Datasets:

cornstack

/

cornstack-python-v1

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CoRNStack 8

{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }

[ "def load_stop_table(self, filename):\n self.stop_table = HashTable(191)\n with open(filename, 'r') as f:\n for word in f.readlines():\n self.stop_table.insert(word.replace('\\n',''),None)", "def load_stop_words(stop_word_file):\n stop_words = []\n for line in open(stop_word_file):\n if line.strip()[0:1] != \"#\":\n for word in line.split(): # in case more than one per line\n stop_words.append(word)\n return stop_words", "def load():\n for line in open(config.filepath, 'r'):\n line = line.strip()\n line_sorted = ''.join(sorted(line))\n\n if line_sorted not in Words.hashed:\n Words.hashed[line_sorted] = []\n\n # Store the real hashed as a list\n # We need line_sorted as the key for fast lookup later\n Words.hashed[line_sorted].append(line)\n\n # Also add the word to a standard list\n # We'll use this to quickly determine wordiness later\n Words.words.append(line)", "def _stopwords():\n global _stopword_set\n if _stopword_set:\n return _stopword_set\n f_name = \"stopword.list\"\n if os.path.isfile(f_name):\n res = set()\n with open(f_name) as f:\n for line in f:\n res.add(line.strip())\n _stopword_set = res\n return res\n else:\n error(\"stop words - not a file: %s\" % f_name)", "def read_text_file(self, filepath: str):\n with open(filepath) as fh:\n for line in fh:\n for word in re.split('\\W+', line):\n word = word.lower()\n if len(word):\n l = self.hash_map.lookup(word)\n self.hash_map.insert(word, l + 1 if l > 0 else 1)", "def read_stopwords(fileName='stopwords.txt', lower_case=True):\n stopwords = set()\n with open(fileName) as f:\n for w in f:\n w = w.strip()\n if w:\n if lower_case:\n w = w.lower()\n stopwords.add(w)\n return stopwords", "def make_word_dict():\n d = dict()\n for line in open('words.txt'):\n word = line.strip().lower()\n d[word] = None\n\n return d", "def load_wordlist(self, filename):\n reg1 = re.compile(\"^([1-6]{5})[ \\t]+(.*)$\")\n f = open(filename, 'r')\n \n if(self.generate):\n wordlist = []\n reg2 = re.compile(\"^(\\S*)$\")\n for line in f:\n m1 = reg1.match(line)\n m2 = reg2.match(line)\n \n if(m1):\n wordlist.append(m1.group(2))\n elif(m2):\n wordlist.append(m2.group(1))\n \n else:\n wordlist = {}\n for line in f:\n m = reg1.match(line)\n if(m):\n wordlist[int(m.group(1))] = m.group(2)\n \n if((not self.generate and len(wordlist) < 7776) or \n (self.generate and len(wordlist) < 2**13)):\n stderr.write(\"Word list is too short\\n\")\n exit(5)\n \n self.wordlist = wordlist", "def __init__(self):\n stopwords_file = open(self.filepath, \"r\")\n for line in stopwords_file.readlines():\n line2 = line.replace(\"\\n\", \"\") \n self.add(line2)", "def mkwrddct(inputfile):\n fin = open(inputfile)\n words = dict()\n for line in fin:\n w = line.strip()\n words[w] = w\n return words", "def rm_stopwords(file_path, word_dict):\n\n # read stop word dict and save in stop_dict\n stop_dict = {}\n with open(word_dict) as d:\n for word in d:\n stop_dict[word.strip(\"\\n\")] = 1\n # remove tmp file if exists\n if os.path.exists(file_path + \".tmp\"):\n os.remove(file_path + \".tmp\")\n\n print(\"now remove stop words in %s.\" % file_path)\n # read source file and rm stop word for each line.\n with open(file_path) as f1, open(file_path + \".tmp\", \"w\") as f2:\n for line in f1:\n tmp_list = [] # save words not in stop dict\n words = line.split()\n for word in words:\n if word not in stop_dict:\n tmp_list.append(word)\n words_without_stop = \" \".join(tmp_list)\n to_write = words_without_stop + \"\\n\"\n f2.write(to_write)\n\n # overwrite origin file with file been removed stop words\n shutil.move(file_path + \".tmp\", file_path)\n print(\"stop words in %s has been removed.\" % file_path)", "def load_stop_words() -> list:\r\n with open(f'{ENGINE}/stop_words.txt', 'r') as i:\r\n stop_words = i.read().splitlines()\r\n stop_words = list(map(lambda x: x.upper(), stop_words)) # Force all stop words to UPPER case.\r\n return stop_words", "def load_stop_words():\n with open('../data/stop_words.txt', 'r') as stop_words_file:\n return stop_words_file.read().split()", "def load_words(file_path: str) -> List[Word]:\n \n words = load_words_raw(file_path)\n \n \n words = remove_stop_words(words)\n\n \n words = remove_duplicates(words)\n \n return words", "def readFile(filename):\n listOfWords = []\n currentLine = 1\n f = open(filename, \"r\")\n for line in f:\n line = stripPunctuation(line)\n for word in line.split():\n word = word.lower()\n if len(word) > 1:\n if not word[0].isdigit():\n tempObj = contains(listOfWords, word)\n if tempObj != None:\n tempObj.incOccurrence(currentLine)\n else:\n temp = Word(word, currentLine)\n listOfWords.append(temp)\n currentLine = currentLine + 1\n return listOfWords", "def load_wordlist(filename):\n # YOUR CODE HERE\n words = {}\n f = open(filename, 'rU')\n text = f.read()\n text = text.split('\\n')\n for line in text:\n words[line] = 1\n f.close()\n return words", "def create_dictionary(filename):\n\tword_set = set()\n\tif os.path.isfile(filename):\n\t\twith open(filename, 'r') as f:\n\t\t\tfor line in iter(f):\n\t\t\t\tword_set.add(line.strip('\\n'))\n\telse:\n\t\tprint \"File not found!\"\n\treturn word_set", "def get_stop_words(stop_file_path):\n \n with open(stop_file_path, 'r', encoding=\"utf-8\") as f:\n stopwords = f.readlines()\n stop_set = set(m.strip() for m in stopwords)\n return frozenset(stop_set)", "def create_word_map(tokenized_descriptions_file_path, word_dictionary_output_path):\n if os.path.exists(word_dictionary_output_path):\n print(\"Word map already exists in workspace. Will be reused.\")\n return\n\n print(\"Word map not found. Generating....\")\n\n words_list = []\n words_to_id = {}\n\n with open(tokenized_descriptions_file_path, 'r') as file:\n for line in file:\n tokens = line.strip().split(\",\")\n words_list.extend(tokens[1:])\n\n # remove duplicate words\n words_list = list(set(words_list))\n\n # sorting the words\n words_list = sorted(words_list)\n for i in range(len(words_list)):\n words_to_id[words_list[i]] = i\n\n with open(word_dictionary_output_path, 'w') as f:\n [f.write('{0},{1}'.format(key, value) + \"\\n\") for key, value in words_to_id.items()]", "def read_dictionary():\n\tglobal dictionary\n\twith open(FILE, \"r\") as f:\n\t\tfor words in f:\n\t\t\tdictionary += words.split()", "def make_word_dict():\n d = dict()\n fin = open(\"words.txt\")\n for line in fin:\n word = line.strip().lower()\n d[word] = None\n #have to add single letter words to the word list;\n #also, the empty string is considered a word.\n for letter in ['a', 'i', '']:\n d[letter] = letter\n return d", "def read_dictionary():\n with open(FILE, 'r') as f:\n for line in f:\n words_lst = line.split()\n for word in words_lst:\n dict_list.append(word)", "def train(self, filename):\n with open(filename, 'r') as f:\n phrases_and_words = []\n\n for index, line in enumerate(f):\n # decoding, since input is not unicode\n cleaned_line = self.get_cleaned_line(line.decode('utf-8', 'ignore'))\n\n if cleaned_line:\n phrases_and_words.extend(self.get_phrase_and_words_from_line(cleaned_line))\n\n if index % 10000 == 0:\n self.db_storage.store_phrases_and_words(phrases_and_words)\n phrases_and_words = []\n\n self.db_storage.store_phrases_and_words(phrases_and_words)", "def word_dict():\n fin = open('words.txt')\n w_dict = {}\n for line in fin:\n word = line.strip()\n w_dict[word] = word\n return w_dict", "def load_dictionary(hash_table, filename):\n\n file = open(filename)\n lines = file.readlines()\n start = timeit.default_timer()\n for line in lines:\n hash_table.insert(line.rstrip(),1)\n if timeit.default_timer() - start > 4:\n break\n file.close()", "def create_index(path):\n words = {}\n\n for l in open(path):\n linewords = l.strip().split(\" \")\n student = linewords[0]\n linewords = linewords[1:]\n\n for word in linewords:\n if word in words:\n if int(student) not in words[word]:\n words[word].append(int(student))\n else:\n words[word] = [int(student)]\n\n return words", "def word_frequency_in_file(filename):\n words = {}\n fin = open(filename)\n punctuation = string.punctuation\n for line in fin:\n line = line.translate( # Replace punctuation with spaces\n str.maketrans(punctuation, ' ' * len(punctuation)))\n line = line.lower()\n line_words = line.split()\n for word in line_words: # Process each word in the line.\n if word in words:\n words[word] += 1\n else:\n words[word] = 1\n return words", "def load_file(self, file_path):\n f = open(file_path, \"r\")\n sentences = f.readlines()\n \n word_count = 0\n\n for sentence in sentence: \n for word in sentence.strip().split(\" \"):\n if not (word in self.word_id): #word not in dictionary\n word_id[word] = word_count\n word_count += 1\n\n #self.doc = [[self.word_id[word] for word in sentence.strip().split(\" \")] for sentence in sentences]", "def load_dictionary(filename):\n\n word_list = []\n freq_sum = 0\n\n # nacitanie zo suboru\n with open(filename) as f:\n for line in f:\n freq, val = line.split()\n word_list.append(Word(int(freq), val))\n freq_sum += int(freq)\n\n # lexikograficke usporiadanie slov\n word_list_sorted = sorted(word_list, key=operator.attrgetter('value'))\n\n return word_list_sorted, freq_sum", "def load_concordance_table(self, filename):\n self.concordance_table = HashTable(191)\n with open(filename, 'r') as f:\n for linenum,words in enumerate(f.readlines()):\n for i in words.translate(self.ttable).split():\n i = i.casefold()\n if not self.stop_table.in_table(i):\n self.concordance_table.insert(i,linenum + 1)", "def read_dictionary():\n global dic\n with open(FILE, 'r') as f:\n for line in f:\n word_list = line.split()\n word = word_list[0].strip()\n dic.append(word)", "def read_words(f, words):\n with open(f) as file:\n for line in file:\n w = tokenizer.tokenize(line.strip())\n for word in w:\n try:\n words[word] += 1\n except:\n words[word] = 1", "def index_embedding_words(embedding_file):\n words = set()\n with open(embedding_file) as f:\n for line in f:\n w = Dictionary.normalize(line.rstrip().split(' ')[0])\n words.add(w)\n return words", "def load_input_word_list(file_path):\n if not os.path.isfile(file_path):\n return False\n\n word_list = list()\n\n with open(file_path, 'r') as fp:\n while True:\n line = fp.readline()\n if not line:\n break\n\n data = line.split(' ')\n text = data[0].lower().strip(Setting.NONWORD_CHARACTERS)\n\n if not text:\n continue\n\n text = text.replace('_', ' ')\n\n score = float(data[1])\n\n if score < 0:\n kind = WordKindEnum.NEG\n else:\n kind = WordKindEnum.POS\n\n word = Word(text, score, kind)\n word_list.append(word)\n\n return word_list", "def __init__(self, filename):\n\n self.term_dict = {}\n for line in open(filename):\n if line.startswith(\"#\"):\n continue\n\n #print line\n word, w_type = line.strip().split(\"\\t\")\n self.term_dict[word.strip().lower()] = \"CHESS_\" + w_type.strip().lower()", "def read_dictionary():\n with open(FILE, 'r') as f:\n for vocabulary in f:\n if vocabulary[0].strip() not in dict_txt:\n dict_txt[vocabulary[0].strip()] = [vocabulary.strip()]\n else:\n dict_txt[vocabulary[0].strip()].append(vocabulary.strip())", "def __init__(self, vocab_file, max_size):\n\t\tself._word_to_id = {}\n\t\tself._id_to_word = {}\n\t\tself._count = 0 # keeps track of total number of words in the Vocab\n\n\t\t# [UNK], [PAD], [START] and [STOP] get the ids 0,1,2,3.\n\t\tfor w in [UNKNOWN_TOKEN, PAD_TOKEN, START_DECODING, STOP_DECODING]:\n\t\t\tself._word_to_id[w] = self._count\n\t\t\tself._id_to_word[self._count] = w\n\t\t\tself._count += 1\n\n\t\t# Read the vocab file and add words up to max_size\n\t\twith open(vocab_file, 'r') as vocab_f:\n\t\t\tfor line in vocab_f:\n\t\t\t\tpieces = line.split()\n\t\t\t\tif len(pieces) != 2:\n\t\t\t\t\tprint ('Warning: incorrectly formatted line in vocabulary file: %s\\n' % line)\n\t\t\t\t\tcontinue\n\t\t\t\tw = pieces[0]\n\t\t\t\tif w in [SENTENCE_START, SENTENCE_END, UNKNOWN_TOKEN, PAD_TOKEN, START_DECODING, STOP_DECODING]:\n\t\t\t\t\traise Exception(\n\t\t\t\t\t\t'<s>, </s>, [UNK], [PAD], [START] and [STOP] shouldn\\'t be in the vocab file, but %s is' % w)\n\t\t\t\tif w in self._word_to_id:\n\t\t\t\t\traise Exception('Duplicated word in vocabulary file: %s' % w)\n\t\t\t\tself._word_to_id[w] = self._count\n\t\t\t\tself._id_to_word[self._count] = w\n\t\t\t\tself._count += 1\n\t\t\t\tif max_size != 0 and self._count >= max_size:\n\t\t\t\t\tprint (\"max_size of vocab was specified as %i; we now have %i words. Stopping reading.