \\\"sum of\\\" etc.\\n # except: if only one child exists, just mention this child.\\n # Separate out the negative and positive terms,\\n # negative terms begin with \\\"subtracted by\\\"\\n \\n # if parent==None:\\n # current_phrase.append(\\\"sum of\\\")\\n # elif len(l_children) == 1\\\\\\n # or len([_ for _ in l_children if l_children[_]==0])==1:\\n # pass\\n # else:\\n # current_phrase.append(\\\"sum of\\\")\\n \\n texts = {\\\"single\\\":[], \\\"subtree\\\": []}\\n for child in l_children:\\n # Check if the children of node are leaves or not\\n if l_children[child] == 0:\\n if debug:\\n print(f\\\"at {node}--> child:{child} phrase:{child_phrases_list[child]}\\\")\\n texts[\\\"single\\\"].append(child_phrases_list[child])\\n elif l_children[child] > 0:\\n texts[\\\"subtree\\\"].append(child_phrases_list[child])\\n \\n if len(texts[\\\"single\\\"]) + len(texts[\\\"subtree\\\"]) > 1:\\n current_phrase.append(\\\"sum of\\\")\\n \\n if len(texts[\\\"single\\\"]):\\n current_phrase\\\\\\n .append(\\\",\\\".join(texts[\\\"single\\\"]))\\n \\n if len(texts[\\\"single\\\"]) and len(texts[\\\"subtree\\\"]):\\n current_phrase.append(\\\"and\\\")\\n \\n if len(texts[\\\"subtree\\\"]):\\n current_phrase\\\\\\n .append(\\\",\\\".join(texts[\\\"subtree\\\"]))\\n \\n elif tree.nodes[node]['label'] == '*':\\n labels_children = [child_phrases_list[_] for _ in child_phrases_list]\\n if \\\"-1\\\" in labels_children:\\n if parent==None:\\n # Top level product with a negative sign\\n current_phrase.append(\\\"negative\\\")\\n elif tree.nodes[parent]['label'] == '+':\\n # The parent above node is +, so this must be a negative term\\n current_phrase.append(\\\"subtracted by\\\")\\n # removing -1 from future considerations, we've already accounted for this.\\n l_children = {_:l_children[_] for _ in l_children if tree.nodes[_]['label'] != '-1'}\\n \\n # if parent==None:\\n # current_phrase.append(\\\"product of\\\")\\n # elif len(l_children) == 1\\\\\\n # or len([_ for _ in l_children if l_children[_]==0])==1:\\n # pass\\n # else:\\n # current_phrase.append('product of')\\n \\n # directly append the leaf node labels,\\n # then separately append the results of the sum on the next level\\n texts = {\\\"single\\\":[], \\\"subtree\\\": []}\\n for child in l_children:\\n # Check if the children of node are leaves or not\\n if l_children[child] == 0:\\n texts[\\\"single\\\"].append(child_phrases_list[child])\\n elif l_children[child] > 0:\\n texts[\\\"subtree\\\"].append(child_phrases_list[child])\\n \\n if len(texts[\\\"single\\\"]) + len(texts[\\\"subtree\\\"]) > 1:\\n current_phrase.append(\\\"product of\\\")\\n \\n if len(texts[\\\"single\\\"]):\\n current_phrase\\\\\\n .append(\\\",\\\".join(texts[\\\"single\\\"]))\\n \\n if len(texts[\\\"single\\\"]) and len(texts[\\\"subtree\\\"]):\\n current_phrase.append(\\\"and\\\")\\n \\n if len(texts[\\\"subtree\\\"]):\\n current_phrase\\\\\\n .append(\\\",\\\".join(texts[\\\"subtree\\\"]))\\n \\n elif tree.nodes[node]['label'] == '^':\\n labels_children = [child_phrases_list[_] for _ in child_phrases_list]\\n if \\\"-1\\\" in labels_children:\\n if parent!=None and tree.nodes[parent]['label'] == '*':\\n # The parent above node is +, so this must be a negative term\\n current_phrase.append(\\\"divided by\\\")\\n current_phrase.extend([\\n child_phrases_list[_] \\n for _ in l_children if tree.nodes[_]['label'] != '-1'\\n ])\\n if parent==None:\\n # Top level product with a negative sign\\n current_phrase.append(\\\"raised to power (-1)\\\")\\n # Update:\\n # If power is integer and <0\\n # If -1, say divided by\\n # If <-1, say divided by __ raised to the power\\n # Otherwise, Say raised to power of (integer or expr)\\n # Eg: MediumProblem\\n \\n return \\\" \\\".join(current_phrase)\\n ##########################################################################\\n \\n return construct_phrase_desc_rec(find_root(tree), None)\",\n \"def compute_idfs(documents):\\n from math import log\\n\\n # number of documents\\n TotalDocuments = len(documents)\\n\\n # create set of all words in all docs\\n words = set()\\n for words_list in documents.values():\\n for word in words_list:\\n words.add(word)\\n\\n # calculate how many doc containing each words, then calculate idfs\\n nDocsContain = dict()\\n idfs = dict()\\n for word in words:\\n nDocsContain[word] = 0\\n for words_list in documents.values():\\n if word in words_list:\\n nDocsContain[word] += 1 \\n idfs[word] = log(TotalDocuments/nDocsContain[word])\\n\\n return idfs\",\n \"def evaluate(self, feats, tag_set):\\n corect=0\\n incorect=0 \\n for (tokens, tags) in zip(feats, tag_set):\\n yyhat= self.tag(tokens)\\n for pre, tag in zip(yyhat, tags):\\n if pre==tag:\\n corect+=1\\n else:\\n incorect+=1\\n\\n return corect/(corect+incorect)\",\n \"def _log_fold_change_pairs(self, idx0, idx1, base):\\n logfc = np.zeros(shape=(len(idx0), len(idx1), self._theta_mle.shape[1]))\\n for i, xi in enumerate(idx0):\\n for j, xj in enumerate(idx1):\\n logfc[i, j, :] = self._theta_mle[xi, :] - self._theta_mle[xj, :]\\n\\n if base == np.e:\\n return logfc\\n else:\\n return logfc / np.log(base)\",\n \"def get_similarities(tags=None):\\n tags = tags or _get_tags()\\n # do your thing ...\\n similar_tags = list()\\n for item in itertools.permutations(tags, 2):\\n diffchecker = SequenceMatcher(isjunk=None, a=item[0], b=item[1], autojunk=True)\\n similarity = diffchecker.quick_ratio()\\n if similarity >= SIMILAR:\\n similar_tags.append(item)\\n return similar_tags\",\n \"def get_stats(sents, **dummy_args):\\n from collections import Counter\\n statsmode_results = Counter() \\n # first, put the relevant trees into temp file\\n\\n for sent in sents:\\n statsmode_results['Sentences'] += 1\\n deps = get_deps(sent, dep_type)\\n numpass = len([x for x in deps.links if x.type.endswith('pass')])\\n statsmode_results['Passives'] += numpass\\n statsmode_results['Tokens'] += len(sent.tokens)\\n words = [w.word for w in sent.tokens if w.word is not None and w.word.isalnum()]\\n statsmode_results['Words'] += len(words)\\n statsmode_results['Characters'] += len(''.join(words))\\n\\n to_open = '\\\\n'.join(s.parse_string.strip() for s in sents)\\n\\n from corpkit.dictionaries.process_types import processes\\n from corpkit.other import as_regex\\n tregex_qs = {'Imperative': r'ROOT < (/(S|SBAR)/ < (VP !< VBD !< VBG !$ NP !$ SBAR < NP !$-- S !$-- VP !$ VP)) !<< (/\\\\?/ !< __) !<<- /-R.B-/ !<<, /(?i)^(-l.b-|hi|hey|hello|oh|wow|thank|thankyou|thanks|welcome)$/',\\n 'Open interrogative': r'ROOT < SBARQ <<- (/\\\\?/ !< __)', \\n 'Closed interrogative': r'ROOT ( < (SQ < (NP $+ VP)) << (/\\\\?/ !< __) | < (/(S|SBAR)/ < (VP $+ NP)) <<- (/\\\\?/ !< __))',\\n 'Unmodalised declarative': r'ROOT < (S < (/(NP|SBAR|VP)/ $+ (VP !< MD)))',\\n 'Modalised declarative': r'ROOT < (S < (/(NP|SBAR|VP)/ $+ (VP < MD)))',\\n 'Open class': r'/^(NN|JJ|VB|RB)/ < __',\\n 'Closed class': r'__ !< __ !> /^(NN|JJ|VB|RB)/',\\n 'Clauses': r'/^S/ < __',\\n 'Interrogative': r'ROOT << (/\\\\?/ !< __)',\\n 'Mental processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % \\\\\\n as_regex(processes.mental, boundaries='w'),\\n 'Verbal processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % \\\\\\n as_regex(processes.verbal, boundaries='w'),\\n 'Relational processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % \\\\\\n as_regex(processes.relational, boundaries='w'),\\n 'Verbless clause': r'/^S/ !<< /^VB.?/'}\\n\\n for name, q in sorted(tregex_qs.items()):\\n res = tregex_engine(query=q, \\n options=['-o', '-C'], \\n corpus=to_open, \\n root=root\\n )\\n statsmode_results[name] += int(res)\\n if root:\\n root.update()\\n return statsmode_results, []\",\n \"def Log_OB(xref,x):\\n\\n nX = np.shape(x)\\n\\n m = nX[0]\\n n = nX[1]\\n t = nX[2]\\n\\n G = np.zeros((m,n,t))\\n\\n for r in range(t):\\n\\n # Correct for permuations\\n\\n Xout,PiA= CorrectPerm(xref,x[:,:,r])\\n\\n G[:,:,r] = Xout - np.dot(xref,np.dot(PiA,Xout))\\n\\n return G\",\n \"def compute_my_variability(event_log: Log) -> float:\\n prefixes: List[List[Event]] = []\\n bar: Bar = IncrementalBar(\\\"Prefix generation\\\", max=len(event_log.trace_list))\\n for trace in event_log.trace_list:\\n trace_prefixes: List[List[Event]] = trace.get_all_prefixes()\\n\\n for prefix in trace_prefixes:\\n if prefix not in prefixes:\\n prefixes.append(prefix)\\n bar.next()\\n bar.finish()\\n\\n entropy: float = 0\\n\\n bar = ShadyBar(\\\"Prefix likelihood estimation\\\", max=len(prefixes))\\n for prefix in prefixes:\\n\\n p: float = _prefix_likelihood_estimator(event_log, prefix)\\n entropy += p * logarithm(p, 10)\\n\\n bar.next()\\n bar.finish()\\n\\n entropy *= -1\\n\\n return entropy\",\n \"def extract_entities_from_dependency_parse(dtrees, postag):\\n sents = []\\n for x in range(0,len(dtrees)):\\n tok_list = []\\n for node_index in dtrees[x].nodes:\\n if node_index != 0:\\n node = dtrees[x].nodes[node_index]\\n if node['ctag'] == postag:\\n tok_list.append((node['word'],postag))\\n else:\\n tok_list.append((node['word'],'O'))\\n sents.append(tok_list)\\n return sents\",\n \"def substructure_sim_exact(subtrees_1, subtrees_2):\\n assert(len(subtrees_1) == len(subtrees_2))\\n n = len(subtrees_1)\\n f1 = np.zeros(n)\\n for i in range(n):\\n f1[i] = subtrees_1[i] == subtrees_2[i] # calculate the number of matching pairs\\n\\n return float(np.count_nonzero(f1)) / float(len(f1))\",\n \"def treeThreat():\\n\\tglobal treeThreatList\\n\\ttreeThreatList = []\\n\\tcount = 0\\n\\tfor name in threatList:\\n\\t\\tif name in treeList:\\n\\t\\t\\tcount += 1\\n\\t\\t\\ttreeThreatList.append(name)\\n\\treturn count\",\n \"def likelihood(A, B, word_list):\\n\\tstr_buf = []\\n\\tloglh = 0.0\\n\\tlh = 1\\n\\n\\t# read each line and compute likelihood.\\n\\tfor line in sys.stdin:\\n\\t\\tline = line.split()\\n\\t\\t# print(line)\\n\\t\\tif len(line) == 3:\\n\\t\\t\\tstr_buf.append((str(line[0]), str(line[1])))\\n\\n\\t\\telse:\\n\\t\\t\\t# if come to the end of a sentence\\n\\t\\t\\tif len(str_buf) != 0:\\n\\t\\t\\t\\tstr_buf = [('<s>','BOS')] + str_buf + [('</s>', 'EOS')]\\n\\t\\t\\t\\tfor i in range(len(str_buf) - 1):\\n\\t\\t\\t\\t\\t# print(str_buf[i][0], str_buf[i+1][0])\\n\\t\\t\\t\\t\\tif str_buf[i+1][0] in word_list:\\n\\t\\t\\t\\t\\t\\t# print('debug: A[',str_buf[i][1],'][', str_buf[i+1][1],']:', A[ str_buf[i][1] ][ str_buf[i+1][1] ])\\n\\t\\t\\t\\t\\t\\t# print('debug: B[',str_buf[i+1][1],'][', [str_buf[i+1][0]], ']:', B[ str_buf[i+1][1] ][str_buf[i+1][0]])\\n\\t\\t\\t\\t\\t\\tloglh += ( log(A[ str_buf[i][1] ][ str_buf[i+1][1] ]) + log(B[ str_buf[i+1][1] ][str_buf[i+1][0]]) )\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t# print('debug: else A[',str_buf[i][1],'][', str_buf[i+1][1],']:', A[ str_buf[i][1] ][ str_buf[i+1][1] ])\\n\\t\\t\\t\\t\\t\\t# print('debug: else B[',str_buf[i+1][1],'][', '<UNK>', ']:', B[ str_buf[i+1][1] ][ '<UNK>' ])\\n\\t\\t\\t\\t\\t\\tloglh += ( log(A[ str_buf[i][1] ][ str_buf[i+1][1] ]) + log(B[ str_buf[i+1][1] ]['<UNK>']) )\\n\\n\\t\\t\\t\\tlh = e ** loglh\\n\\t\\t\\t\\t# for s in str_buf:\\n\\t\\t\\t\\t# \\tprint(s[0], end=\\\" \\\")\\n\\t\\t\\t\\t# print(':', lh)\\n\\t\\t\\t\\tprint(lh)\\n\\n\\t\\t\\t\\tstr_buf = []\\t\\n\\t\\t\\t\\tloglh = 0\\n\\t\\t\\t\\tlh = 1\",\n \"def kld(mu, log_var):\\n return (mu + log_var).sum() # TODO Your code goes here.\",\n \"def labelled_attachment(gold_trees, pred_trees):\\n count_match, count_total = 0, 0\\n for gold, pred in zip(gold_trees, pred_trees):\\n triples_pairs = zip(\\n gold.get_triples(include_root=True),\\n pred.get_triples(include_root=True),\\n )\\n for (g_src, g_trg, g_rel), (p_src, p_trg, p_rel) in triples_pairs:\\n assert g_src == p_src\\n count_total += 1\\n if g_trg == p_trg and g_rel == p_rel:\\n count_match += 1\\n if count_match == 0 or count_total == 0:\\n return 0.0\\n else:\\n return float(count_match) / count_total\",\n \"def get_stats(sents):\\n import os\\n import re \\n # first, put the relevant trees into temp file\\n if 'outname' in kwargs.keys():\\n to_open = 'tmp-%s.txt' % kwargs['outname']\\n else:\\n to_open = 'tmp.txt'\\n with open(to_open, \\\"w\\\") as fo:\\n for sent in sents:\\n statsmode_results['Sentences'] += 1\\n fo.write(sent.parse_string.rstrip().encode('utf-8', errors = 'ignore') + '\\\\n')\\n deps = get_deps(sent, dep_type)\\n numpass = len([x for x in deps.links if x.type.endswith('pass')])\\n statsmode_results['Passives'] += numpass\\n statsmode_results['Tokens'] += len(sent.tokens)\\n statsmode_results['Words'] += len([w for w in sent.tokens if w.word.isalnum()])\\n #statsmode_results['Unique words'] += len(set([w.word.lower() for w in sent.tokens if w.word.isalnum()]))\\n #statsmode_results['Unique lemmata'] += len(set([w.lemma.lower() for w in sent.tokens if w.word.isalnum()]))\\n\\n # count moods via trees (/\\\\?/ !< __)\\n from dictionaries.process_types import processes\\n from corpkit.other import as_regex\\n tregex_qs = {'Imperative': r'ROOT < (/(S|SBAR)/ < (VP !< VBD !< VBG !$ NP !$ SBAR < NP !$-- S !$-- VP !$ VP)) !<< (/\\\\?/ !< __) !<<- /-R.B-/ !<<, /(?i)^(-l.b-|hi|hey|hello|oh|wow|thank|thankyou|thanks|welcome)$/',\\n #'Open interrogative': r'ROOT < SBARQ <<- (/\\\\?/ !< __)', \\n #'Closed interrogative': r'ROOT ( < (SQ < (NP $+ VP)) << (/\\\\?/ !< __) | < (/(S|SBAR)/ < (VP $+ NP)) <<- (/\\\\?/ !< __))',\\n 'Unmodalised declarative': r'ROOT < (S < (/(NP|SBAR|VP)/ $+ (VP !< MD)))',\\n 'Modalised declarative': r'ROOT < (S < (/(NP|SBAR|VP)/ $+ (VP < MD)))',\\n 'Open class words': r'/^(NN|JJ|VB|RB)/ < __',\\n 'Closed class words': r'__ !< __ !> /^(NN|JJ|VB|RB)/',\\n 'Clauses': r'/^S/ < __',\\n 'Interrogative': r'ROOT << (/\\\\?/ !< __)',\\n 'Mental processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % as_regex(processes.mental, boundaries = 'w'),\\n 'Verbal processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % as_regex(processes.verbal, boundaries = 'w'),\\n 'Relational processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % as_regex(processes.relational, boundaries = 'w')}\\n\\n for name, q in sorted(tregex_qs.items()):\\n res = tregex_engine(query = q, \\n options = ['-o', '-C'], \\n corpus = to_open, \\n root = root)\\n statsmode_results[name] += int(res)\\n global numdone\\n numdone += 1\\n if root:\\n root.update()\\n if not root:\\n tot_string = str(numdone + 1) + '/' + str(total_files * len(tregex_qs.keys()))\\n if 'outname' in kwargs.keys():\\n tot_string = '%s: %s' % (kwargs['outname'], tot_string)\\n animator(p, numdone, tot_string, **par_args)\\n if 'note' in kwargs.keys() and kwargs['note'] is not False:\\n kwargs['note'].progvar.set((numdone * 100.0 / (total_files * len(tregex_qs.keys())) / denom) + startnum)\\n os.remove(to_open)\",\n \"def find_matches(params, tol = 1+1e-5):\\n attempt=params['attempt']\\n attempt_tree=params['att_tree']\\n attempt_list=flatten(attempt_tree,'t')\\n \\n answer=params['answer']\\n answer_tree=params['ans_tree']\\n answer_list=flatten(answer_tree,'c')\\n\\n # sort by value\\n combined_list=sorted(answer_list+attempt_list,key=lambda x: (x[0],len(x[2])))\\n # find all hits\\n Hits=find_Hits(combined_list, tol)\\n # eliminate dominating hits\\n final_matches=find_dominating_hits(Hits,answer,attempt)\\n \\n return final_matches\",\n \"def dlog_score(self, T, pos, m, psi):\\n dpsi = tr.zeros_like(psi)\\n for (i, j, lab) in T:\\n if j != 0:\\n m_i = self.get_tag_index(m[i - 1])\\n pos1 = pos[i - 1]\\n m_j = self.get_tag_index(m[j - 1])\\n pos2 = pos[j - 1]\\n dpsi[pos1, pos2, lab, m_i, m_j] += 1\\n return dpsi\",\n \"def calc_tdf(docs):\\r\\n terms = set()\\r\\n for doc in docs:\\r\\n for term in doc:\\r\\n terms.add(term)\\r\\n tdf = {}\\r\\n for term in terms:\\r\\n doc_count = 0\\r\\n for doc in docs:\\r\\n doc_count += 1 if term in doc else 0\\r\\n tdf[term] = doc_count\\r\\n return tdf\",\n \"def Log_OB_S1_mat(xref,x):\\n\\n nX = np.shape(x)\\n\\n m = nX[0]\\n n = nX[1]\\n t = nX[2]\\n\\n G = np.zeros((n,t,t))\\n Gv = np.zeros((m,n,t,t))\\n\\n for rx in range(t):\\n for ry in range(t):\\n\\n # Correct for permuations\\n\\n Xout = dp(x[:,:,rx,ry])\\n\\n for q in range(n):\\n\\n a = np.sum(Xout[:,q]*xref[:,q,rx,ry])/np.sqrt(1e-12 + np.sum(xref[:,q,rx,ry]**2)*np.sum(Xout[:,q]**2)) # Should have unit L2 norm\\n if a > 1:\\n a = 1\\n if a < -1:\\n a = -1\\n G[q,rx,ry] = np.arccos(a) # Computing the angles\\n v = Xout[:,q] - a*xref[:,q,rx,ry]\\n Gv[:,q,rx,ry] = v / (1e-24 + np.linalg.norm(v)) # Unit vector in the tangent subspace\\n\\n return G,Gv\",\n \"def calc_ent(x, dist):\\n\\n assert(len(x) == len(dist))\\n x_value_list = set([x[i] for i in range(x.shape[0])])\\n dist_sum = np.sum(dist)\\n ent = 0.0\\n for x_value in x_value_list:\\n index = (x == x_value)\\n sub_dist = dist[index]\\n sub_dist_sum = np.sum(sub_dist)\\n p = sub_dist_sum / dist_sum\\n logp = np.log2(p)\\n ent -= p * logp\\n\\n return ent\",\n \"def denom(self, tokens):\\n\\n sum = 0\\n A_set = self.A(tokens)\\n # heuristic\\n for elem in A_set:\\n sum += self.cond_prob(elem, tokens[1:])\\n return 1 - sum\",\n \"def calc_tf_log(doc):\\r\\n tf = calc_tf(doc)\\r\\n max_tf = tf[max(tf, key=tf.get)]\\r\\n tf_log = {}\\r\\n for key, val in tf.items():\\r\\n tf_log[key] = (1 + math.log(val)) / (1 + math.log(max_tf))\\r\\n return tf_log\",\n \"def alleles(gt1, gt2):\\n\\n\\t# Count genotypes 1 and 2\\n\\tcount = {}\\n\\tfor g in gt1 :\\n\\t\\tcount[g] = count.get(g, 0) + 1\\n\\n\\tfor g in gt2 :\\n\\t\\tcount[g] = count.get(g, 0) + 1\\n\\n\\t# Find mayor allele (we call it 'ref')\\n\\tmaxCount = 0\\n\\tref = ''\\n\\tfor g in count:\\n\\t\\tif count[g] > maxCount:\\n\\t\\t\\tmaxCount = count[g]\\n\\t\\t\\tref = g\\n\\n\\t# Find minor allele (we call these one 'alt')\\n\\talt = ''\\n\\tfor g in count:\\n\\t\\tif g != ref and g != '0': alt = g\\n\\n\\t# Create a genotype string (VCF style)\\n\\tgtstr = \\\"\\\"\\n\\tfor i in range(len(gt1)):\\n\\t\\tgtstr += \\\"\\\\t\\\" + gtVcf(ref, alt, gt1[i]) + \\\"/\\\" + gtVcf(ref, alt, gt2[i])\\n\\n\\treturn ref, alt, count[alt], gtstr\",\n \"def score(self, tree):\\n probas = [self.prod_freq.get(prod, 0.5) /\\n self.source_freq.get(prod.source, 1)\\n for prod in tree]\\n logger.debug(\\\"Scoring {0}:\\\".format(tree))\\n for prod, proba in zip(tree, probas):\\n logger.debug(\\n \\\"\\\\t- {0} {1}({2})\\\"\\n .format(prod, round(proba, 3), round(log(proba, 10), 3)))\\n log_proba = sum(log(proba, 10) for proba in probas)\\n return {\\n 'log_proba': log_proba,\\n 'productions': len(probas)\\n }\",\n \"def total(tree):\\n if tree is None:\\n return 0\\n return total(tree.left) + total(tree.right) + tree.cargo\",\n \"def find_entropy(less_than_threshold,more_than_threshold):\\n\\n ''' Storing total number of records '''\\n total_records = len(less_than_threshold) + len(more_than_threshold)\\n\\n ''' Calculating the probability '''\\n less_than_probability = len(less_than_threshold) / total_records\\n more_than_probability = len(more_than_threshold) / total_records\\n\\n ''' Converting the dataframe to numpy arrays '''\\n less_than_threshold_values = less_than_threshold.values\\n more_than_threshold_values = more_than_threshold.values\\n\\n ''' Storing the target attribute values (Muffin or Cupcake) for threshold values '''\\n target_for_less_than = less_than_threshold_values[:, -1]\\n target_for_more_than = more_than_threshold_values[:, -1]\\n\\n ''' Finding the counts of muffin and cupcake for values lower than and greater than threshold value '''\\n recipe_type, less_than_cupcake_muffin_count = np.unique(target_for_less_than, return_counts=True)\\n recipe_type, more_than_cupcake_muffin_count = np.unique(target_for_more_than, return_counts=True)\\n\\n # print(recipe_type, more_than_cupcake_muffin_count, len(more_than_cupcake_muffin_count))\\n ''' To ensure there are at least 5 records in each node '''\\n if less_than_cupcake_muffin_count.sum() < 5 or more_than_cupcake_muffin_count.sum() < 5:\\n ''' Return horrible badness '''\\n return math.inf\\n else:\\n ''' Find the entropies for less than threshold values and more than threshold values '''\\n less_than_entropy = sum((less_than_cupcake_muffin_count / less_than_cupcake_muffin_count.sum()) * - np.log2(\\n less_than_cupcake_muffin_count / less_than_cupcake_muffin_count.sum()))\\n more_than_entropy = sum((more_than_cupcake_muffin_count / more_than_cupcake_muffin_count.sum()) * - np.log2(\\n more_than_cupcake_muffin_count / more_than_cupcake_muffin_count.sum()))\\n\\n ''' Calculate the total weighted entropy '''\\n total_weighted_entropy = less_than_probability * less_than_entropy + more_than_probability * more_than_entropy\\n\\n return total_weighted_entropy\",\n \"def compare_dictionaries(d1, d2):\\r\\n score = 0\\r\\n gef = 0\\r\\n for z in d1:\\r\\n gef += d1[z]\\r\\n total = gef\\r\\n \\r\\n for x in d2:\\r\\n if x in d1:\\r\\n score += math.log(d1[x] / total) * d2[x] \\r\\n else:\\r\\n score += math.log(0.5/total) * d2[x]\\r\\n return score\",\n \"def node_significance(log):\\n caseF = {a: sum(a in case_log for case_log in log.flat_log.values())\\n for a in log.activities}\\n # Activities (node) significance\\n S = {a: caseF[a] / len(log.cases) for a in caseF}\\n return S\",\n \"def draw_tre_and_aln(aln_region, tre_str, og, target_sp):\\n #site, aa, score=ps_site\\n\\n # get leaves from tree string\\n tstax = getleaf(tre_str)\\n\\n ts = TreeStyle()\\n ts.margin_left = 5\\n ts.margin_right = 30\\n ts.margin_top = 20\\n ts.tree_width = 50\\n\\n aln_region_str = aln_region.format(\\\"fasta\\\")\\n\\n t = PhyloTree(tre_str, alignment=aln_region_str, alg_format=\\\"fasta\\\")\\n\\n # interfact\\n def _set_style(t):\\n # input an t with alignment, add label to it\\n \\\"\\\"\\\"\\n info = TextFace(\\\"{}\\\\nCodon:{}\\\\nScore:{}\\\".format(og,site,score), fsize=8, fgcolor='black', ftype='Arial')\\n info.margin_top = 10\\n info.margin_right = 20\\n info.margin_left = 5\\n t.add_face(info, column=0, position=\\\"branch-bottom\\\")\\n #t.add_face(TextFace(\\\"Codon:{}\\\".format(site)),column=0,position=\\\"branch-bottom\\\")\\n \\\"\\\"\\\"\\n ## label the longbranch\\n nstyle = NodeStyle()\\n # red line\\n #nstyle[\\\"bgcolor\\\"] = \\\"DarkSeaGreen\\\"\\n #nstyle[\\\"bgcolor\\\"] = \\\"LightSalmon\\\"\\n nstyle[\\\"hz_line_type\\\"] = 0\\n #nstyle[\\\"hz_line_color\\\"] = \\\"#ff0000\\\"\\n for tst in tstax:\\n tsnode = t.get_leaves_by_name(name=tst)[0]\\n tsnode.set_style(nstyle)\\n # add #1 to target species\\n if target_sp in tsnode.name:\\n tsnode.name = tsnode.name + \\\"_#1\\\"\\n return t\\n t = _set_style(t)\\n ## add AA alignment\\n def _trans_aln(aln_region):\\n # input an coding aln\\n aa_aln_str = \\\"\\\"\\n for seq_obj in aln_region:\\n dna = Seq(str(seq_obj.seq))\\n aa_s = dna.translate(gap=\\\"-\\\")\\n aa_aln_str += '>{}\\\\n{}\\\\n'.format(seq_obj.id, aa_s)\\n #print(aa_aln_str)\\n return aa_aln_str\\n\\n aa_aln_region_str = _trans_aln(aln_region)\\n t_aa = PhyloTree(tre_str, alignment=aa_aln_region_str, alg_format=\\\"fasta\\\")\\n t_aa = _set_style(t_aa)\\n\\n return t, t_aa, ts\",\n \"def rel_agreement_index(self) -> float:\\n a = ((self.predicted - self.true) / self.true) ** 2\\n b = np.abs(self.predicted - np.mean(self.true))\\n c = np.abs(self.true - np.mean(self.true))\\n e = ((b + c) / np.mean(self.true)) ** 2\\n return float(1 - (np.sum(a) / np.sum(e)))\",\n \"def get_idf(term, documents):\\n\\n number_of_docs = len(documents)\\n documents_containing_term = len([document for document in documents if term in document])\\n\\n idf = math.log10(number_of_docs / documents_containing_term)\\n\\n return round(idf, 5)\",\n \"def log_likelihood(mu, sigma, y, T):\\n ll = 0.\\n for yi, Ti in zip(y, T):\\n d = yi.size\\n log_det_cov = np.linalg.slogdet(sigma[Ti])[1]\\n y_minus_mean = yi - mu[Ti]\\n term3 = np.dot(y_minus_mean.T.ravel(),\\n np.linalg.solve(sigma[Ti], y_minus_mean.T).ravel())\\n ll += (-0.5 * d * np.log(2 * np.pi) - 0.5 * log_det_cov - 0.5 * term3)\\n return ll\",\n \"def tf_idf_score():\\n\\n global final_doc_set\\n global final_dictionary\\n final_score = []\\n\\n for doc_id in final_doc_set:\\n score = 0\\n for query_term in final_dictionary.keys():\\n if final_dictionary[query_term][1].get(doc_id):\\n tf = final_dictionary[query_term][1][doc_id][0]\\n df = final_dictionary[query_term][0]\\n\\n score += ((1 + log10(tf)) * log10(TOTAL_DOCS / df))\\n\\n final_score.append([doc_id, score])\\n\\n return final_score\",\n \"def mutual_info(l1, l2):\\n return entropy(l1) + entropy(l2) - entropy(joint_dataset(l1, l2))\",\n \"def cstree_predict(value_dict, tree, order, sample, i, data):\\n numerator = cstree_likelihood(sample, order, tree, data)\\n order_of_var = order.index(i)\\n samples = [np.array(list(sample[:order_of_var])+[val]+list(sample[order_of_var+1:]))\\n for val in value_dict[i]]\\n # sum_j P(X1=x1,...,Xi-1=xi-1,Xi+1=xi+1,...,Xn|Xi=j)P(Xi=j)\\n denominator = sum([cstree_likelihood(s, order, tree, data) for s in samples])\\n return numerator/denominator\",\n \"def _alternative(words):\\n wordlist = list(words)\\n counter = []\\n for word in wordlist:\\n used = set()\\n counter.append(__geterate_tree(word, wordlist, used, 1))\\n return max(counter) == len(wordlist)\",\n \"def logrels(rets):\\n return np.log(rets + 1)\",\n \"def chunked_tags(train):\\n cfdist = nltk.ConditionalFreqDist()\\n for t in train:\\n for word, tag, chtag in tree2conlltags(t):\\n if chtag == \\\"O\\\":\\n cfdist[tag].inc(False)\\n else:\\n cfdist[tag].inc(True)\\n return [tag for tag in cfdist.conditions() if cfdist[tag].max() == True]\",\n \"def log_score(self, X_state_tree_sequence, pi=None, T=None, E=None):\\n if pi is None:\\n pi = self.estimate.pi\\n if T is None:\\n T = self.estimate.T\\n if E is None:\\n E = self.estimate.E\\n\\n log_scores = []\\n for idx, lineageObj in enumerate(self.X):\\n log_score = 0\\n # the first term is the value of pi for the state of the first cell\\n log_score += np.log(pi[X_state_tree_sequence[idx][0]])\\n log_score += log_T_score(T, X_state_tree_sequence[idx], lineageObj)\\n log_score += log_E_score(get_Emission_Likelihoods(self, E)\\n [idx], X_state_tree_sequence[idx])\\n assert np.all(np.isfinite(log_score))\\n log_scores.append(log_score)\\n return log_scores\",\n \"def eta_r(self, children, t_coef):\\n with tf.compat.v1.name_scope('coef_r'):\\n # children is shape (batch_size x max_tree_size x max_children)\\n children = tf.cast(children, tf.float32)\\n batch_size = tf.shape(input=children)[0]\\n max_tree_size = tf.shape(input=children)[1]\\n max_children = tf.shape(input=children)[2]\\n\\n # num_siblings is shape (batch_size x max_tree_size x 1)\\n num_siblings = tf.cast(\\n tf.math.count_nonzero(children, axis=2, keepdims=True),\\n dtype=tf.float32\\n )\\n # num_siblings is shape (batch_size x max_tree_size x max_children + 1)\\n num_siblings = tf.tile(\\n num_siblings, [1, 1, max_children + 1], name='num_siblings'\\n )\\n # creates a mask of 1's and 0's where 1 means there is a child there\\n # has shape (batch_size x max_tree_size x max_children + 1)\\n mask = tf.concat(\\n [tf.zeros((batch_size, max_tree_size, 1)),\\n tf.minimum(children, tf.ones(tf.shape(input=children)))],\\n axis=2, name='mask'\\n )\\n\\n # child indices for every tree (batch_size x max_tree_size x max_children + 1)\\n child_indices = tf.multiply(tf.tile(\\n tf.expand_dims(\\n tf.expand_dims(\\n tf.range(-1.0, tf.cast(max_children, tf.float32), 1.0, dtype=tf.float32),\\n axis=0\\n ),\\n axis=0\\n ),\\n [batch_size, max_tree_size, 1]\\n ), mask, name='child_indices')\\n\\n # weights for every tree node in the case that num_siblings = 0\\n # shape is (batch_size x max_tree_size x max_children + 1)\\n singles = tf.concat(\\n [tf.zeros((batch_size, max_tree_size, 1)),\\n tf.fill((batch_size, max_tree_size, 1), 0.5),\\n tf.zeros((batch_size, max_tree_size, max_children - 1))],\\n axis=2, name='singles')\\n\\n # eta_r is shape (batch_size x max_tree_size x max_children + 1)\\n return tf.compat.v1.where(\\n tf.equal(num_siblings, 1.0),\\n # avoid division by 0 when num_siblings == 1\\n singles,\\n # the normal case where num_siblings != 1\\n tf.multiply((1.0 - t_coef), tf.divide(child_indices, num_siblings - 1.0)),\\n name='coef_r'\\n )\",\n \"def get_diff_parent_age(natal_history_adult, natal_history_pediatric, natalhist, logger):\\r\\n nhp=np.array(list(natal_history_pediatric[natalhist]))\\r\\n nhp=[nhp[i] for i in range(len(nhp)) if not(np.isnan(nhp)[i])]\\r\\n nha=np.array(list(natal_history_adult[natalhist]))\\r\\n nha=[nha[i] for i in range(len(nha)) if not(np.isnan(nha)[i])]\\r\\n logger.info(\\\"Difference between maternal age for pediatric and adult onset : {}\\\".format(mannwhitneyu(nhp,nha)))\",\n \"def _calculate_log_det(self, var):\\n log_det = []\\n\\n for k in range(self.n_components):\\n evals, evecs = tf.linalg.eig(var[0, k])\\n\\n log_det.append(tf.reduce_sum(tf.math.log(tf.math.real(evals))))\\n log_det = tf.convert_to_tensor(log_det)\\n return tf.expand_dims(log_det, -1)\",\n \"def likelihood(self, log_occr_array=None):\\n\\n if log_occr_array is not None:\\n # Catch invalid occurrence rates for zero likelihood\\n try:\\n self.log_occr = log_occr_array\\n except InvalidOccurrenceRate:\\n return 0.0\\n\\n # Procedure: calculate N_exp in all cases, calculate detection\\n # term if there are detections\\n\\n # N_exp\\n N_exp = self.calc_integral() * self._N_stars\\n\\n # Product terms\\n # TODO:Check that the array broadcasting works here\\n # Shape of s_terms should be [N_planets, NR, NP]\\n s_terms = self.H_array * self.F_array * self.occr\\n\\n if tf.is_tensor(self.occr):\\n ps_terms = tf.reduce_sum(s_terms, axis=(-1, -2))\\n product_term = tf.reduce_prod(ps_terms)\\n ll_value = product_term * tf.exp(product_term)\\n else:\\n product_term = s_terms.sum(axis=(-1, -2)).prod()\\n ll_value = product_term * np.exp(-N_exp)\\n\\n # BUG\\n if np.isnan(ll_value):\\n warnings.warn(\\\".likelihood value is nan.\\\")\\n import pdb; pdb.set_trace()\\n\\n # A nan value is possible when some of the occr are too high\\n return ll_value if not np.isnan(ll_value) else 0.0\",\n \"def square_tree(t):\\n return tree(label(t)**2,[square_tree(b) for b in branches(t)])\",\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]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.54515827","0.5316931","0.5275841","0.5213601","0.52042305","0.51962835","0.5176608","0.50949377","0.50766563","0.5068682","0.5062237","0.5042272","0.5036879","0.5017808","0.4995817","0.49750316","0.49472603","0.49305362","0.49264774","0.48480722","0.48410428","0.48357734","0.4825944","0.48159447","0.47776973","0.4777408","0.4772853","0.47607905","0.4756927","0.4750655","0.47503585","0.47401693","0.47346497","0.473342","0.47286436","0.47256646","0.4722207","0.47199306","0.47196907","0.47174382","0.4715106","0.4708923","0.4702089","0.4686276","0.46845657","0.46830264","0.46624812","0.4657641","0.46398625","0.46388564","0.4631006","0.46241492","0.46199164","0.46144637","0.46142814","0.4609613","0.4609172","0.45937297","0.45883957","0.45842466","0.4581132","0.4579416","0.45756933","0.4574676","0.45714754","0.4569191","0.4563715","0.45491183","0.45458195","0.4543364","0.45432913","0.45368868","0.45334497","0.4532797","0.45240837","0.4521502","0.45194617","0.45168743","0.45156136","0.45147145","0.4514233","0.45123458","0.4508196","0.45074612","0.45051858","0.45004445","0.44990766","0.44943565","0.44889608","0.44880012","0.4484412","0.4483457","0.44804773","0.44793504","0.4476933","0.44733924","0.44729596","0.44684663","0.44676828","0.4464292","0.44617793"],"string":"[\n \"0.54515827\",\n \"0.5316931\",\n \"0.5275841\",\n \"0.5213601\",\n \"0.52042305\",\n \"0.51962835\",\n \"0.5176608\",\n \"0.50949377\",\n \"0.50766563\",\n \"0.5068682\",\n \"0.5062237\",\n \"0.5042272\",\n \"0.5036879\",\n \"0.5017808\",\n \"0.4995817\",\n \"0.49750316\",\n \"0.49472603\",\n \"0.49305362\",\n \"0.49264774\",\n \"0.48480722\",\n \"0.48410428\",\n \"0.48357734\",\n \"0.4825944\",\n \"0.48159447\",\n \"0.47776973\",\n \"0.4777408\",\n \"0.4772853\",\n \"0.47607905\",\n \"0.4756927\",\n \"0.4750655\",\n \"0.47503585\",\n \"0.47401693\",\n \"0.47346497\",\n \"0.473342\",\n \"0.47286436\",\n \"0.47256646\",\n \"0.4722207\",\n \"0.47199306\",\n \"0.47196907\",\n \"0.47174382\",\n \"0.4715106\",\n \"0.4708923\",\n \"0.4702089\",\n \"0.4686276\",\n \"0.46845657\",\n \"0.46830264\",\n \"0.46624812\",\n \"0.4657641\",\n \"0.46398625\",\n \"0.46388564\",\n \"0.4631006\",\n \"0.46241492\",\n \"0.46199164\",\n \"0.46144637\",\n \"0.46142814\",\n \"0.4609613\",\n \"0.4609172\",\n \"0.45937297\",\n \"0.45883957\",\n \"0.45842466\",\n \"0.4581132\",\n \"0.4579416\",\n \"0.45756933\",\n \"0.4574676\",\n \"0.45714754\",\n \"0.4569191\",\n \"0.4563715\",\n \"0.45491183\",\n \"0.45458195\",\n \"0.4543364\",\n \"0.45432913\",\n \"0.45368868\",\n \"0.45334497\",\n \"0.4532797\",\n \"0.45240837\",\n \"0.4521502\",\n \"0.45194617\",\n \"0.45168743\",\n \"0.45156136\",\n \"0.45147145\",\n \"0.4514233\",\n \"0.45123458\",\n \"0.4508196\",\n \"0.45074612\",\n \"0.45051858\",\n \"0.45004445\",\n \"0.44990766\",\n \"0.44943565\",\n \"0.44889608\",\n \"0.44880012\",\n \"0.4484412\",\n \"0.4483457\",\n \"0.44804773\",\n \"0.44793504\",\n \"0.4476933\",\n \"0.44733924\",\n \"0.44729596\",\n \"0.44684663\",\n \"0.44676828\",\n \"0.4464292\",\n \"0.44617793\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":11183,"cells":{"query":{"kind":"string","value":"Calculate the gradient of the log score for a given tree with respect to the psi and phi parameters"},"document":{"kind":"string","value":"def dlog_score(self, T, pos, m, psi):\n dpsi = tr.zeros_like(psi)\n for (i, j, lab) in T:\n if j != 0:\n m_i = self.get_tag_index(m[i - 1])\n pos1 = pos[i - 1]\n m_j = self.get_tag_index(m[j - 1])\n pos2 = pos[j - 1]\n dpsi[pos1, pos2, lab, m_i, m_j] += 1\n return dpsi"},"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 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 dlogZ(self, T, pos, psi, phi):\n msgs = belief_propagation(T, pos, psi, phi, True)\n dpsi = calculate_gradient(msgs, T, pos, psi, True, True)\n return dpsi","def grad_log_likelihood(self, log_occr_array=None):\n\n if log_occr_array is not None:\n # Catch invalid occurrence rates for zero likelihood\n try:\n self.log_occr = log_occr_array\n except InvalidOccurrenceRate:\n return -np.inf * np.ones_like(self.occr, dtype=float)\n\n # Calculate components first\n N_exp = self.calc_integral() * self._N_stars # perhaps not needed\n nexp_terms = self._N_stars * self.calc_bin_volumes() * self.H_bar_array\n s_terms = self.H_array * self.F_array * self.occr\n numerator_terms = self.H_array * self.F_array\n\n if not tf.is_tensor(self.occr):\n # Checking shapes of intermediate terms,\n # numerator_terms vs s_terms.sum(-1, -2) and vs v factors.\n intermediate_terms = numerator_terms / s_terms.sum(axis=(-1, -2))\n # TODO: v_factor changed to negative, I think a minus\n # sign had been missed\n grad_log_array = - nexp_terms + intermediate_terms.sum(axis=0)\n\n # BUG TODO\n if np.isnan(grad_log_array).any():\n warnings.warn(\".grad_log_likelihood value is nan.\")\n import pdb; pdb.set_trace()\n grad_log_array = -np.inf * grad_log_array\n else:\n raise NotImplementedError(\"Manual gradient calculate with \"\n \"tensorflow objects isn't \"\n \"implemented, and seems a bit \"\n \"redundant.\")\n\n return grad_log_array","def fd_grad(self, T, pos, psi, phi, eps=1e-5):\n dpsi = tr.zeros_like(psi)\n dphi = tr.zeros_like(phi)\n for pos1 in range(psi.shape[0]):\n for pos2 in range(psi.shape[1]):\n for lab in range(psi.shape[2]):\n for i in range(psi.shape[3]):\n for j in range(psi.shape[4]):\n psi[pos1, pos2, lab, i, j] += eps\n val1 = self.logZ(T, pos, psi, phi)\n psi[pos1, pos2, lab, i, j] -= 2 * eps\n val2 = self.logZ(T, pos, psi, phi)\n psi[pos1, pos2, lab, i, j] += eps\n dpsi[pos1, pos2, lab, i, j] = (val1 - val2) / (2 * eps)\n for p in range(phi.shape[0]):\n for i in range(phi.shape[1]):\n phi[p, i] += eps\n val1 = self.logZ(T, pos, psi, phi)\n phi[p, i] -= 2 * eps\n val2 = self.logZ(T, pos, psi, phi)\n phi[p, i] += eps\n dphi[p, i] = (val1 - val2) / (2 * eps)\n return dpsi, dphi","def logp_grad(self, xs, ys, fs, **kwargs):","def logit_cost_grad(self, theta, X, y):\n\n grad = np.zeros(len(theta))\n\n ### YOUR CODE HERE\n sig = utils.sigmoid(theta)\n # sig = np.subtract(sig, y)\n sig = sig - y\n grad = np.dot(X.T, sig) + 2 * self.params['lamb'] * self.regularizer[1](self.weights)\n ### END YOUR CODE\n\n return grad","def _log_likelihood_gradients(self):\r\n return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))","def grad_llh(self, params):\n grad = np.clip(self.grad_log_likelihood(params[0], params[1], params[2:]), SMALLEST_NUMBER,\n LARGEST_NUMBER)\n\n return grad","def log_score(self, T, pos, m, psi, phi):\n log_score = 0\n for (i, j, lab) in T:\n m_i = m[i - 1]\n pos1 = pos[i - 1]\n log_score += self.get_phi_score(phi, i - 1, m_i)\n if j != 0:\n m_j = m[j - 1]\n pos2 = pos[j - 1]\n log_score += self.get_psi_score(psi, pos1, pos2, lab, m_i, m_j)\n return log_score","def log_prior_grad(self, inputs):","def gradient(self, theta):\n a = -(6 * self.scale ** 2)\n b = 3 * self.scale ** 2 + np.exp(2 * theta)\n b *= np.log(3 * self.scale ** 2 * np.exp(-2 * theta) + 1)\n return a / b","def _log_likelihood_gradients(self):\n return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK,X=self.X,slices1=self.Xslices,slices2=self.Xslices), self.likelihood._gradients(partial=np.diag(self.dL_dK))))","def dlog_prob(self, T, pos, m, psi, phi=tr.Tensor()):\n if phi.size() == tr.Size([0]):\n phi = self.create_phi(T, pos, m)\n dpsi_score = self.dlog_score(T, pos, m, psi)\n dpsi_Z = self.dlogZ(T, pos, psi, phi)\n return dpsi_score - dpsi_Z","def grad_log(self, X):\n g = autograd.elementwise_grad(self.log_den)\n G = g(X)\n return G","def Gradient(Walker,particle):\n\n h=0.001\n dPsi = zeros(shape=shape(Walker.Re[particle]))\n for i in range(Walker.sys_dim):\n Y=Walker.Re[particle][i]\n Walker.Re[particle][i]-=h\n wfs1=wfs(Walker)\n Walker.Re[particle][i]+=2.0*h\n wfs2=wfs(Walker)\n dPsi[i] = (wfs2-wfs1)/2/h\n Walker.Re[particle][i]=Y\n\n return dPsi","def log_likelihood_gradients_(self, y, f):\n log_lik = self.evaluate_log_likelihood(y, f)\n f = np.squeeze(f)\n J = jacrev(self.evaluate_log_likelihood, argnums=1)\n H = jacrev(J, argnums=1)\n return log_lik, J(y, f), H(y, f)","def grad_log_likelihood(g_t):\n # state vector [g1, g2, g3, ... , gn-1, gn]\n state_vector = np.zeros(N)\n\n # factor that will be added to one node and subtracted from another\n def alphaij(gj, gi, deltaij, varij):\n return ((gj - gi) - deltaij) / varij\n\n # indices of state vector\n # Iterate over all connections\n for r in self.graph.index:\n state1, state2, value, variance, ligand, standard_state = self.graph.iloc[r]\n i = self.states[self.states.name == state1].index[0]\n j = self.states[self.states.name == state2].index[0]\n\n gj = g_t[j]\n gi = g_t[i]\n\n edge_attr = self.cycle.edges()[(state1, state2)]\n deltaij = edge_attr['energy'] # measured difference\n varij = edge_attr['weight'] # measured variance\n\n shared_alpha = alphaij(gj, gi, deltaij, varij)\n\n state_vector[i] += shared_alpha\n state_vector[j] -= shared_alpha\n\n return state_vector","def MvNormalLogp():\n cov = pt.matrix(\"cov\")\n cov.tag.test_value = floatX(np.eye(3))\n delta = pt.matrix(\"delta\")\n delta.tag.test_value = floatX(np.zeros((2, 3)))\n\n cholesky = Cholesky(lower=True, on_error=\"nan\")\n\n n, k = delta.shape\n n, k = f(n), f(k)\n chol_cov = cholesky(cov)\n diag = pt.diag(chol_cov)\n ok = pt.all(diag > 0)\n\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\n delta_trans = solve_lower(chol_cov, delta.T).T\n\n result = n * k * pt.log(f(2) * np.pi)\n result += f(2) * n * pt.sum(pt.log(diag))\n result += (delta_trans ** f(2)).sum()\n result = f(-0.5) * result\n logp = pt.switch(ok, result, -np.inf)\n\n def dlogp(inputs, gradients):\n (g_logp,) = gradients\n cov, delta = inputs\n\n g_logp.tag.test_value = floatX(1.0)\n n, k = delta.shape\n\n chol_cov = cholesky(cov)\n diag = pt.diag(chol_cov)\n ok = pt.all(diag > 0)\n\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\n delta_trans = solve_lower(chol_cov, delta.T).T\n\n inner = n * pt.eye(k) - pt.dot(delta_trans.T, delta_trans)\n g_cov = solve_upper(chol_cov.T, inner)\n g_cov = solve_upper(chol_cov.T, g_cov.T)\n\n tau_delta = solve_upper(chol_cov.T, delta_trans.T)\n g_delta = tau_delta.T\n\n g_cov = pt.switch(ok, g_cov, -np.nan)\n g_delta = pt.switch(ok, g_delta, -np.nan)\n\n return [-0.5 * g_cov * g_logp, -g_delta * g_logp]\n\n return OpFromGraph([cov, delta], [logp], grad_overrides=dlogp, inline=True)","def fgrad_y(self, y, psi, return_precalc = False):\r\n\r\n\r\n mpsi = psi.copy()\r\n d = psi[-1]\r\n mpsi = mpsi[:self.num_parameters-1].reshape(self.n_terms, 3)\r\n\r\n # vectorized version\r\n\r\n S = (mpsi[:,1]*(y[:,:,None] + mpsi[:,2])).T\r\n R = np.tanh(S)\r\n D = 1-R**2\r\n\r\n GRAD = (d + (mpsi[:,0:1][:,:,None]*mpsi[:,1:2][:,:,None]*D).sum(axis=0)).T\r\n\r\n if return_precalc:\r\n return GRAD, S, R, D\r\n\r\n\r\n return GRAD","def calc_grad(X, Y, theta):\n m, n = X.shape\n\n margins = Y * X.dot(theta)\n probs = 1. / (1 + np.exp(margins))\n grad = -(1./m) * (X.T.dot(probs * Y))\n\n return grad","def costFunction(theta,X,y):\n m = X.shape[0]\n J = 0\n h = sigmoid (np.dot(X,theta))\n \n J = (1/m)* ((-np.dot(y.T,(np.log(h)))) - np.dot((1 - y).T,(np.log(1-h))))\n \n #grad = (1/m) * np.dot(X.T,(h-y))\n grad = (1/m) * np.dot((h.T - y), X).T\n \n return J, grad","def gradient(self, theta):\n pass","def gradient(self, theta):\n pass","def logZ(self, T, pos, psi, phi):\n msgs = belief_propagation(T, pos, psi, phi, True)\n log_z = calculate_belief_sum(msgs, True)\n return log_z","def log_prob(self, T, pos, m, psi, phi=tr.Tensor()):\n if phi.size() == tr.Size([0]):\n phi = self.create_phi(T, pos, m)\n return self.log_score(T, pos, m, psi, phi) - self.logZ(T, pos, psi, phi)","def log_likelihood(self, X, Y, theta):\n \n alphas, logZ = self.alpha_chain(X, theta)\n total = 0\n \n s_prev = self.S\n \n for t,s in enumerate(Y):\n total += self.log_psi(theta, t, s_prev, s, X[t]) \n s_prev = s\n \n total -= logZ\n return total / X.shape[0]","def grad_log_likelihood(self, log_occr_array=None):\n\n if log_occr_array is not None:\n # Catch invalid occurrence rates for zero likelihood\n try:\n self.log_occr = log_occr_array\n except InvalidOccurrenceRate:\n return -np.inf * np.ones_like(self.occr, dtype=float)\n\n # The Poisson Jacobian\n # --------------------\n # Calculate components first\n N_exp = self.calc_integral() * self._N_stars # perhaps not needed\n v_factors = self._N_stars * self.calc_bin_volumes() * self.H_bar_array\n # TODO: broadcast self.occr into the same shape as self.F_array\n # Reduce it later through the chain-rule\n s_terms = self.H_array * self.F_array * self.occr_grid\n numerator_terms = self.H_array * self.F_array\n\n if not tf.is_tensor(self.occr):\n # Checking shapes of intermediate terms,\n # numerator_terms vs s_terms.sum(-1, -2) and vs v factors.\n intermediate_terms = numerator_terms / s_terms.sum(axis=(-1, -2))\n # TODO: v_factor changed to negative, I think a minus\n # sign had been missed\n poisson_jacobian = intermediate_terms.sum(axis=0) - v_factors\n poisson_jacobian = poisson_jacobian.reshape(-1)\n param_jacobian = self.get_occr_jacobian(flattened=True)\n grad_log_array = np.matmul(poisson_jacobian, param_jacobian)\n\n # BUG TODO\n if np.isnan(grad_log_array).any():\n warnings.warn(\".grad_log_likelihood value is nan.\")\n import pdb; pdb.set_trace()\n grad_log_array = -np.inf * grad_log_array\n else:\n raise NotImplementedError(\"Manual gradient calculate with \"\n \"tensorflow objects isn't \"\n \"implemented, and seems a bit \"\n \"redundant.\")\n\n return grad_log_array","def ComputeLogGradientTerm(self, *args):\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF2F_ComputeLogGradientTerm(self, *args)","def _log_prior_gradients(self):\n if self.priors.size == 0:\n return 0.\n x = self.param_array\n ret = np.zeros(x.size)\n #compute derivate of prior density\n [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]\n #add in jacobian derivatives if transformed\n priored_indexes = np.hstack([i for p, i in self.priors.items()])\n for c,j in self.constraints.items():\n if not isinstance(c, Transformation):continue\n for jj in j:\n if jj in priored_indexes:\n ret[jj] += c.log_jacobian_grad(x[jj])\n return ret","def _forward_log_det_jacobian(self, x):\n d = self._compute_shared(x=x)\n relx = (x - d.x_k) / d.w_k\n relx = relx # tf.where(d.out_of_bounds, 0.5*tf.ones_like(x), relx)\n grad = (\n 2 * tf.math.log(d.s_k) +\n tf.math.log(d.d_kp1 * relx**2 + 2 * d.s_k * relx * (1 - relx) + # newln\n d.d_k * (1 - relx)**2) -\n 2 * tf.math.log((d.d_kp1 + d.d_k - 2 * d.s_k) * relx *\n (1 - relx) + d.s_k))\n return grad # tf.where(d.out_of_bounds, tf.zeros_like(grad), grad)","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 compute_gradient(theta, X, y):\n m = X.shape[0]\n grad_theta = np.dot(X.transpose(), (np.dot(X, theta) - y)) / m\n #print theta, grad_theta, objective_function(theta, X, y)\n return grad_theta","def compute_loss_gradient(theta_vector, *args):\n\n psi = args[0] # feed psi as a parameter\n circ_depth = args[1]\n num_qbits = args[2]\n theta = np.reshape(theta_vector, (circ_depth, num_qbits)) # reshapes the flat theta vector\n fidelity = get_fidelity(theta, psi)\n\n # the derivative of the loss wrt fidelity\n dl_df = -0.5 * fidelity ** (-0.5)\n\n df_dtheta = [] # a list of partial derivatives of the fidelity wrt the theta parameters\n\n for index in range(len(theta_vector)):\n layer_index = index // num_qbits\n qbit_index = index % num_qbits\n\n theta_plus = np.copy(theta)\n theta_plus[layer_index][qbit_index] += np.pi / 2 # added pi/2 to the ith theta parameter\n\n theta_minus = np.copy(theta)\n theta_minus[layer_index][qbit_index] -= np.pi / 2 # subtracted pi/2 to the ith theta parameter\n\n df_dtheta_i = 0.5 * (get_fidelity(theta_plus, psi) - get_fidelity(theta_minus, psi)) # ith derivative\n df_dtheta.append(df_dtheta_i)\n\n df_dtheta = np.array(df_dtheta)\n dl_dtheta = dl_df * df_dtheta # chain rule to get partial derivative of loss wrt theta parameters\n\n return dl_dtheta","def grad_neg_log_like(params):\n gp.set_parameter_vector(params)\n return -gp.grad_log_likelihood(y, quiet=True)","def logZ_brute(self, T, pos, psi, phi):\n ms = self.get_all_tag_seq(len(T))\n log_scores = tr.zeros(len(ms), dtype=tr.float64)\n for i in range(len(ms)):\n log_scores[i] = self.log_score(T, pos, ms[i], psi, phi)\n log_z = logsumexp(log_scores)\n return log_z","def get_F(self,X,y=None):\n if len(self.trees) == 0:\n # print ('init')\n assert not (y is None), 'tree0 need y to initialize,y:{}'.format(y)\n pos = y.sum()\n neg = y.shape[0] - pos\n F = np.zeros([X.shape[0],])\n F[:] = math.log(pos / neg)\n # print (pos,neg,F)\n return F\n else:\n F = np.zeros([X.shape[0],])\n for tree in self.trees:\n F += self.learning_rate*tree.predict(X)\n # print (F)\n return F","def log_marginal_likelihood(self, theta=None, gradient=True, opt_flag=False):\n if theta != None:\n self.kernel.hyper = np.exp(theta) if opt_flag else theta\n # self.kernel.hyper\n if gradient:\n K, grad = self.kernel.estimate(self.X_train, gradient=True)\n else:\n K = self.kernel.estimate(self.X_train)\n \n K += self.id_mat * self.sigma_n\n\n try:\n L = np.linalg.cholesky(K)\n except np.linalg.LinAlgError:\n return (np.inf, np.array([0])) if gradient else np.inf\n \n alpha = cho_solve((L, True), self.Y_train)\n \n logl = float(self.Y_train.T.dot(alpha)) / 2\n logl += np.log(np.diag(L)).sum()\n logl += self.ntrain * np.log(2 * np.pi) / 2\n\n \n if gradient:\n logl_grad = alpha.dot(alpha.T) # einsum is slower\n logl_grad -= cho_solve((L, True), self.id_mat)\n logl_grad = 0.5 * np.einsum('ij,ji -> ', logl_grad, grad) #dot prod and trace combined\n return logl, -np.array([logl_grad])\n return logl","def ComputeLogGradientTerm(self, *args):\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF3F_ComputeLogGradientTerm(self, *args)","def _log_prior_gradients(self):\n x = self._get_params()\n ret = np.zeros(x.size)\n [np.put(ret,i,p.lnpdf_grad(xx)) for i,(p,xx) in enumerate(zip(self.priors,x)) if not p is None]\n return ret","def log_likelihood(self, theta=None, phi=None):\n theta = theta if theta is not None else self.theta\n phi = phi if phi is not None else self.phi\n ret = 0.\n for m, dm in enumerate(self.corpus):\n for n, w_mn in enumerate(dm):\n tp = 0.\n for k in range(self.n_components):\n tp += theta[m, k] * phi[k, w_mn]\n ret += np.log(tp)\n return ret","def log_likelihood_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w))-y)","def gradient(self, theta):\n return (1 / (self.sigma * np.sqrt(2 * np.pi))) * (\n -theta / (self.sigma ** 2) * np.exp(-(theta ** 2) / (2 * self.sigma ** 2))\n )","def compute_grad_likelihood(sensor_loc, obj_loc, distance):\n\tgrad = np.zeros(sensor_loc.shape)\n\t# Your code: finish the grad loglike\n\t\t\n\treturn grad","def _log_prior_gradients(self):\r\n if self.priors is None:\r\n return 0.\r\n x = self._get_params()\r\n ret = np.zeros(x.size)\r\n [np.put(ret, i, p.lnpdf_grad(xx)) for i, (p, xx) in enumerate(zip(self.priors, x)) if not p is None]\r\n return ret","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 ComputeLogGradientTerm(self, *args):\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseID2D_ComputeLogGradientTerm(self, *args)","def get_log_likelihood(phi, pred, t, dot_product, weight, reg= 1):\n prior = -0.5* np.sum(np.multiply(weight, weight))\n likelihood = np.multiply(t, np.log(pred+TOLERANCE)) + np.multiply(1.0- t, np.log(1.0-pred+TOLERANCE))\n likelihood = np.sum(likelihood)\n\n return prior + likelihood","def log_likelihood_gradients(self, y, f):\n # align shapes and compute mask\n y = y.reshape(-1, 1)\n f = f.reshape(-1, 1)\n mask = np.isnan(y)\n y = np.where(mask, f, y)\n\n # compute gradients of the log likelihood\n log_lik, J, H = vmap(self.log_likelihood_gradients_)(y, f)\n\n # apply mask\n mask = np.squeeze(mask)\n log_lik = np.where(mask, 0., log_lik)\n J = np.where(mask, np.nan, J)\n H = np.where(mask, np.nan, H)\n\n return log_lik, J, np.diag(H)","def loss_gradient(self, targets, scores):\n m = targets * scores\n numer = 4. * (2. * numpy.arctan(m) - 1.)\n denom = 1. + m**2\n return numer/denom","def costFunction(self,theta, X, y): \n m = len(y)\n h = self.sigmoid(X@theta)\n J = 1 / m * (- y.T @ self.log(h) - (1-y).T @ self.log(1-h)) \n # grad = 1/ m * X.T @ (h - y)\n return J","def grad(theta, X, y, lambda_):\n # ... dopolnite (naloga 1, naloga 2)\n\n l = []\n for i, e in enumerate(theta):\n l.append(1 / len(y) * sum([(h(x, theta) - yi) * x[i] for x, yi in zip(X, y)]) + 2 * lambda_ * e)\n\n return np.array(l)","def num_grad(theta, X, y, lambda_, e=1e-3):\n # ... dopolnite (naloga 1, naloga 2)\n return np.array([(cost(theta + eps, X, y, lambda_) - cost(theta - eps, X, y, lambda_)) / (2 * e)\n for eps in np.identity(len(theta)) * e])","def log_probability(theta):\n global priors\n global logp\n lp = np.sum([priors[p].logpdf(x) for p, x in zip(logp.parnames, theta)])\n if not np.isfinite(lp) or np.isnan(lp):\n return -np.inf\n ll = logp(theta)\n if not np.isfinite(ll):\n return -np.inf\n return lp + ll","def log_probability(theta):\n global priors\n global logp\n lp = np.sum([priors[p].logpdf(x) for p, x in zip(logp.parnames, theta)])\n if not np.isfinite(lp) or np.isnan(lp):\n return -np.inf\n ll = logp(theta)\n if not np.isfinite(ll):\n return -np.inf\n return lp + ll","def gradient(theta, X, y, learning_rate):\n m = len(y)\n\n theta = theta.reshape((-1,1))\n grad = np.zeros(theta.shape)\n h = sigmoid(np.dot(X, theta)) \n \n grad = np.dot((h-y).T, X)/m\n grad = grad.T\n grad[1:] += (learning_rate/m)*theta[1:]\n return grad","def compute_gradient_logreg_regl2(y, tx, w, lambda_):\n grad = compute_gradient_logreg(y, tx, w)\n penal_grad = grad + 2 * lambda_ * w\n\n return penal_grad","def calc_tf_log(doc):\r\n tf = calc_tf(doc)\r\n max_tf = tf[max(tf, key=tf.get)]\r\n tf_log = {}\r\n for key, val in tf.items():\r\n tf_log[key] = (1 + math.log(val)) / (1 + math.log(max_tf))\r\n return tf_log","def lnlike(theta, dtarray, dmagarray, sigmaarray):\n gamma, A = theta\n\n aux=np.sum(np.log(like_one(theta,dtarray,dmagarray,sigmaarray)))\n\n return aux","def log_prob(self):\n res = -self.L_h/2*np.log(2*np.pi*self.la)\n res = res + self.L_h*(self.L_h-1)/2*self.a\n\n\n res = res - 1/(2*self.la)*np.square(np.linalg.norm(self.e*self.pie))\n\n res = res - 1/(2*self.la)*np.sum(self.e2*self.pie_var)\n\n res = res - self.L_h/2*np.log(2*np.pi*self.sigma2)\n res = res - 1/(2*self.sigma2)*(np.square(np.linalg.norm(self.w))+np.trace(self.R))\n\n print(\"Log-probability difference = {}\".format(res - self.LP), file=self.logfile)\n self.LP = res\n return res","def log(self, x_old, grad, x, curr_iter, tol_g_val, tol_x_val, tol_f_val):\n\n print(\"-----------------------------------\")\n print(\"\\n Iter: \", curr_iter)\n print(\"\\n x_old: \", x_old)\n print(\"\\n gradient: \", grad)\n print(\"\\n x: \", x)\n print(\"\\n tol_x_val: \", tol_x_val)\n print(\"\\n tol_f_val: \", tol_f_val)\n print(\"\\n tol_g_val: %s \\n \" % tol_g_val)","def ComputeLogGradientTerm(self, *args):\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseID3D_ComputeLogGradientTerm(self, *args)","def _log_likelihood(self, theta, f, x, y, yerr):\n sigma2 = yerr**2\n return -0.5*np.sum((y - f(theta, x))**2 / sigma2 + 2*np.log(sigma2))","def log_LxP(theta, D):\r\n p1 = np.exp( -((theta-D[0])**2)/2 )\r\n p2 = np.exp( -((theta-D[1])**2)/2 )\r\n if np.abs(theta) <= 1:\r\n LxP = p1*p2\r\n else:\r\n LxP = 0.0\r\n return np.log(LxP)","def passDerivative(self):\n # this will happen when this SumNode is unused\n if self.getLogDerivative() == Node.ZERO:\n return \n for decomp_id in self.getChildren():\n prodNode = self.getChild(decomp_id)\n tmp = self.getLogDerivative() \\\n + np.log(float(self.getChildCounts(decomp_id)) / self.getCounts())\n\n if prodNode.getLogDerivative() == Node.ZERO:\n prodNode.setLogDerivative(tmp)\n else:\n prodNode.setLogDerivative( \\\n np.logaddexp(tmp, self.getLogDerivative()))","def __log_likelihood(self, params, *args):\n\t\tX, y, feature_set, lambda_reg, empirical_weights, verbose, sign = args\n\n\t\tno_example = len(X)\n\t\ttotal_logZ = 0\n\t\ttotal_logProb = 0\n\t\texpected_weights = np.zeros(len(feature_set))\n\t\tfor t in range(len(X)):\n\t\t\t# example_features = X[t], example_labels = y[t]\n\n\t\t\tpotential = np.zeros(len(X[t]))\n\t\t\tfor i in range(len(X[t])):\n\t\t\t\t#candidate_features = X[t][i], candidate_label = y[t][i]\n\t\t\t\tpotential[i] = feature_set.calc_inner_product(X[t][i], params)\n\n\t\t\t#scaling\n\t\t\tpotential = potential - np.max(potential, keepdims=True)\n\n\t\t\tfor i in range(len(X[t])):\n\t\t\t\ttotal_logProb += potential[i] * y[t][i]\n\n\t\t\tpotential, Z = self.__softmax(potential)\n\n\t\t\tfor i in range(len(X[t])):\n\t\t\t\tfeature_set.calc_inner_sum(expected_weights, X[t][i], potential[i])\n\n\t\t\ttotal_logZ += log(Z)\n\n\t\t# _params = feature_set.get_regularized_params(params, 'bias')\n\t\t_params = params\n\t\tlog_likelihood = total_logProb - total_logZ - (lambda_reg/2) * np.sum(np.multiply(_params,_params))\n\t\tgradients = empirical_weights - expected_weights - lambda_reg * _params\n\n\t\tglobal SUB_ITERATION_NUM\n\t\tif verbose:\n\t\t\tsub_iteration_str = ' '\n\t\t\tif SUB_ITERATION_NUM > 0:\n\t\t\t\tsub_iteration_str = '(' + '{0:02d}'.format(SUB_ITERATION_NUM) + ')'\n\t\t\tprint(' ', '{0:03d}'.format(ITERATION_NUM), sub_iteration_str, ':', log_likelihood * sign)\n\n\t\tSUB_ITERATION_NUM += 1\n\n\t\treturn sign * log_likelihood, sign * gradients","def grad_log_prior(self, log_occr_array=None):\n\n if log_occr_array is not None:\n # Catch invalid occurrence rates for zero likelihood\n try:\n self.log_occr = log_occr_array\n except InvalidOccurrenceRate:\n return -np.inf * np.ones_like(self.occr, dtype=float)\n\n if tf.is_tensor(self.occr):\n raise NotImplementedError(\"Manual gradient calculate with \"\n \"tensorflow objects isn't \"\n \"implemented, and seems a bit \"\n \"redundant.\")\n else:\n # Written in terms of occr for ease.\n grad = 1 / (2*self.occr)\n # At this point, it's still possible for occr to be so\n # small that it's underflowing, where value will be nan\n grad = grad if not np.isnan(grad).any() else -np.inf * grad\n\n return grad","def log_likelihood(self, theta, active=None):\n return sum(self.log_likelihood_term(theta, active=active))","def log_marginal_likelihood(X_train,y_train,phi,tau=1.,Ve=1.e-10):","def ga_log(R):\n phiP, t_normal_n, t_perpendicular_n = extractRotorComponents(R)\n return phiP + t_normal_n + t_perpendicular_n","def fgrad_y(self, y, psi, return_precalc = False):\r\n\r\n mpsi = psi.copy()\r\n\r\n # vectorized version\r\n\r\n # S = (mpsi[:,1]*(y + mpsi[:,2])).T\r\n S = (mpsi[:,1]*(y[:,:,None] + mpsi[:,2])).T\r\n R = np.tanh(S)\r\n D = 1-R**2\r\n\r\n # GRAD = (1+(mpsi[:,0:1]*mpsi[:,1:2]*D).sum(axis=0))[:,np.newaxis]\r\n GRAD = (1+(mpsi[:,0:1][:,:,None]*mpsi[:,1:2][:,:,None]*D).sum(axis=0)).T\r\n\r\n if return_precalc:\r\n # return GRAD,S.sum(axis=1),R.sum(axis=1),D.sum(axis=1)\r\n return GRAD, S, R, D\r\n\r\n\r\n return GRAD","def f(p, phi, phib, df):\n\treturn -log(p) - df + (p-1)*phi + \\\n\t ( phi*(1-p) + phib + \\\n\t 5./4*alpha*(phi*p)**(9./4)-(9./4)*alpha*(p*phi)**(5./4) - \\\n\t (1./2)(1-p*phi)**2 - (phib/Nb)-5./4*alpha*(phi+phib)**(9./4) + \\\n\t (9./4)*alpha*(phi+phib)**(5.4) + \\\n\t 1./2*(1-phi-phib)**2 ) * Ns","def lr_cost_function_grad(theta: np.ndarray, X: np.ndarray, y: np.ndarray, l: float) -> np.ndarray:\n # Initialize some useful values\n m = len(y) # number of training examples\n\n # You need to return the following variable correctly.\n grad = np.zeros(theta.shape)\n\n # ====================== YOUR CODE HERE ======================\n # Instructions: Compute the partial derivatives and set grad to the partial\n # derivatives of the cost w.r.t. each parameter in theta.\n\n # =============================================================\n return grad","def newton(flogl, start, fgrad, fhess, maxiter):\r\n warn = 0\r\n iteration = 0\r\n par_hat0 = start\r\n m = 1\r\n while (iteration < maxiter and m >= 1e-04):\r\n H = -la.inv(fhess(par_hat0))\r\n g = fgrad(par_hat0).reshape(start.shape)\r\n Hg = np.dot(H, g)\r\n par_hat0 = par_hat0 + Hg\r\n iteration += 1\r\n m = np.dot(g.T, Hg)\r\n if iteration == maxiter:\r\n warn = 1\r\n logl = flogl(par_hat0)\r\n return (par_hat0, logl, warn)","def compute_tree(self, tree):\n g_list_val, g_list_h = self._build_graph(tree) # return theano variable of each node\n list_val = self._traversal_tree(tree) #\n f = theano.function(g_list_val, g_list_h, allow_input_downcast=True)\n result = f(*list_val)\n return result","def compute_gradient_logreg(y, tx, w):\n assert len(set(y).difference({0., 1.})) == 0, \"Class labels must be encoded as {0, 1}\"\n\n s = sigmoid(tx.dot(w)) - y\n grad = tx.T.dot(s)\n\n return grad","def gradientFunctionReg(theta, X, y, Lambda):\n m = len(y) # number of training examples\n grad = np.zeros(theta.shape[0])\n theta = np.transpose(theta)\n sum_1 = 0\n X = X.values\n y = y.values\n #calcuate the theta_0 \n# ====================== YOUR CODE HERE ======================\n# Instructions: Compute the gradient of a particular choice of theta.\n# Compute the partial derivatives and set grad to the partial\n# derivatives of the cost w.r.t. each parameter in theta\n for i in range(theta.shape[0]):\n if i == 0:\n for j in range(m):\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i]\n else:\n for j in range(m):\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i] + Lambda*theta[i]\n grad[i] = sum_1/m\n sum_1 = 0\n\n# =============================================================\n\n return grad","def compute_gradients(self, logits, target):\n\n target_length = target.shape[0]\n num_time_steps = logits.shape[0]\n\n\t\t######################\n\t\t### YOUR CODE HERE ###\n\t\t######################\n\n # expand labels by inserting a blank between each pair\n normalized_logits = softmax(logits)\n blank_label = normalized_logits.shape[1] - 1\n l = add_blanks(target, blank_label)\n target_length = l.shape[0]\n\n alpha = self.compute_forward_variables(normalized_logits, target) \n beta = self.compute_backward_variables(normalized_logits, target)\n\n # rescale\n alpha = alpha / np.sum(alpha, axis=0)\n beta = beta / np.sum(beta, axis=0)\n alphabeta = alpha * beta\n print \"alpha\"\n print alpha\n\n # compute zt\n z = Counter()\n for t in xrange(num_time_steps):\n for s, k in enumerate(l):\n z[t] += alphabeta[s, t] / normalized_logits[t, k]\n \n # normalized_logits is time steps t by labels k\n # alpha is 2 * target_length - 1 by time steps\n lab_zk = np.zeros_like(normalized_logits)\n for s, k in enumerate(l):\n for t in xrange(num_time_steps):\n lab_zk[t, k] += alphabeta[s, t]\n\n grad = normalized_logits\n for k in xrange(target.shape[0]):\n for t in xrange(num_time_steps):\n ytk = normalized_logits[t, k]\n constant = 1.0 / (ytk * z[t])\n grad[t, k] = ytk - constant * lab_zk[t, k]\n \n return grad","def gradient_function(theta, X, y):\n\n grad = None\n #######################################################################\n # TODO: #\n # Compute the gradient for a particular choice of theta. #\n # Compute the partial derivatives and set grad to the partial #\n # derivatives of the cost w.r.t. each parameter in theta #\n # #\n #######################################################################\n \n theta = theta[:, np.newaxis]\n \n thetatrans = theta.T\n Xtrans = X.T\n \n MulThetaX = np.dot(thetatrans, Xtrans)\n \n h = sigmoid(MulThetaX)\n \n grad = (y - h) * Xtrans\n\n #######################################################################\n # END OF YOUR CODE #\n #######################################################################\n return grad","def log_marginal_likelihood(self, theta=None, eval_gradient=False):\n if theta is None:\n if eval_gradient:\n raise ValueError(\n \"Gradient can only be evaluated for theta!=None\")\n return self.log_marginal_likelihood_value_\n\n kernel = self.kernel_.clone_with_theta(theta)\n\n if eval_gradient:\n K, K_gradient = kernel(self.X_train_, eval_gradient=True)\n else:\n K = kernel(self.X_train_)\n\n K[np.diag_indices_from(K)] += self.alpha\n try:\n L = cholesky(K, lower=True) # Line 2\n except np.linalg.LinAlgError:\n return (-np.inf, np.zeros_like(theta)) \\\n if eval_gradient else -np.inf\n\n # Support multi-dimensional output of self.y_train_\n y_train = self.y_train_\n if y_train.ndim == 1:\n y_train = y_train[:, np.newaxis]\n X_train = self.X_train_\n P = self.P_train_\n Q = np.dot(np.transpose(P), cho_solve((L, True), P))\n try:\n M = cholesky(Q, lower=True)\n except np.linalg.LinAlgError:\n return (-np.inf, np.zeros_like(theta)) \\\n if eval_gradient else -np.inf\n\n alpha = cho_solve((L, True), y_train)\n beta = cho_solve((M,True), np.dot(np.transpose(P), alpha) )\n y_shift = y_train - np.dot(P, beta)\n alpha_ = cho_solve((L, True), y_shift)\n self.y_shift = y_shift\n\n # Compute log-likelihood (compare line 7)\n log_likelihood_dims = -0.5 * np.einsum(\"ik,ik->k\", y_shift, alpha_)\n log_likelihood_dims -= np.log(np.diag(L)).sum()\n log_likelihood_dims -= K.shape[0] / 2 * np.log(2 * np.pi)\n log_likelihood = log_likelihood_dims.sum(-1) # sum over dimensions\n\n if eval_gradient: # compare Equation 5.9 from GPML\n tmp = np.einsum(\"ik,jk->ijk\", alpha_, alpha_) # k: output-dimension\n tmp -= cho_solve((L, True), np.eye(K.shape[0]))[:, :, np.newaxis]\n # Compute \"0.5 * trace(tmp.dot(K_gradient))\" without\n # constructing the full matrix tmp.dot(K_gradient) since only\n # its diagonal is required\n log_likelihood_gradient_dims = \\\n 0.5 * np.einsum(\"ijl,ijk->kl\", tmp, K_gradient)\n log_likelihood_gradient = log_likelihood_gradient_dims.sum(-1)\n\n if eval_gradient:\n return log_likelihood, log_likelihood_gradient\n else:\n return log_likelihood","def log_likelihood(self):\r\n if self.likelihood.is_heteroscedastic:\r\n A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)\r\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self._A))\r\n else:\r\n A = -0.5 * self.num_data * self.output_dim * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT\r\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self._A))\r\n C = -self.output_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * np.log(sf2))\r\n D = 0.5 * self.data_fit\r\n self._A_part, self._B_part, self._C_part, self._D_part = A, B, C, D\r\n return A + B + C + D + self.likelihood.Z","def log_deriv(error):\n return logistic(error) * (1 - logistic(error))","def leapfrog(theta, r, grad, epsilon, f):\n # make half step in r\n rprime = r + 0.5 * epsilon * grad\n # make new step in theta\n thetaprime = theta + epsilon * rprime\n #compute new gradient\n logpprime, gradprime = f(thetaprime)\n # make half step in r again\n rprime = rprime + 0.5 * epsilon * gradprime\n return thetaprime, rprime, gradprime, logpprime","def update(self, returns, log_probs):\n policy_gradient = []\n for log_prob, Gt in zip(log_probs, returns):\n policy_gradient.append(-log_prob * Gt)\n\n loss = torch.stack(policy_gradient).sum()\n self.optimizer.zero_grad()\n loss.backward()\n self.optimizer.step()","def gradient_descent(f, df, x, sigma=0.5, epsilon=1e-8):\n pass","def log_likelihood_grad_rew(self, data, reward_model, bias_params):","def _log_likelihood_colour(self, df, dfo):\n pdf = fit_colour_gaussian(df[\"colour_obs\"].values)\n return np.log(pdf(dfo[\"g_r\"].values)).sum()","def logsum_safe(prob_ll, atl):\n logpdf = prob_ll + K.log(atl + K.epsilon())\n alpha = tf.reduce_max(logpdf, -1, keepdims=True)\n return alpha + tf.log(tf.reduce_sum(K.exp(logpdf-alpha), -1, keepdims=True) + K.epsilon())","def gradientFunction(theta, X, y):\n y = y[:, 0]\n m = y.shape # number of training samples\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\n grad /= m\n return grad","def fast_loss_and_grad(self, X, y):\n loss = 0.0\n grad = np.zeros(self.W.shape) # initialize the gradient as zero\n \n # ================================================================ #\n # YOUR CODE HERE:\n # Calculate the softmax loss and gradient WITHOUT any for loops.\n # ================================================================ #\n \n num_train = X.shape[0]\n num_classes = self.W.shape[0]\n \n# # vectorized loss calculation #\n class_scores_matrix = np.dot(self.W,X.T) # calculating class scores matrix (C x m): rows are class scores transposes\n class_scores_matrix -= np.max(class_scores_matrix) # considering the possible issue for numerical instability and account for it\n exp_a = np.exp(class_scores_matrix) # calculating the exponents\n \n# y_exp = np.array(exp_a[y, np.arange(0, class_scores_matrix.shape[1])])\n# #print(exp_a[:,:3])\n# #print(y[:3])\n# #print(y_exp[:3])\n \n# tt = np.sum(exp_a,axis=0)\n# tt2 = np.divide(tt,y_exp)\n# print(num_train)\n# tt3 = np.power(tt2,1/num_train)\n# loss = np.log(np.prod(tt3))\n \n \n \n \n (C, D) = self.W.shape\n N = X.shape[0]\n\n scores = np.dot(self.W, X.T)\n scores -= np.max(scores) # shift by log C to avoid numerical instability\n\n y_mat = np.zeros(shape = (C, N))\n y_mat[y, range(N)] = 1\n\n # matrix of all zeros except for a single wx + log C value in each column that corresponds to the\n # quantity we need to subtract from each row of scores\n correct_wx = np.multiply(y_mat, scores)\n\n # create a single row of the correct wx_y + log C values for each data point\n sums_wy = np.sum(correct_wx, axis=0) # sum over each column\n\n exp_scores = np.exp(scores)\n sums_exp = np.sum(exp_scores, axis=0) # sum over each column\n result = np.log(sums_exp)\n\n result -= sums_wy\n\n loss = np.sum(result)\n loss /= num_train\n \n \n # vectorized gradient calculation #\n exp_a_sum = np.sum(exp_a,axis=0)\n\n y_mat_corres = np.zeros(shape = (num_classes, num_train))\n y_mat_corres[y, range(num_train)] = 1\n sum_exp_scores = np.sum(exp_a, axis=0) \n sum_exp_scores = 1.0 / exp_a_sum # division by sum over columns\n exp_a *= sum_exp_scores\n grad = np.dot(exp_a, X)\n grad -= np.dot(y_mat_corres, X)\n grad /= num_train\n \n\n # ================================================================ #\n # END YOUR CODE HERE\n # ================================================================ #\n\n return loss, grad","def _grad(V):\n dv = diag(V)\n weights, A, _, AinvB = _weights(dv)\n Ey = (weights.T.dot(Y_control) - Y_treated).getA()\n dGamma0_dV_term2 = zeros(K)\n #dPI_dV = zeros((N0, N1)) # stupid notation: PI = W.T\n #Ai = A.I\n for k in range(K):\n if verbose: # for large sample sizes, linalg.solve is a huge bottle neck,\n print(\"Calculating gradient, linalg.solve() call %s of %s\" % (k ,K,))\n #dPI_dV.fill(0) # faster than re-allocating the memory each loop.\n dA = dA_dV_ki[k]\n dB = dB_dV_ki[k]\n dPI_dV = linalg.solve(A,(dB - dA.dot(AinvB))) \n #dPI_dV = Ai.dot(dB - dA.dot(AinvB))\n dGamma0_dV_term2[k] = np.einsum(\"ij,kj,ki->\",Ey, Y_control, dPI_dV) # (Ey * Y_control.T.dot(dPI_dV).T.getA()).sum()\n return LAMBDA + 2 * dGamma0_dV_term2","def _core_calc_degrad(self,bd,Ld) :\n\t\tdegrad = np.dot(Ld,bd) # Do matrix multiplication \n\t\tdegrad = np.exp(degrad) # Exponentiate to convert log to real\n\t\treturn degrad","def approx_grad(theta, X, y):\n grad_a = np.array([(cost(theta + e, X, y) - cost(theta - e, X, y)) / (2 * 1e-5)\n for e in np.identity(len(theta)) * 1e-5])\n return grad_a","def _Le(self, p):\n a = norm(p - self.edges[:, 0, :], axis=1)\n b = norm(p - self.edges[:, 1, :], axis=1)\n le = norm(self.edges[:, 0, :] - self.edges[:, 1, :], axis=1)\n q = (a + b + le) / (a + b - le) # TODO: set the quotient to 0 whenever (a + b - le) == 0\n return np.log(q)","def _loglike(self, y, f):\n ll = y * tf.log(pos(f)) + (1 - y) * tf.log(pos(1 - f))\n return ll","def get_gradient(phi, pred, t, dot_product, weight, reg= 1, regression= \"logistic\"):\n if regression == \"logistic\":\n gradient = np.matmul(phi.T, pred - t)\n elif regression == \"probit\":\n R = np.eye(pred.shape[0])\n for i in range(pred.shape[0]):\n y_n = pred[i,0]\n dotp = dot_product[i, 0]\n pdf = norm.pdf(dotp)\n R[i,i] = pdf/(y_n*(1-y_n) + TOLERANCE)\n gradient = np.matmul(np.matmul(phi.T, R), pred-t)\n elif regression == \"multiclass\":\n gradient = np.matmul(phi.T, pred - t)\n\n # Add regularization\n gradient += weight/ reg\n return gradient","def expected_log_g(self):\n raise NotImplementedError()\n E_lng = np.zeros_like(self.mf_gamma)\n\n # \\psi(\\sum_{b} \\gamma_b)\n trm2 = psi(self.mf_gamma.sum(axis=2))\n for b in xrange(self.B):\n E_lng[:,:,b] = psi(self.mf_gamma[:,:,b]) - trm2\n\n return E_lng","def log_prob(self):","def lr_cost_function(theta, X, y, learning_rate):\n m = len(y) # number of training examples\n # You need to return the following variables correctly \n J = 0\n grad = np.zeros(theta.shape)\n h = sigmoid(np.dot(X, theta))\n\n #Compute cost:\n first = (1/m)*np.sum(np.dot(-y.T, np.log(h)) - np.dot((1-y).T, np.log(1-h)), axis=0)\n second = (learning_rate/(2*m))*np.sum(theta[1:]**2)\n J = first + second\n \n #Compute gradient\n grad = np.dot((h-y).T, X)/m\n grad = grad.T\n grad[1:] += (learning_rate/m)*theta[1:]\n return float(J), grad","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 f(p, phi, phib, df):\n\treturn - p + exp( - df + Ns*(log((1 - p*phi)/(1 - phi - phib)) + \\\n\t\t(p - 1)*phi - phib + (9./4)*alpha*((phi + phib)**(5./4) - (p*phi)**(5./4))))"],"string":"[\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 dlogZ(self, T, pos, psi, phi):\\n msgs = belief_propagation(T, pos, psi, phi, True)\\n dpsi = calculate_gradient(msgs, T, pos, psi, True, True)\\n return dpsi\",\n \"def grad_log_likelihood(self, log_occr_array=None):\\n\\n if log_occr_array is not None:\\n # Catch invalid occurrence rates for zero likelihood\\n try:\\n self.log_occr = log_occr_array\\n except InvalidOccurrenceRate:\\n return -np.inf * np.ones_like(self.occr, dtype=float)\\n\\n # Calculate components first\\n N_exp = self.calc_integral() * self._N_stars # perhaps not needed\\n nexp_terms = self._N_stars * self.calc_bin_volumes() * self.H_bar_array\\n s_terms = self.H_array * self.F_array * self.occr\\n numerator_terms = self.H_array * self.F_array\\n\\n if not tf.is_tensor(self.occr):\\n # Checking shapes of intermediate terms,\\n # numerator_terms vs s_terms.sum(-1, -2) and vs v factors.\\n intermediate_terms = numerator_terms / s_terms.sum(axis=(-1, -2))\\n # TODO: v_factor changed to negative, I think a minus\\n # sign had been missed\\n grad_log_array = - nexp_terms + intermediate_terms.sum(axis=0)\\n\\n # BUG TODO\\n if np.isnan(grad_log_array).any():\\n warnings.warn(\\\".grad_log_likelihood value is nan.\\\")\\n import pdb; pdb.set_trace()\\n grad_log_array = -np.inf * grad_log_array\\n else:\\n raise NotImplementedError(\\\"Manual gradient calculate with \\\"\\n \\\"tensorflow objects isn't \\\"\\n \\\"implemented, and seems a bit \\\"\\n \\\"redundant.\\\")\\n\\n return grad_log_array\",\n \"def fd_grad(self, T, pos, psi, phi, eps=1e-5):\\n dpsi = tr.zeros_like(psi)\\n dphi = tr.zeros_like(phi)\\n for pos1 in range(psi.shape[0]):\\n for pos2 in range(psi.shape[1]):\\n for lab in range(psi.shape[2]):\\n for i in range(psi.shape[3]):\\n for j in range(psi.shape[4]):\\n psi[pos1, pos2, lab, i, j] += eps\\n val1 = self.logZ(T, pos, psi, phi)\\n psi[pos1, pos2, lab, i, j] -= 2 * eps\\n val2 = self.logZ(T, pos, psi, phi)\\n psi[pos1, pos2, lab, i, j] += eps\\n dpsi[pos1, pos2, lab, i, j] = (val1 - val2) / (2 * eps)\\n for p in range(phi.shape[0]):\\n for i in range(phi.shape[1]):\\n phi[p, i] += eps\\n val1 = self.logZ(T, pos, psi, phi)\\n phi[p, i] -= 2 * eps\\n val2 = self.logZ(T, pos, psi, phi)\\n phi[p, i] += eps\\n dphi[p, i] = (val1 - val2) / (2 * eps)\\n return dpsi, dphi\",\n \"def logp_grad(self, xs, ys, fs, **kwargs):\",\n \"def logit_cost_grad(self, theta, X, y):\\n\\n grad = np.zeros(len(theta))\\n\\n ### YOUR CODE HERE\\n sig = utils.sigmoid(theta)\\n # sig = np.subtract(sig, y)\\n sig = sig - y\\n grad = np.dot(X.T, sig) + 2 * self.params['lamb'] * self.regularizer[1](self.weights)\\n ### END YOUR CODE\\n\\n return grad\",\n \"def _log_likelihood_gradients(self):\\r\\n return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))\",\n \"def grad_llh(self, params):\\n grad = np.clip(self.grad_log_likelihood(params[0], params[1], params[2:]), SMALLEST_NUMBER,\\n LARGEST_NUMBER)\\n\\n return grad\",\n \"def log_score(self, T, pos, m, psi, phi):\\n log_score = 0\\n for (i, j, lab) in T:\\n m_i = m[i - 1]\\n pos1 = pos[i - 1]\\n log_score += self.get_phi_score(phi, i - 1, m_i)\\n if j != 0:\\n m_j = m[j - 1]\\n pos2 = pos[j - 1]\\n log_score += self.get_psi_score(psi, pos1, pos2, lab, m_i, m_j)\\n return log_score\",\n \"def log_prior_grad(self, inputs):\",\n \"def gradient(self, theta):\\n a = -(6 * self.scale ** 2)\\n b = 3 * self.scale ** 2 + np.exp(2 * theta)\\n b *= np.log(3 * self.scale ** 2 * np.exp(-2 * theta) + 1)\\n return a / b\",\n \"def _log_likelihood_gradients(self):\\n return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK,X=self.X,slices1=self.Xslices,slices2=self.Xslices), self.likelihood._gradients(partial=np.diag(self.dL_dK))))\",\n \"def dlog_prob(self, T, pos, m, psi, phi=tr.Tensor()):\\n if phi.size() == tr.Size([0]):\\n phi = self.create_phi(T, pos, m)\\n dpsi_score = self.dlog_score(T, pos, m, psi)\\n dpsi_Z = self.dlogZ(T, pos, psi, phi)\\n return dpsi_score - dpsi_Z\",\n \"def grad_log(self, X):\\n g = autograd.elementwise_grad(self.log_den)\\n G = g(X)\\n return G\",\n \"def Gradient(Walker,particle):\\n\\n h=0.001\\n dPsi = zeros(shape=shape(Walker.Re[particle]))\\n for i in range(Walker.sys_dim):\\n Y=Walker.Re[particle][i]\\n Walker.Re[particle][i]-=h\\n wfs1=wfs(Walker)\\n Walker.Re[particle][i]+=2.0*h\\n wfs2=wfs(Walker)\\n dPsi[i] = (wfs2-wfs1)/2/h\\n Walker.Re[particle][i]=Y\\n\\n return dPsi\",\n \"def log_likelihood_gradients_(self, y, f):\\n log_lik = self.evaluate_log_likelihood(y, f)\\n f = np.squeeze(f)\\n J = jacrev(self.evaluate_log_likelihood, argnums=1)\\n H = jacrev(J, argnums=1)\\n return log_lik, J(y, f), H(y, f)\",\n \"def grad_log_likelihood(g_t):\\n # state vector [g1, g2, g3, ... , gn-1, gn]\\n state_vector = np.zeros(N)\\n\\n # factor that will be added to one node and subtracted from another\\n def alphaij(gj, gi, deltaij, varij):\\n return ((gj - gi) - deltaij) / varij\\n\\n # indices of state vector\\n # Iterate over all connections\\n for r in self.graph.index:\\n state1, state2, value, variance, ligand, standard_state = self.graph.iloc[r]\\n i = self.states[self.states.name == state1].index[0]\\n j = self.states[self.states.name == state2].index[0]\\n\\n gj = g_t[j]\\n gi = g_t[i]\\n\\n edge_attr = self.cycle.edges()[(state1, state2)]\\n deltaij = edge_attr['energy'] # measured difference\\n varij = edge_attr['weight'] # measured variance\\n\\n shared_alpha = alphaij(gj, gi, deltaij, varij)\\n\\n state_vector[i] += shared_alpha\\n state_vector[j] -= shared_alpha\\n\\n return state_vector\",\n \"def MvNormalLogp():\\n cov = pt.matrix(\\\"cov\\\")\\n cov.tag.test_value = floatX(np.eye(3))\\n delta = pt.matrix(\\\"delta\\\")\\n delta.tag.test_value = floatX(np.zeros((2, 3)))\\n\\n cholesky = Cholesky(lower=True, on_error=\\\"nan\\\")\\n\\n n, k = delta.shape\\n n, k = f(n), f(k)\\n chol_cov = cholesky(cov)\\n diag = pt.diag(chol_cov)\\n ok = pt.all(diag > 0)\\n\\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\\n delta_trans = solve_lower(chol_cov, delta.T).T\\n\\n result = n * k * pt.log(f(2) * np.pi)\\n result += f(2) * n * pt.sum(pt.log(diag))\\n result += (delta_trans ** f(2)).sum()\\n result = f(-0.5) * result\\n logp = pt.switch(ok, result, -np.inf)\\n\\n def dlogp(inputs, gradients):\\n (g_logp,) = gradients\\n cov, delta = inputs\\n\\n g_logp.tag.test_value = floatX(1.0)\\n n, k = delta.shape\\n\\n chol_cov = cholesky(cov)\\n diag = pt.diag(chol_cov)\\n ok = pt.all(diag > 0)\\n\\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\\n delta_trans = solve_lower(chol_cov, delta.T).T\\n\\n inner = n * pt.eye(k) - pt.dot(delta_trans.T, delta_trans)\\n g_cov = solve_upper(chol_cov.T, inner)\\n g_cov = solve_upper(chol_cov.T, g_cov.T)\\n\\n tau_delta = solve_upper(chol_cov.T, delta_trans.T)\\n g_delta = tau_delta.T\\n\\n g_cov = pt.switch(ok, g_cov, -np.nan)\\n g_delta = pt.switch(ok, g_delta, -np.nan)\\n\\n return [-0.5 * g_cov * g_logp, -g_delta * g_logp]\\n\\n return OpFromGraph([cov, delta], [logp], grad_overrides=dlogp, inline=True)\",\n \"def fgrad_y(self, y, psi, return_precalc = False):\\r\\n\\r\\n\\r\\n mpsi = psi.copy()\\r\\n d = psi[-1]\\r\\n mpsi = mpsi[:self.num_parameters-1].reshape(self.n_terms, 3)\\r\\n\\r\\n # vectorized version\\r\\n\\r\\n S = (mpsi[:,1]*(y[:,:,None] + mpsi[:,2])).T\\r\\n R = np.tanh(S)\\r\\n D = 1-R**2\\r\\n\\r\\n GRAD = (d + (mpsi[:,0:1][:,:,None]*mpsi[:,1:2][:,:,None]*D).sum(axis=0)).T\\r\\n\\r\\n if return_precalc:\\r\\n return GRAD, S, R, D\\r\\n\\r\\n\\r\\n return GRAD\",\n \"def calc_grad(X, Y, theta):\\n m, n = X.shape\\n\\n margins = Y * X.dot(theta)\\n probs = 1. / (1 + np.exp(margins))\\n grad = -(1./m) * (X.T.dot(probs * Y))\\n\\n return grad\",\n \"def costFunction(theta,X,y):\\n m = X.shape[0]\\n J = 0\\n h = sigmoid (np.dot(X,theta))\\n \\n J = (1/m)* ((-np.dot(y.T,(np.log(h)))) - np.dot((1 - y).T,(np.log(1-h))))\\n \\n #grad = (1/m) * np.dot(X.T,(h-y))\\n grad = (1/m) * np.dot((h.T - y), X).T\\n \\n return J, grad\",\n \"def gradient(self, theta):\\n pass\",\n \"def gradient(self, theta):\\n pass\",\n \"def logZ(self, T, pos, psi, phi):\\n msgs = belief_propagation(T, pos, psi, phi, True)\\n log_z = calculate_belief_sum(msgs, True)\\n return log_z\",\n \"def log_prob(self, T, pos, m, psi, phi=tr.Tensor()):\\n if phi.size() == tr.Size([0]):\\n phi = self.create_phi(T, pos, m)\\n return self.log_score(T, pos, m, psi, phi) - self.logZ(T, pos, psi, phi)\",\n \"def log_likelihood(self, X, Y, theta):\\n \\n alphas, logZ = self.alpha_chain(X, theta)\\n total = 0\\n \\n s_prev = self.S\\n \\n for t,s in enumerate(Y):\\n total += self.log_psi(theta, t, s_prev, s, X[t]) \\n s_prev = s\\n \\n total -= logZ\\n return total / X.shape[0]\",\n \"def grad_log_likelihood(self, log_occr_array=None):\\n\\n if log_occr_array is not None:\\n # Catch invalid occurrence rates for zero likelihood\\n try:\\n self.log_occr = log_occr_array\\n except InvalidOccurrenceRate:\\n return -np.inf * np.ones_like(self.occr, dtype=float)\\n\\n # The Poisson Jacobian\\n # --------------------\\n # Calculate components first\\n N_exp = self.calc_integral() * self._N_stars # perhaps not needed\\n v_factors = self._N_stars * self.calc_bin_volumes() * self.H_bar_array\\n # TODO: broadcast self.occr into the same shape as self.F_array\\n # Reduce it later through the chain-rule\\n s_terms = self.H_array * self.F_array * self.occr_grid\\n numerator_terms = self.H_array * self.F_array\\n\\n if not tf.is_tensor(self.occr):\\n # Checking shapes of intermediate terms,\\n # numerator_terms vs s_terms.sum(-1, -2) and vs v factors.\\n intermediate_terms = numerator_terms / s_terms.sum(axis=(-1, -2))\\n # TODO: v_factor changed to negative, I think a minus\\n # sign had been missed\\n poisson_jacobian = intermediate_terms.sum(axis=0) - v_factors\\n poisson_jacobian = poisson_jacobian.reshape(-1)\\n param_jacobian = self.get_occr_jacobian(flattened=True)\\n grad_log_array = np.matmul(poisson_jacobian, param_jacobian)\\n\\n # BUG TODO\\n if np.isnan(grad_log_array).any():\\n warnings.warn(\\\".grad_log_likelihood value is nan.\\\")\\n import pdb; pdb.set_trace()\\n grad_log_array = -np.inf * grad_log_array\\n else:\\n raise NotImplementedError(\\\"Manual gradient calculate with \\\"\\n \\\"tensorflow objects isn't \\\"\\n \\\"implemented, and seems a bit \\\"\\n \\\"redundant.\\\")\\n\\n return grad_log_array\",\n \"def ComputeLogGradientTerm(self, *args):\\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF2F_ComputeLogGradientTerm(self, *args)\",\n \"def _log_prior_gradients(self):\\n if self.priors.size == 0:\\n return 0.\\n x = self.param_array\\n ret = np.zeros(x.size)\\n #compute derivate of prior density\\n [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]\\n #add in jacobian derivatives if transformed\\n priored_indexes = np.hstack([i for p, i in self.priors.items()])\\n for c,j in self.constraints.items():\\n if not isinstance(c, Transformation):continue\\n for jj in j:\\n if jj in priored_indexes:\\n ret[jj] += c.log_jacobian_grad(x[jj])\\n return ret\",\n \"def _forward_log_det_jacobian(self, x):\\n d = self._compute_shared(x=x)\\n relx = (x - d.x_k) / d.w_k\\n relx = relx # tf.where(d.out_of_bounds, 0.5*tf.ones_like(x), relx)\\n grad = (\\n 2 * tf.math.log(d.s_k) +\\n tf.math.log(d.d_kp1 * relx**2 + 2 * d.s_k * relx * (1 - relx) + # newln\\n d.d_k * (1 - relx)**2) -\\n 2 * tf.math.log((d.d_kp1 + d.d_k - 2 * d.s_k) * relx *\\n (1 - relx) + d.s_k))\\n return grad # tf.where(d.out_of_bounds, tf.zeros_like(grad), grad)\",\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 compute_gradient(theta, X, y):\\n m = X.shape[0]\\n grad_theta = np.dot(X.transpose(), (np.dot(X, theta) - y)) / m\\n #print theta, grad_theta, objective_function(theta, X, y)\\n return grad_theta\",\n \"def compute_loss_gradient(theta_vector, *args):\\n\\n psi = args[0] # feed psi as a parameter\\n circ_depth = args[1]\\n num_qbits = args[2]\\n theta = np.reshape(theta_vector, (circ_depth, num_qbits)) # reshapes the flat theta vector\\n fidelity = get_fidelity(theta, psi)\\n\\n # the derivative of the loss wrt fidelity\\n dl_df = -0.5 * fidelity ** (-0.5)\\n\\n df_dtheta = [] # a list of partial derivatives of the fidelity wrt the theta parameters\\n\\n for index in range(len(theta_vector)):\\n layer_index = index // num_qbits\\n qbit_index = index % num_qbits\\n\\n theta_plus = np.copy(theta)\\n theta_plus[layer_index][qbit_index] += np.pi / 2 # added pi/2 to the ith theta parameter\\n\\n theta_minus = np.copy(theta)\\n theta_minus[layer_index][qbit_index] -= np.pi / 2 # subtracted pi/2 to the ith theta parameter\\n\\n df_dtheta_i = 0.5 * (get_fidelity(theta_plus, psi) - get_fidelity(theta_minus, psi)) # ith derivative\\n df_dtheta.append(df_dtheta_i)\\n\\n df_dtheta = np.array(df_dtheta)\\n dl_dtheta = dl_df * df_dtheta # chain rule to get partial derivative of loss wrt theta parameters\\n\\n return dl_dtheta\",\n \"def grad_neg_log_like(params):\\n gp.set_parameter_vector(params)\\n return -gp.grad_log_likelihood(y, quiet=True)\",\n \"def logZ_brute(self, T, pos, psi, phi):\\n ms = self.get_all_tag_seq(len(T))\\n log_scores = tr.zeros(len(ms), dtype=tr.float64)\\n for i in range(len(ms)):\\n log_scores[i] = self.log_score(T, pos, ms[i], psi, phi)\\n log_z = logsumexp(log_scores)\\n return log_z\",\n \"def get_F(self,X,y=None):\\n if len(self.trees) == 0:\\n # print ('init')\\n assert not (y is None), 'tree0 need y to initialize,y:{}'.format(y)\\n pos = y.sum()\\n neg = y.shape[0] - pos\\n F = np.zeros([X.shape[0],])\\n F[:] = math.log(pos / neg)\\n # print (pos,neg,F)\\n return F\\n else:\\n F = np.zeros([X.shape[0],])\\n for tree in self.trees:\\n F += self.learning_rate*tree.predict(X)\\n # print (F)\\n return F\",\n \"def log_marginal_likelihood(self, theta=None, gradient=True, opt_flag=False):\\n if theta != None:\\n self.kernel.hyper = np.exp(theta) if opt_flag else theta\\n # self.kernel.hyper\\n if gradient:\\n K, grad = self.kernel.estimate(self.X_train, gradient=True)\\n else:\\n K = self.kernel.estimate(self.X_train)\\n \\n K += self.id_mat * self.sigma_n\\n\\n try:\\n L = np.linalg.cholesky(K)\\n except np.linalg.LinAlgError:\\n return (np.inf, np.array([0])) if gradient else np.inf\\n \\n alpha = cho_solve((L, True), self.Y_train)\\n \\n logl = float(self.Y_train.T.dot(alpha)) / 2\\n logl += np.log(np.diag(L)).sum()\\n logl += self.ntrain * np.log(2 * np.pi) / 2\\n\\n \\n if gradient:\\n logl_grad = alpha.dot(alpha.T) # einsum is slower\\n logl_grad -= cho_solve((L, True), self.id_mat)\\n logl_grad = 0.5 * np.einsum('ij,ji -> ', logl_grad, grad) #dot prod and trace combined\\n return logl, -np.array([logl_grad])\\n return logl\",\n \"def ComputeLogGradientTerm(self, *args):\\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF3F_ComputeLogGradientTerm(self, *args)\",\n \"def _log_prior_gradients(self):\\n x = self._get_params()\\n ret = np.zeros(x.size)\\n [np.put(ret,i,p.lnpdf_grad(xx)) for i,(p,xx) in enumerate(zip(self.priors,x)) if not p is None]\\n return ret\",\n \"def log_likelihood(self, theta=None, phi=None):\\n theta = theta if theta is not None else self.theta\\n phi = phi if phi is not None else self.phi\\n ret = 0.\\n for m, dm in enumerate(self.corpus):\\n for n, w_mn in enumerate(dm):\\n tp = 0.\\n for k in range(self.n_components):\\n tp += theta[m, k] * phi[k, w_mn]\\n ret += np.log(tp)\\n return ret\",\n \"def log_likelihood_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w))-y)\",\n \"def gradient(self, theta):\\n return (1 / (self.sigma * np.sqrt(2 * np.pi))) * (\\n -theta / (self.sigma ** 2) * np.exp(-(theta ** 2) / (2 * self.sigma ** 2))\\n )\",\n \"def compute_grad_likelihood(sensor_loc, obj_loc, distance):\\n\\tgrad = np.zeros(sensor_loc.shape)\\n\\t# Your code: finish the grad loglike\\n\\t\\t\\n\\treturn grad\",\n \"def _log_prior_gradients(self):\\r\\n if self.priors is None:\\r\\n return 0.\\r\\n x = self._get_params()\\r\\n ret = np.zeros(x.size)\\r\\n [np.put(ret, i, p.lnpdf_grad(xx)) for i, (p, xx) in enumerate(zip(self.priors, x)) if not p is None]\\r\\n return ret\",\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 ComputeLogGradientTerm(self, *args):\\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseID2D_ComputeLogGradientTerm(self, *args)\",\n \"def get_log_likelihood(phi, pred, t, dot_product, weight, reg= 1):\\n prior = -0.5* np.sum(np.multiply(weight, weight))\\n likelihood = np.multiply(t, np.log(pred+TOLERANCE)) + np.multiply(1.0- t, np.log(1.0-pred+TOLERANCE))\\n likelihood = np.sum(likelihood)\\n\\n return prior + likelihood\",\n \"def log_likelihood_gradients(self, y, f):\\n # align shapes and compute mask\\n y = y.reshape(-1, 1)\\n f = f.reshape(-1, 1)\\n mask = np.isnan(y)\\n y = np.where(mask, f, y)\\n\\n # compute gradients of the log likelihood\\n log_lik, J, H = vmap(self.log_likelihood_gradients_)(y, f)\\n\\n # apply mask\\n mask = np.squeeze(mask)\\n log_lik = np.where(mask, 0., log_lik)\\n J = np.where(mask, np.nan, J)\\n H = np.where(mask, np.nan, H)\\n\\n return log_lik, J, np.diag(H)\",\n \"def loss_gradient(self, targets, scores):\\n m = targets * scores\\n numer = 4. * (2. * numpy.arctan(m) - 1.)\\n denom = 1. + m**2\\n return numer/denom\",\n \"def costFunction(self,theta, X, y): \\n m = len(y)\\n h = self.sigmoid(X@theta)\\n J = 1 / m * (- y.T @ self.log(h) - (1-y).T @ self.log(1-h)) \\n # grad = 1/ m * X.T @ (h - y)\\n return J\",\n \"def grad(theta, X, y, lambda_):\\n # ... dopolnite (naloga 1, naloga 2)\\n\\n l = []\\n for i, e in enumerate(theta):\\n l.append(1 / len(y) * sum([(h(x, theta) - yi) * x[i] for x, yi in zip(X, y)]) + 2 * lambda_ * e)\\n\\n return np.array(l)\",\n \"def num_grad(theta, X, y, lambda_, e=1e-3):\\n # ... dopolnite (naloga 1, naloga 2)\\n return np.array([(cost(theta + eps, X, y, lambda_) - cost(theta - eps, X, y, lambda_)) / (2 * e)\\n for eps in np.identity(len(theta)) * e])\",\n \"def log_probability(theta):\\n global priors\\n global logp\\n lp = np.sum([priors[p].logpdf(x) for p, x in zip(logp.parnames, theta)])\\n if not np.isfinite(lp) or np.isnan(lp):\\n return -np.inf\\n ll = logp(theta)\\n if not np.isfinite(ll):\\n return -np.inf\\n return lp + ll\",\n \"def log_probability(theta):\\n global priors\\n global logp\\n lp = np.sum([priors[p].logpdf(x) for p, x in zip(logp.parnames, theta)])\\n if not np.isfinite(lp) or np.isnan(lp):\\n return -np.inf\\n ll = logp(theta)\\n if not np.isfinite(ll):\\n return -np.inf\\n return lp + ll\",\n \"def gradient(theta, X, y, learning_rate):\\n m = len(y)\\n\\n theta = theta.reshape((-1,1))\\n grad = np.zeros(theta.shape)\\n h = sigmoid(np.dot(X, theta)) \\n \\n grad = np.dot((h-y).T, X)/m\\n grad = grad.T\\n grad[1:] += (learning_rate/m)*theta[1:]\\n return grad\",\n \"def compute_gradient_logreg_regl2(y, tx, w, lambda_):\\n grad = compute_gradient_logreg(y, tx, w)\\n penal_grad = grad + 2 * lambda_ * w\\n\\n return penal_grad\",\n \"def calc_tf_log(doc):\\r\\n tf = calc_tf(doc)\\r\\n max_tf = tf[max(tf, key=tf.get)]\\r\\n tf_log = {}\\r\\n for key, val in tf.items():\\r\\n tf_log[key] = (1 + math.log(val)) / (1 + math.log(max_tf))\\r\\n return tf_log\",\n \"def lnlike(theta, dtarray, dmagarray, sigmaarray):\\n gamma, A = theta\\n\\n aux=np.sum(np.log(like_one(theta,dtarray,dmagarray,sigmaarray)))\\n\\n return aux\",\n \"def log_prob(self):\\n res = -self.L_h/2*np.log(2*np.pi*self.la)\\n res = res + self.L_h*(self.L_h-1)/2*self.a\\n\\n\\n res = res - 1/(2*self.la)*np.square(np.linalg.norm(self.e*self.pie))\\n\\n res = res - 1/(2*self.la)*np.sum(self.e2*self.pie_var)\\n\\n res = res - self.L_h/2*np.log(2*np.pi*self.sigma2)\\n res = res - 1/(2*self.sigma2)*(np.square(np.linalg.norm(self.w))+np.trace(self.R))\\n\\n print(\\\"Log-probability difference = {}\\\".format(res - self.LP), file=self.logfile)\\n self.LP = res\\n return res\",\n \"def log(self, x_old, grad, x, curr_iter, tol_g_val, tol_x_val, tol_f_val):\\n\\n print(\\\"-----------------------------------\\\")\\n print(\\\"\\\\n Iter: \\\", curr_iter)\\n print(\\\"\\\\n x_old: \\\", x_old)\\n print(\\\"\\\\n gradient: \\\", grad)\\n print(\\\"\\\\n x: \\\", x)\\n print(\\\"\\\\n tol_x_val: \\\", tol_x_val)\\n print(\\\"\\\\n tol_f_val: \\\", tol_f_val)\\n print(\\\"\\\\n tol_g_val: %s \\\\n \\\" % tol_g_val)\",\n \"def ComputeLogGradientTerm(self, *args):\\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseID3D_ComputeLogGradientTerm(self, *args)\",\n \"def _log_likelihood(self, theta, f, x, y, yerr):\\n sigma2 = yerr**2\\n return -0.5*np.sum((y - f(theta, x))**2 / sigma2 + 2*np.log(sigma2))\",\n \"def log_LxP(theta, D):\\r\\n p1 = np.exp( -((theta-D[0])**2)/2 )\\r\\n p2 = np.exp( -((theta-D[1])**2)/2 )\\r\\n if np.abs(theta) <= 1:\\r\\n LxP = p1*p2\\r\\n else:\\r\\n LxP = 0.0\\r\\n return np.log(LxP)\",\n \"def passDerivative(self):\\n # this will happen when this SumNode is unused\\n if self.getLogDerivative() == Node.ZERO:\\n return \\n for decomp_id in self.getChildren():\\n prodNode = self.getChild(decomp_id)\\n tmp = self.getLogDerivative() \\\\\\n + np.log(float(self.getChildCounts(decomp_id)) / self.getCounts())\\n\\n if prodNode.getLogDerivative() == Node.ZERO:\\n prodNode.setLogDerivative(tmp)\\n else:\\n prodNode.setLogDerivative( \\\\\\n np.logaddexp(tmp, self.getLogDerivative()))\",\n \"def __log_likelihood(self, params, *args):\\n\\t\\tX, y, feature_set, lambda_reg, empirical_weights, verbose, sign = args\\n\\n\\t\\tno_example = len(X)\\n\\t\\ttotal_logZ = 0\\n\\t\\ttotal_logProb = 0\\n\\t\\texpected_weights = np.zeros(len(feature_set))\\n\\t\\tfor t in range(len(X)):\\n\\t\\t\\t# example_features = X[t], example_labels = y[t]\\n\\n\\t\\t\\tpotential = np.zeros(len(X[t]))\\n\\t\\t\\tfor i in range(len(X[t])):\\n\\t\\t\\t\\t#candidate_features = X[t][i], candidate_label = y[t][i]\\n\\t\\t\\t\\tpotential[i] = feature_set.calc_inner_product(X[t][i], params)\\n\\n\\t\\t\\t#scaling\\n\\t\\t\\tpotential = potential - np.max(potential, keepdims=True)\\n\\n\\t\\t\\tfor i in range(len(X[t])):\\n\\t\\t\\t\\ttotal_logProb += potential[i] * y[t][i]\\n\\n\\t\\t\\tpotential, Z = self.__softmax(potential)\\n\\n\\t\\t\\tfor i in range(len(X[t])):\\n\\t\\t\\t\\tfeature_set.calc_inner_sum(expected_weights, X[t][i], potential[i])\\n\\n\\t\\t\\ttotal_logZ += log(Z)\\n\\n\\t\\t# _params = feature_set.get_regularized_params(params, 'bias')\\n\\t\\t_params = params\\n\\t\\tlog_likelihood = total_logProb - total_logZ - (lambda_reg/2) * np.sum(np.multiply(_params,_params))\\n\\t\\tgradients = empirical_weights - expected_weights - lambda_reg * _params\\n\\n\\t\\tglobal SUB_ITERATION_NUM\\n\\t\\tif verbose:\\n\\t\\t\\tsub_iteration_str = ' '\\n\\t\\t\\tif SUB_ITERATION_NUM > 0:\\n\\t\\t\\t\\tsub_iteration_str = '(' + '{0:02d}'.format(SUB_ITERATION_NUM) + ')'\\n\\t\\t\\tprint(' ', '{0:03d}'.format(ITERATION_NUM), sub_iteration_str, ':', log_likelihood * sign)\\n\\n\\t\\tSUB_ITERATION_NUM += 1\\n\\n\\t\\treturn sign * log_likelihood, sign * gradients\",\n \"def grad_log_prior(self, log_occr_array=None):\\n\\n if log_occr_array is not None:\\n # Catch invalid occurrence rates for zero likelihood\\n try:\\n self.log_occr = log_occr_array\\n except InvalidOccurrenceRate:\\n return -np.inf * np.ones_like(self.occr, dtype=float)\\n\\n if tf.is_tensor(self.occr):\\n raise NotImplementedError(\\\"Manual gradient calculate with \\\"\\n \\\"tensorflow objects isn't \\\"\\n \\\"implemented, and seems a bit \\\"\\n \\\"redundant.\\\")\\n else:\\n # Written in terms of occr for ease.\\n grad = 1 / (2*self.occr)\\n # At this point, it's still possible for occr to be so\\n # small that it's underflowing, where value will be nan\\n grad = grad if not np.isnan(grad).any() else -np.inf * grad\\n\\n return grad\",\n \"def log_likelihood(self, theta, active=None):\\n return sum(self.log_likelihood_term(theta, active=active))\",\n \"def log_marginal_likelihood(X_train,y_train,phi,tau=1.,Ve=1.e-10):\",\n \"def ga_log(R):\\n phiP, t_normal_n, t_perpendicular_n = extractRotorComponents(R)\\n return phiP + t_normal_n + t_perpendicular_n\",\n \"def fgrad_y(self, y, psi, return_precalc = False):\\r\\n\\r\\n mpsi = psi.copy()\\r\\n\\r\\n # vectorized version\\r\\n\\r\\n # S = (mpsi[:,1]*(y + mpsi[:,2])).T\\r\\n S = (mpsi[:,1]*(y[:,:,None] + mpsi[:,2])).T\\r\\n R = np.tanh(S)\\r\\n D = 1-R**2\\r\\n\\r\\n # GRAD = (1+(mpsi[:,0:1]*mpsi[:,1:2]*D).sum(axis=0))[:,np.newaxis]\\r\\n GRAD = (1+(mpsi[:,0:1][:,:,None]*mpsi[:,1:2][:,:,None]*D).sum(axis=0)).T\\r\\n\\r\\n if return_precalc:\\r\\n # return GRAD,S.sum(axis=1),R.sum(axis=1),D.sum(axis=1)\\r\\n return GRAD, S, R, D\\r\\n\\r\\n\\r\\n return GRAD\",\n \"def f(p, phi, phib, df):\\n\\treturn -log(p) - df + (p-1)*phi + \\\\\\n\\t ( phi*(1-p) + phib + \\\\\\n\\t 5./4*alpha*(phi*p)**(9./4)-(9./4)*alpha*(p*phi)**(5./4) - \\\\\\n\\t (1./2)(1-p*phi)**2 - (phib/Nb)-5./4*alpha*(phi+phib)**(9./4) + \\\\\\n\\t (9./4)*alpha*(phi+phib)**(5.4) + \\\\\\n\\t 1./2*(1-phi-phib)**2 ) * Ns\",\n \"def lr_cost_function_grad(theta: np.ndarray, X: np.ndarray, y: np.ndarray, l: float) -> np.ndarray:\\n # Initialize some useful values\\n m = len(y) # number of training examples\\n\\n # You need to return the following variable correctly.\\n grad = np.zeros(theta.shape)\\n\\n # ====================== YOUR CODE HERE ======================\\n # Instructions: Compute the partial derivatives and set grad to the partial\\n # derivatives of the cost w.r.t. each parameter in theta.\\n\\n # =============================================================\\n return grad\",\n \"def newton(flogl, start, fgrad, fhess, maxiter):\\r\\n warn = 0\\r\\n iteration = 0\\r\\n par_hat0 = start\\r\\n m = 1\\r\\n while (iteration < maxiter and m >= 1e-04):\\r\\n H = -la.inv(fhess(par_hat0))\\r\\n g = fgrad(par_hat0).reshape(start.shape)\\r\\n Hg = np.dot(H, g)\\r\\n par_hat0 = par_hat0 + Hg\\r\\n iteration += 1\\r\\n m = np.dot(g.T, Hg)\\r\\n if iteration == maxiter:\\r\\n warn = 1\\r\\n logl = flogl(par_hat0)\\r\\n return (par_hat0, logl, warn)\",\n \"def compute_tree(self, tree):\\n g_list_val, g_list_h = self._build_graph(tree) # return theano variable of each node\\n list_val = self._traversal_tree(tree) #\\n f = theano.function(g_list_val, g_list_h, allow_input_downcast=True)\\n result = f(*list_val)\\n return result\",\n \"def compute_gradient_logreg(y, tx, w):\\n assert len(set(y).difference({0., 1.})) == 0, \\\"Class labels must be encoded as {0, 1}\\\"\\n\\n s = sigmoid(tx.dot(w)) - y\\n grad = tx.T.dot(s)\\n\\n return grad\",\n \"def gradientFunctionReg(theta, X, y, Lambda):\\n m = len(y) # number of training examples\\n grad = np.zeros(theta.shape[0])\\n theta = np.transpose(theta)\\n sum_1 = 0\\n X = X.values\\n y = y.values\\n #calcuate the theta_0 \\n# ====================== YOUR CODE HERE ======================\\n# Instructions: Compute the gradient of a particular choice of theta.\\n# Compute the partial derivatives and set grad to the partial\\n# derivatives of the cost w.r.t. each parameter in theta\\n for i in range(theta.shape[0]):\\n if i == 0:\\n for j in range(m):\\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i]\\n else:\\n for j in range(m):\\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i] + Lambda*theta[i]\\n grad[i] = sum_1/m\\n sum_1 = 0\\n\\n# =============================================================\\n\\n return grad\",\n \"def compute_gradients(self, logits, target):\\n\\n target_length = target.shape[0]\\n num_time_steps = logits.shape[0]\\n\\n\\t\\t######################\\n\\t\\t### YOUR CODE HERE ###\\n\\t\\t######################\\n\\n # expand labels by inserting a blank between each pair\\n normalized_logits = softmax(logits)\\n blank_label = normalized_logits.shape[1] - 1\\n l = add_blanks(target, blank_label)\\n target_length = l.shape[0]\\n\\n alpha = self.compute_forward_variables(normalized_logits, target) \\n beta = self.compute_backward_variables(normalized_logits, target)\\n\\n # rescale\\n alpha = alpha / np.sum(alpha, axis=0)\\n beta = beta / np.sum(beta, axis=0)\\n alphabeta = alpha * beta\\n print \\\"alpha\\\"\\n print alpha\\n\\n # compute zt\\n z = Counter()\\n for t in xrange(num_time_steps):\\n for s, k in enumerate(l):\\n z[t] += alphabeta[s, t] / normalized_logits[t, k]\\n \\n # normalized_logits is time steps t by labels k\\n # alpha is 2 * target_length - 1 by time steps\\n lab_zk = np.zeros_like(normalized_logits)\\n for s, k in enumerate(l):\\n for t in xrange(num_time_steps):\\n lab_zk[t, k] += alphabeta[s, t]\\n\\n grad = normalized_logits\\n for k in xrange(target.shape[0]):\\n for t in xrange(num_time_steps):\\n ytk = normalized_logits[t, k]\\n constant = 1.0 / (ytk * z[t])\\n grad[t, k] = ytk - constant * lab_zk[t, k]\\n \\n return grad\",\n \"def gradient_function(theta, X, y):\\n\\n grad = None\\n #######################################################################\\n # TODO: #\\n # Compute the gradient for a particular choice of theta. #\\n # Compute the partial derivatives and set grad to the partial #\\n # derivatives of the cost w.r.t. each parameter in theta #\\n # #\\n #######################################################################\\n \\n theta = theta[:, np.newaxis]\\n \\n thetatrans = theta.T\\n Xtrans = X.T\\n \\n MulThetaX = np.dot(thetatrans, Xtrans)\\n \\n h = sigmoid(MulThetaX)\\n \\n grad = (y - h) * Xtrans\\n\\n #######################################################################\\n # END OF YOUR CODE #\\n #######################################################################\\n return grad\",\n \"def log_marginal_likelihood(self, theta=None, eval_gradient=False):\\n if theta is None:\\n if eval_gradient:\\n raise ValueError(\\n \\\"Gradient can only be evaluated for theta!=None\\\")\\n return self.log_marginal_likelihood_value_\\n\\n kernel = self.kernel_.clone_with_theta(theta)\\n\\n if eval_gradient:\\n K, K_gradient = kernel(self.X_train_, eval_gradient=True)\\n else:\\n K = kernel(self.X_train_)\\n\\n K[np.diag_indices_from(K)] += self.alpha\\n try:\\n L = cholesky(K, lower=True) # Line 2\\n except np.linalg.LinAlgError:\\n return (-np.inf, np.zeros_like(theta)) \\\\\\n if eval_gradient else -np.inf\\n\\n # Support multi-dimensional output of self.y_train_\\n y_train = self.y_train_\\n if y_train.ndim == 1:\\n y_train = y_train[:, np.newaxis]\\n X_train = self.X_train_\\n P = self.P_train_\\n Q = np.dot(np.transpose(P), cho_solve((L, True), P))\\n try:\\n M = cholesky(Q, lower=True)\\n except np.linalg.LinAlgError:\\n return (-np.inf, np.zeros_like(theta)) \\\\\\n if eval_gradient else -np.inf\\n\\n alpha = cho_solve((L, True), y_train)\\n beta = cho_solve((M,True), np.dot(np.transpose(P), alpha) )\\n y_shift = y_train - np.dot(P, beta)\\n alpha_ = cho_solve((L, True), y_shift)\\n self.y_shift = y_shift\\n\\n # Compute log-likelihood (compare line 7)\\n log_likelihood_dims = -0.5 * np.einsum(\\\"ik,ik->k\\\", y_shift, alpha_)\\n log_likelihood_dims -= np.log(np.diag(L)).sum()\\n log_likelihood_dims -= K.shape[0] / 2 * np.log(2 * np.pi)\\n log_likelihood = log_likelihood_dims.sum(-1) # sum over dimensions\\n\\n if eval_gradient: # compare Equation 5.9 from GPML\\n tmp = np.einsum(\\\"ik,jk->ijk\\\", alpha_, alpha_) # k: output-dimension\\n tmp -= cho_solve((L, True), np.eye(K.shape[0]))[:, :, np.newaxis]\\n # Compute \\\"0.5 * trace(tmp.dot(K_gradient))\\\" without\\n # constructing the full matrix tmp.dot(K_gradient) since only\\n # its diagonal is required\\n log_likelihood_gradient_dims = \\\\\\n 0.5 * np.einsum(\\\"ijl,ijk->kl\\\", tmp, K_gradient)\\n log_likelihood_gradient = log_likelihood_gradient_dims.sum(-1)\\n\\n if eval_gradient:\\n return log_likelihood, log_likelihood_gradient\\n else:\\n return log_likelihood\",\n \"def log_likelihood(self):\\r\\n if self.likelihood.is_heteroscedastic:\\r\\n A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)\\r\\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self._A))\\r\\n else:\\r\\n A = -0.5 * self.num_data * self.output_dim * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT\\r\\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self._A))\\r\\n C = -self.output_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * np.log(sf2))\\r\\n D = 0.5 * self.data_fit\\r\\n self._A_part, self._B_part, self._C_part, self._D_part = A, B, C, D\\r\\n return A + B + C + D + self.likelihood.Z\",\n \"def log_deriv(error):\\n return logistic(error) * (1 - logistic(error))\",\n \"def leapfrog(theta, r, grad, epsilon, f):\\n # make half step in r\\n rprime = r + 0.5 * epsilon * grad\\n # make new step in theta\\n thetaprime = theta + epsilon * rprime\\n #compute new gradient\\n logpprime, gradprime = f(thetaprime)\\n # make half step in r again\\n rprime = rprime + 0.5 * epsilon * gradprime\\n return thetaprime, rprime, gradprime, logpprime\",\n \"def update(self, returns, log_probs):\\n policy_gradient = []\\n for log_prob, Gt in zip(log_probs, returns):\\n policy_gradient.append(-log_prob * Gt)\\n\\n loss = torch.stack(policy_gradient).sum()\\n self.optimizer.zero_grad()\\n loss.backward()\\n self.optimizer.step()\",\n \"def gradient_descent(f, df, x, sigma=0.5, epsilon=1e-8):\\n pass\",\n \"def log_likelihood_grad_rew(self, data, reward_model, bias_params):\",\n \"def _log_likelihood_colour(self, df, dfo):\\n pdf = fit_colour_gaussian(df[\\\"colour_obs\\\"].values)\\n return np.log(pdf(dfo[\\\"g_r\\\"].values)).sum()\",\n \"def logsum_safe(prob_ll, atl):\\n logpdf = prob_ll + K.log(atl + K.epsilon())\\n alpha = tf.reduce_max(logpdf, -1, keepdims=True)\\n return alpha + tf.log(tf.reduce_sum(K.exp(logpdf-alpha), -1, keepdims=True) + K.epsilon())\",\n \"def gradientFunction(theta, X, y):\\n y = y[:, 0]\\n m = y.shape # number of training samples\\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\\n grad /= m\\n return grad\",\n \"def fast_loss_and_grad(self, X, y):\\n loss = 0.0\\n grad = np.zeros(self.W.shape) # initialize the gradient as zero\\n \\n # ================================================================ #\\n # YOUR CODE HERE:\\n # Calculate the softmax loss and gradient WITHOUT any for loops.\\n # ================================================================ #\\n \\n num_train = X.shape[0]\\n num_classes = self.W.shape[0]\\n \\n# # vectorized loss calculation #\\n class_scores_matrix = np.dot(self.W,X.T) # calculating class scores matrix (C x m): rows are class scores transposes\\n class_scores_matrix -= np.max(class_scores_matrix) # considering the possible issue for numerical instability and account for it\\n exp_a = np.exp(class_scores_matrix) # calculating the exponents\\n \\n# y_exp = np.array(exp_a[y, np.arange(0, class_scores_matrix.shape[1])])\\n# #print(exp_a[:,:3])\\n# #print(y[:3])\\n# #print(y_exp[:3])\\n \\n# tt = np.sum(exp_a,axis=0)\\n# tt2 = np.divide(tt,y_exp)\\n# print(num_train)\\n# tt3 = np.power(tt2,1/num_train)\\n# loss = np.log(np.prod(tt3))\\n \\n \\n \\n \\n (C, D) = self.W.shape\\n N = X.shape[0]\\n\\n scores = np.dot(self.W, X.T)\\n scores -= np.max(scores) # shift by log C to avoid numerical instability\\n\\n y_mat = np.zeros(shape = (C, N))\\n y_mat[y, range(N)] = 1\\n\\n # matrix of all zeros except for a single wx + log C value in each column that corresponds to the\\n # quantity we need to subtract from each row of scores\\n correct_wx = np.multiply(y_mat, scores)\\n\\n # create a single row of the correct wx_y + log C values for each data point\\n sums_wy = np.sum(correct_wx, axis=0) # sum over each column\\n\\n exp_scores = np.exp(scores)\\n sums_exp = np.sum(exp_scores, axis=0) # sum over each column\\n result = np.log(sums_exp)\\n\\n result -= sums_wy\\n\\n loss = np.sum(result)\\n loss /= num_train\\n \\n \\n # vectorized gradient calculation #\\n exp_a_sum = np.sum(exp_a,axis=0)\\n\\n y_mat_corres = np.zeros(shape = (num_classes, num_train))\\n y_mat_corres[y, range(num_train)] = 1\\n sum_exp_scores = np.sum(exp_a, axis=0) \\n sum_exp_scores = 1.0 / exp_a_sum # division by sum over columns\\n exp_a *= sum_exp_scores\\n grad = np.dot(exp_a, X)\\n grad -= np.dot(y_mat_corres, X)\\n grad /= num_train\\n \\n\\n # ================================================================ #\\n # END YOUR CODE HERE\\n # ================================================================ #\\n\\n return loss, grad\",\n \"def _grad(V):\\n dv = diag(V)\\n weights, A, _, AinvB = _weights(dv)\\n Ey = (weights.T.dot(Y_control) - Y_treated).getA()\\n dGamma0_dV_term2 = zeros(K)\\n #dPI_dV = zeros((N0, N1)) # stupid notation: PI = W.T\\n #Ai = A.I\\n for k in range(K):\\n if verbose: # for large sample sizes, linalg.solve is a huge bottle neck,\\n print(\\\"Calculating gradient, linalg.solve() call %s of %s\\\" % (k ,K,))\\n #dPI_dV.fill(0) # faster than re-allocating the memory each loop.\\n dA = dA_dV_ki[k]\\n dB = dB_dV_ki[k]\\n dPI_dV = linalg.solve(A,(dB - dA.dot(AinvB))) \\n #dPI_dV = Ai.dot(dB - dA.dot(AinvB))\\n dGamma0_dV_term2[k] = np.einsum(\\\"ij,kj,ki->\\\",Ey, Y_control, dPI_dV) # (Ey * Y_control.T.dot(dPI_dV).T.getA()).sum()\\n return LAMBDA + 2 * dGamma0_dV_term2\",\n \"def _core_calc_degrad(self,bd,Ld) :\\n\\t\\tdegrad = np.dot(Ld,bd) # Do matrix multiplication \\n\\t\\tdegrad = np.exp(degrad) # Exponentiate to convert log to real\\n\\t\\treturn degrad\",\n \"def approx_grad(theta, X, y):\\n grad_a = np.array([(cost(theta + e, X, y) - cost(theta - e, X, y)) / (2 * 1e-5)\\n for e in np.identity(len(theta)) * 1e-5])\\n return grad_a\",\n \"def _Le(self, p):\\n a = norm(p - self.edges[:, 0, :], axis=1)\\n b = norm(p - self.edges[:, 1, :], axis=1)\\n le = norm(self.edges[:, 0, :] - self.edges[:, 1, :], axis=1)\\n q = (a + b + le) / (a + b - le) # TODO: set the quotient to 0 whenever (a + b - le) == 0\\n return np.log(q)\",\n \"def _loglike(self, y, f):\\n ll = y * tf.log(pos(f)) + (1 - y) * tf.log(pos(1 - f))\\n return ll\",\n \"def get_gradient(phi, pred, t, dot_product, weight, reg= 1, regression= \\\"logistic\\\"):\\n if regression == \\\"logistic\\\":\\n gradient = np.matmul(phi.T, pred - t)\\n elif regression == \\\"probit\\\":\\n R = np.eye(pred.shape[0])\\n for i in range(pred.shape[0]):\\n y_n = pred[i,0]\\n dotp = dot_product[i, 0]\\n pdf = norm.pdf(dotp)\\n R[i,i] = pdf/(y_n*(1-y_n) + TOLERANCE)\\n gradient = np.matmul(np.matmul(phi.T, R), pred-t)\\n elif regression == \\\"multiclass\\\":\\n gradient = np.matmul(phi.T, pred - t)\\n\\n # Add regularization\\n gradient += weight/ reg\\n return gradient\",\n \"def expected_log_g(self):\\n raise NotImplementedError()\\n E_lng = np.zeros_like(self.mf_gamma)\\n\\n # \\\\psi(\\\\sum_{b} \\\\gamma_b)\\n trm2 = psi(self.mf_gamma.sum(axis=2))\\n for b in xrange(self.B):\\n E_lng[:,:,b] = psi(self.mf_gamma[:,:,b]) - trm2\\n\\n return E_lng\",\n \"def log_prob(self):\",\n \"def lr_cost_function(theta, X, y, learning_rate):\\n m = len(y) # number of training examples\\n # You need to return the following variables correctly \\n J = 0\\n grad = np.zeros(theta.shape)\\n h = sigmoid(np.dot(X, theta))\\n\\n #Compute cost:\\n first = (1/m)*np.sum(np.dot(-y.T, np.log(h)) - np.dot((1-y).T, np.log(1-h)), axis=0)\\n second = (learning_rate/(2*m))*np.sum(theta[1:]**2)\\n J = first + second\\n \\n #Compute gradient\\n grad = np.dot((h-y).T, X)/m\\n grad = grad.T\\n grad[1:] += (learning_rate/m)*theta[1:]\\n return float(J), grad\",\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 f(p, phi, phib, df):\\n\\treturn - p + exp( - df + Ns*(log((1 - p*phi)/(1 - phi - phib)) + \\\\\\n\\t\\t(p - 1)*phi - phib + (9./4)*alpha*((phi + phib)**(5./4) - (p*phi)**(5./4))))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67526263","0.6612029","0.6377196","0.63414973","0.6340336","0.63349605","0.62160623","0.6211794","0.62073463","0.6133842","0.612299","0.6117252","0.6114974","0.60872525","0.6031914","0.60047233","0.60036635","0.5983822","0.59582996","0.5957819","0.59035105","0.58988154","0.58988154","0.58887506","0.587498","0.58729506","0.5856494","0.58464295","0.58177024","0.5814162","0.57942694","0.5792702","0.5786519","0.57865053","0.5779964","0.57796574","0.5764944","0.5754007","0.5746566","0.57415354","0.5725429","0.572259","0.5714633","0.57068366","0.56775534","0.5675698","0.56701213","0.5669693","0.56683624","0.5664008","0.56557316","0.5647101","0.56370527","0.56370527","0.5629849","0.56292766","0.56248176","0.5618903","0.5599047","0.55946875","0.5591364","0.5586639","0.5582552","0.557797","0.5576528","0.55683696","0.5553974","0.5552054","0.55451083","0.5544402","0.55426866","0.5538449","0.55300134","0.5525409","0.55130327","0.5502817","0.5502589","0.5493569","0.54913497","0.5488076","0.5486876","0.5485317","0.547325","0.54701555","0.5467628","0.5463643","0.54576606","0.5452626","0.54501384","0.544742","0.5442494","0.5439575","0.5439214","0.5436345","0.5430904","0.54210734","0.54203147","0.54116863","0.54008895","0.5400209"],"string":"[\n \"0.67526263\",\n \"0.6612029\",\n \"0.6377196\",\n \"0.63414973\",\n \"0.6340336\",\n \"0.63349605\",\n \"0.62160623\",\n \"0.6211794\",\n \"0.62073463\",\n \"0.6133842\",\n \"0.612299\",\n \"0.6117252\",\n \"0.6114974\",\n \"0.60872525\",\n \"0.6031914\",\n \"0.60047233\",\n \"0.60036635\",\n \"0.5983822\",\n \"0.59582996\",\n \"0.5957819\",\n \"0.59035105\",\n \"0.58988154\",\n \"0.58988154\",\n \"0.58887506\",\n \"0.587498\",\n \"0.58729506\",\n \"0.5856494\",\n \"0.58464295\",\n \"0.58177024\",\n \"0.5814162\",\n \"0.57942694\",\n \"0.5792702\",\n \"0.5786519\",\n \"0.57865053\",\n \"0.5779964\",\n \"0.57796574\",\n \"0.5764944\",\n \"0.5754007\",\n \"0.5746566\",\n \"0.57415354\",\n \"0.5725429\",\n \"0.572259\",\n \"0.5714633\",\n \"0.57068366\",\n \"0.56775534\",\n \"0.5675698\",\n \"0.56701213\",\n \"0.5669693\",\n \"0.56683624\",\n \"0.5664008\",\n \"0.56557316\",\n \"0.5647101\",\n \"0.56370527\",\n \"0.56370527\",\n \"0.5629849\",\n \"0.56292766\",\n \"0.56248176\",\n \"0.5618903\",\n \"0.5599047\",\n \"0.55946875\",\n \"0.5591364\",\n \"0.5586639\",\n \"0.5582552\",\n \"0.557797\",\n \"0.5576528\",\n \"0.55683696\",\n \"0.5553974\",\n \"0.5552054\",\n \"0.55451083\",\n \"0.5544402\",\n \"0.55426866\",\n \"0.5538449\",\n \"0.55300134\",\n \"0.5525409\",\n \"0.55130327\",\n \"0.5502817\",\n \"0.5502589\",\n \"0.5493569\",\n \"0.54913497\",\n \"0.5488076\",\n \"0.5486876\",\n \"0.5485317\",\n \"0.547325\",\n \"0.54701555\",\n \"0.5467628\",\n \"0.5463643\",\n \"0.54576606\",\n \"0.5452626\",\n \"0.54501384\",\n \"0.544742\",\n \"0.5442494\",\n \"0.5439575\",\n \"0.5439214\",\n \"0.5436345\",\n \"0.5430904\",\n \"0.54210734\",\n \"0.54203147\",\n \"0.54116863\",\n \"0.54008895\",\n \"0.5400209\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5909217"},"document_rank":{"kind":"string","value":"20"}}},{"rowIdx":11184,"cells":{"query":{"kind":"string","value":"Belief propagation algorithm for calculating the log of the partition function Z"},"document":{"kind":"string","value":"def logZ(self, T, pos, psi, phi):\n msgs = belief_propagation(T, pos, psi, phi, True)\n log_z = calculate_belief_sum(msgs, True)\n return log_z"},"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_lhood(X, Z, Y, a, ep, lamb):\n \n K = Z.shape[1]\n N, T = X.shape\n \n # p(X)\n ZY = np.dot(Z,Y) \n log_pX = 0\n log_pX = log_pX + np.sum(X * np.log(1 - ((1 - lamb) ** ZY) * (1 - ep)))\n log_pX = log_pX + np.sum((1 - X) * np.log(((1 - lamb) ** ZY) * (1 - ep))) \n \n # p(Z)\n HN = 0\n for n in range(1, N+1):\n HN += 1.0/n\n m = Z.sum(axis=0)\n log_pZ = (K * np.log(a) - (a * HN)) + np.sum(gammaln(m) +\n gammaln(N - m + 1) - gammaln(N + 1)) \n\n return log_pZ + log_pX","def log_prob(self):","def calculate_log_perplexity(self, output, flat_labels): #completed, expensive, should be compiled\n return -np.sum(np.log2(np.clip(output, a_min=1E-12, a_max=1.0))[np.arange(flat_labels.shape[0]), flat_labels[:,1]])","def log_likelihood(self):\r\n if self.likelihood.is_heteroscedastic:\r\n A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)\r\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self._A))\r\n else:\r\n A = -0.5 * self.num_data * self.output_dim * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT\r\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self._A))\r\n C = -self.output_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * np.log(sf2))\r\n D = 0.5 * self.data_fit\r\n self._A_part, self._B_part, self._C_part, self._D_part = A, B, C, D\r\n return A + B + C + D + self.likelihood.Z","def log_prob(self, z, x):\n n_samples = z.shape[0].value\n log_prob = 0\n for i in range(self.time_steps):\n lat_dim = self.transition.dim_x\n obs_dim = self.emmision.dim_x\n # Current time step latent state and observation\n z_t = tf.slice(z, [0, i * lat_dim], [-1, lat_dim])\n x_t = tf.slice(x, [0, i * obs_dim], [-1, obs_dim])\n if i == 0:\n log_prob += tf.reduce_sum(\n self.init_state_p.log_prob(z_t), axis=1)\n else:\n log_prob += self.transition.log_prob(z_t, z_tminus)\n log_prob += self.emmision.log_prob(x_t, z_t)\n # Update the previous latent state for next iteration\n z_tminus = z_t\n return log_prob","def log_p(self,z): \n return np.array([self.log_p_blanket(i) for i in z])","def log_p(self,z):\n return mb_log_p_posterior(z, self.neg_posterior, self.mini_batch)","def __log_likelihood(self, params, *args):\n\t\tX, y, feature_set, lambda_reg, empirical_weights, verbose, sign = args\n\n\t\tno_example = len(X)\n\t\ttotal_logZ = 0\n\t\ttotal_logProb = 0\n\t\texpected_weights = np.zeros(len(feature_set))\n\t\tfor t in range(len(X)):\n\t\t\t# example_features = X[t], example_labels = y[t]\n\n\t\t\tpotential = np.zeros(len(X[t]))\n\t\t\tfor i in range(len(X[t])):\n\t\t\t\t#candidate_features = X[t][i], candidate_label = y[t][i]\n\t\t\t\tpotential[i] = feature_set.calc_inner_product(X[t][i], params)\n\n\t\t\t#scaling\n\t\t\tpotential = potential - np.max(potential, keepdims=True)\n\n\t\t\tfor i in range(len(X[t])):\n\t\t\t\ttotal_logProb += potential[i] * y[t][i]\n\n\t\t\tpotential, Z = self.__softmax(potential)\n\n\t\t\tfor i in range(len(X[t])):\n\t\t\t\tfeature_set.calc_inner_sum(expected_weights, X[t][i], potential[i])\n\n\t\t\ttotal_logZ += log(Z)\n\n\t\t# _params = feature_set.get_regularized_params(params, 'bias')\n\t\t_params = params\n\t\tlog_likelihood = total_logProb - total_logZ - (lambda_reg/2) * np.sum(np.multiply(_params,_params))\n\t\tgradients = empirical_weights - expected_weights - lambda_reg * _params\n\n\t\tglobal SUB_ITERATION_NUM\n\t\tif verbose:\n\t\t\tsub_iteration_str = ' '\n\t\t\tif SUB_ITERATION_NUM > 0:\n\t\t\t\tsub_iteration_str = '(' + '{0:02d}'.format(SUB_ITERATION_NUM) + ')'\n\t\t\tprint(' ', '{0:03d}'.format(ITERATION_NUM), sub_iteration_str, ':', log_likelihood * sign)\n\n\t\tSUB_ITERATION_NUM += 1\n\n\t\treturn sign * log_likelihood, sign * gradients","def compute_log_prob(self,params: ndarray) -> float:\n return self.compute_log_prior(params) + self.compute_log_likelihood(params)","def MvNormalLogp():\n cov = pt.matrix(\"cov\")\n cov.tag.test_value = floatX(np.eye(3))\n delta = pt.matrix(\"delta\")\n delta.tag.test_value = floatX(np.zeros((2, 3)))\n\n cholesky = Cholesky(lower=True, on_error=\"nan\")\n\n n, k = delta.shape\n n, k = f(n), f(k)\n chol_cov = cholesky(cov)\n diag = pt.diag(chol_cov)\n ok = pt.all(diag > 0)\n\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\n delta_trans = solve_lower(chol_cov, delta.T).T\n\n result = n * k * pt.log(f(2) * np.pi)\n result += f(2) * n * pt.sum(pt.log(diag))\n result += (delta_trans ** f(2)).sum()\n result = f(-0.5) * result\n logp = pt.switch(ok, result, -np.inf)\n\n def dlogp(inputs, gradients):\n (g_logp,) = gradients\n cov, delta = inputs\n\n g_logp.tag.test_value = floatX(1.0)\n n, k = delta.shape\n\n chol_cov = cholesky(cov)\n diag = pt.diag(chol_cov)\n ok = pt.all(diag > 0)\n\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\n delta_trans = solve_lower(chol_cov, delta.T).T\n\n inner = n * pt.eye(k) - pt.dot(delta_trans.T, delta_trans)\n g_cov = solve_upper(chol_cov.T, inner)\n g_cov = solve_upper(chol_cov.T, g_cov.T)\n\n tau_delta = solve_upper(chol_cov.T, delta_trans.T)\n g_delta = tau_delta.T\n\n g_cov = pt.switch(ok, g_cov, -np.nan)\n g_delta = pt.switch(ok, g_delta, -np.nan)\n\n return [-0.5 * g_cov * g_logp, -g_delta * g_logp]\n\n return OpFromGraph([cov, delta], [logp], grad_overrides=dlogp, inline=True)","def log_p(self,z):\n return log_p_posterior(z, self.neg_posterior)","def compute_log_prior(self,params: ndarray) -> float:\n ln_tE = params[0]\n ln_A0 = params[1]\n ln_deltaT = params[2]\n fbl = params[3]\n mb = params[4]\n\n # Equation (16,15,17) (note that Albrow uses \"log\" for log10)\n log10e = np.log10(np.exp(1))\n ln_pr_ln_tE = np.log(0.476) - ((log10e*ln_tE - 1.333)**2 / 0.330) + np.log(log10e)\n ln_pr_ln_A0 = np.log(0.660) - (1.289*log10e*ln_A0) + np.log(log10e)\n ln_pr_ln_deltaT = np.log(0.156) - ((log10e*ln_deltaT - 1.432)**2 / 0.458) +\\\n np.log(log10e)\n \n # Paper doesnt mention the prior used, but I assume it to be uniform\n ln_pr_fbl = uniform.logpdf(fbl,0.0,1.0)\n\n # Paper doesnr mention the prior used but I will asuumed it to be uniform\n ln_pr_mb = uniform.logpdf(mb,self.mag_min - 1.0, self.mag_max + 1.0)\n \n \n return ln_pr_fbl + ln_pr_ln_A0 + ln_pr_ln_deltaT + ln_pr_ln_tE + ln_pr_mb","def _estimate_log_prob(self, x):\n x = self.check_size(x)\n\n if self.covariance_type == \"full\":\n mu = self.mu\n var = self.var\n precision = tf.linalg.inv(var)\n d = x.shape[-1]\n\n log_2pi = d * tf.math.log(2. * pi)\n log_2pi = tf.cast(log_2pi, tf.double)\n log_det = self._calculate_log_det(precision)\n log_det = tf.cast(log_det, tf.double)\n\n\n x = tf.cast(x, tf.double)\n mu = tf.cast(mu, tf.double)\n x_mu_T = tf.expand_dims((x - mu), -2)\n x_mu = tf.expand_dims((x - mu), -1)\n\n x_mu_T_precision = calculate_matmul_n_times(self.n_components, x_mu_T, precision)\n # print(x_mu_T_precision, x_mu_T, x, mu)\n x_mu_T_precision_x_mu = calculate_matmul(x_mu_T_precision, x_mu)\n\n return -.5 * (log_2pi - log_det + x_mu_T_precision_x_mu)\n\n elif self.covariance_type == \"diag\":\n mu = self.mu\n prec = tf.math.rsqrt(self.var)\n\n log_p = tf.math.reduce_sum((mu * mu + x * x - 2 * x * mu) * (prec ** 2), axis=2, keepdims=True)\n log_det = tf.math.reduce_sum(tf.math.log(prec), axis=2, keepdims=True)\n\n return -.5 * (self.n_features * tf.math.log(2. * pi) + log_p) + log_det","def compute_loglike(self, tools: ModelingTools) -> float:\n return -1.5","def compute_edge_logits(self):","def calc_probs(log_p):\n\n N = log_p.shape[0]\n\n log_Z_per_N = np.zeros(shape=(N, 1))\n\n for i in range(N):\n\n log_Z_per_N[i] = log_norm(log_p[i])\n\n log_p_new = log_p - log_Z_per_N\n\n p = np.exp(log_p_new)\n\n # log_Z = log_norm(log_p)\n\n # p = np.exp(log_p - log_Z)\n\n return p","def log_marg(self):\n log_prob_X_given_z = 0.\n for k in range(self.K):\n log_prob_X_given_z += self.log_marg_k(k)\n return log_prob_X_given_z","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 statePosteriors(log_alpha, log_beta):\n return log_alpha + log_beta - logsumexp(log_alpha[-1,:])","def _logprob(self, sample):\n cur_y, cur_z = sample\n log_prior = 0\n log_lik = 0\n if cur_z.shape[1] == 0: return -99999999.9\n \n if self.cl_mode:\n a_time = time()\n d_cur_z = cl.Buffer(self.ctx, self.mf.READ_ONLY | self.mf.COPY_HOST_PTR, hostbuf = cur_z.astype(np.int32))\n d_cur_y = cl.Buffer(self.ctx, self.mf.READ_ONLY | self.mf.COPY_HOST_PTR, hostbuf = cur_y.astype(np.int32))\n d_logprob = cl.array.empty(self.queue, (cur_z.shape[0],), np.float32, allocator=self.mem_pool)\n\n if cur_z.shape[0] % self.p_mul_logprob_z_data == 0: wg = (self.p_mul_logprob_z_data,)\n else: wg = None\n \n self.prg.logprob_z_data(self.queue, (cur_z.shape[0],), wg,\n d_cur_z, d_cur_y, self.d_obs, d_logprob.data, #d_novel_f.data,\n np.int32(self.N), np.int32(cur_y.shape[1]), np.int32(cur_z.shape[1]), \n np.float32(self.alpha), np.float32(self.lam), np.float32(self.epislon))\n log_lik = d_logprob.get().sum()\n self.gpu_time += time() - a_time\n\n # calculate the prior probability of Y\n num_on = (cur_y == 1).sum()\n num_off = (cur_y == 0).sum()\n log_prior = num_on * np.log(self.theta) + num_off * np.log(1 - self.theta)\n\n else:\n # calculate the prior probability of Z\n for n in xrange(cur_z.shape[0]):\n num_novel = 0\n for k in xrange(cur_z.shape[1]):\n m = cur_z[:n,k].sum()\n if m > 0:\n if cur_z[n,k] == 1: log_prior += np.log(m / (n+1.0))\n else: log_prior += np.log(1 - m / (n + 1.0))\n else: \n if cur_z[n,k] == 1: num_novel += 1\n if num_novel > 0:\n log_prior += poisson.logpmf(num_novel, self.alpha / (n+1.0))\n # calculate the prior probability of Y\n num_on = (cur_y == 1).sum()\n num_off = (cur_y == 0).sum()\n log_prior += num_on * np.log(self.theta) + num_off * np.log(1 - self.theta)\n # calculate the logliklihood\n log_lik = self._loglik(cur_y = cur_y, cur_z = cur_z)\n return log_prior + log_lik","def logprob(self):\n assert len(self._added_rows) == self._num_rows\n V, E, K, M = self._VEKM\n vertex_logits = logprob_dc(self._vert_ss, self._vert_prior, axis=1)\n logprob = vertex_logits.sum()\n for e, v1, v2 in self._tree.tree_grid.T:\n logprob += (logprob_dc(self._edge_ss[e, :, :], self._edge_prior) -\n vertex_logits[v1] - vertex_logits[v2])\n for v in range(V):\n beg, end = self._table.ragged_index[v:v + 2]\n logprob += logprob_dc(self._feat_ss[beg:end, :], self._feat_prior)\n logprob -= logprob_dc(self._meas_ss[v, :], self._meas_prior[v])\n return logprob","def log_prior_grad(self, inputs):","def log_prob(self, x):\n z, log_det = self.backward_p(x)\n return self.prior.log_prob(z) + log_det","def forward(log_emlik, log_startprob, log_transmat):\n logPi=log_startprob[:-1]\n logB=log_emlik\n logA=log_transmat[:-1,:-1]\n alpha = np.zeros_like(logB)\n alpha[0]=logB[0]+logPi\n for i in range(1,logB.shape[0]):\n for j in range(logA.shape[0]):\n alpha[i][j]=logsumexp(alpha[i-1]+logA[:,j]+logB[i][j])\n return alpha","def logbarrierfunc(delta, z, use_sigma):\n if use_sigma:\n z = np.where(z >= 0, np.tanh(z), z)\n k = 2\n return np.where(z > delta, -np.log(np.abs(z)),\n ((k - 1) / k) * (((z - k * delta) / ((k - 1) * delta)) ** k - 1) - np.log(delta))","def _perplexity(self, X, log_w):\n return np.exp(-log_w/X.sum())","def logP(self):\n raise NotImplementedError","def _log_fold_change_pairs(self, idx0, idx1, base):\n assert np.all([x < self._pval.shape[1] for x in idx0])\n assert np.all([x < self._pval.shape[1] for x in idx1])\n if base == np.e:\n return self._logfc[idx0, :, :][:, idx1, :]\n else:\n return self._logfc[idx0, :, :][:, idx1, :] / np.log(base)","def compute_loglike(self, tools: ModelingTools) -> float:\n return -3.0 * self.placeholder","def log_pseudo_joint(self, data: Tensor, states: Tensor) -> Tensor: # type: ignore\n K = states\n Y = data\n assert K.dtype == to.uint8 and Y.dtype == to.uint8\n pi = self.theta[\"pies\"]\n W = self.theta[\"W\"]\n batch_size, S, H = K.shape\n D = W.shape[0]\n dev = pi.device\n\n logPriors = to.matmul(K.type_as(pi), to.log(pi / (1 - pi)))\n\n logPy = to.empty((batch_size, S), device=dev, dtype=self.precision)\n # We will manually set the lpjs of all-zero states to the appropriate value.\n # For now, transform all-zero states in all-one states, to avoid computation of log(0).\n zeroStatesInd = to.nonzero((K == 0).all(dim=2))\n # https://discuss.pytorch.org/t/use-torch-nonzero-as-index/33218\n zeroStatesInd = (zeroStatesInd[:, 0], zeroStatesInd[:, 1])\n K[zeroStatesInd] = 1\n # prods_nsd = prod{h}{1-W_dh*K_nkh}\n prods = (W * K.type_as(W).unsqueeze(2)).neg_().add_(1).prod(dim=-1)\n to.clamp(prods, self.eps, 1 - self.eps, out=prods)\n # logPy_nk = sum{d}{y_nd*log(1/prods_nkd - 1) + log(prods_nkd)}\n f1 = to.log(1.0 / prods - 1.0)\n indeces = 1 - Y[:, None, :].expand(batch_size, S, D)\n # convert to BoolTensor in pytorch>=1.2, leave it as ByteTensor in earlier versions\n indeces = indeces.type_as(to.empty(0) < 0)\n f1[indeces] = 0.0\n logPy[:, :] = to.sum(f1, dim=-1) + to.sum(to.log(prods), dim=2)\n K[zeroStatesInd] = 0\n\n lpj = logPriors + logPy\n # for all-zero states, set lpj to arbitrary very low value if y!=0, 0 otherwise\n # in the end we want exp(lpj(y,s=0)) = 1 if y=0, 0 otherwise\n lpj[zeroStatesInd] = -1e30 * data[zeroStatesInd[0]].any(dim=1).type_as(lpj)\n assert (\n not to.isnan(lpj).any() and not to.isinf(lpj).any()\n ), \"some NoisyOR lpj values are invalid!\"\n return lpj.to(device=states.device) # (N, S)","def log_sum_exp(Z):\n return np.max(Z) + np.log(np.sum(np.exp(Z - np.max(Z))))","def log_prob(self):\n res = -self.L_h/2*np.log(2*np.pi*self.la)\n res = res + self.L_h*(self.L_h-1)/2*self.a\n\n\n res = res - 1/(2*self.la)*np.square(np.linalg.norm(self.e*self.pie))\n\n res = res - 1/(2*self.la)*np.sum(self.e2*self.pie_var)\n\n res = res - self.L_h/2*np.log(2*np.pi*self.sigma2)\n res = res - 1/(2*self.sigma2)*(np.square(np.linalg.norm(self.w))+np.trace(self.R))\n\n print(\"Log-probability difference = {}\".format(res - self.LP), file=self.logfile)\n self.LP = res\n return res","def LikelihoodFromTrace (trace, A, B, prec=1e-14):\n\n if A.shape[0]==1:\n # uniformization based solution\n tr = np.sort(trace)\n lambd = np.max(np.abs(np.diag(B)))\n loglambda = math.log(lambd)\n P = B/lambd + ml.eye(B.shape[0])\n a = np.sum(-B,1)\n eps = max(prec, 10**(math.log(prec)/math.log(10.0) + math.log(lambd)/math.log(10.0)))\n lpoi = -lambd*tr\n logtr = np.log(tr)\n poi = np.exp(lpoi)\n spoi = np.array(poi)\n fx = poi*(A*a)[0,0]\n k = 1\n first = 0\n coeffv = ml.matrix(A)\n maxIter = 10000\n while k<maxIter:\n coeffv = coeffv * P\n lpoi[first:] += loglambda + logtr[first:] - math.log(k)\n poi[first:] = np.exp(lpoi[first:])\n spoi[first:] += poi[first:]\n fx[first:] += poi[first:] * (coeffv*a)[0,0]\n k += 1\n nfirst = (spoi[first:]<1-eps).nonzero()[0]\n if len(nfirst)==0:\n break\n first += nfirst[0]\n return np.sum(np.log(fx))/len(logtr)\n else:\n D0 = A\n D1 = B\n N = D0.shape[0]\n L = len(trace)\n \n # first we calculate matrix e^(D0*x(i))*D1 for each sample\n ix = np.argsort(trace)\n tr = trace[ix]\n lambd = np.max(np.abs(np.diag(D0)))\n loglambda = math.log(lambd)\n P = D0/lambd + ml.eye(N)\n eps = max(prec, 10**(math.log(prec)/math.log(10.0) + math.log(lambd)/math.log(10.0)))\n lpoi = -lambd*tr;\n logtr = np.log(tr)\n poi = np.exp(lpoi)\n spoi = np.array(poi)\n coeffv = ml.matrix(D1)\n fx = np.kron(poi,coeffv)\n k = 1\n first = 0\n maxIter = 10000\n while k<maxIter:\n coeffv = P * coeffv\n lpoi[first:] += loglambda + logtr[first:] - math.log(k)\n poi[first:] = np.exp(lpoi[first:])\n spoi[first:] += poi[first:] \n fx[:,first*N:] += np.kron(poi[first:],coeffv)\n k += 1 \n nfirst = (spoi[first:]<1-eps).nonzero()[0]\n if len(nfirst)==0:\n break\n first += nfirst[0]\n alpha = DTMCSolve ((-D0).I*D1)\n l = np.array(alpha)\n sc = 0\n ixrev = np.argsort(ix)\n for i in range(L):\n l = l.dot(fx[:,ixrev[i]*N:(ixrev[i]+1)*N])\n if i % 10 ==0:\n # sometimes we need to rescale the results to avoid \"nan\"s\n scale = math.ceil(math.log2(np.sum(l)))\n if scale>1:\n l /= 2**scale\n sc += scale\n if scale<-10:\n scale += 10\n l /= 2**scale\n sc += scale\n return (math.log(np.sum(l))+sc*math.log(2)) / len(logtr)","def backward(log_emlik, log_startprob, log_transmat):\n #print(log_transmat)\n beta = np.zeros(log_emlik.shape)\n n = len(log_emlik)-2\n log_transmat = log_transmat[0:-1,0:-1];\n\n\n\n while n >= 0:\n\n for j in range(0,len(log_emlik[0])):\n\n beta[n,j] = logsumexp(log_transmat[j,:] + log_emlik[n+1,:] + beta[n+1,:])\n\n n = n -1\n #print(beta[n,:])\n #print(beta)\n return beta","def logistic_function(z):\n\tg = 1/(1+np.exp(-z))\n\t#g = np.exp(z)/(1+np.exp(z))\n\treturn g","def backward(log_emlik, log_startprob, log_transmat):\n N, M = log_emlik.shape\n backward_prob = np.zeros((N,M))\n\n backward_prob[N-1, :] = 0.0\n\n for i in range(N-2,-1,-1):\n for k in range(M):\n # probability of transitioning from k to state l * probability of emitting symbol at state l at ts i+1 * recursive backward probability\n backward_prob[i,k] = logsumexp(log_transmat[k,:] + log_emlik[i+1,:] + backward_prob[i+1,:])\n\n return backward_prob","def viterbi(obs: List[int], pi: List[float], A: np.ndarray, B: np.ndarray) -> List[int]:\n pi_log = np.log(pi)\n A_log = np.log(A)\n B_log = np.log(B)\n states = A.shape[0]\n n = len(obs)\n\n D_log = np.zeros((states, n))\n backtrack = np.zeros((states, n - 1)).astype(int)\n D_log[:, 0] = pi_log + B_log[:, obs[0]]\n\n for j in range(1, n):\n for i in range(states):\n temp_sum = A_log[:, i] + D_log[:, j - 1]\n D_log[i, j] = np.max(temp_sum) + B_log[i, obs[j]]\n backtrack[i, j - 1] = np.argmax(temp_sum)\n\n state = np.zeros(n).astype(int)\n state[-1] = np.argmax(D_log[:, -1])\n for n in range(n - 2, -1, -1):\n state[n] = backtrack[int(state[n + 1]), n]\n state = state.tolist()\n return state","def _call(self, z): \n self.a = F.softplus(self.a)\n self.w = F.softmax(self.w, dim=1)\n # Compute\n pre_sigm = self.a * z + self.b\n sigm = torch.sigmoid(pre_sigm)\n x_pre = self.w * sigm\n if (len(z.shape) > 2):\n x_pre = torch.sum(self.w * sigm, dim=1)\n x_pre_clipped = x_pre * (1 - self.eps) + self.eps * 0.5\n zp = torch.log(x_pre_clipped) - torch.log(1 - x_pre_clipped)\n return zp","def baum_welch(self, O):\n alpha, beta, gamma, B, _Z_ = self.forward_backward(O)\n #\n \"\"\"\n gamma_tr = numpy.zeros([len(O) - 1], len(self), len(self)])\n for t in range(gamma_tr.shape[0]):\n for i in range(gamma_tr.shape[1]):\n for j in range(gamma_tr.shape[1]):\n gamma_tr[t, i, j] = alpha[t, i] + self.A.get_log_prob(i, j) + B[j, t + 1] + beta[t + 1,j] - gamma[t, i]\n #\n for i in range(gamma_tr.shape[1]):\n for j in range(gamma_tr.shape[1]):\n _weight_ = HMM.log_add(gamma_tr[:, i, j])\n temp_hmm.A.accumulate_transition(i, j, numpy.exp(_weight_)) # This line is candidate to be modified for accumulating logarithms\n #\n \"\"\"\n\n # UPDATE OF THE STATE-TRANSITION PROBABILITIES\n if len(O) > 1:\n for i in range(len(self)):\n _log_den_ = HMM.log_add(gamma[ : -1, i]) # sum(t = 1..T-1, gamma[t, i])\n for j in range(len(self)):\n gamma_tr = numpy.zeros(len(O) - 1)\n for t in range(gamma_tr.shape[0]):\n gamma_tr[t] = alpha[t, i] + self.A.get_log_prob(i, j) + B[j, t + 1] + beta[t + 1, j] - _Z_\n _weight_ = numpy.exp(HMM.log_add(gamma_tr[:]) - _log_den_)\n self.A.accumulate_transition(i, j, value = _weight_) # This line is candidate to be modified for accumulating logarithms\n #\n # UDPDATE OF THE STATE STARTING PROBABILITIES\n if self.P_accumulator is not None:\n self.P_accumulator[:] += gamma[0, :]\n\n # UDPDATE OF THE OUTPUT PROBABILITIES\n if self.modality in ['Discrete']:\n #\n for i in range(gamma.shape[1]):\n #\n _log_den_ = HMM.log_add(gamma[:, i]) # sum(t = 1..T, gamma[t, i])\n _den_ = numpy.exp(_log_den_)\n #\n for k in numpy.unique(O): # range(self.num_symbols)\n _log_num_ = HMM.log_add(gamma[O == k, i])\n _weight_ = numpy.exp(_log_num_ - _log_den_)\n self.S[i].accumulate_sample(k, _weight_, numpy.exp(_log_num_), _den_) # This line is candidate to be modified for accumulating logarithms\n #\n elif self.modality in ['Continuous']:\n #\n for j in range(len(self)):\n #\n _log_denominator_ = HMM.log_add(gamma[:, j]) # sum(t = 1..T, gamma[t, i])\n _denominator_ = numpy.exp(_log_denominator_)\n #\n _log_densities_ = numpy.zeros([len(O), self.S[j].gmm.n_components])\n for t in range(len(O)):\n _log_densities_[t, :] = self.S[j].gmm.log_densities(O[t]) # log(c_j_k * g_j_k(O_t))\n #\n log_xi = numpy.zeros(len(O)) # A one-dimensional vector for computing _xi_t_j_k_ for fixed 'j' and 'k'\n for k in range(_log_densities_.shape[1]):\n log_xi[0] = self.log_P[j] + _log_densities_[0, k] + beta[0, j] # _xi_0_j_k_\n #\n for t in range(1, len(O)):\n _temp_ = numpy.zeros(len(self))\n for i in range(len(self)): # For all the states in the HMM\n _temp_[i] = alpha[t - 1, i] + self.A.get_log_prob(i, j) + _log_densities_[t, k] + beta[t, j]\n log_xi[t] = HMM.log_add(_temp_) # _xi_t_j_k_ for all t > 0\n #\n log_xi -= _Z_ # Dividing by P(O|lambda)\n #\n _xi_t_j_k_ = numpy.exp(log_xi)\n #\n # In the following lines the code of Baum-Welch directly modifies the accumulators\n # of the GMM of each state 'j'\n #\n self.S[j].gmm_accumulator.acc_posteriors[k] += _xi_t_j_k_.sum() # This value is correct because is used as the denominator for updating mean vectors and covariance matrices\n self.S[j].gmm_accumulator.acc_sample_counter[k] += _denominator_ / self.S[j].gmm_accumulator.n_components\n #\n for t in range(len(O)):\n self.S[j].gmm_accumulator.mu[k] += _xi_t_j_k_[t] * O[t]\n if self.S[j].gmm_accumulator.covar_type in GMM.covar_diagonal_types:\n self.S[j].gmm_accumulator.sigma[k] += _xi_t_j_k_[t] * (O[t] * O[t]) # numpy.diagonal(O[t] * O[t])\n else:\n self.S[j].gmm_accumulator.sigma[k] += _xi_t_j_k_[t] * numpy.outer(O[t], O[t])\n else:\n raise Exception('Modality ' + self.modality + ' is not valid or not implemented yet!')","def log_likelihood_z_lognormal(self, std=1.0):\n #return self.log_det_Jxz - self.dim * tf.log(std) - (0.5 / (std**2)) * tf.reduce_sum(self.output_z**2, axis=1)\n from deep_boltzmann.util import logreg\n logz = logreg(self.output_z, a=0.001, tf=True)\n ll = self.log_det_Jxz \\\n - (0.5 / (std**2)) * tf.reduce_sum(logz**2, axis=1) \\\n - tf.reduce_sum(logz, axis=1)\n return ll","def log_Distribution_volume(self, z):\n if type(z)==np.ndarray:\n ind = z<0\n pv = np.log(self.Distribution_volume(z))\n pv[ind] = -1e99\n return pv\n else:\n if z<0:\n return -1e99\n else:\n return np.log(self.Distribution_volume(z))","def log_likelihood(self):\r\n A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.beta_star)) - 0.5 * np.sum(self.V_star * self.likelihood.Y)\r\n C = -self.output_dim * (np.sum(np.log(np.diag(self.LB))))\r\n D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))\r\n return A + C + D + self.likelihood.Z","def logp(X, nu, V):\n\n p = V.shape[0]\n\n IVI = det(V)\n IXI = det(X)\n\n return check_parameters(\n (\n (nu - p - 1) * pt.log(IXI)\n - trace(matrix_inverse(V).dot(X))\n - nu * p * pt.log(2)\n - nu * pt.log(IVI)\n - 2 * multigammaln(nu / 2.0, p)\n )\n / 2,\n matrix_pos_def(X),\n pt.eq(X, X.T),\n nu > (p - 1),\n )","def logZ_brute(self, T, pos, psi, phi):\n ms = self.get_all_tag_seq(len(T))\n log_scores = tr.zeros(len(ms), dtype=tr.float64)\n for i in range(len(ms)):\n log_scores[i] = self.log_score(T, pos, ms[i], psi, phi)\n log_z = logsumexp(log_scores)\n return log_z","def backward(log_emlik, log_startprob, log_transmat):\n N, M = log_emlik.shape\n logPi=log_startprob[:-1]\n logB=log_emlik\n logA=log_transmat[:-1,:-1]\n beta = np.zeros_like(logB)\n for t in range(N-2,-1,-1):\n for j in range(M):\n beta[t][j]=logsumexp(beta[t+1]+logA[j]+logB[t+1])\n\n return beta","def _log_fold_change_pairs(self, idx0, idx1, base):\n logfc = np.tile(np.NaN, [len(idx0), len(idx1), self.model_estim.x.shape[1]])\n for i, xi in enumerate(idx0):\n for j, xj in enumerate(idx1):\n logfc[i, j, :] = self._theta_mle[xj, :] - self._theta_mle[xi, :]\n logfc[j, i, :] = -logfc[i, j, :]\n\n if base == np.e:\n return logfc\n else:\n return logfc / np.log(base)","def testLogPartitionInfinityIsAccurate(self):\n alpha = float('inf')\n log_z_true = 0.70526025442 # From mathematica.\n log_z = self.variant(self._distribution.log_base_partition_function)(alpha)\n chex.assert_tree_all_close(log_z, log_z_true, atol=1e-7, rtol=1e-7)","def log_abs_det_jacobian(self, z):\n self.a = F.softplus(self.a)\n self.w = F.softmax(self.w, dim=1)\n pre_sigm = self.a * z + self.b\n sigm = torch.sigmoid(pre_sigm)\n x_pre = self.w * sigm\n if (len(z.shape) > 2):\n x_pre = torch.sum(self.w * sigm, dim=1)\n x_pre_clipped = x_pre * (1 - self.eps) + self.eps * 0.5\n logj = F.log_softmax(self.w, dim=1) + logsigmoid(pre_sigm) + logsigmoid(-pre_sigm) + torch.log(self.a)\n logj = torch.log(torch.sum(torch.exp(logj)))#,2).sum(2)\n logdet = logj + np.log(1 - self.eps) - (torch.log(x_pre_clipped) + torch.log(-x_pre_clipped + 1))\n return sum_dims(logdet)","def partition_function(array, temp):\r\n\r\n # Constants imported from scipy.constants\r\n h = scipy.constants.h # Planck's constant\r\n # speed of light must be in cm/s as wavenumber is in cm-1\r\n c = scipy.constants.c * 100\r\n k = scipy.constants.k # Boltzmann constant\r\n T = temp # extracted from log file using extract_temp()\r\n\r\n # check if inputs are numpy arrays and convert if not.\r\n if not isinstance(array, np.ndarray):\r\n np.asarray(array)\r\n\r\n # conversion to exponent\r\n u = (h * array * c) / (k * T)\r\n\r\n # calculates natural log of an individual frequency contribution to the partition function\r\n Q_ = np.log(np.exp(-(u / 2)) / (1 - np.exp(-u)))\r\n # sums all the contributions together, giving the final result.\r\n Q = np.sum(Q_)\r\n return Q","def log_prob_easier(self, x):\n normalization_const = -0.5 * tf.math.log(2 * np.pi) - tf.math.log(self.stddev)\n sq_term = - 0.5 * ((x - self.mean) / self.stddev) ** 2\n l_prob = tf.math.reduce_sum(normalization_const + sq_term, axis=1)\n return l_prob","def log_abs_det_jacobian(self, z):\n pre_u = self.u_ + self.u\n pre_w = self.w_ + self.w\n a = F.softplus(self.a + self.inv)\n w = F.softmax(pre_w, dim=3)\n u = F.softmax(pre_u, dim=3)\n # Perform computation\n pre_sigm = torch.sum(u * a * z, 3) + self.b\n sigm = torch.sigmoid(pre_sigm)\n x_pre = torch.sum(w * sigm, dim=3)\n x_pre_clipped = x_pre * (1 - self.eps) + self.eps * 0.5\n logj = F.log_softmax(pre_w, dim=3) + logsigmoid(pre_sigm) + logsigmoid(-pre_sigm) + torch.log(a)\n # n, d, d2, dh\n logj = logj + F.log_softmax(pre_u, dim=3)\n # n, d, d2, dh, d1\n logj = torch.log(torch.sum(torch.exp(logj),3))\n # n, d, d2, d1\n logdet_ = logj + np.log(1 - self.eps) - (torch.log(x_pre_clipped) + torch.log(-x_pre_clipped + 1))\n return logdet_","def compute_expected_log_prob(self):\n for (w, t), val in np.ndenumerate(self.e_log_prob):\n self.e_log_prob[w][t] = self.mean[w][t + 1] - np.log(self.zeta[t])\n return self.e_log_prob","def _log_prior_gradients(self):\n x = self._get_params()\n ret = np.zeros(x.size)\n [np.put(ret,i,p.lnpdf_grad(xx)) for i,(p,xx) in enumerate(zip(self.priors,x)) if not p is None]\n return ret","def _log_fold_change_pairs(self, idx0, idx1, base):\n logfc = np.zeros(shape=(len(idx0), len(idx1), self._theta_mle.shape[1]))\n for i, xi in enumerate(idx0):\n for j, xj in enumerate(idx1):\n logfc[i, j, :] = self._theta_mle[xi, :] - self._theta_mle[xj, :]\n\n if base == np.e:\n return logfc\n else:\n return logfc / np.log(base)","def statePosteriors(log_alpha, log_beta):","def vbe_step(self):\n\n digam_alpha = digamma(np.sum(self.alpha_k))\n for k in range(self.k):\n\n # compute estimate over ln det(lamb)\n tmp = sum(digamma((self.nu_k[k] + 1 - j) / 2) for j in range(self.dim))\n\n det = np.linalg.det(self.w_k[k])\n self.log_lamb[k] = tmp + self.dim * np.log(2) + np.log(det)\n\n # compute estimate for ln pi\n self.log_pi[k] = digamma(self.alpha_k[k]) - digam_alpha\n\n for n in range(self.n):\n tmp = self.x[n] - self.m_k[k]\n # compute estimate over mu and lambda\n self.estimate[n, k] = self.dim * (1 / self.beta_k[k]) + self.nu_k[k] * (tmp.T @ self.w_k[k] @ tmp)","def logsum_safe(prob_ll, atl):\n logpdf = prob_ll + K.log(atl + K.epsilon())\n alpha = tf.reduce_max(logpdf, -1, keepdims=True)\n return alpha + tf.log(tf.reduce_sum(K.exp(logpdf-alpha), -1, keepdims=True) + K.epsilon())","def logrels(rets):\n return np.log(rets + 1)","def compute_Bayes(BIC, BIC_wo, BIC_white):\n from scipy.misc import logsumexp\n lnprob = -0.5*BIC - np.logaddexp(-0.5*BIC, -0.5*BIC_wo)\n # BIC of H1 - BIC H0\n # larger value favours H1\n logBayes = 0.5 * (-1.0*BIC + BIC_wo)\n #lnprob = np.log(1./3.) - 0.5*BIC - logsumexp([BIC, BIC_wo, BIC_white])\n #print(np.log(1./3.), - 0.5*BIC, - logsumexp([BIC, BIC_wo, BIC_white]))\n logprob = logBayes - logsumexp([logBayes, 1.])\n #print(\"2lnK: \", 2.0*logBayes)\n lnprob_w = -0.5 * BIC - logsumexp([-0.5*BIC, -0.5*BIC_wo, -0.5*BIC_white])\n lnprob_wo = -0.5 * BIC_wo - logsumexp([-0.5*BIC, -0.5*BIC_wo, -0.5*BIC_white])\n lnprob_white = -0.5 * BIC_white - logsumexp([-0.5*BIC, -0.5*BIC_wo, -0.5*BIC_white])\n #print(0.5 * (BIC_wo - BIC))\n #prob = np.exp(-0.5*BIC) / (np.exp(-0.5*BIC) + np.exp(-0.5*BIC_wo))\n return np.exp(lnprob_w), np.exp(lnprob_wo), np.exp(lnprob_white)","def NMF(model, maxIter=100, beliefs=None, verbose=False):\n if beliefs is None: beliefs = [Factor([Xi],1.0/Xi.states) for Xi in model.X]\n \n lnZ = sum([beliefs[Xi].entropy() for Xi in model.X])\n for f in model.factors:\n m = f.log()\n for v in f.vars: m *= beliefs[v]\n lnZ += m.sum()\n if verbose: print(\"Iter 0: \"+str(lnZ))\n\n for t in xrange(1,maxIter+1): # for each iteration:\n # Update all the beliefs via coordinate ascent:\n for Xi in model.X: # for each variable, \n bNew = 0.0 # compute E[ log f ] as a function of Xi:\n for f in model.factorsWith(Xi,copy=False): # for each factor f_a, compute:\n m = f.log() # E[log f_a] = \\sum \\log f_a \\prod b_v\n for v in f.vars - [Xi]: m *= beliefs[v]\n bNew += m.marginal([Xi]) # sum them up to get E[log f]\n bNew -= bNew.max() # (numerical issues)\n bNew = bNew.exp()\n bNew /= bNew.sum() # set b(Xi) = exp( E[log f] ) / Z\n beliefs[Xi] = bNew\n #\n # Compute the lower bound on the partition function:\n # E_b [ log f ] + H(b) = \\sum_a E[log f_a] + \\sum_i H(b_i) for independent beliefs\n lnZ = sum([beliefs[Xi].entropy() for Xi in model.X])\n for f in model.factors:\n m = f.log()\n for v in f.vars: m *= beliefs[v]\n lnZ += m.sum()\n if verbose: print(\"Iter \"+str(t)+\": \"+str(lnZ))\n return lnZ,beliefs","def logdprior(parameters, hyperparameters):\n sigma_w_part = parameters[0] + invgamma_logpdf(parameters[0],\n hyperparameters[\"sigma_w_shape\"], hyperparameters[\"sigma_w_scale\"])\n sigma_v_part = parameters[1] + invgamma_logpdf(parameters[1], hyperparameters[\"sigma_v_shape\"], hyperparameters[\"sigma_v_scale\"])\n return sigma_w_part + sigma_v_part","def kullbackLeibler(mu, log_sigma):\n # (tf.Tensor, tf.Tensor) -> tf.Tensor\n # = -0.5 * (1 + log(sigma**2) - mu**2 - sigma**2)\n return -0.5 * tf.reduce_sum(1 + 2 * log_sigma - mu**2 - tf.exp(2 * log_sigma), 1)","def logp(self, x):\n pass","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 expectation(N,K,log_M):\n\tg0 = log_M[0,0]\n\tg = log_M[1:]\n\ta = forward(g0,g,N,K)\n\tb = backward(g,N,K)\n\tprint \"Forward:\"\n\tprint a\n\tprint \"Backward:\"\n\tprint b\n\t# log-normalizing constant\n\tlogZ = misc.logsumexp(a[N-1,:])\n\n\tE = defaultdict(float)\n\n\t# The first factor needs to be special case'd\n\t# E[ f( y_0 ) ] = p(y_0 | y_[1:N], x) * f(y_0)\n\tc = exp(g0 + b[0,:] - logZ).clip(0.0, 1.0)\n\tfor y in xrange(K):\n\t\tp = c[y]\n\t\tif p < 1e-40: continue # skip really small updates.\n\t\tfor k in f[0, None, y]:\n\t\t\tE[k] += p\n\n\tfor t in xrange(1,N):\n\t\t# vectorized computation of the marginal for this transition factor\n\t\tc = exp((add.outer(a[t-1,:], b[t,:]) + g[t-1,:,:] - logZ)).clip(0.0, 1.0)\n\n\t\tfor yp in xrange(K):\n\t\t\tfor y in xrange(K):\n\t\t\t\t# we can also use the following to compute ``p`` but its quite\n\t\t\t\t# a bit slower than the computation of vectorized quantity ``c``.\n\t\t\t\t#p = exp(a[t-1,yp] + g[t-1,yp,y] + b[t,y] - logZ).clip(0.0, 1.0)\n\t\t\t\tp = c[yp, y]\n\t\t\t\tif p < 1e-40: continue # skip really small updates.\n\t\t\t\t# expectation of this factor is p*f(t, yp, y)\n\t\t\t\tfor k in f[t, yp, y]:\n\t\t\t\t\tE[k] += p\n\n\treturn E","def _loglike(self, y, f):\n ll = -0.5 * (tf.log(2 * self.variance * np.pi) +\n (y - f)**2 / self.variance)\n return ll","def log_post_pred(self, i):\n k_Ns = self.prior.k_0 + self.counts[:self.K]\n v_Ns = self.prior.v_0 + self.counts[:self.K]\n m_Ns = self.m_N_numerators[:self.K]/k_Ns[:, np.newaxis]\n\n studentt_gammas = self._cached_gammaln_by_2[v_Ns + 1] - self._cached_gammaln_by_2[v_Ns]\n\n deltas = m_Ns - self.X[i]\n\n return (\n self.D * (\n studentt_gammas\n - 0.5*self._cached_log_v[v_Ns] - 0.5*self._cached_log_pi\n )\n - 0.5*self.log_prod_vars[:self.K]\n - (v_Ns + 1)/2. * sum_axis1(np.log(\n 1 + np.square(deltas)*self.inv_vars[:self.K]*(1./v_Ns[:, np.newaxis])\n ))\n )","def calculate_negative_log_likelihood(self):\n data = self.played_points_hist[:self.t]\n kernel_matrix = self.kernel_fn(data, data, self.best_ard_params)\n c_matrix = kernel_matrix + (self.noise_sigma ** 2) * np.eye(data.shape[0])\n c_matrix_inv = np.linalg.inv(c_matrix)\n first_term = np.matmul(self.rews_hist[:self.t].T, np.matmul(c_matrix_inv, self.rews_hist[:self.t]))\n second_term = np.log(np.linalg.det(c_matrix))\n return first_term + second_term","def forward(log_emlik, log_startprob, log_transmat):\n \n alpha = np.zeros(np.shape(log_emlik))\n N=len(alpha)\n # recursiveAlpha(N-1, alpha, log_emlik, log_startprob, log_transmat)\n alpha[0][:] = log_startprob.T + log_emlik[0]\n\n for n in range(1,len(alpha)):\n for i in range(alpha.shape[1]):\n alpha[n, i] = logsumexp(alpha[n - 1] + log_transmat[:,i]) + log_emlik[n,i]\n return alpha, logsumexp(alpha[N-1])","def log_likelihood(self) -> tf.Tensor:\n # K⁻¹ + GᵀΣ⁻¹G = LLᵀ.\n l_post = self._k_inv_post.cholesky\n num_data = self.observations_index.shape[0]\n\n # Hμ [..., num_transitions + 1, output_dim]\n marginal = self.emission.project_state_to_f(self.prior_ssm.marginal_means)\n marginal = self._drop_batch_shape(marginal)\n\n # y = obs - Hμ [..., num_transitions + 1, output_dim]\n disp = self.observations - marginal\n disp_data = self.sparse_observations - self.dense_to_sparse(marginal)\n\n # cst is the constant term for a gaussian log likelihood\n cst = (\n -0.5 * np.log(2 * np.pi) * tf.cast(self.emission.output_dim * num_data, default_float())\n )\n\n term1 = -0.5 * tf.reduce_sum(\n input_tensor=tf.einsum(\"...op,...p,...o->...o\", self._r_inv_data, disp_data, disp_data), axis=[-1, -2]\n )\n\n # term 2 is: ½|L⁻¹(GᵀΣ⁻¹)y|²\n # (GᵀΣ⁻¹)y [..., num_transitions + 1, state_dim]\n obs_proj = self._back_project_y_to_state(disp)\n\n # ½|L⁻¹(GᵀΣ⁻¹)y|² [...]\n term2 = 0.5 * tf.reduce_sum(\n input_tensor=tf.square(l_post.solve(obs_proj, transpose_left=False)), axis=[-1, -2]\n )\n\n ## term 3 is: ½log |K⁻¹| - log |L| + ½ log |Σ⁻¹|\n # where log |Σ⁻¹| = num_data * log|R⁻¹|\n term3 = (\n 0.5 * self.prior_ssm.log_det_precision()\n - l_post.abs_log_det()\n + 0.5 * self._log_det_observation_precision\n )\n\n return tf.reduce_sum(cst + term1 + term2 + term3)","def log(self): # just use base?\n return Factor().__build( VarSet(self.v) , np.log(self.t) )","def neglogli(preds, actual):\n preds = np.reshape(preds, (-1,))\n nll = np.sum((preds - actual*np.log(preds + 1e-07) + np.log(sp.special.factorial(actual))))\n return nll","def statePosteriors(log_alpha, log_beta):\n\n gamma = np.zeros(log_alpha.shape)\n\n gamma = log_alpha + log_beta\n\n gamma = gamma - logsumexp(log_alpha[-1,:])\n\n return gamma","def _log_prior_gradients(self):\r\n if self.priors is None:\r\n return 0.\r\n x = self._get_params()\r\n ret = np.zeros(x.size)\r\n [np.put(ret, i, p.lnpdf_grad(xx)) for i, (p, xx) in enumerate(zip(self.priors, x)) if not p is None]\r\n return ret","def nloglikeobs(self, params):\n lambda_ = params[0]\n\n ll_output = self._LL(self.endog, rate=lambda_)\n\n return -np.log(ll_output)","def log_prior(self, params):\n # log likelihood function, see:\n # https://en.wikipedia.org/wiki/Multivariate_normal_distribution#Likelihood_function\n variance = self.std ** 2\n ndim = params.ndim\n mean_diff = params - self.mean\n scaled_sq_err = jnp.dot(mean_diff, mean_diff) / variance\n # log determinant of covariance matrix\n log_det_cov = 2 * ndim * jnp.log(self.std)\n norm_term = ndim * jnp.log(2 * jnp.pi)\n return -0.5 * (log_det_cov + scaled_sq_err + norm_term)","def log_prior(x):\n logp = (-0.5 * x.pow(2) - torch.tensor(2 * math.pi).sqrt().log()).sum(dim=1)\n return logp","def kullback_leibler_iaf(z, logqz_x, beta=1., **args):\n \n logpz = -tf.reduce_sum(input_tensor=0.5 * np.log(2*np.pi) + 0.5 * tf.square(z), axis=-1)\n kl = beta * tf.reduce_mean(input_tensor=logqz_x - logpz)\n return kl","def _log_likelihood_gradient(self, z, K, H, B, Kinv):\n\n nparams = 4\n grad = np.zeros((nparams,))\n\n #t0 = time.time()\n tmp = np.dot(self.invc, self.HKinv)\n #t1 = time.time()\n K_HBH_inv = Kinv - np.dot(tmp.T, tmp)\n #t2 = time.time()\n alpha_z = np.dot(K_HBH_inv, z)\n #t3 = time.time()\n\n #print \"gradient: %f %f %f\" % (t1-t0, t2-t1, t3-t2)\n\n for i in range(nparams):\n tA = time.time()\n if (i == 0):\n dKdi = np.eye(self.n)\n else:\n dKdi = self.predict_tree.kernel_deriv_wrt_i(self.X, self.X, \"se\", self.wfn_params, i-1)\n\n dlldi = .5 * np.dot(alpha_z.T, np.dot(dKdi, alpha_z))\n tB = time.time()\n # here we use the fact:\n # trace(AB) = sum_{ij} A_ij * B_ij\n dlldi -= .5 * np.sum(np.sum(K_HBH_inv.T * dKdi))\n\n grad[i] = dlldi\n tC = time.time()\n print \" %d: %f %f\" % (i, tB-tA, tC-tB)\n\n return grad","def log_boltzmann_dist(Q, temperature):\n return nn.LogSoftmax(dim=0)(Q/temperature)","def _log_prior_gradients(self):\n if self.priors.size == 0:\n return 0.\n x = self.param_array\n ret = np.zeros(x.size)\n #compute derivate of prior density\n [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]\n #add in jacobian derivatives if transformed\n priored_indexes = np.hstack([i for p, i in self.priors.items()])\n for c,j in self.constraints.items():\n if not isinstance(c, Transformation):continue\n for jj in j:\n if jj in priored_indexes:\n ret[jj] += c.log_jacobian_grad(x[jj])\n return ret","def neg_log_prob(self,params: ndarray) -> float:\n return -self.compute_log_prob(params)","def logistic_loss(z):\n idx_pos = np.where(z >= 0.)\n idx_neg = np.where(z < 0.)\n res = np.empty(z.shape)\n res[idx_pos] = np.log(1. + np.exp(-z[idx_pos]))\n z_neg = z[idx_neg]\n res[idx_neg] = -z_neg + np.log(1. + np.exp(z_neg))\n return res","def kullback_leibler_iaf(z, logqz_x, beta=1.):\n \n logpz = -tf.reduce_sum(0.5 * np.log(2*np.pi) + 0.5 * tf.square(z), axis=-1)\n kl = beta * tf.reduce_mean(logqz_x - logpz)\n return kl","def log_Schechter_log(self, logl, alpha, logls, logl0):\n phi = (logl - logls) * (alpha+1) * np.log(10.) - np.power(10., logl-logls)\n lik = phi.copy()\n lik [logl < logl0] = -1e99\n return lik","def log_likelihood(self):\r\n assert not self.likelihood.is_heteroscedastic\r\n A = -0.5*self.batchsize*self.output_dim*(np.log(2.*np.pi) - np.log(self.likelihood.precision))\r\n B = -0.5*self.likelihood.precision*self.output_dim*self.trace_K\r\n Kmm_logdet = 2.*np.sum(np.log(np.diag(self.Lm)))\r\n C = -0.5*self.output_dim*self.data_prop*(Kmm_logdet-self.q_u_logdet - self.num_inducing)\r\n C += -0.5*np.sum(self.LQL * self.B)\r\n D = -0.5*self.likelihood.precision*self.likelihood.trYYT\r\n E = np.sum(self.V*self.projected_mean)\r\n return (A+B+C+D+E)/self.data_prop","def grad_log_q(self,z): \n param_count = 0\n grad = np.zeros((np.sum(self.approx_param_no),self.sims))\n for core_param in range(len(self.q)):\n for approx_param in range(self.q[core_param].param_no):\n grad[param_count] = self.q[core_param].vi_score(z[core_param],approx_param) \n param_count += 1\n return grad","def log_prob_from_logits(x):\n axis = len(x.shape) - 1\n m = x.max(dim=axis, keepdim=True)[0]\n return x - m - torch.log(torch.exp(x - m).sum(dim=axis, keepdim=True))","def _loglike(self, y, f):\n bincoef = tf.lgamma(self.n + 1) - tf.lgamma(y + 1) \\\n - tf.lgamma(self.n - y + 1)\n ll = bincoef + y * tf.log(pos(f)) + (self.n - y) * tf.log(pos(1 - f))\n return ll","def evaluate(observations, model, states=None, log=False):\r\n N = model.N\r\n T = observations.shape[0]\r\n A = numpy.log(model.A)\r\n B = numpy.log(model.B)\r\n\r\n if states is None:\r\n alphas = forward_path(observations, numpy.log(model.pi), A, B, T, N)\r\n\r\n \"\"\" Termination \"\"\"\r\n result = add_logs(alphas[T-1, :])\r\n if log:\r\n return result\r\n else:\r\n return math.exp(result)\r\n\r\n else:\r\n result = 0\r\n for i in range(T):\r\n result += B[states[i], observations[i]]\r\n\r\n if log:\r\n return result\r\n else:\r\n return math.exp(result)","def weight_log(val):\n return val * math.log(val)","def log_prob(self, scores : torch.Tensor, permutations):\n s = torch.log(select_indices(scores, permutations))\n n = len(scores)\n p = self.upto if self.upto is not None else n - 1\n return -sum(\n torch.log(torch.exp((s[k:] - s[k]) * self.shape).sum(dim=0))\n for k in range(p))","def nloglikeobs(self, params):\n #print len(params),\n beta = params[:-2]\n df = params[-2]\n scale = params[-1]\n loc = np.dot(self.exog, beta)\n endog = self.endog\n x = (endog - loc)/scale\n #next part is stats.t._logpdf\n lPx = sps_gamln((df+1)/2) - sps_gamln(df/2.)\n lPx -= 0.5*np_log(df*np_pi) + (df+1)/2.*np_log(1+(x**2)/df)\n lPx -= np_log(scale) # correction for scale\n return -lPx","def get_log_lik(cov_evals, rank, shape):\n\n n_samples, n_features = shape\n if not 1 <= rank <= n_features - 1:\n raise ValueError(\"the tested rank should be in [1, n_features - 1]\")\n\n eps = 1e-15\n\n if cov_evals[rank - 1] < eps:\n # When the tested rank is associated with a small eigenvalue, there's\n # no point in computing the log-likelihood: it's going to be very\n # small and won't be the max anyway. Also, it can lead to numerical\n # issues below when computing pa, in particular in log((spectrum[i] -\n # spectrum[j]) because this will take the log of something very small.\n return -np.inf\n\n pu = -rank * log(2.)\n for i in range(1, rank + 1):\n pu += (gammaln((n_features - i + 1) / 2.) -\n log(np.pi) * (n_features - i + 1) / 2.)\n\n pl = np.sum(np.log(cov_evals[:rank]))\n pl = -pl * n_samples / 2.\n\n v = max(eps, np.sum(cov_evals[rank:]) / (n_features - rank))\n pv = -np.log(v) * n_samples * (n_features - rank) / 2.\n\n m = n_features * rank - rank * (rank + 1.) / 2.\n pp = log(2. * np.pi) * (m + rank) / 2.\n\n pa = 0.\n spectrum_ = cov_evals.copy()\n spectrum_[rank:n_features] = v\n for i in range(rank):\n for j in range(i + 1, len(cov_evals)):\n pa += log((cov_evals[i] - cov_evals[j]) *\n (1. / spectrum_[j] - 1. / spectrum_[i])) + log(n_samples)\n\n ll = pu + pl + pv + pp - pa / 2. - rank * log(n_samples) / 2.\n\n return ll","def Laplace_evidence(self):\r\n A = self.Laplace_covariance()\r\n try:\r\n hld = np.sum(np.log(np.diag(jitchol(A)[0])))\r\n except:\r\n return np.nan\r\n return 0.5 * self._get_params().size * np.log(2 * np.pi) + self.log_likelihood() - hld","def calc_partition_function_one(states, T):\n B = beta(T)\n Z = sum(np.exp(-B * states))\n\n return Z","def TCVAE_loss_function(input, recons, mu, log_var, z, dataset_size, batch_iter, \n anneal_steps=200., alpha=1., beta=6., gamma=1., train=True):\n weight = 1 # Account for the minibatch samples from the dataset\n batch_size, z_dim = z.shape\n \n recons_loss = F.mse_loss(recons, input, reduction='sum')/(19*batch_size)\n\n log_q_zx = log_density_gaussian(z, mu, log_var).sum(dim = 1)\n\n zeros = torch.zeros_like(z)\n log_p_z = log_density_gaussian(z, zeros, zeros).sum(dim = 1)\n\n mat_log_q_z = log_density_gaussian( z.view(batch_size, 1, z_dim),\n mu.view(1, batch_size, z_dim),\n log_var.view(1, batch_size, z_dim))\n\n # Reference\n # [1] https://github.com/YannDubs/disentangling-vae/blob/535bbd2e9aeb5a200663a4f82f1d34e084c4ba8d/disvae/utils/math.py#L54\n strat_weight = (dataset_size - batch_size + 1) / (dataset_size * (batch_size - 1))\n importance_weights = torch.Tensor(batch_size, batch_size).fill_(1 / (batch_size -1)).to(input.device)\n importance_weights.view(-1)[::batch_size] = 1 / dataset_size\n importance_weights.view(-1)[1::batch_size] = strat_weight\n importance_weights[batch_size - 2, 0] = strat_weight\n log_importance_weights = importance_weights.log()\n\n mat_log_q_z += log_importance_weights.view(batch_size, batch_size, 1)\n\n log_q_z = torch.logsumexp(mat_log_q_z.sum(2), dim=1, keepdim=False)\n log_prod_q_z = torch.logsumexp(mat_log_q_z, dim=1, keepdim=False).sum(1)\n\n mi_loss = (log_q_zx - log_q_z).mean()/19\n tc_loss = (log_q_z - log_prod_q_z).mean()/19\n kld_loss = (log_prod_q_z - log_p_z).mean()/19\n\n if train:\n batch_iter += 1\n anneal_rate = min(0 + 1 * batch_iter / anneal_steps, 1)\n else:\n anneal_rate = 1.\n\n loss = recons_loss + alpha*mi_loss + weight*(beta*tc_loss + \n anneal_rate*gamma*kld_loss)\n \n return loss, recons_loss, kld_loss, tc_loss, mi_loss","def compute_log(tx, index_log, mean=[], std=[]):\n tx_new = np.log10(3+abs(tx[:,index_log]))\n return standardize(tx_new,mean,std)","def mak2(n,par=None,D0=1e-2,k=.5,Fb=0):\n if par is not None:\n D0,k,Fb,slope=par\n D=np.zeros(n)\n D[0]=D0\n for i in range(n-1):\n D[i+1]=D[i]+k*np.log(1+D[i]/k)\n F=D+Fb\n E[1:]=k*np.log(1+D[:-1]/k)/D[:-1]\n return F,D,E","def ExponentialPosteriorSample(bs, ls) :\n def exps(args) :\n lamb = args\n eps = K.random_uniform(shape=(bs, ls))\n ans = (-1./lamb) * K.log(-eps + 1)\n return ans\n return exps"],"string":"[\n \"def log_lhood(X, Z, Y, a, ep, lamb):\\n \\n K = Z.shape[1]\\n N, T = X.shape\\n \\n # p(X)\\n ZY = np.dot(Z,Y) \\n log_pX = 0\\n log_pX = log_pX + np.sum(X * np.log(1 - ((1 - lamb) ** ZY) * (1 - ep)))\\n log_pX = log_pX + np.sum((1 - X) * np.log(((1 - lamb) ** ZY) * (1 - ep))) \\n \\n # p(Z)\\n HN = 0\\n for n in range(1, N+1):\\n HN += 1.0/n\\n m = Z.sum(axis=0)\\n log_pZ = (K * np.log(a) - (a * HN)) + np.sum(gammaln(m) +\\n gammaln(N - m + 1) - gammaln(N + 1)) \\n\\n return log_pZ + log_pX\",\n \"def log_prob(self):\",\n \"def calculate_log_perplexity(self, output, flat_labels): #completed, expensive, should be compiled\\n return -np.sum(np.log2(np.clip(output, a_min=1E-12, a_max=1.0))[np.arange(flat_labels.shape[0]), flat_labels[:,1]])\",\n \"def log_likelihood(self):\\r\\n if self.likelihood.is_heteroscedastic:\\r\\n A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)\\r\\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self._A))\\r\\n else:\\r\\n A = -0.5 * self.num_data * self.output_dim * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT\\r\\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self._A))\\r\\n C = -self.output_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * np.log(sf2))\\r\\n D = 0.5 * self.data_fit\\r\\n self._A_part, self._B_part, self._C_part, self._D_part = A, B, C, D\\r\\n return A + B + C + D + self.likelihood.Z\",\n \"def log_prob(self, z, x):\\n n_samples = z.shape[0].value\\n log_prob = 0\\n for i in range(self.time_steps):\\n lat_dim = self.transition.dim_x\\n obs_dim = self.emmision.dim_x\\n # Current time step latent state and observation\\n z_t = tf.slice(z, [0, i * lat_dim], [-1, lat_dim])\\n x_t = tf.slice(x, [0, i * obs_dim], [-1, obs_dim])\\n if i == 0:\\n log_prob += tf.reduce_sum(\\n self.init_state_p.log_prob(z_t), axis=1)\\n else:\\n log_prob += self.transition.log_prob(z_t, z_tminus)\\n log_prob += self.emmision.log_prob(x_t, z_t)\\n # Update the previous latent state for next iteration\\n z_tminus = z_t\\n return log_prob\",\n \"def log_p(self,z): \\n return np.array([self.log_p_blanket(i) for i in z])\",\n \"def log_p(self,z):\\n return mb_log_p_posterior(z, self.neg_posterior, self.mini_batch)\",\n \"def __log_likelihood(self, params, *args):\\n\\t\\tX, y, feature_set, lambda_reg, empirical_weights, verbose, sign = args\\n\\n\\t\\tno_example = len(X)\\n\\t\\ttotal_logZ = 0\\n\\t\\ttotal_logProb = 0\\n\\t\\texpected_weights = np.zeros(len(feature_set))\\n\\t\\tfor t in range(len(X)):\\n\\t\\t\\t# example_features = X[t], example_labels = y[t]\\n\\n\\t\\t\\tpotential = np.zeros(len(X[t]))\\n\\t\\t\\tfor i in range(len(X[t])):\\n\\t\\t\\t\\t#candidate_features = X[t][i], candidate_label = y[t][i]\\n\\t\\t\\t\\tpotential[i] = feature_set.calc_inner_product(X[t][i], params)\\n\\n\\t\\t\\t#scaling\\n\\t\\t\\tpotential = potential - np.max(potential, keepdims=True)\\n\\n\\t\\t\\tfor i in range(len(X[t])):\\n\\t\\t\\t\\ttotal_logProb += potential[i] * y[t][i]\\n\\n\\t\\t\\tpotential, Z = self.__softmax(potential)\\n\\n\\t\\t\\tfor i in range(len(X[t])):\\n\\t\\t\\t\\tfeature_set.calc_inner_sum(expected_weights, X[t][i], potential[i])\\n\\n\\t\\t\\ttotal_logZ += log(Z)\\n\\n\\t\\t# _params = feature_set.get_regularized_params(params, 'bias')\\n\\t\\t_params = params\\n\\t\\tlog_likelihood = total_logProb - total_logZ - (lambda_reg/2) * np.sum(np.multiply(_params,_params))\\n\\t\\tgradients = empirical_weights - expected_weights - lambda_reg * _params\\n\\n\\t\\tglobal SUB_ITERATION_NUM\\n\\t\\tif verbose:\\n\\t\\t\\tsub_iteration_str = ' '\\n\\t\\t\\tif SUB_ITERATION_NUM > 0:\\n\\t\\t\\t\\tsub_iteration_str = '(' + '{0:02d}'.format(SUB_ITERATION_NUM) + ')'\\n\\t\\t\\tprint(' ', '{0:03d}'.format(ITERATION_NUM), sub_iteration_str, ':', log_likelihood * sign)\\n\\n\\t\\tSUB_ITERATION_NUM += 1\\n\\n\\t\\treturn sign * log_likelihood, sign * gradients\",\n \"def compute_log_prob(self,params: ndarray) -> float:\\n return self.compute_log_prior(params) + self.compute_log_likelihood(params)\",\n \"def MvNormalLogp():\\n cov = pt.matrix(\\\"cov\\\")\\n cov.tag.test_value = floatX(np.eye(3))\\n delta = pt.matrix(\\\"delta\\\")\\n delta.tag.test_value = floatX(np.zeros((2, 3)))\\n\\n cholesky = Cholesky(lower=True, on_error=\\\"nan\\\")\\n\\n n, k = delta.shape\\n n, k = f(n), f(k)\\n chol_cov = cholesky(cov)\\n diag = pt.diag(chol_cov)\\n ok = pt.all(diag > 0)\\n\\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\\n delta_trans = solve_lower(chol_cov, delta.T).T\\n\\n result = n * k * pt.log(f(2) * np.pi)\\n result += f(2) * n * pt.sum(pt.log(diag))\\n result += (delta_trans ** f(2)).sum()\\n result = f(-0.5) * result\\n logp = pt.switch(ok, result, -np.inf)\\n\\n def dlogp(inputs, gradients):\\n (g_logp,) = gradients\\n cov, delta = inputs\\n\\n g_logp.tag.test_value = floatX(1.0)\\n n, k = delta.shape\\n\\n chol_cov = cholesky(cov)\\n diag = pt.diag(chol_cov)\\n ok = pt.all(diag > 0)\\n\\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\\n delta_trans = solve_lower(chol_cov, delta.T).T\\n\\n inner = n * pt.eye(k) - pt.dot(delta_trans.T, delta_trans)\\n g_cov = solve_upper(chol_cov.T, inner)\\n g_cov = solve_upper(chol_cov.T, g_cov.T)\\n\\n tau_delta = solve_upper(chol_cov.T, delta_trans.T)\\n g_delta = tau_delta.T\\n\\n g_cov = pt.switch(ok, g_cov, -np.nan)\\n g_delta = pt.switch(ok, g_delta, -np.nan)\\n\\n return [-0.5 * g_cov * g_logp, -g_delta * g_logp]\\n\\n return OpFromGraph([cov, delta], [logp], grad_overrides=dlogp, inline=True)\",\n \"def log_p(self,z):\\n return log_p_posterior(z, self.neg_posterior)\",\n \"def compute_log_prior(self,params: ndarray) -> float:\\n ln_tE = params[0]\\n ln_A0 = params[1]\\n ln_deltaT = params[2]\\n fbl = params[3]\\n mb = params[4]\\n\\n # Equation (16,15,17) (note that Albrow uses \\\"log\\\" for log10)\\n log10e = np.log10(np.exp(1))\\n ln_pr_ln_tE = np.log(0.476) - ((log10e*ln_tE - 1.333)**2 / 0.330) + np.log(log10e)\\n ln_pr_ln_A0 = np.log(0.660) - (1.289*log10e*ln_A0) + np.log(log10e)\\n ln_pr_ln_deltaT = np.log(0.156) - ((log10e*ln_deltaT - 1.432)**2 / 0.458) +\\\\\\n np.log(log10e)\\n \\n # Paper doesnt mention the prior used, but I assume it to be uniform\\n ln_pr_fbl = uniform.logpdf(fbl,0.0,1.0)\\n\\n # Paper doesnr mention the prior used but I will asuumed it to be uniform\\n ln_pr_mb = uniform.logpdf(mb,self.mag_min - 1.0, self.mag_max + 1.0)\\n \\n \\n return ln_pr_fbl + ln_pr_ln_A0 + ln_pr_ln_deltaT + ln_pr_ln_tE + ln_pr_mb\",\n \"def _estimate_log_prob(self, x):\\n x = self.check_size(x)\\n\\n if self.covariance_type == \\\"full\\\":\\n mu = self.mu\\n var = self.var\\n precision = tf.linalg.inv(var)\\n d = x.shape[-1]\\n\\n log_2pi = d * tf.math.log(2. * pi)\\n log_2pi = tf.cast(log_2pi, tf.double)\\n log_det = self._calculate_log_det(precision)\\n log_det = tf.cast(log_det, tf.double)\\n\\n\\n x = tf.cast(x, tf.double)\\n mu = tf.cast(mu, tf.double)\\n x_mu_T = tf.expand_dims((x - mu), -2)\\n x_mu = tf.expand_dims((x - mu), -1)\\n\\n x_mu_T_precision = calculate_matmul_n_times(self.n_components, x_mu_T, precision)\\n # print(x_mu_T_precision, x_mu_T, x, mu)\\n x_mu_T_precision_x_mu = calculate_matmul(x_mu_T_precision, x_mu)\\n\\n return -.5 * (log_2pi - log_det + x_mu_T_precision_x_mu)\\n\\n elif self.covariance_type == \\\"diag\\\":\\n mu = self.mu\\n prec = tf.math.rsqrt(self.var)\\n\\n log_p = tf.math.reduce_sum((mu * mu + x * x - 2 * x * mu) * (prec ** 2), axis=2, keepdims=True)\\n log_det = tf.math.reduce_sum(tf.math.log(prec), axis=2, keepdims=True)\\n\\n return -.5 * (self.n_features * tf.math.log(2. * pi) + log_p) + log_det\",\n \"def compute_loglike(self, tools: ModelingTools) -> float:\\n return -1.5\",\n \"def compute_edge_logits(self):\",\n \"def calc_probs(log_p):\\n\\n N = log_p.shape[0]\\n\\n log_Z_per_N = np.zeros(shape=(N, 1))\\n\\n for i in range(N):\\n\\n log_Z_per_N[i] = log_norm(log_p[i])\\n\\n log_p_new = log_p - log_Z_per_N\\n\\n p = np.exp(log_p_new)\\n\\n # log_Z = log_norm(log_p)\\n\\n # p = np.exp(log_p - log_Z)\\n\\n return p\",\n \"def log_marg(self):\\n log_prob_X_given_z = 0.\\n for k in range(self.K):\\n log_prob_X_given_z += self.log_marg_k(k)\\n return log_prob_X_given_z\",\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 statePosteriors(log_alpha, log_beta):\\n return log_alpha + log_beta - logsumexp(log_alpha[-1,:])\",\n \"def _logprob(self, sample):\\n cur_y, cur_z = sample\\n log_prior = 0\\n log_lik = 0\\n if cur_z.shape[1] == 0: return -99999999.9\\n \\n if self.cl_mode:\\n a_time = time()\\n d_cur_z = cl.Buffer(self.ctx, self.mf.READ_ONLY | self.mf.COPY_HOST_PTR, hostbuf = cur_z.astype(np.int32))\\n d_cur_y = cl.Buffer(self.ctx, self.mf.READ_ONLY | self.mf.COPY_HOST_PTR, hostbuf = cur_y.astype(np.int32))\\n d_logprob = cl.array.empty(self.queue, (cur_z.shape[0],), np.float32, allocator=self.mem_pool)\\n\\n if cur_z.shape[0] % self.p_mul_logprob_z_data == 0: wg = (self.p_mul_logprob_z_data,)\\n else: wg = None\\n \\n self.prg.logprob_z_data(self.queue, (cur_z.shape[0],), wg,\\n d_cur_z, d_cur_y, self.d_obs, d_logprob.data, #d_novel_f.data,\\n np.int32(self.N), np.int32(cur_y.shape[1]), np.int32(cur_z.shape[1]), \\n np.float32(self.alpha), np.float32(self.lam), np.float32(self.epislon))\\n log_lik = d_logprob.get().sum()\\n self.gpu_time += time() - a_time\\n\\n # calculate the prior probability of Y\\n num_on = (cur_y == 1).sum()\\n num_off = (cur_y == 0).sum()\\n log_prior = num_on * np.log(self.theta) + num_off * np.log(1 - self.theta)\\n\\n else:\\n # calculate the prior probability of Z\\n for n in xrange(cur_z.shape[0]):\\n num_novel = 0\\n for k in xrange(cur_z.shape[1]):\\n m = cur_z[:n,k].sum()\\n if m > 0:\\n if cur_z[n,k] == 1: log_prior += np.log(m / (n+1.0))\\n else: log_prior += np.log(1 - m / (n + 1.0))\\n else: \\n if cur_z[n,k] == 1: num_novel += 1\\n if num_novel > 0:\\n log_prior += poisson.logpmf(num_novel, self.alpha / (n+1.0))\\n # calculate the prior probability of Y\\n num_on = (cur_y == 1).sum()\\n num_off = (cur_y == 0).sum()\\n log_prior += num_on * np.log(self.theta) + num_off * np.log(1 - self.theta)\\n # calculate the logliklihood\\n log_lik = self._loglik(cur_y = cur_y, cur_z = cur_z)\\n return log_prior + log_lik\",\n \"def logprob(self):\\n assert len(self._added_rows) == self._num_rows\\n V, E, K, M = self._VEKM\\n vertex_logits = logprob_dc(self._vert_ss, self._vert_prior, axis=1)\\n logprob = vertex_logits.sum()\\n for e, v1, v2 in self._tree.tree_grid.T:\\n logprob += (logprob_dc(self._edge_ss[e, :, :], self._edge_prior) -\\n vertex_logits[v1] - vertex_logits[v2])\\n for v in range(V):\\n beg, end = self._table.ragged_index[v:v + 2]\\n logprob += logprob_dc(self._feat_ss[beg:end, :], self._feat_prior)\\n logprob -= logprob_dc(self._meas_ss[v, :], self._meas_prior[v])\\n return logprob\",\n \"def log_prior_grad(self, inputs):\",\n \"def log_prob(self, x):\\n z, log_det = self.backward_p(x)\\n return self.prior.log_prob(z) + log_det\",\n \"def forward(log_emlik, log_startprob, log_transmat):\\n logPi=log_startprob[:-1]\\n logB=log_emlik\\n logA=log_transmat[:-1,:-1]\\n alpha = np.zeros_like(logB)\\n alpha[0]=logB[0]+logPi\\n for i in range(1,logB.shape[0]):\\n for j in range(logA.shape[0]):\\n alpha[i][j]=logsumexp(alpha[i-1]+logA[:,j]+logB[i][j])\\n return alpha\",\n \"def logbarrierfunc(delta, z, use_sigma):\\n if use_sigma:\\n z = np.where(z >= 0, np.tanh(z), z)\\n k = 2\\n return np.where(z > delta, -np.log(np.abs(z)),\\n ((k - 1) / k) * (((z - k * delta) / ((k - 1) * delta)) ** k - 1) - np.log(delta))\",\n \"def _perplexity(self, X, log_w):\\n return np.exp(-log_w/X.sum())\",\n \"def logP(self):\\n raise NotImplementedError\",\n \"def _log_fold_change_pairs(self, idx0, idx1, base):\\n assert np.all([x < self._pval.shape[1] for x in idx0])\\n assert np.all([x < self._pval.shape[1] for x in idx1])\\n if base == np.e:\\n return self._logfc[idx0, :, :][:, idx1, :]\\n else:\\n return self._logfc[idx0, :, :][:, idx1, :] / np.log(base)\",\n \"def compute_loglike(self, tools: ModelingTools) -> float:\\n return -3.0 * self.placeholder\",\n \"def log_pseudo_joint(self, data: Tensor, states: Tensor) -> Tensor: # type: ignore\\n K = states\\n Y = data\\n assert K.dtype == to.uint8 and Y.dtype == to.uint8\\n pi = self.theta[\\\"pies\\\"]\\n W = self.theta[\\\"W\\\"]\\n batch_size, S, H = K.shape\\n D = W.shape[0]\\n dev = pi.device\\n\\n logPriors = to.matmul(K.type_as(pi), to.log(pi / (1 - pi)))\\n\\n logPy = to.empty((batch_size, S), device=dev, dtype=self.precision)\\n # We will manually set the lpjs of all-zero states to the appropriate value.\\n # For now, transform all-zero states in all-one states, to avoid computation of log(0).\\n zeroStatesInd = to.nonzero((K == 0).all(dim=2))\\n # https://discuss.pytorch.org/t/use-torch-nonzero-as-index/33218\\n zeroStatesInd = (zeroStatesInd[:, 0], zeroStatesInd[:, 1])\\n K[zeroStatesInd] = 1\\n # prods_nsd = prod{h}{1-W_dh*K_nkh}\\n prods = (W * K.type_as(W).unsqueeze(2)).neg_().add_(1).prod(dim=-1)\\n to.clamp(prods, self.eps, 1 - self.eps, out=prods)\\n # logPy_nk = sum{d}{y_nd*log(1/prods_nkd - 1) + log(prods_nkd)}\\n f1 = to.log(1.0 / prods - 1.0)\\n indeces = 1 - Y[:, None, :].expand(batch_size, S, D)\\n # convert to BoolTensor in pytorch>=1.2, leave it as ByteTensor in earlier versions\\n indeces = indeces.type_as(to.empty(0) < 0)\\n f1[indeces] = 0.0\\n logPy[:, :] = to.sum(f1, dim=-1) + to.sum(to.log(prods), dim=2)\\n K[zeroStatesInd] = 0\\n\\n lpj = logPriors + logPy\\n # for all-zero states, set lpj to arbitrary very low value if y!=0, 0 otherwise\\n # in the end we want exp(lpj(y,s=0)) = 1 if y=0, 0 otherwise\\n lpj[zeroStatesInd] = -1e30 * data[zeroStatesInd[0]].any(dim=1).type_as(lpj)\\n assert (\\n not to.isnan(lpj).any() and not to.isinf(lpj).any()\\n ), \\\"some NoisyOR lpj values are invalid!\\\"\\n return lpj.to(device=states.device) # (N, S)\",\n \"def log_sum_exp(Z):\\n return np.max(Z) + np.log(np.sum(np.exp(Z - np.max(Z))))\",\n \"def log_prob(self):\\n res = -self.L_h/2*np.log(2*np.pi*self.la)\\n res = res + self.L_h*(self.L_h-1)/2*self.a\\n\\n\\n res = res - 1/(2*self.la)*np.square(np.linalg.norm(self.e*self.pie))\\n\\n res = res - 1/(2*self.la)*np.sum(self.e2*self.pie_var)\\n\\n res = res - self.L_h/2*np.log(2*np.pi*self.sigma2)\\n res = res - 1/(2*self.sigma2)*(np.square(np.linalg.norm(self.w))+np.trace(self.R))\\n\\n print(\\\"Log-probability difference = {}\\\".format(res - self.LP), file=self.logfile)\\n self.LP = res\\n return res\",\n \"def LikelihoodFromTrace (trace, A, B, prec=1e-14):\\n\\n if A.shape[0]==1:\\n # uniformization based solution\\n tr = np.sort(trace)\\n lambd = np.max(np.abs(np.diag(B)))\\n loglambda = math.log(lambd)\\n P = B/lambd + ml.eye(B.shape[0])\\n a = np.sum(-B,1)\\n eps = max(prec, 10**(math.log(prec)/math.log(10.0) + math.log(lambd)/math.log(10.0)))\\n lpoi = -lambd*tr\\n logtr = np.log(tr)\\n poi = np.exp(lpoi)\\n spoi = np.array(poi)\\n fx = poi*(A*a)[0,0]\\n k = 1\\n first = 0\\n coeffv = ml.matrix(A)\\n maxIter = 10000\\n while k<maxIter:\\n coeffv = coeffv * P\\n lpoi[first:] += loglambda + logtr[first:] - math.log(k)\\n poi[first:] = np.exp(lpoi[first:])\\n spoi[first:] += poi[first:]\\n fx[first:] += poi[first:] * (coeffv*a)[0,0]\\n k += 1\\n nfirst = (spoi[first:]<1-eps).nonzero()[0]\\n if len(nfirst)==0:\\n break\\n first += nfirst[0]\\n return np.sum(np.log(fx))/len(logtr)\\n else:\\n D0 = A\\n D1 = B\\n N = D0.shape[0]\\n L = len(trace)\\n \\n # first we calculate matrix e^(D0*x(i))*D1 for each sample\\n ix = np.argsort(trace)\\n tr = trace[ix]\\n lambd = np.max(np.abs(np.diag(D0)))\\n loglambda = math.log(lambd)\\n P = D0/lambd + ml.eye(N)\\n eps = max(prec, 10**(math.log(prec)/math.log(10.0) + math.log(lambd)/math.log(10.0)))\\n lpoi = -lambd*tr;\\n logtr = np.log(tr)\\n poi = np.exp(lpoi)\\n spoi = np.array(poi)\\n coeffv = ml.matrix(D1)\\n fx = np.kron(poi,coeffv)\\n k = 1\\n first = 0\\n maxIter = 10000\\n while k<maxIter:\\n coeffv = P * coeffv\\n lpoi[first:] += loglambda + logtr[first:] - math.log(k)\\n poi[first:] = np.exp(lpoi[first:])\\n spoi[first:] += poi[first:] \\n fx[:,first*N:] += np.kron(poi[first:],coeffv)\\n k += 1 \\n nfirst = (spoi[first:]<1-eps).nonzero()[0]\\n if len(nfirst)==0:\\n break\\n first += nfirst[0]\\n alpha = DTMCSolve ((-D0).I*D1)\\n l = np.array(alpha)\\n sc = 0\\n ixrev = np.argsort(ix)\\n for i in range(L):\\n l = l.dot(fx[:,ixrev[i]*N:(ixrev[i]+1)*N])\\n if i % 10 ==0:\\n # sometimes we need to rescale the results to avoid \\\"nan\\\"s\\n scale = math.ceil(math.log2(np.sum(l)))\\n if scale>1:\\n l /= 2**scale\\n sc += scale\\n if scale<-10:\\n scale += 10\\n l /= 2**scale\\n sc += scale\\n return (math.log(np.sum(l))+sc*math.log(2)) / len(logtr)\",\n \"def backward(log_emlik, log_startprob, log_transmat):\\n #print(log_transmat)\\n beta = np.zeros(log_emlik.shape)\\n n = len(log_emlik)-2\\n log_transmat = log_transmat[0:-1,0:-1];\\n\\n\\n\\n while n >= 0:\\n\\n for j in range(0,len(log_emlik[0])):\\n\\n beta[n,j] = logsumexp(log_transmat[j,:] + log_emlik[n+1,:] + beta[n+1,:])\\n\\n n = n -1\\n #print(beta[n,:])\\n #print(beta)\\n return beta\",\n \"def logistic_function(z):\\n\\tg = 1/(1+np.exp(-z))\\n\\t#g = np.exp(z)/(1+np.exp(z))\\n\\treturn g\",\n \"def backward(log_emlik, log_startprob, log_transmat):\\n N, M = log_emlik.shape\\n backward_prob = np.zeros((N,M))\\n\\n backward_prob[N-1, :] = 0.0\\n\\n for i in range(N-2,-1,-1):\\n for k in range(M):\\n # probability of transitioning from k to state l * probability of emitting symbol at state l at ts i+1 * recursive backward probability\\n backward_prob[i,k] = logsumexp(log_transmat[k,:] + log_emlik[i+1,:] + backward_prob[i+1,:])\\n\\n return backward_prob\",\n \"def viterbi(obs: List[int], pi: List[float], A: np.ndarray, B: np.ndarray) -> List[int]:\\n pi_log = np.log(pi)\\n A_log = np.log(A)\\n B_log = np.log(B)\\n states = A.shape[0]\\n n = len(obs)\\n\\n D_log = np.zeros((states, n))\\n backtrack = np.zeros((states, n - 1)).astype(int)\\n D_log[:, 0] = pi_log + B_log[:, obs[0]]\\n\\n for j in range(1, n):\\n for i in range(states):\\n temp_sum = A_log[:, i] + D_log[:, j - 1]\\n D_log[i, j] = np.max(temp_sum) + B_log[i, obs[j]]\\n backtrack[i, j - 1] = np.argmax(temp_sum)\\n\\n state = np.zeros(n).astype(int)\\n state[-1] = np.argmax(D_log[:, -1])\\n for n in range(n - 2, -1, -1):\\n state[n] = backtrack[int(state[n + 1]), n]\\n state = state.tolist()\\n return state\",\n \"def _call(self, z): \\n self.a = F.softplus(self.a)\\n self.w = F.softmax(self.w, dim=1)\\n # Compute\\n pre_sigm = self.a * z + self.b\\n sigm = torch.sigmoid(pre_sigm)\\n x_pre = self.w * sigm\\n if (len(z.shape) > 2):\\n x_pre = torch.sum(self.w * sigm, dim=1)\\n x_pre_clipped = x_pre * (1 - self.eps) + self.eps * 0.5\\n zp = torch.log(x_pre_clipped) - torch.log(1 - x_pre_clipped)\\n return zp\",\n \"def baum_welch(self, O):\\n alpha, beta, gamma, B, _Z_ = self.forward_backward(O)\\n #\\n \\\"\\\"\\\"\\n gamma_tr = numpy.zeros([len(O) - 1], len(self), len(self)])\\n for t in range(gamma_tr.shape[0]):\\n for i in range(gamma_tr.shape[1]):\\n for j in range(gamma_tr.shape[1]):\\n gamma_tr[t, i, j] = alpha[t, i] + self.A.get_log_prob(i, j) + B[j, t + 1] + beta[t + 1,j] - gamma[t, i]\\n #\\n for i in range(gamma_tr.shape[1]):\\n for j in range(gamma_tr.shape[1]):\\n _weight_ = HMM.log_add(gamma_tr[:, i, j])\\n temp_hmm.A.accumulate_transition(i, j, numpy.exp(_weight_)) # This line is candidate to be modified for accumulating logarithms\\n #\\n \\\"\\\"\\\"\\n\\n # UPDATE OF THE STATE-TRANSITION PROBABILITIES\\n if len(O) > 1:\\n for i in range(len(self)):\\n _log_den_ = HMM.log_add(gamma[ : -1, i]) # sum(t = 1..T-1, gamma[t, i])\\n for j in range(len(self)):\\n gamma_tr = numpy.zeros(len(O) - 1)\\n for t in range(gamma_tr.shape[0]):\\n gamma_tr[t] = alpha[t, i] + self.A.get_log_prob(i, j) + B[j, t + 1] + beta[t + 1, j] - _Z_\\n _weight_ = numpy.exp(HMM.log_add(gamma_tr[:]) - _log_den_)\\n self.A.accumulate_transition(i, j, value = _weight_) # This line is candidate to be modified for accumulating logarithms\\n #\\n # UDPDATE OF THE STATE STARTING PROBABILITIES\\n if self.P_accumulator is not None:\\n self.P_accumulator[:] += gamma[0, :]\\n\\n # UDPDATE OF THE OUTPUT PROBABILITIES\\n if self.modality in ['Discrete']:\\n #\\n for i in range(gamma.shape[1]):\\n #\\n _log_den_ = HMM.log_add(gamma[:, i]) # sum(t = 1..T, gamma[t, i])\\n _den_ = numpy.exp(_log_den_)\\n #\\n for k in numpy.unique(O): # range(self.num_symbols)\\n _log_num_ = HMM.log_add(gamma[O == k, i])\\n _weight_ = numpy.exp(_log_num_ - _log_den_)\\n self.S[i].accumulate_sample(k, _weight_, numpy.exp(_log_num_), _den_) # This line is candidate to be modified for accumulating logarithms\\n #\\n elif self.modality in ['Continuous']:\\n #\\n for j in range(len(self)):\\n #\\n _log_denominator_ = HMM.log_add(gamma[:, j]) # sum(t = 1..T, gamma[t, i])\\n _denominator_ = numpy.exp(_log_denominator_)\\n #\\n _log_densities_ = numpy.zeros([len(O), self.S[j].gmm.n_components])\\n for t in range(len(O)):\\n _log_densities_[t, :] = self.S[j].gmm.log_densities(O[t]) # log(c_j_k * g_j_k(O_t))\\n #\\n log_xi = numpy.zeros(len(O)) # A one-dimensional vector for computing _xi_t_j_k_ for fixed 'j' and 'k'\\n for k in range(_log_densities_.shape[1]):\\n log_xi[0] = self.log_P[j] + _log_densities_[0, k] + beta[0, j] # _xi_0_j_k_\\n #\\n for t in range(1, len(O)):\\n _temp_ = numpy.zeros(len(self))\\n for i in range(len(self)): # For all the states in the HMM\\n _temp_[i] = alpha[t - 1, i] + self.A.get_log_prob(i, j) + _log_densities_[t, k] + beta[t, j]\\n log_xi[t] = HMM.log_add(_temp_) # _xi_t_j_k_ for all t > 0\\n #\\n log_xi -= _Z_ # Dividing by P(O|lambda)\\n #\\n _xi_t_j_k_ = numpy.exp(log_xi)\\n #\\n # In the following lines the code of Baum-Welch directly modifies the accumulators\\n # of the GMM of each state 'j'\\n #\\n self.S[j].gmm_accumulator.acc_posteriors[k] += _xi_t_j_k_.sum() # This value is correct because is used as the denominator for updating mean vectors and covariance matrices\\n self.S[j].gmm_accumulator.acc_sample_counter[k] += _denominator_ / self.S[j].gmm_accumulator.n_components\\n #\\n for t in range(len(O)):\\n self.S[j].gmm_accumulator.mu[k] += _xi_t_j_k_[t] * O[t]\\n if self.S[j].gmm_accumulator.covar_type in GMM.covar_diagonal_types:\\n self.S[j].gmm_accumulator.sigma[k] += _xi_t_j_k_[t] * (O[t] * O[t]) # numpy.diagonal(O[t] * O[t])\\n else:\\n self.S[j].gmm_accumulator.sigma[k] += _xi_t_j_k_[t] * numpy.outer(O[t], O[t])\\n else:\\n raise Exception('Modality ' + self.modality + ' is not valid or not implemented yet!')\",\n \"def log_likelihood_z_lognormal(self, std=1.0):\\n #return self.log_det_Jxz - self.dim * tf.log(std) - (0.5 / (std**2)) * tf.reduce_sum(self.output_z**2, axis=1)\\n from deep_boltzmann.util import logreg\\n logz = logreg(self.output_z, a=0.001, tf=True)\\n ll = self.log_det_Jxz \\\\\\n - (0.5 / (std**2)) * tf.reduce_sum(logz**2, axis=1) \\\\\\n - tf.reduce_sum(logz, axis=1)\\n return ll\",\n \"def log_Distribution_volume(self, z):\\n if type(z)==np.ndarray:\\n ind = z<0\\n pv = np.log(self.Distribution_volume(z))\\n pv[ind] = -1e99\\n return pv\\n else:\\n if z<0:\\n return -1e99\\n else:\\n return np.log(self.Distribution_volume(z))\",\n \"def log_likelihood(self):\\r\\n A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.beta_star)) - 0.5 * np.sum(self.V_star * self.likelihood.Y)\\r\\n C = -self.output_dim * (np.sum(np.log(np.diag(self.LB))))\\r\\n D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))\\r\\n return A + C + D + self.likelihood.Z\",\n \"def logp(X, nu, V):\\n\\n p = V.shape[0]\\n\\n IVI = det(V)\\n IXI = det(X)\\n\\n return check_parameters(\\n (\\n (nu - p - 1) * pt.log(IXI)\\n - trace(matrix_inverse(V).dot(X))\\n - nu * p * pt.log(2)\\n - nu * pt.log(IVI)\\n - 2 * multigammaln(nu / 2.0, p)\\n )\\n / 2,\\n matrix_pos_def(X),\\n pt.eq(X, X.T),\\n nu > (p - 1),\\n )\",\n \"def logZ_brute(self, T, pos, psi, phi):\\n ms = self.get_all_tag_seq(len(T))\\n log_scores = tr.zeros(len(ms), dtype=tr.float64)\\n for i in range(len(ms)):\\n log_scores[i] = self.log_score(T, pos, ms[i], psi, phi)\\n log_z = logsumexp(log_scores)\\n return log_z\",\n \"def backward(log_emlik, log_startprob, log_transmat):\\n N, M = log_emlik.shape\\n logPi=log_startprob[:-1]\\n logB=log_emlik\\n logA=log_transmat[:-1,:-1]\\n beta = np.zeros_like(logB)\\n for t in range(N-2,-1,-1):\\n for j in range(M):\\n beta[t][j]=logsumexp(beta[t+1]+logA[j]+logB[t+1])\\n\\n return beta\",\n \"def _log_fold_change_pairs(self, idx0, idx1, base):\\n logfc = np.tile(np.NaN, [len(idx0), len(idx1), self.model_estim.x.shape[1]])\\n for i, xi in enumerate(idx0):\\n for j, xj in enumerate(idx1):\\n logfc[i, j, :] = self._theta_mle[xj, :] - self._theta_mle[xi, :]\\n logfc[j, i, :] = -logfc[i, j, :]\\n\\n if base == np.e:\\n return logfc\\n else:\\n return logfc / np.log(base)\",\n \"def testLogPartitionInfinityIsAccurate(self):\\n alpha = float('inf')\\n log_z_true = 0.70526025442 # From mathematica.\\n log_z = self.variant(self._distribution.log_base_partition_function)(alpha)\\n chex.assert_tree_all_close(log_z, log_z_true, atol=1e-7, rtol=1e-7)\",\n \"def log_abs_det_jacobian(self, z):\\n self.a = F.softplus(self.a)\\n self.w = F.softmax(self.w, dim=1)\\n pre_sigm = self.a * z + self.b\\n sigm = torch.sigmoid(pre_sigm)\\n x_pre = self.w * sigm\\n if (len(z.shape) > 2):\\n x_pre = torch.sum(self.w * sigm, dim=1)\\n x_pre_clipped = x_pre * (1 - self.eps) + self.eps * 0.5\\n logj = F.log_softmax(self.w, dim=1) + logsigmoid(pre_sigm) + logsigmoid(-pre_sigm) + torch.log(self.a)\\n logj = torch.log(torch.sum(torch.exp(logj)))#,2).sum(2)\\n logdet = logj + np.log(1 - self.eps) - (torch.log(x_pre_clipped) + torch.log(-x_pre_clipped + 1))\\n return sum_dims(logdet)\",\n \"def partition_function(array, temp):\\r\\n\\r\\n # Constants imported from scipy.constants\\r\\n h = scipy.constants.h # Planck's constant\\r\\n # speed of light must be in cm/s as wavenumber is in cm-1\\r\\n c = scipy.constants.c * 100\\r\\n k = scipy.constants.k # Boltzmann constant\\r\\n T = temp # extracted from log file using extract_temp()\\r\\n\\r\\n # check if inputs are numpy arrays and convert if not.\\r\\n if not isinstance(array, np.ndarray):\\r\\n np.asarray(array)\\r\\n\\r\\n # conversion to exponent\\r\\n u = (h * array * c) / (k * T)\\r\\n\\r\\n # calculates natural log of an individual frequency contribution to the partition function\\r\\n Q_ = np.log(np.exp(-(u / 2)) / (1 - np.exp(-u)))\\r\\n # sums all the contributions together, giving the final result.\\r\\n Q = np.sum(Q_)\\r\\n return Q\",\n \"def log_prob_easier(self, x):\\n normalization_const = -0.5 * tf.math.log(2 * np.pi) - tf.math.log(self.stddev)\\n sq_term = - 0.5 * ((x - self.mean) / self.stddev) ** 2\\n l_prob = tf.math.reduce_sum(normalization_const + sq_term, axis=1)\\n return l_prob\",\n \"def log_abs_det_jacobian(self, z):\\n pre_u = self.u_ + self.u\\n pre_w = self.w_ + self.w\\n a = F.softplus(self.a + self.inv)\\n w = F.softmax(pre_w, dim=3)\\n u = F.softmax(pre_u, dim=3)\\n # Perform computation\\n pre_sigm = torch.sum(u * a * z, 3) + self.b\\n sigm = torch.sigmoid(pre_sigm)\\n x_pre = torch.sum(w * sigm, dim=3)\\n x_pre_clipped = x_pre * (1 - self.eps) + self.eps * 0.5\\n logj = F.log_softmax(pre_w, dim=3) + logsigmoid(pre_sigm) + logsigmoid(-pre_sigm) + torch.log(a)\\n # n, d, d2, dh\\n logj = logj + F.log_softmax(pre_u, dim=3)\\n # n, d, d2, dh, d1\\n logj = torch.log(torch.sum(torch.exp(logj),3))\\n # n, d, d2, d1\\n logdet_ = logj + np.log(1 - self.eps) - (torch.log(x_pre_clipped) + torch.log(-x_pre_clipped + 1))\\n return logdet_\",\n \"def compute_expected_log_prob(self):\\n for (w, t), val in np.ndenumerate(self.e_log_prob):\\n self.e_log_prob[w][t] = self.mean[w][t + 1] - np.log(self.zeta[t])\\n return self.e_log_prob\",\n \"def _log_prior_gradients(self):\\n x = self._get_params()\\n ret = np.zeros(x.size)\\n [np.put(ret,i,p.lnpdf_grad(xx)) for i,(p,xx) in enumerate(zip(self.priors,x)) if not p is None]\\n return ret\",\n \"def _log_fold_change_pairs(self, idx0, idx1, base):\\n logfc = np.zeros(shape=(len(idx0), len(idx1), self._theta_mle.shape[1]))\\n for i, xi in enumerate(idx0):\\n for j, xj in enumerate(idx1):\\n logfc[i, j, :] = self._theta_mle[xi, :] - self._theta_mle[xj, :]\\n\\n if base == np.e:\\n return logfc\\n else:\\n return logfc / np.log(base)\",\n \"def statePosteriors(log_alpha, log_beta):\",\n \"def vbe_step(self):\\n\\n digam_alpha = digamma(np.sum(self.alpha_k))\\n for k in range(self.k):\\n\\n # compute estimate over ln det(lamb)\\n tmp = sum(digamma((self.nu_k[k] + 1 - j) / 2) for j in range(self.dim))\\n\\n det = np.linalg.det(self.w_k[k])\\n self.log_lamb[k] = tmp + self.dim * np.log(2) + np.log(det)\\n\\n # compute estimate for ln pi\\n self.log_pi[k] = digamma(self.alpha_k[k]) - digam_alpha\\n\\n for n in range(self.n):\\n tmp = self.x[n] - self.m_k[k]\\n # compute estimate over mu and lambda\\n self.estimate[n, k] = self.dim * (1 / self.beta_k[k]) + self.nu_k[k] * (tmp.T @ self.w_k[k] @ tmp)\",\n \"def logsum_safe(prob_ll, atl):\\n logpdf = prob_ll + K.log(atl + K.epsilon())\\n alpha = tf.reduce_max(logpdf, -1, keepdims=True)\\n return alpha + tf.log(tf.reduce_sum(K.exp(logpdf-alpha), -1, keepdims=True) + K.epsilon())\",\n \"def logrels(rets):\\n return np.log(rets + 1)\",\n \"def compute_Bayes(BIC, BIC_wo, BIC_white):\\n from scipy.misc import logsumexp\\n lnprob = -0.5*BIC - np.logaddexp(-0.5*BIC, -0.5*BIC_wo)\\n # BIC of H1 - BIC H0\\n # larger value favours H1\\n logBayes = 0.5 * (-1.0*BIC + BIC_wo)\\n #lnprob = np.log(1./3.) - 0.5*BIC - logsumexp([BIC, BIC_wo, BIC_white])\\n #print(np.log(1./3.), - 0.5*BIC, - logsumexp([BIC, BIC_wo, BIC_white]))\\n logprob = logBayes - logsumexp([logBayes, 1.])\\n #print(\\\"2lnK: \\\", 2.0*logBayes)\\n lnprob_w = -0.5 * BIC - logsumexp([-0.5*BIC, -0.5*BIC_wo, -0.5*BIC_white])\\n lnprob_wo = -0.5 * BIC_wo - logsumexp([-0.5*BIC, -0.5*BIC_wo, -0.5*BIC_white])\\n lnprob_white = -0.5 * BIC_white - logsumexp([-0.5*BIC, -0.5*BIC_wo, -0.5*BIC_white])\\n #print(0.5 * (BIC_wo - BIC))\\n #prob = np.exp(-0.5*BIC) / (np.exp(-0.5*BIC) + np.exp(-0.5*BIC_wo))\\n return np.exp(lnprob_w), np.exp(lnprob_wo), np.exp(lnprob_white)\",\n \"def NMF(model, maxIter=100, beliefs=None, verbose=False):\\n if beliefs is None: beliefs = [Factor([Xi],1.0/Xi.states) for Xi in model.X]\\n \\n lnZ = sum([beliefs[Xi].entropy() for Xi in model.X])\\n for f in model.factors:\\n m = f.log()\\n for v in f.vars: m *= beliefs[v]\\n lnZ += m.sum()\\n if verbose: print(\\\"Iter 0: \\\"+str(lnZ))\\n\\n for t in xrange(1,maxIter+1): # for each iteration:\\n # Update all the beliefs via coordinate ascent:\\n for Xi in model.X: # for each variable, \\n bNew = 0.0 # compute E[ log f ] as a function of Xi:\\n for f in model.factorsWith(Xi,copy=False): # for each factor f_a, compute:\\n m = f.log() # E[log f_a] = \\\\sum \\\\log f_a \\\\prod b_v\\n for v in f.vars - [Xi]: m *= beliefs[v]\\n bNew += m.marginal([Xi]) # sum them up to get E[log f]\\n bNew -= bNew.max() # (numerical issues)\\n bNew = bNew.exp()\\n bNew /= bNew.sum() # set b(Xi) = exp( E[log f] ) / Z\\n beliefs[Xi] = bNew\\n #\\n # Compute the lower bound on the partition function:\\n # E_b [ log f ] + H(b) = \\\\sum_a E[log f_a] + \\\\sum_i H(b_i) for independent beliefs\\n lnZ = sum([beliefs[Xi].entropy() for Xi in model.X])\\n for f in model.factors:\\n m = f.log()\\n for v in f.vars: m *= beliefs[v]\\n lnZ += m.sum()\\n if verbose: print(\\\"Iter \\\"+str(t)+\\\": \\\"+str(lnZ))\\n return lnZ,beliefs\",\n \"def logdprior(parameters, hyperparameters):\\n sigma_w_part = parameters[0] + invgamma_logpdf(parameters[0],\\n hyperparameters[\\\"sigma_w_shape\\\"], hyperparameters[\\\"sigma_w_scale\\\"])\\n sigma_v_part = parameters[1] + invgamma_logpdf(parameters[1], hyperparameters[\\\"sigma_v_shape\\\"], hyperparameters[\\\"sigma_v_scale\\\"])\\n return sigma_w_part + sigma_v_part\",\n \"def kullbackLeibler(mu, log_sigma):\\n # (tf.Tensor, tf.Tensor) -> tf.Tensor\\n # = -0.5 * (1 + log(sigma**2) - mu**2 - sigma**2)\\n return -0.5 * tf.reduce_sum(1 + 2 * log_sigma - mu**2 - tf.exp(2 * log_sigma), 1)\",\n \"def logp(self, x):\\n pass\",\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 expectation(N,K,log_M):\\n\\tg0 = log_M[0,0]\\n\\tg = log_M[1:]\\n\\ta = forward(g0,g,N,K)\\n\\tb = backward(g,N,K)\\n\\tprint \\\"Forward:\\\"\\n\\tprint a\\n\\tprint \\\"Backward:\\\"\\n\\tprint b\\n\\t# log-normalizing constant\\n\\tlogZ = misc.logsumexp(a[N-1,:])\\n\\n\\tE = defaultdict(float)\\n\\n\\t# The first factor needs to be special case'd\\n\\t# E[ f( y_0 ) ] = p(y_0 | y_[1:N], x) * f(y_0)\\n\\tc = exp(g0 + b[0,:] - logZ).clip(0.0, 1.0)\\n\\tfor y in xrange(K):\\n\\t\\tp = c[y]\\n\\t\\tif p < 1e-40: continue # skip really small updates.\\n\\t\\tfor k in f[0, None, y]:\\n\\t\\t\\tE[k] += p\\n\\n\\tfor t in xrange(1,N):\\n\\t\\t# vectorized computation of the marginal for this transition factor\\n\\t\\tc = exp((add.outer(a[t-1,:], b[t,:]) + g[t-1,:,:] - logZ)).clip(0.0, 1.0)\\n\\n\\t\\tfor yp in xrange(K):\\n\\t\\t\\tfor y in xrange(K):\\n\\t\\t\\t\\t# we can also use the following to compute ``p`` but its quite\\n\\t\\t\\t\\t# a bit slower than the computation of vectorized quantity ``c``.\\n\\t\\t\\t\\t#p = exp(a[t-1,yp] + g[t-1,yp,y] + b[t,y] - logZ).clip(0.0, 1.0)\\n\\t\\t\\t\\tp = c[yp, y]\\n\\t\\t\\t\\tif p < 1e-40: continue # skip really small updates.\\n\\t\\t\\t\\t# expectation of this factor is p*f(t, yp, y)\\n\\t\\t\\t\\tfor k in f[t, yp, y]:\\n\\t\\t\\t\\t\\tE[k] += p\\n\\n\\treturn E\",\n \"def _loglike(self, y, f):\\n ll = -0.5 * (tf.log(2 * self.variance * np.pi) +\\n (y - f)**2 / self.variance)\\n return ll\",\n \"def log_post_pred(self, i):\\n k_Ns = self.prior.k_0 + self.counts[:self.K]\\n v_Ns = self.prior.v_0 + self.counts[:self.K]\\n m_Ns = self.m_N_numerators[:self.K]/k_Ns[:, np.newaxis]\\n\\n studentt_gammas = self._cached_gammaln_by_2[v_Ns + 1] - self._cached_gammaln_by_2[v_Ns]\\n\\n deltas = m_Ns - self.X[i]\\n\\n return (\\n self.D * (\\n studentt_gammas\\n - 0.5*self._cached_log_v[v_Ns] - 0.5*self._cached_log_pi\\n )\\n - 0.5*self.log_prod_vars[:self.K]\\n - (v_Ns + 1)/2. * sum_axis1(np.log(\\n 1 + np.square(deltas)*self.inv_vars[:self.K]*(1./v_Ns[:, np.newaxis])\\n ))\\n )\",\n \"def calculate_negative_log_likelihood(self):\\n data = self.played_points_hist[:self.t]\\n kernel_matrix = self.kernel_fn(data, data, self.best_ard_params)\\n c_matrix = kernel_matrix + (self.noise_sigma ** 2) * np.eye(data.shape[0])\\n c_matrix_inv = np.linalg.inv(c_matrix)\\n first_term = np.matmul(self.rews_hist[:self.t].T, np.matmul(c_matrix_inv, self.rews_hist[:self.t]))\\n second_term = np.log(np.linalg.det(c_matrix))\\n return first_term + second_term\",\n \"def forward(log_emlik, log_startprob, log_transmat):\\n \\n alpha = np.zeros(np.shape(log_emlik))\\n N=len(alpha)\\n # recursiveAlpha(N-1, alpha, log_emlik, log_startprob, log_transmat)\\n alpha[0][:] = log_startprob.T + log_emlik[0]\\n\\n for n in range(1,len(alpha)):\\n for i in range(alpha.shape[1]):\\n alpha[n, i] = logsumexp(alpha[n - 1] + log_transmat[:,i]) + log_emlik[n,i]\\n return alpha, logsumexp(alpha[N-1])\",\n \"def log_likelihood(self) -> tf.Tensor:\\n # K⁻¹ + GᵀΣ⁻¹G = LLᵀ.\\n l_post = self._k_inv_post.cholesky\\n num_data = self.observations_index.shape[0]\\n\\n # Hμ [..., num_transitions + 1, output_dim]\\n marginal = self.emission.project_state_to_f(self.prior_ssm.marginal_means)\\n marginal = self._drop_batch_shape(marginal)\\n\\n # y = obs - Hμ [..., num_transitions + 1, output_dim]\\n disp = self.observations - marginal\\n disp_data = self.sparse_observations - self.dense_to_sparse(marginal)\\n\\n # cst is the constant term for a gaussian log likelihood\\n cst = (\\n -0.5 * np.log(2 * np.pi) * tf.cast(self.emission.output_dim * num_data, default_float())\\n )\\n\\n term1 = -0.5 * tf.reduce_sum(\\n input_tensor=tf.einsum(\\\"...op,...p,...o->...o\\\", self._r_inv_data, disp_data, disp_data), axis=[-1, -2]\\n )\\n\\n # term 2 is: ½|L⁻¹(GᵀΣ⁻¹)y|²\\n # (GᵀΣ⁻¹)y [..., num_transitions + 1, state_dim]\\n obs_proj = self._back_project_y_to_state(disp)\\n\\n # ½|L⁻¹(GᵀΣ⁻¹)y|² [...]\\n term2 = 0.5 * tf.reduce_sum(\\n input_tensor=tf.square(l_post.solve(obs_proj, transpose_left=False)), axis=[-1, -2]\\n )\\n\\n ## term 3 is: ½log |K⁻¹| - log |L| + ½ log |Σ⁻¹|\\n # where log |Σ⁻¹| = num_data * log|R⁻¹|\\n term3 = (\\n 0.5 * self.prior_ssm.log_det_precision()\\n - l_post.abs_log_det()\\n + 0.5 * self._log_det_observation_precision\\n )\\n\\n return tf.reduce_sum(cst + term1 + term2 + term3)\",\n \"def log(self): # just use base?\\n return Factor().__build( VarSet(self.v) , np.log(self.t) )\",\n \"def neglogli(preds, actual):\\n preds = np.reshape(preds, (-1,))\\n nll = np.sum((preds - actual*np.log(preds + 1e-07) + np.log(sp.special.factorial(actual))))\\n return nll\",\n \"def statePosteriors(log_alpha, log_beta):\\n\\n gamma = np.zeros(log_alpha.shape)\\n\\n gamma = log_alpha + log_beta\\n\\n gamma = gamma - logsumexp(log_alpha[-1,:])\\n\\n return gamma\",\n \"def _log_prior_gradients(self):\\r\\n if self.priors is None:\\r\\n return 0.\\r\\n x = self._get_params()\\r\\n ret = np.zeros(x.size)\\r\\n [np.put(ret, i, p.lnpdf_grad(xx)) for i, (p, xx) in enumerate(zip(self.priors, x)) if not p is None]\\r\\n return ret\",\n \"def nloglikeobs(self, params):\\n lambda_ = params[0]\\n\\n ll_output = self._LL(self.endog, rate=lambda_)\\n\\n return -np.log(ll_output)\",\n \"def log_prior(self, params):\\n # log likelihood function, see:\\n # https://en.wikipedia.org/wiki/Multivariate_normal_distribution#Likelihood_function\\n variance = self.std ** 2\\n ndim = params.ndim\\n mean_diff = params - self.mean\\n scaled_sq_err = jnp.dot(mean_diff, mean_diff) / variance\\n # log determinant of covariance matrix\\n log_det_cov = 2 * ndim * jnp.log(self.std)\\n norm_term = ndim * jnp.log(2 * jnp.pi)\\n return -0.5 * (log_det_cov + scaled_sq_err + norm_term)\",\n \"def log_prior(x):\\n logp = (-0.5 * x.pow(2) - torch.tensor(2 * math.pi).sqrt().log()).sum(dim=1)\\n return logp\",\n \"def kullback_leibler_iaf(z, logqz_x, beta=1., **args):\\n \\n logpz = -tf.reduce_sum(input_tensor=0.5 * np.log(2*np.pi) + 0.5 * tf.square(z), axis=-1)\\n kl = beta * tf.reduce_mean(input_tensor=logqz_x - logpz)\\n return kl\",\n \"def _log_likelihood_gradient(self, z, K, H, B, Kinv):\\n\\n nparams = 4\\n grad = np.zeros((nparams,))\\n\\n #t0 = time.time()\\n tmp = np.dot(self.invc, self.HKinv)\\n #t1 = time.time()\\n K_HBH_inv = Kinv - np.dot(tmp.T, tmp)\\n #t2 = time.time()\\n alpha_z = np.dot(K_HBH_inv, z)\\n #t3 = time.time()\\n\\n #print \\\"gradient: %f %f %f\\\" % (t1-t0, t2-t1, t3-t2)\\n\\n for i in range(nparams):\\n tA = time.time()\\n if (i == 0):\\n dKdi = np.eye(self.n)\\n else:\\n dKdi = self.predict_tree.kernel_deriv_wrt_i(self.X, self.X, \\\"se\\\", self.wfn_params, i-1)\\n\\n dlldi = .5 * np.dot(alpha_z.T, np.dot(dKdi, alpha_z))\\n tB = time.time()\\n # here we use the fact:\\n # trace(AB) = sum_{ij} A_ij * B_ij\\n dlldi -= .5 * np.sum(np.sum(K_HBH_inv.T * dKdi))\\n\\n grad[i] = dlldi\\n tC = time.time()\\n print \\\" %d: %f %f\\\" % (i, tB-tA, tC-tB)\\n\\n return grad\",\n \"def log_boltzmann_dist(Q, temperature):\\n return nn.LogSoftmax(dim=0)(Q/temperature)\",\n \"def _log_prior_gradients(self):\\n if self.priors.size == 0:\\n return 0.\\n x = self.param_array\\n ret = np.zeros(x.size)\\n #compute derivate of prior density\\n [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]\\n #add in jacobian derivatives if transformed\\n priored_indexes = np.hstack([i for p, i in self.priors.items()])\\n for c,j in self.constraints.items():\\n if not isinstance(c, Transformation):continue\\n for jj in j:\\n if jj in priored_indexes:\\n ret[jj] += c.log_jacobian_grad(x[jj])\\n return ret\",\n \"def neg_log_prob(self,params: ndarray) -> float:\\n return -self.compute_log_prob(params)\",\n \"def logistic_loss(z):\\n idx_pos = np.where(z >= 0.)\\n idx_neg = np.where(z < 0.)\\n res = np.empty(z.shape)\\n res[idx_pos] = np.log(1. + np.exp(-z[idx_pos]))\\n z_neg = z[idx_neg]\\n res[idx_neg] = -z_neg + np.log(1. + np.exp(z_neg))\\n return res\",\n \"def kullback_leibler_iaf(z, logqz_x, beta=1.):\\n \\n logpz = -tf.reduce_sum(0.5 * np.log(2*np.pi) + 0.5 * tf.square(z), axis=-1)\\n kl = beta * tf.reduce_mean(logqz_x - logpz)\\n return kl\",\n \"def log_Schechter_log(self, logl, alpha, logls, logl0):\\n phi = (logl - logls) * (alpha+1) * np.log(10.) - np.power(10., logl-logls)\\n lik = phi.copy()\\n lik [logl < logl0] = -1e99\\n return lik\",\n \"def log_likelihood(self):\\r\\n assert not self.likelihood.is_heteroscedastic\\r\\n A = -0.5*self.batchsize*self.output_dim*(np.log(2.