MAX_TEMP:\\n print 'High temperature detected, truncating. ' + line,\\n truncated = True\\n break\\n if temp is not None and temp < MIN_TEMP:\\n print 'Low temperature detected, truncating. ' + line,\\n truncated = True\\n break\\n outf.write(line)\\n\\n # back up original and rename new truncated file\\n outf.close()\\n inf.close()\\n if not truncated:\\n os.remove(outfname)\\n else:\\n print 'Renaming truncated %s to %s .' % (os.path.basename(fname), os.path.basename(permfname))\\n os.rename(fname, bakfname)\\n os.rename(outfname, permfname)\",\n \"def test_default_config():\\n clean_tables() \\n config = set_configuration() \\n assert config['age']['value'] == \\\"72\\\"\\n assert config['retainUnsent']['value'] == \\\"False\\\" \\n \\n insert_into_reading()\\n row_count = get_count() \\n min_id, max_id = min_max_id() \\n update_last_object(min_id=min_id, max_id=max_id)\\n total_purged, unsent_purged = purge(config, _READING_TABLE)\\n\\n log = get_log() \\n\\n assert total_purged == 0\\n assert total_purged == log['rowsRemoved']\\n assert unsent_purged == 0 \\n assert unsent_purged == log['unsentRowsRemoved'] \\n assert log['failedRemovals'] == 0 \\n assert log['rowsRemaining'] == row_count - total_purged \\n clean_tables()\",\n \"def merge_configs(configFile:str, oldSampleFile:str, newSampleFile:str, unsafeAttributesFile:str, filetype:str):\\n upgrade_config(configFile, oldSampleFile, newSampleFile, unsafeAttributesFile, filetype)\",\n \"def Truncate(self, f, fcut, below=True):\\n fout = copy.copy(f)\\n ind = thresh(f,fcut)\\n if below:\\n fout = fout[0:ind]\\n else:\\n fout = fout[ind:]\\n \\n keys=['Gxx','Gyy','Gxy']\\n\\n for curkey in keys:\\n curitem = colwise(getattr(self,curkey))\\n\\n if below:\\n curitem = curitem[0:ind,:]\\n else:\\n curitem = curitem[ind:,:]\\n \\n setattr(self,curkey,squeeze(curitem))\\n return fout\",\n \"def overwrite_file(self):\\n\\n new_file = open(self.temp_filename, 'r')\\n file = open(self.filename, 'w')\\n file.writelines(new_file.readlines())\\n new_file.close()\\n file.close()\\n os.remove(self.temp_filename)\",\n \"def write_config_file():\\n\\tif not config_parser:\\n\\t\\tprint \\\"Config module not loaded. I don't save anything.\\\"\\n\\t\\treturn\\n\\n\\tf = file(config_file, \\\"w\\\")\\n\\tconfig_parser.write(f)\\n\\tf.close()\",\n \"def setup_metrics_file(self):\\n\\n with open(self.metrics_path, \\\"w+\\\") as f_metrics:\\n\\n f_metrics.write(get_metrics_file_form())\",\n \"async def _msgvote_threshold(self, ctx, threshold: int):\\n\\n if threshold < 0:\\n await self.bot.say(\\\"Invalid threshold. Must be a positive \\\"\\n \\\"integer, or 0 to disable.\\\")\\n elif threshold == 0:\\n self.settings[\\\"threshold\\\"] = threshold\\n dataIO.save_json(self.settings_path, self.settings)\\n await self.bot.say(\\\"Message deletion disabled.\\\")\\n else:\\n self.settings[\\\"threshold\\\"] = threshold\\n dataIO.save_json(self.settings_path, self.settings)\\n await self.bot.say(\\\"Messages will be deleted if [downvotes - \\\"\\n \\\"upvotes] reaches {}.\\\".format(threshold))\",\n \"def conf_update(self):\\n pass\",\n \"def load_and_clean_file(self, path):\\n pass\",\n \"def write_config(self, config_file):\\n \\n # write root paths\\n \\n # write reference data\\n \\n # write tool paths\\n \\n pass\",\n \"def _write_elasticluster_config(config, out_file):\\n orig_file = os.path.join(sys.prefix, \\\"share\\\", \\\"bcbio-vm\\\", \\\"elasticluster\\\", \\\"config\\\")\\n if not os.path.exists(os.path.dirname(out_file)):\\n os.makedirs(os.path.dirname(out_file))\\n if os.path.exists(out_file):\\n bak_file = out_file + \\\".bak%s\\\" % datetime.datetime.now().strftime(\\\"%Y-%m-%d-%H-%M-%S\\\")\\n shutil.move(out_file, bak_file)\\n with open(orig_file) as in_handle:\\n with open(out_file, \\\"w\\\") as out_handle:\\n for line in in_handle:\\n if line.startswith(tuple(config.keys())):\\n name, val = line.strip().split(\\\"=\\\")\\n out_handle.write(\\\"%s=%s\\\\n\\\" % (name, config[name]))\\n else:\\n out_handle.write(line)\\n return out_file\",\n \"def tokenize_descriptions_with_threshold(input_file_path, output_file_path):\\n if os.path.exists(output_file_path):\\n print(\\\"Tokenized descriptions found. Will not be generated.\\\")\\n return\\n\\n print(\\\"Generating tokenized descriptions\\\")\\n f = open(output_file_path, 'a')\\n with open(input_file_path, 'r') as file:\\n word_count_threshold = 4\\n word_counts = {}\\n sequences = []\\n for line in file:\\n if line.strip():\\n sequence = line.strip().replace(\\\" '\\\",\\\"'\\\").split()\\n sequence[1:] = clean_tokens(sequence[1:])\\n sequence.insert(1, '<START>')\\n sequence.append('<END>')\\n sequences.append(sequence)\\n for w in sequence[1:]:\\n word_counts[w] = word_counts.get(w, 0) + 1\\n vocab = [w for w in word_counts if word_counts[w] >= word_count_threshold]\\n for sequence in sequences:\\n sequence[1:] = ['<UNK>' if x not in vocab else x for x in sequence[1:]]\\n f.write(\\\",\\\".join(sequence) + \\\"\\\\n\\\") \\n f.close()\\n print(\\\"Finished generating tokenized descriptions\\\")\",\n \"def save_config() -> None:\\n with open(_config_file, \\\"w\\\", newline=\\\"\\\") as config_file:\\n json.dump(_config, config_file, indent=4)\\n config_file.truncate()\",\n \"def new_config_file(filepath):\\n config_container = gather_default()\\n ind = \\\" \\\"\\n try:\\n\\n if _pix.write_data_to_file(config_container, filepath, ind):\\n return filepath\\n\\n except PermissionError:\\n\\n # NOTE: as config.txt is crucial for running blender tools we have to warn user (even in 3D viewport)\\n # so he can not miss the problem with creation of config file; solution also provided in message\\n lprint(\\\"E Cannot create configuration file (permission denied), please ensure read/write permissions for:\\\\n\\\\t %r\\\\n\\\\n\\\\t \\\"\\n \\\"Without configuration file Blender Tools might not work as expected!\\\",\\n (os.path.dirname(filepath),),\\n report_errors=1,\\n report_warnings=1)\\n\\n return None\",\n \"async def before_cleanup(self, invoker: PluginInvoker):\\n config_file = invoker.files[\\\"config\\\"]\\n try:\\n config_file.unlink()\\n except FileNotFoundError:\\n pass\\n logging.debug(f\\\"Deleted configuration at {config_file}\\\")\",\n \"def apply(self, opened_file):\",\n \"def update_from_file(self):\\n config_path = os.environ.get('MINDINSIGHT_CONFIG', '')\\n if not config_path:\\n return\\n\\n config_module = None\\n\\n # python:full.path.for.config.module\\n if config_path.startswith('python:'):\\n config_module = import_module(config_path[len('python:'):])\\n\\n # file:full/path/for/config.py\\n elif config_path.startswith('file:'):\\n config_path = config_path[len('file:'):]\\n module_name = '__mindinsightconfig__'\\n config_module = types.ModuleType(module_name)\\n machinery = import_module('importlib.machinery')\\n loader = machinery.SourceFileLoader(module_name, config_path)\\n loader.exec_module(config_module)\\n\\n if config_module is None:\\n return\\n\\n for setting in dir(config_module):\\n if setting.isupper() and setting in self._default_settings:\\n setting_value = getattr(config_module, setting)\\n setattr(self, setting, setting_value)\\n self._explicit_settings.add(setting)\",\n \"def read_config_file(self, filename):\\n self.LO1_pwr = []\\n self.LO1_freq_pwr = []\\n fd = open(filename, \\\"r\\\")\\n for line in fd:\\n if line[0:8] == \\\"LO1pwr.N\\\":\\n trash, num = line.split(\\\"=\\\")\\n self.num_pwr_readings = string.atoi(num)\\n break\\n for i in range(0, self.num_pwr_readings):\\n self.LO1_pwr.append(0)\\n self.LO1_freq_pwr.append(0)\\n\\n for line in fd:\\n if line[0:7] == \\\"LO1pwr[\\\":\\n ind = line.find(\\\"frequency\\\")\\n if ind != -1:\\n trash, num = line.split(\\\"[\\\")\\n index, trash = num.split(\\\"]\\\")\\n trash, freq = line.split(\\\"=\\\")\\n index = string.atoi(index)\\n freq.lstrip()\\n self.LO1_freq_pwr[index] = string.atof(freq)\\n else:\\n ind = line.find(\\\"power\\\")\\n if ind != -1:\\n trash, num = line.split(\\\"[\\\")\\n index, trash = num.split(\\\"]\\\")\\n index = string.atoi(index)\\n trash, pwr = line.split(\\\"=\\\")\\n pwr.lstrip()\\n self.LO1_pwr[index] = string.atof(pwr)\",\n \"def setconfig(filepath, param, value):\\n\\n with open(filepath, 'rb') as f:\\n lines = f.readlines()\\n with open(filepath, 'wb') as f:\\n updated = False\\n for line in lines:\\n if line.strip().startswith('#') or '=' not in line:\\n # keep comments and other non informative lines unchanged\\n f.write(line)\\n continue\\n k, v = line.split('=', 1)\\n if k.strip() == param:\\n # update with new value\\n f.write('%s=%s\\\\n' % (param, value))\\n updated = True\\n else:\\n # keep line unchanged\\n f.write(line)\\n if not updated:\\n # append the new param at the end of the file\\n f.write('%s=%s\\\\n' % (param, value))\",\n \"def _write_config(self, config_path: Path):\\n with open(config_path, \\\"w\\\") as f:\\n json.dump(self.config_overrides, f)\",\n \"def test_writing(self):\\n with contextlib.closing(RiggedDailyLogFile(self.name, self.dir)) as log:\\n log.write(\\\"123\\\")\\n log.write(\\\"456\\\")\\n log.flush()\\n log.write(\\\"7890\\\")\\n\\n with open(self.path) as f:\\n self.assertEqual(f.read(), \\\"1234567890\\\")\",\n \"def refresh_config(self):\\n with open(config_name, 'rb') as f:\\n self.CONFIG = simplejson.load(f)\\n\\n return self\",\n \"def file_and_malware_syslog_config(self, file_and_malware_syslog_config):\\n\\n self._file_and_malware_syslog_config = file_and_malware_syslog_config\",\n \"def handle_file(self, f, settings):\\n ext = splitext(f.dest_file)[1][1:]\\n if not ext.lower() == \\\"xml\\\": # Not a XML file\\n # Ignore the file. It is not an XML file.\\n return\\n\\n path = f.get_patched_file_path()\\n if not path: # Ignore the file.\\n return\\n\\n notAllowedWords = settings['ReservedWords'].split(',')\\n attributesToCheckLenght = settings['AttributesToCheck'].split(',')\\n maxLength = settings['MaxLength']\\n headerIdentifierRegex = settings['LiquibaseHeaderRegex']\\n isLiquibaseFile = False\\n\\n with open(path, 'rb') as content_test:\\n for line in content_test:\\n if re.findall(headerIdentifierRegex, line):\\n isLiquibaseFile = True\\n if not isLiquibaseFile:\\n # isNotALiquibaseFile\\n return\\n with open(path, 'rb') as content:\\n line_num = 0\\n for line in content:\\n line_num += 1\\n self.checkLine(f, line, line_num, attributesToCheckLenght,\\n notAllowedWords, maxLength)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63270384","0.60728574","0.57777673","0.5709095","0.52786064","0.51557845","0.5135187","0.5053703","0.5041154","0.5027755","0.5015283","0.5009702","0.49824095","0.49750274","0.49732378","0.49518126","0.49454418","0.49447972","0.49405065","0.49233353","0.49216795","0.4916753","0.49041644","0.4902163","0.4885355","0.4884826","0.4877091","0.48757622","0.48717347","0.48696283","0.4863006","0.48627517","0.48447827","0.48403448","0.4834889","0.48313788","0.48297983","0.48233858","0.48036334","0.47951692","0.47937337","0.47924054","0.47869846","0.47862282","0.47828266","0.4779825","0.47712123","0.47630313","0.47613415","0.47588754","0.47512126","0.47508568","0.474808","0.47360313","0.47180617","0.47148475","0.4712992","0.46986407","0.46964324","0.4693388","0.4684574","0.46820784","0.46803665","0.46603006","0.46591735","0.4655229","0.465177","0.46491504","0.46460867","0.4629997","0.46263924","0.46251214","0.46177134","0.46164605","0.461281","0.4611691","0.46002126","0.45968542","0.45917505","0.4589358","0.45872706","0.4586634","0.45831543","0.45739353","0.45726126","0.4567329","0.45630276","0.4560371","0.45561662","0.4553101","0.45501444","0.45480505","0.45420265","0.4541693","0.45318258","0.45300898","0.4525396","0.4523285","0.45223218","0.4521187"],"string":"[\n \"0.63270384\",\n \"0.60728574\",\n \"0.57777673\",\n \"0.5709095\",\n \"0.52786064\",\n \"0.51557845\",\n \"0.5135187\",\n \"0.5053703\",\n \"0.5041154\",\n \"0.5027755\",\n \"0.5015283\",\n \"0.5009702\",\n \"0.49824095\",\n \"0.49750274\",\n \"0.49732378\",\n \"0.49518126\",\n \"0.49454418\",\n \"0.49447972\",\n \"0.49405065\",\n \"0.49233353\",\n \"0.49216795\",\n \"0.4916753\",\n \"0.49041644\",\n \"0.4902163\",\n \"0.4885355\",\n \"0.4884826\",\n \"0.4877091\",\n \"0.48757622\",\n \"0.48717347\",\n \"0.48696283\",\n \"0.4863006\",\n \"0.48627517\",\n \"0.48447827\",\n \"0.48403448\",\n \"0.4834889\",\n \"0.48313788\",\n \"0.48297983\",\n \"0.48233858\",\n \"0.48036334\",\n \"0.47951692\",\n \"0.47937337\",\n \"0.47924054\",\n \"0.47869846\",\n \"0.47862282\",\n \"0.47828266\",\n \"0.4779825\",\n \"0.47712123\",\n \"0.47630313\",\n \"0.47613415\",\n \"0.47588754\",\n \"0.47512126\",\n \"0.47508568\",\n \"0.474808\",\n \"0.47360313\",\n \"0.47180617\",\n \"0.47148475\",\n \"0.4712992\",\n \"0.46986407\",\n \"0.46964324\",\n \"0.4693388\",\n \"0.4684574\",\n \"0.46820784\",\n \"0.46803665\",\n \"0.46603006\",\n \"0.46591735\",\n \"0.4655229\",\n \"0.465177\",\n \"0.46491504\",\n \"0.46460867\",\n \"0.4629997\",\n \"0.46263924\",\n \"0.46251214\",\n \"0.46177134\",\n \"0.46164605\",\n \"0.461281\",\n \"0.4611691\",\n \"0.46002126\",\n \"0.45968542\",\n \"0.45917505\",\n \"0.4589358\",\n \"0.45872706\",\n \"0.4586634\",\n \"0.45831543\",\n \"0.45739353\",\n \"0.45726126\",\n \"0.4567329\",\n \"0.45630276\",\n \"0.4560371\",\n \"0.45561662\",\n \"0.4553101\",\n \"0.45501444\",\n \"0.45480505\",\n \"0.45420265\",\n \"0.4541693\",\n \"0.45318258\",\n \"0.45300898\",\n \"0.4525396\",\n \"0.4523285\",\n \"0.45223218\",\n \"0.4521187\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.55548596"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":2995,"cells":{"query":{"kind":"string","value":"Return sny vowels founded in a supplied word."},"document":{"kind":"string","value":"def search4vowels(word):\n vowels = set('aeiou')\n found = vowels.intersection(set(word))\n #return found\n for vowels in found:\n print(vowels)"},"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 _rv_standard(self, word, vowels):\n rv = \"\"\n if len(word) >= 2:\n if word[1] not in vowels:\n for i in range(2, len(word)):\n if word[i] in vowels:\n rv = word[i + 1 :]\n break\n\n elif word[0] in vowels and word[1] in vowels:\n for i in range(2, len(word)):\n if word[i] not in vowels:\n rv = word[i + 1 :]\n break\n else:\n rv = word[3:]\n\n return rv","def find_words_using_all_vowels():\n pass","def find_vowels(s):\n \"*** YOUR CODE HERE ***\"","def censor_vowels(word):\n\n chars = []\n\n for letter in word:\n if letter in \"aeiou\":\n chars.append(\"*\")\n chars.append(letter)\n\n return \"\".join(vowels)","def censor_vowels(word):\n\n chars = []\n\n for letter in word:\n if letter in \"aeiou\":\n chars.append(\"*\")\n else:\n chars.append(letter)\n \n return \"\".join(chars)","def replace_vowels(word):\n variants = []\n for c in word:\n if c in vowels:\n for vowel in vowels:\n variants.append(word.replace(c, vowel))\n return variants","def fry(word):\n\n # looks for a Y or y which will be (captured) followed and ended by an 'ou'\n match_you = re.match('([Yy])ou$', word)\n\n # First group will be the (captured) group so either 'Y' or 'y'\n if match_you:\n return match_you.group(1) + \"'all\"\n\n # looks for anyword ending in 'ing'\n match_ing = re.search('(.+)ing$', word)\n\n # checks if vowel exists before the 'ing'\n if match_ing:\n vowel_check = re.search('[aeiouy]', match_ing.group(1))\n # First group will be the (captured) group so everything before the 'ing'\n if vowel_check:\n return match_ing.group(1) + \"in'\"\n\n return word","def main():\n word = input(\"Give me a word! \\n\\n\")\n vowels = ['a', 'e', 'i', 'o', 'u']\n if word[0].lower() in vowels:\n print(f\"\\n\\nPig latin: {word}way\")\n else:\n print(f\"\\n\\nPig latin: {word[1:]}{word[0]}ay\")","def stem(self, word):\n word = word.lower()\n\n if word in self.__special_words:\n return self.__special_words[word]\n\n # Map the different apostrophe characters to a single consistent one\n word = (word.replace(u(\"\\u2019\"), u(\"\\x27\"))\n .replace(u(\"\\u2018\"), u(\"\\x27\"))\n .replace(u(\"\\u201B\"), u(\"\\x27\")))\n\n if word.startswith(u(\"\\x27\")):\n word = word[1:]\n\n if word.startswith(\"y\"):\n word = \"\".join((\"Y\", word[1:]))\n\n for i in range(1, len(word)):\n if word[i - 1] in self.__vowels and word[i] == \"y\":\n word = \"\".join((word[:i], \"Y\", word[i + 1:]))\n\n step1a_vowel_found = False\n step1b_vowel_found = False\n\n r1 = \"\"\n r2 = \"\"\n\n if word.startswith((\"gener\", \"commun\", \"arsen\")):\n if word.startswith((\"gener\", \"arsen\")):\n r1 = word[5:]\n else:\n r1 = word[6:]\n\n for i in range(1, len(r1)):\n if r1[i] not in self.__vowels and r1[i - 1] in self.__vowels:\n r2 = r1[i + 1:]\n break\n else:\n r1, r2 = self._r1r2_standard(word, self.__vowels)\n\n # STEP 0\n for suffix in self.__step0_suffixes:\n if word.endswith(suffix):\n word = word[:-len(suffix)]\n r1 = r1[:-len(suffix)]\n r2 = r2[:-len(suffix)]\n break\n\n # STEP 1a\n for suffix in self.__step1a_suffixes:\n if word.endswith(suffix):\n\n if suffix == \"sses\":\n word = word[:-2]\n r1 = r1[:-2]\n r2 = r2[:-2]\n\n elif suffix in (\"ied\", \"ies\"):\n if len(word[:-len(suffix)]) > 1:\n word = word[:-2]\n r1 = r1[:-2]\n r2 = r2[:-2]\n else:\n word = word[:-1]\n r1 = r1[:-1]\n r2 = r2[:-1]\n\n elif suffix == \"s\":\n for letter in word[:-2]:\n if letter in self.__vowels:\n step1a_vowel_found = True\n break\n\n if step1a_vowel_found:\n word = word[:-1]\n r1 = r1[:-1]\n r2 = r2[:-1]\n break\n\n # STEP 1b\n for suffix in self.__step1b_suffixes:\n if word.endswith(suffix):\n if suffix in (\"eed\", \"eedly\"):\n\n if r1.endswith(suffix):\n word = \"\".join((word[:-len(suffix)], \"ee\"))\n\n if len(r1) >= len(suffix):\n r1 = \"\".join((r1[:-len(suffix)], \"ee\"))\n else:\n r1 = \"\"\n\n if len(r2) >= len(suffix):\n r2 = \"\".join((r2[:-len(suffix)], \"ee\"))\n else:\n r2 = \"\"\n else:\n for letter in word[:-len(suffix)]:\n if letter in self.__vowels:\n step1b_vowel_found = True\n break\n\n if step1b_vowel_found:\n word = word[:-len(suffix)]\n r1 = r1[:-len(suffix)]\n r2 = r2[:-len(suffix)]\n\n if word.endswith((\"at\", \"bl\", \"iz\")):\n word = \"\".join((word, \"e\"))\n r1 = \"\".join((r1, \"e\"))\n\n if len(word) > 5 or len(r1) >= 3:\n r2 = \"\".join((r2, \"e\"))\n\n elif word.endswith(self.__double_consonants):\n word = word[:-1]\n r1 = r1[:-1]\n r2 = r2[:-1]\n\n elif ((r1 == \"\" and len(word) >= 3 and\n word[-1] not in self.__vowels and\n word[-1] not in \"wxY\" and\n word[-2] in self.__vowels and\n word[-3] not in self.__vowels)\n or\n (r1 == \"\" and len(word) == 2 and\n word[0] in self.__vowels and\n word[1] not in self.__vowels)):\n\n word = \"\".join((word, \"e\"))\n\n if len(r1) > 0:\n r1 = \"\".join((r1, \"e\"))\n\n if len(r2) > 0:\n r2 = \"\".join((r2, \"e\"))\n break\n\n # STEP 1c\n if (len(word) > 2\n and word[-1] in \"yY\"\n and word[-2] not in self.__vowels):\n word = \"\".join((word[:-1], \"i\"))\n if len(r1) >= 1:\n r1 = \"\".join((r1[:-1], \"i\"))\n else:\n r1 = \"\"\n\n if len(r2) >= 1:\n r2 = \"\".join((r2[:-1], \"i\"))\n else:\n r2 = \"\"\n\n # STEP 2\n for suffix in self.__step2_suffixes:\n if word.endswith(suffix):\n if r1.endswith(suffix):\n if suffix == \"tional\":\n word = word[:-2]\n r1 = r1[:-2]\n r2 = r2[:-2]\n\n elif suffix in (\"enci\", \"anci\", \"abli\"):\n word = \"\".join((word[:-1], \"e\"))\n\n if len(r1) >= 1:\n r1 = \"\".join((r1[:-1], \"e\"))\n else:\n r1 = \"\"\n\n if len(r2) >= 1:\n r2 = \"\".join((r2[:-1], \"e\"))\n else:\n r2 = \"\"\n\n elif suffix == \"entli\":\n word = word[:-2]\n r1 = r1[:-2]\n r2 = r2[:-2]\n\n elif suffix in (\"izer\", \"ization\"):\n word = \"\".join((word[:-len(suffix)], \"ize\"))\n\n if len(r1) >= len(suffix):\n r1 = \"\".join((r1[:-len(suffix)], \"ize\"))\n else:\n r1 = \"\"\n\n if len(r2) >= len(suffix):\n r2 = \"\".join((r2[:-len(suffix)], \"ize\"))\n else:\n r2 = \"\"\n\n elif suffix in (\"ational\", \"ation\", \"ator\"):\n word = \"\".join((word[:-len(suffix)], \"ate\"))\n\n if len(r1) >= len(suffix):\n r1 = \"\".join((r1[:-len(suffix)], \"ate\"))\n else:\n r1 = \"\"\n\n if len(r2) >= len(suffix):\n r2 = \"\".join((r2[:-len(suffix)], \"ate\"))\n else:\n r2 = \"e\"\n\n elif suffix in (\"alism\", \"aliti\", \"alli\"):\n word = \"\".join((word[:-len(suffix)], \"al\"))\n\n if len(r1) >= len(suffix):\n r1 = \"\".join((r1[:-len(suffix)], \"al\"))\n else:\n r1 = \"\"\n\n if len(r2) >= len(suffix):\n r2 = \"\".join((r2[:-len(suffix)], \"al\"))\n else:\n r2 = \"\"\n\n elif suffix == \"fulness\":\n word = word[:-4]\n r1 = r1[:-4]\n r2 = r2[:-4]\n\n elif suffix in (\"ousli\", \"ousness\"):\n word = \"\".join((word[:-len(suffix)], \"ous\"))\n\n if len(r1) >= len(suffix):\n r1 = \"\".join((r1[:-len(suffix)], \"ous\"))\n else:\n r1 = \"\"\n\n if len(r2) >= len(suffix):\n r2 = \"\".join((r2[:-len(suffix)], \"ous\"))\n else:\n r2 = \"\"\n\n elif suffix in (\"iveness\", \"iviti\"):\n word = \"\".join((word[:-len(suffix)], \"ive\"))\n\n if len(r1) >= len(suffix):\n r1 = \"\".join((r1[:-len(suffix)], \"ive\"))\n else:\n r1 = \"\"\n\n if len(r2) >= len(suffix):\n r2 = \"\".join((r2[:-len(suffix)], \"ive\"))\n else:\n r2 = \"e\"\n\n elif suffix in (\"biliti\", \"bli\"):\n word = \"\".join((word[:-len(suffix)], \"ble\"))\n\n if len(r1) >= len(suffix):\n r1 = \"\".join((r1[:-len(suffix)], \"ble\"))\n else:\n r1 = \"\"\n\n if len(r2) >= len(suffix):\n r2 = \"\".join((r2[:-len(suffix)], \"ble\"))\n else:\n r2 = \"\"\n\n elif suffix == \"ogi\" and word[-4] == \"l\":\n word = word[:-1]\n r1 = r1[:-1]\n r2 = r2[:-1]\n\n elif suffix in (\"fulli\", \"lessli\"):\n word = word[:-2]\n r1 = r1[:-2]\n r2 = r2[:-2]\n\n elif suffix == \"li\" and word[-3] in self.__li_ending:\n word = word[:-2]\n r1 = r1[:-2]\n r2 = r2[:-2]\n break\n\n # STEP 3\n for suffix in self.__step3_suffixes:\n if word.endswith(suffix):\n if r1.endswith(suffix):\n if suffix == \"tional\":\n word = word[:-2]\n r1 = r1[:-2]\n r2 = r2[:-2]\n\n elif suffix == \"ational\":\n word = \"\".join((word[:-len(suffix)], \"ate\"))\n\n if len(r1) >= len(suffix):\n r1 = \"\".join((r1[:-len(suffix)], \"ate\"))\n else:\n r1 = \"\"\n\n if len(r2) >= len(suffix):\n r2 = \"\".join((r2[:-len(suffix)], \"ate\"))\n else:\n r2 = \"\"\n\n elif suffix == \"alize\":\n word = word[:-3]\n r1 = r1[:-3]\n r2 = r2[:-3]\n\n elif suffix in (\"icate\", \"iciti\", \"ical\"):\n word = \"\".join((word[:-len(suffix)], \"ic\"))\n\n if len(r1) >= len(suffix):\n r1 = \"\".join((r1[:-len(suffix)], \"ic\"))\n else:\n r1 = \"\"\n\n if len(r2) >= len(suffix):\n r2 = \"\".join((r2[:-len(suffix)], \"ic\"))\n else:\n r2 = \"\"\n\n elif suffix in (\"ful\", \"ness\"):\n word = word[:-len(suffix)]\n r1 = r1[:-len(suffix)]\n r2 = r2[:-len(suffix)]\n\n elif suffix == \"ative\" and r2.endswith(suffix):\n word = word[:-5]\n r1 = r1[:-5]\n r2 = r2[:-5]\n break\n\n # STEP 4\n for suffix in self.__step4_suffixes:\n if word.endswith(suffix):\n if r2.endswith(suffix):\n if suffix == \"ion\":\n if word[-4] in \"st\":\n word = word[:-3]\n r1 = r1[:-3]\n r2 = r2[:-3]\n else:\n word = word[:-len(suffix)]\n r1 = r1[:-len(suffix)]\n r2 = r2[:-len(suffix)]\n break\n\n # STEP 5\n if r2.endswith(\"l\") and word[-2] == \"l\":\n word = word[:-1]\n elif r2.endswith(\"e\"):\n word = word[:-1]\n elif r1.endswith(\"e\"):\n if len(word) >= 4 and (word[-2] in self.__vowels or\n word[-2] in \"wxY\" or\n word[-3] not in self.__vowels or\n word[-4] in self.__vowels):\n word = word[:-1]\n\n word = word.replace(\"Y\", \"y\")\n return word","def count_syllables(word):\n vowels = \"aeiouy\"\n count = 0\n last_was_vowel = False\n for letter in word:\n found_vowel = False\n for v in vowels:\n if v == letter:\n if not last_was_vowel: count += 1 # don't count diphthongs\n found_vowel = last_was_vowel = True\n break\n if not found_vowel: # If full cycle and no vowel found, set last_was_vowel to false\n last_was_vowel = False\n\n\n if len(word) > 2 and word[-2:] == \"es\" and count > 1: # Remove es - it's \"usually\" silent (?)\n count -= 1\n\n if len(word) > 4 and word[-1:] == \"e\": # remove silent e\n count -= 1\n\n if len(word) > 1 and word[-2:] == \"ee\": # adds 1 for na\n count += 1\n\n if len(word) > 1 and word[-2:] == \"na\": # adds 1 for na\n count += 1\n\n # Check for special case words\n special_case = ['eloise','i']\n if word in special_case:\n count += 1\n\n return count","def remove_vowels(phrase):\n vowels = ['a', 'e', 'i', 'o', 'u', 'A', 'E', 'I', 'O', 'U']\n cons_word = \"\".join([char for char in phrase if char not in vowels])\n return cons_word","def count_vowels(word):\n\n vowels = re.compile(r'[aeouy]', re.IGNORECASE)\n return len(vowels.findall(word))","def get_possible_vowels(self, word_set):\r\n \r\n vowels = \"\"\r\n for word in word_set:\r\n # Check if existing vowel is in word.\r\n if any(vowel in word for vowel in vowels):\r\n continue\r\n # Find most common letter and assume it's a vowel\r\n vowel, probability = '', 0\r\n for c in word:\r\n _, number = self.letters.get_value(c)\r\n if number > probability:\r\n vowel = c\r\n probability = number\r\n vowels += vowel\r\n return vowels","def find_vowel(text: str) -> str:\r\n\r\n vowel = text.count('a') + text.count('o') + text.count('u') +\\\r\n text.count('i') + text.count('e') + text.count(\"y\") +\\\r\n text.count('A') + text.count('O') + text.count('U') +\\\r\n text.count('I') + text.count('E') + text.count('Y')\r\n\r\n return(vowel)","def get_vowel_names():","def calculate_construction(self, word):\r\n \r\n construction = \"\"\r\n for c in word.lower():\r\n if c in self.vowels:\r\n construction += \"v\"\r\n elif c in letters:\r\n construction += \"c\"\r\n return construction","def needs_aou(word):\n return re.search(\"([aouAOU])[^yäöYÄÖ]*$\", word)","def countsyllables_nlde(word):\r\n\tresult = 0\r\n\tprev_was_vowel = word[0] in VOWELS\r\n\tfor char in word[1:]:\r\n\t\tis_vowel = char in VOWELS\r\n\t\tif prev_was_vowel and not is_vowel:\r\n\t\t\tresult += 1\r\n\t\tprev_was_vowel = is_vowel\r\n\r\n\tif (len(word) > 1 and word[0] in VOWELS\r\n\t\t\tand word.endswith('e') and not word[-2] in VOWELS):\r\n\t\tresult += 1\r\n\treturn result or 1","def find_words_no_vowels():\n f = open('session09/words.txt')\n num_no_e = 0\n num_words = 0\n for line in f:\n num_words += 1\n word = line.strip()\n if avoids(word, 'aeiou'):\n # print(word)\n num_no_e += 1\n # print(num_no_e, num_words)\n return num_no_e/num_words","def search4vowels(phrase: str) -> set:\n vowels = set('aeiou')\n found = vowels.intersection(set(phrase))\n for vowel in found:\n print(vowel)","def starts_with_vowel(word):\n return True if word[0] in 'aeiou' else False","def _r1r2_standard(self, word, vowels):\n r1 = \"\"\n r2 = \"\"\n for i in range(1, len(word)):\n if word[i] not in vowels and word[i - 1] in vowels:\n r1 = word[i + 1 :]\n break\n\n for i in range(1, len(r1)):\n if r1[i] not in vowels and r1[i - 1] in vowels:\n r2 = r1[i + 1 :]\n break\n\n return (r1, r2)","def analyse_vowels(self, source):\r\n\r\n word_set = set()\r\n with open(source) as f:\r\n for line in f:\r\n words = [word.lower().strip() for word in line.split()]\r\n for word in words:\r\n map(self.parse_character, word)\r\n stripped = ''.join(c for c in word if c in letters)\r\n if stripped:\r\n word_set.add(stripped)\r\n vowels = self.get_possible_vowels(word_set)\r\n return self.filter_vowels(vowels, word_set)","def translate(self):\n\t\tvowels = \"aeiou\"\n\n\t\tif (self.word[0] not in vowels) and (self.word[1] in vowels):\n\t\t\tnew_word = self.word[1:] + self.word[0] + \"ay\"\n\t\telif self.word[0] in vowels:\n\t\t\tnew_word = self.word + \"way\"\n\t\telse:\n\t\t\tnew_word = self.word[2:] + self.word[:2] + \"ay\"\n\n\t\tprint(new_word)","def estimate(word):\n parts = re.split(r'[^aeiouy]+', word)\n valid_parts = []\n\n for part in parts:\n if part != '':\n valid_parts.append(part)\n\n syllables = 0\n\n for p in re_subsyllables:\n if p.match(word):\n syllables -= 1\n\n for p in re_addsyllables:\n if p.match(word):\n syllables += 1\n\n syllables += len(valid_parts)\n\n if syllables <= 0:\n syllables = 1\n\n return syllables","def step1c(self, word):\r\n\r\n if word.endswith('y'):\r\n result = word.rfind('y')\r\n base = word[:result]\r\n if self.containsVowel(base):\r\n word = base\r\n word += 'i'\r\n return word","def disemvowel(string):\n to_return = ''\n for char in string:\n if char not in 'aeiouAEIOU':\n to_return += char\n return to_return","def search_for_vowels(phrase:str) -> set:\n vowels = set('aeiou')\n return vowels.intersection(set(phrase))","def equal_vowel_and_consonant(words):\n # print(words)\n vowel_consonant_equal = []\n for word in words:\n vowel_counts, consonant_counts = 0, 0\n for char in word:\n if char in 'aeiou':\n vowel_counts += 1\n elif char in 'bcdfghjklmnpqrstvwxyz':\n consonant_counts += 1\n\n if vowel_counts == consonant_counts:\n vowel_consonant_equal.append(word)\n return vowel_consonant_equal","def search4vowels(phrase:str) -> set:\n vowels = set('aeiou')\n #word = input('Enter a word to search for vowels:')\n found = vowels.intersection(set(phrase))\n #for vowel in found:\n #print(vowel)\n\n return (found)","def word_syllables(word):\n\n count = 0\n endings = '!@#$%^&*()_+[]{}:;,.eE\"'+\"'\"\n\n while word[-1] in endings:\n word = word[: -1]\n\n if len(word) <= 3:\n return 1\n\n vows = 'aeiouAEIOU'\n prev_char_vow = False\n for char in word:\n if char in vows:\n if not prev_char_vow:\n count = count + 1\n prev_char_vow = True\n else:\n prev_char_vow = False\n\n if word[-1] in 'Yy':\n count = count + 1\n\n return count","def num_syllables(self, word):\n \"\"\"\n using the logic of vowel counting, count all vowels in the pronunciations\n \"\"\"\n dictionary = self._pronunciations;\n # check if word is present in the CMU dictionary\n if word in dictionary :\n word_pronunciations = dictionary[word.lower()]\n else :\n return 1\n \n vowels = ['A', 'E', 'I', 'O', 'U']\n \n ## find the shorter pronunciation for word\n shorter_arr = [];\n for pronunciation in word_pronunciations :\n if len(pronunciation) > len(shorter_arr) : shorter_arr = pronunciation\n \n num_length = 0\n \n for phoneme in shorter_arr :\n if phoneme[:1] in vowels : num_length += 1\n \n return num_length","def most_repeating_vowels(words):\n return ' '.join([x for x in WORDS if len(x) == len(sv_count(words)[-1])])","def has_more_vowels(word):\n\n# If the phrase is over half vowels, it should return True:\n\n # intialize a vowel count variable \n # Loop through the letters of the word:\n # if the letter is in the set of vowels, increment the vowel count\n # if vowel count is greater than length of the word divided by 2, return True\n # else return false\n\n\n vowel_count = 0\n\n for letter in word:\n if letter.lower() in {\"a\", \"e\", \"i\", \"o\", \"u\"}:\n vowel_count += 1\n\n if vowel_count > (len(word) / 2):\n return True\n\n return False","def get_nyx(words, vowels):\n nyx_list = \"\"\n for word in words:\n word = word.lower()\n if not bool(vowels.intersection(word)) and (\"x\" in word or \"y\" in word) and \"'\" not in word:\n nyx_list += word.strip() + \", \"\n return nyx_list","def stem(self, word):\n word = word.lower()\n\n step1_success = False\n\n # All acute accents are replaced by grave accents.\n word = (word.replace(u(\"\\xE1\"), u(\"\\xE0\"))\n .replace(u(\"\\xE9\"), u(\"\\xE8\"))\n .replace(u(\"\\xED\"), u(\"\\xEC\"))\n .replace(u(\"\\xF3\"), u(\"\\xF2\"))\n .replace(u(\"\\xFA\"), u(\"\\xF9\")))\n\n # Every occurrence of 'u' after 'q'\n # is put into upper case.\n for i in range(1, len(word)):\n if word[i - 1] == \"q\" and word[i] == \"u\":\n word = \"\".join((word[:i], \"U\", word[i + 1:]))\n\n # Every occurrence of 'u' and 'i'\n # between vowels is put into upper case.\n for i in range(1, len(word) - 1):\n if word[i - 1] in self.__vowels and word[i + 1] in self.__vowels:\n if word[i] == \"u\":\n word = \"\".join((word[:i], \"U\", word[i + 1:]))\n elif word[i] == \"i\":\n word = \"\".join((word[:i], \"I\", word[i + 1:]))\n\n r1, r2 = self._r1r2_standard(word, self.__vowels)\n rv = self._rv_standard(word, self.__vowels)\n\n # STEP 0: Attached pronoun\n for suffix in self.__step0_suffixes:\n if rv.endswith(suffix):\n if rv[-len(suffix) - 4:-len(suffix)] in (\"ando\", \"endo\"):\n word = word[:-len(suffix)]\n r1 = r1[:-len(suffix)]\n r2 = r2[:-len(suffix)]\n rv = rv[:-len(suffix)]\n\n elif (rv[-len(suffix) - 2:-len(suffix)] in\n (\"ar\", \"er\", \"ir\")):\n word = \"\".join((word[:-len(suffix)], \"e\"))\n r1 = \"\".join((r1[:-len(suffix)], \"e\"))\n r2 = \"\".join((r2[:-len(suffix)], \"e\"))\n rv = \"\".join((rv[:-len(suffix)], \"e\"))\n break\n\n # STEP 1: Standard suffix removal\n for suffix in self.__step1_suffixes:\n if word.endswith(suffix):\n if suffix == \"amente\" and r1.endswith(suffix):\n step1_success = True\n word = word[:-6]\n r2 = r2[:-6]\n rv = rv[:-6]\n\n if r2.endswith(\"iv\"):\n word = word[:-2]\n r2 = r2[:-2]\n rv = rv[:-2]\n\n if r2.endswith(\"at\"):\n word = word[:-2]\n rv = rv[:-2]\n\n elif r2.endswith((\"os\", \"ic\")):\n word = word[:-2]\n rv = rv[:-2]\n\n elif r2 .endswith(\"abil\"):\n word = word[:-4]\n rv = rv[:-4]\n\n elif (suffix in (\"amento\", \"amenti\",\n \"imento\", \"imenti\") and\n rv.endswith(suffix)):\n step1_success = True\n word = word[:-6]\n rv = rv[:-6]\n\n elif r2.endswith(suffix):\n step1_success = True\n if suffix in (\"azione\", \"azioni\", \"atore\", \"atori\"):\n word = word[:-len(suffix)]\n r2 = r2[:-len(suffix)]\n rv = rv[:-len(suffix)]\n\n if r2.endswith(\"ic\"):\n word = word[:-2]\n rv = rv[:-2]\n\n elif suffix in (\"logia\", \"logie\"):\n word = word[:-2]\n rv = word[:-2]\n\n elif suffix in (\"uzione\", \"uzioni\",\n \"usione\", \"usioni\"):\n word = word[:-5]\n rv = rv[:-5]\n\n elif suffix in (\"enza\", \"enze\"):\n word = \"\".join((word[:-2], \"te\"))\n rv = \"\".join((rv[:-2], \"te\"))\n\n elif suffix == u(\"it\\xE0\"):\n word = word[:-3]\n r2 = r2[:-3]\n rv = rv[:-3]\n\n if r2.endswith((\"ic\", \"iv\")):\n word = word[:-2]\n rv = rv[:-2]\n\n elif r2.endswith(\"abil\"):\n word = word[:-4]\n rv = rv[:-4]\n\n elif suffix in (\"ivo\", \"ivi\", \"iva\", \"ive\"):\n word = word[:-3]\n r2 = r2[:-3]\n rv = rv[:-3]\n\n if r2.endswith(\"at\"):\n word = word[:-2]\n r2 = r2[:-2]\n rv = rv[:-2]\n\n if r2.endswith(\"ic\"):\n word = word[:-2]\n rv = rv[:-2]\n else:\n word = word[:-len(suffix)]\n rv = rv[:-len(suffix)]\n break\n\n # STEP 2: Verb suffixes\n if not step1_success:\n for suffix in self.__step2_suffixes:\n if rv.endswith(suffix):\n word = word[:-len(suffix)]\n rv = rv[:-len(suffix)]\n break\n\n # STEP 3a\n if rv.endswith((\"a\", \"e\", \"i\", \"o\", u(\"\\xE0\"), u(\"\\xE8\"),\n u(\"\\xEC\"), u(\"\\xF2\"))):\n word = word[:-1]\n rv = rv[:-1]\n\n if rv.endswith(\"i\"):\n word = word[:-1]\n rv = rv[:-1]\n\n # STEP 3b\n if rv.endswith((\"ch\", \"gh\")):\n word = word[:-1]\n\n word = word.replace(\"I\", \"i\").replace(\"U\", \"u\")\n return word","def syllable_count(word):\n # Count the vowels in the word\n # Subtract one vowel from every dipthong\n count = len(re.findall(r'([aeiouyAEIOUY]+)', word))\n # Subtract any silent vowels\n if len(word) > 2:\n if word[-1] == 'e' and \\\n not is_vowel(word[-2]) and \\\n is_vowel(word[-3]):\n count = count - 1\n return count","def remove_vowels(string: str) -> str:\n return \" \".join([word for word in string.split() if word[0].lower() not in ['a', 'e', 'i', 'o', 'u']])","def pig_latinify(word):\n\n first_letter = word[0]\n\n if first_letter in VOWELS:\n output_word = word + \"yay\"\n else:\n #scan for vowel if word starts with a consonant\n for i in range(len(word)):\n individual_letter = word[i]\n if individual_letter in VOWELS:\n output_word = word[i:] + word[:i] + \"ay\"\n break\n else:\n continue\n\n return output_word","def makePigLatin(word): \n m = len(word)\n vowels = \"a\", \"e\", \"i\", \"o\", \"u\", \"y\" \n # short words are not converted \n if m<3 or word==\"the\":\n return word\n else:\n for i in vowels:\n if word.find(i) < m and word.find(i) != -1:\n m = word.find(i)\n if m==0:\n return word+\"way\" \n else:\n return word[m:]+word[:m]+\"ay\"","def inner(word):\n return word + '!!!'","def pig_word(self, original):\n word = original.lower()\n if word[0] in \"aeiou\":\n new_word = word + 'ay'\n else:\n new_word = word[1:] + word[0] + 'ay'\n return new_word","def _get_vowels(sequence: str) -> list:\n vowels = []\n for char in sequence:\n if char in VOWELS:\n vowels.append(char)\n return vowels","def disemvowel(val):\n c_list = list(val)\n for char in list(val):\n if char.lower() in [\"a\", \"e\", \"i\", \"o\", \"u\"]:\n c_list.remove(char)\n return \"\".join(c_list)","def search(self, word):","def stem(self, word):\n word = word.lower()\n\n if word in self.stopwords:\n return word\n\n step1_success = False\n\n r1, r2 = self._r1r2_standard(word, self.__vowels)\n rv = self._rv_standard(word, self.__vowels)\n\n # STEP 0: Attached pronoun\n for suffix in self.__step0_suffixes:\n if not (word.endswith(suffix) and rv.endswith(suffix)):\n continue\n\n if (\n rv[: -len(suffix)].endswith(\n (\n \"ando\",\n \"ar\",\n \"er\",\n \"iendo\",\n \"ir\",\n )\n )\n ) or (\n rv[: -len(suffix)].endswith(\"yendo\")\n and word[: -len(suffix)].endswith(\"uyendo\")\n ):\n\n word = self.__replace_accented(word[: -len(suffix)])\n r1 = self.__replace_accented(r1[: -len(suffix)])\n r2 = self.__replace_accented(r2[: -len(suffix)])\n rv = self.__replace_accented(rv[: -len(suffix)])\n break\n\n # STEP 1: Standard suffix removal\n for suffix in self.__step1_suffixes:\n if not word.endswith(suffix):\n continue\n\n if suffix == \"amente\" and r1.endswith(suffix):\n step1_success = True\n word = word[:-6]\n r2 = r2[:-6]\n rv = rv[:-6]\n\n if r2.endswith(\"iv\"):\n word = word[:-2]\n r2 = r2[:-2]\n rv = rv[:-2]\n\n if r2.endswith(\"at\"):\n word = word[:-2]\n rv = rv[:-2]\n\n elif r2.endswith((\"os\", \"ic\", \"ad\")):\n word = word[:-2]\n rv = rv[:-2]\n\n elif r2.endswith(suffix):\n step1_success = True\n if suffix in (\n \"adora\",\n \"ador\",\n \"acion\",\n \"adoras\",\n \"adores\",\n \"aciones\",\n \"ante\",\n \"antes\",\n \"ancia\",\n \"ancias\",\n ):\n word = word[: -len(suffix)]\n r2 = r2[: -len(suffix)]\n rv = rv[: -len(suffix)]\n\n if r2.endswith(\"ic\"):\n word = word[:-2]\n rv = rv[:-2]\n\n elif suffix in (\"logia\", \"logias\"):\n word = suffix_replace(word, suffix, \"log\")\n rv = suffix_replace(rv, suffix, \"log\")\n\n elif suffix in (\"ucion\", \"uciones\"):\n word = suffix_replace(word, suffix, \"u\")\n rv = suffix_replace(rv, suffix, \"u\")\n\n elif suffix in (\"encia\", \"encias\"):\n word = suffix_replace(word, suffix, \"ente\")\n rv = suffix_replace(rv, suffix, \"ente\")\n\n elif suffix == \"mente\":\n word = word[: -len(suffix)]\n r2 = r2[: -len(suffix)]\n rv = rv[: -len(suffix)]\n\n if r2.endswith((\"ante\", \"able\", \"ible\")):\n word = word[:-4]\n rv = rv[:-4]\n\n elif suffix in (\"idad\", \"idades\"):\n word = word[: -len(suffix)]\n r2 = r2[: -len(suffix)]\n rv = rv[: -len(suffix)]\n\n for pre_suff in (\"abil\", \"ic\", \"iv\"):\n if r2.endswith(pre_suff):\n word = word[: -len(pre_suff)]\n rv = rv[: -len(pre_suff)]\n\n elif suffix in (\"ivo\", \"iva\", \"ivos\", \"ivas\"):\n word = word[: -len(suffix)]\n r2 = r2[: -len(suffix)]\n rv = rv[: -len(suffix)]\n if r2.endswith(\"at\"):\n word = word[:-2]\n rv = rv[:-2]\n else:\n word = word[: -len(suffix)]\n rv = rv[: -len(suffix)]\n break\n\n # STEP 2a: Verb suffixes beginning 'y'\n if not step1_success:\n for suffix in self.__step2a_suffixes:\n if rv.endswith(suffix) and word[-len(suffix) - 1 : -len(suffix)] == \"u\":\n word = word[: -len(suffix)]\n rv = rv[: -len(suffix)]\n break\n\n # STEP 2b: Other verb suffixes\n for suffix in self.__step2b_suffixes:\n if rv.endswith(suffix):\n word = word[: -len(suffix)]\n rv = rv[: -len(suffix)]\n if suffix in (\"en\", \"es\", \"eis\", \"emos\"):\n if word.endswith(\"gu\"):\n word = word[:-1]\n\n if rv.endswith(\"gu\"):\n rv = rv[:-1]\n break\n\n # STEP 3: Residual suffix\n for suffix in self.__step3_suffixes:\n if rv.endswith(suffix):\n word = word[: -len(suffix)]\n if suffix in (\"e\", \"\\xE9\"):\n rv = rv[: -len(suffix)]\n\n if word[-2:] == \"gu\" and rv.endswith(\"u\"):\n word = word[:-1]\n break\n\n word = self.__replace_accented(word)\n\n return word","def vowel_with_for(character):\r\n\tif character in vowels:\r\n\t\tprint(\"Entered character is vowel..!\")\r\n\telse:\r\n\t\tprint(\"Not a Vowel\")","def replaceOOV(word):\n if word in self.vocab: return word\n else: return self.oov","def generate_vowel():\n return random.sample(['a', 'e', 'i', 'o', 'u', 'y'], 1)","def vowels(self):\n vas = []\n file = self.read()\n words = re.sub(\"[aeiouAEIOU]\",\" \", file).split(\" \")\n for h_u in words:\n if h_u != \"\":\n vas.append(h_u)\n self.print(vas)\n self.write(vas)\n logging.debug(\"Starting with to\")\n return vas","def number_of_vowels(find_vowels):\n v = [\"a\", \"e\", \"i\", \"o\", \"u\", \"y\"]\n new_vowels = find_vowels.lower()\n vowels = \"\"\n for x in new_vowels:\n if x in v:\n vowels += x\n return len(vowels)","def avg_vowels(self, text):\n val = 0\n if text:\n text = text.replace(\"\\n\", \"\")\n text = text.replace(\",\", \"\")\n text = text.replace(\"'\", \"\")\n it = (map(text.lower().count, \"aeiouyæøå\"))\n word_count = len(text.split(\" \"))\n it_sum = 0\n for x in it:\n it_sum += +x\n if word_count == 0:\n return 0\n val = round(it_sum/word_count, 2)\n print(\"avg vowels returned\", val)\n return val","def print_upper_words_e(words):\n\n for word in words:\n if(word[0] == 'e' or word[0] == 'E'):\n print(word.upper())","def print_upper_words2(words):\n for word in words:\n if word.startswith('e') or word.startswith('E'):\n print(word.upper())","def form_ing(word):\n\n # last char of the word\n last = word[-1]\n\n if last == 'e':\n return word[:-1] + 'ing'\n elif last == 'r':\n if word[-2] == 'a': \n return word + \"ring\"\n elif last in ['b', 'd', 'g', 'm', 'n', 'p', 't']:\n if _is_vowel(word[-2]) and not (_is_vowel(word[-3])):\n return word + word[-1] + \"ing\"\n\n return word + \"ing\"","def vowelcount(s):\n s = s.lower()\n nv = 0\n for v in 'aeiou':\n nv += s.count(v)\n return nv","def pig_latinify(word):\n result = \"\"\n if len(word) > 0 and word.isalpha():\n first = word[0]\n if is_vowel(first): # starts with a vowel\n result = str(word) + \"yay\"\n else: # starts with non-vowel\n cut = position_of_vowel(word) # where to cut the word\n if cut > 0: # \"street\"-->\"eet+str+ay\"\n result = word[cut:] + word[:cut] + \"ay\"\n else: # no vowel found\n result = word + \"ay\"\n else:\n result = 'Only letters allowed!'\n\n return result","def filter_vowels(self, vowels, word_set, iterations=10):\r\n \r\n true_vowels = vowels\r\n for i in range(iterations):\r\n vowels = true_vowels\r\n # Go backwards as the last ones are least likely.\r\n for vowel in vowels[::-1]:\r\n uses = 0\r\n for word in word_set:\r\n if vowel not in word or len(word) < 4:\r\n continue\r\n word_ = word.replace(vowel, '')\r\n # Check if no other vowels are in this word.\r\n if not any(v in word_ for v in true_vowels):\r\n uses += 1\r\n if uses > i:\r\n break\r\n else:\r\n true_vowels = true_vowels.replace(vowel, '')\r\n return vowels","def process_word(self, word: str) -> list[str]:\n d = self.d\n if not d:\n return None\n if d.check(word):\n return None\n # Speed doesn't matter here. The more we find, the more convenient.\n # Remove all digits.\n word = ''.join([i for i in word if not i.isdigit()])\n if d.check(word) or d.check(word.lower()):\n return None\n if word.find('_') > -1:\n # Snake case.\n words = word.split('_')\n for word2 in words:\n if not d.check(word2) and not d.check(word2.lower()):\n return d.suggest(word)\n return None\n words = g.unCamel(word)\n if words:\n for word2 in words:\n if not d.check(word2) and not d.check(word2.lower()):\n return d.suggest(word)\n return None\n return d.suggest(word)","def process_word(self, word: str) -> list[str]:\n d = self.d\n if not d:\n return None\n if d.check(word):\n return None\n # Speed doesn't matter here. The more we find, the more convenient.\n # Remove all digits.\n word = ''.join([i for i in word if not i.isdigit()])\n if d.check(word) or d.check(word.lower()):\n return None\n if word.find('_') > -1:\n # Snake case.\n words = word.split('_')\n for word2 in words:\n if not d.check(word2) and not d.check(word2.lower()):\n return d.suggest(word)\n return None\n words = g.unCamel(word)\n if words:\n for word2 in words:\n if not d.check(word2) and not d.check(word2.lower()):\n return d.suggest(word)\n return None\n return d.suggest(word)","async def owoify(self, ctx: Message, *, owome: str = None):\n\t\tif owome == None:\n\t\t\treturn await self.send(\n\t\t\t f\"{ctx.author.mention} Missing Required Argument - Usage: d!owoify <message> • Example: d!owoify dont hurt me!\"\n\t\t\t)\n\t\telse:\n\t\t\towome = owome.replace(\"r\", \"w\")\n\t\t\towome = owome.replace(\"l\", \"w\")\n\t\t\towome = owome.replace(\"g\", \"w\")\n\t\t\towome = owome.replace(\"R\", \"W\")\n\t\t\towome = owome.replace(\"L\", \"W\")\n\t\t\towome = owome.replace(\"G\", \"W\")\n\t\t\towome = owome.replace(\"ove\", \"uv\")\n\n\t\t\treplacing_words = 'aeiou'\n\t\t\tupperreplacing_words = 'AEIOU'\n\t\t\treplacewithuwu = '!?/_.+'\n\n\t\t\tuwufaces = [\n\t\t\t \"ᓀ˵▾˵ᓂ\", \">_<\", \"＾▽＾\", \"❛ ᴗ ❛\", \"UwU\", \"OwO\", \"QwQ\", \"≧▽≦\"\n\t\t\t]\n\t\t\tfor i in owome:\n\t\t\t\tif i in replacewithuwu:\n\t\t\t\t\towomee = owome.replace(i, random.choice(uwufaces))\n\n\t\t\tfor i in owome:\n\t\t\t\tif i in upperreplacing_words:\n\t\t\t\t\towomee = owome.replace(i, f\"Y{i}\")\n\n\t\t\tfor i in owome:\n\t\t\t\tif i in replacing_words:\n\t\t\t\t\towomee = owome.replace(i, f\"y{i}\")\n\n\t\t\tawait self.send(f\"{owomee}ㅤㅤ• {ctx.author.mention}\")","def countsyllables_en(word):\r\n\tif not word:\r\n\t\treturn 0\r\n\r\n\t# Remove final silent 'e'\r\n\tif word[-1] == \"e\":\r\n\t\tword = word[:-1]\r\n\r\n\t# Check for a cached syllable count\r\n\tif word in fallback_cache:\r\n\t\treturn fallback_cache[word]\r\n\r\n\t# Count vowel groups\r\n\tresult = 0\r\n\tprev_was_vowel = False\r\n\tfor char in word:\r\n\t\tis_vowel = char in VOWELS or char == 'y'\r\n\t\tif is_vowel and not prev_was_vowel:\r\n\t\t\tresult += 1\r\n\t\tprev_was_vowel = is_vowel\r\n\r\n\t# Add & subtract syllables\r\n\tfor r in fallback_addsyl:\r\n\t\tif r.search(word):\r\n\t\t\tresult += 1\r\n\tfor r in fallback_subsyl:\r\n\t\tif r.search(word):\r\n\t\t\tresult -= 1\r\n\r\n\t# Cache the syllable count\r\n\tfallback_cache[word] = result\r\n\r\n\treturn result","def replace_vowels(chars):\n\n replaced = []\n vowels = {'a', 'e', 'i', 'o', 'u'}\n\n for char in chars:\n if char.lower() in vowels:\n replaced.append('*')\n else:\n replaced.append(char)\n\n return replaced","def is_english_vowel(c):\n # y was included in the vowel set guided by the tests.\n return c in 'aeiouyAEIOUY'","def is_vowel(text):\n return text.lower() in AVRO_VOWELS","def check(word):\n if 'ie' in word:\n print('{} doesn\\'t follow the rule'.format(word))\n elif 'cie' in word:\n print('{} doesn\\'t follow the rule'.format(word))\n else:\n print('{} does follow the rule'.format(word))","def test_one_disemvowel_code_wars():\n from disemvowel_trolls import disemvowel\n tests = [(\"This website is for losers LOL!\", \"Ths wbst s fr lsrs LL!\"),\n (\"No offense but,\\nYour writing is among the worst I've everread\",\n \"N ffns bt,\\nYr wrtng s mng th wrst 'v vrrd\"),\n (\"What are you, a communist?\", \"Wht r y, cmmnst?\")]\n\n for case in tests:\n assert disemvowel(case[0]) == case[1]","def removeVowels(self, S: str) -> str:\n \n vowel_list = ['a', 'e', 'i', 'o', 'u']\n output = \"\"\n \n for letter in S:\n \n if letter not in vowel_list:\n \n output += letter\n \n else:\n continue\n \n return output","def eval_word(word):\n\tbasis = Generators.standard_basis((2,1))\n\tproduct = Automorphism(basis, basis)\n\tassert product.is_identity()\n\twhile len(word) >= 2:\n\t\tletter, sub, word = word[0], int(word[1]), word[2:]\n\t\taut = genrs[sub]\n\t\tif letter == 'y':\n\t\t\taut = ~aut\n\t\tproduct *= aut\n\treturn product","def count_vowels(string):\n \n vowel = 0\n \n for i in string:\n if i.lower() in 'aeiou':\n vowel = vowel + 1\n \n return vowel","def upper_vowel(s):\n for k, v in REPLACED_MAP.iteritems():\n s = s.replace(k, v)\n return s","def position_of_vowel(s):\n for i in range(len(s)):\n if is_vowel(s[i]):\n return i\n return -1 # no vowel at all","def does_it_have_3_vowels(word):\n vowel_count = 0\n vowels = \"aeiou\"\n for char in word:\n if char in vowels:\n vowel_count += 1\n\n if vowel_count > 2:\n return True\n else:\n return False","def get_value(word):\n letters = 'abcdefghijklmnopqrstuvwxyz'\n sum = 0\n for letter in word:\n letter_value = letters.find(letter)\n if letter_value == -1:\n letter_value = 0\n sum += letter_value\n return sum","def count_syllables_in_word(word):\n\n count = 0\n\n endings = '!,;.?:'\n last_char = word[-1]\n\n if last_char in endings:\n processed_word = word[0:-1]\n else:\n processed_word = word\n\n\n if len(processed_word) <= 3:\n return 1\n if processed_word[-1] in 'Ee':\n processed_word = processed_word[0:-1]\n\n vowels = 'aeiouAEIOU'\n prev_char_was_vowel = False\n\n for char in processed_word:\n if char in vowels:\n if not prev_char_was_vowel:\n count += 1\n prev_char_was_vowel = True\n\n else:\n prev_char_was_vowel = False\n\n if processed_word[-1] in 'yY':\n count += 1\n \n\n return count","def a_or_an(s):\n if s[0].lower() in 'aeiou':\n return 'an'\n return 'a'","def get_word(w):\n return ''.join(c for c in w if c.isalpha()).lower()","def disambiguate(self, word):\n matches = re.match(r'^pen([cdjz])(.*)$', word)\n if matches:\n return matches.group(1) + matches.group(2)","def filter(self, word):\n \n word = word.lower()\n try:\n self.engine.fetch(word)\n except socket.error:\n raise LemmaAPIError\n part_of_speeches = self.engine.part_of_speeches\n\n \n self.basic_form = word\n for part in part_of_speeches:\n if part == 'verb':\n if self.engine.is_verb_conjugated():\n if not self.conEngine.is_verb_regular(word, self.engine.get_basic_verb()):\n self.basic_form = self.engine.get_basic_verb()\n return word\n else:\n self.basic_form = self.engine.get_basic_verb()\n\n elif part == 'noun':\n if self.engine.is_noun_plural():\n if not self.conEngine.is_noun_regular(word, self.engine.get_singular_noun()):\n self.basic_form = self.engine.get_singular_noun() \n return word\n else:\n self.basic_form = self.engine.get_singular_noun()\n\n return self.basic_form","def vowelcount (x):\n x.lower ()\n print (x.count('a')+x.count('e')+x.count('i')+x.count('o')+x.count('u'))","def num_vowels(s):\n if s == '':\n return 0\n else:\n num_in_rest = num_vowels(s[1:])\n if s[0] in 'aeiou':\n return 1 + num_in_rest\n else:\n return 0 + num_in_rest","def learn_vowels(self, data=None):\n #pdb.set_trace()\n if not data:\n data = self.memory\n # find acoustic prototypes by clustering over stored acoustic reps\n raw_data = data.reshape(4 * len(self.stems), 2)\n ac_vowels, ac_spread = vq.kmeans(raw_data, 4)\n # find articulatory reps by comparing synthesized output vowels to\n # acoustic prototypes\n # start with candidate list of \"all possible\" articulations\n tmp_ar = N.empty((1, 3))\n rd = 0.0\n for hi in [0.0, 1.0]:\n for bk in [0.0, 1.0]:\n tmp_ar = N.vstack((tmp_ar, N.array([hi, bk, rd])))\n tmp_ar = tmp_ar[1:]\n while len(self.vowel_map) < 4:\n # no noise (since this shouldn't be running through the \"mouth\")\n tmp_ac = self.perceive(self.acoustify(tmp_ar))\n for v in ac_vowels:\n dists = N.sqrt(N.sum((v - tmp_ac)**2, axis=1))\n d = 0\n while True:\n if dists[d] < (2 * ac_spread):\n # found an articulatory prototype\n self.vowel_map[tuple(v)] = tmp_ar[d]\n # remove it from the candidate list\n tmp_ar = N.vstack((tmp_ar[:d], tmp_ar[d + 1:]))\n tmp_ac = N.vstack((tmp_ac[:d], tmp_ac[d + 1:]))\n break\n d += 1\n if d == len(dists):\n # take the best of the bad ones\n index = N.argmin(dists)\n self.vowel_map[tuple(v)] = tmp_ar[index]\n break\n self.vowel_spread = ac_spread\n return self.vowel_map","def last_char_to_vowel(word):\n assert isinstance(word, str)\n # We iterate over characters of the word, because the last might be a\n # punctuation, perhaps.\n for last in reversed(word):\n last = last.lower()\n for ch, prev in ((\"a\", \"a/+£\"),\n (\"e\", \"eébcçdgptvwz&*:.\"),\n (\"o\", \"ohk€å\"),\n (\"ä\", \"äflmnrsx§\"),\n (\"ö\", \"ö\"),\n (\"i\", \"ij%$\"),\n (\"u\", \"uq,\"),\n (\"y\", \"yü\")):\n if last in prev:\n return ch\n return \"e\"","def main():\n sample_text = \"\\/\\/ |-| @ -|- ! $ -|- |-| ! $ ?\"\n vowel_frequencies = get_vowel_frequency(sample_text)\n print(vowel_frequencies)","def reverse_vowels(s):\n\n phrase = \"\"\n vowels = []\n for letter in s:\n if letter.lower() in \"aeiou\":\n phrase += \"~\"\n vowels.append(letter)\n else: \n phrase += letter\n \n index = 0\n new_phrase = \"\"\n vowels = vowels[-1:-len(vowels)-1:-1]\n \n for letter in phrase:\n\n if letter == \"~\":\n new_phrase += vowels[index]\n index += 1\n else:\n new_phrase += letter\n\n return new_phrase","def test_convert_single_vowel():\n for vowel in \"aeiou\":\n result = convert(vowel)\n assert result == vowel + \"way\"","def good_word(self, word):\r\n return word.strip().lower()","def map_word(self, word):\n for invariance in self.invariances:\n word = invariance.map_word(word)\n return word","def verb_lemma(word):\n if word.endswith(\"ed\"):\n if word[:-2].endswith(\"v\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"at\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"it\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"et\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"ut\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"ac\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"i\"):\n return word[:-3].lower() + \"y\"\n elif word[:-2].endswith(\"ir\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"ag\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"nc\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"nu\"):\n return word[:-2].lower() + \"e\"\n else:\n return word[:-2].lower() \n elif word.endswith(\"ing\"):\n if word[:-3].endswith(\"v\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"at\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"it\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"et\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"ut\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"ac\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"i\"):\n return word[:-4].lower() + \"y\"\n elif word[:-3].endswith(\"ir\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"ag\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"nc\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"nu\"):\n return word[:-3].lower() + \"e\"\n else:\n return word[:-3].lower()\n elif re.match(r\"(does|did|done)\", word):\n return (\"do\")\n elif re.match(r\"(is|are|am|was|will|were|been)\", word):\n return (\"be\")\n elif word == (\"'s\"):\n return (\"be\")\n elif re.match(r\"(had|has|'ve)\", word):\n return (\"have\")\n else:\n return word.lower()","def make_anki_cloze(sentence: str, word: str) -> str:\n\n return re.subn(\n r\"(^\\W*| \\W*)(%s)(\\W* |\\W*$)\" % word,\n r\"\\1{{c1::\\2}}\\3\",\n sentence,\n flags=re.IGNORECASE,\n )[0]","def find_abecedarian_words():\n pass","def calc_word_value(word):\n return sum([LETTER_SCORES.get(letter.upper(), 0) for letter in word])","def words_with_3_or_more_vowel(words):\n return [vowel for vowel in words if re.match(r'(\\w*[aeiou]\\w*){3,}', vowel)]","def profanity_word_handler(word):\n return word[0] + ''.join([settings.CENSOR_PROFANITY_REPLACEMENT_CHARACTER for I in range(len(word)-2)]) + word [-1]","def test_find_word(self):\n self.assertEqual(find_word('GREEN'), [(1, 1), (1, 1), (0, 9)])\n self.assertEqual(find_word('ABSENT'), [])\n self.assertEqual(find_word('PW'), [(1, 7), (3, 7), (0, 8)])","def analyze_word(s):\n\n a = {}\n a['word'] = s\n a['n_letters'] = len(s)\n a['n_vowels'] = count_vowels(s)\n \n return a","def translate_leet(phrase):","def correctWord (w):\n\n if len(re.findall(ur\"[а-я]\",w))>len(re.findall(ur\"[a-z]\",w)):\n return w.translate(eng_rusTranslateTable)\n else:\n return w.translate(rus_engTranslateTable)","def _process(self, word: str) -> List[str]:\n # if a blank arrives from splitting, just return an empty list\n if len(word.strip()) == 0:\n return []\n word = self.convert_consonantal_i(word)\n my_word = \" \" + word + \" \"\n letters = list(my_word)\n positions = []\n for dipth in self.diphthongs:\n if dipth in my_word:\n dipth_matcher = re.compile(\"{}\".format(dipth))\n matches = dipth_matcher.finditer(my_word)\n for match in matches:\n (start, end) = match.span()\n positions.append(start)\n matches = self.kw_matcher.finditer(my_word)\n for match in matches:\n (start, end) = match.span()\n positions.append(start)\n letters = string_utils.merge_next(letters, positions)\n letters = string_utils.remove_blanks(letters)\n positions.clear()\n if not self._contains_vowels(\"\".join(letters)):\n return [\n \"\".join(letters).strip()\n ] # occurs when only 'qu' appears by ellision\n positions = self._starting_consonants_only(letters)\n while len(positions) > 0:\n letters = string_utils.move_consonant_right(letters, positions)\n letters = string_utils.remove_blanks(letters)\n positions = self._starting_consonants_only(letters)\n positions = self._ending_consonants_only(letters)\n while len(positions) > 0:\n letters = string_utils.move_consonant_left(letters, positions)\n letters = string_utils.remove_blanks(letters)\n positions = self._ending_consonants_only(letters)\n positions = self._find_solo_consonant(letters)\n while len(positions) > 0:\n letters = self._move_consonant(letters, positions)\n letters = string_utils.remove_blanks(letters)\n positions = self._find_solo_consonant(letters)\n positions = self._find_consonant_cluster(letters)\n while len(positions) > 0:\n letters = self._move_consonant(letters, positions)\n letters = string_utils.remove_blanks(letters)\n positions = self._find_consonant_cluster(letters)\n return letters","def fun4vowels(value:str)->set:\r\n vowels = set('aeiou')\r\n return vowels.intersection(set(value))"],"string":"[\n \"def _rv_standard(self, word, vowels):\\n rv = \\\"\\\"\\n if len(word) >= 2:\\n if word[1] not in vowels:\\n for i in range(2, len(word)):\\n if word[i] in vowels:\\n rv = word[i + 1 :]\\n break\\n\\n elif word[0] in vowels and word[1] in vowels:\\n for i in range(2, len(word)):\\n if word[i] not in vowels:\\n rv = word[i + 1 :]\\n break\\n else:\\n rv = word[3:]\\n\\n return rv\",\n \"def find_words_using_all_vowels():\\n pass\",\n \"def find_vowels(s):\\n \\\"*** YOUR CODE HERE ***\\\"\",\n \"def censor_vowels(word):\\n\\n chars = []\\n\\n for letter in word:\\n if letter in \\\"aeiou\\\":\\n chars.append(\\\"*\\\")\\n chars.append(letter)\\n\\n return \\\"\\\".join(vowels)\",\n \"def censor_vowels(word):\\n\\n chars = []\\n\\n for letter in word:\\n if letter in \\\"aeiou\\\":\\n chars.append(\\\"*\\\")\\n else:\\n chars.append(letter)\\n \\n return \\\"\\\".join(chars)\",\n \"def replace_vowels(word):\\n variants = []\\n for c in word:\\n if c in vowels:\\n for vowel in vowels:\\n variants.append(word.replace(c, vowel))\\n return variants\",\n \"def fry(word):\\n\\n # looks for a Y or y which will be (captured) followed and ended by an 'ou'\\n match_you = re.match('([Yy])ou$', word)\\n\\n # First group will be the (captured) group so either 'Y' or 'y'\\n if match_you:\\n return match_you.group(1) + \\\"'all\\\"\\n\\n # looks for anyword ending in 'ing'\\n match_ing = re.search('(.+)ing$', word)\\n\\n # checks if vowel exists before the 'ing'\\n if match_ing:\\n vowel_check = re.search('[aeiouy]', match_ing.group(1))\\n # First group will be the (captured) group so everything before the 'ing'\\n if vowel_check:\\n return match_ing.group(1) + \\\"in'\\\"\\n\\n return word\",\n \"def main():\\n word = input(\\\"Give me a word! \\\\n\\\\n\\\")\\n vowels = ['a', 'e', 'i', 'o', 'u']\\n if word[0].lower() in vowels:\\n print(f\\\"\\\\n\\\\nPig latin: {word}way\\\")\\n else:\\n print(f\\\"\\\\n\\\\nPig latin: {word[1:]}{word[0]}ay\\\")\",\n \"def stem(self, word):\\n word = word.lower()\\n\\n if word in self.__special_words:\\n return self.__special_words[word]\\n\\n # Map the different apostrophe characters to a single consistent one\\n word = (word.replace(u(\\\"\\\\u2019\\\"), u(\\\"\\\\x27\\\"))\\n .replace(u(\\\"\\\\u2018\\\"), u(\\\"\\\\x27\\\"))\\n .replace(u(\\\"\\\\u201B\\\"), u(\\\"\\\\x27\\\")))\\n\\n if word.startswith(u(\\\"\\\\x27\\\")):\\n word = word[1:]\\n\\n if word.startswith(\\\"y\\\"):\\n word = \\\"\\\".join((\\\"Y\\\", word[1:]))\\n\\n for i in range(1, len(word)):\\n if word[i - 1] in self.__vowels and word[i] == \\\"y\\\":\\n word = \\\"\\\".join((word[:i], \\\"Y\\\", word[i + 1:]))\\n\\n step1a_vowel_found = False\\n step1b_vowel_found = False\\n\\n r1 = \\\"\\\"\\n r2 = \\\"\\\"\\n\\n if word.startswith((\\\"gener\\\", \\\"commun\\\", \\\"arsen\\\")):\\n if word.startswith((\\\"gener\\\", \\\"arsen\\\")):\\n r1 = word[5:]\\n else:\\n r1 = word[6:]\\n\\n for i in range(1, len(r1)):\\n if r1[i] not in self.__vowels and r1[i - 1] in self.__vowels:\\n r2 = r1[i + 1:]\\n break\\n else:\\n r1, r2 = self._r1r2_standard(word, self.__vowels)\\n\\n # STEP 0\\n for suffix in self.__step0_suffixes:\\n if word.endswith(suffix):\\n word = word[:-len(suffix)]\\n r1 = r1[:-len(suffix)]\\n r2 = r2[:-len(suffix)]\\n break\\n\\n # STEP 1a\\n for suffix in self.__step1a_suffixes:\\n if word.endswith(suffix):\\n\\n if suffix == \\\"sses\\\":\\n word = word[:-2]\\n r1 = r1[:-2]\\n r2 = r2[:-2]\\n\\n elif suffix in (\\\"ied\\\", \\\"ies\\\"):\\n if len(word[:-len(suffix)]) > 1:\\n word = word[:-2]\\n r1 = r1[:-2]\\n r2 = r2[:-2]\\n else:\\n word = word[:-1]\\n r1 = r1[:-1]\\n r2 = r2[:-1]\\n\\n elif suffix == \\\"s\\\":\\n for letter in word[:-2]:\\n if letter in self.__vowels:\\n step1a_vowel_found = True\\n break\\n\\n if step1a_vowel_found:\\n word = word[:-1]\\n r1 = r1[:-1]\\n r2 = r2[:-1]\\n break\\n\\n # STEP 1b\\n for suffix in self.__step1b_suffixes:\\n if word.endswith(suffix):\\n if suffix in (\\\"eed\\\", \\\"eedly\\\"):\\n\\n if r1.endswith(suffix):\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ee\\\"))\\n\\n if len(r1) >= len(suffix):\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ee\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= len(suffix):\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ee\\\"))\\n else:\\n r2 = \\\"\\\"\\n else:\\n for letter in word[:-len(suffix)]:\\n if letter in self.__vowels:\\n step1b_vowel_found = True\\n break\\n\\n if step1b_vowel_found:\\n word = word[:-len(suffix)]\\n r1 = r1[:-len(suffix)]\\n r2 = r2[:-len(suffix)]\\n\\n if word.endswith((\\\"at\\\", \\\"bl\\\", \\\"iz\\\")):\\n word = \\\"\\\".join((word, \\\"e\\\"))\\n r1 = \\\"\\\".join((r1, \\\"e\\\"))\\n\\n if len(word) > 5 or len(r1) >= 3:\\n r2 = \\\"\\\".join((r2, \\\"e\\\"))\\n\\n elif word.endswith(self.__double_consonants):\\n word = word[:-1]\\n r1 = r1[:-1]\\n r2 = r2[:-1]\\n\\n elif ((r1 == \\\"\\\" and len(word) >= 3 and\\n word[-1] not in self.__vowels and\\n word[-1] not in \\\"wxY\\\" and\\n word[-2] in self.__vowels and\\n word[-3] not in self.__vowels)\\n or\\n (r1 == \\\"\\\" and len(word) == 2 and\\n word[0] in self.__vowels and\\n word[1] not in self.__vowels)):\\n\\n word = \\\"\\\".join((word, \\\"e\\\"))\\n\\n if len(r1) > 0:\\n r1 = \\\"\\\".join((r1, \\\"e\\\"))\\n\\n if len(r2) > 0:\\n r2 = \\\"\\\".join((r2, \\\"e\\\"))\\n break\\n\\n # STEP 1c\\n if (len(word) > 2\\n and word[-1] in \\\"yY\\\"\\n and word[-2] not in self.__vowels):\\n word = \\\"\\\".join((word[:-1], \\\"i\\\"))\\n if len(r1) >= 1:\\n r1 = \\\"\\\".join((r1[:-1], \\\"i\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= 1:\\n r2 = \\\"\\\".join((r2[:-1], \\\"i\\\"))\\n else:\\n r2 = \\\"\\\"\\n\\n # STEP 2\\n for suffix in self.__step2_suffixes:\\n if word.endswith(suffix):\\n if r1.endswith(suffix):\\n if suffix == \\\"tional\\\":\\n word = word[:-2]\\n r1 = r1[:-2]\\n r2 = r2[:-2]\\n\\n elif suffix in (\\\"enci\\\", \\\"anci\\\", \\\"abli\\\"):\\n word = \\\"\\\".join((word[:-1], \\\"e\\\"))\\n\\n if len(r1) >= 1:\\n r1 = \\\"\\\".join((r1[:-1], \\\"e\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= 1:\\n r2 = \\\"\\\".join((r2[:-1], \\\"e\\\"))\\n else:\\n r2 = \\\"\\\"\\n\\n elif suffix == \\\"entli\\\":\\n word = word[:-2]\\n r1 = r1[:-2]\\n r2 = r2[:-2]\\n\\n elif suffix in (\\\"izer\\\", \\\"ization\\\"):\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ize\\\"))\\n\\n if len(r1) >= len(suffix):\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ize\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= len(suffix):\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ize\\\"))\\n else:\\n r2 = \\\"\\\"\\n\\n elif suffix in (\\\"ational\\\", \\\"ation\\\", \\\"ator\\\"):\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ate\\\"))\\n\\n if len(r1) >= len(suffix):\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ate\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= len(suffix):\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ate\\\"))\\n else:\\n r2 = \\\"e\\\"\\n\\n elif suffix in (\\\"alism\\\", \\\"aliti\\\", \\\"alli\\\"):\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"al\\\"))\\n\\n if len(r1) >= len(suffix):\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"al\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= len(suffix):\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"al\\\"))\\n else:\\n r2 = \\\"\\\"\\n\\n elif suffix == \\\"fulness\\\":\\n word = word[:-4]\\n r1 = r1[:-4]\\n r2 = r2[:-4]\\n\\n elif suffix in (\\\"ousli\\\", \\\"ousness\\\"):\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ous\\\"))\\n\\n if len(r1) >= len(suffix):\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ous\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= len(suffix):\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ous\\\"))\\n else:\\n r2 = \\\"\\\"\\n\\n elif suffix in (\\\"iveness\\\", \\\"iviti\\\"):\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ive\\\"))\\n\\n if len(r1) >= len(suffix):\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ive\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= len(suffix):\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ive\\\"))\\n else:\\n r2 = \\\"e\\\"\\n\\n elif suffix in (\\\"biliti\\\", \\\"bli\\\"):\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ble\\\"))\\n\\n if len(r1) >= len(suffix):\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ble\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= len(suffix):\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ble\\\"))\\n else:\\n r2 = \\\"\\\"\\n\\n elif suffix == \\\"ogi\\\" and word[-4] == \\\"l\\\":\\n word = word[:-1]\\n r1 = r1[:-1]\\n r2 = r2[:-1]\\n\\n elif suffix in (\\\"fulli\\\", \\\"lessli\\\"):\\n word = word[:-2]\\n r1 = r1[:-2]\\n r2 = r2[:-2]\\n\\n elif suffix == \\\"li\\\" and word[-3] in self.__li_ending:\\n word = word[:-2]\\n r1 = r1[:-2]\\n r2 = r2[:-2]\\n break\\n\\n # STEP 3\\n for suffix in self.__step3_suffixes:\\n if word.endswith(suffix):\\n if r1.endswith(suffix):\\n if suffix == \\\"tional\\\":\\n word = word[:-2]\\n r1 = r1[:-2]\\n r2 = r2[:-2]\\n\\n elif suffix == \\\"ational\\\":\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ate\\\"))\\n\\n if len(r1) >= len(suffix):\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ate\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= len(suffix):\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ate\\\"))\\n else:\\n r2 = \\\"\\\"\\n\\n elif suffix == \\\"alize\\\":\\n word = word[:-3]\\n r1 = r1[:-3]\\n r2 = r2[:-3]\\n\\n elif suffix in (\\\"icate\\\", \\\"iciti\\\", \\\"ical\\\"):\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"ic\\\"))\\n\\n if len(r1) >= len(suffix):\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"ic\\\"))\\n else:\\n r1 = \\\"\\\"\\n\\n if len(r2) >= len(suffix):\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"ic\\\"))\\n else:\\n r2 = \\\"\\\"\\n\\n elif suffix in (\\\"ful\\\", \\\"ness\\\"):\\n word = word[:-len(suffix)]\\n r1 = r1[:-len(suffix)]\\n r2 = r2[:-len(suffix)]\\n\\n elif suffix == \\\"ative\\\" and r2.endswith(suffix):\\n word = word[:-5]\\n r1 = r1[:-5]\\n r2 = r2[:-5]\\n break\\n\\n # STEP 4\\n for suffix in self.__step4_suffixes:\\n if word.endswith(suffix):\\n if r2.endswith(suffix):\\n if suffix == \\\"ion\\\":\\n if word[-4] in \\\"st\\\":\\n word = word[:-3]\\n r1 = r1[:-3]\\n r2 = r2[:-3]\\n else:\\n word = word[:-len(suffix)]\\n r1 = r1[:-len(suffix)]\\n r2 = r2[:-len(suffix)]\\n break\\n\\n # STEP 5\\n if r2.endswith(\\\"l\\\") and word[-2] == \\\"l\\\":\\n word = word[:-1]\\n elif r2.endswith(\\\"e\\\"):\\n word = word[:-1]\\n elif r1.endswith(\\\"e\\\"):\\n if len(word) >= 4 and (word[-2] in self.__vowels or\\n word[-2] in \\\"wxY\\\" or\\n word[-3] not in self.__vowels or\\n word[-4] in self.__vowels):\\n word = word[:-1]\\n\\n word = word.replace(\\\"Y\\\", \\\"y\\\")\\n return word\",\n \"def count_syllables(word):\\n vowels = \\\"aeiouy\\\"\\n count = 0\\n last_was_vowel = False\\n for letter in word:\\n found_vowel = False\\n for v in vowels:\\n if v == letter:\\n if not last_was_vowel: count += 1 # don't count diphthongs\\n found_vowel = last_was_vowel = True\\n break\\n if not found_vowel: # If full cycle and no vowel found, set last_was_vowel to false\\n last_was_vowel = False\\n\\n\\n if len(word) > 2 and word[-2:] == \\\"es\\\" and count > 1: # Remove es - it's \\\"usually\\\" silent (?)\\n count -= 1\\n\\n if len(word) > 4 and word[-1:] == \\\"e\\\": # remove silent e\\n count -= 1\\n\\n if len(word) > 1 and word[-2:] == \\\"ee\\\": # adds 1 for na\\n count += 1\\n\\n if len(word) > 1 and word[-2:] == \\\"na\\\": # adds 1 for na\\n count += 1\\n\\n # Check for special case words\\n special_case = ['eloise','i']\\n if word in special_case:\\n count += 1\\n\\n return count\",\n \"def remove_vowels(phrase):\\n vowels = ['a', 'e', 'i', 'o', 'u', 'A', 'E', 'I', 'O', 'U']\\n cons_word = \\\"\\\".join([char for char in phrase if char not in vowels])\\n return cons_word\",\n \"def count_vowels(word):\\n\\n vowels = re.compile(r'[aeouy]', re.IGNORECASE)\\n return len(vowels.findall(word))\",\n \"def get_possible_vowels(self, word_set):\\r\\n \\r\\n vowels = \\\"\\\"\\r\\n for word in word_set:\\r\\n # Check if existing vowel is in word.\\r\\n if any(vowel in word for vowel in vowels):\\r\\n continue\\r\\n # Find most common letter and assume it's a vowel\\r\\n vowel, probability = '', 0\\r\\n for c in word:\\r\\n _, number = self.letters.get_value(c)\\r\\n if number > probability:\\r\\n vowel = c\\r\\n probability = number\\r\\n vowels += vowel\\r\\n return vowels\",\n \"def find_vowel(text: str) -> str:\\r\\n\\r\\n vowel = text.count('a') + text.count('o') + text.count('u') +\\\\\\r\\n text.count('i') + text.count('e') + text.count(\\\"y\\\") +\\\\\\r\\n text.count('A') + text.count('O') + text.count('U') +\\\\\\r\\n text.count('I') + text.count('E') + text.count('Y')\\r\\n\\r\\n return(vowel)\",\n \"def get_vowel_names():\",\n \"def calculate_construction(self, word):\\r\\n \\r\\n construction = \\\"\\\"\\r\\n for c in word.lower():\\r\\n if c in self.vowels:\\r\\n construction += \\\"v\\\"\\r\\n elif c in letters:\\r\\n construction += \\\"c\\\"\\r\\n return construction\",\n \"def needs_aou(word):\\n return re.search(\\\"([aouAOU])[^yäöYÄÖ]*$\\\", word)\",\n \"def countsyllables_nlde(word):\\r\\n\\tresult = 0\\r\\n\\tprev_was_vowel = word[0] in VOWELS\\r\\n\\tfor char in word[1:]:\\r\\n\\t\\tis_vowel = char in VOWELS\\r\\n\\t\\tif prev_was_vowel and not is_vowel:\\r\\n\\t\\t\\tresult += 1\\r\\n\\t\\tprev_was_vowel = is_vowel\\r\\n\\r\\n\\tif (len(word) > 1 and word[0] in VOWELS\\r\\n\\t\\t\\tand word.endswith('e') and not word[-2] in VOWELS):\\r\\n\\t\\tresult += 1\\r\\n\\treturn result or 1\",\n \"def find_words_no_vowels():\\n f = open('session09/words.txt')\\n num_no_e = 0\\n num_words = 0\\n for line in f:\\n num_words += 1\\n word = line.strip()\\n if avoids(word, 'aeiou'):\\n # print(word)\\n num_no_e += 1\\n # print(num_no_e, num_words)\\n return num_no_e/num_words\",\n \"def search4vowels(phrase: str) -> set:\\n vowels = set('aeiou')\\n found = vowels.intersection(set(phrase))\\n for vowel in found:\\n print(vowel)\",\n \"def starts_with_vowel(word):\\n return True if word[0] in 'aeiou' else False\",\n \"def _r1r2_standard(self, word, vowels):\\n r1 = \\\"\\\"\\n r2 = \\\"\\\"\\n for i in range(1, len(word)):\\n if word[i] not in vowels and word[i - 1] in vowels:\\n r1 = word[i + 1 :]\\n break\\n\\n for i in range(1, len(r1)):\\n if r1[i] not in vowels and r1[i - 1] in vowels:\\n r2 = r1[i + 1 :]\\n break\\n\\n return (r1, r2)\",\n \"def analyse_vowels(self, source):\\r\\n\\r\\n word_set = set()\\r\\n with open(source) as f:\\r\\n for line in f:\\r\\n words = [word.lower().strip() for word in line.split()]\\r\\n for word in words:\\r\\n map(self.parse_character, word)\\r\\n stripped = ''.join(c for c in word if c in letters)\\r\\n if stripped:\\r\\n word_set.add(stripped)\\r\\n vowels = self.get_possible_vowels(word_set)\\r\\n return self.filter_vowels(vowels, word_set)\",\n \"def translate(self):\\n\\t\\tvowels = \\\"aeiou\\\"\\n\\n\\t\\tif (self.word[0] not in vowels) and (self.word[1] in vowels):\\n\\t\\t\\tnew_word = self.word[1:] + self.word[0] + \\\"ay\\\"\\n\\t\\telif self.word[0] in vowels:\\n\\t\\t\\tnew_word = self.word + \\\"way\\\"\\n\\t\\telse:\\n\\t\\t\\tnew_word = self.word[2:] + self.word[:2] + \\\"ay\\\"\\n\\n\\t\\tprint(new_word)\",\n \"def estimate(word):\\n parts = re.split(r'[^aeiouy]+', word)\\n valid_parts = []\\n\\n for part in parts:\\n if part != '':\\n valid_parts.append(part)\\n\\n syllables = 0\\n\\n for p in re_subsyllables:\\n if p.match(word):\\n syllables -= 1\\n\\n for p in re_addsyllables:\\n if p.match(word):\\n syllables += 1\\n\\n syllables += len(valid_parts)\\n\\n if syllables <= 0:\\n syllables = 1\\n\\n return syllables\",\n \"def step1c(self, word):\\r\\n\\r\\n if word.endswith('y'):\\r\\n result = word.rfind('y')\\r\\n base = word[:result]\\r\\n if self.containsVowel(base):\\r\\n word = base\\r\\n word += 'i'\\r\\n return word\",\n \"def disemvowel(string):\\n to_return = ''\\n for char in string:\\n if char not in 'aeiouAEIOU':\\n to_return += char\\n return to_return\",\n \"def search_for_vowels(phrase:str) -> set:\\n vowels = set('aeiou')\\n return vowels.intersection(set(phrase))\",\n \"def equal_vowel_and_consonant(words):\\n # print(words)\\n vowel_consonant_equal = []\\n for word in words:\\n vowel_counts, consonant_counts = 0, 0\\n for char in word:\\n if char in 'aeiou':\\n vowel_counts += 1\\n elif char in 'bcdfghjklmnpqrstvwxyz':\\n consonant_counts += 1\\n\\n if vowel_counts == consonant_counts:\\n vowel_consonant_equal.append(word)\\n return vowel_consonant_equal\",\n \"def search4vowels(phrase:str) -> set:\\n vowels = set('aeiou')\\n #word = input('Enter a word to search for vowels:')\\n found = vowels.intersection(set(phrase))\\n #for vowel in found:\\n #print(vowel)\\n\\n return (found)\",\n \"def word_syllables(word):\\n\\n count = 0\\n endings = '!@#$%^&*()_+[]{}:;,.eE\\\"'+\\\"'\\\"\\n\\n while word[-1] in endings:\\n word = word[: -1]\\n\\n if len(word) <= 3:\\n return 1\\n\\n vows = 'aeiouAEIOU'\\n prev_char_vow = False\\n for char in word:\\n if char in vows:\\n if not prev_char_vow:\\n count = count + 1\\n prev_char_vow = True\\n else:\\n prev_char_vow = False\\n\\n if word[-1] in 'Yy':\\n count = count + 1\\n\\n return count\",\n \"def num_syllables(self, word):\\n \\\"\\\"\\\"\\n using the logic of vowel counting, count all vowels in the pronunciations\\n \\\"\\\"\\\"\\n dictionary = self._pronunciations;\\n # check if word is present in the CMU dictionary\\n if word in dictionary :\\n word_pronunciations = dictionary[word.lower()]\\n else :\\n return 1\\n \\n vowels = ['A', 'E', 'I', 'O', 'U']\\n \\n ## find the shorter pronunciation for word\\n shorter_arr = [];\\n for pronunciation in word_pronunciations :\\n if len(pronunciation) > len(shorter_arr) : shorter_arr = pronunciation\\n \\n num_length = 0\\n \\n for phoneme in shorter_arr :\\n if phoneme[:1] in vowels : num_length += 1\\n \\n return num_length\",\n \"def most_repeating_vowels(words):\\n return ' '.join([x for x in WORDS if len(x) == len(sv_count(words)[-1])])\",\n \"def has_more_vowels(word):\\n\\n# If the phrase is over half vowels, it should return True:\\n\\n # intialize a vowel count variable \\n # Loop through the letters of the word:\\n # if the letter is in the set of vowels, increment the vowel count\\n # if vowel count is greater than length of the word divided by 2, return True\\n # else return false\\n\\n\\n vowel_count = 0\\n\\n for letter in word:\\n if letter.lower() in {\\\"a\\\", \\\"e\\\", \\\"i\\\", \\\"o\\\", \\\"u\\\"}:\\n vowel_count += 1\\n\\n if vowel_count > (len(word) / 2):\\n return True\\n\\n return False\",\n \"def get_nyx(words, vowels):\\n nyx_list = \\\"\\\"\\n for word in words:\\n word = word.lower()\\n if not bool(vowels.intersection(word)) and (\\\"x\\\" in word or \\\"y\\\" in word) and \\\"'\\\" not in word:\\n nyx_list += word.strip() + \\\", \\\"\\n return nyx_list\",\n \"def stem(self, word):\\n word = word.lower()\\n\\n step1_success = False\\n\\n # All acute accents are replaced by grave accents.\\n word = (word.replace(u(\\\"\\\\xE1\\\"), u(\\\"\\\\xE0\\\"))\\n .replace(u(\\\"\\\\xE9\\\"), u(\\\"\\\\xE8\\\"))\\n .replace(u(\\\"\\\\xED\\\"), u(\\\"\\\\xEC\\\"))\\n .replace(u(\\\"\\\\xF3\\\"), u(\\\"\\\\xF2\\\"))\\n .replace(u(\\\"\\\\xFA\\\"), u(\\\"\\\\xF9\\\")))\\n\\n # Every occurrence of 'u' after 'q'\\n # is put into upper case.\\n for i in range(1, len(word)):\\n if word[i - 1] == \\\"q\\\" and word[i] == \\\"u\\\":\\n word = \\\"\\\".join((word[:i], \\\"U\\\", word[i + 1:]))\\n\\n # Every occurrence of 'u' and 'i'\\n # between vowels is put into upper case.\\n for i in range(1, len(word) - 1):\\n if word[i - 1] in self.__vowels and word[i + 1] in self.__vowels:\\n if word[i] == \\\"u\\\":\\n word = \\\"\\\".join((word[:i], \\\"U\\\", word[i + 1:]))\\n elif word[i] == \\\"i\\\":\\n word = \\\"\\\".join((word[:i], \\\"I\\\", word[i + 1:]))\\n\\n r1, r2 = self._r1r2_standard(word, self.__vowels)\\n rv = self._rv_standard(word, self.__vowels)\\n\\n # STEP 0: Attached pronoun\\n for suffix in self.__step0_suffixes:\\n if rv.endswith(suffix):\\n if rv[-len(suffix) - 4:-len(suffix)] in (\\\"ando\\\", \\\"endo\\\"):\\n word = word[:-len(suffix)]\\n r1 = r1[:-len(suffix)]\\n r2 = r2[:-len(suffix)]\\n rv = rv[:-len(suffix)]\\n\\n elif (rv[-len(suffix) - 2:-len(suffix)] in\\n (\\\"ar\\\", \\\"er\\\", \\\"ir\\\")):\\n word = \\\"\\\".join((word[:-len(suffix)], \\\"e\\\"))\\n r1 = \\\"\\\".join((r1[:-len(suffix)], \\\"e\\\"))\\n r2 = \\\"\\\".join((r2[:-len(suffix)], \\\"e\\\"))\\n rv = \\\"\\\".join((rv[:-len(suffix)], \\\"e\\\"))\\n break\\n\\n # STEP 1: Standard suffix removal\\n for suffix in self.