\" % (\n\t\t\t\t\tmax_size, self._count))\n\t\t\t\t\tbreak\n\n\t\tprint (\"Finished constructing vocabulary of %i total words. Last word added: %s\" % (\n\t\tself._count, self._id_to_word[self._count - 1]))", "def word_list():\n\n d = {}\n with open('words.txt') as fin:\n for line in fin.readlines():\n word = line.strip().lower()\n d[word] = True\n return d", "def make_stopwords(filepath='stopwords.txt'):\n sw = open(filepath, \"r\")\n my_stopwords = sw.read()\n my_stopwords = my_stopwords.split(\", \")\n sw.close()\n\n all_stopwords = stopwords.words('english')\n all_stopwords.extend(my_stopwords)\n return all_stopwords", "def _read_words(self, path):\r\n\r\n word_file = open(path)\r\n for line in word_file.readlines():\r\n pair = line.split('::')\r\n self.insert(pair[0], pair[1].rstrip())\r\n word_file.close()", "def index_embedding_words(embedding_file):\n words = set()\n with open(embedding_file, encoding='utf-8') as f:\n for line in f:\n w = Dictionary.normalize(line.rstrip().split(' ')[0])\n words.add(w)\n return words", "def loadStopWordList(swFile):\n f = open(swFile, 'r')\n lines = f.readlines()\n f.close()\n result = list()\n for line in lines:\n sWord = line.strip('\\n')\n result.append(sWord)\n return result", "def import_words(file_name):\n with open(file_name) as word_list:\n words = []\n for line in word_list:\n number, word = line.strip().split(\"\\t\")\n words.append(word.strip())\n # print(f\"Imported {(len(word_dict))} words\")\n\n return words", "def __init__(self,dir_stopwords):\n \n arc = open(dir_stopwords, \"r\", encoding='utf-8')\n self.stp_wrds = [line.strip() for line in arc]\n arc.close()", "def init(wordlist_filename):\n global WORDS\n if WORDS == None:\n WORDS = []\n bad_line = lambda x: x.strip() == '' or x.startswith('#')\n with codecs.open(wordlist_filename, 'r', 'utf-8') as filehandle:\n lines = filehandle.readlines()\n WORDS = set([x.lower().strip() for x in lines if not bad_line(x)])", "def preprocess(in_file: str, file_out: str) -> tuple:\r\n try:\r\n stop_words = []\r\n if os.path.isfile(\"StopWords.pkl\"):\r\n with open(\"StopWords.pkl\", mode=\"rb\") as pkl:\r\n stop_words = pickle.load(pkl)\r\n else:\r\n with open(\"StopWords.txt\", mode=\"r\") as f1:\r\n for line in f1:\r\n stop_words.append(line.strip(\"\\n\"))\r\n with open(\"StopWords.pkl\", mode=\"wb\") as pkl:\r\n pickle.dump(stop_words, pkl)\r\n\r\n vocab = []\r\n corpus = []\r\n with open(in_file, mode=\"r\") as f:\r\n for line in f:\r\n line = line.rsplit(sep=\"\\t\")\r\n sent = line[1].strip(\" \\n\")\r\n sent = int(sent)\r\n\r\n sentence = process_sentence(line[0])\r\n\r\n [sentence.remove(w) for w in sentence if w in stop_words or w == \"\"]\r\n corpus.append([sentence, sent])\r\n vocab += list(set(sentence) - set(vocab))\r\n\r\n # Sort lists alphabetically\r\n corpus = sorted(corpus)\r\n vocab = sorted(vocab)\r\n vocab.append(\"classlabel\")\r\n to_file(file_out, vocab, corpus)\r\n\r\n return vocab, corpus\r\n except:\r\n catch_err()", "def get_glove_dictionary(self, file_path=\"./glove.twitter.27B.25d.txt\"):\n file = open(file_path, \"r\",encoding='utf-8')\n dictionary = {}\n keys = []\n for word_vector in file:\n dictionary[word_vector.split()[0]] = word_vector.split()[1:]\n keys.append(word_vector.split()[0])\n file.close()\n\n file = open(\"./Glove_dict.txt\", \"a\",encoding='utf-8')\n for word in keys:\n file.write(word + '\\n')\n file.close()", "def build_stopwords():\r\n\tprint('\\nbuilding stopwords')\r\n\t\r\n\tif load_stopwords():\r\n\t\treturn\r\n\r\n\tglobal stopwords\r\n\tstopwords = nltk.corpus.stopwords.words('english')\r\n\tfor f in os.listdir(paths.path_data_stopwords):\r\n\t\tpath_stopwords = paths.path_data_stopwords + '/' + f\r\n\t\twith open(path_stopwords,'r') as f:\r\n\t\t\tfor l in f:\r\n\t\t\t\tw = l.strip()\r\n\t\t\t\tw = re.sub(r\"[\\x80-\\xff]\",\" \",w)\r\n\t\t\t\tif (w not in stopwords):\r\n\t\t\t\t\tstopwords.append(w)\r\n\t\r\n\t# wip improve with POS and remove numbers\r\n\twith open(paths.path_data_stopwords_txt,'w') as outf:\r\n\t\toutf.write('\\n'.join(stopwords))\r\n\t\r\n\tprint('\\nstopword count : ' + str(len(stopwords)))", "def create_dictionary(file_dir):\r\n\tword_list = []\r\n\tfile_list = read_files(file_dir, \"lab\") # step 7\r\n\tfor file in file_list:\r\n\t\twith open(file, 'r') as f:\r\n\t\t\ttext = f.read()\r\n\t\tword_list = store_to_dictionary(text, word_list) # step 8cii\r\n\tmake_dictionary_file(file_dir, word_list) # step 9\r", "def loadWords():\n inFile = open(wordFile, 'r')\n wordlist = []\n for line in inFile:\n wordlist.append(line)\n return wordlist", "def buildCorpus(self, filename, stopwords_file=None):\n with open(filename, 'r') as infile:\n # use pattern.subs\n # doclines = [line.rstrip().lower().split(' ') for line in infile]\n doclines = [self.help_clean(line) for line in infile]\n n_docs = len(doclines)\n self.vocab = list({v for doc in doclines for v in doc})\n if stopwords_file:\n with open(stopwords_file, 'r') as stopfile:\n stops = stopfile.read().split()\n self.vocab = [x for x in self.vocab if x not in stops]\n self.vocab.sort()\n self.documents = []\n for i in range(n_docs):\n self.documents.append({})\n for j in range(len(doclines[i])):\n if doclines[i][j] in self.vocab:\n self.documents[i][j] = self.vocab.index(doclines[i][j])", "def load_keywords():\n keywords = set()\n with open(os.path.join(BASE, \"data/keywords.txt\")) as fp:\n for line in fp:\n keywords.add(line.strip().lower())\n return keywords", "def __init__(self, file_name=None):\n self.word_list = {} # Dict of {word: frequency}\n self.replacement_words = {}\n self.ignored_words = []\n [self.add_word(w) for w in self.ADDITIONAL_VALID_WORDS]\n if file_name:\n self.load(file_name)", "def get_word_freq(filein):\n freq = {}\n\n # Open file handles with context manager\n with open(filein) as f:\n\n # Read a single line at a time so as not to crush memory\n for line in f:\n\n # Tokenize and iterate\n for word in line.split():\n\n # Use try/except instead of if/then for performance\n # Likely after the first 1M tweets that the key will be contained\n try:\n freq[word] += 1\n except KeyError:\n freq[word] = 1\n\n return freq", "def parse_file(input_file):\n # Automatically close the file after being used\n with open(input_file) as text:\n # Read file and split each word into an element in a list\n data = text.read().split()\n\n # Sort the list\n # Python sort automatically does lexical sorting\n data.sort()\n\n # For each word, use as Dictionary key and count the occurrences of the word and use as value\n frequency_table = {word: data.count(word) for word in data}\n\n # Return the frequency table\n return frequency_table", "def load_words():\n with open(DICTIONARY) as f:\n return [line.strip() for line in f]", "def read_data(filename,words):\n try:\n f = open(filename)\n reader = f.read().splitlines()\n for line in reader:\n #print(line[0])\n words.add(line.lower())\n f.close()\n except IOError:\n print 'Input file reading failed,'\n return words", "def index_embedding_words(self, embedding_file):\n words = set()\n with open(embedding_file) as f:\n for line in f:\n w = TokenDictionary.normalize(line.rstrip().split(' ')[0])\n words.add(w)\n return words", "def assignWordList(filename, thisDataEntry):\n oldArr = []\n newArr = []\n try:\n with open(filename, encoding=\"latin-1\") as file:\n lines = [line.rstrip() for line in file]\n idx = 0\n while(lines[idx] != \"***\"):\n oldArr.append(lines[idx].lower())\n idx += 1\n idx += 1 #Skip the delimitter\n for x in range(idx, len(lines)):\n newArr.append(lines[x].lower())\n file.close()\n except IOError:\n print(\"Error opening: \" + str(filename))\n for x in oldArr:\n thisDataEntry.old[x] = 0\n for y in newArr:\n thisDataEntry.new[y] = 0", "def read_file(filename):\n print(\"Reading dictionary: \" +filename)\n word_dict = set()\n\n dictionary = open(filename)\n\n # Read each word from the dictionary\n for word in dictionary:\n # Remove the trailing newline character\n word = word.rstrip('\\n')\n\n # Convert to lowercase\n word = word.lower()\n\n word_dict.add(word)\n\n dictionary.close()\n\n return word_dict", "def generate_dictionary(location):\n f = open('../data/wordlist.txt', 'rb')\n words = Counter(re.findall('[a-z]+', f.read().lower().decode()))\n joblib.dump(words, location)", "def create_dictionary(filename):\n file = open(filename, 'r')\n text = file.read()\n file.close()\n words = text.split()\n d = {}\n current_word = '$'\n \n for next_word in words:\n if current_word not in d:\n d[current_word] = [next_word]\n else:\n d[current_word] += [next_word]\n if next_word[-1] == '.' or next_word[-1] == '!' or next_word[-1] == '?':\n current_word = '$'\n else:\n current_word = next_word\n return d", "def construct_dict(self):\n i = 0\n self.word2idx = dict()\n fi = open(self.config.word_vec_fi_glove, 'r')\n\n for line in fi:\n self.word2idx[line.split(\" \")[0]] = i\n i += 1\n\n self.vocab_size = i\n self.write_dict()\n fi.close()", "def getstopwords():\n file = open('stopWords.txt', 'r')\n stoplist = []\n for word in file.readlines():\n word = word.strip('\\n')\n stoplist.append(word)\n return stoplist", "def word_counts(file):\n words = defaultdict(int)\n regex = re.compile('[' + string.punctuation + ']')\n for line in open(file):\n for word in [regex.sub('', w) for w in line.lower().split()]:\n words[word] += 1\n\n return words", "def learn(filename):\n word_dict = {} # Create empty dictionary\n first = None\n prev = None\n with open(filename, 'r', encoding='utf-8') as file:\n for line in file:\n list_words = line.lower().split()\n text = []\n for word in list_words:\n # take out leading and trailing punctuation characters\n words = word.strip(string.punctuation + string.digits)\n word_len = len(words)\n if word_len >= 1:\n text.append(words)\n\n if first is None:\n # Get the first word in the text file\n first = text[0]\n # iterate over text\n if prev:\n text.insert(0, prev)\n for counter, word in enumerate(text):\n if word not in word_dict:\n word_dict[word] = list()\n if counter < (len(text) - 1):\n following = counter + 1\n word_dict[word].append(text[following])\n prev = text[-1]\n return first, word_dict # return a tuple", "def load_cows(filename):\r\n print(\"Loading words from file...\")\r\n # inFile: file\r\n inFile = open(filename, 'r')\r\n # wordlist: list of strings\r\n wordlist = {}\r\n for line in inFile:\r\n cow = line.split(',')\r\n wordlist[cow[0]] = int(cow[1]) # 0: name, 1: weight\r\n inFile.close()\r\n print(\" \", len(wordlist), \"words loaded.\")\r\n return wordlist", "def print_word_freq(file):\n # with open(file, 'r') as text the r as the second arguement means that my intentions are to read the file\n with open(file, 'r') as text:\n # this reads the entire file and puts this into text string\n text_string = text.read()\n # returns the string respresentation of text string without removing special characters so you can see what you need to remove\n # print(repr(text_string))\n # this removes the specified characters from the text string\n text_string = text_string.replace(\",\", \"\")\n text_string = text_string.replace(\".\", \"\")\n text_string = text_string.replace(\"—\", \" \")\n text_string = text_string.replace(\"-\", \" \")\n text_string = text_string.replace(\"?\", \"\")\n text_string = text_string.replace(\":\", \"\")\n text_string = text_string.replace(\"'\", \"\")\n text_string = text_string.replace(\"\\\\n\", \"\")\n text_string = text_string.replace(\"’\", \"\")\n text_string = text_string.replace(\"]\", \"\")\n text_string = text_string.replace(\"[\", \"\")\n text_string = text_string.replace(\"\\\"\", \"\")\n # takes the text string and makes all the characters lower case\n text_string = text_string.lower()\n # takes the text string and splits all the words into a list this splits from space to space\n words_list = text_string.split()\n # a dictionary is a key and a value\n no_stop_words = {}\n # for loop that will cycle through the words list\n for word in words_list:\n # checking to see if the word is stop words\n if word not in STOP_WORDS:\n # if the word is already in the dictionary no stop words increment the value by 1\n if word in no_stop_words:\n no_stop_words[word] += 1\n # if the word is not in the dictionary no stop words add this to the dictionary and give it a value of 1\n else:\n no_stop_words[word] = 1\n \n sorted_dict = {}\n sorted_keys = sorted(no_stop_words, key=no_stop_words.get, reverse=True)\n \n for w in sorted_keys:\n sorted_dict[w] = no_stop_words[w]\n \n for key in sorted_dict:\n print(f\"{key:>15} | {sorted_dict[key]:2} {'*' * sorted_dict[key]}\")\n \n # good practice to ensure that we are properly closing the file in use at the end of the function\n text.close()", "def get_word_list(file_name, to_skip_or_not_to_skip):\n fin = open(file_name) #opening file\n histogram={} \n if to_skip_or_not_to_skip == True: #if I want to skip the header this is set to True\n skip_first_part(fin)\n for line in fin: #runs through lines of book file\n line = line.replace(\"-\",\" \") #takes out dashed, underscroes, numbers, whitespaces, and punctuation\n line = line.replace(\"_\",\" \")\n to_remove = string.punctuation + string.whitespace + '0123456789' \n for word in line.split():\n word = word.strip(to_remove) #running through all words in each line \n if word == 'God' or 'Lord':\n pass\n else:\n word = word.lower()\n histogram[word] = histogram.get(word, 0)+1\n return histogram", "def read_dictionary_from_file(self, stem_flag):\n file_name = \"/dictionary.txt\" if not stem_flag else \"/dictionaryWithStemming.txt\"\n with open(self.posting_and_dictionary_path + file_name, \"r\") as f:\n txt = f.readlines()\n for line in txt:\n l = line.split(\":\")\n pos = l[1].split(\",\")\n e = DictionaryElement(pos[0])\n e.pointer = int(pos[1])\n e.corpus_tf = int(pos[2])\n if not stem_flag:\n self.term_dictionary[l[0]] = e\n else:\n self.term_dictionary_with_stemming[l[0]] = e\n f.close()", "def ReadAndTokenize(filename):\n global CACHE\n global VOCABULARY\n if filename in CACHE:\n return CACHE[filename]\n comment = open(filename).read()\n words = Tokenize(comment)\n\n terms = collections.Counter()\n for w in words:\n VOCABULARY[w] += 1\n terms[w] += 1\n\n CACHE[filename] = terms\n return terms", "def importBrainstormWordsFile(filename):\n #init the list with all words in the file\n allWords = []\n \n #open the brainstorming words file and read the lines\n with open(filename, 'r') as fp:\n lines = fp.read().splitlines()\n \n #split the lines for the idiots that didn't read the instructions and add them to the output\n for curLine in lines:\n if curLine.startswith('Please type one'):\n continue\n cutLines = curLine.replace(',',' ').split()\n \n #cycle the word and add them\n for curWord in cutLines:\n allWords.append(curWord.strip().lower())\n \n return allWords", "def analyzeFile(filename): \n fileData = open(filename, encoding=\"utf-8\") # open the file\n \n counts = {}\n\n for line in fileData:\t\t # iterates over every line of the file\n words = line.split() # turns each line into a list\n for word in words: #iterates over the words in each line list\n word = word.lower().strip(string.whitespace+string.punctuation)\n if len(word) > 0: #make sure word is longer than 0 before adding it to the dictionary\n counts[word] = counts.get(word, 0) + 1 #look up if the dictionary has that word and if not then it'll add that word with the value 0 associated with it and then add one to that, if it has seen it it'll add 1 to the value stored in the counts dictionary\n #when it gets here for the first line it goes back up to the top and repeats for the 2nd line\n mostCommonWord = [word]\n leastCommonWord = [word]\n shortestWord = [word]\n longestWord = [word]\n \n for item in counts:\n if counts[mostCommonWord[0]] < counts[item]:\n mostCommonWord = [item]\n elif counts[mostCommonWord[0]] == counts[item]:\n mostCommonWord.append(item)\n if counts[leastCommonWord[0]] > counts[item]:\n leastCommonWord = [item]\n elif counts[leastCommonWord[0]] == counts[item]:\n leastCommonWord.append(item)\n if len(shortestWord[0]) > len(item):\n shortestWord = [item] \n elif len((shortestWord[0])) == len(item):\n shortestWord.append(item)\n if len(longestWord[0]) < len(item):\n longestWord = [item]\n elif len(longestWord[0]) == len(item):\n longestWord.append(item)\n \n return (mostCommonWord, leastCommonWord, shortestWord, longestWord)", "def load_stopwords():\r\n\tglobal stopwords\r\n\tif os.path.exists(paths.path_data_stopwords_txt):\r\n\t\tprint('\\nloading stopwords')\r\n\t\twith open(paths.path_data_stopwords_txt,'r') as inf:\r\n\t\t\tstopwords = inf.read().split('\\n')\r\n\t\treturn True\r\n\telse:\r\n\t\treturn False", "def load_stop_list():\n stop_list = []\n with open(STOP_LIST, \"r\") as f:\n lines = f.readlines()\n stop_list = [word.strip() for word in lines]\n return stop_list", "def LoadWords(self,FileName) :\r\n\t\ttry :\r\n\t\t\twith open(FileName,'r') as fhan :\r\n\t\t\t\tWords = fhan.read()\r\n\t\texcept Exception as detail :\r\n\t\t\tlogging.error(\"Failed to read file %s: %s\"%(FileName,detail))\r\n\t\ttry :\r\n\t\t\tWordList = Words.rstrip().split('\\n')\r\n\t\t\tWordList = filter(None,WordList)\r\n\t\t\tWordList = [(Word,) for Word in WordList]\r\n\t\t\tDictRef = self.CreateDict(FileName)\r\n\t\t\tself.DB_Cursor.execute(self.SQLCMDs['SelectDictTable'],(DictRef,))\r\n\t\t\tDictName = self.DB_Cursor.fetchone()[0]\r\n\t\t\tself.DB_Cursor.executemany(self.SQLCMDs['InsertAllWordsToDict']%DictName,WordList)\r\n\t\t\tself.DB_Connect.commit()\r\n\t\t\tlist_id = self.CreateWordList(FileName,DictRef)\r\n\t\t\tself.UpdateWordList(list_id,False)\r\n\t\texcept Exception as detail :\r\n\t\t\tlogging.error(\"Failed to add words to the new dictionary: %s\"%detail)\r\n\t\t\tself.DB_Connect.rollback()\r\n\t\treturn DictRef", "def load_words():\r\n## print \"Loading word list from file...\"\r\n # inFile: file\r\n inFile = open(WORDLIST_FILENAME, 'r', 0)\r\n # wordlist: list of strings\r\n wordlist = []\r\n for line in inFile:\r\n wordlist.append(line.strip().lower())\r\n## print \" \", len(wordlist), \"words loaded.\"\r\n return wordlist", "def print_word_freq(file):\n with open(file) as text:\n text_string = str(text.readlines())\n text_string = text_string.replace(\",\", \"\")\n text_string = text_string.replace(\".\", \"\")\n text_string = text_string.replace(\"-\", \"\")\n text_string = text_string.replace(\"?\", \"\")\n text_string = text_string.replace(\":\", \"\")\n text_string = text_string.replace(\"'\", \"\")\n text_string = text_string.replace(\"\\\\n\", \"\")\n text_string = text_string.replace(\"[\", \"\")\n text_string = text_string.replace(\"]\", \"\")\n word_list = text_string.split()\n no_stop_words = []\n for word in word_list:\n if word in STOP_WORDS:\n pass\n else: no_stop_words.append(word)\n clean_list = {}\n for word in no_stop_words:\n clean_list[word] = no_stop_words.count(word) \n print(clean_list)", "def get_words_from_file(filename):\n words_by_len = {}\n f = open(filename, \"r\", 1, \"utf8\")\n for word in f:\n word = word.strip().lower()\n w_len = len(word)\n if w_len > 1:\n words_by_len[w_len] = words_by_len.get(w_len, []) + [word]\n return words_by_len", "def build_dict(fname):\n\t\n\twith open(fname) as file:\n\n\t\tword_count_dict = {}\n\n\t\tfor line in file:\n\t\t\tline = line.rstrip()\n\t\t\tline =line.split(' ')\n\t\t\tfor word in line:\n\t\t\t\tword = word.strip('\"!.,?_;():')\n\t\t\t\tword = word.lower()\n\t\t\t\tword_count_dict[word] = word_count_dict.get(word, 0) + 1\n\t\t#return word_count_dict\n\n\t\tfor each in word_count_dict:\n\t\t\tcount = word_count_dict[each]\n\t\t\tprint(each, count)\n\n\t\treturn", "def read_data(file_path):\n words=[]\n dic_word={}\n actual_text=[]\n for line in open(file_path,encoding='utf-8'):\n words_line=line.strip().split(' ')\n for ite in words_line:\n if ite not in dic_word:\n dic_word[ite]=1\n words.extend(words_line)\n actual_text.append(words_line)\n\n\n #with zipfile.ZipFile(file_path) as f:\n #words = tf.compat.as_str(f.read(f.namelist()[0])).split()\n\n return words,len(dic_word),actual_text", "def make_mimic_dict(filename):\r\n with open(filename, 'r') as file:\r\n text = file.read().lower().replace(\"'\",'').split()\r\n mimic_dict = {}\r\n prev = ''\r\n for word in text:\r\n if not prev in mimic_dict:\r\n mimic_dict[prev] = [word]\r\n else:\r\n mimic_dict[prev].append(word)\r\n prev = word\r\n return mimic_dict", "def getDictionary(tsvFile, wordIndex, ngram):\r\n \r\n reader = DescriptionReader(tsvFile, wordIndex, ngram)\r\n dictionary = corpora.Dictionary( d for d in reader )\r\n\r\n # remove stop words and words that appear only once\r\n stoplist = [] # might be specified in the furture\r\n stop_ids = [dictionary.token2id[stopword] for stopword in stoplist\r\n if stopword in dictionary.token2id]\r\n once_ids = [tokenid for tokenid, docfreq in dictionary.dfs.items() if docfreq == 1]\r\n dictionary.filter_tokens(stop_ids + once_ids) # remove stop words and words that appear only once\r\n dictionary.compactify() # remove gaps in id sequence after words that were removed\r\n \r\n return dictionary", "def removeOwnStopWords(self, sort=True, lc=False):\n\t\tself.textFile = self.removeStopWords(text=self.textFile, sort=sort, lc=lc)", "def read_file(fp):\n scrabble_words_dict = {}\n \n for line in fp:\n line = line.lower()\n line = line.strip()\n if len(line) < 3:\n continue\n elif \"-\" in line or \"'\" in line:\n continue\n else:\n scrabble_words_dict[line] = 1\n return scrabble_words_dict", "def load_defs():\n # Load word definitions\n fname = 'word-definitions.txt'\n with open(fname) as fh:\n lines = fh.readlines()\n \n # Create dictionary keyed by lowercase word\n def_tbl = dict()\n for line in lines:\n # split the dictionary line at the first space\n word, word_def = line.split(sep=None, maxsplit=1)\n # add this entry to the dictionary\n word = word.lower()\n def_tbl[word] = word_def.rstrip()\n return def_tbl", "def load_words():\n # Load all the words from the scrabble dictionary into a python list, words\n fname = 'words.txt'\n with open(fname) as fh:\n words = fh.readlines()\n \n # Create a python dict keyed by sorted letters, with value equal to a list\n # of all the anagrams of that collection of letters\n anagram_tbl = dict()\n for word in words:\n word_lc = word.rstrip().lower()\n key = word_key(word_lc)\n value = anagram_tbl.get(key, []) + [word_lc]\n anagram_tbl[key] = value\n return anagram_tbl", "def load_words():\n print(\"Loading word list from file..\")\n WORDLIST_FILENAME = \"words.txt\"\n # with open('words.txt', 'r') as f:\n # inFile = f.read()\n inFile = open(WORDLIST_FILENAME, 'r')\n wordlist = []\n\n for line in inFile:\n wordlist.append(line.strip().lower())\n return wordlist", "def dictionary_creation(filename):\n\tfp = open(filename)\n\td = dict()\n\tfor line in fp:\n\t\t# print line\n\t\tfor word in line.split():\n\t\t\tword = word.strip(string.punctuation + string.whitespace)\n\t\t\t# print word\n\t\t\tif len(word) >5:\n\t\t\t\tif word not in d:\n\t\t\t\t\t# print 'in'\n\t\t\t\t\td[word] = 1\n\t\t\t\telse:\n\t\t\t\t\t# print 'not in'\n\t\t\t\t\td[word] += 1\n\treturn d\n\n\tfp.close()", "def word_tag_counts (count_file):\r\n wordtagcounts = defaultdict(list)\r\n f = open(count_file, 'r')\r\n for line in f:\r\n fields = line.split(\" \")\r\n if fields[1] != 'WORDTAG':\r\n continue\r\n count = int(fields[0].strip())\r\n tag = fields[2].strip()\r\n word = fields[3].strip()\r\n wordtagcounts[word].append((tag, count)) \r\n f.close() \r\n return wordtagcounts", "def setKeys():\n keywords['c++'] = {}\n with open('cppkeywords.txt', 'r') as f:\n for i in f:\n i = i.strip('\\n')\n words = map(str, i.split())\n key = words[0]\n words.pop(0)\n keywords['c++'][key] = list(words)\n for j in words:\n MyDict.insert(j)\n keywords['py'] = {}\n with open('pykeywords.txt', 'r') as f:\n for i in f:\n i = i.strip('\\n')\n words = map(str, i.split())\n key = words[0]\n words.pop(0)\n keywords['py'][key] = list(words)\n for j in words:\n MyDict.insert(j)", "def read_files(self,corpus):\n\n file = open(corpus)\n markov_dictionary = {}\n word_key = ['None', 'None']\n word_list = []\n lastword = \"\"\n #use for loop to make lines in file a list\n for line in file:\n line = line.strip()\n words = line.split(\" \")\n \n # generate keys\n word_key[0] = lastword\n word_key[1] = words[0]\n \n if lastword:\n markov_dictionary[tuple(word_key)] = self.make_values(corpus, word_key)\n\n i = 0\n while i < len(words) - 1:\n word_key[0] = words[i]\n word_key[1] = words[i + 1]\n \n markov_dictionary[tuple(word_key)] = self.make_values(corpus, word_key)\n\n i += 1\n\n lastword = words[len(words) - 1]\n\n # print \"make_chains\", markov_dictionary\n return markov_dictionary", "def read_word_list(file_name):\r\n\twith open(file_name) as word_list_file:\r\n\t\treturn set(word.strip() for word in word_list_file)", "def load_dictionary(filename, encoding='utf_8', skip=0, max_words=50000):\n\n d = dict()\n with codecs.open(filename, 'r', encoding=encoding) as f:\n line_counter = 0\n index_counter = 1 # we use 1 for the <EOS> symbol in both languages and 0 for <UNK> words\n\n d['<EOS>'] = index_counter\n index_counter += 1\n\n for line in f.readlines():\n\n line_counter += 1\n\n # check if we have to skip something\n if line_counter > skip:\n # split the line\n s = line.split()\n # get word and its index\n # if index > max. number of words, set it to 0\n if index_counter < max_words:\n word = s[0]\n d[word] = index_counter\n index_counter += 1\n return d", "def print_word_freq(file):\n with open(file) as text:\n text = text.read().lower()\n text = text.replace(\"\\n\", \" \")\n text = text.replace(\"’\", \"\")\n # text = \" \".join(text.split())\n # print(text)\n for character in string.punctuation:\n text = text.replace(character, \"\")\n word_list = text.split()\n clean_list = []\n for word in word_list:\n if word not in STOP_WORDS:\n clean_list.append(word)\n \n\n # for stop_word in STOP_WORDS:\n # if stop_word in word_list:\n # word_list.remove(stop_word)\n\n\n new_dict = {}\n for word in clean_list:\n new_dict[word] = clean_list.count(word)\n sorted_dict = sorted(new_dict.items())\n print(sorted_dict)\n\n # print(f\"{key} | {value} {'*' * value}\")\n\n \n # for stop_word in STOP_WORDS:\n # text = text.replace(stop_word, \"\")\n\n # for word in word_list:\n # if word in string.punctuation:\n # #do something\n # if word in STOP_WORDS:\n\n \n # for stop_word in STOP_WORDS:\n # text = text.replace(stop_word, \"\")\n # print(text)", "def countWords(words, filename):\n\ttry:\n\t\tfile = codecs.open(filename, \"r\", \"utf8\")\n\t\ttokens = [ string.strip(string.lower(i)) for i in file.read().split() ]\n\t\tfor i in tokens:\n\t\t\twords[i] = words.get(i, 0) + 1\n\t\tfile.close()\n\texcept IOError:\n\t\tprint \"Cannot read from file:\", filename\n\treturn words", "def process_file(self, filename, order=2):\n fp = open(filename)\n self.skip_gutenberg_header(fp)\n\n for line in fp:\n for word in line.rstrip().split():\n self.process_word(word, order)\n\n #print(\">>>DEBUG the suffix map\")\n #i = 0\n #for k,v in self.suffix_map.items():\n # print(\"key is {}, value is {}\".format(k, v))\n # i += 1\n # if i > 10:\n # break", "def create_english_word_list(filename):\n global global_english_word_list\n\n if not global_english_word_list:\n with open(filename) as f:\n for line in f:\n global_english_word_list.append(re.sub(r'\\s+', '', line))", "def load_vocabulary():\n global vocabulary_list, vocabulary_dict\n vocabulary_list = []\n vocabulary_dict = {}\n\n with open(_VOCABULARY_PATH, 'r') as f:\n for index, line in enumerate(f):\n line = line.strip()\n vocabulary_dict[line] = index\n vocabulary_list.append(line)", "def load_word_counts(filename):\n raw_rows = csv_rows(filename)\n word_counts = defaultdict(lambda: 0)\n\n for line_number, raw_row in enumerate(raw_rows, 2):\n count = int(raw_row[\"count\"])\n ipa = raw_row[\"IPA\"]\n if '*' in ipa:\n continue\n\n # Fixes random badness.. hopefully doesn't hide anything?\n mod_ipa = ipa.replace('(', '').replace(')', '')\n\n # Work around a passage with an error in it:\n gloss = raw_row[\"Gloss\"] or raw_row[\"Text\"]\n\n category = raw_row[\"Category\"]\n\n skipword_characters = {'?'}\n try:\n for i, g in izip(mod_ipa.split('/'), gloss.split('/')):\n word = make_word(i, g, category)\n word_counts[word] += count\n except WordParseError as e:\n print (u\"Error on line %d: %s [%s || %s]\" %\n (line_number, repr(e), ipa, gloss)).encode('utf-8')\n except IndexError as e:\n unknown_index = e.args[0]\n if unknown_index in skipword_characters:\n print (u\"Bad char on line %d: %s [%s || %s]\" %\n (line_number, repr(e), ipa, gloss)).encode('utf-8')\n else:\n print \"FATAL ERROR ON LINE %d\" % line_number\n raise\n except:\n print \"FATAL ERROR ON LINE %d\" % line_number\n raise\n return word_counts" ]