*np.pi) - np.log(self.likelihood.precision))\\r\\n B = -0.5*self.likelihood.precision*self.output_dim*self.trace_K\\r\\n Kmm_logdet = 2.*np.sum(np.log(np.diag(self.Lm)))\\r\\n C = -0.5*self.output_dim*self.data_prop*(Kmm_logdet-self.q_u_logdet - self.num_inducing)\\r\\n C += -0.5*np.sum(self.LQL * self.B)\\r\\n D = -0.5*self.likelihood.precision*self.likelihood.trYYT\\r\\n E = np.sum(self.V*self.projected_mean)\\r\\n return (A+B+C+D+E)/self.data_prop\",\n \"def grad_log_q(self,z): \\n param_count = 0\\n grad = np.zeros((np.sum(self.approx_param_no),self.sims))\\n for core_param in range(len(self.q)):\\n for approx_param in range(self.q[core_param].param_no):\\n grad[param_count] = self.q[core_param].vi_score(z[core_param],approx_param) \\n param_count += 1\\n return grad\",\n \"def log_prob_from_logits(x):\\n axis = len(x.shape) - 1\\n m = x.max(dim=axis, keepdim=True)[0]\\n return x - m - torch.log(torch.exp(x - m).sum(dim=axis, keepdim=True))\",\n \"def _loglike(self, y, f):\\n bincoef = tf.lgamma(self.n + 1) - tf.lgamma(y + 1) \\\\\\n - tf.lgamma(self.n - y + 1)\\n ll = bincoef + y * tf.log(pos(f)) + (self.n - y) * tf.log(pos(1 - f))\\n return ll\",\n \"def evaluate(observations, model, states=None, log=False):\\r\\n N = model.N\\r\\n T = observations.shape[0]\\r\\n A = numpy.log(model.A)\\r\\n B = numpy.log(model.B)\\r\\n\\r\\n if states is None:\\r\\n alphas = forward_path(observations, numpy.log(model.pi), A, B, T, N)\\r\\n\\r\\n \\\"\\\"\\\" Termination \\\"\\\"\\\"\\r\\n result = add_logs(alphas[T-1, :])\\r\\n if log:\\r\\n return result\\r\\n else:\\r\\n return math.exp(result)\\r\\n\\r\\n else:\\r\\n result = 0\\r\\n for i in range(T):\\r\\n result += B[states[i], observations[i]]\\r\\n\\r\\n if log:\\r\\n return result\\r\\n else:\\r\\n return math.exp(result)\",\n \"def weight_log(val):\\n return val * math.log(val)\",\n \"def log_prob(self, scores : torch.Tensor, permutations):\\n s = torch.log(select_indices(scores, permutations))\\n n = len(scores)\\n p = self.upto if self.upto is not None else n - 1\\n return -sum(\\n torch.log(torch.exp((s[k:] - s[k]) * self.shape).sum(dim=0))\\n for k in range(p))\",\n \"def nloglikeobs(self, params):\\n #print len(params),\\n beta = params[:-2]\\n df = params[-2]\\n scale = params[-1]\\n loc = np.dot(self.exog, beta)\\n endog = self.endog\\n x = (endog - loc)/scale\\n #next part is stats.t._logpdf\\n lPx = sps_gamln((df+1)/2) - sps_gamln(df/2.)\\n lPx -= 0.5*np_log(df*np_pi) + (df+1)/2.*np_log(1+(x**2)/df)\\n lPx -= np_log(scale) # correction for scale\\n return -lPx\",\n \"def get_log_lik(cov_evals, rank, shape):\\n\\n n_samples, n_features = shape\\n if not 1 <= rank <= n_features - 1:\\n raise ValueError(\\\"the tested rank should be in [1, n_features - 1]\\\")\\n\\n eps = 1e-15\\n\\n if cov_evals[rank - 1] < eps:\\n # When the tested rank is associated with a small eigenvalue, there's\\n # no point in computing the log-likelihood: it's going to be very\\n # small and won't be the max anyway. Also, it can lead to numerical\\n # issues below when computing pa, in particular in log((spectrum[i] -\\n # spectrum[j]) because this will take the log of something very small.\\n return -np.inf\\n\\n pu = -rank * log(2.)\\n for i in range(1, rank + 1):\\n pu += (gammaln((n_features - i + 1) / 2.) -\\n log(np.pi) * (n_features - i + 1) / 2.)\\n\\n pl = np.sum(np.log(cov_evals[:rank]))\\n pl = -pl * n_samples / 2.\\n\\n v = max(eps, np.sum(cov_evals[rank:]) / (n_features - rank))\\n pv = -np.log(v) * n_samples * (n_features - rank) / 2.\\n\\n m = n_features * rank - rank * (rank + 1.) / 2.\\n pp = log(2. * np.pi) * (m + rank) / 2.\\n\\n pa = 0.\\n spectrum_ = cov_evals.copy()\\n spectrum_[rank:n_features] = v\\n for i in range(rank):\\n for j in range(i + 1, len(cov_evals)):\\n pa += log((cov_evals[i] - cov_evals[j]) *\\n (1. / spectrum_[j] - 1. / spectrum_[i])) + log(n_samples)\\n\\n ll = pu + pl + pv + pp - pa / 2. - rank * log(n_samples) / 2.\\n\\n return ll\",\n \"def Laplace_evidence(self):\\r\\n A = self.Laplace_covariance()\\r\\n try:\\r\\n hld = np.sum(np.log(np.diag(jitchol(A)[0])))\\r\\n except:\\r\\n return np.nan\\r\\n return 0.5 * self._get_params().size * np.log(2 * np.pi) + self.log_likelihood() - hld\",\n \"def calc_partition_function_one(states, T):\\n B = beta(T)\\n Z = sum(np.exp(-B * states))\\n\\n return Z\",\n \"def TCVAE_loss_function(input, recons, mu, log_var, z, dataset_size, batch_iter, \\n anneal_steps=200., alpha=1., beta=6., gamma=1., train=True):\\n weight = 1 # Account for the minibatch samples from the dataset\\n batch_size, z_dim = z.shape\\n \\n recons_loss = F.mse_loss(recons, input, reduction='sum')/(19*batch_size)\\n\\n log_q_zx = log_density_gaussian(z, mu, log_var).sum(dim = 1)\\n\\n zeros = torch.zeros_like(z)\\n log_p_z = log_density_gaussian(z, zeros, zeros).sum(dim = 1)\\n\\n mat_log_q_z = log_density_gaussian( z.view(batch_size, 1, z_dim),\\n mu.view(1, batch_size, z_dim),\\n log_var.view(1, batch_size, z_dim))\\n\\n # Reference\\n # [1] https://github.com/YannDubs/disentangling-vae/blob/535bbd2e9aeb5a200663a4f82f1d34e084c4ba8d/disvae/utils/math.py#L54\\n strat_weight = (dataset_size - batch_size + 1) / (dataset_size * (batch_size - 1))\\n importance_weights = torch.Tensor(batch_size, batch_size).fill_(1 / (batch_size -1)).to(input.device)\\n importance_weights.view(-1)[::batch_size] = 1 / dataset_size\\n importance_weights.view(-1)[1::batch_size] = strat_weight\\n importance_weights[batch_size - 2, 0] = strat_weight\\n log_importance_weights = importance_weights.log()\\n\\n mat_log_q_z += log_importance_weights.view(batch_size, batch_size, 1)\\n\\n log_q_z = torch.logsumexp(mat_log_q_z.sum(2), dim=1, keepdim=False)\\n log_prod_q_z = torch.logsumexp(mat_log_q_z, dim=1, keepdim=False).sum(1)\\n\\n mi_loss = (log_q_zx - log_q_z).mean()/19\\n tc_loss = (log_q_z - log_prod_q_z).mean()/19\\n kld_loss = (log_prod_q_z - log_p_z).mean()/19\\n\\n if train:\\n batch_iter += 1\\n anneal_rate = min(0 + 1 * batch_iter / anneal_steps, 1)\\n else:\\n anneal_rate = 1.\\n\\n loss = recons_loss + alpha*mi_loss + weight*(beta*tc_loss + \\n anneal_rate*gamma*kld_loss)\\n \\n return loss, recons_loss, kld_loss, tc_loss, mi_loss\",\n \"def compute_log(tx, index_log, mean=[], std=[]):\\n tx_new = np.log10(3+abs(tx[:,index_log]))\\n return standardize(tx_new,mean,std)\",\n \"def mak2(n,par=None,D0=1e-2,k=.5,Fb=0):\\n if par is not None:\\n D0,k,Fb,slope=par\\n D=np.zeros(n)\\n D[0]=D0\\n for i in range(n-1):\\n D[i+1]=D[i]+k*np.log(1+D[i]/k)\\n F=D+Fb\\n E[1:]=k*np.log(1+D[:-1]/k)/D[:-1]\\n return F,D,E\",\n \"def ExponentialPosteriorSample(bs, ls) :\\n def exps(args) :\\n lamb = args\\n eps = K.random_uniform(shape=(bs, ls))\\n ans = (-1./lamb) * K.log(-eps + 1)\\n return ans\\n return exps\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68426955","0.67924553","0.65790004","0.641989","0.6392089","0.63153124","0.62316066","0.62185025","0.6198411","0.61977243","0.6184737","0.6161985","0.61484474","0.61339176","0.6121981","0.61210907","0.6119032","0.6114617","0.61016893","0.6100129","0.60950714","0.60828876","0.6078643","0.60670483","0.60524416","0.60429835","0.6040253","0.6039869","0.59924424","0.59566146","0.59541893","0.59532","0.5922642","0.5911423","0.59086764","0.5907098","0.5906583","0.59057695","0.5903592","0.590278","0.59001136","0.58957374","0.58936906","0.58873266","0.58870757","0.5886952","0.58835495","0.5883528","0.58834773","0.58821607","0.58782774","0.5875062","0.5872699","0.5867696","0.5861295","0.5851445","0.58339804","0.5814084","0.58093065","0.5808798","0.5807203","0.58036757","0.58018774","0.57949334","0.57760805","0.57737327","0.57727915","0.577238","0.57699054","0.57684785","0.5764699","0.5764664","0.57644355","0.57641107","0.57490724","0.5747807","0.5739754","0.5732266","0.57274455","0.57265544","0.5725549","0.5719959","0.5710254","0.57085043","0.57009894","0.5698979","0.5697501","0.56900495","0.56874","0.56832397","0.5683026","0.56804496","0.5680347","0.5678637","0.5678044","0.5676393","0.5675967","0.5673634","0.5673061","0.5672972"],"string":"[\n \"0.68426955\",\n \"0.67924553\",\n \"0.65790004\",\n \"0.641989\",\n \"0.6392089\",\n \"0.63153124\",\n \"0.62316066\",\n \"0.62185025\",\n \"0.6198411\",\n \"0.61977243\",\n \"0.6184737\",\n \"0.6161985\",\n \"0.61484474\",\n \"0.61339176\",\n \"0.6121981\",\n \"0.61210907\",\n \"0.6119032\",\n \"0.6114617\",\n \"0.61016893\",\n \"0.6100129\",\n \"0.60950714\",\n \"0.60828876\",\n \"0.6078643\",\n \"0.60670483\",\n \"0.60524416\",\n \"0.60429835\",\n \"0.6040253\",\n \"0.6039869\",\n \"0.59924424\",\n \"0.59566146\",\n \"0.59541893\",\n \"0.59532\",\n \"0.5922642\",\n \"0.5911423\",\n \"0.59086764\",\n \"0.5907098\",\n \"0.5906583\",\n \"0.59057695\",\n \"0.5903592\",\n \"0.590278\",\n \"0.59001136\",\n \"0.58957374\",\n \"0.58936906\",\n \"0.58873266\",\n \"0.58870757\",\n \"0.5886952\",\n \"0.58835495\",\n \"0.5883528\",\n \"0.58834773\",\n \"0.58821607\",\n \"0.58782774\",\n \"0.5875062\",\n \"0.5872699\",\n \"0.5867696\",\n \"0.5861295\",\n \"0.5851445\",\n \"0.58339804\",\n \"0.5814084\",\n \"0.58093065\",\n \"0.5808798\",\n \"0.5807203\",\n \"0.58036757\",\n \"0.58018774\",\n \"0.57949334\",\n \"0.57760805\",\n \"0.57737327\",\n \"0.57727915\",\n \"0.577238\",\n \"0.57699054\",\n \"0.57684785\",\n \"0.5764699\",\n \"0.5764664\",\n \"0.57644355\",\n \"0.57641107\",\n \"0.57490724\",\n \"0.5747807\",\n \"0.5739754\",\n \"0.5732266\",\n \"0.57274455\",\n \"0.57265544\",\n \"0.5725549\",\n \"0.5719959\",\n \"0.5710254\",\n \"0.57085043\",\n \"0.57009894\",\n \"0.5698979\",\n \"0.5697501\",\n \"0.56900495\",\n \"0.56874\",\n \"0.56832397\",\n \"0.5683026\",\n \"0.56804496\",\n \"0.5680347\",\n \"0.5678637\",\n \"0.5678044\",\n \"0.5676393\",\n \"0.5675967\",\n \"0.5673634\",\n \"0.5673061\",\n \"0.5672972\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6328785"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":11185,"cells":{"query":{"kind":"string","value":"Belief propagation algorithm for calculating the gradient of the log of the partition function Z"},"document":{"kind":"string","value":"def dlogZ(self, T, pos, psi, phi):\n msgs = belief_propagation(T, pos, psi, phi, True)\n dpsi = calculate_gradient(msgs, T, pos, psi, True, True)\n return dpsi"},"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 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 log_prior_grad(self, inputs):","def _log_likelihood_gradient(self, z, K, H, B, Kinv):\n\n nparams = 4\n grad = np.zeros((nparams,))\n\n #t0 = time.time()\n tmp = np.dot(self.invc, self.HKinv)\n #t1 = time.time()\n K_HBH_inv = Kinv - np.dot(tmp.T, tmp)\n #t2 = time.time()\n alpha_z = np.dot(K_HBH_inv, z)\n #t3 = time.time()\n\n #print \"gradient: %f %f %f\" % (t1-t0, t2-t1, t3-t2)\n\n for i in range(nparams):\n tA = time.time()\n if (i == 0):\n dKdi = np.eye(self.n)\n else:\n dKdi = self.predict_tree.kernel_deriv_wrt_i(self.X, self.X, \"se\", self.wfn_params, i-1)\n\n dlldi = .5 * np.dot(alpha_z.T, np.dot(dKdi, alpha_z))\n tB = time.time()\n # here we use the fact:\n # trace(AB) = sum_{ij} A_ij * B_ij\n dlldi -= .5 * np.sum(np.sum(K_HBH_inv.T * dKdi))\n\n grad[i] = dlldi\n tC = time.time()\n print \" %d: %f %f\" % (i, tB-tA, tC-tB)\n\n return grad","def logistic_grad(z):\n idx_pos = np.where(z >= 0.)\n idx_neg = np.where(z < 0.)\n res = np.empty(z.shape)\n res[idx_pos] = 1. / (1. + np.exp(-z[idx_pos]))\n res[idx_neg] = 1 - 1. / (1. + np.exp(z[idx_neg]))\n return res","def logistic_grad(z):\n idx_pos = np.where(z >= 0.)\n idx_neg = np.where(z < 0.)\n res = np.empty(z.shape)\n res[idx_pos] = 1. / (1. + np.exp(-z[idx_pos]))\n res[idx_neg] = 1 - 1. / (1. + np.exp(z[idx_neg]))\n return res","def compute_gradients(self, logits, target):\n\n target_length = target.shape[0]\n num_time_steps = logits.shape[0]\n\n\t\t######################\n\t\t### YOUR CODE HERE ###\n\t\t######################\n\n # expand labels by inserting a blank between each pair\n normalized_logits = softmax(logits)\n blank_label = normalized_logits.shape[1] - 1\n l = add_blanks(target, blank_label)\n target_length = l.shape[0]\n\n alpha = self.compute_forward_variables(normalized_logits, target) \n beta = self.compute_backward_variables(normalized_logits, target)\n\n # rescale\n alpha = alpha / np.sum(alpha, axis=0)\n beta = beta / np.sum(beta, axis=0)\n alphabeta = alpha * beta\n print \"alpha\"\n print alpha\n\n # compute zt\n z = Counter()\n for t in xrange(num_time_steps):\n for s, k in enumerate(l):\n z[t] += alphabeta[s, t] / normalized_logits[t, k]\n \n # normalized_logits is time steps t by labels k\n # alpha is 2 * target_length - 1 by time steps\n lab_zk = np.zeros_like(normalized_logits)\n for s, k in enumerate(l):\n for t in xrange(num_time_steps):\n lab_zk[t, k] += alphabeta[s, t]\n\n grad = normalized_logits\n for k in xrange(target.shape[0]):\n for t in xrange(num_time_steps):\n ytk = normalized_logits[t, k]\n constant = 1.0 / (ytk * z[t])\n grad[t, k] = ytk - constant * lab_zk[t, k]\n \n return grad","def logp_grad(self, xs, ys, fs, **kwargs):","def _log_prior_gradients(self):\n x = self._get_params()\n ret = np.zeros(x.size)\n [np.put(ret,i,p.lnpdf_grad(xx)) for i,(p,xx) in enumerate(zip(self.priors,x)) if not p is None]\n return ret","def cv_gradient(self, z): \n z_t = np.transpose(z)\n log_q = self.normal_log_q(z_t)\n log_p = self.log_p(z_t)\n grad_log_q = self.grad_log_q(z)\n gradient = grad_log_q*np.repeat((log_p - log_q).T,2,axis=0)\n\n alpha0 = alpha_recursion(np.zeros(np.sum(self.approx_param_no)), grad_log_q, gradient, np.sum(self.approx_param_no)) \n \n vectorized = gradient - ((alpha0/np.var(grad_log_q,axis=1))*grad_log_q.T).T\n\n return np.mean(vectorized,axis=1)","def _log_prior_gradients(self):\n if self.priors.size == 0:\n return 0.\n x = self.param_array\n ret = np.zeros(x.size)\n #compute derivate of prior density\n [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]\n #add in jacobian derivatives if transformed\n priored_indexes = np.hstack([i for p, i in self.priors.items()])\n for c,j in self.constraints.items():\n if not isinstance(c, Transformation):continue\n for jj in j:\n if jj in priored_indexes:\n ret[jj] += c.log_jacobian_grad(x[jj])\n return ret","def _log_prior_gradients(self):\r\n if self.priors is None:\r\n return 0.\r\n x = self._get_params()\r\n ret = np.zeros(x.size)\r\n [np.put(ret, i, p.lnpdf_grad(xx)) for i, (p, xx) in enumerate(zip(self.priors, x)) if not p is None]\r\n return ret","def _log_likelihood_gradients(self):\n return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK,X=self.X,slices1=self.Xslices,slices2=self.Xslices), self.likelihood._gradients(partial=np.diag(self.dL_dK))))","def __log_likelihood(self, params, *args):\n\t\tX, y, feature_set, lambda_reg, empirical_weights, verbose, sign = args\n\n\t\tno_example = len(X)\n\t\ttotal_logZ = 0\n\t\ttotal_logProb = 0\n\t\texpected_weights = np.zeros(len(feature_set))\n\t\tfor t in range(len(X)):\n\t\t\t# example_features = X[t], example_labels = y[t]\n\n\t\t\tpotential = np.zeros(len(X[t]))\n\t\t\tfor i in range(len(X[t])):\n\t\t\t\t#candidate_features = X[t][i], candidate_label = y[t][i]\n\t\t\t\tpotential[i] = feature_set.calc_inner_product(X[t][i], params)\n\n\t\t\t#scaling\n\t\t\tpotential = potential - np.max(potential, keepdims=True)\n\n\t\t\tfor i in range(len(X[t])):\n\t\t\t\ttotal_logProb += potential[i] * y[t][i]\n\n\t\t\tpotential, Z = self.__softmax(potential)\n\n\t\t\tfor i in range(len(X[t])):\n\t\t\t\tfeature_set.calc_inner_sum(expected_weights, X[t][i], potential[i])\n\n\t\t\ttotal_logZ += log(Z)\n\n\t\t# _params = feature_set.get_regularized_params(params, 'bias')\n\t\t_params = params\n\t\tlog_likelihood = total_logProb - total_logZ - (lambda_reg/2) * np.sum(np.multiply(_params,_params))\n\t\tgradients = empirical_weights - expected_weights - lambda_reg * _params\n\n\t\tglobal SUB_ITERATION_NUM\n\t\tif verbose:\n\t\t\tsub_iteration_str = ' '\n\t\t\tif SUB_ITERATION_NUM > 0:\n\t\t\t\tsub_iteration_str = '(' + '{0:02d}'.format(SUB_ITERATION_NUM) + ')'\n\t\t\tprint(' ', '{0:03d}'.format(ITERATION_NUM), sub_iteration_str, ':', log_likelihood * sign)\n\n\t\tSUB_ITERATION_NUM += 1\n\n\t\treturn sign * log_likelihood, sign * gradients","def _log_likelihood_gradients(self):\r\n return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))","def cv_gradient_initial(self,z): \n z_t = np.transpose(z)\n log_q = self.normal_log_q_initial(z_t)\n log_p = self.log_p(z_t)\n grad_log_q = self.grad_log_q(z)\n gradient = grad_log_q*np.repeat((log_p - log_q).T,2,axis=0)\n\n alpha0 = alpha_recursion(np.zeros(np.sum(self.approx_param_no)), grad_log_q, gradient, np.sum(self.approx_param_no))\n\n vectorized = gradient - ((alpha0/np.var(grad_log_q,axis=1))*grad_log_q.T).T\n\n return np.mean(vectorized,axis=1)","def backward(log_emlik, log_startprob, log_transmat):\n #print(log_transmat)\n beta = np.zeros(log_emlik.shape)\n n = len(log_emlik)-2\n log_transmat = log_transmat[0:-1,0:-1];\n\n\n\n while n >= 0:\n\n for j in range(0,len(log_emlik[0])):\n\n beta[n,j] = logsumexp(log_transmat[j,:] + log_emlik[n+1,:] + beta[n+1,:])\n\n n = n -1\n #print(beta[n,:])\n #print(beta)\n return beta","def cv_gradient(self,z):\n gradient = np.zeros(np.sum(self.approx_param_no))\n z_t = z.T \n log_q = self.normal_log_q(z.T)\n log_p = self.log_p(z.T)\n grad_log_q = self.grad_log_q(z)\n gradient = grad_log_q*(log_p-log_q)\n\n alpha0 = alpha_recursion(np.zeros(np.sum(self.approx_param_no)), grad_log_q, gradient, np.sum(self.approx_param_no)) \n\n vectorized = gradient - ((alpha0/np.var(grad_log_q,axis=1))*grad_log_q.T).T\n\n return np.mean(vectorized,axis=1)","def log_lhood(X, Z, Y, a, ep, lamb):\n \n K = Z.shape[1]\n N, T = X.shape\n \n # p(X)\n ZY = np.dot(Z,Y) \n log_pX = 0\n log_pX = log_pX + np.sum(X * np.log(1 - ((1 - lamb) ** ZY) * (1 - ep)))\n log_pX = log_pX + np.sum((1 - X) * np.log(((1 - lamb) ** ZY) * (1 - ep))) \n \n # p(Z)\n HN = 0\n for n in range(1, N+1):\n HN += 1.0/n\n m = Z.sum(axis=0)\n log_pZ = (K * np.log(a) - (a * HN)) + np.sum(gammaln(m) +\n gammaln(N - m + 1) - gammaln(N + 1)) \n\n return log_pZ + log_pX","def _forward_log_det_jacobian(self, x):\n d = self._compute_shared(x=x)\n relx = (x - d.x_k) / d.w_k\n relx = relx # tf.where(d.out_of_bounds, 0.5*tf.ones_like(x), relx)\n grad = (\n 2 * tf.math.log(d.s_k) +\n tf.math.log(d.d_kp1 * relx**2 + 2 * d.s_k * relx * (1 - relx) + # newln\n d.d_k * (1 - relx)**2) -\n 2 * tf.math.log((d.d_kp1 + d.d_k - 2 * d.s_k) * relx *\n (1 - relx) + d.s_k))\n return grad # tf.where(d.out_of_bounds, tf.zeros_like(grad), grad)","def MvNormalLogp():\n cov = pt.matrix(\"cov\")\n cov.tag.test_value = floatX(np.eye(3))\n delta = pt.matrix(\"delta\")\n delta.tag.test_value = floatX(np.zeros((2, 3)))\n\n cholesky = Cholesky(lower=True, on_error=\"nan\")\n\n n, k = delta.shape\n n, k = f(n), f(k)\n chol_cov = cholesky(cov)\n diag = pt.diag(chol_cov)\n ok = pt.all(diag > 0)\n\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\n delta_trans = solve_lower(chol_cov, delta.T).T\n\n result = n * k * pt.log(f(2) * np.pi)\n result += f(2) * n * pt.sum(pt.log(diag))\n result += (delta_trans ** f(2)).sum()\n result = f(-0.5) * result\n logp = pt.switch(ok, result, -np.inf)\n\n def dlogp(inputs, gradients):\n (g_logp,) = gradients\n cov, delta = inputs\n\n g_logp.tag.test_value = floatX(1.0)\n n, k = delta.shape\n\n chol_cov = cholesky(cov)\n diag = pt.diag(chol_cov)\n ok = pt.all(diag > 0)\n\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\n delta_trans = solve_lower(chol_cov, delta.T).T\n\n inner = n * pt.eye(k) - pt.dot(delta_trans.T, delta_trans)\n g_cov = solve_upper(chol_cov.T, inner)\n g_cov = solve_upper(chol_cov.T, g_cov.T)\n\n tau_delta = solve_upper(chol_cov.T, delta_trans.T)\n g_delta = tau_delta.T\n\n g_cov = pt.switch(ok, g_cov, -np.nan)\n g_delta = pt.switch(ok, g_delta, -np.nan)\n\n return [-0.5 * g_cov * g_logp, -g_delta * g_logp]\n\n return OpFromGraph([cov, delta], [logp], grad_overrides=dlogp, inline=True)","def grad_log_q(self,z): \n param_count = 0\n grad = np.zeros((np.sum(self.approx_param_no),self.sims))\n for core_param in range(len(self.q)):\n for approx_param in range(self.q[core_param].param_no):\n grad[param_count] = self.q[core_param].vi_score(z[core_param],approx_param) \n param_count += 1\n return grad","def backward(log_emlik, log_startprob, log_transmat):\n N, M = log_emlik.shape\n logPi=log_startprob[:-1]\n logB=log_emlik\n logA=log_transmat[:-1,:-1]\n beta = np.zeros_like(logB)\n for t in range(N-2,-1,-1):\n for j in range(M):\n beta[t][j]=logsumexp(beta[t+1]+logA[j]+logB[t+1])\n\n return beta","def b_gradient_descent(self, LB,UB,eta, tol,iter):\n bgd=[]\n bgd_x=[LB]\n iteration=0\n # current_pt=X\n first_derivative=sym.diff(self.gdfunc)\n #print(first_derivative)\n x=sym.Symbol('x')\n first_derivative=sym.lambdify(x,first_derivative)\n learn_rate=eta\n \n new_x=LB\n bgd_x.append(LB)\n \n for i in range(iter):\n for j in np.arange(LB,UB,0.1):\n prev_x=new_x\n new_x=prev_x-(learn_rate*first_derivative(prev_x))\n #print(\"i = \",j,\"gradient =\",(learn_rate*first_derivative(j)),iteration)\n iteration=iteration+1\n #print(iteration)\n if iteration >=iter:\n break \n if new_x <= tol:\n #print(\"new_x = \",new_x,\"gradient =\",(learn_rate*first_derivative(prev_x)), iteration) \n break\n \n \n \n \n #print(new_x)\n bgd_x.append(new_x)\n \n \n bgd.append(bgd_x)\n bgd.append(new_x)\n bgd.append(iteration)\n\n return bgd","def fast_loss_and_grad(self, X, y):\n loss = 0.0\n grad = np.zeros(self.W.shape) # initialize the gradient as zero\n \n # ================================================================ #\n # YOUR CODE HERE:\n # Calculate the softmax loss and gradient WITHOUT any for loops.\n # ================================================================ #\n \n num_train = X.shape[0]\n num_classes = self.W.shape[0]\n \n# # vectorized loss calculation #\n class_scores_matrix = np.dot(self.W,X.T) # calculating class scores matrix (C x m): rows are class scores transposes\n class_scores_matrix -= np.max(class_scores_matrix) # considering the possible issue for numerical instability and account for it\n exp_a = np.exp(class_scores_matrix) # calculating the exponents\n \n# y_exp = np.array(exp_a[y, np.arange(0, class_scores_matrix.shape[1])])\n# #print(exp_a[:,:3])\n# #print(y[:3])\n# #print(y_exp[:3])\n \n# tt = np.sum(exp_a,axis=0)\n# tt2 = np.divide(tt,y_exp)\n# print(num_train)\n# tt3 = np.power(tt2,1/num_train)\n# loss = np.log(np.prod(tt3))\n \n \n \n \n (C, D) = self.W.shape\n N = X.shape[0]\n\n scores = np.dot(self.W, X.T)\n scores -= np.max(scores) # shift by log C to avoid numerical instability\n\n y_mat = np.zeros(shape = (C, N))\n y_mat[y, range(N)] = 1\n\n # matrix of all zeros except for a single wx + log C value in each column that corresponds to the\n # quantity we need to subtract from each row of scores\n correct_wx = np.multiply(y_mat, scores)\n\n # create a single row of the correct wx_y + log C values for each data point\n sums_wy = np.sum(correct_wx, axis=0) # sum over each column\n\n exp_scores = np.exp(scores)\n sums_exp = np.sum(exp_scores, axis=0) # sum over each column\n result = np.log(sums_exp)\n\n result -= sums_wy\n\n loss = np.sum(result)\n loss /= num_train\n \n \n # vectorized gradient calculation #\n exp_a_sum = np.sum(exp_a,axis=0)\n\n y_mat_corres = np.zeros(shape = (num_classes, num_train))\n y_mat_corres[y, range(num_train)] = 1\n sum_exp_scores = np.sum(exp_a, axis=0) \n sum_exp_scores = 1.0 / exp_a_sum # division by sum over columns\n exp_a *= sum_exp_scores\n grad = np.dot(exp_a, X)\n grad -= np.dot(y_mat_corres, X)\n grad /= num_train\n \n\n # ================================================================ #\n # END YOUR CODE HERE\n # ================================================================ #\n\n return loss, grad","def forward(log_emlik, log_startprob, log_transmat):\n logPi=log_startprob[:-1]\n logB=log_emlik\n logA=log_transmat[:-1,:-1]\n alpha = np.zeros_like(logB)\n alpha[0]=logB[0]+logPi\n for i in range(1,logB.shape[0]):\n for j in range(logA.shape[0]):\n alpha[i][j]=logsumexp(alpha[i-1]+logA[:,j]+logB[i][j])\n return alpha","def cv_gradient_initial(self,z):\n gradient = np.zeros(np.sum(self.approx_param_no))\n z_t = z.T \n log_q = self.normal_log_q_initial(z.T)\n log_p = self.log_p(z.T)\n grad_log_q = self.grad_log_q(z)\n gradient = grad_log_q*(log_p-log_q)\n\n alpha0 = alpha_recursion(np.zeros(np.sum(self.approx_param_no)), grad_log_q, gradient, np.sum(self.approx_param_no))\n\n vectorized = gradient - ((alpha0/np.var(grad_log_q,axis=1))*grad_log_q.T).T\n\n return np.mean(vectorized,axis=1)","def backward(log_emlik, log_startprob, log_transmat):\n N, M = log_emlik.shape\n backward_prob = np.zeros((N,M))\n\n backward_prob[N-1, :] = 0.0\n\n for i in range(N-2,-1,-1):\n for k in range(M):\n # probability of transitioning from k to state l * probability of emitting symbol at state l at ts i+1 * recursive backward probability\n backward_prob[i,k] = logsumexp(log_transmat[k,:] + log_emlik[i+1,:] + backward_prob[i+1,:])\n\n return backward_prob","def logZ(self, T, pos, psi, phi):\n msgs = belief_propagation(T, pos, psi, phi, True)\n log_z = calculate_belief_sum(msgs, True)\n return log_z","def log_prob(self):","def TCVAE_loss_function(input, recons, mu, log_var, z, dataset_size, batch_iter, \n anneal_steps=200., alpha=1., beta=6., gamma=1., train=True):\n weight = 1 # Account for the minibatch samples from the dataset\n batch_size, z_dim = z.shape\n \n recons_loss = F.mse_loss(recons, input, reduction='sum')/(19*batch_size)\n\n log_q_zx = log_density_gaussian(z, mu, log_var).sum(dim = 1)\n\n zeros = torch.zeros_like(z)\n log_p_z = log_density_gaussian(z, zeros, zeros).sum(dim = 1)\n\n mat_log_q_z = log_density_gaussian( z.view(batch_size, 1, z_dim),\n mu.view(1, batch_size, z_dim),\n log_var.view(1, batch_size, z_dim))\n\n # Reference\n # [1] https://github.com/YannDubs/disentangling-vae/blob/535bbd2e9aeb5a200663a4f82f1d34e084c4ba8d/disvae/utils/math.py#L54\n strat_weight = (dataset_size - batch_size + 1) / (dataset_size * (batch_size - 1))\n importance_weights = torch.Tensor(batch_size, batch_size).fill_(1 / (batch_size -1)).to(input.device)\n importance_weights.view(-1)[::batch_size] = 1 / dataset_size\n importance_weights.view(-1)[1::batch_size] = strat_weight\n importance_weights[batch_size - 2, 0] = strat_weight\n log_importance_weights = importance_weights.log()\n\n mat_log_q_z += log_importance_weights.view(batch_size, batch_size, 1)\n\n log_q_z = torch.logsumexp(mat_log_q_z.sum(2), dim=1, keepdim=False)\n log_prod_q_z = torch.logsumexp(mat_log_q_z, dim=1, keepdim=False).sum(1)\n\n mi_loss = (log_q_zx - log_q_z).mean()/19\n tc_loss = (log_q_z - log_prod_q_z).mean()/19\n kld_loss = (log_prod_q_z - log_p_z).mean()/19\n\n if train:\n batch_iter += 1\n anneal_rate = min(0 + 1 * batch_iter / anneal_steps, 1)\n else:\n anneal_rate = 1.\n\n loss = recons_loss + alpha*mi_loss + weight*(beta*tc_loss + \n anneal_rate*gamma*kld_loss)\n \n return loss, recons_loss, kld_loss, tc_loss, mi_loss","def statePosteriors(log_alpha, log_beta):\n return log_alpha + log_beta - logsumexp(log_alpha[-1,:])","def update(self, returns, log_probs):\n policy_gradient = []\n for log_prob, Gt in zip(log_probs, returns):\n policy_gradient.append(-log_prob * Gt)\n\n loss = torch.stack(policy_gradient).sum()\n self.optimizer.zero_grad()\n loss.backward()\n self.optimizer.step()","def log_likelihood(self):\r\n if self.likelihood.is_heteroscedastic:\r\n A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)\r\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self._A))\r\n else:\r\n A = -0.5 * self.num_data * self.output_dim * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT\r\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self._A))\r\n C = -self.output_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * np.log(sf2))\r\n D = 0.5 * self.data_fit\r\n self._A_part, self._B_part, self._C_part, self._D_part = A, B, C, D\r\n return A + B + C + D + self.likelihood.Z","def log_likelihood_gradients_(self, y, f):\n log_lik = self.evaluate_log_likelihood(y, f)\n f = np.squeeze(f)\n J = jacrev(self.evaluate_log_likelihood, argnums=1)\n H = jacrev(J, argnums=1)\n return log_lik, J(y, f), H(y, f)","def bprop(self, input, output):\n\n\t\tgradient_biases = [self.array(i, 1) for i in self.netSize[1:]]\n\t\tgradient_weights = [self.array(i, j) for i, j in zip(self.netSize[:-1], self.netSize[1:])]\n\t\t\n\t\t\"\"\"\n\t\tFirst we should obtain our activations and z for a given input\n\t\t\"\"\"\n\t\ta, z = self.fprop(input)\n\t\t\n\t\tprint \"a: \"\n\t\tpprint(a)\n\t\tprint \"z: \"\n\t\tpprint(z)\n\t\t\n\t\t\"\"\"\n\t\t1) Initialization\n\t\t\"\"\"\n\t\t\n\t\t#Initialize delta\n\t\tdelta = [self.array(i, 1) for i in self.netSize[1:]]\n\t\t\t\t\n\t\t\"\"\"\n\t\t2) Compute output error, delta\n\t\t\"\"\"\n\t\tdelta[-1] = self.hadmardProduct((self.matrixSubtraction(a[-1], output)), self.sigmoidPrimeArray(z[-1]))\n\t\tprint \"DELTA -1\"\n\t\tprint delta[-1]\n\n\n\t\t#We want to go from the second last value until the second value!\n\t\tinterval = range(len(self.netSize) - 2, 0, -1)\n\t\n\t\t\"\"\"\n\t\t3) Backpropagate the error to all previous layers.\n\t\t\"\"\"\n\t\t#Now it's time to backpropagate\n\t\t#Remember, only weights for in between each layer!\n\t\t#BE VERY CAREFUL WHEN USING LIST COMPREHENSIONS!\n\t\tfor i in interval:\n\t\t\tdelta[i - 1] = self.hadmardProduct(self.matrixMultiply(self.weights[i], delta[i]), self.sigmoidPrimeArray(z[i])) \n\t\n\t\t\"\"\"\n\t\t4) Compute Gradients.\n\t\t\"\"\"\n\t\t#We have weights for every layer except for the last\n\t\tnumLayers = len(self.netSize)\n\t\t\n\t\t\"\"\"\n\t\tRemember how you arrange weights.\n\t\tYou arrange weights from the jth neuron in the lth layer, to the kth neuron in the lth + 1 layer\n\t\t -\n\t\t- \n\t\t -\n\t\t-\n\t\t -\n\t\tBecause of the way you have it arranged, the weights only exist for every layer except for the last!\t \n\t\tSo for the gradient with respect to the weight, the formula is d/dw (l) = a(l) * delta(i + 1)\n\t\tIt isn't the same as in the book!\n\t\t\n\t\tThis should also be activations * delta ^T (delta transpose)\n\t\t\"\"\"\n\t\tfor i in range(0, numLayers - 1):\n\t\t\tgradient_weights[i] =self.matrixMultiply(a[i], self.transpose(delta[i])) \n\t\t\n\t\t#We have biases for every layer but the first, and the last layer\n\t\tgradient_biases = delta\n\t\n\t\tprint \"\\n DELTA FINAL: \\n\"\n\t\tpprint(delta)\n\t\tprint \"\\n GRADIENT WEIGHTS: \\n\"\n\t\tpprint(gradient_weights)\n\t\tprint \"\\n GRADIENT BIASES: \\n\"\n\t\tpprint(gradient_biases)\n\t\n\t\treturn gradient_biases, gradient_weights","def calculate_log_perplexity(self, output, flat_labels): #completed, expensive, should be compiled\n return -np.sum(np.log2(np.clip(output, a_min=1E-12, a_max=1.0))[np.arange(flat_labels.shape[0]), flat_labels[:,1]])","def backward(log_emlik, log_startprob, log_transmat):\n N = log_emlik.shape[0]\n M = log_emlik.shape[1]\n backward_prob = np.zeros((N, M))\n for n in reversed(range(N-1)):\n for i in range(M):\n backward_prob[n, i] = logsumexp(backward_prob[n + 1, :M] + log_transmat[i, :M] + log_emlik[n + 1, :M])\n\n return backward_prob","def grad_log(self, X):\n g = autograd.elementwise_grad(self.log_den)\n G = g(X)\n return G","def logistic_pen(weights, data, targets, hyperparameters):\n\n wr = hyperparameters['weight_regularization']\n \n t = np.transpose(np.repeat(np.reshape(weights[:-1], (len(weights)-1, 1)), len(data), axis = 1))\n f_e = data * t\n z_sums = np.sum(f_e, axis=1)\n y = sigmoid(z_sums +weights[-1])\n f = np.sum(np.log(1 + np.exp(-z_sums - weights[-1])) + (1 - np.transpose(targets)) * (z_sums + weights[-1]))\n df = np.sum(data * np.transpose(((-np.exp(-z_sums - weights[-1]) / (1 + np.exp(-z_sums - weights[-1]))) + (1 - np.transpose(targets)))), axis = 0)\n df = np.append(df, np.sum(np.transpose(((-np.exp(-z_sums - weights[-1]) / (1 + np.exp(-z_sums - weights[-1]))) + (1 - np.transpose(targets)))), axis = 0))\n\n f += np.dot(weights[:-1].transpose()[0], weights[:-1].transpose()[0]) * wr / 2\n df = np.reshape(df, ((len(df), 1)))\n df += np.reshape(np.append(weights[:-1] * wr, 0), (len(weights), 1))\n\n f += (weights[-1, 0] ** 2) * wr / 2\n df[-1] += weights[-1,0] * wr \n\n return f, df, np.reshape(y, (len(y), 1))","def grad_llh(self, params):\n grad = np.clip(self.grad_log_likelihood(params[0], params[1], params[2:]), SMALLEST_NUMBER,\n LARGEST_NUMBER)\n\n return grad","def _grad(V):\n dv = diag(V)\n weights, A, _, AinvB = _weights(dv)\n Ey = (weights.T.dot(Y_control) - Y_treated).getA()\n dGamma0_dV_term2 = zeros(K)\n #dPI_dV = zeros((N0, N1)) # stupid notation: PI = W.T\n #Ai = A.I\n for k in range(K):\n if verbose: # for large sample sizes, linalg.solve is a huge bottle neck,\n print(\"Calculating gradient, linalg.solve() call %s of %s\" % (k ,K,))\n #dPI_dV.fill(0) # faster than re-allocating the memory each loop.\n dA = dA_dV_ki[k]\n dB = dB_dV_ki[k]\n dPI_dV = linalg.solve(A,(dB - dA.dot(AinvB))) \n #dPI_dV = Ai.dot(dB - dA.dot(AinvB))\n dGamma0_dV_term2[k] = np.einsum(\"ij,kj,ki->\",Ey, Y_control, dPI_dV) # (Ey * Y_control.T.dot(dPI_dV).T.getA()).sum()\n return LAMBDA + 2 * dGamma0_dV_term2","def kullback_leibler_gaussian(z_mean, z_logvar, beta=1., **args):\n \n loss = 1 + z_logvar - tf.square(z_mean) - tf.exp(z_logvar)\n loss = -0.5 * tf.reduce_sum(input_tensor=loss, axis=-1)\n return beta * tf.reduce_mean(input_tensor=loss)","def compute_edge_logits(self):","def forward(log_emlik, log_startprob, log_transmat):\n \n alpha = np.zeros(np.shape(log_emlik))\n N=len(alpha)\n # recursiveAlpha(N-1, alpha, log_emlik, log_startprob, log_transmat)\n alpha[0][:] = log_startprob.T + log_emlik[0]\n\n for n in range(1,len(alpha)):\n for i in range(alpha.shape[1]):\n alpha[n, i] = logsumexp(alpha[n - 1] + log_transmat[:,i]) + log_emlik[n,i]\n return alpha, logsumexp(alpha[N-1])","def backward_p(self, x):\n log_det_jacob, z = x.new_zeros(x.shape[0]), x\n for i in reversed(range(len(self.t))):\n z_ = self.mask[i] * z\n s = self.s[i](z_) * (1 - self.mask[i])\n t = self.t[i](z_) * (1 - self.mask[i])\n z = (1 - self.mask[i]) * (z - t) * torch.exp(-s) + z_\n log_det_jacob -= s.sum(dim=1)\n return z, log_det_jacob","def kullback_leibler_gaussian(z_mean, z_logvar, beta=1.):\n \n loss = 1 + z_logvar - tf.square(z_mean) - tf.exp(z_logvar)\n loss = -0.5 * tf.reduce_sum(loss, axis=-1)\n return beta * tf.reduce_mean(loss)","def backward(g, N, K):\n\tb = np.zeros((N,K))\n\tfor t in reversed(xrange(0,N-1)):\n\t\tby = b[t+1,:]\n\t\tfor yp in xrange(K):\n\t\t\tb[t,yp] = misc.logsumexp(by + g[t,yp,:])\n\treturn b","def log_prob(self, z, x):\n n_samples = z.shape[0].value\n log_prob = 0\n for i in range(self.time_steps):\n lat_dim = self.transition.dim_x\n obs_dim = self.emmision.dim_x\n # Current time step latent state and observation\n z_t = tf.slice(z, [0, i * lat_dim], [-1, lat_dim])\n x_t = tf.slice(x, [0, i * obs_dim], [-1, obs_dim])\n if i == 0:\n log_prob += tf.reduce_sum(\n self.init_state_p.log_prob(z_t), axis=1)\n else:\n log_prob += self.transition.log_prob(z_t, z_tminus)\n log_prob += self.emmision.log_prob(x_t, z_t)\n # Update the previous latent state for next iteration\n z_tminus = z_t\n return log_prob","def _finite_diff_gradient(self, parameters):\n assert isinstance(parameters,np.ndarray)\n # get the free indicies\n free_inds = np.nonzero(np.logical_not(self._fixed_indicies))[0]\n\n # first take a forward step in each direction\n step = 1e-6 # finite difference step\n log_like_fs = np.zeros(free_inds.size)\n for i,param_idx in enumerate(free_inds):\n p_fs = parameters.copy()\n p_fs[param_idx] += step # take a step forward\n log_like_fs[i] = self._compute_log_likelihood(p_fs)\n\n # compute the log likelihood at current point\n log_like = self._compute_log_likelihood(parameters)\n\n # compute the gradient\n gradient = np.zeros(parameters.shape) # default gradient is zero\n gradient[free_inds] = (log_like_fs-log_like)\n gradient[free_inds] = gradient[free_inds]/step # divide by step length\n return log_like, gradient","def log_abs_det_jacobian(self, z):\n pre_u = self.u_ + self.u\n pre_w = self.w_ + self.w\n a = F.softplus(self.a + self.inv)\n w = F.softmax(pre_w, dim=3)\n u = F.softmax(pre_u, dim=3)\n # Perform computation\n pre_sigm = torch.sum(u * a * z, 3) + self.b\n sigm = torch.sigmoid(pre_sigm)\n x_pre = torch.sum(w * sigm, dim=3)\n x_pre_clipped = x_pre * (1 - self.eps) + self.eps * 0.5\n logj = F.log_softmax(pre_w, dim=3) + logsigmoid(pre_sigm) + logsigmoid(-pre_sigm) + torch.log(a)\n # n, d, d2, dh\n logj = logj + F.log_softmax(pre_u, dim=3)\n # n, d, d2, dh, d1\n logj = torch.log(torch.sum(torch.exp(logj),3))\n # n, d, d2, d1\n logdet_ = logj + np.log(1 - self.eps) - (torch.log(x_pre_clipped) + torch.log(-x_pre_clipped + 1))\n return logdet_","def _call(self, z): \n self.a = F.softplus(self.a)\n self.w = F.softmax(self.w, dim=1)\n # Compute\n pre_sigm = self.a * z + self.b\n sigm = torch.sigmoid(pre_sigm)\n x_pre = self.w * sigm\n if (len(z.shape) > 2):\n x_pre = torch.sum(self.w * sigm, dim=1)\n x_pre_clipped = x_pre * (1 - self.eps) + self.eps * 0.5\n zp = torch.log(x_pre_clipped) - torch.log(1 - x_pre_clipped)\n return zp","def _log_bessel_ive_bwd(aux, g):\n v, z = aux\n grad_z = g * (bessel_iv_ratio(v + 1., z) + (v / z - tf.math.sign(z)))\n _, grad_z = tfp_math.fix_gradient_for_broadcasting(\n [v, z], [tf.ones_like(grad_z), grad_z])\n\n # No gradient for v at the moment. This is a complicated expression\n # The gradient with respect to the parameter doesn't have an easy closed\n # form. More work will need to be done to ensure good numerics for the\n # gradient.\n # TODO(b/169357627): Implement gradients of modified bessel functions with\n # respect to parameters.\n\n return None, grad_z","def kullbackLeibler(mu, log_sigma):\n # (tf.Tensor, tf.Tensor) -> tf.Tensor\n # = -0.5 * (1 + log(sigma**2) - mu**2 - sigma**2)\n return -0.5 * tf.reduce_sum(1 + 2 * log_sigma - mu**2 - tf.exp(2 * log_sigma), 1)","def log_marginal_likelihood(self, eval_gradient=False):\n A, B, mu_tilda, gamma_tilda, r_grid, n_grid = self.calc_tau(return_all=True)\n\n gamma = self.GAMMA\n gamma_y = self.GAMMA_Y\n\n A1 = np.copy(self.baseTau0)\n A1[np.diag_indices_from(A1)] = 2 * self.ndim\n\n # A1 = (A - B) / gamma_y\n A2 = np.diag(n_grid)\n\n B_inv = np.linalg.inv(B)\n A_inv = np.linalg.inv(A)\n\n M_lambda = B - B.dot(np.linalg.inv(A).dot(B))\n\n # log_likelihood = mu_tilda.dot(M_lambda).dot(\n # mu_tilda[:, np.newaxis]) - np.nan_to_num(np.log(np.linalg.det(M_lambda)))\n log_likelihood = mu_tilda.dot(M_lambda).dot(mu_tilda[:, np.newaxis])\n\n # log_likelihood = -log_likelihood\n print('log_likelihood: %f' % log_likelihood)\n\n Lambda = B - B.dot(A_inv.dot(B))\n Lambda_inv = np.linalg.inv(Lambda)\n\n M_gamma = A2 - A2.dot(2 * gamma * A_inv - (gamma ** 2) * A_inv.dot(A2).dot(A_inv)).dot(A2)\n\n mean_r_grid = np.nan_to_num(np.array([r.sum() for r in r_grid]) / n_grid)\n\n log_likelihood_grad_gamma = mean_r_grid.dot(M_gamma.dot(mean_r_grid[:, np.newaxis]))[0]\n\n tmpMat = A2.dot(A_inv).dot(A1).dot(A_inv).dot(A2)\n\n log_likelihood_grad_gamma_y = (gamma ** 2) * (\n np.trace(Lambda_inv.dot(tmpMat) + mean_r_grid.dot(tmpMat.dot(mean_r_grid[:, np.newaxis]))[0]))\n\n log_likelihood_gradient = np.array([log_likelihood_grad_gamma, log_likelihood_grad_gamma_y])\n print('log_likelihood_grad: %s' % log_likelihood_gradient)\n\n if eval_gradient:\n return log_likelihood, log_likelihood_gradient\n else:\n return log_likelihood","def log(self, x_old, grad, x, curr_iter, tol_g_val, tol_x_val, tol_f_val):\n\n print(\"-----------------------------------\")\n print(\"\\n Iter: \", curr_iter)\n print(\"\\n x_old: \", x_old)\n print(\"\\n gradient: \", grad)\n print(\"\\n x: \", x)\n print(\"\\n tol_x_val: \", tol_x_val)\n print(\"\\n tol_f_val: \", tol_f_val)\n print(\"\\n tol_g_val: %s \\n \" % tol_g_val)","def gradient_ascent(f, df, theta_init, step_size, max_iter):\n\n fs = []\n xs = []\n thetas = theta_init\n for i in range(max_iter): #for each data example\n fs.append(f(thetas))\n\n temp = step_size*df(thetas)\n thetas = step_size*df(thetas) #modify that feature by using the derivative of log likelihood\n xs.append(thetas.flatten())\n if i % 10 == 0:\n print(i, thetas)\n\n return thetas, fs, xs","def _log_fold_change_pairs(self, idx0, idx1, base):\n logfc = np.zeros(shape=(len(idx0), len(idx1), self._theta_mle.shape[1]))\n for i, xi in enumerate(idx0):\n for j, xj in enumerate(idx1):\n logfc[i, j, :] = self._theta_mle[xi, :] - self._theta_mle[xj, :]\n\n if base == np.e:\n return logfc\n else:\n return logfc / np.log(base)","def log_likelihood_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w))-y)","def propagate(w, b, X, Y):\n\n m = X.shape[1];\n\n # A.shape should be (1, m), m -- number of training examples\n A = sigmoid(np.dot(w.T, X) + b);\n # print(\"A = \" +str(A))\n\n # single training exmaple cost = - (y * log(A) + (1-y) * log(1-A))\n # cost = (-1/m) * (np.dot(Y, np.log(A).T) + np.dot((1-Y), np.log(1-A).T));\n cost = (-1/m)*(np.dot(Y, np.log(A).T) + np.dot((1-Y), np.log(1-A).T));\n # print(\"cost in function: \" +str(cost))\n\n # Backward propagation \n dw = (1/m) * np.dot(X, (A - Y).T)\n # np.sum() : axis = 0 means along the column and axis = 1 means working along the row.\n db = (1/m) * ((A - Y).sum(1))\n\n # dw.shape should be (num_px * nump_px * 3, 1), db should be a scalar\n assert(dw.shape == w.shape)\n assert(db.dtype == float)\n\n # np.squeeze(arr) -- remove one dimentional entry from the shape of given array. \n # -- arr: input array\n cost = np.squeeze(cost)\n assert(cost.shape == ())\n \n grads = {\"dw\": dw, \"db\": db}\n \n return grads, cost","def ComputeLogGradientTerm(self, *args):\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF2F_ComputeLogGradientTerm(self, *args)","def statePosteriors(log_alpha, log_beta):\n\n gamma = np.zeros(log_alpha.shape)\n\n gamma = log_alpha + log_beta\n\n gamma = gamma - logsumexp(log_alpha[-1,:])\n\n return gamma","def _log_fold_change_pairs(self, idx0, idx1, base):\n logfc = np.tile(np.NaN, [len(idx0), len(idx1), self.model_estim.x.shape[1]])\n for i, xi in enumerate(idx0):\n for j, xj in enumerate(idx1):\n logfc[i, j, :] = self._theta_mle[xj, :] - self._theta_mle[xi, :]\n logfc[j, i, :] = -logfc[i, j, :]\n\n if base == np.e:\n return logfc\n else:\n return logfc / np.log(base)","def _log_bessel_kve_bwd(aux, g):\n v, z = aux\n dtype = dtype_util.common_dtype([v, z], tf.float32)\n numpy_dtype = dtype_util.as_numpy_dtype(dtype)\n\n log_kve = _log_bessel_kve_custom_gradient(v, z)\n grad_z = tfp_math.log_add_exp(\n _log_bessel_kve_custom_gradient(v - 1., z),\n _log_bessel_kve_custom_gradient(v + 1., z)) - numpy_dtype(\n np.log(2.)) - log_kve\n grad_z = g * -tf.math.expm1(grad_z)\n _, grad_z = tfp_math.fix_gradient_for_broadcasting(\n [v, z], [tf.ones_like(grad_z), grad_z])\n\n # No gradient for v at the moment. This is a complicated expression\n # The gradient with respect to the parameter doesn't have an easy closed\n # form. More work will need to be done to ensure good numerics for the\n # gradient.\n # TODO(b/169357627): Implement gradients of modified bessel functions with\n # respect to parameters.\n\n return None, grad_z","def log_likelihood(self) -> tf.Tensor:\n # K⁻¹ + GᵀΣ⁻¹G = LLᵀ.\n l_post = self._k_inv_post.cholesky\n num_data = self.observations_index.shape[0]\n\n # Hμ [..., num_transitions + 1, output_dim]\n marginal = self.emission.project_state_to_f(self.prior_ssm.marginal_means)\n marginal = self._drop_batch_shape(marginal)\n\n # y = obs - Hμ [..., num_transitions + 1, output_dim]\n disp = self.observations - marginal\n disp_data = self.sparse_observations - self.dense_to_sparse(marginal)\n\n # cst is the constant term for a gaussian log likelihood\n cst = (\n -0.5 * np.log(2 * np.pi) * tf.cast(self.emission.output_dim * num_data, default_float())\n )\n\n term1 = -0.5 * tf.reduce_sum(\n input_tensor=tf.einsum(\"...op,...p,...o->...o\", self._r_inv_data, disp_data, disp_data), axis=[-1, -2]\n )\n\n # term 2 is: ½|L⁻¹(GᵀΣ⁻¹)y|²\n # (GᵀΣ⁻¹)y [..., num_transitions + 1, state_dim]\n obs_proj = self._back_project_y_to_state(disp)\n\n # ½|L⁻¹(GᵀΣ⁻¹)y|² [...]\n term2 = 0.5 * tf.reduce_sum(\n input_tensor=tf.square(l_post.solve(obs_proj, transpose_left=False)), axis=[-1, -2]\n )\n\n ## term 3 is: ½log |K⁻¹| - log |L| + ½ log |Σ⁻¹|\n # where log |Σ⁻¹| = num_data * log|R⁻¹|\n term3 = (\n 0.5 * self.prior_ssm.log_det_precision()\n - l_post.abs_log_det()\n + 0.5 * self._log_det_observation_precision\n )\n\n return tf.reduce_sum(cst + term1 + term2 + term3)","def gradient_descent(self, x, y):\n # Initialize weights vector\n self.weights = np.zeros(len(x[0]))\n\n # Storing number of training example in a variable \n n = len(x)\n\n # Initiate variables to keep track of the current and smallest loss recorded\n lowest_loss = sys.float_info.max\n current_loss = sys.float_info.max\n\n # Initiate variables to keep track of step sizes\n norm = sys.float_info.max\n smallest_norm = sys.float_info.max\n\n # Initiate list variable that stores all previous weights\n prev_weights = []\n\n # Initiate list that stores all the errors. \n errors = []\n \n # Variable to keep track of the number of iterations that returns a bigger loss than current loss\n k_loss_iteration = 1\n\n # Learning loop\n for i in range(self.max_iter):\n\n # Append current weights\n prev_weights.append(np.array(self.weights))\n \n # Minimizing Loss Function Error by adjusting weights using Gradient Descent\n self.weights += self.learning_rate * (sum([x[i] * (y[i] - self.logistic_function(self.weights.dot(x[i]))) for i in range(n)]) - 2 * self.l2 * self.weights)\n\n # Compute the error of the Cost Function and store it in a list\n current_loss = self.cost(x,y)\n\n if len(errors) > 1 and current_loss > errors[-1]:\n k_loss_iteration += 1\n else: \n k_loss_iteration = 1\n\n errors.append(current_loss)\n \n # Track smallest loss\n if current_loss < lowest_loss:\n lowest_loss = current_loss\n\n # Compute the L2 Norm of the difference between current weights and previous weights\n norm = np.linalg.norm(self.weights - prev_weights[-1])\n\n # Track smallest step size and set it as error threshold\n if norm < smallest_norm:\n smallest_norm = norm\n\n # If this L2 norm is smaller than the error_threshold it means that it converged, hence we can break. In other words, repeat until the step size is too small\n if self.error_threshold != None and norm < self.error_threshold:\n print(\"Converged after {} iterations!\".format(i))\n break\n\n # stop if error hasn't gone down in k iterations\n if k_loss_iteration >= 10:\n print(k_loss_iteration + \" iterations of loss not decreasing on {}th itertion.\".format(i))\n break\n\n # Log final weights\n print(\"Final norm: \" + str(norm) + \"\\nSmallest step size recorded: \" + str(smallest_norm) + \"\\nFinal error: \" + str(current_loss) + \"\\nLowest error recorded: \" + str(lowest_loss) + \"\\nNumber of epochs: \" + str(len(errors)) + \"\\nFinal weights: \" + str(self.weights))","def logprob(self):\n assert len(self._added_rows) == self._num_rows\n V, E, K, M = self._VEKM\n vertex_logits = logprob_dc(self._vert_ss, self._vert_prior, axis=1)\n logprob = vertex_logits.sum()\n for e, v1, v2 in self._tree.tree_grid.T:\n logprob += (logprob_dc(self._edge_ss[e, :, :], self._edge_prior) -\n vertex_logits[v1] - vertex_logits[v2])\n for v in range(V):\n beg, end = self._table.ragged_index[v:v + 2]\n logprob += logprob_dc(self._feat_ss[beg:end, :], self._feat_prior)\n logprob -= logprob_dc(self._meas_ss[v, :], self._meas_prior[v])\n return logprob","def compute_log_prob(self,params: ndarray) -> float:\n return self.compute_log_prior(params) + self.compute_log_likelihood(params)","def forward(log_emlik, log_startprob, log_transmat):\n alpha = np.zeros(log_emlik.shape)\n\n alpha[0,:] = log_startprob[0:-1] + log_emlik[0,:]\n\n sum_row = 0;\n log_transmat = log_transmat[0:-1];\n\n\n for frame in range(1,len(log_emlik)):\n\n for state in range(0,len(log_emlik[0])):\n\n alpha[frame,state] = logsumexp(alpha[frame-1,:] + log_transmat[:,state]) + log_emlik[frame,state]\n #print(alpha[frame,state])\n #print(alpha[frame,:])\n\n return alpha","def _perplexity(self, X, log_w):\n return np.exp(-log_w/X.sum())","def log_abs_det_jacobian(self, z):\n self.a = F.softplus(self.a)\n self.w = F.softmax(self.w, dim=1)\n pre_sigm = self.a * z + self.b\n sigm = torch.sigmoid(pre_sigm)\n x_pre = self.w * sigm\n if (len(z.shape) > 2):\n x_pre = torch.sum(self.w * sigm, dim=1)\n x_pre_clipped = x_pre * (1 - self.eps) + self.eps * 0.5\n logj = F.log_softmax(self.w, dim=1) + logsigmoid(pre_sigm) + logsigmoid(-pre_sigm) + torch.log(self.a)\n logj = torch.log(torch.sum(torch.exp(logj)))#,2).sum(2)\n logdet = logj + np.log(1 - self.eps) - (torch.log(x_pre_clipped) + torch.log(-x_pre_clipped + 1))\n return sum_dims(logdet)","def compute_log_prior(self,params: ndarray) -> float:\n ln_tE = params[0]\n ln_A0 = params[1]\n ln_deltaT = params[2]\n fbl = params[3]\n mb = params[4]\n\n # Equation (16,15,17) (note that Albrow uses \"log\" for log10)\n log10e = np.log10(np.exp(1))\n ln_pr_ln_tE = np.log(0.476) - ((log10e*ln_tE - 1.333)**2 / 0.330) + np.log(log10e)\n ln_pr_ln_A0 = np.log(0.660) - (1.289*log10e*ln_A0) + np.log(log10e)\n ln_pr_ln_deltaT = np.log(0.156) - ((log10e*ln_deltaT - 1.432)**2 / 0.458) +\\\n np.log(log10e)\n \n # Paper doesnt mention the prior used, but I assume it to be uniform\n ln_pr_fbl = uniform.logpdf(fbl,0.0,1.0)\n\n # Paper doesnr mention the prior used but I will asuumed it to be uniform\n ln_pr_mb = uniform.logpdf(mb,self.mag_min - 1.0, self.mag_max + 1.0)\n \n \n return ln_pr_fbl + ln_pr_ln_A0 + ln_pr_ln_deltaT + ln_pr_ln_tE + ln_pr_mb","def Map_Gradients(post_eval,q,InvV,m_points):\n m = InvV.n\n N = m_points.num\n d = InvV.d\n \n ds_dq = np.zeros([m,N])\n dr_dq = np.zeros([m,N])\n \n ds_db = np.zeros([m,d,N])\n dr_db = np.zeros([m,d,N])\n \n ds_dL = np.zeros([m,d,d,N])\n dr_dL = np.zeros([m,d,d,N])\n \n dB_dL = np.zeros([m,d,d,d,N])\n dM_dL = Cholesky_Derivs(InvV,m_points)\n Q = Partitioner(q, InvV, post_eval, m_points)\n \n for j in range(m):\n backtrack = m_points.map(InvV,j)\n ds_dq[j,:] = - Q[j,:] / q[j]\n dr_dq[j,:] = ds_dq[j,:] - np.mean(ds_dq[j,:])\n \n for k in range(d):\n ds_db[j,k,:] = Q[j,:] * backtrack.all[:,k].T\n dr_db[j,k,:] = ds_db[j,k,:] - np.mean(ds_db[j,k,:])\n \n for l in range(d):\n for i in range(N):\n for row in range(d):\n for col in range(d):\n dB_dL[j,row,k,l,i] += m_points.pick(i)[col] * dM_dL[j,row,col,k,l]\n ds_dL[j,k,l,i] = Q[j,i] * np.inner(backtrack.pick(i),dB_dL[j,:,k,l,i])\n if k == l:\n ds_dL[j,k,l,:] += (2/InvV.L[j,k,l])\n \n dr_dL[j,k,l,:] = ds_dL[j,k,l,:] - np.mean(ds_dL[j,k,l,:])\n \n return dr_dq, dr_db, dr_dL","def _log_fold_change_pairs(self, idx0, idx1, base):\n assert np.all([x < self._pval.shape[1] for x in idx0])\n assert np.all([x < self._pval.shape[1] for x in idx1])\n if base == np.e:\n return self._logfc[idx0, :, :][:, idx1, :]\n else:\n return self._logfc[idx0, :, :][:, idx1, :] / np.log(base)","def _back_prop(self,W,X,labels,f=sigmoid,fprime=sigmoid_prime,lam=0.000001):\n \n Wl1,bl1,Wl2,bl2 = self._extract_weights(W)\n \n \n \n layers_outputs = self._forward_prop(W,X,f)\n \n \n y = self.make_1_of_c_encoding(labels)\n num_samples = X.shape[0] # layers_outputs[-1].shape[0]\n \n # Dot product return Numsamples (N) x Outputs (No CLasses)\n # Y is NxNo Clases\n # Layers output to\n # small_delta_nl = NxNo_Outputs\n\n big_delta = np.zeros(Wl2.size + bl2.size + Wl1.size + bl1.size)\n big_delta_wl1, big_delta_bl1, big_delta_wl2, big_delta_bl2 = self._extract_weights(big_delta)\n \n \n dE_dy = layers_outputs[-1] - y \n \n big_delta_bl2 = dE_dy.sum(axis=0)\n\n \n dE_dhl = dE_dy.dot(Wl2.T)\n \n\n small_delta_hl = dE_dhl*fprime(layers_outputs[-2])\n big_delta_bl1 = small_delta_hl.sum(axis=0)\n \n big_delta_wl2 = np.dot(layers_outputs[-2].T,dE_dy)\n big_delta_wl1 = np.dot(X.T,small_delta_hl)\n \n \n big_delta_wl2 = np.true_divide(big_delta_wl2,num_samples) + lam*Wl2*2\n big_delta_bl2 = np.true_divide(big_delta_bl2,num_samples)\n big_delta_wl1 = np.true_divide(big_delta_wl1,num_samples) + lam*Wl1*2\n big_delta_bl1 = np.true_divide(big_delta_bl1,num_samples)\n \n #return big_delta\n \n return np.concatenate([big_delta_wl1.ravel(),\n big_delta_bl1,\n big_delta_wl2.ravel(),\n big_delta_bl2])","def logsum_safe(prob_ll, atl):\n logpdf = prob_ll + K.log(atl + K.epsilon())\n alpha = tf.reduce_max(logpdf, -1, keepdims=True)\n return alpha + tf.log(tf.reduce_sum(K.exp(logpdf-alpha), -1, keepdims=True) + K.epsilon())","def logistic_function(z):\n\tg = 1/(1+np.exp(-z))\n\t#g = np.exp(z)/(1+np.exp(z))\n\treturn g","def kl_div_prior_gradient(self, posterior_logits, posterior_binary_samples):\n #DVAE Eq11 - gradient of prior\n #gradient of the KLD between posterior and prior wrt to prior\n #parameters theta, i.e. generative model parameters.\n #logits to probabilities\n posterior_probs=torch.sigmoid(posterior_logits)\n positive_probs=posterior_probs.detach()\n \n #samples from posterior are labelled positive\n positive_samples=posterior_binary_samples.detach()\n\n n_split=positive_samples.size()[1]//2\n positive_samples_left,positive_samples_right=torch.split(positive_samples,split_size_or_sections=int(n_split),dim=1)\n \n #-z_left^t J z_right\n pos_first_term=torch.matmul(positive_samples_left,self.prior.get_weights())*positive_samples_right\n \n rbm_bias_left=self.prior.get_visible_bias()\n rbm_bias_right=self.prior.get_hidden_bias()\n rbm_bias=torch.cat([rbm_bias_left,rbm_bias_right])#self._h\n \n #this gives [42,400] size\n #- z^t h\n #TODO this uses positive probs. Should it not use positive samples?\n # FIXME an indication are the negative ones where samples are used! On\n #other hand this is the only place this this used\n pos_sec_term=positive_probs*rbm_bias\n # pos_sec_term=positive_samples*rbm_bias\n\n # Energy = -z_left^t J z_right - z^t h\n pos_kld_per_sample=-(torch.sum(pos_first_term,axis=1)+torch.sum(pos_sec_term,axis=1))\n #samples from rbm are labelled negative\n\n #rbm_samples Tensor(\"zeros:0\", shape=(200, 200), dtype=float32)\n #this returns the full RBM set: left and right nodes concatenated\n\n #TODO What are these samples here?\n #TODO what's the impact of doing gibbs sampling here? does this make\n #sense?\n rbm_samples=self.prior.get_samples_kld(approx_post_samples=positive_samples_left,n_gibbs_sampling_steps=1)\n negative_samples=rbm_samples.detach()\n\n # print(self.prior.get_weights())\n n_split=negative_samples.size()[1]//2\n negative_samples_left,negative_samples_right=torch.split(negative_samples,split_size_or_sections=int(n_split),dim=1)\n neg_first_term=torch.matmul(negative_samples_left,self.prior.get_weights())*negative_samples_right\n \n #FIXME see above, the positive case looks different. Why?\n neg_sec_term=negative_samples*rbm_bias\n neg_kld_per_sample=(torch.sum(neg_first_term,axis=1)+torch.sum(neg_sec_term,axis=1))\n \n kld_per_sample=pos_kld_per_sample+neg_kld_per_sample\n\n return kld_per_sample","def compute_log_gradient(self, stateFeatures, action):\n\t\t'''\n\t\tassert(len(stateFeatures) == self.nStateFeatures)\n\t\tassert(len(action) == self.actionDim)\n\n\t\tmean = np.dot(self.params,stateFeatures)\n\t\tlog_gradient = np.tile(stateFeatures,(self.actionDim,1))\n\t\taa = (action-mean)/np.square(np.diag(self.covarianceMatrix))\n\t\tfor i,a in enumerate(aa):\n\t\t\tlog_gradient[i]*=a\n\t\treturn log_gradient\n\t\t'''\n\n\t\tif np.ndim(action) == 1:\n\t\t\taction = action[:, None]\n\n\t\tmean = np.dot(stateFeatures, self.params.T)\n\t\tratio = (action - mean) / self.cov_diag[None, :]\n\t\tlog_gradient = ratio[:, :, None] * stateFeatures[:, None, :]\n\t\treturn log_gradient","def gradient(self):\n functional = self\n\n class KLCrossEntropyGradient(Operator):\n\n \"\"\"The gradient operator of this functional.\"\"\"\n\n def __init__(self):\n \"\"\"Initialize a new instance.\"\"\"\n super().__init__(functional.domain, functional.domain,\n linear=False)\n\n def _call(self, x):\n \"\"\"Apply the gradient operator to the given point.\n\n The gradient is not defined in for points with components less\n than or equal to zero.\n \"\"\"\n if functional.prior is None:\n tmp = np.log(x)\n else:\n tmp = np.log(x / functional.prior)\n\n if np.all(np.isfinite(tmp)):\n return tmp\n else:\n # The derivative is not defined.\n raise ValueError('The gradient of the Kullback-Leibler '\n 'Cross Entropy functional is not defined '\n 'for `x` with one or more components '\n 'less than or equal to zero.'.format(x))\n\n return KLCrossEntropyGradient()","def compute_gradient(self): # TODO: try to change to square loss since it's hessian is easier to obtain\n A = np.dot(self.X, self.w)\n m = self.t.shape[0]\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\n return (1 / m) * np.dot(self.X.T, C)","def _logprob(self, sample):\n cur_y, cur_z = sample\n log_prior = 0\n log_lik = 0\n if cur_z.shape[1] == 0: return -99999999.9\n \n if self.cl_mode:\n a_time = time()\n d_cur_z = cl.Buffer(self.ctx, self.mf.READ_ONLY | self.mf.COPY_HOST_PTR, hostbuf = cur_z.astype(np.int32))\n d_cur_y = cl.Buffer(self.ctx, self.mf.READ_ONLY | self.mf.COPY_HOST_PTR, hostbuf = cur_y.astype(np.int32))\n d_logprob = cl.array.empty(self.queue, (cur_z.shape[0],), np.float32, allocator=self.mem_pool)\n\n if cur_z.shape[0] % self.p_mul_logprob_z_data == 0: wg = (self.p_mul_logprob_z_data,)\n else: wg = None\n \n self.prg.logprob_z_data(self.queue, (cur_z.shape[0],), wg,\n d_cur_z, d_cur_y, self.d_obs, d_logprob.data, #d_novel_f.data,\n np.int32(self.N), np.int32(cur_y.shape[1]), np.int32(cur_z.shape[1]), \n np.float32(self.alpha), np.float32(self.lam), np.float32(self.epislon))\n log_lik = d_logprob.get().sum()\n self.gpu_time += time() - a_time\n\n # calculate the prior probability of Y\n num_on = (cur_y == 1).sum()\n num_off = (cur_y == 0).sum()\n log_prior = num_on * np.log(self.theta) + num_off * np.log(1 - self.theta)\n\n else:\n # calculate the prior probability of Z\n for n in xrange(cur_z.shape[0]):\n num_novel = 0\n for k in xrange(cur_z.shape[1]):\n m = cur_z[:n,k].sum()\n if m > 0:\n if cur_z[n,k] == 1: log_prior += np.log(m / (n+1.0))\n else: log_prior += np.log(1 - m / (n + 1.0))\n else: \n if cur_z[n,k] == 1: num_novel += 1\n if num_novel > 0:\n log_prior += poisson.logpmf(num_novel, self.alpha / (n+1.0))\n # calculate the prior probability of Y\n num_on = (cur_y == 1).sum()\n num_off = (cur_y == 0).sum()\n log_prior += num_on * np.log(self.theta) + num_off * np.log(1 - self.theta)\n # calculate the logliklihood\n log_lik = self._loglik(cur_y = cur_y, cur_z = cur_z)\n return log_prior + log_lik","def softmax_derivative(Z):\n\treturn None","def log_distr_fdmw(self, dnu, logflux, dme, logw, alpha, logls, logl0, mu, sigma, gtype=None):\n #stepdms = 100/1000.\n #vdms = np.arange(0, 100, stepdm)\n stepz = (np.log(self.Zmax) - np.log(self.Zmin)) / 1000\n vz = np.exp(np.arange(np.log(self.Zmin), np.log(self.Zmax), stepz))\n lik = 0\n for z in vz:\n likv = np.exp(self.log_distr_fdmwz(dnu, logflux, dme, logw, z, alpha, logls, logl0, mu, sigma, gtype=gtype))\n lik += z * stepz * likv\n ind = lik > 0\n ind2 = lik <= 0\n loglik = lik.copy()\n loglik[ind] = np.log(lik[ind])\n loglik[ind2] = np.ones(loglik[ind2].shape) * -1e99\n return loglik","def _compute_func_grad(self, w):\n W = w.reshape((self.X.shape[1], self.Y.shape[1]))\n self.nll_, self.grad_ = calculate_gradient(self.X, self.Y, W, self.prior, self.weighted,0)","def _estimate_log_prob(self, x):\n x = self.check_size(x)\n\n if self.covariance_type == \"full\":\n mu = self.mu\n var = self.var\n precision = tf.linalg.inv(var)\n d = x.shape[-1]\n\n log_2pi = d * tf.math.log(2. * pi)\n log_2pi = tf.cast(log_2pi, tf.double)\n log_det = self._calculate_log_det(precision)\n log_det = tf.cast(log_det, tf.double)\n\n\n x = tf.cast(x, tf.double)\n mu = tf.cast(mu, tf.double)\n x_mu_T = tf.expand_dims((x - mu), -2)\n x_mu = tf.expand_dims((x - mu), -1)\n\n x_mu_T_precision = calculate_matmul_n_times(self.n_components, x_mu_T, precision)\n # print(x_mu_T_precision, x_mu_T, x, mu)\n x_mu_T_precision_x_mu = calculate_matmul(x_mu_T_precision, x_mu)\n\n return -.5 * (log_2pi - log_det + x_mu_T_precision_x_mu)\n\n elif self.covariance_type == \"diag\":\n mu = self.mu\n prec = tf.math.rsqrt(self.var)\n\n log_p = tf.math.reduce_sum((mu * mu + x * x - 2 * x * mu) * (prec ** 2), axis=2, keepdims=True)\n log_det = tf.math.reduce_sum(tf.math.log(prec), axis=2, keepdims=True)\n\n return -.5 * (self.n_features * tf.math.log(2. * pi) + log_p) + log_det","def logit_cost_grad(self, theta, X, y):\n\n grad = np.zeros(len(theta))\n\n ### YOUR CODE HERE\n sig = utils.sigmoid(theta)\n # sig = np.subtract(sig, y)\n sig = sig - y\n grad = np.dot(X.T, sig) + 2 * self.params['lamb'] * self.regularizer[1](self.weights)\n ### END YOUR CODE\n\n return grad","def loss_and_grad(self, X, y):\n\n # Initialize the loss and gradient to zero.\n loss = 0.0\n grad = np.zeros_like(self.W)\n grad_tmp = np.zeros_like(self.W)\n num_classes = self.W.shape[0] # C = num_classes\n num_train = X.shape[0]\n \n # ================================================================ #\n # YOUR CODE HERE:\n # Calculate the softmax loss and the gradient. Store the gradient\n # as the variable grad.\n # ================================================================ #\n \n exp_a = np.zeros((num_classes,num_train))\n for i in np.arange(num_train):\n \n Loss = 0.0\n\n class_scores = np.dot(self.W,X[i,:].T) # calculating class scores (C x 1 vector)\n class_scores -= np.max(class_scores) # considering the possible issue for numerical instability and account for it\n\n exp_a[:,i] = np.exp(class_scores) # turning class scores to probabilities (C x 1 vector), without normalization\n\n Loss -= np.log(exp_a[y[i],i]/np.sum(exp_a[:,i]))\n \n \n #if i==0:\n grada = np.zeros(X.shape[1])\n \n for j in range(num_classes):\n if j != y[i]:\n grad_tmp[j,:] = X[i,:].T * (exp_a[j,i] / np.sum(exp_a[:,i])) \n else: \n grad_tmp[j,:] = X[i,:].T * (exp_a[j,i] / np.sum(exp_a[:,i])) - X[i,:].T \n\n grad += grad_tmp\n loss += Loss \n \n pass\n\n\n loss /= num_train\n grad /= num_train\n # ================================================================ #\n # END YOUR CODE HERE\n # ================================================================ #\n\n return loss, grad","def grad_neg_log_like(params):\n gp.set_parameter_vector(params)\n return -gp.grad_log_likelihood(y, quiet=True)","def collect_gradients(log_prob, params, pass_grad = None):\n\n if isinstance(log_prob, tuple):\n log_prob[0].backward()\n params_list = list(log_prob[1])\n params = torch.cat([p.flatten() for p in params_list])\n params.grad = torch.cat([p.grad.flatten() for p in params_list])\n elif pass_grad is not None:\n if callable(pass_grad):\n params.grad = pass_grad(params)\n else:\n params.grad = pass_grad\n else:\n params.grad = torch.autograd.grad(log_prob,params)[0]\n return params","def softmax_back_propagate(da, cache):\n z = cache\n y_hat = np.exp(z) / np.exp(z).sum()\n dz = da * (1 - y_hat)\n assert (dz.shape == z.shape)\n return dz","def gradient_descent(self, X ,eta, tol,iter):\n gd=[]\n gd_x=[X]\n iteration=0\n # current_pt=X\n first_derivative=sym.diff(self.gdfunc)\n #print(first_derivative)\n x=sym.Symbol('x')\n first_derivative=sym.lambdify(x,first_derivative)\n learn_rate=eta\n \n \n prev_x=X\n new_x=prev_x -(learn_rate*first_derivative(prev_x))\n gd_x.append(new_x)\n #print(\"prev_x = \",prev_x,\" Next x = \",new_x)\n for i in range(iter):\n prev_x=new_x\n #print(prev_x)\n new_x=prev_x -(learn_rate*first_derivative(prev_x))\n gd_x.append(new_x)\n # print(\"x = \",new_x,\"Gradient =\",learn_rate*self.func(prev_x))\n if abs(self.func(new_x)) <= self.func(tol) :\n break\n iteration=iteration+1\n #print(\"Best at GD x= \",new_x)\n gd.append(gd_x)\n gd.append(new_x)\n gd.append(iteration)\n\n return gd","def get_gradient_minus_log_joint(z, mean, Sigma):\n data_minus_mean = z[np.newaxis,:] - mean# 1 x 2\n Sigma_inv = np.linalg.inv(Sigma) # 2 x 2\n gradient = - np.matmul(Sigma_inv, data_minus_mean.T) #2 x 1\n gradient = gradient[0]\n return gradient","def calc_probs(log_p):\n\n N = log_p.shape[0]\n\n log_Z_per_N = np.zeros(shape=(N, 1))\n\n for i in range(N):\n\n log_Z_per_N[i] = log_norm(log_p[i])\n\n log_p_new = log_p - log_Z_per_N\n\n p = np.exp(log_p_new)\n\n # log_Z = log_norm(log_p)\n\n # p = np.exp(log_p - log_Z)\n\n return p","def compute_loglike(self, tools: ModelingTools) -> float:\n return -1.5","def forward_backward_prop(data, labels, params, dimensions):\n\n ### Unpack network parameters (do not modify)\n ofs = 0\n Dx, H, Dy = (dimensions[0], dimensions[1], dimensions[2])\n\n W1 = np.reshape(params[ofs:ofs+ Dx * H], (Dx, H))\n ofs += Dx * H\n b1 = np.reshape(params[ofs:ofs + H], (1, H))\n ofs += H\n W2 = np.reshape(params[ofs:ofs + H * Dy], (H, Dy))\n ofs += H * Dy\n b2 = np.reshape(params[ofs:ofs + Dy], (1, Dy))\n\n #print W2.shape, b2.shape\n\n #print len(W1), data[0], b1, len(data)\n\n ### YOUR CODE HERE: forward propagation\n #Eg, find the cost function. Save some intermediate stuff though, seems like it'd be useful\n #h = sigmoid(x * w1 + b1)\n # y = (softmax( h * w2 + b2)\n # hence the cost function will be labels * log(y) and then sum it all up\n\n z_1 = np.matrix(data) * W1 + b1\n h = sigmoid(z_1)\n y_prime = softmax(h * W2 + b2)\n logs = np.log(y_prime)\n\n #print y_prime.shape\n\n #print np.array(logs) * labels\n\n cost = - np.sum(np.array(logs) * labels, axis = 1)\n cost = np.sum(cost) # lets add up each instance fo the cost for now and see what happens\n\n # My question is then do we just sum up the costs of each function\n #print cost #somethign is printing so I'm gonan say i'm a genius right here duh\n\n #Cost(y, y') = -sum of (y * log Y')\n ### END YOUR CODE\n\n ### YOUR CODE HERE: backward propagation\n\n # you'll need gradients for each parameter except for the input vectors. Right now this isn't even a word2vec\n delta_1 = y_prime - labels\n delta_2 = delta_1 * W2.T\n #print sigmoid_grad(h).shape\n delta_3 = np.array(delta_2) * sigmoid_grad(h)\n\n gradW2 = np.array(h.T * delta_1) # i dunno or its reverse OMG I HASTE EVERYONE why is it that np.array fixes everything. Sigh\n gradb2 = np.array(np.sum(delta_1, axis=0)) # main issue is that this is a 20 x 5 vector when it should be a 1 x 5\n gradW1 = data.T.dot(delta_3)\n gradb1 = np.sum(delta_3, axis=0) # this should be 1 x10 not 20 x 5\n\n\n\n ### END YOUR CODE\n\n #print gradW1, gradW1.flatten()\n # print 'jee'\n\n ### Stack gradients (do not modify)\n grad = np.concatenate((\n gradW1.flatten(),\n gradb1.flatten(),\n gradW2.flatten(),\n gradb2.flatten())\n )\n #print grad\n #print cost\n return cost, grad","def compute_loglike(self, tools: ModelingTools) -> float:\n return -3.0 * self.placeholder","def grad_margin(x0, preds, y, model, n_class, weights, L, loss_function):\n theta = x0\n loss_fd = weights[y.ravel().astype(int)]\n\n Alpha = theta - preds # (n_class - 1, n_samples)\n S = np.sign(np.arange(n_class - 1) - y + 0.5)\n\n if(loss_function == 'logistic'):\n lf = sigmoid\n elif(loss_function == 'hinge'):\n lf = hinge_derive\n \n Sigma = np.dot(S, loss_fd.T)\n Sigma = np.dot(Sigma, lf(np.multiply(-S, Alpha)))\n\n grad_theta = -Sigma.sum(0)\n return grad_theta","def scipy_minus_gradient(w,all_vector_graphs,all_correct_rows,\\\n all_batches,sigma=None,perceptron=None):\n if perceptron:\n perceptron._gradient_iter += 1\n g = None\n index = 0\n for vector_graphs,correct_rows,batches in zip(all_vector_graphs,all_correct_rows,all_batches):\n first_term = vector_graphs[correct_rows,:].sum(axis=0)\n all_scores = vector_graphs * w\n all_probs = []\n for batch in batches:\n batch_scores = all_scores[batch]\n S = logsumexp(batch_scores)\n all_probs.append(np.exp(batch_scores - S))\n all_probs = numpy.hstack(all_probs)\n second_term = all_probs * vector_graphs\n if g is None:\n g = second_term - first_term\n else:\n g = g + second_term - first_term\n index += 1\n if index % 100 == 0:\n print('Gradient '+str(index)+' processed')\n g = numpy.ndarray.flatten(numpy.asarray(g)) / len(all_vector_graphs)\n if sigma != None:\n g = g + sigma * w\n print('Gradient norm:'+str(scipy.linalg.norm(g)))\n sys.stdout.flush()\n if perceptron and perceptron._model_pickle:\n if perceptron._gradient_iter % 5 == 0:\n perceptron._weights = numpy.reshape(w,(1,perceptron._num_features))\n perceptron.save(perceptron._model_pickle+'_'+str(perceptron._gradient_iter))\n return g","def compute_backward_variables(self, normalized_logits, target):\n\n target_length = target.shape[0]\n num_time_steps = normalized_logits.shape[0]\n\n\t\t######################\n\t\t### YOUR CODE HERE ###\n\t\t######################\n\n blank_label = normalized_logits.shape[1] - 1\n l = add_blanks(target, blank_label)\n target_length = l.shape[0]\n\n beta = np.zeros((target_length, num_time_steps))\n\n # init\n beta[target_length - 1, num_time_steps - 1] = normalized_logits[num_time_steps - 1, l[-1]]\n beta[target_length - 2, num_time_steps - 1] = normalized_logits[num_time_steps - 1, l[-2]]\n\n for s in xrange(target_length - 2):\n beta[s, num_time_steps - 2] = 0.0\n\n # recursive case\n for t in xrange(num_time_steps - 2, -1, -1):\n for s in xrange(target_length - 3, -1, -1):\n b_bar = beta[s, t+1] + beta[s+1, t+1]\n \n if l[s] == blank_label or l[s+2] == l[s]:\n beta[s, t] = b_bar * normalized_logits[t, l[s]]\n else:\n beta[s, t] = (b_bar + beta[s+2, t+1]) * normalized_logits[t, l[2]]\n\n return beta","def compute_logistic_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w)) - y) / len(y)"],"string":"[\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 log_prior_grad(self, inputs):\",\n \"def _log_likelihood_gradient(self, z, K, H, B, Kinv):\\n\\n nparams = 4\\n grad = np.zeros((nparams,))\\n\\n #t0 = time.time()\\n tmp = np.dot(self.invc, self.HKinv)\\n #t1 = time.time()\\n K_HBH_inv = Kinv - np.dot(tmp.T, tmp)\\n #t2 = time.time()\\n alpha_z = np.dot(K_HBH_inv, z)\\n #t3 = time.time()\\n\\n #print \\\"gradient: %f %f %f\\\" % (t1-t0, t2-t1, t3-t2)\\n\\n for i in range(nparams):\\n tA = time.time()\\n if (i == 0):\\n dKdi = np.eye(self.n)\\n else:\\n dKdi = self.predict_tree.kernel_deriv_wrt_i(self.X, self.X, \\\"se\\\", self.wfn_params, i-1)\\n\\n dlldi = .5 * np.dot(alpha_z.T, np.dot(dKdi, alpha_z))\\n tB = time.time()\\n # here we use the fact:\\n # trace(AB) = sum_{ij} A_ij * B_ij\\n dlldi -= .5 * np.sum(np.sum(K_HBH_inv.T * dKdi))\\n\\n grad[i] = dlldi\\n tC = time.time()\\n print \\\" %d: %f %f\\\" % (i, tB-tA, tC-tB)\\n\\n return grad\",\n \"def logistic_grad(z):\\n idx_pos = np.where(z >= 0.)\\n idx_neg = np.where(z < 0.)\\n res = np.empty(z.shape)\\n res[idx_pos] = 1. / (1. + np.exp(-z[idx_pos]))\\n res[idx_neg] = 1 - 1. / (1. + np.exp(z[idx_neg]))\\n return res\",\n \"def logistic_grad(z):\\n idx_pos = np.where(z >= 0.)\\n idx_neg = np.where(z < 0.)\\n res = np.empty(z.shape)\\n res[idx_pos] = 1. / (1. + np.exp(-z[idx_pos]))\\n res[idx_neg] = 1 - 1. / (1. + np.exp(z[idx_neg]))\\n return res\",\n \"def compute_gradients(self, logits, target):\\n\\n target_length = target.shape[0]\\n num_time_steps = logits.shape[0]\\n\\n\\t\\t######################\\n\\t\\t### YOUR CODE HERE ###\\n\\t\\t######################\\n\\n # expand labels by inserting a blank between each pair\\n normalized_logits = softmax(logits)\\n blank_label = normalized_logits.shape[1] - 1\\n l = add_blanks(target, blank_label)\\n target_length = l.shape[0]\\n\\n alpha = self.compute_forward_variables(normalized_logits, target) \\n beta = self.compute_backward_variables(normalized_logits, target)\\n\\n # rescale\\n alpha = alpha / np.sum(alpha, axis=0)\\n beta = beta / np.sum(beta, axis=0)\\n alphabeta = alpha * beta\\n print \\\"alpha\\\"\\n print alpha\\n\\n # compute zt\\n z = Counter()\\n for t in xrange(num_time_steps):\\n for s, k in enumerate(l):\\n z[t] += alphabeta[s, t] / normalized_logits[t, k]\\n \\n # normalized_logits is time steps t by labels k\\n # alpha is 2 * target_length - 1 by time steps\\n lab_zk = np.zeros_like(normalized_logits)\\n for s, k in enumerate(l):\\n for t in xrange(num_time_steps):\\n lab_zk[t, k] += alphabeta[s, t]\\n\\n grad = normalized_logits\\n for k in xrange(target.shape[0]):\\n for t in xrange(num_time_steps):\\n ytk = normalized_logits[t, k]\\n constant = 1.0 / (ytk * z[t])\\n grad[t, k] = ytk - constant * lab_zk[t, k]\\n \\n return grad\",\n \"def logp_grad(self, xs, ys, fs, **kwargs):\",\n \"def _log_prior_gradients(self):\\n x = self._get_params()\\n ret = np.zeros(x.size)\\n [np.put(ret,i,p.lnpdf_grad(xx)) for i,(p,xx) in enumerate(zip(self.priors,x)) if not p is None]\\n return ret\",\n \"def cv_gradient(self, z): \\n z_t = np.transpose(z)\\n log_q = self.normal_log_q(z_t)\\n log_p = self.log_p(z_t)\\n grad_log_q = self.grad_log_q(z)\\n gradient = grad_log_q*np.repeat((log_p - log_q).T,2,axis=0)\\n\\n alpha0 = alpha_recursion(np.zeros(np.sum(self.approx_param_no)), grad_log_q, gradient, np.sum(self.approx_param_no)) \\n \\n vectorized = gradient - ((alpha0/np.var(grad_log_q,axis=1))*grad_log_q.T).T\\n\\n return np.mean(vectorized,axis=1)\",\n \"def _log_prior_gradients(self):\\n if self.priors.size == 0:\\n return 0.\\n x = self.param_array\\n ret = np.zeros(x.size)\\n #compute derivate of prior density\\n [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]\\n #add in jacobian derivatives if transformed\\n priored_indexes = np.hstack([i for p, i in self.priors.items()])\\n for c,j in self.constraints.items():\\n if not isinstance(c, Transformation):continue\\n for jj in j:\\n if jj in priored_indexes:\\n ret[jj] += c.log_jacobian_grad(x[jj])\\n return ret\",\n \"def _log_prior_gradients(self):\\r\\n if self.priors is None:\\r\\n return 0.\\r\\n x = self._get_params()\\r\\n ret = np.zeros(x.size)\\r\\n [np.put(ret, i, p.lnpdf_grad(xx)) for i, (p, xx) in enumerate(zip(self.priors, x)) if not p is None]\\r\\n return ret\",\n \"def _log_likelihood_gradients(self):\\n return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK,X=self.X,slices1=self.Xslices,slices2=self.Xslices), self.likelihood._gradients(partial=np.diag(self.dL_dK))))\",\n \"def __log_likelihood(self, params, *args):\\n\\t\\tX, y, feature_set, lambda_reg, empirical_weights, verbose, sign = args\\n\\n\\t\\tno_example = len(X)\\n\\t\\ttotal_logZ = 0\\n\\t\\ttotal_logProb = 0\\n\\t\\texpected_weights = np.zeros(len(feature_set))\\n\\t\\tfor t in range(len(X)):\\n\\t\\t\\t# example_features = X[t], example_labels = y[t]\\n\\n\\t\\t\\tpotential = np.zeros(len(X[t]))\\n\\t\\t\\tfor i in range(len(X[t])):\\n\\t\\t\\t\\t#candidate_features = X[t][i], candidate_label = y[t][i]\\n\\t\\t\\t\\tpotential[i] = feature_set.calc_inner_product(X[t][i], params)\\n\\n\\t\\t\\t#scaling\\n\\t\\t\\tpotential = potential - np.max(potential, keepdims=True)\\n\\n\\t\\t\\tfor i in range(len(X[t])):\\n\\t\\t\\t\\ttotal_logProb += potential[i] * y[t][i]\\n\\n\\t\\t\\tpotential, Z = self.__softmax(potential)\\n\\n\\t\\t\\tfor i in range(len(X[t])):\\n\\t\\t\\t\\tfeature_set.calc_inner_sum(expected_weights, X[t][i], potential[i])\\n\\n\\t\\t\\ttotal_logZ += log(Z)\\n\\n\\t\\t# _params = feature_set.get_regularized_params(params, 'bias')\\n\\t\\t_params = params\\n\\t\\tlog_likelihood = total_logProb - total_logZ - (lambda_reg/2) * np.sum(np.multiply(_params,_params))\\n\\t\\tgradients = empirical_weights - expected_weights - lambda_reg * _params\\n\\n\\t\\tglobal SUB_ITERATION_NUM\\n\\t\\tif verbose:\\n\\t\\t\\tsub_iteration_str = ' '\\n\\t\\t\\tif SUB_ITERATION_NUM > 0:\\n\\t\\t\\t\\tsub_iteration_str = '(' + '{0:02d}'.format(SUB_ITERATION_NUM) + ')'\\n\\t\\t\\tprint(' ', '{0:03d}'.format(ITERATION_NUM), sub_iteration_str, ':', log_likelihood * sign)\\n\\n\\t\\tSUB_ITERATION_NUM += 1\\n\\n\\t\\treturn sign * log_likelihood, sign * gradients\",\n \"def _log_likelihood_gradients(self):\\r\\n return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))\",\n \"def cv_gradient_initial(self,z): \\n z_t = np.transpose(z)\\n log_q = self.normal_log_q_initial(z_t)\\n log_p = self.log_p(z_t)\\n grad_log_q = self.grad_log_q(z)\\n gradient = grad_log_q*np.repeat((log_p - log_q).T,2,axis=0)\\n\\n alpha0 = alpha_recursion(np.zeros(np.sum(self.approx_param_no)), grad_log_q, gradient, np.sum(self.approx_param_no))\\n\\n vectorized = gradient - ((alpha0/np.var(grad_log_q,axis=1))*grad_log_q.T).T\\n\\n return np.mean(vectorized,axis=1)\",\n \"def backward(log_emlik, log_startprob, log_transmat):\\n #print(log_transmat)\\n beta = np.zeros(log_emlik.shape)\\n n = len(log_emlik)-2\\n log_transmat = log_transmat[0:-1,0:-1];\\n\\n\\n\\n while n >= 0:\\n\\n for j in range(0,len(log_emlik[0])):\\n\\n beta[n,j] = logsumexp(log_transmat[j,:] + log_emlik[n+1,:] + beta[n+1,:])\\n\\n n = n -1\\n #print(beta[n,:])\\n #print(beta)\\n return beta\",\n \"def cv_gradient(self,z):\\n gradient = np.zeros(np.sum(self.approx_param_no))\\n z_t = z.T \\n log_q = self.normal_log_q(z.T)\\n log_p = self.log_p(z.T)\\n grad_log_q = self.grad_log_q(z)\\n gradient = grad_log_q*(log_p-log_q)\\n\\n alpha0 = alpha_recursion(np.zeros(np.sum(self.approx_param_no)), grad_log_q, gradient, np.sum(self.approx_param_no)) \\n\\n vectorized = gradient - ((alpha0/np.var(grad_log_q,axis=1))*grad_log_q.T).T\\n\\n return np.mean(vectorized,axis=1)\",\n \"def log_lhood(X, Z, Y, a, ep, lamb):\\n \\n K = Z.shape[1]\\n N, T = X.shape\\n \\n # p(X)\\n ZY = np.dot(Z,Y) \\n log_pX = 0\\n log_pX = log_pX + np.sum(X * np.log(1 - ((1 - lamb) ** ZY) * (1 - ep)))\\n log_pX = log_pX + np.sum((1 - X) * np.log(((1 - lamb) ** ZY) * (1 - ep))) \\n \\n # p(Z)\\n HN = 0\\n for n in range(1, N+1):\\n HN += 1.0/n\\n m = Z.sum(axis=0)\\n log_pZ = (K * np.log(a) - (a * HN)) + np.sum(gammaln(m) +\\n gammaln(N - m + 1) - gammaln(N + 1)) \\n\\n return log_pZ + log_pX\",\n \"def _forward_log_det_jacobian(self, x):\\n d = self._compute_shared(x=x)\\n relx = (x - d.x_k) / d.w_k\\n relx = relx # tf.where(d.out_of_bounds, 0.5*tf.ones_like(x), relx)\\n grad = (\\n 2 * tf.math.log(d.s_k) +\\n tf.math.log(d.d_kp1 * relx**2 + 2 * d.s_k * relx * (1 - relx) + # newln\\n d.d_k * (1 - relx)**2) -\\n 2 * tf.math.log((d.d_kp1 + d.d_k - 2 * d.s_k) * relx *\\n (1 - relx) + d.s_k))\\n return grad # tf.where(d.out_of_bounds, tf.zeros_like(grad), grad)\",\n \"def MvNormalLogp():\\n cov = pt.matrix(\\\"cov\\\")\\n cov.tag.test_value = floatX(np.eye(3))\\n delta = pt.matrix(\\\"delta\\\")\\n delta.tag.test_value = floatX(np.zeros((2, 3)))\\n\\n cholesky = Cholesky(lower=True, on_error=\\\"nan\\\")\\n\\n n, k = delta.shape\\n n, k = f(n), f(k)\\n chol_cov = cholesky(cov)\\n diag = pt.diag(chol_cov)\\n ok = pt.all(diag > 0)\\n\\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\\n delta_trans = solve_lower(chol_cov, delta.T).T\\n\\n result = n * k * pt.log(f(2) * np.pi)\\n result += f(2) * n * pt.sum(pt.log(diag))\\n result += (delta_trans ** f(2)).sum()\\n result = f(-0.5) * result\\n logp = pt.switch(ok, result, -np.inf)\\n\\n def dlogp(inputs, gradients):\\n (g_logp,) = gradients\\n cov, delta = inputs\\n\\n g_logp.tag.test_value = floatX(1.0)\\n n, k = delta.shape\\n\\n chol_cov = cholesky(cov)\\n diag = pt.diag(chol_cov)\\n ok = pt.all(diag > 0)\\n\\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\\n delta_trans = solve_lower(chol_cov, delta.T).T\\n\\n inner = n * pt.eye(k) - pt.dot(delta_trans.T, delta_trans)\\n g_cov = solve_upper(chol_cov.T, inner)\\n g_cov = solve_upper(chol_cov.T, g_cov.T)\\n\\n tau_delta = solve_upper(chol_cov.T, delta_trans.T)\\n g_delta = tau_delta.T\\n\\n g_cov = pt.switch(ok, g_cov, -np.nan)\\n g_delta = pt.switch(ok, g_delta, -np.nan)\\n\\n return [-0.5 * g_cov * g_logp, -g_delta * g_logp]\\n\\n return OpFromGraph([cov, delta], [logp], grad_overrides=dlogp, inline=True)\",\n \"def grad_log_q(self,z): \\n param_count = 0\\n grad = np.zeros((np.sum(self.approx_param_no),self.sims))\\n for core_param in range(len(self.q)):\\n for approx_param in range(self.q[core_param].param_no):\\n grad[param_count] = self.q[core_param].vi_score(z[core_param],approx_param) \\n param_count += 1\\n return grad\",\n \"def backward(log_emlik, log_startprob, log_transmat):\\n N, M = log_emlik.shape\\n logPi=log_startprob[:-1]\\n logB=log_emlik\\n logA=log_transmat[:-1,:-1]\\n beta = np.zeros_like(logB)\\n for t in range(N-2,-1,-1):\\n for j in range(M):\\n beta[t][j]=logsumexp(beta[t+1]+logA[j]+logB[t+1])\\n\\n return beta\",\n \"def b_gradient_descent(self, LB,UB,eta, tol,iter):\\n bgd=[]\\n bgd_x=[LB]\\n iteration=0\\n # current_pt=X\\n first_derivative=sym.diff(self.gdfunc)\\n #print(first_derivative)\\n x=sym.Symbol('x')\\n first_derivative=sym.lambdify(x,first_derivative)\\n learn_rate=eta\\n \\n new_x=LB\\n bgd_x.append(LB)\\n \\n for i in range(iter):\\n for j in np.arange(LB,UB,0.1):\\n prev_x=new_x\\n new_x=prev_x-(learn_rate*first_derivative(prev_x))\\n #print(\\\"i = \\\",j,\\\"gradient =\\\",(learn_rate*first_derivative(j)),iteration)\\n iteration=iteration+1\\n #print(iteration)\\n if iteration >=iter:\\n break \\n if new_x <= tol:\\n #print(\\\"new_x = \\\",new_x,\\\"gradient =\\\",(learn_rate*first_derivative(prev_x)), iteration) \\n break\\n \\n \\n \\n \\n #print(new_x)\\n bgd_x.append(new_x)\\n \\n \\n bgd.append(bgd_x)\\n bgd.append(new_x)\\n bgd.append(iteration)\\n\\n return bgd\",\n \"def fast_loss_and_grad(self, X, y):\\n loss = 0.0\\n grad = np.zeros(self.W.shape) # initialize the gradient as zero\\n \\n # ================================================================ #\\n # YOUR CODE HERE:\\n # Calculate the softmax loss and gradient WITHOUT any for loops.\\n # ================================================================ #\\n \\n num_train = X.shape[0]\\n num_classes = self.W.shape[0]\\n \\n# # vectorized loss calculation #\\n class_scores_matrix = np.dot(self.W,X.T) # calculating class scores matrix (C x m): rows are class scores transposes\\n class_scores_matrix -= np.max(class_scores_matrix) # considering the possible issue for numerical instability and account for it\\n exp_a = np.exp(class_scores_matrix) # calculating the exponents\\n \\n# y_exp = np.array(exp_a[y, np.arange(0, class_scores_matrix.shape[1])])\\n# #print(exp_a[:,:3])\\n# #print(y[:3])\\n# #print(y_exp[:3])\\n \\n# tt = np.sum(exp_a,axis=0)\\n# tt2 = np.divide(tt,y_exp)\\n# print(num_train)\\n# tt3 = np.power(tt2,1/num_train)\\n# loss = np.log(np.prod(tt3))\\n \\n \\n \\n \\n (C, D) = self.W.shape\\n N = X.shape[0]\\n\\n scores = np.dot(self.W, X.T)\\n scores -= np.max(scores) # shift by log C to avoid numerical instability\\n\\n y_mat = np.zeros(shape = (C, N))\\n y_mat[y, range(N)] = 1\\n\\n # matrix of all zeros except for a single wx + log C value in each column that corresponds to the\\n # quantity we need to subtract from each row of scores\\n correct_wx = np.multiply(y_mat, scores)\\n\\n # create a single row of the correct wx_y + log C values for each data point\\n sums_wy = np.sum(correct_wx, axis=0) # sum over each column\\n\\n exp_scores = np.exp(scores)\\n sums_exp = np.sum(exp_scores, axis=0) # sum over each column\\n result = np.log(sums_exp)\\n\\n result -= sums_wy\\n\\n loss = np.sum(result)\\n loss /= num_train\\n \\n \\n # vectorized gradient calculation #\\n exp_a_sum = np.sum(exp_a,axis=0)\\n\\n y_mat_corres = np.zeros(shape = (num_classes, num_train))\\n y_mat_corres[y, range(num_train)] = 1\\n sum_exp_scores = np.sum(exp_a, axis=0) \\n sum_exp_scores = 1.0 / exp_a_sum # division by sum over columns\\n exp_a *= sum_exp_scores\\n grad = np.dot(exp_a, X)\\n grad -= np.dot(y_mat_corres, X)\\n grad /= num_train\\n \\n\\n # ================================================================ #\\n # END YOUR CODE HERE\\n # ================================================================ #\\n\\n return loss, grad\",\n \"def forward(log_emlik, log_startprob, log_transmat):\\n logPi=log_startprob[:-1]\\n logB=log_emlik\\n logA=log_transmat[:-1,:-1]\\n alpha = np.zeros_like(logB)\\n alpha[0]=logB[0]+logPi\\n for i in range(1,logB.shape[0]):\\n for j in range(logA.shape[0]):\\n alpha[i][j]=logsumexp(alpha[i-1]+logA[:,j]+logB[i][j])\\n return alpha\",\n \"def cv_gradient_initial(self,z):\\n gradient = np.zeros(np.sum(self.approx_param_no))\\n z_t = z.T \\n log_q = self.normal_log_q_initial(z.T)\\n log_p = self.log_p(z.T)\\n grad_log_q = self.grad_log_q(z)\\n gradient = grad_log_q*(log_p-log_q)\\n\\n alpha0 = alpha_recursion(np.zeros(np.sum(self.approx_param_no)), grad_log_q, gradient, np.sum(self.approx_param_no))\\n\\n vectorized = gradient - ((alpha0/np.var(grad_log_q,axis=1))*grad_log_q.T).T\\n\\n return np.mean(vectorized,axis=1)\",\n \"def backward(log_emlik, log_startprob, log_transmat):\\n N, M = log_emlik.shape\\n backward_prob = np.zeros((N,M))\\n\\n backward_prob[N-1, :] = 0.0\\n\\n for i in range(N-2,-1,-1):\\n for k in range(M):\\n # probability of transitioning from k to state l * probability of emitting symbol at state l at ts i+1 * recursive backward probability\\n backward_prob[i,k] = logsumexp(log_transmat[k,:] + log_emlik[i+1,:] + backward_prob[i+1,:])\\n\\n return backward_prob\",\n \"def logZ(self, T, pos, psi, phi):\\n msgs = belief_propagation(T, pos, psi, phi, True)\\n log_z = calculate_belief_sum(msgs, True)\\n return log_z\",\n \"def log_prob(self):\",\n \"def TCVAE_loss_function(input, recons, mu, log_var, z, dataset_size, batch_iter, \\n anneal_steps=200., alpha=1., beta=6., gamma=1., train=True):\\n weight = 1 # Account for the minibatch samples from the dataset\\n batch_size, z_dim = z.shape\\n \\n recons_loss = F.mse_loss(recons, input, reduction='sum')/(19*batch_size)\\n\\n log_q_zx = log_density_gaussian(z, mu, log_var).sum(dim = 1)\\n\\n zeros = torch.zeros_like(z)\\n log_p_z = log_density_gaussian(z, zeros, zeros).sum(dim = 1)\\n\\n mat_log_q_z = log_density_gaussian( z.view(batch_size, 1, z_dim),\\n mu.view(1, batch_size, z_dim),\\n log_var.view(1, batch_size, z_dim))\\n\\n # Reference\\n # [1] https://github.com/YannDubs/disentangling-vae/blob/535bbd2e9aeb5a200663a4f82f1d34e084c4ba8d/disvae/utils/math.py#L54\\n strat_weight = (dataset_size - batch_size + 1) / (dataset_size * (batch_size - 1))\\n importance_weights = torch.Tensor(batch_size, batch_size).fill_(1 / (batch_size -1)).to(input.device)\\n importance_weights.view(-1)[::batch_size] = 1 / dataset_size\\n importance_weights.view(-1)[1::batch_size] = strat_weight\\n importance_weights[batch_size - 2, 0] = strat_weight\\n log_importance_weights = importance_weights.log()\\n\\n mat_log_q_z += log_importance_weights.view(batch_size, batch_size, 1)\\n\\n log_q_z = torch.logsumexp(mat_log_q_z.sum(2), dim=1, keepdim=False)\\n log_prod_q_z = torch.logsumexp(mat_log_q_z, dim=1, keepdim=False).sum(1)\\n\\n mi_loss = (log_q_zx - log_q_z).mean()/19\\n tc_loss = (log_q_z - log_prod_q_z).mean()/19\\n kld_loss = (log_prod_q_z - log_p_z).mean()/19\\n\\n if train:\\n batch_iter += 1\\n anneal_rate = min(0 + 1 * batch_iter / anneal_steps, 1)\\n else:\\n anneal_rate = 1.\\n\\n loss = recons_loss + alpha*mi_loss + weight*(beta*tc_loss + \\n anneal_rate*gamma*kld_loss)\\n \\n return loss, recons_loss, kld_loss, tc_loss, mi_loss\",\n \"def statePosteriors(log_alpha, log_beta):\\n return log_alpha + log_beta - logsumexp(log_alpha[-1,:])\",\n \"def update(self, returns, log_probs):\\n policy_gradient = []\\n for log_prob, Gt in zip(log_probs, returns):\\n policy_gradient.append(-log_prob * Gt)\\n\\n loss = torch.stack(policy_gradient).sum()\\n self.optimizer.zero_grad()\\n loss.backward()\\n self.optimizer.step()\",\n \"def log_likelihood(self):\\r\\n if self.likelihood.is_heteroscedastic:\\r\\n A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)\\r\\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self._A))\\r\\n else:\\r\\n A = -0.5 * self.num_data * self.output_dim * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT\\r\\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self._A))\\r\\n C = -self.output_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * np.log(sf2))\\r\\n D = 0.5 * self.data_fit\\r\\n self._A_part, self._B_part, self._C_part, self._D_part = A, B, C, D\\r\\n return A + B + C + D + self.likelihood.Z\",\n \"def log_likelihood_gradients_(self, y, f):\\n log_lik = self.evaluate_log_likelihood(y, f)\\n f = np.squeeze(f)\\n J = jacrev(self.evaluate_log_likelihood, argnums=1)\\n H = jacrev(J, argnums=1)\\n return log_lik, J(y, f), H(y, f)\",\n \"def bprop(self, input, output):\\n\\n\\t\\tgradient_biases = [self.array(i, 1) for i in self.netSize[1:]]\\n\\t\\tgradient_weights = [self.array(i, j) for i, j in zip(self.netSize[:-1], self.netSize[1:])]\\n\\t\\t\\n\\t\\t\\\"\\\"\\\"\\n\\t\\tFirst we should obtain our activations and z for a given input\\n\\t\\t\\\"\\\"\\\"\\n\\t\\ta, z = self.fprop(input)\\n\\t\\t\\n\\t\\tprint \\\"a: \\\"\\n\\t\\tpprint(a)\\n\\t\\tprint \\\"z: \\\"\\n\\t\\tpprint(z)\\n\\t\\t\\n\\t\\t\\\"\\\"\\\"\\n\\t\\t1) Initialization\\n\\t\\t\\\"\\\"\\\"\\n\\t\\t\\n\\t\\t#Initialize delta\\n\\t\\tdelta = [self.array(i, 1) for i in self.netSize[1:]]\\n\\t\\t\\t\\t\\n\\t\\t\\\"\\\"\\\"\\n\\t\\t2) Compute output error, delta\\n\\t\\t\\\"\\\"\\\"\\n\\t\\tdelta[-1] = self.hadmardProduct((self.matrixSubtraction(a[-1], output)), self.sigmoidPrimeArray(z[-1]))\\n\\t\\tprint \\\"DELTA -1\\\"\\n\\t\\tprint delta[-1]\\n\\n\\n\\t\\t#We want to go from the second last value until the second value!\\n\\t\\tinterval = range(len(self.netSize) - 2, 0, -1)\\n\\t\\n\\t\\t\\\"\\\"\\\"\\n\\t\\t3) Backpropagate the error to all previous layers.\\n\\t\\t\\\"\\\"\\\"\\n\\t\\t#Now it's time to backpropagate\\n\\t\\t#Remember, only weights for in between each layer!\\n\\t\\t#BE VERY CAREFUL WHEN USING LIST COMPREHENSIONS!\\n\\t\\tfor i in interval:\\n\\t\\t\\tdelta[i - 1] = self.hadmardProduct(self.matrixMultiply(self.weights[i], delta[i]), self.sigmoidPrimeArray(z[i])) \\n\\t\\n\\t\\t\\\"\\\"\\\"\\n\\t\\t4) Compute Gradients.\\n\\t\\t\\\"\\\"\\\"\\n\\t\\t#We have weights for every layer except for the last\\n\\t\\tnumLayers = len(self.netSize)\\n\\t\\t\\n\\t\\t\\\"\\\"\\\"\\n\\t\\tRemember how you arrange weights.\\n\\t\\tYou arrange weights from the jth neuron in the lth layer, to the kth neuron in the lth + 1 layer\\n\\t\\t -\\n\\t\\t- \\n\\t\\t -\\n\\t\\t-\\n\\t\\t -\\n\\t\\tBecause of the way you have it arranged, the weights only exist for every layer except for the last!\\t \\n\\t\\tSo for the gradient with respect to the weight, the formula is d/dw (l) = a(l) * delta(i + 1)\\n\\t\\tIt isn't the same as in the book!\\n\\t\\t\\n\\t\\tThis should also be activations * delta ^T (delta transpose)\\n\\t\\t\\\"\\\"\\\"\\n\\t\\tfor i in range(0, numLayers - 1):\\n\\t\\t\\tgradient_weights[i] =self.matrixMultiply(a[i], self.transpose(delta[i])) \\n\\t\\t\\n\\t\\t#We have biases for every layer but the first, and the last layer\\n\\t\\tgradient_biases = delta\\n\\t\\n\\t\\tprint \\\"\\\\n DELTA FINAL: \\\\n\\\"\\n\\t\\tpprint(delta)\\n\\t\\tprint \\\"\\\\n GRADIENT WEIGHTS: \\\\n\\\"\\n\\t\\tpprint(gradient_weights)\\n\\t\\tprint \\\"\\\\n GRADIENT BIASES: \\\\n\\\"\\n\\t\\tpprint(gradient_biases)\\n\\t\\n\\t\\treturn gradient_biases, gradient_weights\",\n \"def calculate_log_perplexity(self, output, flat_labels): #completed, expensive, should be compiled\\n return -np.sum(np.log2(np.clip(output, a_min=1E-12, a_max=1.0))[np.arange(flat_labels.shape[0]), flat_labels[:,1]])\",\n \"def backward(log_emlik, log_startprob, log_transmat):\\n N = log_emlik.shape[0]\\n M = log_emlik.shape[1]\\n backward_prob = np.zeros((N, M))\\n for n in reversed(range(N-1)):\\n for i in range(M):\\n backward_prob[n, i] = logsumexp(backward_prob[n + 1, :M] + log_transmat[i, :M] + log_emlik[n + 1, :M])\\n\\n return backward_prob\",\n \"def grad_log(self, X):\\n g = autograd.elementwise_grad(self.log_den)\\n G = g(X)\\n return G\",\n \"def logistic_pen(weights, data, targets, hyperparameters):\\n\\n wr = hyperparameters['weight_regularization']\\n \\n t = np.transpose(np.repeat(np.reshape(weights[:-1], (len(weights)-1, 1)), len(data), axis = 1))\\n f_e = data * t\\n z_sums = np.sum(f_e, axis=1)\\n y = sigmoid(z_sums +weights[-1])\\n f = np.sum(np.log(1 + np.exp(-z_sums - weights[-1])) + (1 - np.transpose(targets)) * (z_sums + weights[-1]))\\n df = np.sum(data * np.transpose(((-np.exp(-z_sums - weights[-1]) / (1 + np.exp(-z_sums - weights[-1]))) + (1 - np.transpose(targets)))), axis = 0)\\n df = np.append(df, np.sum(np.transpose(((-np.exp(-z_sums - weights[-1]) / (1 + np.exp(-z_sums - weights[-1]))) + (1 - np.transpose(targets)))), axis = 0))\\n\\n f += np.dot(weights[:-1].transpose()[0], weights[:-1].transpose()[0]) * wr / 2\\n df = np.reshape(df, ((len(df), 1)))\\n df += np.reshape(np.append(weights[:-1] * wr, 0), (len(weights), 1))\\n\\n f += (weights[-1, 0] ** 2) * wr / 2\\n df[-1] += weights[-1,0] * wr \\n\\n return f, df, np.reshape(y, (len(y), 1))\",\n \"def grad_llh(self, params):\\n grad = np.clip(self.grad_log_likelihood(params[0], params[1], params[2:]), SMALLEST_NUMBER,\\n LARGEST_NUMBER)\\n\\n return grad\",\n \"def _grad(V):\\n dv = diag(V)\\n weights, A, _, AinvB = _weights(dv)\\n Ey = (weights.T.dot(Y_control) - Y_treated).getA()\\n dGamma0_dV_term2 = zeros(K)\\n #dPI_dV = zeros((N0, N1)) # stupid notation: PI = W.T\\n #Ai = A.I\\n for k in range(K):\\n if verbose: # for large sample sizes, linalg.solve is a huge bottle neck,\\n print(\\\"Calculating gradient, linalg.solve() call %s of %s\\\" % (k ,K,))\\n #dPI_dV.fill(0) # faster than re-allocating the memory each loop.\\n dA = dA_dV_ki[k]\\n dB = dB_dV_ki[k]\\n dPI_dV = linalg.solve(A,(dB - dA.dot(AinvB))) \\n #dPI_dV = Ai.dot(dB - dA.dot(AinvB))\\n dGamma0_dV_term2[k] = np.einsum(\\\"ij,kj,ki->\\\",Ey, Y_control, dPI_dV) # (Ey * Y_control.T.dot(dPI_dV).T.getA()).sum()\\n return LAMBDA + 2 * dGamma0_dV_term2\",\n \"def kullback_leibler_gaussian(z_mean, z_logvar, beta=1., **args):\\n \\n loss = 1 + z_logvar - tf.square(z_mean) - tf.exp(z_logvar)\\n loss = -0.5 * tf.reduce_sum(input_tensor=loss, axis=-1)\\n return beta * tf.reduce_mean(input_tensor=loss)\",\n \"def compute_edge_logits(self):\",\n \"def forward(log_emlik, log_startprob, log_transmat):\\n \\n alpha = np.zeros(np.shape(log_emlik))\\n N=len(alpha)\\n # recursiveAlpha(N-1, alpha, log_emlik, log_startprob, log_transmat)\\n alpha[0][:] = log_startprob.T + log_emlik[0]\\n\\n for n in range(1,len(alpha)):\\n for i in range(alpha.shape[1]):\\n alpha[n, i] = logsumexp(alpha[n - 1] + log_transmat[:,i]) + log_emlik[n,i]\\n return alpha, logsumexp(alpha[N-1])\",\n \"def backward_p(self, x):\\n log_det_jacob, z = x.new_zeros(x.shape[0]), x\\n for i in reversed(range(len(self.t))):\\n z_ = self.mask[i] * z\\n s = self.s[i](z_) * (1 - self.mask[i])\\n t = self.t[i](z_) * (1 - self.mask[i])\\n z = (1 - self.mask[i]) * (z - t) * torch.exp(-s) + z_\\n log_det_jacob -= s.sum(dim=1)\\n return z, log_det_jacob\",\n \"def kullback_leibler_gaussian(z_mean, z_logvar, beta=1.):\\n \\n loss = 1 + z_logvar - tf.square(z_mean) - tf.exp(z_logvar)\\n loss = -0.5 * tf.reduce_sum(loss, axis=-1)\\n return beta * tf.reduce_mean(loss)\",\n \"def backward(g, N, K):\\n\\tb = np.zeros((N,K))\\n\\tfor t in reversed(xrange(0,N-1)):\\n\\t\\tby = b[t+1,:]\\n\\t\\tfor yp in xrange(K):\\n\\t\\t\\tb[t,yp] = misc.logsumexp(by + g[t,yp,:])\\n\\treturn b\",\n \"def log_prob(self, z, x):\\n n_samples = z.shape[0].value\\n log_prob = 0\\n for i in range(self.time_steps):\\n lat_dim = self.transition.dim_x\\n obs_dim = self.emmision.dim_x\\n # Current time step latent state and observation\\n z_t = tf.slice(z, [0, i * lat_dim], [-1, lat_dim])\\n x_t = tf.slice(x, [0, i * obs_dim], [-1, obs_dim])\\n if i == 0:\\n log_prob += tf.reduce_sum(\\n self.init_state_p.log_prob(z_t), axis=1)\\n else:\\n log_prob += self.transition.log_prob(z_t, z_tminus)\\n log_prob += self.emmision.log_prob(x_t, z_t)\\n # Update the previous latent state for next iteration\\n z_tminus = z_t\\n return log_prob\",\n \"def _finite_diff_gradient(self, parameters):\\n assert isinstance(parameters,np.ndarray)\\n # get the free indicies\\n free_inds = np.nonzero(np.logical_not(self._fixed_indicies))[0]\\n\\n # first take a forward step in each direction\\n step = 1e-6 # finite difference step\\n log_like_fs = np.zeros(free_inds.size)\\n for i,param_idx in enumerate(free_inds):\\n p_fs = parameters.copy()\\n p_fs[param_idx] += step # take a step forward\\n log_like_fs[i] = self._compute_log_likelihood(p_fs)\\n\\n # compute the log likelihood at current point\\n log_like = self._compute_log_likelihood(parameters)\\n\\n # compute the gradient\\n gradient = np.zeros(parameters.shape) # default gradient is zero\\n gradient[free_inds] = (log_like_fs-log_like)\\n gradient[free_inds] = gradient[free_inds]/step # divide by step length\\n return log_like, gradient\",\n \"def log_abs_det_jacobian(self, z):\\n pre_u = self.u_ + self.u\\n pre_w = self.w_ + self.w\\n a = F.softplus(self.a + self.inv)\\n w = F.softmax(pre_w, dim=3)\\n u = F.softmax(pre_u, dim=3)\\n # Perform computation\\n pre_sigm = torch.sum(u * a * z, 3) + self.b\\n sigm = torch.sigmoid(pre_sigm)\\n x_pre = torch.sum(w * sigm, dim=3)\\n x_pre_clipped = x_pre * (1 - self.eps) + self.eps * 0.5\\n logj = F.log_softmax(pre_w, dim=3) + logsigmoid(pre_sigm) + logsigmoid(-pre_sigm) + torch.log(a)\\n # n, d, d2, dh\\n logj = logj + F.log_softmax(pre_u, dim=3)\\n # n, d, d2, dh, d1\\n logj = torch.log(torch.sum(torch.exp(logj),3))\\n # n, d, d2, d1\\n logdet_ = logj + np.log(1 - self.eps) - (torch.log(x_pre_clipped) + torch.log(-x_pre_clipped + 1))\\n return logdet_\",\n \"def _call(self, z): \\n self.a = F.softplus(self.a)\\n self.w = F.softmax(self.w, dim=1)\\n # Compute\\n pre_sigm = self.a * z + self.b\\n sigm = torch.sigmoid(pre_sigm)\\n x_pre = self.w * sigm\\n if (len(z.shape) > 2):\\n x_pre = torch.sum(self.w * sigm, dim=1)\\n x_pre_clipped = x_pre * (1 - self.eps) + self.eps * 0.5\\n zp = torch.log(x_pre_clipped) - torch.log(1 - x_pre_clipped)\\n return zp\",\n \"def _log_bessel_ive_bwd(aux, g):\\n v, z = aux\\n grad_z = g * (bessel_iv_ratio(v + 1., z) + (v / z - tf.math.sign(z)))\\n _, grad_z = tfp_math.fix_gradient_for_broadcasting(\\n [v, z], [tf.ones_like(grad_z), grad_z])\\n\\n # No gradient for v at the moment. This is a complicated expression\\n # The gradient with respect to the parameter doesn't have an easy closed\\n # form. More work will need to be done to ensure good numerics for the\\n # gradient.\\n # TODO(b/169357627): Implement gradients of modified bessel functions with\\n # respect to parameters.\\n\\n return None, grad_z\",\n \"def kullbackLeibler(mu, log_sigma):\\n # (tf.Tensor, tf.Tensor) -> tf.Tensor\\n # = -0.5 * (1 + log(sigma**2) - mu**2 - sigma**2)\\n return -0.5 * tf.reduce_sum(1 + 2 * log_sigma - mu**2 - tf.exp(2 * log_sigma), 1)\",\n \"def log_marginal_likelihood(self, eval_gradient=False):\\n A, B, mu_tilda, gamma_tilda, r_grid, n_grid = self.calc_tau(return_all=True)\\n\\n gamma = self.GAMMA\\n gamma_y = self.GAMMA_Y\\n\\n A1 = np.copy(self.baseTau0)\\n A1[np.diag_indices_from(A1)] = 2 * self.ndim\\n\\n # A1 = (A - B) / gamma_y\\n A2 = np.diag(n_grid)\\n\\n B_inv = np.linalg.inv(B)\\n A_inv = np.linalg.inv(A)\\n\\n M_lambda = B - B.dot(np.linalg.inv(A).dot(B))\\n\\n # log_likelihood = mu_tilda.dot(M_lambda).dot(\\n # mu_tilda[:, np.newaxis]) - np.nan_to_num(np.log(np.linalg.det(M_lambda)))\\n log_likelihood = mu_tilda.dot(M_lambda).dot(mu_tilda[:, np.newaxis])\\n\\n # log_likelihood = -log_likelihood\\n print('log_likelihood: %f' % log_likelihood)\\n\\n Lambda = B - B.dot(A_inv.dot(B))\\n Lambda_inv = np.linalg.inv(Lambda)\\n\\n M_gamma = A2 - A2.dot(2 * gamma * A_inv - (gamma ** 2) * A_inv.dot(A2).dot(A_inv)).dot(A2)\\n\\n mean_r_grid = np.nan_to_num(np.array([r.sum() for r in r_grid]) / n_grid)\\n\\n log_likelihood_grad_gamma = mean_r_grid.dot(M_gamma.dot(mean_r_grid[:, np.newaxis]))[0]\\n\\n tmpMat = A2.dot(A_inv).dot(A1).dot(A_inv).dot(A2)\\n\\n log_likelihood_grad_gamma_y = (gamma ** 2) * (\\n np.trace(Lambda_inv.dot(tmpMat) + mean_r_grid.dot(tmpMat.dot(mean_r_grid[:, np.newaxis]))[0]))\\n\\n log_likelihood_gradient = np.array([log_likelihood_grad_gamma, log_likelihood_grad_gamma_y])\\n print('log_likelihood_grad: %s' % log_likelihood_gradient)\\n\\n if eval_gradient:\\n return log_likelihood, log_likelihood_gradient\\n else:\\n return log_likelihood\",\n \"def log(self, x_old, grad, x, curr_iter, tol_g_val, tol_x_val, tol_f_val):\\n\\n print(\\\"-----------------------------------\\\")\\n print(\\\"\\\\n Iter: \\\", curr_iter)\\n print(\\\"\\\\n x_old: \\\", x_old)\\n print(\\\"\\\\n gradient: \\\", grad)\\n print(\\\"\\\\n x: \\\", x)\\n print(\\\"\\\\n tol_x_val: \\\", tol_x_val)\\n print(\\\"\\\\n tol_f_val: \\\", tol_f_val)\\n print(\\\"\\\\n tol_g_val: %s \\\\n \\\" % tol_g_val)\",\n \"def gradient_ascent(f, df, theta_init, step_size, max_iter):\\n\\n fs = []\\n xs = []\\n thetas = theta_init\\n for i in range(max_iter): #for each data example\\n fs.append(f(thetas))\\n\\n temp = step_size*df(thetas)\\n thetas = step_size*df(thetas) #modify that feature by using the derivative of log likelihood\\n xs.append(thetas.flatten())\\n if i % 10 == 0:\\n print(i, thetas)\\n\\n return thetas, fs, xs\",\n \"def _log_fold_change_pairs(self, idx0, idx1, base):\\n logfc = np.zeros(shape=(len(idx0), len(idx1), self._theta_mle.shape[1]))\\n for i, xi in enumerate(idx0):\\n for j, xj in enumerate(idx1):\\n logfc[i, j, :] = self._theta_mle[xi, :] - self._theta_mle[xj, :]\\n\\n if base == np.e:\\n return logfc\\n else:\\n return logfc / np.log(base)\",\n \"def log_likelihood_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w))-y)\",\n \"def propagate(w, b, X, Y):\\n\\n m = X.shape[1];\\n\\n # A.shape should be (1, m), m -- number of training examples\\n A = sigmoid(np.dot(w.T, X) + b);\\n # print(\\\"A = \\\" +str(A))\\n\\n # single training exmaple cost = - (y * log(A) + (1-y) * log(1-A))\\n # cost = (-1/m) * (np.dot(Y, np.log(A).T) + np.dot((1-Y), np.log(1-A).T));\\n cost = (-1/m)*(np.dot(Y, np.log(A).T) + np.dot((1-Y), np.log(1-A).T));\\n # print(\\\"cost in function: \\\" +str(cost))\\n\\n # Backward propagation \\n dw = (1/m) * np.dot(X, (A - Y).T)\\n # np.sum() : axis = 0 means along the column and axis = 1 means working along the row.\\n db = (1/m) * ((A - Y).sum(1))\\n\\n # dw.shape should be (num_px * nump_px * 3, 1), db should be a scalar\\n assert(dw.shape == w.shape)\\n assert(db.dtype == float)\\n\\n # np.squeeze(arr) -- remove one dimentional entry from the shape of given array. \\n # -- arr: input array\\n cost = np.squeeze(cost)\\n assert(cost.shape == ())\\n \\n grads = {\\\"dw\\\": dw, \\\"db\\\": db}\\n \\n return grads, cost\",\n \"def ComputeLogGradientTerm(self, *args):\\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF2F_ComputeLogGradientTerm(self, *args)\",\n \"def statePosteriors(log_alpha, log_beta):\\n\\n gamma = np.zeros(log_alpha.shape)\\n\\n gamma = log_alpha + log_beta\\n\\n gamma = gamma - logsumexp(log_alpha[-1,:])\\n\\n return gamma\",\n \"def _log_fold_change_pairs(self, idx0, idx1, base):\\n logfc = np.tile(np.NaN, [len(idx0), len(idx1), self.model_estim.x.shape[1]])\\n for i, xi in enumerate(idx0):\\n for j, xj in enumerate(idx1):\\n logfc[i, j, :] = self._theta_mle[xj, :] - self._theta_mle[xi, :]\\n logfc[j, i, :] = -logfc[i, j, :]\\n\\n if base == np.e:\\n return logfc\\n else:\\n return logfc / np.log(base)\",\n \"def _log_bessel_kve_bwd(aux, g):\\n v, z = aux\\n dtype = dtype_util.common_dtype([v, z], tf.float32)\\n numpy_dtype = dtype_util.as_numpy_dtype(dtype)\\n\\n log_kve = _log_bessel_kve_custom_gradient(v, z)\\n grad_z = tfp_math.log_add_exp(\\n _log_bessel_kve_custom_gradient(v - 1., z),\\n _log_bessel_kve_custom_gradient(v + 1., z)) - numpy_dtype(\\n np.log(2.)) - log_kve\\n grad_z = g * -tf.math.expm1(grad_z)\\n _, grad_z = tfp_math.fix_gradient_for_broadcasting(\\n [v, z], [tf.ones_like(grad_z), grad_z])\\n\\n # No gradient for v at the moment. This is a complicated expression\\n # The gradient with respect to the parameter doesn't have an easy closed\\n # form. More work will need to be done to ensure good numerics for the\\n # gradient.\\n # TODO(b/169357627): Implement gradients of modified bessel functions with\\n # respect to parameters.\\n\\n return None, grad_z\",\n \"def log_likelihood(self) -> tf.Tensor:\\n # K⁻¹ + GᵀΣ⁻¹G = LLᵀ.\\n l_post = self._k_inv_post.cholesky\\n num_data = self.observations_index.shape[0]\\n\\n # Hμ [..., num_transitions + 1, output_dim]\\n marginal = self.emission.project_state_to_f(self.prior_ssm.marginal_means)\\n marginal = self._drop_batch_shape(marginal)\\n\\n # y = obs - Hμ [..., num_transitions + 1, output_dim]\\n disp = self.observations - marginal\\n disp_data = self.sparse_observations - self.dense_to_sparse(marginal)\\n\\n # cst is the constant term for a gaussian log likelihood\\n cst = (\\n -0.5 * np.log(2 * np.pi) * tf.cast(self.emission.output_dim * num_data, default_float())\\n )\\n\\n term1 = -0.5 * tf.reduce_sum(\\n input_tensor=tf.einsum(\\\"...op,...p,...o->...o\\\", self._r_inv_data, disp_data, disp_data), axis=[-1, -2]\\n )\\n\\n # term 2 is: ½|L⁻¹(GᵀΣ⁻¹)y|²\\n # (GᵀΣ⁻¹)y [..., num_transitions + 1, state_dim]\\n obs_proj = self._back_project_y_to_state(disp)\\n\\n # ½|L⁻¹(GᵀΣ⁻¹)y|² [...]\\n term2 = 0.5 * tf.reduce_sum(\\n input_tensor=tf.square(l_post.solve(obs_proj, transpose_left=False)), axis=[-1, -2]\\n )\\n\\n ## term 3 is: ½log |K⁻¹| - log |L| + ½ log |Σ⁻¹|\\n # where log |Σ⁻¹| = num_data * log|R⁻¹|\\n term3 = (\\n 0.5 * self.prior_ssm.log_det_precision()\\n - l_post.abs_log_det()\\n + 0.5 * self._log_det_observation_precision\\n )\\n\\n return tf.reduce_sum(cst + term1 + term2 + term3)\",\n \"def gradient_descent(self, x, y):\\n # Initialize weights vector\\n self.weights = np.zeros(len(x[0]))\\n\\n # Storing number of training example in a variable \\n n = len(x)\\n\\n # Initiate variables to keep track of the current and smallest loss recorded\\n lowest_loss = sys.float_info.max\\n current_loss = sys.float_info.max\\n\\n # Initiate variables to keep track of step sizes\\n norm = sys.float_info.max\\n smallest_norm = sys.float_info.max\\n\\n # Initiate list variable that stores all previous weights\\n prev_weights = []\\n\\n # Initiate list that stores all the errors. \\n errors = []\\n \\n # Variable to keep track of the number of iterations that returns a bigger loss than current loss\\n k_loss_iteration = 1\\n\\n # Learning loop\\n for i in range(self.max_iter):\\n\\n # Append current weights\\n prev_weights.append(np.array(self.weights))\\n \\n # Minimizing Loss Function Error by adjusting weights using Gradient Descent\\n self.weights += self.learning_rate * (sum([x[i] * (y[i] - self.logistic_function(self.weights.dot(x[i]))) for i in range(n)]) - 2 * self.l2 * self.weights)\\n\\n # Compute the error of the Cost Function and store it in a list\\n current_loss = self.cost(x,y)\\n\\n if len(errors) > 1 and current_loss > errors[-1]:\\n k_loss_iteration += 1\\n else: \\n k_loss_iteration = 1\\n\\n errors.append(current_loss)\\n \\n # Track smallest loss\\n if current_loss < lowest_loss:\\n lowest_loss = current_loss\\n\\n # Compute the L2 Norm of the difference between current weights and previous weights\\n norm = np.linalg.norm(self.weights - prev_weights[-1])\\n\\n # Track smallest step size and set it as error threshold\\n if norm < smallest_norm:\\n smallest_norm = norm\\n\\n # If this L2 norm is smaller than the error_threshold it means that it converged, hence we can break. In other words, repeat until the step size is too small\\n if self.error_threshold != None and norm < self.error_threshold:\\n print(\\\"Converged after {} iterations!\\\".format(i))\\n break\\n\\n # stop if error hasn't gone down in k iterations\\n if k_loss_iteration >= 10:\\n print(k_loss_iteration + \\\" iterations of loss not decreasing on {}th itertion.\\\".format(i))\\n break\\n\\n # Log final weights\\n print(\\\"Final norm: \\\" + str(norm) + \\\"\\\\nSmallest step size recorded: \\\" + str(smallest_norm) + \\\"\\\\nFinal error: \\\" + str(current_loss) + \\\"\\\\nLowest error recorded: \\\" + str(lowest_loss) + \\\"\\\\nNumber of epochs: \\\" + str(len(errors)) + \\\"\\\\nFinal weights: \\\" + str(self.weights))\",\n \"def logprob(self):\\n assert len(self._added_rows) == self._num_rows\\n V, E, K, M = self._VEKM\\n vertex_logits = logprob_dc(self._vert_ss, self._vert_prior, axis=1)\\n logprob = vertex_logits.sum()\\n for e, v1, v2 in self._tree.tree_grid.T:\\n logprob += (logprob_dc(self._edge_ss[e, :, :], self._edge_prior) -\\n vertex_logits[v1] - vertex_logits[v2])\\n for v in range(V):\\n beg, end = self._table.ragged_index[v:v + 2]\\n logprob += logprob_dc(self._feat_ss[beg:end, :], self._feat_prior)\\n logprob -= logprob_dc(self._meas_ss[v, :], self._meas_prior[v])\\n return logprob\",\n \"def compute_log_prob(self,params: ndarray) -> float:\\n return self.compute_log_prior(params) + self.compute_log_likelihood(params)\",\n \"def forward(log_emlik, log_startprob, log_transmat):\\n alpha = np.zeros(log_emlik.shape)\\n\\n alpha[0,:] = log_startprob[0:-1] + log_emlik[0,:]\\n\\n sum_row = 0;\\n log_transmat = log_transmat[0:-1];\\n\\n\\n for frame in range(1,len(log_emlik)):\\n\\n for state in range(0,len(log_emlik[0])):\\n\\n alpha[frame,state] = logsumexp(alpha[frame-1,:] + log_transmat[:,state]) + log_emlik[frame,state]\\n #print(alpha[frame,state])\\n #print(alpha[frame,:])\\n\\n return alpha\",\n \"def _perplexity(self, X, log_w):\\n return np.exp(-log_w/X.sum())\",\n \"def log_abs_det_jacobian(self, z):\\n self.a = F.softplus(self.a)\\n self.w = F.softmax(self.w, dim=1)\\n pre_sigm = self.a * z + self.b\\n sigm = torch.sigmoid(pre_sigm)\\n x_pre = self.w * sigm\\n if (len(z.shape) > 2):\\n x_pre = torch.sum(self.w * sigm, dim=1)\\n x_pre_clipped = x_pre * (1 - self.eps) + self.eps * 0.5\\n logj = F.log_softmax(self.w, dim=1) + logsigmoid(pre_sigm) + logsigmoid(-pre_sigm) + torch.log(self.a)\\n logj = torch.log(torch.sum(torch.exp(logj)))#,2).sum(2)\\n logdet = logj + np.log(1 - self.eps) - (torch.log(x_pre_clipped) + torch.log(-x_pre_clipped + 1))\\n return sum_dims(logdet)\",\n \"def compute_log_prior(self,params: ndarray) -> float:\\n ln_tE = params[0]\\n ln_A0 = params[1]\\n ln_deltaT = params[2]\\n fbl = params[3]\\n mb = params[4]\\n\\n # Equation (16,15,17) (note that Albrow uses \\\"log\\\" for log10)\\n log10e = np.log10(np.exp(1))\\n ln_pr_ln_tE = np.log(0.476) - ((log10e*ln_tE - 1.333)**2 / 0.330) + np.log(log10e)\\n ln_pr_ln_A0 = np.log(0.660) - (1.289*log10e*ln_A0) + np.log(log10e)\\n ln_pr_ln_deltaT = np.log(0.156) - ((log10e*ln_deltaT - 1.432)**2 / 0.458) +\\\\\\n np.log(log10e)\\n \\n # Paper doesnt mention the prior used, but I assume it to be uniform\\n ln_pr_fbl = uniform.logpdf(fbl,0.0,1.0)\\n\\n # Paper doesnr mention the prior used but I will asuumed it to be uniform\\n ln_pr_mb = uniform.logpdf(mb,self.mag_min - 1.0, self.mag_max + 1.0)\\n \\n \\n return ln_pr_fbl + ln_pr_ln_A0 + ln_pr_ln_deltaT + ln_pr_ln_tE + ln_pr_mb\",\n \"def Map_Gradients(post_eval,q,InvV,m_points):\\n m = InvV.n\\n N = m_points.num\\n d = InvV.d\\n \\n ds_dq = np.zeros([m,N])\\n dr_dq = np.zeros([m,N])\\n \\n ds_db = np.zeros([m,d,N])\\n dr_db = np.zeros([m,d,N])\\n \\n ds_dL = np.zeros([m,d,d,N])\\n dr_dL = np.zeros([m,d,d,N])\\n \\n dB_dL = np.zeros([m,d,d,d,N])\\n dM_dL = Cholesky_Derivs(InvV,m_points)\\n Q = Partitioner(q, InvV, post_eval, m_points)\\n \\n for j in range(m):\\n backtrack = m_points.map(InvV,j)\\n ds_dq[j,:] = - Q[j,:] / q[j]\\n dr_dq[j,:] = ds_dq[j,:] - np.mean(ds_dq[j,:])\\n \\n for k in range(d):\\n ds_db[j,k,:] = Q[j,:] * backtrack.all[:,k].T\\n dr_db[j,k,:] = ds_db[j,k,:] - np.mean(ds_db[j,k,:])\\n \\n for l in range(d):\\n for i in range(N):\\n for row in range(d):\\n for col in range(d):\\n dB_dL[j,row,k,l,i] += m_points.pick(i)[col] * dM_dL[j,row,col,k,l]\\n ds_dL[j,k,l,i] = Q[j,i] * np.inner(backtrack.pick(i),dB_dL[j,:,k,l,i])\\n if k == l:\\n ds_dL[j,k,l,:] += (2/InvV.L[j,k,l])\\n \\n dr_dL[j,k,l,:] = ds_dL[j,k,l,:] - np.mean(ds_dL[j,k,l,:])\\n \\n return dr_dq, dr_db, dr_dL\",\n \"def _log_fold_change_pairs(self, idx0, idx1, base):\\n assert np.all([x < self._pval.shape[1] for x in idx0])\\n assert np.all([x < self._pval.shape[1] for x in idx1])\\n if base == np.e:\\n return self._logfc[idx0, :, :][:, idx1, :]\\n else:\\n return self._logfc[idx0, :, :][:, idx1, :] / np.log(base)\",\n \"def _back_prop(self,W,X,labels,f=sigmoid,fprime=sigmoid_prime,lam=0.000001):\\n \\n Wl1,bl1,Wl2,bl2 = self._extract_weights(W)\\n \\n \\n \\n layers_outputs = self._forward_prop(W,X,f)\\n \\n \\n y = self.make_1_of_c_encoding(labels)\\n num_samples = X.shape[0] # layers_outputs[-1].shape[0]\\n \\n # Dot product return Numsamples (N) x Outputs (No CLasses)\\n # Y is NxNo Clases\\n # Layers output to\\n # small_delta_nl = NxNo_Outputs\\n\\n big_delta = np.zeros(Wl2.size + bl2.size + Wl1.size + bl1.size)\\n big_delta_wl1, big_delta_bl1, big_delta_wl2, big_delta_bl2 = self._extract_weights(big_delta)\\n \\n \\n dE_dy = layers_outputs[-1] - y \\n \\n big_delta_bl2 = dE_dy.sum(axis=0)\\n\\n \\n dE_dhl = dE_dy.dot(Wl2.T)\\n \\n\\n small_delta_hl = dE_dhl*fprime(layers_outputs[-2])\\n big_delta_bl1 = small_delta_hl.sum(axis=0)\\n \\n big_delta_wl2 = np.dot(layers_outputs[-2].T,dE_dy)\\n big_delta_wl1 = np.dot(X.T,small_delta_hl)\\n \\n \\n big_delta_wl2 = np.true_divide(big_delta_wl2,num_samples) + lam*Wl2*2\\n big_delta_bl2 = np.true_divide(big_delta_bl2,num_samples)\\n big_delta_wl1 = np.true_divide(big_delta_wl1,num_samples) + lam*Wl1*2\\n big_delta_bl1 = np.true_divide(big_delta_bl1,num_samples)\\n \\n #return big_delta\\n \\n return np.concatenate([big_delta_wl1.ravel(),\\n big_delta_bl1,\\n big_delta_wl2.ravel(),\\n big_delta_bl2])\",\n \"def logsum_safe(prob_ll, atl):\\n logpdf = prob_ll + K.log(atl + K.epsilon())\\n alpha = tf.reduce_max(logpdf, -1, keepdims=True)\\n return alpha + tf.log(tf.reduce_sum(K.exp(logpdf-alpha), -1, keepdims=True) + K.epsilon())\",\n \"def logistic_function(z):\\n\\tg = 1/(1+np.exp(-z))\\n\\t#g = np.exp(z)/(1+np.exp(z))\\n\\treturn g\",\n \"def kl_div_prior_gradient(self, posterior_logits, posterior_binary_samples):\\n #DVAE Eq11 - gradient of prior\\n #gradient of the KLD between posterior and prior wrt to prior\\n #parameters theta, i.e. generative model parameters.\\n #logits to probabilities\\n posterior_probs=torch.sigmoid(posterior_logits)\\n positive_probs=posterior_probs.detach()\\n \\n #samples from posterior are labelled positive\\n positive_samples=posterior_binary_samples.detach()\\n\\n n_split=positive_samples.size()[1]//2\\n positive_samples_left,positive_samples_right=torch.split(positive_samples,split_size_or_sections=int(n_split),dim=1)\\n \\n #-z_left^t J z_right\\n pos_first_term=torch.matmul(positive_samples_left,self.prior.get_weights())*positive_samples_right\\n \\n rbm_bias_left=self.prior.get_visible_bias()\\n rbm_bias_right=self.prior.get_hidden_bias()\\n rbm_bias=torch.cat([rbm_bias_left,rbm_bias_right])#self._h\\n \\n #this gives [42,400] size\\n #- z^t h\\n #TODO this uses positive probs. Should it not use positive samples?\\n # FIXME an indication are the negative ones where samples are used! On\\n #other hand this is the only place this this used\\n pos_sec_term=positive_probs*rbm_bias\\n # pos_sec_term=positive_samples*rbm_bias\\n\\n # Energy = -z_left^t J z_right - z^t h\\n pos_kld_per_sample=-(torch.sum(pos_first_term,axis=1)+torch.sum(pos_sec_term,axis=1))\\n #samples from rbm are labelled negative\\n\\n #rbm_samples Tensor(\\\"zeros:0\\\", shape=(200, 200), dtype=float32)\\n #this returns the full RBM set: left and right nodes concatenated\\n\\n #TODO What are these samples here?\\n #TODO what's the impact of doing gibbs sampling here? does this make\\n #sense?\\n rbm_samples=self.prior.get_samples_kld(approx_post_samples=positive_samples_left,n_gibbs_sampling_steps=1)\\n negative_samples=rbm_samples.detach()\\n\\n # print(self.prior.get_weights())\\n n_split=negative_samples.size()[1]//2\\n negative_samples_left,negative_samples_right=torch.split(negative_samples,split_size_or_sections=int(n_split),dim=1)\\n neg_first_term=torch.matmul(negative_samples_left,self.prior.get_weights())*negative_samples_right\\n \\n #FIXME see above, the positive case looks different. Why?\\n neg_sec_term=negative_samples*rbm_bias\\n neg_kld_per_sample=(torch.sum(neg_first_term,axis=1)+torch.sum(neg_sec_term,axis=1))\\n \\n kld_per_sample=pos_kld_per_sample+neg_kld_per_sample\\n\\n return kld_per_sample\",\n \"def compute_log_gradient(self, stateFeatures, action):\\n\\t\\t'''\\n\\t\\tassert(len(stateFeatures) == self.nStateFeatures)\\n\\t\\tassert(len(action) == self.actionDim)\\n\\n\\t\\tmean = np.dot(self.params,stateFeatures)\\n\\t\\tlog_gradient = np.tile(stateFeatures,(self.actionDim,1))\\n\\t\\taa = (action-mean)/np.square(np.diag(self.covarianceMatrix))\\n\\t\\tfor i,a in enumerate(aa):\\n\\t\\t\\tlog_gradient[i]*=a\\n\\t\\treturn log_gradient\\n\\t\\t'''\\n\\n\\t\\tif np.ndim(action) == 1:\\n\\t\\t\\taction = action[:, None]\\n\\n\\t\\tmean = np.dot(stateFeatures, self.params.T)\\n\\t\\tratio = (action - mean) / self.cov_diag[None, :]\\n\\t\\tlog_gradient = ratio[:, :, None] * stateFeatures[:, None, :]\\n\\t\\treturn log_gradient\",\n \"def gradient(self):\\n functional = self\\n\\n class KLCrossEntropyGradient(Operator):\\n\\n \\\"\\\"\\\"The gradient operator of this functional.\\\"\\\"\\\"\\n\\n def __init__(self):\\n \\\"\\\"\\\"Initialize a new instance.\\\"\\\"\\\"\\n super().__init__(functional.domain, functional.domain,\\n linear=False)\\n\\n def _call(self, x):\\n \\\"\\\"\\\"Apply the gradient operator to the given point.\\n\\n The gradient is not defined in for points with components less\\n than or equal to zero.\\n \\\"\\\"\\\"\\n if functional.prior is None:\\n tmp = np.log(x)\\n else:\\n tmp = np.log(x / functional.prior)\\n\\n if np.all(np.isfinite(tmp)):\\n return tmp\\n else:\\n # The derivative is not defined.\\n raise ValueError('The gradient of the Kullback-Leibler '\\n 'Cross Entropy functional is not defined '\\n 'for `x` with one or more components '\\n 'less than or equal to zero.'.format(x))\\n\\n return KLCrossEntropyGradient()\",\n \"def compute_gradient(self): # TODO: try to change to square loss since it's hessian is easier to obtain\\n A = np.dot(self.X, self.w)\\n m = self.t.shape[0]\\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\\n return (1 / m) * np.dot(self.X.T, C)\",\n \"def _logprob(self, sample):\\n cur_y, cur_z = sample\\n log_prior = 0\\n log_lik = 0\\n if cur_z.shape[1] == 0: return -99999999.9\\n \\n if self.cl_mode:\\n a_time = time()\\n d_cur_z = cl.Buffer(self.ctx, self.mf.READ_ONLY | self.mf.COPY_HOST_PTR, hostbuf = cur_z.astype(np.int32))\\n d_cur_y = cl.Buffer(self.ctx, self.mf.READ_ONLY | self.mf.COPY_HOST_PTR, hostbuf = cur_y.astype(np.int32))\\n d_logprob = cl.array.empty(self.queue, (cur_z.shape[0],), np.float32, allocator=self.mem_pool)\\n\\n if cur_z.shape[0] % self.p_mul_logprob_z_data == 0: wg = (self.p_mul_logprob_z_data,)\\n else: wg = None\\n \\n self.prg.logprob_z_data(self.queue, (cur_z.shape[0],), wg,\\n d_cur_z, d_cur_y, self.d_obs, d_logprob.data, #d_novel_f.data,\\n np.int32(self.N), np.int32(cur_y.shape[1]), np.int32(cur_z.shape[1]), \\n np.float32(self.alpha), np.float32(self.lam), np.float32(self.epislon))\\n log_lik = d_logprob.get().sum()\\n self.gpu_time += time() - a_time\\n\\n # calculate the prior probability of Y\\n num_on = (cur_y == 1).sum()\\n num_off = (cur_y == 0).sum()\\n log_prior = num_on * np.log(self.theta) + num_off * np.log(1 - self.theta)\\n\\n else:\\n # calculate the prior probability of Z\\n for n in xrange(cur_z.shape[0]):\\n num_novel = 0\\n for k in xrange(cur_z.shape[1]):\\n m = cur_z[:n,k].sum()\\n if m > 0:\\n if cur_z[n,k] == 1: log_prior += np.log(m / (n+1.0))\\n else: log_prior += np.log(1 - m / (n + 1.0))\\n else: \\n if cur_z[n,k] == 1: num_novel += 1\\n if num_novel > 0:\\n log_prior += poisson.logpmf(num_novel, self.alpha / (n+1.0))\\n # calculate the prior probability of Y\\n num_on = (cur_y == 1).sum()\\n num_off = (cur_y == 0).sum()\\n log_prior += num_on * np.log(self.theta) + num_off * np.log(1 - self.theta)\\n # calculate the logliklihood\\n log_lik = self._loglik(cur_y = cur_y, cur_z = cur_z)\\n return log_prior + log_lik\",\n \"def softmax_derivative(Z):\\n\\treturn None\",\n \"def log_distr_fdmw(self, dnu, logflux, dme, logw, alpha, logls, logl0, mu, sigma, gtype=None):\\n #stepdms = 100/1000.\\n #vdms = np.arange(0, 100, stepdm)\\n stepz = (np.log(self.Zmax) - np.log(self.Zmin)) / 1000\\n vz = np.exp(np.arange(np.log(self.Zmin), np.log(self.Zmax), stepz))\\n lik = 0\\n for z in vz:\\n likv = np.exp(self.log_distr_fdmwz(dnu, logflux, dme, logw, z, alpha, logls, logl0, mu, sigma, gtype=gtype))\\n lik += z * stepz * likv\\n ind = lik > 0\\n ind2 = lik <= 0\\n loglik = lik.copy()\\n loglik[ind] = np.log(lik[ind])\\n loglik[ind2] = np.ones(loglik[ind2].shape) * -1e99\\n return loglik\",\n \"def _compute_func_grad(self, w):\\n W = w.reshape((self.X.shape[1], self.Y.shape[1]))\\n self.nll_, self.grad_ = calculate_gradient(self.X, self.Y, W, self.prior, self.weighted,0)\",\n \"def _estimate_log_prob(self, x):\\n x = self.check_size(x)\\n\\n if self.covariance_type == \\\"full\\\":\\n mu = self.mu\\n var = self.var\\n precision = tf.linalg.inv(var)\\n d = x.shape[-1]\\n\\n log_2pi = d * tf.math.log(2. * pi)\\n log_2pi = tf.cast(log_2pi, tf.double)\\n log_det = self._calculate_log_det(precision)\\n log_det = tf.cast(log_det, tf.double)\\n\\n\\n x = tf.cast(x, tf.double)\\n mu = tf.cast(mu, tf.double)\\n x_mu_T = tf.expand_dims((x - mu), -2)\\n x_mu = tf.expand_dims((x - mu), -1)\\n\\n x_mu_T_precision = calculate_matmul_n_times(self.n_components, x_mu_T, precision)\\n # print(x_mu_T_precision, x_mu_T, x, mu)\\n x_mu_T_precision_x_mu = calculate_matmul(x_mu_T_precision, x_mu)\\n\\n return -.5 * (log_2pi - log_det + x_mu_T_precision_x_mu)\\n\\n elif self.covariance_type == \\\"diag\\\":\\n mu = self.mu\\n prec = tf.math.rsqrt(self.var)\\n\\n log_p = tf.math.reduce_sum((mu * mu + x * x - 2 * x * mu) * (prec ** 2), axis=2, keepdims=True)\\n log_det = tf.math.reduce_sum(tf.math.log(prec), axis=2, keepdims=True)\\n\\n return -.5 * (self.n_features * tf.math.log(2. * pi) + log_p) + log_det\",\n \"def logit_cost_grad(self, theta, X, y):\\n\\n grad = np.zeros(len(theta))\\n\\n ### YOUR CODE HERE\\n sig = utils.sigmoid(theta)\\n # sig = np.subtract(sig, y)\\n sig = sig - y\\n grad = np.dot(X.T, sig) + 2 * self.params['lamb'] * self.regularizer[1](self.weights)\\n ### END YOUR CODE\\n\\n return grad\",\n \"def loss_and_grad(self, X, y):\\n\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n grad = np.zeros_like(self.W)\\n grad_tmp = np.zeros_like(self.W)\\n num_classes = self.W.shape[0] # C = num_classes\\n num_train = X.shape[0]\\n \\n # ================================================================ #\\n # YOUR CODE HERE:\\n # Calculate the softmax loss and the gradient. Store the gradient\\n # as the variable grad.\\n # ================================================================ #\\n \\n exp_a = np.zeros((num_classes,num_train))\\n for i in np.arange(num_train):\\n \\n Loss = 0.0\\n\\n class_scores = np.dot(self.W,X[i,:].T) # calculating class scores (C x 1 vector)\\n class_scores -= np.max(class_scores) # considering the possible issue for numerical instability and account for it\\n\\n exp_a[:,i] = np.exp(class_scores) # turning class scores to probabilities (C x 1 vector), without normalization\\n\\n Loss -= np.log(exp_a[y[i],i]/np.sum(exp_a[:,i]))\\n \\n \\n #if i==0:\\n grada = np.zeros(X.shape[1])\\n \\n for j in range(num_classes):\\n if j != y[i]:\\n grad_tmp[j,:] = X[i,:].T * (exp_a[j,i] / np.sum(exp_a[:,i])) \\n else: \\n grad_tmp[j,:] = X[i,:].T * (exp_a[j,i] / np.sum(exp_a[:,i])) - X[i,:].T \\n\\n grad += grad_tmp\\n loss += Loss \\n \\n pass\\n\\n\\n loss /= num_train\\n grad /= num_train\\n # ================================================================ #\\n # END YOUR CODE HERE\\n # ================================================================ #\\n\\n return loss, grad\",\n \"def grad_neg_log_like(params):\\n gp.set_parameter_vector(params)\\n return -gp.grad_log_likelihood(y, quiet=True)\",\n \"def collect_gradients(log_prob, params, pass_grad = None):\\n\\n if isinstance(log_prob, tuple):\\n log_prob[0].backward()\\n params_list = list(log_prob[1])\\n params = torch.cat([p.flatten() for p in params_list])\\n params.grad = torch.cat([p.grad.flatten() for p in params_list])\\n elif pass_grad is not None:\\n if callable(pass_grad):\\n params.grad = pass_grad(params)\\n else:\\n params.grad = pass_grad\\n else:\\n params.grad = torch.autograd.grad(log_prob,params)[0]\\n return params\",\n \"def softmax_back_propagate(da, cache):\\n z = cache\\n y_hat = np.exp(z) / np.exp(z).sum()\\n dz = da * (1 - y_hat)\\n assert (dz.shape == z.shape)\\n return dz\",\n \"def gradient_descent(self, X ,eta, tol,iter):\\n gd=[]\\n gd_x=[X]\\n iteration=0\\n # current_pt=X\\n first_derivative=sym.diff(self.gdfunc)\\n #print(first_derivative)\\n x=sym.Symbol('x')\\n first_derivative=sym.lambdify(x,first_derivative)\\n learn_rate=eta\\n \\n \\n prev_x=X\\n new_x=prev_x -(learn_rate*first_derivative(prev_x))\\n gd_x.append(new_x)\\n #print(\\\"prev_x = \\\",prev_x,\\\" Next x = \\\",new_x)\\n for i in range(iter):\\n prev_x=new_x\\n #print(prev_x)\\n new_x=prev_x -(learn_rate*first_derivative(prev_x))\\n gd_x.append(new_x)\\n # print(\\\"x = \\\",new_x,\\\"Gradient =\\\",learn_rate*self.func(prev_x))\\n if abs(self.func(new_x)) <= self.func(tol) :\\n break\\n iteration=iteration+1\\n #print(\\\"Best at GD x= \\\",new_x)\\n gd.append(gd_x)\\n gd.append(new_x)\\n gd.append(iteration)\\n\\n return gd\",\n \"def get_gradient_minus_log_joint(z, mean, Sigma):\\n data_minus_mean = z[np.newaxis,:] - mean# 1 x 2\\n Sigma_inv = np.linalg.inv(Sigma) # 2 x 2\\n gradient = - np.matmul(Sigma_inv, data_minus_mean.T) #2 x 1\\n gradient = gradient[0]\\n return gradient\",\n \"def calc_probs(log_p):\\n\\n N = log_p.shape[0]\\n\\n log_Z_per_N = np.zeros(shape=(N, 1))\\n\\n for i in range(N):\\n\\n log_Z_per_N[i] = log_norm(log_p[i])\\n\\n log_p_new = log_p - log_Z_per_N\\n\\n p = np.exp(log_p_new)\\n\\n # log_Z = log_norm(log_p)\\n\\n # p = np.exp(log_p - log_Z)\\n\\n return p\",\n \"def compute_loglike(self, tools: ModelingTools) -> float:\\n return -1.5\",\n \"def forward_backward_prop(data, labels, params, dimensions):\\n\\n ### Unpack network parameters (do not modify)\\n ofs = 0\\n Dx, H, Dy = (dimensions[0], dimensions[1], dimensions[2])\\n\\n W1 = np.reshape(params[ofs:ofs+ Dx * H], (Dx, H))\\n ofs += Dx * H\\n b1 = np.reshape(params[ofs:ofs + H], (1, H))\\n ofs += H\\n W2 = np.reshape(params[ofs:ofs + H * Dy], (H, Dy))\\n ofs += H * Dy\\n b2 = np.reshape(params[ofs:ofs + Dy], (1, Dy))\\n\\n #print W2.shape, b2.shape\\n\\n #print len(W1), data[0], b1, len(data)\\n\\n ### YOUR CODE HERE: forward propagation\\n #Eg, find the cost function. Save some intermediate stuff though, seems like it'd be useful\\n #h = sigmoid(x * w1 + b1)\\n # y = (softmax( h * w2 + b2)\\n # hence the cost function will be labels * log(y) and then sum it all up\\n\\n z_1 = np.matrix(data) * W1 + b1\\n h = sigmoid(z_1)\\n y_prime = softmax(h * W2 + b2)\\n logs = np.log(y_prime)\\n\\n #print y_prime.shape\\n\\n #print np.array(logs) * labels\\n\\n cost = - np.sum(np.array(logs) * labels, axis = 1)\\n cost = np.sum(cost) # lets add up each instance fo the cost for now and see what happens\\n\\n # My question is then do we just sum up the costs of each function\\n #print cost #somethign is printing so I'm gonan say i'm a genius right here duh\\n\\n #Cost(y, y') = -sum of (y * log Y')\\n ### END YOUR CODE\\n\\n ### YOUR CODE HERE: backward propagation\\n\\n # you'll need gradients for each parameter except for the input vectors. Right now this isn't even a word2vec\\n delta_1 = y_prime - labels\\n delta_2 = delta_1 * W2.T\\n #print sigmoid_grad(h).shape\\n delta_3 = np.array(delta_2) * sigmoid_grad(h)\\n\\n gradW2 = np.array(h.T * delta_1) # i dunno or its reverse OMG I HASTE EVERYONE why is it that np.array fixes everything. Sigh\\n gradb2 = np.array(np.sum(delta_1, axis=0)) # main issue is that this is a 20 x 5 vector when it should be a 1 x 5\\n gradW1 = data.T.dot(delta_3)\\n gradb1 = np.sum(delta_3, axis=0) # this should be 1 x10 not 20 x 5\\n\\n\\n\\n ### END YOUR CODE\\n\\n #print gradW1, gradW1.flatten()\\n # print 'jee'\\n\\n ### Stack gradients (do not modify)\\n grad = np.concatenate((\\n gradW1.flatten(),\\n gradb1.flatten(),\\n gradW2.flatten(),\\n gradb2.flatten())\\n )\\n #print grad\\n #print cost\\n return cost, grad\",\n \"def compute_loglike(self, tools: ModelingTools) -> float:\\n return -3.0 * self.placeholder\",\n \"def grad_margin(x0, preds, y, model, n_class, weights, L, loss_function):\\n theta = x0\\n loss_fd = weights[y.ravel().astype(int)]\\n\\n Alpha = theta - preds # (n_class - 1, n_samples)\\n S = np.sign(np.arange(n_class - 1) - y + 0.5)\\n\\n if(loss_function == 'logistic'):\\n lf = sigmoid\\n elif(loss_function == 'hinge'):\\n lf = hinge_derive\\n \\n Sigma = np.dot(S, loss_fd.T)\\n Sigma = np.dot(Sigma, lf(np.multiply(-S, Alpha)))\\n\\n grad_theta = -Sigma.sum(0)\\n return grad_theta\",\n \"def scipy_minus_gradient(w,all_vector_graphs,all_correct_rows,\\\\\\n all_batches,sigma=None,perceptron=None):\\n if perceptron:\\n perceptron._gradient_iter += 1\\n g = None\\n index = 0\\n for vector_graphs,correct_rows,batches in zip(all_vector_graphs,all_correct_rows,all_batches):\\n first_term = vector_graphs[correct_rows,:].sum(axis=0)\\n all_scores = vector_graphs * w\\n all_probs = []\\n for batch in batches:\\n batch_scores = all_scores[batch]\\n S = logsumexp(batch_scores)\\n all_probs.append(np.exp(batch_scores - S))\\n all_probs = numpy.hstack(all_probs)\\n second_term = all_probs * vector_graphs\\n if g is None:\\n g = second_term - first_term\\n else:\\n g = g + second_term - first_term\\n index += 1\\n if index % 100 == 0:\\n print('Gradient '+str(index)+' processed')\\n g = numpy.ndarray.flatten(numpy.asarray(g)) / len(all_vector_graphs)\\n if sigma != None:\\n g = g + sigma * w\\n print('Gradient norm:'+str(scipy.linalg.norm(g)))\\n sys.stdout.flush()\\n if perceptron and perceptron._model_pickle:\\n if perceptron._gradient_iter % 5 == 0:\\n perceptron._weights = numpy.reshape(w,(1,perceptron._num_features))\\n perceptron.save(perceptron._model_pickle+'_'+str(perceptron._gradient_iter))\\n return g\",\n \"def compute_backward_variables(self, normalized_logits, target):\\n\\n target_length = target.shape[0]\\n num_time_steps = normalized_logits.shape[0]\\n\\n\\t\\t######################\\n\\t\\t### YOUR CODE HERE ###\\n\\t\\t######################\\n\\n blank_label = normalized_logits.shape[1] - 1\\n l = add_blanks(target, blank_label)\\n target_length = l.shape[0]\\n\\n beta = np.zeros((target_length, num_time_steps))\\n\\n # init\\n beta[target_length - 1, num_time_steps - 1] = normalized_logits[num_time_steps - 1, l[-1]]\\n beta[target_length - 2, num_time_steps - 1] = normalized_logits[num_time_steps - 1, l[-2]]\\n\\n for s in xrange(target_length - 2):\\n beta[s, num_time_steps - 2] = 0.0\\n\\n # recursive case\\n for t in xrange(num_time_steps - 2, -1, -1):\\n for s in xrange(target_length - 3, -1, -1):\\n b_bar = beta[s, t+1] + beta[s+1, t+1]\\n \\n if l[s] == blank_label or l[s+2] == l[s]:\\n beta[s, t] = b_bar * normalized_logits[t, l[s]]\\n else:\\n beta[s, t] = (b_bar + beta[s+2, t+1]) * normalized_logits[t, l[2]]\\n\\n return beta\",\n \"def compute_logistic_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w)) - y) / len(y)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72906494","0.69248664","0.6920769","0.663529","0.663529","0.662517","0.6602322","0.660013","0.6522516","0.64752764","0.6431578","0.64274055","0.6426478","0.6416801","0.64133984","0.6344967","0.63444275","0.63398236","0.6332006","0.6312257","0.62948793","0.62902164","0.62888885","0.62695324","0.6242809","0.6240489","0.6211932","0.6202686","0.6181409","0.61691386","0.6163054","0.61619514","0.6147129","0.61262745","0.6111148","0.6047703","0.6045359","0.6041987","0.60375345","0.6037036","0.60356826","0.60248065","0.60178655","0.60143065","0.5986822","0.5978784","0.5971393","0.596431","0.59551376","0.5938273","0.59273726","0.59273684","0.5923098","0.592102","0.5910803","0.59099483","0.5907938","0.5906338","0.5897433","0.5881384","0.5874959","0.5870025","0.5869626","0.5865132","0.5856859","0.58490413","0.58453214","0.5844607","0.58405167","0.5839238","0.58366823","0.5835541","0.5829329","0.5824168","0.58216214","0.58197284","0.5818359","0.58166254","0.5811133","0.58036023","0.58032274","0.5801706","0.58012766","0.5801268","0.57967","0.57950664","0.5794609","0.57944286","0.57861376","0.57821935","0.57810205","0.57806027","0.5778332","0.57669824","0.5764032","0.57635605","0.57624143","0.57618773","0.57591164","0.57573354"],"string":"[\n \"0.72906494\",\n \"0.69248664\",\n \"0.6920769\",\n \"0.663529\",\n \"0.663529\",\n \"0.662517\",\n \"0.6602322\",\n \"0.660013\",\n \"0.6522516\",\n \"0.64752764\",\n \"0.6431578\",\n \"0.64274055\",\n \"0.6426478\",\n \"0.6416801\",\n \"0.64133984\",\n \"0.6344967\",\n \"0.63444275\",\n \"0.63398236\",\n \"0.6332006\",\n \"0.6312257\",\n \"0.62948793\",\n \"0.62902164\",\n \"0.62888885\",\n \"0.62695324\",\n \"0.6242809\",\n \"0.6240489\",\n \"0.6211932\",\n \"0.6202686\",\n \"0.6181409\",\n \"0.61691386\",\n \"0.6163054\",\n \"0.61619514\",\n \"0.6147129\",\n \"0.61262745\",\n \"0.6111148\",\n \"0.6047703\",\n \"0.6045359\",\n \"0.6041987\",\n \"0.60375345\",\n \"0.6037036\",\n \"0.60356826\",\n \"0.60248065\",\n \"0.60178655\",\n \"0.60143065\",\n \"0.5986822\",\n \"0.5978784\",\n \"0.5971393\",\n \"0.596431\",\n \"0.59551376\",\n \"0.5938273\",\n \"0.59273726\",\n \"0.59273684\",\n \"0.5923098\",\n \"0.592102\",\n \"0.5910803\",\n \"0.59099483\",\n \"0.5907938\",\n \"0.5906338\",\n \"0.5897433\",\n \"0.5881384\",\n \"0.5874959\",\n \"0.5870025\",\n \"0.5869626\",\n \"0.5865132\",\n \"0.5856859\",\n \"0.58490413\",\n \"0.58453214\",\n \"0.5844607\",\n \"0.58405167\",\n \"0.5839238\",\n \"0.58366823\",\n \"0.5835541\",\n \"0.5829329\",\n \"0.5824168\",\n \"0.58216214\",\n \"0.58197284\",\n \"0.5818359\",\n \"0.58166254\",\n \"0.5811133\",\n \"0.58036023\",\n \"0.58032274\",\n \"0.5801706\",\n \"0.58012766\",\n \"0.5801268\",\n \"0.57967\",\n \"0.57950664\",\n \"0.5794609\",\n \"0.57944286\",\n \"0.57861376\",\n \"0.57821935\",\n \"0.57810205\",\n \"0.57806027\",\n \"0.5778332\",\n \"0.57669824\",\n \"0.5764032\",\n \"0.57635605\",\n \"0.57624143\",\n \"0.57618773\",\n \"0.57591164\",\n \"0.57573354\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6327207"},"document_rank":{"kind":"string","value":"19"}}},{"rowIdx":11186,"cells":{"query":{"kind":"string","value":"Calculate the conditional log probability of the tags given the tree"},"document":{"kind":"string","value":"def log_prob(self, T, pos, m, psi, phi=tr.Tensor()):\n if phi.size() == tr.Size([0]):\n phi = self.create_phi(T, pos, m)\n return self.log_score(T, pos, m, psi, phi) - self.logZ(T, pos, psi, phi)"},"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_prob(self):","def probability(self, tokens):\n\n return 2 ** self.log_probability(tokens)","def log_probability(self, sequence):\n sequence = self._transform(sequence)\n\n T = len(sequence)\n\n if T > 0 and sequence[0][_TAG]:\n last_state = sequence[0][_TAG]\n p = self._priors.logprob(last_state) + self._output_logprob(\n last_state, sequence[0][_TEXT]\n )\n for t in range(1, T):\n state = sequence[t][_TAG]\n p += self._transitions[last_state].logprob(\n state\n ) + self._output_logprob(state, sequence[t][_TEXT])\n last_state = state\n return p\n else:\n alpha = self._forward_probability(sequence)\n p = logsumexp2(alpha[T - 1])\n return p","def logprob(predictions, labels):\n predictions[predictions < 1e-10] = 1e-10\n return np.sum(np.multiply(labels, -np.log(predictions))) / labels.shape[0]","def logprob(predictions, labels):\n predictions[predictions < 1e-10] = 1e-10\n return np.sum(np.multiply(labels, -np.log(predictions))) / labels.shape[0]","def logprob(predictions, labels):\n # prevent negative probability\n predictions[predictions < 1e-10] = 1e-10\n return np.sum(np.multiply(labels, -np.log(predictions))) / labels.shape[0]","def tag_log_prob(self, y, add_end_token=True):\n p = 0\n n = self._n\n y = (START_TAG,) * (n - 1) + tuple(y) + (END_TAG,) * add_end_token\n for i in range(len(y) - self._n + 1):\n tag = y[i + n - 1]\n prev_tags = y[i:i + n - 1]\n if self.trans_prob(tag, prev_tags) == 0:\n return -math.inf\n\n p += math.log2(self.trans_prob(tag, prev_tags))\n\n return p","def logprob(predictions, labels):\n predictions[predictions < 1e-10] = 1e-10\n return np.sum(np.multiply(labels, -np.log(predictions)))","def calculateLogJointProbabilities(self, datum):\n\tlogJoint = util.Counter()\n\t#want to calculate log(P(y)) + log(sum(P(fi|y)))\n\t#where y is a label\n\tfor label in self.legalLabels:\n\t\tlogJoint[label] = math.log(self.prior_distribution_prob[label])\n\t\tfor feature, value in datum.items():\n\t\t\tcp = self.conditional_prob[label][feature][value]\n\t\t\tif cp > 0: #condition check for values < 0 because log(0) is undefined and math domain error occurs\n\t\t\t\tlogJoint[label] += math.log(cp) #summing up\n\t\t\t\t\n\treturn logJoint","def calculate_entropy(self, tags):\n tags_counter = Counter()\n\n if not tags:\n return 0\n\n for tag in tags:\n tags_counter[tag] += 1\n classes_probs = [tags_counter[tag] / float(len(tags)) for tag in tags_counter]\n if 0.0 in classes_probs:\n return 0\n\n entropy = 0\n for prob in classes_probs:\n entropy -= prob * math.log(prob, 2)\n\n return entropy","def likelihood(self, data, hypo):\n tagged, n, k = data\n if hypo < tagged + n - k:\n return 0\n\n p = tagged / hypo\n like = thinkbayes.eval_binomial_pmf(k, n, p)\n return like","def compute_log_prob(self,params: ndarray) -> float:\n return self.compute_log_prior(params) + self.compute_log_likelihood(params)","def logprob(self):\n assert len(self._added_rows) == self._num_rows\n TODO('https://github.com/posterior/treecat/issues/27')","def sentence_logprob(self, sentence):\n line = get_ngrams(sentence,3)\n log_por = 0.0\n for item in line:\n raw_por = self.smoothed_trigram_probability(item)\n log_por = log_por+math.log2(raw_por)\n\n return float(log_por)","def logprob(self):\n assert len(self._added_rows) == self._num_rows\n TODO('https://github.com/posterior/treecat/issues/26')","def calculateLogJointProbabilities(self, datum):\n logJoint = util.Counter()\n \n \"*** YOUR CODE HERE ***\"\n \n # -- OUR CODE HERE\n \n \n import math\n for label in self.legalLabels:\n sumThing = 0.0\n for pixel in self.conditionalProb[label]:\n if datum[pixel] is 1:\n #assert self.conditionalProb[label][pixel] < 1.0 # -- sanity check that the probability is valid\n sumThing += math.log((self.conditionalProb[label][pixel]*1.0))\n else:\n sumThing+=math.log(1-self.conditionalProb[label][pixel]*1.0)\n logJoint[label] = math.log(self.prior[label]*1.0) + sumThing*1.0\n \n\n \n \n import time\n #print \"logJoint is :: \", logJoint\n #time.sleep(2)\n \n \n # -- uses the conditional probability tables computed in the current iteration\n # -- in train and tune\n \n return logJoint","def caculate_prob(self):\n t_H = self.tree.depth()\n t_h = 1\n while(t_h <= t_H):\n t_hnodes = self.get_h(t_h)\n t_sum = 0\n t_hpro = []\n t_cpro = []\n for t_n in t_hnodes:\n t_sum = self.tree.get_node(t_n).data[0] + t_sum\n t_node = self.tree.get_node(t_n)\n if t_node.is_leaf():\n t_node.data.append(0)\n continue\n t_childrens = self.tree.children(t_n)\n t_shang = 0\n for child in t_childrens:\n t_shang = t_shang + (child.data[0]/t_node.data[0])*np.log(child.data[0]/t_node.data[0])\n t_node.data.append(-t_shang)\n for t_n in t_hnodes:\n t_node = self.tree.get_node(t_n)\n t_parentnode = self.tree.parent(t_n)\n if t_h > 1:\n t_node.data.append((t_node.data[0] / t_sum) * (t_node.data[0]/t_parentnode.data[0]))\n t_hpro.append((t_node.data[0]/t_sum) * (t_node.data[0]/t_parentnode.data[0]))\n else:\n t_node.data.append((t_node.data[0] / t_sum))\n t_hpro.append((t_node.data[0] / t_sum))\n\n t_cpro.append(t_node.data[1])\n t_ndata = np.array(t_hpro)\n mean = np.mean(t_ndata)\n std = np.std(t_ndata,ddof=1)\n t_sdata = np.array(t_cpro)\n mean_s = np.mean(t_sdata)\n std_s = np.std(t_sdata,ddof=1)\n for t_n in t_hnodes:\n t_node = self.tree.get_node(t_n)\n if(std != 0):\n t_node.data[2] = (t_node.data[2] - mean)/std\n else:\n t_node.data[2] = (t_node.data[2] - mean)\n if(mean_s == 0 and std_s ==0):\n t_node.data[1] = -100.0\n continue\n t_node.data[1] = (t_node.data[1] - mean_s)/std_s\n t_h = t_h + 1","def calc_marginal_entropy(data_stat):\n label_col = data_stat.keys()[-1]\n label_info = data_stat[label_col]\n label_lengths = []\n for label_poses in label_info.values():\n label_lengths.append(len(label_poses))\n # print(label_lengths)\n majority = max(label_lengths)\n minority = min(label_lengths)\n total = float(majority + minority)\n entropy = -(minority/total*math.log(minority/total,2)+majority/total*math.log(majority/total,2))\n return entropy","def log_probability(self, tokens):\n\n log_sum = 0\n for leader, token in generate_ngrams(tokens, self.size, include_terminator = self.include_terminator):\n if not leader in self.frequencies:\n return float(\"-inf\")\n\n word_frequency = self.frequencies[leader][token]\n leader_frequency = self.frequencies[leader].total\n\n probability = (word_frequency + self.smoothing) / (leader_frequency + len(self.vocab) * self.smoothing)\n\n if probability == 0:\n return float(\"-inf\")\n\n log_sum += math.log2(probability)\n \n return log_sum","def get_probability(self, sentence):\n if len(sentence) == 1:\n return Decimal(10) ** self.get_unigram_log_prob(sentence)\n elif len(sentence) == 2:\n return Decimal(10) ** self.get_bigram_log_prob(sentence)\n else:\n log_prob = Decimal(0.0)\n for w1, w2, w3 in zip(sentence, sentence[1:], sentence[2:]):\n log_prob += self.get_trigram_log_prob((w1, w2, w3))\n log_prob = Decimal(log_prob)\n return Decimal(10) ** log_prob","def log_probability(self, samples):\n pass","def log_prob(self):\n if not RerankingParser._parser_model_loaded:\n raise ValueError(\"You need to have loaded a parser model in \"\n \"order to get the log probability.\")\n return parser.treeLogProb(self._tree)","def compute_joint_probability(token_list, token_probabilities, use_log_prob=False):\n\n log_prob = 0\n\n for word in token_list:\n\n # do not allow zero probabilites\n assert word in token_probabilities\n\n if use_log_prob:\n log_prob += token_probabilities[word]\n else:\n log_prob += log10(token_probabilities[word])\n\n if use_log_prob:\n return log_prob\n\n return 10**log_prob","def calculate_entropy(prob):\n return -(prob * math.log(prob,2))","def conditional_entropy(P):\n P_nan = P.copy()\n P_nan[P_nan == 0] = np.nan\n\n marginals = np.nansum(P_nan, axis=1)\n P_cond = P_nan / marginals[:, None]\n\n return np.nansum(np.multiply(P_nan, np.log2(1 / P_cond)))","def compute_my_variability(event_log: Log) -> float:\n prefixes: List[List[Event]] = []\n bar: Bar = IncrementalBar(\"Prefix generation\", max=len(event_log.trace_list))\n for trace in event_log.trace_list:\n trace_prefixes: List[List[Event]] = trace.get_all_prefixes()\n\n for prefix in trace_prefixes:\n if prefix not in prefixes:\n prefixes.append(prefix)\n bar.next()\n bar.finish()\n\n entropy: float = 0\n\n bar = ShadyBar(\"Prefix likelihood estimation\", max=len(prefixes))\n for prefix in prefixes:\n\n p: float = _prefix_likelihood_estimator(event_log, prefix)\n entropy += p * logarithm(p, 10)\n\n bar.next()\n bar.finish()\n\n entropy *= -1\n\n return entropy","def entropy(*args):\n\n\n values = []\n leaf = -1\n\n for i, val in enumerate(args):\n if(val != 0):\n values.append(val * math.log(val, len(args)))\n if(val == 1):\n leaf = i\n \n return -sum(values), leaf","def sentence_logprob(self, sentence):\n sum_logprob = 0\n trigrams = get_ngrams(sentence, 3)\n for trigram in trigrams :\n sum_logprob += math.log2(self.smoothed_trigram_probability(trigram))\n\n return sum_logprob","def sentence_logprob(self, sentence):\n grams = get_ngrams(sentence, 3)\n p = 1\n\n for gram in grams:\n p *= np.longfloat(self.smoothed_trigram_probability(gram))\n\n return np.log2(p)","def test_that_most_probable_always_returns_a_tree_of_the_same_logprob():\n\n grammar = Grammar(rulebooks)\n actual_tree = grammar.sample_tree()\n sentence = actual_tree.terminals\n inner = grammar.compute_inside_probabilities(sentence)\n best_tree = grammar.compute_most_likely_tree(sentence, inner, root=0)\n best_logprob = grammar.logprob_tree(best_tree)\n\n for _ in range(20):\n tree = grammar.compute_most_likely_tree(sentence, inner, root=0)\n assert np.isclose(grammar.logprob_tree(tree), best_logprob)","def log_prior(x):\n logp = (-0.5 * x.pow(2) - torch.tensor(2 * math.pi).sqrt().log()).sum(dim=1)\n return logp","def evaluate(self, test_data):\n log_like = 0\n for sequence in test_data:\n for event, context in self.extract_ngrams(sequence):\n log_like += math.log(self.cond_prob(event, context))\n return log_like","def test_posterior_logprobs(self):\n x = list(product([True, False], repeat=2))\n xs = list(e for e in product(x, repeat=3))\n all_obs = list(o for o in xs\n if all(any(e) and not all(e) for e in o))\n total = logsumexp(list(posterior_logprobs(np.array(obs), self.S, self.A, self.E)[1]\n for obs in all_obs))\n assert_allclose(total, np.log(1))","def get_entropy(dictionary):\n my_sum = 0\n weighted_sum_of_logs = 0\n for freq in dictionary.values():\n if freq:\n my_sum += freq\n weighted_sum_of_logs += freq * math.log(freq)\n return math.log(my_sum) - weighted_sum_of_logs / my_sum","def get_entropy(self, rows):\n label_count = defaultdict(int)\n total_count = 0\n for row in rows:\n label = row[self.target_attribute]\n label_count[label] += 1\n total_count += 1\n return sum([-(float(label_count[label]) /\n total_count) * np.log2(float(label_count[label]) / total_count)\n for label in label_count.keys()])","def multinomial_likelihood(m_true, alpha, alpha0, m_probs):\n\n ll = tf.reduce_sum(input_tensor=m_true * (tf.math.log(alpha0) - tf.math.log(alpha)), axis=1, keepdims=True)\n ll = tf.reduce_mean(input_tensor=ll)\n return ll","def log_T_score(T, state_tree_sequence, lineageObj):\n log_T_score_holder = 0\n log_T = np.log(T)\n # we start with the first transition, from the root cell\n for level in lineageObj.output_list_of_gens[1:]:\n for cell in level:\n cell_idx = lineageObj.output_lineage.index(cell)\n cell_state = state_tree_sequence[cell_idx]\n if not cell.isLeaf():\n for daughter in cell.get_daughters():\n child_idx = lineageObj.output_lineage.index(daughter)\n daughter_state = state_tree_sequence[child_idx]\n log_T_score_holder += log_T[cell_state, daughter_state]\n return log_T_score_holder","def calculateLogJointProbabilities(self, datum):\n logJoint = util.Counter()\n \"*** YOUR CODE HERE ***\"\n\t#Adds log(P(y)) to calculate P(y|f1,f2...)\n for label in self.legalLabels:\n\t\tlogJoint[label] += math.log(self.prior[label])\n\t#Adds log(P(f1|y)), log(P(f2|y))... to calculate P(y|f1, f2...)\n for key in datum:\n\t\t#if key == (7, 3):\n\t\t\t#print self.condprobs[key, 0]\n\t\tfor label in self.legalLabels:\n\t\t\t#print str(key) + str(datum[key])\n\t\t\tlogJoint[label] += math.log(self.condprobs[key, label][datum[key]])\n return logJoint","def cond_prob(self, event, context):\n count = self.table[event, context] + self.prior\n norm = self.margin[context] + (self.prior * len(self.alphabet))\n return count / norm","def node_conditional_likelihood(self, node):\n # get transition probabilities over each branch length\n prob_child0 = expm(self.qmat * node.children[0].dist)\n prob_child1 = expm(self.qmat * node.children[1].dist)\n\n # likelihood that child 0 observation occurs if anc==0\n child0_is0 = (\n prob_child0[0, 0] * node.children[0].likelihood[0] + \n prob_child0[0, 1] * node.children[0].likelihood[1]\n )\n # likelihood that child 1 observation occurs if anc==0\n child1_is0 = (\n prob_child1[0, 0] * node.children[1].likelihood[0] + \n prob_child1[0, 1] * node.children[1].likelihood[1]\n )\n anc_lik_0 = child0_is0 * child1_is0\n\n # likelihood that child 0 observation occurs if anc==1\n child0_is1 = (\n prob_child0[1, 0] * node.children[0].likelihood[0] + \n prob_child0[1, 1] * node.children[0].likelihood[1]\n )\n child1_is1 = (\n prob_child1[1, 0] * node.children[1].likelihood[0] + \n prob_child1[1, 1] * node.children[1].likelihood[1]\n )\n anc_lik_1 = child0_is1 * child1_is1\n\n # set estimated conditional likelihood on this node\n node.likelihood = [anc_lik_0, anc_lik_1]","def compute_probability_of_state(state):\n p = compute_log_probability_of_text(state[\"text\"], state[\"char_to_ix\"], \n state[\"frequency_statistics\"], state[\"transition_matrix\"])\n \n return p","def get_log_likelihood(phi, pred, t, dot_product, weight, reg= 1):\n prior = -0.5* np.sum(np.multiply(weight, weight))\n likelihood = np.multiply(t, np.log(pred+TOLERANCE)) + np.multiply(1.0- t, np.log(1.0-pred+TOLERANCE))\n likelihood = np.sum(likelihood)\n\n return prior + likelihood","def calculate_log_perplexity(self, output, flat_labels): #completed, expensive, should be compiled\n return -np.sum(np.log2(np.clip(output, a_min=1E-12, a_max=1.0))[np.arange(flat_labels.shape[0]), flat_labels[:,1]])","def sumLogProb(a, b):\n if a > b:\n return a + log1p(exp(b - a))\n else:\n return b + log1p(exp(a - b))","def __logprob__(self, cv, vsense):\n return 1.0 / (1.0 + np.exp(-np.dot(cv, vsense)))","def cond_entropy(joint_prob, cond_prob):\n # Computing log2(P cond)\n log2_p = (np.ma.log2(cond_prob)).filled(0)\n # Multipling element wise the arrays\n prod_entropy = np.multiply(joint_prob, log2_p)\n # Getting the - sum of the resulting array.\n H = -( np.sum(prod_entropy))\n return H","def log_prob(sentence, LM, smoothing=False, delta=0, vocabSize=0):\n word_list = sentence.split()\n log_prob = 0\n for i in range(len(word_list)-1):\n print(word_list[i], word_list[i+1])\n bi_count = LM['bi'][word_list[i]][word_list[i+1]]\n uni_count = LM['uni'][word_list[i]]\n if uni_count == 0 and smoothing:\n return float('-inf')\n log_prob += log(((bi_count + delta)/(uni_count + delta * vocabSize)))\n return log_prob","def get_ngramlogprobs(freqdict):\n return","def cond_prob(self, token, prev_tokens=None):\n\n if not prev_tokens:\n assert self.n == 1\n prev_tokens = tuple()\n # ngram condicional probs are based on relative counts\n hits = self.count((tuple(prev_tokens)+(token,)))\n sub_count = self.count(tuple(prev_tokens))\n\n return hits / float(sub_count)","def log_probability(self, X):\n\n\t\treturn self.__log_probability(X)","def _logprobratio(prob1, prob2):\n return log(prob1) / log(prob2)","def classify(self, documents):\n predictions = []\n for doc in documents:\n\n score_sod = math.log(self.priorSOD)\n score_pop = math.log(self.priorPOP)\n for term in doc.tokens:\n if term in self.cond_prob_sod.keys():\n score_sod += math.log(self.cond_prob_sod[term])\n if term in self.cond_prob_pop.keys():\n score_pop += math.log(self.cond_prob_pop[term])\n if(score_pop >= score_sod): #defaults to ham if score = even \n predictions.append('pop')\n else:\n predictions.append('sod')\n \n return predictions \n pass","def cond_prob(self, token, prev_tokens=()):\n return float(self.count(list(prev_tokens) + [token]) + 1) / float(self.count(prev_tokens) + self._V)","def log_prob(self, th):\n\n\t\tmask = self.__low <= th <= self.__high\n\t\tif len(th.shape) == 1:\n\t\t\tlength = 1\n\t\telif len(th.shape) == 2:\n\t\t\tlength = th.shape[0]\n\t\telse:\n\t\t\traise RuntimeError(\"th must be either (d,) or (n_samples, d)\")\n\t\tlogprobs = np.empty(length)\n\t\tlogprobs[mask] = 0.\n\t\tlogprobs[~mask] = -float('inf')\n\t\treturn logprobs","def log_likelihood_function(self, instance) -> float:\n return self.prior.factor(instance[0])","def logprob(self, word, context):\n\n return -log(self.prob(word, context), 2)","def calc_conditional_entropy(map,data_stat,attribute):\n #acquire the data info of the attribute stored in data_stat\n data_info = data_stat[attribute]\n #acquire the label info\n # label_col = len(data_stat)-1\n label_col = data_stat.keys()[-1]\n # print(data_stat.keys())\n label_info = data_stat[label_col]\n #acquire the data \n data = map[attribute]\n labels = map[label_col]\n conditional_entropy =0\n for data_type in data_info:\n specific_entropy = 0\n for label_type in label_info: \n #attribute data indices where all data entries are equal to a speicifc value\n data_with_spec_val_idx = data_info[data_type]\n #label indices where all labels are of same value\n spec_label_idx = label_info[label_type]\n #the intersection of the two indices above\n intersect_idx = np.intersect1d(data_with_spec_val_idx,spec_label_idx)\n #conditional probability of label being of specific value given speicific data value\n temp_prob = len(intersect_idx)/float(len(data_with_spec_val_idx))\n if temp_prob!=0:\n specific_entropy += temp_prob*math.log(temp_prob,2)\n specific_entropy = -specific_entropy\n prob = len(data_with_spec_val_idx)/float(len(data))\n conditional_entropy += prob * specific_entropy\n return conditional_entropy","def log_marginal_likelihood(X_train,y_train,phi,tau=1.,Ve=1.e-10):","def likelihood(self, data, hypo):\n n_total_population = hypo\n k_tagged, n_caught, k_caught_tagged = data\n\n if hypo < k_tagged + (n_caught - k_caught_tagged):\n return 0\n\n like = thinkbayes.eval_hypergeom_pmf(k_caught_tagged, n_total_population, k_tagged, n_caught)\n return like","def proba_from_log_odds(self, log_odds):\n return (1/(1 + math.exp(log_odds)))","def entropy_(P):\n res = 0.0\n\n mask = P != 0.0 # avoid 0 in log\n f = lambda x: x*np.log2(x)\n # map-reduce strategy (likely to be more optimized than loops)\n temp = list(map(f, P[mask]))\n res = -np.sum(temp, dtype=float)\n return res","def classify(self, event):\n\n results = np.zeros(self.ntrees, dtype=float)\n\n for i,dt in enumerate(self.dTrees):\n results[i] = self.treeWeights[i]*dt.classify(event)\n\n return np.sum(results)*(1.0/np.sum(self.treeWeights))","def cond_prob(self, token, prev_tokens=()):\n assert len(prev_tokens) < self._n\n if self.count(prev_tokens) == 0:\n return 0.0\n return float(self.count(list(prev_tokens) + [token])) / float(self.count(prev_tokens))","def log_likelihood(data, probs):\n # Assume data is given as counts\n return _np.sum([nlogp(n, p) for n, p in zip(data, probs) if n > 0])","def prob(self, sequence):\n prob = 1\n for event, context in self.extract_ngrams(sequence):\n prob *= self.cond_prob(event, context)\n return prob","def pruning_algorithm(self):\n # traverse tree to get conditional likelihood estimate at root.\n self.set_qmat()\n for node in self.tree.treenode.traverse(\"postorder\"):\n if not node.is_leaf(): \n self.node_conditional_likelihood(node)\n logger.debug(\n f\"node={node.idx}; likelihood=[{node.likelihood[0]:.6f}, {node.likelihood[1]:.6f}]\") \n\n # multiply root prior times the conditional likelihood at root\n root = self.tree.treenode\n lik = (\n (1 - self.prior_root_is_1) * root.likelihood[0] + \n self.prior_root_is_1 * root.likelihood[1]\n )\n return lik","def entropy(probabilities):\n return -(sum([p * log(p, 2) if p > 0 else 0 for p in probabilities]))","def log_probability(theta):\n global priors\n global logp\n lp = np.sum([priors[p].logpdf(x) for p, x in zip(logp.parnames, theta)])\n if not np.isfinite(lp) or np.isnan(lp):\n return -np.inf\n ll = logp(theta)\n if not np.isfinite(ll):\n return -np.inf\n return lp + ll","def log_probability(theta):\n global priors\n global logp\n lp = np.sum([priors[p].logpdf(x) for p, x in zip(logp.parnames, theta)])\n if not np.isfinite(lp) or np.isnan(lp):\n return -np.inf\n ll = logp(theta)\n if not np.isfinite(ll):\n return -np.inf\n return lp + ll","def dlog_score(self, T, pos, m, psi):\n dpsi = tr.zeros_like(psi)\n for (i, j, lab) in T:\n if j != 0:\n m_i = self.get_tag_index(m[i - 1])\n pos1 = pos[i - 1]\n m_j = self.get_tag_index(m[j - 1])\n pos2 = pos[j - 1]\n dpsi[pos1, pos2, lab, m_i, m_j] += 1\n return dpsi","def _conditional_entropy_compute(confmat: Tensor) ->Tensor:\n confmat = _drop_empty_rows_and_cols(confmat)\n total_occurrences = confmat.sum()\n p_xy_m = confmat / total_occurrences\n p_y = confmat.sum(1) / total_occurrences\n p_y_m = p_y.unsqueeze(1).repeat(1, p_xy_m.shape[1])\n return torch.nansum(p_xy_m * torch.log(p_y_m / p_xy_m))","def log_prob(theta):\n params = parse_params(theta)\n prior = log_prior(**params)\n if not np.isfinite(prior):\n return -np.inf, -np.inf\n like = log_like(**params)\n if not np.isfinite(like):\n return -np.inf, prior\n return prior + like, prior","def find_entropy(less_than_threshold,more_than_threshold):\n\n ''' Storing total number of records '''\n total_records = len(less_than_threshold) + len(more_than_threshold)\n\n ''' Calculating the probability '''\n less_than_probability = len(less_than_threshold) / total_records\n more_than_probability = len(more_than_threshold) / total_records\n\n ''' Converting the dataframe to numpy arrays '''\n less_than_threshold_values = less_than_threshold.values\n more_than_threshold_values = more_than_threshold.values\n\n ''' Storing the target attribute values (Muffin or Cupcake) for threshold values '''\n target_for_less_than = less_than_threshold_values[:, -1]\n target_for_more_than = more_than_threshold_values[:, -1]\n\n ''' Finding the counts of muffin and cupcake for values lower than and greater than threshold value '''\n recipe_type, less_than_cupcake_muffin_count = np.unique(target_for_less_than, return_counts=True)\n recipe_type, more_than_cupcake_muffin_count = np.unique(target_for_more_than, return_counts=True)\n\n # print(recipe_type, more_than_cupcake_muffin_count, len(more_than_cupcake_muffin_count))\n ''' To ensure there are at least 5 records in each node '''\n if less_than_cupcake_muffin_count.sum() < 5 or more_than_cupcake_muffin_count.sum() < 5:\n ''' Return horrible badness '''\n return math.inf\n else:\n ''' Find the entropies for less than threshold values and more than threshold values '''\n less_than_entropy = sum((less_than_cupcake_muffin_count / less_than_cupcake_muffin_count.sum()) * - np.log2(\n less_than_cupcake_muffin_count / less_than_cupcake_muffin_count.sum()))\n more_than_entropy = sum((more_than_cupcake_muffin_count / more_than_cupcake_muffin_count.sum()) * - np.log2(\n more_than_cupcake_muffin_count / more_than_cupcake_muffin_count.sum()))\n\n ''' Calculate the total weighted entropy '''\n total_weighted_entropy = less_than_probability * less_than_entropy + more_than_probability * more_than_entropy\n\n return total_weighted_entropy","def prior(self, c, labeled):\n return log(len(labeled[c])/self.N_features)","def get_log_prior(samples):\n prior_distribution = tfp.distributions.Normal(loc=0., scale=1.)\n log_prior = tf.reduce_sum(prior_distribution.log_prob(samples), axis=1)\n return log_prior","def decision_tree_prediction(example, root, attributes):\n # If reached a leaf node, return the label\n if isinstance(root, str):\n return root\n\n # Attribute that was split on\n attribute = root.attribute\n # Column of the attribute that was split on\n i = get_index(attribute, attributes)\n testValue = example[i]\n # Check every child to see what path the example must take in the decision tree\n for child in root.children:\n if isinstance(child.branch, int):\n if int(testValue) <= child.branch:\n return decision_tree_prediction(example, child.nextTree, attributes)\n elif isinstance(child.branch, float):\n if int(testValue) > child.branch:\n return decision_tree_prediction(example, child.nextTree, attributes)\n# -----------------------------------------------Naive Bayes-------------------------------------------------\n # Naive bayes\n elif child.branch == \"Naive\":\n yes_probability = child.histogram[0]\n no_probability = child.histogram[2]\n i = 0\n for feature in example:\n if feature == \"yes\" or feature == \"no\":\n continue\n if i == 0 or i == 2 or i == 4 or i == 10 or i == 11 or i == 12:\n j = 0\n # Its a float so check\n for key in child.histogram[1][attribute_index[i]]:\n if float(feature) <= float(key) and j == 0:\n yes_probability = yes_probability * child.histogram[1][attribute_index[i]][key]\n elif j == 1:\n yes_probability = yes_probability * child.histogram[1][attribute_index[i]][key]\n j += 1\n for key in child.histogram[1][attribute_index[i]]:\n if float(feature) <= float(key) and j == 0:\n no_probability = no_probability * child.histogram[1][attribute_index[i]][key]\n elif j == 1:\n no_probability = no_probability * child.histogram[1][attribute_index[i]][key]\n j += 1\n else:\n yes_probability = yes_probability * child.histogram[1][attribute_index[i]][feature]\n no_probability = no_probability * child.histogram[3][attribute_index[i]][feature]\n i += 1\n if yes_probability > no_probability:\n return \"yes\"\n elif no_probability >= yes_probability:\n return \"no\"\n# -----------------------------------------------End Naive Bayes-------------------------------------------------\n else:\n if child.branch == testValue:\n return decision_tree_prediction(example, child.nextTree, attributes)","def calculate_log_p_multinomial(self, n_counts, nbins, batch_size):\n\n n_counts = tf.cast(n_counts, tf.float32)\n nbins = tf.cast(nbins, tf.float32)\n batch_size = tf.cast(batch_size, tf.float32)\n\n term_a = tf.lgamma(batch_size + 1)\n term_b = tf.reduce_sum(tf.lgamma(n_counts + 1))\n term_c = batch_size * tf.log(nbins)\n\n return term_a - term_b - term_c","def logdprior(parameters, hyperparameters):\n sigma_w_part = parameters[0] + invgamma_logpdf(parameters[0],\n hyperparameters[\"sigma_w_shape\"], hyperparameters[\"sigma_w_scale\"])\n sigma_v_part = parameters[1] + invgamma_logpdf(parameters[1], hyperparameters[\"sigma_v_shape\"], hyperparameters[\"sigma_v_scale\"])\n return sigma_w_part + sigma_v_part","def get_probability(fields, dic):\r\n sum_ = sum(dic.values())\r\n p = 0.0\r\n for f in fields:\r\n value = dic.get(f, 0.0) + 0.0001\r\n p = p + math.log(float(value)/float(sum_))\r\n return p","def geometric_brownian_motion_levels(param):\n return convert_to_prices(param, geometric_brownian_motion_log_returns(param))","def get_trigram_log_prob(self, trigram):\n w1, w2, w3 = trigram\n if (\n w1 in self.ngrams[3][\"data\"]\n and w2 in self.ngrams[3][\"data\"][w1]\n and w3 in self.ngrams[3][\"data\"][w1][w2]\n ):\n return self.ngrams[3][\"data\"][w1][w2][w3]\n return Decimal(1.0).log10() - Decimal(int(self.ngrams[1][\"count\"]) ** 2).log10()","def probOfAllNodeObservations( self ):\n aLeaf = list( self._hyperGraph.leaves )[ 0 ]\n\n total = LogVar( 0 )\n for i in range( aLeaf.N ):\n _u = aLeaf.getFullJoint( i )\n total += _u\n return total","def get_log_likelihood(response_probability, response):\n pass","def cond_prob(self, token, prev_tokens=None):\n if not prev_tokens:\n assert self.n == 1\n prev_tokens = tuple()\n\n hits = self.count((tuple(prev_tokens)+(token,)))\n sub_count = self.count(tuple(prev_tokens))\n # heuristic\n return (hits+1) / (float(sub_count)+self.V())","def cond_prob(self, token, prev_tokens=()):\n n = self._n\n lambdas = [0] * n\n res = 0\n for i in range(0, n - 1):\n lambdas[i] = (1 - sum(lambdas[:i])) * float(self.count(prev_tokens[i:])) / (\n float(self.count(prev_tokens[i:])) + self._gamma)\n res = res + lambdas[i] * self.sub_cond_prob(token, prev_tokens[i:])\n lambdas[n - 1] = (1 - sum(lambdas))\n assert (sum(lambdas) == 1.0)\n if (self._addone):\n return float(res + lambdas[n - 1] * self.sub_cond_prob_addone(token))\n else:\n return float(res + lambdas[n - 1] * self.sub_cond_prob(token))","def lnprior(theta):\n gamma, A = theta\n\n if 0.0 < gamma and 0.0 < A < 2.0 :\n return ( np.log(1.0/A) + np.log(1.0/(1.0+(gamma**2.0))) )\n\n return -np.inf","def bernoulli_logpmf(X, p):\n return -T.nnet.binary_crossentropy(p, X).sum(axis=-1)","def likelihood(self, log_occr_array=None):\n\n if log_occr_array is not None:\n # Catch invalid occurrence rates for zero likelihood\n try:\n self.log_occr = log_occr_array\n except InvalidOccurrenceRate:\n return 0.0\n\n # Procedure: calculate N_exp in all cases, calculate detection\n # term if there are detections\n\n # N_exp\n N_exp = self.calc_integral() * self._N_stars\n\n # Product terms\n # TODO:Check that the array broadcasting works here\n # Shape of s_terms should be [N_planets, NR, NP]\n s_terms = self.H_array * self.F_array * self.occr\n\n if tf.is_tensor(self.occr):\n ps_terms = tf.reduce_sum(s_terms, axis=(-1, -2))\n product_term = tf.reduce_prod(ps_terms)\n ll_value = product_term * tf.exp(product_term)\n else:\n product_term = s_terms.sum(axis=(-1, -2)).prod()\n ll_value = product_term * np.exp(-N_exp)\n\n # BUG\n if np.isnan(ll_value):\n warnings.warn(\".likelihood value is nan.\")\n import pdb; pdb.set_trace()\n\n # A nan value is possible when some of the occr are too high\n return ll_value if not np.isnan(ll_value) else 0.0","def cond_prob(self, token, prev_tokens=None):\n\n addone = self.addone\n n = self.n\n gamma = self.gamma\n\n if not prev_tokens:\n prev_tokens = []\n assert len(prev_tokens) == n - 1\n\n lambdas = []\n for i in range(0, n-1):\n # 1 - sum(previous lambdas)\n aux_lambda = 1 - sum(lambdas)\n # counts for numerator\n counts_top = self.count(tuple(prev_tokens[i:n-1]))\n # counts plus gamma (denominator)\n counts_w_gamma = self.count(tuple(prev_tokens[i:n-1])) + gamma\n # save the computed i-th lambda\n lambdas.append(aux_lambda * (counts_top / counts_w_gamma))\n # last lambda, by hand\n lambdas.append(1-sum(lambdas))\n\n # Maximum likelihood probs\n ML_probs = dict()\n for i in range(0, n):\n hits = self.count((tuple(prev_tokens[i:])+(token,)))\n sub_count = self.count(tuple(prev_tokens[i:]))\n result = 0\n if addone and not len(prev_tokens[i:]):\n result = (hits+1) / (float(sub_count) + len(self.voc))\n else:\n if sub_count:\n result = hits / float(sub_count)\n # the (i+1)-th element in ML_probs holds the q_ML value\n # for a (n-i)-gram\n ML_probs[i+1] = result\n\n prob = 0\n # ML_probs dict starts in 1\n for j in range(0, n):\n prob += ML_probs[j+1] * lambdas[j]\n return prob","def IDCG_p(topic, p):\n lbl_scores = gold_topic_labels[topic].items() # (label, score) list\n top_p = sorted(lbl_scores, key=lambda x: x[1], reverse=True)[:p]\n idcg = 0\n for idx, (label, rel) in enumerate(top_p):\n rank = idx + 1\n if idx == 0:\n idcg += rel\n continue\n idcg += rel/ math.log(rank,2)\n return idcg","def h(self, probs):\n\n return np.sum(-p*np.log2(p) if p > 0 else 0 for p in np.nditer(probs))","def herdan(n_terms, n_words):\n if n_words <= 1:\n return 0\n return math.log(n_terms) / math.log(n_words)","def nll_logprobs(self, input, target_idx):\n raise NotImplementedError()","def entropy(self, unlabeled_sequence):\n unlabeled_sequence = self._transform(unlabeled_sequence)\n\n T = len(unlabeled_sequence)\n N = len(self._states)\n\n alpha = self._forward_probability(unlabeled_sequence)\n beta = self._backward_probability(unlabeled_sequence)\n normalisation = logsumexp2(alpha[T - 1])\n\n entropy = normalisation\n\n # starting state, t = 0\n for i, state in enumerate(self._states):\n p = 2 ** (alpha[0, i] + beta[0, i] - normalisation)\n entropy -= p * self._priors.logprob(state)\n # print('p(s_0 = %s) =' % state, p)\n\n # state transitions\n for t0 in range(T - 1):\n t1 = t0 + 1\n for i0, s0 in enumerate(self._states):\n for i1, s1 in enumerate(self._states):\n p = 2 ** (\n alpha[t0, i0]\n + self._transitions[s0].logprob(s1)\n + self._outputs[s1].logprob(unlabeled_sequence[t1][_TEXT])\n + beta[t1, i1]\n - normalisation\n )\n entropy -= p * self._transitions[s0].logprob(s1)\n # print('p(s_%d = %s, s_%d = %s) =' % (t0, s0, t1, s1), p)\n\n # symbol emissions\n for t in range(T):\n for i, state in enumerate(self._states):\n p = 2 ** (alpha[t, i] + beta[t, i] - normalisation)\n entropy -= p * self._outputs[state].logprob(\n unlabeled_sequence[t][_TEXT]\n )\n # print('p(s_%d = %s) =' % (t, state), p)\n\n return entropy","def log_score(self, X_state_tree_sequence, pi=None, T=None, E=None):\n if pi is None:\n pi = self.estimate.pi\n if T is None:\n T = self.estimate.T\n if E is None:\n E = self.estimate.E\n\n log_scores = []\n for idx, lineageObj in enumerate(self.X):\n log_score = 0\n # the first term is the value of pi for the state of the first cell\n log_score += np.log(pi[X_state_tree_sequence[idx][0]])\n log_score += log_T_score(T, X_state_tree_sequence[idx], lineageObj)\n log_score += log_E_score(get_Emission_Likelihoods(self, E)\n [idx], X_state_tree_sequence[idx])\n assert np.all(np.isfinite(log_score))\n log_scores.append(log_score)\n return log_scores","def prior(training_data, label_list):\n\n smooth = 1 # smoothing factor\n logprob = {}\n # TODO: add your code here\n numfile1 = 0\n numfile2 = 0\n for dic in training_data:\n if(dic[\"label\"] == label_list[0]):\n numfile1 += 1\n elif(dic[\"label\"] == label_list[1]):\n numfile2 += 1\n numtotal = numfile1 + numfile2\n\n prob1 = (numfile1+smooth)/(numtotal+2)\n prob2 = (numfile2 + smooth) / (numtotal + 2)\n\n logprob[label_list[0]] = math.log(prob1)\n logprob[label_list[1]] = math.log(prob2)\n\n\n return logprob","def log_prob(self, samples):\n return -0.5 * sum_except_batch(\n np.log(2 * np.pi) + self.logstd + \\\n tf.exp(-2 * self.logstd) * tf.square(samples - self.mean))","def score(self, tree):\n probas = [self.prod_freq.get(prod, 0.5) /\n self.source_freq.get(prod.source, 1)\n for prod in tree]\n logger.debug(\"Scoring {0}:\".format(tree))\n for prod, proba in zip(tree, probas):\n logger.debug(\n \"\\t- {0} {1}({2})\"\n .format(prod, round(proba, 3), round(log(proba, 10), 3)))\n log_proba = sum(log(proba, 10) for proba in probas)\n return {\n 'log_proba': log_proba,\n 'productions': len(probas)\n }","def get_log(p):\n if p==0:\n return 0.\n return p*np.log2(p)","def log_prob(self, sents):\n log_prob = 0\n for sent in sents:\n log_prob += self.sent_log_prob(sent)\n return log_prob","def _evaluate_log_prob(self, mock_data):\n d_counts = self.data\n\n return -mock_data.sum() + numpy.sum(d_counts * numpy.log(mock_data))"],"string":"[\n \"def log_prob(self):\",\n \"def probability(self, tokens):\\n\\n return 2 ** self.log_probability(tokens)\",\n \"def log_probability(self, sequence):\\n sequence = self._transform(sequence)\\n\\n T = len(sequence)\\n\\n if T > 0 and sequence[0][_TAG]:\\n last_state = sequence[0][_TAG]\\n p = self._priors.logprob(last_state) + self._output_logprob(\\n last_state, sequence[0][_TEXT]\\n )\\n for t in range(1, T):\\n state = sequence[t][_TAG]\\n p += self._transitions[last_state].logprob(\\n state\\n ) + self._output_logprob(state, sequence[t][_TEXT])\\n last_state = state\\n return p\\n else:\\n alpha = self._forward_probability(sequence)\\n p = logsumexp2(alpha[T - 1])\\n return p\",\n \"def logprob(predictions, labels):\\n predictions[predictions < 1e-10] = 1e-10\\n return np.sum(np.multiply(labels, -np.log(predictions))) / labels.shape[0]\",\n \"def logprob(predictions, labels):\\n predictions[predictions < 1e-10] = 1e-10\\n return np.sum(np.multiply(labels, -np.log(predictions))) / labels.shape[0]\",\n \"def logprob(predictions, labels):\\n # prevent negative probability\\n predictions[predictions < 1e-10] = 1e-10\\n return np.sum(np.multiply(labels, -np.log(predictions))) / labels.shape[0]\",\n \"def tag_log_prob(self, y, add_end_token=True):\\n p = 0\\n n = self._n\\n y = (START_TAG,) * (n - 1) + tuple(y) + (END_TAG,) * add_end_token\\n for i in range(len(y) - self._n + 1):\\n tag = y[i + n - 1]\\n prev_tags = y[i:i + n - 1]\\n if self.trans_prob(tag, prev_tags) == 0:\\n return -math.inf\\n\\n p += math.log2(self.trans_prob(tag, prev_tags))\\n\\n return p\",\n \"def logprob(predictions, labels):\\n predictions[predictions < 1e-10] = 1e-10\\n return np.sum(np.multiply(labels, -np.log(predictions)))\",\n \"def calculateLogJointProbabilities(self, datum):\\n\\tlogJoint = util.Counter()\\n\\t#want to calculate log(P(y)) + log(sum(P(fi|y)))\\n\\t#where y is a label\\n\\tfor label in self.legalLabels:\\n\\t\\tlogJoint[label] = math.log(self.prior_distribution_prob[label])\\n\\t\\tfor feature, value in datum.items():\\n\\t\\t\\tcp = self.conditional_prob[label][feature][value]\\n\\t\\t\\tif cp > 0: #condition check for values < 0 because log(0) is undefined and math domain error occurs\\n\\t\\t\\t\\tlogJoint[label] += math.log(cp) #summing up\\n\\t\\t\\t\\t\\n\\treturn logJoint\",\n \"def calculate_entropy(self, tags):\\n tags_counter = Counter()\\n\\n if not tags:\\n return 0\\n\\n for tag in tags:\\n tags_counter[tag] += 1\\n classes_probs = [tags_counter[tag] / float(len(tags)) for tag in tags_counter]\\n if 0.0 in classes_probs:\\n return 0\\n\\n entropy = 0\\n for prob in classes_probs:\\n entropy -= prob * math.log(prob, 2)\\n\\n return entropy\",\n \"def likelihood(self, data, hypo):\\n tagged, n, k = data\\n if hypo < tagged + n - k:\\n return 0\\n\\n p = tagged / hypo\\n like = thinkbayes.eval_binomial_pmf(k, n, p)\\n return like\",\n \"def compute_log_prob(self,params: ndarray) -> float:\\n return self.compute_log_prior(params) + self.compute_log_likelihood(params)\",\n \"def logprob(self):\\n assert len(self._added_rows) == self._num_rows\\n TODO('https://github.com/posterior/treecat/issues/27')\",\n \"def sentence_logprob(self, sentence):\\n line = get_ngrams(sentence,3)\\n log_por = 0.0\\n for item in line:\\n raw_por = self.smoothed_trigram_probability(item)\\n log_por = log_por+math.log2(raw_por)\\n\\n return float(log_por)\",\n \"def logprob(self):\\n assert len(self._added_rows) == self._num_rows\\n TODO('https://github.com/posterior/treecat/issues/26')\",\n \"def calculateLogJointProbabilities(self, datum):\\n logJoint = util.Counter()\\n \\n \\\"*** YOUR CODE HERE ***\\\"\\n \\n # -- OUR CODE HERE\\n \\n \\n import math\\n for label in self.legalLabels:\\n sumThing = 0.0\\n for pixel in self.conditionalProb[label]:\\n if datum[pixel] is 1:\\n #assert self.conditionalProb[label][pixel] < 1.0 # -- sanity check that the probability is valid\\n sumThing += math.log((self.conditionalProb[label][pixel]*1.0))\\n else:\\n sumThing+=math.log(1-self.conditionalProb[label][pixel]*1.0)\\n logJoint[label] = math.log(self.prior[label]*1.0) + sumThing*1.0\\n \\n\\n \\n \\n import time\\n #print \\\"logJoint is :: \\\", logJoint\\n #time.sleep(2)\\n \\n \\n # -- uses the conditional probability tables computed in the current iteration\\n # -- in train and tune\\n \\n return logJoint\",\n \"def caculate_prob(self):\\n t_H = self.tree.depth()\\n t_h = 1\\n while(t_h <= t_H):\\n t_hnodes = self.get_h(t_h)\\n t_sum = 0\\n t_hpro = []\\n t_cpro = []\\n for t_n in t_hnodes:\\n t_sum = self.tree.get_node(t_n).data[0] + t_sum\\n t_node = self.tree.get_node(t_n)\\n if t_node.is_leaf():\\n t_node.data.append(0)\\n continue\\n t_childrens = self.tree.children(t_n)\\n t_shang = 0\\n for child in t_childrens:\\n t_shang = t_shang + (child.data[0]/t_node.data[0])*np.log(child.data[0]/t_node.data[0])\\n t_node.data.append(-t_shang)\\n for t_n in t_hnodes:\\n t_node = self.tree.get_node(t_n)\\n t_parentnode = self.tree.parent(t_n)\\n if t_h > 1:\\n t_node.data.append((t_node.data[0] / t_sum) * (t_node.data[0]/t_parentnode.data[0]))\\n t_hpro.append((t_node.data[0]/t_sum) * (t_node.data[0]/t_parentnode.data[0]))\\n else:\\n t_node.data.append((t_node.data[0] / t_sum))\\n t_hpro.append((t_node.data[0] / t_sum))\\n\\n t_cpro.append(t_node.data[1])\\n t_ndata = np.array(t_hpro)\\n mean = np.mean(t_ndata)\\n std = np.std(t_ndata,ddof=1)\\n t_sdata = np.array(t_cpro)\\n mean_s = np.mean(t_sdata)\\n std_s = np.std(t_sdata,ddof=1)\\n for t_n in t_hnodes:\\n t_node = self.tree.get_node(t_n)\\n if(std != 0):\\n t_node.data[2] = (t_node.data[2] - mean)/std\\n else:\\n t_node.data[2] = (t_node.data[2] - mean)\\n if(mean_s == 0 and std_s ==0):\\n t_node.data[1] = -100.0\\n continue\\n t_node.data[1] = (t_node.data[1] - mean_s)/std_s\\n t_h = t_h + 1\",\n \"def calc_marginal_entropy(data_stat):\\n label_col = data_stat.keys()[-1]\\n label_info = data_stat[label_col]\\n label_lengths = []\\n for label_poses in label_info.values():\\n label_lengths.append(len(label_poses))\\n # print(label_lengths)\\n majority = max(label_lengths)\\n minority = min(label_lengths)\\n total = float(majority + minority)\\n entropy = -(minority/total*math.log(minority/total,2)+majority/total*math.log(majority/total,2))\\n return entropy\",\n \"def log_probability(self, tokens):\\n\\n log_sum = 0\\n for leader, token in generate_ngrams(tokens, self.size, include_terminator = self.include_terminator):\\n if not leader in self.frequencies:\\n return float(\\\"-inf\\\")\\n\\n word_frequency = self.frequencies[leader][token]\\n leader_frequency = self.frequencies[leader].total\\n\\n probability = (word_frequency + self.smoothing) / (leader_frequency + len(self.vocab) * self.smoothing)\\n\\n if probability == 0:\\n return float(\\\"-inf\\\")\\n\\n log_sum += math.log2(probability)\\n \\n return log_sum\",\n \"def get_probability(self, sentence):\\n if len(sentence) == 1:\\n return Decimal(10) ** self.get_unigram_log_prob(sentence)\\n elif len(sentence) == 2:\\n return Decimal(10) ** self.get_bigram_log_prob(sentence)\\n else:\\n log_prob = Decimal(0.0)\\n for w1, w2, w3 in zip(sentence, sentence[1:], sentence[2:]):\\n log_prob += self.get_trigram_log_prob((w1, w2, w3))\\n log_prob = Decimal(log_prob)\\n return Decimal(10) ** log_prob\",\n \"def log_probability(self, samples):\\n pass\",\n \"def log_prob(self):\\n if not RerankingParser._parser_model_loaded:\\n raise ValueError(\\\"You need to have loaded a parser model in \\\"\\n \\\"order to get the log probability.\\\")\\n return parser.treeLogProb(self._tree)\",\n \"def compute_joint_probability(token_list, token_probabilities, use_log_prob=False):\\n\\n log_prob = 0\\n\\n for word in token_list:\\n\\n # do not allow zero probabilites\\n assert word in token_probabilities\\n\\n if use_log_prob:\\n log_prob += token_probabilities[word]\\n else:\\n log_prob += log10(token_probabilities[word])\\n\\n if use_log_prob:\\n return log_prob\\n\\n return 10**log_prob\",\n \"def calculate_entropy(prob):\\n return -(prob * math.log(prob,2))\",\n \"def conditional_entropy(P):\\n P_nan = P.copy()\\n P_nan[P_nan == 0] = np.nan\\n\\n marginals = np.nansum(P_nan, axis=1)\\n P_cond = P_nan / marginals[:, None]\\n\\n return np.nansum(np.multiply(P_nan, np.log2(1 / P_cond)))\",\n \"def compute_my_variability(event_log: Log) -> float:\\n prefixes: List[List[Event]] = []\\n bar: Bar = IncrementalBar(\\\"Prefix generation\\\", max=len(event_log.trace_list))\\n for trace in event_log.trace_list:\\n trace_prefixes: List[List[Event]] = trace.get_all_prefixes()\\n\\n for prefix in trace_prefixes:\\n if prefix not in prefixes:\\n prefixes.append(prefix)\\n bar.next()\\n bar.finish()\\n\\n entropy: float = 0\\n\\n bar = ShadyBar(\\\"Prefix likelihood estimation\\\", max=len(prefixes))\\n for prefix in prefixes:\\n\\n p: float = _prefix_likelihood_estimator(event_log, prefix)\\n entropy += p * logarithm(p, 10)\\n\\n bar.next()\\n bar.finish()\\n\\n entropy *= -1\\n\\n return entropy\",\n \"def entropy(*args):\\n\\n\\n values = []\\n leaf = -1\\n\\n for i, val in enumerate(args):\\n if(val != 0):\\n values.append(val * math.log(val, len(args)))\\n if(val == 1):\\n leaf = i\\n \\n return -sum(values), leaf\",\n \"def sentence_logprob(self, sentence):\\n sum_logprob = 0\\n trigrams = get_ngrams(sentence, 3)\\n for trigram in trigrams :\\n sum_logprob += math.log2(self.smoothed_trigram_probability(trigram))\\n\\n return sum_logprob\",\n \"def sentence_logprob(self, sentence):\\n grams = get_ngrams(sentence, 3)\\n p = 1\\n\\n for gram in grams:\\n p *= np.longfloat(self.smoothed_trigram_probability(gram))\\n\\n return np.log2(p)\",\n \"def test_that_most_probable_always_returns_a_tree_of_the_same_logprob():\\n\\n grammar = Grammar(rulebooks)\\n actual_tree = grammar.sample_tree()\\n sentence = actual_tree.terminals\\n inner = grammar.compute_inside_probabilities(sentence)\\n best_tree = grammar.compute_most_likely_tree(sentence, inner, root=0)\\n best_logprob = grammar.logprob_tree(best_tree)\\n\\n for _ in range(20):\\n tree = grammar.compute_most_likely_tree(sentence, inner, root=0)\\n assert np.isclose(grammar.logprob_tree(tree), best_logprob)\",\n \"def log_prior(x):\\n logp = (-0.5 * x.pow(2) - torch.tensor(2 * math.pi).sqrt().log()).sum(dim=1)\\n return logp\",\n \"def evaluate(self, test_data):\\n log_like = 0\\n for sequence in test_data:\\n for event, context in self.extract_ngrams(sequence):\\n log_like += math.log(self.cond_prob(event, context))\\n return log_like\",\n \"def test_posterior_logprobs(self):\\n x = list(product([True, False], repeat=2))\\n xs = list(e for e in product(x, repeat=3))\\n all_obs = list(o for o in xs\\n if all(any(e) and not all(e) for e in o))\\n total = logsumexp(list(posterior_logprobs(np.array(obs), self.S, self.A, self.E)[1]\\n for obs in all_obs))\\n assert_allclose(total, np.log(1))\",\n \"def get_entropy(dictionary):\\n my_sum = 0\\n weighted_sum_of_logs = 0\\n for freq in dictionary.values():\\n if freq:\\n my_sum += freq\\n weighted_sum_of_logs += freq * math.log(freq)\\n return math.log(my_sum) - weighted_sum_of_logs / my_sum\",\n \"def get_entropy(self, rows):\\n label_count = defaultdict(int)\\n total_count = 0\\n for row in rows:\\n label = row[self.target_attribute]\\n label_count[label] += 1\\n total_count += 1\\n return sum([-(float(label_count[label]) /\\n total_count) * np.log2(float(label_count[label]) / total_count)\\n for label in label_count.keys()])\",\n \"def multinomial_likelihood(m_true, alpha, alpha0, m_probs):\\n\\n ll = tf.reduce_sum(input_tensor=m_true * (tf.math.log(alpha0) - tf.math.log(alpha)), axis=1, keepdims=True)\\n ll = tf.reduce_mean(input_tensor=ll)\\n return ll\",\n \"def log_T_score(T, state_tree_sequence, lineageObj):\\n log_T_score_holder = 0\\n log_T = np.log(T)\\n # we start with the first transition, from the root cell\\n for level in lineageObj.output_list_of_gens[1:]:\\n for cell in level:\\n cell_idx = lineageObj.output_lineage.index(cell)\\n cell_state = state_tree_sequence[cell_idx]\\n if not cell.isLeaf():\\n for daughter in cell.get_daughters():\\n child_idx = lineageObj.output_lineage.index(daughter)\\n daughter_state = state_tree_sequence[child_idx]\\n log_T_score_holder += log_T[cell_state, daughter_state]\\n return log_T_score_holder\",\n \"def calculateLogJointProbabilities(self, datum):\\n logJoint = util.Counter()\\n \\\"*** YOUR CODE HERE ***\\\"\\n\\t#Adds log(P(y)) to calculate P(y|f1,f2...)\\n for label in self.legalLabels:\\n\\t\\tlogJoint[label] += math.log(self.prior[label])\\n\\t#Adds log(P(f1|y)), log(P(f2|y))... to calculate P(y|f1, f2...)\\n for key in datum:\\n\\t\\t#if key == (7, 3):\\n\\t\\t\\t#print self.condprobs[key, 0]\\n\\t\\tfor label in self.legalLabels:\\n\\t\\t\\t#print str(key) + str(datum[key])\\n\\t\\t\\tlogJoint[label] += math.log(self.condprobs[key, label][datum[key]])\\n return logJoint\",\n \"def cond_prob(self, event, context):\\n count = self.table[event, context] + self.prior\\n norm = self.margin[context] + (self.prior * len(self.alphabet))\\n return count / norm\",\n \"def node_conditional_likelihood(self, node):\\n # get transition probabilities over each branch length\\n prob_child0 = expm(self.qmat * node.children[0].dist)\\n prob_child1 = expm(self.qmat * node.children[1].dist)\\n\\n # likelihood that child 0 observation occurs if anc==0\\n child0_is0 = (\\n prob_child0[0, 0] * node.children[0].likelihood[0] + \\n prob_child0[0, 1] * node.children[0].likelihood[1]\\n )\\n # likelihood that child 1 observation occurs if anc==0\\n child1_is0 = (\\n prob_child1[0, 0] * node.children[1].likelihood[0] + \\n prob_child1[0, 1] * node.children[1].likelihood[1]\\n )\\n anc_lik_0 = child0_is0 * child1_is0\\n\\n # likelihood that child 0 observation occurs if anc==1\\n child0_is1 = (\\n prob_child0[1, 0] * node.children[0].likelihood[0] + \\n prob_child0[1, 1] * node.children[0].likelihood[1]\\n )\\n child1_is1 = (\\n prob_child1[1, 0] * node.children[1].likelihood[0] + \\n prob_child1[1, 1] * node.children[1].likelihood[1]\\n )\\n anc_lik_1 = child0_is1 * child1_is1\\n\\n # set estimated conditional likelihood on this node\\n node.likelihood = [anc_lik_0, anc_lik_1]\",\n \"def compute_probability_of_state(state):\\n p = compute_log_probability_of_text(state[\\\"text\\\"], state[\\\"char_to_ix\\\"], \\n state[\\\"frequency_statistics\\\"], state[\\\"transition_matrix\\\"])\\n \\n return p\",\n \"def get_log_likelihood(phi, pred, t, dot_product, weight, reg= 1):\\n prior = -0.5* np.sum(np.multiply(weight, weight))\\n likelihood = np.multiply(t, np.log(pred+TOLERANCE)) + np.multiply(1.0- t, np.log(1.0-pred+TOLERANCE))\\n likelihood = np.sum(likelihood)\\n\\n return prior + likelihood\",\n \"def calculate_log_perplexity(self, output, flat_labels): #completed, expensive, should be compiled\\n return -np.sum(np.log2(np.clip(output, a_min=1E-12, a_max=1.0))[np.arange(flat_labels.shape[0]), flat_labels[:,1]])\",\n \"def sumLogProb(a, b):\\n if a > b:\\n return a + log1p(exp(b - a))\\n else:\\n return b + log1p(exp(a - b))\",\n \"def __logprob__(self, cv, vsense):\\n return 1.0 / (1.0 + np.exp(-np.dot(cv, vsense)))\",\n \"def cond_entropy(joint_prob, cond_prob):\\n # Computing log2(P cond)\\n log2_p = (np.ma.log2(cond_prob)).filled(0)\\n # Multipling element wise the arrays\\n prod_entropy = np.multiply(joint_prob, log2_p)\\n # Getting the - sum of the resulting array.\\n H = -( np.sum(prod_entropy))\\n return H\",\n \"def log_prob(sentence, LM, smoothing=False, delta=0, vocabSize=0):\\n word_list = sentence.split()\\n log_prob = 0\\n for i in range(len(word_list)-1):\\n print(word_list[i], word_list[i+1])\\n bi_count = LM['bi'][word_list[i]][word_list[i+1]]\\n uni_count = LM['uni'][word_list[i]]\\n if uni_count == 0 and smoothing:\\n return float('-inf')\\n log_prob += log(((bi_count + delta)/(uni_count + delta * vocabSize)))\\n return log_prob\",\n \"def get_ngramlogprobs(freqdict):\\n return\",\n \"def cond_prob(self, token, prev_tokens=None):\\n\\n if not prev_tokens:\\n assert self.n == 1\\n prev_tokens = tuple()\\n # ngram condicional probs are based on relative counts\\n hits = self.count((tuple(prev_tokens)+(token,)))\\n sub_count = self.count(tuple(prev_tokens))\\n\\n return hits / float(sub_count)\",\n \"def log_probability(self, X):\\n\\n\\t\\treturn self.__log_probability(X)\",\n \"def _logprobratio(prob1, prob2):\\n return log(prob1) / log(prob2)\",\n \"def classify(self, documents):\\n predictions = []\\n for doc in documents:\\n\\n score_sod = math.log(self.priorSOD)\\n score_pop = math.log(self.priorPOP)\\n for term in doc.tokens:\\n if term in self.cond_prob_sod.keys():\\n score_sod += math.log(self.cond_prob_sod[term])\\n if term in self.cond_prob_pop.keys():\\n score_pop += math.log(self.cond_prob_pop[term])\\n if(score_pop >= score_sod): #defaults to ham if score = even \\n predictions.append('pop')\\n else:\\n predictions.append('sod')\\n \\n return predictions \\n pass\",\n \"def cond_prob(self, token, prev_tokens=()):\\n return float(self.count(list(prev_tokens) + [token]) + 1) / float(self.count(prev_tokens) + self._V)\",\n \"def log_prob(self, th):\\n\\n\\t\\tmask = self.__low <= th <= self.__high\\n\\t\\tif len(th.shape) == 1:\\n\\t\\t\\tlength = 1\\n\\t\\telif len(th.shape) == 2:\\n\\t\\t\\tlength = th.shape[0]\\n\\t\\telse:\\n\\t\\t\\traise RuntimeError(\\\"th must be either (d,) or (n_samples, d)\\\")\\n\\t\\tlogprobs = np.empty(length)\\n\\t\\tlogprobs[mask] = 0.\\n\\t\\tlogprobs[~mask] = -float('inf')\\n\\t\\treturn logprobs\",\n \"def log_likelihood_function(self, instance) -> float:\\n return self.prior.factor(instance[0])\",\n \"def logprob(self, word, context):\\n\\n return -log(self.prob(word, context), 2)\",\n \"def calc_conditional_entropy(map,data_stat,attribute):\\n #acquire the data info of the attribute stored in data_stat\\n data_info = data_stat[attribute]\\n #acquire the label info\\n # label_col = len(data_stat)-1\\n label_col = data_stat.keys()[-1]\\n # print(data_stat.keys())\\n label_info = data_stat[label_col]\\n #acquire the data \\n data = map[attribute]\\n labels = map[label_col]\\n conditional_entropy =0\\n for data_type in data_info:\\n specific_entropy = 0\\n for label_type in label_info: \\n #attribute data indices where all data entries are equal to a speicifc value\\n data_with_spec_val_idx = data_info[data_type]\\n #label indices where all labels are of same value\\n spec_label_idx = label_info[label_type]\\n #the intersection of the two indices above\\n intersect_idx = np.intersect1d(data_with_spec_val_idx,spec_label_idx)\\n #conditional probability of label being of specific value given speicific data value\\n temp_prob = len(intersect_idx)/float(len(data_with_spec_val_idx))\\n if temp_prob!=0:\\n specific_entropy += temp_prob*math.log(temp_prob,2)\\n specific_entropy = -specific_entropy\\n prob = len(data_with_spec_val_idx)/float(len(data))\\n conditional_entropy += prob * specific_entropy\\n return conditional_entropy\",\n \"def log_marginal_likelihood(X_train,y_train,phi,tau=1.,Ve=1.e-10):\",\n \"def likelihood(self, data, hypo):\\n n_total_population = hypo\\n k_tagged, n_caught, k_caught_tagged = data\\n\\n if hypo < k_tagged + (n_caught - k_caught_tagged):\\n return 0\\n\\n like = thinkbayes.eval_hypergeom_pmf(k_caught_tagged, n_total_population, k_tagged, n_caught)\\n return like\",\n \"def proba_from_log_odds(self, log_odds):\\n return (1/(1 + math.exp(log_odds)))\",\n \"def entropy_(P):\\n res = 0.0\\n\\n mask = P != 0.0 # avoid 0 in log\\n f = lambda x: x*np.log2(x)\\n # map-reduce strategy (likely to be more optimized than loops)\\n temp = list(map(f, P[mask]))\\n res = -np.sum(temp, dtype=float)\\n return res\",\n \"def classify(self, event):\\n\\n results = np.zeros(self.ntrees, dtype=float)\\n\\n for i,dt in enumerate(self.dTrees):\\n results[i] = self.treeWeights[i]*dt.classify(event)\\n\\n return np.sum(results)*(1.0/np.sum(self.treeWeights))\",\n \"def cond_prob(self, token, prev_tokens=()):\\n assert len(prev_tokens) < self._n\\n if self.count(prev_tokens) == 0:\\n return 0.0\\n return float(self.count(list(prev_tokens) + [token])) / float(self.count(prev_tokens))\",\n \"def log_likelihood(data, probs):\\n # Assume data is given as counts\\n return _np.sum([nlogp(n, p) for n, p in zip(data, probs) if n > 0])\",\n \"def prob(self, sequence):\\n prob = 1\\n for event, context in self.extract_ngrams(sequence):\\n prob *= self.cond_prob(event, context)\\n return prob\",\n \"def pruning_algorithm(self):\\n # traverse tree to get conditional likelihood estimate at root.\\n self.set_qmat()\\n for node in self.tree.treenode.traverse(\\\"postorder\\\"):\\n if not node.is_leaf(): \\n self.node_conditional_likelihood(node)\\n logger.debug(\\n f\\\"node={node.idx}; likelihood=[{node.likelihood[0]:.6f}, {node.likelihood[1]:.6f}]\\\") \\n\\n # multiply root prior times the conditional likelihood at root\\n root = self.tree.treenode\\n lik = (\\n (1 - self.prior_root_is_1) * root.likelihood[0] + \\n self.prior_root_is_1 * root.likelihood[1]\\n )\\n return lik\",\n \"def entropy(probabilities):\\n return -(sum([p * log(p, 2) if p > 0 else 0 for p in probabilities]))\",\n \"def log_probability(theta):\\n global priors\\n global logp\\n lp = np.sum([priors[p].logpdf(x) for p, x in zip(logp.parnames, theta)])\\n if not np.isfinite(lp) or np.isnan(lp):\\n return -np.inf\\n ll = logp(theta)\\n if not np.isfinite(ll):\\n return -np.inf\\n return lp + ll\",\n \"def log_probability(theta):\\n global priors\\n global logp\\n lp = np.sum([priors[p].logpdf(x) for p, x in zip(logp.parnames, theta)])\\n if not np.isfinite(lp) or np.isnan(lp):\\n return -np.inf\\n ll = logp(theta)\\n if not np.isfinite(ll):\\n return -np.inf\\n return lp + ll\",\n \"def dlog_score(self, T, pos, m, psi):\\n dpsi = tr.zeros_like(psi)\\n for (i, j, lab) in T:\\n if j != 0:\\n m_i = self.get_tag_index(m[i - 1])\\n pos1 = pos[i - 1]\\n m_j = self.get_tag_index(m[j - 1])\\n pos2 = pos[j - 1]\\n dpsi[pos1, pos2, lab, m_i, m_j] += 1\\n return dpsi\",\n \"def _conditional_entropy_compute(confmat: Tensor) ->Tensor:\\n confmat = _drop_empty_rows_and_cols(confmat)\\n total_occurrences = confmat.sum()\\n p_xy_m = confmat / total_occurrences\\n p_y = confmat.sum(1) / total_occurrences\\n p_y_m = p_y.unsqueeze(1).repeat(1, p_xy_m.shape[1])\\n return torch.nansum(p_xy_m * torch.log(p_y_m / p_xy_m))\",\n \"def log_prob(theta):\\n params = parse_params(theta)\\n prior = log_prior(**params)\\n if not np.isfinite(prior):\\n return -np.inf, -np.inf\\n like = log_like(**params)\\n if not np.isfinite(like):\\n return -np.inf, prior\\n return prior + like, prior\",\n \"def find_entropy(less_than_threshold,more_than_threshold):\\n\\n ''' Storing total number of records '''\\n total_records = len(less_than_threshold) + len(more_than_threshold)\\n\\n ''' Calculating the probability '''\\n less_than_probability = len(less_than_threshold) / total_records\\n more_than_probability = len(more_than_threshold) / total_records\\n\\n ''' Converting the dataframe to numpy arrays '''\\n less_than_threshold_values = less_than_threshold.values\\n more_than_threshold_values = more_than_threshold.values\\n\\n ''' Storing the target attribute values (Muffin or Cupcake) for threshold values '''\\n target_for_less_than = less_than_threshold_values[:, -1]\\n target_for_more_than = more_than_threshold_values[:, -1]\\n\\n ''' Finding the counts of muffin and cupcake for values lower than and greater than threshold value '''\\n recipe_type, less_than_cupcake_muffin_count = np.unique(target_for_less_than, return_counts=True)\\n recipe_type, more_than_cupcake_muffin_count = np.unique(target_for_more_than, return_counts=True)\\n\\n # print(recipe_type, more_than_cupcake_muffin_count, len(more_than_cupcake_muffin_count))\\n ''' To ensure there are at least 5 records in each node '''\\n if less_than_cupcake_muffin_count.sum() < 5 or more_than_cupcake_muffin_count.sum() < 5:\\n ''' Return horrible badness '''\\n return math.inf\\n else:\\n ''' Find the entropies for less than threshold values and more than threshold values '''\\n less_than_entropy = sum((less_than_cupcake_muffin_count / less_than_cupcake_muffin_count.sum()) * - np.log2(\\n less_than_cupcake_muffin_count / less_than_cupcake_muffin_count.sum()))\\n more_than_entropy = sum((more_than_cupcake_muffin_count / more_than_cupcake_muffin_count.sum()) * - np.log2(\\n more_than_cupcake_muffin_count / more_than_cupcake_muffin_count.sum()))\\n\\n ''' Calculate the total weighted entropy '''\\n total_weighted_entropy = less_than_probability * less_than_entropy + more_than_probability * more_than_entropy\\n\\n return total_weighted_entropy\",\n \"def prior(self, c, labeled):\\n return log(len(labeled[c])/self.N_features)\",\n \"def get_log_prior(samples):\\n prior_distribution = tfp.distributions.Normal(loc=0., scale=1.)\\n log_prior = tf.reduce_sum(prior_distribution.log_prob(samples), axis=1)\\n return log_prior\",\n \"def decision_tree_prediction(example, root, attributes):\\n # If reached a leaf node, return the label\\n if isinstance(root, str):\\n return root\\n\\n # Attribute that was split on\\n attribute = root.attribute\\n # Column of the attribute that was split on\\n i = get_index(attribute, attributes)\\n testValue = example[i]\\n # Check every child to see what path the example must take in the decision tree\\n for child in root.children:\\n if isinstance(child.branch, int):\\n if int(testValue) <= child.branch:\\n return decision_tree_prediction(example, child.nextTree, attributes)\\n elif isinstance(child.branch, float):\\n if int(testValue) > child.branch:\\n return decision_tree_prediction(example, child.nextTree, attributes)\\n# -----------------------------------------------Naive Bayes-------------------------------------------------\\n # Naive bayes\\n elif child.branch == \\\"Naive\\\":\\n yes_probability = child.histogram[0]\\n no_probability = child.histogram[2]\\n i = 0\\n for feature in example:\\n if feature == \\\"yes\\\" or feature == \\\"no\\\":\\n continue\\n if i == 0 or i == 2 or i == 4 or i == 10 or i == 11 or i == 12:\\n j = 0\\n # Its a float so check\\n for key in child.histogram[1][attribute_index[i]]:\\n if float(feature) <= float(key) and j == 0:\\n yes_probability = yes_probability * child.histogram[1][attribute_index[i]][key]\\n elif j == 1:\\n yes_probability = yes_probability * child.histogram[1][attribute_index[i]][key]\\n j += 1\\n for key in child.histogram[1][attribute_index[i]]:\\n if float(feature) <= float(key) and j == 0:\\n no_probability = no_probability * child.histogram[1][attribute_index[i]][key]\\n elif j == 1:\\n no_probability = no_probability * child.histogram[1][attribute_index[i]][key]\\n j += 1\\n else:\\n yes_probability = yes_probability * child.histogram[1][attribute_index[i]][feature]\\n no_probability = no_probability * child.histogram[3][attribute_index[i]][feature]\\n i += 1\\n if yes_probability > no_probability:\\n return \\\"yes\\\"\\n elif no_probability >= yes_probability:\\n return \\\"no\\\"\\n# -----------------------------------------------End Naive Bayes-------------------------------------------------\\n else:\\n if child.branch == testValue:\\n return decision_tree_prediction(example, child.nextTree, attributes)\",\n \"def calculate_log_p_multinomial(self, n_counts, nbins, batch_size):\\n\\n n_counts = tf.cast(n_counts, tf.float32)\\n nbins = tf.cast(nbins, tf.float32)\\n batch_size = tf.cast(batch_size, tf.float32)\\n\\n term_a = tf.lgamma(batch_size + 1)\\n term_b = tf.reduce_sum(tf.lgamma(n_counts + 1))\\n term_c = batch_size * tf.log(nbins)\\n\\n return term_a - term_b - term_c\",\n \"def logdprior(parameters, hyperparameters):\\n sigma_w_part = parameters[0] + invgamma_logpdf(parameters[0],\\n hyperparameters[\\\"sigma_w_shape\\\"], hyperparameters[\\\"sigma_w_scale\\\"])\\n sigma_v_part = parameters[1] + invgamma_logpdf(parameters[1], hyperparameters[\\\"sigma_v_shape\\\"], hyperparameters[\\\"sigma_v_scale\\\"])\\n return sigma_w_part + sigma_v_part\",\n \"def get_probability(fields, dic):\\r\\n sum_ = sum(dic.values())\\r\\n p = 0.0\\r\\n for f in fields:\\r\\n value = dic.get(f, 0.0) + 0.0001\\r\\n p = p + math.log(float(value)/float(sum_))\\r\\n return p\",\n \"def geometric_brownian_motion_levels(param):\\n return convert_to_prices(param, geometric_brownian_motion_log_returns(param))\",\n \"def get_trigram_log_prob(self, trigram):\\n w1, w2, w3 = trigram\\n if (\\n w1 in self.ngrams[3][\\\"data\\\"]\\n and w2 in self.ngrams[3][\\\"data\\\"][w1]\\n and w3 in self.ngrams[3][\\\"data\\\"][w1][w2]\\n ):\\n return self.ngrams[3][\\\"data\\\"][w1][w2][w3]\\n return Decimal(1.0).log10() - Decimal(int(self.ngrams[1][\\\"count\\\"]) ** 2).log10()\",\n \"def probOfAllNodeObservations( self ):\\n aLeaf = list( self._hyperGraph.leaves )[ 0 ]\\n\\n total = LogVar( 0 )\\n for i in range( aLeaf.N ):\\n _u = aLeaf.getFullJoint( i )\\n total += _u\\n return total\",\n \"def get_log_likelihood(response_probability, response):\\n pass\",\n \"def cond_prob(self, token, prev_tokens=None):\\n if not prev_tokens:\\n assert self.n == 1\\n prev_tokens = tuple()\\n\\n hits = self.count((tuple(prev_tokens)+(token,)))\\n sub_count = self.count(tuple(prev_tokens))\\n # heuristic\\n return (hits+1) / (float(sub_count)+self.V())\",\n \"def cond_prob(self, token, prev_tokens=()):\\n n = self._n\\n lambdas = [0] * n\\n res = 0\\n for i in range(0, n - 1):\\n lambdas[i] = (1 - sum(lambdas[:i])) * float(self.count(prev_tokens[i:])) / (\\n float(self.count(prev_tokens[i:])) + self._gamma)\\n res = res + lambdas[i] * self.sub_cond_prob(token, prev_tokens[i:])\\n lambdas[n - 1] = (1 - sum(lambdas))\\n assert (sum(lambdas) == 1.0)\\n if (self._addone):\\n return float(res + lambdas[n - 1] * self.sub_cond_prob_addone(token))\\n else:\\n return float(res + lambdas[n - 1] * self.sub_cond_prob(token))\",\n \"def lnprior(theta):\\n gamma, A = theta\\n\\n if 0.0 < gamma and 0.0 < A < 2.0 :\\n return ( np.log(1.0/A) + np.log(1.0/(1.0+(gamma**2.0))) )\\n\\n return -np.inf\",\n \"def bernoulli_logpmf(X, p):\\n return -T.nnet.binary_crossentropy(p, X).sum(axis=-1)\",\n \"def likelihood(self, log_occr_array=None):\\n\\n if log_occr_array is not None:\\n # Catch invalid occurrence rates for zero likelihood\\n try:\\n self.log_occr = log_occr_array\\n except InvalidOccurrenceRate:\\n return 0.0\\n\\n # Procedure: calculate N_exp in all cases, calculate detection\\n # term if there are detections\\n\\n # N_exp\\n N_exp = self.calc_integral() * self._N_stars\\n\\n # Product terms\\n # TODO:Check that the array broadcasting works here\\n # Shape of s_terms should be [N_planets, NR, NP]\\n s_terms = self.H_array * self.F_array * self.occr\\n\\n if tf.is_tensor(self.occr):\\n ps_terms = tf.reduce_sum(s_terms, axis=(-1, -2))\\n product_term = tf.reduce_prod(ps_terms)\\n ll_value = product_term * tf.exp(product_term)\\n else:\\n product_term = s_terms.sum(axis=(-1, -2)).prod()\\n ll_value = product_term * np.exp(-N_exp)\\n\\n # BUG\\n if np.isnan(ll_value):\\n warnings.warn(\\\".likelihood value is nan.