__step1_suffixes:\\n if word.endswith(suffix):\\n if suffix == \\\"amente\\\" and r1.endswith(suffix):\\n step1_success = True\\n word = word[:-6]\\n r2 = r2[:-6]\\n rv = rv[:-6]\\n\\n if r2.endswith(\\\"iv\\\"):\\n word = word[:-2]\\n r2 = r2[:-2]\\n rv = rv[:-2]\\n\\n if r2.endswith(\\\"at\\\"):\\n word = word[:-2]\\n rv = rv[:-2]\\n\\n elif r2.endswith((\\\"os\\\", \\\"ic\\\")):\\n word = word[:-2]\\n rv = rv[:-2]\\n\\n elif r2 .endswith(\\\"abil\\\"):\\n word = word[:-4]\\n rv = rv[:-4]\\n\\n elif (suffix in (\\\"amento\\\", \\\"amenti\\\",\\n \\\"imento\\\", \\\"imenti\\\") and\\n rv.endswith(suffix)):\\n step1_success = True\\n word = word[:-6]\\n rv = rv[:-6]\\n\\n elif r2.endswith(suffix):\\n step1_success = True\\n if suffix in (\\\"azione\\\", \\\"azioni\\\", \\\"atore\\\", \\\"atori\\\"):\\n word = word[:-len(suffix)]\\n r2 = r2[:-len(suffix)]\\n rv = rv[:-len(suffix)]\\n\\n if r2.endswith(\\\"ic\\\"):\\n word = word[:-2]\\n rv = rv[:-2]\\n\\n elif suffix in (\\\"logia\\\", \\\"logie\\\"):\\n word = word[:-2]\\n rv = word[:-2]\\n\\n elif suffix in (\\\"uzione\\\", \\\"uzioni\\\",\\n \\\"usione\\\", \\\"usioni\\\"):\\n word = word[:-5]\\n rv = rv[:-5]\\n\\n elif suffix in (\\\"enza\\\", \\\"enze\\\"):\\n word = \\\"\\\".join((word[:-2], \\\"te\\\"))\\n rv = \\\"\\\".join((rv[:-2], \\\"te\\\"))\\n\\n elif suffix == u(\\\"it\\\\xE0\\\"):\\n word = word[:-3]\\n r2 = r2[:-3]\\n rv = rv[:-3]\\n\\n if r2.endswith((\\\"ic\\\", \\\"iv\\\")):\\n word = word[:-2]\\n rv = rv[:-2]\\n\\n elif r2.endswith(\\\"abil\\\"):\\n word = word[:-4]\\n rv = rv[:-4]\\n\\n elif suffix in (\\\"ivo\\\", \\\"ivi\\\", \\\"iva\\\", \\\"ive\\\"):\\n word = word[:-3]\\n r2 = r2[:-3]\\n rv = rv[:-3]\\n\\n if r2.endswith(\\\"at\\\"):\\n word = word[:-2]\\n r2 = r2[:-2]\\n rv = rv[:-2]\\n\\n if r2.endswith(\\\"ic\\\"):\\n word = word[:-2]\\n rv = rv[:-2]\\n else:\\n word = word[:-len(suffix)]\\n rv = rv[:-len(suffix)]\\n break\\n\\n # STEP 2: Verb suffixes\\n if not step1_success:\\n for suffix in self.__step2_suffixes:\\n if rv.endswith(suffix):\\n word = word[:-len(suffix)]\\n rv = rv[:-len(suffix)]\\n break\\n\\n # STEP 3a\\n if rv.endswith((\\\"a\\\", \\\"e\\\", \\\"i\\\", \\\"o\\\", u(\\\"\\\\xE0\\\"), u(\\\"\\\\xE8\\\"),\\n u(\\\"\\\\xEC\\\"), u(\\\"\\\\xF2\\\"))):\\n word = word[:-1]\\n rv = rv[:-1]\\n\\n if rv.endswith(\\\"i\\\"):\\n word = word[:-1]\\n rv = rv[:-1]\\n\\n # STEP 3b\\n if rv.endswith((\\\"ch\\\", \\\"gh\\\")):\\n word = word[:-1]\\n\\n word = word.replace(\\\"I\\\", \\\"i\\\").replace(\\\"U\\\", \\\"u\\\")\\n return word\",\n \"def syllable_count(word):\\n # Count the vowels in the word\\n # Subtract one vowel from every dipthong\\n count = len(re.findall(r'([aeiouyAEIOUY]+)', word))\\n # Subtract any silent vowels\\n if len(word) > 2:\\n if word[-1] == 'e' and \\\\\\n not is_vowel(word[-2]) and \\\\\\n is_vowel(word[-3]):\\n count = count - 1\\n return count\",\n \"def remove_vowels(string: str) -> str:\\n return \\\" \\\".join([word for word in string.split() if word[0].lower() not in ['a', 'e', 'i', 'o', 'u']])\",\n \"def pig_latinify(word):\\n\\n first_letter = word[0]\\n\\n if first_letter in VOWELS:\\n output_word = word + \\\"yay\\\"\\n else:\\n #scan for vowel if word starts with a consonant\\n for i in range(len(word)):\\n individual_letter = word[i]\\n if individual_letter in VOWELS:\\n output_word = word[i:] + word[:i] + \\\"ay\\\"\\n break\\n else:\\n continue\\n\\n return output_word\",\n \"def makePigLatin(word): \\n m = len(word)\\n vowels = \\\"a\\\", \\\"e\\\", \\\"i\\\", \\\"o\\\", \\\"u\\\", \\\"y\\\" \\n # short words are not converted \\n if m<3 or word==\\\"the\\\":\\n return word\\n else:\\n for i in vowels:\\n if word.find(i) < m and word.find(i) != -1:\\n m = word.find(i)\\n if m==0:\\n return word+\\\"way\\\" \\n else:\\n return word[m:]+word[:m]+\\\"ay\\\"\",\n \"def inner(word):\\n return word + '!!!'\",\n \"def pig_word(self, original):\\n word = original.lower()\\n if word[0] in \\\"aeiou\\\":\\n new_word = word + 'ay'\\n else:\\n new_word = word[1:] + word[0] + 'ay'\\n return new_word\",\n \"def _get_vowels(sequence: str) -> list:\\n vowels = []\\n for char in sequence:\\n if char in VOWELS:\\n vowels.append(char)\\n return vowels\",\n \"def disemvowel(val):\\n c_list = list(val)\\n for char in list(val):\\n if char.lower() in [\\\"a\\\", \\\"e\\\", \\\"i\\\", \\\"o\\\", \\\"u\\\"]:\\n c_list.remove(char)\\n return \\\"\\\".join(c_list)\",\n \"def search(self, word):\",\n \"def stem(self, word):\\n word = word.lower()\\n\\n if word in self.stopwords:\\n return word\\n\\n step1_success = False\\n\\n r1, r2 = self._r1r2_standard(word, self.__vowels)\\n rv = self._rv_standard(word, self.__vowels)\\n\\n # STEP 0: Attached pronoun\\n for suffix in self.__step0_suffixes:\\n if not (word.endswith(suffix) and rv.endswith(suffix)):\\n continue\\n\\n if (\\n rv[: -len(suffix)].endswith(\\n (\\n \\\"ando\\\",\\n \\\"ar\\\",\\n \\\"er\\\",\\n \\\"iendo\\\",\\n \\\"ir\\\",\\n )\\n )\\n ) or (\\n rv[: -len(suffix)].endswith(\\\"yendo\\\")\\n and word[: -len(suffix)].endswith(\\\"uyendo\\\")\\n ):\\n\\n word = self.__replace_accented(word[: -len(suffix)])\\n r1 = self.__replace_accented(r1[: -len(suffix)])\\n r2 = self.__replace_accented(r2[: -len(suffix)])\\n rv = self.__replace_accented(rv[: -len(suffix)])\\n break\\n\\n # STEP 1: Standard suffix removal\\n for suffix in self.__step1_suffixes:\\n if not word.endswith(suffix):\\n continue\\n\\n if suffix == \\\"amente\\\" and r1.endswith(suffix):\\n step1_success = True\\n word = word[:-6]\\n r2 = r2[:-6]\\n rv = rv[:-6]\\n\\n if r2.endswith(\\\"iv\\\"):\\n word = word[:-2]\\n r2 = r2[:-2]\\n rv = rv[:-2]\\n\\n if r2.endswith(\\\"at\\\"):\\n word = word[:-2]\\n rv = rv[:-2]\\n\\n elif r2.endswith((\\\"os\\\", \\\"ic\\\", \\\"ad\\\")):\\n word = word[:-2]\\n rv = rv[:-2]\\n\\n elif r2.endswith(suffix):\\n step1_success = True\\n if suffix in (\\n \\\"adora\\\",\\n \\\"ador\\\",\\n \\\"acion\\\",\\n \\\"adoras\\\",\\n \\\"adores\\\",\\n \\\"aciones\\\",\\n \\\"ante\\\",\\n \\\"antes\\\",\\n \\\"ancia\\\",\\n \\\"ancias\\\",\\n ):\\n word = word[: -len(suffix)]\\n r2 = r2[: -len(suffix)]\\n rv = rv[: -len(suffix)]\\n\\n if r2.endswith(\\\"ic\\\"):\\n word = word[:-2]\\n rv = rv[:-2]\\n\\n elif suffix in (\\\"logia\\\", \\\"logias\\\"):\\n word = suffix_replace(word, suffix, \\\"log\\\")\\n rv = suffix_replace(rv, suffix, \\\"log\\\")\\n\\n elif suffix in (\\\"ucion\\\", \\\"uciones\\\"):\\n word = suffix_replace(word, suffix, \\\"u\\\")\\n rv = suffix_replace(rv, suffix, \\\"u\\\")\\n\\n elif suffix in (\\\"encia\\\", \\\"encias\\\"):\\n word = suffix_replace(word, suffix, \\\"ente\\\")\\n rv = suffix_replace(rv, suffix, \\\"ente\\\")\\n\\n elif suffix == \\\"mente\\\":\\n word = word[: -len(suffix)]\\n r2 = r2[: -len(suffix)]\\n rv = rv[: -len(suffix)]\\n\\n if r2.endswith((\\\"ante\\\", \\\"able\\\", \\\"ible\\\")):\\n word = word[:-4]\\n rv = rv[:-4]\\n\\n elif suffix in (\\\"idad\\\", \\\"idades\\\"):\\n word = word[: -len(suffix)]\\n r2 = r2[: -len(suffix)]\\n rv = rv[: -len(suffix)]\\n\\n for pre_suff in (\\\"abil\\\", \\\"ic\\\", \\\"iv\\\"):\\n if r2.endswith(pre_suff):\\n word = word[: -len(pre_suff)]\\n rv = rv[: -len(pre_suff)]\\n\\n elif suffix in (\\\"ivo\\\", \\\"iva\\\", \\\"ivos\\\", \\\"ivas\\\"):\\n word = word[: -len(suffix)]\\n r2 = r2[: -len(suffix)]\\n rv = rv[: -len(suffix)]\\n if r2.endswith(\\\"at\\\"):\\n word = word[:-2]\\n rv = rv[:-2]\\n else:\\n word = word[: -len(suffix)]\\n rv = rv[: -len(suffix)]\\n break\\n\\n # STEP 2a: Verb suffixes beginning 'y'\\n if not step1_success:\\n for suffix in self.__step2a_suffixes:\\n if rv.endswith(suffix) and word[-len(suffix) - 1 : -len(suffix)] == \\\"u\\\":\\n word = word[: -len(suffix)]\\n rv = rv[: -len(suffix)]\\n break\\n\\n # STEP 2b: Other verb suffixes\\n for suffix in self.__step2b_suffixes:\\n if rv.endswith(suffix):\\n word = word[: -len(suffix)]\\n rv = rv[: -len(suffix)]\\n if suffix in (\\\"en\\\", \\\"es\\\", \\\"eis\\\", \\\"emos\\\"):\\n if word.endswith(\\\"gu\\\"):\\n word = word[:-1]\\n\\n if rv.endswith(\\\"gu\\\"):\\n rv = rv[:-1]\\n break\\n\\n # STEP 3: Residual suffix\\n for suffix in self.__step3_suffixes:\\n if rv.endswith(suffix):\\n word = word[: -len(suffix)]\\n if suffix in (\\\"e\\\", \\\"\\\\xE9\\\"):\\n rv = rv[: -len(suffix)]\\n\\n if word[-2:] == \\\"gu\\\" and rv.endswith(\\\"u\\\"):\\n word = word[:-1]\\n break\\n\\n word = self.__replace_accented(word)\\n\\n return word\",\n \"def vowel_with_for(character):\\r\\n\\tif character in vowels:\\r\\n\\t\\tprint(\\\"Entered character is vowel..!\\\")\\r\\n\\telse:\\r\\n\\t\\tprint(\\\"Not a Vowel\\\")\",\n \"def replaceOOV(word):\\n if word in self.vocab: return word\\n else: return self.oov\",\n \"def generate_vowel():\\n return random.sample(['a', 'e', 'i', 'o', 'u', 'y'], 1)\",\n \"def vowels(self):\\n vas = []\\n file = self.read()\\n words = re.sub(\\\"[aeiouAEIOU]\\\",\\\" \\\", file).split(\\\" \\\")\\n for h_u in words:\\n if h_u != \\\"\\\":\\n vas.append(h_u)\\n self.print(vas)\\n self.write(vas)\\n logging.debug(\\\"Starting with to\\\")\\n return vas\",\n \"def number_of_vowels(find_vowels):\\n v = [\\\"a\\\", \\\"e\\\", \\\"i\\\", \\\"o\\\", \\\"u\\\", \\\"y\\\"]\\n new_vowels = find_vowels.lower()\\n vowels = \\\"\\\"\\n for x in new_vowels:\\n if x in v:\\n vowels += x\\n return len(vowels)\",\n \"def avg_vowels(self, text):\\n val = 0\\n if text:\\n text = text.replace(\\\"\\\\n\\\", \\\"\\\")\\n text = text.replace(\\\",\\\", \\\"\\\")\\n text = text.replace(\\\"'\\\", \\\"\\\")\\n it = (map(text.lower().count, \\\"aeiouyæøå\\\"))\\n word_count = len(text.split(\\\" \\\"))\\n it_sum = 0\\n for x in it:\\n it_sum += +x\\n if word_count == 0:\\n return 0\\n val = round(it_sum/word_count, 2)\\n print(\\\"avg vowels returned\\\", val)\\n return val\",\n \"def print_upper_words_e(words):\\n\\n for word in words:\\n if(word[0] == 'e' or word[0] == 'E'):\\n print(word.upper())\",\n \"def print_upper_words2(words):\\n for word in words:\\n if word.startswith('e') or word.startswith('E'):\\n print(word.upper())\",\n \"def form_ing(word):\\n\\n # last char of the word\\n last = word[-1]\\n\\n if last == 'e':\\n return word[:-1] + 'ing'\\n elif last == 'r':\\n if word[-2] == 'a': \\n return word + \\\"ring\\\"\\n elif last in ['b', 'd', 'g', 'm', 'n', 'p', 't']:\\n if _is_vowel(word[-2]) and not (_is_vowel(word[-3])):\\n return word + word[-1] + \\\"ing\\\"\\n\\n return word + \\\"ing\\\"\",\n \"def vowelcount(s):\\n s = s.lower()\\n nv = 0\\n for v in 'aeiou':\\n nv += s.count(v)\\n return nv\",\n \"def pig_latinify(word):\\n result = \\\"\\\"\\n if len(word) > 0 and word.isalpha():\\n first = word[0]\\n if is_vowel(first): # starts with a vowel\\n result = str(word) + \\\"yay\\\"\\n else: # starts with non-vowel\\n cut = position_of_vowel(word) # where to cut the word\\n if cut > 0: # \\\"street\\\"-->\\\"eet+str+ay\\\"\\n result = word[cut:] + word[:cut] + \\\"ay\\\"\\n else: # no vowel found\\n result = word + \\\"ay\\\"\\n else:\\n result = 'Only letters allowed!'\\n\\n return result\",\n \"def filter_vowels(self, vowels, word_set, iterations=10):\\r\\n \\r\\n true_vowels = vowels\\r\\n for i in range(iterations):\\r\\n vowels = true_vowels\\r\\n # Go backwards as the last ones are least likely.\\r\\n for vowel in vowels[::-1]:\\r\\n uses = 0\\r\\n for word in word_set:\\r\\n if vowel not in word or len(word) < 4:\\r\\n continue\\r\\n word_ = word.replace(vowel, '')\\r\\n # Check if no other vowels are in this word.\\r\\n if not any(v in word_ for v in true_vowels):\\r\\n uses += 1\\r\\n if uses > i:\\r\\n break\\r\\n else:\\r\\n true_vowels = true_vowels.replace(vowel, '')\\r\\n return vowels\",\n \"def process_word(self, word: str) -> list[str]:\\n d = self.d\\n if not d:\\n return None\\n if d.check(word):\\n return None\\n # Speed doesn't matter here. The more we find, the more convenient.\\n # Remove all digits.\\n word = ''.join([i for i in word if not i.isdigit()])\\n if d.check(word) or d.check(word.lower()):\\n return None\\n if word.find('_') > -1:\\n # Snake case.\\n words = word.split('_')\\n for word2 in words:\\n if not d.check(word2) and not d.check(word2.lower()):\\n return d.suggest(word)\\n return None\\n words = g.unCamel(word)\\n if words:\\n for word2 in words:\\n if not d.check(word2) and not d.check(word2.lower()):\\n return d.suggest(word)\\n return None\\n return d.suggest(word)\",\n \"def process_word(self, word: str) -> list[str]:\\n d = self.d\\n if not d:\\n return None\\n if d.check(word):\\n return None\\n # Speed doesn't matter here. The more we find, the more convenient.\\n # Remove all digits.\\n word = ''.join([i for i in word if not i.isdigit()])\\n if d.check(word) or d.check(word.lower()):\\n return None\\n if word.find('_') > -1:\\n # Snake case.\\n words = word.split('_')\\n for word2 in words:\\n if not d.check(word2) and not d.check(word2.lower()):\\n return d.suggest(word)\\n return None\\n words = g.unCamel(word)\\n if words:\\n for word2 in words:\\n if not d.check(word2) and not d.check(word2.lower()):\\n return d.suggest(word)\\n return None\\n return d.suggest(word)\",\n \"async def owoify(self, ctx: Message, *, owome: str = None):\\n\\t\\tif owome == None:\\n\\t\\t\\treturn await self.send(\\n\\t\\t\\t f\\\"{ctx.author.mention} Missing Required Argument - Usage: d!owoify <message> • Example: d!owoify dont hurt me!\\\"\\n\\t\\t\\t)\\n\\t\\telse:\\n\\t\\t\\towome = owome.replace(\\\"r\\\", \\\"w\\\")\\n\\t\\t\\towome = owome.replace(\\\"l\\\", \\\"w\\\")\\n\\t\\t\\towome = owome.replace(\\\"g\\\", \\\"w\\\")\\n\\t\\t\\towome = owome.replace(\\\"R\\\", \\\"W\\\")\\n\\t\\t\\towome = owome.replace(\\\"L\\\", \\\"W\\\")\\n\\t\\t\\towome = owome.replace(\\\"G\\\", \\\"W\\\")\\n\\t\\t\\towome = owome.replace(\\\"ove\\\", \\\"uv\\\")\\n\\n\\t\\t\\treplacing_words = 'aeiou'\\n\\t\\t\\tupperreplacing_words = 'AEIOU'\\n\\t\\t\\treplacewithuwu = '!?/_.+'\\n\\n\\t\\t\\tuwufaces = [\\n\\t\\t\\t \\\"ᓀ˵▾˵ᓂ\\\", \\\">_<\\\", \\\"＾▽＾\\\", \\\"❛ ᴗ ❛\\\", \\\"UwU\\\", \\\"OwO\\\", \\\"QwQ\\\", \\\"≧▽≦\\\"\\n\\t\\t\\t]\\n\\t\\t\\tfor i in owome:\\n\\t\\t\\t\\tif i in replacewithuwu:\\n\\t\\t\\t\\t\\towomee = owome.replace(i, random.choice(uwufaces))\\n\\n\\t\\t\\tfor i in owome:\\n\\t\\t\\t\\tif i in upperreplacing_words:\\n\\t\\t\\t\\t\\towomee = owome.replace(i, f\\\"Y{i}\\\")\\n\\n\\t\\t\\tfor i in owome:\\n\\t\\t\\t\\tif i in replacing_words:\\n\\t\\t\\t\\t\\towomee = owome.replace(i, f\\\"y{i}\\\")\\n\\n\\t\\t\\tawait self.send(f\\\"{owomee}ㅤㅤ• {ctx.author.mention}\\\")\",\n \"def countsyllables_en(word):\\r\\n\\tif not word:\\r\\n\\t\\treturn 0\\r\\n\\r\\n\\t# Remove final silent 'e'\\r\\n\\tif word[-1] == \\\"e\\\":\\r\\n\\t\\tword = word[:-1]\\r\\n\\r\\n\\t# Check for a cached syllable count\\r\\n\\tif word in fallback_cache:\\r\\n\\t\\treturn fallback_cache[word]\\r\\n\\r\\n\\t# Count vowel groups\\r\\n\\tresult = 0\\r\\n\\tprev_was_vowel = False\\r\\n\\tfor char in word:\\r\\n\\t\\tis_vowel = char in VOWELS or char == 'y'\\r\\n\\t\\tif is_vowel and not prev_was_vowel:\\r\\n\\t\\t\\tresult += 1\\r\\n\\t\\tprev_was_vowel = is_vowel\\r\\n\\r\\n\\t# Add & subtract syllables\\r\\n\\tfor r in fallback_addsyl:\\r\\n\\t\\tif r.search(word):\\r\\n\\t\\t\\tresult += 1\\r\\n\\tfor r in fallback_subsyl:\\r\\n\\t\\tif r.search(word):\\r\\n\\t\\t\\tresult -= 1\\r\\n\\r\\n\\t# Cache the syllable count\\r\\n\\tfallback_cache[word] = result\\r\\n\\r\\n\\treturn result\",\n \"def replace_vowels(chars):\\n\\n replaced = []\\n vowels = {'a', 'e', 'i', 'o', 'u'}\\n\\n for char in chars:\\n if char.lower() in vowels:\\n replaced.append('*')\\n else:\\n replaced.append(char)\\n\\n return replaced\",\n \"def is_english_vowel(c):\\n # y was included in the vowel set guided by the tests.\\n return c in 'aeiouyAEIOUY'\",\n \"def is_vowel(text):\\n return text.lower() in AVRO_VOWELS\",\n \"def check(word):\\n if 'ie' in word:\\n print('{} doesn\\\\'t follow the rule'.format(word))\\n elif 'cie' in word:\\n print('{} doesn\\\\'t follow the rule'.format(word))\\n else:\\n print('{} does follow the rule'.format(word))\",\n \"def test_one_disemvowel_code_wars():\\n from disemvowel_trolls import disemvowel\\n tests = [(\\\"This website is for losers LOL!\\\", \\\"Ths wbst s fr lsrs LL!\\\"),\\n (\\\"No offense but,\\\\nYour writing is among the worst I've everread\\\",\\n \\\"N ffns bt,\\\\nYr wrtng s mng th wrst 'v vrrd\\\"),\\n (\\\"What are you, a communist?\\\", \\\"Wht r y, cmmnst?\\\")]\\n\\n for case in tests:\\n assert disemvowel(case[0]) == case[1]\",\n \"def removeVowels(self, S: str) -> str:\\n \\n vowel_list = ['a', 'e', 'i', 'o', 'u']\\n output = \\\"\\\"\\n \\n for letter in S:\\n \\n if letter not in vowel_list:\\n \\n output += letter\\n \\n else:\\n continue\\n \\n return output\",\n \"def eval_word(word):\\n\\tbasis = Generators.standard_basis((2,1))\\n\\tproduct = Automorphism(basis, basis)\\n\\tassert product.is_identity()\\n\\twhile len(word) >= 2:\\n\\t\\tletter, sub, word = word[0], int(word[1]), word[2:]\\n\\t\\taut = genrs[sub]\\n\\t\\tif letter == 'y':\\n\\t\\t\\taut = ~aut\\n\\t\\tproduct *= aut\\n\\treturn product\",\n \"def count_vowels(string):\\n \\n vowel = 0\\n \\n for i in string:\\n if i.lower() in 'aeiou':\\n vowel = vowel + 1\\n \\n return vowel\",\n \"def upper_vowel(s):\\n for k, v in REPLACED_MAP.iteritems():\\n s = s.replace(k, v)\\n return s\",\n \"def position_of_vowel(s):\\n for i in range(len(s)):\\n if is_vowel(s[i]):\\n return i\\n return -1 # no vowel at all\",\n \"def does_it_have_3_vowels(word):\\n vowel_count = 0\\n vowels = \\\"aeiou\\\"\\n for char in word:\\n if char in vowels:\\n vowel_count += 1\\n\\n if vowel_count > 2:\\n return True\\n else:\\n return False\",\n \"def get_value(word):\\n letters = 'abcdefghijklmnopqrstuvwxyz'\\n sum = 0\\n for letter in word:\\n letter_value = letters.find(letter)\\n if letter_value == -1:\\n letter_value = 0\\n sum += letter_value\\n return sum\",\n \"def count_syllables_in_word(word):\\n\\n count = 0\\n\\n endings = '!,;.?:'\\n last_char = word[-1]\\n\\n if last_char in endings:\\n processed_word = word[0:-1]\\n else:\\n processed_word = word\\n\\n\\n if len(processed_word) <= 3:\\n return 1\\n if processed_word[-1] in 'Ee':\\n processed_word = processed_word[0:-1]\\n\\n vowels = 'aeiouAEIOU'\\n prev_char_was_vowel = False\\n\\n for char in processed_word:\\n if char in vowels:\\n if not prev_char_was_vowel:\\n count += 1\\n prev_char_was_vowel = True\\n\\n else:\\n prev_char_was_vowel = False\\n\\n if processed_word[-1] in 'yY':\\n count += 1\\n \\n\\n return count\",\n \"def a_or_an(s):\\n if s[0].lower() in 'aeiou':\\n return 'an'\\n return 'a'\",\n \"def get_word(w):\\n return ''.join(c for c in w if c.isalpha()).lower()\",\n \"def disambiguate(self, word):\\n matches = re.match(r'^pen([cdjz])(.*)$', word)\\n if matches:\\n return matches.group(1) + matches.group(2)\",\n \"def filter(self, word):\\n \\n word = word.lower()\\n try:\\n self.engine.fetch(word)\\n except socket.error:\\n raise LemmaAPIError\\n part_of_speeches = self.engine.part_of_speeches\\n\\n \\n self.basic_form = word\\n for part in part_of_speeches:\\n if part == 'verb':\\n if self.engine.is_verb_conjugated():\\n if not self.conEngine.is_verb_regular(word, self.engine.get_basic_verb()):\\n self.basic_form = self.engine.get_basic_verb()\\n return word\\n else:\\n self.basic_form = self.engine.get_basic_verb()\\n\\n elif part == 'noun':\\n if self.engine.is_noun_plural():\\n if not self.conEngine.is_noun_regular(word, self.engine.get_singular_noun()):\\n self.basic_form = self.engine.get_singular_noun() \\n return word\\n else:\\n self.basic_form = self.engine.get_singular_noun()\\n\\n return self.basic_form\",\n \"def vowelcount (x):\\n x.lower ()\\n print (x.count('a')+x.count('e')+x.count('i')+x.count('o')+x.count('u'))\",\n \"def num_vowels(s):\\n if s == '':\\n return 0\\n else:\\n num_in_rest = num_vowels(s[1:])\\n if s[0] in 'aeiou':\\n return 1 + num_in_rest\\n else:\\n return 0 + num_in_rest\",\n \"def learn_vowels(self, data=None):\\n #pdb.set_trace()\\n if not data:\\n data = self.memory\\n # find acoustic prototypes by clustering over stored acoustic reps\\n raw_data = data.reshape(4 * len(self.stems), 2)\\n ac_vowels, ac_spread = vq.kmeans(raw_data, 4)\\n # find articulatory reps by comparing synthesized output vowels to\\n # acoustic prototypes\\n # start with candidate list of \\\"all possible\\\" articulations\\n tmp_ar = N.empty((1, 3))\\n rd = 0.0\\n for hi in [0.0, 1.0]:\\n for bk in [0.0, 1.0]:\\n tmp_ar = N.vstack((tmp_ar, N.array([hi, bk, rd])))\\n tmp_ar = tmp_ar[1:]\\n while len(self.vowel_map) < 4:\\n # no noise (since this shouldn't be running through the \\\"mouth\\\")\\n tmp_ac = self.perceive(self.acoustify(tmp_ar))\\n for v in ac_vowels:\\n dists = N.sqrt(N.sum((v - tmp_ac)**2, axis=1))\\n d = 0\\n while True:\\n if dists[d] < (2 * ac_spread):\\n # found an articulatory prototype\\n self.vowel_map[tuple(v)] = tmp_ar[d]\\n # remove it from the candidate list\\n tmp_ar = N.vstack((tmp_ar[:d], tmp_ar[d + 1:]))\\n tmp_ac = N.vstack((tmp_ac[:d], tmp_ac[d + 1:]))\\n break\\n d += 1\\n if d == len(dists):\\n # take the best of the bad ones\\n index = N.argmin(dists)\\n self.vowel_map[tuple(v)] = tmp_ar[index]\\n break\\n self.vowel_spread = ac_spread\\n return self.vowel_map\",\n \"def last_char_to_vowel(word):\\n assert isinstance(word, str)\\n # We iterate over characters of the word, because the last might be a\\n # punctuation, perhaps.\\n for last in reversed(word):\\n last = last.lower()\\n for ch, prev in ((\\\"a\\\", \\\"a/+£\\\"),\\n (\\\"e\\\", \\\"eébcçdgptvwz&*:.\\\"),\\n (\\\"o\\\", \\\"ohk€å\\\"),\\n (\\\"ä\\\", \\\"äflmnrsx§\\\"),\\n (\\\"ö\\\", \\\"ö\\\"),\\n (\\\"i\\\", \\\"ij%$\\\"),\\n (\\\"u\\\", \\\"uq,\\\"),\\n (\\\"y\\\", \\\"yü\\\")):\\n if last in prev:\\n return ch\\n return \\\"e\\\"\",\n \"def main():\\n sample_text = \\\"\\\\/\\\\/ |-| @ -|- ! $ -|- |-| ! $ ?\\\"\\n vowel_frequencies = get_vowel_frequency(sample_text)\\n print(vowel_frequencies)\",\n \"def reverse_vowels(s):\\n\\n phrase = \\\"\\\"\\n vowels = []\\n for letter in s:\\n if letter.lower() in \\\"aeiou\\\":\\n phrase += \\\"~\\\"\\n vowels.append(letter)\\n else: \\n phrase += letter\\n \\n index = 0\\n new_phrase = \\\"\\\"\\n vowels = vowels[-1:-len(vowels)-1:-1]\\n \\n for letter in phrase:\\n\\n if letter == \\\"~\\\":\\n new_phrase += vowels[index]\\n index += 1\\n else:\\n new_phrase += letter\\n\\n return new_phrase\",\n \"def test_convert_single_vowel():\\n for vowel in \\\"aeiou\\\":\\n result = convert(vowel)\\n assert result == vowel + \\\"way\\\"\",\n \"def good_word(self, word):\\r\\n return word.strip().lower()\",\n \"def map_word(self, word):\\n for invariance in self.invariances:\\n word = invariance.map_word(word)\\n return word\",\n \"def verb_lemma(word):\\n if word.endswith(\\\"ed\\\"):\\n if word[:-2].endswith(\\\"v\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"at\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"it\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"et\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"ut\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"ac\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"i\\\"):\\n return word[:-3].lower() + \\\"y\\\"\\n elif word[:-2].endswith(\\\"ir\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"ag\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"nc\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"nu\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n else:\\n return word[:-2].lower() \\n elif word.endswith(\\\"ing\\\"):\\n if word[:-3].endswith(\\\"v\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"at\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"it\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"et\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"ut\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"ac\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"i\\\"):\\n return word[:-4].lower() + \\\"y\\\"\\n elif word[:-3].endswith(\\\"ir\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"ag\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"nc\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"nu\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n else:\\n return word[:-3].lower()\\n elif re.match(r\\\"(does|did|done)\\\", word):\\n return (\\\"do\\\")\\n elif re.match(r\\\"(is|are|am|was|will|were|been)\\\", word):\\n return (\\\"be\\\")\\n elif word == (\\\"'s\\\"):\\n return (\\\"be\\\")\\n elif re.match(r\\\"(had|has|'ve)\\\", word):\\n return (\\\"have\\\")\\n else:\\n return word.lower()\",\n \"def make_anki_cloze(sentence: str, word: str) -> str:\\n\\n return re.subn(\\n r\\\"(^\\\\W*| \\\\W*)(%s)(\\\\W* |\\\\W*$)\\\" % word,\\n r\\\"\\\\1{{c1::\\\\2}}\\\\3\\\",\\n sentence,\\n flags=re.IGNORECASE,\\n )[0]\",\n \"def find_abecedarian_words():\\n pass\",\n \"def calc_word_value(word):\\n return sum([LETTER_SCORES.get(letter.upper(), 0) for letter in word])\",\n \"def words_with_3_or_more_vowel(words):\\n return [vowel for vowel in words if re.match(r'(\\\\w*[aeiou]\\\\w*){3,}', vowel)]\",\n \"def profanity_word_handler(word):\\n return word[0] + ''.join([settings.CENSOR_PROFANITY_REPLACEMENT_CHARACTER for I in range(len(word)-2)]) + word [-1]\",\n \"def test_find_word(self):\\n self.assertEqual(find_word('GREEN'), [(1, 1), (1, 1), (0, 9)])\\n self.assertEqual(find_word('ABSENT'), [])\\n self.assertEqual(find_word('PW'), [(1, 7), (3, 7), (0, 8)])\",\n \"def analyze_word(s):\\n\\n a = {}\\n a['word'] = s\\n a['n_letters'] = len(s)\\n a['n_vowels'] = count_vowels(s)\\n \\n return a\",\n \"def translate_leet(phrase):\",\n \"def correctWord (w):\\n\\n if len(re.findall(ur\\\"[а-я]\\\",w))>len(re.findall(ur\\\"[a-z]\\\",w)):\\n return w.translate(eng_rusTranslateTable)\\n else:\\n return w.translate(rus_engTranslateTable)\",\n \"def _process(self, word: str) -> List[str]:\\n # if a blank arrives from splitting, just return an empty list\\n if len(word.strip()) == 0:\\n return []\\n word = self.convert_consonantal_i(word)\\n my_word = \\\" \\\" + word + \\\" \\\"\\n letters = list(my_word)\\n positions = []\\n for dipth in self.diphthongs:\\n if dipth in my_word:\\n dipth_matcher = re.compile(\\\"{}\\\".format(dipth))\\n matches = dipth_matcher.finditer(my_word)\\n for match in matches:\\n (start, end) = match.span()\\n positions.append(start)\\n matches = self.kw_matcher.finditer(my_word)\\n for match in matches:\\n (start, end) = match.span()\\n positions.append(start)\\n letters = string_utils.merge_next(letters, positions)\\n letters = string_utils.remove_blanks(letters)\\n positions.clear()\\n if not self._contains_vowels(\\\"\\\".join(letters)):\\n return [\\n \\\"\\\".join(letters).strip()\\n ] # occurs when only 'qu' appears by ellision\\n positions = self._starting_consonants_only(letters)\\n while len(positions) > 0:\\n letters = string_utils.move_consonant_right(letters, positions)\\n letters = string_utils.remove_blanks(letters)\\n positions = self._starting_consonants_only(letters)\\n positions = self._ending_consonants_only(letters)\\n while len(positions) > 0:\\n letters = string_utils.move_consonant_left(letters, positions)\\n letters = string_utils.remove_blanks(letters)\\n positions = self._ending_consonants_only(letters)\\n positions = self._find_solo_consonant(letters)\\n while len(positions) > 0:\\n letters = self._move_consonant(letters, positions)\\n letters = string_utils.remove_blanks(letters)\\n positions = self._find_solo_consonant(letters)\\n positions = self._find_consonant_cluster(letters)\\n while len(positions) > 0:\\n letters = self._move_consonant(letters, positions)\\n letters = string_utils.remove_blanks(letters)\\n positions = self._find_consonant_cluster(letters)\\n return letters\",\n \"def fun4vowels(value:str)->set:\\r\\n vowels = set('aeiou')\\r\\n return vowels.intersection(set(value))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74471885","0.7248238","0.71690327","0.68447524","0.6721554","0.670467","0.6704543","0.64083433","0.6355867","0.6346289","0.6267494","0.62225914","0.6183491","0.61348367","0.60796964","0.604393","0.60383445","0.603059","0.59944683","0.5991076","0.5983007","0.59746933","0.5971418","0.5955748","0.5945857","0.59315956","0.5917575","0.5914967","0.59126544","0.5890969","0.5883844","0.58820546","0.5863154","0.58493847","0.5814235","0.58020264","0.579921","0.5781883","0.5777419","0.57649887","0.5746775","0.5745656","0.5729128","0.5727515","0.5726614","0.57235444","0.570914","0.5693651","0.5664359","0.56639755","0.5614951","0.5607894","0.55961204","0.5594828","0.5587138","0.5582752","0.55791837","0.5569781","0.5546237","0.5546237","0.5530814","0.5524702","0.5518182","0.55123246","0.5506708","0.550327","0.5498983","0.54936653","0.54918367","0.548504","0.54781896","0.54780483","0.54553604","0.5450381","0.54429495","0.5441067","0.54331976","0.54236174","0.5423246","0.5422364","0.54086107","0.5407726","0.54056746","0.5405594","0.5389495","0.53761625","0.5373883","0.53412354","0.53375244","0.532456","0.53092676","0.5308572","0.53076404","0.52976036","0.52951694","0.5291071","0.5276347","0.5275294","0.52634907","0.5258589"],"string":"[\n \"0.74471885\",\n \"0.7248238\",\n \"0.71690327\",\n \"0.68447524\",\n \"0.6721554\",\n \"0.670467\",\n \"0.6704543\",\n \"0.64083433\",\n \"0.6355867\",\n \"0.6346289\",\n \"0.6267494\",\n \"0.62225914\",\n \"0.6183491\",\n \"0.61348367\",\n \"0.60796964\",\n \"0.604393\",\n \"0.60383445\",\n \"0.603059\",\n \"0.59944683\",\n \"0.5991076\",\n \"0.5983007\",\n \"0.59746933\",\n \"0.5971418\",\n \"0.5955748\",\n \"0.5945857\",\n \"0.59315956\",\n \"0.5917575\",\n \"0.5914967\",\n \"0.59126544\",\n \"0.5890969\",\n \"0.5883844\",\n \"0.58820546\",\n \"0.5863154\",\n \"0.58493847\",\n \"0.5814235\",\n \"0.58020264\",\n \"0.579921\",\n \"0.5781883\",\n \"0.5777419\",\n \"0.57649887\",\n \"0.5746775\",\n \"0.5745656\",\n \"0.5729128\",\n \"0.5727515\",\n \"0.5726614\",\n \"0.57235444\",\n \"0.570914\",\n \"0.5693651\",\n \"0.5664359\",\n \"0.56639755\",\n \"0.5614951\",\n \"0.5607894\",\n \"0.55961204\",\n \"0.5594828\",\n \"0.5587138\",\n \"0.5582752\",\n \"0.55791837\",\n \"0.5569781\",\n \"0.5546237\",\n \"0.5546237\",\n \"0.5530814\",\n \"0.5524702\",\n \"0.5518182\",\n \"0.55123246\",\n \"0.5506708\",\n \"0.550327\",\n \"0.5498983\",\n \"0.54936653\",\n \"0.54918367\",\n \"0.548504\",\n \"0.54781896\",\n \"0.54780483\",\n \"0.54553604\",\n \"0.5450381\",\n \"0.54429495\",\n \"0.5441067\",\n \"0.54331976\",\n \"0.54236174\",\n \"0.5423246\",\n \"0.5422364\",\n \"0.54086107\",\n \"0.5407726\",\n \"0.54056746\",\n \"0.5405594\",\n \"0.5389495\",\n \"0.53761625\",\n \"0.5373883\",\n \"0.53412354\",\n \"0.53375244\",\n \"0.532456\",\n \"0.53092676\",\n \"0.5308572\",\n \"0.53076404\",\n \"0.52976036\",\n \"0.52951694\",\n \"0.5291071\",\n \"0.5276347\",\n \"0.5275294\",\n \"0.52634907\",\n \"0.5258589\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6320444"},"document_rank":{"kind":"string","value":"10"}}},{"rowIdx":2996,"cells":{"query":{"kind":"string","value":"Constructor. Create the settings objects"},"document":{"kind":"string","value":"def __init__(self):\n self.s = QSettings()\n self.p = QgsProject.instance()"},"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__( settings={} ):","def __init__(self, settings):\n\n # store settings\n self.settings = settings","def __init__(self, settings):\n \n # storing otmbs settings\n self.settings = settings","def __init__(self, settings):\n self._settings = settings","def __init__(self, settings):\n self._read_config(settings)","def __init__(self, settings):\n self._settings = settings\n self._stats = None","def __init__(self):\n\n self.logger = utils.get_logger()\n\n # set constants\n constants = models.get_asset_dicts('preferences')\n for key, value in constants.items():\n setattr(self, key, value)","def create_settings():\n\n settings = {}\n\n settings['induction'] = {'type': 'DT'}\n\n settings['selection'] = {'type': 'Base',\n 'its': 1,\n 'param': 1}\n\n settings['prediction'] = {'type': 'MI',\n 'its': 0.1,\n 'param': 0.95}\n\n settings['queries'] = {}\n\n settings['metadata'] = {}\n\n settings['model_data'] = {}\n\n return settings","def __init__(self):\n super(sppasPathSettings, self).__init__()\n\n sppas_dir = os.path.dirname(os.path.dirname(\n os.path.dirname(os.path.abspath(__file__))))\n\n self.__dict__ = dict(\n sppas=sppas_dir,\n cli=os.path.join(sppas_dir, \"bin\"),\n etc=os.path.join(sppas_dir, \"etc\"),\n po=os.path.join(sppas_dir, \"po\"),\n src=os.path.join(sppas_dir, \"src\"),\n plugins=os.path.join(os.path.dirname(sppas_dir), \"plugins\"),\n resources=os.path.join(os.path.dirname(sppas_dir), \"resources\"),\n samples=os.path.join(os.path.dirname(sppas_dir), \"samples\"),\n logs=os.path.join(os.path.dirname(sppas_dir), \".logs\"),\n wkps=os.path.join(os.path.dirname(sppas_dir), \"workspaces\"),\n trash=os.path.join(os.path.dirname(sppas_dir), \".trash\"),\n )","def __init__(self, settings_xml):\n # The list of setting ids.\n #\n # XXX This is redundant. We could just get the ids from\n # getting the values of any of our dicts.\n #\n self.ids = []\n self.values = { }\n self.types = { }\n self.defaults = { }\n self.labels = { }\n\n if settings_xml:\n dom = parseString(settings_xml)\n s = dom.firstChild\n\n setting = first_child(s, \"setting\")\n while setting:\n setting_id = setting.getAttribute(\"id\")\n\n # I know the 'sep' setting has no id. I am not sure what it is\n # used for so I am just going to skip it.\n #\n if setting_id != \"\":\n self.ids.append(setting_id)\n self.labels[setting_id] = setting.getAttribute(\"label\")\n self.types[setting_id] = setting.getAttribute(\"type\")\n\n # For bool's actually set the default value to True or\n # False. otherwise it is all strings to us.\n #\n default = setting.getAttribute(\"default\")\n if self.types[setting_id] == \"bool\":\n self.defaults[setting_id] = (default.lower() == 'true')\n else:\n self.defaults[setting_id] = default\n\n # Settings start out with their default value.\n #\n self.values[setting_id] = self.defaults[setting_id]\n setting = next_sibling(setting, \"setting\")\n\n dom.unlink()\n dom = None\n\n # There is always an 'override' setting - \"override\", which is\n # set based on the Language Override setting in the scraper.\n #\n if 'override' not in self.ids:\n self.ids.append(\"override\")\n self.values[\"override\"] = False\n self.types[\"override\"] = \"bool\"\n self.defaults[\"override\"] = False\n self.labels[\"override\"] = \"Language Override\"\n\n # The default language for now is english!\n #\n if 'language' not in self.ids:\n self.ids.append(\"language\")\n self.values[\"language\"] = \"en\"\n self.types[\"language\"] = \"string\"\n self.defaults[\"language\"] = \"en\"\n self.labels[\"language\"] = \"Language\"\n\n return","def __init__(self) -> None:\n self._settings = {}\n\n # Load values from global_settings (only uppercase)\n self.filter_and_set(global_settings)\n\n settings_env_value: str = os.environ.get(SETTINGS_ENV)\n if settings_env_value:\n # Load values from custom settings\n try:\n module = importlib.import_module(settings_env_value)\n except ModuleNotFoundError:\n msg = \"Can't import custom settings. Is it under PYTHONPATH?\"\n raise ModuleError(msg)\n self.filter_and_set(module)","def __init__(self, settings, valid, defaults=None):\n\n try:\n with open(settings, 'r') as settings_file:\n self._settings = json.load(settings_file)\n except TypeError:\n self._settings = dict(settings)\n self._settings = Settings._inject_defaults(self._settings, defaults)\n Settings._validity_check(self._settings, valid)","def __init__(self, values: dict):\n self.instantaneous = InstantaneousSettings\n self.infinite_duration = InfiniteDurationSettings\n self.fixed_duration = FixedDurationSettings","def __init__(self, settings_folder):\n\t\tself.SettingsFolder = settings_folder\n\t\tself.__load_settings()","def load_settings(self):\n\n self.std = settings.settings","def __init__(self):\n self.settings = {}\n self.settings[\"user_agent\"] = u\"Cerabot/0.1 (Python/{0}; {1}; {2}; {3}; {4})\"\n self.settings[\"user_agent\"] = self.settings[\"user_agent\"].format(pyv(), dist()[0],\n version(), dist()[1], machine())\n self.settings[\"wiki\"] = {}\n self.settings[\"irc\"] = {}\n self.settings[\"sql\"] = {}\n self.settings[\"watcher\"] = {}\n\n #Wiki settings\n self.settings[\"wiki\"][\"user\"] = u\"Cerabot\"\n self.settings[\"wiki\"][\"site\"] = [(u\"wikipedia\", u\"en\")]\n self.settings[\"wiki\"][\"passwd\"] = u\"\"\n self.settings[\"wiki\"][\"passwd_file\"] = u\".passwd\"\n self.settings[\"wiki\"][\"run_base\"] = u\"User:Cerabot/Run/Task {task}\"\n self.settings[\"wiki\"][\"summary\"] = u\"Task {task}: {comment}. \"\n self.settings[\"wiki\"][\"summary\"] += \"([[User:Cerabot/Run/Task {task}|bot]])\"\n \n #IRC settings\n self.settings[\"irc\"][\"nick\"] = u\"Cerabot\"\n self.settings[\"irc\"][\"passwd\"] = u\"\"\n self.settings[\"irc\"][\"passwd_file\"] = u\".passwd\"\n self.settings[\"irc\"][\"server\"] = u\"irc.freenode.net\", 6667\n self.settings[\"irc\"][\"realname\"] = u\"IRC extension to Pyhton robot Cerabot.\"\n self.settings[\"irc\"][\"ident\"] = u\"cerabot\"\n self.settings[\"irc\"][\"channels\"] = [\"##cerabot\", \"##ceradon\"]\n\n #Watcher settings\n self.settings[\"watcher\"][\"nick\"] = u\"Cerabot\"\n self.settings[\"watcher\"][\"server\"] = u\"irc.wikimedia.org\", 6667\n self.settings[\"watcher\"][\"realname\"] = u\"IRC extension to Python robot Cerabot.\"\n self.settings[\"watcher\"][\"ident\"] = u\"cerabot\"\n self.settings[\"watcher\"][\"channels\"] = [\"#en.wikipedia\"]\n self.settings[\"watcher\"][\"report_chans\"] = [\"##ceradon-recent\"]\n\n #Database settings\n self.settings[\"sql\"][\"host\"] = u\"bots-bsql01\"\n self.settings[\"sql\"][\"port\"] = 1433\n self.settings[\"sql\"][\"user\"] = u\"ceradon\"\n self.settings[\"sql\"][\"password\"] = u\"\"","def _define_settings(self):\n\n self.settings = {}\n\n ##### ORIGINALLY IN THE DOMAIN FILE #######\n\n # Maximum input in the C-Space : no constituent can be more than 100% present\n self.settings['maxInp'] = 1\n\n #### ORIGINALLY IN THE SETTINGS FILE #####\n self.settings[\"epochs\"] = 3 # Training epochs\n self.settings[\"tgtStd\"] = 12e-6\n self.settings['TInit'] = 1e-6\n self.settings[\"TMin\"] = 0\n self.settings[\"TDecayRate\"] = 0.05\n self.settings[\"lambdaInit\"] = 0.011387\n self.settings['lambdaMin'] = 0.0001\n self.settings[\"lambdaDecayRate\"] = 0.60\n self.settings[\"maxSteps\"] = 300000\n self.settings[\"emaSpeedTol\"] = 0.009\n self.settings[\"emaFactor\"] = .005\n self.settings[\"printInterval\"] = 3000\n self.settings[\"summary_file\"] = \"data/summary.txt\"\n mean = torch.ones(self.grammar.bind.nF,\n self.grammar.bind.nR)/self.grammar.bind.nF\n self.settings[\"initStateMean\"] = mean\n self.settings[\"initStateStdev\"] = .025\n self.settings['clamp'] = False\n\n if self.custom_settings is not None:\n for key, value in self.custom_settings.items():\n if key in self.settings:\n self.settings[key] = value","def __init__(self, settings):\n\n self.r = redis.Redis(\n host=settings['hostname'],\n port=settings['port']\n )\n\n # set the redis list name for storing jobs\n self.joblist = settings['joblistname']","def __init__(self, settings):\n\n \tself.redis = Redis(host=settings.redis.bind_address, port=settings.redis.port)\n \tself.store = Store(settings.content.path)\n self.get_all_pages() # page modification times\n self.get_all_aliases() # page aliases","def __init__(self):\n for name, default in self.defaults.items():\n value = getattr(django.conf.settings, name, default)\n setattr(self, name, value)","def settings() -> Settings:\n return Settings()","def __init__(self, settings):\n\n #self.e_tree = settings.e_tree # Don't do this!!!! change all calls to e_tree to self.settings.e_tree!\n #self.k_tree = settings.k_tree\n self.settings = settings\n self.made_parents = {}\n self.added_files = {}\n self.edit_files = {}","def __init__(self, configParserObject):\n section = 'settings'\n self.debug = configParserObject.getboolean(section, 'debug')\n self.application_path = configParserObject.get(section, 'application_path')\n self.log_path = configParserObject.get(section, 'log_path')\n self.chat_enabled = configParserObject.getboolean(section, 'chat_enabled')\n self.token_validation_enabled = configParserObject.getboolean(section, 'token_validation_enabled')\n if not os.path.isdir(self.application_path):\n raise ErrorLoadingData('Path %s does not exists' % self.application_path)\n if not os.path.isdir(self.log_path):\n raise ErrorLoadingData('Path %s does not exists' % self.log_path)","def initialize(cls, settings: Settings) -> Settings:\n\n settings_obj = SettingsService.load_game_conf()\n\n for entry in SettingsService.GAME_SETTINGS:\n value = settings_obj.get(SettingsService.GAME_SETTINGS_ROOT, {}).get(\n entry, None\n )\n if value is None:\n raise RuntimeError(f\"Entry {entry} is missing in settings.\")\n\n setattr(settings, entry, value)\n\n for entry in SettingsService.INITIALS:\n value = settings_obj.get(SettingsService.INITIALS_ROOT, {}).get(entry, None)\n if value is None:\n raise RuntimeError(f\"Entry {entry} is missing in settings.\")\n\n settings.initials[entry] = value\n\n return settings","def __init__(self, SETTINGS_FILE):\n try:\n self.__setup(SETTINGS_FILE)\n Loger.write(self)\n if self.data.user.enable:\n if self.data.user.timeEnabled:\n now = datetime.datetime.now().time().replace(second=0)\n if not (self.data.user.timeFrom < now < self.data.user.timeTo):\n raise UserDisabledTime(self.data.user)\n self.data.user.lastLogin = datetime.datetime.now()\n self.data.user.save()\n else:\n raise UserDisabled(self.data.user)\n self.blackHoleBrowser = gui.BlackHoleBrowser(self)\n BlackHole.instance = self\n except Exception as e:\n raise e","def build_settings(self, settings):\n \n settings.add_json_panel(\"Network\", self.config, data=network_json)\n settings.add_json_panel(\"Camera\", self.config, data=camera_json)\n settings.add_json_panel(\"CV\", self.config, data=cv_json)\n settings.add_json_panel(\"Admin\", self.config, data=admin_json)","def from_settings(settings):","def __init__(self):\n\n # Initialize cache.\n self._cache = {}\n\n # Initialize default settings.\n defaults = {\n 'api_url': 'http://127.0.0.1:8000/api',\n 'client_id': '',\n 'client_secret': '',\n 'refresh_token': '',\n 'verbose': 'false',\n }\n self._defaults = Parser(defaults=defaults)\n self._defaults.add_section('general')\n\n # Initialize a parser for the user settings file.\n self._user = Parser()\n self._user.add_section('general')\n\n # If the user settings file exists, read it into the parser object.\n user_filename = os.path.expanduser('~/.mezzanine.cfg')\n self._user.read(user_filename)","def __init__(self, _confFixture, _settings):\n self._conf = _confFixture\n self._settings = _settings","def __init__(self, domain='com.markfickett.gors'):\n\t\tsettingsDir = os.path.expanduser(self.__SETTINGS_DIR)\n\t\tif not os.path.isdir(settingsDir):\n\t\t\tos.makedirs(settingsDir)\n\t\tself.__settingsFileName = os.path.join(settingsDir,\n\t\t\tdomain + '.plist')\n\t\tif os.path.isfile(self.__settingsFileName):\n\t\t\tself.__settings = plistlib.readPlist(\n\t\t\t\tself.__settingsFileName)\n\t\telse:\n\t\t\tself.clear()\n\t\tself.__currentGroupNames = []","def __setup(self, SETTINGS_FILE):\n config = ConfigParser()\n try:\n config.read(SETTINGS_FILE)\n self.settings = Settings(config)\n self.data = Data()\n except IOError:\n raise FileMissing(SETTINGS_FILE)\n except Exception as e:\n raise e","def __init__(self):\n\t\t\n\t\tsettings = configparser.SafeConfigParser(allow_no_value=True)\n\t\tlist=settings.read('data/settings.cfg')\n\t\tif not 'data/settings.cfg' in list:\n\t\t\tprint('no configuration file present.. making one')\n\t\t\tself.makeConfigFile(settings)\n\t\t\tshare = ['']\n\t\t\tself.nodes = []\n\t\telse:\n\t\t\tshare, nodes = self.openConfig(settings)\n\t\t\tself.nodes = nodes\n\t\t\n\t\t\n\t\tself.files = self.loadFiles(share)\t\t\n\t\tself.share = share\n\t\tself.kill= False\n\t\tself.downloads = {}\n\t\tself.currentVersion = (0,2,1)\n\t\tself.totalDownloads = 0\n\t\tself.current = 0\n\t\tself.config = settings","def test_constructor(self):\n # Build the Settings objects\n self.assertEqual(self.extension, self.settings.extension)\n\n # Ensure that the registration settings dict gets\n # added to this Settings\n self.assertEqual(self.test_dict['test_key1'],\n self.settings['test_key1'])","def __init__(self, settings):\n super().__init__(settings, self.player_info_url, Player)","def __init__(self):\n self.config = {}","def initialize(self):\n my_setting = self.settings.get('my_setting')","def __init__(self, bot, name, default_settings=None):\n if default_settings is None:\n default_settings = {}\n self.bot = bot\n self.name = name\n self.default_settings = default_settings\n\n # set up storage for settings and load from persistent file\n self.settings_path = pathlib.Path(\".settings\", f\"{self.name}.yml\")\n self.id_dict = load_persistent_settings(self.settings_path)","def build_settings(self, settings):\n settings.add_json_panel('Makesmith Settings', self.config, data=self.json)","def __init__(self, settings):\n # Requirement ID: 7.0.0\n\n self.settings = settings\n self.ResetScore()\n self.game_active = False\n self.high_score = 0\n self.number_of_mega_bullets = settings.number_of_mega_bullets\n self.shot_bullets = 0\n self.remaining_bullets = settings.number_of_mega_bullets","def settings(self):\r\n return settings.Settings(self)","def settings(self):\r\n return SettingResource(self)","def settings(self):\n return {}","def settings(self):\n from hubspot3.settings import SettingsClient\n\n return SettingsClient(**self.auth, **self.options)","def _init_key_settings(self):\n self.minKeySize = 1023\n self.maxKeySize = 8193\n self.rsaSigHashes = list(RSA_SIGNATURE_HASHES)\n self.rsaSchemes = list(RSA_SCHEMES)\n self.dsaSigHashes = list(DSA_SIGNATURE_HASHES)\n self.virtual_hosts = []\n # DH key settings\n self.eccCurves = list(CURVE_NAMES)\n self.dhParams = None\n self.dhGroups = list(ALL_DH_GROUP_NAMES)\n self.defaultCurve = \"secp256r1\"\n self.keyShares = [\"secp256r1\", \"x25519\"]\n self.padding_cb = None\n self.use_heartbeat_extension = True\n self.heartbeat_response_callback = None","def __init__(\n self,\n settings: Optional[Union[Dict[str, Any], MpConfigFile, str]] = None,\n conf_filepath: str = None,\n ):\n self._lbl_loading = widgets.Label(value=\"Loading. Please wait.\")\n display(self._lbl_loading)\n if isinstance(settings, MpConfigFile):\n self.mp_conf_file = MpConfigFile(\n settings=settings.settings, file=conf_filepath\n )\n elif isinstance(settings, dict):\n self.mp_conf_file = MpConfigFile(settings=settings, file=conf_filepath)\n elif isinstance(settings, str):\n self.mp_conf_file = MpConfigFile(file=settings)\n else:\n # This is the default if neither settings nor conf_filepath are passed.\n self.mp_conf_file = MpConfigFile(file=conf_filepath)\n self.mp_conf_file.load_default()\n self.tool_buttons: Dict[str, widgets.Widget] = {}\n self._inc_loading_label()\n\n # Get the settings definitions and Config controls object\n mp_def_dict = get_mpconfig_definitions()\n self.mp_controls = MpConfigControls(mp_def_dict, self.mp_conf_file.settings)\n self._inc_loading_label()\n\n # Set up the tabs\n self.tab_ctrl = CompEditTabs(self._get_tab_definitions())\n self._inc_loading_label()\n\n self.txt_current_file = widgets.Text(\n description=\"Conf File\",\n value=self.current_config_file,\n layout=widgets.Layout(width=\"75%\"),\n )\n self.btn_save = widgets.Button(\n description=\"Save Settings\",\n tooltip=\"Save current settings to your config file.\",\n )\n self.btn_save.on_click(self._save_file)\n self.btn_validate = widgets.Button(\n description=\"Validate Settings\",\n tooltip=\"Run basic sanity checks on current settings.\",\n )\n self.btn_validate.on_click(self._validate_config)\n self.cb_backup = widgets.Checkbox(description=\"Create backup\", value=False)\n self.cb_refresh = widgets.Checkbox(description=\"Refresh on save\", value=True)\n vbox = widgets.VBox(\n [\n self.txt_current_file,\n widgets.HBox(\n [self.btn_save, self.cb_refresh, self.cb_backup, self.btn_validate]\n ),\n self.mp_conf_file.viewer,\n ]\n )\n self.layout = widgets.VBox([self.tab_ctrl.layout, vbox])\n self._lbl_loading.layout.visibility = \"hidden\"","def __init__(self):\n self._create_options()\n self._create_sections()","def settings(self) -> Dict[str, Any]:\n return {}","def __init__(self, settings_file_name):\n with open(settings_file_name, 'r') as f:\n # load config file\n self.settings = yaml.load(f)\n\n # get key values\n sit_names = self.settings[HNF.Consts.SIT_NAMES]\n row_action_names = self.settings[HNF.Consts.ROW_ACT_NAMES]\n column_action_names = self.settings[HNF.Consts.COL_ACT_NAMES]\n name = self.settings[HNF.Consts.NAME]\n\n # init HNG object\n self.HNFOut = HNF.HNFInstance(sit_names, row_action_names, column_action_names, name)\n\n # set the values found in the settings\n self.__initFromFile()\n\n # calc the summary and expected utility\n self.HNFOut.initSummaryBelief()\n self.HNFOut.initExpectedUtility()\n self.HNFOut.calcHypergameExpectedUtility()\n self.HNFOut.calcModelingOpponentUtility()","def settings_init(self):\n config_console = configparser.ConfigParser()\n config_console.read(CONFIG_FILE_NAME)\n self.logmode = config_console[\"LOG\"][\"log_mode\"]","def __init__(self, settings: ActorSettings) -> None:\n super().__init__()\n self.settings = settings\n\n self._critic = None","def __init__(self, *args, **kwargs):\r\n super().__init__()\r\n self._cfg = ConfigDict() # current configuration\r\n self._default_config = ConfigDict() # default configuration\r\n self._temp_config = OrderedDict() # temporary configuration\r\n self._path = Path() # current configuration path\r\n self._default_path = Path() # default configuration path\r\n self._conversion_dict = None\r\n self._auto_cast = None\r\n self._write_flags = None\r\n self._force_load = None\r\n self._load_empty = None\r\n self._ask_path = None\r\n self._search_in_default_config = None\r\n self._init_count = 0\r\n self._policies = defaultdict(bool) # by default every modification is forbidden # WIP\r\n if args or kwargs:\r\n self.init(*args, **kwargs)\r\n logger.debug(\"Config object created.\")","def __init__(self):\n self.__load_settings()\n self.__load_history()\n self.__clear_context()\n\n if self.DELETE_ALL_ON_STARTUP:\n sublime.set_timeout_async(lambda: self.delete_all_history(), 0)\n elif self.CLEANUP_ON_STARTUP:\n sublime.set_timeout_async(lambda: self.clean_history(False), 0)","def __init__(self, configs):\n\n self.__configs = configs","def __init__(self, os_creds, keypair_settings):\n super(self.__class__, self).__init__(os_creds)\n\n self.keypair_settings = keypair_settings\n self.__delete_keys_on_clean = True\n\n # Attributes instantiated on create()\n self.__keypair = None","def test_settings_instantiation(self):\n ## no settings passed on instantiation\n bd = BorrowDirect() # no settings info\n self.assertEqual(\n True, isinstance(bd, BorrowDirect) )\n ## dict settings\n settings_dict = {} ## empty dct\n bd = BorrowDirect( settings_dict )\n self.assertEqual(\n None, bd.UNIVERSITY_CODE )\n settings_dict = { 'UNIVERSITY_CODE': '123' } ## populated dct\n bd = BorrowDirect( settings_dict )\n self.assertEqual(\n '123', bd.UNIVERSITY_CODE )\n ## module settings\n s = imp.new_module( 'settings' ) ## empty module\n bd = BorrowDirect( s )\n self.assertEqual(\n None, bd.UNIVERSITY_CODE )\n s = imp.new_module( 'settings' ) ## populated module\n s.UNIVERSITY_CODE = '234'\n bd = BorrowDirect( s )\n self.assertEqual(\n '234', bd.UNIVERSITY_CODE )","def initialize(cls, settings):\n\n settings_obj = SettingsService.load_game_conf()\n\n for entry in SettingsService.GAME_SETTINGS:\n value = settings_obj.get(SettingsService.GAME_SETTINGS_ROOT, {}).get(entry, None)\n if value is None:\n raise RuntimeError(f\"Entry {entry} is missing in settings.\")\n\n settings[entry] = float(value)\n\n return settings","def config_init(self):\n\n game_opts = [\n\n # Execution Options\n ('debug',False), # Toggle Debug Messaging\n ('log_path',False), # Turn on logging (w/path)\n ('log_lvl',logging.DEBUG), # Set log level\n\n # World Generation Options\n ('flex_limit',3) # Sets the maximum variance\n\n ]\n\n # Attempts to pull each value from the configuration\n # if not in config, the default value defined above\n # is set instead\n for opt in game_opts:\n try:\n setattr(self,opt[0],self.conf.conf_dict[opt[0]])\n except:\n setattr(self,opt[0],opt[1])\n continue","def __init__(self, *args):\n this = _libsbml.new_L3ParserSettings(*args)\n try: self.this.append(this)\n except: self.this = this","def __init__(self, *args, **kwds):\n if args or kwds:\n super(KltSettings, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.max_features is None:\n self.max_features = 0\n if self.window_size is None:\n self.window_size = 0\n if self.quality is None:\n self.quality = 0.\n if self.min_distance is None:\n self.min_distance = 0.\n if self.harris is None:\n self.harris = 0.\n if self.size_block is None:\n self.size_block = 0\n if self.pyramid_lvl is None:\n self.pyramid_lvl = 0\n if self.mask_border is None:\n self.mask_border = 0\n else:\n self.max_features = 0\n self.window_size = 0\n self.quality = 0.\n self.min_distance = 0.\n self.harris = 0.\n self.size_block = 0\n self.pyramid_lvl = 0\n self.mask_border = 0","def __init__(self):\n # Read configuration into dictionary\n self.directories = general.config_directories()\n self.config = general.read_yaml_files(self.directories)","def __init__(self, text=None, settings=None, style='General', language='en'):\n\n self._text = None\n self._settings = None\n self._style = None\n self._language = None\n\n self.text = text\n self.settings = settings\n self.style = style\n self.language = language","def __init__(self):\n self.__parameters: ConfigParams = ConfigParams()","def settings(self, settings):\n\n self._settings = settings","def __init__(\n self,\n *,\n file_name: str,\n klass: Type[TSettingsType],\n ) -> None:\n self._file_name = file_name\n self._klass = klass","def __init__(self):\n # Screen settings\n self.screen_width = 1860\n self.screen_height = 1020\n self.screen_size = self.screen_width, self.screen_height\n self.bg_color = 230, 230, 230\n\n # Ship static settings\n self.ship_limit = 3\n\n # Bullet static settings\n self.bullet_limit = 3\n self.bullet_width = 3\n self.bullet_height = 15\n self.bullet_color = 60, 60, 60\n\n # Alien static settings\n self.fleet_drop_speed = 10\n\n self.speed_up_scale = 1.1\n self.initialize_dynamic_settings()","def __init__(self):\n\n # open json config file that reads in information\n config_path = open(\"config.json\", \"r\")\n config_json = config_path.read()\n config_dict = json.loads(config_json)\n\n # assign object variables\n self.project_id = config_dict[\"project-id\"]\n self.bucket_name = config_dict[\"bucket-name\"]\n self.location_id = config_dict[\"key-location\"]\n self.key_ring_id = config_dict[\"key-ring-id\"]\n self.crypto_key_id = config_dict[\"crypto-key-id\"]\n self.service_account_email = config_dict[\"service-account-email\"]\n\n # close the file\n config_path.close()","def __init__(self, window, settings_dir, n_trials=0):\r\n self.stimuli = OrderedDict()\r\n self.triggers = {}\r\n\r\n self.settings_dir = settings_dir\r\n self.num_trials = n_trials\r\n self.win = window","def __init__(self, settings, **kwargs):\n super().__init__(settings=settings, **kwargs)\n self.vr_status = 'off' #off, ready, waiting or recording\n for v_attr in self.saveable_defaults.keys():\n setattr(self, v_attr, self.resolve_attr(v_attr, settings, self.saveable_defaults[v_attr]))\n self.vr_splitter_port = None\n self.vr_recordcount = 0\n self.vr_lastrecording = 0\n self.vr_activefile = '-'\n self.vr_protect = threading.Lock()\n self.vr_monthread = None\n self.procthread = None\n self.vr_web_trigger = None\n self.vr_trig_queue = queue.Queue()\n # and this to support web browser front end\n self.camhand.add_url_rule('/flip-trigger', view_func=self.flip_record_trigger, methods=('REQUEST',))\n self.camhand.add_url_rule('/flip-record', view_func=self.record_now, methods=('REQUEST',))","def __init__(self, values = None):\n TCInit(self)\n if values is not None:\n self.set_opts(values)","def __init__(self):\n default_config = Config()\n query = Query(default_config)\n database = Database(default_config)\n common_util = CommonUtil(default_config, database)\n self.config = default_config\n self.query = query\n self.database = database\n self.common_util = common_util","def __init__(self, argv: list, company: str, appname: str, Liststr=None):\n if company is None:\n company = self.__class__.__name__\n QSettings.setPath(QSettings.IniFormat, QSettings.UserScope, str(FOLDER.parent / \"settings\"))\n self.settings = QSettings(QSettings.IniFormat, QSettings.UserScope, company, appname)\n super().__init__(argv)","def __init__(self, generateConf=False):\n super(Settings, self).__init__()\n if generateConf:\n self.writeSettingsFile(DEFAULT_CONF, path=DEFAULT_SETTINGS_PATH)\n else:\n try:\n self.settingsFilePath = DEFAULT_SETTINGS_PATH\n except IOError:\n self.writeSettingsFile(\n DEFAULT_CONF,\n path=DEFAULT_SETTINGS_PATH\n )\n util.write(\n \"No configuration file found, a default one has been generated at %s.\" % (DEFAULT_SETTINGS_PATH,),\n priority=9\n )\n sys.exit(0)","def initialize_from_config(self):","def __init__(self, settings={}, prefix=\"\"):\n self.settings = settings\n self.mapper = routes.Mapper()\n self.setup_handlers(self.mapper)","def init_from_settings(self, settings):\n\n # Initializes screen.\n self.screen = pygame.display.set_mode(settings.resolution)\n self.screen.set_clip(settings.game_info)\n self.screen.fill(settings.colors[5])\n \n self.screen.blit(settings.help_text1, (165, 250))\n self.screen.blit(settings.help_text2, (165, 270))\n self.screen.blit(settings.help_text3, (165, 290))\n \n pygame.draw.rect(self.screen, settings.colors[6], start_rect, 0)\n pygame.draw.rect(self.screen, settings.colors[6], reset_rect, 0)\n pygame.draw.rect(self.screen, settings.colors[6], settings.box_display, 2)\n \n self.screen.blit(settings.start_text, (171, 188))\n self.screen.blit(settings.reset_text, (251, 188))\n \n self.screen.set_clip(settings.game_area)\n \n pygame.display.set_caption(settings.title)\n pygame.mouse.set_visible(settings.mouse_enabled)\n\n # Initializes background.\n background = pygame.Surface(self.screen.get_size())\n self.background = background.convert()\n self.background.fill(settings.background)\n\n pygame.display.update()","def _generate_settings(self):\n settings = {}\n settings[\"api_client_id\"] = input(\"(OPTIONAL) Please enter your Twitch API Client ID: \") #Get API Client ID first so I can use API to get user ID\n #Save JSON\n fileIO.save_json(\"settings.json\", settings)\n name = False\n while not name: #While name not set\n name = input(\"Please enter the username of your Twitch account: \").lower()\n userID = self._get_user_id(name)\n if not userID:\n name = False\n settings[\"userid\"] = userID\n settings[\"oauth\"] = input(\"Please enter the oauth token for your Twitch account: \")\n if settings[\"oauth\"].startswith(\"oauth:\"): #If the oauth token starts with oauth:, remove it\n settings[\"oauth\"] = settings[\"oauth\"][6:]\n settings[\"error_webhook\"] = input(\"Please enter the Discord WebHook URL you would like errors to be sent to: \")\n #Save JSON\n fileIO.save_json(\"settings.json\", settings)","def build_mail_settings():\n mail_settings = MailSettings()\n mail_settings.bcc_settings = BCCSettings(True, Email(\"test@example.com\"))\n mail_settings.bypass_list_management = BypassListManagement(True)\n mail_settings.footer_settings = FooterSettings(True, \"Footer Text\",\n (\"<html><body>Footer \"\n \"Text</body></html>\"))\n mail_settings.sandbox_mode = SandBoxMode(True)\n mail_settings.spam_check = SpamCheck(True, 1,\n \"https://spamcatcher.sendgrid.com\")\n return mail_settings","def load_settings(self):\n LogConfiguration.initialize(self._db)\n self.analytics = Analytics(self._db)\n self.auth = Authenticator(self._db, self.analytics)\n\n self.setup_external_search()\n\n # Track the Lane configuration for each library by mapping its\n # short name to the top-level lane.\n new_top_level_lanes = {}\n # Create a CirculationAPI for each library.\n new_circulation_apis = {}\n\n # Potentially load a CustomIndexView for each library\n new_custom_index_views = {}\n\n # Make sure there's a site-wide public/private key pair.\n self.sitewide_key_pair\n\n for library in self._db.query(Library):\n lanes = load_lanes(self._db, library)\n\n new_top_level_lanes[library.id] = lanes\n\n new_custom_index_views[library.id] = CustomIndexView.for_library(\n library\n )\n\n new_circulation_apis[library.id] = self.setup_circulation(\n library, self.analytics\n )\n self.top_level_lanes = new_top_level_lanes\n self.circulation_apis = new_circulation_apis\n self.custom_index_views = new_custom_index_views\n self.shared_collection_api = self.setup_shared_collection()\n\n # Assemble the list of patron web client domains from individual\n # library registration settings as well as a sitewide setting.\n patron_web_domains = set()\n admin_web_domains = set()\n\n def get_domain(url):\n url = url.strip()\n if url == \"*\":\n return url\n scheme, netloc, path, parameters, query, fragment = urllib.parse.urlparse(\n url)\n if scheme and netloc:\n return scheme + \"://\" + netloc\n else:\n return None\n\n sitewide_patron_web_client_urls = ConfigurationSetting.sitewide(\n self._db, Configuration.PATRON_WEB_HOSTNAMES).value\n if sitewide_patron_web_client_urls:\n for url in sitewide_patron_web_client_urls.split('|'):\n domain = get_domain(url)\n if domain:\n patron_web_domains.add(domain)\n\n sitewide_admin_web_client_urls = ConfigurationSetting.sitewide(\n self._db, Configuration.ADMIN_WEB_HOSTNAMES).value\n if sitewide_admin_web_client_urls:\n for url in sitewide_admin_web_client_urls.split('|'):\n domain = get_domain(url)\n if domain:\n admin_web_domains.add(domain)\n\n from .registry import Registration\n for setting in self._db.query(\n ConfigurationSetting).filter(\n ConfigurationSetting.key == Registration.LIBRARY_REGISTRATION_WEB_CLIENT):\n if setting.value:\n patron_web_domains.add(get_domain(setting.value))\n\n self.patron_web_domains = patron_web_domains\n self.admin_web_domains = admin_web_domains\n self.setup_configuration_dependent_controllers()\n authentication_document_cache_time = int(\n ConfigurationSetting.sitewide(\n self._db, Configuration.AUTHENTICATION_DOCUMENT_CACHE_TIME\n ).value_or_default(0)\n )\n self.authentication_for_opds_documents = ExpiringDict(\n max_len=1000, max_age_seconds=authentication_document_cache_time\n )\n self.wsgi_debug = ConfigurationSetting.sitewide(\n self._db, Configuration.WSGI_DEBUG_KEY\n ).bool_value or False","def __init__(\n self,\n settings=None,\n scenario=None,\n living_expenses_strategy=None,\n saving_strategy=None,\n withdrawal_strategy=None,\n tax_treatment=None\n ):\n # Set up instance:\n super().__init__()\n self.default_values = copy(DEFAULTVALUES)\n self.default_types = copy(DEFAULTTYPES)\n self.default_builders = copy(DEFAULTBUILDERS)\n # Store args as attributes:\n # For `settings` specifically, use the default values provided\n # by the class if none are provided explicitly.\n if settings is None:\n self.settings = Settings()\n else:\n self.settings = settings\n # For the rest, None is allowed:\n self.scenario = scenario\n self.living_expenses_strategy = living_expenses_strategy\n self.saving_strategy = saving_strategy\n self.withdrawal_strategy = withdrawal_strategy\n self.tax_treatment = tax_treatment\n # Some params aren't used to build Forecast and so are not\n # received as input to __init__. Create attrs for them here:\n self.allocation_strategy = None","def __init__(self, *kwargs):\n self.session = requests.Session()\n self.config_path = os.path.join(\n os.path.dirname(__file__), 'config.json')\n self.load_config()\n if self.application_token == '':\n self.set_application_token()\n self.token = self.get_token()\n self.get_settings()","def __init__(self):\n self.collectorName = COLLECTOR_NAME\n self.configCycleInterval = 20 # minutes\n self.cycleInterval = 5 * 60 # seconds\n\n # The configurationService attribute is the fully qualified class-name\n # of our configuration service that runs within ZenHub\n self.configurationService = 'NullConfig'\n\n # Will be filled in based on buildOptions\n self.options = None\n\n self.configCycleInterval = 20*60","def _post_initialisations(self):\n # Init the settings module.\n self.dummy_for_settings = SectionConfig(self.app.id, self.__class__.__name__)\n global settings\n settings = self.dummy_for_settings\n\n self.dummy_for_options = OptionConfig(self.app.id)\n global options\n options = self.dummy_for_options\n\n # Bind message boxes.\n self.MessageBox = MessageBox(self)\n self.msg = self.MessageBox.Message\n self.are_you_sure = self.MessageBox.are_you_sure\n\n # Set previous size and state.\n width = settings.get('width', 350)\n height = settings.get('height', 350)\n self.set_title(self.app.localizedname)\n self.resize(width, height)\n if settings.get_bool('maximized', False):\n self.maximize()\n # Load any other settings here.\n self.load_xinput_devices()","def __init__(self, config):\n\n self.mode9 = config[sC.PROJECT_DETAILS][sC.MODE] == '9'\n self.admins = eval(handler.config[sC.PROJECT_DETAILS][sC.ADMIN_IDS])\n self.approvers = eval(handler.config[sC.COUNTER_STRIKE_ADMINS][sC.APPROVERS])","def __init__(self, configfile='settings.cfg'):\n \n self.configfile = configfile\n \n # Load parameters from config file\n config = ConfigParser.RawConfigParser()\n config.read(self.configfile)\n \n # Set parameters to default if not in config file \n self.title=config.get('Settings','title') if config.has_option(\n 'Settings','title') else 'REDPy Catalog'\n self.filename=config.get('Settings','filename') if config.has_option(\n 'Settings','filename') else 'redpytable.h5'\n self.groupName=config.get('Settings','groupName') if config.has_option(\n 'Settings','groupName') else 'default'\n self.groupDesc=config.get('Settings','groupDesc') if config.has_option(\n 'Settings','groupDesc') else 'Default Test Run'\n self.nsta=config.getint('Settings','nsta') if config.has_option(\n 'Settings','nsta') else 8 \n self.station=config.get('Settings','station') if config.has_option(\n 'Settings','station') else 'SEP,YEL,HSR,SHW,EDM,STD,JUN,SOS'\n self.channel=config.get('Settings','channel') if config.has_option(\n 'Settings','channel') else 'EHZ,EHZ,EHZ,EHZ,EHZ,EHZ,EHZ,EHZ'\n self.network=config.get('Settings','network') if config.has_option(\n 'Settings','network') else 'UW,UW,UW,UW,UW,UW,UW,UW'\n self.location=config.get('Settings','location') if config.has_option(\n 'Settings','location') else '--,--,--,--,--,--,--,--'\n self.samprate=config.getfloat('Settings','samprate') if config.has_option(\n 'Settings','samprate') else 100.\n self.nstaC=config.getint('Settings','nstaC') if config.has_option(\n 'Settings','nstaC') else 5\n self.printsta=config.getint('Settings','printsta') if config.has_option(\n 'Settings','printsta') else 2\n self.server=config.get('Settings','server') if config.has_option(\n 'Settings','server') else 'IRIS'\n self.port=config.getint('Settings','port') if config.has_option(\n 'Settings','port') else 16017\n self.nsec=config.getint('Settings','nsec') if config.has_option(\n 'Settings','nsec') else 3600\n self.lwin=config.getfloat('Settings','lwin') if config.has_option(\n 'Settings','lwin') else 7.\n self.swin=config.getfloat('Settings','swin') if config.has_option(\n 'Settings','swin') else 0.8\n self.trigon=config.getfloat('Settings','trigon') if config.has_option(\n 'Settings','trigon') else 3.\n self.trigoff=config.getfloat('Settings','trigoff') if config.has_option(\n 'Settings','trigoff') else 2.\n self.kurtmax=config.getfloat('Settings','kurtmax') if config.has_option(\n 'Settings','kurtmax') else 80.\n self.kurtfmax=config.getfloat('Settings','kurtfmax') if config.has_option(\n 'Settings','kurtfmax') else 150.\n self.oratiomax=config.getfloat('Settings','oratiomax') if config.has_option(\n 'Settings','oratiomax') else 0.06\n self.kurtwin=config.getfloat('Settings','kurtwin') if config.has_option(\n 'Settings','kurtwin') else 5.\n self.winlen=config.getint('Settings','winlen') if config.has_option(\n 'Settings','winlen') else 1024\n self.fmin=config.getfloat('Settings','fmin') if config.has_option(\n 'Settings','fmin') else 1.\n self.fmax=config.getfloat('Settings','fmax') if config.has_option(\n 'Settings','fmax') else 10.\n self.filomin=config.getfloat('Settings','filomin') if config.has_option(\n 'Settings','filomin') else 1.\n self.filomax=config.getfloat('Settings','filomax') if config.has_option(\n 'Settings','filomax') else 2.5\n self.fiupmin=config.getfloat('Settings','fiupmin') if config.has_option(\n 'Settings','fiupmin') else 5.\n self.fiupmax=config.getfloat('Settings','fiupmax') if config.has_option(\n 'Settings','fiupmax') else 10.\n self.telefi=config.getfloat('Settings','telefi') if config.has_option(\n 'Settings','telefi') else -1.\n self.teleok=config.getint('Settings','teleok') if config.has_option(\n 'Settings','teleok') else 1 \n self.cmin=config.getfloat('Settings','cmin') if config.has_option(\n 'Settings','cmin') else 0.7\n self.ncor=config.getint('Settings','ncor') if config.has_option(\n 'Settings','ncor') else 4\n self.minorph=config.getfloat('Settings','minorph') if config.has_option(\n 'Settings','minorph') else 0.05\n self.maxorph=config.getfloat('Settings','maxorph') if config.has_option(\n 'Settings','maxorph') else 7.\n self.minplot=config.getint('Settings','minplot') if config.has_option(\n 'Settings','minplot') else 3\n self.dybin=config.getfloat('Settings','dybin') if config.has_option(\n 'Settings','dybin') else 1.\n self.hrbin=config.getfloat('Settings','hrbin') if config.has_option(\n 'Settings','hrbin') else 1.\n self.recplot=config.getfloat('Settings','recplot') if config.has_option(\n 'Settings','recplot') else 14.\n \n # Derived Settings\n self.ptrig=1.5*self.winlen/self.samprate\n self.atrig=3*self.winlen/self.samprate\n self.mintrig=self.winlen/self.samprate\n self.wshape = int((self.ptrig + self.atrig)*self.samprate) + 1","def init_game_setting(self):\r\n pass","def config(settings):\n\n #T = current.T\n\n # PrePopulate data\n settings.base.prepopulate += (\"SHARE/LK\",)\n settings.base.prepopulate_demo += (\"SHARE/Demo\",)\n\n # Finance settings\n settings.fin.currencies = {\n #\"EUR\" : \"Euros\",\n #\"GBP\" : \"Great British Pounds\",\n \"LKR\" : \"Sri Lanka Rupees\",\n \"USD\" : \"United States Dollars\",\n }\n settings.fin.currency_default = \"USD\"","def init(self):\n\n if self.has_settings:\n print(\n TERM.bold_red('Error:'),\n 'Settings file already exists. Doing nothing.'\n )\n return\n\n new_settings = {\n 'strategy': self.ns.strategy,\n 'branch': self.ns.branch,\n 'scoring': self.ns.scoring,\n }\n\n with open(self.settings, 'w') as f:\n f.write(yaml.dump(new_settings, default_flow_style=False))\n\n print(\n TERM.bold_green('Yay!'),\n 'Wrote settings file {0}'.format(self.settings)\n )","def __init__(self, config_file=None):\n\t\tself.options = {}\n\n\t\tif config_file:\n\t\t\tself.set_file(config_file)","def get_settings():\n return SettingCollection.build()","def default_settings(self, settings):\n return {}","def __init__(self, setting):\n self.setting.update(setting)\n self.base = (self.setting.get('base') + '/').lower()\n self.client = Box(self.setting.get('access_token'))\n self.client.users_get_current_account()","def __init__(self):\n self._init_key_settings()\n self._init_misc_extensions()\n self.minVersion = (3, 1)\n self.maxVersion = (3, 4)\n self.versions = [(3, 4), (3, 3), (3, 2), (3, 1)]\n self.cipherNames = list(CIPHER_NAMES)\n self.macNames = list(MAC_NAMES)\n self.keyExchangeNames = list(KEY_EXCHANGE_NAMES)\n self.cipherImplementations = list(CIPHER_IMPLEMENTATIONS)","def _initialise(self, **settings):\n if settings:\n self.settings = self.clean_settings(settings)\n else:\n # same as self._fill_from_preset('commoner')\n self._initialise(name=\"Commoner\", alignment=\"neutral\",\n ac=10, hp=4,\n attack_parameters=[['club', 2, 0, 4]])\n print(\"EMPTY CREATURE GIVEN. SETTING TO COMMONER\")\n return 0\n\n # Mod of preexisting\n if 'base' in self.settings:\n # Sanify first and make victim\n if type(self.settings['base']) is str:\n # generate a preset and get its attributes. Seems a bit wasteful.\n victim = self.__class__(self.settings['base'])\n elif isinstance(self.settings['base'], self.__class__):\n victim = self.settings['base']\n else:\n raise TypeError\n # copy all\n # victim.ability_bonuses #in case the user provided with ability scores,\n # which are overridden by adbility bonues\n base = {x: getattr(victim, x) for x in dir(victim) if\n getattr(victim, x) and x.find(\"__\") == -1 and x.find(\"_\") != 0 and x != 'beastiary'}\n base['ability_bonuses'] = {}\n # base.update(**self.settings)\n for (k, v) in self.settings.items():\n if type(v) is dict:\n base[k].update(v)\n else:\n base[k] = v\n self.settings = base\n\n # Name etc.\n # subscript assigns it or self.settings if it has a value\n self['name'] = 'nameless'\n self['level'] = 0\n self['xp'] = 0\n # proficiency. Will be overridden if hp is provided?\n self['proficiency'] = 1 + round(self.level / 4) # TODO check maths on PH\n\n # set abilities\n self.set_ability_dice()\n\n # Get HD\n self.hit_die = None\n if 'hd' in self.settings.keys():\n if type(self.settings['hd']) is Dice:\n self.hit_die = self.settings['hd'] # we're dealing with a copy of a beastiary obj.\n else:\n self.hit_die = Dice(num_faces=int(self.settings['hd']),\n bonus=self.con.bonus,\n avg=True,\n role=\"hd\")\n elif 'size' in self.settings.keys():\n size_cat = {\"small\": 6, \"medium\": 8, \"large\": 10, \"huge\": 12}\n if self.settings['size'] in size_cat.keys():\n self.hit_die = Dice(bonus=self.con.bonus,\n num_faces=size_cat[self.settings['size']],\n avg=True,\n role=\"hd\")\n elif 'hp' in self.settings and 'level' in self.settings:\n # Guess based on hp and level. It is not that dodgy really as the manual does not use odd dice.\n # hp =approx. 0.5 HD * (level-1) + HD + con * level\n # HD * (0.5* (level-1)+1) = hp - con*level\n # HD = (hp - con*level)/(level+1)\n bestchoice = (int(self.settings['hp']) - self.con.bonus * int(self.settings['level'])) / (\n (int(self.settings['level']) + 1))\n print(int(self.settings['hp']), int(self.ability_bonuses['con']), int(self.settings['level']))\n print(\"choice HD...\", bestchoice)\n # print(\"diagnosis...\",self.ability_bonuses)\n self.log.warning('Unfinished case to guess HD. so Defaulting hit dice to d8 instead') # TODO finish\n self.hit_die = Dice(bonus=self.con.bonus, num_faces=8, avg=True, role=\"hd\")\n else:\n # defaulting to d8\n self.log.warning('Insufficient info: defaulting hit dice to d8')\n self.hit_die = Dice(bonus=self.con.bonus, num_faces=8, avg=True, role=\"hd\")\n\n # Get HP\n if 'hp' in self.settings.keys():\n self.hp = int(self.settings['hp'])\n self.starting_hp = self.hp\n elif self.settings['level']:\n self.set_level()\n else:\n raise Exception('Cannot make character without hp or hd + level provided')\n\n # AC\n if not 'ac' in self.settings.keys():\n self.settings['ac'] = 10 + self.dex.bonus\n self.ac = int(self.settings['ac'])\n\n # init\n if not 'initiative_bonus' in self.settings:\n self.settings['initiative_bonus'] = self.dex.bonus\n self.initiative = Dice(bonus=int(self.settings['initiative_bonus']),\n num_faces=20,\n role=\"initiative\")\n\n ##spell casting ability_bonuses\n if 'sc_ability' in self.settings:\n self.spellcasting_ability_name = self.settings['sc_ability'].lower()\n elif 'healing_spells' in self.settings or 'buff_spells' in self.settings:\n self.spellcasting_ability_name = max('wis', 'int', 'cha',\n key=lambda ab: self[ab].bonus) # Going for highest. seriously?!\n print(\n \"Please specify spellcasting ability of \" +\n self.name +\n \" next time, this time \" +\n self.spellcasting_ability_name +\n \" was used as it was biggest.\")\n else:\n self.spellcasting_ability_name = 'con' # TODO fix this botch up.\n if not 'healing_bonus' in self.settings:\n self.settings['healing_bonus'] = self[self.spellcasting_ability_name].bonus\n if 'healing_spells' in self.settings:\n self.starting_healing_spells = int(self.settings['healing_spells'])\n self.healing_spells = self.starting_healing_spells\n if not 'healing_dice' in self.settings:\n self.settings['healing_dice'] = 4 # healing word.\n self.healing = Dice(bonus=int(self.settings['healing_bonus']),\n num_faces=int(self.settings['healing_dice']),\n role=\"healing\") ##Healing dice can't crit or have adv.\n else:\n self.starting_healing_spells = 0\n self.healing_spells = 0\n # not a healer\n\n # attacks\n self.attacks = []\n self.hurtful = 0\n if not 'attack_parameters' in self.settings:\n # Benefit of doubt. Given 'em a dagger .\n self.settings['attack_parameters'] = 'dagger'\n if type(self.settings['attack_parameters']) is str:\n try:\n x = json.loads(self.settings['attack_parameters'].replace(\"*\", \"\\\"\"))\n self._attack_parse(x)\n self.attack_parameters = x\n except:\n # These have to be readable by _attack_parse\n weapons = {'club': 4, 'greatclub': 8,\n 'dagger': 4, 'shortsword': 6, 'longsword': 8, 'bastardsword': 10, 'greatsword': 12,\n 'rapier': 8, 'scimitar': 6, 'sickle': 4,\n 'handaxe': 6, 'battleaxe': 8, 'waraxe': 10, 'greataxe': 12,\n 'javelin': 6, 'spear': 6, 'flail': 8, 'glaive': 10, 'halberd': 10, 'lance': 12, 'pike': 10,\n 'trident': 6,\n 'war pick': 8,\n 'lighthammer': 4, 'mace': 6, 'warhammer': 8,\n 'quaterstaff': 6, 'morningstar': 8, 'punch': 1,\n 'whip': 4} # parsing of strings for dice not implemented yet, so punch is d1 for now.\n # TODO weapons removed as they gave trouble:\n # 'maul':[6,6],\n # 'brütal war pick': [8, 8], # okay, I could not resist it.\n\n # bastard sword and war axe are no more due to the versatile rule,\n # however they were kept here to keep it simple\n # ranged weapons are missing for now...\n for w in weapons.keys():\n if self.settings['attack_parameters'].lower().find(w) > -1:\n # TODO fix the fact that a it gives the finesse option to all.\n if self.dex.bonus > self.str.bonus:\n chosen_ab = 'dex'\n else:\n chosen_ab = 'str'\n self.attack_parameters = [[w, self.proficiency + chosen_ab, chosen_ab, weapons[w]]]\n self._attack_parse(self.attack_parameters)\n self.log += \"Weapon matched by str to {w}\\n\"\n break\n else:\n raise Exception(\"Cannot figure out what is: \" + self.settings['attack_parameters'] + str(\n type(self.settings['attack_parameters'])))\n elif type(self.settings['attack_parameters']) is list:\n self.attack_parameters = self.settings['attack_parameters']\n self._attack_parse(self.attack_parameters)\n else:\n raise Exception('Could not determine weapon')\n ##Weird bit needing upgrade.\n if 'alt_attack' in self.settings and type(self.settings['alt_attack']) is list:\n self.alt_attack = {'name': self.settings['alt_attack'][0],\n 'attack': Dice(bonus=self.settings['alt_attack'][1],\n num_faces=20)} # CURRENTLY ONLY NETTING IS OPTION!\n else:\n self.alt_attack = {'name': None, 'attack': None}\n # last but not least\n if 'alignment' not in self.settings:\n self.settings['alignment'] = \"unassigned mercenaries\" # hahaha!\n self.alignment = self.settings['alignment']\n # internal stuff\n self.tally = {'damage': 0, 'hits': 0, 'dead': 0, 'misses': 0, 'battles': 0, 'rounds': 0, 'hp': 0,\n 'healing_spells': 0}\n self.copy_index = 1\n self.condition = 'normal'\n\n self.dodge = 0\n self.concentrating = 0\n self.temp = 0\n\n self.buff_spells = None\n if 'buff_spells' in self.settings:\n self.buff_spells = int(self.settings['buff_spells'])\n self.conc_fx = getattr(self, self.settings['buff'])\n else:\n self.buff_spells = 0\n\n if 'cr' in self.settings:\n self.cr = self.settings['cr']\n elif 'level' in self.settings:\n # TODO check maths on MM.