[ "0.7675772", "0.6984068", "0.67051345", "0.65770996", "0.6555971", "0.65203714", "0.6489752", "0.6475122", "0.6467671", "0.6451879", "0.6377066", "0.63083595", "0.63079983", "0.6283576", "0.6268957", "0.6258872", "0.6253571", "0.6214078", "0.61646724", "0.6143819", "0.608474", "0.6073526", "0.6068786", "0.6065902", "0.60613567", "0.6052064", "0.6046372", "0.6042853", "0.60415167", "0.6040906", "0.60295177", "0.59983766", "0.59936625", "0.5988736", "0.5987943", "0.5976255", "0.59729934", "0.5966749", "0.59628665", "0.59528106", "0.5945419", "0.59452385", "0.5942639", "0.59383506", "0.5925877", "0.5908141", "0.5902478", "0.58904344", "0.588735", "0.5849045", "0.58342755", "0.5830125", "0.5798432", "0.57905126", "0.5788999", "0.5785569", "0.5784483", "0.5779273", "0.5775147", "0.5769087", "0.57593477", "0.57565105", "0.5749934", "0.57453054", "0.57395786", "0.5739182", "0.5737352", "0.5734947", "0.57284075", "0.5726179", "0.570873", "0.5707878", "0.57050043", "0.5704106", "0.568509", "0.56668484", "0.566292", "0.56574434", "0.5651551", "0.565081", "0.56492203", "0.56425697", "0.56345403", "0.5618813", "0.56128454", "0.56111825", "0.5608342", "0.56067157", "0.55998313", "0.55942154", "0.55927545", "0.55899787", "0.55746", "0.5572658", "0.5571997", "0.5567334", "0.55641556", "0.55524105", "0.55454993", "0.5521379" ]

{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }

[ "def read_text_file(self, filepath: str):\n with open(filepath) as fh:\n for line in fh:\n for word in re.split('\\W+', line):\n word = word.lower()\n if len(word):\n l = self.hash_map.lookup(word)\n self.hash_map.insert(word, l + 1 if l > 0 else 1)", "def load_concordance_table(self, filename):\n self.concordance_table = HashTable(191)\n with open(filename, 'r') as f:\n for linenum,words in enumerate(f.readlines()):\n for i in words.translate(self.ttable).split():\n i = i.casefold()\n if not self.stop_table.in_table(i):\n self.concordance_table.insert(i,linenum + 1)", "def analyzeFile(filename): \n fileData = open(filename, encoding=\"utf-8\") # open the file\n \n counts = {}\n\n for line in fileData:\t\t # iterates over every line of the file\n words = line.split() # turns each line into a list\n for word in words: #iterates over the words in each line list\n word = word.lower().strip(string.whitespace+string.punctuation)\n if len(word) > 0: #make sure word is longer than 0 before adding it to the dictionary\n counts[word] = counts.get(word, 0) + 1 #look up if the dictionary has that word and if not then it'll add that word with the value 0 associated with it and then add one to that, if it has seen it it'll add 1 to the value stored in the counts dictionary\n #when it gets here for the first line it goes back up to the top and repeats for the 2nd line\n mostCommonWord = [word]\n leastCommonWord = [word]\n shortestWord = [word]\n longestWord = [word]\n \n for item in counts:\n if counts[mostCommonWord[0]] < counts[item]:\n mostCommonWord = [item]\n elif counts[mostCommonWord[0]] == counts[item]:\n mostCommonWord.append(item)\n if counts[leastCommonWord[0]] > counts[item]:\n leastCommonWord = [item]\n elif counts[leastCommonWord[0]] == counts[item]:\n leastCommonWord.append(item)\n if len(shortestWord[0]) > len(item):\n shortestWord = [item] \n elif len((shortestWord[0])) == len(item):\n shortestWord.append(item)\n if len(longestWord[0]) < len(item):\n longestWord = [item]\n elif len(longestWord[0]) == len(item):\n longestWord.append(item)\n \n return (mostCommonWord, leastCommonWord, shortestWord, longestWord)", "def readFile(filename):\n listOfWords = []\n currentLine = 1\n f = open(filename, \"r\")\n for line in f:\n line = stripPunctuation(line)\n for word in line.split():\n word = word.lower()\n if len(word) > 1:\n if not word[0].isdigit():\n tempObj = contains(listOfWords, word)\n if tempObj != None:\n tempObj.incOccurrence(currentLine)\n else:\n temp = Word(word, currentLine)\n listOfWords.append(temp)\n currentLine = currentLine + 1\n return listOfWords", "def load():\n for line in open(config.filepath, 'r'):\n line = line.strip()\n line_sorted = ''.join(sorted(line))\n\n if line_sorted not in Words.hashed:\n Words.hashed[line_sorted] = []\n\n # Store the real hashed as a list\n # We need line_sorted as the key for fast lookup later\n Words.hashed[line_sorted].append(line)\n\n # Also add the word to a standard list\n # We'll use this to quickly determine wordiness later\n Words.words.append(line)", "def top_words(source, number):\n\n keys = set()\n\n ht = HashMap(2500,hash_function_2)\n\n # This block of code will read a file one word as a time and\n # put the word in `w`. It should be left as starter code.\n with open(source) as f:\n for line in f:\n words = rgx.findall(line)\n for w in words:\n # set up index for hash map\n key = w.lower()\n hash = ht._hash_function(key)\n hash_index = hash % ht.capacity\n cur_bucket = ht._buckets[hash_index]\n new_node = cur_bucket.head\n # if key already exists in hash map, find and increment value\n if ht.contains_key(key):\n while new_node is not None:\n if new_node.key == key:\n new_node.value = new_node.value + 1\n new_node = new_node.next\n # else, add key to hashmap with value of 1\n else:\n cur_bucket.add_front(key, 1)\n # make empty list\n list = []\n # add all buckets to list as tuples\n for i in range(ht.capacity):\n bucket = ht._buckets[i]\n if bucket.head is not None:\n new_node = bucket.head\n while new_node is not None:\n list.append((new_node.key, new_node.value))\n new_node = new_node.next\n # Sort list in reverse by key value (word count)\n # Source: https://www.geeksforgeeks.org/python-program-to-sort-a-list-of-tuples-by-second-item/\n list.sort(key = lambda x: x[1], reverse=True)\n # Return list from 0 to user number\n return(list[0:number])", "def train(self, filename):\n with open(filename, 'r') as f:\n phrases_and_words = []\n\n for index, line in enumerate(f):\n # decoding, since input is not unicode\n cleaned_line = self.get_cleaned_line(line.decode('utf-8', 'ignore'))\n\n if cleaned_line:\n phrases_and_words.extend(self.get_phrase_and_words_from_line(cleaned_line))\n\n if index % 10000 == 0:\n self.db_storage.store_phrases_and_words(phrases_and_words)\n phrases_and_words = []\n\n self.db_storage.store_phrases_and_words(phrases_and_words)", "def top_words(source, number):\n\n keys = set()\n\n ht = HashMap(2500, hash_function_2)\n\n # This block of code will read a file one word at a time and\n # put the word in `w`. It should be left as starter code.\n with open(source) as f:\n for line in f:\n words = rgx.findall(line)\n for w in words:\n # convert word to lowercase to avoid inconsistent hash values\n # due to different cases of the same word.\n w = w.lower()\n\n # check if the current word already exists as a key\n if w in keys:\n current_count = ht.get(w) # fetch the current count for that word\n current_count += 1 # increment count by one\n ht.put(w, current_count) # update value for the key\n else:\n # word does not exist in hash map\n keys.add(w) # add current word to keys set\n ht.put(w, 1) # insert key into hash map with value of 1\n\n # fetch unsorted list of tuples from parsed data\n word_count_list = compile_list(ht, keys)\n\n # sort word count tuple list\n word_count_list = word_count_sort(word_count_list)\n\n # initialize and fill final word list\n final_list = []\n\n for index in range(0, number):\n final_list.append(word_count_list[index])\n\n return final_list", "def index_embedding_words(embedding_file):\n words = set()\n with open(embedding_file) as f:\n for line in f:\n w = Dictionary.normalize(line.rstrip().split(' ')[0])\n words.add(w)\n return words", "def make_word_dict():\n d = dict()\n for line in open('words.txt'):\n word = line.strip().lower()\n d[word] = None\n\n return d", "def load_stop_table(self, filename):\n self.stop_table = HashTable(191)\n with open(filename, 'r') as f:\n for word in f.readlines():\n self.stop_table.insert(word.replace('\\n',''),None)", "def collate(filename):\r\n x=open(filename,\"r\")\r\n total_words=[]\r\n for line in x:\r\n line=line.strip(\"\\n\")\r\n line=line.split(\":\")\r\n if len(total_words)<1:\r\n total_words.append(line)\r\n else:\r\n x= len(total_words)\r\n if line[0] == total_words[x-1][0]:\r\n if int(line[1]) > int(total_words[x-1][len(total_words[x-1])-1]):\r\n total_words[x-1].append(line[1])\r\n else:\r\n total_words.append(line)\r\n y = open(\"collated_ids.txt\", \"w\")\r\n # for i in range(len(total_words)):\r\n # if len(total_words[i])<3:\r\n # total_words[i]=\":\".join(total_words[i])+\"\\n\"\r\n # else:\r\n # id=\" \".join(total_words[i][1:])\r\n # total_words[i]=total_words[i][0]+\":\"+id+\"\\n\"\r\n # y.writelines(total_words)\r\n for i in range(len(total_words)):\r\n id=\"\"\r\n for j in range(1,len(total_words[i])):\r\n id=id +total_words[i][j] +\" \"\r\n y.write(str(total_words[i][0]) + \":\" +str(id) + \"\\n\")", "def index_embedding_words(embedding_file):\n words = set()\n with open(embedding_file, encoding='utf-8') as f:\n for line in f:\n w = Dictionary.normalize(line.rstrip().split(' ')[0])\n words.add(w)\n return words", "def word_frequency_in_file(filename):\n words = {}\n fin = open(filename)\n punctuation = string.punctuation\n for line in fin:\n line = line.translate( # Replace punctuation with spaces\n str.maketrans(punctuation, ' ' * len(punctuation)))\n line = line.lower()\n line_words = line.split()\n for word in line_words: # Process each word in the line.\n if word in words:\n words[word] += 1\n else:\n words[word] = 1\n return words", "def create_index(path):\n words = {}\n\n for l in open(path):\n linewords = l.strip().split(\" \")\n student = linewords[0]\n linewords = linewords[1:]\n\n for word in linewords:\n if word in words:\n if int(student) not in words[word]:\n words[word].append(int(student))\n else:\n words[word] = [int(student)]\n\n return words", "def load_file(self, file_path):\n f = open(file_path, \"r\")\n sentences = f.readlines()\n \n word_count = 0\n\n for sentence in sentence: \n for word in sentence.strip().split(\" \"):\n if not (word in self.word_id): #word not in dictionary\n word_id[word] = word_count\n word_count += 1\n\n #self.doc = [[self.word_id[word] for word in sentence.strip().split(\" \")] for sentence in sentences]", "def load_word_counts(filename):\n raw_rows = csv_rows(filename)\n word_counts = defaultdict(lambda: 0)\n\n for line_number, raw_row in enumerate(raw_rows, 2):\n count = int(raw_row[\"count\"])\n ipa = raw_row[\"IPA\"]\n if '*' in ipa:\n continue\n\n # Fixes random badness.. hopefully doesn't hide anything?\n mod_ipa = ipa.replace('(', '').replace(')', '')\n\n # Work around a passage with an error in it:\n gloss = raw_row[\"Gloss\"] or raw_row[\"Text\"]\n\n category = raw_row[\"Category\"]\n\n skipword_characters = {'?'