\\\")\\n import pdb; pdb.set_trace()\\n\\n # A nan value is possible when some of the occr are too high\\n return ll_value if not np.isnan(ll_value) else 0.0\",\n \"def cond_prob(self, token, prev_tokens=None):\\n\\n addone = self.addone\\n n = self.n\\n gamma = self.gamma\\n\\n if not prev_tokens:\\n prev_tokens = []\\n assert len(prev_tokens) == n - 1\\n\\n lambdas = []\\n for i in range(0, n-1):\\n # 1 - sum(previous lambdas)\\n aux_lambda = 1 - sum(lambdas)\\n # counts for numerator\\n counts_top = self.count(tuple(prev_tokens[i:n-1]))\\n # counts plus gamma (denominator)\\n counts_w_gamma = self.count(tuple(prev_tokens[i:n-1])) + gamma\\n # save the computed i-th lambda\\n lambdas.append(aux_lambda * (counts_top / counts_w_gamma))\\n # last lambda, by hand\\n lambdas.append(1-sum(lambdas))\\n\\n # Maximum likelihood probs\\n ML_probs = dict()\\n for i in range(0, n):\\n hits = self.count((tuple(prev_tokens[i:])+(token,)))\\n sub_count = self.count(tuple(prev_tokens[i:]))\\n result = 0\\n if addone and not len(prev_tokens[i:]):\\n result = (hits+1) / (float(sub_count) + len(self.voc))\\n else:\\n if sub_count:\\n result = hits / float(sub_count)\\n # the (i+1)-th element in ML_probs holds the q_ML value\\n # for a (n-i)-gram\\n ML_probs[i+1] = result\\n\\n prob = 0\\n # ML_probs dict starts in 1\\n for j in range(0, n):\\n prob += ML_probs[j+1] * lambdas[j]\\n return prob\",\n \"def IDCG_p(topic, p):\\n lbl_scores = gold_topic_labels[topic].items() # (label, score) list\\n top_p = sorted(lbl_scores, key=lambda x: x[1], reverse=True)[:p]\\n idcg = 0\\n for idx, (label, rel) in enumerate(top_p):\\n rank = idx + 1\\n if idx == 0:\\n idcg += rel\\n continue\\n idcg += rel/ math.log(rank,2)\\n return idcg\",\n \"def h(self, probs):\\n\\n return np.sum(-p*np.log2(p) if p > 0 else 0 for p in np.nditer(probs))\",\n \"def herdan(n_terms, n_words):\\n if n_words <= 1:\\n return 0\\n return math.log(n_terms) / math.log(n_words)\",\n \"def nll_logprobs(self, input, target_idx):\\n raise NotImplementedError()\",\n \"def entropy(self, unlabeled_sequence):\\n unlabeled_sequence = self._transform(unlabeled_sequence)\\n\\n T = len(unlabeled_sequence)\\n N = len(self._states)\\n\\n alpha = self._forward_probability(unlabeled_sequence)\\n beta = self._backward_probability(unlabeled_sequence)\\n normalisation = logsumexp2(alpha[T - 1])\\n\\n entropy = normalisation\\n\\n # starting state, t = 0\\n for i, state in enumerate(self._states):\\n p = 2 ** (alpha[0, i] + beta[0, i] - normalisation)\\n entropy -= p * self._priors.logprob(state)\\n # print('p(s_0 = %s) =' % state, p)\\n\\n # state transitions\\n for t0 in range(T - 1):\\n t1 = t0 + 1\\n for i0, s0 in enumerate(self._states):\\n for i1, s1 in enumerate(self._states):\\n p = 2 ** (\\n alpha[t0, i0]\\n + self._transitions[s0].logprob(s1)\\n + self._outputs[s1].logprob(unlabeled_sequence[t1][_TEXT])\\n + beta[t1, i1]\\n - normalisation\\n )\\n entropy -= p * self._transitions[s0].logprob(s1)\\n # print('p(s_%d = %s, s_%d = %s) =' % (t0, s0, t1, s1), p)\\n\\n # symbol emissions\\n for t in range(T):\\n for i, state in enumerate(self._states):\\n p = 2 ** (alpha[t, i] + beta[t, i] - normalisation)\\n entropy -= p * self._outputs[state].logprob(\\n unlabeled_sequence[t][_TEXT]\\n )\\n # print('p(s_%d = %s) =' % (t, state), p)\\n\\n return entropy\",\n \"def log_score(self, X_state_tree_sequence, pi=None, T=None, E=None):\\n if pi is None:\\n pi = self.estimate.pi\\n if T is None:\\n T = self.estimate.T\\n if E is None:\\n E = self.estimate.E\\n\\n log_scores = []\\n for idx, lineageObj in enumerate(self.X):\\n log_score = 0\\n # the first term is the value of pi for the state of the first cell\\n log_score += np.log(pi[X_state_tree_sequence[idx][0]])\\n log_score += log_T_score(T, X_state_tree_sequence[idx], lineageObj)\\n log_score += log_E_score(get_Emission_Likelihoods(self, E)\\n [idx], X_state_tree_sequence[idx])\\n assert np.all(np.isfinite(log_score))\\n log_scores.append(log_score)\\n return log_scores\",\n \"def prior(training_data, label_list):\\n\\n smooth = 1 # smoothing factor\\n logprob = {}\\n # TODO: add your code here\\n numfile1 = 0\\n numfile2 = 0\\n for dic in training_data:\\n if(dic[\\\"label\\\"] == label_list[0]):\\n numfile1 += 1\\n elif(dic[\\\"label\\\"] == label_list[1]):\\n numfile2 += 1\\n numtotal = numfile1 + numfile2\\n\\n prob1 = (numfile1+smooth)/(numtotal+2)\\n prob2 = (numfile2 + smooth) / (numtotal + 2)\\n\\n logprob[label_list[0]] = math.log(prob1)\\n logprob[label_list[1]] = math.log(prob2)\\n\\n\\n return logprob\",\n \"def log_prob(self, samples):\\n return -0.5 * sum_except_batch(\\n np.log(2 * np.pi) + self.logstd + \\\\\\n tf.exp(-2 * self.logstd) * tf.square(samples - self.mean))\",\n \"def score(self, tree):\\n probas = [self.prod_freq.get(prod, 0.5) /\\n self.source_freq.get(prod.source, 1)\\n for prod in tree]\\n logger.debug(\\\"Scoring {0}:\\\".format(tree))\\n for prod, proba in zip(tree, probas):\\n logger.debug(\\n \\\"\\\\t- {0} {1}({2})\\\"\\n .format(prod, round(proba, 3), round(log(proba, 10), 3)))\\n log_proba = sum(log(proba, 10) for proba in probas)\\n return {\\n 'log_proba': log_proba,\\n 'productions': len(probas)\\n }\",\n \"def get_log(p):\\n if p==0:\\n return 0.\\n return p*np.log2(p)\",\n \"def log_prob(self, sents):\\n log_prob = 0\\n for sent in sents:\\n log_prob += self.sent_log_prob(sent)\\n return log_prob\",\n \"def _evaluate_log_prob(self, mock_data):\\n d_counts = self.data\\n\\n return -mock_data.sum() + numpy.sum(d_counts * numpy.log(mock_data))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6678612","0.6432416","0.63992906","0.633939","0.633939","0.6339248","0.6256718","0.6255451","0.623174","0.6225141","0.6221682","0.619902","0.61507213","0.61484003","0.61439043","0.60917944","0.6077829","0.6073268","0.60723597","0.6044864","0.60433054","0.604024","0.60035455","0.5990469","0.59813815","0.5981123","0.59752923","0.59669024","0.59632385","0.59607065","0.5930449","0.59142494","0.5901886","0.59017277","0.5883845","0.588004","0.5869644","0.5855146","0.58456033","0.5839067","0.58294284","0.5815644","0.5812913","0.581066","0.58079445","0.58031774","0.57925564","0.5788786","0.57872146","0.5785752","0.57709795","0.57689553","0.57529783","0.5744579","0.5741878","0.57298845","0.57289404","0.5728621","0.5720095","0.5716352","0.571599","0.5709488","0.57079273","0.57036984","0.57027","0.57022464","0.5691451","0.5689225","0.5689225","0.56804883","0.5666911","0.56578124","0.56486803","0.5639593","0.5634813","0.5629536","0.5626212","0.56232923","0.5623146","0.56170726","0.5613873","0.5608615","0.56083995","0.5604739","0.5604275","0.5585453","0.5582583","0.5574825","0.557477","0.557075","0.5570348","0.5569173","0.55691195","0.5568786","0.55669945","0.55646026","0.5562148","0.5560663","0.556047","0.555785","0.55523014"],"string":"[\n \"0.6678612\",\n \"0.6432416\",\n \"0.63992906\",\n \"0.633939\",\n \"0.633939\",\n \"0.6339248\",\n \"0.6256718\",\n \"0.6255451\",\n \"0.623174\",\n \"0.6225141\",\n \"0.6221682\",\n \"0.619902\",\n \"0.61507213\",\n \"0.61484003\",\n \"0.61439043\",\n \"0.60917944\",\n \"0.6077829\",\n \"0.6073268\",\n \"0.60723597\",\n \"0.6044864\",\n \"0.60433054\",\n \"0.604024\",\n \"0.60035455\",\n \"0.5990469\",\n \"0.59813815\",\n \"0.5981123\",\n \"0.59752923\",\n \"0.59669024\",\n \"0.59632385\",\n \"0.59607065\",\n \"0.5930449\",\n \"0.59142494\",\n \"0.5901886\",\n \"0.59017277\",\n \"0.5883845\",\n \"0.588004\",\n \"0.5869644\",\n \"0.5855146\",\n \"0.58456033\",\n \"0.5839067\",\n \"0.58294284\",\n \"0.5815644\",\n \"0.5812913\",\n \"0.581066\",\n \"0.58079445\",\n \"0.58031774\",\n \"0.57925564\",\n \"0.5788786\",\n \"0.57872146\",\n \"0.5785752\",\n \"0.57709795\",\n \"0.57689553\",\n \"0.57529783\",\n \"0.5744579\",\n \"0.5741878\",\n \"0.57298845\",\n \"0.57289404\",\n \"0.5728621\",\n \"0.5720095\",\n \"0.5716352\",\n \"0.571599\",\n \"0.5709488\",\n \"0.57079273\",\n \"0.57036984\",\n \"0.57027\",\n \"0.57022464\",\n \"0.5691451\",\n \"0.5689225\",\n \"0.5689225\",\n \"0.56804883\",\n \"0.5666911\",\n \"0.56578124\",\n \"0.56486803\",\n \"0.5639593\",\n \"0.5634813\",\n \"0.5629536\",\n \"0.5626212\",\n \"0.56232923\",\n \"0.5623146\",\n \"0.56170726\",\n \"0.5613873\",\n \"0.5608615\",\n \"0.56083995\",\n \"0.5604739\",\n \"0.5604275\",\n \"0.5585453\",\n \"0.5582583\",\n \"0.5574825\",\n \"0.557477\",\n \"0.557075\",\n \"0.5570348\",\n \"0.5569173\",\n \"0.55691195\",\n \"0.5568786\",\n \"0.55669945\",\n \"0.55646026\",\n \"0.5562148\",\n \"0.5560663\",\n \"0.556047\",\n \"0.555785\",\n \"0.55523014\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":11187,"cells":{"query":{"kind":"string","value":"Calculate the gradient of the log probability for a given tree with respect to the psi and phi parameters"},"document":{"kind":"string","value":"def dlog_prob(self, T, pos, m, psi, phi=tr.Tensor()):\n if phi.size() == tr.Size([0]):\n phi = self.create_phi(T, pos, m)\n dpsi_score = self.dlog_score(T, pos, m, psi)\n dpsi_Z = self.dlogZ(T, pos, psi, phi)\n return dpsi_score - dpsi_Z"},"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 dlogZ(self, T, pos, psi, phi):\n msgs = belief_propagation(T, pos, psi, phi, True)\n dpsi = calculate_gradient(msgs, T, pos, psi, True, True)\n return dpsi","def fd_grad(self, T, pos, psi, phi, eps=1e-5):\n dpsi = tr.zeros_like(psi)\n dphi = tr.zeros_like(phi)\n for pos1 in range(psi.shape[0]):\n for pos2 in range(psi.shape[1]):\n for lab in range(psi.shape[2]):\n for i in range(psi.shape[3]):\n for j in range(psi.shape[4]):\n psi[pos1, pos2, lab, i, j] += eps\n val1 = self.logZ(T, pos, psi, phi)\n psi[pos1, pos2, lab, i, j] -= 2 * eps\n val2 = self.logZ(T, pos, psi, phi)\n psi[pos1, pos2, lab, i, j] += eps\n dpsi[pos1, pos2, lab, i, j] = (val1 - val2) / (2 * eps)\n for p in range(phi.shape[0]):\n for i in range(phi.shape[1]):\n phi[p, i] += eps\n val1 = self.logZ(T, pos, psi, phi)\n phi[p, i] -= 2 * eps\n val2 = self.logZ(T, pos, psi, phi)\n phi[p, i] += eps\n dphi[p, i] = (val1 - val2) / (2 * eps)\n return dpsi, dphi","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 Gradient(Walker,particle):\n\n h=0.001\n dPsi = zeros(shape=shape(Walker.Re[particle]))\n for i in range(Walker.sys_dim):\n Y=Walker.Re[particle][i]\n Walker.Re[particle][i]-=h\n wfs1=wfs(Walker)\n Walker.Re[particle][i]+=2.0*h\n wfs2=wfs(Walker)\n dPsi[i] = (wfs2-wfs1)/2/h\n Walker.Re[particle][i]=Y\n\n return dPsi","def logp_grad(self, xs, ys, fs, **kwargs):","def _log_likelihood_gradients(self):\r\n return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))","def MvNormalLogp():\n cov = pt.matrix(\"cov\")\n cov.tag.test_value = floatX(np.eye(3))\n delta = pt.matrix(\"delta\")\n delta.tag.test_value = floatX(np.zeros((2, 3)))\n\n cholesky = Cholesky(lower=True, on_error=\"nan\")\n\n n, k = delta.shape\n n, k = f(n), f(k)\n chol_cov = cholesky(cov)\n diag = pt.diag(chol_cov)\n ok = pt.all(diag > 0)\n\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\n delta_trans = solve_lower(chol_cov, delta.T).T\n\n result = n * k * pt.log(f(2) * np.pi)\n result += f(2) * n * pt.sum(pt.log(diag))\n result += (delta_trans ** f(2)).sum()\n result = f(-0.5) * result\n logp = pt.switch(ok, result, -np.inf)\n\n def dlogp(inputs, gradients):\n (g_logp,) = gradients\n cov, delta = inputs\n\n g_logp.tag.test_value = floatX(1.0)\n n, k = delta.shape\n\n chol_cov = cholesky(cov)\n diag = pt.diag(chol_cov)\n ok = pt.all(diag > 0)\n\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\n delta_trans = solve_lower(chol_cov, delta.T).T\n\n inner = n * pt.eye(k) - pt.dot(delta_trans.T, delta_trans)\n g_cov = solve_upper(chol_cov.T, inner)\n g_cov = solve_upper(chol_cov.T, g_cov.T)\n\n tau_delta = solve_upper(chol_cov.T, delta_trans.T)\n g_delta = tau_delta.T\n\n g_cov = pt.switch(ok, g_cov, -np.nan)\n g_delta = pt.switch(ok, g_delta, -np.nan)\n\n return [-0.5 * g_cov * g_logp, -g_delta * g_logp]\n\n return OpFromGraph([cov, delta], [logp], grad_overrides=dlogp, inline=True)","def log_prob(self, T, pos, m, psi, phi=tr.Tensor()):\n if phi.size() == tr.Size([0]):\n phi = self.create_phi(T, pos, m)\n return self.log_score(T, pos, m, psi, phi) - self.logZ(T, pos, psi, phi)","def _log_likelihood_gradients(self):\n return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK,X=self.X,slices1=self.Xslices,slices2=self.Xslices), self.likelihood._gradients(partial=np.diag(self.dL_dK))))","def grad_log(self, X):\n g = autograd.elementwise_grad(self.log_den)\n G = g(X)\n return G","def grad_log_likelihood(self, log_occr_array=None):\n\n if log_occr_array is not None:\n # Catch invalid occurrence rates for zero likelihood\n try:\n self.log_occr = log_occr_array\n except InvalidOccurrenceRate:\n return -np.inf * np.ones_like(self.occr, dtype=float)\n\n # Calculate components first\n N_exp = self.calc_integral() * self._N_stars # perhaps not needed\n nexp_terms = self._N_stars * self.calc_bin_volumes() * self.H_bar_array\n s_terms = self.H_array * self.F_array * self.occr\n numerator_terms = self.H_array * self.F_array\n\n if not tf.is_tensor(self.occr):\n # Checking shapes of intermediate terms,\n # numerator_terms vs s_terms.sum(-1, -2) and vs v factors.\n intermediate_terms = numerator_terms / s_terms.sum(axis=(-1, -2))\n # TODO: v_factor changed to negative, I think a minus\n # sign had been missed\n grad_log_array = - nexp_terms + intermediate_terms.sum(axis=0)\n\n # BUG TODO\n if np.isnan(grad_log_array).any():\n warnings.warn(\".grad_log_likelihood value is nan.\")\n import pdb; pdb.set_trace()\n grad_log_array = -np.inf * grad_log_array\n else:\n raise NotImplementedError(\"Manual gradient calculate with \"\n \"tensorflow objects isn't \"\n \"implemented, and seems a bit \"\n \"redundant.\")\n\n return grad_log_array","def gradient(self, theta):\n a = -(6 * self.scale ** 2)\n b = 3 * self.scale ** 2 + np.exp(2 * theta)\n b *= np.log(3 * self.scale ** 2 * np.exp(-2 * theta) + 1)\n return a / b","def log_probability(theta):\n global priors\n global logp\n lp = np.sum([priors[p].logpdf(x) for p, x in zip(logp.parnames, theta)])\n if not np.isfinite(lp) or np.isnan(lp):\n return -np.inf\n ll = logp(theta)\n if not np.isfinite(ll):\n return -np.inf\n return lp + ll","def log_probability(theta):\n global priors\n global logp\n lp = np.sum([priors[p].logpdf(x) for p, x in zip(logp.parnames, theta)])\n if not np.isfinite(lp) or np.isnan(lp):\n return -np.inf\n ll = logp(theta)\n if not np.isfinite(ll):\n return -np.inf\n return lp + ll","def logZ(self, T, pos, psi, phi):\n msgs = belief_propagation(T, pos, psi, phi, True)\n log_z = calculate_belief_sum(msgs, True)\n return log_z","def log_prior_grad(self, inputs):","def grad_llh(self, params):\n grad = np.clip(self.grad_log_likelihood(params[0], params[1], params[2:]), SMALLEST_NUMBER,\n LARGEST_NUMBER)\n\n return grad","def logit_cost_grad(self, theta, X, y):\n\n grad = np.zeros(len(theta))\n\n ### YOUR CODE HERE\n sig = utils.sigmoid(theta)\n # sig = np.subtract(sig, y)\n sig = sig - y\n grad = np.dot(X.T, sig) + 2 * self.params['lamb'] * self.regularizer[1](self.weights)\n ### END YOUR CODE\n\n return grad","def _log_prior_gradients(self):\n if self.priors.size == 0:\n return 0.\n x = self.param_array\n ret = np.zeros(x.size)\n #compute derivate of prior density\n [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]\n #add in jacobian derivatives if transformed\n priored_indexes = np.hstack([i for p, i in self.priors.items()])\n for c,j in self.constraints.items():\n if not isinstance(c, Transformation):continue\n for jj in j:\n if jj in priored_indexes:\n ret[jj] += c.log_jacobian_grad(x[jj])\n return ret","def _log_prior_gradients(self):\n x = self._get_params()\n ret = np.zeros(x.size)\n [np.put(ret,i,p.lnpdf_grad(xx)) for i,(p,xx) in enumerate(zip(self.priors,x)) if not p is None]\n return ret","def grad_log_likelihood(g_t):\n # state vector [g1, g2, g3, ... , gn-1, gn]\n state_vector = np.zeros(N)\n\n # factor that will be added to one node and subtracted from another\n def alphaij(gj, gi, deltaij, varij):\n return ((gj - gi) - deltaij) / varij\n\n # indices of state vector\n # Iterate over all connections\n for r in self.graph.index:\n state1, state2, value, variance, ligand, standard_state = self.graph.iloc[r]\n i = self.states[self.states.name == state1].index[0]\n j = self.states[self.states.name == state2].index[0]\n\n gj = g_t[j]\n gi = g_t[i]\n\n edge_attr = self.cycle.edges()[(state1, state2)]\n deltaij = edge_attr['energy'] # measured difference\n varij = edge_attr['weight'] # measured variance\n\n shared_alpha = alphaij(gj, gi, deltaij, varij)\n\n state_vector[i] += shared_alpha\n state_vector[j] -= shared_alpha\n\n return state_vector","def get_log_likelihood(phi, pred, t, dot_product, weight, reg= 1):\n prior = -0.5* np.sum(np.multiply(weight, weight))\n likelihood = np.multiply(t, np.log(pred+TOLERANCE)) + np.multiply(1.0- t, np.log(1.0-pred+TOLERANCE))\n likelihood = np.sum(likelihood)\n\n return prior + likelihood","def log_likelihood_gradients_(self, y, f):\n log_lik = self.evaluate_log_likelihood(y, f)\n f = np.squeeze(f)\n J = jacrev(self.evaluate_log_likelihood, argnums=1)\n H = jacrev(J, argnums=1)\n return log_lik, J(y, f), H(y, f)","def log_LxP(theta, D):\r\n p1 = np.exp( -((theta-D[0])**2)/2 )\r\n p2 = np.exp( -((theta-D[1])**2)/2 )\r\n if np.abs(theta) <= 1:\r\n LxP = p1*p2\r\n else:\r\n LxP = 0.0\r\n return np.log(LxP)","def _log_prior_gradients(self):\r\n if self.priors is None:\r\n return 0.\r\n x = self._get_params()\r\n ret = np.zeros(x.size)\r\n [np.put(ret, i, p.lnpdf_grad(xx)) for i, (p, xx) in enumerate(zip(self.priors, x)) if not p is None]\r\n return ret","def calc_grad(X, Y, theta):\n m, n = X.shape\n\n margins = Y * X.dot(theta)\n probs = 1. / (1 + np.exp(margins))\n grad = -(1./m) * (X.T.dot(probs * Y))\n\n return grad","def log_likelihood(self, theta=None, phi=None):\n theta = theta if theta is not None else self.theta\n phi = phi if phi is not None else self.phi\n ret = 0.\n for m, dm in enumerate(self.corpus):\n for n, w_mn in enumerate(dm):\n tp = 0.\n for k in range(self.n_components):\n tp += theta[m, k] * phi[k, w_mn]\n ret += np.log(tp)\n return ret","def f(p, phi, phib, df):\n\treturn -log(p) - df + (p-1)*phi + \\\n\t ( phi*(1-p) + phib + \\\n\t 5./4*alpha*(phi*p)**(9./4)-(9./4)*alpha*(p*phi)**(5./4) - \\\n\t (1./2)(1-p*phi)**2 - (phib/Nb)-5./4*alpha*(phi+phib)**(9./4) + \\\n\t (9./4)*alpha*(phi+phib)**(5.4) + \\\n\t 1./2*(1-phi-phib)**2 ) * Ns","def _forward_log_det_jacobian(self, x):\n d = self._compute_shared(x=x)\n relx = (x - d.x_k) / d.w_k\n relx = relx # tf.where(d.out_of_bounds, 0.5*tf.ones_like(x), relx)\n grad = (\n 2 * tf.math.log(d.s_k) +\n tf.math.log(d.d_kp1 * relx**2 + 2 * d.s_k * relx * (1 - relx) + # newln\n d.d_k * (1 - relx)**2) -\n 2 * tf.math.log((d.d_kp1 + d.d_k - 2 * d.s_k) * relx *\n (1 - relx) + d.s_k))\n return grad # tf.where(d.out_of_bounds, tf.zeros_like(grad), grad)","def log_prob(self):\n res = -self.L_h/2*np.log(2*np.pi*self.la)\n res = res + self.L_h*(self.L_h-1)/2*self.a\n\n\n res = res - 1/(2*self.la)*np.square(np.linalg.norm(self.e*self.pie))\n\n res = res - 1/(2*self.la)*np.sum(self.e2*self.pie_var)\n\n res = res - self.L_h/2*np.log(2*np.pi*self.sigma2)\n res = res - 1/(2*self.sigma2)*(np.square(np.linalg.norm(self.w))+np.trace(self.R))\n\n print(\"Log-probability difference = {}\".format(res - self.LP), file=self.logfile)\n self.LP = res\n return res","def gradient(self, theta):\n return (1 / (self.sigma * np.sqrt(2 * np.pi))) * (\n -theta / (self.sigma ** 2) * np.exp(-(theta ** 2) / (2 * self.sigma ** 2))\n )","def gradient(self, theta):\n pass","def gradient(self, theta):\n pass","def log_prob(theta):\n params = parse_params(theta)\n prior = log_prior(**params)\n if not np.isfinite(prior):\n return -np.inf, -np.inf\n like = log_like(**params)\n if not np.isfinite(like):\n return -np.inf, prior\n return prior + like, prior","def log_deriv(error):\n return logistic(error) * (1 - logistic(error))","def grad_log_likelihood(self, log_occr_array=None):\n\n if log_occr_array is not None:\n # Catch invalid occurrence rates for zero likelihood\n try:\n self.log_occr = log_occr_array\n except InvalidOccurrenceRate:\n return -np.inf * np.ones_like(self.occr, dtype=float)\n\n # The Poisson Jacobian\n # --------------------\n # Calculate components first\n N_exp = self.calc_integral() * self._N_stars # perhaps not needed\n v_factors = self._N_stars * self.calc_bin_volumes() * self.H_bar_array\n # TODO: broadcast self.occr into the same shape as self.F_array\n # Reduce it later through the chain-rule\n s_terms = self.H_array * self.F_array * self.occr_grid\n numerator_terms = self.H_array * self.F_array\n\n if not tf.is_tensor(self.occr):\n # Checking shapes of intermediate terms,\n # numerator_terms vs s_terms.sum(-1, -2) and vs v factors.\n intermediate_terms = numerator_terms / s_terms.sum(axis=(-1, -2))\n # TODO: v_factor changed to negative, I think a minus\n # sign had been missed\n poisson_jacobian = intermediate_terms.sum(axis=0) - v_factors\n poisson_jacobian = poisson_jacobian.reshape(-1)\n param_jacobian = self.get_occr_jacobian(flattened=True)\n grad_log_array = np.matmul(poisson_jacobian, param_jacobian)\n\n # BUG TODO\n if np.isnan(grad_log_array).any():\n warnings.warn(\".grad_log_likelihood value is nan.\")\n import pdb; pdb.set_trace()\n grad_log_array = -np.inf * grad_log_array\n else:\n raise NotImplementedError(\"Manual gradient calculate with \"\n \"tensorflow objects isn't \"\n \"implemented, and seems a bit \"\n \"redundant.\")\n\n return grad_log_array","def dphi(x):\n return grad(phi)(x)","def log_likelihood_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w))-y)","def log_marginal_likelihood(X_train,y_train,phi,tau=1.,Ve=1.e-10):","def compute_loss_gradient(theta_vector, *args):\n\n psi = args[0] # feed psi as a parameter\n circ_depth = args[1]\n num_qbits = args[2]\n theta = np.reshape(theta_vector, (circ_depth, num_qbits)) # reshapes the flat theta vector\n fidelity = get_fidelity(theta, psi)\n\n # the derivative of the loss wrt fidelity\n dl_df = -0.5 * fidelity ** (-0.5)\n\n df_dtheta = [] # a list of partial derivatives of the fidelity wrt the theta parameters\n\n for index in range(len(theta_vector)):\n layer_index = index // num_qbits\n qbit_index = index % num_qbits\n\n theta_plus = np.copy(theta)\n theta_plus[layer_index][qbit_index] += np.pi / 2 # added pi/2 to the ith theta parameter\n\n theta_minus = np.copy(theta)\n theta_minus[layer_index][qbit_index] -= np.pi / 2 # subtracted pi/2 to the ith theta parameter\n\n df_dtheta_i = 0.5 * (get_fidelity(theta_plus, psi) - get_fidelity(theta_minus, psi)) # ith derivative\n df_dtheta.append(df_dtheta_i)\n\n df_dtheta = np.array(df_dtheta)\n dl_dtheta = dl_df * df_dtheta # chain rule to get partial derivative of loss wrt theta parameters\n\n return dl_dtheta","def logZ_brute(self, T, pos, psi, phi):\n ms = self.get_all_tag_seq(len(T))\n log_scores = tr.zeros(len(ms), dtype=tr.float64)\n for i in range(len(ms)):\n log_scores[i] = self.log_score(T, pos, ms[i], psi, phi)\n log_z = logsumexp(log_scores)\n return log_z","def grad(theta, X, y, lambda_):\n # ... dopolnite (naloga 1, naloga 2)\n\n l = []\n for i, e in enumerate(theta):\n l.append(1 / len(y) * sum([(h(x, theta) - yi) * x[i] for x, yi in zip(X, y)]) + 2 * lambda_ * e)\n\n return np.array(l)","def f(p, phi, phib, df):\n\treturn - p + exp( - df + Ns*(log((1 - p*phi)/(1 - phi - phib)) + \\\n\t\t(p - 1)*phi - phib + (9./4)*alpha*((phi + phib)**(5./4) - (p*phi)**(5./4))))","def ga_log(R):\n phiP, t_normal_n, t_perpendicular_n = extractRotorComponents(R)\n return phiP + t_normal_n + t_perpendicular_n","def fgrad_y(self, y, psi, return_precalc = False):\r\n\r\n\r\n mpsi = psi.copy()\r\n d = psi[-1]\r\n mpsi = mpsi[:self.num_parameters-1].reshape(self.n_terms, 3)\r\n\r\n # vectorized version\r\n\r\n S = (mpsi[:,1]*(y[:,:,None] + mpsi[:,2])).T\r\n R = np.tanh(S)\r\n D = 1-R**2\r\n\r\n GRAD = (d + (mpsi[:,0:1][:,:,None]*mpsi[:,1:2][:,:,None]*D).sum(axis=0)).T\r\n\r\n if return_precalc:\r\n return GRAD, S, R, D\r\n\r\n\r\n return GRAD","def collect_gradients(log_prob, params, pass_grad = None):\n\n if isinstance(log_prob, tuple):\n log_prob[0].backward()\n params_list = list(log_prob[1])\n params = torch.cat([p.flatten() for p in params_list])\n params.grad = torch.cat([p.grad.flatten() for p in params_list])\n elif pass_grad is not None:\n if callable(pass_grad):\n params.grad = pass_grad(params)\n else:\n params.grad = pass_grad\n else:\n params.grad = torch.autograd.grad(log_prob,params)[0]\n return params","def lnlike(theta, dtarray, dmagarray, sigmaarray):\n gamma, A = theta\n\n aux=np.sum(np.log(like_one(theta,dtarray,dmagarray,sigmaarray)))\n\n return aux","def log_prob(self):","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 derivative(self, theta):\n diag_gamma = np.dot(theta.T, self.X.T)\n logistic_term = self.logistic_fn(diag_gamma)\n diag_gamma = logistic_term * (1.0 - logistic_term)\n gamma = np.diag(diag_gamma)\n\n # v computation\n diags_v = 1.0 - 2*self.logistic_fn(np.dot(theta.T, self.X.T))\n diags_v = diags_v.reshape((-1, 1))\n diags_v = diags_v*self.X\n assert diags_v.shape == self.X.shape #N*d shape\n\n XtGamma = np.dot(self.X.T, gamma) # d*N shape\n\n # TODO: Verifier car pas sur de mon coup ... et surtout plus long...\n # id = np.eye(self.n_examples).reshape((self.n_examples, self.n_examples, 1))\n # diags_v = diags_v.reshape((self.n_examples, 1, self.dim))\n # v = id*diags_v # n*n*d tensor\n # left = np.tensordot(XtGamma, v, axes=(1, 0)) # shape d*N*d\n # assert left.shape == (self.dim, self.n_examples, self.dim)\n # dg = np.tensordot(left, self.X, axes=(1, 0))\n # dg = np.swapaxes(dg, axis1=-2, axis2=-1)\n\n dg = np.zeros((self.dim, self.dim, self.dim))\n for idx, v_i_diag in enumerate(diags_v.T):\n v_i = np.diag(v_i_diag)\n dg_di = np.dot(np.dot(XtGamma, v_i), self.X)\n dg[:, :, idx] = dg_di\n return dg","def log_likelihood(self, X, Y, theta):\n \n alphas, logZ = self.alpha_chain(X, theta)\n total = 0\n \n s_prev = self.S\n \n for t,s in enumerate(Y):\n total += self.log_psi(theta, t, s_prev, s, X[t]) \n s_prev = s\n \n total -= logZ\n return total / X.shape[0]","def ComputeLogGradientTerm(self, *args):\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF2F_ComputeLogGradientTerm(self, *args)","def grad_neg_log_like(params):\n gp.set_parameter_vector(params)\n return -gp.grad_log_likelihood(y, quiet=True)","def log_prior_psi(self, params_opt, *args):\n\n rand_node_name = list(self.tree.keys())[0]\n params_node = self.get_node_params(params_opt, rand_node_name)\n ms_psi = self.get_matrices(params_node, 'Psi')\n prob_psi = invwishart.logpdf(ms_psi, self.p_psi_df, self.p_psi_cov)\n return prob_psi","def leapfrog(theta, r, grad, epsilon, f):\n # make half step in r\n rprime = r + 0.5 * epsilon * grad\n # make new step in theta\n thetaprime = theta + epsilon * rprime\n #compute new gradient\n logpprime, gradprime = f(thetaprime)\n # make half step in r again\n rprime = rprime + 0.5 * epsilon * gradprime\n return thetaprime, rprime, gradprime, logpprime","def log_density_gradient(\n self,\n theta_unc: FloatArray,\n *,\n propto: bool = True,\n jacobian: bool = True,\n out: Optional[FloatArray] = None,\n ) -> Tuple[float, FloatArray]:\n dims = self.param_unc_num()\n if out is None:\n out = np.zeros(shape=dims)\n elif out.size != dims:\n raise ValueError(f\"out size = {out.size} != params size = {dims}\")\n lp = ctypes.pointer(ctypes.c_double())\n err = ctypes.pointer(ctypes.c_char_p())\n rc = self._log_density_gradient(\n self.model, int(propto), int(jacobian), theta_unc, lp, out, err\n )\n if rc:\n raise self._handle_error(err.contents, \"log_density_gradient\")\n return lp.contents.value, out","def num_grad(theta, X, y, lambda_, e=1e-3):\n # ... dopolnite (naloga 1, naloga 2)\n return np.array([(cost(theta + eps, X, y, lambda_) - cost(theta - eps, X, y, lambda_)) / (2 * e)\n for eps in np.identity(len(theta)) * e])","def compute_log_prob(self,params: ndarray) -> float:\n return self.compute_log_prior(params) + self.compute_log_likelihood(params)","def get_gradient(phi, pred, t, dot_product, weight, reg= 1, regression= \"logistic\"):\n if regression == \"logistic\":\n gradient = np.matmul(phi.T, pred - t)\n elif regression == \"probit\":\n R = np.eye(pred.shape[0])\n for i in range(pred.shape[0]):\n y_n = pred[i,0]\n dotp = dot_product[i, 0]\n pdf = norm.pdf(dotp)\n R[i,i] = pdf/(y_n*(1-y_n) + TOLERANCE)\n gradient = np.matmul(np.matmul(phi.T, R), pred-t)\n elif regression == \"multiclass\":\n gradient = np.matmul(phi.T, pred - t)\n\n # Add regularization\n gradient += weight/ reg\n return gradient","def passDerivative(self):\n # this will happen when this SumNode is unused\n if self.getLogDerivative() == Node.ZERO:\n return \n for decomp_id in self.getChildren():\n prodNode = self.getChild(decomp_id)\n tmp = self.getLogDerivative() \\\n + np.log(float(self.getChildCounts(decomp_id)) / self.getCounts())\n\n if prodNode.getLogDerivative() == Node.ZERO:\n prodNode.setLogDerivative(tmp)\n else:\n prodNode.setLogDerivative( \\\n np.logaddexp(tmp, self.getLogDerivative()))","def log_pdf_derivative(x):\n return gs.autodiff.jacobian(log_pdf_at_x(x))(base_point)","def gradient(E: np.ndarray, p: np.ndarray) -> np.ndarray:\n\n adj = np.abs(np.sign(E))\n F_pot = np.array(adj, copy=True, dtype=float)\n F_pot[F_pot.nonzero()] = gradop(adj) * p / E[E.nonzero()] ** 2\n return F_pot","def compute_gradient_logreg_regl2(y, tx, w, lambda_):\n grad = compute_gradient_logreg(y, tx, w)\n penal_grad = grad + 2 * lambda_ * w\n\n return penal_grad","def costFunction(theta,X,y):\n m = X.shape[0]\n J = 0\n h = sigmoid (np.dot(X,theta))\n \n J = (1/m)* ((-np.dot(y.T,(np.log(h)))) - np.dot((1 - y).T,(np.log(1-h))))\n \n #grad = (1/m) * np.dot(X.T,(h-y))\n grad = (1/m) * np.dot((h.T - y), X).T\n \n return J, grad","def gradient_function(theta, X, y):\n\n grad = None\n #######################################################################\n # TODO: #\n # Compute the gradient for a particular choice of theta. #\n # Compute the partial derivatives and set grad to the partial #\n # derivatives of the cost w.r.t. each parameter in theta #\n # #\n #######################################################################\n \n theta = theta[:, np.newaxis]\n \n thetatrans = theta.T\n Xtrans = X.T\n \n MulThetaX = np.dot(thetatrans, Xtrans)\n \n h = sigmoid(MulThetaX)\n \n grad = (y - h) * Xtrans\n\n #######################################################################\n # END OF YOUR CODE #\n #######################################################################\n return grad","def log_prior_theta(self, params_opt):\n # As this parameter is the same through all of the nodes,\n # let take theta from the Theta parameters from the random node\n rand_node_name = list(self.tree.keys())[0]\n params_theta = [params_opt[pos.id_opt] for pos in self.param_pos\n if pos.mx_type == 'Theta' and pos.node_name == rand_node_name]\n invgamma_theta = partial(invgamma.logpdf,\n a=self.p_theta_alpha,\n scale=self.p_theta_beta)\n prob_theta = reduce(lambda x, y: x+y,\n map(invgamma_theta, params_theta))\n return prob_theta","def log_marginal_likelihood(self, theta=None, gradient=True, opt_flag=False):\n if theta != None:\n self.kernel.hyper = np.exp(theta) if opt_flag else theta\n # self.kernel.hyper\n if gradient:\n K, grad = self.kernel.estimate(self.X_train, gradient=True)\n else:\n K = self.kernel.estimate(self.X_train)\n \n K += self.id_mat * self.sigma_n\n\n try:\n L = np.linalg.cholesky(K)\n except np.linalg.LinAlgError:\n return (np.inf, np.array([0])) if gradient else np.inf\n \n alpha = cho_solve((L, True), self.Y_train)\n \n logl = float(self.Y_train.T.dot(alpha)) / 2\n logl += np.log(np.diag(L)).sum()\n logl += self.ntrain * np.log(2 * np.pi) / 2\n\n \n if gradient:\n logl_grad = alpha.dot(alpha.T) # einsum is slower\n logl_grad -= cho_solve((L, True), self.id_mat)\n logl_grad = 0.5 * np.einsum('ij,ji -> ', logl_grad, grad) #dot prod and trace combined\n return logl, -np.array([logl_grad])\n return logl","def gravity_gradients(self, p):\n Le = self._Le(p)\n wf = self._wf(p)\n EeLe = np.einsum(\"i...,i\", self.Ee, Le)\n FfWf = np.einsum(\"i...,i\", self.Ff, wf)\n return G * self.d * (EeLe - FfWf)","def log_likelihood_gradients(self, y, f):\n # align shapes and compute mask\n y = y.reshape(-1, 1)\n f = f.reshape(-1, 1)\n mask = np.isnan(y)\n y = np.where(mask, f, y)\n\n # compute gradients of the log likelihood\n log_lik, J, H = vmap(self.log_likelihood_gradients_)(y, f)\n\n # apply mask\n mask = np.squeeze(mask)\n log_lik = np.where(mask, 0., log_lik)\n J = np.where(mask, np.nan, J)\n H = np.where(mask, np.nan, H)\n\n return log_lik, J, np.diag(H)","def ComputeLogGradientTerm(self, *args):\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF3F_ComputeLogGradientTerm(self, *args)","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 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 from_grad_log(d, g):\n return UDFromCallable(d, fgrad_log=g)","def ComputeLogGradientTerm(self, *args):\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseID2D_ComputeLogGradientTerm(self, *args)","def log_likelihood(self, theta, x, **kwargs):\n\n u, logdet_dudx, log_a = self.forward(theta, x, **kwargs)\n\n constant = float(-0.5 * self.n_inputs * np.log(2.0 * np.pi))\n # log_likelihood = torch.log(torch.sum(torch.exp(log_a - 0.5 * u ** 2 + logdet_dudx), dim=2))\n log_likelihood = torch.logsumexp(log_a - 0.5 * u**2 + logdet_dudx, dim=2)\n log_likelihood = constant + torch.sum(log_likelihood, dim=1)\n\n return u, log_likelihood","def log_likelihood_BP(y_t, theta_f_t, R, N):\n psi_bethe = compute_eta_BP(theta_f_t, N, return_psi=1)[1]\n log_p = R * (numpy.dot(y_t, theta_f_t) - psi_bethe)\n return log_p","def compute_gradient(theta, X, y):\n m = X.shape[0]\n grad_theta = np.dot(X.transpose(), (np.dot(X, theta) - y)) / m\n #print theta, grad_theta, objective_function(theta, X, y)\n return grad_theta","def calculate_log_q_prior(theta, mu, rho):\n\n sigma = tf.nn.softplus(rho)\n log_qw = tm.log_gaussian(theta, mu, sigma)\n return tf.reduce_sum(log_qw)","def log(self, x_old, grad, x, curr_iter, tol_g_val, tol_x_val, tol_f_val):\n\n print(\"-----------------------------------\")\n print(\"\\n Iter: \", curr_iter)\n print(\"\\n x_old: \", x_old)\n print(\"\\n gradient: \", grad)\n print(\"\\n x: \", x)\n print(\"\\n tol_x_val: \", tol_x_val)\n print(\"\\n tol_f_val: \", tol_f_val)\n print(\"\\n tol_g_val: %s \\n \" % tol_g_val)","def expected_log_g(self):\n raise NotImplementedError()\n E_lng = np.zeros_like(self.mf_gamma)\n\n # \\psi(\\sum_{b} \\gamma_b)\n trm2 = psi(self.mf_gamma.sum(axis=2))\n for b in xrange(self.B):\n E_lng[:,:,b] = psi(self.mf_gamma[:,:,b]) - trm2\n\n return E_lng","def grad_log_prior(self, log_occr_array=None):\n\n if log_occr_array is not None:\n # Catch invalid occurrence rates for zero likelihood\n try:\n self.log_occr = log_occr_array\n except InvalidOccurrenceRate:\n return -np.inf * np.ones_like(self.occr, dtype=float)\n\n if tf.is_tensor(self.occr):\n raise NotImplementedError(\"Manual gradient calculate with \"\n \"tensorflow objects isn't \"\n \"implemented, and seems a bit \"\n \"redundant.\")\n else:\n # Written in terms of occr for ease.\n grad = 1 / (2*self.occr)\n # At this point, it's still possible for occr to be so\n # small that it's underflowing, where value will be nan\n grad = grad if not np.isnan(grad).any() else -np.inf * grad\n\n return grad","def compute_grad_likelihood(sensor_loc, obj_loc, distance):\n\tgrad = np.zeros(sensor_loc.shape)\n\t# Your code: finish the grad loglike\n\t\t\n\treturn grad","def log_pdf_derivative_squared(x):\n dlog = log_pdf_derivative(x)\n return gs.einsum(\"...i, ...j -> ...ij\", dlog, dlog)","def logp(self, value: TensorType, **kwargs) -> TensorType:","def logp(self, value: TensorType, **kwargs) -> TensorType:","def gradientFunctionReg(theta, X, y, Lambda):\n m = len(y) # number of training examples\n grad = np.zeros(theta.shape[0])\n theta = np.transpose(theta)\n sum_1 = 0\n X = X.values\n y = y.values\n #calcuate the theta_0 \n# ====================== YOUR CODE HERE ======================\n# Instructions: Compute the gradient of a particular choice of theta.\n# Compute the partial derivatives and set grad to the partial\n# derivatives of the cost w.r.t. each parameter in theta\n for i in range(theta.shape[0]):\n if i == 0:\n for j in range(m):\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i]\n else:\n for j in range(m):\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i] + Lambda*theta[i]\n grad[i] = sum_1/m\n sum_1 = 0\n\n# =============================================================\n\n return grad","def logp(self, F, Y):\n raise NotImplementedError(\"implement the logp function\\\n for this likelihood\")","def get_F(self,X,y=None):\n if len(self.trees) == 0:\n # print ('init')\n assert not (y is None), 'tree0 need y to initialize,y:{}'.format(y)\n pos = y.sum()\n neg = y.shape[0] - pos\n F = np.zeros([X.shape[0],])\n F[:] = math.log(pos / neg)\n # print (pos,neg,F)\n return F\n else:\n F = np.zeros([X.shape[0],])\n for tree in self.trees:\n F += self.learning_rate*tree.predict(X)\n # print (F)\n return F","def log_marginal_likelihood(self, eval_gradient=False):\n A, B, mu_tilda, gamma_tilda, r_grid, n_grid = self.calc_tau(return_all=True)\n\n gamma = self.GAMMA\n gamma_y = self.GAMMA_Y\n\n A1 = np.copy(self.baseTau0)\n A1[np.diag_indices_from(A1)] = 2 * self.ndim\n\n # A1 = (A - B) / gamma_y\n A2 = np.diag(n_grid)\n\n B_inv = np.linalg.inv(B)\n A_inv = np.linalg.inv(A)\n\n M_lambda = B - B.dot(np.linalg.inv(A).dot(B))\n\n # log_likelihood = mu_tilda.dot(M_lambda).dot(\n # mu_tilda[:, np.newaxis]) - np.nan_to_num(np.log(np.linalg.det(M_lambda)))\n log_likelihood = mu_tilda.dot(M_lambda).dot(mu_tilda[:, np.newaxis])\n\n # log_likelihood = -log_likelihood\n print('log_likelihood: %f' % log_likelihood)\n\n Lambda = B - B.dot(A_inv.dot(B))\n Lambda_inv = np.linalg.inv(Lambda)\n\n M_gamma = A2 - A2.dot(2 * gamma * A_inv - (gamma ** 2) * A_inv.dot(A2).dot(A_inv)).dot(A2)\n\n mean_r_grid = np.nan_to_num(np.array([r.sum() for r in r_grid]) / n_grid)\n\n log_likelihood_grad_gamma = mean_r_grid.dot(M_gamma.dot(mean_r_grid[:, np.newaxis]))[0]\n\n tmpMat = A2.dot(A_inv).dot(A1).dot(A_inv).dot(A2)\n\n log_likelihood_grad_gamma_y = (gamma ** 2) * (\n np.trace(Lambda_inv.dot(tmpMat) + mean_r_grid.dot(tmpMat.dot(mean_r_grid[:, np.newaxis]))[0]))\n\n log_likelihood_gradient = np.array([log_likelihood_grad_gamma, log_likelihood_grad_gamma_y])\n print('log_likelihood_grad: %s' % log_likelihood_gradient)\n\n if eval_gradient:\n return log_likelihood, log_likelihood_gradient\n else:\n return log_likelihood","def ComputeLogGradientTerm(self, *args):\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseID3D_ComputeLogGradientTerm(self, *args)","def gradient_ascent(f, df, theta_init, step_size, max_iter):\n\n fs = []\n xs = []\n thetas = theta_init\n for i in range(max_iter): #for each data example\n fs.append(f(thetas))\n\n temp = step_size*df(thetas)\n thetas = step_size*df(thetas) #modify that feature by using the derivative of log likelihood\n xs.append(thetas.flatten())\n if i % 10 == 0:\n print(i, thetas)\n\n return thetas, fs, xs","def gradient(theta, X, y, Lambda=0.0):\n m = X.shape[0] # number of samples\n\n h = hypothesis(theta, X)\n\n if Lambda:\n g_0 = (1/m)*(X.T@(h - y))[0]\n g_1 = (1/m)*(X.T@(h - y))[1:] + (Lambda/m)*theta[1:] # skip theta-0\n \n return np.append(g_0, g_1)\n else:\n return (1/m)*(X.T@(h - y))","def logpost(self, theta, t=None):\n return self.prior.logpdf(theta) + self.loglik(theta, t)","def _log_likelihood(self, theta, f, x, y, yerr):\n sigma2 = yerr**2\n return -0.5*np.sum((y - f(theta, x))**2 / sigma2 + 2*np.log(sigma2))","def log_likelihood(self, theta, active=None):\n return sum(self.log_likelihood_term(theta, active=active))","def log_density(\n self,\n theta_unc: FloatArray,\n *,\n propto: bool = True,\n jacobian: bool = True,\n ) -> float:\n lp = ctypes.pointer(ctypes.c_double())\n err = ctypes.pointer(ctypes.c_char_p())\n rc = self._log_density(\n self.model, int(propto), int(jacobian), theta_unc, lp, err\n )\n if rc:\n raise self._handle_error(err.contents, \"log_density\")\n return lp.contents.value","def __log_likelihood(self, params, *args):\n\t\tX, y, feature_set, lambda_reg, empirical_weights, verbose, sign = args\n\n\t\tno_example = len(X)\n\t\ttotal_logZ = 0\n\t\ttotal_logProb = 0\n\t\texpected_weights = np.zeros(len(feature_set))\n\t\tfor t in range(len(X)):\n\t\t\t# example_features = X[t], example_labels = y[t]\n\n\t\t\tpotential = np.zeros(len(X[t]))\n\t\t\tfor i in range(len(X[t])):\n\t\t\t\t#candidate_features = X[t][i], candidate_label = y[t][i]\n\t\t\t\tpotential[i] = feature_set.calc_inner_product(X[t][i], params)\n\n\t\t\t#scaling\n\t\t\tpotential = potential - np.max(potential, keepdims=True)\n\n\t\t\tfor i in range(len(X[t])):\n\t\t\t\ttotal_logProb += potential[i] * y[t][i]\n\n\t\t\tpotential, Z = self.__softmax(potential)\n\n\t\t\tfor i in range(len(X[t])):\n\t\t\t\tfeature_set.calc_inner_sum(expected_weights, X[t][i], potential[i])\n\n\t\t\ttotal_logZ += log(Z)\n\n\t\t# _params = feature_set.get_regularized_params(params, 'bias')\n\t\t_params = params\n\t\tlog_likelihood = total_logProb - total_logZ - (lambda_reg/2) * np.sum(np.multiply(_params,_params))\n\t\tgradients = empirical_weights - expected_weights - lambda_reg * _params\n\n\t\tglobal SUB_ITERATION_NUM\n\t\tif verbose:\n\t\t\tsub_iteration_str = ' '\n\t\t\tif SUB_ITERATION_NUM > 0:\n\t\t\t\tsub_iteration_str = '(' + '{0:02d}'.format(SUB_ITERATION_NUM) + ')'\n\t\t\tprint(' ', '{0:03d}'.format(ITERATION_NUM), sub_iteration_str, ':', log_likelihood * sign)\n\n\t\tSUB_ITERATION_NUM += 1\n\n\t\treturn sign * log_likelihood, sign * gradients","def log_prob(self, value, pre_tanh_value=None, epsilon=1e-06):\n if pre_tanh_value is None:\n pre_tanh_value = torch.log((1 + epsilon + value) / (1 + epsilon - value)) / 2\n norm_lp = self._normal.log_prob(pre_tanh_value)\n ret = norm_lp - torch.sum(torch.log(self._clip_but_pass_gradient(1.0 - value ** 2) + epsilon), axis=-1)\n return ret","def costFunction(self,theta, X, y): \n m = len(y)\n h = self.sigmoid(X@theta)\n J = 1 / m * (- y.T @ self.log(h) - (1-y).T @ self.log(1-h)) \n # grad = 1/ m * X.T @ (h - y)\n return J","def logg_trigomonetric(teff, mass, v, bc, par, dpar, dteff, dmass):\n #np.geterr()\n if mass == 'nan':\n logg, dlogg = 'nan', 'nan'\n else:\n e = 2.718281828\n logg = 4.44 + np.log10(mass) + (4.0*np.log10(teff/5777.)) + (0.4*(v + bc)) + (2.0*np.log10(par/1000.0)) + 0.108\n logg = np.round(logg, 2)\n dlogg = np.sqrt(((dmass*np.log10(e))/mass)**2 + ((4.*dteff*np.log10(e))/teff)**2 + ((2.*0.05*np.log10(e))/par)**2)\n dlogg = np.round(dlogg, 2)\n return logg, dlogg"],"string":"[\n \"def dlogZ(self, T, pos, psi, phi):\\n msgs = belief_propagation(T, pos, psi, phi, True)\\n dpsi = calculate_gradient(msgs, T, pos, psi, True, True)\\n return dpsi\",\n \"def fd_grad(self, T, pos, psi, phi, eps=1e-5):\\n dpsi = tr.zeros_like(psi)\\n dphi = tr.zeros_like(phi)\\n for pos1 in range(psi.shape[0]):\\n for pos2 in range(psi.shape[1]):\\n for lab in range(psi.shape[2]):\\n for i in range(psi.shape[3]):\\n for j in range(psi.shape[4]):\\n psi[pos1, pos2, lab, i, j] += eps\\n val1 = self.logZ(T, pos, psi, phi)\\n psi[pos1, pos2, lab, i, j] -= 2 * eps\\n val2 = self.logZ(T, pos, psi, phi)\\n psi[pos1, pos2, lab, i, j] += eps\\n dpsi[pos1, pos2, lab, i, j] = (val1 - val2) / (2 * eps)\\n for p in range(phi.shape[0]):\\n for i in range(phi.shape[1]):\\n phi[p, i] += eps\\n val1 = self.logZ(T, pos, psi, phi)\\n phi[p, i] -= 2 * eps\\n val2 = self.logZ(T, pos, psi, phi)\\n phi[p, i] += eps\\n dphi[p, i] = (val1 - val2) / (2 * eps)\\n return dpsi, dphi\",\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 Gradient(Walker,particle):\\n\\n h=0.001\\n dPsi = zeros(shape=shape(Walker.Re[particle]))\\n for i in range(Walker.sys_dim):\\n Y=Walker.Re[particle][i]\\n Walker.Re[particle][i]-=h\\n wfs1=wfs(Walker)\\n Walker.Re[particle][i]+=2.0*h\\n wfs2=wfs(Walker)\\n dPsi[i] = (wfs2-wfs1)/2/h\\n Walker.Re[particle][i]=Y\\n\\n return dPsi\",\n \"def logp_grad(self, xs, ys, fs, **kwargs):\",\n \"def _log_likelihood_gradients(self):\\r\\n return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))\",\n \"def MvNormalLogp():\\n cov = pt.matrix(\\\"cov\\\")\\n cov.tag.test_value = floatX(np.eye(3))\\n delta = pt.matrix(\\\"delta\\\")\\n delta.tag.test_value = floatX(np.zeros((2, 3)))\\n\\n cholesky = Cholesky(lower=True, on_error=\\\"nan\\\")\\n\\n n, k = delta.shape\\n n, k = f(n), f(k)\\n chol_cov = cholesky(cov)\\n diag = pt.diag(chol_cov)\\n ok = pt.all(diag > 0)\\n\\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\\n delta_trans = solve_lower(chol_cov, delta.T).T\\n\\n result = n * k * pt.log(f(2) * np.pi)\\n result += f(2) * n * pt.sum(pt.log(diag))\\n result += (delta_trans ** f(2)).sum()\\n result = f(-0.5) * result\\n logp = pt.switch(ok, result, -np.inf)\\n\\n def dlogp(inputs, gradients):\\n (g_logp,) = gradients\\n cov, delta = inputs\\n\\n g_logp.tag.test_value = floatX(1.0)\\n n, k = delta.shape\\n\\n chol_cov = cholesky(cov)\\n diag = pt.diag(chol_cov)\\n ok = pt.all(diag > 0)\\n\\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\\n delta_trans = solve_lower(chol_cov, delta.T).T\\n\\n inner = n * pt.eye(k) - pt.dot(delta_trans.T, delta_trans)\\n g_cov = solve_upper(chol_cov.T, inner)\\n g_cov = solve_upper(chol_cov.T, g_cov.T)\\n\\n tau_delta = solve_upper(chol_cov.T, delta_trans.T)\\n g_delta = tau_delta.T\\n\\n g_cov = pt.switch(ok, g_cov, -np.nan)\\n g_delta = pt.switch(ok, g_delta, -np.nan)\\n\\n return [-0.5 * g_cov * g_logp, -g_delta * g_logp]\\n\\n return OpFromGraph([cov, delta], [logp], grad_overrides=dlogp, inline=True)\",\n \"def log_prob(self, T, pos, m, psi, phi=tr.Tensor()):\\n if phi.size() == tr.Size([0]):\\n phi = self.create_phi(T, pos, m)\\n return self.log_score(T, pos, m, psi, phi) - self.logZ(T, pos, psi, phi)\",\n \"def _log_likelihood_gradients(self):\\n return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK,X=self.X,slices1=self.Xslices,slices2=self.Xslices), self.likelihood._gradients(partial=np.diag(self.dL_dK))))\",\n \"def grad_log(self, X):\\n g = autograd.elementwise_grad(self.log_den)\\n G = g(X)\\n return G\",\n \"def grad_log_likelihood(self, log_occr_array=None):\\n\\n if log_occr_array is not None:\\n # Catch invalid occurrence rates for zero likelihood\\n try:\\n self.log_occr = log_occr_array\\n except InvalidOccurrenceRate:\\n return -np.inf * np.ones_like(self.occr, dtype=float)\\n\\n # Calculate components first\\n N_exp = self.calc_integral() * self._N_stars # perhaps not needed\\n nexp_terms = self._N_stars * self.calc_bin_volumes() * self.H_bar_array\\n s_terms = self.H_array * self.F_array * self.occr\\n numerator_terms = self.H_array * self.F_array\\n\\n if not tf.is_tensor(self.occr):\\n # Checking shapes of intermediate terms,\\n # numerator_terms vs s_terms.sum(-1, -2) and vs v factors.\\n intermediate_terms = numerator_terms / s_terms.sum(axis=(-1, -2))\\n # TODO: v_factor changed to negative, I think a minus\\n # sign had been missed\\n grad_log_array = - nexp_terms + intermediate_terms.sum(axis=0)\\n\\n # BUG TODO\\n if np.isnan(grad_log_array).any():\\n warnings.warn(\\\".grad_log_likelihood value is nan.\\\")\\n import pdb; pdb.set_trace()\\n grad_log_array = -np.inf * grad_log_array\\n else:\\n raise NotImplementedError(\\\"Manual gradient calculate with \\\"\\n \\\"tensorflow objects isn't \\\"\\n \\\"implemented, and seems a bit \\\"\\n \\\"redundant.\\\")\\n\\n return grad_log_array\",\n \"def gradient(self, theta):\\n a = -(6 * self.scale ** 2)\\n b = 3 * self.scale ** 2 + np.exp(2 * theta)\\n b *= np.log(3 * self.scale ** 2 * np.exp(-2 * theta) + 1)\\n return a / b\",\n \"def log_probability(theta):\\n global priors\\n global logp\\n lp = np.sum([priors[p].logpdf(x) for p, x in zip(logp.parnames, theta)])\\n if not np.isfinite(lp) or np.isnan(lp):\\n return -np.inf\\n ll = logp(theta)\\n if not np.isfinite(ll):\\n return -np.inf\\n return lp + ll\",\n \"def log_probability(theta):\\n global priors\\n global logp\\n lp = np.sum([priors[p].logpdf(x) for p, x in zip(logp.parnames, theta)])\\n if not np.isfinite(lp) or np.isnan(lp):\\n return -np.inf\\n ll = logp(theta)\\n if not np.isfinite(ll):\\n return -np.inf\\n return lp + ll\",\n \"def logZ(self, T, pos, psi, phi):\\n msgs = belief_propagation(T, pos, psi, phi, True)\\n log_z = calculate_belief_sum(msgs, True)\\n return log_z\",\n \"def log_prior_grad(self, inputs):\",\n \"def grad_llh(self, params):\\n grad = np.clip(self.grad_log_likelihood(params[0], params[1], params[2:]), SMALLEST_NUMBER,\\n LARGEST_NUMBER)\\n\\n return grad\",\n \"def logit_cost_grad(self, theta, X, y):\\n\\n grad = np.zeros(len(theta))\\n\\n ### YOUR CODE HERE\\n sig = utils.sigmoid(theta)\\n # sig = np.subtract(sig, y)\\n sig = sig - y\\n grad = np.dot(X.T, sig) + 2 * self.params['lamb'] * self.regularizer[1](self.weights)\\n ### END YOUR CODE\\n\\n return grad\",\n \"def _log_prior_gradients(self):\\n if self.priors.size == 0:\\n return 0.\\n x = self.param_array\\n ret = np.zeros(x.size)\\n #compute derivate of prior density\\n [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]\\n #add in jacobian derivatives if transformed\\n priored_indexes = np.hstack([i for p, i in self.priors.items()])\\n for c,j in self.constraints.items():\\n if not isinstance(c, Transformation):continue\\n for jj in j:\\n if jj in priored_indexes:\\n ret[jj] += c.log_jacobian_grad(x[jj])\\n return ret\",\n \"def _log_prior_gradients(self):\\n x = self._get_params()\\n ret = np.zeros(x.size)\\n [np.put(ret,i,p.lnpdf_grad(xx)) for i,(p,xx) in enumerate(zip(self.priors,x)) if not p is None]\\n return ret\",\n \"def grad_log_likelihood(g_t):\\n # state vector [g1, g2, g3, ... , gn-1, gn]\\n state_vector = np.zeros(N)\\n\\n # factor that will be added to one node and subtracted from another\\n def alphaij(gj, gi, deltaij, varij):\\n return ((gj - gi) - deltaij) / varij\\n\\n # indices of state vector\\n # Iterate over all connections\\n for r in self.graph.index:\\n state1, state2, value, variance, ligand, standard_state = self.graph.iloc[r]\\n i = self.states[self.states.name == state1].index[0]\\n j = self.states[self.states.name == state2].index[0]\\n\\n gj = g_t[j]\\n gi = g_t[i]\\n\\n edge_attr = self.cycle.edges()[(state1, state2)]\\n deltaij = edge_attr['energy'] # measured difference\\n varij = edge_attr['weight'] # measured variance\\n\\n shared_alpha = alphaij(gj, gi, deltaij, varij)\\n\\n state_vector[i] += shared_alpha\\n state_vector[j] -= shared_alpha\\n\\n return state_vector\",\n \"def get_log_likelihood(phi, pred, t, dot_product, weight, reg= 1):\\n prior = -0.5* np.sum(np.multiply(weight, weight))\\n likelihood = np.multiply(t, np.log(pred+TOLERANCE)) + np.multiply(1.0- t, np.log(1.0-pred+TOLERANCE))\\n likelihood = np.sum(likelihood)\\n\\n return prior + likelihood\",\n \"def log_likelihood_gradients_(self, y, f):\\n log_lik = self.evaluate_log_likelihood(y, f)\\n f = np.squeeze(f)\\n J = jacrev(self.evaluate_log_likelihood, argnums=1)\\n H = jacrev(J, argnums=1)\\n return log_lik, J(y, f), H(y, f)\",\n \"def log_LxP(theta, D):\\r\\n p1 = np.exp( -((theta-D[0])**2)/2 )\\r\\n p2 = np.exp( -((theta-D[1])**2)/2 )\\r\\n if np.abs(theta) <= 1:\\r\\n LxP = p1*p2\\r\\n else:\\r\\n LxP = 0.0\\r\\n return np.log(LxP)\",\n \"def _log_prior_gradients(self):\\r\\n if self.priors is None:\\r\\n return 0.\\r\\n x = self._get_params()\\r\\n ret = np.zeros(x.size)\\r\\n [np.put(ret, i, p.lnpdf_grad(xx)) for i, (p, xx) in enumerate(zip(self.priors, x)) if not p is None]\\r\\n return ret\",\n \"def calc_grad(X, Y, theta):\\n m, n = X.shape\\n\\n margins = Y * X.dot(theta)\\n probs = 1. / (1 + np.exp(margins))\\n grad = -(1./m) * (X.T.dot(probs * Y))\\n\\n return grad\",\n \"def log_likelihood(self, theta=None, phi=None):\\n theta = theta if theta is not None else self.theta\\n phi = phi if phi is not None else self.phi\\n ret = 0.\\n for m, dm in enumerate(self.corpus):\\n for n, w_mn in enumerate(dm):\\n tp = 0.\\n for k in range(self.n_components):\\n tp += theta[m, k] * phi[k, w_mn]\\n ret += np.log(tp)\\n return ret\",\n \"def f(p, phi, phib, df):\\n\\treturn -log(p) - df + (p-1)*phi + \\\\\\n\\t ( phi*(1-p) + phib + \\\\\\n\\t 5./4*alpha*(phi*p)**(9./4)-(9./4)*alpha*(p*phi)**(5./4) - \\\\\\n\\t (1./2)(1-p*phi)**2 - (phib/Nb)-5./4*alpha*(phi+phib)**(9./4) + \\\\\\n\\t (9./4)*alpha*(phi+phib)**(5.4) + \\\\\\n\\t 1./2*(1-phi-phib)**2 ) * Ns\",\n \"def _forward_log_det_jacobian(self, x):\\n d = self._compute_shared(x=x)\\n relx = (x - d.x_k) / d.w_k\\n relx = relx # tf.where(d.out_of_bounds, 0.5*tf.ones_like(x), relx)\\n grad = (\\n 2 * tf.math.log(d.s_k) +\\n tf.math.log(d.d_kp1 * relx**2 + 2 * d.s_k * relx * (1 - relx) + # newln\\n d.d_k * (1 - relx)**2) -\\n 2 * tf.math.log((d.d_kp1 + d.d_k - 2 * d.s_k) * relx *\\n (1 - relx) + d.s_k))\\n return grad # tf.where(d.out_of_bounds, tf.zeros_like(grad), grad)\",\n \"def log_prob(self):\\n res = -self.L_h/2*np.log(2*np.pi*self.la)\\n res = res + self.L_h*(self.L_h-1)/2*self.a\\n\\n\\n res = res - 1/(2*self.la)*np.square(np.linalg.norm(self.e*self.pie))\\n\\n res = res - 1/(2*self.la)*np.sum(self.e2*self.pie_var)\\n\\n res = res - self.L_h/2*np.log(2*np.pi*self.sigma2)\\n res = res - 1/(2*self.sigma2)*(np.square(np.linalg.norm(self.w))+np.trace(self.R))\\n\\n print(\\\"Log-probability difference = {}\\\".format(res - self.LP), file=self.logfile)\\n self.LP = res\\n return res\",\n \"def gradient(self, theta):\\n return (1 / (self.sigma * np.sqrt(2 * np.pi))) * (\\n -theta / (self.sigma ** 2) * np.exp(-(theta ** 2) / (2 * self.sigma ** 2))\\n )\",\n \"def gradient(self, theta):\\n pass\",\n \"def gradient(self, theta):\\n pass\",\n \"def log_prob(theta):\\n params = parse_params(theta)\\n prior = log_prior(**params)\\n if not np.isfinite(prior):\\n return -np.inf, -np.inf\\n like = log_like(**params)\\n if not np.isfinite(like):\\n return -np.inf, prior\\n return prior + like, prior\",\n \"def log_deriv(error):\\n return logistic(error) * (1 - logistic(error))\",\n \"def grad_log_likelihood(self, log_occr_array=None):\\n\\n if log_occr_array is not None:\\n # Catch invalid occurrence rates for zero likelihood\\n try:\\n self.log_occr = log_occr_array\\n except InvalidOccurrenceRate:\\n return -np.inf * np.ones_like(self.occr, dtype=float)\\n\\n # The Poisson Jacobian\\n # --------------------\\n # Calculate components first\\n N_exp = self.calc_integral() * self._N_stars # perhaps not needed\\n v_factors = self._N_stars * self.calc_bin_volumes() * self.H_bar_array\\n # TODO: broadcast self.occr into the same shape as self.F_array\\n # Reduce it later through the chain-rule\\n s_terms = self.H_array * self.F_array * self.occr_grid\\n numerator_terms = self.H_array * self.F_array\\n\\n if not tf.is_tensor(self.occr):\\n # Checking shapes of intermediate terms,\\n # numerator_terms vs s_terms.sum(-1, -2) and vs v factors.\\n intermediate_terms = numerator_terms / s_terms.sum(axis=(-1, -2))\\n # TODO: v_factor changed to negative, I think a minus\\n # sign had been missed\\n poisson_jacobian = intermediate_terms.sum(axis=0) - v_factors\\n poisson_jacobian = poisson_jacobian.reshape(-1)\\n param_jacobian = self.get_occr_jacobian(flattened=True)\\n grad_log_array = np.matmul(poisson_jacobian, param_jacobian)\\n\\n # BUG TODO\\n if np.isnan(grad_log_array).any():\\n warnings.warn(\\\".grad_log_likelihood value is nan.\\\")\\n import pdb; pdb.set_trace()\\n grad_log_array = -np.inf * grad_log_array\\n else:\\n raise NotImplementedError(\\\"Manual gradient calculate with \\\"\\n \\\"tensorflow objects isn't \\\"\\n \\\"implemented, and seems a bit \\\"\\n \\\"redundant.\\\")\\n\\n return grad_log_array\",\n \"def dphi(x):\\n return grad(phi)(x)\",\n \"def log_likelihood_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w))-y)\",\n \"def log_marginal_likelihood(X_train,y_train,phi,tau=1.,Ve=1.e-10):\",\n \"def compute_loss_gradient(theta_vector, *args):\\n\\n psi = args[0] # feed psi as a parameter\\n circ_depth = args[1]\\n num_qbits = args[2]\\n theta = np.reshape(theta_vector, (circ_depth, num_qbits)) # reshapes the flat theta vector\\n fidelity = get_fidelity(theta, psi)\\n\\n # the derivative of the loss wrt fidelity\\n dl_df = -0.5 * fidelity ** (-0.5)\\n\\n df_dtheta = [] # a list of partial derivatives of the fidelity wrt the theta parameters\\n\\n for index in range(len(theta_vector)):\\n layer_index = index // num_qbits\\n qbit_index = index % num_qbits\\n\\n theta_plus = np.copy(theta)\\n theta_plus[layer_index][qbit_index] += np.pi / 2 # added pi/2 to the ith theta parameter\\n\\n theta_minus = np.copy(theta)\\n theta_minus[layer_index][qbit_index] -= np.pi / 2 # subtracted pi/2 to the ith theta parameter\\n\\n df_dtheta_i = 0.5 * (get_fidelity(theta_plus, psi) - get_fidelity(theta_minus, psi)) # ith derivative\\n df_dtheta.append(df_dtheta_i)\\n\\n df_dtheta = np.array(df_dtheta)\\n dl_dtheta = dl_df * df_dtheta # chain rule to get partial derivative of loss wrt theta parameters\\n\\n return dl_dtheta\",\n \"def logZ_brute(self, T, pos, psi, phi):\\n ms = self.get_all_tag_seq(len(T))\\n log_scores = tr.zeros(len(ms), dtype=tr.float64)\\n for i in range(len(ms)):\\n log_scores[i] = self.log_score(T, pos, ms[i], psi, phi)\\n log_z = logsumexp(log_scores)\\n return log_z\",\n \"def grad(theta, X, y, lambda_):\\n # ... dopolnite (naloga 1, naloga 2)\\n\\n l = []\\n for i, e in enumerate(theta):\\n l.append(1 / len(y) * sum([(h(x, theta) - yi) * x[i] for x, yi in zip(X, y)]) + 2 * lambda_ * e)\\n\\n return np.array(l)\",\n \"def f(p, phi, phib, df):\\n\\treturn - p + exp( - df + Ns*(log((1 - p*phi)/(1 - phi - phib)) + \\\\\\n\\t\\t(p - 1)*phi - phib + (9./4)*alpha*((phi + phib)**(5./4) - (p*phi)**(5./4))))\",\n \"def ga_log(R):\\n phiP, t_normal_n, t_perpendicular_n = extractRotorComponents(R)\\n return phiP + t_normal_n + t_perpendicular_n\",\n \"def fgrad_y(self, y, psi, return_precalc = False):\\r\\n\\r\\n\\r\\n mpsi = psi.copy()\\r\\n d = psi[-1]\\r\\n mpsi = mpsi[:self.num_parameters-1].reshape(self.n_terms, 3)\\r\\n\\r\\n # vectorized version\\r\\n\\r\\n S = (mpsi[:,1]*(y[:,:,None] + mpsi[:,2])).T\\r\\n R = np.tanh(S)\\r\\n D = 1-R**2\\r\\n\\r\\n GRAD = (d + (mpsi[:,0:1][:,:,None]*mpsi[:,1:2][:,:,None]*D).sum(axis=0)).T\\r\\n\\r\\n if return_precalc:\\r\\n return GRAD, S, R, D\\r\\n\\r\\n\\r\\n return GRAD\",\n \"def collect_gradients(log_prob, params, pass_grad = None):\\n\\n if isinstance(log_prob, tuple):\\n log_prob[0].backward()\\n params_list = list(log_prob[1])\\n params = torch.cat([p.flatten() for p in params_list])\\n params.grad = torch.cat([p.grad.flatten() for p in params_list])\\n elif pass_grad is not None:\\n if callable(pass_grad):\\n params.grad = pass_grad(params)\\n else:\\n params.grad = pass_grad\\n else:\\n params.grad = torch.autograd.grad(log_prob,params)[0]\\n return params\",\n \"def lnlike(theta, dtarray, dmagarray, sigmaarray):\\n gamma, A = theta\\n\\n aux=np.sum(np.log(like_one(theta,dtarray,dmagarray,sigmaarray)))\\n\\n return aux\",\n \"def log_prob(self):\",\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 derivative(self, theta):\\n diag_gamma = np.dot(theta.T, self.X.T)\\n logistic_term = self.logistic_fn(diag_gamma)\\n diag_gamma = logistic_term * (1.0 - logistic_term)\\n gamma = np.diag(diag_gamma)\\n\\n # v computation\\n diags_v = 1.0 - 2*self.logistic_fn(np.dot(theta.T, self.X.T))\\n diags_v = diags_v.reshape((-1, 1))\\n diags_v = diags_v*self.X\\n assert diags_v.shape == self.X.shape #N*d shape\\n\\n XtGamma = np.dot(self.X.T, gamma) # d*N shape\\n\\n # TODO: Verifier car pas sur de mon coup ... et surtout plus long...\\n # id = np.eye(self.n_examples).reshape((self.n_examples, self.n_examples, 1))\\n # diags_v = diags_v.reshape((self.n_examples, 1, self.dim))\\n # v = id*diags_v # n*n*d tensor\\n # left = np.tensordot(XtGamma, v, axes=(1, 0)) # shape d*N*d\\n # assert left.shape == (self.dim, self.n_examples, self.dim)\\n # dg = np.tensordot(left, self.X, axes=(1, 0))\\n # dg = np.swapaxes(dg, axis1=-2, axis2=-1)\\n\\n dg = np.zeros((self.dim, self.dim, self.dim))\\n for idx, v_i_diag in enumerate(diags_v.T):\\n v_i = np.diag(v_i_diag)\\n dg_di = np.dot(np.dot(XtGamma, v_i), self.X)\\n dg[:, :, idx] = dg_di\\n return dg\",\n \"def log_likelihood(self, X, Y, theta):\\n \\n alphas, logZ = self.alpha_chain(X, theta)\\n total = 0\\n \\n s_prev = self.S\\n \\n for t,s in enumerate(Y):\\n total += self.log_psi(theta, t, s_prev, s, X[t]) \\n s_prev = s\\n \\n total -= logZ\\n return total / X.shape[0]\",\n \"def ComputeLogGradientTerm(self, *args):\\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF2F_ComputeLogGradientTerm(self, *args)\",\n \"def grad_neg_log_like(params):\\n gp.set_parameter_vector(params)\\n return -gp.grad_log_likelihood(y, quiet=True)\",\n \"def log_prior_psi(self, params_opt, *args):\\n\\n rand_node_name = list(self.tree.keys())[0]\\n params_node = self.get_node_params(params_opt, rand_node_name)\\n ms_psi = self.get_matrices(params_node, 'Psi')\\n prob_psi = invwishart.logpdf(ms_psi, self.p_psi_df, self.p_psi_cov)\\n return prob_psi\",\n \"def leapfrog(theta, r, grad, epsilon, f):\\n # make half step in r\\n rprime = r + 0.5 * epsilon * grad\\n # make new step in theta\\n thetaprime = theta + epsilon * rprime\\n #compute new gradient\\n logpprime, gradprime = f(thetaprime)\\n # make half step in r again\\n rprime = rprime + 0.5 * epsilon * gradprime\\n return thetaprime, rprime, gradprime, logpprime\",\n \"def log_density_gradient(\\n self,\\n theta_unc: FloatArray,\\n *,\\n propto: bool = True,\\n jacobian: bool = True,\\n out: Optional[FloatArray] = None,\\n ) -> Tuple[float, FloatArray]:\\n dims = self.param_unc_num()\\n if out is None:\\n out = np.zeros(shape=dims)\\n elif out.size != dims:\\n raise ValueError(f\\\"out size = {out.size} != params size = {dims}\\\")\\n lp = ctypes.pointer(ctypes.c_double())\\n err = ctypes.pointer(ctypes.c_char_p())\\n rc = self._log_density_gradient(\\n self.model, int(propto), int(jacobian), theta_unc, lp, out, err\\n )\\n if rc:\\n raise self._handle_error(err.contents, \\\"log_density_gradient\\\")\\n return lp.contents.value, out\",\n \"def num_grad(theta, X, y, lambda_, e=1e-3):\\n # ... dopolnite (naloga 1, naloga 2)\\n return np.array([(cost(theta + eps, X, y, lambda_) - cost(theta - eps, X, y, lambda_)) / (2 * e)\\n for eps in np.identity(len(theta)) * e])\",\n \"def compute_log_prob(self,params: ndarray) -> float:\\n return self.compute_log_prior(params) + self.compute_log_likelihood(params)\",\n \"def get_gradient(phi, pred, t, dot_product, weight, reg= 1, regression= \\\"logistic\\\"):\\n if regression == \\\"logistic\\\":\\n gradient = np.matmul(phi.T, pred - t)\\n elif regression == \\\"probit\\\":\\n R = np.eye(pred.shape[0])\\n for i in range(pred.shape[0]):\\n y_n = pred[i,0]\\n dotp = dot_product[i, 0]\\n pdf = norm.pdf(dotp)\\n R[i,i] = pdf/(y_n*(1-y_n) + TOLERANCE)\\n gradient = np.matmul(np.matmul(phi.T, R), pred-t)\\n elif regression == \\\"multiclass\\\":\\n gradient = np.matmul(phi.T, pred - t)\\n\\n # Add regularization\\n gradient += weight/ reg\\n return gradient\",\n \"def passDerivative(self):\\n # this will happen when this SumNode is unused\\n if self.getLogDerivative() == Node.ZERO:\\n return \\n for decomp_id in self.getChildren():\\n prodNode = self.getChild(decomp_id)\\n tmp = self.getLogDerivative() \\\\\\n + np.log(float(self.getChildCounts(decomp_id)) / self.getCounts())\\n\\n if prodNode.getLogDerivative() == Node.ZERO:\\n prodNode.setLogDerivative(tmp)\\n else:\\n prodNode.setLogDerivative( \\\\\\n np.logaddexp(tmp, self.getLogDerivative()))\",\n \"def log_pdf_derivative(x):\\n return gs.autodiff.jacobian(log_pdf_at_x(x))(base_point)\",\n \"def gradient(E: np.ndarray, p: np.ndarray) -> np.ndarray:\\n\\n adj = np.abs(np.sign(E))\\n F_pot = np.array(adj, copy=True, dtype=float)\\n F_pot[F_pot.nonzero()] = gradop(adj) * p / E[E.nonzero()] ** 2\\n return F_pot\",\n \"def compute_gradient_logreg_regl2(y, tx, w, lambda_):\\n grad = compute_gradient_logreg(y, tx, w)\\n penal_grad = grad + 2 * lambda_ * w\\n\\n return penal_grad\",\n \"def costFunction(theta,X,y):\\n m = X.shape[0]\\n J = 0\\n h = sigmoid (np.dot(X,theta))\\n \\n J = (1/m)* ((-np.dot(y.T,(np.log(h)))) - np.dot((1 - y).T,(np.log(1-h))))\\n \\n #grad = (1/m) * np.dot(X.T,(h-y))\\n grad = (1/m) * np.dot((h.T - y), X).T\\n \\n return J, grad\",\n \"def gradient_function(theta, X, y):\\n\\n grad = None\\n #######################################################################\\n # TODO: #\\n # Compute the gradient for a particular choice of theta. #\\n # Compute the partial derivatives and set grad to the partial #\\n # derivatives of the cost w.r.t. each parameter in theta #\\n # #\\n #######################################################################\\n \\n theta = theta[:, np.newaxis]\\n \\n thetatrans = theta.T\\n Xtrans = X.T\\n \\n MulThetaX = np.dot(thetatrans, Xtrans)\\n \\n h = sigmoid(MulThetaX)\\n \\n grad = (y - h) * Xtrans\\n\\n #######################################################################\\n # END OF YOUR CODE #\\n #######################################################################\\n return grad\",\n \"def log_prior_theta(self, params_opt):\\n # As this parameter is the same through all of the nodes,\\n # let take theta from the Theta parameters from the random node\\n rand_node_name = list(self.tree.keys())[0]\\n params_theta = [params_opt[pos.id_opt] for pos in self.param_pos\\n if pos.mx_type == 'Theta' and pos.node_name == rand_node_name]\\n invgamma_theta = partial(invgamma.logpdf,\\n a=self.p_theta_alpha,\\n scale=self.p_theta_beta)\\n prob_theta = reduce(lambda x, y: x+y,\\n map(invgamma_theta, params_theta))\\n return prob_theta\",\n \"def log_marginal_likelihood(self, theta=None, gradient=True, opt_flag=False):\\n if theta != None:\\n self.kernel.hyper = np.exp(theta) if opt_flag else theta\\n # self.kernel.hyper\\n if gradient:\\n K, grad = self.kernel.estimate(self.X_train, gradient=True)\\n else:\\n K = self.kernel.estimate(self.X_train)\\n \\n K += self.id_mat * self.sigma_n\\n\\n try:\\n L = np.linalg.cholesky(K)\\n except np.linalg.LinAlgError:\\n return (np.inf, np.array([0])) if gradient else np.inf\\n \\n alpha = cho_solve((L, True), self.Y_train)\\n \\n logl = float(self.Y_train.T.dot(alpha)) / 2\\n logl += np.log(np.diag(L)).sum()\\n logl += self.ntrain * np.log(2 * np.pi) / 2\\n\\n \\n if gradient:\\n logl_grad = alpha.dot(alpha.T) # einsum is slower\\n logl_grad -= cho_solve((L, True), self.id_mat)\\n logl_grad = 0.5 * np.einsum('ij,ji -> ', logl_grad, grad) #dot prod and trace combined\\n return logl, -np.array([logl_grad])\\n return logl\",\n \"def gravity_gradients(self, p):\\n Le = self._Le(p)\\n wf = self._wf(p)\\n EeLe = np.einsum(\\\"i...,i\\\", self.Ee, Le)\\n FfWf = np.einsum(\\\"i...,i\\\", self.Ff, wf)\\n return G * self.d * (EeLe - FfWf)\",\n \"def log_likelihood_gradients(self, y, f):\\n # align shapes and compute mask\\n y = y.reshape(-1, 1)\\n f = f.reshape(-1, 1)\\n mask = np.isnan(y)\\n y = np.where(mask, f, y)\\n\\n # compute gradients of the log likelihood\\n log_lik, J, H = vmap(self.log_likelihood_gradients_)(y, f)\\n\\n # apply mask\\n mask = np.squeeze(mask)\\n log_lik = np.where(mask, 0., log_lik)\\n J = np.where(mask, np.nan, J)\\n H = np.where(mask, np.nan, H)\\n\\n return log_lik, J, np.diag(H)\",\n \"def ComputeLogGradientTerm(self, *args):\\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF3F_ComputeLogGradientTerm(self, *args)\",\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 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 from_grad_log(d, g):\\n return UDFromCallable(d, fgrad_log=g)\",\n \"def ComputeLogGradientTerm(self, *args):\\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseID2D_ComputeLogGradientTerm(self, *args)\",\n \"def log_likelihood(self, theta, x, **kwargs):\\n\\n u, logdet_dudx, log_a = self.forward(theta, x, **kwargs)\\n\\n constant = float(-0.5 * self.n_inputs * np.log(2.0 * np.pi))\\n # log_likelihood = torch.log(torch.sum(torch.exp(log_a - 0.5 * u ** 2 + logdet_dudx), dim=2))\\n log_likelihood = torch.logsumexp(log_a - 0.5 * u**2 + logdet_dudx, dim=2)\\n log_likelihood = constant + torch.sum(log_likelihood, dim=1)\\n\\n return u, log_likelihood\",\n \"def log_likelihood_BP(y_t, theta_f_t, R, N):\\n psi_bethe = compute_eta_BP(theta_f_t, N, return_psi=1)[1]\\n log_p = R * (numpy.dot(y_t, theta_f_t) - psi_bethe)\\n return log_p\",\n \"def compute_gradient(theta, X, y):\\n m = X.shape[0]\\n grad_theta = np.dot(X.transpose(), (np.dot(X, theta) - y)) / m\\n #print theta, grad_theta, objective_function(theta, X, y)\\n return grad_theta\",\n \"def calculate_log_q_prior(theta, mu, rho):\\n\\n sigma = tf.nn.softplus(rho)\\n log_qw = tm.log_gaussian(theta, mu, sigma)\\n return tf.reduce_sum(log_qw)\",\n \"def log(self, x_old, grad, x, curr_iter, tol_g_val, tol_x_val, tol_f_val):\\n\\n print(\\\"-----------------------------------\\\")\\n print(\\\"\\\\n Iter: \\\", curr_iter)\\n print(\\\"\\\\n x_old: \\\", x_old)\\n print(\\\"\\\\n gradient: \\\", grad)\\n print(\\\"\\\\n x: \\\", x)\\n print(\\\"\\\\n tol_x_val: \\\", tol_x_val)\\n print(\\\"\\\\n tol_f_val: \\\", tol_f_val)\\n print(\\\"\\\\n tol_g_val: %s \\\\n \\\" % tol_g_val)\",\n \"def expected_log_g(self):\\n raise NotImplementedError()\\n E_lng = np.zeros_like(self.mf_gamma)\\n\\n # \\\\psi(\\\\sum_{b} \\\\gamma_b)\\n trm2 = psi(self.mf_gamma.sum(axis=2))\\n for b in xrange(self.B):\\n E_lng[:,:,b] = psi(self.mf_gamma[:,:,b]) - trm2\\n\\n return E_lng\",\n \"def grad_log_prior(self, log_occr_array=None):\\n\\n if log_occr_array is not None:\\n # Catch invalid occurrence rates for zero likelihood\\n try:\\n self.log_occr = log_occr_array\\n except InvalidOccurrenceRate:\\n return -np.inf * np.ones_like(self.occr, dtype=float)\\n\\n if tf.is_tensor(self.occr):\\n raise NotImplementedError(\\\"Manual gradient calculate with \\\"\\n \\\"tensorflow objects isn't \\\"\\n \\\"implemented, and seems a bit \\\"\\n \\\"redundant.\\\")\\n else:\\n # Written in terms of occr for ease.\\n grad = 1 / (2*self.occr)\\n # At this point, it's still possible for occr to be so\\n # small that it's underflowing, where value will be nan\\n grad = grad if not np.isnan(grad).any() else -np.inf * grad\\n\\n return grad\",\n \"def compute_grad_likelihood(sensor_loc, obj_loc, distance):\\n\\tgrad = np.zeros(sensor_loc.shape)\\n\\t# Your code: finish the grad loglike\\n\\t\\t\\n\\treturn grad\",\n \"def log_pdf_derivative_squared(x):\\n dlog = log_pdf_derivative(x)\\n return gs.einsum(\\\"...i, ...j -> ...ij\\\", dlog, dlog)\",\n \"def logp(self, value: TensorType, **kwargs) -> TensorType:\",\n \"def logp(self, value: TensorType, **kwargs) -> TensorType:\",\n \"def gradientFunctionReg(theta, X, y, Lambda):\\n m = len(y) # number of training examples\\n grad = np.zeros(theta.shape[0])\\n theta = np.transpose(theta)\\n sum_1 = 0\\n X = X.values\\n y = y.values\\n #calcuate the theta_0 \\n# ====================== YOUR CODE HERE ======================\\n# Instructions: Compute the gradient of a particular choice of theta.\\n# Compute the partial derivatives and set grad to the partial\\n# derivatives of the cost w.r.t. each parameter in theta\\n for i in range(theta.shape[0]):\\n if i == 0:\\n for j in range(m):\\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i]\\n else:\\n for j in range(m):\\n sum_1 += (sigmoid(np.dot(X[j],theta)) - y[j]) * X[j,i] + Lambda*theta[i]\\n grad[i] = sum_1/m\\n sum_1 = 0\\n\\n# =============================================================\\n\\n return grad\",\n \"def logp(self, F, Y):\\n raise NotImplementedError(\\\"implement the logp function\\\\\\n for this likelihood\\\")\",\n \"def get_F(self,X,y=None):\\n if len(self.trees) == 0:\\n # print ('init')\\n assert not (y is None), 'tree0 need y to initialize,y:{}'.format(y)\\n pos = y.sum()\\n neg = y.shape[0] - pos\\n F = np.zeros([X.shape[0],])\\n F[:] = math.log(pos / neg)\\n # print (pos,neg,F)\\n return F\\n else:\\n F = np.zeros([X.shape[0],])\\n for tree in self.trees:\\n F += self.learning_rate*tree.predict(X)\\n # print (F)\\n return F\",\n \"def log_marginal_likelihood(self, eval_gradient=False):\\n A, B, mu_tilda, gamma_tilda, r_grid, n_grid = self.calc_tau(return_all=True)\\n\\n gamma = self.GAMMA\\n gamma_y = self.GAMMA_Y\\n\\n A1 = np.copy(self.baseTau0)\\n A1[np.diag_indices_from(A1)] = 2 * self.ndim\\n\\n # A1 = (A - B) / gamma_y\\n A2 = np.diag(n_grid)\\n\\n B_inv = np.linalg.inv(B)\\n A_inv = np.linalg.inv(A)\\n\\n M_lambda = B - B.dot(np.linalg.inv(A).dot(B))\\n\\n # log_likelihood = mu_tilda.dot(M_lambda).dot(\\n # mu_tilda[:, np.newaxis]) - np.nan_to_num(np.log(np.linalg.det(M_lambda)))\\n log_likelihood = mu_tilda.dot(M_lambda).dot(mu_tilda[:, np.newaxis])\\n\\n # log_likelihood = -log_likelihood\\n print('log_likelihood: %f' % log_likelihood)\\n\\n Lambda = B - B.dot(A_inv.dot(B))\\n Lambda_inv = np.linalg.inv(Lambda)\\n\\n M_gamma = A2 - A2.dot(2 * gamma * A_inv - (gamma ** 2) * A_inv.dot(A2).dot(A_inv)).dot(A2)\\n\\n mean_r_grid = np.nan_to_num(np.array([r.sum() for r in r_grid]) / n_grid)\\n\\n log_likelihood_grad_gamma = mean_r_grid.dot(M_gamma.dot(mean_r_grid[:, np.newaxis]))[0]\\n\\n tmpMat = A2.dot(A_inv).dot(A1).dot(A_inv).dot(A2)\\n\\n log_likelihood_grad_gamma_y = (gamma ** 2) * (\\n np.trace(Lambda_inv.dot(tmpMat) + mean_r_grid.dot(tmpMat.dot(mean_r_grid[:, np.newaxis]))[0]))\\n\\n log_likelihood_gradient = np.array([log_likelihood_grad_gamma, log_likelihood_grad_gamma_y])\\n print('log_likelihood_grad: %s' % log_likelihood_gradient)\\n\\n if eval_gradient:\\n return log_likelihood, log_likelihood_gradient\\n else:\\n return log_likelihood\",\n \"def ComputeLogGradientTerm(self, *args):\\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseID3D_ComputeLogGradientTerm(self, *args)\",\n \"def gradient_ascent(f, df, theta_init, step_size, max_iter):\\n\\n fs = []\\n xs = []\\n thetas = theta_init\\n for i in range(max_iter): #for each data example\\n fs.append(f(thetas))\\n\\n temp = step_size*df(thetas)\\n thetas = step_size*df(thetas) #modify that feature by using the derivative of log likelihood\\n xs.append(thetas.flatten())\\n if i % 10 == 0:\\n print(i, thetas)\\n\\n return thetas, fs, xs\",\n \"def gradient(theta, X, y, Lambda=0.0):\\n m = X.shape[0] # number of samples\\n\\n h = hypothesis(theta, X)\\n\\n if Lambda:\\n g_0 = (1/m)*(X.T@(h - y))[0]\\n g_1 = (1/m)*(X.T@(h - y))[1:] + (Lambda/m)*theta[1:] # skip theta-0\\n \\n return np.append(g_0, g_1)\\n else:\\n return (1/m)*(X.T@(h - y))\",\n \"def logpost(self, theta, t=None):\\n return self.prior.logpdf(theta) + self.loglik(theta, t)\",\n \"def _log_likelihood(self, theta, f, x, y, yerr):\\n sigma2 = yerr**2\\n return -0.5*np.sum((y - f(theta, x))**2 / sigma2 + 2*np.log(sigma2))\",\n \"def log_likelihood(self, theta, active=None):\\n return sum(self.log_likelihood_term(theta, active=active))\",\n \"def log_density(\\n self,\\n theta_unc: FloatArray,\\n *,\\n propto: bool = True,\\n jacobian: bool = True,\\n ) -> float:\\n lp = ctypes.pointer(ctypes.c_double())\\n err = ctypes.pointer(ctypes.c_char_p())\\n rc = self._log_density(\\n self.model, int(propto), int(jacobian), theta_unc, lp, err\\n )\\n if rc:\\n raise self._handle_error(err.contents, \\\"log_density\\\")\\n return lp.contents.value\",\n \"def __log_likelihood(self, params, *args):\\n\\t\\tX, y, feature_set, lambda_reg, empirical_weights, verbose, sign = args\\n\\n\\t\\tno_example = len(X)\\n\\t\\ttotal_logZ = 0\\n\\t\\ttotal_logProb = 0\\n\\t\\texpected_weights = np.zeros(len(feature_set))\\n\\t\\tfor t in range(len(X)):\\n\\t\\t\\t# example_features = X[t], example_labels = y[t]\\n\\n\\t\\t\\tpotential = np.zeros(len(X[t]))\\n\\t\\t\\tfor i in range(len(X[t])):\\n\\t\\t\\t\\t#candidate_features = X[t][i], candidate_label = y[t][i]\\n\\t\\t\\t\\tpotential[i] = feature_set.calc_inner_product(X[t][i], params)\\n\\n\\t\\t\\t#scaling\\n\\t\\t\\tpotential = potential - np.max(potential, keepdims=True)\\n\\n\\t\\t\\tfor i in range(len(X[t])):\\n\\t\\t\\t\\ttotal_logProb += potential[i] * y[t][i]\\n\\n\\t\\t\\tpotential, Z = self.__softmax(potential)\\n\\n\\t\\t\\tfor i in range(len(X[t])):\\n\\t\\t\\t\\tfeature_set.calc_inner_sum(expected_weights, X[t][i], potential[i])\\n\\n\\t\\t\\ttotal_logZ += log(Z)\\n\\n\\t\\t# _params = feature_set.get_regularized_params(params, 'bias')\\n\\t\\t_params = params\\n\\t\\tlog_likelihood = total_logProb - total_logZ - (lambda_reg/2) * np.sum(np.multiply(_params,_params))\\n\\t\\tgradients = empirical_weights - expected_weights - lambda_reg * _params\\n\\n\\t\\tglobal SUB_ITERATION_NUM\\n\\t\\tif verbose:\\n\\t\\t\\tsub_iteration_str = ' '\\n\\t\\t\\tif SUB_ITERATION_NUM > 0:\\n\\t\\t\\t\\tsub_iteration_str = '(' + '{0:02d}'.format(SUB_ITERATION_NUM) + ')'\\n\\t\\t\\tprint(' ', '{0:03d}'.format(ITERATION_NUM), sub_iteration_str, ':', log_likelihood * sign)\\n\\n\\t\\tSUB_ITERATION_NUM += 1\\n\\n\\t\\treturn sign * log_likelihood, sign * gradients\",\n \"def log_prob(self, value, pre_tanh_value=None, epsilon=1e-06):\\n if pre_tanh_value is None:\\n pre_tanh_value = torch.log((1 + epsilon + value) / (1 + epsilon - value)) / 2\\n norm_lp = self._normal.log_prob(pre_tanh_value)\\n ret = norm_lp - torch.sum(torch.log(self._clip_but_pass_gradient(1.0 - value ** 2) + epsilon), axis=-1)\\n return ret\",\n \"def costFunction(self,theta, X, y): \\n m = len(y)\\n h = self.sigmoid(X@theta)\\n J = 1 / m * (- y.T @ self.log(h) - (1-y).T @ self.log(1-h)) \\n # grad = 1/ m * X.T @ (h - y)\\n return J\",\n \"def logg_trigomonetric(teff, mass, v, bc, par, dpar, dteff, dmass):\\n #np.geterr()\\n if mass == 'nan':\\n logg, dlogg = 'nan', 'nan'\\n else:\\n e = 2.718281828\\n logg = 4.44 + np.log10(mass) + (4.0*np.log10(teff/5777.)) + (0.4*(v + bc)) + (2.0*np.log10(par/1000.0)) + 0.108\\n logg = np.round(logg, 2)\\n dlogg = np.sqrt(((dmass*np.log10(e))/mass)**2 + ((4.*dteff*np.log10(e))/teff)**2 + ((2.*0.05*np.log10(e))/par)**2)\\n dlogg = np.round(dlogg, 2)\\n return logg, dlogg\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7159313","0.6766048","0.6629792","0.6519036","0.6494689","0.6489138","0.6413346","0.64006495","0.6371672","0.63623327","0.63360333","0.63232714","0.6310686","0.6310686","0.6260827","0.62424856","0.62325174","0.62135476","0.61898154","0.61844623","0.61440015","0.6114388","0.6110577","0.6110501","0.60843813","0.6071816","0.6047368","0.60419714","0.6017729","0.60096514","0.60076016","0.6006773","0.6006773","0.59986335","0.5995104","0.59873563","0.5985749","0.59806585","0.59673953","0.5948539","0.590245","0.589995","0.5886045","0.58834344","0.58759516","0.5852535","0.58474606","0.5843807","0.58371586","0.58354884","0.58289236","0.58273715","0.58237135","0.5822867","0.5818234","0.5813569","0.580619","0.5803491","0.5796232","0.57924724","0.5785461","0.5781482","0.57792455","0.5773008","0.5768084","0.5751284","0.57505244","0.5732688","0.5726727","0.5725875","0.5701997","0.5694859","0.5683221","0.5674768","0.56681925","0.56649566","0.5659276","0.5649877","0.5637533","0.5635855","0.56330127","0.56308407","0.5624894","0.56176394","0.56176394","0.5614318","0.56024057","0.55988234","0.55816656","0.5578296","0.5573564","0.55717826","0.5567318","0.55673033","0.55667","0.55664736","0.5566362","0.556293","0.5558266","0.55579096"],"string":"[\n \"0.7159313\",\n \"0.6766048\",\n \"0.6629792\",\n \"0.6519036\",\n \"0.6494689\",\n \"0.6489138\",\n \"0.6413346\",\n \"0.64006495\",\n \"0.6371672\",\n \"0.63623327\",\n \"0.63360333\",\n \"0.63232714\",\n \"0.6310686\",\n \"0.6310686\",\n \"0.6260827\",\n \"0.62424856\",\n \"0.62325174\",\n \"0.62135476\",\n \"0.61898154\",\n \"0.61844623\",\n \"0.61440015\",\n \"0.6114388\",\n \"0.6110577\",\n \"0.6110501\",\n \"0.60843813\",\n \"0.6071816\",\n \"0.6047368\",\n \"0.60419714\",\n \"0.6017729\",\n \"0.60096514\",\n \"0.60076016\",\n \"0.6006773\",\n \"0.6006773\",\n \"0.59986335\",\n \"0.5995104\",\n \"0.59873563\",\n \"0.5985749\",\n \"0.59806585\",\n \"0.59673953\",\n \"0.5948539\",\n \"0.590245\",\n \"0.589995\",\n \"0.5886045\",\n \"0.58834344\",\n \"0.58759516\",\n \"0.5852535\",\n \"0.58474606\",\n \"0.5843807\",\n \"0.58371586\",\n \"0.58354884\",\n \"0.58289236\",\n \"0.58273715\",\n \"0.58237135\",\n \"0.5822867\",\n \"0.5818234\",\n \"0.5813569\",\n \"0.580619\",\n \"0.5803491\",\n \"0.5796232\",\n \"0.57924724\",\n \"0.5785461\",\n \"0.5781482\",\n \"0.57792455\",\n \"0.5773008\",\n \"0.5768084\",\n \"0.5751284\",\n \"0.57505244\",\n \"0.5732688\",\n \"0.5726727\",\n \"0.5725875\",\n \"0.5701997\",\n \"0.5694859\",\n \"0.5683221\",\n \"0.5674768\",\n \"0.56681925\",\n \"0.56649566\",\n \"0.5659276\",\n \"0.5649877\",\n \"0.5637533\",\n \"0.5635855\",\n \"0.56330127\",\n \"0.56308407\",\n \"0.5624894\",\n \"0.56176394\",\n \"0.56176394\",\n \"0.5614318\",\n \"0.56024057\",\n \"0.55988234\",\n \"0.55816656\",\n \"0.5578296\",\n \"0.5573564\",\n \"0.55717826\",\n \"0.5567318\",\n \"0.55673033\",\n \"0.55667\",\n \"0.55664736\",\n \"0.5566362\",\n \"0.556293\",\n \"0.5558266\",\n \"0.55579096\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6783176"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11188,"cells":{"query":{"kind":"string","value":"Belief propagation (maxproduct) algorithm for calculating the best tag sequence for a tree"},"document":{"kind":"string","value":"def best_sequence(self, T, pos, psi, phi, fix_tags=[]):\n for idx, m in fix_tags:\n phi[idx - 1, m] = 100\n # if fix_idx:\n # phi[fix_idx - 1, fix_m] = 100\n msgs, pointers = max_product(T, pos, psi, phi, True)\n tags_dict = get_best_tags(T, msgs, pointers)\n tags = []\n for i in range(1, len(T) + 1):\n tags.append(self.get_tag(tags_dict[str(i)]))\n return tags"},"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 viterbi_tags (untagged_sentences, h):\n transitions = h[0]\n emissions = h[1]\n tags = h[2]\n maxtags = []\n #print tags\n\n for untaggedsent in untagged_sentences:\n #Create empty probtable\n words = untaggedsent.split()\n r = len(tags)\n c = len(words)\n probtable = [None]*r\n for i in range(r):\n probtable[i] = [None]*c\n for j in range(c):\n probtable[i][j] = [None]*2\n\n #Initialize zeroth column of probtable\n prevtag = '<START>'\n word = words[0]\n for i in range(r):\n tag = tags[i]\n\n transition = transitions[prevtag][tag]\n if word in emissions[tag]:\n emission = emissions[tag][word]\n else:\n emission = .0001*emissions[tag]['<UNKNOWN>']\n\n probtable[i][0][0] = transition*emission\n \n #Fill in probtable\n for j in range(1, c):\n word = words[j]\n for i in range(r):\n tag = tags[i]\n maxprob = 0\n maxtag = None\n\n if word in emissions[tag]:\n emission = emissions[tag][word]\n else:\n emission = .0001*emissions[tag]['<UNKNOWN>']\n\n for k in range(r):\n prevtag = tags[k]\n transition = transitions[prevtag][tag]\n prob = probtable[k][j-1][0]*transition*emission\n \n if (prob > maxprob):\n maxprob = prob\n maxtag = k\n\n probtable[i][j][0] = maxprob\n probtable[i][j][1] = maxtag\n\n #Find most likely sequence of POS tags of this sentence\n sentmaxtags = maxsequence(probtable, tags)\n maxtags.extend(sentmaxtags)\n\n #Return most likely sequence of POS tags of all sentences\n return maxtags","def maxsequence(probtable, tags):\n r = len(probtable)\n c = len(probtable[0])\n\n maxfinalprob = 0\n maxfinaltag = None\n for i in range(r):\n if (probtable[i][c-1][0] > maxfinalprob):\n maxfinalprob = probtable[i][c-1][0]\n maxfinaltag = i\n\n #print maxfinaltag\n\n maxsequence = []\n prevmaxtag = maxfinaltag\n for j in range(c-1, -1, -1):\n maxsequence.insert(0, tags[prevmaxtag])\n #print probtable[prevmaxtag][j][1]\n prevmaxtag = probtable[prevmaxtag][j][1]\n\t \n return maxsequence","def id3(x, y, attributes, max_depth, weight, depth=0):\r\n tree = {}\r\n new_attr = []\r\n arr, count = np.unique(y, return_counts=True)\r\n # checking edge cases - reached maximum depth, or no attributes\r\n if len(attributes) == 0 or depth == max_depth or len(x) == 0:\r\n return np.bincount(y).argmax()\r\n # if all the values of y are one return one\r\n elif len(np.unique(y)) == 1:\r\n return arr[0]\r\n else:\r\n # calculating mutual information values\r\n info_values = {} \r\n # over number of columns\r\n for i in range(data.dim):\r\n oldX = partition(x[:,i])\r\n oldKeys = oldX.keys()\r\n # check in attributes recieved from bagging\r\n for attr in attributes:\r\n binX = []\r\n key , value = attr\r\n # check for key and value\r\n if i == key and value in oldKeys:\r\n # get the index\r\n index = oldX[value]\r\n for n in range(len(x)):\r\n if n in index:\r\n # if match binary classification 1 / 0 and appending to binX list\r\n binX.append(1)\r\n else:\r\n binX.append(0)\r\n # adding to a dictionary \r\n info_values[(i, value)] = mutual_information(binX, y, weight)\r\n \r\n # getting the maximum feature value\r\n best_feature_index = max(info_values, key=info_values.get) \r\n best_feature, best_val = best_feature_index\r\n # creating the best partition\r\n x_best_part = partition(x[:,best_feature])\r\n #selecting other than the best feature value from the dictionary\r\n new_attr = list(filter(lambda x: x!= (best_feature, best_val), attributes))\r\n # increasing depth\r\n depth += 1\r\n\r\n # Calling id3 recursively, checking over 0,1 making a prediction as True / False \r\n for n in range(0,2):\r\n if n == 0:\r\n # recursively calling id3 over the best values of the x partition\r\n bestX = x[x_best_part[best_val]]\r\n bestY = y[x_best_part[best_val]]\r\n tree[best_feature, best_val, 'True'] = id3(bestX, bestY, new_attr, max_depth,weight, depth)\r\n else:\r\n \t # recursively calling id3 selecting other than best features\r\n othr_idx = []\r\n for i in x_best_part:\r\n if i != best_val:\r\n othr_idx.extend(x_best_part[i])\r\n\r\n otherX = x[othr_idx]\r\n otherY = y[othr_idx]\r\n tree[best_feature, best_val, 'False'] = id3(otherX, otherY, new_attr, max_depth,weight, depth)\r\n return tree","def test_that_most_probable_always_returns_a_tree_of_the_same_logprob():\n\n grammar = Grammar(rulebooks)\n actual_tree = grammar.sample_tree()\n sentence = actual_tree.terminals\n inner = grammar.compute_inside_probabilities(sentence)\n best_tree = grammar.compute_most_likely_tree(sentence, inner, root=0)\n best_logprob = grammar.logprob_tree(best_tree)\n\n for _ in range(20):\n tree = grammar.compute_most_likely_tree(sentence, inner, root=0)\n assert np.isclose(grammar.logprob_tree(tree), best_logprob)","def viterbi_bigrams(transition_probabilities, label_matches, prev_tag, word, tag_possibilities):\r\n\tmax_prob = 0\r\n\tbest_tag = \"\"\r\n\ttag_counts = get_tag_counts(label_matches)\r\n\tfor tag in tag_possibilities:\r\n\t\temissions_probability = get_emissions_probability(label_matches, tag, word, tag_counts)\r\n\t\ttag_bigram = (prev_tag,tag)\r\n\t\ttransition_probability = transition_probabilities.get(tag_bigram, 0.000027)\r\n\t\tprob = emissions_probability * transition_probability\r\n\t\tif prob > max_prob:\r\n\t\t\tmax_prob = prob\r\n\t\t\tbest_tag = tag\r\n\tif best_tag == \"\":\r\n\t\tbest_tag = \"o\"\r\n\treturn best_tag","def best_B(Ag):\n top = 0\n for i in range(len(Ag)):\n etop = np.min(cf.TD20[int(Ag[i]) - 1])\n top += etop\n return top","def viterbi_trigrams(transition_probabilities, label_matches, prev_tag, prev_prev_tag, word, tag_possibilities):\r\n\tmax_prob = 0\r\n\tbest_tag = \"\"\r\n\ttag_counts = get_tag_counts(label_matches)\r\n\tfor tag in tag_possibilities:\r\n\t\temissions_probability = get_emissions_probability(label_matches, tag, word, tag_counts)\r\n\t\ttag_trigram = (prev_prev_tag, prev_tag,tag)\r\n\t\ttransition_probability = transition_probabilities.get(tag_trigram, 0.000027)\r\n\t\tprob = emissions_probability * transition_probability\r\n\t\tif prob > max_prob:\r\n\t\t\tmax_prob = prob\r\n\t\t\tbest_tag = tag\r\n\tif best_tag == \"\":\r\n\t\tbest_tag = \"o\"\r\n\treturn best_tag","def kuhn_munkres(G): # maximum profit bipartite matching in O(n^4)\n assert len(G) == len(G[0])\n n = len(G)\n mu = [None] * n # Empty matching\n mv = [None] * n\n lu = [max(row) for row in G] # Trivial labels\n lv = [0] * n\n for u0 in range(n):\n if mu[u0] is None: # Free node\n while True:\n au = [False] * n # Empty alternating tree\n av = [False] * n\n if improve_matching(G, u0, mu, mv, au, av, lu, lv):\n break\n improve_labels(G, au, av, lu, lv)\n return (mu, sum(lu) + sum(lv))","def greedy_policy(self):\n # print(self.weights)\n policy = defaultdict(lambda: 0)\n\n for entry, values in self.weights.items():\n policy[entry] = np.argmax(self.weights[entry])\n # print(policy)\n\n return policy","def predict_greedy(self, tokens: TokenSeq) -> Tuple[NDArray, PosSeq]:\n \n #array to hold predictions\n predictions = np.zeros((len(tokens), len(self.l), len(self.l)))\n for i in range(len(tokens)):\n\n for prev_tag in self.l:\n new_feature_matrix = []\n temp_dict = {}\n feature_dict = add_features(tokens, prev_tag ,i, temp_dict)\n new_feature_matrix.append(feature_dict)\n new_feature_matrix = self.vectorizer.transform(new_feature_matrix)\n \n probabilities = self.clf.predict_proba(new_feature_matrix)\n predictions[i, self.label_index(prev_tag)] = probabilities\n \n cur = len(probabilities[0])- 1\n final_predictions = []\n for i in range(len(predictions)):\n cur_pred = np.argmax(predictions[i, cur])\n final_predictions.append(cur_pred)\n cur = cur_pred\n ret_matrix = []\n #print(tokens)\n final_predictions = self.le.inverse_transform(final_predictions)\n #print(final_predictions)\n new_pos = final_predictions.tolist()\n new_pos.insert(0, \"<s>\")\n new_pos.pop(len(new_pos) - 1)\n for i in range(len(new_pos)):\n feature_dict = add_features(tokens, new_pos[i] ,i, temp_dict)\n ret_matrix.append(feature_dict)\n ret_matrix = self.vectorizer.transform(ret_matrix)\n\n return (ret_matrix, final_predictions)","def _get_max_bootstrap_genus(self, seq, repeats):\n word_posteriors = self._word_posteriors\n word_idxs = self._word_idxs\n word_size = self._word_size\n\n all_words = list(unique_words(seq, word_size))\n print sorted(map(word_idxs.get, all_words))\n decisions = [] #genera idxs\n for words in bootstrap(all_words, len(seq)//word_size, repeats):\n decisions.append(self._get_max_likelihood_genus(words,\n word_posteriors, word_idxs))\n freqs = calc_freqs(concatenate(decisions))\n sorted_freqs = sorted(freqs.items(), key=itemgetter(1))\n return sorted_freqs[-1] #what if a tie here?","def aux_phrase_subtree(tree, tag_list):\n highest_subtree = max([(0, None)]+[(subtree.height(), subtree) for subtree in tree.subtrees(lambda x: x.node in tag_list) if subtree != tree[0]])[1]\n # print highest_subtree\n return highest_subtree","def predict(self, data):\t\t\n\t\tpredictions = {}\n\t\tfor tree in self.trees:\n\t\t\tprediction, prob = tree.predict(data)\n\t\t\tif prediction in predictions:\n\t\t\t\tpredictions[prediction] += prob\n\t\t\telse:\n\t\t\t\tpredictions[prediction] = prob\n\t\treturn max(predictions, key=predictions.get)","def lazy_greedy_max(self, budget):\r\n\r\n classes, no_elements = torch.unique(self.y_trn, return_counts=True)\r\n len_unique_elements = no_elements.shape[0]\r\n per_class_bud = int(budget / len(classes))\r\n final_per_class_bud = []\r\n _, sorted_indices = torch.sort(no_elements, descending = True)\r\n\r\n if self.selection_type == 'PerClass':\r\n \r\n total_idxs = 0\r\n for n_element in no_elements:\r\n final_per_class_bud.append(min(per_class_bud, torch.IntTensor.item(n_element)))\r\n total_idxs += min(per_class_bud, torch.IntTensor.item(n_element))\r\n \r\n if total_idxs < budget:\r\n bud_difference = budget - total_idxs\r\n for i in range(len_unique_elements):\r\n available_idxs = torch.IntTensor.item(no_elements[sorted_indices[i]])-per_class_bud \r\n final_per_class_bud[sorted_indices[i]] += min(bud_difference, available_idxs)\r\n total_idxs += min(bud_difference, available_idxs)\r\n bud_difference = budget - total_idxs\r\n if bud_difference == 0:\r\n break\r\n\r\n total_greedy_list = []\r\n for i in range(len_unique_elements):\r\n idxs = torch.where(self.y_trn == classes[i])[0]\r\n \r\n if self.submod == 'facility_location':\r\n self.compute_score(idxs)\r\n fl = apricot.functions.facilityLocation.FacilityLocationSelection(random_state=0, metric='precomputed',\r\n n_samples=final_per_class_bud[i])\r\n elif self.submod == 'graph_cut':\r\n self.compute_score(idxs)\r\n fl = apricot.functions.graphCut.GraphCutSelection(random_state=0, metric='precomputed',\r\n n_samples=final_per_class_bud[i])\r\n elif self.submod == 'saturated_coverage':\r\n self.compute_score(idxs)\r\n fl = apricot.functions.saturatedCoverage.SaturatedCoverageSelection(random_state=0, metric='precomputed',\r\n n_samples=final_per_class_bud[i])\r\n elif self.submod == 'sum_redundancy':\r\n self.compute_score(idxs)\r\n fl = apricot.functions.sumRedundancy.SumRedundancySelection(random_state=0, metric='precomputed',\r\n n_samples=final_per_class_bud[i])\r\n elif self.submod == 'feature_based':\r\n fl = apricot.functions.featureBased.FeatureBasedSelection(random_state=0, n_samples=final_per_class_bud[i])\r\n\r\n if self.submod == 'feature_based':\r\n\r\n x_sub = fl.fit_transform(self.x_trn[idxs].numpy())\r\n greedyList = self.get_index(self.x_trn[idxs].numpy(), x_sub)\r\n total_greedy_list.extend(idxs[greedyList])\r\n\r\n else: \r\n\r\n sim_sub = fl.fit_transform(self.dist_mat.cpu().numpy())\r\n greedyList = list(np.argmax(sim_sub, axis=1))\r\n total_greedy_list.extend(idxs[greedyList])\r\n\r\n elif self.selection_type == 'Supervised':\r\n \r\n \r\n if self.submod == 'feature_based':\r\n \r\n class_map = {}\r\n for i in range(len_unique_elements):\r\n class_map[torch.IntTensor.item(classes[i])] = i #Mapping classes from 0 to n\r\n \r\n sparse_data = torch.zeros([self.x_trn.shape[0], self.x_trn.shape[1]*len_unique_elements])\r\n for i in range(self.x_trn.shape[0]):\r\n \r\n start_col = class_map[torch.IntTensor.item(self.y_trn[i])]*self.x_trn.shape[1]\r\n end_col = start_col+self.x_trn.shape[1]\r\n sparse_data[i, start_col:end_col] = self.x_trn[i, :]\r\n\r\n fl = apricot.functions.featureBased.FeatureBasedSelection(random_state=0, n_samples=budget)\r\n x_sub = fl.fit_transform(sparse_data.numpy())\r\n total_greedy_list = self.get_index(sparse_data.numpy(), x_sub)\r\n\r\n else:\r\n for i in range(len(classes)):\r\n \r\n if i == 0:\r\n idxs = torch.where(self.y_trn == classes[i])[0]\r\n N = len(idxs)\r\n self.compute_score(idxs)\r\n row = idxs.repeat_interleave(N)\r\n col = idxs.repeat(N)\r\n data = self.dist_mat.cpu().numpy().flatten()\r\n else:\r\n idxs = torch.where(self.y_trn == classes[i])[0]\r\n N = len(idxs)\r\n self.compute_score(idxs)\r\n row = torch.cat((row, idxs.repeat_interleave(N)), dim=0)\r\n col = torch.cat((col, idxs.repeat(N)), dim=0)\r\n data = np.concatenate([data, self.dist_mat.cpu().numpy().flatten()], axis=0)\r\n \r\n \r\n sparse_simmat = csr_matrix((data, (row.numpy(), col.numpy())), shape=(self.N_trn, self.N_trn))\r\n #self.dist_mat = sparse_simmat\r\n\r\n if self.submod == 'facility_location':\r\n fl = apricot.functions.facilityLocation.FacilityLocationSelection(random_state=0, metric='precomputed',\r\n n_samples=budget)\r\n elif self.submod == 'graph_cut':\r\n fl = apricot.functions.graphCut.GraphCutSelection(random_state=0, metric='precomputed',\r\n n_samples=budget)\r\n elif self.submod == 'saturated_coverage':\r\n fl = apricot.functions.saturatedCoverage.SaturatedCoverageSelection(random_state=0, metric='precomputed',\r\n n_samples=budget)\r\n elif self.submod == 'sum_redundancy':\r\n fl = apricot.functions.sumRedundancy.SumRedundancySelection(random_state=0, metric='precomputed',\r\n n_samples=budget)\r\n sim_sub = fl.fit_transform(sparse_simmat)\r\n total_greedy_list = list(np.array(np.argmax(sim_sub, axis=1)).reshape(-1))\r\n\r\n\r\n if self.selection_type == 'Full':\r\n \r\n\r\n total_greedy_list = []\r\n idx_end = self.x_trn.shape[0] - 1\r\n idxs = torch.linspace(0, idx_end, self.x_trn.shape[0]).long()\r\n\r\n if self.submod == 'facility_location':\r\n self.compute_score(idxs)\r\n fl = apricot.functions.facilityLocation.FacilityLocationSelection(random_state=0, metric='precomputed',\r\n n_samples=budget)\r\n elif self.submod == 'graph_cut':\r\n self.compute_score(idxs)\r\n fl = apricot.functions.graphCut.GraphCutSelection(random_state=0, metric='precomputed',\r\n n_samples=budget)\r\n elif self.submod == 'saturated_coverage':\r\n self.compute_score(idxs)\r\n fl = apricot.functions.saturatedCoverage.SaturatedCoverageSelection(random_state=0, metric='precomputed',\r\n n_samples=budget)\r\n elif self.submod == 'sum_redundancy':\r\n self.compute_score(idxs)\r\n fl = apricot.functions.sumRedundancy.SumRedundancySelection(random_state=0, metric='precomputed',\r\n n_samples=budget)\r\n elif self.submod == 'feature_based':\r\n fl = apricot.functions.featureBased.FeatureBasedSelection(random_state=0, n_samples=budget)\r\n\r\n if self.submod == 'feature_based':\r\n\r\n x_sub = fl.fit_transform(self.x_trn.numpy())\r\n total_greedy_list = self.get_index(self.x_trn.numpy(), x_sub)\r\n\r\n else: \r\n\r\n sim_sub = fl.fit_transform(self.dist_mat.cpu().numpy())\r\n total_greedy_list = list(np.argmax(sim_sub, axis=1))\r\n\r\n return total_greedy_list","def run_viterbi(emission_scores, trans_scores, start_scores, end_scores):\r\n\r\n L = start_scores.shape[0]\r\n assert end_scores.shape[0] == L\r\n assert trans_scores.shape[0] == L\r\n assert trans_scores.shape[1] == L\r\n assert emission_scores.shape[1] == L\r\n N = emission_scores.shape[0]\r\n\r\n #T - Score matrix same as in assignement pdf\r\n T = np.zeros(shape=(L,N))\r\n #Back pointers - to store the previous best tag for word at (i-1)th position\r\n #that resulted into current best tag for (i)th word \r\n back_pointer = np.full((L,N), -1)\r\n\r\n for i in xrange(L):\r\n emission = emission_scores[0][i]\r\n combined = emission + start_scores[i]\r\n T[i][0] = combined\r\n\r\n # Loop over all the words in a sequesnce\r\n for i in xrange(1, N):\r\n # Loop over all the tags for the word at index i \r\n for j in xrange(L):\r\n # Varibale for maximum tag score from previous word (word at i-1)\r\n tmp_max = float('-inf')\r\n tmp_max_idx = -1\r\n #Emission value of word at idx i from state (i.e tag) j\r\n emission = emission_scores[i][j]\r\n #Loop over all the possibile tags for previous word T[tag (1..L), word at i-1]\r\n #and get max among them. Store the corresponding back pointer for there T[tag (1..L), word at i-1]\r\n for k in xrange(L):\r\n transition = trans_scores[k][j]\r\n prev_path = T[k][i-1]\r\n combined = transition + prev_path\r\n if (tmp_max < combined):\r\n tmp_max = combined\r\n tmp_max_idx = k\r\n\r\n back_pointer[j][i] = tmp_max_idx\r\n T[j][i] = tmp_max + emission\r\n\r\n # Doing this step outside because if N == 1 then above loop will not run\r\n # Variable for maximum tag score\r\n tag_max = float('-inf')\r\n # Variable for back pointer(previous T[tag, word])\r\n tag_max_idx = -1\r\n for i in xrange(L):\r\n T[i][N-1] = T[i][N-1] + end_scores[i]\r\n if (tag_max < T[i][N-1]):\r\n tag_max = T[i][N-1]\r\n tag_max_idx = i\r\n # print(\"Max tag -> \" + str(tag_max_idx))\r\n\r\n #Variable to track the path length - should be equal to N\r\n path_length = 0\r\n #Variable to back track on the tags\r\n tag_idx = tag_max_idx\r\n #Varibale to track the word index in N\r\n word_idx = N-1 \r\n #Path strored using backtracking\r\n y = []\r\n\r\n #Getting the best path using backtracking on back_pointers\r\n while path_length != N-1:\r\n y.append(back_pointer[tag_idx][word_idx])\r\n tag_idx = back_pointer[tag_idx][word_idx]\r\n word_idx = word_idx - 1\r\n path_length = path_length + 1\r\n\r\n #Reversing the backtracked path\r\n y = y[::-1]\r\n #Adding the tag for the last word idx in N\r\n y.append(tag_max_idx)\r\n # print(\"Path -> \" + str(y))\r\n\r\n return (tag_max, y)","def leaf_prediction(self, node_id): #returns y_idx\n values = self.tree.value[node_id]\n return np.argmax(values)","def calcMostEdgesToLeaves(unprocN, leaf, TG):\n\t\tmostLeaves = -1\n\t\tretNode = None\n\t\tl_zero = []\n\t\tfor l in leaf:\n\t\t\tl_zero.append(l[0])\n\t\tfor n in unprocN:\n\t\t\te_count = 0\n\t\t\tfor e in TG[n]:\n\t\t\t\tif e in l_zero:\n\t\t\t\t\te_count += 1\n\t\t\tif e_count > mostLeaves:\n\t\t\t\tmostLeaves = e_count\n\t\t\t\tretNode = n\n\t\t\tif e_count == 2:\n\t\t\t\treturn (retNode, mostLeaves)\n\t\treturn (retNode, mostLeaves)","def greedy_learn_search(self,db,labels):\n queue = PriorityQueue()\n dolowmem = (self.lowmem == True)\n numidsets = 0\n root_ids = range(len(labels))\n queue.push((self.root,root_ids),len(labels))\n numnodes = 1\n deepest = 0\n err = 0\n while len(queue) > 0 and numnodes+2 <= self.maxnodes:\n #print \"%d nodes, priority %d\"%(numnodes,queue.nextkey())\n nerr = queue.nextkey()\n (node,trainingset) = queue.pop()\n #print \"Greedy learn\",len(trainingset)\n if trainingset is None:\n trainingset = self.identify_examples(db,labels,node)\n if node.depth >= self.maxdepth or len(trainingset) <= self.minexamples:\n #print \" Hit depth or training set limit\"\n node.pick_best_label(db,labels,trainingset)\n err += misclassification_error([labels[id] for id in trainingset])\n continue\n features = self.feature_subset(node,db,labels,trainingset)\n cost = node.pick_best_split(db,labels,trainingset,features)\n numidsets -= len(trainingset)\n #do a split\n if node.type == 'v':\n continue\n elif node.type == 's':\n #discrete split\n node.children = dict()\n #select sub-indices\n Eids = defaultdict(list)\n noneids = []\n for id in trainingset:\n v = db[node.feature,id]\n if v is None:\n #item doesn't exist, it's a missing value\n noneids.append(id)\n else:\n Eids[v].append(id)\n #determine whether to switch to low-memory mode\n if not dolowmem and self.lowmem=='auto':\n for v,vids in Eids.iteritems():\n numidsets += len(vids)+len(noneids)\n if numidsets > self.lowmem_threshold:\n print \"Decision tree learner switching to low-memory mode\"\n dolowmem = True\n trainingset = None\n\n\n numnodes += len(Eids)\n #print \"Split sizes\",[len(v) for v in Eids.itervalues()]\n #print \"None size\",len(noneids)\n for v,vids in Eids.iteritems():\n #print \"->\",len(vids),\"+\",len(noneids)\n #recurse\n c = DecisionTreeNode(node)\n node.children[v] = c\n err = misclassification_error([labels[id] for id in vids+noneids])\n cids = (None if dolowmem else vids+noneids)\n queue.push((c,cids),err)\n if c.depth > deepest:\n deepest = c.depth\n print \"Decision tree learner: Reached node with depth\",deepest\n else:\n #do an inequality split\n assert node.type == 'i',\"Got a weird type? \"+str(node.type)\n leftids = []\n rightids = []\n for id in trainingset:\n val = db[node.feature,id]\n if val is not None:\n if val <= node.value: leftids.append(id)\n else: rightids.append(id)\n else:\n leftids.append(id)\n rightids.append(id)\n if len(leftids)==0 or len(rightids)==0:\n print \"node feature \"+str(node.feature)+\" doesn't have a valid split value \"+str(node.value)\n vals = [db[node.feature,id] for id in trainingset if db[node.feature,id]!=None]\n print \"min,max of training set:\",min(vals),max(vals)\n print \"cost is\",cost\n raw_input()\n assert len(leftids) > 0 and len(rightids) > 0\n if not dolowmem and self.lowmem=='auto':\n numidsets += len(leftids) + len(rightids)\n if numidsets > self.lowmem_threshold:\n print \"Decision tree learner switching to low-memory mode\"\n dolowmem = True\n trainingset = None\n numnodes += 2\n c1 = DecisionTreeNode(node)\n c2 = DecisionTreeNode(node)\n node.children = {0:c1,1:c2}\n #print \"->\",len(leftids)\n #print \"->\",len(rightids)\n err1 = misclassification_error([labels[id] for id in leftids])\n err2 = misclassification_error([labels[id] for id in rightids])\n if dolowmem:\n leftids = None\n rightids = None\n queue.push((c1,leftids),err1)\n queue.push((c2,rightids),err2)\n if c1.depth > deepest:\n deepest = c1.depth\n print \"Decision tree learner: Reached node with depth\",deepest\n #end of recursion. for the rest of the nodes still in the queue, make them leaf nodes\n if len(queue) > 0:\n print \"%d nodes remaining in queue, setting to leaves\"%(len(queue),)\n for (node,trainingset) in queue:\n node.pick_best_label(db,labels,trainingset)\n err += misclassification_error([labels[id] for id in trainingset])\n return err","def find_max_triples(st_prob, ed_prob, top_n=5, prob_thd=None, tensor_type=\"torch\"):\n if tensor_type == \"torch\":\n st_prob, ed_prob = st_prob.data.numpy(), ed_prob.data.numpy()\n product = np.einsum(\"bm,bn->bmn\", st_prob, ed_prob)\n # (N, L, L) the lower part becomes zeros, start_idx < ed_idx\n upper_product = np.triu(product, k=1)\n return find_max_triples_from_upper_triangle_product(upper_product, top_n=top_n, prob_thd=prob_thd)","def max_value(tree):\n max_utility = float(\"-inf\")\n \n if (is_terminal(tree)):\n return tree\n else:\n #options = []\n for node in tree:\n #options.append(max_value(node))\n max_utility = max(max_utility, min_value(node))\n return max_utility","def most_probable_class(text, weights):\n\n pos_weights = weights['positive']\n neg_weights = weights['negative']\n neu_weights = weights['neutral']\n features = calculate_features(text)\n pos_numerator = 0.0\n neg_numerator = 0.0\n neu_numerator = 0.0\n denominator = 0.0\n for f in features:\n if f in pos_weights and f in neg_weights and f in neu_weights:\n pos_numerator += pos_weights[f] * features[f]\n neg_numerator += neg_weights[f] * features[f]\n neu_numerator += neu_weights[f] * features[f]\n denominator += pos_numerator + neg_numerator + neu_numerator\n else:\n pos_numerator += 0\n neg_numerator += 0\n neu_numerator += 0\n denominator += pos_numerator + neg_numerator + neu_numerator\n\n pos_prob = (\"positive\", exp(pos_numerator))# /exp(denominator))\n neg_prob = (\"negative\", exp(neg_numerator))# /exp(denominator))\n neu_prob = (\"neutral\", exp(neu_numerator))# /exp(denominator))\n return max(neu_prob, neg_prob, pos_prob, key=lambda x: x[1])","def MaxParsimonyNoTable(X, Tree, tip_row_dict, do_tips=False, naming=False):\n # 2 represents {0,1} set\n sp_to_arr = lambda sp_arr: np.array(sp_arr.todense().astype(np.int8))[0]\n wrap = lambda x: sp_to_arr(X[tip_row_dict[x.name]]) if x.is_leaf() and not do_tips else sp_to_arr(x.genotype)\n tree_len = 0\n for _ in Tree.traverse(): tree_len += 1\n for i, node in tqdm.tqdm(enumerate(Tree.traverse('postorder')), total=tree_len,\n desc='Ancestral Reconstruction: 1st pass'):\n if node.is_leaf():\n if not do_tips:\n node.genotype = X[tip_row_dict[node.name]]\n continue\n if naming: node.name = i\n children = [wrap(c) for c in node.children]\n res = children[0].copy()\n eq = np.equal(*children)\n res[children[0] == 2] = children[1][children[0] == 2] # 2 is the union {0,1}\n res[children[1] == 2] = children[0][children[1] == 2]\n res[(children[0] != 2) & (children[1] != 2) & ~eq] = 2\n node.genotype = sp.csr_matrix(res)\n\n post = Tree.traverse('preorder')\n root = next(post)\n root.random = (wrap(root) == 2)\n root.genotype[root.genotype == 2] = np.random.choice([1, 0], size=(root.genotype == 2).sum())\n for node in tqdm.tqdm(post, total=tree_len - 1, desc='Ancestral Reconstruction: 2nd pass'):\n if node.is_leaf(): continue\n parent_ = wrap(node.up)\n node_ = wrap(node)\n res = node_.copy()\n res[node_ == 2] = parent_[node_ == 2]\n node.random = (node.up.random) & (node_ == 2) # these are unstable positions - will not be counted\n node.genotype = sp.csr_matrix(res)\n\n return Tree","def best_sequence_brute(self, T, pos, psi, phi):\n ms = self.get_all_tag_seq(len(T))\n log_scores = tr.zeros(len(ms), dtype=tr.float64)\n for i in range(len(ms)):\n log_scores[i] = self.log_score(T, pos, ms[i], psi, phi)\n best = ms[tr.argmax(log_scores)]\n tags = []\n for i in range(len(T)):\n tags.append(self.get_tag_index(best[i]))\n return tags","def findMaximal(freqSet):","def most_discriminating( features_df, labels_df, top=5):\n \n columns = features_df.shape[1]\n labels_df = labels_df[['file', 'candy_id']].set_index('file')\n qualities = np.zeros(columns)\n \n _left = 0\n _right = 1\n\n _c = 0\n _h = 1\n\n # globals\n cases = float(labels_df['candy_id'].count()) # total cases\n\n p_c_A = (labels_df['candy_id'] == 0).sum() / cases\n p_h_A = 1.0 - p_c_A\n\n\n for feature in range(columns):\n\n branch_cases = np.zeros(2) # total on each branch\n pi = np.zeros(2) # proportion on each branch\n\n split = np.array([\n #c, h\n [0, 0], #left\n [0, 0] #right\n ])\n\n for index, value in features_df[feature].iteritems():\n split[value][labels_df.loc[index][0]] += 1\n\n branch_cases[_left] = split[_left].sum()\n branch_cases[_right] = split[_right].sum()\n \n if branch_cases[_left] == 0.0 or branch_cases[_right] == 0.0:\n qualities[feature] = 0\n continue\n \n pi[_left] = branch_cases[_left] / cases\n pi[_right] = branch_cases[_right] / cases\n\n p_c_B = split[_left][_c] / branch_cases[_left]\n p_h_B = split[_left][_h] / branch_cases[_left]\n\n p_c_C = split[_right][_c] / branch_cases[_right]\n p_h_C = split[_right][_h] / branch_cases[_right]\n\n gini_tree = 1.0 - (math.pow(p_c_A, 2) + math.pow(p_h_A, 2))\n\n gini_left = 1.0 - (math.pow(p_c_B, 2) + math.pow(p_h_B, 2))\n gini_right = 1.0 - (math.pow(p_c_C, 2) + math.pow(p_h_C, 2))\n\n quality = gini_tree - pi[_left] * gini_left - pi[_right] * gini_right\n\n qualities[feature] = quality\n return list(reversed(qualities.argsort()))[:top]","def decode(self, input):\n S = [s[0] for s in self.ngram_counts[0].keys()] # Set of tags\n _S = [s[0] for s in self.ngram_counts[0].keys()]\n _S.append('*') # _S includes '*' tag\n X = ['*'] # X stores each sentence. X[0] = '*', X[i] = xi\n for l in input:\n x = l.strip()\n if x: # Word\n X.append(x)\n else: # End of a sentence\n n = len(X) - 1 # the length of the sentence\n pi = defaultdict(float) # DP table PI\n bp = {} # back pointer\n\n # Initialize DP table\n for u in _S:\n for v in _S:\n pi[tuple([0, u, v])] = 0\n pi[tuple([0, '*', '*'])] = 1\n\n # Viterbi algorithm\n for k in xrange(1, n + 1):\n for u in _S:\n for v in S: # v will not be '*' \n max_score = 0\n tag = None\n for w in _S:\n if sum([self.emission_counts[tuple([y, X[k]])] for y in S]) < 5: # If the word X[k] is rare word or unseen word in the training corpus,\n x = symbolize(X[k], self.symbolize_option) # use RARE word probability\n else:\n x = X[k]\n try:\n score = pi[tuple([k-1, w, u])] * self.q(v, w, u) * self.e(x, v)\n if max_score < score:\n max_score = score\n tag = w\n except:\n pass\n pi[tuple([k, u, v])] = max_score # Update DP table entry\n bp[tuple([k, u, v])] = tag\n\n # Find tag sequence\n Y = ['*'] # Y stores tag sequence for X. Y[0] = '*', Y[i] = yi\n Y.extend(n * [None])\n max_score = None\n tag = None\n for u in _S:\n for v in _S:\n if self.ngram_counts[1][tuple([u, v])]:\n score = pi[tuple([n, u, v])] * self.q('STOP', u, v)\n if max_score is None or max_score < score:\n max_score = score\n tag = [u, v]\n Y[n-1] = tag[0]\n Y[n] = tag[1]\n for k in xrange(n - 2, 0, -1):\n Y[k] = bp[tuple([k + 2, Y[k + 1], Y[k + 2]])]\n\n # Write result\n prev = '*'\n for k in xrange(1, n + 1):\n print X[k], Y[k], log(pi[tuple([k, prev, Y[k]])])\n prev = Y[k]\n print ''\n\n X = ['*'] # set for the next sentence","def optimize(seqlen, trees, nstates=2):\n num_params = nstates * (nstates + 1)\n guess = np.log(np.random.rand(num_params))\n # guess = np.log(np.array([0.5, 0.5, 0.13, 0.37, 0.37, 0.13,\n # 0.32, 0.18, 0.18, 0.32, 0.95, 0.05, 0.05, 0.95]))\n i, t, s = unfold_params(guess, nstates=nstates)\n i = np.log(np.ones(nstates) / nstates)\n i, t = normalize(i), normalize(t)\n\n i = np.log(np.array([0.5, 0.5]))\n # t = np.log(np.array([\n # [1 - 0.01, 0.01],\n # [0.01, 1 - 0.01]\n # ]))\n t = np.log(np.array([\n [0.5, 0.5],\n [0.5, 0.5]\n ]))\n s = 1.0 / trees[0].tot_branch_len()\n\n guess = np.concatenate((i, np.ndarray.flatten(t), [1]))\n res = minimize(getProb, guess, args=(nstates, seqlen, trees), method=\"L-BFGS-B\",\n options={\"maxiter\": 250, \"disp\": True},\n bounds=(([(np.NINF, 0)] * num_params) + [(0, np.Inf)]))\n i, t, s = unfold_params(res.x, nstates=nstates)\n i, t = normalize(i), normalize(t)\n print(forward(seqlen, t, i, (trees * s)))\n return np.exp(i), np.exp(t), s","def predict_bagging_example(x, h_ens):\r\n arr = []\r\n for y in h_ens:\r\n \t# calls predict example repeatedly and stores them in an array\r\n tst_pred = predict_example(x, h_ens[y][1])\r\n arr.append(tst_pred)\r\n # returning the maximum voted\r\n predict_egz = max(set(arr), key=arr.count)\r\n return predict_egz","def find_B(self):\n max_lb = 0\n for arc in self.arcs():\n lb = self.arc_info[arc[0]]['lower_bound']\n max_lb = max(max_lb, lb)\n n = len(self)\n m = len(list(self.edges()))\n return((m - n + 2)*max_lb)","def compute_edge_logits(self):","def Viterbi(words:Sequence[str], train_bag:Sequence[Tuple[str, str]]=train_tagged_words)-> Sequence[Tuple[str, str]]:\n state = []\n tags_set = list(set([pair[1] for pair in train_bag]))\n\n for key, word in enumerate(words):\n # initialise list of probability column for a given observation\n p = []\n for tag in tags_set:\n if key == 0:\n transition_p = tags_df.loc['.', tag]\n else:\n transition_p = tags_df.loc[state[-1], tag]\n\n # compute emission and state probabilities\n emission_p_parts = word_given_tag(word, tag)\n emission_p = emission_p_parts[0]/emission_p_parts[1]\n state_probability = emission_p * transition_p\n p.append(state_probability)\n\n p_max = max(p)\n # getting state for which probability is maximum\n state_max = tags_set[p.index(p_max)]\n state.append(state_max)\n return list(zip(words, state))","def viterbi(prob_matrix):\n TINY = 1e-6 # to avoid NaNs in logs\n\n # if prob_matrix is 1D, make it 2D\n if len(np.shape(prob_matrix)) == 1:\n prob_matrix = [prob_matrix]\n \n length = len(prob_matrix)\n\n probs = np.zeros_like(prob_matrix)\n backpt = np.ones_like(prob_matrix, dtype=np.int32) * -1\n \n for i in [0,1,2,3,4]:\n probs[0][i] = np.log(prob_matrix[0][i]+TINY)\n \n # {B, M, E, S} <=== 0:begin, 1:middle, 2:end, 3:single\n for t in range(1, length):\n # E, S -> B | B, M -> M | B, M -> E | E, S -> S\n previous_of = [[0,0], [3,4], [1,2], [1,2], [3,4]]\n for i in range(5):\n prevs = previous_of[i]\n max_id = prevs[np.argmax([probs[t-1][prevs[0]], probs[t-1][prevs[1]]])]\n backpt[t][i] = max_id\n probs[t][i] = np.log(prob_matrix[t][i]+TINY) + probs[t-1][max_id]\n\n seq = np.ones(length, 'int32') * -1\n #print(probs[length-1])\n seq[length-1] = np.argmax(probs[length-1])\n #print(seq[length-1])\n max_prob = probs[length-1][seq[length-1]]\n for t in range(1, length):\n seq[length-1-t] = backpt[length-t][seq[length-t]]\n \n return seq","def max_diffs(state):\n # your code here\n return best_action(state, pig_actions, Q_pig, win_diff)","def max_ntf(self):\n deg_diffs = set()\n for k in self.get_fd_part_j(self.id):\n deg_diff = (self.degree(k) - self.degree(self.id)) % 6\n deg_diffs.add(deg_diff)\n if not (deg_diffs <= {0, 1}):\n return self.get_prp_j(self.id)\n else:\n max_lex_set = constants.BOTTOM\n for k in self.get_fd_part_j(self.id):\n max_lex_set = self.max_lex(max_lex_set, self.get_prp_j(k)[1])\n return (self.mod_max(), max_lex_set)","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 _height1(self): #works but n^2 time\n return max(self.depth(p) for p in self.positions() if self.is_leaf(p))","def get_relevances(self):\n\n # Backpropagate softmax value\n # relevances = [tf.nn.softmax(self.activations[0])*tf.cast(self.y, tf.float32)]\n\n # Backpropagate a value from given labels y\n relevances = [tf.cast(self.y, tf.float32)]\n\n loc_poly = [pol for pol in self.polynomials]\n loc_pooling = [p for p in self.model.p]\n print(\"\\n Relevance calculation:\")\n for i in range(1, len(\n self.activations)): # start from 1 (not 0) penultimate activations since relevances already contains logits.\n name = self.activations[i - 1].name.split('/')\n if 'logits' in name[0] or 'fc' in name[0]:\n print(\"\\tFully connected:\", name[0])\n relevances.append(self.prop_fc(name[0], self.activations[i], relevances[-1]))\n elif 'flatten' in name[0]:\n print(\"\\tFlatten layer:\", name[0])\n relevances.append(self.prop_flatten(self.activations[i], relevances[-1]))\n # print(\"\\n\")\n elif 'pooling' in name[1]:\n # TODO: incorporate pooling type and value into name\n print(\"\\tPooling:\", name[0] + \" \" + name[1])\n p = loc_pooling.pop()\n relevances.append(self.prop_max_pool(self.activations[i], relevances[-1], ksize=[1, p, 1, 1],\n strides=[1, p, 1, 1]))\n elif 'conv' in name[0]:\n if len(loc_poly) > 1:\n print(\"\\tConvolution: \", name[0], \"\\n\")\n relevances.append(self.prop_gconv(name[0], self.activations[i], relevances[-1],\n polynomials=loc_poly.pop()))\n else:\n print(\"\\tConvolution, the first layer:\", name[0], \"\\n\")\n relevances.append(self.prop_gconv_first_conv_layer(name[0], self.activations[i], relevances[-1],\n polynomials=loc_poly.pop()))\n else:\n raise 'Error parsing layer'\n\n return relevances","def puct_choice(node):\n return np.argmax(puct_distribution(node))","def viterbi_score(confusion_networks):\n for confusion_network in confusion_networks:\n prev, score = [-infinity] * len(confusion_network), [-infinity] + [0.0] * len(confusion_network)\n for t in range(0, len(confusion_network)): # t: words in the sentence (\"bfs\")\n prev, score = score, prev\n for j in range(0, len(confusion_network[t])): # Iterates deep-first in a CN position (\"dfs\")\n score[j] = max([prev[i] +\n confusion_network[i][j][2]\n for i in range(0, len(confusion_network[t]))])\n return max([score[i] for i in range(1, len(confusion_network[t]))])","def analyze_belief_strength_with_bias(self, G):\r\n n = []\r\n nbs_list = []\r\n for node in G.nodes: #cycles through the nodes of the graph to mine the attributes\r\n n.append(node) #appends each node to a list that will be put into a dictionary\r\n pbs_list = []\r\n og_bs = G.nodes[node]['belief_strength'] #mines the numerical value for a nodes belief strength, from a pre-set node attribute\r\n unc = G.nodes[node]['uncertainty'] #mines the numerical value for a nodes belief uncertainty, from a pre-set node attribute\r\n prob = G.nodes[node]['probability']\r\n for pre in G.predecessors(node):\r\n ew = G.edges[pre, node]['weight'] #mines the numerical value of an edge's weight, from a pre-set edge attribute\r\n pre_bs = G.nodes[pre]['belief_strength'] #mines the numerical value for a predecessors belief strength, from a pre-set node attribute\r\n x = ew * pre_bs #determines how much a node values its neighbor's opinion.\r\n pbs_list.append(x) #puts all values for predecessor belief strangths in a list\r\n if len(pbs_list) == 0:\r\n nbs = og_bs\r\n nbs = int(nbs)\r\n else:\r\n apbs = sum(pbs_list)/len(pbs_list) #calculates the average predecessor belief strength value for a node\r\n if apbs*og_bs > 0:\r\n if apbs > 0:\r\n nbs = min(og_bs + (0.1*prob*unc*apbs), 100)\r\n else:\r\n nbs = max(og_bs + (0.1*prob*unc*apbs), -100)\r\n nbs = int(nbs)\r\n else:\r\n nbs = og_bs\r\n nbs = int(nbs)\r\n nbs_list.append(nbs) #the new belief strengths are appended to a list that will be put into adictionary\r\n change = dict(zip(n, nbs_list)) #creates a dictionary from two lists which stores the nodes as keys and their new belief strengths as values\r\n print(change)\r\n return change #this will be used to update the list in a different function\r","def score_max_depths(graph, max_depths):\n ###TODO\n pass","def tag(self, tokens):\n numRows = len(self.tags)\n numCols = len(tokens)\n # initialize tables for dynamic programming\n table = array([[0] * numCols] * numRows, dtype=float32)\n trace = array([[None] * numCols] * numRows)\n \n # fill in the base cases, i.e. the first column\n for row in range(numRows):\n currentTag = self.tags[row]\n currentWord = tokens[0] if tokens[0] in self.vocab else '<OOV>'\n table[row][0] = self.tag_tag_probs['<START>'].prob(currentTag) * self.tag_word_probs[currentTag].prob(currentWord)\n trace[row][0] = '<START>'\n \n # fill the rest of the table\n # iterate through by columns\n for col in range(1, numCols):\n for row in range(numRows):\n currentTag = self.tags[row]\n currentWord = tokens[col] if tokens[col] in self.vocab else '<OOV>'\n maxProbability = 0.0;\n maxPrevRow = 0\n \n # iterate through the previous column and find the maximum probability\n # as well as the previous tag that led to the maximum probability\n for prevRow in range(numRows):\n prevTag = self.tags[prevRow]\n probability = table[prevRow][col-1] * self.tag_tag_probs[prevTag].prob(currentTag) * self.tag_word_probs[currentTag].prob(currentWord)\n if probability > maxProbability:\n maxProbability = probability\n maxPrevRow = prevRow\n \n table[row][col] = maxProbability\n trace[row][col] = maxPrevRow\n \n returnList = []\n # retrace and construct the tag list\n maxIndex = argmax(table, axis=0)[-1]\n # insert the last (token, tag) pair\n returnList.insert(0, (tokens[-1], self.tags[maxIndex]))\n # loop through the trace table and prepend each (token, tag) pair\n i = numCols - 1\n index = trace[maxIndex][numCols-1]\n while i > 0:\n returnList.insert(0, (tokens[i-1], self.tags[index]))\n i -= 1\n index = trace[index][i]\n \n return returnList","def _decode_by_maxprob(self):\n self.best_path = np.zeros(self.N, dtype=np.int)\n P = self.prob.copy()\n \n for i in range(self.N):\n idx = np.unravel_index(np.argmax(P, axis=None), P.shape)\n self.best_path[idx[0]] = idx[1]\n P[idx[0],:] = 0\n P[:,idx[1]] = 0","def worst_B(Ag):\n bottom = 0\n for i in range(len(Ag)):\n etop = np.max(cf.TD20[int(Ag[i]) - 1])\n bottom += etop\n return bottom","def greedy_learn(self,node,db,labels,ids):\n if node.depth >= self.maxdepth or len(ids) <= self.minexamples:\n #terminate recursion\n node.pick_best_label(db,labels,ids)\n err = misclassification_error([labels[id] for id in ids])\n if err > 0:\n print \"Reached a leaf and had to make some sacrifices, cost\",err\n print \" depth\",node.depth\n print \" labels\",[labels[id] for id in ids]\n return err\n\n features = self.feature_subset(node,db,labels,ids)\n cost = node.pick_best_split(db,labels,ids,features)\n \n #do a split\n if node.type == 'v':\n #base case: no misclassifications\n \"\"\"\n if cost>0:\n print \"greedy_learn: Warning, pick_best_split indicates a leaf but the cost is nonzero\"\n print \"cost=\",cost,\"misclassification=\",misclassification_error([labels[id] for id in ids])\n print \"# of ids:\",len(ids)\n for i in ids:\n print \"id\",i,\",\",\n for k in range(db.numFeatures()):\n if db[k,i] != None:\n print k,\"=\",db[k,i],\",\",\n print \"label\",labels[i]\n raw_input()\n \"\"\"\n return 0\n elif node.type == 's':\n #print \"Picked feature\",node.feature,\"split\"\n #do a discrete split\n node.children = dict()\n #select sub-indices\n Eids = defaultdict(list)\n noneids = []\n for id in ids:\n v = db[node.feature,id]\n if v is None:\n #item doesn't exist, it's a missing value\n noneids.append(id)\n else:\n Eids[v].append(id)\n #print \" split sizes:\",[len(x) for x in Eids.values()]\n #print \" None ids:\",len(noneids)\n ids = None\n errors = 0\n for v,vids in Eids.iteritems():\n #recurse\n c = DecisionTreeNode(node)\n #print \"Recursing on value\",v\n #print \" ids:\",vids\n errors += self.greedy_learn(c,db,labels,vids+noneids)\n node.children[v] = c\n if c.depth > self.deepest:\n self.deepest = c.depth\n print \"Decision tree learner: Reached node with depth\",self.deepest\n return errors\n else:\n #do an inequality split\n assert node.type == 'i'\n #print \"Picked feature\",node.feature,\"inequality value\",node.value,\"cost\",cost\n leftids = []\n rightids = []\n for id in ids:\n if db[node.feature,id] is not None:\n if db[node.feature,id] <= node.value: leftids.append(id)\n else: rightids.append(id)\n else:\n leftids.append(id)\n rightids.append(id)\n if len(rightids) == len(ids) or len(leftids) == len(ids):\n #due to missing values, this split is useless\n errors = misclassification_error([labels[id] for id in ids])\n print \"useless split on feature\",node.feature,\"value\",node.value,\"misclassification error\",errors\n print \"Left size\",len(leftids),\"right size\",len(rightids)\n raw_input()\n node.pick_best_label(db,labels,ids)\n return errors\n #clear memory associated with ids list\n del ids[:]\n ids = None\n #print \"Left size\",len(leftids),\"right size\",len(rightids)\n c1 = DecisionTreeNode(node)\n c2 = DecisionTreeNode(node)\n #left side\n errors = self.greedy_learn(c1,db,labels,leftids)\n #right side\n errors += self.greedy_learn(c2,db,labels,rightids)\n #restore index\n node.children = {0:c1,1:c2}\n if c1.depth > self.deepest:\n self.deepest = c1.depth\n print \"Decision tree learner: Reached node with depth\",self.deepest\n return errors","def build(self):\n # weights to apply to training samples, updated on each\n # iteration of the boosting algo, normalised to 1\n sigWeights = np.ones(self.nSig, dtype=float)\n bkgWeights = np.ones(self.nBkg, dtype=float)\n reweight = 1.0/(np.sum(sigWeights)+np.sum(bkgWeights))\n sigWeights *= reweight\n bkgWeights *= reweight \n\n # Weight of each tree, strong classifers have higher weight\n self.treeWeights = np.zeros(self.ntrees, dtype=float)\n\n for i in xrange(self.ntrees):\n\n # build new tree\n newTree = Tree()\n newTree.load(self.sigData,self.bkgData,weights=(sigWeights,bkgWeights))\n newTree.build()\n self.dTrees.append(newTree) \n\n # evaluate trees\n # keep track of each event\n err = 0.0\n sigWrong = np.zeros(self.nSig)\n bkgWrong = np.zeros(self.nBkg)\n\n for j in range(self.nSig):\n if newTree.classify(np.array((self.sigData[j,])))<0:\n sigWrong[i]=1\n err+=sigWeights[j]\n\n for j in range(self.nBkg):\n if newTree.classify(np.array((self.bkgData[j,])))>0:\n bkgWrong[i]=1\n err+=bkgWeights[j]\n\n alpha = self.beta*math.log((1.0-err)/err)\n print err,alpha\n corFactor = math.exp(-alpha)\n wrongFactor = math.exp(alpha)\n\n if (err<1e-20 or err >= 0.5):\n print \"SOEMTHING WRONG!!\"\n\n self.treeWeights[i] = alpha\n\n # reweight training samples\n for j in range(self.nSig):\n if sigWrong[j]:\n sigWeights[j]*=wrongFactor\n else :\n sigWeights[j]*=corFactor\n\n for j in range(self.nBkg):\n if bkgWrong[j]:\n bkgWeights[j]*=wrongFactor\n else :\n bkgWeights[j]*=corFactor\n\n # normalise weights\n reweight = 1.0/(np.sum(sigWeights)+np.sum(bkgWeights))\n sigWeights *= reweight\n bkgWeights *= reweight","def maximal_chain_binary_trees(self):\n for it in self.maximal_chain_tamari_intervals():\n yield it.lower_binary_tree()","def evaluate(self):\n max_decomp = 0\n max_value = 0.0\n v = 0.0\n # find the largest log-value in children\n # for computing the log-exponentials\n for d in self.__children:\n # should be Product Node\n node = self.__children[d]\n value = node.getLogValue()\n if value == Node.ZERO:\n continue\n if max_decomp == 0 or value > max_value:\n max_decomp = d\n max_value = value\n\n if max_decomp == 0:\n self.setLogValue(Node.ZERO)\n\n for d in self.__children:\n if d not in self.__children_counts:\n continue\n cnts = self.__children_counts[d]\n node = self.__children[d]\n value = node.getLogValue()\n if value == Node.ZERO:\n continue\n v += cnts * np.exp(value - max_value)\n self.setLogValue(np.log(v / self.__counts) + max_value)","def most_frequent_eval(test_set, pred_tags):\n gold_tag_seqs = []\n pred_tag_seqs = []\n for sent in test_set:\n words, true_tags = zip(*sent)\n gold_tag_seqs.append(true_tags)\n\n ### YOUR CODE HERE\n DEFAULT_TAG = 'O'\n \n pred_tags_list = []\n for word in words:\n tag = DEFAULT_TAG\n if word in pred_tags:\n tag = pred_tags[word]\n pred_tags_list.append(tag)\n pred_tag_seqs.append(tuple(pred_tags_list)) \n ### END CODE HERE\n\n return evaluate_ner(gold_tag_seqs, pred_tag_seqs)","def hmm_mle(self):\n\n b = Baseline(self.pos_tags, self.words, self.training_set)\n self.e = b.e\n self.q = {tag: 0 for tag in self.pos_tags}\n first_tag = [sentence[0][1] for sentence in self.training_set]\n for tag in first_tag:\n self.q[tag] += 1\n for tag in self.pos_tags:\n self.q[tag] /= len(first_tag)\n\n t = {}\n tag_count = {}\n # initialize all to zero\n for tag1 in self.pos_tags:\n tag_count[tag1] = 0\n for tag2 in self.pos_tags:\n t[tag1, tag2] = 0\n\n # count i before j\n for sentence in self.training_set:\n sentence_tags = sentence[0]\n for i in range(len(sentence[0])-1):\n t[sentence_tags[i+1], sentence_tags[i]] += 1\n tag_count[sentence_tags[i]] += 1\n # tag_count[sentence_tags[-1]]+=1\n self.t = t\n # divide by some of tag1 count\n for tag1 in self.pos_tags:\n for tag2 in self.pos_tags:\n if tag1 == \"END\" or tag2 ==\"START\":\n continue\n t[tag2, tag1] /= tag_count[tag1]\n\n return self.q, self.e, self.t","def viterbi(sent, dqml, eqml, S, V_CASE=-1):\n\n if type(sent) is list:\n sent_words = sent\n else:\n sent_words = word_tokenize(sent)\n n = len(sent_words)\n\n # define and initialize PI table\n pi = defaultdict(Counter)\n pi[0]['*'] = 1\n bp = {}\n\n for k in range(1, n+1):\n bp[k] = {}\n for v in S:\n eml = compute_eml(V_CASE, eqml, k, sent_words, v)\n if k-1 is 0: # w e S_0 -> w = '*'\n qmlr = compute_qml(dqml, v, '*')\n pival = pi[0]['*'] * qmlr * eml\n pi[k][v] = pival\n bp[k][v] = '*'\n else: # for w e S_k, S_k = S\n max_S = None\n max_w = -1\n for w in S:\n qmlr = compute_qml(dqml, v, w)\n currmax = pi[k-1][w] * qmlr * eml\n if currmax > 0 and currmax > max_w:\n max_w = currmax\n max_S = w\n # if word is unknown use tag 'NN'\n if max_S is None:\n max_w = 0.0\n max_S = UNKNOWN_TAG\n pi[k][v] = max_w\n bp[k][v] = max_S\n\n # calculate y_n\n max_y = -1\n yn = None\n for v in S:\n nextmax = pi[n][v] * compute_propability('STOP', v, dqml)\n if nextmax > max_y:\n max_y = nextmax\n yn = v\n\n # calculate y_n-1....y1\n yk1 = yn\n tagSequence = list()\n tagSequence.append(yn)\n for k in range(n-1,0,-1):\n yk = bp[k+1][yk1]\n tagSequence.append(yk)\n yk1 = yk\n\n tagSequence.reverse()\n return tagSequence","def max_evidence(self):\n self.A = np.linalg.inv(self.Sn)\n A_eigval = np.linalg.eigvals(self.A)\n gamma = 0\n for i in range(len(A_eigval)):\n gamma += A_eigval[i]/(self.alpha + A_eigval[i])\n new_alpha = gamma/(self.mn.T@self.mn)\n\n sum = 0\n for i in range(self.n):\n sum +=(self.t[i]-self.mn.T@self.design_matrix[i])**2\n new_beta = 1/((1/(self.n-gamma))*sum)\n\n return new_alpha, new_beta","def greedy_build(nodes, priors=None, cutoff=200, considered=set(), uniq='', targets=[]):\n\n\t# Tracks frequency of states for each character in nodes\n\tcharacter_mutation_mapping = defaultdict(int)\n\n\t# G models the network that is returned recursively\n\tG = nx.DiGraph()\n\n\troot = root_finder(nodes)\n\n\t# Base case check for recursion, returns a graph with one node corresponding to the root of the remaining nodes\n\tif len(nodes) <= cutoff or len(nodes) == 1:\n\t\troot = root_finder(nodes)\n\t\tG.add_node(root)\n\t\treturn G, [[root, nodes]]\n\n\t# Accounting for frequency of mutated states per character, in order to choose the best split\n\tfor node in nodes:\n\t\tnode_list = node.split(\"_\")[0].split('|')\n\t\tfor i in range(0, len(node_list)):\n\t\t\tchar = node_list[i]\n\t\t\tif char != '0' and char != '-':\n\t\t\t\tcharacter_mutation_mapping[(str(i), char)] += 1\n #if char != '0':\n # if char == \"-\":\n # character_mutation_mapping[(str(i), char)] -= 1\n # else:\n # character_mutation_mapping[(str(i), char)] += 1\n\n\t# Choosing the best mutation to split on (ie character and state)\n\tcharacter, state = 0, 0\n\tmax_cost = 0\n\n\tmin_prior = 1\n\tif priors:\n\t\tfor i in priors.keys():\n\t\t\tfor j in priors[i].keys():\n\t\t\t\tmin_prior = min(min_prior, priors[i][j])\n\n\tfor i,j in character_mutation_mapping:\n\t\tif not (i,j) in considered:\n\t\t\tif not priors:\n\t\t\t\tif max_cost < character_mutation_mapping[(i, j)]:\n\t\t\t\t\tmax_cost = character_mutation_mapping[(i, j)]\n\t\t\t\t\tcharacter, state = i, j\n\t\t\telse:\n\t\t\t\tif j not in priors[int(i)]:\n\t\t\t\t\tpriors[int(i)][j] = min_prior\n\t\t\t\tif max_cost < -np.log(priors[int(i)][j]) * character_mutation_mapping[(i, j)]:\n\t\t\t\t\tmax_cost = -np.log(priors[int(i)][j]) * character_mutation_mapping[(i, j)]\n\t\t\t\t\tcharacter, state = i, j\n\tcharacter = int(character)\n\n\n\t# If there is no good split left, stop the process and return a graph with the remainder of nodes\n\tif character == 0 and state == 0:\n\t\tif len(nodes) == 1:\n\t\t\tG.add_node(nodes[0])\n\t\telse:\n\t\t\tfor i in range(0, len(nodes)):\n\t\t\t\tif nodes[i] != root:\n\t\t\t\t\tG.add_edge(root, nodes[i])\n\t\treturn G, []\n\n\t# Splitting nodes based on whether they have the mutation, don't have the mutation, or are NA('-') in that character\n\t# Right split is where nodes with the mutation go, everyone else goes to left split or NA chars\n\tleft_split, right_split, NA_chars = [], [], []\n\tright_split_temp = []\n\tleft_split_temp = []\n\tfor node in nodes:\n\t\tnode_list = node.split('|')\n\t\tif node_list[character] == state:\n\t\t\tright_split.append(node)\n\t\telif node_list[character] == '-':\n\t\t\tNA_chars.append(node)\n\t\telse:\n\t\t\tleft_split.append(node)\n\n\n\t# Seperates all nodes with NA in the character chosen to be split upon\n\t# Puts in right split or left split based on which list shares more mutated characters with this string\n\tfor node in NA_chars:\n\t\tright_split_score = 0\n\t\tleft_split_score = 0\n\t\tnode_list = node.split('|')\n\t\tnum_not_missing = len([n for n in node_list if n != \"-\"])\n\t\tfor i in range(0, len(node_list)):\n\t\t\tif node_list[i] != '0' and node_list[i] != '-':\n\t\t\t\tfor node_2 in left_split:\n\t\t\t\t\tnode2_list = node_2.split('|')\n\t\t\t\t\tif node_list[i] == node2_list[i]:\n\t\t\t\t\t\tleft_split_score += 1\n\t\t\t\tfor node_2 in right_split:\n\t\t\t\t\tnode2_list = node_2.split('|')\n\t\t\t\t\tif node_list[i] == node2_list[i]:\n\t\t\t\t\t\tright_split_score += 1\n\n\t\tavg_left_split_score = left_split_score / float(len(left_split) * num_not_missing + 1)\n\t\tavg_right_split_score = right_split_score / float(len(right_split) * num_not_missing + 1)\n\n\t\tif avg_left_split_score < avg_right_split_score:\n\t\t\tright_split_temp.append(node)\n\t\telse:\n\t\t\tleft_split_temp.append(node)\n\n\tright_split += right_split_temp\n\tleft_split += left_split_temp\n\n\t# Add character, state that split occurred to already considered mutations\n\tconsidered.add((str(character), state))\n\tG = nx.DiGraph()\n\t#splitter = str(character) + \" \" + str(state) + \" (\" + uniq + \")\"\n\tsplitter = root\n\n\t# Recursively build left side of network (ie side that did not mutation at the character with the specific state)\n\tG.add_node(splitter)\n\tleft_subproblems = []\n\tleft_network = None\n\tif len(left_split) != 0:\n\t\tleft_root = root_finder(left_split)\n\t\t# if left_root not in left_split and left_root in targets:\n\t\t# \tleft_root = left_root + \"_unique\"\n\n\t\tleft_network, left_subproblems = greedy_build(left_split, priors, cutoff, considered.copy(), uniq + \"0\", targets=targets)\n\n\t\tleft_nodes = [node for node in left_network.nodes() if left_network.in_degree(node) == 0]\n\t\tdup_dict = {}\n\t\tfor n in left_network:\n\t\t\tif n in list(G.nodes()) and n != left_root:\n\t\t\t\tdup_dict[n] = n + \"_\" + str(hashlib.md5(left_root.encode('utf-8')).hexdigest())\n\t\tleft_network = nx.relabel_nodes(left_network, dup_dict)\n\t\tG = nx.compose(G, left_network)\n\t\tif root != left_root:\n\t\t\tG.add_edge(splitter, left_root, weight=0, label=\"None\")\n\n\t# Recursively build right side of network\n\tright_network, right_subproblems = greedy_build(right_split, priors, cutoff, considered.copy(), uniq + \"1\", targets=targets)\n\tright_nodes = [node for node in right_network.nodes() if right_network.in_degree(node) == 0]\n\tright_root = root_finder(right_split)\n\n\tdup_dict = {}\n\tfor n in right_network:\n\t\tif n in list(G.nodes()) and n != right_root:\n\t\t\tdup_dict[n] = n + \"_\" + str(hashlib.md5(right_root.encode('utf-8')).hexdigest())\n\tfor n in dup_dict:\n\t\trename_dict = {n: dup_dict[n]}\n\t\tif right_network.out_degree(n) != 0:\n\t\t\tright_network = nx.relabel_nodes(right_network, rename_dict)\n\t\telse:\n\t\t\trename_dict = {n: dup_dict[n]}\n\t\t\tG = nx.relabel_nodes(G, rename_dict)\n\n\tG = nx.compose(G, right_network)\n\t# if right_root not in right_split and right_root in targets:\n\t# \tright_root = right_root + \"_unique\"\n\t#for node in right_nodes:\n\tif root != right_root:\n\t\tif not priors:\n\t\t\tG.add_edge(splitter, right_root, weight=1, label = str(character) + \": 0 -> \" + str(state))\n\t\telse:\n\t\t\tG.add_edge(splitter, right_root, weight=-np.log(priors[int(character)][state]), label=str(character) + \": 0 -> \" + str(state))\n\n\n\treturn G, left_subproblems + right_subproblems","def max_diffs(state):\n return best_action(state, pig_actions, Q_pig, win_diff)","def classify(self, instance):\n \"\"\"best stores the pointer to the sequence with maximum final probability from the Viterbi trellis.\n Iterate through the corresponding row in the backtrace_pointers matrix backwards and add its labels to best_sequence list.\n Return the reversed best_sequence list.\n \"\"\"\n backtrace_pointers = self.dynamic_programming_on_trellis(instance, False)\n best_sequence = []\n trellis = backtrace_pointers[0]\n best = trellis[:,len(instance.data)-1].argmax()\n i = best\n best_sequence = [self.labels[best]]\n for j in xrange(len(instance.data)-1, 0, -1):\n i = backtrace_pointers[1][i][j]\n best_sequence.append(self.labels[i])\n return best_sequence[::-1]","def max_gain(self):\n if self.val1:\n val1_gain_tuple, val0_gain_tuple = self.val1.max_gain(), self.val0.max_gain()\n if val1_gain_tuple.gain > val0_gain_tuple.gain:\n return val1_gain_tuple\n else:\n return val0_gain_tuple\n elif self.attributes:\n filtered_data = filter_data(self.data,self.ancestors)\n max_attribute, max_gain = max([(attribute,\n self.heuristic(self,attribute)) for attribute in self.attributes],\n key = lambda x: x[1])\n return gain_tuple(self, max_attribute, max_gain)\n return gain_tuple(None, '', 0)","def propagate(self, game_state, maximizing, max_hitherto, depth, max_depth):\n \n self.moves += 1\n \n\n if depth >= max_depth:\n rating = self.rate_game_state(game_state)\n return (rating, None, None)\n \n else:\n \n color = self.color if maximizing else (1 if self.color==0 else 0)\n\n self.timer.start(\"\")\n choices = []\n for line in self.get_all_lines(game_state, color):\n for vector in LEGAL_VECTORS:\n\n new_game_state = self.get_new_game_state(game_state, line, vector, color)\n if new_game_state is not None:\n choices.append( (new_game_state, self.rate_game_state(new_game_state), line, vector) )\n\n \n\n choices = sorted(choices, key=lambda x: x[1], reverse=maximizing)\n \n final_choice = choices[0][1:]\n del choices[0]\n\n for choice in choices:\n\n new_game_state, estimated_rating, line, vector = choice\n actual_rating, _, _ = self.propagate(new_game_state, not maximizing, final_choice[0], depth+1, max_depth)\n \n if maximizing:\n if final_choice[0] < actual_rating:\n final_choice = (actual_rating, line, vector)\n\n else: #minimizing\n if (max_hitherto is not None) and (actual_rating < max_hitherto): return (actual_rating, line, vector)\n\n if actual_rating < final_choice[0]:\n final_choice = (actual_rating, line, vector)\n\n return final_choice","def extract_belief(ontology, state, threshold=0.3):\n\n \"\"\"\n need to check if the value at argmax is bigger than a threshold\n \"\"\"\n\n request_idx = np.argmax([item[1] for item in state[\"request\"].items()])\n frequency_idx = np.argmax([item[1] for item in state[\"frequency\"].items()])\n illness_type_idx = np.argmax([item[1] for item in state[\"type\"].items()])\n symptom_idx = np.argmax([item[1] for item in state[\"symptom\"].items()])\n escalation_idx = np.argmax([item[1]\n for item in state[\"escalation\"].items()])\n duration_idx = np.argmax([item[1] for item in state[\"duration\"].items()])\n confirmation_idx = np.argmax([item[1]\n for item in state[\"confirmation\"].items()])\n\n # then it is neccessary to map the indices back to words\n\n request = is_plausible(state[\"request\"].items(\n ), ontology, \"request\", request_idx, threshold)\n frequency = is_plausible(\n state[\"frequency\"].items(), ontology, \"frequency\", frequency_idx, threshold)\n illness_type = is_plausible(\n state[\"type\"].items(), ontology, \"type\", illness_type_idx, threshold)\n symptom = is_plausible(state[\"symptom\"].items(\n ), ontology, \"symptom\", symptom_idx, threshold)\n\n escalation = is_plausible(state[\"escalation\"].items(\n ), ontology, \"escalation\", escalation_idx, threshold)\n\n duration = is_plausible(state[\"duration\"].items(\n ), ontology, \"duration\", duration_idx, threshold)\n confirmation = is_plausible(state[\"confirmation\"].items(\n ), ontology, \"confirmation\", confirmation_idx, threshold)\n\n if request is not None:\n request = \"request \"+request\n if frequency is not None:\n frequency = \"frequency \"+frequency\n if illness_type is not None:\n illness_type = \"type \"+illness_type\n if symptom is not None:\n symptom = \"symptom \"+symptom\n if escalation is not None:\n escalation = \"escalation \"+escalation\n if duration is not None:\n duration = \"duration \"+duration\n if confirmation is not None:\n confirmation = \"confirmation \"+confirmation\n values = [request, frequency, illness_type,\n symptom, escalation, duration, confirmation]\n values = [val for val in values if val is not None]\n return sorted(values)","def _height1(self): # works, but O(n^2) worst-case time\n return max(self.depth(p) for p in self.positions() if self.is_leaf(p))","def _get_lip_best(self) -> float:\n pass","def bagging(x, y, max_depth, num_trees):\r\n\r\n hypotheses = {}\r\n # attribute_idx = np.array(range(data.dim))\r\n # generating attributes\r\n attributes = []\r\n cols = data.dim\r\n for i in range(cols):\r\n arr = np.unique(x[:, i])\r\n for value in arr:\r\n attributes.append((i, value))\r\n lena = len(x)\r\n # initializing weights to 1 for boosting\r\n alpha = 1\r\n w = np.ones((lena, 1), dtype=int) \r\n # iterating over j number of trees\r\n for j in range(num_trees):\r\n # generating a random array of indicies with replacement over the length of x\r\n new_array = np.random.choice(lena,size =lena,replace=True)\r\n #calling id3 over the indices of the new array\r\n tree = id3(x[new_array],y[new_array],attributes, max_depth, w)\r\n # appending to a global tree as a weighted pair\r\n hypotheses[j] = (alpha, tree)\r\n\r\n return hypotheses","def beam_search_stack_decode(self, src_sentence):\n for i in xrange(len(src_sentence)):\n for j in xrange(i+1, len(src_sentence)):\n foreign_phrase = src_sentence[i:j]\n best_phrase = \"\"\n highest_prob = float('-inf')\n for native_phrase, translation_probability in self.phrase_table[foreign_phrase]:\n prob = translation_probability + self.language_model.score(native_phrase.split())\n if prob > highest_prob:\n highest_prob = prob\n best_phrase = native_phrase\n self.heuristic_table[i][j] = (best_phrase, highest_prob)\n\n\n\n hypStacks = [ HypoStack() for i in len(src_sentence)]\n \"\"\"\n What will the cost of NULL Hypothesis be? Right now I don't know what it calculates but I'm enqueueing it with FLT_MAX\n \"\"\"\n hypStacks[0].add(Hypothesis([], '0' * len(src_sentence), 0, 0, None, self.heuristic_table, self.language_model, self.phrase_table), float('inf'))\n\n for hypStack in hypStacks:\n while len(hypStack) > 0:\n hyp = hypStack.pop()\n new_hyps = self.derive_new_hyps(hyp)\n for new_hyp in new_hyps:\n nf_new_hyp = new_hyp.covering.count('1')\n \"\"\"\n DONE\n For recombination, whenever you add a new hypothesis, look at the other hypotheses in the same stack and do this:\n – same number of foreign words translated\n – same last three English words in output\n – same last foreign word translated\n \"\"\"\n new_hyp_trigram = new_hyp.get_trigram()\n new_hyp_last_word = src_sentence[new_hyp.fp_end]\n add_new_hyp = True\n for other_hyp in hypStacks[nf_new_hyp]:\n if other_hyp.get_trigram() == new_hyp_trigram and src_sentence[other_hyp.fp_end] == new_hyp_last_word:\n if other_hyp.cost < new_hyp.cost:\n add_new_hyp = False\n break\n else:\n hypStacks[nf_new_hyp].remove(other_hyp)\n if not add_new_hyp: continue\n hypStacks[nf_new_hyp].add(new_hyp, new_hyp.cost)\n if len(hypStacks[nf_new_hyp]) > MAX_STACK_LEN:\n hypStacks[nf_new_hyp].remove_worst()\n return hypStacks[-1].pop()","def comp_probs_and_labels(self, x_feats):\n scores = sparse.dot(x_feats, self.W) + self.Wb # [l, d] x [d, m] + [m] => [l, m]\n relation_probs = T.nnet.softmax(scores)\n labels = T.argmax(scores, axis=1) # [l, m] => [l]\n return labels, relation_probs","def naiveBayesMixture(train_set, train_labels, dev_set, bigram_lambda,unigram_smoothing_parameter, bigram_smoothing_parameter, pos_prior):\n\n # TODO: Write your code here\n # return predicted labels of development set\n\n # counters for Training Phase\n ham = Counter()\n ham_bi = Counter()\n spam = Counter()\n spam_bi = Counter()\n\n for string, label in zip(train_set, train_labels):\n for i in range(len(string)):\n word = string[i]\n if i != len(string)-1:\n word_bi = string[i] + ' ' + string[i+1]\n if label == 1:\n ham_bi.update({word_bi:1})\n else:\n spam_bi.update({word_bi:1})\n if label == 1:\n ham.update({word:1})\n else:\n spam.update({word:1})\n\n ham_len = 0\n for w in ham:\n ham_len += ham[w]\n spam_len = 0\n for w in spam:\n spam_len += spam[w]\n \n hambi_len = 0\n for w in ham_bi:\n hambi_len += ham_bi[w]\n spambi_len = 0\n for w in spam_bi:\n spambi_len += spam_bi[w]\n\n # labels for Development Phase\n dev_labels = []\n # dicts for P(word|ham) and P(word|spam)\n p_ham = {}\n p_spam = {}\n p_hambi = {}\n p_spambi = {}\n\n # develop likelihoods based on dev_set\n for word in ham:\n numerator = ham[word] + unigram_smoothing_parameter\n denominator = ham_len + unigram_smoothing_parameter*(len(ham))\n p_ham[word] = numerator / denominator\n for word in spam:\n numerator = spam[word] + unigram_smoothing_parameter\n denominator = spam_len + unigram_smoothing_parameter*(len(spam))\n p_spam[word] = numerator / denominator\n\n for word_bi in ham_bi:\n numerator = ham_bi[word_bi] + bigram_smoothing_parameter\n denominator = hambi_len + bigram_smoothing_parameter*(len(ham_bi))\n p_hambi[word_bi] = numerator / denominator\n for word_bi in spam_bi:\n numerator = spam_bi[word_bi] + bigram_smoothing_parameter\n denominator = spambi_len + bigram_smoothing_parameter*(len(spam_bi))\n p_spambi[word_bi] = numerator / denominator\n \n numerator = unigram_smoothing_parameter\n denominator = ham_len + unigram_smoothing_parameter*(len(ham))\n p_ham_zero = numerator / denominator\n denominator = spam_len + unigram_smoothing_parameter*(len(spam))\n p_spam_zero = numerator / denominator\n\n numerator = bigram_smoothing_parameter\n denominator = hambi_len + bigram_smoothing_parameter*(len(ham_bi))\n p_hambi_zero = numerator / denominator\n denominator = spambi_len + bigram_smoothing_parameter*(len(spam_bi))\n p_spambi_zero = numerator / denominator\n\n for string in dev_set:\n p_words_ham = math.log(pos_prior)\n p_words_spam = math.log(1 - pos_prior)\n\n p_words_hambi = math.log(pos_prior)\n p_words_spambi = math.log(1 - pos_prior)\n \n for i in range(len(string)):\n word = string[i]\n if word in p_ham:\n p_words_ham += math.log(p_ham[word])\n else:\n p_words_ham += math.log(p_ham_zero)\n if word in p_spam:\n p_words_spam += math.log(p_spam[word])\n else:\n p_words_spam += math.log(p_spam_zero)\n\n if i != len(string)-1:\n word_bi = string[i] + ' ' + string[i+1]\n if word_bi in p_hambi:\n p_words_hambi += math.log(p_hambi[word_bi])\n else:\n p_words_hambi += math.log(p_hambi_zero)\n if word_bi in p_spambi:\n p_words_spambi += math.log(p_spambi[word_bi])\n else:\n p_words_spambi += math.log(p_spambi_zero)\n\n p_ham_mix = p_words_ham*(1-bigram_lambda) + p_words_hambi*bigram_lambda\n p_spam_mix = p_words_spam*(1-bigram_lambda) + p_words_spambi*bigram_lambda\n\n dev_labels.append(p_ham_mix >= p_spam_mix)\n\n return dev_labels","def Viterbi(self, sent):\n viterbi = defaultdict(dict)\n backpointer = defaultdict(dict)\n sent_tag = []\n pos_list = [end_token]\n viterbi['0'] = 1.0\n\n # Initialization step\n # This loop will run for all the tags of each first word (sent[1][0])(word next to <S>) in dictionary\n for tag in self.dictionary[sent[1][0]]:\n # if any sentance in our trained data starts with a word that has same tag as \"state\"\n if (start_token, tag) in self.transitions:\n viterbi[str(1)][tag] = self.transitions[(start_token, tag)] + self.emissions[(sent[1][0], tag)]\n else:\n viterbi[str(1)][tag] = -float('inf')\n backpointer[str(1)][tag] = start_token\n\n # Recursion step\n # This loop will run for rest of the tuples (word, pos) after first tuple in \"sent\"\n for i in xrange(2, len(sent)):\n # This loop will run for all the tags of each word (sent[idx][0]) in dictionary\n for tag in self.dictionary[sent[i][0]]:\n maximum_value = -float(\"inf\")\n maximum_loc = []\n # This loop will run for all the tags in previous word (sent[idx-1][0]) in dictionary\n for prev_tag in self.dictionary[sent[i - 1][0]]:\n # if any sentance in our trained data has (privious tag, current tag) or (pre_state, state) of given word\n if (prev_tag, tag) in self.transitions:\n t = viterbi[str(i - 1)][prev_tag] + self.transitions[(prev_tag, tag)]\n else:\n t = -float('inf')\n if t >= maximum_value:\n maximum_value = t\n maximum_loc = prev_tag\n\n viterbi[str(i)][tag] = maximum_value + self.emissions[(sent[i][0], tag)]\n backpointer[str(i)][tag] = maximum_loc\n\n t = end_token\n for i in xrange(1, len(sent)):\n t = backpointer[str(len(sent) - i)][t]\n pos_list.append(t)\n\n for tup in sent:\n sent_tag.append((tup[0], pos_list.pop()))\n\n #print \"viterbi:\", viterbi\n #print \"backpointer:\", backpointer\n #print \"sent_tagged\", sent_tag\n\n return sent_tag","def get_n_best(self):\n pass","def dfs_maximizing(state) :\n #print state.describe_previous_move()\n global state_evals, path, _path, _score, level, _state;\n\n level+=1\n path.append(state)\n for stt in state.generate_next_states():\n score=0\n agenda.append((stt, level))\n \n if stt.is_game_over():\n state_evals+=1\n score=stt.get_endgame_score()\n if score>_score:\n _score=score\n _path = path[0:]\n _state = stt\n if not agenda:\n\n _path.append(_state)\n return [_path, _score, state_evals];\n else:\n new_state, level=agenda.pop()\n path=path[0:level]\n level-=1\n return dfs_maximizing(new_state)","def prior_params_tree(self):\n id = {name:i for i, name in enumerate(list(self.tree.keys()))}\n n_nodes = len(id)\n dist_mx = np.zeros((n_nodes, n_nodes))\n\n for node1, edges in self.tree.items():\n for node2, dist in edges.dist:\n dist_mx[id[node1], id[node2]] = dist\n dist_mx[id[node2], id[node1]] = dist\n\n # while np.count_nonzero(dist_mx) < (n_nodes ** 2 - n_nodes):\n for _ in range(20):\n for i, j in combinations(range(n_nodes), 2):\n if dist_mx[i,j] > 0:\n continue\n row_i = dist_mx[i]\n row_j = dist_mx[j]\n value = (row_i + row_j) * (row_i > 0) * (row_j > 0)\n dist_mx[i, j] = dist_mx[j, i] = - max(np.unique(value))\n dist_mx = np.abs(dist_mx)\n\n evolve_rate = []\n for node1, node2 in combinations(self.m_cov.keys(), 2):\n mx_cov_dist = np.abs(self.m_cov[node1] - self.m_cov[node2])\n elements = mx_cov_dist[np.triu_indices(len(mx_cov_dist))]\n norm_elements = elements / dist_mx[id[node2], id[node1]]\n evolve_rate += list(norm_elements)\n\n\n\n df = np.mean([p.shape[0] for _, p in self.m_profiles.items()])\n p_theta_alpha = df/2\n # p_theta_alpha = 4\n p_theta_beta = np.percentile(evolve_rate, 75) * (p_theta_alpha - 1)\n # print(p_theta_alpha, p_theta_beta)\n return p_theta_alpha, p_theta_beta","def _find_max(self, root):\n while root.right:\n root = root.right\n return root","def max_v_greedy():\n\n S1=Spectrum.Spectrum()\n S1.add_peak(50.4,16)\n S1.add_peak(50.7,36)\n S1.add_peak(74.8,25)\n S1.add_peak(96.2,23)\n S1.pep_mass=100\n S1.euclidean_scale()\n\n S2=Spectrum.Spectrum()\n S2.add_peak(50.6,49)\n S2.add_peak(50.9,25)\n S2.add_peak(74.6,9)\n S2.add_peak(102.4,17)\n S2.pep_mass=100\n S2.euclidean_scale()\n\n score,peaks=similarity.cosine_score_max(S1,S2)\n g_score,g_peaks=similarity.cosine_score_greedy(S1,S2)\n\n assert score>=g_score, \"Maximum weighted method did not get higher score than greedy method\"\n assert peaks>=g_peaks, \"Maximum weighted method did not match more peaks than greedy method\"\n\n assert peaks==3, \"Incorrect number of peaks matched with greedy method\"\n assert math.isclose(score,0.73), \"Incorrect score with greedy method\"\n\n assert g_peaks==2, \"Incorrect number of peaks matched with maximum weighted method\"\n assert math.isclose(g_score,0.57), \"Incorrect score with maximum weighted method\"","def maxProduct2(nums):\n\n maxSubseq = nums[0]\n minSubseq = nums[0]\n res = nums[0]\n for i in range(1, len(nums)):\n if nums[i] < 0:\n minSubseq, maxSubseq = maxSubseq, minSubseq\n maxSubseq = max(nums[i], maxSubseq*nums[i])\n minSubseq = min(nums[i], minSubseq*nums[i])\n res = max(res, maxSubseq)\n return res","def maxQ(self,feat):\r\n \r\n maxQ = float('-inf')\r\n maxA = 0\r\n for a in self.actions:\r\n q = self.Q(feat,a)\r\n print(q,a)\r\n if q > maxQ:\r\n maxQ = q\r\n maxA = a\r\n return(maxQ,maxA)","def smooth_tag_model(cls, heldout_data):\n\n # bi_transition_counts = defaultdict(int)\n n = 0 # count word-tags\n e = .0001 # stopping condition\n L = [.25, .25, .25, .25] # initialize lambdas uniformly\n i = 1 # iteration\n while True:\n # E Step (Step 1)\n # Iterate through all occurring trigrams\n # in the heldout.txt data (H), i.e. minimizing\n # log likelihood\n counts = [0, 0, 0, 0]\n ratio = [0, 0, 0, 0]\n nextL = 4 * [0] # next lambda\n\n for sent in heldout_data:\n\n # Handle beginning of sentence\n t = '<s>'\n u = '<s>'\n # bi_transition_counts[t, u] += 1\n\n # if first == '<s>' and second == '<s>': # add bigram for nulls\n\n for word, tag in sent:\n v = tag\n # tri_transitions[t, u, v] += 1\n # bi_transitions[u, v] += 1\n # # if first == '<s>' and second == '<s>': # add bigram for nulls\n # # tagger._bi_transitions[('<s>', '<s>')] += 1\n # uni_transitions[v] += 1\n # cls._emissions[word, tag] += 1\n # tags.add(v)\n # words.add(word)\n # uni_words[word] += 1\n\n\n # Calculate expected counts of lambdas\n ratio = cls.calc_tag_ratio(t, u, v, L)\n\n # M-step (Step 2)\n # Calculate expected counts of lambdas, i.e. weight, taking\n # into account the number of occurrences of each trigram (cnt)\n for j in range(len(L)):\n counts[j] += ratio[j] # weight of lambda in whole equation (count)\n\n t = u\n u = v\n # n += 1\n\n # Handle end of sentence\n # tri_transitions[t, u, '</s>'] += 1\n v = '</s>'\n ratio = cls.calc_tag_ratio(t, u, v, L)\n for j in range(len(L)):\n counts[j] += ratio[j] # weight of lambda in whole equation (count)\n\n # cls._bi_transitions[first, second] += 1\n # cls._tri_transitions[second, '</s>', '</s>'] += 1\n\n\n # Update values for parameters given current distribution\n for k in range(len(L)):\n total = np.sum(counts)\n nextL[k] = counts[k] / total # next lambda\n\n # Check if lambda values have converged\n converged = True\n for l in range(len(L)):\n if np.abs(nextL[l] - L[l]) > e: # tolerance = e\n converged = False\n L = nextL\n\n # Return values if lambdas have converged\n if converged:\n break\n\n i += 1 # increment iteration counter\n\n\n return L # copy lambdas passed by reference","def naiveBayes(train_set, train_labels, dev_set, smoothing_parameter, pos_prior):\n # TODO: Write your code here\n # return predicted labels of development set\n #\n ### len(train_set) 8000, len(dev) = 5000 P(pos) = 0.8 \n #### 0.55, 4.0, 0.30 ----------- 0.766\n #### 0.25 3.5 0.3 -------------- 0.766\n print(pos_prior)\n smoothing_parameter = 3.5\n pos_total_word = 0\n neg_total_word = 0\n pos_word_dict = {}\n neg_word_dict = {}\n dicts = [neg_word_dict, pos_word_dict]\n for i, sentence in enumerate(train_set):\n\n if train_labels[i] == 1: # positive reviews\n for word in sentence:\n pos_total_word += 1 \n if word in stop_words:\n continue\n if word in pos_word_dict:\n pos_word_dict[word] += 1\n else :\n pos_word_dict[word] = 1\n\n else: # negative reviews\n for word in sentence:\n neg_total_word += 1 \n if word in stop_words:\n continue\n if word in neg_word_dict:\n neg_word_dict[word] += 1\n else :\n neg_word_dict[word] = 1\n\n\n prob = {}\n denominator_pos = pos_total_word + smoothing_parameter * (len(pos_word_dict) + 1)\n denominator_neg = neg_total_word + smoothing_parameter * (len(neg_word_dict) + 1)\n de = [denominator_neg, denominator_pos]\n\n for t, dictionary in enumerate(dicts):\n for key, value in dictionary.items():\n if key not in prob:\n prob[key] = {0 : 0, 1 : 0}\n if smoothing_parameter != 0:\n prob[key][1 - t] = -1 * np.log(smoothing_parameter / de[t]) \n # print(prob[key][1 - t])\n\n prob[key][t] = -1 * np.log((value + smoothing_parameter) / de[t]) \n \n\n revised_prob = {}\n for key, value in prob.items():\n if np.abs(value[0] - value[1]) >= 0.25:\n revised_prob[key] = value \n\n print(len(revised_prob))\n\n dev_labels = []\n num_0 = 0\n for i, sentence in enumerate(dev_set):\n pos_odd = -1 * np.log(pos_prior)\n neg_odd = -1 * np.log(1.0 - pos_prior)\n for word in sentence:\n if word in revised_prob:\n pos_odd += revised_prob[word][1]\n neg_odd += revised_prob[word][0]\n \n if pos_odd > neg_odd:\n num_0 += 1\n dev_labels.append(1 if pos_odd <= neg_odd else 0)\n print(num_0)\n\n \n #### bigram model \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n return dev_labels","def contraction_max_algos():\n return cutensor.contractionMaxAlgos()","def youngest(self):\n # Your implementation here","def ensembleVote(x, classes, ensemble):\n votes = np.array([0 for kk in range(len(classes))])\n for i in ensemble:\n votes = votes + classProbs(x, ensemble[i][\"tree\"], classes)\n maxVote = 0\n loc = None\n for ind, vote in enumerate(votes):\n if vote > maxVote:\n maxVote = vote\n loc = ind\n prediction = classes[loc]\n return prediction","def Maximize(stateTup,A,B):\n self.emergency()\n t = Terminal(stateTup)\n if t != None:\n return (None, t)\n\n maxChild , maxUtility = None,-999999999\n state = stateTup[0]\n Map = self.dict.get(str(state.map))\n if Map == None:\n children = []\n for M in range(4):\n g = state.clone()\n if g.move(M):\n children.append(g)\n self.dict[str(state.map)] = children\n else:\n children = Map\n for child in children:\n childTup = (child,stateTup[1]+1)\n utility = Minimize(childTup,A,B)[1]\n if utility > maxUtility:\n maxChild , maxUtility = child , utility\n if maxUtility >= B:\n# global prune\n# prune +=1\n break\n if maxUtility > A:\n A = maxUtility\n\n return (maxChild,maxUtility)","def _bert_encoder(self, tokens, attn_masks):\n output = self.bert(tokens, attn_masks)\n embedding = output['hidden_states'][-2]\n tag_prob = self.Softmax(self.hidden2tag(embedding))\n return tag_prob","def cal_max_dup_tags ( genome_size, tags_number, p=1e-5 ):\n return binomial_cdf_inv(1-p,tags_number,1.0/genome_size)","def cal_max_dup_tags ( genome_size, tags_number, p=1e-5 ):\n return binomial_cdf_inv(1-p,tags_number,1.0/genome_size)","def max_target(board, depth, alpha, beta):\n if terminal(board) or depth == DEPTH:\n return utility(board)\n\n best_val = -math.inf\n for action in actions(board):\n val = min_target(result(board, action), depth+1, alpha, beta)\n best_val = max(best_val, val)\n alpha = max(alpha, best_val)\n if beta <= alpha:\n break\n\n return best_val","def evaluate_ngrams(eval_dataset, trigram_counts, bigram_counts, unigram_counts, train_token_count, lambda1, lambda2):\n perplexity = 0\n\n ### YOUR CODE HERE\n def calc_prob(sentense, i, word, trigram_counts, bigram_counts, unigram_counts, train_token_count, model):\n prob = 0.0\n prev_word = sentense[i - 1]\n prev_to_prev_word = sentense[i - 2]\n\n if model == \"unigram\":\n if word in unigram_counts:\n prob = (unigram_counts[word] + 0.0) / train_token_count\n else:\n prob = (unigram_counts[word_to_num['UUUNKKK']] + 0.0) / \\\n train_token_count\n\n if model == \"bigram\":\n if (prev_word, word) in bigram_counts:\n prob = (bigram_counts[(prev_word, word)] + 0.0) / \\\n unigram_counts[prev_word]\n # print(num_to_word[prev_word] ,num_to_word[word])\n # print(bigram_counts[(prev_word, word)])\n # print(unigram_counts[prev_word])\n # print(\"---------------------------\")\n else:\n prob = 0.0\n\n if model == \"trigram\":\n if (prev_to_prev_word, prev_word, word) in trigram_counts:\n prob = (trigram_counts[(prev_to_prev_word, prev_word, word)] + 0.0) \\\n / bigram_counts[(prev_to_prev_word, prev_word)]\n # / bigram_counts[(prev_word, word)] #this according to lecture notes slide 27\n else:\n prob = 0.0\n\n return prob\n\n l = 0\n num_of_words = 0\n\n ##########3\n better_than_chance = 0\n ###########\n\n for sentense in eval_dataset:\n for i, word in enumerate(sentense[2:]):\n num_of_words += 1\n prob = lambda1 * calc_prob(sentense, i + 2, word, trigram_counts, bigram_counts, unigram_counts,\n train_token_count, \"trigram\") + \\\n lambda2 * calc_prob(sentense, i + 2, word, trigram_counts, bigram_counts, unigram_counts,\n train_token_count, \"bigram\") + \\\n (1 - lambda1 - lambda2) * calc_prob(sentense, i + 2, word, trigram_counts, bigram_counts,\n unigram_counts, train_token_count, \"unigram\")\n ######################################\n if prob > (1.0 / vocabsize):\n better_than_chance += 1\n #########################\n l += np.log2(prob)\n l /= num_of_words\n perplexity = 2 ** -l\n\n print(\"better_than_chance:\", (better_than_chance + 0.0) / num_of_words)\n\n ### END YOUR CODE\n return perplexity","def batch_predict(tree_adj, training_signs, edge_weight):\n # since shazoo use the revealed signs as-is, it's ok to use the same name\n training_signs, l2_values, rta_signs = training_signs\n all_nodes_to_predict = set(tree_adj) - set(training_signs)\n logging.debug('batch_predict has %d nodes to predict', len(all_nodes_to_predict))\n methods = ['l2cost', 'rta', 'shazoo']\n # fields are current_closest_hinge, current_sign, current_dst_to_closest_hinge\n node_predictions = {m: defaultdict(lambda: (None, None, 2e9)) for m in methods}\n hinge_value = {m: {} for m in methods}\n total_iter = 0\n while all_nodes_to_predict:\n some_root_of_a_border_tree = next(iter(all_nodes_to_predict))\n hinge_nodes, border_tree_nodes = find_hinge_nodes(tree_adj, edge_weight, training_signs,\n some_root_of_a_border_tree,\n with_visited=True)\n unmarked = border_tree_nodes - hinge_nodes\n for u in hinge_nodes:\n if u in hinge_value['shazoo']:\n continue\n vals, _, status = flep(tree_adj, (training_signs, rta_signs), edge_weight, u)\n hinge_value['shazoo'][u] = sgn(vals[0])\n hinge_value['rta'][u] = sgn(vals[1])\n if not USE_SCIPY:\n continue\n border_tree = build_border_tree_from_mincut_run(status, edge_weight)\n _, E, El, leaves_sign, _, _ = border_tree\n L = {u: l2_values[u] for u in leaves_sign}\n mapped_E, mapped_El_L, mapping = preprocess_edge_and_leaves(E, El, L)\n val = solve_by_zeroing_derivative(mapped_E, mapped_El_L, mapping, L,\n reorder=False)[0][u]\n hinge_value['l2cost'][u] = sgn(val)\n predicted_in_that_border_tree = set()\n inner_iter = 0\n # to avoid the same fork being picked again and again\n unmarked.add(some_root_of_a_border_tree)\n while unmarked:\n one_to_predict = next(iter(unmarked))\n hinge_tree = get_hinge_tree(one_to_predict, tree_adj, hinge_nodes)\n other_predicted = set()\n for h, h_val in iteritems(hinge_value['shazoo']):\n if h not in hinge_tree:\n continue\n predicted = propagate_hinge(hinge_tree, h, h_val, node_predictions['shazoo'],\n edge_weight)\n for u in predicted:\n prediction_info = node_predictions['shazoo'][u]\n used_hinge = prediction_info[0]\n node_predictions['rta'][u] = (used_hinge, hinge_value['rta'][used_hinge],\n prediction_info[2])\n if not USE_SCIPY:\n continue\n node_predictions['l2cost'][u] = (used_hinge, hinge_value['l2cost'][used_hinge],\n prediction_info[2])\n other_predicted.update(predicted)\n predicted_in_that_border_tree.update(other_predicted)\n unmarked -= other_predicted\n inner_iter += 1\n if inner_iter > len(tree_adj):\n import time\n logging.critical('batch predict failed in the inner loop')\n persistent.save_var('__fail_{}.my'.format(int(time.time())), (tree_adj, (training_signs, l2_values, rta_signs), edge_weight))\n raise RuntimeError('batch predict failed in the inner loop')\n all_nodes_to_predict -= predicted_in_that_border_tree\n total_iter += 1\n if total_iter > len(tree_adj):\n import time\n logging.critical('batch predict failed in the outer loop')\n persistent.save_var('__fail_{}.my'.format(int(time.time())), (tree_adj, (training_signs, l2_values, rta_signs), edge_weight))\n raise RuntimeError('batch predict failed in the outer loop')\n logging.debug('batch_predict has actually predicted %d nodes', len(node_predictions) - len(training_signs))\n return {m: {u: v[1] for u, v in iteritems(node_predictions[m]) if u not in training_signs}\n for m in methods}","def caculate_prob(self):\n t_H = self.tree.depth()\n t_h = 1\n while(t_h <= t_H):\n t_hnodes = self.get_h(t_h)\n t_sum = 0\n t_hpro = []\n t_cpro = []\n for t_n in t_hnodes:\n t_sum = self.tree.get_node(t_n).data[0] + t_sum\n t_node = self.tree.get_node(t_n)\n if t_node.is_leaf():\n t_node.data.append(0)\n continue\n t_childrens = self.tree.children(t_n)\n t_shang = 0\n for child in t_childrens:\n t_shang = t_shang + (child.data[0]/t_node.data[0])*np.log(child.data[0]/t_node.data[0])\n t_node.data.append(-t_shang)\n for t_n in t_hnodes:\n t_node = self.tree.get_node(t_n)\n t_parentnode = self.tree.parent(t_n)\n if t_h > 1:\n t_node.data.append((t_node.data[0] / t_sum) * (t_node.data[0]/t_parentnode.data[0]))\n t_hpro.append((t_node.data[0]/t_sum) * (t_node.data[0]/t_parentnode.data[0]))\n else:\n t_node.data.append((t_node.data[0] / t_sum))\n t_hpro.append((t_node.data[0] / t_sum))\n\n t_cpro.append(t_node.data[1])\n t_ndata = np.array(t_hpro)\n mean = np.mean(t_ndata)\n std = np.std(t_ndata,ddof=1)\n t_sdata = np.array(t_cpro)\n mean_s = np.mean(t_sdata)\n std_s = np.std(t_sdata,ddof=1)\n for t_n in t_hnodes:\n t_node = self.tree.get_node(t_n)\n if(std != 0):\n t_node.data[2] = (t_node.data[2] - mean)/std\n else:\n t_node.data[2] = (t_node.data[2] - mean)\n if(mean_s == 0 and std_s ==0):\n t_node.data[1] = -100.0\n continue\n t_node.data[1] = (t_node.data[1] - mean_s)/std_s\n t_h = t_h + 1","def children_impurity(self):\n\n\t\tentropy_left = 0.0\n\t\tentropy_right = 0.0\n\n\t\tfor c in range(self.n_classes):\n\t\t\ttmp = self.label_count_left[c]\n\t\t\tif tmp > 0.0:\n\t\t\t\ttmp /= self.weighted_n_left\n\t\t\t\tentropy_left -= tmp * log(tmp)\n\n\t\t\ttmp = self.label_count_right[c]\n\t\t\tif tmp > 0.0:\n\t\t\t\ttmp /= self.weighted_n_right\n\t\t\t\tentropy_right -= tmp * log(tmp)\n\n\t\treturn entropy_left, entropy_right","def naiveBayes(train_set, train_labels, dev_set, smoothing_parameter, pos_prior):\r\n # TODO: Write your code here\r\n # return predicted labels of development set\r\n retval = []\r\n smoothing_parameter = 0.0055\r\n # Generate a unigram BOW for both positive and negative reviews, choose the top 2500 words\r\n pos_bow, neg_bow = generate_unigram_BOW(train_set, train_labels)\r\n sorted_pos = sorted(pos_bow.items(), key=lambda x: x[1], reverse = True)\r\n sorted_neg = sorted(neg_bow.items(), key=lambda x: x[1], reverse = True)\r\n pos_words = sorted_pos[:].copy()\r\n neg_words = sorted_neg[:].copy()\r\n\r\n pos_bi_bow, neg_bi_bow = generate_bigram_BOW(train_set, train_labels)\r\n sorted_bi_pos = sorted(pos_bi_bow.items(), key=lambda x: x[1], reverse = True)\r\n sorted_bi_neg = sorted(neg_bi_bow.items(), key=lambda x: x[1], reverse = True)\r\n bi_pos_words = sorted_bi_pos[:].copy()\r\n bi_neg_words = sorted_bi_neg[:].copy()\r\n\r\n # Calculate the log probabilities each word given type\r\n pos_count = sum(pair[1] for pair in pos_words)\r\n neg_count = sum(pair[1] for pair in neg_words)\r\n bi_pos_count = sum(pair[1] for pair in bi_pos_words)\r\n bi_neg_count = sum(pair[1] for pair in bi_neg_words)\r\n\r\n log_probability_pos = {} #(word)->P(word|positive)\r\n log_probability_neg = {} #(word)->P(word|negative)\r\n log_prob_bi_pos = {}\r\n log_prob_bi_neg = {}\r\n\r\n for pair in pos_words:\r\n pos_prob = np.log((pair[1]+smoothing_parameter)/(pos_count+smoothing_parameter*(len(pos_words) + 1)))\r\n log_probability_pos[pair[0]] = pos_prob\r\n\r\n for pair in neg_words:\r\n neg_prob = np.log((pair[1]+smoothing_parameter)/(neg_count+smoothing_parameter*(len(neg_words) + 1)))\r\n log_probability_neg[pair[0]] = neg_prob\r\n\r\n for pair in bi_pos_words:\r\n bi_pos_prob = np.log((pair[1]+smoothing_parameter)/(bi_pos_count+smoothing_parameter*(len(bi_pos_words) + 1)))\r\n log_prob_bi_pos[pair[0]] = bi_pos_prob\r\n\r\n for pair in bi_neg_words:\r\n bi_neg_prob = np.log((pair[1]+smoothing_parameter)/(bi_neg_count+smoothing_parameter*(len(bi_neg_words) + 1)))\r\n log_prob_bi_neg[pair[0]] = bi_neg_prob\r\n # Finished training\r\n\r\n # For each of the new reviews from development data\r\n for review in dev_set:\r\n uni_pos = np.log(pos_prior)\r\n uni_neg = np.log(1 - pos_prior)\r\n for word in review:\r\n if word in log_probability_pos:\r\n uni_pos += log_probability_pos[word]\r\n elif word not in log_probability_pos:\r\n uni_pos += np.log(smoothing_parameter/(pos_count+smoothing_parameter*(len(pos_words) + 1)))\r\n\r\n if word in log_probability_neg:\r\n uni_neg += log_probability_neg[word]\r\n elif word not in log_probability_neg:\r\n uni_neg += np.log(smoothing_parameter/(neg_count+smoothing_parameter*(len(neg_words) + 1)))\r\n\r\n bi_pos = np.log(pos_prior)\r\n bi_neg = np.log(1 - pos_prior)\r\n for i in range(len(review)-1):\r\n currTuple = (review[i], review[i+1])\r\n if currTuple in log_prob_bi_pos:\r\n bi_pos += log_prob_bi_pos[currTuple]\r\n elif currTuple not in log_prob_bi_pos:\r\n bi_pos += np.log(smoothing_parameter/(bi_pos_count+smoothing_parameter*(len(bi_pos_words) + 1)))\r\n\r\n if currTuple in log_prob_bi_neg:\r\n bi_neg += log_prob_bi_neg[currTuple]\r\n elif currTuple not in log_prob_bi_neg:\r\n bi_neg += np.log(smoothing_parameter/(bi_neg_count+smoothing_parameter*(len(bi_neg_words) + 1)))\r\n\r\n MAP_pos = (1-0.4)*uni_pos + 0.4*bi_pos\r\n MAP_neg = (1-0.4)*uni_neg + 0.4*bi_neg\r\n\r\n if MAP_pos >= MAP_neg:\r\n retval.append(1)\r\n else:\r\n retval.append(0)\r\n\r\n return retval","def haplotype(self, ploidy):\n \n i = 0 # counter for ploidy\n final = [] #to store seq\n got_bubble = True\n \n while i < ploidy:\n \n frag = []\n longest_path = []\n\n sorted_node = self.topological_sort()\n # find greatest weight path\n path = self.longest_path(sorted_node)\n # last node in the graph\n curr_max = max(path, key=path.get)\n next_node = ''\n str_temp = ''\n\n # while it is not the source node\n while len(self.prefix[curr_max]) != 0:\n # prev max node\n prev = path[curr_max][1]\n\n # if current node longer than prev and next node\n if len(curr_max) >= len(prev) and len(curr_max) >= len(next_node):\n str_temp = curr_max \n\n # if current node shorter than prev and next node \n elif len(curr_max) < len(prev) and len(curr_max) < len(next_node):\n start = self.suffix[prev][curr_max][2]\n end = self.prefix[next_node][curr_max][1]\n str_temp = curr_max[start:end]\n\n # if current node shorter than prev but longer than the next node \n elif len(curr_max) < len(prev) and len(curr_max) >= len(next_node):\n start = self.suffix[prev][curr_max][2]\n str_temp = curr_max[start:]\n\n # if current node longer than prev but shorter than the next node \n else:\n end = self.prefix[next_node][curr_max][1]\n str_temp = curr_max[:end]\n\n frag.insert(0, str_temp)\n longest_path.insert(0, curr_max)\n next_node = curr_max\n curr_max = prev\n\n\n # if this is the first node\n if len(self.prefix[curr_max]) == 0:\n\n # if current longer than next node\n if len(curr_max) > len(next_node):\n frag.insert(0, curr_max)\n next_node = curr_max\n\n else: \n end = self.prefix[next_node][curr_max][1]\n str_temp = curr_max[:end]\n frag.insert(0, str_temp)\n next_node = curr_max\n \n longest_path.insert(0, curr_max) \n\n\n # combine all string\n seq = ''.join(frag)\n final.append(seq)\n \n got_bubble = self.reduce_graph(longest_path, sorted_node)\n if got_bubble is False:\n break\n i += 1\n \n \n return final","def argmax(X):\n\tN,K,_ = X.shape\n\tg0 = X[0,0]\n\tg = X[1:]\n\n\tB = ones((N,K), dtype=int32) * -1\n\t# compute max-marginals and backtrace matrix\n\tV = g0\n\tfor t in xrange(1,N):\n\t\tU = empty(K)\n\t\tfor y in xrange(K):\n\t\t\tw = V + g[t-1,:,y]\n\t\t\tB[t,y] = b = w.argmax()\n\t\t\tU[y] = w[b]\n\t\tV = U\n\t# extract the best path by brack-tracking\n\ty = V.argmax()\n\ttrace = []\n\tfor t in reversed(xrange(N)):\n\t\ttrace.append(y)\n\t\ty = B[t, y]\n\ttrace.reverse()\n\treturn trace","def get_greatest_probability(self):\n greatest = -1\n for i in range(self.dim):\n for j in range(self.dim):\n if self.kb[i][j] > greatest:\n greatest = self.kb[i][j]\n\n return greatest","def naive_bn(data, attributes):\n bn = []\n attr = attributes['attr'].tolist()\n # each attribute is only dependent on the class node\n i = 0\n while (i < len(attr)-1):\n row = [attr[i], attr[-1]]\n bn.append(row)\n i= i + 1\n # frequency table \n freq = counts_table(data, attributes)\n # conditional probabilities and prior probabilities\n cond_probs, prior0, prior1 = conditional_probability(data, attributes, freq)\n\n return bn, cond_probs, prior0, prior1","def baseline_mle(self):\n tag_word_tapples = self.data_to_taples(self.training_set)\n e = np.zeros((self.pos_size, self.words_size))\n for tag, word in tag_word_tapples:\n e[self.pos2i[tag], self.word2i[word]] += 1\n sum = e.sum(axis=1)[..., np.newaxis]\n e = e / sum\n return e","def most_frequent_train(train_data):\n ### YOUR CODE HERE\n tags_counts_for_each_word = {}\n # Filling a dictionary from words and tag tags to their counters\n # Going over the words and counting their tags appearances\n for sentance in train_data:\n for word, tag in sentance:\n # If first time seeing word, adding it's tags count dictionary\n if word not in tags_counts_for_each_word:\n tags_counts_for_each_word[word] = {}\n # Fetching word tags count dictionary\n word_tags_count_dictionary = tags_counts_for_each_word[word]\n # If tag not in word's tags dictionary, initializing the counter\n if tag not in word_tags_count_dictionary:\n word_tags_count_dictionary[tag] = 0\n # Incrementing word tag counter\n word_tags_count_dictionary[tag] += 1\n \n words_maximal_tags = {}\n # Going over each word and finding it's maximal tag\n for word in tags_counts_for_each_word:\n # Fetching all word tags counts\n word_tags_count_dictionary = tags_counts_for_each_word[word]\n \n maximal_tag, maximal_tag_counter = '', 0\n # Finding word tag with maximal tag counter\n for curent_tag, current_counter in word_tags_count_dictionary.items():\n if current_counter > maximal_tag_counter:\n maximal_tag, maximal_tag_counter = curent_tag, current_counter\n \n # Setting the maximal tag for current word\n words_maximal_tags[word] = maximal_tag\n \n return words_maximal_tags\n ### END CODE HERE","def find_max_independent_set(graph, params):\r\n\r\n max_ind_set = []\r\n\r\n # QHACK #\r\n\r\n # function that takes in a graph and outputs the hamiltonians\r\n cost_h, mixer_h = qaoa.max_independent_set(graph, constrained=True) # Assume the graph they give me is good\r\n\r\n def qaoa_layer(gamma, alpha):\r\n qaoa.cost_layer(gamma, cost_h)\r\n qaoa.mixer_layer(alpha, mixer_h)\r\n\r\n dev = qml.device(\"default.qubit\", wires=range(NODES))\r\n\r\n def circuit(params, **kwargs): \r\n qml.layer(qaoa_layer, N_LAYERS, params[0], params[1]) \r\n\r\n @qml.qnode(dev)\r\n def probability_circuit(gamma, alpha):\r\n circuit([gamma, alpha])\r\n return qml.probs(wires=range(NODES))\r\n\r\n answer = probability_circuit(params[0], params[1])\r\n\r\n maxn = 0\r\n maxn = max(answer)\r\n\r\n for i in range(len(answer)):\r\n if maxn == answer[i]:\r\n decimal = i\r\n \r\n binary_num = []\r\n def DecimalToBinary(decimal):\r\n if decimal >= 1:\r\n DecimalToBinary(decimal // 2)\r\n binary_num.append(decimal % 2)\r\n \r\n DecimalToBinary(decimal)\r\n\r\n if len(binary_num) < 6:\r\n if len(binary_num) < 5:\r\n if len(binary_num) < 4:\r\n if len(binary_num) < 3:\r\n if len(binary_num) < 2:\r\n binary_num.insert(0, 0) # At beginning append 0\r\n binary_num.insert(0, 0)\r\n binary_num.insert(0, 0)\r\n binary_num.insert(0, 0)\r\n binary_num.insert(0, 0)\r\n\r\n for i in range(6):\r\n if binary_num[i] == 1:\r\n max_ind_set.append(i)\r\n\r\n # QHACK #\r\n\r\n return max_ind_set","def _update_max(self):\n tmp = self\n while tmp.right is not None:\n tmp = tmp.right\n return tmp.parent.key","def _calculate_tag_weight(weight, max_weight, distribution):\r\n if distribution == LINEAR or max_weight == 1:\r\n return weight\r\n elif distribution == LOGARITHMIC:\r\n return math.log(weight) * max_weight / math.log(max_weight)\r\n raise ValueError(_('Invalid distribution algorithm specified: %s.') % distribution)","def __findBestLogProbability(self):\n best_model = None\n highest_log_probability = -sys.maxsize# (np.finfo(float).eps)\n\n # Find the highest model\n for item in self.data_array:\n if item[1] > highest_log_probability:\n best_model = item\n highest_log_probability = item[1]\n\n return best_model","def id3(x, y, attribute_value_pairs=None, depth=0, max_depth=5):\r\n\r\n # INSERT YOUR CODE HERE. NOTE: THIS IS A RECURSIVE FUNCTION.\r\n # To make a list of all attribute value pair\r\n root = {}\r\n \r\n x1 = []\r\n if attribute_value_pairs is None:\r\n for i in range(x.shape[1]):\r\n for v in np.unique(x[:, i]):\r\n x1.append([i, v])\r\n attribute_value_pairs = np.vstack((x1))\r\n \r\n # To compute no of records classified into each class of target column\r\n yvalue, ycount = np.unique(y, return_counts=True)\r\n \r\n # if all the records belong to the same class return the label of the class\r\n if len(yvalue) == 1:\r\n return yvalue[0]\r\n \r\n # If no more attributes are available for branching, output majority class\r\n if len(attribute_value_pairs) == 0:\r\n return yvalue[np.argmax(ycount)]\r\n \r\n # max depth is reached, output majority class\r\n if depth == max_depth:\r\n return yvalue[np.argmax(ycount)]\r\n \r\n # To find best attribute for branching criteria\r\n Mutual_info_list = []\r\n for (i, v) in attribute_value_pairs:\r\n Mutual_info_list.append(mutual_information(np.array(x[:, i] == v).astype(int),y))\r\n \r\n (bestattribute, bestvalue) = attribute_value_pairs[np.argmax(Mutual_info_list)]\r\n \r\n partitions = partition(np.array(x[:, bestattribute] == bestvalue).astype(int))\r\n \r\n dropindex = np.all(attribute_value_pairs == (bestattribute, bestvalue), axis=1)\r\n \r\n attribute_value_pairs = np.delete(attribute_value_pairs, np.argwhere(dropindex), 0)\r\n \r\n for decision_attribute, index in partitions.items():\r\n xsubset = x.take(index, axis=0)\r\n ysubset = y.take(index, axis=0)\r\n decision = bool(decision_attribute)\r\n\r\n root[(bestattribute, bestvalue, decision)] = id3(xsubset, ysubset, attribute_value_pairs=attribute_value_pairs,max_depth=max_depth, depth=depth + 1)\r\n \r\n return root\r\n raise Exception('Function not yet implemented!')","def maxWeightMatching(edges, maxcardinality=False):\n\n #\n # Vertices are numbered 0 .. (nvertex-1).\n # Non-trivial blossoms are numbered nvertex .. (2*nvertex-1)\n #\n # Edges are numbered 0 .. (nedge-1).\n # Edge endpoints are numbered 0 .. (2*nedge-1), such that endpoints\n # (2*k) and (2*k+1) both belong to edge k.\n #\n # Many terms used in the comments (sub-blossom, T-vertex) come from\n # the paper by Galil; read the paper before reading this code.\n #\n\n # Deal swiftly with empty graphs.\n if not edges:\n return [ ]\n\n # Count vertices.\n nedge = len(edges)\n nvertex = 0\n for (i, j, w) in edges:\n assert i >= 0 and j >= 0 and i != j\n if i >= nvertex:\n nvertex = i + 1\n if j >= nvertex:\n nvertex = j + 1\n\n # Find the maximum edge weight.\n maxweight = max(0, max([ wt for (i, j, wt) in edges ]))\n\n # If p is an edge endpoint,\n # endpoint[p] is the vertex to which endpoint p is attached.\n # Not modified by the algorithm.\n endpoint = [ edges[p//2][p%2] for p in range (2*nedge) ]\n\n # If v is a vertex,\n # neighbend[v] is the list of remote endpoints of the edges attached to v.\n # Not modified by the algorithm.\n neighbend = [ [ ] for i in range(nvertex) ]\n for k in xrange(len(edges)):\n (i, j, w) = edges[k]\n neighbend[i].append(2*k+1)\n neighbend[j].append(2*k)\n\n # If v is a vertex,\n # mate[v] is the remote endpoint of its matched edge, or -1 if it is single\n # (i.e. endpoint[mate[v]] is v's partner vertex).\n # Initially all vertices are single; updated during augmentation.\n mate = nvertex * [ -1 ]\n\n # If b is a top-level blossom,\n # label[b] is 0 if b is unlabeled (free);\n # 1 if b is an S-vertex/blossom;\n # 2 if b is a T-vertex/blossom.\n # The label of a vertex is found by looking at the label of its\n # top-level containing blossom.\n # If v is a vertex inside a T-blossom,\n # label[v] is 2 iff v is reachable from an S-vertex outside the blossom.\n # Labels are assigned during a stage and reset after each augmentation.\n label = (2 * nvertex) * [ 0 ]\n\n # If b is a labeled top-level blossom,\n # labelend[b] is the remote endpoint of the edge through which b obtained\n # its label, or -1 if b's base vertex is single.\n # If v is a vertex inside a T-blossom and label[v] == 2,\n # labelend[v] is the remote endpoint of the edge through which v is\n # reachable from outside the blossom.\n labelend = (2 * nvertex) * [ -1 ]\n\n # If v is a vertex,\n # inblossom[v] is the top-level blossom to which v belongs.\n # If v is a top-level vertex, v is itself a blossom (a trivial blossom)\n # and inblossom[v] == v.\n # Initially all vertices are top-level trivial blossoms.\n inblossom = range(nvertex)\n\n # If b is a sub-blossom,\n # blossomparent[b] is its immediate parent (sub-)blossom.\n # If b is a top-level blossom, blossomparent[b] is -1.\n blossomparent = (2 * nvertex) * [ -1 ]\n\n # If b is a non-trivial (sub-)blossom,\n # blossomchilds[b] is an ordered list of its sub-blossoms, starting with\n # the base and going round the blossom.\n blossomchilds = (2 * nvertex) * [ None ]\n\n # If b is a (sub-)blossom,\n # blossombase[b] is its base VERTEX (i.e. recursive sub-blossom).\n blossombase = range(nvertex) + nvertex * [ -1 ]\n\n # If b is a non-trivial (sub-)blossom,\n # blossomendps[b] is a list of endpoints on its connecting edges,\n # such that blossomendps[b][i] is the local endpoint of blossomchilds[b][i]\n # on the edge that connects it to blossomchilds[b][wrap(i+1)].\n blossomendps = (2 * nvertex) * [ None ]\n\n # If v is a free vertex (or an unreached vertex inside a T-blossom),\n # bestedge[v] is the edge to an S-vertex with least slack,\n # or -1 if there is no such edge.\n # If b is a (possibly trivial) top-level S-blossom,\n # bestedge[b] is the least-slack edge to a different S-blossom,\n # or -1 if there is no such edge.\n # This is used for efficient computation of delta2 and delta3.\n bestedge = (2 * nvertex) * [ -1 ]\n\n # If b is a non-trivial top-level S-blossom,\n # blossombestedges[b] is a list of least-slack edges to neighbouring\n # S-blossoms, or None if no such list has been computed yet.\n # This is used for efficient computation of delta3.\n blossombestedges = (2 * nvertex) * [ None ]\n\n # List of currently unused blossom numbers.\n unusedblossoms = range(nvertex, 2*nvertex)\n\n # If v is a vertex,\n # dualvar[v] = 2 * u(v) where u(v) is the v's variable in the dual\n # optimization problem (multiplication by two ensures integer values\n # throughout the algorithm if all edge weights are integers).\n # If b is a non-trivial blossom,\n # dualvar[b] = z(b) where z(b) is b's variable in the dual optimization\n # problem.\n dualvar = nvertex * [ maxweight ] + nvertex * [ 0 ]\n\n # If allowedge[k] is true, edge k has zero slack in the optimization\n # problem; if allowedge[k] is false, the edge's slack may or may not\n # be zero.\n allowedge = nedge * [ False ]\n\n # Queue of newly discovered S-vertices.\n queue = [ ]\n\n # Return 2 * slack of edge k (does not work inside blossoms).\n def slack(k):\n (i, j, wt) = edges[k]\n return dualvar[i] + dualvar[j] - 2 * wt\n\n # Generate the leaf vertices of a blossom.\n def blossomLeaves(b):\n if b < nvertex:\n yield b\n else:\n for t in blossomchilds[b]:\n if t < nvertex:\n yield t\n else:\n for v in blossomLeaves(t):\n yield v\n\n # Assign label t to the top-level blossom containing vertex w\n # and record the fact that w was reached through the edge with\n # remote endpoint p.\n def assignLabel(w, t, p):\n if DEBUG: DEBUG('assignLabel(%d,%d,%d)' % (w, t, p))\n b = inblossom[w]\n assert label[w] == 0 and label[b] == 0\n label[w] = label[b] = t\n labelend[w] = labelend[b] = p\n bestedge[w] = bestedge[b] = -1\n if t == 1:\n # b became an S-vertex/blossom; add it(s vertices) to the queue.\n queue.extend(blossomLeaves(b))\n if DEBUG: DEBUG('PUSH ' + str(list(blossomLeaves(b))))\n elif t == 2:\n # b became a T-vertex/blossom; assign label S to its mate.\n # (If b is a non-trivial blossom, its base is the only vertex\n # with an external mate.)\n base = blossombase[b]\n assert mate[base] >= 0\n assignLabel(endpoint[mate[base]], 1, mate[base] ^ 1)\n\n # Trace back from vertices v and w to discover either a new blossom\n # or an augmenting path. Return the base vertex of the new blossom or -1.\n def scanBlossom(v, w):\n if DEBUG: DEBUG('scanBlossom(%d,%d)' % (v, w))\n # Trace back from v and w, placing breadcrumbs as we go.\n path = [ ]\n base = -1\n while v != -1 or w != -1:\n # Look for a breadcrumb in v's blossom or put a new breadcrumb.\n b = inblossom[v]\n if label[b] & 4:\n base = blossombase[b]\n break\n assert label[b] == 1\n path.append(b)\n label[b] = 5\n # Trace one step back.\n assert labelend[b] == mate[blossombase[b]]\n if labelend[b] == -1:\n # The base of blossom b is single; stop tracing this path.\n v = -1\n else:\n v = endpoint[labelend[b]]\n b = inblossom[v]\n assert label[b] == 2\n # b is a T-blossom; trace one more step back.\n assert labelend[b] >= 0\n v = endpoint[labelend[b]]\n # Swap v and w so that we alternate between both paths.\n if w != -1:\n v, w = w, v\n # Remove breadcrumbs.\n for b in path:\n label[b] = 1\n # Return base vertex, if we found one.\n return base\n\n # Construct a new blossom with given base, containing edge k which\n # connects a pair of S vertices. Label the new blossom as S; set its dual\n # variable to zero; relabel its T-vertices to S and add them to the queue.\n def addBlossom(base, k):\n (v, w, wt) = edges[k]\n bb = inblossom[base]\n bv = inblossom[v]\n bw = inblossom[w]\n # Create blossom.\n b = unusedblossoms.pop()\n if DEBUG: DEBUG('addBlossom(%d,%d) (v=%d w=%d) -> %d' % (base, k, v, w, b))\n blossombase[b] = base\n blossomparent[b] = -1\n blossomparent[bb] = b\n # Make list of sub-blossoms and their interconnecting edge endpoints.\n blossomchilds[b] = path = [ ]\n blossomendps[b] = endps = [ ]\n # Trace back from v to base.\n while bv != bb:\n # Add bv to the new blossom.\n blossomparent[bv] = b\n path.append(bv)\n endps.append(labelend[bv])\n assert (label[bv] == 2 or\n (label[bv] == 1 and labelend[bv] == mate[blossombase[bv]]))\n # Trace one step back.\n assert labelend[bv] >= 0\n v = endpoint[labelend[bv]]\n bv = inblossom[v]\n # Reverse lists, add endpoint that connects the pair of S vertices.\n path.append(bb)\n path.reverse()\n endps.reverse()\n endps.append(2*k)\n # Trace back from w to base.\n while bw != bb:\n # Add bw to the new blossom.\n blossomparent[bw] = b\n path.append(bw)\n endps.append(labelend[bw] ^ 1)\n assert (label[bw] == 2 or\n (label[bw] == 1 and labelend[bw] == mate[blossombase[bw]]))\n # Trace one step back.\n assert labelend[bw] >= 0\n w = endpoint[labelend[bw]]\n bw = inblossom[w]\n # Set label to S.\n assert label[bb] == 1\n label[b] = 1\n labelend[b] = labelend[bb]\n # Set dual variable to zero.\n dualvar[b] = 0\n # Relabel vertices.\n for v in blossomLeaves(b):\n if label[inblossom[v]] == 2:\n # This T-vertex now turns into an S-vertex because it becomes\n # part of an S-blossom; add it to the queue.\n queue.append(v)\n inblossom[v] = b\n # Compute blossombestedges[b].\n bestedgeto = (2 * nvertex) * [ -1 ]\n for bv in path:\n if blossombestedges[bv] is None:\n # This subblossom does not have a list of least-slack edges;\n # get the information from the vertices.\n nblists = [ [ p // 2 for p in neighbend[v] ]\n for v in blossomLeaves(bv) ]\n else:\n # Walk this subblossom's least-slack edges.\n nblists = [ blossombestedges[bv] ]\n for nblist in nblists:\n for k in nblist:\n (i, j, wt) = edges[k]\n if inblossom[j] == b:\n i, j = j, i\n bj = inblossom[j]\n if (bj != b and label[bj] == 1 and\n (bestedgeto[bj] == -1 or\n slack(k) < slack(bestedgeto[bj]))):\n bestedgeto[bj] = k\n # Forget about least-slack edges of the subblossom.\n blossombestedges[bv] = None\n bestedge[bv] = -1\n blossombestedges[b] = [ k for k in bestedgeto if k != -1 ]\n # Select bestedge[b].\n bestedge[b] = -1\n for k in blossombestedges[b]:\n if bestedge[b] == -1 or slack(k) < slack(bestedge[b]):\n bestedge[b] = k\n if DEBUG: DEBUG('blossomchilds[%d]=' % b + repr(blossomchilds[b]))\n\n # Expand the given top-level blossom.\n def expandBlossom(b, endstage):\n if DEBUG: DEBUG('expandBlossom(%d,%d) %s' % (b, endstage, repr(blossomchilds[b])))\n # Convert sub-blossoms into top-level blossoms.\n for s in blossomchilds[b]:\n blossomparent[s] = -1\n if s < nvertex:\n inblossom[s] = s\n elif endstage and dualvar[s] == 0:\n # Recursively expand this sub-blossom.\n expandBlossom(s, endstage)\n else:\n for v in blossomLeaves(s):\n inblossom[v] = s\n # If we expand a T-blossom during a stage, its sub-blossoms must be\n # relabeled.\n if (not endstage) and label[b] == 2:\n # Start at the sub-blossom through which the expanding\n # blossom obtained its label, and relabel sub-blossoms untili\n # we reach the base.\n # Figure out through which sub-blossom the expanding blossom\n # obtained its label initially.\n assert labelend[b] >= 0\n entrychild = inblossom[endpoint[labelend[b] ^ 1]]\n # Decide in which direction we will go round the blossom.\n j = blossomchilds[b].index(entrychild)\n if j & 1:\n # Start index is odd; go forward and wrap.\n j -= len(blossomchilds[b])\n jstep = 1\n endptrick = 0\n else:\n # Start index is even; go backward.\n jstep = -1\n endptrick = 1\n # Move along the blossom until we get to the base.\n p = labelend[b]\n while j != 0:\n # Relabel the T-sub-blossom.\n label[endpoint[p ^ 1]] = 0\n label[endpoint[blossomendps[b][j-endptrick]^endptrick^1]] = 0\n assignLabel(endpoint[p ^ 1], 2, p)\n # Step to the next S-sub-blossom and note its forward endpoint.\n allowedge[blossomendps[b][j-endptrick]//2] = True\n j += jstep\n p = blossomendps[b][j-endptrick] ^ endptrick\n # Step to the next T-sub-blossom.\n allowedge[p//2] = True\n j += jstep\n # Relabel the base T-sub-blossom WITHOUT stepping through to\n # its mate (so don't call assignLabel).\n bv = blossomchilds[b][j]\n label[endpoint[p ^ 1]] = label[bv] = 2\n labelend[endpoint[p ^ 1]] = labelend[bv] = p\n bestedge[bv] = -1\n # Continue along the blossom until we get back to entrychild.\n j += jstep\n while blossomchilds[b][j] != entrychild:\n # Examine the vertices of the sub-blossom to see whether\n # it is reachable from a neighbouring S-vertex outside the\n # expanding blossom.\n bv = blossomchilds[b][j]\n if label[bv] == 1:\n # This sub-blossom just got label S through one of its\n # neighbours; leave it.\n j += jstep\n continue\n for v in blossomLeaves(bv):\n if label[v] != 0:\n break\n # If the sub-blossom contains a reachable vertex, assign\n # label T to the sub-blossom.\n if label[v] != 0:\n assert label[v] == 2\n assert inblossom[v] == bv\n label[v] = 0\n label[endpoint[mate[blossombase[bv]]]] = 0\n assignLabel(v, 2, labelend[v])\n j += jstep\n # Recycle the blossom number.\n label[b] = labelend[b] = -1\n blossomchilds[b] = blossomendps[b] = None\n blossombase[b] = -1\n blossombestedges[b] = None\n bestedge[b] = -1\n unusedblossoms.append(b)\n\n # Swap matched/unmatched edges over an alternating path through blossom b\n # between vertex v and the base vertex. Keep blossom bookkeeping consistent.\n def augmentBlossom(b, v):\n if DEBUG: DEBUG('augmentBlossom(%d,%d)' % (b, v))\n # Bubble up through the blossom tree from vertex v to an immediate\n # sub-blossom of b.\n t = v\n while blossomparent[t] != b:\n t = blossomparent[t]\n # Recursively deal with the first sub-blossom.\n if t >= nvertex:\n augmentBlossom(t, v)\n # Decide in which direction we will go round the blossom.\n i = j = blossomchilds[b].index(t)\n if i & 1:\n # Start index is odd; go forward and wrap.\n j -= len(blossomchilds[b])\n jstep = 1\n endptrick = 0\n else:\n # Start index is even; go backward.\n jstep = -1\n endptrick = 1\n # Move along the blossom until we get to the base.\n while j != 0:\n # Step to the next sub-blossom and augment it recursively.\n j += jstep\n t = blossomchilds[b][j]\n p = blossomendps[b][j-endptrick] ^ endptrick\n if t >= nvertex:\n augmentBlossom(t, endpoint[p])\n # Step to the next sub-blossom and augment it recursively.\n j += jstep\n t = blossomchilds[b][j]\n if t >= nvertex:\n augmentBlossom(t, endpoint[p ^ 1])\n # Match the edge connecting those sub-blossoms.\n mate[endpoint[p]] = p ^ 1\n mate[endpoint[p ^ 1]] = p\n if DEBUG: DEBUG('PAIR %d %d (k=%d)' % (endpoint[p], endpoint[p^1], p//2))\n # Rotate the list of sub-blossoms to put the new base at the front.\n blossomchilds[b] = blossomchilds[b][i:] + blossomchilds[b][:i]\n blossomendps[b] = blossomendps[b][i:] + blossomendps[b][:i]\n blossombase[b] = blossombase[blossomchilds[b][0]]\n assert blossombase[b] == v\n\n # Swap matched/unmatched edges over an alternating path between two\n # single vertices. The augmenting path runs through edge k, which\n # connects a pair of S vertices.\n def augmentMatching(k):\n (v, w, wt) = edges[k]\n if DEBUG: DEBUG('augmentMatching(%d) (v=%d w=%d)' % (k, v, w))\n if DEBUG: DEBUG('PAIR %d %d (k=%d)' % (v, w, k))\n for (s, p) in ((v, 2*k+1), (w, 2*k)):\n # Match vertex s to remote endpoint p. Then trace back from s\n # until we find a single vertex, swapping matched and unmatched\n # edges as we go.\n while 1:\n bs = inblossom[s]\n assert label[bs] == 1\n assert labelend[bs] == mate[blossombase[bs]]\n # Augment through the S-blossom from s to base.\n if bs >= nvertex:\n augmentBlossom(bs, s)\n # Update mate[s]\n mate[s] = p\n # Trace one step back.\n if labelend[bs] == -1:\n # Reached single vertex; stop.\n break\n t = endpoint[labelend[bs]]\n bt = inblossom[t]\n assert label[bt] == 2\n # Trace one step back.\n assert labelend[bt] >= 0\n s = endpoint[labelend[bt]]\n j = endpoint[labelend[bt] ^ 1]\n # Augment through the T-blossom from j to base.\n assert blossombase[bt] == t\n if bt >= nvertex:\n augmentBlossom(bt, j)\n # Update mate[j]\n mate[j] = labelend[bt]\n # Keep the opposite endpoint;\n # it will be assigned to mate[s] in the next step.\n p = labelend[bt] ^ 1\n if DEBUG: DEBUG('PAIR %d %d (k=%d)' % (s, t, p//2))\n\n # Verify that the optimum solution has been reached.\n def verifyOptimum():\n if maxcardinality:\n # Vertices may have negative dual;\n # find a constant non-negative number to add to all vertex duals.\n vdualoffset = max(0, -min(dualvar[:nvertex]))\n else:\n vdualoffset = 0\n # 0. all dual variables are non-negative\n assert min(dualvar[:nvertex]) + vdualoffset >= 0\n assert min(dualvar[nvertex:]) >= 0\n # 0. all edges have non-negative slack and\n # 1. all matched edges have zero slack;\n for k in xrange(nedge):\n (i, j, wt) = edges[k]\n s = dualvar[i] + dualvar[j] - 2 * wt\n iblossoms = [ i ]\n jblossoms = [ j ]\n while blossomparent[iblossoms[-1]] != -1:\n iblossoms.append(blossomparent[iblossoms[-1]])\n while blossomparent[jblossoms[-1]] != -1:\n jblossoms.append(blossomparent[jblossoms[-1]])\n iblossoms.reverse()\n jblossoms.reverse()\n for (bi, bj) in zip(iblossoms, jblossoms):\n if bi != bj:\n break\n s += 2 * dualvar[bi]\n assert s >= 0\n if mate[i] // 2 == k or mate[j] // 2 == k:\n assert mate[i] // 2 == k and mate[j] // 2 == k\n assert s == 0\n # 2. all single vertices have zero dual value;\n for v in xrange(nvertex):\n assert mate[v] >= 0 or dualvar[v] + vdualoffset == 0\n # 3. all blossoms with positive dual value are full.\n for b in xrange(nvertex, 2*nvertex):\n if blossombase[b] >= 0 and dualvar[b] > 0:\n assert len(blossomendps[b]) % 2 == 1\n for p in blossomendps[b][1::2]:\n assert mate[endpoint[p]] == p ^ 1\n assert mate[endpoint[p ^ 1]] == p\n # Ok.\n\n # Check optimized delta2 against a trivial computation.\n def checkDelta2():\n for v in xrange(nvertex):\n if label[inblossom[v]] == 0:\n bd = None\n bk = -1\n for p in neighbend[v]:\n k = p // 2\n w = endpoint[p]\n if label[inblossom[w]] == 1:\n d = slack(k)\n if bk == -1 or d < bd:\n bk = k\n bd = d\n if DEBUG and (bestedge[v] != -1 or bk != -1) and (bestedge[v] == -1 or bd != slack(bestedge[v])):\n DEBUG('v=' + str(v) + ' bk=' + str(bk) + ' bd=' + str(bd) + ' bestedge=' + str(bestedge[v]) + ' slack=' + str(slack(bestedge[v])))\n assert (bk == -1 and bestedge[v] == -1) or (bestedge[v] != -1 and bd == slack(bestedge[v]))\n\n # Check optimized delta3 against a trivial computation.\n def checkDelta3():\n bk = -1\n bd = None\n tbk = -1\n tbd = None\n for b in xrange(2 * nvertex):\n if blossomparent[b] == -1 and label[b] == 1:\n for v in blossomLeaves(b):\n for p in neighbend[v]:\n k = p // 2\n w = endpoint[p]\n if inblossom[w] != b and label[inblossom[w]] == 1:\n d = slack(k)\n if bk == -1 or d < bd:\n bk = k\n bd = d\n if bestedge[b] != -1:\n (i, j, wt) = edges[bestedge[b]]\n assert inblossom[i] == b or inblossom[j] == b\n assert inblossom[i] != b or inblossom[j] != b\n assert label[inblossom[i]] == 1 and label[inblossom[j]] == 1\n if tbk == -1 or slack(bestedge[b]) < tbd:\n tbk = bestedge[b]\n tbd = slack(bestedge[b])\n if DEBUG and bd != tbd:\n DEBUG('bk=%d tbk=%d bd=%s tbd=%s' % (bk, tbk, repr(bd), repr(tbd)))\n assert bd == tbd\n\n # Main loop: continue until no further improvement is possible.\n for t in xrange(nvertex):\n\n # Each iteration of this loop is a \"stage\".\n # A stage finds an augmenting path and uses that to improve\n # the matching.\n if DEBUG: DEBUG('STAGE %d' % t)\n\n # Remove labels from top-level blossoms/vertices.\n label[:] = (2 * nvertex) * [ 0 ]\n\n # Forget all about least-slack edges.\n bestedge[:] = (2 * nvertex) * [ -1 ]\n blossombestedges[nvertex:] = nvertex * [ None ]\n\n # Loss of labeling means that we can not be sure that currently\n # allowable edges remain allowable througout this stage.\n allowedge[:] = nedge * [ False ]\n\n # Make queue empty.\n queue[:] = [ ]\n \n # Label single blossoms/vertices with S and put them in the queue.\n for v in xrange(nvertex):\n if mate[v] == -1 and label[inblossom[v]] == 0:\n assignLabel(v, 1, -1)\n\n # Loop until we succeed in augmenting the matching.\n augmented = 0\n while 1:\n\n # Each iteration of this loop is a \"substage\".\n # A substage tries to find an augmenting path;\n # if found, the path is used to improve the matching and\n # the stage ends. If there is no augmenting path, the\n # primal-dual method is used to pump some slack out of\n # the dual variables.