\n if int(self.settings['level']) > 1:\n self.cr = int(self.settings['level']) - 1\n else:\n self.cr = 0.5\n else:\n self.cr = None # vermin\n\n ##backdoor and overider\n self['custom'] = []\n for other in self.custom:\n if other == \"conc_fx\":\n getattr(self, self.settings['conc_fx'])\n else:\n self[other] = self.settings[other] # force it to be set.\n\n self.arena = None\n self.settings = {}","def __init__(self):\n\n if Config._instance:\n raise Exception('Config singleton is already instantiated. User Config.get_instance() obtain it.')\n\n parser = configparser.ConfigParser()\n parser.read('C:\\\\Users\\\\Akatosh\\\\PythonProjects\\\\note-it\\\\config\\\\config.ini')\n\n self.sections = {}\n\n for section in parser:\n self.sections[section] = _Section(parser[section])\n\n Config._instance = self","def setup_settings():\n # pylint: disable=import-outside-toplevel\n from django.conf import settings\n import tiny_erp.settings as defaults\n\n for name in dir(defaults):\n if name.isupper() and not hasattr(settings, name):\n setattr(settings, name, getattr(defaults, name))","def load_settings(self):\n # Set the default settings. In case in a later version of this script the settings change, new default variables will be added automatically\n self.settings = {\n # Connection settings to OBS Studio websockets plugin\n \"host\": \"localhost\",\n \"port\": 4444,\n \"password\": \"\",\n \"update_frequency\": 1, # seconds, how often the script loads the SC2 UI location\n }\n if os.path.isfile(self.settings_path):\n with open(self.settings_path) as f:\n self.settings.update(json.load(f))","def __init__(self):\n # Try to get the Bulma settings. The user may not have created this dict.\n try:\n self.bulma_settings = settings.BULMA_SETTINGS\n except AttributeError:\n self.bulma_settings = {}\n\n self.bulma_submodule_path = simple_bulma_path / \"bulma\" / \"sass\"\n self.custom_scss = self.bulma_settings.get(\"custom_scss\", [])\n self.variables = self.bulma_settings.get(\"variables\", {})\n self.output_style = self.bulma_settings.get(\"output_style\", \"nested\")\n self.storage = FileSystemStorage(simple_bulma_path)\n\n # Make a list of all the finders except this one.\n # We use this in the custom SCSS handler.\n other_finders = settings.STATICFILES_FINDERS.copy()\n other_finders.remove(\"django_simple_bulma.finders.SimpleBulmaFinder\")\n self.other_finders = [get_finder(finder) for finder in other_finders]","def initialize_bot_settings(settings: Config) -> Config:\n\n settings.define_section(\"ctxreminders\", ContextualRemindersSection)\n\n settings.ctxreminders.configure_setting(\n \"persistence_dir\",\n \"In which folder do you want to store the reminders file?\",\n default=settings.core.homedir)\n\n settings.ctxreminders.configure_setting(\n \"context_capture_min_duration\",\n \"What is the minimum duration (in seconds) for reminders to save contextual chat logs? \" \\\n \"Default 30 days (2592000)\",\n default=2592000)\n\n settings.ctxreminders.configure_setting(\n \"context_capture_max_duration\",\n \"What is the minimum duration (in seconds) for reminders to save contextual chat logs? \" \\\n \"Default no limit (inf)\",\n default=math.inf)\n\n settings.ctxreminders.configure_setting(\n \"context_capture_chat_lines\",\n \"How many lines of chat to save with reminders that have context? Default 20\",\n default=20)\n\n settings.ctxreminders.configure_setting(\n \"pastebin_url\",\n \"What is the pastebin url (PrivateBin)? Default ''\",\n default=20)\n\n settings.ctxreminders.configure_setting(\n \"pastebin_expiration\",\n \"Expiration of pastebin pastes? Default 5min\",\n default=\"5min\")\n\n return settings","def __init__(self, config):\n self._config = config\n self.logging = logging.getLogger(\"Settings\")\n self.logging.propagate = False\n level = logging.INFO\n if \"DEBUG\" in os.environ and (\n os.environ[\"DEBUG\"]\n or os.environ[\"DEBUG\"].lower() in (\"true\", \"t\", \"yes\", \"y\")\n ):\n level = logging.DEBUG\n self.logging.setLevel(level)\n handler = logging.StreamHandler()\n handler.setLevel(level)\n handler.setFormatter(logging.Formatter(\"%(asctime)s [Settings] %(message)s\"))\n self.logging.addHandler(handler)\n self.logging.debug(\"Running in debug mode.\")","def __init__(self, filename=None, use_argv=True):\n self._init_filename = filename\n if use_argv:\n self.options, self.args = [self.get_parser().parse_args()] * 2\n else:\n self.options = self.args = None\n self._wrapped = self.load(file=self.settings_file)\n # build a special dynamic section for things the user wants,\n # ie, things that have been passed into the option\n # parser but are not useful in the .ini\n if not self.get_section('user'):\n self['user'] = {}\n if self.options is not None:\n self['user']['shell'] = self.options.shell and 'true' or ''\n else:\n self['user']['shell'] = ''"],"string":"[\n \"def __init__( settings={} ):\",\n \"def __init__(self, settings):\\n\\n # store settings\\n self.settings = settings\",\n \"def __init__(self, settings):\\n \\n # storing otmbs settings\\n self.settings = settings\",\n \"def __init__(self, settings):\\n self._settings = settings\",\n \"def __init__(self, settings):\\n self._read_config(settings)\",\n \"def __init__(self, settings):\\n self._settings = settings\\n self._stats = None\",\n \"def __init__(self):\\n\\n self.logger = utils.get_logger()\\n\\n # set constants\\n constants = models.get_asset_dicts('preferences')\\n for key, value in constants.items():\\n setattr(self, key, value)\",\n \"def create_settings():\\n\\n settings = {}\\n\\n settings['induction'] = {'type': 'DT'}\\n\\n settings['selection'] = {'type': 'Base',\\n 'its': 1,\\n 'param': 1}\\n\\n settings['prediction'] = {'type': 'MI',\\n 'its': 0.1,\\n 'param': 0.95}\\n\\n settings['queries'] = {}\\n\\n settings['metadata'] = {}\\n\\n settings['model_data'] = {}\\n\\n return settings\",\n \"def __init__(self):\\n super(sppasPathSettings, self).__init__()\\n\\n sppas_dir = os.path.dirname(os.path.dirname(\\n os.path.dirname(os.path.abspath(__file__))))\\n\\n self.__dict__ = dict(\\n sppas=sppas_dir,\\n cli=os.path.join(sppas_dir, \\\"bin\\\"),\\n etc=os.path.join(sppas_dir, \\\"etc\\\"),\\n po=os.path.join(sppas_dir, \\\"po\\\"),\\n src=os.path.join(sppas_dir, \\\"src\\\"),\\n plugins=os.path.join(os.path.dirname(sppas_dir), \\\"plugins\\\"),\\n resources=os.path.join(os.path.dirname(sppas_dir), \\\"resources\\\"),\\n samples=os.path.join(os.path.dirname(sppas_dir), \\\"samples\\\"),\\n logs=os.path.join(os.path.dirname(sppas_dir), \\\".logs\\\"),\\n wkps=os.path.join(os.path.dirname(sppas_dir), \\\"workspaces\\\"),\\n trash=os.path.join(os.path.dirname(sppas_dir), \\\".trash\\\"),\\n )\",\n \"def __init__(self, settings_xml):\\n # The list of setting ids.\\n #\\n # XXX This is redundant. We could just get the ids from\\n # getting the values of any of our dicts.\\n #\\n self.ids = []\\n self.values = { }\\n self.types = { }\\n self.defaults = { }\\n self.labels = { }\\n\\n if settings_xml:\\n dom = parseString(settings_xml)\\n s = dom.firstChild\\n\\n setting = first_child(s, \\\"setting\\\")\\n while setting:\\n setting_id = setting.getAttribute(\\\"id\\\")\\n\\n # I know the 'sep' setting has no id. I am not sure what it is\\n # used for so I am just going to skip it.\\n #\\n if setting_id != \\\"\\\":\\n self.ids.append(setting_id)\\n self.labels[setting_id] = setting.getAttribute(\\\"label\\\")\\n self.types[setting_id] = setting.getAttribute(\\\"type\\\")\\n\\n # For bool's actually set the default value to True or\\n # False. otherwise it is all strings to us.\\n #\\n default = setting.getAttribute(\\\"default\\\")\\n if self.types[setting_id] == \\\"bool\\\":\\n self.defaults[setting_id] = (default.lower() == 'true')\\n else:\\n self.defaults[setting_id] = default\\n\\n # Settings start out with their default value.\\n #\\n self.values[setting_id] = self.defaults[setting_id]\\n setting = next_sibling(setting, \\\"setting\\\")\\n\\n dom.unlink()\\n dom = None\\n\\n # There is always an 'override' setting - \\\"override\\\", which is\\n # set based on the Language Override setting in the scraper.\\n #\\n if 'override' not in self.ids:\\n self.ids.append(\\\"override\\\")\\n self.values[\\\"override\\\"] = False\\n self.types[\\\"override\\\"] = \\\"bool\\\"\\n self.defaults[\\\"override\\\"] = False\\n self.labels[\\\"override\\\"] = \\\"Language Override\\\"\\n\\n # The default language for now is english!\\n #\\n if 'language' not in self.ids:\\n self.ids.append(\\\"language\\\")\\n self.values[\\\"language\\\"] = \\\"en\\\"\\n self.types[\\\"language\\\"] = \\\"string\\\"\\n self.defaults[\\\"language\\\"] = \\\"en\\\"\\n self.labels[\\\"language\\\"] = \\\"Language\\\"\\n\\n return\",\n \"def __init__(self) -> None:\\n self._settings = {}\\n\\n # Load values from global_settings (only uppercase)\\n self.filter_and_set(global_settings)\\n\\n settings_env_value: str = os.environ.get(SETTINGS_ENV)\\n if settings_env_value:\\n # Load values from custom settings\\n try:\\n module = importlib.import_module(settings_env_value)\\n except ModuleNotFoundError:\\n msg = \\\"Can't import custom settings. Is it under PYTHONPATH?\\\"\\n raise ModuleError(msg)\\n self.filter_and_set(module)\",\n \"def __init__(self, settings, valid, defaults=None):\\n\\n try:\\n with open(settings, 'r') as settings_file:\\n self._settings = json.load(settings_file)\\n except TypeError:\\n self._settings = dict(settings)\\n self._settings = Settings._inject_defaults(self._settings, defaults)\\n Settings._validity_check(self._settings, valid)\",\n \"def __init__(self, values: dict):\\n self.instantaneous = InstantaneousSettings\\n self.infinite_duration = InfiniteDurationSettings\\n self.fixed_duration = FixedDurationSettings\",\n \"def __init__(self, settings_folder):\\n\\t\\tself.SettingsFolder = settings_folder\\n\\t\\tself.__load_settings()\",\n \"def load_settings(self):\\n\\n self.std = settings.settings\",\n \"def __init__(self):\\n self.settings = {}\\n self.settings[\\\"user_agent\\\"] = u\\\"Cerabot/0.1 (Python/{0}; {1}; {2}; {3}; {4})\\\"\\n self.settings[\\\"user_agent\\\"] = self.settings[\\\"user_agent\\\"].format(pyv(), dist()[0],\\n version(), dist()[1], machine())\\n self.settings[\\\"wiki\\\"] = {}\\n self.settings[\\\"irc\\\"] = {}\\n self.settings[\\\"sql\\\"] = {}\\n self.settings[\\\"watcher\\\"] = {}\\n\\n #Wiki settings\\n self.settings[\\\"wiki\\\"][\\\"user\\\"] = u\\\"Cerabot\\\"\\n self.settings[\\\"wiki\\\"][\\\"site\\\"] = [(u\\\"wikipedia\\\", u\\\"en\\\")]\\n self.settings[\\\"wiki\\\"][\\\"passwd\\\"] = u\\\"\\\"\\n self.settings[\\\"wiki\\\"][\\\"passwd_file\\\"] = u\\\".passwd\\\"\\n self.settings[\\\"wiki\\\"][\\\"run_base\\\"] = u\\\"User:Cerabot/Run/Task {task}\\\"\\n self.settings[\\\"wiki\\\"][\\\"summary\\\"] = u\\\"Task {task}: {comment}. \\\"\\n self.settings[\\\"wiki\\\"][\\\"summary\\\"] += \\\"([[User:Cerabot/Run/Task {task}|bot]])\\\"\\n \\n #IRC settings\\n self.settings[\\\"irc\\\"][\\\"nick\\\"] = u\\\"Cerabot\\\"\\n self.settings[\\\"irc\\\"][\\\"passwd\\\"] = u\\\"\\\"\\n self.settings[\\\"irc\\\"][\\\"passwd_file\\\"] = u\\\".passwd\\\"\\n self.settings[\\\"irc\\\"][\\\"server\\\"] = u\\\"irc.freenode.net\\\", 6667\\n self.settings[\\\"irc\\\"][\\\"realname\\\"] = u\\\"IRC extension to Pyhton robot Cerabot.\\\"\\n self.settings[\\\"irc\\\"][\\\"ident\\\"] = u\\\"cerabot\\\"\\n self.settings[\\\"irc\\\"][\\\"channels\\\"] = [\\\"##cerabot\\\", \\\"##ceradon\\\"]\\n\\n #Watcher settings\\n self.settings[\\\"watcher\\\"][\\\"nick\\\"] = u\\\"Cerabot\\\"\\n self.settings[\\\"watcher\\\"][\\\"server\\\"] = u\\\"irc.wikimedia.org\\\", 6667\\n self.settings[\\\"watcher\\\"][\\\"realname\\\"] = u\\\"IRC extension to Python robot Cerabot.\\\"\\n self.settings[\\\"watcher\\\"][\\\"ident\\\"] = u\\\"cerabot\\\"\\n self.settings[\\\"watcher\\\"][\\\"channels\\\"] = [\\\"#en.wikipedia\\\"]\\n self.settings[\\\"watcher\\\"][\\\"report_chans\\\"] = [\\\"##ceradon-recent\\\"]\\n\\n #Database settings\\n self.settings[\\\"sql\\\"][\\\"host\\\"] = u\\\"bots-bsql01\\\"\\n self.settings[\\\"sql\\\"][\\\"port\\\"] = 1433\\n self.settings[\\\"sql\\\"][\\\"user\\\"] = u\\\"ceradon\\\"\\n self.settings[\\\"sql\\\"][\\\"password\\\"] = u\\\"\\\"\",\n \"def _define_settings(self):\\n\\n self.settings = {}\\n\\n ##### ORIGINALLY IN THE DOMAIN FILE #######\\n\\n # Maximum input in the C-Space : no constituent can be more than 100% present\\n self.settings['maxInp'] = 1\\n\\n #### ORIGINALLY IN THE SETTINGS FILE #####\\n self.settings[\\\"epochs\\\"] = 3 # Training epochs\\n self.settings[\\\"tgtStd\\\"] = 12e-6\\n self.settings['TInit'] = 1e-6\\n self.settings[\\\"TMin\\\"] = 0\\n self.settings[\\\"TDecayRate\\\"] = 0.05\\n self.settings[\\\"lambdaInit\\\"] = 0.011387\\n self.settings['lambdaMin'] = 0.0001\\n self.settings[\\\"lambdaDecayRate\\\"] = 0.60\\n self.settings[\\\"maxSteps\\\"] = 300000\\n self.settings[\\\"emaSpeedTol\\\"] = 0.009\\n self.settings[\\\"emaFactor\\\"] = .005\\n self.settings[\\\"printInterval\\\"] = 3000\\n self.settings[\\\"summary_file\\\"] = \\\"data/summary.txt\\\"\\n mean = torch.ones(self.grammar.bind.nF,\\n self.grammar.bind.nR)/self.grammar.bind.nF\\n self.settings[\\\"initStateMean\\\"] = mean\\n self.settings[\\\"initStateStdev\\\"] = .025\\n self.settings['clamp'] = False\\n\\n if self.custom_settings is not None:\\n for key, value in self.custom_settings.items():\\n if key in self.settings:\\n self.settings[key] = value\",\n \"def __init__(self, settings):\\n\\n self.r = redis.Redis(\\n host=settings['hostname'],\\n port=settings['port']\\n )\\n\\n # set the redis list name for storing jobs\\n self.joblist = settings['joblistname']\",\n \"def __init__(self, settings):\\n\\n \\tself.redis = Redis(host=settings.redis.bind_address, port=settings.redis.port)\\n \\tself.store = Store(settings.content.path)\\n self.get_all_pages() # page modification times\\n self.get_all_aliases() # page aliases\",\n \"def __init__(self):\\n for name, default in self.defaults.items():\\n value = getattr(django.conf.settings, name, default)\\n setattr(self, name, value)\",\n \"def settings() -> Settings:\\n return Settings()\",\n \"def __init__(self, settings):\\n\\n #self.e_tree = settings.e_tree # Don't do this!!!! change all calls to e_tree to self.settings.e_tree!\\n #self.k_tree = settings.k_tree\\n self.settings = settings\\n self.made_parents = {}\\n self.added_files = {}\\n self.edit_files = {}\",\n \"def __init__(self, configParserObject):\\n section = 'settings'\\n self.debug = configParserObject.getboolean(section, 'debug')\\n self.application_path = configParserObject.get(section, 'application_path')\\n self.log_path = configParserObject.get(section, 'log_path')\\n self.chat_enabled = configParserObject.getboolean(section, 'chat_enabled')\\n self.token_validation_enabled = configParserObject.getboolean(section, 'token_validation_enabled')\\n if not os.path.isdir(self.application_path):\\n raise ErrorLoadingData('Path %s does not exists' % self.application_path)\\n if not os.path.isdir(self.log_path):\\n raise ErrorLoadingData('Path %s does not exists' % self.log_path)\",\n \"def initialize(cls, settings: Settings) -> Settings:\\n\\n settings_obj = SettingsService.load_game_conf()\\n\\n for entry in SettingsService.GAME_SETTINGS:\\n value = settings_obj.get(SettingsService.GAME_SETTINGS_ROOT, {}).get(\\n entry, None\\n )\\n if value is None:\\n raise RuntimeError(f\\\"Entry {entry} is missing in settings.\\\")\\n\\n setattr(settings, entry, value)\\n\\n for entry in SettingsService.INITIALS:\\n value = settings_obj.get(SettingsService.INITIALS_ROOT, {}).get(entry, None)\\n if value is None:\\n raise RuntimeError(f\\\"Entry {entry} is missing in settings.\\\")\\n\\n settings.initials[entry] = value\\n\\n return settings\",\n \"def __init__(self, SETTINGS_FILE):\\n try:\\n self.__setup(SETTINGS_FILE)\\n Loger.write(self)\\n if self.data.user.enable:\\n if self.data.user.timeEnabled:\\n now = datetime.datetime.now().time().replace(second=0)\\n if not (self.data.user.timeFrom < now < self.data.user.timeTo):\\n raise UserDisabledTime(self.data.user)\\n self.data.user.lastLogin = datetime.datetime.now()\\n self.data.user.save()\\n else:\\n raise UserDisabled(self.data.user)\\n self.blackHoleBrowser = gui.BlackHoleBrowser(self)\\n BlackHole.instance = self\\n except Exception as e:\\n raise e\",\n \"def build_settings(self, settings):\\n \\n settings.add_json_panel(\\\"Network\\\", self.config, data=network_json)\\n settings.add_json_panel(\\\"Camera\\\", self.config, data=camera_json)\\n settings.add_json_panel(\\\"CV\\\", self.config, data=cv_json)\\n settings.add_json_panel(\\\"Admin\\\", self.config, data=admin_json)\",\n \"def from_settings(settings):\",\n \"def __init__(self):\\n\\n # Initialize cache.\\n self._cache = {}\\n\\n # Initialize default settings.\\n defaults = {\\n 'api_url': 'http://127.0.0.1:8000/api',\\n 'client_id': '',\\n 'client_secret': '',\\n 'refresh_token': '',\\n 'verbose': 'false',\\n }\\n self._defaults = Parser(defaults=defaults)\\n self._defaults.add_section('general')\\n\\n # Initialize a parser for the user settings file.\\n self._user = Parser()\\n self._user.add_section('general')\\n\\n # If the user settings file exists, read it into the parser object.\\n user_filename = os.path.expanduser('~/.mezzanine.cfg')\\n self._user.read(user_filename)\",\n \"def __init__(self, _confFixture, _settings):\\n self._conf = _confFixture\\n self._settings = _settings\",\n \"def __init__(self, domain='com.markfickett.gors'):\\n\\t\\tsettingsDir = os.path.expanduser(self.__SETTINGS_DIR)\\n\\t\\tif not os.path.isdir(settingsDir):\\n\\t\\t\\tos.makedirs(settingsDir)\\n\\t\\tself.__settingsFileName = os.path.join(settingsDir,\\n\\t\\t\\tdomain + '.plist')\\n\\t\\tif os.path.isfile(self.__settingsFileName):\\n\\t\\t\\tself.__settings = plistlib.readPlist(\\n\\t\\t\\t\\tself.__settingsFileName)\\n\\t\\telse:\\n\\t\\t\\tself.clear()\\n\\t\\tself.__currentGroupNames = []\",\n \"def __setup(self, SETTINGS_FILE):\\n config = ConfigParser()\\n try:\\n config.read(SETTINGS_FILE)\\n self.settings = Settings(config)\\n self.data = Data()\\n except IOError:\\n raise FileMissing(SETTINGS_FILE)\\n except Exception as e:\\n raise e\",\n \"def __init__(self):\\n\\t\\t\\n\\t\\tsettings = configparser.SafeConfigParser(allow_no_value=True)\\n\\t\\tlist=settings.read('data/settings.cfg')\\n\\t\\tif not 'data/settings.cfg' in list:\\n\\t\\t\\tprint('no configuration file present.. making one')\\n\\t\\t\\tself.makeConfigFile(settings)\\n\\t\\t\\tshare = ['']\\n\\t\\t\\tself.nodes = []\\n\\t\\telse:\\n\\t\\t\\tshare, nodes = self.openConfig(settings)\\n\\t\\t\\tself.nodes = nodes\\n\\t\\t\\n\\t\\t\\n\\t\\tself.files = self.loadFiles(share)\\t\\t\\n\\t\\tself.share = share\\n\\t\\tself.kill= False\\n\\t\\tself.downloads = {}\\n\\t\\tself.currentVersion = (0,2,1)\\n\\t\\tself.totalDownloads = 0\\n\\t\\tself.current = 0\\n\\t\\tself.config = settings\",\n \"def test_constructor(self):\\n # Build the Settings objects\\n self.assertEqual(self.extension, self.settings.extension)\\n\\n # Ensure that the registration settings dict gets\\n # added to this Settings\\n self.assertEqual(self.test_dict['test_key1'],\\n self.settings['test_key1'])\",\n \"def __init__(self, settings):\\n super().__init__(settings, self.player_info_url, Player)\",\n \"def __init__(self):\\n self.config = {}\",\n \"def initialize(self):\\n my_setting = self.settings.get('my_setting')\",\n \"def __init__(self, bot, name, default_settings=None):\\n if default_settings is None:\\n default_settings = {}\\n self.bot = bot\\n self.name = name\\n self.default_settings = default_settings\\n\\n # set up storage for settings and load from persistent file\\n self.settings_path = pathlib.Path(\\\".settings\\\", f\\\"{self.name}.yml\\\")\\n self.id_dict = load_persistent_settings(self.settings_path)\",\n \"def build_settings(self, settings):\\n settings.add_json_panel('Makesmith Settings', self.config, data=self.json)\",\n \"def __init__(self, settings):\\n # Requirement ID: 7.0.0\\n\\n self.settings = settings\\n self.ResetScore()\\n self.game_active = False\\n self.high_score = 0\\n self.number_of_mega_bullets = settings.number_of_mega_bullets\\n self.shot_bullets = 0\\n self.remaining_bullets = settings.number_of_mega_bullets\",\n \"def settings(self):\\r\\n return settings.Settings(self)\",\n \"def settings(self):\\r\\n return SettingResource(self)\",\n \"def settings(self):\\n return {}\",\n \"def settings(self):\\n from hubspot3.settings import SettingsClient\\n\\n return SettingsClient(**self.auth, **self.options)\",\n \"def _init_key_settings(self):\\n self.minKeySize = 1023\\n self.maxKeySize = 8193\\n self.rsaSigHashes = list(RSA_SIGNATURE_HASHES)\\n self.rsaSchemes = list(RSA_SCHEMES)\\n self.dsaSigHashes = list(DSA_SIGNATURE_HASHES)\\n self.virtual_hosts = []\\n # DH key settings\\n self.eccCurves = list(CURVE_NAMES)\\n self.dhParams = None\\n self.dhGroups = list(ALL_DH_GROUP_NAMES)\\n self.defaultCurve = \\\"secp256r1\\\"\\n self.keyShares = [\\\"secp256r1\\\", \\\"x25519\\\"]\\n self.padding_cb = None\\n self.use_heartbeat_extension = True\\n self.heartbeat_response_callback = None\",\n \"def __init__(\\n self,\\n settings: Optional[Union[Dict[str, Any], MpConfigFile, str]] = None,\\n conf_filepath: str = None,\\n ):\\n self._lbl_loading = widgets.Label(value=\\\"Loading. Please wait.\\\")\\n display(self._lbl_loading)\\n if isinstance(settings, MpConfigFile):\\n self.mp_conf_file = MpConfigFile(\\n settings=settings.settings, file=conf_filepath\\n )\\n elif isinstance(settings, dict):\\n self.mp_conf_file = MpConfigFile(settings=settings, file=conf_filepath)\\n elif isinstance(settings, str):\\n self.mp_conf_file = MpConfigFile(file=settings)\\n else:\\n # This is the default if neither settings nor conf_filepath are passed.\\n self.mp_conf_file = MpConfigFile(file=conf_filepath)\\n self.mp_conf_file.load_default()\\n self.tool_buttons: Dict[str, widgets.Widget] = {}\\n self._inc_loading_label()\\n\\n # Get the settings definitions and Config controls object\\n mp_def_dict = get_mpconfig_definitions()\\n self.mp_controls = MpConfigControls(mp_def_dict, self.mp_conf_file.settings)\\n self._inc_loading_label()\\n\\n # Set up the tabs\\n self.tab_ctrl = CompEditTabs(self._get_tab_definitions())\\n self._inc_loading_label()\\n\\n self.txt_current_file = widgets.Text(\\n description=\\\"Conf File\\\",\\n value=self.current_config_file,\\n layout=widgets.Layout(width=\\\"75%\\\"),\\n )\\n self.btn_save = widgets.Button(\\n description=\\\"Save Settings\\\",\\n tooltip=\\\"Save current settings to your config file.\\\",\\n )\\n self.btn_save.on_click(self._save_file)\\n self.btn_validate = widgets.Button(\\n description=\\\"Validate Settings\\\",\\n tooltip=\\\"Run basic sanity checks on current settings.\\\",\\n )\\n self.btn_validate.on_click(self._validate_config)\\n self.cb_backup = widgets.Checkbox(description=\\\"Create backup\\\", value=False)\\n self.cb_refresh = widgets.Checkbox(description=\\\"Refresh on save\\\", value=True)\\n vbox = widgets.VBox(\\n [\\n self.txt_current_file,\\n widgets.HBox(\\n [self.btn_save, self.cb_refresh, self.cb_backup, self.btn_validate]\\n ),\\n self.mp_conf_file.viewer,\\n ]\\n )\\n self.layout = widgets.VBox([self.tab_ctrl.layout, vbox])\\n self._lbl_loading.layout.visibility = \\\"hidden\\\"\",\n \"def __init__(self):\\n self._create_options()\\n self._create_sections()\",\n \"def settings(self) -> Dict[str, Any]:\\n return {}\",\n \"def __init__(self, settings_file_name):\\n with open(settings_file_name, 'r') as f:\\n # load config file\\n self.settings = yaml.load(f)\\n\\n # get key values\\n sit_names = self.settings[HNF.Consts.SIT_NAMES]\\n row_action_names = self.settings[HNF.Consts.ROW_ACT_NAMES]\\n column_action_names = self.settings[HNF.Consts.COL_ACT_NAMES]\\n name = self.settings[HNF.Consts.NAME]\\n\\n # init HNG object\\n self.HNFOut = HNF.HNFInstance(sit_names, row_action_names, column_action_names, name)\\n\\n # set the values found in the settings\\n self.__initFromFile()\\n\\n # calc the summary and expected utility\\n self.HNFOut.initSummaryBelief()\\n self.HNFOut.initExpectedUtility()\\n self.HNFOut.calcHypergameExpectedUtility()\\n self.HNFOut.calcModelingOpponentUtility()\",\n \"def settings_init(self):\\n config_console = configparser.ConfigParser()\\n config_console.read(CONFIG_FILE_NAME)\\n self.logmode = config_console[\\\"LOG\\\"][\\\"log_mode\\\"]\",\n \"def __init__(self, settings: ActorSettings) -> None:\\n super().__init__()\\n self.settings = settings\\n\\n self._critic = None\",\n \"def __init__(self, *args, **kwargs):\\r\\n super().__init__()\\r\\n self._cfg = ConfigDict() # current configuration\\r\\n self._default_config = ConfigDict() # default configuration\\r\\n self._temp_config = OrderedDict() # temporary configuration\\r\\n self._path = Path() # current configuration path\\r\\n self._default_path = Path() # default configuration path\\r\\n self._conversion_dict = None\\r\\n self._auto_cast = None\\r\\n self._write_flags = None\\r\\n self._force_load = None\\r\\n self._load_empty = None\\r\\n self._ask_path = None\\r\\n self._search_in_default_config = None\\r\\n self._init_count = 0\\r\\n self._policies = defaultdict(bool) # by default every modification is forbidden # WIP\\r\\n if args or kwargs:\\r\\n self.init(*args, **kwargs)\\r\\n logger.debug(\\\"Config object created.\\\")\",\n \"def __init__(self):\\n self.__load_settings()\\n self.__load_history()\\n self.__clear_context()\\n\\n if self.DELETE_ALL_ON_STARTUP:\\n sublime.set_timeout_async(lambda: self.delete_all_history(), 0)\\n elif self.CLEANUP_ON_STARTUP:\\n sublime.set_timeout_async(lambda: self.clean_history(False), 0)\",\n \"def __init__(self, configs):\\n\\n self.__configs = configs\",\n \"def __init__(self, os_creds, keypair_settings):\\n super(self.__class__, self).__init__(os_creds)\\n\\n self.keypair_settings = keypair_settings\\n self.__delete_keys_on_clean = True\\n\\n # Attributes instantiated on create()\\n self.__keypair = None\",\n \"def test_settings_instantiation(self):\\n ## no settings passed on instantiation\\n bd = BorrowDirect() # no settings info\\n self.assertEqual(\\n True, isinstance(bd, BorrowDirect) )\\n ## dict settings\\n settings_dict = {} ## empty dct\\n bd = BorrowDirect( settings_dict )\\n self.assertEqual(\\n None, bd.UNIVERSITY_CODE )\\n settings_dict = { 'UNIVERSITY_CODE': '123' } ## populated dct\\n bd = BorrowDirect( settings_dict )\\n self.assertEqual(\\n '123', bd.UNIVERSITY_CODE )\\n ## module settings\\n s = imp.new_module( 'settings' ) ## empty module\\n bd = BorrowDirect( s )\\n self.assertEqual(\\n None, bd.UNIVERSITY_CODE )\\n s = imp.new_module( 'settings' ) ## populated module\\n s.UNIVERSITY_CODE = '234'\\n bd = BorrowDirect( s )\\n self.assertEqual(\\n '234', bd.UNIVERSITY_CODE )\",\n \"def initialize(cls, settings):\\n\\n settings_obj = SettingsService.load_game_conf()\\n\\n for entry in SettingsService.GAME_SETTINGS:\\n value = settings_obj.get(SettingsService.GAME_SETTINGS_ROOT, {}).get(entry, None)\\n if value is None:\\n raise RuntimeError(f\\\"Entry {entry} is missing in settings.\\\")\\n\\n settings[entry] = float(value)\\n\\n return settings\",\n \"def config_init(self):\\n\\n game_opts = [\\n\\n # Execution Options\\n ('debug',False), # Toggle Debug Messaging\\n ('log_path',False), # Turn on logging (w/path)\\n ('log_lvl',logging.DEBUG), # Set log level\\n\\n # World Generation Options\\n ('flex_limit',3) # Sets the maximum variance\\n\\n ]\\n\\n # Attempts to pull each value from the configuration\\n # if not in config, the default value defined above\\n # is set instead\\n for opt in game_opts:\\n try:\\n setattr(self,opt[0],self.conf.conf_dict[opt[0]])\\n except:\\n setattr(self,opt[0],opt[1])\\n continue\",\n \"def __init__(self, *args):\\n this = _libsbml.new_L3ParserSettings(*args)\\n try: self.this.append(this)\\n except: self.this = this\",\n \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(KltSettings, self).__init__(*args, **kwds)\\n #message fields cannot be None, assign default values for those that are\\n if self.max_features is None:\\n self.max_features = 0\\n if self.window_size is None:\\n self.window_size = 0\\n if self.quality is None:\\n self.quality = 0.\\n if self.min_distance is None:\\n self.min_distance = 0.\\n if self.harris is None:\\n self.harris = 0.\\n if self.size_block is None:\\n self.size_block = 0\\n if self.pyramid_lvl is None:\\n self.pyramid_lvl = 0\\n if self.mask_border is None:\\n self.mask_border = 0\\n else:\\n self.max_features = 0\\n self.window_size = 0\\n self.quality = 0.\\n self.min_distance = 0.\\n self.harris = 0.\\n self.size_block = 0\\n self.pyramid_lvl = 0\\n self.mask_border = 0\",\n \"def __init__(self):\\n # Read configuration into dictionary\\n self.directories = general.config_directories()\\n self.config = general.read_yaml_files(self.directories)\",\n \"def __init__(self, text=None, settings=None, style='General', language='en'):\\n\\n self._text = None\\n self._settings = None\\n self._style = None\\n self._language = None\\n\\n self.text = text\\n self.settings = settings\\n self.style = style\\n self.language = language\",\n \"def __init__(self):\\n self.__parameters: ConfigParams = ConfigParams()\",\n \"def settings(self, settings):\\n\\n self._settings = settings\",\n \"def __init__(\\n self,\\n *,\\n file_name: str,\\n klass: Type[TSettingsType],\\n ) -> None:\\n self._file_name = file_name\\n self._klass = klass\",\n \"def __init__(self):\\n # Screen settings\\n self.screen_width = 1860\\n self.screen_height = 1020\\n self.screen_size = self.screen_width, self.screen_height\\n self.bg_color = 230, 230, 230\\n\\n # Ship static settings\\n self.ship_limit = 3\\n\\n # Bullet static settings\\n self.bullet_limit = 3\\n self.bullet_width = 3\\n self.bullet_height = 15\\n self.bullet_color = 60, 60, 60\\n\\n # Alien static settings\\n self.fleet_drop_speed = 10\\n\\n self.speed_up_scale = 1.1\\n self.initialize_dynamic_settings()\",\n \"def __init__(self):\\n\\n # open json config file that reads in information\\n config_path = open(\\\"config.json\\\", \\\"r\\\")\\n config_json = config_path.read()\\n config_dict = json.loads(config_json)\\n\\n # assign object variables\\n self.project_id = config_dict[\\\"project-id\\\"]\\n self.bucket_name = config_dict[\\\"bucket-name\\\"]\\n self.location_id = config_dict[\\\"key-location\\\"]\\n self.key_ring_id = config_dict[\\\"key-ring-id\\\"]\\n self.crypto_key_id = config_dict[\\\"crypto-key-id\\\"]\\n self.service_account_email = config_dict[\\\"service-account-email\\\"]\\n\\n # close the file\\n config_path.close()\",\n \"def __init__(self, window, settings_dir, n_trials=0):\\r\\n self.stimuli = OrderedDict()\\r\\n self.triggers = {}\\r\\n\\r\\n self.settings_dir = settings_dir\\r\\n self.num_trials = n_trials\\r\\n self.win = window\",\n \"def __init__(self, settings, **kwargs):\\n super().__init__(settings=settings, **kwargs)\\n self.vr_status = 'off' #off, ready, waiting or recording\\n for v_attr in self.saveable_defaults.keys():\\n setattr(self, v_attr, self.resolve_attr(v_attr, settings, self.saveable_defaults[v_attr]))\\n self.vr_splitter_port = None\\n self.vr_recordcount = 0\\n self.vr_lastrecording = 0\\n self.vr_activefile = '-'\\n self.vr_protect = threading.Lock()\\n self.vr_monthread = None\\n self.procthread = None\\n self.vr_web_trigger = None\\n self.vr_trig_queue = queue.Queue()\\n # and this to support web browser front end\\n self.camhand.add_url_rule('/flip-trigger', view_func=self.flip_record_trigger, methods=('REQUEST',))\\n self.camhand.add_url_rule('/flip-record', view_func=self.record_now, methods=('REQUEST',))\",\n \"def __init__(self, values = None):\\n TCInit(self)\\n if values is not None:\\n self.set_opts(values)\",\n \"def __init__(self):\\n default_config = Config()\\n query = Query(default_config)\\n database = Database(default_config)\\n common_util = CommonUtil(default_config, database)\\n self.config = default_config\\n self.query = query\\n self.database = database\\n self.common_util = common_util\",\n \"def __init__(self, argv: list, company: str, appname: str, Liststr=None):\\n if company is None:\\n company = self.__class__.__name__\\n QSettings.setPath(QSettings.IniFormat, QSettings.UserScope, str(FOLDER.parent / \\\"settings\\\"))\\n self.settings = QSettings(QSettings.IniFormat, QSettings.UserScope, company, appname)\\n super().__init__(argv)\",\n \"def __init__(self, generateConf=False):\\n super(Settings, self).__init__()\\n if generateConf:\\n self.writeSettingsFile(DEFAULT_CONF, path=DEFAULT_SETTINGS_PATH)\\n else:\\n try:\\n self.settingsFilePath = DEFAULT_SETTINGS_PATH\\n except IOError:\\n self.writeSettingsFile(\\n DEFAULT_CONF,\\n path=DEFAULT_SETTINGS_PATH\\n )\\n util.write(\\n \\\"No configuration file found, a default one has been generated at %s.\\\" % (DEFAULT_SETTINGS_PATH,),\\n priority=9\\n )\\n sys.exit(0)\",\n \"def initialize_from_config(self):\",\n \"def __init__(self, settings={}, prefix=\\\"\\\"):\\n self.settings = settings\\n self.mapper = routes.Mapper()\\n self.setup_handlers(self.mapper)\",\n \"def init_from_settings(self, settings):\\n\\n # Initializes screen.\\n self.screen = pygame.display.set_mode(settings.resolution)\\n self.screen.set_clip(settings.game_info)\\n self.screen.fill(settings.colors[5])\\n \\n self.screen.blit(settings.help_text1, (165, 250))\\n self.screen.blit(settings.help_text2, (165, 270))\\n self.screen.blit(settings.help_text3, (165, 290))\\n \\n pygame.draw.rect(self.screen, settings.colors[6], start_rect, 0)\\n pygame.draw.rect(self.screen, settings.colors[6], reset_rect, 0)\\n pygame.draw.rect(self.screen, settings.colors[6], settings.box_display, 2)\\n \\n self.screen.blit(settings.start_text, (171, 188))\\n self.screen.blit(settings.reset_text, (251, 188))\\n \\n self.screen.set_clip(settings.game_area)\\n \\n pygame.display.set_caption(settings.title)\\n pygame.mouse.set_visible(settings.mouse_enabled)\\n\\n # Initializes background.\\n background = pygame.Surface(self.screen.get_size())\\n self.background = background.convert()\\n self.background.fill(settings.background)\\n\\n pygame.display.update()\",\n \"def _generate_settings(self):\\n settings = {}\\n settings[\\\"api_client_id\\\"] = input(\\\"(OPTIONAL) Please enter your Twitch API Client ID: \\\") #Get API Client ID first so I can use API to get user ID\\n #Save JSON\\n fileIO.save_json(\\\"settings.json\\\", settings)\\n name = False\\n while not name: #While name not set\\n name = input(\\\"Please enter the username of your Twitch account: \\\").lower()\\n userID = self._get_user_id(name)\\n if not userID:\\n name = False\\n settings[\\\"userid\\\"] = userID\\n settings[\\\"oauth\\\"] = input(\\\"Please enter the oauth token for your Twitch account: \\\")\\n if settings[\\\"oauth\\\"].startswith(\\\"oauth:\\\"): #If the oauth token starts with oauth:, remove it\\n settings[\\\"oauth\\\"] = settings[\\\"oauth\\\"][6:]\\n settings[\\\"error_webhook\\\"] = input(\\\"Please enter the Discord WebHook URL you would like errors to be sent to: \\\")\\n #Save JSON\\n fileIO.save_json(\\\"settings.json\\\", settings)\",\n \"def build_mail_settings():\\n mail_settings = MailSettings()\\n mail_settings.bcc_settings = BCCSettings(True, Email(\\\"test@example.com\\\"))\\n mail_settings.bypass_list_management = BypassListManagement(True)\\n mail_settings.footer_settings = FooterSettings(True, \\\"Footer Text\\\",\\n (\\\"<html><body>Footer \\\"\\n \\\"Text</body></html>\\\"))\\n mail_settings.sandbox_mode = SandBoxMode(True)\\n mail_settings.spam_check = SpamCheck(True, 1,\\n \\\"https://spamcatcher.sendgrid.com\\\")\\n return mail_settings\",\n \"def load_settings(self):\\n LogConfiguration.initialize(self._db)\\n self.analytics = Analytics(self._db)\\n self.auth = Authenticator(self._db, self.analytics)\\n\\n self.setup_external_search()\\n\\n # Track the Lane configuration for each library by mapping its\\n # short name to the top-level lane.\\n new_top_level_lanes = {}\\n # Create a CirculationAPI for each library.\\n new_circulation_apis = {}\\n\\n # Potentially load a CustomIndexView for each library\\n new_custom_index_views = {}\\n\\n # Make sure there's a site-wide public/private key pair.\\n self.sitewide_key_pair\\n\\n for library in self._db.query(Library):\\n lanes = load_lanes(self._db, library)\\n\\n new_top_level_lanes[library.id] = lanes\\n\\n new_custom_index_views[library.id] = CustomIndexView.for_library(\\n library\\n )\\n\\n new_circulation_apis[library.id] = self.setup_circulation(\\n library, self.analytics\\n )\\n self.top_level_lanes = new_top_level_lanes\\n self.circulation_apis = new_circulation_apis\\n self.custom_index_views = new_custom_index_views\\n self.shared_collection_api = self.setup_shared_collection()\\n\\n # Assemble the list of patron web client domains from individual\\n # library registration settings as well as a sitewide setting.\\n patron_web_domains = set()\\n admin_web_domains = set()\\n\\n def get_domain(url):\\n url = url.strip()\\n if url == \\\"*\\\":\\n return url\\n scheme, netloc, path, parameters, query, fragment = urllib.parse.urlparse(\\n url)\\n if scheme and netloc:\\n return scheme + \\\"://\\\" + netloc\\n else:\\n return None\\n\\n sitewide_patron_web_client_urls = ConfigurationSetting.sitewide(\\n self._db, Configuration.PATRON_WEB_HOSTNAMES).value\\n if sitewide_patron_web_client_urls:\\n for url in sitewide_patron_web_client_urls.split('|'):\\n domain = get_domain(url)\\n if domain:\\n patron_web_domains.add(domain)\\n\\n sitewide_admin_web_client_urls = ConfigurationSetting.sitewide(\\n self._db, Configuration.ADMIN_WEB_HOSTNAMES).value\\n if sitewide_admin_web_client_urls:\\n for url in sitewide_admin_web_client_urls.split('|'):\\n domain = get_domain(url)\\n if domain:\\n admin_web_domains.add(domain)\\n\\n from .registry import Registration\\n for setting in self._db.query(\\n ConfigurationSetting).filter(\\n ConfigurationSetting.key == Registration.LIBRARY_REGISTRATION_WEB_CLIENT):\\n if setting.value:\\n patron_web_domains.add(get_domain(setting.value))\\n\\n self.patron_web_domains = patron_web_domains\\n self.admin_web_domains = admin_web_domains\\n self.setup_configuration_dependent_controllers()\\n authentication_document_cache_time = int(\\n ConfigurationSetting.sitewide(\\n self._db, Configuration.AUTHENTICATION_DOCUMENT_CACHE_TIME\\n ).value_or_default(0)\\n )\\n self.authentication_for_opds_documents = ExpiringDict(\\n max_len=1000, max_age_seconds=authentication_document_cache_time\\n )\\n self.wsgi_debug = ConfigurationSetting.sitewide(\\n self._db, Configuration.WSGI_DEBUG_KEY\\n ).bool_value or False\",\n \"def __init__(\\n self,\\n settings=None,\\n scenario=None,\\n living_expenses_strategy=None,\\n saving_strategy=None,\\n withdrawal_strategy=None,\\n tax_treatment=None\\n ):\\n # Set up instance:\\n super().__init__()\\n self.default_values = copy(DEFAULTVALUES)\\n self.default_types = copy(DEFAULTTYPES)\\n self.default_builders = copy(DEFAULTBUILDERS)\\n # Store args as attributes:\\n # For `settings` specifically, use the default values provided\\n # by the class if none are provided explicitly.\\n if settings is None:\\n self.settings = Settings()\\n else:\\n self.settings = settings\\n # For the rest, None is allowed:\\n self.scenario = scenario\\n self.living_expenses_strategy = living_expenses_strategy\\n self.saving_strategy = saving_strategy\\n self.withdrawal_strategy = withdrawal_strategy\\n self.tax_treatment = tax_treatment\\n # Some params aren't used to build Forecast and so are not\\n # received as input to __init__. Create attrs for them here:\\n self.allocation_strategy = None\",\n \"def __init__(self, *kwargs):\\n self.session = requests.Session()\\n self.config_path = os.path.join(\\n os.path.dirname(__file__), 'config.json')\\n self.load_config()\\n if self.application_token == '':\\n self.set_application_token()\\n self.token = self.get_token()\\n self.get_settings()\",\n \"def __init__(self):\\n self.collectorName = COLLECTOR_NAME\\n self.configCycleInterval = 20 # minutes\\n self.cycleInterval = 5 * 60 # seconds\\n\\n # The configurationService attribute is the fully qualified class-name\\n # of our configuration service that runs within ZenHub\\n self.configurationService = 'NullConfig'\\n\\n # Will be filled in based on buildOptions\\n self.options = None\\n\\n self.configCycleInterval = 20*60\",\n \"def _post_initialisations(self):\\n # Init the settings module.\\n self.dummy_for_settings = SectionConfig(self.app.id, self.__class__.__name__)\\n global settings\\n settings = self.dummy_for_settings\\n\\n self.dummy_for_options = OptionConfig(self.app.id)\\n global options\\n options = self.dummy_for_options\\n\\n # Bind message boxes.\\n self.MessageBox = MessageBox(self)\\n self.msg = self.MessageBox.Message\\n self.are_you_sure = self.MessageBox.are_you_sure\\n\\n # Set previous size and state.\\n width = settings.get('width', 350)\\n height = settings.get('height', 350)\\n self.set_title(self.app.localizedname)\\n self.resize(width, height)\\n if settings.get_bool('maximized', False):\\n self.maximize()\\n # Load any other settings here.\\n self.load_xinput_devices()\",\n \"def __init__(self, config):\\n\\n self.mode9 = config[sC.PROJECT_DETAILS][sC.MODE] == '9'\\n self.admins = eval(handler.config[sC.PROJECT_DETAILS][sC.ADMIN_IDS])\\n self.approvers = eval(handler.config[sC.COUNTER_STRIKE_ADMINS][sC.APPROVERS])\",\n \"def __init__(self, configfile='settings.cfg'):\\n \\n self.configfile = configfile\\n \\n # Load parameters from config file\\n config = ConfigParser.RawConfigParser()\\n config.read(self.configfile)\\n \\n # Set parameters to default if not in config file \\n self.title=config.get('Settings','title') if config.has_option(\\n 'Settings','title') else 'REDPy Catalog'\\n self.filename=config.get('Settings','filename') if config.has_option(\\n 'Settings','filename') else 'redpytable.h5'\\n self.groupName=config.get('Settings','groupName') if config.has_option(\\n 'Settings','groupName') else 'default'\\n self.groupDesc=config.get('Settings','groupDesc') if config.has_option(\\n 'Settings','groupDesc') else 'Default Test Run'\\n self.nsta=config.getint('Settings','nsta') if config.has_option(\\n 'Settings','nsta') else 8 \\n self.station=config.get('Settings','station') if config.has_option(\\n 'Settings','station') else 'SEP,YEL,HSR,SHW,EDM,STD,JUN,SOS'\\n self.channel=config.get('Settings','channel') if config.has_option(\\n 'Settings','channel') else 'EHZ,EHZ,EHZ,EHZ,EHZ,EHZ,EHZ,EHZ'\\n self.network=config.get('Settings','network') if config.has_option(\\n 'Settings','network') else 'UW,UW,UW,UW,UW,UW,UW,UW'\\n self.location=config.get('Settings','location') if config.has_option(\\n 'Settings','location') else '--,--,--,--,--,--,--,--'\\n self.samprate=config.getfloat('Settings','samprate') if config.has_option(\\n 'Settings','samprate') else 100.\\n self.nstaC=config.getint('Settings','nstaC') if config.has_option(\\n 'Settings','nstaC') else 5\\n self.printsta=config.getint('Settings','printsta') if config.has_option(\\n 'Settings','printsta') else 2\\n self.server=config.get('Settings','server') if config.has_option(\\n 'Settings','server') else 'IRIS'\\n self.port=config.getint('Settings','port') if config.has_option(\\n 'Settings','port') else 16017\\n self.nsec=config.getint('Settings','nsec') if config.has_option(\\n 'Settings','nsec') else 3600\\n self.lwin=config.getfloat('Settings','lwin') if config.has_option(\\n 'Settings','lwin') else 7.\\n self.swin=config.getfloat('Settings','swin') if config.has_option(\\n 'Settings','swin') else 0.8\\n self.trigon=config.getfloat('Settings','trigon') if config.has_option(\\n 'Settings','trigon') else 3.\\n self.trigoff=config.getfloat('Settings','trigoff') if config.has_option(\\n 'Settings','trigoff') else 2.\\n self.kurtmax=config.getfloat('Settings','kurtmax') if config.has_option(\\n 'Settings','kurtmax') else 80.\\n self.kurtfmax=config.getfloat('Settings','kurtfmax') if config.has_option(\\n 'Settings','kurtfmax') else 150.\\n self.oratiomax=config.getfloat('Settings','oratiomax') if config.has_option(\\n 'Settings','oratiomax') else 0.06\\n self.kurtwin=config.getfloat('Settings','kurtwin') if config.has_option(\\n 'Settings','kurtwin') else 5.\\n self.winlen=config.getint('Settings','winlen') if config.has_option(\\n 'Settings','winlen') else 1024\\n self.fmin=config.getfloat('Settings','fmin') if config.has_option(\\n 'Settings','fmin') else 1.\\n self.fmax=config.getfloat('Settings','fmax') if config.has_option(\\n 'Settings','fmax') else 10.\\n self.filomin=config.getfloat('Settings','filomin') if config.has_option(\\n 'Settings','filomin') else 1.\\n self.filomax=config.getfloat('Settings','filomax') if config.has_option(\\n 'Settings','filomax') else 2.5\\n self.fiupmin=config.getfloat('Settings','fiupmin') if config.has_option(\\n 'Settings','fiupmin') else 5.\\n self.fiupmax=config.getfloat('Settings','fiupmax') if config.has_option(\\n 'Settings','fiupmax') else 10.\\n self.telefi=config.getfloat('Settings','telefi') if config.has_option(\\n 'Settings','telefi') else -1.\\n self.teleok=config.getint('Settings','teleok') if config.has_option(\\n 'Settings','teleok') else 1 \\n self.cmin=config.getfloat('Settings','cmin') if config.has_option(\\n 'Settings','cmin') else 0.7\\n self.ncor=config.getint('Settings','ncor') if config.has_option(\\n 'Settings','ncor') else 4\\n self.minorph=config.getfloat('Settings','minorph') if config.has_option(\\n 'Settings','minorph') else 0.05\\n self.maxorph=config.getfloat('Settings','maxorph') if config.has_option(\\n 'Settings','maxorph') else 7.\\n self.minplot=config.getint('Settings','minplot') if config.has_option(\\n 'Settings','minplot') else 3\\n self.dybin=config.getfloat('Settings','dybin') if config.has_option(\\n 'Settings','dybin') else 1.\\n self.hrbin=config.getfloat('Settings','hrbin') if config.has_option(\\n 'Settings','hrbin') else 1.\\n self.recplot=config.getfloat('Settings','recplot') if config.has_option(\\n 'Settings','recplot') else 14.\\n \\n # Derived Settings\\n self.ptrig=1.5*self.winlen/self.samprate\\n self.atrig=3*self.winlen/self.samprate\\n self.mintrig=self.winlen/self.samprate\\n self.wshape = int((self.ptrig + self.atrig)*self.samprate) + 1\",\n \"def init_game_setting(self):\\r\\n pass\",\n \"def config(settings):\\n\\n #T = current.T\\n\\n # PrePopulate data\\n settings.base.prepopulate += (\\\"SHARE/LK\\\",)\\n settings.base.prepopulate_demo += (\\\"SHARE/Demo\\\",)\\n\\n # Finance settings\\n settings.fin.currencies = {\\n #\\\"EUR\\\" : \\\"Euros\\\",\\n #\\\"GBP\\\" : \\\"Great British Pounds\\\",\\n \\\"LKR\\\" : \\\"Sri Lanka Rupees\\\",\\n \\\"USD\\\" : \\\"United States Dollars\\\",\\n }\\n settings.fin.currency_default = \\\"USD\\\"\",\n \"def init(self):\\n\\n if self.has_settings:\\n print(\\n TERM.bold_red('Error:'),\\n 'Settings file already exists. Doing nothing.'\\n )\\n return\\n\\n new_settings = {\\n 'strategy': self.ns.strategy,\\n 'branch': self.ns.branch,\\n 'scoring': self.ns.scoring,\\n }\\n\\n with open(self.settings, 'w') as f:\\n f.write(yaml.dump(new_settings, default_flow_style=False))\\n\\n print(\\n TERM.bold_green('Yay!'),\\n 'Wrote settings file {0}'.format(self.settings)\\n )\",\n \"def __init__(self, config_file=None):\\n\\t\\tself.options = {}\\n\\n\\t\\tif config_file:\\n\\t\\t\\tself.set_file(config_file)\",\n \"def get_settings():\\n return SettingCollection.build()\",\n \"def default_settings(self, settings):\\n return {}\",\n \"def __init__(self, setting):\\n self.setting.update(setting)\\n self.base = (self.setting.get('base') + '/').lower()\\n self.client = Box(self.setting.get('access_token'))\\n self.client.users_get_current_account()\",\n \"def __init__(self):\\n self._init_key_settings()\\n self._init_misc_extensions()\\n self.minVersion = (3, 1)\\n self.maxVersion = (3, 4)\\n self.versions = [(3, 4), (3, 3), (3, 2), (3, 1)]\\n self.cipherNames = list(CIPHER_NAMES)\\n self.macNames = list(MAC_NAMES)\\n self.keyExchangeNames = list(KEY_EXCHANGE_NAMES)\\n self.cipherImplementations = list(CIPHER_IMPLEMENTATIONS)\",\n \"def _initialise(self, **settings):\\n if settings:\\n self.settings = self.clean_settings(settings)\\n else:\\n # same as self._fill_from_preset('commoner')\\n self._initialise(name=\\\"Commoner\\\", alignment=\\\"neutral\\\",\\n ac=10, hp=4,\\n attack_parameters=[['club', 2, 0, 4]])\\n print(\\\"EMPTY CREATURE GIVEN. SETTING TO COMMONER\\\")\\n return 0\\n\\n # Mod of preexisting\\n if 'base' in self.settings:\\n # Sanify first and make victim\\n if type(self.settings['base']) is str:\\n # generate a preset and get its attributes. Seems a bit wasteful.\\n victim = self.__class__(self.settings['base'])\\n elif isinstance(self.settings['base'], self.__class__):\\n victim = self.settings['base']\\n else:\\n raise TypeError\\n # copy all\\n # victim.ability_bonuses #in case the user provided with ability scores,\\n # which are overridden by adbility bonues\\n base = {x: getattr(victim, x) for x in dir(victim) if\\n getattr(victim, x) and x.find(\\\"__\\\") == -1 and x.find(\\\"_\\\") != 0 and x != 'beastiary'}\\n base['ability_bonuses'] = {}\\n # base.update(**self.settings)\\n for (k, v) in self.settings.items():\\n if type(v) is dict:\\n base[k].update(v)\\n else:\\n base[k] = v\\n self.settings = base\\n\\n # Name etc.\\n # subscript assigns it or self.settings if it has a value\\n self['name'] = 'nameless'\\n self['level'] = 0\\n self['xp'] = 0\\n # proficiency. Will be overridden if hp is provided?\\n self['proficiency'] = 1 + round(self.level / 4) # TODO check maths on PH\\n\\n # set abilities\\n self.set_ability_dice()\\n\\n # Get HD\\n self.hit_die = None\\n if 'hd' in self.settings.keys():\\n if type(self.settings['hd']) is Dice:\\n self.hit_die = self.settings['hd'] # we're dealing with a copy of a beastiary obj.\\n else:\\n self.hit_die = Dice(num_faces=int(self.settings['hd']),\\n bonus=self.con.bonus,\\n avg=True,\\n role=\\\"hd\\\")\\n elif 'size' in self.settings.keys():\\n size_cat = {\\\"small\\\": 6, \\\"medium\\\": 8, \\\"large\\\": 10, \\\"huge\\\": 12}\\n if self.settings['size'] in size_cat.keys():\\n self.hit_die = Dice(bonus=self.con.bonus,\\n num_faces=size_cat[self.settings['size']],\\n avg=True,\\n role=\\\"hd\\\")\\n elif 'hp' in self.settings and 'level' in self.settings:\\n # Guess based on hp and level. It is not that dodgy really as the manual does not use odd dice.\\n # hp =approx. 0.5 HD * (level-1) + HD + con * level\\n # HD * (0.5* (level-1)+1) = hp - con*level\\n # HD = (hp - con*level)/(level+1)\\n bestchoice = (int(self.settings['hp']) - self.con.bonus * int(self.settings['level'])) / (\\n (int(self.settings['level']) + 1))\\n print(int(self.settings['hp']), int(self.ability_bonuses['con']), int(self.settings['level']))\\n print(\\\"choice HD...\\\", bestchoice)\\n # print(\\\"diagnosis...\\\",self.ability_bonuses)\\n self.log.warning('Unfinished case to guess HD. so Defaulting hit dice to d8 instead') # TODO finish\\n self.hit_die = Dice(bonus=self.con.bonus, num_faces=8, avg=True, role=\\\"hd\\\")\\n else:\\n # defaulting to d8\\n self.log.warning('Insufficient info: defaulting hit dice to d8')\\n self.hit_die = Dice(bonus=self.con.bonus, num_faces=8, avg=True, role=\\\"hd\\\")\\n\\n # Get HP\\n if 'hp' in self.settings.keys():\\n self.hp = int(self.settings['hp'])\\n self.starting_hp = self.hp\\n elif self.settings['level']:\\n self.set_level()\\n else:\\n raise Exception('Cannot make character without hp or hd + level provided')\\n\\n # AC\\n if not 'ac' in self.settings.keys():\\n self.settings['ac'] = 10 + self.dex.bonus\\n self.ac = int(self.settings['ac'])\\n\\n # init\\n if not 'initiative_bonus' in self.settings:\\n self.settings['initiative_bonus'] = self.dex.bonus\\n self.initiative = Dice(bonus=int(self.settings['initiative_bonus']),\\n num_faces=20,\\n role=\\\"initiative\\\")\\n\\n ##spell casting ability_bonuses\\n if 'sc_ability' in self.settings:\\n self.spellcasting_ability_name = self.settings['sc_ability'].lower()\\n elif 'healing_spells' in self.settings or 'buff_spells' in self.settings:\\n self.spellcasting_ability_name = max('wis', 'int', 'cha',\\n key=lambda ab: self[ab].bonus) # Going for highest. seriously?!\\n print(\\n \\\"Please specify spellcasting ability of \\\" +\\n self.name +\\n \\\" next time, this time \\\" +\\n self.spellcasting_ability_name +\\n \\\" was used as it was biggest.\\\")\\n else:\\n self.spellcasting_ability_name = 'con' # TODO fix this botch up.\\n if not 'healing_bonus' in self.settings:\\n self.settings['healing_bonus'] = self[self.spellcasting_ability_name].bonus\\n if 'healing_spells' in self.settings:\\n self.starting_healing_spells = int(self.settings['healing_spells'])\\n self.healing_spells = self.starting_healing_spells\\n if not 'healing_dice' in self.settings:\\n self.settings['healing_dice'] = 4 # healing word.\\n self.healing = Dice(bonus=int(self.settings['healing_bonus']),\\n num_faces=int(self.settings['healing_dice']),\\n role=\\\"healing\\\") ##Healing dice can't crit or have adv.\\n else:\\n self.starting_healing_spells = 0\\n self.healing_spells = 0\\n # not a healer\\n\\n # attacks\\n self.attacks = []\\n self.hurtful = 0\\n if not 'attack_parameters' in self.settings:\\n # Benefit of doubt. Given 'em a dagger .\\n self.settings['attack_parameters'] = 'dagger'\\n if type(self.settings['attack_parameters']) is str:\\n try:\\n x = json.loads(self.settings['attack_parameters'].replace(\\\"*\\\", \\\"\\\\\\\"\\\"))\\n self._attack_parse(x)\\n self.attack_parameters = x\\n except:\\n # These have to be readable by _attack_parse\\n weapons = {'club': 4, 'greatclub': 8,\\n 'dagger': 4, 'shortsword': 6, 'longsword': 8, 'bastardsword': 10, 'greatsword': 12,\\n 'rapier': 8, 'scimitar': 6, 'sickle': 4,\\n 'handaxe': 6, 'battleaxe': 8, 'waraxe': 10, 'greataxe': 12,\\n 'javelin': 6, 'spear': 6, 'flail': 8, 'glaive': 10, 'halberd': 10, 'lance': 12, 'pike': 10,\\n 'trident': 6,\\n 'war pick': 8,\\n 'lighthammer': 4, 'mace': 6, 'warhammer': 8,\\n 'quaterstaff': 6, 'morningstar': 8, 'punch': 1,\\n 'whip': 4} # parsing of strings for dice not implemented yet, so punch is d1 for now.\\n # TODO weapons removed as they gave trouble:\\n # 'maul':[6,6],\\n # 'brütal war pick': [8, 8], # okay, I could not resist it.\\n\\n # bastard sword and war axe are no more due to the versatile rule,\\n # however they were kept here to keep it simple\\n # ranged weapons are missing for now...\\n for w in weapons.keys():\\n if self.settings['attack_parameters'].lower().find(w) > -1:\\n # TODO fix the fact that a it gives the finesse option to all.\\n if self.dex.bonus > self.str.bonus:\\n chosen_ab = 'dex'\\n else:\\n chosen_ab = 'str'\\n self.attack_parameters = [[w, self.proficiency + chosen_ab, chosen_ab, weapons[w]]]\\n self._attack_parse(self.attack_parameters)\\n self.log += \\\"Weapon matched by str to {w}\\\\n\\\"\\n break\\n else:\\n raise Exception(\\\"Cannot figure out what is: \\\" + self.settings['attack_parameters'] + str(\\n type(self.settings['attack_parameters'])))\\n elif type(self.settings['attack_parameters']) is list:\\n self.attack_parameters = self.settings['attack_parameters']\\n self._attack_parse(self.attack_parameters)\\n else:\\n raise Exception('Could not determine weapon')\\n ##Weird bit needing upgrade.\\n if 'alt_attack' in self.settings and type(self.settings['alt_attack']) is list:\\n self.alt_attack = {'name': self.settings['alt_attack'][0],\\n 'attack': Dice(bonus=self.settings['alt_attack'][1],\\n num_faces=20)} # CURRENTLY ONLY NETTING IS OPTION!\\n else:\\n self.alt_attack = {'name': None, 'attack': None}\\n # last but not least\\n if 'alignment' not in self.settings:\\n self.settings['alignment'] = \\\"unassigned mercenaries\\\" # hahaha!\\n self.alignment = self.settings['alignment']\\n # internal stuff\\n self.tally = {'damage': 0, 'hits': 0, 'dead': 0, 'misses': 0, 'battles': 0, 'rounds': 0, 'hp': 0,\\n 'healing_spells': 0}\\n self.copy_index = 1\\n self.condition = 'normal'\\n\\n self.dodge = 0\\n self.concentrating = 0\\n self.temp = 0\\n\\n self.buff_spells = None\\n if 'buff_spells' in self.settings:\\n self.buff_spells = int(self.settings['buff_spells'])\\n self.conc_fx = getattr(self, self.settings['buff'])\\n else:\\n self.buff_spells = 0\\n\\n if 'cr' in self.settings:\\n self.cr = self.settings['cr']\\n elif 'level' in self.settings:\\n # TODO check maths on MM.\\n if int(self.settings['level']) > 1:\\n self.cr = int(self.settings['level']) - 1\\n else:\\n self.cr = 0.5\\n else:\\n self.cr = None # vermin\\n\\n ##backdoor and overider\\n self['custom'] = []\\n for other in self.custom:\\n if other == \\\"conc_fx\\\":\\n getattr(self, self.settings['conc_fx'])\\n else:\\n self[other] = self.settings[other] # force it to be set.\\n\\n self.arena = None\\n self.settings = {}\",\n \"def __init__(self):\\n\\n if Config._instance:\\n raise Exception('Config singleton is already instantiated. User Config.get_instance() obtain it.')\\n\\n parser = configparser.ConfigParser()\\n parser.read('C:\\\\\\\\Users\\\\\\\\Akatosh\\\\\\\\PythonProjects\\\\\\\\note-it\\\\\\\\config\\\\\\\\config.ini')\\n\\n self.sections = {}\\n\\n for section in parser:\\n self.sections[section] = _Section(parser[section])\\n\\n Config._instance = self\",\n \"def setup_settings():\\n # pylint: disable=import-outside-toplevel\\n from django.conf import settings\\n import tiny_erp.settings as defaults\\n\\n for name in dir(defaults):\\n if name.isupper() and not hasattr(settings, name):\\n setattr(settings, name, getattr(defaults, name))\",\n \"def load_settings(self):\\n # Set the default settings. In case in a later version of this script the settings change, new default variables will be added automatically\\n self.settings = {\\n # Connection settings to OBS Studio websockets plugin\\n \\\"host\\\": \\\"localhost\\\",\\n \\\"port\\\": 4444,\\n \\\"password\\\": \\\"\\\",\\n \\\"update_frequency\\\": 1, # seconds, how often the script loads the SC2 UI location\\n }\\n if os.path.isfile(self.settings_path):\\n with open(self.settings_path) as f:\\n self.settings.update(json.load(f))\",\n \"def __init__(self):\\n # Try to get the Bulma settings. The user may not have created this dict.\\n try:\\n self.bulma_settings = settings.BULMA_SETTINGS\\n except AttributeError:\\n self.bulma_settings = {}\\n\\n self.bulma_submodule_path = simple_bulma_path / \\\"bulma\\\" / \\\"sass\\\"\\n self.custom_scss = self.bulma_settings.get(\\\"custom_scss\\\", [])\\n self.variables = self.bulma_settings.get(\\\"variables\\\", {})\\n self.output_style = self.bulma_settings.get(\\\"output_style\\\", \\\"nested\\\")\\n self.storage = FileSystemStorage(simple_bulma_path)\\n\\n # Make a list of all the finders except this one.\\n # We use this in the custom SCSS handler.\\n other_finders = settings.STATICFILES_FINDERS.copy()\\n other_finders.remove(\\\"django_simple_bulma.finders.SimpleBulmaFinder\\\")\\n self.other_finders = [get_finder(finder) for finder in other_finders]\",\n \"def initialize_bot_settings(settings: Config) -> Config:\\n\\n settings.define_section(\\\"ctxreminders\\\", ContextualRemindersSection)\\n\\n settings.ctxreminders.configure_setting(\\n \\\"persistence_dir\\\",\\n \\\"In which folder do you want to store the reminders file?\\\",\\n default=settings.core.homedir)\\n\\n settings.ctxreminders.configure_setting(\\n \\\"context_capture_min_duration\\\",\\n \\\"What is the minimum duration (in seconds) for reminders to save contextual chat logs? \\\" \\\\\\n \\\"Default 30 days (2592000)\\\",\\n default=2592000)\\n\\n settings.ctxreminders.configure_setting(\\n \\\"context_capture_max_duration\\\",\\n \\\"What is the minimum duration (in seconds) for reminders to save contextual chat logs? \\\" \\\\\\n \\\"Default no limit (inf)\\\",\\n default=math.inf)\\n\\n settings.ctxreminders.configure_setting(\\n \\\"context_capture_chat_lines\\\",\\n \\\"How many lines of chat to save with reminders that have context? Default 20\\\",\\n default=20)\\n\\n settings.ctxreminders.configure_setting(\\n \\\"pastebin_url\\\",\\n \\\"What is the pastebin url (PrivateBin)? Default ''\\\",\\n default=20)\\n\\n settings.ctxreminders.configure_setting(\\n \\\"pastebin_expiration\\\",\\n \\\"Expiration of pastebin pastes? Default 5min\\\",\\n default=\\\"5min\\\")\\n\\n return settings\",\n \"def __init__(self, config):\\n self._config = config\\n self.logging = logging.getLogger(\\\"Settings\\\")\\n self.logging.propagate = False\\n level = logging.INFO\\n if \\\"DEBUG\\\" in os.environ and (\\n os.environ[\\\"DEBUG\\\"]\\n or os.environ[\\\"DEBUG\\\"].lower() in (\\\"true\\\", \\\"t\\\", \\\"yes\\\", \\\"y\\\")\\n ):\\n level = logging.DEBUG\\n self.logging.setLevel(level)\\n handler = logging.StreamHandler()\\n handler.setLevel(level)\\n handler.setFormatter(logging.Formatter(\\\"%(asctime)s [Settings] %(message)s\\\"))\\n self.logging.addHandler(handler)\\n self.logging.debug(\\\"Running in debug mode.\\\")\",\n \"def __init__(self, filename=None, use_argv=True):\\n self._init_filename = filename\\n if use_argv:\\n self.options, self.args = [self.get_parser().parse_args()] * 2\\n else:\\n self.options = self.args = None\\n self._wrapped = self.load(file=self.settings_file)\\n # build a special dynamic section for things the user wants,\\n # ie, things that have been passed into the option\\n # parser but are not useful in the .ini\\n if not self.get_section('user'):\\n self['user'] = {}\\n if self.options is not None:\\n self['user']['shell'] = self.options.shell and 'true' or ''\\n else:\\n self['user']['shell'] = ''\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.81398326","0.8127764","0.81223667","0.8024526","0.75850004","0.7424904","0.7297364","0.727927","0.7227605","0.7210535","0.7177579","0.7173619","0.71518964","0.7086663","0.695417","0.6928098","0.69154674","0.6878284","0.6834534","0.6787074","0.67606455","0.6758912","0.6748137","0.6731232","0.6717663","0.6697846","0.66729206","0.66554177","0.6643146","0.6621014","0.6619936","0.659823","0.6592204","0.65862024","0.65728766","0.6546619","0.6544933","0.6533024","0.6487919","0.6480176","0.6473921","0.64459866","0.64372915","0.6423492","0.6413758","0.6411492","0.63983417","0.6369448","0.63606113","0.63606083","0.63580656","0.63486755","0.63437945","0.63424826","0.63396674","0.63156","0.62853354","0.62812006","0.62799835","0.6275392","0.62692064","0.6266638","0.6265068","0.62566024","0.624254","0.62355435","0.6233606","0.62323874","0.62239194","0.62191945","0.62123764","0.6195576","0.61916375","0.6187056","0.6178486","0.61685586","0.6168261","0.61567235","0.61534995","0.6152272","0.6152243","0.61508256","0.6149036","0.61482686","0.6145459","0.6136797","0.61335075","0.6128961","0.61260414","0.6113223","0.6113038","0.61114275","0.6086795","0.6084148","0.60823786","0.60778606","0.6073026","0.60699093","0.605438","0.6049942"],"string":"[\n \"0.81398326\",\n \"0.8127764\",\n \"0.81223667\",\n \"0.8024526\",\n \"0.75850004\",\n \"0.7424904\",\n \"0.7297364\",\n \"0.727927\",\n \"0.7227605\",\n \"0.7210535\",\n \"0.7177579\",\n \"0.7173619\",\n \"0.71518964\",\n \"0.7086663\",\n \"0.695417\",\n \"0.6928098\",\n \"0.69154674\",\n \"0.6878284\",\n \"0.6834534\",\n \"0.6787074\",\n \"0.67606455\",\n \"0.6758912\",\n \"0.6748137\",\n \"0.6731232\",\n \"0.6717663\",\n \"0.6697846\",\n \"0.66729206\",\n \"0.66554177\",\n \"0.6643146\",\n \"0.6621014\",\n \"0.6619936\",\n \"0.659823\",\n \"0.6592204\",\n \"0.65862024\",\n \"0.65728766\",\n \"0.6546619\",\n \"0.6544933\",\n \"0.6533024\",\n \"0.6487919\",\n \"0.6480176\",\n \"0.6473921\",\n \"0.64459866\",\n \"0.64372915\",\n \"0.6423492\",\n \"0.6413758\",\n \"0.6411492\",\n \"0.63983417\",\n \"0.6369448\",\n \"0.63606113\",\n \"0.63606083\",\n \"0.63580656\",\n \"0.63486755\",\n \"0.63437945\",\n \"0.63424826\",\n \"0.63396674\",\n \"0.63156\",\n \"0.62853354\",\n \"0.62812006\",\n \"0.62799835\",\n \"0.6275392\",\n \"0.62692064\",\n \"0.6266638\",\n \"0.6265068\",\n \"0.62566024\",\n \"0.624254\",\n \"0.62355435\",\n \"0.6233606\",\n \"0.62323874\",\n \"0.62239194\",\n \"0.62191945\",\n \"0.62123764\",\n \"0.6195576\",\n \"0.61916375\",\n \"0.6187056\",\n \"0.6178486\",\n \"0.61685586\",\n \"0.6168261\",\n \"0.61567235\",\n \"0.61534995\",\n \"0.6152272\",\n \"0.6152243\",\n \"0.61508256\",\n \"0.6149036\",\n \"0.61482686\",\n \"0.6145459\",\n \"0.6136797\",\n \"0.61335075\",\n \"0.6128961\",\n \"0.61260414\",\n \"0.6113223\",\n \"0.6113038\",\n \"0.61114275\",\n \"0.6086795\",\n \"0.6084148\",\n \"0.60823786\",\n \"0.60778606\",\n \"0.6073026\",\n \"0.60699093\",\n \"0.605438\",\n \"0.6049942\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6513789"},"document_rank":{"kind":"string","value":"38"}}},{"rowIdx":2997,"cells":{"query":{"kind":"string","value":"Transform x elementwise through an affine function y = exp(s)x + t where s = st[...,0] and t = st[...,1] with s.shape == x.shape == t.shape The Jacobian for this transformation is the coordinatewise product of the scaling factors J = prod(es[...,i],i)"},"document":{"kind":"string","value":"def element_wise_affine(x, st, compute_jacobian=True):\n es = torch.exp(st[..., 0])\n t = st[..., 1]\n logj = None\n if compute_jacobian:\n logj = torch.sum(torch.log(es), dim=-1)\n\n return es * x + t, logj"},"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 inverse_element_wise_affine(x, st, compute_jacobian=True):\n es = torch.exp(-st[..., 0])\n t = st[..., 1]\n logj = None\n if compute_jacobian:\n logj = torch.sum(torch.log(es), dim=-1)\n\n return es * (x - t), logj","def affine(params, x):\n return np.dot(params['w'], x) + params['b']","def transform(fn):\n def _(vec, dt):\n return np.einsum(\n 'ji,i,ki,k...->j...',\n evecs, fn(evals, dt), evecs, vec, optimize=True)\n\n return _","def affineTransform(x,output_dim):\n w=tf.get_variable(\"w\", [x.get_shape()[1], output_dim])\n b=tf.get_variable(\"b\", [output_dim], initializer=tf.constant_initializer(0.0))\n return tf.matmul(x,w)+b","def affine_transform(trans_mat, p0):\r\n n_data, n_dim = np.shape(p0)\r\n p0 = np.hstack((p0, np.ones((n_data, 1))))\r\n #return np.transpose(np.dot(np.transpose(trans_mat), np.transpose(p0)))\r\n return np.dot(p0, trans_mat)","def affine_transform(x, output_dim, name=None):\n\n w = tf.get_variable(name + \"_w\", [x.get_shape()[1], output_dim], initializer=tf.truncated_normal_initializer(stddev=0.02))\n b = tf.get_variable(name + \"_b\", [output_dim], initializer=tf.constant_initializer(0.0))\n\n return tf.matmul(x, w) + b","def affine_transform(geom, matrix):\n if geom.is_empty:\n return geom\n if len(matrix) == 6:\n ndim = 2\n a, b, d, e, xoff, yoff = matrix\n if geom.has_z:\n ndim = 3\n i = 1.0\n c = f = g = h = zoff = 0.0\n matrix = a, b, c, d, e, f, g, h, i, xoff, yoff, zoff\n elif len(matrix) == 12:\n ndim = 3\n a, b, c, d, e, f, g, h, i, xoff, yoff, zoff = matrix\n if not geom.has_z:\n ndim = 2\n matrix = a, b, d, e, xoff, yoff\n else:\n raise ValueError(\"'matrix' expects either 6 or 12 coefficients\")\n\n def affine_pts(pts):\n \"\"\"Internal function to yield affine transform of coordinate tuples\"\"\"\n if ndim == 2:\n for x, y in pts:\n xp = a * x + b * y + xoff\n yp = d * x + e * y + yoff\n yield (xp, yp)\n elif ndim == 3:\n for x, y, z in pts:\n xp = a * x + b * y + c * z + xoff\n yp = d * x + e * y + f * z + yoff\n zp = g * x + h * y + i * z + zoff\n yield (xp, yp, zp)\n\n # Process coordinates from each supported geometry type\n if geom.type in ('Point', 'LineString', 'LinearRing'):\n return type(geom)(list(affine_pts(geom.coords)))\n elif geom.type == 'Polygon':\n ring = geom.exterior\n shell = type(ring)(list(affine_pts(ring.coords)))\n holes = list(geom.interiors)\n for pos, ring in enumerate(holes):\n holes[pos] = type(ring)(list(affine_pts(ring.coords)))\n return type(geom)(shell, holes)\n elif geom.type.startswith('Multi') or geom.type == 'GeometryCollection':\n # Recursive call\n # TODO: fix GeometryCollection constructor\n return type(geom)([affine_transform(part, matrix)\n for part in geom.geoms])\n else:\n raise ValueError('Type %r not recognized' % geom.type)","def affine_mult(affine, coordinates):\n return np.dot(coordinates, affine[:3, :3].T) + affine[:3, -1]","def apply_affine_transform(x, M):\n is1d = len(x.shape) == 1\n if is1d:\n x = np.expand_dims(x, axis=0)\n\n x_hom = np.concatenate(\n [x, np.ones((x.shape[0], 1), dtype=x.dtype)], axis=-1\n )\n x_out = x_hom @ M.T\n if is1d:\n x_out = np.squeeze(x_out, axis=0)\n return x_out","def transformAffine(self, coords):\n coordsshape = coords.shape\n dims = coordsshape[0] + 1\n coords = coords.reshape((len(coords), -1))\n coords = np.concatenate((coords, np.ones((1, len(coords[0])))), 0)\n affine = np.eye(dims)\n # now transform first to center:\n meanvec = np.mean(coords, 1)\n center = np.eye(dims)\n center[:-1, -1] = -meanvec[:-1]\n affine = np.matmul(center, affine)\n\n if np.sum(self.shift):\n affine[:-1, -1] += (self.deformrandomstate.rand(dims - 1) - 0.5) * np.float32(self.shift)\n if np.max(self.scaling) > 1:\n scales = np.ones(dims)\n # scales[:-1] = (self.deformrandomstate.rand(dims-1)-0.5)*(self.scaling-1.0/self.scaling)+(self.scaling+1/self.scaling)/2\n scales[:-1] = self.scaling ** (self.deformrandomstate.rand(dims - 1) * 2 - 1)\n scales = np.diag(scales)\n # print(scales)\n affine = np.matmul(scales, affine)\n if np.sum(self.rotation):\n affine = self._rotate(affine)\n # move back to location:\n center[:-1, -1] = -center[:-1, -1]\n affine = np.matmul(center, affine)\n # now appyl to coords:\n coords = np.matmul(affine, coords)\n coords = coords[:-1]\n coords = coords.reshape(coordsshape)\n return coords","def transAffine2D( iScale=(1, 1), iTrans=(0, 0), iRot=0, iShear=(0, 0) ): \n iRot = iRot * np.pi / 180\n oMatScale = np.matrix( ((iScale[0],0,0),(0,iScale[1],0),(0,0,1)) )\n oMatTrans = np.matrix( ((1,0,iTrans[0]),(0,1,iTrans[1]),(0,0,1)) )\n oMatRot = np.matrix( ((np.cos(iRot),-np.sin(iRot),0),\\\n (np.sin(iRot),np.cos(iRot),0),(0,0,1)) )\n oMatShear = np.matrix( ((1,iShear[0],0),(iShear[1],1,0),(0,0,1)) )\n # ustvari izhodno matriko\n oMat2D = oMatTrans * oMatShear * oMatRot * oMatScale\n return oMat2D","def AffineTransform( from_pts, to_pts ):\n \n # check that there are match points\n if len(from_pts) != len(to_pts) or len(to_pts)<1:\n print \"from_pts and to_pts must be of same size.\"\n return False\n\n # check the dimensions\n dim = len(from_pts[0]) # num of dimensions\n if len(from_pts) < dim:\n print \"Too few points => under-determined system.\"\n return False\n elif len(from_pts) > dim + 1:\n print \"Too many points => over-determined system.\"\n return False\n\n \n #segregate the x and y coordinages\n from_pts_x, from_pts_y = zip(*from_pts)\n to_pts_x, to_pts_y = zip(*to_pts)\n \n #create the Matricies for processing\n I = np.matrix([from_pts_x, from_pts_y, [1,1,1]])\n P = np.matrix([to_pts_x, to_pts_y])\n \n #Calculate the 2D affine transform matrix (A)\n A = P * linalg.pinv(I) \n\n # Make a result object\n class Transformation:\n \"\"\"Result object that represents the transformation\n from affine fitter.\"\"\"\n\n def To_Str(self):\n res = \"\"\n for j in range(dim):\n str1 = \"x%d' = \" % j\n for i in range(dim):\n str1 +=\"x%d * %f + \" % (i, A[i][j+dim+1])\n str1 += \"%f\" % A[dim][j+dim+1]\n res += str1 + \"\\n\"\n return res\n\n def Transform(self, pt_x, pt_y):\n pt_vector = np.matrix([[pt_x], [pt_y], [1]])\n transformed_pt = A * pt_vector\n return map(itemgetter(0), transformed_pt.tolist())\n return Transformation()","def estimate_stage_affine(t0, t1):\n src = np.array([t.tforms[0].translation for t in t0])\n dst = np.array([t.tforms[1].translation for t in t1])\n aff = renderapi.transform.AffineModel()\n aff.estimate(src, dst)\n return aff","def affine_forward(x, W, b):\r\n x2d = np.reshape(x, (x.shape[0], -1)) # convert 4D input matrix to 2D \r\n out = np.dot(x2d, W) + b # linear transformation\r\n cache = (x, W, b) # keep for backward step (stay with us)\r\n return out, cache","def calc_affine(df):\n\tx0 = df.columns[0]\n\ty0 = df.index[0]\n\tdx = df.columns[1] - df.columns[0]\n\tdy = df.index[1] - df.index[0]\n\t\n\tt = affine.Affine(dx, 0, x0 , 0, dy ,y0 - dy) \n\t# y0 - dy because anker point is in the south!\n\treturn t","def temporal_affine_forward(x, w, b):\n N, T, D = x.shape\n M = b.shape[0]\n out = x.reshape(N * T, D).dot(w).reshape(N, T, M) + b\n cache = x, w, b, out\n return out, cache","def affine_forward(x, w, b):\n out = None\n ###########################################################################\n # TODO: Implement the affine forward pass. Store the result in out. You #\n # will need to reshape the input into rows. #\n ###########################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n t = np.reshape(x,(x.shape[0],np.prod(np.shape(x)[1:])))\n \n\n out = np.dot(t,w) + b\n \n #print(np.shape(out))\n\n pass\n\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n cache = (x, w, b)\n return out, cache","def estimate_affine_matrix_3d_to_2d(X, x):\n assert x.shape[0] == X.shape[0]\n assert x.shape[0] >= 4\n X = X.T # (3, n)\n x = x.T # (2, n)\n n = x.shape[1]\n\n ###---- 1. normalization\n ## 2d points\n mean = np.mean(x, 1) # (2, )\n x = x - np.tile(mean[:, np.newaxis], [1, n]) # (2, n)\n average_norm = np.mean(np.sqrt(np.sum(x ** 2, 0)))\n scale = np.sqrt(2) / average_norm\n x = scale * x\n\n # T = [[scale, 0, -mean * scale], \n # [ 0, scale, -mean * scale], \n # [ 0, 0, 1 ]]\n T = np.zeros((3, 3), dtype=np.float32)\n T[0, 0] = T[1, 1] = scale\n T[:2, 2] = -mean * scale\n T[2, 2] = 1\n\n ## 3d points\n X_homo = np.vstack((X, np.ones((1, n)))) # (4, n)\n mean = np.mean(X, 1) # (3, )\n X = X - np.tile(mean[:, np.newaxis], [1, n]) # (3, n)\n m = X_homo[: 3, :] - X\n average_norm = np.mean(np.sqrt(np.sum(X ** 2, 0)))\n scale = np.sqrt(3) / average_norm\n X = scale * X\n\n U = np.zeros((4, 4), dtype=np.float32)\n U[0, 0] = U[1, 1] = U[2, 2] = scale\n U[: 3, 3] = -mean * scale\n U[3, 3] = 1\n\n ###---- 2. equations\n A = np.zeros((n * 2, 8), dtype=np.float32)\n X_homo = np.vstack((X, np.ones((1, n)))).T\n A[: n, : 4] = X_homo\n A[n: , 4: ] = X_homo\n b = np.reshape(x, [-1, 1]) # (2n, 1)\n\n ###---- 3.solution\n p_8 = np.linalg.pinv(A).dot(b) # (8, 2n) x (2n, 1) -> (8, 1)\n p = np.zeros((3, 4), dtype=np.float32)\n p[0, :] = p_8[:4, 0]\n p[1, :] = p_8[4:, 0]\n p[-1, -1] = 1\n\n ###---- 4. denormalization\n P_Affine = np.linalg.inv(T).dot(p.dot(U))\n return P_Affine","def get_affine_matrix2d(\n translations: torch.Tensor,\n center: torch.Tensor,\n scale: torch.Tensor,\n angle: torch.Tensor,\n sx: Optional[torch.Tensor] = None,\n sy: Optional[torch.Tensor] = None,\n) -> torch.Tensor:\n transform: torch.Tensor = get_rotation_matrix2d(center, -angle, scale)\n transform[..., 2] += translations # tx/ty\n\n # pad transform to get Bx3x3\n transform_h = convert_affinematrix_to_homography(transform)\n\n if any(s is not None for s in [sx, sy]):\n shear_mat = get_shear_matrix2d(center, sx, sy)\n transform_h = transform_h @ shear_mat\n\n return transform_h","def get_affine_matrix2d(\n translations: Tensor,\n center: Tensor,\n scale: Tensor,\n angle: Tensor,\n sx: Tensor | None = None,\n sy: Tensor | None = None,\n) -> Tensor:\n transform: Tensor = get_rotation_matrix2d(center, -angle, scale)\n transform[..., 2] += translations # tx/ty\n\n # pad transform to get Bx3x3\n transform_h = convert_affinematrix_to_homography(transform)\n\n if any(s is not None for s in [sx, sy]):\n shear_mat = get_shear_matrix2d(center, sx, sy)\n transform_h = transform_h @ shear_mat\n\n return transform_h","def apply_affine_transform(x, theta=0, tx=0, ty=0, shear=0, zx=1, zy=1,\n row_axis=0, col_axis=1, channel_axis=2,\n fill_mode='nearest', cval=0., order=1):\n if scipy is None:\n raise ImportError('Image transformations require SciPy. '\n 'Install SciPy.')