}\n try:\n for i, g in izip(mod_ipa.split('/'), gloss.split('/')):\n word = make_word(i, g, category)\n word_counts[word] += count\n except WordParseError as e:\n print (u\"Error on line %d: %s [%s || %s]\" %\n (line_number, repr(e), ipa, gloss)).encode('utf-8')\n except IndexError as e:\n unknown_index = e.args[0]\n if unknown_index in skipword_characters:\n print (u\"Bad char on line %d: %s [%s || %s]\" %\n (line_number, repr(e), ipa, gloss)).encode('utf-8')\n else:\n print \"FATAL ERROR ON LINE %d\" % line_number\n raise\n except:\n print \"FATAL ERROR ON LINE %d\" % line_number\n raise\n return word_counts", "def process_file(filename, skip_header):\n hist = {}\n fp = open(filename, encoding='utf8')\n\n if skip_header:\n skip_gutenberg_header(fp)\n\n\n for line in fp:\n if line.startswith('*** END OF THIS PROJECT'):\n break\n line = line.replace('-', ' ')\n strippables = string.punctuation + string.whitespace\n\n for word in line.split():\n # remove punctuation and convert to lowercase\n word = word.strip(strippables)\n word = word.lower()\n\n #update the histrogram\n hist[word] = hist.get(word, 0) + 1\n\n\n return hist", "def mkwrddct(inputfile):\n fin = open(inputfile)\n words = dict()\n for line in fin:\n w = line.strip()\n words[w] = w\n return words", "def read_file(fp):\n scrabble_words_dict = {}\n \n for line in fp:\n line = line.lower()\n line = line.strip()\n if len(line) < 3:\n continue\n elif \"-\" in line or \"'\" in line:\n continue\n else:\n scrabble_words_dict[line] = 1\n return scrabble_words_dict", "def process(filename):\r\n x = open(filename, \"r\")\r\n words_from_songs=[]\r\n for line in x:\r\n array =line.split(\":\")\r\n songid= array[0]\r\n lyrics=array[1]\r\n lyrics=lyrics.replace(\"\\n\", \"\")\r\n lyrics=lyrics.split(\" \")\r\n for i in range(len(lyrics)):\r\n words_from_songs.append((lyrics[i],songid))\r\n words_from_songs=radixSortNumbers(words_from_songs)\r\n max1 = longestWord(words_from_songs)\r\n counting = []\r\n for _ in range(max1+1):\r\n counting.append([])\r\n for k in range(len(words_from_songs)-1,0,-1):\r\n counting[len(words_from_songs[k][0])].append(words_from_songs[k])\r\n new_list = []\r\n # for i in range(len(counting)-1,0,-1):\r\n # for k in range(len(counting[i])):\r\n # new_list.insert(0,counting[i][k])\r\n # for i in range(len(counting) - 1, 0, -1):\r\n # new_list = countingSort(new_list, i - 1)\r\n\r\n for i in range(len(counting)-1,0,-1):\r\n for k in range(len(counting[i])):\r\n new_list.insert(0,counting[i][k])\r\n new_list = countingSort(new_list,i-1)\r\n y = open(\"sorted_words.txt\",\"w\")\r\n for i in range(len(new_list)):\r\n y.write(str(new_list[i][0])+\":\"+str(new_list[i][1]+\"\\n\"))", "def dictionary_creation(filename):\n\tfp = open(filename)\n\td = dict()\n\tfor line in fp:\n\t\t# print line\n\t\tfor word in line.split():\n\t\t\tword = word.strip(string.punctuation + string.whitespace)\n\t\t\t# print word\n\t\t\tif len(word) >5:\n\t\t\t\tif word not in d:\n\t\t\t\t\t# print 'in'\n\t\t\t\t\td[word] = 1\n\t\t\t\telse:\n\t\t\t\t\t# print 'not in'\n\t\t\t\t\td[word] += 1\n\treturn d\n\n\tfp.close()", "def get_word_list(file_name, to_skip_or_not_to_skip):\n fin = open(file_name) #opening file\n histogram={} \n if to_skip_or_not_to_skip == True: #if I want to skip the header this is set to True\n skip_first_part(fin)\n for line in fin: #runs through lines of book file\n line = line.replace(\"-\",\" \") #takes out dashed, underscroes, numbers, whitespaces, and punctuation\n line = line.replace(\"_\",\" \")\n to_remove = string.punctuation + string.whitespace + '0123456789' \n for word in line.split():\n word = word.strip(to_remove) #running through all words in each line \n if word == 'God' or 'Lord':\n pass\n else:\n word = word.lower()\n histogram[word] = histogram.get(word, 0)+1\n return histogram", "def read_words(f, words):\n with open(f) as file:\n for line in file:\n w = tokenizer.tokenize(line.strip())\n for word in w:\n try:\n words[word] += 1\n except:\n words[word] = 1", "def load_wordlist(self, filename):\n reg1 = re.compile(\"^([1-6]{5})[ \\t]+(.*)$\")\n f = open(filename, 'r')\n \n if(self.generate):\n wordlist = []\n reg2 = re.compile(\"^(\\S*)$\")\n for line in f:\n m1 = reg1.match(line)\n m2 = reg2.match(line)\n \n if(m1):\n wordlist.append(m1.group(2))\n elif(m2):\n wordlist.append(m2.group(1))\n \n else:\n wordlist = {}\n for line in f:\n m = reg1.match(line)\n if(m):\n wordlist[int(m.group(1))] = m.group(2)\n \n if((not self.generate and len(wordlist) < 7776) or \n (self.generate and len(wordlist) < 2**13)):\n stderr.write(\"Word list is too short\\n\")\n exit(5)\n \n self.wordlist = wordlist", "def print_word_freq(file):\n # with open(file, 'r') as text the r as the second arguement means that my intentions are to read the file\n with open(file, 'r') as text:\n # this reads the entire file and puts this into text string\n text_string = text.read()\n # returns the string respresentation of text string without removing special characters so you can see what you need to remove\n # print(repr(text_string))\n # this removes the specified characters from the text string\n text_string = text_string.replace(\",\", \"\")\n text_string = text_string.replace(\".\", \"\")\n text_string = text_string.replace(\"—\", \" \")\n text_string = text_string.replace(\"-\", \" \")\n text_string = text_string.replace(\"?\", \"\")\n text_string = text_string.replace(\":\", \"\")\n text_string = text_string.replace(\"'\", \"\")\n text_string = text_string.replace(\"\\\\n\", \"\")\n text_string = text_string.replace(\"’\", \"\")\n text_string = text_string.replace(\"]\", \"\")\n text_string = text_string.replace(\"[\", \"\")\n text_string = text_string.replace(\"\\\"\", \"\")\n # takes the text string and makes all the characters lower case\n text_string = text_string.lower()\n # takes the text string and splits all the words into a list this splits from space to space\n words_list = text_string.split()\n # a dictionary is a key and a value\n no_stop_words = {}\n # for loop that will cycle through the words list\n for word in words_list:\n # checking to see if the word is stop words\n if word not in STOP_WORDS:\n # if the word is already in the dictionary no stop words increment the value by 1\n if word in no_stop_words:\n no_stop_words[word] += 1\n # if the word is not in the dictionary no stop words add this to the dictionary and give it a value of 1\n else:\n no_stop_words[word] = 1\n \n sorted_dict = {}\n sorted_keys = sorted(no_stop_words, key=no_stop_words.get, reverse=True)\n \n for w in sorted_keys:\n sorted_dict[w] = no_stop_words[w]\n \n for key in sorted_dict:\n print(f\"{key:>15} | {sorted_dict[key]:2} {'*' * sorted_dict[key]}\")\n \n # good practice to ensure that we are properly closing the file in use at the end of the function\n text.close()", "def importBrainstormWordsFile(filename):\n #init the list with all words in the file\n allWords = []\n \n #open the brainstorming words file and read the lines\n with open(filename, 'r') as fp:\n lines = fp.read().splitlines()\n \n #split the lines for the idiots that didn't read the instructions and add them to the output\n for curLine in lines:\n if curLine.startswith('Please type one'):\n continue\n cutLines = curLine.replace(',',' ').split()\n \n #cycle the word and add them\n for curWord in cutLines:\n allWords.append(curWord.strip().lower())\n \n return allWords", "def getDictionary(tsvFile, wordIndex, ngram):\r\n \r\n reader = DescriptionReader(tsvFile, wordIndex, ngram)\r\n dictionary = corpora.Dictionary( d for d in reader )\r\n\r\n # remove stop words and words that appear only once\r\n stoplist = [] # might be specified in the furture\r\n stop_ids = [dictionary.token2id[stopword] for stopword in stoplist\r\n if stopword in dictionary.token2id]\r\n once_ids = [tokenid for tokenid, docfreq in dictionary.dfs.items() if docfreq == 1]\r\n dictionary.filter_tokens(stop_ids + once_ids) # remove stop words and words that appear only once\r\n dictionary.compactify() # remove gaps in id sequence after words that were removed\r\n \r\n return dictionary", "def learn(filename):\n word_dict = {} # Create empty dictionary\n first = None\n prev = None\n with open(filename, 'r', encoding='utf-8') as file:\n for line in file:\n list_words = line.lower().split()\n text = []\n for word in list_words:\n # take out leading and trailing punctuation characters\n words = word.strip(string.punctuation + string.digits)\n word_len = len(words)\n if word_len >= 1:\n text.append(words)\n\n if first is None:\n # Get the first word in the text file\n first = text[0]\n # iterate over text\n if prev:\n text.insert(0, prev)\n for counter, word in enumerate(text):\n if word not in word_dict:\n word_dict[word] = list()\n if counter < (len(text) - 1):\n following = counter + 1\n word_dict[word].append(text[following])\n prev = text[-1]\n return first, word_dict # return a tuple", "def read_dictionary():\n with open(FILE, 'r') as f:\n for vocabulary in f:\n if vocabulary[0].strip() not in dict_txt:\n dict_txt[vocabulary[0].strip()] = [vocabulary.strip()]\n else:\n dict_txt[vocabulary[0].strip()].append(vocabulary.strip())", "def ReadAndTokenize(filename):\n global CACHE\n global VOCABULARY\n if filename in CACHE:\n return CACHE[filename]\n comment = open(filename).read()\n words = Tokenize(comment)\n\n terms = collections.Counter()\n for w in words:\n VOCABULARY[w] += 1\n terms[w] += 1\n\n CACHE[filename] = terms\n return terms", "def file_preprocessing(input_file, output_file):\n # print(\"processing file \" + input_file)z\n # replace the punctuations with space\n replace_punctuation = str.maketrans(string.punctuation, ' '*len(string.punctuation))\n # stemming\n stemmer = PorterStemmer()\n\n with open(input_file, 'r', encoding='utf-8', errors='replace') as inFile, open(output_file,'w') as outFile:\n for line in inFile:\n # replace punctuations\n # convert camel case into space separated\n # convert snake case into space separated\n # remove language keywords\n custom_stopwords = [\"ENDCOND\",\"PVSCL\", \"IFCOND\", \"EVAL\", \"ENDCOND\", \"ELSECOND\", \"ELSEIFCOND\", \"WINDOW\", \"FUNCTION\",\n \"CALLBACK\", \"ABWA\", \"ERROR\", \"TODO\", \"RESOLVE\", \"DOCUMENT\", \"CLASS\", \"LINE\", \"ELEMENT\", \"UTILS\",\n \"NEW\", \"IS\", \"EMPTY\",\"ANNOTATIONS\",\"ANNOTATION\",\"UTILS\",\"CURRENT\",\"TEXT\",\"GET\",\"NAME\",\"LISTERNER\",\n \"ADD\", \"EVENT\", \"CREATE\",\"FOR\", \"FIND\", \"LENGTH\", \"USER\", \"VALUE\", \"ALERT\", \"ALERTS\", \"ID\", \"HANDLER\",\n \"MESSAGE\", \"GROUP\", \"RETRIEVE\", \"MANAGER\", \"LANGUAGE\", \"CONTENT\", \"INIT\"]\n line_witout_puncs = ' '.join([snake_to_spaces(camel_to_spaces(word))\n for word in line.translate(replace_punctuation).split()\n if len(word) >=4 and word not in stopwords.words('english') #and #word.upper() not in (name.upper() for name in custom_stopwords)\n and word not in all_keywords])\n\n\n # stemming\n # singles = []\n # for plural in line_witout_puncs.split():\n # try:\n # singles.append(stemmer.stem(plural))\n # except UnicodeDecodeError:\n # print(plural)\n\n # line_stemmed = ' '.join(singles)\n # print(line_stemmed, file=outFile)\n print(line_witout_puncs.encode(\"utf-8\"), file=outFile)", "def twitter_data(filename, dictionary):\r\n new_data = []\r\n with codecs.open(filename, 'r', 'utf8') as f:\r\n for line in f:\r\n new_line = []\r\n stuff = [x for x in line.lower().split() if\r\n ((has_letter(x) or len(x) >= 1) and keep_word(x, num_words, count_dict))]\r\n for word in stuff:\r\n new_line.append(dictionary.get(word, 1))\r\n if len(new_line) > 0:\r\n new_data.append(new_line)\r\n return new_data", "def index_embedding_words(self, embedding_file):\n words = set()\n with open(embedding_file) as f:\n for line in f:\n w = TokenDictionary.normalize(line.rstrip().split(' ')[0])\n words.add(w)\n return words", "def buildGraph(file):\r\n dict = {}\r\n graph = Graph()\r\n wfile = open(file,'r')\r\n for line in wfile:\r\n word = line[:-1]\r\n for i in range(len(word)):\r\n bucket = word[:i] + '_' + word[i+1:]\r\n if bucket in dict:\r\n dict[bucket].append(word)\r\n else:\r\n dict[bucket] = [word]\r\n for bucket in dict.keys():\r\n for word1 in dict[bucket]:\r\n for word2 in dict[bucket]:\r\n if word1 != word2:\r\n graph.addEdge(word1,word2)\r\n return graph", "def init(wordlist_filename):\n global WORDS\n if WORDS == None:\n WORDS = []\n bad_line = lambda x: x.strip() == '' or x.startswith('#')\n with codecs.open(wordlist_filename, 'r', 'utf-8') as filehandle:\n lines = filehandle.readlines()\n WORDS = set([x.lower().strip() for x in lines if not bad_line(x)])", "def import_words(file_name):\n with open(file_name) as word_list:\n words = []\n for line in word_list:\n number, word = line.strip().split(\"\\t\")\n words.append(word.strip())\n # print(f\"Imported {(len(word_dict))} words\")\n\n return words", "def tokenize_and_split_bis(sms_file):\n \n dic = {}\n list1 = []\n list2 = []\n list3 = []\n list4 = []\n i = -1\n document = 0\n terms = 0\n new_document = True\n ham = True\n for line in open(sms_file, 'r').readlines():\n w = []\n document += 1\n new_document = True\n for word in line.split():\n i = i + 1\n if word == \"ham\":\n ham = True\n i = i - 1\n elif word == \"spam\":\n ham = False\n i = i - 1\n else:\n if word not in dic:\n dic[word] = i\n w.append(dic[word])\n list3.append(1)\n list4.append(1)\n new_document = False\n terms += 1\n else : \n i = i - 1\n w.append(dic[word])\n list4[dic[word]] += 1\n terms += 1\n if new_document: \n list3[dic[word]] += 1\n new_document = False\n \n if ham and w !=[]:\n list2.append(w)\n elif ham == False and w !=[]:\n list1.append(w)\n\n moy = 0\n len_dic = len(dic.keys())\n list5 = [0 for x in range(len_dic)]\n for key in dic.keys():\n if list4[dic[key]] > 0:\n tf = list4[dic[key]] / terms\n idf = math.log(document / list3[dic[key]])\n tfIdf = tf * idf\n list5[dic[key]] = tfIdf\n # print(\"the word \" + str(key) + \" appairs \" + str(list4[dic[key]]) + \" times.\")\n # print(\"his frequency is \" + str(list4[dic[key]] / terms) )\n # print(\"the word \" + str(key) + \" appairs \" + str(list3[dic[key]]) + \" times in each document.