\n if DEBUG: DEBUG('SUBSTAGE')\n\n # Continue labeling until all vertices which are reachable\n # through an alternating path have got a label.\n while queue and not augmented:\n\n # Take an S vertex from the queue.\n v = queue.pop()\n if DEBUG: DEBUG('POP v=%d' % v)\n assert label[inblossom[v]] == 1\n\n # Scan its neighbours:\n for p in neighbend[v]:\n k = p // 2\n w = endpoint[p]\n # w is a neighbour to v\n if inblossom[v] == inblossom[w]:\n # this edge is internal to a blossom; ignore it\n continue\n if not allowedge[k]:\n kslack = slack(k)\n if kslack <= 0:\n # edge k has zero slack => it is allowable\n allowedge[k] = True\n if allowedge[k]:\n if label[inblossom[w]] == 0:\n # (C1) w is a free vertex;\n # label w with T and label its mate with S (R12).\n assignLabel(w, 2, p ^ 1)\n elif label[inblossom[w]] == 1:\n # (C2) w is an S-vertex (not in the same blossom);\n # follow back-links to discover either an\n # augmenting path or a new blossom.\n base = scanBlossom(v, w)\n if base >= 0:\n # Found a new blossom; add it to the blossom\n # bookkeeping and turn it into an S-blossom.\n addBlossom(base, k)\n else:\n # Found an augmenting path; augment the\n # matching and end this stage.\n augmentMatching(k)\n augmented = 1\n break\n elif label[w] == 0:\n # w is inside a T-blossom, but w itself has not\n # yet been reached from outside the blossom;\n # mark it as reached (we need this to relabel\n # during T-blossom expansion).\n assert label[inblossom[w]] == 2\n label[w] = 2\n labelend[w] = p ^ 1\n elif label[inblossom[w]] == 1:\n # keep track of the least-slack non-allowable edge to\n # a different S-blossom.\n b = inblossom[v]\n if bestedge[b] == -1 or kslack < slack(bestedge[b]):\n bestedge[b] = k\n elif label[w] == 0:\n # w is a free vertex (or an unreached vertex inside\n # a T-blossom) but we can not reach it yet;\n # keep track of the least-slack edge that reaches w.\n if bestedge[w] == -1 or kslack < slack(bestedge[w]):\n bestedge[w] = k\n\n if augmented:\n break\n\n # There is no augmenting path under these constraints;\n # compute delta and reduce slack in the optimization problem.\n # (Note that our vertex dual variables, edge slacks and delta's\n # are pre-multiplied by two.)\n deltatype = -1\n delta = deltaedge = deltablossom = None\n\n # Verify data structures for delta2/delta3 computation.\n if CHECK_DELTA:\n checkDelta2()\n checkDelta3()\n\n # Compute delta1: the minumum value of any vertex dual.\n if not maxcardinality:\n deltatype = 1\n delta = min(dualvar[:nvertex])\n\n # Compute delta2: the minimum slack on any edge between\n # an S-vertex and a free vertex.\n for v in xrange(nvertex):\n if label[inblossom[v]] == 0 and bestedge[v] != -1:\n d = slack(bestedge[v])\n if deltatype == -1 or d < delta:\n delta = d\n deltatype = 2\n deltaedge = bestedge[v]\n\n # Compute delta3: half the minimum slack on any edge between\n # a pair of S-blossoms.\n for b in xrange(2 * nvertex):\n if ( blossomparent[b] == -1 and label[b] == 1 and\n bestedge[b] != -1 ):\n kslack = slack(bestedge[b])\n if type(kslack) in (int, long):\n assert (kslack % 2) == 0\n d = kslack // 2\n else:\n d = kslack / 2\n if deltatype == -1 or d < delta:\n delta = d\n deltatype = 3\n deltaedge = bestedge[b]\n\n # Compute delta4: minimum z variable of any T-blossom.\n for b in xrange(nvertex, 2*nvertex):\n if ( blossombase[b] >= 0 and blossomparent[b] == -1 and\n label[b] == 2 and\n (deltatype == -1 or dualvar[b] < delta) ):\n delta = dualvar[b]\n deltatype = 4\n deltablossom = b\n\n if deltatype == -1:\n # No further improvement possible; max-cardinality optimum\n # reached. Do a final delta update to make the optimum\n # verifyable.\n assert maxcardinality\n deltatype = 1\n delta = max(0, min(dualvar[:nvertex]))\n\n # Update dual variables according to delta.\n for v in xrange(nvertex):\n if label[inblossom[v]] == 1:\n # S-vertex: 2*u = 2*u - 2*delta\n dualvar[v] -= delta\n elif label[inblossom[v]] == 2:\n # T-vertex: 2*u = 2*u + 2*delta\n dualvar[v] += delta\n for b in xrange(nvertex, 2*nvertex):\n if blossombase[b] >= 0 and blossomparent[b] == -1:\n if label[b] == 1:\n # top-level S-blossom: z = z + 2*delta\n dualvar[b] += delta\n elif label[b] == 2:\n # top-level T-blossom: z = z - 2*delta\n dualvar[b] -= delta\n\n # Take action at the point where minimum delta occurred.\n if DEBUG: DEBUG('delta%d=%f' % (deltatype, delta))\n if deltatype == 1: \n # No further improvement possible; optimum reached.\n break\n elif deltatype == 2:\n # Use the least-slack edge to continue the search.\n allowedge[deltaedge] = True\n (i, j, wt) = edges[deltaedge]\n if label[inblossom[i]] == 0:\n i, j = j, i\n assert label[inblossom[i]] == 1\n queue.append(i)\n elif deltatype == 3:\n # Use the least-slack edge to continue the search.\n allowedge[deltaedge] = True\n (i, j, wt) = edges[deltaedge]\n assert label[inblossom[i]] == 1\n queue.append(i)\n elif deltatype == 4:\n # Expand the least-z blossom.\n expandBlossom(deltablossom, False)\n\n # End of a this substage.\n\n # Stop when no more augmenting path can be found.\n if not augmented:\n break\n\n # End of a stage; expand all S-blossoms which have dualvar = 0.\n for b in xrange(nvertex, 2*nvertex):\n if ( blossomparent[b] == -1 and blossombase[b] >= 0 and\n label[b] == 1 and dualvar[b] == 0 ):\n expandBlossom(b, True)\n\n # Verify that we reached the optimum solution.\n if CHECK_OPTIMUM:\n verifyOptimum()\n\n # Transform mate[] such that mate[v] is the vertex to which v is paired.\n for v in xrange(nvertex):\n if mate[v] >= 0:\n mate[v] = endpoint[mate[v]]\n totalweight = 0\n for v in xrange(nvertex):\n assert mate[v] == -1 or mate[mate[v]] == v \n if v < mate[v]:\t\n \tfor (i, j, w) in edges:\n \t\tif i == v and j == mate[v]:\n \tprint (v, mate[v], w)\n #maxweighthere = max(0, max([ wt for (i == v, j == mate[v], wt) in edges ]))\n #print maxweighthere\n print (\"totalweight = \",totalweight)\n return mate","def Viterbi_Transition(words:Sequence[str], train_bag:Sequence[Tuple[str, str]]=train_tagged_words)-> Sequence[Tuple[str, str]]:\n state = []\n all_tags = list(set([pair[1] for pair in train_bag]))\n\n for word_idx, word in enumerate(words):\n # initialise list of probability column for a given observation\n p = []\n for tag in all_tags:\n if word_idx == 0:\n transition_p = tags_df.loc['.', tag]\n else:\n transition_p = tags_df.loc[state[-1], tag]\n\n # compute emission and state probabilities\n emission_p_parts = word_given_tag(word, tag)\n emission_p = emission_p_parts[0]/emission_p_parts[1]\n\n if word in V:\n state_probability = transition_p * emission_p\n else:\n state_probability = transition_p\n\n p.append(state_probability)\n\n p_max = max(p)\n # getting state for which probability is maximum\n state_max = all_tags[p.index(p_max)]\n state.append(state_max)\n return list(zip(words, state))"],"string":"[\n \"def viterbi_tags (untagged_sentences, h):\\n transitions = h[0]\\n emissions = h[1]\\n tags = h[2]\\n maxtags = []\\n #print tags\\n\\n for untaggedsent in untagged_sentences:\\n #Create empty probtable\\n words = untaggedsent.split()\\n r = len(tags)\\n c = len(words)\\n probtable = [None]*r\\n for i in range(r):\\n probtable[i] = [None]*c\\n for j in range(c):\\n probtable[i][j] = [None]*2\\n\\n #Initialize zeroth column of probtable\\n prevtag = '<START>'\\n word = words[0]\\n for i in range(r):\\n tag = tags[i]\\n\\n transition = transitions[prevtag][tag]\\n if word in emissions[tag]:\\n emission = emissions[tag][word]\\n else:\\n emission = .0001*emissions[tag]['<UNKNOWN>']\\n\\n probtable[i][0][0] = transition*emission\\n \\n #Fill in probtable\\n for j in range(1, c):\\n word = words[j]\\n for i in range(r):\\n tag = tags[i]\\n maxprob = 0\\n maxtag = None\\n\\n if word in emissions[tag]:\\n emission = emissions[tag][word]\\n else:\\n emission = .0001*emissions[tag]['<UNKNOWN>']\\n\\n for k in range(r):\\n prevtag = tags[k]\\n transition = transitions[prevtag][tag]\\n prob = probtable[k][j-1][0]*transition*emission\\n \\n if (prob > maxprob):\\n maxprob = prob\\n maxtag = k\\n\\n probtable[i][j][0] = maxprob\\n probtable[i][j][1] = maxtag\\n\\n #Find most likely sequence of POS tags of this sentence\\n sentmaxtags = maxsequence(probtable, tags)\\n maxtags.extend(sentmaxtags)\\n\\n #Return most likely sequence of POS tags of all sentences\\n return maxtags\",\n \"def maxsequence(probtable, tags):\\n r = len(probtable)\\n c = len(probtable[0])\\n\\n maxfinalprob = 0\\n maxfinaltag = None\\n for i in range(r):\\n if (probtable[i][c-1][0] > maxfinalprob):\\n maxfinalprob = probtable[i][c-1][0]\\n maxfinaltag = i\\n\\n #print maxfinaltag\\n\\n maxsequence = []\\n prevmaxtag = maxfinaltag\\n for j in range(c-1, -1, -1):\\n maxsequence.insert(0, tags[prevmaxtag])\\n #print probtable[prevmaxtag][j][1]\\n prevmaxtag = probtable[prevmaxtag][j][1]\\n\\t \\n return maxsequence\",\n \"def id3(x, y, attributes, max_depth, weight, depth=0):\\r\\n tree = {}\\r\\n new_attr = []\\r\\n arr, count = np.unique(y, return_counts=True)\\r\\n # checking edge cases - reached maximum depth, or no attributes\\r\\n if len(attributes) == 0 or depth == max_depth or len(x) == 0:\\r\\n return np.bincount(y).argmax()\\r\\n # if all the values of y are one return one\\r\\n elif len(np.unique(y)) == 1:\\r\\n return arr[0]\\r\\n else:\\r\\n # calculating mutual information values\\r\\n info_values = {} \\r\\n # over number of columns\\r\\n for i in range(data.dim):\\r\\n oldX = partition(x[:,i])\\r\\n oldKeys = oldX.keys()\\r\\n # check in attributes recieved from bagging\\r\\n for attr in attributes:\\r\\n binX = []\\r\\n key , value = attr\\r\\n # check for key and value\\r\\n if i == key and value in oldKeys:\\r\\n # get the index\\r\\n index = oldX[value]\\r\\n for n in range(len(x)):\\r\\n if n in index:\\r\\n # if match binary classification 1 / 0 and appending to binX list\\r\\n binX.append(1)\\r\\n else:\\r\\n binX.append(0)\\r\\n # adding to a dictionary \\r\\n info_values[(i, value)] = mutual_information(binX, y, weight)\\r\\n \\r\\n # getting the maximum feature value\\r\\n best_feature_index = max(info_values, key=info_values.get) \\r\\n best_feature, best_val = best_feature_index\\r\\n # creating the best partition\\r\\n x_best_part = partition(x[:,best_feature])\\r\\n #selecting other than the best feature value from the dictionary\\r\\n new_attr = list(filter(lambda x: x!= (best_feature, best_val), attributes))\\r\\n # increasing depth\\r\\n depth += 1\\r\\n\\r\\n # Calling id3 recursively, checking over 0,1 making a prediction as True / False \\r\\n for n in range(0,2):\\r\\n if n == 0:\\r\\n # recursively calling id3 over the best values of the x partition\\r\\n bestX = x[x_best_part[best_val]]\\r\\n bestY = y[x_best_part[best_val]]\\r\\n tree[best_feature, best_val, 'True'] = id3(bestX, bestY, new_attr, max_depth,weight, depth)\\r\\n else:\\r\\n \\t # recursively calling id3 selecting other than best features\\r\\n othr_idx = []\\r\\n for i in x_best_part:\\r\\n if i != best_val:\\r\\n othr_idx.extend(x_best_part[i])\\r\\n\\r\\n otherX = x[othr_idx]\\r\\n otherY = y[othr_idx]\\r\\n tree[best_feature, best_val, 'False'] = id3(otherX, otherY, new_attr, max_depth,weight, depth)\\r\\n return tree\",\n \"def test_that_most_probable_always_returns_a_tree_of_the_same_logprob():\\n\\n grammar = Grammar(rulebooks)\\n actual_tree = grammar.sample_tree()\\n sentence = actual_tree.terminals\\n inner = grammar.compute_inside_probabilities(sentence)\\n best_tree = grammar.compute_most_likely_tree(sentence, inner, root=0)\\n best_logprob = grammar.logprob_tree(best_tree)\\n\\n for _ in range(20):\\n tree = grammar.compute_most_likely_tree(sentence, inner, root=0)\\n assert np.isclose(grammar.logprob_tree(tree), best_logprob)\",\n \"def viterbi_bigrams(transition_probabilities, label_matches, prev_tag, word, tag_possibilities):\\r\\n\\tmax_prob = 0\\r\\n\\tbest_tag = \\\"\\\"\\r\\n\\ttag_counts = get_tag_counts(label_matches)\\r\\n\\tfor tag in tag_possibilities:\\r\\n\\t\\temissions_probability = get_emissions_probability(label_matches, tag, word, tag_counts)\\r\\n\\t\\ttag_bigram = (prev_tag,tag)\\r\\n\\t\\ttransition_probability = transition_probabilities.get(tag_bigram, 0.000027)\\r\\n\\t\\tprob = emissions_probability * transition_probability\\r\\n\\t\\tif prob > max_prob:\\r\\n\\t\\t\\tmax_prob = prob\\r\\n\\t\\t\\tbest_tag = tag\\r\\n\\tif best_tag == \\\"\\\":\\r\\n\\t\\tbest_tag = \\\"o\\\"\\r\\n\\treturn best_tag\",\n \"def best_B(Ag):\\n top = 0\\n for i in range(len(Ag)):\\n etop = np.min(cf.TD20[int(Ag[i]) - 1])\\n top += etop\\n return top\",\n \"def viterbi_trigrams(transition_probabilities, label_matches, prev_tag, prev_prev_tag, word, tag_possibilities):\\r\\n\\tmax_prob = 0\\r\\n\\tbest_tag = \\\"\\\"\\r\\n\\ttag_counts = get_tag_counts(label_matches)\\r\\n\\tfor tag in tag_possibilities:\\r\\n\\t\\temissions_probability = get_emissions_probability(label_matches, tag, word, tag_counts)\\r\\n\\t\\ttag_trigram = (prev_prev_tag, prev_tag,tag)\\r\\n\\t\\ttransition_probability = transition_probabilities.get(tag_trigram, 0.000027)\\r\\n\\t\\tprob = emissions_probability * transition_probability\\r\\n\\t\\tif prob > max_prob:\\r\\n\\t\\t\\tmax_prob = prob\\r\\n\\t\\t\\tbest_tag = tag\\r\\n\\tif best_tag == \\\"\\\":\\r\\n\\t\\tbest_tag = \\\"o\\\"\\r\\n\\treturn best_tag\",\n \"def kuhn_munkres(G): # maximum profit bipartite matching in O(n^4)\\n assert len(G) == len(G[0])\\n n = len(G)\\n mu = [None] * n # Empty matching\\n mv = [None] * n\\n lu = [max(row) for row in G] # Trivial labels\\n lv = [0] * n\\n for u0 in range(n):\\n if mu[u0] is None: # Free node\\n while True:\\n au = [False] * n # Empty alternating tree\\n av = [False] * n\\n if improve_matching(G, u0, mu, mv, au, av, lu, lv):\\n break\\n improve_labels(G, au, av, lu, lv)\\n return (mu, sum(lu) + sum(lv))\",\n \"def greedy_policy(self):\\n # print(self.weights)\\n policy = defaultdict(lambda: 0)\\n\\n for entry, values in self.weights.items():\\n policy[entry] = np.argmax(self.weights[entry])\\n # print(policy)\\n\\n return policy\",\n \"def predict_greedy(self, tokens: TokenSeq) -> Tuple[NDArray, PosSeq]:\\n \\n #array to hold predictions\\n predictions = np.zeros((len(tokens), len(self.l), len(self.l)))\\n for i in range(len(tokens)):\\n\\n for prev_tag in self.l:\\n new_feature_matrix = []\\n temp_dict = {}\\n feature_dict = add_features(tokens, prev_tag ,i, temp_dict)\\n new_feature_matrix.append(feature_dict)\\n new_feature_matrix = self.vectorizer.transform(new_feature_matrix)\\n \\n probabilities = self.clf.predict_proba(new_feature_matrix)\\n predictions[i, self.label_index(prev_tag)] = probabilities\\n \\n cur = len(probabilities[0])- 1\\n final_predictions = []\\n for i in range(len(predictions)):\\n cur_pred = np.argmax(predictions[i, cur])\\n final_predictions.append(cur_pred)\\n cur = cur_pred\\n ret_matrix = []\\n #print(tokens)\\n final_predictions = self.le.inverse_transform(final_predictions)\\n #print(final_predictions)\\n new_pos = final_predictions.tolist()\\n new_pos.insert(0, \\\"<s>\\\")\\n new_pos.pop(len(new_pos) - 1)\\n for i in range(len(new_pos)):\\n feature_dict = add_features(tokens, new_pos[i] ,i, temp_dict)\\n ret_matrix.append(feature_dict)\\n ret_matrix = self.vectorizer.transform(ret_matrix)\\n\\n return (ret_matrix, final_predictions)\",\n \"def _get_max_bootstrap_genus(self, seq, repeats):\\n word_posteriors = self._word_posteriors\\n word_idxs = self._word_idxs\\n word_size = self._word_size\\n\\n all_words = list(unique_words(seq, word_size))\\n print sorted(map(word_idxs.get, all_words))\\n decisions = [] #genera idxs\\n for words in bootstrap(all_words, len(seq)//word_size, repeats):\\n decisions.append(self._get_max_likelihood_genus(words,\\n word_posteriors, word_idxs))\\n freqs = calc_freqs(concatenate(decisions))\\n sorted_freqs = sorted(freqs.items(), key=itemgetter(1))\\n return sorted_freqs[-1] #what if a tie here?\",\n \"def aux_phrase_subtree(tree, tag_list):\\n highest_subtree = max([(0, None)]+[(subtree.height(), subtree) for subtree in tree.subtrees(lambda x: x.node in tag_list) if subtree != tree[0]])[1]\\n # print highest_subtree\\n return highest_subtree\",\n \"def predict(self, data):\\t\\t\\n\\t\\tpredictions = {}\\n\\t\\tfor tree in self.trees:\\n\\t\\t\\tprediction, prob = tree.predict(data)\\n\\t\\t\\tif prediction in predictions:\\n\\t\\t\\t\\tpredictions[prediction] += prob\\n\\t\\t\\telse:\\n\\t\\t\\t\\tpredictions[prediction] = prob\\n\\t\\treturn max(predictions, key=predictions.get)\",\n \"def lazy_greedy_max(self, budget):\\r\\n\\r\\n classes, no_elements = torch.unique(self.y_trn, return_counts=True)\\r\\n len_unique_elements = no_elements.shape[0]\\r\\n per_class_bud = int(budget / len(classes))\\r\\n final_per_class_bud = []\\r\\n _, sorted_indices = torch.sort(no_elements, descending = True)\\r\\n\\r\\n if self.selection_type == 'PerClass':\\r\\n \\r\\n total_idxs = 0\\r\\n for n_element in no_elements:\\r\\n final_per_class_bud.append(min(per_class_bud, torch.IntTensor.item(n_element)))\\r\\n total_idxs += min(per_class_bud, torch.IntTensor.item(n_element))\\r\\n \\r\\n if total_idxs < budget:\\r\\n bud_difference = budget - total_idxs\\r\\n for i in range(len_unique_elements):\\r\\n available_idxs = torch.IntTensor.item(no_elements[sorted_indices[i]])-per_class_bud \\r\\n final_per_class_bud[sorted_indices[i]] += min(bud_difference, available_idxs)\\r\\n total_idxs += min(bud_difference, available_idxs)\\r\\n bud_difference = budget - total_idxs\\r\\n if bud_difference == 0:\\r\\n break\\r\\n\\r\\n total_greedy_list = []\\r\\n for i in range(len_unique_elements):\\r\\n idxs = torch.where(self.y_trn == classes[i])[0]\\r\\n \\r\\n if self.submod == 'facility_location':\\r\\n self.compute_score(idxs)\\r\\n fl = apricot.functions.facilityLocation.FacilityLocationSelection(random_state=0, metric='precomputed',\\r\\n n_samples=final_per_class_bud[i])\\r\\n elif self.submod == 'graph_cut':\\r\\n self.compute_score(idxs)\\r\\n fl = apricot.functions.graphCut.GraphCutSelection(random_state=0, metric='precomputed',\\r\\n n_samples=final_per_class_bud[i])\\r\\n elif self.submod == 'saturated_coverage':\\r\\n self.compute_score(idxs)\\r\\n fl = apricot.functions.saturatedCoverage.SaturatedCoverageSelection(random_state=0, metric='precomputed',\\r\\n n_samples=final_per_class_bud[i])\\r\\n elif self.submod == 'sum_redundancy':\\r\\n self.compute_score(idxs)\\r\\n fl = apricot.functions.sumRedundancy.SumRedundancySelection(random_state=0, metric='precomputed',\\r\\n n_samples=final_per_class_bud[i])\\r\\n elif self.submod == 'feature_based':\\r\\n fl = apricot.functions.featureBased.FeatureBasedSelection(random_state=0, n_samples=final_per_class_bud[i])\\r\\n\\r\\n if self.submod == 'feature_based':\\r\\n\\r\\n x_sub = fl.fit_transform(self.x_trn[idxs].numpy())\\r\\n greedyList = self.get_index(self.x_trn[idxs].numpy(), x_sub)\\r\\n total_greedy_list.extend(idxs[greedyList])\\r\\n\\r\\n else: \\r\\n\\r\\n sim_sub = fl.fit_transform(self.dist_mat.cpu().numpy())\\r\\n greedyList = list(np.argmax(sim_sub, axis=1))\\r\\n total_greedy_list.extend(idxs[greedyList])\\r\\n\\r\\n elif self.selection_type == 'Supervised':\\r\\n \\r\\n \\r\\n if self.submod == 'feature_based':\\r\\n \\r\\n class_map = {}\\r\\n for i in range(len_unique_elements):\\r\\n class_map[torch.IntTensor.item(classes[i])] = i #Mapping classes from 0 to n\\r\\n \\r\\n sparse_data = torch.zeros([self.x_trn.shape[0], self.x_trn.shape[1]*len_unique_elements])\\r\\n for i in range(self.x_trn.shape[0]):\\r\\n \\r\\n start_col = class_map[torch.IntTensor.item(self.y_trn[i])]*self.x_trn.shape[1]\\r\\n end_col = start_col+self.x_trn.shape[1]\\r\\n sparse_data[i, start_col:end_col] = self.x_trn[i, :]\\r\\n\\r\\n fl = apricot.functions.featureBased.FeatureBasedSelection(random_state=0, n_samples=budget)\\r\\n x_sub = fl.fit_transform(sparse_data.numpy())\\r\\n total_greedy_list = self.get_index(sparse_data.numpy(), x_sub)\\r\\n\\r\\n else:\\r\\n for i in range(len(classes)):\\r\\n \\r\\n if i == 0:\\r\\n idxs = torch.where(self.y_trn == classes[i])[0]\\r\\n N = len(idxs)\\r\\n self.compute_score(idxs)\\r\\n row = idxs.repeat_interleave(N)\\r\\n col = idxs.repeat(N)\\r\\n data = self.dist_mat.cpu().numpy().flatten()\\r\\n else:\\r\\n idxs = torch.where(self.y_trn == classes[i])[0]\\r\\n N = len(idxs)\\r\\n self.compute_score(idxs)\\r\\n row = torch.cat((row, idxs.repeat_interleave(N)), dim=0)\\r\\n col = torch.cat((col, idxs.repeat(N)), dim=0)\\r\\n data = np.concatenate([data, self.dist_mat.cpu().numpy().flatten()], axis=0)\\r\\n \\r\\n \\r\\n sparse_simmat = csr_matrix((data, (row.numpy(), col.numpy())), shape=(self.N_trn, self.N_trn))\\r\\n #self.dist_mat = sparse_simmat\\r\\n\\r\\n if self.submod == 'facility_location':\\r\\n fl = apricot.functions.facilityLocation.FacilityLocationSelection(random_state=0, metric='precomputed',\\r\\n n_samples=budget)\\r\\n elif self.submod == 'graph_cut':\\r\\n fl = apricot.functions.graphCut.GraphCutSelection(random_state=0, metric='precomputed',\\r\\n n_samples=budget)\\r\\n elif self.submod == 'saturated_coverage':\\r\\n fl = apricot.functions.saturatedCoverage.SaturatedCoverageSelection(random_state=0, metric='precomputed',\\r\\n n_samples=budget)\\r\\n elif self.submod == 'sum_redundancy':\\r\\n fl = apricot.functions.sumRedundancy.SumRedundancySelection(random_state=0, metric='precomputed',\\r\\n n_samples=budget)\\r\\n sim_sub = fl.fit_transform(sparse_simmat)\\r\\n total_greedy_list = list(np.array(np.argmax(sim_sub, axis=1)).reshape(-1))\\r\\n\\r\\n\\r\\n if self.selection_type == 'Full':\\r\\n \\r\\n\\r\\n total_greedy_list = []\\r\\n idx_end = self.x_trn.shape[0] - 1\\r\\n idxs = torch.linspace(0, idx_end, self.x_trn.shape[0]).long()\\r\\n\\r\\n if self.submod == 'facility_location':\\r\\n self.compute_score(idxs)\\r\\n fl = apricot.functions.facilityLocation.FacilityLocationSelection(random_state=0, metric='precomputed',\\r\\n n_samples=budget)\\r\\n elif self.submod == 'graph_cut':\\r\\n self.compute_score(idxs)\\r\\n fl = apricot.functions.graphCut.GraphCutSelection(random_state=0, metric='precomputed',\\r\\n n_samples=budget)\\r\\n elif self.submod == 'saturated_coverage':\\r\\n self.compute_score(idxs)\\r\\n fl = apricot.functions.saturatedCoverage.SaturatedCoverageSelection(random_state=0, metric='precomputed',\\r\\n n_samples=budget)\\r\\n elif self.submod == 'sum_redundancy':\\r\\n self.compute_score(idxs)\\r\\n fl = apricot.functions.sumRedundancy.SumRedundancySelection(random_state=0, metric='precomputed',\\r\\n n_samples=budget)\\r\\n elif self.submod == 'feature_based':\\r\\n fl = apricot.functions.featureBased.FeatureBasedSelection(random_state=0, n_samples=budget)\\r\\n\\r\\n if self.submod == 'feature_based':\\r\\n\\r\\n x_sub = fl.fit_transform(self.x_trn.numpy())\\r\\n total_greedy_list = self.get_index(self.x_trn.numpy(), x_sub)\\r\\n\\r\\n else: \\r\\n\\r\\n sim_sub = fl.fit_transform(self.dist_mat.cpu().numpy())\\r\\n total_greedy_list = list(np.argmax(sim_sub, axis=1))\\r\\n\\r\\n return total_greedy_list\",\n \"def run_viterbi(emission_scores, trans_scores, start_scores, end_scores):\\r\\n\\r\\n L = start_scores.shape[0]\\r\\n assert end_scores.shape[0] == L\\r\\n assert trans_scores.shape[0] == L\\r\\n assert trans_scores.shape[1] == L\\r\\n assert emission_scores.shape[1] == L\\r\\n N = emission_scores.shape[0]\\r\\n\\r\\n #T - Score matrix same as in assignement pdf\\r\\n T = np.zeros(shape=(L,N))\\r\\n #Back pointers - to store the previous best tag for word at (i-1)th position\\r\\n #that resulted into current best tag for (i)th word \\r\\n back_pointer = np.full((L,N), -1)\\r\\n\\r\\n for i in xrange(L):\\r\\n emission = emission_scores[0][i]\\r\\n combined = emission + start_scores[i]\\r\\n T[i][0] = combined\\r\\n\\r\\n # Loop over all the words in a sequesnce\\r\\n for i in xrange(1, N):\\r\\n # Loop over all the tags for the word at index i \\r\\n for j in xrange(L):\\r\\n # Varibale for maximum tag score from previous word (word at i-1)\\r\\n tmp_max = float('-inf')\\r\\n tmp_max_idx = -1\\r\\n #Emission value of word at idx i from state (i.e tag) j\\r\\n emission = emission_scores[i][j]\\r\\n #Loop over all the possibile tags for previous word T[tag (1..L), word at i-1]\\r\\n #and get max among them. Store the corresponding back pointer for there T[tag (1..L), word at i-1]\\r\\n for k in xrange(L):\\r\\n transition = trans_scores[k][j]\\r\\n prev_path = T[k][i-1]\\r\\n combined = transition + prev_path\\r\\n if (tmp_max < combined):\\r\\n tmp_max = combined\\r\\n tmp_max_idx = k\\r\\n\\r\\n back_pointer[j][i] = tmp_max_idx\\r\\n T[j][i] = tmp_max + emission\\r\\n\\r\\n # Doing this step outside because if N == 1 then above loop will not run\\r\\n # Variable for maximum tag score\\r\\n tag_max = float('-inf')\\r\\n # Variable for back pointer(previous T[tag, word])\\r\\n tag_max_idx = -1\\r\\n for i in xrange(L):\\r\\n T[i][N-1] = T[i][N-1] + end_scores[i]\\r\\n if (tag_max < T[i][N-1]):\\r\\n tag_max = T[i][N-1]\\r\\n tag_max_idx = i\\r\\n # print(\\\"Max tag -> \\\" + str(tag_max_idx))\\r\\n\\r\\n #Variable to track the path length - should be equal to N\\r\\n path_length = 0\\r\\n #Variable to back track on the tags\\r\\n tag_idx = tag_max_idx\\r\\n #Varibale to track the word index in N\\r\\n word_idx = N-1 \\r\\n #Path strored using backtracking\\r\\n y = []\\r\\n\\r\\n #Getting the best path using backtracking on back_pointers\\r\\n while path_length != N-1:\\r\\n y.append(back_pointer[tag_idx][word_idx])\\r\\n tag_idx = back_pointer[tag_idx][word_idx]\\r\\n word_idx = word_idx - 1\\r\\n path_length = path_length + 1\\r\\n\\r\\n #Reversing the backtracked path\\r\\n y = y[::-1]\\r\\n #Adding the tag for the last word idx in N\\r\\n y.append(tag_max_idx)\\r\\n # print(\\\"Path -> \\\" + str(y))\\r\\n\\r\\n return (tag_max, y)\",\n \"def leaf_prediction(self, node_id): #returns y_idx\\n values = self.tree.value[node_id]\\n return np.argmax(values)\",\n \"def calcMostEdgesToLeaves(unprocN, leaf, TG):\\n\\t\\tmostLeaves = -1\\n\\t\\tretNode = None\\n\\t\\tl_zero = []\\n\\t\\tfor l in leaf:\\n\\t\\t\\tl_zero.append(l[0])\\n\\t\\tfor n in unprocN:\\n\\t\\t\\te_count = 0\\n\\t\\t\\tfor e in TG[n]:\\n\\t\\t\\t\\tif e in l_zero:\\n\\t\\t\\t\\t\\te_count += 1\\n\\t\\t\\tif e_count > mostLeaves:\\n\\t\\t\\t\\tmostLeaves = e_count\\n\\t\\t\\t\\tretNode = n\\n\\t\\t\\tif e_count == 2:\\n\\t\\t\\t\\treturn (retNode, mostLeaves)\\n\\t\\treturn (retNode, mostLeaves)\",\n \"def greedy_learn_search(self,db,labels):\\n queue = PriorityQueue()\\n dolowmem = (self.lowmem == True)\\n numidsets = 0\\n root_ids = range(len(labels))\\n queue.push((self.root,root_ids),len(labels))\\n numnodes = 1\\n deepest = 0\\n err = 0\\n while len(queue) > 0 and numnodes+2 <= self.maxnodes:\\n #print \\\"%d nodes, priority %d\\\"%(numnodes,queue.nextkey())\\n nerr = queue.nextkey()\\n (node,trainingset) = queue.pop()\\n #print \\\"Greedy learn\\\",len(trainingset)\\n if trainingset is None:\\n trainingset = self.identify_examples(db,labels,node)\\n if node.depth >= self.maxdepth or len(trainingset) <= self.minexamples:\\n #print \\\" Hit depth or training set limit\\\"\\n node.pick_best_label(db,labels,trainingset)\\n err += misclassification_error([labels[id] for id in trainingset])\\n continue\\n features = self.feature_subset(node,db,labels,trainingset)\\n cost = node.pick_best_split(db,labels,trainingset,features)\\n numidsets -= len(trainingset)\\n #do a split\\n if node.type == 'v':\\n continue\\n elif node.type == 's':\\n #discrete split\\n node.children = dict()\\n #select sub-indices\\n Eids = defaultdict(list)\\n noneids = []\\n for id in trainingset:\\n v = db[node.feature,id]\\n if v is None:\\n #item doesn't exist, it's a missing value\\n noneids.append(id)\\n else:\\n Eids[v].append(id)\\n #determine whether to switch to low-memory mode\\n if not dolowmem and self.lowmem=='auto':\\n for v,vids in Eids.iteritems():\\n numidsets += len(vids)+len(noneids)\\n if numidsets > self.lowmem_threshold:\\n print \\\"Decision tree learner switching to low-memory mode\\\"\\n dolowmem = True\\n trainingset = None\\n\\n\\n numnodes += len(Eids)\\n #print \\\"Split sizes\\\",[len(v) for v in Eids.itervalues()]\\n #print \\\"None size\\\",len(noneids)\\n for v,vids in Eids.iteritems():\\n #print \\\"->\\\",len(vids),\\\"+\\\",len(noneids)\\n #recurse\\n c = DecisionTreeNode(node)\\n node.children[v] = c\\n err = misclassification_error([labels[id] for id in vids+noneids])\\n cids = (None if dolowmem else vids+noneids)\\n queue.push((c,cids),err)\\n if c.depth > deepest:\\n deepest = c.depth\\n print \\\"Decision tree learner: Reached node with depth\\\",deepest\\n else:\\n #do an inequality split\\n assert node.type == 'i',\\\"Got a weird type? \\\"+str(node.type)\\n leftids = []\\n rightids = []\\n for id in trainingset:\\n val = db[node.feature,id]\\n if val is not None:\\n if val <= node.value: leftids.append(id)\\n else: rightids.append(id)\\n else:\\n leftids.append(id)\\n rightids.append(id)\\n if len(leftids)==0 or len(rightids)==0:\\n print \\\"node feature \\\"+str(node.feature)+\\\" doesn't have a valid split value \\\"+str(node.value)\\n vals = [db[node.feature,id] for id in trainingset if db[node.feature,id]!=None]\\n print \\\"min,max of training set:\\\",min(vals),max(vals)\\n print \\\"cost is\\\",cost\\n raw_input()\\n assert len(leftids) > 0 and len(rightids) > 0\\n if not dolowmem and self.lowmem=='auto':\\n numidsets += len(leftids) + len(rightids)\\n if numidsets > self.lowmem_threshold:\\n print \\\"Decision tree learner switching to low-memory mode\\\"\\n dolowmem = True\\n trainingset = None\\n numnodes += 2\\n c1 = DecisionTreeNode(node)\\n c2 = DecisionTreeNode(node)\\n node.children = {0:c1,1:c2}\\n #print \\\"->\\\",len(leftids)\\n #print \\\"->\\\",len(rightids)\\n err1 = misclassification_error([labels[id] for id in leftids])\\n err2 = misclassification_error([labels[id] for id in rightids])\\n if dolowmem:\\n leftids = None\\n rightids = None\\n queue.push((c1,leftids),err1)\\n queue.push((c2,rightids),err2)\\n if c1.depth > deepest:\\n deepest = c1.depth\\n print \\\"Decision tree learner: Reached node with depth\\\",deepest\\n #end of recursion. for the rest of the nodes still in the queue, make them leaf nodes\\n if len(queue) > 0:\\n print \\\"%d nodes remaining in queue, setting to leaves\\\"%(len(queue),)\\n for (node,trainingset) in queue:\\n node.pick_best_label(db,labels,trainingset)\\n err += misclassification_error([labels[id] for id in trainingset])\\n return err\",\n \"def find_max_triples(st_prob, ed_prob, top_n=5, prob_thd=None, tensor_type=\\\"torch\\\"):\\n if tensor_type == \\\"torch\\\":\\n st_prob, ed_prob = st_prob.data.numpy(), ed_prob.data.numpy()\\n product = np.einsum(\\\"bm,bn->bmn\\\", st_prob, ed_prob)\\n # (N, L, L) the lower part becomes zeros, start_idx < ed_idx\\n upper_product = np.triu(product, k=1)\\n return find_max_triples_from_upper_triangle_product(upper_product, top_n=top_n, prob_thd=prob_thd)\",\n \"def max_value(tree):\\n max_utility = float(\\\"-inf\\\")\\n \\n if (is_terminal(tree)):\\n return tree\\n else:\\n #options = []\\n for node in tree:\\n #options.append(max_value(node))\\n max_utility = max(max_utility, min_value(node))\\n return max_utility\",\n \"def most_probable_class(text, weights):\\n\\n pos_weights = weights['positive']\\n neg_weights = weights['negative']\\n neu_weights = weights['neutral']\\n features = calculate_features(text)\\n pos_numerator = 0.0\\n neg_numerator = 0.0\\n neu_numerator = 0.0\\n denominator = 0.0\\n for f in features:\\n if f in pos_weights and f in neg_weights and f in neu_weights:\\n pos_numerator += pos_weights[f] * features[f]\\n neg_numerator += neg_weights[f] * features[f]\\n neu_numerator += neu_weights[f] * features[f]\\n denominator += pos_numerator + neg_numerator + neu_numerator\\n else:\\n pos_numerator += 0\\n neg_numerator += 0\\n neu_numerator += 0\\n denominator += pos_numerator + neg_numerator + neu_numerator\\n\\n pos_prob = (\\\"positive\\\", exp(pos_numerator))# /exp(denominator))\\n neg_prob = (\\\"negative\\\", exp(neg_numerator))# /exp(denominator))\\n neu_prob = (\\\"neutral\\\", exp(neu_numerator))# /exp(denominator))\\n return max(neu_prob, neg_prob, pos_prob, key=lambda x: x[1])\",\n \"def MaxParsimonyNoTable(X, Tree, tip_row_dict, do_tips=False, naming=False):\\n # 2 represents {0,1} set\\n sp_to_arr = lambda sp_arr: np.array(sp_arr.todense().astype(np.int8))[0]\\n wrap = lambda x: sp_to_arr(X[tip_row_dict[x.name]]) if x.is_leaf() and not do_tips else sp_to_arr(x.genotype)\\n tree_len = 0\\n for _ in Tree.traverse(): tree_len += 1\\n for i, node in tqdm.tqdm(enumerate(Tree.traverse('postorder')), total=tree_len,\\n desc='Ancestral Reconstruction: 1st pass'):\\n if node.is_leaf():\\n if not do_tips:\\n node.genotype = X[tip_row_dict[node.name]]\\n continue\\n if naming: node.name = i\\n children = [wrap(c) for c in node.children]\\n res = children[0].copy()\\n eq = np.equal(*children)\\n res[children[0] == 2] = children[1][children[0] == 2] # 2 is the union {0,1}\\n res[children[1] == 2] = children[0][children[1] == 2]\\n res[(children[0] != 2) & (children[1] != 2) & ~eq] = 2\\n node.genotype = sp.csr_matrix(res)\\n\\n post = Tree.traverse('preorder')\\n root = next(post)\\n root.random = (wrap(root) == 2)\\n root.genotype[root.genotype == 2] = np.random.choice([1, 0], size=(root.genotype == 2).sum())\\n for node in tqdm.tqdm(post, total=tree_len - 1, desc='Ancestral Reconstruction: 2nd pass'):\\n if node.is_leaf(): continue\\n parent_ = wrap(node.up)\\n node_ = wrap(node)\\n res = node_.copy()\\n res[node_ == 2] = parent_[node_ == 2]\\n node.random = (node.up.random) & (node_ == 2) # these are unstable positions - will not be counted\\n node.genotype = sp.csr_matrix(res)\\n\\n return Tree\",\n \"def best_sequence_brute(self, T, pos, psi, phi):\\n ms = self.get_all_tag_seq(len(T))\\n log_scores = tr.zeros(len(ms), dtype=tr.float64)\\n for i in range(len(ms)):\\n log_scores[i] = self.log_score(T, pos, ms[i], psi, phi)\\n best = ms[tr.argmax(log_scores)]\\n tags = []\\n for i in range(len(T)):\\n tags.append(self.get_tag_index(best[i]))\\n return tags\",\n \"def findMaximal(freqSet):\",\n \"def most_discriminating( features_df, labels_df, top=5):\\n \\n columns = features_df.shape[1]\\n labels_df = labels_df[['file', 'candy_id']].set_index('file')\\n qualities = np.zeros(columns)\\n \\n _left = 0\\n _right = 1\\n\\n _c = 0\\n _h = 1\\n\\n # globals\\n cases = float(labels_df['candy_id'].count()) # total cases\\n\\n p_c_A = (labels_df['candy_id'] == 0).sum() / cases\\n p_h_A = 1.0 - p_c_A\\n\\n\\n for feature in range(columns):\\n\\n branch_cases = np.zeros(2) # total on each branch\\n pi = np.zeros(2) # proportion on each branch\\n\\n split = np.array([\\n #c, h\\n [0, 0], #left\\n [0, 0] #right\\n ])\\n\\n for index, value in features_df[feature].iteritems():\\n split[value][labels_df.loc[index][0]] += 1\\n\\n branch_cases[_left] = split[_left].sum()\\n branch_cases[_right] = split[_right].sum()\\n \\n if branch_cases[_left] == 0.0 or branch_cases[_right] == 0.0:\\n qualities[feature] = 0\\n continue\\n \\n pi[_left] = branch_cases[_left] / cases\\n pi[_right] = branch_cases[_right] / cases\\n\\n p_c_B = split[_left][_c] / branch_cases[_left]\\n p_h_B = split[_left][_h] / branch_cases[_left]\\n\\n p_c_C = split[_right][_c] / branch_cases[_right]\\n p_h_C = split[_right][_h] / branch_cases[_right]\\n\\n gini_tree = 1.0 - (math.pow(p_c_A, 2) + math.pow(p_h_A, 2))\\n\\n gini_left = 1.0 - (math.pow(p_c_B, 2) + math.pow(p_h_B, 2))\\n gini_right = 1.0 - (math.pow(p_c_C, 2) + math.pow(p_h_C, 2))\\n\\n quality = gini_tree - pi[_left] * gini_left - pi[_right] * gini_right\\n\\n qualities[feature] = quality\\n return list(reversed(qualities.argsort()))[:top]\",\n \"def decode(self, input):\\n S = [s[0] for s in self.ngram_counts[0].keys()] # Set of tags\\n _S = [s[0] for s in self.ngram_counts[0].keys()]\\n _S.append('*') # _S includes '*' tag\\n X = ['*'] # X stores each sentence. X[0] = '*', X[i] = xi\\n for l in input:\\n x = l.strip()\\n if x: # Word\\n X.append(x)\\n else: # End of a sentence\\n n = len(X) - 1 # the length of the sentence\\n pi = defaultdict(float) # DP table PI\\n bp = {} # back pointer\\n\\n # Initialize DP table\\n for u in _S:\\n for v in _S:\\n pi[tuple([0, u, v])] = 0\\n pi[tuple([0, '*', '*'])] = 1\\n\\n # Viterbi algorithm\\n for k in xrange(1, n + 1):\\n for u in _S:\\n for v in S: # v will not be '*' \\n max_score = 0\\n tag = None\\n for w in _S:\\n if sum([self.emission_counts[tuple([y, X[k]])] for y in S]) < 5: # If the word X[k] is rare word or unseen word in the training corpus,\\n x = symbolize(X[k], self.symbolize_option) # use RARE word probability\\n else:\\n x = X[k]\\n try:\\n score = pi[tuple([k-1, w, u])] * self.q(v, w, u) * self.e(x, v)\\n if max_score < score:\\n max_score = score\\n tag = w\\n except:\\n pass\\n pi[tuple([k, u, v])] = max_score # Update DP table entry\\n bp[tuple([k, u, v])] = tag\\n\\n # Find tag sequence\\n Y = ['*'] # Y stores tag sequence for X. Y[0] = '*', Y[i] = yi\\n Y.extend(n * [None])\\n max_score = None\\n tag = None\\n for u in _S:\\n for v in _S:\\n if self.ngram_counts[1][tuple([u, v])]:\\n score = pi[tuple([n, u, v])] * self.q('STOP', u, v)\\n if max_score is None or max_score < score:\\n max_score = score\\n tag = [u, v]\\n Y[n-1] = tag[0]\\n Y[n] = tag[1]\\n for k in xrange(n - 2, 0, -1):\\n Y[k] = bp[tuple([k + 2, Y[k + 1], Y[k + 2]])]\\n\\n # Write result\\n prev = '*'\\n for k in xrange(1, n + 1):\\n print X[k], Y[k], log(pi[tuple([k, prev, Y[k]])])\\n prev = Y[k]\\n print ''\\n\\n X = ['*'] # set for the next sentence\",\n \"def optimize(seqlen, trees, nstates=2):\\n num_params = nstates * (nstates + 1)\\n guess = np.log(np.random.rand(num_params))\\n # guess = np.log(np.array([0.5, 0.5, 0.13, 0.37, 0.37, 0.13,\\n # 0.32, 0.18, 0.18, 0.32, 0.95, 0.05, 0.05, 0.95]))\\n i, t, s = unfold_params(guess, nstates=nstates)\\n i = np.log(np.ones(nstates) / nstates)\\n i, t = normalize(i), normalize(t)\\n\\n i = np.log(np.array([0.5, 0.5]))\\n # t = np.log(np.array([\\n # [1 - 0.01, 0.01],\\n # [0.01, 1 - 0.01]\\n # ]))\\n t = np.log(np.array([\\n [0.5, 0.5],\\n [0.5, 0.5]\\n ]))\\n s = 1.0 / trees[0].tot_branch_len()\\n\\n guess = np.concatenate((i, np.ndarray.flatten(t), [1]))\\n res = minimize(getProb, guess, args=(nstates, seqlen, trees), method=\\\"L-BFGS-B\\\",\\n options={\\\"maxiter\\\": 250, \\\"disp\\\": True},\\n bounds=(([(np.NINF, 0)] * num_params) + [(0, np.Inf)]))\\n i, t, s = unfold_params(res.x, nstates=nstates)\\n i, t = normalize(i), normalize(t)\\n print(forward(seqlen, t, i, (trees * s)))\\n return np.exp(i), np.exp(t), s\",\n \"def predict_bagging_example(x, h_ens):\\r\\n arr = []\\r\\n for y in h_ens:\\r\\n \\t# calls predict example repeatedly and stores them in an array\\r\\n tst_pred = predict_example(x, h_ens[y][1])\\r\\n arr.append(tst_pred)\\r\\n # returning the maximum voted\\r\\n predict_egz = max(set(arr), key=arr.count)\\r\\n return predict_egz\",\n \"def find_B(self):\\n max_lb = 0\\n for arc in self.arcs():\\n lb = self.arc_info[arc[0]]['lower_bound']\\n max_lb = max(max_lb, lb)\\n n = len(self)\\n m = len(list(self.edges()))\\n return((m - n + 2)*max_lb)\",\n \"def compute_edge_logits(self):\",\n \"def Viterbi(words:Sequence[str], train_bag:Sequence[Tuple[str, str]]=train_tagged_words)-> Sequence[Tuple[str, str]]:\\n state = []\\n tags_set = list(set([pair[1] for pair in train_bag]))\\n\\n for key, word in enumerate(words):\\n # initialise list of probability column for a given observation\\n p = []\\n for tag in tags_set:\\n if key == 0:\\n transition_p = tags_df.loc['.', tag]\\n else:\\n transition_p = tags_df.loc[state[-1], tag]\\n\\n # compute emission and state probabilities\\n emission_p_parts = word_given_tag(word, tag)\\n emission_p = emission_p_parts[0]/emission_p_parts[1]\\n state_probability = emission_p * transition_p\\n p.append(state_probability)\\n\\n p_max = max(p)\\n # getting state for which probability is maximum\\n state_max = tags_set[p.index(p_max)]\\n state.append(state_max)\\n return list(zip(words, state))\",\n \"def viterbi(prob_matrix):\\n TINY = 1e-6 # to avoid NaNs in logs\\n\\n # if prob_matrix is 1D, make it 2D\\n if len(np.shape(prob_matrix)) == 1:\\n prob_matrix = [prob_matrix]\\n \\n length = len(prob_matrix)\\n\\n probs = np.zeros_like(prob_matrix)\\n backpt = np.ones_like(prob_matrix, dtype=np.int32) * -1\\n \\n for i in [0,1,2,3,4]:\\n probs[0][i] = np.log(prob_matrix[0][i]+TINY)\\n \\n # {B, M, E, S} <=== 0:begin, 1:middle, 2:end, 3:single\\n for t in range(1, length):\\n # E, S -> B | B, M -> M | B, M -> E | E, S -> S\\n previous_of = [[0,0], [3,4], [1,2], [1,2], [3,4]]\\n for i in range(5):\\n prevs = previous_of[i]\\n max_id = prevs[np.argmax([probs[t-1][prevs[0]], probs[t-1][prevs[1]]])]\\n backpt[t][i] = max_id\\n probs[t][i] = np.log(prob_matrix[t][i]+TINY) + probs[t-1][max_id]\\n\\n seq = np.ones(length, 'int32') * -1\\n #print(probs[length-1])\\n seq[length-1] = np.argmax(probs[length-1])\\n #print(seq[length-1])\\n max_prob = probs[length-1][seq[length-1]]\\n for t in range(1, length):\\n seq[length-1-t] = backpt[length-t][seq[length-t]]\\n \\n return seq\",\n \"def max_diffs(state):\\n # your code here\\n return best_action(state, pig_actions, Q_pig, win_diff)\",\n \"def max_ntf(self):\\n deg_diffs = set()\\n for k in self.get_fd_part_j(self.id):\\n deg_diff = (self.degree(k) - self.degree(self.id)) % 6\\n deg_diffs.add(deg_diff)\\n if not (deg_diffs <= {0, 1}):\\n return self.get_prp_j(self.id)\\n else:\\n max_lex_set = constants.BOTTOM\\n for k in self.get_fd_part_j(self.id):\\n max_lex_set = self.max_lex(max_lex_set, self.get_prp_j(k)[1])\\n return (self.mod_max(), max_lex_set)\",\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 _height1(self): #works but n^2 time\\n return max(self.depth(p) for p in self.positions() if self.is_leaf(p))\",\n \"def get_relevances(self):\\n\\n # Backpropagate softmax value\\n # relevances = [tf.nn.softmax(self.activations[0])*tf.cast(self.y, tf.float32)]\\n\\n # Backpropagate a value from given labels y\\n relevances = [tf.cast(self.y, tf.float32)]\\n\\n loc_poly = [pol for pol in self.polynomials]\\n loc_pooling = [p for p in self.model.p]\\n print(\\\"\\\\n Relevance calculation:\\\")\\n for i in range(1, len(\\n self.activations)): # start from 1 (not 0) penultimate activations since relevances already contains logits.\\n name = self.activations[i - 1].name.split('/')\\n if 'logits' in name[0] or 'fc' in name[0]:\\n print(\\\"\\\\tFully connected:\\\", name[0])\\n relevances.append(self.prop_fc(name[0], self.activations[i], relevances[-1]))\\n elif 'flatten' in name[0]:\\n print(\\\"\\\\tFlatten layer:\\\", name[0])\\n relevances.append(self.prop_flatten(self.activations[i], relevances[-1]))\\n # print(\\\"\\\\n\\\")\\n elif 'pooling' in name[1]:\\n # TODO: incorporate pooling type and value into name\\n print(\\\"\\\\tPooling:\\\", name[0] + \\\" \\\" + name[1])\\n p = loc_pooling.pop()\\n relevances.append(self.prop_max_pool(self.activations[i], relevances[-1], ksize=[1, p, 1, 1],\\n strides=[1, p, 1, 1]))\\n elif 'conv' in name[0]:\\n if len(loc_poly) > 1:\\n print(\\\"\\\\tConvolution: \\\", name[0], \\\"\\\\n\\\")\\n relevances.append(self.prop_gconv(name[0], self.activations[i], relevances[-1],\\n polynomials=loc_poly.pop()))\\n else:\\n print(\\\"\\\\tConvolution, the first layer:\\\", name[0], \\\"\\\\n\\\")\\n relevances.append(self.prop_gconv_first_conv_layer(name[0], self.activations[i], relevances[-1],\\n polynomials=loc_poly.pop()))\\n else:\\n raise 'Error parsing layer'\\n\\n return relevances\",\n \"def puct_choice(node):\\n return np.argmax(puct_distribution(node))\",\n \"def viterbi_score(confusion_networks):\\n for confusion_network in confusion_networks:\\n prev, score = [-infinity] * len(confusion_network), [-infinity] + [0.0] * len(confusion_network)\\n for t in range(0, len(confusion_network)): # t: words in the sentence (\\\"bfs\\\")\\n prev, score = score, prev\\n for j in range(0, len(confusion_network[t])): # Iterates deep-first in a CN position (\\\"dfs\\\")\\n score[j] = max([prev[i] +\\n confusion_network[i][j][2]\\n for i in range(0, len(confusion_network[t]))])\\n return max([score[i] for i in range(1, len(confusion_network[t]))])\",\n \"def analyze_belief_strength_with_bias(self, G):\\r\\n n = []\\r\\n nbs_list = []\\r\\n for node in G.nodes: #cycles through the nodes of the graph to mine the attributes\\r\\n n.append(node) #appends each node to a list that will be put into a dictionary\\r\\n pbs_list = []\\r\\n og_bs = G.nodes[node]['belief_strength'] #mines the numerical value for a nodes belief strength, from a pre-set node attribute\\r\\n unc = G.nodes[node]['uncertainty'] #mines the numerical value for a nodes belief uncertainty, from a pre-set node attribute\\r\\n prob = G.nodes[node]['probability']\\r\\n for pre in G.predecessors(node):\\r\\n ew = G.edges[pre, node]['weight'] #mines the numerical value of an edge's weight, from a pre-set edge attribute\\r\\n pre_bs = G.nodes[pre]['belief_strength'] #mines the numerical value for a predecessors belief strength, from a pre-set node attribute\\r\\n x = ew * pre_bs #determines how much a node values its neighbor's opinion.\\r\\n pbs_list.append(x) #puts all values for predecessor belief strangths in a list\\r\\n if len(pbs_list) == 0:\\r\\n nbs = og_bs\\r\\n nbs = int(nbs)\\r\\n else:\\r\\n apbs = sum(pbs_list)/len(pbs_list) #calculates the average predecessor belief strength value for a node\\r\\n if apbs*og_bs > 0:\\r\\n if apbs > 0:\\r\\n nbs = min(og_bs + (0.1*prob*unc*apbs), 100)\\r\\n else:\\r\\n nbs = max(og_bs + (0.1*prob*unc*apbs), -100)\\r\\n nbs = int(nbs)\\r\\n else:\\r\\n nbs = og_bs\\r\\n nbs = int(nbs)\\r\\n nbs_list.append(nbs) #the new belief strengths are appended to a list that will be put into adictionary\\r\\n change = dict(zip(n, nbs_list)) #creates a dictionary from two lists which stores the nodes as keys and their new belief strengths as values\\r\\n print(change)\\r\\n return change #this will be used to update the list in a different function\\r\",\n \"def score_max_depths(graph, max_depths):\\n ###TODO\\n pass\",\n \"def tag(self, tokens):\\n numRows = len(self.tags)\\n numCols = len(tokens)\\n # initialize tables for dynamic programming\\n table = array([[0] * numCols] * numRows, dtype=float32)\\n trace = array([[None] * numCols] * numRows)\\n \\n # fill in the base cases, i.e. the first column\\n for row in range(numRows):\\n currentTag = self.tags[row]\\n currentWord = tokens[0] if tokens[0] in self.vocab else '<OOV>'\\n table[row][0] = self.tag_tag_probs['<START>'].prob(currentTag) * self.tag_word_probs[currentTag].prob(currentWord)\\n trace[row][0] = '<START>'\\n \\n # fill the rest of the table\\n # iterate through by columns\\n for col in range(1, numCols):\\n for row in range(numRows):\\n currentTag = self.tags[row]\\n currentWord = tokens[col] if tokens[col] in self.vocab else '<OOV>'\\n maxProbability = 0.0;\\n maxPrevRow = 0\\n \\n # iterate through the previous column and find the maximum probability\\n # as well as the previous tag that led to the maximum probability\\n for prevRow in range(numRows):\\n prevTag = self.tags[prevRow]\\n probability = table[prevRow][col-1] * self.tag_tag_probs[prevTag].prob(currentTag) * self.tag_word_probs[currentTag].prob(currentWord)\\n if probability > maxProbability:\\n maxProbability = probability\\n maxPrevRow = prevRow\\n \\n table[row][col] = maxProbability\\n trace[row][col] = maxPrevRow\\n \\n returnList = []\\n # retrace and construct the tag list\\n maxIndex = argmax(table, axis=0)[-1]\\n # insert the last (token, tag) pair\\n returnList.insert(0, (tokens[-1], self.tags[maxIndex]))\\n # loop through the trace table and prepend each (token, tag) pair\\n i = numCols - 1\\n index = trace[maxIndex][numCols-1]\\n while i > 0:\\n returnList.insert(0, (tokens[i-1], self.tags[index]))\\n i -= 1\\n index = trace[index][i]\\n \\n return returnList\",\n \"def _decode_by_maxprob(self):\\n self.best_path = np.zeros(self.N, dtype=np.int)\\n P = self.prob.copy()\\n \\n for i in range(self.N):\\n idx = np.unravel_index(np.argmax(P, axis=None), P.shape)\\n self.best_path[idx[0]] = idx[1]\\n P[idx[0],:] = 0\\n P[:,idx[1]] = 0\",\n \"def worst_B(Ag):\\n bottom = 0\\n for i in range(len(Ag)):\\n etop = np.max(cf.TD20[int(Ag[i]) - 1])\\n bottom += etop\\n return bottom\",\n \"def greedy_learn(self,node,db,labels,ids):\\n if node.depth >= self.maxdepth or len(ids) <= self.minexamples:\\n #terminate recursion\\n node.pick_best_label(db,labels,ids)\\n err = misclassification_error([labels[id] for id in ids])\\n if err > 0:\\n print \\\"Reached a leaf and had to make some sacrifices, cost\\\",err\\n print \\\" depth\\\",node.depth\\n print \\\" labels\\\",[labels[id] for id in ids]\\n return err\\n\\n features = self.feature_subset(node,db,labels,ids)\\n cost = node.pick_best_split(db,labels,ids,features)\\n \\n #do a split\\n if node.type == 'v':\\n #base case: no misclassifications\\n \\\"\\\"\\\"\\n if cost>0:\\n print \\\"greedy_learn: Warning, pick_best_split indicates a leaf but the cost is nonzero\\\"\\n print \\\"cost=\\\",cost,\\\"misclassification=\\\",misclassification_error([labels[id] for id in ids])\\n print \\\"# of ids:\\\",len(ids)\\n for i in ids:\\n print \\\"id\\\",i,\\\",\\\",\\n for k in range(db.numFeatures()):\\n if db[k,i] != None:\\n print k,\\\"=\\\",db[k,i],\\\",\\\",\\n print \\\"label\\\",labels[i]\\n raw_input()\\n \\\"\\\"\\\"\\n return 0\\n elif node.type == 's':\\n #print \\\"Picked feature\\\",node.feature,\\\"split\\\"\\n #do a discrete split\\n node.children = dict()\\n #select sub-indices\\n Eids = defaultdict(list)\\n noneids = []\\n for id in ids:\\n v = db[node.feature,id]\\n if v is None:\\n #item doesn't exist, it's a missing value\\n noneids.append(id)\\n else:\\n Eids[v].append(id)\\n #print \\\" split sizes:\\\",[len(x) for x in Eids.values()]\\n #print \\\" None ids:\\\",len(noneids)\\n ids = None\\n errors = 0\\n for v,vids in Eids.iteritems():\\n #recurse\\n c = DecisionTreeNode(node)\\n #print \\\"Recursing on value\\\",v\\n #print \\\" ids:\\\",vids\\n errors += self.greedy_learn(c,db,labels,vids+noneids)\\n node.children[v] = c\\n if c.depth > self.deepest:\\n self.deepest = c.depth\\n print \\\"Decision tree learner: Reached node with depth\\\",self.deepest\\n return errors\\n else:\\n #do an inequality split\\n assert node.type == 'i'\\n #print \\\"Picked feature\\\",node.feature,\\\"inequality value\\\",node.value,\\\"cost\\\",cost\\n leftids = []\\n rightids = []\\n for id in ids:\\n if db[node.feature,id] is not None:\\n if db[node.feature,id] <= node.value: leftids.append(id)\\n else: rightids.append(id)\\n else:\\n leftids.append(id)\\n rightids.append(id)\\n if len(rightids) == len(ids) or len(leftids) == len(ids):\\n #due to missing values, this split is useless\\n errors = misclassification_error([labels[id] for id in ids])\\n print \\\"useless split on feature\\\",node.feature,\\\"value\\\",node.value,\\\"misclassification error\\\",errors\\n print \\\"Left size\\\",len(leftids),\\\"right size\\\",len(rightids)\\n raw_input()\\n node.pick_best_label(db,labels,ids)\\n return errors\\n #clear memory associated with ids list\\n del ids[:]\\n ids = None\\n #print \\\"Left size\\\",len(leftids),\\\"right size\\\",len(rightids)\\n c1 = DecisionTreeNode(node)\\n c2 = DecisionTreeNode(node)\\n #left side\\n errors = self.greedy_learn(c1,db,labels,leftids)\\n #right side\\n errors += self.greedy_learn(c2,db,labels,rightids)\\n #restore index\\n node.children = {0:c1,1:c2}\\n if c1.depth > self.deepest:\\n self.deepest = c1.depth\\n print \\\"Decision tree learner: Reached node with depth\\\",self.deepest\\n return errors\",\n \"def build(self):\\n # weights to apply to training samples, updated on each\\n # iteration of the boosting algo, normalised to 1\\n sigWeights = np.ones(self.nSig, dtype=float)\\n bkgWeights = np.ones(self.nBkg, dtype=float)\\n reweight = 1.0/(np.sum(sigWeights)+np.sum(bkgWeights))\\n sigWeights *= reweight\\n bkgWeights *= reweight \\n\\n # Weight of each tree, strong classifers have higher weight\\n self.treeWeights = np.zeros(self.ntrees, dtype=float)\\n\\n for i in xrange(self.ntrees):\\n\\n # build new tree\\n newTree = Tree()\\n newTree.load(self.sigData,self.bkgData,weights=(sigWeights,bkgWeights))\\n newTree.build()\\n self.dTrees.append(newTree) \\n\\n # evaluate trees\\n # keep track of each event\\n err = 0.0\\n sigWrong = np.zeros(self.nSig)\\n bkgWrong = np.zeros(self.nBkg)\\n\\n for j in range(self.nSig):\\n if newTree.classify(np.array((self.sigData[j,])))<0:\\n sigWrong[i]=1\\n err+=sigWeights[j]\\n\\n for j in range(self.nBkg):\\n if newTree.classify(np.array((self.bkgData[j,])))>0:\\n bkgWrong[i]=1\\n err+=bkgWeights[j]\\n\\n alpha = self.beta*math.log((1.0-err)/err)\\n print err,alpha\\n corFactor = math.exp(-alpha)\\n wrongFactor = math.exp(alpha)\\n\\n if (err<1e-20 or err >= 0.5):\\n print \\\"SOEMTHING WRONG!!\\\"\\n\\n self.treeWeights[i] = alpha\\n\\n # reweight training samples\\n for j in range(self.nSig):\\n if sigWrong[j]:\\n sigWeights[j]*=wrongFactor\\n else :\\n sigWeights[j]*=corFactor\\n\\n for j in range(self.nBkg):\\n if bkgWrong[j]:\\n bkgWeights[j]*=wrongFactor\\n else :\\n bkgWeights[j]*=corFactor\\n\\n # normalise weights\\n reweight = 1.0/(np.sum(sigWeights)+np.sum(bkgWeights))\\n sigWeights *= reweight\\n bkgWeights *= reweight\",\n \"def maximal_chain_binary_trees(self):\\n for it in self.maximal_chain_tamari_intervals():\\n yield it.lower_binary_tree()\",\n \"def evaluate(self):\\n max_decomp = 0\\n max_value = 0.0\\n v = 0.0\\n # find the largest log-value in children\\n # for computing the log-exponentials\\n for d in self.__children:\\n # should be Product Node\\n node = self.__children[d]\\n value = node.getLogValue()\\n if value == Node.ZERO:\\n continue\\n if max_decomp == 0 or value > max_value:\\n max_decomp = d\\n max_value = value\\n\\n if max_decomp == 0:\\n self.setLogValue(Node.ZERO)\\n\\n for d in self.__children:\\n if d not in self.__children_counts:\\n continue\\n cnts = self.__children_counts[d]\\n node = self.__children[d]\\n value = node.getLogValue()\\n if value == Node.ZERO:\\n continue\\n v += cnts * np.exp(value - max_value)\\n self.setLogValue(np.log(v / self.__counts) + max_value)\",\n \"def most_frequent_eval(test_set, pred_tags):\\n gold_tag_seqs = []\\n pred_tag_seqs = []\\n for sent in test_set:\\n words, true_tags = zip(*sent)\\n gold_tag_seqs.append(true_tags)\\n\\n ### YOUR CODE HERE\\n DEFAULT_TAG = 'O'\\n \\n pred_tags_list = []\\n for word in words:\\n tag = DEFAULT_TAG\\n if word in pred_tags:\\n tag = pred_tags[word]\\n pred_tags_list.append(tag)\\n pred_tag_seqs.append(tuple(pred_tags_list)) \\n ### END CODE HERE\\n\\n return evaluate_ner(gold_tag_seqs, pred_tag_seqs)\",\n \"def hmm_mle(self):\\n\\n b = Baseline(self.pos_tags, self.words, self.training_set)\\n self.e = b.e\\n self.q = {tag: 0 for tag in self.pos_tags}\\n first_tag = [sentence[0][1] for sentence in self.training_set]\\n for tag in first_tag:\\n self.q[tag] += 1\\n for tag in self.pos_tags:\\n self.q[tag] /= len(first_tag)\\n\\n t = {}\\n tag_count = {}\\n # initialize all to zero\\n for tag1 in self.pos_tags:\\n tag_count[tag1] = 0\\n for tag2 in self.pos_tags:\\n t[tag1, tag2] = 0\\n\\n # count i before j\\n for sentence in self.training_set:\\n sentence_tags = sentence[0]\\n for i in range(len(sentence[0])-1):\\n t[sentence_tags[i+1], sentence_tags[i]] += 1\\n tag_count[sentence_tags[i]] += 1\\n # tag_count[sentence_tags[-1]]+=1\\n self.t = t\\n # divide by some of tag1 count\\n for tag1 in self.pos_tags:\\n for tag2 in self.pos_tags:\\n if tag1 == \\\"END\\\" or tag2 ==\\\"START\\\":\\n continue\\n t[tag2, tag1] /= tag_count[tag1]\\n\\n return self.q, self.e, self.t\",\n \"def viterbi(sent, dqml, eqml, S, V_CASE=-1):\\n\\n if type(sent) is list:\\n sent_words = sent\\n else:\\n sent_words = word_tokenize(sent)\\n n = len(sent_words)\\n\\n # define and initialize PI table\\n pi = defaultdict(Counter)\\n pi[0]['*'] = 1\\n bp = {}\\n\\n for k in range(1, n+1):\\n bp[k] = {}\\n for v in S:\\n eml = compute_eml(V_CASE, eqml, k, sent_words, v)\\n if k-1 is 0: # w e S_0 -> w = '*'\\n qmlr = compute_qml(dqml, v, '*')\\n pival = pi[0]['*'] * qmlr * eml\\n pi[k][v] = pival\\n bp[k][v] = '*'\\n else: # for w e S_k, S_k = S\\n max_S = None\\n max_w = -1\\n for w in S:\\n qmlr = compute_qml(dqml, v, w)\\n currmax = pi[k-1][w] * qmlr * eml\\n if currmax > 0 and currmax > max_w:\\n max_w = currmax\\n max_S = w\\n # if word is unknown use tag 'NN'\\n if max_S is None:\\n max_w = 0.0\\n max_S = UNKNOWN_TAG\\n pi[k][v] = max_w\\n bp[k][v] = max_S\\n\\n # calculate y_n\\n max_y = -1\\n yn = None\\n for v in S:\\n nextmax = pi[n][v] * compute_propability('STOP', v, dqml)\\n if nextmax > max_y:\\n max_y = nextmax\\n yn = v\\n\\n # calculate y_n-1....y1\\n yk1 = yn\\n tagSequence = list()\\n tagSequence.append(yn)\\n for k in range(n-1,0,-1):\\n yk = bp[k+1][yk1]\\n tagSequence.append(yk)\\n yk1 = yk\\n\\n tagSequence.reverse()\\n return tagSequence\",\n \"def max_evidence(self):\\n self.A = np.linalg.inv(self.Sn)\\n A_eigval = np.linalg.eigvals(self.A)\\n gamma = 0\\n for i in range(len(A_eigval)):\\n gamma += A_eigval[i]/(self.alpha + A_eigval[i])\\n new_alpha = gamma/(self.mn.T@self.mn)\\n\\n sum = 0\\n for i in range(self.n):\\n sum +=(self.t[i]-self.mn.T@self.design_matrix[i])**2\\n new_beta = 1/((1/(self.n-gamma))*sum)\\n\\n return new_alpha, new_beta\",\n \"def greedy_build(nodes, priors=None, cutoff=200, considered=set(), uniq='', targets=[]):\\n\\n\\t# Tracks frequency of states for each character in nodes\\n\\tcharacter_mutation_mapping = defaultdict(int)\\n\\n\\t# G models the network that is returned recursively\\n\\tG = nx.DiGraph()\\n\\n\\troot = root_finder(nodes)\\n\\n\\t# Base case check for recursion, returns a graph with one node corresponding to the root of the remaining nodes\\n\\tif len(nodes) <= cutoff or len(nodes) == 1:\\n\\t\\troot = root_finder(nodes)\\n\\t\\tG.add_node(root)\\n\\t\\treturn G, [[root, nodes]]\\n\\n\\t# Accounting for frequency of mutated states per character, in order to choose the best split\\n\\tfor node in nodes:\\n\\t\\tnode_list = node.split(\\\"_\\\")[0].split('|')\\n\\t\\tfor i in range(0, len(node_list)):\\n\\t\\t\\tchar = node_list[i]\\n\\t\\t\\tif char != '0' and char != '-':\\n\\t\\t\\t\\tcharacter_mutation_mapping[(str(i), char)] += 1\\n #if char != '0':\\n # if char == \\\"-\\\":\\n # character_mutation_mapping[(str(i), char)] -= 1\\n # else:\\n # character_mutation_mapping[(str(i), char)] += 1\\n\\n\\t# Choosing the best mutation to split on (ie character and state)\\n\\tcharacter, state = 0, 0\\n\\tmax_cost = 0\\n\\n\\tmin_prior = 1\\n\\tif priors:\\n\\t\\tfor i in priors.keys():\\n\\t\\t\\tfor j in priors[i].keys():\\n\\t\\t\\t\\tmin_prior = min(min_prior, priors[i][j])\\n\\n\\tfor i,j in character_mutation_mapping:\\n\\t\\tif not (i,j) in considered:\\n\\t\\t\\tif not priors:\\n\\t\\t\\t\\tif max_cost < character_mutation_mapping[(i, j)]:\\n\\t\\t\\t\\t\\tmax_cost = character_mutation_mapping[(i, j)]\\n\\t\\t\\t\\t\\tcharacter, state = i, j\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif j not in priors[int(i)]:\\n\\t\\t\\t\\t\\tpriors[int(i)][j] = min_prior\\n\\t\\t\\t\\tif max_cost < -np.log(priors[int(i)][j]) * character_mutation_mapping[(i, j)]:\\n\\t\\t\\t\\t\\tmax_cost = -np.log(priors[int(i)][j]) * character_mutation_mapping[(i, j)]\\n\\t\\t\\t\\t\\tcharacter, state = i, j\\n\\tcharacter = int(character)\\n\\n\\n\\t# If there is no good split left, stop the process and return a graph with the remainder of nodes\\n\\tif character == 0 and state == 0:\\n\\t\\tif len(nodes) == 1:\\n\\t\\t\\tG.add_node(nodes[0])\\n\\t\\telse:\\n\\t\\t\\tfor i in range(0, len(nodes)):\\n\\t\\t\\t\\tif nodes[i] != root:\\n\\t\\t\\t\\t\\tG.add_edge(root, nodes[i])\\n\\t\\treturn G, []\\n\\n\\t# Splitting nodes based on whether they have the mutation, don't have the mutation, or are NA('-') in that character\\n\\t# Right split is where nodes with the mutation go, everyone else goes to left split or NA chars\\n\\tleft_split, right_split, NA_chars = [], [], []\\n\\tright_split_temp = []\\n\\tleft_split_temp = []\\n\\tfor node in nodes:\\n\\t\\tnode_list = node.split('|')\\n\\t\\tif node_list[character] == state:\\n\\t\\t\\tright_split.append(node)\\n\\t\\telif node_list[character] == '-':\\n\\t\\t\\tNA_chars.append(node)\\n\\t\\telse:\\n\\t\\t\\tleft_split.append(node)\\n\\n\\n\\t# Seperates all nodes with NA in the character chosen to be split upon\\n\\t# Puts in right split or left split based on which list shares more mutated characters with this string\\n\\tfor node in NA_chars:\\n\\t\\tright_split_score = 0\\n\\t\\tleft_split_score = 0\\n\\t\\tnode_list = node.split('|')\\n\\t\\tnum_not_missing = len([n for n in node_list if n != \\\"-\\\"])\\n\\t\\tfor i in range(0, len(node_list)):\\n\\t\\t\\tif node_list[i] != '0' and node_list[i] != '-':\\n\\t\\t\\t\\tfor node_2 in left_split:\\n\\t\\t\\t\\t\\tnode2_list = node_2.split('|')\\n\\t\\t\\t\\t\\tif node_list[i] == node2_list[i]:\\n\\t\\t\\t\\t\\t\\tleft_split_score += 1\\n\\t\\t\\t\\tfor node_2 in right_split:\\n\\t\\t\\t\\t\\tnode2_list = node_2.split('|')\\n\\t\\t\\t\\t\\tif node_list[i] == node2_list[i]:\\n\\t\\t\\t\\t\\t\\tright_split_score += 1\\n\\n\\t\\tavg_left_split_score = left_split_score / float(len(left_split) * num_not_missing + 1)\\n\\t\\tavg_right_split_score = right_split_score / float(len(right_split) * num_not_missing + 1)\\n\\n\\t\\tif avg_left_split_score < avg_right_split_score:\\n\\t\\t\\tright_split_temp.append(node)\\n\\t\\telse:\\n\\t\\t\\tleft_split_temp.append(node)\\n\\n\\tright_split += right_split_temp\\n\\tleft_split += left_split_temp\\n\\n\\t# Add character, state that split occurred to already considered mutations\\n\\tconsidered.add((str(character), state))\\n\\tG = nx.DiGraph()\\n\\t#splitter = str(character) + \\\" \\\" + str(state) + \\\" (\\\" + uniq + \\\")\\\"\\n\\tsplitter = root\\n\\n\\t# Recursively build left side of network (ie side that did not mutation at the character with the specific state)\\n\\tG.add_node(splitter)\\n\\tleft_subproblems = []\\n\\tleft_network = None\\n\\tif len(left_split) != 0:\\n\\t\\tleft_root = root_finder(left_split)\\n\\t\\t# if left_root not in left_split and left_root in targets:\\n\\t\\t# \\tleft_root = left_root + \\\"_unique\\\"\\n\\n\\t\\tleft_network, left_subproblems = greedy_build(left_split, priors, cutoff, considered.copy(), uniq + \\\"0\\\", targets=targets)\\n\\n\\t\\tleft_nodes = [node for node in left_network.nodes() if left_network.in_degree(node) == 0]\\n\\t\\tdup_dict = {}\\n\\t\\tfor n in left_network:\\n\\t\\t\\tif n in list(G.nodes()) and n != left_root:\\n\\t\\t\\t\\tdup_dict[n] = n + \\\"_\\\" + str(hashlib.md5(left_root.encode('utf-8')).hexdigest())\\n\\t\\tleft_network = nx.relabel_nodes(left_network, dup_dict)\\n\\t\\tG = nx.compose(G, left_network)\\n\\t\\tif root != left_root:\\n\\t\\t\\tG.add_edge(splitter, left_root, weight=0, label=\\\"None\\\")\\n\\n\\t# Recursively build right side of network\\n\\tright_network, right_subproblems = greedy_build(right_split, priors, cutoff, considered.copy(), uniq + \\\"1\\\", targets=targets)\\n\\tright_nodes = [node for node in right_network.nodes() if right_network.in_degree(node) == 0]\\n\\tright_root = root_finder(right_split)\\n\\n\\tdup_dict = {}\\n\\tfor n in right_network:\\n\\t\\tif n in list(G.nodes()) and n != right_root:\\n\\t\\t\\tdup_dict[n] = n + \\\"_\\\" + str(hashlib.md5(right_root.encode('utf-8')).hexdigest())\\n\\tfor n in dup_dict:\\n\\t\\trename_dict = {n: dup_dict[n]}\\n\\t\\tif right_network.out_degree(n) != 0:\\n\\t\\t\\tright_network = nx.relabel_nodes(right_network, rename_dict)\\n\\t\\telse:\\n\\t\\t\\trename_dict = {n: dup_dict[n]}\\n\\t\\t\\tG = nx.relabel_nodes(G, rename_dict)\\n\\n\\tG = nx.compose(G, right_network)\\n\\t# if right_root not in right_split and right_root in targets:\\n\\t# \\tright_root = right_root + \\\"_unique\\\"\\n\\t#for node in right_nodes:\\n\\tif root != right_root:\\n\\t\\tif not priors:\\n\\t\\t\\tG.add_edge(splitter, right_root, weight=1, label = str(character) + \\\": 0 -> \\\" + str(state))\\n\\t\\telse:\\n\\t\\t\\tG.add_edge(splitter, right_root, weight=-np.log(priors[int(character)][state]), label=str(character) + \\\": 0 -> \\\" + str(state))\\n\\n\\n\\treturn G, left_subproblems + right_subproblems\",\n \"def max_diffs(state):\\n return best_action(state, pig_actions, Q_pig, win_diff)\",\n \"def classify(self, instance):\\n \\\"\\\"\\\"best stores the pointer to the sequence with maximum final probability from the Viterbi trellis.\\n Iterate through the corresponding row in the backtrace_pointers matrix backwards and add its labels to best_sequence list.\\n Return the reversed best_sequence list.\\n \\\"\\\"\\\"\\n backtrace_pointers = self.dynamic_programming_on_trellis(instance, False)\\n best_sequence = []\\n trellis = backtrace_pointers[0]\\n best = trellis[:,len(instance.data)-1].argmax()\\n i = best\\n best_sequence = [self.labels[best]]\\n for j in xrange(len(instance.data)-1, 0, -1):\\n i = backtrace_pointers[1][i][j]\\n best_sequence.append(self.labels[i])\\n return best_sequence[::-1]\",\n \"def max_gain(self):\\n if self.val1:\\n val1_gain_tuple, val0_gain_tuple = self.val1.max_gain(), self.val0.max_gain()\\n if val1_gain_tuple.gain > val0_gain_tuple.gain:\\n return val1_gain_tuple\\n else:\\n return val0_gain_tuple\\n elif self.attributes:\\n filtered_data = filter_data(self.data,self.ancestors)\\n max_attribute, max_gain = max([(attribute,\\n self.heuristic(self,attribute)) for attribute in self.attributes],\\n key = lambda x: x[1])\\n return gain_tuple(self, max_attribute, max_gain)\\n return gain_tuple(None, '', 0)\",\n \"def propagate(self, game_state, maximizing, max_hitherto, depth, max_depth):\\n \\n self.moves += 1\\n \\n\\n if depth >= max_depth:\\n rating = self.rate_game_state(game_state)\\n return (rating, None, None)\\n \\n else:\\n \\n color = self.color if maximizing else (1 if self.color==0 else 0)\\n\\n self.timer.start(\\\"\\\")\\n choices = []\\n for line in self.get_all_lines(game_state, color):\\n for vector in LEGAL_VECTORS:\\n\\n new_game_state = self.get_new_game_state(game_state, line, vector, color)\\n if new_game_state is not None:\\n choices.append( (new_game_state, self.rate_game_state(new_game_state), line, vector) )\\n\\n \\n\\n choices = sorted(choices, key=lambda x: x[1], reverse=maximizing)\\n \\n final_choice = choices[0][1:]\\n del choices[0]\\n\\n for choice in choices:\\n\\n new_game_state, estimated_rating, line, vector = choice\\n actual_rating, _, _ = self.propagate(new_game_state, not maximizing, final_choice[0], depth+1, max_depth)\\n \\n if maximizing:\\n if final_choice[0] < actual_rating:\\n final_choice = (actual_rating, line, vector)\\n\\n else: #minimizing\\n if (max_hitherto is not None) and (actual_rating < max_hitherto): return (actual_rating, line, vector)\\n\\n if actual_rating < final_choice[0]:\\n final_choice = (actual_rating, line, vector)\\n\\n return final_choice\",\n \"def extract_belief(ontology, state, threshold=0.3):\\n\\n \\\"\\\"\\\"\\n need to check if the value at argmax is bigger than a threshold\\n \\\"\\\"\\\"\\n\\n request_idx = np.argmax([item[1] for item in state[\\\"request\\\"].items()])\\n frequency_idx = np.argmax([item[1] for item in state[\\\"frequency\\\"].items()])\\n illness_type_idx = np.argmax([item[1] for item in state[\\\"type\\\"].items()])\\n symptom_idx = np.argmax([item[1] for item in state[\\\"symptom\\\"].items()])\\n escalation_idx = np.argmax([item[1]\\n for item in state[\\\"escalation\\\"].items()])\\n duration_idx = np.argmax([item[1] for item in state[\\\"duration\\\"].items()])\\n confirmation_idx = np.argmax([item[1]\\n for item in state[\\\"confirmation\\\"].items()])\\n\\n # then it is neccessary to map the indices back to words\\n\\n request = is_plausible(state[\\\"request\\\"].items(\\n ), ontology, \\\"request\\\", request_idx, threshold)\\n frequency = is_plausible(\\n state[\\\"frequency\\\"].items(), ontology, \\\"frequency\\\", frequency_idx, threshold)\\n illness_type = is_plausible(\\n state[\\\"type\\\"].items(), ontology, \\\"type\\\", illness_type_idx, threshold)\\n symptom = is_plausible(state[\\\"symptom\\\"].items(\\n ), ontology, \\\"symptom\\\", symptom_idx, threshold)\\n\\n escalation = is_plausible(state[\\\"escalation\\\"].items(\\n ), ontology, \\\"escalation\\\", escalation_idx, threshold)\\n\\n duration = is_plausible(state[\\\"duration\\\"].items(\\n ), ontology, \\\"duration\\\", duration_idx, threshold)\\n confirmation = is_plausible(state[\\\"confirmation\\\"].items(\\n ), ontology, \\\"confirmation\\\", confirmation_idx, threshold)\\n\\n if request is not None:\\n request = \\\"request \\\"+request\\n if frequency is not None:\\n frequency = \\\"frequency \\\"+frequency\\n if illness_type is not None:\\n illness_type = \\\"type \\\"+illness_type\\n if symptom is not None:\\n symptom = \\\"symptom \\\"+symptom\\n if escalation is not None:\\n escalation = \\\"escalation \\\"+escalation\\n if duration is not None:\\n duration = \\\"duration \\\"+duration\\n if confirmation is not None:\\n confirmation = \\\"confirmation \\\"+confirmation\\n values = [request, frequency, illness_type,\\n symptom, escalation, duration, confirmation]\\n values = [val for val in values if val is not None]\\n return sorted(values)\",\n \"def _height1(self): # works, but O(n^2) worst-case time\\n return max(self.depth(p) for p in self.positions() if self.is_leaf(p))\",\n \"def _get_lip_best(self) -> float:\\n pass\",\n \"def bagging(x, y, max_depth, num_trees):\\r\\n\\r\\n hypotheses = {}\\r\\n # attribute_idx = np.array(range(data.dim))\\r\\n # generating attributes\\r\\n attributes = []\\r\\n cols = data.dim\\r\\n for i in range(cols):\\r\\n arr = np.unique(x[:, i])\\r\\n for value in arr:\\r\\n attributes.append((i, value))\\r\\n lena = len(x)\\r\\n # initializing weights to 1 for boosting\\r\\n alpha = 1\\r\\n w = np.ones((lena, 1), dtype=int) \\r\\n # iterating over j number of trees\\r\\n for j in range(num_trees):\\r\\n # generating a random array of indicies with replacement over the length of x\\r\\n new_array = np.random.choice(lena,size =lena,replace=True)\\r\\n #calling id3 over the indices of the new array\\r\\n tree = id3(x[new_array],y[new_array],attributes, max_depth, w)\\r\\n # appending to a global tree as a weighted pair\\r\\n hypotheses[j] = (alpha, tree)\\r\\n\\r\\n return hypotheses\",\n \"def beam_search_stack_decode(self, src_sentence):\\n for i in xrange(len(src_sentence)):\\n for j in xrange(i+1, len(src_sentence)):\\n foreign_phrase = src_sentence[i:j]\\n best_phrase = \\\"\\\"\\n highest_prob = float('-inf')\\n for native_phrase, translation_probability in self.phrase_table[foreign_phrase]:\\n prob = translation_probability + self.language_model.score(native_phrase.split())\\n if prob > highest_prob:\\n highest_prob = prob\\n best_phrase = native_phrase\\n self.heuristic_table[i][j] = (best_phrase, highest_prob)\\n\\n\\n\\n hypStacks = [ HypoStack() for i in len(src_sentence)]\\n \\\"\\\"\\\"\\n What will the cost of NULL Hypothesis be? Right now I don't know what it calculates but I'm enqueueing it with FLT_MAX\\n \\\"\\\"\\\"\\n hypStacks[0].add(Hypothesis([], '0' * len(src_sentence), 0, 0, None, self.heuristic_table, self.language_model, self.phrase_table), float('inf'))\\n\\n for hypStack in hypStacks:\\n while len(hypStack) > 0:\\n hyp = hypStack.pop()\\n new_hyps = self.derive_new_hyps(hyp)\\n for new_hyp in new_hyps:\\n nf_new_hyp = new_hyp.covering.count('1')\\n \\\"\\\"\\\"\\n DONE\\n For recombination, whenever you add a new hypothesis, look at the other hypotheses in the same stack and do this:\\n – same number of foreign words translated\\n – same last three English words in output\\n – same last foreign word translated\\n \\\"\\\"\\\"\\n new_hyp_trigram = new_hyp.get_trigram()\\n new_hyp_last_word = src_sentence[new_hyp.fp_end]\\n add_new_hyp = True\\n for other_hyp in hypStacks[nf_new_hyp]:\\n if other_hyp.get_trigram() == new_hyp_trigram and src_sentence[other_hyp.fp_end] == new_hyp_last_word:\\n if other_hyp.cost < new_hyp.cost:\\n add_new_hyp = False\\n break\\n else:\\n hypStacks[nf_new_hyp].remove(other_hyp)\\n if not add_new_hyp: continue\\n hypStacks[nf_new_hyp].add(new_hyp, new_hyp.cost)\\n if len(hypStacks[nf_new_hyp]) > MAX_STACK_LEN:\\n hypStacks[nf_new_hyp].remove_worst()\\n return hypStacks[-1].pop()\",\n \"def comp_probs_and_labels(self, x_feats):\\n scores = sparse.dot(x_feats, self.W) + self.Wb # [l, d] x [d, m] + [m] => [l, m]\\n relation_probs = T.nnet.softmax(scores)\\n labels = T.argmax(scores, axis=1) # [l, m] => [l]\\n return labels, relation_probs\",\n \"def naiveBayesMixture(train_set, train_labels, dev_set, bigram_lambda,unigram_smoothing_parameter, bigram_smoothing_parameter, pos_prior):\\n\\n # TODO: Write your code here\\n # return predicted labels of development set\\n\\n # counters for Training Phase\\n ham = Counter()\\n ham_bi = Counter()\\n spam = Counter()\\n spam_bi = Counter()\\n\\n for string, label in zip(train_set, train_labels):\\n for i in range(len(string)):\\n word = string[i]\\n if i != len(string)-1:\\n word_bi = string[i] + ' ' + string[i+1]\\n if label == 1:\\n ham_bi.update({word_bi:1})\\n else:\\n spam_bi.update({word_bi:1})\\n if label == 1:\\n ham.update({word:1})\\n else:\\n spam.update({word:1})\\n\\n ham_len = 0\\n for w in ham:\\n ham_len += ham[w]\\n spam_len = 0\\n for w in spam:\\n spam_len += spam[w]\\n \\n hambi_len = 0\\n for w in ham_bi:\\n hambi_len += ham_bi[w]\\n spambi_len = 0\\n for w in spam_bi:\\n spambi_len += spam_bi[w]\\n\\n # labels for Development Phase\\n dev_labels = []\\n # dicts for P(word|ham) and P(word|spam)\\n p_ham = {}\\n p_spam = {}\\n p_hambi = {}\\n p_spambi = {}\\n\\n # develop likelihoods based on dev_set\\n for word in ham:\\n numerator = ham[word] + unigram_smoothing_parameter\\n denominator = ham_len + unigram_smoothing_parameter*(len(ham))\\n p_ham[word] = numerator / denominator\\n for word in spam:\\n numerator = spam[word] + unigram_smoothing_parameter\\n denominator = spam_len + unigram_smoothing_parameter*(len(spam))\\n p_spam[word] = numerator / denominator\\n\\n for word_bi in ham_bi:\\n numerator = ham_bi[word_bi] + bigram_smoothing_parameter\\n denominator = hambi_len + bigram_smoothing_parameter*(len(ham_bi))\\n p_hambi[word_bi] = numerator / denominator\\n for word_bi in spam_bi:\\n numerator = spam_bi[word_bi] + bigram_smoothing_parameter\\n denominator = spambi_len + bigram_smoothing_parameter*(len(spam_bi))\\n p_spambi[word_bi] = numerator / denominator\\n \\n numerator = unigram_smoothing_parameter\\n denominator = ham_len + unigram_smoothing_parameter*(len(ham))\\n p_ham_zero = numerator / denominator\\n denominator = spam_len + unigram_smoothing_parameter*(len(spam))\\n p_spam_zero = numerator / denominator\\n\\n numerator = bigram_smoothing_parameter\\n denominator = hambi_len + bigram_smoothing_parameter*(len(ham_bi))\\n p_hambi_zero = numerator / denominator\\n denominator = spambi_len + bigram_smoothing_parameter*(len(spam_bi))\\n p_spambi_zero = numerator / denominator\\n\\n for string in dev_set:\\n p_words_ham = math.log(pos_prior)\\n p_words_spam = math.log(1 - pos_prior)\\n\\n p_words_hambi = math.log(pos_prior)\\n p_words_spambi = math.log(1 - pos_prior)\\n \\n for i in range(len(string)):\\n word = string[i]\\n if word in p_ham:\\n p_words_ham += math.log(p_ham[word])\\n else:\\n p_words_ham += math.log(p_ham_zero)\\n if word in p_spam:\\n p_words_spam += math.log(p_spam[word])\\n else:\\n p_words_spam += math.log(p_spam_zero)\\n\\n if i != len(string)-1:\\n word_bi = string[i] + ' ' + string[i+1]\\n if word_bi in p_hambi:\\n p_words_hambi += math.log(p_hambi[word_bi])\\n else:\\n p_words_hambi += math.log(p_hambi_zero)\\n if word_bi in p_spambi:\\n p_words_spambi += math.log(p_spambi[word_bi])\\n else:\\n p_words_spambi += math.log(p_spambi_zero)\\n\\n p_ham_mix = p_words_ham*(1-bigram_lambda) + p_words_hambi*bigram_lambda\\n p_spam_mix = p_words_spam*(1-bigram_lambda) + p_words_spambi*bigram_lambda\\n\\n dev_labels.append(p_ham_mix >= p_spam_mix)\\n\\n return dev_labels\",\n \"def Viterbi(self, sent):\\n viterbi = defaultdict(dict)\\n backpointer = defaultdict(dict)\\n sent_tag = []\\n pos_list = [end_token]\\n viterbi['0'] = 1.0\\n\\n # Initialization step\\n # This loop will run for all the tags of each first word (sent[1][0])(word next to <S>) in dictionary\\n for tag in self.dictionary[sent[1][0]]:\\n # if any sentance in our trained data starts with a word that has same tag as \\\"state\\\"\\n if (start_token, tag) in self.transitions:\\n viterbi[str(1)][tag] = self.transitions[(start_token, tag)] + self.emissions[(sent[1][0], tag)]\\n else:\\n viterbi[str(1)][tag] = -float('inf')\\n backpointer[str(1)][tag] = start_token\\n\\n # Recursion step\\n # This loop will run for rest of the tuples (word, pos) after first tuple in \\\"sent\\\"\\n for i in xrange(2, len(sent)):\\n # This loop will run for all the tags of each word (sent[idx][0]) in dictionary\\n for tag in self.dictionary[sent[i][0]]:\\n maximum_value = -float(\\\"inf\\\")\\n maximum_loc = []\\n # This loop will run for all the tags in previous word (sent[idx-1][0]) in dictionary\\n for prev_tag in self.dictionary[sent[i - 1][0]]:\\n # if any sentance in our trained data has (privious tag, current tag) or (pre_state, state) of given word\\n if (prev_tag, tag) in self.transitions:\\n t = viterbi[str(i - 1)][prev_tag] + self.transitions[(prev_tag, tag)]\\n else:\\n t = -float('inf')\\n if t >= maximum_value:\\n maximum_value = t\\n maximum_loc = prev_tag\\n\\n viterbi[str(i)][tag] = maximum_value + self.emissions[(sent[i][0], tag)]\\n backpointer[str(i)][tag] = maximum_loc\\n\\n t = end_token\\n for i in xrange(1, len(sent)):\\n t = backpointer[str(len(sent) - i)][t]\\n pos_list.append(t)\\n\\n for tup in sent:\\n sent_tag.append((tup[0], pos_list.pop()))\\n\\n #print \\\"viterbi:\\\", viterbi\\n #print \\\"backpointer:\\\", backpointer\\n #print \\\"sent_tagged\\\", sent_tag\\n\\n return sent_tag\",\n \"def get_n_best(self):\\n pass\",\n \"def dfs_maximizing(state) :\\n #print state.describe_previous_move()\\n global state_evals, path, _path, _score, level, _state;\\n\\n level+=1\\n path.append(state)\\n for stt in state.generate_next_states():\\n score=0\\n agenda.append((stt, level))\\n \\n if stt.is_game_over():\\n state_evals+=1\\n score=stt.get_endgame_score()\\n if score>_score:\\n _score=score\\n _path = path[0:]\\n _state = stt\\n if not agenda:\\n\\n _path.append(_state)\\n return [_path, _score, state_evals];\\n else:\\n new_state, level=agenda.pop()\\n path=path[0:level]\\n level-=1\\n return dfs_maximizing(new_state)\",\n \"def prior_params_tree(self):\\n id = {name:i for i, name in enumerate(list(self.tree.keys()))}\\n n_nodes = len(id)\\n dist_mx = np.zeros((n_nodes, n_nodes))\\n\\n for node1, edges in self.tree.items():\\n for node2, dist in edges.dist:\\n dist_mx[id[node1], id[node2]] = dist\\n dist_mx[id[node2], id[node1]] = dist\\n\\n # while np.count_nonzero(dist_mx) < (n_nodes ** 2 - n_nodes):\\n for _ in range(20):\\n for i, j in combinations(range(n_nodes), 2):\\n if dist_mx[i,j] > 0:\\n continue\\n row_i = dist_mx[i]\\n row_j = dist_mx[j]\\n value = (row_i + row_j) * (row_i > 0) * (row_j > 0)\\n dist_mx[i, j] = dist_mx[j, i] = - max(np.unique(value))\\n dist_mx = np.abs(dist_mx)\\n\\n evolve_rate = []\\n for node1, node2 in combinations(self.m_cov.keys(), 2):\\n mx_cov_dist = np.abs(self.m_cov[node1] - self.m_cov[node2])\\n elements = mx_cov_dist[np.triu_indices(len(mx_cov_dist))]\\n norm_elements = elements / dist_mx[id[node2], id[node1]]\\n evolve_rate += list(norm_elements)\\n\\n\\n\\n df = np.mean([p.shape[0] for _, p in self.m_profiles.items()])\\n p_theta_alpha = df/2\\n # p_theta_alpha = 4\\n p_theta_beta = np.percentile(evolve_rate, 75) * (p_theta_alpha - 1)\\n # print(p_theta_alpha, p_theta_beta)\\n return p_theta_alpha, p_theta_beta\",\n \"def _find_max(self, root):\\n while root.right:\\n root = root.right\\n return root\",\n \"def max_v_greedy():\\n\\n S1=Spectrum.Spectrum()\\n S1.add_peak(50.4,16)\\n S1.add_peak(50.7,36)\\n S1.add_peak(74.8,25)\\n S1.add_peak(96.2,23)\\n S1.pep_mass=100\\n S1.euclidean_scale()\\n\\n S2=Spectrum.Spectrum()\\n S2.add_peak(50.6,49)\\n S2.add_peak(50.9,25)\\n S2.add_peak(74.6,9)\\n S2.add_peak(102.4,17)\\n S2.pep_mass=100\\n S2.euclidean_scale()\\n\\n score,peaks=similarity.cosine_score_max(S1,S2)\\n g_score,g_peaks=similarity.cosine_score_greedy(S1,S2)\\n\\n assert score>=g_score, \\\"Maximum weighted method did not get higher score than greedy method\\\"\\n assert peaks>=g_peaks, \\\"Maximum weighted method did not match more peaks than greedy method\\\"\\n\\n assert peaks==3, \\\"Incorrect number of peaks matched with greedy method\\\"\\n assert math.isclose(score,0.73), \\\"Incorrect score with greedy method\\\"\\n\\n assert g_peaks==2, \\\"Incorrect number of peaks matched with maximum weighted method\\\"\\n assert math.isclose(g_score,0.57), \\\"Incorrect score with maximum weighted method\\\"\",\n \"def maxProduct2(nums):\\n\\n maxSubseq = nums[0]\\n minSubseq = nums[0]\\n res = nums[0]\\n for i in range(1, len(nums)):\\n if nums[i] < 0:\\n minSubseq, maxSubseq = maxSubseq, minSubseq\\n maxSubseq = max(nums[i], maxSubseq*nums[i])\\n minSubseq = min(nums[i], minSubseq*nums[i])\\n res = max(res, maxSubseq)\\n return res\",\n \"def maxQ(self,feat):\\r\\n \\r\\n maxQ = float('-inf')\\r\\n maxA = 0\\r\\n for a in self.actions:\\r\\n q = self.Q(feat,a)\\r\\n print(q,a)\\r\\n if q > maxQ:\\r\\n maxQ = q\\r\\n maxA = a\\r\\n return(maxQ,maxA)\",\n \"def smooth_tag_model(cls, heldout_data):\\n\\n # bi_transition_counts = defaultdict(int)\\n n = 0 # count word-tags\\n e = .0001 # stopping condition\\n L = [.25, .25, .25, .25] # initialize lambdas uniformly\\n i = 1 # iteration\\n while True:\\n # E Step (Step 1)\\n # Iterate through all occurring trigrams\\n # in the heldout.txt data (H), i.e. minimizing\\n # log likelihood\\n counts = [0, 0, 0, 0]\\n ratio = [0, 0, 0, 0]\\n nextL = 4 * [0] # next lambda\\n\\n for sent in heldout_data:\\n\\n # Handle beginning of sentence\\n t = '<s>'\\n u = '<s>'\\n # bi_transition_counts[t, u] += 1\\n\\n # if first == '<s>' and second == '<s>': # add bigram for nulls\\n\\n for word, tag in sent:\\n v = tag\\n # tri_transitions[t, u, v] += 1\\n # bi_transitions[u, v] += 1\\n # # if first == '<s>' and second == '<s>': # add bigram for nulls\\n # # tagger._bi_transitions[('<s>', '<s>')] += 1\\n # uni_transitions[v] += 1\\n # cls._emissions[word, tag] += 1\\n # tags.add(v)\\n # words.add(word)\\n # uni_words[word] += 1\\n\\n\\n # Calculate expected counts of lambdas\\n ratio = cls.calc_tag_ratio(t, u, v, L)\\n\\n # M-step (Step 2)\\n # Calculate expected counts of lambdas, i.e. weight, taking\\n # into account the number of occurrences of each trigram (cnt)\\n for j in range(len(L)):\\n counts[j] += ratio[j] # weight of lambda in whole equation (count)\\n\\n t = u\\n u = v\\n # n += 1\\n\\n # Handle end of sentence\\n # tri_transitions[t, u, '</s>'] += 1\\n v = '</s>'\\n ratio = cls.calc_tag_ratio(t, u, v, L)\\n for j in range(len(L)):\\n counts[j] += ratio[j] # weight of lambda in whole equation (count)\\n\\n # cls._bi_transitions[first, second] += 1\\n # cls._tri_transitions[second, '</s>', '</s>'] += 1\\n\\n\\n # Update values for parameters given current distribution\\n for k in range(len(L)):\\n total = np.sum(counts)\\n nextL[k] = counts[k] / total # next lambda\\n\\n # Check if lambda values have converged\\n converged = True\\n for l in range(len(L)):\\n if np.abs(nextL[l] - L[l]) > e: # tolerance = e\\n converged = False\\n L = nextL\\n\\n # Return values if lambdas have converged\\n if converged:\\n break\\n\\n i += 1 # increment iteration counter\\n\\n\\n return L # copy lambdas passed by reference\",\n \"def naiveBayes(train_set, train_labels, dev_set, smoothing_parameter, pos_prior):\\n # TODO: Write your code here\\n # return predicted labels of development set\\n #\\n ### len(train_set) 8000, len(dev) = 5000 P(pos) = 0.8 \\n #### 0.55, 4.0, 0.30 ----------- 0.766\\n #### 0.25 3.5 0.3 -------------- 0.766\\n print(pos_prior)\\n smoothing_parameter = 3.5\\n pos_total_word = 0\\n neg_total_word = 0\\n pos_word_dict = {}\\n neg_word_dict = {}\\n dicts = [neg_word_dict, pos_word_dict]\\n for i, sentence in enumerate(train_set):\\n\\n if train_labels[i] == 1: # positive reviews\\n for word in sentence:\\n pos_total_word += 1 \\n if word in stop_words:\\n continue\\n if word in pos_word_dict:\\n pos_word_dict[word] += 1\\n else :\\n pos_word_dict[word] = 1\\n\\n else: # negative reviews\\n for word in sentence:\\n neg_total_word += 1 \\n if word in stop_words:\\n continue\\n if word in neg_word_dict:\\n neg_word_dict[word] += 1\\n else :\\n neg_word_dict[word] = 1\\n\\n\\n prob = {}\\n denominator_pos = pos_total_word + smoothing_parameter * (len(pos_word_dict) + 1)\\n denominator_neg = neg_total_word + smoothing_parameter * (len(neg_word_dict) + 1)\\n de = [denominator_neg, denominator_pos]\\n\\n for t, dictionary in enumerate(dicts):\\n for key, value in dictionary.items():\\n if key not in prob:\\n prob[key] = {0 : 0, 1 : 0}\\n if smoothing_parameter != 0:\\n prob[key][1 - t] = -1 * np.log(smoothing_parameter / de[t]) \\n # print(prob[key][1 - t])\\n\\n prob[key][t] = -1 * np.log((value + smoothing_parameter) / de[t]) \\n \\n\\n revised_prob = {}\\n for key, value in prob.items():\\n if np.abs(value[0] - value[1]) >= 0.25:\\n revised_prob[key] = value \\n\\n print(len(revised_prob))\\n\\n dev_labels = []\\n num_0 = 0\\n for i, sentence in enumerate(dev_set):\\n pos_odd = -1 * np.log(pos_prior)\\n neg_odd = -1 * np.log(1.0 - pos_prior)\\n for word in sentence:\\n if word in revised_prob:\\n pos_odd += revised_prob[word][1]\\n neg_odd += revised_prob[word][0]\\n \\n if pos_odd > neg_odd:\\n num_0 += 1\\n dev_labels.append(1 if pos_odd <= neg_odd else 0)\\n print(num_0)\\n\\n \\n #### bigram model \\n \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n return dev_labels\",\n \"def contraction_max_algos():\\n return cutensor.contractionMaxAlgos()\",\n \"def youngest(self):\\n # Your implementation here\",\n \"def ensembleVote(x, classes, ensemble):\\n votes = np.array([0 for kk in range(len(classes))])\\n for i in ensemble:\\n votes = votes + classProbs(x, ensemble[i][\\\"tree\\\"], classes)\\n maxVote = 0\\n loc = None\\n for ind, vote in enumerate(votes):\\n if vote > maxVote:\\n maxVote = vote\\n loc = ind\\n prediction = classes[loc]\\n return prediction\",\n \"def Maximize(stateTup,A,B):\\n self.emergency()\\n t = Terminal(stateTup)\\n if t != None:\\n return (None, t)\\n\\n maxChild , maxUtility = None,-999999999\\n state = stateTup[0]\\n Map = self.dict.get(str(state.map))\\n if Map == None:\\n children = []\\n for M in range(4):\\n g = state.clone()\\n if g.move(M):\\n children.append(g)\\n self.dict[str(state.map)] = children\\n else:\\n children = Map\\n for child in children:\\n childTup = (child,stateTup[1]+1)\\n utility = Minimize(childTup,A,B)[1]\\n if utility > maxUtility:\\n maxChild , maxUtility = child , utility\\n if maxUtility >= B:\\n# global prune\\n# prune +=1\\n break\\n if maxUtility > A:\\n A = maxUtility\\n\\n return (maxChild,maxUtility)\",\n \"def _bert_encoder(self, tokens, attn_masks):\\n output = self.bert(tokens, attn_masks)\\n embedding = output['hidden_states'][-2]\\n tag_prob = self.Softmax(self.hidden2tag(embedding))\\n return tag_prob\",\n \"def cal_max_dup_tags ( genome_size, tags_number, p=1e-5 ):\\n return binomial_cdf_inv(1-p,tags_number,1.0/genome_size)\",\n \"def cal_max_dup_tags ( genome_size, tags_number, p=1e-5 ):\\n return binomial_cdf_inv(1-p,tags_number,1.0/genome_size)\",\n \"def max_target(board, depth, alpha, beta):\\n if terminal(board) or depth == DEPTH:\\n return utility(board)\\n\\n best_val = -math.inf\\n for action in actions(board):\\n val = min_target(result(board, action), depth+1, alpha, beta)\\n best_val = max(best_val, val)\\n alpha = max(alpha, best_val)\\n if beta <= alpha:\\n break\\n\\n return best_val\",\n \"def evaluate_ngrams(eval_dataset, trigram_counts, bigram_counts, unigram_counts, train_token_count, lambda1, lambda2):\\n perplexity = 0\\n\\n ### YOUR CODE HERE\\n def calc_prob(sentense, i, word, trigram_counts, bigram_counts, unigram_counts, train_token_count, model):\\n prob = 0.0\\n prev_word = sentense[i - 1]\\n prev_to_prev_word = sentense[i - 2]\\n\\n if model == \\\"unigram\\\":\\n if word in unigram_counts:\\n prob = (unigram_counts[word] + 0.0) / train_token_count\\n else:\\n prob = (unigram_counts[word_to_num['UUUNKKK']] + 0.0) / \\\\\\n train_token_count\\n\\n if model == \\\"bigram\\\":\\n if (prev_word, word) in bigram_counts:\\n prob = (bigram_counts[(prev_word, word)] + 0.0) / \\\\\\n unigram_counts[prev_word]\\n # print(num_to_word[prev_word] ,num_to_word[word])\\n # print(bigram_counts[(prev_word, word)])\\n # print(unigram_counts[prev_word])\\n # print(\\\"---------------------------\\\")\\n else:\\n prob = 0.0\\n\\n if model == \\\"trigram\\\":\\n if (prev_to_prev_word, prev_word, word) in trigram_counts:\\n prob = (trigram_counts[(prev_to_prev_word, prev_word, word)] + 0.0) \\\\\\n / bigram_counts[(prev_to_prev_word, prev_word)]\\n # / bigram_counts[(prev_word, word)] #this according to lecture notes slide 27\\n else:\\n prob = 0.0\\n\\n return prob\\n\\n l = 0\\n num_of_words = 0\\n\\n ##########3\\n better_than_chance = 0\\n ###########\\n\\n for sentense in eval_dataset:\\n for i, word in enumerate(sentense[2:]):\\n num_of_words += 1\\n prob = lambda1 * calc_prob(sentense, i + 2, word, trigram_counts, bigram_counts, unigram_counts,\\n train_token_count, \\\"trigram\\\") + \\\\\\n lambda2 * calc_prob(sentense, i + 2, word, trigram_counts, bigram_counts, unigram_counts,\\n train_token_count, \\\"bigram\\\") + \\\\\\n (1 - lambda1 - lambda2) * calc_prob(sentense, i + 2, word, trigram_counts, bigram_counts,\\n unigram_counts, train_token_count, \\\"unigram\\\")\\n ######################################\\n if prob > (1.0 / vocabsize):\\n better_than_chance += 1\\n #########################\\n l += np.log2(prob)\\n l /= num_of_words\\n perplexity = 2 ** -l\\n\\n print(\\\"better_than_chance:\\\", (better_than_chance + 0.0) / num_of_words)\\n\\n ### END YOUR CODE\\n return perplexity\",\n \"def batch_predict(tree_adj, training_signs, edge_weight):\\n # since shazoo use the revealed signs as-is, it's ok to use the same name\\n training_signs, l2_values, rta_signs = training_signs\\n all_nodes_to_predict = set(tree_adj) - set(training_signs)\\n logging.debug('batch_predict has %d nodes to predict', len(all_nodes_to_predict))\\n methods = ['l2cost', 'rta', 'shazoo']\\n # fields are current_closest_hinge, current_sign, current_dst_to_closest_hinge\\n node_predictions = {m: defaultdict(lambda: (None, None, 2e9)) for m in methods}\\n hinge_value = {m: {} for m in methods}\\n total_iter = 0\\n while all_nodes_to_predict:\\n some_root_of_a_border_tree = next(iter(all_nodes_to_predict))\\n hinge_nodes, border_tree_nodes = find_hinge_nodes(tree_adj, edge_weight, training_signs,\\n some_root_of_a_border_tree,\\n with_visited=True)\\n unmarked = border_tree_nodes - hinge_nodes\\n for u in hinge_nodes:\\n if u in hinge_value['shazoo']:\\n continue\\n vals, _, status = flep(tree_adj, (training_signs, rta_signs), edge_weight, u)\\n hinge_value['shazoo'][u] = sgn(vals[0])\\n hinge_value['rta'][u] = sgn(vals[1])\\n if not USE_SCIPY:\\n continue\\n border_tree = build_border_tree_from_mincut_run(status, edge_weight)\\n _, E, El, leaves_sign, _, _ = border_tree\\n L = {u: l2_values[u] for u in leaves_sign}\\n mapped_E, mapped_El_L, mapping = preprocess_edge_and_leaves(E, El, L)\\n val = solve_by_zeroing_derivative(mapped_E, mapped_El_L, mapping, L,\\n reorder=False)[0][u]\\n hinge_value['l2cost'][u] = sgn(val)\\n predicted_in_that_border_tree = set()\\n inner_iter = 0\\n # to avoid the same fork being picked again and again\\n unmarked.add(some_root_of_a_border_tree)\\n while unmarked:\\n one_to_predict = next(iter(unmarked))\\n hinge_tree = get_hinge_tree(one_to_predict, tree_adj, hinge_nodes)\\n other_predicted = set()\\n for h, h_val in iteritems(hinge_value['shazoo']):\\n if h not in hinge_tree:\\n continue\\n predicted = propagate_hinge(hinge_tree, h, h_val, node_predictions['shazoo'],\\n edge_weight)\\n for u in predicted:\\n prediction_info = node_predictions['shazoo'][u]\\n used_hinge = prediction_info[0]\\n node_predictions['rta'][u] = (used_hinge, hinge_value['rta'][used_hinge],\\n prediction_info[2])\\n if not USE_SCIPY:\\n continue\\n node_predictions['l2cost'][u] = (used_hinge, hinge_value['l2cost'][used_hinge],\\n prediction_info[2])\\n other_predicted.update(predicted)\\n predicted_in_that_border_tree.update(other_predicted)\\n unmarked -= other_predicted\\n inner_iter += 1\\n if inner_iter > len(tree_adj):\\n import time\\n logging.critical('batch predict failed in the inner loop')\\n persistent.save_var('__fail_{}.my'.format(int(time.time())), (tree_adj, (training_signs, l2_values, rta_signs), edge_weight))\\n raise RuntimeError('batch predict failed in the inner loop')\\n all_nodes_to_predict -= predicted_in_that_border_tree\\n total_iter += 1\\n if total_iter > len(tree_adj):\\n import time\\n logging.critical('batch predict failed in the outer loop')\\n persistent.save_var('__fail_{}.my'.format(int(time.time())), (tree_adj, (training_signs, l2_values, rta_signs), edge_weight))\\n raise RuntimeError('batch predict failed in the outer loop')\\n logging.debug('batch_predict has actually predicted %d nodes', len(node_predictions) - len(training_signs))\\n return {m: {u: v[1] for u, v in iteritems(node_predictions[m]) if u not in training_signs}\\n for m in methods}\",\n \"def caculate_prob(self):\\n t_H = self.tree.depth()\\n t_h = 1\\n while(t_h <= t_H):\\n t_hnodes = self.get_h(t_h)\\n t_sum = 0\\n t_hpro = []\\n t_cpro = []\\n for t_n in t_hnodes:\\n t_sum = self.tree.get_node(t_n).data[0] + t_sum\\n t_node = self.tree.get_node(t_n)\\n if t_node.is_leaf():\\n t_node.data.append(0)\\n continue\\n t_childrens = self.tree.children(t_n)\\n t_shang = 0\\n for child in t_childrens:\\n t_shang = t_shang + (child.data[0]/t_node.data[0])*np.log(child.data[0]/t_node.data[0])\\n t_node.data.append(-t_shang)\\n for t_n in t_hnodes:\\n t_node = self.tree.get_node(t_n)\\n t_parentnode = self.tree.parent(t_n)\\n if t_h > 1:\\n t_node.data.append((t_node.data[0] / t_sum) * (t_node.data[0]/t_parentnode.data[0]))\\n t_hpro.append((t_node.data[0]/t_sum) * (t_node.data[0]/t_parentnode.data[0]))\\n else:\\n t_node.data.append((t_node.data[0] / t_sum))\\n t_hpro.append((t_node.data[0] / t_sum))\\n\\n t_cpro.append(t_node.data[1])\\n t_ndata = np.array(t_hpro)\\n mean = np.mean(t_ndata)\\n std = np.std(t_ndata,ddof=1)\\n t_sdata = np.array(t_cpro)\\n mean_s = np.mean(t_sdata)\\n std_s = np.std(t_sdata,ddof=1)\\n for t_n in t_hnodes:\\n t_node = self.tree.get_node(t_n)\\n if(std != 0):\\n t_node.data[2] = (t_node.data[2] - mean)/std\\n else:\\n t_node.data[2] = (t_node.data[2] - mean)\\n if(mean_s == 0 and std_s ==0):\\n t_node.data[1] = -100.0\\n continue\\n t_node.data[1] = (t_node.data[1] - mean_s)/std_s\\n t_h = t_h + 1\",\n \"def children_impurity(self):\\n\\n\\t\\tentropy_left = 0.0\\n\\t\\tentropy_right = 0.0\\n\\n\\t\\tfor c in range(self.n_classes):\\n\\t\\t\\ttmp = self.label_count_left[c]\\n\\t\\t\\tif tmp > 0.0:\\n\\t\\t\\t\\ttmp /= self.weighted_n_left\\n\\t\\t\\t\\tentropy_left -= tmp * log(tmp)\\n\\n\\t\\t\\ttmp = self.label_count_right[c]\\n\\t\\t\\tif tmp > 0.0:\\n\\t\\t\\t\\ttmp /= self.weighted_n_right\\n\\t\\t\\t\\tentropy_right -= tmp * log(tmp)\\n\\n\\t\\treturn entropy_left, entropy_right\",\n \"def naiveBayes(train_set, train_labels, dev_set, smoothing_parameter, pos_prior):\\r\\n # TODO: Write your code here\\r\\n # return predicted labels of development set\\r\\n retval = []\\r\\n smoothing_parameter = 0.0055\\r\\n # Generate a unigram BOW for both positive and negative reviews, choose the top 2500 words\\r\\n pos_bow, neg_bow = generate_unigram_BOW(train_set, train_labels)\\r\\n sorted_pos = sorted(pos_bow.items(), key=lambda x: x[1], reverse = True)\\r\\n sorted_neg = sorted(neg_bow.items(), key=lambda x: x[1], reverse = True)\\r\\n pos_words = sorted_pos[:].copy()\\r\\n neg_words = sorted_neg[:].copy()\\r\\n\\r\\n pos_bi_bow, neg_bi_bow = generate_bigram_BOW(train_set, train_labels)\\r\\n sorted_bi_pos = sorted(pos_bi_bow.items(), key=lambda x: x[1], reverse = True)\\r\\n sorted_bi_neg = sorted(neg_bi_bow.items(), key=lambda x: x[1], reverse = True)\\r\\n bi_pos_words = sorted_bi_pos[:].copy()\\r\\n bi_neg_words = sorted_bi_neg[:].copy()\\r\\n\\r\\n # Calculate the log probabilities each word given type\\r\\n pos_count = sum(pair[1] for pair in pos_words)\\r\\n neg_count = sum(pair[1] for pair in neg_words)\\r\\n bi_pos_count = sum(pair[1] for pair in bi_pos_words)\\r\\n bi_neg_count = sum(pair[1] for pair in bi_neg_words)\\r\\n\\r\\n log_probability_pos = {} #(word)->P(word|positive)\\r\\n log_probability_neg = {} #(word)->P(word|negative)\\r\\n log_prob_bi_pos = {}\\r\\n log_prob_bi_neg = {}\\r\\n\\r\\n for pair in pos_words:\\r\\n pos_prob = np.log((pair[1]+smoothing_parameter)/(pos_count+smoothing_parameter*(len(pos_words) + 1)))\\r\\n log_probability_pos[pair[0]] = pos_prob\\r\\n\\r\\n for pair in neg_words:\\r\\n neg_prob = np.log((pair[1]+smoothing_parameter)/(neg_count+smoothing_parameter*(len(neg_words) + 1)))\\r\\n log_probability_neg[pair[0]] = neg_prob\\r\\n\\r\\n for pair in bi_pos_words:\\r\\n bi_pos_prob = np.log((pair[1]+smoothing_parameter)/(bi_pos_count+smoothing_parameter*(len(bi_pos_words) + 1)))\\r\\n log_prob_bi_pos[pair[0]] = bi_pos_prob\\r\\n\\r\\n for pair in bi_neg_words:\\r\\n bi_neg_prob = np.log((pair[1]+smoothing_parameter)/(bi_neg_count+smoothing_parameter*(len(bi_neg_words) + 1)))\\r\\n log_prob_bi_neg[pair[0]] = bi_neg_prob\\r\\n # Finished training\\r\\n\\r\\n # For each of the new reviews from development data\\r\\n for review in dev_set:\\r\\n uni_pos = np.log(pos_prior)\\r\\n uni_neg = np.log(1 - pos_prior)\\r\\n for word in review:\\r\\n if word in log_probability_pos:\\r\\n uni_pos += log_probability_pos[word]\\r\\n elif word not in log_probability_pos:\\r\\n uni_pos += np.log(smoothing_parameter/(pos_count+smoothing_parameter*(len(pos_words) + 1)))\\r\\n\\r\\n if word in log_probability_neg:\\r\\n uni_neg += log_probability_neg[word]\\r\\n elif word not in log_probability_neg:\\r\\n uni_neg += np.log(smoothing_parameter/(neg_count+smoothing_parameter*(len(neg_words) + 1)))\\r\\n\\r\\n bi_pos = np.log(pos_prior)\\r\\n bi_neg = np.log(1 - pos_prior)\\r\\n for i in range(len(review)-1):\\r\\n currTuple = (review[i], review[i+1])\\r\\n if currTuple in log_prob_bi_pos:\\r\\n bi_pos += log_prob_bi_pos[currTuple]\\r\\n elif currTuple not in log_prob_bi_pos:\\r\\n bi_pos += np.log(smoothing_parameter/(bi_pos_count+smoothing_parameter*(len(bi_pos_words) + 1)))\\r\\n\\r\\n if currTuple in log_prob_bi_neg:\\r\\n bi_neg += log_prob_bi_neg[currTuple]\\r\\n elif currTuple not in log_prob_bi_neg:\\r\\n bi_neg += np.log(smoothing_parameter/(bi_neg_count+smoothing_parameter*(len(bi_neg_words) + 1)))\\r\\n\\r\\n MAP_pos = (1-0.4)*uni_pos + 0.4*bi_pos\\r\\n MAP_neg = (1-0.4)*uni_neg + 0.4*bi_neg\\r\\n\\r\\n if MAP_pos >= MAP_neg:\\r\\n retval.append(1)\\r\\n else:\\r\\n retval.append(0)\\r\\n\\r\\n return retval\",\n \"def haplotype(self, ploidy):\\n \\n i = 0 # counter for ploidy\\n final = [] #to store seq\\n got_bubble = True\\n \\n while i < ploidy:\\n \\n frag = []\\n longest_path = []\\n\\n sorted_node = self.topological_sort()\\n # find greatest weight path\\n path = self.longest_path(sorted_node)\\n # last node in the graph\\n curr_max = max(path, key=path.get)\\n next_node = ''\\n str_temp = ''\\n\\n # while it is not the source node\\n while len(self.prefix[curr_max]) != 0:\\n # prev max node\\n prev = path[curr_max][1]\\n\\n # if current node longer than prev and next node\\n if len(curr_max) >= len(prev) and len(curr_max) >= len(next_node):\\n str_temp = curr_max \\n\\n # if current node shorter than prev and next node \\n elif len(curr_max) < len(prev) and len(curr_max) < len(next_node):\\n start = self.suffix[prev][curr_max][2]\\n end = self.prefix[next_node][curr_max][1]\\n str_temp = curr_max[start:end]\\n\\n # if current node shorter than prev but longer than the next node \\n elif len(curr_max) < len(prev) and len(curr_max) >= len(next_node):\\n start = self.suffix[prev][curr_max][2]\\n str_temp = curr_max[start:]\\n\\n # if current node longer than prev but shorter than the next node \\n else:\\n end = self.prefix[next_node][curr_max][1]\\n str_temp = curr_max[:end]\\n\\n frag.insert(0, str_temp)\\n longest_path.insert(0, curr_max)\\n next_node = curr_max\\n curr_max = prev\\n\\n\\n # if this is the first node\\n if len(self.prefix[curr_max]) == 0:\\n\\n # if current longer than next node\\n if len(curr_max) > len(next_node):\\n frag.insert(0, curr_max)\\n next_node = curr_max\\n\\n else: \\n end = self.prefix[next_node][curr_max][1]\\n str_temp = curr_max[:end]\\n frag.insert(0, str_temp)\\n next_node = curr_max\\n \\n longest_path.insert(0, curr_max) \\n\\n\\n # combine all string\\n seq = ''.join(frag)\\n final.append(seq)\\n \\n got_bubble = self.reduce_graph(longest_path, sorted_node)\\n if got_bubble is False:\\n break\\n i += 1\\n \\n \\n return final\",\n \"def argmax(X):\\n\\tN,K,_ = X.shape\\n\\tg0 = X[0,0]\\n\\tg = X[1:]\\n\\n\\tB = ones((N,K), dtype=int32) * -1\\n\\t# compute max-marginals and backtrace matrix\\n\\tV = g0\\n\\tfor t in xrange(1,N):\\n\\t\\tU = empty(K)\\n\\t\\tfor y in xrange(K):\\n\\t\\t\\tw = V + g[t-1,:,y]\\n\\t\\t\\tB[t,y] = b = w.argmax()\\n\\t\\t\\tU[y] = w[b]\\n\\t\\tV = U\\n\\t# extract the best path by brack-tracking\\n\\ty = V.argmax()\\n\\ttrace = []\\n\\tfor t in reversed(xrange(N)):\\n\\t\\ttrace.append(y)\\n\\t\\ty = B[t, y]\\n\\ttrace.reverse()\\n\\treturn trace\",\n \"def get_greatest_probability(self):\\n greatest = -1\\n for i in range(self.dim):\\n for j in range(self.dim):\\n if self.kb[i][j] > greatest:\\n greatest = self.kb[i][j]\\n\\n return greatest\",\n \"def naive_bn(data, attributes):\\n bn = []\\n attr = attributes['attr'].tolist()\\n # each attribute is only dependent on the class node\\n i = 0\\n while (i < len(attr)-1):\\n row = [attr[i], attr[-1]]\\n bn.append(row)\\n i= i + 1\\n # frequency table \\n freq = counts_table(data, attributes)\\n # conditional probabilities and prior probabilities\\n cond_probs, prior0, prior1 = conditional_probability(data, attributes, freq)\\n\\n return bn, cond_probs, prior0, prior1\",\n \"def baseline_mle(self):\\n tag_word_tapples = self.data_to_taples(self.training_set)\\n e = np.zeros((self.pos_size, self.words_size))\\n for tag, word in tag_word_tapples:\\n e[self.pos2i[tag], self.word2i[word]] += 1\\n sum = e.sum(axis=1)[..., np.newaxis]\\n e = e / sum\\n return e\",\n \"def most_frequent_train(train_data):\\n ### YOUR CODE HERE\\n tags_counts_for_each_word = {}\\n # Filling a dictionary from words and tag tags to their counters\\n # Going over the words and counting their tags appearances\\n for sentance in train_data:\\n for word, tag in sentance:\\n # If first time seeing word, adding it's tags count dictionary\\n if word not in tags_counts_for_each_word:\\n tags_counts_for_each_word[word] = {}\\n # Fetching word tags count dictionary\\n word_tags_count_dictionary = tags_counts_for_each_word[word]\\n # If tag not in word's tags dictionary, initializing the counter\\n if tag not in word_tags_count_dictionary:\\n word_tags_count_dictionary[tag] = 0\\n # Incrementing word tag counter\\n word_tags_count_dictionary[tag] += 1\\n \\n words_maximal_tags = {}\\n # Going over each word and finding it's maximal tag\\n for word in tags_counts_for_each_word:\\n # Fetching all word tags counts\\n word_tags_count_dictionary = tags_counts_for_each_word[word]\\n \\n maximal_tag, maximal_tag_counter = '', 0\\n # Finding word tag with maximal tag counter\\n for curent_tag, current_counter in word_tags_count_dictionary.items():\\n if current_counter > maximal_tag_counter:\\n maximal_tag, maximal_tag_counter = curent_tag, current_counter\\n \\n # Setting the maximal tag for current word\\n words_maximal_tags[word] = maximal_tag\\n \\n return words_maximal_tags\\n ### END CODE HERE\",\n \"def find_max_independent_set(graph, params):\\r\\n\\r\\n max_ind_set = []\\r\\n\\r\\n # QHACK #\\r\\n\\r\\n # function that takes in a graph and outputs the hamiltonians\\r\\n cost_h, mixer_h = qaoa.max_independent_set(graph, constrained=True) # Assume the graph they give me is good\\r\\n\\r\\n def qaoa_layer(gamma, alpha):\\r\\n qaoa.cost_layer(gamma, cost_h)\\r\\n qaoa.mixer_layer(alpha, mixer_h)\\r\\n\\r\\n dev = qml.device(\\\"default.qubit\\\", wires=range(NODES))\\r\\n\\r\\n def circuit(params, **kwargs): \\r\\n qml.layer(qaoa_layer, N_LAYERS, params[0], params[1]) \\r\\n\\r\\n @qml.qnode(dev)\\r\\n def probability_circuit(gamma, alpha):\\r\\n circuit([gamma, alpha])\\r\\n return qml.probs(wires=range(NODES))\\r\\n\\r\\n answer = probability_circuit(params[0], params[1])\\r\\n\\r\\n maxn = 0\\r\\n maxn = max(answer)\\r\\n\\r\\n for i in range(len(answer)):\\r\\n if maxn == answer[i]:\\r\\n decimal = i\\r\\n \\r\\n binary_num = []\\r\\n def DecimalToBinary(decimal):\\r\\n if decimal >= 1:\\r\\n DecimalToBinary(decimal // 2)\\r\\n binary_num.append(decimal % 2)\\r\\n \\r\\n DecimalToBinary(decimal)\\r\\n\\r\\n if len(binary_num) < 6:\\r\\n if len(binary_num) < 5:\\r\\n if len(binary_num) < 4:\\r\\n if len(binary_num) < 3:\\r\\n if len(binary_num) < 2:\\r\\n binary_num.insert(0, 0) # At beginning append 0\\r\\n binary_num.insert(0, 0)\\r\\n binary_num.insert(0, 0)\\r\\n binary_num.insert(0, 0)\\r\\n binary_num.insert(0, 0)\\r\\n\\r\\n for i in range(6):\\r\\n if binary_num[i] == 1:\\r\\n max_ind_set.append(i)\\r\\n\\r\\n # QHACK #\\r\\n\\r\\n return max_ind_set\",\n \"def _update_max(self):\\n tmp = self\\n while tmp.right is not None:\\n tmp = tmp.right\\n return tmp.parent.key\",\n \"def _calculate_tag_weight(weight, max_weight, distribution):\\r\\n if distribution == LINEAR or max_weight == 1:\\r\\n return weight\\r\\n elif distribution == LOGARITHMIC:\\r\\n return math.log(weight) * max_weight / math.log(max_weight)\\r\\n raise ValueError(_('Invalid distribution algorithm specified: %s.') % distribution)\",\n \"def __findBestLogProbability(self):\\n best_model = None\\n highest_log_probability = -sys.maxsize# (np.finfo(float).eps)\\n\\n # Find the highest model\\n for item in self.data_array:\\n if item[1] > highest_log_probability:\\n best_model = item\\n highest_log_probability = item[1]\\n\\n return best_model\",\n \"def id3(x, y, attribute_value_pairs=None, depth=0, max_depth=5):\\r\\n\\r\\n # INSERT YOUR CODE HERE. NOTE: THIS IS A RECURSIVE FUNCTION.\\r\\n # To make a list of all attribute value pair\\r\\n root = {}\\r\\n \\r\\n x1 = []\\r\\n if attribute_value_pairs is None:\\r\\n for i in range(x.shape[1]):\\r\\n for v in np.unique(x[:, i]):\\r\\n x1.append([i, v])\\r\\n attribute_value_pairs = np.vstack((x1))\\r\\n \\r\\n # To compute no of records classified into each class of target column\\r\\n yvalue, ycount = np.unique(y, return_counts=True)\\r\\n \\r\\n # if all the records belong to the same class return the label of the class\\r\\n if len(yvalue) == 1:\\r\\n return yvalue[0]\\r\\n \\r\\n # If no more attributes are available for branching, output majority class\\r\\n if len(attribute_value_pairs) == 0:\\r\\n return yvalue[np.argmax(ycount)]\\r\\n \\r\\n # max depth is reached, output majority class\\r\\n if depth == max_depth:\\r\\n return yvalue[np.argmax(ycount)]\\r\\n \\r\\n # To find best attribute for branching criteria\\r\\n Mutual_info_list = []\\r\\n for (i, v) in attribute_value_pairs:\\r\\n Mutual_info_list.append(mutual_information(np.array(x[:, i] == v).astype(int),y))\\r\\n \\r\\n (bestattribute, bestvalue) = attribute_value_pairs[np.argmax(Mutual_info_list)]\\r\\n \\r\\n partitions = partition(np.array(x[:, bestattribute] == bestvalue).astype(int))\\r\\n \\r\\n dropindex = np.all(attribute_value_pairs == (bestattribute, bestvalue), axis=1)\\r\\n \\r\\n attribute_value_pairs = np.delete(attribute_value_pairs, np.argwhere(dropindex), 0)\\r\\n \\r\\n for decision_attribute, index in partitions.items():\\r\\n xsubset = x.take(index, axis=0)\\r\\n ysubset = y.take(index, axis=0)\\r\\n decision = bool(decision_attribute)\\r\\n\\r\\n root[(bestattribute, bestvalue, decision)] = id3(xsubset, ysubset, attribute_value_pairs=attribute_value_pairs,max_depth=max_depth, depth=depth + 1)\\r\\n \\r\\n return root\\r\\n raise Exception('Function not yet implemented!')\",\n \"def maxWeightMatching(edges, maxcardinality=False):\\n\\n #\\n # Vertices are numbered 0 .. (nvertex-1).\\n # Non-trivial blossoms are numbered nvertex .. (2*nvertex-1)\\n #\\n # Edges are numbered 0 .. (nedge-1).\\n # Edge endpoints are numbered 0 .. (2*nedge-1), such that endpoints\\n # (2*k) and (2*k+1) both belong to edge k.\\n #\\n # Many terms used in the comments (sub-blossom, T-vertex) come from\\n # the paper by Galil; read the paper before reading this code.\\n #\\n\\n # Deal swiftly with empty graphs.\\n if not edges:\\n return [ ]\\n\\n # Count vertices.\\n nedge = len(edges)\\n nvertex = 0\\n for (i, j, w) in edges:\\n assert i >= 0 and j >= 0 and i != j\\n if i >= nvertex:\\n nvertex = i + 1\\n if j >= nvertex:\\n nvertex = j + 1\\n\\n # Find the maximum edge weight.\\n maxweight = max(0, max([ wt for (i, j, wt) in edges ]))\\n\\n # If p is an edge endpoint,\\n # endpoint[p] is the vertex to which endpoint p is attached.\\n # Not modified by the algorithm.\\n endpoint = [ edges[p//2][p%2] for p in range (2*nedge) ]\\n\\n # If v is a vertex,\\n # neighbend[v] is the list of remote endpoints of the edges attached to v.\\n # Not modified by the algorithm.\\n neighbend = [ [ ] for i in range(nvertex) ]\\n for k in xrange(len(edges)):\\n (i, j, w) = edges[k]\\n neighbend[i].append(2*k+1)\\n neighbend[j].append(2*k)\\n\\n # If v is a vertex,\\n # mate[v] is the remote endpoint of its matched edge, or -1 if it is single\\n # (i.e. endpoint[mate[v]] is v's partner vertex).\\n # Initially all vertices are single; updated during augmentation.\\n mate = nvertex * [ -1 ]\\n\\n # If b is a top-level blossom,\\n # label[b] is 0 if b is unlabeled (free);\\n # 1 if b is an S-vertex/blossom;\\n # 2 if b is a T-vertex/blossom.\\n # The label of a vertex is found by looking at the label of its\\n # top-level containing blossom.\\n # If v is a vertex inside a T-blossom,\\n # label[v] is 2 iff v is reachable from an S-vertex outside the blossom.\\n # Labels are assigned during a stage and reset after each augmentation.\\n label = (2 * nvertex) * [ 0 ]\\n\\n # If b is a labeled top-level blossom,\\n # labelend[b] is the remote endpoint of the edge through which b obtained\\n # its label, or -1 if b's base vertex is single.\\n # If v is a vertex inside a T-blossom and label[v] == 2,\\n # labelend[v] is the remote endpoint of the edge through which v is\\n # reachable from outside the blossom.\\n labelend = (2 * nvertex) * [ -1 ]\\n\\n # If v is a vertex,\\n # inblossom[v] is the top-level blossom to which v belongs.\\n # If v is a top-level vertex, v is itself a blossom (a trivial blossom)\\n # and inblossom[v] == v.\\n # Initially all vertices are top-level trivial blossoms.\\n inblossom = range(nvertex)\\n\\n # If b is a sub-blossom,\\n # blossomparent[b] is its immediate parent (sub-)blossom.\\n # If b is a top-level blossom, blossomparent[b] is -1.\\n blossomparent = (2 * nvertex) * [ -1 ]\\n\\n # If b is a non-trivial (sub-)blossom,\\n # blossomchilds[b] is an ordered list of its sub-blossoms, starting with\\n # the base and going round the blossom.\\n blossomchilds = (2 * nvertex) * [ None ]\\n\\n # If b is a (sub-)blossom,\\n # blossombase[b] is its base VERTEX (i.e. recursive sub-blossom).\\n blossombase = range(nvertex) + nvertex * [ -1 ]\\n\\n # If b is a non-trivial (sub-)blossom,\\n # blossomendps[b] is a list of endpoints on its connecting edges,\\n # such that blossomendps[b][i] is the local endpoint of blossomchilds[b][i]\\n # on the edge that connects it to blossomchilds[b][wrap(i+1)].\\n blossomendps = (2 * nvertex) * [ None ]\\n\\n # If v is a free vertex (or an unreached vertex inside a T-blossom),\\n # bestedge[v] is the edge to an S-vertex with least slack,\\n # or -1 if there is no such edge.\\n # If b is a (possibly trivial) top-level S-blossom,\\n # bestedge[b] is the least-slack edge to a different S-blossom,\\n # or -1 if there is no such edge.\\n # This is used for efficient computation of delta2 and delta3.\\n bestedge = (2 * nvertex) * [ -1 ]\\n\\n # If b is a non-trivial top-level S-blossom,\\n # blossombestedges[b] is a list of least-slack edges to neighbouring\\n # S-blossoms, or None if no such list has been computed yet.\\n # This is used for efficient computation of delta3.\\n blossombestedges = (2 * nvertex) * [ None ]\\n\\n # List of currently unused blossom numbers.\\n unusedblossoms = range(nvertex, 2*nvertex)\\n\\n # If v is a vertex,\\n # dualvar[v] = 2 * u(v) where u(v) is the v's variable in the dual\\n # optimization problem (multiplication by two ensures integer values\\n # throughout the algorithm if all edge weights are integers).\\n # If b is a non-trivial blossom,\\n # dualvar[b] = z(b) where z(b) is b's variable in the dual optimization\\n # problem.\\n dualvar = nvertex * [ maxweight ] + nvertex * [ 0 ]\\n\\n # If allowedge[k] is true, edge k has zero slack in the optimization\\n # problem; if allowedge[k] is false, the edge's slack may or may not\\n # be zero.\\n allowedge = nedge * [ False ]\\n\\n # Queue of newly discovered S-vertices.\\n queue = [ ]\\n\\n # Return 2 * slack of edge k (does not work inside blossoms).\\n def slack(k):\\n (i, j, wt) = edges[k]\\n return dualvar[i] + dualvar[j] - 2 * wt\\n\\n # Generate the leaf vertices of a blossom.\\n def blossomLeaves(b):\\n if b < nvertex:\\n yield b\\n else:\\n for t in blossomchilds[b]:\\n if t < nvertex:\\n yield t\\n else:\\n for v in blossomLeaves(t):\\n yield v\\n\\n # Assign label t to the top-level blossom containing vertex w\\n # and record the fact that w was reached through the edge with\\n # remote endpoint p.\\n def assignLabel(w, t, p):\\n if DEBUG: DEBUG('assignLabel(%d,%d,%d)' % (w, t, p))\\n b = inblossom[w]\\n assert label[w] == 0 and label[b] == 0\\n label[w] = label[b] = t\\n labelend[w] = labelend[b] = p\\n bestedge[w] = bestedge[b] = -1\\n if t == 1:\\n # b became an S-vertex/blossom; add it(s vertices) to the queue.\\n queue.extend(blossomLeaves(b))\\n if DEBUG: DEBUG('PUSH ' + str(list(blossomLeaves(b))))\\n elif t == 2:\\n # b became a T-vertex/blossom; assign label S to its mate.\\n # (If b is a non-trivial blossom, its base is the only vertex\\n # with an external mate.)\\n base = blossombase[b]\\n assert mate[base] >= 0\\n assignLabel(endpoint[mate[base]], 1, mate[base] ^ 1)\\n\\n # Trace back from vertices v and w to discover either a new blossom\\n # or an augmenting path. Return the base vertex of the new blossom or -1.\\n def scanBlossom(v, w):\\n if DEBUG: DEBUG('scanBlossom(%d,%d)' % (v, w))\\n # Trace back from v and w, placing breadcrumbs as we go.\\n path = [ ]\\n base = -1\\n while v != -1 or w != -1:\\n # Look for a breadcrumb in v's blossom or put a new breadcrumb.\\n b = inblossom[v]\\n if label[b] & 4:\\n base = blossombase[b]\\n break\\n assert label[b] == 1\\n path.append(b)\\n label[b] = 5\\n # Trace one step back.\\n assert labelend[b] == mate[blossombase[b]]\\n if labelend[b] == -1:\\n # The base of blossom b is single; stop tracing this path.\\n v = -1\\n else:\\n v = endpoint[labelend[b]]\\n b = inblossom[v]\\n assert label[b] == 2\\n # b is a T-blossom; trace one more step back.\\n assert labelend[b] >= 0\\n v = endpoint[labelend[b]]\\n # Swap v and w so that we alternate between both paths.\\n if w != -1:\\n v, w = w, v\\n # Remove breadcrumbs.\\n for b in path:\\n label[b] = 1\\n # Return base vertex, if we found one.\\n return base\\n\\n # Construct a new blossom with given base, containing edge k which\\n # connects a pair of S vertices. Label the new blossom as S; set its dual\\n # variable to zero; relabel its T-vertices to S and add them to the queue.\\n def addBlossom(base, k):\\n (v, w, wt) = edges[k]\\n bb = inblossom[base]\\n bv = inblossom[v]\\n bw = inblossom[w]\\n # Create blossom.\\n b = unusedblossoms.pop()\\n if DEBUG: DEBUG('addBlossom(%d,%d) (v=%d w=%d) -> %d' % (base, k, v, w, b))\\n blossombase[b] = base\\n blossomparent[b] = -1\\n blossomparent[bb] = b\\n # Make list of sub-blossoms and their interconnecting edge endpoints.\\n blossomchilds[b] = path = [ ]\\n blossomendps[b] = endps = [ ]\\n # Trace back from v to base.\\n while bv != bb:\\n # Add bv to the new blossom.\\n blossomparent[bv] = b\\n path.append(bv)\\n endps.append(labelend[bv])\\n assert (label[bv] == 2 or\\n (label[bv] == 1 and labelend[bv] == mate[blossombase[bv]]))\\n # Trace one step back.\\n assert labelend[bv] >= 0\\n v = endpoint[labelend[bv]]\\n bv = inblossom[v]\\n # Reverse lists, add endpoint that connects the pair of S vertices.\\n path.append(bb)\\n path.reverse()\\n endps.reverse()\\n endps.append(2*k)\\n # Trace back from w to base.\\n while bw != bb:\\n # Add bw to the new blossom.\\n blossomparent[bw] = b\\n path.append(bw)\\n endps.append(labelend[bw] ^ 1)\\n assert (label[bw] == 2 or\\n (label[bw] == 1 and labelend[bw] == mate[blossombase[bw]]))\\n # Trace one step back.\\n assert labelend[bw] >= 0\\n w = endpoint[labelend[bw]]\\n bw = inblossom[w]\\n # Set label to S.\\n assert label[bb] == 1\\n label[b] = 1\\n labelend[b] = labelend[bb]\\n # Set dual variable to zero.\\n dualvar[b] = 0\\n # Relabel vertices.\\n for v in blossomLeaves(b):\\n if label[inblossom[v]] == 2:\\n # This T-vertex now turns into an S-vertex because it becomes\\n # part of an S-blossom; add it to the queue.\\n queue.append(v)\\n inblossom[v] = b\\n # Compute blossombestedges[b].\\n bestedgeto = (2 * nvertex) * [ -1 ]\\n for bv in path:\\n if blossombestedges[bv] is None:\\n # This subblossom does not have a list of least-slack edges;\\n # get the information from the vertices.\\n nblists = [ [ p // 2 for p in neighbend[v] ]\\n for v in blossomLeaves(bv) ]\\n else:\\n # Walk this subblossom's least-slack edges.\\n nblists = [ blossombestedges[bv] ]\\n for nblist in nblists:\\n for k in nblist:\\n (i, j, wt) = edges[k]\\n if inblossom[j] == b:\\n i, j = j, i\\n bj = inblossom[j]\\n if (bj != b and label[bj] == 1 and\\n (bestedgeto[bj] == -1 or\\n slack(k) < slack(bestedgeto[bj]))):\\n bestedgeto[bj] = k\\n # Forget about least-slack edges of the subblossom.\\n blossombestedges[bv] = None\\n bestedge[bv] = -1\\n blossombestedges[b] = [ k for k in bestedgeto if k != -1 ]\\n # Select bestedge[b].\\n bestedge[b] = -1\\n for k in blossombestedges[b]:\\n if bestedge[b] == -1 or slack(k) < slack(bestedge[b]):\\n bestedge[b] = k\\n if DEBUG: DEBUG('blossomchilds[%d]=' % b + repr(blossomchilds[b]))\\n\\n # Expand the given top-level blossom.\\n def expandBlossom(b, endstage):\\n if DEBUG: DEBUG('expandBlossom(%d,%d) %s' % (b, endstage, repr(blossomchilds[b])))\\n # Convert sub-blossoms into top-level blossoms.\\n for s in blossomchilds[b]:\\n blossomparent[s] = -1\\n if s < nvertex:\\n inblossom[s] = s\\n elif endstage and dualvar[s] == 0:\\n # Recursively expand this sub-blossom.\\n expandBlossom(s, endstage)\\n else:\\n for v in blossomLeaves(s):\\n inblossom[v] = s\\n # If we expand a T-blossom during a stage, its sub-blossoms must be\\n # relabeled.\\n if (not endstage) and label[b] == 2:\\n # Start at the sub-blossom through which the expanding\\n # blossom obtained its label, and relabel sub-blossoms untili\\n # we reach the base.\\n # Figure out through which sub-blossom the expanding blossom\\n # obtained its label initially.\\n assert labelend[b] >= 0\\n entrychild = inblossom[endpoint[labelend[b] ^ 1]]\\n # Decide in which direction we will go round the blossom.\\n j = blossomchilds[b].index(entrychild)\\n if j & 1:\\n # Start index is odd; go forward and wrap.\\n j -= len(blossomchilds[b])\\n jstep = 1\\n endptrick = 0\\n else:\\n # Start index is even; go backward.\\n jstep = -1\\n endptrick = 1\\n # Move along the blossom until we get to the base.\\n p = labelend[b]\\n while j != 0:\\n # Relabel the T-sub-blossom.\\n label[endpoint[p ^ 1]] = 0\\n label[endpoint[blossomendps[b][j-endptrick]^endptrick^1]] = 0\\n assignLabel(endpoint[p ^ 1], 2, p)\\n # Step to the next S-sub-blossom and note its forward endpoint.\\n allowedge[blossomendps[b][j-endptrick]//2] = True\\n j += jstep\\n p = blossomendps[b][j-endptrick] ^ endptrick\\n # Step to the next T-sub-blossom.\\n allowedge[p//2] = True\\n j += jstep\\n # Relabel the base T-sub-blossom WITHOUT stepping through to\\n # its mate (so don't call assignLabel).\\n bv = blossomchilds[b][j]\\n label[endpoint[p ^ 1]] = label[bv] = 2\\n labelend[endpoint[p ^ 1]] = labelend[bv] = p\\n bestedge[bv] = -1\\n # Continue along the blossom until we get back to entrychild.\\n j += jstep\\n while blossomchilds[b][j] != entrychild:\\n # Examine the vertices of the sub-blossom to see whether\\n # it is reachable from a neighbouring S-vertex outside the\\n # expanding blossom.\\n bv = blossomchilds[b][j]\\n if label[bv] == 1:\\n # This sub-blossom just got label S through one of its\\n # neighbours; leave it.\\n j += jstep\\n continue\\n for v in blossomLeaves(bv):\\n if label[v] != 0:\\n break\\n # If the sub-blossom contains a reachable vertex, assign\\n # label T to the sub-blossom.\\n if label[v] != 0:\\n assert label[v] == 2\\n assert inblossom[v] == bv\\n label[v] = 0\\n label[endpoint[mate[blossombase[bv]]]] = 0\\n assignLabel(v, 2, labelend[v])\\n j += jstep\\n # Recycle the blossom number.\\n label[b] = labelend[b] = -1\\n blossomchilds[b] = blossomendps[b] = None\\n blossombase[b] = -1\\n blossombestedges[b] = None\\n bestedge[b] = -1\\n unusedblossoms.append(b)\\n\\n # Swap matched/unmatched edges over an alternating path through blossom b\\n # between vertex v and the base vertex. Keep blossom bookkeeping consistent.\\n def augmentBlossom(b, v):\\n if DEBUG: DEBUG('augmentBlossom(%d,%d)' % (b, v))\\n # Bubble up through the blossom tree from vertex v to an immediate\\n # sub-blossom of b.\\n t = v\\n while blossomparent[t] != b:\\n t = blossomparent[t]\\n # Recursively deal with the first sub-blossom.\\n if t >= nvertex:\\n augmentBlossom(t, v)\\n # Decide in which direction we will go round the blossom.\\n i = j = blossomchilds[b].index(t)\\n if i & 1:\\n # Start index is odd; go forward and wrap.\\n j -= len(blossomchilds[b])\\n jstep = 1\\n endptrick = 0\\n else:\\n # Start index is even; go backward.\\n jstep = -1\\n endptrick = 1\\n # Move along the blossom until we get to the base.\\n while j != 0:\\n # Step to the next sub-blossom and augment it recursively.\\n j += jstep\\n t = blossomchilds[b][j]\\n p = blossomendps[b][j-endptrick] ^ endptrick\\n if t >= nvertex:\\n augmentBlossom(t, endpoint[p])\\n # Step to the next sub-blossom and augment it recursively.\\n j += jstep\\n t = blossomchilds[b][j]\\n if t >= nvertex:\\n augmentBlossom(t, endpoint[p ^ 1])\\n # Match the edge connecting those sub-blossoms.\\n mate[endpoint[p]] = p ^ 1\\n mate[endpoint[p ^ 1]] = p\\n if DEBUG: DEBUG('PAIR %d %d (k=%d)' % (endpoint[p], endpoint[p^1], p//2))\\n # Rotate the list of sub-blossoms to put the new base at the front.\\n blossomchilds[b] = blossomchilds[b][i:] + blossomchilds[b][:i]\\n blossomendps[b] = blossomendps[b][i:] + blossomendps[b][:i]\\n blossombase[b] = blossombase[blossomchilds[b][0]]\\n assert blossombase[b] == v\\n\\n # Swap matched/unmatched edges over an alternating path between two\\n # single vertices. The augmenting path runs through edge k, which\\n # connects a pair of S vertices.\\n def augmentMatching(k):\\n (v, w, wt) = edges[k]\\n if DEBUG: DEBUG('augmentMatching(%d) (v=%d w=%d)' % (k, v, w))\\n if DEBUG: DEBUG('PAIR %d %d (k=%d)' % (v, w, k))\\n for (s, p) in ((v, 2*k+1), (w, 2*k)):\\n # Match vertex s to remote endpoint p. Then trace back from s\\n # until we find a single vertex, swapping matched and unmatched\\n # edges as we go.\\n while 1:\\n bs = inblossom[s]\\n assert label[bs] == 1\\n assert labelend[bs] == mate[blossombase[bs]]\\n # Augment through the S-blossom from s to base.\\n if bs >= nvertex:\\n augmentBlossom(bs, s)\\n # Update mate[s]\\n mate[s] = p\\n # Trace one step back.\\n if labelend[bs] == -1:\\n # Reached single vertex; stop.\\n break\\n t = endpoint[labelend[bs]]\\n bt = inblossom[t]\\n assert label[bt] == 2\\n # Trace one step back.\\n assert labelend[bt] >= 0\\n s = endpoint[labelend[bt]]\\n j = endpoint[labelend[bt] ^ 1]\\n # Augment through the T-blossom from j to base.\\n assert blossombase[bt] == t\\n if bt >= nvertex:\\n augmentBlossom(bt, j)\\n # Update mate[j]\\n mate[j] = labelend[bt]\\n # Keep the opposite endpoint;\\n # it will be assigned to mate[s] in the next step.\\n p = labelend[bt] ^ 1\\n if DEBUG: DEBUG('PAIR %d %d (k=%d)' % (s, t, p//2))\\n\\n # Verify that the optimum solution has been reached.\\n def verifyOptimum():\\n if maxcardinality:\\n # Vertices may have negative dual;\\n # find a constant non-negative number to add to all vertex duals.\\n vdualoffset = max(0, -min(dualvar[:nvertex]))\\n else:\\n vdualoffset = 0\\n # 0. all dual variables are non-negative\\n assert min(dualvar[:nvertex]) + vdualoffset >= 0\\n assert min(dualvar[nvertex:]) >= 0\\n # 0. all edges have non-negative slack and\\n # 1. all matched edges have zero slack;\\n for k in xrange(nedge):\\n (i, j, wt) = edges[k]\\n s = dualvar[i] + dualvar[j] - 2 * wt\\n iblossoms = [ i ]\\n jblossoms = [ j ]\\n while blossomparent[iblossoms[-1]] != -1:\\n iblossoms.append(blossomparent[iblossoms[-1]])\\n while blossomparent[jblossoms[-1]] != -1:\\n jblossoms.append(blossomparent[jblossoms[-1]])\\n iblossoms.reverse()\\n jblossoms.reverse()\\n for (bi, bj) in zip(iblossoms, jblossoms):\\n if bi != bj:\\n break\\n s += 2 * dualvar[bi]\\n assert s >= 0\\n if mate[i] // 2 == k or mate[j] // 2 == k:\\n assert mate[i] // 2 == k and mate[j] // 2 == k\\n assert s == 0\\n # 2. all single vertices have zero dual value;\\n for v in xrange(nvertex):\\n assert mate[v] >= 0 or dualvar[v] + vdualoffset == 0\\n # 3. all blossoms with positive dual value are full.\\n for b in xrange(nvertex, 2*nvertex):\\n if blossombase[b] >= 0 and dualvar[b] > 0:\\n assert len(blossomendps[b]) % 2 == 1\\n for p in blossomendps[b][1::2]:\\n assert mate[endpoint[p]] == p ^ 1\\n assert mate[endpoint[p ^ 1]] == p\\n # Ok.\\n\\n # Check optimized delta2 against a trivial computation.\\n def checkDelta2():\\n for v in xrange(nvertex):\\n if label[inblossom[v]] == 0:\\n bd = None\\n bk = -1\\n for p in neighbend[v]:\\n k = p // 2\\n w = endpoint[p]\\n if label[inblossom[w]] == 1:\\n d = slack(k)\\n if bk == -1 or d < bd:\\n bk = k\\n bd = d\\n if DEBUG and (bestedge[v] != -1 or bk != -1) and (bestedge[v] == -1 or bd != slack(bestedge[v])):\\n DEBUG('v=' + str(v) + ' bk=' + str(bk) + ' bd=' + str(bd) + ' bestedge=' + str(bestedge[v]) + ' slack=' + str(slack(bestedge[v])))\\n assert (bk == -1 and bestedge[v] == -1) or (bestedge[v] != -1 and bd == slack(bestedge[v]))\\n\\n # Check optimized delta3 against a trivial computation.\\n def checkDelta3():\\n bk = -1\\n bd = None\\n tbk = -1\\n tbd = None\\n for b in xrange(2 * nvertex):\\n if blossomparent[b] == -1 and label[b] == 1:\\n for v in blossomLeaves(b):\\n for p in neighbend[v]:\\n k = p // 2\\n w = endpoint[p]\\n if inblossom[w] != b and label[inblossom[w]] == 1:\\n d = slack(k)\\n if bk == -1 or d < bd:\\n bk = k\\n bd = d\\n if bestedge[b] != -1:\\n (i, j, wt) = edges[bestedge[b]]\\n assert inblossom[i] == b or inblossom[j] == b\\n assert inblossom[i] != b or inblossom[j] != b\\n assert label[inblossom[i]] == 1 and label[inblossom[j]] == 1\\n if tbk == -1 or slack(bestedge[b]) < tbd:\\n tbk = bestedge[b]\\n tbd = slack(bestedge[b])\\n if DEBUG and bd != tbd:\\n DEBUG('bk=%d tbk=%d bd=%s tbd=%s' % (bk, tbk, repr(bd), repr(tbd)))\\n assert bd == tbd\\n\\n # Main loop: continue until no further improvement is possible.\\n for t in xrange(nvertex):\\n\\n # Each iteration of this loop is a \\\"stage\\\".\\n # A stage finds an augmenting path and uses that to improve\\n # the matching.\\n if DEBUG: DEBUG('STAGE %d' % t)\\n\\n # Remove labels from top-level blossoms/vertices.\\n label[:] = (2 * nvertex) * [ 0 ]\\n\\n # Forget all about least-slack edges.\\n bestedge[:] = (2 * nvertex) * [ -1 ]\\n blossombestedges[nvertex:] = nvertex * [ None ]\\n\\n # Loss of labeling means that we can not be sure that currently\\n # allowable edges remain allowable througout this stage.\\n allowedge[:] = nedge * [ False ]\\n\\n # Make queue empty.\\n queue[:] = [ ]\\n \\n # Label single blossoms/vertices with S and put them in the queue.\\n for v in xrange(nvertex):\\n if mate[v] == -1 and label[inblossom[v]] == 0:\\n assignLabel(v, 1, -1)\\n\\n # Loop until we succeed in augmenting the matching.\\n augmented = 0\\n while 1:\\n\\n # Each iteration of this loop is a \\\"substage\\\".\\n # A substage tries to find an augmenting path;\\n # if found, the path is used to improve the matching and\\n # the stage ends. If there is no augmenting path, the\\n # primal-dual method is used to pump some slack out of\\n # the dual variables.\\n if DEBUG: DEBUG('SUBSTAGE')\\n\\n # Continue labeling until all vertices which are reachable\\n # through an alternating path have got a label.\\n while queue and not augmented:\\n\\n # Take an S vertex from the queue.\\n v = queue.pop()\\n if DEBUG: DEBUG('POP v=%d' % v)\\n assert label[inblossom[v]] == 1\\n\\n # Scan its neighbours:\\n for p in neighbend[v]:\\n k = p // 2\\n w = endpoint[p]\\n # w is a neighbour to v\\n if inblossom[v] == inblossom[w]:\\n # this edge is internal to a blossom; ignore it\\n continue\\n if not allowedge[k]:\\n kslack = slack(k)\\n if kslack <= 0:\\n # edge k has zero slack => it is allowable\\n allowedge[k] = True\\n if allowedge[k]:\\n if label[inblossom[w]] == 0:\\n # (C1) w is a free vertex;\\n # label w with T and label its mate with S (R12).\\n assignLabel(w, 2, p ^ 1)\\n elif label[inblossom[w]] == 1:\\n # (C2) w is an S-vertex (not in the same blossom);\\n # follow back-links to discover either an\\n # augmenting path or a new blossom.\\n base = scanBlossom(v, w)\\n if base >= 0:\\n # Found a new blossom; add it to the blossom\\n # bookkeeping and turn it into an S-blossom.\\n addBlossom(base, k)\\n else:\\n # Found an augmenting path; augment the\\n # matching and end this stage.\\n augmentMatching(k)\\n augmented = 1\\n break\\n elif label[w] == 0:\\n # w is inside a T-blossom, but w itself has not\\n # yet been reached from outside the blossom;\\n # mark it as reached (we need this to relabel\\n # during T-blossom expansion).\\n assert label[inblossom[w]] == 2\\n label[w] = 2\\n labelend[w] = p ^ 1\\n elif label[inblossom[w]] == 1:\\n # keep track of the least-slack non-allowable edge to\\n # a different S-blossom.\\n b = inblossom[v]\\n if bestedge[b] == -1 or kslack < slack(bestedge[b]):\\n bestedge[b] = k\\n elif label[w] == 0:\\n # w is a free vertex (or an unreached vertex inside\\n # a T-blossom) but we can not reach it yet;\\n # keep track of the least-slack edge that reaches w.\\n if bestedge[w] == -1 or kslack < slack(bestedge[w]):\\n bestedge[w] = k\\n\\n if augmented:\\n break\\n\\n # There is no augmenting path under these constraints;\\n # compute delta and reduce slack in the optimization problem.\\n # (Note that our vertex dual variables, edge slacks and delta's\\n # are pre-multiplied by two.)\\n deltatype = -1\\n delta = deltaedge = deltablossom = None\\n\\n # Verify data structures for delta2/delta3 computation.\\n if CHECK_DELTA:\\n checkDelta2()\\n checkDelta3()\\n\\n # Compute delta1: the minumum value of any vertex dual.\\n if not maxcardinality:\\n deltatype = 1\\n delta = min(dualvar[:nvertex])\\n\\n # Compute delta2: the minimum slack on any edge between\\n # an S-vertex and a free vertex.\\n for v in xrange(nvertex):\\n if label[inblossom[v]] == 0 and bestedge[v] != -1:\\n d = slack(bestedge[v])\\n if deltatype == -1 or d < delta:\\n delta = d\\n deltatype = 2\\n deltaedge = bestedge[v]\\n\\n # Compute delta3: half the minimum slack on any edge between\\n # a pair of S-blossoms.\\n for b in xrange(2 * nvertex):\\n if ( blossomparent[b] == -1 and label[b] == 1 and\\n bestedge[b] != -1 ):\\n kslack = slack(bestedge[b])\\n if type(kslack) in (int, long):\\n assert (kslack % 2) == 0\\n d = kslack // 2\\n else:\\n d = kslack / 2\\n if deltatype == -1 or d < delta:\\n delta = d\\n deltatype = 3\\n deltaedge = bestedge[b]\\n\\n # Compute delta4: minimum z variable of any T-blossom.\\n for b in xrange(nvertex, 2*nvertex):\\n if ( blossombase[b] >= 0 and blossomparent[b] == -1 and\\n label[b] == 2 and\\n (deltatype == -1 or dualvar[b] < delta) ):\\n delta = dualvar[b]\\n deltatype = 4\\n deltablossom = b\\n\\n if deltatype == -1:\\n # No further improvement possible; max-cardinality optimum\\n # reached. Do a final delta update to make the optimum\\n # verifyable.\\n assert maxcardinality\\n deltatype = 1\\n delta = max(0, min(dualvar[:nvertex]))\\n\\n # Update dual variables according to delta.\\n for v in xrange(nvertex):\\n if label[inblossom[v]] == 1:\\n # S-vertex: 2*u = 2*u - 2*delta\\n dualvar[v] -= delta\\n elif label[inblossom[v]] == 2:\\n # T-vertex: 2*u = 2*u + 2*delta\\n dualvar[v] += delta\\n for b in xrange(nvertex, 2*nvertex):\\n if blossombase[b] >= 0 and blossomparent[b] == -1:\\n if label[b] == 1:\\n # top-level S-blossom: z = z + 2*delta\\n dualvar[b] += delta\\n elif label[b] == 2:\\n # top-level T-blossom: z = z - 2*delta\\n dualvar[b] -= delta\\n\\n # Take action at the point where minimum delta occurred.\\n if DEBUG: DEBUG('delta%d=%f' % (deltatype, delta))\\n if deltatype == 1: \\n # No further improvement possible; optimum reached.\\n break\\n elif deltatype == 2:\\n # Use the least-slack edge to continue the search.\\n allowedge[deltaedge] = True\\n (i, j, wt) = edges[deltaedge]\\n if label[inblossom[i]] == 0:\\n i, j = j, i\\n assert label[inblossom[i]] == 1\\n queue.append(i)\\n elif deltatype == 3:\\n # Use the least-slack edge to continue the search.\\n allowedge[deltaedge] = True\\n (i, j, wt) = edges[deltaedge]\\n assert label[inblossom[i]] == 1\\n queue.append(i)\\n elif deltatype == 4:\\n # Expand the least-z blossom.\\n expandBlossom(deltablossom, False)\\n\\n # End of a this substage.\\n\\n # Stop when no more augmenting path can be found.\\n if not augmented:\\n break\\n\\n # End of a stage; expand all S-blossoms which have dualvar = 0.\\n for b in xrange(nvertex, 2*nvertex):\\n if ( blossomparent[b] == -1 and blossombase[b] >= 0 and\\n label[b] == 1 and dualvar[b] == 0 ):\\n expandBlossom(b, True)\\n\\n # Verify that we reached the optimum solution.\\n if CHECK_OPTIMUM:\\n verifyOptimum()\\n\\n # Transform mate[] such that mate[v] is the vertex to which v is paired.\\n for v in xrange(nvertex):\\n if mate[v] >= 0:\\n mate[v] = endpoint[mate[v]]\\n totalweight = 0\\n for v in xrange(nvertex):\\n assert mate[v] == -1 or mate[mate[v]] == v \\n if v < mate[v]:\\t\\n \\tfor (i, j, w) in edges:\\n \\t\\tif i == v and j == mate[v]:\\n \\tprint (v, mate[v], w)\\n #maxweighthere = max(0, max([ wt for (i == v, j == mate[v], wt) in edges ]))\\n #print maxweighthere\\n print (\\\"totalweight = \\\",totalweight)\\n return mate\",\n \"def Viterbi_Transition(words:Sequence[str], train_bag:Sequence[Tuple[str, str]]=train_tagged_words)-> Sequence[Tuple[str, str]]:\\n state = []\\n all_tags = list(set([pair[1] for pair in train_bag]))\\n\\n for word_idx, word in enumerate(words):\\n # initialise list of probability column for a given observation\\n p = []\\n for tag in all_tags:\\n if word_idx == 0:\\n transition_p = tags_df.loc['.', tag]\\n else:\\n transition_p = tags_df.loc[state[-1], tag]\\n\\n # compute emission and state probabilities\\n emission_p_parts = word_given_tag(word, tag)\\n emission_p = emission_p_parts[0]/emission_p_parts[1]\\n\\n if word in V:\\n state_probability = transition_p * emission_p\\n else:\\n state_probability = transition_p\\n\\n p.append(state_probability)\\n\\n p_max = max(p)\\n # getting state for which probability is maximum\\n state_max = all_tags[p.index(p_max)]\\n state.append(state_max)\\n return list(zip(words, state))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6412046","0.6118864","0.6081776","0.6072963","0.59527105","0.58929455","0.58275664","0.5798937","0.57968605","0.5785027","0.57746196","0.57544744","0.5740327","0.570907","0.5700903","0.5691829","0.56907284","0.56797606","0.56477773","0.564019","0.56395525","0.560942","0.56069916","0.55892706","0.55862474","0.5575909","0.55613136","0.55573356","0.554948","0.55320555","0.5529094","0.55236894","0.5519785","0.548761","0.54849434","0.548086","0.5459565","0.5435145","0.54291797","0.5421523","0.5414345","0.54109067","0.5410388","0.54063094","0.539674","0.5393821","0.539377","0.5391346","0.53854144","0.53781044","0.5369883","0.5367611","0.53668135","0.53412104","0.53348225","0.533084","0.5325907","0.5320447","0.53164774","0.53148866","0.5313618","0.53076154","0.53073794","0.5307089","0.53015333","0.52979136","0.5296247","0.5286005","0.5285509","0.5285003","0.5278175","0.5278157","0.5275765","0.52738184","0.5272849","0.52677757","0.52607137","0.52606356","0.5251904","0.52490425","0.52490425","0.5239612","0.5238837","0.5238387","0.52293086","0.52242327","0.5224139","0.52239496","0.52213377","0.521854","0.5217374","0.52169985","0.52095425","0.5208239","0.5208164","0.5205326","0.5193544","0.5191494","0.51877993","0.51874834"],"string":"[\n \"0.6412046\",\n \"0.6118864\",\n \"0.6081776\",\n \"0.6072963\",\n \"0.59527105\",\n \"0.58929455\",\n \"0.58275664\",\n \"0.5798937\",\n \"0.57968605\",\n \"0.5785027\",\n \"0.57746196\",\n \"0.57544744\",\n \"0.5740327\",\n \"0.570907\",\n \"0.5700903\",\n \"0.5691829\",\n \"0.56907284\",\n \"0.56797606\",\n \"0.56477773\",\n \"0.564019\",\n \"0.56395525\",\n \"0.560942\",\n \"0.56069916\",\n \"0.55892706\",\n \"0.55862474\",\n \"0.5575909\",\n \"0.55613136\",\n \"0.55573356\",\n \"0.554948\",\n \"0.55320555\",\n \"0.5529094\",\n \"0.55236894\",\n \"0.5519785\",\n \"0.548761\",\n \"0.54849434\",\n \"0.548086\",\n \"0.5459565\",\n \"0.5435145\",\n \"0.54291797\",\n \"0.5421523\",\n \"0.5414345\",\n \"0.54109067\",\n \"0.5410388\",\n \"0.54063094\",\n \"0.539674\",\n \"0.5393821\",\n \"0.539377\",\n \"0.5391346\",\n \"0.53854144\",\n \"0.53781044\",\n \"0.5369883\",\n \"0.5367611\",\n \"0.53668135\",\n \"0.53412104\",\n \"0.53348225\",\n \"0.533084\",\n \"0.5325907\",\n \"0.5320447\",\n \"0.53164774\",\n \"0.53148866\",\n \"0.5313618\",\n \"0.53076154\",\n \"0.53073794\",\n \"0.5307089\",\n \"0.53015333\",\n \"0.52979136\",\n \"0.5296247\",\n \"0.5286005\",\n \"0.5285509\",\n \"0.5285003\",\n \"0.5278175\",\n \"0.5278157\",\n \"0.5275765\",\n \"0.52738184\",\n \"0.5272849\",\n \"0.52677757\",\n \"0.52607137\",\n \"0.52606356\",\n \"0.5251904\",\n \"0.52490425\",\n \"0.52490425\",\n \"0.5239612\",\n \"0.5238837\",\n \"0.5238387\",\n \"0.52293086\",\n \"0.52242327\",\n \"0.5224139\",\n \"0.52239496\",\n \"0.52213377\",\n \"0.521854\",\n \"0.5217374\",\n \"0.52169985\",\n \"0.52095425\",\n \"0.5208239\",\n \"0.5208164\",\n \"0.5205326\",\n \"0.5193544\",\n \"0.5191494\",\n \"0.51877993\",\n \"0.51874834\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.58380824"},"document_rank":{"kind":"string","value":"6"}}},{"rowIdx":11189,"cells":{"query":{"kind":"string","value":"Brute force algorithm for calculating the log of the partition function Z"},"document":{"kind":"string","value":"def logZ_brute(self, T, pos, psi, phi):\n ms = self.get_all_tag_seq(len(T))\n log_scores = tr.zeros(len(ms), dtype=tr.float64)\n for i in range(len(ms)):\n log_scores[i] = self.log_score(T, pos, ms[i], psi, phi)\n log_z = logsumexp(log_scores)\n return log_z"},"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 partition_function(array, temp):\r\n\r\n # Constants imported from scipy.constants\r\n h = scipy.constants.h # Planck's constant\r\n # speed of light must be in cm/s as wavenumber is in cm-1\r\n c = scipy.constants.c * 100\r\n k = scipy.constants.k # Boltzmann constant\r\n T = temp # extracted from log file using extract_temp()\r\n\r\n # check if inputs are numpy arrays and convert if not.\r\n if not isinstance(array, np.ndarray):\r\n np.asarray(array)\r\n\r\n # conversion to exponent\r\n u = (h * array * c) / (k * T)\r\n\r\n # calculates natural log of an individual frequency contribution to the partition function\r\n Q_ = np.log(np.exp(-(u / 2)) / (1 - np.exp(-u)))\r\n # sums all the contributions together, giving the final result.\r\n Q = np.sum(Q_)\r\n return Q","def log_lhood(X, Z, Y, a, ep, lamb):\n \n K = Z.shape[1]\n N, T = X.shape\n \n # p(X)\n ZY = np.dot(Z,Y) \n log_pX = 0\n log_pX = log_pX + np.sum(X * np.log(1 - ((1 - lamb) ** ZY) * (1 - ep)))\n log_pX = log_pX + np.sum((1 - X) * np.log(((1 - lamb) ** ZY) * (1 - ep))) \n \n # p(Z)\n HN = 0\n for n in range(1, N+1):\n HN += 1.0/n\n m = Z.sum(axis=0)\n log_pZ = (K * np.log(a) - (a * HN)) + np.sum(gammaln(m) +\n gammaln(N - m + 1) - gammaln(N + 1)) \n\n return log_pZ + log_pX","def log_p(self,z): \n return np.array([self.log_p_blanket(i) for i in z])","def calculate_log_perplexity(self, output, flat_labels): #completed, expensive, should be compiled\n return -np.sum(np.log2(np.clip(output, a_min=1E-12, a_max=1.0))[np.arange(flat_labels.shape[0]), flat_labels[:,1]])","def log2(a):","def log_prob(self, z, x):\n n_samples = z.shape[0].value\n log_prob = 0\n for i in range(self.time_steps):\n lat_dim = self.transition.dim_x\n obs_dim = self.emmision.dim_x\n # Current time step latent state and observation\n z_t = tf.slice(z, [0, i * lat_dim], [-1, lat_dim])\n x_t = tf.slice(x, [0, i * obs_dim], [-1, obs_dim])\n if i == 0:\n log_prob += tf.reduce_sum(\n self.init_state_p.log_prob(z_t), axis=1)\n else:\n log_prob += self.transition.log_prob(z_t, z_tminus)\n log_prob += self.emmision.log_prob(x_t, z_t)\n # Update the previous latent state for next iteration\n z_tminus = z_t\n return log_prob","def testLogPartitionInfinityIsAccurate(self):\n alpha = float('inf')\n log_z_true = 0.70526025442 # From mathematica.\n log_z = self.variant(self._distribution.log_base_partition_function)(alpha)\n chex.assert_tree_all_close(log_z, log_z_true, atol=1e-7, rtol=1e-7)","def log_marg(self):\n log_prob_X_given_z = 0.\n for k in range(self.K):\n log_prob_X_given_z += self.log_marg_k(k)\n return log_prob_X_given_z","def log_Distribution_volume(self, z):\n if type(z)==np.ndarray:\n ind = z<0\n pv = np.log(self.Distribution_volume(z))\n pv[ind] = -1e99\n return pv\n else:\n if z<0:\n return -1e99\n else:\n return np.log(self.Distribution_volume(z))","def log_p(self,z):\n return mb_log_p_posterior(z, self.neg_posterior, self.mini_batch)","def save_exact_log_Z(expt):\n if isinstance(expt, str):\n expt = get_experiment(expt)\n tr_expt = get_training_expt(expt)\n \n for it in tr_expt.save_after:\n for avg in AVG_VALS:\n print 'iteration', it, avg\n try:\n rbm = load_rbm(expt, it, avg)\n except:\n continue\n\n log_Z = tractable.exact_partition_function(rbm)\n storage.dump(log_Z, expt.log_Z_file(it, avg))","def logrels(rets):\n return np.log(rets + 1)","def logbarrierfunc(delta, z, use_sigma):\n if use_sigma:\n z = np.where(z >= 0, np.tanh(z), z)\n k = 2\n return np.where(z > delta, -np.log(np.abs(z)),\n ((k - 1) / k) * (((z - k * delta) / ((k - 1) * delta)) ** k - 1) - np.log(delta))","def log_sum_exp(Z):\n return np.max(Z) + np.log(np.sum(np.exp(Z - np.max(Z))))","def _log_fold_change_pairs(self, idx0, idx1, base):\n assert np.all([x < self._pval.shape[1] for x in idx0])\n assert np.all([x < self._pval.shape[1] for x in idx1])\n if base == np.e:\n return self._logfc[idx0, :, :][:, idx1, :]\n else:\n return self._logfc[idx0, :, :][:, idx1, :] / np.log(base)","def log_prob(self):","def log_likelihood(self):\r\n if self.likelihood.is_heteroscedastic:\r\n A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)\r\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self._A))\r\n else:\r\n A = -0.5 * self.num_data * self.output_dim * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT\r\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self._A))\r\n C = -self.output_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * np.log(sf2))\r\n D = 0.5 * self.data_fit\r\n self._A_part, self._B_part, self._C_part, self._D_part = A, B, C, D\r\n return A + B + C + D + self.likelihood.Z","def log_p(self,z):\n return log_p_posterior(z, self.neg_posterior)","def log2_inplace(a):","def grad_log_q(self,z): \n param_count = 0\n grad = np.zeros((np.sum(self.approx_param_no),self.sims))\n for core_param in range(len(self.q)):\n for approx_param in range(self.q[core_param].param_no):\n grad[param_count] = self.q[core_param].vi_score(z[core_param],approx_param) \n param_count += 1\n return grad","def compute_log(tx, index_log, mean=[], std=[]):\n tx_new = np.log10(3+abs(tx[:,index_log]))\n return standardize(tx_new,mean,std)","def kullback_leibler_iaf(z, logqz_x, beta=1.):\n \n logpz = -tf.reduce_sum(0.5 * np.log(2*np.pi) + 0.5 * tf.square(z), axis=-1)\n kl = beta * tf.reduce_mean(logqz_x - logpz)\n return kl","def kullback_leibler_iaf(z, logqz_x, beta=1., **args):\n \n logpz = -tf.reduce_sum(input_tensor=0.5 * np.log(2*np.pi) + 0.5 * tf.square(z), axis=-1)\n kl = beta * tf.reduce_mean(input_tensor=logqz_x - logpz)\n return kl","def logP(self):\n raise NotImplementedError","def _log_util(chips: float,\n bet_size: float,\n payout: float) -> float:\n if chips <= 0 or chips + payout*bet_size <= 0:\n return MIN_REWARD\n return max(math.log(1.0 + chips + payout*bet_size) - math.log(1.0 + chips),\n MIN_REWARD)","def log_likelihood_z_lognormal(self, std=1.0):\n #return self.log_det_Jxz - self.dim * tf.log(std) - (0.5 / (std**2)) * tf.reduce_sum(self.output_z**2, axis=1)\n from deep_boltzmann.util import logreg\n logz = logreg(self.output_z, a=0.001, tf=True)\n ll = self.log_det_Jxz \\\n - (0.5 / (std**2)) * tf.reduce_sum(logz**2, axis=1) \\\n - tf.reduce_sum(logz, axis=1)\n return ll","def logp(self, x):\n pass","def log(a):","def _logprob(self, sample):\n cur_y, cur_z = sample\n log_prior = 0\n log_lik = 0\n if cur_z.shape[1] == 0: return -99999999.9\n \n if self.cl_mode:\n a_time = time()\n d_cur_z = cl.Buffer(self.ctx, self.mf.READ_ONLY | self.mf.COPY_HOST_PTR, hostbuf = cur_z.astype(np.int32))\n d_cur_y = cl.Buffer(self.ctx, self.mf.READ_ONLY | self.mf.COPY_HOST_PTR, hostbuf = cur_y.astype(np.int32))\n d_logprob = cl.array.empty(self.queue, (cur_z.shape[0],), np.float32, allocator=self.mem_pool)\n\n if cur_z.shape[0] % self.p_mul_logprob_z_data == 0: wg = (self.p_mul_logprob_z_data,)\n else: wg = None\n \n self.prg.logprob_z_data(self.queue, (cur_z.shape[0],), wg,\n d_cur_z, d_cur_y, self.d_obs, d_logprob.data, #d_novel_f.data,\n np.int32(self.N), np.int32(cur_y.shape[1]), np.int32(cur_z.shape[1]), \n np.float32(self.alpha), np.float32(self.lam), np.float32(self.epislon))\n log_lik = d_logprob.get().sum()\n self.gpu_time += time() - a_time\n\n # calculate the prior probability of Y\n num_on = (cur_y == 1).sum()\n num_off = (cur_y == 0).sum()\n log_prior = num_on * np.log(self.theta) + num_off * np.log(1 - self.theta)\n\n else:\n # calculate the prior probability of Z\n for n in xrange(cur_z.shape[0]):\n num_novel = 0\n for k in xrange(cur_z.shape[1]):\n m = cur_z[:n,k].sum()\n if m > 0:\n if cur_z[n,k] == 1: log_prior += np.log(m / (n+1.0))\n else: log_prior += np.log(1 - m / (n + 1.0))\n else: \n if cur_z[n,k] == 1: num_novel += 1\n if num_novel > 0:\n log_prior += poisson.logpmf(num_novel, self.alpha / (n+1.0))\n # calculate the prior probability of Y\n num_on = (cur_y == 1).sum()\n num_off = (cur_y == 0).sum()\n log_prior += num_on * np.log(self.theta) + num_off * np.log(1 - self.theta)\n # calculate the logliklihood\n log_lik = self._loglik(cur_y = cur_y, cur_z = cur_z)\n return log_prior + log_lik","def _log_fold_change_pairs(self, idx0, idx1, base):\n logfc = np.tile(np.NaN, [len(idx0), len(idx1), self.model_estim.x.shape[1]])\n for i, xi in enumerate(idx0):\n for j, xj in enumerate(idx1):\n logfc[i, j, :] = self._theta_mle[xj, :] - self._theta_mle[xi, :]\n logfc[j, i, :] = -logfc[i, j, :]\n\n if base == np.e:\n return logfc\n else:\n return logfc / np.log(base)","def log10(a):","def log_Schechter_log(self, logl, alpha, logls, logl0):\n phi = (logl - logls) * (alpha+1) * np.log(10.) - np.power(10., logl-logls)\n lik = phi.copy()\n lik [logl < logl0] = -1e99\n return lik","def _log_fold_change_pairs(self, idx0, idx1, base):\n logfc = np.zeros(shape=(len(idx0), len(idx1), self._theta_mle.shape[1]))\n for i, xi in enumerate(idx0):\n for j, xj in enumerate(idx1):\n logfc[i, j, :] = self._theta_mle[xi, :] - self._theta_mle[xj, :]\n\n if base == np.e:\n return logfc\n else:\n return logfc / np.log(base)","def padic_reduce(self, diff_bound):\n Z_bounds = []\n for i in range(len(self.constants.primes)):\n p = self.constants.primes[i]\n logging.info(\"Examining the prime %d for the p-adic reduction.\" % p)\n r = math.ceil(math.log(self.coefficients[\"n1_bound\"], p))\n prec = r + self.tries + 10\n L = Qp(p, prec)\n\n current_z_bound = -1\n\n # Pre-computation step (i.e. memoization)\n alpha, beta = self.calculate_alphabeta(p, prec)\n alpha_t = self.precompute_t_power(alpha, diff_bound)\n beta_t = self.precompute_t_power(beta, diff_bound)\n z0_right_term = L((alpha - beta).norm()).ordp()\n log_alpha_over_beta = log(alpha / beta)\n\n # Pre-computations for tau == 1 \n p_order = log_alpha_over_beta.norm().ordp() / 2\n\n for t_vec in combinations_with_replacement(list(range(1, diff_bound + 1)), self.constants.num_terms - 1):\n z0_left_term = self.constants.a * (1 + sum([alpha_t[t - 1] for t in t_vec]))\n z0 = L(z0_left_term.norm()).ordp() - z0_right_term\n tau = self.calculate_tau(t_vec, alpha_t, beta_t)\n\n # Unlikely case, and we may assume the p-adic log is injective for our purposes.\n if tau == 1:\n new_bound = math.log(self.coefficients[\"n1_bound\"], p) + p_order + z0\n Z_bounds.append(max(z0 + 3/2, new_bound))\n print('executing edge case')\n else:\n # Remember that zeta is in Q_p, not in the field extension.\n log_tau = tau.log()\n zeta = log_tau / log_alpha_over_beta\n vzeta = zeta.norm().ordp()\n zeta_list = self.get_expansion(prec, zeta)\n\n R_max = self.find_Rmax(r, vzeta, zeta_list)\n if R_max == -1:\n print('R does not exist')\n m0 = self.find_m0(p, zeta_list)\n z_bound = math.log(self.coefficients[\"n1_bound\"] - m0, p) + (alpha / beta).ordp() + z0\n else:\n zeta_inverse = log_alpha_over_beta / log_tau\n vzeta_inverse = zeta_inverse.ordp()\n z_bound = log_tau.ordp() + R_max + vzeta_inverse\n current_z_bound = max(current_z_bound, z_bound)\n Z_bounds.append(current_z_bound)\n return Z_bounds","def log_boltzmann_dist(Q, temperature):\n return nn.LogSoftmax(dim=0)(Q/temperature)","def log_zgrid(zr=[0.7,3.4], dz=0.01):\n zgrid = np.exp(np.arange(np.log(1+zr[0]), np.log(1+zr[1]), dz))-1\n return zgrid","def kl(p, q):\n return np.sum(np.where(p != 0, p * np.log(p / q), 0))","def _vertical_log(self, X: np.ndarray) -> (np.ndarray, np.ndarray):\n ret_p = np.zeros_like(X)\n ret_n = np.zeros_like(X)\n log_p = self.manifold.log(X[:-1], X[1:])\n log_n = self.manifold.log(X[1:], X[:-1])\n ret_p[:-1] = log_p\n ret_n[1:] = log_n\n return ret_p, ret_n","def logistic_function(z):\n\tg = 1/(1+np.exp(-z))\n\t#g = np.exp(z)/(1+np.exp(z))\n\treturn g","def compute_approx_log_Z(self, data_term, non_data_term, axis= None):\n \n if (axis == 1) and (non_data_term != None):\n \n approx_Z = T.concatenate([non_data_term, data_term], axis=1)\n \n max_vals = T.max(approx_Z, axis=1)\n\n max_vals = T.reshape(max_vals,[self.batch_size,1])\n \n if self.mf_steps ==0:\n \n max_vals_tiled = T.tile(max_vals,\n (1,self.num_samples+self.batch_size))\n \n else:\n \n max_vals_tiled= T.tile(max_vals,(1,self.num_samples+1))\n \n approx_Z = approx_Z - max_vals_tiled\n \n approx_Z = max_vals + T.log(T.sum(T.exp(approx_Z), axis=1))\n \n approx_Z = T.mean(approx_Z)\n \n if (axis == None) and (non_data_term != None):\n \n approx_Z = T.concatenate([non_data_term, data_term])\n \n max_val = T.max(approx_Z)\n \n ### self. added to enable reporting p_tilda instead of\n ### pseudo likelihood cost\n self.approx_Z = approx_Z - max_val\n \n approx_Z = max_val + T.log(T.sum(T.exp(self.approx_Z)))\n \n if (non_data_term == None) and (axis == None):\n \n approx_Z = data_term\n \n max_val = T.max(approx_Z)\n \n approx_Z = max_val + T.log(T.sum(T.exp(approx_Z -max_val)))\n \n return approx_Z","def fn(val):\n if val < x: return min(2*val-1, 2*(x-val))\n k = int(log(val)//log(x))\n ans = k + fn(val - x**k)\n if x**(k+1) < 2*val: \n ans = min(ans, k + 1 + fn(x**(k+1) - val))\n return ans","def MvNormalLogp():\n cov = pt.matrix(\"cov\")\n cov.tag.test_value = floatX(np.eye(3))\n delta = pt.matrix(\"delta\")\n delta.tag.test_value = floatX(np.zeros((2, 3)))\n\n cholesky = Cholesky(lower=True, on_error=\"nan\")\n\n n, k = delta.shape\n n, k = f(n), f(k)\n chol_cov = cholesky(cov)\n diag = pt.diag(chol_cov)\n ok = pt.all(diag > 0)\n\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\n delta_trans = solve_lower(chol_cov, delta.T).T\n\n result = n * k * pt.log(f(2) * np.pi)\n result += f(2) * n * pt.sum(pt.log(diag))\n result += (delta_trans ** f(2)).sum()\n result = f(-0.5) * result\n logp = pt.switch(ok, result, -np.inf)\n\n def dlogp(inputs, gradients):\n (g_logp,) = gradients\n cov, delta = inputs\n\n g_logp.tag.test_value = floatX(1.0)\n n, k = delta.shape\n\n chol_cov = cholesky(cov)\n diag = pt.diag(chol_cov)\n ok = pt.all(diag > 0)\n\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\n delta_trans = solve_lower(chol_cov, delta.T).T\n\n inner = n * pt.eye(k) - pt.dot(delta_trans.T, delta_trans)\n g_cov = solve_upper(chol_cov.T, inner)\n g_cov = solve_upper(chol_cov.T, g_cov.T)\n\n tau_delta = solve_upper(chol_cov.T, delta_trans.T)\n g_delta = tau_delta.T\n\n g_cov = pt.switch(ok, g_cov, -np.nan)\n g_delta = pt.switch(ok, g_delta, -np.nan)\n\n return [-0.5 * g_cov * g_logp, -g_delta * g_logp]\n\n return OpFromGraph([cov, delta], [logp], grad_overrides=dlogp, inline=True)","def logp(X, nu, V):\n\n p = V.shape[0]\n\n IVI = det(V)\n IXI = det(X)\n\n return check_parameters(\n (\n (nu - p - 1) * pt.log(IXI)\n - trace(matrix_inverse(V).dot(X))\n - nu * p * pt.log(2)\n - nu * pt.log(IVI)\n - 2 * multigammaln(nu / 2.0, p)\n )\n / 2,\n matrix_pos_def(X),\n pt.eq(X, X.T),\n nu > (p - 1),\n )","def kl_heuristic(A, z):\n n = len(A)\n n_round = 10\n P = defaultdict(list)\n L = defaultdict(list)\n bestL = 0\n bestP = None\n for j in range(1, (n+1)/2):\n z_init = rand_partition(n, j)\n l_init = sbm.likelihood(A, z_init, log_scale=True)\n z_round = z_init\n for _round in range(n_round):\n z_values = []\n l_values = []\n for index, pair in enumerate(swap_pairs(z_round)):\n z_round[pair[0]], z_round[pair[1]] = \\\n z_init[pair[1]], z_init[pair[0]]\n z_values.append(z_round)\n l_values.append(sbm.likelihood(A, z_round, log_scale=True))\n l_max_index = l_values.index(max(l_values))\n z_max_round = z_values[l_max_index]\n z_round = z_max_round\n L[j].append(l_values)\n bestL = max(l_values)\n bestP = z_max_round\n return bestL, bestP, L, P","def hankelp(j, nu, z):\n return hankel(j, nu-1, z) - nu*hankel(j, nu, z)/z","def IntegratePolynomialPartLogExt(poly, fieldTower):\n #print(poly)\n #print(poly.degree)\n #print(poly.fieldTower)\n if poly == 0 or poly.isZero():\n return Int.Integral()\n red = poly.reduceToLowestPossibleFieldTower()\n if red.fieldTower.towerHeight<fieldTower.towerHeight:\n return Integrate(red)\n p = poly.getCoefficients()\n l = len(p)-1\n q = [ZERO]*(len(p)+1)\n b = [ZERO]*(len(p)+1)\n d = [ZERO]*(len(p)+1)\n \n \n u = fieldTower.getLastExtension().argFunction # last fieldExtension with T=log(u)\n log_diff_u = u.logDifferentiate()#u.differentiate()/u # u'/u\n \n # p_l *T^l + p_(l-1)*T^(l-1) + ... + p_0 = (q_(l+1)*T^(l+1) + q_l*T^l + ... + q_0)' + (c_1 *v_1'/v_i + ... + c_m *v_m'/v_m)\n # (q_i*T^i)' = q_i'*T^i + q_i*i*T'*T^(i-1)\n # -> 0 = q_(l+1)'\n # p_l = (l+1)*q_(l+1)*T' + q_l'\n # ...\n # p_0 = q_1*T' + (qe_0)', qe_0 = q_0+(c_1 *log(v_1) + ... + c_m *log(v_m))\n for i in range(l,-1,-1):\n # q_(i+1) = d_(i+1) + b_(i+1)\n # integral(p_i-(i+1)*d_(i+1)*T') = l*b_(i+1)*T' + q_i\n #i = Rational.fromFloat(i)\n integrand = p[i]+(-ONE)*(i+1)*d[i+1]*log_diff_u\n if type(integrand)==float or type(integrand)==int:\n integrand = Rational.fromFloat(integrand)\n int_reduced = integrand.reduceToLowestPossibleFieldTower()\n if int_reduced!=None:\n integrand = int_reduced\n P_i = Integrate(integrand)\n if i>0:\n IntegratePolynomialPartLogExtCheckIntegralConditions(P_i, fieldTower)\n else:\n if P_i==None:\n raise Int.IntegralNotElementaryError()\n \n prev = fieldTower.prevTower()\n #logs = P_i.getNewLogExpressionsInFieldTower(prev,fieldTower)\n logTerm = P_i.getLogExpression(u)\n otherLogs = [log for log in P_i.logExpressions if not log==logTerm]#in logs]\n ci = ZERO if logTerm==None else logTerm.factor # integral = P_i = c_i*T+d_i, d_i = P_i\\logs\n d[i] = Int.Integral(poly_rationals=P_i.poly_rational_partExpressions,logs=otherLogs,rootSums=P_i.rootSums).asFunction()\n b[i+1] = ci/(i+1)\n q[i+1] = d[i+1]+b[i+1]\n \n #integrand = p[0]+(-1)*d[1]*log_diff_u\n #P0 = Integrate(integrand)\n #if P0==None:\n # raise Int.IntegralNotElementaryError()\n #b1 = 0\n #for log in P0.logExpressions:\n # if log.argFunction==u:\n # b1 = log.factor\n \n if b[1]==0:\n q_0 = P_i.asFunction()\n else:\n q_0 = (P_i+Int.Integral(logs=[Int.LogFunction(u,(-1)*b[1])])).asFunction()\n q[0] = q_0\n \n integralPoly = Pol.Polynomial(variable=fieldTower.getLastVariable())\n for i in range(l+1,-1,-1):\n integralPoly.setCoefficient(i,q[i], callUpdates=False)\n integralPoly.updateCoefficientsAll()\n \n return Int.Integral(poly_rationals=[integralPoly])","def calc_probs(log_p):\n\n N = log_p.shape[0]\n\n log_Z_per_N = np.zeros(shape=(N, 1))\n\n for i in range(N):\n\n log_Z_per_N[i] = log_norm(log_p[i])\n\n log_p_new = log_p - log_Z_per_N\n\n p = np.exp(log_p_new)\n\n # log_Z = log_norm(log_p)\n\n # p = np.exp(log_p - log_Z)\n\n return p","def U(xs):\n ret = 0\n for x in xs:\n ret += log(x)\n return ret","def logsum_safe(prob_ll, atl):\n logpdf = prob_ll + K.log(atl + K.epsilon())\n alpha = tf.reduce_max(logpdf, -1, keepdims=True)\n return alpha + tf.log(tf.reduce_sum(K.exp(logpdf-alpha), -1, keepdims=True) + K.epsilon())","def log_inplace(a):","def empirical_counts(a_log, p_log, pi0_log, init_data, alpha_list, beta_list):\n # Calculate total marginal probability for each z_it\n seq_log_probs = []\n full_seq_probs = []\n for t, init_vec in enumerate(init_data):\n alpha_array = alpha_list[t]\n beta_array = beta_list[t]\n seq_log_probs.append(logsumexp(alpha_array[:, -1]))\n seq_probs = alpha_array + beta_array\n seq_probs = seq_probs - logsumexp(seq_probs, axis=0)\n full_seq_probs.append(seq_probs)\n\n # Now calculate effective number of transitions (empirical transition matrix)\n a_log_empirical = np.zeros_like(a_log) - np.Inf\n K = len(a_log[:, 0])\n # transition counts\n for k in range(K):\n for l in range(K):\n # store log probs for each sequence\n a_probs = []\n # current transition prob from k to l\n akl = a_log[l, k]\n for i, init_vec in enumerate(init_data):\n T = len(init_vec)\n a = alpha_list[i]\n b = beta_list[i]\n event_list = [a[k, t] + b[l, t + 1] + akl + p_log[int(init_vec[t + 1]), l] for t in range(T - 1)]\n a_probs.append(logsumexp(event_list) - seq_log_probs[i]) # NL: why do this?\n\n a_log_empirical[l, k] = logsumexp(a_probs)\n\n p_empirical = np.zeros_like(p_log)\n for i, init_vec in enumerate(init_data):\n seq_probs = np.exp(full_seq_probs[i])\n for t in range(len(init_vec)):\n p_empirical[int(init_vec[t]), :] += seq_probs[:, t]\n\n pi0_log_empirical = np.zeros_like(pi0_log)\n for i, init_vec in enumerate(init_data):\n seq_probs = full_seq_probs[i]\n pi0_log_empirical += seq_probs[:, 0]\n\n return a_log_empirical, np.log(p_empirical), pi0_log_empirical, seq_log_probs, full_seq_probs","def _x(rho, z):\r\n a = (1 - 2*rho*z + z**2)**.5 + z - rho\r\n b = 1 - rho\r\n return np.log(a / b)","def rundit_fold_logredst(S,nu_lines,cnbeta,gammaL,nu_grid,ngammaL_grid,dLarray,dv_lines,dv_grid):\n\n Ng_nu=len(nu_grid)\n Ng_gammaL=len(ngammaL_grid)\n\n ngammaL=gammaL/dv_lines\n log_nstbeta=jnp.log(cnbeta)\n log_ngammaL=jnp.log(ngammaL)\n \n log_ngammaL_grid = jnp.log(ngammaL_grid)\n\n k = jnp.fft.rfftfreq(2*Ng_nu,1)\n val=inc2D(S,nu_lines,log_ngammaL,nu_grid,log_ngammaL_grid)\n valbuf=jnp.vstack([val,jnp.zeros_like(val)])\n fftval = jnp.fft.rfft(valbuf,axis=0)\n vk=folded_voigt_kernel_logst(k, log_nstbeta,log_ngammaL_grid,dLarray)\n fftvalsum = jnp.sum(fftval*vk,axis=(1,))\n xs=jnp.fft.irfft(fftvalsum)[:Ng_nu]/dv_grid\n \n return xs","def log_likelihood_z_uniform(self, std=1.0):\n return self.log_det_Jxz - self.dim * (tf.log(std) + 0.5*tf.log(12.0))","def _estimate_log_prob(self, x):\n x = self.check_size(x)\n\n if self.covariance_type == \"full\":\n mu = self.mu\n var = self.var\n precision = tf.linalg.inv(var)\n d = x.shape[-1]\n\n log_2pi = d * tf.math.log(2. * pi)\n log_2pi = tf.cast(log_2pi, tf.double)\n log_det = self._calculate_log_det(precision)\n log_det = tf.cast(log_det, tf.double)\n\n\n x = tf.cast(x, tf.double)\n mu = tf.cast(mu, tf.double)\n x_mu_T = tf.expand_dims((x - mu), -2)\n x_mu = tf.expand_dims((x - mu), -1)\n\n x_mu_T_precision = calculate_matmul_n_times(self.n_components, x_mu_T, precision)\n # print(x_mu_T_precision, x_mu_T, x, mu)\n x_mu_T_precision_x_mu = calculate_matmul(x_mu_T_precision, x_mu)\n\n return -.5 * (log_2pi - log_det + x_mu_T_precision_x_mu)\n\n elif self.covariance_type == \"diag\":\n mu = self.mu\n prec = tf.math.rsqrt(self.var)\n\n log_p = tf.math.reduce_sum((mu * mu + x * x - 2 * x * mu) * (prec ** 2), axis=2, keepdims=True)\n log_det = tf.math.reduce_sum(tf.math.log(prec), axis=2, keepdims=True)\n\n return -.5 * (self.n_features * tf.math.log(2. * pi) + log_p) + log_det","def idcg(k):\n res = sum([1.0 / math.log(i + 2, 2) for i in range(k)])\n if not res:\n return 1.0\n else:\n return res","def log10_inplace(a):","def test_safe_sum_p_log_p(self):\r\n m = array([2, 4, 0, 8])\r\n self.assertEqual(safe_sum_p_log_p(m, 2), 2 * 1 + 4 * 2 + 8 * 3)","def MaxHks(N): \n return np.log2(N-1)/2","def estimate(i,x,z):\n\n b = 2 * sqrt(2.0) / 9801\n a = abs(1.0/x-pi)\n if a >= 1e-15:\n z = calc(i)+z\n i += 1\n x = b*z\n estimate(i,x,z)\n else:\n p =1.0/x\n print 1.0/x\n return p","def compute_log_prob(self,params: ndarray) -> float:\n return self.compute_log_prior(params) + self.compute_log_likelihood(params)","def ransac_iters(w=0.5, d=min_num_pairs(), z=0.99):\n\n return int(np.ceil(np.log(1 - z) / np.log(1 - np.power(w,d))))","def __pll(L,h,x, L2=0):\n if len(x.shape)>1: h = h.reshape(-1,1);\n pll = -np.log(1+np.exp(-2*x*(L.dot(x)+h))).sum(0)\n if L2>0: pll += L2*(L**2).sum()\n return pll","def KL(p, q):\n return np.sum(p * np.log(p / q))","def eq_padic_reduce(self, diff_bound):\n Z_bounds = []\n for i in range(len(self.constants.primes)):\n p = self.constants.primes[i]\n logging.info(\"Examining the prime %d for the p-adic reduction.\" % p)\n r = math.ceil(math.log(self.coefficients[\"eq_n1_bound\"], p))\n prec = r + self.tries + 10\n L = Qp(p, prec)\n\n current_z_bound = -1\n\n # Pre-computation step (i.e. memoization)\n alpha, beta = self.calculate_alphabeta(p, prec)\n alpha_t = self.precompute_t_power(alpha, diff_bound)\n beta_t = self.precompute_t_power(beta, diff_bound)\n z0_right_term = L((alpha - beta).norm()).ordp()\n log_alpha_over_beta = log(alpha / beta)\n\n # Pre-computations for tau == 1 \n p_order = log_alpha_over_beta.norm().ordp() / 2\n\n t_vec = (0,0,0)\n z0_left_term = self.constants.a * (1 + sum([alpha_t[t] for t in t_vec]))\n z0 = L(z0_left_term.norm()).ordp() - z0_right_term\n tau = self.calculate_tau(t_vec, alpha_t, beta_t)\n print('tau',tau)\n\n # Unlikely case, and we may assume the p-adic log is injective for our purposes.\n if tau == 1:\n new_bound = math.log(self.coefficients[\"eq_n1_bound\"], p) + p_order + z0\n Z_bounds.append(max(z0 + 3/2, new_bound))\n print('executing edge case')\n else:\n # Remember that zeta is in Q_p, not in the field extension.\n log_tau = tau.log()\n zeta = log_tau / log_alpha_over_beta\n vzeta = zeta.norm().ordp()\n zeta_list = self.get_expansion(prec, zeta)\n\n R_max = self.find_Rmax(r, vzeta, zeta_list)\n if R_max == -1:\n print('R does not exist')\n m0 = self.find_m0(p, zeta_list)\n z_bound = math.log(self.coefficients[\"n1_bound\"] - m0, p) + (alpha / beta).ordp() + z0\n else:\n zeta_inverse = log_alpha_over_beta / log_tau\n vzeta_inverse = zeta_inverse.ordp()\n z_bound = log_tau.ordp() + R_max + vzeta_inverse\n current_z_bound = max(current_z_bound, z_bound)\n return Z_bounds","def logsumexp(logv):\n res = logzero()\n for val in logv:\n res = logsum_pair(res, val)\n return res","def compute_log_prior(self,params: ndarray) -> float:\n ln_tE = params[0]\n ln_A0 = params[1]\n ln_deltaT = params[2]\n fbl = params[3]\n mb = params[4]\n\n # Equation (16,15,17) (note that Albrow uses \"log\" for log10)\n log10e = np.log10(np.exp(1))\n ln_pr_ln_tE = np.log(0.476) - ((log10e*ln_tE - 1.333)**2 / 0.330) + np.log(log10e)\n ln_pr_ln_A0 = np.log(0.660) - (1.289*log10e*ln_A0) + np.log(log10e)\n ln_pr_ln_deltaT = np.log(0.156) - ((log10e*ln_deltaT - 1.432)**2 / 0.458) +\\\n np.log(log10e)\n \n # Paper doesnt mention the prior used, but I assume it to be uniform\n ln_pr_fbl = uniform.logpdf(fbl,0.0,1.0)\n\n # Paper doesnr mention the prior used but I will asuumed it to be uniform\n ln_pr_mb = uniform.logpdf(mb,self.mag_min - 1.0, self.mag_max + 1.0)\n \n \n return ln_pr_fbl + ln_pr_ln_A0 + ln_pr_ln_deltaT + ln_pr_ln_tE + ln_pr_mb","def growth_factor_scale_independent(self, z):\n z = np.atleast_1d(z)\n nz = len(z)\n #if self.M_nu_tot == 0. and self.w0 == -1. and self.wa==0.:\n # aa = 1./(1.+z)\n # ww = self.w0 + (1.-aa)*self.wa\n # d1 = aa*ss.hyp2f1(1/3., 1., 11/6., -aa**3/self.Omega_m*(1.-self.Omega_m))/ss.hyp2f1(1/3., 1., 11/6., -(1.-self.Omega_m)/self.Omega_m)\n #else:\n # d1 = np.zeros(nz)\n # for i in range(nz):\n # LCDM, _ = sint.quad(lambda x: (1+x)*(self.H0/self.H_massive(x))**3., z[i], np.inf)\n # d1[i] = LCDM*self.H_massive(z[i])/self.H0\n # LCDM0, _ = sint.quad(lambda x: (1+x)*(self.H0/self.H_massive(x))**3., 0., np.inf)\n # d1 = d1/LCDM0\n d1 = np.zeros(nz)\n for i in range(nz):\n LCDM, _ = sint.quad(lambda x: (1+x)*(self.H0/self.H_massive(x))**3., z[i], np.inf)\n d1[i] = LCDM*self.H_massive(z[i])/self.H0\n LCDM0, _ = sint.quad(lambda x: (1+x)*(self.H0/self.H_massive(x))**3., 0., np.inf)\n d1 = d1/LCDM0\n return d1","def levin(x):\n summ = 0\n for t, l in x: # for the time and length of each algorithm\n summ += l + np.log(t)\n return summ","def calc_partition_function_one(states, T):\n B = beta(T)\n Z = sum(np.exp(-B * states))\n\n return Z","def smhm_behroozi(logMstar, z, Mstar00 = 10.72, Mstar0a = 0.55,\n M10 = 12.35, M1a = 0.28, beta0 = 0.44,\n abeta = 0.18, delta0 = 0.57, adelta = 0.17,\n gamma0 = 1.56, agamma = 2.51 ):\n\n # Warning\n if np.any(logMstar<8.8):\n print \"Warning: LogMstar value less than Behroozi et al. 2010 Figure 2 range [8.8, 11.6].\"\n elif np.any(logMstar>11.6):\n print \"Warning: LogMstar value greater than Behroozi et al. 2010 Figure 2 range [8.8, 11.6].\"\n \n #scale factor\n a = 1/(1+z)\n \n #Equation 21 of Behroozi et al\n logM1 = M10 + M1a*(a-1)\n logMstar0 = Mstar00 + Mstar0a*(a-1)\n beta = beta0 + abeta*(a-1)\n delta = delta0 + adelta*(a-1)\n gamma = gamma0 + agamma*(a-1) \n MstarbyMstar0 = 10**( logMstar - logMstar0 ) \n return logM1 + beta*np.log10( MstarbyMstar0 ) +\\\n (MstarbyMstar0**delta)/(1+(MstarbyMstar0)**-gamma) - 0.5","def monotonicity(self, grid):\n\t\ttotal = [0] * 4;\n\n\t\tfor x in range(0,4):\n\t\t\tcurrent = 0\n\t\t\tnext = current + 1\n\t\t\twhile next < 4 :\n\t\t\t\t\"\"\"move to next non zero value\"\"\"\n\t\t\t\twhile next < 4 and not grid.getCellValue((x, next)):\n\t\t\t\t\tnext += 1\n\t\t\t\t\"\"\"if the non zero cell was last cell of grid\"\"\"\n\t\t\t\tif next >= 4:\n\t\t\t\t\tnext -= 1\n\t\t\t\t\n\t\t\t\tcurrent_value = grid.getCellValue((x, current))\n\t\t\t\tif current_value:\n\t\t\t\t\tcurrent_value = log(current_value, 2)\n\t\t\t\tnext_value = grid.getCellValue((x, next))\n\t\t\t\tif next_value:\n\t\t\t\t\tnext_value = log(next_value, 2)\n\t\t\t\t\n\t\t\t\tif current_value > next_value :\n\t\t\t\t\ttotal[0] += next_value - current_value\n\t\t\t\telif next_value > current_value :\n\t\t\t\t\ttotal[1] += current_value - next_value\n\t\t\t\tcurrent = next\n\t\t\t\tnext += 1\n\n\t\tfor y in range(0,4):\n\t\t\tcurrent = 0\n\t\t\tnext = current + 1\n\t\t\twhile next < 4 :\n\t\t\t\twhile next < 4 and not grid.getCellValue((next, y)):\n\t\t\t\t\tnext += 1\n\t\t\t\tif next >= 4:\n\t\t\t\t\tnext -= 1\n\t\t\t\t\n\t\t\t\tcurrent_value = grid.getCellValue((current, y))\n\t\t\t\tif current_value:\n\t\t\t\t\tcurrent_value = log(current_value, 2)\n\t\t\t\tnext_value = grid.getCellValue((next, y))\n\t\t\t\tif next_value:\n\t\t\t\t\tnext_value = log(next_value, 2)\n\t\t\t\t\n\t\t\t\tif current_value > next_value :\n\t\t\t\t\ttotal[2] += next_value - current_value\n\t\t\t\telif next_value > current_value :\n\t\t\t\t\ttotal[3] += current_value - next_value\n\t\t\t\tcurrent = next\n\t\t\t\tnext += 1\n\n\t\treturn max(total[0], total[1]) + max(total[2],total[3])","def get_log(p):\n if p==0:\n return 0.\n return p*np.log2(p)","def main(l, k):\n S = 0\n T = product(xrange(2), repeat=k)\n for ts in T:\n tmp = []\n\n for t, c in zip(ts, cs):\n tmp.append(((-1)*c)**t)\n\n S += (sum(tmp)**l)\n val = (sum(tmp)**l)\n print val\n return S / float(2**(k))","def main(l, k):\n S = 0\n T = product(xrange(2), repeat=k)\n for ts in T:\n tmp = []\n\n for t, c in zip(ts, cs):\n tmp.append(((-1)*c)**t)\n\n S += (sum(tmp)**l)\n val = (sum(tmp)**l)\n print val\n return S / float(2**(k))","def viterbi(obs: List[int], pi: List[float], A: np.ndarray, B: np.ndarray) -> List[int]:\n pi_log = np.log(pi)\n A_log = np.log(A)\n B_log = np.log(B)\n states = A.shape[0]\n n = len(obs)\n\n D_log = np.zeros((states, n))\n backtrack = np.zeros((states, n - 1)).astype(int)\n D_log[:, 0] = pi_log + B_log[:, obs[0]]\n\n for j in range(1, n):\n for i in range(states):\n temp_sum = A_log[:, i] + D_log[:, j - 1]\n D_log[i, j] = np.max(temp_sum) + B_log[i, obs[j]]\n backtrack[i, j - 1] = np.argmax(temp_sum)\n\n state = np.zeros(n).astype(int)\n state[-1] = np.argmax(D_log[:, -1])\n for n in range(n - 2, -1, -1):\n state[n] = backtrack[int(state[n + 1]), n]\n state = state.tolist()\n return state","def neglogli(preds, actual):\n preds = np.reshape(preds, (-1,))\n nll = np.sum((preds - actual*np.log(preds + 1e-07) + np.log(sp.special.factorial(actual))))\n return nll","def calc_mi(self, input_batch, graph_latent_vec=None, tree_latent_vec=None):\n # [x_batch, nz]\n if graph_latent_vec is None:\n graph_latent_vec, tree_latent_vec = self.encode(*input_batch)\n g_z_mean = self.g_mean(graph_latent_vec)\n g_z_log_var = -torch.abs(self.g_var(graph_latent_vec))\n t_z_mean = self.t_mean(tree_latent_vec)\n t_z_log_var = -torch.abs(self.t_var(tree_latent_vec))\n z_mean = torch.cat([g_z_mean, t_z_mean], dim=-1)\n z_log_var = torch.cat([g_z_log_var, t_z_log_var], dim=-1)\n\n x_batch, nz = z_mean.size()\n # E_{q(z|x)}log(q(z|x)) = -0.5*nz*log(2*\\pi) - 0.5*(1+logvar).sum(-1)\n neg_entropy = (-0.5 * nz * np.log(2 * np.pi) - 0.5 * (1 + z_log_var).sum(-1)).mean()\n # [z_batch, 1, nz]\n z_samples = self.reparameterize(z_mean, z_log_var, nsamples=1)\n # [1, x_batch, nz]\n mu, logvar = z_mean.unsqueeze(0), z_log_var.unsqueeze(0)\n var = logvar.exp()\n # (z_batch, x_batch, nz)\n dev = z_samples - mu\n # (z_batch, x_batch)\n log_density = -0.5 * ((dev ** 2) / var).sum(dim=-1) - \\\n 0.5 * (nz * np.log(2 * np.pi) + logvar.sum(-1))\n # log q(z): aggregate posterior\n # [z_batch]\n log_qz = log_sum_exp(log_density, dim=1) - np.log(x_batch)\n return (neg_entropy - log_qz.mean(-1)).item()","def f(z):\n a=1./(1.+z)\n #da=0.01\n da=0.01*a\n #da=1e-7\n gp,g,gm=[D(1./ia-1.) for ia in [a+da,a,a-da]]\n f=a*(gp-gm)/(2*g*da)\n #dz=0.01\n #gp,g,gm=[D(zi) for zi in [z+dz,z,z-dz]]\n #f=(z)*(gp-gm)/(2.*g*dz)\n return f","def toeplitz_slogdet(r):\n n = len(r)\n r_0 = r[0]\n \n r = np.concatenate((r, np.array([r_0])))\n r /= r_0 # normalize the system so that the T matrix has diagonal of ones\n \n logdet = n*np.log(np.abs(r_0))\n sign = np.sign(r_0)**n\n \n if n == 1:\n return (sign, logdet)\n \n # now on is a modification of Levinson algorithm\n y = zeros((n,))\n x = zeros((n,))\n\n b = -r[1:n+1] \n r = r[:n]\n \n y[0] = -r[1]\n x[0] = b[0]\n beta = 1\n alpha = -r[1]\n \n d = 1 + dot(-b[0], x[0])\n sign *= np.sign(d)\n logdet += np.log(np.abs(d))\n \n for k in range(0,n-2):\n \n beta = (1 - alpha*alpha)*beta\n mu = (b[k+1] - dot(r[1:k+2], x[k::-1])) /beta\n x[0:k+1] = x[0:k+1] + mu*y[k::-1]\n x[k+1] = mu\n \n d = 1 + dot(-b[0:k+2], x[0:k+2])\n sign *= np.sign(d)\n logdet += np.log(np.abs(d))\n \n if k < n-2:\n alpha = -(r[k+2] + dot(r[1:k+2], y[k::-1]))/beta\n y[0:k+1] = y[0:k+1] + alpha * y[k::-1]\n y[k+1] = alpha \n\n return(sign, logdet)","def log1p(x):\n return 0.0","def get_log_lik(cov_evals, rank, shape):\n\n n_samples, n_features = shape\n if not 1 <= rank <= n_features - 1:\n raise ValueError(\"the tested rank should be in [1, n_features - 1]\")\n\n eps = 1e-15\n\n if cov_evals[rank - 1] < eps:\n # When the tested rank is associated with a small eigenvalue, there's\n # no point in computing the log-likelihood: it's going to be very\n # small and won't be the max anyway. Also, it can lead to numerical\n # issues below when computing pa, in particular in log((spectrum[i] -\n # spectrum[j]) because this will take the log of something very small.\n return -np.inf\n\n pu = -rank * log(2.)\n for i in range(1, rank + 1):\n pu += (gammaln((n_features - i + 1) / 2.) -\n log(np.pi) * (n_features - i + 1) / 2.)\n\n pl = np.sum(np.log(cov_evals[:rank]))\n pl = -pl * n_samples / 2.\n\n v = max(eps, np.sum(cov_evals[rank:]) / (n_features - rank))\n pv = -np.log(v) * n_samples * (n_features - rank) / 2.\n\n m = n_features * rank - rank * (rank + 1.) / 2.\n pp = log(2. * np.pi) * (m + rank) / 2.\n\n pa = 0.\n spectrum_ = cov_evals.copy()\n spectrum_[rank:n_features] = v\n for i in range(rank):\n for j in range(i + 1, len(cov_evals)):\n pa += log((cov_evals[i] - cov_evals[j]) *\n (1. / spectrum_[j] - 1. / spectrum_[i])) + log(n_samples)\n\n ll = pu + pl + pv + pp - pa / 2. - rank * log(n_samples) / 2.\n\n return ll","def log_norm(log_x):\n c = np.max(log_x)\n\n if np.isinf(c):\n return c\n\n sum_exp = 0\n\n for x in log_x:\n sum_exp += np.exp(x - c)\n\n log_sum_exp = np.log(sum_exp)\n\n log_Z = log_sum_exp + c\n\n return log_Z","def logsumexp2(z):\n return logsumexp(z, axis=2)","def log2(x):\n raise NotImplementedError","def safelog(x):\n #return np.log(x)\n return np.log(np.clip(x,floor,np.inf))","def normal_log_q_initial(self,z):\n means, scale = self.get_means_and_scales_from_q()\n return (ss.norm.logpdf(z,loc=means,scale=scale)).sum(axis=1)","def log(self): # just use base?\n return Factor().__build( VarSet(self.v) , np.log(self.t) )","def mak2(n,par=None,D0=1e-2,k=.5,Fb=0):\n if par is not None:\n D0,k,Fb,slope=par\n D=np.zeros(n)\n D[0]=D0\n for i in range(n-1):\n D[i+1]=D[i]+k*np.log(1+D[i]/k)\n F=D+Fb\n E[1:]=k*np.log(1+D[:-1]/k)/D[:-1]\n return F,D,E","def log_abs_det_jacobian(self, z):\n pre_u = self.u_ + self.u\n pre_w = self.w_ + self.w\n a = F.softplus(self.a + self.inv)\n w = F.softmax(pre_w, dim=3)\n u = F.softmax(pre_u, dim=3)\n # Perform computation\n pre_sigm = torch.sum(u * a * z, 3) + self.b\n sigm = torch.sigmoid(pre_sigm)\n x_pre = torch.sum(w * sigm, dim=3)\n x_pre_clipped = x_pre * (1 - self.eps) + self.eps * 0.5\n logj = F.log_softmax(pre_w, dim=3) + logsigmoid(pre_sigm) + logsigmoid(-pre_sigm) + torch.log(a)\n # n, d, d2, dh\n logj = logj + F.log_softmax(pre_u, dim=3)\n # n, d, d2, dh, d1\n logj = torch.log(torch.sum(torch.exp(logj),3))\n # n, d, d2, d1\n logdet_ = logj + np.log(1 - self.eps) - (torch.log(x_pre_clipped) + torch.log(-x_pre_clipped + 1))\n return logdet_","def zkl_divergence(x, y, gamma):\n return np.sum([p_i*np.log(p_i/q_i) if q_i > 0 and p_i > 0 else p_i*gamma for (p_i, q_i) in zip(x, y)])","def entropy(self):\n Z = self.sum()\n assert (Z > 0), 'Non-normalizable factor (perhaps log factor?)' # also check for positivity?\n H = 0.0\n for x in np.nditer(self.t, op_flags=['readonly']):\n p = x/Z\n H += 0.0 if p==0 else -p*np.log(p)\n return H","def kl(p, q):\n p = np.asarray(p, dtype=float)\n q = np.asarray(q, dtype=float)\n\n return np.where(p != 0, p * np.log(p / q), 0).sum()","def compute_loglike(self, tools: ModelingTools) -> float:\n return -3.0 * self.placeholder","def log(base, real):\n return math.log(real, base)","def find_z(q):\n z = np.zeros(len(q) - 1, int)\n for i in range(len(q) - 1):\n z[i] = np.ceil(q[i]/2 + q[i+1]/2) # formula from class\n return np.concatenate([[0], z, [BITS-1]])","def log_poisson(k, l):\n return k*np.log(l) -l - gammaln(k+1)","def safe_sum_p_log_p(a, base=None):\r\n flat = ravel(a)\r\n nz = take(flat, nonzero(flat)[0])\r\n logs = log(nz)\r\n if base:\r\n logs /= log(base)\r\n return sum(nz * logs, 0)","def find_metallicity_distribution(redshifts, min_logZ_COMPAS, max_logZ_COMPAS,\n mu0=0.035, muz=-0.23, sigma_0=0.39, sigma_z=0.0, alpha =0.0,\n min_logZ =-12.0, max_logZ =0.0, step_logZ = 0.01): \n ##################################\n # Log-Linear redshift dependence of sigma\n sigma = sigma_0* 10**(sigma_z*redshifts)\n \n ##################################\n # Follow Langer & Norman 2006 in assuming that mean metallicities evolve in z as:\n mean_metallicities = mu0 * 10**(muz * redshifts) \n \n # Now we re-write the expected value of the log-skew-normal to retrieve mu\n beta = alpha/(np.sqrt(1 + (alpha)**2))\n PHI = NormDist.cdf(beta * sigma) \n mu_metallicities = np.log(mean_metallicities/2. * 1./(np.exp(0.5*sigma**2) * PHI ) ) \n\n ##################################\n # create a range of metallicities (the x-values, or random variables)\n log_metallicities = np.arange(min_logZ, max_logZ + step_logZ, step_logZ)\n metallicities = np.exp(log_metallicities)\n\n\n ##################################\n # probabilities of log-skew-normal (without the factor of 1/Z since this is dp/dlogZ not dp/dZ)\n dPdlogZ = 2./(sigma[:,np.newaxis]) * NormDist.pdf((log_metallicities - mu_metallicities[:,np.newaxis])/sigma[:,np.newaxis]) * NormDist.cdf(alpha * (log_metallicities - mu_metallicities[:,np.newaxis])/sigma[:,np.newaxis] )\n\n ##################################\n # normalise the distribution over all metallicities; this choice of normalisation assumes that metallicities outside the COMPAS range have yields of zero\n norm = dPdlogZ.sum(axis=-1) * step_logZ\n dPdlogZ = dPdlogZ /norm[:,np.newaxis]\n\n ##################################\n # assume a flat in log distribution in sampled metallicity to find probability of drawing Z in COMPAS\n p_draw_metallicity = 1 / (max_logZ_COMPAS - min_logZ_COMPAS)\n \n return dPdlogZ, metallicities, p_draw_metallicity","def compute_log_likehood_markov(markov_n, markov_table, start_i, seq):\n ll = 0\n for cur_i in range(start_i, len(seq)):\n cur_mem = seq[cur_i]\n past_mems = tuple(seq[cur_i-markov_n:cur_i])\n prob = markov_table[past_mems][cur_mem]\n # We shouldn't get prob = 0 because we get the markov table from the sequence themselves\n ll += np.log(prob)\n return ll"],"string":"[\n \"def partition_function(array, temp):\\r\\n\\r\\n # Constants imported from scipy.constants\\r\\n h = scipy.constants.h # Planck's constant\\r\\n # speed of light must be in cm/s as wavenumber is in cm-1\\r\\n c = scipy.constants.c * 100\\r\\n k = scipy.constants.k # Boltzmann constant\\r\\n T = temp # extracted from log file using extract_temp()\\r\\n\\r\\n # check if inputs are numpy arrays and convert if not.\\r\\n if not isinstance(array, np.ndarray):\\r\\n np.asarray(array)\\r\\n\\r\\n # conversion to exponent\\r\\n u = (h * array * c) / (k * T)\\r\\n\\r\\n # calculates natural log of an individual frequency contribution to the partition function\\r\\n Q_ = np.log(np.exp(-(u / 2)) / (1 - np.exp(-u)))\\r\\n # sums all the contributions together, giving the final result.\\r\\n Q = np.sum(Q_)\\r\\n return Q\",\n \"def log_lhood(X, Z, Y, a, ep, lamb):\\n \\n K = Z.shape[1]\\n N, T = X.shape\\n \\n # p(X)\\n ZY = np.dot(Z,Y) \\n log_pX = 0\\n log_pX = log_pX + np.sum(X * np.log(1 - ((1 - lamb) ** ZY) * (1 - ep)))\\n log_pX = log_pX + np.sum((1 - X) * np.log(((1 - lamb) ** ZY) * (1 - ep))) \\n \\n # p(Z)\\n HN = 0\\n for n in range(1, N+1):\\n HN += 1.0/n\\n m = Z.sum(axis=0)\\n log_pZ = (K * np.log(a) - (a * HN)) + np.sum(gammaln(m) +\\n gammaln(N - m + 1) - gammaln(N + 1)) \\n\\n return log_pZ + log_pX\",\n \"def log_p(self,z): \\n return np.array([self.log_p_blanket(i) for i in z])\",\n \"def calculate_log_perplexity(self, output, flat_labels): #completed, expensive, should be compiled\\n return -np.sum(np.log2(np.clip(output, a_min=1E-12, a_max=1.0))[np.arange(flat_labels.shape[0]), flat_labels[:,1]])\",\n \"def log2(a):\",\n \"def log_prob(self, z, x):\\n n_samples = z.shape[0].value\\n log_prob = 0\\n for i in range(self.time_steps):\\n lat_dim = self.transition.dim_x\\n obs_dim = self.emmision.dim_x\\n # Current time step latent state and observation\\n z_t = tf.slice(z, [0, i * lat_dim], [-1, lat_dim])\\n x_t = tf.slice(x, [0, i * obs_dim], [-1, obs_dim])\\n if i == 0:\\n log_prob += tf.reduce_sum(\\n self.init_state_p.log_prob(z_t), axis=1)\\n else:\\n log_prob += self.transition.log_prob(z_t, z_tminus)\\n log_prob += self.emmision.log_prob(x_t, z_t)\\n # Update the previous latent state for next iteration\\n z_tminus = z_t\\n return log_prob\",\n \"def testLogPartitionInfinityIsAccurate(self):\\n alpha = float('inf')\\n log_z_true = 0.70526025442 # From mathematica.\\n log_z = self.variant(self._distribution.log_base_partition_function)(alpha)\\n chex.assert_tree_all_close(log_z, log_z_true, atol=1e-7, rtol=1e-7)\",\n \"def log_marg(self):\\n log_prob_X_given_z = 0.\\n for k in range(self.K):\\n log_prob_X_given_z += self.log_marg_k(k)\\n return log_prob_X_given_z\",\n \"def log_Distribution_volume(self, z):\\n if type(z)==np.ndarray:\\n ind = z<0\\n pv = np.log(self.Distribution_volume(z))\\n pv[ind] = -1e99\\n return pv\\n else:\\n if z<0:\\n return -1e99\\n else:\\n return np.log(self.Distribution_volume(z))\",\n \"def log_p(self,z):\\n return mb_log_p_posterior(z, self.neg_posterior, self.mini_batch)\",\n \"def save_exact_log_Z(expt):\\n if isinstance(expt, str):\\n expt = get_experiment(expt)\\n tr_expt = get_training_expt(expt)\\n \\n for it in tr_expt.save_after:\\n for avg in AVG_VALS:\\n print 'iteration', it, avg\\n try:\\n rbm = load_rbm(expt, it, avg)\\n except:\\n continue\\n\\n log_Z = tractable.exact_partition_function(rbm)\\n storage.dump(log_Z, expt.log_Z_file(it, avg))\",\n \"def logrels(rets):\\n return np.log(rets + 1)\",\n \"def logbarrierfunc(delta, z, use_sigma):\\n if use_sigma:\\n z = np.where(z >= 0, np.tanh(z), z)\\n k = 2\\n return np.where(z > delta, -np.log(np.abs(z)),\\n ((k - 1) / k) * (((z - k * delta) / ((k - 1) * delta)) ** k - 1) - np.log(delta))\",\n \"def log_sum_exp(Z):\\n return np.max(Z) + np.log(np.sum(np.exp(Z - np.max(Z))))\",\n \"def _log_fold_change_pairs(self, idx0, idx1, base):\\n assert np.all([x < self._pval.shape[1] for x in idx0])\\n assert np.all([x < self._pval.shape[1] for x in idx1])\\n if base == np.e:\\n return self._logfc[idx0, :, :][:, idx1, :]\\n else:\\n return self._logfc[idx0, :, :][:, idx1, :] / np.log(base)\",\n \"def log_prob(self):\",\n \"def log_likelihood(self):\\r\\n if self.likelihood.is_heteroscedastic:\\r\\n A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)\\r\\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self._A))\\r\\n else:\\r\\n A = -0.5 * self.num_data * self.output_dim * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT\\r\\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self._A))\\r\\n C = -self.output_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * np.log(sf2))\\r\\n D = 0.5 * self.data_fit\\r\\n self._A_part, self._B_part, self._C_part, self._D_part = A, B, C, D\\r\\n return A + B + C + D + self.likelihood.Z\",\n \"def log_p(self,z):\\n return log_p_posterior(z, self.neg_posterior)\",\n \"def log2_inplace(a):\",\n \"def grad_log_q(self,z): \\n param_count = 0\\n grad = np.zeros((np.sum(self.approx_param_no),self.sims))\\n for core_param in range(len(self.q)):\\n for approx_param in range(self.q[core_param].param_no):\\n grad[param_count] = self.q[core_param].vi_score(z[core_param],approx_param) \\n param_count += 1\\n return grad\",\n \"def compute_log(tx, index_log, mean=[], std=[]):\\n tx_new = np.log10(3+abs(tx[:,index_log]))\\n return standardize(tx_new,mean,std)\",\n \"def kullback_leibler_iaf(z, logqz_x, beta=1.):\\n \\n logpz = -tf.reduce_sum(0.5 * np.log(2*np.pi) + 0.5 * tf.square(z), axis=-1)\\n kl = beta * tf.reduce_mean(logqz_x - logpz)\\n return kl\",\n \"def kullback_leibler_iaf(z, logqz_x, beta=1., **args):\\n \\n logpz = -tf.reduce_sum(input_tensor=0.5 * np.log(2*np.pi) + 0.5 * tf.square(z), axis=-1)\\n kl = beta * tf.reduce_mean(input_tensor=logqz_x - logpz)\\n return kl\",\n \"def logP(self):\\n raise NotImplementedError\",\n \"def _log_util(chips: float,\\n bet_size: float,\\n payout: float) -> float:\\n if chips <= 0 or chips + payout*bet_size <= 0:\\n return MIN_REWARD\\n return max(math.log(1.0 + chips + payout*bet_size) - math.log(1.0 + chips),\\n MIN_REWARD)\",\n \"def log_likelihood_z_lognormal(self, std=1.0):\\n #return self.log_det_Jxz - self.dim * tf.log(std) - (0.5 / (std**2)) * tf.reduce_sum(self.output_z**2, axis=1)\\n from deep_boltzmann.util import logreg\\n logz = logreg(self.output_z, a=0.001, tf=True)\\n ll = self.log_det_Jxz \\\\\\n - (0.5 / (std**2)) * tf.reduce_sum(logz**2, axis=1) \\\\\\n - tf.reduce_sum(logz, axis=1)\\n return ll\",\n \"def logp(self, x):\\n pass\",\n \"def log(a):\",\n \"def _logprob(self, sample):\\n cur_y, cur_z = sample\\n log_prior = 0\\n log_lik = 0\\n if cur_z.shape[1] == 0: return -99999999.9\\n \\n if self.cl_mode:\\n a_time = time()\\n d_cur_z = cl.Buffer(self.ctx, self.mf.READ_ONLY | self.mf.COPY_HOST_PTR, hostbuf = cur_z.astype(np.int32))\\n d_cur_y = cl.Buffer(self.ctx, self.mf.READ_ONLY | self.mf.COPY_HOST_PTR, hostbuf = cur_y.astype(np.int32))\\n d_logprob = cl.array.empty(self.queue, (cur_z.shape[0],), np.float32, allocator=self.mem_pool)\\n\\n if cur_z.shape[0] % self.p_mul_logprob_z_data == 0: wg = (self.p_mul_logprob_z_data,)\\n else: wg = None\\n \\n self.prg.logprob_z_data(self.queue, (cur_z.shape[0],), wg,\\n d_cur_z, d_cur_y, self.d_obs, d_logprob.data, #d_novel_f.data,\\n np.int32(self.N), np.int32(cur_y.shape[1]), np.int32(cur_z.shape[1]), \\n np.float32(self.alpha), np.float32(self.lam), np.float32(self.epislon))\\n log_lik = d_logprob.get().sum()\\n self.gpu_time += time() - a_time\\n\\n # calculate the prior probability of Y\\n num_on = (cur_y == 1).sum()\\n num_off = (cur_y == 0).sum()\\n log_prior = num_on * np.log(self.theta) + num_off * np.log(1 - self.theta)\\n\\n else:\\n # calculate the prior probability of Z\\n for n in xrange(cur_z.shape[0]):\\n num_novel = 0\\n for k in xrange(cur_z.shape[1]):\\n m = cur_z[:n,k].sum()\\n if m > 0:\\n if cur_z[n,k] == 1: log_prior += np.log(m / (n+1.0))\\n else: log_prior += np.log(1 - m / (n + 1.0))\\n else: \\n if cur_z[n,k] == 1: num_novel += 1\\n if num_novel > 0:\\n log_prior += poisson.logpmf(num_novel, self.alpha / (n+1.0))\\n # calculate the prior probability of Y\\n num_on = (cur_y == 1).sum()\\n num_off = (cur_y == 0).sum()\\n log_prior += num_on * np.log(self.theta) + num_off * np.log(1 - self.theta)\\n # calculate the logliklihood\\n log_lik = self._loglik(cur_y = cur_y, cur_z = cur_z)\\n return log_prior + log_lik\",\n \"def _log_fold_change_pairs(self, idx0, idx1, base):\\n logfc = np.tile(np.NaN, [len(idx0), len(idx1), self.model_estim.x.shape[1]])\\n for i, xi in enumerate(idx0):\\n for j, xj in enumerate(idx1):\\n logfc[i, j, :] = self._theta_mle[xj, :] - self._theta_mle[xi, :]\\n logfc[j, i, :] = -logfc[i, j, :]\\n\\n if base == np.e:\\n return logfc\\n else:\\n return logfc / np.log(base)\",\n \"def log10(a):\",\n \"def log_Schechter_log(self, logl, alpha, logls, logl0):\\n phi = (logl - logls) * (alpha+1) * np.log(10.) - np.power(10., logl-logls)\\n lik = phi.copy()\\n lik [logl < logl0] = -1e99\\n return lik\",\n \"def _log_fold_change_pairs(self, idx0, idx1, base):\\n logfc = np.zeros(shape=(len(idx0), len(idx1), self._theta_mle.shape[1]))\\n for i, xi in enumerate(idx0):\\n for j, xj in enumerate(idx1):\\n logfc[i, j, :] = self._theta_mle[xi, :] - self._theta_mle[xj, :]\\n\\n if base == np.e:\\n return logfc\\n else:\\n return logfc / np.log(base)\",\n \"def padic_reduce(self, diff_bound):\\n Z_bounds = []\\n for i in range(len(self.constants.primes)):\\n p = self.constants.primes[i]\\n logging.info(\\\"Examining the prime %d for the p-adic reduction.\\\" % p)\\n r = math.ceil(math.log(self.coefficients[\\\"n1_bound\\\"], p))\\n prec = r + self.tries + 10\\n L = Qp(p, prec)\\n\\n current_z_bound = -1\\n\\n # Pre-computation step (i.e. memoization)\\n alpha, beta = self.calculate_alphabeta(p, prec)\\n alpha_t = self.precompute_t_power(alpha, diff_bound)\\n beta_t = self.precompute_t_power(beta, diff_bound)\\n z0_right_term = L((alpha - beta).norm()).ordp()\\n log_alpha_over_beta = log(alpha / beta)\\n\\n # Pre-computations for tau == 1 \\n p_order = log_alpha_over_beta.norm().ordp() / 2\\n\\n for t_vec in combinations_with_replacement(list(range(1, diff_bound + 1)), self.constants.num_terms - 1):\\n z0_left_term = self.constants.a * (1 + sum([alpha_t[t - 1] for t in t_vec]))\\n z0 = L(z0_left_term.norm()).ordp() - z0_right_term\\n tau = self.calculate_tau(t_vec, alpha_t, beta_t)\\n\\n # Unlikely case, and we may assume the p-adic log is injective for our purposes.\\n if tau == 1:\\n new_bound = math.log(self.coefficients[\\\"n1_bound\\\"], p) + p_order + z0\\n Z_bounds.append(max(z0 + 3/2, new_bound))\\n print('executing edge case')\\n else:\\n # Remember that zeta is in Q_p, not in the field extension.\\n log_tau = tau.log()\\n zeta = log_tau / log_alpha_over_beta\\n vzeta = zeta.norm().ordp()\\n zeta_list = self.get_expansion(prec, zeta)\\n\\n R_max = self.find_Rmax(r, vzeta, zeta_list)\\n if R_max == -1:\\n print('R does not exist')\\n m0 = self.find_m0(p, zeta_list)\\n z_bound = math.log(self.coefficients[\\\"n1_bound\\\"] - m0, p) + (alpha / beta).ordp() + z0\\n else:\\n zeta_inverse = log_alpha_over_beta / log_tau\\n vzeta_inverse = zeta_inverse.ordp()\\n z_bound = log_tau.ordp() + R_max + vzeta_inverse\\n current_z_bound = max(current_z_bound, z_bound)\\n Z_bounds.append(current_z_bound)\\n return Z_bounds\",\n \"def log_boltzmann_dist(Q, temperature):\\n return nn.LogSoftmax(dim=0)(Q/temperature)\",\n \"def log_zgrid(zr=[0.7,3.4], dz=0.01):\\n zgrid = np.exp(np.arange(np.log(1+zr[0]), np.log(1+zr[1]), dz))-1\\n return zgrid\",\n \"def kl(p, q):\\n return np.sum(np.where(p != 0, p * np.log(p / q), 0))\",\n \"def _vertical_log(self, X: np.ndarray) -> (np.ndarray, np.ndarray):\\n ret_p = np.zeros_like(X)\\n ret_n = np.zeros_like(X)\\n log_p = self.manifold.log(X[:-1], X[1:])\\n log_n = self.manifold.log(X[1:], X[:-1])\\n ret_p[:-1] = log_p\\n ret_n[1:] = log_n\\n return ret_p, ret_n\",\n \"def logistic_function(z):\\n\\tg = 1/(1+np.exp(-z))\\n\\t#g = np.exp(z)/(1+np.exp(z))\\n\\treturn g\",\n \"def compute_approx_log_Z(self, data_term, non_data_term, axis= None):\\n \\n if (axis == 1) and (non_data_term != None):\\n \\n approx_Z = T.concatenate([non_data_term, data_term], axis=1)\\n \\n max_vals = T.max(approx_Z, axis=1)\\n\\n max_vals = T.reshape(max_vals,[self.batch_size,1])\\n \\n if self.mf_steps ==0:\\n \\n max_vals_tiled = T.tile(max_vals,\\n (1,self.num_samples+self.batch_size))\\n \\n else:\\n \\n max_vals_tiled= T.tile(max_vals,(1,self.num_samples+1))\\n \\n approx_Z = approx_Z - max_vals_tiled\\n \\n approx_Z = max_vals + T.log(T.sum(T.exp(approx_Z), axis=1))\\n \\n approx_Z = T.mean(approx_Z)\\n \\n if (axis == None) and (non_data_term != None):\\n \\n approx_Z = T.concatenate([non_data_term, data_term])\\n \\n max_val = T.max(approx_Z)\\n \\n ### self. added to enable reporting p_tilda instead of\\n ### pseudo likelihood cost\\n self.approx_Z = approx_Z - max_val\\n \\n approx_Z = max_val + T.log(T.sum(T.exp(self.approx_Z)))\\n \\n if (non_data_term == None) and (axis == None):\\n \\n approx_Z = data_term\\n \\n max_val = T.max(approx_Z)\\n \\n approx_Z = max_val + T.log(T.sum(T.exp(approx_Z -max_val)))\\n \\n return approx_Z\",\n \"def fn(val):\\n if val < x: return min(2*val-1, 2*(x-val))\\n k = int(log(val)//log(x))\\n ans = k + fn(val - x**k)\\n if x**(k+1) < 2*val: \\n ans = min(ans, k + 1 + fn(x**(k+1) - val))\\n return ans\",\n \"def MvNormalLogp():\\n cov = pt.matrix(\\\"cov\\\")\\n cov.tag.test_value = floatX(np.eye(3))\\n delta = pt.matrix(\\\"delta\\\")\\n delta.tag.test_value = floatX(np.zeros((2, 3)))\\n\\n cholesky = Cholesky(lower=True, on_error=\\\"nan\\\")\\n\\n n, k = delta.shape\\n n, k = f(n), f(k)\\n chol_cov = cholesky(cov)\\n diag = pt.diag(chol_cov)\\n ok = pt.all(diag > 0)\\n\\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\\n delta_trans = solve_lower(chol_cov, delta.T).T\\n\\n result = n * k * pt.log(f(2) * np.pi)\\n result += f(2) * n * pt.sum(pt.log(diag))\\n result += (delta_trans ** f(2)).sum()\\n result = f(-0.5) * result\\n logp = pt.switch(ok, result, -np.inf)\\n\\n def dlogp(inputs, gradients):\\n (g_logp,) = gradients\\n cov, delta = inputs\\n\\n g_logp.tag.test_value = floatX(1.0)\\n n, k = delta.shape\\n\\n chol_cov = cholesky(cov)\\n diag = pt.diag(chol_cov)\\n ok = pt.all(diag > 0)\\n\\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\\n delta_trans = solve_lower(chol_cov, delta.T).T\\n\\n inner = n * pt.eye(k) - pt.dot(delta_trans.T, delta_trans)\\n g_cov = solve_upper(chol_cov.T, inner)\\n g_cov = solve_upper(chol_cov.T, g_cov.T)\\n\\n tau_delta = solve_upper(chol_cov.T, delta_trans.T)\\n g_delta = tau_delta.T\\n\\n g_cov = pt.switch(ok, g_cov, -np.nan)\\n g_delta = pt.switch(ok, g_delta, -np.nan)\\n\\n return [-0.5 * g_cov * g_logp, -g_delta * g_logp]\\n\\n return OpFromGraph([cov, delta], [logp], grad_overrides=dlogp, inline=True)\",\n \"def logp(X, nu, V):\\n\\n p = V.shape[0]\\n\\n IVI = det(V)\\n IXI = det(X)\\n\\n return check_parameters(\\n (\\n (nu - p - 1) * pt.log(IXI)\\n - trace(matrix_inverse(V).dot(X))\\n - nu * p * pt.log(2)\\n - nu * pt.log(IVI)\\n - 2 * multigammaln(nu / 2.0, p)\\n )\\n / 2,\\n matrix_pos_def(X),\\n pt.eq(X, X.T),\\n nu > (p - 1),\\n )\",\n \"def kl_heuristic(A, z):\\n n = len(A)\\n n_round = 10\\n P = defaultdict(list)\\n L = defaultdict(list)\\n bestL = 0\\n bestP = None\\n for j in range(1, (n+1)/2):\\n z_init = rand_partition(n, j)\\n l_init = sbm.likelihood(A, z_init, log_scale=True)\\n z_round = z_init\\n for _round in range(n_round):\\n z_values = []\\n l_values = []\\n for index, pair in enumerate(swap_pairs(z_round)):\\n z_round[pair[0]], z_round[pair[1]] = \\\\\\n z_init[pair[1]], z_init[pair[0]]\\n z_values.append(z_round)\\n l_values.append(sbm.likelihood(A, z_round, log_scale=True))\\n l_max_index = l_values.index(max(l_values))\\n z_max_round = z_values[l_max_index]\\n z_round = z_max_round\\n L[j].append(l_values)\\n bestL = max(l_values)\\n bestP = z_max_round\\n return bestL, bestP, L, P\",\n \"def hankelp(j, nu, z):\\n return hankel(j, nu-1, z) - nu*hankel(j, nu, z)/z\",\n \"def IntegratePolynomialPartLogExt(poly, fieldTower):\\n #print(poly)\\n #print(poly.degree)\\n #print(poly.fieldTower)\\n if poly == 0 or poly.isZero():\\n return Int.Integral()\\n red = poly.reduceToLowestPossibleFieldTower()\\n if red.fieldTower.towerHeight<fieldTower.towerHeight:\\n return Integrate(red)\\n p = poly.getCoefficients()\\n l = len(p)-1\\n q = [ZERO]*(len(p)+1)\\n b = [ZERO]*(len(p)+1)\\n d = [ZERO]*(len(p)+1)\\n \\n \\n u = fieldTower.getLastExtension().argFunction # last fieldExtension with T=log(u)\\n log_diff_u = u.logDifferentiate()#u.differentiate()/u # u'/u\\n \\n # p_l *T^l + p_(l-1)*T^(l-1) + ... + p_0 = (q_(l+1)*T^(l+1) + q_l*T^l + ... + q_0)' + (c_1 *v_1'/v_i + ... + c_m *v_m'/v_m)\\n # (q_i*T^i)' = q_i'*T^i + q_i*i*T'*T^(i-1)\\n # -> 0 = q_(l+1)'\\n # p_l = (l+1)*q_(l+1)*T' + q_l'\\n # ...\\n # p_0 = q_1*T' + (qe_0)', qe_0 = q_0+(c_1 *log(v_1) + ... + c_m *log(v_m))\\n for i in range(l,-1,-1):\\n # q_(i+1) = d_(i+1) + b_(i+1)\\n # integral(p_i-(i+1)*d_(i+1)*T') = l*b_(i+1)*T' + q_i\\n #i = Rational.fromFloat(i)\\n integrand = p[i]+(-ONE)*(i+1)*d[i+1]*log_diff_u\\n if type(integrand)==float or type(integrand)==int:\\n integrand = Rational.fromFloat(integrand)\\n int_reduced = integrand.reduceToLowestPossibleFieldTower()\\n if int_reduced!=None:\\n integrand = int_reduced\\n P_i = Integrate(integrand)\\n if i>0:\\n IntegratePolynomialPartLogExtCheckIntegralConditions(P_i, fieldTower)\\n else:\\n if P_i==None:\\n raise Int.IntegralNotElementaryError()\\n \\n prev = fieldTower.prevTower()\\n #logs = P_i.getNewLogExpressionsInFieldTower(prev,fieldTower)\\n logTerm = P_i.getLogExpression(u)\\n otherLogs = [log for log in P_i.logExpressions if not log==logTerm]#in logs]\\n ci = ZERO if logTerm==None else logTerm.factor # integral = P_i = c_i*T+d_i, d_i = P_i\\\\logs\\n d[i] = Int.Integral(poly_rationals=P_i.poly_rational_partExpressions,logs=otherLogs,rootSums=P_i.rootSums).asFunction()\\n b[i+1] = ci/(i+1)\\n q[i+1] = d[i+1]+b[i+1]\\n \\n #integrand = p[0]+(-1)*d[1]*log_diff_u\\n #P0 = Integrate(integrand)\\n #if P0==None:\\n # raise Int.IntegralNotElementaryError()\\n #b1 = 0\\n #for log in P0.logExpressions:\\n # if log.argFunction==u:\\n # b1 = log.factor\\n \\n if b[1]==0:\\n q_0 = P_i.asFunction()\\n else:\\n q_0 = (P_i+Int.Integral(logs=[Int.LogFunction(u,(-1)*b[1])])).asFunction()\\n q[0] = q_0\\n \\n integralPoly = Pol.Polynomial(variable=fieldTower.getLastVariable())\\n for i in range(l+1,-1,-1):\\n integralPoly.setCoefficient(i,q[i], callUpdates=False)\\n integralPoly.updateCoefficientsAll()\\n \\n return Int.Integral(poly_rationals=[integralPoly])\",\n \"def calc_probs(log_p):\\n\\n N = log_p.shape[0]\\n\\n log_Z_per_N = np.zeros(shape=(N, 1))\\n\\n for i in range(N):\\n\\n log_Z_per_N[i] = log_norm(log_p[i])\\n\\n log_p_new = log_p - log_Z_per_N\\n\\n p = np.exp(log_p_new)\\n\\n # log_Z = log_norm(log_p)\\n\\n # p = np.exp(log_p - log_Z)\\n\\n return p\",\n \"def U(xs):\\n ret = 0\\n for x in xs:\\n ret += log(x)\\n return ret\",\n \"def logsum_safe(prob_ll, atl):\\n logpdf = prob_ll + K.log(atl + K.epsilon())\\n alpha = tf.reduce_max(logpdf, -1, keepdims=True)\\n return alpha + tf.log(tf.reduce_sum(K.exp(logpdf-alpha), -1, keepdims=True) + K.epsilon())\",\n \"def log_inplace(a):\",\n \"def empirical_counts(a_log, p_log, pi0_log, init_data, alpha_list, beta_list):\\n # Calculate total marginal probability for each z_it\\n seq_log_probs = []\\n full_seq_probs = []\\n for t, init_vec in enumerate(init_data):\\n alpha_array = alpha_list[t]\\n beta_array = beta_list[t]\\n seq_log_probs.append(logsumexp(alpha_array[:, -1]))\\n seq_probs = alpha_array + beta_array\\n seq_probs = seq_probs - logsumexp(seq_probs, axis=0)\\n full_seq_probs.append(seq_probs)\\n\\n # Now calculate effective number of transitions (empirical transition matrix)\\n a_log_empirical = np.zeros_like(a_log) - np.Inf\\n K = len(a_log[:, 0])\\n # transition counts\\n for k in range(K):\\n for l in range(K):\\n # store log probs for each sequence\\n a_probs = []\\n # current transition prob from k to l\\n akl = a_log[l, k]\\n for i, init_vec in enumerate(init_data):\\n T = len(init_vec)\\n a = alpha_list[i]\\n b = beta_list[i]\\n event_list = [a[k, t] + b[l, t + 1] + akl + p_log[int(init_vec[t + 1]), l] for t in range(T - 1)]\\n a_probs.append(logsumexp(event_list) - seq_log_probs[i]) # NL: why do this?