\n transform_matrix = None\n if theta != 0:\n theta = np.deg2rad(theta)\n rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],\n [np.sin(theta), np.cos(theta), 0],\n [0, 0, 1]])\n transform_matrix = rotation_matrix\n\n if tx != 0 or ty != 0:\n shift_matrix = np.array([[1, 0, tx],\n [0, 1, ty],\n [0, 0, 1]])\n if transform_matrix is None:\n transform_matrix = shift_matrix\n else:\n transform_matrix = np.dot(transform_matrix, shift_matrix)\n\n if shear != 0:\n shear = np.deg2rad(shear)\n shear_matrix = np.array([[1, -np.sin(shear), 0],\n [0, np.cos(shear), 0],\n [0, 0, 1]])\n if transform_matrix is None:\n transform_matrix = shear_matrix\n else:\n transform_matrix = np.dot(transform_matrix, shear_matrix)\n\n if zx != 1 or zy != 1:\n zoom_matrix = np.array([[zx, 0, 0],\n [0, zy, 0],\n [0, 0, 1]])\n if transform_matrix is None:\n transform_matrix = zoom_matrix\n else:\n transform_matrix = np.dot(transform_matrix, zoom_matrix)\n\n if transform_matrix is not None:\n h, w = x.shape[row_axis], x.shape[col_axis]\n transform_matrix = transform_matrix_offset_center(\n transform_matrix, h, w)\n x = np.rollaxis(x, channel_axis, 0)\n final_affine_matrix = transform_matrix[:2, :2]\n final_offset = transform_matrix[:2, 2]\n\n channel_images = [ndimage.interpolation.affine_transform(\n x_channel,\n final_affine_matrix,\n final_offset,\n order=order,\n mode=fill_mode,\n cval=cval) for x_channel in x]\n x = np.stack(channel_images, axis=0)\n x = np.rollaxis(x, 0, channel_axis + 1)\n return x","def __affine_geo_transformation(x, y, gtr):\n\n # https://gdal.org/user/raster_data_model.html#affine-geotransform\n # Affine transformation rewritten for rasterio:\n gtr_x = gtr[2] + (x + 0.5) * gtr[0] + (y + 0.5) * gtr[1]\n gtr_y = gtr[5] + (x + 0.5) * gtr[3] + (y + 0.5) * gtr[4]\n\n return gtr_x, gtr_y","def fit_transform(self, x: Array2D) -> Array2D:","def get_affine(x, m, c):\n x = m*x + c\n return x","def affine_forward(x,w,b):\n out=None\n N=x.shape[0]\n x_row=x.reshape(N,-1)\n out=np.dot(x_row,w)+b\n cache=(x,w,b)\n return out,cache","def affine_forward(X, W, b):\n return np.dot(X, W) + b","def apply_affine(A: Affine, x: np.ndarray, y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:\n\n shape = x.shape\n\n A = np.asarray(A).reshape(3, 3) # type: ignore[assignment]\n t = A[:2, -1].reshape((2, 1)) # type: ignore[index]\n A = A[:2, :2] # type: ignore[index]\n\n x, y = A @ np.vstack([x.ravel(), y.ravel()]) + t\n x, y = (a.reshape(shape) for a in (x, y))\n return (x, y)","def affine_forward(x, w, b):\n #raise NotImplementedError\n #######################################################################\n # #\n # #\n # TODO: YOUR CODE HERE #\n # #\n # #\n #######################################################################\n out=np.dot(x,w)+b\n cache=(x,w,b)\n return(out, cache)","def affine_forward(x, w, b):\n ############################################################################\n # TODO: Implement the affine forward pass. Store the result in 'out'. You #\n # will need to reshape the input into rows. #\n ############################################################################\n ############################################################################\n # START OF YOUR CODE #\n ############################################################################\n N = len(x)\n D,M = w.shape\n # reshape get a new x\n new_x = x.reshape(N,D)\n # get the output\n out = np.dot(new_x,w) + np.expand_dims(b,axis=0)\n \n ############################################################################\n # END OF YOUR CODE #\n ############################################################################\n return out","def get_transform(ds):\n\n if 'transform' in ds.attrs:\n ds_trans = ds.attrs['transform']\n if isinstance(ds_trans, Affine):\n return ds_trans\n else:\n return Affine(*ds_trans)\n\n elif 'crs' in ds.data_vars and 'i2m' in ds.data_vars['crs'].attrs:\n transf_str = ds.data_vars['crs'].attrs['i2m']\n a = list(map(float, transf_str.split(',')))\n return Affine(a[0], a[2], a[4], a[1], a[3], a[5])\n\n else:\n resx, resy = get_resolution(ds)\n xoff = ds['x'].values.min()\n yoff = ds['y'].values.max()\n return Affine(resx, 0, xoff, 0, resy, yoff)","def _apply_transform(self, x, transform_parameters):\n # x is a single image, so it doesn't have image number at index 0\n img_row_axis = self.row_axis - 1\n img_col_axis = self.col_axis - 1\n img_channel_axis = self.channel_axis - 1\n\n x = apply_affine_transform(x, transform_parameters.get('theta', 0),\n transform_parameters.get('tx', 0),\n transform_parameters.get('ty', 0),\n transform_parameters.get('shear', 0),\n transform_parameters.get('zx', 1),\n transform_parameters.get('zy', 1),\n row_axis=img_row_axis,\n col_axis=img_col_axis,\n channel_axis=img_channel_axis,\n fill_mode=self.fill_mode,\n cval=self.cval)\n\n if transform_parameters.get('channel_shift_intensity') is not None:\n x = apply_channel_shift(x,\n transform_parameters['channel_shift_intensity'],\n img_channel_axis)\n\n if transform_parameters.get('flip_horizontal', False):\n x = self._flip_axis(x, img_col_axis)\n\n if transform_parameters.get('flip_vertical', False):\n x = self._flip_axis(x, img_row_axis)\n\n if transform_parameters.get('brightness') is not None:\n x = apply_brightness_shift(x, transform_parameters['brightness'])\n\n return x","def affine_forward(x, w, b):\n out = None\n ###########################################################################\n # TODO: Implement the affine forward pass. Store the result in out. You #\n # will need to reshape the input into rows. #\n ###########################################################################\n reshaped_inp = np.reshape(x,(int(x.shape[0]),int(np.prod(x.shape) / x.shape[0])))\n out = reshaped_inp.dot(w) + b\n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n cache = (x, w, b)\n return out, cache","def fit_transform(self, x):\n self.fit(x)\n\n if self.method == \"svd\":\n return self._u * self._s\n else:\n return self._transform_eig(x)","def affine_transform(x, transform_matrix, channel_index=2, fill_mode='nearest', cval=0., order=1):\n # transform_matrix = transform_matrix_offset_center()\n # asdihasid\n # asd\n\n x = np.rollaxis(x, channel_index, 0)\n final_affine_matrix = transform_matrix[:2, :2]\n final_offset = transform_matrix[:2, 2]\n channel_images = [\n ndi.interpolation.affine_transform(\n x_channel, final_affine_matrix, final_offset, order=order, mode=fill_mode, cval=cval\n ) for x_channel in x\n ]\n x = np.stack(channel_images, axis=0)\n x = np.rollaxis(x, 0, channel_index + 1)\n return x","def affine_forward(x, w, b):\n out = None\n ###########################################################################\n # TODO: Implement the affine forward pass. Store the result in out. You #\n # will need to reshape the input into rows. #\n ###########################################################################\n dim_size = x[0].shape\n X = x.reshape(x.shape[0], np.prod(dim_size))\n out = X.dot(w) + b\n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n cache = (x, w, b)\n return out, cache","def affine_forward(x, w, b):\n out = None\n ########################################################################\n # TODO: Implement the affine forward pass. Store the result in out. #\n # You will need to reshape the input into rows. #\n ########################################################################\n\n x_reshaped = x.reshape(x.shape[:1] + (-1,))\n out = x_reshaped.dot(w) + b\n\n ########################################################################\n # END OF YOUR CODE #\n ########################################################################\n cache = (x, w, b)\n return out, cache","def affine_forward(x, w, b):\n N = x.shape[0]\n\n # reshape input into rows\n output = x.reshape([N, -1]).dot(w) + b\n cache = (x, w, b)\n\n return output, cache","def transforms_multiply(t0s, t1s):\r\n \r\n return ut.matrix_multiply(t0s, t1s)","def affine_forward(x, w, b):\n out = None\n x_shape = x.shape\n x_reshaped = x.reshape(x_shape[0], np.prod(x_shape[1:]))\n out = np.dot(x_reshaped, w) + b\n cache = (x, w, b)\n return out, cache","def affine_forward(x, w, b):\n out = None\n \n # reshape the input into (N, d_1 *...* d_k)\n input_shape = x.shape\n prod = 1\n for i in range(1,len(input_shape)):\n prod *= input_shape[i]\n\n a = x.reshape(x.shape[0],prod)\n out = np.dot(a,w) + b\n \n cache = (x, w, b)\n return out, cache","def transform(self, x):\n return self._transform_eig(x)","def elastic_transform(image, alpha, sigma, alpha_affine, random_state=None):\n if random_state is None:\n random_state = np.random.RandomState(None)\n\n shape = image.shape\n shape_size = shape[:2]\n \n # Random affine\n center_square = np.float32(shape_size) // 2\n square_size = min(shape_size) // 3\n pts1 = np.float32([center_square + square_size, [center_square[0]+square_size, center_square[1]-square_size], center_square - square_size])\n pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine, size=pts1.shape).astype(np.float32)\n M = cv2.getAffineTransform(pts1, pts2)\n image = cv2.warpAffine(image, M, shape_size[::-1], borderMode=cv2.BORDER_REFLECT_101)\n\n dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma) * alpha\n dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma) * alpha\n dz = np.zeros_like(dx)\n\n x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))\n indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1)), np.reshape(z, (-1, 1))\n\n return map_coordinates(image, indices, order=1, mode='reflect').reshape(shape)","def affine_trans(self):\n h, w, _ = self.img.shape\n\n \"\"\"\n pts1 = np.float32(\n [\n [randint(0, rows), randint(0, cols)],\n [randint(0, rows), randint(0, cols)],\n [randint(0, rows), randint(0, cols)],\n ]\n )\n pts2 = np.float32(\n [\n [randint(0, rows), randint(0, cols)],\n [randint(0, rows), randint(0, cols)],\n [randint(0, rows), randint(0, cols)],\n ]\n )\n \"\"\"\n\n pts1 = np.float32([[50, 50], [200, 50], [50, 200]])\n pts2 = np.float32([[10, 100], [200, 50], [100, 250]])\n\n M = cv2.getAffineTransform(pts1, pts2)\n\n self.img = cv2.warpAffine(self.img, M, (w, h))\n\n self.edits.append(\"affine\")\n return self","def affine(img, angle, translate, scale, shear):\n if not _is_numpy(img):\n raise TypeError('img should be Numpy Image. Got {}'.format(type(img)))\n\n assert isinstance(translate, (tuple, list)) and len(translate) == 2, \\\n \"Argument translate should be a list or tuple of length 2\"\n\n assert scale > 0.0, \"Argument scale should be positive\"\n\n aug = iaa.Affine(scale=scale, rotate=angle, translate_px=translate, shear=shear)\n return aug.augment_image(img)","def fit_transform(self, Xs, y=None):\n return self.fit(Xs, y).transform(Xs)","def setrans(Bi, t):\n\n x,v=mat2set(Bi)\n Bo = set2mat((x+t,v))\n Bo = Bo.astype(Bi.dtype)\n return Bo","def get_affine_transform(gps_coords, pdr_coords):\n # Compute similarity Xp = s A X + b\n X = np.array(pdr_coords)\n Xp = np.array(gps_coords)\n T = tf.superimposition_matrix(X.T, Xp.T, scale=True)\n\n A, b = T[:3, :3], T[:3, 3]\n s = np.linalg.det(A)**(1. / 3)\n A /= s\n return s, A, b","def translateEuler(self,trans):\n return np.array([[1,0,0,trans[0]],[0,1,0,trans[1]],[0,0,1,trans[2]],[0,0,0,1]])","def model_1exp(a, t, s, t0=0):\n a, t, s = physicond(a, t, s)\n\n # auxilary function taking as argument a time array.\n def aux(array_t, details=False):\n\n # applying time offset\n t_var = array_t - t0\n\n # model expression coming from symbolic calculation\n pulse = np.heaviside(t_var, 1.)\n ind = np.where(pulse != 0.)\n pulse[ind] = a * (np.exp(-t_var[ind]/t)-np.exp(-t_var[ind]/s))\n\n # same behavior as model_2exp and model_3exp\n if details == True:\n return pulse, pulse\n elif details == False:\n return pulse\n\n return aux","def compute_e(f_mat, m_mat):\r\n return m_mat.T @ f_mat @ m_mat","def convert_affine(ref, t, out):\n args = [\n transform_exe,\n '-d', '3',\n '-r', ref,\n '-t', '[{},0]'.format(t),\n '-o', '[{},1]'.format(out),\n '--float'\n ]\n subprocess.check_call(args)","def elastic_transform(X, min_alpha=36, max_alpha=38, min_sigma=5, max_sigma=6, random_state=None, n_jobs=1):\n if random_state is None:\n rng = np.random\n else:\n rng = np.random.RandomState(random_state)\n alphas = rng.uniform(min_alpha, max_alpha, size=X.shape[0])\n sigmas = rng.uniform(min_sigma, max_sigma, size=X.shape[0])\n X_elas = Parallel(n_jobs=n_jobs)(delayed(elastic_transform_one)(X[i], alphas[i], sigmas[i]) for i in range(X.shape[0]))\n return np.array(X_elas, dtype='float32')","def elastic_transform(image, alpha=1000, sigma=30, spline_order=1, mode='nearest', random_state=np.random):\n assert image.ndim == 2\n shape = image.shape[:2]\n\n dx = gaussian_filter((random_state.rand(*shape) * 2 - 1),\n sigma, mode=\"constant\", cval=0) * alpha\n dy = gaussian_filter((random_state.rand(*shape) * 2 - 1),\n sigma, mode=\"constant\", cval=0) * alpha\n\n x, y = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]), indexing='ij')\n indices = [np.reshape(x + dx, (-1, 1)), np.reshape(y + dy, (-1, 1))]\n result = map_coordinates(\n image, indices, order=spline_order, mode=mode).reshape(shape)\n return result","def eval(self, x):\n y = list(x)\n if not self.biased:\n y.insert(0, 1.0)\n y = np.array(y).reshape((self.Ws[0].shape[1], 1))\n for W, g in zip(self.Ws, self.gs):\n y = g(np.dot(W, y))\n return y.flatten()","def map(self,Affine,i):\n map_x = np.zeros([self.num,self.d])\n for k in range(self.num):\n map_x[k,:] = Affine.apply(i,self.pick(k))\n Mapped = Model_Points(map_x)\n return Mapped","def affine_transform(initialize=False, fixed=None, moments=True) :\n \n transform = itk.AffineTransform[itk.D, fixed.ndim].New()\n \n if initialize :\n fixed_itk = medipy.itk.medipy_image_to_itk_image(fixed, False)\n #moving_itk = medipy.itk.medipy_image_to_itk_image(moving, False)\n\n fixedIndex = fixed_itk.GetLargestPossibleRegion().GetIndex()\n fixedSize = fixed_itk.GetLargestPossibleRegion().GetSize()\n\n centerIndex = (int(fixedIndex[0] + fixedSize[0] / 2.0), \\\n int(fixedIndex[1] + fixedSize[1] / 2.0), \\\n int(fixedIndex[2] + fixedSize[2] / 2.0))\n\n rotationCenter = fixed_itk.TransformIndexToPhysicalPoint(centerIndex)\n\n transform.SetIdentity()\n transform.SetCenter(rotationCenter)\n\n #initial_transform = itk.VersorRigid3DTransform[itk.D].New()\n #initializer = itk.CenteredTransformInitializer[\n # initial_transform, fixed_itk, moving_itk].New(\n # Transform=initial_transform, FixedImage=fixed_itk, MovingImage=moving_itk) \n #if moments :\n # initializer.MomentsOn()\n #else :\n # initializer.GeometryOn()\n #initializer.InitializeTransform()\n #transform.SetCenter(initial_transform.GetCenter())\n #transform.SetOffset(initial_transform.GetOffset())\n \n return transform","def apply_transform(x,\n transform_matrix,\n channel_axis=0,\n fill_mode='nearest',\n cval=0.,\n interp_order=0):\n x = np.rollaxis(x, channel_axis, 0)\n final_affine_matrix = transform_matrix[:3, :3]\n final_offset = transform_matrix[:3, -1]\n channel_volumes = [ndi.interpolation.affine_transform(\n x_channel,\n final_affine_matrix,\n final_offset,\n order=interp_order, # NOTE: The order of the spline interpolation\n mode=fill_mode,\n cval=cval) for x_channel in x]\n x = np.stack(channel_volumes, axis=0)\n x = np.rollaxis(x, 0, channel_axis + 1)\n return x","def affine_2Dtransform(img, t_mat, height, width, h_offset=0, w_offset=0, nh_flag=False, nw_flag=False):\n # transform matrix must be validated\n if(np.shape(t_mat) != (2, 2)):\n return img\n\n # implementing matrix multiplication to a default map of source data in order to apply transform\n # and to achieve coordination/location of transformed matrix according to source data(data map)\n coord_map = transform_calcualtion(\n height, width, t_mat, h_offset, w_offset, nh_flag, nw_flag)\n\n # transformed image data construction\n t_img = np.full((height+h_offset, width+w_offset, 3), 255, dtype='uint8')\n\n # applying new map to image inorder to complete the transform\n try:\n for i in range(height):\n for j in range(width):\n [i_new_coord, j_new_coord] = coord_map[i, j, :]\n # unhandled bound-jumpout\n t_img[i_new_coord, j_new_coord, :] = img[i, j, :]\n except:\n print(\"not enough offset/negative coordination pushed\")\n return img\n return t_img","def translateEuler(trans):\n return np.array([[1,0,0,trans[0]],[0,1,0,trans[1]],[0,0,1,trans[2]],[0,0,0,1]])","def affine_sigmoid(xin, m= 10, c= 3):\n if type(xin) != np.ndarray:\n x = np.array([xin])\n else:\n x = xin\n\n x = get_affine(x, m, c)\n output = get_sigmoid(x)\n\n if type(xin) != np.ndarray:\n return output[0]\n else:\n return output","def transform(self, x):","def _transform(\n self, x: \"torch.Tensor\", y: Optional[\"torch.Tensor\"], **kwargs\n ) -> Tuple[\"torch.Tensor\", Optional[\"torch.Tensor\"]]:\n import torch\n import torchvision.transforms.functional as F\n\n img_size = x.shape[:2]\n\n angle = float(\n torch.empty(1)\n .uniform_(float(self.degree_range[0]), float(self.degree_range[1]))\n .item()\n )\n\n max_dx = float(self.translate[0] * img_size[1])\n max_dy = float(self.translate[1] * img_size[0])\n tx = int(round(torch.empty(1).uniform_(-max_dx, max_dx).item()))\n ty = int(round(torch.empty(1).uniform_(-max_dy, max_dy).item()))\n translations = (tx, ty)\n\n scale = float(torch.empty(1).uniform_(self.scale[0], self.scale[1]).item())\n\n # x needs to have channel first\n x = x.permute(2, 0, 1)\n x = F.affine(\n img=x, angle=angle, translate=translations, scale=scale, shear=(0.0, 0.0)\n )\n x = x.permute(1, 2, 0)\n\n return torch.clamp(x, min=self.clip_values[0], max=self.clip_values[1]), y","def jacobian(self, dt):\n if dt not in self._F_cache:\n d = self._dimension\n with torch.no_grad():\n F = eye_like(self.sa2, d)\n F[: d // 2, d // 2 :] = dt * eye_like(self.sa2, d // 2)\n self._F_cache[dt] = F\n\n return self._F_cache[dt]","def __compose_transformation(self):\n s = self.scale\n rotR = self.rotation\n t = self.translation\n T = np.eye(4)\n T[0:3, 3] = t\n R = np.eye(4)\n R[0:3, 0:3] = rotR\n M = T.dot(R)\n if s == 1:\n M = T.dot(R)\n else:\n S = np.eye(4)\n S[0:3, 0:3] = np.diag([s, s, s])\n M = T.dot(R).dot(S)\n return M","def seToSE( x ):\n x = asarray(x,dtype=float)\n if x.shape != (6,):\n raise ValueError(\"shape must be (6,); got %s\" % str(x.shape))\n #\n return expM(screw(x))","def J_direct_transform(om, consts, taus):\n ndecay=len(consts) ; noms=len(om)\n Jmat = np.zeros( (ndecay, noms ) )\n for i in range(ndecay):\n Jmat[i] = consts[i]*taus[i] /(1 + (taus[i]*om)**2.)\n return Jmat.sum(axis=0)","def exp(tensor):\n return _elementary_op(tensor, np.exp, np.exp)","def affineTransform(img, pts, newPts):\n\ttmp = img.copy()\n\tif len(img.shape) is 3:\n\t\trows, cols, ch = img.shape\n\telse:\n\t\trows, cols = img.shape\n\tpts1 = np.float32(pts)\n\tpts2 = np.float32(newPts)\n\tM = cv2.getAffineTransform(pts1, pts2)\n\tdst = cv2.warpAffine(tmp, M, (cols, rows))\n\treturn dst","def sfunc(self,x,y):\n return np.exp(-(x-self.x_0)**2.0-(y-self.y_0)**2.0)","def apply_T(T, points):\n flipped = False\n if points.shape[0] != 3:\n assert points.shape[1] == 3, \"Points must be 3xN or Nx3\"\n points = points.T\n flipped = True\n points_h = np.vstack((points, np.ones_like(points[0, :])))\n points_transformed_h = np.dot(T, points_h)\n points_transformed = points_transformed_h[:-1]\n if flipped:\n return points_transformed.T\n return points_transformed","def transform_and_compute_jacobian(self, xj):\n x = xj[:, :self.d].detach()\n log_j = xj[:, -1]\n\n x.requires_grad = True\n y = self.flow_(x)\n\n n_batch = xj.shape[0]\n\n jx = torch.zeros(n_batch, self.d, self.d).to(log_j.device)\n directions = torch.eye(self.d).to(log_j).unsqueeze(0).repeat(n_batch, 1, 1)\n\n for i in range(self.d):\n jx[:, i, :] = torch.autograd.grad(y, x, directions[:, i, :],\n allow_unused=True, create_graph=True, retain_graph=True)[0]\n x.requires_grad = False\n x.grad = None\n\n log_det_j = torch.log(torch.abs(torch.det(jx)))\n return torch.cat([y.detach(), (log_j + log_det_j).unsqueeze(1)], 1)","def minimize_transform(params, points_dest, points_src, constraints):\n # initialize dof\n dof = [0, 0, 0, 0, 0, 0, 1, 1, 1]\n # initialize dictionary to relate constraints index to dof\n dict_dof = {'Tx': 0, 'Ty': 1, 'Tz': 2, 'Rx': 3, 'Ry': 4, 'Rz': 5, 'Sx': 6, 'Sy': 7, 'Sz': 8}\n # extract constraints\n list_constraints = constraints.split('_')\n # loop across constraints and update dof\n for i in range(len(list_constraints)):\n dof[dict_dof[list_constraints[i]]] = params[i]\n # convert dof to more intuitive variables\n tx, ty, tz, alpha, beta, gamma, scx, scy, scz = dof[0], dof[1], dof[2], dof[3], dof[4], dof[5], dof[6], dof[7], dof[8]\n # build rotation matrix\n rotation_matrix = matrix([[cos(alpha)*cos(beta), cos(alpha)*sin(beta)*sin(gamma)-sin(alpha)*cos(gamma), cos(alpha)*sin(beta)*cos(gamma)+sin(alpha)*sin(gamma)],\n [sin(alpha)*cos(beta), sin(alpha)*sin(beta)*sin(gamma)+cos(alpha)*cos(gamma), sin(alpha)*sin(beta)*cos(gamma)-cos(alpha)*sin(gamma)],\n [-sin(beta), cos(beta)*sin(gamma), cos(beta)*cos(gamma)]])\n # build scaling matrix\n scaling_matrix = matrix([[scx, 0.0, 0.0], [0.0, scy, 0.0], [0.0, 0.0, scz]])\n # compute rotation+scaling matrix\n rotsc_matrix = scaling_matrix * rotation_matrix\n # compute center of mass from moving points (src)\n points_src_barycenter = mean(points_src, axis=0)\n # apply transformation to moving points (src)\n points_src_reg = ((rotsc_matrix * (matrix(points_src) - points_src_barycenter).T).T + points_src_barycenter) + matrix([tx, ty, tz])\n # record SSE for later display\n sse_results.append(SSE(matrix(points_dest), points_src_reg))\n # return SSE\n return SSE(matrix(points_dest), points_src_reg)","def affineSchur(self):\n return AffineSchurFunctions(self)","def transformAffine(self, path=None, src=None, dst=None):\n if path is not None:\n landmarks = pd.read_csv(path, skiprows=1,engine=\"c\", na_filter=False, header=None, delim_whitespace=True, dtype=np.float32).as_matrix()\n dst = landmarks[:,3:5]\n src = landmarks[:,1:3]\n affine = transform.estimate_transform(\"affine\",src,dst)\n data = self.stormData[0][:,0:2]\n data = affine(data)\n self.stormData[0][:,0:2] = data","def attrTransform(self, matrix, transform):\n for ttype, targs in self.reTransformFind.findall(transform):\n targs = list(map(lambda x: float(x), self.reNumberFind.findall(targs)))\n if ttype == 'matrix':\n newmatrix = [ targs[0], targs[1],\n targs[2], targs[3],\n targs[4], targs[5] ]\n self.matrixMul(matrix, newmatrix)\n elif ttype == 'translate':\n tx = targs[0]\n ty = targs[1] if len(targs) > 1 else 0\n newmatrix = [ 1, 0, 0, 1, tx, ty ]\n self.matrixMul(matrix, newmatrix)\n elif ttype == 'scale':\n sx = targs[0]\n sy = targs[1] if len(targs) > 1 else sx\n newmatrix = [ sx, 0, 0, sy, 0, 0 ]\n self.matrixMul(matrix, newmatrix)\n elif ttype == 'rotate':\n if len(targs) == 1:\n alpha = targs[0]\n newmatrix = [ math.cos(alpha), math.sin(alpha),\n -math.sin(alpha), math.cos(alpha),\n 0, 0]\n self.matrixMul(matrix, newmatrix)\n else:\n alpha = targs[0]\n newmatrix = [ 1, 0, 0, 1, targs[1], targs[2] ]\n self.matrixMul(matrix, newmatrix)\n newmatrix = [ math.cos(alpha), math.sin(alpha),\n -math.sin(alpha), math.cos(alpha),\n 0, 0]\n self.matrixMul(matrix, newmatrix)\n newmatrix = [ 1, 0, 0, 1, -targs[1], -targs[2] ]\n self.matrixMul(matrix, newmatrix)\n elif ttype == 'skewX' or ttype == 'skewY':\n self.alert(\"skewX and skewY transformations are not supported\", elem)\n else:\n print('unknown transform type: ', ttype)\n return matrix","def _apply(self, x, **kwargs):\n return reduce(lambda x_i, tr: tr._apply(x_i), self.transforms, x)","def translate(self, x=0, y=0, z=0):\n\t\ttranslation = np.identity(4)\n\t\ttranslation[0, 3] += x\n\t\ttranslation[1, 3] += y\n\t\ttranslation[2, 3] += z\n\t\t\n\t\tself.matrix = np.matmul(self.matrix, translation)","def affine_matrix(self) -> np.ndarray:\n return self._tf_matrix","def _apply_sx(self, state, axes, inverse=False):\n if inverse:\n return 0.5 * ((1 - 1j) * state + (1 + 1j) * self._apply_x(state, axes))\n\n return 0.5 * ((1 + 1j) * state + (1 - 1j) * self._apply_x(state, axes))","def elastic_transform_approx(\n img: np.ndarray,\n alpha: float,\n sigma: float,\n alpha_affine: float,\n interpolation: int = cv2.INTER_LINEAR,\n border_mode: int = cv2.BORDER_REFLECT_101,\n value: Optional[ImageColorType] = None,\n random_state: Optional[np.random.RandomState] = None,\n) -> np.ndarray:\n height, width = img.shape[:2]\n\n # Random affine\n center_square = np.array((height, width), dtype=np.float32) // 2\n square_size = min((height, width)) // 3\n alpha = float(alpha)\n sigma = float(sigma)\n alpha_affine = float(alpha_affine)\n\n pts1 = np.array(\n [\n center_square + square_size,\n [center_square[0] + square_size, center_square[1] - square_size],\n center_square - square_size,\n ],\n dtype=np.float32,\n )\n pts2 = pts1 + random_utils.uniform(-alpha_affine, alpha_affine, size=pts1.shape, random_state=random_state).astype(\n np.float32\n )\n matrix = cv2.getAffineTransform(pts1, pts2)\n\n warp_fn = _maybe_process_in_chunks(\n cv2.warpAffine,\n M=matrix,\n dsize=(width, height),\n flags=interpolation,\n borderMode=border_mode,\n borderValue=value,\n )\n img = warp_fn(img)\n\n dx = random_utils.rand(height, width, random_state=random_state).astype(np.float32) * 2 - 1\n cv2.GaussianBlur(dx, (17, 17), sigma, dst=dx)\n dx *= alpha\n\n dy = random_utils.rand(height, width, random_state=random_state).astype(np.float32) * 2 - 1\n cv2.GaussianBlur(dy, (17, 17), sigma, dst=dy)\n dy *= alpha\n\n x, y = np.meshgrid(np.arange(width), np.arange(height))\n\n map_x = np.float32(x + dx)\n map_y = np.float32(y + dy)\n\n remap_fn = _maybe_process_in_chunks(\n cv2.remap,\n map1=map_x,\n map2=map_y,\n interpolation=interpolation,\n borderMode=border_mode,\n borderValue=value,\n )\n return remap_fn(img)","def transform(self, x: Array2D) -> Array2D:","def elastic_transform(self, image, random_state=None):\n if random_state is None:\n random_state = np.random.RandomState(None)\n\n image = self.affine(image, random_state)\n #from ipdb import set_trace; set_trace()\n indices = self.stretch_indices(image, random_state)\n\n return map_coordinates(image, indices, order=1, mode='reflect').reshape(image.shape)","def affine(self, image, random_state):\n shape = image.shape\n shape_size = shape[:2]\n transform_std = self.alpha_affine*image.shape[1]\n\n center_square = np.float32(shape[:2]) // 2\n square_size = min(shape[:2]) // 3\n\n source = np.float32([center_square + square_size, [center_square[0]+square_size, center_square[1]-square_size], center_square - square_size])\n destination = source + random_state.uniform(-transform_std, transform_std, size=source.shape).astype(np.float32)\n M = cv2.getAffineTransform(source, destination)\n\n return cv2.warpAffine(image, M, shape_size[::-1], borderMode=cv2.BORDER_REPLICATE)","def EvaluateJacobian(x):\n j = np.zeros((NOBSERVATIONS, 3))\n\n for i in range(NOBSERVATIONS):\n base = np.exp(-x[0] * t[i]) / (x[1] + x[2] * t[i])\n\n j[i][0] = t[i] * base\n j[i][1] = base / (x[1] + x[2] * t[i])\n j[i][2] = base * t[i] / (x[1] + x[2] * t[i])\n\n return j","def f(self,t,svect):\n s1, vectors = spinlib.get_s_and_vectors(svect, self.N)\n heff = -spinlib.cycleLeft3D(s1,self.N) - spinlib.cycleRight3D(s1,self.N)\n \n # some transpose magic - basically want to matrix multiply the right things\n if len(vectors) == 0:\n retvect = vectors\n else:\n retvect = np.dot(self.jac(s1, heff), vectors).T.ravel()\n return self.J * np.concatenate((-spinlib.cross(s1, heff), retvect), axis = 0)","def quantize_affine_given_quant_params(\n input: torch.Tensor,\n quantize_params: QuantizeAffineParams2,\n) -> torch.Tensor:\n return QuantizeAffineFunction.apply(input, quantize_params)","def temperature_scaling(x,t):\n n,d = x.shape\n res = np.copy(x)\n\n res *= (1./t)\n for i in range(n):\n res[i] = softmax(res[i])\n return(res)","def analytic(x, t, D, x0, xend, logx=False, c_s=1, use_log2=False):\n import scipy.special\n if t.ndim == 1:\n t = t.reshape((t.size, 1))\n expb = (lambda arg: 2**arg) if use_log2 else np.exp\n x = expb(x) if logx else x\n return c_s * scipy.special.erfc(x/(2*(D*t)**0.5))","def transformation_matrix(self, s1, s2, s3, t1, t2, t3):\n\n s1 = np.array(s1)\n s2 = np.array(s2)\n s3 = np.array(s3)\n t1 = np.array(t1)\n t2 = np.array(t2)\n t3 = np.array(t3)\n\n Q = np.array(\n [\n [t2[0] - t1[0], t2[1] - t1[1], t2[2] - t1[2]],\n [t3[0] - t1[0], t3[1] - t1[1], t3[2] - t1[2]],\n ]\n )\n\n P = np.array([[s2[0] - s1[0], s2[1] - s1[1]], [s3[0] - s1[0], s3[1] - s1[1]]])\n\n try:\n # Invert the P matrix\n Pinv = inv(P)\n\n # Build the dot product\n T = np.dot(Pinv, Q)\n\n # Offset\n V0 = np.subtract(t2, np.transpose(s2[0:2]).dot(T))\n except Exception as e:\n self.log.error(\"An error occured during the transformation.\", exc_info=True)\n return -1, -1\n\n return T, V0","def transform(self, x:generic_array, dense=True) -> generic_array:\n if type(x) == np.ndarray and not dense:\n warnings.warn(\"For Numpy transform it is best to use dense=True\")\n \n K_nq = self._pairwise_kernels(x, self.components_, dense=dense)\n x_new = K_nq @ self.normalization\n return x_new","def nonlinear_eom_to_ss(aircraft, x_ss, u_ss, x_0, u_0, m, j, dx=0.1, du=0.1):\n \"\"\"return jacobians a, b wrt to x_ss and output matrices c, and d wrt u_ss.\"\"\"\n x = x_0\n u = u_0\n a = zeros((len(x_0), len(x_0)))\n b = zeros((len(x_0), len(u_0)))\n for ii in range(0, len(x_0)):\n x[ii] = x[ii] + dx\n c = c_f_m(aircraft, x, u_0)\n dxdt_1 = nonlinear_eom(x, m, j, c)\n\n x[ii] = x[ii] - dx\n c = c_f_m(aircraft, x, u_0)\n dxdt_2 = nonlinear_eom(x, m, j, c)\n ddx_dx = (dxdt_1 - dxdt_2)/(2*dx)\n a[:, ii] = transpose(ddx_dx)\n x = x_0\n\n for ii in range(0, len(u_0)):\n u[ii] = u[ii] + du\n c = c_f_m(aircraft, x_0, u)\n dxdt_1 = nonlinear_eom(x, m, j, c)\n\n u[ii] = u[ii] - du\n c = c_f_m(aircraft, x_0, u)\n dxdt_2 = nonlinear_eom(x, m, j, c)\n ddx_dx = (dxdt_1 - dxdt_2)/(2*du)\n b[:, ii] = transpose(ddx_dx)\n u = u_0\n\n a_out = a[x_ss, :]\n a_out = a_out[:, x_ss]\n\n b_out = b[x_ss, :]\n b_out = b_out[:, u_ss]\n\n c_out = identity(len(x_ss))\n d_out = zeros((len(x_ss), len(u_ss)))\n return a_out, b_out, c_out, d_out","def elastic_transform(\n img: np.ndarray,\n alpha: float,\n sigma: float,\n alpha_affine: float,\n interpolation: int = cv2.INTER_LINEAR,\n border_mode: int = cv2.BORDER_REFLECT_101,\n value: Optional[ImageColorType] = None,\n random_state: Optional[np.random.RandomState] = None,\n approximate: bool = False,\n same_dxdy: bool = False,\n):\n height, width = img.shape[:2]\n\n # Random affine\n center_square = np.array((height, width), dtype=np.float32) // 2\n square_size = min((height, width)) // 3\n alpha = float(alpha)\n sigma = float(sigma)\n alpha_affine = float(alpha_affine)\n\n pts1 = np.array(\n [\n center_square + square_size,\n [center_square[0] + square_size, center_square[1] - square_size],\n center_square - square_size,\n ],\n dtype=np.float32,\n )\n pts2 = pts1 + random_utils.uniform(-alpha_affine, alpha_affine, size=pts1.shape, random_state=random_state).astype(\n np.float32\n )\n matrix = cv2.getAffineTransform(pts1, pts2)\n\n warp_fn = _maybe_process_in_chunks(\n cv2.warpAffine, M=matrix, dsize=(width, height), flags=interpolation, borderMode=border_mode, borderValue=value\n )\n img = warp_fn(img)\n\n if approximate:\n # Approximate computation smooth displacement map with a large enough kernel.\n # On large images (512+) this is approximately 2X times faster\n dx = random_utils.rand(height, width, random_state=random_state).astype(np.float32) * 2 - 1\n cv2.GaussianBlur(dx, (17, 17), sigma, dst=dx)\n dx *= alpha\n if same_dxdy:\n # Speed up even more\n dy = dx\n else:\n dy = random_utils.rand(height, width, random_state=random_state).astype(np.float32) * 2 - 1\n cv2.GaussianBlur(dy, (17, 17), sigma, dst=dy)\n dy *= alpha\n else:\n dx = np.float32(\n gaussian_filter((random_utils.rand(height, width, random_state=random_state) * 2 - 1), sigma) * alpha\n )\n if same_dxdy:\n # Speed up\n dy = dx\n else:\n dy = np.float32(\n gaussian_filter((random_utils.rand(height, width, random_state=random_state) * 2 - 1), sigma) * alpha\n )\n\n x, y = np.meshgrid(np.arange(width), np.arange(height))\n\n map_x = np.float32(x + dx)\n map_y = np.float32(y + dy)\n\n remap_fn = _maybe_process_in_chunks(\n cv2.remap, map1=map_x, map2=map_y, interpolation=interpolation, borderMode=border_mode, borderValue=value\n )\n return remap_fn(img)","def affine_transform_2d(v, mapping, alpha = 1):\r\n p_wgt = vec2(0, 0)\r\n q_wgt = vec2(0, 0)\r\n w = len(mapping)*[None]\r\n w_sum = 0\r\n for i in range(len(mapping)):\r\n mp = mapping[i]\r\n x = mp[0].x - v.x\r\n y = mp[0].y - v.y\r\n if (x == 0 and y == 0): return mp[1]\r\n w[i] = 1/((x*x + y*y) ** alpha)\r\n p_wgt += mp[0]*w[i]\r\n q_wgt += mp[1]*w[i]\r\n w_sum += w[i]\r\n p_wgt /= w_sum\r\n q_wgt /= w_sum\r\n M1 = mat2(0)\r\n M2 = mat2(0)\r\n for i in range(len(mapping)):\r\n mp = mapping[i]\r\n p_adj = mp[0] - p_wgt\r\n q_adj = mp[1] - q_wgt\r\n M1 += p_adj.transpose_multiply(p_adj)*w[i]\r\n M2 += p_adj.transpose_multiply(q_adj)*w[i]\r\n M1 = M1.inverse()\r\n M = M1*M2\r\n M = M.transpose()\r\n v_out = M*(v - p_wgt) + q_wgt\r\n return v_out","def apply_transform_to_image(self,img, transform, center=None):\n \n if center is None:\n center = (np.array(img.shape)[::-1]-1)/2.0\n \n displacement = np.dot(transform, center)\n shift = center - displacement\n \n img_tf = ndimage.interpolation.affine_transform(img, transform, offset=shift, mode=\"constant\", order=3, cval=0.0)\n return img_tf","def apply_transformation(self, points):\n assert (points.shape[0] == 3)\n n = points.shape[1]\n points_ = np.vstack((points, np.ones((1, n))))\n points_trans_ = np.matmul(self.pose_mat, points_)\n points_transformed = np.true_divide(points_trans_[:3, :], points_trans_[[-1], :])\n return points_transformed","def transform(self) -> Affine:\n transform = (\n Affine.translation(*self.origin)\n * Affine.rotation(self.rotation)\n * Affine.scale(*self.res)\n )\n return transform","def augmentAffine(img_in, seg_in, strength=0.05):\n B,C,D,H,W = img_in.size()\n affine_matrix = (torch.eye(3,4).unsqueeze(0) + torch.randn(B, 3, 4) * strength).to(img_in.device)\n\n meshgrid = F.affine_grid(affine_matrix,torch.Size((B,1,D,H,W)))\n\n img_out = F.grid_sample(img_in, meshgrid,padding_mode='border')\n seg_out = F.grid_sample(seg_in.float().unsqueeze(1), meshgrid, mode='nearest').long().squeeze(1)\n\n return img_out, seg_out","def scaling(sx,sy,Mat):\r\n # SM is the Scaling Matrix ( 3 X 3 )\r\n SM = [[sx,0,0],[0,sy,0],[0,0,1]]\r\n Scaled = Multiply(SM,Mat)\r\n # Scaled[0][0] is the updated x coordinate\r\n # Scaled[1][0] is the updated y coordinate\r\n return Scaled[0][0],Scaled[1][0],Scaled[2][0]","def get_affine_transform(center, scale, rot, output_size, shift=(0.0, 0.0), inv=False):\n assert len(center) == 2\n assert len(scale) == 2\n assert len(output_size) == 2\n assert len(shift) == 2\n scale_tmp = scale * 200.0\n shift = np.array(shift)\n src_w = scale_tmp[0]\n dst_w = output_size[0]\n dst_h = output_size[1]\n rot_rad = np.pi * rot / 180\n src_dir = rotate_point([0.0, src_w * -0.5], rot_rad)\n dst_dir = np.array([0.0, dst_w * -0.5])\n src = np.zeros((3, 2), dtype=np.float32)\n src[0, :] = center + scale_tmp * shift\n src[1, :] = center + src_dir + scale_tmp * shift\n src[2, :] = _get_3rd_point(src[0, :], src[1, :])\n dst = np.zeros((3, 2), dtype=np.float32)\n dst[0, :] = [dst_w * 0.5, dst_h * 0.5]\n dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir\n dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])\n if inv:\n trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))\n else:\n trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))\n return trans","def transformCoordinates(self,x,incoordsys=None,outcoordsys=None):\n if incoordsys is None:\n incoordsys = self.incoordsys\n if outcoordsys is None:\n outcoordsys = self._fcoordsys\n if incoordsys == outcoordsys:\n return x\n else:\n return self._inputtransforms[incoordsys][outcoordsys](*x)"],"string":"[\n \"def inverse_element_wise_affine(x, st, compute_jacobian=True):\\n es = torch.exp(-st[..., 0])\\n t = st[..., 1]\\n logj = None\\n if compute_jacobian:\\n logj = torch.sum(torch.log(es), dim=-1)\\n\\n return es * (x - t), logj\",\n \"def affine(params, x):\\n return np.dot(params['w'], x) + params['b']\",\n \"def transform(fn):\\n def _(vec, dt):\\n return np.einsum(\\n 'ji,i,ki,k...->j...',\\n evecs, fn(evals, dt), evecs, vec, optimize=True)\\n\\n return _\",\n \"def affineTransform(x,output_dim):\\n w=tf.get_variable(\\\"w\\\", [x.get_shape()[1], output_dim])\\n b=tf.get_variable(\\\"b\\\", [output_dim], initializer=tf.constant_initializer(0.0))\\n return tf.matmul(x,w)+b\",\n \"def affine_transform(trans_mat, p0):\\r\\n n_data, n_dim = np.shape(p0)\\r\\n p0 = np.hstack((p0, np.ones((n_data, 1))))\\r\\n #return np.transpose(np.dot(np.transpose(trans_mat), np.transpose(p0)))\\r\\n return np.dot(p0, trans_mat)\",\n \"def affine_transform(x, output_dim, name=None):\\n\\n w = tf.get_variable(name + \\\"_w\\\", [x.get_shape()[1], output_dim], initializer=tf.truncated_normal_initializer(stddev=0.02))\\n b = tf.get_variable(name + \\\"_b\\\", [output_dim], initializer=tf.constant_initializer(0.0))\\n\\n return tf.matmul(x, w) + b\",\n \"def affine_transform(geom, matrix):\\n if geom.is_empty:\\n return geom\\n if len(matrix) == 6:\\n ndim = 2\\n a, b, d, e, xoff, yoff = matrix\\n if geom.has_z:\\n ndim = 3\\n i = 1.0\\n c = f = g = h = zoff = 0.0\\n matrix = a, b, c, d, e, f, g, h, i, xoff, yoff, zoff\\n elif len(matrix) == 12:\\n ndim = 3\\n a, b, c, d, e, f, g, h, i, xoff, yoff, zoff = matrix\\n if not geom.has_z:\\n ndim = 2\\n matrix = a, b, d, e, xoff, yoff\\n else:\\n raise ValueError(\\\"'matrix' expects either 6 or 12 coefficients\\\")\\n\\n def affine_pts(pts):\\n \\\"\\\"\\\"Internal function to yield affine transform of coordinate tuples\\\"\\\"\\\"\\n if ndim == 2:\\n for x, y in pts:\\n xp = a * x + b * y + xoff\\n yp = d * x + e * y + yoff\\n yield (xp, yp)\\n elif ndim == 3:\\n for x, y, z in pts:\\n xp = a * x + b * y + c * z + xoff\\n yp = d * x + e * y + f * z + yoff\\n zp = g * x + h * y + i * z + zoff\\n yield (xp, yp, zp)\\n\\n # Process coordinates from each supported geometry type\\n if geom.type in ('Point', 'LineString', 'LinearRing'):\\n return type(geom)(list(affine_pts(geom.coords)))\\n elif geom.type == 'Polygon':\\n ring = geom.exterior\\n shell = type(ring)(list(affine_pts(ring.coords)))\\n holes = list(geom.interiors)\\n for pos, ring in enumerate(holes):\\n holes[pos] = type(ring)(list(affine_pts(ring.coords)))\\n return type(geom)(shell, holes)\\n elif geom.type.startswith('Multi') or geom.type == 'GeometryCollection':\\n # Recursive call\\n # TODO: fix GeometryCollection constructor\\n return type(geom)([affine_transform(part, matrix)\\n for part in geom.geoms])\\n else:\\n raise ValueError('Type %r not recognized' % geom.type)\",\n \"def affine_mult(affine, coordinates):\\n return np.dot(coordinates, affine[:3, :3].T) + affine[:3, -1]\",\n \"def apply_affine_transform(x, M):\\n is1d = len(x.shape) == 1\\n if is1d:\\n x = np.expand_dims(x, axis=0)\\n\\n x_hom = np.concatenate(\\n [x, np.ones((x.shape[0], 1), dtype=x.dtype)], axis=-1\\n )\\n x_out = x_hom @ M.T\\n if is1d:\\n x_out = np.squeeze(x_out, axis=0)\\n return x_out\",\n \"def transformAffine(self, coords):\\n coordsshape = coords.shape\\n dims = coordsshape[0] + 1\\n coords = coords.reshape((len(coords), -1))\\n coords = np.concatenate((coords, np.ones((1, len(coords[0])))), 0)\\n affine = np.eye(dims)\\n # now transform first to center:\\n meanvec = np.mean(coords, 1)\\n center = np.eye(dims)\\n center[:-1, -1] = -meanvec[:-1]\\n affine = np.matmul(center, affine)\\n\\n if np.sum(self.shift):\\n affine[:-1, -1] += (self.deformrandomstate.rand(dims - 1) - 0.5) * np.float32(self.shift)\\n if np.max(self.scaling) > 1:\\n scales = np.ones(dims)\\n # scales[:-1] = (self.deformrandomstate.rand(dims-1)-0.5)*(self.scaling-1.0/self.scaling)+(self.scaling+1/self.scaling)/2\\n scales[:-1] = self.scaling ** (self.deformrandomstate.rand(dims - 1) * 2 - 1)\\n scales = np.diag(scales)\\n # print(scales)\\n affine = np.matmul(scales, affine)\\n if np.sum(self.rotation):\\n affine = self._rotate(affine)\\n # move back to location:\\n center[:-1, -1] = -center[:-1, -1]\\n affine = np.matmul(center, affine)\\n # now appyl to coords:\\n coords = np.matmul(affine, coords)\\n coords = coords[:-1]\\n coords = coords.reshape(coordsshape)\\n return coords\",\n \"def transAffine2D( iScale=(1, 1), iTrans=(0, 0), iRot=0, iShear=(0, 0) ): \\n iRot = iRot * np.pi / 180\\n oMatScale = np.matrix( ((iScale[0],0,0),(0,iScale[1],0),(0,0,1)) )\\n oMatTrans = np.matrix( ((1,0,iTrans[0]),(0,1,iTrans[1]),(0,0,1)) )\\n oMatRot = np.matrix( ((np.cos(iRot),-np.sin(iRot),0),\\\\\\n (np.sin(iRot),np.cos(iRot),0),(0,0,1)) )\\n oMatShear = np.matrix( ((1,iShear[0],0),(iShear[1],1,0),(0,0,1)) )\\n # ustvari izhodno matriko\\n oMat2D = oMatTrans * oMatShear * oMatRot * oMatScale\\n return oMat2D\",\n \"def AffineTransform( from_pts, to_pts ):\\n \\n # check that there are match points\\n if len(from_pts) != len(to_pts) or len(to_pts)<1:\\n print \\\"from_pts and to_pts must be of same size.\\\"\\n return False\\n\\n # check the dimensions\\n dim = len(from_pts[0]) # num of dimensions\\n if len(from_pts) < dim:\\n print \\\"Too few points => under-determined system.\\\"\\n return False\\n elif len(from_pts) > dim + 1:\\n print \\\"Too many points => over-determined system.\\\"\\n return False\\n\\n \\n #segregate the x and y coordinages\\n from_pts_x, from_pts_y = zip(*from_pts)\\n to_pts_x, to_pts_y = zip(*to_pts)\\n \\n #create the Matricies for processing\\n I = np.matrix([from_pts_x, from_pts_y, [1,1,1]])\\n P = np.matrix([to_pts_x, to_pts_y])\\n \\n #Calculate the 2D affine transform matrix (A)\\n A = P * linalg.pinv(I) \\n\\n # Make a result object\\n class Transformation:\\n \\\"\\\"\\\"Result object that represents the transformation\\n from affine fitter.\\\"\\\"\\\"\\n\\n def To_Str(self):\\n res = \\\"\\\"\\n for j in range(dim):\\n str1 = \\\"x%d' = \\\" % j\\n for i in range(dim):\\n str1 +=\\\"x%d * %f + \\\" % (i, A[i][j+dim+1])\\n str1 += \\\"%f\\\" % A[dim][j+dim+1]\\n res += str1 + \\\"\\\\n\\\"\\n return res\\n\\n def Transform(self, pt_x, pt_y):\\n pt_vector = np.matrix([[pt_x], [pt_y], [1]])\\n transformed_pt = A * pt_vector\\n return map(itemgetter(0), transformed_pt.tolist())\\n return Transformation()\",\n \"def estimate_stage_affine(t0, t1):\\n src = np.array([t.tforms[0].translation for t in t0])\\n dst = np.array([t.tforms[1].translation for t in t1])\\n aff = renderapi.transform.AffineModel()\\n aff.estimate(src, dst)\\n return aff\",\n \"def affine_forward(x, W, b):\\r\\n x2d = np.reshape(x, (x.shape[0], -1)) # convert 4D input matrix to 2D \\r\\n out = np.dot(x2d, W) + b # linear transformation\\r\\n cache = (x, W, b) # keep for backward step (stay with us)\\r\\n return out, cache\",\n \"def calc_affine(df):\\n\\tx0 = df.columns[0]\\n\\ty0 = df.index[0]\\n\\tdx = df.columns[1] - df.columns[0]\\n\\tdy = df.index[1] - df.index[0]\\n\\t\\n\\tt = affine.Affine(dx, 0, x0 , 0, dy ,y0 - dy) \\n\\t# y0 - dy because anker point is in the south!\\n\\treturn t\",\n \"def temporal_affine_forward(x, w, b):\\n N, T, D = x.shape\\n M = b.shape[0]\\n out = x.reshape(N * T, D).dot(w).reshape(N, T, M) + b\\n cache = x, w, b, out\\n return out, cache\",\n \"def affine_forward(x, w, b):\\n out = None\\n ###########################################################################\\n # TODO: Implement the affine forward pass. Store the result in out. You #\\n # will need to reshape the input into rows. #\\n ###########################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n t = np.reshape(x,(x.shape[0],np.prod(np.shape(x)[1:])))\\n \\n\\n out = np.dot(t,w) + b\\n \\n #print(np.shape(out))\\n\\n pass\\n\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n ###########################################################################\\n # END OF YOUR CODE #\\n ###########################################################################\\n cache = (x, w, b)\\n return out, cache\",\n \"def estimate_affine_matrix_3d_to_2d(X, x):\\n assert x.shape[0] == X.shape[0]\\n assert x.shape[0] >= 4\\n X = X.T # (3, n)\\n x = x.T # (2, n)\\n n = x.shape[1]\\n\\n ###---- 1. normalization\\n ## 2d points\\n mean = np.mean(x, 1) # (2, )\\n x = x - np.tile(mean[:, np.newaxis], [1, n]) # (2, n)\\n average_norm = np.mean(np.sqrt(np.sum(x ** 2, 0)))\\n scale = np.sqrt(2) / average_norm\\n x = scale * x\\n\\n # T = [[scale, 0, -mean * scale], \\n # [ 0, scale, -mean * scale], \\n # [ 0, 0, 1 ]]\\n T = np.zeros((3, 3), dtype=np.float32)\\n T[0, 0] = T[1, 1] = scale\\n T[:2, 2] = -mean * scale\\n T[2, 2] = 1\\n\\n ## 3d points\\n X_homo = np.vstack((X, np.ones((1, n)))) # (4, n)\\n mean = np.mean(X, 1) # (3, )\\n X = X - np.tile(mean[:, np.newaxis], [1, n]) # (3, n)\\n m = X_homo[: 3, :] - X\\n average_norm = np.mean(np.sqrt(np.sum(X ** 2, 0)))\\n scale = np.sqrt(3) / average_norm\\n X = scale * X\\n\\n U = np.zeros((4, 4), dtype=np.float32)\\n U[0, 0] = U[1, 1] = U[2, 2] = scale\\n U[: 3, 3] = -mean * scale\\n U[3, 3] = 1\\n\\n ###---- 2. equations\\n A = np.zeros((n * 2, 8), dtype=np.float32)\\n X_homo = np.vstack((X, np.ones((1, n)))).T\\n A[: n, : 4] = X_homo\\n A[n: , 4: ] = X_homo\\n b = np.reshape(x, [-1, 1]) # (2n, 1)\\n\\n ###---- 3.solution\\n p_8 = np.linalg.pinv(A).dot(b) # (8, 2n) x (2n, 1) -> (8, 1)\\n p = np.zeros((3, 4), dtype=np.float32)\\n p[0, :] = p_8[:4, 0]\\n p[1, :] = p_8[4:, 0]\\n p[-1, -1] = 1\\n\\n ###---- 4. denormalization\\n P_Affine = np.linalg.inv(T).dot(p.dot(U))\\n return P_Affine\",\n \"def get_affine_matrix2d(\\n translations: torch.Tensor,\\n center: torch.Tensor,\\n scale: torch.Tensor,\\n angle: torch.Tensor,\\n sx: Optional[torch.Tensor] = None,\\n sy: Optional[torch.Tensor] = None,\\n) -> torch.Tensor:\\n transform: torch.Tensor = get_rotation_matrix2d(center, -angle, scale)\\n transform[..., 2] += translations # tx/ty\\n\\n # pad transform to get Bx3x3\\n transform_h = convert_affinematrix_to_homography(transform)\\n\\n if any(s is not None for s in [sx, sy]):\\n shear_mat = get_shear_matrix2d(center, sx, sy)\\n transform_h = transform_h @ shear_mat\\n\\n return transform_h\",\n \"def get_affine_matrix2d(\\n translations: Tensor,\\n center: Tensor,\\n scale: Tensor,\\n angle: Tensor,\\n sx: Tensor | None = None,\\n sy: Tensor | None = None,\\n) -> Tensor:\\n transform: Tensor = get_rotation_matrix2d(center, -angle, scale)\\n transform[..., 2] += translations # tx/ty\\n\\n # pad transform to get Bx3x3\\n transform_h = convert_affinematrix_to_homography(transform)\\n\\n if any(s is not None for s in [sx, sy]):\\n shear_mat = get_shear_matrix2d(center, sx, sy)\\n transform_h = transform_h @ shear_mat\\n\\n return transform_h\",\n \"def apply_affine_transform(x, theta=0, tx=0, ty=0, shear=0, zx=1, zy=1,\\n row_axis=0, col_axis=1, channel_axis=2,\\n fill_mode='nearest', cval=0., order=1):\\n if scipy is None:\\n raise ImportError('Image transformations require SciPy. '\\n 'Install SciPy.')\\n transform_matrix = None\\n if theta != 0:\\n theta = np.deg2rad(theta)\\n rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],\\n [np.sin(theta), np.cos(theta), 0],\\n [0, 0, 1]])\\n transform_matrix = rotation_matrix\\n\\n if tx != 0 or ty != 0:\\n shift_matrix = np.array([[1, 0, tx],\\n [0, 1, ty],\\n [0, 0, 1]])\\n if transform_matrix is None:\\n transform_matrix = shift_matrix\\n else:\\n transform_matrix = np.dot(transform_matrix, shift_matrix)\\n\\n if shear != 0:\\n shear = np.deg2rad(shear)\\n shear_matrix = np.array([[1, -np.sin(shear), 0],\\n [0, np.cos(shear), 0],\\n [0, 0, 1]])\\n if transform_matrix is None:\\n transform_matrix = shear_matrix\\n else:\\n transform_matrix = np.dot(transform_matrix, shear_matrix)\\n\\n if zx != 1 or zy != 1:\\n zoom_matrix = np.array([[zx, 0, 0],\\n [0, zy, 0],\\n [0, 0, 1]])\\n if transform_matrix is None:\\n transform_matrix = zoom_matrix\\n else:\\n transform_matrix = np.dot(transform_matrix, zoom_matrix)\\n\\n if transform_matrix is not None:\\n h, w = x.shape[row_axis], x.shape[col_axis]\\n transform_matrix = transform_matrix_offset_center(\\n transform_matrix, h, w)\\n x = np.rollaxis(x, channel_axis, 0)\\n final_affine_matrix = transform_matrix[:2, :2]\\n final_offset = transform_matrix[:2, 2]\\n\\n channel_images = [ndimage.interpolation.affine_transform(\\n x_channel,\\n final_affine_matrix,\\n final_offset,\\n order=order,\\n mode=fill_mode,\\n cval=cval) for x_channel in x]\\n x = np.stack(channel_images, axis=0)\\n x = np.rollaxis(x, 0, channel_axis + 1)\\n return x\",\n \"def __affine_geo_transformation(x, y, gtr):\\n\\n # https://gdal.org/user/raster_data_model.html#affine-geotransform\\n # Affine transformation rewritten for rasterio:\\n gtr_x = gtr[2] + (x + 0.5) * gtr[0] + (y + 0.5) * gtr[1]\\n gtr_y = gtr[5] + (x + 0.5) * gtr[3] + (y + 0.5) * gtr[4]\\n\\n return gtr_x, gtr_y\",\n \"def fit_transform(self, x: Array2D) -> Array2D:\",\n \"def get_affine(x, m, c):\\n x = m*x + c\\n return x\",\n \"def affine_forward(x,w,b):\\n out=None\\n N=x.shape[0]\\n x_row=x.reshape(N,-1)\\n out=np.dot(x_row,w)+b\\n cache=(x,w,b)\\n return out,cache\",\n \"def affine_forward(X, W, b):\\n return np.dot(X, W) + b\",\n \"def apply_affine(A: Affine, x: np.ndarray, y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:\\n\\n shape = x.shape\\n\\n A = np.asarray(A).reshape(3, 3) # type: ignore[assignment]\\n t = A[:2, -1].reshape((2, 1)) # type: ignore[index]\\n A = A[:2, :2] # type: ignore[index]\\n\\n x, y = A @ np.vstack([x.ravel(), y.ravel()]) + t\\n x, y = (a.reshape(shape) for a in (x, y))\\n return (x, y)\",\n \"def affine_forward(x, w, b):\\n #raise NotImplementedError\\n #######################################################################\\n # #\\n # #\\n # TODO: YOUR CODE HERE #\\n # #\\n # #\\n #######################################################################\\n out=np.dot(x,w)+b\\n cache=(x,w,b)\\n return(out, cache)\",\n \"def affine_forward(x, w, b):\\n ############################################################################\\n # TODO: Implement the affine forward pass. Store the result in 'out'. You #\\n # will need to reshape the input into rows. #\\n ############################################################################\\n ############################################################################\\n # START OF YOUR CODE #\\n ############################################################################\\n N = len(x)\\n D,M = w.shape\\n # reshape get a new x\\n new_x = x.reshape(N,D)\\n # get the output\\n out = np.dot(new_x,w) + np.expand_dims(b,axis=0)\\n \\n ############################################################################\\n # END OF YOUR CODE #\\n ############################################################################\\n return out\",\n \"def get_transform(ds):\\n\\n if 'transform' in ds.attrs:\\n ds_trans = ds.attrs['transform']\\n if isinstance(ds_trans, Affine):\\n return ds_trans\\n else:\\n return Affine(*ds_trans)\\n\\n elif 'crs' in ds.data_vars and 'i2m' in ds.data_vars['crs'].attrs:\\n transf_str = ds.data_vars['crs'].attrs['i2m']\\n a = list(map(float, transf_str.split(',')))\\n return Affine(a[0], a[2], a[4], a[1], a[3], a[5])\\n\\n else:\\n resx, resy = get_resolution(ds)\\n xoff = ds['x'].values.min()\\n yoff = ds['y'].values.max()\\n return Affine(resx, 0, xoff, 0, resy, yoff)\",\n \"def _apply_transform(self, x, transform_parameters):\\n # x is a single image, so it doesn't have image number at index 0\\n img_row_axis = self.row_axis - 1\\n img_col_axis = self.col_axis - 1\\n img_channel_axis = self.channel_axis - 1\\n\\n x = apply_affine_transform(x, transform_parameters.get('theta', 0),\\n transform_parameters.get('tx', 0),\\n transform_parameters.get('ty', 0),\\n transform_parameters.get('shear', 0),\\n transform_parameters.get('zx', 1),\\n transform_parameters.get('zy', 1),\\n row_axis=img_row_axis,\\n col_axis=img_col_axis,\\n channel_axis=img_channel_axis,\\n fill_mode=self.fill_mode,\\n cval=self.cval)\\n\\n if transform_parameters.get('channel_shift_intensity') is not None:\\n x = apply_channel_shift(x,\\n transform_parameters['channel_shift_intensity'],\\n img_channel_axis)\\n\\n if transform_parameters.get('flip_horizontal', False):\\n x = self._flip_axis(x, img_col_axis)\\n\\n if transform_parameters.get('flip_vertical', False):\\n x = self._flip_axis(x, img_row_axis)\\n\\n if transform_parameters.get('brightness') is not None:\\n x = apply_brightness_shift(x, transform_parameters['brightness'])\\n\\n return x\",\n \"def affine_forward(x, w, b):\\n out = None\\n ###########################################################################\\n # TODO: Implement the affine forward pass. Store the result in out. You #\\n # will need to reshape the input into rows. #\\n ###########################################################################\\n reshaped_inp = np.reshape(x,(int(x.shape[0]),int(np.prod(x.shape) / x.shape[0])))\\n out = reshaped_inp.dot(w) + b\\n ###########################################################################\\n # END OF YOUR CODE #\\n ###########################################################################\\n cache = (x, w, b)\\n return out, cache\",\n \"def fit_transform(self, x):\\n self.fit(x)\\n\\n if self.method == \\\"svd\\\":\\n return self._u * self._s\\n else:\\n return self._transform_eig(x)\",\n \"def affine_transform(x, transform_matrix, channel_index=2, fill_mode='nearest', cval=0., order=1):\\n # transform_matrix = transform_matrix_offset_center()\\n # asdihasid\\n # asd\\n\\n x = np.rollaxis(x, channel_index, 0)\\n final_affine_matrix = transform_matrix[:2, :2]\\n final_offset = transform_matrix[:2, 2]\\n channel_images = [\\n ndi.interpolation.affine_transform(\\n x_channel, final_affine_matrix, final_offset, order=order, mode=fill_mode, cval=cval\\n ) for x_channel in x\\n ]\\n x = np.stack(channel_images, axis=0)\\n x = np.rollaxis(x, 0, channel_index + 1)\\n return x\",\n \"def affine_forward(x, w, b):\\n out = None\\n ###########################################################################\\n # TODO: Implement the affine forward pass. Store the result in out. You #\\n # will need to reshape the input into rows. #\\n ###########################################################################\\n dim_size = x[0].shape\\n X = x.reshape(x.shape[0], np.prod(dim_size))\\n out = X.dot(w) + b\\n ###########################################################################\\n # END OF YOUR CODE #\\n ###########################################################################\\n cache = (x, w, b)\\n return out, cache\",\n \"def affine_forward(x, w, b):\\n out = None\\n ########################################################################\\n # TODO: Implement the affine forward pass. Store the result in out. #\\n # You will need to reshape the input into rows. #\\n ########################################################################\\n\\n x_reshaped = x.reshape(x.shape[:1] + (-1,))\\n out = x_reshaped.dot(w) + b\\n\\n ########################################################################\\n # END OF YOUR CODE #\\n ########################################################################\\n cache = (x, w, b)\\n return out, cache\",\n \"def affine_forward(x, w, b):\\n N = x.shape[0]\\n\\n # reshape input into rows\\n output = x.reshape([N, -1]).dot(w) + b\\n cache = (x, w, b)\\n\\n return output, cache\",\n \"def transforms_multiply(t0s, t1s):\\r\\n \\r\\n return ut.matrix_multiply(t0s, t1s)\",\n \"def affine_forward(x, w, b):\\n out = None\\n x_shape = x.shape\\n x_reshaped = x.reshape(x_shape[0], np.prod(x_shape[1:]))\\n out = np.dot(x_reshaped, w) + b\\n cache = (x, w, b)\\n return out, cache\",\n \"def affine_forward(x, w, b):\\n out = None\\n \\n # reshape the input into (N, d_1 *...* d_k)\\n input_shape = x.shape\\n prod = 1\\n for i in range(1,len(input_shape)):\\n prod *= input_shape[i]\\n\\n a = x.reshape(x.shape[0],prod)\\n out = np.dot(a,w) + b\\n \\n cache = (x, w, b)\\n return out, cache\",\n \"def transform(self, x):\\n return self._transform_eig(x)\",\n \"def elastic_transform(image, alpha, sigma, alpha_affine, random_state=None):\\n if random_state is None:\\n random_state = np.random.RandomState(None)\\n\\n shape = image.shape\\n shape_size = shape[:2]\\n \\n # Random affine\\n center_square = np.float32(shape_size) // 2\\n square_size = min(shape_size) // 3\\n pts1 = np.float32([center_square + square_size, [center_square[0]+square_size, center_square[1]-square_size], center_square - square_size])\\n pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine, size=pts1.shape).astype(np.float32)\\n M = cv2.getAffineTransform(pts1, pts2)\\n image = cv2.warpAffine(image, M, shape_size[::-1], borderMode=cv2.BORDER_REFLECT_101)\\n\\n dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma) * alpha\\n dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma) * alpha\\n dz = np.zeros_like(dx)\\n\\n x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))\\n indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1)), np.reshape(z, (-1, 1))\\n\\n return map_coordinates(image, indices, order=1, mode='reflect').reshape(shape)\",\n \"def affine_trans(self):\\n h, w, _ = self.img.shape\\n\\n \\\"\\\"\\\"\\n pts1 = np.float32(\\n [\\n [randint(0, rows), randint(0, cols)],\\n [randint(0, rows), randint(0, cols)],\\n [randint(0, rows), randint(0, cols)],\\n ]\\n )\\n pts2 = np.float32(\\n [\\n [randint(0, rows), randint(0, cols)],\\n [randint(0, rows), randint(0, cols)],\\n [randint(0, rows), randint(0, cols)],\\n ]\\n )\\n \\\"\\\"\\\"\\n\\n pts1 = np.float32([[50, 50], [200, 50], [50, 200]])\\n pts2 = np.float32([[10, 100], [200, 50], [100, 250]])\\n\\n M = cv2.getAffineTransform(pts1, pts2)\\n\\n self.img = cv2.warpAffine(self.img, M, (w, h))\\n\\n self.edits.append(\\\"affine\\\")\\n return self\",\n \"def affine(img, angle, translate, scale, shear):\\n if not _is_numpy(img):\\n raise TypeError('img should be Numpy Image. Got {}'.format(type(img)))\\n\\n assert isinstance(translate, (tuple, list)) and len(translate) == 2, \\\\\\n \\\"Argument translate should be a list or tuple of length 2\\\"\\n\\n assert scale > 0.0, \\\"Argument scale should be positive\\\"\\n\\n aug = iaa.Affine(scale=scale, rotate=angle, translate_px=translate, shear=shear)\\n return aug.augment_image(img)\",\n \"def fit_transform(self, Xs, y=None):\\n return self.fit(Xs, y).transform(Xs)\",\n \"def setrans(Bi, t):\\n\\n x,v=mat2set(Bi)\\n Bo = set2mat((x+t,v))\\n Bo = Bo.astype(Bi.dtype)\\n return Bo\",\n \"def get_affine_transform(gps_coords, pdr_coords):\\n # Compute similarity Xp = s A X + b\\n X = np.array(pdr_coords)\\n Xp = np.array(gps_coords)\\n T = tf.superimposition_matrix(X.T, Xp.T, scale=True)\\n\\n A, b = T[:3, :3], T[:3, 3]\\n s = np.linalg.det(A)**(1. / 3)\\n A /= s\\n return s, A, b\",\n \"def translateEuler(self,trans):\\n return np.array([[1,0,0,trans[0]],[0,1,0,trans[1]],[0,0,1,trans[2]],[0,0,0,1]])\",\n \"def model_1exp(a, t, s, t0=0):\\n a, t, s = physicond(a, t, s)\\n\\n # auxilary function taking as argument a time array.\\n def aux(array_t, details=False):\\n\\n # applying time offset\\n t_var = array_t - t0\\n\\n # model expression coming from symbolic calculation\\n pulse = np.heaviside(t_var, 1.)\\n ind = np.where(pulse != 0.)\\n pulse[ind] = a * (np.exp(-t_var[ind]/t)-np.exp(-t_var[ind]/s))\\n\\n # same behavior as model_2exp and model_3exp\\n if details == True:\\n return pulse, pulse\\n elif details == False:\\n return pulse\\n\\n return aux\",\n \"def compute_e(f_mat, m_mat):\\r\\n return m_mat.T @ f_mat @ m_mat\",\n \"def convert_affine(ref, t, out):\\n args = [\\n transform_exe,\\n '-d', '3',\\n '-r', ref,\\n '-t', '[{},0]'.format(t),\\n '-o', '[{},1]'.format(out),\\n '--float'\\n ]\\n subprocess.check_call(args)\",\n \"def elastic_transform(X, min_alpha=36, max_alpha=38, min_sigma=5, max_sigma=6, random_state=None, n_jobs=1):\\n if random_state is None:\\n rng = np.random\\n else:\\n rng = np.random.RandomState(random_state)\\n alphas = rng.uniform(min_alpha, max_alpha, size=X.shape[0])\\n sigmas = rng.uniform(min_sigma, max_sigma, size=X.shape[0])\\n X_elas = Parallel(n_jobs=n_jobs)(delayed(elastic_transform_one)(X[i], alphas[i], sigmas[i]) for i in range(X.shape[0]))\\n return np.array(X_elas, dtype='float32')\",\n \"def elastic_transform(image, alpha=1000, sigma=30, spline_order=1, mode='nearest', random_state=np.random):\\n assert image.ndim == 2\\n shape = image.shape[:2]\\n\\n dx = gaussian_filter((random_state.rand(*shape) * 2 - 1),\\n sigma, mode=\\\"constant\\\", cval=0) * alpha\\n dy = gaussian_filter((random_state.rand(*shape) * 2 - 1),\\n sigma, mode=\\\"constant\\\", cval=0) * alpha\\n\\n x, y = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]), indexing='ij')\\n indices = [np.reshape(x + dx, (-1, 1)), np.reshape(y + dy, (-1, 1))]\\n result = map_coordinates(\\n image, indices, order=spline_order, mode=mode).reshape(shape)\\n return result\",\n \"def eval(self, x):\\n y = list(x)\\n if not self.biased:\\n y.insert(0, 1.0)\\n y = np.array(y).reshape((self.Ws[0].shape[1], 1))\\n for W, g in zip(self.Ws, self.gs):\\n y = g(np.dot(W, y))\\n return y.flatten()\",\n \"def map(self,Affine,i):\\n map_x = np.zeros([self.num,self.d])\\n for k in range(self.num):\\n map_x[k,:] = Affine.apply(i,self.pick(k))\\n Mapped = Model_Points(map_x)\\n return Mapped\",\n \"def affine_transform(initialize=False, fixed=None, moments=True) :\\n \\n transform = itk.AffineTransform[itk.D, fixed.ndim].New()\\n \\n if initialize :\\n fixed_itk = medipy.itk.medipy_image_to_itk_image(fixed, False)\\n #moving_itk = medipy.itk.medipy_image_to_itk_image(moving, False)\\n\\n fixedIndex = fixed_itk.GetLargestPossibleRegion().GetIndex()\\n fixedSize = fixed_itk.GetLargestPossibleRegion().GetSize()\\n\\n centerIndex = (int(fixedIndex[0] + fixedSize[0] / 2.0), \\\\\\n int(fixedIndex[1] + fixedSize[1] / 2.0), \\\\\\n int(fixedIndex[2] + fixedSize[2] / 2.0))\\n\\n rotationCenter = fixed_itk.TransformIndexToPhysicalPoint(centerIndex)\\n\\n transform.SetIdentity()\\n transform.SetCenter(rotationCenter)\\n\\n #initial_transform = itk.VersorRigid3DTransform[itk.D].New()\\n #initializer = itk.CenteredTransformInitializer[\\n # initial_transform, fixed_itk, moving_itk].New(\\n # Transform=initial_transform, FixedImage=fixed_itk, MovingImage=moving_itk) \\n #if moments :\\n # initializer.MomentsOn()\\n #else :\\n # initializer.GeometryOn()\\n #initializer.InitializeTransform()\\n #transform.SetCenter(initial_transform.GetCenter())\\n #transform.SetOffset(initial_transform.GetOffset())\\n \\n return transform\",\n \"def apply_transform(x,\\n transform_matrix,\\n channel_axis=0,\\n fill_mode='nearest',\\n cval=0.,\\n interp_order=0):\\n x = np.rollaxis(x, channel_axis, 0)\\n final_affine_matrix = transform_matrix[:3, :3]\\n final_offset = transform_matrix[:3, -1]\\n channel_volumes = [ndi.interpolation.affine_transform(\\n x_channel,\\n final_affine_matrix,\\n final_offset,\\n order=interp_order, # NOTE: The order of the spline interpolation\\n mode=fill_mode,\\n cval=cval) for x_channel in x]\\n x = np.stack(channel_volumes, axis=0)\\n x = np.rollaxis(x, 0, channel_axis + 1)\\n return x\",\n \"def affine_2Dtransform(img, t_mat, height, width, h_offset=0, w_offset=0, nh_flag=False, nw_flag=False):\\n # transform matrix must be validated\\n if(np.shape(t_mat) != (2, 2)):\\n return img\\n\\n # implementing matrix multiplication to a default map of source data in order to apply transform\\n # and to achieve coordination/location of transformed matrix according to source data(data map)\\n coord_map = transform_calcualtion(\\n height, width, t_mat, h_offset, w_offset, nh_flag, nw_flag)\\n\\n # transformed image data construction\\n t_img = np.full((height+h_offset, width+w_offset, 3), 255, dtype='uint8')\\n\\n # applying new map to image inorder to complete the transform\\n try:\\n for i in range(height):\\n for j in range(width):\\n [i_new_coord, j_new_coord] = coord_map[i, j, :]\\n # unhandled bound-jumpout\\n t_img[i_new_coord, j_new_coord, :] = img[i, j, :]\\n except:\\n print(\\\"not enough offset/negative coordination pushed\\\")\\n return img\\n return t_img\",\n \"def translateEuler(trans):\\n return np.array([[1,0,0,trans[0]],[0,1,0,trans[1]],[0,0,1,trans[2]],[0,0,0,1]])\",\n \"def affine_sigmoid(xin, m= 10, c= 3):\\n if type(xin) != np.ndarray:\\n x = np.array([xin])\\n else:\\n x = xin\\n\\n x = get_affine(x, m, c)\\n output = get_sigmoid(x)\\n\\n if type(xin) != np.ndarray:\\n return output[0]\\n else:\\n return output\",\n \"def transform(self, x):\",\n \"def _transform(\\n self, x: \\\"torch.Tensor\\\", y: Optional[\\\"torch.Tensor\\\"], **kwargs\\n ) -> Tuple[\\\"torch.Tensor\\\", Optional[\\\"torch.Tensor\\\"]]:\\n import torch\\n import torchvision.transforms.functional as F\\n\\n img_size = x.shape[:2]\\n\\n angle = float(\\n torch.empty(1)\\n .uniform_(float(self.degree_range[0]), float(self.degree_range[1]))\\n .item()\\n )\\n\\n max_dx = float(self.translate[0] * img_size[1])\\n max_dy = float(self.translate[1] * img_size[0])\\n tx = int(round(torch.empty(1).uniform_(-max_dx, max_dx).item()))\\n ty = int(round(torch.empty(1).uniform_(-max_dy, max_dy).item()))\\n translations = (tx, ty)\\n\\n scale = float(torch.empty(1).uniform_(self.scale[0], self.scale[1]).item())\\n\\n # x needs to have channel first\\n x = x.permute(2, 0, 1)\\n x = F.affine(\\n img=x, angle=angle, translate=translations, scale=scale, shear=(0.0, 0.0)\\n )\\n x = x.permute(1, 2, 0)\\n\\n return torch.clamp(x, min=self.clip_values[0], max=self.clip_values[1]), y\",\n \"def jacobian(self, dt):\\n if dt not in self._F_cache:\\n d = self._dimension\\n with torch.no_grad():\\n F = eye_like(self.sa2, d)\\n F[: d // 2, d // 2 :] = dt * eye_like(self.sa2, d // 2)\\n self._F_cache[dt] = F\\n\\n return self._F_cache[dt]\",\n \"def __compose_transformation(self):\\n s = self.scale\\n rotR = self.rotation\\n t = self.translation\\n T = np.eye(4)\\n T[0:3, 3] = t\\n R = np.eye(4)\\n R[0:3, 0:3] = rotR\\n M = T.dot(R)\\n if s == 1:\\n M = T.dot(R)\\n else:\\n S = np.eye(4)\\n S[0:3, 0:3] = np.diag([s, s, s])\\n M = T.dot(R).dot(S)\\n return M\",\n \"def seToSE( x ):\\n x = asarray(x,dtype=float)\\n if x.shape != (6,):\\n raise ValueError(\\\"shape must be (6,); got %s\\\" % str(x.shape))\\n #\\n return expM(screw(x))\",\n \"def J_direct_transform(om, consts, taus):\\n ndecay=len(consts) ; noms=len(om)\\n Jmat = np.zeros( (ndecay, noms ) )\\n for i in range(ndecay):\\n Jmat[i] = consts[i]*taus[i] /(1 + (taus[i]*om)**2.)\\n return Jmat.sum(axis=0)\",\n \"def exp(tensor):\\n return _elementary_op(tensor, np.exp, np.exp)\",\n \"def affineTransform(img, pts, newPts):\\n\\ttmp = img.copy()\\n\\tif len(img.shape) is 3:\\n\\t\\trows, cols, ch = img.shape\\n\\telse:\\n\\t\\trows, cols = img.shape\\n\\tpts1 = np.float32(pts)\\n\\tpts2 = np.float32(newPts)\\n\\tM = cv2.getAffineTransform(pts1, pts2)\\n\\tdst = cv2.warpAffine(tmp, M, (cols, rows))\\n\\treturn dst\",\n \"def sfunc(self,x,y):\\n return np.exp(-(x-self.x_0)**2.0-(y-self.y_0)**2.0)\",\n \"def apply_T(T, points):\\n flipped = False\\n if points.shape[0] != 3:\\n assert points.shape[1] == 3, \\\"Points must be 3xN or Nx3\\\"\\n points = points.T\\n flipped = True\\n points_h = np.vstack((points, np.ones_like(points[0, :])))\\n points_transformed_h = np.dot(T, points_h)\\n points_transformed = points_transformed_h[:-1]\\n if flipped:\\n return points_transformed.T\\n return points_transformed\",\n \"def transform_and_compute_jacobian(self, xj):\\n x = xj[:, :self.d].detach()\\n log_j = xj[:, -1]\\n\\n x.requires_grad = True\\n y = self.flow_(x)\\n\\n n_batch = xj.shape[0]\\n\\n jx = torch.zeros(n_batch, self.d, self.d).to(log_j.device)\\n directions = torch.eye(self.d).to(log_j).unsqueeze(0).repeat(n_batch, 1, 1)\\n\\n for i in range(self.d):\\n jx[:, i, :] = torch.autograd.grad(y, x, directions[:, i, :],\\n allow_unused=True, create_graph=True, retain_graph=True)[0]\\n x.requires_grad = False\\n x.grad = None\\n\\n log_det_j = torch.log(torch.abs(torch.det(jx)))\\n return torch.cat([y.detach(), (log_j + log_det_j).unsqueeze(1)], 1)\",\n \"def minimize_transform(params, points_dest, points_src, constraints):\\n # initialize dof\\n dof = [0, 0, 0, 0, 0, 0, 1, 1, 1]\\n # initialize dictionary to relate constraints index to dof\\n dict_dof = {'Tx': 0, 'Ty': 1, 'Tz': 2, 'Rx': 3, 'Ry': 4, 'Rz': 5, 'Sx': 6, 'Sy': 7, 'Sz': 8}\\n # extract constraints\\n list_constraints = constraints.split('_')\\n # loop across constraints and update dof\\n for i in range(len(list_constraints)):\\n dof[dict_dof[list_constraints[i]]] = params[i]\\n # convert dof to more intuitive variables\\n tx, ty, tz, alpha, beta, gamma, scx, scy, scz = dof[0], dof[1], dof[2], dof[3], dof[4], dof[5], dof[6], dof[7], dof[8]\\n # build rotation matrix\\n rotation_matrix = matrix([[cos(alpha)*cos(beta), cos(alpha)*sin(beta)*sin(gamma)-sin(alpha)*cos(gamma), cos(alpha)*sin(beta)*cos(gamma)+sin(alpha)*sin(gamma)],\\n [sin(alpha)*cos(beta), sin(alpha)*sin(beta)*sin(gamma)+cos(alpha)*cos(gamma), sin(alpha)*sin(beta)*cos(gamma)-cos(alpha)*sin(gamma)],\\n [-sin(beta), cos(beta)*sin(gamma), cos(beta)*cos(gamma)]])\\n # build scaling matrix\\n scaling_matrix = matrix([[scx, 0.0, 0.0], [0.0, scy, 0.0], [0.0, 0.0, scz]])\\n # compute rotation+scaling matrix\\n rotsc_matrix = scaling_matrix * rotation_matrix\\n # compute center of mass from moving points (src)\\n points_src_barycenter = mean(points_src, axis=0)\\n # apply transformation to moving points (src)\\n points_src_reg = ((rotsc_matrix * (matrix(points_src) - points_src_barycenter).T).T + points_src_barycenter) + matrix([tx, ty, tz])\\n # record SSE for later display\\n sse_results.append(SSE(matrix(points_dest), points_src_reg))\\n # return SSE\\n return SSE(matrix(points_dest), points_src_reg)\",\n \"def affineSchur(self):\\n return AffineSchurFunctions(self)\",\n \"def transformAffine(self, path=None, src=None, dst=None):\\n if path is not None:\\n landmarks = pd.read_csv(path, skiprows=1,engine=\\\"c\\\", na_filter=False, header=None, delim_whitespace=True, dtype=np.float32).as_matrix()\\n dst = landmarks[:,3:5]\\n src = landmarks[:,1:3]\\n affine = transform.estimate_transform(\\\"affine\\\",src,dst)\\n data = self.stormData[0][:,0:2]\\n data = affine(data)\\n self.stormData[0][:,0:2] = data\",\n \"def attrTransform(self, matrix, transform):\\n for ttype, targs in self.reTransformFind.findall(transform):\\n targs = list(map(lambda x: float(x), self.reNumberFind.findall(targs)))\\n if ttype == 'matrix':\\n newmatrix = [ targs[0], targs[1],\\n targs[2], targs[3],\\n targs[4], targs[5] ]\\n self.matrixMul(matrix, newmatrix)\\n elif ttype == 'translate':\\n tx = targs[0]\\n ty = targs[1] if len(targs) > 1 else 0\\n newmatrix = [ 1, 0, 0, 1, tx, ty ]\\n self.matrixMul(matrix, newmatrix)\\n elif ttype == 'scale':\\n sx = targs[0]\\n sy = targs[1] if len(targs) > 1 else sx\\n newmatrix = [ sx, 0, 0, sy, 0, 0 ]\\n self.matrixMul(matrix, newmatrix)\\n elif ttype == 'rotate':\\n if len(targs) == 1:\\n alpha = targs[0]\\n newmatrix = [ math.cos(alpha), math.sin(alpha),\\n -math.sin(alpha), math.cos(alpha),\\n 0, 0]\\n self.matrixMul(matrix, newmatrix)\\n else:\\n alpha = targs[0]\\n newmatrix = [ 1, 0, 0, 1, targs[1], targs[2] ]\\n self.matrixMul(matrix, newmatrix)\\n newmatrix = [ math.cos(alpha), math.sin(alpha),\\n -math.sin(alpha), math.cos(alpha),\\n 0, 0]\\n self.matrixMul(matrix, newmatrix)\\n newmatrix = [ 1, 0, 0, 1, -targs[1], -targs[2] ]\\n self.matrixMul(matrix, newmatrix)\\n elif ttype == 'skewX' or ttype == 'skewY':\\n self.alert(\\\"skewX and skewY transformations are not supported\\\", elem)\\n else:\\n print('unknown transform type: ', ttype)\\n return matrix\",\n \"def _apply(self, x, **kwargs):\\n return reduce(lambda x_i, tr: tr._apply(x_i), self.transforms, x)\",\n \"def translate(self, x=0, y=0, z=0):\\n\\t\\ttranslation = np.identity(4)\\n\\t\\ttranslation[0, 3] += x\\n\\t\\ttranslation[1, 3] += y\\n\\t\\ttranslation[2, 3] += z\\n\\t\\t\\n\\t\\tself.matrix = np.matmul(self.matrix, translation)\",\n \"def affine_matrix(self) -> np.ndarray:\\n return self._tf_matrix\",\n \"def _apply_sx(self, state, axes, inverse=False):\\n if inverse:\\n return 0.5 * ((1 - 1j) * state + (1 + 1j) * self._apply_x(state, axes))\\n\\n return 0.5 * ((1 + 1j) * state + (1 - 1j) * self._apply_x(state, axes))\",\n \"def elastic_transform_approx(\\n img: np.ndarray,\\n alpha: float,\\n sigma: float,\\n alpha_affine: float,\\n interpolation: int = cv2.INTER_LINEAR,\\n border_mode: int = cv2.BORDER_REFLECT_101,\\n value: Optional[ImageColorType] = None,\\n random_state: Optional[np.random.RandomState] = None,\\n) -> np.ndarray:\\n height, width = img.shape[:2]\\n\\n # Random affine\\n center_square = np.array((height, width), dtype=np.float32) // 2\\n square_size = min((height, width)) // 3\\n alpha = float(alpha)\\n sigma = float(sigma)\\n alpha_affine = float(alpha_affine)\\n\\n pts1 = np.array(\\n [\\n center_square + square_size,\\n [center_square[0] + square_size, center_square[1] - square_size],\\n center_square - square_size,\\n ],\\n dtype=np.float32,\\n )\\n pts2 = pts1 + random_utils.uniform(-alpha_affine, alpha_affine, size=pts1.shape, random_state=random_state).astype(\\n np.float32\\n )\\n matrix = cv2.getAffineTransform(pts1, pts2)\\n\\n warp_fn = _maybe_process_in_chunks(\\n cv2.warpAffine,\\n M=matrix,\\n dsize=(width, height),\\n flags=interpolation,\\n borderMode=border_mode,\\n borderValue=value,\\n )\\n img = warp_fn(img)\\n\\n dx = random_utils.rand(height, width, random_state=random_state).astype(np.float32) * 2 - 1\\n cv2.GaussianBlur(dx, (17, 17), sigma, dst=dx)\\n dx *= alpha\\n\\n dy = random_utils.rand(height, width, random_state=random_state).astype(np.float32) * 2 - 1\\n cv2.GaussianBlur(dy, (17, 17), sigma, dst=dy)\\n dy *= alpha\\n\\n x, y = np.meshgrid(np.arange(width), np.arange(height))\\n\\n map_x = np.float32(x + dx)\\n map_y = np.float32(y + dy)\\n\\n remap_fn = _maybe_process_in_chunks(\\n cv2.remap,\\n map1=map_x,\\n map2=map_y,\\n interpolation=interpolation,\\n borderMode=border_mode,\\n borderValue=value,\\n )\\n return remap_fn(img)\",\n \"def transform(self, x: Array2D) -> Array2D:\",\n \"def elastic_transform(self, image, random_state=None):\\n if random_state is None:\\n random_state = np.random.RandomState(None)\\n\\n image = self.affine(image, random_state)\\n #from ipdb import set_trace; set_trace()\\n indices = self.stretch_indices(image, random_state)\\n\\n return map_coordinates(image, indices, order=1, mode='reflect').reshape(image.shape)\",\n \"def affine(self, image, random_state):\\n shape = image.shape\\n shape_size = shape[:2]\\n transform_std = self.alpha_affine*image.shape[1]\\n\\n center_square = np.float32(shape[:2]) // 2\\n square_size = min(shape[:2]) // 3\\n\\n source = np.float32([center_square + square_size, [center_square[0]+square_size, center_square[1]-square_size], center_square - square_size])\\n destination = source + random_state.uniform(-transform_std, transform_std, size=source.shape).astype(np.float32)\\n M = cv2.getAffineTransform(source, destination)\\n\\n return cv2.warpAffine(image, M, shape_size[::-1], borderMode=cv2.BORDER_REPLICATE)\",\n \"def EvaluateJacobian(x):\\n j = np.zeros((NOBSERVATIONS, 3))\\n\\n for i in range(NOBSERVATIONS):\\n base = np.exp(-x[0] * t[i]) / (x[1] + x[2] * t[i])\\n\\n j[i][0] = t[i] * base\\n j[i][1] = base / (x[1] + x[2] * t[i])\\n j[i][2] = base * t[i] / (x[1] + x[2] * t[i])\\n\\n return j\",\n \"def f(self,t,svect):\\n s1, vectors = spinlib.get_s_and_vectors(svect, self.N)\\n heff = -spinlib.cycleLeft3D(s1,self.N) - spinlib.cycleRight3D(s1,self.N)\\n \\n # some transpose magic - basically want to matrix multiply the right things\\n if len(vectors) == 0:\\n retvect = vectors\\n else:\\n retvect = np.dot(self.jac(s1, heff), vectors).T.ravel()\\n return self.J * np.concatenate((-spinlib.cross(s1, heff), retvect), axis = 0)\",\n \"def quantize_affine_given_quant_params(\\n input: torch.Tensor,\\n quantize_params: QuantizeAffineParams2,\\n) -> torch.Tensor:\\n return QuantizeAffineFunction.apply(input, quantize_params)\",\n \"def temperature_scaling(x,t):\\n n,d = x.shape\\n res = np.copy(x)\\n\\n res *= (1./t)\\n for i in range(n):\\n res[i] = softmax(res[i])\\n return(res)\",\n \"def analytic(x, t, D, x0, xend, logx=False, c_s=1, use_log2=False):\\n import scipy.special\\n if t.ndim == 1:\\n t = t.reshape((t.size, 1))\\n expb = (lambda arg: 2**arg) if use_log2 else np.exp\\n x = expb(x) if logx else x\\n return c_s * scipy.special.erfc(x/(2*(D*t)**0.5))\",\n \"def transformation_matrix(self, s1, s2, s3, t1, t2, t3):\\n\\n s1 = np.array(s1)\\n s2 = np.array(s2)\\n s3 = np.array(s3)\\n t1 = np.array(t1)\\n t2 = np.array(t2)\\n t3 = np.array(t3)\\n\\n Q = np.array(\\n [\\n [t2[0] - t1[0], t2[1] - t1[1], t2[2] - t1[2]],\\n [t3[0] - t1[0], t3[1] - t1[1], t3[2] - t1[2]],\\n ]\\n )\\n\\n P = np.array([[s2[0] - s1[0], s2[1] - s1[1]], [s3[0] - s1[0], s3[1] - s1[1]]])\\n\\n try:\\n # Invert the P matrix\\n Pinv = inv(P)\\n\\n # Build the dot product\\n T = np.dot(Pinv, Q)\\n\\n # Offset\\n V0 = np.subtract(t2, np.transpose(s2[0:2]).dot(T))\\n except Exception as e:\\n self.log.error(\\\"An error occured during the transformation.\\\", exc_info=True)\\n return -1, -1\\n\\n return T, V0\",\n \"def transform(self, x:generic_array, dense=True) -> generic_array:\\n if type(x) == np.ndarray and not dense:\\n warnings.warn(\\\"For Numpy transform it is best to use dense=True\\\")\\n \\n K_nq = self._pairwise_kernels(x, self.components_, dense=dense)\\n x_new = K_nq @ self.normalization\\n return x_new\",\n \"def nonlinear_eom_to_ss(aircraft, x_ss, u_ss, x_0, u_0, m, j, dx=0.1, du=0.1):\\n \\\"\\\"\\\"return jacobians a, b wrt to x_ss and output matrices c, and d wrt u_ss.\\\"\\\"\\\"\\n x = x_0\\n u = u_0\\n a = zeros((len(x_0), len(x_0)))\\n b = zeros((len(x_0), len(u_0)))\\n for ii in range(0, len(x_0)):\\n x[ii] = x[ii] + dx\\n c = c_f_m(aircraft, x, u_0)\\n dxdt_1 = nonlinear_eom(x, m, j, c)\\n\\n x[ii] = x[ii] - dx\\n c = c_f_m(aircraft, x, u_0)\\n dxdt_2 = nonlinear_eom(x, m, j, c)\\n ddx_dx = (dxdt_1 - dxdt_2)/(2*dx)\\n a[:, ii] = transpose(ddx_dx)\\n x = x_0\\n\\n for ii in range(0, len(u_0)):\\n u[ii] = u[ii] + du\\n c = c_f_m(aircraft, x_0, u)\\n dxdt_1 = nonlinear_eom(x, m, j, c)\\n\\n u[ii] = u[ii] - du\\n c = c_f_m(aircraft, x_0, u)\\n dxdt_2 = nonlinear_eom(x, m, j, c)\\n ddx_dx = (dxdt_1 - dxdt_2)/(2*du)\\n b[:, ii] = transpose(ddx_dx)\\n u = u_0\\n\\n a_out = a[x_ss, :]\\n a_out = a_out[:, x_ss]\\n\\n b_out = b[x_ss, :]\\n b_out = b_out[:, u_ss]\\n\\n c_out = identity(len(x_ss))\\n d_out = zeros((len(x_ss), len(u_ss)))\\n return a_out, b_out, c_out, d_out\",\n \"def elastic_transform(\\n img: np.ndarray,\\n alpha: float,\\n sigma: float,\\n alpha_affine: float,\\n interpolation: int = cv2.INTER_LINEAR,\\n border_mode: int = cv2.BORDER_REFLECT_101,\\n value: Optional[ImageColorType] = None,\\n random_state: Optional[np.random.RandomState] = None,\\n approximate: bool = False,\\n same_dxdy: bool = False,\\n):\\n height, width = img.shape[:2]\\n\\n # Random affine\\n center_square = np.array((height, width), dtype=np.float32) // 2\\n square_size = min((height, width)) // 3\\n alpha = float(alpha)\\n sigma = float(sigma)\\n alpha_affine = float(alpha_affine)\\n\\n pts1 = np.array(\\n [\\n center_square + square_size,\\n [center_square[0] + square_size, center_square[1] - square_size],\\n center_square - square_size,\\n ],\\n dtype=np.float32,\\n )\\n pts2 = pts1 + random_utils.uniform(-alpha_affine, alpha_affine, size=pts1.shape, random_state=random_state).astype(\\n np.float32\\n )\\n matrix = cv2.getAffineTransform(pts1, pts2)\\n\\n warp_fn = _maybe_process_in_chunks(\\n cv2.warpAffine, M=matrix, dsize=(width, height), flags=interpolation, borderMode=border_mode, borderValue=value\\n )\\n img = warp_fn(img)\\n\\n if approximate:\\n # Approximate computation smooth displacement map with a large enough kernel.