\")\n # print(\"his frequency is \" + str(idf))\n # print(\"utility \" + str(tfIdf))\n moy += tfIdf\n \n moy = moy / len_dic \n # print(moy)\n dic_bis = {}\n i = -1\n for key in dic.keys():\n value = list5[dic[key]]\n # print(str(value))\n if (value > oracle * moy):\n i += 1\n dic_bis[key] = i\n # else:\n # print(\"not pass \" + key + \" \" + str(value))\n \n \n # print(dic_bis == dic)\n # print(dic)\n return dic_bis,list1,list2", "def load_dictionary(hash_table, filename):\n\n file = open(filename)\n lines = file.readlines()\n start = timeit.default_timer()\n for line in lines:\n hash_table.insert(line.rstrip(),1)\n if timeit.default_timer() - start > 4:\n break\n file.close()", "def top_words(source, number):\n\n keys = set()\n\n ht = HashMap(2500, hash_function_2)\n\n # This block of code will read a file one word at a time and\n # put the word in `w`\n with open(source) as f:\n for line in f:\n words = rgx.findall(line)\n for w in words:\n current_word = w.lower()\n #get a count for current word\n current_count = ht.get(current_word)\n if current_count is None:\n ht.put(current_word, 1)\n else:\n ht.put(current_word, current_count + 1)\n\n #create an empty list to store top words in\n tuple_list = []\n\n #traverse hash_map to find most used words\n for i in range(ht.capacity):\n if ht._buckets[i] is not None:\n #traverse links at each bucket\n current = ht._buckets[i].head\n while current is not None:\n tuple_list.append((current.key, current.value))\n current = current.next\n\n #create an ordered list out of items\n iter_tuple_quick_sort(tuple_list, len(tuple_list) - 1, 0)\n\n #create a new list to return with passed number arg\n return_list = []\n list_counter = 0\n while list_counter <= number - 1:\n if list_counter == len(tuple_list) - 1:\n break\n else:\n return_list.append(tuple_list[list_counter])\n list_counter += 1\n\n return return_list", "def word_counts(file):\n words = defaultdict(int)\n regex = re.compile('[' + string.punctuation + ']')\n for line in open(file):\n for word in [regex.sub('', w) for w in line.lower().split()]:\n words[word] += 1\n\n return words", "def parse_file(input_file):\n # Automatically close the file after being used\n with open(input_file) as text:\n # Read file and split each word into an element in a list\n data = text.read().split()\n\n # Sort the list\n # Python sort automatically does lexical sorting\n data.sort()\n\n # For each word, use as Dictionary key and count the occurrences of the word and use as value\n frequency_table = {word: data.count(word) for word in data}\n\n # Return the frequency table\n return frequency_table", "def print_word_freq(file):\n with open(file) as text:\n text = text.read().lower()\n text = text.replace(\"\\n\", \" \")\n text = text.replace(\"’\", \"\")\n # text = \" \".join(text.split())\n # print(text)\n for character in string.punctuation:\n text = text.replace(character, \"\")\n word_list = text.split()\n clean_list = []\n for word in word_list:\n if word not in STOP_WORDS:\n clean_list.append(word)\n \n\n # for stop_word in STOP_WORDS:\n # if stop_word in word_list:\n # word_list.remove(stop_word)\n\n\n new_dict = {}\n for word in clean_list:\n new_dict[word] = clean_list.count(word)\n sorted_dict = sorted(new_dict.items())\n print(sorted_dict)\n\n # print(f\"{key} | {value} {'*' * value}\")\n\n \n # for stop_word in STOP_WORDS:\n # text = text.replace(stop_word, \"\")\n\n # for word in word_list:\n # if word in string.punctuation:\n # #do something\n # if word in STOP_WORDS:\n\n \n # for stop_word in STOP_WORDS:\n # text = text.replace(stop_word, \"\")\n # print(text)", "def spell_file(fn, wordcost, maxword):\n\n def infer_spaces(s):\n \"\"\"Uses dynamic programming to infer the location of spaces in a string\n without spaces.\"\"\"\n global unfolded\n if s in unfolded:\n return unfolded[s]\n\n # Find the best match for the i first characters, assuming cost has\n # been built for the i-1 first characters.\n # Returns a pair (match_cost, match_length).\n def best_match(i):\n candidates = enumerate(reversed(cost[max(0, i-maxword):i]))\n return min((c + wordcost.get(s[i-k-1:i], 9e999), k+1) for k,c in candidates)\n\n # Build the cost array.\n cost = [0]\n for i in range(1,len(s)+1):\n c,k = best_match(i)\n cost.append(c)\n\n # Backtrack to recover the minimal-cost string.\n out = []\n i = len(s)\n while i>0:\n c,k = best_match(i)\n assert c == cost[i]\n out.append(s[i-k:i])\n i -= k\n \n unfolded[s] = ' '.join(reversed(out))\n return ' '.join(reversed(out))\n\n\n\n speller = aspell.Speller('lang', 'en')\n for w in slang:\n speller.addtoSession(w)\n \n with open(tweet_tmp1_dir + fn, 'r') as fin:\n with open(tweet_tmp2_dir + fn, 'w') as fout:\n res = []\n for l in fin:\n prefix = ''\n if 'test' in fn:\n comma = l.find(',')\n prefix = l[:comma].strip()\n l = l[comma+1:]\n try:\n assert(prefix.isdigit())\n except:\n print(prefix, l)\n prefix += ','\n \n ll = ''\n \n ws = [w for w in l.strip().split(' ') if len(w) > 0]\n for w in ws:\n if w in correct_word:\n nw = correct_word[w]\n elif (w.startswith('<') and w.endswith('>')) or w in whitelist or speller.check(w):\n nw = w\n else:\n try:\n nw1, nw2 = speller.suggest(w)[:2]\n nwdist1 = jellyfish.levenshtein_distance(w,nw1)\n nwdist2 = jellyfish.levenshtein_distance(w,nw2)\n \n if nw2.count(' ') < nw1.count(' ') or (nwdist1 > MAX_DIST_CORRECTION and nwdist2 < nwdist1) :\n nw1 = nw2\n nwdist1 = nwdist2\n if nwdist1 <= MAX_DIST_CORRECTION:\n nw = nw1.lower()\n else:\n nw = w.lower()\n except:\n nw = infer_spaces(w)\n if nw.count('.') >= nw.count(' ')/3:\n nw = nw.replace('.', '')\n elif nw.count('-') >= nw.count(' ')/3:\n nw = nw.replace('-', '')\n nw = nw.replace(' ', ' ').lower()\n ll += nw + ' '\n correct_word[w] = nw\n res.append(prefix+ll.strip())\n# fout.write(prefix+ll.strip()+'\\n')\n fout.write('\\n'.join(res))", "def process_file(filename, skip_header=True):\n hist = {}\n fp = file(filename)\n fullwordlist=[]\n # if skip_header:\n # skip_gutenberg_header(fp)\n\n for line in fp:\n holder=process_line(line,hist)\n #print holder\n fullwordlist.extend(holder)\n return fullwordlist", "def make_word_dict():\n d = dict()\n fin = open(\"words.txt\")\n for line in fin:\n word = line.strip().lower()\n d[word] = None\n #have to add single letter words to the word list;\n #also, the empty string is considered a word.\n for letter in ['a', 'i', '']:\n d[letter] = letter\n return d", "def load_wordlist(filename):\n # YOUR CODE HERE\n words = {}\n f = open(filename, 'rU')\n text = f.read()\n text = text.split('\\n')\n for line in text:\n words[line] = 1\n f.close()\n return words", "def __init__(self, vocab_file, max_size):\n\t\tself._word_to_id = {}\n\t\tself._id_to_word = {}\n\t\tself._count = 0 # keeps track of total number of words in the Vocab\n\n\t\t# [UNK], [PAD], [START] and [STOP] get the ids 0,1,2,3.\n\t\tfor w in [UNKNOWN_TOKEN, PAD_TOKEN, START_DECODING, STOP_DECODING]:\n\t\t\tself._word_to_id[w] = self._count\n\t\t\tself._id_to_word[self._count] = w\n\t\t\tself._count += 1\n\n\t\t# Read the vocab file and add words up to max_size\n\t\twith open(vocab_file, 'r') as vocab_f:\n\t\t\tfor line in vocab_f:\n\t\t\t\tpieces = line.split()\n\t\t\t\tif len(pieces) != 2:\n\t\t\t\t\tprint ('Warning: incorrectly formatted line in vocabulary file: %s\\n' % line)\n\t\t\t\t\tcontinue\n\t\t\t\tw = pieces[0]\n\t\t\t\tif w in [SENTENCE_START, SENTENCE_END, UNKNOWN_TOKEN, PAD_TOKEN, START_DECODING, STOP_DECODING]:\n\t\t\t\t\traise Exception(\n\t\t\t\t\t\t'<s>, </s>, [UNK], [PAD], [START] and [STOP] shouldn\\'t be in the vocab file, but %s is' % w)\n\t\t\t\tif w in self._word_to_id:\n\t\t\t\t\traise Exception('Duplicated word in vocabulary file: %s' % w)\n\t\t\t\tself._word_to_id[w] = self._count\n\t\t\t\tself._id_to_word[self._count] = w\n\t\t\t\tself._count += 1\n\t\t\t\tif max_size != 0 and self._count >= max_size:\n\t\t\t\t\tprint (\"max_size of vocab was specified as %i; we now have %i words. Stopping reading.\" % (\n\t\t\t\t\tmax_size, self._count))\n\t\t\t\t\tbreak\n\n\t\tprint (\"Finished constructing vocabulary of %i total words. Last word added: %s\" % (\n\t\tself._count, self._id_to_word[self._count - 1]))", "def create_dictionary(filename):\n\tword_set = set()\n\tif os.path.isfile(filename):\n\t\twith open(filename, 'r') as f:\n\t\t\tfor line in iter(f):\n\t\t\t\tword_set.add(line.strip('\\n'))\n\telse:\n\t\tprint \"File not found!\"\n\treturn word_set", "def read_dictionary():\n global dic\n with open(FILE, 'r') as f:\n for line in f:\n word_list = line.split()\n word = word_list[0].strip()\n dic.append(word)", "def read_data(filename,words):\n try:\n f = open(filename)\n reader = f.read().splitlines()\n for line in reader:\n #print(line[0])\n words.add(line.lower())\n f.close()\n except IOError:\n print 'Input file reading failed,'\n return words", "def read_dictionary():\n\tglobal dictionary\n\twith open(FILE, \"r\") as f:\n\t\tfor words in f:\n\t\t\tdictionary += words.split()", "def create_word_map(tokenized_descriptions_file_path, word_dictionary_output_path):\n if os.path.exists(word_dictionary_output_path):\n print(\"Word map already exists in workspace. Will be reused.\")\n return\n\n print(\"Word map not found. Generating....\")\n\n words_list = []\n words_to_id = {}\n\n with open(tokenized_descriptions_file_path, 'r') as file:\n for line in file:\n tokens = line.strip().split(\",\")\n words_list.extend(tokens[1:])\n\n # remove duplicate words\n words_list = list(set(words_list))\n\n # sorting the words\n words_list = sorted(words_list)\n for i in range(len(words_list)):\n words_to_id[words_list[i]] = i\n\n with open(word_dictionary_output_path, 'w') as f:\n [f.write('{0},{1}'.format(key, value) + \"\\n\") for key, value in words_to_id.items()]", "def __init__(self):\n stopwords_file = open(self.filepath, \"r\")\n for line in stopwords_file.readlines():\n line2 = line.replace(\"\\n\", \"\") \n self.add(line2)", "def print_word_freq(file):\n with open(file) as text:\n text_string = str(text.readlines())\n text_string = text_string.replace(\",\", \"\")\n text_string = text_string.replace(\".\", \"\")\n text_string = text_string.replace(\"-\", \"\")\n text_string = text_string.replace(\"?\", \"\")\n text_string = text_string.replace(\":\", \"\")\n text_string = text_string.replace(\"'\", \"\")\n text_string = text_string.replace(\"\\\\n\", \"\")\n text_string = text_string.replace(\"[\", \"\")\n text_string = text_string.replace(\"]\", \"\")\n word_list = text_string.split()\n no_stop_words = []\n for word in word_list:\n if word in STOP_WORDS:\n pass\n else: no_stop_words.append(word)\n clean_list = {}\n for word in no_stop_words:\n clean_list[word] = no_stop_words.count(word) \n print(clean_list)", "def load_words(file_path: str) -> List[Word]:\n \n words = load_words_raw(file_path)\n \n \n words = remove_stop_words(words)\n\n \n words = remove_duplicates(words)\n \n return words", "def word_frequency():\n\n song = open(\"data/yellow_submarine.txt\")\n d = dict()\n for line in song:\n line = line.strip()\n line = line.lower()\n punctuations = \"\"\"!()-[]{};:'\"\\,<>./?@#$%^&*_~\"\"\" # remove punctuation https://www.programiz.com/python-programming/examples/remove-punctuation\n no_punct = \"\" # remove punctuation\n for char in line: # remove punctuation\n if char not in punctuations: # remove punctuation\n no_punct = no_punct + char # remove punctuation\n words = line.split(\" \")\n for word in words:\n d[word] = d.get(word, 0) + 1\n return d", "def create_dict(fd):\n # initialize an empty dictionary\n full_dict = {}\n # loop through file\n for line in fd:\n # lowercase everything in line, then split line into a list\n line = line.lower().split()\n # loop through elements in the list of words in the splitted line\n for word in line:\n # strip words from puncuation using string module\n word = word.strip(string.punctuation)\n # if words contains only alphabatic characters and of length > 1\n if word.isalpha() and len(word)!= 1:\n if len(word) in full_dict:\n full_dict[len(word)].add(word)\n else:\n full_dict[len(word)] = set()\n full_dict[len(word)].add(word)\n return full_dict", "def __init__(self, filename):\n\n self.term_dict = {}\n for line in open(filename):\n if line.startswith(\"#\"):\n continue\n\n #print line\n word, w_type = line.strip().split(\"\\t\")\n self.term_dict[word.strip().lower()] = \"CHESS_\" + w_type.strip().lower()", "def print_word_freq(file):\n# Opening file to be read\n with open(file, \"r\") as f:\n file_contents = f.read()\n\n\n# # Taking away punctuation and lowercase all words\n word_list = file_contents.lower().replace(',',' ').replace('.',' ').replace('!',' ').split()\n # print(word_list)\n\n nice_list = []\n for word in word_list:\n if word not in STOP_WORDS:\n nice_list.append(word)\n # print(nice_list)\n\n d = {}\n for word in nice_list:\n if word not in d.keys():\n d[word] = 1\n else:\n d[word] += 1 \n # print(sorted(d, key=d.get, reverse=True)\n # sorted(d, key=d.get, reverse=true)\n # print(d)\n\n # for word in sorted(d):\n # print((word, d[word]), end = \" \")\n\n d_filtered = sorted(d, key=d.get, reverse=True)\n for x in d_filtered:\n print(x, d[x])", "def get_words(txtfile):\n\n global _wordset\n global _postrie\n\n f = open(txtfile,'r')\n _wordset = set([x.lower() for x in set(f.read().split()) \\\n if not re.match('.*[\\W,\\d]|^.$',x)])\n\n #print('building suffix trie')\n _postrie = trienode(pre = False)\n _postrie.grow(_wordset)\n\n # Since this will be recursed through later, take care of it now.