\\n\\n a_log_empirical[l, k] = logsumexp(a_probs)\\n\\n p_empirical = np.zeros_like(p_log)\\n for i, init_vec in enumerate(init_data):\\n seq_probs = np.exp(full_seq_probs[i])\\n for t in range(len(init_vec)):\\n p_empirical[int(init_vec[t]), :] += seq_probs[:, t]\\n\\n pi0_log_empirical = np.zeros_like(pi0_log)\\n for i, init_vec in enumerate(init_data):\\n seq_probs = full_seq_probs[i]\\n pi0_log_empirical += seq_probs[:, 0]\\n\\n return a_log_empirical, np.log(p_empirical), pi0_log_empirical, seq_log_probs, full_seq_probs\",\n \"def _x(rho, z):\\r\\n a = (1 - 2*rho*z + z**2)**.5 + z - rho\\r\\n b = 1 - rho\\r\\n return np.log(a / b)\",\n \"def rundit_fold_logredst(S,nu_lines,cnbeta,gammaL,nu_grid,ngammaL_grid,dLarray,dv_lines,dv_grid):\\n\\n Ng_nu=len(nu_grid)\\n Ng_gammaL=len(ngammaL_grid)\\n\\n ngammaL=gammaL/dv_lines\\n log_nstbeta=jnp.log(cnbeta)\\n log_ngammaL=jnp.log(ngammaL)\\n \\n log_ngammaL_grid = jnp.log(ngammaL_grid)\\n\\n k = jnp.fft.rfftfreq(2*Ng_nu,1)\\n val=inc2D(S,nu_lines,log_ngammaL,nu_grid,log_ngammaL_grid)\\n valbuf=jnp.vstack([val,jnp.zeros_like(val)])\\n fftval = jnp.fft.rfft(valbuf,axis=0)\\n vk=folded_voigt_kernel_logst(k, log_nstbeta,log_ngammaL_grid,dLarray)\\n fftvalsum = jnp.sum(fftval*vk,axis=(1,))\\n xs=jnp.fft.irfft(fftvalsum)[:Ng_nu]/dv_grid\\n \\n return xs\",\n \"def log_likelihood_z_uniform(self, std=1.0):\\n return self.log_det_Jxz - self.dim * (tf.log(std) + 0.5*tf.log(12.0))\",\n \"def _estimate_log_prob(self, x):\\n x = self.check_size(x)\\n\\n if self.covariance_type == \\\"full\\\":\\n mu = self.mu\\n var = self.var\\n precision = tf.linalg.inv(var)\\n d = x.shape[-1]\\n\\n log_2pi = d * tf.math.log(2. * pi)\\n log_2pi = tf.cast(log_2pi, tf.double)\\n log_det = self._calculate_log_det(precision)\\n log_det = tf.cast(log_det, tf.double)\\n\\n\\n x = tf.cast(x, tf.double)\\n mu = tf.cast(mu, tf.double)\\n x_mu_T = tf.expand_dims((x - mu), -2)\\n x_mu = tf.expand_dims((x - mu), -1)\\n\\n x_mu_T_precision = calculate_matmul_n_times(self.n_components, x_mu_T, precision)\\n # print(x_mu_T_precision, x_mu_T, x, mu)\\n x_mu_T_precision_x_mu = calculate_matmul(x_mu_T_precision, x_mu)\\n\\n return -.5 * (log_2pi - log_det + x_mu_T_precision_x_mu)\\n\\n elif self.covariance_type == \\\"diag\\\":\\n mu = self.mu\\n prec = tf.math.rsqrt(self.var)\\n\\n log_p = tf.math.reduce_sum((mu * mu + x * x - 2 * x * mu) * (prec ** 2), axis=2, keepdims=True)\\n log_det = tf.math.reduce_sum(tf.math.log(prec), axis=2, keepdims=True)\\n\\n return -.5 * (self.n_features * tf.math.log(2. * pi) + log_p) + log_det\",\n \"def idcg(k):\\n res = sum([1.0 / math.log(i + 2, 2) for i in range(k)])\\n if not res:\\n return 1.0\\n else:\\n return res\",\n \"def log10_inplace(a):\",\n \"def test_safe_sum_p_log_p(self):\\r\\n m = array([2, 4, 0, 8])\\r\\n self.assertEqual(safe_sum_p_log_p(m, 2), 2 * 1 + 4 * 2 + 8 * 3)\",\n \"def MaxHks(N): \\n return np.log2(N-1)/2\",\n \"def estimate(i,x,z):\\n\\n b = 2 * sqrt(2.0) / 9801\\n a = abs(1.0/x-pi)\\n if a >= 1e-15:\\n z = calc(i)+z\\n i += 1\\n x = b*z\\n estimate(i,x,z)\\n else:\\n p =1.0/x\\n print 1.0/x\\n return p\",\n \"def compute_log_prob(self,params: ndarray) -> float:\\n return self.compute_log_prior(params) + self.compute_log_likelihood(params)\",\n \"def ransac_iters(w=0.5, d=min_num_pairs(), z=0.99):\\n\\n return int(np.ceil(np.log(1 - z) / np.log(1 - np.power(w,d))))\",\n \"def __pll(L,h,x, L2=0):\\n if len(x.shape)>1: h = h.reshape(-1,1);\\n pll = -np.log(1+np.exp(-2*x*(L.dot(x)+h))).sum(0)\\n if L2>0: pll += L2*(L**2).sum()\\n return pll\",\n \"def KL(p, q):\\n return np.sum(p * np.log(p / q))\",\n \"def eq_padic_reduce(self, diff_bound):\\n Z_bounds = []\\n for i in range(len(self.constants.primes)):\\n p = self.constants.primes[i]\\n logging.info(\\\"Examining the prime %d for the p-adic reduction.\\\" % p)\\n r = math.ceil(math.log(self.coefficients[\\\"eq_n1_bound\\\"], p))\\n prec = r + self.tries + 10\\n L = Qp(p, prec)\\n\\n current_z_bound = -1\\n\\n # Pre-computation step (i.e. memoization)\\n alpha, beta = self.calculate_alphabeta(p, prec)\\n alpha_t = self.precompute_t_power(alpha, diff_bound)\\n beta_t = self.precompute_t_power(beta, diff_bound)\\n z0_right_term = L((alpha - beta).norm()).ordp()\\n log_alpha_over_beta = log(alpha / beta)\\n\\n # Pre-computations for tau == 1 \\n p_order = log_alpha_over_beta.norm().ordp() / 2\\n\\n t_vec = (0,0,0)\\n z0_left_term = self.constants.a * (1 + sum([alpha_t[t] for t in t_vec]))\\n z0 = L(z0_left_term.norm()).ordp() - z0_right_term\\n tau = self.calculate_tau(t_vec, alpha_t, beta_t)\\n print('tau',tau)\\n\\n # Unlikely case, and we may assume the p-adic log is injective for our purposes.\\n if tau == 1:\\n new_bound = math.log(self.coefficients[\\\"eq_n1_bound\\\"], p) + p_order + z0\\n Z_bounds.append(max(z0 + 3/2, new_bound))\\n print('executing edge case')\\n else:\\n # Remember that zeta is in Q_p, not in the field extension.\\n log_tau = tau.log()\\n zeta = log_tau / log_alpha_over_beta\\n vzeta = zeta.norm().ordp()\\n zeta_list = self.get_expansion(prec, zeta)\\n\\n R_max = self.find_Rmax(r, vzeta, zeta_list)\\n if R_max == -1:\\n print('R does not exist')\\n m0 = self.find_m0(p, zeta_list)\\n z_bound = math.log(self.coefficients[\\\"n1_bound\\\"] - m0, p) + (alpha / beta).ordp() + z0\\n else:\\n zeta_inverse = log_alpha_over_beta / log_tau\\n vzeta_inverse = zeta_inverse.ordp()\\n z_bound = log_tau.ordp() + R_max + vzeta_inverse\\n current_z_bound = max(current_z_bound, z_bound)\\n return Z_bounds\",\n \"def logsumexp(logv):\\n res = logzero()\\n for val in logv:\\n res = logsum_pair(res, val)\\n return res\",\n \"def compute_log_prior(self,params: ndarray) -> float:\\n ln_tE = params[0]\\n ln_A0 = params[1]\\n ln_deltaT = params[2]\\n fbl = params[3]\\n mb = params[4]\\n\\n # Equation (16,15,17) (note that Albrow uses \\\"log\\\" for log10)\\n log10e = np.log10(np.exp(1))\\n ln_pr_ln_tE = np.log(0.476) - ((log10e*ln_tE - 1.333)**2 / 0.330) + np.log(log10e)\\n ln_pr_ln_A0 = np.log(0.660) - (1.289*log10e*ln_A0) + np.log(log10e)\\n ln_pr_ln_deltaT = np.log(0.156) - ((log10e*ln_deltaT - 1.432)**2 / 0.458) +\\\\\\n np.log(log10e)\\n \\n # Paper doesnt mention the prior used, but I assume it to be uniform\\n ln_pr_fbl = uniform.logpdf(fbl,0.0,1.0)\\n\\n # Paper doesnr mention the prior used but I will asuumed it to be uniform\\n ln_pr_mb = uniform.logpdf(mb,self.mag_min - 1.0, self.mag_max + 1.0)\\n \\n \\n return ln_pr_fbl + ln_pr_ln_A0 + ln_pr_ln_deltaT + ln_pr_ln_tE + ln_pr_mb\",\n \"def growth_factor_scale_independent(self, z):\\n z = np.atleast_1d(z)\\n nz = len(z)\\n #if self.M_nu_tot == 0. and self.w0 == -1. and self.wa==0.:\\n # aa = 1./(1.+z)\\n # ww = self.w0 + (1.-aa)*self.wa\\n # d1 = aa*ss.hyp2f1(1/3., 1., 11/6., -aa**3/self.Omega_m*(1.-self.Omega_m))/ss.hyp2f1(1/3., 1., 11/6., -(1.-self.Omega_m)/self.Omega_m)\\n #else:\\n # d1 = np.zeros(nz)\\n # for i in range(nz):\\n # LCDM, _ = sint.quad(lambda x: (1+x)*(self.H0/self.H_massive(x))**3., z[i], np.inf)\\n # d1[i] = LCDM*self.H_massive(z[i])/self.H0\\n # LCDM0, _ = sint.quad(lambda x: (1+x)*(self.H0/self.H_massive(x))**3., 0., np.inf)\\n # d1 = d1/LCDM0\\n d1 = np.zeros(nz)\\n for i in range(nz):\\n LCDM, _ = sint.quad(lambda x: (1+x)*(self.H0/self.H_massive(x))**3., z[i], np.inf)\\n d1[i] = LCDM*self.H_massive(z[i])/self.H0\\n LCDM0, _ = sint.quad(lambda x: (1+x)*(self.H0/self.H_massive(x))**3., 0., np.inf)\\n d1 = d1/LCDM0\\n return d1\",\n \"def levin(x):\\n summ = 0\\n for t, l in x: # for the time and length of each algorithm\\n summ += l + np.log(t)\\n return summ\",\n \"def calc_partition_function_one(states, T):\\n B = beta(T)\\n Z = sum(np.exp(-B * states))\\n\\n return Z\",\n \"def smhm_behroozi(logMstar, z, Mstar00 = 10.72, Mstar0a = 0.55,\\n M10 = 12.35, M1a = 0.28, beta0 = 0.44,\\n abeta = 0.18, delta0 = 0.57, adelta = 0.17,\\n gamma0 = 1.56, agamma = 2.51 ):\\n\\n # Warning\\n if np.any(logMstar<8.8):\\n print \\\"Warning: LogMstar value less than Behroozi et al. 2010 Figure 2 range [8.8, 11.6].\\\"\\n elif np.any(logMstar>11.6):\\n print \\\"Warning: LogMstar value greater than Behroozi et al. 2010 Figure 2 range [8.8, 11.6].\\\"\\n \\n #scale factor\\n a = 1/(1+z)\\n \\n #Equation 21 of Behroozi et al\\n logM1 = M10 + M1a*(a-1)\\n logMstar0 = Mstar00 + Mstar0a*(a-1)\\n beta = beta0 + abeta*(a-1)\\n delta = delta0 + adelta*(a-1)\\n gamma = gamma0 + agamma*(a-1) \\n MstarbyMstar0 = 10**( logMstar - logMstar0 ) \\n return logM1 + beta*np.log10( MstarbyMstar0 ) +\\\\\\n (MstarbyMstar0**delta)/(1+(MstarbyMstar0)**-gamma) - 0.5\",\n \"def monotonicity(self, grid):\\n\\t\\ttotal = [0] * 4;\\n\\n\\t\\tfor x in range(0,4):\\n\\t\\t\\tcurrent = 0\\n\\t\\t\\tnext = current + 1\\n\\t\\t\\twhile next < 4 :\\n\\t\\t\\t\\t\\\"\\\"\\\"move to next non zero value\\\"\\\"\\\"\\n\\t\\t\\t\\twhile next < 4 and not grid.getCellValue((x, next)):\\n\\t\\t\\t\\t\\tnext += 1\\n\\t\\t\\t\\t\\\"\\\"\\\"if the non zero cell was last cell of grid\\\"\\\"\\\"\\n\\t\\t\\t\\tif next >= 4:\\n\\t\\t\\t\\t\\tnext -= 1\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tcurrent_value = grid.getCellValue((x, current))\\n\\t\\t\\t\\tif current_value:\\n\\t\\t\\t\\t\\tcurrent_value = log(current_value, 2)\\n\\t\\t\\t\\tnext_value = grid.getCellValue((x, next))\\n\\t\\t\\t\\tif next_value:\\n\\t\\t\\t\\t\\tnext_value = log(next_value, 2)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif current_value > next_value :\\n\\t\\t\\t\\t\\ttotal[0] += next_value - current_value\\n\\t\\t\\t\\telif next_value > current_value :\\n\\t\\t\\t\\t\\ttotal[1] += current_value - next_value\\n\\t\\t\\t\\tcurrent = next\\n\\t\\t\\t\\tnext += 1\\n\\n\\t\\tfor y in range(0,4):\\n\\t\\t\\tcurrent = 0\\n\\t\\t\\tnext = current + 1\\n\\t\\t\\twhile next < 4 :\\n\\t\\t\\t\\twhile next < 4 and not grid.getCellValue((next, y)):\\n\\t\\t\\t\\t\\tnext += 1\\n\\t\\t\\t\\tif next >= 4:\\n\\t\\t\\t\\t\\tnext -= 1\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tcurrent_value = grid.getCellValue((current, y))\\n\\t\\t\\t\\tif current_value:\\n\\t\\t\\t\\t\\tcurrent_value = log(current_value, 2)\\n\\t\\t\\t\\tnext_value = grid.getCellValue((next, y))\\n\\t\\t\\t\\tif next_value:\\n\\t\\t\\t\\t\\tnext_value = log(next_value, 2)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif current_value > next_value :\\n\\t\\t\\t\\t\\ttotal[2] += next_value - current_value\\n\\t\\t\\t\\telif next_value > current_value :\\n\\t\\t\\t\\t\\ttotal[3] += current_value - next_value\\n\\t\\t\\t\\tcurrent = next\\n\\t\\t\\t\\tnext += 1\\n\\n\\t\\treturn max(total[0], total[1]) + max(total[2],total[3])\",\n \"def get_log(p):\\n if p==0:\\n return 0.\\n return p*np.log2(p)\",\n \"def main(l, k):\\n S = 0\\n T = product(xrange(2), repeat=k)\\n for ts in T:\\n tmp = []\\n\\n for t, c in zip(ts, cs):\\n tmp.append(((-1)*c)**t)\\n\\n S += (sum(tmp)**l)\\n val = (sum(tmp)**l)\\n print val\\n return S / float(2**(k))\",\n \"def main(l, k):\\n S = 0\\n T = product(xrange(2), repeat=k)\\n for ts in T:\\n tmp = []\\n\\n for t, c in zip(ts, cs):\\n tmp.append(((-1)*c)**t)\\n\\n S += (sum(tmp)**l)\\n val = (sum(tmp)**l)\\n print val\\n return S / float(2**(k))\",\n \"def viterbi(obs: List[int], pi: List[float], A: np.ndarray, B: np.ndarray) -> List[int]:\\n pi_log = np.log(pi)\\n A_log = np.log(A)\\n B_log = np.log(B)\\n states = A.shape[0]\\n n = len(obs)\\n\\n D_log = np.zeros((states, n))\\n backtrack = np.zeros((states, n - 1)).astype(int)\\n D_log[:, 0] = pi_log + B_log[:, obs[0]]\\n\\n for j in range(1, n):\\n for i in range(states):\\n temp_sum = A_log[:, i] + D_log[:, j - 1]\\n D_log[i, j] = np.max(temp_sum) + B_log[i, obs[j]]\\n backtrack[i, j - 1] = np.argmax(temp_sum)\\n\\n state = np.zeros(n).astype(int)\\n state[-1] = np.argmax(D_log[:, -1])\\n for n in range(n - 2, -1, -1):\\n state[n] = backtrack[int(state[n + 1]), n]\\n state = state.tolist()\\n return state\",\n \"def neglogli(preds, actual):\\n preds = np.reshape(preds, (-1,))\\n nll = np.sum((preds - actual*np.log(preds + 1e-07) + np.log(sp.special.factorial(actual))))\\n return nll\",\n \"def calc_mi(self, input_batch, graph_latent_vec=None, tree_latent_vec=None):\\n # [x_batch, nz]\\n if graph_latent_vec is None:\\n graph_latent_vec, tree_latent_vec = self.encode(*input_batch)\\n g_z_mean = self.g_mean(graph_latent_vec)\\n g_z_log_var = -torch.abs(self.g_var(graph_latent_vec))\\n t_z_mean = self.t_mean(tree_latent_vec)\\n t_z_log_var = -torch.abs(self.t_var(tree_latent_vec))\\n z_mean = torch.cat([g_z_mean, t_z_mean], dim=-1)\\n z_log_var = torch.cat([g_z_log_var, t_z_log_var], dim=-1)\\n\\n x_batch, nz = z_mean.size()\\n # E_{q(z|x)}log(q(z|x)) = -0.5*nz*log(2*\\\\pi) - 0.5*(1+logvar).sum(-1)\\n neg_entropy = (-0.5 * nz * np.log(2 * np.pi) - 0.5 * (1 + z_log_var).sum(-1)).mean()\\n # [z_batch, 1, nz]\\n z_samples = self.reparameterize(z_mean, z_log_var, nsamples=1)\\n # [1, x_batch, nz]\\n mu, logvar = z_mean.unsqueeze(0), z_log_var.unsqueeze(0)\\n var = logvar.exp()\\n # (z_batch, x_batch, nz)\\n dev = z_samples - mu\\n # (z_batch, x_batch)\\n log_density = -0.5 * ((dev ** 2) / var).sum(dim=-1) - \\\\\\n 0.5 * (nz * np.log(2 * np.pi) + logvar.sum(-1))\\n # log q(z): aggregate posterior\\n # [z_batch]\\n log_qz = log_sum_exp(log_density, dim=1) - np.log(x_batch)\\n return (neg_entropy - log_qz.mean(-1)).item()\",\n \"def f(z):\\n a=1./(1.+z)\\n #da=0.01\\n da=0.01*a\\n #da=1e-7\\n gp,g,gm=[D(1./ia-1.) for ia in [a+da,a,a-da]]\\n f=a*(gp-gm)/(2*g*da)\\n #dz=0.01\\n #gp,g,gm=[D(zi) for zi in [z+dz,z,z-dz]]\\n #f=(z)*(gp-gm)/(2.*g*dz)\\n return f\",\n \"def toeplitz_slogdet(r):\\n n = len(r)\\n r_0 = r[0]\\n \\n r = np.concatenate((r, np.array([r_0])))\\n r /= r_0 # normalize the system so that the T matrix has diagonal of ones\\n \\n logdet = n*np.log(np.abs(r_0))\\n sign = np.sign(r_0)**n\\n \\n if n == 1:\\n return (sign, logdet)\\n \\n # now on is a modification of Levinson algorithm\\n y = zeros((n,))\\n x = zeros((n,))\\n\\n b = -r[1:n+1] \\n r = r[:n]\\n \\n y[0] = -r[1]\\n x[0] = b[0]\\n beta = 1\\n alpha = -r[1]\\n \\n d = 1 + dot(-b[0], x[0])\\n sign *= np.sign(d)\\n logdet += np.log(np.abs(d))\\n \\n for k in range(0,n-2):\\n \\n beta = (1 - alpha*alpha)*beta\\n mu = (b[k+1] - dot(r[1:k+2], x[k::-1])) /beta\\n x[0:k+1] = x[0:k+1] + mu*y[k::-1]\\n x[k+1] = mu\\n \\n d = 1 + dot(-b[0:k+2], x[0:k+2])\\n sign *= np.sign(d)\\n logdet += np.log(np.abs(d))\\n \\n if k < n-2:\\n alpha = -(r[k+2] + dot(r[1:k+2], y[k::-1]))/beta\\n y[0:k+1] = y[0:k+1] + alpha * y[k::-1]\\n y[k+1] = alpha \\n\\n return(sign, logdet)\",\n \"def log1p(x):\\n return 0.0\",\n \"def get_log_lik(cov_evals, rank, shape):\\n\\n n_samples, n_features = shape\\n if not 1 <= rank <= n_features - 1:\\n raise ValueError(\\\"the tested rank should be in [1, n_features - 1]\\\")\\n\\n eps = 1e-15\\n\\n if cov_evals[rank - 1] < eps:\\n # When the tested rank is associated with a small eigenvalue, there's\\n # no point in computing the log-likelihood: it's going to be very\\n # small and won't be the max anyway. Also, it can lead to numerical\\n # issues below when computing pa, in particular in log((spectrum[i] -\\n # spectrum[j]) because this will take the log of something very small.\\n return -np.inf\\n\\n pu = -rank * log(2.)\\n for i in range(1, rank + 1):\\n pu += (gammaln((n_features - i + 1) / 2.) -\\n log(np.pi) * (n_features - i + 1) / 2.)\\n\\n pl = np.sum(np.log(cov_evals[:rank]))\\n pl = -pl * n_samples / 2.\\n\\n v = max(eps, np.sum(cov_evals[rank:]) / (n_features - rank))\\n pv = -np.log(v) * n_samples * (n_features - rank) / 2.\\n\\n m = n_features * rank - rank * (rank + 1.) / 2.\\n pp = log(2. * np.pi) * (m + rank) / 2.\\n\\n pa = 0.\\n spectrum_ = cov_evals.copy()\\n spectrum_[rank:n_features] = v\\n for i in range(rank):\\n for j in range(i + 1, len(cov_evals)):\\n pa += log((cov_evals[i] - cov_evals[j]) *\\n (1. / spectrum_[j] - 1. / spectrum_[i])) + log(n_samples)\\n\\n ll = pu + pl + pv + pp - pa / 2. - rank * log(n_samples) / 2.\\n\\n return ll\",\n \"def log_norm(log_x):\\n c = np.max(log_x)\\n\\n if np.isinf(c):\\n return c\\n\\n sum_exp = 0\\n\\n for x in log_x:\\n sum_exp += np.exp(x - c)\\n\\n log_sum_exp = np.log(sum_exp)\\n\\n log_Z = log_sum_exp + c\\n\\n return log_Z\",\n \"def logsumexp2(z):\\n return logsumexp(z, axis=2)\",\n \"def log2(x):\\n raise NotImplementedError\",\n \"def safelog(x):\\n #return np.log(x)\\n return np.log(np.clip(x,floor,np.inf))\",\n \"def normal_log_q_initial(self,z):\\n means, scale = self.get_means_and_scales_from_q()\\n return (ss.norm.logpdf(z,loc=means,scale=scale)).sum(axis=1)\",\n \"def log(self): # just use base?\\n return Factor().__build( VarSet(self.v) , np.log(self.t) )\",\n \"def mak2(n,par=None,D0=1e-2,k=.5,Fb=0):\\n if par is not None:\\n D0,k,Fb,slope=par\\n D=np.zeros(n)\\n D[0]=D0\\n for i in range(n-1):\\n D[i+1]=D[i]+k*np.log(1+D[i]/k)\\n F=D+Fb\\n E[1:]=k*np.log(1+D[:-1]/k)/D[:-1]\\n return F,D,E\",\n \"def log_abs_det_jacobian(self, z):\\n pre_u = self.u_ + self.u\\n pre_w = self.w_ + self.w\\n a = F.softplus(self.a + self.inv)\\n w = F.softmax(pre_w, dim=3)\\n u = F.softmax(pre_u, dim=3)\\n # Perform computation\\n pre_sigm = torch.sum(u * a * z, 3) + self.b\\n sigm = torch.sigmoid(pre_sigm)\\n x_pre = torch.sum(w * sigm, dim=3)\\n x_pre_clipped = x_pre * (1 - self.eps) + self.eps * 0.5\\n logj = F.log_softmax(pre_w, dim=3) + logsigmoid(pre_sigm) + logsigmoid(-pre_sigm) + torch.log(a)\\n # n, d, d2, dh\\n logj = logj + F.log_softmax(pre_u, dim=3)\\n # n, d, d2, dh, d1\\n logj = torch.log(torch.sum(torch.exp(logj),3))\\n # n, d, d2, d1\\n logdet_ = logj + np.log(1 - self.eps) - (torch.log(x_pre_clipped) + torch.log(-x_pre_clipped + 1))\\n return logdet_\",\n \"def zkl_divergence(x, y, gamma):\\n return np.sum([p_i*np.log(p_i/q_i) if q_i > 0 and p_i > 0 else p_i*gamma for (p_i, q_i) in zip(x, y)])\",\n \"def entropy(self):\\n Z = self.sum()\\n assert (Z > 0), 'Non-normalizable factor (perhaps log factor?)' # also check for positivity?\\n H = 0.0\\n for x in np.nditer(self.t, op_flags=['readonly']):\\n p = x/Z\\n H += 0.0 if p==0 else -p*np.log(p)\\n return H\",\n \"def kl(p, q):\\n p = np.asarray(p, dtype=float)\\n q = np.asarray(q, dtype=float)\\n\\n return np.where(p != 0, p * np.log(p / q), 0).sum()\",\n \"def compute_loglike(self, tools: ModelingTools) -> float:\\n return -3.0 * self.placeholder\",\n \"def log(base, real):\\n return math.log(real, base)\",\n \"def find_z(q):\\n z = np.zeros(len(q) - 1, int)\\n for i in range(len(q) - 1):\\n z[i] = np.ceil(q[i]/2 + q[i+1]/2) # formula from class\\n return np.concatenate([[0], z, [BITS-1]])\",\n \"def log_poisson(k, l):\\n return k*np.log(l) -l - gammaln(k+1)\",\n \"def safe_sum_p_log_p(a, base=None):\\r\\n flat = ravel(a)\\r\\n nz = take(flat, nonzero(flat)[0])\\r\\n logs = log(nz)\\r\\n if base:\\r\\n logs /= log(base)\\r\\n return sum(nz * logs, 0)\",\n \"def find_metallicity_distribution(redshifts, min_logZ_COMPAS, max_logZ_COMPAS,\\n mu0=0.035, muz=-0.23, sigma_0=0.39, sigma_z=0.0, alpha =0.0,\\n min_logZ =-12.0, max_logZ =0.0, step_logZ = 0.01): \\n ##################################\\n # Log-Linear redshift dependence of sigma\\n sigma = sigma_0* 10**(sigma_z*redshifts)\\n \\n ##################################\\n # Follow Langer & Norman 2006 in assuming that mean metallicities evolve in z as:\\n mean_metallicities = mu0 * 10**(muz * redshifts) \\n \\n # Now we re-write the expected value of the log-skew-normal to retrieve mu\\n beta = alpha/(np.sqrt(1 + (alpha)**2))\\n PHI = NormDist.cdf(beta * sigma) \\n mu_metallicities = np.log(mean_metallicities/2. * 1./(np.exp(0.5*sigma**2) * PHI ) ) \\n\\n ##################################\\n # create a range of metallicities (the x-values, or random variables)\\n log_metallicities = np.arange(min_logZ, max_logZ + step_logZ, step_logZ)\\n metallicities = np.exp(log_metallicities)\\n\\n\\n ##################################\\n # probabilities of log-skew-normal (without the factor of 1/Z since this is dp/dlogZ not dp/dZ)\\n dPdlogZ = 2./(sigma[:,np.newaxis]) * NormDist.pdf((log_metallicities - mu_metallicities[:,np.newaxis])/sigma[:,np.newaxis]) * NormDist.cdf(alpha * (log_metallicities - mu_metallicities[:,np.newaxis])/sigma[:,np.newaxis] )\\n\\n ##################################\\n # normalise the distribution over all metallicities; this choice of normalisation assumes that metallicities outside the COMPAS range have yields of zero\\n norm = dPdlogZ.sum(axis=-1) * step_logZ\\n dPdlogZ = dPdlogZ /norm[:,np.newaxis]\\n\\n ##################################\\n # assume a flat in log distribution in sampled metallicity to find probability of drawing Z in COMPAS\\n p_draw_metallicity = 1 / (max_logZ_COMPAS - min_logZ_COMPAS)\\n \\n return dPdlogZ, metallicities, p_draw_metallicity\",\n \"def compute_log_likehood_markov(markov_n, markov_table, start_i, seq):\\n ll = 0\\n for cur_i in range(start_i, len(seq)):\\n cur_mem = seq[cur_i]\\n past_mems = tuple(seq[cur_i-markov_n:cur_i])\\n prob = markov_table[past_mems][cur_mem]\\n # We shouldn't get prob = 0 because we get the markov table from the sequence themselves\\n ll += np.log(prob)\\n return ll\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65710264","0.6284432","0.61714786","0.59657145","0.59184784","0.5914684","0.5879645","0.58559495","0.5837734","0.5820538","0.5819314","0.57771146","0.57759076","0.57748866","0.57500154","0.57367206","0.56673867","0.56623465","0.5645668","0.5631098","0.5629977","0.56249344","0.5616786","0.5611232","0.55960584","0.55942136","0.5581019","0.5571306","0.5568434","0.5555853","0.5530221","0.5519178","0.55177075","0.5505236","0.55001366","0.5485298","0.54847133","0.54749817","0.54706985","0.5466124","0.54499","0.5449548","0.54475117","0.5445403","0.5439931","0.54354024","0.5435149","0.542799","0.54269314","0.5426642","0.54115903","0.5405169","0.5403385","0.54007244","0.5400368","0.53972316","0.53913134","0.5387972","0.5382282","0.5371846","0.53717947","0.53696644","0.5365044","0.5363579","0.53541464","0.534634","0.5345014","0.5339028","0.5335341","0.5325734","0.5303504","0.52963626","0.5296195","0.5295978","0.5295978","0.52954036","0.5295403","0.5289935","0.5285413","0.5282823","0.52775085","0.5257809","0.52569085","0.5252142","0.5241772","0.5240479","0.5240244","0.5238706","0.5237453","0.523625","0.52349615","0.5231648","0.5231506","0.52287024","0.5225842","0.5223261","0.5219407","0.5217569","0.5212435","0.5212341"],"string":"[\n \"0.65710264\",\n \"0.6284432\",\n \"0.61714786\",\n \"0.59657145\",\n \"0.59184784\",\n \"0.5914684\",\n \"0.5879645\",\n \"0.58559495\",\n \"0.5837734\",\n \"0.5820538\",\n \"0.5819314\",\n \"0.57771146\",\n \"0.57759076\",\n \"0.57748866\",\n \"0.57500154\",\n \"0.57367206\",\n \"0.56673867\",\n \"0.56623465\",\n \"0.5645668\",\n \"0.5631098\",\n \"0.5629977\",\n \"0.56249344\",\n \"0.5616786\",\n \"0.5611232\",\n \"0.55960584\",\n \"0.55942136\",\n \"0.5581019\",\n \"0.5571306\",\n \"0.5568434\",\n \"0.5555853\",\n \"0.5530221\",\n \"0.5519178\",\n \"0.55177075\",\n \"0.5505236\",\n \"0.55001366\",\n \"0.5485298\",\n \"0.54847133\",\n \"0.54749817\",\n \"0.54706985\",\n \"0.5466124\",\n \"0.54499\",\n \"0.5449548\",\n \"0.54475117\",\n \"0.5445403\",\n \"0.5439931\",\n \"0.54354024\",\n \"0.5435149\",\n \"0.542799\",\n \"0.54269314\",\n \"0.5426642\",\n \"0.54115903\",\n \"0.5405169\",\n \"0.5403385\",\n \"0.54007244\",\n \"0.5400368\",\n \"0.53972316\",\n \"0.53913134\",\n \"0.5387972\",\n \"0.5382282\",\n \"0.5371846\",\n \"0.53717947\",\n \"0.53696644\",\n \"0.5365044\",\n \"0.5363579\",\n \"0.53541464\",\n \"0.534634\",\n \"0.5345014\",\n \"0.5339028\",\n \"0.5335341\",\n \"0.5325734\",\n \"0.5303504\",\n \"0.52963626\",\n \"0.5296195\",\n \"0.5295978\",\n \"0.5295978\",\n \"0.52954036\",\n \"0.5295403\",\n \"0.5289935\",\n \"0.5285413\",\n \"0.5282823\",\n \"0.52775085\",\n \"0.5257809\",\n \"0.52569085\",\n \"0.5252142\",\n \"0.5241772\",\n \"0.5240479\",\n \"0.5240244\",\n \"0.5238706\",\n \"0.5237453\",\n \"0.523625\",\n \"0.52349615\",\n \"0.5231648\",\n \"0.5231506\",\n \"0.52287024\",\n \"0.5225842\",\n \"0.5223261\",\n \"0.5219407\",\n \"0.5217569\",\n \"0.5212435\",\n \"0.5212341\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6006404"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":11190,"cells":{"query":{"kind":"string","value":"Brute force algorithm for calculating the log of the partition function Z"},"document":{"kind":"string","value":"def best_sequence_brute(self, T, pos, psi, phi):\n ms = self.get_all_tag_seq(len(T))\n log_scores = tr.zeros(len(ms), dtype=tr.float64)\n for i in range(len(ms)):\n log_scores[i] = self.log_score(T, pos, ms[i], psi, phi)\n best = ms[tr.argmax(log_scores)]\n tags = []\n for i in range(len(T)):\n tags.append(self.get_tag_index(best[i]))\n return tags"},"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 partition_function(array, temp):\r\n\r\n # Constants imported from scipy.constants\r\n h = scipy.constants.h # Planck's constant\r\n # speed of light must be in cm/s as wavenumber is in cm-1\r\n c = scipy.constants.c * 100\r\n k = scipy.constants.k # Boltzmann constant\r\n T = temp # extracted from log file using extract_temp()\r\n\r\n # check if inputs are numpy arrays and convert if not.\r\n if not isinstance(array, np.ndarray):\r\n np.asarray(array)\r\n\r\n # conversion to exponent\r\n u = (h * array * c) / (k * T)\r\n\r\n # calculates natural log of an individual frequency contribution to the partition function\r\n Q_ = np.log(np.exp(-(u / 2)) / (1 - np.exp(-u)))\r\n # sums all the contributions together, giving the final result.\r\n Q = np.sum(Q_)\r\n return Q","def log_lhood(X, Z, Y, a, ep, lamb):\n \n K = Z.shape[1]\n N, T = X.shape\n \n # p(X)\n ZY = np.dot(Z,Y) \n log_pX = 0\n log_pX = log_pX + np.sum(X * np.log(1 - ((1 - lamb) ** ZY) * (1 - ep)))\n log_pX = log_pX + np.sum((1 - X) * np.log(((1 - lamb) ** ZY) * (1 - ep))) \n \n # p(Z)\n HN = 0\n for n in range(1, N+1):\n HN += 1.0/n\n m = Z.sum(axis=0)\n log_pZ = (K * np.log(a) - (a * HN)) + np.sum(gammaln(m) +\n gammaln(N - m + 1) - gammaln(N + 1)) \n\n return log_pZ + log_pX","def log_p(self,z): \n return np.array([self.log_p_blanket(i) for i in z])","def logZ_brute(self, T, pos, psi, phi):\n ms = self.get_all_tag_seq(len(T))\n log_scores = tr.zeros(len(ms), dtype=tr.float64)\n for i in range(len(ms)):\n log_scores[i] = self.log_score(T, pos, ms[i], psi, phi)\n log_z = logsumexp(log_scores)\n return log_z","def calculate_log_perplexity(self, output, flat_labels): #completed, expensive, should be compiled\n return -np.sum(np.log2(np.clip(output, a_min=1E-12, a_max=1.0))[np.arange(flat_labels.shape[0]), flat_labels[:,1]])","def log2(a):","def log_prob(self, z, x):\n n_samples = z.shape[0].value\n log_prob = 0\n for i in range(self.time_steps):\n lat_dim = self.transition.dim_x\n obs_dim = self.emmision.dim_x\n # Current time step latent state and observation\n z_t = tf.slice(z, [0, i * lat_dim], [-1, lat_dim])\n x_t = tf.slice(x, [0, i * obs_dim], [-1, obs_dim])\n if i == 0:\n log_prob += tf.reduce_sum(\n self.init_state_p.log_prob(z_t), axis=1)\n else:\n log_prob += self.transition.log_prob(z_t, z_tminus)\n log_prob += self.emmision.log_prob(x_t, z_t)\n # Update the previous latent state for next iteration\n z_tminus = z_t\n return log_prob","def testLogPartitionInfinityIsAccurate(self):\n alpha = float('inf')\n log_z_true = 0.70526025442 # From mathematica.\n log_z = self.variant(self._distribution.log_base_partition_function)(alpha)\n chex.assert_tree_all_close(log_z, log_z_true, atol=1e-7, rtol=1e-7)","def log_marg(self):\n log_prob_X_given_z = 0.\n for k in range(self.K):\n log_prob_X_given_z += self.log_marg_k(k)\n return log_prob_X_given_z","def log_Distribution_volume(self, z):\n if type(z)==np.ndarray:\n ind = z<0\n pv = np.log(self.Distribution_volume(z))\n pv[ind] = -1e99\n return pv\n else:\n if z<0:\n return -1e99\n else:\n return np.log(self.Distribution_volume(z))","def log_p(self,z):\n return mb_log_p_posterior(z, self.neg_posterior, self.mini_batch)","def save_exact_log_Z(expt):\n if isinstance(expt, str):\n expt = get_experiment(expt)\n tr_expt = get_training_expt(expt)\n \n for it in tr_expt.save_after:\n for avg in AVG_VALS:\n print 'iteration', it, avg\n try:\n rbm = load_rbm(expt, it, avg)\n except:\n continue\n\n log_Z = tractable.exact_partition_function(rbm)\n storage.dump(log_Z, expt.log_Z_file(it, avg))","def logrels(rets):\n return np.log(rets + 1)","def logbarrierfunc(delta, z, use_sigma):\n if use_sigma:\n z = np.where(z >= 0, np.tanh(z), z)\n k = 2\n return np.where(z > delta, -np.log(np.abs(z)),\n ((k - 1) / k) * (((z - k * delta) / ((k - 1) * delta)) ** k - 1) - np.log(delta))","def log_sum_exp(Z):\n return np.max(Z) + np.log(np.sum(np.exp(Z - np.max(Z))))","def _log_fold_change_pairs(self, idx0, idx1, base):\n assert np.all([x < self._pval.shape[1] for x in idx0])\n assert np.all([x < self._pval.shape[1] for x in idx1])\n if base == np.e:\n return self._logfc[idx0, :, :][:, idx1, :]\n else:\n return self._logfc[idx0, :, :][:, idx1, :] / np.log(base)","def log_prob(self):","def log_likelihood(self):\r\n if self.likelihood.is_heteroscedastic:\r\n A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)\r\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self._A))\r\n else:\r\n A = -0.5 * self.num_data * self.output_dim * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT\r\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self._A))\r\n C = -self.output_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * np.log(sf2))\r\n D = 0.5 * self.data_fit\r\n self._A_part, self._B_part, self._C_part, self._D_part = A, B, C, D\r\n return A + B + C + D + self.likelihood.Z","def log_p(self,z):\n return log_p_posterior(z, self.neg_posterior)","def log2_inplace(a):","def grad_log_q(self,z): \n param_count = 0\n grad = np.zeros((np.sum(self.approx_param_no),self.sims))\n for core_param in range(len(self.q)):\n for approx_param in range(self.q[core_param].param_no):\n grad[param_count] = self.q[core_param].vi_score(z[core_param],approx_param) \n param_count += 1\n return grad","def compute_log(tx, index_log, mean=[], std=[]):\n tx_new = np.log10(3+abs(tx[:,index_log]))\n return standardize(tx_new,mean,std)","def kullback_leibler_iaf(z, logqz_x, beta=1.):\n \n logpz = -tf.reduce_sum(0.5 * np.log(2*np.pi) + 0.5 * tf.square(z), axis=-1)\n kl = beta * tf.reduce_mean(logqz_x - logpz)\n return kl","def kullback_leibler_iaf(z, logqz_x, beta=1., **args):\n \n logpz = -tf.reduce_sum(input_tensor=0.5 * np.log(2*np.pi) + 0.5 * tf.square(z), axis=-1)\n kl = beta * tf.reduce_mean(input_tensor=logqz_x - logpz)\n return kl","def logP(self):\n raise NotImplementedError","def _log_util(chips: float,\n bet_size: float,\n payout: float) -> float:\n if chips <= 0 or chips + payout*bet_size <= 0:\n return MIN_REWARD\n return max(math.log(1.0 + chips + payout*bet_size) - math.log(1.0 + chips),\n MIN_REWARD)","def log_likelihood_z_lognormal(self, std=1.0):\n #return self.log_det_Jxz - self.dim * tf.log(std) - (0.5 / (std**2)) * tf.reduce_sum(self.output_z**2, axis=1)\n from deep_boltzmann.util import logreg\n logz = logreg(self.output_z, a=0.001, tf=True)\n ll = self.log_det_Jxz \\\n - (0.5 / (std**2)) * tf.reduce_sum(logz**2, axis=1) \\\n - tf.reduce_sum(logz, axis=1)\n return ll","def logp(self, x):\n pass","def log(a):","def _logprob(self, sample):\n cur_y, cur_z = sample\n log_prior = 0\n log_lik = 0\n if cur_z.shape[1] == 0: return -99999999.9\n \n if self.cl_mode:\n a_time = time()\n d_cur_z = cl.Buffer(self.ctx, self.mf.READ_ONLY | self.mf.COPY_HOST_PTR, hostbuf = cur_z.astype(np.int32))\n d_cur_y = cl.Buffer(self.ctx, self.mf.READ_ONLY | self.mf.COPY_HOST_PTR, hostbuf = cur_y.astype(np.int32))\n d_logprob = cl.array.empty(self.queue, (cur_z.shape[0],), np.float32, allocator=self.mem_pool)\n\n if cur_z.shape[0] % self.p_mul_logprob_z_data == 0: wg = (self.p_mul_logprob_z_data,)\n else: wg = None\n \n self.prg.logprob_z_data(self.queue, (cur_z.shape[0],), wg,\n d_cur_z, d_cur_y, self.d_obs, d_logprob.data, #d_novel_f.data,\n np.int32(self.N), np.int32(cur_y.shape[1]), np.int32(cur_z.shape[1]), \n np.float32(self.alpha), np.float32(self.lam), np.float32(self.epislon))\n log_lik = d_logprob.get().sum()\n self.gpu_time += time() - a_time\n\n # calculate the prior probability of Y\n num_on = (cur_y == 1).sum()\n num_off = (cur_y == 0).sum()\n log_prior = num_on * np.log(self.theta) + num_off * np.log(1 - self.theta)\n\n else:\n # calculate the prior probability of Z\n for n in xrange(cur_z.shape[0]):\n num_novel = 0\n for k in xrange(cur_z.shape[1]):\n m = cur_z[:n,k].sum()\n if m > 0:\n if cur_z[n,k] == 1: log_prior += np.log(m / (n+1.0))\n else: log_prior += np.log(1 - m / (n + 1.0))\n else: \n if cur_z[n,k] == 1: num_novel += 1\n if num_novel > 0:\n log_prior += poisson.logpmf(num_novel, self.alpha / (n+1.0))\n # calculate the prior probability of Y\n num_on = (cur_y == 1).sum()\n num_off = (cur_y == 0).sum()\n log_prior += num_on * np.log(self.theta) + num_off * np.log(1 - self.theta)\n # calculate the logliklihood\n log_lik = self._loglik(cur_y = cur_y, cur_z = cur_z)\n return log_prior + log_lik","def _log_fold_change_pairs(self, idx0, idx1, base):\n logfc = np.tile(np.NaN, [len(idx0), len(idx1), self.model_estim.x.shape[1]])\n for i, xi in enumerate(idx0):\n for j, xj in enumerate(idx1):\n logfc[i, j, :] = self._theta_mle[xj, :] - self._theta_mle[xi, :]\n logfc[j, i, :] = -logfc[i, j, :]\n\n if base == np.e:\n return logfc\n else:\n return logfc / np.log(base)","def log10(a):","def log_Schechter_log(self, logl, alpha, logls, logl0):\n phi = (logl - logls) * (alpha+1) * np.log(10.) - np.power(10., logl-logls)\n lik = phi.copy()\n lik [logl < logl0] = -1e99\n return lik","def _log_fold_change_pairs(self, idx0, idx1, base):\n logfc = np.zeros(shape=(len(idx0), len(idx1), self._theta_mle.shape[1]))\n for i, xi in enumerate(idx0):\n for j, xj in enumerate(idx1):\n logfc[i, j, :] = self._theta_mle[xi, :] - self._theta_mle[xj, :]\n\n if base == np.e:\n return logfc\n else:\n return logfc / np.log(base)","def padic_reduce(self, diff_bound):\n Z_bounds = []\n for i in range(len(self.constants.primes)):\n p = self.constants.primes[i]\n logging.info(\"Examining the prime %d for the p-adic reduction.\" % p)\n r = math.ceil(math.log(self.coefficients[\"n1_bound\"], p))\n prec = r + self.tries + 10\n L = Qp(p, prec)\n\n current_z_bound = -1\n\n # Pre-computation step (i.e. memoization)\n alpha, beta = self.calculate_alphabeta(p, prec)\n alpha_t = self.precompute_t_power(alpha, diff_bound)\n beta_t = self.precompute_t_power(beta, diff_bound)\n z0_right_term = L((alpha - beta).norm()).ordp()\n log_alpha_over_beta = log(alpha / beta)\n\n # Pre-computations for tau == 1 \n p_order = log_alpha_over_beta.norm().ordp() / 2\n\n for t_vec in combinations_with_replacement(list(range(1, diff_bound + 1)), self.constants.num_terms - 1):\n z0_left_term = self.constants.a * (1 + sum([alpha_t[t - 1] for t in t_vec]))\n z0 = L(z0_left_term.norm()).ordp() - z0_right_term\n tau = self.calculate_tau(t_vec, alpha_t, beta_t)\n\n # Unlikely case, and we may assume the p-adic log is injective for our purposes.\n if tau == 1:\n new_bound = math.log(self.coefficients[\"n1_bound\"], p) + p_order + z0\n Z_bounds.append(max(z0 + 3/2, new_bound))\n print('executing edge case')\n else:\n # Remember that zeta is in Q_p, not in the field extension.\n log_tau = tau.log()\n zeta = log_tau / log_alpha_over_beta\n vzeta = zeta.norm().ordp()\n zeta_list = self.get_expansion(prec, zeta)\n\n R_max = self.find_Rmax(r, vzeta, zeta_list)\n if R_max == -1:\n print('R does not exist')\n m0 = self.find_m0(p, zeta_list)\n z_bound = math.log(self.coefficients[\"n1_bound\"] - m0, p) + (alpha / beta).ordp() + z0\n else:\n zeta_inverse = log_alpha_over_beta / log_tau\n vzeta_inverse = zeta_inverse.ordp()\n z_bound = log_tau.ordp() + R_max + vzeta_inverse\n current_z_bound = max(current_z_bound, z_bound)\n Z_bounds.append(current_z_bound)\n return Z_bounds","def log_boltzmann_dist(Q, temperature):\n return nn.LogSoftmax(dim=0)(Q/temperature)","def log_zgrid(zr=[0.7,3.4], dz=0.01):\n zgrid = np.exp(np.arange(np.log(1+zr[0]), np.log(1+zr[1]), dz))-1\n return zgrid","def kl(p, q):\n return np.sum(np.where(p != 0, p * np.log(p / q), 0))","def _vertical_log(self, X: np.ndarray) -> (np.ndarray, np.ndarray):\n ret_p = np.zeros_like(X)\n ret_n = np.zeros_like(X)\n log_p = self.manifold.log(X[:-1], X[1:])\n log_n = self.manifold.log(X[1:], X[:-1])\n ret_p[:-1] = log_p\n ret_n[1:] = log_n\n return ret_p, ret_n","def logistic_function(z):\n\tg = 1/(1+np.exp(-z))\n\t#g = np.exp(z)/(1+np.exp(z))\n\treturn g","def compute_approx_log_Z(self, data_term, non_data_term, axis= None):\n \n if (axis == 1) and (non_data_term != None):\n \n approx_Z = T.concatenate([non_data_term, data_term], axis=1)\n \n max_vals = T.max(approx_Z, axis=1)\n\n max_vals = T.reshape(max_vals,[self.batch_size,1])\n \n if self.mf_steps ==0:\n \n max_vals_tiled = T.tile(max_vals,\n (1,self.num_samples+self.batch_size))\n \n else:\n \n max_vals_tiled= T.tile(max_vals,(1,self.num_samples+1))\n \n approx_Z = approx_Z - max_vals_tiled\n \n approx_Z = max_vals + T.log(T.sum(T.exp(approx_Z), axis=1))\n \n approx_Z = T.mean(approx_Z)\n \n if (axis == None) and (non_data_term != None):\n \n approx_Z = T.concatenate([non_data_term, data_term])\n \n max_val = T.max(approx_Z)\n \n ### self. added to enable reporting p_tilda instead of\n ### pseudo likelihood cost\n self.approx_Z = approx_Z - max_val\n \n approx_Z = max_val + T.log(T.sum(T.exp(self.approx_Z)))\n \n if (non_data_term == None) and (axis == None):\n \n approx_Z = data_term\n \n max_val = T.max(approx_Z)\n \n approx_Z = max_val + T.log(T.sum(T.exp(approx_Z -max_val)))\n \n return approx_Z","def fn(val):\n if val < x: return min(2*val-1, 2*(x-val))\n k = int(log(val)//log(x))\n ans = k + fn(val - x**k)\n if x**(k+1) < 2*val: \n ans = min(ans, k + 1 + fn(x**(k+1) - val))\n return ans","def MvNormalLogp():\n cov = pt.matrix(\"cov\")\n cov.tag.test_value = floatX(np.eye(3))\n delta = pt.matrix(\"delta\")\n delta.tag.test_value = floatX(np.zeros((2, 3)))\n\n cholesky = Cholesky(lower=True, on_error=\"nan\")\n\n n, k = delta.shape\n n, k = f(n), f(k)\n chol_cov = cholesky(cov)\n diag = pt.diag(chol_cov)\n ok = pt.all(diag > 0)\n\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\n delta_trans = solve_lower(chol_cov, delta.T).T\n\n result = n * k * pt.log(f(2) * np.pi)\n result += f(2) * n * pt.sum(pt.log(diag))\n result += (delta_trans ** f(2)).sum()\n result = f(-0.5) * result\n logp = pt.switch(ok, result, -np.inf)\n\n def dlogp(inputs, gradients):\n (g_logp,) = gradients\n cov, delta = inputs\n\n g_logp.tag.test_value = floatX(1.0)\n n, k = delta.shape\n\n chol_cov = cholesky(cov)\n diag = pt.diag(chol_cov)\n ok = pt.all(diag > 0)\n\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\n delta_trans = solve_lower(chol_cov, delta.T).T\n\n inner = n * pt.eye(k) - pt.dot(delta_trans.T, delta_trans)\n g_cov = solve_upper(chol_cov.T, inner)\n g_cov = solve_upper(chol_cov.T, g_cov.T)\n\n tau_delta = solve_upper(chol_cov.T, delta_trans.T)\n g_delta = tau_delta.T\n\n g_cov = pt.switch(ok, g_cov, -np.nan)\n g_delta = pt.switch(ok, g_delta, -np.nan)\n\n return [-0.5 * g_cov * g_logp, -g_delta * g_logp]\n\n return OpFromGraph([cov, delta], [logp], grad_overrides=dlogp, inline=True)","def logp(X, nu, V):\n\n p = V.shape[0]\n\n IVI = det(V)\n IXI = det(X)\n\n return check_parameters(\n (\n (nu - p - 1) * pt.log(IXI)\n - trace(matrix_inverse(V).dot(X))\n - nu * p * pt.log(2)\n - nu * pt.log(IVI)\n - 2 * multigammaln(nu / 2.0, p)\n )\n / 2,\n matrix_pos_def(X),\n pt.eq(X, X.T),\n nu > (p - 1),\n )","def kl_heuristic(A, z):\n n = len(A)\n n_round = 10\n P = defaultdict(list)\n L = defaultdict(list)\n bestL = 0\n bestP = None\n for j in range(1, (n+1)/2):\n z_init = rand_partition(n, j)\n l_init = sbm.likelihood(A, z_init, log_scale=True)\n z_round = z_init\n for _round in range(n_round):\n z_values = []\n l_values = []\n for index, pair in enumerate(swap_pairs(z_round)):\n z_round[pair[0]], z_round[pair[1]] = \\\n z_init[pair[1]], z_init[pair[0]]\n z_values.append(z_round)\n l_values.append(sbm.likelihood(A, z_round, log_scale=True))\n l_max_index = l_values.index(max(l_values))\n z_max_round = z_values[l_max_index]\n z_round = z_max_round\n L[j].append(l_values)\n bestL = max(l_values)\n bestP = z_max_round\n return bestL, bestP, L, P","def hankelp(j, nu, z):\n return hankel(j, nu-1, z) - nu*hankel(j, nu, z)/z","def IntegratePolynomialPartLogExt(poly, fieldTower):\n #print(poly)\n #print(poly.degree)\n #print(poly.fieldTower)\n if poly == 0 or poly.isZero():\n return Int.Integral()\n red = poly.reduceToLowestPossibleFieldTower()\n if red.fieldTower.towerHeight<fieldTower.towerHeight:\n return Integrate(red)\n p = poly.getCoefficients()\n l = len(p)-1\n q = [ZERO]*(len(p)+1)\n b = [ZERO]*(len(p)+1)\n d = [ZERO]*(len(p)+1)\n \n \n u = fieldTower.getLastExtension().argFunction # last fieldExtension with T=log(u)\n log_diff_u = u.logDifferentiate()#u.differentiate()/u # u'/u\n \n # p_l *T^l + p_(l-1)*T^(l-1) + ... + p_0 = (q_(l+1)*T^(l+1) + q_l*T^l + ... + q_0)' + (c_1 *v_1'/v_i + ... + c_m *v_m'/v_m)\n # (q_i*T^i)' = q_i'*T^i + q_i*i*T'*T^(i-1)\n # -> 0 = q_(l+1)'\n # p_l = (l+1)*q_(l+1)*T' + q_l'\n # ...\n # p_0 = q_1*T' + (qe_0)', qe_0 = q_0+(c_1 *log(v_1) + ... + c_m *log(v_m))\n for i in range(l,-1,-1):\n # q_(i+1) = d_(i+1) + b_(i+1)\n # integral(p_i-(i+1)*d_(i+1)*T') = l*b_(i+1)*T' + q_i\n #i = Rational.fromFloat(i)\n integrand = p[i]+(-ONE)*(i+1)*d[i+1]*log_diff_u\n if type(integrand)==float or type(integrand)==int:\n integrand = Rational.fromFloat(integrand)\n int_reduced = integrand.reduceToLowestPossibleFieldTower()\n if int_reduced!=None:\n integrand = int_reduced\n P_i = Integrate(integrand)\n if i>0:\n IntegratePolynomialPartLogExtCheckIntegralConditions(P_i, fieldTower)\n else:\n if P_i==None:\n raise Int.IntegralNotElementaryError()\n \n prev = fieldTower.prevTower()\n #logs = P_i.getNewLogExpressionsInFieldTower(prev,fieldTower)\n logTerm = P_i.getLogExpression(u)\n otherLogs = [log for log in P_i.logExpressions if not log==logTerm]#in logs]\n ci = ZERO if logTerm==None else logTerm.factor # integral = P_i = c_i*T+d_i, d_i = P_i\\logs\n d[i] = Int.Integral(poly_rationals=P_i.poly_rational_partExpressions,logs=otherLogs,rootSums=P_i.rootSums).asFunction()\n b[i+1] = ci/(i+1)\n q[i+1] = d[i+1]+b[i+1]\n \n #integrand = p[0]+(-1)*d[1]*log_diff_u\n #P0 = Integrate(integrand)\n #if P0==None:\n # raise Int.IntegralNotElementaryError()\n #b1 = 0\n #for log in P0.logExpressions:\n # if log.argFunction==u:\n # b1 = log.factor\n \n if b[1]==0:\n q_0 = P_i.asFunction()\n else:\n q_0 = (P_i+Int.Integral(logs=[Int.LogFunction(u,(-1)*b[1])])).asFunction()\n q[0] = q_0\n \n integralPoly = Pol.Polynomial(variable=fieldTower.getLastVariable())\n for i in range(l+1,-1,-1):\n integralPoly.setCoefficient(i,q[i], callUpdates=False)\n integralPoly.updateCoefficientsAll()\n \n return Int.Integral(poly_rationals=[integralPoly])","def calc_probs(log_p):\n\n N = log_p.shape[0]\n\n log_Z_per_N = np.zeros(shape=(N, 1))\n\n for i in range(N):\n\n log_Z_per_N[i] = log_norm(log_p[i])\n\n log_p_new = log_p - log_Z_per_N\n\n p = np.exp(log_p_new)\n\n # log_Z = log_norm(log_p)\n\n # p = np.exp(log_p - log_Z)\n\n return p","def U(xs):\n ret = 0\n for x in xs:\n ret += log(x)\n return ret","def logsum_safe(prob_ll, atl):\n logpdf = prob_ll + K.log(atl + K.epsilon())\n alpha = tf.reduce_max(logpdf, -1, keepdims=True)\n return alpha + tf.log(tf.reduce_sum(K.exp(logpdf-alpha), -1, keepdims=True) + K.epsilon())","def log_inplace(a):","def empirical_counts(a_log, p_log, pi0_log, init_data, alpha_list, beta_list):\n # Calculate total marginal probability for each z_it\n seq_log_probs = []\n full_seq_probs = []\n for t, init_vec in enumerate(init_data):\n alpha_array = alpha_list[t]\n beta_array = beta_list[t]\n seq_log_probs.append(logsumexp(alpha_array[:, -1]))\n seq_probs = alpha_array + beta_array\n seq_probs = seq_probs - logsumexp(seq_probs, axis=0)\n full_seq_probs.append(seq_probs)\n\n # Now calculate effective number of transitions (empirical transition matrix)\n a_log_empirical = np.zeros_like(a_log) - np.Inf\n K = len(a_log[:, 0])\n # transition counts\n for k in range(K):\n for l in range(K):\n # store log probs for each sequence\n a_probs = []\n # current transition prob from k to l\n akl = a_log[l, k]\n for i, init_vec in enumerate(init_data):\n T = len(init_vec)\n a = alpha_list[i]\n b = beta_list[i]\n event_list = [a[k, t] + b[l, t + 1] + akl + p_log[int(init_vec[t + 1]), l] for t in range(T - 1)]\n a_probs.append(logsumexp(event_list) - seq_log_probs[i]) # NL: why do this?\n\n a_log_empirical[l, k] = logsumexp(a_probs)\n\n p_empirical = np.zeros_like(p_log)\n for i, init_vec in enumerate(init_data):\n seq_probs = np.exp(full_seq_probs[i])\n for t in range(len(init_vec)):\n p_empirical[int(init_vec[t]), :] += seq_probs[:, t]\n\n pi0_log_empirical = np.zeros_like(pi0_log)\n for i, init_vec in enumerate(init_data):\n seq_probs = full_seq_probs[i]\n pi0_log_empirical += seq_probs[:, 0]\n\n return a_log_empirical, np.log(p_empirical), pi0_log_empirical, seq_log_probs, full_seq_probs","def _x(rho, z):\r\n a = (1 - 2*rho*z + z**2)**.5 + z - rho\r\n b = 1 - rho\r\n return np.log(a / b)","def rundit_fold_logredst(S,nu_lines,cnbeta,gammaL,nu_grid,ngammaL_grid,dLarray,dv_lines,dv_grid):\n\n Ng_nu=len(nu_grid)\n Ng_gammaL=len(ngammaL_grid)\n\n ngammaL=gammaL/dv_lines\n log_nstbeta=jnp.log(cnbeta)\n log_ngammaL=jnp.log(ngammaL)\n \n log_ngammaL_grid = jnp.log(ngammaL_grid)\n\n k = jnp.fft.rfftfreq(2*Ng_nu,1)\n val=inc2D(S,nu_lines,log_ngammaL,nu_grid,log_ngammaL_grid)\n valbuf=jnp.vstack([val,jnp.zeros_like(val)])\n fftval = jnp.fft.rfft(valbuf,axis=0)\n vk=folded_voigt_kernel_logst(k, log_nstbeta,log_ngammaL_grid,dLarray)\n fftvalsum = jnp.sum(fftval*vk,axis=(1,))\n xs=jnp.fft.irfft(fftvalsum)[:Ng_nu]/dv_grid\n \n return xs","def log_likelihood_z_uniform(self, std=1.0):\n return self.log_det_Jxz - self.dim * (tf.log(std) + 0.5*tf.log(12.0))","def _estimate_log_prob(self, x):\n x = self.check_size(x)\n\n if self.covariance_type == \"full\":\n mu = self.mu\n var = self.var\n precision = tf.linalg.inv(var)\n d = x.shape[-1]\n\n log_2pi = d * tf.math.log(2. * pi)\n log_2pi = tf.cast(log_2pi, tf.double)\n log_det = self._calculate_log_det(precision)\n log_det = tf.cast(log_det, tf.double)\n\n\n x = tf.cast(x, tf.double)\n mu = tf.cast(mu, tf.double)\n x_mu_T = tf.expand_dims((x - mu), -2)\n x_mu = tf.expand_dims((x - mu), -1)\n\n x_mu_T_precision = calculate_matmul_n_times(self.n_components, x_mu_T, precision)\n # print(x_mu_T_precision, x_mu_T, x, mu)\n x_mu_T_precision_x_mu = calculate_matmul(x_mu_T_precision, x_mu)\n\n return -.5 * (log_2pi - log_det + x_mu_T_precision_x_mu)\n\n elif self.covariance_type == \"diag\":\n mu = self.mu\n prec = tf.math.rsqrt(self.var)\n\n log_p = tf.math.reduce_sum((mu * mu + x * x - 2 * x * mu) * (prec ** 2), axis=2, keepdims=True)\n log_det = tf.math.reduce_sum(tf.math.log(prec), axis=2, keepdims=True)\n\n return -.5 * (self.n_features * tf.math.log(2. * pi) + log_p) + log_det","def idcg(k):\n res = sum([1.0 / math.log(i + 2, 2) for i in range(k)])\n if not res:\n return 1.0\n else:\n return res","def log10_inplace(a):","def test_safe_sum_p_log_p(self):\r\n m = array([2, 4, 0, 8])\r\n self.assertEqual(safe_sum_p_log_p(m, 2), 2 * 1 + 4 * 2 + 8 * 3)","def MaxHks(N): \n return np.log2(N-1)/2","def estimate(i,x,z):\n\n b = 2 * sqrt(2.0) / 9801\n a = abs(1.0/x-pi)\n if a >= 1e-15:\n z = calc(i)+z\n i += 1\n x = b*z\n estimate(i,x,z)\n else:\n p =1.0/x\n print 1.0/x\n return p","def compute_log_prob(self,params: ndarray) -> float:\n return self.compute_log_prior(params) + self.compute_log_likelihood(params)","def ransac_iters(w=0.5, d=min_num_pairs(), z=0.99):\n\n return int(np.ceil(np.log(1 - z) / np.log(1 - np.power(w,d))))","def __pll(L,h,x, L2=0):\n if len(x.shape)>1: h = h.reshape(-1,1);\n pll = -np.log(1+np.exp(-2*x*(L.dot(x)+h))).sum(0)\n if L2>0: pll += L2*(L**2).sum()\n return pll","def KL(p, q):\n return np.sum(p * np.log(p / q))","def eq_padic_reduce(self, diff_bound):\n Z_bounds = []\n for i in range(len(self.constants.primes)):\n p = self.constants.primes[i]\n logging.info(\"Examining the prime %d for the p-adic reduction.\" % p)\n r = math.ceil(math.log(self.coefficients[\"eq_n1_bound\"], p))\n prec = r + self.tries + 10\n L = Qp(p, prec)\n\n current_z_bound = -1\n\n # Pre-computation step (i.e. memoization)\n alpha, beta = self.calculate_alphabeta(p, prec)\n alpha_t = self.precompute_t_power(alpha, diff_bound)\n beta_t = self.precompute_t_power(beta, diff_bound)\n z0_right_term = L((alpha - beta).norm()).ordp()\n log_alpha_over_beta = log(alpha / beta)\n\n # Pre-computations for tau == 1 \n p_order = log_alpha_over_beta.norm().ordp() / 2\n\n t_vec = (0,0,0)\n z0_left_term = self.constants.a * (1 + sum([alpha_t[t] for t in t_vec]))\n z0 = L(z0_left_term.norm()).ordp() - z0_right_term\n tau = self.calculate_tau(t_vec, alpha_t, beta_t)\n print('tau',tau)\n\n # Unlikely case, and we may assume the p-adic log is injective for our purposes.\n if tau == 1:\n new_bound = math.log(self.coefficients[\"eq_n1_bound\"], p) + p_order + z0\n Z_bounds.append(max(z0 + 3/2, new_bound))\n print('executing edge case')\n else:\n # Remember that zeta is in Q_p, not in the field extension.\n log_tau = tau.log()\n zeta = log_tau / log_alpha_over_beta\n vzeta = zeta.norm().ordp()\n zeta_list = self.get_expansion(prec, zeta)\n\n R_max = self.find_Rmax(r, vzeta, zeta_list)\n if R_max == -1:\n print('R does not exist')\n m0 = self.find_m0(p, zeta_list)\n z_bound = math.log(self.coefficients[\"n1_bound\"] - m0, p) + (alpha / beta).ordp() + z0\n else:\n zeta_inverse = log_alpha_over_beta / log_tau\n vzeta_inverse = zeta_inverse.ordp()\n z_bound = log_tau.ordp() + R_max + vzeta_inverse\n current_z_bound = max(current_z_bound, z_bound)\n return Z_bounds","def logsumexp(logv):\n res = logzero()\n for val in logv:\n res = logsum_pair(res, val)\n return res","def compute_log_prior(self,params: ndarray) -> float:\n ln_tE = params[0]\n ln_A0 = params[1]\n ln_deltaT = params[2]\n fbl = params[3]\n mb = params[4]\n\n # Equation (16,15,17) (note that Albrow uses \"log\" for log10)\n log10e = np.log10(np.exp(1))\n ln_pr_ln_tE = np.log(0.476) - ((log10e*ln_tE - 1.333)**2 / 0.330) + np.log(log10e)\n ln_pr_ln_A0 = np.log(0.660) - (1.289*log10e*ln_A0) + np.log(log10e)\n ln_pr_ln_deltaT = np.log(0.156) - ((log10e*ln_deltaT - 1.432)**2 / 0.458) +\\\n np.log(log10e)\n \n # Paper doesnt mention the prior used, but I assume it to be uniform\n ln_pr_fbl = uniform.logpdf(fbl,0.0,1.0)\n\n # Paper doesnr mention the prior used but I will asuumed it to be uniform\n ln_pr_mb = uniform.logpdf(mb,self.mag_min - 1.0, self.mag_max + 1.0)\n \n \n return ln_pr_fbl + ln_pr_ln_A0 + ln_pr_ln_deltaT + ln_pr_ln_tE + ln_pr_mb","def growth_factor_scale_independent(self, z):\n z = np.atleast_1d(z)\n nz = len(z)\n #if self.M_nu_tot == 0. and self.w0 == -1. and self.wa==0.:\n # aa = 1./(1.+z)\n # ww = self.w0 + (1.-aa)*self.wa\n # d1 = aa*ss.hyp2f1(1/3., 1., 11/6., -aa**3/self.Omega_m*(1.-self.Omega_m))/ss.hyp2f1(1/3., 1., 11/6., -(1.-self.Omega_m)/self.Omega_m)\n #else:\n # d1 = np.zeros(nz)\n # for i in range(nz):\n # LCDM, _ = sint.quad(lambda x: (1+x)*(self.H0/self.H_massive(x))**3., z[i], np.inf)\n # d1[i] = LCDM*self.H_massive(z[i])/self.H0\n # LCDM0, _ = sint.quad(lambda x: (1+x)*(self.H0/self.H_massive(x))**3., 0., np.inf)\n # d1 = d1/LCDM0\n d1 = np.zeros(nz)\n for i in range(nz):\n LCDM, _ = sint.quad(lambda x: (1+x)*(self.H0/self.H_massive(x))**3., z[i], np.inf)\n d1[i] = LCDM*self.H_massive(z[i])/self.H0\n LCDM0, _ = sint.quad(lambda x: (1+x)*(self.H0/self.H_massive(x))**3., 0., np.inf)\n d1 = d1/LCDM0\n return d1","def levin(x):\n summ = 0\n for t, l in x: # for the time and length of each algorithm\n summ += l + np.log(t)\n return summ","def calc_partition_function_one(states, T):\n B = beta(T)\n Z = sum(np.exp(-B * states))\n\n return Z","def smhm_behroozi(logMstar, z, Mstar00 = 10.72, Mstar0a = 0.55,\n M10 = 12.35, M1a = 0.28, beta0 = 0.44,\n abeta = 0.18, delta0 = 0.57, adelta = 0.17,\n gamma0 = 1.56, agamma = 2.51 ):\n\n # Warning\n if np.any(logMstar<8.8):\n print \"Warning: LogMstar value less than Behroozi et al. 2010 Figure 2 range [8.8, 11.6].\"\n elif np.any(logMstar>11.6):\n print \"Warning: LogMstar value greater than Behroozi et al. 2010 Figure 2 range [8.8, 11.6].\"\n \n #scale factor\n a = 1/(1+z)\n \n #Equation 21 of Behroozi et al\n logM1 = M10 + M1a*(a-1)\n logMstar0 = Mstar00 + Mstar0a*(a-1)\n beta = beta0 + abeta*(a-1)\n delta = delta0 + adelta*(a-1)\n gamma = gamma0 + agamma*(a-1) \n MstarbyMstar0 = 10**( logMstar - logMstar0 ) \n return logM1 + beta*np.log10( MstarbyMstar0 ) +\\\n (MstarbyMstar0**delta)/(1+(MstarbyMstar0)**-gamma) - 0.5","def monotonicity(self, grid):\n\t\ttotal = [0] * 4;\n\n\t\tfor x in range(0,4):\n\t\t\tcurrent = 0\n\t\t\tnext = current + 1\n\t\t\twhile next < 4 :\n\t\t\t\t\"\"\"move to next non zero value\"\"\"\n\t\t\t\twhile next < 4 and not grid.getCellValue((x, next)):\n\t\t\t\t\tnext += 1\n\t\t\t\t\"\"\"if the non zero cell was last cell of grid\"\"\"\n\t\t\t\tif next >= 4:\n\t\t\t\t\tnext -= 1\n\t\t\t\t\n\t\t\t\tcurrent_value = grid.getCellValue((x, current))\n\t\t\t\tif current_value:\n\t\t\t\t\tcurrent_value = log(current_value, 2)\n\t\t\t\tnext_value = grid.getCellValue((x, next))\n\t\t\t\tif next_value:\n\t\t\t\t\tnext_value = log(next_value, 2)\n\t\t\t\t\n\t\t\t\tif current_value > next_value :\n\t\t\t\t\ttotal[0] += next_value - current_value\n\t\t\t\telif next_value > current_value :\n\t\t\t\t\ttotal[1] += current_value - next_value\n\t\t\t\tcurrent = next\n\t\t\t\tnext += 1\n\n\t\tfor y in range(0,4):\n\t\t\tcurrent = 0\n\t\t\tnext = current + 1\n\t\t\twhile next < 4 :\n\t\t\t\twhile next < 4 and not grid.getCellValue((next, y)):\n\t\t\t\t\tnext += 1\n\t\t\t\tif next >= 4:\n\t\t\t\t\tnext -= 1\n\t\t\t\t\n\t\t\t\tcurrent_value = grid.getCellValue((current, y))\n\t\t\t\tif current_value:\n\t\t\t\t\tcurrent_value = log(current_value, 2)\n\t\t\t\tnext_value = grid.getCellValue((next, y))\n\t\t\t\tif next_value:\n\t\t\t\t\tnext_value = log(next_value, 2)\n\t\t\t\t\n\t\t\t\tif current_value > next_value :\n\t\t\t\t\ttotal[2] += next_value - current_value\n\t\t\t\telif next_value > current_value :\n\t\t\t\t\ttotal[3] += current_value - next_value\n\t\t\t\tcurrent = next\n\t\t\t\tnext += 1\n\n\t\treturn max(total[0], total[1]) + max(total[2],total[3])","def get_log(p):\n if p==0:\n return 0.\n return p*np.log2(p)","def main(l, k):\n S = 0\n T = product(xrange(2), repeat=k)\n for ts in T:\n tmp = []\n\n for t, c in zip(ts, cs):\n tmp.append(((-1)*c)**t)\n\n S += (sum(tmp)**l)\n val = (sum(tmp)**l)\n print val\n return S / float(2**(k))","def main(l, k):\n S = 0\n T = product(xrange(2), repeat=k)\n for ts in T:\n tmp = []\n\n for t, c in zip(ts, cs):\n tmp.append(((-1)*c)**t)\n\n S += (sum(tmp)**l)\n val = (sum(tmp)**l)\n print val\n return S / float(2**(k))","def viterbi(obs: List[int], pi: List[float], A: np.ndarray, B: np.ndarray) -> List[int]:\n pi_log = np.log(pi)\n A_log = np.log(A)\n B_log = np.log(B)\n states = A.shape[0]\n n = len(obs)\n\n D_log = np.zeros((states, n))\n backtrack = np.zeros((states, n - 1)).astype(int)\n D_log[:, 0] = pi_log + B_log[:, obs[0]]\n\n for j in range(1, n):\n for i in range(states):\n temp_sum = A_log[:, i] + D_log[:, j - 1]\n D_log[i, j] = np.max(temp_sum) + B_log[i, obs[j]]\n backtrack[i, j - 1] = np.argmax(temp_sum)\n\n state = np.zeros(n).astype(int)\n state[-1] = np.argmax(D_log[:, -1])\n for n in range(n - 2, -1, -1):\n state[n] = backtrack[int(state[n + 1]), n]\n state = state.tolist()\n return state","def neglogli(preds, actual):\n preds = np.reshape(preds, (-1,))\n nll = np.sum((preds - actual*np.log(preds + 1e-07) + np.log(sp.special.factorial(actual))))\n return nll","def calc_mi(self, input_batch, graph_latent_vec=None, tree_latent_vec=None):\n # [x_batch, nz]\n if graph_latent_vec is None:\n graph_latent_vec, tree_latent_vec = self.encode(*input_batch)\n g_z_mean = self.g_mean(graph_latent_vec)\n g_z_log_var = -torch.abs(self.g_var(graph_latent_vec))\n t_z_mean = self.t_mean(tree_latent_vec)\n t_z_log_var = -torch.abs(self.t_var(tree_latent_vec))\n z_mean = torch.cat([g_z_mean, t_z_mean], dim=-1)\n z_log_var = torch.cat([g_z_log_var, t_z_log_var], dim=-1)\n\n x_batch, nz = z_mean.size()\n # E_{q(z|x)}log(q(z|x)) = -0.5*nz*log(2*\\pi) - 0.5*(1+logvar).sum(-1)\n neg_entropy = (-0.5 * nz * np.log(2 * np.pi) - 0.5 * (1 + z_log_var).sum(-1)).mean()\n # [z_batch, 1, nz]\n z_samples = self.reparameterize(z_mean, z_log_var, nsamples=1)\n # [1, x_batch, nz]\n mu, logvar = z_mean.unsqueeze(0), z_log_var.unsqueeze(0)\n var = logvar.exp()\n # (z_batch, x_batch, nz)\n dev = z_samples - mu\n # (z_batch, x_batch)\n log_density = -0.5 * ((dev ** 2) / var).sum(dim=-1) - \\\n 0.5 * (nz * np.log(2 * np.pi) + logvar.sum(-1))\n # log q(z): aggregate posterior\n # [z_batch]\n log_qz = log_sum_exp(log_density, dim=1) - np.log(x_batch)\n return (neg_entropy - log_qz.mean(-1)).item()","def f(z):\n a=1./(1.+z)\n #da=0.01\n da=0.01*a\n #da=1e-7\n gp,g,gm=[D(1./ia-1.) for ia in [a+da,a,a-da]]\n f=a*(gp-gm)/(2*g*da)\n #dz=0.01\n #gp,g,gm=[D(zi) for zi in [z+dz,z,z-dz]]\n #f=(z)*(gp-gm)/(2.*g*dz)\n return f","def toeplitz_slogdet(r):\n n = len(r)\n r_0 = r[0]\n \n r = np.concatenate((r, np.array([r_0])))\n r /= r_0 # normalize the system so that the T matrix has diagonal of ones\n \n logdet = n*np.log(np.abs(r_0))\n sign = np.sign(r_0)**n\n \n if n == 1:\n return (sign, logdet)\n \n # now on is a modification of Levinson algorithm\n y = zeros((n,))\n x = zeros((n,))\n\n b = -r[1:n+1] \n r = r[:n]\n \n y[0] = -r[1]\n x[0] = b[0]\n beta = 1\n alpha = -r[1]\n \n d = 1 + dot(-b[0], x[0])\n sign *= np.sign(d)\n logdet += np.log(np.abs(d))\n \n for k in range(0,n-2):\n \n beta = (1 - alpha*alpha)*beta\n mu = (b[k+1] - dot(r[1:k+2], x[k::-1])) /beta\n x[0:k+1] = x[0:k+1] + mu*y[k::-1]\n x[k+1] = mu\n \n d = 1 + dot(-b[0:k+2], x[0:k+2])\n sign *= np.sign(d)\n logdet += np.log(np.abs(d))\n \n if k < n-2:\n alpha = -(r[k+2] + dot(r[1:k+2], y[k::-1]))/beta\n y[0:k+1] = y[0:k+1] + alpha * y[k::-1]\n y[k+1] = alpha \n\n return(sign, logdet)","def log1p(x):\n return 0.0","def get_log_lik(cov_evals, rank, shape):\n\n n_samples, n_features = shape\n if not 1 <= rank <= n_features - 1:\n raise ValueError(\"the tested rank should be in [1, n_features - 1]\")\n\n eps = 1e-15\n\n if cov_evals[rank - 1] < eps:\n # When the tested rank is associated with a small eigenvalue, there's\n # no point in computing the log-likelihood: it's going to be very\n # small and won't be the max anyway. Also, it can lead to numerical\n # issues below when computing pa, in particular in log((spectrum[i] -\n # spectrum[j]) because this will take the log of something very small.\n return -np.inf\n\n pu = -rank * log(2.)\n for i in range(1, rank + 1):\n pu += (gammaln((n_features - i + 1) / 2.) -\n log(np.pi) * (n_features - i + 1) / 2.)\n\n pl = np.sum(np.log(cov_evals[:rank]))\n pl = -pl * n_samples / 2.\n\n v = max(eps, np.sum(cov_evals[rank:]) / (n_features - rank))\n pv = -np.log(v) * n_samples * (n_features - rank) / 2.\n\n m = n_features * rank - rank * (rank + 1.) / 2.\n pp = log(2. * np.pi) * (m + rank) / 2.\n\n pa = 0.\n spectrum_ = cov_evals.copy()\n spectrum_[rank:n_features] = v\n for i in range(rank):\n for j in range(i + 1, len(cov_evals)):\n pa += log((cov_evals[i] - cov_evals[j]) *\n (1. / spectrum_[j] - 1. / spectrum_[i])) + log(n_samples)\n\n ll = pu + pl + pv + pp - pa / 2. - rank * log(n_samples) / 2.\n\n return ll","def log_norm(log_x):\n c = np.max(log_x)\n\n if np.isinf(c):\n return c\n\n sum_exp = 0\n\n for x in log_x:\n sum_exp += np.exp(x - c)\n\n log_sum_exp = np.log(sum_exp)\n\n log_Z = log_sum_exp + c\n\n return log_Z","def logsumexp2(z):\n return logsumexp(z, axis=2)","def log2(x):\n raise NotImplementedError","def safelog(x):\n #return np.log(x)\n return np.log(np.clip(x,floor,np.inf))","def normal_log_q_initial(self,z):\n means, scale = self.get_means_and_scales_from_q()\n return (ss.norm.logpdf(z,loc=means,scale=scale)).sum(axis=1)","def log(self): # just use base?\n return Factor().__build( VarSet(self.v) , np.log(self.t) )","def mak2(n,par=None,D0=1e-2,k=.5,Fb=0):\n if par is not None:\n D0,k,Fb,slope=par\n D=np.zeros(n)\n D[0]=D0\n for i in range(n-1):\n D[i+1]=D[i]+k*np.log(1+D[i]/k)\n F=D+Fb\n E[1:]=k*np.log(1+D[:-1]/k)/D[:-1]\n return F,D,E","def log_abs_det_jacobian(self, z):\n pre_u = self.u_ + self.u\n pre_w = self.w_ + self.w\n a = F.softplus(self.a + self.inv)\n w = F.softmax(pre_w, dim=3)\n u = F.softmax(pre_u, dim=3)\n # Perform computation\n pre_sigm = torch.sum(u * a * z, 3) + self.b\n sigm = torch.sigmoid(pre_sigm)\n x_pre = torch.sum(w * sigm, dim=3)\n x_pre_clipped = x_pre * (1 - self.eps) + self.eps * 0.5\n logj = F.log_softmax(pre_w, dim=3) + logsigmoid(pre_sigm) + logsigmoid(-pre_sigm) + torch.log(a)\n # n, d, d2, dh\n logj = logj + F.log_softmax(pre_u, dim=3)\n # n, d, d2, dh, d1\n logj = torch.log(torch.sum(torch.exp(logj),3))\n # n, d, d2, d1\n logdet_ = logj + np.log(1 - self.eps) - (torch.log(x_pre_clipped) + torch.log(-x_pre_clipped + 1))\n return logdet_","def zkl_divergence(x, y, gamma):\n return np.sum([p_i*np.log(p_i/q_i) if q_i > 0 and p_i > 0 else p_i*gamma for (p_i, q_i) in zip(x, y)])","def entropy(self):\n Z = self.sum()\n assert (Z > 0), 'Non-normalizable factor (perhaps log factor?)' # also check for positivity?\n H = 0.0\n for x in np.nditer(self.t, op_flags=['readonly']):\n p = x/Z\n H += 0.0 if p==0 else -p*np.log(p)\n return H","def kl(p, q):\n p = np.asarray(p, dtype=float)\n q = np.asarray(q, dtype=float)\n\n return np.where(p != 0, p * np.log(p / q), 0).sum()","def compute_loglike(self, tools: ModelingTools) -> float:\n return -3.0 * self.placeholder","def log(base, real):\n return math.log(real, base)","def find_z(q):\n z = np.zeros(len(q) - 1, int)\n for i in range(len(q) - 1):\n z[i] = np.ceil(q[i]/2 + q[i+1]/2) # formula from class\n return np.concatenate([[0], z, [BITS-1]])","def log_poisson(k, l):\n return k*np.log(l) -l - gammaln(k+1)","def safe_sum_p_log_p(a, base=None):\r\n flat = ravel(a)\r\n nz = take(flat, nonzero(flat)[0])\r\n logs = log(nz)\r\n if base:\r\n logs /= log(base)\r\n return sum(nz * logs, 0)","def find_metallicity_distribution(redshifts, min_logZ_COMPAS, max_logZ_COMPAS,\n mu0=0.035, muz=-0.23, sigma_0=0.39, sigma_z=0.0, alpha =0.0,\n min_logZ =-12.0, max_logZ =0.0, step_logZ = 0.01): \n ##################################\n # Log-Linear redshift dependence of sigma\n sigma = sigma_0* 10**(sigma_z*redshifts)\n \n ##################################\n # Follow Langer & Norman 2006 in assuming that mean metallicities evolve in z as:\n mean_metallicities = mu0 * 10**(muz * redshifts) \n \n # Now we re-write the expected value of the log-skew-normal to retrieve mu\n beta = alpha/(np.sqrt(1 + (alpha)**2))\n PHI = NormDist.cdf(beta * sigma) \n mu_metallicities = np.log(mean_metallicities/2. * 1./(np.exp(0.5*sigma**2) * PHI ) ) \n\n ##################################\n # create a range of metallicities (the x-values, or random variables)\n log_metallicities = np.arange(min_logZ, max_logZ + step_logZ, step_logZ)\n metallicities = np.exp(log_metallicities)\n\n\n ##################################\n # probabilities of log-skew-normal (without the factor of 1/Z since this is dp/dlogZ not dp/dZ)\n dPdlogZ = 2./(sigma[:,np.newaxis]) * NormDist.pdf((log_metallicities - mu_metallicities[:,np.newaxis])/sigma[:,np.newaxis]) * NormDist.cdf(alpha * (log_metallicities - mu_metallicities[:,np.newaxis])/sigma[:,np.newaxis] )\n\n ##################################\n # normalise the distribution over all metallicities; this choice of normalisation assumes that metallicities outside the COMPAS range have yields of zero\n norm = dPdlogZ.sum(axis=-1) * step_logZ\n dPdlogZ = dPdlogZ /norm[:,np.newaxis]\n\n ##################################\n # assume a flat in log distribution in sampled metallicity to find probability of drawing Z in COMPAS\n p_draw_metallicity = 1 / (max_logZ_COMPAS - min_logZ_COMPAS)\n \n return dPdlogZ, metallicities, p_draw_metallicity","def compute_log_likehood_markov(markov_n, markov_table, start_i, seq):\n ll = 0\n for cur_i in range(start_i, len(seq)):\n cur_mem = seq[cur_i]\n past_mems = tuple(seq[cur_i-markov_n:cur_i])\n prob = markov_table[past_mems][cur_mem]\n # We shouldn't get prob = 0 because we get the markov table from the sequence themselves\n ll += np.log(prob)\n return ll"],"string":"[\n \"def partition_function(array, temp):\\r\\n\\r\\n # Constants imported from scipy.constants\\r\\n h = scipy.constants.h # Planck's constant\\r\\n # speed of light must be in cm/s as wavenumber is in cm-1\\r\\n c = scipy.constants.c * 100\\r\\n k = scipy.constants.k # Boltzmann constant\\r\\n T = temp # extracted from log file using extract_temp()\\r\\n\\r\\n # check if inputs are numpy arrays and convert if not.\\r\\n if not isinstance(array, np.ndarray):\\r\\n np.asarray(array)\\r\\n\\r\\n # conversion to exponent\\r\\n u = (h * array * c) / (k * T)\\r\\n\\r\\n # calculates natural log of an individual frequency contribution to the partition function\\r\\n Q_ = np.log(np.exp(-(u / 2)) / (1 - np.exp(-u)))\\r\\n # sums all the contributions together, giving the final result.\\r\\n Q = np.sum(Q_)\\r\\n return Q\",\n \"def log_lhood(X, Z, Y, a, ep, lamb):\\n \\n K = Z.shape[1]\\n N, T = X.shape\\n \\n # p(X)\\n ZY = np.dot(Z,Y) \\n log_pX = 0\\n log_pX = log_pX + np.sum(X * np.log(1 - ((1 - lamb) ** ZY) * (1 - ep)))\\n log_pX = log_pX + np.sum((1 - X) * np.log(((1 - lamb) ** ZY) * (1 - ep))) \\n \\n # p(Z)\\n HN = 0\\n for n in range(1, N+1):\\n HN += 1.0/n\\n m = Z.sum(axis=0)\\n log_pZ = (K * np.log(a) - (a * HN)) + np.sum(gammaln(m) +\\n gammaln(N - m + 1) - gammaln(N + 1)) \\n\\n return log_pZ + log_pX\",\n \"def log_p(self,z): \\n return np.array([self.log_p_blanket(i) for i in z])\",\n \"def logZ_brute(self, T, pos, psi, phi):\\n ms = self.get_all_tag_seq(len(T))\\n log_scores = tr.zeros(len(ms), dtype=tr.float64)\\n for i in range(len(ms)):\\n log_scores[i] = self.log_score(T, pos, ms[i], psi, phi)\\n log_z = logsumexp(log_scores)\\n return log_z\",\n \"def calculate_log_perplexity(self, output, flat_labels): #completed, expensive, should be compiled\\n return -np.sum(np.log2(np.clip(output, a_min=1E-12, a_max=1.0))[np.arange(flat_labels.shape[0]), flat_labels[:,1]])\",\n \"def log2(a):\",\n \"def log_prob(self, z, x):\\n n_samples = z.shape[0].value\\n log_prob = 0\\n for i in range(self.time_steps):\\n lat_dim = self.transition.dim_x\\n obs_dim = self.emmision.dim_x\\n # Current time step latent state and observation\\n z_t = tf.slice(z, [0, i * lat_dim], [-1, lat_dim])\\n x_t = tf.slice(x, [0, i * obs_dim], [-1, obs_dim])\\n if i == 0:\\n log_prob += tf.reduce_sum(\\n self.init_state_p.log_prob(z_t), axis=1)\\n else:\\n log_prob += self.transition.log_prob(z_t, z_tminus)\\n log_prob += self.emmision.log_prob(x_t, z_t)\\n # Update the previous latent state for next iteration\\n z_tminus = z_t\\n return log_prob\",\n \"def testLogPartitionInfinityIsAccurate(self):\\n alpha = float('inf')\\n log_z_true = 0.70526025442 # From mathematica.\\n log_z = self.variant(self._distribution.log_base_partition_function)(alpha)\\n chex.assert_tree_all_close(log_z, log_z_true, atol=1e-7, rtol=1e-7)\",\n \"def log_marg(self):\\n log_prob_X_given_z = 0.\\n for k in range(self.K):\\n log_prob_X_given_z += self.log_marg_k(k)\\n return log_prob_X_given_z\",\n \"def log_Distribution_volume(self, z):\\n if type(z)==np.ndarray:\\n ind = z<0\\n pv = np.log(self.Distribution_volume(z))\\n pv[ind] = -1e99\\n return pv\\n else:\\n if z<0:\\n return -1e99\\n else:\\n return np.log(self.Distribution_volume(z))\",\n \"def log_p(self,z):\\n return mb_log_p_posterior(z, self.neg_posterior, self.mini_batch)\",\n \"def save_exact_log_Z(expt):\\n if isinstance(expt, str):\\n expt = get_experiment(expt)\\n tr_expt = get_training_expt(expt)\\n \\n for it in tr_expt.save_after:\\n for avg in AVG_VALS:\\n print 'iteration', it, avg\\n try:\\n rbm = load_rbm(expt, it, avg)\\n except:\\n continue\\n\\n log_Z = tractable.exact_partition_function(rbm)\\n storage.dump(log_Z, expt.log_Z_file(it, avg))\",\n \"def logrels(rets):\\n return np.log(rets + 1)\",\n \"def logbarrierfunc(delta, z, use_sigma):\\n if use_sigma:\\n z = np.where(z >= 0, np.tanh(z), z)\\n k = 2\\n return np.where(z > delta, -np.log(np.abs(z)),\\n ((k - 1) / k) * (((z - k * delta) / ((k - 1) * delta)) ** k - 1) - np.log(delta))\",\n \"def log_sum_exp(Z):\\n return np.max(Z) + np.log(np.sum(np.exp(Z - np.max(Z))))\",\n \"def _log_fold_change_pairs(self, idx0, idx1, base):\\n assert np.all([x < self._pval.shape[1] for x in idx0])\\n assert np.all([x < self._pval.shape[1] for x in idx1])\\n if base == np.e:\\n return self._logfc[idx0, :, :][:, idx1, :]\\n else:\\n return self._logfc[idx0, :, :][:, idx1, :] / np.log(base)\",\n \"def log_prob(self):\",\n \"def log_likelihood(self):\\r\\n if self.likelihood.is_heteroscedastic:\\r\\n A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)\\r\\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self._A))\\r\\n else:\\r\\n A = -0.5 * self.num_data * self.output_dim * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT\\r\\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self._A))\\r\\n C = -self.output_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * np.log(sf2))\\r\\n D = 0.5 * self.data_fit\\r\\n self._A_part, self._B_part, self._C_part, self._D_part = A, B, C, D\\r\\n return A + B + C + D + self.likelihood.Z\",\n \"def log_p(self,z):\\n return log_p_posterior(z, self.neg_posterior)\",\n \"def log2_inplace(a):\",\n \"def grad_log_q(self,z): \\n param_count = 0\\n grad = np.zeros((np.sum(self.approx_param_no),self.sims))\\n for core_param in range(len(self.q)):\\n for approx_param in range(self.q[core_param].param_no):\\n grad[param_count] = self.q[core_param].vi_score(z[core_param],approx_param) \\n param_count += 1\\n return grad\",\n \"def compute_log(tx, index_log, mean=[], std=[]):\\n tx_new = np.log10(3+abs(tx[:,index_log]))\\n return standardize(tx_new,mean,std)\",\n \"def kullback_leibler_iaf(z, logqz_x, beta=1.):\\n \\n logpz = -tf.reduce_sum(0.5 * np.log(2*np.pi) + 0.5 * tf.square(z), axis=-1)\\n kl = beta * tf.reduce_mean(logqz_x - logpz)\\n return kl\",\n \"def kullback_leibler_iaf(z, logqz_x, beta=1., **args):\\n \\n logpz = -tf.reduce_sum(input_tensor=0.5 * np.log(2*np.pi) + 0.5 * tf.square(z), axis=-1)\\n kl = beta * tf.reduce_mean(input_tensor=logqz_x - logpz)\\n return kl\",\n \"def logP(self):\\n raise NotImplementedError\",\n \"def _log_util(chips: float,\\n bet_size: float,\\n payout: float) -> float:\\n if chips <= 0 or chips + payout*bet_size <= 0:\\n return MIN_REWARD\\n return max(math.log(1.0 + chips + payout*bet_size) - math.log(1.0 + chips),\\n MIN_REWARD)\",\n \"def log_likelihood_z_lognormal(self, std=1.0):\\n #return self.log_det_Jxz - self.dim * tf.log(std) - (0.5 / (std**2)) * tf.reduce_sum(self.output_z**2, axis=1)\\n from deep_boltzmann.util import logreg\\n logz = logreg(self.output_z, a=0.001, tf=True)\\n ll = self.log_det_Jxz \\\\\\n - (0.5 / (std**2)) * tf.reduce_sum(logz**2, axis=1) \\\\\\n - tf.reduce_sum(logz, axis=1)\\n return ll\",\n \"def logp(self, x):\\n pass\",\n \"def log(a):\",\n \"def _logprob(self, sample):\\n cur_y, cur_z = sample\\n log_prior = 0\\n log_lik = 0\\n if cur_z.shape[1] == 0: return -99999999.9\\n \\n if self.cl_mode:\\n a_time = time()\\n d_cur_z = cl.Buffer(self.ctx, self.mf.READ_ONLY | self.mf.COPY_HOST_PTR, hostbuf = cur_z.astype(np.int32))\\n d_cur_y = cl.Buffer(self.ctx, self.mf.READ_ONLY | self.mf.COPY_HOST_PTR, hostbuf = cur_y.astype(np.int32))\\n d_logprob = cl.array.empty(self.queue, (cur_z.shape[0],), np.float32, allocator=self.mem_pool)\\n\\n if cur_z.shape[0] % self.p_mul_logprob_z_data == 0: wg = (self.p_mul_logprob_z_data,)\\n else: wg = None\\n \\n self.prg.logprob_z_data(self.queue, (cur_z.shape[0],), wg,\\n d_cur_z, d_cur_y, self.d_obs, d_logprob.data, #d_novel_f.data,\\n np.int32(self.N), np.int32(cur_y.shape[1]), np.int32(cur_z.shape[1]), \\n np.float32(self.alpha), np.float32(self.lam), np.float32(self.epislon))\\n log_lik = d_logprob.get().sum()\\n self.gpu_time += time() - a_time\\n\\n # calculate the prior probability of Y\\n num_on = (cur_y == 1).sum()\\n num_off = (cur_y == 0).sum()\\n log_prior = num_on * np.log(self.theta) + num_off * np.log(1 - self.theta)\\n\\n else:\\n # calculate the prior probability of Z\\n for n in xrange(cur_z.shape[0]):\\n num_novel = 0\\n for k in xrange(cur_z.shape[1]):\\n m = cur_z[:n,k].sum()\\n if m > 0:\\n if cur_z[n,k] == 1: log_prior += np.log(m / (n+1.0))\\n else: log_prior += np.log(1 - m / (n + 1.0))\\n else: \\n if cur_z[n,k] == 1: num_novel += 1\\n if num_novel > 0:\\n log_prior += poisson.logpmf(num_novel, self.alpha / (n+1.0))\\n # calculate the prior probability of Y\\n num_on = (cur_y == 1).sum()\\n num_off = (cur_y == 0).sum()\\n log_prior += num_on * np.log(self.theta) + num_off * np.log(1 - self.theta)\\n # calculate the logliklihood\\n log_lik = self._loglik(cur_y = cur_y, cur_z = cur_z)\\n return log_prior + log_lik\",\n \"def _log_fold_change_pairs(self, idx0, idx1, base):\\n logfc = np.tile(np.NaN, [len(idx0), len(idx1), self.model_estim.x.shape[1]])\\n for i, xi in enumerate(idx0):\\n for j, xj in enumerate(idx1):\\n logfc[i, j, :] = self._theta_mle[xj, :] - self._theta_mle[xi, :]\\n logfc[j, i, :] = -logfc[i, j, :]\\n\\n if base == np.e:\\n return logfc\\n else:\\n return logfc / np.log(base)\",\n \"def log10(a):\",\n \"def log_Schechter_log(self, logl, alpha, logls, logl0):\\n phi = (logl - logls) * (alpha+1) * np.log(10.) - np.power(10., logl-logls)\\n lik = phi.copy()\\n lik [logl < logl0] = -1e99\\n return lik\",\n \"def _log_fold_change_pairs(self, idx0, idx1, base):\\n logfc = np.zeros(shape=(len(idx0), len(idx1), self._theta_mle.shape[1]))\\n for i, xi in enumerate(idx0):\\n for j, xj in enumerate(idx1):\\n logfc[i, j, :] = self._theta_mle[xi, :] - self._theta_mle[xj, :]\\n\\n if base == np.e:\\n return logfc\\n else:\\n return logfc / np.log(base)\",\n \"def padic_reduce(self, diff_bound):\\n Z_bounds = []\\n for i in range(len(self.constants.primes)):\\n p = self.constants.primes[i]\\n logging.info(\\\"Examining the prime %d for the p-adic reduction.\\\" % p)\\n r = math.ceil(math.log(self.coefficients[\\\"n1_bound\\\"], p))\\n prec = r + self.tries + 10\\n L = Qp(p, prec)\\n\\n current_z_bound = -1\\n\\n # Pre-computation step (i.e. memoization)\\n alpha, beta = self.calculate_alphabeta(p, prec)\\n alpha_t = self.precompute_t_power(alpha, diff_bound)\\n beta_t = self.precompute_t_power(beta, diff_bound)\\n z0_right_term = L((alpha - beta).norm()).ordp()\\n log_alpha_over_beta = log(alpha / beta)\\n\\n # Pre-computations for tau == 1 \\n p_order = log_alpha_over_beta.norm().ordp() / 2\\n\\n for t_vec in combinations_with_replacement(list(range(1, diff_bound + 1)), self.constants.num_terms - 1):\\n z0_left_term = self.constants.a * (1 + sum([alpha_t[t - 1] for t in t_vec]))\\n z0 = L(z0_left_term.norm()).ordp() - z0_right_term\\n tau = self.calculate_tau(t_vec, alpha_t, beta_t)\\n\\n # Unlikely case, and we may assume the p-adic log is injective for our purposes.\\n if tau == 1:\\n new_bound = math.log(self.coefficients[\\\"n1_bound\\\"], p) + p_order + z0\\n Z_bounds.append(max(z0 + 3/2, new_bound))\\n print('executing edge case')\\n else:\\n # Remember that zeta is in Q_p, not in the field extension.\\n log_tau = tau.log()\\n zeta = log_tau / log_alpha_over_beta\\n vzeta = zeta.norm().ordp()\\n zeta_list = self.get_expansion(prec, zeta)\\n\\n R_max = self.find_Rmax(r, vzeta, zeta_list)\\n if R_max == -1:\\n print('R does not exist')\\n m0 = self.find_m0(p, zeta_list)\\n z_bound = math.log(self.coefficients[\\\"n1_bound\\\"] - m0, p) + (alpha / beta).ordp() + z0\\n else:\\n zeta_inverse = log_alpha_over_beta / log_tau\\n vzeta_inverse = zeta_inverse.ordp()\\n z_bound = log_tau.ordp() + R_max + vzeta_inverse\\n current_z_bound = max(current_z_bound, z_bound)\\n Z_bounds.append(current_z_bound)\\n return Z_bounds\",\n \"def log_boltzmann_dist(Q, temperature):\\n return nn.LogSoftmax(dim=0)(Q/temperature)\",\n \"def log_zgrid(zr=[0.7,3.4], dz=0.01):\\n zgrid = np.exp(np.arange(np.log(1+zr[0]), np.log(1+zr[1]), dz))-1\\n return zgrid\",\n \"def kl(p, q):\\n return np.sum(np.where(p != 0, p * np.log(p / q), 0))\",\n \"def _vertical_log(self, X: np.ndarray) -> (np.ndarray, np.ndarray):\\n ret_p = np.zeros_like(X)\\n ret_n = np.zeros_like(X)\\n log_p = self.manifold.log(X[:-1], X[1:])\\n log_n = self.manifold.log(X[1:], X[:-1])\\n ret_p[:-1] = log_p\\n ret_n[1:] = log_n\\n return ret_p, ret_n\",\n \"def logistic_function(z):\\n\\tg = 1/(1+np.exp(-z))\\n\\t#g = np.exp(z)/(1+np.exp(z))\\n\\treturn g\",\n \"def compute_approx_log_Z(self, data_term, non_data_term, axis= None):\\n \\n if (axis == 1) and (non_data_term != None):\\n \\n approx_Z = T.concatenate([non_data_term, data_term], axis=1)\\n \\n max_vals = T.max(approx_Z, axis=1)\\n\\n max_vals = T.reshape(max_vals,[self.batch_size,1])\\n \\n if self.mf_steps ==0:\\n \\n max_vals_tiled = T.tile(max_vals,\\n (1,self.num_samples+self.batch_size))\\n \\n else:\\n \\n max_vals_tiled= T.tile(max_vals,(1,self.num_samples+1))\\n \\n approx_Z = approx_Z - max_vals_tiled\\n \\n approx_Z = max_vals + T.log(T.sum(T.exp(approx_Z), axis=1))\\n \\n approx_Z = T.mean(approx_Z)\\n \\n if (axis == None) and (non_data_term != None):\\n \\n approx_Z = T.concatenate([non_data_term, data_term])\\n \\n max_val = T.max(approx_Z)\\n \\n ### self. added to enable reporting p_tilda instead of\\n ### pseudo likelihood cost\\n self.approx_Z = approx_Z - max_val\\n \\n approx_Z = max_val + T.log(T.sum(T.exp(self.approx_Z)))\\n \\n if (non_data_term == None) and (axis == None):\\n \\n approx_Z = data_term\\n \\n max_val = T.max(approx_Z)\\n \\n approx_Z = max_val + T.log(T.sum(T.exp(approx_Z -max_val)))\\n \\n return approx_Z\",\n \"def fn(val):\\n if val < x: return min(2*val-1, 2*(x-val))\\n k = int(log(val)//log(x))\\n ans = k + fn(val - x**k)\\n if x**(k+1) < 2*val: \\n ans = min(ans, k + 1 + fn(x**(k+1) - val))\\n return ans\",\n \"def MvNormalLogp():\\n cov = pt.matrix(\\\"cov\\\")\\n cov.tag.test_value = floatX(np.eye(3))\\n delta = pt.matrix(\\\"delta\\\")\\n delta.tag.test_value = floatX(np.zeros((2, 3)))\\n\\n cholesky = Cholesky(lower=True, on_error=\\\"nan\\\")\\n\\n n, k = delta.shape\\n n, k = f(n), f(k)\\n chol_cov = cholesky(cov)\\n diag = pt.diag(chol_cov)\\n ok = pt.all(diag > 0)\\n\\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\\n delta_trans = solve_lower(chol_cov, delta.T).T\\n\\n result = n * k * pt.log(f(2) * np.pi)\\n result += f(2) * n * pt.sum(pt.log(diag))\\n result += (delta_trans ** f(2)).sum()\\n result = f(-0.5) * result\\n logp = pt.switch(ok, result, -np.inf)\\n\\n def dlogp(inputs, gradients):\\n (g_logp,) = gradients\\n cov, delta = inputs\\n\\n g_logp.tag.test_value = floatX(1.0)\\n n, k = delta.shape\\n\\n chol_cov = cholesky(cov)\\n diag = pt.diag(chol_cov)\\n ok = pt.all(diag > 0)\\n\\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\\n delta_trans = solve_lower(chol_cov, delta.T).T\\n\\n inner = n * pt.eye(k) - pt.dot(delta_trans.T, delta_trans)\\n g_cov = solve_upper(chol_cov.T, inner)\\n g_cov = solve_upper(chol_cov.T, g_cov.T)\\n\\n tau_delta = solve_upper(chol_cov.T, delta_trans.T)\\n g_delta = tau_delta.T\\n\\n g_cov = pt.switch(ok, g_cov, -np.nan)\\n g_delta = pt.switch(ok, g_delta, -np.nan)\\n\\n return [-0.5 * g_cov * g_logp, -g_delta * g_logp]\\n\\n return OpFromGraph([cov, delta], [logp], grad_overrides=dlogp, inline=True)\",\n \"def logp(X, nu, V):\\n\\n p = V.shape[0]\\n\\n IVI = det(V)\\n IXI = det(X)\\n\\n return check_parameters(\\n (\\n (nu - p - 1) * pt.log(IXI)\\n - trace(matrix_inverse(V).dot(X))\\n - nu * p * pt.log(2)\\n - nu * pt.log(IVI)\\n - 2 * multigammaln(nu / 2.0, p)\\n )\\n / 2,\\n matrix_pos_def(X),\\n pt.eq(X, X.T),\\n nu > (p - 1),\\n )\",\n \"def kl_heuristic(A, z):\\n n = len(A)\\n n_round = 10\\n P = defaultdict(list)\\n L = defaultdict(list)\\n bestL = 0\\n bestP = None\\n for j in range(1, (n+1)/2):\\n z_init = rand_partition(n, j)\\n l_init = sbm.likelihood(A, z_init, log_scale=True)\\n z_round = z_init\\n for _round in range(n_round):\\n z_values = []\\n l_values = []\\n for index, pair in enumerate(swap_pairs(z_round)):\\n z_round[pair[0]], z_round[pair[1]] = \\\\\\n z_init[pair[1]], z_init[pair[0]]\\n z_values.append(z_round)\\n l_values.append(sbm.likelihood(A, z_round, log_scale=True))\\n l_max_index = l_values.index(max(l_values))\\n z_max_round = z_values[l_max_index]\\n z_round = z_max_round\\n L[j].append(l_values)\\n bestL = max(l_values)\\n bestP = z_max_round\\n return bestL, bestP, L, P\",\n \"def hankelp(j, nu, z):\\n return hankel(j, nu-1, z) - nu*hankel(j, nu, z)/z\",\n \"def IntegratePolynomialPartLogExt(poly, fieldTower):\\n #print(poly)\\n #print(poly.degree)\\n #print(poly.fieldTower)\\n if poly == 0 or poly.isZero():\\n return Int.Integral()\\n red = poly.reduceToLowestPossibleFieldTower()\\n if red.fieldTower.towerHeight<fieldTower.towerHeight:\\n return Integrate(red)\\n p = poly.getCoefficients()\\n l = len(p)-1\\n q = [ZERO]*(len(p)+1)\\n b = [ZERO]*(len(p)+1)\\n d = [ZERO]*(len(p)+1)\\n \\n \\n u = fieldTower.getLastExtension().argFunction # last fieldExtension with T=log(u)\\n log_diff_u = u.logDifferentiate()#u.differentiate()/u # u'/u\\n \\n # p_l *T^l + p_(l-1)*T^(l-1) + ... + p_0 = (q_(l+1)*T^(l+1) + q_l*T^l + ... + q_0)' + (c_1 *v_1'/v_i + ... + c_m *v_m'/v_m)\\n # (q_i*T^i)' = q_i'*T^i + q_i*i*T'*T^(i-1)\\n # -> 0 = q_(l+1)'\\n # p_l = (l+1)*q_(l+1)*T' + q_l'\\n # ...\\n # p_0 = q_1*T' + (qe_0)', qe_0 = q_0+(c_1 *log(v_1) + ... + c_m *log(v_m))\\n for i in range(l,-1,-1):\\n # q_(i+1) = d_(i+1) + b_(i+1)\\n # integral(p_i-(i+1)*d_(i+1)*T') = l*b_(i+1)*T' + q_i\\n #i = Rational.fromFloat(i)\\n integrand = p[i]+(-ONE)*(i+1)*d[i+1]*log_diff_u\\n if type(integrand)==float or type(integrand)==int:\\n integrand = Rational.fromFloat(integrand)\\n int_reduced = integrand.reduceToLowestPossibleFieldTower()\\n if int_reduced!=None:\\n integrand = int_reduced\\n P_i = Integrate(integrand)\\n if i>0:\\n IntegratePolynomialPartLogExtCheckIntegralConditions(P_i, fieldTower)\\n else:\\n if P_i==None:\\n raise Int.IntegralNotElementaryError()\\n \\n prev = fieldTower.prevTower()\\n #logs = P_i.getNewLogExpressionsInFieldTower(prev,fieldTower)\\n logTerm = P_i.getLogExpression(u)\\n otherLogs = [log for log in P_i.logExpressions if not log==logTerm]#in logs]\\n ci = ZERO if logTerm==None else logTerm.factor # integral = P_i = c_i*T+d_i, d_i = P_i\\\\logs\\n d[i] = Int.Integral(poly_rationals=P_i.poly_rational_partExpressions,logs=otherLogs,rootSums=P_i.rootSums).asFunction()\\n b[i+1] = ci/(i+1)\\n q[i+1] = d[i+1]+b[i+1]\\n \\n #integrand = p[0]+(-1)*d[1]*log_diff_u\\n #P0 = Integrate(integrand)\\n #if P0==None:\\n # raise Int.IntegralNotElementaryError()\\n #b1 = 0\\n #for log in P0.logExpressions:\\n # if log.argFunction==u:\\n # b1 = log.factor\\n \\n if b[1]==0:\\n q_0 = P_i.asFunction()\\n else:\\n q_0 = (P_i+Int.Integral(logs=[Int.LogFunction(u,(-1)*b[1])])).asFunction()\\n q[0] = q_0\\n \\n integralPoly = Pol.Polynomial(variable=fieldTower.getLastVariable())\\n for i in range(l+1,-1,-1):\\n integralPoly.setCoefficient(i,q[i], callUpdates=False)\\n integralPoly.updateCoefficientsAll()\\n \\n return Int.Integral(poly_rationals=[integralPoly])\",\n \"def calc_probs(log_p):\\n\\n N = log_p.shape[0]\\n\\n log_Z_per_N = np.zeros(shape=(N, 1))\\n\\n for i in range(N):\\n\\n log_Z_per_N[i] = log_norm(log_p[i])\\n\\n log_p_new = log_p - log_Z_per_N\\n\\n p = np.exp(log_p_new)\\n\\n # log_Z = log_norm(log_p)\\n\\n # p = np.exp(log_p - log_Z)\\n\\n return p\",\n \"def U(xs):\\n ret = 0\\n for x in xs:\\n ret += log(x)\\n return ret\",\n \"def logsum_safe(prob_ll, atl):\\n logpdf = prob_ll + K.log(atl + K.epsilon())\\n alpha = tf.reduce_max(logpdf, -1, keepdims=True)\\n return alpha + tf.log(tf.reduce_sum(K.exp(logpdf-alpha), -1, keepdims=True) + K.epsilon())\",\n \"def log_inplace(a):\",\n \"def empirical_counts(a_log, p_log, pi0_log, init_data, alpha_list, beta_list):\\n # Calculate total marginal probability for each z_it\\n seq_log_probs = []\\n full_seq_probs = []\\n for t, init_vec in enumerate(init_data):\\n alpha_array = alpha_list[t]\\n beta_array = beta_list[t]\\n seq_log_probs.append(logsumexp(alpha_array[:, -1]))\\n seq_probs = alpha_array + beta_array\\n seq_probs = seq_probs - logsumexp(seq_probs, axis=0)\\n full_seq_probs.append(seq_probs)\\n\\n # Now calculate effective number of transitions (empirical transition matrix)\\n a_log_empirical = np.zeros_like(a_log) - np.Inf\\n K = len(a_log[:, 0])\\n # transition counts\\n for k in range(K):\\n for l in range(K):\\n # store log probs for each sequence\\n a_probs = []\\n # current transition prob from k to l\\n akl = a_log[l, k]\\n for i, init_vec in enumerate(init_data):\\n T = len(init_vec)\\n a = alpha_list[i]\\n b = beta_list[i]\\n event_list = [a[k, t] + b[l, t + 1] + akl + p_log[int(init_vec[t + 1]), l] for t in range(T - 1)]\\n a_probs.append(logsumexp(event_list) - seq_log_probs[i]) # NL: why do this?\\n\\n a_log_empirical[l, k] = logsumexp(a_probs)\\n\\n p_empirical = np.zeros_like(p_log)\\n for i, init_vec in enumerate(init_data):\\n seq_probs = np.exp(full_seq_probs[i])\\n for t in range(len(init_vec)):\\n p_empirical[int(init_vec[t]), :] += seq_probs[:, t]\\n\\n pi0_log_empirical = np.zeros_like(pi0_log)\\n for i, init_vec in enumerate(init_data):\\n seq_probs = full_seq_probs[i]\\n pi0_log_empirical += seq_probs[:, 0]\\n\\n return a_log_empirical, np.log(p_empirical), pi0_log_empirical, seq_log_probs, full_seq_probs\",\n \"def _x(rho, z):\\r\\n a = (1 - 2*rho*z + z**2)**.5 + z - rho\\r\\n b = 1 - rho\\r\\n return np.log(a / b)\",\n \"def rundit_fold_logredst(S,nu_lines,cnbeta,gammaL,nu_grid,ngammaL_grid,dLarray,dv_lines,dv_grid):\\n\\n Ng_nu=len(nu_grid)\\n Ng_gammaL=len(ngammaL_grid)\\n\\n ngammaL=gammaL/dv_lines\\n log_nstbeta=jnp.log(cnbeta)\\n log_ngammaL=jnp.log(ngammaL)\\n \\n log_ngammaL_grid = jnp.log(ngammaL_grid)\\n\\n k = jnp.fft.rfftfreq(2*Ng_nu,1)\\n val=inc2D(S,nu_lines,log_ngammaL,nu_grid,log_ngammaL_grid)\\n valbuf=jnp.vstack([val,jnp.zeros_like(val)])\\n fftval = jnp.fft.rfft(valbuf,axis=0)\\n vk=folded_voigt_kernel_logst(k, log_nstbeta,log_ngammaL_grid,dLarray)\\n fftvalsum = jnp.sum(fftval*vk,axis=(1,))\\n xs=jnp.fft.irfft(fftvalsum)[:Ng_nu]/dv_grid\\n \\n return xs\",\n \"def log_likelihood_z_uniform(self, std=1.0):\\n return self.log_det_Jxz - self.dim * (tf.log(std) + 0.5*tf.log(12.0))\",\n \"def _estimate_log_prob(self, x):\\n x = self.check_size(x)\\n\\n if self.covariance_type == \\\"full\\\":\\n mu = self.mu\\n var = self.var\\n precision = tf.linalg.inv(var)\\n d = x.shape[-1]\\n\\n log_2pi = d * tf.math.log(2. * pi)\\n log_2pi = tf.cast(log_2pi, tf.double)\\n log_det = self._calculate_log_det(precision)\\n log_det = tf.cast(log_det, tf.double)\\n\\n\\n x = tf.cast(x, tf.double)\\n mu = tf.cast(mu, tf.double)\\n x_mu_T = tf.expand_dims((x - mu), -2)\\n x_mu = tf.expand_dims((x - mu), -1)\\n\\n x_mu_T_precision = calculate_matmul_n_times(self.n_components, x_mu_T, precision)\\n # print(x_mu_T_precision, x_mu_T, x, mu)\\n x_mu_T_precision_x_mu = calculate_matmul(x_mu_T_precision, x_mu)\\n\\n return -.5 * (log_2pi - log_det + x_mu_T_precision_x_mu)\\n\\n elif self.covariance_type == \\\"diag\\\":\\n mu = self.mu\\n prec = tf.math.rsqrt(self.var)\\n\\n log_p = tf.math.reduce_sum((mu * mu + x * x - 2 * x * mu) * (prec ** 2), axis=2, keepdims=True)\\n log_det = tf.math.reduce_sum(tf.math.log(prec), axis=2, keepdims=True)\\n\\n return -.5 * (self.n_features * tf.math.log(2. * pi) + log_p) + log_det\",\n \"def idcg(k):\\n res = sum([1.0 / math.log(i + 2, 2) for i in range(k)])\\n if not res:\\n return 1.0\\n else:\\n return res\",\n \"def log10_inplace(a):\",\n \"def test_safe_sum_p_log_p(self):\\r\\n m = array([2, 4, 0, 8])\\r\\n self.assertEqual(safe_sum_p_log_p(m, 2), 2 * 1 + 4 * 2 + 8 * 3)\",\n \"def MaxHks(N): \\n return np.log2(N-1)/2\",\n \"def estimate(i,x,z):\\n\\n b = 2 * sqrt(2.0) / 9801\\n a = abs(1.0/x-pi)\\n if a >= 1e-15:\\n z = calc(i)+z\\n i += 1\\n x = b*z\\n estimate(i,x,z)\\n else:\\n p =1.0/x\\n print 1.0/x\\n return p\",\n \"def compute_log_prob(self,params: ndarray) -> float:\\n return self.compute_log_prior(params) + self.compute_log_likelihood(params)\",\n \"def ransac_iters(w=0.5, d=min_num_pairs(), z=0.99):\\n\\n return int(np.ceil(np.log(1 - z) / np.log(1 - np.power(w,d))))\",\n \"def __pll(L,h,x, L2=0):\\n if len(x.shape)>1: h = h.reshape(-1,1);\\n pll = -np.log(1+np.exp(-2*x*(L.dot(x)+h))).sum(0)\\n if L2>0: pll += L2*(L**2).sum()\\n return pll\",\n \"def KL(p, q):\\n return np.sum(p * np.log(p / q))\",\n \"def eq_padic_reduce(self, diff_bound):\\n Z_bounds = []\\n for i in range(len(self.constants.primes)):\\n p = self.constants.primes[i]\\n logging.info(\\\"Examining the prime %d for the p-adic reduction.\\\" % p)\\n r = math.ceil(math.log(self.coefficients[\\\"eq_n1_bound\\\"], p))\\n prec = r + self.tries + 10\\n L = Qp(p, prec)\\n\\n current_z_bound = -1\\n\\n # Pre-computation step (i.e. memoization)\\n alpha, beta = self.calculate_alphabeta(p, prec)\\n alpha_t = self.precompute_t_power(alpha, diff_bound)\\n beta_t = self.precompute_t_power(beta, diff_bound)\\n z0_right_term = L((alpha - beta).norm()).ordp()\\n log_alpha_over_beta = log(alpha / beta)\\n\\n # Pre-computations for tau == 1 \\n p_order = log_alpha_over_beta.norm().ordp() / 2\\n\\n t_vec = (0,0,0)\\n z0_left_term = self.constants.a * (1 + sum([alpha_t[t] for t in t_vec]))\\n z0 = L(z0_left_term.norm()).ordp() - z0_right_term\\n tau = self.calculate_tau(t_vec, alpha_t, beta_t)\\n print('tau',tau)\\n\\n # Unlikely case, and we may assume the p-adic log is injective for our purposes.\\n if tau == 1:\\n new_bound = math.log(self.coefficients[\\\"eq_n1_bound\\\"], p) + p_order + z0\\n Z_bounds.append(max(z0 + 3/2, new_bound))\\n print('executing edge case')\\n else:\\n # Remember that zeta is in Q_p, not in the field extension.\\n log_tau = tau.log()\\n zeta = log_tau / log_alpha_over_beta\\n vzeta = zeta.norm().ordp()\\n zeta_list = self.get_expansion(prec, zeta)\\n\\n R_max = self.find_Rmax(r, vzeta, zeta_list)\\n if R_max == -1:\\n print('R does not exist')\\n m0 = self.find_m0(p, zeta_list)\\n z_bound = math.log(self.coefficients[\\\"n1_bound\\\"] - m0, p) + (alpha / beta).ordp() + z0\\n else:\\n zeta_inverse = log_alpha_over_beta / log_tau\\n vzeta_inverse = zeta_inverse.ordp()\\n z_bound = log_tau.ordp() + R_max + vzeta_inverse\\n current_z_bound = max(current_z_bound, z_bound)\\n return Z_bounds\",\n \"def logsumexp(logv):\\n res = logzero()\\n for val in logv:\\n res = logsum_pair(res, val)\\n return res\",\n \"def compute_log_prior(self,params: ndarray) -> float:\\n ln_tE = params[0]\\n ln_A0 = params[1]\\n ln_deltaT = params[2]\\n fbl = params[3]\\n mb = params[4]\\n\\n # Equation (16,15,17) (note that Albrow uses \\\"log\\\" for log10)\\n log10e = np.log10(np.exp(1))\\n ln_pr_ln_tE = np.log(0.476) - ((log10e*ln_tE - 1.333)**2 / 0.330) + np.log(log10e)\\n ln_pr_ln_A0 = np.log(0.660) - (1.289*log10e*ln_A0) + np.log(log10e)\\n ln_pr_ln_deltaT = np.log(0.156) - ((log10e*ln_deltaT - 1.432)**2 / 0.458) +\\\\\\n np.log(log10e)\\n \\n # Paper doesnt mention the prior used, but I assume it to be uniform\\n ln_pr_fbl = uniform.logpdf(fbl,0.0,1.0)\\n\\n # Paper doesnr mention the prior used but I will asuumed it to be uniform\\n ln_pr_mb = uniform.logpdf(mb,self.mag_min - 1.0, self.mag_max + 1.0)\\n \\n \\n return ln_pr_fbl + ln_pr_ln_A0 + ln_pr_ln_deltaT + ln_pr_ln_tE + ln_pr_mb\",\n \"def growth_factor_scale_independent(self, z):\\n z = np.atleast_1d(z)\\n nz = len(z)\\n #if self.M_nu_tot == 0. and self.w0 == -1. and self.wa==0.:\\n # aa = 1./(1.+z)\\n # ww = self.w0 + (1.-aa)*self.wa\\n # d1 = aa*ss.hyp2f1(1/3., 1., 11/6., -aa**3/self.Omega_m*(1.-self.Omega_m))/ss.hyp2f1(1/3., 1., 11/6., -(1.-self.Omega_m)/self.Omega_m)\\n #else:\\n # d1 = np.zeros(nz)\\n # for i in range(nz):\\n # LCDM, _ = sint.quad(lambda x: (1+x)*(self.H0/self.H_massive(x))**3., z[i], np.inf)\\n # d1[i] = LCDM*self.H_massive(z[i])/self.H0\\n # LCDM0, _ = sint.quad(lambda x: (1+x)*(self.H0/self.H_massive(x))**3., 0., np.inf)\\n # d1 = d1/LCDM0\\n d1 = np.zeros(nz)\\n for i in range(nz):\\n LCDM, _ = sint.quad(lambda x: (1+x)*(self.H0/self.H_massive(x))**3., z[i], np.inf)\\n d1[i] = LCDM*self.H_massive(z[i])/self.H0\\n LCDM0, _ = sint.quad(lambda x: (1+x)*(self.H0/self.H_massive(x))**3., 0., np.inf)\\n d1 = d1/LCDM0\\n return d1\",\n \"def levin(x):\\n summ = 0\\n for t, l in x: # for the time and length of each algorithm\\n summ += l + np.log(t)\\n return summ\",\n \"def calc_partition_function_one(states, T):\\n B = beta(T)\\n Z = sum(np.exp(-B * states))\\n\\n return Z\",\n \"def smhm_behroozi(logMstar, z, Mstar00 = 10.72, Mstar0a = 0.55,\\n M10 = 12.35, M1a = 0.28, beta0 = 0.44,\\n abeta = 0.18, delta0 = 0.57, adelta = 0.17,\\n gamma0 = 1.56, agamma = 2.51 ):\\n\\n # Warning\\n if np.any(logMstar<8.8):\\n print \\\"Warning: LogMstar value less than Behroozi et al. 2010 Figure 2 range [8.8, 11.6].\\\"\\n elif np.any(logMstar>11.6):\\n print \\\"Warning: LogMstar value greater than Behroozi et al. 2010 Figure 2 range [8.8, 11.6].\\\"\\n \\n #scale factor\\n a = 1/(1+z)\\n \\n #Equation 21 of Behroozi et al\\n logM1 = M10 + M1a*(a-1)\\n logMstar0 = Mstar00 + Mstar0a*(a-1)\\n beta = beta0 + abeta*(a-1)\\n delta = delta0 + adelta*(a-1)\\n gamma = gamma0 + agamma*(a-1) \\n MstarbyMstar0 = 10**( logMstar - logMstar0 ) \\n return logM1 + beta*np.log10( MstarbyMstar0 ) +\\\\\\n (MstarbyMstar0**delta)/(1+(MstarbyMstar0)**-gamma) - 0.5\",\n \"def monotonicity(self, grid):\\n\\t\\ttotal = [0] * 4;\\n\\n\\t\\tfor x in range(0,4):\\n\\t\\t\\tcurrent = 0\\n\\t\\t\\tnext = current + 1\\n\\t\\t\\twhile next < 4 :\\n\\t\\t\\t\\t\\\"\\\"\\\"move to next non zero value\\\"\\\"\\\"\\n\\t\\t\\t\\twhile next < 4 and not grid.getCellValue((x, next)):\\n\\t\\t\\t\\t\\tnext += 1\\n\\t\\t\\t\\t\\\"\\\"\\\"if the non zero cell was last cell of grid\\\"\\\"\\\"\\n\\t\\t\\t\\tif next >= 4:\\n\\t\\t\\t\\t\\tnext -= 1\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tcurrent_value = grid.getCellValue((x, current))\\n\\t\\t\\t\\tif current_value:\\n\\t\\t\\t\\t\\tcurrent_value = log(current_value, 2)\\n\\t\\t\\t\\tnext_value = grid.getCellValue((x, next))\\n\\t\\t\\t\\tif next_value:\\n\\t\\t\\t\\t\\tnext_value = log(next_value, 2)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif current_value > next_value :\\n\\t\\t\\t\\t\\ttotal[0] += next_value - current_value\\n\\t\\t\\t\\telif next_value > current_value :\\n\\t\\t\\t\\t\\ttotal[1] += current_value - next_value\\n\\t\\t\\t\\tcurrent = next\\n\\t\\t\\t\\tnext += 1\\n\\n\\t\\tfor y in range(0,4):\\n\\t\\t\\tcurrent = 0\\n\\t\\t\\tnext = current + 1\\n\\t\\t\\twhile next < 4 :\\n\\t\\t\\t\\twhile next < 4 and not grid.getCellValue((next, y)):\\n\\t\\t\\t\\t\\tnext += 1\\n\\t\\t\\t\\tif next >= 4:\\n\\t\\t\\t\\t\\tnext -= 1\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tcurrent_value = grid.getCellValue((current, y))\\n\\t\\t\\t\\tif current_value:\\n\\t\\t\\t\\t\\tcurrent_value = log(current_value, 2)\\n\\t\\t\\t\\tnext_value = grid.getCellValue((next, y))\\n\\t\\t\\t\\tif next_value:\\n\\t\\t\\t\\t\\tnext_value = log(next_value, 2)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif current_value > next_value :\\n\\t\\t\\t\\t\\ttotal[2] += next_value - current_value\\n\\t\\t\\t\\telif next_value > current_value :\\n\\t\\t\\t\\t\\ttotal[3] += current_value - next_value\\n\\t\\t\\t\\tcurrent = next\\n\\t\\t\\t\\tnext += 1\\n\\n\\t\\treturn max(total[0], total[1]) + max(total[2],total[3])\",\n \"def get_log(p):\\n if p==0:\\n return 0.\\n return p*np.log2(p)\",\n \"def main(l, k):\\n S = 0\\n T = product(xrange(2), repeat=k)\\n for ts in T:\\n tmp = []\\n\\n for t, c in zip(ts, cs):\\n tmp.append(((-1)*c)**t)\\n\\n S += (sum(tmp)**l)\\n val = (sum(tmp)**l)\\n print val\\n return S / float(2**(k))\",\n \"def main(l, k):\\n S = 0\\n T = product(xrange(2), repeat=k)\\n for ts in T:\\n tmp = []\\n\\n for t, c in zip(ts, cs):\\n tmp.append(((-1)*c)**t)\\n\\n S += (sum(tmp)**l)\\n val = (sum(tmp)**l)\\n print val\\n return S / float(2**(k))\",\n \"def viterbi(obs: List[int], pi: List[float], A: np.ndarray, B: np.ndarray) -> List[int]:\\n pi_log = np.log(pi)\\n A_log = np.log(A)\\n B_log = np.log(B)\\n states = A.shape[0]\\n n = len(obs)\\n\\n D_log = np.zeros((states, n))\\n backtrack = np.zeros((states, n - 1)).astype(int)\\n D_log[:, 0] = pi_log + B_log[:, obs[0]]\\n\\n for j in range(1, n):\\n for i in range(states):\\n temp_sum = A_log[:, i] + D_log[:, j - 1]\\n D_log[i, j] = np.max(temp_sum) + B_log[i, obs[j]]\\n backtrack[i, j - 1] = np.argmax(temp_sum)\\n\\n state = np.zeros(n).astype(int)\\n state[-1] = np.argmax(D_log[:, -1])\\n for n in range(n - 2, -1, -1):\\n state[n] = backtrack[int(state[n + 1]), n]\\n state = state.tolist()\\n return state\",\n \"def neglogli(preds, actual):\\n preds = np.reshape(preds, (-1,))\\n nll = np.sum((preds - actual*np.log(preds + 1e-07) + np.log(sp.special.factorial(actual))))\\n return nll\",\n \"def calc_mi(self, input_batch, graph_latent_vec=None, tree_latent_vec=None):\\n # [x_batch, nz]\\n if graph_latent_vec is None:\\n graph_latent_vec, tree_latent_vec = self.encode(*input_batch)\\n g_z_mean = self.g_mean(graph_latent_vec)\\n g_z_log_var = -torch.abs(self.g_var(graph_latent_vec))\\n t_z_mean = self.t_mean(tree_latent_vec)\\n t_z_log_var = -torch.abs(self.t_var(tree_latent_vec))\\n z_mean = torch.cat([g_z_mean, t_z_mean], dim=-1)\\n z_log_var = torch.cat([g_z_log_var, t_z_log_var], dim=-1)\\n\\n x_batch, nz = z_mean.size()\\n # E_{q(z|x)}log(q(z|x)) = -0.5*nz*log(2*\\\\pi) - 0.5*(1+logvar).sum(-1)\\n neg_entropy = (-0.5 * nz * np.log(2 * np.pi) - 0.5 * (1 + z_log_var).sum(-1)).mean()\\n # [z_batch, 1, nz]\\n z_samples = self.reparameterize(z_mean, z_log_var, nsamples=1)\\n # [1, x_batch, nz]\\n mu, logvar = z_mean.unsqueeze(0), z_log_var.unsqueeze(0)\\n var = logvar.exp()\\n # (z_batch, x_batch, nz)\\n dev = z_samples - mu\\n # (z_batch, x_batch)\\n log_density = -0.5 * ((dev ** 2) / var).sum(dim=-1) - \\\\\\n 0.5 * (nz * np.log(2 * np.pi) + logvar.sum(-1))\\n # log q(z): aggregate posterior\\n # [z_batch]\\n log_qz = log_sum_exp(log_density, dim=1) - np.log(x_batch)\\n return (neg_entropy - log_qz.mean(-1)).item()\",\n \"def f(z):\\n a=1./(1.+z)\\n #da=0.01\\n da=0.01*a\\n #da=1e-7\\n gp,g,gm=[D(1./ia-1.) for ia in [a+da,a,a-da]]\\n f=a*(gp-gm)/(2*g*da)\\n #dz=0.01\\n #gp,g,gm=[D(zi) for zi in [z+dz,z,z-dz]]\\n #f=(z)*(gp-gm)/(2.*g*dz)\\n return f\",\n \"def toeplitz_slogdet(r):\\n n = len(r)\\n r_0 = r[0]\\n \\n r = np.concatenate((r, np.array([r_0])))\\n r /= r_0 # normalize the system so that the T matrix has diagonal of ones\\n \\n logdet = n*np.log(np.abs(r_0))\\n sign = np.sign(r_0)**n\\n \\n if n == 1:\\n return (sign, logdet)\\n \\n # now on is a modification of Levinson algorithm\\n y = zeros((n,))\\n x = zeros((n,))\\n\\n b = -r[1:n+1] \\n r = r[:n]\\n \\n y[0] = -r[1]\\n x[0] = b[0]\\n beta = 1\\n alpha = -r[1]\\n \\n d = 1 + dot(-b[0], x[0])\\n sign *= np.sign(d)\\n logdet += np.log(np.abs(d))\\n \\n for k in range(0,n-2):\\n \\n beta = (1 - alpha*alpha)*beta\\n mu = (b[k+1] - dot(r[1:k+2], x[k::-1])) /beta\\n x[0:k+1] = x[0:k+1] + mu*y[k::-1]\\n x[k+1] = mu\\n \\n d = 1 + dot(-b[0:k+2], x[0:k+2])\\n sign *= np.sign(d)\\n logdet += np.log(np.abs(d))\\n \\n if k < n-2:\\n alpha = -(r[k+2] + dot(r[1:k+2], y[k::-1]))/beta\\n y[0:k+1] = y[0:k+1] + alpha * y[k::-1]\\n y[k+1] = alpha \\n\\n return(sign, logdet)\",\n \"def log1p(x):\\n return 0.0\",\n \"def get_log_lik(cov_evals, rank, shape):\\n\\n n_samples, n_features = shape\\n if not 1 <= rank <= n_features - 1:\\n raise ValueError(\\\"the tested rank should be in [1, n_features - 1]\\\")\\n\\n eps = 1e-15\\n\\n if cov_evals[rank - 1] < eps:\\n # When the tested rank is associated with a small eigenvalue, there's\\n # no point in computing the log-likelihood: it's going to be very\\n # small and won't be the max anyway. Also, it can lead to numerical\\n # issues below when computing pa, in particular in log((spectrum[i] -\\n # spectrum[j]) because this will take the log of something very small.\\n return -np.inf\\n\\n pu = -rank * log(2.)\\n for i in range(1, rank + 1):\\n pu += (gammaln((n_features - i + 1) / 2.) -\\n log(np.pi) * (n_features - i + 1) / 2.)\\n\\n pl = np.sum(np.log(cov_evals[:rank]))\\n pl = -pl * n_samples / 2.\\n\\n v = max(eps, np.sum(cov_evals[rank:]) / (n_features - rank))\\n pv = -np.log(v) * n_samples * (n_features - rank) / 2.\\n\\n m = n_features * rank - rank * (rank + 1.) / 2.\\n pp = log(2. * np.pi) * (m + rank) / 2.\\n\\n pa = 0.\\n spectrum_ = cov_evals.copy()\\n spectrum_[rank:n_features] = v\\n for i in range(rank):\\n for j in range(i + 1, len(cov_evals)):\\n pa += log((cov_evals[i] - cov_evals[j]) *\\n (1. / spectrum_[j] - 1. / spectrum_[i])) + log(n_samples)\\n\\n ll = pu + pl + pv + pp - pa / 2. - rank * log(n_samples) / 2.\\n\\n return ll\",\n \"def log_norm(log_x):\\n c = np.max(log_x)\\n\\n if np.isinf(c):\\n return c\\n\\n sum_exp = 0\\n\\n for x in log_x:\\n sum_exp += np.exp(x - c)\\n\\n log_sum_exp = np.log(sum_exp)\\n\\n log_Z = log_sum_exp + c\\n\\n return log_Z\",\n \"def logsumexp2(z):\\n return logsumexp(z, axis=2)\",\n \"def log2(x):\\n raise NotImplementedError\",\n \"def safelog(x):\\n #return np.log(x)\\n return np.log(np.clip(x,floor,np.inf))\",\n \"def normal_log_q_initial(self,z):\\n means, scale = self.get_means_and_scales_from_q()\\n return (ss.norm.logpdf(z,loc=means,scale=scale)).sum(axis=1)\",\n \"def log(self): # just use base?\\n return Factor().__build( VarSet(self.v) , np.log(self.t) )\",\n \"def mak2(n,par=None,D0=1e-2,k=.5,Fb=0):\\n if par is not None:\\n D0,k,Fb,slope=par\\n D=np.zeros(n)\\n D[0]=D0\\n for i in range(n-1):\\n D[i+1]=D[i]+k*np.log(1+D[i]/k)\\n F=D+Fb\\n E[1:]=k*np.log(1+D[:-1]/k)/D[:-1]\\n return F,D,E\",\n \"def log_abs_det_jacobian(self, z):\\n pre_u = self.u_ + self.u\\n pre_w = self.w_ + self.w\\n a = F.softplus(self.a + self.inv)\\n w = F.softmax(pre_w, dim=3)\\n u = F.softmax(pre_u, dim=3)\\n # Perform computation\\n pre_sigm = torch.sum(u * a * z, 3) + self.b\\n sigm = torch.sigmoid(pre_sigm)\\n x_pre = torch.sum(w * sigm, dim=3)\\n x_pre_clipped = x_pre * (1 - self.eps) + self.eps * 0.5\\n logj = F.log_softmax(pre_w, dim=3) + logsigmoid(pre_sigm) + logsigmoid(-pre_sigm) + torch.log(a)\\n # n, d, d2, dh\\n logj = logj + F.log_softmax(pre_u, dim=3)\\n # n, d, d2, dh, d1\\n logj = torch.log(torch.sum(torch.exp(logj),3))\\n # n, d, d2, d1\\n logdet_ = logj + np.log(1 - self.eps) - (torch.log(x_pre_clipped) + torch.log(-x_pre_clipped + 1))\\n return logdet_\",\n \"def zkl_divergence(x, y, gamma):\\n return np.sum([p_i*np.log(p_i/q_i) if q_i > 0 and p_i > 0 else p_i*gamma for (p_i, q_i) in zip(x, y)])\",\n \"def entropy(self):\\n Z = self.sum()\\n assert (Z > 0), 'Non-normalizable factor (perhaps log factor?)' # also check for positivity?\\n H = 0.0\\n for x in np.nditer(self.t, op_flags=['readonly']):\\n p = x/Z\\n H += 0.0 if p==0 else -p*np.log(p)\\n return H\",\n \"def kl(p, q):\\n p = np.asarray(p, dtype=float)\\n q = np.asarray(q, dtype=float)\\n\\n return np.where(p != 0, p * np.log(p / q), 0).sum()\",\n \"def compute_loglike(self, tools: ModelingTools) -> float:\\n return -3.0 * self.placeholder\",\n \"def log(base, real):\\n return math.log(real, base)\",\n \"def find_z(q):\\n z = np.zeros(len(q) - 1, int)\\n for i in range(len(q) - 1):\\n z[i] = np.ceil(q[i]/2 + q[i+1]/2) # formula from class\\n return np.concatenate([[0], z, [BITS-1]])\",\n \"def log_poisson(k, l):\\n return k*np.log(l) -l - gammaln(k+1)\",\n \"def safe_sum_p_log_p(a, base=None):\\r\\n flat = ravel(a)\\r\\n nz = take(flat, nonzero(flat)[0])\\r\\n logs = log(nz)\\r\\n if base:\\r\\n logs /= log(base)\\r\\n return sum(nz * logs, 0)\",\n \"def find_metallicity_distribution(redshifts, min_logZ_COMPAS, max_logZ_COMPAS,\\n mu0=0.035, muz=-0.23, sigma_0=0.39, sigma_z=0.0, alpha =0.0,\\n min_logZ =-12.0, max_logZ =0.0, step_logZ = 0.01): \\n ##################################\\n # Log-Linear redshift dependence of sigma\\n sigma = sigma_0* 10**(sigma_z*redshifts)\\n \\n ##################################\\n # Follow Langer & Norman 2006 in assuming that mean metallicities evolve in z as:\\n mean_metallicities = mu0 * 10**(muz * redshifts) \\n \\n # Now we re-write the expected value of the log-skew-normal to retrieve mu\\n beta = alpha/(np.sqrt(1 + (alpha)**2))\\n PHI = NormDist.cdf(beta * sigma) \\n mu_metallicities = np.log(mean_metallicities/2. * 1./(np.exp(0.5*sigma**2) * PHI ) ) \\n\\n ##################################\\n # create a range of metallicities (the x-values, or random variables)\\n log_metallicities = np.arange(min_logZ, max_logZ + step_logZ, step_logZ)\\n metallicities = np.exp(log_metallicities)\\n\\n\\n ##################################\\n # probabilities of log-skew-normal (without the factor of 1/Z since this is dp/dlogZ not dp/dZ)\\n dPdlogZ = 2./(sigma[:,np.newaxis]) * NormDist.pdf((log_metallicities - mu_metallicities[:,np.newaxis])/sigma[:,np.newaxis]) * NormDist.cdf(alpha * (log_metallicities - mu_metallicities[:,np.newaxis])/sigma[:,np.newaxis] )\\n\\n ##################################\\n # normalise the distribution over all metallicities; this choice of normalisation assumes that metallicities outside the COMPAS range have yields of zero\\n norm = dPdlogZ.sum(axis=-1) * step_logZ\\n dPdlogZ = dPdlogZ /norm[:,np.newaxis]\\n\\n ##################################\\n # assume a flat in log distribution in sampled metallicity to find probability of drawing Z in COMPAS\\n p_draw_metallicity = 1 / (max_logZ_COMPAS - min_logZ_COMPAS)\\n \\n return dPdlogZ, metallicities, p_draw_metallicity\",\n \"def compute_log_likehood_markov(markov_n, markov_table, start_i, seq):\\n ll = 0\\n for cur_i in range(start_i, len(seq)):\\n cur_mem = seq[cur_i]\\n past_mems = tuple(seq[cur_i-markov_n:cur_i])\\n prob = markov_table[past_mems][cur_mem]\\n # We shouldn't get prob = 0 because we get the markov table from the sequence themselves\\n ll += np.log(prob)\\n return ll\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65710264","0.6284432","0.61714786","0.6006404","0.59657145","0.59184784","0.5914684","0.5879645","0.58559495","0.5837734","0.5820538","0.5819314","0.57771146","0.57759076","0.57748866","0.57500154","0.57367206","0.56673867","0.56623465","0.5645668","0.5631098","0.5629977","0.56249344","0.5616786","0.5611232","0.55960584","0.55942136","0.5581019","0.5571306","0.5568434","0.5555853","0.5530221","0.5519178","0.55177075","0.5505236","0.55001366","0.5485298","0.54847133","0.54749817","0.54706985","0.5466124","0.54499","0.5449548","0.54475117","0.5445403","0.5439931","0.54354024","0.5435149","0.542799","0.54269314","0.5426642","0.54115903","0.5405169","0.5403385","0.54007244","0.5400368","0.53972316","0.53913134","0.5387972","0.5382282","0.5371846","0.53717947","0.53696644","0.5365044","0.5363579","0.53541464","0.534634","0.5345014","0.5339028","0.5335341","0.5325734","0.5303504","0.52963626","0.5296195","0.5295978","0.5295978","0.52954036","0.5295403","0.5289935","0.5285413","0.5282823","0.52775085","0.5257809","0.52569085","0.5252142","0.5241772","0.5240479","0.5240244","0.5238706","0.5237453","0.523625","0.52349615","0.5231648","0.5231506","0.52287024","0.5225842","0.5223261","0.5219407","0.5217569","0.5212435","0.5212341"],"string":"[\n \"0.65710264\",\n \"0.6284432\",\n \"0.61714786\",\n \"0.6006404\",\n \"0.59657145\",\n \"0.59184784\",\n \"0.5914684\",\n \"0.5879645\",\n \"0.58559495\",\n \"0.5837734\",\n \"0.5820538\",\n \"0.5819314\",\n \"0.57771146\",\n \"0.57759076\",\n \"0.57748866\",\n \"0.57500154\",\n \"0.57367206\",\n \"0.56673867\",\n \"0.56623465\",\n \"0.5645668\",\n \"0.5631098\",\n \"0.5629977\",\n \"0.56249344\",\n \"0.5616786\",\n \"0.5611232\",\n \"0.55960584\",\n \"0.55942136\",\n \"0.5581019\",\n \"0.5571306\",\n \"0.5568434\",\n \"0.5555853\",\n \"0.5530221\",\n \"0.5519178\",\n \"0.55177075\",\n \"0.5505236\",\n \"0.55001366\",\n \"0.5485298\",\n \"0.54847133\",\n \"0.54749817\",\n \"0.54706985\",\n \"0.5466124\",\n \"0.54499\",\n \"0.5449548\",\n \"0.54475117\",\n \"0.5445403\",\n \"0.5439931\",\n \"0.54354024\",\n \"0.5435149\",\n \"0.542799\",\n \"0.54269314\",\n \"0.5426642\",\n \"0.54115903\",\n \"0.5405169\",\n \"0.5403385\",\n \"0.54007244\",\n \"0.5400368\",\n \"0.53972316\",\n \"0.53913134\",\n \"0.5387972\",\n \"0.5382282\",\n \"0.5371846\",\n \"0.53717947\",\n \"0.53696644\",\n \"0.5365044\",\n \"0.5363579\",\n \"0.53541464\",\n \"0.534634\",\n \"0.5345014\",\n \"0.5339028\",\n \"0.5335341\",\n \"0.5325734\",\n \"0.5303504\",\n \"0.52963626\",\n \"0.5296195\",\n \"0.5295978\",\n \"0.5295978\",\n \"0.52954036\",\n \"0.5295403\",\n \"0.5289935\",\n \"0.5285413\",\n \"0.5282823\",\n \"0.52775085\",\n \"0.5257809\",\n \"0.52569085\",\n \"0.5252142\",\n \"0.5241772\",\n \"0.5240479\",\n \"0.5240244\",\n \"0.5238706\",\n \"0.5237453\",\n \"0.523625\",\n \"0.52349615\",\n \"0.5231648\",\n \"0.5231506\",\n \"0.52287024\",\n \"0.5225842\",\n \"0.5223261\",\n \"0.5219407\",\n \"0.5217569\",\n \"0.5212435\",\n \"0.5212341\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":11191,"cells":{"query":{"kind":"string","value":"Finite Difference gradient computation for logZ"},"document":{"kind":"string","value":"def fd_grad(self, T, pos, psi, phi, eps=1e-5):\n dpsi = tr.zeros_like(psi)\n dphi = tr.zeros_like(phi)\n for pos1 in range(psi.shape[0]):\n for pos2 in range(psi.shape[1]):\n for lab in range(psi.shape[2]):\n for i in range(psi.shape[3]):\n for j in range(psi.shape[4]):\n psi[pos1, pos2, lab, i, j] += eps\n val1 = self.logZ(T, pos, psi, phi)\n psi[pos1, pos2, lab, i, j] -= 2 * eps\n val2 = self.logZ(T, pos, psi, phi)\n psi[pos1, pos2, lab, i, j] += eps\n dpsi[pos1, pos2, lab, i, j] = (val1 - val2) / (2 * eps)\n for p in range(phi.shape[0]):\n for i in range(phi.shape[1]):\n phi[p, i] += eps\n val1 = self.logZ(T, pos, psi, phi)\n phi[p, i] -= 2 * eps\n val2 = self.logZ(T, pos, psi, phi)\n phi[p, i] += eps\n dphi[p, i] = (val1 - val2) / (2 * eps)\n return dpsi, dphi"},"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 logistic_grad(z):\n idx_pos = np.where(z >= 0.)\n idx_neg = np.where(z < 0.)\n res = np.empty(z.shape)\n res[idx_pos] = 1. / (1. + np.exp(-z[idx_pos]))\n res[idx_neg] = 1 - 1. / (1. + np.exp(z[idx_neg]))\n return res","def logistic_grad(z):\n idx_pos = np.where(z >= 0.)\n idx_neg = np.where(z < 0.)\n res = np.empty(z.shape)\n res[idx_pos] = 1. / (1. + np.exp(-z[idx_pos]))\n res[idx_neg] = 1 - 1. / (1. + np.exp(z[idx_neg]))\n return res","def dlogZ(self, T, pos, psi, phi):\n msgs = belief_propagation(T, pos, psi, phi, True)\n dpsi = calculate_gradient(msgs, T, pos, psi, True, True)\n return dpsi","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 _finite_diff_gradient(self, parameters):\n assert isinstance(parameters,np.ndarray)\n # get the free indicies\n free_inds = np.nonzero(np.logical_not(self._fixed_indicies))[0]\n\n # first take a forward step in each direction\n step = 1e-6 # finite difference step\n log_like_fs = np.zeros(free_inds.size)\n for i,param_idx in enumerate(free_inds):\n p_fs = parameters.copy()\n p_fs[param_idx] += step # take a step forward\n log_like_fs[i] = self._compute_log_likelihood(p_fs)\n\n # compute the log likelihood at current point\n log_like = self._compute_log_likelihood(parameters)\n\n # compute the gradient\n gradient = np.zeros(parameters.shape) # default gradient is zero\n gradient[free_inds] = (log_like_fs-log_like)\n gradient[free_inds] = gradient[free_inds]/step # divide by step length\n return log_like, gradient","def sigmoid_gradient(z):\n #derivative of sigmoid\n return z * (1 - z)","def _log_likelihood_gradient(self, z, K, H, B, Kinv):\n\n nparams = 4\n grad = np.zeros((nparams,))\n\n #t0 = time.time()\n tmp = np.dot(self.invc, self.HKinv)\n #t1 = time.time()\n K_HBH_inv = Kinv - np.dot(tmp.T, tmp)\n #t2 = time.time()\n alpha_z = np.dot(K_HBH_inv, z)\n #t3 = time.time()\n\n #print \"gradient: %f %f %f\" % (t1-t0, t2-t1, t3-t2)\n\n for i in range(nparams):\n tA = time.time()\n if (i == 0):\n dKdi = np.eye(self.n)\n else:\n dKdi = self.predict_tree.kernel_deriv_wrt_i(self.X, self.X, \"se\", self.wfn_params, i-1)\n\n dlldi = .5 * np.dot(alpha_z.T, np.dot(dKdi, alpha_z))\n tB = time.time()\n # here we use the fact:\n # trace(AB) = sum_{ij} A_ij * B_ij\n dlldi -= .5 * np.sum(np.sum(K_HBH_inv.T * dKdi))\n\n grad[i] = dlldi\n tC = time.time()\n print \" %d: %f %f\" % (i, tB-tA, tC-tB)\n\n return grad","def log_prior_grad(self, inputs):","def cv_gradient_initial(self,z): \n z_t = np.transpose(z)\n log_q = self.normal_log_q_initial(z_t)\n log_p = self.log_p(z_t)\n grad_log_q = self.grad_log_q(z)\n gradient = grad_log_q*np.repeat((log_p - log_q).T,2,axis=0)\n\n alpha0 = alpha_recursion(np.zeros(np.sum(self.approx_param_no)), grad_log_q, gradient, np.sum(self.approx_param_no))\n\n vectorized = gradient - ((alpha0/np.var(grad_log_q,axis=1))*grad_log_q.T).T\n\n return np.mean(vectorized,axis=1)","def cv_gradient(self, z): \n z_t = np.transpose(z)\n log_q = self.normal_log_q(z_t)\n log_p = self.log_p(z_t)\n grad_log_q = self.grad_log_q(z)\n gradient = grad_log_q*np.repeat((log_p - log_q).T,2,axis=0)\n\n alpha0 = alpha_recursion(np.zeros(np.sum(self.approx_param_no)), grad_log_q, gradient, np.sum(self.approx_param_no)) \n \n vectorized = gradient - ((alpha0/np.var(grad_log_q,axis=1))*grad_log_q.T).T\n\n return np.mean(vectorized,axis=1)","def log_likelihood_gradients_(self, y, f):\n log_lik = self.evaluate_log_likelihood(y, f)\n f = np.squeeze(f)\n J = jacrev(self.evaluate_log_likelihood, argnums=1)\n H = jacrev(J, argnums=1)\n return log_lik, J(y, f), H(y, f)","def logp_grad(self, xs, ys, fs, **kwargs):","def sigmoid_grad(z):\n return Sigmoid(z) * (1 - Sigmoid(z))","def _log_likelihood_gradients(self):\r\n return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))","def cv_gradient(self,z):\n gradient = np.zeros(np.sum(self.approx_param_no))\n z_t = z.T \n log_q = self.normal_log_q(z.T)\n log_p = self.log_p(z.T)\n grad_log_q = self.grad_log_q(z)\n gradient = grad_log_q*(log_p-log_q)\n\n alpha0 = alpha_recursion(np.zeros(np.sum(self.approx_param_no)), grad_log_q, gradient, np.sum(self.approx_param_no)) \n\n vectorized = gradient - ((alpha0/np.var(grad_log_q,axis=1))*grad_log_q.T).T\n\n return np.mean(vectorized,axis=1)","def cv_gradient_initial(self,z):\n gradient = np.zeros(np.sum(self.approx_param_no))\n z_t = z.T \n log_q = self.normal_log_q_initial(z.T)\n log_p = self.log_p(z.T)\n grad_log_q = self.grad_log_q(z)\n gradient = grad_log_q*(log_p-log_q)\n\n alpha0 = alpha_recursion(np.zeros(np.sum(self.approx_param_no)), grad_log_q, gradient, np.sum(self.approx_param_no))\n\n vectorized = gradient - ((alpha0/np.var(grad_log_q,axis=1))*grad_log_q.T).T\n\n return np.mean(vectorized,axis=1)","def _log_likelihood_gradients(self):\n return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK,X=self.X,slices1=self.Xslices,slices2=self.Xslices), self.likelihood._gradients(partial=np.diag(self.dL_dK))))","def relu_grad(z):\n index = z >= 0\n result = np.zeros(z.shape)\n result[index] = 1.0\n return result","def grad_log(self, X):\n g = autograd.elementwise_grad(self.log_den)\n G = g(X)\n return G","def numerical_gradient(f, x: np.ndarray):\n h = 1e-4\n grad = np.zeros_like(x)\n for i in range(x.size):\n tmp_val = x.flat[i]\n x.flat[i] = tmp_val + h\n fxh1 = f(x)\n\n x.flat[i] = tmp_val - h\n fxh2 = f(x)\n grad.flat[i] = (fxh1 - fxh2) / (2 * h)\n x.flat[i] = tmp_val\n return grad","def compute_gradients(self, logits, target):\n\n target_length = target.shape[0]\n num_time_steps = logits.shape[0]\n\n\t\t######################\n\t\t### YOUR CODE HERE ###\n\t\t######################\n\n # expand labels by inserting a blank between each pair\n normalized_logits = softmax(logits)\n blank_label = normalized_logits.shape[1] - 1\n l = add_blanks(target, blank_label)\n target_length = l.shape[0]\n\n alpha = self.compute_forward_variables(normalized_logits, target) \n beta = self.compute_backward_variables(normalized_logits, target)\n\n # rescale\n alpha = alpha / np.sum(alpha, axis=0)\n beta = beta / np.sum(beta, axis=0)\n alphabeta = alpha * beta\n print \"alpha\"\n print alpha\n\n # compute zt\n z = Counter()\n for t in xrange(num_time_steps):\n for s, k in enumerate(l):\n z[t] += alphabeta[s, t] / normalized_logits[t, k]\n \n # normalized_logits is time steps t by labels k\n # alpha is 2 * target_length - 1 by time steps\n lab_zk = np.zeros_like(normalized_logits)\n for s, k in enumerate(l):\n for t in xrange(num_time_steps):\n lab_zk[t, k] += alphabeta[s, t]\n\n grad = normalized_logits\n for k in xrange(target.shape[0]):\n for t in xrange(num_time_steps):\n ytk = normalized_logits[t, k]\n constant = 1.0 / (ytk * z[t])\n grad[t, k] = ytk - constant * lab_zk[t, k]\n \n return grad","def logistic_function(z):\n\tg = 1/(1+np.exp(-z))\n\t#g = np.exp(z)/(1+np.exp(z))\n\treturn g","def log_likelihood_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w))-y)","def _forward_log_det_jacobian(self, x):\n d = self._compute_shared(x=x)\n relx = (x - d.x_k) / d.w_k\n relx = relx # tf.where(d.out_of_bounds, 0.5*tf.ones_like(x), relx)\n grad = (\n 2 * tf.math.log(d.s_k) +\n tf.math.log(d.d_kp1 * relx**2 + 2 * d.s_k * relx * (1 - relx) + # newln\n d.d_k * (1 - relx)**2) -\n 2 * tf.math.log((d.d_kp1 + d.d_k - 2 * d.s_k) * relx *\n (1 - relx) + d.s_k))\n return grad # tf.where(d.out_of_bounds, tf.zeros_like(grad), grad)","def MvNormalLogp():\n cov = pt.matrix(\"cov\")\n cov.tag.test_value = floatX(np.eye(3))\n delta = pt.matrix(\"delta\")\n delta.tag.test_value = floatX(np.zeros((2, 3)))\n\n cholesky = Cholesky(lower=True, on_error=\"nan\")\n\n n, k = delta.shape\n n, k = f(n), f(k)\n chol_cov = cholesky(cov)\n diag = pt.diag(chol_cov)\n ok = pt.all(diag > 0)\n\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\n delta_trans = solve_lower(chol_cov, delta.T).T\n\n result = n * k * pt.log(f(2) * np.pi)\n result += f(2) * n * pt.sum(pt.log(diag))\n result += (delta_trans ** f(2)).sum()\n result = f(-0.5) * result\n logp = pt.switch(ok, result, -np.inf)\n\n def dlogp(inputs, gradients):\n (g_logp,) = gradients\n cov, delta = inputs\n\n g_logp.tag.test_value = floatX(1.0)\n n, k = delta.shape\n\n chol_cov = cholesky(cov)\n diag = pt.diag(chol_cov)\n ok = pt.all(diag > 0)\n\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\n delta_trans = solve_lower(chol_cov, delta.T).T\n\n inner = n * pt.eye(k) - pt.dot(delta_trans.T, delta_trans)\n g_cov = solve_upper(chol_cov.T, inner)\n g_cov = solve_upper(chol_cov.T, g_cov.T)\n\n tau_delta = solve_upper(chol_cov.T, delta_trans.T)\n g_delta = tau_delta.T\n\n g_cov = pt.switch(ok, g_cov, -np.nan)\n g_delta = pt.switch(ok, g_delta, -np.nan)\n\n return [-0.5 * g_cov * g_logp, -g_delta * g_logp]\n\n return OpFromGraph([cov, delta], [logp], grad_overrides=dlogp, inline=True)","def log_likelihood_gradients(self, y, f):\n # align shapes and compute mask\n y = y.reshape(-1, 1)\n f = f.reshape(-1, 1)\n mask = np.isnan(y)\n y = np.where(mask, f, y)\n\n # compute gradients of the log likelihood\n log_lik, J, H = vmap(self.log_likelihood_gradients_)(y, f)\n\n # apply mask\n mask = np.squeeze(mask)\n log_lik = np.where(mask, 0., log_lik)\n J = np.where(mask, np.nan, J)\n H = np.where(mask, np.nan, H)\n\n return log_lik, J, np.diag(H)","def compute_logistic_gradient(y, tx, w):\n return tx.T.dot(sigmoid(tx.dot(w)) - y) / len(y)","def grad_llh(self, params):\n grad = np.clip(self.grad_log_likelihood(params[0], params[1], params[2:]), SMALLEST_NUMBER,\n LARGEST_NUMBER)\n\n return grad","def get_gradient_minus_log_joint(z, mean, Sigma):\n data_minus_mean = z[np.newaxis,:] - mean# 1 x 2\n Sigma_inv = np.linalg.inv(Sigma) # 2 x 2\n gradient = - np.matmul(Sigma_inv, data_minus_mean.T) #2 x 1\n gradient = gradient[0]\n return gradient","def compute_gradient(self): # TODO: try to change to square loss since it's hessian is easier to obtain\n A = np.dot(self.X, self.w)\n m = self.t.shape[0]\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\n return (1 / m) * np.dot(self.X.T, C)","def compute_gradient(self):\n A = np.dot(self.X, self.w)\n m = self.t.shape[0]\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\n return (1 / m) * np.dot(self.X.T, C)","def _log_prior_gradients(self):\n x = self._get_params()\n ret = np.zeros(x.size)\n [np.put(ret,i,p.lnpdf_grad(xx)) for i,(p,xx) in enumerate(zip(self.priors,x)) if not p is None]\n return ret","def gradient(self, theta):\n a = -(6 * self.scale ** 2)\n b = 3 * self.scale ** 2 + np.exp(2 * theta)\n b *= np.log(3 * self.scale ** 2 * np.exp(-2 * theta) + 1)\n return a / b","def _core_calc_degrad(self,bd,Ld) :\n\t\tdegrad = np.dot(Ld,bd) # Do matrix multiplication \n\t\tdegrad = np.exp(degrad) # Exponentiate to convert log to real\n\t\treturn degrad","def gradient_ascent(f, df, theta_init, step_size, max_iter):\n\n fs = []\n xs = []\n thetas = theta_init\n for i in range(max_iter): #for each data example\n fs.append(f(thetas))\n\n temp = step_size*df(thetas)\n thetas = step_size*df(thetas) #modify that feature by using the derivative of log likelihood\n xs.append(thetas.flatten())\n if i % 10 == 0:\n print(i, thetas)\n\n return thetas, fs, xs","def logit_cost_grad(self, theta, X, y):\n\n grad = np.zeros(len(theta))\n\n ### YOUR CODE HERE\n sig = utils.sigmoid(theta)\n # sig = np.subtract(sig, y)\n sig = sig - y\n grad = np.dot(X.T, sig) + 2 * self.params['lamb'] * self.regularizer[1](self.weights)\n ### END YOUR CODE\n\n return grad","def grad_log_q(self,z): \n param_count = 0\n grad = np.zeros((np.sum(self.approx_param_no),self.sims))\n for core_param in range(len(self.q)):\n for approx_param in range(self.q[core_param].param_no):\n grad[param_count] = self.q[core_param].vi_score(z[core_param],approx_param) \n param_count += 1\n return grad","def from_grad_log(d, g):\n return UDFromCallable(d, fgrad_log=g)","def gradient(cls, x):\n return 1 - TanH.apply(x) ** 2","def log_deriv(error):\n return logistic(error) * (1 - logistic(error))","def calculate_maxL_gradient(y, tx, w):\n sig=sigmoid(tx.dot(w))\n grad=tx.T.dot(sig-y)\n #divides with number of samples so that learning rate is not dependant on number of samples\n grad=grad/len(y) \n return grad","def f(z):\n a=1./(1.+z)\n #da=0.01\n da=0.01*a\n #da=1e-7\n gp,g,gm=[D(1./ia-1.) for ia in [a+da,a,a-da]]\n f=a*(gp-gm)/(2*g*da)\n #dz=0.01\n #gp,g,gm=[D(zi) for zi in [z+dz,z,z-dz]]\n #f=(z)*(gp-gm)/(2.*g*dz)\n return f","def natural_gradient(params):\n\n natural_grad = np.zeros(6)\n\n F = np.zeros((6,6))\n shift = np.eye(len(params))\n gradient = np.zeros([6])\n\n for i in range(len(F)):\n for j in range(len(F[i])):\n shifted = params.copy() + (np.pi/2)*(shift[i] + shift[j])\n f0 = measurement(params, shifted)\n shifted = params.copy() + (np.pi/2)*(shift[i] - shift[j])\n f1 = measurement(params, shifted)\n shifted = params.copy() + (np.pi/2)*(-shift[i] + shift[j])\n f2 = measurement(params, shifted)\n shifted = params.copy() - (np.pi/2)*(shift[i] + shift[j])\n f3 = measurement(params, shifted)\n F[i][j] = float((- f0 + f1 + f2 - f3)/8)\n\n\n for i in range(len(gradient)):\n gradient[i] = parameter_shift_term(qnode, params, i)\n\n F_inv = np.linalg.inv(F)\n QNG = np.dot(F_inv,gradient.T)\n natural_grad = QNG.T\n\n return natural_grad","def _log_prior_gradients(self):\r\n if self.priors is None:\r\n return 0.\r\n x = self._get_params()\r\n ret = np.zeros(x.size)\r\n [np.put(ret, i, p.lnpdf_grad(xx)) for i, (p, xx) in enumerate(zip(self.priors, x)) if not p is None]\r\n return ret","def logZ(self, T, pos, psi, phi):\n msgs = belief_propagation(T, pos, psi, phi, True)\n log_z = calculate_belief_sum(msgs, True)\n return log_z","def grad_log_likelihood(self, log_occr_array=None):\n\n if log_occr_array is not None:\n # Catch invalid occurrence rates for zero likelihood\n try:\n self.log_occr = log_occr_array\n except InvalidOccurrenceRate:\n return -np.inf * np.ones_like(self.occr, dtype=float)\n\n # Calculate components first\n N_exp = self.calc_integral() * self._N_stars # perhaps not needed\n nexp_terms = self._N_stars * self.calc_bin_volumes() * self.H_bar_array\n s_terms = self.H_array * self.F_array * self.occr\n numerator_terms = self.H_array * self.F_array\n\n if not tf.is_tensor(self.occr):\n # Checking shapes of intermediate terms,\n # numerator_terms vs s_terms.sum(-1, -2) and vs v factors.\n intermediate_terms = numerator_terms / s_terms.sum(axis=(-1, -2))\n # TODO: v_factor changed to negative, I think a minus\n # sign had been missed\n grad_log_array = - nexp_terms + intermediate_terms.sum(axis=0)\n\n # BUG TODO\n if np.isnan(grad_log_array).any():\n warnings.warn(\".grad_log_likelihood value is nan.\")\n import pdb; pdb.set_trace()\n grad_log_array = -np.inf * grad_log_array\n else:\n raise NotImplementedError(\"Manual gradient calculate with \"\n \"tensorflow objects isn't \"\n \"implemented, and seems a bit \"\n \"redundant.\")\n\n return grad_log_array","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 log_distr_fdmw(self, dnu, logflux, dme, logw, alpha, logls, logl0, mu, sigma, gtype=None):\n #stepdms = 100/1000.\n #vdms = np.arange(0, 100, stepdm)\n stepz = (np.log(self.Zmax) - np.log(self.Zmin)) / 1000\n vz = np.exp(np.arange(np.log(self.Zmin), np.log(self.Zmax), stepz))\n lik = 0\n for z in vz:\n likv = np.exp(self.log_distr_fdmwz(dnu, logflux, dme, logw, z, alpha, logls, logl0, mu, sigma, gtype=gtype))\n lik += z * stepz * likv\n ind = lik > 0\n ind2 = lik <= 0\n loglik = lik.copy()\n loglik[ind] = np.log(lik[ind])\n loglik[ind2] = np.ones(loglik[ind2].shape) * -1e99\n return loglik","def grad_neg_log_like(params):\n gp.set_parameter_vector(params)\n return -gp.grad_log_likelihood(y, quiet=True)","def _log_prior_gradients(self):\n if self.priors.size == 0:\n return 0.\n x = self.param_array\n ret = np.zeros(x.size)\n #compute derivate of prior density\n [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]\n #add in jacobian derivatives if transformed\n priored_indexes = np.hstack([i for p, i in self.priors.items()])\n for c,j in self.constraints.items():\n if not isinstance(c, Transformation):continue\n for jj in j:\n if jj in priored_indexes:\n ret[jj] += c.log_jacobian_grad(x[jj])\n return ret","def gradient_descent(x0,df,rate=0.1,max_iters=1000,min_step=1e-6,max_step=1e5,\n projection=None,trajectory=False,step_history=False,f=None,\n cost_history=False,feedback=False,plot_history=False):\n if feedback is True:\n print(\"gd.gradient_descent():\")\n if f is not None:\n assert callable(f)\n fx0 = f(x0)\n if feedback is True:\n print(f\" initial cost = {fx0:.2e}\")\n if projection is not None:\n assert callable(projection)\n project = True\n else:\n project = False\n if trajectory is True:\n xx = [x0.copy()]\n if step_history is True:\n steps = []\n if cost_history is True:\n assert callable(f)\n fx = [fx0]\n\n x = x0.copy()\n for i in range(max_iters):\n dx = -rate*df(x)\n if project is True:\n x0 = x.copy()\n x = projection(x0+dx)\n dx = x-x0\n else:\n x += dx\n if trajectory is True:\n xx.append(x.copy())\n if cost_history is True:\n fx += [f(x)]\n step_size = np.linalg.norm(dx)\n if step_history is True:\n steps += [step_size]\n if step_size < min_step or step_size > max_step:\n break\n\n results = dict()\n results['output'] = x\n if trajectory is True:\n results['trajectory'] = xx\n if cost_history is True:\n results['cost_history'] = fx\n if step_history is True:\n results['step_history'] = steps\n if plot_history is True:\n assert step_history is True or cost_history is True\n plt.figure()\n if step_history is True:\n plt.semilogy(steps,label='step size')\n if cost_history is True:\n plt.semilogy(fx,label='cost')\n plt.xlabel('iteration number')\n plt.title('Gradient Descent')\n plt.legend()\n results['figure'] = plt\n plt.show(block=False)\n \n if feedback is True:\n if f is not None:\n print(f\" final cost = {f(x):.2e}\")\n \n return results","def g(z):\n return 1. / (1. + np.exp(-z))","def gradient(f,h,X):\n # X = list(X)# flip to a list from tuple so we can modify elements\n fx = f(*X) # only need this once\n dX = []\n for i in range(len(X)):\n # Make a vector of Value(X_i, [0 ... 1 ... 0]) with 1 in ith position\n X[i] += h # tweak in dimension i\n y = f(*X)\n X[i] -=h # undo the tweak for next round\n dx = (y - fx)/h\n dX.append(dx)\n return dX","def gradient(self):\n result = np.zeros(len(self.variables))\n result[self.bivariateGradInd] = (self.shape-1)/self.variable - self.rate\n return result","def statePosteriors(log_alpha, log_beta):\n return log_alpha + log_beta - logsumexp(log_alpha[-1,:])","def grad(x):\n\n return x - np.arange(1, cfg.n + 1, dtype=np.float_)","def gradient(self, x):\n return 0.0","def compute_gradient_logreg(y, tx, w):\n assert len(set(y).difference({0., 1.})) == 0, \"Class labels must be encoded as {0, 1}\"\n\n s = sigmoid(tx.dot(w)) - y\n grad = tx.T.dot(s)\n\n return grad","def statePosteriors(log_alpha, log_beta):\n\n gamma = np.zeros(log_alpha.shape)\n\n gamma = log_alpha + log_beta\n\n gamma = gamma - logsumexp(log_alpha[-1,:])\n\n return gamma","def compute_grad_likelihood(sensor_loc, obj_loc, distance):\n\tgrad = np.zeros(sensor_loc.shape)\n\t# Your code: finish the grad loglike\n\t\t\n\treturn grad","def gradient(self, x_in):\r\n\r\n return 1 - torch.pow(self.forward(x_in), 2)","def backward_energy(im):\n\n xgrad = ndi.convolve1d(im, np.array([1, 0, -1]), axis=1, mode='wrap')\n ygrad = ndi.convolve1d(im, np.array([1, 0, -1]), axis=0, mode='wrap')\n\n grad_mag = np.sqrt(np.sum(xgrad ** 2, axis=2) + np.sum(ygrad ** 2, axis=2))\n\n return grad_mag","def extrapolate_fwds(h, ufr, llfr, alpha=0.10):\n fwd_fsp_fsp_plus_h = np.log(1 + ufr) + (llfr - np.log(1 + ufr)) * big_b(h, alpha)\n return fwd_fsp_fsp_plus_h","def gradient(self, x):\n pass","def softmax_derivative(Z):\n\treturn None","def log_pdf_derivative(x):\n return gs.autodiff.jacobian(log_pdf_at_x(x))(base_point)","def derivative_activation(z):\n return activation(z) * (1 - activation(z))","def log_zgrid(zr=[0.7,3.4], dz=0.01):\n zgrid = np.exp(np.arange(np.log(1+zr[0]), np.log(1+zr[1]), dz))-1\n return zgrid","def Gradient(Walker,particle):\n\n h=0.001\n dPsi = zeros(shape=shape(Walker.Re[particle]))\n for i in range(Walker.sys_dim):\n Y=Walker.Re[particle][i]\n Walker.Re[particle][i]-=h\n wfs1=wfs(Walker)\n Walker.Re[particle][i]+=2.0*h\n wfs2=wfs(Walker)\n dPsi[i] = (wfs2-wfs1)/2/h\n Walker.Re[particle][i]=Y\n\n return dPsi","def sigmoid_derivative(z):\n s = 1./(1. + np.exp(-z))\n ds = s * (1 - s)\n return ds","def gradient(f, *varargs):\n N = len(f.shape) # number of dimensions\n n = len(varargs)\n if n == 0:\n dx = [1.0]*N\n elif n == 1:\n dx = [varargs[0]]*N\n elif n == N:\n dx = list(varargs)\n else:\n raise SyntaxError, \"invalid number of arguments\"\n\n # use central differences on interior and first differences on endpoints\n\n outvals = []\n\n # create slice objects --- initially all are [:, :, ..., :]\n slice1 = [slice(None)]*N\n slice2 = [slice(None)]*N\n slice3 = [slice(None)]*N\n\n otype = f.dtype.char\n if otype not in ['f', 'd', 'F', 'D']:\n otype = 'd'\n\n for axis in range(N):\n # select out appropriate parts for this dimension\n out = zeros(f.shape, f.dtype.char)\n slice1[axis] = slice(1, -1)\n slice2[axis] = slice(2, None)\n slice3[axis] = slice(None, -2)\n # 1D equivalent -- out[1:-1] = (f[2:] - f[:-2])/2.0\n out[slice1] = (f[slice2] - f[slice3])/2.0\n slice1[axis] = 0\n slice2[axis] = 1\n slice3[axis] = 0\n # 1D equivalent -- out[0] = (f[1] - f[0])\n out[slice1] = (f[slice2] - f[slice3])\n slice1[axis] = -1\n slice2[axis] = -1\n slice3[axis] = -2\n # 1D equivalent -- out[-1] = (f[-1] - f[-2])\n out[slice1] = (f[slice2] - f[slice3])\n\n # divide by step size\n outvals.append(out / dx[axis])\n\n # reset the slice object in this dimension to \":\"\n slice1[axis] = slice(None)\n slice2[axis] = slice(None)\n slice3[axis] = slice(None)\n\n if N == 1:\n return outvals[0]\n else:\n return outvals","def dalf(x):\n return grad(alf)(x)","def compute_gradient(self, function, arguments):","def linear_backward(dZ, cache):\n m = dZ.shape[1]\n dW = np.dot(dZ, cache[0].transpose())/m\n db = np.sum(dZ, axis=1, keepdims=True)/m\n dA_prev = np.dot(cache[1].transpose(), dZ)\n return dA_prev, dW, db","def calculate_gradient(y, tx, w): \n return tx.T@(sigmoid(tx@w)-y)","def logistic_derivative(errors):\n return [log_deriv(error) for error in errors]","def gradient_nD(stack):\n # Convert for 64-bit to avoid large number problems in squares.\n stack = np.copy(stack)\n stack = stack.astype(np.float64)\n sumsq = ndi.filters.sobel(stack, axis=0) ** 2\n for d in range(1, stack.ndim):\n sumsq = sumsq + (ndi.filters.sobel(stack, axis=d) ** 2)\n gradient = np.sqrt(sumsq)\n return gradient","def linear_backward(dZ, cache):\n A_prev, W, b = cache\n m = A_prev.shape[1]\n\n dW = np.dot(dZ,A_prev.T) / m\n db = np.sum(dZ,axis = 1,keepdims = True) / m\n dA_prev = np.dot(W.T,dZ)\n\n return dA_prev,dW,db","def log_abs_det_jacobian(self, z):\n pre_u = self.u_ + self.u\n pre_w = self.w_ + self.w\n a = F.softplus(self.a + self.inv)\n w = F.softmax(pre_w, dim=3)\n u = F.softmax(pre_u, dim=3)\n # Perform computation\n pre_sigm = torch.sum(u * a * z, 3) + self.b\n sigm = torch.sigmoid(pre_sigm)\n x_pre = torch.sum(w * sigm, dim=3)\n x_pre_clipped = x_pre * (1 - self.eps) + self.eps * 0.5\n logj = F.log_softmax(pre_w, dim=3) + logsigmoid(pre_sigm) + logsigmoid(-pre_sigm) + torch.log(a)\n # n, d, d2, dh\n logj = logj + F.log_softmax(pre_u, dim=3)\n # n, d, d2, dh, d1\n logj = torch.log(torch.sum(torch.exp(logj),3))\n # n, d, d2, d1\n logdet_ = logj + np.log(1 - self.eps) - (torch.log(x_pre_clipped) + torch.log(-x_pre_clipped + 1))\n return logdet_","def compute_grad(beta, lambdat, X, y):\n return -2/len(y)*(np.maximum(0, 1-(\n (y[:, np.newaxis]*X).dot(beta)))).dot(\n y[:, np.newaxis]*X) + 2 * lambdat * beta","def relu(z: float) -> float:\n return z if z > 0 else 0.01 * z","def newton(flogl, start, fgrad, fhess, maxiter):\r\n warn = 0\r\n iteration = 0\r\n par_hat0 = start\r\n m = 1\r\n while (iteration < maxiter and m >= 1e-04):\r\n H = -la.inv(fhess(par_hat0))\r\n g = fgrad(par_hat0).reshape(start.shape)\r\n Hg = np.dot(H, g)\r\n par_hat0 = par_hat0 + Hg\r\n iteration += 1\r\n m = np.dot(g.T, Hg)\r\n if iteration == maxiter:\r\n warn = 1\r\n logl = flogl(par_hat0)\r\n return (par_hat0, logl, warn)","def ComputeLogGradientTerm(self, *args):\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF2F_ComputeLogGradientTerm(self, *args)","def log_likelihood_z_lognormal(self, std=1.0):\n #return self.log_det_Jxz - self.dim * tf.log(std) - (0.5 / (std**2)) * tf.reduce_sum(self.output_z**2, axis=1)\n from deep_boltzmann.util import logreg\n logz = logreg(self.output_z, a=0.001, tf=True)\n ll = self.log_det_Jxz \\\n - (0.5 / (std**2)) * tf.reduce_sum(logz**2, axis=1) \\\n - tf.reduce_sum(logz, axis=1)\n return ll","def backward_p(self, x):\n log_det_jacob, z = x.new_zeros(x.shape[0]), x\n for i in reversed(range(len(self.t))):\n z_ = self.mask[i] * z\n s = self.s[i](z_) * (1 - self.mask[i])\n t = self.t[i](z_) * (1 - self.mask[i])\n z = (1 - self.mask[i]) * (z - t) * torch.exp(-s) + z_\n log_det_jacob -= s.sum(dim=1)\n return z, log_det_jacob","def log_distr_fdmwz(self, dnu, logflux, dme, logw, z, alpha, logls, logl0, mu, sigma, gtype=None):\n flux = np.power(10., logflux)\n logl = np.log10(self.cos.Luminosity(z, f=flux, dnu=dnu))\n logint1 = self.log_IntBeam(logl, alpha, logls, logl0)\n #print logint1\n logw0 = logw - np.log10(1+z)\n logfw = self.log_dis_logw(logw0, mu, sigma)\n logfz = self.log_Distribution_volume(z)\n dmi = self.cos.DispersionMeasure_IGM(z)\n u1 = (dme-dmi)*(1+z)*self.kappa(z)\n u2 = ((dme-dmi)*(1+z)-self.DMsmax)*self.kappa(z)\n #print u2\n logint2 = np.ones(u2.shape) * (-1e99)\n ind = u2 > 0\n logint2[ind] = self.log_IntDMsrc(u1[ind],u2[ind],gtype=gtype)\n #print logint2\n loglikv = logint1 + logfz + logfw + logint2 + np.log(1+z)\n return loglikv","def gradient(self,i,f):\n\n diff = self.points[f, :] - self.points[i, :]\n gradient = diff[1]/diff[0]\n\n return gradient","def gradient(self):\n functional = self\n\n class KLCrossEntropyGradient(Operator):\n\n \"\"\"The gradient operator of this functional.\"\"\"\n\n def __init__(self):\n \"\"\"Initialize a new instance.\"\"\"\n super().__init__(functional.domain, functional.domain,\n linear=False)\n\n def _call(self, x):\n \"\"\"Apply the gradient operator to the given point.\n\n The gradient is not defined in for points with components less\n than or equal to zero.\n \"\"\"\n if functional.prior is None:\n tmp = np.log(x)\n else:\n tmp = np.log(x / functional.prior)\n\n if np.all(np.isfinite(tmp)):\n return tmp\n else:\n # The derivative is not defined.\n raise ValueError('The gradient of the Kullback-Leibler '\n 'Cross Entropy functional is not defined '\n 'for `x` with one or more components '\n 'less than or equal to zero.'.format(x))\n\n return KLCrossEntropyGradient()","def backward(log_emlik, log_startprob, log_transmat):\n #print(log_transmat)\n beta = np.zeros(log_emlik.shape)\n n = len(log_emlik)-2\n log_transmat = log_transmat[0:-1,0:-1];\n\n\n\n while n >= 0:\n\n for j in range(0,len(log_emlik[0])):\n\n beta[n,j] = logsumexp(log_transmat[j,:] + log_emlik[n+1,:] + beta[n+1,:])\n\n n = n -1\n #print(beta[n,:])\n #print(beta)\n return beta","def calculate_gradient(scaled_noise: torch.Tensor) -> torch.Tensor:\n grad = torch.sum(scaled_noise, 0)\n return grad","def gradient_descent(f, df, x, sigma=0.5, epsilon=1e-8):\n pass","def compute_gradient_newton(resid, green, lamb):\n # Get infos\n nr, nt = green.shape\n\n # FFT of residuals and green functions\n RES = fft(resid, axis=1)\n GRE = fft(green, axis=1)\n\n # Compute gradient (full wavelet estimation)\n num = np.sum(RES * np.conj(GRE), axis=0)\n den = np.sum(GRE * np.conj(GRE), axis=0)\n\n # Waterlevel?\n wl = lamb * np.max(np.abs(den))\n grad = np.real(ifft(num / (den + wl)))\n\n # Step value\n hmax = 1\n\n return grad, hmax","def _de_exp_const_w(z,w):\n return np.log((z+1.)**(3.*(1.+w)))/3.","def grad_log_likelihood(g_t):\n # state vector [g1, g2, g3, ... , gn-1, gn]\n state_vector = np.zeros(N)\n\n # factor that will be added to one node and subtracted from another\n def alphaij(gj, gi, deltaij, varij):\n return ((gj - gi) - deltaij) / varij\n\n # indices of state vector\n # Iterate over all connections\n for r in self.graph.index:\n state1, state2, value, variance, ligand, standard_state = self.graph.iloc[r]\n i = self.states[self.states.name == state1].index[0]\n j = self.states[self.states.name == state2].index[0]\n\n gj = g_t[j]\n gi = g_t[i]\n\n edge_attr = self.cycle.edges()[(state1, state2)]\n deltaij = edge_attr['energy'] # measured difference\n varij = edge_attr['weight'] # measured variance\n\n shared_alpha = alphaij(gj, gi, deltaij, varij)\n\n state_vector[i] += shared_alpha\n state_vector[j] -= shared_alpha\n\n return state_vector","def ComputeLogGradientTerm(self, *args):\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF3F_ComputeLogGradientTerm(self, *args)","def gradient(self, x):\n u = np.asarray([x[0]])\n C = self.C_func(u)\n dC = self.dC_func(u, order=1)\n P = self.P\n numerator = np.sum((C - P) * dC, axis=0)\n denominator = np.sum(np.sum((C - P) ** 2, axis=0) ** (1 / 2))\n if np.abs(denominator) > 0:\n gradient = numerator/denominator\n else:\n gradient = np.asarray(0)[np.newaxis]\n return gradient","def delta_n(n, zeros):\n #return log(zeros[n]/2.0/pi/e)/2.0/pi*(zeros[n+1]-zeros[n])\n return log(zeros[n]/2.0/pi)/2.0/pi*(zeros[n+1]-zeros[n])","def eval_numerical_gradient_array(f, x, df, h=1e-5):\n grad = np.zeros_like(x)\n it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])\n while not it.finished:\n ix = it.multi_index\n\n oldval = x[ix]\n x[ix] = oldval + h\n pos = f(x).copy()\n x[ix] = oldval - h\n neg = f(x).copy()\n x[ix] = oldval\n\n grad[ix] = np.sum((pos - neg) * df) / (2 * h)\n it.iternext()\n return grad","def grad_ReLU(self):\n grad = np.where(self.x > 0, 1, 0)\n return grad","def gradientFunction(theta, X, y):\n y = y[:, 0]\n m = y.shape # number of training samples\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\n grad /= m\n return grad"],"string":"[\n \"def logistic_grad(z):\\n idx_pos = np.where(z >= 0.)\\n idx_neg = np.where(z < 0.)\\n res = np.empty(z.shape)\\n res[idx_pos] = 1. / (1. + np.exp(-z[idx_pos]))\\n res[idx_neg] = 1 - 1. / (1. + np.exp(z[idx_neg]))\\n return res\",\n \"def logistic_grad(z):\\n idx_pos = np.where(z >= 0.)\\n idx_neg = np.where(z < 0.)\\n res = np.empty(z.shape)\\n res[idx_pos] = 1. / (1. + np.exp(-z[idx_pos]))\\n res[idx_neg] = 1 - 1. / (1. + np.exp(z[idx_neg]))\\n return res\",\n \"def dlogZ(self, T, pos, psi, phi):\\n msgs = belief_propagation(T, pos, psi, phi, True)\\n dpsi = calculate_gradient(msgs, T, pos, psi, True, True)\\n return dpsi\",\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 _finite_diff_gradient(self, parameters):\\n assert isinstance(parameters,np.ndarray)\\n # get the free indicies\\n free_inds = np.nonzero(np.logical_not(self._fixed_indicies))[0]\\n\\n # first take a forward step in each direction\\n step = 1e-6 # finite difference step\\n log_like_fs = np.zeros(free_inds.size)\\n for i,param_idx in enumerate(free_inds):\\n p_fs = parameters.copy()\\n p_fs[param_idx] += step # take a step forward\\n log_like_fs[i] = self._compute_log_likelihood(p_fs)\\n\\n # compute the log likelihood at current point\\n log_like = self._compute_log_likelihood(parameters)\\n\\n # compute the gradient\\n gradient = np.zeros(parameters.shape) # default gradient is zero\\n gradient[free_inds] = (log_like_fs-log_like)\\n gradient[free_inds] = gradient[free_inds]/step # divide by step length\\n return log_like, gradient\",\n \"def sigmoid_gradient(z):\\n #derivative of sigmoid\\n return z * (1 - z)\",\n \"def _log_likelihood_gradient(self, z, K, H, B, Kinv):\\n\\n nparams = 4\\n grad = np.zeros((nparams,))\\n\\n #t0 = time.time()\\n tmp = np.dot(self.invc, self.HKinv)\\n #t1 = time.time()\\n K_HBH_inv = Kinv - np.dot(tmp.T, tmp)\\n #t2 = time.time()\\n alpha_z = np.dot(K_HBH_inv, z)\\n #t3 = time.time()\\n\\n #print \\\"gradient: %f %f %f\\\" % (t1-t0, t2-t1, t3-t2)\\n\\n for i in range(nparams):\\n tA = time.time()\\n if (i == 0):\\n dKdi = np.eye(self.n)\\n else:\\n dKdi = self.predict_tree.kernel_deriv_wrt_i(self.X, self.X, \\\"se\\\", self.wfn_params, i-1)\\n\\n dlldi = .5 * np.dot(alpha_z.T, np.dot(dKdi, alpha_z))\\n tB = time.time()\\n # here we use the fact:\\n # trace(AB) = sum_{ij} A_ij * B_ij\\n dlldi -= .5 * np.sum(np.sum(K_HBH_inv.T * dKdi))\\n\\n grad[i] = dlldi\\n tC = time.time()\\n print \\\" %d: %f %f\\\" % (i, tB-tA, tC-tB)\\n\\n return grad\",\n \"def log_prior_grad(self, inputs):\",\n \"def cv_gradient_initial(self,z): \\n z_t = np.transpose(z)\\n log_q = self.normal_log_q_initial(z_t)\\n log_p = self.log_p(z_t)\\n grad_log_q = self.grad_log_q(z)\\n gradient = grad_log_q*np.repeat((log_p - log_q).T,2,axis=0)\\n\\n alpha0 = alpha_recursion(np.zeros(np.sum(self.approx_param_no)), grad_log_q, gradient, np.sum(self.approx_param_no))\\n\\n vectorized = gradient - ((alpha0/np.var(grad_log_q,axis=1))*grad_log_q.T).T\\n\\n return np.mean(vectorized,axis=1)\",\n \"def cv_gradient(self, z): \\n z_t = np.transpose(z)\\n log_q = self.normal_log_q(z_t)\\n log_p = self.log_p(z_t)\\n grad_log_q = self.grad_log_q(z)\\n gradient = grad_log_q*np.repeat((log_p - log_q).T,2,axis=0)\\n\\n alpha0 = alpha_recursion(np.zeros(np.sum(self.approx_param_no)), grad_log_q, gradient, np.sum(self.approx_param_no)) \\n \\n vectorized = gradient - ((alpha0/np.var(grad_log_q,axis=1))*grad_log_q.T).T\\n\\n return np.mean(vectorized,axis=1)\",\n \"def log_likelihood_gradients_(self, y, f):\\n log_lik = self.evaluate_log_likelihood(y, f)\\n f = np.squeeze(f)\\n J = jacrev(self.evaluate_log_likelihood, argnums=1)\\n H = jacrev(J, argnums=1)\\n return log_lik, J(y, f), H(y, f)\",\n \"def logp_grad(self, xs, ys, fs, **kwargs):\",\n \"def sigmoid_grad(z):\\n return Sigmoid(z) * (1 - Sigmoid(z))\",\n \"def _log_likelihood_gradients(self):\\r\\n return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))\",\n \"def cv_gradient(self,z):\\n gradient = np.zeros(np.sum(self.approx_param_no))\\n z_t = z.T \\n log_q = self.normal_log_q(z.T)\\n log_p = self.log_p(z.T)\\n grad_log_q = self.grad_log_q(z)\\n gradient = grad_log_q*(log_p-log_q)\\n\\n alpha0 = alpha_recursion(np.zeros(np.sum(self.approx_param_no)), grad_log_q, gradient, np.sum(self.approx_param_no)) \\n\\n vectorized = gradient - ((alpha0/np.var(grad_log_q,axis=1))*grad_log_q.T).T\\n\\n return np.mean(vectorized,axis=1)\",\n \"def cv_gradient_initial(self,z):\\n gradient = np.zeros(np.sum(self.approx_param_no))\\n z_t = z.T \\n log_q = self.normal_log_q_initial(z.T)\\n log_p = self.log_p(z.T)\\n grad_log_q = self.grad_log_q(z)\\n gradient = grad_log_q*(log_p-log_q)\\n\\n alpha0 = alpha_recursion(np.zeros(np.sum(self.approx_param_no)), grad_log_q, gradient, np.sum(self.approx_param_no))\\n\\n vectorized = gradient - ((alpha0/np.var(grad_log_q,axis=1))*grad_log_q.T).T\\n\\n return np.mean(vectorized,axis=1)\",\n \"def _log_likelihood_gradients(self):\\n return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK,X=self.X,slices1=self.Xslices,slices2=self.Xslices), self.likelihood._gradients(partial=np.diag(self.dL_dK))))\",\n \"def relu_grad(z):\\n index = z >= 0\\n result = np.zeros(z.shape)\\n result[index] = 1.0\\n return result\",\n \"def grad_log(self, X):\\n g = autograd.elementwise_grad(self.log_den)\\n G = g(X)\\n return G\",\n \"def numerical_gradient(f, x: np.ndarray):\\n h = 1e-4\\n grad = np.zeros_like(x)\\n for i in range(x.size):\\n tmp_val = x.flat[i]\\n x.flat[i] = tmp_val + h\\n fxh1 = f(x)\\n\\n x.flat[i] = tmp_val - h\\n fxh2 = f(x)\\n grad.flat[i] = (fxh1 - fxh2) / (2 * h)\\n x.flat[i] = tmp_val\\n return grad\",\n \"def compute_gradients(self, logits, target):\\n\\n target_length = target.shape[0]\\n num_time_steps = logits.shape[0]\\n\\n\\t\\t######################\\n\\t\\t### YOUR CODE HERE ###\\n\\t\\t######################\\n\\n # expand labels by inserting a blank between each pair\\n normalized_logits = softmax(logits)\\n blank_label = normalized_logits.shape[1] - 1\\n l = add_blanks(target, blank_label)\\n target_length = l.shape[0]\\n\\n alpha = self.compute_forward_variables(normalized_logits, target) \\n beta = self.compute_backward_variables(normalized_logits, target)\\n\\n # rescale\\n alpha = alpha / np.sum(alpha, axis=0)\\n beta = beta / np.sum(beta, axis=0)\\n alphabeta = alpha * beta\\n print \\\"alpha\\\"\\n print alpha\\n\\n # compute zt\\n z = Counter()\\n for t in xrange(num_time_steps):\\n for s, k in enumerate(l):\\n z[t] += alphabeta[s, t] / normalized_logits[t, k]\\n \\n # normalized_logits is time steps t by labels k\\n # alpha is 2 * target_length - 1 by time steps\\n lab_zk = np.zeros_like(normalized_logits)\\n for s, k in enumerate(l):\\n for t in xrange(num_time_steps):\\n lab_zk[t, k] += alphabeta[s, t]\\n\\n grad = normalized_logits\\n for k in xrange(target.shape[0]):\\n for t in xrange(num_time_steps):\\n ytk = normalized_logits[t, k]\\n constant = 1.0 / (ytk * z[t])\\n grad[t, k] = ytk - constant * lab_zk[t, k]\\n \\n return grad\",\n \"def logistic_function(z):\\n\\tg = 1/(1+np.exp(-z))\\n\\t#g = np.exp(z)/(1+np.exp(z))\\n\\treturn g\",\n \"def log_likelihood_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w))-y)\",\n \"def _forward_log_det_jacobian(self, x):\\n d = self._compute_shared(x=x)\\n relx = (x - d.x_k) / d.w_k\\n relx = relx # tf.where(d.out_of_bounds, 0.5*tf.ones_like(x), relx)\\n grad = (\\n 2 * tf.math.log(d.s_k) +\\n tf.math.log(d.d_kp1 * relx**2 + 2 * d.s_k * relx * (1 - relx) + # newln\\n d.d_k * (1 - relx)**2) -\\n 2 * tf.math.log((d.d_kp1 + d.d_k - 2 * d.s_k) * relx *\\n (1 - relx) + d.s_k))\\n return grad # tf.where(d.out_of_bounds, tf.zeros_like(grad), grad)\",\n \"def MvNormalLogp():\\n cov = pt.matrix(\\\"cov\\\")\\n cov.tag.test_value = floatX(np.eye(3))\\n delta = pt.matrix(\\\"delta\\\")\\n delta.tag.test_value = floatX(np.zeros((2, 3)))\\n\\n cholesky = Cholesky(lower=True, on_error=\\\"nan\\\")\\n\\n n, k = delta.shape\\n n, k = f(n), f(k)\\n chol_cov = cholesky(cov)\\n diag = pt.diag(chol_cov)\\n ok = pt.all(diag > 0)\\n\\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\\n delta_trans = solve_lower(chol_cov, delta.T).T\\n\\n result = n * k * pt.log(f(2) * np.pi)\\n result += f(2) * n * pt.sum(pt.log(diag))\\n result += (delta_trans ** f(2)).sum()\\n result = f(-0.5) * result\\n logp = pt.switch(ok, result, -np.inf)\\n\\n def dlogp(inputs, gradients):\\n (g_logp,) = gradients\\n cov, delta = inputs\\n\\n g_logp.tag.test_value = floatX(1.0)\\n n, k = delta.shape\\n\\n chol_cov = cholesky(cov)\\n diag = pt.diag(chol_cov)\\n ok = pt.all(diag > 0)\\n\\n chol_cov = pt.switch(ok, chol_cov, pt.fill(chol_cov, 1))\\n delta_trans = solve_lower(chol_cov, delta.T).T\\n\\n inner = n * pt.eye(k) - pt.dot(delta_trans.T, delta_trans)\\n g_cov = solve_upper(chol_cov.T, inner)\\n g_cov = solve_upper(chol_cov.T, g_cov.T)\\n\\n tau_delta = solve_upper(chol_cov.T, delta_trans.T)\\n g_delta = tau_delta.T\\n\\n g_cov = pt.switch(ok, g_cov, -np.nan)\\n g_delta = pt.switch(ok, g_delta, -np.nan)\\n\\n return [-0.5 * g_cov * g_logp, -g_delta * g_logp]\\n\\n return OpFromGraph([cov, delta], [logp], grad_overrides=dlogp, inline=True)\",\n \"def log_likelihood_gradients(self, y, f):\\n # align shapes and compute mask\\n y = y.reshape(-1, 1)\\n f = f.reshape(-1, 1)\\n mask = np.isnan(y)\\n y = np.where(mask, f, y)\\n\\n # compute gradients of the log likelihood\\n log_lik, J, H = vmap(self.log_likelihood_gradients_)(y, f)\\n\\n # apply mask\\n mask = np.squeeze(mask)\\n log_lik = np.where(mask, 0., log_lik)\\n J = np.where(mask, np.nan, J)\\n H = np.where(mask, np.nan, H)\\n\\n return log_lik, J, np.diag(H)\",\n \"def compute_logistic_gradient(y, tx, w):\\n return tx.T.dot(sigmoid(tx.dot(w)) - y) / len(y)\",\n \"def grad_llh(self, params):\\n grad = np.clip(self.grad_log_likelihood(params[0], params[1], params[2:]), SMALLEST_NUMBER,\\n LARGEST_NUMBER)\\n\\n return grad\",\n \"def get_gradient_minus_log_joint(z, mean, Sigma):\\n data_minus_mean = z[np.newaxis,:] - mean# 1 x 2\\n Sigma_inv = np.linalg.inv(Sigma) # 2 x 2\\n gradient = - np.matmul(Sigma_inv, data_minus_mean.T) #2 x 1\\n gradient = gradient[0]\\n return gradient\",\n \"def compute_gradient(self): # TODO: try to change to square loss since it's hessian is easier to obtain\\n A = np.dot(self.X, self.w)\\n m = self.t.shape[0]\\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\\n return (1 / m) * np.dot(self.X.T, C)\",\n \"def compute_gradient(self):\\n A = np.dot(self.X, self.w)\\n m = self.t.shape[0]\\n C = -1 * self.t * (1 / (1 + np.exp(A * self.t)))\\n return (1 / m) * np.dot(self.X.T, C)\",\n \"def _log_prior_gradients(self):\\n x = self._get_params()\\n ret = np.zeros(x.size)\\n [np.put(ret,i,p.lnpdf_grad(xx)) for i,(p,xx) in enumerate(zip(self.priors,x)) if not p is None]\\n return ret\",\n \"def gradient(self, theta):\\n a = -(6 * self.scale ** 2)\\n b = 3 * self.scale ** 2 + np.exp(2 * theta)\\n b *= np.log(3 * self.scale ** 2 * np.exp(-2 * theta) + 1)\\n return a / b\",\n \"def _core_calc_degrad(self,bd,Ld) :\\n\\t\\tdegrad = np.dot(Ld,bd) # Do matrix multiplication \\n\\t\\tdegrad = np.exp(degrad) # Exponentiate to convert log to real\\n\\t\\treturn degrad\",\n \"def gradient_ascent(f, df, theta_init, step_size, max_iter):\\n\\n fs = []\\n xs = []\\n thetas = theta_init\\n for i in range(max_iter): #for each data example\\n fs.append(f(thetas))\\n\\n temp = step_size*df(thetas)\\n thetas = step_size*df(thetas) #modify that feature by using the derivative of log likelihood\\n xs.append(thetas.flatten())\\n if i % 10 == 0:\\n print(i, thetas)\\n\\n return thetas, fs, xs\",\n \"def logit_cost_grad(self, theta, X, y):\\n\\n grad = np.zeros(len(theta))\\n\\n ### YOUR CODE HERE\\n sig = utils.sigmoid(theta)\\n # sig = np.subtract(sig, y)\\n sig = sig - y\\n grad = np.dot(X.T, sig) + 2 * self.params['lamb'] * self.regularizer[1](self.weights)\\n ### END YOUR CODE\\n\\n return grad\",\n \"def grad_log_q(self,z): \\n param_count = 0\\n grad = np.zeros((np.sum(self.approx_param_no),self.sims))\\n for core_param in range(len(self.q)):\\n for approx_param in range(self.q[core_param].param_no):\\n grad[param_count] = self.q[core_param].vi_score(z[core_param],approx_param) \\n param_count += 1\\n return grad\",\n \"def from_grad_log(d, g):\\n return UDFromCallable(d, fgrad_log=g)\",\n \"def gradient(cls, x):\\n return 1 - TanH.apply(x) ** 2\",\n \"def log_deriv(error):\\n return logistic(error) * (1 - logistic(error))\",\n \"def calculate_maxL_gradient(y, tx, w):\\n sig=sigmoid(tx.dot(w))\\n grad=tx.T.dot(sig-y)\\n #divides with number of samples so that learning rate is not dependant on number of samples\\n grad=grad/len(y) \\n return grad\",\n \"def f(z):\\n a=1./(1.+z)\\n #da=0.01\\n da=0.01*a\\n #da=1e-7\\n gp,g,gm=[D(1./ia-1.) for ia in [a+da,a,a-da]]\\n f=a*(gp-gm)/(2*g*da)\\n #dz=0.01\\n #gp,g,gm=[D(zi) for zi in [z+dz,z,z-dz]]\\n #f=(z)*(gp-gm)/(2.*g*dz)\\n return f\",\n \"def natural_gradient(params):\\n\\n natural_grad = np.zeros(6)\\n\\n F = np.zeros((6,6))\\n shift = np.eye(len(params))\\n gradient = np.zeros([6])\\n\\n for i in range(len(F)):\\n for j in range(len(F[i])):\\n shifted = params.copy() + (np.pi/2)*(shift[i] + shift[j])\\n f0 = measurement(params, shifted)\\n shifted = params.copy() + (np.pi/2)*(shift[i] - shift[j])\\n f1 = measurement(params, shifted)\\n shifted = params.copy() + (np.pi/2)*(-shift[i] + shift[j])\\n f2 = measurement(params, shifted)\\n shifted = params.copy() - (np.pi/2)*(shift[i] + shift[j])\\n f3 = measurement(params, shifted)\\n F[i][j] = float((- f0 + f1 + f2 - f3)/8)\\n\\n\\n for i in range(len(gradient)):\\n gradient[i] = parameter_shift_term(qnode, params, i)\\n\\n F_inv = np.linalg.inv(F)\\n QNG = np.dot(F_inv,gradient.T)\\n natural_grad = QNG.T\\n\\n return natural_grad\",\n \"def _log_prior_gradients(self):\\r\\n if self.priors is None:\\r\\n return 0.\\r\\n x = self._get_params()\\r\\n ret = np.zeros(x.size)\\r\\n [np.put(ret, i, p.lnpdf_grad(xx)) for i, (p, xx) in enumerate(zip(self.priors, x)) if not p is None]\\r\\n return ret\",\n \"def logZ(self, T, pos, psi, phi):\\n msgs = belief_propagation(T, pos, psi, phi, True)\\n log_z = calculate_belief_sum(msgs, True)\\n return log_z\",\n \"def grad_log_likelihood(self, log_occr_array=None):\\n\\n if log_occr_array is not None:\\n # Catch invalid occurrence rates for zero likelihood\\n try:\\n self.log_occr = log_occr_array\\n except InvalidOccurrenceRate:\\n return -np.inf * np.ones_like(self.occr, dtype=float)\\n\\n # Calculate components first\\n N_exp = self.calc_integral() * self._N_stars # perhaps not needed\\n nexp_terms = self._N_stars * self.calc_bin_volumes() * self.H_bar_array\\n s_terms = self.H_array * self.F_array * self.occr\\n numerator_terms = self.H_array * self.F_array\\n\\n if not tf.is_tensor(self.occr):\\n # Checking shapes of intermediate terms,\\n # numerator_terms vs s_terms.sum(-1, -2) and vs v factors.\\n intermediate_terms = numerator_terms / s_terms.sum(axis=(-1, -2))\\n # TODO: v_factor changed to negative, I think a minus\\n # sign had been missed\\n grad_log_array = - nexp_terms + intermediate_terms.sum(axis=0)\\n\\n # BUG TODO\\n if np.isnan(grad_log_array).any():\\n warnings.warn(\\\".grad_log_likelihood value is nan.\\\")\\n import pdb; pdb.set_trace()\\n grad_log_array = -np.inf * grad_log_array\\n else:\\n raise NotImplementedError(\\\"Manual gradient calculate with \\\"\\n \\\"tensorflow objects isn't \\\"\\n \\\"implemented, and seems a bit \\\"\\n \\\"redundant.\\\")\\n\\n return grad_log_array\",\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 log_distr_fdmw(self, dnu, logflux, dme, logw, alpha, logls, logl0, mu, sigma, gtype=None):\\n #stepdms = 100/1000.\\n #vdms = np.arange(0, 100, stepdm)\\n stepz = (np.log(self.Zmax) - np.log(self.Zmin)) / 1000\\n vz = np.exp(np.arange(np.log(self.Zmin), np.log(self.Zmax), stepz))\\n lik = 0\\n for z in vz:\\n likv = np.exp(self.log_distr_fdmwz(dnu, logflux, dme, logw, z, alpha, logls, logl0, mu, sigma, gtype=gtype))\\n lik += z * stepz * likv\\n ind = lik > 0\\n ind2 = lik <= 0\\n loglik = lik.copy()\\n loglik[ind] = np.log(lik[ind])\\n loglik[ind2] = np.ones(loglik[ind2].shape) * -1e99\\n return loglik\",\n \"def grad_neg_log_like(params):\\n gp.set_parameter_vector(params)\\n return -gp.grad_log_likelihood(y, quiet=True)\",\n \"def _log_prior_gradients(self):\\n if self.priors.size == 0:\\n return 0.\\n x = self.param_array\\n ret = np.zeros(x.size)\\n #compute derivate of prior density\\n [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]\\n #add in jacobian derivatives if transformed\\n priored_indexes = np.hstack([i for p, i in self.priors.items()])\\n for c,j in self.constraints.items():\\n if not isinstance(c, Transformation):continue\\n for jj in j:\\n if jj in priored_indexes:\\n ret[jj] += c.log_jacobian_grad(x[jj])\\n return ret\",\n \"def gradient_descent(x0,df,rate=0.1,max_iters=1000,min_step=1e-6,max_step=1e5,\\n projection=None,trajectory=False,step_history=False,f=None,\\n cost_history=False,feedback=False,plot_history=False):\\n if feedback is True:\\n print(\\\"gd.gradient_descent():\\\")\\n if f is not None:\\n assert callable(f)\\n fx0 = f(x0)\\n if feedback is True:\\n print(f\\\" initial cost = {fx0:.2e}\\\")\\n if projection is not None:\\n assert callable(projection)\\n project = True\\n else:\\n project = False\\n if trajectory is True:\\n xx = [x0.copy()]\\n if step_history is True:\\n steps = []\\n if cost_history is True:\\n assert callable(f)\\n fx = [fx0]\\n\\n x = x0.copy()\\n for i in range(max_iters):\\n dx = -rate*df(x)\\n if project is True:\\n x0 = x.copy()\\n x = projection(x0+dx)\\n dx = x-x0\\n else:\\n x += dx\\n if trajectory is True:\\n xx.append(x.copy())\\n if cost_history is True:\\n fx += [f(x)]\\n step_size = np.linalg.norm(dx)\\n if step_history is True:\\n steps += [step_size]\\n if step_size < min_step or step_size > max_step:\\n break\\n\\n results = dict()\\n results['output'] = x\\n if trajectory is True:\\n results['trajectory'] = xx\\n if cost_history is True:\\n results['cost_history'] = fx\\n if step_history is True:\\n results['step_history'] = steps\\n if plot_history is True:\\n assert step_history is True or cost_history is True\\n plt.figure()\\n if step_history is True:\\n plt.semilogy(steps,label='step size')\\n if cost_history is True:\\n plt.semilogy(fx,label='cost')\\n plt.xlabel('iteration number')\\n plt.title('Gradient Descent')\\n plt.legend()\\n results['figure'] = plt\\n plt.show(block=False)\\n \\n if feedback is True:\\n if f is not None:\\n print(f\\\" final cost = {f(x):.2e}\\\")\\n \\n return results\",\n \"def g(z):\\n return 1. / (1. + np.exp(-z))\",\n \"def gradient(f,h,X):\\n # X = list(X)# flip to a list from tuple so we can modify elements\\n fx = f(*X) # only need this once\\n dX = []\\n for i in range(len(X)):\\n # Make a vector of Value(X_i, [0 ... 1 ... 0]) with 1 in ith position\\n X[i] += h # tweak in dimension i\\n y = f(*X)\\n X[i] -=h # undo the tweak for next round\\n dx = (y - fx)/h\\n dX.append(dx)\\n return dX\",\n \"def gradient(self):\\n result = np.zeros(len(self.variables))\\n result[self.bivariateGradInd] = (self.shape-1)/self.variable - self.rate\\n return result\",\n \"def statePosteriors(log_alpha, log_beta):\\n return log_alpha + log_beta - logsumexp(log_alpha[-1,:])\",\n \"def grad(x):\\n\\n return x - np.arange(1, cfg.n + 1, dtype=np.float_)\",\n \"def gradient(self, x):\\n return 0.0\",\n \"def compute_gradient_logreg(y, tx, w):\\n assert len(set(y).difference({0., 1.})) == 0, \\\"Class labels must be encoded as {0, 1}\\\"\\n\\n s = sigmoid(tx.dot(w)) - y\\n grad = tx.T.dot(s)\\n\\n return grad\",\n \"def statePosteriors(log_alpha, log_beta):\\n\\n gamma = np.zeros(log_alpha.shape)\\n\\n gamma = log_alpha + log_beta\\n\\n gamma = gamma - logsumexp(log_alpha[-1,:])\\n\\n return gamma\",\n \"def compute_grad_likelihood(sensor_loc, obj_loc, distance):\\n\\tgrad = np.zeros(sensor_loc.shape)\\n\\t# Your code: finish the grad loglike\\n\\t\\t\\n\\treturn grad\",\n \"def gradient(self, x_in):\\r\\n\\r\\n return 1 - torch.pow(self.forward(x_in), 2)\",\n \"def backward_energy(im):\\n\\n xgrad = ndi.convolve1d(im, np.array([1, 0, -1]), axis=1, mode='wrap')\\n ygrad = ndi.convolve1d(im, np.array([1, 0, -1]), axis=0, mode='wrap')\\n\\n grad_mag = np.sqrt(np.sum(xgrad ** 2, axis=2) + np.sum(ygrad ** 2, axis=2))\\n\\n return grad_mag\",\n \"def extrapolate_fwds(h, ufr, llfr, alpha=0.10):\\n fwd_fsp_fsp_plus_h = np.log(1 + ufr) + (llfr - np.log(1 + ufr)) * big_b(h, alpha)\\n return fwd_fsp_fsp_plus_h\",\n \"def gradient(self, x):\\n pass\",\n \"def softmax_derivative(Z):\\n\\treturn None\",\n \"def log_pdf_derivative(x):\\n return gs.autodiff.jacobian(log_pdf_at_x(x))(base_point)\",\n \"def derivative_activation(z):\\n return activation(z) * (1 - activation(z))\",\n \"def log_zgrid(zr=[0.7,3.4], dz=0.01):\\n zgrid = np.exp(np.arange(np.log(1+zr[0]), np.log(1+zr[1]), dz))-1\\n return zgrid\",\n \"def Gradient(Walker,particle):\\n\\n h=0.001\\n dPsi = zeros(shape=shape(Walker.Re[particle]))\\n for i in range(Walker.sys_dim):\\n Y=Walker.Re[particle][i]\\n Walker.Re[particle][i]-=h\\n wfs1=wfs(Walker)\\n Walker.Re[particle][i]+=2.0*h\\n wfs2=wfs(Walker)\\n dPsi[i] = (wfs2-wfs1)/2/h\\n Walker.Re[particle][i]=Y\\n\\n return dPsi\",\n \"def sigmoid_derivative(z):\\n s = 1./(1. + np.exp(-z))\\n ds = s * (1 - s)\\n return ds\",\n \"def gradient(f, *varargs):\\n N = len(f.shape) # number of dimensions\\n n = len(varargs)\\n if n == 0:\\n dx = [1.0]*N\\n elif n == 1:\\n dx = [varargs[0]]*N\\n elif n == N:\\n dx = list(varargs)\\n else:\\n raise SyntaxError, \\\"invalid number of arguments\\\"\\n\\n # use central differences on interior and first differences on endpoints\\n\\n outvals = []\\n\\n # create slice objects --- initially all are [:, :, ..., :]\\n slice1 = [slice(None)]*N\\n slice2 = [slice(None)]*N\\n slice3 = [slice(None)]*N\\n\\n otype = f.dtype.char\\n if otype not in ['f', 'd', 'F', 'D']:\\n otype = 'd'\\n\\n for axis in range(N):\\n # select out appropriate parts for this dimension\\n out = zeros(f.shape, f.dtype.char)\\n slice1[axis] = slice(1, -1)\\n slice2[axis] = slice(2, None)\\n slice3[axis] = slice(None, -2)\\n # 1D equivalent -- out[1:-1] = (f[2:] - f[:-2])/2.0\\n out[slice1] = (f[slice2] - f[slice3])/2.0\\n slice1[axis] = 0\\n slice2[axis] = 1\\n slice3[axis] = 0\\n # 1D equivalent -- out[0] = (f[1] - f[0])\\n out[slice1] = (f[slice2] - f[slice3])\\n slice1[axis] = -1\\n slice2[axis] = -1\\n slice3[axis] = -2\\n # 1D equivalent -- out[-1] = (f[-1] - f[-2])\\n out[slice1] = (f[slice2] - f[slice3])\\n\\n # divide by step size\\n outvals.append(out / dx[axis])\\n\\n # reset the slice object in this dimension to \\\":\\\"\\n slice1[axis] = slice(None)\\n slice2[axis] = slice(None)\\n slice3[axis] = slice(None)\\n\\n if N == 1:\\n return outvals[0]\\n else:\\n return outvals\",\n \"def dalf(x):\\n return grad(alf)(x)\",\n \"def compute_gradient(self, function, arguments):\",\n \"def linear_backward(dZ, cache):\\n m = dZ.shape[1]\\n dW = np.dot(dZ, cache[0].transpose())/m\\n db = np.sum(dZ, axis=1, keepdims=True)/m\\n dA_prev = np.dot(cache[1].transpose(), dZ)\\n return dA_prev, dW, db\",\n \"def calculate_gradient(y, tx, w): \\n return tx.T@(sigmoid(tx@w)-y)\",\n \"def logistic_derivative(errors):\\n return [log_deriv(error) for error in errors]\",\n \"def gradient_nD(stack):\\n # Convert for 64-bit to avoid large number problems in squares.\\n stack = np.copy(stack)\\n stack = stack.astype(np.float64)\\n sumsq = ndi.filters.sobel(stack, axis=0) ** 2\\n for d in range(1, stack.ndim):\\n sumsq = sumsq + (ndi.filters.sobel(stack, axis=d) ** 2)\\n gradient = np.sqrt(sumsq)\\n return gradient\",\n \"def linear_backward(dZ, cache):\\n A_prev, W, b = cache\\n m = A_prev.shape[1]\\n\\n dW = np.dot(dZ,A_prev.T) / m\\n db = np.sum(dZ,axis = 1,keepdims = True) / m\\n dA_prev = np.dot(W.T,dZ)\\n\\n return dA_prev,dW,db\",\n \"def log_abs_det_jacobian(self, z):\\n pre_u = self.u_ + self.u\\n pre_w = self.w_ + self.w\\n a = F.softplus(self.a + self.inv)\\n w = F.softmax(pre_w, dim=3)\\n u = F.softmax(pre_u, dim=3)\\n # Perform computation\\n pre_sigm = torch.sum(u * a * z, 3) + self.b\\n sigm = torch.sigmoid(pre_sigm)\\n x_pre = torch.sum(w * sigm, dim=3)\\n x_pre_clipped = x_pre * (1 - self.eps) + self.eps * 0.5\\n logj = F.log_softmax(pre_w, dim=3) + logsigmoid(pre_sigm) + logsigmoid(-pre_sigm) + torch.log(a)\\n # n, d, d2, dh\\n logj = logj + F.log_softmax(pre_u, dim=3)\\n # n, d, d2, dh, d1\\n logj = torch.log(torch.sum(torch.exp(logj),3))\\n # n, d, d2, d1\\n logdet_ = logj + np.log(1 - self.eps) - (torch.log(x_pre_clipped) + torch.log(-x_pre_clipped + 1))\\n return logdet_\",\n \"def compute_grad(beta, lambdat, X, y):\\n return -2/len(y)*(np.maximum(0, 1-(\\n (y[:, np.newaxis]*X).dot(beta)))).dot(\\n y[:, np.newaxis]*X) + 2 * lambdat * beta\",\n \"def relu(z: float) -> float:\\n return z if z > 0 else 0.01 * z\",\n \"def newton(flogl, start, fgrad, fhess, maxiter):\\r\\n warn = 0\\r\\n iteration = 0\\r\\n par_hat0 = start\\r\\n m = 1\\r\\n while (iteration < maxiter and m >= 1e-04):\\r\\n H = -la.inv(fhess(par_hat0))\\r\\n g = fgrad(par_hat0).reshape(start.shape)\\r\\n Hg = np.dot(H, g)\\r\\n par_hat0 = par_hat0 + Hg\\r\\n iteration += 1\\r\\n m = np.dot(g.T, Hg)\\r\\n if iteration == maxiter:\\r\\n warn = 1\\r\\n logl = flogl(par_hat0)\\r\\n return (par_hat0, logl, warn)\",\n \"def ComputeLogGradientTerm(self, *args):\\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF2F_ComputeLogGradientTerm(self, *args)\",\n \"def log_likelihood_z_lognormal(self, std=1.0):\\n #return self.log_det_Jxz - self.dim * tf.log(std) - (0.5 / (std**2)) * tf.reduce_sum(self.output_z**2, axis=1)\\n from deep_boltzmann.util import logreg\\n logz = logreg(self.output_z, a=0.001, tf=True)\\n ll = self.log_det_Jxz \\\\\\n - (0.5 / (std**2)) * tf.reduce_sum(logz**2, axis=1) \\\\\\n - tf.reduce_sum(logz, axis=1)\\n return ll\",\n \"def backward_p(self, x):\\n log_det_jacob, z = x.new_zeros(x.shape[0]), x\\n for i in reversed(range(len(self.t))):\\n z_ = self.mask[i] * z\\n s = self.s[i](z_) * (1 - self.mask[i])\\n t = self.t[i](z_) * (1 - self.mask[i])\\n z = (1 - self.mask[i]) * (z - t) * torch.exp(-s) + z_\\n log_det_jacob -= s.sum(dim=1)\\n return z, log_det_jacob\",\n \"def log_distr_fdmwz(self, dnu, logflux, dme, logw, z, alpha, logls, logl0, mu, sigma, gtype=None):\\n flux = np.power(10., logflux)\\n logl = np.log10(self.cos.Luminosity(z, f=flux, dnu=dnu))\\n logint1 = self.log_IntBeam(logl, alpha, logls, logl0)\\n #print logint1\\n logw0 = logw - np.log10(1+z)\\n logfw = self.log_dis_logw(logw0, mu, sigma)\\n logfz = self.log_Distribution_volume(z)\\n dmi = self.cos.DispersionMeasure_IGM(z)\\n u1 = (dme-dmi)*(1+z)*self.kappa(z)\\n u2 = ((dme-dmi)*(1+z)-self.DMsmax)*self.kappa(z)\\n #print u2\\n logint2 = np.ones(u2.shape) * (-1e99)\\n ind = u2 > 0\\n logint2[ind] = self.log_IntDMsrc(u1[ind],u2[ind],gtype=gtype)\\n #print logint2\\n loglikv = logint1 + logfz + logfw + logint2 + np.log(1+z)\\n return loglikv\",\n \"def gradient(self,i,f):\\n\\n diff = self.points[f, :] - self.points[i, :]\\n gradient = diff[1]/diff[0]\\n\\n return gradient\",\n \"def gradient(self):\\n functional = self\\n\\n class KLCrossEntropyGradient(Operator):\\n\\n \\\"\\\"\\\"The gradient operator of this functional.\\\"\\\"\\\"\\n\\n def __init__(self):\\n \\\"\\\"\\\"Initialize a new instance.\\\"\\\"\\\"\\n super().__init__(functional.domain, functional.domain,\\n linear=False)\\n\\n def _call(self, x):\\n \\\"\\\"\\\"Apply the gradient operator to the given point.\\n\\n The gradient is not defined in for points with components less\\n than or equal to zero.\\n \\\"\\\"\\\"\\n if functional.prior is None:\\n tmp = np.log(x)\\n else:\\n tmp = np.log(x / functional.prior)\\n\\n if np.all(np.isfinite(tmp)):\\n return tmp\\n else:\\n # The derivative is not defined.\\n raise ValueError('The gradient of the Kullback-Leibler '\\n 'Cross Entropy functional is not defined '\\n 'for `x` with one or more components '\\n 'less than or equal to zero.'.format(x))\\n\\n return KLCrossEntropyGradient()\",\n \"def backward(log_emlik, log_startprob, log_transmat):\\n #print(log_transmat)\\n beta = np.zeros(log_emlik.shape)\\n n = len(log_emlik)-2\\n log_transmat = log_transmat[0:-1,0:-1];\\n\\n\\n\\n while n >= 0:\\n\\n for j in range(0,len(log_emlik[0])):\\n\\n beta[n,j] = logsumexp(log_transmat[j,:] + log_emlik[n+1,:] + beta[n+1,:])\\n\\n n = n -1\\n #print(beta[n,:])\\n #print(beta)\\n return beta\",\n \"def calculate_gradient(scaled_noise: torch.Tensor) -> torch.Tensor:\\n grad = torch.sum(scaled_noise, 0)\\n return grad\",\n \"def gradient_descent(f, df, x, sigma=0.5, epsilon=1e-8):\\n pass\",\n \"def compute_gradient_newton(resid, green, lamb):\\n # Get infos\\n nr, nt = green.shape\\n\\n # FFT of residuals and green functions\\n RES = fft(resid, axis=1)\\n GRE = fft(green, axis=1)\\n\\n # Compute gradient (full wavelet estimation)\\n num = np.sum(RES * np.conj(GRE), axis=0)\\n den = np.sum(GRE * np.conj(GRE), axis=0)\\n\\n # Waterlevel?\\n wl = lamb * np.max(np.abs(den))\\n grad = np.real(ifft(num / (den + wl)))\\n\\n # Step value\\n hmax = 1\\n\\n return grad, hmax\",\n \"def _de_exp_const_w(z,w):\\n return np.log((z+1.)**(3.*(1.+w)))/3.\",\n \"def grad_log_likelihood(g_t):\\n # state vector [g1, g2, g3, ... , gn-1, gn]\\n state_vector = np.zeros(N)\\n\\n # factor that will be added to one node and subtracted from another\\n def alphaij(gj, gi, deltaij, varij):\\n return ((gj - gi) - deltaij) / varij\\n\\n # indices of state vector\\n # Iterate over all connections\\n for r in self.graph.index:\\n state1, state2, value, variance, ligand, standard_state = self.graph.iloc[r]\\n i = self.states[self.states.name == state1].index[0]\\n j = self.states[self.states.name == state2].index[0]\\n\\n gj = g_t[j]\\n gi = g_t[i]\\n\\n edge_attr = self.cycle.edges()[(state1, state2)]\\n deltaij = edge_attr['energy'] # measured difference\\n varij = edge_attr['weight'] # measured variance\\n\\n shared_alpha = alphaij(gj, gi, deltaij, varij)\\n\\n state_vector[i] += shared_alpha\\n state_vector[j] -= shared_alpha\\n\\n return state_vector\",\n \"def ComputeLogGradientTerm(self, *args):\\n return _ITKCostFunctionsPython.itkShapePriorMAPCostFunctionBaseIF3F_ComputeLogGradientTerm(self, *args)\",\n \"def gradient(self, x):\\n u = np.asarray([x[0]])\\n C = self.C_func(u)\\n dC = self.dC_func(u, order=1)\\n P = self.P\\n numerator = np.sum((C - P) * dC, axis=0)\\n denominator = np.sum(np.sum((C - P) ** 2, axis=0) ** (1 / 2))\\n if np.abs(denominator) > 0:\\n gradient = numerator/denominator\\n else:\\n gradient = np.asarray(0)[np.newaxis]\\n return gradient\",\n \"def delta_n(n, zeros):\\n #return log(zeros[n]/2.0/pi/e)/2.0/pi*(zeros[n+1]-zeros[n])\\n return log(zeros[n]/2.0/pi)/2.0/pi*(zeros[n+1]-zeros[n])\",\n \"def eval_numerical_gradient_array(f, x, df, h=1e-5):\\n grad = np.zeros_like(x)\\n it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])\\n while not it.finished:\\n ix = it.multi_index\\n\\n oldval = x[ix]\\n x[ix] = oldval + h\\n pos = f(x).copy()\\n x[ix] = oldval - h\\n neg = f(x).copy()\\n x[ix] = oldval\\n\\n grad[ix] = np.sum((pos - neg) * df) / (2 * h)\\n it.iternext()\\n return grad\",\n \"def grad_ReLU(self):\\n grad = np.where(self.x > 0, 1, 0)\\n return grad\",\n \"def gradientFunction(theta, X, y):\\n y = y[:, 0]\\n m = y.shape # number of training samples\\n grad = X.T.dot(sigmoid(theta.dot(X.T))-1*y)\\n grad /= m\\n return grad\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.75813544","0.75813544","0.7266053","0.70977503","0.7045134","0.6986021","0.69778025","0.6907271","0.689076","0.6876","0.6797919","0.6774386","0.67495555","0.67483854","0.67364544","0.67208683","0.67097276","0.668161","0.6634446","0.6579385","0.65332234","0.6509687","0.6506646","0.6478691","0.6450477","0.6386328","0.63828313","0.63724256","0.63638514","0.63565606","0.6353765","0.63466173","0.63144183","0.6297534","0.62960154","0.6287333","0.6280169","0.6265978","0.6247902","0.62318444","0.6218716","0.6213542","0.62127596","0.6198737","0.61934257","0.6185303","0.6183505","0.61823744","0.61798316","0.61726403","0.6165441","0.6158007","0.6129448","0.61020637","0.60982","0.6098188","0.6092768","0.60876745","0.60865146","0.6085451","0.6078223","0.6075569","0.6074967","0.6066125","0.6066073","0.60654926","0.6055849","0.6039766","0.6032898","0.6030729","0.6030167","0.6026904","0.6023563","0.6023184","0.602242","0.60196704","0.6018451","0.6015396","0.6014966","0.6014578","0.6013493","0.6008795","0.600547","0.599886","0.5992631","0.59778637","0.59731776","0.5972313","0.59695673","0.59652025","0.59642965","0.5961014","0.59547335","0.5951176","0.59401107","0.5937101","0.59341544","0.59324014","0.59298724","0.5929581"],"string":"[\n \"0.75813544\",\n \"0.75813544\",\n \"0.7266053\",\n \"0.70977503\",\n \"0.7045134\",\n \"0.6986021\",\n \"0.69778025\",\n \"0.6907271\",\n \"0.689076\",\n \"0.6876\",\n \"0.6797919\",\n \"0.6774386\",\n \"0.67495555\",\n \"0.67483854\",\n \"0.67364544\",\n \"0.67208683\",\n \"0.67097276\",\n \"0.668161\",\n \"0.6634446\",\n \"0.6579385\",\n \"0.65332234\",\n \"0.6509687\",\n \"0.6506646\",\n \"0.6478691\",\n \"0.6450477\",\n \"0.6386328\",\n \"0.63828313\",\n \"0.63724256\",\n \"0.63638514\",\n \"0.63565606\",\n \"0.6353765\",\n \"0.63466173\",\n \"0.63144183\",\n \"0.6297534\",\n \"0.62960154\",\n \"0.6287333\",\n \"0.6280169\",\n \"0.6265978\",\n \"0.6247902\",\n \"0.62318444\",\n \"0.6218716\",\n \"0.6213542\",\n \"0.62127596\",\n \"0.6198737\",\n \"0.61934257\",\n \"0.6185303\",\n \"0.6183505\",\n \"0.61823744\",\n \"0.61798316\",\n \"0.61726403\",\n \"0.6165441\",\n \"0.6158007\",\n \"0.6129448\",\n \"0.61020637\",\n \"0.60982\",\n \"0.6098188\",\n \"0.6092768\",\n \"0.60876745\",\n \"0.60865146\",\n \"0.6085451\",\n \"0.6078223\",\n \"0.6075569\",\n \"0.6074967\",\n \"0.6066125\",\n \"0.6066073\",\n \"0.60654926\",\n \"0.6055849\",\n \"0.6039766\",\n \"0.6032898\",\n \"0.6030729\",\n \"0.6030167\",\n \"0.6026904\",\n \"0.6023563\",\n \"0.6023184\",\n \"0.602242\",\n \"0.60196704\",\n \"0.6018451\",\n \"0.6015396\",\n \"0.6014966\",\n \"0.6014578\",\n \"0.6013493\",\n \"0.6008795\",\n \"0.600547\",\n \"0.599886\",\n \"0.5992631\",\n \"0.59778637\",\n \"0.59731776\",\n \"0.5972313\",\n \"0.59695673\",\n \"0.59652025\",\n \"0.59642965\",\n \"0.5961014\",\n \"0.59547335\",\n \"0.5951176\",\n \"0.59401107\",\n \"0.5937101\",\n \"0.59341544\",\n \"0.59324014\",\n \"0.59298724\",\n \"0.5929581\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6227096"},"document_rank":{"kind":"string","value":"40"}}},{"rowIdx":11192,"cells":{"query":{"kind":"string","value":"returns collection of beat times when given start/end/length"},"document":{"kind":"string","value":"def Calc(self, a, b, size):\n self.eq = lambda x: (60000/((b-a)/size*x+a))\n points = []\n names = [str(self.offset)]\n points.append(0)\n for j in range(1, int(size)):\n points.append(integrate.quad(self.eq,0,j)[0])\n names.append(str(points[-1]+self.offset))\n self.beatstr = ' '.join(names)\n return points"},"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 list_times(self, start: int = None, end: int = None) -> List:\n return [i.time for i in self.data[start:end]]","def get_target_timestamps(self):\n times=[]\n curr = self.begin_ts\n while curr<=self.end_ts:\n times.append(curr)\n curr = curr + 24 * 60 * 60\n return times","def get_obs_times(start_time, duration, interval=0.2):\n return start_time + np.arange(0.0, duration+interval/2., interval)*u.h","def stadates(self,start=False,end=False):\n #{{{ function to return start and end times for a station\n\n if not start: return self.stachan_cache.keys()\n\n\n cache = {}\n\n if not end: end = stock.now()\n if start > end: end = stock.now()\n start = float(start)\n end = float(end)\n\n for sta in self.stachan_cache:\n for chan in self.stachan_cache[sta]:\n for date in self.stachan_cache[sta][chan]['dates']:\n\n if date[1] == -1:\n\n if date[0] <= start: cache[sta] = 1\n if date[0] <= end: cache[sta] = 1\n\n else:\n\n if date[0] <= start and start <= date[1]: cache[sta] = 1\n if date[0] <= end and end <= date[1]: cache[sta] = 1\n if start <= date[0] and date[1] <= end: cache[sta] = 1\n\n print cache.keys()\n return cache.keys()","def getTimes():","def getTimes():","def getTimes():","def get_times(self):\n raise NotImplementedError(\"Abstract method not implemented.\")","def filter_time_range(start: int, end: int, time_slots: Optional[Container[int]] = None) -> Iterator[int]:\n if time_slots is None:\n time_slots = range(24)\n for time in range(start, end):\n if time in time_slots:\n yield time","def _hist_filter_ts(commands, start_time, end_time):\n for cmd in commands:\n if start_time <= cmd[1] < end_time:\n yield cmd","def gen_start_end_times(start_time=[6, 0, 0], end_time=[23, 0, 0]):\n\n now = datetime.now()\n year = now.year\n month = now.month\n day = now.day\n\n start_time = datetime(\n year, month, day, start_time[0], start_time[1], start_time[2], 0\n )\n\n end_time = datetime(year, month, day, end_time[0], end_time[1], end_time[2], 0)\n\n if end_time < now:\n end_time += timedelta(days=1)\n start_time += timedelta(days=1)\n\n return start_time, end_time","def sample_times():\n\tthe_times = []\n\tday = config.window_start_date\n\twhile day <= config.window_end_date:\n\t\t# times from start of window on day to end of window \n\t\ttime = config.tz.localize( datetime.combine( \n\t\t\tday, config.window_start_time \n\t\t) )\n\t\tend_time = config.tz.localize( datetime.combine( \n\t\t\tday, config.window_end_time \n\t\t) )\n\t\twhile time < end_time: # While still in the time window\n\t\t\tthe_times.append( time )\n\t\t\ttime += timedelta(minutes=1)\n\t\tday += timedelta(days=1)\n\treturn the_times","def make_times(self, start, end, delta):\n assert type(start) is float or type(start) is int, \\\n 'Start time not specified with float'\n assert type(end) is float or type(end) is int, \\\n 'End time not specified with a number'\n assert type(delta) is float or type(delta) is int, \\\n 'Time increment not specified with a number'\n assert end >= start, 'End time is before start time'\n self.target_times = []\n step = start\n while step <= end:\n self.target_times.append(step)\n step += delta","def intervals_between(self, start: dt.datetime, end: dt.datetime) -> List[Interval]:\n relevant_intervals: List[Interval] = []\n\n for interval in self.intervals:\n if start <= interval.start_time < end:\n relevant_intervals.append(interval)\n\n return relevant_intervals","def time_slice(self, start=None, end=None):\n if (start is None) and (end is None):\n return self\n else:\n crit = True\n if start is not None:\n crit &= self.data.time >= start\n if end is not None:\n crit &= self.data.time <= end\n # Create a copy of this TrackRun\n result = self.__class__()\n result.extend(self)\n # Replace data with TrackRun.data sliced by start or end\n result.data = result.data[crit]\n # Clear up sources to avoid confusion\n result.sources = []\n result.dirname = None\n result.filelist = []\n try:\n result.conf.dt_start = None\n result.conf.dt_end = None\n except AttributeError:\n pass\n return result","def get_timings(beat, max_length, rand_beat):\n BEATS = [int(beat/4),\n int(beat/2),\n int(beat),\n int(beat*2),\n int(beat*4)]\n\n total_steps = beat * max_length\n timings = [beat] * max_length\n\n if rand_beat == True:\n timings = []\n while total_steps > 0:\n duration = random.sample(BEATS, 1)[0]\n total_steps -= duration\n timings.append(duration)\n\n return timings","def _intervals(parts, duration) -> list:\n part_duration = duration / parts\n return [(floor(i * part_duration), floor((i + 1) * part_duration)) for i in range(parts)]","def get_times(self):\n times = []\n for i in range(1, len(self.events)):\n times.append(self.events[i-1].elapsed_time(self.events[i]))\n return times","def set_start_stop_time(self):\n start_acq, stop_acq = [], []\n \n for fit_id in self.raw_results:\n print(fit_id)\n ts, _ = self.get_spec_times(fit_id)\n if len(ts) > 0:\n start_acq.append(ts.min())\n stop_acq.append(ts.max())\n \n self.setup.start = min(start_acq)\n self.setup.stop = max(stop_acq)\n return self.start, self.stop","def __get_times(self):\n data = self.simulate_file.readlines()\n data = list(map(str.strip, data))\n data = list(map(float, data))\n start = data[0]\n times = data[1:]\n return (start, times)","def datetime_range(start_timestamp, end_timestamp, step_size):\n r = []\n ts = start_timestamp\n while ts <= end_timestamp:\n r.append(ts)\n ts += timedelta(minutes=step_size)\n return r","def _zipTimes(self, user):\n starts = sorted(self._start_times[user])\n ends = sorted(self._end_times[user])\n result = []\n if len(starts) == len(ends):\n for i in xrange(len(starts)):\n interval = self._adjustRange(starts[i], ends[i])\n if interval:\n result.append(interval)\n return result\n\n start_idx = 0\n end_idx = 0\n while end_idx < len(ends):\n interval = self._adjustRange(starts[start_idx], ends[end_idx])\n if start_idx == len(starts) - 1:\n if interval:\n result.append(interval)\n break\n else:\n if interval:\n result.append(interval)\n if end_idx == len(ends) - 1:\n break\n while starts[start_idx + 1] < ends[end_idx]:\n start_idx += 1\n start_idx += 1\n end_idx += 1\n \n return result","def timeRange(self):\r\n _times = self.getTimes()\r\n return _times[0], _times[-1]","def times(self):\n return list(range(self._max_time + 1))","def getTimeSegments(segments,bounds,radius,starttime,endtime,magrange,catalog,contributor):\n stime = starttime\n etime = endtime\n \n dt = etime - stime\n dtseconds = dt.days*86400 + dt.seconds\n #segment 1\n newstime = stime\n newetime = stime + timedelta(seconds=dtseconds/2)\n nevents,maxevents = getEventCount(bounds=bounds,radius=radius,starttime=newstime,endtime=newetime,\n magrange=magrange,catalog=catalog,contributor=contributor)\n if nevents < maxevents:\n segments.append((newstime,newetime))\n else:\n segments = getTimeSegments(segments,bounds,radius,newstime,newetime,\n magrange,catalog,contributor)\n #segment 2\n newstime = newetime\n newetime = etime\n nevents,maxevents = getEventCount(bounds=bounds,radius=radius,\n starttime=newstime,endtime=newetime,\n magrange=magrange,catalog=catalog,\n contributor=contributor)\n if nevents < maxevents:\n segments.append((newstime,newetime))\n else:\n segments = getTimeSegments(segments,bounds,radius,newstime,newetime,\n magrange,catalog,contributor)\n\n return segments","def find_start_end_rests(audio_data, sr, hop_length=HOP_LENGTH, n_fft=N_FFT):\r\n \r\n # Compute the 3rd percentile of the envelope and \r\n # deem anything below this value as silence \r\n envelope = frame(audio_data, hop_length=hop_length, frame_length=n_fft).max(axis=0)\r\n lower_bound = np.percentile(envelope, 5.0)\r\n \r\n # Implement the search as loop, this should be faster than vectorisation\r\n k = 0\r\n while envelope[k] <= lower_bound:\r\n k += 1\r\n \r\n # Return 0 if there is no start rest\r\n if k == 0:\r\n time_start = 0.0\r\n else:\r\n # The first value of the output of the frame function correspond to the time of\r\n # n_fft, then the times are spaced according to hop_length \r\n time_start = ((k-1)*hop_length + n_fft)/float(sr)\r\n \r\n j = len(envelope)-1\r\n while envelope[j] <= lower_bound:\r\n j -= 1\r\n \r\n # Return the length of the track if the is no end rest\r\n if j == len(envelope)-1:\r\n time_end = len(audio_data)/float(sr)\r\n else:\r\n time_end = ((j-1)*hop_length + n_fft)/float(sr)\r\n \r\n times_start_end_rests = [time_start, time_end]\r\n \r\n return(times_start_end_rests)","def get_spec_times(self, fit):\n\n start = asarray(self.raw_results[fit][\"start\"])\n stop = asarray(self.raw_results[fit][\"stop\"])\n return start, stop","def get_marked_slots_times(self, format_string=None):\n\n marked_slots_times = []\n for i in range(0,self._bits_map.length()):\n\n if self._bits_map[i]:\n slot_start_time = self.start_time + timedelta(seconds=self.slot_unit_size_seconds * i)\n slot_end_time = slot_start_time + timedelta(seconds=self.slot_unit_size_seconds)\n\n if format_string:\n marked_slots_times.append((datetime.strftime(slot_start_time,format_string),\n datetime.strftime(slot_end_time, format_string)))\n else:\n marked_slots_times.append((slot_start_time,slot_end_time))\n\n return marked_slots_times","def span_range(cls, frame, start, end):\n if not isinstance(start, cls):\n start = cls.parse(start)\n\n if not isinstance(end, cls):\n end = cls.parse(end)\n\n if start > end:\n # Return empty items when start is greater than end.\n return\n\n # The next starting value to span from.\n next_start = start\n\n while True:\n # We use the span() method to bring the actual tuple with the\n # given time frame.\n span = next_start.span(frame)\n\n if span[1] <= end:\n yield span\n\n # All span-ends have 999999 microseconds set. Shift to the next\n # microsecond to \"turn-over\" to the next start value of the\n # frame.\n next_start = span[1].shift(microseconds=1)\n else:\n break","def find_time_beats(dw1, dw2, dw3, dw4, dw5, dw6, p1, p2, p3, p4, p5, p6):\n time_beats = numpy.concatenate((dw1[p1, 0], dw2[p2, 0],\n dw3[p3, 0], dw4[p4, 0],\n dw5[p5, 0], dw6[p6, 0]))\n\n time_beats = numpy.ndarray.tolist(time_beats)\n\n logging.info('Calculated time of beats: %s', time_beats)\n return time_beats","def times(self, runs):\n times = []\n for run in runs:\n query = \"\"\"SELECT start, stop FROM runs WHERE run = '%i'\"\"\" % run\n #print query\n response = self.query(query)\n if len(response) > 0:\n #print response\n times.append(response[0])\n else:\n times.append((None, None))\n return times","def _get_time_range(self, data):\n time = data.coords[self.time_field]\n if time.size == 0:\n raise ProviderNoDataError()\n else:\n start = _to_datetime_string(data[self.time_field].values.min())\n end = _to_datetime_string(data[self.time_field].values.max())\n return [start, end]","def get_speeds(start: str, end: str) -> Generator[Dict[str, str], None, None]:\n return map(\n lambda date: _api_request(date, WINDSPEED_API_URL),\n _date_range(start, end)\n )","def get_all_measurements(self, start_time, end_time):\n return","def range(self):\n\n return time_stat(self, stat=\"range\")","def dates(self):\n #{{{ function to return start and end times for a station\n return self.wfdates.keys()","def startEndPoints(start, end, num):\n ll = np.linspace(0,1,num)\n xxs = start[0]*(1-ll)+end[0]*ll\n tts = start[1]*(1-ll)+end[1]*ll\n return( np.array([xxs, tts]) )","def _makespan(sched_mapping_list):\n start = reduce(min, [x.start_time for x in sched_mapping_list], 0.)\n end = reduce(max, [x.end_time for x in sched_mapping_list], 0.)\n return end - start","def get_duration_heat(hobo, start, stop):\n alltimes = []\n heat = 0\n numsecs = 0\n for i, t in enumerate(hobo[\"Temp\"][start:stop]):\n if t > TEMP_FLOOR:\n alltimes.append(hobo[\"Time\"][start:stop][i])\n heat += 1/60.0 * t\n numsecs+=1\n if len(alltimes) < 1 : return (timedelta(0),0,0)\n begin = min(alltimes)\n end = max(alltimes)\n return(end-begin,numsecs, heat)","def times(self) -> Mapping[str, float]:\n if len(self._clock_starts) > 0:\n warnings.warn(\n \"Retrieved times while clocks are still going, \"\n \"incomplete times are not included: \"\n f\"{list(self._clock_starts.keys())}\",\n RuntimeWarning,\n )\n return self._accumulated_time.copy()","def calculate_intervals(tick_times: List[float]) -> List[float]:\n return [tick_times[i] - tick_times[i - 1] for i in range(1, len(tick_times))]","def get_start_end_times(frequency, fname):\n\n\n if fname.endswith('.nc'):\n\n ncfile = os.path.basename(fname)\n timestamp = ncfile.strip('.nc').split('_')[-1]\n\n # IF timestamp is of the form YYYYMMDDHHMM-YYYYMMDDHHMM\n if len(timestamp) == 25:\n start_time = datetime.date(int(timestamp[:4]), int(timestamp[4:6]), int(timestamp[7:8]))\n end_time = datetime.date(int(timestamp[-12:-8]), int(timestamp[-8:-6]), int(timestamp[-6:-4]))\n\n # IF timestamp is of the form YYYYMMDDHH-YYYYMMDDHH\n if len(timestamp) == 21:\n start_time = datetime.date(int(timestamp[:4]), int(timestamp[4:6]), int(timestamp[7:8]))\n end_time = datetime.date(int(timestamp[-10:-6]), int(timestamp[-6:-4]), int(timestamp[-4:-2]))\n\n if frequency == 'mon':\n start_time = datetime.date(int(fname[-16:-12]), int(fname[-12:-10]), 01)\n end_mon = fname[-5:-3]\n if end_mon == '02':\n end_day = 28\n elif end_mon in ['04', '06', '09', '11']:\n end_day = 30\n else:\n end_day = 31\n end_time = datetime.date(int(fname[-9:-5]), int(fname[-5:-3]), end_day)\n\n if frequency == 'day':\n start_time = datetime.date(int(fname[-20:-16]), int(fname[-16:-14]), int(fname[-14:-12]))\n end_time = datetime.date(int(fname[-11:-7]), int(fname[-7:-5]), int(fname[-5:-3]))\n else:\n start_time = datetime.date(1900, 1, 1)\n end_time = datetime.date(1999, 12, 31)\n\n return start_time, end_time","def interval(start, end):\n return seconds_since_midnight(end) - seconds_since_midnight(start)","def timepoints(self, session):\n tps = session.query(Timepoint).filter(\n Timepoint.id.between(\n self.start_timepoint_id, self.end_timepoint_id)).all()\n\n return tps","def get_included_trials(trial_times, data_range, t_start=0, t_stop=0):\n return trial_times[\n (trial_times + t_start >= data_range[0]) &\n (trial_times + t_stop < data_range[1])\n ].index","def getStarts(self) -> List[int]:\n ...","def getEnds(self) -> List[int]:\n ...","def file_times(self, cycle_start, fc_start, fc_hours):\n\n logging.info('period_hours = %d' % self.period_hours)\n if self.period_hours not in [1, 3]:\n raise GribError('period_hours = %d must be 1 or 3' % self.period_hours) \n\n \n g=self.grib_forecast_hours_periods\n fc_seq = [] \n for i in range(0, len(g)):\n\t fc_seq += list(range(max(int(fc_start), 0 if i is 0 else g[i-1]['hours'] + g[i]['period']), \n\t g[i]['hours'] + g[i]['period'], g[i]['period']))\n # get all time points up to fc_hours plus one (we must cover entire timespan)\n fc_list = [x for x in fc_seq if x < fc_hours]\n fc_list.append(fc_seq[len(fc_list)])\n\n colmet_files_utc = [cycle_start + timedelta(hours = x) for x in range(int(fc_start), fc_list[-1] +1, self.period_hours)]\n \n return fc_list, colmet_files_utc","def test_total_time_with_start_and_end_times(time_record_factory):\n ts = datetime.datetime(2018, 10, 1, 9, 0)\n te = datetime.datetime(2018, 10, 1, 17, 0)\n\n t = time_record_factory(time_start=ts, time_end=te)\n\n expected = te-ts\n\n assert t.total_time == expected","def compute_blocks(start: time, end: time) -> int:\n end_hour = 24 if end == time.min else end.hour\n blocks = (end_hour - start.hour) // 2\n if blocks <= 0:\n raise Exception(\"Non available blocks to use\")\n \n return blocks","def findguidingstop(starttime, event_list):\n for r in event_list:\n if r[0]==6 and r[1]+datetime.timedelta(seconds=0)>starttime: return r[1]\n return None","def get_hour_offsets(self):\n starttime = self.parameters['startlocaltime']\n stoptime = self.parameters['stoplocaltime']\n timediff = (stoptime - starttime)\n logger.debug(\"Start time: {} | Stop time: {}\".format(starttime, stoptime))\n if timediff > config.TIME_THRESHOLD:\n logger.debug(\"Time delta is {}. This is significantly larger than anticipated\".format(timediff))\n else:\n logger.debug(\"Time delta is {}. Using start time as the global time\".format(timediff))\n\n \"\"\"\n timediff = (stoptime - starttime).total_seconds()\n logger.debug(\"Start time: {} | Stop time: {}\".format(starttime, stoptime))\n #TODO: How do we want to handle large images with huge time differences?\n if timediff > config.TIME_THRESHOLD:\n logger.debug(\"Time delta is {}. This is significantly larger than anticipated\".format(timediff))\n starttime = starttime\n else:\n logger.debug(\"Time delta is {}. Using start time as the global time\".format(timediff))\n \"\"\"\n #Given the image start time, find the nearest index and set to the middle,\n # then find the adjacent two nodes in both directions to get allow a\n # cubic interpolation.\n #image_time = starttime.hour + starttime.minute / 60.0\n # This grabs the hour that is nearest, but hour is circular\n image_time = starttime\n if abs(image_time - 24) < abs(image_time - 23.5):\n image_time -= 24\n mididx, midhour = utils.getnearest(self.times, image_time)\n logger.debug(\"Time is {}. The nearest lookup node is {}\".format(image_time, mididx))\n minidx = mididx - 2\n maxidx = mididx + 2\n\n hourslice = np.arange(minidx, maxidx + 1, dtype=np.int8)\n\n hourslice[hourslice < 0] += 18\n\n if hourslice[-1] >= len(self.times):\n #The hour slice needs to be shifted over the time break\n hourslice[hourslice >= len(self.times)] -= len(self.times)\n logger.debug(\"Using indices {} and start time of {}.\".format(hourslice, image_time))\n return hourslice, image_time","def test_overlap():\n events = [['Event', '2017-11-21T10:00:00-08:00', '2017-11-21T11:00:00-08:00'],\n ['Event', '2017-11-21T10:30:00-08:00', '2017-11-21T11:20:00-08:00']]\n freetimes, _ = free(events, 9, 0, 17, 0, day_range, 30)\n fmt_freetime = output_format(freetimes)\n for i in fmt_freetime:\n print(i)\n assert fmt_freetime == ['Tue, Nov 21, 9:00 am to Tue, Nov 21, 10:00 am.',\n 'Tue, Nov 21, 11:20 am to Tue, Nov 21, 5:00 pm.',\n 'Wed, Nov 22, 9:00 am to Wed, Nov 22, 5:00 pm.',\n 'Thu, Nov 23, 9:00 am to Thu, Nov 23, 5:00 pm.',\n 'Fri, Nov 24, 9:00 am to Fri, Nov 24, 5:00 pm.',\n 'Sat, Nov 25, 9:00 am to Sat, Nov 25, 5:00 pm.',\n 'Sun, Nov 26, 9:00 am to Sun, Nov 26, 5:00 pm.',\n 'Mon, Nov 27, 9:00 am to Mon, Nov 27, 5:00 pm.']","def segments_to_labels(start_times, end_times, labels, window):\n flags = []\n class_names = list(set(labels))\n index = window / 2.0\n while index < end_times[-1]:\n for i in range(len(start_times)):\n if start_times[i] < index <= end_times[i]:\n break\n flags.append(class_names.index(labels[i]))\n index += window\n return np.array(flags), class_names","def burst_time(temp, bstart, bstop):\n to_sum = []\n for b in range(len(bstart)):\n if bstop[b]-bstart[b] >= 0:\n to_sum.append(bstop[b]-bstart[b])\n elif bstop[b]-bstart[b] < 0 and b == len(bstop)+1: # Make it go to end\n to_sum.append(temp['length']/1000.-bstart[b])\n else:\n pass\n return np.mean(to_sum), np.std(to_sum)/np.mean(to_sum), sum(to_sum)/(temp['length']/1000.)","def get_times(my_vars):\n base_time = my_vars['base_time'].getValue()\n try:\n times=my_vars['time']\n except KeyError:\n times = my_vars['time_offset']\n\n ts = []\n for time in times:\n temp = datetime.utcfromtimestamp(base_time+time)\n if (temp.minute == 0) :\n ts.append(temp)\n return ts","def test_save_slot_time_frames(self):\n coupon = COUPON_FACTORY.create_coupon()\n slot = Slot.objects.create(site_id=2,\n business_id=coupon.offer.business.id,\n start_date=datetime.datetime.today(),\n end_date = datetime.datetime.today() + datetime.timedelta(1))\n now = datetime.datetime.now()\n datetime_list = [\n # test iteration 1: Cannot start time frame before the slot starts.\n (now - datetime.timedelta(1), now, False),\n # 2. Cannot end time frame before starting it.\n (now + datetime.timedelta(1), now, False), \n # 3. Cannot start and end at the same time.\n (now, now, False), \n (now, now + datetime.timedelta(1), True), # Becomes slot id = 1.\n # 5. Cannot begin at same time.\n (now, now + datetime.timedelta(1), False), \n (now + datetime.timedelta(1), now + datetime.timedelta(2), True), #2\n (now + datetime.timedelta(2), now + datetime.timedelta(3), True), #3\n (now + datetime.timedelta(5), now + datetime.timedelta(8), True), #4\n # 9. Cannot be a subset of a timeframe.\n (now + datetime.timedelta(6), now + datetime.timedelta(7), False),\n # 10. Cannot wholly include a timeframe.\n (now + datetime.timedelta(4), now + datetime.timedelta(9), False),\n # 11. Cannot straddle a start datetime.\n (now + datetime.timedelta(4), now + datetime.timedelta(6), False),\n # 12. Cannot straddle an end datetime.\n (now + datetime.timedelta(7), now + datetime.timedelta(9), False),\n # 13. Cannot end at the same time.\n (now + datetime.timedelta(6), now + datetime.timedelta(8), False),\n (now + datetime.timedelta(10), None, True), # Becomes slot id = 5.\n (now + datetime.timedelta(9), None, True), # Becomes slot id = 6\n (now + datetime.timedelta(11), None, True), # Becomes slot id = 7\n (now + datetime.timedelta(12), now + datetime.timedelta(15), True),\n ]\n counter = 0\n for start_datetime, end_datetime, is_valid in datetime_list:\n counter += 1\n LOG.debug('test iteration: %s' % counter)\n LOG.debug('test start_datetime: %s' % start_datetime)\n LOG.debug('test end_datetime: %s' % end_datetime)\n slot_time_frame = SlotTimeFrame(slot=slot, coupon_id=coupon.id,\n start_datetime=start_datetime, end_datetime=end_datetime)\n if is_valid:\n slot_time_frame.save()\n self.assertTrue(slot_time_frame.id)\n else:\n with self.assertRaises(ValidationError) as context_manager:\n slot_time_frame.save()\n self.fail('Invalid time frame saved.')\n LOG.debug(context_manager.exception)","def get_kaalas(start_span, end_span, part_start, num_parts):\n start_fraction = part_start / num_parts\n end_fraction = (part_start + 1) / num_parts\n\n start_time = start_span + (end_span - start_span) * start_fraction\n end_time = start_span + (end_span - start_span) * end_fraction\n\n return (start_time, end_time)","def _get_stamps(self, start_stamp, stamp_step):\n\t\tif start_stamp >= self._stamp_left:\n\t\t\tstamp = start_stamp\n\t\telse:\n\t\t\tstamp = start_stamp\\\n\t\t\t + int((self._stamp_left - start_stamp) / stamp_step)\\\n\t\t\t * stamp_step\\\n\t\t\t + stamp_step\n\n\t\twhile stamp < self._end_stamp:\n\t\t\tyield stamp\n\t\t\tstamp += stamp_step","def get_start_end_times(frequency, fname):\n\n if fname.endswith('.nc'):\n\n ncfile = os.path.basename(fname)\n timestamp = ncfile.strip('.nc').split('_')[-1]\n\n # IF timestamp is of the form YYYYMMDDHHMM-YYYYMMDDHHMM\n if len(timestamp) == 25:\n start_time = datetime.date(int(timestamp[:4]), int(timestamp[4:6]), int(timestamp[7:8]))\n end_time = datetime.date(int(timestamp[-12:-8]), int(timestamp[-8:-6]), int(timestamp[-6:-4]))\n\n # IF timestamp is of the form YYYYMMDDHH-YYYYMMDDHH\n if len(timestamp) == 21:\n start_time = datetime.date(int(timestamp[:4]), int(timestamp[4:6]), int(timestamp[7:8]))\n end_time = datetime.date(int(timestamp[-10:-6]), int(timestamp[-6:-4]), int(timestamp[-4:-2]))\n\n if frequency == 'mon':\n start_time = datetime.date(int(fname[-16:-12]), int(fname[-12:-10]), 01)\n end_mon = fname[-5:-3]\n if end_mon == '02':\n end_day = 28\n elif end_mon in ['04', '06', '09', '11']:\n end_day = 30\n else:\n end_day = 31\n end_time = datetime.date(int(fname[-9:-5]), int(fname[-5:-3]), end_day)\n\n if frequency == 'day':\n start_time = datetime.date(int(fname[-20:-16]), int(fname[-16:-14]), int(fname[-14:-12]))\n end_time = datetime.date(int(fname[-11:-7]), int(fname[-7:-5]), int(fname[-5:-3]))\n else:\n start_time = datetime.date(1900, 1, 1)\n end_time = datetime.date(1999, 12, 31)\n\n return start_time, end_time","def _get_timestamps(self, time_interval: RawTimeIntervalType | None, bbox: BBox) -> list[dt.datetime]:","def chunk_periods(start, end):\n\n logging.debug(f'chunking {start} to {end}')\n # convert the strings to datetime objects\n #start = dt.datetime.strptime(''.join(start.rsplit(':', 1)), '%Y-%m-%dT%H:%M:%S-%z')\n start = dt.datetime.strptime(start, '%Y-%m-%dT%H:%M:%S-%z')\n logging.debug(f'start: {start}')\n periods = []\n\n # if the year and month of the period are the same, just return the dates as we got them\n\n\n\n return periods","def start_and_end_time():\n (shour, smin) = START_TIME.split(\":\")\n stime = arrow.now()\n start_time = arrow.Arrow(stime.year, stime.month, stime.day, int(shour), int(smin), tzinfo=stime.tzinfo)\n if stime > start_time:\n # If start time is in the past, start now\n start_time = stime\n if __debug__:\n end_time = start_time.shift(minutes=+2)\n else:\n end_time = start_time.shift(hours=+END_AFTER_HOURS)\n return start_time, end_time","def even_dist(start_time: typing.Union[int, float], end_time: typing.Union[int, float], size: int):\n time_vals = np.arange(start_time, end_time, (end_time - start_time) / size)\n return time_vals","def _build_intervals(self) -> List[Tuple[datetime.datetime, datetime.datetime]]:\n if self.granularity == 'HOUR':\n days = max(min((self.bounds[1] - self.bounds[0]).days,\n self.GRANULARITIES['HOUR'][1]),\n self.GRANULARITIES['HOUR'][0])\n interval_length = datetime.timedelta(days=days)\n offset = datetime.timedelta(hours=1)\n elif self.granularity == 'MONTH':\n # no need to split requests for monthly data\n days = max((self.bounds[1] - self.bounds[0]).days,\n self.GRANULARITIES['MONTH'][0])\n interval_length = datetime.timedelta(days=days)\n offset = datetime.timedelta(days=1)\n else:\n days = max(min((self.bounds[1] - self.bounds[0]).days,\n self.GRANULARITIES['DAY'][1]),\n self.GRANULARITIES['DAY'][0])\n interval_length = datetime.timedelta(days=days)\n offset = datetime.timedelta(days=1)\n\n time_pointer = self.bounds[1]\n intervals = []\n while time_pointer > self.bounds[0]:\n upper = time_pointer\n time_pointer -= interval_length\n intervals.append((time_pointer, upper))\n time_pointer -= offset\n return intervals","def spikesperburst(tdf, bstart, bstop):\n tms = list(tdf.times.dropna().values)\n bursts = [[tms[u] for u in range(len(tms)) if bstart[k]<(tms[u]/1000.)