\\n # On large images (512+) this is approximately 2X times faster\\n dx = random_utils.rand(height, width, random_state=random_state).astype(np.float32) * 2 - 1\\n cv2.GaussianBlur(dx, (17, 17), sigma, dst=dx)\\n dx *= alpha\\n if same_dxdy:\\n # Speed up even more\\n dy = dx\\n else:\\n dy = random_utils.rand(height, width, random_state=random_state).astype(np.float32) * 2 - 1\\n cv2.GaussianBlur(dy, (17, 17), sigma, dst=dy)\\n dy *= alpha\\n else:\\n dx = np.float32(\\n gaussian_filter((random_utils.rand(height, width, random_state=random_state) * 2 - 1), sigma) * alpha\\n )\\n if same_dxdy:\\n # Speed up\\n dy = dx\\n else:\\n dy = np.float32(\\n gaussian_filter((random_utils.rand(height, width, random_state=random_state) * 2 - 1), sigma) * alpha\\n )\\n\\n x, y = np.meshgrid(np.arange(width), np.arange(height))\\n\\n map_x = np.float32(x + dx)\\n map_y = np.float32(y + dy)\\n\\n remap_fn = _maybe_process_in_chunks(\\n cv2.remap, map1=map_x, map2=map_y, interpolation=interpolation, borderMode=border_mode, borderValue=value\\n )\\n return remap_fn(img)\",\n \"def affine_transform_2d(v, mapping, alpha = 1):\\r\\n p_wgt = vec2(0, 0)\\r\\n q_wgt = vec2(0, 0)\\r\\n w = len(mapping)*[None]\\r\\n w_sum = 0\\r\\n for i in range(len(mapping)):\\r\\n mp = mapping[i]\\r\\n x = mp[0].x - v.x\\r\\n y = mp[0].y - v.y\\r\\n if (x == 0 and y == 0): return mp[1]\\r\\n w[i] = 1/((x*x + y*y) ** alpha)\\r\\n p_wgt += mp[0]*w[i]\\r\\n q_wgt += mp[1]*w[i]\\r\\n w_sum += w[i]\\r\\n p_wgt /= w_sum\\r\\n q_wgt /= w_sum\\r\\n M1 = mat2(0)\\r\\n M2 = mat2(0)\\r\\n for i in range(len(mapping)):\\r\\n mp = mapping[i]\\r\\n p_adj = mp[0] - p_wgt\\r\\n q_adj = mp[1] - q_wgt\\r\\n M1 += p_adj.transpose_multiply(p_adj)*w[i]\\r\\n M2 += p_adj.transpose_multiply(q_adj)*w[i]\\r\\n M1 = M1.inverse()\\r\\n M = M1*M2\\r\\n M = M.transpose()\\r\\n v_out = M*(v - p_wgt) + q_wgt\\r\\n return v_out\",\n \"def apply_transform_to_image(self,img, transform, center=None):\\n \\n if center is None:\\n center = (np.array(img.shape)[::-1]-1)/2.0\\n \\n displacement = np.dot(transform, center)\\n shift = center - displacement\\n \\n img_tf = ndimage.interpolation.affine_transform(img, transform, offset=shift, mode=\\\"constant\\\", order=3, cval=0.0)\\n return img_tf\",\n \"def apply_transformation(self, points):\\n assert (points.shape[0] == 3)\\n n = points.shape[1]\\n points_ = np.vstack((points, np.ones((1, n))))\\n points_trans_ = np.matmul(self.pose_mat, points_)\\n points_transformed = np.true_divide(points_trans_[:3, :], points_trans_[[-1], :])\\n return points_transformed\",\n \"def transform(self) -> Affine:\\n transform = (\\n Affine.translation(*self.origin)\\n * Affine.rotation(self.rotation)\\n * Affine.scale(*self.res)\\n )\\n return transform\",\n \"def augmentAffine(img_in, seg_in, strength=0.05):\\n B,C,D,H,W = img_in.size()\\n affine_matrix = (torch.eye(3,4).unsqueeze(0) + torch.randn(B, 3, 4) * strength).to(img_in.device)\\n\\n meshgrid = F.affine_grid(affine_matrix,torch.Size((B,1,D,H,W)))\\n\\n img_out = F.grid_sample(img_in, meshgrid,padding_mode='border')\\n seg_out = F.grid_sample(seg_in.float().unsqueeze(1), meshgrid, mode='nearest').long().squeeze(1)\\n\\n return img_out, seg_out\",\n \"def scaling(sx,sy,Mat):\\r\\n # SM is the Scaling Matrix ( 3 X 3 )\\r\\n SM = [[sx,0,0],[0,sy,0],[0,0,1]]\\r\\n Scaled = Multiply(SM,Mat)\\r\\n # Scaled[0][0] is the updated x coordinate\\r\\n # Scaled[1][0] is the updated y coordinate\\r\\n return Scaled[0][0],Scaled[1][0],Scaled[2][0]\",\n \"def get_affine_transform(center, scale, rot, output_size, shift=(0.0, 0.0), inv=False):\\n assert len(center) == 2\\n assert len(scale) == 2\\n assert len(output_size) == 2\\n assert len(shift) == 2\\n scale_tmp = scale * 200.0\\n shift = np.array(shift)\\n src_w = scale_tmp[0]\\n dst_w = output_size[0]\\n dst_h = output_size[1]\\n rot_rad = np.pi * rot / 180\\n src_dir = rotate_point([0.0, src_w * -0.5], rot_rad)\\n dst_dir = np.array([0.0, dst_w * -0.5])\\n src = np.zeros((3, 2), dtype=np.float32)\\n src[0, :] = center + scale_tmp * shift\\n src[1, :] = center + src_dir + scale_tmp * shift\\n src[2, :] = _get_3rd_point(src[0, :], src[1, :])\\n dst = np.zeros((3, 2), dtype=np.float32)\\n dst[0, :] = [dst_w * 0.5, dst_h * 0.5]\\n dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir\\n dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])\\n if inv:\\n trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))\\n else:\\n trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))\\n return trans\",\n \"def transformCoordinates(self,x,incoordsys=None,outcoordsys=None):\\n if incoordsys is None:\\n incoordsys = self.incoordsys\\n if outcoordsys is None:\\n outcoordsys = self._fcoordsys\\n if incoordsys == outcoordsys:\\n return x\\n else:\\n return self._inputtransforms[incoordsys][outcoordsys](*x)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7174713","0.6328652","0.6287944","0.6152823","0.6144677","0.5968129","0.5957975","0.5921042","0.5909272","0.5798729","0.5789589","0.5777053","0.56403214","0.5585435","0.55693203","0.5553606","0.5544687","0.5482424","0.5461267","0.54540503","0.54315287","0.5431089","0.54214513","0.5414109","0.541142","0.5406962","0.53979105","0.53966075","0.5387316","0.53590506","0.53474116","0.531475","0.53132176","0.5309061","0.53039515","0.5298219","0.52731067","0.52626914","0.5262444","0.5262215","0.52503246","0.5246775","0.5241218","0.5227229","0.5211313","0.51990587","0.519675","0.5184051","0.51559013","0.5151995","0.514902","0.5138682","0.5134257","0.51302505","0.51272625","0.5122011","0.5118679","0.5098131","0.5095631","0.5091359","0.50878257","0.5085916","0.50805473","0.50737447","0.5071047","0.5056921","0.50530255","0.5050616","0.50383174","0.5037148","0.5034865","0.5009804","0.50043106","0.50017273","0.49965462","0.49963453","0.49912074","0.49882534","0.49836805","0.49808493","0.49803478","0.49787912","0.49754673","0.4973556","0.49674076","0.4966571","0.49656722","0.4965338","0.4964089","0.4963674","0.49623898","0.49605146","0.49542803","0.495026","0.49478838","0.49416018","0.49410886","0.4940748","0.4935474","0.49321038"],"string":"[\n \"0.7174713\",\n \"0.6328652\",\n \"0.6287944\",\n \"0.6152823\",\n \"0.6144677\",\n \"0.5968129\",\n \"0.5957975\",\n \"0.5921042\",\n \"0.5909272\",\n \"0.5798729\",\n \"0.5789589\",\n \"0.5777053\",\n \"0.56403214\",\n \"0.5585435\",\n \"0.55693203\",\n \"0.5553606\",\n \"0.5544687\",\n \"0.5482424\",\n \"0.5461267\",\n \"0.54540503\",\n \"0.54315287\",\n \"0.5431089\",\n \"0.54214513\",\n \"0.5414109\",\n \"0.541142\",\n \"0.5406962\",\n \"0.53979105\",\n \"0.53966075\",\n \"0.5387316\",\n \"0.53590506\",\n \"0.53474116\",\n \"0.531475\",\n \"0.53132176\",\n \"0.5309061\",\n \"0.53039515\",\n \"0.5298219\",\n \"0.52731067\",\n \"0.52626914\",\n \"0.5262444\",\n \"0.5262215\",\n \"0.52503246\",\n \"0.5246775\",\n \"0.5241218\",\n \"0.5227229\",\n \"0.5211313\",\n \"0.51990587\",\n \"0.519675\",\n \"0.5184051\",\n \"0.51559013\",\n \"0.5151995\",\n \"0.514902\",\n \"0.5138682\",\n \"0.5134257\",\n \"0.51302505\",\n \"0.51272625\",\n \"0.5122011\",\n \"0.5118679\",\n \"0.5098131\",\n \"0.5095631\",\n \"0.5091359\",\n \"0.50878257\",\n \"0.5085916\",\n \"0.50805473\",\n \"0.50737447\",\n \"0.5071047\",\n \"0.5056921\",\n \"0.50530255\",\n \"0.5050616\",\n \"0.50383174\",\n \"0.5037148\",\n \"0.5034865\",\n \"0.5009804\",\n \"0.50043106\",\n \"0.50017273\",\n \"0.49965462\",\n \"0.49963453\",\n \"0.49912074\",\n \"0.49882534\",\n \"0.49836805\",\n \"0.49808493\",\n \"0.49803478\",\n \"0.49787912\",\n \"0.49754673\",\n \"0.4973556\",\n \"0.49674076\",\n \"0.4966571\",\n \"0.49656722\",\n \"0.4965338\",\n \"0.4964089\",\n \"0.4963674\",\n \"0.49623898\",\n \"0.49605146\",\n \"0.49542803\",\n \"0.495026\",\n \"0.49478838\",\n \"0.49416018\",\n \"0.49410886\",\n \"0.4940748\",\n \"0.4935474\",\n \"0.49321038\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8046413"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2998,"cells":{"query":{"kind":"string","value":"Transform x elementwise through an affine function y = exp(s)(x t) where s = st[...,0] and t = st[...,1] with s.shape == x.shape == t.shape This is the inverse of `element_wise_affine` above for the same set of parameters st The Jacobian for this transformation is the coordinatewise product of the scaling factors J = prod(es[...,i],i)"},"document":{"kind":"string","value":"def inverse_element_wise_affine(x, st, compute_jacobian=True):\n es = torch.exp(-st[..., 0])\n t = st[..., 1]\n logj = None\n if compute_jacobian:\n logj = torch.sum(torch.log(es), dim=-1)\n\n return es * (x - t), logj"},"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 element_wise_affine(x, st, compute_jacobian=True):\n es = torch.exp(st[..., 0])\n t = st[..., 1]\n logj = None\n if compute_jacobian:\n logj = torch.sum(torch.log(es), dim=-1)\n\n return es * x + t, logj","def transform(fn):\n def _(vec, dt):\n return np.einsum(\n 'ji,i,ki,k...->j...',\n evecs, fn(evals, dt), evecs, vec, optimize=True)\n\n return _","def affine(params, x):\n return np.dot(params['w'], x) + params['b']","def affine_mult(affine, coordinates):\n return np.dot(coordinates, affine[:3, :3].T) + affine[:3, -1]","def affineTransform(x,output_dim):\n w=tf.get_variable(\"w\", [x.get_shape()[1], output_dim])\n b=tf.get_variable(\"b\", [output_dim], initializer=tf.constant_initializer(0.0))\n return tf.matmul(x,w)+b","def affine_transform(x, output_dim, name=None):\n\n w = tf.get_variable(name + \"_w\", [x.get_shape()[1], output_dim], initializer=tf.truncated_normal_initializer(stddev=0.02))\n b = tf.get_variable(name + \"_b\", [output_dim], initializer=tf.constant_initializer(0.0))\n\n return tf.matmul(x, w) + b","def apply_affine_transform(x, M):\n is1d = len(x.shape) == 1\n if is1d:\n x = np.expand_dims(x, axis=0)\n\n x_hom = np.concatenate(\n [x, np.ones((x.shape[0], 1), dtype=x.dtype)], axis=-1\n )\n x_out = x_hom @ M.T\n if is1d:\n x_out = np.squeeze(x_out, axis=0)\n return x_out","def affine_transform(geom, matrix):\n if geom.is_empty:\n return geom\n if len(matrix) == 6:\n ndim = 2\n a, b, d, e, xoff, yoff = matrix\n if geom.has_z:\n ndim = 3\n i = 1.0\n c = f = g = h = zoff = 0.0\n matrix = a, b, c, d, e, f, g, h, i, xoff, yoff, zoff\n elif len(matrix) == 12:\n ndim = 3\n a, b, c, d, e, f, g, h, i, xoff, yoff, zoff = matrix\n if not geom.has_z:\n ndim = 2\n matrix = a, b, d, e, xoff, yoff\n else:\n raise ValueError(\"'matrix' expects either 6 or 12 coefficients\")\n\n def affine_pts(pts):\n \"\"\"Internal function to yield affine transform of coordinate tuples\"\"\"\n if ndim == 2:\n for x, y in pts:\n xp = a * x + b * y + xoff\n yp = d * x + e * y + yoff\n yield (xp, yp)\n elif ndim == 3:\n for x, y, z in pts:\n xp = a * x + b * y + c * z + xoff\n yp = d * x + e * y + f * z + yoff\n zp = g * x + h * y + i * z + zoff\n yield (xp, yp, zp)\n\n # Process coordinates from each supported geometry type\n if geom.type in ('Point', 'LineString', 'LinearRing'):\n return type(geom)(list(affine_pts(geom.coords)))\n elif geom.type == 'Polygon':\n ring = geom.exterior\n shell = type(ring)(list(affine_pts(ring.coords)))\n holes = list(geom.interiors)\n for pos, ring in enumerate(holes):\n holes[pos] = type(ring)(list(affine_pts(ring.coords)))\n return type(geom)(shell, holes)\n elif geom.type.startswith('Multi') or geom.type == 'GeometryCollection':\n # Recursive call\n # TODO: fix GeometryCollection constructor\n return type(geom)([affine_transform(part, matrix)\n for part in geom.geoms])\n else:\n raise ValueError('Type %r not recognized' % geom.type)","def affine_transform(trans_mat, p0):\r\n n_data, n_dim = np.shape(p0)\r\n p0 = np.hstack((p0, np.ones((n_data, 1))))\r\n #return np.transpose(np.dot(np.transpose(trans_mat), np.transpose(p0)))\r\n return np.dot(p0, trans_mat)","def apply_affine(A: Affine, x: np.ndarray, y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:\n\n shape = x.shape\n\n A = np.asarray(A).reshape(3, 3) # type: ignore[assignment]\n t = A[:2, -1].reshape((2, 1)) # type: ignore[index]\n A = A[:2, :2] # type: ignore[index]\n\n x, y = A @ np.vstack([x.ravel(), y.ravel()]) + t\n x, y = (a.reshape(shape) for a in (x, y))\n return (x, y)","def affine_forward(x, W, b):\r\n x2d = np.reshape(x, (x.shape[0], -1)) # convert 4D input matrix to 2D \r\n out = np.dot(x2d, W) + b # linear transformation\r\n cache = (x, W, b) # keep for backward step (stay with us)\r\n return out, cache","def affine_forward(x, w, b):\n out = None\n ###########################################################################\n # TODO: Implement the affine forward pass. Store the result in out. You #\n # will need to reshape the input into rows. #\n ###########################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n t = np.reshape(x,(x.shape[0],np.prod(np.shape(x)[1:])))\n \n\n out = np.dot(t,w) + b\n \n #print(np.shape(out))\n\n pass\n\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n cache = (x, w, b)\n return out, cache","def exp(tensor):\n return _elementary_op(tensor, np.exp, np.exp)","def affine_forward(x,w,b):\n out=None\n N=x.shape[0]\n x_row=x.reshape(N,-1)\n out=np.dot(x_row,w)+b\n cache=(x,w,b)\n return out,cache","def temporal_affine_forward(x, w, b):\n N, T, D = x.shape\n M = b.shape[0]\n out = x.reshape(N * T, D).dot(w).reshape(N, T, M) + b\n cache = x, w, b, out\n return out, cache","def affine_forward(X, W, b):\n return np.dot(X, W) + b","def affine_forward(x, w, b):\n #raise NotImplementedError\n #######################################################################\n # #\n # #\n # TODO: YOUR CODE HERE #\n # #\n # #\n #######################################################################\n out=np.dot(x,w)+b\n cache=(x,w,b)\n return(out, cache)","def affine_forward(x, w, b):\n out = None\n ###########################################################################\n # TODO: Implement the affine forward pass. Store the result in out. You #\n # will need to reshape the input into rows. #\n ###########################################################################\n reshaped_inp = np.reshape(x,(int(x.shape[0]),int(np.prod(x.shape) / x.shape[0])))\n out = reshaped_inp.dot(w) + b\n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n cache = (x, w, b)\n return out, cache","def affine_forward(x, w, b):\n ############################################################################\n # TODO: Implement the affine forward pass. Store the result in 'out'. You #\n # will need to reshape the input into rows. #\n ############################################################################\n ############################################################################\n # START OF YOUR CODE #\n ############################################################################\n N = len(x)\n D,M = w.shape\n # reshape get a new x\n new_x = x.reshape(N,D)\n # get the output\n out = np.dot(new_x,w) + np.expand_dims(b,axis=0)\n \n ############################################################################\n # END OF YOUR CODE #\n ############################################################################\n return out","def affine_forward(x, w, b):\n out = None\n \n # reshape the input into (N, d_1 *...* d_k)\n input_shape = x.shape\n prod = 1\n for i in range(1,len(input_shape)):\n prod *= input_shape[i]\n\n a = x.reshape(x.shape[0],prod)\n out = np.dot(a,w) + b\n \n cache = (x, w, b)\n return out, cache","def affine_forward(x, w, b):\n out = None\n ###########################################################################\n # TODO: Implement the affine forward pass. Store the result in out. You #\n # will need to reshape the input into rows. #\n ###########################################################################\n dim_size = x[0].shape\n X = x.reshape(x.shape[0], np.prod(dim_size))\n out = X.dot(w) + b\n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n cache = (x, w, b)\n return out, cache","def transformAffine(self, coords):\n coordsshape = coords.shape\n dims = coordsshape[0] + 1\n coords = coords.reshape((len(coords), -1))\n coords = np.concatenate((coords, np.ones((1, len(coords[0])))), 0)\n affine = np.eye(dims)\n # now transform first to center:\n meanvec = np.mean(coords, 1)\n center = np.eye(dims)\n center[:-1, -1] = -meanvec[:-1]\n affine = np.matmul(center, affine)\n\n if np.sum(self.shift):\n affine[:-1, -1] += (self.deformrandomstate.rand(dims - 1) - 0.5) * np.float32(self.shift)\n if np.max(self.scaling) > 1:\n scales = np.ones(dims)\n # scales[:-1] = (self.deformrandomstate.rand(dims-1)-0.5)*(self.scaling-1.0/self.scaling)+(self.scaling+1/self.scaling)/2\n scales[:-1] = self.scaling ** (self.deformrandomstate.rand(dims - 1) * 2 - 1)\n scales = np.diag(scales)\n # print(scales)\n affine = np.matmul(scales, affine)\n if np.sum(self.rotation):\n affine = self._rotate(affine)\n # move back to location:\n center[:-1, -1] = -center[:-1, -1]\n affine = np.matmul(center, affine)\n # now appyl to coords:\n coords = np.matmul(affine, coords)\n coords = coords[:-1]\n coords = coords.reshape(coordsshape)\n return coords","def affine_forward(x, w, b):\n out = None\n ########################################################################\n # TODO: Implement the affine forward pass. Store the result in out. #\n # You will need to reshape the input into rows. #\n ########################################################################\n\n x_reshaped = x.reshape(x.shape[:1] + (-1,))\n out = x_reshaped.dot(w) + b\n\n ########################################################################\n # END OF YOUR CODE #\n ########################################################################\n cache = (x, w, b)\n return out, cache","def affine_forward(x, w, b):\n N = x.shape[0]\n\n # reshape input into rows\n output = x.reshape([N, -1]).dot(w) + b\n cache = (x, w, b)\n\n return output, cache","def affine_forward(x, w, b):\n out = None\n x_shape = x.shape\n x_reshaped = x.reshape(x_shape[0], np.prod(x_shape[1:]))\n out = np.dot(x_reshaped, w) + b\n cache = (x, w, b)\n return out, cache","def AffineTransform( from_pts, to_pts ):\n \n # check that there are match points\n if len(from_pts) != len(to_pts) or len(to_pts)<1:\n print \"from_pts and to_pts must be of same size.\"\n return False\n\n # check the dimensions\n dim = len(from_pts[0]) # num of dimensions\n if len(from_pts) < dim:\n print \"Too few points => under-determined system.\"\n return False\n elif len(from_pts) > dim + 1:\n print \"Too many points => over-determined system.\"\n return False\n\n \n #segregate the x and y coordinages\n from_pts_x, from_pts_y = zip(*from_pts)\n to_pts_x, to_pts_y = zip(*to_pts)\n \n #create the Matricies for processing\n I = np.matrix([from_pts_x, from_pts_y, [1,1,1]])\n P = np.matrix([to_pts_x, to_pts_y])\n \n #Calculate the 2D affine transform matrix (A)\n A = P * linalg.pinv(I) \n\n # Make a result object\n class Transformation:\n \"\"\"Result object that represents the transformation\n from affine fitter.\"\"\"\n\n def To_Str(self):\n res = \"\"\n for j in range(dim):\n str1 = \"x%d' = \" % j\n for i in range(dim):\n str1 +=\"x%d * %f + \" % (i, A[i][j+dim+1])\n str1 += \"%f\" % A[dim][j+dim+1]\n res += str1 + \"\\n\"\n return res\n\n def Transform(self, pt_x, pt_y):\n pt_vector = np.matrix([[pt_x], [pt_y], [1]])\n transformed_pt = A * pt_vector\n return map(itemgetter(0), transformed_pt.tolist())\n return Transformation()","def affine_transform(x, transform_matrix, channel_index=2, fill_mode='nearest', cval=0., order=1):\n # transform_matrix = transform_matrix_offset_center()\n # asdihasid\n # asd\n\n x = np.rollaxis(x, channel_index, 0)\n final_affine_matrix = transform_matrix[:2, :2]\n final_offset = transform_matrix[:2, 2]\n channel_images = [\n ndi.interpolation.affine_transform(\n x_channel, final_affine_matrix, final_offset, order=order, mode=fill_mode, cval=cval\n ) for x_channel in x\n ]\n x = np.stack(channel_images, axis=0)\n x = np.rollaxis(x, 0, channel_index + 1)\n return x","def estimate_stage_affine(t0, t1):\n src = np.array([t.tforms[0].translation for t in t0])\n dst = np.array([t.tforms[1].translation for t in t1])\n aff = renderapi.transform.AffineModel()\n aff.estimate(src, dst)\n return aff","def get_affine(x, m, c):\n x = m*x + c\n return x","def elastic_transform(image, alpha, sigma, alpha_affine, random_state=None):\n if random_state is None:\n random_state = np.random.RandomState(None)\n\n shape = image.shape\n shape_size = shape[:2]\n \n # Random affine\n center_square = np.float32(shape_size) // 2\n square_size = min(shape_size) // 3\n pts1 = np.float32([center_square + square_size, [center_square[0]+square_size, center_square[1]-square_size], center_square - square_size])\n pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine, size=pts1.shape).astype(np.float32)\n M = cv2.getAffineTransform(pts1, pts2)\n image = cv2.warpAffine(image, M, shape_size[::-1], borderMode=cv2.BORDER_REFLECT_101)\n\n dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma) * alpha\n dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma) * alpha\n dz = np.zeros_like(dx)\n\n x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))\n indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1)), np.reshape(z, (-1, 1))\n\n return map_coordinates(image, indices, order=1, mode='reflect').reshape(shape)","def apply_affine_transform(x, theta=0, tx=0, ty=0, shear=0, zx=1, zy=1,\n row_axis=0, col_axis=1, channel_axis=2,\n fill_mode='nearest', cval=0., order=1):\n if scipy is None:\n raise ImportError('Image transformations require SciPy. '\n 'Install SciPy.')\n transform_matrix = None\n if theta != 0:\n theta = np.deg2rad(theta)\n rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],\n [np.sin(theta), np.cos(theta), 0],\n [0, 0, 1]])\n transform_matrix = rotation_matrix\n\n if tx != 0 or ty != 0:\n shift_matrix = np.array([[1, 0, tx],\n [0, 1, ty],\n [0, 0, 1]])\n if transform_matrix is None:\n transform_matrix = shift_matrix\n else:\n transform_matrix = np.dot(transform_matrix, shift_matrix)\n\n if shear != 0:\n shear = np.deg2rad(shear)\n shear_matrix = np.array([[1, -np.sin(shear), 0],\n [0, np.cos(shear), 0],\n [0, 0, 1]])\n if transform_matrix is None:\n transform_matrix = shear_matrix\n else:\n transform_matrix = np.dot(transform_matrix, shear_matrix)\n\n if zx != 1 or zy != 1:\n zoom_matrix = np.array([[zx, 0, 0],\n [0, zy, 0],\n [0, 0, 1]])\n if transform_matrix is None:\n transform_matrix = zoom_matrix\n else:\n transform_matrix = np.dot(transform_matrix, zoom_matrix)\n\n if transform_matrix is not None:\n h, w = x.shape[row_axis], x.shape[col_axis]\n transform_matrix = transform_matrix_offset_center(\n transform_matrix, h, w)\n x = np.rollaxis(x, channel_axis, 0)\n final_affine_matrix = transform_matrix[:2, :2]\n final_offset = transform_matrix[:2, 2]\n\n channel_images = [ndimage.interpolation.affine_transform(\n x_channel,\n final_affine_matrix,\n final_offset,\n order=order,\n mode=fill_mode,\n cval=cval) for x_channel in x]\n x = np.stack(channel_images, axis=0)\n x = np.rollaxis(x, 0, channel_axis + 1)\n return x","def __affine_geo_transformation(x, y, gtr):\n\n # https://gdal.org/user/raster_data_model.html#affine-geotransform\n # Affine transformation rewritten for rasterio:\n gtr_x = gtr[2] + (x + 0.5) * gtr[0] + (y + 0.5) * gtr[1]\n gtr_y = gtr[5] + (x + 0.5) * gtr[3] + (y + 0.5) * gtr[4]\n\n return gtr_x, gtr_y","def transAffine2D( iScale=(1, 1), iTrans=(0, 0), iRot=0, iShear=(0, 0) ): \n iRot = iRot * np.pi / 180\n oMatScale = np.matrix( ((iScale[0],0,0),(0,iScale[1],0),(0,0,1)) )\n oMatTrans = np.matrix( ((1,0,iTrans[0]),(0,1,iTrans[1]),(0,0,1)) )\n oMatRot = np.matrix( ((np.cos(iRot),-np.sin(iRot),0),\\\n (np.sin(iRot),np.cos(iRot),0),(0,0,1)) )\n oMatShear = np.matrix( ((1,iShear[0],0),(iShear[1],1,0),(0,0,1)) )\n # ustvari izhodno matriko\n oMat2D = oMatTrans * oMatShear * oMatRot * oMatScale\n return oMat2D","def calc_affine(df):\n\tx0 = df.columns[0]\n\ty0 = df.index[0]\n\tdx = df.columns[1] - df.columns[0]\n\tdy = df.index[1] - df.index[0]\n\t\n\tt = affine.Affine(dx, 0, x0 , 0, dy ,y0 - dy) \n\t# y0 - dy because anker point is in the south!\n\treturn t","def affine(img, angle, translate, scale, shear):\n if not _is_numpy(img):\n raise TypeError('img should be Numpy Image. Got {}'.format(type(img)))\n\n assert isinstance(translate, (tuple, list)) and len(translate) == 2, \\\n \"Argument translate should be a list or tuple of length 2\"\n\n assert scale > 0.0, \"Argument scale should be positive\"\n\n aug = iaa.Affine(scale=scale, rotate=angle, translate_px=translate, shear=shear)\n return aug.augment_image(img)","def estimate_affine_matrix_3d_to_2d(X, x):\n assert x.shape[0] == X.shape[0]\n assert x.shape[0] >= 4\n X = X.T # (3, n)\n x = x.T # (2, n)\n n = x.shape[1]\n\n ###---- 1. normalization\n ## 2d points\n mean = np.mean(x, 1) # (2, )\n x = x - np.tile(mean[:, np.newaxis], [1, n]) # (2, n)\n average_norm = np.mean(np.sqrt(np.sum(x ** 2, 0)))\n scale = np.sqrt(2) / average_norm\n x = scale * x\n\n # T = [[scale, 0, -mean * scale], \n # [ 0, scale, -mean * scale], \n # [ 0, 0, 1 ]]\n T = np.zeros((3, 3), dtype=np.float32)\n T[0, 0] = T[1, 1] = scale\n T[:2, 2] = -mean * scale\n T[2, 2] = 1\n\n ## 3d points\n X_homo = np.vstack((X, np.ones((1, n)))) # (4, n)\n mean = np.mean(X, 1) # (3, )\n X = X - np.tile(mean[:, np.newaxis], [1, n]) # (3, n)\n m = X_homo[: 3, :] - X\n average_norm = np.mean(np.sqrt(np.sum(X ** 2, 0)))\n scale = np.sqrt(3) / average_norm\n X = scale * X\n\n U = np.zeros((4, 4), dtype=np.float32)\n U[0, 0] = U[1, 1] = U[2, 2] = scale\n U[: 3, 3] = -mean * scale\n U[3, 3] = 1\n\n ###---- 2. equations\n A = np.zeros((n * 2, 8), dtype=np.float32)\n X_homo = np.vstack((X, np.ones((1, n)))).T\n A[: n, : 4] = X_homo\n A[n: , 4: ] = X_homo\n b = np.reshape(x, [-1, 1]) # (2n, 1)\n\n ###---- 3.solution\n p_8 = np.linalg.pinv(A).dot(b) # (8, 2n) x (2n, 1) -> (8, 1)\n p = np.zeros((3, 4), dtype=np.float32)\n p[0, :] = p_8[:4, 0]\n p[1, :] = p_8[4:, 0]\n p[-1, -1] = 1\n\n ###---- 4. denormalization\n P_Affine = np.linalg.inv(T).dot(p.dot(U))\n return P_Affine","def elastic_transform_approx(\n img: np.ndarray,\n alpha: float,\n sigma: float,\n alpha_affine: float,\n interpolation: int = cv2.INTER_LINEAR,\n border_mode: int = cv2.BORDER_REFLECT_101,\n value: Optional[ImageColorType] = None,\n random_state: Optional[np.random.RandomState] = None,\n) -> np.ndarray:\n height, width = img.shape[:2]\n\n # Random affine\n center_square = np.array((height, width), dtype=np.float32) // 2\n square_size = min((height, width)) // 3\n alpha = float(alpha)\n sigma = float(sigma)\n alpha_affine = float(alpha_affine)\n\n pts1 = np.array(\n [\n center_square + square_size,\n [center_square[0] + square_size, center_square[1] - square_size],\n center_square - square_size,\n ],\n dtype=np.float32,\n )\n pts2 = pts1 + random_utils.uniform(-alpha_affine, alpha_affine, size=pts1.shape, random_state=random_state).astype(\n np.float32\n )\n matrix = cv2.getAffineTransform(pts1, pts2)\n\n warp_fn = _maybe_process_in_chunks(\n cv2.warpAffine,\n M=matrix,\n dsize=(width, height),\n flags=interpolation,\n borderMode=border_mode,\n borderValue=value,\n )\n img = warp_fn(img)\n\n dx = random_utils.rand(height, width, random_state=random_state).astype(np.float32) * 2 - 1\n cv2.GaussianBlur(dx, (17, 17), sigma, dst=dx)\n dx *= alpha\n\n dy = random_utils.rand(height, width, random_state=random_state).astype(np.float32) * 2 - 1\n cv2.GaussianBlur(dy, (17, 17), sigma, dst=dy)\n dy *= alpha\n\n x, y = np.meshgrid(np.arange(width), np.arange(height))\n\n map_x = np.float32(x + dx)\n map_y = np.float32(y + dy)\n\n remap_fn = _maybe_process_in_chunks(\n cv2.remap,\n map1=map_x,\n map2=map_y,\n interpolation=interpolation,\n borderMode=border_mode,\n borderValue=value,\n )\n return remap_fn(img)","def affine_transform(initialize=False, fixed=None, moments=True) :\n \n transform = itk.AffineTransform[itk.D, fixed.ndim].New()\n \n if initialize :\n fixed_itk = medipy.itk.medipy_image_to_itk_image(fixed, False)\n #moving_itk = medipy.itk.medipy_image_to_itk_image(moving, False)\n\n fixedIndex = fixed_itk.GetLargestPossibleRegion().GetIndex()\n fixedSize = fixed_itk.GetLargestPossibleRegion().GetSize()\n\n centerIndex = (int(fixedIndex[0] + fixedSize[0] / 2.0), \\\n int(fixedIndex[1] + fixedSize[1] / 2.0), \\\n int(fixedIndex[2] + fixedSize[2] / 2.0))\n\n rotationCenter = fixed_itk.TransformIndexToPhysicalPoint(centerIndex)\n\n transform.SetIdentity()\n transform.SetCenter(rotationCenter)\n\n #initial_transform = itk.VersorRigid3DTransform[itk.D].New()\n #initializer = itk.CenteredTransformInitializer[\n # initial_transform, fixed_itk, moving_itk].New(\n # Transform=initial_transform, FixedImage=fixed_itk, MovingImage=moving_itk) \n #if moments :\n # initializer.MomentsOn()\n #else :\n # initializer.GeometryOn()\n #initializer.InitializeTransform()\n #transform.SetCenter(initial_transform.GetCenter())\n #transform.SetOffset(initial_transform.GetOffset())\n \n return transform","def elastic_transform(\n img: np.ndarray,\n alpha: float,\n sigma: float,\n alpha_affine: float,\n interpolation: int = cv2.INTER_LINEAR,\n border_mode: int = cv2.BORDER_REFLECT_101,\n value: Optional[ImageColorType] = None,\n random_state: Optional[np.random.RandomState] = None,\n approximate: bool = False,\n same_dxdy: bool = False,\n):\n height, width = img.shape[:2]\n\n # Random affine\n center_square = np.array((height, width), dtype=np.float32) // 2\n square_size = min((height, width)) // 3\n alpha = float(alpha)\n sigma = float(sigma)\n alpha_affine = float(alpha_affine)\n\n pts1 = np.array(\n [\n center_square + square_size,\n [center_square[0] + square_size, center_square[1] - square_size],\n center_square - square_size,\n ],\n dtype=np.float32,\n )\n pts2 = pts1 + random_utils.uniform(-alpha_affine, alpha_affine, size=pts1.shape, random_state=random_state).astype(\n np.float32\n )\n matrix = cv2.getAffineTransform(pts1, pts2)\n\n warp_fn = _maybe_process_in_chunks(\n cv2.warpAffine, M=matrix, dsize=(width, height), flags=interpolation, borderMode=border_mode, borderValue=value\n )\n img = warp_fn(img)\n\n if approximate:\n # Approximate computation smooth displacement map with a large enough kernel.\n # On large images (512+) this is approximately 2X times faster\n dx = random_utils.rand(height, width, random_state=random_state).astype(np.float32) * 2 - 1\n cv2.GaussianBlur(dx, (17, 17), sigma, dst=dx)\n dx *= alpha\n if same_dxdy:\n # Speed up even more\n dy = dx\n else:\n dy = random_utils.rand(height, width, random_state=random_state).astype(np.float32) * 2 - 1\n cv2.GaussianBlur(dy, (17, 17), sigma, dst=dy)\n dy *= alpha\n else:\n dx = np.float32(\n gaussian_filter((random_utils.rand(height, width, random_state=random_state) * 2 - 1), sigma) * alpha\n )\n if same_dxdy:\n # Speed up\n dy = dx\n else:\n dy = np.float32(\n gaussian_filter((random_utils.rand(height, width, random_state=random_state) * 2 - 1), sigma) * alpha\n )\n\n x, y = np.meshgrid(np.arange(width), np.arange(height))\n\n map_x = np.float32(x + dx)\n map_y = np.float32(y + dy)\n\n remap_fn = _maybe_process_in_chunks(\n cv2.remap, map1=map_x, map2=map_y, interpolation=interpolation, borderMode=border_mode, borderValue=value\n )\n return remap_fn(img)","def convert_affine(ref, t, out):\n args = [\n transform_exe,\n '-d', '3',\n '-r', ref,\n '-t', '[{},0]'.format(t),\n '-o', '[{},1]'.format(out),\n '--float'\n ]\n subprocess.check_call(args)","def affine_trans(self):\n h, w, _ = self.img.shape\n\n \"\"\"\n pts1 = np.float32(\n [\n [randint(0, rows), randint(0, cols)],\n [randint(0, rows), randint(0, cols)],\n [randint(0, rows), randint(0, cols)],\n ]\n )\n pts2 = np.float32(\n [\n [randint(0, rows), randint(0, cols)],\n [randint(0, rows), randint(0, cols)],\n [randint(0, rows), randint(0, cols)],\n ]\n )\n \"\"\"\n\n pts1 = np.float32([[50, 50], [200, 50], [50, 200]])\n pts2 = np.float32([[10, 100], [200, 50], [100, 250]])\n\n M = cv2.getAffineTransform(pts1, pts2)\n\n self.img = cv2.warpAffine(self.img, M, (w, h))\n\n self.edits.append(\"affine\")\n return self","def affine_sigmoid(xin, m= 10, c= 3):\n if type(xin) != np.ndarray:\n x = np.array([xin])\n else:\n x = xin\n\n x = get_affine(x, m, c)\n output = get_sigmoid(x)\n\n if type(xin) != np.ndarray:\n return output[0]\n else:\n return output","def model_1exp(a, t, s, t0=0):\n a, t, s = physicond(a, t, s)\n\n # auxilary function taking as argument a time array.\n def aux(array_t, details=False):\n\n # applying time offset\n t_var = array_t - t0\n\n # model expression coming from symbolic calculation\n pulse = np.heaviside(t_var, 1.)\n ind = np.where(pulse != 0.)\n pulse[ind] = a * (np.exp(-t_var[ind]/t)-np.exp(-t_var[ind]/s))\n\n # same behavior as model_2exp and model_3exp\n if details == True:\n return pulse, pulse\n elif details == False:\n return pulse\n\n return aux","def _apply_transform(self, x, transform_parameters):\n # x is a single image, so it doesn't have image number at index 0\n img_row_axis = self.row_axis - 1\n img_col_axis = self.col_axis - 1\n img_channel_axis = self.channel_axis - 1\n\n x = apply_affine_transform(x, transform_parameters.get('theta', 0),\n transform_parameters.get('tx', 0),\n transform_parameters.get('ty', 0),\n transform_parameters.get('shear', 0),\n transform_parameters.get('zx', 1),\n transform_parameters.get('zy', 1),\n row_axis=img_row_axis,\n col_axis=img_col_axis,\n channel_axis=img_channel_axis,\n fill_mode=self.fill_mode,\n cval=self.cval)\n\n if transform_parameters.get('channel_shift_intensity') is not None:\n x = apply_channel_shift(x,\n transform_parameters['channel_shift_intensity'],\n img_channel_axis)\n\n if transform_parameters.get('flip_horizontal', False):\n x = self._flip_axis(x, img_col_axis)\n\n if transform_parameters.get('flip_vertical', False):\n x = self._flip_axis(x, img_row_axis)\n\n if transform_parameters.get('brightness') is not None:\n x = apply_brightness_shift(x, transform_parameters['brightness'])\n\n return x","def elastic_transform(image, mask, alpha, sigma, alpha_affine,\n random_state=None):\n if random_state is None:\n random_state = np.random.RandomState(None)\n\n shape = image.shape\n shape_size = shape[:2]\n\n # Random affine\n center_square = np.float32(shape_size) // 2\n square_size = min(shape_size) // 3\n pts1 = np.float32([center_square + square_size,\n [center_square[0] + square_size,\n center_square[1] - square_size],\n center_square - square_size])\n pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine,\n size=pts1.shape).astype(np.float32)\n M = cv2.getAffineTransform(pts1, pts2)\n\n dx = gaussian_filter((random_state.rand(*shape) * 2 - 1),\n sigma) * alpha\n dy = gaussian_filter((random_state.rand(*shape) * 2 - 1),\n sigma) * alpha\n\n x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]),\n np.arange(shape[2]))\n indices = np.reshape(y + dy, (-1, 1)), np.reshape(\n x + dx, (-1, 1)), np.reshape(z, (-1, 1))\n\n image = cv2.warpAffine(image, M, shape_size[::-1],\n borderMode=cv2.BORDER_REFLECT_101)\n\n image = map_coordinates(image, indices, order=1,\n mode='reflect').reshape(shape)\n if mask is not None:\n mask = cv2.warpAffine(mask, M, shape_size[::-1],\n borderMode=cv2.BORDER_REFLECT_101)\n mask = map_coordinates(\n mask, indices, order=1, mode='reflect'\n ).reshape(shape)\n\n return image, mask","def get_affine_matrix2d(\n translations: Tensor,\n center: Tensor,\n scale: Tensor,\n angle: Tensor,\n sx: Tensor | None = None,\n sy: Tensor | None = None,\n) -> Tensor:\n transform: Tensor = get_rotation_matrix2d(center, -angle, scale)\n transform[..., 2] += translations # tx/ty\n\n # pad transform to get Bx3x3\n transform_h = convert_affinematrix_to_homography(transform)\n\n if any(s is not None for s in [sx, sy]):\n shear_mat = get_shear_matrix2d(center, sx, sy)\n transform_h = transform_h @ shear_mat\n\n return transform_h","def get_affine_matrix2d(\n translations: torch.Tensor,\n center: torch.Tensor,\n scale: torch.Tensor,\n angle: torch.Tensor,\n sx: Optional[torch.Tensor] = None,\n sy: Optional[torch.Tensor] = None,\n) -> torch.Tensor:\n transform: torch.Tensor = get_rotation_matrix2d(center, -angle, scale)\n transform[..., 2] += translations # tx/ty\n\n # pad transform to get Bx3x3\n transform_h = convert_affinematrix_to_homography(transform)\n\n if any(s is not None for s in [sx, sy]):\n shear_mat = get_shear_matrix2d(center, sx, sy)\n transform_h = transform_h @ shear_mat\n\n return transform_h","def elastic_transform(image, alpha=1000, sigma=30, spline_order=1, mode='nearest', random_state=np.random):\n assert image.ndim == 2\n shape = image.shape[:2]\n\n dx = gaussian_filter((random_state.rand(*shape) * 2 - 1),\n sigma, mode=\"constant\", cval=0) * alpha\n dy = gaussian_filter((random_state.rand(*shape) * 2 - 1),\n sigma, mode=\"constant\", cval=0) * alpha\n\n x, y = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]), indexing='ij')\n indices = [np.reshape(x + dx, (-1, 1)), np.reshape(y + dy, (-1, 1))]\n result = map_coordinates(\n image, indices, order=spline_order, mode=mode).reshape(shape)\n return result","def elastic_transform(image, alpha_param, sigma_param, alpha_affine_param, random_state=None):\n image = np.asarray(image)\n #im_np = im_np.transpose(1,0,2) # when we go from pil to numpy array the W and L dimensions are swaped\n\n if len(image.shape) < 3: # if there is less than 3 channels (black&white) we triplicate the image\n image = np.concatenate((image[:,:, np.newaxis], image[:,:, np.newaxis], image[:,:, np.newaxis]), axis = 2)\n\n alpha = image.shape[1] * alpha_param\n sigma = image.shape[1] * sigma_param\n alpha_affine = image.shape[1] * alpha_affine_param\n\n if random_state is None:\n random_state = np.random.RandomState(None)\n\n shape = image.shape\n shape_size = shape[:2]\n\n # Random affine\n center_square = np.float32(shape_size) // 2\n square_size = min(shape_size) // 3\n pts1 = np.float32([center_square + square_size, [center_square[0]+square_size, center_square[1]-square_size], center_square - square_size])\n pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine, size=pts1.shape).astype(np.float32)\n M = cv2.getAffineTransform(pts1, pts2)\n image = cv2.warpAffine(image, M, shape_size[::-1], borderMode=cv2.BORDER_REFLECT_101)\n\n dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma) * alpha\n dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma) * alpha\n dz = np.zeros_like(dx)\n\n x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))\n indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1)), np.reshape(z, (-1, 1))\n\n im_elastic = map_coordinates(image, indices, order=1, mode='reflect').reshape(shape)\n\n return Image.fromarray(np.uint8(im_elastic))","def apply_transform(x,\n transform_matrix,\n channel_axis=0,\n fill_mode='nearest',\n cval=0.,\n interp_order=0):\n x = np.rollaxis(x, channel_axis, 0)\n final_affine_matrix = transform_matrix[:3, :3]\n final_offset = transform_matrix[:3, -1]\n channel_volumes = [ndi.interpolation.affine_transform(\n x_channel,\n final_affine_matrix,\n final_offset,\n order=interp_order, # NOTE: The order of the spline interpolation\n mode=fill_mode,\n cval=cval) for x_channel in x]\n x = np.stack(channel_volumes, axis=0)\n x = np.rollaxis(x, 0, channel_axis + 1)\n return x","def affine(self, image, random_state):\n shape = image.shape\n shape_size = shape[:2]\n transform_std = self.alpha_affine*image.shape[1]\n\n center_square = np.float32(shape[:2]) // 2\n square_size = min(shape[:2]) // 3\n\n source = np.float32([center_square + square_size, [center_square[0]+square_size, center_square[1]-square_size], center_square - square_size])\n destination = source + random_state.uniform(-transform_std, transform_std, size=source.shape).astype(np.float32)\n M = cv2.getAffineTransform(source, destination)\n\n return cv2.warpAffine(image, M, shape_size[::-1], borderMode=cv2.BORDER_REPLICATE)","def compute_e(f_mat, m_mat):\r\n return m_mat.T @ f_mat @ m_mat","def elasticTransform_legacy(image, mask, sigma, alpha_affine, random_seed=None):\n\t\n\trandom_state = np.random.RandomState(random_seed)\n\t\n\tif len(image.shape)<3:\n\t\timage = np.expand_dims(image,-1)\n\tif len(mask.shape)<3:\n\t\tmask = np.expand_dims(mask,-1)\n\t\n\tshape = image.shape\n\tshape_size = shape[:2]\n\n\t# Random affine\n\tcenter_square = np.float32(shape_size) // 2\n\tsquare_size = min(shape_size) // 3\n\tpts1 = np.float32([center_square + square_size, [center_square[0] + square_size,center_square[1] - square_size], center_square - square_size])\n\tpts2 = pts1 + random_state.uniform(-alpha_affine,alpha_affine, size=pts1.shape).astype(np.float32)\n\t\n\tM = cv2.getAffineTransform(pts1, pts2)\n\t\n\timage_w = np.zeros_like(image)\n\tfor i in range(image.shape[-1]):\n\t\timage_w[...,i] = cv2.warpAffine(image[...,i], M, shape_size[::-1], borderMode=cv2.BORDER_CONSTANT, borderValue=int(np.min(image[...,i])))\n\t\n\tmask_w = np.zeros_like(mask)\n\tfor i in range(mask.shape[-1]):\n\t\tmask_w[...,i] = cv2.warpAffine(mask[...,i] , M, shape_size[::-1], borderMode=cv2.BORDER_REPLICATE)\n\n\tblur_size = int(2*sigma) | 1\n\tdx = cv2.GaussianBlur((random_state.rand(*shape) * 2 - 1), ksize=(blur_size, blur_size), sigmaX=sigma)\n\tdy = cv2.GaussianBlur((random_state.rand(*shape) * 2 - 1), ksize=(blur_size, blur_size), sigmaX=sigma)\n\t\n\tif len(dx.shape) < 3:\n\t\tdx = np.expand_dims(dx,-1)\n\t\tdy = np.expand_dims(dy,-1)\n\n\tgx, gy = np.meshgrid(np.arange(shape_size[1]), np.arange(shape_size[0]))\n\tgx = np.expand_dims(gx,-1)\n\tgy = np.expand_dims(gy,-1)\n\n\tgx = np.repeat(gx,dx.shape[-1], -1)\n\tgy = np.repeat(gy,dy.shape[-1], -1)\n\n\tgx = (gx + dx).astype(np.float32)\n\tgy = (gy + dy).astype(np.float32)\n\n\timage_d = np.zeros_like(image_w)\n\tmask_d = np.zeros_like(mask_w)\n\n\tfor i in range(image.shape[-1]):\n\t\timage_d[...,i] = cv2.remap(image_w[...,i], gx[...,i], gy[...,i], interpolation=cv2.INTER_LINEAR)\n\t\n\tradix = gx.shape[-1]\n\tfor i in range(mask.shape[-1]):\n\t\tmask_d[...,i] = cv2.remap(mask_w[...,i], gx[...,i%radix], gy[...,i%radix], interpolation=cv2.INTER_LINEAR)\n\t\n\treturn image_d, mask_d","def get_affine_transform(gps_coords, pdr_coords):\n # Compute similarity Xp = s A X + b\n X = np.array(pdr_coords)\n Xp = np.array(gps_coords)\n T = tf.superimposition_matrix(X.T, Xp.T, scale=True)\n\n A, b = T[:3, :3], T[:3, 3]\n s = np.linalg.det(A)**(1. / 3)\n A /= s\n return s, A, b","def affine_2Dtransform(img, t_mat, height, width, h_offset=0, w_offset=0, nh_flag=False, nw_flag=False):\n # transform matrix must be validated\n if(np.shape(t_mat) != (2, 2)):\n return img\n\n # implementing matrix multiplication to a default map of source data in order to apply transform\n # and to achieve coordination/location of transformed matrix according to source data(data map)\n coord_map = transform_calcualtion(\n height, width, t_mat, h_offset, w_offset, nh_flag, nw_flag)\n\n # transformed image data construction\n t_img = np.full((height+h_offset, width+w_offset, 3), 255, dtype='uint8')\n\n # applying new map to image inorder to complete the transform\n try:\n for i in range(height):\n for j in range(width):\n [i_new_coord, j_new_coord] = coord_map[i, j, :]\n # unhandled bound-jumpout\n t_img[i_new_coord, j_new_coord, :] = img[i, j, :]\n except:\n print(\"not enough offset/negative coordination pushed\")\n return img\n return t_img","def get_transform(ds):\n\n if 'transform' in ds.attrs:\n ds_trans = ds.attrs['transform']\n if isinstance(ds_trans, Affine):\n return ds_trans\n else:\n return Affine(*ds_trans)\n\n elif 'crs' in ds.data_vars and 'i2m' in ds.data_vars['crs'].attrs:\n transf_str = ds.data_vars['crs'].attrs['i2m']\n a = list(map(float, transf_str.split(',')))\n return Affine(a[0], a[2], a[4], a[1], a[3], a[5])\n\n else:\n resx, resy = get_resolution(ds)\n xoff = ds['x'].values.min()\n yoff = ds['y'].values.max()\n return Affine(resx, 0, xoff, 0, resy, yoff)","def apply_transform(x,\n transform_matrix,\n channel_axis=0,\n fill_mode='constant',\n cval=0.):\n x = np.rollaxis(x, channel_axis, 0)\n final_affine_matrix = transform_matrix[:2, :2]\n final_offset = transform_matrix[:2, 2]\n channel_images = [\n interpolation.affine_transform(\n x_channel,\n final_affine_matrix,\n final_offset,\n order=0,\n mode=fill_mode,\n cval=cval) for x_channel in x\n ]\n x = np.stack(channel_images, axis=0)\n x = np.rollaxis(x, 0, channel_axis + 1)\n return x","def apply_transform(x,\n transform_matrix,\n channel_axis=0,\n fill_mode='constant',\n cval=0.):\n x = np.rollaxis(x, channel_axis, 0)\n final_affine_matrix = transform_matrix[:2, :2]\n final_offset = transform_matrix[:2, 2]\n channel_images = [\n interpolation.affine_transform(\n x_channel,\n final_affine_matrix,\n final_offset,\n order=0,\n mode=fill_mode,\n cval=cval) for x_channel in x\n ]\n x = np.stack(channel_images, axis=0)\n x = np.rollaxis(x, 0, channel_axis + 1)\n return x","def softmax(x):\n if type(x) == list:\n dim=len(x)\n norm = np.sum(np.exp(x))\n for idx in range(dim):\n x[idx] = np.exp(x[idx])/norm\n elif type(x) == np.ndarray:\n dim=x.shape\n for col in range(dim[1]):\n norm = np.sum(np.exp(x[:, col]))\n for idx in range(dim[0]):\n x[idx, col] = np.exp(x[idx, col])/norm\n else:\n raise Exception('incorrect input')\n return x","def affineSchur(self):\n return AffineSchurFunctions(self)","def seToSE( x ):\n x = asarray(x,dtype=float)\n if x.shape != (6,):\n raise ValueError(\"shape must be (6,); got %s\" % str(x.shape))\n #\n return expM(screw(x))","def affine_sigmoid_forward(x, w, b):\n a, fc_cache = affine_forward(x, w, b)\n out, sigmoid_cache = sigmoid_forward(a)\n cache = (fc_cache, sigmoid_cache)\n return out, cache","def augmentAffine(img_in, seg_in, strength=0.05):\n B,C,D,H,W = img_in.size()\n affine_matrix = (torch.eye(3,4).unsqueeze(0) + torch.randn(B, 3, 4) * strength).to(img_in.device)\n\n meshgrid = F.affine_grid(affine_matrix,torch.Size((B,1,D,H,W)))\n\n img_out = F.grid_sample(img_in, meshgrid,padding_mode='border')\n seg_out = F.grid_sample(seg_in.float().unsqueeze(1), meshgrid, mode='nearest').long().squeeze(1)\n\n return img_out, seg_out","def func_exp(x, a, b, c):\n return a * np.exp(b * x) + c","def _eigen_fns(mat, fns):\n evals, evecs = _eigh(mat)\n\n def transform(fn):\n \"\"\"Generates a transform given a function on the eigenvalues.\"\"\"\n def _(vec, dt):\n return np.einsum(\n 'ji,i,ki,k...->j...',\n evecs, fn(evals, dt), evecs, vec, optimize=True)\n\n return _\n\n return tuple(transform(fn) for fn in fns)","def elastic_transform(self, image, random_state=None):\n if random_state is None:\n random_state = np.random.RandomState(None)\n\n image = self.affine(image, random_state)\n #from ipdb import set_trace; set_trace()\n indices = self.stretch_indices(image, random_state)\n\n return map_coordinates(image, indices, order=1, mode='reflect').reshape(image.shape)","def fused_elemwise_activation(x,\n y,\n functor_list,\n axis=-1,\n scale=0.0,\n save_intermediate_out=True):\n if isinstance(functor_list, str):\n functor_list = functor_list.split(',')\n\n if not isinstance(functor_list, list) or len(functor_list) != 2:\n raise ValueError(\n 'functor_list should be a list of str, and the length should be 2.')\n\n helper = LayerHelper('fused_elemwise_activation', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n intermediate_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='fused_elemwise_activation',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out,\n 'IntermediateOut': intermediate_out},\n attrs={\n 'axis': axis,\n 'scale': scale,\n 'save_intermediate_out': save_intermediate_out,\n 'functor_list': functor_list\n })\n return out","def _transform(\n self, x: \"torch.Tensor\", y: Optional[\"torch.Tensor\"], **kwargs\n ) -> Tuple[\"torch.Tensor\", Optional[\"torch.Tensor\"]]:\n import torch\n import torchvision.transforms.functional as F\n\n img_size = x.shape[:2]\n\n angle = float(\n torch.empty(1)\n .uniform_(float(self.degree_range[0]), float(self.degree_range[1]))\n .item()\n )\n\n max_dx = float(self.translate[0] * img_size[1])\n max_dy = float(self.translate[1] * img_size[0])\n tx = int(round(torch.empty(1).uniform_(-max_dx, max_dx).item()))\n ty = int(round(torch.empty(1).uniform_(-max_dy, max_dy).item()))\n translations = (tx, ty)\n\n scale = float(torch.empty(1).uniform_(self.scale[0], self.scale[1]).item())\n\n # x needs to have channel first\n x = x.permute(2, 0, 1)\n x = F.affine(\n img=x, angle=angle, translate=translations, scale=scale, shear=(0.0, 0.0)\n )\n x = x.permute(1, 2, 0)\n\n return torch.clamp(x, min=self.clip_values[0], max=self.clip_values[1]), y","def gen_affine_map(Ab, img_sz, dim=3):\n Ab = Ab.view(Ab.shape[0], dim+1, dim)\n phi = gen_identity_map(img_sz).to(Ab.device)\n phi_cp = phi.view(dim, -1)\n affine_map = torch.matmul(Ab[:, :dim, :], phi_cp)\n affine_map = Ab[:, dim, :].contiguous().view(-1, dim, 1) + affine_map\n affine_map = affine_map.view([Ab.shape[0]] + list(phi.shape))\n return affine_map","def transpose_as_einsum(x: JaxExpression, params: Params) -> Einsum:\n x_ndim = len(x.shape)\n x_dims = ''.join(it.islice(einsum.einsum_letters(), x_ndim))\n out_dims = ''.join([x_dims[dim] for dim in params['permutation']])\n return Einsum(f'{x_dims}->{out_dims}', (x,))","def elastic_transform(X, min_alpha=36, max_alpha=38, min_sigma=5, max_sigma=6, random_state=None, n_jobs=1):\n if random_state is None:\n rng = np.random\n else:\n rng = np.random.RandomState(random_state)\n alphas = rng.uniform(min_alpha, max_alpha, size=X.shape[0])\n sigmas = rng.uniform(min_sigma, max_sigma, size=X.shape[0])\n X_elas = Parallel(n_jobs=n_jobs)(delayed(elastic_transform_one)(X[i], alphas[i], sigmas[i]) for i in range(X.shape[0]))\n return np.array(X_elas, dtype='float32')","def get_affine_transform(center, scale, rot, output_size, shift=(0.0, 0.0), inv=False):\n assert len(center) == 2\n assert len(scale) == 2\n assert len(output_size) == 2\n assert len(shift) == 2\n scale_tmp = scale * 200.0\n shift = np.array(shift)\n src_w = scale_tmp[0]\n dst_w = output_size[0]\n dst_h = output_size[1]\n rot_rad = np.pi * rot / 180\n src_dir = rotate_point([0.0, src_w * -0.5], rot_rad)\n dst_dir = np.array([0.0, dst_w * -0.5])\n src = np.zeros((3, 2), dtype=np.float32)\n src[0, :] = center + scale_tmp * shift\n src[1, :] = center + src_dir + scale_tmp * shift\n src[2, :] = _get_3rd_point(src[0, :], src[1, :])\n dst = np.zeros((3, 2), dtype=np.float32)\n dst[0, :] = [dst_w * 0.5, dst_h * 0.5]\n dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir\n dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])\n if inv:\n trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))\n else:\n trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))\n return trans","def quantize_affine_given_quant_params(\n input: torch.Tensor,\n quantize_params: QuantizeAffineParams2,\n) -> torch.Tensor:\n return QuantizeAffineFunction.apply(input, quantize_params)","def apply_transform(x, transform_matrix, channel_index=0, fill_mode='nearest', cval=0.):\n x = np.rollaxis(x, channel_index, 0)\n final_affine_matrix = transform_matrix[:2, :2]\n final_offset = transform_matrix[:2, 2]\n channel_images = [ndi.interpolation.affine_transform(x_channel,\n final_affine_matrix,\n final_offset, order=0, mode=fill_mode, cval=cval)\n for x_channel in x]\n x = np.stack(channel_images, axis=0)\n x = np.rollaxis(x, 0, channel_index+1)\n return x","def affineTransform(img, pts, newPts):\n\ttmp = img.copy()\n\tif len(img.shape) is 3:\n\t\trows, cols, ch = img.shape\n\telse:\n\t\trows, cols = img.shape\n\tpts1 = np.float32(pts)\n\tpts2 = np.float32(newPts)\n\tM = cv2.getAffineTransform(pts1, pts2)\n\tdst = cv2.warpAffine(tmp, M, (cols, rows))\n\treturn dst","def transform(self, x):\n return self._transform_eig(x)","def apply_transform(x,\n transform_matrix,\n channel_axis=0,\n fill_mode='nearest',\n cval=0.):\n x = np.rollaxis(x, channel_axis, 0)\n final_affine_matrix = transform_matrix[:2, :2]\n final_offset = transform_matrix[:2, 2]\n channel_images = [ndi.interpolation.affine_transform(\n x_channel,\n final_affine_matrix,\n final_offset,\n order=1,\n mode=fill_mode,\n cval=cval) for x_channel in x]\n x = np.stack(channel_images, axis=0)\n x = np.rollaxis(x, 0, channel_axis + 1)\n return x","def singleexp(params, t):\n # 2011-05-18 20:58 IJMC: Created\n # 2011-06-03 11:49 IJMC: Normalized to unity.\n\n if len(params)==2:\n return 1. - params[0] * exp(-t/params[1]) \n else:\n return params[2] * (1. - params[0] * exp(-t/params[1]) )","def apply_transform(x,\n transform_matrix,\n channel_axis=0,\n fill_mode='nearest',\n cval=0.):\n x = np.rollaxis(x, channel_axis, 0)\n final_affine_matrix = transform_matrix[:2, :2]\n final_offset = transform_matrix[:2, 2]\n channel_images = [ndi.interpolation.affine_transform(\n x_channel,\n final_affine_matrix,\n final_offset,\n order=0,\n mode=fill_mode,\n cval=cval) for x_channel in x]\n x = np.stack(channel_images, axis=0)\n x = np.rollaxis(x, 0, channel_axis + 1)\n return x","def affine(img, matrix, interpolation=\"nearest\", fill=None, data_format='CHW'):\n ndim = len(img.shape)\n if ndim == 3:\n img = img.unsqueeze(0)\n\n img = img if data_format.lower() == 'chw' else img.transpose((0, 3, 1, 2))\n\n matrix = paddle.to_tensor(matrix, place=img.place)\n matrix = matrix.reshape((1, 2, 3))\n shape = img.shape\n\n grid = _affine_grid(\n matrix, w=shape[-1], h=shape[-2], ow=shape[-1], oh=shape[-2]\n )\n\n if isinstance(fill, int):\n fill = tuple([fill] * 3)\n\n out = _grid_transform(img, grid, mode=interpolation, fill=fill)\n\n out = out if data_format.lower() == 'chw' else out.transpose((0, 2, 3, 1))\n out = out.squeeze(0) if ndim == 3 else out\n\n return out","def exp2eval(self, p, x, y=None, C=None, sumsq=False, weights=None):\n yd = p[0] + (p[1] * (1.0 - numpy.exp(-x / p[2])) ** 2.0) + \\\n (p[3] * (1.0 - numpy.exp(-x / p[4])))\n if y is None:\n return yd\n else:\n if sumsq is True:\n ss = numpy.sqrt(numpy.sum((y - yd) ** 2.0))\n # if p[4] < 3.0*p[2]:\n # ss = ss*1e6 # penalize them being too close\n return ss\n else:\n return y - yd\n\n # @autojit","def map(self,Affine,i):\n map_x = np.zeros([self.num,self.d])\n for k in range(self.num):\n map_x[k,:] = Affine.apply(i,self.pick(k))\n Mapped = Model_Points(map_x)\n return Mapped","def apply_transform(img,\n transform_matrix):\n rows,cols = img.shape[:2]\n dst = cv2.warpAffine(img,transform_matrix,(cols,rows))\n\n\n return dst","def affine_transform_2d(v, mapping, alpha = 1):\r\n p_wgt = vec2(0, 0)\r\n q_wgt = vec2(0, 0)\r\n w = len(mapping)*[None]\r\n w_sum = 0\r\n for i in range(len(mapping)):\r\n mp = mapping[i]\r\n x = mp[0].x - v.x\r\n y = mp[0].y - v.y\r\n if (x == 0 and y == 0): return mp[1]\r\n w[i] = 1/((x*x + y*y) ** alpha)\r\n p_wgt += mp[0]*w[i]\r\n q_wgt += mp[1]*w[i]\r\n w_sum += w[i]\r\n p_wgt /= w_sum\r\n q_wgt /= w_sum\r\n M1 = mat2(0)\r\n M2 = mat2(0)\r\n for i in range(len(mapping)):\r\n mp = mapping[i]\r\n p_adj = mp[0] - p_wgt\r\n q_adj = mp[1] - q_wgt\r\n M1 += p_adj.transpose_multiply(p_adj)*w[i]\r\n M2 += p_adj.transpose_multiply(q_adj)*w[i]\r\n M1 = M1.inverse()\r\n M = M1*M2\r\n M = M.transpose()\r\n v_out = M*(v - p_wgt) + q_wgt\r\n return v_out","def transforms_multiply(t0s, t1s):\r\n \r\n return ut.matrix_multiply(t0s, t1s)","def expms(A, eig=np.linalg.eigh):\r\n # TODO: check that this works reliably for low rank matrices\r\n # first: symmetrize A\r\n D, B = eig(A)\r\n return np.dot(B, (np.exp(D) * B).T)","def expm1_inplace(a):","def softmax(x, axis=1):\n sf = np.exp(x)\n sf = sf/np.sum(sf, axis=axis)[:,np.newaxis]\n return sf","def fit_transform(self, Xs, y=None):\n return self.fit(Xs, y).transform(Xs)","def attrTransform(self, matrix, transform):\n for ttype, targs in self.reTransformFind.findall(transform):\n targs = list(map(lambda x: float(x), self.reNumberFind.findall(targs)))\n if ttype == 'matrix':\n newmatrix = [ targs[0], targs[1],\n targs[2], targs[3],\n targs[4], targs[5] ]\n self.matrixMul(matrix, newmatrix)\n elif ttype == 'translate':\n tx = targs[0]\n ty = targs[1] if len(targs) > 1 else 0\n newmatrix = [ 1, 0, 0, 1, tx, ty ]\n self.matrixMul(matrix, newmatrix)\n elif ttype == 'scale':\n sx = targs[0]\n sy = targs[1] if len(targs) > 1 else sx\n newmatrix = [ sx, 0, 0, sy, 0, 0 ]\n self.matrixMul(matrix, newmatrix)\n elif ttype == 'rotate':\n if len(targs) == 1:\n alpha = targs[0]\n newmatrix = [ math.cos(alpha), math.sin(alpha),\n -math.sin(alpha), math.cos(alpha),\n 0, 0]\n self.matrixMul(matrix, newmatrix)\n else:\n alpha = targs[0]\n newmatrix = [ 1, 0, 0, 1, targs[1], targs[2] ]\n self.matrixMul(matrix, newmatrix)\n newmatrix = [ math.cos(alpha), math.sin(alpha),\n -math.sin(alpha), math.cos(alpha),\n 0, 0]\n self.matrixMul(matrix, newmatrix)\n newmatrix = [ 1, 0, 0, 1, -targs[1], -targs[2] ]\n self.matrixMul(matrix, newmatrix)\n elif ttype == 'skewX' or ttype == 'skewY':\n self.alert(\"skewX and skewY transformations are not supported\", elem)\n else:\n print('unknown transform type: ', ttype)\n return matrix","def setrans(Bi, t):\n\n x,v=mat2set(Bi)\n Bo = set2mat((x+t,v))\n Bo = Bo.astype(Bi.dtype)\n return Bo","def affine(img, angle=0, translate=(0, 0), scale=1, shear=0, resample='BILINEAR', fillcolor=(0,0,0)):\n if not _is_numpy_image(img):\n raise TypeError('img should be CV Image. Got {}'.format(type(img)))\n\n assert isinstance(translate, (tuple, list)) and len(translate) == 2, \\\n \"Argument translate should be a list or tuple of length 2\"\n\n assert scale > 0.0, \"Argument scale should be positive\"\n\n rows, cols, _ = img.shape\n center = (cols * 0.5, rows * 0.5)\n angle = math.radians(angle)\n shear = math.radians(shear)\n M00 = math.cos(angle)*scale\n M01 = -math.sin(angle+shear)*scale\n M10 = math.sin(angle)*scale\n M11 = math.cos(angle+shear)*scale\n M02 = center[0] - center[0]*M00 - center[1]*M01 + translate[0]\n M12 = center[1] - center[0]*M10 - center[1]*M11 + translate[1]\n affine_matrix = np.array([[M00, M01, M02], [M10, M11, M12]], dtype=np.float32)\n dst_img = cv2.warpAffine(img, affine_matrix, (cols, rows), flags=INTER_MODE[resample],\n borderMode=cv2.BORDER_CONSTANT, borderValue=fillcolor)\n return dst_img","def expIP(self):\n np.exp(self.t, out=self.t)\n return self","def translateEuler(trans):\n return np.array([[1,0,0,trans[0]],[0,1,0,trans[1]],[0,0,1,trans[2]],[0,0,0,1]])","def transform(self, x:generic_array, dense=True) -> generic_array:\n if type(x) == np.ndarray and not dense:\n warnings.warn(\"For Numpy transform it is best to use dense=True\")\n \n K_nq = self._pairwise_kernels(x, self.components_, dense=dense)\n x_new = K_nq @ self.normalization\n return x_new","def analytic(x, t, D, x0, xend, logx=False, c_s=1, use_log2=False):\n import scipy.special\n if t.ndim == 1:\n t = t.reshape((t.size, 1))\n expb = (lambda arg: 2**arg) if use_log2 else np.exp\n x = expb(x) if logx else x\n return c_s * scipy.special.erfc(x/(2*(D*t)**0.5))","def testTranslateAffine(self):\n affineClass = xyTransformRegistry[\"affine\"]\n affineConfig = affineClass.ConfigClass()\n affineConfig.translation = (1.2, -3.4)\n with lsst.utils.tests.getTempFilePath(\".py\") as filePath:\n self.checkConfig(affineClass, affineConfig, filePath)\n affine = affineClass(affineConfig)\n for fromPoint in self.fromIter():\n toPoint = affine.forwardTransform(fromPoint)\n predToPoint = fromPoint + Extent2D(*affineConfig.translation)\n for i in range(2):\n self.assertAlmostEqual(toPoint[i], predToPoint[i])","def translateEuler(self,trans):\n return np.array([[1,0,0,trans[0]],[0,1,0,trans[1]],[0,0,1,trans[2]],[0,0,0,1]])","def softmax(x):\n sf = np.exp(x)\n sf = sf / np.sum(sf, axis=0)\n return sf","def softmax(x):\n sf = np.exp(x)\n sf = sf / np.sum(sf, axis=0)\n return sf"],"string":"[\n \"def element_wise_affine(x, st, compute_jacobian=True):\\n es = torch.exp(st[..., 0])\\n t = st[..., 1]\\n logj = None\\n if compute_jacobian:\\n logj = torch.sum(torch.log(es), dim=-1)\\n\\n return es * x + t, logj\",\n \"def transform(fn):\\n def _(vec, dt):\\n return np.einsum(\\n 'ji,i,ki,k...->j...',\\n evecs, fn(evals, dt), evecs, vec, optimize=True)\\n\\n return _\",\n \"def affine(params, x):\\n return np.dot(params['w'], x) + params['b']\",\n \"def affine_mult(affine, coordinates):\\n return np.dot(coordinates, affine[:3, :3].T) + affine[:3, -1]\",\n \"def affineTransform(x,output_dim):\\n w=tf.get_variable(\\\"w\\\", [x.get_shape()[1], output_dim])\\n b=tf.get_variable(\\\"b\\\", [output_dim], initializer=tf.constant_initializer(0.0))\\n return tf.matmul(x,w)+b\",\n \"def affine_transform(x, output_dim, name=None):\\n\\n w = tf.get_variable(name + \\\"_w\\\", [x.get_shape()[1], output_dim], initializer=tf.truncated_normal_initializer(stddev=0.02))\\n b = tf.get_variable(name + \\\"_b\\\", [output_dim], initializer=tf.constant_initializer(0.0))\\n\\n return tf.matmul(x, w) + b\",\n \"def apply_affine_transform(x, M):\\n is1d = len(x.shape) == 1\\n if is1d:\\n x = np.expand_dims(x, axis=0)\\n\\n x_hom = np.concatenate(\\n [x, np.ones((x.shape[0], 1), dtype=x.dtype)], axis=-1\\n )\\n x_out = x_hom @ M.T\\n if is1d:\\n x_out = np.squeeze(x_out, axis=0)\\n return x_out\",\n \"def affine_transform(geom, matrix):\\n if geom.is_empty:\\n return geom\\n if len(matrix) == 6:\\n ndim = 2\\n a, b, d, e, xoff, yoff = matrix\\n if geom.has_z:\\n ndim = 3\\n i = 1.0\\n c = f = g = h = zoff = 0.0\\n matrix = a, b, c, d, e, f, g, h, i, xoff, yoff, zoff\\n elif len(matrix) == 12:\\n ndim = 3\\n a, b, c, d, e, f, g, h, i, xoff, yoff, zoff = matrix\\n if not geom.has_z:\\n ndim = 2\\n matrix = a, b, d, e, xoff, yoff\\n else:\\n raise ValueError(\\\"'matrix' expects either 6 or 12 coefficients\\\")\\n\\n def affine_pts(pts):\\n \\\"\\\"\\\"Internal function to yield affine transform of coordinate tuples\\\"\\\"\\\"\\n if ndim == 2:\\n for x, y in pts:\\n xp = a * x + b * y + xoff\\n yp = d * x + e * y + yoff\\n yield (xp, yp)\\n elif ndim == 3:\\n for x, y, z in pts:\\n xp = a * x + b * y + c * z + xoff\\n yp = d * x + e * y + f * z + yoff\\n zp = g * x + h * y + i * z + zoff\\n yield (xp, yp, zp)\\n\\n # Process coordinates from each supported geometry type\\n if geom.type in ('Point', 'LineString', 'LinearRing'):\\n return type(geom)(list(affine_pts(geom.coords)))\\n elif geom.type == 'Polygon':\\n ring = geom.exterior\\n shell = type(ring)(list(affine_pts(ring.coords)))\\n holes = list(geom.interiors)\\n for pos, ring in enumerate(holes):\\n holes[pos] = type(ring)(list(affine_pts(ring.coords)))\\n return type(geom)(shell, holes)\\n elif geom.type.startswith('Multi') or geom.type == 'GeometryCollection':\\n # Recursive call\\n # TODO: fix GeometryCollection constructor\\n return type(geom)([affine_transform(part, matrix)\\n for part in geom.geoms])\\n else:\\n raise ValueError('Type %r not recognized' % geom.type)\",\n \"def affine_transform(trans_mat, p0):\\r\\n n_data, n_dim = np.shape(p0)\\r\\n p0 = np.hstack((p0, np.ones((n_data, 1))))\\r\\n #return np.transpose(np.dot(np.transpose(trans_mat), np.transpose(p0)))\\r\\n return np.dot(p0, trans_mat)\",\n \"def apply_affine(A: Affine, x: np.ndarray, y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:\\n\\n shape = x.shape\\n\\n A = np.asarray(A).reshape(3, 3) # type: ignore[assignment]\\n t = A[:2, -1].reshape((2, 1)) # type: ignore[index]\\n A = A[:2, :2] # type: ignore[index]\\n\\n x, y = A @ np.vstack([x.ravel(), y.ravel()]) + t\\n x, y = (a.reshape(shape) for a in (x, y))\\n return (x, y)\",\n \"def affine_forward(x, W, b):\\r\\n x2d = np.reshape(x, (x.shape[0], -1)) # convert 4D input matrix to 2D \\r\\n out = np.dot(x2d, W) + b # linear transformation\\r\\n cache = (x, W, b) # keep for backward step (stay with us)\\r\\n return out, cache\",\n \"def affine_forward(x, w, b):\\n out = None\\n ###########################################################################\\n # TODO: Implement the affine forward pass. Store the result in out. You #\\n # will need to reshape the input into rows. #\\n ###########################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n t = np.reshape(x,(x.shape[0],np.prod(np.shape(x)[1:])))\\n \\n\\n out = np.dot(t,w) + b\\n \\n #print(np.shape(out))\\n\\n pass\\n\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n ###########################################################################\\n # END OF YOUR CODE #\\n ###########################################################################\\n cache = (x, w, b)\\n return out, cache\",\n \"def exp(tensor):\\n return _elementary_op(tensor, np.exp, np.exp)\",\n \"def affine_forward(x,w,b):\\n out=None\\n N=x.shape[0]\\n x_row=x.reshape(N,-1)\\n out=np.dot(x_row,w)+b\\n cache=(x,w,b)\\n return out,cache\",\n \"def temporal_affine_forward(x, w, b):\\n N, T, D = x.shape\\n M = b.shape[0]\\n out = x.reshape(N * T, D).dot(w).reshape(N, T, M) + b\\n cache = x, w, b, out\\n return out, cache\",\n \"def affine_forward(X, W, b):\\n return np.dot(X, W) + b\",\n \"def affine_forward(x, w, b):\\n #raise NotImplementedError\\n #######################################################################\\n # #\\n # #\\n # TODO: YOUR CODE HERE #\\n # #\\n # #\\n #######################################################################\\n out=np.dot(x,w)+b\\n cache=(x,w,b)\\n return(out, cache)\",\n \"def affine_forward(x, w, b):\\n out = None\\n ###########################################################################\\n # TODO: Implement the affine forward pass. Store the result in out. You #\\n # will need to reshape the input into rows. #\\n ###########################################################################\\n reshaped_inp = np.reshape(x,(int(x.shape[0]),int(np.prod(x.shape) / x.shape[0])))\\n out = reshaped_inp.dot(w) + b\\n ###########################################################################\\n # END OF YOUR CODE #\\n ###########################################################################\\n cache = (x, w, b)\\n return out, cache\",\n \"def affine_forward(x, w, b):\\n ############################################################################\\n # TODO: Implement the affine forward pass. Store the result in 'out'. You #\\n # will need to reshape the input into rows. #\\n ############################################################################\\n ############################################################################\\n # START OF YOUR CODE #\\n ############################################################################\\n N = len(x)\\n D,M = w.shape\\n # reshape get a new x\\n new_x = x.reshape(N,D)\\n # get the output\\n out = np.dot(new_x,w) + np.expand_dims(b,axis=0)\\n \\n ############################################################################\\n # END OF YOUR CODE #\\n ############################################################################\\n return out\",\n \"def affine_forward(x, w, b):\\n out = None\\n \\n # reshape the input into (N, d_1 *...* d_k)\\n input_shape = x.shape\\n prod = 1\\n for i in range(1,len(input_shape)):\\n prod *= input_shape[i]\\n\\n a = x.reshape(x.shape[0],prod)\\n out = np.dot(a,w) + b\\n \\n cache = (x, w, b)\\n return out, cache\",\n \"def affine_forward(x, w, b):\\n out = None\\n ###########################################################################\\n # TODO: Implement the affine forward pass. Store the result in out. You #\\n # will need to reshape the input into rows. #\\n ###########################################################################\\n dim_size = x[0].shape\\n X = x.reshape(x.shape[0], np.prod(dim_size))\\n out = X.dot(w) + b\\n ###########################################################################\\n # END OF YOUR CODE #\\n ###########################################################################\\n cache = (x, w, b)\\n return out, cache\",\n \"def transformAffine(self, coords):\\n coordsshape = coords.shape\\n dims = coordsshape[0] + 1\\n coords = coords.reshape((len(coords), -1))\\n coords = np.concatenate((coords, np.ones((1, len(coords[0])))), 0)\\n affine = np.eye(dims)\\n # now transform first to center:\\n meanvec = np.mean(coords, 1)\\n center = np.eye(dims)\\n center[:-1, -1] = -meanvec[:-1]\\n affine = np.matmul(center, affine)\\n\\n if np.sum(self.shift):\\n affine[:-1, -1] += (self.deformrandomstate.rand(dims - 1) - 0.5) * np.float32(self.shift)\\n if np.max(self.scaling) > 1:\\n scales = np.ones(dims)\\n # scales[:-1] = (self.deformrandomstate.rand(dims-1)-0.5)*(self.scaling-1.0/self.scaling)+(self.scaling+1/self.scaling)/2\\n scales[:-1] = self.scaling ** (self.deformrandomstate.rand(dims - 1) * 2 - 1)\\n scales = np.diag(scales)\\n # print(scales)\\n affine = np.matmul(scales, affine)\\n if np.sum(self.rotation):\\n affine = self._rotate(affine)\\n # move back to location:\\n center[:-1, -1] = -center[:-1, -1]\\n affine = np.matmul(center, affine)\\n # now appyl to coords:\\n coords = np.matmul(affine, coords)\\n coords = coords[:-1]\\n coords = coords.reshape(coordsshape)\\n return coords\",\n \"def affine_forward(x, w, b):\\n out = None\\n ########################################################################\\n # TODO: Implement the affine forward pass. Store the result in out. #\\n # You will need to reshape the input into rows. #\\n ########################################################################\\n\\n x_reshaped = x.reshape(x.shape[:1] + (-1,))\\n out = x_reshaped.dot(w) + b\\n\\n ########################################################################\\n # END OF YOUR CODE #\\n ########################################################################\\n cache = (x, w, b)\\n return out, cache\",\n \"def affine_forward(x, w, b):\\n N = x.shape[0]\\n\\n # reshape input into rows\\n output = x.reshape([N, -1]).dot(w) + b\\n cache = (x, w, b)\\n\\n return output, cache\",\n \"def affine_forward(x, w, b):\\n out = None\\n x_shape = x.shape\\n x_reshaped = x.reshape(x_shape[0], np.prod(x_shape[1:]))\\n out = np.dot(x_reshaped, w) + b\\n cache = (x, w, b)\\n return out, cache\",\n \"def AffineTransform( from_pts, to_pts ):\\n \\n # check that there are match points\\n if len(from_pts) != len(to_pts) or len(to_pts)<1:\\n print \\\"from_pts and to_pts must be of same size.\\\"\\n return False\\n\\n # check the dimensions\\n dim = len(from_pts[0]) # num of dimensions\\n if len(from_pts) < dim:\\n print \\\"Too few points => under-determined system.\\\"\\n return False\\n elif len(from_pts) > dim + 1:\\n print \\\"Too many points => over-determined system.\\\"\\n return False\\n\\n \\n #segregate the x and y coordinages\\n from_pts_x, from_pts_y = zip(*from_pts)\\n to_pts_x, to_pts_y = zip(*to_pts)\\n \\n #create the Matricies for processing\\n I = np.matrix([from_pts_x, from_pts_y, [1,1,1]])\\n P = np.matrix([to_pts_x, to_pts_y])\\n \\n #Calculate the 2D affine transform matrix (A)\\n A = P * linalg.pinv(I) \\n\\n # Make a result object\\n class Transformation:\\n \\\"\\\"\\\"Result object that represents the transformation\\n from affine fitter.\\\"\\\"\\\"\\n\\n def To_Str(self):\\n res = \\\"\\\"\\n for j in range(dim):\\n str1 = \\\"x%d' = \\\" % j\\n for i in range(dim):\\n str1 +=\\\"x%d * %f + \\\" % (i, A[i][j+dim+1])\\n str1 += \\\"%f\\\" % A[dim][j+dim+1]\\n res += str1 + \\\"\\\\n\\\"\\n return res\\n\\n def Transform(self, pt_x, pt_y):\\n pt_vector = np.matrix([[pt_x], [pt_y], [1]])\\n transformed_pt = A * pt_vector\\n return map(itemgetter(0), transformed_pt.tolist())\\n return Transformation()\",\n \"def affine_transform(x, transform_matrix, channel_index=2, fill_mode='nearest', cval=0., order=1):\\n # transform_matrix = transform_matrix_offset_center()\\n # asdihasid\\n # asd\\n\\n x = np.rollaxis(x, channel_index, 0)\\n final_affine_matrix = transform_matrix[:2, :2]\\n final_offset = transform_matrix[:2, 2]\\n channel_images = [\\n ndi.interpolation.affine_transform(\\n x_channel, final_affine_matrix, final_offset, order=order, mode=fill_mode, cval=cval\\n ) for x_channel in x\\n ]\\n x = np.stack(channel_images, axis=0)\\n x = np.rollaxis(x, 0, channel_index + 1)\\n return x\",\n \"def estimate_stage_affine(t0, t1):\\n src = np.array([t.tforms[0].translation for t in t0])\\n dst = np.array([t.tforms[1].translation for t in t1])\\n aff = renderapi.transform.AffineModel()\\n aff.estimate(src, dst)\\n return aff\",\n \"def get_affine(x, m, c):\\n x = m*x + c\\n return x\",\n \"def elastic_transform(image, alpha, sigma, alpha_affine, random_state=None):\\n if random_state is None:\\n random_state = np.random.RandomState(None)\\n\\n shape = image.shape\\n shape_size = shape[:2]\\n \\n # Random affine\\n center_square = np.float32(shape_size) // 2\\n square_size = min(shape_size) // 3\\n pts1 = np.float32([center_square + square_size, [center_square[0]+square_size, center_square[1]-square_size], center_square - square_size])\\n pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine, size=pts1.shape).astype(np.float32)\\n M = cv2.getAffineTransform(pts1, pts2)\\n image = cv2.warpAffine(image, M, shape_size[::-1], borderMode=cv2.BORDER_REFLECT_101)\\n\\n dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma) * alpha\\n dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma) * alpha\\n dz = np.zeros_like(dx)\\n\\n x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))\\n indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1)), np.reshape(z, (-1, 1))\\n\\n return map_coordinates(image, indices, order=1, mode='reflect').reshape(shape)\",\n \"def apply_affine_transform(x, theta=0, tx=0, ty=0, shear=0, zx=1, zy=1,\\n row_axis=0, col_axis=1, channel_axis=2,\\n fill_mode='nearest', cval=0., order=1):\\n if scipy is None:\\n raise ImportError('Image transformations require SciPy. '\\n 'Install SciPy.')\\n transform_matrix = None\\n if theta != 0:\\n theta = np.deg2rad(theta)\\n rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],\\n [np.sin(theta), np.cos(theta), 0],\\n [0, 0, 1]])\\n transform_matrix = rotation_matrix\\n\\n if tx != 0 or ty != 0:\\n shift_matrix = np.array([[1, 0, tx],\\n [0, 1, ty],\\n [0, 0, 1]])\\n if transform_matrix is None:\\n transform_matrix = shift_matrix\\n else:\\n transform_matrix = np.dot(transform_matrix, shift_matrix)\\n\\n if shear != 0:\\n shear = np.deg2rad(shear)\\n shear_matrix = np.array([[1, -np.sin(shear), 0],\\n [0, np.cos(shear), 0],\\n [0, 0, 1]])\\n if transform_matrix is None:\\n transform_matrix = shear_matrix\\n else:\\n transform_matrix = np.dot(transform_matrix, shear_matrix)\\n\\n if zx != 1 or zy != 1:\\n zoom_matrix = np.array([[zx, 0, 0],\\n [0, zy, 0],\\n [0, 0, 1]])\\n if transform_matrix is None:\\n transform_matrix = zoom_matrix\\n else:\\n transform_matrix = np.dot(transform_matrix, zoom_matrix)\\n\\n if transform_matrix is not None:\\n h, w = x.shape[row_axis], x.shape[col_axis]\\n transform_matrix = transform_matrix_offset_center(\\n transform_matrix, h, w)\\n x = np.rollaxis(x, channel_axis, 0)\\n final_affine_matrix = transform_matrix[:2, :2]\\n final_offset = transform_matrix[:2, 2]\\n\\n channel_images = [ndimage.interpolation.affine_transform(\\n x_channel,\\n final_affine_matrix,\\n final_offset,\\n order=order,\\n mode=fill_mode,\\n cval=cval) for x_channel in x]\\n x = np.stack(channel_images, axis=0)\\n x = np.rollaxis(x, 0, channel_axis + 1)\\n return x\",\n \"def __affine_geo_transformation(x, y, gtr):\\n\\n # https://gdal.org/user/raster_data_model.html#affine-geotransform\\n # Affine transformation rewritten for rasterio:\\n gtr_x = gtr[2] + (x + 0.5) * gtr[0] + (y + 0.5) * gtr[1]\\n gtr_y = gtr[5] + (x + 0.5) * gtr[3] + (y + 0.5) * gtr[4]\\n\\n return gtr_x, gtr_y\",\n \"def transAffine2D( iScale=(1, 1), iTrans=(0, 0), iRot=0, iShear=(0, 0) ): \\n iRot = iRot * np.pi / 180\\n oMatScale = np.matrix( ((iScale[0],0,0),(0,iScale[1],0),(0,0,1)) )\\n oMatTrans = np.matrix( ((1,0,iTrans[0]),(0,1,iTrans[1]),(0,0,1)) )\\n oMatRot = np.matrix( ((np.cos(iRot),-np.sin(iRot),0),\\\\\\n (np.sin(iRot),np.cos(iRot),0),(0,0,1)) )\\n oMatShear = np.matrix( ((1,iShear[0],0),(iShear[1],1,0),(0,0,1)) )\\n # ustvari izhodno matriko\\n oMat2D = oMatTrans * oMatShear * oMatRot * oMatScale\\n return oMat2D\",\n \"def calc_affine(df):\\n\\tx0 = df.columns[0]\\n\\ty0 = df.index[0]\\n\\tdx = df.columns[1] - df.columns[0]\\n\\tdy = df.index[1] - df.index[0]\\n\\t\\n\\tt = affine.Affine(dx, 0, x0 , 0, dy ,y0 - dy) \\n\\t# y0 - dy because anker point is in the south!\\n\\treturn t\",\n \"def affine(img, angle, translate, scale, shear):\\n if not _is_numpy(img):\\n raise TypeError('img should be Numpy Image. Got {}'.format(type(img)))\\n\\n assert isinstance(translate, (tuple, list)) and len(translate) == 2, \\\\\\n \\\"Argument translate should be a list or tuple of length 2\\\"\\n\\n assert scale > 0.0, \\\"Argument scale should be positive\\\"\\n\\n aug = iaa.Affine(scale=scale, rotate=angle, translate_px=translate, shear=shear)\\n return aug.augment_image(img)\",\n \"def estimate_affine_matrix_3d_to_2d(X, x):\\n assert x.shape[0] == X.shape[0]\\n assert x.shape[0] >= 4\\n X = X.T # (3, n)\\n x = x.T # (2, n)\\n n = x.shape[1]\\n\\n ###---- 1. normalization\\n ## 2d points\\n mean = np.mean(x, 1) # (2, )\\n x = x - np.tile(mean[:, np.newaxis], [1, n]) # (2, n)\\n average_norm = np.mean(np.sqrt(np.sum(x ** 2, 0)))\\n scale = np.sqrt(2) / average_norm\\n x = scale * x\\n\\n # T = [[scale, 0, -mean * scale], \\n # [ 0, scale, -mean * scale], \\n # [ 0, 0, 1 ]]\\n T = np.zeros((3, 3), dtype=np.float32)\\n T[0, 0] = T[1, 1] = scale\\n T[:2, 2] = -mean * scale\\n T[2, 2] = 1\\n\\n ## 3d points\\n X_homo = np.vstack((X, np.ones((1, n)))) # (4, n)\\n mean = np.mean(X, 1) # (3, )\\n X = X - np.tile(mean[:, np.newaxis], [1, n]) # (3, n)\\n m = X_homo[: 3, :] - X\\n average_norm = np.mean(np.sqrt(np.sum(X ** 2, 0)))\\n scale = np.sqrt(3) / average_norm\\n X = scale * X\\n\\n U = np.zeros((4, 4), dtype=np.float32)\\n U[0, 0] = U[1, 1] = U[2, 2] = scale\\n U[: 3, 3] = -mean * scale\\n U[3, 3] = 1\\n\\n ###---- 2. equations\\n A = np.zeros((n * 2, 8), dtype=np.float32)\\n X_homo = np.vstack((X, np.ones((1, n)))).T\\n A[: n, : 4] = X_homo\\n A[n: , 4: ] = X_homo\\n b = np.reshape(x, [-1, 1]) # (2n, 1)\\n\\n ###---- 3.solution\\n p_8 = np.linalg.pinv(A).dot(b) # (8, 2n) x (2n, 1) -> (8, 1)\\n p = np.zeros((3, 4), dtype=np.float32)\\n p[0, :] = p_8[:4, 0]\\n p[1, :] = p_8[4:, 0]\\n p[-1, -1] = 1\\n\\n ###---- 4. denormalization\\n P_Affine = np.linalg.inv(T).dot(p.dot(U))\\n return P_Affine\",\n \"def elastic_transform_approx(\\n img: np.ndarray,\\n alpha: float,\\n sigma: float,\\n alpha_affine: float,\\n interpolation: int = cv2.INTER_LINEAR,\\n border_mode: int = cv2.BORDER_REFLECT_101,\\n value: Optional[ImageColorType] = None,\\n random_state: Optional[np.random.RandomState] = None,\\n) -> np.ndarray:\\n height, width = img.shape[:2]\\n\\n # Random affine\\n center_square = np.array((height, width), dtype=np.float32) // 2\\n square_size = min((height, width)) // 3\\n alpha = float(alpha)\\n sigma = float(sigma)\\n alpha_affine = float(alpha_affine)\\n\\n pts1 = np.array(\\n [\\n center_square + square_size,\\n [center_square[0] + square_size, center_square[1] - square_size],\\n center_square - square_size,\\n ],\\n dtype=np.float32,\\n )\\n pts2 = pts1 + random_utils.uniform(-alpha_affine, alpha_affine, size=pts1.shape, random_state=random_state).astype(\\n np.float32\\n )\\n matrix = cv2.getAffineTransform(pts1, pts2)\\n\\n warp_fn = _maybe_process_in_chunks(\\n cv2.warpAffine,\\n M=matrix,\\n dsize=(width, height),\\n flags=interpolation,\\n borderMode=border_mode,\\n borderValue=value,\\n )\\n img = warp_fn(img)\\n\\n dx = random_utils.rand(height, width, random_state=random_state).astype(np.float32) * 2 - 1\\n cv2.GaussianBlur(dx, (17, 17), sigma, dst=dx)\\n dx *= alpha\\n\\n dy = random_utils.rand(height, width, random_state=random_state).astype(np.float32) * 2 - 1\\n cv2.GaussianBlur(dy, (17, 17), sigma, dst=dy)\\n dy *= alpha\\n\\n x, y = np.meshgrid(np.arange(width), np.arange(height))\\n\\n map_x = np.float32(x + dx)\\n map_y = np.float32(y + dy)\\n\\n remap_fn = _maybe_process_in_chunks(\\n cv2.remap,\\n map1=map_x,\\n map2=map_y,\\n interpolation=interpolation,\\n borderMode=border_mode,\\n borderValue=value,\\n )\\n return remap_fn(img)\",\n \"def affine_transform(initialize=False, fixed=None, moments=True) :\\n \\n transform = itk.AffineTransform[itk.D, fixed.ndim].New()\\n \\n if initialize :\\n fixed_itk = medipy.itk.medipy_image_to_itk_image(fixed, False)\\n #moving_itk = medipy.itk.medipy_image_to_itk_image(moving, False)\\n\\n fixedIndex = fixed_itk.GetLargestPossibleRegion().GetIndex()\\n fixedSize = fixed_itk.GetLargestPossibleRegion().GetSize()\\n\\n centerIndex = (int(fixedIndex[0] + fixedSize[0] / 2.0), \\\\\\n int(fixedIndex[1] + fixedSize[1] / 2.0), \\\\\\n int(fixedIndex[2] + fixedSize[2] / 2.0))\\n\\n rotationCenter = fixed_itk.TransformIndexToPhysicalPoint(centerIndex)\\n\\n transform.SetIdentity()\\n transform.SetCenter(rotationCenter)\\n\\n #initial_transform = itk.VersorRigid3DTransform[itk.D].New()\\n #initializer = itk.CenteredTransformInitializer[\\n # initial_transform, fixed_itk, moving_itk].New(\\n # Transform=initial_transform, FixedImage=fixed_itk, MovingImage=moving_itk) \\n #if moments :\\n # initializer.MomentsOn()\\n #else :\\n # initializer.GeometryOn()\\n #initializer.InitializeTransform()\\n #transform.SetCenter(initial_transform.GetCenter())\\n #transform.SetOffset(initial_transform.GetOffset())\\n \\n return transform\",\n \"def elastic_transform(\\n img: np.ndarray,\\n alpha: float,\\n sigma: float,\\n alpha_affine: float,\\n interpolation: int = cv2.INTER_LINEAR,\\n border_mode: int = cv2.BORDER_REFLECT_101,\\n value: Optional[ImageColorType] = None,\\n random_state: Optional[np.random.RandomState] = None,\\n approximate: bool = False,\\n same_dxdy: bool = False,\\n):\\n height, width = img.shape[:2]\\n\\n # Random affine\\n center_square = np.array((height, width), dtype=np.float32) // 2\\n square_size = min((height, width)) // 3\\n alpha = float(alpha)\\n sigma = float(sigma)\\n alpha_affine = float(alpha_affine)\\n\\n pts1 = np.array(\\n [\\n center_square + square_size,\\n [center_square[0] + square_size, center_square[1] - square_size],\\n center_square - square_size,\\n ],\\n dtype=np.float32,\\n )\\n pts2 = pts1 + random_utils.uniform(-alpha_affine, alpha_affine, size=pts1.shape, random_state=random_state).astype(\\n np.float32\\n )\\n matrix = cv2.getAffineTransform(pts1, pts2)\\n\\n warp_fn = _maybe_process_in_chunks(\\n cv2.warpAffine, M=matrix, dsize=(width, height), flags=interpolation, borderMode=border_mode, borderValue=value\\n )\\n img = warp_fn(img)\\n\\n if approximate:\\n # Approximate computation smooth displacement map with a large enough kernel.