\n if len(_wordset) > sys.getrecursionlimit():\n sys.setrecursionlimit(len(_wordset))", "def loadBrainstormingCorrectAnswersFile( filename ):\n #read the file and init the output struct\n with open(filename, 'r') as fp:\n lines = fp.readlines()\n synonymTable = {}\n curCategory = ''\n \n for curLine in lines:\n #skip empty lines and lines that start with # as they are comments\n curLine = curLine.strip().lower()\n if not curLine or curLine.startswith('#'):\n continue\n \n #the > symbol indicates a new category all other lines are synonys for this cateogry\n if curLine.startswith('>'):\n curCategory = curLine[1:].strip()\n synonymTable[curCategory] = [curCategory]\n continue\n \n synonymTable[curCategory].append(curLine)\n \n return synonymTable", "def get_analyze_per_file(self):\n \"\"\"Exclude tags, exclude binary (img), count words without non literal characters and digits\"\"\"\n filename = f'{self.path}/{self.filename}'\n file = open(filename, 'r', encoding='utf-8')\n df_tmp = pd.DataFrame(columns=['word', 'cnt', 'word_low'])\n w_cnt = 0\n word_counter = {}\n data = file.read()\n head, sep, tail = data.partition('<binary')\n head = re.sub('\\\\s\\\\s*', ' ', (re.sub('\\\\W|\\\\d', ' ', re.sub('<.*?>', '', head))))\n word_list = head.split()\n for word in word_list:\n\n if word not in word_counter:\n word_counter[word] = 1\n else:\n word_counter[word] = word_counter[word] + 1\n w_cnt += 1\n\n for word, occurance in word_counter.items():\n df_tmp = df_tmp.append({'word': '{:15}'.format(word), 'cnt': '{:3}'.format(occurance),\n 'word_low': '{:15}'.format(word).lower()}, ignore_index=True)\n df_tmp = df_tmp.sort_values(by='word_low')\n df_tmp.loc[(df_tmp.word != df_tmp.word_low), 'word'] = df_tmp.cnt\n df_tmp.loc[(df_tmp.word == df_tmp.cnt), 'cnt'] = 0\n df_tmp.loc[(df_tmp.word == df_tmp.word_low), 'word'] = 0\n df_tmp['word'] = df_tmp.word.astype(int)\n df_tmp['cnt'] = df_tmp.cnt.astype(int)\n df_tmp = df_tmp.groupby(['word_low'])['cnt', 'word'].sum().reset_index()\n conn = sqlite3.connect('for_python_ht.db')\n try:\n try:\n sqlite_for_ht.CreateTableSingle.delete_table(f_3, self.filename)\n print(datetime.now(), '-', self.filename, 'Table deleted at the start point')\n except Exception:\n print(datetime.now(), '-', 'Something went wrong')\n traceback.print_exc()\n df_tmp.to_sql(name=self.filename, con=conn, index=False)\n print(datetime.now(), '-', self.filename, 'Table created and filled with data')\n except Exception:\n print(datetime.now(), '-', 'file with name {} already exists'.format(self.filename))\n traceback.print_exc()\n print(datetime.now(), '-', 'word analyse for', self.filename, 'done')\n sqlite_for_ht.HandleTemp.update_table(f_2, 'status', 'Done', self.filename)\n return None", "def convert_from_text(self, file_name):\n with open(file_name, 'r') as reader:\n words_list = []\n for line in reader:\n words_list.extend(line.split())\n\n for word in set(words_list):\n if word.isalpha():\n self.insert_word(word.lower())\n else:\n self.insert_word(''.join([c for c in word if c.isalpha()]).lower())", "def create_B_words(path_to_pairs,\n path_to_librispeech_text,\n path_to_phonemes,\n path_save,\n freq_sim,\n len_sim,\n edit_sim):\n for i in range(len(path_to_pairs)):\n \n pairs = []\n dic_cl_eq = {} # Classe d'equivalence pour le sens des mots\n \n with open(path_to_pairs[i]) as f:\n for line in f:\n line = line.replace('\\n', '').split(' ')\n pairs.append(line)\n if line[0] in dic_cl_eq:\n dic_cl_eq[line[0]].add(line[1])\n else:\n dic_cl_eq[line[0]] = {line[1]}\n if line[1] in dic_cl_eq:\n dic_cl_eq[line[1]].add(line[0])\n else:\n dic_cl_eq[line[1]] = {line[0]}\n \n dic_cl_eq_prev = {}\n while dic_cl_eq_prev != dic_cl_eq:\n dic_cl_eq_prev = copy.deepcopy(dic_cl_eq)\n for word in dic_cl_eq:\n for syn in dic_cl_eq[word]:\n dic_cl_eq[word] = set.union(dic_cl_eq[word], dic_cl_eq[syn])\n \n with open(path_to_librispeech_text) as f:\n text_librispeech = f.read()\n text_librispeech_split = text_librispeech.replace('\\n', ' ').split(' ')\n freq_libri = {}\n for word in text_librispeech_split:\n if word in dic_cl_eq:\n if word in freq_libri:\n freq_libri[word] += 1\n else:\n freq_libri[word] = 1\n \n phonemes = []\n with open(path_to_phonemes[i]) as f:\n for line in f:\n line = line.replace('\\n', '').split(' ')\n phonemes.append(line)\n \n dic_word_phonemes = {}\n for j in range(len(pairs)):\n dic_word_phonemes[pairs[j][0]] = phonemes[j][0]\n dic_word_phonemes[pairs[j][1]] = phonemes[j][1]\n \n file = open(path_save[i], 'w+')\n file.truncate(0)\n \n for j in range(len(pairs)):\n A, X = pairs[j]\n B_0 = []\n for word in dic_cl_eq:\n if word not in dic_cl_eq[A]:\n if np.abs(np.log(freq_libri[word])/np.log(freq_sim) \\\n - np.log(freq_libri[A])/np.log(freq_sim)) <= 1:\n if (len(word) > (1-len_sim)*len(A)) and \\\n (len(word) < (1+len_sim)*len(A)):\n p_A = dic_word_phonemes[A]\n p_X = dic_word_phonemes[X]\n p_word = dic_word_phonemes[word]\n if np.abs(dist(p_A, p_X) - dist(p_X, p_word)) < edit_sim:\n B_0.append(word)\n line_0 = ' '.join([A, X] + B_0)\n \n X, A = pairs[j]\n B_1 = []\n for word in dic_cl_eq:\n if word not in dic_cl_eq[A]:\n if np.abs(np.log(freq_libri[word])/np.log(freq_sim) \\\n - np.log(freq_libri[A])/np.log(freq_sim)) <= 1:\n if (len(word) > np.around((1-len_sim)*len(A), decimals=2)) and \\\n (len(word) < np.around((1+len_sim)*len(A), decimals=2)):\n p_A = dic_word_phonemes[A]\n p_X = dic_word_phonemes[X]\n p_word = dic_word_phonemes[word]\n if np.abs(dist(p_A, p_X) - dist(p_X, p_word)) < edit_sim:\n B_1.append(word)\n line_1 = ' '.join([A, X] + B_1)\n \n if max(len(B_0), len(B_1)) == 0:\n print(X, A)\n \n line = line_0 if len(line_0) > len(line_1) else line_1\n if j < len(pairs) - 1:\n line += '\\n'\n file.write(line)\n \n file.close()", "def process_dict(text, frequency_threshold):\n\n # Trying to load previous unique_words (pickle file)\n UNIQUE_WORDS_PICKLE = \"unique_words_with_frequency_\" + str(frequency_threshold) + \".pickle\"\n \n unique_words = None\n if os.path.isfile(UNIQUE_WORDS_PICKLE):\n try:\n with open(UNIQUE_WORDS_PICKLE, 'r') as f:\n unique_words = pickle.load(f)\n except:\n os.remove(UNIQUE_WORDS_PICKLE)\n unique_words = None\n\n if (type(unique_words) == list):\n return unique_words\n\n\n WORD_COUNT_PICKLE = \"word_count.pickle\"\n WORD_COUNT = 253855\n\n print(\"Processing dictionary. This will take a while.\")\n\n # Trying to load previous word_count (pickle file)\n word_count = None\n if os.path.isfile(WORD_COUNT_PICKLE):\n try:\n with open(WORD_COUNT_PICKLE, 'r') as f:\n word_count = pickle.load(f)\n if len(word_count) != WORD_COUNT:\n os.remove(WORD_COUNT_PICKLE)\n word_count = None\n except:\n raise\n os.remove(WORD_COUNT_PICKLE)\n word_count = None\n\n # count words\n if word_count == None:\n print(\"Pickle file not found. Counting word occurence...\")\n\n # grab all the words\n words = text.split(\" \")\n\n # counting word occurence\n word_count = dict(Counter(words).most_common())\n \n # saving word count for future reuse\n with open(WORD_COUNT_PICKLE, 'w') as f:\n pickle.dump(word_count, f)\n print(\"Word count saved for future reuse.\")\n \n # making sure we have the correct count loaded\n assert(type(word_count) == dict)\n assert(len(word_count) == WORD_COUNT)\n\n # remove the duplicates and single-character words.\n unique_words = [w for w in word_count.keys() if len(w) > 1]\n vocab_size = len(unique_words)\n print(\"Vocab size:\", vocab_size)\n\n # remove words with frequency lower than 1%\n unique_words = [word for word in unique_words if float(word_count[word]) / vocab_size > frequency_threshold]\n print(\"Vocab size (>%.3f%% frequency): %d\" % ((frequency_threshold * 100), len(unique_words)))\n\n unique_words.sort(key=lambda word: len(word), reverse=True)\n unique_words.append('a')\n unique_words.append('i')\n\n # save unique words for future reuse\n with open(UNIQUE_WORDS_PICKLE, 'w') as f:\n pickle.dump(unique_words, f)\n print(\"unique_words saved for future reuse.\")\n\n return unique_words", "def load_cows(filename):\r\n print(\"Loading words from file...\")\r\n # inFile: file\r\n inFile = open(filename, 'r')\r\n # wordlist: list of strings\r\n wordlist = {}\r\n for line in inFile:\r\n cow = line.split(',')\r\n wordlist[cow[0]] = int(cow[1]) # 0: name, 1: weight\r\n inFile.close()\r\n print(\" \", len(wordlist), \"words loaded.\")\r\n return wordlist", "def create_dictionary(filename):\n file = open(filename, 'r')\n text = file.read()\n file.close()\n words = text.split()\n d = {}\n current_word = '$'\n \n for next_word in words:\n if current_word not in d:\n d[current_word] = [next_word]\n else:\n d[current_word] += [next_word]\n if next_word[-1] == '.' or next_word[-1] == '!' or next_word[-1] == '?':\n current_word = '$'\n else:\n current_word = next_word\n return d", "def get_word_freq(filein):\n freq = {}\n\n # Open file handles with context manager\n with open(filein) as f:\n\n # Read a single line at a time so as not to crush memory\n for line in f:\n\n # Tokenize and iterate\n for word in line.split():\n\n # Use try/except instead of if/then for performance\n # Likely after the first 1M tweets that the key will be contained\n try:\n freq[word] += 1\n except KeyError:\n freq[word] = 1\n\n return freq", "def word_list():\n\n d = {}\n with open('words.txt') as fin:\n for line in fin.readlines():\n word = line.strip().lower()\n d[word] = True\n return d", "def set_words(data_path):\n w_df = pd.read_csv(data_path, names=['es','gn','syn1','syn2'], encoding='iso-8859-1') # file -i\n gn_df = w_df[['gn','syn1','syn2']].drop_duplicates()\n gn_lst = gn_df['gn'].tolist()+gn_df['syn1'].tolist()+gn_df['syn2'].tolist()\n cleanedList = [x for x in gn_lst if str(x) != 'nan' and len(x)>=3]\n gn_set = set(cleanedList)\n \n print(len(gn_set))\n \n f = open(data_path[:-4]+\".txt\", 'w')\n for w in gn_set:\n f.write('{}\\n'.format(w))\n f.close()\n \n return list(gn_set)", "def fill_words_table(self, statistics, path, filemoving, conn, logg, parser):\n logg.writing_log(conn, 'Starting filling words table')\n c = conn.cursor()\n val1 = statistics.book_name(path, filemoving, parser).replace(' ', '_')\n sql1 = \"CREATE TABLE \" + val1 + \" (word text, count integer, count_uppercase integer)\"\n c.execute(sql1)\n val2 = statistics.frequency(path, filemoving, parser)\n sql2 = \"INSERT INTO \" + val1 + \" VALUES(?,?,?)\"\n for key, value in val2.items():\n if not key.istitle():\n c.execute(sql2, (key, value, (0 if val2.get(key.capitalize()) == None else val2.get(key.capitalize()))))\n logg.writing_log(conn, 'Words table is filled')\n conn.commit()", "def generate_input_with_unknown_words(file_path):\r\n\tseen_tuples = []\r\n\tlabel_matches = dict()\r\n\tfile_lines = []\r\n\twith open(file_path) as f:\r\n\t\tfor line in f:\r\n\t\t\tfile_lines = file_lines + [line.lower().split()]\r\n\t\tword_tuples = zip(file_lines[0::3], file_lines[1::3], file_lines[2::3])\r\n\t\tfor (words, part_of_speech, word_type) in word_tuples:\r\n\t\t\ttype_tuple = zip(words, word_type)\r\n\t\t\tfor word_and_tag in type_tuple:\r\n\t\t\t\tif word_and_tag in seen_tuples:\r\n\t\t\t\t\tlabel_matches.update({word_and_tag : (label_matches.get(word_and_tag, 0) + 1)})\r\n\t\t\t\telse:\r\n\t\t\t\t\ttag = word_and_tag[1]\r\n\t\t\t\t\tunknown_entry = (\"<UNK>\", tag)\r\n\t\t\t\t\tlabel_matches.update({unknown_entry : (label_matches.get(unknown_entry, 0) + 1)})\r\n\t\t\t\t\tseen_tuples.append(word_and_tag)\r\n\treturn label_matches", "def read_dictionary():\n with open(FILE, 'r') as f:\n for line in f:\n words_lst = line.split()\n for word in words_lst:\n dict_list.append(word)", "def uniquewords(self):\n vas = set({})\n file = self.read1()\n for line in file:\n line = line.strip()\n string = re.sub(\"[^0-9a-zA-Z]\", \" \", line).split(\" \")\n for s_i in string:\n if s_i != \"\":\n vas.add(s_i)\n l_i = list(vas)\n self.print(l_i)\n self.write(l_i)\n logging.debug(\"Starting with to\")\n return l_i", "def read_data(file_path):\n words=[]\n dic_word={}\n actual_text=[]\n for line in open(file_path,encoding='utf-8'):\n words_line=line.strip().split(' ')\n for ite in words_line:\n if ite not in dic_word:\n dic_word[ite]=1\n words.extend(words_line)\n actual_text.append(words_line)\n\n\n #with zipfile.ZipFile(file_path) as f:\n #words = tf.compat.as_str(f.read(f.namelist()[0])).split()\n\n return words,len(dic_word),actual_text", "def get_words(f, letters):\n # lettrs = []\n # okay = True\n # words = []\n # nline = ''\n # with open(f, 'r') as vocabulary:\n # for line in vocabulary.readlines():\n # nline = line.replace(\"\\n\", \"\").lower()\n # if 4 <= len(nline) <= 9 and letters[4] in nline:\n # lettrs = list(nline)\n # for lettr in lettrs:\n # if lettr not in letters:\n # okay = False\n # break\n # else:\n # okay = True\n # if okay is True:\n # words.append(nline)\n #\n # lettrs = copy.copy(letters)\n # nwords = []\n # okay = True\n # for word in words[::1]:\n # lettrs = copy.copy(letters)\n # for letter in word:\n # if letter in lettrs:\n # lettrs[lettrs.index(letter)] = '0'\n # else:\n # okay = False\n # break\n # if okay is True:\n # nwords.append(word)\n # okay = True\n #\n # unique = True\n # words = []\n # for word in nwords:\n # if nwords.count(word) > 1:\n # nwords.remove(word)\n # nwords.sort()\n # return nwords\n res = []\n cort_letters = []\n our_letters = []\n res = []\n f = open(f, 'r')\n for line in f:\n line = line.replace(\"\\n\", \"\").strip().lower()\n if 4 <= len(line) <= 9:\n if letters[4] in line:\n count = 0\n for each_letter in line:\n if each_letter in letters:\n count += 1\n if count == len(line):\n our_letters.append(line)\n f.close()\n for each_word in our_letters:\n count_let = 0\n for each_letter in each_word:\n if each_word.count(each_letter) <= letters.count(each_letter):\n count_let += 1\n if count_let == len(each_word):\n res.append(each_word)\n for each in res:\n if res.count(each) > 1:\n res.remove(each)\n return sorted(res)", "def add_encryptors(word):\r\n\r\n assert isinstance(word, str), 'Strings only!'