<bstop[k] ]\n for k in range(len(bstart))]\n bursts = [len(i) for i in bursts]\n return np.mean(bursts), np.std(bursts)/np.mean(bursts)","def test_split_ranges(self):\n start = datetime.utcnow() - pd.Timedelta(\"5H\")\n end = datetime.utcnow() + pd.Timedelta(\"5min\")\n delta = pd.Timedelta(\"1H\")\n\n ranges = QueryProvider._calc_split_ranges(start, end, delta)\n self.assertEqual(len(ranges), 5)\n self.assertEqual(ranges[0][0], start)\n self.assertEqual(ranges[-1][1], end)\n\n st_times = [start_tm[0] for start_tm in ranges]\n for end_time in (end_tm[1] for end_tm in ranges):\n self.assertNotIn(end_time, st_times)\n\n end = end + pd.Timedelta(\"20min\")\n ranges = QueryProvider._calc_split_ranges(start, end, delta)\n self.assertEqual(len(ranges), 5)\n self.assertEqual(ranges[0][0], start)\n self.assertEqual(ranges[-1][1], end)","def get_range(self):\n return time_to_range(self.get_time())","def tstamps_for_daterange(self, start_date, end_date):\n\n img_offsets = np.array([timedelta(hours=h) for h in self.h_steps])\n\n timestamps = []\n diff = end_date - start_date\n for i in range(diff.days + 1):\n daily_dates = start_date + timedelta(days=i) + img_offsets\n timestamps.extend(daily_dates.tolist())\n\n return timestamps","def tstamps_for_daterange(self, start_date, end_date):\n img_offsets = np.array([timedelta(hours=h) for h in self.h_steps])\n\n timestamps = []\n diff = end_date - start_date\n for i in range(diff.days + 1):\n daily_dates = start_date + timedelta(days=i) + img_offsets\n timestamps.extend(daily_dates.tolist())\n\n return timestamps","def calculate_time_results(\n time_start,\n trigger_time,\n):\n return {\n 'sample_time': (time_start + (trigger_time - time_start) / 2).unix*u.s,\n 'sample_time_min': time_start.unix*u.s,\n 'sample_time_max': trigger_time.unix*u.s,\n }","def getInterval(self, from_stamp, to_stamp):\n assert from_stamp <= to_stamp\n\n return [msg for (msg, time) in zip(self.cache_msgs, self.cache_times)\n if from_stamp <= time <= to_stamp]","def get_times(level, ns, nt, no, ne, nf):\r\n\r\n automaton = search_automaton(ns, nt, no, ne, nf, level, method3_2)\r\n if type(automaton) is str:\r\n print automaton\r\n exit(-1)\r\n times = list()\r\n for method in (method1, method2, method3_1, method3_2):\r\n start = time.clock()\r\n method(automaton, level)\r\n stop = time.clock()\r\n times.append(stop - start)\r\n return times","def get_event_count(event_times, start, end):\n mask = (event_times > start) & (event_times <= end)\n return event_times[mask].size","def read_times(self, slices=None):\n times = netCDF4.num2date(\n datetime.strptime(\n self.get_handler().SOURCE_START_DATE.split('.')[0],\n '%Y%m%d%H%M%S'\n )\n )\n return numpy.ma.array([times])","def UTC_times(times, \n trace, \n diff_thres = 30.0):\n # set times values to seconds\n \n #AUTOMATE THIS SECTION!\n #CHECK THAT THIS IS CORRECT\n times = times / trace.stats.sampling_rate\n #remove unwanted parts of times numpy array \n times = times[:,0]\n \n #remove the first instance of time because it is \n #somehow always of the wrong format!\n #times = np.delete(times, 0) \n \n event_times = []\n event = [times[0]]\n \n start_time = trace.stats.starttime\n \n #for item in times:\n # print start_time + item\n\n for i in range(1, len(times)):\n \n # check if two events in times array have a difference < diff_thres, \n #if not, run average of those times, if so append that events to a \n #new events_times list\n \n #time_diff = times[i + 1] - times[i]\n \n time_diff = times[i] - times[i-1]\n\n #save info until events are far enough apart! \n if time_diff < diff_thres:\n\n event.append(times[i])\n \n \n #raise conditional for if events are far enough apart! \n else:\n\n event_start = event[0] - 2 #minus 5 seconds\n event_end = max(event) + 2 #add 5 seconds\n\n event_times.append([event_start, event_end])\n \n event = [] \n \n event.append(times[i])\n\n #if event still contains something for any reason, add it to event times\n if len(event) > 0: \n event_start = event[0] - 2 #minus 5 seconds\n event_end = max(event) + 2 #add 5 seconds\n event_times.append([event_start, event_end])\n event = [] \n \n\n\n #if len(event_times) == 0 and len(event) > 0 or time_diff > diff_thres and len(event) > 0:\n \n #event_times.append(sum(event) / len(event))\n \n # event_start = event[0] - 2 #minus 5 seconds\n # event_end = event[-1] + 2 #add 5 seconds\n \n # event_times.append([event_start, event_end])\n \n # event = []\n \n #event_times.append(times[i])\n \n # else:\n # event.append(times[i])\n \n\n UTC_events = []\n\n #earthquake length threshold is 10 seconds and above!\n eq_len = 0#5.0\n\n for i in event_times:\n estart = start_time + i[0]\n eend = start_time + i[1]\n \n if eend - estart > eq_len:\n UTC_events.append([estart, eend])\n \n #UTC_events = np.unique(np.asarray(UTC_events))\n\n \n return UTC_events","def GetTimeCompletenessList(self):\n raise NotImplementedError()","def get_timings(self):\n exp=lib.is_Exposure_d8(self.hcam,7)*1E-3\n frame_rate=lib.is_SetFrameRate(self.hcam,0x8000)\n return self.AcqTimes(exp,1./frame_rate)","def ticks(self, start, end, desired_ticks=None):\n if start == end or isnan(start) or isnan(end):\n return []\n res = self.resolution\n start -= self.zero\n end -= self.zero\n start_tick = int(ceil(start / res))\n end_tick = int(floor(end / res))\n ticks = [i*res for i in range(start_tick, end_tick+1)]\n return ticks","def sfreq_to_times(gaze_array, sfreq, start_time=0):\n return np.arange(0, len(gaze_array) / sfreq, 1. / sfreq) + start_time","def get_pump_times(self, start):\n pumps_dict = {}\n for pump in self.pumps:\n dataframe_ = pd.DataFrame()\n time = []\n command = []\n for i in range(len(pump.start_intervals)):\n t_on = pump.start_intervals[i].epanet_on_time\n t_off = pump.start_intervals[i].epanet_off_time\n time += [start + t_on * pd.Timedelta(\"1S\"),\n start + t_off * pd.Timedelta(\"1S\")]\n command += [1, 0]\n dataframe_['Time'] = time\n dataframe_[pump.link_id] = command\n pumps_dict[pump.link_id] = dataframe_\n return pumps_dict","def hit_time(tmat,start_list,targ_list,ntraj = 1000, cutoff=1000):\n \n # get state names\n nstates = tmat.shape[0]\n states = array([ii for ii in range(nstates)])\n \n times = list()\n \n for ii in range(ntraj):\n curr = choice(start_list)\n tm = 0\n while curr not in targ_list:\n weights = copy(tmat[curr, :])\n curr = discrete_dist(states, weights, nn=1)\n tm += 1\n if tm==cutoff:\n tm = nan\n break\n \n times.append(tm)\n\n return array(times)","def compute_all_minutes(\n opens_in_ns: np.ndarray,\n break_starts_in_ns: np.ndarray,\n break_ends_in_ns: np.ndarray,\n closes_in_ns: np.ndarray,\n) -> np.ndarray:\n pieces = []\n for open_time, break_start_time, break_end_time, close_time in zip(\n opens_in_ns, break_starts_in_ns, break_ends_in_ns, closes_in_ns\n ):\n if break_start_time != NP_NAT:\n pieces.append(\n np.arange(\n open_time,\n break_start_time + NANOSECONDS_PER_MINUTE,\n NANOSECONDS_PER_MINUTE,\n )\n )\n pieces.append(\n np.arange(\n break_end_time,\n close_time + NANOSECONDS_PER_MINUTE,\n NANOSECONDS_PER_MINUTE,\n )\n )\n else:\n pieces.append(\n np.arange(\n open_time,\n close_time + NANOSECONDS_PER_MINUTE,\n NANOSECONDS_PER_MINUTE,\n )\n )\n out = np.concatenate(pieces).view(\"datetime64[ns]\")\n return out","def beaten(self):\r\n return [self.trainer.beaten()]","def probe_times(self):\r\n probe_times = []\r\n for probe in self.__probes.values():\r\n if probe.complete():\r\n if probe.round_trip_time() > 20:\r\n \"Long probe: %s \" %self.__id\r\n probe_times.append(probe.round_trip_time())\r\n return probe_times","def test_lengthen_synthetic_interval(self):\n now = datetime.now()\n now_utc = now.utcnow()\n\n three_hours_before = now - timedelta(hours=3)\n four_hours_before = now - timedelta(hours=4)\n five_hours_before = now - timedelta(hours=5)\n\n three_hours_before_utc = now_utc - timedelta(hours=3)\n four_hours_before_utc = now_utc - timedelta(hours=4)\n five_hours_before_utc = now_utc - timedelta(hours=5)\n\n self.t.configure_exclusions((four_hours_before.time(), three_hours_before.time()))\n\n self.t(\"start {:%Y-%m-%dT%H:%M:%S}\".format(five_hours_before))\n\n self.t(\"lengthen @2 5min\")\n\n j = self.t.export()\n\n self.assertEqual(len(j), 2)\n self.assertClosedInterval(j[0],\n expectedStart=\"{:%Y%m%dT%H%M%S}Z\".format(five_hours_before_utc),\n expectedEnd=\"{:%Y%m%dT%H%M%S}Z\".format(four_hours_before_utc + timedelta(minutes=5)),\n expectedTags=[],\n description=\"lengthened interval\")\n self.assertOpenInterval(j[1],\n expectedStart=\"{:%Y%m%dT%H%M%S}Z\".format(three_hours_before_utc),\n expectedTags=[],\n description=\"unmodified interval\")","def ablation_times(self):\n ats = {}\n for n in np.arange(self.n) + 1:\n t = self.Time[self.ns == n]\n ats[n - 1] = t.max() - t.min()\n return ats","def times():\n MST = pytz.timezone('MST')\n return pd.date_range(start=datetime(2018, 6, 15, 12, 0, 0, tzinfo=MST),\n end=datetime(2018, 6, 15, 13, 0, 0, tzinfo=MST),\n freq='10T')","def _all_dist_to_end(args):\n idx = args[0]\n spiketrains = args[1]\n start = args[2]\n end = args[3]\n N = args[4]\n num_spiketrains = len(spiketrains)\n distances = []\n for jdx in range(idx+1, num_spiketrains):\n dist = distance(spiketrains[idx], spiketrains[jdx], start, end, N)\n times = dist[0] # should be the same for all\n distances.append(dist[1])\n return times, distances","def create_event_dur_score(self):\n for inst in self.instruments:\n #[rest/midipitch, dur, vel]\n inst_score=[]\n running_clock = 0\n for n, note in enumerate(inst.notes):\n freq = mp_to_adjusted_freq(note[0], self.ratios)\n if type(freq) != int: freq = np.asscalar(freq)\n if type(note[0]) != int: inst.notes[n][0] = np.asscalar(note[0])\n if type(note[1]) != int: inst.notes[n][1] = np.asscalar(note[1])\n if type(note[2]) != int: inst.notes[n][2] = np.asscalar(note[2])\n # if type(note[3]) != int: inst.notes[n][3] = np.asscalar(note[3])\n if note[1] != running_clock:\n inst_score.append(['Rest()', note[1] - running_clock, 0])\n inst_score.append([freq, note[2], note[3]])\n running_clock = note[1] + note[2]\n inst.event_dur_score = inst_score","def _fill_time(beats, shard, *args, **kwargs):\n total = 0\n for delay in shard(*args, **kwargs):\n total += delay.count\n yield delay\n yield Beat(beats - total)","def range(self):\n return self.times[0], self.times[-1]","def samples(self):\n return np.linspace(self._start, self._stop, num=self._duration)","def time_in_range(start, end, time):\n if start <= end:\n return start <= time <= end\n else:\n return start <= time or time <= end","def timeSlice(requestContext, seriesList, startSliceAt, endSliceAt=\"now\"):\n\n results = []\n start = time.mktime(parseATTime(startSliceAt).timetuple())\n end = time.mktime(parseATTime(endSliceAt).timetuple())\n\n for slicedSeries in seriesList:\n slicedSeries.name = 'timeSlice(%s, %s, %s)' % (slicedSeries.name, int(start), int(end))\n\n curr = time.mktime(requestContext[\"startTime\"].timetuple())\n for i, v in enumerate(slicedSeries):\n if v is None or curr < start or curr > end:\n slicedSeries[i] = None\n curr += slicedSeries.step\n\n results.append(slicedSeries)\n\n return results","def getIntervals(t_start,t_end,interval):\n timestamp_start = pd.Timestamp(t_start)\n timestamp_end = pd.Timestamp(t_end)\n nro_elements=int(int((timestamp_end-timestamp_start).total_seconds())/(60*interval))\n list_dates = []\n list_dates.append(t_start)\n for i in range(nro_elements):\n date_time_str = list_dates[i]\n Current_Date = dt.datetime.strptime(date_time_str, '%Y-%m-%d %H:%M:%S')\n Next_Date = Current_Date + datetime.timedelta(minutes=interval)\n list_dates.append(str(Next_Date))\n return list_dates","def get_spikes(spiketimes=None, t1=None, t2=None):\n indices = np.where((spiketimes[:,1] > t1) & (spiketimes[:,1] < t2))\n timed_spikes = spiketimes[indices]\n return timed_spikes","def times(self):\n if self._times is None:\n self._times = np.zeros((self.ntimestep), dtype=np.float64)\n for record in range(self.ntimestep):\n self._times[record] = self.get_data_time(record)\n\n return self._times","def calc_overlap(self, start, stop):\n\n overlaps = []\n for s in self.map:\n e = self.map[s]\n if s >= start or s <= stop:\n # We found an overlap\n if e <= stop:\n overlaps.append({\"start\": s, \"stop\": e})\n else:\n overlaps.append({\"start\": s, \"stop\": stop})\n elif e >= start or e <= stop:\n if s >= start:\n overlaps.append({\"start\": s, \"stop\": e})\n else:\n overlaps.append({\"start\": start, \"stop\": e})\n return overlaps","def get_times_list(binout):\r\n return sorted([float(\"{0:15.6f}\".format(t)) for t in\r\n binout.recordarray[\"totim\"]])","def test_start_end_hour():\n # sh = None\n # eh = None\n # data = None\n # result = makesky.start_end_hour(sh, eh, data)\n pass"],"string":"[\n \"def list_times(self, start: int = None, end: int = None) -> List:\\n return [i.time for i in self.data[start:end]]\",\n \"def get_target_timestamps(self):\\n times=[]\\n curr = self.begin_ts\\n while curr<=self.end_ts:\\n times.append(curr)\\n curr = curr + 24 * 60 * 60\\n return times\",\n \"def get_obs_times(start_time, duration, interval=0.2):\\n return start_time + np.arange(0.0, duration+interval/2., interval)*u.h\",\n \"def stadates(self,start=False,end=False):\\n #{{{ function to return start and end times for a station\\n\\n if not start: return self.stachan_cache.keys()\\n\\n\\n cache = {}\\n\\n if not end: end = stock.now()\\n if start > end: end = stock.now()\\n start = float(start)\\n end = float(end)\\n\\n for sta in self.stachan_cache:\\n for chan in self.stachan_cache[sta]:\\n for date in self.stachan_cache[sta][chan]['dates']:\\n\\n if date[1] == -1:\\n\\n if date[0] <= start: cache[sta] = 1\\n if date[0] <= end: cache[sta] = 1\\n\\n else:\\n\\n if date[0] <= start and start <= date[1]: cache[sta] = 1\\n if date[0] <= end and end <= date[1]: cache[sta] = 1\\n if start <= date[0] and date[1] <= end: cache[sta] = 1\\n\\n print cache.keys()\\n return cache.keys()\",\n \"def getTimes():\",\n \"def getTimes():\",\n \"def getTimes():\",\n \"def get_times(self):\\n raise NotImplementedError(\\\"Abstract method not implemented.\\\")\",\n \"def filter_time_range(start: int, end: int, time_slots: Optional[Container[int]] = None) -> Iterator[int]:\\n if time_slots is None:\\n time_slots = range(24)\\n for time in range(start, end):\\n if time in time_slots:\\n yield time\",\n \"def _hist_filter_ts(commands, start_time, end_time):\\n for cmd in commands:\\n if start_time <= cmd[1] < end_time:\\n yield cmd\",\n \"def gen_start_end_times(start_time=[6, 0, 0], end_time=[23, 0, 0]):\\n\\n now = datetime.now()\\n year = now.year\\n month = now.month\\n day = now.day\\n\\n start_time = datetime(\\n year, month, day, start_time[0], start_time[1], start_time[2], 0\\n )\\n\\n end_time = datetime(year, month, day, end_time[0], end_time[1], end_time[2], 0)\\n\\n if end_time < now:\\n end_time += timedelta(days=1)\\n start_time += timedelta(days=1)\\n\\n return start_time, end_time\",\n \"def sample_times():\\n\\tthe_times = []\\n\\tday = config.window_start_date\\n\\twhile day <= config.window_end_date:\\n\\t\\t# times from start of window on day to end of window \\n\\t\\ttime = config.tz.localize( datetime.combine( \\n\\t\\t\\tday, config.window_start_time \\n\\t\\t) )\\n\\t\\tend_time = config.tz.localize( datetime.combine( \\n\\t\\t\\tday, config.window_end_time \\n\\t\\t) )\\n\\t\\twhile time < end_time: # While still in the time window\\n\\t\\t\\tthe_times.append( time )\\n\\t\\t\\ttime += timedelta(minutes=1)\\n\\t\\tday += timedelta(days=1)\\n\\treturn the_times\",\n \"def make_times(self, start, end, delta):\\n assert type(start) is float or type(start) is int, \\\\\\n 'Start time not specified with float'\\n assert type(end) is float or type(end) is int, \\\\\\n 'End time not specified with a number'\\n assert type(delta) is float or type(delta) is int, \\\\\\n 'Time increment not specified with a number'\\n assert end >= start, 'End time is before start time'\\n self.target_times = []\\n step = start\\n while step <= end:\\n self.target_times.append(step)\\n step += delta\",\n \"def intervals_between(self, start: dt.datetime, end: dt.datetime) -> List[Interval]:\\n relevant_intervals: List[Interval] = []\\n\\n for interval in self.intervals:\\n if start <= interval.start_time < end:\\n relevant_intervals.append(interval)\\n\\n return relevant_intervals\",\n \"def time_slice(self, start=None, end=None):\\n if (start is None) and (end is None):\\n return self\\n else:\\n crit = True\\n if start is not None:\\n crit &= self.data.time >= start\\n if end is not None:\\n crit &= self.data.time <= end\\n # Create a copy of this TrackRun\\n result = self.__class__()\\n result.extend(self)\\n # Replace data with TrackRun.data sliced by start or end\\n result.data = result.data[crit]\\n # Clear up sources to avoid confusion\\n result.sources = []\\n result.dirname = None\\n result.filelist = []\\n try:\\n result.conf.dt_start = None\\n result.conf.dt_end = None\\n except AttributeError:\\n pass\\n return result\",\n \"def get_timings(beat, max_length, rand_beat):\\n BEATS = [int(beat/4),\\n int(beat/2),\\n int(beat),\\n int(beat*2),\\n int(beat*4)]\\n\\n total_steps = beat * max_length\\n timings = [beat] * max_length\\n\\n if rand_beat == True:\\n timings = []\\n while total_steps > 0:\\n duration = random.sample(BEATS, 1)[0]\\n total_steps -= duration\\n timings.append(duration)\\n\\n return timings\",\n \"def _intervals(parts, duration) -> list:\\n part_duration = duration / parts\\n return [(floor(i * part_duration), floor((i + 1) * part_duration)) for i in range(parts)]\",\n \"def get_times(self):\\n times = []\\n for i in range(1, len(self.events)):\\n times.append(self.events[i-1].elapsed_time(self.events[i]))\\n return times\",\n \"def set_start_stop_time(self):\\n start_acq, stop_acq = [], []\\n \\n for fit_id in self.raw_results:\\n print(fit_id)\\n ts, _ = self.get_spec_times(fit_id)\\n if len(ts) > 0:\\n start_acq.append(ts.min())\\n stop_acq.append(ts.max())\\n \\n self.setup.start = min(start_acq)\\n self.setup.stop = max(stop_acq)\\n return self.start, self.stop\",\n \"def __get_times(self):\\n data = self.simulate_file.readlines()\\n data = list(map(str.strip, data))\\n data = list(map(float, data))\\n start = data[0]\\n times = data[1:]\\n return (start, times)\",\n \"def datetime_range(start_timestamp, end_timestamp, step_size):\\n r = []\\n ts = start_timestamp\\n while ts <= end_timestamp:\\n r.append(ts)\\n ts += timedelta(minutes=step_size)\\n return r\",\n \"def _zipTimes(self, user):\\n starts = sorted(self._start_times[user])\\n ends = sorted(self._end_times[user])\\n result = []\\n if len(starts) == len(ends):\\n for i in xrange(len(starts)):\\n interval = self._adjustRange(starts[i], ends[i])\\n if interval:\\n result.append(interval)\\n return result\\n\\n start_idx = 0\\n end_idx = 0\\n while end_idx < len(ends):\\n interval = self._adjustRange(starts[start_idx], ends[end_idx])\\n if start_idx == len(starts) - 1:\\n if interval:\\n result.append(interval)\\n break\\n else:\\n if interval:\\n result.append(interval)\\n if end_idx == len(ends) - 1:\\n break\\n while starts[start_idx + 1] < ends[end_idx]:\\n start_idx += 1\\n start_idx += 1\\n end_idx += 1\\n \\n return result\",\n \"def timeRange(self):\\r\\n _times = self.getTimes()\\r\\n return _times[0], _times[-1]\",\n \"def times(self):\\n return list(range(self._max_time + 1))\",\n \"def getTimeSegments(segments,bounds,radius,starttime,endtime,magrange,catalog,contributor):\\n stime = starttime\\n etime = endtime\\n \\n dt = etime - stime\\n dtseconds = dt.days*86400 + dt.seconds\\n #segment 1\\n newstime = stime\\n newetime = stime + timedelta(seconds=dtseconds/2)\\n nevents,maxevents = getEventCount(bounds=bounds,radius=radius,starttime=newstime,endtime=newetime,\\n magrange=magrange,catalog=catalog,contributor=contributor)\\n if nevents < maxevents:\\n segments.append((newstime,newetime))\\n else:\\n segments = getTimeSegments(segments,bounds,radius,newstime,newetime,\\n magrange,catalog,contributor)\\n #segment 2\\n newstime = newetime\\n newetime = etime\\n nevents,maxevents = getEventCount(bounds=bounds,radius=radius,\\n starttime=newstime,endtime=newetime,\\n magrange=magrange,catalog=catalog,\\n contributor=contributor)\\n if nevents < maxevents:\\n segments.append((newstime,newetime))\\n else:\\n segments = getTimeSegments(segments,bounds,radius,newstime,newetime,\\n magrange,catalog,contributor)\\n\\n return segments\",\n \"def find_start_end_rests(audio_data, sr, hop_length=HOP_LENGTH, n_fft=N_FFT):\\r\\n \\r\\n # Compute the 3rd percentile of the envelope and \\r\\n # deem anything below this value as silence \\r\\n envelope = frame(audio_data, hop_length=hop_length, frame_length=n_fft).max(axis=0)\\r\\n lower_bound = np.percentile(envelope, 5.0)\\r\\n \\r\\n # Implement the search as loop, this should be faster than vectorisation\\r\\n k = 0\\r\\n while envelope[k] <= lower_bound:\\r\\n k += 1\\r\\n \\r\\n # Return 0 if there is no start rest\\r\\n if k == 0:\\r\\n time_start = 0.0\\r\\n else:\\r\\n # The first value of the output of the frame function correspond to the time of\\r\\n # n_fft, then the times are spaced according to hop_length \\r\\n time_start = ((k-1)*hop_length + n_fft)/float(sr)\\r\\n \\r\\n j = len(envelope)-1\\r\\n while envelope[j] <= lower_bound:\\r\\n j -= 1\\r\\n \\r\\n # Return the length of the track if the is no end rest\\r\\n if j == len(envelope)-1:\\r\\n time_end = len(audio_data)/float(sr)\\r\\n else:\\r\\n time_end = ((j-1)*hop_length + n_fft)/float(sr)\\r\\n \\r\\n times_start_end_rests = [time_start, time_end]\\r\\n \\r\\n return(times_start_end_rests)\",\n \"def get_spec_times(self, fit):\\n\\n start = asarray(self.raw_results[fit][\\\"start\\\"])\\n stop = asarray(self.raw_results[fit][\\\"stop\\\"])\\n return start, stop\",\n \"def get_marked_slots_times(self, format_string=None):\\n\\n marked_slots_times = []\\n for i in range(0,self._bits_map.length()):\\n\\n if self._bits_map[i]:\\n slot_start_time = self.start_time + timedelta(seconds=self.slot_unit_size_seconds * i)\\n slot_end_time = slot_start_time + timedelta(seconds=self.slot_unit_size_seconds)\\n\\n if format_string:\\n marked_slots_times.append((datetime.strftime(slot_start_time,format_string),\\n datetime.strftime(slot_end_time, format_string)))\\n else:\\n marked_slots_times.append((slot_start_time,slot_end_time))\\n\\n return marked_slots_times\",\n \"def span_range(cls, frame, start, end):\\n if not isinstance(start, cls):\\n start = cls.parse(start)\\n\\n if not isinstance(end, cls):\\n end = cls.parse(end)\\n\\n if start > end:\\n # Return empty items when start is greater than end.\\n return\\n\\n # The next starting value to span from.\\n next_start = start\\n\\n while True:\\n # We use the span() method to bring the actual tuple with the\\n # given time frame.\\n span = next_start.span(frame)\\n\\n if span[1] <= end:\\n yield span\\n\\n # All span-ends have 999999 microseconds set. Shift to the next\\n # microsecond to \\\"turn-over\\\" to the next start value of the\\n # frame.\\n next_start = span[1].shift(microseconds=1)\\n else:\\n break\",\n \"def find_time_beats(dw1, dw2, dw3, dw4, dw5, dw6, p1, p2, p3, p4, p5, p6):\\n time_beats = numpy.concatenate((dw1[p1, 0], dw2[p2, 0],\\n dw3[p3, 0], dw4[p4, 0],\\n dw5[p5, 0], dw6[p6, 0]))\\n\\n time_beats = numpy.ndarray.tolist(time_beats)\\n\\n logging.info('Calculated time of beats: %s', time_beats)\\n return time_beats\",\n \"def times(self, runs):\\n times = []\\n for run in runs:\\n query = \\\"\\\"\\\"SELECT start, stop FROM runs WHERE run = '%i'\\\"\\\"\\\" % run\\n #print query\\n response = self.query(query)\\n if len(response) > 0:\\n #print response\\n times.append(response[0])\\n else:\\n times.append((None, None))\\n return times\",\n \"def _get_time_range(self, data):\\n time = data.coords[self.time_field]\\n if time.size == 0:\\n raise ProviderNoDataError()\\n else:\\n start = _to_datetime_string(data[self.time_field].values.min())\\n end = _to_datetime_string(data[self.time_field].values.max())\\n return [start, end]\",\n \"def get_speeds(start: str, end: str) -> Generator[Dict[str, str], None, None]:\\n return map(\\n lambda date: _api_request(date, WINDSPEED_API_URL),\\n _date_range(start, end)\\n )\",\n \"def get_all_measurements(self, start_time, end_time):\\n return\",\n \"def range(self):\\n\\n return time_stat(self, stat=\\\"range\\\")\",\n \"def dates(self):\\n #{{{ function to return start and end times for a station\\n return self.wfdates.keys()\",\n \"def startEndPoints(start, end, num):\\n ll = np.linspace(0,1,num)\\n xxs = start[0]*(1-ll)+end[0]*ll\\n tts = start[1]*(1-ll)+end[1]*ll\\n return( np.array([xxs, tts]) )\",\n \"def _makespan(sched_mapping_list):\\n start = reduce(min, [x.start_time for x in sched_mapping_list], 0.)\\n end = reduce(max, [x.end_time for x in sched_mapping_list], 0.)\\n return end - start\",\n \"def get_duration_heat(hobo, start, stop):\\n alltimes = []\\n heat = 0\\n numsecs = 0\\n for i, t in enumerate(hobo[\\\"Temp\\\"][start:stop]):\\n if t > TEMP_FLOOR:\\n alltimes.append(hobo[\\\"Time\\\"][start:stop][i])\\n heat += 1/60.0 * t\\n numsecs+=1\\n if len(alltimes) < 1 : return (timedelta(0),0,0)\\n begin = min(alltimes)\\n end = max(alltimes)\\n return(end-begin,numsecs, heat)\",\n \"def times(self) -> Mapping[str, float]:\\n if len(self._clock_starts) > 0:\\n warnings.warn(\\n \\\"Retrieved times while clocks are still going, \\\"\\n \\\"incomplete times are not included: \\\"\\n f\\\"{list(self._clock_starts.keys())}\\\",\\n RuntimeWarning,\\n )\\n return self._accumulated_time.copy()\",\n \"def calculate_intervals(tick_times: List[float]) -> List[float]:\\n return [tick_times[i] - tick_times[i - 1] for i in range(1, len(tick_times))]\",\n \"def get_start_end_times(frequency, fname):\\n\\n\\n if fname.endswith('.nc'):\\n\\n ncfile = os.path.basename(fname)\\n timestamp = ncfile.strip('.nc').split('_')[-1]\\n\\n # IF timestamp is of the form YYYYMMDDHHMM-YYYYMMDDHHMM\\n if len(timestamp) == 25:\\n start_time = datetime.date(int(timestamp[:4]), int(timestamp[4:6]), int(timestamp[7:8]))\\n end_time = datetime.date(int(timestamp[-12:-8]), int(timestamp[-8:-6]), int(timestamp[-6:-4]))\\n\\n # IF timestamp is of the form YYYYMMDDHH-YYYYMMDDHH\\n if len(timestamp) == 21:\\n start_time = datetime.date(int(timestamp[:4]), int(timestamp[4:6]), int(timestamp[7:8]))\\n end_time = datetime.date(int(timestamp[-10:-6]), int(timestamp[-6:-4]), int(timestamp[-4:-2]))\\n\\n if frequency == 'mon':\\n start_time = datetime.date(int(fname[-16:-12]), int(fname[-12:-10]), 01)\\n end_mon = fname[-5:-3]\\n if end_mon == '02':\\n end_day = 28\\n elif end_mon in ['04', '06', '09', '11']:\\n end_day = 30\\n else:\\n end_day = 31\\n end_time = datetime.date(int(fname[-9:-5]), int(fname[-5:-3]), end_day)\\n\\n if frequency == 'day':\\n start_time = datetime.date(int(fname[-20:-16]), int(fname[-16:-14]), int(fname[-14:-12]))\\n end_time = datetime.date(int(fname[-11:-7]), int(fname[-7:-5]), int(fname[-5:-3]))\\n else:\\n start_time = datetime.date(1900, 1, 1)\\n end_time = datetime.date(1999, 12, 31)\\n\\n return start_time, end_time\",\n \"def interval(start, end):\\n return seconds_since_midnight(end) - seconds_since_midnight(start)\",\n \"def timepoints(self, session):\\n tps = session.query(Timepoint).filter(\\n Timepoint.id.between(\\n self.start_timepoint_id, self.end_timepoint_id)).all()\\n\\n return tps\",\n \"def get_included_trials(trial_times, data_range, t_start=0, t_stop=0):\\n return trial_times[\\n (trial_times + t_start >= data_range[0]) &\\n (trial_times + t_stop < data_range[1])\\n ].index\",\n \"def getStarts(self) -> List[int]:\\n ...\",\n \"def getEnds(self) -> List[int]:\\n ...\",\n \"def file_times(self, cycle_start, fc_start, fc_hours):\\n\\n logging.info('period_hours = %d' % self.period_hours)\\n if self.period_hours not in [1, 3]:\\n raise GribError('period_hours = %d must be 1 or 3' % self.period_hours) \\n\\n \\n g=self.grib_forecast_hours_periods\\n fc_seq = [] \\n for i in range(0, len(g)):\\n\\t fc_seq += list(range(max(int(fc_start), 0 if i is 0 else g[i-1]['hours'] + g[i]['period']), \\n\\t g[i]['hours'] + g[i]['period'], g[i]['period']))\\n # get all time points up to fc_hours plus one (we must cover entire timespan)\\n fc_list = [x for x in fc_seq if x < fc_hours]\\n fc_list.append(fc_seq[len(fc_list)])\\n\\n colmet_files_utc = [cycle_start + timedelta(hours = x) for x in range(int(fc_start), fc_list[-1] +1, self.period_hours)]\\n \\n return fc_list, colmet_files_utc\",\n \"def test_total_time_with_start_and_end_times(time_record_factory):\\n ts = datetime.datetime(2018, 10, 1, 9, 0)\\n te = datetime.datetime(2018, 10, 1, 17, 0)\\n\\n t = time_record_factory(time_start=ts, time_end=te)\\n\\n expected = te-ts\\n\\n assert t.total_time == expected\",\n \"def compute_blocks(start: time, end: time) -> int:\\n end_hour = 24 if end == time.min else end.hour\\n blocks = (end_hour - start.hour) // 2\\n if blocks <= 0:\\n raise Exception(\\\"Non available blocks to use\\\")\\n \\n return blocks\",\n \"def findguidingstop(starttime, event_list):\\n for r in event_list:\\n if r[0]==6 and r[1]+datetime.timedelta(seconds=0)>starttime: return r[1]\\n return None\",\n \"def get_hour_offsets(self):\\n starttime = self.parameters['startlocaltime']\\n stoptime = self.parameters['stoplocaltime']\\n timediff = (stoptime - starttime)\\n logger.debug(\\\"Start time: {} | Stop time: {}\\\".format(starttime, stoptime))\\n if timediff > config.TIME_THRESHOLD:\\n logger.debug(\\\"Time delta is {}. This is significantly larger than anticipated\\\".format(timediff))\\n else:\\n logger.debug(\\\"Time delta is {}. Using start time as the global time\\\".format(timediff))\\n\\n \\\"\\\"\\\"\\n timediff = (stoptime - starttime).total_seconds()\\n logger.debug(\\\"Start time: {} | Stop time: {}\\\".format(starttime, stoptime))\\n #TODO: How do we want to handle large images with huge time differences?\\n if timediff > config.TIME_THRESHOLD:\\n logger.debug(\\\"Time delta is {}. This is significantly larger than anticipated\\\".format(timediff))\\n starttime = starttime\\n else:\\n logger.debug(\\\"Time delta is {}. Using start time as the global time\\\".format(timediff))\\n \\\"\\\"\\\"\\n #Given the image start time, find the nearest index and set to the middle,\\n # then find the adjacent two nodes in both directions to get allow a\\n # cubic interpolation.\\n #image_time = starttime.hour + starttime.minute / 60.0\\n # This grabs the hour that is nearest, but hour is circular\\n image_time = starttime\\n if abs(image_time - 24) < abs(image_time - 23.5):\\n image_time -= 24\\n mididx, midhour = utils.getnearest(self.times, image_time)\\n logger.debug(\\\"Time is {}. The nearest lookup node is {}\\\".format(image_time, mididx))\\n minidx = mididx - 2\\n maxidx = mididx + 2\\n\\n hourslice = np.arange(minidx, maxidx + 1, dtype=np.int8)\\n\\n hourslice[hourslice < 0] += 18\\n\\n if hourslice[-1] >= len(self.times):\\n #The hour slice needs to be shifted over the time break\\n hourslice[hourslice >= len(self.times)] -= len(self.times)\\n logger.debug(\\\"Using indices {} and start time of {}.\\\".format(hourslice, image_time))\\n return hourslice, image_time\",\n \"def test_overlap():\\n events = [['Event', '2017-11-21T10:00:00-08:00', '2017-11-21T11:00:00-08:00'],\\n ['Event', '2017-11-21T10:30:00-08:00', '2017-11-21T11:20:00-08:00']]\\n freetimes, _ = free(events, 9, 0, 17, 0, day_range, 30)\\n fmt_freetime = output_format(freetimes)\\n for i in fmt_freetime:\\n print(i)\\n assert fmt_freetime == ['Tue, Nov 21, 9:00 am to Tue, Nov 21, 10:00 am.',\\n 'Tue, Nov 21, 11:20 am to Tue, Nov 21, 5:00 pm.',\\n 'Wed, Nov 22, 9:00 am to Wed, Nov 22, 5:00 pm.',\\n 'Thu, Nov 23, 9:00 am to Thu, Nov 23, 5:00 pm.',\\n 'Fri, Nov 24, 9:00 am to Fri, Nov 24, 5:00 pm.',\\n 'Sat, Nov 25, 9:00 am to Sat, Nov 25, 5:00 pm.',\\n 'Sun, Nov 26, 9:00 am to Sun, Nov 26, 5:00 pm.',\\n 'Mon, Nov 27, 9:00 am to Mon, Nov 27, 5:00 pm.']\",\n \"def segments_to_labels(start_times, end_times, labels, window):\\n flags = []\\n class_names = list(set(labels))\\n index = window / 2.0\\n while index < end_times[-1]:\\n for i in range(len(start_times)):\\n if start_times[i] < index <= end_times[i]:\\n break\\n flags.append(class_names.index(labels[i]))\\n index += window\\n return np.array(flags), class_names\",\n \"def burst_time(temp, bstart, bstop):\\n to_sum = []\\n for b in range(len(bstart)):\\n if bstop[b]-bstart[b] >= 0:\\n to_sum.append(bstop[b]-bstart[b])\\n elif bstop[b]-bstart[b] < 0 and b == len(bstop)+1: # Make it go to end\\n to_sum.append(temp['length']/1000.-bstart[b])\\n else:\\n pass\\n return np.mean(to_sum), np.std(to_sum)/np.mean(to_sum), sum(to_sum)/(temp['length']/1000.)\",\n \"def get_times(my_vars):\\n base_time = my_vars['base_time'].getValue()\\n try:\\n times=my_vars['time']\\n except KeyError:\\n times = my_vars['time_offset']\\n\\n ts = []\\n for time in times:\\n temp = datetime.utcfromtimestamp(base_time+time)\\n if (temp.minute == 0) :\\n ts.append(temp)\\n return ts\",\n \"def test_save_slot_time_frames(self):\\n coupon = COUPON_FACTORY.create_coupon()\\n slot = Slot.objects.create(site_id=2,\\n business_id=coupon.offer.business.id,\\n start_date=datetime.datetime.today(),\\n end_date = datetime.datetime.today() + datetime.timedelta(1))\\n now = datetime.datetime.now()\\n datetime_list = [\\n # test iteration 1: Cannot start time frame before the slot starts.\\n (now - datetime.timedelta(1), now, False),\\n # 2. Cannot end time frame before starting it.\\n (now + datetime.timedelta(1), now, False), \\n # 3. Cannot start and end at the same time.\\n (now, now, False), \\n (now, now + datetime.timedelta(1), True), # Becomes slot id = 1.\\n # 5. Cannot begin at same time.\\n (now, now + datetime.timedelta(1), False), \\n (now + datetime.timedelta(1), now + datetime.timedelta(2), True), #2\\n (now + datetime.timedelta(2), now + datetime.timedelta(3), True), #3\\n (now + datetime.timedelta(5), now + datetime.timedelta(8), True), #4\\n # 9. Cannot be a subset of a timeframe.\\n (now + datetime.timedelta(6), now + datetime.timedelta(7), False),\\n # 10. Cannot wholly include a timeframe.\\n (now + datetime.timedelta(4), now + datetime.timedelta(9), False),\\n # 11. Cannot straddle a start datetime.\\n (now + datetime.timedelta(4), now + datetime.timedelta(6), False),\\n # 12. Cannot straddle an end datetime.\\n (now + datetime.timedelta(7), now + datetime.timedelta(9), False),\\n # 13. Cannot end at the same time.\\n (now + datetime.timedelta(6), now + datetime.timedelta(8), False),\\n (now + datetime.timedelta(10), None, True), # Becomes slot id = 5.\\n (now + datetime.timedelta(9), None, True), # Becomes slot id = 6\\n (now + datetime.timedelta(11), None, True), # Becomes slot id = 7\\n (now + datetime.timedelta(12), now + datetime.timedelta(15), True),\\n ]\\n counter = 0\\n for start_datetime, end_datetime, is_valid in datetime_list:\\n counter += 1\\n LOG.debug('test iteration: %s' % counter)\\n LOG.debug('test start_datetime: %s' % start_datetime)\\n LOG.debug('test end_datetime: %s' % end_datetime)\\n slot_time_frame = SlotTimeFrame(slot=slot, coupon_id=coupon.id,\\n start_datetime=start_datetime, end_datetime=end_datetime)\\n if is_valid:\\n slot_time_frame.save()\\n self.assertTrue(slot_time_frame.id)\\n else:\\n with self.assertRaises(ValidationError) as context_manager:\\n slot_time_frame.save()\\n self.fail('Invalid time frame saved.')\\n LOG.debug(context_manager.exception)\",\n \"def get_kaalas(start_span, end_span, part_start, num_parts):\\n start_fraction = part_start / num_parts\\n end_fraction = (part_start + 1) / num_parts\\n\\n start_time = start_span + (end_span - start_span) * start_fraction\\n end_time = start_span + (end_span - start_span) * end_fraction\\n\\n return (start_time, end_time)\",\n \"def _get_stamps(self, start_stamp, stamp_step):\\n\\t\\tif start_stamp >= self._stamp_left:\\n\\t\\t\\tstamp = start_stamp\\n\\t\\telse:\\n\\t\\t\\tstamp = start_stamp\\\\\\n\\t\\t\\t + int((self._stamp_left - start_stamp) / stamp_step)\\\\\\n\\t\\t\\t * stamp_step\\\\\\n\\t\\t\\t + stamp_step\\n\\n\\t\\twhile stamp < self._end_stamp:\\n\\t\\t\\tyield stamp\\n\\t\\t\\tstamp += stamp_step\",\n \"def get_start_end_times(frequency, fname):\\n\\n if fname.endswith('.nc'):\\n\\n ncfile = os.path.basename(fname)\\n timestamp = ncfile.strip('.nc').split('_')[-1]\\n\\n # IF timestamp is of the form YYYYMMDDHHMM-YYYYMMDDHHMM\\n if len(timestamp) == 25:\\n start_time = datetime.date(int(timestamp[:4]), int(timestamp[4:6]), int(timestamp[7:8]))\\n end_time = datetime.date(int(timestamp[-12:-8]), int(timestamp[-8:-6]), int(timestamp[-6:-4]))\\n\\n # IF timestamp is of the form YYYYMMDDHH-YYYYMMDDHH\\n if len(timestamp) == 21:\\n start_time = datetime.date(int(timestamp[:4]), int(timestamp[4:6]), int(timestamp[7:8]))\\n end_time = datetime.date(int(timestamp[-10:-6]), int(timestamp[-6:-4]), int(timestamp[-4:-2]))\\n\\n if frequency == 'mon':\\n start_time = datetime.date(int(fname[-16:-12]), int(fname[-12:-10]), 01)\\n end_mon = fname[-5:-3]\\n if end_mon == '02':\\n end_day = 28\\n elif end_mon in ['04', '06', '09', '11']:\\n end_day = 30\\n else:\\n end_day = 31\\n end_time = datetime.date(int(fname[-9:-5]), int(fname[-5:-3]), end_day)\\n\\n if frequency == 'day':\\n start_time = datetime.date(int(fname[-20:-16]), int(fname[-16:-14]), int(fname[-14:-12]))\\n end_time = datetime.date(int(fname[-11:-7]), int(fname[-7:-5]), int(fname[-5:-3]))\\n else:\\n start_time = datetime.date(1900, 1, 1)\\n end_time = datetime.date(1999, 12, 31)\\n\\n return start_time, end_time\",\n \"def _get_timestamps(self, time_interval: RawTimeIntervalType | None, bbox: BBox) -> list[dt.datetime]:\",\n \"def chunk_periods(start, end):\\n\\n logging.debug(f'chunking {start} to {end}')\\n # convert the strings to datetime objects\\n #start = dt.datetime.strptime(''.join(start.rsplit(':', 1)), '%Y-%m-%dT%H:%M:%S-%z')\\n start = dt.datetime.strptime(start, '%Y-%m-%dT%H:%M:%S-%z')\\n logging.debug(f'start: {start}')\\n periods = []\\n\\n # if the year and month of the period are the same, just return the dates as we got them\\n\\n\\n\\n return periods\",\n \"def start_and_end_time():\\n (shour, smin) = START_TIME.split(\\\":\\\")\\n stime = arrow.now()\\n start_time = arrow.Arrow(stime.year, stime.month, stime.day, int(shour), int(smin), tzinfo=stime.tzinfo)\\n if stime > start_time:\\n # If start time is in the past, start now\\n start_time = stime\\n if __debug__:\\n end_time = start_time.shift(minutes=+2)\\n else:\\n end_time = start_time.shift(hours=+END_AFTER_HOURS)\\n return start_time, end_time\",\n \"def even_dist(start_time: typing.Union[int, float], end_time: typing.Union[int, float], size: int):\\n time_vals = np.arange(start_time, end_time, (end_time - start_time) / size)\\n return time_vals\",\n \"def _build_intervals(self) -> List[Tuple[datetime.datetime, datetime.datetime]]:\\n if self.granularity == 'HOUR':\\n days = max(min((self.bounds[1] - self.bounds[0]).days,\\n self.GRANULARITIES['HOUR'][1]),\\n self.GRANULARITIES['HOUR'][0])\\n interval_length = datetime.timedelta(days=days)\\n offset = datetime.timedelta(hours=1)\\n elif self.granularity == 'MONTH':\\n # no need to split requests for monthly data\\n days = max((self.bounds[1] - self.bounds[0]).days,\\n self.GRANULARITIES['MONTH'][0])\\n interval_length = datetime.timedelta(days=days)\\n offset = datetime.timedelta(days=1)\\n else:\\n days = max(min((self.bounds[1] - self.bounds[0]).days,\\n self.GRANULARITIES['DAY'][1]),\\n self.GRANULARITIES['DAY'][0])\\n interval_length = datetime.timedelta(days=days)\\n offset = datetime.timedelta(days=1)\\n\\n time_pointer = self.bounds[1]\\n intervals = []\\n while time_pointer > self.bounds[0]:\\n upper = time_pointer\\n time_pointer -= interval_length\\n intervals.append((time_pointer, upper))\\n time_pointer -= offset\\n return intervals\",\n \"def spikesperburst(tdf, bstart, bstop):\\n tms = list(tdf.times.dropna().values)\\n bursts = [[tms[u] for u in range(len(tms)) if bstart[k]<(tms[u]/1000.)<bstop[k] ]\\n for k in range(len(bstart))]\\n bursts = [len(i) for i in bursts]\\n return np.mean(bursts), np.std(bursts)/np.mean(bursts)\",\n \"def test_split_ranges(self):\\n start = datetime.utcnow() - pd.Timedelta(\\\"5H\\\")\\n end = datetime.utcnow() + pd.Timedelta(\\\"5min\\\")\\n delta = pd.Timedelta(\\\"1H\\\")\\n\\n ranges = QueryProvider._calc_split_ranges(start, end, delta)\\n self.assertEqual(len(ranges), 5)\\n self.assertEqual(ranges[0][0], start)\\n self.assertEqual(ranges[-1][1], end)\\n\\n st_times = [start_tm[0] for start_tm in ranges]\\n for end_time in (end_tm[1] for end_tm in ranges):\\n self.assertNotIn(end_time, st_times)\\n\\n end = end + pd.Timedelta(\\\"20min\\\")\\n ranges = QueryProvider._calc_split_ranges(start, end, delta)\\n self.assertEqual(len(ranges), 5)\\n self.assertEqual(ranges[0][0], start)\\n self.assertEqual(ranges[-1][1], end)\",\n \"def get_range(self):\\n return time_to_range(self.get_time())\",\n \"def tstamps_for_daterange(self, start_date, end_date):\\n\\n img_offsets = np.array([timedelta(hours=h) for h in self.h_steps])\\n\\n timestamps = []\\n diff = end_date - start_date\\n for i in range(diff.days + 1):\\n daily_dates = start_date + timedelta(days=i) + img_offsets\\n timestamps.extend(daily_dates.tolist())\\n\\n return timestamps\",\n \"def tstamps_for_daterange(self, start_date, end_date):\\n img_offsets = np.array([timedelta(hours=h) for h in self.h_steps])\\n\\n timestamps = []\\n diff = end_date - start_date\\n for i in range(diff.days + 1):\\n daily_dates = start_date + timedelta(days=i) + img_offsets\\n timestamps.extend(daily_dates.tolist())\\n\\n return timestamps\",\n \"def calculate_time_results(\\n time_start,\\n trigger_time,\\n):\\n return {\\n 'sample_time': (time_start + (trigger_time - time_start) / 2).unix*u.s,\\n 'sample_time_min': time_start.unix*u.s,\\n 'sample_time_max': trigger_time.unix*u.s,\\n }\",\n \"def getInterval(self, from_stamp, to_stamp):\\n assert from_stamp <= to_stamp\\n\\n return [msg for (msg, time) in zip(self.cache_msgs, self.cache_times)\\n if from_stamp <= time <= to_stamp]\",\n \"def get_times(level, ns, nt, no, ne, nf):\\r\\n\\r\\n automaton = search_automaton(ns, nt, no, ne, nf, level, method3_2)\\r\\n if type(automaton) is str:\\r\\n print automaton\\r\\n exit(-1)\\r\\n times = list()\\r\\n for method in (method1, method2, method3_1, method3_2):\\r\\n start = time.clock()\\r\\n method(automaton, level)\\r\\n stop = time.clock()\\r\\n times.append(stop - start)\\r\\n return times\",\n \"def get_event_count(event_times, start, end):\\n mask = (event_times > start) & (event_times <= end)\\n return event_times[mask].size\",\n \"def read_times(self, slices=None):\\n times = netCDF4.num2date(\\n datetime.strptime(\\n self.get_handler().SOURCE_START_DATE.split('.')[0],\\n '%Y%m%d%H%M%S'\\n )\\n )\\n return numpy.ma.array([times])\",\n \"def UTC_times(times, \\n trace, \\n diff_thres = 30.0):\\n # set times values to seconds\\n \\n #AUTOMATE THIS SECTION!\\n #CHECK THAT THIS IS CORRECT\\n times = times / trace.stats.sampling_rate\\n #remove unwanted parts of times numpy array \\n times = times[:,0]\\n \\n #remove the first instance of time because it is \\n #somehow always of the wrong format!\\n #times = np.delete(times, 0) \\n \\n event_times = []\\n event = [times[0]]\\n \\n start_time = trace.stats.starttime\\n \\n #for item in times:\\n # print start_time + item\\n\\n for i in range(1, len(times)):\\n \\n # check if two events in times array have a difference < diff_thres, \\n #if not, run average of those times, if so append that events to a \\n #new events_times list\\n \\n #time_diff = times[i + 1] - times[i]\\n \\n time_diff = times[i] - times[i-1]\\n\\n #save info until events are far enough apart! \\n if time_diff < diff_thres:\\n\\n event.append(times[i])\\n \\n \\n #raise conditional for if events are far enough apart! \\n else:\\n\\n event_start = event[0] - 2 #minus 5 seconds\\n event_end = max(event) + 2 #add 5 seconds\\n\\n event_times.append([event_start, event_end])\\n \\n event = [] \\n \\n event.append(times[i])\\n\\n #if event still contains something for any reason, add it to event times\\n if len(event) > 0: \\n event_start = event[0] - 2 #minus 5 seconds\\n event_end = max(event) + 2 #add 5 seconds\\n event_times.append([event_start, event_end])\\n event = [] \\n \\n\\n\\n #if len(event_times) == 0 and len(event) > 0 or time_diff > diff_thres and len(event) > 0:\\n \\n #event_times.append(sum(event) / len(event))\\n \\n # event_start = event[0] - 2 #minus 5 seconds\\n # event_end = event[-1] + 2 #add 5 seconds\\n \\n # event_times.append([event_start, event_end])\\n \\n # event = []\\n \\n #event_times.append(times[i])\\n \\n # else:\\n # event.append(times[i])\\n \\n\\n UTC_events = []\\n\\n #earthquake length threshold is 10 seconds and above!\\n eq_len = 0#5.0\\n\\n for i in event_times:\\n estart = start_time + i[0]\\n eend = start_time + i[1]\\n \\n if eend - estart > eq_len:\\n UTC_events.append([estart, eend])\\n \\n #UTC_events = np.unique(np.asarray(UTC_events))\\n\\n \\n return UTC_events\",\n \"def GetTimeCompletenessList(self):\\n raise NotImplementedError()\",\n \"def get_timings(self):\\n exp=lib.is_Exposure_d8(self.hcam,7)*1E-3\\n frame_rate=lib.is_SetFrameRate(self.hcam,0x8000)\\n return self.AcqTimes(exp,1./frame_rate)\",\n \"def ticks(self, start, end, desired_ticks=None):\\n if start == end or isnan(start) or isnan(end):\\n return []\\n res = self.resolution\\n start -= self.zero\\n end -= self.zero\\n start_tick = int(ceil(start / res))\\n end_tick = int(floor(end / res))\\n ticks = [i*res for i in range(start_tick, end_tick+1)]\\n return ticks\",\n \"def sfreq_to_times(gaze_array, sfreq, start_time=0):\\n return np.arange(0, len(gaze_array) / sfreq, 1. / sfreq) + start_time\",\n \"def get_pump_times(self, start):\\n pumps_dict = {}\\n for pump in self.pumps:\\n dataframe_ = pd.DataFrame()\\n time = []\\n command = []\\n for i in range(len(pump.start_intervals)):\\n t_on = pump.start_intervals[i].epanet_on_time\\n t_off = pump.start_intervals[i].epanet_off_time\\n time += [start + t_on * pd.Timedelta(\\\"1S\\\"),\\n start + t_off * pd.Timedelta(\\\"1S\\\")]\\n command += [1, 0]\\n dataframe_['Time'] = time\\n dataframe_[pump.link_id] = command\\n pumps_dict[pump.link_id] = dataframe_\\n return pumps_dict\",\n \"def hit_time(tmat,start_list,targ_list,ntraj = 1000, cutoff=1000):\\n \\n # get state names\\n nstates = tmat.shape[0]\\n states = array([ii for ii in range(nstates)])\\n \\n times = list()\\n \\n for ii in range(ntraj):\\n curr = choice(start_list)\\n tm = 0\\n while curr not in targ_list:\\n weights = copy(tmat[curr, :])\\n curr = discrete_dist(states, weights, nn=1)\\n tm += 1\\n if tm==cutoff:\\n tm = nan\\n break\\n \\n times.append(tm)\\n\\n return array(times)\",\n \"def compute_all_minutes(\\n opens_in_ns: np.ndarray,\\n break_starts_in_ns: np.ndarray,\\n break_ends_in_ns: np.ndarray,\\n closes_in_ns: np.ndarray,\\n) -> np.ndarray:\\n pieces = []\\n for open_time, break_start_time, break_end_time, close_time in zip(\\n opens_in_ns, break_starts_in_ns, break_ends_in_ns, closes_in_ns\\n ):\\n if break_start_time != NP_NAT:\\n pieces.append(\\n np.arange(\\n open_time,\\n break_start_time + NANOSECONDS_PER_MINUTE,\\n NANOSECONDS_PER_MINUTE,\\n )\\n )\\n pieces.append(\\n np.arange(\\n break_end_time,\\n close_time + NANOSECONDS_PER_MINUTE,\\n NANOSECONDS_PER_MINUTE,\\n )\\n )\\n else:\\n pieces.append(\\n np.arange(\\n open_time,\\n close_time + NANOSECONDS_PER_MINUTE,\\n NANOSECONDS_PER_MINUTE,\\n )\\n )\\n out = np.concatenate(pieces).view(\\\"datetime64[ns]\\\")\\n return out\",\n \"def beaten(self):\\r\\n return [self.trainer.beaten()]\",\n \"def probe_times(self):\\r\\n probe_times = []\\r\\n for probe in self.__probes.values():\\r\\n if probe.complete():\\r\\n if probe.round_trip_time() > 20:\\r\\n \\\"Long probe: %s \\\" %self.__id\\r\\n probe_times.append(probe.round_trip_time())\\r\\n return probe_times\",\n \"def test_lengthen_synthetic_interval(self):\\n now = datetime.now()\\n now_utc = now.utcnow()\\n\\n three_hours_before = now - timedelta(hours=3)\\n four_hours_before = now - timedelta(hours=4)\\n five_hours_before = now - timedelta(hours=5)\\n\\n three_hours_before_utc = now_utc - timedelta(hours=3)\\n four_hours_before_utc = now_utc - timedelta(hours=4)\\n five_hours_before_utc = now_utc - timedelta(hours=5)\\n\\n self.t.configure_exclusions((four_hours_before.time(), three_hours_before.time()))\\n\\n self.t(\\\"start {:%Y-%m-%dT%H:%M:%S}\\\".format(five_hours_before))\\n\\n self.t(\\\"lengthen @2 5min\\\")\\n\\n j = self.t.export()\\n\\n self.assertEqual(len(j), 2)\\n self.assertClosedInterval(j[0],\\n expectedStart=\\\"{:%Y%m%dT%H%M%S}Z\\\".format(five_hours_before_utc),\\n expectedEnd=\\\"{:%Y%m%dT%H%M%S}Z\\\".format(four_hours_before_utc + timedelta(minutes=5)),\\n expectedTags=[],\\n description=\\\"lengthened interval\\\")\\n self.assertOpenInterval(j[1],\\n expectedStart=\\\"{:%Y%m%dT%H%M%S}Z\\\".format(three_hours_before_utc),\\n expectedTags=[],\\n description=\\\"unmodified interval\\\")\",\n \"def ablation_times(self):\\n ats = {}\\n for n in np.arange(self.n) + 1:\\n t = self.Time[self.ns == n]\\n ats[n - 1] = t.max() - t.min()\\n return ats\",\n \"def times():\\n MST = pytz.timezone('MST')\\n return pd.date_range(start=datetime(2018, 6, 15, 12, 0, 0, tzinfo=MST),\\n end=datetime(2018, 6, 15, 13, 0, 0, tzinfo=MST),\\n freq='10T')\",\n \"def _all_dist_to_end(args):\\n idx = args[0]\\n spiketrains = args[1]\\n start = args[2]\\n end = args[3]\\n N = args[4]\\n num_spiketrains = len(spiketrains)\\n distances = []\\n for jdx in range(idx+1, num_spiketrains):\\n dist = distance(spiketrains[idx], spiketrains[jdx], start, end, N)\\n times = dist[0] # should be the same for all\\n distances.append(dist[1])\\n return times, distances\",\n \"def create_event_dur_score(self):\\n for inst in self.instruments:\\n #[rest/midipitch, dur, vel]\\n inst_score=[]\\n running_clock = 0\\n for n, note in enumerate(inst.notes):\\n freq = mp_to_adjusted_freq(note[0], self.ratios)\\n if type(freq) != int: freq = np.asscalar(freq)\\n if type(note[0]) != int: inst.notes[n][0] = np.asscalar(note[0])\\n if type(note[1]) != int: inst.notes[n][1] = np.asscalar(note[1])\\n if type(note[2]) != int: inst.notes[n][2] = np.asscalar(note[2])\\n # if type(note[3]) != int: inst.notes[n][3] = np.asscalar(note[3])\\n if note[1] != running_clock:\\n inst_score.append(['Rest()', note[1] - running_clock, 0])\\n inst_score.append([freq, note[2], note[3]])\\n running_clock = note[1] + note[2]\\n inst.event_dur_score = inst_score\",\n \"def _fill_time(beats, shard, *args, **kwargs):\\n total = 0\\n for delay in shard(*args, **kwargs):\\n total += delay.count\\n yield delay\\n yield Beat(beats - total)\",\n \"def range(self):\\n return self.times[0], self.times[-1]\",\n \"def samples(self):\\n return np.linspace(self._start, self._stop, num=self._duration)\",\n \"def time_in_range(start, end, time):\\n if start <= end:\\n return start <= time <= end\\n else:\\n return start <= time or time <= end\",\n \"def timeSlice(requestContext, seriesList, startSliceAt, endSliceAt=\\\"now\\\"):\\n\\n results = []\\n start = time.mktime(parseATTime(startSliceAt).timetuple())\\n end = time.mktime(parseATTime(endSliceAt).timetuple())\\n\\n for slicedSeries in seriesList:\\n slicedSeries.name = 'timeSlice(%s, %s, %s)' % (slicedSeries.name, int(start), int(end))\\n\\n curr = time.mktime(requestContext[\\\"startTime\\\"].timetuple())\\n for i, v in enumerate(slicedSeries):\\n if v is None or curr < start or curr > end:\\n slicedSeries[i] = None\\n curr += slicedSeries.step\\n\\n results.append(slicedSeries)\\n\\n return results\",\n \"def getIntervals(t_start,t_end,interval):\\n timestamp_start = pd.Timestamp(t_start)\\n timestamp_end = pd.Timestamp(t_end)\\n nro_elements=int(int((timestamp_end-timestamp_start).total_seconds())/(60*interval))\\n list_dates = []\\n list_dates.append(t_start)\\n for i in range(nro_elements):\\n date_time_str = list_dates[i]\\n Current_Date = dt.datetime.strptime(date_time_str, '%Y-%m-%d %H:%M:%S')\\n Next_Date = Current_Date + datetime.timedelta(minutes=interval)\\n list_dates.append(str(Next_Date))\\n return list_dates\",\n \"def get_spikes(spiketimes=None, t1=None, t2=None):\\n indices = np.where((spiketimes[:,1] > t1) & (spiketimes[:,1] < t2))\\n timed_spikes = spiketimes[indices]\\n return timed_spikes\",\n \"def times(self):\\n if self._times is None:\\n self._times = np.zeros((self.ntimestep), dtype=np.float64)\\n for record in range(self.ntimestep):\\n self._times[record] = self.get_data_time(record)\\n\\n return self._times\",\n \"def calc_overlap(self, start, stop):\\n\\n overlaps = []\\n for s in self.map:\\n e = self.map[s]\\n if s >= start or s <= stop:\\n # We found an overlap\\n if e <= stop:\\n overlaps.append({\\\"start\\\": s, \\\"stop\\\": e})\\n else:\\n overlaps.append({\\\"start\\\": s, \\\"stop\\\": stop})\\n elif e >= start or e <= stop:\\n if s >= start:\\n overlaps.append({\\\"start\\\": s, \\\"stop\\\": e})\\n else:\\n overlaps.append({\\\"start\\\": start, \\\"stop\\\": e})\\n return overlaps\",\n \"def get_times_list(binout):\\r\\n return sorted([float(\\\"{0:15.6f}\\\".format(t)) for t in\\r\\n binout.recordarray[\\\"totim\\\"]])\",\n \"def test_start_end_hour():\\n # sh = None\\n # eh = None\\n # data = None\\n # result = makesky.start_end_hour(sh, eh, data)\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6811883","0.66345775","0.61763304","0.6161527","0.61594856","0.61594856","0.61594856","0.60631555","0.5961605","0.59090525","0.5891341","0.58558464","0.5800605","0.57836986","0.5770535","0.57627267","0.5715411","0.56829864","0.56808645","0.5678318","0.5642978","0.56403667","0.5622419","0.5611325","0.56024724","0.5590601","0.5545839","0.55417025","0.5527246","0.5520706","0.55017114","0.54986584","0.54640007","0.5461902","0.5456338","0.5437937","0.5431802","0.5428996","0.54193974","0.54144233","0.54068995","0.5405029","0.54049295","0.53725535","0.5372069","0.53701377","0.5365834","0.5364541","0.53563595","0.5348177","0.5345886","0.5341176","0.5336783","0.5323469","0.5322871","0.53196645","0.5309433","0.5292535","0.52837163","0.5280947","0.5275005","0.5274645","0.5270314","0.5238954","0.52337456","0.52279633","0.5227856","0.5227614","0.52183664","0.52180576","0.52122366","0.52087855","0.52046645","0.52011406","0.5200743","0.51947623","0.5186244","0.51855356","0.51837677","0.5175841","0.51736736","0.5161155","0.51591","0.5155939","0.51544756","0.51527184","0.51512295","0.51485384","0.5145468","0.51444244","0.5143649","0.51406175","0.513915","0.51348364","0.5134563","0.5128865","0.5125131","0.51245975","0.5122134","0.51211685","0.5118955"],"string":"[\n \"0.6811883\",\n \"0.66345775\",\n \"0.61763304\",\n \"0.6161527\",\n \"0.61594856\",\n \"0.61594856\",\n \"0.61594856\",\n \"0.60631555\",\n \"0.5961605\",\n \"0.59090525\",\n \"0.5891341\",\n \"0.58558464\",\n \"0.5800605\",\n \"0.57836986\",\n \"0.5770535\",\n \"0.57627267\",\n \"0.5715411\",\n \"0.56829864\",\n \"0.56808645\",\n \"0.5678318\",\n \"0.5642978\",\n \"0.56403667\",\n \"0.5622419\",\n \"0.5611325\",\n \"0.56024724\",\n \"0.5590601\",\n \"0.5545839\",\n \"0.55417025\",\n \"0.5527246\",\n \"0.5520706\",\n \"0.55017114\",\n \"0.54986584\",\n \"0.54640007\",\n \"0.5461902\",\n \"0.5456338\",\n \"0.5437937\",\n \"0.5431802\",\n \"0.5428996\",\n \"0.54193974\",\n \"0.54144233\",\n \"0.54068995\",\n \"0.5405029\",\n \"0.54049295\",\n \"0.53725535\",\n \"0.5372069\",\n \"0.53701377\",\n \"0.5365834\",\n \"0.5364541\",\n \"0.53563595\",\n \"0.5348177\",\n \"0.5345886\",\n \"0.5341176\",\n \"0.5336783\",\n \"0.5323469\",\n \"0.5322871\",\n \"0.53196645\",\n \"0.5309433\",\n \"0.5292535\",\n \"0.52837163\",\n \"0.5280947\",\n \"0.5275005\",\n \"0.5274645\",\n \"0.5270314\",\n \"0.5238954\",\n \"0.52337456\",\n \"0.52279633\",\n \"0.5227856\",\n \"0.5227614\",\n \"0.52183664\",\n \"0.52180576\",\n \"0.52122366\",\n \"0.52087855\",\n \"0.52046645\",\n \"0.52011406\",\n \"0.5200743\",\n \"0.51947623\",\n \"0.5186244\",\n \"0.51855356\",\n \"0.51837677\",\n \"0.5175841\",\n \"0.51736736\",\n \"0.5161155\",\n \"0.51591\",\n \"0.5155939\",\n \"0.51544756\",\n \"0.51527184\",\n \"0.51512295\",\n \"0.51485384\",\n \"0.5145468\",\n \"0.51444244\",\n \"0.5143649\",\n \"0.51406175\",\n \"0.513915\",\n \"0.51348364\",\n \"0.5134563\",\n \"0.5128865\",\n \"0.5125131\",\n \"0.51245975\",\n \"0.5122134\",\n \"0.51211685\",\n \"0.5118955\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":11193,"cells":{"query":{"kind":"string","value":"returns beat info as string"},"document":{"kind":"string","value":"def Beat_disp(self):\n return ' '.join(str(x+self.offset) for x in self.beats)"},"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 info(self):\n out = f\"sec: {self.em_sec()}\\nmin: {self.em_min()}\"\n out += f\"\\nhora: {self.em_hora()}\\ndia: {self.em_dia()}\"\n return out","def get_at_as_string(self):\n\n return self.at.strftime(\"%Y-%m-%dT%H:%M:%S.000Z\")","def __str__(self):\n return_text = \"Time-Triggered Frame information =>\\n\"\n return_text += \" Sender id : \" + str(self.__sender_id) + \"\\n\"\n return_text += \" Receivers ids : \" + str(self.__receivers_id) + \"\\n\"\n return_text += \" Path : \" + str(self.__paths) + \"\\n\"\n return_text += \" End_to_End : \" + str(self.__end_to_end_delay) + \" nanoseconds\\n\"\n return_text += \" Period : \" + str(self.__period) + \" nanoseconds\\n\"\n return_text += \" Starting : \" + str(self.__starting_time) + \" nanoseconds\\n\"\n return_text += \" Deadline : \" + str(self.__deadline) + \" nanoseconds\\n\"\n return_text += \" Size : \" + str(self.__size) + \" bytes\"\n return return_text","def coverage_time_str(info_df: DataFrame) -> str:\n start = attribute_value(info_df, \"time_coverage_start\")\n start_dt = parse_time(start)\n\n now = datetime.now()\n now = now.replace(hour=0, minute=0, second=0, microsecond=0)\n\n if start_dt < now:\n start = now.isoformat() + \"Z\"\n end = attribute_value(info_df, \"time_coverage_end\")\n\n return f\"[({start}):1:({end})]\"","def get_now_time():\r\n return '[' + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f') + ']: '","def human_readable_info(self) -> str:\n next_session = unix_str(self._stat.next_session)\n last_session = unix_str(self._stat.last_session)\n return \"\"\"\n Next Session: {}\n Last Session: {}\n Repetitions: {}\n Health: {}\n ------------------------\n Past Quality (last 20):\n ------------------------\n {}\n \"\"\".format(\n next_session,\n last_session,\n self._stat.actual_repetitions,\n self._health(),\n self._past_quality_graph(),\n )","def get_entry_string(self):\n return f\"{self.get_time_string()} {self.mode} {self.radar}\"","def getDebugText(self):\n timeDifference = time.time() - self.time_created\n hours = math.floor(timeDifference / 3600)\n minutes = math.floor((timeDifference % 3600) / 60)\n seconds = math.floor(timeDifference % 3600 % 60)\n\n output = \"\\n\" * 50\n output += \"Time started: %s\\n\" % time.ctime(self.time_created)\n output += \"Time now: %s\\n\" % time.ctime()\n output += \"Time elapsed: %02d:%02d:%02d\\n\" % (hours, minutes, seconds)\n output += (\"=\" * 80) + \"\\n\"\n output += \"Health potions used: %d\\n\" % self.hp_pots_used\n output += \"Health potions per hour: %d\\n\" % (self.hp_pots_used / (\n timeDifference / 3600))\n output += \"Mana potions used: %d\\n\" % self.mana_pots_used\n output += \"Mana potions per hour: %d\\n\" % (self.mana_pots_used / (\n timeDifference / 3600))\n return output","def get_info(self) -> str:\n return self.info","def output(self):\n if self.after_sunrise:\n return \"%02d:%02d:%02dR\" % self.time\n if self.after_sunset:\n return \"%02d:%02d:%02dT\" % self.time\n return \"%02d:%02d:%02d\" % self.time","def beatTracker(inputFile):\n beats, downbeats, ose, sig = analyse(inputFile)\n return beats, downbeats","def to_str(self):\n return self.PATTERN % (self.hours, self.minutes, self.seconds, self.milliseconds)","def msg(self):\n ended = time.time()\n started_wait = datetime.datetime.fromtimestamp(self.started).strftime(\n \"%Y-%m-%d %H:%M:%S\"\n )\n raised_date = datetime.datetime.fromtimestamp(ended).strftime(\n \"%Y-%m-%d %H:%M:%S\"\n )\n duration = ended - self.started\n return \"Info[started at {}, raised at {} after {}s]\".format(\n started_wait, raised_date, round(duration, 2)\n )","def __str__(self):\n\n string = ''\n string += \"Battery Voltage: \" + \"{0:.2f}\".format(self.getBatteryVoltage()) + '\\n'\n string += \"Data Logging: \" + str(self.getDataLoggingStatus()) + '\\n'\n string += \"Data Filename: \" + self.getFilename() +'\\n'\n\n string += \"Time Since Instrument Reset (s): \" + \"{0:.2f}\".format(self.getResetTime()) + '\\n'\n string += \"Data Age (s): \" + \"{0:.2f}\".format(self.getDataAge()) + '\\n'\n string += '\\n'\n string += \"Interior Temperature 1 (F): \" + \"{0:.2f}\".format(self.getInteriorTemperature1()) + '\\n'\n string += \"Interior Temperature 2 (F): \" + \"{0:.2f}\".format(self.getInteriorTemperature2()) + '\\n'\n string += \"Interior Temperature 3 (F): \" + \"{0:.2f}\".format(self.getInteriorTemperature3()) + '\\n'\n string += \"Exterior Temperature (F): \" + \"{0:.2f}\".format(self.getExteriorTemperature()) + '\\n'\n string += \"Pressure (PSI): \" + \"{0:.2f}\".format(self.getPressure()) + '\\n'\n string += \"Humidity (%): \" + \"{0:.2f}\".format(self.getHumidity()) + '\\n'\n string += '\\n'\n\n string += \"GPS Time: \" + str(self.getGpsTime1()) + '\\n'\n string += \"Latitude: \" + \"{0:.9f}\".format(self.getLatitude1()) + '\\n'\n string += \"Longitude: \" + \"{0:.9f}\".format(self.getLongitude1()) + '\\n'\n string += \"Altitude (ft): \" + \"{0:.2f}\".format(self.getAltitude1()) + '\\n'\n string += \"Speed (MPH): \" + \"{0:.2f}\".format(self.getSpeed()) + '\\n'\n string += '\\n'\n\n string += \"GPS Time: \" + str(self.getGpsTime2()) + '\\n'\n string += \"Latitude: \" + \"{0:.9f}\".format(self.getLatitude2()) + '\\n'\n string += \"Longitude: \" + \"{0:.9f}\".format(self.getLongitude2()) + '\\n'\n string += \"Altitude (ft): \" + \"{0:.2f}\".format(self.getAltitude2()) + '\\n'\n string += '\\n'\n\n ax, ay, az = self.getAcceleration()\n string += \"Acceleration (x, y, z): \"\n string += \"{0:.2f}\".format(ax) + \", \"\n string += \"{0:.2f}\".format(ay) + \", \"\n string += \"{0:.2f}\".format(az) + '\\n'\n\n gx, gy, gz = self.getRates()\n string += \"Rates (x, y, z): \"\n string += \"{0:.2f}\".format(gx) + \", \"\n string += \"{0:.2f}\".format(gy) + \", \"\n string += \"{0:.2f}\".format(gz) + '\\n'\n\n mx, my, mz = self.getMagneticReading()\n string += \"Magnetic Field (x, y, z): \"\n string += \"{0:.2f}\".format(mx) + \", \"\n string += \"{0:.2f}\".format(my) + \", \"\n string += \"{0:.2f}\".format(mz) + '\\n'\n\n roll, pitch, yaw = self.getAttitude()\n string += \"Roll (deg): \" + \"{0:.2f}\".format(roll) + '\\n'\n string += \"Pitch (deg): \" + \"{0:.2f}\".format(pitch) + '\\n'\n string += \"Yaw (deg): \" + \"{0:.2f}\".format(yaw) + '\\n'\n string += '\\n'\n relayStates = self.getRelayStates()\n \n\n string += \"Relay States: \" \n string += (( \"ON \") if relayStates[0] else ( \"OFF \")) \n string += (( \"ON \") if relayStates[1] else ( \"OFF \"))\n string += (( \"ON \") if relayStates[2] else ( \"OFF \"))\n string += (( \"ON \") if relayStates[3] else ( \"OFF \"))\n string += '\\n'\n\n\n return string","def _player_info(self):\n return \"%r %s seat:%s m:%r c:%s b:%s \" % (self.name, self.serial, self.seat, self.money, self._chips, self._bet)","def info(cls):\n return 'Snapshot (i.e. hydro variables at given time) plotting module.'","def getFormattedText(self):\r\n h = \"00\"\r\n m = \"00\"\r\n s = \"00\"\r\n if(self.seconds < 10):\r\n s = \"0\" + str(self.seconds)\r\n else:\r\n s = str(self.seconds)\r\n\r\n if(self.minutes < 10):\r\n m = \"0\" + str(self.minutes)\r\n else:\r\n m = str(self.minutes)\r\n\r\n if(self.hours < 10):\r\n h = \"0\" + str(self.hours)\r\n else:\r\n h = str(self.hours)\r\n\r\n return h + \":\" + m + \":\" + s","def __str__(self):\n\n return self.date.strftime('%y/%m/%d') + \" \" + self.time.strftime(\"%I:%M %p\") + \" \" + self.activity + \" \" + self.description[:40]","def __str__(self):\n return '%.2d:%.2d:%.2d' % (self.hour, self.minute, self.second)","def getinfo(timestamp):\n datetime, message = timestamp.split(']')\n\n date, time = datetime.split()\n date = date.strip('[')\n hour, minute = time.split(':')\n\n message = message.split()\n extra = message[1] # either 'asleep', 'up', or '#XXX'\n\n return date, int(hour), int(minute), extra","def ctime(self):\n return \"\"","def ctime(self):\n return \"\"","def _timestamp(self):\n\n retval = []\n\n if self.log_level >= _Log.DEBUG:\n retval.append('%f: ' % (time.time() - self.start_time,))\n\n return ''.join(retval)","def __repr__(self: object) -> str:\n measstring: str = \"Tatort - {:04d} - {} - {} - {} - {}\".format(self.episode_id, self.episode_name, self.episode_inspectors, self.episode_sequence, self.episode_broadcast)\n return measstring","def info(self):\n return (f\"Match id: {self._id}\\n\"\n f\"dire_score: {self.dire_score}\\n\"\n f\"dire_team: {self.dire_team}\\n\"\n f\"duration: {self.duration}\\n\"\n f\"game_mode: {self.game_mode}\\n\"\n f\"patch: {self.patch}\\n\"\n f\"radiant_score: {self.radiant_score}\\n\"\n f\"radiant_team: {self.radiant_team}\\n\"\n f\"radiant_win: {self.radiant_win}\\n\"\n f\"skill: {self.skill}\\n\"\n f\"start_time: {self.start_time}\\n\")","def info(self) -> str:\n return self._info","def info(self) -> str:\n return self._info","def timestr():\n return dt.strftime(dt.now(),'%H:%M:%S')","def summary_string(self) -> str:","def __str__(self) -> str:\n\n # Get current time\n t = timer()\n # Length of label field, calculated from max label length\n fldlen = [len(lbl) for lbl in self.t0] + [\n len(self.default_label),\n ]\n lfldln = max(fldlen) + 2\n # Header string for table of timers\n s = f\"{'Label':{lfldln}s} Accum. Current\\n\"\n s += \"-\" * (lfldln + 25) + \"\\n\"\n # Construct table of timer details\n for lbl in sorted(self.t0):\n td = self.td[lbl]\n if self.t0[lbl] is None:\n ts = \" Stopped\"\n else:\n ts = f\" {(t - self.t0[lbl]):.2e} s\" % (t - self.t0[lbl]) # type: ignore\n s += f\"{lbl:{lfldln}s} {td:.2e} s {ts}\\n\"\n\n return s","def stats_get_str(self):\n return self.stats.get_all_str()","def stats_get_str(self):\n return self.stats.get_all_str()","def get_info(self) -> str:\n raise NotImplementedError()","def time(self):\n return self.raw[\"logTime\"]","def info(self):\n self.update_info()\n print('Number of electrodes: ' + str(self.n_elecs))\n print('Recording time in seconds: ' + str(self.dur))\n print('Sample Rate in Hz: '+ str(self.sample_rate))\n print('Number of sessions: ' + str(self.n_sessions))\n print('Date created: ' + str(self.date_created))\n print('Meta data: ' + str(self.meta))","def _get_debug_message(self):\r\n if DEBUG:\r\n return 'fps = %d' % self.timer.get_fps()","def get_time(self):\n return ''","def time_str(self):\n return datetime.now().strftime('%c')","def info(self) -> str:\n return pulumi.get(self, \"info\")","def get_str_metadata(self):\n return \"\\n\".join([\"Guessed by {}\".format(self.guessed_by), \"{} metaphors used\".format(self.metaphors_used)])","def __str__(self):\n return '<TuebingenMEG: %i samples, %i timepoints, %i channels>' \\\n % (self.nsamples, self.ntimepoints, len(self.channelids))","def __str__(self):\n return 'elasped: %s (%s)' % (str(self.diff()), self.times())","def output(self):\n time = \"%04d-%02d-%02d %02d:%02d:%02d\" % (self.year, self.month, \\\n self.date, self.hours, self.minutes, self.seconds)\n return {\n \"id\": self.id,\n \"name\": self.name,\n \"type\": 4,\n \"enabled\": 1,\n \"abstime\": time\n }","def getchrony():\n \n filename = \"/var/log/chrony/tracking.log\"\n fileNotOK = True\n try:\n if os.path.isfile(filename):\n fileNotOK = False\n except:\n fileNotOK = True\n # if file is not OK, return default\n if fileNotOK:\n return( \"2020-02-20T02:02:02.000\", 0., 0.)\n \n #get the very last line in the filea\n line = subprocess.check_output(['tail', '-1', filename])\n parts = line.split()\n nparts = len(parts)\n\n if nparts < 10:\n return( \"\", 0., 0.)\n \n date = parts[0]\n time = parts[1]\n ip = parts[2]\n #print(\"Offset: %s\" % (parts[9]))\n offset = float(parts[6])\n offsetrms = float(parts[9])\n datestr = \"%sT%s\" % (date, time)\n return( datestr, offset, offsetrms)","def get_time_info(self):\n\n raise NotImplementedError","def __repr__(self):\n if self._microsecond != 0:\n s = \", %d, %d\" % (self._second, self._microsecond)\n elif self._second != 0:\n s = \", %d\" % self._second\n else:\n s = \"\"\n s = \"%s.%s(%d, %d%s)\" % (\n type(self).__module__,\n self.__class__.__qualname__,\n self._hour,\n self._minute,\n s,\n )\n if self._tzinfo is not None:\n assert s[-1:] == \")\"\n s = s[:-1] + \", tzinfo=%r\" % self._tzinfo + \")\"\n if self._fold:\n assert s[-1:] == \")\"\n s = s[:-1] + \", fold=1)\"\n return s","def getisotime():\n ct = datetime.utcnow()\n return ct.strftime(\"%Y%m%d%H%M\")","def gen_beat_output(e):\n return [playback_char(e,t) for t in range(70000)]","def __str__(self):\n return \"{0:02}:{1:02}\".format(self.min, self.sec)","def __str__(self):\n return str(self.elapsed) + ' sec'","def report(self) -> str:\n return REPORT_TIMEFRAME.format(self.name,\n self.times_run,\n self.average_runtime)","def get_monitor_string(self):\n\n return self.reporter.get_overview_string(self.info)","def get_info(self):\n out = ''\n for k in sorted(self.components.keys()):\n out += '{:s}: {:s}'.format(k, self.info[k]) + '\\n'\n return(out)","def __str__(self):\n if self.minutes == 0:\n hm = 4 * ''\n else:\n hours, minutes = divmod(self.minutes, 60)\n hm = \"{0:0>2d}{1:0>2d}\".format(hours, minutes)\n\n return hm","def _get_timestamp() -> str:\n\n dt = timezone(\"UTC\").localize(datetime.utcnow()).strftime(\"%b. %d, %Y#%H:%M UTC\")\n date, time = dt.split(\"#\")\n return f\"Event Timestamp: 📅 {date} 🕒 {time}\"","def info(self) -> str:\n info = \"\"\n\n # Ensure we have a latitude and a longitude\n if self.latitude is None:\n raise GenerateError(\"Missing latitude\")\n elif self.longitude is None:\n raise GenerateError(\"Missing longitude\")\n\n # Ensure we have a symbol table and symbol ID\n if self.symbol_table is None:\n raise GenerateError(\"Missing symbol table\")\n elif self.symbol_id is None:\n raise GenerateError(\"Missing symbol ID\")\n\n # Set data type ID\n if self.timestamp is None:\n if self.messaging is False:\n self.data_type_id = \"!\"\n else:\n self.data_type_id = \"=\"\n else:\n if self.messaging is False:\n self.data_type_id = \"/\"\n else:\n self.data_type_id = \"@\"\n\n # Set the timestamp\n info += APRSUtils.encode_timestamp(self.timestamp, self.timestamp_type)\n\n if self.compressed:\n # Add the position in a compressed format\n info += self._generate_compressed_position(\n self.latitude, self.longitude, self.symbol_table, self.symbol_id, self.altitude,\n self.course, self.speed, self.radio_range, self.compression_fix,\n self.compression_source, self.compression_origin)\n\n # PHG, etc is not supported for compressed formats (see APRS 1.01 C9 P36)\n if self.comment:\n info += self.comment\n\n else:\n # Add the position in an uncompressed format\n # TODO: handle BRG/NRQ\n info += self._generate_uncompressed_position(\n self.latitude, self.longitude, self.symbol_table, self.symbol_id, self.ambiguity\n )\n\n # Handle PHG\n if self.power is not None and self.height is not None and self.gain is not None \\\n and self.directivity is not None:\n phg = APRSUtils.encode_phg(self.power, self.height, self.gain, self.directivity)\n info += \"PHG{}\".format(\n self._generate_data(phg=phg, altitude=self.altitude, comment=self.comment)\n )\n\n # Handle DFS\n elif self.strength is not None and self.height is not None and self.gain is not None \\\n and self.directivity is not None:\n dfs = APRSUtils.encode_dfs(self.strength, self.height, self.gain, self.directivity)\n info += \"DFS{}\".format(\n self._generate_data(dfs=dfs, altitude=self.altitude, comment=self.comment)\n )\n\n # Handle course/speed\n elif self.course is not None and self.speed is not None:\n info += \"{}/{}\".format(\n str(self.course).zfill(3),\n str(self.speed).zfill(3)\n )\n info += self._generate_data(altitude=self.altitude, comment=self.comment)\n\n # Handle RNG\n elif self.radio_range is not None:\n info += \"RNG{}\".format(\n str(self.radio_range).zfill(4)\n )\n info += self._generate_data(altitude=self.altitude, comment=self.comment)\n\n else:\n info += self._generate_data(altitude=self.altitude, comment=self.comment)\n\n return info","def __str__(self):\n y, m, d = self._year, self._month, self._day\n h, mn, s, t = self._hour, self._minute, self._second, self._tz\n if s == int(s):\n # A whole number of seconds -- suppress milliseconds.\n return '%4.4d/%2.2d/%2.2d %2.2d:%2.2d:%2.2d %s' % (\n y, m, d, h, mn, s, t)\n else:\n # s is already rounded to the nearest microsecond, and\n # it's not a whole number of seconds. Be sure to print\n # 2 digits before the decimal point.\n return '%4.4d/%2.2d/%2.2d %2.2d:%2.2d:%06.6f %s' % (\n y, m, d, h, mn, s, t)","def __str__(self) -> str:\r\n\r\n return 'RawData obect version {}\\n- {:>5} : {}\\n- {:>5} : {}\\n- {:>5} : {}'.format(self.version,\r\n 'Time',\r\n strftime('%m/%d/%Y %H:%M:%S', localtime(self.timestamp)),\r\n 'Label',\r\n self.label,\r\n 'Value',\r\n self.value)","def to_string(self):\n if self.is_power_onoff():\n return 'Power On/Off'\n else:\n gain = str(hex(int(self['gain_speed'])))\n out = self['target'].ljust(20) + ' ' + self['filters'].ljust(11) + ' ' + self['x_bin'] + 'x' + self['y_bin'] + ' ' + gain[2:].upper()\n \n \n if self.number_windows() > 0:\n out += ' ' + self['x1_size'].rjust(4) + 'x' + self['y1_size'].ljust(4) + ' ' + self['x1_start'].ljust(3) + ' ' + self['y1_start'].ljust(4)\n if self.number_windows() > 1:\n out += ' ' + self['x2_size'].rjust(4) + 'x' + self['y2_size'].ljust(4) + ' ' + self['x2_start'].ljust(3) + ' ' + self['y2_start'].ljust(4)\n \n if 'Comment' in self:\n out += ' ' + self['Comment']\n return out","def time( self, mess, args):\n return str(datetime.datetime.now())","def time( self, mess, args):\n return str(datetime.datetime.now())","def extract_beat(audio, sample_rate = 44100):\n beat_tracker = BeatTrackerDegara()\n beats_time = beat_tracker(audio)\n beats = np.array(map(lambda time : round(time * sample_rate), beats_time))\n beats = np.append(0, beats)\n beats_time = np.append(0, beats_time)\n\n return beats, beats_time","def __str__(self) -> str:\n return self.__repr__() + \"\\n\" + \"\\n\".join(self.regimes())","def __get_timestamp() -> str:\n return str(datetime.now().astimezone())","def __get_timestamp() -> str:\n return str(datetime.now().astimezone())","def info(self):\n\n if self.running:\n return INFO_RUNNING_FORMAT.format(**self.__dict__)\n else:\n return INFO_ENDED_FORMAT.format(**self.__dict__)","def nice_output(self):\n return 'Pitch: {0} at {1}: {2}'.format(\n self.pitch_type, self.start_speed, self.des)","def get_time(self):\n return \"%02u:%02u:%02u (%d)\" % self.rtc.datetime()[4:8]","def __str__(self):\n out_str = \"\\n\".join(`\"%.5f, %.5f, %.1f, %s, %s\" % (point[0], point[1], point[2], point[3], point[4])` for point in self.__traectory_list)\n return \"\\'x, y, altitude, capture time, capture date'\\n\"+out_str","def _printable(self):\n toPrint = \"Time Info header. \"\n toPrint += \"timestamp: \" + str(self.datetime) + \" \"\n\n return toPrint","def time():\n return datetime.datetime.now().strftime(\"%Y%m%dT%H%M%SZ\")","def __str__(self):\n iso_time = str(datetime.datetime.fromtimestamp(self.next_time))\n return \"<Job(%s, %ss, %s)>\" % (iso_time, self.interval, self.func)","def __str__(self):\n struct_repr = \", \".join([\n \"num_satellites: \" + str(self.num_satellites),\n \"fix_type: \" + str(self.fix_type)\n ])\n\n return f\"GpsInfo: [{struct_repr}]\"","def heartbeat():\n return jsonify(int(time.time()))","def __str__(self):\n return str(self.date) + \" \" + str(self.time_of_day)","def now(self):\n return time.strftime(r'[%d/%b/%Y:%H:%M:%S]')","def get_info_string(self) -> str:\n return \"Not implemented\"","def __str__(self):\n return \"({0}:{1}:{2})\".format(self.hours, self.minutes, self.seconds)","def time_now() -> str:\n return datetime_to_str(datetime_now())","def __str__(self) -> str:\n\n return f\"{_time_to_pretty_string(self.opening)} - {_time_to_pretty_string(self.closing)}\"","def timeStamp():\n import time\n return str(time.strftime(\"%a %d %b %Y %I:%M:%S %p\"))","def info():\n return r\"\"\"Lin-Yu Tseng and Chun Chen, \"Multiple trajectory search for Large Scale Global Optimization,\" 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), Hong Kong, 2008, pp. 3052-3059. doi: 10.1109/CEC.2008.4631210\"\"\"","def __str__(self) -> str:\n return self.customer.name + ' arrives at ' + str(self.timestamp)","def get_wav_info(file_name):\n wr = wave.open(file_name, 'r')\n sample_width = wr.getsampwidth()\n frame_rate = wr.getframerate()\n num_frames = wr.getnframes()\n n_channels = wr.getnchannels()\n s = \"sample width: {} bytes\\n\".format(sample_width) + \\\n \"frame rate: {} Hz\\n\".format(frame_rate) + \\\n \"num frames: {}\\n\".format(num_frames) + \\\n \"track length: {} s\\n\".format(num_frames / frame_rate) + \\\n \"num channels: {}\\n\".format(n_channels)\n\n return s","def print(self):\n return '{:<32s}{:>25s}{:>8.3f}{:>10d}{:>15d}{:>12d}{:>12.3f}{:>10d}{:>10d}'.format(\n self.image_path, list_to_str(self.coord), self.depth, self.priority, self.enqueue_time,\n self.exec_time, self.response_time, self.deadline, self.missed)","def __str__(self):\n return ', '.join([self.yftf_data, self.info_hash, str(self.num_pieces), str(self.peers)])","def __str__(self):\n return \"Boat with the maximum speed of \" + str(self.max_speed) \\\n + \" knots\"","def format(cls):\n return super().format() + 'Hhiihh'","def massage_addinfo(self) -> str:\n self.message_str = f'{self.time}\\n{self.sent_by}\\n'\n return self.message_str","def __str__(self):\n return \"\\nCurrent time:\" + str(self._time) +\\\n \"\\nCurrent cookies:\" + str(self._cur_cookies) +\\\n \"\\nCurrent CPS:\"+ str(self._cps) +\\\n \"\\nTotoal cookies:\" + str(self._tot_cookies)","def __str__(self):\n\n # Print the class and address.\n msg = \"{0} at {1}\\n\".format(str(self.__class__), str(hex(id(self))))\n\n # Print some other basic information.\n msg = \"{0} line name: ({1})\\n\".format(msg, self.name)\n msg = \"{0} ping_time: ({1})\\n\".format(\n msg,\n self.ping_time.shape[0])\n msg = \"{0} data: ({1})\\n\".format(\n msg,\n self.data.shape[0])\n msg = \"{0} start time: {1}\\n\".format(msg,\n self.ping_time[0])\n msg = \"{0} end time: {1}\\n\" .format(msg,\n self.ping_time[-1])\n\n return msg","def __str__(self):\n return f\"{self._desc:16s}\"","def to_event(self):\n return f'{self.gem_pack_id},{self.event_time_in_milliseconds},{self.price}\\n'","def info():\n\n\t info = \"This package determines the day of the week.\"\n\t print(info)","def wattsString(self):\n return self.watts is None and \"unknown\" or str(self.watts)","def _cap_title(self, ndx):\n\n return '%d/%d @ %d:%02d:%02d' % (ndx, self.dpar.n_caps,\n self.dpar.frame_timestamp[ndx].hour,\n self.dpar.frame_timestamp[ndx].minute,\n self.dpar.frame_timestamp[ndx].second)","def getBatteryInfo():\n \n # WMIC Win32_Battery fields to query\n battery_vars = ['BatteryStatus', 'EstimatedChargeRemaining', 'EstimatedRunTime',\n 'Status']\n\n # Start data with timestamp\n result = datetime.now().strftime(DATE_FORMAT)\n\n for var in battery_vars:\n \n #execute wmic command and capture output\n temp = subprocess.check_output([\"wmic\", \"path\", \"Win32_Battery\", \"get\", var, \"/value\"]) \n \n result += ', ' + temp.strip()\n \n result = '[' + result + ']'\n \n return result","def __str__(self):\n\n strme = \"fed method {} {} {} {}\"\\\n .format(WangLandau.key, self.delta0, self.c_upd, self.n_upd)\n if self.smooth:\n strme = \"{} {}\".format(strme, self.smooth)\n\n return strme","def printInfo(totaltime, vid, cpath):\r\n infotxt = open(cpath + 'Resize Info' + '.txt', 'a')\r\n info = str('executeTime: %f' % totaltime + '\\n')\r\n converageRate = totaltime / (vid.get(7))\r\n info += str('average converage rate is: %f' % converageRate + 'f/s' + '\\n')\r\n frameNum = vid.get(7)\r\n info += str('frame number is %d' % frameNum + '\\n')\r\n fps = vid.get(5)\r\n info += str('frame rate is %f' % fps + '\\n')\r\n\r\n infotxt.write(info)\r\n infotxt.close()\r\n\r\n # print(info)\r\n vid.release()\r\n return info","def __str__(self):\n return self.fmt('DMY', '.')"],"string":"[\n \"def info(self):\\n out = f\\\"sec: {self.em_sec()}\\\\nmin: {self.em_min()}\\\"\\n out += f\\\"\\\\nhora: {self.em_hora()}\\\\ndia: {self.em_dia()}\\\"\\n return out\",\n \"def get_at_as_string(self):\\n\\n return self.at.strftime(\\\"%Y-%m-%dT%H:%M:%S.000Z\\\")\",\n \"def __str__(self):\\n return_text = \\\"Time-Triggered Frame information =>\\\\n\\\"\\n return_text += \\\" Sender id : \\\" + str(self.__sender_id) + \\\"\\\\n\\\"\\n return_text += \\\" Receivers ids : \\\" + str(self.__receivers_id) + \\\"\\\\n\\\"\\n return_text += \\\" Path : \\\" + str(self.__paths) + \\\"\\\\n\\\"\\n return_text += \\\" End_to_End : \\\" + str(self.__end_to_end_delay) + \\\" nanoseconds\\\\n\\\"\\n return_text += \\\" Period : \\\" + str(self.__period) + \\\" nanoseconds\\\\n\\\"\\n return_text += \\\" Starting : \\\" + str(self.__starting_time) + \\\" nanoseconds\\\\n\\\"\\n return_text += \\\" Deadline : \\\" + str(self.__deadline) + \\\" nanoseconds\\\\n\\\"\\n return_text += \\\" Size : \\\" + str(self.__size) + \\\" bytes\\\"\\n return return_text\",\n \"def coverage_time_str(info_df: DataFrame) -> str:\\n start = attribute_value(info_df, \\\"time_coverage_start\\\")\\n start_dt = parse_time(start)\\n\\n now = datetime.now()\\n now = now.replace(hour=0, minute=0, second=0, microsecond=0)\\n\\n if start_dt < now:\\n start = now.isoformat() + \\\"Z\\\"\\n end = attribute_value(info_df, \\\"time_coverage_end\\\")\\n\\n return f\\\"[({start}):1:({end})]\\\"\",\n \"def get_now_time():\\r\\n return '[' + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f') + ']: '\",\n \"def human_readable_info(self) -> str:\\n next_session = unix_str(self._stat.next_session)\\n last_session = unix_str(self._stat.last_session)\\n return \\\"\\\"\\\"\\n Next Session: {}\\n Last Session: {}\\n Repetitions: {}\\n Health: {}\\n ------------------------\\n Past Quality (last 20):\\n ------------------------\\n {}\\n \\\"\\\"\\\".format(\\n next_session,\\n last_session,\\n self._stat.actual_repetitions,\\n self._health(),\\n self._past_quality_graph(),\\n )\",\n \"def get_entry_string(self):\\n return f\\\"{self.get_time_string()} {self.mode} {self.radar}\\\"\",\n \"def getDebugText(self):\\n timeDifference = time.time() - self.time_created\\n hours = math.floor(timeDifference / 3600)\\n minutes = math.floor((timeDifference % 3600) / 60)\\n seconds = math.floor(timeDifference % 3600 % 60)\\n\\n output = \\\"\\\\n\\\" * 50\\n output += \\\"Time started: %s\\\\n\\\" % time.ctime(self.time_created)\\n output += \\\"Time now: %s\\\\n\\\" % time.ctime()\\n output += \\\"Time elapsed: %02d:%02d:%02d\\\\n\\\" % (hours, minutes, seconds)\\n output += (\\\"=\\\" * 80) + \\\"\\\\n\\\"\\n output += \\\"Health potions used: %d\\\\n\\\" % self.hp_pots_used\\n output += \\\"Health potions per hour: %d\\\\n\\\" % (self.hp_pots_used / (\\n timeDifference / 3600))\\n output += \\\"Mana potions used: %d\\\\n\\\" % self.mana_pots_used\\n output += \\\"Mana potions per hour: %d\\\\n\\\" % (self.mana_pots_used / (\\n timeDifference / 3600))\\n return output\",\n \"def get_info(self) -> str:\\n return self.info\",\n \"def output(self):\\n if self.after_sunrise:\\n return \\\"%02d:%02d:%02dR\\\" % self.time\\n if self.after_sunset:\\n return \\\"%02d:%02d:%02dT\\\" % self.time\\n return \\\"%02d:%02d:%02d\\\" % self.time\",\n \"def beatTracker(inputFile):\\n beats, downbeats, ose, sig = analyse(inputFile)\\n return beats, downbeats\",\n \"def to_str(self):\\n return self.PATTERN % (self.hours, self.minutes, self.seconds, self.milliseconds)\",\n \"def msg(self):\\n ended = time.time()\\n started_wait = datetime.datetime.fromtimestamp(self.started).strftime(\\n \\\"%Y-%m-%d %H:%M:%S\\\"\\n )\\n raised_date = datetime.datetime.fromtimestamp(ended).strftime(\\n \\\"%Y-%m-%d %H:%M:%S\\\"\\n )\\n duration = ended - self.started\\n return \\\"Info[started at {}, raised at {} after {}s]\\\".format(\\n started_wait, raised_date, round(duration, 2)\\n )\",\n \"def __str__(self):\\n\\n string = ''\\n string += \\\"Battery Voltage: \\\" + \\\"{0:.2f}\\\".format(self.getBatteryVoltage()) + '\\\\n'\\n string += \\\"Data Logging: \\\" + str(self.getDataLoggingStatus()) + '\\\\n'\\n string += \\\"Data Filename: \\\" + self.getFilename() +'\\\\n'\\n\\n string += \\\"Time Since Instrument Reset (s): \\\" + \\\"{0:.2f}\\\".format(self.getResetTime()) + '\\\\n'\\n string += \\\"Data Age (s): \\\" + \\\"{0:.2f}\\\".format(self.getDataAge()) + '\\\\n'\\n string += '\\\\n'\\n string += \\\"Interior Temperature 1 (F): \\\" + \\\"{0:.2f}\\\".format(self.getInteriorTemperature1()) + '\\\\n'\\n string += \\\"Interior Temperature 2 (F): \\\" + \\\"{0:.2f}\\\".format(self.getInteriorTemperature2()) + '\\\\n'\\n string += \\\"Interior Temperature 3 (F): \\\" + \\\"{0:.2f}\\\".format(self.getInteriorTemperature3()) + '\\\\n'\\n string += \\\"Exterior Temperature (F): \\\" + \\\"{0:.2f}\\\".format(self.getExteriorTemperature()) + '\\\\n'\\n string += \\\"Pressure (PSI): \\\" + \\\"{0:.2f}\\\".format(self.getPressure()) + '\\\\n'\\n string += \\\"Humidity (%): \\\" + \\\"{0:.2f}\\\".format(self.getHumidity()) + '\\\\n'\\n string += '\\\\n'\\n\\n string += \\\"GPS Time: \\\" + str(self.getGpsTime1()) + '\\\\n'\\n string += \\\"Latitude: \\\" + \\\"{0:.9f}\\\".format(self.getLatitude1()) + '\\\\n'\\n string += \\\"Longitude: \\\" + \\\"{0:.9f}\\\".format(self.getLongitude1()) + '\\\\n'\\n string += \\\"Altitude (ft): \\\" + \\\"{0:.2f}\\\".format(self.getAltitude1()) + '\\\\n'\\n string += \\\"Speed (MPH): \\\" + \\\"{0:.2f}\\\".format(self.getSpeed()) + '\\\\n'\\n string += '\\\\n'\\n\\n string += \\\"GPS Time: \\\" + str(self.getGpsTime2()) + '\\\\n'\\n string += \\\"Latitude: \\\" + \\\"{0:.9f}\\\".format(self.getLatitude2()) + '\\\\n'\\n string += \\\"Longitude: \\\" + \\\"{0:.9f}\\\".format(self.getLongitude2()) + '\\\\n'\\n string += \\\"Altitude (ft): \\\" + \\\"{0:.2f}\\\".format(self.getAltitude2()) + '\\\\n'\\n string += '\\\\n'\\n\\n ax, ay, az = self.getAcceleration()\\n string += \\\"Acceleration (x, y, z): \\\"\\n string += \\\"{0:.2f}\\\".format(ax) + \\\", \\\"\\n string += \\\"{0:.2f}\\\".format(ay) + \\\", \\\"\\n string += \\\"{0:.2f}\\\".format(az) + '\\\\n'\\n\\n gx, gy, gz = self.getRates()\\n string += \\\"Rates (x, y, z): \\\"\\n string += \\\"{0:.2f}\\\".format(gx) + \\\", \\\"\\n string += \\\"{0:.2f}\\\".format(gy) + \\\", \\\"\\n string += \\\"{0:.2f}\\\".format(gz) + '\\\\n'\\n\\n mx, my, mz = self.getMagneticReading()\\n string += \\\"Magnetic Field (x, y, z): \\\"\\n string += \\\"{0:.2f}\\\".format(mx) + \\\", \\\"\\n string += \\\"{0:.2f}\\\".format(my) + \\\", \\\"\\n string += \\\"{0:.2f}\\\".format(mz) + '\\\\n'\\n\\n roll, pitch, yaw = self.getAttitude()\\n string += \\\"Roll (deg): \\\" + \\\"{0:.2f}\\\".format(roll) + '\\\\n'\\n string += \\\"Pitch (deg): \\\" + \\\"{0:.2f}\\\".format(pitch) + '\\\\n'\\n string += \\\"Yaw (deg): \\\" + \\\"{0:.2f}\\\".format(yaw) + '\\\\n'\\n string += '\\\\n'\\n relayStates = self.getRelayStates()\\n \\n\\n string += \\\"Relay States: \\\" \\n string += (( \\\"ON \\\") if relayStates[0] else ( \\\"OFF \\\")) \\n string += (( \\\"ON \\\") if relayStates[1] else ( \\\"OFF \\\"))\\n string += (( \\\"ON \\\") if relayStates[2] else ( \\\"OFF \\\"))\\n string += (( \\\"ON \\\") if relayStates[3] else ( \\\"OFF \\\"))\\n string += '\\\\n'\\n\\n\\n return string\",\n \"def _player_info(self):\\n return \\\"%r %s seat:%s m:%r c:%s b:%s \\\" % (self.name, self.serial, self.seat, self.money, self._chips, self._bet)\",\n \"def info(cls):\\n return 'Snapshot (i.e. hydro variables at given time) plotting module.'\",\n \"def getFormattedText(self):\\r\\n h = \\\"00\\\"\\r\\n m = \\\"00\\\"\\r\\n s = \\\"00\\\"\\r\\n if(self.seconds < 10):\\r\\n s = \\\"0\\\" + str(self.seconds)\\r\\n else:\\r\\n s = str(self.seconds)\\r\\n\\r\\n if(self.minutes < 10):\\r\\n m = \\\"0\\\" + str(self.minutes)\\r\\n else:\\r\\n m = str(self.minutes)\\r\\n\\r\\n if(self.hours < 10):\\r\\n h = \\\"0\\\" + str(self.hours)\\r\\n else:\\r\\n h = str(self.hours)\\r\\n\\r\\n return h + \\\":\\\" + m + \\\":\\\" + s\",\n \"def __str__(self):\\n\\n return self.date.strftime('%y/%m/%d') + \\\" \\\" + self.time.strftime(\\\"%I:%M %p\\\") + \\\" \\\" + self.activity + \\\" \\\" + self.description[:40]\",\n \"def __str__(self):\\n return '%.2d:%.2d:%.2d' % (self.hour, self.minute, self.second)\",\n \"def getinfo(timestamp):\\n datetime, message = timestamp.split(']')\\n\\n date, time = datetime.split()\\n date = date.strip('[')\\n hour, minute = time.split(':')\\n\\n message = message.split()\\n extra = message[1] # either 'asleep', 'up', or '#XXX'\\n\\n return date, int(hour), int(minute), extra\",\n \"def ctime(self):\\n return \\\"\\\"\",\n \"def ctime(self):\\n return \\\"\\\"\",\n \"def _timestamp(self):\\n\\n retval = []\\n\\n if self.log_level >= _Log.DEBUG:\\n retval.append('%f: ' % (time.time() - self.start_time,))\\n\\n return ''.join(retval)\",\n \"def __repr__(self: object) -> str:\\n measstring: str = \\\"Tatort - {:04d} - {} - {} - {} - {}\\\".format(self.episode_id, self.episode_name, self.episode_inspectors, self.episode_sequence, self.episode_broadcast)\\n return measstring\",\n \"def info(self):\\n return (f\\\"Match id: {self._id}\\\\n\\\"\\n f\\\"dire_score: {self.dire_score}\\\\n\\\"\\n f\\\"dire_team: {self.dire_team}\\\\n\\\"\\n f\\\"duration: {self.duration}\\\\n\\\"\\n f\\\"game_mode: {self.game_mode}\\\\n\\\"\\n f\\\"patch: {self.patch}\\\\n\\\"\\n f\\\"radiant_score: {self.radiant_score}\\\\n\\\"\\n f\\\"radiant_team: {self.radiant_team}\\\\n\\\"\\n f\\\"radiant_win: {self.radiant_win}\\\\n\\\"\\n f\\\"skill: {self.skill}\\\\n\\\"\\n f\\\"start_time: {self.start_time}\\\\n\\\")\",\n \"def info(self) -> str:\\n return self._info\",\n \"def info(self) -> str:\\n return self._info\",\n \"def timestr():\\n return dt.strftime(dt.now(),'%H:%M:%S')\",\n \"def summary_string(self) -> str:\",\n \"def __str__(self) -> str:\\n\\n # Get current time\\n t = timer()\\n # Length of label field, calculated from max label length\\n fldlen = [len(lbl) for lbl in self.t0] + [\\n len(self.default_label),\\n ]\\n lfldln = max(fldlen) + 2\\n # Header string for table of timers\\n s = f\\\"{'Label':{lfldln}s} Accum. Current\\\\n\\\"\\n s += \\\"-\\\" * (lfldln + 25) + \\\"\\\\n\\\"\\n # Construct table of timer details\\n for lbl in sorted(self.t0):\\n td = self.td[lbl]\\n if self.t0[lbl] is None:\\n ts = \\\" Stopped\\\"\\n else:\\n ts = f\\\" {(t - self.t0[lbl]):.2e} s\\\" % (t - self.t0[lbl]) # type: ignore\\n s += f\\\"{lbl:{lfldln}s} {td:.2e} s {ts}\\\\n\\\"\\n\\n return s\",\n \"def stats_get_str(self):\\n return self.stats.get_all_str()\",\n \"def stats_get_str(self):\\n return self.stats.get_all_str()\",\n \"def get_info(self) -> str:\\n raise NotImplementedError()\",\n \"def time(self):\\n return self.raw[\\\"logTime\\\"]\",\n \"def info(self):\\n self.update_info()\\n print('Number of electrodes: ' + str(self.n_elecs))\\n print('Recording time in seconds: ' + str(self.dur))\\n print('Sample Rate in Hz: '+ str(self.sample_rate))\\n print('Number of sessions: ' + str(self.n_sessions))\\n print('Date created: ' + str(self.date_created))\\n print('Meta data: ' + str(self.meta))\",\n \"def _get_debug_message(self):\\r\\n if DEBUG:\\r\\n return 'fps = %d' % self.timer.get_fps()\",\n \"def get_time(self):\\n return ''\",\n \"def time_str(self):\\n return datetime.now().strftime('%c')\",\n \"def info(self) -> str:\\n return pulumi.get(self, \\\"info\\\")\",\n \"def get_str_metadata(self):\\n return \\\"\\\\n\\\".join([\\\"Guessed by {}\\\".format(self.guessed_by), \\\"{} metaphors used\\\".format(self.metaphors_used)])\",\n \"def __str__(self):\\n return '<TuebingenMEG: %i samples, %i timepoints, %i channels>' \\\\\\n % (self.nsamples, self.ntimepoints, len(self.channelids))\",\n \"def __str__(self):\\n return 'elasped: %s (%s)' % (str(self.diff()), self.times())\",\n \"def output(self):\\n time = \\\"%04d-%02d-%02d %02d:%02d:%02d\\\" % (self.year, self.month, \\\\\\n self.date, self.hours, self.minutes, self.seconds)\\n return {\\n \\\"id\\\": self.id,\\n \\\"name\\\": self.name,\\n \\\"type\\\": 4,\\n \\\"enabled\\\": 1,\\n \\\"abstime\\\": time\\n }\",\n \"def getchrony():\\n \\n filename = \\\"/var/log/chrony/tracking.log\\\"\\n fileNotOK = True\\n try:\\n if os.path.isfile(filename):\\n fileNotOK = False\\n except:\\n fileNotOK = True\\n # if file is not OK, return default\\n if fileNotOK:\\n return( \\\"2020-02-20T02:02:02.000\\\", 0., 0.)\\n \\n #get the very last line in the filea\\n line = subprocess.check_output(['tail', '-1', filename])\\n parts = line.split()\\n nparts = len(parts)\\n\\n if nparts < 10:\\n return( \\\"\\\", 0., 0.)\\n \\n date = parts[0]\\n time = parts[1]\\n ip = parts[2]\\n #print(\\\"Offset: %s\\\" % (parts[9]))\\n offset = float(parts[6])\\n offsetrms = float(parts[9])\\n datestr = \\\"%sT%s\\\" % (date, time)\\n return( datestr, offset, offsetrms)\",\n \"def get_time_info(self):\\n\\n raise NotImplementedError\",\n \"def __repr__(self):\\n if self._microsecond != 0:\\n s = \\\", %d, %d\\\" % (self._second, self._microsecond)\\n elif self._second != 0:\\n s = \\\", %d\\\" % self._second\\n else:\\n s = \\\"\\\"\\n s = \\\"%s.%s(%d, %d%s)\\\" % (\\n type(self).__module__,\\n self.__class__.__qualname__,\\n self._hour,\\n self._minute,\\n s,\\n )\\n if self._tzinfo is not None:\\n assert s[-1:] == \\\")\\\"\\n s = s[:-1] + \\\", tzinfo=%r\\\" % self._tzinfo + \\\")\\\"\\n if self._fold:\\n assert s[-1:] == \\\")\\\"\\n s = s[:-1] + \\\", fold=1)\\\"\\n return s\",\n \"def getisotime():\\n ct = datetime.utcnow()\\n return ct.strftime(\\\"%Y%m%d%H%M\\\")\",\n \"def gen_beat_output(e):\\n return [playback_char(e,t) for t in range(70000)]\",\n \"def __str__(self):\\n return \\\"{0:02}:{1:02}\\\".format(self.min, self.sec)\",\n \"def __str__(self):\\n return str(self.elapsed) + ' sec'\",\n \"def report(self) -> str:\\n return REPORT_TIMEFRAME.format(self.name,\\n self.times_run,\\n self.average_runtime)\",\n \"def get_monitor_string(self):\\n\\n return self.reporter.get_overview_string(self.info)\",\n \"def get_info(self):\\n out = ''\\n for k in sorted(self.components.keys()):\\n out += '{:s}: {:s}'.format(k, self.info[k]) + '\\\\n'\\n return(out)\",\n \"def __str__(self):\\n if self.minutes == 0:\\n hm = 4 * ''\\n else:\\n hours, minutes = divmod(self.minutes, 60)\\n hm = \\\"{0:0>2d}{1:0>2d}\\\".format(hours, minutes)\\n\\n return hm\",\n \"def _get_timestamp() -> str:\\n\\n dt = timezone(\\\"UTC\\\").localize(datetime.utcnow()).strftime(\\\"%b. %d, %Y#%H:%M UTC\\\")\\n date, time = dt.split(\\\"#\\\")\\n return f\\\"Event Timestamp: 📅 {date} 🕒 {time}\\\"\",\n \"def info(self) -> str:\\n info = \\\"\\\"\\n\\n # Ensure we have a latitude and a longitude\\n if self.latitude is None:\\n raise GenerateError(\\\"Missing latitude\\\")\\n elif self.longitude is None:\\n raise GenerateError(\\\"Missing longitude\\\")\\n\\n # Ensure we have a symbol table and symbol ID\\n if self.symbol_table is None:\\n raise GenerateError(\\\"Missing symbol table\\\")\\n elif self.symbol_id is None:\\n raise GenerateError(\\\"Missing symbol ID\\\")\\n\\n # Set data type ID\\n if self.timestamp is None:\\n if self.messaging is False:\\n self.data_type_id = \\\"!\\\"\\n else:\\n self.data_type_id = \\\"=\\\"\\n else:\\n if self.messaging is False:\\n self.data_type_id = \\\"/\\\"\\n else:\\n self.data_type_id = \\\"@\\\"\\n\\n # Set the timestamp\\n info += APRSUtils.encode_timestamp(self.timestamp, self.timestamp_type)\\n\\n if self.compressed:\\n # Add the position in a compressed format\\n info += self._generate_compressed_position(\\n self.latitude, self.longitude, self.symbol_table, self.symbol_id, self.altitude,\\n self.course, self.speed, self.radio_range, self.compression_fix,\\n self.compression_source, self.compression_origin)\\n\\n # PHG, etc is not supported for compressed formats (see APRS 1.01 C9 P36)\\n if self.comment:\\n info += self.comment\\n\\n else:\\n # Add the position in an uncompressed format\\n # TODO: handle BRG/NRQ\\n info += self._generate_uncompressed_position(\\n self.latitude, self.longitude, self.symbol_table, self.symbol_id, self.ambiguity\\n )\\n\\n # Handle PHG\\n if self.power is not None and self.height is not None and self.gain is not None \\\\\\n and self.directivity is not None:\\n phg = APRSUtils.encode_phg(self.power, self.height, self.gain, self.directivity)\\n info += \\\"PHG{}\\\".format(\\n self._generate_data(phg=phg, altitude=self.altitude, comment=self.comment)\\n )\\n\\n # Handle DFS\\n elif self.strength is not None and self.height is not None and self.gain is not None \\\\\\n and self.directivity is not None:\\n dfs = APRSUtils.encode_dfs(self.strength, self.height, self.gain, self.directivity)\\n info += \\\"DFS{}\\\".format(\\n self._generate_data(dfs=dfs, altitude=self.altitude, comment=self.comment)\\n )\\n\\n # Handle course/speed\\n elif self.course is not None and self.speed is not None:\\n info += \\\"{}/{}\\\".format(\\n str(self.course).zfill(3),\\n str(self.speed).zfill(3)\\n )\\n info += self._generate_data(altitude=self.altitude, comment=self.comment)\\n\\n # Handle RNG\\n elif self.radio_range is not None:\\n info += \\\"RNG{}\\\".format(\\n str(self.radio_range).zfill(4)\\n )\\n info += self._generate_data(altitude=self.altitude, comment=self.comment)\\n\\n else:\\n info += self._generate_data(altitude=self.altitude, comment=self.comment)\\n\\n return info\",\n \"def __str__(self):\\n y, m, d = self._year, self._month, self._day\\n h, mn, s, t = self._hour, self._minute, self._second, self._tz\\n if s == int(s):\\n # A whole number of seconds -- suppress milliseconds.\\n return '%4.4d/%2.2d/%2.2d %2.2d:%2.2d:%2.2d %s' % (\\n y, m, d, h, mn, s, t)\\n else:\\n # s is already rounded to the nearest microsecond, and\\n # it's not a whole number of seconds. Be sure to print\\n # 2 digits before the decimal point.\\n return '%4.4d/%2.2d/%2.2d %2.2d:%2.2d:%06.6f %s' % (\\n y, m, d, h, mn, s, t)\",\n \"def __str__(self) -> str:\\r\\n\\r\\n return 'RawData obect version {}\\\\n- {:>5} : {}\\\\n- {:>5} : {}\\\\n- {:>5} : {}'.format(self.version,\\r\\n 'Time',\\r\\n strftime('%m/%d/%Y %H:%M:%S', localtime(self.timestamp)),\\r\\n 'Label',\\r\\n self.label,\\r\\n 'Value',\\r\\n self.value)\",\n \"def to_string(self):\\n if self.is_power_onoff():\\n return 'Power On/Off'\\n else:\\n gain = str(hex(int(self['gain_speed'])))\\n out = self['target'].ljust(20) + ' ' + self['filters'].ljust(11) + ' ' + self['x_bin'] + 'x' + self['y_bin'] + ' ' + gain[2:].upper()\\n \\n \\n if self.number_windows() > 0:\\n out += ' ' + self['x1_size'].rjust(4) + 'x' + self['y1_size'].ljust(4) + ' ' + self['x1_start'].ljust(3) + ' ' + self['y1_start'].ljust(4)\\n if self.number_windows() > 1:\\n out += ' ' + self['x2_size'].rjust(4) + 'x' + self['y2_size'].ljust(4) + ' ' + self['x2_start'].ljust(3) + ' ' + self['y2_start'].ljust(4)\\n \\n if 'Comment' in self:\\n out += ' ' + self['Comment']\\n return out\",\n \"def time( self, mess, args):\\n return str(datetime.datetime.now())\",\n \"def time( self, mess, args):\\n return str(datetime.datetime.now())\",\n \"def extract_beat(audio, sample_rate = 44100):\\n beat_tracker = BeatTrackerDegara()\\n beats_time = beat_tracker(audio)\\n beats = np.array(map(lambda time : round(time * sample_rate), beats_time))\\n beats = np.append(0, beats)\\n beats_time = np.append(0, beats_time)\\n\\n return beats, beats_time\",\n \"def __str__(self) -> str:\\n return self.__repr__() + \\\"\\\\n\\\" + \\\"\\\\n\\\".join(self.regimes())\",\n \"def __get_timestamp() -> str:\\n return str(datetime.now().astimezone())\",\n \"def __get_timestamp() -> str:\\n return str(datetime.now().astimezone())\",\n \"def info(self):\\n\\n if self.running:\\n return INFO_RUNNING_FORMAT.format(**self.__dict__)\\n else:\\n return INFO_ENDED_FORMAT.format(**self.__dict__)\",\n \"def nice_output(self):\\n return 'Pitch: {0} at {1}: {2}'.format(\\n self.pitch_type, self.start_speed, self.des)\",\n \"def get_time(self):\\n return \\\"%02u:%02u:%02u (%d)\\\" % self.rtc.datetime()[4:8]\",\n \"def __str__(self):\\n out_str = \\\"\\\\n\\\".join(`\\\"%.5f, %.5f, %.1f, %s, %s\\\" % (point[0], point[1], point[2], point[3], point[4])` for point in self.__traectory_list)\\n return \\\"\\\\'x, y, altitude, capture time, capture date'\\\\n\\\"+out_str\",\n \"def _printable(self):\\n toPrint = \\\"Time Info header. \\\"\\n toPrint += \\\"timestamp: \\\" + str(self.datetime) + \\\" \\\"\\n\\n return toPrint\",\n \"def time():\\n return datetime.datetime.now().strftime(\\\"%Y%m%dT%H%M%SZ\\\")\",\n \"def __str__(self):\\n iso_time = str(datetime.datetime.fromtimestamp(self.next_time))\\n return \\\"<Job(%s, %ss, %s)>\\\" % (iso_time, self.interval, self.func)\",\n \"def __str__(self):\\n struct_repr = \\\", \\\".join([\\n \\\"num_satellites: \\\" + str(self.num_satellites),\\n \\\"fix_type: \\\" + str(self.fix_type)\\n ])\\n\\n return f\\\"GpsInfo: [{struct_repr}]\\\"\",\n \"def heartbeat():\\n return jsonify(int(time.time()))\",\n \"def __str__(self):\\n return str(self.date) + \\\" \\\" + str(self.time_of_day)\",\n \"def now(self):\\n return time.strftime(r'[%d/%b/%Y:%H:%M:%S]')\",\n \"def get_info_string(self) -> str:\\n return \\\"Not implemented\\\"\",\n \"def __str__(self):\\n return \\\"({0}:{1}:{2})\\\".format(self.hours, self.minutes, self.seconds)\",\n \"def time_now() -> str:\\n return datetime_to_str(datetime_now())\",\n \"def __str__(self) -> str:\\n\\n return f\\\"{_time_to_pretty_string(self.opening)} - {_time_to_pretty_string(self.closing)}\\\"\",\n \"def timeStamp():\\n import time\\n return str(time.strftime(\\\"%a %d %b %Y %I:%M:%S %p\\\"))\",\n \"def info():\\n return r\\\"\\\"\\\"Lin-Yu Tseng and Chun Chen, \\\"Multiple trajectory search for Large Scale Global Optimization,\\\" 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), Hong Kong, 2008, pp. 3052-3059. doi: 10.1109/CEC.2008.4631210\\\"\\\"\\\"\",\n \"def __str__(self) -> str:\\n return self.customer.name + ' arrives at ' + str(self.timestamp)\",\n \"def get_wav_info(file_name):\\n wr = wave.open(file_name, 'r')\\n sample_width = wr.getsampwidth()\\n frame_rate = wr.getframerate()\\n num_frames = wr.getnframes()\\n n_channels = wr.getnchannels()\\n s = \\\"sample width: {} bytes\\\\n\\\".format(sample_width) + \\\\\\n \\\"frame rate: {} Hz\\\\n\\\".format(frame_rate) + \\\\\\n \\\"num frames: {}\\\\n\\\".format(num_frames) + \\\\\\n \\\"track length: {} s\\\\n\\\".format(num_frames / frame_rate) + \\\\\\n \\\"num channels: {}\\\\n\\\".format(n_channels)\\n\\n return s\",\n \"def print(self):\\n return '{:<32s}{:>25s}{:>8.3f}{:>10d}{:>15d}{:>12d}{:>12.3f}{:>10d}{:>10d}'.format(\\n self.image_path, list_to_str(self.coord), self.depth, self.priority, self.enqueue_time,\\n self.exec_time, self.response_time, self.deadline, self.missed)\",\n \"def __str__(self):\\n return ', '.join([self.yftf_data, self.info_hash, str(self.num_pieces), str(self.peers)])\",\n \"def __str__(self):\\n return \\\"Boat with the maximum speed of \\\" + str(self.max_speed) \\\\\\n + \\\" knots\\\"\",\n \"def format(cls):\\n return super().format() + 'Hhiihh'\",\n \"def massage_addinfo(self) -> str:\\n self.message_str = f'{self.time}\\\\n{self.sent_by}\\\\n'\\n return self.message_str\",\n \"def __str__(self):\\n return \\\"\\\\nCurrent time:\\\" + str(self._time) +\\\\\\n \\\"\\\\nCurrent cookies:\\\" + str(self._cur_cookies) +\\\\\\n \\\"\\\\nCurrent CPS:\\\"+ str(self._cps) +\\\\\\n \\\"\\\\nTotoal cookies:\\\" + str(self._tot_cookies)\",\n \"def __str__(self):\\n\\n # Print the class and address.\\n msg = \\\"{0} at {1}\\\\n\\\".format(str(self.__class__), str(hex(id(self))))\\n\\n # Print some other basic information.\\n msg = \\\"{0} line name: ({1})\\\\n\\\".format(msg, self.name)\\n msg = \\\"{0} ping_time: ({1})\\\\n\\\".format(\\n msg,\\n self.ping_time.shape[0])\\n msg = \\\"{0} data: ({1})\\\\n\\\".format(\\n msg,\\n self.data.shape[0])\\n msg = \\\"{0} start time: {1}\\\\n\\\".format(msg,\\n self.ping_time[0])\\n msg = \\\"{0} end time: {1}\\\\n\\\" .format(msg,\\n self.ping_time[-1])\\n\\n return msg\",\n \"def __str__(self):\\n return f\\\"{self._desc:16s}\\\"\",\n \"def to_event(self):\\n return f'{self.gem_pack_id},{self.event_time_in_milliseconds},{self.price}\\\\n'\",\n \"def info():\\n\\n\\t info = \\\"This package determines the day of the week.\\\"\\n\\t print(info)\",\n \"def wattsString(self):\\n return self.watts is None and \\\"unknown\\\" or str(self.watts)\",\n \"def _cap_title(self, ndx):\\n\\n return '%d/%d @ %d:%02d:%02d' % (ndx, self.dpar.n_caps,\\n self.dpar.frame_timestamp[ndx].hour,\\n self.dpar.frame_timestamp[ndx].minute,\\n self.dpar.frame_timestamp[ndx].second)\",\n \"def getBatteryInfo():\\n \\n # WMIC Win32_Battery fields to query\\n battery_vars = ['BatteryStatus', 'EstimatedChargeRemaining', 'EstimatedRunTime',\\n 'Status']\\n\\n # Start data with timestamp\\n result = datetime.now().strftime(DATE_FORMAT)\\n\\n for var in battery_vars:\\n \\n #execute wmic command and capture output\\n temp = subprocess.check_output([\\\"wmic\\\", \\\"path\\\", \\\"Win32_Battery\\\", \\\"get\\\", var, \\\"/value\\\"]) \\n \\n result += ', ' + temp.strip()\\n \\n result = '[' + result + ']'\\n \\n return result\",\n \"def __str__(self):\\n\\n strme = \\\"fed method {} {} {} {}\\\"\\\\\\n .format(WangLandau.key, self.delta0, self.c_upd, self.n_upd)\\n if self.smooth:\\n strme = \\\"{} {}\\\".format(strme, self.smooth)\\n\\n return strme\",\n \"def printInfo(totaltime, vid, cpath):\\r\\n infotxt = open(cpath + 'Resize Info' + '.txt', 'a')\\r\\n info = str('executeTime: %f' % totaltime + '\\\\n')\\r\\n converageRate = totaltime / (vid.get(7))\\r\\n info += str('average converage rate is: %f' % converageRate + 'f/s' + '\\\\n')\\r\\n frameNum = vid.get(7)\\r\\n info += str('frame number is %d' % frameNum + '\\\\n')\\r\\n fps = vid.get(5)\\r\\n info += str('frame rate is %f' % fps + '\\\\n')\\r\\n\\r\\n infotxt.write(info)\\r\\n infotxt.close()\\r\\n\\r\\n # print(info)\\r\\n vid.release()\\r\\n return info\",\n \"def __str__(self):\\n return self.fmt('DMY', '.')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66582495","0.6518425","0.6161863","0.6112195","0.6085194","0.6056986","0.6040513","0.59848976","0.5980764","0.59712094","0.5916152","0.59127504","0.5898801","0.5861905","0.58609194","0.5855922","0.58121693","0.5801718","0.5774646","0.5771938","0.574199","0.574199","0.5730006","0.57110345","0.5678113","0.56704986","0.56704986","0.5667893","0.5653733","0.5648271","0.56328386","0.56328386","0.5628988","0.56260693","0.561943","0.5605078","0.56010723","0.56010467","0.5596043","0.5595272","0.5594745","0.5583202","0.55648375","0.5564155","0.55633116","0.5559619","0.5559283","0.5557892","0.55528384","0.55406994","0.5523992","0.5511505","0.55002916","0.5491661","0.5486412","0.5476742","0.54742706","0.5471277","0.5468429","0.54669","0.54669","0.54633975","0.54594904","0.5452521","0.5452521","0.54484636","0.54389936","0.54312366","0.54262453","0.5426052","0.541205","0.5411829","0.539993","0.53955406","0.5395195","0.5394015","0.5393499","0.5383987","0.536254","0.5360917","0.5352964","0.5349415","0.5344921","0.53431195","0.5338612","0.5319831","0.53160095","0.5315268","0.53131455","0.5307858","0.53013927","0.5284715","0.5280565","0.5279921","0.527974","0.52722514","0.52694356","0.5268141","0.5267707","0.526598"],"string":"[\n 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index(request):\n\n username = request.session.get('username', False)\n profile = request.session.get('profile', False)\n baseurl = ''\n workpath = os.path.dirname(os.path.abspath(__file__)) # Returns the Path your .py file is in\n with open((os.path.join(workpath,'../baseurl.txt')),'r',encoding='utf-8') as f:\n baseurl = f.readline()\n baseurl = baseurl.replace('\\n','').strip()\n print(baseurl)\n if not baseurl.endswith('/'):\n baseurl = baseurl +'/'\n if(username and baseurl != ''):\n context = {'username': username,'profile':profile, 'baseurl':baseurl}\n # if(username):\n # context = {'username': username,'profile':profile}\n return render(request, 'MedTAG_sket_dock_App/index.html', context)\n else:\n return redirect('MedTAG_sket_dock_App:login')"},"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 home():\n\n\treturn render_template('index.html', title='Home Page',\n\t\t\t\t\t\t year=datetime.now().year)","def home():\r\n return render_template(\r\n 'index.html',\r\n title='Home Page',\r\n year=datetime.now().year,\r\n )","def homepage():\n return render_template('home/index.html', \n title=\"Bem vindo!\")","def home():\n return render_template(\n 'index.html',\n title='Home Page',\n year=datetime.now().year,\n )","def home():\n return render_template(\n 'index.html',\n title='Home Page',\n year=datetime.now().year,\n )","def home():\n return render_template(\n 'index.html',\n title='Home Page',\n year=datetime.now().year,\n )","def home():\n return render_template(\n 'index.html',\n title='Home Page',\n year=datetime.now().year,\n )","def home():\n return render_template(\n 'index.html',\n title='Home Page',\n year=datetime.now().year,\n )","def home():\n return render_template(\n 'index.html',\n title='Home Page',\n year=datetime.now().year,\n )","def home():\n return 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home():\n return render_template('home.html', title=\"Home\")","def home():\n\n return render_template('home_page.html')","def home(self, *args, **kwargs):\n pass","def home():\n\n return render_template(\"home.html\")","def home():\n return render_template(\n 'index.html',\n title='Sairam sai baba',\n year=datetime.now().year,\n )","def home():\n return render_template(\n 'index.html'\n )","def homepage():\n return render_template(\"home/index.html\", title=\"Welcome\")","def home():\n\n\treturn render_template('solai.html')","def home():\n payload = manager.get_payload()\n return render_template('index.html', payload=payload)","def homepage():\n return render_template('home/index.html', title=\"Welcome\")","def homepage():\n return render_template('home/index.html', title=\"Welcome\")","def homepage():\n return render_template(\"home/index.html\")","def homepage():\n\treturn render_template(\"home/a_homepage.html\", title=\"Welcome\")","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template('home.html')","def home():\n return render_template(\"home.html\")","def home():\n return render_template(\"home.html\")","def home():\n\n return render_template('index.html')","def index():\n response.view_title = myconf.get('app.name') + ' Home Page'\n return dict(message='')","def home(environ):\n return render_template(template_name=\"index.html\", context={})","def home():\n return render_template('main.html')","def home():\n return render_template('home.html',\n face=session.get(app.config['SESSION_KEY'], None))","def home(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'AscensionESports_Baseline/index.html',\n {\n 'background': getSiteBackground(),\n 'color': getSiteColor(),\n 'title':'Home Page',\n 'year':datetime.now().year,\n }\n )","def home():\n return render_template('index.html')","def home():\n return render_template('index.html')","def home():\n return render_template('index.html')","def home():\n return render_template('index.html')","def home():\n return render_template('index.html')","def home():\n return render_template('index.html')","def index():\r\n return render_template('home.html')","def Home():\n resp = render_template('index.html')\n return resp","def home():\n return render_template(\"index.html\")","def home():\n return render_template(\"index.html\")","def home():\n return render_template(\"index.html\")","def home():\n return render_template(\"index.html\")","def home():\n return render_template(\n 'index.html',\n year=datetime.now().year,\n )","def home():\n return render_template('Main_Page.html')","def home(request):\n return render_template('core/home.html')","def home():\n\n return render_template('index.html',\n date = d,\n mood = mood,\n header_color = color,\n day = day,\n week = week,\n month = month,\n year = year)","def homepage(request):\n\treturn render(request, 'core/homepage.html')","def home():\n template_stories = [\"silly_story\", \"excited_story\"]\n\n return render_template(\"homepage.html\", story_list = template_stories)","def index():\r\n\r\n # initializes page title\r\n page_title = 'Home'\r\n\r\n # renders the landing page\r\n return render_template('index.html', page_title=page_title)","def render_home():\r\n\treturn render_template(\"index.html\")","def home_page():\n\n return render_template('index.html')","def homepage():\n return render_template('homepage.html')","def index():\n\n return render_template('home.html')","def index():\n return render_template(\"home.html\")","def home_page() -> str:\r\n return render_template(\"home.html\")","def homepage():\n\n return render_template('rosemead_homepage.html')","def index():\n return render_template('home.html')","def home(request):\r\n return render(request, 'home.html')","def home(request):\n return render_to_response('home.html', {}, RequestContext(request))","def index():\n\treturn render_template(\"index.html\", title=\"Home\")","def home(request):\r\n assert isinstance(request, HttpRequest)\r\n return render(\r\n request,\r\n 'app/index.html',\r\n {\r\n 'title':'Home Page',\r\n 'year':datetime.now().year,\r\n }\r\n )","def home():\n\n # sets the page to load depending on the type of user\n # if none specified the login screen will be displayed\n pageName = ''\n userType = session.get('UserType', None)\n if userType == None:\n pageName = 'anonHome.jade'\n elif userType == 'Seeker':\n pageName = 'indexJob.jade'\n elif userType == 'Manager':\n pageName = 'indexManager.jade'\n\n frogHop = url_for('static', filename='loop frog.gif')\n uName = session.get('UserName', 'Unknown') # load a default value if retrieval fails\n return render_template(\n pageName,\n title='Home',\n name=uName,\n getFrog=frogHop,\n year=datetime.now().year,\n )","def a_homepage():\n\n\treturn render_template('home/a_homepage.html', title=\"Homepage Admin\")","def home():\n logging.info('Entering route: HOME')\n\n logging.info('Rendering template: main.html')\n return render_template('main.html')","def home():\n return render_template('index.html', year=datetime.now().year)","def home(request):\n # Test Comment\n assert isinstance(request, HttpRequest)\n return render(\n 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app"},"document":{"kind":"string","value":"def credits(request):\n\n username = request.session.get('username', False)\n profile = request.session.get('profile', False)\n if(username):\n context = {'username': username,'profile':profile}\n return render(request, 'MedTAG_sket_dock_App/index.html', context)\n else:\n return redirect('MedTAG_sket_dock_App:login')"},"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 credits():\n return render_template('credits.html')","async def credits(self, ctx: commands.Context):\r\n await ctx.send(embed=CREDITS_EMBED)","def credits(cls):\n print(\"Thank you for playing.\\nCreated by \" + cls.author+\".\\n\")","def contact_linkup(self, request, pk):\n obj_api = api()\n title_contact = \"Tu contacto Linkup\"\n token = request.session['token']\n resp = obj_api.get(slug='sellers/' + pk + \"/\", token=token)\n return render(request, 'frontend/actors/client/my_account.html', {'data_user': resp, \n 'title_contact': title_contact})","async def credits(self, ctx):\n embed = discord.Embed(title='{}:'.format(ctx.message.author.name),\n description='\\n:ok_hand: :laughing:\\n :telephone_receiver::shirt::call_me:\\n :jeans: :fire:',\n colour=0xf20006)\n last_message = await self.bot.say(embed=embed)\n await self.bot.add_reaction(last_message, self.emojiUnicode['succes'])","def links(request):\n cart = Cart(request)\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/links.html',\n {\n \t'cart': cart,\n 'title':'Полезные ресурсы',\n 'message':'Your contact page.',\n 'year':datetime.now().year,\n }\n )","def copyright(request):\n username = request.session.get('username')\n try:\n index_card = IndexCard.objects.get(username=username)\n except:\n index_card = None\n template = loader.get_template('crppindexcard/copyright.html')\n context = RequestContext(request, {\n 'username': username,\n 'index_card': index_card,\n 'is_copyright': 'is_copyright',\n })\n return HttpResponse(template.render(context))","def credits(self, **kwargs):\n\n path = self._get_movie_id_path('credits')\n resp = self._get_method(path, kwargs)\n return resp","def thanks(request):\n assert isinstance(request, HttpRequest)\n\n return render(\n request,\n 'AscensionESports_Baseline/Thanks.html',\n {\n 'background': getSiteBackground(),\n 'color': getSiteColor(),\n 'title':\"You are now signed up!\",\n 'year': datetime.now().year,\n }\n )","def credits_purchase(request):\n if request.method==\"POST\":\n \"\"\" POST request can come from card form submit or from initial\n credits amount selection page \"\"\"\n try:\n credits_amount = int(request.POST.get('credits_amount', None))\n except TypeError:\n messages.error(request, \"Amount was invalid\")\n return redirect('add_credits')\n if credits_amount or credits_amount == 0:\n if (credits_amount % 10) is not 0:\n # Credits amount wasn't a multiple of 10, so user bypassed\n # JavaScript form validation\n messages.error(\n request, \n \"Credits can only be added in multiples of 10\"\n )\n return redirect('add_credits')\n credits_cost = \\\n settings.COST_PER_TEN_CREDITS * (credits_amount / 10)\n if \"stripeToken\" in request.POST:\n # POST request came from card form submit\n try:\n customer = stripe.Charge.create(\n amount = int(credits_cost*100),\n currency = \"EUR\",\n description = request.user.email,\n source = request.POST['stripeToken'],\n )\n except stripe.error.CardError:\n messages.error(request, \"Your card was declined!\")\n return redirect('credits_purchase')\n if customer.paid:\n # All is good, so add the chosen amount of credits\n profile = request.user.profile\n profile.credits += credits_amount\n profile.save()\n return render(request, 'credits_added.html')\n else:\n messages.error(request, \"Unable to take payment\")\n return redirect('credits_purchase')\n else:\n \"\"\" POST request came from initial credits selection page\n so now render Stripe card form \"\"\"\n return render(request, \"credits_checkout.html\", {\n 'publishable': settings.STRIPE_PUBLISHABLE,\n 'ten_credit_cost': settings.COST_PER_TEN_CREDITS,\n 'credits_amount': credits_amount,\n 'total': credits_cost,\n })\n else:\n messages.error(request, \"No amounts of credits selected\")\n return redirect('add_credits')\n else:\n return redirect('add_credits')","def _credits_dialogue(self):\n credits_dialogue = _Credits(self)\n self.root.wait_window(credits_dialogue.top)","def account():\n\n return render_template('account_page.html', title='Account')","def credits(self):\r\n return credit.Credits(self)","def contributors(request):\n return render(request, 'metro_app/contributors.html')","def credits_window():\n credits = tk.Toplevel()\n credits_lbl = tk.Label(credits, text='Software Developed By Allan / SpideyKeiiti\\n'\n 'Made for Prototype purposes for Streets Of Rage Remake Community!')\n\n credits_lbl.pack()","def about(request):\n return render(request, 'code_challenge/about.html', {})","def get(self, request ):\n return render(request, \"main_display_cards.html\")","def about():\n return render_template('about.html', name=\"COMP3161\")","def about_us(request):\n\treturn render(request, 'about_us.html')","def index(request):\r\n return render(request, 'coin/index.html')","def about(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'csa/about.html',\n {\n 'title':'About',\n 'message':'Your application description page.',\n 'year':datetime.now().year,\n }\n )","def contact():\n img.compare_images()\n return render_template(\n 'contact.html',\n title='Contact',\n #year=datetime.now().year,\n year=datetime.now().year,\n message='Your contact page.'\n )","def about(request):\n cart = Cart(request)\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/about.html',\n {\n \t'cart': cart,\n 'title':'Автосервис Локнянский район',\n 'message':'Вас приветствует компания «СТО»!',\n 'year':datetime.now().year,\n }\n )","def aboutus(request):\r\n\treturn render(request, 'aboutus.html')","def index(request):\n copy = '2018 ' + author\n\n context = dict(author=author, copyright=copy, repo_url=repo_url)\n\n return render(request, 'index.html', context)","def contact(request):\r\n assert isinstance(request, HttpRequest)\r\n return render(\r\n request,\r\n 'app/contact.html',\r\n {\r\n 'title':'Iletisim',\r\n\r\n 'year':datetime.now().year,\r\n }\r\n )","def thanks(request):\n # Let's grab that temporary authorization code Slack's sent us from\n # the request's parameters.\n code_arg = request.GET['code']\n # The bot's auth method to handles exchanging the code for an OAuth token\n pyBot.auth(code_arg)\n return render(request, \"thanks.html\")","def main_page():\n pages=get_accounts()\n return render_template('disp.html',pages=pages)","def contact(request):\n cart = Cart(request)\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/contact.html',\n {\n 'title':'Контакты',\n 'cart': cart,\n 'message':'Your contact page.',\n 'year':datetime.now().year,\n }\n )","def about(request):\n assert isinstance(request, HttpRequest)\n\n return render(\n request,\n 'AscensionESports_Baseline/about.html',\n {\n 'background': getSiteBackground(),\n 'color': getSiteColor(),\n 'title':'About Us',\n 'year': datetime.now().year,\n }\n )","def my_account(request):\n context = {}\n\n return render(request, 'classroom_main/my_account.html', context)","def about_us():\r\n return render_template(\"about_us.html\")","def dashboard(request):\r\n profile = get_object_or_404(Profile, user=request.user)\r\n wallet = Wallet.objects.get(user=request.user)\r\n history = History.objects.get(pk=1)\r\n referrals = Referral.objects.filter(referee=request.user).count()\r\n invoices = Invoice.objects.filter(issuer=request.user).count()\r\n return render(request, 'coin/dashboard.html', {'profile': profile, \r\n 'wallet': wallet, 'history': history, 'referrals': referrals, \r\n 'invoices': invoices})","def credit(self):\n return pn_link_credit(self._impl)","def contact(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'csa/contact.html',\n {\n 'title':'Contact',\n 'message':'Your contact page.',\n 'year':datetime.now().year,\n }\n )","def about(request):\n context = {}\n return render(request, 'store/about.html', context)","def about(request):\n\n context = {\n\n }\n\n return render(request, 'about.html', context=context)","def Credits(master):\n from spam.__init__ import __version__\n from sys import argv\n from os import path\n CRW = 450 # credits window width\n CRH = 350 # credits window height\n\n header_text = (\n \"SPAM.py is a wrapper that uses cpptraj to calculate and identify\\n\"\n \"water density peaks, NAMD to calculate the energies of individual\\n\"\n \"water molecules, and SciPy to analyze the data and calculate SPAM\\n\"\n \"Free Energies to identify water hotspots.\"\n )\n tailer_text = (\n \"SPAM.Pie Version %s\\n\\n\" % __version__ +\n \"Cpptraj modifications and SPAM.Pie\\n\"\n \"by Jason Swails and Guanglei Cui\\n\"\n \"GlaxoSmithKline, Copyright 2012\"\n )\n credits = tk.Toplevel(master)\n credits.resizable(False, False)\n credits.title('About SPAM.Pie')\n # Get the file name\n fname = path.join(path.abspath(path.split(__file__)[0]), 'spam.gif')\n canv = tk.Canvas(credits, width=CRW, height=CRH)\n canv.pack()\n image = tk.PhotoImage(name='SPAM', master=canv, file=fname)\n canv.create_image(CRW//2, CRH//2, anchor=tk.CENTER, image=image)\n canv.create_text(CRW//2, 10, anchor=tk.N, text=header_text,\n justify=tk.CENTER)\n canv.create_text(CRW//2, CRH-10, anchor=tk.S, text=tailer_text,\n justify=tk.CENTER)\n credits.grab_set()\n credits.mainloop()","def about(request):\n return render(request, 'wantedly_app/about.html')","def thanks(request):\n return render(request, 'SNP_Feature_View/thanks.html')","def about_page(request):\r\n return render(request, 'ez_main/about_page.html')","def about(request):\r\n assert isinstance(request, HttpRequest)\r\n return render(\r\n request,\r\n 'app/about.html',\r\n {\r\n 'title':'Hakkimizda',\r\n 'year':datetime.now().year,\r\n }\r\n )","def about_us():\n\n return render_template('about_us.html')","def users_page(request):","def about(request):\r\n assert isinstance(request, HttpRequest)\r\n return render(\r\n request,\r\n 'app/about.html',\r\n context = \r\n {\r\n 'title':_('About'),\r\n 'year':datetime.now().year,\r\n }\r\n )","def about():\n data = []\n with open(\"data/company.json\", \"r\", encoding=\"utf-8\") as json_data:\n data = json.load(json_data)\n return render_template(\"about.html\", page_title=\"About Us\", company=data)","def about():\n\n return render_template('about_page.html', title='About')","def contact(request):\r\n assert isinstance(request, HttpRequest)\r\n return render(\r\n request,\r\n 'app/contact.html',\r\n {\r\n 'title':'Contact',\r\n 'message':'Your contact page.',\r\n 'year':datetime.now().year,\r\n }\r\n )","def account(request):\n return render(request, 'registration/account.html')","def about():\n\n\treturn render_template(\"about.html\")","def on_about(self):\n render_about_window()","def ZeusPaypalAccounts(request):\n\n\tif request.method == \"GET\":\n\t\treturn render(request, \"lost-empire/site_templates/zeus/paypal_accounts.html\")","def about():\n return render_template(\n 'about.html',\n title='About',\n year=datetime.now().year,\n message='about page.'\n )","def get(self, request, *args, **kwargs):\n context = {\"token\": request.user.auth_token.key}\n return render(request, \"payment/landing_payment.html\", context=context)","def about(request):\r\n assert isinstance(request, HttpRequest)\r\n return render(\r\n request,\r\n 'app/about.html',\r\n {\r\n 'title':'About',\r\n 'message':'Your application description page.',\r\n 'year':datetime.now().year,\r\n }\r\n )","def about(request):\n assert isinstance(request, HttpRequest)\n Description = models.AboutUs.objects.all()\n return render(\n request,\n 'app/about.html',\n {\n 'title':'About',\n 'message':'Your application description page.',\n 'year':datetime.now().year,\n 'Description': Description\n \n }\n )","def contact(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/contact.html',\n {\n 'title':'Contact',\n 'message':'Your contact page.',\n 'year':datetime.now().year,\n }\n )","def contact(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/contact.html',\n {\n 'title':'Contact',\n 'message':'Your contact page.',\n 'year':datetime.now().year,\n }\n )","def contact(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/contact.html',\n {\n 'title':'Contact',\n 'message':'Your contact page.',\n 'year':datetime.now().year,\n }\n )","def contact(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/contact.html',\n {\n 'title':'Contact',\n 'message':'Your contact page.',\n 'year':datetime.now().year,\n }\n )","def thank_you(request):\n return render(request, 'core/thank_you.html')","def about_page(request):\n\n return render(request, 'about_page/about_page.html')","def contact(request):\r\n assert isinstance(request, HttpRequest)\r\n return render(\r\n request,\r\n 'app/contact.html',\r\n context = \r\n {\r\n 'title':_('Contact'),\r\n 'message':'Pour nous contacter.',\r\n 'year':datetime.now().year,\r\n }\r\n )","def about_view(request):\n return {'message': 'Info about us.'}","def about():\n return render_template('about.html', name=\"Nadrine Simms\")","def about(request):\n return render(request, \"about.html\", {})","def about(request):\r\n assert isinstance(request, HttpRequest)\r\n return render(\r\n request,\r\n 'app/about.html',\r\n context_instance = RequestContext(request,\r\n {\r\n 'title': 'About',\r\n 'message': 'Your application description page.',\r\n 'year': datetime.now().year,\r\n 'contact': Contact.objects.get(pk=1),\r\n\r\n })\r\n )","def about(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/about.html',\n {\n 'title':'About',\n 'message':'Your application description page.',\n 'year':datetime.now().year,\n }\n )","def about(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/about.html',\n {\n 'title':'About',\n 'message':'Your application description page.',\n 'year':datetime.now().year,\n }\n )","def about(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/about.html',\n {\n 'title':'About',\n 'message':'Your application description page.',\n 'year':datetime.now().year,\n }\n )","def about(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/about.html',\n {\n 'title':'About',\n 'message':'Your application description page.',\n 'year':datetime.now().year,\n }\n )","def contact(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/contact.html',\n context_instance = RequestContext(request,\n {\n 'title':'Contact',\n 'message':'Your contact page.',\n 'year':datetime.now().year,\n })\n )","def aboutus():\n return render_template(\"aboutUs.html\")","def home(request):\r\n\t# u = User.objects.get(username=\"mohit2\")\r\n\t# google_u = u.socialaccount_set.all()[0]\r\n\t# google_u_prof_pic = google_u.extra_data['picture'];\r\n\r\n\r\n\tproducts = Product.objects.all()\r\n\tcontext = {'products':products}\r\n\tif request.user.is_authenticated:\r\n\t\tif request.user:\r\n\t\t\t# print(request.user)\r\n\t\t\tusername = request.user.username\r\n\t\t\tcustomer = Customer.objects.get(user=request.user)\r\n\t\t\tbio = customer.bio\r\n\t\t\tcontext['username'] = username\r\n\t\t\tcontext['user_bio'] = bio\r\n\treturn render(request, 'small/home.html', context)","def aboutme():\n return render_template('about.html')","def __add_credit_cc(self):\n log.debug(\"Displaying __add_credit_cc\")\n # Create a keyboard to be sent later\n presets = self.cfg.ccard[\"payment_presets\"]\n keyboard = [[telegram.KeyboardButton(str(self.Price(preset)))] for preset in presets]\n keyboard.append([telegram.KeyboardButton(self.loc.get(\"menu_all_cancel\"))])\n # Boolean variable to check if the user has cancelled the action\n cancelled = False\n # Loop used to continue asking if there's an error during the input\n while not cancelled:\n # Send the message and the keyboard\n self.bot.send_message(self.chat.id, self.loc.get(\"payment_cc_amount\"),\n reply_markup=telegram.ReplyKeyboardMarkup(keyboard, one_time_keyboard=True))\n # Wait until a valid amount is sent\n selection = self.__wait_for_regex(r\"([0-9]+(?:[.,][0-9]+)?|\" + self.loc.get(\"menu_all_cancel\") + r\")\",\n cancellable=True)\n # If the user cancelled the action\n if isinstance(selection, CancelSignal):\n # Exit the loop\n cancelled = True\n continue\n # Convert the amount to an integer\n value = self.Price(selection)\n # Ensure the amount is within the range\n if value > self.Price(self.cfg.ccard[\"max_amount\"]):\n self.bot.send_message(self.chat.id,\n self.loc.get(\"error_payment_amount_over_max\",\n max_amount=self.Price(self.cfg.ccard[\"max_amount\"])))\n continue\n elif value < self.Price(self.cfg.ccard[\"min_amount\"]):\n self.bot.send_message(self.chat.id,\n self.loc.get(\"error_payment_amount_under_min\",\n min_amount=self.Price(self.cfg.ccard[\"min_amount\"])))\n continue\n break\n # If the user cancelled the action...\n else:\n # Exit the function\n return\n # Issue the payment invoice\n self.__make_payment(amount=value)","def about(request):\n return render(request, \"about/about.html\")","def about(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/about.html',\n context_instance = RequestContext(request,\n {\n 'title':'About',\n 'message':'Your application description page.',\n 'year':datetime.now().year,\n })\n )","def about(request):\n return render(request,\"encyclopedia/about.html\")","def about():\n return render_template('about.html', title='About')","def about(request):\n return render(request, \"about.html\")","def about():\n\n return render_template('about.html')","def about(request):\n context = get_context()\n return render(request, 'about.html', context)","def buyshares():\n # Initialise buy form\n buyform = BuyShareForm()\n # Validate and process form data\n if(buyform.validate_on_submit()):\n # Buys shares\n issuerID = buyform.buysharecode.data\n quantity = buyform.buyquantity.data\n userID = current_user.userID\n # Call buyshare API\n buyshare = gdb.buyshare(userID, issuerID, quantity)\n if(buyshare):\n # Flash with success message\n flash(\"Share purchase successful!\", category=\"success\")\n else:\n # Flash with warning message\n flash(\"Share purchase unsuccessful!\", category=\"error\")\n # Redirect to reffering page or dashboard\n return redirect(request.referrer or url_for('main.dashboard'))","def about():\n\n return render_template('about.html', title=\"About\")","def about(request):\n return render(request, 'about.html')","def about():\n return render_template(\"pages/about.html\")","def handler(request):\n context = {\"request\": request}\n return render_to_response(\n 'accounts.html',\n context,\n context_instance=RequestContext(request))","def about():\n return render_template('about.html', title='About')","def account(request):\n context = {'user': request.user}\n return render(request, 'registration/account.html', context)","def about(request):\n context = {'client_id': settings.OPENHUMANS_CLIENT_ID,\n 'oh_proj_page': settings.OH_ACTIVITY_PAGE}\n return render(request, 'main/about.html', context=context)","def contact():\r\n return render_template(\r\n 'contact.html',\r\n title='Contact',\r\n year=datetime.now().year,\r\n message='Your contact page.'\r\n )","def contact():\r\n return render_template(\r\n 'contact.html',\r\n title='Contact',\r\n year=datetime.now().year,\r\n message='Your contact page.'\r\n )","def about(request):\n # Test Comment\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'about.html',\n context_instance=RequestContext(request, {})\n )","def about(request):\n # Test Comment\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'about.html',\n context_instance=RequestContext(request, {})\n )","def about():\n return render_template('about.html')","def about():\n return render_template('about.html')","def about():\n return render_template('about.html')","def about():\n return render_template('about.html')","def about():\n return render_template('about.html')"],"string":"[\n \"def credits():\\n return render_template('credits.html')\",\n \"async def credits(self, ctx: commands.Context):\\r\\n await ctx.send(embed=CREDITS_EMBED)\",\n \"def credits(cls):\\n print(\\\"Thank you for playing.\\\\nCreated by \\\" + cls.author+\\\".\\\\n\\\")\",\n \"def contact_linkup(self, request, pk):\\n obj_api = api()\\n title_contact = \\\"Tu contacto Linkup\\\"\\n token = request.session['token']\\n resp = obj_api.get(slug='sellers/' + pk + \\\"/\\\", token=token)\\n return render(request, 'frontend/actors/client/my_account.html', {'data_user': resp, \\n 'title_contact': title_contact})\",\n \"async def credits(self, ctx):\\n embed = discord.Embed(title='{}:'.format(ctx.message.author.name),\\n description='\\\\n:ok_hand: :laughing:\\\\n :telephone_receiver::shirt::call_me:\\\\n :jeans: :fire:',\\n colour=0xf20006)\\n last_message = await self.bot.say(embed=embed)\\n await self.bot.add_reaction(last_message, self.emojiUnicode['succes'])\",\n \"def links(request):\\n cart = Cart(request)\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/links.html',\\n {\\n \\t'cart': cart,\\n 'title':'Полезные ресурсы',\\n 'message':'Your contact page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def copyright(request):\\n username = request.session.get('username')\\n try:\\n index_card = IndexCard.objects.get(username=username)\\n except:\\n index_card = None\\n template = loader.get_template('crppindexcard/copyright.html')\\n context = RequestContext(request, {\\n 'username': username,\\n 'index_card': index_card,\\n 'is_copyright': 'is_copyright',\\n })\\n return HttpResponse(template.render(context))\",\n \"def credits(self, **kwargs):\\n\\n path = self._get_movie_id_path('credits')\\n resp = self._get_method(path, kwargs)\\n return resp\",\n \"def thanks(request):\\n assert isinstance(request, HttpRequest)\\n\\n return render(\\n request,\\n 'AscensionESports_Baseline/Thanks.html',\\n {\\n 'background': getSiteBackground(),\\n 'color': getSiteColor(),\\n 'title':\\\"You are now signed up!\\\",\\n 'year': datetime.now().year,\\n }\\n )\",\n \"def credits_purchase(request):\\n if request.method==\\\"POST\\\":\\n \\\"\\\"\\\" POST request can come from card form submit or from initial\\n credits amount selection page \\\"\\\"\\\"\\n try:\\n credits_amount = int(request.POST.get('credits_amount', None))\\n except TypeError:\\n messages.error(request, \\\"Amount was invalid\\\")\\n return redirect('add_credits')\\n if credits_amount or credits_amount == 0:\\n if (credits_amount % 10) is not 0:\\n # Credits amount wasn't a multiple of 10, so user bypassed\\n # JavaScript form validation\\n messages.error(\\n request, \\n \\\"Credits can only be added in multiples of 10\\\"\\n )\\n return redirect('add_credits')\\n credits_cost = \\\\\\n settings.COST_PER_TEN_CREDITS * (credits_amount / 10)\\n if \\\"stripeToken\\\" in request.POST:\\n # POST request came from card form submit\\n try:\\n customer = stripe.Charge.create(\\n amount = int(credits_cost*100),\\n currency = \\\"EUR\\\",\\n description = request.user.email,\\n source = request.POST['stripeToken'],\\n )\\n except stripe.error.CardError:\\n messages.error(request, \\\"Your card was declined!\\\")\\n return redirect('credits_purchase')\\n if customer.paid:\\n # All is good, so add the chosen amount of credits\\n profile = request.user.profile\\n profile.credits += credits_amount\\n profile.save()\\n return render(request, 'credits_added.html')\\n else:\\n messages.error(request, \\\"Unable to take payment\\\")\\n return redirect('credits_purchase')\\n else:\\n \\\"\\\"\\\" POST request came from initial credits selection page\\n so now render Stripe card form \\\"\\\"\\\"\\n return render(request, \\\"credits_checkout.html\\\", {\\n 'publishable': settings.STRIPE_PUBLISHABLE,\\n 'ten_credit_cost': settings.COST_PER_TEN_CREDITS,\\n 'credits_amount': credits_amount,\\n 'total': credits_cost,\\n })\\n else:\\n messages.error(request, \\\"No amounts of credits selected\\\")\\n return redirect('add_credits')\\n else:\\n return redirect('add_credits')\",\n \"def _credits_dialogue(self):\\n credits_dialogue = _Credits(self)\\n self.root.wait_window(credits_dialogue.top)\",\n \"def account():\\n\\n return render_template('account_page.html', title='Account')\",\n \"def credits(self):\\r\\n return credit.Credits(self)\",\n \"def contributors(request):\\n return render(request, 'metro_app/contributors.html')\",\n \"def credits_window():\\n credits = tk.Toplevel()\\n credits_lbl = tk.Label(credits, text='Software Developed By Allan / SpideyKeiiti\\\\n'\\n 'Made for Prototype purposes for Streets Of Rage Remake Community!')\\n\\n credits_lbl.pack()\",\n \"def about(request):\\n return render(request, 'code_challenge/about.html', {})\",\n \"def get(self, request ):\\n return render(request, \\\"main_display_cards.html\\\")\",\n \"def about():\\n return render_template('about.html', name=\\\"COMP3161\\\")\",\n \"def about_us(request):\\n\\treturn render(request, 'about_us.html')\",\n \"def index(request):\\r\\n return render(request, 'coin/index.html')\",\n \"def about(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'csa/about.html',\\n {\\n 'title':'About',\\n 'message':'Your application description page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def contact():\\n img.compare_images()\\n return render_template(\\n 'contact.html',\\n title='Contact',\\n #year=datetime.now().year,\\n year=datetime.now().year,\\n message='Your contact page.'\\n )\",\n \"def about(request):\\n cart = Cart(request)\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/about.html',\\n {\\n \\t'cart': cart,\\n 'title':'Автосервис Локнянский район',\\n 'message':'Вас приветствует компания «СТО»!',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def aboutus(request):\\r\\n\\treturn render(request, 'aboutus.html')\",\n \"def index(request):\\n copy = '2018 ' + author\\n\\n context = dict(author=author, copyright=copy, repo_url=repo_url)\\n\\n return render(request, 'index.html', context)\",\n \"def contact(request):\\r\\n assert isinstance(request, HttpRequest)\\r\\n return render(\\r\\n request,\\r\\n 'app/contact.html',\\r\\n {\\r\\n 'title':'Iletisim',\\r\\n\\r\\n 'year':datetime.now().year,\\r\\n }\\r\\n )\",\n \"def thanks(request):\\n # Let's grab that temporary authorization code Slack's sent us from\\n # the request's parameters.\\n code_arg = request.GET['code']\\n # The bot's auth method to handles exchanging the code for an OAuth token\\n pyBot.auth(code_arg)\\n return render(request, \\\"thanks.html\\\")\",\n \"def main_page():\\n pages=get_accounts()\\n return render_template('disp.html',pages=pages)\",\n \"def contact(request):\\n cart = Cart(request)\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/contact.html',\\n {\\n 'title':'Контакты',\\n 'cart': cart,\\n 'message':'Your contact page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def about(request):\\n assert isinstance(request, HttpRequest)\\n\\n return render(\\n request,\\n 'AscensionESports_Baseline/about.html',\\n {\\n 'background': getSiteBackground(),\\n 'color': getSiteColor(),\\n 'title':'About Us',\\n 'year': datetime.now().year,\\n }\\n )\",\n \"def my_account(request):\\n context = {}\\n\\n return render(request, 'classroom_main/my_account.html', context)\",\n \"def about_us():\\r\\n return render_template(\\\"about_us.html\\\")\",\n \"def dashboard(request):\\r\\n profile = get_object_or_404(Profile, user=request.user)\\r\\n wallet = Wallet.objects.get(user=request.user)\\r\\n history = History.objects.get(pk=1)\\r\\n referrals = Referral.objects.filter(referee=request.user).count()\\r\\n invoices = Invoice.objects.filter(issuer=request.user).count()\\r\\n return render(request, 'coin/dashboard.html', {'profile': profile, \\r\\n 'wallet': wallet, 'history': history, 'referrals': referrals, \\r\\n 'invoices': invoices})\",\n \"def credit(self):\\n return pn_link_credit(self._impl)\",\n \"def contact(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'csa/contact.html',\\n {\\n 'title':'Contact',\\n 'message':'Your contact page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def about(request):\\n context = {}\\n return render(request, 'store/about.html', context)\",\n \"def about(request):\\n\\n context = {\\n\\n }\\n\\n return render(request, 'about.html', context=context)\",\n \"def Credits(master):\\n from spam.__init__ import __version__\\n from sys import argv\\n from os import path\\n CRW = 450 # credits window width\\n CRH = 350 # credits window height\\n\\n header_text = (\\n \\\"SPAM.py is a wrapper that uses cpptraj to calculate and identify\\\\n\\\"\\n \\\"water density peaks, NAMD to calculate the energies of individual\\\\n\\\"\\n \\\"water molecules, and SciPy to analyze the data and calculate SPAM\\\\n\\\"\\n \\\"Free Energies to identify water hotspots.\\\"\\n )\\n tailer_text = (\\n \\\"SPAM.Pie Version %s\\\\n\\\\n\\\" % __version__ +\\n \\\"Cpptraj modifications and SPAM.Pie\\\\n\\\"\\n \\\"by Jason Swails and Guanglei Cui\\\\n\\\"\\n \\\"GlaxoSmithKline, Copyright 2012\\\"\\n )\\n credits = tk.Toplevel(master)\\n credits.resizable(False, False)\\n credits.title('About SPAM.Pie')\\n # Get the file name\\n fname = path.join(path.abspath(path.split(__file__)[0]), 'spam.gif')\\n canv = tk.Canvas(credits, width=CRW, height=CRH)\\n canv.pack()\\n image = tk.PhotoImage(name='SPAM', master=canv, file=fname)\\n canv.create_image(CRW//2, CRH//2, anchor=tk.CENTER, image=image)\\n canv.create_text(CRW//2, 10, anchor=tk.N, text=header_text,\\n justify=tk.CENTER)\\n canv.create_text(CRW//2, CRH-10, anchor=tk.S, text=tailer_text,\\n justify=tk.CENTER)\\n credits.grab_set()\\n credits.mainloop()\",\n \"def about(request):\\n return render(request, 'wantedly_app/about.html')\",\n \"def thanks(request):\\n return render(request, 'SNP_Feature_View/thanks.html')\",\n \"def about_page(request):\\r\\n return render(request, 'ez_main/about_page.html')\",\n \"def about(request):\\r\\n assert isinstance(request, HttpRequest)\\r\\n return render(\\r\\n request,\\r\\n 'app/about.html',\\r\\n {\\r\\n 'title':'Hakkimizda',\\r\\n 'year':datetime.now().year,\\r\\n }\\r\\n )\",\n \"def about_us():\\n\\n return render_template('about_us.html')\",\n \"def users_page(request):\",\n \"def about(request):\\r\\n assert isinstance(request, HttpRequest)\\r\\n return render(\\r\\n request,\\r\\n 'app/about.html',\\r\\n context = \\r\\n {\\r\\n 'title':_('About'),\\r\\n 'year':datetime.now().year,\\r\\n }\\r\\n )\",\n \"def about():\\n data = []\\n with open(\\\"data/company.json\\\", \\\"r\\\", encoding=\\\"utf-8\\\") as json_data:\\n data = json.load(json_data)\\n return render_template(\\\"about.html\\\", page_title=\\\"About Us\\\", company=data)\",\n \"def about():\\n\\n return render_template('about_page.html', title='About')\",\n \"def contact(request):\\r\\n assert isinstance(request, HttpRequest)\\r\\n return render(\\r\\n request,\\r\\n 'app/contact.html',\\r\\n {\\r\\n 'title':'Contact',\\r\\n 'message':'Your contact page.',\\r\\n 'year':datetime.now().year,\\r\\n }\\r\\n )\",\n \"def account(request):\\n return render(request, 'registration/account.html')\",\n \"def about():\\n\\n\\treturn render_template(\\\"about.html\\\")\",\n \"def on_about(self):\\n render_about_window()\",\n \"def ZeusPaypalAccounts(request):\\n\\n\\tif request.method == \\\"GET\\\":\\n\\t\\treturn render(request, \\\"lost-empire/site_templates/zeus/paypal_accounts.html\\\")\",\n \"def about():\\n return render_template(\\n 'about.html',\\n title='About',\\n year=datetime.now().year,\\n message='about page.'\\n )\",\n \"def get(self, request, *args, **kwargs):\\n context = {\\\"token\\\": request.user.auth_token.key}\\n return render(request, \\\"payment/landing_payment.html\\\", context=context)\",\n \"def about(request):\\r\\n assert isinstance(request, HttpRequest)\\r\\n return render(\\r\\n request,\\r\\n 'app/about.html',\\r\\n {\\r\\n 'title':'About',\\r\\n 'message':'Your application description page.',\\r\\n 'year':datetime.now().year,\\r\\n }\\r\\n )\",\n \"def about(request):\\n assert isinstance(request, HttpRequest)\\n Description = models.AboutUs.objects.all()\\n return render(\\n request,\\n 'app/about.html',\\n {\\n 'title':'About',\\n 'message':'Your application description page.',\\n 'year':datetime.now().year,\\n 'Description': Description\\n \\n }\\n )\",\n \"def contact(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/contact.html',\\n {\\n 'title':'Contact',\\n 'message':'Your contact page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def contact(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/contact.html',\\n {\\n 'title':'Contact',\\n 'message':'Your contact page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def contact(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/contact.html',\\n {\\n 'title':'Contact',\\n 'message':'Your contact page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def contact(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/contact.html',\\n {\\n 'title':'Contact',\\n 'message':'Your contact page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def thank_you(request):\\n return render(request, 'core/thank_you.html')\",\n \"def about_page(request):\\n\\n return render(request, 'about_page/about_page.html')\",\n \"def contact(request):\\r\\n assert isinstance(request, HttpRequest)\\r\\n return render(\\r\\n request,\\r\\n 'app/contact.html',\\r\\n context = \\r\\n {\\r\\n 'title':_('Contact'),\\r\\n 'message':'Pour nous contacter.',\\r\\n 'year':datetime.now().year,\\r\\n }\\r\\n )\",\n \"def about_view(request):\\n return {'message': 'Info about us.'}\",\n \"def about():\\n return render_template('about.html', name=\\\"Nadrine Simms\\\")\",\n \"def about(request):\\n return render(request, \\\"about.html\\\", {})\",\n \"def about(request):\\r\\n assert isinstance(request, HttpRequest)\\r\\n return render(\\r\\n request,\\r\\n 'app/about.html',\\r\\n context_instance = RequestContext(request,\\r\\n {\\r\\n 'title': 'About',\\r\\n 'message': 'Your application description page.',\\r\\n 'year': datetime.now().year,\\r\\n 'contact': Contact.objects.get(pk=1),\\r\\n\\r\\n })\\r\\n )\",\n \"def about(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/about.html',\\n {\\n 'title':'About',\\n 'message':'Your application description page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def about(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/about.html',\\n {\\n 'title':'About',\\n 'message':'Your application description page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def about(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/about.html',\\n {\\n 'title':'About',\\n 'message':'Your application description page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def about(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/about.html',\\n {\\n 'title':'About',\\n 'message':'Your application description page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def contact(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/contact.html',\\n context_instance = RequestContext(request,\\n {\\n 'title':'Contact',\\n 'message':'Your contact page.',\\n 'year':datetime.now().year,\\n })\\n )\",\n \"def aboutus():\\n return render_template(\\\"aboutUs.html\\\")\",\n \"def home(request):\\r\\n\\t# u = User.objects.get(username=\\\"mohit2\\\")\\r\\n\\t# google_u = u.socialaccount_set.all()[0]\\r\\n\\t# google_u_prof_pic = google_u.extra_data['picture'];\\r\\n\\r\\n\\r\\n\\tproducts = Product.objects.all()\\r\\n\\tcontext = {'products':products}\\r\\n\\tif request.user.is_authenticated:\\r\\n\\t\\tif request.user:\\r\\n\\t\\t\\t# print(request.user)\\r\\n\\t\\t\\tusername = request.user.username\\r\\n\\t\\t\\tcustomer = Customer.objects.get(user=request.user)\\r\\n\\t\\t\\tbio = customer.bio\\r\\n\\t\\t\\tcontext['username'] = username\\r\\n\\t\\t\\tcontext['user_bio'] = bio\\r\\n\\treturn render(request, 'small/home.html', context)\",\n \"def aboutme():\\n return render_template('about.html')\",\n \"def __add_credit_cc(self):\\n log.debug(\\\"Displaying __add_credit_cc\\\")\\n # Create a keyboard to be sent later\\n presets = self.cfg.ccard[\\\"payment_presets\\\"]\\n keyboard = [[telegram.KeyboardButton(str(self.Price(preset)))] for preset in presets]\\n keyboard.append([telegram.KeyboardButton(self.loc.get(\\\"menu_all_cancel\\\"))])\\n # Boolean variable to check if the user has cancelled the action\\n cancelled = False\\n # Loop used to continue asking if there's an error during the input\\n while not cancelled:\\n # Send the message and the keyboard\\n self.bot.send_message(self.chat.id, self.loc.get(\\\"payment_cc_amount\\\"),\\n reply_markup=telegram.ReplyKeyboardMarkup(keyboard, one_time_keyboard=True))\\n # Wait until a valid amount is sent\\n selection = self.__wait_for_regex(r\\\"([0-9]+(?:[.,][0-9]+)?|\\\" + self.loc.get(\\\"menu_all_cancel\\\") + r\\\")\\\",\\n cancellable=True)\\n # If the user cancelled the action\\n if isinstance(selection, CancelSignal):\\n # Exit the loop\\n cancelled = True\\n continue\\n # Convert the amount to an integer\\n value = self.Price(selection)\\n # Ensure the amount is within the range\\n if value > self.Price(self.cfg.ccard[\\\"max_amount\\\"]):\\n self.bot.send_message(self.chat.id,\\n self.loc.get(\\\"error_payment_amount_over_max\\\",\\n max_amount=self.Price(self.cfg.ccard[\\\"max_amount\\\"])))\\n continue\\n elif value < self.Price(self.cfg.ccard[\\\"min_amount\\\"]):\\n self.bot.send_message(self.chat.id,\\n self.loc.get(\\\"error_payment_amount_under_min\\\",\\n min_amount=self.Price(self.cfg.ccard[\\\"min_amount\\\"])))\\n continue\\n break\\n # If the user cancelled the action...\\n else:\\n # Exit the function\\n return\\n # Issue the payment invoice\\n self.__make_payment(amount=value)\",\n \"def about(request):\\n return render(request, \\\"about/about.html\\\")\",\n \"def about(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/about.html',\\n context_instance = RequestContext(request,\\n {\\n 'title':'About',\\n 'message':'Your application description page.',\\n 'year':datetime.now().year,\\n })\\n )\",\n \"def about(request):\\n return render(request,\\\"encyclopedia/about.html\\\")\",\n \"def about():\\n return render_template('about.html', title='About')\",\n \"def about(request):\\n return render(request, \\\"about.html\\\")\",\n \"def about():\\n\\n return render_template('about.html')\",\n \"def about(request):\\n context = get_context()\\n return render(request, 'about.html', context)\",\n \"def buyshares():\\n # Initialise buy form\\n buyform = BuyShareForm()\\n # Validate and process form data\\n if(buyform.validate_on_submit()):\\n # Buys shares\\n issuerID = buyform.buysharecode.data\\n quantity = buyform.buyquantity.data\\n userID = current_user.userID\\n # Call buyshare API\\n buyshare = gdb.buyshare(userID, issuerID, quantity)\\n if(buyshare):\\n # Flash with success message\\n flash(\\\"Share purchase successful!\\\", category=\\\"success\\\")\\n else:\\n # Flash with warning message\\n flash(\\\"Share purchase unsuccessful!\\\", category=\\\"error\\\")\\n # Redirect to reffering page or dashboard\\n return redirect(request.referrer or url_for('main.dashboard'))\",\n \"def about():\\n\\n return render_template('about.html', title=\\\"About\\\")\",\n \"def about(request):\\n return render(request, 'about.html')\",\n \"def about():\\n return render_template(\\\"pages/about.html\\\")\",\n \"def handler(request):\\n context = {\\\"request\\\": request}\\n return render_to_response(\\n 'accounts.html',\\n context,\\n context_instance=RequestContext(request))\",\n \"def about():\\n return render_template('about.html', title='About')\",\n \"def account(request):\\n context = {'user': request.user}\\n return render(request, 'registration/account.html', context)\",\n \"def about(request):\\n context = {'client_id': settings.OPENHUMANS_CLIENT_ID,\\n 'oh_proj_page': settings.OH_ACTIVITY_PAGE}\\n return render(request, 'main/about.html', context=context)\",\n \"def contact():\\r\\n return render_template(\\r\\n 'contact.html',\\r\\n title='Contact',\\r\\n year=datetime.now().year,\\r\\n message='Your contact page.'\\r\\n )\",\n \"def contact():\\r\\n return render_template(\\r\\n 'contact.html',\\r\\n title='Contact',\\r\\n year=datetime.now().year,\\r\\n message='Your contact page.'\\r\\n )\",\n \"def about(request):\\n # Test Comment\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'about.html',\\n context_instance=RequestContext(request, {})\\n )\",\n \"def about(request):\\n # Test Comment\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'about.html',\\n context_instance=RequestContext(request, {})\\n )\",\n \"def about():\\n return render_template('about.html')\",\n \"def about():\\n return render_template('about.html')\",\n \"def about():\\n return render_template('about.html')\",\n \"def about():\\n return render_template('about.html')\",\n \"def about():\\n return render_template('about.html')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7859985","0.7057577","0.6672508","0.62410414","0.6223819","0.61764616","0.61620957","0.61559856","0.61495495","0.6127247","0.6028435","0.6007418","0.5978635","0.59492284","0.5912205","0.5789936","0.57580626","0.5750479","0.5719444","0.5716057","0.57144773","0.5701521","0.56994194","0.5693802","0.5693193","0.5690752","0.5684356","0.56841236","0.5682127","0.5664405","0.5664048","0.565327","0.5637385","0.5621486","0.560556","0.56","0.5598349","0.55818987","0.55749065","0.5566225","0.55659944","0.5564588","0.5559204","0.555026","0.55471134","0.55402213","0.55393654","0.553797","0.5532919","0.55279744","0.55137825","0.55103856","0.5498521","0.5494301","0.549111","0.5490074","0.54836464","0.54836464","0.54836464","0.54836464","0.5476614","0.5476584","0.5474678","0.54736906","0.5472121","0.5471756","0.5470322","0.5465557","0.5465557","0.5465557","0.5465557","0.54567677","0.5454274","0.54394144","0.54371434","0.54215896","0.5417619","0.54066503","0.5401639","0.540095","0.54001886","0.5399094","0.5397426","0.5394968","0.5392777","0.539088","0.5389388","0.5385413","0.5382134","0.5379451","0.5378281","0.5375231","0.5375231","0.53719944","0.53719944","0.537169","0.537169","0.537169","0.537169","0.537169"],"string":"[\n \"0.7859985\",\n \"0.7057577\",\n \"0.6672508\",\n \"0.62410414\",\n \"0.6223819\",\n \"0.61764616\",\n \"0.61620957\",\n \"0.61559856\",\n \"0.61495495\",\n \"0.6127247\",\n \"0.6028435\",\n \"0.6007418\",\n \"0.5978635\",\n \"0.59492284\",\n \"0.5912205\",\n \"0.5789936\",\n \"0.57580626\",\n \"0.5750479\",\n \"0.5719444\",\n \"0.5716057\",\n \"0.57144773\",\n \"0.5701521\",\n \"0.56994194\",\n \"0.5693802\",\n \"0.5693193\",\n \"0.5690752\",\n \"0.5684356\",\n \"0.56841236\",\n \"0.5682127\",\n \"0.5664405\",\n \"0.5664048\",\n \"0.565327\",\n \"0.5637385\",\n \"0.5621486\",\n \"0.560556\",\n \"0.56\",\n \"0.5598349\",\n \"0.55818987\",\n \"0.55749065\",\n \"0.5566225\",\n \"0.55659944\",\n \"0.5564588\",\n \"0.5559204\",\n \"0.555026\",\n \"0.55471134\",\n \"0.55402213\",\n \"0.55393654\",\n \"0.553797\",\n \"0.5532919\",\n \"0.55279744\",\n \"0.55137825\",\n \"0.55103856\",\n \"0.5498521\",\n \"0.5494301\",\n \"0.549111\",\n \"0.5490074\",\n \"0.54836464\",\n \"0.54836464\",\n \"0.54836464\",\n \"0.54836464\",\n \"0.5476614\",\n \"0.5476584\",\n \"0.5474678\",\n \"0.54736906\",\n \"0.5472121\",\n \"0.5471756\",\n \"0.5470322\",\n \"0.5465557\",\n \"0.5465557\",\n \"0.5465557\",\n \"0.5465557\",\n \"0.54567677\",\n \"0.5454274\",\n \"0.54394144\",\n \"0.54371434\",\n \"0.54215896\",\n \"0.5417619\",\n \"0.54066503\",\n \"0.5401639\",\n \"0.540095\",\n \"0.54001886\",\n \"0.5399094\",\n \"0.5397426\",\n \"0.5394968\",\n \"0.5392777\",\n \"0.539088\",\n \"0.5389388\",\n \"0.5385413\",\n \"0.5382134\",\n \"0.5379451\",\n \"0.5378281\",\n \"0.5375231\",\n \"0.5375231\",\n \"0.53719944\",\n \"0.53719944\",\n \"0.537169\",\n \"0.537169\",\n \"0.537169\",\n \"0.537169\",\n \"0.537169\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6755606"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":11196,"cells":{"query":{"kind":"string","value":"Credits page for app"},"document":{"kind":"string","value":"def uploadFile(request):\n\n username = request.session.get('username', False)\n profile = request.session.get('profile', False)\n if(username):\n context = {'username': username,'profile':profile}\n return render(request, 'MedTAG_sket_dock_App/index.html', context)\n else:\n return redirect('MedTAG_sket_dock_App:login')"},"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 credits():\n return render_template('credits.html')","async def credits(self, ctx: commands.Context):\r\n await ctx.send(embed=CREDITS_EMBED)","def credits(request):\n\n username = request.session.get('username', False)\n profile = request.session.get('profile', False)\n if(username):\n context = {'username': username,'profile':profile}\n return render(request, 'MedTAG_sket_dock_App/index.html', context)\n else:\n return redirect('MedTAG_sket_dock_App:login')","def credits(cls):\n print(\"Thank you for playing.\\nCreated by \" + cls.author+\".\\n\")","def contact_linkup(self, request, pk):\n obj_api = api()\n title_contact = \"Tu contacto Linkup\"\n token = request.session['token']\n resp = obj_api.get(slug='sellers/' + pk + \"/\", token=token)\n return render(request, 'frontend/actors/client/my_account.html', {'data_user': resp, \n 'title_contact': title_contact})","async def credits(self, ctx):\n embed = discord.Embed(title='{}:'.format(ctx.message.author.name),\n description='\\n:ok_hand: :laughing:\\n :telephone_receiver::shirt::call_me:\\n :jeans: :fire:',\n colour=0xf20006)\n last_message = await self.bot.say(embed=embed)\n await self.bot.add_reaction(last_message, self.emojiUnicode['succes'])","def links(request):\n cart = Cart(request)\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/links.html',\n {\n \t'cart': cart,\n 'title':'Полезные ресурсы',\n 'message':'Your contact page.',\n 'year':datetime.now().year,\n }\n )","def copyright(request):\n username = request.session.get('username')\n try:\n index_card = IndexCard.objects.get(username=username)\n except:\n index_card = None\n template = loader.get_template('crppindexcard/copyright.html')\n context = RequestContext(request, {\n 'username': username,\n 'index_card': index_card,\n 'is_copyright': 'is_copyright',\n })\n return HttpResponse(template.render(context))","def credits(self, **kwargs):\n\n path = self._get_movie_id_path('credits')\n resp = self._get_method(path, kwargs)\n return resp","def thanks(request):\n assert isinstance(request, HttpRequest)\n\n return render(\n request,\n 'AscensionESports_Baseline/Thanks.html',\n {\n 'background': getSiteBackground(),\n 'color': getSiteColor(),\n 'title':\"You are now signed up!\",\n 'year': datetime.now().year,\n }\n )","def credits_purchase(request):\n if request.method==\"POST\":\n \"\"\" POST request can come from card form submit or from initial\n credits amount selection page \"\"\"\n try:\n credits_amount = int(request.POST.get('credits_amount', None))\n except TypeError:\n messages.error(request, \"Amount was invalid\")\n return redirect('add_credits')\n if credits_amount or credits_amount == 0:\n if (credits_amount % 10) is not 0:\n # Credits amount wasn't a multiple of 10, so user bypassed\n # JavaScript form validation\n messages.error(\n request, \n \"Credits can only be added in multiples of 10\"\n )\n return redirect('add_credits')\n credits_cost = \\\n settings.COST_PER_TEN_CREDITS * (credits_amount / 10)\n if \"stripeToken\" in request.POST:\n # POST request came from card form submit\n try:\n customer = stripe.Charge.create(\n amount = int(credits_cost*100),\n currency = \"EUR\",\n description = request.user.email,\n source = request.POST['stripeToken'],\n )\n except stripe.error.CardError:\n messages.error(request, \"Your card was declined!\")\n return redirect('credits_purchase')\n if customer.paid:\n # All is good, so add the chosen amount of credits\n profile = request.user.profile\n profile.credits += credits_amount\n profile.save()\n return render(request, 'credits_added.html')\n else:\n messages.error(request, \"Unable to take payment\")\n return redirect('credits_purchase')\n else:\n \"\"\" POST request came from initial credits selection page\n so now render Stripe card form \"\"\"\n return render(request, \"credits_checkout.html\", {\n 'publishable': settings.STRIPE_PUBLISHABLE,\n 'ten_credit_cost': settings.COST_PER_TEN_CREDITS,\n 'credits_amount': credits_amount,\n 'total': credits_cost,\n })\n else:\n messages.error(request, \"No amounts of credits selected\")\n return redirect('add_credits')\n else:\n return redirect('add_credits')","def _credits_dialogue(self):\n credits_dialogue = _Credits(self)\n self.root.wait_window(credits_dialogue.top)","def account():\n\n return render_template('account_page.html', title='Account')","def credits(self):\r\n return credit.Credits(self)","def contributors(request):\n return render(request, 'metro_app/contributors.html')","def credits_window():\n credits = tk.Toplevel()\n credits_lbl = tk.Label(credits, text='Software Developed By Allan / SpideyKeiiti\\n'\n 'Made for Prototype purposes for Streets Of Rage Remake Community!')\n\n credits_lbl.pack()","def about(request):\n return render(request, 'code_challenge/about.html', {})","def get(self, request ):\n return render(request, \"main_display_cards.html\")","def about():\n return render_template('about.html', name=\"COMP3161\")","def about_us(request):\n\treturn render(request, 'about_us.html')","def index(request):\r\n return render(request, 'coin/index.html')","def about(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'csa/about.html',\n {\n 'title':'About',\n 'message':'Your application description page.',\n 'year':datetime.now().year,\n }\n )","def contact():\n img.compare_images()\n return render_template(\n 'contact.html',\n title='Contact',\n #year=datetime.now().year,\n year=datetime.now().year,\n message='Your contact page.'\n )","def about(request):\n cart = Cart(request)\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/about.html',\n {\n \t'cart': cart,\n 'title':'Автосервис Локнянский район',\n 'message':'Вас приветствует компания «СТО»!',\n 'year':datetime.now().year,\n }\n )","def aboutus(request):\r\n\treturn render(request, 'aboutus.html')","def index(request):\n copy = '2018 ' + author\n\n context = dict(author=author, copyright=copy, repo_url=repo_url)\n\n return render(request, 'index.html', context)","def contact(request):\r\n assert isinstance(request, HttpRequest)\r\n return render(\r\n request,\r\n 'app/contact.html',\r\n {\r\n 'title':'Iletisim',\r\n\r\n 'year':datetime.now().year,\r\n }\r\n )","def thanks(request):\n # Let's grab that temporary authorization code Slack's sent us from\n # the request's parameters.\n code_arg = request.GET['code']\n # The bot's auth method to handles exchanging the code for an OAuth token\n pyBot.auth(code_arg)\n return render(request, \"thanks.html\")","def main_page():\n pages=get_accounts()\n return render_template('disp.html',pages=pages)","def contact(request):\n cart = Cart(request)\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/contact.html',\n {\n 'title':'Контакты',\n 'cart': cart,\n 'message':'Your contact page.',\n 'year':datetime.now().year,\n }\n )","def about(request):\n assert isinstance(request, HttpRequest)\n\n return render(\n request,\n 'AscensionESports_Baseline/about.html',\n {\n 'background': getSiteBackground(),\n 'color': getSiteColor(),\n 'title':'About Us',\n 'year': datetime.now().year,\n }\n )","def my_account(request):\n context = {}\n\n return render(request, 'classroom_main/my_account.html', context)","def about_us():\r\n return render_template(\"about_us.html\")","def dashboard(request):\r\n profile = get_object_or_404(Profile, user=request.user)\r\n wallet = Wallet.objects.get(user=request.user)\r\n history = History.objects.get(pk=1)\r\n referrals = Referral.objects.filter(referee=request.user).count()\r\n invoices = Invoice.objects.filter(issuer=request.user).count()\r\n return render(request, 'coin/dashboard.html', {'profile': profile, \r\n 'wallet': wallet, 'history': history, 'referrals': referrals, \r\n 'invoices': invoices})","def credit(self):\n return pn_link_credit(self._impl)","def contact(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'csa/contact.html',\n {\n 'title':'Contact',\n 'message':'Your contact page.',\n 'year':datetime.now().year,\n }\n )","def about(request):\n context = {}\n return render(request, 'store/about.html', context)","def about(request):\n\n context = {\n\n }\n\n return render(request, 'about.html', context=context)","def Credits(master):\n from spam.__init__ import __version__\n from sys import argv\n from os import path\n CRW = 450 # credits window width\n CRH = 350 # credits window height\n\n header_text = (\n \"SPAM.py is a wrapper that uses cpptraj to calculate and identify\\n\"\n \"water density peaks, NAMD to calculate the energies of individual\\n\"\n \"water molecules, and SciPy to analyze the data and calculate SPAM\\n\"\n \"Free Energies to identify water hotspots.\"\n )\n tailer_text = (\n \"SPAM.Pie Version %s\\n\\n\" % __version__ +\n \"Cpptraj modifications and SPAM.Pie\\n\"\n \"by Jason Swails and Guanglei Cui\\n\"\n \"GlaxoSmithKline, Copyright 2012\"\n )\n credits = tk.Toplevel(master)\n credits.resizable(False, False)\n credits.title('About SPAM.Pie')\n # Get the file name\n fname = path.join(path.abspath(path.split(__file__)[0]), 'spam.gif')\n canv = tk.Canvas(credits, width=CRW, height=CRH)\n canv.pack()\n image = tk.PhotoImage(name='SPAM', master=canv, file=fname)\n canv.create_image(CRW//2, CRH//2, anchor=tk.CENTER, image=image)\n canv.create_text(CRW//2, 10, anchor=tk.N, text=header_text,\n justify=tk.CENTER)\n canv.create_text(CRW//2, CRH-10, anchor=tk.S, text=tailer_text,\n justify=tk.CENTER)\n credits.grab_set()\n credits.mainloop()","def about(request):\n return render(request, 'wantedly_app/about.html')","def thanks(request):\n return render(request, 'SNP_Feature_View/thanks.html')","def about_page(request):\r\n return render(request, 'ez_main/about_page.html')","def about(request):\r\n assert isinstance(request, HttpRequest)\r\n return render(\r\n request,\r\n 'app/about.html',\r\n {\r\n 'title':'Hakkimizda',\r\n 'year':datetime.now().year,\r\n }\r\n )","def about_us():\n\n return render_template('about_us.html')","def users_page(request):","def about(request):\r\n assert isinstance(request, HttpRequest)\r\n return render(\r\n request,\r\n 'app/about.html',\r\n context = \r\n {\r\n 'title':_('About'),\r\n 'year':datetime.now().year,\r\n }\r\n )","def about():\n data = []\n with open(\"data/company.json\", \"r\", encoding=\"utf-8\") as json_data:\n data = json.load(json_data)\n return render_template(\"about.html\", page_title=\"About Us\", company=data)","def about():\n\n return render_template('about_page.html', title='About')","def contact(request):\r\n assert isinstance(request, HttpRequest)\r\n return render(\r\n request,\r\n 'app/contact.html',\r\n {\r\n 'title':'Contact',\r\n 'message':'Your contact page.',\r\n 'year':datetime.now().year,\r\n }\r\n )","def account(request):\n return render(request, 'registration/account.html')","def about():\n\n\treturn render_template(\"about.html\")","def on_about(self):\n render_about_window()","def ZeusPaypalAccounts(request):\n\n\tif request.method == \"GET\":\n\t\treturn render(request, \"lost-empire/site_templates/zeus/paypal_accounts.html\")","def about():\n return render_template(\n 'about.html',\n title='About',\n year=datetime.now().year,\n message='about page.'\n )","def get(self, request, *args, **kwargs):\n context = {\"token\": request.user.auth_token.key}\n return render(request, \"payment/landing_payment.html\", context=context)","def about(request):\r\n assert isinstance(request, HttpRequest)\r\n return render(\r\n request,\r\n 'app/about.html',\r\n {\r\n 'title':'About',\r\n 'message':'Your application description page.',\r\n 'year':datetime.now().year,\r\n }\r\n )","def about(request):\n assert isinstance(request, HttpRequest)\n Description = models.AboutUs.objects.all()\n return render(\n request,\n 'app/about.html',\n {\n 'title':'About',\n 'message':'Your application description page.',\n 'year':datetime.now().year,\n 'Description': Description\n \n }\n )","def contact(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/contact.html',\n {\n 'title':'Contact',\n 'message':'Your contact page.',\n 'year':datetime.now().year,\n }\n )","def contact(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/contact.html',\n {\n 'title':'Contact',\n 'message':'Your contact page.',\n 'year':datetime.now().year,\n }\n )","def contact(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/contact.html',\n {\n 'title':'Contact',\n 'message':'Your contact page.',\n 'year':datetime.now().year,\n }\n )","def contact(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/contact.html',\n {\n 'title':'Contact',\n 'message':'Your contact page.',\n 'year':datetime.now().year,\n }\n )","def thank_you(request):\n return render(request, 'core/thank_you.html')","def about_page(request):\n\n return render(request, 'about_page/about_page.html')","def contact(request):\r\n assert isinstance(request, HttpRequest)\r\n return render(\r\n request,\r\n 'app/contact.html',\r\n context = \r\n {\r\n 'title':_('Contact'),\r\n 'message':'Pour nous contacter.',\r\n 'year':datetime.now().year,\r\n }\r\n )","def about_view(request):\n return {'message': 'Info about us.'}","def about():\n return render_template('about.html', name=\"Nadrine Simms\")","def about(request):\n return render(request, \"about.html\", {})","def about(request):\r\n assert isinstance(request, HttpRequest)\r\n return render(\r\n request,\r\n 'app/about.html',\r\n context_instance = RequestContext(request,\r\n {\r\n 'title': 'About',\r\n 'message': 'Your application description page.',\r\n 'year': datetime.now().year,\r\n 'contact': Contact.objects.get(pk=1),\r\n\r\n })\r\n )","def about(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/about.html',\n {\n 'title':'About',\n 'message':'Your application description page.',\n 'year':datetime.now().year,\n }\n )","def about(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/about.html',\n {\n 'title':'About',\n 'message':'Your application description page.',\n 'year':datetime.now().year,\n }\n )","def about(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/about.html',\n {\n 'title':'About',\n 'message':'Your application description page.',\n 'year':datetime.now().year,\n }\n )","def about(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/about.html',\n {\n 'title':'About',\n 'message':'Your application description page.',\n 'year':datetime.now().year,\n }\n )","def contact(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/contact.html',\n context_instance = RequestContext(request,\n {\n 'title':'Contact',\n 'message':'Your contact page.',\n 'year':datetime.now().year,\n })\n )","def aboutus():\n return render_template(\"aboutUs.html\")","def home(request):\r\n\t# u = User.objects.get(username=\"mohit2\")\r\n\t# google_u = u.socialaccount_set.all()[0]\r\n\t# google_u_prof_pic = google_u.extra_data['picture'];\r\n\r\n\r\n\tproducts = Product.objects.all()\r\n\tcontext = {'products':products}\r\n\tif request.user.is_authenticated:\r\n\t\tif request.user:\r\n\t\t\t# print(request.user)\r\n\t\t\tusername = request.user.username\r\n\t\t\tcustomer = Customer.objects.get(user=request.user)\r\n\t\t\tbio = customer.bio\r\n\t\t\tcontext['username'] = username\r\n\t\t\tcontext['user_bio'] = bio\r\n\treturn render(request, 'small/home.html', context)","def aboutme():\n return render_template('about.html')","def __add_credit_cc(self):\n log.debug(\"Displaying __add_credit_cc\")\n # Create a keyboard to be sent later\n presets = self.cfg.ccard[\"payment_presets\"]\n keyboard = [[telegram.KeyboardButton(str(self.Price(preset)))] for preset in presets]\n keyboard.append([telegram.KeyboardButton(self.loc.get(\"menu_all_cancel\"))])\n # Boolean variable to check if the user has cancelled the action\n cancelled = False\n # Loop used to continue asking if there's an error during the input\n while not cancelled:\n # Send the message and the keyboard\n self.bot.send_message(self.chat.id, self.loc.get(\"payment_cc_amount\"),\n reply_markup=telegram.ReplyKeyboardMarkup(keyboard, one_time_keyboard=True))\n # Wait until a valid amount is sent\n selection = self.__wait_for_regex(r\"([0-9]+(?:[.,][0-9]+)?|\" + self.loc.get(\"menu_all_cancel\") + r\")\",\n cancellable=True)\n # If the user cancelled the action\n if isinstance(selection, CancelSignal):\n # Exit the loop\n cancelled = True\n continue\n # Convert the amount to an integer\n value = self.Price(selection)\n # Ensure the amount is within the range\n if value > self.Price(self.cfg.ccard[\"max_amount\"]):\n self.bot.send_message(self.chat.id,\n self.loc.get(\"error_payment_amount_over_max\",\n max_amount=self.Price(self.cfg.ccard[\"max_amount\"])))\n continue\n elif value < self.Price(self.cfg.ccard[\"min_amount\"]):\n self.bot.send_message(self.chat.id,\n self.loc.get(\"error_payment_amount_under_min\",\n min_amount=self.Price(self.cfg.ccard[\"min_amount\"])))\n continue\n break\n # If the user cancelled the action...\n else:\n # Exit the function\n return\n # Issue the payment invoice\n self.__make_payment(amount=value)","def about(request):\n return render(request, \"about/about.html\")","def about(request):\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/about.html',\n context_instance = RequestContext(request,\n {\n 'title':'About',\n 'message':'Your application description page.',\n 'year':datetime.now().year,\n })\n )","def about(request):\n return render(request,\"encyclopedia/about.html\")","def about():\n return render_template('about.html', title='About')","def about(request):\n return render(request, \"about.html\")","def about():\n\n return render_template('about.html')","def about(request):\n context = get_context()\n return render(request, 'about.html', context)","def buyshares():\n # Initialise buy form\n buyform = BuyShareForm()\n # Validate and process form data\n if(buyform.validate_on_submit()):\n # Buys shares\n issuerID = buyform.buysharecode.data\n quantity = buyform.buyquantity.data\n userID = current_user.userID\n # Call buyshare API\n buyshare = gdb.buyshare(userID, issuerID, quantity)\n if(buyshare):\n # Flash with success message\n flash(\"Share purchase successful!\", category=\"success\")\n else:\n # Flash with warning message\n flash(\"Share purchase unsuccessful!\", category=\"error\")\n # Redirect to reffering page or dashboard\n return redirect(request.referrer or url_for('main.dashboard'))","def about():\n\n return render_template('about.html', title=\"About\")","def about(request):\n return render(request, 'about.html')","def about():\n return render_template(\"pages/about.html\")","def handler(request):\n context = {\"request\": request}\n return render_to_response(\n 'accounts.html',\n context,\n context_instance=RequestContext(request))","def about():\n return render_template('about.html', title='About')","def account(request):\n context = {'user': request.user}\n return render(request, 'registration/account.html', context)","def about(request):\n context = {'client_id': settings.OPENHUMANS_CLIENT_ID,\n 'oh_proj_page': settings.OH_ACTIVITY_PAGE}\n return render(request, 'main/about.html', context=context)","def contact():\r\n return render_template(\r\n 'contact.html',\r\n title='Contact',\r\n year=datetime.now().year,\r\n message='Your contact page.'\r\n )","def contact():\r\n return render_template(\r\n 'contact.html',\r\n title='Contact',\r\n year=datetime.now().year,\r\n message='Your contact page.'\r\n )","def about(request):\n # Test Comment\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'about.html',\n context_instance=RequestContext(request, {})\n )","def about(request):\n # Test Comment\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'about.html',\n context_instance=RequestContext(request, {})\n )","def about():\n return render_template('about.html')","def about():\n return render_template('about.html')","def about():\n return render_template('about.html')","def about():\n return render_template('about.html')","def about():\n return render_template('about.html')"],"string":"[\n \"def credits():\\n return render_template('credits.html')\",\n \"async def credits(self, ctx: commands.Context):\\r\\n await ctx.send(embed=CREDITS_EMBED)\",\n \"def credits(request):\\n\\n username = request.session.get('username', False)\\n profile = request.session.get('profile', False)\\n if(username):\\n context = {'username': username,'profile':profile}\\n return render(request, 'MedTAG_sket_dock_App/index.html', context)\\n else:\\n return redirect('MedTAG_sket_dock_App:login')\",\n \"def credits(cls):\\n print(\\\"Thank you for playing.\\\\nCreated by \\\" + cls.author+\\\".\\\\n\\\")\",\n \"def contact_linkup(self, request, pk):\\n obj_api = api()\\n title_contact = \\\"Tu contacto Linkup\\\"\\n token = request.session['token']\\n resp = obj_api.get(slug='sellers/' + pk + \\\"/\\\", token=token)\\n return render(request, 'frontend/actors/client/my_account.html', {'data_user': resp, \\n 'title_contact': title_contact})\",\n \"async def credits(self, ctx):\\n embed = discord.Embed(title='{}:'.format(ctx.message.author.name),\\n description='\\\\n:ok_hand: :laughing:\\\\n :telephone_receiver::shirt::call_me:\\\\n :jeans: :fire:',\\n colour=0xf20006)\\n last_message = await self.bot.say(embed=embed)\\n await self.bot.add_reaction(last_message, self.emojiUnicode['succes'])\",\n \"def links(request):\\n cart = Cart(request)\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/links.html',\\n {\\n \\t'cart': cart,\\n 'title':'Полезные ресурсы',\\n 'message':'Your contact page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def copyright(request):\\n username = request.session.get('username')\\n try:\\n index_card = IndexCard.objects.get(username=username)\\n except:\\n index_card = None\\n template = loader.get_template('crppindexcard/copyright.html')\\n context = RequestContext(request, {\\n 'username': username,\\n 'index_card': index_card,\\n 'is_copyright': 'is_copyright',\\n })\\n return HttpResponse(template.render(context))\",\n \"def credits(self, **kwargs):\\n\\n path = self._get_movie_id_path('credits')\\n resp = self._get_method(path, kwargs)\\n return resp\",\n \"def thanks(request):\\n assert isinstance(request, HttpRequest)\\n\\n return render(\\n request,\\n 'AscensionESports_Baseline/Thanks.html',\\n {\\n 'background': getSiteBackground(),\\n 'color': getSiteColor(),\\n 'title':\\\"You are now signed up!\\\",\\n 'year': datetime.now().year,\\n }\\n )\",\n \"def credits_purchase(request):\\n if request.method==\\\"POST\\\":\\n \\\"\\\"\\\" POST request can come from card form submit or from initial\\n credits amount selection page \\\"\\\"\\\"\\n try:\\n credits_amount = int(request.POST.get('credits_amount', None))\\n except TypeError:\\n messages.error(request, \\\"Amount was invalid\\\")\\n return redirect('add_credits')\\n if credits_amount or credits_amount == 0:\\n if (credits_amount % 10) is not 0:\\n # Credits amount wasn't a multiple of 10, so user bypassed\\n # JavaScript form validation\\n messages.error(\\n request, \\n \\\"Credits can only be added in multiples of 10\\\"\\n )\\n return redirect('add_credits')\\n credits_cost = \\\\\\n settings.COST_PER_TEN_CREDITS * (credits_amount / 10)\\n if \\\"stripeToken\\\" in request.POST:\\n # POST request came from card form submit\\n try:\\n customer = stripe.Charge.create(\\n amount = int(credits_cost*100),\\n currency = \\\"EUR\\\",\\n description = request.user.email,\\n source = request.POST['stripeToken'],\\n )\\n except stripe.error.CardError:\\n messages.error(request, \\\"Your card was declined!\\\")\\n return redirect('credits_purchase')\\n if customer.paid:\\n # All is good, so add the chosen amount of credits\\n profile = request.user.profile\\n profile.credits += credits_amount\\n profile.save()\\n return render(request, 'credits_added.html')\\n else:\\n messages.error(request, \\\"Unable to take payment\\\")\\n return redirect('credits_purchase')\\n else:\\n \\\"\\\"\\\" POST request came from initial credits selection page\\n so now render Stripe card form \\\"\\\"\\\"\\n return render(request, \\\"credits_checkout.html\\\", {\\n 'publishable': settings.STRIPE_PUBLISHABLE,\\n 'ten_credit_cost': settings.COST_PER_TEN_CREDITS,\\n 'credits_amount': credits_amount,\\n 'total': credits_cost,\\n })\\n else:\\n messages.error(request, \\\"No amounts of credits selected\\\")\\n return redirect('add_credits')\\n else:\\n return redirect('add_credits')\",\n \"def _credits_dialogue(self):\\n credits_dialogue = _Credits(self)\\n self.root.wait_window(credits_dialogue.top)\",\n \"def account():\\n\\n return render_template('account_page.html', title='Account')\",\n \"def credits(self):\\r\\n return credit.Credits(self)\",\n \"def contributors(request):\\n return render(request, 'metro_app/contributors.html')\",\n \"def credits_window():\\n credits = tk.Toplevel()\\n credits_lbl = tk.Label(credits, text='Software Developed By Allan / SpideyKeiiti\\\\n'\\n 'Made for Prototype purposes for Streets Of Rage Remake Community!')\\n\\n credits_lbl.pack()\",\n \"def about(request):\\n return render(request, 'code_challenge/about.html', {})\",\n \"def get(self, request ):\\n return render(request, \\\"main_display_cards.html\\\")\",\n \"def about():\\n return render_template('about.html', name=\\\"COMP3161\\\")\",\n \"def about_us(request):\\n\\treturn render(request, 'about_us.html')\",\n \"def index(request):\\r\\n return render(request, 'coin/index.html')\",\n \"def about(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'csa/about.html',\\n {\\n 'title':'About',\\n 'message':'Your application description page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def contact():\\n img.compare_images()\\n return render_template(\\n 'contact.html',\\n title='Contact',\\n #year=datetime.now().year,\\n year=datetime.now().year,\\n message='Your contact page.'\\n )\",\n \"def about(request):\\n cart = Cart(request)\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/about.html',\\n {\\n \\t'cart': cart,\\n 'title':'Автосервис Локнянский район',\\n 'message':'Вас приветствует компания «СТО»!',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def aboutus(request):\\r\\n\\treturn render(request, 'aboutus.html')\",\n \"def index(request):\\n copy = '2018 ' + author\\n\\n context = dict(author=author, copyright=copy, repo_url=repo_url)\\n\\n return render(request, 'index.html', context)\",\n \"def contact(request):\\r\\n assert isinstance(request, HttpRequest)\\r\\n return render(\\r\\n request,\\r\\n 'app/contact.html',\\r\\n {\\r\\n 'title':'Iletisim',\\r\\n\\r\\n 'year':datetime.now().year,\\r\\n }\\r\\n )\",\n \"def thanks(request):\\n # Let's grab that temporary authorization code Slack's sent us from\\n # the request's parameters.\\n code_arg = request.GET['code']\\n # The bot's auth method to handles exchanging the code for an OAuth token\\n pyBot.auth(code_arg)\\n return render(request, \\\"thanks.html\\\")\",\n \"def main_page():\\n pages=get_accounts()\\n return render_template('disp.html',pages=pages)\",\n \"def contact(request):\\n cart = Cart(request)\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/contact.html',\\n {\\n 'title':'Контакты',\\n 'cart': cart,\\n 'message':'Your contact page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def about(request):\\n assert isinstance(request, HttpRequest)\\n\\n return render(\\n request,\\n 'AscensionESports_Baseline/about.html',\\n {\\n 'background': getSiteBackground(),\\n 'color': getSiteColor(),\\n 'title':'About Us',\\n 'year': datetime.now().year,\\n }\\n )\",\n \"def my_account(request):\\n context = {}\\n\\n return render(request, 'classroom_main/my_account.html', context)\",\n \"def about_us():\\r\\n return render_template(\\\"about_us.html\\\")\",\n \"def dashboard(request):\\r\\n profile = get_object_or_404(Profile, user=request.user)\\r\\n wallet = Wallet.objects.get(user=request.user)\\r\\n history = History.objects.get(pk=1)\\r\\n referrals = Referral.objects.filter(referee=request.user).count()\\r\\n invoices = Invoice.objects.filter(issuer=request.user).count()\\r\\n return render(request, 'coin/dashboard.html', {'profile': profile, \\r\\n 'wallet': wallet, 'history': history, 'referrals': referrals, \\r\\n 'invoices': invoices})\",\n \"def credit(self):\\n return pn_link_credit(self._impl)\",\n \"def contact(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'csa/contact.html',\\n {\\n 'title':'Contact',\\n 'message':'Your contact page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def about(request):\\n context = {}\\n return render(request, 'store/about.html', context)\",\n \"def about(request):\\n\\n context = {\\n\\n }\\n\\n return render(request, 'about.html', context=context)\",\n \"def Credits(master):\\n from spam.__init__ import __version__\\n from sys import argv\\n from os import path\\n CRW = 450 # credits window width\\n CRH = 350 # credits window height\\n\\n header_text = (\\n \\\"SPAM.py is a wrapper that uses cpptraj to calculate and identify\\\\n\\\"\\n \\\"water density peaks, NAMD to calculate the energies of individual\\\\n\\\"\\n \\\"water molecules, and SciPy to analyze the data and calculate SPAM\\\\n\\\"\\n \\\"Free Energies to identify water hotspots.\\\"\\n )\\n tailer_text = (\\n \\\"SPAM.Pie Version %s\\\\n\\\\n\\\" % __version__ +\\n \\\"Cpptraj modifications and SPAM.Pie\\\\n\\\"\\n \\\"by Jason Swails and Guanglei Cui\\\\n\\\"\\n \\\"GlaxoSmithKline, Copyright 2012\\\"\\n )\\n credits = tk.Toplevel(master)\\n credits.resizable(False, False)\\n credits.title('About SPAM.Pie')\\n # Get the file name\\n fname = path.join(path.abspath(path.split(__file__)[0]), 'spam.gif')\\n canv = tk.Canvas(credits, width=CRW, height=CRH)\\n canv.pack()\\n image = tk.PhotoImage(name='SPAM', master=canv, file=fname)\\n canv.create_image(CRW//2, CRH//2, anchor=tk.CENTER, image=image)\\n canv.create_text(CRW//2, 10, anchor=tk.N, text=header_text,\\n justify=tk.CENTER)\\n canv.create_text(CRW//2, CRH-10, anchor=tk.S, text=tailer_text,\\n justify=tk.CENTER)\\n credits.grab_set()\\n credits.mainloop()\",\n \"def about(request):\\n return render(request, 'wantedly_app/about.html')\",\n \"def thanks(request):\\n return render(request, 'SNP_Feature_View/thanks.html')\",\n \"def about_page(request):\\r\\n return render(request, 'ez_main/about_page.html')\",\n \"def about(request):\\r\\n assert isinstance(request, HttpRequest)\\r\\n return render(\\r\\n request,\\r\\n 'app/about.html',\\r\\n {\\r\\n 'title':'Hakkimizda',\\r\\n 'year':datetime.now().year,\\r\\n }\\r\\n )\",\n \"def about_us():\\n\\n return render_template('about_us.html')\",\n \"def users_page(request):\",\n \"def about(request):\\r\\n assert isinstance(request, HttpRequest)\\r\\n return render(\\r\\n request,\\r\\n 'app/about.html',\\r\\n context = \\r\\n {\\r\\n 'title':_('About'),\\r\\n 'year':datetime.now().year,\\r\\n }\\r\\n )\",\n \"def about():\\n data = []\\n with open(\\\"data/company.json\\\", \\\"r\\\", encoding=\\\"utf-8\\\") as json_data:\\n data = json.load(json_data)\\n return render_template(\\\"about.html\\\", page_title=\\\"About Us\\\", company=data)\",\n \"def about():\\n\\n return render_template('about_page.html', title='About')\",\n \"def contact(request):\\r\\n assert isinstance(request, HttpRequest)\\r\\n return render(\\r\\n request,\\r\\n 'app/contact.html',\\r\\n {\\r\\n 'title':'Contact',\\r\\n 'message':'Your contact page.',\\r\\n 'year':datetime.now().year,\\r\\n }\\r\\n )\",\n \"def account(request):\\n return render(request, 'registration/account.html')\",\n \"def about():\\n\\n\\treturn render_template(\\\"about.html\\\")\",\n \"def on_about(self):\\n render_about_window()\",\n \"def ZeusPaypalAccounts(request):\\n\\n\\tif request.method == \\\"GET\\\":\\n\\t\\treturn render(request, \\\"lost-empire/site_templates/zeus/paypal_accounts.html\\\")\",\n \"def about():\\n return render_template(\\n 'about.html',\\n title='About',\\n year=datetime.now().year,\\n message='about page.'\\n )\",\n \"def get(self, request, *args, **kwargs):\\n context = {\\\"token\\\": request.user.auth_token.key}\\n return render(request, \\\"payment/landing_payment.html\\\", context=context)\",\n \"def about(request):\\r\\n assert isinstance(request, HttpRequest)\\r\\n return render(\\r\\n request,\\r\\n 'app/about.html',\\r\\n {\\r\\n 'title':'About',\\r\\n 'message':'Your application description page.',\\r\\n 'year':datetime.now().year,\\r\\n }\\r\\n )\",\n \"def about(request):\\n assert isinstance(request, HttpRequest)\\n Description = models.AboutUs.objects.all()\\n return render(\\n request,\\n 'app/about.html',\\n {\\n 'title':'About',\\n 'message':'Your application description page.',\\n 'year':datetime.now().year,\\n 'Description': Description\\n \\n }\\n )\",\n \"def contact(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/contact.html',\\n {\\n 'title':'Contact',\\n 'message':'Your contact page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def contact(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/contact.html',\\n {\\n 'title':'Contact',\\n 'message':'Your contact page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def contact(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/contact.html',\\n {\\n 'title':'Contact',\\n 'message':'Your contact page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def contact(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/contact.html',\\n {\\n 'title':'Contact',\\n 'message':'Your contact page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def thank_you(request):\\n return render(request, 'core/thank_you.html')\",\n \"def about_page(request):\\n\\n return render(request, 'about_page/about_page.html')\",\n \"def contact(request):\\r\\n assert isinstance(request, HttpRequest)\\r\\n return render(\\r\\n request,\\r\\n 'app/contact.html',\\r\\n context = \\r\\n {\\r\\n 'title':_('Contact'),\\r\\n 'message':'Pour nous contacter.',\\r\\n 'year':datetime.now().year,\\r\\n }\\r\\n )\",\n \"def about_view(request):\\n return {'message': 'Info about us.'}\",\n \"def about():\\n return render_template('about.html', name=\\\"Nadrine Simms\\\")\",\n \"def about(request):\\n return render(request, \\\"about.html\\\", {})\",\n \"def about(request):\\r\\n assert isinstance(request, HttpRequest)\\r\\n return render(\\r\\n request,\\r\\n 'app/about.html',\\r\\n context_instance = RequestContext(request,\\r\\n {\\r\\n 'title': 'About',\\r\\n 'message': 'Your application description page.',\\r\\n 'year': datetime.now().year,\\r\\n 'contact': Contact.objects.get(pk=1),\\r\\n\\r\\n })\\r\\n )\",\n \"def about(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/about.html',\\n {\\n 'title':'About',\\n 'message':'Your application description page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def about(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/about.html',\\n {\\n 'title':'About',\\n 'message':'Your application description page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def about(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/about.html',\\n {\\n 'title':'About',\\n 'message':'Your application description page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def about(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/about.html',\\n {\\n 'title':'About',\\n 'message':'Your application description page.',\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def contact(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/contact.html',\\n context_instance = RequestContext(request,\\n {\\n 'title':'Contact',\\n 'message':'Your contact page.',\\n 'year':datetime.now().year,\\n })\\n )\",\n \"def aboutus():\\n return render_template(\\\"aboutUs.html\\\")\",\n \"def home(request):\\r\\n\\t# u = User.objects.get(username=\\\"mohit2\\\")\\r\\n\\t# google_u = u.socialaccount_set.all()[0]\\r\\n\\t# google_u_prof_pic = google_u.extra_data['picture'];\\r\\n\\r\\n\\r\\n\\tproducts = Product.objects.all()\\r\\n\\tcontext = {'products':products}\\r\\n\\tif request.user.is_authenticated:\\r\\n\\t\\tif request.user:\\r\\n\\t\\t\\t# print(request.user)\\r\\n\\t\\t\\tusername = request.user.username\\r\\n\\t\\t\\tcustomer = Customer.objects.get(user=request.user)\\r\\n\\t\\t\\tbio = customer.bio\\r\\n\\t\\t\\tcontext['username'] = username\\r\\n\\t\\t\\tcontext['user_bio'] = bio\\r\\n\\treturn render(request, 'small/home.html', context)\",\n \"def aboutme():\\n return render_template('about.html')\",\n \"def __add_credit_cc(self):\\n log.debug(\\\"Displaying __add_credit_cc\\\")\\n # Create a keyboard to be sent later\\n presets = self.cfg.ccard[\\\"payment_presets\\\"]\\n keyboard = [[telegram.KeyboardButton(str(self.Price(preset)))] for preset in presets]\\n keyboard.append([telegram.KeyboardButton(self.loc.get(\\\"menu_all_cancel\\\"))])\\n # Boolean variable to check if the user has cancelled the action\\n cancelled = False\\n # Loop used to continue asking if there's an error during the input\\n while not cancelled:\\n # Send the message and the keyboard\\n self.bot.send_message(self.chat.id, self.loc.get(\\\"payment_cc_amount\\\"),\\n reply_markup=telegram.ReplyKeyboardMarkup(keyboard, one_time_keyboard=True))\\n # Wait until a valid amount is sent\\n selection = self.__wait_for_regex(r\\\"([0-9]+(?:[.,][0-9]+)?|\\\" + self.loc.get(\\\"menu_all_cancel\\\") + r\\\")\\\",\\n cancellable=True)\\n # If the user cancelled the action\\n if isinstance(selection, CancelSignal):\\n # Exit the loop\\n cancelled = True\\n continue\\n # Convert the amount to an integer\\n value = self.Price(selection)\\n # Ensure the amount is within the range\\n if value > self.Price(self.cfg.ccard[\\\"max_amount\\\"]):\\n self.bot.send_message(self.chat.id,\\n self.loc.get(\\\"error_payment_amount_over_max\\\",\\n max_amount=self.Price(self.cfg.ccard[\\\"max_amount\\\"])))\\n continue\\n elif value < self.Price(self.cfg.ccard[\\\"min_amount\\\"]):\\n self.bot.send_message(self.chat.id,\\n self.loc.get(\\\"error_payment_amount_under_min\\\",\\n min_amount=self.Price(self.cfg.ccard[\\\"min_amount\\\"])))\\n continue\\n break\\n # If the user cancelled the action...\\n else:\\n # Exit the function\\n return\\n # Issue the payment invoice\\n self.