\\n # On large images (512+) this is approximately 2X times faster\\n dx = random_utils.rand(height, width, random_state=random_state).astype(np.float32) * 2 - 1\\n cv2.GaussianBlur(dx, (17, 17), sigma, dst=dx)\\n dx *= alpha\\n if same_dxdy:\\n # Speed up even more\\n dy = dx\\n else:\\n dy = random_utils.rand(height, width, random_state=random_state).astype(np.float32) * 2 - 1\\n cv2.GaussianBlur(dy, (17, 17), sigma, dst=dy)\\n dy *= alpha\\n else:\\n dx = np.float32(\\n gaussian_filter((random_utils.rand(height, width, random_state=random_state) * 2 - 1), sigma) * alpha\\n )\\n if same_dxdy:\\n # Speed up\\n dy = dx\\n else:\\n dy = np.float32(\\n gaussian_filter((random_utils.rand(height, width, random_state=random_state) * 2 - 1), sigma) * alpha\\n )\\n\\n x, y = np.meshgrid(np.arange(width), np.arange(height))\\n\\n map_x = np.float32(x + dx)\\n map_y = np.float32(y + dy)\\n\\n remap_fn = _maybe_process_in_chunks(\\n cv2.remap, map1=map_x, map2=map_y, interpolation=interpolation, borderMode=border_mode, borderValue=value\\n )\\n return remap_fn(img)\",\n \"def convert_affine(ref, t, out):\\n args = [\\n transform_exe,\\n '-d', '3',\\n '-r', ref,\\n '-t', '[{},0]'.format(t),\\n '-o', '[{},1]'.format(out),\\n '--float'\\n ]\\n subprocess.check_call(args)\",\n \"def affine_trans(self):\\n h, w, _ = self.img.shape\\n\\n \\\"\\\"\\\"\\n pts1 = np.float32(\\n [\\n [randint(0, rows), randint(0, cols)],\\n [randint(0, rows), randint(0, cols)],\\n [randint(0, rows), randint(0, cols)],\\n ]\\n )\\n pts2 = np.float32(\\n [\\n [randint(0, rows), randint(0, cols)],\\n [randint(0, rows), randint(0, cols)],\\n [randint(0, rows), randint(0, cols)],\\n ]\\n )\\n \\\"\\\"\\\"\\n\\n pts1 = np.float32([[50, 50], [200, 50], [50, 200]])\\n pts2 = np.float32([[10, 100], [200, 50], [100, 250]])\\n\\n M = cv2.getAffineTransform(pts1, pts2)\\n\\n self.img = cv2.warpAffine(self.img, M, (w, h))\\n\\n self.edits.append(\\\"affine\\\")\\n return self\",\n \"def affine_sigmoid(xin, m= 10, c= 3):\\n if type(xin) != np.ndarray:\\n x = np.array([xin])\\n else:\\n x = xin\\n\\n x = get_affine(x, m, c)\\n output = get_sigmoid(x)\\n\\n if type(xin) != np.ndarray:\\n return output[0]\\n else:\\n return output\",\n \"def model_1exp(a, t, s, t0=0):\\n a, t, s = physicond(a, t, s)\\n\\n # auxilary function taking as argument a time array.\\n def aux(array_t, details=False):\\n\\n # applying time offset\\n t_var = array_t - t0\\n\\n # model expression coming from symbolic calculation\\n pulse = np.heaviside(t_var, 1.)\\n ind = np.where(pulse != 0.)\\n pulse[ind] = a * (np.exp(-t_var[ind]/t)-np.exp(-t_var[ind]/s))\\n\\n # same behavior as model_2exp and model_3exp\\n if details == True:\\n return pulse, pulse\\n elif details == False:\\n return pulse\\n\\n return aux\",\n \"def _apply_transform(self, x, transform_parameters):\\n # x is a single image, so it doesn't have image number at index 0\\n img_row_axis = self.row_axis - 1\\n img_col_axis = self.col_axis - 1\\n img_channel_axis = self.channel_axis - 1\\n\\n x = apply_affine_transform(x, transform_parameters.get('theta', 0),\\n transform_parameters.get('tx', 0),\\n transform_parameters.get('ty', 0),\\n transform_parameters.get('shear', 0),\\n transform_parameters.get('zx', 1),\\n transform_parameters.get('zy', 1),\\n row_axis=img_row_axis,\\n col_axis=img_col_axis,\\n channel_axis=img_channel_axis,\\n fill_mode=self.fill_mode,\\n cval=self.cval)\\n\\n if transform_parameters.get('channel_shift_intensity') is not None:\\n x = apply_channel_shift(x,\\n transform_parameters['channel_shift_intensity'],\\n img_channel_axis)\\n\\n if transform_parameters.get('flip_horizontal', False):\\n x = self._flip_axis(x, img_col_axis)\\n\\n if transform_parameters.get('flip_vertical', False):\\n x = self._flip_axis(x, img_row_axis)\\n\\n if transform_parameters.get('brightness') is not None:\\n x = apply_brightness_shift(x, transform_parameters['brightness'])\\n\\n return x\",\n \"def elastic_transform(image, mask, alpha, sigma, alpha_affine,\\n random_state=None):\\n if random_state is None:\\n random_state = np.random.RandomState(None)\\n\\n shape = image.shape\\n shape_size = shape[:2]\\n\\n # Random affine\\n center_square = np.float32(shape_size) // 2\\n square_size = min(shape_size) // 3\\n pts1 = np.float32([center_square + square_size,\\n [center_square[0] + square_size,\\n center_square[1] - square_size],\\n center_square - square_size])\\n pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine,\\n size=pts1.shape).astype(np.float32)\\n M = cv2.getAffineTransform(pts1, pts2)\\n\\n dx = gaussian_filter((random_state.rand(*shape) * 2 - 1),\\n sigma) * alpha\\n dy = gaussian_filter((random_state.rand(*shape) * 2 - 1),\\n sigma) * alpha\\n\\n x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]),\\n np.arange(shape[2]))\\n indices = np.reshape(y + dy, (-1, 1)), np.reshape(\\n x + dx, (-1, 1)), np.reshape(z, (-1, 1))\\n\\n image = cv2.warpAffine(image, M, shape_size[::-1],\\n borderMode=cv2.BORDER_REFLECT_101)\\n\\n image = map_coordinates(image, indices, order=1,\\n mode='reflect').reshape(shape)\\n if mask is not None:\\n mask = cv2.warpAffine(mask, M, shape_size[::-1],\\n borderMode=cv2.BORDER_REFLECT_101)\\n mask = map_coordinates(\\n mask, indices, order=1, mode='reflect'\\n ).reshape(shape)\\n\\n return image, mask\",\n \"def get_affine_matrix2d(\\n translations: Tensor,\\n center: Tensor,\\n scale: Tensor,\\n angle: Tensor,\\n sx: Tensor | None = None,\\n sy: Tensor | None = None,\\n) -> Tensor:\\n transform: Tensor = get_rotation_matrix2d(center, -angle, scale)\\n transform[..., 2] += translations # tx/ty\\n\\n # pad transform to get Bx3x3\\n transform_h = convert_affinematrix_to_homography(transform)\\n\\n if any(s is not None for s in [sx, sy]):\\n shear_mat = get_shear_matrix2d(center, sx, sy)\\n transform_h = transform_h @ shear_mat\\n\\n return transform_h\",\n \"def get_affine_matrix2d(\\n translations: torch.Tensor,\\n center: torch.Tensor,\\n scale: torch.Tensor,\\n angle: torch.Tensor,\\n sx: Optional[torch.Tensor] = None,\\n sy: Optional[torch.Tensor] = None,\\n) -> torch.Tensor:\\n transform: torch.Tensor = get_rotation_matrix2d(center, -angle, scale)\\n transform[..., 2] += translations # tx/ty\\n\\n # pad transform to get Bx3x3\\n transform_h = convert_affinematrix_to_homography(transform)\\n\\n if any(s is not None for s in [sx, sy]):\\n shear_mat = get_shear_matrix2d(center, sx, sy)\\n transform_h = transform_h @ shear_mat\\n\\n return transform_h\",\n \"def elastic_transform(image, alpha=1000, sigma=30, spline_order=1, mode='nearest', random_state=np.random):\\n assert image.ndim == 2\\n shape = image.shape[:2]\\n\\n dx = gaussian_filter((random_state.rand(*shape) * 2 - 1),\\n sigma, mode=\\\"constant\\\", cval=0) * alpha\\n dy = gaussian_filter((random_state.rand(*shape) * 2 - 1),\\n sigma, mode=\\\"constant\\\", cval=0) * alpha\\n\\n x, y = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]), indexing='ij')\\n indices = [np.reshape(x + dx, (-1, 1)), np.reshape(y + dy, (-1, 1))]\\n result = map_coordinates(\\n image, indices, order=spline_order, mode=mode).reshape(shape)\\n return result\",\n \"def elastic_transform(image, alpha_param, sigma_param, alpha_affine_param, random_state=None):\\n image = np.asarray(image)\\n #im_np = im_np.transpose(1,0,2) # when we go from pil to numpy array the W and L dimensions are swaped\\n\\n if len(image.shape) < 3: # if there is less than 3 channels (black&white) we triplicate the image\\n image = np.concatenate((image[:,:, np.newaxis], image[:,:, np.newaxis], image[:,:, np.newaxis]), axis = 2)\\n\\n alpha = image.shape[1] * alpha_param\\n sigma = image.shape[1] * sigma_param\\n alpha_affine = image.shape[1] * alpha_affine_param\\n\\n if random_state is None:\\n random_state = np.random.RandomState(None)\\n\\n shape = image.shape\\n shape_size = shape[:2]\\n\\n # Random affine\\n center_square = np.float32(shape_size) // 2\\n square_size = min(shape_size) // 3\\n pts1 = np.float32([center_square + square_size, [center_square[0]+square_size, center_square[1]-square_size], center_square - square_size])\\n pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine, size=pts1.shape).astype(np.float32)\\n M = cv2.getAffineTransform(pts1, pts2)\\n image = cv2.warpAffine(image, M, shape_size[::-1], borderMode=cv2.BORDER_REFLECT_101)\\n\\n dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma) * alpha\\n dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma) * alpha\\n dz = np.zeros_like(dx)\\n\\n x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))\\n indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1)), np.reshape(z, (-1, 1))\\n\\n im_elastic = map_coordinates(image, indices, order=1, mode='reflect').reshape(shape)\\n\\n return Image.fromarray(np.uint8(im_elastic))\",\n \"def apply_transform(x,\\n transform_matrix,\\n channel_axis=0,\\n fill_mode='nearest',\\n cval=0.,\\n interp_order=0):\\n x = np.rollaxis(x, channel_axis, 0)\\n final_affine_matrix = transform_matrix[:3, :3]\\n final_offset = transform_matrix[:3, -1]\\n channel_volumes = [ndi.interpolation.affine_transform(\\n x_channel,\\n final_affine_matrix,\\n final_offset,\\n order=interp_order, # NOTE: The order of the spline interpolation\\n mode=fill_mode,\\n cval=cval) for x_channel in x]\\n x = np.stack(channel_volumes, axis=0)\\n x = np.rollaxis(x, 0, channel_axis + 1)\\n return x\",\n \"def affine(self, image, random_state):\\n shape = image.shape\\n shape_size = shape[:2]\\n transform_std = self.alpha_affine*image.shape[1]\\n\\n center_square = np.float32(shape[:2]) // 2\\n square_size = min(shape[:2]) // 3\\n\\n source = np.float32([center_square + square_size, [center_square[0]+square_size, center_square[1]-square_size], center_square - square_size])\\n destination = source + random_state.uniform(-transform_std, transform_std, size=source.shape).astype(np.float32)\\n M = cv2.getAffineTransform(source, destination)\\n\\n return cv2.warpAffine(image, M, shape_size[::-1], borderMode=cv2.BORDER_REPLICATE)\",\n \"def compute_e(f_mat, m_mat):\\r\\n return m_mat.T @ f_mat @ m_mat\",\n \"def elasticTransform_legacy(image, mask, sigma, alpha_affine, random_seed=None):\\n\\t\\n\\trandom_state = np.random.RandomState(random_seed)\\n\\t\\n\\tif len(image.shape)<3:\\n\\t\\timage = np.expand_dims(image,-1)\\n\\tif len(mask.shape)<3:\\n\\t\\tmask = np.expand_dims(mask,-1)\\n\\t\\n\\tshape = image.shape\\n\\tshape_size = shape[:2]\\n\\n\\t# Random affine\\n\\tcenter_square = np.float32(shape_size) // 2\\n\\tsquare_size = min(shape_size) // 3\\n\\tpts1 = np.float32([center_square + square_size, [center_square[0] + square_size,center_square[1] - square_size], center_square - square_size])\\n\\tpts2 = pts1 + random_state.uniform(-alpha_affine,alpha_affine, size=pts1.shape).astype(np.float32)\\n\\t\\n\\tM = cv2.getAffineTransform(pts1, pts2)\\n\\t\\n\\timage_w = np.zeros_like(image)\\n\\tfor i in range(image.shape[-1]):\\n\\t\\timage_w[...,i] = cv2.warpAffine(image[...,i], M, shape_size[::-1], borderMode=cv2.BORDER_CONSTANT, borderValue=int(np.min(image[...,i])))\\n\\t\\n\\tmask_w = np.zeros_like(mask)\\n\\tfor i in range(mask.shape[-1]):\\n\\t\\tmask_w[...,i] = cv2.warpAffine(mask[...,i] , M, shape_size[::-1], borderMode=cv2.BORDER_REPLICATE)\\n\\n\\tblur_size = int(2*sigma) | 1\\n\\tdx = cv2.GaussianBlur((random_state.rand(*shape) * 2 - 1), ksize=(blur_size, blur_size), sigmaX=sigma)\\n\\tdy = cv2.GaussianBlur((random_state.rand(*shape) * 2 - 1), ksize=(blur_size, blur_size), sigmaX=sigma)\\n\\t\\n\\tif len(dx.shape) < 3:\\n\\t\\tdx = np.expand_dims(dx,-1)\\n\\t\\tdy = np.expand_dims(dy,-1)\\n\\n\\tgx, gy = np.meshgrid(np.arange(shape_size[1]), np.arange(shape_size[0]))\\n\\tgx = np.expand_dims(gx,-1)\\n\\tgy = np.expand_dims(gy,-1)\\n\\n\\tgx = np.repeat(gx,dx.shape[-1], -1)\\n\\tgy = np.repeat(gy,dy.shape[-1], -1)\\n\\n\\tgx = (gx + dx).astype(np.float32)\\n\\tgy = (gy + dy).astype(np.float32)\\n\\n\\timage_d = np.zeros_like(image_w)\\n\\tmask_d = np.zeros_like(mask_w)\\n\\n\\tfor i in range(image.shape[-1]):\\n\\t\\timage_d[...,i] = cv2.remap(image_w[...,i], gx[...,i], gy[...,i], interpolation=cv2.INTER_LINEAR)\\n\\t\\n\\tradix = gx.shape[-1]\\n\\tfor i in range(mask.shape[-1]):\\n\\t\\tmask_d[...,i] = cv2.remap(mask_w[...,i], gx[...,i%radix], gy[...,i%radix], interpolation=cv2.INTER_LINEAR)\\n\\t\\n\\treturn image_d, mask_d\",\n \"def get_affine_transform(gps_coords, pdr_coords):\\n # Compute similarity Xp = s A X + b\\n X = np.array(pdr_coords)\\n Xp = np.array(gps_coords)\\n T = tf.superimposition_matrix(X.T, Xp.T, scale=True)\\n\\n A, b = T[:3, :3], T[:3, 3]\\n s = np.linalg.det(A)**(1. / 3)\\n A /= s\\n return s, A, b\",\n \"def affine_2Dtransform(img, t_mat, height, width, h_offset=0, w_offset=0, nh_flag=False, nw_flag=False):\\n # transform matrix must be validated\\n if(np.shape(t_mat) != (2, 2)):\\n return img\\n\\n # implementing matrix multiplication to a default map of source data in order to apply transform\\n # and to achieve coordination/location of transformed matrix according to source data(data map)\\n coord_map = transform_calcualtion(\\n height, width, t_mat, h_offset, w_offset, nh_flag, nw_flag)\\n\\n # transformed image data construction\\n t_img = np.full((height+h_offset, width+w_offset, 3), 255, dtype='uint8')\\n\\n # applying new map to image inorder to complete the transform\\n try:\\n for i in range(height):\\n for j in range(width):\\n [i_new_coord, j_new_coord] = coord_map[i, j, :]\\n # unhandled bound-jumpout\\n t_img[i_new_coord, j_new_coord, :] = img[i, j, :]\\n except:\\n print(\\\"not enough offset/negative coordination pushed\\\")\\n return img\\n return t_img\",\n \"def get_transform(ds):\\n\\n if 'transform' in ds.attrs:\\n ds_trans = ds.attrs['transform']\\n if isinstance(ds_trans, Affine):\\n return ds_trans\\n else:\\n return Affine(*ds_trans)\\n\\n elif 'crs' in ds.data_vars and 'i2m' in ds.data_vars['crs'].attrs:\\n transf_str = ds.data_vars['crs'].attrs['i2m']\\n a = list(map(float, transf_str.split(',')))\\n return Affine(a[0], a[2], a[4], a[1], a[3], a[5])\\n\\n else:\\n resx, resy = get_resolution(ds)\\n xoff = ds['x'].values.min()\\n yoff = ds['y'].values.max()\\n return Affine(resx, 0, xoff, 0, resy, yoff)\",\n \"def apply_transform(x,\\n transform_matrix,\\n channel_axis=0,\\n fill_mode='constant',\\n cval=0.):\\n x = np.rollaxis(x, channel_axis, 0)\\n final_affine_matrix = transform_matrix[:2, :2]\\n final_offset = transform_matrix[:2, 2]\\n channel_images = [\\n interpolation.affine_transform(\\n x_channel,\\n final_affine_matrix,\\n final_offset,\\n order=0,\\n mode=fill_mode,\\n cval=cval) for x_channel in x\\n ]\\n x = np.stack(channel_images, axis=0)\\n x = np.rollaxis(x, 0, channel_axis + 1)\\n return x\",\n \"def apply_transform(x,\\n transform_matrix,\\n channel_axis=0,\\n fill_mode='constant',\\n cval=0.):\\n x = np.rollaxis(x, channel_axis, 0)\\n final_affine_matrix = transform_matrix[:2, :2]\\n final_offset = transform_matrix[:2, 2]\\n channel_images = [\\n interpolation.affine_transform(\\n x_channel,\\n final_affine_matrix,\\n final_offset,\\n order=0,\\n mode=fill_mode,\\n cval=cval) for x_channel in x\\n ]\\n x = np.stack(channel_images, axis=0)\\n x = np.rollaxis(x, 0, channel_axis + 1)\\n return x\",\n \"def softmax(x):\\n if type(x) == list:\\n dim=len(x)\\n norm = np.sum(np.exp(x))\\n for idx in range(dim):\\n x[idx] = np.exp(x[idx])/norm\\n elif type(x) == np.ndarray:\\n dim=x.shape\\n for col in range(dim[1]):\\n norm = np.sum(np.exp(x[:, col]))\\n for idx in range(dim[0]):\\n x[idx, col] = np.exp(x[idx, col])/norm\\n else:\\n raise Exception('incorrect input')\\n return x\",\n \"def affineSchur(self):\\n return AffineSchurFunctions(self)\",\n \"def seToSE( x ):\\n x = asarray(x,dtype=float)\\n if x.shape != (6,):\\n raise ValueError(\\\"shape must be (6,); got %s\\\" % str(x.shape))\\n #\\n return expM(screw(x))\",\n \"def affine_sigmoid_forward(x, w, b):\\n a, fc_cache = affine_forward(x, w, b)\\n out, sigmoid_cache = sigmoid_forward(a)\\n cache = (fc_cache, sigmoid_cache)\\n return out, cache\",\n \"def augmentAffine(img_in, seg_in, strength=0.05):\\n B,C,D,H,W = img_in.size()\\n affine_matrix = (torch.eye(3,4).unsqueeze(0) + torch.randn(B, 3, 4) * strength).to(img_in.device)\\n\\n meshgrid = F.affine_grid(affine_matrix,torch.Size((B,1,D,H,W)))\\n\\n img_out = F.grid_sample(img_in, meshgrid,padding_mode='border')\\n seg_out = F.grid_sample(seg_in.float().unsqueeze(1), meshgrid, mode='nearest').long().squeeze(1)\\n\\n return img_out, seg_out\",\n \"def func_exp(x, a, b, c):\\n return a * np.exp(b * x) + c\",\n \"def _eigen_fns(mat, fns):\\n evals, evecs = _eigh(mat)\\n\\n def transform(fn):\\n \\\"\\\"\\\"Generates a transform given a function on the eigenvalues.\\\"\\\"\\\"\\n def _(vec, dt):\\n return np.einsum(\\n 'ji,i,ki,k...->j...',\\n evecs, fn(evals, dt), evecs, vec, optimize=True)\\n\\n return _\\n\\n return tuple(transform(fn) for fn in fns)\",\n \"def elastic_transform(self, image, random_state=None):\\n if random_state is None:\\n random_state = np.random.RandomState(None)\\n\\n image = self.affine(image, random_state)\\n #from ipdb import set_trace; set_trace()\\n indices = self.stretch_indices(image, random_state)\\n\\n return map_coordinates(image, indices, order=1, mode='reflect').reshape(image.shape)\",\n \"def fused_elemwise_activation(x,\\n y,\\n functor_list,\\n axis=-1,\\n scale=0.0,\\n save_intermediate_out=True):\\n if isinstance(functor_list, str):\\n functor_list = functor_list.split(',')\\n\\n if not isinstance(functor_list, list) or len(functor_list) != 2:\\n raise ValueError(\\n 'functor_list should be a list of str, and the length should be 2.')\\n\\n helper = LayerHelper('fused_elemwise_activation', **locals())\\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\\n intermediate_out = helper.create_variable_for_type_inference(dtype=x.dtype)\\n helper.append_op(\\n type='fused_elemwise_activation',\\n inputs={'X': x,\\n 'Y': y},\\n outputs={'Out': out,\\n 'IntermediateOut': intermediate_out},\\n attrs={\\n 'axis': axis,\\n 'scale': scale,\\n 'save_intermediate_out': save_intermediate_out,\\n 'functor_list': functor_list\\n })\\n return out\",\n \"def _transform(\\n self, x: \\\"torch.Tensor\\\", y: Optional[\\\"torch.Tensor\\\"], **kwargs\\n ) -> Tuple[\\\"torch.Tensor\\\", Optional[\\\"torch.Tensor\\\"]]:\\n import torch\\n import torchvision.transforms.functional as F\\n\\n img_size = x.shape[:2]\\n\\n angle = float(\\n torch.empty(1)\\n .uniform_(float(self.degree_range[0]), float(self.degree_range[1]))\\n .item()\\n )\\n\\n max_dx = float(self.translate[0] * img_size[1])\\n max_dy = float(self.translate[1] * img_size[0])\\n tx = int(round(torch.empty(1).uniform_(-max_dx, max_dx).item()))\\n ty = int(round(torch.empty(1).uniform_(-max_dy, max_dy).item()))\\n translations = (tx, ty)\\n\\n scale = float(torch.empty(1).uniform_(self.scale[0], self.scale[1]).item())\\n\\n # x needs to have channel first\\n x = x.permute(2, 0, 1)\\n x = F.affine(\\n img=x, angle=angle, translate=translations, scale=scale, shear=(0.0, 0.0)\\n )\\n x = x.permute(1, 2, 0)\\n\\n return torch.clamp(x, min=self.clip_values[0], max=self.clip_values[1]), y\",\n \"def gen_affine_map(Ab, img_sz, dim=3):\\n Ab = Ab.view(Ab.shape[0], dim+1, dim)\\n phi = gen_identity_map(img_sz).to(Ab.device)\\n phi_cp = phi.view(dim, -1)\\n affine_map = torch.matmul(Ab[:, :dim, :], phi_cp)\\n affine_map = Ab[:, dim, :].contiguous().view(-1, dim, 1) + affine_map\\n affine_map = affine_map.view([Ab.shape[0]] + list(phi.shape))\\n return affine_map\",\n \"def transpose_as_einsum(x: JaxExpression, params: Params) -> Einsum:\\n x_ndim = len(x.shape)\\n x_dims = ''.join(it.islice(einsum.einsum_letters(), x_ndim))\\n out_dims = ''.join([x_dims[dim] for dim in params['permutation']])\\n return Einsum(f'{x_dims}->{out_dims}', (x,))\",\n \"def elastic_transform(X, min_alpha=36, max_alpha=38, min_sigma=5, max_sigma=6, random_state=None, n_jobs=1):\\n if random_state is None:\\n rng = np.random\\n else:\\n rng = np.random.RandomState(random_state)\\n alphas = rng.uniform(min_alpha, max_alpha, size=X.shape[0])\\n sigmas = rng.uniform(min_sigma, max_sigma, size=X.shape[0])\\n X_elas = Parallel(n_jobs=n_jobs)(delayed(elastic_transform_one)(X[i], alphas[i], sigmas[i]) for i in range(X.shape[0]))\\n return np.array(X_elas, dtype='float32')\",\n \"def get_affine_transform(center, scale, rot, output_size, shift=(0.0, 0.0), inv=False):\\n assert len(center) == 2\\n assert len(scale) == 2\\n assert len(output_size) == 2\\n assert len(shift) == 2\\n scale_tmp = scale * 200.0\\n shift = np.array(shift)\\n src_w = scale_tmp[0]\\n dst_w = output_size[0]\\n dst_h = output_size[1]\\n rot_rad = np.pi * rot / 180\\n src_dir = rotate_point([0.0, src_w * -0.5], rot_rad)\\n dst_dir = np.array([0.0, dst_w * -0.5])\\n src = np.zeros((3, 2), dtype=np.float32)\\n src[0, :] = center + scale_tmp * shift\\n src[1, :] = center + src_dir + scale_tmp * shift\\n src[2, :] = _get_3rd_point(src[0, :], src[1, :])\\n dst = np.zeros((3, 2), dtype=np.float32)\\n dst[0, :] = [dst_w * 0.5, dst_h * 0.5]\\n dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir\\n dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])\\n if inv:\\n trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))\\n else:\\n trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))\\n return trans\",\n \"def quantize_affine_given_quant_params(\\n input: torch.Tensor,\\n quantize_params: QuantizeAffineParams2,\\n) -> torch.Tensor:\\n return QuantizeAffineFunction.apply(input, quantize_params)\",\n \"def apply_transform(x, transform_matrix, channel_index=0, fill_mode='nearest', cval=0.):\\n x = np.rollaxis(x, channel_index, 0)\\n final_affine_matrix = transform_matrix[:2, :2]\\n final_offset = transform_matrix[:2, 2]\\n channel_images = [ndi.interpolation.affine_transform(x_channel,\\n final_affine_matrix,\\n final_offset, order=0, mode=fill_mode, cval=cval)\\n for x_channel in x]\\n x = np.stack(channel_images, axis=0)\\n x = np.rollaxis(x, 0, channel_index+1)\\n return x\",\n \"def affineTransform(img, pts, newPts):\\n\\ttmp = img.copy()\\n\\tif len(img.shape) is 3:\\n\\t\\trows, cols, ch = img.shape\\n\\telse:\\n\\t\\trows, cols = img.shape\\n\\tpts1 = np.float32(pts)\\n\\tpts2 = np.float32(newPts)\\n\\tM = cv2.getAffineTransform(pts1, pts2)\\n\\tdst = cv2.warpAffine(tmp, M, (cols, rows))\\n\\treturn dst\",\n \"def transform(self, x):\\n return self._transform_eig(x)\",\n \"def apply_transform(x,\\n transform_matrix,\\n channel_axis=0,\\n fill_mode='nearest',\\n cval=0.):\\n x = np.rollaxis(x, channel_axis, 0)\\n final_affine_matrix = transform_matrix[:2, :2]\\n final_offset = transform_matrix[:2, 2]\\n channel_images = [ndi.interpolation.affine_transform(\\n x_channel,\\n final_affine_matrix,\\n final_offset,\\n order=1,\\n mode=fill_mode,\\n cval=cval) for x_channel in x]\\n x = np.stack(channel_images, axis=0)\\n x = np.rollaxis(x, 0, channel_axis + 1)\\n return x\",\n \"def singleexp(params, t):\\n # 2011-05-18 20:58 IJMC: Created\\n # 2011-06-03 11:49 IJMC: Normalized to unity.\\n\\n if len(params)==2:\\n return 1. - params[0] * exp(-t/params[1]) \\n else:\\n return params[2] * (1. - params[0] * exp(-t/params[1]) )\",\n \"def apply_transform(x,\\n transform_matrix,\\n channel_axis=0,\\n fill_mode='nearest',\\n cval=0.):\\n x = np.rollaxis(x, channel_axis, 0)\\n final_affine_matrix = transform_matrix[:2, :2]\\n final_offset = transform_matrix[:2, 2]\\n channel_images = [ndi.interpolation.affine_transform(\\n x_channel,\\n final_affine_matrix,\\n final_offset,\\n order=0,\\n mode=fill_mode,\\n cval=cval) for x_channel in x]\\n x = np.stack(channel_images, axis=0)\\n x = np.rollaxis(x, 0, channel_axis + 1)\\n return x\",\n \"def affine(img, matrix, interpolation=\\\"nearest\\\", fill=None, data_format='CHW'):\\n ndim = len(img.shape)\\n if ndim == 3:\\n img = img.unsqueeze(0)\\n\\n img = img if data_format.lower() == 'chw' else img.transpose((0, 3, 1, 2))\\n\\n matrix = paddle.to_tensor(matrix, place=img.place)\\n matrix = matrix.reshape((1, 2, 3))\\n shape = img.shape\\n\\n grid = _affine_grid(\\n matrix, w=shape[-1], h=shape[-2], ow=shape[-1], oh=shape[-2]\\n )\\n\\n if isinstance(fill, int):\\n fill = tuple([fill] * 3)\\n\\n out = _grid_transform(img, grid, mode=interpolation, fill=fill)\\n\\n out = out if data_format.lower() == 'chw' else out.transpose((0, 2, 3, 1))\\n out = out.squeeze(0) if ndim == 3 else out\\n\\n return out\",\n \"def exp2eval(self, p, x, y=None, C=None, sumsq=False, weights=None):\\n yd = p[0] + (p[1] * (1.0 - numpy.exp(-x / p[2])) ** 2.0) + \\\\\\n (p[3] * (1.0 - numpy.exp(-x / p[4])))\\n if y is None:\\n return yd\\n else:\\n if sumsq is True:\\n ss = numpy.sqrt(numpy.sum((y - yd) ** 2.0))\\n # if p[4] < 3.0*p[2]:\\n # ss = ss*1e6 # penalize them being too close\\n return ss\\n else:\\n return y - yd\\n\\n # @autojit\",\n \"def map(self,Affine,i):\\n map_x = np.zeros([self.num,self.d])\\n for k in range(self.num):\\n map_x[k,:] = Affine.apply(i,self.pick(k))\\n Mapped = Model_Points(map_x)\\n return Mapped\",\n \"def apply_transform(img,\\n transform_matrix):\\n rows,cols = img.shape[:2]\\n dst = cv2.warpAffine(img,transform_matrix,(cols,rows))\\n\\n\\n return dst\",\n \"def affine_transform_2d(v, mapping, alpha = 1):\\r\\n p_wgt = vec2(0, 0)\\r\\n q_wgt = vec2(0, 0)\\r\\n w = len(mapping)*[None]\\r\\n w_sum = 0\\r\\n for i in range(len(mapping)):\\r\\n mp = mapping[i]\\r\\n x = mp[0].x - v.x\\r\\n y = mp[0].y - v.y\\r\\n if (x == 0 and y == 0): return mp[1]\\r\\n w[i] = 1/((x*x + y*y) ** alpha)\\r\\n p_wgt += mp[0]*w[i]\\r\\n q_wgt += mp[1]*w[i]\\r\\n w_sum += w[i]\\r\\n p_wgt /= w_sum\\r\\n q_wgt /= w_sum\\r\\n M1 = mat2(0)\\r\\n M2 = mat2(0)\\r\\n for i in range(len(mapping)):\\r\\n mp = mapping[i]\\r\\n p_adj = mp[0] - p_wgt\\r\\n q_adj = mp[1] - q_wgt\\r\\n M1 += p_adj.transpose_multiply(p_adj)*w[i]\\r\\n M2 += p_adj.transpose_multiply(q_adj)*w[i]\\r\\n M1 = M1.inverse()\\r\\n M = M1*M2\\r\\n M = M.transpose()\\r\\n v_out = M*(v - p_wgt) + q_wgt\\r\\n return v_out\",\n \"def transforms_multiply(t0s, t1s):\\r\\n \\r\\n return ut.matrix_multiply(t0s, t1s)\",\n \"def expms(A, eig=np.linalg.eigh):\\r\\n # TODO: check that this works reliably for low rank matrices\\r\\n # first: symmetrize A\\r\\n D, B = eig(A)\\r\\n return np.dot(B, (np.exp(D) * B).T)\",\n \"def expm1_inplace(a):\",\n \"def softmax(x, axis=1):\\n sf = np.exp(x)\\n sf = sf/np.sum(sf, axis=axis)[:,np.newaxis]\\n return sf\",\n \"def fit_transform(self, Xs, y=None):\\n return self.fit(Xs, y).transform(Xs)\",\n \"def attrTransform(self, matrix, transform):\\n for ttype, targs in self.reTransformFind.findall(transform):\\n targs = list(map(lambda x: float(x), self.reNumberFind.findall(targs)))\\n if ttype == 'matrix':\\n newmatrix = [ targs[0], targs[1],\\n targs[2], targs[3],\\n targs[4], targs[5] ]\\n self.matrixMul(matrix, newmatrix)\\n elif ttype == 'translate':\\n tx = targs[0]\\n ty = targs[1] if len(targs) > 1 else 0\\n newmatrix = [ 1, 0, 0, 1, tx, ty ]\\n self.matrixMul(matrix, newmatrix)\\n elif ttype == 'scale':\\n sx = targs[0]\\n sy = targs[1] if len(targs) > 1 else sx\\n newmatrix = [ sx, 0, 0, sy, 0, 0 ]\\n self.matrixMul(matrix, newmatrix)\\n elif ttype == 'rotate':\\n if len(targs) == 1:\\n alpha = targs[0]\\n newmatrix = [ math.cos(alpha), math.sin(alpha),\\n -math.sin(alpha), math.cos(alpha),\\n 0, 0]\\n self.matrixMul(matrix, newmatrix)\\n else:\\n alpha = targs[0]\\n newmatrix = [ 1, 0, 0, 1, targs[1], targs[2] ]\\n self.matrixMul(matrix, newmatrix)\\n newmatrix = [ math.cos(alpha), math.sin(alpha),\\n -math.sin(alpha), math.cos(alpha),\\n 0, 0]\\n self.matrixMul(matrix, newmatrix)\\n newmatrix = [ 1, 0, 0, 1, -targs[1], -targs[2] ]\\n self.matrixMul(matrix, newmatrix)\\n elif ttype == 'skewX' or ttype == 'skewY':\\n self.alert(\\\"skewX and skewY transformations are not supported\\\", elem)\\n else:\\n print('unknown transform type: ', ttype)\\n return matrix\",\n \"def setrans(Bi, t):\\n\\n x,v=mat2set(Bi)\\n Bo = set2mat((x+t,v))\\n Bo = Bo.astype(Bi.dtype)\\n return Bo\",\n \"def affine(img, angle=0, translate=(0, 0), scale=1, shear=0, resample='BILINEAR', fillcolor=(0,0,0)):\\n if not _is_numpy_image(img):\\n raise TypeError('img should be CV Image. Got {}'.format(type(img)))\\n\\n assert isinstance(translate, (tuple, list)) and len(translate) == 2, \\\\\\n \\\"Argument translate should be a list or tuple of length 2\\\"\\n\\n assert scale > 0.0, \\\"Argument scale should be positive\\\"\\n\\n rows, cols, _ = img.shape\\n center = (cols * 0.5, rows * 0.5)\\n angle = math.radians(angle)\\n shear = math.radians(shear)\\n M00 = math.cos(angle)*scale\\n M01 = -math.sin(angle+shear)*scale\\n M10 = math.sin(angle)*scale\\n M11 = math.cos(angle+shear)*scale\\n M02 = center[0] - center[0]*M00 - center[1]*M01 + translate[0]\\n M12 = center[1] - center[0]*M10 - center[1]*M11 + translate[1]\\n affine_matrix = np.array([[M00, M01, M02], [M10, M11, M12]], dtype=np.float32)\\n dst_img = cv2.warpAffine(img, affine_matrix, (cols, rows), flags=INTER_MODE[resample],\\n borderMode=cv2.BORDER_CONSTANT, borderValue=fillcolor)\\n return dst_img\",\n \"def expIP(self):\\n np.exp(self.t, out=self.t)\\n return self\",\n \"def translateEuler(trans):\\n return np.array([[1,0,0,trans[0]],[0,1,0,trans[1]],[0,0,1,trans[2]],[0,0,0,1]])\",\n \"def transform(self, x:generic_array, dense=True) -> generic_array:\\n if type(x) == np.ndarray and not dense:\\n warnings.warn(\\\"For Numpy transform it is best to use dense=True\\\")\\n \\n K_nq = self._pairwise_kernels(x, self.components_, dense=dense)\\n x_new = K_nq @ self.normalization\\n return x_new\",\n \"def analytic(x, t, D, x0, xend, logx=False, c_s=1, use_log2=False):\\n import scipy.special\\n if t.ndim == 1:\\n t = t.reshape((t.size, 1))\\n expb = (lambda arg: 2**arg) if use_log2 else np.exp\\n x = expb(x) if logx else x\\n return c_s * scipy.special.erfc(x/(2*(D*t)**0.5))\",\n \"def testTranslateAffine(self):\\n affineClass = xyTransformRegistry[\\\"affine\\\"]\\n affineConfig = affineClass.ConfigClass()\\n affineConfig.translation = (1.2, -3.4)\\n with lsst.utils.tests.getTempFilePath(\\\".py\\\") as filePath:\\n self.checkConfig(affineClass, affineConfig, filePath)\\n affine = affineClass(affineConfig)\\n for fromPoint in self.fromIter():\\n toPoint = affine.forwardTransform(fromPoint)\\n predToPoint = fromPoint + Extent2D(*affineConfig.translation)\\n for i in range(2):\\n self.assertAlmostEqual(toPoint[i], predToPoint[i])\",\n \"def translateEuler(self,trans):\\n return np.array([[1,0,0,trans[0]],[0,1,0,trans[1]],[0,0,1,trans[2]],[0,0,0,1]])\",\n \"def softmax(x):\\n sf = np.exp(x)\\n sf = sf / np.sum(sf, axis=0)\\n return sf\",\n \"def softmax(x):\\n sf = np.exp(x)\\n sf = sf / np.sum(sf, axis=0)\\n return sf\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.83168423","0.6524651","0.6448697","0.6208682","0.6185584","0.60859853","0.60077345","0.6005692","0.6002416","0.585732","0.5842322","0.57764435","0.5741281","0.5718799","0.57156426","0.57109356","0.5676722","0.56560165","0.5641633","0.56377053","0.56344235","0.5631245","0.56272626","0.56263775","0.5614629","0.55554545","0.5553882","0.5552548","0.55399966","0.55360585","0.5484923","0.5471292","0.54649603","0.5416288","0.5415288","0.5394227","0.5341808","0.533065","0.5316358","0.5300863","0.5298905","0.52985513","0.52416044","0.5240841","0.5236045","0.52254987","0.5216815","0.52064705","0.52002156","0.51893497","0.5172894","0.5152543","0.51522845","0.51458055","0.5132458","0.5124474","0.51092124","0.51092124","0.5107944","0.51038617","0.50998217","0.50988835","0.5096238","0.5093721","0.5076671","0.50761884","0.5075886","0.50730413","0.5069762","0.50534934","0.5047299","0.50407267","0.5039684","0.503877","0.50288457","0.502724","0.5020765","0.50171196","0.5015357","0.5012609","0.50116163","0.5007974","0.50024426","0.4995894","0.4971688","0.49713078","0.49635506","0.49580705","0.4956665","0.4940032","0.49307853","0.4921124","0.4918872","0.49187765","0.49152216","0.4911592","0.49082","0.49071148","0.49047735","0.49047735"],"string":"[\n \"0.83168423\",\n \"0.6524651\",\n \"0.6448697\",\n \"0.6208682\",\n \"0.6185584\",\n \"0.60859853\",\n \"0.60077345\",\n \"0.6005692\",\n \"0.6002416\",\n \"0.585732\",\n \"0.5842322\",\n \"0.57764435\",\n \"0.5741281\",\n \"0.5718799\",\n \"0.57156426\",\n \"0.57109356\",\n \"0.5676722\",\n \"0.56560165\",\n \"0.5641633\",\n \"0.56377053\",\n \"0.56344235\",\n \"0.5631245\",\n \"0.56272626\",\n \"0.56263775\",\n \"0.5614629\",\n \"0.55554545\",\n \"0.5553882\",\n \"0.5552548\",\n \"0.55399966\",\n \"0.55360585\",\n \"0.5484923\",\n \"0.5471292\",\n \"0.54649603\",\n \"0.5416288\",\n \"0.5415288\",\n \"0.5394227\",\n \"0.5341808\",\n \"0.533065\",\n \"0.5316358\",\n \"0.5300863\",\n \"0.5298905\",\n \"0.52985513\",\n \"0.52416044\",\n \"0.5240841\",\n \"0.5236045\",\n \"0.52254987\",\n \"0.5216815\",\n \"0.52064705\",\n \"0.52002156\",\n \"0.51893497\",\n \"0.5172894\",\n \"0.5152543\",\n \"0.51522845\",\n \"0.51458055\",\n \"0.5132458\",\n \"0.5124474\",\n \"0.51092124\",\n \"0.51092124\",\n \"0.5107944\",\n \"0.51038617\",\n \"0.50998217\",\n \"0.50988835\",\n \"0.5096238\",\n \"0.5093721\",\n \"0.5076671\",\n \"0.50761884\",\n \"0.5075886\",\n \"0.50730413\",\n \"0.5069762\",\n \"0.50534934\",\n \"0.5047299\",\n \"0.50407267\",\n \"0.5039684\",\n \"0.503877\",\n \"0.50288457\",\n \"0.502724\",\n \"0.5020765\",\n \"0.50171196\",\n \"0.5015357\",\n \"0.5012609\",\n \"0.50116163\",\n \"0.5007974\",\n \"0.50024426\",\n \"0.4995894\",\n \"0.4971688\",\n \"0.49713078\",\n \"0.49635506\",\n \"0.49580705\",\n \"0.4956665\",\n \"0.4940032\",\n \"0.49307853\",\n \"0.4921124\",\n \"0.4918872\",\n \"0.49187765\",\n \"0.49152216\",\n \"0.4911592\",\n \"0.49082\",\n \"0.49071148\",\n \"0.49047735\",\n \"0.49047735\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77893174"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":2999,"cells":{"query":{"kind":"string","value":"Softmax loss function, naive implementation (with loops) Inputs have dimension D, there are C classes, and we operate on minibatches of N examples."},"document":{"kind":"string","value":"def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n num_train = X.shape[0]\n num_classes = W.shape[1]\n\n # Calculate loss for each example\n f = np.zeros((num_train, num_classes))\n f_max = np.zeros((num_train, 1))\n for i in xrange(num_train):\n for j in xrange(num_classes):\n f[i, j] = np.dot(X[i, :], W[:, j])\n if f[i, j] > f_max[i]:\n f_max[i] = f[i, j]\n\n exp_f = np.zeros_like(f)\n sum_exp_f = np.zeros((num_train, 1))\n for i in xrange(num_train):\n for j in xrange(num_classes):\n f[i, j] -= f_max[i]\n exp_f[i, j] = math.exp(f[i, j])\n sum_exp_f[i] += exp_f[i, j]\n\n for i in xrange(num_train):\n loss += -math.log(exp_f[i, y[i]] / sum_exp_f[i])\n\n loss /= num_train\n\n # Calculate regularization term\n reg_term = 0.0\n for i in xrange(W.shape[0]):\n for j in xrange(W.shape[1]):\n reg_term += W[i, j]**2\n\n loss += reg * reg_term\n\n # Calculate gradient\n P = np.zeros((num_train, num_classes))\n for i in xrange(num_train):\n for j in xrange(num_classes):\n P[i, j] = exp_f[i, j] / sum_exp_f[i]\n P[i, y[i]] -= 1\n\n for i in xrange(dW.shape[0]):\n for j in xrange(dW.shape[1]):\n dW[i, j] = 1 / num_train * np.dot(X[:, i].T, P[:, j])\n \n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW"},"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 softmax_classifier(W, input, label, lamda):\n\n ############################################################################\n # TODO: Put your code here\n\n loss = 0.0\n num_train = input.shape[0]\n num_classes = W.shape[1]\n\n score = np.dot(input, W) # (N,C)\n prediction = np.argmax(score, axis=1)\n score -= np.max(score, axis=1, keepdims=True)\n\n # # cross entropy loss\n # # take exponent of the score and normalized with sum of all exponents.\n probs = np.exp(score) # (N,C)\n e_y = np.sum(np.multiply(probs,label), axis=1) # (N,) probability for correct class\n e_sum = np.sum(probs, axis=1) # (N,) sum of probability over all classes\n\n # implementation of loss equivalent l_i = -f_y_i + log sum_j(e^(f_j))\n # loss = np.sum(-np.log(e_y/e_sum)) # sum of -log across all samples.\n # loss /= num_train # average loss\n loss = np.sum(-1 * e_y) + np.sum(np.log(e_sum))\n loss /= num_train\n\n loss += lamda * np.sum(W * W) # regularization \n\n # Gradient\n delta_score = probs / e_sum.reshape(num_train,1) # (N,C)\n delta_score -= label # (NxC)\n gradient = np.dot(input.T, delta_score)\n gradient /= num_train\n gradient += lamda * 2 * W\n\n ############################################################################\n\n return loss, gradient, prediction","def softmax(x):\n # (n_samples, n_classes)\n if len(x.shape) == 2:\n row_max = np.max(x, axis=1)\n x -= row_max.reshape((x.shape[0], 1))\n x = np.exp(x)\n row_sum = np.sum(x, axis=1)\n x /= row_sum.reshape((x.shape[0], 1))\n # (n_samples, n_tasks, n_classes)\n elif len(x.shape) == 3:\n row_max = np.max(x, axis=2)\n x -= row_max.reshape(x.shape[:2] + (1,))\n x = np.exp(x)\n row_sum = np.sum(x, axis=2)\n x /= row_sum.reshape(x.shape[:2] + (1,))\n return x","def softmax_loss_naive(W, X, y, reg):\r\n # Initialize the loss and gradient to zero.\r\n loss = 0.0\r\n dW = np.zeros_like(W)\r\n num_train = X.shape[1] # d*n\r\n num_class = W.shape[0]\r\n\r\n #############################################################################\r\n # Compute the softmax loss and its gradient using explicit loops. #\r\n # Store the loss in loss and the gradient in dW. If you are not careful #\r\n # here, it is easy to run into numeric instability. Don't forget the #\r\n # regularization! #\r\n #############################################################################\r\n loss = 0.0\r\n for i in range(num_train):\r\n X_i = X[:,i] # D*1\r\n score_i = W.dot(X_i)\r\n score_i -= np.max(score_i) #C*1 but keepdims = false so it becomes 1*C\r\n exp_score_i = np.exp(score_i)\r\n probs_i = exp_score_i/np.sum(exp_score_i) #1*C\r\n correct_logprobs_i = -np.log(probs_i[y[i]])\r\n loss += correct_logprobs_i\r\n \r\n dscore_i = probs_i.reshape(num_class,-1)#c*1\r\n dscore_i[y[i]] -= 1 #C*1\r\n X_i = X_i.reshape(1,-1)# 1*D\r\n dW += dscore_i.dot(X_i)\r\n \r\n loss /= num_train\r\n loss += 0.5*reg*np.sum(W*W)\r\n\r\n dW /= num_train\r\n dW += reg*W\r\n \r\n return loss, dW","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n num_train = X.shape[0]\n # print(\"num_train:\", num_train)\n num_classes = W.shape[1]\n # print(\"num_classes:\", num_classes)\n \n for i in range(num_train):\n scores = X[i].dot(W) # scores is 1 * C\n correct_class = y[i]\n \n # LOSS DUE TO TRAINING SAMPLE = -log(exp^correct_score / sum(exp^all_other_scores))\n log_c = np.max(scores)\n scores -= log_c\n correct_class_score = scores[correct_class]\n exp_scores = np.exp(scores)\n sum_exp_scores = np.sum(np.exp(scores))\n proportion = np.exp(correct_class_score) / sum_exp_scores\n loss -= np.log(proportion)\n # print(proportion)\n \n # ALTERNATIVELY: (we split the log)\n# loss -= scores[y[i]]\n# loss += np.log(np.sum(np.exp(X[i].dot(W))))\n \n # UPDATE GRADIENT\n for j in range(num_classes):\n p = np.exp(scores[j]) / sum_exp_scores # \"probability\" of class j\n dW[:,j] += (p - (j == y[i])) * X[i,:]\n # dW is D by C\n\n loss /= num_train\n loss += reg * np.sum(W * W) \n dW /= num_train\n dW += reg * 2 * W\n\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n \n return loss, dW","def softmax_loss(x, y):\n # softmax\n num = np.exp(x)\n den = np.sum(num, axis=1)\n softmax = num/den[:, None]\n N = x.shape[0]\n\n # compute the los per class\n loss = softmax[np.arange(N), y]\n loss = -np.log(loss)\n\n # sum all the losses and divide by number of class\n # Also add the regularization loss term\n loss = np.sum(loss)/N \n \n dscores = softmax\n dscores[np.arange(N), y] -= 1\n dscores /= N\n\n return loss, dscores","def SoftmaxLayer(inputs, n_classes):\n\n l = Conv2DLayer(inputs, n_classes, filter_size=1, nonlinearity=linear, W=HeUniform(gain='relu'), pad='same',\n flip_filters=False, stride=1)\n\n # We perform the softmax nonlinearity in 2 steps :\n # 1. Reshape from (batch_size, n_classes, n_rows, n_cols) to (batch_size * n_rows * n_cols, n_classes)\n # 2. Apply softmax\n\n l = DimshuffleLayer(l, (0, 2, 3, 1))\n batch_size, n_rows, n_cols, _ = get_output(l).shape\n l = ReshapeLayer(l, (batch_size * n_rows * n_cols, n_classes))\n l = NonlinearityLayer(l, softmax)\n\n return l\n\n # Note : we also tried to apply deep supervision using intermediate outputs at lower resolutions but didn't see\n # any improvements. Our guess is that FC-DenseNet naturally permits this multiscale approach","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n num_train = X.shape[0]\n num_classe = W.shape[1]\n loss = 0.0\n\n for i in range(num_train): #pour chaque image de l'ensemble d'entrainement\n scores = X[i].dot(W)\n scores -= max(scores)\n\n correct_class_score = scores[y[i]] #y[i]=c\n e_syi = np.exp(correct_class_score)\n e_sj = np.sum(np.exp(scores))\n\n loss -= np.log(e_syi/e_sj)\n\n for k in range(num_classe): #pour chaque classe\n dW[:, k] += ((np.exp(scores[k])/e_sj) - (k == y[i])) * X[i].T\n\n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n dW/= num_train\n\n # Add regularization to the loss.\n loss += reg * np.sum(W * W)\n dW += 2 * reg * W\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n num_train=X.shape[0]\n num_class=W.shape[1]\n num_feature=X.shape[1]\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n for i in range(num_train):\n #W*Xi C*1\n x=np.exp(np.dot(W.T,X[i,:]))\n denominator=np.sum(x)\n numerator=x[y[i]]\n loss-=np.log(numerator/denominator)\n #numerator and denominator\n #for j in range(num_class):\n normalize_score=x/denominator\n nm=np.reshape(normalize_score, (num_class, 1))\n \n #CxD\n dscore=nm.dot(np.reshape(X[i,:],(1,num_feature)))\n #print(dscore.shape)\n\n dscore[y[i],:]-=X[i,:]\n dW+=dscore.T\n\n loss/=num_train\n dW = dW/num_train + reg*W\n #\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n (num_class, D), (D, num_train) = (W.shape, X.shape)\n class_scores = np.dot(W, X)\n \n # Subtract maximum unnormalized score from each set of class scores\n for i in range(num_train):\n max_class_score = np.max(class_scores[:, i])\n for j in range(num_class):\n class_scores[j, i] -= max_class_score\n \n # Compute softmax and update gradient\n for i in range(num_train):\n normalization_term = sum(np.exp(class_score) for class_score in class_scores[:, i])\n for j in range(num_class):\n class_scores[j, i] = np.exp(class_scores[j, i]) / normalization_term\n # Thanks again to MyHumbleSelf for making me examine this further and discover a bug in my derivation of the softmax gradient!\n dW[j] += (class_scores[j, i] - (j==y[i])) * X[:, i]\n \n # Compute cross-entropy errors and total loss from that\n losses = [np.log(class_scores[y[i], i]) for i in range(num_train)]\n loss = -sum(losses) / num_train\n\n # Add regularization to loss and normalize dW\n loss += 0.5 * reg * np.sum(W * W)\n dW /= num_train\n dW += reg * W\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n # softmax P(Y=k|X=x_i) = e^{s_k}/∑e^{s_j} softmax loss = -log(softmax)\n num_train = X.shape[0]\n num_class = W.shape[1]\n for i in range(num_train):\n scores = X[i].dot(W) # get scores\n max_score = np.max(scores)\n scores -= max_score # 考虑数值计算稳定性 softmax = (e^s_c - max)/∑(e^s_j - max)\n correct_score = scores[y[i]] # score_correct\n P_ic = np.exp(correct_score)/np.sum(np.exp(scores))\n loss += -np.log(P_ic)\n for j in range(num_class):\n if j == y[i]:\n dW[:, j] += (P_ic - 1) * X[i].T\n else:\n P_ij = np.exp(scores[j])/np.sum(np.exp(scores))\n dW[:, j] += P_ij * X[i].T\n \n \n loss /= num_train\n loss += reg*np.sum(W*W)\n dW /= num_train\n dW += 2 * reg * W\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n return loss, dW","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n num_train = X.shape[0]\n num_class = W.shape[1]\n #scores = np.zeros(num_train,num_class)\n scores = X.dot(W)\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n for i in range(num_train):\n # compute Li\n fmax= np.max(scores[i])\n scores[i] -= fmax\n correct_class_score = scores[i,y[i]]\n M = np.exp(correct_class_score)/np.sum(np.exp(scores[i]))\n loss += -np.log(M)\n for j in range(num_class):\n N = np.exp(scores[i,j])/np.sum(np.exp(scores[i]))\n if j ==y[i]:\n dW[:,y[i]]+= (M-1)*X[i].T\n else:\n dW[:,j] += N*X[i].T \n loss /= num_train\n loss += reg*np.sum(W*W)\n dW /= num_train \n dW += 2*reg*W \n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","def softmax(inputs):\n probs = np.exp(inputs)\n # print(probs.shape)\n # t = np.sum(probs, axis=0)\n # print(t.shape)\n\n probs /= np.sum(probs, axis=0)[np.newaxis,:]\n return probs","def softmax(self, scores):\n\n\n # for each sample, for each class ,caclulate\n # np.exp(scores) : still (n_samples, n_classes)\n\n # axis = 1\n # a00, a01, a02 as a sinlge one to perfrom np_sum\n # which is the same sample \n # sum_exp : still (n_samples, 1)\n\n # softmax = (n_samples, n_classes) / (n_samples, 1) = (n_samples, n_classes) \n\n sum_exp = np.sum(np.exp(scores), axis=1, keepdims=True)\n softmax = np.exp(scores) / sum_exp\n \n return softmax","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n # print \"dW's shape\", dW.shape\n # compute the loss and the gradient\n num_classes = W.shape[1]\n num_train = X.shape[0]\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax.ipynb loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n # For every training image\n for train_image in xrange(num_train):\n # Multiply the weights by the image to get the scores\n scores = X[train_image].dot(W)\n # print(scores)\n # And then get the correct score\n correct_label = y[train_image]\n correct_score = scores[correct_label]\n # TODO: Right up to here\n # And then get the score of every other classifier\n all_scores = np.sum(scores)\n # Add a normalizing factor for numeric stability\n normalizing_constant = np.max(scores)\n scores -= normalizing_constant\n correct_score -= normalizing_constant\n #Calculating the softmax values\n softmax = np.exp(correct_score)/np.sum(np.exp(scores))\n\n # print(\"Correct score softmax\",softmax)\n\n # And calculating the loss\n loss += -1*np.log(softmax)\n # print loss\n #TODO: Loss computation is also correct\n\n # And calculating the gradient\n\n # First, update the Weight matrix with the correct example's derivative\n dW[:,correct_label] += (softmax-1)*np.transpose(X[train_image])\n\n # Then do the same for the wrong cases\n incorrect_labels = [x for x in xrange(num_classes) if x != correct_label]\n # Now, update the weights\n for label_index in incorrect_labels:\n #Calculating the softmax for a wrong label\n incorrect_label_softmax = np.exp(scores[label_index])/(np.sum(np.exp(scores)))\n # Calculating the derivative\n necessary_weight = incorrect_label_softmax*np.transpose(X[train_image])\n # Updating the weights\n dW[:,label_index] += necessary_weight\n\n\n # Divide the loss\n loss /= num_train\n dW /= num_train\n\n # Now, do regularization\n loss += 0.5*reg*np.sum(W*W)# Penalize big weights\n dW += reg*W\n\n\n\n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n num_train = X.shape[1]\n num_classes = W.shape[0]\n #############################################################################\n # Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n for i in range(num_train): # for each image\n # compute the score\n scores = W.dot(X[:, i])\n\n # shift the values of f so that the highest number is 0:\n scores -= np.max(scores)\n\n # compute the loss\n loss += -np.log(np.exp(scores[y[i]]) / np.sum(np.exp(scores)))\n\n # gradient(https://github.com/seyedamo/cs231n/blob/master/assignment1/cs231n/classifiers/softmax.py)\n scores = np.exp(scores)\n scores /= np.sum(scores)\n for j in range(num_classes): # for each class\n dW[j, :] += scores[j] * X[:, i].T\n\n # dW wrt correct class scores w_yi\n dW[y[i], :] += -X[:, i].T\n\n # Average the loss \n loss /= num_train\n\n # Add regularization to the loss.\n loss += 0.5 * reg * np.sum(W * W)\n\n # average of the gradient\n dW /= num_train\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n return loss, dW","def softmax_loss(x, y):\n N, C = x.shape\n loss, dx = 0, np.zeros(x.shape) \n for i in range(N):\n loss += -np.log(np.exp(x[i,y[i]])/np.sum(np.exp(x[i,:])))\n dx[i,:] = np.exp(x[i,:])/np.sum(np.exp(x[i,:]))\n dx[i,y[i]] += (-1)\n \n loss /= N\n dx /= N\n return loss, dx","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n num_classes = W.shape[1]\n #print('num_classes = ', num_classes)\n num_train = X.shape[0]\n #print('num_train = ', num_train)\n \n min_score = 0.0\n shifted_scores = np.zeros(W.shape[1])\n #max_score = np.zeros(W.shape[1])\n max_score = 0.0\n \n loss_array = np.zeros(y.shape[0])\n for i in range(num_train):\n scores = X[i].dot(W)\n #print('scores dimensions = ', scores.shape)\n #print('scores = ', scores)\n #print('i =', i, 'y = ', y[i])\n min_score = np.min(scores)\n max_score = np.max(scores)\n #print(min_score,max_score)\n shifted_scores = np.multiply(-1,scores + abs(min_score))\n #print(scores)\n #print(shifted_scores)\n exp_scores = np.exp(shifted_scores)\n norm = np.amax(exp_scores)\n norm_scores = np.divide(exp_scores,norm)\n loss_array[i] = np.multiply(-1,np.log(norm_scores[y[i]]/(np.sum(norm_scores)-norm_scores[y[i]])))\n #print(loss_array)\n for j in range(num_classes): \n\t\n if j == y[i]: \n dW[:,j] = np.multiply(norm_scores[y[i]],1-norm_scores[y[i]])\n else:\n dW[:,j] = np.multiply(-1,np.multiply(norm_scores[y[i]],norm_scores[y[j]]))\n\t\t\t\n\t\t\t\n loss = np.amax(loss_array)\n\n # Add regularization to the loss.\n loss = 0.5 * reg * np.sum(W * W) + loss\n \n \n pass\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","def softmax(self, X):\n\n n_samples, _ = X.shape\n\n X_ = np.empty((n_samples, self.n_classes_))\n old_ind = 0\n for i, n in enumerate(self.n_weights_per_class):\n new_ind = int(old_ind + n + 1)\n X_[:, i] = np.dot(X[:, old_ind: new_ind], self.weights_[old_ind: new_ind])\n old_ind = new_ind\n\n exp_X = np.exp(X_ - np.max(X_, axis=1).reshape(-1, 1))\n softmax = exp_X / (np.sum(exp_X, axis=1)).reshape((n_samples, 1))\n\n return softmax","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n pass\n num_tran = X.shape[0]\n num_classes = W.shape[1]\n loss_par =np.zeros(num_tran)\n\n Score = np.dot(X,W)\n expS = np.exp(Score)\n # for i in num_tran:\n sumS = np.sum(expS,axis=1)\n sumS = sumS.reshape(sumS.shape[0],1)\n normalize = np.divide(expS,sumS)\n softmax = -np.log(normalize)\n\n for i in np.arange(num_tran):\n loss_par[i]=softmax[i, y[i]]\n for j in np.arange(num_classes) :\n if j!=y[i]:\n # dW[:,j]+=1/normalize[i,y[i]]*expS[i,y[i]]*expS[i,j]/np.power(sumS[i],2) *X[i,:]\n dW[:,j]+=expS[i,j]/sumS[i] *X[i,:]\n else:\n # dW[:,y[i]]+=-1/normalize[i,y[i]]*expS[i,y[i]]*(sumS[i]-expS[i,y[i]])/np.power(sumS[i],2) *X[i,:]\n dW[:,y[i]]+=-(sumS[i]-expS[i,y[i]])/sumS[i] *X[i,:]\n\n dW /=num_tran\n\n loss = np.sum(loss_par) / num_tran\n # print num_tran,loss\n\n dW+=reg*W\n loss+=0.5*reg*np.sum(W*W)\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","def softmax_loss(x, y):\n\n eps = 1e-5\n \n N,C = x.shape\n p = softmax(x)\n llikelihood = -np.log(p[range(N),y] + eps)\n# print(llikelihood)\n loss = np.sum(llikelihood) / N\n\n dx = p\n dx[range(N),y] -= 1\n dx = dx/N\n \n return loss, dx","def softmax_loss_vectorized(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using no explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n num_train = X.shape[0]\n # print(\"num_train:\", num_train)\n num_classes = W.shape[1]\n # print(\"num_classes:\", num_classes)\n \n scores = X.dot(W) # scores is N*D x D*C -> N*C \n log_c = np.max(scores, axis=1).T\n scores -= log_c[:,None]\n correct_class_score = scores[np.arange(num_train),y]\n exp_scores = np.exp(scores)\n sum_exp_scores = np.sum(np.exp(scores), axis=1)\n proportion = np.exp(correct_class_score) / sum_exp_scores\n loss -= np.sum(np.log(proportion))\n \n # calculating dW = (p - (c = correct c ? 1 : 0)) * x\n correct_class_one_hot = np.zeros_like(scores)\n correct_class_one_hot[np.arange(num_train),y] += 1\n p = np.exp(scores) / sum_exp_scores[:,None] - correct_class_one_hot # N*C / N:1 -> N*C\n dW += X.T.dot(p) # D*N x N*C -> D*C\n\n loss /= num_train\n loss += 0.5 * reg * np.sum(W * W) \n dW /= num_train\n dW += reg * W\n\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n return loss, dW","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n num_classes = W.shape[1]\n num_train = X.shape[0]\n\n for i in xrange(num_train):\n scores = X[i].dot(W)\n\n # Normalization trick to resolve numerical instability\n # when dealing with the large exponential terms.\n scores -= np.max(scores)\n\n # Cache some terms that are used repeatedly.\n exp_scores = np.exp(scores)\n sum_exp_scores = np.sum(exp_scores)\n correct_class_score = scores[y[i]]\n \n # Update the loss \n loss -= correct_class_score\n loss += np.log(sum_exp_scores)\n\n # Update the gradient\n dW[:,y[i]] -= X[i,:].T\n for j in xrange(num_classes):\n dW[:,j] += ((X[i,:].T * exp_scores[j]) / sum_exp_scores)\n\n \n # Right now the loss is a sum over all training examples, but we want it\n # to be an average instead so we divide by num_train.\n loss /= num_train\n\n dW /= num_train\n\n # Add regularization to the loss.\n loss += 0.5 * reg * np.sum(W * W)\n\n dW += reg*W\n \n pass\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","def softmax(x):\r\n sum_c = np.sum(np.exp(x), axis=1)\r\n sum_c = np.expand_dims(sum_c, axis=1)\r\n pred_x = np.divide(np.exp(x), sum_c)\r\n return pred_x","def test_softmax_base():\n x = np.array([[[2.0, 3.0, 4.0, 5.0],\n [3.0, 4.0, 5.0, 6.0],\n [7.0, 8.0, 8.0, 9.0]],\n [[1.0, 2.0, 3.0, 4.0],\n [5.0, 6.0, 7.0, 8.0],\n [6.0, 7.0, 8.0, 9.0]]])\n res = np.array([[[0.0320586, 0.08714432, 0.23688282, 0.64391426],\n [0.0320586, 0.08714432, 0.23688282, 0.64391426],\n [0.07232949, 0.19661193, 0.19661193, 0.53444665]],\n [[0.0320586, 0.08714432, 0.23688282, 0.64391426],\n [0.0320586, 0.08714432, 0.23688282, 0.64391426],\n [0.0320586, 0.08714432, 0.23688282, 0.64391426]]])\n obj.run(res=res, input=x)","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n num_cases = X.shape[0]\n num_class = W.shape[1]\n y_label = np.zeros((num_cases,num_class))\n for i in range(num_cases):\n h1 = np.exp(X[i].dot(W))\n h = h1/np.sum(h1)\n y_label[i] = (np.arange(h.shape[0]) == y[i]) + 0\n loss -= (np.sum(y_label[i] * np.log(h) + (1 - y_label[i]) * np.log(1 - h)))\n delta = np.zeros(W.shape)\n for j in range(num_class):\n delta[:,j] += X[i]\n delta[:,j] *= h1[j]\n delta[:,j] *= (np.sum(h1) - h1[j])/(np.sum(h1) ** 2)\n delta[:,j] = y_label[i][j] / h[j] * delta[:,j] - (1 - y_label[i][j]) / (1 - h[j]) * delta[:,j]\n dW -= delta\n loss /= num_cases\n loss += reg * np.sum(W * W)\n dW /= num_cases\n dW += 2 * reg * W\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n num_train = X.shape[0]\n num_class = W.shape[1]\n l = np.zeros([num_train,1])\n for i in range(num_train):\n scores = np.dot(X[i], W)\n f_yi = scores[y[i]]\n exp_num = np.exp(f_yi)\n exp = np.exp(scores)\n exp_deno = np.sum(exp)\n for j in range(num_class):\n if (j == y[i]):\n dW[:,j] -= X[i,:].transpose()\n dW[:,j] += (np.exp(scores[j]) / exp_deno) * X[i,:].transpose()\n l[i] = -np.log(exp_num/exp_deno)\n\n loss = np.sum(l)/num_train\n loss += reg * np.sum(W*W)\n dW /= num_train \n dW += 2 * reg * W\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n return loss, dW","def softmax(input, dim, inplace=False):\n return FunctionLib.apply(\n 'Softmax', input.device, [input],\n outputs=[input if inplace else None], axis=dim)","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n for i in range(X.shape[0]):\n# c = np.matmul(X[i],W)\n# c -= np.amax(c)\n# e_c = np.exp(c)\n# denom = np.sum(e_c)\n# #Nice fact: we know that the largest element in c will also be the largest softmax value, so we only\n# # need to transform that one value. \n# sm_c = e_c/denom\n# \n# loss1 += -np.log(sm_c[y[i]])\n\n # Need to make this whole dang thing more numerically stable. \n c = np.matmul(X[i],W)\n c -= np.amax(c)\n e_c = np.exp(c)\n denom = np.sum(e_c)\n sm_c = e_c/denom\n\n loss += np.log(denom) - c[y[i]]\n# print(-np.log(sm_c[y[i]]) - (np.log(denom)-c[y[i]]))\n\n \"\"\"They are basically the same value\"\"\"\n\n # now computing some gradients\n dL_ds = sm_c\n dL_ds[y[i]] -= 1\n #note that sm_c is modified now!\n \"\"\" #ah, something fundamentally different is happening with numpy. When an array element\n is changed, it's really changed for good. And it changes for all pointers pointing to same object.\n yikes. Actually it's the same with python lists. Anything pointing to And underlying object can\n change that underlying object for all things that point to it. Alas.\"\"\"\n# import pdb; pdb.set_trace()\n \"\"\"Okay I just coudln't bear the for loops...\"\"\"\n dW_update = np.matmul(X[i].reshape(1,X.shape[1]).T,dL_ds[np.newaxis,:])\n dW+=dW_update\n # for n in range(W.shape[0]):\n# for m in range(W.shape[1]):\n# if m == y[i]:\n# dW[n,m] += X[i,n]*(sm_c[m]-e_c[m])\n# else:\n# dW[n,m] += X[i,n]*sm_c[m]\n\n # should be numerically unstable I think.\n\n loss /= X.shape[0]\n loss += reg*np.sum(W*W)\n\n dW /= X.shape[0]\n dW += reg*2*W\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n return loss, dW","def softmax_loss_vectorized(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n num_train = X.shape[0]\n dim = dW.shape[0]\n num_classe = W.shape[1]\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using no explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n S = X.dot(W)\n # ajouter le - max a la fin\n indexes=np.arange(num_train)\n #c = correct class score\n c = S[indexes, y]\n\n e_syi = np.exp(c)\n e_sj = np.sum(np.exp(S), axis=1)\n Li = - np.log(e_syi/e_sj)\n loss = np.sum(Li) / num_train + reg * np.sum(W * W)\n\n\n M = np.exp(S)/(np.repeat(e_sj, num_classe).reshape(num_train, num_classe)) #(500,10)\n M[indexes, y] -= 1 #bonnes classes\n dW = X.T.dot(M)\n\n dW = dW/num_train + 2 * reg * W\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","def softmax(x):\n shape = x.shape\n probs = np.exp(x - np.max(x, axis=len(shape) - 1, keepdims=True))\n probs /= np.sum(probs, axis=len(shape) - 1, keepdims=True)\n return probs","def softmax_my(x):\n dim = 1\n try:\n dim = x.ndim\n except AttributeError:\n return cal_1D_softmax(x)\n if dim == 1:\n return cal_1D_softmax(x)\n elif dim == 2:\n return cal_2D_softmax(x)","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n num_train = X.shape[0]\n num_classes = W.shape[1]\n for i in xrange(num_train):\n scores = X[i, :].dot(W)\n scores -= np.max(scores)\n correct_scores = scores[y[i]]\n score_sum = np.sum(np.exp(scores))\n h = np.exp(correct_scores) / score_sum\n loss += -np.log(h)\n for j in xrange(num_classes):\n if j == y[i]:\n dW[:, y[i]] += (np.exp(scores[j]) / score_sum - 1) * X[i, :]\n else:\n dW[:, j] += (np.exp(scores[j]) / score_sum) * X[i, :]\n \n \n loss /= num_train + ( reg * np.sum(W * W))\n dW /= num_train\n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","def softmax(inputs):\n # Your code pass\n # 对向量的每个元素归一化，先把每个元素作为e的指数求结果\n input_exp = np.exp(inputs)\n # 返回 每个元素作为e的指数求结果 与所在列所有的这些结果和的比值，另外添加一个维度\n return input_exp / np.sum(input_exp, axis=1)[:, np.newaxis]","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n num_train = X.shape[0]\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n scores = X.dot(W)\n scores_exp = np.exp(scores-np.max(scores, axis=1, keepdims=True))\n\n sum = np.sum(scores_exp, axis=1, keepdims=True)\n probability = scores_exp/sum\n #list containing the correct classification\n indices = [range(num_train), y]\n correct_class_score = probability[indices]\n\n #calculate -log(prob_y) and take the sum across all training examples\n loss = np.sum(-np.log(correct_class_score))\n loss /= num_train\n loss += 0.5 * reg * np.sum(W * W)\n\n #Compute Gradient\n probability[indices] -=1\n dW = X.T.dot(probability)\n dW /= num_train\n dW += .5 * reg * W\n\n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","def _softmax(x):\n e = K.exp(x - K.max(x, axis=-1, keepdims=True))\n s = K.sum(e, axis=-1, keepdims=True)\n return e / s","def softmax_loss1(x, y):\n # tmp = np.max(x, axis=1, keepdims=True)\n shifted_logits = x - np.max(x, axis=1, keepdims=True)\n Z = np.sum(np.exp(shifted_logits), axis=1, keepdims=True)\n log_probs = shifted_logits - np.log(Z)\n probs = np.exp(log_probs)\n N = x.shape[0]\n # tmp2 = np.arange(N)\n tmp3 = log_probs[np.arange(N), y]\n # tmp4 = log_probs[[0,1,2],[2,5,0]]\n loss = -np.sum(log_probs[np.arange(N), y]) / N\n dx = probs.copy()\n dx[np.arange(N), y] -= 1\n dx /= N\n return loss, dx","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n num_train = X.shape[0]\n num_classes = W.shape[1]\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n scores = np.dot(X,W)\n scores = (scores.T - np.max(scores,1)).T\n for i in xrange(num_train):\n nominator = np.exp(scores[i,:])\n denominator = np.sum(np.exp(scores[i,:]))\n loss -= np.log(nominator[y[i]]/denominator)\n for j in xrange(num_classes):\n dW[:,j] += (nominator[j]/denominator)*X[i,:]\n dW[:,y[i]] -= X[i,:]\n\n loss /= num_train\n dW /= num_train\n loss += 0.5*reg*np.sum(W*W)\n dW += reg*W\n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","def softmax(input):\n list_value = []\n len_compute = input.shape[-1]\n shape_input = input.shape\n for x in input.reshape(-1, len_compute):\n # print(x)\n e_x = np.exp(x - np.max(x))\n res = e_x / e_x.sum(axis=0)\n list_value.append(res)\n\n return np.array(list_value).reshape(shape_input)","def softmax(inputs):\n return np.exp(inputs) / float(sum(np.exp(inputs)))","def softmax(inputs):\n return np.exp(inputs) / float(sum(np.exp(inputs)))","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=1, keepdims=True)","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n num_classes = W.shape[1]\n num_train = X.shape[0]\n for i in range(num_train):\n score = X[i].dot(W)\n exp_score = np.exp(score)\n probability = exp_score[y[i]] / exp_score.sum()\n loss += -np.log(probability)\n dp = -1 / probability\n for j in range(num_classes):\n ds = np.exp(score[j])\n if j == y[i]:\n des = (exp_score.sum() - exp_score[y[i]]) / np.square(exp_score.sum())\n else:\n des = -(exp_score[y[i]]) / np.square(exp_score.sum())\n dW[:, j] += X[i].T * ds * des * dp # chain rule\n\n loss /= num_train\n dW /= num_train\n\n loss += 0.5 * reg * np.sum(W * W)\n dW += reg * W\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","def softmax4(x):\n ndim = K.ndim(x)\n if ndim == 2:\n return K.softmax(x)\n elif ndim == 3:\n e = K.exp(x - K.max(x, axis=-1, keepdims=True))\n s = K.sum(e, axis=-1, keepdims=True)\n return e / s\n elif ndim == 4:\n e = K.exp(x - K.max(x, axis=1, keepdims=True))\n s = K.sum(e, axis=1, keepdims=True)\n return e / s\n else:\n raise Exception('Cannot apply softmax to a tensor that is not 2D or 3D. ' +\n 'Here, ndim=' + str(ndim))","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n # needed for calculations\n num_train = X.shape[1]\n\n for i in xrange(num_train):\n # calculate the scores for the current training example with the current weights\n scores = W.dot(X[:, i])\n # scale by the max for numerical stability\n scores -= np.max(scores)\n # calculate the loss\n loss += -scores[y[i]] + np.log(np.sum(np.exp(scores)))\n\n ## L' = -1_y + 1/(\\sum_{}^{} e^f) * e^f\n # e^f\n scores = np.exp(scores)\n # 1/(\\sum_{}^{} e^f)\n scores /= np.sum(scores)\n # -1_y\n scores[y[i]] -= 1\n\n # now scale it by the data\n # we need to use [:, np.newaxis] because when you make a X by 1 dimension slices in numpy the 1 dimension is null\n dW += scores[:, np.newaxis].dot(X[:, i][:, np.newaxis].T)\n\n\n # get the average loss\n loss /= num_train\n # get the average gradient\n dW /= num_train\n\n # regularize the loss function\n loss += 0.5 * reg * np.sum(W * W)\n\n return loss, dW","def softmax(x):\r\n e_x = np.exp(x - np.expand_dims(np.max(x, axis=-1), axis=-1))\r\n return e_x / np.expand_dims(e_x.sum(axis=-1), axis=-1) # only difference\r","def softmax_loss(x, y):\n loss, dx = None, None\n ###########################################################################\n # TODO: Implement the loss and gradient for softmax classification. This #\n # will be similar to the softmax loss vectorized implementation in #\n # cs231n/classifiers/softmax.py. #\n ###########################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n num_train = x.shape[0]\n\n x = np.exp(x)\n temp_sum = np.sum(x, axis = 1, keepdims = True)\n x = x / temp_sum\n softmax_result = x\n trans_y = np.zeros((x.shape[0],x.shape[1]))\n trans_y[np.arange(x.shape[0]), y] += 1\n x = - np.log(x)\n x = x * trans_y\n x_sum = np.sum(x)\n loss = x_sum / num_train\n loss = loss + \n\n dx = softmax_result - trans_y\n dx = dx / num_train\n\n\n pass\n\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n return loss, dx","def softmax_loss(x, y):\n #raise NotImplementedError\n #######################################################################\n # #\n # #\n # TODO: YOUR CODE HERE #\n # #\n # #\n #######################################################################\n N=x.shape[0]\n\n \n x-=np.max(x,axis=1,keepdims=True)\n temp=np.exp(x)\n dr_vec=np.sum(temp,axis=1,keepdims=True)\n\n nr=(x[np.arange(N),y]).reshape([N,1])\n loss=np.sum(-(nr)+np.log(dr_vec))\n \n loss=(loss/N)\n temp/=dr_vec\n temp[np.arange(N),y] -= 1\n \n dx = temp/N\n \n return loss, dx","def softmax_loss(x, y):\n probs = np.exp(x - np.max(x, axis=1, keepdims=True))\n probs /= np.sum(probs, axis=1, keepdims=True)\n N = x.shape[0]\n loss = -np.sum(np.log(probs[np.arange(N), y])) / N\n dx = probs.copy()\n dx[np.arange(N), y] -= 1\n dx /= N\n return loss, dx","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=1, keepdims=True)","def softmax_loss(x, y):\n ############################################################################\n # TODO: You can use the previous softmax loss function here. # \n # Hint: Be careful on overflow problem #\n ############################################################################\n ############################################################################\n # START OF YOUR CODE #\n ############################################################################\n N = len(x)\n # We want to get the real y\n log_C = -np.max(x,axis=1,keepdims = True)\n # Get numerator\n e_all = np.exp(x+log_C)\n # Get the final prob\n prob = e_all/e_all.sum(axis=1,keepdims=True)\n # Find final loss\n loss = np.sum(-np.log(prob)[np.arange(N),y])/N\n # Get dx\n dx = prob\n dx[np.arange(N),y] -= 1\n dx /= N\n \n ############################################################################\n # END OF YOUR CODE #\n ############################################################################\n return loss, dx","def softmax(x):\r\n output = np.exp(x)\r\n return output / np.sum(output, axis=1, keepdims=True)","def softmax(x):\n #pass # TODO: Compute and return softmax(x)\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n #############################################################################\n # START OF YOUR CODE #\n #############################################################################\n # construct a one-hot vector for y\n onehot_y = np.zeros((y.size, W.shape[1]))\n onehot_y[np.arange(y.size), y] = 1\n dW = dW.T\n for i in range(y.shape[0]):\n f = np.dot(X[i], W)\n \n for j in range(W.shape[1]):\n e_f = np.exp(f - np.max(f))\n softmax = e_f / e_f.sum()\n loss -= onehot_y[i][j] * np.log(softmax[j])\n dW[j] -= X[i] * (onehot_y[i][j] - softmax[j])\n \n loss = loss / y.shape[0] + reg * np.linalg.norm(W)\n dW = dW.T / y.shape[0] + 2 * reg * W\n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n \n\n return loss, dW","def loss(self, X, y):\n\n # Initialize the loss to zero.\n loss = 0.0\n num_classes = self.W.shape[0] # C = num_classes\n num_train = X.shape[0]\n \n exp_a = np.zeros((num_classes,num_train))\n # ================================================================ #\n # YOUR CODE HERE:\n # Calculate the normalized softmax loss. Store it as the variable loss.\n # (That is, calculate the sum of the losses of all the training \n # set margins, and then normalize the loss by the number of \n # training examples.)\n # ================================================================ #\n \n \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 #p[:,i] = exp_a[:,i]/np.sum(exp_a[:,i]) # p now is a valid probability matrix\n #print(p[:,i])\n\n loss += Loss \n #print(Loss,i) \n \n pass\n loss /= num_train\n # ================================================================ #\n # END YOUR CODE HERE\n # ================================================================ #\n\n return loss","def softmax(x):\n pass # TODO: Compute and return softmax(x)\n\n exp_x = np.exp(x)\n sum_x = np.sum(exp_x, axis=0)\n softmax = exp_x/sum_x\n \n return softmax","def softmax(x): \n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax_loss_naive(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n for i in range(X.shape[0]):\n scores = X[i].dot(W)\n \n idx_max = np.argmax(scores)\n s_max = scores[idx_max]\n scores -= s_max # shift for numerical stability\n \n temp = np.exp(scores)\n summation = np.sum(temp)\n loss += (- scores[y[i]] + np.log(summation))\n \n # computing gradients\n # (1) an explicit version:\n# for j in range(W.shape[1]):\n# if j == y[i]:\n# dW[:, j] -= X[i]\n# dW[:, idx_max] -= (-X[i])\n \n# dW[:, j] += (1 / summation) * temp[j] * X[i]\n# dW[:, idx_max] += (1 / summation) * temp[j] * (-X[i])\n# elif j == idx_max:\n# dW[:, j] += 0 # X[i] + (-X[i]) = 0\n# else:\n# dW[:, j] += (1 / summation) * temp[j] * X[i]\n# dW[:, idx_max] += (1 / summation) * temp[j] * (-X[i])\n \n # (2) a more concise version:\n softmax_scores = temp / summation\n for j in range(W.shape[1]):\n if j == y[i]:\n dW[:, j] += (-1 + softmax_scores[j]) * X[i]\n else:\n dW[:, j] += softmax_scores[j] * X[i]\n \n loss /= X.shape[0]\n dW /= X.shape[0]\n \n loss += reg * np.sum(W * W)\n dW += 2 * reg * W\n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","def softmax_loss(x, y):\n def softmax(x):\n exps = np.exp(x)\n return exps / np.sum(exps, axis=1)[:,None]\n\n N = y.shape[0]\n p = softmax(x)\n log_likelihood = -np.log(p[range(N),y])\n loss = np.sum(log_likelihood) / N\n\n dx = p.copy()\n dx[range(N),y] -= 1\n dx = dx/N\n\n return loss, dx","def softmax_loss_vectorized(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using no explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n scores = X@W # 500,10\n# print(scores.shape)\n max_scores = np.max(scores, axis=1).reshape(-1,1) # 500, numeric instablity\n# print(max_scores.shape)\n scores -= max_scores # numeric instablity\n# print(scores.shape)\n correct_scores = scores[np.arange(scores.shape[0]), y] # 500,\n P_ic = np.exp(correct_scores)/np.sum(np.exp(scores), axis=1)\n# print(P)\n loss += np.sum(-np.log(P_ic))/scores.shape[0] # L = ∑L_i/N\n loss += reg * np.sum(W * W) # regularization\n # 向量化梯度：用scores构建一个P [500, 10]，首先取exp(scores)得到每一个位置的exp,然后对每个位置除以这一行的exp和\n # 上面的操作会得到500，10的矩阵，每个位置都是softmax之后的结果\n # !重点：对于[i,y[i]]位置，根据P_ic - 1, 要减1 \n P = np.exp(scores) # 正确分类的梯度, 位于梯度矩阵所有c的行\n P /= np.sum(np.exp(scores),axis=1).reshape(-1, 1)\n P[np.arange(scores.shape[0]), y] -= 1 # 将 i, y[i] -= 1\n \n # 得到这个矩阵之后，与X.T相乘即可得到dL/dW P(500,10) X(500,3073) X.T (3073, 500) W(3073, 10)\n dW += X.T@P\n dW /= scores.shape[0] # *1/N\n dW += 2*reg*W # 正则化梯度\n \n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n\n return loss, dW","def softmax(x):\n \"\"\"\"\"\"\n return exp(x) / sum(exp(x), axis=0)","def softmax(x):\n x = np.array(x)\n if x.ndim == 1:\n return column_softmax(x)\n else:\n ret_arr = np.array([column_softmax(col) for col in x.T])\n return ret_arr.T","def softmax(x):\n shape = np.shape(x)\n numDim = len(shape)\n result = np.zeros_like(x, dtype=float)\n \n if numDim == 1:\n totalSum = 0\n # Sum (e^yi)\n for i in range(len(x)):\n totalSum = totalSum + exp(x[i])\n # e^yi / Sum (e^yi)\n for i in range(len(x)):\n result[i] = exp(x[i])/totalSum\n else:\n xDim = len(x[0])\n totalSum = np.zeros(xDim, dtype=float)\n \n # e^yi\n for i in range(len(x)):\n for j in range(len(x[i])):\n result[i,j] = exp(x[i,j])\n totalSum[j] = totalSum[j] + result[i,j]\n # e^yi / Sum (e^yi)\n for i in range(len(result)):\n for j in range(len(result[i])):\n result[i,j] = result[i,j] / totalSum[j]\n \n return result","def softmax(x):\n scoreMatExp = np.exp(np.asarray(x))\n return scoreMatExp / scoreMatExp.sum(0)","def softmax(x):\n # https://stackoverflow.com/questions/34968722/softmax-function-python\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax_loss_vectorized(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n num_train = X.shape[1]\n num_classes = W.shape[0]\n #############################################################################\n # Compute the softmax loss and its gradient using no explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n \n # compute scores\n scores = W.dot(X)\n scores -= np.max(scores)\n\n # softmax function\n softmax = np.exp(scores) / np.sum(np.exp(scores), 0) # 10 x 49000 | C x D\n \n # cross entropy loss\n loss = -np.log(softmax[y, range(num_train)]) # 49000\n loss = np.sum(loss) / num_train\n\n # regularisation\n loss += 0.5 * reg * np.sum(W*W)\n\n # gradient (source:https://github.com/MyHumbleSelf/cs231n/blob/master/assignment1/cs231n/classifiers/softmax.py)\n ind = np.zeros(softmax.shape)\n ind[y, range(num_train)] = 1\n dW = np.dot((softmax-ind), X.T)\n dW /= num_train\n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","def softmax_loss_vectorized(W, X, y, reg):\n num_train = X.shape[0]\n num_class = W.shape[1]\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n scores = X.dot(W)\n temp_matrix = np.zeros(scores.shape)\n \n max_each_row = np.max(scores,axis=1).reshape(-1,1)\n scores -= max_each_row\n summation = np.sum(np.exp(scores),axis=1).reshape(-1,1)\n scores = np.exp(scores)\n scores = np.divide(scores,summation)\n temp_matrix[range(num_train),list(y)] =-1\n scores += temp_matrix\n dW = X.T.dot(scores) / num_train + 2*reg*W \n log_summation = np.log(summation)\n vector = scores[range(num_train),list(y)].reshape(-1,1) \n L = -vector+ log_summation \n loss = np.sum(L)/num_train + reg*np.sum(W*W)\n \n #############################################################################\n # TODO: Compute the softmax loss and its gradient using no explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n \n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","def softmax(self,Weights,X,b):\n N = X.shape[0]\n D = X.shape[1]\n C = Weights.shape[1]\n \n #P = np.zeros((N,C))\n #print P.shape\n\n P1 = np.dot(X,Weights) + b\n P1 = np.exp(P1)\n \n for i in range(N):\n P1[i,:] = P1[i,:]/P1[i,:].sum()\n # print P1\n return P1","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=1)","def softmax_loss(x, y):\n shifted_logits = x - np.max(x, axis=1, keepdims=True)\n Z = np.sum(np.exp(shifted_logits), axis=1, keepdims=True)\n log_probs = shifted_logits - np.log(Z)\n probs = np.exp(log_probs)\n N = x.shape[0]\n loss = -np.sum(log_probs[np.arange(N), y]) / N\n dx = probs.copy()\n dx[np.arange(N), y] -= 1\n dx /= N\n return loss, dx","def softmax(x):\n return np.exp(x)/np.sum(np.exp(x),axis=0)","def softmax(x):\n return np.exp(x)/np.sum(np.exp(x),axis=0)","def softmax(X):\n num = np.exp(X)\n den = np.sum(np.exp(X))\n return num / den","def softmax(x):\n npX = np.array(x)\n expX = np.exp(x)\n\n return expX/sum(expX)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)\n # return ( x / np.sum(x, axis=0) )","def test_softmax(n_epochs=250, optimizer='cg'):\n n_hidden = 10\n n_in = 5\n n_steps = 10\n n_seq = 10 # per batch\n n_batches = 50\n n_classes = 3\n n_out = n_classes # restricted to single softmax per time step\n\n np.random.seed(0)\n # simple lag test\n seq = np.random.randn(n_steps, n_seq * n_batches, n_in)\n targets = np.zeros((n_steps, n_seq * n_batches), dtype=np.int)\n\n thresh = 0.5\n # if lag 1 (dim 3) is greater than lag 2 (dim 0) + thresh\n # class 1\n # if lag 1 (dim 3) is less than lag 2 (dim 0) - thresh\n # class 2\n # if lag 2(dim0) - thresh <= lag 1 (dim 3) <= lag2(dim0) + thresh\n # class 0\n targets[2:, :][seq[1:-1, :, 3] > seq[:-2, :, 0] + thresh] = 1\n targets[2:, :][seq[1:-1, :, 3] < seq[:-2, :, 0] - thresh] = 2\n #targets[:, 2:, 0] = np.cast[np.int](seq[:, 1:-1, 3] > seq[:, :-2, 0])\n\n model = MetaRNN(n_in=n_in, n_hidden=n_hidden, n_out=n_out,\n learning_rate=0.005, learning_rate_decay=0.999,\n n_epochs=n_epochs, batch_size=n_seq, activation='tanh',\n output_type='softmax')\n\n model.fit(seq, targets, validate_every=10, compute_zero_one=True,\n optimizer=optimizer)\n\n seqs = xrange(10)\n\n plt.close('all')\n for seq_num in seqs:\n fig = plt.figure()\n ax1 = plt.subplot(211)\n plt.plot(seq[:, seq_num])\n ax1.set_title('input')\n ax2 = plt.subplot(212)\n\n # blue line will represent true classes\n true_targets = plt.step(xrange(n_steps), targets[:, seq_num],\n marker='o')\n\n # show probabilities (in b/w) output by model\n guess = model.predict_proba(seq[:, seq_num][:, np.newaxis])\n guessed_probs = plt.imshow(guess.squeeze().T, interpolation='nearest',\n cmap='gray')\n ax2.set_title('blue: true class, grayscale: probs assigned by model')","def softmax(x):\n orig_shape = x.shape\n\n if len(x.shape) > 1:\n # Matrix\n tmp = np.max(x, axis=1)\n x -= tmp.reshape((x.shape[0], 1))\n x = np.exp(x)\n tmp = np.sum(x, axis=1)\n x /= tmp.reshape((x.shape[0], 1))\n else:\n # Vector\n tmp = np.max(x)\n x -= tmp\n x = np.exp(x)\n tmp = np.sum(x)\n x /= tmp\n\n assert x.shape == orig_shape\n return x","def softmax(X):\n _X = X - np.max(X, axis=1).reshape(-1, 1)\n ep = np.exp(_X)\n return ep / np.sum(ep, axis=1).reshape(-1, 1)","def softmax(x):\n xx = x\n x = x.reshape((-1, x.shape[-1]))\n e_x = np.exp(x - np.max(x, 1).reshape(-1, 1))\n res = e_x / e_x.sum(axis=1).reshape(-1, 1)\n return res.reshape(xx.shape)","def softmax(x: npt.NDArray) -> npt.NDArray:\n row_wise_max = np.max(x, axis=1).reshape(-1, 1)\n exp_x = np.exp(x - row_wise_max)\n return exp_x / np.sum(exp_x, axis=1).reshape(-1, 1)","def _softmax(self,x):\n e_x = np.exp(x - np.max(x))\n return np.nan_to_num(e_x / np.nan_to_num(e_x.sum(axis=0)))","def softmax_loss_vectorized(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n # Compute class scores\n (num_class, D), (D, num_train) = W.shape, X.shape\n class_scores = np.dot(W, X)\n\n # Softmax them\n e_x = np.exp(class_scores - class_scores.max(axis=0))\n class_scores = e_x / e_x.sum(axis=0)\n \n # Create mask of ys\n gold_class_matrix = np.zeros((num_class, num_train))\n gold_class_matrix[y, range(num_train)] = 1\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using no explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n # Cross entropy loss\n loss = -(gold_class_matrix * np.log(class_scores)).sum()\n \n # Add regularization and normalize\n loss += 0.5 * reg * np.sum(W * W)\n loss /= num_train\n \n # Gradients\n augmented_scores = class_scores - gold_class_matrix\n (num_class, num_train), (num_train, D) = augmented_scores.shape, X.T.shape\n dW = np.dot(augmented_scores, X.T)\n \n # Add regularization and normalize\n dW += reg * W\n dW /= num_train\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","def softmax(x): \n e_x = np.exp(x - np.max(x)) \n return e_x / e_x.sum()","def softmax_loss_vectorized(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n num_train, num_dim = X.shape\n num_classes = W.shape[1]\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using no explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n scores = np.dot(X,W)\n # scores = scores.T - np.max(scores,1)\n # f = np.exp(scores.T) \n # correct_scores = f[range(num_train),y] #1*N\n # col_sum = np.sum(f,1)\n # loss = np.sum(-np.log(correct_scores/col_sum))\n\n # mat = f.T/col_sum #\n # mat = mat.T\n # y_pred = np.zeros(mat.shape)\n # y_pred[range(num_train),y] = 1\n # dW = np.dot(X.T,mat-y_pred)\n\n # loss/=num_train\n # loss += 0.5*reg*np.sum(W*W)\n # dW /= num_train\n # dW += reg*W\n f = scores.T - np.max(scores,1)\n f = f.T\n f_correct = scores[range(num_train),y]\n \n sum_col = np.log(np.sum(np.exp(scores),1)) # N*1\n \n loss = sum_col - f_correct # N*1\n loss = np.sum(loss)/num_train + 0.5*reg*np.sum(W*W)\n\n prob = np.exp(f).T / np.sum(np.exp(f),1)\n prob = prob.T\n y_pred = np.zeros(scores.shape)\n y_pred[range(num_train),y] = 1\n dW = X.T.dot(prob - y_pred)\n dW = dW/float(num_train) + reg*W\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","def softmax(H, V, d):\n postActivation = np.dot(H,V) + d\n expVector = np.exp(postActivation)\n return expVector/(np.sum(expVector, axis=1)[:,np.newaxis])","def softmax(X, Y, w, HProp = None, arg=None, reg=None, batchsize=None):\n if reg == None:\n reg_f = 0\n reg_g = 0\n reg_Hv = lambda v: 0\n else:\n reg_f, reg_g, reg_Hv = reg(w)\n global d, C\n n, d = X.shape\n \n if batchsize is not None:\n n_mini = np.int(np.floor(n*batchsize))\n index_batch = np.random.choice(n, n_mini, replace = False)\n# print(index_batch[:5])\n X = X[index_batch,:]\n Y = Y[index_batch]\n n = n_mini\n \n C = int(len(w)/d)\n w = w.reshape(d*C,1) #[d*C x 1]\n W = w.reshape(C,d).T #[d x C]\n XW = np.dot(X,W) #[n x C]\n large_vals = np.amax(XW,axis = 1).reshape(n, 1) #[n,1 ]\n large_vals = np.maximum(0,large_vals) #M(x), [n, 1]\n #XW - M(x)/<Xi,Wc> - M(x), [n x C]\n XW_trick = XW - np.tile(large_vals, (1, C))\n #sum over b to calc alphax, [n x total_C]\n XW_1_trick = np.append(-large_vals, XW_trick,axis = 1)\n #alphax, [n, ]\n sum_exp_trick = np.sum(np.exp(XW_1_trick), axis = 1).reshape(n, 1)\n log_sum_exp_trick = large_vals + np.log(sum_exp_trick) #[n, 1]\n \n f = np.sum(log_sum_exp_trick)/n - np.sum(np.sum(XW*Y,axis=1))/n + reg_f\n if arg == 'f': \n return f\n inv_sum_exp = 1./sum_exp_trick\n inv_sum_exp = np.tile(inv_sum_exp,(1,np.size(W,axis = 1)))\n S = inv_sum_exp*np.exp(XW_trick) #h(x,w), [n x C] \n g = np.dot(X.T, S-Y)/n #[d x C]\n g = g.T.flatten().reshape(d*C,1) + reg_g#[d*C, ] \n\n if arg == 'g':\n return g \n \n if arg == 'fg':\n return f, g\n\n if HProp == None:\n Hv = lambda v: hessvec(X, S, n, v) + reg_Hv(v) \n return f, g, Hv\n else:\n n_H = np.int(np.floor(n*HProp))\n idx_H = np.random.choice(n, n_H, replace = False)\n inv_sum_exp_H = 1./(sum_exp_trick[idx_H,:])\n inv_sum_exp_H = np.tile(inv_sum_exp_H,(1,np.size(W,axis = 1)))\n S_H = inv_sum_exp_H*np.exp(XW_trick[idx_H,:]) #h(x,w), [S x C] \n Hv = lambda v: hessvec(X[idx_H,:], S_H, n_H, v) + reg_Hv(v)\n return f, g, Hv\n \n if arg == 'explicit':\n f = np.sum(log_sum_exp_trick) - np.sum(np.sum(XW*Y,axis=1)) + reg_f\n g = np.dot(X.T, S-Y) #[d x C]\n g = g.T.flatten().reshape(d*C,1) + reg_g #[d*C, ]\n Hv = lambda v: hessvec(X, S, v, reg)\n #S is divided into C parts {1:b}U{c}, [n, ] * C\n S_cell = np.split(S.T,C) \n SX_cell = np.array([]).reshape(n,0) #empty [n x 0] array\n SX_self_cell = np.array([]).reshape(0,0)\n for column in S_cell:\n c = spdiags(column,0,n,n) #value of the b/c class\n SX_1_cell = np.dot(c.A,X) #WX = W x X,half of W, [n x d]\n #fill results from columns, [n x d*C]\n SX_cell = np.c_[SX_cell, SX_1_cell] \n SX_cross = np.dot(SX_cell.T,SX_cell) #take square, [d*C x d*C] \n #X.T x WX half of W, [d x d]\n SX_1self_cell = np.dot(X.T,SX_1_cell) \n #put [d x d] in diag, W_cc, [d*C x d*C] \n SX_self_cell = block_diag(SX_self_cell,SX_1self_cell) \n H = SX_self_cell - SX_cross #compute W_cc, [d*C x d*C]\n H = H + 2*reg*identity(d*C)\n return f, g, Hv, H","def softmaxCostAndGradient(predicted, target, outputVectors, dataset):\n\n ### YOUR CODE HERE\n scores = outputVectors.dot(predicted.T) # shape = (V, 1)\n y_hat = softmax(scores)\n cost = -scores[target] + np.log(np.sum(np.exp(scores)))\n one_hot_target = np.zeros_like(y_hat)\n one_hot_target[target] = 1\n grad = np.outer((y_hat - one_hot_target), predicted)\n gradPred = outputVectors.T.dot(y_hat - one_hot_target)\n \n '''\n final_predicted = predicted.dot(outputVectors.T)\n probability = softmax(final_predicted)\n cost = -np.log(probability[target])\n \n one_hot_target = np.zeros_like(probability)\n one_hot_target[target] += 1\n dlogits = probability - one_hot_target\n grad = np.outer(predicted, dlogits).T\n gradPred = outputVectors.T.dot(dlogits)\n '''\n ### END YOUR CODE\n\n return cost, gradPred, grad","def pred(W, X):\n A = softmax_stable(X.dot(W))\n return np.argmax(A, axis = 1)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax_loss_vectorized(W, X, y, reg):\n\n #############################################################################\n # TODO: Compute the softmax.ipynb loss and its gradient using no explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n train_images = X.shape[0]\n # Store all the scores in a matrix\n all_scores = np.dot(X,W)\n #First, calculate the normalizing constant for numeric stability\n constant = np.max(all_scores,axis=1)\n normalized_scores = np.transpose(np.subtract(np.transpose(all_scores),constant))\n\n #Then, calculate softmax for the correct scores\n exp_scores = np.exp(all_scores)\n # First, keep track of the sum of values per row\n exp_sum = np.sum(exp_scores,axis=1)\n\n # Finally, calculate the softmax score for every entry\n softmax_scores = np.transpose(exp_scores)/exp_sum # useful when computing gradient\n softmax_scores = np.transpose(softmax_scores)\n # And then, compute the loss\n loss_score = softmax_scores[range(train_images),y]\n loss_score = -1 * np.log(loss_score) #taking the logarithm\n loss += np.sum(loss_score)\n\n #Normalize and regularize the loss\n loss /= train_images\n loss += 0.5*reg*np.sum(W*W)\n\n #Finally, calculate a vectorized gradient\n\n # Calculate the derivative at the correct label\n softmax_scores[range(train_images),y] -= 1\n # Then, make a matrix containing all the gradient values\n gradient_values = np.dot(np.transpose(X),softmax_scores)\n gradient_values = gradient_values\n\n #FINALLY, update the gradient\n dW+= gradient_values\n #And normalize and regularize it\n dW /= train_images\n dW += reg*W\n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW","def softmax_loss(scores, y):\r\n N = scores.shape[0] # number of input data\r\n\r\n # compute data loss\r\n shifted_logits = scores - np.max(scores, axis=1, keepdims=True)\r\n Z = np.sum(np.exp(shifted_logits), axis=1, keepdims=True)\r\n log_probs = shifted_logits - np.log(Z)\r\n probs = np.exp(log_probs)\r\n loss = -np.sum(log_probs[range(N), y]) / N\r\n\r\n # Compute gradient of loss function w.r.t. scores\r\n dscores = probs.copy()\r\n dscores[range(N), y] -= 1\r\n dscores /= N\r\n \r\n return loss, dscores","def softmax(x):\n x_exp = np.exp(x)\n x_sum = np.sum(x_exp, axis=1, keepdims=True)\n s = x_exp / x_sum\n \n return s","def softmax_loss_vectorized(W, X, y, reg):\n # Initialize the loss and gradient to zero.