__make_payment(amount=value)\",\n \"def about(request):\\n return render(request, \\\"about/about.html\\\")\",\n \"def about(request):\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/about.html',\\n context_instance = RequestContext(request,\\n {\\n 'title':'About',\\n 'message':'Your application description page.',\\n 'year':datetime.now().year,\\n })\\n )\",\n \"def about(request):\\n return render(request,\\\"encyclopedia/about.html\\\")\",\n \"def about():\\n return render_template('about.html', title='About')\",\n \"def about(request):\\n return render(request, \\\"about.html\\\")\",\n \"def about():\\n\\n return render_template('about.html')\",\n \"def about(request):\\n context = get_context()\\n return render(request, 'about.html', context)\",\n \"def buyshares():\\n # Initialise buy form\\n buyform = BuyShareForm()\\n # Validate and process form data\\n if(buyform.validate_on_submit()):\\n # Buys shares\\n issuerID = buyform.buysharecode.data\\n quantity = buyform.buyquantity.data\\n userID = current_user.userID\\n # Call buyshare API\\n buyshare = gdb.buyshare(userID, issuerID, quantity)\\n if(buyshare):\\n # Flash with success message\\n flash(\\\"Share purchase successful!\\\", category=\\\"success\\\")\\n else:\\n # Flash with warning message\\n flash(\\\"Share purchase unsuccessful!\\\", category=\\\"error\\\")\\n # Redirect to reffering page or dashboard\\n return redirect(request.referrer or url_for('main.dashboard'))\",\n \"def about():\\n\\n return render_template('about.html', title=\\\"About\\\")\",\n \"def about(request):\\n return render(request, 'about.html')\",\n \"def about():\\n return render_template(\\\"pages/about.html\\\")\",\n \"def handler(request):\\n context = {\\\"request\\\": request}\\n return render_to_response(\\n 'accounts.html',\\n context,\\n context_instance=RequestContext(request))\",\n \"def about():\\n return render_template('about.html', title='About')\",\n \"def account(request):\\n context = {'user': request.user}\\n return render(request, 'registration/account.html', context)\",\n \"def about(request):\\n context = {'client_id': settings.OPENHUMANS_CLIENT_ID,\\n 'oh_proj_page': settings.OH_ACTIVITY_PAGE}\\n return render(request, 'main/about.html', context=context)\",\n \"def contact():\\r\\n return render_template(\\r\\n 'contact.html',\\r\\n title='Contact',\\r\\n year=datetime.now().year,\\r\\n message='Your contact page.'\\r\\n )\",\n \"def contact():\\r\\n return render_template(\\r\\n 'contact.html',\\r\\n title='Contact',\\r\\n year=datetime.now().year,\\r\\n message='Your contact page.'\\r\\n )\",\n \"def about(request):\\n # Test Comment\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'about.html',\\n context_instance=RequestContext(request, {})\\n )\",\n \"def about(request):\\n # Test Comment\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'about.html',\\n context_instance=RequestContext(request, {})\\n )\",\n \"def about():\\n return render_template('about.html')\",\n \"def about():\\n return render_template('about.html')\",\n \"def about():\\n return render_template('about.html')\",\n \"def about():\\n return render_template('about.html')\",\n \"def about():\\n return render_template('about.html')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7859985","0.7057577","0.6755606","0.6672508","0.62410414","0.6223819","0.61764616","0.61620957","0.61559856","0.61495495","0.6127247","0.6028435","0.6007418","0.5978635","0.59492284","0.5912205","0.5789936","0.57580626","0.5750479","0.5719444","0.5716057","0.57144773","0.5701521","0.56994194","0.5693802","0.5693193","0.5690752","0.5684356","0.56841236","0.5682127","0.5664405","0.5664048","0.565327","0.5637385","0.5621486","0.560556","0.56","0.5598349","0.55818987","0.55749065","0.5566225","0.55659944","0.5564588","0.5559204","0.555026","0.55471134","0.55402213","0.55393654","0.553797","0.5532919","0.55279744","0.55137825","0.55103856","0.5498521","0.5494301","0.549111","0.5490074","0.54836464","0.54836464","0.54836464","0.54836464","0.5476614","0.5476584","0.5474678","0.54736906","0.5472121","0.5471756","0.5470322","0.5465557","0.5465557","0.5465557","0.5465557","0.54567677","0.5454274","0.54394144","0.54371434","0.54215896","0.5417619","0.54066503","0.5401639","0.540095","0.54001886","0.5399094","0.5397426","0.5394968","0.5392777","0.539088","0.5389388","0.5385413","0.5382134","0.5379451","0.5378281","0.5375231","0.5375231","0.53719944","0.53719944","0.537169","0.537169","0.537169","0.537169","0.537169"],"string":"[\n \"0.7859985\",\n \"0.7057577\",\n \"0.6755606\",\n \"0.6672508\",\n \"0.62410414\",\n \"0.6223819\",\n \"0.61764616\",\n \"0.61620957\",\n \"0.61559856\",\n \"0.61495495\",\n \"0.6127247\",\n \"0.6028435\",\n \"0.6007418\",\n \"0.5978635\",\n \"0.59492284\",\n \"0.5912205\",\n \"0.5789936\",\n \"0.57580626\",\n \"0.5750479\",\n \"0.5719444\",\n \"0.5716057\",\n \"0.57144773\",\n \"0.5701521\",\n \"0.56994194\",\n \"0.5693802\",\n \"0.5693193\",\n \"0.5690752\",\n \"0.5684356\",\n \"0.56841236\",\n \"0.5682127\",\n \"0.5664405\",\n \"0.5664048\",\n \"0.565327\",\n \"0.5637385\",\n \"0.5621486\",\n \"0.560556\",\n \"0.56\",\n \"0.5598349\",\n \"0.55818987\",\n \"0.55749065\",\n \"0.5566225\",\n \"0.55659944\",\n \"0.5564588\",\n \"0.5559204\",\n \"0.555026\",\n \"0.55471134\",\n \"0.55402213\",\n \"0.55393654\",\n \"0.553797\",\n \"0.5532919\",\n \"0.55279744\",\n \"0.55137825\",\n \"0.55103856\",\n \"0.5498521\",\n \"0.5494301\",\n \"0.549111\",\n \"0.5490074\",\n \"0.54836464\",\n 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configure(request):\n\n username = request.session.get('username', False)\n profile = request.session.get('profile', False)\n if(username):\n context = {'username': username,'profile':profile}\n return render(request, 'MedTAG_sket_dock_App/index.html', context)\n else:\n return redirect('MedTAG_sket_dock_App:login')"},"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 configuration():","def config():","def config():","def config(self):\n pass","def config(self):\n pass","def config():\n config_django()\n config_svisor()","def configuration_view(project):\n project_query = Project.select().where(Project.slug == project).first()\n if project_query is None:\n flash(\"invalid project\")\n return redirect(url_for(\"projects\"))\n session[\"project\"] = project_query\n\n g.selected_tab = \"configuration\"\n\n settings = None\n if request.method == \"GET\":\n settings = Anemone.abcfile.parse(path(project_query.path, \"build.abc\"))\n elif request.method == \"POST\":\n configuration_post(project_query, request)\n\n return render_template(\"configure.html\", ssh=open(app.config[\"SSH_PUBLIC\"]).readline(),\n build=settings, unity=app.config[\"UNITY_PATH\"])","def config():\n if app.args.ui_mode == \"jinja\":\n ui_config = {\n \"p1\": {\n \"options\": {\n \"lineNumbers\": True,\n \"theme\":\"material\",\n \"lineWrapping\" : True,\n \"mode\": \"yaml\",\n \"indentUnit\": 2,\n \"tabSize\": 2\n },\n \"title\": \"DATA\",\n \"inventory\": bool(app.args.inventory_source),\n \"b1\": {\n \"icon\": None,\n \"show\": False,\n \"text\": None,\n \"url\": None\n }\n },\n \"p2\": {\n \"options\": {\n \"lineNumbers\": True,\n \"theme\": \"material\",\n \"lineWrapping\" : True,\n \"mode\": \"jinja2\"\n },\n \"title\": \"RENDER\",\n \"b1\": {\n \"icon\": \"create\",\n \"show\": True,\n \"text\": \"Render\",\n \"url\": \"/render\"\n }\n },\n \"p3\": {\n \"options\": {\n \"lineNumbers\": True,\n \"theme\": \"material\",\n \"lineWrapping\" : True,\n \"mode\": 'text'\n },\n \"title\": \"RESULT\",\n \"b1\": {\n \"icon\": \"link\",\n \"show\": bool(app.args.url),\n \"text\": \"link\"\n }\n }\n }\n elif app.args.ui_mode == \"schema\":\n ui_config = {\n \"p1\": {\n \"options\": {\n \"lineNumbers\": True,\n \"theme\":\"material\",\n \"lineWrapping\" : True,\n \"mode\": \"yaml\",\n \"indentUnit\": 2,\n \"tabSize\": 2\n },\n \"title\": \"DATA\",\n \"inventory\": bool(app.args.inventory_source),\n \"b1\": {\n \"icon\": \"create\",\n \"show\": True,\n \"text\": \"schema\",\n \"url\": \"/schema\"\n }\n },\n \"p2\": {\n \"options\": {\n \"lineNumbers\": True,\n \"theme\": \"material\",\n \"lineWrapping\" : True,\n \"mode\": \"yaml\"\n },\n \"title\": \"SCHEMA\",\n \"b1\": {\n \"icon\": \"check\",\n \"show\": True,\n \"text\": \"Validate\",\n \"url\": \"/validate\"\n }\n },\n \"p3\": {\n \"options\": {\n \"lineNumbers\": True,\n \"theme\": \"material\",\n \"lineWrapping\" : True,\n \"mode\": \"yaml\"\n },\n \"title\": \"VALIDATION SUCCESS/ERRORS\",\n \"b1\": {\n \"icon\": \"link\",\n \"show\": bool(app.args.url),\n \"text\": \"link\"\n }\n }\n }\n return jsonify(ui_config)","def config(ctx):\n return","def __configure(self):\n e5App().getObject(\"UserInterface\").showPreferences(\"logViewerPage\")","def settings_view():\n return template('settings.html')","def config(self):\n return None","def configure(self):\n inject(self.urls, self.names_for(\"urls\"))\n inject(self.models, self.names_for(\"models\"))\n self.load_admin()","def config(self):\n raise NotImplementedError","def home(request):\n page=1\n span = 15\n order = \"-created_at\"\n page = request.GET.get('page', page)\n span = request.GET.get('span', span)\n config_list,entry_count = Config.get_items(span=span, page=page)\n\n temp_values = {\n \"target\":\"config\",\n \"title\":u\"Config定義一覧ページ\",\n \"config_list\":config_list,\n \"subscroll\":True,\n }\n return render(request, 'server/index.html', temp_values)","def configure(self):\r\n pass","def configure(self):\n pass","def configure(self):\n pass","def configure(self):\n\n pass","def configure(self):","def configure(self):","def configure(self):","def configure(self):","def config(self):\n return {}","def config (self):\n import wikicode\n class Config (wikicode.extension):\n def run (self):\n self.send_page (\"Generic DC Setup\")\n wikicode.run_extension (Config)","def configure(self, conf):\n return","def config( **kwargs ):","def __writeConfig(self):\n page = None\n\n #TODO: get values of configurations here\n particles = \"#f\" if not base.particleMgrEnabled else \"#t\"\n volume = str(round(base.musicManager.getVolume(), 2))\n mute = \"#f\" if base.AppHasAudioFocus else \"#t\"\n #TODO: add any configuration variable name that you have added\n customConfigVariables = [\n \"\", \"particles-enabled\", \"audio-mute\", \"audio-volume\"]\n if os.path.exists(prcFile):\n # open the config file and change values according to current\n # application settings\n page = loadPrcFile(Filename.fromOsSpecific(prcFile))\n removeDecls = []\n for dec in range(page.getNumDeclarations()):\n # Check if our variables are given.\n # NOTE: This check has to be done to not loose our base or other\n # manual config changes by the user\n if page.getVariableName(dec) in customConfigVariables:\n decl = page.modifyDeclaration(dec)\n removeDecls.append(decl)\n for dec in removeDecls:\n page.deleteDeclaration(dec)\n # NOTE: particles-enabled and audio-mute are custom variables and\n # have to be loaded by hand at startup\n # Particles\n page.makeDeclaration(\"particles-enabled\", particles)\n # audio\n page.makeDeclaration(\"audio-volume\", volume)\n page.makeDeclaration(\"audio-mute\", mute)\n else:\n # Create a config file and set default values\n cpMgr = ConfigPageManager.getGlobalPtr()\n page = cpMgr.makeExplicitPage(\"%s Pandaconfig\"%appName)\n # set OpenGL to be the default\n page.makeDeclaration(\"load-display\", \"pandagl\")\n # get the displays width and height\n w = self.pipe.getDisplayWidth()\n h = self.pipe.getDisplayHeight()\n # set the window size in the config file\n page.makeDeclaration(\"win-size\", \"%d %d\"%(w, h))\n # set the default to fullscreen in the config file\n page.makeDeclaration(\"fullscreen\", \"1\")\n # particles\n page.makeDeclaration(\"particles-enabled\", \"#t\")\n # audio\n page.makeDeclaration(\"audio-volume\", volume)\n page.makeDeclaration(\"audio-mute\", \"#f\")\n # create a stream to the specified config file\n configfile = OFileStream(prcFile)\n # and now write it out\n page.write(configfile)\n # close the stream\n configfile.close()","def __init__(self):\n self.html = conf.get('HTML', 'simplePage').format(title='Services',\n h1='Services', message='')","def config(self) -> Dict[str, Any]:","def getConfigData():\n return {\n \"djangoPage\": [\n QCoreApplication.translate(\"ProjectDjangoPlugin\", \"Django\"),\n os.path.join(\"ProjectDjango\", \"icons\", \"django.png\"),\n createDjangoPage, None, None],\n }","def __gitConfigure(self):\n e5App().getObject(\"UserInterface\").showPreferences(\"zzz_gitPage\")","def app_config(self):\n return self._app_conf[\"aiscalator\"]","def configure(self) -> None:","def configure(self, section):","def options(self):\n self.title = self.action\n accept = self.request.accept.best_match(['text/html', 'application/json'])\n\n if accept == 'application/json':\n self.render(json=list(self.ConfigService.items()))\n else:\n if self.request.method == 'POST':\n params = self.params\n self.ConfigService.set(params['id'], params['value'])\n self.render(text=params['value'])\n return\n # if we made it here, render the html","def show_config() -> None:\n with _config_lock:\n config_util.show_config(\n _section_descriptions, cast(Dict[str, ConfigOption], _config_options)\n )","def config(self):\n\t\tsys.stderr.write(\"Base class method called: config() This souldn't happen.\\n\")","def configure(self, options, conf):","async def config(self, ctx):\n await ctx.send_help(ctx.command)","def _init_config(self):\n self.config = self.config_template.specialize()\n print('MMH CONFIG:\\n' + str(self.config))","def printConfig():\n # Why not log instead? Are we asking user to confirm settings?\n pass # until implemented","def settings(request):\n from django.conf import settings\n return {\n 'exhibitBaseTemplate': settings.EXHIBIT_TEMPLATE,\n 'thumbnailUrl': settings.THUMBNAIL_URL,\n 'calisphere': settings.CALISPHERE\n }","def _configure(self):\n pass","def generate_config():\n\n return {\n \"email_subject\": DEFAULT_EMAIL_SUBJECT,\n \"from_email\": DEFAULT_FROM_EMAIL,\n \"to_email\": DEFAULT_TO_EMAIL,\n \"url\": DEFAULT_URL,\n \"start_value\": DEFAULT_START_VALUE,\n \"look_ahead\": DEFAULT_LOOK_AHEAD,\n \"slide_window\": DEFAULT_SLIDE_WINDOW,\n }","def app_settings():\n return {\n 'app_wksp_path': os.path.join(App.get_app_workspace().path, ''),\n 'threddsdatadir': App.get_custom_setting(\"thredds_path\"),\n 'threddsurl': App.get_custom_setting(\"thredds_url\"),\n 'logfile': os.path.join(App.get_app_workspace().path, 'workflow.log')\n }","async def dashboard(request):\n return [\n {'name': 'application config', 'value': {k: str(v) for k, v in app.cfg}},\n {'name': 'request headers', 'value': dict(request.headers)},\n ]","def _configure(self):\n Application._configure(self)\n\n return","def setting():\n return render_template('setting.html', year=datetime.now().year)","def getConfigPanel():\n\treturn None","def showconfig():\n print(yaml.dump(CONFIG))","def magic_config(self,parameter_s=''):\n \n page('Current configuration structure:\\n'+\n pformat(self.rc.dict()))","def write_config(self):\n cfg = {\n 'ALERT_API_KEY':self.api_key,\n 'APP_NAME':self.title,\n 'alertes':self.alertes\n }\n write_conf(self.CONF_FILE,cfg)","def main(global_config, **settings):\n config = Configurator(settings=settings)\n config.include('clldmpg')\n config.registry.registerUtility(link_attrs, interfaces.ILinkAttrs)\n home_comp = config.registry.settings['home_comp']\n home_comp.append('software')\n home_comp.append('contribute')\n config.add_settings(home_comp=home_comp)\n config.add_route('software', '/software')\n config.add_route('contribute', '/contribute')\n return config.make_wsgi_app()","def do_display_config(self, *arg):\n try:\n if self.pocs and self.pocs.config:\n pprint(self.pocs.config)\n else:\n print_warning(\"No config file for POCS.\")\n\n except AttributeError:\n print_warning(\"Please run `setup_pocs` before trying to run `display_config`\")","def configure(self):\n\n log.info(\"Loading configuration from the database...\")\n settings = dict(db.query(\"\"\"SELECT `key`, `value` FROM settings\"\"\"))\n \n log.info(\"Config loaded\")\n log.info(\"HoN Version: %s Chat Port: %s Protocol: %s\" % (settings['honver'], settings['chatport'], settings['chatver']))\n if 'username' in settings:\n acc_config['username'] = settings['username']\n \n if 'password' in settings:\n acc_config['password'] = settings['password']\n \n if 'invis' in settings:\n settings['invis'] = True if settings['invis'] == \"True\" else False\n \n if 'chatport' in settings:\n settings['chatport'] = int(settings['chatport'])\n \n if 'chatver' in settings:\n settings['chatver'] = int(settings['chatver'])\n \n for key in settings:\n if key in basic_config:\n basic_config[key] = settings[key]\n \n self._configure(chatport=settings['chatport'], protocol=settings['chatver'], invis=settings['invis'],\n masterserver=settings['masterserver'], basicserver=settings['basicserver'], honver=settings['honver'])","def config(self):\n return self[CONFIG_KEY]","def _get_config(self, *args, **kwargs):\n # Just need to show the parameter screen...the parser for the command\n # does the update_many()\n self._go_to_root_menu()\n self._navigate(SubMenu.SHOW_PARAM)\n self._go_to_root_menu()","def show_config(): \n\n #Is single or multi node?\n nodes = hl.getAllNodes()\n if nodes:\n if request.method == \"POST\":\n nodeID = request.form['node1']\n\n else:\n nodeID = 1\n\n nodeReq = hl.getNode(\"ID\", nodeID)\n \n if nodeReq[\"Address\"] == \"self\":\n node = hl.getIptablesRules()\n else:\n res = hl.nodeGet(nodeReq[\"Address\"]+\"/getrules/\")\n if 'result' in res and res['result']:\n node = res[\"rules\"] \n else:\n node = None\n\n if node:\n return render_template('config.html', firewall = node, nodes = nodes, nodeID = hl.getNode(\"ID\", nodeID))\n else:\n flash(\"Error: cannot retrieve iptable rules from node\")\n else:\n return render_template('config.html', firewall = hl.getIptablesRules(), nodes = -1, nodeID = -1)","def read_config(self):\n cfg = read_conf(self.CONF_FILE)\n self.api_key = cfg[\"ALERT_API_KEY\"]\n self.title = cfg[\"APP_NAME\"]\n if type(cfg[\"alertes\"]) is dict:\n self.alertes = cfg[\"alertes\"]\n else:\n self.alertes = dict()","def app_config_cli(**kwargs):\n\n @signals.config_complete.connect\n def set_config(app):\n app.config.update(kwargs)\n\n yield","def getConfig(self):\n pass","def configure(cls):\n pass","def view_to_config(self):\n raise NotImplementedError","def getConfigPage():\r\n\r\n\r\n #print os.getcwd()\r\n #os.chdir('..')\r\n #os.chdir('D:\\Stone\\Python\\Python_Insurance')\r\n #print os.getcwd()\r\n\r\n #configPage = ConfigObj('pagesConf.conf')\r\n configPage = ConfigObj('thirdevalConf.conf')\r\n\r\n #configPagePolicyHolder_Usr = configPage['PolicyHolder_Usr']\r\n #dictFirst = configPagePolicyHolder_Usr.dict()\r\n #print dictFirst\r\n #j = 2\r\n #for j in range(j):\r\n # for member in dictFirst:\r\n # if 'titleText' in dictFirst[member]:\r\n # print member\r\n # dictSecond = dictFirst[member]\r\n # print dictSecond\r\n # break\r\n # dictFirst.pop(member)\r\n #i = i+1\r\n #print i\r\n #for item in configPagePolicyHolder_Usr[member]:\r\n # print configPagePolicyHolder_Usr[member][item]\r\n #for item in member:\r\n # print member[item]\r\n #print configPage\r\n #print configPagePolicyHolder_Usr\r\n #configPagePolicyHolder_Usr = configPage['PolicyHolder_Usr']\r\n #print configPagePolicyHolder_Usr\r\n\r\n #print configPagePolicyHolder_Usr.as_int( 'rowNumber')\r\n #print configPage.as_bool(\"hasPolicyHolder_Usr\")\r\n return configPage","def showConf(self):\n\n return json.dumps(\n self.config, sort_keys=True, indent=4, separators=(',', ': '))","def conf():\n global config\n return config","def _config(request):\n return request.environ['adminish']","def display_config_info():\n print(\"Merlin Configuration\")\n print(\"-\" * 25)\n print(\"\")\n\n conf = default_config_info()\n sconf = {}\n excpts = {}\n try:\n conf[\"broker server\"] = broker.get_connection_string(include_password=False)\n sconf[\"broker server\"] = broker.get_connection_string()\n conf[\"broker ssl\"] = broker.get_ssl_config()\n except Exception as e:\n conf[\"broker server\"] = \"Broker server error.\"\n excpts[\"broker server\"] = e\n\n try:\n conf[\"results server\"] = results_backend.get_connection_string(include_password=False)\n sconf[\"results server\"] = results_backend.get_connection_string()\n conf[\"results ssl\"] = results_backend.get_ssl_config()\n except Exception as e:\n conf[\"results server\"] = \"No results server configured or error.\"\n excpts[\"results server\"] = e\n\n print(tabulate(conf.items(), tablefmt=\"presto\"))\n\n if excpts:\n print(\"\\nExceptions:\")\n for k, v in excpts.items():\n print(f\"{k}: {v}\")\n\n check_server_access(sconf)","def configure(self, options, conf):\n pass","def configureAims(self):\n self._config_ui.showDialog()","def main(global_config, **settings):\n with open('config.yaml', 'r') as fp:\n local_config = yaml.safe_load(fp)['config']\n Calendar.initialize(local_config['calendar'])\n ICloud.initialize(local_config['icloud'])\n Weather.initialize(local_config['weather'])\n\n config = Configurator(settings=settings)\n config.include('pyramid_jinja2')\n config.add_static_view('static', 'static', cache_max_age=3600)\n config.add_route('home', '/')\n config.add_route('oauthCallback', '/oauthCallback')\n config.scan()\n return config.make_wsgi_app()","def _update_config(self):\n \n # app size\n if self.IsMaximized():\n config.SETTINGS['app_maximized'] = True\n else:\n size = self.GetSize()\n config.SETTINGS['app_width'] = size[0]\n config.SETTINGS['app_height'] = size[1]\n config.SETTINGS['app_maximized'] = False\n \n # panes size\n pane = self.AUIManager.GetPane(self._collections_view)\n config.SETTINGS['collections_view_enabled'] = pane.IsShown()\n if pane.IsShown():\n config.SETTINGS['collections_view_width'] = pane.window.GetClientSize()[0]\n \n pane = self.AUIManager.GetPane(self._pdf_view)\n config.SETTINGS['pdf_view_enabled'] = pane.IsShown()\n if pane.IsShown():\n config.SETTINGS['pdf_view_height'] = pane.window.GetClientSize()[1]\n \n pane = self.AUIManager.GetPane(self._details_view)\n config.SETTINGS['details_view_enabled'] = pane.IsShown()\n if pane.IsShown():\n config.SETTINGS['details_view_width'] = pane.window.GetClientSize()[0]\n \n # article view\n config.SETTINGS['articles_view_columns'] = self._articles_view.GetColumnsSettings()","def render_settings_view():\n return render_template('settings_screen.html', realsense_device_status=realsense_enabled, detector_enabled=enabled_detector)","def configure(self):\n self.configureLinks()\n self.isConfigured = True\n return","def index():\n response.view_title = myconf.get('app.name') + ' Home Page'\n return dict(message='')","def config(self, **kw):\n self.cfg_fixture.config(**kw)","def widget_load_config(self, plugman):\r\n pass","def main(global_config, **settings):\n config = Configurator(settings=settings)\n config.include('pyramid_jinja2')\n config.add_static_view('static', 'static', cache_max_age=3600)\n config.add_route('home', '/')\n config.add_route('total_users', '/totalusers/')\n config.add_route('pageviews_weekly', '/pageviews/weekly/')\n config.add_route('pageviews_monthly', '/pageviews/monthly/')\n config.add_route('pageviews', '/pageviews/')\n config.add_route('devices', '/devices/')\n config.add_route('moreinfo', '/moreinfo/{profile_id}')\n config.scan()\n return config.make_wsgi_app()","def main(global_config, **settings):\n # add settings in here?\n config = Configurator(settings=settings)\n config.include('pyramid_jinja2')\n config.include('.models')\n config.include('.routes')\n config.include('.security')\n config.scan()\n return config.make_wsgi_app()","def init_config(self):\n pass","def __init__(self):\n self.config = {}","def __init__(self) -> None:\n\n self.config_keys = ['APPS_HOST', 'APPS_PORT']\n super().__init__()\n\n self.APPS_HOST = str(self.APPS_HOST)\n \"\"\"Host where the server will be served\"\"\"\n\n self.APPS_PORT = int(self.APPS_PORT)\n \"\"\"Port where the server will be served\"\"\"","def config(self) -> \"AutomationConfig\":","def config(self) -> \"AutomationConfig\":","async def _show_config_form(\n self,\n ):\n return self.async_show_form(\n step_id=\"user\",\n data_schema=vol.Schema(\n {\n vol.Required(CONF_DEVICEID): str,\n }\n ),\n errors=self._errors,\n )","def render_config(self, *args):\n # ensure that a directory we need is there\n ch_host.mkdir(APACHE_LOCATION)\n\n self.render_configs(self.string_templates.keys())\n\n core.templating.render(\n source=APACHE_CONF_TEMPLATE,\n template_loader=os_templating.get_loader(\n 'templates/', self.release),\n target='{}/{}'.format(APACHE_LOCATION, APACHE_CONF_TEMPLATE),\n context=self.adapters_class(args, charm_instance=self),\n )\n\n core.templating.render(\n source=KERBEROS_CONF_TEMPLATE,\n template_loader=os_templating.get_loader(\n 'templates/', self.release),\n target=\"/etc/krb5.conf\",\n context=self.adapters_class(args, charm_instance=self),\n )","def configure_app(flask_app):\n flask_app.config['RESTPLUS_SWAGGER_UI_DOC_EXPANSION'] = \\\n settings.RESTPLUS_SWAGGER_UI_DOC_EXPANSION\n flask_app.config['RESTPLUS_VALIDATE'] = \\\n settings.RESTPLUS_VALIDATE\n\n flask_app.config['SQLALCHEMY_DATABASE_URI'] = \\\n settings.SQLALCHEMY_DATABASE_URI\n flask_app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = \\\n settings.SQLALCHEMY_TRACK_MODIFICATIONS","def configure_app(self):\n self.app.route('/', callback=self.get_api)","def init_app(self, app):\n app.config.setdefault('YAWT_INDEXER_IFC', 'yawtext.whoosh')\n app.config.setdefault('YAWT_INDEXER_WHOOSH_INFO_FIELDS', {})\n app.config.setdefault('YAWT_INDEXER_WHOOSH_FIELDS',\n {'content': TEXT()})","def includeme(config): # pragma: no cover\n config.add_route('home', '/')\n config.add_view('kotti.views.edit.actions.contents',\n route_name=u'home',\n permission=u'view',\n renderer='kotti:templates/edit/contents.pt',\n )","def config_show(cibfile=None):\n return item_show(item=\"config\", item_id=None, extra_args=None, cibfile=cibfile)","def config_skeleton():\n config = Config()\n config.set_to_default()\n config.save()","def main(global_config, **settings):\n config = Configurator(settings=settings)\n config.include('pyramid_chameleon')\n config.add_static_view('static', 'static', cache_max_age=3600)\n config.add_route('home', '/')\n \n config.registry.registerUtility(Basic(settings), INotification, 'basic')\n config.registry.registerUtility(Twitter(settings), INotification, 'twitter')\n\n #config.registry.registerUtility(MailService(settings), INotification, 'mail')\n\n #config.registry.registerUtility(Facebook(settings), INotification, 'facebook')\n\n #config.registry.registerUtility(Twitter(settings), INotification, 'twitter')\n\n\n config.scan()\n return config.make_wsgi_app()","async def settings(self, ctx: BBContext):\n pass","def config():\n return Config()","def config():\n return Config()","def configure(self, args):\n pass","def app():\n # Add balloons animation when page opens\n st.balloons()\n\n # Add title\n st.title(\"Welcome to the about me page\")\n \n # Add Contact Details\n st.header('Contact Us')\n\n # Add email\n st.markdown('''### Name:\n Khushal Mehrotra''')\n\n # Add name\n st.markdown('''### Email:\n kmehrotra07@gmail.com''')\n\n # Add github\n st.markdown('''### GitHub: [Khushal Mehrotra](https://github.com/km-07)''')\n\n # Add linkedin\n st.markdown('''### Linkedin: [Khushal Mehrotra](https://www.linkedin.com/in/khushal-mehrotra-5276091a9/)''')","def configure(username, password, domain):\n art = r'''\nWelcome! __ ___. .__\n_____ ____ ____ ____ __ __ _____/ |______ \\_ |__ | | ____\n\\__ \\ _/ ___\\/ ___\\/ _ \\| | \\/ \\ __\\__ \\ | __ \\| | _/ __ \\\n / __ \\\\ \\__\\ \\__( <_> ) | / | \\ | / __ \\| \\_\\ \\ |_\\ ___/\n(____ /\\___ >___ >____/|____/|___| /__| (____ /___ /____/\\___ >\n \\/ \\/ \\/ \\/ \\/ \\/ \\/\n '''\n click.secho(art, fg='blue')\n Config(username=username, password=password, domain=domain)","def main(global_config, **settings):\n SETTINGS = settings\n config = Configurator(settings=settings,)\n config.include('pyramid_jinja2')\n config.include('.models')\n config.include('.cors')\n config.add_cors_preflight_handler()\n config.include('.routes')\n config.include('.security')\n config.include('..greggo')\n config.add_static_view('static', path='repoll:static')\n config.scan()\n return config.make_wsgi_app()"],"string":"[\n \"def configuration():\",\n \"def config():\",\n \"def config():\",\n \"def config(self):\\n pass\",\n \"def config(self):\\n pass\",\n \"def config():\\n config_django()\\n config_svisor()\",\n \"def configuration_view(project):\\n project_query = Project.select().where(Project.slug == project).first()\\n if project_query is None:\\n flash(\\\"invalid project\\\")\\n return redirect(url_for(\\\"projects\\\"))\\n session[\\\"project\\\"] = project_query\\n\\n g.selected_tab = \\\"configuration\\\"\\n\\n settings = None\\n if request.method == \\\"GET\\\":\\n settings = Anemone.abcfile.parse(path(project_query.path, \\\"build.abc\\\"))\\n elif request.method == \\\"POST\\\":\\n configuration_post(project_query, request)\\n\\n return render_template(\\\"configure.html\\\", ssh=open(app.config[\\\"SSH_PUBLIC\\\"]).readline(),\\n build=settings, unity=app.config[\\\"UNITY_PATH\\\"])\",\n \"def config():\\n if app.args.ui_mode == \\\"jinja\\\":\\n ui_config = {\\n \\\"p1\\\": {\\n \\\"options\\\": {\\n \\\"lineNumbers\\\": True,\\n \\\"theme\\\":\\\"material\\\",\\n \\\"lineWrapping\\\" : True,\\n \\\"mode\\\": \\\"yaml\\\",\\n \\\"indentUnit\\\": 2,\\n \\\"tabSize\\\": 2\\n },\\n \\\"title\\\": \\\"DATA\\\",\\n \\\"inventory\\\": bool(app.args.inventory_source),\\n \\\"b1\\\": {\\n \\\"icon\\\": None,\\n \\\"show\\\": False,\\n \\\"text\\\": None,\\n \\\"url\\\": None\\n }\\n },\\n \\\"p2\\\": {\\n \\\"options\\\": {\\n \\\"lineNumbers\\\": True,\\n \\\"theme\\\": \\\"material\\\",\\n \\\"lineWrapping\\\" : True,\\n \\\"mode\\\": \\\"jinja2\\\"\\n },\\n \\\"title\\\": \\\"RENDER\\\",\\n \\\"b1\\\": {\\n \\\"icon\\\": \\\"create\\\",\\n \\\"show\\\": True,\\n \\\"text\\\": \\\"Render\\\",\\n \\\"url\\\": \\\"/render\\\"\\n }\\n },\\n \\\"p3\\\": {\\n \\\"options\\\": {\\n \\\"lineNumbers\\\": True,\\n \\\"theme\\\": \\\"material\\\",\\n \\\"lineWrapping\\\" : True,\\n \\\"mode\\\": 'text'\\n },\\n \\\"title\\\": \\\"RESULT\\\",\\n \\\"b1\\\": {\\n \\\"icon\\\": \\\"link\\\",\\n \\\"show\\\": bool(app.args.url),\\n \\\"text\\\": \\\"link\\\"\\n }\\n }\\n }\\n elif app.args.ui_mode == \\\"schema\\\":\\n ui_config = {\\n \\\"p1\\\": {\\n \\\"options\\\": {\\n \\\"lineNumbers\\\": True,\\n \\\"theme\\\":\\\"material\\\",\\n \\\"lineWrapping\\\" : True,\\n \\\"mode\\\": \\\"yaml\\\",\\n \\\"indentUnit\\\": 2,\\n \\\"tabSize\\\": 2\\n },\\n \\\"title\\\": \\\"DATA\\\",\\n \\\"inventory\\\": bool(app.args.inventory_source),\\n \\\"b1\\\": {\\n \\\"icon\\\": \\\"create\\\",\\n \\\"show\\\": True,\\n \\\"text\\\": \\\"schema\\\",\\n \\\"url\\\": \\\"/schema\\\"\\n }\\n },\\n \\\"p2\\\": {\\n \\\"options\\\": {\\n \\\"lineNumbers\\\": True,\\n \\\"theme\\\": \\\"material\\\",\\n \\\"lineWrapping\\\" : True,\\n \\\"mode\\\": \\\"yaml\\\"\\n },\\n \\\"title\\\": \\\"SCHEMA\\\",\\n \\\"b1\\\": {\\n \\\"icon\\\": \\\"check\\\",\\n \\\"show\\\": True,\\n \\\"text\\\": \\\"Validate\\\",\\n \\\"url\\\": \\\"/validate\\\"\\n }\\n },\\n \\\"p3\\\": {\\n \\\"options\\\": {\\n \\\"lineNumbers\\\": True,\\n \\\"theme\\\": \\\"material\\\",\\n \\\"lineWrapping\\\" : True,\\n \\\"mode\\\": \\\"yaml\\\"\\n },\\n \\\"title\\\": \\\"VALIDATION SUCCESS/ERRORS\\\",\\n \\\"b1\\\": {\\n \\\"icon\\\": \\\"link\\\",\\n \\\"show\\\": bool(app.args.url),\\n \\\"text\\\": \\\"link\\\"\\n }\\n }\\n }\\n return jsonify(ui_config)\",\n \"def config(ctx):\\n return\",\n \"def __configure(self):\\n e5App().getObject(\\\"UserInterface\\\").showPreferences(\\\"logViewerPage\\\")\",\n \"def settings_view():\\n return template('settings.html')\",\n \"def config(self):\\n return None\",\n \"def configure(self):\\n inject(self.urls, self.names_for(\\\"urls\\\"))\\n inject(self.models, self.names_for(\\\"models\\\"))\\n self.load_admin()\",\n \"def config(self):\\n raise NotImplementedError\",\n \"def home(request):\\n page=1\\n span = 15\\n order = \\\"-created_at\\\"\\n page = request.GET.get('page', page)\\n span = request.GET.get('span', span)\\n config_list,entry_count = Config.get_items(span=span, page=page)\\n\\n temp_values = {\\n \\\"target\\\":\\\"config\\\",\\n \\\"title\\\":u\\\"Config定義一覧ページ\\\",\\n \\\"config_list\\\":config_list,\\n \\\"subscroll\\\":True,\\n }\\n return render(request, 'server/index.html', temp_values)\",\n \"def configure(self):\\r\\n pass\",\n \"def configure(self):\\n pass\",\n \"def configure(self):\\n pass\",\n \"def configure(self):\\n\\n pass\",\n \"def configure(self):\",\n \"def configure(self):\",\n \"def configure(self):\",\n \"def configure(self):\",\n \"def config(self):\\n return {}\",\n \"def config (self):\\n import wikicode\\n class Config (wikicode.extension):\\n def run (self):\\n self.send_page (\\\"Generic DC Setup\\\")\\n wikicode.run_extension (Config)\",\n \"def configure(self, conf):\\n return\",\n \"def config( **kwargs ):\",\n \"def __writeConfig(self):\\n page = None\\n\\n #TODO: get values of configurations here\\n particles = \\\"#f\\\" if not base.particleMgrEnabled else \\\"#t\\\"\\n volume = str(round(base.musicManager.getVolume(), 2))\\n mute = \\\"#f\\\" if base.AppHasAudioFocus else \\\"#t\\\"\\n #TODO: add any configuration variable name that you have added\\n customConfigVariables = [\\n \\\"\\\", \\\"particles-enabled\\\", \\\"audio-mute\\\", \\\"audio-volume\\\"]\\n if os.path.exists(prcFile):\\n # open the config file and change values according to current\\n # application settings\\n page = loadPrcFile(Filename.fromOsSpecific(prcFile))\\n removeDecls = []\\n for dec in range(page.getNumDeclarations()):\\n # Check if our variables are given.\\n # NOTE: This check has to be done to not loose our base or other\\n # manual config changes by the user\\n if page.getVariableName(dec) in customConfigVariables:\\n decl = page.modifyDeclaration(dec)\\n removeDecls.append(decl)\\n for dec in removeDecls:\\n page.deleteDeclaration(dec)\\n # NOTE: particles-enabled and audio-mute are custom variables and\\n # have to be loaded by hand at startup\\n # Particles\\n page.makeDeclaration(\\\"particles-enabled\\\", particles)\\n # audio\\n page.makeDeclaration(\\\"audio-volume\\\", volume)\\n page.makeDeclaration(\\\"audio-mute\\\", mute)\\n else:\\n # Create a config file and set default values\\n cpMgr = ConfigPageManager.getGlobalPtr()\\n page = cpMgr.makeExplicitPage(\\\"%s Pandaconfig\\\"%appName)\\n # set OpenGL to be the default\\n page.makeDeclaration(\\\"load-display\\\", \\\"pandagl\\\")\\n # get the displays width and height\\n w = self.pipe.getDisplayWidth()\\n h = self.pipe.getDisplayHeight()\\n # set the window size in the config file\\n page.makeDeclaration(\\\"win-size\\\", \\\"%d %d\\\"%(w, h))\\n # set the default to fullscreen in the config file\\n page.makeDeclaration(\\\"fullscreen\\\", \\\"1\\\")\\n # particles\\n page.makeDeclaration(\\\"particles-enabled\\\", \\\"#t\\\")\\n # audio\\n page.makeDeclaration(\\\"audio-volume\\\", volume)\\n page.makeDeclaration(\\\"audio-mute\\\", \\\"#f\\\")\\n # create a stream to the specified config file\\n configfile = OFileStream(prcFile)\\n # and now write it out\\n page.write(configfile)\\n # close the stream\\n configfile.close()\",\n \"def __init__(self):\\n self.html = conf.get('HTML', 'simplePage').format(title='Services',\\n h1='Services', message='')\",\n \"def config(self) -> Dict[str, Any]:\",\n \"def getConfigData():\\n return {\\n \\\"djangoPage\\\": [\\n QCoreApplication.translate(\\\"ProjectDjangoPlugin\\\", \\\"Django\\\"),\\n os.path.join(\\\"ProjectDjango\\\", \\\"icons\\\", \\\"django.png\\\"),\\n createDjangoPage, None, None],\\n }\",\n \"def __gitConfigure(self):\\n e5App().getObject(\\\"UserInterface\\\").showPreferences(\\\"zzz_gitPage\\\")\",\n \"def app_config(self):\\n return self._app_conf[\\\"aiscalator\\\"]\",\n \"def configure(self) -> None:\",\n \"def configure(self, section):\",\n \"def options(self):\\n self.title = self.action\\n accept = self.request.accept.best_match(['text/html', 'application/json'])\\n\\n if accept == 'application/json':\\n self.render(json=list(self.ConfigService.items()))\\n else:\\n if self.request.method == 'POST':\\n params = self.params\\n self.ConfigService.set(params['id'], params['value'])\\n self.render(text=params['value'])\\n return\\n # if we made it here, render the html\",\n \"def show_config() -> None:\\n with _config_lock:\\n config_util.show_config(\\n _section_descriptions, cast(Dict[str, ConfigOption], _config_options)\\n )\",\n \"def config(self):\\n\\t\\tsys.stderr.write(\\\"Base class method called: config() This souldn't happen.\\\\n\\\")\",\n \"def configure(self, options, conf):\",\n \"async def config(self, ctx):\\n await ctx.send_help(ctx.command)\",\n \"def _init_config(self):\\n self.config = self.config_template.specialize()\\n print('MMH CONFIG:\\\\n' + str(self.config))\",\n \"def printConfig():\\n # Why not log instead? Are we asking user to confirm settings?\\n pass # until implemented\",\n \"def settings(request):\\n from django.conf import settings\\n return {\\n 'exhibitBaseTemplate': settings.EXHIBIT_TEMPLATE,\\n 'thumbnailUrl': settings.THUMBNAIL_URL,\\n 'calisphere': settings.CALISPHERE\\n }\",\n \"def _configure(self):\\n pass\",\n \"def generate_config():\\n\\n return {\\n \\\"email_subject\\\": DEFAULT_EMAIL_SUBJECT,\\n \\\"from_email\\\": DEFAULT_FROM_EMAIL,\\n \\\"to_email\\\": DEFAULT_TO_EMAIL,\\n \\\"url\\\": DEFAULT_URL,\\n \\\"start_value\\\": DEFAULT_START_VALUE,\\n \\\"look_ahead\\\": DEFAULT_LOOK_AHEAD,\\n \\\"slide_window\\\": DEFAULT_SLIDE_WINDOW,\\n }\",\n \"def app_settings():\\n return {\\n 'app_wksp_path': os.path.join(App.get_app_workspace().path, ''),\\n 'threddsdatadir': App.get_custom_setting(\\\"thredds_path\\\"),\\n 'threddsurl': App.get_custom_setting(\\\"thredds_url\\\"),\\n 'logfile': os.path.join(App.get_app_workspace().path, 'workflow.log')\\n }\",\n \"async def dashboard(request):\\n return [\\n {'name': 'application config', 'value': {k: str(v) for k, v in app.cfg}},\\n {'name': 'request headers', 'value': dict(request.headers)},\\n ]\",\n \"def _configure(self):\\n Application._configure(self)\\n\\n return\",\n \"def setting():\\n return render_template('setting.html', year=datetime.now().year)\",\n \"def getConfigPanel():\\n\\treturn None\",\n \"def showconfig():\\n print(yaml.dump(CONFIG))\",\n \"def magic_config(self,parameter_s=''):\\n \\n page('Current configuration structure:\\\\n'+\\n pformat(self.rc.dict()))\",\n \"def write_config(self):\\n cfg = {\\n 'ALERT_API_KEY':self.api_key,\\n 'APP_NAME':self.title,\\n 'alertes':self.alertes\\n }\\n write_conf(self.CONF_FILE,cfg)\",\n \"def main(global_config, **settings):\\n config = Configurator(settings=settings)\\n config.include('clldmpg')\\n config.registry.registerUtility(link_attrs, interfaces.ILinkAttrs)\\n home_comp = config.registry.settings['home_comp']\\n home_comp.append('software')\\n home_comp.append('contribute')\\n config.add_settings(home_comp=home_comp)\\n config.add_route('software', '/software')\\n config.add_route('contribute', '/contribute')\\n return config.make_wsgi_app()\",\n \"def do_display_config(self, *arg):\\n try:\\n if self.pocs and self.pocs.config:\\n pprint(self.pocs.config)\\n else:\\n print_warning(\\\"No config file for POCS.\\\")\\n\\n except AttributeError:\\n print_warning(\\\"Please run `setup_pocs` before trying to run `display_config`\\\")\",\n \"def configure(self):\\n\\n log.info(\\\"Loading configuration from the database...\\\")\\n settings = dict(db.query(\\\"\\\"\\\"SELECT `key`, `value` FROM settings\\\"\\\"\\\"))\\n \\n log.info(\\\"Config loaded\\\")\\n log.info(\\\"HoN Version: %s Chat Port: %s Protocol: %s\\\" % (settings['honver'], settings['chatport'], settings['chatver']))\\n if 'username' in settings:\\n acc_config['username'] = settings['username']\\n \\n if 'password' in settings:\\n acc_config['password'] = settings['password']\\n \\n if 'invis' in settings:\\n settings['invis'] = True if settings['invis'] == \\\"True\\\" else False\\n \\n if 'chatport' in settings:\\n settings['chatport'] = int(settings['chatport'])\\n \\n if 'chatver' in settings:\\n settings['chatver'] = int(settings['chatver'])\\n \\n for key in settings:\\n if key in basic_config:\\n basic_config[key] = settings[key]\\n \\n self._configure(chatport=settings['chatport'], protocol=settings['chatver'], invis=settings['invis'],\\n masterserver=settings['masterserver'], basicserver=settings['basicserver'], honver=settings['honver'])\",\n \"def config(self):\\n return self[CONFIG_KEY]\",\n \"def _get_config(self, *args, **kwargs):\\n # Just need to show the parameter screen...the parser for the command\\n # does the update_many()\\n self._go_to_root_menu()\\n self._navigate(SubMenu.SHOW_PARAM)\\n self._go_to_root_menu()\",\n \"def show_config(): \\n\\n #Is single or multi node?\\n nodes = hl.getAllNodes()\\n if nodes:\\n if request.method == \\\"POST\\\":\\n nodeID = request.form['node1']\\n\\n else:\\n nodeID = 1\\n\\n nodeReq = hl.getNode(\\\"ID\\\", nodeID)\\n \\n if nodeReq[\\\"Address\\\"] == \\\"self\\\":\\n node = hl.getIptablesRules()\\n else:\\n res = hl.nodeGet(nodeReq[\\\"Address\\\"]+\\\"/getrules/\\\")\\n if 'result' in res and res['result']:\\n node = res[\\\"rules\\\"] \\n else:\\n node = None\\n\\n if node:\\n return render_template('config.html', firewall = node, nodes = nodes, nodeID = hl.getNode(\\\"ID\\\", nodeID))\\n else:\\n flash(\\\"Error: cannot retrieve iptable rules from node\\\")\\n else:\\n return render_template('config.html', firewall = hl.getIptablesRules(), nodes = -1, nodeID = -1)\",\n \"def read_config(self):\\n cfg = read_conf(self.CONF_FILE)\\n self.api_key = cfg[\\\"ALERT_API_KEY\\\"]\\n self.title = cfg[\\\"APP_NAME\\\"]\\n if type(cfg[\\\"alertes\\\"]) is dict:\\n self.alertes = cfg[\\\"alertes\\\"]\\n else:\\n self.alertes = dict()\",\n \"def app_config_cli(**kwargs):\\n\\n @signals.config_complete.connect\\n def set_config(app):\\n app.config.update(kwargs)\\n\\n yield\",\n \"def getConfig(self):\\n pass\",\n \"def configure(cls):\\n pass\",\n \"def view_to_config(self):\\n raise NotImplementedError\",\n \"def getConfigPage():\\r\\n\\r\\n\\r\\n #print os.getcwd()\\r\\n #os.chdir('..')\\r\\n #os.chdir('D:\\\\Stone\\\\Python\\\\Python_Insurance')\\r\\n #print os.getcwd()\\r\\n\\r\\n #configPage = ConfigObj('pagesConf.conf')\\r\\n configPage = ConfigObj('thirdevalConf.conf')\\r\\n\\r\\n #configPagePolicyHolder_Usr = configPage['PolicyHolder_Usr']\\r\\n #dictFirst = configPagePolicyHolder_Usr.dict()\\r\\n #print dictFirst\\r\\n #j = 2\\r\\n #for j in range(j):\\r\\n # for member in dictFirst:\\r\\n # if 'titleText' in dictFirst[member]:\\r\\n # print member\\r\\n # dictSecond = dictFirst[member]\\r\\n # print dictSecond\\r\\n # break\\r\\n # dictFirst.pop(member)\\r\\n #i = i+1\\r\\n #print i\\r\\n #for item in configPagePolicyHolder_Usr[member]:\\r\\n # print configPagePolicyHolder_Usr[member][item]\\r\\n #for item in member:\\r\\n # print member[item]\\r\\n #print configPage\\r\\n #print configPagePolicyHolder_Usr\\r\\n #configPagePolicyHolder_Usr = configPage['PolicyHolder_Usr']\\r\\n #print configPagePolicyHolder_Usr\\r\\n\\r\\n #print configPagePolicyHolder_Usr.as_int( 'rowNumber')\\r\\n #print configPage.as_bool(\\\"hasPolicyHolder_Usr\\\")\\r\\n return configPage\",\n \"def showConf(self):\\n\\n return json.dumps(\\n self.config, sort_keys=True, indent=4, separators=(',', ': '))\",\n \"def conf():\\n global config\\n return config\",\n \"def _config(request):\\n return request.environ['adminish']\",\n \"def display_config_info():\\n print(\\\"Merlin Configuration\\\")\\n print(\\\"-\\\" * 25)\\n print(\\\"\\\")\\n\\n conf = default_config_info()\\n sconf = {}\\n excpts = {}\\n try:\\n conf[\\\"broker server\\\"] = broker.get_connection_string(include_password=False)\\n sconf[\\\"broker server\\\"] = broker.get_connection_string()\\n conf[\\\"broker ssl\\\"] = broker.get_ssl_config()\\n except Exception as e:\\n conf[\\\"broker server\\\"] = \\\"Broker server error.\\\"\\n excpts[\\\"broker server\\\"] = e\\n\\n try:\\n conf[\\\"results server\\\"] = results_backend.get_connection_string(include_password=False)\\n sconf[\\\"results server\\\"] = results_backend.get_connection_string()\\n conf[\\\"results ssl\\\"] = results_backend.get_ssl_config()\\n except Exception as e:\\n conf[\\\"results server\\\"] = \\\"No results server configured or error.\\\"\\n excpts[\\\"results server\\\"] = e\\n\\n print(tabulate(conf.items(), tablefmt=\\\"presto\\\"))\\n\\n if excpts:\\n print(\\\"\\\\nExceptions:\\\")\\n for k, v in excpts.items():\\n print(f\\\"{k}: {v}\\\")\\n\\n check_server_access(sconf)\",\n \"def configure(self, options, conf):\\n pass\",\n \"def configureAims(self):\\n self._config_ui.showDialog()\",\n \"def main(global_config, **settings):\\n with open('config.yaml', 'r') as fp:\\n local_config = yaml.safe_load(fp)['config']\\n Calendar.initialize(local_config['calendar'])\\n ICloud.initialize(local_config['icloud'])\\n Weather.initialize(local_config['weather'])\\n\\n config = Configurator(settings=settings)\\n config.include('pyramid_jinja2')\\n config.add_static_view('static', 'static', cache_max_age=3600)\\n config.add_route('home', '/')\\n config.add_route('oauthCallback', '/oauthCallback')\\n config.scan()\\n return config.make_wsgi_app()\",\n \"def _update_config(self):\\n \\n # app size\\n if self.IsMaximized():\\n config.SETTINGS['app_maximized'] = True\\n else:\\n size = self.GetSize()\\n config.SETTINGS['app_width'] = size[0]\\n config.SETTINGS['app_height'] = size[1]\\n config.SETTINGS['app_maximized'] = False\\n \\n # panes size\\n pane = self.AUIManager.GetPane(self._collections_view)\\n config.SETTINGS['collections_view_enabled'] = pane.IsShown()\\n if pane.IsShown():\\n config.SETTINGS['collections_view_width'] = pane.window.GetClientSize()[0]\\n \\n pane = self.AUIManager.GetPane(self._pdf_view)\\n config.SETTINGS['pdf_view_enabled'] = pane.IsShown()\\n if pane.IsShown():\\n config.SETTINGS['pdf_view_height'] = pane.window.GetClientSize()[1]\\n \\n pane = self.AUIManager.GetPane(self._details_view)\\n config.SETTINGS['details_view_enabled'] = pane.IsShown()\\n if pane.IsShown():\\n config.SETTINGS['details_view_width'] = pane.window.GetClientSize()[0]\\n \\n # article view\\n config.SETTINGS['articles_view_columns'] = self._articles_view.GetColumnsSettings()\",\n \"def render_settings_view():\\n return render_template('settings_screen.html', realsense_device_status=realsense_enabled, detector_enabled=enabled_detector)\",\n \"def configure(self):\\n self.configureLinks()\\n self.isConfigured = True\\n return\",\n \"def index():\\n response.view_title = myconf.get('app.name') + ' Home Page'\\n return dict(message='')\",\n \"def config(self, **kw):\\n self.cfg_fixture.config(**kw)\",\n \"def widget_load_config(self, plugman):\\r\\n pass\",\n \"def main(global_config, **settings):\\n config = Configurator(settings=settings)\\n config.include('pyramid_jinja2')\\n config.add_static_view('static', 'static', cache_max_age=3600)\\n config.add_route('home', '/')\\n config.add_route('total_users', '/totalusers/')\\n config.add_route('pageviews_weekly', '/pageviews/weekly/')\\n config.add_route('pageviews_monthly', '/pageviews/monthly/')\\n config.add_route('pageviews', '/pageviews/')\\n config.add_route('devices', '/devices/')\\n config.add_route('moreinfo', '/moreinfo/{profile_id}')\\n config.scan()\\n return config.make_wsgi_app()\",\n \"def main(global_config, **settings):\\n # add settings in here?\\n config = Configurator(settings=settings)\\n config.include('pyramid_jinja2')\\n config.include('.models')\\n config.include('.routes')\\n config.include('.security')\\n config.scan()\\n return config.make_wsgi_app()\",\n \"def init_config(self):\\n pass\",\n \"def __init__(self):\\n self.config = {}\",\n \"def __init__(self) -> None:\\n\\n self.config_keys = ['APPS_HOST', 'APPS_PORT']\\n super().__init__()\\n\\n self.APPS_HOST = str(self.APPS_HOST)\\n \\\"\\\"\\\"Host where the server will be served\\\"\\\"\\\"\\n\\n self.APPS_PORT = int(self.APPS_PORT)\\n \\\"\\\"\\\"Port where the server will be served\\\"\\\"\\\"\",\n \"def config(self) -> \\\"AutomationConfig\\\":\",\n \"def config(self) -> \\\"AutomationConfig\\\":\",\n \"async def _show_config_form(\\n self,\\n ):\\n return self.async_show_form(\\n step_id=\\\"user\\\",\\n data_schema=vol.Schema(\\n {\\n vol.Required(CONF_DEVICEID): str,\\n }\\n ),\\n errors=self._errors,\\n )\",\n \"def render_config(self, *args):\\n # ensure that a directory we need is there\\n ch_host.mkdir(APACHE_LOCATION)\\n\\n self.render_configs(self.string_templates.keys())\\n\\n core.templating.render(\\n source=APACHE_CONF_TEMPLATE,\\n template_loader=os_templating.get_loader(\\n 'templates/', self.release),\\n target='{}/{}'.format(APACHE_LOCATION, APACHE_CONF_TEMPLATE),\\n context=self.adapters_class(args, charm_instance=self),\\n )\\n\\n core.templating.render(\\n source=KERBEROS_CONF_TEMPLATE,\\n template_loader=os_templating.get_loader(\\n 'templates/', self.release),\\n target=\\\"/etc/krb5.conf\\\",\\n context=self.adapters_class(args, charm_instance=self),\\n )\",\n \"def configure_app(flask_app):\\n flask_app.config['RESTPLUS_SWAGGER_UI_DOC_EXPANSION'] = \\\\\\n settings.RESTPLUS_SWAGGER_UI_DOC_EXPANSION\\n flask_app.config['RESTPLUS_VALIDATE'] = \\\\\\n settings.RESTPLUS_VALIDATE\\n\\n flask_app.config['SQLALCHEMY_DATABASE_URI'] = \\\\\\n settings.SQLALCHEMY_DATABASE_URI\\n flask_app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = \\\\\\n settings.SQLALCHEMY_TRACK_MODIFICATIONS\",\n \"def configure_app(self):\\n self.app.route('/', callback=self.get_api)\",\n \"def init_app(self, app):\\n app.config.setdefault('YAWT_INDEXER_IFC', 'yawtext.whoosh')\\n app.config.setdefault('YAWT_INDEXER_WHOOSH_INFO_FIELDS', {})\\n app.config.setdefault('YAWT_INDEXER_WHOOSH_FIELDS',\\n {'content': TEXT()})\",\n \"def includeme(config): # pragma: no cover\\n config.add_route('home', '/')\\n config.add_view('kotti.views.edit.actions.contents',\\n route_name=u'home',\\n permission=u'view',\\n renderer='kotti:templates/edit/contents.pt',\\n )\",\n \"def config_show(cibfile=None):\\n return item_show(item=\\\"config\\\", item_id=None, extra_args=None, cibfile=cibfile)\",\n \"def config_skeleton():\\n config = Config()\\n config.set_to_default()\\n config.save()\",\n \"def main(global_config, **settings):\\n config = Configurator(settings=settings)\\n config.include('pyramid_chameleon')\\n config.add_static_view('static', 'static', cache_max_age=3600)\\n config.add_route('home', '/')\\n \\n config.registry.registerUtility(Basic(settings), INotification, 'basic')\\n config.registry.registerUtility(Twitter(settings), INotification, 'twitter')\\n\\n #config.registry.registerUtility(MailService(settings), INotification, 'mail')\\n\\n #config.registry.registerUtility(Facebook(settings), INotification, 'facebook')\\n\\n #config.registry.registerUtility(Twitter(settings), INotification, 'twitter')\\n\\n\\n config.scan()\\n return config.make_wsgi_app()\",\n \"async def settings(self, ctx: BBContext):\\n pass\",\n \"def config():\\n return Config()\",\n \"def config():\\n return Config()\",\n \"def configure(self, args):\\n pass\",\n \"def app():\\n # Add balloons animation when page opens\\n st.balloons()\\n\\n # Add title\\n st.title(\\\"Welcome to the about me page\\\")\\n \\n # Add Contact Details\\n st.header('Contact Us')\\n\\n # Add email\\n st.markdown('''### Name:\\n Khushal Mehrotra''')\\n\\n # Add name\\n st.markdown('''### Email:\\n kmehrotra07@gmail.com''')\\n\\n # Add github\\n st.markdown('''### GitHub: [Khushal Mehrotra](https://github.com/km-07)''')\\n\\n # Add linkedin\\n st.markdown('''### Linkedin: [Khushal Mehrotra](https://www.linkedin.com/in/khushal-mehrotra-5276091a9/)''')\",\n \"def configure(username, password, domain):\\n art = r'''\\nWelcome! __ ___. .__\\n_____ ____ ____ ____ __ __ _____/ |______ \\\\_ |__ | | ____\\n\\\\__ \\\\ _/ ___\\\\/ ___\\\\/ _ \\\\| | \\\\/ \\\\ __\\\\__ \\\\ | __ \\\\| | _/ __ \\\\\\n / __ \\\\\\\\ \\\\__\\\\ \\\\__( <_> ) | / | \\\\ | / __ \\\\| \\\\_\\\\ \\\\ |_\\\\ ___/\\n(____ /\\\\___ >___ >____/|____/|___| /__| (____ /___ /____/\\\\___ >\\n \\\\/ \\\\/ \\\\/ \\\\/ \\\\/ \\\\/ \\\\/\\n '''\\n click.secho(art, fg='blue')\\n Config(username=username, password=password, domain=domain)\",\n \"def main(global_config, **settings):\\n SETTINGS = settings\\n config = Configurator(settings=settings,)\\n config.include('pyramid_jinja2')\\n config.include('.models')\\n config.include('.cors')\\n config.add_cors_preflight_handler()\\n config.include('.routes')\\n config.include('.security')\\n config.include('..greggo')\\n config.add_static_view('static', path='repoll:static')\\n config.scan()\\n return config.make_wsgi_app()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7428864","0.73023534","0.73023534","0.7212623","0.7212623","0.6980258","0.68614465","0.68128914","0.6723701","0.66743726","0.66153085","0.66144913","0.6602604","0.6585713","0.6577487","0.6575138","0.6562094","0.6562094","0.65005267","0.64869064","0.64869064","0.64869064","0.64869064","0.64560694","0.6449693","0.64012897","0.6398548","0.6394537","0.63332653","0.6294562","0.62793666","0.6277572","0.6266794","0.6258634","0.62481946","0.6241684","0.61889994","0.61873275","0.61831844","0.61738265","0.61614203","0.6159278","0.61530185","0.61508507","0.61504227","0.6138133","0.6127724","0.61251837","0.61226195","0.61087793","0.6094087","0.60851794","0.6082965","0.6082427","0.6050426","0.60411185","0.6037555","0.6021287","0.6012311","0.60109514","0.60086876","0.6000778","0.59904605","0.59875673","0.59815973","0.598122","0.5980755","0.5969937","0.595853","0.5958452","0.59455705","0.5935184","0.5931379","0.5930646","0.59293646","0.59273773","0.5924168","0.5920476","0.59200096","0.5919641","0.59160644","0.5915969","0.59052014","0.5904809","0.5904809","0.58930886","0.58922136","0.58921427","0.5891684","0.5888778","0.58864933","0.587932","0.58709204","0.58694506","0.586161","0.58423406","0.58423406","0.5842238","0.58368176","0.58340067","0.58332205"],"string":"[\n \"0.7428864\",\n \"0.73023534\",\n \"0.73023534\",\n \"0.7212623\",\n \"0.7212623\",\n \"0.6980258\",\n \"0.68614465\",\n \"0.68128914\",\n \"0.6723701\",\n \"0.66743726\",\n \"0.66153085\",\n \"0.66144913\",\n \"0.6602604\",\n \"0.6585713\",\n \"0.6577487\",\n \"0.6575138\",\n \"0.6562094\",\n \"0.6562094\",\n \"0.65005267\",\n \"0.64869064\",\n \"0.64869064\",\n \"0.64869064\",\n \"0.64869064\",\n \"0.64560694\",\n \"0.6449693\",\n \"0.64012897\",\n \"0.6398548\",\n \"0.6394537\",\n \"0.63332653\",\n \"0.6294562\",\n \"0.62793666\",\n \"0.6277572\",\n \"0.6266794\",\n \"0.6258634\",\n \"0.62481946\",\n \"0.6241684\",\n \"0.61889994\",\n \"0.61873275\",\n \"0.61831844\",\n \"0.61738265\",\n \"0.61614203\",\n \"0.6159278\",\n \"0.61530185\",\n \"0.61508507\",\n \"0.61504227\",\n \"0.6138133\",\n \"0.6127724\",\n \"0.61251837\",\n \"0.61226195\",\n \"0.61087793\",\n \"0.6094087\",\n \"0.60851794\",\n \"0.6082965\",\n \"0.6082427\",\n \"0.6050426\",\n \"0.60411185\",\n \"0.6037555\",\n \"0.6021287\",\n \"0.6012311\",\n \"0.60109514\",\n \"0.60086876\",\n \"0.6000778\",\n \"0.59904605\",\n \"0.59875673\",\n \"0.59815973\",\n \"0.598122\",\n \"0.5980755\",\n \"0.5969937\",\n \"0.595853\",\n \"0.5958452\",\n \"0.59455705\",\n \"0.5935184\",\n \"0.5931379\",\n \"0.5930646\",\n \"0.59293646\",\n \"0.59273773\",\n \"0.5924168\",\n \"0.5920476\",\n \"0.59200096\",\n \"0.5919641\",\n \"0.59160644\",\n \"0.5915969\",\n \"0.59052014\",\n \"0.5904809\",\n \"0.5904809\",\n \"0.58930886\",\n \"0.58922136\",\n \"0.58921427\",\n \"0.5891684\",\n \"0.5888778\",\n \"0.58864933\",\n \"0.587932\",\n \"0.58709204\",\n \"0.58694506\",\n \"0.586161\",\n \"0.58423406\",\n \"0.58423406\",\n \"0.5842238\",\n \"0.58368176\",\n \"0.58340067\",\n \"0.58332205\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":11198,"cells":{"query":{"kind":"string","value":"Team members' stats page for app"},"document":{"kind":"string","value":"def team_members_stats(request):\n\n username = request.session.get('username', False)\n profile = request.session.get('profile', False)\n if (username):\n context = {'username': username, 'profile': profile}\n return render(request, 'MedTAG_sket_dock_App/index.html', context)\n else:\n return redirect('MedTAG_sket_dock_App:login')"},"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 show_stats(self):\n print(self.team_one.name + \" stats: \")\n self.team_one.stats()\n print(self.team_two.name + \" stats: \")\n self.team_two.stats()","def info():\n print 'Loading info page'\n\n team_list = datastore.get_all_teams(engine)\n\n return render_template('info.html', rows=team_list)","def baron_stats(request, name):\n assert isinstance(request, HttpRequest)\n query = Baron_Players.objects.filter(summoner_name=name)\n \n return render(\n request,\n 'AscensionESports_Baseline/player_stats.html',\n {\n 'background': getBaronBackground(),\n 'color': getBaronColor(),\n 'title':'Baron League Stats',\n 'query_results': query,\n 'year': datetime.now().year,\n }\n )","def stats(request):\n \n return render(request, 'stats.html')","def showStats(population, masterList, index):\n count = 0\n if index == \"all\":\n for team in population:\n print (\"Team at index\", count)\n print(\"Tot Avg\", team.totAvg)\n print(\"Tot Runs\", team.totRuns)\n print(\"Tot HRs\", team.totHr)\n print(\"Tot RBIs\", team.totRbi)\n print(\"Tot SB\", team.totSb)\n print(\"Tot points\", team.points, '\\n')\n count += 1\n else:\n print(\"Team at index\", index)\n print(\"Tot Avg\", population[index].totAvg)\n print(\"Tot Runs\", population[index].totRuns)\n print(\"Tot HRs\", population[index].totHr)\n print(\"Tot RBIs\", population[0].totRbi)\n print(\"Tot SB\", population[0].totSb)\n print(\"Tot points\", population[0].points, '\\n')","def teams(request):\n if request.method == 'GET':\n # Validates if the member exists. If members logon successfully they are created on the teams-app.\n email = request.session.get('email', None)\n full_name = request.session.get('full_name', None)\n try: \n member = Member.objects.get(email=email)\n except ObjectDoesNotExist:\n member = Member(email=email, full_name=full_name)\n member.save()\n\n member_teams = member.teams.all() \n context = {\n 'email': email,\n 'full_name': full_name,\n 'member_teams': member_teams\n }\n return render(request, 'teamsapp/teams.html', context)\n else:\n raise Http404('Not allowed')","async def stats(ctx):\n pythonVersion = platform.python_version()\n dpyVersion = discord.__version__\n serverCount = len(bot.guilds)\n memberCount = len(set(bot.get_all_members()))\n\n embed = discord.Embed(\n title=f\"{bot.user.name} Stats\",\n description=\"\\uFEFF\",\n colour=ctx.author.colour,\n timestamp=ctx.message.created_at,\n )\n\n embed.add_field(name=\"Bot Version:\", value=\"0.0.1\")\n embed.add_field(name=\"Python Version:\", value=pythonVersion)\n embed.add_field(name=\"Discord.Py Version\", value=dpyVersion)\n embed.add_field(name=\"Total Guilds:\", value=serverCount)\n embed.add_field(name=\"Total Users:\", value=memberCount)\n embed.add_field(name=\"Bot Developers:\", value=\"<@271612318947868673>\")\n\n embed.set_footer(text=f\"Carpe Noctem | {bot.user.name}\")\n embed.set_author(name=bot.user.name, icon_url=bot.user.avatar_url)\n\n await ctx.send(embed=embed)","async def view_stats(self, ctx):\n app_info = await self.bot.application_info()\n total_ram = (psutil.virtual_memory().total >> 30) + 1\n embed = discord.Embed(\n title=\"Bot Stats\",\n description=f\"Running on a dedicated server with {total_ram}GB RAM \\n provided by RandomGhost#0666.\",\n )\n\n embed.add_field(name=\"**__General Info__**\", inline=False, value=\"\\u200b\")\n embed.add_field(name=\"Latency\", value=f\"{self.bot.latency*1000:.03f}ms\")\n embed.add_field(name=\"Guild Count\", value=f\"{len(self.bot.guilds):,}\")\n embed.add_field(name=\"User Count\", value=f\"{len(self.bot.users):,}\")\n\n embed.add_field(name=\"**__Technical Info__**\", inline=False, value=\"\\u200b\")\n embed.add_field(name=\"System CPU Usage\", value=f\"{psutil.cpu_percent():.02f}%\")\n embed.add_field(name=\"System RAM Usage\", value=f\"{psutil.virtual_memory().used/1048576:.02f} MB\")\n embed.add_field(name=\"System Uptime\", value=f\"{timedelta(seconds=int(time.time() - psutil.boot_time()))}\")\n embed.add_field(name=\"Bot CPU Usage\", value=f\"{process.cpu_percent():.02f}%\")\n embed.add_field(name=\"Bot RAM Usage\", value=f\"{process.memory_info().rss / 1048576:.02f} MB\")\n embed.add_field(name=\"Bot Uptime\", value=f\"{timedelta(seconds=int(time.time() - process.create_time()))}\")\n\n embed.add_field(name=\"**__Links__**\", inline=False, value=\"\\u200b\")\n embed.add_field(name=\"Support Server\", value=\"[https://discord.swaglyrics.dev](https://discord.swaglyrics.dev)\")\n embed.add_field(name=\"Invite\", value=\"[https://invite.swaglyrics.dev](https://invite.swaglyrics.dev)\")\n embed.add_field(\n name=\"Source\",\n value=\"[https://swaglyrics.dev/SwagLyrics-Discord-Bot]\" \"(https://swaglyrics.dev/SwagLyrics-discord-bot)\",\n )\n\n embed.set_footer(\n text=f\"Made by {app_info.owner} • {self.bot.get_user(512708394994368548)}\",\n icon_url=[\n app_info.owner.avatar_url_as(size=128),\n self.bot.get_user(512708394994368548).avatar_url_as(size=128),\n ][getrandbits(1)],\n ) # randomize clash or flabbet avatar\n\n await ctx.send(embed=embed)","def index():\r\n\r\n title = \"Global Statistics\"\r\n\r\n n_auth = n_auth_users()\r\n\r\n n_anon = n_anon_users()\r\n\r\n n_total_users = n_anon + n_auth\r\n\r\n n_published_apps = cached_apps.n_published()\r\n n_draft_apps = cached_apps.n_draft()\r\n n_total_apps = n_published_apps + n_draft_apps\r\n\r\n n_tasks = n_tasks_site()\r\n\r\n n_task_runs = n_task_runs_site()\r\n\r\n top5_apps_24_hours = get_top5_apps_24_hours()\r\n\r\n top5_users_24_hours = get_top5_users_24_hours()\r\n\r\n locs = get_locs()\r\n\r\n show_locs = False\r\n if len(locs) > 0:\r\n show_locs = True\r\n\r\n stats = dict(n_total_users=n_total_users, n_auth=n_auth, n_anon=n_anon,\r\n n_published_apps=n_published_apps,\r\n n_draft_apps=n_draft_apps,\r\n n_total_apps=n_total_apps,\r\n n_tasks=n_tasks,\r\n n_task_runs=n_task_runs)\r\n\r\n users = dict(label=\"User Statistics\",\r\n values=[\r\n dict(label='Anonymous', value=[0, n_anon]),\r\n dict(label='Authenticated', value=[0, n_auth])])\r\n\r\n apps = dict(label=\"Apps Statistics\",\r\n values=[\r\n dict(label='Published', value=[0, n_published_apps]),\r\n dict(label='Draft', value=[0, n_draft_apps])])\r\n\r\n tasks = dict(label=\"Task and Task Run Statistics\",\r\n values=[\r\n dict(label='Tasks', value=[0, n_tasks]),\r\n dict(label='Answers', value=[1, n_task_runs])])\r\n\r\n return render_template('/stats/global.html', title=title,\r\n users=json.dumps(users),\r\n apps=json.dumps(apps),\r\n tasks=json.dumps(tasks),\r\n locs=json.dumps(locs),\r\n show_locs=show_locs,\r\n top5_users_24_hours=top5_users_24_hours,\r\n top5_apps_24_hours=top5_apps_24_hours,\r\n stats=stats)","def index():\r\n if current_user.is_authenticated():\r\n user_id = current_user.id\r\n else:\r\n user_id = 'anonymous'\r\n top_users = cached_users.get_leaderboard(current_app.config['LEADERBOARD'],\r\n user_id=user_id)\r\n\r\n return render_template('/stats/index.html', title=\"Community Leaderboard\",\r\n top_users=top_users)","def fetch_teams_stats():\n teams_scraper = TeamStatsScraper(API_URL, API_HEADERS)\n result = teams_scraper.save_objects()\n return result","def showUserStats(self) :\n self.getAllStats()\n self.getNbTotalLevelsPlayed()\n Scenario.messageAllStats(self.level_history[0].created_at)\n self.showBestStats()\n self.showWorstStats()\n self.showAverageStats()","async def stats(self, ctx: commands.Context):\n users = len(self.bot.users)\n guilds = len(self.bot.guilds)\n\n embed = Embed(color=Color.dark_green())\n embed.add_field(name=\"Guilds\", value=guilds)\n embed.add_field(name=\"Users\", value=users)\n embed.set_thumbnail(url=ctx.guild.me.avatar_url)\n\n await ctx.send(embed=embed)","def statistics():\n return render_template('statistics.html'), 200","def stats_page():\n import alltheitems.stats\n return alltheitems.stats.index()","def overview(request):\n LOGGER.info('Rendering WMT16 HIT overview for user \"{0}\".'.format(\n request.user.username or \"Anonymous\"))\n \n # Re-initialise random number generator.\n seed(None)\n \n # Collect available language pairs for the current user.\n language_codes = set([x[0] for x in LANGUAGE_PAIR_CHOICES])\n language_pairs = request.user.groups.filter(name__in=language_codes)\n \n # Collect available annotation projects for the current user.\n annotation_projects = request.user.project_set.all()\n \n hit_data = []\n total = [0, 0, 0]\n\n for language_pair in language_pairs:\n for annotation_project in annotation_projects:\n hit = _compute_next_task_for_user(request.user, annotation_project, language_pair)\n user_status = HIT.compute_status_for_user(request.user, annotation_project, language_pair)\n for i in range(3):\n total[i] = total[i] + user_status[i]\n \n if hit:\n # Convert status seconds back into datetime.time instances.\n for i in range(2):\n user_status[i+1] = seconds_to_timedelta(int(user_status[i+1]))\n \n hit_data.append(\n (hit.get_language_pair_display(), hit.get_absolute_url(),\n hit.hit_id, user_status, annotation_project)\n )\n \n # Convert total seconds back into datetime.timedelta instances.\n total[1] = seconds_to_timedelta(int(total[2]) / float(int(total[0]) or 1))\n \n # Remove microseconds to get a nicer timedelta rendering in templates.\n total[1] = total[1] - timedelta(microseconds=total[1].microseconds)\n \n total[2] = seconds_to_timedelta(int(total[2]))\n \n groups = _identify_groups_for_user(request.user)\n group = None\n if len(groups) > 1:\n LOGGER.debug(u'User \"{0}\" assigned to multiple annotation groups: {1}'.format(\n request.user.username or u'Anonymous',\n u', '.join([x.name for x in groups]))\n )\n group = groups[0]\n \n if group is not None:\n group_name = group.name\n group_status = HIT.compute_status_for_group(group)\n for i in range(2):\n group_status[i+1] = seconds_to_timedelta(int(group_status[i+1]))\n \n else:\n group_status = None\n group_name = None\n \n LOGGER.debug(u'\\n\\nHIT data for user \"{0}\":\\n\\n{1}\\n'.format(\n request.user.username or \"Anonymous\",\n u'\\n'.join([u'{0}\\t{1}\\t{2}\\t{3}'.format(*x) for x in hit_data])))\n\n # Compute admin URL for super users.\n admin_url = None\n if request.user.is_superuser:\n admin_url = reverse('admin:index')\n \n dictionary = {\n 'active_page': \"OVERVIEW\",\n 'hit_data': hit_data,\n 'total': total,\n 'group_name': group_name,\n 'group_status': group_status,\n 'admin_url': admin_url,\n 'title': 'WMT16 Dashboard',\n 'annotation_groups': [x.name for x in groups],\n }\n dictionary.update(BASE_CONTEXT)\n \n LOGGER.info(dictionary.values())\n \n return render(request, 'wmt16/overview.html', dictionary)","def test_05_app_stats_index(self):\r\n # As Anonymou user\r\n url = \"/app/%s/stats\" % self.app_short_name\r\n res = self.app.get(url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"App Stats page should be shown to anonymous users\"\r\n assert dom.find(id='enforce_privacy') is None, err_msg\r\n # As Authenticated user but NOT ADMIN\r\n self.signin()\r\n res = self.app.get(url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"App Stats page should be shown to authenticated users\"\r\n assert dom.find(id='enforce_privacy') is None, err_msg\r\n self.signout\r\n # As Authenticated user but ADMIN\r\n self.signin(email=self.root_addr, password=self.root_password)\r\n res = self.app.get(url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"App Stats page should be shown to admin users\"\r\n assert dom.find(id='enforce_privacy') is None, err_msg\r\n self.signout()","def test_05_app_stats_index(self):\r\n # As Anonymou user\r\n url = \"/app/%s/stats\" % self.app_short_name\r\n res = self.app.get(url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"App Stats page should not be shown to anonymous users\"\r\n assert dom.find(id='enforce_privacy') is not None, err_msg\r\n # As Authenticated user but NOT ADMIN\r\n self.signin()\r\n res = self.app.get(url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"App Stats page should not be shown to authenticated users\"\r\n assert dom.find(id='enforce_privacy') is not None, err_msg\r\n self.signout\r\n # As Authenticated user but ADMIN\r\n self.signin(email=self.root_addr, password=self.root_password)\r\n res = self.app.get(url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"App Stats page should be shown to admin users\"\r\n assert dom.find(id='enforce_privacy') is None, err_msg\r\n self.signout()","async def cmd_stats(self, ctx):\n\n guild = ctx.guild\n members = await guild.fetch_members().flatten()\n answer = f''\n embed = discord.Embed(title=\"Statistiken\",\n description=f'Wir haben aktuell {len(members)} Mitglieder auf diesem Server, verteilt auf folgende Rollen:')\n\n for role in guild.roles:\n if not self.get_key(role):\n continue\n role_members = role.members\n if len(role_members) > 0 and not role.name.startswith(\"Farbe\"):\n embed.add_field(name=role.name, value=f'{len(role_members)} Mitglieder', inline=False)\n\n no_role = 0\n for member in members:\n # ToDo Search for study roles only!\n if len(member.roles) == 1:\n no_role += 1\n\n embed.add_field(name=\"\\u200B\", value=\"\\u200b\", inline=False)\n embed.add_field(name=\"Mitglieder ohne Rolle\", value=str(no_role), inline=False)\n\n await ctx.channel.send(answer, embed=embed)","def test_04_global_stats_index(self):\r\n # As Anonymou user\r\n url = \"/stats\"\r\n res = self.app.get(url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"Stats page should be shown to anonymous users\"\r\n assert dom.find(id='enforce_privacy') is None, err_msg\r\n # As Authenticated user but NOT ADMIN\r\n self.signin()\r\n res = self.app.get(url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"Stats page should be shown to authenticated users\"\r\n assert dom.find(id='enforce_privacy') is None, err_msg\r\n self.signout\r\n # As Authenticated user but ADMIN\r\n self.signin(email=self.root_addr, password=self.root_password)\r\n res = self.app.get(url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"Stats page should be shown to admin users\"\r\n assert dom.find(id='enforce_privacy') is None, err_msg\r\n self.signout()","def analyze_team_page(auth, db, id_team):\n session = login(auth)\n\n url = \"http://es.ibasketmanager.com/equipo.php?id=\" + id_team\n r = session.get(url)\n load_status = 0\n while load_status != 200:\n load_status = r.status_code\n \n print(show(\"profile\") + \" > Analizando perfil del equipo\")\n\n soup = BeautifulSoup(r.content, \"html.parser\")\n \n trs2 = soup.find_all(\"tr\", {\"class\": \"tipo2\"})\n\n id_user = trs2[0].find(\"a\")[\"href\"].split(\"=\")[1]\n streak = trs2[2].find_all(\"td\")[1].text\n club_seats = trs2[3].find_all(\"td\")[1].text.replace(\".\",\"\").strip()\n ranking = trs2[4].find_all(\"td\")[1].text.replace(\"Ranking\",\"\").strip()\n \n trs1 = soup.find_all(\"tr\", {\"class\": \"tipo1\"})\n fans = trs1[3].find_all(\"td\")[1].text.replace(\".\",\"\").strip()\n\n return [id_user, club_seats, fans, ranking, streak]","def status(request):\n LOGGER.info('Rendering WMT16 HIT status for user \"{0}\".'.format(\n request.user.username or \"Anonymous\"))\n \n if not STATUS_CACHE.has_key('global_stats'):\n update_status(key='global_stats')\n \n if not STATUS_CACHE.has_key('language_pair_stats'):\n update_status(key='language_pair_stats')\n \n if not STATUS_CACHE.has_key('group_stats'):\n update_status(key='group_stats')\n \n if not STATUS_CACHE.has_key('user_stats'):\n update_status(key='user_stats')\n \n # Compute admin URL for super users.\n admin_url = None\n if request.user.is_superuser:\n admin_url = reverse('admin:index')\n \n dictionary = {\n 'active_page': \"STATUS\",\n 'global_stats': STATUS_CACHE['global_stats'],\n 'language_pair_stats': STATUS_CACHE['language_pair_stats'],\n 'group_stats': STATUS_CACHE['group_stats'],\n 'user_stats': STATUS_CACHE['user_stats'],\n 'clusters': RANKINGS_CACHE.get('clusters', []),\n 'admin_url': admin_url,\n 'title': 'WMT16 Status',\n }\n dictionary.update(BASE_CONTEXT)\n \n return render(request, 'wmt16/status.html', dictionary)","def test_04_global_stats_index(self):\r\n # As Anonymou user\r\n url = \"/stats\"\r\n res = self.app.get(url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"Stats page should not be shown to anonymous users\"\r\n assert dom.find(id='enforce_privacy') is not None, err_msg\r\n # As Authenticated user but NOT ADMIN\r\n self.signin()\r\n res = self.app.get(url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"Stats page should not be shown to authenticated users\"\r\n assert dom.find(id='enforce_privacy') is not None, err_msg\r\n self.signout\r\n # As Authenticated user but ADMIN\r\n self.signin(email=self.root_addr, password=self.root_password)\r\n res = self.app.get(url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"Stats page should be shown to admin users\"\r\n assert dom.find(id='enforce_privacy') is None, err_msg\r\n self.signout()","def teams(teamid):\n team_summary = team.TeamSummary(teamid)\n team_summary_info = team_summary.info()\n team_season_ranks = team_summary.season_ranks()\n\n team_common_roster = team.TeamCommonRoster(teamid)\n roster = team_common_roster.roster()\n coaches = team_common_roster.coaches()\n\n season = team_summary_info[0][\"SEASON_YEAR\"]\n\n team_game_log = team.TeamGameLogs(teamid,\n season=season)\n team_games = team_game_log.info()\n\n playoffs_teamgamelogs = team.TeamGameLogs(teamid,\n season=season,\n season_type=\"Playoffs\")\n playoffs_team_games = playoffs_teamgamelogs.info()\n\n team_season = team.TeamSeasons(teamid)\n team_season_info = team_season.info()\n\n for i in team_season_info:\n if (i[\"YEAR\"] == season):\n current_season_info = i\n\n return render_template(\"teams.html\",\n title=team_summary_info[0][\"TEAM_CITY\"] + \" \" + team_summary_info[0][\"TEAM_NAME\"],\n teamid=teamid,\n team_summary_info=team_summary_info,\n team_season_ranks=team_season_ranks,\n season=season,\n team_games=team_games,\n playoffs_team_games=playoffs_team_games,\n team_season=team_season_info,\n roster=roster,\n coaches=coaches,\n current_season_info=current_season_info,\n team_img=TEAM_ID_DATA)","def friend_stats(request):\n \n r = {}\n fb_ids = FacebookProfile.objects.all().values(\"facebook_id\") \n for u in FacebookProfile.objects.all():\n friends = Friends.objects.filter(facebook_id__in=fb_ids)\n num_friends = Friends.objects.filter(facebook_id=u.facebook_id, friends__in=friends).count()\n participant = OTNUser.objects.get(facebook_profile__facebook_id=u.facebook_id)\n r[u.facebook_id]=\"%s (%d): %d\"%(participant.name, participant.id, num_friends)\n\n return JSONHttpResponse(r)","def stats(request):\n stats = []\n activeProject = None\n activity = get_activity()\n if activity:\n activeProject = activity.project\n projects = Project.objects.filter(company__id=1).order_by('name')\n for project in projects:\n isCurrent = (activeProject != None) and (project.id == activeProject.id)\n # If this is NOT the currently selected project...\n if (not isCurrent) or (not activeProject):\n # If this project is password protected, skip it.\n if (project.password != None) and (len(project.password) > 0):\n continue\n sessions = Session.objects.filter(project=project,\n endtime__gt=F('starttime') +\n timedelta(minutes=3))\n files = File.objects.filter(project=project)\n fileactions = Fileaction.objects.filter(file__in=files)\n events = Event.objects.filter(session__in=sessions)\n sQuery = {\n 'avg': 'SUM(TIMESTAMPDIFF(SECOND, starttime, endtime)) / COUNT(*)',\n 'min': 'MIN(TIMESTAMPDIFF(SECOND, starttime, endtime))',\n 'max': 'MAX(TIMESTAMPDIFF(SECOND, starttime, endtime))',\n 'count': 'COUNT(*)'\n }\n sessions = sessions.extra(select=sQuery)\n sessions = sessions.values_list('avg', 'min', 'max', 'count').get()\n session_average_duration = 0\n session_min_duration = 0\n session_max_duration = 0\n if sessions[0] is not None:\n session_average_duration = int(sessions[0])\n if sessions[1] is not None:\n session_min_duration = int(sessions[1])\n if sessions[2] is not None:\n session_max_duration = int(sessions[2])\n session_count = sessions[3]\n statsdata = {\n 'selected': isCurrent,\n 'name': project.name,\n 'session_average_duration': session_average_duration,\n 'session_min_duration': session_min_duration,\n 'session_max_duration': session_max_duration,\n 'session_count': session_count,\n 'file_count': files.count(),\n 'fileaction_count': fileactions.count(),\n 'event_count': events.count()\n }\n stats.append(statsdata)\n return render_to_response(\n 'stats.html',\n {\n 'stats': stats,\n 'tab': 'stats'\n },\n context_instance=RequestContext(request)\n )","def view_team_page(request, team_pk):\n\t\n\tselected_team = ChallengeTeam.objects.get(pk = team_pk)\n\t\n\tusers = selected_team.team_members.all()\n\t\n\tteam_name = selected_team.team_name\n\t\n\tall_results = get_team_results(users, selected_team.challenge.schedule)\n\tteam_consistency = all_results[\"consistency\"]\n\tteam_completion = all_results[\"completion\"]\n\t\n\tmember_names = []\n\tfor usr in users:\n\t\tprint usr.first_name + \" \" + usr.last_name\n\t\tmember_names.append(usr.first_name + \" \" + usr.last_name)\n\t\t\n\tjoin_control = \"join\"\n\tif(request.user in selected_team.team_members.all()):\n\t\tjoin_control = \"leave\"\n\telif(selected_team.challenge.invite_only and not request.user in selected_team.invited.all()):\n\t\tjoin_control = \"invite\"\n\t\n\tcontext = RequestContext(request, { \"team_pk\" : team_pk, \"name\" : team_name, \"members\" : member_names, \"consistency\" : team_consistency, \"completion\" : team_completion, \"join_control\" : join_control, \"messages\" : messages })\n\treturn render_to_response(\"encourage/view_team.html\", context)","def teamstats(team_id, conn, curr):\n \n title = \"Team Points Breakdown for Team {}\".format(team_id)\n cmd = \"\"\"\n SELECT user_id, event_id, team_id, score FROM PlayerStats\n WHERE team_id={}\n \"\"\".format(team_id)\n curr.execute(cmd)\n teamscores = curr.fetchall()\n player_dict = {}\n event_set = set()\n data = []\n for player in teamscores:\n user_id = player[0]\n event_id = player[1]\n score = player[3]\n if user_id in player_dict:\n player_dict[user_id][event_id] = score\n else:\n player_dict[user_id] = {event_id: score}\n event_set.add(event_id)\n\n color_index = 0\n for player, score in player_dict.items():\n scores = []\n for event_id in event_set:\n scores.append(player_dict[player][event_id])\n width = 0\n if len(event_set) == 1:\n data.append(\n Bar(\n x=['event {}'.format(str(event_id)) for event_id in list(event_set)],\n y=scores,\n name='Player ID {}'.format(player),\n width=.4,\n marker=dict(\n color=COLORS[color_index]\n )\n )\n )\n else:\n data.append(\n Bar(\n x=['event {}'.format(str(event_id)) for event_id in list(event_set)],\n y=scores,\n name='Player ID {}'.format(player),\n marker=dict(\n color=COLORS[color_index]\n )\n )\n )\n color_index += 1\n\n layout = Layout(\n barmode='stack',\n title=title,\n yaxis=dict(\n title='Points'\n ),\n xaxis=dict(\n title='Events'\n )\n )\n fig = plotly.graph_objs.Figure(data=data, layout=layout)\n return fig","def team_tester(request):\n\n\t# Look for the team size entered by the user\n\tteam_size = int(request.GET.get('team_size', False))\n\n\t# If user has entered information...\n\tif team_size:\n\n\t\t# Get the rest of the information from the form\n\t\tscores_up = int(request.GET.get('scores_up', False))\n\t\tscores_count = int(request.GET.get('scores_count', False))\n\t\tsumstat = request.GET.get('sumstat', False)\n\t\ttime = request.GET.get('time', False)\n\t\tgymnast_list = []\n\t\tfor i in range(1, team_size+1):\n\t\t\tgymnast_search_id = \"gymnast_search\" + str(i)\n\t\t\tgymnast_list.append(request.GET.get(gymnast_search_id, False))\n\n\t\t# Set the date range \n\t\tnow = datetime.datetime.now()\n\t\tif time==\"year\":\n\t\t\tdate_range = [now-relativedelta(years=1), now]\n\t\telif time == \"season\":\n\t\t\tdate_range = [datetime.date(2019, 10, 13), now] # Since last world championships\n\t\telse:\n\t\t\tdate_range = [datetime.date(2016, 8, 21), now] # Since last olympics\n\n\t\t# Loop through the list of gymnasts and get scores\n\t\ttable_data = []\n\t\tfor gymnast in gymnast_list:\n\t\t\tgymnast = Gymnast.objects.get(name=gymnast)\n\t\t\tthis_gymnast_scores = []\n\t\t\tthis_gymnast_scores.append(gymnast)\n\t\t\tfor sub_event in [\"VT\", \"UB\", \"BB\", \"FX\"]:\n\t\t\t\tscores = Score.objects.filter(gymnast=gymnast, \n\t\t\t\t\tmeet__in=Meet.objects.filter(start_date__range=date_range), event__in=Event.objects.filter(name=sub_event), score_num=1)\n\t\t\t\tif scores.count() > 0:\n\t\t\t\t\tif sumstat == \"avg\":\n\t\t\t\t\t\tscores_sumstat = scores.aggregate(Avg('score'))['score__avg']\n\t\t\t\t\telif sumstat == \"max\":\n\t\t\t\t\t\tscores_sumstat = scores.aggregate(Max('score'))['score__max']\n\t\t\t\telse:\n\t\t\t\t\tscores_sumstat = \"\"\n\t\t\t\tthis_gymnast_scores.append(scores_sumstat)\n\t\t\ttable_data.append(this_gymnast_scores)\n\n\t\t# Select the scores that go up and the scores that count\n\t\tfor i in range(1, 5):\n\t\t\t# Get the list of all scores on this event\n\t\t\tevent_scores = [col[i] for col in table_data]\n\t\t\t# Get the sort order of these scores\n\t\t\tsort_order = np.argsort(np.argsort(event_scores)) # See https://github.com/numpy/numpy/issues/8757\n\t\t\tsort_order = team_size - 1 - sort_order\n\t\t\t# Replace each score with a tuple of the score and the class that we'll use for the td of each score\n\t\t\tfor j, row in enumerate(table_data):\n\t\t\t\t# For scores that count\n\t\t\t\tif sort_order[j] < scores_count:\n\t\t\t\t\ttable_data[j][i] = [table_data[j][i], \"counts\"]\n\t\t\t\telif sort_order[j] < scores_up:\n\t\t\t\t\ttable_data[j][i] = [table_data[j][i], \"up\"]\n\t\t\t\telse:\n\t\t\t\t\ttable_data[j][i] = [table_data[j][i], \"not_used\"]\n\n\t\t# Calculate total row\n\t\ttotal_row = [\"Team Total\", 0, 0, 0, 0]\n\t\tfor row in table_data:\n\t\t\tfor i in range(1, 5):\n\t\t\t\tif row[i][1] == \"counts\" and (not isinstance(row[i][0], str)):\n\t\t\t\t\ttotal_row[i] = total_row[i] + row[i][0]\n\t\ttable_data.append(total_row)\n\t\tteam_total = sum(total_row[1:5])\n\t\tprint(table_data)\n\telse:\n\t\tteam_size=5\n\t\tscores_up=4\n\t\tscores_count=3\n\t\tsumstat = \"avg\"\n\t\ttime = \"year\"\n\t\tgymnast_list = []\n\t\ttable_data = []\n\t\tteam_total = \"\"\n\n\n\n\tcontext = {\n\t\t'team_size': team_size,\n\t\t'scores_up': scores_up,\n\t\t'scores_count': scores_count,\n\t\t'sumstat': sumstat,\n\t\t'time': time,\n\t\t'gymnast_list': gymnast_list,\n\t\t'table_data': table_data,\n\t\t'team_total': team_total,\n\t}\n\n\treturn render(request, 'team_tester.html', context=context)","def team_list(request):\n template = loader.get_template('team/team_list.html')\n teams_list = Team.objects.all().order_by('name')\n\n if not request.user.is_authenticated:\n team = None\n else:\n team = request.user.profile.team\n\n paginator = Paginator(teams_list, 6)\n\n page = request.GET.get('page')\n try:\n teams = paginator.page(page)\n except PageNotAnInteger:\n # If page is not an integer, deliver first page.\n teams = paginator.page(1)\n except EmptyPage:\n # If page is out of range (e.g. 9999), deliver last page of results.\n teams = paginator.page(paginator.num_pages)\n\n context = {\n 'teams': teams,\n 'team': team\n }\n\n return CustomHttpResponse.send(template, context, request)","async def stats(self, ctx: Message):\n\t\tawait self.open_account(ctx.author.id, ctx.author.username)\n\t\tuserid = ctx.author.id\n\t\tusers = await self.get_stats_data()\n\n\t\trancmds = users[str(userid)][\"rancmd\"]\n\t\txp = users[str(userid)][\"xp\"]\n\t\tmsgs = users[str(userid)][\"sentmsgs\"]\n\t\twhisperto = [ctx.author.id]\n\t\tawait self.send(message=f\"{ctx.author.mention} Here are your stats! • Ran {rancmds} DogeBoss commands • XP: {xp} • Sent {msgs} messages\", whisper=whisperto)","def team_details(request, id):\n template = loader.get_template('team/details.html')\n\n try:\n team = Team.objects.get(pk=id)\n team_members = User.objects.filter(profile__team=team)\n\n context = {\n 'team_name': team.name,\n 'team_info': team.information,\n 'team_logo': team.logo,\n 'team_members': team_members,\n 'days': Information.getDaysToContest()\n }\n\n except Team.DoesNotExist:\n context = None\n\n return CustomHttpResponse.send(template, context, request)","def get_team_stats(self):\n if self.team_id is None:\n raise RuntimeError(\"Please set team_id before calling\"\n \"get_team_stats\")\n url = '%s/api/v4/teams/%s/stats' % (self.server_url, self.team_id)\n response = self._request(self._session.get, url)\n return json.loads(response.content)","def index(request):\r\n return render(request, 'team_tasks_managers/index.html')","def statistics(self, request):\n context = {\"uid\": request.session.get(\"uid\", None),\n \"is_auth\": request.session.get(\"is_auth\", False),\n \"is_daily\": request.session.get(\"is_daily\", False),\n \"is_leader\": request.session.get(\"is_leader\", False),\n }\n\n context.update({\"statistics\": self.utils.get_statistics()})\n # \"dates\": self.utils.get_artefacts_dates()})\n return render_to_response(\"crisis/statistics.html\",\n context,\n context_instance=RequestContext(request))","def getTeamStat(self, year = 2014):\r\n \r\n year_next = (year % 100) + 1\r\n season = str(year) + '-' + str(year_next)\r\n \r\n stat_url = 'http://stats.nba.com/stats/leaguedashteamstats?Conference=&'\\\r\n 'DateFrom=&DateTo=&Division=&GameScope=&GameSegment=&'\\\r\n 'LastNGames=0&LeagueID=00&Location=&MeasureType=Base&'\\\r\n 'Month=0&OpponentTeamID=0&Outcome=&PORound=0&PaceAdjust=N&'\\\r\n 'PerMode=PerGame&Period=0&PlayerExperience=&PlayerPosition=&'\\\r\n 'PlusMinus=N&Rank=N&Season=' + season + '&SeasonSegment=&'\\\r\n 'SeasonType=Regular+Season&ShotClockRange=&StarterBench=&'\\\r\n 'TeamID=0&VsConference=&VsDivision='\r\n \r\n response = requests.get(stat_url)\r\n data = json.loads(response.text)\r\n \r\n headers = data['resultSets'][0]['headers']\r\n stat_data = data['resultSets'][0]['rowSet']\r\n df = pd.DataFrame(stat_data,columns=headers) \r\n \r\n team_df = df[[\"TEAM_ID\",\"TEAM_NAME\",\"GP\",\"W\",\"L\",\"W_PCT\",\"MIN\",\"FGM\",\r\n \"FGA\",\"FG_PCT\",\"FG3M\",\"FG3A\",\"FG3_PCT\",\"FTM\",\"FTA\",\"FT_PCT\",\r\n \"OREB\",\"DREB\",\"REB\",\"AST\",\"TOV\",\"STL\",\"BLK\",\"BLKA\",\"PF\",\r\n \"PFD\",\"PTS\",\"PLUS_MINUS\"]]\r\n \r\n return team_df","def userstats(request):\r\n with ReqAuthorize(request):\r\n user = UserMgr.get(username=request.user.username)\r\n return {\r\n 'user': user,\r\n 'username': user.username,\r\n }","def get_teams():","def fullleaderboard (request):\n # Define views here\n score_submit = EventEntryModel.objects.exclude(winner__isnull=True).count()\n active_players = PlayerModel.objects.all()\n\n loaded_points = list(EventEntryModel.objects.aggregate(Sum('points')).values())[0]\n awarded_points = list(EventEntryModel.objects.exclude(winner__isnull=True).aggregate(Sum('points')).values())[0]\n\n context = {\n 'score_submit': score_submit,\n 'active_players': active_players,\n 'loaded_points': loaded_points,\n 'awarded_points': awarded_points,\n }\n\n return render(request, 'fullLeaderboard.html', context=context)","def display_user_stats(self):\n\n self.user_frame = tk.LabelFrame(\n self.stats_frame, text=\"User Types\", padx=5, pady=5\n )\n self.user_frame.grid(row=3, padx=5, pady=5, sticky=\"w\")\n self.user_stats_data = tk.Label(self.user_frame, justify=\"left\")\n self.user_stats_data.pack()\n\n self.gender_frame = tk.LabelFrame(\n self.stats_frame, text=\"User Gender\", padx=5, pady=5\n )\n self.gender_frame.grid(row=3, column=1, padx=5, pady=5, sticky=\"w\")\n self.gender_stats_data = tk.Label(self.gender_frame, justify=\"left\")\n self.gender_stats_data.pack()\n\n self.age_frame = stat_display_labels(\n self.stats_frame,\n \"Age Stats\",\n [\n \"Oldest Rider Birth Year:\",\n \"Youngest Rider Birth Year:\",\n \"Most common birth year:\",\n ],\n row=4,\n columnspan=2,\n )\n self.age_stats_data = tk.Label(self.age_frame, justify=\"left\")\n self.age_stats_data.grid(row=0, column=1)","async def report(self, ctx):\n try:\n members = self.bot.get_all_members()\n online, offline, other = 0,0,0\n for member in members:\n if member.status.online:\n online += 1\n elif member.status.offline:\n offline += 1\n else:\n other += 1\n message = discord.Embed(title='Server report',type='rich', colour=discord.Color(0xffb6c1))\n message.add_field(name='Online',value='**{}** online members'.format(online))\n message.add_field(name='Offline',value='**{}** offline members'.format(offline))\n message.add_field(name='Other',value='**{}** other members'.format(other))\n await self.bot.say(embed=message)\n\n except Exception as error:\n await self.bot.say('The report has failed !')\n self.logger.error(error)","def statistics(request, id):\n election = get_object_or_404(Election, pk=id)\n return render_to_response('django_elect/statistics.html', {\n 'title': \"Election Statistics\",\n 'election': election,\n })","def appointment_stats(request):\n # TODO: Consider/Look into Django cache framework\n # Default is to load up yesterday's stats\n data = {}\n if request.GET.get('lookback'):\n data['lookback'] = request.GET.get('lookback')\n appointments = get_appointments_word(request.GET.get('lookback'))\n else:\n data['lookback'] = 'yesterday'\n appointments = get_appointments_word('yesterday')\n data.update(get_appointment_stats(appointments))\n return render(request, 'doctor/stats.html', data)","def statistics(username, root_wf_id, wf_id):\n dashboard = Dashboard(g.master_db_url, root_wf_id, wf_id)\n summary_times = dashboard.workflow_summary_stats(wf_id)\n\n for key, value in summary_times.items():\n summary_times[key] = filters.time_to_str(value)\n\n workflow_stats = dashboard.workflow_stats()\n\n return render_template('workflow/statistics.html', root_wf_id=root_wf_id, wf_id=wf_id, summary_stats=summary_times, workflow_stats=workflow_stats)","def get_team_stats() -> List[BaseStat]:\n return [PossessionStat(),\n TeamTendencies(),\n RelativeTendencies(),\n PerPossessionStat(),\n RumbleItemStat(),\n PreRumbleGoals(),\n DropshotStats()\n ]","def user_stats(request):\r\n user_count = UserMgr.count()\r\n pending_activations = ActivationMgr.count()\r\n users_with_bookmarks = BmarkMgr.count(distinct_users=True)\r\n return _api_response(request, {\r\n 'count': user_count,\r\n 'activations': pending_activations,\r\n 'with_bookmarks': users_with_bookmarks\r\n })","def get_player_stats_from_game(team, year, week):","def index(request):\n home_user = request.user.profile\n \"\"\"num_activities = Activity.objects.count()\"\"\"\n Cactivity = CompletedActivity.objects.filter(user=home_user)\n UActivity = Cactivity.values('activity_id', 'activity__name', 'activity__value', 'activity__group').annotate \\\n (count=Count('activity__name'), earned=Sum('activity__value'))\n TimesCompelted = Cactivity.annotate(count=Count('activity__name'))\n # Generate counts of some of the main objects\n\n context = {\n 'huser': home_user,\n 'Lname' : home_user.user.last_name,\n 'Fname': home_user.user.first_name,\n 'num_activities': 1,\n 'activity_list' : UActivity,\n \"times_completed\" : TimesCompelted\n }\n\n # Render the HTML template index.html with the data in the context variable\n return render(request, 'index.html', context=context)","def get(self):\n\n usrs = get_mapviewers(24)\n for usr in usrs:\n logging.info(\"Mapviewer: \" + usr.loginuser);\n\n template_values = {\n 'mapviewers': usrs,\n }\n\n logging.info(\"Showusers visited.\")\n template = JINJA_ENVIRONMENT.get_template('showusers.html')\n self.response.write(template.render(template_values))","async def get_team_embed(self):\r\n embed = discord.Embed(title=\"Valorant 10 Man Bot\",colour=discord.Colour(0x470386))\r\n team_a_strings = [get_member_name(m,lower=False) for m in self.teams[\"A\"]]\r\n team_b_strings = [get_member_name(m,lower=False) for m in self.teams[\"B\"]] \r\n embed.add_field(name=\"Defenders\", value=\"{}\".format(\"\\n\".join(team_a_strings)), inline=True)\r\n embed.add_field(name=\"Attackers\", value=\"{}\".format(\"\\n\".join(team_b_strings)), inline=True)\r\n return embed","def stats(self):","def reports_stats(request):\n\n username = request.session.get('username', False)\n profile = request.session.get('profile', False)\n if(username):\n context = {'username': username,'profile':profile}\n return render(request, 'MedTAG_sket_dock_App/index.html', context)\n else:\n return redirect('MedTAG_sket_dock_App:login')","def get_stats(self):\n _url = f\"{self.connector.base_url}/projects/{self.project_id}/stats\"\n\n _response = self.connector.http_call(\"get\", _url)\n\n # Update object\n self.stats = _response.json()","async def team_show(self, ctx: commands.Context, team_id: int):\n try:\n if team_id not in self.teams:\n self.teams[team_id] = await self._get_team_data(team_id)\n team = self.teams[team_id]\n except KeyError:\n await ctx.send(f'Unrecognized team ID {team_id}. If you think this is a '\n 'valid team ID, perhaps no one from that team has '\n 'registered a Discord account yet.')\n return\n\n if ctx.guild:\n members, users = self._get_members_if_possible(\n [user.id for user in team.users], ctx.guild)\n else:\n members, users = [], team.users\n\n pages = paginate_team_data(members, users,\n [channel for channel in team.channels\n if channel and channel.guild == ctx.guild])\n\n embeds = [\n discord.Embed(title=f'**{team.display_name} (ID: {team.team_id})**',\n color=discord.Color(0x22aaff),\n description=content)\n for content in pages]\n if len(embeds) == 1:\n await ctx.send(embed=embeds[0])\n else:\n await menu(ctx, embeds, DEFAULT_CONTROLS, timeout=120)","def team_wakacms_membership(request):\n team = Team.objects.get(slug=TEAM_SLUG)\n return {\n 'is_wakacms_team_member': team.user_is_member(request.user),\n }","def printPlayerStats(self):\n\t\tplayerStats = ['Name = ' + self.name, \n\t\t\t\t\t 'Agility = ' + str(self.agility), \n\t\t\t\t\t 'Personality = ' + str(self.personality), \n\t\t\t\t\t 'Sanity = ' + str(self.sanity), \n\t\t\t\t\t 'Strength = ' + str(self.strength), \n\t\t\t\t\t 'Progress = ' + str(self.progress)]\n\t\tprint playerStats","def get_users_info(self):\r\n headers = {\"Content-type\": \"application/x-www-form-urlencoded\", \"Accept\": \"text/plain\",\r\n 'Referer': 'http://' + self.domain + '/standings.phtml', \"User-Agent\": user_agent}\r\n soup = BeautifulSoup(self.session.get('http://' + self.domain + '/teamInfo.phtml?tid=' + str(self.community_id),\r\n headers=headers).content, \"html.parser\")\r\n\r\n headers_zo = {'Accept': '*/*', 'Referer': 'http://www.comuniazo.com/comunio/dinero',\r\n 'Host': 'www.comuniazo.com', 'X-Requested-With': 'XMLHttpRequest'}\r\n money = requests.session()\r\n money.get('http://www.comuniazo.com/comunio/dinero', headers=headers_zo)\r\n money_bids = json.loads(\r\n money.get('http://www.comuniazo.com/ajax/dinero.php?user=%s&dinero=20000000' % self.username,\r\n headers=headers_zo).content)\r\n\r\n info = list()\r\n for row in soup.find('table', cellpadding=2).find_all('tr')[1:]:\r\n money, max_bid = [0, 0]\r\n name = row.a.text\r\n user_id = row.find('a')['href'].split('pid=')[1]\r\n username = re.findall('\\((.+)\\)', BeautifulSoup(self.session.get('http://'+self.domain+'/playerInfo.phtml?pid='+user_id, headers=headers).content, \"html.parser\").h1.text)[0]\r\n user_points = int(row.find_all('td')[3].text)\r\n team_value = int(row.find_all('td')[4].text.replace('.', ''))\r\n for user in money_bids['lista']['players']:\r\n if user['id'] == user_id:\r\n money = int(user['dinero'].replace('.', ''))\r\n max_bid = int(user['puja'].replace('.', ''))\r\n info.append([name, username, int(user_id), user_points, team_value, money, max_bid])\r\n return info","def teams(request):\n locales = Locale.objects.available().prefetch_related(\"latest_translation__user\")\n\n form = LocaleRequestForm()\n\n if not locales:\n return render(request, \"no_projects.html\", {\"title\": \"Teams\"})\n\n return render(\n request,\n \"teams/teams.html\",\n {\n \"locales\": locales,\n \"form\": form,\n \"top_instances\": locales.get_top_instances(),\n },\n )","async def dotabuff(self, ctx, *, member: discord.Member=None):\n if member is None:\n member = ctx.message.author\n\n steam_ids = self.bot.steam_info.get(member.id)\n\n if steam_ids is None:\n await self.bot.say(\"{0.name} has not linked their Steam account to MT5ABot.\".format(member))\n return\n\n msg = \"__Dotabuff page(s) for {0.name}:__\\n\\n\".format(member)\n try:\n response = self.steam_api.get_player_summaries(steam_ids)['response']\n except:\n await self.bot.say(\"The Steam Web API is down. Please try again later.\")\n # Response isn't in a guaranteed order.\n for steam_id in steam_ids:\n for player in response['players']:\n if player['steamid'] == steam_id:\n dota_id = steamapi.ID.steam_to_dota(steam_id)\n msg += \"{0} - <https://dotabuff.com/players/{1}>\\n\".format(player['personaname'], dota_id)\n await self.bot.say(msg)","def meta_members():\n with open(\"./team_members.txt\") as f:\n team_members = f.read().strip().split(\"\\n\")\n return make_json_response(team_members)","async def teams(self, ctx, teamsize: int, *member: discord.Member):\r\n d = dict()\r\n for x in member:\r\n if x.name in player.keys():\r\n d[x.name] = player[x.name]\r\n else:\r\n await ctx.send('{} is not a registered player'.format(x.name))\r\n return\r\n\r\n teams = itertools.combinations(d.items(), teamsize) # divide the players up into all possible teams: AB, AC...\r\n matchups = itertools.combinations(teams, 2) # all possible match ups from all possible teams\r\n\r\n report = '```md' + u\"\\u000A\"\r\n\r\n for x in matchups:\r\n t1 = x[0] # first team\r\n t2 = x[1] # second team\r\n t1_ratings, t2_ratings, t1_names, t2_names = ([] for n in range(4))\r\n for i in range(teamsize):\r\n t1_ratings.append(t1[i][1])\r\n t2_ratings.append(t2[i][1])\r\n t1_names.append(t1[i][0])\r\n t2_names.append(t2[i][0])\r\n qual = quality([t1_ratings, t2_ratings])\r\n # ignore all match ups with players on the same team...\r\n if len(set(t1_names).intersection(set(t2_names))) == 0:\r\n report += ', '.join(t1_names) + ' <vs> ' + ', '.join(t2_names) + u\"\\u000A\" + '> Quality: ' + str(\r\n round(qual, 3)) + u\"\\u000A\"\r\n\r\n await ctx.send(report + u\"\\u000A\" + '```')","def summary_page() :\r\n logger.debug(\"\")\r\n model = session_info.get_user_model(session)\r\n return render_template( \"summary_page.html\" , model=model ,\r\n stat_types=param_stats.StatTypes )","def main_screen():\n if current_user.is_authenticated:\n\n activities = Run.query.filter_by(user_id=current_user.get_id())\n\n total_duration = sum([a.duration for a in activities.all()])\n hours = str(int(total_duration//3600))\n minutes = str(int((total_duration % 3600)//60)).rjust(2, '0')\n seconds = str(int(total_duration % 60)).rjust(2, '0')\n\n stats = {'count': len(activities.all()),\n 'total_distance': sum([a.distance for a in activities.all()]),\n 'total_hours': hours,\n 'total_minutes': minutes,\n 'total_seconds': seconds,\n 'total_calories': sum([a.calories for a in activities.all()])}\n\n return render_template('main.html', stats=stats)\n else:\n return render_template('main.html')","def get(self, request, *args, **kwargs):\n\n # Common for all members views ===================================================\n l_categories = Category.objects.all()\n l_attributes = Attribute.objects.all()\n try:\n logged_member = Member.objects.get(django_user__username=request.user)\n show_describe_button = logged_member.is_editor(request)\n is_monthly_member = logged_member.is_monthly_member()\n is_yearly_member = logged_member.is_yearly_member()\n except ObjectDoesNotExist:\n logged_member = None\n except:\n raise HttpResponseNotFound\n\n\n # END Common for all members views ===============================================\n l_squarefollowing_queryset = SquareFollowing.objects.filter(member_id2=logged_member)\n\n #for x in l_squarefollowing_queryset:\n # for y in x.\n\n # Limit calculation --------------------------------------------------------------\n logged_member.refresh_api_limits(request)\n x_ratelimit_remaining, x_ratelimit = logged_member.get_api_limits()\n\n x_ratelimit_used = x_ratelimit - x_ratelimit_remaining\n if x_ratelimit != 0:\n x_limit_pct = (x_ratelimit_used / x_ratelimit) * 100\n else:\n x_limit_pct = 100\n # END Limit calculation ----------------------------------------------------------\n\n return render(request,\n self.template_name,\n dict(\n l_squarefollowing_queryset=l_squarefollowing_queryset,\n\n is_monthly_member=is_monthly_member,\n is_yearly_member=is_yearly_member,\n logged_member=logged_member,\n x_ratelimit_remaining=x_ratelimit_remaining,\n x_ratelimit=x_ratelimit,\n x_limit_pct=x_limit_pct,\n categories=l_categories,\n attributes=l_attributes,\n )\n )","def stats(userid, args):\r\n if len(args):\r\n playerToTest = es.getuserid(str(args))\r\n if playerToTest is None:\r\n playerToTest = userid\r\n buildStatsMenu(userid, playerToTest)\r\n else:\r\n buildStatsMenu(userid, userid)","def stats(self):\n try:\n slack_id = MENTION_RE.search(self.command_body).groups()[0]\n except AttributeError:\n slack_id = None\n\n if slack_id:\n users = [UserManager.get_by_slack_id(slack_id)]\n else:\n users = self.session.query(User).filter(User.tea_type.isnot(None)).all()\n\n results = []\n\n for user in users:\n results.append({\n 'real_name': user.real_name,\n 'teas_drunk': user.teas_drunk,\n 'teas_brewed': user.teas_brewed,\n 'times_brewed': user.times_brewed,\n 'teas_received': user.teas_received\n })\n\n return post_message('', self.channel, attachments=[\n {\n \"fallback\": \"Teabot Stats\",\n \"pretext\": \"\",\n \"author_name\": \"%s\" % result['real_name'],\n \"fields\": [\n {\n \"value\": \"Number of tea cups consumed -> %(teas_drunk)s\\nNumber of tea cups brewed -> %(teas_brewed)s\\nNumber of times you've brewed tea -> %(times_brewed)s\\nNumber of tea cups you were served -> %(teas_received)s\" % result,\n \"short\": False\n },\n ]\n }\n for result in results\n ])","def leaderboard(request):\r\n\tMEDIA_URL = '/media/'\r\n\tgames = Game.objects.all()\r\n\tuser_high_scores = []\r\n\tgame_high_scores = []\r\n\tnew = {}\r\n\t# Get global scores\r\n\tfor game in games:\r\n\t\tgame_intermediate_high = Score.objects.filter(game = game.id).order_by('-current_score').values('game__name', 'player__user__username', 'current_score')[:1]\r\n\t\tif (game_intermediate_high.count() > 0):\r\n\t\t\tgame_high_scores.append(game_intermediate_high)\r\n\t# Check if user is authenticated and get user's scores\r\n\tif (request.user.is_authenticated):\r\n\t\tfor game in games:\r\n\t\t\t\tuser_intermediate_high = Score.objects.filter(game=game.id, player = request.user.profile).order_by('-current_score').values('player__user__username','game__name', 'current_score').distinct()[:1]\r\n\t\t\t\tif (user_intermediate_high.count() > 0):\r\n\t\t\t\t\tuser_high_scores.append(user_intermediate_high)\r\n\r\n\treturn render(request, 'leaderboard.html',{'MEDIA_URL' : MEDIA_URL,'games': games, 'user_high_scores': user_high_scores, 'game_high_scores': game_high_scores})","async def userinfo(self, ctx: \"IceTeaContext\", target: discord.Member = None):\n target = target or ctx.author\n target_data = ctx.author_data if target == ctx.author else await ctx.get_user_data(target)\n if target_data:\n nicknames = await target_data.get_nicknames()\n else:\n nicknames = []\n shared_servers = len([member for member in ctx.bot.get_all_members() if member == target])\n embed = discord.Embed(title=f\"{target.nick or target.name} Profile\")\n embed.set_author(name=f\"{target.name} ({target.id})\", icon_url=target.avatar_url)\n embed.set_thumbnail(url=target.avatar_url)\n embed.add_field(name=\"Shared Servers\", value=f\"{shared_servers} Shared\")\n embed.add_field(name=\"Created\",\n value=f\"\"\"{timeago.format(target.created_at)} ({target.created_at.strftime(\"%b %d, %Y\")})\"\"\")\n embed.add_field(name=\"Joined\",\n value=f\"\"\"{timeago.format(target.joined_at)} ({target.joined_at.strftime(\"%b %d, %Y\")})\"\"\")\n embed.set_footer(text=\"Last Spoke In server\")\n if target_data:\n embed.timestamp = target_data.last_spoke\n else:\n embed.timestamp = ctx.message.created_at\n if len(nicknames) > 0:\n embed.add_field(name=\"Nicknames\", value=\" , \".join(str(nick) for nick in nicknames[:5]), inline=False)\n embed.add_field(name=\"Roles\", value=\" , \".join([role.name for role in target.roles[:5] if len(role.name) > 0]),\n inline=False)\n if target.activity:\n if isinstance(target.activity, discord.Spotify):\n embed.add_field(name=\"Currently Listening to\",\n value=f\"**{target.activity.title}** by {target.activity.artist} \")\n else:\n embed.add_field(name=\"Currently Playing Since\",\n value=f\"{target.activity.name}\\n{target.activity.details}\\n{target.activity.state}\")\n await ctx.send(embed=embed)","def general_timeline():\n return render_template('timeline.html', general=True, show_username=True)","async def stats(self, ctx) -> None:\n stats = self.bot.numbers.statistics\n await ctx.send(\n \"\",\n embed=NumEmbed(\n title=\"Number Statistics\",\n colour=0x007E80,\n fields={\n \"Numbers Given\": stats[\"numbers_given\"],\n \"Even Numbers\": stats[\"evens\"],\n \"Odd Numbers\": stats[\"odds\"],\n\n \"Highest Number\": stats[\"highest_info\"],\n \"Lowest Positive\": stats[\"lowest_positive_info\"],\n \"Lowest Number\": stats[\"lowest_info\"],\n\n \"Sum of Numbers\": stats[\"sum\"],\n \"Mean\": stats[\"mean\"],\n \"Median\": stats[\"median\"],\n\n \"Below 500\": stats[\"below_500\"],\n \"Below 1000\": stats[\"below_1000\"],\n \"Below 2500\": stats[\"below_2500\"],\n },\n user=ctx.author,\n ),\n )","async def team_show_all(self, ctx: commands.Context, n: int=3):\n lines = [team.table_line(ctx.guild, n) for team in self.teams.values()]\n lines = [line for line in lines if line]\n for page in paginate_table(lines):\n await ctx.send(page)","async def stat(ctx):\r\n await bot.request_offline_members(ctx.message.server)\r\n before = time.time()\r\n nicknames = [m.display_name for m in ctx.message.server.members]\r\n roles = [m.roles for m in ctx.message.server.members]\r\n rn = [[m.name for m in line] for line in roles]\r\n with open('temp.csv', mode='w', encoding='utf-8', newline='') as f:\r\n writer = csv.writer(f, dialect='excel')\r\n for i in range(len(rn)):\r\n writer.writerow([nicknames[i], rn[i]])\r\n after = time.time()\r\n await bot.send_file(ctx.message.author, 'temp.csv', filename='stats.csv',\r\n content=\"Here you go! Check your PM's. Generated in {:.4}ms.\".format((after - before) * 1000))","def print_summary_stats(self) -> None:\n print(\"Number of Users: {}\".format(len(self.all_users)))\n print(\"Number of Utterances: {}\".format(len(self.utterances)))\n print(\"Number of Conversations: {}\".format(len(self.conversations)))","def stats(self):\n pass","def nflteamrankings(self, irc, msg, args, optteam):\n \n optteam = optteam.upper().strip()\n\n if optteam not in self._validteams():\n irc.reply(\"Team not found. Must be one of: %s\" % self._validteams())\n return\n \n url = self._b64decode('aHR0cDovL2VzcG4uZ28uY29tL25mbC90ZWFtL18vbmFtZQ==') + '/%s/' % optteam\n\n try:\n request = urllib2.Request(url)\n html = (urllib2.urlopen(request)).read()\n except:\n irc.reply(\"Cannot open page: %s\" % url)\n return\n \n soup = BeautifulSoup(html)\n div = soup.find('div', attrs={'class':'mod-container mod-stat'}) \n h3 = div.find('h3')\n statsfind = div.findAll('div', attrs={'class':re.compile('span-1.*?')})\n\n append_list = []\n \n for stats in statsfind:\n header = stats.find('h4')\n stat = stats.find('span', attrs={'class':'stat'})\n rank = stat.findNext('strong')\n append_list.append(ircutils.bold(header.text) + \" \" + stat.text + \" (\" + rank.text + \")\")\n \n descstring = string.join([item for item in append_list], \" | \")\n irc.reply(ircutils.mircColor(optteam,'red') + \" :: \" + ircutils.underline(h3.text) + \" :: \" + descstring)","def users_page(request):","def get_stats(self):\n self.stats = self.soup.find('table', id='statsTable')","def mlbrosterstats(self, irc, msg, args, optteam):\n\n if optteam:\n optteam = optteam.upper()\n if optteam not in self._validteams():\n irc.reply(\"Team not found. Must be one of: %s\" % self._validteams())\n return\n \n url = self._b64decode('aHR0cDovL2VzcG4uZ28uY29tL21sYi9zdGF0cy9yb3N0ZXJz')\n\n try:\n req = urllib2.Request(url)\n html = (urllib2.urlopen(req)).read()\n except:\n irc.reply(\"Failed to open: %s\" % url)\n return\n \n soup = BeautifulSoup(html)\n table = soup.find('table', attrs={'class':'tablehead'})\n rows = table.findAll('tr')[2:]\n\n object_list = []\n\n for row in rows:\n rank = row.find('td')\n team = rank.findNext('td')\n rhb = team.findNext('td')\n lhb = rhb.findNext('td')\n sh = lhb.findNext('td')\n rhp = sh.findNext('td')\n lhp = rhp.findNext('td')\n ht = lhp.findNext('td')\n wt = ht.findNext('td')\n age = wt.findNext('td')\n young = age.findNext('td')\n old = young.findNext('td')\n \n aString = str(\"RHB: \" + rhb.getText() + \" LHB: \" + lhb.getText() + \" SH: \" + sh.getText() + \" RHP: \" + rhp.getText() + \" LHP: \" + lhp.getText()\\\n + \" AVG HT: \" + ht.getText() + \" AVG WEIGHT: \" + wt.getText() + \" AVG AGE: \" + age.getText() + \" YOUNGEST: \" + young.getText() + \" OLDEST: \" + old.getText())\n \n d = collections.OrderedDict()\n d['team'] = str(self._translateTeam('team', 'ename', team.getText()))\n d['data'] = str(aString)\n object_list.append(d)\n \n if optteam:\n for each in object_list:\n if each['team'] == optteam: # list will have all teams so we don't need to check\n output = \"{0} Roster Stats :: {1}\".format(ircutils.bold(each['team']), each['data'])\n \n irc.reply(output)\n \n else:\n \n youngest_list = []\n oldest_list = []\n \n for each in object_list[0:5]:\n youngest_list.append(each['team'])\n for each in object_list[-6:-1]:\n oldest_list.append(each['team'])\n \n output = \"{0} :: {1}\".format(ircutils.bold(\"5 Youngest MLB Teams:\"), string.join([item for item in youngest_list], \" | \"))\n irc.reply(output)\n \n output = \"{0} :: {1}\".format(ircutils.bold(\"5 Oldest MLB Teams:\"), string.join([item for item in oldest_list], \" | \"))\n irc.reply(output)","def seasonStats(personId,type = 'gameLog',group = 'hitting'):\n\n #playerInfo = get('people', {'personIds':personId})\n\n\n teamStats = get('person',{ 'ver':'v1' , 'personId':personId,'hydrate':['stats(group={},type={})'.format(group,type),'currentTeam']})\n return teamStats\n #iterate of stats and find the right player id\n #career stats broken\n #fix the season :2019\n #make function to get team id","async def _stats_heist(self, ctx):\r\n author = ctx.message.author\r\n avatar = ctx.message.author.avatar_url\r\n guild = ctx.guild\r\n config = await self.thief.get_guild_settings(guild)\r\n theme = await self.thief.get_guild_theme(guild)\r\n\r\n await self.thief.check_member_settings(author)\r\n\r\n # Theme variables\r\n sentencing = \"{} {}\".format(theme[\"Jail\"], theme[\"Sentence\"])\r\n t_bail = \"{} Cost\".format(theme[\"Bail\"])\r\n\r\n # Sentence Time Remaining\r\n sentence = await self.thief.get_member_sentence(author)\r\n time_served = await self.thief.get_member_timeserved(author)\r\n jail_fmt = self.thief.cooldown_calculator(time_served, sentence)\r\n\r\n # Death Time Remaining\r\n death_timer = await self.thief.get_member_deathtimer(author)\r\n base_death_timer = config[\"Death\"]\r\n death_fmt = self.thief.cooldown_calculator(death_timer, base_death_timer)\r\n\r\n rank = self.thief.criminal_level(await self.thief.get_member_crimlevel(author))\r\n\r\n embed = discord.Embed(colour=0x0066FF, description=rank)\r\n embed.title = author.name\r\n embed.set_thumbnail(url=avatar)\r\n embed.add_field(name=\"Status\", value=await self.thief.get_member_status(author))\r\n embed.add_field(name=\"Spree\", value=await self.thief.get_member_spree(author))\r\n embed.add_field(name=t_bail, value=await self.thief.get_member_bailcost(author))\r\n embed.add_field(name=theme[\"OOB\"], value=await self.thief.get_member_oob(author))\r\n embed.add_field(name=sentencing, value=jail_fmt)\r\n embed.add_field(name=\"Apprehended\", value=await self.thief.get_member_jailcounter(author))\r\n embed.add_field(name=\"Death Timer\", value=death_fmt)\r\n embed.add_field(name=\"Total Deaths\", value=await self.thief.get_member_totaldeaths(author))\r\n embed.add_field(name=\"Lifetime Apprehensions\", value=await self.thief.get_member_totaljails(author))\r\n\r\n await ctx.send(embed=embed)","def show_players_specific_tournament(self) -> None:\n id_choice = check.request_id(TOURNAMENTS)\n tournament_data = TOURNAMENTS.get(doc_id=id_choice)\n if tournament_data.get(\"players\") == {}:\n print(\"\\n This tournaments has no players yet\")\n else:\n players_list = tournament_data.get(\"players\")\n deserialized_player_list = []\n for player_data in players_list:\n deserialized_player = Player(**json.loads(player_data))\n deserialized_player_list.append(deserialized_player)\n utils.clear_terminal()\n print(\n \"Do you want the list of players by alphabetical order or by ranking ? \\n\"\n \"1 - Ranking players list \\n\"\n \"2 - Alphabetical players list\"\n )\n choice = check.request_selection_with_number(\"alphabetical\", \"ranking\", \"None\")\n if choice == \"alphabetical\":\n utils.clear_terminal()\n deserialized_player_list = sorted(deserialized_player_list, key=lambda player: player.first_name)\n for deserialized_player in deserialized_player_list:\n print(deserialized_player)\n elif choice == \"ranking\":\n utils.clear_terminal()\n deserialized_player_list = sorted(deserialized_player_list, key=lambda player: player.ranking)\n for deserialized_player in deserialized_player_list:\n print(deserialized_player)","def global_stats(request):\n\n if request.user.is_authenticated:\n profile: IntervalsProfile = request.user.profile\n\n global_correct = 0\n global_answered = 0\n\n for r in profile.all_recent_results().values():\n global_correct += r.total_correct\n global_answered += r.total_completed\n\n print(global_correct, global_answered)\n\n return Response({\"global_correct\": global_correct,\n \"global_answered\": global_answered},\n status=status.HTTP_201_CREATED)\n return Response(status=status.HTTP_401_UNAUTHORIZED)","def my_stats(request):\n\n username = request.session.get('username', False)\n profile = request.session.get('profile', False)\n if(username):\n context = {'username': username,'profile':profile}\n return render(request, 'MedTAG_sket_dock_App/index.html', context)\n else:\n return redirect('MedTAG_sket_dock_App:login')","def standings():\n teams = Team.query.all()\n teams = list(reversed(sorted(teams, key=lambda team: team.points)))\n for team in teams:\n team.logo = url_for('static', filename='images/teams/{}'.format(team.logo_image))\n\n return render_template('standings/standings.html', teams=teams, title='Standings')","def stats(self):\n\n for hero in self.heroes:\n print(\"{} has {} kills and {} deaths.\".format(hero.name, hero.kills, hero.deaths))","def index(page):\r\n per_page = 24\r\n count = cached_users.get_total_users()\r\n accounts = cached_users.get_users_page(page, per_page)\r\n if not accounts and page != 1:\r\n abort(404)\r\n pagination = Pagination(page, per_page, count)\r\n if current_user.is_authenticated():\r\n user_id = current_user.id\r\n else:\r\n user_id = 'anonymous'\r\n top_users = cached_users.get_leaderboard(current_app.config['LEADERBOARD'],\r\n user_id)\r\n return render_template('account/index.html', accounts=accounts,\r\n total=count,\r\n top_users=top_users,\r\n title=\"Community\", pagination=pagination)","def display_tournament_list():\r\n for tournament in tournaments_table:\r\n print(tournament['Nom'])","def main_page(request):\n\t\n\tall_joined_teams = request.user.joined_teams.all()\n\t\n\tchallenge_status = []\n\t\n\tfor team in all_joined_teams:\n\t\tschedule = team.challenge.schedule\n\t\tchallenge = team.challenge\n\t\t\n\t\tresult = get_consistency(request.user, schedule)\n\t\tchallenge_status.append((challenge.pk, team.pk, schedule.pk, challenge.name, team.team_name, schedule.title, result[\"consistency\"], result[\"completion\"]))\n\t\t\n\tcontext = RequestContext(request, {\"challenge_status\" : challenge_status, \"messages\" : messages})\n\treturn render_to_response(\"encourage/challenge_main.html\", context)","def my_team(request):\n template = loader.get_template('team/my_team.html')\n team = request.user.profile.team\n\n if team is not None:\n team_members = User.objects.filter(profile__team=team)\n\n context = {\n 'team_name': team.name,\n 'team_members': team_members,\n 'team_logo': team.logo,\n 'team_info': team.information\n }\n if request.POST.get('save'):\n if request.POST.get('new_name') != '':\n new_name = request.POST.get('new_name')\n team.name = new_name\n if 'logo_image' in request.FILES:\n team.logo = request.FILES['logo_image']\n new_info = request.POST.get('new_info')\n team.information = new_info\n team.save()\n\n context['team_name'] = team.name\n context['team_info'] = team.information\n context['team_logo'] = team.logo\n\n if request.POST.get('save_name'):\n new_name = request.POST.get('new_name')\n team.name = new_name\n team.save()\n context['team_name'] = team.name\n\n if request.POST.get('save_info'):\n new_info = request.POST.get('new_info')\n team.information = new_info\n team.save()\n context['team_info'] = team.information\n\n if request.POST.get('save_logo'):\n team.logo = request.FILES['logo_image']\n team.save()\n context['team_logo'] = team.logo\n\n if request.POST.get('leave_team'):\n request.user.profile.team = None\n request.user.profile.save()\n context = None\n return redirect('/')\n\n return CustomHttpResponse.send(template, context, request)\n\n else:\n return redirect('/team/new')","def show_stats(self):\n if checkPermission('genweb.webmaster', self.context):\n return True","def display_trip_stats(self):\n\n self.trip_frame = stat_display_labels(\n self.stats_frame,\n \"Trip Stats\",\n [\"The total travel time was:\", \"The mean travel time was:\"],\n row=0,\n column=2,\n )\n self.trip_stats_data = tk.Label(self.trip_frame, justify=\"left\")\n self.trip_stats_data.grid(row=0, column=1)","def team(request,league_id = 1,team_id = -1):\n\n\t#define variables\n\targs = {}\n\n\t#getting data form models\n\tthis_team = get_object_or_404(Team, pk = team_id)\n\tnews = this_team.news_set.all().order_by('date')\n\tthis_league = get_object_or_404(League, pk = league_id)\n\tteams = this_league.team_set.all().exclude(pk = team_id).order_by('name')\n\tleagues = League.objects.all().exclude(pk = league_id).order_by('name')\n\n\t#context data initialization into dictionary 'args'\n\targs['this_team'] = this_team\n\targs['this_league'] = this_league\n\targs['teams'] = teams\n\targs['leagues'] = leagues\n\targs['news'] = news\n\t\n\treturn render_to_response('news/team.html',args)","async def stats(self, ctx, user: discord.Member = None):\n if user is None:\n await self.guild_tag_stats(ctx)\n else:\n await self.member_tag_stats(ctx, user)","def fetch_players_stats():\n players_scraper = PlayerStatsScraper(API_URL, API_HEADERS)\n result = players_scraper.save_objects()\n return result","def get_people(team):","def render(self, mode='human'):\n index = self._get_current_index()\n teams = self.teams.iloc[index]\n teams = teams.itertuples() if isinstance(teams, pandas.DataFrame) else [teams]\n teams_str = ', '.join(['Home Team {} VS Away Team {}'.format(row.home_team, row.away_team)\n for row in teams])\n teams_str = teams_str + \".\"\n print(teams_str)\n super().render(mode)","def GetStats(self):\r\n\t\tArg1 = self.href\r\n\t\treturn self._execute('GetStats', payload=locals(), response_object=None)","async def userstats(self, ctx, *, user):\n try:\n converter = commands.MemberConverter()\n user = await converter.convert(ctx, user)\n roles = user.roles\n printedRoles = []\n for role in roles:\n if role.name == \"@everyone\":\n printedRoles.append(\"@everyone\")\n else:\n printedRoles.append(\"<@&{}>\".format(role.id))\n createDate = user.created_at\n joinDate = user.joined_at\n rolesStr = \", \".join(printedRoles)\n memActivity = user.activity.name if user.activity is not None else \"None\"\n embed_userstats = discord.Embed(title=\"User Statistics\", description=f\"This embed will show some general and guild information about {user}!\", color=0x0000FF)\n embed_userstats.set_thumbnail(url=user.avatar_url)\n embed_userstats.add_field(name=\"Username\", value=user.display_name, inline=False)\n embed_userstats.add_field(name=\"ID\", value=user.id, inline=False)\n embed_userstats.add_field(name=\"Nickname\", value=user.nick, inline=False)\n embed_userstats.add_field(name=\"Activity\", value=memActivity, inline=False)\n embed_userstats.add_field(name=\"Roles\", value=rolesStr, inline=False)\n embed_userstats.add_field(name=\"Date of Account Creation\", value=createDate.strftime(\"%A, %d. %B %Y %H:%M\"), inline=False)\n embed_userstats.add_field(name=\"Date of Guild Join\", value=joinDate.strftime(\"%A, %d. %B %Y %H:%M\"), inline=False)\n await esay(ctx, embed_userstats)\n except commands.CommandError:\n if user.isdigit():\n user = await self.bot.get_user_info(user_id=user)\n createDate = user.created_at\n embed_userstats_out = discord.Embed(title=\"User Statistics\", description=f\"{user} has not been found inside of the current guild, which means that the global user list has been used to identify the user. The data this instance can deliver is limited.\", color=0x0000FF)\n embed_userstats_out.set_thumbnail(url=user.avatar_url)\n embed_userstats_out.add_field(name=\"Username#Discriminator\", value=user)\n embed_userstats_out.add_field(name=\"ID\", value=user.id)\n embed_userstats_out.add_field(name=\"Date of Account Creation\", value=createDate.strftime(\"%A, %d. %B %Y %H:%M\"))\n await esay(ctx, embed_userstats_out)\n else:\n await say(ctx, \":interrobang: - The given information hasn't resulted a guild member. If your intention is to get an User, please use a valid ID!\")","def home(request):\n if not request.user.is_authenticated():\n soccer = Game.objects.filter(sport=Sport.objects.get(sport=\"soccer\"), active=\"true\")\n volleyball = Game.objects.filter(sport=Sport.objects.get(sport=\"volleyball\"), active=\"true\")\n baseball = Game.objects.filter(sport=Sport.objects.get(sport=\"baseball\"), active=\"true\")\n hockey = Game.objects.filter(sport=Sport.objects.get(sport=\"hockey\"), active=\"true\")\n basketball = Game.objects.filter(sport=Sport.objects.get(sport=\"basketball\"), active=\"true\")\n return render(request, 'index.html', {'soccer': soccer, 'hockey': hockey, 'basketball': basketball, 'baseball': baseball, 'volleyball': volleyball})\n\n if request.user.is_staff:\n return HttpResponseRedirect('/admin/')\n\n player = Player.objects.get(user=request.user)\n sports = player.sports.all()\n joined_games = player.game_set.all()\n my_games = Game.objects.filter(owner=request.user)\n profile_pic_url = player.image_url\n return render(request, 'profile.html', {'player': player, 'profile_pic_url': profile_pic_url, 'sports': sports, 'games': my_games | joined_games})","def community_stats(request):\n stats = cache.get(STATS_CACHE_KEY, None)\n if not stats:\n\n stats = fetch(PEOPLE_STATS_URL)\n packages_data = fetch(PACKAGES_STATS_URL)\n if 'meta' in packages_data:\n stats.update({'packages': packages_data['meta']['total_count']})\n\n stats = {'community_stats': stats}\n\n cache.add(STATS_CACHE_KEY, stats, 60 * 60 * 12) # for half a day\n\n return stats"],"string":"[\n \"def show_stats(self):\\n print(self.team_one.name + \\\" stats: \\\")\\n self.team_one.stats()\\n print(self.team_two.name + \\\" stats: \\\")\\n self.team_two.stats()\",\n \"def info():\\n print 'Loading info page'\\n\\n team_list = datastore.get_all_teams(engine)\\n\\n return render_template('info.html', rows=team_list)\",\n \"def baron_stats(request, name):\\n assert isinstance(request, HttpRequest)\\n query = Baron_Players.objects.filter(summoner_name=name)\\n \\n return render(\\n request,\\n 'AscensionESports_Baseline/player_stats.html',\\n {\\n 'background': getBaronBackground(),\\n 'color': getBaronColor(),\\n 'title':'Baron League Stats',\\n 'query_results': query,\\n 'year': datetime.now().year,\\n }\\n )\",\n \"def stats(request):\\n \\n return render(request, 'stats.html')\",\n \"def showStats(population, masterList, index):\\n count = 0\\n if index == \\\"all\\\":\\n for team in population:\\n print (\\\"Team at index\\\", count)\\n print(\\\"Tot Avg\\\", team.totAvg)\\n print(\\\"Tot Runs\\\", team.totRuns)\\n print(\\\"Tot HRs\\\", team.totHr)\\n print(\\\"Tot RBIs\\\", team.totRbi)\\n print(\\\"Tot SB\\\", team.totSb)\\n print(\\\"Tot points\\\", team.points, '\\\\n')\\n count += 1\\n else:\\n print(\\\"Team at index\\\", index)\\n print(\\\"Tot Avg\\\", population[index].totAvg)\\n print(\\\"Tot Runs\\\", population[index].totRuns)\\n print(\\\"Tot HRs\\\", population[index].totHr)\\n print(\\\"Tot RBIs\\\", population[0].totRbi)\\n print(\\\"Tot SB\\\", population[0].totSb)\\n print(\\\"Tot points\\\", population[0].points, '\\\\n')\",\n \"def teams(request):\\n if request.method == 'GET':\\n # Validates if the member exists. If members logon successfully they are created on the teams-app.\\n email = request.session.get('email', None)\\n full_name = request.session.get('full_name', None)\\n try: \\n member = Member.objects.get(email=email)\\n except ObjectDoesNotExist:\\n member = Member(email=email, full_name=full_name)\\n member.save()\\n\\n member_teams = member.teams.all() \\n context = {\\n 'email': email,\\n 'full_name': full_name,\\n 'member_teams': member_teams\\n }\\n return render(request, 'teamsapp/teams.html', context)\\n else:\\n raise Http404('Not allowed')\",\n \"async def stats(ctx):\\n pythonVersion = platform.python_version()\\n dpyVersion = discord.__version__\\n serverCount = len(bot.guilds)\\n memberCount = len(set(bot.get_all_members()))\\n\\n embed = discord.Embed(\\n title=f\\\"{bot.user.name} Stats\\\",\\n description=\\\"\\\\uFEFF\\\",\\n colour=ctx.author.colour,\\n timestamp=ctx.message.created_at,\\n )\\n\\n embed.add_field(name=\\\"Bot Version:\\\", value=\\\"0.0.1\\\")\\n embed.add_field(name=\\\"Python Version:\\\", value=pythonVersion)\\n embed.add_field(name=\\\"Discord.Py Version\\\", value=dpyVersion)\\n embed.add_field(name=\\\"Total Guilds:\\\", value=serverCount)\\n embed.add_field(name=\\\"Total Users:\\\", value=memberCount)\\n embed.add_field(name=\\\"Bot Developers:\\\", value=\\\"<@271612318947868673>\\\")\\n\\n embed.set_footer(text=f\\\"Carpe Noctem | {bot.user.name}\\\")\\n embed.set_author(name=bot.user.name, icon_url=bot.user.avatar_url)\\n\\n await ctx.send(embed=embed)\",\n \"async def view_stats(self, ctx):\\n app_info = await self.bot.application_info()\\n total_ram = (psutil.virtual_memory().total >> 30) + 1\\n embed = discord.Embed(\\n title=\\\"Bot Stats\\\",\\n description=f\\\"Running on a dedicated server with {total_ram}GB RAM \\\\n provided by RandomGhost#0666.\\\",\\n )\\n\\n embed.add_field(name=\\\"**__General Info__**\\\", inline=False, value=\\\"\\\\u200b\\\")\\n embed.add_field(name=\\\"Latency\\\", value=f\\\"{self.bot.latency*1000:.03f}ms\\\")\\n embed.add_field(name=\\\"Guild Count\\\", value=f\\\"{len(self.bot.guilds):,}\\\")\\n embed.add_field(name=\\\"User Count\\\", value=f\\\"{len(self.bot.users):,}\\\")\\n\\n embed.add_field(name=\\\"**__Technical Info__**\\\", inline=False, value=\\\"\\\\u200b\\\")\\n embed.add_field(name=\\\"System CPU Usage\\\", value=f\\\"{psutil.cpu_percent():.02f}%\\\")\\n embed.add_field(name=\\\"System RAM Usage\\\", value=f\\\"{psutil.virtual_memory().used/1048576:.02f} MB\\\")\\n embed.add_field(name=\\\"System Uptime\\\", value=f\\\"{timedelta(seconds=int(time.time() - psutil.boot_time()))}\\\")\\n embed.add_field(name=\\\"Bot CPU Usage\\\", value=f\\\"{process.cpu_percent():.02f}%\\\")\\n embed.add_field(name=\\\"Bot RAM Usage\\\", value=f\\\"{process.memory_info().rss / 1048576:.02f} MB\\\")\\n embed.add_field(name=\\\"Bot Uptime\\\", value=f\\\"{timedelta(seconds=int(time.time() - process.create_time()))}\\\")\\n\\n embed.add_field(name=\\\"**__Links__**\\\", inline=False, value=\\\"\\\\u200b\\\")\\n embed.add_field(name=\\\"Support Server\\\", value=\\\"[https://discord.swaglyrics.dev](https://discord.swaglyrics.dev)\\\")\\n embed.add_field(name=\\\"Invite\\\", value=\\\"[https://invite.swaglyrics.dev](https://invite.swaglyrics.dev)\\\")\\n embed.add_field(\\n name=\\\"Source\\\",\\n value=\\\"[https://swaglyrics.dev/SwagLyrics-Discord-Bot]\\\" \\\"(https://swaglyrics.dev/SwagLyrics-discord-bot)\\\",\\n )\\n\\n embed.set_footer(\\n text=f\\\"Made by {app_info.owner} • {self.bot.get_user(512708394994368548)}\\\",\\n icon_url=[\\n app_info.owner.avatar_url_as(size=128),\\n self.bot.get_user(512708394994368548).avatar_url_as(size=128),\\n ][getrandbits(1)],\\n ) # randomize clash or flabbet avatar\\n\\n await ctx.send(embed=embed)\",\n \"def index():\\r\\n\\r\\n title = \\\"Global Statistics\\\"\\r\\n\\r\\n n_auth = n_auth_users()\\r\\n\\r\\n n_anon = n_anon_users()\\r\\n\\r\\n n_total_users = n_anon + n_auth\\r\\n\\r\\n n_published_apps = cached_apps.n_published()\\r\\n n_draft_apps = cached_apps.n_draft()\\r\\n n_total_apps = n_published_apps + n_draft_apps\\r\\n\\r\\n n_tasks = n_tasks_site()\\r\\n\\r\\n n_task_runs = n_task_runs_site()\\r\\n\\r\\n top5_apps_24_hours = get_top5_apps_24_hours()\\r\\n\\r\\n top5_users_24_hours = get_top5_users_24_hours()\\r\\n\\r\\n locs = get_locs()\\r\\n\\r\\n show_locs = False\\r\\n if len(locs) > 0:\\r\\n show_locs = True\\r\\n\\r\\n stats = dict(n_total_users=n_total_users, n_auth=n_auth, n_anon=n_anon,\\r\\n n_published_apps=n_published_apps,\\r\\n n_draft_apps=n_draft_apps,\\r\\n n_total_apps=n_total_apps,\\r\\n n_tasks=n_tasks,\\r\\n n_task_runs=n_task_runs)\\r\\n\\r\\n users = dict(label=\\\"User Statistics\\\",\\r\\n values=[\\r\\n dict(label='Anonymous', value=[0, n_anon]),\\r\\n dict(label='Authenticated', value=[0, n_auth])])\\r\\n\\r\\n apps = dict(label=\\\"Apps Statistics\\\",\\r\\n values=[\\r\\n dict(label='Published', value=[0, n_published_apps]),\\r\\n dict(label='Draft', value=[0, n_draft_apps])])\\r\\n\\r\\n tasks = dict(label=\\\"Task and Task Run Statistics\\\",\\r\\n values=[\\r\\n dict(label='Tasks', value=[0, n_tasks]),\\r\\n dict(label='Answers', value=[1, n_task_runs])])\\r\\n\\r\\n return render_template('/stats/global.html', title=title,\\r\\n users=json.dumps(users),\\r\\n apps=json.dumps(apps),\\r\\n tasks=json.dumps(tasks),\\r\\n locs=json.dumps(locs),\\r\\n show_locs=show_locs,\\r\\n top5_users_24_hours=top5_users_24_hours,\\r\\n top5_apps_24_hours=top5_apps_24_hours,\\r\\n stats=stats)\",\n \"def index():\\r\\n if current_user.is_authenticated():\\r\\n user_id = current_user.id\\r\\n else:\\r\\n user_id = 'anonymous'\\r\\n top_users = cached_users.get_leaderboard(current_app.config['LEADERBOARD'],\\r\\n user_id=user_id)\\r\\n\\r\\n return render_template('/stats/index.html', title=\\\"Community Leaderboard\\\",\\r\\n top_users=top_users)\",\n \"def fetch_teams_stats():\\n teams_scraper = TeamStatsScraper(API_URL, API_HEADERS)\\n result = teams_scraper.save_objects()\\n return result\",\n \"def showUserStats(self) :\\n self.getAllStats()\\n self.getNbTotalLevelsPlayed()\\n Scenario.messageAllStats(self.level_history[0].created_at)\\n self.showBestStats()\\n self.showWorstStats()\\n self.showAverageStats()\",\n \"async def stats(self, ctx: commands.Context):\\n users = len(self.bot.users)\\n guilds = len(self.bot.guilds)\\n\\n embed = Embed(color=Color.dark_green())\\n embed.add_field(name=\\\"Guilds\\\", value=guilds)\\n embed.add_field(name=\\\"Users\\\", value=users)\\n embed.set_thumbnail(url=ctx.guild.me.avatar_url)\\n\\n await ctx.send(embed=embed)\",\n \"def statistics():\\n return render_template('statistics.html'), 200\",\n \"def stats_page():\\n import alltheitems.stats\\n return alltheitems.stats.index()\",\n \"def overview(request):\\n LOGGER.info('Rendering WMT16 HIT overview for user \\\"{0}\\\".'.format(\\n request.user.username or \\\"Anonymous\\\"))\\n \\n # Re-initialise random number generator.\\n seed(None)\\n \\n # Collect available language pairs for the current user.\\n language_codes = set([x[0] for x in LANGUAGE_PAIR_CHOICES])\\n language_pairs = request.user.groups.filter(name__in=language_codes)\\n \\n # Collect available annotation projects for the current user.\\n annotation_projects = request.user.project_set.all()\\n \\n hit_data = []\\n total = [0, 0, 0]\\n\\n for language_pair in language_pairs:\\n for annotation_project in annotation_projects:\\n hit = _compute_next_task_for_user(request.user, annotation_project, language_pair)\\n user_status = HIT.compute_status_for_user(request.user, annotation_project, language_pair)\\n for i in range(3):\\n total[i] = total[i] + user_status[i]\\n \\n if hit:\\n # Convert status seconds back into datetime.time instances.\\n for i in range(2):\\n user_status[i+1] = seconds_to_timedelta(int(user_status[i+1]))\\n \\n hit_data.append(\\n (hit.get_language_pair_display(), hit.get_absolute_url(),\\n hit.hit_id, user_status, annotation_project)\\n )\\n \\n # Convert total seconds back into datetime.timedelta instances.\\n total[1] = seconds_to_timedelta(int(total[2]) / float(int(total[0]) or 1))\\n \\n # Remove microseconds to get a nicer timedelta rendering in templates.\\n total[1] = total[1] - timedelta(microseconds=total[1].microseconds)\\n \\n total[2] = seconds_to_timedelta(int(total[2]))\\n \\n groups = _identify_groups_for_user(request.user)\\n group = None\\n if len(groups) > 1:\\n LOGGER.debug(u'User \\\"{0}\\\" assigned to multiple annotation groups: {1}'.format(\\n request.user.username or u'Anonymous',\\n u', '.join([x.name for x in groups]))\\n )\\n group = groups[0]\\n \\n if group is not None:\\n group_name = group.name\\n group_status = HIT.compute_status_for_group(group)\\n for i in range(2):\\n group_status[i+1] = seconds_to_timedelta(int(group_status[i+1]))\\n \\n else:\\n group_status = None\\n group_name = None\\n \\n LOGGER.debug(u'\\\\n\\\\nHIT data for user \\\"{0}\\\":\\\\n\\\\n{1}\\\\n'.format(\\n request.user.username or \\\"Anonymous\\\",\\n u'\\\\n'.join([u'{0}\\\\t{1}\\\\t{2}\\\\t{3}'.format(*x) for x in hit_data])))\\n\\n # Compute admin URL for super users.\\n admin_url = None\\n if request.user.is_superuser:\\n admin_url = reverse('admin:index')\\n \\n dictionary = {\\n 'active_page': \\\"OVERVIEW\\\",\\n 'hit_data': hit_data,\\n 'total': total,\\n 'group_name': group_name,\\n 'group_status': group_status,\\n 'admin_url': admin_url,\\n 'title': 'WMT16 Dashboard',\\n 'annotation_groups': [x.name for x in groups],\\n }\\n dictionary.update(BASE_CONTEXT)\\n \\n LOGGER.info(dictionary.values())\\n \\n return render(request, 'wmt16/overview.html', dictionary)\",\n \"def test_05_app_stats_index(self):\\r\\n # As Anonymou user\\r\\n url = \\\"/app/%s/stats\\\" % self.app_short_name\\r\\n res = self.app.get(url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"App Stats page should be shown to anonymous users\\\"\\r\\n assert dom.find(id='enforce_privacy') is None, err_msg\\r\\n # As Authenticated user but NOT ADMIN\\r\\n self.signin()\\r\\n res = self.app.get(url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"App Stats page should be shown to authenticated users\\\"\\r\\n assert dom.find(id='enforce_privacy') is None, err_msg\\r\\n self.signout\\r\\n # As Authenticated user but ADMIN\\r\\n self.signin(email=self.root_addr, password=self.root_password)\\r\\n res = self.app.get(url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"App Stats page should be shown to admin users\\\"\\r\\n assert dom.find(id='enforce_privacy') is None, err_msg\\r\\n self.signout()\",\n \"def test_05_app_stats_index(self):\\r\\n # As Anonymou user\\r\\n url = \\\"/app/%s/stats\\\" % self.app_short_name\\r\\n res = self.app.get(url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"App Stats page should not be shown to anonymous users\\\"\\r\\n assert dom.find(id='enforce_privacy') is not None, err_msg\\r\\n # As Authenticated user but NOT ADMIN\\r\\n self.signin()\\r\\n res = self.app.get(url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"App Stats page should not be shown to authenticated users\\\"\\r\\n assert dom.find(id='enforce_privacy') is not None, err_msg\\r\\n self.signout\\r\\n # As Authenticated user but ADMIN\\r\\n self.signin(email=self.root_addr, password=self.root_password)\\r\\n res = self.app.get(url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"App Stats page should be shown to admin users\\\"\\r\\n assert dom.find(id='enforce_privacy') is None, err_msg\\r\\n self.signout()\",\n \"async def cmd_stats(self, ctx):\\n\\n guild = ctx.guild\\n members = await guild.fetch_members().flatten()\\n answer = f''\\n embed = discord.Embed(title=\\\"Statistiken\\\",\\n description=f'Wir haben aktuell {len(members)} Mitglieder auf diesem Server, verteilt auf folgende Rollen:')\\n\\n for role in guild.roles:\\n if not self.get_key(role):\\n continue\\n role_members = role.members\\n if len(role_members) > 0 and not role.name.startswith(\\\"Farbe\\\"):\\n embed.add_field(name=role.name, value=f'{len(role_members)} Mitglieder', inline=False)\\n\\n no_role = 0\\n for member in members:\\n # ToDo Search for study roles only!\\n if len(member.roles) == 1:\\n no_role += 1\\n\\n embed.add_field(name=\\\"\\\\u200B\\\", value=\\\"\\\\u200b\\\", inline=False)\\n embed.add_field(name=\\\"Mitglieder ohne Rolle\\\", value=str(no_role), inline=False)\\n\\n await ctx.channel.send(answer, embed=embed)\",\n \"def test_04_global_stats_index(self):\\r\\n # As Anonymou user\\r\\n url = \\\"/stats\\\"\\r\\n res = self.app.get(url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"Stats page should be shown to anonymous users\\\"\\r\\n assert dom.find(id='enforce_privacy') is None, err_msg\\r\\n # As Authenticated user but NOT ADMIN\\r\\n self.signin()\\r\\n res = self.app.get(url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"Stats page should be shown to authenticated users\\\"\\r\\n assert dom.find(id='enforce_privacy') is None, err_msg\\r\\n self.signout\\r\\n # As Authenticated user but ADMIN\\r\\n self.signin(email=self.root_addr, password=self.root_password)\\r\\n res = self.app.get(url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"Stats page should be shown to admin users\\\"\\r\\n assert dom.find(id='enforce_privacy') is None, err_msg\\r\\n self.signout()\",\n \"def analyze_team_page(auth, db, id_team):\\n session = login(auth)\\n\\n url = \\\"http://es.ibasketmanager.com/equipo.php?id=\\\" + id_team\\n r = session.get(url)\\n load_status = 0\\n while load_status != 200:\\n load_status = r.status_code\\n \\n print(show(\\\"profile\\\") + \\\" > Analizando perfil del equipo\\\")\\n\\n soup = BeautifulSoup(r.content, \\\"html.parser\\\")\\n \\n trs2 = soup.find_all(\\\"tr\\\", {\\\"class\\\": \\\"tipo2\\\"})\\n\\n id_user = trs2[0].find(\\\"a\\\")[\\\"href\\\"].split(\\\"=\\\")[1]\\n streak = trs2[2].find_all(\\\"td\\\")[1].text\\n club_seats = trs2[3].find_all(\\\"td\\\")[1].text.replace(\\\".\\\",\\\"\\\").strip()\\n ranking = trs2[4].find_all(\\\"td\\\")[1].text.replace(\\\"Ranking\\\",\\\"\\\").strip()\\n \\n trs1 = soup.find_all(\\\"tr\\\", {\\\"class\\\": \\\"tipo1\\\"})\\n fans = trs1[3].find_all(\\\"td\\\")[1].text.replace(\\\".\\\",\\\"\\\").strip()\\n\\n return [id_user, club_seats, fans, ranking, streak]\",\n \"def status(request):\\n LOGGER.info('Rendering WMT16 HIT status for user \\\"{0}\\\".'.format(\\n request.user.username or \\\"Anonymous\\\"))\\n \\n if not STATUS_CACHE.has_key('global_stats'):\\n update_status(key='global_stats')\\n \\n if not STATUS_CACHE.has_key('language_pair_stats'):\\n update_status(key='language_pair_stats')\\n \\n if not STATUS_CACHE.has_key('group_stats'):\\n update_status(key='group_stats')\\n \\n if not STATUS_CACHE.has_key('user_stats'):\\n update_status(key='user_stats')\\n \\n # Compute admin URL for super users.\\n admin_url = None\\n if request.user.is_superuser:\\n admin_url = reverse('admin:index')\\n \\n dictionary = {\\n 'active_page': \\\"STATUS\\\",\\n 'global_stats': STATUS_CACHE['global_stats'],\\n 'language_pair_stats': STATUS_CACHE['language_pair_stats'],\\n 'group_stats': STATUS_CACHE['group_stats'],\\n 'user_stats': STATUS_CACHE['user_stats'],\\n 'clusters': RANKINGS_CACHE.get('clusters', []),\\n 'admin_url': admin_url,\\n 'title': 'WMT16 Status',\\n }\\n dictionary.update(BASE_CONTEXT)\\n \\n return render(request, 'wmt16/status.html', dictionary)\",\n \"def test_04_global_stats_index(self):\\r\\n # As Anonymou user\\r\\n url = \\\"/stats\\\"\\r\\n res = self.app.get(url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"Stats page should not be shown to anonymous users\\\"\\r\\n assert dom.find(id='enforce_privacy') is not None, err_msg\\r\\n # As Authenticated user but NOT ADMIN\\r\\n self.signin()\\r\\n res = self.app.get(url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"Stats page should not be shown to authenticated users\\\"\\r\\n assert dom.find(id='enforce_privacy') is not None, err_msg\\r\\n self.signout\\r\\n # As Authenticated user but ADMIN\\r\\n self.signin(email=self.root_addr, password=self.root_password)\\r\\n res = self.app.get(url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"Stats page should be shown to admin users\\\"\\r\\n assert dom.find(id='enforce_privacy') is None, err_msg\\r\\n self.signout()\",\n \"def teams(teamid):\\n team_summary = team.TeamSummary(teamid)\\n team_summary_info = team_summary.info()\\n team_season_ranks = team_summary.season_ranks()\\n\\n team_common_roster = team.TeamCommonRoster(teamid)\\n roster = team_common_roster.roster()\\n coaches = team_common_roster.coaches()\\n\\n season = team_summary_info[0][\\\"SEASON_YEAR\\\"]\\n\\n team_game_log = team.TeamGameLogs(teamid,\\n season=season)\\n team_games = team_game_log.info()\\n\\n playoffs_teamgamelogs = team.TeamGameLogs(teamid,\\n season=season,\\n season_type=\\\"Playoffs\\\")\\n playoffs_team_games = playoffs_teamgamelogs.info()\\n\\n team_season = team.TeamSeasons(teamid)\\n team_season_info = team_season.info()\\n\\n for i in team_season_info:\\n if (i[\\\"YEAR\\\"] == season):\\n current_season_info = i\\n\\n return render_template(\\\"teams.html\\\",\\n title=team_summary_info[0][\\\"TEAM_CITY\\\"] + \\\" \\\" + team_summary_info[0][\\\"TEAM_NAME\\\"],\\n teamid=teamid,\\n team_summary_info=team_summary_info,\\n team_season_ranks=team_season_ranks,\\n season=season,\\n team_games=team_games,\\n playoffs_team_games=playoffs_team_games,\\n team_season=team_season_info,\\n roster=roster,\\n coaches=coaches,\\n current_season_info=current_season_info,\\n team_img=TEAM_ID_DATA)\",\n \"def friend_stats(request):\\n \\n r = {}\\n fb_ids = FacebookProfile.objects.all().values(\\\"facebook_id\\\") \\n for u in FacebookProfile.objects.all():\\n friends = Friends.objects.filter(facebook_id__in=fb_ids)\\n num_friends = Friends.objects.filter(facebook_id=u.facebook_id, friends__in=friends).count()\\n participant = OTNUser.objects.get(facebook_profile__facebook_id=u.facebook_id)\\n r[u.facebook_id]=\\\"%s (%d): %d\\\"%(participant.name, participant.id, num_friends)\\n\\n return JSONHttpResponse(r)\",\n \"def stats(request):\\n stats = []\\n activeProject = None\\n activity = get_activity()\\n if activity:\\n activeProject = activity.project\\n projects = Project.objects.filter(company__id=1).order_by('name')\\n for project in projects:\\n isCurrent = (activeProject != None) and (project.id == activeProject.id)\\n # If this is NOT the currently selected project...\\n if (not isCurrent) or (not activeProject):\\n # If this project is password protected, skip it.\\n if (project.password != None) and (len(project.password) > 0):\\n continue\\n sessions = Session.objects.filter(project=project,\\n endtime__gt=F('starttime') +\\n timedelta(minutes=3))\\n files = File.objects.filter(project=project)\\n fileactions = Fileaction.objects.filter(file__in=files)\\n events = Event.objects.filter(session__in=sessions)\\n sQuery = {\\n 'avg': 'SUM(TIMESTAMPDIFF(SECOND, starttime, endtime)) / COUNT(*)',\\n 'min': 'MIN(TIMESTAMPDIFF(SECOND, starttime, endtime))',\\n 'max': 'MAX(TIMESTAMPDIFF(SECOND, starttime, endtime))',\\n 'count': 'COUNT(*)'\\n }\\n sessions = sessions.extra(select=sQuery)\\n sessions = sessions.values_list('avg', 'min', 'max', 'count').get()\\n session_average_duration = 0\\n session_min_duration = 0\\n session_max_duration = 0\\n if sessions[0] is not None:\\n session_average_duration = int(sessions[0])\\n if sessions[1] is not None:\\n session_min_duration = int(sessions[1])\\n if sessions[2] is not None:\\n session_max_duration = int(sessions[2])\\n session_count = sessions[3]\\n statsdata = {\\n 'selected': isCurrent,\\n 'name': project.name,\\n 'session_average_duration': session_average_duration,\\n 'session_min_duration': session_min_duration,\\n 'session_max_duration': session_max_duration,\\n 'session_count': session_count,\\n 'file_count': files.count(),\\n 'fileaction_count': fileactions.count(),\\n 'event_count': events.count()\\n }\\n stats.append(statsdata)\\n return render_to_response(\\n 'stats.html',\\n {\\n 'stats': stats,\\n 'tab': 'stats'\\n },\\n context_instance=RequestContext(request)\\n )\",\n \"def view_team_page(request, team_pk):\\n\\t\\n\\tselected_team = ChallengeTeam.objects.get(pk = team_pk)\\n\\t\\n\\tusers = selected_team.team_members.all()\\n\\t\\n\\tteam_name = selected_team.team_name\\n\\t\\n\\tall_results = get_team_results(users, selected_team.challenge.schedule)\\n\\tteam_consistency = all_results[\\\"consistency\\\"]\\n\\tteam_completion = all_results[\\\"completion\\\"]\\n\\t\\n\\tmember_names = []\\n\\tfor usr in users:\\n\\t\\tprint usr.first_name + \\\" \\\" + usr.last_name\\n\\t\\tmember_names.append(usr.first_name + \\\" \\\" + usr.last_name)\\n\\t\\t\\n\\tjoin_control = \\\"join\\\"\\n\\tif(request.user in selected_team.team_members.all()):\\n\\t\\tjoin_control = \\\"leave\\\"\\n\\telif(selected_team.challenge.invite_only and not request.user in selected_team.invited.all()):\\n\\t\\tjoin_control = \\\"invite\\\"\\n\\t\\n\\tcontext = RequestContext(request, { \\\"team_pk\\\" : team_pk, \\\"name\\\" : team_name, \\\"members\\\" : member_names, \\\"consistency\\\" : team_consistency, \\\"completion\\\" : team_completion, \\\"join_control\\\" : join_control, \\\"messages\\\" : messages })\\n\\treturn render_to_response(\\\"encourage/view_team.html\\\", context)\",\n \"def teamstats(team_id, conn, curr):\\n \\n title = \\\"Team Points Breakdown for Team {}\\\".format(team_id)\\n cmd = \\\"\\\"\\\"\\n SELECT user_id, event_id, team_id, score FROM PlayerStats\\n WHERE team_id={}\\n \\\"\\\"\\\".format(team_id)\\n curr.execute(cmd)\\n teamscores = curr.fetchall()\\n player_dict = {}\\n event_set = set()\\n data = []\\n for player in teamscores:\\n user_id = player[0]\\n event_id = player[1]\\n score = player[3]\\n if user_id in player_dict:\\n player_dict[user_id][event_id] = score\\n else:\\n player_dict[user_id] = {event_id: score}\\n event_set.add(event_id)\\n\\n color_index = 0\\n for player, score in player_dict.items():\\n scores = []\\n for event_id in event_set:\\n scores.append(player_dict[player][event_id])\\n width = 0\\n if len(event_set) == 1:\\n data.append(\\n Bar(\\n x=['event {}'.format(str(event_id)) for event_id in list(event_set)],\\n y=scores,\\n name='Player ID {}'.format(player),\\n width=.4,\\n marker=dict(\\n color=COLORS[color_index]\\n )\\n )\\n )\\n else:\\n data.append(\\n Bar(\\n x=['event {}'.format(str(event_id)) for event_id in list(event_set)],\\n y=scores,\\n name='Player ID {}'.format(player),\\n marker=dict(\\n color=COLORS[color_index]\\n )\\n )\\n )\\n color_index += 1\\n\\n layout = Layout(\\n barmode='stack',\\n title=title,\\n yaxis=dict(\\n title='Points'\\n ),\\n xaxis=dict(\\n title='Events'\\n )\\n )\\n fig = plotly.graph_objs.Figure(data=data, layout=layout)\\n return fig\",\n \"def team_tester(request):\\n\\n\\t# Look for the team size entered by the user\\n\\tteam_size = int(request.GET.get('team_size', False))\\n\\n\\t# If user has entered information...\\n\\tif team_size:\\n\\n\\t\\t# Get the rest of the information from the form\\n\\t\\tscores_up = int(request.GET.get('scores_up', False))\\n\\t\\tscores_count = int(request.GET.get('scores_count', False))\\n\\t\\tsumstat = request.GET.get('sumstat', False)\\n\\t\\ttime = request.GET.get('time', False)\\n\\t\\tgymnast_list = []\\n\\t\\tfor i in range(1, team_size+1):\\n\\t\\t\\tgymnast_search_id = \\\"gymnast_search\\\" + str(i)\\n\\t\\t\\tgymnast_list.append(request.GET.get(gymnast_search_id, False))\\n\\n\\t\\t# Set the date range \\n\\t\\tnow = datetime.datetime.now()\\n\\t\\tif time==\\\"year\\\":\\n\\t\\t\\tdate_range = [now-relativedelta(years=1), now]\\n\\t\\telif time == \\\"season\\\":\\n\\t\\t\\tdate_range = [datetime.date(2019, 10, 13), now] # Since last world championships\\n\\t\\telse:\\n\\t\\t\\tdate_range = [datetime.date(2016, 8, 21), now] # Since last olympics\\n\\n\\t\\t# Loop through the list of gymnasts and get scores\\n\\t\\ttable_data = []\\n\\t\\tfor gymnast in gymnast_list:\\n\\t\\t\\tgymnast = Gymnast.objects.get(name=gymnast)\\n\\t\\t\\tthis_gymnast_scores = []\\n\\t\\t\\tthis_gymnast_scores.append(gymnast)\\n\\t\\t\\tfor sub_event in [\\\"VT\\\", \\\"UB\\\", \\\"BB\\\", \\\"FX\\\"]:\\n\\t\\t\\t\\tscores = Score.objects.filter(gymnast=gymnast, \\n\\t\\t\\t\\t\\tmeet__in=Meet.objects.filter(start_date__range=date_range), event__in=Event.objects.filter(name=sub_event), score_num=1)\\n\\t\\t\\t\\tif scores.count() > 0:\\n\\t\\t\\t\\t\\tif sumstat == \\\"avg\\\":\\n\\t\\t\\t\\t\\t\\tscores_sumstat = scores.aggregate(Avg('score'))['score__avg']\\n\\t\\t\\t\\t\\telif sumstat == \\\"max\\\":\\n\\t\\t\\t\\t\\t\\tscores_sumstat = scores.aggregate(Max('score'))['score__max']\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tscores_sumstat = \\\"\\\"\\n\\t\\t\\t\\tthis_gymnast_scores.append(scores_sumstat)\\n\\t\\t\\ttable_data.append(this_gymnast_scores)\\n\\n\\t\\t# Select the scores that go up and the scores that count\\n\\t\\tfor i in range(1, 5):\\n\\t\\t\\t# Get the list of all scores on this event\\n\\t\\t\\tevent_scores = [col[i] for col in table_data]\\n\\t\\t\\t# Get the sort order of these scores\\n\\t\\t\\tsort_order = np.argsort(np.argsort(event_scores)) # See https://github.com/numpy/numpy/issues/8757\\n\\t\\t\\tsort_order = team_size - 1 - sort_order\\n\\t\\t\\t# Replace each score with a tuple of the score and the class that we'll use for the td of each score\\n\\t\\t\\tfor j, row in enumerate(table_data):\\n\\t\\t\\t\\t# For scores that count\\n\\t\\t\\t\\tif sort_order[j] < scores_count:\\n\\t\\t\\t\\t\\ttable_data[j][i] = [table_data[j][i], \\\"counts\\\"]\\n\\t\\t\\t\\telif sort_order[j] < scores_up:\\n\\t\\t\\t\\t\\ttable_data[j][i] = [table_data[j][i], \\\"up\\\"]\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\ttable_data[j][i] = [table_data[j][i], \\\"not_used\\\"]\\n\\n\\t\\t# Calculate total row\\n\\t\\ttotal_row = [\\\"Team Total\\\", 0, 0, 0, 0]\\n\\t\\tfor row in table_data:\\n\\t\\t\\tfor i in range(1, 5):\\n\\t\\t\\t\\tif row[i][1] == \\\"counts\\\" and (not isinstance(row[i][0], str)):\\n\\t\\t\\t\\t\\ttotal_row[i] = total_row[i] + row[i][0]\\n\\t\\ttable_data.append(total_row)\\n\\t\\tteam_total = sum(total_row[1:5])\\n\\t\\tprint(table_data)\\n\\telse:\\n\\t\\tteam_size=5\\n\\t\\tscores_up=4\\n\\t\\tscores_count=3\\n\\t\\tsumstat = \\\"avg\\\"\\n\\t\\ttime = \\\"year\\\"\\n\\t\\tgymnast_list = []\\n\\t\\ttable_data = []\\n\\t\\tteam_total = \\\"\\\"\\n\\n\\n\\n\\tcontext = {\\n\\t\\t'team_size': team_size,\\n\\t\\t'scores_up': scores_up,\\n\\t\\t'scores_count': scores_count,\\n\\t\\t'sumstat': sumstat,\\n\\t\\t'time': time,\\n\\t\\t'gymnast_list': gymnast_list,\\n\\t\\t'table_data': table_data,\\n\\t\\t'team_total': team_total,\\n\\t}\\n\\n\\treturn render(request, 'team_tester.html', context=context)\",\n \"def team_list(request):\\n template = loader.get_template('team/team_list.html')\\n teams_list = Team.objects.all().order_by('name')\\n\\n if not request.user.is_authenticated:\\n team = None\\n else:\\n team = request.user.profile.team\\n\\n paginator = Paginator(teams_list, 6)\\n\\n page = request.GET.get('page')\\n try:\\n teams = paginator.page(page)\\n except PageNotAnInteger:\\n # If page is not an integer, deliver first page.\\n teams = paginator.page(1)\\n except EmptyPage:\\n # If page is out of range (e.g. 9999), deliver last page of results.\\n teams = paginator.page(paginator.num_pages)\\n\\n context = {\\n 'teams': teams,\\n 'team': team\\n }\\n\\n return CustomHttpResponse.send(template, context, request)\",\n \"async def stats(self, ctx: Message):\\n\\t\\tawait self.open_account(ctx.author.id, ctx.author.username)\\n\\t\\tuserid = ctx.author.id\\n\\t\\tusers = await self.get_stats_data()\\n\\n\\t\\trancmds = users[str(userid)][\\\"rancmd\\\"]\\n\\t\\txp = users[str(userid)][\\\"xp\\\"]\\n\\t\\tmsgs = users[str(userid)][\\\"sentmsgs\\\"]\\n\\t\\twhisperto = [ctx.author.id]\\n\\t\\tawait self.send(message=f\\\"{ctx.author.mention} Here are your stats! • Ran {rancmds} DogeBoss commands • XP: {xp} • Sent {msgs} messages\\\", whisper=whisperto)\",\n \"def team_details(request, id):\\n template = loader.get_template('team/details.html')\\n\\n try:\\n team = Team.objects.get(pk=id)\\n team_members = User.objects.filter(profile__team=team)\\n\\n context = {\\n 'team_name': team.name,\\n 'team_info': team.information,\\n 'team_logo': team.logo,\\n 'team_members': team_members,\\n 'days': Information.getDaysToContest()\\n }\\n\\n except Team.DoesNotExist:\\n context = None\\n\\n return CustomHttpResponse.send(template, context, request)\",\n \"def get_team_stats(self):\\n if self.team_id is None:\\n raise RuntimeError(\\\"Please set team_id before calling\\\"\\n \\\"get_team_stats\\\")\\n url = '%s/api/v4/teams/%s/stats' % (self.server_url, self.team_id)\\n response = self._request(self._session.get, url)\\n return json.loads(response.content)\",\n \"def index(request):\\r\\n return render(request, 'team_tasks_managers/index.html')\",\n \"def statistics(self, request):\\n context = {\\\"uid\\\": request.session.get(\\\"uid\\\", None),\\n \\\"is_auth\\\": request.session.get(\\\"is_auth\\\", False),\\n \\\"is_daily\\\": request.session.get(\\\"is_daily\\\", False),\\n \\\"is_leader\\\": request.session.get(\\\"is_leader\\\", False),\\n }\\n\\n context.update({\\\"statistics\\\": self.utils.get_statistics()})\\n # \\\"dates\\\": self.utils.get_artefacts_dates()})\\n return render_to_response(\\\"crisis/statistics.html\\\",\\n context,\\n context_instance=RequestContext(request))\",\n \"def getTeamStat(self, year = 2014):\\r\\n \\r\\n year_next = (year % 100) + 1\\r\\n season = str(year) + '-' + str(year_next)\\r\\n \\r\\n stat_url = 'http://stats.nba.com/stats/leaguedashteamstats?Conference=&'\\\\\\r\\n 'DateFrom=&DateTo=&Division=&GameScope=&GameSegment=&'\\\\\\r\\n 'LastNGames=0&LeagueID=00&Location=&MeasureType=Base&'\\\\\\r\\n 'Month=0&OpponentTeamID=0&Outcome=&PORound=0&PaceAdjust=N&'\\\\\\r\\n 'PerMode=PerGame&Period=0&PlayerExperience=&PlayerPosition=&'\\\\\\r\\n 'PlusMinus=N&Rank=N&Season=' + season + '&SeasonSegment=&'\\\\\\r\\n 'SeasonType=Regular+Season&ShotClockRange=&StarterBench=&'\\\\\\r\\n 'TeamID=0&VsConference=&VsDivision='\\r\\n \\r\\n response = requests.get(stat_url)\\r\\n data = json.loads(response.text)\\r\\n \\r\\n headers = data['resultSets'][0]['headers']\\r\\n stat_data = data['resultSets'][0]['rowSet']\\r\\n df = pd.DataFrame(stat_data,columns=headers) \\r\\n \\r\\n team_df = df[[\\\"TEAM_ID\\\",\\\"TEAM_NAME\\\",\\\"GP\\\",\\\"W\\\",\\\"L\\\",\\\"W_PCT\\\",\\\"MIN\\\",\\\"FGM\\\",\\r\\n \\\"FGA\\\",\\\"FG_PCT\\\",\\\"FG3M\\\",\\\"FG3A\\\",\\\"FG3_PCT\\\",\\\"FTM\\\",\\\"FTA\\\",\\\"FT_PCT\\\",\\r\\n \\\"OREB\\\",\\\"DREB\\\",\\\"REB\\\",\\\"AST\\\",\\\"TOV\\\",\\\"STL\\\",\\\"BLK\\\",\\\"BLKA\\\",\\\"PF\\\",\\r\\n \\\"PFD\\\",\\\"PTS\\\",\\\"PLUS_MINUS\\\"]]\\r\\n \\r\\n return team_df\",\n \"def userstats(request):\\r\\n with ReqAuthorize(request):\\r\\n user = UserMgr.get(username=request.user.username)\\r\\n return {\\r\\n 'user': user,\\r\\n 'username': user.username,\\r\\n }\",\n \"def get_teams():\",\n \"def fullleaderboard (request):\\n # Define views here\\n score_submit = EventEntryModel.objects.exclude(winner__isnull=True).count()\\n active_players = PlayerModel.objects.all()\\n\\n loaded_points = list(EventEntryModel.objects.aggregate(Sum('points')).values())[0]\\n awarded_points = list(EventEntryModel.objects.exclude(winner__isnull=True).aggregate(Sum('points')).values())[0]\\n\\n context = {\\n 'score_submit': score_submit,\\n 'active_players': active_players,\\n 'loaded_points': loaded_points,\\n 'awarded_points': awarded_points,\\n }\\n\\n return render(request, 'fullLeaderboard.html', context=context)\",\n \"def display_user_stats(self):\\n\\n self.user_frame = tk.LabelFrame(\\n self.stats_frame, text=\\\"User Types\\\", padx=5, pady=5\\n )\\n self.user_frame.grid(row=3, padx=5, pady=5, sticky=\\\"w\\\")\\n self.user_stats_data = tk.Label(self.user_frame, justify=\\\"left\\\")\\n self.user_stats_data.pack()\\n\\n self.gender_frame = tk.LabelFrame(\\n self.stats_frame, text=\\\"User Gender\\\", padx=5, pady=5\\n )\\n self.gender_frame.grid(row=3, column=1, padx=5, pady=5, sticky=\\\"w\\\")\\n self.gender_stats_data = tk.Label(self.gender_frame, justify=\\\"left\\\")\\n self.gender_stats_data.pack()\\n\\n self.age_frame = stat_display_labels(\\n self.stats_frame,\\n \\\"Age Stats\\\",\\n [\\n \\\"Oldest Rider Birth Year:\\\",\\n \\\"Youngest Rider Birth Year:\\\",\\n \\\"Most common birth year:\\\",\\n ],\\n row=4,\\n columnspan=2,\\n )\\n self.age_stats_data = tk.Label(self.age_frame, justify=\\\"left\\\")\\n self.age_stats_data.grid(row=0, column=1)\",\n \"async def report(self, ctx):\\n try:\\n members = self.bot.get_all_members()\\n online, offline, other = 0,0,0\\n for member in members:\\n if member.status.online:\\n online += 1\\n elif member.status.offline:\\n offline += 1\\n else:\\n other += 1\\n message = discord.Embed(title='Server report',type='rich', colour=discord.Color(0xffb6c1))\\n message.add_field(name='Online',value='**{}** online members'.format(online))\\n message.add_field(name='Offline',value='**{}** offline members'.format(offline))\\n message.add_field(name='Other',value='**{}** other members'.format(other))\\n await self.bot.say(embed=message)\\n\\n except Exception as error:\\n await self.bot.say('The report has failed !')\\n self.logger.error(error)\",\n \"def statistics(request, id):\\n election = get_object_or_404(Election, pk=id)\\n return render_to_response('django_elect/statistics.html', {\\n 'title': \\\"Election Statistics\\\",\\n 'election': election,\\n })\",\n \"def appointment_stats(request):\\n # TODO: Consider/Look into Django cache framework\\n # Default is to load up yesterday's stats\\n data = {}\\n if request.GET.get('lookback'):\\n data['lookback'] = request.GET.get('lookback')\\n appointments = get_appointments_word(request.GET.get('lookback'))\\n else:\\n data['lookback'] = 'yesterday'\\n appointments = get_appointments_word('yesterday')\\n data.update(get_appointment_stats(appointments))\\n return render(request, 'doctor/stats.html', data)\",\n \"def statistics(username, root_wf_id, wf_id):\\n dashboard = Dashboard(g.master_db_url, root_wf_id, wf_id)\\n summary_times = dashboard.workflow_summary_stats(wf_id)\\n\\n for key, value in summary_times.items():\\n summary_times[key] = filters.time_to_str(value)\\n\\n workflow_stats = dashboard.workflow_stats()\\n\\n return render_template('workflow/statistics.html', root_wf_id=root_wf_id, wf_id=wf_id, summary_stats=summary_times, workflow_stats=workflow_stats)\",\n \"def get_team_stats() -> List[BaseStat]:\\n return [PossessionStat(),\\n TeamTendencies(),\\n RelativeTendencies(),\\n PerPossessionStat(),\\n RumbleItemStat(),\\n PreRumbleGoals(),\\n DropshotStats()\\n ]\",\n \"def user_stats(request):\\r\\n user_count = UserMgr.count()\\r\\n pending_activations = ActivationMgr.count()\\r\\n users_with_bookmarks = BmarkMgr.count(distinct_users=True)\\r\\n return _api_response(request, {\\r\\n 'count': user_count,\\r\\n 'activations': pending_activations,\\r\\n 'with_bookmarks': users_with_bookmarks\\r\\n })\",\n \"def get_player_stats_from_game(team, year, week):\",\n \"def index(request):\\n home_user = request.user.profile\\n \\\"\\\"\\\"num_activities = Activity.objects.count()\\\"\\\"\\\"\\n Cactivity = CompletedActivity.objects.filter(user=home_user)\\n UActivity = Cactivity.values('activity_id', 'activity__name', 'activity__value', 'activity__group').annotate \\\\\\n (count=Count('activity__name'), earned=Sum('activity__value'))\\n TimesCompelted = Cactivity.annotate(count=Count('activity__name'))\\n # Generate counts of some of the main objects\\n\\n context = {\\n 'huser': home_user,\\n 'Lname' : home_user.user.last_name,\\n 'Fname': home_user.user.first_name,\\n 'num_activities': 1,\\n 'activity_list' : UActivity,\\n \\\"times_completed\\\" : TimesCompelted\\n }\\n\\n # Render the HTML template index.html with the data in the context variable\\n return render(request, 'index.html', context=context)\",\n \"def get(self):\\n\\n usrs = get_mapviewers(24)\\n for usr in usrs:\\n logging.info(\\\"Mapviewer: \\\" + usr.loginuser);\\n\\n template_values = {\\n 'mapviewers': usrs,\\n }\\n\\n logging.info(\\\"Showusers visited.\\\")\\n template = JINJA_ENVIRONMENT.get_template('showusers.html')\\n self.response.write(template.render(template_values))\",\n \"async def get_team_embed(self):\\r\\n embed = discord.Embed(title=\\\"Valorant 10 Man Bot\\\",colour=discord.Colour(0x470386))\\r\\n team_a_strings = [get_member_name(m,lower=False) for m in self.teams[\\\"A\\\"]]\\r\\n team_b_strings = [get_member_name(m,lower=False) for m in self.teams[\\\"B\\\"]] \\r\\n embed.add_field(name=\\\"Defenders\\\", value=\\\"{}\\\".format(\\\"\\\\n\\\".join(team_a_strings)), inline=True)\\r\\n embed.add_field(name=\\\"Attackers\\\", value=\\\"{}\\\".format(\\\"\\\\n\\\".join(team_b_strings)), inline=True)\\r\\n return embed\",\n \"def stats(self):\",\n \"def reports_stats(request):\\n\\n username = request.session.get('username', False)\\n profile = request.session.get('profile', False)\\n if(username):\\n context = {'username': username,'profile':profile}\\n return render(request, 'MedTAG_sket_dock_App/index.html', context)\\n else:\\n return redirect('MedTAG_sket_dock_App:login')\",\n \"def get_stats(self):\\n _url = f\\\"{self.connector.base_url}/projects/{self.project_id}/stats\\\"\\n\\n _response = self.connector.http_call(\\\"get\\\", _url)\\n\\n # Update object\\n self.stats = _response.json()\",\n \"async def team_show(self, ctx: commands.Context, team_id: int):\\n try:\\n if team_id not in self.teams:\\n self.teams[team_id] = await self._get_team_data(team_id)\\n team = self.teams[team_id]\\n except KeyError:\\n await ctx.send(f'Unrecognized team ID {team_id}. If you think this is a '\\n 'valid team ID, perhaps no one from that team has '\\n 'registered a Discord account yet.')\\n return\\n\\n if ctx.guild:\\n members, users = self._get_members_if_possible(\\n [user.id for user in team.users], ctx.guild)\\n else:\\n members, users = [], team.users\\n\\n pages = paginate_team_data(members, users,\\n [channel for channel in team.channels\\n if channel and channel.guild == ctx.guild])\\n\\n embeds = [\\n discord.Embed(title=f'**{team.display_name} (ID: {team.team_id})**',\\n color=discord.Color(0x22aaff),\\n description=content)\\n for content in pages]\\n if len(embeds) == 1:\\n await ctx.send(embed=embeds[0])\\n else:\\n await menu(ctx, embeds, DEFAULT_CONTROLS, timeout=120)\",\n \"def team_wakacms_membership(request):\\n team = Team.objects.get(slug=TEAM_SLUG)\\n return {\\n 'is_wakacms_team_member': team.user_is_member(request.user),\\n }\",\n \"def printPlayerStats(self):\\n\\t\\tplayerStats = ['Name = ' + self.name, \\n\\t\\t\\t\\t\\t 'Agility = ' + str(self.agility), \\n\\t\\t\\t\\t\\t 'Personality = ' + str(self.personality), \\n\\t\\t\\t\\t\\t 'Sanity = ' + str(self.sanity), \\n\\t\\t\\t\\t\\t 'Strength = ' + str(self.strength), \\n\\t\\t\\t\\t\\t 'Progress = ' + str(self.progress)]\\n\\t\\tprint playerStats\",\n \"def get_users_info(self):\\r\\n headers = {\\\"Content-type\\\": \\\"application/x-www-form-urlencoded\\\", \\\"Accept\\\": \\\"text/plain\\\",\\r\\n 'Referer': 'http://' + self.domain + '/standings.phtml', \\\"User-Agent\\\": user_agent}\\r\\n soup = BeautifulSoup(self.session.get('http://' + self.domain + '/teamInfo.phtml?tid=' + str(self.community_id),\\r\\n headers=headers).content, \\\"html.parser\\\")\\r\\n\\r\\n headers_zo = {'Accept': '*/*', 'Referer': 'http://www.comuniazo.com/comunio/dinero',\\r\\n 'Host': 'www.comuniazo.com', 'X-Requested-With': 'XMLHttpRequest'}\\r\\n money = requests.session()\\r\\n money.get('http://www.comuniazo.com/comunio/dinero', headers=headers_zo)\\r\\n money_bids = json.loads(\\r\\n money.get('http://www.comuniazo.com/ajax/dinero.php?user=%s&dinero=20000000' % self.username,\\r\\n headers=headers_zo).content)\\r\\n\\r\\n info = list()\\r\\n for row in soup.find('table', cellpadding=2).find_all('tr')[1:]:\\r\\n money, max_bid = [0, 0]\\r\\n name = row.a.text\\r\\n user_id = row.find('a')['href'].split('pid=')[1]\\r\\n username = re.findall('\\\\((.+)\\\\)', BeautifulSoup(self.session.get('http://'+self.domain+'/playerInfo.phtml?pid='+user_id, headers=headers).content, \\\"html.parser\\\").h1.text)[0]\\r\\n user_points = int(row.find_all('td')[3].text)\\r\\n team_value = int(row.find_all('td')[4].text.replace('.', ''))\\r\\n for user in money_bids['lista']['players']:\\r\\n if user['id'] == user_id:\\r\\n money = int(user['dinero'].replace('.', ''))\\r\\n max_bid = int(user['puja'].replace('.', ''))\\r\\n info.append([name, username, int(user_id), user_points, team_value, money, max_bid])\\r\\n return info\",\n \"def teams(request):\\n locales = Locale.objects.available().prefetch_related(\\\"latest_translation__user\\\")\\n\\n form = LocaleRequestForm()\\n\\n if not locales:\\n return render(request, \\\"no_projects.html\\\", {\\\"title\\\": \\\"Teams\\\"})\\n\\n return render(\\n request,\\n \\\"teams/teams.html\\\",\\n {\\n \\\"locales\\\": locales,\\n \\\"form\\\": form,\\n \\\"top_instances\\\": locales.get_top_instances(),\\n },\\n )\",\n \"async def dotabuff(self, ctx, *, member: discord.Member=None):\\n if member is None:\\n member = ctx.message.author\\n\\n steam_ids = self.bot.steam_info.get(member.id)\\n\\n if steam_ids is None:\\n await self.bot.say(\\\"{0.name} has not linked their Steam account to MT5ABot.\\\".format(member))\\n return\\n\\n msg = \\\"__Dotabuff page(s) for {0.name}:__\\\\n\\\\n\\\".format(member)\\n try:\\n response = self.steam_api.get_player_summaries(steam_ids)['response']\\n except:\\n await self.bot.say(\\\"The Steam Web API is down. Please try again later.\\\")\\n # Response isn't in a guaranteed order.\\n for steam_id in steam_ids:\\n for player in response['players']:\\n if player['steamid'] == steam_id:\\n dota_id = steamapi.ID.steam_to_dota(steam_id)\\n msg += \\\"{0} - <https://dotabuff.com/players/{1}>\\\\n\\\".format(player['personaname'], dota_id)\\n await self.bot.say(msg)\",\n \"def meta_members():\\n with open(\\\"./team_members.txt\\\") as f:\\n team_members = f.read().strip().split(\\\"\\\\n\\\")\\n return make_json_response(team_members)\",\n \"async def teams(self, ctx, teamsize: int, *member: discord.Member):\\r\\n d = dict()\\r\\n for x in member:\\r\\n if x.name in player.keys():\\r\\n d[x.name] = player[x.name]\\r\\n else:\\r\\n await ctx.send('{} is not a registered player'.format(x.name))\\r\\n return\\r\\n\\r\\n teams = itertools.combinations(d.items(), teamsize) # divide the players up into all possible teams: AB, AC...\\r\\n matchups = itertools.combinations(teams, 2) # all possible match ups from all possible teams\\r\\n\\r\\n report = '```md' + u\\\"\\\\u000A\\\"\\r\\n\\r\\n for x in matchups:\\r\\n t1 = x[0] # first team\\r\\n t2 = x[1] # second team\\r\\n t1_ratings, t2_ratings, t1_names, t2_names = ([] for n in range(4))\\r\\n for i in range(teamsize):\\r\\n t1_ratings.append(t1[i][1])\\r\\n t2_ratings.append(t2[i][1])\\r\\n t1_names.append(t1[i][0])\\r\\n t2_names.append(t2[i][0])\\r\\n qual = quality([t1_ratings, t2_ratings])\\r\\n # ignore all match ups with players on the same team...\\r\\n if len(set(t1_names).intersection(set(t2_names))) == 0:\\r\\n report += ', '.join(t1_names) + ' <vs> ' + ', '.join(t2_names) + u\\\"\\\\u000A\\\" + '> Quality: ' + str(\\r\\n round(qual, 3)) + u\\\"\\\\u000A\\\"\\r\\n\\r\\n await ctx.send(report + u\\\"\\\\u000A\\\" + '```')\",\n \"def summary_page() :\\r\\n logger.debug(\\\"\\\")\\r\\n model = session_info.get_user_model(session)\\r\\n return render_template( \\\"summary_page.html\\\" , model=model ,\\r\\n stat_types=param_stats.StatTypes )\",\n \"def main_screen():\\n if current_user.is_authenticated:\\n\\n activities = Run.query.filter_by(user_id=current_user.get_id())\\n\\n total_duration = sum([a.duration for a in activities.all()])\\n hours = str(int(total_duration//3600))\\n minutes = str(int((total_duration % 3600)//60)).rjust(2, '0')\\n seconds = str(int(total_duration % 60)).rjust(2, '0')\\n\\n stats = {'count': len(activities.all()),\\n 'total_distance': sum([a.distance for a in activities.all()]),\\n 'total_hours': hours,\\n 'total_minutes': minutes,\\n 'total_seconds': seconds,\\n 'total_calories': sum([a.calories for a in activities.all()])}\\n\\n return render_template('main.html', stats=stats)\\n else:\\n return render_template('main.html')\",\n \"def get(self, request, *args, **kwargs):\\n\\n # Common for all members views ===================================================\\n l_categories = Category.objects.all()\\n l_attributes = Attribute.objects.all()\\n try:\\n logged_member = Member.objects.get(django_user__username=request.user)\\n show_describe_button = logged_member.is_editor(request)\\n is_monthly_member = logged_member.is_monthly_member()\\n is_yearly_member = logged_member.is_yearly_member()\\n except ObjectDoesNotExist:\\n logged_member = None\\n except:\\n raise HttpResponseNotFound\\n\\n\\n # END Common for all members views ===============================================\\n l_squarefollowing_queryset = SquareFollowing.objects.filter(member_id2=logged_member)\\n\\n #for x in l_squarefollowing_queryset:\\n # for y in x.\\n\\n # Limit calculation --------------------------------------------------------------\\n logged_member.refresh_api_limits(request)\\n x_ratelimit_remaining, x_ratelimit = logged_member.get_api_limits()\\n\\n x_ratelimit_used = x_ratelimit - x_ratelimit_remaining\\n if x_ratelimit != 0:\\n x_limit_pct = (x_ratelimit_used / x_ratelimit) * 100\\n else:\\n x_limit_pct = 100\\n # END Limit calculation ----------------------------------------------------------\\n\\n return render(request,\\n self.template_name,\\n dict(\\n l_squarefollowing_queryset=l_squarefollowing_queryset,\\n\\n is_monthly_member=is_monthly_member,\\n is_yearly_member=is_yearly_member,\\n logged_member=logged_member,\\n x_ratelimit_remaining=x_ratelimit_remaining,\\n x_ratelimit=x_ratelimit,\\n x_limit_pct=x_limit_pct,\\n categories=l_categories,\\n attributes=l_attributes,\\n )\\n )\",\n \"def stats(userid, args):\\r\\n if len(args):\\r\\n playerToTest = es.getuserid(str(args))\\r\\n if playerToTest is None:\\r\\n playerToTest = userid\\r\\n buildStatsMenu(userid, playerToTest)\\r\\n else:\\r\\n buildStatsMenu(userid, userid)\",\n \"def stats(self):\\n try:\\n slack_id = MENTION_RE.search(self.command_body).groups()[0]\\n except AttributeError:\\n slack_id = None\\n\\n if slack_id:\\n users = [UserManager.get_by_slack_id(slack_id)]\\n else:\\n users = self.session.query(User).filter(User.tea_type.isnot(None)).all()\\n\\n results = []\\n\\n for user in users:\\n results.append({\\n 'real_name': user.real_name,\\n 'teas_drunk': user.teas_drunk,\\n 'teas_brewed': user.teas_brewed,\\n 'times_brewed': user.times_brewed,\\n 'teas_received': user.teas_received\\n })\\n\\n return post_message('', self.channel, attachments=[\\n {\\n \\\"fallback\\\": \\\"Teabot Stats\\\",\\n \\\"pretext\\\": \\\"\\\",\\n \\\"author_name\\\": \\\"%s\\\" % result['real_name'],\\n \\\"fields\\\": [\\n {\\n \\\"value\\\": \\\"Number of tea cups consumed -> %(teas_drunk)s\\\\nNumber of tea cups brewed -> %(teas_brewed)s\\\\nNumber of times you've brewed tea -> %(times_brewed)s\\\\nNumber of tea cups you were served -> %(teas_received)s\\\" % result,\\n \\\"short\\\": False\\n },\\n ]\\n }\\n for result in results\\n ])\",\n \"def leaderboard(request):\\r\\n\\tMEDIA_URL = '/media/'\\r\\n\\tgames = Game.objects.all()\\r\\n\\tuser_high_scores = []\\r\\n\\tgame_high_scores = []\\r\\n\\tnew = {}\\r\\n\\t# Get global scores\\r\\n\\tfor game in games:\\r\\n\\t\\tgame_intermediate_high = Score.objects.filter(game = game.id).order_by('-current_score').values('game__name', 'player__user__username', 'current_score')[:1]\\r\\n\\t\\tif (game_intermediate_high.count() > 0):\\r\\n\\t\\t\\tgame_high_scores.append(game_intermediate_high)\\r\\n\\t# Check if user is authenticated and get user's scores\\r\\n\\tif (request.user.is_authenticated):\\r\\n\\t\\tfor game in games:\\r\\n\\t\\t\\t\\tuser_intermediate_high = Score.objects.filter(game=game.id, player = request.user.profile).order_by('-current_score').values('player__user__username','game__name', 'current_score').distinct()[:1]\\r\\n\\t\\t\\t\\tif (user_intermediate_high.count() > 0):\\r\\n\\t\\t\\t\\t\\tuser_high_scores.append(user_intermediate_high)\\r\\n\\r\\n\\treturn render(request, 'leaderboard.html',{'MEDIA_URL' : MEDIA_URL,'games': games, 'user_high_scores': user_high_scores, 'game_high_scores': game_high_scores})\",\n \"async def userinfo(self, ctx: \\\"IceTeaContext\\\", target: discord.Member = None):\\n target = target or ctx.author\\n target_data = ctx.author_data if target == ctx.author else await ctx.get_user_data(target)\\n if target_data:\\n nicknames = await target_data.get_nicknames()\\n else:\\n nicknames = []\\n shared_servers = len([member for member in ctx.bot.get_all_members() if member == target])\\n embed = discord.Embed(title=f\\\"{target.nick or target.name} Profile\\\")\\n embed.set_author(name=f\\\"{target.name} ({target.id})\\\", icon_url=target.avatar_url)\\n embed.set_thumbnail(url=target.avatar_url)\\n embed.add_field(name=\\\"Shared Servers\\\", value=f\\\"{shared_servers} Shared\\\")\\n embed.add_field(name=\\\"Created\\\",\\n value=f\\\"\\\"\\\"{timeago.format(target.created_at)} ({target.created_at.strftime(\\\"%b %d, %Y\\\")})\\\"\\\"\\\")\\n embed.add_field(name=\\\"Joined\\\",\\n value=f\\\"\\\"\\\"{timeago.format(target.joined_at)} ({target.joined_at.strftime(\\\"%b %d, %Y\\\")})\\\"\\\"\\\")\\n embed.set_footer(text=\\\"Last Spoke In server\\\")\\n if target_data:\\n embed.timestamp = target_data.last_spoke\\n else:\\n embed.timestamp = ctx.message.created_at\\n if len(nicknames) > 0:\\n embed.add_field(name=\\\"Nicknames\\\", value=\\\" , \\\".join(str(nick) for nick in nicknames[:5]), inline=False)\\n embed.add_field(name=\\\"Roles\\\", value=\\\" , \\\".join([role.name for role in target.roles[:5] if len(role.name) > 0]),\\n inline=False)\\n if target.activity:\\n if isinstance(target.activity, discord.Spotify):\\n embed.add_field(name=\\\"Currently Listening to\\\",\\n value=f\\\"**{target.activity.title}** by {target.activity.artist} \\\")\\n else:\\n embed.add_field(name=\\\"Currently Playing Since\\\",\\n value=f\\\"{target.activity.name}\\\\n{target.activity.details}\\\\n{target.activity.state}\\\")\\n await ctx.send(embed=embed)\",\n \"def general_timeline():\\n return render_template('timeline.html', general=True, show_username=True)\",\n \"async def stats(self, ctx) -> None:\\n stats = self.bot.numbers.statistics\\n await ctx.send(\\n \\\"\\\",\\n embed=NumEmbed(\\n title=\\\"Number Statistics\\\",\\n colour=0x007E80,\\n fields={\\n \\\"Numbers Given\\\": stats[\\\"numbers_given\\\"],\\n \\\"Even Numbers\\\": stats[\\\"evens\\\"],\\n \\\"Odd Numbers\\\": stats[\\\"odds\\\"],\\n\\n \\\"Highest Number\\\": stats[\\\"highest_info\\\"],\\n \\\"Lowest Positive\\\": stats[\\\"lowest_positive_info\\\"],\\n \\\"Lowest Number\\\": stats[\\\"lowest_info\\\"],\\n\\n \\\"Sum of Numbers\\\": stats[\\\"sum\\\"],\\n \\\"Mean\\\": stats[\\\"mean\\\"],\\n \\\"Median\\\": stats[\\\"median\\\"],\\n\\n \\\"Below 500\\\": stats[\\\"below_500\\\"],\\n \\\"Below 1000\\\": stats[\\\"below_1000\\\"],\\n \\\"Below 2500\\\": stats[\\\"below_2500\\\"],\\n },\\n user=ctx.author,\\n ),\\n )\",\n \"async def team_show_all(self, ctx: commands.Context, n: int=3):\\n lines = [team.table_line(ctx.guild, n) for team in self.teams.values()]\\n lines = [line for line in lines if line]\\n for page in paginate_table(lines):\\n await ctx.send(page)\",\n \"async def stat(ctx):\\r\\n await bot.request_offline_members(ctx.message.server)\\r\\n before = time.time()\\r\\n nicknames = [m.display_name for m in ctx.message.server.members]\\r\\n roles = [m.roles for m in ctx.message.server.members]\\r\\n rn = [[m.name for m in line] for line in roles]\\r\\n with open('temp.csv', mode='w', encoding='utf-8', newline='') as f:\\r\\n writer = csv.writer(f, dialect='excel')\\r\\n for i in range(len(rn)):\\r\\n writer.writerow([nicknames[i], rn[i]])\\r\\n after = time.time()\\r\\n await bot.send_file(ctx.message.author, 'temp.csv', filename='stats.csv',\\r\\n content=\\\"Here you go! Check your PM's. Generated in {:.4}ms.\\\".format((after - before) * 1000))\",\n \"def print_summary_stats(self) -> None:\\n print(\\\"Number of Users: {}\\\".format(len(self.all_users)))\\n print(\\\"Number of Utterances: {}\\\".format(len(self.utterances)))\\n print(\\\"Number of Conversations: {}\\\".format(len(self.conversations)))\",\n \"def stats(self):\\n pass\",\n \"def nflteamrankings(self, irc, msg, args, optteam):\\n \\n optteam = optteam.upper().strip()\\n\\n if optteam not in self._validteams():\\n irc.reply(\\\"Team not found. Must be one of: %s\\\" % self._validteams())\\n return\\n \\n url = self._b64decode('aHR0cDovL2VzcG4uZ28uY29tL25mbC90ZWFtL18vbmFtZQ==') + '/%s/' % optteam\\n\\n try:\\n request = urllib2.Request(url)\\n html = (urllib2.urlopen(request)).read()\\n except:\\n irc.reply(\\\"Cannot open page: %s\\\" % url)\\n return\\n \\n soup = BeautifulSoup(html)\\n div = soup.find('div', attrs={'class':'mod-container mod-stat'}) \\n h3 = div.find('h3')\\n statsfind = div.findAll('div', attrs={'class':re.compile('span-1.*?')})\\n\\n append_list = []\\n \\n for stats in statsfind:\\n header = stats.find('h4')\\n stat = stats.find('span', attrs={'class':'stat'})\\n rank = stat.findNext('strong')\\n append_list.append(ircutils.bold(header.text) + \\\" \\\" + stat.text + \\\" (\\\" + rank.text + \\\")\\\")\\n \\n descstring = string.join([item for item in append_list], \\\" | \\\")\\n irc.reply(ircutils.mircColor(optteam,'red') + \\\" :: \\\" + ircutils.underline(h3.text) + \\\" :: \\\" + descstring)\",\n \"def users_page(request):\",\n \"def get_stats(self):\\n self.stats = self.soup.find('table', id='statsTable')\",\n \"def mlbrosterstats(self, irc, msg, args, optteam):\\n\\n if optteam:\\n optteam = optteam.upper()\\n if optteam not in self._validteams():\\n irc.reply(\\\"Team not found. Must be one of: %s\\\" % self._validteams())\\n return\\n \\n url = self._b64decode('aHR0cDovL2VzcG4uZ28uY29tL21sYi9zdGF0cy9yb3N0ZXJz')\\n\\n try:\\n req = urllib2.Request(url)\\n html = (urllib2.urlopen(req)).read()\\n except:\\n irc.reply(\\\"Failed to open: %s\\\" % url)\\n return\\n \\n soup = BeautifulSoup(html)\\n table = soup.find('table', attrs={'class':'tablehead'})\\n rows = table.findAll('tr')[2:]\\n\\n object_list = []\\n\\n for row in rows:\\n rank = row.find('td')\\n team = rank.findNext('td')\\n rhb = team.findNext('td')\\n lhb = rhb.findNext('td')\\n sh = lhb.findNext('td')\\n rhp = sh.findNext('td')\\n lhp = rhp.findNext('td')\\n ht = lhp.findNext('td')\\n wt = ht.findNext('td')\\n age = wt.findNext('td')\\n young = age.findNext('td')\\n old = young.findNext('td')\\n \\n aString = str(\\\"RHB: \\\" + rhb.getText() + \\\" LHB: \\\" + lhb.getText() + \\\" SH: \\\" + sh.getText() + \\\" RHP: \\\" + rhp.getText() + \\\" LHP: \\\" + lhp.getText()\\\\\\n + \\\" AVG HT: \\\" + ht.getText() + \\\" AVG WEIGHT: \\\" + wt.getText() + \\\" AVG AGE: \\\" + age.getText() + \\\" YOUNGEST: \\\" + young.getText() + \\\" OLDEST: \\\" + old.getText())\\n \\n d = collections.OrderedDict()\\n d['team'] = str(self._translateTeam('team', 'ename', team.getText()))\\n d['data'] = str(aString)\\n object_list.append(d)\\n \\n if optteam:\\n for each in object_list:\\n if each['team'] == optteam: # list will have all teams so we don't need to check\\n output = \\\"{0} Roster Stats :: {1}\\\".format(ircutils.bold(each['team']), each['data'])\\n \\n irc.reply(output)\\n \\n else:\\n \\n youngest_list = []\\n oldest_list = []\\n \\n for each in object_list[0:5]:\\n youngest_list.append(each['team'])\\n for each in object_list[-6:-1]:\\n oldest_list.append(each['team'])\\n \\n output = \\\"{0} :: {1}\\\".format(ircutils.bold(\\\"5 Youngest MLB Teams:\\\"), string.join([item for item in youngest_list], \\\" | \\\"))\\n irc.reply(output)\\n \\n output = \\\"{0} :: {1}\\\".format(ircutils.bold(\\\"5 Oldest MLB Teams:\\\"), string.join([item for item in oldest_list], \\\" | \\\"))\\n irc.reply(output)\",\n \"def seasonStats(personId,type = 'gameLog',group = 'hitting'):\\n\\n #playerInfo = get('people', {'personIds':personId})\\n\\n\\n teamStats = get('person',{ 'ver':'v1' , 'personId':personId,'hydrate':['stats(group={},type={})'.format(group,type),'currentTeam']})\\n return teamStats\\n #iterate of stats and find the right player id\\n #career stats broken\\n #fix the season :2019\\n #make function to get team id\",\n \"async def _stats_heist(self, ctx):\\r\\n author = ctx.message.author\\r\\n avatar = ctx.message.author.avatar_url\\r\\n guild = ctx.guild\\r\\n config = await self.thief.get_guild_settings(guild)\\r\\n theme = await self.thief.get_guild_theme(guild)\\r\\n\\r\\n await self.thief.check_member_settings(author)\\r\\n\\r\\n # Theme variables\\r\\n sentencing = \\\"{} {}\\\".format(theme[\\\"Jail\\\"], theme[\\\"Sentence\\\"])\\r\\n t_bail = \\\"{} Cost\\\".format(theme[\\\"Bail\\\"])\\r\\n\\r\\n # Sentence Time Remaining\\r\\n sentence = await self.thief.get_member_sentence(author)\\r\\n time_served = await self.thief.get_member_timeserved(author)\\r\\n jail_fmt = self.thief.cooldown_calculator(time_served, sentence)\\r\\n\\r\\n # Death Time Remaining\\r\\n death_timer = await self.thief.get_member_deathtimer(author)\\r\\n base_death_timer = config[\\\"Death\\\"]\\r\\n death_fmt = self.thief.cooldown_calculator(death_timer, base_death_timer)\\r\\n\\r\\n rank = self.thief.criminal_level(await self.thief.get_member_crimlevel(author))\\r\\n\\r\\n embed = discord.Embed(colour=0x0066FF, description=rank)\\r\\n embed.title = author.name\\r\\n embed.set_thumbnail(url=avatar)\\r\\n embed.add_field(name=\\\"Status\\\", value=await self.thief.get_member_status(author))\\r\\n embed.add_field(name=\\\"Spree\\\", value=await self.thief.get_member_spree(author))\\r\\n embed.add_field(name=t_bail, value=await self.thief.get_member_bailcost(author))\\r\\n embed.add_field(name=theme[\\\"OOB\\\"], value=await self.thief.get_member_oob(author))\\r\\n embed.add_field(name=sentencing, value=jail_fmt)\\r\\n embed.add_field(name=\\\"Apprehended\\\", value=await self.thief.get_member_jailcounter(author))\\r\\n embed.add_field(name=\\\"Death Timer\\\", value=death_fmt)\\r\\n embed.add_field(name=\\\"Total Deaths\\\", value=await self.thief.get_member_totaldeaths(author))\\r\\n embed.add_field(name=\\\"Lifetime Apprehensions\\\", value=await self.thief.get_member_totaljails(author))\\r\\n\\r\\n await ctx.send(embed=embed)\",\n \"def show_players_specific_tournament(self) -> None:\\n id_choice = check.request_id(TOURNAMENTS)\\n tournament_data = TOURNAMENTS.get(doc_id=id_choice)\\n if tournament_data.get(\\\"players\\\") == {}:\\n print(\\\"\\\\n This tournaments has no players yet\\\")\\n else:\\n players_list = tournament_data.get(\\\"players\\\")\\n deserialized_player_list = []\\n for player_data in players_list:\\n deserialized_player = Player(**json.loads(player_data))\\n deserialized_player_list.append(deserialized_player)\\n utils.clear_terminal()\\n print(\\n \\\"Do you want the list of players by alphabetical order or by ranking ? \\\\n\\\"\\n \\\"1 - Ranking players list \\\\n\\\"\\n \\\"2 - Alphabetical players list\\\"\\n )\\n choice = check.request_selection_with_number(\\\"alphabetical\\\", \\\"ranking\\\", \\\"None\\\")\\n if choice == \\\"alphabetical\\\":\\n utils.clear_terminal()\\n deserialized_player_list = sorted(deserialized_player_list, key=lambda player: player.first_name)\\n for deserialized_player in deserialized_player_list:\\n print(deserialized_player)\\n elif choice == \\\"ranking\\\":\\n utils.clear_terminal()\\n deserialized_player_list = sorted(deserialized_player_list, key=lambda player: player.ranking)\\n for deserialized_player in deserialized_player_list:\\n print(deserialized_player)\",\n \"def global_stats(request):\\n\\n if request.user.is_authenticated:\\n profile: IntervalsProfile = request.user.profile\\n\\n global_correct = 0\\n global_answered = 0\\n\\n for r in profile.all_recent_results().values():\\n global_correct += r.total_correct\\n global_answered += r.total_completed\\n\\n print(global_correct, global_answered)\\n\\n return Response({\\\"global_correct\\\": global_correct,\\n \\\"global_answered\\\": global_answered},\\n status=status.HTTP_201_CREATED)\\n return Response(status=status.HTTP_401_UNAUTHORIZED)\",\n \"def my_stats(request):\\n\\n username = request.session.get('username', False)\\n profile = request.session.get('profile', False)\\n if(username):\\n context = {'username': username,'profile':profile}\\n return render(request, 'MedTAG_sket_dock_App/index.html', context)\\n else:\\n return redirect('MedTAG_sket_dock_App:login')\",\n \"def standings():\\n teams = Team.query.all()\\n teams = list(reversed(sorted(teams, key=lambda team: team.points)))\\n for team in teams:\\n team.logo = url_for('static', filename='images/teams/{}'.format(team.logo_image))\\n\\n return render_template('standings/standings.html', teams=teams, title='Standings')\",\n \"def stats(self):\\n\\n for hero in self.heroes:\\n print(\\\"{} has {} kills and {} deaths.\\\".format(hero.name, hero.kills, hero.deaths))\",\n \"def index(page):\\r\\n per_page = 24\\r\\n count = cached_users.get_total_users()\\r\\n accounts = cached_users.get_users_page(page, per_page)\\r\\n if not accounts and page != 1:\\r\\n abort(404)\\r\\n pagination = Pagination(page, per_page, count)\\r\\n if current_user.is_authenticated():\\r\\n user_id = current_user.id\\r\\n else:\\r\\n user_id = 'anonymous'\\r\\n top_users = cached_users.get_leaderboard(current_app.config['LEADERBOARD'],\\r\\n user_id)\\r\\n return render_template('account/index.html', accounts=accounts,\\r\\n total=count,\\r\\n top_users=top_users,\\r\\n title=\\\"Community\\\", pagination=pagination)\",\n \"def display_tournament_list():\\r\\n for tournament in tournaments_table:\\r\\n print(tournament['Nom'])\",\n \"def main_page(request):\\n\\t\\n\\tall_joined_teams = request.user.joined_teams.all()\\n\\t\\n\\tchallenge_status = []\\n\\t\\n\\tfor team in all_joined_teams:\\n\\t\\tschedule = team.challenge.schedule\\n\\t\\tchallenge = team.challenge\\n\\t\\t\\n\\t\\tresult = get_consistency(request.user, schedule)\\n\\t\\tchallenge_status.append((challenge.pk, team.pk, schedule.pk, challenge.name, team.team_name, schedule.title, result[\\\"consistency\\\"], result[\\\"completion\\\"]))\\n\\t\\t\\n\\tcontext = RequestContext(request, {\\\"challenge_status\\\" : challenge_status, \\\"messages\\\" : messages})\\n\\treturn render_to_response(\\\"encourage/challenge_main.html\\\", context)\",\n \"def my_team(request):\\n template = loader.get_template('team/my_team.html')\\n team = request.user.profile.team\\n\\n if team is not None:\\n team_members = User.objects.filter(profile__team=team)\\n\\n context = {\\n 'team_name': team.name,\\n 'team_members': team_members,\\n 'team_logo': team.logo,\\n 'team_info': team.information\\n }\\n if request.POST.get('save'):\\n if request.POST.get('new_name') != '':\\n new_name = request.POST.get('new_name')\\n team.name = new_name\\n if 'logo_image' in request.FILES:\\n team.logo = request.FILES['logo_image']\\n new_info = request.POST.get('new_info')\\n team.information = new_info\\n team.save()\\n\\n context['team_name'] = team.name\\n context['team_info'] = team.information\\n context['team_logo'] = team.logo\\n\\n if request.POST.get('save_name'):\\n new_name = request.POST.get('new_name')\\n team.name = new_name\\n team.save()\\n context['team_name'] = team.name\\n\\n if request.POST.get('save_info'):\\n new_info = request.POST.get('new_info')\\n team.information = new_info\\n team.save()\\n context['team_info'] = team.information\\n\\n if request.POST.get('save_logo'):\\n team.logo = request.FILES['logo_image']\\n team.save()\\n context['team_logo'] = team.logo\\n\\n if request.POST.get('leave_team'):\\n request.user.profile.team = None\\n request.user.profile.save()\\n context = None\\n return redirect('/')\\n\\n return CustomHttpResponse.send(template, context, request)\\n\\n else:\\n return redirect('/team/new')\",\n \"def show_stats(self):\\n if checkPermission('genweb.webmaster', self.context):\\n return True\",\n \"def display_trip_stats(self):\\n\\n self.trip_frame = stat_display_labels(\\n self.stats_frame,\\n \\\"Trip Stats\\\",\\n [\\\"The total travel time was:\\\", \\\"The mean travel time was:\\\"],\\n row=0,\\n column=2,\\n )\\n self.trip_stats_data = tk.Label(self.trip_frame, justify=\\\"left\\\")\\n self.trip_stats_data.grid(row=0, column=1)\",\n \"def team(request,league_id = 1,team_id = -1):\\n\\n\\t#define variables\\n\\targs = {}\\n\\n\\t#getting data form models\\n\\tthis_team = get_object_or_404(Team, pk = team_id)\\n\\tnews = this_team.news_set.all().order_by('date')\\n\\tthis_league = get_object_or_404(League, pk = league_id)\\n\\tteams = this_league.team_set.all().exclude(pk = team_id).order_by('name')\\n\\tleagues = League.objects.all().exclude(pk = league_id).order_by('name')\\n\\n\\t#context data initialization into dictionary 'args'\\n\\targs['this_team'] = this_team\\n\\targs['this_league'] = this_league\\n\\targs['teams'] = teams\\n\\targs['leagues'] = leagues\\n\\targs['news'] = news\\n\\t\\n\\treturn render_to_response('news/team.html',args)\",\n \"async def stats(self, ctx, user: discord.Member = None):\\n if user is None:\\n await self.guild_tag_stats(ctx)\\n else:\\n await self.member_tag_stats(ctx, user)\",\n \"def fetch_players_stats():\\n players_scraper = PlayerStatsScraper(API_URL, API_HEADERS)\\n result = players_scraper.save_objects()\\n return result\",\n \"def get_people(team):\",\n \"def render(self, mode='human'):\\n index = self._get_current_index()\\n teams = self.teams.iloc[index]\\n teams = teams.itertuples() if isinstance(teams, pandas.DataFrame) else [teams]\\n teams_str = ', '.join(['Home Team {} VS Away Team {}'.format(row.home_team, row.away_team)\\n for row in teams])\\n teams_str = teams_str + \\\".\\\"\\n print(teams_str)\\n super().render(mode)\",\n \"def GetStats(self):\\r\\n\\t\\tArg1 = self.href\\r\\n\\t\\treturn self._execute('GetStats', payload=locals(), response_object=None)\",\n \"async def userstats(self, ctx, *, user):\\n try:\\n converter = commands.MemberConverter()\\n user = await converter.convert(ctx, user)\\n roles = user.roles\\n printedRoles = []\\n for role in roles:\\n if role.name == \\\"@everyone\\\":\\n printedRoles.append(\\\"@everyone\\\")\\n else:\\n printedRoles.append(\\\"<@&{}>\\\".format(role.id))\\n createDate = user.created_at\\n joinDate = user.joined_at\\n rolesStr = \\\", \\\".join(printedRoles)\\n memActivity = user.activity.name if user.activity is not None else \\\"None\\\"\\n embed_userstats = discord.Embed(title=\\\"User Statistics\\\", description=f\\\"This embed will show some general and guild information about {user}!\\\", color=0x0000FF)\\n embed_userstats.set_thumbnail(url=user.avatar_url)\\n embed_userstats.add_field(name=\\\"Username\\\", value=user.display_name, inline=False)\\n embed_userstats.add_field(name=\\\"ID\\\", value=user.id, inline=False)\\n embed_userstats.add_field(name=\\\"Nickname\\\", value=user.nick, inline=False)\\n embed_userstats.add_field(name=\\\"Activity\\\", value=memActivity, inline=False)\\n embed_userstats.add_field(name=\\\"Roles\\\", value=rolesStr, inline=False)\\n embed_userstats.add_field(name=\\\"Date of Account Creation\\\", value=createDate.strftime(\\\"%A, %d. %B %Y %H:%M\\\"), inline=False)\\n embed_userstats.add_field(name=\\\"Date of Guild Join\\\", value=joinDate.strftime(\\\"%A, %d. %B %Y %H:%M\\\"), inline=False)\\n await esay(ctx, embed_userstats)\\n except commands.CommandError:\\n if user.isdigit():\\n user = await self.bot.get_user_info(user_id=user)\\n createDate = user.created_at\\n embed_userstats_out = discord.Embed(title=\\\"User Statistics\\\", description=f\\\"{user} has not been found inside of the current guild, which means that the global user list has been used to identify the user. The data this instance can deliver is limited.\\\", color=0x0000FF)\\n embed_userstats_out.set_thumbnail(url=user.avatar_url)\\n embed_userstats_out.add_field(name=\\\"Username#Discriminator\\\", value=user)\\n embed_userstats_out.add_field(name=\\\"ID\\\", value=user.id)\\n embed_userstats_out.add_field(name=\\\"Date of Account Creation\\\", value=createDate.strftime(\\\"%A, %d. %B %Y %H:%M\\\"))\\n await esay(ctx, embed_userstats_out)\\n else:\\n await say(ctx, \\\":interrobang: - The given information hasn't resulted a guild member. If your intention is to get an User, please use a valid ID!\\\")\",\n \"def home(request):\\n if not request.user.is_authenticated():\\n soccer = Game.objects.filter(sport=Sport.objects.get(sport=\\\"soccer\\\"), active=\\\"true\\\")\\n volleyball = Game.objects.filter(sport=Sport.objects.get(sport=\\\"volleyball\\\"), active=\\\"true\\\")\\n baseball = Game.objects.filter(sport=Sport.objects.get(sport=\\\"baseball\\\"), active=\\\"true\\\")\\n hockey = Game.objects.filter(sport=Sport.objects.get(sport=\\\"hockey\\\"), active=\\\"true\\\")\\n basketball = Game.objects.filter(sport=Sport.objects.get(sport=\\\"basketball\\\"), active=\\\"true\\\")\\n return render(request, 'index.html', {'soccer': soccer, 'hockey': hockey, 'basketball': basketball, 'baseball': baseball, 'volleyball': volleyball})\\n\\n if request.user.is_staff:\\n return HttpResponseRedirect('/admin/')\\n\\n player = Player.objects.get(user=request.user)\\n sports = player.sports.all()\\n joined_games = player.game_set.all()\\n my_games = Game.objects.filter(owner=request.user)\\n profile_pic_url = player.image_url\\n return render(request, 'profile.html', {'player': player, 'profile_pic_url': profile_pic_url, 'sports': sports, 'games': my_games | joined_games})\",\n \"def community_stats(request):\\n stats = cache.get(STATS_CACHE_KEY, None)\\n if not stats:\\n\\n stats = fetch(PEOPLE_STATS_URL)\\n packages_data = fetch(PACKAGES_STATS_URL)\\n if 'meta' in packages_data:\\n stats.update({'packages': packages_data['meta']['total_count']})\\n\\n stats = {'community_stats': stats}\\n\\n cache.add(STATS_CACHE_KEY, stats, 60 * 60 * 12) # for half a day\\n\\n return stats\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72802764","0.7093188","0.6892792","0.66899914","0.65582514","0.6528671","0.64821774","0.6452844","0.64374906","0.64060926","0.63946265","0.63593155","0.63570726","0.6330489","0.6300333","0.62758255","0.6246533","0.6213044","0.6185886","0.6148427","0.6138208","0.6134316","0.613257","0.6104232","0.61014026","0.6097455","0.6097395","0.6093304","0.6079273","0.6071337","0.6064064","0.6061376","0.60492146","0.6039461","0.6010906","0.600176","0.60007876","0.5993664","0.5964823","0.59514123","0.59434026","0.59297675","0.5924234","0.5902881","0.589668","0.586723","0.58644605","0.5843122","0.58378434","0.58302504","0.58232254","0.5821847","0.58206314","0.58071494","0.5806052","0.5800559","0.5799155","0.57924783","0.5789696","0.5785295","0.5783721","0.57797295","0.57743114","0.5759245","0.57454705","0.57326347","0.5715242","0.57070094","0.5704668","0.57022154","0.56938106","0.56902426","0.5689577","0.56887174","0.56685376","0.56652576","0.5658373","0.5657325","0.56498396","0.56467944","0.5644465","0.56428635","0.5642387","0.56392676","0.5624994","0.56165415","0.561367","0.56116104","0.55962527","0.5593952","0.5591184","0.55817515","0.55800617","0.55796266","0.5567272","0.55666","0.5566008","0.55567527","0.55536765","0.554864"],"string":"[\n \"0.72802764\",\n \"0.7093188\",\n \"0.6892792\",\n \"0.66899914\",\n \"0.65582514\",\n \"0.6528671\",\n \"0.64821774\",\n \"0.6452844\",\n \"0.64374906\",\n \"0.64060926\",\n \"0.63946265\",\n \"0.63593155\",\n \"0.63570726\",\n \"0.6330489\",\n \"0.6300333\",\n \"0.62758255\",\n \"0.6246533\",\n \"0.6213044\",\n \"0.6185886\",\n \"0.6148427\",\n \"0.6138208\",\n \"0.6134316\",\n \"0.613257\",\n \"0.6104232\",\n \"0.61014026\",\n \"0.6097455\",\n \"0.6097395\",\n \"0.6093304\",\n \"0.6079273\",\n \"0.6071337\",\n \"0.6064064\",\n \"0.6061376\",\n \"0.60492146\",\n \"0.6039461\",\n \"0.6010906\",\n \"0.600176\",\n \"0.60007876\",\n \"0.5993664\",\n \"0.5964823\",\n \"0.59514123\",\n \"0.59434026\",\n \"0.59297675\",\n \"0.5924234\",\n \"0.5902881\",\n \"0.589668\",\n \"0.586723\",\n \"0.58644605\",\n \"0.5843122\",\n \"0.58378434\",\n \"0.58302504\",\n \"0.58232254\",\n \"0.5821847\",\n \"0.58206314\",\n \"0.58071494\",\n \"0.5806052\",\n \"0.5800559\",\n \"0.5799155\",\n 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app"},"document":{"kind":"string","value":"def updateConfiguration(request):\n\n username = request.session.get('username', False)\n profile = request.session.get('profile', False)\n if(username):\n context = {'username': username,'profile':profile}\n return render(request, 'MedTAG_sket_dock_App/index.html', context)\n else:\n return redirect('MedTAG_sket_dock_App:login')"},"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 conf_update(self):\n pass","def update(self):\n self.save_config_file()","def configuration_view(project):\n project_query = Project.select().where(Project.slug == project).first()\n if project_query is None:\n flash(\"invalid project\")\n return redirect(url_for(\"projects\"))\n session[\"project\"] = project_query\n\n g.selected_tab = \"configuration\"\n\n settings = None\n if request.method == \"GET\":\n settings = Anemone.abcfile.parse(path(project_query.path, \"build.abc\"))\n elif request.method == \"POST\":\n configuration_post(project_query, request)\n\n return render_template(\"configure.html\", ssh=open(app.config[\"SSH_PUBLIC\"]).readline(),\n build=settings, unity=app.config[\"UNITY_PATH\"])","def refresh_configuration(self):\n pass","def update_config(self, config):\n self.config = {\n \"display_name\": \"\",\n \"description\": \"\",\n \"required\": 1,\n \"type\": \"string\"\n }\n self.config.update(config)\n self.API_KEY = self.config['key']","def update_config(self, config):\n self.config = {\n \"display_name\": \"\",\n \"description\": \"\",\n \"required\": 1,\n \"type\": \"string\"\n }\n self.config.update(config)\n self.API_KEY = self.config['key']","def _update_config(self):\n \n # app size\n if self.IsMaximized():\n config.SETTINGS['app_maximized'] = True\n else:\n size = self.GetSize()\n config.SETTINGS['app_width'] = size[0]\n config.SETTINGS['app_height'] = size[1]\n config.SETTINGS['app_maximized'] = False\n \n # panes size\n pane = self.AUIManager.GetPane(self._collections_view)\n config.SETTINGS['collections_view_enabled'] = pane.IsShown()\n if pane.IsShown():\n config.SETTINGS['collections_view_width'] = pane.window.GetClientSize()[0]\n \n pane = self.AUIManager.GetPane(self._pdf_view)\n config.SETTINGS['pdf_view_enabled'] = pane.IsShown()\n if pane.IsShown():\n config.SETTINGS['pdf_view_height'] = pane.window.GetClientSize()[1]\n \n pane = self.AUIManager.GetPane(self._details_view)\n config.SETTINGS['details_view_enabled'] = pane.IsShown()\n if pane.IsShown():\n config.SETTINGS['details_view_width'] = pane.window.GetClientSize()[0]\n \n # article view\n config.SETTINGS['articles_view_columns'] = self._articles_view.GetColumnsSettings()","def update_config(self, config):\n return self._update_config(\"config\", config)","def updateSettingsUI(self):\n\n pass","def update_urls_file(self, app_name):\n\n logger.info(\"\\n--------------------------------------------------------\\n\\t\\tRefreshing application list in urls.py\")\n copyfile(settings.SITE_ROOT + \"/\" + settings.APPLICATION_NAME + \"/urls.py\", settings.SITE_ROOT + \"/\" + settings.APPLICATION_NAME + \"/urls.py.backup\")\n t = loader.get_template('applicationManager/applicationFileTemplates/project_urls_py.txt')\n\n apps = Application.objects.all()\n\n c = {'applist': apps}\n rendered = t.render(c)\n open(settings.SITE_ROOT + \"/\" + settings.APPLICATION_NAME + \"/urls.py\", \"w+\").write(rendered)","def update_flask(self, flask_app):\n flask_app.config.update(self.flask_config_dict)","def on_save(self):\r\n #new_config = ConfigParser.RawConfigParser()\r\n cur_config = self.config.dict_config\r\n #\r\n # update the dict_config\r\n cur_config[\"access_restriction\"][\"ips\"] = self.text_ips.get(1.0, tk.END).strip()\r\n cur_config[\"access_restriction\"][\"ar_url\"] = self.entry_url.get().strip()\r\n #\r\n cur_config[\"email\"][\"relay_server_host\"] = self.entry_server_host.get().strip()\r\n cur_config[\"email\"][\"relay_server_port\"] = self.entry_server_port.get().strip()\r\n cur_config[\"email\"][\"email_from\"] = self.entry_from.get().strip()\r\n cur_config[\"email\"][\"recipients\"] = self.text_recipients.get(1.0, tk.END).strip()\r\n cur_config[\"email\"][\"ar_enabled_subject\"] = self.entry_enabled_subject.get().strip()\r\n cur_config[\"email\"][\"ar_enabled_body\"] = self.text_enabled_body.get(1.0, tk.END).strip()\r\n cur_config[\"email\"][\"ar_disabled_subject\"] = self.entry_disabled_subject.get()\r\n cur_config[\"email\"][\"ar_disabled_body\"] = self.text_disabled_body.get(1.0, tk.END).strip()\r\n\r\n #self.action.save_config()\r\n # # sync dict_config to the gui\r\n # for section in self.config.dict_config:\r\n # new_config.add_section(section)\r\n # for item in self.config.dict_config[section]:\r\n # new_config.set(section, item, self.config.dict_config[section][item])\r\n # #\r\n # # saving to a file\r\n # with open(self.config.file_path, 'w') as newconfigfile:\r\n # new_config.write(newconfigfile)\r\n #\r\n # # mbox.showinfo(\"Information\",\r\n # # \"Current configuration has been successfully saved to '%s'\" % os.path.basename(self.configfile))\r\n # self.console.debug(\"Configuration has been saved to '%s'\" % self.config.file_path)\r","async def update_app_settings(self, app_id: str, data: dict) -> dict:\r\n return await self.put(API_APP_SETTINGS.format(app_id=app_id), data)","def _on_config_changed(self, _):\n self._configure_pod()","def options(self):\n self.title = self.action\n accept = self.request.accept.best_match(['text/html', 'application/json'])\n\n if accept == 'application/json':\n self.render(json=list(self.ConfigService.items()))\n else:\n if self.request.method == 'POST':\n params = self.params\n self.ConfigService.set(params['id'], params['value'])\n self.render(text=params['value'])\n return\n # if we made it here, render the html","def app_config_cli(**kwargs):\n\n @signals.config_complete.connect\n def set_config(app):\n app.config.update(kwargs)\n\n yield","def update_config(self, config):\n toolkit.add_template_directory(config, 'templates')\n toolkit.add_resource('fanstatic', 'mingus')\n return","def update_dashboard_section(self):\n rconfig = configparser.RawConfigParser()\n rconfig.read(self.conf_file)\n if env.get('DASHBOARD_URL'):\n if not rconfig.has_section('dashboard'):\n rconfig.add_section('dashboard')\n rconfig.set('dashboard', 'dashboard_url', env.get('DASHBOARD_URL'))\n else:\n rconfig.set('service_available', 'horizon', False)\n with open(self.conf_file, 'w', encoding='utf-8') as config_file:\n rconfig.write(config_file)","def updateAppMonConf(AppID, requestModel):\n url = \"https://api.ams.fortify.com/api/v3/applications/{applicationId}/application-monitoring/configuration\".format(applicationId=AppID)\n\n req = fodRequest()\n r = req.put(url, params=requestModel)\n return r\n\n #*******************************************************Applications**************************************************************","def configure_app(app, config_dict, config_file_folder):\n # write the config dict to app config as a read-only proxy of a mutable dict\n app.config[APP_CONFIG_JSON] = MappingProxyType(config_dict)\n config_file_folder = config_file_folder\n app.config[CONFIG_FILE_FOLDER] = config_file_folder\n app.config[AVAILABLE_PAGES_DICT] = make_pages_dict(\n config_dict.get(AVAILABLE_PAGES, []), app.config[CONFIG_FILE_FOLDER]\n )\n configure_backend(app)\n return app","def update_global_config(self, config, **kwargs):\n pass","def updateConfig(self, conf=None):\r\n if conf is not None:\r\n self.config.update(conf)\r\n if self.visprotocol is not None:\r\n self.visprotocol.updateSettings(self.getConfigData())\r\n # else:\r\n # _LOGGER.warning(\"Visonic link is not set\")\r\n # make the changes to the platform parameters (used in alarm_control_panel)\r\n # the original idea was to keep these separate for multiple partitions but now i'm not so sure its necessary\r\n\r\n self.hass.data[DOMAIN][\"arm_without_code\"] = self.toBool(self.config.get(CONF_ARM_CODE_AUTO, False))\r\n self.hass.data[DOMAIN][\"force_keypad\"] = self.toBool(self.config.get(CONF_FORCE_KEYPAD, False))\r\n self.hass.data[DOMAIN][\"arm_away_instant\"] = self.toBool(self.config.get(CONF_INSTANT_ARM_AWAY, False))\r\n self.hass.data[DOMAIN][\"arm_home_instant\"] = self.toBool(self.config.get(CONF_INSTANT_ARM_HOME, False))\r\n\r\n _LOGGER.debug(\"[Settings] Log Max Entries %s\", self.config.get(CONF_LOG_MAX_ENTRIES))\r\n _LOGGER.debug(\"[Settings] Log Reverse %s\", self.config.get(CONF_LOG_REVERSE))\r\n _LOGGER.debug(\"[Settings] Log Create Event %s\", self.config.get(CONF_LOG_EVENT))\r\n _LOGGER.debug(\"[Settings] Log Final Event %s\", self.config.get(CONF_LOG_DONE))\r\n _LOGGER.debug(\"[Settings] Log XML Filename %s\", self.config.get(CONF_LOG_XML_FN))\r\n _LOGGER.debug(\"[Settings] Log CSV Filename %s\", self.config.get(CONF_LOG_CSV_FN))\r\n _LOGGER.debug(\"[Settings] Log CSV title Row %s\", self.config.get(CONF_LOG_CSV_TITLE))","def update_application(configurationId=None, name=None, description=None):\n pass","def write_config(self):\n cfg = {\n 'ALERT_API_KEY':self.api_key,\n 'APP_NAME':self.title,\n 'alertes':self.alertes\n }\n write_conf(self.CONF_FILE,cfg)","def _1_apply_config(self):\n \n columns = utils.get_var_details()\n driver = self.driver\n config_name = self.config_name\n driver.get(self.base_url + \"/home/login\")\n driver.find_element_by_id(\"email\").clear()\n driver.find_element_by_id(\"email\").send_keys(columns['email'][1])\n driver.find_element_by_id(\"password\").clear()\n driver.find_element_by_id(\"password\").send_keys(columns['password'][1])\n driver.find_element_by_id(\"btnLogin\").click()\n time.sleep(10)\n \n driver.find_element_by_id(\"menuAccessPoints\").click()\n time.sleep(5)\n \n i = 0\n for option in driver.find_elements_by_css_selector(\"#ap_grid_table tbody tr\"):\n client_name_id = \"AP_Name_\" + str(i)\n status_id = \"Status_\" + str(i)\n select_checkbox_id = \"_\" + str(i)\n if properties.ap_name == str(driver.find_element_by_css_selector(\"#\" + client_name_id + \" a\").get_attribute(\n 'innerHTML')) and driver.find_element_by_css_selector('#' + status_id + ' img').get_attribute(\n 'title') == 'available':\n #print \"pass\"\n driver.find_element_by_css_selector(\"#\" + select_checkbox_id + \" div\").click()\n time.sleep(5)\n driver.find_element_by_id(\"applyTemplateBtn\").click()\n j = 0\n for option1 in driver.find_elements_by_css_selector(\"#config_list_table tbody tr\"):\n template_name_id = \"Template_name_\" + str(j)\n select_radiobutton_id = \"__\" + str(j)\n #print select_radiobutton_id\n if config_name == driver.find_element_by_id(template_name_id).get_attribute('innerHTML'):\n driver.find_element_by_css_selector(\"#\" + select_radiobutton_id + \" input\").click()\n driver.find_element_by_xpath(\"//html/body/div[2]/div/div/div/div[2]/div[2]/a\").click()\n #driver.find_element_by_id(\"btnApply\").click()\n j = j + 1\n time.sleep(5)\n \n ##Checking for API response message which comes up after applying config to AP.\n try:\n actual_message = driver.find_element_by_xpath('//*[@id=\"deleteSuccessMessage\"]').get_attribute('innerHTML')\n expected_message = \"Configuration template change initiated for AP(s).\"\n print \"Actual message = \" + actual_message\n print \"Expected message = \" + expected_message\n #self.assertEqual(actual_message, expected_message, \"Appropriate message '%s' is not displayed\" % expected_message)\n except AssertionError as e: self.verificationErrors.append(str(e))\n time.sleep(40)\n \n ####Checking for the default config running on AP.\n ssid = config_name\n expected_running_config_id = db.search_field_in_configuration_collection('_id', config_name)\n #Get the ap's jid\n ap_jid = utils.get_ap_jid(properties.real_ap_ip)\n print \"AP jid return value is: \" + ap_jid\n ap_jid = ap_jid.strip()\n actual_running_config_id = db.search_field_in_ap_collection('running_config_id', ap_jid)\n print \"Actual running config id of AP is:\" + str(actual_running_config_id) \n print \"Expected running config id of AP is:\" + str(expected_running_config_id)\n try:\n self.assertEqual(str(actual_running_config_id), str(expected_running_config_id),\n \"Expected configuration %s is not running on %s AP.\" % (ssid, ap_jid))\n except AssertionError as e: self.verificationErrors.append(str(e)) \n \n \n #Verify if radios are enabled on the AP\n ssh = paramiko.SSHClient()\n ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n ssh.connect(properties.real_ap_ip, username='root',\n password='password')\n \n try:\n stdin, stdout, stderr = ssh.exec_command(\"uci show | grep disabled\")\n except SSHException, e:\n print e\n data = stdout.readlines()\n for eachLine in data:\n if \"radio0\" in eachLine:\n radio_line = eachLine.split(\"\\n\")\n radio_state = radio_line[0]\n print \"Actual Radio0 state is: \" + radio_state\n expected_radio_state = \"wireless.radio\" + \"0\" + \".disabled=0\"\n print \"Expected radio state is: \" + expected_radio_state\n try:\n self.assertEqual(radio_state.strip(), expected_radio_state,\n \"After applying config to AP Radio0 was not disabled\")\n except AssertionError as e: self.verificationErrors.append(str(e))\n elif \"radio1\" in eachLine:\n radio_line = eachLine.split(\"\\n\")\n radio_state = radio_line[0]\n print \"Actual Radio1 state is: \" + radio_state\n expected_radio_state = \"wireless.radio\" + \"1\" + \".disabled=0\"\n print \"Expected radio state is: \" + expected_radio_state\n try:\n self.assertEqual(radio_state.strip(), expected_radio_state,\n \"After applying config to AP Radio1 was not disabled\")\n except AssertionError as e: self.verificationErrors.append(str(e))\n \n ###Checking for the SSID running on AP \n ssh = paramiko.SSHClient()\n ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n ssh.connect(properties.real_ap_ip, username='root',\n password='password')\n try:\n #To get the ssids running on AP\n (stdin, stdout, stderr) = ssh.exec_command(\"uci show | grep ssid\")\n except SSHException, e:\n print e\n ssid_output = stdout.readlines()\n result = False\n cnt = 0\n for num, each in enumerate(ssid_output):\n each = each.replace(\"\\n\", \"\")\n if cnt == 0:\n expected_ssid = (\"wireless.@wifi-iface[%s].ssid=%s\" % (cnt, ssid))\n elif cnt == 1:\n expected_ssid = (\"wireless.@wifi-iface[%s].ssid=%s\" % (cnt, ssid))\n cnt = cnt + 1\n if expected_ssid == each:\n result = True\n print \"Expected SSID is:\" + expected_ssid\n print \"Actual SSID is:\" + each\n else:\n result = False\n print \"Expected SSID is:\" + expected_ssid\n print \"Actual SSID is:\" + each\n try:\n self.assertEqual(result, True, \"Expected SSID %s is not running on AP.\" % ssid)\n except AssertionError as e: self.verificationErrors.append(str(e))\n #print (\"Expected SSID %s is running on AP.\" % ssid)\n i = i + 1","def update_app(AppId=None, Name=None, Description=None, DataSources=None, Type=None, AppSource=None, Domains=None, EnableSsl=None, SslConfiguration=None, Attributes=None, Environment=None):\n pass","def update_configuration(ConfigurationId=None, Data=None, Description=None):\n pass","def update_config(self, data):\n self.config.data = dict_merge(self.config.data, data)\n self.config.save()","def settings():\n\n mysettings = MySettings(app)\n form = SettingsForm(request.form, obj=mysettings)\n\n if request.method == 'POST' and form.validate():\n mysettings.update(\n app,\n form.influxdb_host.data,\n form.influxdb_port.data,\n form.influxdb_db.data,\n form.influxdb_table.data,\n form.influxdb_user.data,\n form.influxdb_pw.data,\n form.diematicd_host.data,\n form.diematicd_port.data\n )\n return redirect(url_for('controller'))\n\n return render_template('settings.html', form=form, user=current_user)","def configure(self, config: dict):\n self.config.update(config)","def _auto_update_configuration(self) -> None:\n self.config = rasa.utils.train_utils.update_confidence_type(self.config)\n rasa.utils.train_utils.validate_configuration_settings(self.config)\n self.config = rasa.utils.train_utils.update_similarity_type(self.config)\n self.config = rasa.utils.train_utils.update_evaluation_parameters(self.config)","def config():","def config():","def update(self, config_dict):\r\n self._update(config_dict, allow_new_keys=True)","def update_shed_config(self, shed_conf):\n for index, my_shed_tool_conf in enumerate(self._dynamic_tool_confs):\n if shed_conf['config_filename'] == my_shed_tool_conf['config_filename']:\n self._dynamic_tool_confs[index] = shed_conf\n self._save_integrated_tool_panel()","def config(self, config_dict):\r\n self._cfg.config = config_dict","def test_change_config(self):\n browser = Browser(self.app)\n portalURL = self.portal.absolute_url()\n browser.addHeader('Authorization', 'Basic %s:%s' % (SITE_OWNER_NAME, SITE_OWNER_PASSWORD))\n browser.open(portalURL + '/@@overview-controlpanel')\n browser.getLink('Image WatchDog settings').click()\n browser.getControl('Optimize PNG').selected = True\n browser.getControl('Enabled').selected = True\n browser.getControl('Save').click()\n\n registry = getUtility(IRegistry)\n settings = registry.forInterface(IImageWatchDogSettings)\n self.assertTrue(settings.optimize)\n self.assertTrue(settings.enabled)","def config():\n if app.args.ui_mode == \"jinja\":\n ui_config = {\n \"p1\": {\n \"options\": {\n \"lineNumbers\": True,\n \"theme\":\"material\",\n \"lineWrapping\" : True,\n \"mode\": \"yaml\",\n \"indentUnit\": 2,\n \"tabSize\": 2\n },\n \"title\": \"DATA\",\n \"inventory\": bool(app.args.inventory_source),\n \"b1\": {\n \"icon\": None,\n \"show\": False,\n \"text\": None,\n \"url\": None\n }\n },\n \"p2\": {\n \"options\": {\n \"lineNumbers\": True,\n \"theme\": \"material\",\n \"lineWrapping\" : True,\n \"mode\": \"jinja2\"\n },\n \"title\": \"RENDER\",\n \"b1\": {\n \"icon\": \"create\",\n \"show\": True,\n \"text\": \"Render\",\n \"url\": \"/render\"\n }\n },\n \"p3\": {\n \"options\": {\n \"lineNumbers\": True,\n \"theme\": \"material\",\n \"lineWrapping\" : True,\n \"mode\": 'text'\n },\n \"title\": \"RESULT\",\n \"b1\": {\n \"icon\": \"link\",\n \"show\": bool(app.args.url),\n \"text\": \"link\"\n }\n }\n }\n elif app.args.ui_mode == \"schema\":\n ui_config = {\n \"p1\": {\n \"options\": {\n \"lineNumbers\": True,\n \"theme\":\"material\",\n \"lineWrapping\" : True,\n \"mode\": \"yaml\",\n \"indentUnit\": 2,\n \"tabSize\": 2\n },\n \"title\": \"DATA\",\n \"inventory\": bool(app.args.inventory_source),\n \"b1\": {\n \"icon\": \"create\",\n \"show\": True,\n \"text\": \"schema\",\n \"url\": \"/schema\"\n }\n },\n \"p2\": {\n \"options\": {\n \"lineNumbers\": True,\n \"theme\": \"material\",\n \"lineWrapping\" : True,\n \"mode\": \"yaml\"\n },\n \"title\": \"SCHEMA\",\n \"b1\": {\n \"icon\": \"check\",\n \"show\": True,\n \"text\": \"Validate\",\n \"url\": \"/validate\"\n }\n },\n \"p3\": {\n \"options\": {\n \"lineNumbers\": True,\n \"theme\": \"material\",\n \"lineWrapping\" : True,\n \"mode\": \"yaml\"\n },\n \"title\": \"VALIDATION SUCCESS/ERRORS\",\n \"b1\": {\n \"icon\": \"link\",\n \"show\": bool(app.args.url),\n \"text\": \"link\"\n }\n }\n }\n return jsonify(ui_config)","def configure(self, section):","def update_config(self, config):\n p.toolkit.add_template_directory(config, \"templates\")\n p.toolkit.add_public_directory(config, 'public')","def update_config(self, config):\n p.toolkit.add_template_directory(config, 'templates')\n p.toolkit.add_public_directory(config, 'public')","def update_config(self, kv: dict):\n self._configs.update(kv)\n self._save()","def update_config_item(self, elements: Dict[str, Any]) -> None:\n ...","def do_PUT(self):\n if self.path == \"/set_config\" or self.path == \"/set_config/\":\n\n if len(self.post_dict) > 0:\n for key, value in self.post_dict.items():\n\n self.log_message(f\"Set config '{key}' to '{value}'\")\n\n try:\n value = json.loads(value)\n\n except ValueError:\n self.log_message(f\"Could not parse JSON: {value}\")\n self.send_response(400)\n\n else:\n self.server.config[key] = value\n self.send_response(200)\n\n # No parameters sent to configure, so return success by default\n else:\n self.send_response(200)\n\n else:\n self.send_response(404)","def config():\n config_django()\n config_svisor()","def setAppID(self, appid):\n\t\tself.config.APP_ID = appid","def update_config(self, config):\n self.config = {\n \"key\": \"\",\n \"display_name\": \"\",\n \"description\": \"\",\n \"required\": 1,\n \"type\": \"string\"\n }\n self.config.update(config)\n self.API_KEY = self.config['github_api_key']","def updateConfig(self):\n # Make sure to keep the default values in place.\n if self.newConfig['sensor'] == 0:\n self.newConfig['sensor'] = self.config['sensor']\n if self.newConfig['camera'] == 0:\n self.newConfig['camera'] = self.config['camera']\n if not self.newConfig['auto']['times']:\n self.newConfig['auto']['times'] = self.config['auto']['times']\n if not self.newConfig['auto']['days']:\n self.newConfig['auto']['days'] = self.config['auto']['days']\n\n # Show the changes.\n if self.verbosity >= 1:\n print('%s: Updating configuration file...' % self.feederName)\n try:\n for key in self.config.keys():\n if type(self.config[key]) is dict:\n for subkey in self.config[key].keys():\n if self.config[key][subkey] != self.newConfig[key][subkey]:\n print('%s: Updating %s from %s to %s.' % (self.feederName, subkey, self.config[key][subkey], self.newConfig[key][subkey]))\n elif self.config[key] != self.newConfig[key]:\n print('%s: Updating %s from %s to %s.' % (self.feederName, key, self.config[key], self.newConfig[key]))\n except ValueError:\n if self.verbosity >= 1:\n print('%s: Configuration file does not contain a valid JSON object.' % self.feederName)\n if self.verbosity == 2:\n print('%s: Overwriting configuration file to: %s.' % (self.feederName, self.config))\n\n # Change the configuration file.\n self.config = self.newConfig\n self.writeConfig()","def handle_adminreloadconfig(bot, event):\n try:\n bot.cfg.reload()\n getmainconfig().reload()\n except Exception, ex: handle_exception()\n event.done()","def update(self):\n self.application['email'] = 'test@example.spinnaker'","def settings_view():\n return template('settings.html')","def update_website_configuration():\n put('config/supervisor_website.conf', \n '/etc/supervisor/conf.d/gunicorn.conf', \n use_sudo=True)\n sudo('supervisorctl update')\n sudo('supervisorctl reload')","def handleReload(self, confInfo=None):","def reload_config(self):\n pass","def config_update(self, update: io.BytesIO) -> None:\n self.__logger.debug('Eva.config_update called')\n return self.__http_client.config_update(update)","def _configure_ipsec_site(self, name, confs):\n site_conf_tpl = '../config/tpl/ipsec.d/_.conf'\n\n context = {\n 'conn_name': name,\n 'elastic_ip': env.aws_elastic_ip,\n }\n for key, value in confs.items():\n context[key] = value\n\n with hide(*fab_output_hides):\n return upload_template_changed(\n site_conf_tpl,\n '/etc/ipsec.d/%s.conf' % name,\n context=context,\n use_sudo=True,\n mode=0600,\n )","def set_config(self): # called from button_set object \n self.settings['lights_on'] = self.lights_on.get()\n self.settings['lights_off'] = self.lights_off.get()\n self.settings['ambient_min'] = self.ambient_min.get()\n self.settings['soil_1'] = self.smc1.get()\n self.settings['soil_2'] = self.smc2.get()\n self.settings['soil_3'] = self.smc3.get()\n self.settings['soil_4'] = self.smc4.get()\n self.settings['overhead_level'] = self.overhead_level.get()\n\n # Save settings to config file in case of reboot / power-loss\n print \"UPDATING SETTINGS FILE\"\n with open(self.settings_path, 'w') as jsonfile:\n jsonfile.write(json.dumps(self.settings, indent=4))\n self.active_changes = True # (flag) changes are active!","def configure(self, conf):\n return","def edit_cfg(config_file):\n\n GUI().cfgEditor(config_file)","def configuration():","def appconfig():\r\n print('''\\n%s at %s acting as user %s\r\n\\nApp Configuration Menu''' % (PACKETMASTER.model, ADDRESS, USERNAME))\r\n choice = moves.input('''\r\n 1 - List Apps\r\n 2 - List Running Apps\r\n 3 - Start an App instance\r\n 4 - Modify an App instance\r\n 5 - Kill an App instance\r\n 6 - Call a custom App action\r\n 7 - Back\r\n 8 - Quit \\n\r\n Enter selection number: ''')\r\n try:\r\n choice = int(choice)\r\n except ValueError as reason:\r\n print(\"That is not a valid selection.\", reason)\r\n appconfig()\r\n execute = {1: PACKETMASTER.device_apps,\r\n 2: PACKETMASTER.apps_active,\r\n 3: PACKETMASTER.start_app_guided,\r\n 4: PACKETMASTER.mod_app_guided,\r\n 5: PACKETMASTER.kill_app_guided,\r\n 6: PACKETMASTER.call_app_action_guided,\r\n 7: manage,\r\n 8: exit}\r\n if choice in execute:\r\n try:\r\n select = execute[choice]\r\n run = select()\r\n print(run)\r\n appconfig()\r\n except KeyError as reason:\r\n print(reason)\r\n else:\r\n print(\"That is not a valid selection.\")\r\n appconfig()","def __writeConfig(self):\n page = None\n\n #TODO: get values of configurations here\n particles = \"#f\" if not base.particleMgrEnabled else \"#t\"\n volume = str(round(base.musicManager.getVolume(), 2))\n mute = \"#f\" if base.AppHasAudioFocus else \"#t\"\n #TODO: add any configuration variable name that you have added\n customConfigVariables = [\n \"\", \"particles-enabled\", \"audio-mute\", \"audio-volume\"]\n if os.path.exists(prcFile):\n # open the config file and change values according to current\n # application settings\n page = loadPrcFile(Filename.fromOsSpecific(prcFile))\n removeDecls = []\n for dec in range(page.getNumDeclarations()):\n # Check if our variables are given.\n # NOTE: This check has to be done to not loose our base or other\n # manual config changes by the user\n if page.getVariableName(dec) in customConfigVariables:\n decl = page.modifyDeclaration(dec)\n removeDecls.append(decl)\n for dec in removeDecls:\n page.deleteDeclaration(dec)\n # NOTE: particles-enabled and audio-mute are custom variables and\n # have to be loaded by hand at startup\n # Particles\n page.makeDeclaration(\"particles-enabled\", particles)\n # audio\n page.makeDeclaration(\"audio-volume\", volume)\n page.makeDeclaration(\"audio-mute\", mute)\n else:\n # Create a config file and set default values\n cpMgr = ConfigPageManager.getGlobalPtr()\n page = cpMgr.makeExplicitPage(\"%s Pandaconfig\"%appName)\n # set OpenGL to be the default\n page.makeDeclaration(\"load-display\", \"pandagl\")\n # get the displays width and height\n w = self.pipe.getDisplayWidth()\n h = self.pipe.getDisplayHeight()\n # set the window size in the config file\n page.makeDeclaration(\"win-size\", \"%d %d\"%(w, h))\n # set the default to fullscreen in the config file\n page.makeDeclaration(\"fullscreen\", \"1\")\n # particles\n page.makeDeclaration(\"particles-enabled\", \"#t\")\n # audio\n page.makeDeclaration(\"audio-volume\", volume)\n page.makeDeclaration(\"audio-mute\", \"#f\")\n # create a stream to the specified config file\n configfile = OFileStream(prcFile)\n # and now write it out\n page.write(configfile)\n # close the stream\n configfile.close()","def fusion_api_reapply_li_configuration(self, uri, api=None, headers=None):\n param = '/configuration'\n return self.li.update(body=None, uri=uri, api=api, headers=headers, param=param)","def update(self):\n # TO DO for updating urls if changed\n pass","def update_settings(command):\n namespace = app.main(command)\n assert namespace.command == 'u' or namespace.command == \"updatesettings\"","def _refreshconfig(self):\n self.config = ConfigGenerator(os.path.join(self.rundir, const.CONFIG_FILE))","def config_diff_form_page():\n # connect to database\n cur_db = connect_db(\"172.20.38.50\", \"mvelay\", \"user\", \"sandbox\")\n cursor = cur_db.cursor()\n\n modules_query = \"\"\"SELECT DISTINCT module from t_ck5050_ini ORDER BY module ASC\"\"\"\n versions_query = \"\"\"SELECT DISTINCT version from t_ck5050_ini ORDER BY version DESC\"\"\"\n clients_query = \"\"\"SELECT DISTINCT client from t_ck5050_ini ORDER BY client DESC\"\"\"\n\n cursor.execute(modules_query)\n modules = cursor.fetchall()\n\n cursor.execute(versions_query)\n versions = cursor.fetchall()\n\n cursor.execute(clients_query)\n clients = cursor.fetchall()\n\n # Build template page\n with open(\"./header.html\") as header, open('./config_form.tpl') as config_form, open('./footer.html') as footer:\n template_html = header.read() + config_form.read() + footer.read()\n\n html_page = template(template_html, modules=modules, versions=versions,\n clients=clients)\n\n cursor.close()\n\n return html_page","def home(request):\n page=1\n span = 15\n order = \"-created_at\"\n page = request.GET.get('page', page)\n span = request.GET.get('span', span)\n config_list,entry_count = Config.get_items(span=span, page=page)\n\n temp_values = {\n \"target\":\"config\",\n \"title\":u\"Config定義一覧ページ\",\n \"config_list\":config_list,\n \"subscroll\":True,\n }\n return render(request, 'server/index.html', temp_values)","def display_app_info(config, client, app_id):\n try:\n resp = client.get_app_full_info(config.username, app_id)\n result = resp.json()\n app_info = result[\"app_info\"]\n title = click.style(\"App Name : \", fg=\"blue\") + click.style(\n \"{}\".format(app_info[\"title\"]))\n\n if app_info[\"rating_count\"] == 0:\n rating = \"Not yet rated\"\n else:\n rating = \"{:.1f} ({} rating\".format(app_info[\"average_rating\"],\n int(app_info[\"rating_count\"]))\n if app_info[\"rating_count\"] > 1:\n rating += \"s\"\n rating += \")\"\n rating_row = click.style(\"Rating : \", fg=\"blue\") + click.style(\"{}\".format(rating))\n up_status = click.style(\"Status : \", fg=\"blue\")\n if app_info[\"is_up\"]:\n up_status += click.style(\"Up\")\n else:\n up_status += click.style(\"Down\")\n\n last_crawl_str = \"Not yet crawled\"\n if \"last_crawl\" in app_info:\n last_crawl_str = util.format_date(app_info[\"last_crawl\"])\n\n last_crawl = click.style(\"Last Crawl Time : \", fg=\"blue\") + click.style(\n \"{}\".format(last_crawl_str))\n version = click.style(\"Version : \", fg=\"blue\") + click.style(\n \"{}\".format(app_info[\"version\"]))\n\n last_updated_str = util.format_date(app_info[\"updated\"])\n last_update = click.style(\"Last Update : \", fg=\"blue\") + click.style(\n \"{}\".format(last_updated_str))\n\n availability = click.style(\"Availability : \", fg=\"blue\") + click.style(\n \"{:.2f}%\".format(app_info[\"average_uptime\"] * 100))\n\n app_url = click.style(\"Public App URL : \", fg=\"blue\") + click.style(\n \"{}\".format(app_info[\"app_url\"]))\n original_url = click.style(\"Private App URL : \", fg=\"blue\") + click.style(\n \"{}\".format(app_info[\"original_url\"]))\n category = click.style(\"Category : \", fg=\"blue\") + click.style(\n \"{}\".format(app_info[\"category\"]))\n\n desc = click.style(\"Description : \", fg=\"blue\") + click.style(\n \"{}\".format(app_info[\"description\"]))\n price = click.style(\"Price Range : \", fg=\"blue\") + click.style(\n \"{} - {} Satoshis\").format(\n app_info[\"min_price\"], app_info[\"max_price\"])\n doc_url = click.style(\"Docs URL : \", fg=\"blue\") + click.style(\n \"{}\".format(app_info[\"docs_url\"]))\n\n quick_start = click.style(\"Quick Start\\n\\n\", fg=\"blue\") + click.style(\n app_info[\"quick_buy\"])\n\n usage_docs = None\n if \"usage_docs\" in app_info:\n usage_docs = click.style(\"Detailed usage\\n\\n\", fg=\"blue\") + click.style(\n app_info[\"usage_docs\"])\n\n page_components = [title, \"\\n\",\n rating_row, up_status, availability, last_crawl, last_update, version,\n \"\\n\",\n desc, app_url, original_url, doc_url, \"\\n\",\n category, price, \"\\n\", quick_start, \"\\n\"]\n if usage_docs:\n page_components.append(usage_docs + \"\\n\")\n final_str = \"\\n\".join(page_components)\n logger.info(final_str, pager=True)\n\n except ServerRequestError as e:\n if e.status_code == 404:\n logger.info(\n \"The specified id for the app ({}) does not match any apps in the \"\n \"marketplace.\".format(app_id))\n else:\n raise e","def update_config(self, config):\n toolkit.add_template_directory(config, 'templates')\n toolkit.add_public_directory(config, 'public')\n toolkit.add_resource('fanstatic', 'syngenta')","def set_config(app):\n # set config from config.py\n app.config.from_object('config')\n\n # override config from secret conf files\n pi_home = os.path.dirname(app.config['ENVPATH']) # /home/pi\n secret_conf_dir = os.path.join(pi_home, 'CONFIG_CHAUDIERE') # /home/pi/CONFIG_CHAUDIERE\n secret_conf_com_file = 'chaudiere_secret_config.py'\n secret_conf_com = secret_conf_dir+'/'+secret_conf_com_file\n try:\n with open(secret_conf_com) as f:\n json_config = json.load(f)\n for conf in ['Common', app.config['ENVNAME']]:\n app.config.update(json_config[conf])\n except IOError as e:\n print('IOError loading conf file (file not existing?): ' + secret_conf_com + str(e))\n except ValueError as e:\n print('ValueError loading JSON : ' + secret_conf_com + ' ' + str(e))\n\n #app.config['USERS_EMAILS'] = list(map(lambda x: x+'@gmail.com', app.config['USERS'])) \n # app.logger.error('test error') # <-- This works !!! ","def set_config(self, aConfig):\n \n # we update the dict of the existing config with the passed\n # parameter. This means that the new config is merged with\n # the old, but all new members overwrite old one. This is\n # more robust.\n self._config.__dict__.update(aConfig.__dict__)\n # apply the config to the underlying logic\n self.config_to_logic()\n # bring it back all the way up to the view\n self.logic_to_config()\n\n # but only if we are in view mode\n if self.view_initialised:\n self.config_to_view()\n\n # the config has been set, so we assumem that the module has\n # now been modified. \n self._module_manager.modify_module(self)","def update(self, obj):\n\n self.cfg.update(obj)","def getConfigPage():\r\n\r\n\r\n #print os.getcwd()\r\n #os.chdir('..')\r\n #os.chdir('D:\\Stone\\Python\\Python_Insurance')\r\n #print os.getcwd()\r\n\r\n #configPage = ConfigObj('pagesConf.conf')\r\n configPage = ConfigObj('thirdevalConf.conf')\r\n\r\n #configPagePolicyHolder_Usr = configPage['PolicyHolder_Usr']\r\n #dictFirst = configPagePolicyHolder_Usr.dict()\r\n #print dictFirst\r\n #j = 2\r\n #for j in range(j):\r\n # for member in dictFirst:\r\n # if 'titleText' in dictFirst[member]:\r\n # print member\r\n # dictSecond = dictFirst[member]\r\n # print dictSecond\r\n # break\r\n # dictFirst.pop(member)\r\n #i = i+1\r\n #print i\r\n #for item in configPagePolicyHolder_Usr[member]:\r\n # print configPagePolicyHolder_Usr[member][item]\r\n #for item in member:\r\n # print member[item]\r\n #print configPage\r\n #print configPagePolicyHolder_Usr\r\n #configPagePolicyHolder_Usr = configPage['PolicyHolder_Usr']\r\n #print configPagePolicyHolder_Usr\r\n\r\n #print configPagePolicyHolder_Usr.as_int( 'rowNumber')\r\n #print configPage.as_bool(\"hasPolicyHolder_Usr\")\r\n return configPage","def update_zoo_config(self, site_name, virt_path, new_config):\n root_path = self.map_path(site_name, virt_path)\n zoo_config_path = os.path.join(root_path, \".zoo\")\n config = get_zoo_config(zoo_config_path) or {}\n\n app = config.get('application')\n # disabled ability\n if 'selected-engine' in new_config :\n new_engine = new_config.get('selected-engine')\n if 'parameters' in app:\n app['parameters']['selected-engine'] = new_engine\n else:\n app['parameters'] = OrderedDict()\n app['parameters']['selected-engine'] = new_engine\n\n\n if 'engines' in new_config:\n engines = new_config.get('engines')\n app['engines'] = engines\n\n if 'locations' in new_config:\n app['locations'] = new_config['locations']\n\n if \"description\" in app:\n app[\"description\"] = Literal(app[\"description\"])\n\n if \"find_installed_command\" in app:\n app[\"find_installed_command\"] = Literal(app[\"find_installed_command\"])\n\n if \"install_command\" in app:\n app[\"install_command\"] = Literal(app[\"install_command\"])\n\n if \"uninstall_command\" in app:\n app[\"uninstall_command\"] = Literal(app[\"uninstall_command\"])\n\n if \"upgrade_command\" in app:\n app[\"upgrade_command\"] = Literal(app[\"upgrade_command\"])\n\n # save .zoo\n YamlHelper.save(config, zoo_config_path)","def config(ctx):\n return","def _editSysconfig():\n dbUrl = \"jdbc:postgresql://\" + getDbHostName() + \":\" + getDbPort() + \"/\" + basedefs.DB_NAME\n if \"DB_SECURE_CONNECTION\" in controller.CONF.keys() and controller.CONF[\"DB_SECURE_CONNECTION\"] == \"yes\":\n dbUrl = dbUrl + \"?ssl=true&sslfactory=org.postgresql.ssl.NonValidatingFactory\"\n\n proxyEnabled = utils.compareStrIgnoreCase(controller.CONF[\"OVERRIDE_HTTPD_CONFIG\"], \"yes\")\n utils.editEngineSysconfig(proxyEnabled=proxyEnabled,\n dbUrl=dbUrl,\n dbUser=utils.getDbUser(),\n fqdn=controller.CONF[\"HOST_FQDN\"],\n http=controller.CONF[\"HTTP_PORT\"],\n https=controller.CONF[\"HTTPS_PORT\"],\n javaHome=controller.CONF[\"JAVA_HOME\"])","def task_settings(short_name):\r\n (app, owner, n_tasks, n_task_runs,\r\n overall_progress, last_activity) = app_by_shortname(short_name)\r\n try:\r\n require.app.read(app)\r\n require.app.update(app)\r\n return render_template('applications/task_settings.html',\r\n app=app,\r\n owner=owner)\r\n except HTTPException:\r\n if app.hidden:\r\n raise abort(403)\r\n else:\r\n raise","def manage_config(self, action=None, peel=False, force=False,\n as_json=False, path=None):\n if action is None:\n return\n elif action == \"view\":\n if self.config is None:\n raise ValueError(\"No config loaded\")\n flattened = {}\n for cat, bcd in self.config._asdict().items():\n flattened[cat] = bcd.peel(peel=peel)\n return flattened\n #\n nvid = self.GetUser().GetUserName()\n if not hasattr(self, \"_nv_undo_stack\"):\n from collections import deque\n self._nv_undo_stack = deque()\n #\n import os\n from .configgers import default_config\n defver = default_config.settings[\"config_version\"]\n #\n def get_path(path): # noqa: E306\n ext = \"json\" if as_json else \"ini\"\n if path and not force:\n path = os.path.expandvars(os.path.expanduser(path))\n path = os.path.abspath(path)\n # All dirs must exist; \"export\" creates files if absent\n if os.path.isdir(path):\n path = os.path.join(path, f\"config.{ext}\")\n elif not os.path.exists(path):\n parpath = os.path.dirname(path)\n if not os.path.isdir(parpath):\n path = None\n elif not any(path.endswith(e) for e in (\".json\", \".ini\")):\n path = os.path.join(parpath, f\"config.{ext}\")\n if not path:\n path = os.path.join(self.datadir, f\"config.{ext}\")\n return path\n #\n # save/export\n def ensure_defver(peeled): # noqa: E306\n ps = peeled.setdefault(\"settings\", {})\n ps.setdefault(\"config_version\", defver)\n #\n # load/reload\n def handle_outdated(curver): # noqa: E306\n dirname = os.path.dirname(path or get_path(path))\n basename = \"config.{}.new\".format(\"json\" if as_json else \"ini\")\n dest = os.path.join(dirname, basename)\n orig, self.config = self.config, construct_config({})\n # OnHooks will be skipped during this call\n self.manage_config(\"export\", force=True, as_json=as_json,\n path=dest)\n self.config = orig\n msg = \" \".join(\"\"\"\n Your config appears to be outdated. Please update it\n using the latest defaults, which can be found here: {}.\n Make sure to include the new version number. Or, use\n --force to bypass this warning.\n \"\"\".split()).format(dest)\n raise UserWarning(msg)\n #\n if action == \"load\":\n from .configgers import load_config, construct_config\n stringified = self.nv.get(nvid)\n peeled = load_config(stringified) if stringified else {}\n # Could just view/peel, but this should be the only redundant item\n curver = peeled.get(\"settings\", {}).get(\"config_version\")\n if peeled:\n if not curver:\n raise KeyError(\"Required item /settings/config_version \"\n f\"missing from nv[{nvid}]\")\n elif curver == defver:\n del peeled[\"settings\"][\"config_version\"]\n elif not force:\n handle_outdated(curver)\n self.config = construct_config(peeled)\n return\n elif action == \"save\":\n if not self.config.settings[\"host\"] and not force:\n msg = (\"Warning: not caching config because \"\n \"'/settings/host' is empty; use --force to override\")\n raise UserWarning(msg)\n peeled = self.manage_config(action=\"view\", peel=True)\n # Must track version because module may be updated in the interim\n ensure_defver(peeled)\n from .ootil import restring\n if nvid in self.nv:\n MAX_UNDOS = 5\n if len(self._nv_undo_stack) == MAX_UNDOS:\n del self.nv[self._nv_undo_stack.pop()]\n from datetime import datetime\n bakkey = f\"{nvid}.{datetime.now().timestamp()}\"\n self.nv[bakkey] = self.nv[nvid]\n self._nv_undo_stack.appendleft(bakkey)\n self.nv[nvid] = restring(peeled)\n return\n elif action == \"undo\":\n # TODO write tests for this, add to cmd_update\n raise RuntimeError(\"TODO: need tests for this\")\n try:\n lastkey = self._nv_undo_stack.popleft()\n except IndexError:\n raise UserWarning(\"Nothing to undo\")\n self.nv[nvid] = self.nv[lastkey] # TODO see if nv supports pop\n del self.nv[lastkey]\n return self.manage_config(\"load\")\n elif action not in (\"reload\", \"export\"):\n raise ValueError(\"Unrecognized action\")\n #\n path = get_path(path)\n #\n def validate(peeled, skip_dropped=False): # noqa: E306\n from .configgers import validate_config\n warn, info = validate_config(peeled)\n msg = []\n if skip_dropped:\n info = [l for l in info if \"dropped\" not in l]\n if info:\n msg += [f\"\\x02FYI:\\x02\\n\"] + info\n if warn:\n msg += [f\"\\x02Potential problems:\\x02\\n\"] + warn\n if msg:\n self.put_pretty(\"\\n\".join(msg))\n return False if warn else True\n #\n if action == \"reload\":\n if not os.path.exists(path):\n raise FileNotFoundError(f\"No config found at {path}\")\n from .configgers import load_config, construct_config\n loaded = load_config(path)\n if not force:\n curver = loaded.get(\"settings\", {}).get(\"config_version\")\n if curver:\n if curver == defver:\n del loaded[\"settings\"][\"config_version\"]\n elif not force:\n handle_outdated(curver)\n elif as_json:\n msg = (\"Warning: 'config_version' absent from config; \"\n \"use --force to try loading anyway\")\n raise UserWarning(msg)\n if not validate(loaded, as_json):\n return\n self.config = construct_config(loaded)\n return self.manage_config(\"save\")\n elif action == \"export\":\n try:\n peeled = self.manage_config(\"view\", peel=True)\n except Exception:\n if not force:\n raise\n else:\n # \"Emergency\" backup called by OnShutdown(); must peel,\n # unfortunately, since construct_config likely just failed\n as_json = peel = True\n strung = self.nv[nvid]\n import json\n peeled = json.loads(strung)\n version = peeled[\"settings\"][\"config_version\"]\n path = os.path.dirname(path)\n path = os.path.join(path, f\"config.{version}.json.bak\")\n if not force:\n if not peeled:\n msg = (\"Warning: cached config is empty; \"\n \"use --force to export default config\")\n raise UserWarning(msg)\n if not validate(peeled):\n return\n with open(path, \"w\") as flow:\n if as_json:\n # No need to support \"complete\" (redundant) version\n if not peel:\n spread = self.config.conditions.spread\n payload = self.manage_config(\"view\", peel=spread)\n else:\n payload = peeled\n ensure_defver(payload)\n import json\n json.dump(payload, flow, indent=2)\n else:\n from Signal.iniquitous import gen_ini\n formatted = gen_ini(self.config)\n flow.write(formatted)","def UpdateConfig(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","async def do_config():\n\n\n async def cfg_add():\n try:\n if config[message.server.id]:\n await bot.send_message(c, 'The channel is already configured.')\n return\n except:\n config[message.server.id] = {\n 'server_api_1': '',\n 'server_api_2': '',\n 'welcome_msg enabled': False,\n 'welcome_msg': '',\n }\n\n async def cfg_ip():\n ip = message.content.split()[2]\n config[message.server.id]['server_api_1'] = f'https://mcapi.us/server/status?ip={ip}'\n config[message.server.id]['server_api_2'] = f'https://eu.mc-api.net/v3/server/ping/{ip}'\n\n async def cfg_port():\n port = message.content.split()[2]\n try:\n config.get(message.server.id, 'server_api_1')\n config.get(message.server.id, 'server_api_2')\n except:\n await bot.send_message(c, 'IP not configured.')\n return\n config[message.server.id]['server_api_1'] += f'&port={port}'\n config[message.server.id]['server_api_2'] += f'%3A{port}'\n\n async def cfg_wmsg():\n msg = message.content.split('wmsg')[1].strip()\n config[message.server.id]['welcome_msg'] = msg\n\n subcommands = {\n 'add': cfg_add,\n 'ip': cfg_ip,\n 'port': cfg_port,\n 'wmsg': cfg_wmsg,\n }\n\n appinfo = await bot.application_info()\n\n if message.author.display_name != appinfo.owner.name and not message.author.server_permissions.manage_server:\n await bot.send_message(c, 'You don\\'t have the proper permissions to configure this bot.')\n return\n\n for i in subcommands:\n if message.content.split()[1] == i:\n await subcommands[i]()\n with open('dbot_config.ini', 'w') as f:\n config.write(f)\n await bot.send_message(c, 'Success.')","def config(self, **kwargs):\n backup_config = app.config.copy()\n app.config.update(kwargs)\n try:\n yield\n finally:\n app.config = backup_config","def update_config(self, config):\n # add follower public folder to the CKAN's list of public folders\n here = os.path.dirname(__file__)\n public_dir = os.path.join(here, 'public')\n if config.get('extra_public_paths'):\n config['extra_public_paths'] += ',' + public_dir\n else:\n config['extra_public_paths'] = public_dir\n # add follower template folder to the CKAN's list of template folders\n template_dir = os.path.join(here, 'templates')\n if config.get('extra_template_paths'):\n config['extra_template_paths'] += ',' + template_dir\n else:\n config['extra_template_paths'] = template_dir","def config(self):\n pass","def config(self):\n pass","def do_PUT(self):\r\n if self.path == \"/set_config\" or self.path == \"/set_config/\":\r\n\r\n if len(self.post_dict) > 0:\r\n for key, value in self.post_dict.iteritems():\r\n\r\n # Decode the params as UTF-8\r\n try:\r\n key = unicode(key, 'utf-8')\r\n value = unicode(value, 'utf-8')\r\n except UnicodeDecodeError:\r\n self.log_message(\"Could not decode request params as UTF-8\")\r\n\r\n self.log_message(u\"Set config '{0}' to '{1}'\".format(key, value))\r\n\r\n try:\r\n value = json.loads(value)\r\n\r\n except ValueError:\r\n self.log_message(u\"Could not parse JSON: {0}\".format(value))\r\n self.send_response(400)\r\n\r\n else:\r\n self.server.config[key] = value\r\n self.send_response(200)\r\n\r\n # No parameters sent to configure, so return success by default\r\n else:\r\n self.send_response(200)\r\n\r\n else:\r\n self.send_response(404)","def config_updated(self):\n if callable(self.on_config_updated):\n self.on_config_updated(self.config())","def GoToConfig(self):\n self.write_ack(MID.GoToConfig)","def _do_update(self, meta, k, v):\n self.runtime.logger.info('{}: [{}] -> {}'.format(meta.in_group_config_path, k, v))\n meta.config[k] = v\n meta.save()","def open_configuration(self,event):\n configDevFrame = Single_deviceconf(parent=self, ID=996)\n configDevFrame.Centre()\n configDevFrame.Show()\n configDevFrame.ShowModal()\n configDevFrame.Destroy()","def update(_id): \n pages_object = Pages(_id)\n page = pages_object.page\n \n language_name = languages_object.get_languages(3)\n \n # Update page\n if request.method == 'POST':\n if pages_object.update():\n return redirect(url_for('pages.overview'))\n \n len_of_label = len(page['label'])\n \n # Come back a message when there is an error\t\n if not pages_object.message is None:\n message = pages_object.message\n status = pages_object.status\n \n return render_template('{}/update.html'.format(MODULE_DIR), **locals())","def update_conf_file():\n filepath = remote_dir + \"/apache2/conf/httpd.conf\"\n fabric.contrib.files.sed(filepath, 'myproject', project_name)","def _update_site_configuration(self):\n self.site.configuration.site_values = {'THIRD_PARTY_AUTH_ONLY_DOMAIN': self.email_domain_name}\n self.site.configuration.save()","def populate_config(self, config):\n self.use_wine_mappings.set_active(config['use_wine_mappings'])\n self.force_recheck.set_active(config['force_recheck'])\n self._previous_force_recheck = config['force_recheck']\n self.resume.set_active(config['resume'])\n try:\n self.glade.get_widget('time_added_checkbox').set_active(\n 'time_added' in config['transfer_meta'])\n except KeyError:\n pass\n self.resume_dat_entry.set_text(config['previous_resume_dat_path'])","def toggle_conf(self):\n # save the configuration file before updating the incoming config\n if self.old_conf_id == '':\n conf_file = 'config_rec'\n else:\n conf_file = self.old_conf_id + '_config_rec'\n\n conf_map = self.get_rec_config()\n conf_dir = os.path.join(self.main_win.experiment_dir, 'conf')\n\n if write_conf(conf_map, conf_dir, conf_file):\n if str(self.rec_id.currentText()) == 'main':\n self.old_conf_id = ''\n else:\n self.old_conf_id = str(self.rec_id.currentText())\n else:\n msg_window('configuration ' + conf_file + ' was not saved')\n # if a config file corresponding to the rec id exists, load it\n # otherwise read from base configuration and load\n if self.old_conf_id == '':\n conf_file = os.path.join(conf_dir, 'config_rec')\n else:\n conf_file = os.path.join(conf_dir, self.old_conf_id + '_config_rec')\n\n if os.path.isfile(conf_file):\n # load the tab with new configuration, but do not update rec choices\n self.load_tab_common(conf_file, False)\n else:\n self.load_tab_common(os.path.join(conf_dir, 'config_rec'), False)\n self.notify()","def configure( self, csSection, submitPool ):\n\n VMDirector.configure( self, csSection, submitPool )\n self.reloadConfiguration( csSection, submitPool )","def write_config():\n\n e = Element(\"Configuration\")\n r = SubElement(e, \"RepositoryList\")\n r = SubElement(r, \"Repository\", name = \"default\")\n SubElement(r, \"Module\").text = args.driver\n SubElement(r, \"TokenLabel\").text = args.token_label\n SubElement(r, \"PIN\").text = args.pin\n ElementTree(e).write(args.write_config)\n args.write_config.flush()","def update(self, adt=None, url=None, params=None):\n if not self._id_exists():\n abort(404, f\"Application with ID {self.app_id} does not exist\")\n elif not self.engine.app_list:\n abort(404, \"There are no currently running applications\")\n\n path = self._get_path(adt, url)\n tpl, adaps = self._validate(path, params, validate_only=True)\n try:\n self.engine.update(self.app_id, tpl, adaps)\n except Exception as error:\n abort(500, f\"Error while updating: {error}\")\n\n return {\"message\": f\"Application {self.app_id} successfully updated\"}","def manage_edit_save(self, REQUEST):\n self._config.update(ldap_config.read_form(REQUEST.form, edit=True))\n REQUEST.RESPONSE.redirect(self.absolute_url() + '/manage_edit')","def updateconfig(self):\n\n # Initialize the yaml data\n ydata = {\"metadata\": self._metadata, \"nodes\": self._nodes}\n\n # Write the system config file\n filename = self._rootdir + self._metadata[\"system_config_file\"]\n with open(filename, \"w\") as yamlfile:\n yaml.dump(ydata, yamlfile)"],"string":"[\n \"def conf_update(self):\\n pass\",\n \"def update(self):\\n self.save_config_file()\",\n \"def configuration_view(project):\\n project_query = Project.select().where(Project.slug == project).first()\\n if project_query is None:\\n flash(\\\"invalid project\\\")\\n return redirect(url_for(\\\"projects\\\"))\\n session[\\\"project\\\"] = project_query\\n\\n g.selected_tab = \\\"configuration\\\"\\n\\n settings = None\\n if request.method == \\\"GET\\\":\\n settings = Anemone.abcfile.parse(path(project_query.path, \\\"build.abc\\\"))\\n elif request.method == \\\"POST\\\":\\n configuration_post(project_query, request)\\n\\n return render_template(\\\"configure.html\\\", ssh=open(app.config[\\\"SSH_PUBLIC\\\"]).readline(),\\n build=settings, unity=app.config[\\\"UNITY_PATH\\\"])\",\n \"def refresh_configuration(self):\\n pass\",\n \"def update_config(self, config):\\n self.config = {\\n \\\"display_name\\\": \\\"\\\",\\n \\\"description\\\": \\\"\\\",\\n \\\"required\\\": 1,\\n \\\"type\\\": \\\"string\\\"\\n }\\n self.config.update(config)\\n self.API_KEY = self.config['key']\",\n \"def update_config(self, config):\\n self.config = {\\n \\\"display_name\\\": \\\"\\\",\\n \\\"description\\\": \\\"\\\",\\n \\\"required\\\": 1,\\n \\\"type\\\": \\\"string\\\"\\n }\\n self.config.update(config)\\n self.API_KEY = self.config['key']\",\n \"def _update_config(self):\\n \\n # app size\\n if self.IsMaximized():\\n config.SETTINGS['app_maximized'] = True\\n else:\\n size = self.GetSize()\\n config.SETTINGS['app_width'] = size[0]\\n config.SETTINGS['app_height'] = size[1]\\n config.SETTINGS['app_maximized'] = False\\n \\n # panes size\\n pane = self.AUIManager.GetPane(self._collections_view)\\n config.SETTINGS['collections_view_enabled'] = pane.IsShown()\\n if pane.IsShown():\\n config.SETTINGS['collections_view_width'] = pane.window.GetClientSize()[0]\\n \\n pane = self.AUIManager.GetPane(self._pdf_view)\\n config.SETTINGS['pdf_view_enabled'] = pane.IsShown()\\n if pane.IsShown():\\n config.SETTINGS['pdf_view_height'] = pane.window.GetClientSize()[1]\\n \\n pane = self.AUIManager.GetPane(self._details_view)\\n config.SETTINGS['details_view_enabled'] = pane.IsShown()\\n if pane.IsShown():\\n config.SETTINGS['details_view_width'] = pane.window.GetClientSize()[0]\\n \\n # article view\\n config.SETTINGS['articles_view_columns'] = self._articles_view.GetColumnsSettings()\",\n \"def update_config(self, config):\\n return self._update_config(\\\"config\\\", config)\",\n \"def updateSettingsUI(self):\\n\\n pass\",\n \"def update_urls_file(self, app_name):\\n\\n logger.info(\\\"\\\\n--------------------------------------------------------\\\\n\\\\t\\\\tRefreshing application list in urls.py\\\")\\n copyfile(settings.SITE_ROOT + \\\"/\\\" + settings.APPLICATION_NAME + \\\"/urls.py\\\", settings.SITE_ROOT + \\\"/\\\" + settings.APPLICATION_NAME + \\\"/urls.py.backup\\\")\\n t = loader.get_template('applicationManager/applicationFileTemplates/project_urls_py.txt')\\n\\n apps = Application.objects.all()\\n\\n c = {'applist': apps}\\n rendered = t.render(c)\\n open(settings.SITE_ROOT + \\\"/\\\" + settings.APPLICATION_NAME + \\\"/urls.py\\\", \\\"w+\\\").write(rendered)\",\n \"def update_flask(self, flask_app):\\n flask_app.config.update(self.flask_config_dict)\",\n \"def on_save(self):\\r\\n #new_config = ConfigParser.RawConfigParser()\\r\\n cur_config = self.config.dict_config\\r\\n #\\r\\n # update the dict_config\\r\\n cur_config[\\\"access_restriction\\\"][\\\"ips\\\"] = self.text_ips.get(1.0, tk.END).strip()\\r\\n cur_config[\\\"access_restriction\\\"][\\\"ar_url\\\"] = self.entry_url.get().strip()\\r\\n #\\r\\n cur_config[\\\"email\\\"][\\\"relay_server_host\\\"] = self.entry_server_host.get().strip()\\r\\n cur_config[\\\"email\\\"][\\\"relay_server_port\\\"] = self.entry_server_port.get().strip()\\r\\n cur_config[\\\"email\\\"][\\\"email_from\\\"] = self.entry_from.get().strip()\\r\\n cur_config[\\\"email\\\"][\\\"recipients\\\"] = self.text_recipients.get(1.0, tk.END).strip()\\r\\n cur_config[\\\"email\\\"][\\\"ar_enabled_subject\\\"] = self.entry_enabled_subject.get().strip()\\r\\n cur_config[\\\"email\\\"][\\\"ar_enabled_body\\\"] = self.text_enabled_body.get(1.0, tk.END).strip()\\r\\n cur_config[\\\"email\\\"][\\\"ar_disabled_subject\\\"] = self.entry_disabled_subject.get()\\r\\n cur_config[\\\"email\\\"][\\\"ar_disabled_body\\\"] = self.text_disabled_body.get(1.0, tk.END).strip()\\r\\n\\r\\n #self.action.save_config()\\r\\n # # sync dict_config to the gui\\r\\n # for section in self.config.dict_config:\\r\\n # new_config.add_section(section)\\r\\n # for item in self.config.dict_config[section]:\\r\\n # new_config.set(section, item, self.config.dict_config[section][item])\\r\\n # #\\r\\n # # saving to a file\\r\\n # with open(self.config.file_path, 'w') as newconfigfile:\\r\\n # new_config.write(newconfigfile)\\r\\n #\\r\\n # # mbox.showinfo(\\\"Information\\\",\\r\\n # # \\\"Current configuration has been successfully saved to '%s'\\\" % os.path.basename(self.configfile))\\r\\n # self.console.debug(\\\"Configuration has been saved to '%s'\\\" % self.config.file_path)\\r\",\n \"async def update_app_settings(self, app_id: str, data: dict) -> dict:\\r\\n return await self.put(API_APP_SETTINGS.format(app_id=app_id), data)\",\n \"def _on_config_changed(self, _):\\n self._configure_pod()\",\n \"def options(self):\\n self.title = self.action\\n accept = self.request.accept.best_match(['text/html', 'application/json'])\\n\\n if accept == 'application/json':\\n self.render(json=list(self.ConfigService.items()))\\n else:\\n if self.request.method == 'POST':\\n params = self.params\\n self.ConfigService.set(params['id'], params['value'])\\n self.render(text=params['value'])\\n return\\n # if we made it here, render the html\",\n \"def app_config_cli(**kwargs):\\n\\n @signals.config_complete.connect\\n def set_config(app):\\n app.config.update(kwargs)\\n\\n yield\",\n \"def update_config(self, config):\\n toolkit.add_template_directory(config, 'templates')\\n toolkit.add_resource('fanstatic', 'mingus')\\n return\",\n \"def update_dashboard_section(self):\\n rconfig = configparser.RawConfigParser()\\n rconfig.read(self.conf_file)\\n if env.get('DASHBOARD_URL'):\\n if not rconfig.has_section('dashboard'):\\n rconfig.add_section('dashboard')\\n rconfig.set('dashboard', 'dashboard_url', env.get('DASHBOARD_URL'))\\n else:\\n rconfig.set('service_available', 'horizon', False)\\n with open(self.conf_file, 'w', encoding='utf-8') as config_file:\\n rconfig.write(config_file)\",\n \"def updateAppMonConf(AppID, requestModel):\\n url = \\\"https://api.ams.fortify.com/api/v3/applications/{applicationId}/application-monitoring/configuration\\\".format(applicationId=AppID)\\n\\n req = fodRequest()\\n r = req.put(url, params=requestModel)\\n return r\\n\\n #*******************************************************Applications**************************************************************\",\n \"def configure_app(app, config_dict, config_file_folder):\\n # write the config dict to app config as a read-only proxy of a mutable dict\\n app.config[APP_CONFIG_JSON] = MappingProxyType(config_dict)\\n config_file_folder = config_file_folder\\n app.config[CONFIG_FILE_FOLDER] = config_file_folder\\n app.config[AVAILABLE_PAGES_DICT] = make_pages_dict(\\n config_dict.get(AVAILABLE_PAGES, []), app.config[CONFIG_FILE_FOLDER]\\n )\\n configure_backend(app)\\n return app\",\n \"def update_global_config(self, config, **kwargs):\\n pass\",\n \"def updateConfig(self, conf=None):\\r\\n if conf is not None:\\r\\n self.config.update(conf)\\r\\n if self.visprotocol is not None:\\r\\n self.visprotocol.updateSettings(self.getConfigData())\\r\\n # else:\\r\\n # _LOGGER.warning(\\\"Visonic link is not set\\\")\\r\\n # make the changes to the platform parameters (used in alarm_control_panel)\\r\\n # the original idea was to keep these separate for multiple partitions but now i'm not so sure its necessary\\r\\n\\r\\n self.hass.data[DOMAIN][\\\"arm_without_code\\\"] = self.toBool(self.config.get(CONF_ARM_CODE_AUTO, False))\\r\\n self.hass.data[DOMAIN][\\\"force_keypad\\\"] = self.toBool(self.config.get(CONF_FORCE_KEYPAD, False))\\r\\n self.hass.data[DOMAIN][\\\"arm_away_instant\\\"] = self.toBool(self.config.get(CONF_INSTANT_ARM_AWAY, False))\\r\\n self.hass.data[DOMAIN][\\\"arm_home_instant\\\"] = self.toBool(self.config.get(CONF_INSTANT_ARM_HOME, False))\\r\\n\\r\\n _LOGGER.debug(\\\"[Settings] Log Max Entries %s\\\", self.config.get(CONF_LOG_MAX_ENTRIES))\\r\\n _LOGGER.debug(\\\"[Settings] Log Reverse %s\\\", self.config.get(CONF_LOG_REVERSE))\\r\\n _LOGGER.debug(\\\"[Settings] Log Create Event %s\\\", self.config.get(CONF_LOG_EVENT))\\r\\n _LOGGER.debug(\\\"[Settings] Log Final Event %s\\\", self.config.get(CONF_LOG_DONE))\\r\\n _LOGGER.debug(\\\"[Settings] Log XML Filename %s\\\", self.config.get(CONF_LOG_XML_FN))\\r\\n _LOGGER.debug(\\\"[Settings] Log CSV Filename %s\\\", self.config.get(CONF_LOG_CSV_FN))\\r\\n _LOGGER.debug(\\\"[Settings] Log CSV title Row %s\\\", self.config.get(CONF_LOG_CSV_TITLE))\",\n \"def update_application(configurationId=None, name=None, description=None):\\n pass\",\n \"def write_config(self):\\n cfg = {\\n 'ALERT_API_KEY':self.api_key,\\n 'APP_NAME':self.title,\\n 'alertes':self.alertes\\n }\\n write_conf(self.CONF_FILE,cfg)\",\n \"def _1_apply_config(self):\\n \\n columns = utils.get_var_details()\\n driver = self.driver\\n config_name = self.config_name\\n driver.get(self.base_url + \\\"/home/login\\\")\\n driver.find_element_by_id(\\\"email\\\").clear()\\n driver.find_element_by_id(\\\"email\\\").send_keys(columns['email'][1])\\n driver.find_element_by_id(\\\"password\\\").clear()\\n driver.find_element_by_id(\\\"password\\\").send_keys(columns['password'][1])\\n driver.find_element_by_id(\\\"btnLogin\\\").click()\\n time.sleep(10)\\n \\n driver.find_element_by_id(\\\"menuAccessPoints\\\").click()\\n time.sleep(5)\\n \\n i = 0\\n for option in driver.find_elements_by_css_selector(\\\"#ap_grid_table tbody tr\\\"):\\n client_name_id = \\\"AP_Name_\\\" + str(i)\\n status_id = \\\"Status_\\\" + str(i)\\n select_checkbox_id = \\\"_\\\" + str(i)\\n if properties.ap_name == str(driver.find_element_by_css_selector(\\\"#\\\" + client_name_id + \\\" a\\\").get_attribute(\\n 'innerHTML')) and driver.find_element_by_css_selector('#' + status_id + ' img').get_attribute(\\n 'title') == 'available':\\n #print \\\"pass\\\"\\n driver.find_element_by_css_selector(\\\"#\\\" + select_checkbox_id + \\\" div\\\").click()\\n time.sleep(5)\\n driver.find_element_by_id(\\\"applyTemplateBtn\\\").click()\\n j = 0\\n for option1 in driver.find_elements_by_css_selector(\\\"#config_list_table tbody tr\\\"):\\n template_name_id = \\\"Template_name_\\\" + str(j)\\n select_radiobutton_id = \\\"__\\\" + str(j)\\n #print select_radiobutton_id\\n if config_name == driver.find_element_by_id(template_name_id).get_attribute('innerHTML'):\\n driver.find_element_by_css_selector(\\\"#\\\" + select_radiobutton_id + \\\" input\\\").click()\\n driver.find_element_by_xpath(\\\"//html/body/div[2]/div/div/div/div[2]/div[2]/a\\\").click()\\n #driver.find_element_by_id(\\\"btnApply\\\").click()\\n j = j + 1\\n time.sleep(5)\\n \\n ##Checking for API response message which comes up after applying config to AP.\\n try:\\n actual_message = driver.find_element_by_xpath('//*[@id=\\\"deleteSuccessMessage\\\"]').get_attribute('innerHTML')\\n expected_message = \\\"Configuration template change initiated for AP(s).\\\"\\n print \\\"Actual message = \\\" + actual_message\\n print \\\"Expected message = \\\" + expected_message\\n #self.assertEqual(actual_message, expected_message, \\\"Appropriate message '%s' is not displayed\\\" % expected_message)\\n except AssertionError as e: self.verificationErrors.append(str(e))\\n time.sleep(40)\\n \\n ####Checking for the default config running on AP.\\n ssid = config_name\\n expected_running_config_id = db.search_field_in_configuration_collection('_id', config_name)\\n #Get the ap's jid\\n ap_jid = utils.get_ap_jid(properties.real_ap_ip)\\n print \\\"AP jid return value is: \\\" + ap_jid\\n ap_jid = ap_jid.strip()\\n actual_running_config_id = db.search_field_in_ap_collection('running_config_id', ap_jid)\\n print \\\"Actual running config id of AP is:\\\" + str(actual_running_config_id) \\n print \\\"Expected running config id of AP is:\\\" + str(expected_running_config_id)\\n try:\\n self.assertEqual(str(actual_running_config_id), str(expected_running_config_id),\\n \\\"Expected configuration %s is not running on %s AP.\\\" % (ssid, ap_jid))\\n except AssertionError as e: self.verificationErrors.append(str(e)) \\n \\n \\n #Verify if radios are enabled on the AP\\n ssh = paramiko.SSHClient()\\n ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\\n ssh.connect(properties.real_ap_ip, username='root',\\n password='password')\\n \\n try:\\n stdin, stdout, stderr = ssh.exec_command(\\\"uci show | grep disabled\\\")\\n except SSHException, e:\\n print e\\n data = stdout.readlines()\\n for eachLine in data:\\n if \\\"radio0\\\" in eachLine:\\n radio_line = eachLine.split(\\\"\\\\n\\\")\\n radio_state = radio_line[0]\\n print \\\"Actual Radio0 state is: \\\" + radio_state\\n expected_radio_state = \\\"wireless.radio\\\" + \\\"0\\\" + \\\".disabled=0\\\"\\n print \\\"Expected radio state is: \\\" + expected_radio_state\\n try:\\n self.assertEqual(radio_state.strip(), expected_radio_state,\\n \\\"After applying config to AP Radio0 was not disabled\\\")\\n except AssertionError as e: self.verificationErrors.append(str(e))\\n elif \\\"radio1\\\" in eachLine:\\n radio_line = eachLine.split(\\\"\\\\n\\\")\\n radio_state = radio_line[0]\\n print \\\"Actual Radio1 state is: \\\" + radio_state\\n expected_radio_state = \\\"wireless.radio\\\" + \\\"1\\\" + \\\".disabled=0\\\"\\n print \\\"Expected radio state is: \\\" + expected_radio_state\\n try:\\n self.assertEqual(radio_state.strip(), expected_radio_state,\\n \\\"After applying config to AP Radio1 was not disabled\\\")\\n except AssertionError as e: self.verificationErrors.append(str(e))\\n \\n ###Checking for the SSID running on AP \\n ssh = paramiko.SSHClient()\\n ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\\n ssh.connect(properties.real_ap_ip, username='root',\\n password='password')\\n try:\\n #To get the ssids running on AP\\n (stdin, stdout, stderr) = ssh.exec_command(\\\"uci show | grep ssid\\\")\\n except SSHException, e:\\n print e\\n ssid_output = stdout.readlines()\\n result = False\\n cnt = 0\\n for num, each in enumerate(ssid_output):\\n each = each.replace(\\\"\\\\n\\\", \\\"\\\")\\n if cnt == 0:\\n expected_ssid = (\\\"wireless.@wifi-iface[%s].ssid=%s\\\" % (cnt, ssid))\\n elif cnt == 1:\\n expected_ssid = (\\\"wireless.@wifi-iface[%s].ssid=%s\\\" % (cnt, ssid))\\n cnt = cnt + 1\\n if expected_ssid == each:\\n result = True\\n print \\\"Expected SSID is:\\\" + expected_ssid\\n print \\\"Actual SSID is:\\\" + each\\n else:\\n result = False\\n print \\\"Expected SSID is:\\\" + expected_ssid\\n print \\\"Actual SSID is:\\\" + each\\n try:\\n self.assertEqual(result, True, \\\"Expected SSID %s is not running on AP.\\\" % ssid)\\n except AssertionError as e: self.verificationErrors.append(str(e))\\n #print (\\\"Expected SSID %s is running on AP.\\\" % ssid)\\n i = i + 1\",\n \"def update_app(AppId=None, Name=None, Description=None, DataSources=None, Type=None, AppSource=None, Domains=None, EnableSsl=None, SslConfiguration=None, Attributes=None, Environment=None):\\n pass\",\n \"def update_configuration(ConfigurationId=None, Data=None, Description=None):\\n pass\",\n \"def update_config(self, data):\\n self.config.data = dict_merge(self.config.data, data)\\n self.config.save()\",\n \"def settings():\\n\\n mysettings = MySettings(app)\\n form = SettingsForm(request.form, obj=mysettings)\\n\\n if request.method == 'POST' and form.validate():\\n mysettings.update(\\n app,\\n form.influxdb_host.data,\\n form.influxdb_port.data,\\n form.influxdb_db.data,\\n form.influxdb_table.data,\\n form.influxdb_user.data,\\n form.influxdb_pw.data,\\n form.diematicd_host.data,\\n form.diematicd_port.data\\n )\\n return redirect(url_for('controller'))\\n\\n return render_template('settings.html', form=form, user=current_user)\",\n \"def configure(self, config: dict):\\n self.config.update(config)\",\n \"def _auto_update_configuration(self) -> None:\\n self.config = rasa.utils.train_utils.update_confidence_type(self.config)\\n rasa.utils.train_utils.validate_configuration_settings(self.config)\\n self.config = rasa.utils.train_utils.update_similarity_type(self.config)\\n self.config = rasa.utils.train_utils.update_evaluation_parameters(self.config)\",\n \"def config():\",\n \"def config():\",\n \"def update(self, config_dict):\\r\\n self._update(config_dict, allow_new_keys=True)\",\n \"def update_shed_config(self, shed_conf):\\n for index, my_shed_tool_conf in enumerate(self._dynamic_tool_confs):\\n if shed_conf['config_filename'] == my_shed_tool_conf['config_filename']:\\n self._dynamic_tool_confs[index] = shed_conf\\n self._save_integrated_tool_panel()\",\n \"def config(self, config_dict):\\r\\n self._cfg.config = config_dict\",\n \"def test_change_config(self):\\n browser = Browser(self.app)\\n portalURL = self.portal.absolute_url()\\n browser.addHeader('Authorization', 'Basic %s:%s' % (SITE_OWNER_NAME, SITE_OWNER_PASSWORD))\\n browser.open(portalURL + '/@@overview-controlpanel')\\n browser.getLink('Image WatchDog settings').click()\\n browser.getControl('Optimize PNG').selected = True\\n browser.getControl('Enabled').selected = True\\n browser.getControl('Save').click()\\n\\n registry = getUtility(IRegistry)\\n settings = registry.forInterface(IImageWatchDogSettings)\\n self.assertTrue(settings.optimize)\\n self.assertTrue(settings.enabled)\",\n \"def config():\\n if app.args.ui_mode == \\\"jinja\\\":\\n ui_config = {\\n \\\"p1\\\": {\\n \\\"options\\\": {\\n \\\"lineNumbers\\\": True,\\n \\\"theme\\\":\\\"material\\\",\\n \\\"lineWrapping\\\" : True,\\n \\\"mode\\\": \\\"yaml\\\",\\n \\\"indentUnit\\\": 2,\\n \\\"tabSize\\\": 2\\n },\\n \\\"title\\\": \\\"DATA\\\",\\n \\\"inventory\\\": bool(app.args.inventory_source),\\n \\\"b1\\\": {\\n \\\"icon\\\": None,\\n \\\"show\\\": False,\\n \\\"text\\\": None,\\n \\\"url\\\": None\\n }\\n },\\n \\\"p2\\\": {\\n \\\"options\\\": {\\n \\\"lineNumbers\\\": True,\\n \\\"theme\\\": \\\"material\\\",\\n \\\"lineWrapping\\\" : True,\\n \\\"mode\\\": \\\"jinja2\\\"\\n },\\n \\\"title\\\": \\\"RENDER\\\",\\n \\\"b1\\\": {\\n \\\"icon\\\": \\\"create\\\",\\n \\\"show\\\": True,\\n \\\"text\\\": \\\"Render\\\",\\n \\\"url\\\": \\\"/render\\\"\\n }\\n },\\n \\\"p3\\\": {\\n \\\"options\\\": {\\n \\\"lineNumbers\\\": True,\\n \\\"theme\\\": \\\"material\\\",\\n \\\"lineWrapping\\\" : True,\\n \\\"mode\\\": 'text'\\n },\\n \\\"title\\\": \\\"RESULT\\\",\\n \\\"b1\\\": {\\n \\\"icon\\\": \\\"link\\\",\\n \\\"show\\\": bool(app.args.url),\\n \\\"text\\\": \\\"link\\\"\\n }\\n }\\n }\\n elif app.args.ui_mode == \\\"schema\\\":\\n ui_config = {\\n \\\"p1\\\": {\\n \\\"options\\\": {\\n \\\"lineNumbers\\\": True,\\n \\\"theme\\\":\\\"material\\\",\\n \\\"lineWrapping\\\" : True,\\n \\\"mode\\\": \\\"yaml\\\",\\n \\\"indentUnit\\\": 2,\\n \\\"tabSize\\\": 2\\n },\\n \\\"title\\\": \\\"DATA\\\",\\n \\\"inventory\\\": bool(app.args.inventory_source),\\n \\\"b1\\\": {\\n \\\"icon\\\": \\\"create\\\",\\n \\\"show\\\": True,\\n \\\"text\\\": \\\"schema\\\",\\n \\\"url\\\": \\\"/schema\\\"\\n }\\n },\\n \\\"p2\\\": {\\n \\\"options\\\": {\\n \\\"lineNumbers\\\": True,\\n \\\"theme\\\": \\\"material\\\",\\n \\\"lineWrapping\\\" : True,\\n \\\"mode\\\": \\\"yaml\\\"\\n },\\n \\\"title\\\": \\\"SCHEMA\\\",\\n \\\"b1\\\": {\\n \\\"icon\\\": \\\"check\\\",\\n \\\"show\\\": True,\\n \\\"text\\\": \\\"Validate\\\",\\n \\\"url\\\": \\\"/validate\\\"\\n }\\n },\\n \\\"p3\\\": {\\n \\\"options\\\": {\\n \\\"lineNumbers\\\": True,\\n \\\"theme\\\": \\\"material\\\",\\n \\\"lineWrapping\\\" : True,\\n \\\"mode\\\": \\\"yaml\\\"\\n },\\n \\\"title\\\": \\\"VALIDATION SUCCESS/ERRORS\\\",\\n \\\"b1\\\": {\\n \\\"icon\\\": \\\"link\\\",\\n \\\"show\\\": bool(app.args.url),\\n \\\"text\\\": \\\"link\\\"\\n }\\n }\\n }\\n return jsonify(ui_config)\",\n \"def configure(self, section):\",\n \"def update_config(self, config):\\n p.toolkit.add_template_directory(config, \\\"templates\\\")\\n p.toolkit.add_public_directory(config, 'public')\",\n \"def update_config(self, config):\\n p.toolkit.add_template_directory(config, 'templates')\\n p.toolkit.add_public_directory(config, 'public')\",\n \"def update_config(self, kv: dict):\\n self._configs.update(kv)\\n self._save()\",\n \"def update_config_item(self, elements: Dict[str, Any]) -> None:\\n ...\",\n \"def do_PUT(self):\\n if self.path == \\\"/set_config\\\" or self.path == \\\"/set_config/\\\":\\n\\n if len(self.post_dict) > 0:\\n for key, value in self.post_dict.items():\\n\\n self.log_message(f\\\"Set config '{key}' to '{value}'\\\")\\n\\n try:\\n value = json.loads(value)\\n\\n except ValueError:\\n self.log_message(f\\\"Could not parse JSON: {value}\\\")\\n self.send_response(400)\\n\\n else:\\n self.server.config[key] = value\\n self.send_response(200)\\n\\n # No parameters sent to configure, so return success by default\\n else:\\n self.send_response(200)\\n\\n else:\\n self.send_response(404)\",\n \"def config():\\n config_django()\\n config_svisor()\",\n \"def setAppID(self, appid):\\n\\t\\tself.config.APP_ID = appid\",\n \"def update_config(self, config):\\n self.config = {\\n \\\"key\\\": \\\"\\\",\\n \\\"display_name\\\": \\\"\\\",\\n \\\"description\\\": \\\"\\\",\\n \\\"required\\\": 1,\\n \\\"type\\\": \\\"string\\\"\\n }\\n self.config.update(config)\\n self.API_KEY = self.config['github_api_key']\",\n \"def updateConfig(self):\\n # Make sure to keep the default values in place.\\n if self.newConfig['sensor'] == 0:\\n self.newConfig['sensor'] = self.config['sensor']\\n if self.newConfig['camera'] == 0:\\n self.newConfig['camera'] = self.config['camera']\\n if not self.newConfig['auto']['times']:\\n self.newConfig['auto']['times'] = self.config['auto']['times']\\n if not self.newConfig['auto']['days']:\\n self.newConfig['auto']['days'] = self.config['auto']['days']\\n\\n # Show the changes.\\n if self.verbosity >= 1:\\n print('%s: Updating configuration file...' % self.feederName)\\n try:\\n for key in self.config.keys():\\n if type(self.config[key]) is dict:\\n for subkey in self.config[key].keys():\\n if self.config[key][subkey] != self.newConfig[key][subkey]:\\n print('%s: Updating %s from %s to %s.' % (self.feederName, subkey, self.config[key][subkey], self.newConfig[key][subkey]))\\n elif self.config[key] != self.newConfig[key]:\\n print('%s: Updating %s from %s to %s.' % (self.feederName, key, self.config[key], self.newConfig[key]))\\n except ValueError:\\n if self.verbosity >= 1:\\n print('%s: Configuration file does not contain a valid JSON object.' % self.feederName)\\n if self.verbosity == 2:\\n print('%s: Overwriting configuration file to: %s.' % (self.feederName, self.config))\\n\\n # Change the configuration file.\\n self.config = self.newConfig\\n self.writeConfig()\",\n \"def handle_adminreloadconfig(bot, event):\\n try:\\n bot.cfg.reload()\\n getmainconfig().reload()\\n except Exception, ex: handle_exception()\\n event.done()\",\n \"def update(self):\\n self.application['email'] = 'test@example.spinnaker'\",\n \"def settings_view():\\n return template('settings.html')\",\n \"def update_website_configuration():\\n put('config/supervisor_website.conf', \\n '/etc/supervisor/conf.d/gunicorn.conf', \\n use_sudo=True)\\n sudo('supervisorctl update')\\n sudo('supervisorctl reload')\",\n \"def handleReload(self, confInfo=None):\",\n \"def reload_config(self):\\n pass\",\n \"def config_update(self, update: io.BytesIO) -> None:\\n self.__logger.debug('Eva.config_update called')\\n return self.__http_client.config_update(update)\",\n \"def _configure_ipsec_site(self, name, confs):\\n site_conf_tpl = '../config/tpl/ipsec.d/_.conf'\\n\\n context = {\\n 'conn_name': name,\\n 'elastic_ip': env.aws_elastic_ip,\\n }\\n for key, value in confs.items():\\n context[key] = value\\n\\n with hide(*fab_output_hides):\\n return upload_template_changed(\\n site_conf_tpl,\\n '/etc/ipsec.d/%s.conf' % name,\\n context=context,\\n use_sudo=True,\\n mode=0600,\\n )\",\n \"def set_config(self): # called from button_set object \\n self.settings['lights_on'] = self.lights_on.get()\\n self.settings['lights_off'] = self.lights_off.get()\\n self.settings['ambient_min'] = self.ambient_min.get()\\n self.settings['soil_1'] = self.smc1.get()\\n self.settings['soil_2'] = self.smc2.get()\\n self.settings['soil_3'] = self.smc3.get()\\n self.settings['soil_4'] = self.smc4.get()\\n self.settings['overhead_level'] = self.overhead_level.get()\\n\\n # Save settings to config file in case of reboot / power-loss\\n print \\\"UPDATING SETTINGS FILE\\\"\\n with open(self.settings_path, 'w') as jsonfile:\\n jsonfile.write(json.dumps(self.settings, indent=4))\\n self.active_changes = True # (flag) changes are active!\",\n \"def configure(self, conf):\\n return\",\n \"def edit_cfg(config_file):\\n\\n GUI().cfgEditor(config_file)\",\n \"def configuration():\",\n \"def appconfig():\\r\\n print('''\\\\n%s at %s acting as user %s\\r\\n\\\\nApp Configuration Menu''' % (PACKETMASTER.model, ADDRESS, USERNAME))\\r\\n choice = moves.input('''\\r\\n 1 - List Apps\\r\\n 2 - List Running Apps\\r\\n 3 - Start an App instance\\r\\n 4 - Modify an App instance\\r\\n 5 - Kill an App instance\\r\\n 6 - Call a custom App action\\r\\n 7 - Back\\r\\n 8 - Quit \\\\n\\r\\n Enter selection number: ''')\\r\\n try:\\r\\n choice = int(choice)\\r\\n except ValueError as reason:\\r\\n print(\\\"That is not a valid selection.\\\", reason)\\r\\n appconfig()\\r\\n execute = {1: PACKETMASTER.device_apps,\\r\\n 2: PACKETMASTER.apps_active,\\r\\n 3: PACKETMASTER.start_app_guided,\\r\\n 4: PACKETMASTER.mod_app_guided,\\r\\n 5: PACKETMASTER.kill_app_guided,\\r\\n 6: PACKETMASTER.call_app_action_guided,\\r\\n 7: manage,\\r\\n 8: exit}\\r\\n if choice in execute:\\r\\n try:\\r\\n select = execute[choice]\\r\\n run = select()\\r\\n print(run)\\r\\n appconfig()\\r\\n except KeyError as reason:\\r\\n print(reason)\\r\\n else:\\r\\n print(\\\"That is not a valid selection.\\\")\\r\\n appconfig()\",\n \"def __writeConfig(self):\\n page = None\\n\\n #TODO: get values of configurations here\\n particles = \\\"#f\\\" if not base.particleMgrEnabled else \\\"#t\\\"\\n volume = str(round(base.musicManager.getVolume(), 2))\\n mute = \\\"#f\\\" if base.AppHasAudioFocus else \\\"#t\\\"\\n #TODO: add any configuration variable name that you have added\\n customConfigVariables = [\\n \\\"\\\", \\\"particles-enabled\\\", \\\"audio-mute\\\", \\\"audio-volume\\\"]\\n if os.path.exists(prcFile):\\n # open the config file and change values according to current\\n # application settings\\n page = loadPrcFile(Filename.fromOsSpecific(prcFile))\\n removeDecls = []\\n for dec in range(page.getNumDeclarations()):\\n # Check if our variables are given.\\n # NOTE: This check has to be done to not loose our base or other\\n # manual config changes by the user\\n if page.getVariableName(dec) in customConfigVariables:\\n decl = page.modifyDeclaration(dec)\\n removeDecls.append(decl)\\n for dec in removeDecls:\\n page.deleteDeclaration(dec)\\n # NOTE: particles-enabled and audio-mute are custom variables and\\n # have to be loaded by hand at startup\\n # Particles\\n page.makeDeclaration(\\\"particles-enabled\\\", particles)\\n # audio\\n page.makeDeclaration(\\\"audio-volume\\\", volume)\\n page.makeDeclaration(\\\"audio-mute\\\", mute)\\n else:\\n # Create a config file and set default values\\n cpMgr = ConfigPageManager.getGlobalPtr()\\n page = cpMgr.makeExplicitPage(\\\"%s Pandaconfig\\\"%appName)\\n # set OpenGL to be the default\\n page.makeDeclaration(\\\"load-display\\\", \\\"pandagl\\\")\\n # get the displays width and height\\n w = self.pipe.getDisplayWidth()\\n h = self.pipe.getDisplayHeight()\\n # set the window size in the config file\\n page.makeDeclaration(\\\"win-size\\\", \\\"%d %d\\\"%(w, h))\\n # set the default to fullscreen in the config file\\n page.makeDeclaration(\\\"fullscreen\\\", \\\"1\\\")\\n # particles\\n page.makeDeclaration(\\\"particles-enabled\\\", \\\"#t\\\")\\n # audio\\n page.makeDeclaration(\\\"audio-volume\\\", volume)\\n page.makeDeclaration(\\\"audio-mute\\\", \\\"#f\\\")\\n # create a stream to the specified config file\\n configfile = OFileStream(prcFile)\\n # and now write it out\\n page.write(configfile)\\n # close the stream\\n configfile.close()\",\n \"def fusion_api_reapply_li_configuration(self, uri, api=None, headers=None):\\n param = '/configuration'\\n return self.li.update(body=None, uri=uri, api=api, headers=headers, param=param)\",\n \"def update(self):\\n # TO DO for updating urls if changed\\n pass\",\n \"def update_settings(command):\\n namespace = app.main(command)\\n assert namespace.command == 'u' or namespace.command == \\\"updatesettings\\\"\",\n \"def _refreshconfig(self):\\n self.config = ConfigGenerator(os.path.join(self.rundir, const.CONFIG_FILE))\",\n \"def config_diff_form_page():\\n # connect to database\\n cur_db = connect_db(\\\"172.20.38.50\\\", \\\"mvelay\\\", \\\"user\\\", \\\"sandbox\\\")\\n cursor = cur_db.cursor()\\n\\n modules_query = \\\"\\\"\\\"SELECT DISTINCT module from t_ck5050_ini ORDER BY module ASC\\\"\\\"\\\"\\n versions_query = \\\"\\\"\\\"SELECT DISTINCT version from t_ck5050_ini ORDER BY version DESC\\\"\\\"\\\"\\n clients_query = \\\"\\\"\\\"SELECT DISTINCT client from t_ck5050_ini ORDER BY client DESC\\\"\\\"\\\"\\n\\n cursor.execute(modules_query)\\n modules = cursor.fetchall()\\n\\n cursor.execute(versions_query)\\n versions = cursor.fetchall()\\n\\n cursor.execute(clients_query)\\n clients = cursor.fetchall()\\n\\n # Build template page\\n with open(\\\"./header.html\\\") as header, open('./config_form.tpl') as config_form, open('./footer.html') as footer:\\n template_html = header.read() + config_form.read() + footer.read()\\n\\n html_page = template(template_html, modules=modules, versions=versions,\\n clients=clients)\\n\\n cursor.close()\\n\\n return html_page\",\n \"def home(request):\\n page=1\\n span = 15\\n order = \\\"-created_at\\\"\\n page = request.GET.get('page', page)\\n span = request.GET.get('span', span)\\n config_list,entry_count = Config.get_items(span=span, page=page)\\n\\n temp_values = {\\n \\\"target\\\":\\\"config\\\",\\n \\\"title\\\":u\\\"Config定義一覧ページ\\\",\\n \\\"config_list\\\":config_list,\\n \\\"subscroll\\\":True,\\n }\\n return render(request, 'server/index.html', temp_values)\",\n \"def display_app_info(config, client, app_id):\\n try:\\n resp = client.get_app_full_info(config.username, app_id)\\n result = resp.json()\\n app_info = result[\\\"app_info\\\"]\\n title = click.style(\\\"App Name : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{}\\\".format(app_info[\\\"title\\\"]))\\n\\n if app_info[\\\"rating_count\\\"] == 0:\\n rating = \\\"Not yet rated\\\"\\n else:\\n rating = \\\"{:.1f} ({} rating\\\".format(app_info[\\\"average_rating\\\"],\\n int(app_info[\\\"rating_count\\\"]))\\n if app_info[\\\"rating_count\\\"] > 1:\\n rating += \\\"s\\\"\\n rating += \\\")\\\"\\n rating_row = click.style(\\\"Rating : \\\", fg=\\\"blue\\\") + click.style(\\\"{}\\\".format(rating))\\n up_status = click.style(\\\"Status : \\\", fg=\\\"blue\\\")\\n if app_info[\\\"is_up\\\"]:\\n up_status += click.style(\\\"Up\\\")\\n else:\\n up_status += click.style(\\\"Down\\\")\\n\\n last_crawl_str = \\\"Not yet crawled\\\"\\n if \\\"last_crawl\\\" in app_info:\\n last_crawl_str = util.format_date(app_info[\\\"last_crawl\\\"])\\n\\n last_crawl = click.style(\\\"Last Crawl Time : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{}\\\".format(last_crawl_str))\\n version = click.style(\\\"Version : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{}\\\".format(app_info[\\\"version\\\"]))\\n\\n last_updated_str = util.format_date(app_info[\\\"updated\\\"])\\n last_update = click.style(\\\"Last Update : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{}\\\".format(last_updated_str))\\n\\n availability = click.style(\\\"Availability : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{:.2f}%\\\".format(app_info[\\\"average_uptime\\\"] * 100))\\n\\n app_url = click.style(\\\"Public App URL : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{}\\\".format(app_info[\\\"app_url\\\"]))\\n original_url = click.style(\\\"Private App URL : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{}\\\".format(app_info[\\\"original_url\\\"]))\\n category = click.style(\\\"Category : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{}\\\".format(app_info[\\\"category\\\"]))\\n\\n desc = click.style(\\\"Description : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{}\\\".format(app_info[\\\"description\\\"]))\\n price = click.style(\\\"Price Range : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{} - {} Satoshis\\\").format(\\n app_info[\\\"min_price\\\"], app_info[\\\"max_price\\\"])\\n doc_url = click.style(\\\"Docs URL : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{}\\\".format(app_info[\\\"docs_url\\\"]))\\n\\n quick_start = click.style(\\\"Quick Start\\\\n\\\\n\\\", fg=\\\"blue\\\") + click.style(\\n app_info[\\\"quick_buy\\\"])\\n\\n usage_docs = None\\n if \\\"usage_docs\\\" in app_info:\\n usage_docs = click.style(\\\"Detailed usage\\\\n\\\\n\\\", fg=\\\"blue\\\") + click.style(\\n app_info[\\\"usage_docs\\\"])\\n\\n page_components = [title, \\\"\\\\n\\\",\\n rating_row, up_status, availability, last_crawl, last_update, version,\\n \\\"\\\\n\\\",\\n desc, app_url, original_url, doc_url, \\\"\\\\n\\\",\\n category, price, \\\"\\\\n\\\", quick_start, \\\"\\\\n\\\"]\\n if usage_docs:\\n page_components.append(usage_docs + \\\"\\\\n\\\")\\n final_str = \\\"\\\\n\\\".join(page_components)\\n logger.info(final_str, pager=True)\\n\\n except ServerRequestError as e:\\n if e.status_code == 404:\\n logger.info(\\n \\\"The specified id for the app ({}) does not match any apps in the \\\"\\n \\\"marketplace.\\\".format(app_id))\\n else:\\n raise e\",\n \"def update_config(self, config):\\n toolkit.add_template_directory(config, 'templates')\\n toolkit.add_public_directory(config, 'public')\\n toolkit.add_resource('fanstatic', 'syngenta')\",\n \"def set_config(app):\\n # set config from config.py\\n app.config.from_object('config')\\n\\n # override config from secret conf files\\n pi_home = os.path.dirname(app.config['ENVPATH']) # /home/pi\\n secret_conf_dir = os.path.join(pi_home, 'CONFIG_CHAUDIERE') # /home/pi/CONFIG_CHAUDIERE\\n secret_conf_com_file = 'chaudiere_secret_config.py'\\n secret_conf_com = secret_conf_dir+'/'+secret_conf_com_file\\n try:\\n with open(secret_conf_com) as f:\\n json_config = json.load(f)\\n for conf in ['Common', app.config['ENVNAME']]:\\n app.config.update(json_config[conf])\\n except IOError as e:\\n print('IOError loading conf file (file not existing?): ' + secret_conf_com + str(e))\\n except ValueError as e:\\n print('ValueError loading JSON : ' + secret_conf_com + ' ' + str(e))\\n\\n #app.config['USERS_EMAILS'] = list(map(lambda x: x+'@gmail.com', app.config['USERS'])) \\n # app.logger.error('test error') # <-- This works !!! \",\n \"def set_config(self, aConfig):\\n \\n # we update the dict of the existing config with the passed\\n # parameter. This means that the new config is merged with\\n # the old, but all new members overwrite old one. This is\\n # more robust.\\n self._config.__dict__.update(aConfig.__dict__)\\n # apply the config to the underlying logic\\n self.config_to_logic()\\n # bring it back all the way up to the view\\n self.logic_to_config()\\n\\n # but only if we are in view mode\\n if self.view_initialised:\\n self.config_to_view()\\n\\n # the config has been set, so we assumem that the module has\\n # now been modified. \\n self._module_manager.modify_module(self)\",\n \"def update(self, obj):\\n\\n self.cfg.update(obj)\",\n \"def getConfigPage():\\r\\n\\r\\n\\r\\n #print os.getcwd()\\r\\n #os.chdir('..')\\r\\n #os.chdir('D:\\\\Stone\\\\Python\\\\Python_Insurance')\\r\\n #print os.getcwd()\\r\\n\\r\\n #configPage = ConfigObj('pagesConf.conf')\\r\\n configPage = ConfigObj('thirdevalConf.conf')\\r\\n\\r\\n #configPagePolicyHolder_Usr = configPage['PolicyHolder_Usr']\\r\\n #dictFirst = configPagePolicyHolder_Usr.dict()\\r\\n #print dictFirst\\r\\n #j = 2\\r\\n #for j in range(j):\\r\\n # for member in dictFirst:\\r\\n # if 'titleText' in dictFirst[member]:\\r\\n # print member\\r\\n # dictSecond = dictFirst[member]\\r\\n # print dictSecond\\r\\n # break\\r\\n # dictFirst.pop(member)\\r\\n #i = i+1\\r\\n #print i\\r\\n #for item in configPagePolicyHolder_Usr[member]:\\r\\n # print configPagePolicyHolder_Usr[member][item]\\r\\n #for item in member:\\r\\n # print member[item]\\r\\n #print configPage\\r\\n #print configPagePolicyHolder_Usr\\r\\n #configPagePolicyHolder_Usr = configPage['PolicyHolder_Usr']\\r\\n #print configPagePolicyHolder_Usr\\r\\n\\r\\n #print configPagePolicyHolder_Usr.as_int( 'rowNumber')\\r\\n #print configPage.as_bool(\\\"hasPolicyHolder_Usr\\\")\\r\\n return configPage\",\n \"def update_zoo_config(self, site_name, virt_path, new_config):\\n root_path = self.map_path(site_name, virt_path)\\n zoo_config_path = os.path.join(root_path, \\\".zoo\\\")\\n config = get_zoo_config(zoo_config_path) or {}\\n\\n app = config.get('application')\\n # disabled ability\\n if 'selected-engine' in new_config :\\n new_engine = new_config.get('selected-engine')\\n if 'parameters' in app:\\n app['parameters']['selected-engine'] = new_engine\\n else:\\n app['parameters'] = OrderedDict()\\n app['parameters']['selected-engine'] = new_engine\\n\\n\\n if 'engines' in new_config:\\n engines = new_config.get('engines')\\n app['engines'] = engines\\n\\n if 'locations' in new_config:\\n app['locations'] = new_config['locations']\\n\\n if \\\"description\\\" in app:\\n app[\\\"description\\\"] = Literal(app[\\\"description\\\"])\\n\\n if \\\"find_installed_command\\\" in app:\\n app[\\\"find_installed_command\\\"] = Literal(app[\\\"find_installed_command\\\"])\\n\\n if \\\"install_command\\\" in app:\\n app[\\\"install_command\\\"] = Literal(app[\\\"install_command\\\"])\\n\\n if \\\"uninstall_command\\\" in app:\\n app[\\\"uninstall_command\\\"] = Literal(app[\\\"uninstall_command\\\"])\\n\\n if \\\"upgrade_command\\\" in app:\\n app[\\\"upgrade_command\\\"] = Literal(app[\\\"upgrade_command\\\"])\\n\\n # save .zoo\\n YamlHelper.save(config, zoo_config_path)\",\n \"def config(ctx):\\n return\",\n \"def _editSysconfig():\\n dbUrl = \\\"jdbc:postgresql://\\\" + getDbHostName() + \\\":\\\" + getDbPort() + \\\"/\\\" + basedefs.DB_NAME\\n if \\\"DB_SECURE_CONNECTION\\\" in controller.CONF.keys() and controller.CONF[\\\"DB_SECURE_CONNECTION\\\"] == \\\"yes\\\":\\n dbUrl = dbUrl + \\\"?ssl=true&sslfactory=org.postgresql.ssl.NonValidatingFactory\\\"\\n\\n proxyEnabled = utils.compareStrIgnoreCase(controller.CONF[\\\"OVERRIDE_HTTPD_CONFIG\\\"], \\\"yes\\\")\\n utils.editEngineSysconfig(proxyEnabled=proxyEnabled,\\n dbUrl=dbUrl,\\n dbUser=utils.getDbUser(),\\n fqdn=controller.CONF[\\\"HOST_FQDN\\\"],\\n http=controller.CONF[\\\"HTTP_PORT\\\"],\\n https=controller.CONF[\\\"HTTPS_PORT\\\"],\\n javaHome=controller.CONF[\\\"JAVA_HOME\\\"])\",\n \"def task_settings(short_name):\\r\\n (app, owner, n_tasks, n_task_runs,\\r\\n overall_progress, last_activity) = app_by_shortname(short_name)\\r\\n try:\\r\\n require.app.read(app)\\r\\n require.app.update(app)\\r\\n return render_template('applications/task_settings.html',\\r\\n app=app,\\r\\n owner=owner)\\r\\n except HTTPException:\\r\\n if app.hidden:\\r\\n raise abort(403)\\r\\n else:\\r\\n raise\",\n \"def manage_config(self, action=None, peel=False, force=False,\\n as_json=False, path=None):\\n if action is None:\\n return\\n elif action == \\\"view\\\":\\n if self.config is None:\\n raise ValueError(\\\"No config loaded\\\")\\n flattened = {}\\n for cat, bcd in self.config._asdict().items():\\n flattened[cat] = bcd.peel(peel=peel)\\n return flattened\\n #\\n nvid = self.GetUser().GetUserName()\\n if not hasattr(self, \\\"_nv_undo_stack\\\"):\\n from collections import deque\\n self._nv_undo_stack = deque()\\n #\\n import os\\n from .configgers import default_config\\n defver = default_config.settings[\\\"config_version\\\"]\\n #\\n def get_path(path): # noqa: E306\\n ext = \\\"json\\\" if as_json else \\\"ini\\\"\\n if path and not force:\\n path = os.path.expandvars(os.path.expanduser(path))\\n path = os.path.abspath(path)\\n # All dirs must exist; \\\"export\\\" creates files if absent\\n if os.path.isdir(path):\\n path = os.path.join(path, f\\\"config.{ext}\\\")\\n elif not os.path.exists(path):\\n parpath = os.path.dirname(path)\\n if not os.path.isdir(parpath):\\n path = None\\n elif not any(path.endswith(e) for e in (\\\".json\\\", \\\".ini\\\")):\\n path = os.path.join(parpath, f\\\"config.{ext}\\\")\\n if not path:\\n path = os.path.join(self.datadir, f\\\"config.{ext}\\\")\\n return path\\n #\\n # save/export\\n def ensure_defver(peeled): # noqa: E306\\n ps = peeled.setdefault(\\\"settings\\\", {})\\n ps.setdefault(\\\"config_version\\\", defver)\\n #\\n # load/reload\\n def handle_outdated(curver): # noqa: E306\\n dirname = os.path.dirname(path or get_path(path))\\n basename = \\\"config.{}.new\\\".format(\\\"json\\\" if as_json else \\\"ini\\\")\\n dest = os.path.join(dirname, basename)\\n orig, self.config = self.config, construct_config({})\\n # OnHooks will be skipped during this call\\n self.manage_config(\\\"export\\\", force=True, as_json=as_json,\\n path=dest)\\n self.config = orig\\n msg = \\\" \\\".join(\\\"\\\"\\\"\\n Your config appears to be outdated. Please update it\\n using the latest defaults, which can be found here: {}.\\n Make sure to include the new version number. Or, use\\n --force to bypass this warning.\\n \\\"\\\"\\\".split()).format(dest)\\n raise UserWarning(msg)\\n #\\n if action == \\\"load\\\":\\n from .configgers import load_config, construct_config\\n stringified = self.nv.get(nvid)\\n peeled = load_config(stringified) if stringified else {}\\n # Could just view/peel, but this should be the only redundant item\\n curver = peeled.get(\\\"settings\\\", {}).get(\\\"config_version\\\")\\n if peeled:\\n if not curver:\\n raise KeyError(\\\"Required item /settings/config_version \\\"\\n f\\\"missing from nv[{nvid}]\\\")\\n elif curver == defver:\\n del peeled[\\\"settings\\\"][\\\"config_version\\\"]\\n elif not force:\\n handle_outdated(curver)\\n self.config = construct_config(peeled)\\n return\\n elif action == \\\"save\\\":\\n if not self.config.settings[\\\"host\\\"] and not force:\\n msg = (\\\"Warning: not caching config because \\\"\\n \\\"'/settings/host' is empty; use --force to override\\\")\\n raise UserWarning(msg)\\n peeled = self.manage_config(action=\\\"view\\\", peel=True)\\n # Must track version because module may be updated in the interim\\n ensure_defver(peeled)\\n from .ootil import restring\\n if nvid in self.nv:\\n MAX_UNDOS = 5\\n if len(self._nv_undo_stack) == MAX_UNDOS:\\n del self.nv[self._nv_undo_stack.pop()]\\n from datetime import datetime\\n bakkey = f\\\"{nvid}.{datetime.now().timestamp()}\\\"\\n self.nv[bakkey] = self.nv[nvid]\\n self._nv_undo_stack.appendleft(bakkey)\\n self.nv[nvid] = restring(peeled)\\n return\\n elif action == \\\"undo\\\":\\n # TODO write tests for this, add to cmd_update\\n raise RuntimeError(\\\"TODO: need tests for this\\\")\\n try:\\n lastkey = self._nv_undo_stack.popleft()\\n except IndexError:\\n raise UserWarning(\\\"Nothing to undo\\\")\\n self.nv[nvid] = self.nv[lastkey] # TODO see if nv supports pop\\n del self.nv[lastkey]\\n return self.manage_config(\\\"load\\\")\\n elif action not in (\\\"reload\\\", \\\"export\\\"):\\n raise ValueError(\\\"Unrecognized action\\\")\\n #\\n path = get_path(path)\\n #\\n def validate(peeled, skip_dropped=False): # noqa: E306\\n from .configgers import validate_config\\n warn, info = validate_config(peeled)\\n msg = []\\n if skip_dropped:\\n info = [l for l in info if \\\"dropped\\\" not in l]\\n if info:\\n msg += [f\\\"\\\\x02FYI:\\\\x02\\\\n\\\"] + info\\n if warn:\\n msg += [f\\\"\\\\x02Potential problems:\\\\x02\\\\n\\\"] + warn\\n if msg:\\n self.put_pretty(\\\"\\\\n\\\".join(msg))\\n return False if warn else True\\n #\\n if action == \\\"reload\\\":\\n if not os.path.exists(path):\\n raise FileNotFoundError(f\\\"No config found at {path}\\\")\\n from .configgers import load_config, construct_config\\n loaded = load_config(path)\\n if not force:\\n curver = loaded.get(\\\"settings\\\", {}).get(\\\"config_version\\\")\\n if curver:\\n if curver == defver:\\n del loaded[\\\"settings\\\"][\\\"config_version\\\"]\\n elif not force:\\n handle_outdated(curver)\\n elif as_json:\\n msg = (\\\"Warning: 'config_version' absent from config; \\\"\\n \\\"use --force to try loading anyway\\\")\\n raise UserWarning(msg)\\n if not validate(loaded, as_json):\\n return\\n self.config = construct_config(loaded)\\n return self.manage_config(\\\"save\\\")\\n elif action == \\\"export\\\":\\n try:\\n peeled = self.manage_config(\\\"view\\\", peel=True)\\n except Exception:\\n if not force:\\n raise\\n else:\\n # \\\"Emergency\\\" backup called by OnShutdown(); must peel,\\n # unfortunately, since construct_config likely just failed\\n as_json = peel = True\\n strung = self.nv[nvid]\\n import json\\n peeled = json.loads(strung)\\n version = peeled[\\\"settings\\\"][\\\"config_version\\\"]\\n path = os.path.dirname(path)\\n path = os.path.join(path, f\\\"config.{version}.json.bak\\\")\\n if not force:\\n if not peeled:\\n msg = (\\\"Warning: cached config is empty; \\\"\\n \\\"use --force to export default config\\\")\\n raise UserWarning(msg)\\n if not validate(peeled):\\n return\\n with open(path, \\\"w\\\") as flow:\\n if as_json:\\n # No need to support \\\"complete\\\" (redundant) version\\n if not peel:\\n spread = self.config.conditions.spread\\n payload = self.manage_config(\\\"view\\\", peel=spread)\\n else:\\n payload = peeled\\n ensure_defver(payload)\\n import json\\n json.dump(payload, flow, indent=2)\\n else:\\n from Signal.iniquitous import gen_ini\\n formatted = gen_ini(self.config)\\n flow.write(formatted)\",\n \"def UpdateConfig(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"async def do_config():\\n\\n\\n async def cfg_add():\\n try:\\n if config[message.server.id]:\\n await bot.send_message(c, 'The channel is already configured.')\\n return\\n except:\\n config[message.server.id] = {\\n 'server_api_1': '',\\n 'server_api_2': '',\\n 'welcome_msg enabled': False,\\n 'welcome_msg': '',\\n }\\n\\n async def cfg_ip():\\n ip = message.content.split()[2]\\n config[message.server.id]['server_api_1'] = f'https://mcapi.us/server/status?ip={ip}'\\n config[message.server.id]['server_api_2'] = f'https://eu.mc-api.net/v3/server/ping/{ip}'\\n\\n async def cfg_port():\\n port = message.content.split()[2]\\n try:\\n config.get(message.server.id, 'server_api_1')\\n config.get(message.server.id, 'server_api_2')\\n except:\\n await bot.send_message(c, 'IP not configured.')\\n return\\n config[message.server.id]['server_api_1'] += f'&port={port}'\\n config[message.server.id]['server_api_2'] += f'%3A{port}'\\n\\n async def cfg_wmsg():\\n msg = message.content.split('wmsg')[1].strip()\\n config[message.server.id]['welcome_msg'] = msg\\n\\n subcommands = {\\n 'add': cfg_add,\\n 'ip': cfg_ip,\\n 'port': cfg_port,\\n 'wmsg': cfg_wmsg,\\n }\\n\\n appinfo = await bot.application_info()\\n\\n if message.author.display_name != appinfo.owner.name and not message.author.server_permissions.manage_server:\\n await bot.send_message(c, 'You don\\\\'t have the proper permissions to configure this bot.')\\n return\\n\\n for i in subcommands:\\n if message.content.split()[1] == i:\\n await subcommands[i]()\\n with open('dbot_config.ini', 'w') as f:\\n config.write(f)\\n await bot.send_message(c, 'Success.')\",\n \"def config(self, **kwargs):\\n backup_config = app.config.copy()\\n app.config.update(kwargs)\\n try:\\n yield\\n finally:\\n app.config = backup_config\",\n \"def update_config(self, config):\\n # add follower public folder to the CKAN's list of public folders\\n here = os.path.dirname(__file__)\\n public_dir = os.path.join(here, 'public')\\n if config.get('extra_public_paths'):\\n config['extra_public_paths'] += ',' + public_dir\\n else:\\n config['extra_public_paths'] = public_dir\\n # add follower template folder to the CKAN's list of template folders\\n template_dir = os.path.join(here, 'templates')\\n if config.get('extra_template_paths'):\\n config['extra_template_paths'] += ',' + template_dir\\n else:\\n config['extra_template_paths'] = template_dir\",\n \"def config(self):\\n pass\",\n \"def config(self):\\n pass\",\n \"def do_PUT(self):\\r\\n if self.path == \\\"/set_config\\\" or self.path == \\\"/set_config/\\\":\\r\\n\\r\\n if len(self.post_dict) > 0:\\r\\n for key, value in self.post_dict.iteritems():\\r\\n\\r\\n # Decode the params as UTF-8\\r\\n try:\\r\\n key = unicode(key, 'utf-8')\\r\\n value = unicode(value, 'utf-8')\\r\\n except UnicodeDecodeError:\\r\\n self.log_message(\\\"Could not decode request params as UTF-8\\\")\\r\\n\\r\\n self.log_message(u\\\"Set config '{0}' to '{1}'\\\".format(key, value))\\r\\n\\r\\n try:\\r\\n value = json.loads(value)\\r\\n\\r\\n except ValueError:\\r\\n self.log_message(u\\\"Could not parse JSON: {0}\\\".format(value))\\r\\n self.send_response(400)\\r\\n\\r\\n else:\\r\\n self.server.config[key] = value\\r\\n self.send_response(200)\\r\\n\\r\\n # No parameters sent to configure, so return success by default\\r\\n else:\\r\\n self.send_response(200)\\r\\n\\r\\n else:\\r\\n self.send_response(404)\",\n \"def config_updated(self):\\n if callable(self.on_config_updated):\\n self.on_config_updated(self.config())\",\n \"def GoToConfig(self):\\n self.write_ack(MID.GoToConfig)\",\n \"def _do_update(self, meta, k, v):\\n self.runtime.logger.info('{}: [{}] -> {}'.format(meta.in_group_config_path, k, v))\\n meta.config[k] = v\\n meta.save()\",\n \"def open_configuration(self,event):\\n configDevFrame = Single_deviceconf(parent=self, ID=996)\\n configDevFrame.Centre()\\n configDevFrame.Show()\\n configDevFrame.ShowModal()\\n configDevFrame.Destroy()\",\n \"def update(_id): \\n pages_object = Pages(_id)\\n page = pages_object.page\\n \\n language_name = languages_object.get_languages(3)\\n \\n # Update page\\n if request.method == 'POST':\\n if pages_object.update():\\n return redirect(url_for('pages.overview'))\\n \\n len_of_label = len(page['label'])\\n \\n # Come back a message when there is an error\\t\\n if not pages_object.message is None:\\n message = pages_object.message\\n status = pages_object.status\\n \\n return render_template('{}/update.html'.format(MODULE_DIR), **locals())\",\n \"def update_conf_file():\\n filepath = remote_dir + \\\"/apache2/conf/httpd.conf\\\"\\n fabric.contrib.files.sed(filepath, 'myproject', project_name)\",\n \"def _update_site_configuration(self):\\n self.site.configuration.site_values = {'THIRD_PARTY_AUTH_ONLY_DOMAIN': self.email_domain_name}\\n self.site.configuration.save()\",\n \"def populate_config(self, config):\\n self.use_wine_mappings.set_active(config['use_wine_mappings'])\\n self.force_recheck.set_active(config['force_recheck'])\\n self._previous_force_recheck = config['force_recheck']\\n self.resume.set_active(config['resume'])\\n try:\\n self.glade.get_widget('time_added_checkbox').set_active(\\n 'time_added' in config['transfer_meta'])\\n except KeyError:\\n pass\\n self.resume_dat_entry.set_text(config['previous_resume_dat_path'])\",\n \"def toggle_conf(self):\\n # save the configuration file before updating the incoming config\\n if self.old_conf_id == '':\\n conf_file = 'config_rec'\\n else:\\n conf_file = self.old_conf_id + '_config_rec'\\n\\n conf_map = self.get_rec_config()\\n conf_dir = os.path.join(self.main_win.experiment_dir, 'conf')\\n\\n if write_conf(conf_map, conf_dir, conf_file):\\n if str(self.rec_id.currentText()) == 'main':\\n self.old_conf_id = ''\\n else:\\n self.old_conf_id = str(self.rec_id.currentText())\\n else:\\n msg_window('configuration ' + conf_file + ' was not saved')\\n # if a config file corresponding to the rec id exists, load it\\n # otherwise read from base configuration and load\\n if self.old_conf_id == '':\\n conf_file = os.path.join(conf_dir, 'config_rec')\\n else:\\n conf_file = os.path.join(conf_dir, self.old_conf_id + '_config_rec')\\n\\n if os.path.isfile(conf_file):\\n # load the tab with new configuration, but do not update rec choices\\n self.load_tab_common(conf_file, False)\\n else:\\n self.load_tab_common(os.path.join(conf_dir, 'config_rec'), False)\\n self.notify()\",\n \"def configure( self, csSection, submitPool ):\\n\\n VMDirector.configure( self, csSection, submitPool )\\n self.reloadConfiguration( csSection, submitPool )\",\n \"def write_config():\\n\\n e = Element(\\\"Configuration\\\")\\n r = SubElement(e, \\\"RepositoryList\\\")\\n r = SubElement(r, \\\"Repository\\\", name = \\\"default\\\")\\n SubElement(r, \\\"Module\\\").text = args.driver\\n SubElement(r, \\\"TokenLabel\\\").text = args.token_label\\n SubElement(r, \\\"PIN\\\").text = args.pin\\n ElementTree(e).write(args.write_config)\\n args.write_config.flush()\",\n \"def update(self, adt=None, url=None, params=None):\\n if not self._id_exists():\\n abort(404, f\\\"Application with ID {self.app_id} does not exist\\\")\\n elif not self.engine.app_list:\\n abort(404, \\\"There are no currently running applications\\\")\\n\\n path = self._get_path(adt, url)\\n tpl, adaps = self._validate(path, params, validate_only=True)\\n try:\\n self.engine.update(self.app_id, tpl, adaps)\\n except Exception as error:\\n abort(500, f\\\"Error while updating: {error}\\\")\\n\\n return {\\\"message\\\": f\\\"Application {self.app_id} successfully updated\\\"}\",\n \"def manage_edit_save(self, REQUEST):\\n self._config.update(ldap_config.read_form(REQUEST.form, edit=True))\\n REQUEST.RESPONSE.redirect(self.absolute_url() + '/manage_edit')\",\n \"def updateconfig(self):\\n\\n # Initialize the yaml data\\n ydata = {\\\"metadata\\\": self._metadata, \\\"nodes\\\": self._nodes}\\n\\n # Write the system config file\\n filename = self._rootdir + self._metadata[\\\"system_config_file\\\"]\\n with open(filename, \\\"w\\\") as yamlfile:\\n yaml.dump(ydata, yamlfile)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7255445","0.6736466","0.64811534","0.63596797","0.6235038","0.6235038","0.62124354","0.618501","0.60655963","0.5992609","0.59914666","0.59606487","0.5928038","0.59218657","0.5920716","0.5906855","0.58799803","0.58701694","0.58692014","0.58270746","0.5814665","0.58109903","0.5810534","0.58080846","0.57815295","0.5776769","0.5775733","0.5773777","0.5755639","0.57508945","0.57505304","0.57367265","0.57367265","0.5725418","0.5723374","0.5712102","0.5700295","0.569902","0.5691896","0.56875116","0.56848586","0.5679593","0.5673448","0.5672492","0.56627214","0.56521225","0.56366086","0.563621","0.5624081","0.56233597","0.5619004","0.56169564","0.561486","0.5609772","0.5608607","0.5604662","0.56021357","0.5597748","0.5574066","0.55705017","0.55689204","0.55688864","0.5568485","0.55622023","0.5561327","0.55581814","0.5553868","0.55534554","0.5547471","0.55431306","0.5539218","0.5535864","0.5532774","0.553116","0.55245537","0.5520513","0.5514849","0.55121845","0.5502526","0.54970986","0.5491673","0.5488669","0.5483806","0.5479082","0.5479082","0.5473987","0.54586935","0.5455789","0.5449698","0.5448657","0.54401994","0.54380965","0.5435965","0.5432497","0.5425348","0.5424875","0.5422235","0.542177","0.54183084","0.5414841"],"string":"[\n \"0.7255445\",\n \"0.6736466\",\n \"0.64811534\",\n \"0.63596797\",\n \"0.6235038\",\n \"0.6235038\",\n \"0.62124354\",\n \"0.618501\",\n \"0.60655963\",\n \"0.5992609\",\n \"0.59914666\",\n \"0.59606487\",\n \"0.5928038\",\n \"0.59218657\",\n \"0.5920716\",\n \"0.5906855\",\n \"0.58799803\",\n \"0.58701694\",\n \"0.58692014\",\n \"0.58270746\",\n \"0.5814665\",\n \"0.58109903\",\n \"0.5810534\",\n \"0.58080846\",\n \"0.57815295\",\n \"0.5776769\",\n \"0.5775733\",\n \"0.5773777\",\n \"0.5755639\",\n \"0.57508945\",\n \"0.57505304\",\n \"0.57367265\",\n \"0.57367265\",\n \"0.5725418\",\n \"0.5723374\",\n \"0.5712102\",\n \"0.5700295\",\n \"0.569902\",\n \"0.5691896\",\n \"0.56875116\",\n \"0.56848586\",\n \"0.5679593\",\n \"0.5673448\",\n \"0.5672492\",\n \"0.56627214\",\n \"0.56521225\",\n \"0.56366086\",\n \"0.563621\",\n \"0.5624081\",\n \"0.56233597\",\n \"0.5619004\",\n \"0.56169564\",\n \"0.561486\",\n \"0.5609772\",\n \"0.5608607\",\n \"0.5604662\",\n \"0.56021357\",\n \"0.5597748\",\n \"0.5574066\",\n \"0.55705017\",\n \"0.55689204\",\n \"0.55688864\",\n \"0.5568485\",\n \"0.55622023\",\n \"0.5561327\",\n \"0.55581814\",\n \"0.5553868\",\n \"0.55534554\",\n \"0.5547471\",\n \"0.55431306\",\n \"0.5539218\",\n \"0.5535864\",\n \"0.5532774\",\n \"0.553116\",\n \"0.55245537\",\n \"0.5520513\",\n \"0.5514849\",\n \"0.55121845\",\n \"0.5502526\",\n \"0.54970986\",\n \"0.5491673\",\n \"0.5488669\",\n \"0.5483806\",\n \"0.5479082\",\n \"0.5479082\",\n \"0.5473987\",\n \"0.54586935\",\n \"0.5455789\",\n \"0.5449698\",\n \"0.5448657\",\n \"0.54401994\",\n \"0.54380965\",\n \"0.5435965\",\n \"0.5432497\",\n \"0.5425348\",\n \"0.5424875\",\n \"0.5422235\",\n \"0.542177\",\n \"0.54183084\",\n \"0.5414841\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.606258"},"document_rank":{"kind":"string","value":"9"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":111,"numItemsPerPage":100,"numTotalItems":95772,"offset":11100,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NTkwMDg1MCwianRpIjoiMTcyNTU3OGEtYzg2OS00NTI5LTk2MzgtNzBmZWU5NWIyOGJkIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODU5MDQ0NTAsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.bGidY7ivtnzvKpp8HkZVeGt8PDTgYoXEabapP7pBg8O_qPfIq4AG6TcWMIPupyRhob-Quaai09F0_qMWt87jAg","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 |
|---|---|---|---|---|---|---|
Utilities for spatio temporal analysis zed.uchicago.edu For pairwise model generation; pick only the nearest neighbors to track of a point and create models of it | def generateNeighborMap(self):
A=[]
for key,value in self._ts_dict.iteritems():
A.append(np.array([i.replace("#"," ")
.split()[0:4] for i in value.index])
.astype(float))
B=np.array(A[0]).reshape(len(A[0]),4)
print (B[:,0]+... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def closest_points(point, points, nn=1):\n\n eu_dsts = point - points\n eu_dsts = np.sqrt((eu_dsts * eu_dsts).sum(axis=1))\n n_ids = np.argsort(eu_dsts)\n out_points = np.zeros(shape=(nn, 3))\n for i in range(nn):\n out_points[i] = points[n_ids[i], :]\n return out_points",
"def create_po... | [
"0.5707754",
"0.5542335",
"0.5489949",
"0.5440589",
"0.53954345",
"0.5356737",
"0.53348047",
"0.5333644",
"0.532608",
"0.53175616",
"0.53087217",
"0.5277371",
"0.5273548",
"0.5270412",
"0.5265518",
"0.52623814",
"0.5237282",
"0.5209064",
"0.51936203",
"0.5169329",
"0.51646906... | 0.0 | -1 |
plot global distribution of events within time period specified Inputs | def showGlobalPlot(self,fsize=[14,14],cmap='jet',m=None,figname='fig'):
fig=plt.figure(figsize=(14,14))
# read in data to use for plotted points
A=[]
for key,value in self._ts_dict.iteritems():
A.append(np.array([i.replace("#"," ")
.split()[0:... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pixel_ts_distribution(self):\n fig,ax = plt.subplots(figsize=(8,6))\n bins = np.linspace(0,25,501)\n tsvec=self.tsmap.vec\n ax.hist(tsvec, bins, log=True, histtype='step', lw=2, cumulative=-1, label='data');\n # make array corresponding to the hist\n h = np.histogram(t... | [
"0.6123722",
"0.6043641",
"0.58373654",
"0.57921237",
"0.5719745",
"0.57124525",
"0.56260604",
"0.5591529",
"0.5584743",
"0.557683",
"0.5535808",
"0.55103",
"0.55076784",
"0.54803574",
"0.54626095",
"0.5461081",
"0.5432566",
"0.54265696",
"0.54216766",
"0.54160786",
"0.540530... | 0.0 | -1 |
Utility function zed.uchicago.edu Converts list into string separated by dashes or empty string if input list is not list or is empty | def stringify(List):
if List is None:
return ''
if not List:
return ''
return '-'.join(str(elem) for elem in List) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_to_string(in_list):\n if not in_list:\n return \"[]\"\n else:\n return \"\\n- \" + \"\\n- \".join(in_list)",
"def list_to_str( L ):\n if len(L) == 0: return ''\n return L[0] + list_to_str( L[1:] )",
"def list_to_str( L ):\n if len(L) == 0: return ''\n return L[0] + list_to_str(... | [
"0.7693768",
"0.6974077",
"0.6974077",
"0.6955682",
"0.6715619",
"0.6706818",
"0.66814554",
"0.6577999",
"0.6566847",
"0.65541935",
"0.65537137",
"0.65157914",
"0.6480404",
"0.64604145",
"0.6455032",
"0.6404342",
"0.63729745",
"0.63619614",
"0.63449293",
"0.6314343",
"0.62951... | 0.7753648 | 0 |
Utilities for spatio temporal analysis zed.uchicago.edu Reads in output TS logfile into pd.DF and then outputs necessary CSV files in XgenESeSSfriendly format Input | def readTS(TSfile,csvNAME='TS1',BEG=None,END=None):
dfts=pd.read_csv(TSfile,sep=" ",index_col=0)
dfts.columns = pd.to_datetime(dfts.columns)
cols=dfts.columns[np.logical_and(dfts.columns >= pd.to_datetime(BEG),
dfts.columns <= pd.to_datetime(END))]
dfts=dfts[cols]... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_dataset(subs_list, indexing=True):\n\n S = None\n print(f'\\nProcess - {current_process().name} has {len(subs_list)} files to work on.\\n')\n\n try:\n start = time()\n repo = (Subject(sub) for sub in subs_list)\n for sub in repo:\n S = sub\n for i in r... | [
"0.58780235",
"0.5840372",
"0.58105165",
"0.5806595",
"0.5803117",
"0.5729501",
"0.56970805",
"0.5673466",
"0.56693894",
"0.5664184",
"0.5662434",
"0.5633177",
"0.562404",
"0.55828017",
"0.5571171",
"0.5557831",
"0.5554224",
"0.55407476",
"0.5506054",
"0.5502374",
"0.548554",... | 0.55022866 | 20 |
Utilities for spatio temporal analysis zed.uchicago.edu Writes out each row of the pd.DataFrame as a separate CSVfile For XgenESeSS binary No I/O | def splitTS(TSfile,csvNAME='TS1',dirname='./',prefix="@",
BEG=None,END=None):
dfts=pd.read_csv(TSfile,sep=" ",index_col=0)
dfts.columns = pd.to_datetime(dfts.columns)
cols=dfts.columns[np.logical_and(dfts.columns >= pd.to_datetime(BEG),
dfts.columns <= pd.t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_csv(df: pd.DataFrame, name: str, time_series=False) -> None:\n abs_dir = os.path.dirname(__file__)\n if time_series:\n rel_dir = os.path.join(abs_dir, 'eikon_time_series_files')\n else:\n rel_dir = os.path.join(abs_dir, 'eikon_data_files')\n path = ''.join([rel_dir, '/' + name])\n\... | [
"0.662147",
"0.66056687",
"0.64890814",
"0.6471857",
"0.6447726",
"0.6424269",
"0.64069694",
"0.6400947",
"0.63799506",
"0.63763857",
"0.63729095",
"0.63496006",
"0.63397557",
"0.6312564",
"0.6295084",
"0.6286356",
"0.6265861",
"0.6253045",
"0.6251188",
"0.62434536",
"0.62170... | 0.0 | -1 |
Utilities for storing and manipulating XPFSA models inferred by XGenESeSS zed.uchicago.edu Selects the N top models as ranked by var specified value (in reverse order if reverse is True) Inputs | def select(self,var="gamma",n=None,reverse=False,
store=False, outFile="modelselection.json"):
some_model = [val for val in self._models.values()[:1]]
assert var in some_model[0].keys(), \
"Error: Model parameter specified not valid"
this_dict = {value[var]:key for key, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_model_ranking(self, pos, var):\n x = []\n keys = []\n for k in self.pos:\n if (self.pos[k] == pos) & ('_' + var + '_' in k):\n x.append(self.data[k])\n keys.append(k[:k.index(var) - 1]) # model name\n\n x = np.asarray(x)\n keys =... | [
"0.5920686",
"0.5876782",
"0.58111525",
"0.58068573",
"0.5769203",
"0.57331395",
"0.5698059",
"0.56730765",
"0.55201364",
"0.55091244",
"0.54951775",
"0.54919577",
"0.54321176",
"0.5391369",
"0.53734744",
"0.536808",
"0.5328299",
"0.5325843",
"0.53122205",
"0.53112197",
"0.52... | 0.5154817 | 25 |
Utilities for storing and manipulating XPFSA models inferred by XGenESeSS zed.uchicago.edu Calculates the distance between all models and stores them under the distance key of each model; modifies instance in place No I/O | def augmentDistance(self):
for key,value in self._models.iteritems():
src=[float(i) for i in value['src'].replace('#',' ').split()]
tgt=[float(i) for i in value['tgt'].replace('#',' ').split()]
dist = haversine((np.mean(src[0:2]),np.mean(src[2:])),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def distance_train(self):\n\n for self.epoch in range(self.args.epochs):\n # switch to train mode\n self.set_train()\n data_loading_time = 0\n gpu_time = 0\n before_op_time = time.time()\n\n for batch_idx, inputs in enumerate(self.train_loade... | [
"0.5727781",
"0.55241746",
"0.55023146",
"0.5344562",
"0.5287414",
"0.5233412",
"0.5179906",
"0.51727074",
"0.5120181",
"0.51190937",
"0.5085125",
"0.5083492",
"0.50830466",
"0.50505143",
"0.5047023",
"0.5040699",
"0.50249517",
"0.50155187",
"0.50076103",
"0.49986377",
"0.499... | 0.6418641 | 0 |
Utilities for storing and manipulating XPFSA models inferred by XGenESeSS zed.uchicago.edu Writes out updated models json to file Input | def to_json(outFile):
with open(outFile, 'w') as outfile:
json.dump(self._models, outfile) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def makeModel(self):\n\n # Get the script\n modelScript = os.path.join(self.datapath, 'make3FGLxml.py')\n if not os.path.isfile(modelScript):\n # download it\n print(\"\\t=== Downloading make3FGLxml.py ===\")\n os.system('wget https://fermi.gsfc.nasa.gov/ssc/da... | [
"0.6173078",
"0.60120565",
"0.5975275",
"0.5885461",
"0.5875494",
"0.5873646",
"0.58637625",
"0.584942",
"0.5823678",
"0.58222973",
"0.5819418",
"0.5815936",
"0.5812336",
"0.5801755",
"0.57986027",
"0.5793235",
"0.57837665",
"0.5778584",
"0.575814",
"0.575188",
"0.5745891",
... | 0.0 | -1 |
Method collects all biom permissions of current action with default values and permission type | def collect_all_perms(cls):
permissions = filter(lambda perm: perm.startswith('biom_perm') or perm.startswith('entity_perm'), dir(cls))
result = [{
'perm_name': perm,
'description': getattr(cls, perm).__doc__,
'perm_type': getattr(cls, perm).action_type if hasattr(ge... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_permissions(self):\n try:\n # return permission_classes depending on `action`\n return [permission() for permission in self.permission_action\n [self.action]]\n except KeyError:\n # action is not set return default permission_classes\n ... | [
"0.714981",
"0.714981",
"0.6895174",
"0.6862876",
"0.68085474",
"0.6785352",
"0.6735046",
"0.6719133",
"0.67098784",
"0.67098784",
"0.6702679",
"0.66938925",
"0.668023",
"0.668023",
"0.66758317",
"0.66377646",
"0.6611043",
"0.66060686",
"0.6586158",
"0.6580176",
"0.65646994",... | 0.6783265 | 6 |
Make sure login and logout works. | def test_login_logout(self):
rv = login(self.client, app.config['USERNAME'], app.config['PASSWORD'])
self.assertTrue(b'Play' in rv.data)
rv = logout(self.client)
self.assertTrue(b'Sign In' in rv.data)
rv = login(self.client, app.config['USERNAME'] + 'x', app.config['PASSWORD']... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ttest_login_logout(self):\n rv = self.login(\n app.config['USERNAME'],\n app.config['PASSWORD']\n )\n assert b'You were logged in' in rv.data\n rv = self.logout()\n assert b'You were logged out' in rv.data\n rv = self.login(\n app.confi... | [
"0.7865821",
"0.781352",
"0.7775909",
"0.7647303",
"0.74561095",
"0.7432088",
"0.7389897",
"0.73577553",
"0.73564583",
"0.73248774",
"0.73198557",
"0.72890925",
"0.72614825",
"0.7247851",
"0.7239363",
"0.7235257",
"0.72166586",
"0.72166586",
"0.72002006",
"0.7142968",
"0.7130... | 0.77306336 | 3 |
Assesses the model with n_iter different sets of parameters through crossvalidation, choose the best one, train it on the train data and predicts on the test data. | def make_prediction(pipe, X_train, y_train, X_test):
param_grid = {'svc__C': stats.uniform(loc=0, scale=10),
'svc__decision_function_shape': [None, 'ovo', 'ovr'],
'svc__shrinking': [True, False]
}
rand = RandomizedSearchCV(pipe, param_grid, cv=5, scoring='ac... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fit(self,\n X_train,\n y_train, \n X_test, \n y_test,\n max_evals,\n **kwargs,\n ):\n \n self.max_evals = max_evals\n \n for key in self.models_dict.keys():\n \n path_model_dir = self.... | [
"0.7250705",
"0.7142707",
"0.6941888",
"0.6888644",
"0.6884445",
"0.6816108",
"0.679953",
"0.67979425",
"0.6753174",
"0.67356896",
"0.671182",
"0.6653014",
"0.66369843",
"0.6635218",
"0.6623897",
"0.65718347",
"0.6570627",
"0.65526783",
"0.65389687",
"0.6513162",
"0.65088755"... | 0.0 | -1 |
Path of the directory that stores all the instances. | def instance_dir(self):
return os.path.join(self.basedir, self.yml['instdir']) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _InstanceDir(cls, instance_name):\n return utils.PathJoin(cls._ROOT_DIR, instance_name)",
"def store_path(self):\n return path.join(env.store_home, self._store_path)",
"def data_directory(self):\n\n return self.get_raw(\"data_directory\")",
"def path(self):\n return self._containe... | [
"0.78036046",
"0.7167195",
"0.702",
"0.6844666",
"0.6840358",
"0.6783134",
"0.676215",
"0.6758423",
"0.67428887",
"0.6675447",
"0.6670271",
"0.66667795",
"0.66445255",
"0.66307175",
"0.66128314",
"0.6605219",
"0.6593088",
"0.6589724",
"0.6554919",
"0.65276265",
"0.6510622",
... | 0.7848447 | 0 |
Collects all successful runs and optionally parses their output. | def collect_successful_results(self, parse_fn=None):
def successful_runs(verbose=False):
for run in self.discover_all_runs():
finished = os.access(run.output_file_path('status'), os.F_OK)
if not finished:
if verbose:
print("Skipping unfinished run {}/{}[{}]".format(run.experiment.name,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def collect_output(self):\n pass",
"def collect_output(self):\n pass",
"def task_parse_results():\n pass",
"def __parse_success(self, fullname, results):\n match = NUMBER_PASSED_RE.match(results[0])\n if not match:\n raise ValueError(\"All passed line incorrect: '%s'... | [
"0.6443427",
"0.6443427",
"0.6302049",
"0.6148188",
"0.6071832",
"0.6046133",
"0.5980672",
"0.59162134",
"0.5890119",
"0.58879757",
"0.58761954",
"0.5875161",
"0.5846488",
"0.5807116",
"0.57725614",
"0.57704043",
"0.5761875",
"0.57333165",
"0.57006025",
"0.5662879",
"0.565996... | 0.7599388 | 0 |
Exports experiments based on their status. | def export_experiments(self, included_statuses=None):
experiment_list = []
if included_statuses is not None:
for run in self.discover_all_runs():
status = run.get_status()
if status in included_statuses:
experiment_list.append((
run.experiment.name,
tuple(variant.name for variant in run... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def export_comparisons(self):\n print(\"Exporting comparisons:\")\n\n return",
"def run(self,\n example_input : Union[str,Path,None] = None) -> EasyDict :\n outputs = []\n ok = True\n for export_config in self.export_configs :\n exporter = create_exporter(... | [
"0.5686498",
"0.5624677",
"0.5623435",
"0.5498768",
"0.546386",
"0.5412859",
"0.53924197",
"0.5347752",
"0.53329897",
"0.53323716",
"0.52957964",
"0.5271474",
"0.5201375",
"0.5200578",
"0.5200364",
"0.5186541",
"0.5160601",
"0.51597124",
"0.51319706",
"0.5105871",
"0.5100903"... | 0.67712706 | 0 |
devbuilds only have a source directory instead of a repo and clone directory | def source_dir(self):
assert self.revision.is_dev_build
rev = self._get_dev_build_suffix()
return os.path.join(self._cfg.basedir, 'develop', self.name + rev) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fork(args):\n subprocess.check_call([\"git\", \"config\", \"--global\",\n \"--add\", \"safe.directory\", args.src])\n head = subprocess.check_output([\"git\", \"rev-parse\", args.rev], cwd=args.src).strip()\n obj_dir = subprocess.check_output([\"git\", \"rev-parse\", \"--git-... | [
"0.6243398",
"0.6228782",
"0.6211728",
"0.62041354",
"0.6192375",
"0.61694247",
"0.6141966",
"0.60571873",
"0.60399204",
"0.6031721",
"0.6004611",
"0.5989618",
"0.5989434",
"0.59862226",
"0.5955796",
"0.59555477",
"0.5943259",
"0.5915231",
"0.5878496",
"0.58773404",
"0.587551... | 0.67784536 | 0 |
Only account balance detail report | def default_get(self, fields):
defaults = super(ExportBalanceDetailWizard, self).default_get(fields)
active_model = self._context.get('active_model', False)
active_id = self._context.get('active_id', False)
if active_model and active_id:
general_ledger = self.env[active_model... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def view_bank_account_details(self) -> None:\n Menu.prompt_view_bank_account_details()\n print(\"Bank Account Details:\")\n print(self.user.account)\n\n for tx_num, tx_details in \\\n self.user.tx_manager.transaction_records.items():\n print(f\"\\nTransaction #... | [
"0.7012718",
"0.6964789",
"0.6946847",
"0.67337406",
"0.67311573",
"0.6661313",
"0.66381043",
"0.6633519",
"0.66310024",
"0.659897",
"0.65527236",
"0.6493311",
"0.6437109",
"0.63235724",
"0.6307414",
"0.63025016",
"0.6263153",
"0.62554604",
"0.625527",
"0.62519646",
"0.618655... | 0.0 | -1 |
Only account balance detail report | def _compute_is_account_balance_detail_template(self):
self.ensure_one()
template = self.env.ref(
'pabi_general_ledger_extension.account_balance_detail_template')
self.is_account_balance_detail_template = False
if self.template_id == template:
self.is_account_bala... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def view_bank_account_details(self) -> None:\n Menu.prompt_view_bank_account_details()\n print(\"Bank Account Details:\")\n print(self.user.account)\n\n for tx_num, tx_details in \\\n self.user.tx_manager.transaction_records.items():\n print(f\"\\nTransaction #... | [
"0.7012718",
"0.6964789",
"0.6946847",
"0.67337406",
"0.67311573",
"0.6661313",
"0.66381043",
"0.6633519",
"0.66310024",
"0.659897",
"0.65527236",
"0.6493311",
"0.6437109",
"0.63235724",
"0.6307414",
"0.63025016",
"0.6263153",
"0.62554604",
"0.625527",
"0.62519646",
"0.618655... | 0.6018376 | 35 |
Only account balance detail report | def _compute_move_line(self):
self.move_line_ids = False
if self.is_account_balance_detail_template:
TB = self.env['account.general.ledger.report']
_x, moves = TB._get_moves(self.fiscalyear_id.id, self.target_move,
self.reconcile_cond, self.a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def view_bank_account_details(self) -> None:\n Menu.prompt_view_bank_account_details()\n print(\"Bank Account Details:\")\n print(self.user.account)\n\n for tx_num, tx_details in \\\n self.user.tx_manager.transaction_records.items():\n print(f\"\\nTransaction #... | [
"0.7012718",
"0.6964789",
"0.6946847",
"0.67337406",
"0.67311573",
"0.6661313",
"0.66381043",
"0.6633519",
"0.66310024",
"0.659897",
"0.65527236",
"0.6493311",
"0.6437109",
"0.63235724",
"0.6307414",
"0.63025016",
"0.6263153",
"0.62554604",
"0.625527",
"0.62519646",
"0.618655... | 0.0 | -1 |
Constructor for the TemplateWithIdPreview class | def __init__(self,
primary_language=None,
secondary_language=None,
xml_signature=None,
additional_properties = {}):
# Initialize members of the class
self.primary_language = primary_language
self.secondary_language = se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, id):\n \n self.id = id",
"def __init__(self,\n id: str) -> None:\n self.id = id",
"def __init__(self,\n id: str) -> None:\n self.id = id",
"def __init__(self,\n id: str) -> None:\n self.id = id",
"def __in... | [
"0.62204456",
"0.6166899",
"0.6166899",
"0.6166899",
"0.6166899",
"0.6128319",
"0.6017259",
"0.59618884",
"0.59618884",
"0.59460723",
"0.5944425",
"0.5929474",
"0.5929474",
"0.5929474",
"0.5929474",
"0.5845104",
"0.5818631",
"0.581609",
"0.581609",
"0.5777787",
"0.5777787",
... | 0.0 | -1 |
Creates an instance of this model from a dictionary | def from_dictionary(cls,
dictionary):
if dictionary is None:
return None
# Extract variables from the dictionary
primary_language = dictionary.get('PrimaryLanguage')
secondary_language = dictionary.get('SecondaryLanguage')
xml_signatur... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_dictionary(cls,\n dictionary):\n if dictionary is None:\n return None\n\n # Extract variables from the dictionary\n id = dictionary.get('id')\n name = dictionary.get('name')\n mtype = dictionary.get('type')\n usage_bytes = diction... | [
"0.83181584",
"0.8168118",
"0.8168118",
"0.8118749",
"0.8089047",
"0.79787344",
"0.7949278",
"0.79231393",
"0.7898951",
"0.78923255",
"0.7882321",
"0.7882212",
"0.7876749",
"0.78585315",
"0.7836853",
"0.7801407",
"0.7801407",
"0.7801407",
"0.7801407",
"0.7801407",
"0.7801407"... | 0.788838 | 10 |
This func is to select a series of dots with mouse. It is selected from the shown curve. | def sf_dotset():
global fig,ax,ss,statusL,statusM,axh,axv
#get the handle of figure and axis
fig=plt.gcf()
ax=plt.gca()
statusL,statusM=None,None
ss=[]
print 'Drawing a line, you should select at least 2 points'
#
def onmouse(event):
global fig,ax,ss,statusL,statusM,axh,axv
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def selectPointsUnderCursor(self):\n #spw = self.spw\n #sw = spw.windows['Sort']\n #if clear:\n # sw.uslist.clearSelection()\n # sw.nlist.clearSelection()\n x, y = self.cursorPosGL()\n sids = self.pick(x, y, pb=10, multiple=True)\n if sids == None:\n ... | [
"0.7041565",
"0.7023079",
"0.6246638",
"0.62356895",
"0.62014484",
"0.61904097",
"0.6099179",
"0.609854",
"0.6074863",
"0.6068525",
"0.60472685",
"0.5994338",
"0.59378636",
"0.5905589",
"0.59030867",
"0.581273",
"0.5783279",
"0.57704127",
"0.5756903",
"0.5746277",
"0.57343125... | 0.58463234 | 15 |
Initialize an Agent object. | def __init__(self, state_size, action_size, seed):
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size, seed).to(device)
self.qnetwork_target = QNetwork(state_size, a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, agent: AEA) -> None:\n self._agent = agent\n super().__init__()",
"def agent_init(self):\n pass",
"def __init__(self, env, agent, agent_config, remote=None):\n self.env = Environment(env)\n self.episodes = 1000\n self.remote = remote\n\n if re... | [
"0.78424275",
"0.7698366",
"0.7576744",
"0.75063956",
"0.7380445",
"0.7162815",
"0.6989959",
"0.6956423",
"0.69451666",
"0.6873446",
"0.6822198",
"0.6817786",
"0.6772045",
"0.67031044",
"0.66557187",
"0.6651313",
"0.6629612",
"0.66007495",
"0.6569908",
"0.65372974",
"0.652172... | 0.0 | -1 |
Adds the current stateaction value to the memory and lets the agent learn if UPDATE_EVERY many steps are taken and the memory has more entries then BATCH_SIZE. | def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % PARAM.UPDATE_EVERY
if self.t_step == 0:
if le... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(self, action): \n self.memory.pop(-1) \n self.memory.insert(0, [self.last_state.cpu().numpy(), action.cpu().numpy()])\n\n self.last_action = action",
"def _add_to_memory(self, state, action, next_state, reward, done):\n if len(self.memory) >= self.size_max_memory:\n ... | [
"0.7732924",
"0.73804665",
"0.7167032",
"0.7005231",
"0.69732106",
"0.69556653",
"0.6837309",
"0.6820877",
"0.6817679",
"0.6776465",
"0.67520034",
"0.67520034",
"0.67520034",
"0.67520034",
"0.67520034",
"0.67520034",
"0.67520034",
"0.67520034",
"0.67520034",
"0.67520034",
"0.... | 0.66679215 | 24 |
Returns actions for given state as per current policy. | def act(self, state, eps=0.):
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getActions(self, state): \n util.raiseNotDefined()",
"def get_available_actions(self, state):\n pass",
"def getLegalActions(self, state):\n return self.actionFn(state)",
"def getLegalActions(self,state):\n return self.actionFn(state)",
"def get_actions(self, state: TState... | [
"0.81847",
"0.78752404",
"0.77772415",
"0.77585727",
"0.7377628",
"0.73220915",
"0.72467107",
"0.72087854",
"0.7112076",
"0.71091956",
"0.7075793",
"0.706494",
"0.70488673",
"0.7009308",
"0.7006878",
"0.69928527",
"0.6989333",
"0.69823205",
"0.6970077",
"0.695084",
"0.6893704... | 0.0 | -1 |
Gets the stateaction value of the target network. That is, the current estimate of the target network for the next state including the seen reward. | def get_dqg_target(self, next_states, rewards, gamma, dones):
# Get predicted Q values
qtarget_values = self.qnetwork_target(next_states).detach()
# get max of it
best_qtarget_value = qtarget_values.max(1)
# reduce one dimension
best_qtarget_value = best_qtarget_value[0... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_state_action_value(self, state, action):\n state_tensor = torch.from_numpy(state).float().to(self.device)\n output = torch.dot(self.weights[action,:],state_tensor.view(-1))\n return output",
"def get_value(self, state):\n epsilon = self.epsilon\n possible_actions = self... | [
"0.74637866",
"0.738342",
"0.7381812",
"0.72057045",
"0.7147627",
"0.7144865",
"0.7104878",
"0.7084618",
"0.7046648",
"0.7013591",
"0.69931084",
"0.6982195",
"0.69723356",
"0.69467",
"0.6917595",
"0.69127136",
"0.68956476",
"0.68674845",
"0.67943704",
"0.67933476",
"0.6788547... | 0.0 | -1 |
Update value parameters using given batch of experience tuples. | def learn(self, experiences, gamma):
states, actions, rewards, next_states, dones = experiences
Q_targets = self.get_dqg_target(next_states, rewards, gamma, dones)
# Get expected Q values
q_exp = self.qnetwork_local(states)
# gets the q values along dimention 1 according to t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(self, values: List[int]) -> None:\n ...",
"def update(self, values: List[int]) -> None:\n ...",
"def update(self, values):\n pass",
"def update(self, batch):\n for experience in batch:\n # First calculate the expected future reward from the final state\n ... | [
"0.6765197",
"0.6765197",
"0.6508957",
"0.6346006",
"0.62429523",
"0.62217015",
"0.6117239",
"0.6103772",
"0.6048167",
"0.6005078",
"0.5968958",
"0.5968552",
"0.59434044",
"0.5935012",
"0.5893864",
"0.58876216",
"0.5860463",
"0.585012",
"0.5819268",
"0.5778017",
"0.57520366",... | 0.0 | -1 |
drawing original & divided 8 imgs. | def draw_img_old(original, bit_imgs, title, sub_title):
fig = plt.figure(figsize=(17, 7))
fig.suptitle(title)
for i in range(1,10):
if i == 1:
ax = fig.add_subplot(2, 5, i)
ax.imshow(original, cmap='gray')
ax.set_title(sub_title)
elif i < 6:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def displayImg(self):\r\n\r\n\t# If you want to skip n frames, set value to 0 to see all images\r\n\tSKIP = 4500\r\n for idx in range(len(self.centers)):\r\n\t if idx < SKIP:\r\n\t\tcontinue\r\n file_left = self.lefts[idx][5]\r\n file_center = self.centers[idx][5]\r\n fil... | [
"0.63771784",
"0.6351863",
"0.609269",
"0.60603905",
"0.60569733",
"0.6042662",
"0.60385704",
"0.60364",
"0.6027411",
"0.6017766",
"0.5995479",
"0.59777516",
"0.5974092",
"0.59739196",
"0.59285754",
"0.59271795",
"0.5926349",
"0.5906782",
"0.5906068",
"0.59037554",
"0.5899282... | 0.6305598 | 2 |
drawing original & divided 8 imgs. | def draw_img(original, bit_imgs, title, sub_title):
fig, axs = plt.subplots(nrows=2, ncols=5, figsize=(17, 7))
fig.suptitle(title)
print(axs.shape) # (2,5)
img_number = 8
for i, ax in enumerate(axs):
for j, a in enumerate(ax):
img_number -= 1
if i == 0 and ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def displayImg(self):\r\n\r\n\t# If you want to skip n frames, set value to 0 to see all images\r\n\tSKIP = 4500\r\n for idx in range(len(self.centers)):\r\n\t if idx < SKIP:\r\n\t\tcontinue\r\n file_left = self.lefts[idx][5]\r\n file_center = self.centers[idx][5]\r\n fil... | [
"0.63771784",
"0.6351863",
"0.6305598",
"0.609269",
"0.60603905",
"0.60569733",
"0.6042662",
"0.60385704",
"0.6027411",
"0.6017766",
"0.5995479",
"0.59777516",
"0.5974092",
"0.59739196",
"0.59285754",
"0.59271795",
"0.5926349",
"0.5906782",
"0.5906068",
"0.59037554",
"0.58992... | 0.60364 | 8 |
Calculates the correlation coefficients between columns. Displays them in descending order of their absolute values. | def correlation(data, method, caption):
columns = list(data)
coefficients = data.astype(float).corr(method=method)
results = []
for i in range(len(columns)):
for j in range(i + 1, len(columns)):
coefficient = coefficients[columns[i]][columns[j]]
results.append((
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def correlate_columns(matrix):\n return np.dot(matrix.T, matrix) / (la.norm(matrix) ** 2)",
"def get_correlation(df):\n frame_correlation = df.corr()\n return frame_correlation",
"def _calculate_correlation(self, anomaly):\n if self.silence_level <= 1:\n print(\"Calculating partial c... | [
"0.684586",
"0.6635547",
"0.6557074",
"0.65286785",
"0.6519738",
"0.63619393",
"0.63094735",
"0.6283914",
"0.62756026",
"0.627133",
"0.62701637",
"0.62639886",
"0.6246803",
"0.6214507",
"0.620774",
"0.6157019",
"0.6151323",
"0.6137466",
"0.61353064",
"0.6128442",
"0.61145526"... | 0.76043904 | 0 |
Checks if a column exists in a given table. | def table_has_column(table: str, column: str) -> bool:
config = op.get_context().config
engine = engine_from_config(
config.get_section(config.config_ini_section), prefix="sqlalchemy."
)
insp = reflection.Inspector.from_engine(engine)
try:
return any(col["name"] == column for col in ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_column(self, column_name, table, verbose=True): \n assert(self.connected)\n try: \n assert(self.check_table(table, verbose=False)) \n except AssertionError: \n raise TableNotFoundError\n \n \n CHECK_COLUMN_COMMAND = \"SHOW COLUMNS F... | [
"0.8527261",
"0.8314371",
"0.8096708",
"0.80846864",
"0.77050006",
"0.76575935",
"0.73856586",
"0.7372754",
"0.73057866",
"0.73057866",
"0.7105575",
"0.6965715",
"0.68270487",
"0.67641026",
"0.6658639",
"0.6497775",
"0.6388615",
"0.6200891",
"0.6199939",
"0.61866385",
"0.6141... | 0.82262325 | 2 |
Generate new UUIDs for all rows in a table | def assign_uuids(
model: Any, session: Session, batch_size: int = DEFAULT_BATCH_SIZE
) -> None:
bind = op.get_bind()
table_name = model.__tablename__
count = session.query(model).count()
# silently skip if the table is empty (suitable for db initialization)
if count == 0:
return
sta... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def function_uuid():\r\n yield uuid.uuid4()",
"def _generate_uuid(self):\n\n return uuid.uuid4()",
"def generate_uuids():\n uuid_start = str(uuid())\n while uuid_start.startswith(\"zzzzzzzz\"):\n uuid_start = str(uuid())\n uuid_end = list(deepcopy(uuid_start))\n \n char_pool = l... | [
"0.6252362",
"0.6069835",
"0.60602546",
"0.6031163",
"0.5899798",
"0.58836985",
"0.58688223",
"0.58331877",
"0.5799059",
"0.5787476",
"0.57632875",
"0.5743339",
"0.5730291",
"0.57265776",
"0.57037145",
"0.5674246",
"0.5665568",
"0.5646195",
"0.56417024",
"0.56417024",
"0.5639... | 0.65638924 | 0 |
Update models in small batches so we don't have to load everything in memory. | def paginated_update(
query: Query,
print_page_progress: Optional[Union[Callable[[int, int], None], bool]] = None,
batch_size: int = DEFAULT_BATCH_SIZE,
) -> Iterator[Any]:
start = 0
count = query.count()
session: Session = inspect(query).session
if print_page_progress is None or print_page_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_batch(self, *args, **kwargs):\n pass",
"async def update_model(model_updates):\n async for model_update in model_updates:\n model_location = model_update['model_location']\n print(f\"Updating model to: {model_location}\")\n\n # using incrementing version number to keep t... | [
"0.6971209",
"0.69643724",
"0.6669123",
"0.65299875",
"0.6319953",
"0.630288",
"0.62560856",
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"0.61049205",
"0.61014456",
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"0.60398704",
"0.60339373",
"0.6004137",
"0.5917219",
"0.5912759",
"0.5912155",
"0.5853216... | 0.0 | -1 |
Starts the move process | def callback_queue(self, data):
global command_flag, current_command
#grab action command
action = data.action
#Discards pending messages and null/invalid messages
if action.split(' ')[0] in riu.valid_cmds and not data.pending:
if action in riu.interrupts:
self.process_interrupt(action)
elif action.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self):\n self.spawn()\n while self.is_alive:\n self.move()\n time.sleep(.2)",
"def run(self):\n return self.move_bat()",
"def run(self):\n # type: () -> None\n self.move_to(self.location)",
"def _move(self, pos):\n self.put_par(\"drive\", po... | [
"0.7315805",
"0.7224031",
"0.69296336",
"0.6760997",
"0.67485535",
"0.6699276",
"0.652494",
"0.64953417",
"0.6469153",
"0.6459451",
"0.6454574",
"0.64427245",
"0.64338416",
"0.64247763",
"0.64221346",
"0.64221346",
"0.64193857",
"0.6376137",
"0.6372804",
"0.6338717",
"0.63228... | 0.0 | -1 |
Updates the movement status dictionary to the turtlebot's current position and velocity | def callback_odom(self, data):
global move_state
#acquire a lock on global move_state
with self.move_state_lock:
move_state['roll'] = data.roll
move_state['pitch'] = data.pitch
move_state['yaw'] = data.yaw
move_state['twist'] = data.twist
move_state['x'] = data.position.x
move_state['y'] = data... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_vehicle_state(self):\n #vel = self.v + self.commands['throttle']/self.m/self.simulation_rate\n\n vel = self.commands['speed']\n steer = self.commands['steering_angle']\n\n if steer > 0.5:\n steer_cmd = 25\n elif steer < -0.5:\n steer_cmd = 185\n ... | [
"0.70230997",
"0.65851307",
"0.6452682",
"0.6401587",
"0.63653696",
"0.6347469",
"0.6264557",
"0.61921144",
"0.61820686",
"0.6180512",
"0.617782",
"0.60477686",
"0.6040164",
"0.60250515",
"0.59930575",
"0.5981435",
"0.59646755",
"0.59518903",
"0.59491944",
"0.5907636",
"0.590... | 0.0 | -1 |
Checks if action_thread is already running; if not, spawn a new thread to handle command. | def process_command(self, command):
if not Mover.executing_action:
cmd = command.split(' ')[0]
try:
param = float(command.split(' ')[1])
except:
param = None
finally:
Mover.executing_action = True
#Load sets the thread's run target and parameters
self.action_thread.load(getattr(self, c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def action(self):\n self.action_thread = Thread(target=self._action_then_signal, daemon=True)\n self.action_thread.start()",
"def _make_thread(self):\r\n pass",
"def _ensure_thread(self) -> None:\n\n if not self._thread:\n thread = self._thread_factory(self.run)\n ... | [
"0.6012218",
"0.59985614",
"0.5906066",
"0.5879578",
"0.5825946",
"0.5720824",
"0.5614206",
"0.55436355",
"0.55431575",
"0.5424333",
"0.5341071",
"0.53191227",
"0.5313008",
"0.52827656",
"0.52175707",
"0.52173704",
"0.52034307",
"0.5200631",
"0.51673424",
"0.5149323",
"0.5117... | 0.57459235 | 5 |
If param == 0, sets turn angle to default value. Converts current position angle from radians to degrees. Converts negative angles to positive. COntinues to turn left until the current distance to the goal is greater than the previous distance, meaning that the goal has been passed. | def left(self, param):
global estop_flag, move_state
#If input angle is zero, set angle to default
if param:
angle = param
else:
angle = riu.default_angle
signal.alarm(0) #Disable timer interrupt for the duration of the movement
#safely grab current yaw
with self.move_state_lock:
current_yaw = (... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def right(self, param):\n\t\tglobal estop_flag, move_state\n\t\t#If input angle is zero, set angle to default\n\t\tif param:\n\t\t\tangle = param\n\t\telse:\n\t\t\tangle = riu.default_angle\n\n\t\tsignal.alarm(0) #Disable timer interrupt for the duration of the movement\n\t\t#safely grab current yaw\n\t\twith self... | [
"0.7021173",
"0.70137686",
"0.67029786",
"0.65796685",
"0.63092864",
"0.61473304",
"0.6123353",
"0.61182714",
"0.61117864",
"0.60715616",
"0.60414594",
"0.60361344",
"0.59939015",
"0.5975596",
"0.59737754",
"0.5938437",
"0.59375",
"0.59341085",
"0.59288204",
"0.5903714",
"0.5... | 0.7418729 | 0 |
If param == 0, sets turn angle to default value. Converts current position angle from radians to degrees. Converts negative angles to positive. COntinues to turn left until the current distance to the goal is greater than the previous distance, meaning that the goal has been passed. | def right(self, param):
global estop_flag, move_state
#If input angle is zero, set angle to default
if param:
angle = param
else:
angle = riu.default_angle
signal.alarm(0) #Disable timer interrupt for the duration of the movement
#safely grab current yaw
with self.move_state_lock:
current_yaw = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def left(self, param):\n\t\tglobal estop_flag, move_state\n\t\t#If input angle is zero, set angle to default\n\t\tif param:\n\t\t\tangle = param\n\t\telse:\n\t\t\tangle = riu.default_angle\n\n\t\tsignal.alarm(0) #Disable timer interrupt for the duration of the movement\n\t\t#safely grab current yaw\n\t\twith self.... | [
"0.7420052",
"0.70118314",
"0.67038393",
"0.6579593",
"0.6309404",
"0.6147523",
"0.6123622",
"0.61167604",
"0.6110599",
"0.6072676",
"0.6042288",
"0.60363406",
"0.59952015",
"0.59768206",
"0.5973986",
"0.59382665",
"0.59378344",
"0.59349597",
"0.5929142",
"0.59034985",
"0.587... | 0.7022378 | 1 |
Gets shortest angular distance between two positions regardless of direction | def get_abs_dist(self, pos1, pos2):
return min(abs(pos1 - pos2), abs(pos1 - pos2 + 360)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calc_distance(first: Waypoint, second: Waypoint) -> int:\n return int(distance.vincenty(first.coords(), second.coords()).m)",
"def get_position_distance(pos1, pos2, ignore_strand=False):\n NaN = float('nan')\n if pos1 in SPECIAL_POSITIONS.all_undefined: return NaN\n elif pos2 in S... | [
"0.7098709",
"0.70844555",
"0.6993295",
"0.69222057",
"0.68783516",
"0.68510944",
"0.6760011",
"0.6758225",
"0.6641673",
"0.66253275",
"0.66251695",
"0.66108775",
"0.6573693",
"0.65723246",
"0.65262455",
"0.6504858",
"0.6500021",
"0.6487806",
"0.644004",
"0.64247996",
"0.6398... | 0.68771785 | 5 |
Calls left 355 to approximate turning in a complete circle | def scan(self, param):
self.left(355) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def left_twist(self):\n self.turn_by_deg(-179)\n #time.sleep(.1)\n self.stop()\n self.turn_by_deg(-179)\n #time.sleep(.1)\n self.stop()",
"def u_turn(self, direction, diameter_in):\n \n# pdb.set_trace()\n # Calculate radius of turn for the inside wheel.\... | [
"0.67077476",
"0.666737",
"0.64651495",
"0.6459743",
"0.6402393",
"0.6391744",
"0.6298772",
"0.6274763",
"0.6226703",
"0.61825544",
"0.6179941",
"0.6137049",
"0.61068386",
"0.59903145",
"0.5953306",
"0.5945245",
"0.5925505",
"0.5921823",
"0.5917596",
"0.5917394",
"0.5915323",... | 0.0 | -1 |
Calls linear_move. If no parameter, defaults to default_dist | def forward(self, param):
if param:
self.linear_move(param * .3048)
else:
self.linear_move(riu.default_dist * .3048) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def linear_move(self, initial_position, final_position):\n if any(initial_position - final_position):\n # The desired position is not the actual position (would make a 'divide by zero' error otherwise)\n\n # Compute directional vector\n dir_vector = final_position - initial_... | [
"0.6693261",
"0.66415036",
"0.65412503",
"0.6132586",
"0.6122272",
"0.60313886",
"0.5981754",
"0.59595865",
"0.59388167",
"0.58818024",
"0.5879753",
"0.58745044",
"0.58463347",
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"0.5824566",
"0.58189654",
"0.57764554",
"0.5741105",
"0.5720051",
"0.5718488",
"0.569... | 0.7202606 | 0 |
Calls linear_move. Changes the input parameter to a negative value. If no parameter, defaults to default_dist | def backward(self, param):
if param:
self.linear_move(-1 * param * .3048)
else:
self.linear_move(-1 * riu.default_dist * .3048) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def forward(self, param):\n\t\tif param:\n\t\t\tself.linear_move(param * .3048)\n\t\telse:\n\t\t\tself.linear_move(riu.default_dist * .3048)",
"def linear_move(self, dist):\n\t\tglobal estop_flag, move_state\n\t\tsignal.alarm(0) #Disable timer interrupt for the duration of the movement\n\t\thalfway_flag = False\... | [
"0.7253888",
"0.6747373",
"0.64891535",
"0.6274169",
"0.6252882",
"0.62049425",
"0.5926211",
"0.5910371",
"0.58791304",
"0.5832406",
"0.579784",
"0.57928425",
"0.5781144",
"0.5778036",
"0.56768364",
"0.56569725",
"0.5645296",
"0.5622642",
"0.5621369",
"0.5614227",
"0.55908465... | 0.6636399 | 2 |
Checks the tracking variable updated by the tracker callback. If no correction is needed, sends a linear twist message. If correction is needed, sends a left or right angular twist as appropriate. Acquires a lock on the move state to update its position. Checks for estop every cycle. Disables ready messages for duratio... | def linear_track(self, dist):
global estop_flag, move_state
#Disable timer interrupt, reset halfway flag, set target distance
signal.alarm(0)
halfway_flag = False
#Set starting position
with self.move_state_lock:
start_x, start_y, start_z = move_state['x'], move_state['y'], move_state['z']
#Set curr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def linear_move(self, dist):\n\t\tglobal estop_flag, move_state\n\t\tsignal.alarm(0) #Disable timer interrupt for the duration of the movement\n\t\thalfway_flag = False\n\t\t\n\t\twith self.move_state_lock:\n\t\t\tstart_x, start_y, start_z = move_state['x'], move_state['y'], move_state['z']\n\t\tcurrent_x = start_... | [
"0.6857079",
"0.6112145",
"0.5854988",
"0.5841018",
"0.58113366",
"0.57477736",
"0.5714076",
"0.57138836",
"0.5696555",
"0.56912374",
"0.5585474",
"0.5583985",
"0.557774",
"0.5564837",
"0.5528463",
"0.55114955",
"0.5441816",
"0.54202133",
"0.5392405",
"0.53795177",
"0.5377141... | 0.71479756 | 0 |
Moves the robot a distance equal to dist. Checks for estop on each iteration. Publishes a Done message after completion and a Half message when the current distance is equal to half of the goal distance. | def linear_move(self, dist):
global estop_flag, move_state
signal.alarm(0) #Disable timer interrupt for the duration of the movement
halfway_flag = False
with self.move_state_lock:
start_x, start_y, start_z = move_state['x'], move_state['y'], move_state['z']
current_x = start_x
current_y = start_y
c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def drive(self, distance, tolerance=0.0, tolerance_step=0.5,\n max_attempts=10, avoid_targets=True, avoid_home=False,\n use_waypoints=True):\n self.cur_loc = self.swarmie.get_odom_location()\n start = self.cur_loc.get_pose()\n\n goal = Point()\n goal.x = start.... | [
"0.64928085",
"0.6398207",
"0.63323027",
"0.63294876",
"0.62530607",
"0.61761117",
"0.6161925",
"0.6121459",
"0.60939133",
"0.6092826",
"0.60628915",
"0.6010287",
"0.5977998",
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"0.5965475",
"0.59644395",
"0.5960455",
"0.5934109",
"0.5906981",
"0.5897367",
"0.58761... | 0.708726 | 0 |
Returns the username in java system property argument form. | def username(self):
return self._username() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def username(self) -> str:\n return self.get_env_var(self.username_var)",
"def username(self) -> str:\n return self.get_env_var(self.username_var)",
"def _username(self):\n if 'username' not in self._config:\n self._config['username'] = self._UI.get_input(\"Please enter your tra... | [
"0.74624604",
"0.74624604",
"0.7008669",
"0.6944945",
"0.6944945",
"0.6944945",
"0.6944945",
"0.6944945",
"0.6944945",
"0.6944945",
"0.6944945",
"0.6944945",
"0.6944945",
"0.6944945",
"0.6944945",
"0.6933543",
"0.68644357",
"0.68644357",
"0.68644357",
"0.6803364",
"0.6795208"... | 0.65675604 | 47 |
Returns the password in java system property argument form. | def password(self):
return self._password() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def password(self) -> str:\n return self.get_env_var(self.password_var)",
"def password(self) -> str:\n return self.get_env_var(self.password_var)",
"def password(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"password\")",
"def password(self) -> pulumi.Input[str]:\n ret... | [
"0.7942731",
"0.7942731",
"0.7384854",
"0.73838073",
"0.73838073",
"0.73838073",
"0.73838073",
"0.73838073",
"0.73838073",
"0.73838073",
"0.73838073",
"0.73838073",
"0.73838073",
"0.73838073",
"0.73838073",
"0.73838073",
"0.7376234",
"0.7376234",
"0.7376234",
"0.7174086",
"0.... | 0.7163494 | 21 |
Initialize the distributed environment. | def setup(rank, world_size, master_addr='127.0.0.1', master_port=12355):
os.environ['MASTER_ADDR'] = str(master_addr)
os.environ['MASTER_PORT'] = str(int(master_port))
dist.init_process_group("gloo", rank=rank, world_size=world_size) # initialize the process group | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialize():\n environment = Environment()\n environment.setup()",
"def _initialize_distributed():\r\n args = get_args()\r\n\r\n device_count = torch.cuda.device_count()\r\n if torch.distributed.is_initialized():\r\n\r\n if args.rank == 0:\r\n print('torch distributed is alr... | [
"0.7673883",
"0.7542333",
"0.7162409",
"0.71584105",
"0.6866288",
"0.67851424",
"0.67827564",
"0.6744308",
"0.67387545",
"0.67387545",
"0.67387545",
"0.67387545",
"0.67387545",
"0.67387545",
"0.6711211",
"0.6655738",
"0.6625131",
"0.66135144",
"0.6564343",
"0.65406877",
"0.65... | 0.6317892 | 27 |
Clean the distributed compute pipeline. | def cleanup():
dist.destroy_process_group() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean_up(self):\n dist.destroy_process_group()",
"def cleanup(self):\n with hide(\"output\", \"warnings\", \"running\"):\n self.stop_all()\n self._execute_standard(\"rm -rf {model_repo}\".format(model_repo=MODEL_REPO))\n self._execute_root(\"docker rmi --force $(doc... | [
"0.71976703",
"0.6800443",
"0.6734573",
"0.64912385",
"0.63518906",
"0.63412476",
"0.62536824",
"0.6208296",
"0.6204782",
"0.61962974",
"0.6179033",
"0.617475",
"0.61652684",
"0.61405635",
"0.61350846",
"0.6127614",
"0.60969156",
"0.6074719",
"0.6072437",
"0.6032038",
"0.6014... | 0.68549407 | 1 |
Get a data partition to use. | def use(self, partition_id, config, mpii_annotation_handle):
image_scale_factor_range = (float(config.neural_network.train.data_augmentation.image_scale_factor.min), float(config.neural_network.train.data_augmentation.image_scale_factor.max))
input_resolution = int(config.neural_network.train.input_res... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_partition(self, partition_spec):\n return self.partitions[partition_spec]",
"def getPartition(self):\n\t\treturn self.partition",
"def get_partition():\n if selection is None:\n warning(\"You need to pick something first.\")\n return\n if not selection.obj_type in ['partition... | [
"0.74296004",
"0.7092693",
"0.7056794",
"0.6869214",
"0.67465454",
"0.67465454",
"0.66885304",
"0.6422414",
"0.6421582",
"0.63326484",
"0.63326484",
"0.6325958",
"0.6294001",
"0.61344606",
"0.61189866",
"0.61016935",
"0.6048441",
"0.6018342",
"0.6000349",
"0.59874904",
"0.598... | 0.0 | -1 |
Distributed Synchronous SGD Example | def run(rank, world_size, config):
setup(rank, world_size, master_addr=config.neural_network.train.DistributedDataParallel.MASTER_ADDR, master_port=config.neural_network.train.DistributedDataParallel.MASTER_PORT)
torch.manual_seed(int(config.neural_network.train.random_seed))
training_dataloader, validatio... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sgd_optimization(dataset, learning_rate, n_epochs, batch_size):\n datasets = load_data(dataset)\n train_set_x, train_set_y = datasets[0]\n valid_set_x, valid_set_y = datasets[1]\n test_set_x, test_set_y = datasets[2]\n\n #number of minibatches\n n_train_batches = train_set_x.get_value(borrow=... | [
"0.64315987",
"0.6223416",
"0.602052",
"0.59918475",
"0.5853318",
"0.5793804",
"0.5776111",
"0.5755776",
"0.57527995",
"0.57093793",
"0.57017654",
"0.5669587",
"0.5668901",
"0.5660818",
"0.56590956",
"0.5644157",
"0.5641831",
"0.5631096",
"0.56299424",
"0.5627335",
"0.5613017... | 0.0 | -1 |
Returns a list of valid words. Words are strings of lowercase letters. Depending on the size of the word list, this function may take a while to finish. | def loadWords():
print("Loading word list from file...")
# inFile: file
inFile = open(WORDLIST_FILENAME, 'r')
# line: string
line = inFile.readline()
# wordlist: list of strings
wordlist = line.split()
print(" ", len(wordlist), "words loaded.")
return wordlist | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_words(f: str, letters: List[str]) -> List[str]:\r\n forbidden_letters = [i for i in string.ascii_lowercase]\r\n for i in letters:\r\n try:\r\n forbidden_letters.remove(i)\r\n except:\r\n pass\r\n words_file = open(f)\r\n word_list = []\r\n letstr = \"\"\r\... | [
"0.77132916",
"0.71703184",
"0.70881295",
"0.69955045",
"0.6963074",
"0.696277",
"0.69443035",
"0.6916884",
"0.68984777",
"0.6888594",
"0.6887068",
"0.68727845",
"0.684664",
"0.68418354",
"0.6833625",
"0.6832609",
"0.682177",
"0.68213826",
"0.680509",
"0.68021727",
"0.6729428... | 0.0 | -1 |
Manage the OATH application. | def oath(ctx):
dev = ctx.obj["device"]
conn = dev.open_connection(SmartCardConnection)
ctx.call_on_close(conn.close)
ctx.obj["session"] = OathSession(conn)
ctx.obj["oath_keys"] = AppData("oath_keys") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def app():\n return aplicattion",
"def startapp():",
"def main():\n\n root = tk.Tk()\n root.title(\"Exploring US Bikeshare Data\")\n app = Application(master=root)\n print(\"Application loaded! Please use the GUI window to continue...\")\n app.mainloop()",
"def main():\n CLI_APP.run()",
... | [
"0.6161091",
"0.6057929",
"0.6044224",
"0.60303146",
"0.60209566",
"0.6003098",
"0.59859675",
"0.5966284",
"0.5943148",
"0.59315443",
"0.5913396",
"0.5890067",
"0.58656245",
"0.58575094",
"0.5827109",
"0.5824534",
"0.5740286",
"0.5723912",
"0.56984925",
"0.5697103",
"0.569710... | 0.0 | -1 |
Display general status of the OATH application. | def info(ctx):
session = ctx.obj["session"]
version = session.version
click.echo(f"OATH version: {version[0]}.{version[1]}.{version[2]}")
click.echo("Password protection: " + ("enabled" if session.locked else "disabled"))
keys = ctx.obj["oath_keys"]
if session.locked and session.device_id in ke... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_status():\n\n pass",
"def status():\n with spinner():\n is_enabled = is_witness_enabled()\n signing_key = current_signing_key()\n misses = total_missed()\n\n t = PrettyTable([\"Enabled\", \"Misses\", \"Key\"])\n t.align = \"l\"\n t.add_row([is_enabled, mis... | [
"0.74407864",
"0.67104983",
"0.6594999",
"0.65726674",
"0.6557009",
"0.6550992",
"0.6458308",
"0.6412158",
"0.63966596",
"0.63714415",
"0.6368729",
"0.63677835",
"0.63424",
"0.6311805",
"0.6283904",
"0.6280261",
"0.6270306",
"0.6165676",
"0.6147138",
"0.61286515",
"0.61152047... | 0.0 | -1 |
Reset all OATH data. This action will delete all accounts and restore factory settings for the OATH application on the YubiKey. | def reset(ctx, force):
force or click.confirm(
"WARNING! This will delete all stored OATH accounts and restore factory "
"settings. Proceed?",
abort=True,
err=True,
)
session = ctx.obj["session"]
click.echo("Resetting OATH data...")
old_id = session.device_id
se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset(ctx):\n\n controller = ctx.obj['controller']\n click.echo('Resetting OATH data...')\n old_id = controller.id\n controller.reset()\n\n settings = ctx.obj['settings']\n keys = settings.setdefault('keys', {})\n if old_id in keys:\n del keys[old_id]\n settings.write()\n\n ... | [
"0.8467011",
"0.65540415",
"0.63808334",
"0.6317176",
"0.6243645",
"0.62291396",
"0.6131616",
"0.6092173",
"0.6083494",
"0.6074787",
"0.6063735",
"0.6057817",
"0.6048862",
"0.6033866",
"0.60303414",
"0.6019029",
"0.601429",
"0.5997502",
"0.5983064",
"0.59774274",
"0.59740347"... | 0.8467517 | 0 |
Manage password protection for OATH. | def access(): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def password(self, ctx):\n pass",
"def enter_password(self):",
"def password(self) -> str:",
"def setpassword(self, pwd):\n pass",
"def acceptsPassword(self):\r\n raise NotImplementedError()",
"def _encryptDBPass():\n #run encrypt tool on user given password\n controller.... | [
"0.68413633",
"0.6767164",
"0.6509423",
"0.64279366",
"0.6285428",
"0.6279007",
"0.62434596",
"0.6238656",
"0.620061",
"0.6151136",
"0.61161023",
"0.61159134",
"0.609999",
"0.6076949",
"0.6052523",
"0.60466707",
"0.6025319",
"0.5992021",
"0.59835994",
"0.597444",
"0.5940144",... | 0.0 | -1 |
Change the password used to protect OATH accounts. Allows you to set or change a password that will be required to access the OATH accounts stored on the YubiKey. | def change(ctx, password, clear, new_password, remember):
if clear and new_password:
ctx.fail("--clear cannot be combined with --new-password.")
_init_session(ctx, password, False, prompt="Enter the current password")
session = ctx.obj["session"]
keys = ctx.obj["oath_keys"]
device_id = ses... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def change_password(change_account):\n change_data(change_account, changed_data='password')",
"def setpassword(self, pwd):\n pass",
"def change_password(self, new_password):\n dev = self.nearest_pandevice()\n self.password_hash = dev.request_password_hash(new_password)\n self.upd... | [
"0.7618517",
"0.746997",
"0.7434945",
"0.7257479",
"0.7143954",
"0.70882785",
"0.70502394",
"0.7047161",
"0.6986982",
"0.6944256",
"0.6928588",
"0.69195575",
"0.68995327",
"0.68914664",
"0.6882281",
"0.68337",
"0.6824367",
"0.6823735",
"0.6792913",
"0.67883617",
"0.67817914",... | 0.73968214 | 3 |
Store the YubiKeys password on this computer to avoid having to enter it on each use. | def remember(ctx, password):
session = ctx.obj["session"]
device_id = session.device_id
keys = ctx.obj["oath_keys"]
if not session.locked:
if device_id in keys:
del keys[session.device_id]
keys.write()
logger.info("Deleted remembered access key")
clic... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_password(self):\n Credential.passwords.append(self)",
"def save_password(self, new_password):\n # 55 iterations takes about 100 ms on a Netgear WNDR3800 or about 8ms on a\n # Core2 Duo at 1200 MHz.\n hashed = pbkdf2.crypt(new_password, iterations=55)\n self.write(self.... | [
"0.6675744",
"0.65866464",
"0.65817255",
"0.63898677",
"0.6381752",
"0.63418746",
"0.6293246",
"0.6226015",
"0.62100047",
"0.6160657",
"0.61586887",
"0.6145468",
"0.6143356",
"0.6109165",
"0.59605056",
"0.59529567",
"0.5867149",
"0.582038",
"0.580043",
"0.5765957",
"0.5743831... | 0.6156244 | 11 |
Remove a stored password from this computer. | def forget(ctx):
session = ctx.obj["session"]
device_id = session.device_id
keys = ctx.obj["oath_keys"]
if device_id in keys:
del keys[session.device_id]
keys.write()
logger.info("Deleted remembered access key")
click.echo("Password forgotten.")
else:
click.e... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_password(cls, media):\n for password in cls.passwords:\n if password.media.lower() == media.lower():\n cls.passwords.remove(password)",
"def delete_password(self) -> None:\n\n msg = QtWidgets.QMessageBox()\n icon = QtGui.QIcon()\n icon.addPixmap(Qt... | [
"0.6645211",
"0.6478664",
"0.64253193",
"0.6278725",
"0.62367344",
"0.6210454",
"0.61962545",
"0.61674005",
"0.615477",
"0.60955536",
"0.60510457",
"0.60510457",
"0.60510457",
"0.60021687",
"0.5914388",
"0.59058386",
"0.5874533",
"0.57981825",
"0.578934",
"0.57035446",
"0.566... | 0.59053725 | 16 |
Manage and use OATH accounts. | def accounts(): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def accounts():\n pass",
"def open_account():\n print(\"\\n\")\n print(messages.open_account)\n u_id = pyip.inputInt(\"Id: \", greaterThan=0)\n name = pyip.inputCustom(raiseNameError, prompt=\"Name: \")\n address = pyip.inputCustom(raiseAddressError, prompt=\"Address: \")\n ... | [
"0.68266904",
"0.65521026",
"0.6182628",
"0.60371274",
"0.6025271",
"0.6007449",
"0.5981175",
"0.5968835",
"0.59599483",
"0.59120464",
"0.58873284",
"0.58873284",
"0.58796024",
"0.58731365",
"0.5872677",
"0.5826017",
"0.582573",
"0.58219486",
"0.5776078",
"0.57698965",
"0.576... | 0.7143794 | 0 |
Add a new account. This will add a new OATH account to the YubiKey. \b NAME human readable name of the account, such as a username or email address SECRET base32encoded secret/key value provided by the server | def add(
ctx,
secret,
name,
issuer,
period,
oath_type,
digits,
touch,
algorithm,
counter,
force,
password,
remember,
):
digits = int(digits)
if not secret:
while True:
secret = click_prompt("Enter a secret key (base32)")
try:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def newaccount(accountname, account, owner, active, memo, posting, create_claimed_account):\n stm = shared_morphene_instance()\n if mph.rpc is not None:\n mph.rpc.rpcconnect()\n if not account:\n account = mph.config[\"default_account\"]\n if not unlock_wallet(stm):\n return\n a... | [
"0.692764",
"0.6889752",
"0.664424",
"0.66392326",
"0.66027886",
"0.6473702",
"0.646768",
"0.6431205",
"0.6337383",
"0.6320626",
"0.63136363",
"0.62558633",
"0.62369376",
"0.620672",
"0.61543274",
"0.6143997",
"0.6131898",
"0.6082695",
"0.60308576",
"0.59818006",
"0.5971492",... | 0.67763263 | 2 |
List all accounts. List all accounts stored on the YubiKey. | def list(ctx, show_hidden, oath_type, period, password, remember):
_init_session(ctx, password, remember)
session = ctx.obj["session"]
creds = [
cred
for cred in session.list_credentials()
if show_hidden or not is_hidden(cred)
]
creds.sort()
for cred in creds:
cli... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_accounts(self):\n pass",
"def display_accounts(cls):\n return cls.account_list",
"def list_accounts():\n\n try:\n accounts = Account.query.all()\n except NoResultFound:\n print(f\"No account configured yet.\")\n return\n n_len = max([len(a.nickname) for a in... | [
"0.77831614",
"0.7059491",
"0.6992086",
"0.68804544",
"0.6816529",
"0.677024",
"0.67435384",
"0.6720439",
"0.67129004",
"0.66524947",
"0.6590446",
"0.65834236",
"0.65735555",
"0.6564277",
"0.6535469",
"0.64419425",
"0.6433965",
"0.63949704",
"0.6325707",
"0.6317909",
"0.63084... | 0.5570774 | 95 |
Generate codes. Generate codes from OATH accounts stored on the YubiKey. Provide a query string to match one or more specific accounts. Accounts of type HOTP, or those that require touch, requre a single match to be triggered. | def code(ctx, show_hidden, query, single, password, remember):
_init_session(ctx, password, remember)
session = ctx.obj["session"]
entries = session.calculate_all()
creds = _search(entries.keys(), query, show_hidden)
if len(creds) == 1:
cred = creds[0]
code = entries[cred]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def code(ctx, show_hidden, query, single):\n\n ensure_validated(ctx)\n\n controller = ctx.obj['controller']\n creds = [(cr, c)\n for (cr, c) in controller.calculate_all()\n if show_hidden or not cr.is_hidden\n ]\n\n creds = _search(creds, query)\n\n if len(creds) ... | [
"0.5709946",
"0.54599285",
"0.52223974",
"0.5158887",
"0.511579",
"0.49772036",
"0.4926266",
"0.48736855",
"0.482959",
"0.48240364",
"0.47595084",
"0.47581586",
"0.47494113",
"0.47327673",
"0.47232696",
"0.47091013",
"0.47038928",
"0.46369225",
"0.4620117",
"0.46148378",
"0.4... | 0.60336894 | 0 |
Rename an account (requires YubiKey 5.3 or later). \b QUERY a query to match a single account (as shown in "list") | def rename(ctx, query, name, force, password, remember):
_init_session(ctx, password, remember)
session = ctx.obj["session"]
creds = session.list_credentials()
hits = _search(creds, query, True)
if len(hits) == 0:
click.echo("No matches, nothing to be done.")
elif len(hits) == 1:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def change_name(change_account):\n change_data(change_account, changed_data='name')",
"def userRenamed(self, oldname, newname):\n # Send messasge to Server bot.\n self.data_in(text=\"\", type=\"renamed\", oldname=oldname, newname=newname)",
"def change_username(self, accountid, oldusername, ne... | [
"0.680213",
"0.61724126",
"0.61534506",
"0.5984996",
"0.58834755",
"0.5820864",
"0.5812739",
"0.580092",
"0.57732266",
"0.57656634",
"0.5690298",
"0.56819504",
"0.56756616",
"0.5665553",
"0.5652086",
"0.56036377",
"0.55914325",
"0.55752826",
"0.5561572",
"0.5548004",
"0.54920... | 0.78993976 | 0 |
Delete an account. Delete an account from the YubiKey. \b QUERY a query to match a single account (as shown in "list") | def delete(ctx, query, force, password, remember):
_init_session(ctx, password, remember)
session = ctx.obj["session"]
creds = session.list_credentials()
hits = _search(creds, query, True)
if len(hits) == 0:
click.echo("No matches, nothing to be done.")
elif len(hits) == 1:
cred... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_account():\n print(\"\\n\")\n print(messages.delete_account)\n u_id = pyip.inputInt(\"User Id: \", greaterThan=0)\n\n credentials = {\"id\":u_id}\n result = BankOperationsBackend.delete_account(credentials)\n start_again() if result else BankOperationsUi.delete_... | [
"0.7632831",
"0.7553542",
"0.7370156",
"0.7133459",
"0.70442104",
"0.7016698",
"0.70111597",
"0.69728017",
"0.6969365",
"0.69664097",
"0.6955276",
"0.69501173",
"0.6761479",
"0.67169905",
"0.6671774",
"0.66007626",
"0.65042543",
"0.64596933",
"0.6452238",
"0.6445934",
"0.6404... | 0.72511256 | 3 |
Returns True if provided url is valid ATOM or RSS. | def isFeedURLValid(feed_url=None):
# a missing or empty feed url is never valid
if not feed_url:
return False
try:
result = urlfetch.fetch(feed_url)
except urlfetch_errors.Error:
return False
# 200 is the status code for 'all ok'
if result.status_code != 200:
return False
try:
pars... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_url(url):\n\n return bool(re.match(re_url, url))",
"def isValidURL(self, url):\n if \"imdb.com\" in url:\n return True\n else:\n return False",
"def __isUrl(self, url):\n if type(url)==str:\n return url.startswith('http://') or url.startswith('htt... | [
"0.7569633",
"0.7502508",
"0.73820883",
"0.73411655",
"0.7280303",
"0.7181644",
"0.71765673",
"0.71716577",
"0.71481854",
"0.71284044",
"0.71284044",
"0.7101685",
"0.7084717",
"0.70737684",
"0.70601267",
"0.7044573",
"0.7031269",
"0.6966769",
"0.6956776",
"0.6939639",
"0.6937... | 0.7483982 | 2 |
Returns True if link_id is in a valid format. | def isLinkIdFormatValid(link_id):
if linkable.LINK_ID_REGEX.match(link_id):
return True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _validate_item_link(self, item):\n if len(item.link) > 255:\n raise ValueError(\"item.link length too long.\")\n\n return True",
"def isValid(t_id):\n\tstr_id=str(t_id).strip()\n\treturn str_id.isdigit()",
"def is_id_valid(id_code: str) -> bool:\n if id_code.isdigit():\n ... | [
"0.6823712",
"0.6606774",
"0.65906495",
"0.63503116",
"0.63053745",
"0.62947255",
"0.62471926",
"0.6207654",
"0.6194306",
"0.6191876",
"0.6190582",
"0.60711575",
"0.60548055",
"0.6014753",
"0.5996904",
"0.59781694",
"0.5946499",
"0.59353083",
"0.59190065",
"0.5917687",
"0.590... | 0.90292734 | 0 |
Returns True if scope_path is in a valid format. | def isScopePathFormatValid(scope_path):
if linkable.SCOPE_PATH_REGEX.match(scope_path):
return True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def path_validate(path):\n # functionality to be added later\n return path",
"def _IsWellFormattedFilePath(path):\n return path.startswith(SRC) and path.endswith(_OWNERS)",
"def ValidatePath(self, root_path: str) -> bool:\n if 'silver' in root_path:\n return True\n\n return False",
"def val... | [
"0.62401545",
"0.6233845",
"0.61344105",
"0.61142206",
"0.6085311",
"0.6056564",
"0.6019026",
"0.5993043",
"0.59646183",
"0.5931616",
"0.59268624",
"0.57996225",
"0.5798255",
"0.5786526",
"0.5785276",
"0.5777391",
"0.57569396",
"0.5749316",
"0.57310104",
"0.57060814",
"0.5705... | 0.90448207 | 0 |
Get a data schema | def get_schema(schema): # noqa: E501
return 'do some magic!' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_schema(self) -> dict:",
"def _get_schema(self):\n self._pick()\n return Schema()",
"def get_schema(self):\n response = self.client.get(self._get_collection_url('schema'))\n\n return response.get('schema', {})",
"def get_schema(cls):\n return cls.schema()",
"def ge... | [
"0.79035777",
"0.7808428",
"0.774904",
"0.75302166",
"0.7522008",
"0.74665457",
"0.72975564",
"0.72781396",
"0.7229537",
"0.7178495",
"0.71265703",
"0.7107381",
"0.70986634",
"0.70339996",
"0.6992602",
"0.69804245",
"0.69703525",
"0.6958406",
"0.69463897",
"0.69424665",
"0.68... | 0.71875787 | 9 |
Fetch latest crease xml from animation publish folder... | def _crease_XML_latest_publish(self, tk, templateFile = '', id = '', shotNum = ''):
debug(app = self.app, method = '_crease_XML_latest_publish', message = 'Looking for crease xml now...', verbose = False)
getCreaseXMLPublishFolder = tk.paths_from_template( templateFile, {'Step' : 'Anm', 'id' : id, 'Shot' : shot... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _fetchAnimPublish(self, tk, templateFile = '', fields = '', id = '', shotNum = '', inprogressBar = '', filteredPublish = ''):\r\n\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'Fetching latest caches now....', verbose = False)\r\n\t\tdebug(app = self.app, method = '_fetchAnimPublish', messag... | [
"0.6206035",
"0.55234224",
"0.52666616",
"0.5235553",
"0.52091956",
"0.5206474",
"0.52010465",
"0.51887625",
"0.51887625",
"0.5178505",
"0.51600707",
"0.51256144",
"0.5122521",
"0.50846535",
"0.50757486",
"0.5039742",
"0.5022523",
"0.5022523",
"0.5006762",
"0.4999023",
"0.499... | 0.63504636 | 0 |
Func to cleanup the FX aspects of an ocean setup for a clean rebuild | def _removeFX(self):
nodesToClean = [CONST.FOAM_FLUID_SHAPENODE, CONST.WAKE_FLUID_SHAPENODE, 'fluids_hrc']
for eachNode in nodesToClean:
try:
cmds.delete(each)
except:
pass
for eachCache in cmds.ls(type = 'cacheFile'):
cmds.delete(eachCache) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cleanup():",
"def cleanUp(self):\n import evoware.fileutil as F\n F.tryRemove(self.f_project, verbose=(self.VERBOSITY>1), tree=1)",
"def cleanup_step(self):\n self.clean_home_subdir()\n\n super(IntelBase, self).cleanup_step()",
"def env_cleanup(self):\n pass",
"def cl... | [
"0.75079024",
"0.72766304",
"0.7203938",
"0.7149449",
"0.7148618",
"0.7132942",
"0.7098037",
"0.7097166",
"0.70632",
"0.705151",
"0.70513195",
"0.70179564",
"0.6965721",
"0.6965721",
"0.6965721",
"0.6941519",
"0.6903252",
"0.68758154",
"0.68729347",
"0.68729347",
"0.68729347"... | 0.0 | -1 |
Func to clean up all the related ocean stuff for a full clean rebuild. | def _removeOcean(self):
nodesToClean = [CONST.OCEANDISPSHADER, CONST.OCEANANIMSHADER, CONST.OCEAN_ANIM_PREVIEWPLANENAME]
for eachNode in nodesToClean:
try:
cmds.delete(each)
except:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cleanup():",
"def _clean_up(self):",
"def cleanUp(self):\n import evoware.fileutil as F\n F.tryRemove(self.f_project, verbose=(self.VERBOSITY>1), tree=1)",
"def cleanup_step(self):\n self.clean_home_subdir()\n\n super(IntelBase, self).cleanup_step()",
"def cleanUp(self):\r\n... | [
"0.7034238",
"0.6948844",
"0.6938194",
"0.69257504",
"0.68967277",
"0.68826264",
"0.68699753",
"0.68283665",
"0.6806426",
"0.67668396",
"0.67551947",
"0.6735371",
"0.6726853",
"0.67112094",
"0.67112094",
"0.66846704",
"0.6684126",
"0.66598904",
"0.6635282",
"0.65948325",
"0.6... | 0.65006167 | 28 |
Fetches all the latest published fluid containers and their caches for lighting | def _fetchFXPublish(self, tk, templateFile = '', fields = '', id = '', shotNum = '', inprogressBar = ''):
## First clean up any existing caches and fluids
self._removeFX()
## CHECK FOR FX PUBLISHES NOW
getFXVersionFolders = tk.paths_from_template(templateFile, {'Step' : 'FX', 'id' : id, 'Shot' : shotNum... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _fetchAnimPublish(self, tk, templateFile = '', fields = '', id = '', shotNum = '', inprogressBar = '', filteredPublish = ''):\r\n\t\tdebug(app = self.app, method = '_fetchAnimPublish', message = 'Fetching latest caches now....', verbose = False)\r\n\t\tdebug(app = self.app, method = '_fetchAnimPublish', messag... | [
"0.56672907",
"0.5571335",
"0.55697733",
"0.54212946",
"0.5389289",
"0.53722525",
"0.53226024",
"0.5241255",
"0.5167858",
"0.5153116",
"0.5110662",
"0.5107825",
"0.5104006",
"0.5092108",
"0.5091016",
"0.50862485",
"0.507191",
"0.5070526",
"0.50680923",
"0.5056334",
"0.5054478... | 0.51527745 | 10 |
Exposing a tool to help push the ocean into the right location based off the FX published fluid containers fluids_hrc | def _setOceanLocation(self):
## If the fluids_hrc exists
if cmds.objExists('fluids_hrc'):
if cmds.objExists('ocean_srf'):
cmds.connectAttr('fluids_hrc.translateX', 'ocean_srf.translateX', f = True)
cmds.connectAttr('fluids_hrc.translateZ', 'ocean_srf.translateZ', f = True)
else:
cmds.warnin... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n\n\n fab_list = get_fabric_list(SANNAV_IP_ADDRESS, SANNAV_FOS_USERNAME, SANNAV_FOS_PASSWORD)\n\n # Print all known facts about the fabrics and the switches\n # Comment out this print statement if this code will be used to generate\n # an Ansible Tower inventory.\n print(json.dumps(fab_l... | [
"0.5672977",
"0.560191",
"0.55638695",
"0.55382484",
"0.55016917",
"0.5492851",
"0.5487739",
"0.547502",
"0.5456432",
"0.5440167",
"0.54215467",
"0.5404361",
"0.53749293",
"0.53648347",
"0.53563046",
"0.5336703",
"0.53051615",
"0.5297738",
"0.52720964",
"0.52703017",
"0.52670... | 0.5646119 | 1 |
Split out as requested by lighting to just fetch the latest ocean | def _rebuildOcean(self, tk, templateFile = '', fields = '', id = '', shotNum = '', inprogressBar = ''):
debug(app = self.app, method = '_rebuildOcean', message = 'Deleting current Ocean now....', verbose = False)
self._removeOcean()
## Get all the publishes from shotgun now for the blocking or animation step... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getSlaves():",
"def crossWalkGeoBlacklight(data):\n\n dataJsonObj=deep_get(data,\"xml.fgdc\",[])\n if len (dataJsonObj)>0:\n dataJsonObj=deep_get(dataJsonObj[0],\"data\",{})\n else:\n dataJsonObj={}\n layername=os.path.splitext(os.path.basename(data['file']))[0]\n geoserver_layer... | [
"0.5493592",
"0.5329551",
"0.52164626",
"0.5196075",
"0.5143778",
"0.5120439",
"0.5111923",
"0.5093739",
"0.50789034",
"0.5073732",
"0.5064189",
"0.5026506",
"0.50177354",
"0.49574155",
"0.49371618",
"0.49332502",
"0.49277258",
"0.49213284",
"0.4918263",
"0.4880653",
"0.48528... | 0.48307702 | 22 |
Used to fetch most recent cache files | def _fetchAnimPublish(self, tk, templateFile = '', fields = '', id = '', shotNum = '', inprogressBar = '', filteredPublish = ''):
debug(app = self.app, method = '_fetchAnimPublish', message = 'Fetching latest caches now....', verbose = False)
debug(app = self.app, method = '_fetchAnimPublish', message = 'Template... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def files():\n return get_cached(\"files.json\")",
"def fetch(\n self, output_folder: Path, cache: Dict[str, str], fetch_opts: List[Dict[str, Any]]\n ) -> List[str]:\n return [\n download_snapshot(source_config[\"url\"], output_folder, **source_config.get(\"opts\", {}))\n ... | [
"0.6888681",
"0.6708136",
"0.658939",
"0.6533511",
"0.6487432",
"0.6340917",
"0.63322324",
"0.63005716",
"0.6286395",
"0.62659204",
"0.6218005",
"0.6218005",
"0.61635375",
"0.61493427",
"0.61172336",
"0.6110893",
"0.6078489",
"0.60554326",
"0.6053905",
"0.60533607",
"0.601005... | 0.0 | -1 |
Final pass to connect existing ocean caches to the ocean shader if they exist in the scene | def _connectWakeAndFoamToOcean(self, tk, templateFile = '', id = '', shotNum = '', inprogressBar = ''):
debug(app = self.app, method = '_connectWakeAndFoamToOcean', message = 'Connecting fluid textures to ocean shader....', verbose = False)
####################################################
## Straight up ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _on_load_scene_shaders(self):\n\n artellapipe.ShadersMgr().load_scene_shaders()",
"def _on_lowres_assets(self):\n\n scene_assets = artellapipe.AssetsMgr().get_scene_assets()\n if not scene_assets:\n return\n\n for scene_asset in scene_assets:\n scene_asset.sw... | [
"0.5968839",
"0.5813282",
"0.57067865",
"0.56659585",
"0.5622924",
"0.5525419",
"0.5465",
"0.5386124",
"0.53504324",
"0.53005147",
"0.5141424",
"0.511689",
"0.50960606",
"0.50883526",
"0.5087522",
"0.5083337",
"0.50820434",
"0.50608236",
"0.5042474",
"0.50115144",
"0.49761882... | 0.49265927 | 24 |
Return if the selected digits from start in the number are a palindrome | def is_number_palindrome(number, digits, start):
number = str((number // 10**start) % 10**digits).zfill(digits)
return is_palindrome(number) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_palindrome(n):\n d = digits(n)\n r = int(\"\".join([str(i) for i in d]))\n return n == r",
"def isPalindrome(Number):\r\n ListOfDigit=[int(d) for d in str(Number)]\r\n n=len(ListOfDigit)\r\n for i in range(n//2):\r\n if ListOfDigit[i]!=ListOfDigit[-(i+1)]:\r\n return(Fa... | [
"0.7902744",
"0.78919506",
"0.7875554",
"0.78522855",
"0.780591",
"0.77704966",
"0.7650538",
"0.7627759",
"0.7580284",
"0.75686455",
"0.7507331",
"0.75047344",
"0.7498053",
"0.7495534",
"0.74926513",
"0.74589795",
"0.74185145",
"0.73735946",
"0.7350664",
"0.73400944",
"0.7291... | 0.8182947 | 0 |
Model is initialized with tag model m | def __init__(self, tags):
self.tags = tags | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_model(self):\n pass",
"def initialize_model(self):\n pass",
"def initialize(self, model):\n pass",
"def __init__(self, model):\n\t\tself.model = model",
"def __init__(self, model):\n self._model = model",
"def __init__(self, model):\n self.model = model",
"de... | [
"0.77973366",
"0.77588874",
"0.76936555",
"0.731248",
"0.7298934",
"0.7228691",
"0.7228691",
"0.7228691",
"0.7228691",
"0.72052944",
"0.72052944",
"0.70650303",
"0.688462",
"0.6877822",
"0.68087727",
"0.67039186",
"0.6681423",
"0.66007894",
"0.6584187",
"0.6508255",
"0.650825... | 0.6052295 | 75 |
Get all the tag combinations possible for a tree of length n | def get_all_tag_seq(self, n):
tags = list(product(self.tags, repeat=n))
return tags | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fn(n):\n if n == 1: return [TreeNode()]\n ans = []\n for nn in range(1, n, 2): \n for left in fn(nn):\n for right in fn(n-1-nn): \n ans.append(TreeNode(left=left, right=right))\n return ans",
"def get_subs(n)... | [
"0.6263927",
"0.61682093",
"0.60873145",
"0.599515",
"0.596374",
"0.5940209",
"0.5909812",
"0.5883347",
"0.58303434",
"0.58066684",
"0.5776324",
"0.57579505",
"0.57390195",
"0.5715532",
"0.57020915",
"0.5674218",
"0.5645066",
"0.5627819",
"0.56251854",
"0.56197864",
"0.561497... | 0.6972763 | 0 |
Get index of a tag sequence m in self.tags | def get_tag_index(self, m):
return self.tags.index(m) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def index_in_tag(self):\n if hasattr(self, '_m_index_in_tag'):\n return self._m_index_in_tag if hasattr(self, '_m_index_in_tag') else None\n\n self._m_index_in_tag = (self.tag - 35)\n return self._m_index_in_tag if hasattr(self, '_m_index_in_tag') els... | [
"0.7142307",
"0.6958829",
"0.6688589",
"0.66438335",
"0.65356153",
"0.6207758",
"0.62011987",
"0.6198451",
"0.61982673",
"0.6162026",
"0.6146128",
"0.6113043",
"0.60828024",
"0.60632235",
"0.60050696",
"0.5982074",
"0.59499717",
"0.5918507",
"0.5912195",
"0.5911467",
"0.59011... | 0.8766997 | 0 |
Given two tags and a label, return the psi factor of the two tag sequences | def get_psi_score(self, psi, pos1, pos2, lab, m1, m2):
i, j = self.get_tag_index(m1), self.get_tag_index(m2)
return psi[pos1, pos2, lab, i, j] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _psi_function(share1, share2):\n return (share1 - share2) * math.log(share1/share2)",
"def get_emissions_probability(label_matches, given_tag, given_word, tag_counts):\r\n\tlookup_tuple = (given_word, given_tag)\r\n\tword_tag_frequency = label_matches.get(lookup_tuple, 0)\r\n\ttag_frequency = tag_counts[g... | [
"0.59438974",
"0.5719111",
"0.5711388",
"0.5612045",
"0.5503579",
"0.54424065",
"0.54400754",
"0.5393413",
"0.53729504",
"0.53618443",
"0.5353206",
"0.5324507",
"0.5300333",
"0.52868974",
"0.5257343",
"0.52473134",
"0.52161574",
"0.52138144",
"0.5211579",
"0.5206199",
"0.5191... | 0.58502215 | 1 |
Given a word index and a tag, return the corresponding phi factor | def get_phi_score(self, phi, i, m):
m_i = self.get_tag_index(m)
return phi[i, m_i] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_e(word, tag, e_word_tag_counts, q_uni_counts):\n word_tag_tupple = (word, tag)\n\n word_tag_count = 0\n if word_tag_tupple in e_word_tag_counts:\n word_tag_count = e_word_tag_counts[word_tag_tupple]\n\n nof_tag = q_uni_counts[tag]\n return float(word_tag_count) / nof_tag",
"def phi_... | [
"0.60653216",
"0.60379994",
"0.6002697",
"0.5898803",
"0.561196",
"0.5606778",
"0.559027",
"0.55902416",
"0.55877733",
"0.55638826",
"0.5545571",
"0.55339694",
"0.55001754",
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"0.54254514",
"0.5408535",
"0.5381232",
"0.53649646",
"0.53594136",
"0.5357221",
"0.53333... | 0.60124296 | 2 |
Create phi factors for a given tree | def create_phi(self, T, pos, m, alpha=1):
phi = tr.zeros((len(T), self.tag_size()), dtype=tr.float64)
for i, _, _ in T:
m_i = self.get_tag_index(m[i - 1])
phi[i - 1, m_i] = alpha
return phi | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def euler_phi(n):\n\tif n == 1: return 1\n\tif n <= 0: return 0\n\t# For each prime factor p with multiplicity n, a factor of (p**(n-1))*(p-1)\n\treturn functools.reduce(lambda a,x:a*(x[0]**(x[1]-1))*(x[0]-1),factor(n),1)",
"def euler_phi(n):\r\n\t# For each prime factor p with multiplicity n, a factor of (p**(n... | [
"0.6235746",
"0.6235249",
"0.59856504",
"0.59158146",
"0.59023964",
"0.582595",
"0.5666644",
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"0.555361",
"0.54805905",
"0.5480042",
"0.5480042",
"0.5477096",
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"0.5401921",
"0.53897333",
"0.5374874",
"0.53169054",
"0.52634305",
"0.5260795",
"0.5258291... | 0.48233575 | 71 |
Calculate the (log) agreement of a list of tags for a given tree | def log_score(self, T, pos, m, psi, phi):
log_score = 0
for (i, j, lab) in T:
m_i = m[i - 1]
pos1 = pos[i - 1]
log_score += self.get_phi_score(phi, i - 1, m_i)
if j != 0:
m_j = m[j - 1]
pos2 = pos[j - 1]
log_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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([... | [
"0.54515827",
"0.5316931",
"0.5275841",
"0.5213601",
"0.52042305",
"0.51962835",
"0.5176608",
"0.50949377",
"0.50766563",
"0.5068682",
"0.5062237",
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"0.5017808",
"0.4995817",
"0.49750316",
"0.49472603",
"0.49305362",
"0.49264774",
"0.48480722",
"0.484... | 0.0 | -1 |
Calculate the gradient of the log score for a given tree with respect to the psi and phi parameters | def dlog_score(self, T, pos, m, psi):
dpsi = tr.zeros_like(psi)
for (i, j, lab) in T:
if j != 0:
m_i = self.get_tag_index(m[i - 1])
pos1 = pos[i - 1]
m_j = self.get_tag_index(m[j - 1])
pos2 = pos[j - 1]
dpsi[pos1... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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 ... | [
"0.67526263",
"0.6612029",
"0.6377196",
"0.63414973",
"0.6340336",
"0.63349605",
"0.62160623",
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"0.62073463",
"0.6133842",
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"0.6031914",
"0.60047233",
"0.60036635",
"0.5983822",
"0.59582996",
"0.5957819",
"0.59035... | 0.5909217 | 20 |
Belief propagation algorithm for calculating the log of the partition function Z | def logZ(self, T, pos, psi, phi):
msgs = belief_propagation(T, pos, psi, phi, True)
log_z = calculate_belief_sum(msgs, True)
return log_z | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log_lhood(X, Z, Y, a, ep, lamb):\n \n K = Z.shape[1]\n N, T = X.shape\n \n # p(X)\n ZY = np.dot(Z,Y) \n log_pX = 0\n log_pX = log_pX + np.sum(X * np.log(1 - ((1 - lamb) ** ZY) * (1 - ep)))\n log_pX = log_pX + np.sum((1 - X) * np.log(((1 - lamb) ** ZY) * (1 - ep))) \n \n ... | [
"0.68426955",
"0.67924553",
"0.65790004",
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"0.6392089",
"0.63153124",
"0.62316066",
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"0.6121981",
"0.61210907",
"0.6119032",
"0.6114617",
"0.61016893",
"0.6100129",
"0.609... | 0.6328785 | 5 |
Belief propagation algorithm for calculating the gradient of the log of the partition function Z | def dlogZ(self, T, pos, psi, phi):
msgs = belief_propagation(T, pos, psi, phi, True)
dpsi = calculate_gradient(msgs, T, pos, psi, True, True)
return dpsi | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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 ... | [
"0.72906494",
"0.69248664",
"0.6920769",
"0.663529",
"0.663529",
"0.662517",
"0.6602322",
"0.660013",
"0.6522516",
"0.64752764",
"0.6431578",
"0.64274055",
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"0.64133984",
"0.6344967",
"0.63444275",
"0.63398236",
"0.6332006",
"0.6312257",
"0.62948793",... | 0.6327207 | 19 |
Calculate the conditional log probability of the tags given the tree | def log_prob(self, T, pos, m, psi, phi=tr.Tensor()):
if phi.size() == tr.Size([0]):
phi = self.create_phi(T, pos, m)
return self.log_score(T, pos, m, psi, phi) - self.logZ(T, pos, psi, phi) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log_prob(self):",
"def probability(self, tokens):\n\n return 2 ** self.log_probability(tokens)",
"def log_probability(self, sequence):\n sequence = self._transform(sequence)\n\n T = len(sequence)\n\n if T > 0 and sequence[0][_TAG]:\n last_state = sequence[0][_TAG]\n ... | [
"0.6678612",
"0.6432416",
"0.63992906",
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"0.633939",
"0.6339248",
"0.6256718",
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"0.60917944",
"0.6077829",
"0.6073268",
"0.60723597",
"0.6044864",
"0.60433054",
... | 0.0 | -1 |
Calculate the gradient of the log probability for a given tree with respect to the psi and phi parameters | def dlog_prob(self, T, pos, m, psi, phi=tr.Tensor()):
if phi.size() == tr.Size([0]):
phi = self.create_phi(T, pos, m)
dpsi_score = self.dlog_score(T, pos, m, psi)
dpsi_Z = self.dlogZ(T, pos, psi, phi)
return dpsi_score - dpsi_Z | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dlogZ(self, T, pos, psi, phi):\n msgs = belief_propagation(T, pos, psi, phi, True)\n dpsi = calculate_gradient(msgs, T, pos, psi, True, True)\n return dpsi",
"def fd_grad(self, T, pos, psi, phi, eps=1e-5):\n dpsi = tr.zeros_like(psi)\n dphi = tr.zeros_like(phi)\n for... | [
"0.7159313",
"0.6766048",
"0.6629792",
"0.6519036",
"0.6494689",
"0.6489138",
"0.6413346",
"0.64006495",
"0.6371672",
"0.63623327",
"0.63360333",
"0.63232714",
"0.6310686",
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"0.6260827",
"0.62424856",
"0.62325174",
"0.62135476",
"0.61898154",
"0.61844623",
"0.6144... | 0.6783176 | 1 |
Belief propagation (maxproduct) algorithm for calculating the best tag sequence for a tree | def best_sequence(self, T, pos, psi, phi, fix_tags=[]):
for idx, m in fix_tags:
phi[idx - 1, m] = 100
# if fix_idx:
# phi[fix_idx - 1, fix_m] = 100
msgs, pointers = max_product(T, pos, psi, phi, True)
tags_dict = get_best_tags(T, msgs, pointers)
tags = []
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def viterbi_tags (untagged_sentences, h):\n transitions = h[0]\n emissions = h[1]\n tags = h[2]\n maxtags = []\n #print tags\n\n for untaggedsent in untagged_sentences:\n #Create empty probtable\n words = untaggedsent.split()\n r = len(tags)\n c = len(words)\n p... | [
"0.6412046",
"0.6118864",
"0.6081776",
"0.6072963",
"0.59527105",
"0.58929455",
"0.58275664",
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"0.57968605",
"0.5785027",
"0.57746196",
"0.57544744",
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"0.5700903",
"0.5691829",
"0.56907284",
"0.56797606",
"0.56477773",
"0.564019",
"0.563955... | 0.58380824 | 6 |
Brute force algorithm for calculating the log of the partition function Z | def logZ_brute(self, T, pos, psi, phi):
ms = self.get_all_tag_seq(len(T))
log_scores = tr.zeros(len(ms), dtype=tr.float64)
for i in range(len(ms)):
log_scores[i] = self.log_score(T, pos, ms[i], psi, phi)
log_z = logsumexp(log_scores)
return log_z | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def partition_function(array, temp):\r\n\r\n # Constants imported from scipy.constants\r\n h = scipy.constants.h # Planck's constant\r\n # speed of light must be in cm/s as wavenumber is in cm-1\r\n c = scipy.constants.c * 100\r\n k = scipy.constants.k # Boltzmann constant\r\n T = temp # extra... | [
"0.65710264",
"0.6284432",
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"0.59657145",
"0.59184784",
"0.5914684",
"0.5879645",
"0.58559495",
"0.5837734",
"0.5820538",
"0.5819314",
"0.57771146",
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"0.57748866",
"0.57500154",
"0.57367206",
"0.56673867",
"0.56623465",
"0.5645668",
"0.5631098",
"0.5... | 0.6006404 | 3 |
Brute force algorithm for calculating the log of the partition function Z | def best_sequence_brute(self, T, pos, psi, phi):
ms = self.get_all_tag_seq(len(T))
log_scores = tr.zeros(len(ms), dtype=tr.float64)
for i in range(len(ms)):
log_scores[i] = self.log_score(T, pos, ms[i], psi, phi)
best = ms[tr.argmax(log_scores)]
tags = []
for ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def partition_function(array, temp):\r\n\r\n # Constants imported from scipy.constants\r\n h = scipy.constants.h # Planck's constant\r\n # speed of light must be in cm/s as wavenumber is in cm-1\r\n c = scipy.constants.c * 100\r\n k = scipy.constants.k # Boltzmann constant\r\n T = temp # extra... | [
"0.65710264",
"0.6284432",
"0.61714786",
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"0.59184784",
"0.5914684",
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"0.58559495",
"0.5837734",
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"0.57748866",
"0.57500154",
"0.57367206",
"0.56673867",
"0.56623465",
"0.5645668",
"0.5... | 0.0 | -1 |
Finite Difference gradient computation for logZ | def fd_grad(self, T, pos, psi, phi, eps=1e-5):
dpsi = tr.zeros_like(psi)
dphi = tr.zeros_like(phi)
for pos1 in range(psi.shape[0]):
for pos2 in range(psi.shape[1]):
for lab in range(psi.shape[2]):
for i in range(psi.shape[3]):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def logistic_grad(z):\n idx_pos = np.where(z >= 0.)\n idx_neg = np.where(z < 0.)\n res = np.empty(z.shape)\n res[idx_pos] = 1. / (1. + np.exp(-z[idx_pos]))\n res[idx_neg] = 1 - 1. / (1. + np.exp(z[idx_neg]))\n return res",
"def logistic_grad(z):\n idx_pos = np.whe... | [
"0.75813544",
"0.75813544",
"0.7266053",
"0.70977503",
"0.7045134",
"0.6986021",
"0.69778025",
"0.6907271",
"0.689076",
"0.6876",
"0.6797919",
"0.6774386",
"0.67495555",
"0.67483854",
"0.67364544",
"0.67208683",
"0.67097276",
"0.668161",
"0.6634446",
"0.6579385",
"0.65332234"... | 0.6227096 | 40 |
returns collection of beat times when given start/end/length | def Calc(self, a, b, size):
self.eq = lambda x: (60000/((b-a)/size*x+a))
points = []
names = [str(self.offset)]
points.append(0)
for j in range(1, int(size)):
points.append(integrate.quad(self.eq,0,j)[0])
names.append(str(points[-1]+self.offset))
s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_times(self, start: int = None, end: int = None) -> List:\n return [i.time for i in self.data[start:end]]",
"def get_target_timestamps(self):\n times=[]\n curr = self.begin_ts\n while curr<=self.end_ts:\n times.append(curr)\n curr = curr + 24 * 60 * 60\n ... | [
"0.6811883",
"0.66345775",
"0.61763304",
"0.6161527",
"0.61594856",
"0.61594856",
"0.61594856",
"0.60631555",
"0.5961605",
"0.59090525",
"0.5891341",
"0.58558464",
"0.5800605",
"0.57836986",
"0.5770535",
"0.57627267",
"0.5715411",
"0.56829864",
"0.56808645",
"0.5678318",
"0.5... | 0.0 | -1 |
returns beat info as string | def Beat_disp(self):
return ' '.join(str(x+self.offset) for x in self.beats) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def info(self):\n out = f\"sec: {self.em_sec()}\\nmin: {self.em_min()}\"\n out += f\"\\nhora: {self.em_hora()}\\ndia: {self.em_dia()}\"\n return out",
"def get_at_as_string(self):\n\n return self.at.strftime(\"%Y-%m-%dT%H:%M:%S.000Z\")",
"def __str__(self):\n return_text = \"... | [
"0.66582495",
"0.6518425",
"0.6161863",
"0.6112195",
"0.6085194",
"0.6056986",
"0.6040513",
"0.59848976",
"0.5980764",
"0.59712094",
"0.5916152",
"0.59127504",
"0.5898801",
"0.5861905",
"0.58609194",
"0.5855922",
"0.58121693",
"0.5801718",
"0.5774646",
"0.5771938",
"0.574199"... | 0.7030934 | 0 |
Home page for app (and project) | def index(request):
username = request.session.get('username', False)
profile = request.session.get('profile', False)
baseurl = ''
workpath = os.path.dirname(os.path.abspath(__file__)) # Returns the Path your .py file is in
with open((os.path.join(workpath,'../baseurl.txt')),'r',encoding='utf-8') ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def home():\n\n\treturn render_template('index.html', title='Home Page',\n\t\t\t\t\t\t year=datetime.now().year)",
"def home():\r\n return render_template(\r\n 'index.html',\r\n title='Home Page',\r\n year=datetime.now().year,\r\n )",
"def homepage():\n return render_template('h... | [
"0.8311739",
"0.81331044",
"0.81042373",
"0.80418706",
"0.80418706",
"0.80418706",
"0.80418706",
"0.80418706",
"0.80418706",
"0.80418706",
"0.80418706",
"0.80418706",
"0.80418706",
"0.8040029",
"0.799967",
"0.79859835",
"0.7929723",
"0.79114187",
"0.7904756",
"0.7903732",
"0.... | 0.0 | -1 |
Credits page for app | def credits(request):
username = request.session.get('username', False)
profile = request.session.get('profile', False)
if(username):
context = {'username': username,'profile':profile}
return render(request, 'MedTAG_sket_dock_App/index.html', context)
else:
return redirect('MedT... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def credits():\n return render_template('credits.html')",
"async def credits(self, ctx: commands.Context):\r\n await ctx.send(embed=CREDITS_EMBED)",
"def credits(cls):\n print(\"Thank you for playing.\\nCreated by \" + cls.author+\".\\n\")",
"def contact_linkup(self, request, pk):\n o... | [
"0.7859985",
"0.7057577",
"0.6672508",
"0.62410414",
"0.6223819",
"0.61764616",
"0.61620957",
"0.61559856",
"0.61495495",
"0.6127247",
"0.6028435",
"0.6007418",
"0.5978635",
"0.59492284",
"0.5912205",
"0.5789936",
"0.57580626",
"0.5750479",
"0.5719444",
"0.5716057",
"0.571447... | 0.6755606 | 2 |
Credits page for app | def uploadFile(request):
username = request.session.get('username', False)
profile = request.session.get('profile', False)
if(username):
context = {'username': username,'profile':profile}
return render(request, 'MedTAG_sket_dock_App/index.html', context)
else:
return redirect('M... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def credits():\n return render_template('credits.html')",
"async def credits(self, ctx: commands.Context):\r\n await ctx.send(embed=CREDITS_EMBED)",
"def credits(request):\n\n username = request.session.get('username', False)\n profile = request.session.get('profile', False)\n if(username):\... | [
"0.7859985",
"0.7057577",
"0.6755606",
"0.6672508",
"0.62410414",
"0.6223819",
"0.61764616",
"0.61620957",
"0.61559856",
"0.61495495",
"0.6127247",
"0.6028435",
"0.6007418",
"0.5978635",
"0.59492284",
"0.5912205",
"0.5789936",
"0.57580626",
"0.5750479",
"0.5719444",
"0.571605... | 0.0 | -1 |
Configuration page for app | def configure(request):
username = request.session.get('username', False)
profile = request.session.get('profile', False)
if(username):
context = {'username': username,'profile':profile}
return render(request, 'MedTAG_sket_dock_App/index.html', context)
else:
return redirect('Me... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def configuration():",
"def config():",
"def config():",
"def config(self):\n pass",
"def config(self):\n pass",
"def config():\n config_django()\n config_svisor()",
"def configuration_view(project):\n project_query = Project.select().where(Project.slug == project).first()\n i... | [
"0.7428864",
"0.73023534",
"0.73023534",
"0.7212623",
"0.7212623",
"0.6980258",
"0.68614465",
"0.68128914",
"0.6723701",
"0.66743726",
"0.66153085",
"0.66144913",
"0.6602604",
"0.6585713",
"0.6577487",
"0.6575138",
"0.6562094",
"0.6562094",
"0.65005267",
"0.64869064",
"0.6486... | 0.0 | -1 |
Team members' stats page for app | def team_members_stats(request):
username = request.session.get('username', False)
profile = request.session.get('profile', False)
if (username):
context = {'username': username, 'profile': profile}
return render(request, 'MedTAG_sket_dock_App/index.html', context)
else:
return ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_stats(self):\n print(self.team_one.name + \" stats: \")\n self.team_one.stats()\n print(self.team_two.name + \" stats: \")\n self.team_two.stats()",
"def info():\n print 'Loading info page'\n\n team_list = datastore.get_all_teams(engine)\n\n return render_template('i... | [
"0.72802764",
"0.7093188",
"0.6892792",
"0.66899914",
"0.65582514",
"0.6528671",
"0.64821774",
"0.6452844",
"0.64374906",
"0.64060926",
"0.63946265",
"0.63593155",
"0.63570726",
"0.6330489",
"0.6300333",
"0.62758255",
"0.6246533",
"0.6213044",
"0.6185886",
"0.6148427",
"0.613... | 0.7581212 | 0 |
Update Configuration page for app | def updateConfiguration(request):
username = request.session.get('username', False)
profile = request.session.get('profile', False)
if(username):
context = {'username': username,'profile':profile}
return render(request, 'MedTAG_sket_dock_App/index.html', context)
else:
return re... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def conf_update(self):\n pass",
"def update(self):\n self.save_config_file()",
"def configuration_view(project):\n project_query = Project.select().where(Project.slug == project).first()\n if project_query is None:\n flash(\"invalid project\")\n return redirect(url_for(\"proje... | [
"0.7255445",
"0.6736466",
"0.64811534",
"0.63596797",
"0.6235038",
"0.6235038",
"0.62124354",
"0.618501",
"0.60655963",
"0.5992609",
"0.59914666",
"0.59606487",
"0.5928038",
"0.59218657",
"0.5920716",
"0.5906855",
"0.58799803",
"0.58701694",
"0.58692014",
"0.58270746",
"0.581... | 0.606258 | 9 |
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