\n loss = 0.0\n dW = np.zeros_like(W)\n\n #############################################################################\n # TODO: Compute the softmax loss and its gradient using no explicit loops. #\n # Store the loss in loss and the gradient in dW. If you are not careful #\n # here, it is easy to run into numeric instability. Don't forget the #\n # regularization! #\n #############################################################################\n\n num_classes = W.shape[1]\n num_train = X.shape[0]\n\n # Calculate scores for each classifier (column in the weight matrix W)\n # acting on each training sample (row in X)\n scores = X.dot(W)\n \n # Normalization trick to resolve numerical instability\n # when dealing with the large exponential terms.\n scores -= np.max(scores)\n\n # Cache some terms that are used repeatedly.\n exp_scores = np.exp(scores)\n sum_exp_scores = np.sum(exp_scores,axis=1)\n\n # Find the correct classifier scores for each training sample\n correct_class_scores = scores[np.arange(num_train), y]\n\n # Update the loss\n loss = np.sum(-correct_class_scores + np.log(sum_exp_scores))\n\n # Update the gradient\n correct_indices = np.zeros(scores.shape)\n correct_indices[np.arange(num_train), y] = 1\n\n dW -= correct_indices.T.dot(X).T\n dW += X.T.dot((exp_scores.T / sum_exp_scores).T)\n \n # Average over the training samples\n loss /= num_train\n dW /= num_train\n \n # Add regularization.\n loss += 0.5 * reg * np.sum(W * W)\n dW += reg * W\n\n pass\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return loss, dW"],"string":"[\n \"def softmax_classifier(W, input, label, lamda):\\n\\n ############################################################################\\n # TODO: Put your code here\\n\\n loss = 0.0\\n num_train = input.shape[0]\\n num_classes = W.shape[1]\\n\\n score = np.dot(input, W) # (N,C)\\n prediction = np.argmax(score, axis=1)\\n score -= np.max(score, axis=1, keepdims=True)\\n\\n # # cross entropy loss\\n # # take exponent of the score and normalized with sum of all exponents.\\n probs = np.exp(score) # (N,C)\\n e_y = np.sum(np.multiply(probs,label), axis=1) # (N,) probability for correct class\\n e_sum = np.sum(probs, axis=1) # (N,) sum of probability over all classes\\n\\n # implementation of loss equivalent l_i = -f_y_i + log sum_j(e^(f_j))\\n # loss = np.sum(-np.log(e_y/e_sum)) # sum of -log across all samples.\\n # loss /= num_train # average loss\\n loss = np.sum(-1 * e_y) + np.sum(np.log(e_sum))\\n loss /= num_train\\n\\n loss += lamda * np.sum(W * W) # regularization \\n\\n # Gradient\\n delta_score = probs / e_sum.reshape(num_train,1) # (N,C)\\n delta_score -= label # (NxC)\\n gradient = np.dot(input.T, delta_score)\\n gradient /= num_train\\n gradient += lamda * 2 * W\\n\\n ############################################################################\\n\\n return loss, gradient, prediction\",\n \"def softmax(x):\\n # (n_samples, n_classes)\\n if len(x.shape) == 2:\\n row_max = np.max(x, axis=1)\\n x -= row_max.reshape((x.shape[0], 1))\\n x = np.exp(x)\\n row_sum = np.sum(x, axis=1)\\n x /= row_sum.reshape((x.shape[0], 1))\\n # (n_samples, n_tasks, n_classes)\\n elif len(x.shape) == 3:\\n row_max = np.max(x, axis=2)\\n x -= row_max.reshape(x.shape[:2] + (1,))\\n x = np.exp(x)\\n row_sum = np.sum(x, axis=2)\\n x /= row_sum.reshape(x.shape[:2] + (1,))\\n return x\",\n \"def softmax_loss_naive(W, X, y, reg):\\r\\n # Initialize the loss and gradient to zero.\\r\\n loss = 0.0\\r\\n dW = np.zeros_like(W)\\r\\n num_train = X.shape[1] # d*n\\r\\n num_class = W.shape[0]\\r\\n\\r\\n #############################################################################\\r\\n # Compute the softmax loss and its gradient using explicit loops. #\\r\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\r\\n # here, it is easy to run into numeric instability. Don't forget the #\\r\\n # regularization! #\\r\\n #############################################################################\\r\\n loss = 0.0\\r\\n for i in range(num_train):\\r\\n X_i = X[:,i] # D*1\\r\\n score_i = W.dot(X_i)\\r\\n score_i -= np.max(score_i) #C*1 but keepdims = false so it becomes 1*C\\r\\n exp_score_i = np.exp(score_i)\\r\\n probs_i = exp_score_i/np.sum(exp_score_i) #1*C\\r\\n correct_logprobs_i = -np.log(probs_i[y[i]])\\r\\n loss += correct_logprobs_i\\r\\n \\r\\n dscore_i = probs_i.reshape(num_class,-1)#c*1\\r\\n dscore_i[y[i]] -= 1 #C*1\\r\\n X_i = X_i.reshape(1,-1)# 1*D\\r\\n dW += dscore_i.dot(X_i)\\r\\n \\r\\n loss /= num_train\\r\\n loss += 0.5*reg*np.sum(W*W)\\r\\n\\r\\n dW /= num_train\\r\\n dW += reg*W\\r\\n \\r\\n return loss, dW\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n num_train = X.shape[0]\\n # print(\\\"num_train:\\\", num_train)\\n num_classes = W.shape[1]\\n # print(\\\"num_classes:\\\", num_classes)\\n \\n for i in range(num_train):\\n scores = X[i].dot(W) # scores is 1 * C\\n correct_class = y[i]\\n \\n # LOSS DUE TO TRAINING SAMPLE = -log(exp^correct_score / sum(exp^all_other_scores))\\n log_c = np.max(scores)\\n scores -= log_c\\n correct_class_score = scores[correct_class]\\n exp_scores = np.exp(scores)\\n sum_exp_scores = np.sum(np.exp(scores))\\n proportion = np.exp(correct_class_score) / sum_exp_scores\\n loss -= np.log(proportion)\\n # print(proportion)\\n \\n # ALTERNATIVELY: (we split the log)\\n# loss -= scores[y[i]]\\n# loss += np.log(np.sum(np.exp(X[i].dot(W))))\\n \\n # UPDATE GRADIENT\\n for j in range(num_classes):\\n p = np.exp(scores[j]) / sum_exp_scores # \\\"probability\\\" of class j\\n dW[:,j] += (p - (j == y[i])) * X[i,:]\\n # dW is D by C\\n\\n loss /= num_train\\n loss += reg * np.sum(W * W) \\n dW /= num_train\\n dW += reg * 2 * W\\n\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n \\n return loss, dW\",\n \"def softmax_loss(x, y):\\n # softmax\\n num = np.exp(x)\\n den = np.sum(num, axis=1)\\n softmax = num/den[:, None]\\n N = x.shape[0]\\n\\n # compute the los per class\\n loss = softmax[np.arange(N), y]\\n loss = -np.log(loss)\\n\\n # sum all the losses and divide by number of class\\n # Also add the regularization loss term\\n loss = np.sum(loss)/N \\n \\n dscores = softmax\\n dscores[np.arange(N), y] -= 1\\n dscores /= N\\n\\n return loss, dscores\",\n \"def SoftmaxLayer(inputs, n_classes):\\n\\n l = Conv2DLayer(inputs, n_classes, filter_size=1, nonlinearity=linear, W=HeUniform(gain='relu'), pad='same',\\n flip_filters=False, stride=1)\\n\\n # We perform the softmax nonlinearity in 2 steps :\\n # 1. Reshape from (batch_size, n_classes, n_rows, n_cols) to (batch_size * n_rows * n_cols, n_classes)\\n # 2. Apply softmax\\n\\n l = DimshuffleLayer(l, (0, 2, 3, 1))\\n batch_size, n_rows, n_cols, _ = get_output(l).shape\\n l = ReshapeLayer(l, (batch_size * n_rows * n_cols, n_classes))\\n l = NonlinearityLayer(l, softmax)\\n\\n return l\\n\\n # Note : we also tried to apply deep supervision using intermediate outputs at lower resolutions but didn't see\\n # any improvements. Our guess is that FC-DenseNet naturally permits this multiscale approach\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n num_train = X.shape[0]\\n num_classe = W.shape[1]\\n loss = 0.0\\n\\n for i in range(num_train): #pour chaque image de l'ensemble d'entrainement\\n scores = X[i].dot(W)\\n scores -= max(scores)\\n\\n correct_class_score = scores[y[i]] #y[i]=c\\n e_syi = np.exp(correct_class_score)\\n e_sj = np.sum(np.exp(scores))\\n\\n loss -= np.log(e_syi/e_sj)\\n\\n for k in range(num_classe): #pour chaque classe\\n dW[:, k] += ((np.exp(scores[k])/e_sj) - (k == y[i])) * X[i].T\\n\\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n dW/= num_train\\n\\n # Add regularization to the loss.\\n loss += reg * np.sum(W * W)\\n dW += 2 * reg * W\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n num_train=X.shape[0]\\n num_class=W.shape[1]\\n num_feature=X.shape[1]\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n for i in range(num_train):\\n #W*Xi C*1\\n x=np.exp(np.dot(W.T,X[i,:]))\\n denominator=np.sum(x)\\n numerator=x[y[i]]\\n loss-=np.log(numerator/denominator)\\n #numerator and denominator\\n #for j in range(num_class):\\n normalize_score=x/denominator\\n nm=np.reshape(normalize_score, (num_class, 1))\\n \\n #CxD\\n dscore=nm.dot(np.reshape(X[i,:],(1,num_feature)))\\n #print(dscore.shape)\\n\\n dscore[y[i],:]-=X[i,:]\\n dW+=dscore.T\\n\\n loss/=num_train\\n dW = dW/num_train + reg*W\\n #\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n (num_class, D), (D, num_train) = (W.shape, X.shape)\\n class_scores = np.dot(W, X)\\n \\n # Subtract maximum unnormalized score from each set of class scores\\n for i in range(num_train):\\n max_class_score = np.max(class_scores[:, i])\\n for j in range(num_class):\\n class_scores[j, i] -= max_class_score\\n \\n # Compute softmax and update gradient\\n for i in range(num_train):\\n normalization_term = sum(np.exp(class_score) for class_score in class_scores[:, i])\\n for j in range(num_class):\\n class_scores[j, i] = np.exp(class_scores[j, i]) / normalization_term\\n # Thanks again to MyHumbleSelf for making me examine this further and discover a bug in my derivation of the softmax gradient!\\n dW[j] += (class_scores[j, i] - (j==y[i])) * X[:, i]\\n \\n # Compute cross-entropy errors and total loss from that\\n losses = [np.log(class_scores[y[i], i]) for i in range(num_train)]\\n loss = -sum(losses) / num_train\\n\\n # Add regularization to loss and normalize dW\\n loss += 0.5 * reg * np.sum(W * W)\\n dW /= num_train\\n dW += reg * W\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n # softmax P(Y=k|X=x_i) = e^{s_k}/∑e^{s_j} softmax loss = -log(softmax)\\n num_train = X.shape[0]\\n num_class = W.shape[1]\\n for i in range(num_train):\\n scores = X[i].dot(W) # get scores\\n max_score = np.max(scores)\\n scores -= max_score # 考虑数值计算稳定性 softmax = (e^s_c - max)/∑(e^s_j - max)\\n correct_score = scores[y[i]] # score_correct\\n P_ic = np.exp(correct_score)/np.sum(np.exp(scores))\\n loss += -np.log(P_ic)\\n for j in range(num_class):\\n if j == y[i]:\\n dW[:, j] += (P_ic - 1) * X[i].T\\n else:\\n P_ij = np.exp(scores[j])/np.sum(np.exp(scores))\\n dW[:, j] += P_ij * X[i].T\\n \\n \\n loss /= num_train\\n loss += reg*np.sum(W*W)\\n dW /= num_train\\n dW += 2 * reg * W\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n return loss, dW\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n num_train = X.shape[0]\\n num_class = W.shape[1]\\n #scores = np.zeros(num_train,num_class)\\n scores = X.dot(W)\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n for i in range(num_train):\\n # compute Li\\n fmax= np.max(scores[i])\\n scores[i] -= fmax\\n correct_class_score = scores[i,y[i]]\\n M = np.exp(correct_class_score)/np.sum(np.exp(scores[i]))\\n loss += -np.log(M)\\n for j in range(num_class):\\n N = np.exp(scores[i,j])/np.sum(np.exp(scores[i]))\\n if j ==y[i]:\\n dW[:,y[i]]+= (M-1)*X[i].T\\n else:\\n dW[:,j] += N*X[i].T \\n loss /= num_train\\n loss += reg*np.sum(W*W)\\n dW /= num_train \\n dW += 2*reg*W \\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"def softmax(inputs):\\n probs = np.exp(inputs)\\n # print(probs.shape)\\n # t = np.sum(probs, axis=0)\\n # print(t.shape)\\n\\n probs /= np.sum(probs, axis=0)[np.newaxis,:]\\n return probs\",\n \"def softmax(self, scores):\\n\\n\\n # for each sample, for each class ,caclulate\\n # np.exp(scores) : still (n_samples, n_classes)\\n\\n # axis = 1\\n # a00, a01, a02 as a sinlge one to perfrom np_sum\\n # which is the same sample \\n # sum_exp : still (n_samples, 1)\\n\\n # softmax = (n_samples, n_classes) / (n_samples, 1) = (n_samples, n_classes) \\n\\n sum_exp = np.sum(np.exp(scores), axis=1, keepdims=True)\\n softmax = np.exp(scores) / sum_exp\\n \\n return softmax\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n # print \\\"dW's shape\\\", dW.shape\\n # compute the loss and the gradient\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n #############################################################################\\n # TODO: Compute the softmax.ipynb loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n # For every training image\\n for train_image in xrange(num_train):\\n # Multiply the weights by the image to get the scores\\n scores = X[train_image].dot(W)\\n # print(scores)\\n # And then get the correct score\\n correct_label = y[train_image]\\n correct_score = scores[correct_label]\\n # TODO: Right up to here\\n # And then get the score of every other classifier\\n all_scores = np.sum(scores)\\n # Add a normalizing factor for numeric stability\\n normalizing_constant = np.max(scores)\\n scores -= normalizing_constant\\n correct_score -= normalizing_constant\\n #Calculating the softmax values\\n softmax = np.exp(correct_score)/np.sum(np.exp(scores))\\n\\n # print(\\\"Correct score softmax\\\",softmax)\\n\\n # And calculating the loss\\n loss += -1*np.log(softmax)\\n # print loss\\n #TODO: Loss computation is also correct\\n\\n # And calculating the gradient\\n\\n # First, update the Weight matrix with the correct example's derivative\\n dW[:,correct_label] += (softmax-1)*np.transpose(X[train_image])\\n\\n # Then do the same for the wrong cases\\n incorrect_labels = [x for x in xrange(num_classes) if x != correct_label]\\n # Now, update the weights\\n for label_index in incorrect_labels:\\n #Calculating the softmax for a wrong label\\n incorrect_label_softmax = np.exp(scores[label_index])/(np.sum(np.exp(scores)))\\n # Calculating the derivative\\n necessary_weight = incorrect_label_softmax*np.transpose(X[train_image])\\n # Updating the weights\\n dW[:,label_index] += necessary_weight\\n\\n\\n # Divide the loss\\n loss /= num_train\\n dW /= num_train\\n\\n # Now, do regularization\\n loss += 0.5*reg*np.sum(W*W)# Penalize big weights\\n dW += reg*W\\n\\n\\n\\n\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n num_train = X.shape[1]\\n num_classes = W.shape[0]\\n #############################################################################\\n # Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n for i in range(num_train): # for each image\\n # compute the score\\n scores = W.dot(X[:, i])\\n\\n # shift the values of f so that the highest number is 0:\\n scores -= np.max(scores)\\n\\n # compute the loss\\n loss += -np.log(np.exp(scores[y[i]]) / np.sum(np.exp(scores)))\\n\\n # gradient(https://github.com/seyedamo/cs231n/blob/master/assignment1/cs231n/classifiers/softmax.py)\\n scores = np.exp(scores)\\n scores /= np.sum(scores)\\n for j in range(num_classes): # for each class\\n dW[j, :] += scores[j] * X[:, i].T\\n\\n # dW wrt correct class scores w_yi\\n dW[y[i], :] += -X[:, i].T\\n\\n # Average the loss \\n loss /= num_train\\n\\n # Add regularization to the loss.\\n loss += 0.5 * reg * np.sum(W * W)\\n\\n # average of the gradient\\n dW /= num_train\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n return loss, dW\",\n \"def softmax_loss(x, y):\\n N, C = x.shape\\n loss, dx = 0, np.zeros(x.shape) \\n for i in range(N):\\n loss += -np.log(np.exp(x[i,y[i]])/np.sum(np.exp(x[i,:])))\\n dx[i,:] = np.exp(x[i,:])/np.sum(np.exp(x[i,:]))\\n dx[i,y[i]] += (-1)\\n \\n loss /= N\\n dx /= N\\n return loss, dx\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n num_classes = W.shape[1]\\n #print('num_classes = ', num_classes)\\n num_train = X.shape[0]\\n #print('num_train = ', num_train)\\n \\n min_score = 0.0\\n shifted_scores = np.zeros(W.shape[1])\\n #max_score = np.zeros(W.shape[1])\\n max_score = 0.0\\n \\n loss_array = np.zeros(y.shape[0])\\n for i in range(num_train):\\n scores = X[i].dot(W)\\n #print('scores dimensions = ', scores.shape)\\n #print('scores = ', scores)\\n #print('i =', i, 'y = ', y[i])\\n min_score = np.min(scores)\\n max_score = np.max(scores)\\n #print(min_score,max_score)\\n shifted_scores = np.multiply(-1,scores + abs(min_score))\\n #print(scores)\\n #print(shifted_scores)\\n exp_scores = np.exp(shifted_scores)\\n norm = np.amax(exp_scores)\\n norm_scores = np.divide(exp_scores,norm)\\n loss_array[i] = np.multiply(-1,np.log(norm_scores[y[i]]/(np.sum(norm_scores)-norm_scores[y[i]])))\\n #print(loss_array)\\n for j in range(num_classes): \\n\\t\\n if j == y[i]: \\n dW[:,j] = np.multiply(norm_scores[y[i]],1-norm_scores[y[i]])\\n else:\\n dW[:,j] = np.multiply(-1,np.multiply(norm_scores[y[i]],norm_scores[y[j]]))\\n\\t\\t\\t\\n\\t\\t\\t\\n loss = np.amax(loss_array)\\n\\n # Add regularization to the loss.\\n loss = 0.5 * reg * np.sum(W * W) + loss\\n \\n \\n pass\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"def softmax(self, X):\\n\\n n_samples, _ = X.shape\\n\\n X_ = np.empty((n_samples, self.n_classes_))\\n old_ind = 0\\n for i, n in enumerate(self.n_weights_per_class):\\n new_ind = int(old_ind + n + 1)\\n X_[:, i] = np.dot(X[:, old_ind: new_ind], self.weights_[old_ind: new_ind])\\n old_ind = new_ind\\n\\n exp_X = np.exp(X_ - np.max(X_, axis=1).reshape(-1, 1))\\n softmax = exp_X / (np.sum(exp_X, axis=1)).reshape((n_samples, 1))\\n\\n return softmax\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n pass\\n num_tran = X.shape[0]\\n num_classes = W.shape[1]\\n loss_par =np.zeros(num_tran)\\n\\n Score = np.dot(X,W)\\n expS = np.exp(Score)\\n # for i in num_tran:\\n sumS = np.sum(expS,axis=1)\\n sumS = sumS.reshape(sumS.shape[0],1)\\n normalize = np.divide(expS,sumS)\\n softmax = -np.log(normalize)\\n\\n for i in np.arange(num_tran):\\n loss_par[i]=softmax[i, y[i]]\\n for j in np.arange(num_classes) :\\n if j!=y[i]:\\n # dW[:,j]+=1/normalize[i,y[i]]*expS[i,y[i]]*expS[i,j]/np.power(sumS[i],2) *X[i,:]\\n dW[:,j]+=expS[i,j]/sumS[i] *X[i,:]\\n else:\\n # dW[:,y[i]]+=-1/normalize[i,y[i]]*expS[i,y[i]]*(sumS[i]-expS[i,y[i]])/np.power(sumS[i],2) *X[i,:]\\n dW[:,y[i]]+=-(sumS[i]-expS[i,y[i]])/sumS[i] *X[i,:]\\n\\n dW /=num_tran\\n\\n loss = np.sum(loss_par) / num_tran\\n # print num_tran,loss\\n\\n dW+=reg*W\\n loss+=0.5*reg*np.sum(W*W)\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"def softmax_loss(x, y):\\n\\n eps = 1e-5\\n \\n N,C = x.shape\\n p = softmax(x)\\n llikelihood = -np.log(p[range(N),y] + eps)\\n# print(llikelihood)\\n loss = np.sum(llikelihood) / N\\n\\n dx = p\\n dx[range(N),y] -= 1\\n dx = dx/N\\n \\n return loss, dx\",\n \"def softmax_loss_vectorized(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using no explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n num_train = X.shape[0]\\n # print(\\\"num_train:\\\", num_train)\\n num_classes = W.shape[1]\\n # print(\\\"num_classes:\\\", num_classes)\\n \\n scores = X.dot(W) # scores is N*D x D*C -> N*C \\n log_c = np.max(scores, axis=1).T\\n scores -= log_c[:,None]\\n correct_class_score = scores[np.arange(num_train),y]\\n exp_scores = np.exp(scores)\\n sum_exp_scores = np.sum(np.exp(scores), axis=1)\\n proportion = np.exp(correct_class_score) / sum_exp_scores\\n loss -= np.sum(np.log(proportion))\\n \\n # calculating dW = (p - (c = correct c ? 1 : 0)) * x\\n correct_class_one_hot = np.zeros_like(scores)\\n correct_class_one_hot[np.arange(num_train),y] += 1\\n p = np.exp(scores) / sum_exp_scores[:,None] - correct_class_one_hot # N*C / N:1 -> N*C\\n dW += X.T.dot(p) # D*N x N*C -> D*C\\n\\n loss /= num_train\\n loss += 0.5 * reg * np.sum(W * W) \\n dW /= num_train\\n dW += reg * W\\n\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n return loss, dW\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n\\n for i in xrange(num_train):\\n scores = X[i].dot(W)\\n\\n # Normalization trick to resolve numerical instability\\n # when dealing with the large exponential terms.\\n scores -= np.max(scores)\\n\\n # Cache some terms that are used repeatedly.\\n exp_scores = np.exp(scores)\\n sum_exp_scores = np.sum(exp_scores)\\n correct_class_score = scores[y[i]]\\n \\n # Update the loss \\n loss -= correct_class_score\\n loss += np.log(sum_exp_scores)\\n\\n # Update the gradient\\n dW[:,y[i]] -= X[i,:].T\\n for j in xrange(num_classes):\\n dW[:,j] += ((X[i,:].T * exp_scores[j]) / sum_exp_scores)\\n\\n \\n # Right now the loss is a sum over all training examples, but we want it\\n # to be an average instead so we divide by num_train.\\n loss /= num_train\\n\\n dW /= num_train\\n\\n # Add regularization to the loss.\\n loss += 0.5 * reg * np.sum(W * W)\\n\\n dW += reg*W\\n \\n pass\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"def softmax(x):\\r\\n sum_c = np.sum(np.exp(x), axis=1)\\r\\n sum_c = np.expand_dims(sum_c, axis=1)\\r\\n pred_x = np.divide(np.exp(x), sum_c)\\r\\n return pred_x\",\n \"def test_softmax_base():\\n x = np.array([[[2.0, 3.0, 4.0, 5.0],\\n [3.0, 4.0, 5.0, 6.0],\\n [7.0, 8.0, 8.0, 9.0]],\\n [[1.0, 2.0, 3.0, 4.0],\\n [5.0, 6.0, 7.0, 8.0],\\n [6.0, 7.0, 8.0, 9.0]]])\\n res = np.array([[[0.0320586, 0.08714432, 0.23688282, 0.64391426],\\n [0.0320586, 0.08714432, 0.23688282, 0.64391426],\\n [0.07232949, 0.19661193, 0.19661193, 0.53444665]],\\n [[0.0320586, 0.08714432, 0.23688282, 0.64391426],\\n [0.0320586, 0.08714432, 0.23688282, 0.64391426],\\n [0.0320586, 0.08714432, 0.23688282, 0.64391426]]])\\n obj.run(res=res, input=x)\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n num_cases = X.shape[0]\\n num_class = W.shape[1]\\n y_label = np.zeros((num_cases,num_class))\\n for i in range(num_cases):\\n h1 = np.exp(X[i].dot(W))\\n h = h1/np.sum(h1)\\n y_label[i] = (np.arange(h.shape[0]) == y[i]) + 0\\n loss -= (np.sum(y_label[i] * np.log(h) + (1 - y_label[i]) * np.log(1 - h)))\\n delta = np.zeros(W.shape)\\n for j in range(num_class):\\n delta[:,j] += X[i]\\n delta[:,j] *= h1[j]\\n delta[:,j] *= (np.sum(h1) - h1[j])/(np.sum(h1) ** 2)\\n delta[:,j] = y_label[i][j] / h[j] * delta[:,j] - (1 - y_label[i][j]) / (1 - h[j]) * delta[:,j]\\n dW -= delta\\n loss /= num_cases\\n loss += reg * np.sum(W * W)\\n dW /= num_cases\\n dW += 2 * reg * W\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n num_train = X.shape[0]\\n num_class = W.shape[1]\\n l = np.zeros([num_train,1])\\n for i in range(num_train):\\n scores = np.dot(X[i], W)\\n f_yi = scores[y[i]]\\n exp_num = np.exp(f_yi)\\n exp = np.exp(scores)\\n exp_deno = np.sum(exp)\\n for j in range(num_class):\\n if (j == y[i]):\\n dW[:,j] -= X[i,:].transpose()\\n dW[:,j] += (np.exp(scores[j]) / exp_deno) * X[i,:].transpose()\\n l[i] = -np.log(exp_num/exp_deno)\\n\\n loss = np.sum(l)/num_train\\n loss += reg * np.sum(W*W)\\n dW /= num_train \\n dW += 2 * reg * W\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n return loss, dW\",\n \"def softmax(input, dim, inplace=False):\\n return FunctionLib.apply(\\n 'Softmax', input.device, [input],\\n outputs=[input if inplace else None], axis=dim)\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n for i in range(X.shape[0]):\\n# c = np.matmul(X[i],W)\\n# c -= np.amax(c)\\n# e_c = np.exp(c)\\n# denom = np.sum(e_c)\\n# #Nice fact: we know that the largest element in c will also be the largest softmax value, so we only\\n# # need to transform that one value. \\n# sm_c = e_c/denom\\n# \\n# loss1 += -np.log(sm_c[y[i]])\\n\\n # Need to make this whole dang thing more numerically stable. \\n c = np.matmul(X[i],W)\\n c -= np.amax(c)\\n e_c = np.exp(c)\\n denom = np.sum(e_c)\\n sm_c = e_c/denom\\n\\n loss += np.log(denom) - c[y[i]]\\n# print(-np.log(sm_c[y[i]]) - (np.log(denom)-c[y[i]]))\\n\\n \\\"\\\"\\\"They are basically the same value\\\"\\\"\\\"\\n\\n # now computing some gradients\\n dL_ds = sm_c\\n dL_ds[y[i]] -= 1\\n #note that sm_c is modified now!\\n \\\"\\\"\\\" #ah, something fundamentally different is happening with numpy. When an array element\\n is changed, it's really changed for good. And it changes for all pointers pointing to same object.\\n yikes. Actually it's the same with python lists. Anything pointing to And underlying object can\\n change that underlying object for all things that point to it. Alas.\\\"\\\"\\\"\\n# import pdb; pdb.set_trace()\\n \\\"\\\"\\\"Okay I just coudln't bear the for loops...\\\"\\\"\\\"\\n dW_update = np.matmul(X[i].reshape(1,X.shape[1]).T,dL_ds[np.newaxis,:])\\n dW+=dW_update\\n # for n in range(W.shape[0]):\\n# for m in range(W.shape[1]):\\n# if m == y[i]:\\n# dW[n,m] += X[i,n]*(sm_c[m]-e_c[m])\\n# else:\\n# dW[n,m] += X[i,n]*sm_c[m]\\n\\n # should be numerically unstable I think.\\n\\n loss /= X.shape[0]\\n loss += reg*np.sum(W*W)\\n\\n dW /= X.shape[0]\\n dW += reg*2*W\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n return loss, dW\",\n \"def softmax_loss_vectorized(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n num_train = X.shape[0]\\n dim = dW.shape[0]\\n num_classe = W.shape[1]\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using no explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n S = X.dot(W)\\n # ajouter le - max a la fin\\n indexes=np.arange(num_train)\\n #c = correct class score\\n c = S[indexes, y]\\n\\n e_syi = np.exp(c)\\n e_sj = np.sum(np.exp(S), axis=1)\\n Li = - np.log(e_syi/e_sj)\\n loss = np.sum(Li) / num_train + reg * np.sum(W * W)\\n\\n\\n M = np.exp(S)/(np.repeat(e_sj, num_classe).reshape(num_train, num_classe)) #(500,10)\\n M[indexes, y] -= 1 #bonnes classes\\n dW = X.T.dot(M)\\n\\n dW = dW/num_train + 2 * reg * W\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"def softmax(x):\\n shape = x.shape\\n probs = np.exp(x - np.max(x, axis=len(shape) - 1, keepdims=True))\\n probs /= np.sum(probs, axis=len(shape) - 1, keepdims=True)\\n return probs\",\n \"def softmax_my(x):\\n dim = 1\\n try:\\n dim = x.ndim\\n except AttributeError:\\n return cal_1D_softmax(x)\\n if dim == 1:\\n return cal_1D_softmax(x)\\n elif dim == 2:\\n return cal_2D_softmax(x)\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n num_train = X.shape[0]\\n num_classes = W.shape[1]\\n for i in xrange(num_train):\\n scores = X[i, :].dot(W)\\n scores -= np.max(scores)\\n correct_scores = scores[y[i]]\\n score_sum = np.sum(np.exp(scores))\\n h = np.exp(correct_scores) / score_sum\\n loss += -np.log(h)\\n for j in xrange(num_classes):\\n if j == y[i]:\\n dW[:, y[i]] += (np.exp(scores[j]) / score_sum - 1) * X[i, :]\\n else:\\n dW[:, j] += (np.exp(scores[j]) / score_sum) * X[i, :]\\n \\n \\n loss /= num_train + ( reg * np.sum(W * W))\\n dW /= num_train\\n\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"def softmax(inputs):\\n # Your code pass\\n # 对向量的每个元素归一化，先把每个元素作为e的指数求结果\\n input_exp = np.exp(inputs)\\n # 返回 每个元素作为e的指数求结果 与所在列所有的这些结果和的比值，另外添加一个维度\\n return input_exp / np.sum(input_exp, axis=1)[:, np.newaxis]\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n num_train = X.shape[0]\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n scores = X.dot(W)\\n scores_exp = np.exp(scores-np.max(scores, axis=1, keepdims=True))\\n\\n sum = np.sum(scores_exp, axis=1, keepdims=True)\\n probability = scores_exp/sum\\n #list containing the correct classification\\n indices = [range(num_train), y]\\n correct_class_score = probability[indices]\\n\\n #calculate -log(prob_y) and take the sum across all training examples\\n loss = np.sum(-np.log(correct_class_score))\\n loss /= num_train\\n loss += 0.5 * reg * np.sum(W * W)\\n\\n #Compute Gradient\\n probability[indices] -=1\\n dW = X.T.dot(probability)\\n dW /= num_train\\n dW += .5 * reg * W\\n\\n\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"def _softmax(x):\\n e = K.exp(x - K.max(x, axis=-1, keepdims=True))\\n s = K.sum(e, axis=-1, keepdims=True)\\n return e / s\",\n \"def softmax_loss1(x, y):\\n # tmp = np.max(x, axis=1, keepdims=True)\\n shifted_logits = x - np.max(x, axis=1, keepdims=True)\\n Z = np.sum(np.exp(shifted_logits), axis=1, keepdims=True)\\n log_probs = shifted_logits - np.log(Z)\\n probs = np.exp(log_probs)\\n N = x.shape[0]\\n # tmp2 = np.arange(N)\\n tmp3 = log_probs[np.arange(N), y]\\n # tmp4 = log_probs[[0,1,2],[2,5,0]]\\n loss = -np.sum(log_probs[np.arange(N), y]) / N\\n dx = probs.copy()\\n dx[np.arange(N), y] -= 1\\n dx /= N\\n return loss, dx\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n num_train = X.shape[0]\\n num_classes = W.shape[1]\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n scores = np.dot(X,W)\\n scores = (scores.T - np.max(scores,1)).T\\n for i in xrange(num_train):\\n nominator = np.exp(scores[i,:])\\n denominator = np.sum(np.exp(scores[i,:]))\\n loss -= np.log(nominator[y[i]]/denominator)\\n for j in xrange(num_classes):\\n dW[:,j] += (nominator[j]/denominator)*X[i,:]\\n dW[:,y[i]] -= X[i,:]\\n\\n loss /= num_train\\n dW /= num_train\\n loss += 0.5*reg*np.sum(W*W)\\n dW += reg*W\\n\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"def softmax(input):\\n list_value = []\\n len_compute = input.shape[-1]\\n shape_input = input.shape\\n for x in input.reshape(-1, len_compute):\\n # print(x)\\n e_x = np.exp(x - np.max(x))\\n res = e_x / e_x.sum(axis=0)\\n list_value.append(res)\\n\\n return np.array(list_value).reshape(shape_input)\",\n \"def softmax(inputs):\\n return np.exp(inputs) / float(sum(np.exp(inputs)))\",\n \"def softmax(inputs):\\n return np.exp(inputs) / float(sum(np.exp(inputs)))\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=1, keepdims=True)\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n for i in range(num_train):\\n score = X[i].dot(W)\\n exp_score = np.exp(score)\\n probability = exp_score[y[i]] / exp_score.sum()\\n loss += -np.log(probability)\\n dp = -1 / probability\\n for j in range(num_classes):\\n ds = np.exp(score[j])\\n if j == y[i]:\\n des = (exp_score.sum() - exp_score[y[i]]) / np.square(exp_score.sum())\\n else:\\n des = -(exp_score[y[i]]) / np.square(exp_score.sum())\\n dW[:, j] += X[i].T * ds * des * dp # chain rule\\n\\n loss /= num_train\\n dW /= num_train\\n\\n loss += 0.5 * reg * np.sum(W * W)\\n dW += reg * W\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"def softmax4(x):\\n ndim = K.ndim(x)\\n if ndim == 2:\\n return K.softmax(x)\\n elif ndim == 3:\\n e = K.exp(x - K.max(x, axis=-1, keepdims=True))\\n s = K.sum(e, axis=-1, keepdims=True)\\n return e / s\\n elif ndim == 4:\\n e = K.exp(x - K.max(x, axis=1, keepdims=True))\\n s = K.sum(e, axis=1, keepdims=True)\\n return e / s\\n else:\\n raise Exception('Cannot apply softmax to a tensor that is not 2D or 3D. ' +\\n 'Here, ndim=' + str(ndim))\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n # needed for calculations\\n num_train = X.shape[1]\\n\\n for i in xrange(num_train):\\n # calculate the scores for the current training example with the current weights\\n scores = W.dot(X[:, i])\\n # scale by the max for numerical stability\\n scores -= np.max(scores)\\n # calculate the loss\\n loss += -scores[y[i]] + np.log(np.sum(np.exp(scores)))\\n\\n ## L' = -1_y + 1/(\\\\sum_{}^{} e^f) * e^f\\n # e^f\\n scores = np.exp(scores)\\n # 1/(\\\\sum_{}^{} e^f)\\n scores /= np.sum(scores)\\n # -1_y\\n scores[y[i]] -= 1\\n\\n # now scale it by the data\\n # we need to use [:, np.newaxis] because when you make a X by 1 dimension slices in numpy the 1 dimension is null\\n dW += scores[:, np.newaxis].dot(X[:, i][:, np.newaxis].T)\\n\\n\\n # get the average loss\\n loss /= num_train\\n # get the average gradient\\n dW /= num_train\\n\\n # regularize the loss function\\n loss += 0.5 * reg * np.sum(W * W)\\n\\n return loss, dW\",\n \"def softmax(x):\\r\\n e_x = np.exp(x - np.expand_dims(np.max(x, axis=-1), axis=-1))\\r\\n return e_x / np.expand_dims(e_x.sum(axis=-1), axis=-1) # only difference\\r\",\n \"def softmax_loss(x, y):\\n loss, dx = None, None\\n ###########################################################################\\n # TODO: Implement the loss and gradient for softmax classification. This #\\n # will be similar to the softmax loss vectorized implementation in #\\n # cs231n/classifiers/softmax.py. #\\n ###########################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n num_train = x.shape[0]\\n\\n x = np.exp(x)\\n temp_sum = np.sum(x, axis = 1, keepdims = True)\\n x = x / temp_sum\\n softmax_result = x\\n trans_y = np.zeros((x.shape[0],x.shape[1]))\\n trans_y[np.arange(x.shape[0]), y] += 1\\n x = - np.log(x)\\n x = x * trans_y\\n x_sum = np.sum(x)\\n loss = x_sum / num_train\\n loss = loss + \\n\\n dx = softmax_result - trans_y\\n dx = dx / num_train\\n\\n\\n pass\\n\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n ###########################################################################\\n # END OF YOUR CODE #\\n ###########################################################################\\n return loss, dx\",\n \"def softmax_loss(x, y):\\n #raise NotImplementedError\\n #######################################################################\\n # #\\n # #\\n # TODO: YOUR CODE HERE #\\n # #\\n # #\\n #######################################################################\\n N=x.shape[0]\\n\\n \\n x-=np.max(x,axis=1,keepdims=True)\\n temp=np.exp(x)\\n dr_vec=np.sum(temp,axis=1,keepdims=True)\\n\\n nr=(x[np.arange(N),y]).reshape([N,1])\\n loss=np.sum(-(nr)+np.log(dr_vec))\\n \\n loss=(loss/N)\\n temp/=dr_vec\\n temp[np.arange(N),y] -= 1\\n \\n dx = temp/N\\n \\n return loss, dx\",\n \"def softmax_loss(x, y):\\n probs = np.exp(x - np.max(x, axis=1, keepdims=True))\\n probs /= np.sum(probs, axis=1, keepdims=True)\\n N = x.shape[0]\\n loss = -np.sum(np.log(probs[np.arange(N), y])) / N\\n dx = probs.copy()\\n dx[np.arange(N), y] -= 1\\n dx /= N\\n return loss, dx\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=1, keepdims=True)\",\n \"def softmax_loss(x, y):\\n ############################################################################\\n # TODO: You can use the previous softmax loss function here. # \\n # Hint: Be careful on overflow problem #\\n ############################################################################\\n ############################################################################\\n # START OF YOUR CODE #\\n ############################################################################\\n N = len(x)\\n # We want to get the real y\\n log_C = -np.max(x,axis=1,keepdims = True)\\n # Get numerator\\n e_all = np.exp(x+log_C)\\n # Get the final prob\\n prob = e_all/e_all.sum(axis=1,keepdims=True)\\n # Find final loss\\n loss = np.sum(-np.log(prob)[np.arange(N),y])/N\\n # Get dx\\n dx = prob\\n dx[np.arange(N),y] -= 1\\n dx /= N\\n \\n ############################################################################\\n # END OF YOUR CODE #\\n ############################################################################\\n return loss, dx\",\n \"def softmax(x):\\r\\n output = np.exp(x)\\r\\n return output / np.sum(output, axis=1, keepdims=True)\",\n \"def softmax(x):\\n #pass # TODO: Compute and return softmax(x)\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n #############################################################################\\n # START OF YOUR CODE #\\n #############################################################################\\n # construct a one-hot vector for y\\n onehot_y = np.zeros((y.size, W.shape[1]))\\n onehot_y[np.arange(y.size), y] = 1\\n dW = dW.T\\n for i in range(y.shape[0]):\\n f = np.dot(X[i], W)\\n \\n for j in range(W.shape[1]):\\n e_f = np.exp(f - np.max(f))\\n softmax = e_f / e_f.sum()\\n loss -= onehot_y[i][j] * np.log(softmax[j])\\n dW[j] -= X[i] * (onehot_y[i][j] - softmax[j])\\n \\n loss = loss / y.shape[0] + reg * np.linalg.norm(W)\\n dW = dW.T / y.shape[0] + 2 * reg * W\\n\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n \\n\\n return loss, dW\",\n \"def loss(self, X, y):\\n\\n # Initialize the loss to zero.\\n loss = 0.0\\n num_classes = self.W.shape[0] # C = num_classes\\n num_train = X.shape[0]\\n \\n exp_a = np.zeros((num_classes,num_train))\\n # ================================================================ #\\n # YOUR CODE HERE:\\n # Calculate the normalized softmax loss. Store it as the variable loss.\\n # (That is, calculate the sum of the losses of all the training \\n # set margins, and then normalize the loss by the number of \\n # training examples.)\\n # ================================================================ #\\n \\n \\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 #p[:,i] = exp_a[:,i]/np.sum(exp_a[:,i]) # p now is a valid probability matrix\\n #print(p[:,i])\\n\\n loss += Loss \\n #print(Loss,i) \\n \\n pass\\n loss /= num_train\\n # ================================================================ #\\n # END YOUR CODE HERE\\n # ================================================================ #\\n\\n return loss\",\n \"def softmax(x):\\n pass # TODO: Compute and return softmax(x)\\n\\n exp_x = np.exp(x)\\n sum_x = np.sum(exp_x, axis=0)\\n softmax = exp_x/sum_x\\n \\n return softmax\",\n \"def softmax(x): \\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax_loss_naive(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n for i in range(X.shape[0]):\\n scores = X[i].dot(W)\\n \\n idx_max = np.argmax(scores)\\n s_max = scores[idx_max]\\n scores -= s_max # shift for numerical stability\\n \\n temp = np.exp(scores)\\n summation = np.sum(temp)\\n loss += (- scores[y[i]] + np.log(summation))\\n \\n # computing gradients\\n # (1) an explicit version:\\n# for j in range(W.shape[1]):\\n# if j == y[i]:\\n# dW[:, j] -= X[i]\\n# dW[:, idx_max] -= (-X[i])\\n \\n# dW[:, j] += (1 / summation) * temp[j] * X[i]\\n# dW[:, idx_max] += (1 / summation) * temp[j] * (-X[i])\\n# elif j == idx_max:\\n# dW[:, j] += 0 # X[i] + (-X[i]) = 0\\n# else:\\n# dW[:, j] += (1 / summation) * temp[j] * X[i]\\n# dW[:, idx_max] += (1 / summation) * temp[j] * (-X[i])\\n \\n # (2) a more concise version:\\n softmax_scores = temp / summation\\n for j in range(W.shape[1]):\\n if j == y[i]:\\n dW[:, j] += (-1 + softmax_scores[j]) * X[i]\\n else:\\n dW[:, j] += softmax_scores[j] * X[i]\\n \\n loss /= X.shape[0]\\n dW /= X.shape[0]\\n \\n loss += reg * np.sum(W * W)\\n dW += 2 * reg * W\\n\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"def softmax_loss(x, y):\\n def softmax(x):\\n exps = np.exp(x)\\n return exps / np.sum(exps, axis=1)[:,None]\\n\\n N = y.shape[0]\\n p = softmax(x)\\n log_likelihood = -np.log(p[range(N),y])\\n loss = np.sum(log_likelihood) / N\\n\\n dx = p.copy()\\n dx[range(N),y] -= 1\\n dx = dx/N\\n\\n return loss, dx\",\n \"def softmax_loss_vectorized(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using no explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n scores = X@W # 500,10\\n# print(scores.shape)\\n max_scores = np.max(scores, axis=1).reshape(-1,1) # 500, numeric instablity\\n# print(max_scores.shape)\\n scores -= max_scores # numeric instablity\\n# print(scores.shape)\\n correct_scores = scores[np.arange(scores.shape[0]), y] # 500,\\n P_ic = np.exp(correct_scores)/np.sum(np.exp(scores), axis=1)\\n# print(P)\\n loss += np.sum(-np.log(P_ic))/scores.shape[0] # L = ∑L_i/N\\n loss += reg * np.sum(W * W) # regularization\\n # 向量化梯度：用scores构建一个P [500, 10]，首先取exp(scores)得到每一个位置的exp,然后对每个位置除以这一行的exp和\\n # 上面的操作会得到500，10的矩阵，每个位置都是softmax之后的结果\\n # !重点：对于[i,y[i]]位置，根据P_ic - 1, 要减1 \\n P = np.exp(scores) # 正确分类的梯度, 位于梯度矩阵所有c的行\\n P /= np.sum(np.exp(scores),axis=1).reshape(-1, 1)\\n P[np.arange(scores.shape[0]), y] -= 1 # 将 i, y[i] -= 1\\n \\n # 得到这个矩阵之后，与X.T相乘即可得到dL/dW P(500,10) X(500,3073) X.T (3073, 500) W(3073, 10)\\n dW += X.T@P\\n dW /= scores.shape[0] # *1/N\\n dW += 2*reg*W # 正则化梯度\\n \\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n\\n return loss, dW\",\n \"def softmax(x):\\n \\\"\\\"\\\"\\\"\\\"\\\"\\n return exp(x) / sum(exp(x), axis=0)\",\n \"def softmax(x):\\n x = np.array(x)\\n if x.ndim == 1:\\n return column_softmax(x)\\n else:\\n ret_arr = np.array([column_softmax(col) for col in x.T])\\n return ret_arr.T\",\n \"def softmax(x):\\n shape = np.shape(x)\\n numDim = len(shape)\\n result = np.zeros_like(x, dtype=float)\\n \\n if numDim == 1:\\n totalSum = 0\\n # Sum (e^yi)\\n for i in range(len(x)):\\n totalSum = totalSum + exp(x[i])\\n # e^yi / Sum (e^yi)\\n for i in range(len(x)):\\n result[i] = exp(x[i])/totalSum\\n else:\\n xDim = len(x[0])\\n totalSum = np.zeros(xDim, dtype=float)\\n \\n # e^yi\\n for i in range(len(x)):\\n for j in range(len(x[i])):\\n result[i,j] = exp(x[i,j])\\n totalSum[j] = totalSum[j] + result[i,j]\\n # e^yi / Sum (e^yi)\\n for i in range(len(result)):\\n for j in range(len(result[i])):\\n result[i,j] = result[i,j] / totalSum[j]\\n \\n return result\",\n \"def softmax(x):\\n scoreMatExp = np.exp(np.asarray(x))\\n return scoreMatExp / scoreMatExp.sum(0)\",\n \"def softmax(x):\\n # https://stackoverflow.com/questions/34968722/softmax-function-python\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax_loss_vectorized(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n num_train = X.shape[1]\\n num_classes = W.shape[0]\\n #############################################################################\\n # Compute the softmax loss and its gradient using no explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n \\n # compute scores\\n scores = W.dot(X)\\n scores -= np.max(scores)\\n\\n # softmax function\\n softmax = np.exp(scores) / np.sum(np.exp(scores), 0) # 10 x 49000 | C x D\\n \\n # cross entropy loss\\n loss = -np.log(softmax[y, range(num_train)]) # 49000\\n loss = np.sum(loss) / num_train\\n\\n # regularisation\\n loss += 0.5 * reg * np.sum(W*W)\\n\\n # gradient (source:https://github.com/MyHumbleSelf/cs231n/blob/master/assignment1/cs231n/classifiers/softmax.py)\\n ind = np.zeros(softmax.shape)\\n ind[y, range(num_train)] = 1\\n dW = np.dot((softmax-ind), X.T)\\n dW /= num_train\\n\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"def softmax_loss_vectorized(W, X, y, reg):\\n num_train = X.shape[0]\\n num_class = W.shape[1]\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n scores = X.dot(W)\\n temp_matrix = np.zeros(scores.shape)\\n \\n max_each_row = np.max(scores,axis=1).reshape(-1,1)\\n scores -= max_each_row\\n summation = np.sum(np.exp(scores),axis=1).reshape(-1,1)\\n scores = np.exp(scores)\\n scores = np.divide(scores,summation)\\n temp_matrix[range(num_train),list(y)] =-1\\n scores += temp_matrix\\n dW = X.T.dot(scores) / num_train + 2*reg*W \\n log_summation = np.log(summation)\\n vector = scores[range(num_train),list(y)].reshape(-1,1) \\n L = -vector+ log_summation \\n loss = np.sum(L)/num_train + reg*np.sum(W*W)\\n \\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using no explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n \\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"def softmax(self,Weights,X,b):\\n N = X.shape[0]\\n D = X.shape[1]\\n C = Weights.shape[1]\\n \\n #P = np.zeros((N,C))\\n #print P.shape\\n\\n P1 = np.dot(X,Weights) + b\\n P1 = np.exp(P1)\\n \\n for i in range(N):\\n P1[i,:] = P1[i,:]/P1[i,:].sum()\\n # print P1\\n return P1\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=1)\",\n \"def softmax_loss(x, y):\\n shifted_logits = x - np.max(x, axis=1, keepdims=True)\\n Z = np.sum(np.exp(shifted_logits), axis=1, keepdims=True)\\n log_probs = shifted_logits - np.log(Z)\\n probs = np.exp(log_probs)\\n N = x.shape[0]\\n loss = -np.sum(log_probs[np.arange(N), y]) / N\\n dx = probs.copy()\\n dx[np.arange(N), y] -= 1\\n dx /= N\\n return loss, dx\",\n \"def softmax(x):\\n return np.exp(x)/np.sum(np.exp(x),axis=0)\",\n \"def softmax(x):\\n return np.exp(x)/np.sum(np.exp(x),axis=0)\",\n \"def softmax(X):\\n num = np.exp(X)\\n den = np.sum(np.exp(X))\\n return num / den\",\n \"def softmax(x):\\n npX = np.array(x)\\n expX = np.exp(x)\\n\\n return expX/sum(expX)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\\n # return ( x / np.sum(x, axis=0) )\",\n \"def test_softmax(n_epochs=250, optimizer='cg'):\\n n_hidden = 10\\n n_in = 5\\n n_steps = 10\\n n_seq = 10 # per batch\\n n_batches = 50\\n n_classes = 3\\n n_out = n_classes # restricted to single softmax per time step\\n\\n np.random.seed(0)\\n # simple lag test\\n seq = np.random.randn(n_steps, n_seq * n_batches, n_in)\\n targets = np.zeros((n_steps, n_seq * n_batches), dtype=np.int)\\n\\n thresh = 0.5\\n # if lag 1 (dim 3) is greater than lag 2 (dim 0) + thresh\\n # class 1\\n # if lag 1 (dim 3) is less than lag 2 (dim 0) - thresh\\n # class 2\\n # if lag 2(dim0) - thresh <= lag 1 (dim 3) <= lag2(dim0) + thresh\\n # class 0\\n targets[2:, :][seq[1:-1, :, 3] > seq[:-2, :, 0] + thresh] = 1\\n targets[2:, :][seq[1:-1, :, 3] < seq[:-2, :, 0] - thresh] = 2\\n #targets[:, 2:, 0] = np.cast[np.int](seq[:, 1:-1, 3] > seq[:, :-2, 0])\\n\\n model = MetaRNN(n_in=n_in, n_hidden=n_hidden, n_out=n_out,\\n learning_rate=0.005, learning_rate_decay=0.999,\\n n_epochs=n_epochs, batch_size=n_seq, activation='tanh',\\n output_type='softmax')\\n\\n model.fit(seq, targets, validate_every=10, compute_zero_one=True,\\n optimizer=optimizer)\\n\\n seqs = xrange(10)\\n\\n plt.close('all')\\n for seq_num in seqs:\\n fig = plt.figure()\\n ax1 = plt.subplot(211)\\n plt.plot(seq[:, seq_num])\\n ax1.set_title('input')\\n ax2 = plt.subplot(212)\\n\\n # blue line will represent true classes\\n true_targets = plt.step(xrange(n_steps), targets[:, seq_num],\\n marker='o')\\n\\n # show probabilities (in b/w) output by model\\n guess = model.predict_proba(seq[:, seq_num][:, np.newaxis])\\n guessed_probs = plt.imshow(guess.squeeze().T, interpolation='nearest',\\n cmap='gray')\\n ax2.set_title('blue: true class, grayscale: probs assigned by model')\",\n \"def softmax(x):\\n orig_shape = x.shape\\n\\n if len(x.shape) > 1:\\n # Matrix\\n tmp = np.max(x, axis=1)\\n x -= tmp.reshape((x.shape[0], 1))\\n x = np.exp(x)\\n tmp = np.sum(x, axis=1)\\n x /= tmp.reshape((x.shape[0], 1))\\n else:\\n # Vector\\n tmp = np.max(x)\\n x -= tmp\\n x = np.exp(x)\\n tmp = np.sum(x)\\n x /= tmp\\n\\n assert x.shape == orig_shape\\n return x\",\n \"def softmax(X):\\n _X = X - np.max(X, axis=1).reshape(-1, 1)\\n ep = np.exp(_X)\\n return ep / np.sum(ep, axis=1).reshape(-1, 1)\",\n \"def softmax(x):\\n xx = x\\n x = x.reshape((-1, x.shape[-1]))\\n e_x = np.exp(x - np.max(x, 1).reshape(-1, 1))\\n res = e_x / e_x.sum(axis=1).reshape(-1, 1)\\n return res.reshape(xx.shape)\",\n \"def softmax(x: npt.NDArray) -> npt.NDArray:\\n row_wise_max = np.max(x, axis=1).reshape(-1, 1)\\n exp_x = np.exp(x - row_wise_max)\\n return exp_x / np.sum(exp_x, axis=1).reshape(-1, 1)\",\n \"def _softmax(self,x):\\n e_x = np.exp(x - np.max(x))\\n return np.nan_to_num(e_x / np.nan_to_num(e_x.sum(axis=0)))\",\n \"def softmax_loss_vectorized(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n # Compute class scores\\n (num_class, D), (D, num_train) = W.shape, X.shape\\n class_scores = np.dot(W, X)\\n\\n # Softmax them\\n e_x = np.exp(class_scores - class_scores.max(axis=0))\\n class_scores = e_x / e_x.sum(axis=0)\\n \\n # Create mask of ys\\n gold_class_matrix = np.zeros((num_class, num_train))\\n gold_class_matrix[y, range(num_train)] = 1\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using no explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n # Cross entropy loss\\n loss = -(gold_class_matrix * np.log(class_scores)).sum()\\n \\n # Add regularization and normalize\\n loss += 0.5 * reg * np.sum(W * W)\\n loss /= num_train\\n \\n # Gradients\\n augmented_scores = class_scores - gold_class_matrix\\n (num_class, num_train), (num_train, D) = augmented_scores.shape, X.T.shape\\n dW = np.dot(augmented_scores, X.T)\\n \\n # Add regularization and normalize\\n dW += reg * W\\n dW /= num_train\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"def softmax(x): \\n e_x = np.exp(x - np.max(x)) \\n return e_x / e_x.sum()\",\n \"def softmax_loss_vectorized(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n num_train, num_dim = X.shape\\n num_classes = W.shape[1]\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using no explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n scores = np.dot(X,W)\\n # scores = scores.T - np.max(scores,1)\\n # f = np.exp(scores.T) \\n # correct_scores = f[range(num_train),y] #1*N\\n # col_sum = np.sum(f,1)\\n # loss = np.sum(-np.log(correct_scores/col_sum))\\n\\n # mat = f.T/col_sum #\\n # mat = mat.T\\n # y_pred = np.zeros(mat.shape)\\n # y_pred[range(num_train),y] = 1\\n # dW = np.dot(X.T,mat-y_pred)\\n\\n # loss/=num_train\\n # loss += 0.5*reg*np.sum(W*W)\\n # dW /= num_train\\n # dW += reg*W\\n f = scores.T - np.max(scores,1)\\n f = f.T\\n f_correct = scores[range(num_train),y]\\n \\n sum_col = np.log(np.sum(np.exp(scores),1)) # N*1\\n \\n loss = sum_col - f_correct # N*1\\n loss = np.sum(loss)/num_train + 0.5*reg*np.sum(W*W)\\n\\n prob = np.exp(f).T / np.sum(np.exp(f),1)\\n prob = prob.T\\n y_pred = np.zeros(scores.shape)\\n y_pred[range(num_train),y] = 1\\n dW = X.T.dot(prob - y_pred)\\n dW = dW/float(num_train) + reg*W\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"def softmax(H, V, d):\\n postActivation = np.dot(H,V) + d\\n expVector = np.exp(postActivation)\\n return expVector/(np.sum(expVector, axis=1)[:,np.newaxis])\",\n \"def softmax(X, Y, w, HProp = None, arg=None, reg=None, batchsize=None):\\n if reg == None:\\n reg_f = 0\\n reg_g = 0\\n reg_Hv = lambda v: 0\\n else:\\n reg_f, reg_g, reg_Hv = reg(w)\\n global d, C\\n n, d = X.shape\\n \\n if batchsize is not None:\\n n_mini = np.int(np.floor(n*batchsize))\\n index_batch = np.random.choice(n, n_mini, replace = False)\\n# print(index_batch[:5])\\n X = X[index_batch,:]\\n Y = Y[index_batch]\\n n = n_mini\\n \\n C = int(len(w)/d)\\n w = w.reshape(d*C,1) #[d*C x 1]\\n W = w.reshape(C,d).T #[d x C]\\n XW = np.dot(X,W) #[n x C]\\n large_vals = np.amax(XW,axis = 1).reshape(n, 1) #[n,1 ]\\n large_vals = np.maximum(0,large_vals) #M(x), [n, 1]\\n #XW - M(x)/<Xi,Wc> - M(x), [n x C]\\n XW_trick = XW - np.tile(large_vals, (1, C))\\n #sum over b to calc alphax, [n x total_C]\\n XW_1_trick = np.append(-large_vals, XW_trick,axis = 1)\\n #alphax, [n, ]\\n sum_exp_trick = np.sum(np.exp(XW_1_trick), axis = 1).reshape(n, 1)\\n log_sum_exp_trick = large_vals + np.log(sum_exp_trick) #[n, 1]\\n \\n f = np.sum(log_sum_exp_trick)/n - np.sum(np.sum(XW*Y,axis=1))/n + reg_f\\n if arg == 'f': \\n return f\\n inv_sum_exp = 1./sum_exp_trick\\n inv_sum_exp = np.tile(inv_sum_exp,(1,np.size(W,axis = 1)))\\n S = inv_sum_exp*np.exp(XW_trick) #h(x,w), [n x C] \\n g = np.dot(X.T, S-Y)/n #[d x C]\\n g = g.T.flatten().reshape(d*C,1) + reg_g#[d*C, ] \\n\\n if arg == 'g':\\n return g \\n \\n if arg == 'fg':\\n return f, g\\n\\n if HProp == None:\\n Hv = lambda v: hessvec(X, S, n, v) + reg_Hv(v) \\n return f, g, Hv\\n else:\\n n_H = np.int(np.floor(n*HProp))\\n idx_H = np.random.choice(n, n_H, replace = False)\\n inv_sum_exp_H = 1./(sum_exp_trick[idx_H,:])\\n inv_sum_exp_H = np.tile(inv_sum_exp_H,(1,np.size(W,axis = 1)))\\n S_H = inv_sum_exp_H*np.exp(XW_trick[idx_H,:]) #h(x,w), [S x C] \\n Hv = lambda v: hessvec(X[idx_H,:], S_H, n_H, v) + reg_Hv(v)\\n return f, g, Hv\\n \\n if arg == 'explicit':\\n f = np.sum(log_sum_exp_trick) - np.sum(np.sum(XW*Y,axis=1)) + reg_f\\n g = np.dot(X.T, S-Y) #[d x C]\\n g = g.T.flatten().reshape(d*C,1) + reg_g #[d*C, ]\\n Hv = lambda v: hessvec(X, S, v, reg)\\n #S is divided into C parts {1:b}U{c}, [n, ] * C\\n S_cell = np.split(S.T,C) \\n SX_cell = np.array([]).reshape(n,0) #empty [n x 0] array\\n SX_self_cell = np.array([]).reshape(0,0)\\n for column in S_cell:\\n c = spdiags(column,0,n,n) #value of the b/c class\\n SX_1_cell = np.dot(c.A,X) #WX = W x X,half of W, [n x d]\\n #fill results from columns, [n x d*C]\\n SX_cell = np.c_[SX_cell, SX_1_cell] \\n SX_cross = np.dot(SX_cell.T,SX_cell) #take square, [d*C x d*C] \\n #X.T x WX half of W, [d x d]\\n SX_1self_cell = np.dot(X.T,SX_1_cell) \\n #put [d x d] in diag, W_cc, [d*C x d*C] \\n SX_self_cell = block_diag(SX_self_cell,SX_1self_cell) \\n H = SX_self_cell - SX_cross #compute W_cc, [d*C x d*C]\\n H = H + 2*reg*identity(d*C)\\n return f, g, Hv, H\",\n \"def softmaxCostAndGradient(predicted, target, outputVectors, dataset):\\n\\n ### YOUR CODE HERE\\n scores = outputVectors.dot(predicted.T) # shape = (V, 1)\\n y_hat = softmax(scores)\\n cost = -scores[target] + np.log(np.sum(np.exp(scores)))\\n one_hot_target = np.zeros_like(y_hat)\\n one_hot_target[target] = 1\\n grad = np.outer((y_hat - one_hot_target), predicted)\\n gradPred = outputVectors.T.dot(y_hat - one_hot_target)\\n \\n '''\\n final_predicted = predicted.dot(outputVectors.T)\\n probability = softmax(final_predicted)\\n cost = -np.log(probability[target])\\n \\n one_hot_target = np.zeros_like(probability)\\n one_hot_target[target] += 1\\n dlogits = probability - one_hot_target\\n grad = np.outer(predicted, dlogits).T\\n gradPred = outputVectors.T.dot(dlogits)\\n '''\\n ### END YOUR CODE\\n\\n return cost, gradPred, grad\",\n \"def pred(W, X):\\n A = softmax_stable(X.dot(W))\\n return np.argmax(A, axis = 1)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax_loss_vectorized(W, X, y, reg):\\n\\n #############################################################################\\n # TODO: Compute the softmax.ipynb loss and its gradient using no explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n train_images = X.shape[0]\\n # Store all the scores in a matrix\\n all_scores = np.dot(X,W)\\n #First, calculate the normalizing constant for numeric stability\\n constant = np.max(all_scores,axis=1)\\n normalized_scores = np.transpose(np.subtract(np.transpose(all_scores),constant))\\n\\n #Then, calculate softmax for the correct scores\\n exp_scores = np.exp(all_scores)\\n # First, keep track of the sum of values per row\\n exp_sum = np.sum(exp_scores,axis=1)\\n\\n # Finally, calculate the softmax score for every entry\\n softmax_scores = np.transpose(exp_scores)/exp_sum # useful when computing gradient\\n softmax_scores = np.transpose(softmax_scores)\\n # And then, compute the loss\\n loss_score = softmax_scores[range(train_images),y]\\n loss_score = -1 * np.log(loss_score) #taking the logarithm\\n loss += np.sum(loss_score)\\n\\n #Normalize and regularize the loss\\n loss /= train_images\\n loss += 0.5*reg*np.sum(W*W)\\n\\n #Finally, calculate a vectorized gradient\\n\\n # Calculate the derivative at the correct label\\n softmax_scores[range(train_images),y] -= 1\\n # Then, make a matrix containing all the gradient values\\n gradient_values = np.dot(np.transpose(X),softmax_scores)\\n gradient_values = gradient_values\\n\\n #FINALLY, update the gradient\\n dW+= gradient_values\\n #And normalize and regularize it\\n dW /= train_images\\n dW += reg*W\\n\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\",\n \"def softmax_loss(scores, y):\\r\\n N = scores.shape[0] # number of input data\\r\\n\\r\\n # compute data loss\\r\\n shifted_logits = scores - np.max(scores, axis=1, keepdims=True)\\r\\n Z = np.sum(np.exp(shifted_logits), axis=1, keepdims=True)\\r\\n log_probs = shifted_logits - np.log(Z)\\r\\n probs = np.exp(log_probs)\\r\\n loss = -np.sum(log_probs[range(N), y]) / N\\r\\n\\r\\n # Compute gradient of loss function w.r.t. scores\\r\\n dscores = probs.copy()\\r\\n dscores[range(N), y] -= 1\\r\\n dscores /= N\\r\\n \\r\\n return loss, dscores\",\n \"def softmax(x):\\n x_exp = np.exp(x)\\n x_sum = np.sum(x_exp, axis=1, keepdims=True)\\n s = x_exp / x_sum\\n \\n return s\",\n \"def softmax_loss_vectorized(W, X, y, reg):\\n # Initialize the loss and gradient to zero.\\n loss = 0.0\\n dW = np.zeros_like(W)\\n\\n #############################################################################\\n # TODO: Compute the softmax loss and its gradient using no explicit loops. #\\n # Store the loss in loss and the gradient in dW. If you are not careful #\\n # here, it is easy to run into numeric instability. Don't forget the #\\n # regularization! #\\n #############################################################################\\n\\n num_classes = W.shape[1]\\n num_train = X.shape[0]\\n\\n # Calculate scores for each classifier (column in the weight matrix W)\\n # acting on each training sample (row in X)\\n scores = X.dot(W)\\n \\n # Normalization trick to resolve numerical instability\\n # when dealing with the large exponential terms.\\n scores -= np.max(scores)\\n\\n # Cache some terms that are used repeatedly.\\n exp_scores = np.exp(scores)\\n sum_exp_scores = np.sum(exp_scores,axis=1)\\n\\n # Find the correct classifier scores for each training sample\\n correct_class_scores = scores[np.arange(num_train), y]\\n\\n # Update the loss\\n loss = np.sum(-correct_class_scores + np.log(sum_exp_scores))\\n\\n # Update the gradient\\n correct_indices = np.zeros(scores.shape)\\n correct_indices[np.arange(num_train), y] = 1\\n\\n dW -= correct_indices.T.dot(X).T\\n dW += X.T.dot((exp_scores.T / sum_exp_scores).T)\\n \\n # Average over the training samples\\n loss /= num_train\\n dW /= num_train\\n \\n # Add regularization.\\n loss += 0.5 * reg * np.sum(W * W)\\n dW += reg * W\\n\\n pass\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return loss, dW\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77309406","0.76029754","0.75815004","0.7345373","0.7316555","0.7265773","0.72579056","0.72342706","0.7226934","0.72213393","0.7203286","0.71891123","0.713433","0.71218145","0.7092656","0.7017707","0.69664174","0.6941942","0.693169","0.69267535","0.6920247","0.69167215","0.69166845","0.6902661","0.68815243","0.68711543","0.68626106","0.68621993","0.68389505","0.6825831","0.6822717","0.6810713","0.6808892","0.6802318","0.67983186","0.6763175","0.6752769","0.6745803","0.6724247","0.6724247","0.67205447","0.671932","0.6706289","0.67049116","0.6681859","0.66787505","0.6677297","0.6673336","0.665494","0.6631346","0.66088074","0.6607533","0.65828854","0.6567757","0.65631723","0.65579456","0.6535626","0.65345955","0.6526599","0.65238607","0.6512038","0.6511488","0.6508471","0.6505849","0.64962375","0.6494031","0.6483469","0.648092","0.648092","0.648092","0.648092","0.648092","0.648092","0.648092","0.648092","0.6463299","0.6460482","0.64555496","0.64555496","0.644398","0.6443572","0.6439401","0.64138234","0.6411188","0.64003414","0.6399044","0.6396093","0.6394289","0.6384336","0.6382958","0.6377422","0.6353616","0.6352739","0.6345235","0.63434684","0.63374186","0.6330419","0.6324013","0.63219005","0.6318117"],"string":"[\n \"0.77309406\",\n \"0.76029754\",\n \"0.75815004\",\n \"0.7345373\",\n \"0.7316555\",\n \"0.7265773\",\n \"0.72579056\",\n \"0.72342706\",\n \"0.7226934\",\n \"0.72213393\",\n \"0.7203286\",\n \"0.71891123\",\n \"0.713433\",\n \"0.71218145\",\n \"0.7092656\",\n \"0.7017707\",\n \"0.69664174\",\n \"0.6941942\",\n \"0.693169\",\n \"0.69267535\",\n \"0.6920247\",\n \"0.69167215\",\n \"0.69166845\",\n \"0.6902661\",\n \"0.68815243\",\n \"0.68711543\",\n \"0.68626106\",\n \"0.68621993\",\n \"0.68389505\",\n \"0.6825831\",\n \"0.6822717\",\n \"0.6810713\",\n \"0.6808892\",\n \"0.6802318\",\n \"0.67983186\",\n \"0.6763175\",\n \"0.6752769\",\n \"0.6745803\",\n \"0.6724247\",\n \"0.6724247\",\n \"0.67205447\",\n \"0.671932\",\n \"0.6706289\",\n \"0.67049116\",\n \"0.6681859\",\n \"0.66787505\",\n \"0.6677297\",\n \"0.6673336\",\n \"0.665494\",\n \"0.6631346\",\n \"0.66088074\",\n \"0.6607533\",\n \"0.65828854\",\n \"0.6567757\",\n \"0.65631723\",\n \"0.65579456\",\n \"0.6535626\",\n \"0.65345955\",\n \"0.6526599\",\n \"0.65238607\",\n \"0.6512038\",\n \"0.6511488\",\n \"0.6508471\",\n \"0.6505849\",\n \"0.64962375\",\n \"0.6494031\",\n \"0.6483469\",\n \"0.648092\",\n \"0.648092\",\n \"0.648092\",\n \"0.648092\",\n \"0.648092\",\n \"0.648092\",\n \"0.648092\",\n \"0.648092\",\n \"0.6463299\",\n \"0.6460482\",\n \"0.64555496\",\n \"0.64555496\",\n \"0.644398\",\n \"0.6443572\",\n \"0.6439401\",\n \"0.64138234\",\n \"0.6411188\",\n \"0.64003414\",\n \"0.6399044\",\n \"0.6396093\",\n \"0.6394289\",\n \"0.6384336\",\n \"0.6382958\",\n \"0.6377422\",\n \"0.6353616\",\n \"0.6352739\",\n \"0.6345235\",\n \"0.63434684\",\n \"0.63374186\",\n \"0.6330419\",\n \"0.6324013\",\n \"0.63219005\",\n \"0.6318117\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6760264"},"document_rank":{"kind":"string","value":"36"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":29,"numItemsPerPage":100,"numTotalItems":95772,"offset":2900,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NzcxOTQ5Mywic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3Nzc3MjMwOTMsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.s0LxEG3YTrp5ow849HkUEBoN54EdTqum0qSVRtDVe5vokPcbGpp7eBTEHv-x3SWGjOoE3uKpTY2TFuluq2QGDw","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 |
|---|---|---|---|---|---|---|
Make sure all required tags are set | def clean_tags(self):
if hasattr(self.instance, 'get_required_keys') and hasattr(self.instance, 'tags'):
for key in self.instance.get_required_keys():
if key not in self.cleaned_data.get('tags'):
raise forms.ValidationError("Tag %s missing." % key)
return ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _verify_tags(self):\n for tag in self.tags:\n if tag.lower() in VASP_TAG_LIST:\n continue\n else:\n print((\"Warning: unknown INCAR tag '\" + tag + \"' with value '\" + str(self.tags[tag]) + \"'\"))",
"def test_tags(question):\n assert \"tags\" in... | [
"0.6713339",
"0.62802184",
"0.62078756",
"0.61916",
"0.6153197",
"0.6120965",
"0.61105335",
"0.6104445",
"0.60799277",
"0.6070347",
"0.6061206",
"0.5990083",
"0.5961519",
"0.5878047",
"0.5865956",
"0.5823758",
"0.5815917",
"0.577706",
"0.57572424",
"0.57379663",
"0.568212",
... | 0.6199197 | 3 |
function used for writing late checkin record in payslip input tree. | def get_inputs(self, contracts, date_from, date_to):
res = super(PayslipLateCheckIn, self).get_inputs(contracts, date_to, date_from)
late_check_in_type = self.env.ref('employee_late_check_in.late_check_in')
contract = self.contract_id
late_check_in_id = self.env['late.check_in'].search([... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def writeto(self, fileout):\n \n dump_pkl(self.data, fileout)",
"def dump(self,out):\n if self.changed: raise StateError(_('Data changed: ')+ self.name)\n if not self.data: raise StateError(_('Data undefined: ')+self.name)\n out.write(struct.pack('4s3i',self.name,self.size,self... | [
"0.5688454",
"0.54053",
"0.52774614",
"0.52668023",
"0.52515334",
"0.5238117",
"0.5215203",
"0.5178974",
"0.50876176",
"0.506635",
"0.5011479",
"0.4995191",
"0.49629214",
"0.49451622",
"0.49426115",
"0.4934632",
"0.48918885",
"0.48890415",
"0.48604503",
"0.48549697",
"0.48458... | 0.0 | -1 |
function used for marking deducted Late checkin request. | def action_payslip_done(self):
for recd in self.late_check_in_ids:
recd.state = 'deducted'
return super(PayslipLateCheckIn, self).action_payslip_done() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def loan(self):",
"def checkin(self):\n folio = self.folio_id\n if folio.payment_deposits <= 0:\n raise UserError(_(\"\"\"No record of security deposit found on folio {}\n \"\"\".format(folio.name)))\n if folio.state != 'on_queue':\n raise UserError(_(\n ... | [
"0.56058526",
"0.557811",
"0.5529027",
"0.5488796",
"0.52010155",
"0.5192642",
"0.516891",
"0.51390547",
"0.5094012",
"0.50648564",
"0.5062245",
"0.5061174",
"0.49786085",
"0.49332282",
"0.4931753",
"0.49173915",
"0.4892089",
"0.4874537",
"0.48676074",
"0.4837274",
"0.4802804... | 0.5966537 | 0 |
Name of current protocol. | def name(self):
return 'Null' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def protocol(self) -> str:\n return __name__",
"def protocol(self) -> str:\n return pulumi.get(self, \"protocol\")",
"def protocol_name(self):\n self._protocol_name = 'kerberos'\n return self._protocol_name",
"def getProtocol(self) -> str:\n ...",
"def layer_protocol_name... | [
"0.8527097",
"0.8032896",
"0.8025242",
"0.7800471",
"0.7785212",
"0.7730119",
"0.76614213",
"0.76614213",
"0.75701255",
"0.75649047",
"0.75599545",
"0.7478808",
"0.7466269",
"0.7439446",
"0.7334409",
"0.7231231",
"0.71754944",
"0.715286",
"0.70239735",
"0.70239735",
"0.702397... | 0.0 | -1 |
Header length of current protocol. | def length(self):
raise UnsupportedCall(f"'{self.__class__.__name__}' object has no attribute 'length'") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def length(self):\n return struct.unpack('<H', self.pkt.payload[2:4])[0]",
"def length(self):\n return struct.unpack('<H', self.pkt.payload[6:8])[0]",
"def calculated_length(self) -> int:\n return TunnellingRequest.HEADER_LENGTH + len(self.raw_cemi)",
"def header_len(self):\n if s... | [
"0.773718",
"0.7697014",
"0.74317473",
"0.74186736",
"0.72751355",
"0.72751355",
"0.7265553",
"0.72438484",
"0.7240133",
"0.7159681",
"0.7159037",
"0.7113465",
"0.69755954",
"0.69566464",
"0.69399494",
"0.69354415",
"0.68519056",
"0.6833837",
"0.68100524",
"0.6744303",
"0.672... | 0.0 | -1 |
Name of next layer protocol. | def protocol(self):
raise UnsupportedCall(f"'{self.__class__.__name__}' object has no attribute 'protocol'") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def layer_protocol_name(self) -> str:\n return self._layer_protocol_name",
"def protocol(self):\n return self._info.next # pylint: disable=E1101",
"def protocol(self) -> str:\n return __name__",
"def get_name(self):\n \n return 'Loop-Back'",
"def name(self):\n ret... | [
"0.7427126",
"0.7015629",
"0.7010182",
"0.66151756",
"0.6591896",
"0.6542744",
"0.6537509",
"0.6536096",
"0.6428703",
"0.62516314",
"0.6208627",
"0.6208627",
"0.6167117",
"0.6059282",
"0.6015737",
"0.59872663",
"0.597741",
"0.5912",
"0.589437",
"0.5888402",
"0.58677226",
"0... | 0.53521055 | 91 |
Read (parse) packet data. | def read(self, length=None, **kwargs): # pylint: disable=unused-argument
return dict() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_packet(self):\n\n\t\t#self.debug(\"READ BUFFER SIZE: %d\" % len(self.buff))\n\t\tbackup = self.buff[:]\n\t\tpacket = Packet()\n\t\ttry:\n\t\t\tpacket.direction = self.node\n\t\t\tpacket.ident = self.unpack('ubyte')\n\t\t\t\n\t\t\t#Defined structs from huge dict\n\t\t\tfor datatype, name in self.get_struct... | [
"0.73247546",
"0.718705",
"0.6907943",
"0.6843252",
"0.68329656",
"0.6731588",
"0.66780645",
"0.6584551",
"0.6576974",
"0.6557971",
"0.6532748",
"0.64306426",
"0.6358723",
"0.6336314",
"0.6299943",
"0.62733495",
"0.62719905",
"0.6264791",
"0.6221513",
"0.62090373",
"0.6196162... | 0.0 | -1 |
Make (construct) packet data. | def make(self, **kwargs):
return bytes() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_packet(self, type, data): \n return (\"{}\\x00{}\\x00{}\".format(type, data, self.ID)).encode()",
"def _make_data(cls, data: 'Data_ARP') -> 'dict[str, Any]': # type: ignore[override]\n return {\n 'htype': data.htype,\n 'ptype': data.ptype,\n 'hlen':... | [
"0.7320882",
"0.6888568",
"0.6545822",
"0.650796",
"0.6446084",
"0.6386353",
"0.6256809",
"0.6228369",
"0.6189242",
"0.61338437",
"0.6114817",
"0.6103833",
"0.60672927",
"0.60629386",
"0.60616815",
"0.605075",
"0.60235",
"0.5961972",
"0.594975",
"0.59361804",
"0.5934646",
"... | 0.53749585 | 81 |
Numeral registry index of the protocol. | def __index__(cls):
raise UnsupportedCall(f'{cls.__name__!r} object cannot be interpreted as an integer') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def InterfaceIndex(self) -> int:",
"def InterfaceIndex(self) -> int:",
"def get_numkey(self):\n return self._numkey",
"def getPidx(self):\n return int(bytes(self.keeper.getGbl(b\"pidx\")), 16)",
"def idx(self):\n return int(self.__ph.get('idx', 0))",
"def register_index(self) -> int:... | [
"0.66120684",
"0.66120684",
"0.6520881",
"0.6501883",
"0.6485339",
"0.63360345",
"0.6295744",
"0.6161678",
"0.6085867",
"0.603896",
"0.6011073",
"0.6011073",
"0.5929243",
"0.5907422",
"0.5906776",
"0.58790284",
"0.5871748",
"0.58596975",
"0.5855196",
"0.5851725",
"0.58458555"... | 0.0 | -1 |
Decode next layer protocol. | def _decode_next_layer(self, *args, **kwargs): # pylint: disable=signature-differs
raise UnsupportedCall(f"'{self.__class__.__name__}' object has no attribute '_decode_next_layer'") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _decode_next_layer(self, dict_, length=None):\n # make next layer protocol name\n proto = str(self._prot or 'Raw').lower()\n\n # make BytesIO from frame package data\n bytes_ = io.BytesIO(self._file.read(dict_['len']))\n info, protochain = self._import_next_layer(bytes_, leng... | [
"0.75996035",
"0.6549479",
"0.65207684",
"0.6393973",
"0.6335503",
"0.6324604",
"0.6315522",
"0.62680393",
"0.62302494",
"0.6203099",
"0.61799216",
"0.6160706",
"0.6160706",
"0.6118852",
"0.6044695",
"0.6001132",
"0.5993506",
"0.5988984",
"0.59845906",
"0.59568155",
"0.590623... | 0.67941993 | 1 |
Import next layer extractor. | def _import_next_layer(self, *args, **kwargs): # pylint: disable=signature-differs
raise UnsupportedCall(f"'{self.__class__.__name__}' object has no attribute '_import_next_layer'") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _import_next_layer(self, file_, length):\n if self._prot == 'Ethernet':\n from .link import Ethernet as Protocol\n elif self._prot == 'IPv4':\n from .internet import IPv4 as Protocol\n elif self._prot == 'IPv6':\n from .internet import IPv6 as Protocol\n ... | [
"0.6111195",
"0.54837346",
"0.54835594",
"0.5450349",
"0.5450116",
"0.54399455",
"0.5409996",
"0.5303996",
"0.52164537",
"0.52136886",
"0.516096",
"0.51069194",
"0.5101633",
"0.5099417",
"0.5078807",
"0.5042868",
"0.4991108",
"0.49878004",
"0.4973067",
"0.49313796",
"0.491468... | 0.66877997 | 0 |
Class to organize and execute QA for a DESI production | def __init__(self, specprod_dir=None, **kwargs):
if specprod_dir is None:
specprod_dir = specprod_root()
self.specprod_dir = specprod_dir
# Init
QA_MultiExp.__init__(self, specprod_dir=specprod_dir, **kwargs)
# Load up exposures for the full production
nights ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def qa():\n env.config_file = 'config_production.py'\n env.hosts = ['ombu@d2.ombuweb.com:34165']\n env.host_type = 'qa'\n env.user = 'ombu'\n env.host_webserver_user = 'www-data'\n env.host_site_path = '/mnt/main/qa/qa2/public'",
"def main():\n\n # Run all the requirements for part A\n ##... | [
"0.64223295",
"0.6202825",
"0.5899661",
"0.57702637",
"0.56131524",
"0.5524281",
"0.5509671",
"0.5425418",
"0.53912246",
"0.53891236",
"0.53559035",
"0.53552413",
"0.53085",
"0.5271458",
"0.5259535",
"0.5251413",
"0.524817",
"0.5218205",
"0.52015793",
"0.51848274",
"0.5179872... | 0.5289428 | 13 |
Load QA data from night objects on disk | def load_data(self, inroot=None):
self.data = {}
# Load
for night in self.mexp_dict.keys():
qaNight = QA_Night(night, specprod_dir=self.specprod_dir, qaprod_dir=self.qaprod_dir)
qaNight.load_data()
#
self.data[night] = qaNight.data[night] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_data(self):",
"def load_data(self) -> None:",
"def load_rentedout():",
"def _load_test_data(self):\n self._save_test_data()",
"def load_mp_data():\n tree = Artifact.load(os.path.join(PREFIX_DIR, \"rooted-tree.qza\"))\n table = Artifact.load(os.path.join(PREFIX_DIR, \"table.qza\"))\n ... | [
"0.65798295",
"0.6398732",
"0.6165075",
"0.61242807",
"0.5998567",
"0.58897173",
"0.58770794",
"0.58206344",
"0.57725376",
"0.5756605",
"0.5740402",
"0.5737037",
"0.5737037",
"0.57252693",
"0.5663166",
"0.5662534",
"0.56499773",
"0.56499773",
"0.56499773",
"0.56499773",
"0.56... | 0.6338221 | 2 |
Build QA data dict from the nights | def build_data(self):
from desiutil.io import combine_dicts
# Loop on exposures
odict = {}
for qanight in self.qa_nights:
for qaexp in qanight.qa_exps:
# Get the exposure dict
idict = write_qa_exposure('foo', qaexp, ret_dict=True)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_data(self, inroot=None):\n self.data = {}\n # Load\n for night in self.mexp_dict.keys():\n qaNight = QA_Night(night, specprod_dir=self.specprod_dir, qaprod_dir=self.qaprod_dir)\n qaNight.load_data()\n #\n self.data[night] = qaNight.data[nigh... | [
"0.5826197",
"0.55371296",
"0.5417445",
"0.5337191",
"0.52416694",
"0.51694846",
"0.5151711",
"0.5091198",
"0.5085503",
"0.5074048",
"0.5069187",
"0.5063415",
"0.50576264",
"0.5021445",
"0.50159806",
"0.5006675",
"0.4991931",
"0.49876162",
"0.49839446",
"0.49812287",
"0.49771... | 0.6585092 | 0 |
Slurp all the individual QA files, night by night Loops on nights, generating QANight objects along the way | def slurp_nights(self, make_frameqa=False, remove=True, restrict_nights=None,
write_nights=False, **kwargs):
log = get_logger()
# Remake?
if make_frameqa:
self.make_frameqa(**kwargs)
# Reset
log.info("Resetting QA_Night objects")
self.qa_n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_quasar(folder, set_type, doc_size):\n print(\"def process_quasar(folder, set_type, doc_size) ...\")\n\n # create counter for enumeration of batch-files\n counter = 0\n\n # Question File and Path\n question_file = set_type + \"_questions.json\"\n question_file_path = Path(\"/\".join([f... | [
"0.609987",
"0.59429026",
"0.5871865",
"0.55248433",
"0.54379",
"0.5391129",
"0.5366999",
"0.53585476",
"0.53522",
"0.53338474",
"0.5293552",
"0.5277471",
"0.5270402",
"0.5206287",
"0.52051455",
"0.51889557",
"0.5166118",
"0.51528084",
"0.51408404",
"0.51355785",
"0.5130651",... | 0.5576337 | 3 |
Test case for add_or_update_case | def test_add_or_update_case(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_update_case(self):\n pass",
"def test_update_one(self):\n pass",
"def test_update_record(self):\n pass",
"def test_update(self):\n pass",
"def test_update(self):\n pass",
"def test_update(self):\n pass",
"def test_update_scenario(self):\n pass",... | [
"0.79237324",
"0.7314126",
"0.7272837",
"0.72685695",
"0.72685695",
"0.72685695",
"0.712033",
"0.69949913",
"0.6982817",
"0.69368035",
"0.69113",
"0.68479264",
"0.68319446",
"0.6824832",
"0.6721132",
"0.6690393",
"0.6686279",
"0.66511667",
"0.6622248",
"0.6599744",
"0.6579878... | 0.90939504 | 0 |
Test case for delete_case | def test_delete_case(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_delete_run(self):\n pass",
"def test_delete(self):\n pass",
"def test_delete1(self):\n pass",
"def test_delete_record(self):\n pass",
"def test_CovidCase_delete(self):\n # setting up by creating and saving the the database\n del_Covid = self.create_CovidCa... | [
"0.85045195",
"0.83174175",
"0.8187092",
"0.81214935",
"0.7901589",
"0.78933346",
"0.7868206",
"0.7762365",
"0.7726762",
"0.77229226",
"0.7537628",
"0.74579",
"0.73829216",
"0.735922",
"0.7357545",
"0.7353939",
"0.73434424",
"0.7334559",
"0.7333317",
"0.7319243",
"0.72404516"... | 0.94501704 | 0 |
Test case for get_case_by_id | def test_get_case_by_id(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_case(loqusdbapi, mocker):\n # GIVEN a loqusdb api\n case_id = 'a_case'\n # WHEN fetching a case with the adapter\n mocker.patch.object(subprocess, 'check_output')\n loqusdb_output = (b\"{'_id': 'one_case', 'case_id': 'one_case'}\\n\"\n b\"{'_id': 'a_case', 'case_id'... | [
"0.7206168",
"0.7135359",
"0.70535195",
"0.6983086",
"0.69185466",
"0.6779661",
"0.6560765",
"0.6444879",
"0.6441801",
"0.643984",
"0.64307916",
"0.6415146",
"0.63614887",
"0.63151497",
"0.6309817",
"0.6308288",
"0.6291289",
"0.62707704",
"0.62072265",
"0.62045175",
"0.616458... | 0.949092 | 0 |
Test case for get_cases_for_dict | def test_get_cases_for_dict(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def navigate_case_dictionary(case_list_for_run, num_cases):",
"def test_create_results_dict_1(self):\n dict = find_domains.create_results_dict(self.rps_results)\n with self.subTest():\n self.assertEqual(len(dict.keys()), 4)\n with self.subTest():\n self.assertEqual(len(... | [
"0.70761895",
"0.65705884",
"0.647785",
"0.6304288",
"0.6191103",
"0.61260873",
"0.6123775",
"0.60676205",
"0.60428697",
"0.60196096",
"0.59500134",
"0.58859193",
"0.5870065",
"0.5868873",
"0.5839127",
"0.5819128",
"0.57976145",
"0.5794307",
"0.57906353",
"0.57838327",
"0.576... | 0.93498963 | 0 |
Test case for get_sync_history | def test_get_sync_history(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_tracker_getHistory():\n\n trackers, cap = init_tracker()\n tr = trackers[0]\n tr.addHistory([1, 1, 1, 1])\n\n assert tr.getHistory()[1] == [1, 1, 1, 1]",
"def test_get_team_history(self):\n pass",
"def QueryHistory(self):\n return []",
"def testGetHistory(self):\n self.maxDi... | [
"0.692454",
"0.68217295",
"0.6417337",
"0.63609856",
"0.6296297",
"0.6270521",
"0.6258328",
"0.6244751",
"0.62230396",
"0.6212335",
"0.6196629",
"0.60348105",
"0.602882",
"0.5997421",
"0.5959013",
"0.5924428",
"0.5920476",
"0.59072894",
"0.58979166",
"0.58102685",
"0.57995194... | 0.9430264 | 0 |
Test case for update_case | def test_update_case(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_update_scenario(self):\n pass",
"def test_update(self):\n pass",
"def test_update(self):\n pass",
"def test_update(self):\n pass",
"def test_add_or_update_case(self):\n pass",
"def test_update_record(self):\n pass",
"def test_update_one(self):\n ... | [
"0.8599205",
"0.849513",
"0.849513",
"0.849513",
"0.8350617",
"0.8206942",
"0.81498384",
"0.81489325",
"0.78003776",
"0.75835353",
"0.75727797",
"0.7463717",
"0.7440917",
"0.7428357",
"0.7388373",
"0.7385997",
"0.73503935",
"0.7333985",
"0.73062307",
"0.7268944",
"0.72565717"... | 0.93751144 | 0 |
Set state of key exchange process | def _set_state(self, state):
#print("** set state from %d to %d" % (self.state, state))
self.state = state | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __setstate__(self, state):\n state['_lock'] = Lock()\n self.__dict__.update(state)",
"def __setstate__(self, state):\n\n self.set(DER = state)",
"def set_state(self, state: int):",
"def set_state( self ):",
"def set_state(self, state: ProcessStateStr | core.QProcess.ProcessState):\... | [
"0.6264874",
"0.62306124",
"0.6219067",
"0.62150574",
"0.6114401",
"0.609517",
"0.6037395",
"0.6025829",
"0.5982595",
"0.59734386",
"0.5955964",
"0.59348714",
"0.59043247",
"0.5876987",
"0.5874065",
"0.5861183",
"0.58430445",
"0.5808853",
"0.5787678",
"0.5764213",
"0.57516503... | 0.59008133 | 13 |
Set key to the encryptor and decryptor | def set_cipher(self, key_name, hint):
message_key_types.set_cipher(self.shared_key, self.nonce, key_name, hint) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_key(self):\n\n self.key = Fernet.generate_key()\n self.cryptor = Fernet(self.key)",
"def set_encryption(key):\n global_scope['enc'] = Encryption(key.encode())",
"def get_key(self, key_value):\n # Storing the correct key value back to the self.key attributes.\n self.k... | [
"0.75822407",
"0.7209422",
"0.7106368",
"0.66078645",
"0.6590576",
"0.6399073",
"0.6381233",
"0.63626736",
"0.63152766",
"0.62950706",
"0.6283543",
"0.62832546",
"0.62813187",
"0.6264032",
"0.623297",
"0.61464673",
"0.610903",
"0.61076546",
"0.60681397",
"0.6064058",
"0.60416... | 0.612742 | 16 |
Unset key from the encryptor and decryptor | def unset_cipher(self, key_name=None):
if key_name is None:
if self.key_name is not None:
message_key_types.unset_cipher(self.key_name)
if self.pending_key_name is not None:
message_key_types.unset_cipher(self.pending_key_name)
else:
me... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def del_key(self):\n # Deleting the values from the self.key and self.cryptor attributes.\n self.key=None\n self.cryptor=None",
"def clear_key(self, key):\r\n return self.handler.clear_key(key_to_code(key))",
"def tearDown(self) -> None:\n\n del self.private_key\n del self... | [
"0.79658395",
"0.6650825",
"0.6479646",
"0.64384425",
"0.62364286",
"0.6223678",
"0.62217623",
"0.61869067",
"0.61497605",
"0.6120932",
"0.6093871",
"0.60819805",
"0.60426354",
"0.6033569",
"0.6002724",
"0.5995064",
"0.5992837",
"0.5932019",
"0.59261566",
"0.5922496",
"0.5895... | 0.6914403 | 1 |
Set timer for key refreshment | def set_invoke_timer(self, timeout, retry_entry=False):
if self.timer_entry is not None and self.timer_entry.active:
self.timer_entry.deactivate()
#print("(%d) set_invoke_timer:" % int(time.time()), timeout)
self.timer_entry = query_management.QueryEntry(expire_after=timeout,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_time(cls, key):\n key.put()",
"def __updateElapsedTime(self):\n if self._keyCodeTime != 0.0 and \\\n (globalClock.getFrameTime() - self._keyCodeTime) >= self._timeout:\n self.notify.debug(\"Key code timed out. Resetting...\")\n self.reset()\n ... | [
"0.7022178",
"0.66496783",
"0.64718276",
"0.64211446",
"0.6270911",
"0.62581867",
"0.62419593",
"0.62306786",
"0.61925316",
"0.6182545",
"0.61779076",
"0.6164358",
"0.6159859",
"0.6124073",
"0.6121016",
"0.6054378",
"0.6042401",
"0.6031246",
"0.60209733",
"0.6018492",
"0.6016... | 0.58821076 | 29 |
Set timer for key revocation | def _set_delete_timer(self, key_name, timeout):
if key_name is not None:
#print("(%d) _set_delete_timer:" % int(time.time()), key_name.hex()[:10], timeout)
query_management.QueryEntry(expire_after=timeout, callback_expire=remove_old_key,
data={KeyT... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __updateElapsedTime(self):\n if self._keyCodeTime != 0.0 and \\\n (globalClock.getFrameTime() - self._keyCodeTime) >= self._timeout:\n self.notify.debug(\"Key code timed out. Resetting...\")\n self.reset()\n messenger.send(KeyCodes.CLEAR_CODE_E... | [
"0.6193708",
"0.6188828",
"0.61632437",
"0.61356395",
"0.6049411",
"0.5949552",
"0.5939341",
"0.58892876",
"0.5877896",
"0.584273",
"0.58422077",
"0.5806897",
"0.57887334",
"0.57828623",
"0.5779299",
"0.5777707",
"0.571499",
"0.5553746",
"0.55466074",
"0.5524556",
"0.5522834"... | 0.6783776 | 0 |
Perform ECDH key exhange to establish secure channel to the node | def _perform_key_exchange(self, query_entry):
if KeyType.retry_timer in query_entry.data and query_entry.data[KeyType.retry_timer]:
message_key_types.unset_cipher(self.pending_key_name)
self.pending_key_name = None
self._set_state(KeyExchangeManager.STATE_REQUESTING)
#pri... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def exchange_key(connection, pub_key):\r\n\r\n if main.diffe_key_exchange is False:\r\n # Get the server's public key\r\n server_pub_key_bytes = connection.recv(1024)\r\n\r\n # Send public key\r\n connection.sendall(rsa.PublicKey.save_pkcs1(pub_key))\r\n\r\n else:\r\n # Rou... | [
"0.6512742",
"0.6338184",
"0.63009053",
"0.62812775",
"0.62282526",
"0.613101",
"0.6050611",
"0.60426515",
"0.6037874",
"0.5955243",
"0.5955243",
"0.5944734",
"0.5941283",
"0.58977014",
"0.5856448",
"0.5838825",
"0.5827069",
"0.5824423",
"0.5812279",
"0.5808559",
"0.57647914"... | 0.0 | -1 |
Procedure when receiving message with BBcNetwork.REQUEST_KEY_EXCHANGE | def receive_exchange_request(self, pubkey, nonce, random_val, hint):
if self.state != KeyExchangeManager.STATE_REQUESTING:
#print("(%d) receive_exchange_request: processing" % int(time.time()))
self.peer_public_key = pubkey
self.nonce = nonce
self.random = random_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def key_request(self, user):\n\t\tclient_log.debug(f'Запрос публичного ключа для {user}')\n\t\treq = {\n\t\t\tACTION: PUBLIC_KEY_REQUEST,\n\t\t\tTIME: time.time(),\n\t\t\tACCOUNT_NAME: user\n\t\t}\n\t\twith socket_lock:\n\t\t\tsend_message(self.transport, req)\n\t\t\tans = get_message(self.transport)\n\t\tif RESPO... | [
"0.67223096",
"0.6613165",
"0.66030747",
"0.65658045",
"0.6505863",
"0.64924264",
"0.63855624",
"0.63809603",
"0.6345278",
"0.6331297",
"0.6206609",
"0.6176792",
"0.6141044",
"0.61168796",
"0.61084545",
"0.5941246",
"0.5910018",
"0.5898104",
"0.58852327",
"0.5862589",
"0.5856... | 0.6525278 | 4 |
Process ECDH procedure (receiving response) | def receive_exchange_response(self, pubkey, random_val, hint):
#print("(%d) receive_exchange_response:" % int(time.time()))
#print(" **> state:", self.state)
if self.state != KeyExchangeManager.STATE_REQUESTING:
return
rand_time = int(KeyExchangeManager.KEY_REFRESH_INTERVAL*r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def response(self):\n return self._send(bytes([0xef,0xfe,0x02,0x0,0x0,0x0,0x0,0x0]))",
"def process_eas (chan, eas):\n\n text = os.popen('./dsame.py --msg \"' + eas + '\"').read().split(\"\\n\")\n text2 = list(filter(None, text))\n n = len(text2)\n if n:\n print (\"Transmitting...\");\n ... | [
"0.6189765",
"0.59589136",
"0.5955229",
"0.5889146",
"0.57184684",
"0.5701011",
"0.5673351",
"0.5545665",
"0.55449224",
"0.554279",
"0.55046767",
"0.5504035",
"0.55030364",
"0.5494673",
"0.54934955",
"0.5493104",
"0.54797333",
"0.5475469",
"0.5475273",
"0.5459943",
"0.5458891... | 0.0 | -1 |
Confirm that the key has been agreed | def receive_confirmation(self):
#print("(%d) receive_confirmation:" % int(time.time()))
#print(" **> state:", self.state)
if self.state != KeyExchangeManager.STATE_CONFIRMING:
return
rand_time = int(KeyExchangeManager.KEY_REFRESH_INTERVAL*random.uniform(0.9, 1.1))
sel... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def confirm(self, msg, *args):\n if Controller.prev_regex is None:\n await msg.channel.send(**{\n 'content': 'No key change in progress',\n 'reference': msg.to_reference(),\n 'mention_author': True,\n })\n return\n ... | [
"0.70200527",
"0.6937028",
"0.6796732",
"0.65929675",
"0.64999527",
"0.64217055",
"0.6304895",
"0.626149",
"0.6239152",
"0.6135",
"0.61015856",
"0.6096368",
"0.59683174",
"0.5962593",
"0.5954259",
"0.59480214",
"0.59259063",
"0.58750486",
"0.5863333",
"0.58017457",
"0.5787794... | 0.63993216 | 6 |
Euclidean distance between two graph poses | def distance(pose1, pose2):
return (
(pose1["pose"][3] - pose2["pose"][3]) ** 2
+ (pose1["pose"][7] - pose2["pose"][7]) ** 2
+ (pose1["pose"][11] - pose2["pose"][11]) ** 2
) ** 0.5 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def graph_dist(i1: int, g1: nx.Graph, i2: int, g2: nx.Graph) -> t.Tuple[int, int, float]:\n space1, space2 = map(dict, map(mut_space, [g1, g2]))\n d = 0\n for k in set(list(space1) + list(space2)):\n if k in space1 and k in space2:\n d += len(set(space1[k]).symmetric_difference(set(space... | [
"0.7292896",
"0.7110288",
"0.6867631",
"0.68615735",
"0.6761212",
"0.6698261",
"0.66788447",
"0.6668662",
"0.6659703",
"0.66497093",
"0.6615427",
"0.66116714",
"0.66099334",
"0.6598748",
"0.65919584",
"0.65918255",
"0.65908754",
"0.6572982",
"0.6572364",
"0.6497199",
"0.64961... | 0.0 | -1 |
Build a graph from a connectivity json file | def open_graph(scan_id):
infile = "%s%s_connectivity.json" % (connectivity_dir, scan_id)
G = nx.Graph()
with open(infile) as f:
data = json.load(f)
for i, item in enumerate(data):
if item["included"]:
for j, conn in enumerate(item["unobstructed"]):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def open_graph(connectDir, scan_id):\n infile = \"%s%s_connectivity.json\" % (connectDir, scan_id)\n G = nx.Graph()\n with open(infile) as f:\n data = json.load(f)\n for i, item in enumerate(data):\n if item[\"included\"]:\n for j, conn in enumerate(item[\"unobstruc... | [
"0.7250749",
"0.7151497",
"0.69580156",
"0.6932821",
"0.6580459",
"0.6554932",
"0.6549572",
"0.6462024",
"0.6431498",
"0.6411141",
"0.6403866",
"0.6394087",
"0.63446313",
"0.63325745",
"0.63108885",
"0.6290227",
"0.6284171",
"0.62591064",
"0.6194136",
"0.6182282",
"0.6135531"... | 0.7102555 | 2 |
.mfk play Begin the game ||| [+/] Edit the mfk user list ||| marry fuck kill Give the verdict ||| score Retrieves the score of a user | def mfk(inp, db=None):
db_init(db)
inp = inp.lower()
m_regex = re.compile(r"marry\s([a-zA-Z0-9_]+\s|[a-zA-Z0-9_]+)").finditer(inp)
f_regex = re.compile(r"fuck\s([a-zA-Z0-9_]+\s|[a-zA-Z0-9_]+)").finditer(inp)
k_regex = re.compile(r"kill\s([a-zA-Z0-9_]+|[a-zA-Z0-9_]+\s)").finditer(inp)
if inp... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def enter_game_scores():\n pass",
"def update_score():\n pass",
"async def leaderboard(self, ctx):\r\n rank = 1\r\n strlist = []\r\n for k, v in sorted(player.items(), key=lambda x: expose(x[1]), reverse=True): # operator.itemgetter(1)\r\n position = str(rank) + '. ' ... | [
"0.67305034",
"0.63729185",
"0.6132123",
"0.6093871",
"0.60754216",
"0.5949699",
"0.59380084",
"0.59304893",
"0.59282887",
"0.5903168",
"0.58756876",
"0.58682275",
"0.5865948",
"0.58637714",
"0.5862318",
"0.5842622",
"0.58407027",
"0.582625",
"0.5826105",
"0.5816378",
"0.5811... | 0.5794052 | 25 |
Returns the presence for this channel | def presence(self, params=None, timeout=None):
params = params or {}
path = '/channels/%s/presence' % self.__name
return self.__ably._get(path, params=params, timeout=timeout).json() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def presence(self):\n return self.slack_client.api_call(\"users.getPresence?user=\"+self.user_id)",
"def isHumanPresence(self):\n\t\treturn self.humanPresence",
"def online(self):\n api_call = self.presence()\n if api_call.get('ok'):\n # retrieve all users so we can find our bot... | [
"0.78225",
"0.65050405",
"0.63529664",
"0.6173057",
"0.5725983",
"0.5710549",
"0.5505179",
"0.54912657",
"0.54869306",
"0.54740065",
"0.5472166",
"0.5465967",
"0.5449083",
"0.5377885",
"0.5373487",
"0.5356357",
"0.534464",
"0.53218323",
"0.52983725",
"0.52873003",
"0.528721",... | 0.80038244 | 0 |
Returns the history for this channel | def history(self, direction=None, limit=None, start=None, end=None, timeout=None):
params = {}
if direction:
params['direction'] = '%s' % direction
if limit:
params['limit'] = '%d' % limit
if start:
params['start'] = self._format_time_param(start)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_history(self):\n return self.history",
"def history(self):\n return self.board.history",
"def history(self):\n return self.info['history']",
"def get_history(self):\r\n\r\n return self.board_history",
"def history(self):\n return self._history",
"def history(self):\... | [
"0.8223158",
"0.8190711",
"0.8100468",
"0.8018653",
"0.7970339",
"0.7970339",
"0.7960411",
"0.763217",
"0.7524663",
"0.74288756",
"0.7316858",
"0.7306517",
"0.7249383",
"0.72073257",
"0.7187233",
"0.7177014",
"0.71616286",
"0.71466166",
"0.713637",
"0.7119178",
"0.7116252",
... | 0.7255194 | 12 |
Publishes a message on this channel. | def publish(self, name, data, timeout=None):
message = Message(name, data)
if self.encrypted:
message.encrypt(self.__cipher)
if self.ably.options.use_text_protocol:
request_body = message.as_json()
else:
request_body = message.as_thrift()
p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def publish(self, message):\n logger.info(\"Publishing to topic [{0}]: {1}\".format(self._topic_name, message))\n self._executor.send(json.dumps({\n 'op': 'publish',\n 'id': 'publish:{0}:{1}'.format(self._topic_name, self._id),\n 'topic': self._topic_name,\n ... | [
"0.8069301",
"0.7968601",
"0.7785665",
"0.76731753",
"0.7639972",
"0.761992",
"0.75402987",
"0.7448884",
"0.74153394",
"0.7411613",
"0.7408458",
"0.7408458",
"0.73652995",
"0.73333603",
"0.7326912",
"0.72585905",
"0.7211472",
"0.72038764",
"0.7194173",
"0.7134606",
"0.7127167... | 0.6742099 | 38 |
The set of arguments for constructing a Assessment resource. | def __init__(__self__, *,
resource_details: pulumi.Input[Union['AzureResourceDetailsArgs', 'OnPremiseResourceDetailsArgs', 'OnPremiseSqlResourceDetailsArgs']],
resource_id: pulumi.Input[str],
status: pulumi.Input['AssessmentStatusArgs'],
additional_dat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(__self__,\n resource_name: str,\n args: AssessmentArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...",
"def __init__(__self__,\n resource_name: str,\n args: AssessmentPolicyArgs,\n opts:... | [
"0.71892506",
"0.676996",
"0.6377259",
"0.6322052",
"0.63183135",
"0.59458417",
"0.5945797",
"0.57835245",
"0.57723206",
"0.5743007",
"0.5741708",
"0.5738933",
"0.57031816",
"0.5700542",
"0.5700223",
"0.56762666",
"0.5639396",
"0.56337005",
"0.56271166",
"0.56271166",
"0.5627... | 0.6006293 | 5 |
Details of the resource that was assessed | def resource_details(self) -> pulumi.Input[Union['AzureResourceDetailsArgs', 'OnPremiseResourceDetailsArgs', 'OnPremiseSqlResourceDetailsArgs']]:
return pulumi.get(self, "resource_details") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def resource_details(self) -> pulumi.Output[Any]:\n return pulumi.get(self, \"resource_details\")",
"def resource_details(self) -> pulumi.Output[Any]:\n return pulumi.get(self, \"resource_details\")",
"def get_resource_details (self):\n return (f\"[Title:\\\"{self.get_title()}\\\"] [Author... | [
"0.7746103",
"0.7746103",
"0.76440537",
"0.7001793",
"0.6875115",
"0.6875115",
"0.6875115",
"0.6875115",
"0.6875115",
"0.6875115",
"0.6875115",
"0.686545",
"0.6756598",
"0.6750164",
"0.66482615",
"0.6627125",
"0.6553682",
"0.6521818",
"0.64652663",
"0.6450048",
"0.6442991",
... | 0.6483526 | 18 |
The identifier of the resource. | def resource_id(self) -> pulumi.Input[str]:
return pulumi.get(self, "resource_id") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def resource_id(self) -> str:\n return pulumi.get(self, \"resource_id\")",
"def resource_id(self) -> str:\n return pulumi.get(self, \"resource_id\")",
"def resource_id(self) -> str:\n return pulumi.get(self, \"resource_id\")",
"def resourceid(self):",
"def id(self):\n return sel... | [
"0.87842387",
"0.87842387",
"0.87842387",
"0.8679565",
"0.8428031",
"0.8255889",
"0.82200164",
"0.7965054",
"0.7965054",
"0.7965054",
"0.7965054",
"0.7965054",
"0.7965054",
"0.7965054",
"0.7965054",
"0.7965054",
"0.79283077",
"0.79256773",
"0.78568465",
"0.7837528",
"0.777672... | 0.81783426 | 14 |
The result of the assessment | def status(self) -> pulumi.Input['AssessmentStatusArgs']:
return pulumi.get(self, "status") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def result(self):\n return self['result']",
"def results(self):\n\n\t\tresults = {'answer':42}\n\n\t\treturn results",
"def result(self):\n\n print('Ergebnisse: -------------\\n'\n 'Richtige Antworten:{} \\n'\n 'Falsche Antworten:{} \\n'.format(self.answer_right, self.a... | [
"0.7455872",
"0.7048662",
"0.687895",
"0.68733793",
"0.6868782",
"0.6851356",
"0.68279386",
"0.6774118",
"0.67248684",
"0.67248684",
"0.67248684",
"0.65798163",
"0.65798163",
"0.6516882",
"0.6478505",
"0.6459931",
"0.645169",
"0.64418316",
"0.64348304",
"0.6432639",
"0.639982... | 0.0 | -1 |
Additional data regarding the assessment | def additional_data(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:
return pulumi.get(self, "additional_data") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def student_view_data(self, context=None):\n return {\n 'title': self.title,\n 'description': self.description,\n 'embed_code': self.embed_code,\n 'highres_url': self.highres_url,\n 'lowres_url': self.lowres_url,\n }",
"def get_assessment(self)... | [
"0.63733923",
"0.61233914",
"0.60425943",
"0.5986746",
"0.59847146",
"0.5970008",
"0.596638",
"0.59152776",
"0.590361",
"0.5881575",
"0.58584493",
"0.5775308",
"0.56499285",
"0.56463957",
"0.5642021",
"0.5598647",
"0.55952555",
"0.5585957",
"0.5569665",
"0.55653036",
"0.55639... | 0.0 | -1 |
The Assessment Key Unique key for the assessment type | def assessment_name(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "assessment_name") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def key(self):\n return str(self._id)",
"def get_key_id(self):",
"def generate_key(self):\n return str(uuid4())",
"def key(self):\n\n return self.qualifiers.get(\"key\", False)",
"def key(self) -> str:\n return pulumi.get(self, \"key\")",
"def key(self) -> str:\n return... | [
"0.6836664",
"0.6800989",
"0.67905533",
"0.6716157",
"0.66948956",
"0.66948956",
"0.66948956",
"0.66948956",
"0.66948956",
"0.66948956",
"0.66948956",
"0.66948956",
"0.66948956",
"0.66948956",
"0.66948956",
"0.66948956",
"0.66948956",
"0.66948956",
"0.66948956",
"0.66948956",
... | 0.0 | -1 |
Describes properties of an assessment metadata. | def metadata(self) -> Optional[pulumi.Input['SecurityAssessmentMetadataPropertiesArgs']]:
return pulumi.get(self, "metadata") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def metadata(self) -> global___SummaryMetadata:",
"def metadata(self) -> pulumi.Output[Optional['outputs.SecurityAssessmentMetadataPropertiesResponse']]:\n return pulumi.get(self, \"metadata\")",
"def metadata(self) -> pulumi.Output[Optional['outputs.SecurityAssessmentMetadataPropertiesResponse']]:\n ... | [
"0.64961946",
"0.6462961",
"0.6462961",
"0.6290093",
"0.6145428",
"0.6084069",
"0.6051594",
"0.60471356",
"0.5994781",
"0.5933389",
"0.59084606",
"0.58996195",
"0.5870387",
"0.5848383",
"0.5837634",
"0.5826372",
"0.5826372",
"0.5818724",
"0.58181715",
"0.5814273",
"0.5807516"... | 0.64541817 | 3 |
Data regarding 3rd party partner integration | def partners_data(self) -> Optional[pulumi.Input['SecurityAssessmentPartnerDataArgs']]:
return pulumi.get(self, "partners_data") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def info():\n if g.party_id is None:\n # No party is configured for the current site.\n abort(404)\n\n party = party_service.get_party(g.party_id)\n\n return {\n 'party': party,\n }",
"def get(self,\n partner_id):\n abort(501)",
"def get(self,\n par... | [
"0.62221545",
"0.56236756",
"0.56236756",
"0.5579682",
"0.5457257",
"0.5414618",
"0.5382873",
"0.5349557",
"0.53429776",
"0.5338125",
"0.5333075",
"0.53239155",
"0.5318248",
"0.5313936",
"0.53106076",
"0.5300438",
"0.528713",
"0.52863556",
"0.52863556",
"0.528309",
"0.5242493... | 0.5107046 | 28 |
Security assessment on a resource response format | def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
additional_data: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
assessment_name: Optional[pulumi.Input[str]] = None,
metadata: O... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate_response(self, response):\n pass",
"def get_secured():\n return jsonify({'isSecured': config.requires_auth()})",
"def getSecurity(self):\n return self.client.get(self.name +\"/_security\").getBodyData()",
"def ExtractSecurityMarksFromResponse(response, args):\n del args\n list_a... | [
"0.5803334",
"0.57779896",
"0.5722596",
"0.55923194",
"0.5541996",
"0.5463912",
"0.54613954",
"0.54241943",
"0.5412314",
"0.53474534",
"0.53294784",
"0.5312035",
"0.52927554",
"0.5291069",
"0.5280915",
"0.52559435",
"0.5234564",
"0.52308244",
"0.5222391",
"0.5222391",
"0.5207... | 0.0 | -1 |
Security assessment on a resource response format | def __init__(__self__,
resource_name: str,
args: AssessmentArgs,
opts: Optional[pulumi.ResourceOptions] = None):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate_response(self, response):\n pass",
"def get_secured():\n return jsonify({'isSecured': config.requires_auth()})",
"def getSecurity(self):\n return self.client.get(self.name +\"/_security\").getBodyData()",
"def ExtractSecurityMarksFromResponse(response, args):\n del args\n list_a... | [
"0.5803334",
"0.57779896",
"0.5722596",
"0.55923194",
"0.5541996",
"0.5463912",
"0.54613954",
"0.54241943",
"0.5412314",
"0.53474534",
"0.53294784",
"0.5312035",
"0.52927554",
"0.5291069",
"0.5280915",
"0.52559435",
"0.5234564",
"0.52308244",
"0.5222391",
"0.5222391",
"0.5207... | 0.0 | -1 |
Get an existing Assessment resource's state with the given name, id, and optional extra properties used to qualify the lookup. | def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'Assessment':
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = AssessmentArgs.__new__(AssessmentArgs)
__props__.__dict__["additional_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get(resource_name: str,\n id: pulumi.Input[str],\n opts: Optional[pulumi.ResourceOptions] = None) -> 'Assessment':\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\n\n __props__ = dict()\n\n __props__[\"additional_data\"] = None\n __pr... | [
"0.6976486",
"0.608855",
"0.5846438",
"0.5757699",
"0.5648362",
"0.55919707",
"0.5582545",
"0.55817664",
"0.55264586",
"0.55085826",
"0.550386",
"0.5496845",
"0.54861027",
"0.5467891",
"0.54414856",
"0.53757304",
"0.53509825",
"0.53232807",
"0.5311685",
"0.52735114",
"0.52480... | 0.6898669 | 1 |
Additional data regarding the assessment | def additional_data(self) -> pulumi.Output[Optional[Mapping[str, str]]]:
return pulumi.get(self, "additional_data") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def student_view_data(self, context=None):\n return {\n 'title': self.title,\n 'description': self.description,\n 'embed_code': self.embed_code,\n 'highres_url': self.highres_url,\n 'lowres_url': self.lowres_url,\n }",
"def get_assessment(self)... | [
"0.63733923",
"0.61233914",
"0.60425943",
"0.5986746",
"0.59847146",
"0.5970008",
"0.596638",
"0.59152776",
"0.590361",
"0.5881575",
"0.58584493",
"0.5775308",
"0.56499285",
"0.56463957",
"0.5642021",
"0.5598647",
"0.55952555",
"0.5585957",
"0.5569665",
"0.55653036",
"0.55639... | 0.5422631 | 31 |
User friendly display name of the assessment | def display_name(self) -> pulumi.Output[str]:
return pulumi.get(self, "display_name") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def assessment_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"assessment_name\")",
"def __str__(self):\n # Use 'Unknown' if the course instance does not have a term\n if self.course_instance.term:\n term = self.course_instance.term.verbose_name()\n e... | [
"0.76884186",
"0.72017074",
"0.71944445",
"0.71944445",
"0.71944445",
"0.71944445",
"0.71944445",
"0.71944445",
"0.71944445",
"0.71944445",
"0.71944445",
"0.71944445",
"0.71944445",
"0.71944445",
"0.6931914",
"0.69249964",
"0.6901365",
"0.68354696",
"0.6828654",
"0.6815666",
... | 0.6926838 | 21 |
Links relevant to the assessment | def links(self) -> pulumi.Output['outputs.AssessmentLinksResponse']:
return pulumi.get(self, "links") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getLink(self):",
"def exam_url(self, obj):\n request = self.context.get(\"request\")\n return reverse(\"exam-detail\", args=[obj.id], request=request)",
"def get_absolute_url(self):\n return reverse('trait_browser:source:studies:pk:detail', kwargs={'pk': self.pk})",
"def href(self, r... | [
"0.6158211",
"0.5980082",
"0.5797147",
"0.57784724",
"0.5716922",
"0.5695942",
"0.56922674",
"0.56786233",
"0.5670207",
"0.5577552",
"0.5543779",
"0.5527378",
"0.5521695",
"0.5521499",
"0.5518003",
"0.55149287",
"0.551347",
"0.5483872",
"0.5457637",
"0.5448972",
"0.5433619",
... | 0.65279543 | 0 |
Describes properties of an assessment metadata. | def metadata(self) -> pulumi.Output[Optional['outputs.SecurityAssessmentMetadataPropertiesResponse']]:
return pulumi.get(self, "metadata") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def metadata(self) -> global___SummaryMetadata:",
"def metadata(self) -> Optional[pulumi.Input['SecurityAssessmentMetadataPropertiesArgs']]:\n return pulumi.get(self, \"metadata\")",
"def get_assessment_metadata(self):\n return Metadata(**settings.METADATA['assessment_id'])",
"def describe(self... | [
"0.64961946",
"0.64541817",
"0.6290093",
"0.6145428",
"0.6084069",
"0.6051594",
"0.60471356",
"0.5994781",
"0.5933389",
"0.59084606",
"0.58996195",
"0.5870387",
"0.5848383",
"0.5837634",
"0.5826372",
"0.5826372",
"0.5818724",
"0.58181715",
"0.5814273",
"0.5807516",
"0.5801381... | 0.6462961 | 1 |
Data regarding 3rd party partner integration | def partners_data(self) -> pulumi.Output[Optional['outputs.SecurityAssessmentPartnerDataResponse']]:
return pulumi.get(self, "partners_data") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def info():\n if g.party_id is None:\n # No party is configured for the current site.\n abort(404)\n\n party = party_service.get_party(g.party_id)\n\n return {\n 'party': party,\n }",
"def get(self,\n partner_id):\n abort(501)",
"def get(self,\n par... | [
"0.62221545",
"0.56236756",
"0.56236756",
"0.5579682",
"0.5457257",
"0.5414618",
"0.5382873",
"0.5349557",
"0.53429776",
"0.5338125",
"0.5333075",
"0.53239155",
"0.5318248",
"0.5313936",
"0.53106076",
"0.5300438",
"0.528713",
"0.528309",
"0.5242493",
"0.52211165",
"0.52005583... | 0.52863556 | 17 |
Details of the resource that was assessed | def resource_details(self) -> pulumi.Output[Any]:
return pulumi.get(self, "resource_details") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_resource_details (self):\n return (f\"[Title:\\\"{self.get_title()}\\\"] [Author:{self.get_author()}] [Publisher:{self.get_publisher()}] [Year:{self.get_year()}]\")",
"def resource(self):\n return str(self._resource)",
"def resource(self):\n return self._resource",
"def resource(... | [
"0.76440537",
"0.7001793",
"0.6875115",
"0.6875115",
"0.6875115",
"0.6875115",
"0.6875115",
"0.6875115",
"0.6875115",
"0.686545",
"0.6756598",
"0.6750164",
"0.66482615",
"0.6627125",
"0.6553682",
"0.6521818",
"0.6483526",
"0.64652663",
"0.6450048",
"0.6442991",
"0.63966125",
... | 0.7746103 | 0 |
The result of the assessment | def status(self) -> pulumi.Output['outputs.AssessmentStatusResponseResponse']:
return pulumi.get(self, "status") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def result(self):\n return self['result']",
"def results(self):\n\n\t\tresults = {'answer':42}\n\n\t\treturn results",
"def result(self):\n\n print('Ergebnisse: -------------\\n'\n 'Richtige Antworten:{} \\n'\n 'Falsche Antworten:{} \\n'.format(self.answer_right, self.a... | [
"0.7455872",
"0.7048662",
"0.687895",
"0.68733793",
"0.6868782",
"0.6851356",
"0.68279386",
"0.6774118",
"0.67248684",
"0.67248684",
"0.67248684",
"0.65798163",
"0.65798163",
"0.6516882",
"0.6478505",
"0.6459931",
"0.645169",
"0.64418316",
"0.64348304",
"0.6432639",
"0.639982... | 0.6113301 | 36 |
Test get_type_for_key_path with Simple Key Path | def test_get_type_for_key_path_simple_path(test_schema):
assert get_type_for_key_path(test_schema, "Age") == "integer" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_type_for_key_path_multi_level(test_schema):\n assert (\n get_type_for_key_path(test_schema, \"EmploymentInformation.Beneficiary.Name\")\n == \"string\"\n )",
"def test_get_type_for_key_path_invalid_key_path(test_schema):\n assert get_type_for_key_path(test_schema, \"foo.bar\")... | [
"0.7844626",
"0.75669205",
"0.74641645",
"0.7052028",
"0.65783304",
"0.65595055",
"0.65575176",
"0.62750286",
"0.59552884",
"0.59359103",
"0.5933874",
"0.59200144",
"0.58311874",
"0.5820171",
"0.5779001",
"0.5731968",
"0.5724418",
"0.5718408",
"0.5717397",
"0.5695019",
"0.567... | 0.84244055 | 0 |
Test get_type_for_key_path with key path of one level deep | def test_get_type_for_key_path_depth_one_level(test_schema):
assert (
get_type_for_key_path(test_schema, "EmploymentInformation.OriginalHireDate")
== "string"
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_type_for_key_path_multi_level(test_schema):\n assert (\n get_type_for_key_path(test_schema, \"EmploymentInformation.Beneficiary.Name\")\n == \"string\"\n )",
"def test_get_type_for_key_path_simple_path(test_schema):\n assert get_type_for_key_path(test_schema, \"Age\") == \"int... | [
"0.81315565",
"0.7790559",
"0.73348916",
"0.6510662",
"0.63362354",
"0.6123883",
"0.5945233",
"0.59033793",
"0.5871133",
"0.58505154",
"0.58181745",
"0.579614",
"0.5716585",
"0.56624275",
"0.5648696",
"0.56418836",
"0.5635143",
"0.5609441",
"0.5574678",
"0.55583984",
"0.55554... | 0.78562295 | 1 |
Test get_type_for_key_path with multi level key path | def test_get_type_for_key_path_multi_level(test_schema):
assert (
get_type_for_key_path(test_schema, "EmploymentInformation.Beneficiary.Name")
== "string"
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_type_for_key_path_simple_path(test_schema):\n assert get_type_for_key_path(test_schema, \"Age\") == \"integer\"",
"def test_get_type_for_key_path_depth_one_level(test_schema):\n assert (\n get_type_for_key_path(test_schema, \"EmploymentInformation.OriginalHireDate\")\n == \"strin... | [
"0.78702086",
"0.77100694",
"0.718522",
"0.6605864",
"0.6282174",
"0.62626797",
"0.5969291",
"0.59365714",
"0.59263676",
"0.5847719",
"0.5822397",
"0.5740006",
"0.5724306",
"0.57241476",
"0.56666523",
"0.56429887",
"0.563662",
"0.5624624",
"0.56129014",
"0.55840045",
"0.55359... | 0.8355806 | 0 |
Test get_type_for_key_path with invalid key path | def test_get_type_for_key_path_invalid_key_path(test_schema):
assert get_type_for_key_path(test_schema, "foo.bar") == None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_type_for_key_path_simple_path(test_schema):\n assert get_type_for_key_path(test_schema, \"Age\") == \"integer\"",
"def test_get_type_for_key_path_multi_level(test_schema):\n assert (\n get_type_for_key_path(test_schema, \"EmploymentInformation.Beneficiary.Name\")\n == \"string\"\... | [
"0.7941828",
"0.74922115",
"0.73346525",
"0.67035246",
"0.6578848",
"0.64337885",
"0.64240384",
"0.64203644",
"0.6280829",
"0.6259001",
"0.6171811",
"0.61548215",
"0.61433345",
"0.61050516",
"0.6074959",
"0.60654056",
"0.6059521",
"0.6050425",
"0.60475475",
"0.6045783",
"0.60... | 0.8730529 | 0 |
Create a mesh of points to plot in | def make_meshgrid(x, y, h=.02):
x_min, x_max = x.min() - 1, x.max() + 1
y_min, y_max = y.min() - 1, y.max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
return xx, yy | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_mesh(\n self,\n lims_x: array_like = (-1, 1),\n lims_y: array_like = (-1, 1),\n ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n a, b, c, d = self.cartesian()\n x_center, y_center = self.point[:2]\n\n values_x = x_center + lims_x\n values_y = y_center + l... | [
"0.7197377",
"0.6756",
"0.6733346",
"0.6643686",
"0.6442712",
"0.6364836",
"0.63441443",
"0.6343205",
"0.63342935",
"0.62988",
"0.627023",
"0.623843",
"0.62137896",
"0.61971325",
"0.6183295",
"0.6164846",
"0.6139129",
"0.61126757",
"0.61126757",
"0.6105098",
"0.6105098",
"0... | 0.6036985 | 24 |
Plot the decision boundaries for a classifier. | def plot_contours(ax, clf, xx, yy, **params):
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
out = ax.contourf(xx, yy, Z, **params)
return out | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_decision_regions(X, y, classifier, resolution=0.02):\n #setup marker generator and color map\n markers = ('s', 'x', 'o', '^', 'v')\n colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n cmap = ListedColormap(colors[:len(np.unique(y))])\n\n #plot the decision surface\n #just find the ... | [
"0.72929466",
"0.7281941",
"0.7130415",
"0.70138526",
"0.70124936",
"0.69807297",
"0.69738376",
"0.6969092",
"0.6924797",
"0.68875146",
"0.6852066",
"0.6791433",
"0.66879207",
"0.66742694",
"0.66299",
"0.6457393",
"0.628703",
"0.62751436",
"0.62345755",
"0.61874735",
"0.61633... | 0.5346783 | 71 |
Guts for `~trigger.utils.url.parse_url`. Based on Kombu's ``kombu.utils.url``. | def _parse_url(url):
parts = urlparse(url)
scheme = parts.scheme
port = parts.port or None
hostname = parts.hostname
path = parts.path or ''
virtual_host = path[1:] if path and path[0] == '/' else path
return (scheme, unquote(hostname or '') or None, port,
unquote(parts.username ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __ParseUrl(url):\n return urlparse(url)",
"def _parse(url):\n url = url.strip()\n parsed = urlparse(url)\n return _parsed_url_args(parsed)",
"def urlparse(url):\n\tunquote_url=urllib.parse.unquote(url)\n\treturn unquote_url",
"def _parse_url(self, url):\n url_prefix = self.URL_PREFIX\n ... | [
"0.8100496",
"0.74347377",
"0.7177285",
"0.7094402",
"0.69739527",
"0.69297016",
"0.6924397",
"0.68131274",
"0.67186797",
"0.6689108",
"0.66737133",
"0.6653376",
"0.6621237",
"0.6607721",
"0.6598066",
"0.65887487",
"0.652699",
"0.65201724",
"0.65014964",
"0.64695275",
"0.6461... | 0.7226152 | 2 |
Given a ``url`` returns, a dict of its constituent parts. Based on Kombu's ``kombu.utils.url``. | def parse_url(url):
scheme, host, port, user, passwd, path, vhost, qs, qs_dict = _parse_url(url)
return dict(scheme=scheme, hostname=host, port=port, username=user,
password=passwd, path=path, virtual_host=vhost,
query=qs, **qs_dict) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_url(url):\n parsed = urlparse(url)\n return {\n \"scheme\": parsed.scheme,\n \"netloc\": parsed.netloc,\n \"path\": parsed.path,\n \"qs\": parse_qs(parsed.query),\n }",
"def parse_url(url):\n results = NotifyBase.parse_url(url)\n if not results:\n ... | [
"0.7244224",
"0.70891213",
"0.68429947",
"0.6841017",
"0.66689384",
"0.6423777",
"0.6402168",
"0.6328875",
"0.6304817",
"0.62504447",
"0.6184663",
"0.6176373",
"0.6129636",
"0.6107103",
"0.61060536",
"0.60760194",
"0.6045592",
"0.6023665",
"0.6001749",
"0.59867436",
"0.597842... | 0.682851 | 4 |
Test if game is to be won/lost by player. | def test_case_if_row_is_about_to_be_foobar(self, mock_game):
test_game = Game(3, "playerX", "playerO")
test_game.game_id = 1002
test_game.board_size = 3
test_game.your_move = "X"
test_game.board_blob = json.dumps(['X', '', 'O',
'X', '', ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def has_won(board, player):\r\n return False",
"def is_game_won(self):\n return True",
"def has_won(board, player):\n return False",
"def check_game(self):\n gameOver = None\n if self.turn > 4:\n gameOver = self.check_x_won()\n if gameOver is True:\n ... | [
"0.8094866",
"0.8090733",
"0.8032095",
"0.8010544",
"0.79272014",
"0.7739902",
"0.7669931",
"0.75953156",
"0.75601757",
"0.7536703",
"0.7533477",
"0.7526654",
"0.7492473",
"0.7458491",
"0.7454935",
"0.7428993",
"0.73916984",
"0.7306342",
"0.72584236",
"0.7249623",
"0.7238585"... | 0.0 | -1 |
Evaluate and apply formatting on template, apply any art if provided. Any additional parameters are passed as extra variables to the template. The extra variables have priority when there's conflicting variable names. | def run(self, template: str, art: Optional[str] = None, **kwargs: Any) -> str:
variables = self.__dict__
variables.update(kwargs)
template = CustomFormats().format(template, **variables)
if art:
art = art.format(nfo=template)
template = art
for m in re.f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def render(template, variables={}):\r\n\treturn prettify( parse(template).render(dict(variables.items())) )",
"def format_template(template, *args):\n return textwrap.dedent(template % args).strip()",
"def formatEval(self, template, attrs, scale=1, noScale=None):\n # Boat width not stored, so calcula... | [
"0.5875008",
"0.58742577",
"0.58426213",
"0.5590589",
"0.5554982",
"0.5440268",
"0.5436101",
"0.53913677",
"0.5359046",
"0.5331052",
"0.530326",
"0.5283194",
"0.5190745",
"0.51852167",
"0.5137004",
"0.51337534",
"0.5114587",
"0.50861675",
"0.5056899",
"0.5040804",
"0.5000452"... | 0.6679136 | 0 |
Get an IMDB ID from either the media's global tags, or the config. Since IMDB IDs are required for this project, it will bug the user for one interactively if not found. | def get_imdb_id(self, imdb_id: Any) -> str:
if not imdb_id:
general_track = self.media_info.general_tracks[0].to_data()
imdb_id = general_track.get("imdb")
if not imdb_id:
print("No IMDB ID was provided but is required...")
while not imdb_id or not isinstance(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def alternative_media_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"alternative_media_id\")",
"def imdb_id(title):\n pass",
"def _get_id(mf, url=None):\n\n\tprops = mf['properties']\n\n\tif 'uid' in props:\n\t\treturn props['uid'][0]\n\telif 'url' in props:\n\t\treturn props['... | [
"0.5761104",
"0.5743748",
"0.559037",
"0.5428841",
"0.5407892",
"0.5339938",
"0.5334296",
"0.53217006",
"0.5253112",
"0.52254945",
"0.52251637",
"0.5214888",
"0.5210509",
"0.5178844",
"0.5142981",
"0.5113672",
"0.5091264",
"0.5082251",
"0.5070948",
"0.5068511",
"0.5062682",
... | 0.64897996 | 0 |
Get a TMDB ID from either the media's global tags, or the config. It will raise a ValueError if the provided ID is invalid. | def get_tmdb_id(self, tmdb_id: Any) -> Optional[str]:
if not tmdb_id:
general_track = self.media_info.general_tracks[0].to_data()
tmdb_id = general_track.get("tmdb")
if not tmdb_id:
print("Warning: No TMDB ID was provided...")
return None
if not se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_tvdb_id(self, tvdb_id: Any) -> Optional[int]:\n if not tvdb_id:\n general_track = self.media_info.general_tracks[0].to_data()\n tvdb_id = general_track.get(\"tvdb\")\n if not tvdb_id:\n print(\"Warning: No TVDB ID was provided...\")\n return None\n ... | [
"0.64487255",
"0.5630184",
"0.5409801",
"0.54041606",
"0.54024845",
"0.53911626",
"0.5343543",
"0.53333956",
"0.5306317",
"0.5306317",
"0.5306317",
"0.5306317",
"0.5306317",
"0.5306317",
"0.52967757",
"0.52887344",
"0.52816087",
"0.5266011",
"0.52260435",
"0.522252",
"0.52078... | 0.64089936 | 1 |
Get a TVDB ID from either the media's global tags, or the config. It will raise a ValueError if the provided ID is invalid. | def get_tvdb_id(self, tvdb_id: Any) -> Optional[int]:
if not tvdb_id:
general_track = self.media_info.general_tracks[0].to_data()
tvdb_id = general_track.get("tvdb")
if not tvdb_id:
print("Warning: No TVDB ID was provided...")
return None
if isinst... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_tmdb_id(self, tmdb_id: Any) -> Optional[str]:\n if not tmdb_id:\n general_track = self.media_info.general_tracks[0].to_data()\n tmdb_id = general_track.get(\"tmdb\")\n if not tmdb_id:\n print(\"Warning: No TMDB ID was provided...\")\n return None\n ... | [
"0.60287786",
"0.5454019",
"0.5437956",
"0.5360313",
"0.5346196",
"0.5333645",
"0.5321893",
"0.52784383",
"0.5270552",
"0.5240752",
"0.5226761",
"0.5213634",
"0.5184168",
"0.5155326",
"0.5155326",
"0.5155326",
"0.5155326",
"0.5155326",
"0.5155326",
"0.51372594",
"0.5098536",
... | 0.694675 | 0 |
Scrape Title Name and Year (including e.g. 2019) from IMDB | def get_title_name_year(self) -> Tuple[str, str]:
r = self.session.get(f"https://www.imdb.com/title/{self.imdb}")
if r.status_code != 200:
raise ValueError(f"An unexpected error occurred getting IMDB Title Page [{r.status_code}]")
imdb_page = html.unescape(r.text)
imdb_title ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scrape_movie_page(dom):\n # to save the information\n info = []\n\n # find the information block needed\n header = dom.find(\"div\", \"title_wrapper\")\n\n # find the title and strip the string\n name_dom = header.h1.get_text().encode(\"utf-8\")\n name = str(name_dom)[2:-16]\n info.appe... | [
"0.6832312",
"0.6682347",
"0.66435677",
"0.6387017",
"0.637413",
"0.63639027",
"0.633812",
"0.6296725",
"0.61997265",
"0.6172732",
"0.6140767",
"0.61179507",
"0.60745186",
"0.60595584",
"0.59533656",
"0.59527224",
"0.5951921",
"0.5943582",
"0.5937595",
"0.59366393",
"0.592958... | 0.7373919 | 0 |
Calculate total episode count based on neighbouring sameextension files. | def get_tv_episodes(self) -> int:
return len(glob.glob(os.path.join(
os.path.dirname(self.file),
f"*{os.path.splitext(self.file)[-1]}"
))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def n_episodes(self):\n raise NotImplementedError",
"def return_episode_num(name):\n return int(name.split(\".\")[0].split(\"ep_\")[1]) # Use split to return only the episode number needed to sort the files in increasing order",
"def _get_total_games(self) -> int:\n files = get_tfr_filenames(... | [
"0.58617145",
"0.5788146",
"0.5688501",
"0.5621688",
"0.551865",
"0.5483126",
"0.54120064",
"0.54120064",
"0.5410899",
"0.5262325",
"0.52515316",
"0.5230109",
"0.52180594",
"0.52164894",
"0.5211398",
"0.52089113",
"0.52078176",
"0.5205271",
"0.5205262",
"0.5201948",
"0.518634... | 0.6543224 | 0 |
Retrieve the release name based on the file used during MediaInfo. If a season was specified, but an episode number was not, it presumes the release is a Pack. Hence when pack, it uses the parent folder's name as the release name. | def get_release_name(self) -> str:
if self.season is not None and self.episode is None:
return os.path.basename(os.path.dirname(self.file))
return os.path.splitext(os.path.basename(self.file))[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getApplicationReleaseName(self) -> unicode:\n ...",
"def title(self):\n if self.file_name is None:\n return None\n else:\n fname = os.path.split(self.file_name)[-1]\n fname, *ext = fname.rsplit('.', 1)\n procgen = ext and ext[0] in ('json', 'ya... | [
"0.59712",
"0.59365255",
"0.58621913",
"0.57642496",
"0.5731158",
"0.5726277",
"0.57143974",
"0.5686276",
"0.5618006",
"0.557107",
"0.5563853",
"0.5559816",
"0.5543877",
"0.55142844",
"0.55000263",
"0.5495673",
"0.5444121",
"0.5430501",
"0.5416234",
"0.54096",
"0.5329431",
... | 0.81937546 | 0 |
Get a wide banner image from fanart.tv. Currently restricts banners to Englishonly. | def get_banner_image(self, tvdb_id: int) -> Optional[str]:
if not tvdb_id:
return None
if not self.fanart_api_key:
raise ValueError("Need Fanart.tv api key for TV titles!")
r = self.session.get(f"http://webservice.fanart.tv/v3/tv/{tvdb_id}?api_key={self.fanart_api_key}")... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def render_banner(self, width=300, height=85):\n img_path = IMG_PATH + os.sep + CARD_BANNER\n banner_img = Image.open(img_path)\n banner_img = banner_img.resize((width, height))\n return banner_img",
"def banner_wrapper(banner_url):\n # so simple\n return '{url}<img src=... | [
"0.63562655",
"0.5907053",
"0.58398026",
"0.56723255",
"0.55253977",
"0.55232245",
"0.55212253",
"0.5516398",
"0.532808",
"0.5248593",
"0.52476937",
"0.52444804",
"0.5240686",
"0.51916087",
"0.51215434",
"0.5034326",
"0.5020774",
"0.5014858",
"0.49858487",
"0.4975781",
"0.496... | 0.6869307 | 0 |
Return a list of a brief subtitle overview persubtitle. e.g. English, Forced, SubRip (SRT) English, SubRip (SRT) English, SDH, SubRip (SRT) Spanish, Latin American (SDH), SubRip (SRT) The bit of text between the Language and the Subtitle format is the Track Title. It can be of any format, but it is recommended to be us... | def get_subtitle_print(subs: List[Track]) -> List[str]:
data = []
if not subs:
data.append("--")
for sub in subs:
line_items = []
# following sub.title tree checks and supports three different language and title scenarios
# The second scenario is ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_title(self) -> list:\n scanning = False # start of a title is found, this may be the second of later part of that.\n ret = [] # to return\n temp = [] # deal with mutiple line titles.\n for page in self.pdf.pages:\n text = page.extract_text()\n # it's p... | [
"0.6559989",
"0.626816",
"0.61477166",
"0.59452933",
"0.5862041",
"0.58259624",
"0.57575667",
"0.5716959",
"0.57007366",
"0.56698",
"0.566014",
"0.5631774",
"0.56170785",
"0.5601238",
"0.55479985",
"0.55277705",
"0.55247784",
"0.55187654",
"0.5466009",
"0.5463526",
"0.5434021... | 0.73892254 | 0 |
The mins method returns the lower bounds of the action spaces' parameters. | def mins(self) -> Tensor:
return self._ranges[:, 0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mins(self):\n return self._mins",
"def bounds(self) -> Tensor:\n return torch.cat([self.mins, self.mins + self.ranges], dim=-2)",
"def mins(self):\n return self.intervals[:, 0]",
"def argminX( self ):\n min = 1e30\n minX = None\n for i in range( 0, self.GetN() ):\n ... | [
"0.65231216",
"0.63490486",
"0.6329805",
"0.6140933",
"0.604615",
"0.5979384",
"0.59507585",
"0.5943575",
"0.5881969",
"0.5867249",
"0.58545846",
"0.5735611",
"0.5735611",
"0.5695982",
"0.568224",
"0.5630734",
"0.56240106",
"0.55890894",
"0.556751",
"0.55503213",
"0.55368114"... | 0.6706375 | 0 |
The maxs method returns the upper bounds of the action spaces' parameters. | def maxs(self) -> Tensor:
return self._ranges[:, 1] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def maxs(self):\n return self._maxs",
"def get_parameters_max(self):\n maxValues = numpy.zeros(self.get_num_parameters())\n i = 0\n for p in self.parameters:\n maxValues[i] = p.get_max_value()\n i += 1\n return maxValues",
"def get_bounds(self):\n ... | [
"0.6918863",
"0.66844064",
"0.6673597",
"0.6575035",
"0.6552709",
"0.6513679",
"0.6467321",
"0.6414459",
"0.62708175",
"0.62123346",
"0.620865",
"0.62085176",
"0.62085176",
"0.6191484",
"0.6156986",
"0.6142866",
"0.6112747",
"0.6100505",
"0.6096278",
"0.60705197",
"0.60639876... | 0.6770987 | 1 |
The _generate_iterator method creates an iterator which runs over all possible parameter combinations | def _generate_iterator(self) -> Iterable:
params: List[Tensor] = []
for angle_range in self._ranges:
lin_space: Tensor = linspace(angle_range[0], angle_range[1], steps=self._num_steps)
params.append(lin_space)
power: int
dims: int
for i in range(0, self._n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __iter__(self):\n for p in self.param_grid:\n # Always sort the keys of a dictionary, for reproducibility\n items = sorted(p.items())\n if not items:\n yield {}\n else:\n keys, values = zip(*items)\n for v in produc... | [
"0.7333558",
"0.6785712",
"0.67566705",
"0.67345154",
"0.66711015",
"0.66702646",
"0.66659456",
"0.64822334",
"0.6465031",
"0.64270854",
"0.64160955",
"0.6409563",
"0.638886",
"0.638886",
"0.638886",
"0.638886",
"0.6387711",
"0.6350889",
"0.6341186",
"0.6333276",
"0.6317058",... | 0.73833257 | 0 |
computes and returns a complex rotation matrix given by the angles in params. | def operator(self, params: Tensor) -> Tensor:
theta, phi = params
# calculate entries
a: Tensor = exp(1j * phi) * cos(theta / 2)
b: Tensor = sin(theta / 2)
c: Tensor = -b
d: Tensor = exp(-1j * phi) * cos(theta / 2)
# construct the rows of the rotation matrix
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rotation_matrix(theta=0, phi=0, psi=0, units='deg'):\n\n rpy = Rpy(theta,units)\n rmx = Rmx(phi, units)\n rpz = Rpz(psi, units)\n\n return np.matmul(rpy, np.matmul(rmx, rpz))",
"def rotation_matrix3(angle_x=0, angle_y=0, angle_z=0):\n if angle_x != 0:\n c, s = cos(angle_x), sin(angle_x)... | [
"0.6859258",
"0.68380105",
"0.6828468",
"0.67929703",
"0.6778919",
"0.6778919",
"0.6726848",
"0.6723164",
"0.67022794",
"0.67022794",
"0.67022794",
"0.66824156",
"0.6651829",
"0.66406155",
"0.6638365",
"0.66007775",
"0.6590055",
"0.6520805",
"0.6512235",
"0.6512235",
"0.64985... | 0.6610632 | 15 |
Implementation of the triplet loss as defined by formula (3) | def triplet_loss(y_true, y_pred, alpha=0.2):
anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]
# Step 1: Compute the (encoding) distance between the anchor and the positive, you will need to sum over axis=-1
pos_dist = K.sum(K.square(anchor - positive), axis=-1)
# Step 2: Compute the (encod... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def triplet_loss(y_true, y_pred):\n [a,p,n] = tf.unstack(y_pred, num=3)\n pos_dist = tf.reduce_sum((a - p)**2, axis=-1)\n neg_dist = tf.reduce_sum((a - n)**2, axis=-1)\n basic_loss = pos_dist - neg_dist + 0.1\n loss = tf.reduce_sum(tf.maximum(basic_loss, 0.0)) \n return loss",
"de... | [
"0.7934357",
"0.78243315",
"0.76634",
"0.76594365",
"0.75780183",
"0.75132746",
"0.74581045",
"0.7425377",
"0.7383122",
"0.7329623",
"0.731566",
"0.7292533",
"0.7243651",
"0.7238686",
"0.7167928",
"0.714028",
"0.7125176",
"0.7010623",
"0.69959813",
"0.6995111",
"0.69010466",
... | 0.71021456 | 17 |
Function to rotate one vector to another, inspired by vrrotvec.m in MATLAB | def vrrotvec(a,b):
a = normalize(a)
b = normalize(b)
ax = normalize(np.cross(a,b))
angle = np.arccos(np.minimum(np.dot(a,b),[1]))
if not np.any(ax):
absa = np.abs(a)
mind = np.argmin(absa)
c = np.zeros((1,3))
c[mind] = 0
ax = normalize(np.cross(a,c))
r = n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def svecRotate(v, T):\n \n return svec(Rotate(smat(v), T))",
"def _rot(theta, vec):\n\n rmat = scipy.array([[scipy.cos(theta), -1*scipy.sin(theta)],\n [scipy.sin(theta), scipy.cos(theta)]]) \n return scipy.dot(rmat,vec)",
"def rotate_vectors(q, vec):\n rot_vec = []\n fo... | [
"0.73906195",
"0.7337561",
"0.72559744",
"0.70578516",
"0.702117",
"0.6936926",
"0.69332725",
"0.6840454",
"0.68362135",
"0.6819142",
"0.6817099",
"0.67670673",
"0.6754099",
"0.6747304",
"0.67367476",
"0.6716166",
"0.6703668",
"0.6696891",
"0.66868734",
"0.6665173",
"0.660386... | 0.7442148 | 0 |
Convert the axisangle representation to the matrix representation of the rotation | def vrrotvec2mat(r):
s = np.sin(r[3])
c = np.cos(r[3])
t = 1 - c
n = normalize(r[0:3])
x = n[0]
y = n[1]
z = n[2]
m = np.array(
[[t*x*x + c, t*x*y - s*z, t*x*z + s*y],
[t*x*y + s*z, t*y*y + c, t*y*z - s*x],
[t*x*z - s*y, t*y*z + s*x, t*z*z + c]]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def axis2rotmat(axis):\n return quat2rotmat(axis2quat(axis))",
"def rotation_matrix(angle, axis):\n about_z = rotation_about_z(angle)\n z_to_axis = z_to_vector(axis)\n axis_to_z = np.linalg.inv(z_to_axis)\n return reduce(np.dot, [z_to_axis, about_z, axis_to_z])",
"def angle_to_rotation_matrix(an... | [
"0.81078196",
"0.78859186",
"0.74368423",
"0.7374491",
"0.7312507",
"0.7310099",
"0.7273846",
"0.7225561",
"0.71868175",
"0.71868175",
"0.70643413",
"0.70557725",
"0.70138526",
"0.7002238",
"0.6987983",
"0.6980551",
"0.69750994",
"0.6967742",
"0.69532573",
"0.693683",
"0.6926... | 0.0 | -1 |
Sort the buses reversed by their period, having tagged them with their position in the sequence, which is their c value. >>> list(prep_input(EXAMPLE_BUSES)) [(59, 4), (31, 6), (19, 7), (13, 1), (7, 0)] | def prep_input(buses):
return sorted([(bus, offset)
for offset, bus
in enumerate(buses)
if bus], reverse=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_bc_freqs(bc_freqs):\r\n\r\n bcs_list = []\r\n for curr_key in bc_freqs.keys():\r\n bcs_list.append((curr_key, int(bc_freqs[curr_key])))\r\n\r\n bcs_list = sorted(bcs_list, key=itemgetter(1), reverse=True)\r\n\r\n sorted_bcs = []\r\n for curr_bc in bcs_list:\r\n sorted_bcs.a... | [
"0.5366377",
"0.5326399",
"0.532053",
"0.5228897",
"0.5217887",
"0.5176579",
"0.5169902",
"0.51473904",
"0.513209",
"0.5101623",
"0.50995326",
"0.5060153",
"0.50267196",
"0.5010616",
"0.5007883",
"0.4982695",
"0.49244604",
"0.49026328",
"0.49025616",
"0.4894752",
"0.48676687"... | 0.6246531 | 0 |
period of combined signal is lcm of the periods of its components >>> lcm(3, 9) 9 >>> lcm(4, 9) 36 | def lcm(x, y):
return x*y//gcd(x,y) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lcm(a, b):\n\treturn a * b // gcm(a, b)",
"def lcm(a, b):\r\n return a * b / fr.gcd(a, b)",
"def lcm(a, b):\r\n return a * b // gcd(a, b)",
"def lcm(a, b):\n return a * b // gcd(a, b)",
"def lcm(a, b):\n return a * b // gcd(a, b)",
"def lcm(a, b):\n return a * b // gcd(a, b)",
"def l... | [
"0.71418875",
"0.69032246",
"0.6865901",
"0.6818754",
"0.6818754",
"0.6818754",
"0.6818754",
"0.6818754",
"0.6818754",
"0.6818754",
"0.6818754",
"0.6818754",
"0.67949474",
"0.67771405",
"0.67771405",
"0.6775764",
"0.67721605",
"0.67721605",
"0.67721605",
"0.665062",
"0.660152... | 0.6627561 | 20 |
>>> combine_signals((5,2), (3,1)) (15, 7) >>> combine_signals((3,1), (2,0)) (6, 4) >>> combine_signals((13,1),(12,0)) (156, 144) | def combine_signals(longer,shorter):
T_0, c_0 = longer
T_1, c_1 = shorter
# Period is the lcm of the provided periods
T_result = lcm(T_0,T_1)
# Determine phase by searching soutions of longer that fall between the
# start position and start + T_result
for i in range(T_0-c_0,T_result+c_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def combine_signals(series1: pd.Series, series2: pd.Series) -> pd.Series:\n return ((np.sign(series1) == np.sign(series2)) * series1).astype(int, copy=False)",
"def solve_buses(prepared_buses):\n T, c = functools.reduce(combine_signals, prepared_buses)\n return T - c",
"def pick_signals(processor, sou... | [
"0.60273904",
"0.57812446",
"0.5720774",
"0.5197646",
"0.5101901",
"0.50511295",
"0.49429286",
"0.49002105",
"0.48530227",
"0.48525456",
"0.48228434",
"0.48220006",
"0.48124447",
"0.47445247",
"0.4741653",
"0.47265753",
"0.46585917",
"0.4639718",
"0.46313864",
"0.46211886",
"... | 0.5614806 | 3 |
Reduce a bunch of periodic signals to a single signal. The value of x that answers the puzzle is the first place ( c + x ) % T = 0, that is to say, c + x = T, or x = Tc. >>> solve_buses(prep_input(EXAMPLE_BUSES)) 1068781 | def solve_buses(prepared_buses):
T, c = functools.reduce(combine_signals, prepared_buses)
return T - c | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solution2(inp):\n inp = get_lines(inp)\n notes = inp[1].split(\",\")\n\n offsets = {}\n for i, bus in enumerate(notes):\n if bus == 'x':\n continue\n bus = int(bus)\n offsets[bus] = i\n buses = set(offsets)\n old_buses = buses.copy()\n\n def search(bus, offs... | [
"0.5621422",
"0.5476572",
"0.5280086",
"0.5161614",
"0.5074294",
"0.49908358",
"0.49579656",
"0.48839134",
"0.48619267",
"0.48579392",
"0.48389107",
"0.48320952",
"0.4826791",
"0.48213837",
"0.48160282",
"0.4811936",
"0.48002857",
"0.4795177",
"0.47753403",
"0.4768853",
"0.47... | 0.76751333 | 0 |
Generator over all subclasses of a given class, in depth first order. | def itersubclasses(cls, _seen=None):
if not isinstance(cls, type):
raise TypeError('itersubclasses must be called with '
'new-style classes, not %.100r' % cls)
if _seen is None:
_seen = set()
try:
subs = cls.__subclasses__()
except TypeError: # fails onl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def all_subclasses(cls):\n for subclass in cls.__subclasses__():\n yield subclass\n for subc in all_subclasses(subclass):\n yield subc",
"def class_hierarchy(clslist):\n for cls in clslist:\n subclass_list = cls.__subclasses__()\n if subclass_list:\n ... | [
"0.7962573",
"0.79427534",
"0.7785052",
"0.75617856",
"0.7538503",
"0.75220305",
"0.7414212",
"0.7337148",
"0.7327652",
"0.7236801",
"0.70275396",
"0.7026855",
"0.7007763",
"0.6963463",
"0.6906831",
"0.688978",
"0.688137",
"0.6830467",
"0.68002534",
"0.67773235",
"0.6738663",... | 0.7340427 | 7 |
Might be a useful helper | def _get_belt(self, new_score):
for score in reversed(scores):
if new_score >= score:
return BELTS[score].capitalize()
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def regular(self):",
"def sth():",
"def substantiate():",
"def __call__():",
"def __call__():",
"def __call__():",
"def __call__():",
"def __call__():",
"def exo2():",
"def support(self):",
"def __call__(self) -> None:",
"def common(self):",
"def fn():",
"def func():",
"def _prepare(sel... | [
"0.61169916",
"0.5881199",
"0.5851093",
"0.5847512",
"0.5847512",
"0.5847512",
"0.5847512",
"0.5847512",
"0.5819249",
"0.5811447",
"0.579026",
"0.5758938",
"0.57119006",
"0.56060624",
"0.55889446",
"0.55889446",
"0.5582659",
"0.5582659",
"0.5576801",
"0.55566376",
"0.55192375... | 0.0 | -1 |
Method counting photos and creatig list of subfolders with images. | def my_root_listdir(root_dir):
root_listdir = [
images_dir
for images_dir in os.listdir(root_dir)
if not any(
characters in images_dir for characters in [".", "test", "train", "valid"]
)
]
summ = 0
for images_dir in root_listdir:
summ += len(os.listdir... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def appendpics(pathofimg, w_sub, h_sub, step):\n num = 0\n dirlist = []\n images = [] # images in each folder\n for root, dirs, fileswer in os.walk(pathofimg):\n if len(dirs)!= 0:\n for dir in dirs:\n dirlist.append(dir)\n for rooert, dirwerwes, files in... | [
"0.69187784",
"0.6491537",
"0.6410648",
"0.63387346",
"0.63301575",
"0.60764897",
"0.6062333",
"0.6053507",
"0.60286975",
"0.6016352",
"0.59886175",
"0.58583134",
"0.58411384",
"0.5840281",
"0.5804456",
"0.57886666",
"0.57884914",
"0.5774842",
"0.57680684",
"0.5757683",
"0.57... | 0.5316852 | 77 |
Method opening all images to test their validity. | def verify_images(root_dir, root_listdir):
counter = 0
for index, image_dir in enumerate(root_listdir):
images_listdir = os.listdir(root_dir + "/" + image_dir)
list_of_images_indices = [
image_index
for image_index in range(3, len(images_listdir) - 1)
if imag... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_images(self):\n self.roses.save_image()\n all_images = Images.get_all_images()\n self.assertTrue(len(all_images)<1)",
"def load_from_images(self):\n logging.debug(\"load_from_images called\")\n return True",
"def images_exist(self):\n pass",
"def test_rea... | [
"0.6742113",
"0.62553185",
"0.6229397",
"0.6169011",
"0.61681324",
"0.6136686",
"0.60898274",
"0.60487515",
"0.5997627",
"0.5935533",
"0.58759665",
"0.58210576",
"0.5768145",
"0.5766127",
"0.5718156",
"0.5691568",
"0.5679888",
"0.56696707",
"0.5667723",
"0.5602127",
"0.557296... | 0.663587 | 1 |
for a given template and list of extensions, find every file related to that template which has one of the extensions. | def find_template_companion_files(template: Path, extensions: Iterable[str], recurse_up_to: Path = None) -> Set[Path]:
files_to_check = []
# Get a list of all file names to look for in each folder
data_file_names = []
basename = template.name.split('.')[0]
for i in range(len(template.suffixes)):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def filter_files_by_extension(\n files: list ,\n extensions: list\n):\n filtered_files = []\n for file in files:\n file_ext = os.path.splitext(file)[-1].lower()\n file_ext = _remove_dot_from_extension(file_ext)\n for extension in extensions:\n ext = _remove_dot_from_exte... | [
"0.6474418",
"0.63438606",
"0.63118434",
"0.6272479",
"0.6263339",
"0.6136152",
"0.6116507",
"0.6069046",
"0.60550666",
"0.6040801",
"0.599823",
"0.5954619",
"0.5945622",
"0.59430933",
"0.5937887",
"0.58905417",
"0.58869183",
"0.5864783",
"0.58605796",
"0.58385235",
"0.581291... | 0.74726915 | 0 |
The core component of this software is the Yasha class. When used as a commandline tool, a new instance will be create with each invocation. When used as a library, multiple different instances can be created with different configurations | def __init__(self,
root_dir: Path = Path('.'),
variable_files: List[Union[Path,str]] = list(),
inline_variables = dict(),
yasha_extensions_files: List[Union[Path,str]] = list(),
template_lookup_paths: List[Union[Path,str]] = list(),
mode: Union... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main(cls):\n raise NotImplementedError",
"def __init__(self):\r\n self._config = Config.load()\r\n self._bootstrap_jar_url = self._config.get('ivy', 'bootstrap_jar_url',\r\n default=self._DEFAULT_URL)\r\n self._timeout = Amount(self._config.getint... | [
"0.6384319",
"0.5974639",
"0.59633154",
"0.59085184",
"0.58305883",
"0.58266973",
"0.5822428",
"0.58058023",
"0.5801817",
"0.5789056",
"0.57864296",
"0.57859546",
"0.5772526",
"0.5756182",
"0.5749882",
"0.5728382",
"0.57279474",
"0.57279474",
"0.57224405",
"0.57149786",
"0.56... | 0.0 | -1 |
Render a single template | def render_template(self,
template: Union[Path, str],
find_data_files = True,
find_extension_files = True,
jinja_env_overrides = dict(),
output: BinaryIO = None) -> Union[str, BinaryIO]:
if isinstance(template, Path):
# Automatic file... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def render(request, template):\r\n return render_to_response('static_templates/' + template, {})",
"def render(self, template: str, **vars) -> str:",
"def render(self, _template, **context):\n context['_request'] = self.request\n self.response.write(self.jinja2.render_template(_template, **con... | [
"0.73298955",
"0.72438174",
"0.71931773",
"0.7177177",
"0.7177177",
"0.7177177",
"0.7177177",
"0.7177177",
"0.7039566",
"0.70391536",
"0.6924555",
"0.69022024",
"0.68838",
"0.68724924",
"0.6871491",
"0.6865003",
"0.6862738",
"0.68552256",
"0.6819078",
"0.67653996",
"0.6763914... | 0.0 | -1 |
When rendering or working with multiple template files, we load extension files related to those templates, which alters the environment, and we add each template's parent directory to the template loader search path, which also alters the environment. That means processing one template with a Yasha instance alters the... | def _make_isolated_env_for_template(self, template: Union[Path, str]) -> Environment:
if isinstance(template, str):
# string tempaltes have no associated files, and therefore don't alter the environment. They can use the base environment directly
return self.env
# Deplic... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_templates( path=\"boilerplate\" ):\n global template_env\n template_loader = jinja2.FileSystemLoader(searchpath=\"boilerplate\" )\n template_env = jinja2.Environment(\n loader=template_loader,\n lstrip_blocks=True\n )",
"def _buildjinja2_templates(self):\n templates ... | [
"0.71863306",
"0.68339425",
"0.68019456",
"0.64534956",
"0.6438727",
"0.64271533",
"0.63226676",
"0.62789506",
"0.61880404",
"0.61768335",
"0.6093075",
"0.6058683",
"0.60580313",
"0.60333896",
"0.59724724",
"0.59577113",
"0.5878471",
"0.5849247",
"0.5840072",
"0.5812187",
"0.... | 0.5354332 | 61 |
Produces a list of all files that the rendering of this template depends on, including files referenced within {% include %}, {% import %}, and {% extends %} blocks within the template | def get_makefile_dependencies(self, template: Union[Path, str]) -> List[Path]:
if isinstance(template, Path):
template = template.read_text()
dependencies = self.variable_files + self.yasha_extensions_files
referenced_template_partials = find_referenced_templates(self.env.parse(templ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_imports() -> str:\n extension = \"\"\n for js_ in JS_FILES.values():\n extension += f'<script src=\"{js_}\"></script>'\n for css in CSS_FILES.values():\n extension += f'<link rel=\"stylesheet\" href=\"{css}\" is=\"custom-style\">'\n\n return extension",
"... | [
"0.6667309",
"0.649749",
"0.64170736",
"0.63219726",
"0.6255717",
"0.61782795",
"0.6177389",
"0.61124706",
"0.60708463",
"0.60316014",
"0.6030461",
"0.6025385",
"0.60198873",
"0.598662",
"0.59268177",
"0.5915987",
"0.58921176",
"0.58828634",
"0.58777714",
"0.5809368",
"0.5792... | 0.6414305 | 3 |
Distributes an archive to your web servers | def do_deploy(archive_path):
if not os.path.exists(archive_path):
return False
else:
try:
put(archive_path, "/tmp/")
filename = archive_path.split('/')
no_ext = filename[-1].split('.')
archive = no_ext[0]
run("mkdir -p /data/web_static/... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_pack():\n\n now = datetime.now()\n # format the name of the file with the timestamps\n now_year = now.year\n now_month = now.month\n now_day = now.day\n now_hour = now.hour\n now_minute = now.minute\n now_second = now.second\n # apply the format\n file_name = 'versions/web_stat... | [
"0.7481866",
"0.7308439",
"0.717865",
"0.70625347",
"0.70540977",
"0.6997107",
"0.69896996",
"0.6985268",
"0.6972793",
"0.6966511",
"0.6944373",
"0.69133264",
"0.68920314",
"0.6885152",
"0.68583626",
"0.68551016",
"0.6854341",
"0.68525314",
"0.68385327",
"0.681433",
"0.678784... | 0.6309926 | 65 |
To search and replace the text config string the file | def search_and_replace_config(param_dict, config_file):
if param_dict is None:
raise ValueError('could not find parameters to update the configuration: %s' % param_dict)
if config_file is None:
raise ValueError('could not find config file to update the configuration: %s' % config_file)
sea... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def modify_config_file(config_file, search_config, replace_config):\n with open(config_file, 'r+') as f:\n content = f.read()\n f.seek(0)\n f.write(content.replace(search_config, replace_config))\n f.truncate()\n f.close()",
"def replace_includes(self, file_name):\n\n ... | [
"0.7213954",
"0.6664111",
"0.6658277",
"0.644173",
"0.63886225",
"0.6382801",
"0.6320942",
"0.6153013",
"0.60404235",
"0.6038611",
"0.6027211",
"0.592698",
"0.5926587",
"0.5894178",
"0.58916545",
"0.5873578",
"0.58602273",
"0.5843004",
"0.5838578",
"0.57836527",
"0.57670426",... | 0.6405321 | 4 |
To read the config file, modify the threshold content and truncate the file | def modify_config_file(config_file, search_config, replace_config):
with open(config_file, 'r+') as f:
content = f.read()
f.seek(0)
f.write(content.replace(search_config, replace_config))
f.truncate()
f.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reten_log(path):\n try:\n file = open(path, 'r+')\n lines = file.readlines()\n if lines > 200:\n file.truncate()\n file.close()\n else:\n file.close()\n except:\n pass",
"def __write_thresholds_off_config(self, path):\n self.__w... | [
"0.63270384",
"0.60728574",
"0.57777673",
"0.5709095",
"0.52786064",
"0.51557845",
"0.5135187",
"0.5053703",
"0.5041154",
"0.5027755",
"0.5015283",
"0.5009702",
"0.49824095",
"0.49750274",
"0.49732378",
"0.49518126",
"0.49454418",
"0.49447972",
"0.49405065",
"0.49233353",
"0.... | 0.55548596 | 4 |
Return sny vowels founded in a supplied word. | def search4vowels(word):
vowels = set('aeiou')
found = vowels.intersection(set(word))
#return found
for vowels in found:
print(vowels) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _rv_standard(self, word, vowels):\n rv = \"\"\n if len(word) >= 2:\n if word[1] not in vowels:\n for i in range(2, len(word)):\n if word[i] in vowels:\n rv = word[i + 1 :]\n break\n\n elif word[0... | [
"0.74471885",
"0.7248238",
"0.71690327",
"0.68447524",
"0.6721554",
"0.670467",
"0.6704543",
"0.64083433",
"0.6355867",
"0.6346289",
"0.6267494",
"0.62225914",
"0.6183491",
"0.61348367",
"0.60796964",
"0.604393",
"0.60383445",
"0.603059",
"0.59944683",
"0.5991076",
"0.5983007... | 0.6320444 | 10 |
Constructor. Create the settings objects | def __init__(self):
self.s = QSettings()
self.p = QgsProject.instance() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__( settings={} ):",
"def __init__(self, settings):\n\n # store settings\n self.settings = settings",
"def __init__(self, settings):\n \n # storing otmbs settings\n self.settings = settings",
"def __init__(self, settings):\n self._settings = settings",
"d... | [
"0.81398326",
"0.8127764",
"0.81223667",
"0.8024526",
"0.75850004",
"0.7424904",
"0.7297364",
"0.727927",
"0.7227605",
"0.7210535",
"0.7177579",
"0.7173619",
"0.71518964",
"0.7086663",
"0.695417",
"0.6928098",
"0.69154674",
"0.6878284",
"0.6834534",
"0.6787074",
"0.67606455",... | 0.6513789 | 38 |
Transform x elementwise through an affine function y = exp(s)x + t where s = st[...,0] and t = st[...,1] with s.shape == x.shape == t.shape The Jacobian for this transformation is the coordinatewise product of the scaling factors J = prod(es[...,i],i) | def element_wise_affine(x, st, compute_jacobian=True):
es = torch.exp(st[..., 0])
t = st[..., 1]
logj = None
if compute_jacobian:
logj = torch.sum(torch.log(es), dim=-1)
return es * x + t, logj | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inverse_element_wise_affine(x, st, compute_jacobian=True):\n es = torch.exp(-st[..., 0])\n t = st[..., 1]\n logj = None\n if compute_jacobian:\n logj = torch.sum(torch.log(es), dim=-1)\n\n return es * (x - t), logj",
"def affine(params, x):\n return np.dot(params['w'], x) + params['b']... | [
"0.7174713",
"0.6328652",
"0.6287944",
"0.6152823",
"0.6144677",
"0.5968129",
"0.5957975",
"0.5921042",
"0.5909272",
"0.5798729",
"0.5789589",
"0.5777053",
"0.56403214",
"0.5585435",
"0.55693203",
"0.5553606",
"0.5544687",
"0.5482424",
"0.5461267",
"0.54540503",
"0.54315287",... | 0.8046413 | 0 |
Transform x elementwise through an affine function y = exp(s)(x t) where s = st[...,0] and t = st[...,1] with s.shape == x.shape == t.shape This is the inverse of `element_wise_affine` above for the same set of parameters st The Jacobian for this transformation is the coordinatewise product of the scaling factors J = p... | def inverse_element_wise_affine(x, st, compute_jacobian=True):
es = torch.exp(-st[..., 0])
t = st[..., 1]
logj = None
if compute_jacobian:
logj = torch.sum(torch.log(es), dim=-1)
return es * (x - t), logj | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def element_wise_affine(x, st, compute_jacobian=True):\n es = torch.exp(st[..., 0])\n t = st[..., 1]\n logj = None\n if compute_jacobian:\n logj = torch.sum(torch.log(es), dim=-1)\n\n return es * x + t, logj",
"def transform(fn):\n def _(vec, dt):\n return np.einsum(\n 'ji,... | [
"0.83168423",
"0.6524651",
"0.6448697",
"0.6208682",
"0.6185584",
"0.60859853",
"0.60077345",
"0.6005692",
"0.6002416",
"0.585732",
"0.5842322",
"0.57764435",
"0.5741281",
"0.5718799",
"0.57156426",
"0.57109356",
"0.5676722",
"0.56560165",
"0.5641633",
"0.56377053",
"0.563442... | 0.77893174 | 1 |
Softmax loss function, naive implementation (with loops) Inputs have dimension D, there are C classes, and we operate on minibatches of N examples. | def softmax_loss_naive(W, X, y, reg):
# Initialize the loss and gradient to zero.
loss = 0.0
dW = np.zeros_like(W)
#############################################################################
# TODO: Compute the softmax loss and its gradient using explicit loops. #
# Store the loss in loss and the gra... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def softmax_classifier(W, input, label, lamda):\n\n ############################################################################\n # TODO: Put your code here\n\n loss = 0.0\n num_train = input.shape[0]\n num_classes = W.shape[1]\n\n score = np.dot(input, W) # (N,C)\n prediction = np.argmax(sco... | [
"0.77309406",
"0.76029754",
"0.75815004",
"0.7345373",
"0.7316555",
"0.7265773",
"0.72579056",
"0.72342706",
"0.7226934",
"0.72213393",
"0.7203286",
"0.71891123",
"0.713433",
"0.71218145",
"0.7092656",
"0.7017707",
"0.69664174",
"0.6941942",
"0.693169",
"0.69267535",
"0.69202... | 0.6760264 | 36 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.