\r\n if word == \"\": return None\r\n file = open(r'words.txt', 'r')\r\n for line in file:\r\n first_word = line.split()[0]\r\n if word == first_word:\r\n print('Error, word is already added to the list!')\r\n return 'Error, word is already added to the list!'\r\n file.close()\r\n\r\n\r\n new_encryption = str(create_encryptors())\r\n blank = True\r\n while blank == True:\r\n file = open(r'words.txt', 'r')\r\n blank = False\r\n for line in file:\r\n if new_encryption == line.split()[2]:\r\n new_encryption = str(create_encryptors())\r\n blank = True\r\n file.close()\r\n break\r\n file.close()\r\n\r\n\r\n if len(word) < 4: tabs = 3\r\n elif len(word) <8: tabs = 2\r\n else: tabs = 1\r\n\r\n file = open(r'words.txt', 'a')\r\n file.write(word + '\\t'*tabs + \"= \" + new_encryption + \"\\n\")", "def words_occur():\n\n # Get the file name from keyboard\n # file_name = input(\"Enter the the file name: \")\n file_name = 'Untitled.txt'\n\n # File open, read and save in the word_list\n f = open(file_name, 'r')\n word_list = f.read().split()\n f.close()\n\n # Get the unic words inclusion's number\n occurs_dict = {}\n for word in word_list[:10]: # test on first ten\n if word.isalpha: # why isalpha isn't working?\n print(word)\n # Increment the counter\n occurs_dict[word] = occurs_dict.get(word, 0) + 1\n\n # Present results\n print(\"File %s has %d words (%d are unique)\" \\\n % (file_name, len(word_list), len(occurs_dict)))\n List = [occurs_dict.values].sort(reverse=True) # how to sort counts?\n print(List)\n print(occurs_dict)", "def _read_words(self, path):\r\n\r\n word_file = open(path)\r\n for line in word_file.readlines():\r\n pair = line.split('::')\r\n self.insert(pair[0], pair[1].rstrip())\r\n word_file.close()", "def parse_file(input_lst):\n word_dct = {}\n for line in input_lst:\n raw_output = line.split() # these are lists of strings\n for str_ in raw_output: # strings\n str_ = str_.lower()\n str_ = str_.replace(\"-\", \" \")\n str_ = str_.replace(\"?\", \"\")\n str_ = str_.replace(\"!\", \"\")\n str_ = str_.replace(\",\", \"\")\n str_ = str_.replace(\"\\'\", \"\")\n str_ = str_.replace('\\\"', \"\")\n str_ = str_.replace(\".\", \"\")\n if str_ not in word_dct:\n word_dct[str_] = 1\n else:\n word_dct[str_] += 1\n return word_dct", "def __line_parse(index: int, line: list, dictionary: dict, word_list: list):\n\n if index + 2 >= len(line):\n return\n word_1 = line[index + 2]\n word_2 = line[index + 1]\n word_3 = line[index]\n if word_1 == \"\" or word_2 == \"\" or word_3 == \"\":\n return\n\n if word_1 not in dictionary:\n dictionary[word_1] = {\n str(word_1 + \"_1\"): {\n\n },\n str(word_1 + \"_2\"): {\n\n },\n str(word_1 + \"_3\"): {\n\n }\n }\n if word_2 not in dictionary:\n dictionary[word_2] = {\n str(word_2 + \"_1\"): {\n\n },\n str(word_2 + \"_2\"): {\n\n },\n str(word_2 + \"_3\"): {\n\n }\n }\n if word_3 not in dictionary:\n dictionary[word_3] = {\n str(word_3 + \"_1\"): {\n\n },\n str(word_3 + \"_2\"): {\n\n },\n str(word_3 + \"_3\"): {\n\n }\n }\n if word_1 not in word_list:\n word_list.append(word_1)\n if word_2 not in word_list:\n word_list.append(word_2)\n if word_3 not in word_list:\n word_list.append(word_3)\n \"\"\" word_3 word_2 word_1\"\"\"\n if word_2 not in dictionary[word_1][str(word_1 + \"_1\")]:\n dictionary[word_1][str(word_1 + \"_1\")][word_2] = 1\n else:\n dictionary[word_1][str(word_1 + \"_1\")][word_2] = dictionary[word_1][str(word_1 + \"_1\")][word_2] + 1\n if word_3 not in dictionary[word_1][str(word_1 + \"_2\")]:\n dictionary[word_1][str(word_1 + \"_2\")][word_3] = 1\n else:\n dictionary[word_1][str(word_1 + \"_2\")][word_3] = dictionary[word_1][str(word_1 + \"_2\")][word_3] + 1\n if word_3 not in dictionary[word_2][str(word_2 + \"_1\")]:\n dictionary[word_2][str(word_2 + \"_1\")][word_3] = 1\n else:\n dictionary[word_2][str(word_2 + \"_1\")][word_3] = dictionary[word_2][str(word_2 + \"_1\")][word_3] + 1\n if index + 3 >= len(line) or line[index + 3] == \"\":\n return\n word_0 = line[index + 3]\n if word_0 not in dictionary:\n dictionary[word_0] = {\n str(word_0 + \"_1\"): {\n\n },\n str(word_0 + \"_2\"): {\n\n },\n str(word_0 + \"_3\"): {\n\n }\n }\n\n if word_0 not in word_list:\n word_list.append(word_0)\n\n if word_3 not in dictionary[word_0][str(word_0 + \"_3\")]:\n dictionary[word_0][str(word_0 + \"_3\")][word_3] = 1\n else:\n dictionary[word_0][str(word_0 + \"_3\")][word_3] = dictionary[word_0][str(word_0 + \"_3\")][word_3] + 1", "def make_words(self,lm):\n if \" \" in self.corpus[0] and \" \" in self.corpus[1]: \n print \"assuming BLICK\"\n self.corpus = [convert_to_disc(i) for i in self.corpus]\n else:\n self.disc = 1\n print \"assuming Disc\" \n if not os.path.isfile(self.f): ##check if it already exists\n print \"generating 10 million words\"\n outfile = open(self.f, \"w\")\n outfile.write(\"word,blick,ngram,Real,T,disc\\n\")\n for word in self.corpus:\n write_row_of_bigmatch(word, self.disc, outfile, lm, \"Real\", \"1\")\n while len(self.wordlist)<10000000: \n words = lm.generate(100)\n for word in words:\n if word not in self.wordlist and len(word) < 9: #keep only words less than len9\n write_row_of_bigmatch(word, self.disc, outfile, lm, \"Simulated\", \"0\")\n self.wordlist[word] = 0\n return", "def refine_tokens( self, tokens ):\n k = 1.75\n b = 0.75\n stop_words_file = \"stop_words.txt\"\n all_stopwords = list()\n refined_tokens_sources = dict()\n \n # collect all the stopwords\n with open( stop_words_file ) as file:\n lines = file.read()\n all_stopwords = lines.split( \"\\n\" )\n \n for source in tokens:\n refined_tokens = dict()\n files = dict()\n inverted_frequency = dict()\n file_id = -1\n total_file_length = 0\n for item in tokens[ source ]:\n file_id += 1\n file_tokens = tokens[ source ][ item ].split(\" \")\n if source in \"name_desc_edam_help\":\n file_tokens = utils._clean_tokens( file_tokens, all_stopwords )\n total_file_length += len( file_tokens )\n term_frequency = dict()\n for token in file_tokens:\n if token is not '':\n file_ids = list()\n if token not in inverted_frequency:\n file_ids.append( file_id )\n else:\n file_ids = inverted_frequency[ token ]\n if file_id not in file_ids:\n file_ids.append( file_id )\n inverted_frequency[ token ] = file_ids\n # for term frequency\n if token not in term_frequency:\n term_frequency[ token ] = 1\n else:\n term_frequency[ token ] += 1\n files[ item ] = term_frequency\n N = len( files )\n average_file_length = float( total_file_length ) / N\n # find BM25 score for each token of each tool. It helps to determine\n # how important each word is with respect to the tool and other tools\n for item in files:\n file_item = files[ item ]\n file_length = len( file_item )\n for token in file_item:\n tf = file_item[ token ]\n # normalize the term freq of token for each document\n tf = float( tf ) / file_length\n idf = np.log2( N / len( inverted_frequency[ token ] ) )\n alpha = ( 1 - b ) + ( float( b * file_length ) / average_file_length )\n tf_star = tf * float( ( k + 1 ) ) / ( k * alpha + tf )\n tf_idf = tf_star * idf\n file_item[ token ] = tf_idf\n # filter tokens based on the BM25 scores and stop words. Not all tokens are important\n for item in files:\n file_tokens = files[ item ]\n tokens_scores = [ ( token, score ) for ( token, score ) in file_tokens.items() ]\n sorted_tokens = sorted( tokens_scores, key=operator.itemgetter( 1 ), reverse=True )\n refined_tokens[ item ] = sorted_tokens\n tokens_file_name = 'tokens_' + source + '.txt'\n token_file_path = os.path.join( os.path.dirname( self.tools_data_path ) + '/' + tokens_file_name )\n with open( token_file_path, 'w' ) as file:\n file.write( json.dumps( refined_tokens ) )\n file.close()\n refined_tokens_sources[ source ] = refined_tokens\n return refined_tokens_sources", "def process_raw_phrases(file_path):", "def load_input_word_list(file_path):\n if not os.path.isfile(file_path):\n return False\n\n word_list = list()\n\n with open(file_path, 'r') as fp:\n while True:\n line = fp.readline()\n if not line:\n break\n\n data = line.split(' ')\n text = data[0].lower().strip(Setting.NONWORD_CHARACTERS)\n\n if not text:\n continue\n\n text = text.replace('_', ' ')\n\n score = float(data[1])\n\n if score < 0:\n kind = WordKindEnum.NEG\n else:\n kind = WordKindEnum.POS\n\n word = Word(text, score, kind)\n word_list.append(word)\n\n return word_list", "def word_dict():\n fin = open('words.txt')\n w_dict = {}\n for line in fin:\n word = line.strip()\n w_dict[word] = word\n return w_dict", "def word_count(filename):\n \n word_counts = {}\n\n with open(filename) as file_:\n for line in file_:\n # strip white space\n words = line.split()\n # iterate over words and strip excess punctutation then add to dict\n for word in words:\n word = word.strip(\",.\\\";:?_!\").lower()\n word_counts[word] = word_counts.get(word, 0) + 1\n\n # print list of words and count\n for word, count in word_counts.iteritems():\n print \"{} {}\".format(word, count)", "def read_file(filename):\n print(\"Reading dictionary: \" +filename)\n word_dict = set()\n\n dictionary = open(filename)\n\n # Read each word from the dictionary\n for word in dictionary:\n # Remove the trailing newline character\n word = word.rstrip('\\n')\n\n # Convert to lowercase\n word = word.lower()\n\n word_dict.add(word)\n\n dictionary.close()\n\n return word_dict", "def process_line(line, hist):\n # replace hyphens with spaces before splitting\n line = line.replace('-', ' ')\n wordlist=[]\n\n for word in line.split():\n # remove punctuation and convert to lowercase\n word = word.strip(string.punctuation + string.whitespace)\n word = word.lower()\n\n wordlist.append(word)\n # update the histogram\n #hist[word] = hist.get(word, 0) + 1\n return wordlist", "def main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"infile\", help=\"Text file to be analyzed.\")\n args = parser.parse_args()\n with open(args.infile, encoding=\"utf-8\") as f:\n text = f.read()\n words = text.split()\n unique_words(words)", "def analyze_file(file_contents):\r\n load_dict = [];\r\n for a in file_contents:\r\n load_dict.append((char_score(a)[0][0],a))# should \r\n with open(\"./scores.txt\",'w') as scr:\r\n scr.write(str(load_dict)); \r\n return load_dict;", "def buildCorpus(self, filename, stopwords_file=None):\n with open(filename, 'r') as infile:\n # use pattern.subs\n # doclines = [line.rstrip().lower().split(' ') for line in infile]\n doclines = [self.help_clean(line) for line in infile]\n n_docs = len(doclines)\n self.vocab = list({v for doc in doclines for v in doc})\n if stopwords_file:\n with open(stopwords_file, 'r') as stopfile:\n stops = stopfile.read().split()\n self.vocab = [x for x in self.vocab if x not in stops]\n self.vocab.sort()\n self.documents = []\n for i in range(n_docs):\n self.documents.append({})\n for j in range(len(doclines[i])):\n if doclines[i][j] in self.vocab:\n self.documents[i][j] = self.vocab.index(doclines[i][j])", "def tokenize_and_split(sms_file):\n \n dic = {}\n list1 = []\n list2 = []\n i = -1\n ham = True\n for line in open(sms_file, 'r').readlines():\n w = []\n for word in line.split():\n i = i + 1\n if word == \"ham\":\n ham = True\n i = i - 1\n elif word == \"spam\":\n ham = False\n i = i - 1\n else:\n if word not in dic:\n dic[word] = i\n w.append(dic[word])\n else : \n i = i - 1\n w.append(dic[word])\n if ham and w !=[]:\n list2.append(w)\n elif ham == False and w !=[]:\n list1.append(w)\n \n return dic,list1,list2", "def load_train_word_dict():\n train_dict = {}\n with open(TRANSCRIPTION_PATH) as file:\n for line in file:\n if int(line[0:3]) < 300:\n word_id, transcript = str.split(line, \" \")\n train_dict[word_id] = transcript.rstrip('\\n')\n return train_dict", "def read_data(filename, n_words):\n with open(filename) as f:\n filter_set = set()\n unsorted_res = []\n words = []\n count = []\n for line in f:\n word = line.strip()\n if len(word) == 0:\n continue\n word_idx_list = [int(idx) for idx in word.split(',')]\n filter_set.add(tuple(word_idx_list))\n words.append(tuple(sorted(word_idx_list)))\n words_counter = collections.Counter(words)\n most_common_words = dict()\n most_common_words_counter = words_counter.most_common(n_words)\n for item in most_common_words_counter:\n most_common_words[item[0]] = item[1]\n\n unsorted_res = dict()\n for w in filter_set:\n sorted_tuple = tuple(sorted(list(w)))\n if sorted_tuple in most_common_words:\n unsorted_res[w] = most_common_words[sorted_tuple]\n \n del words\n del count\n del filter_set\n del most_common_words\n\n return unsorted_res", "def add_cpd_synonyms_from_datafile(filename = '/Users/wbryant/work/cogzymes/data/SEED/SEED_met_table.csv'):\n \n \n num_syns_added = 0\n num_syns_tested = 0\n \n source = Source.objects.get(name='seed')\n \n met_db_dict = get_model_dictionary(Metabolite, 'id')\n syn_met_dict = get_synonym_met_dict()\n \n counter = loop_counter(count_lines(filename), 'Adding CPD IDs to database')\n \n f_in = open(filename, 'r')\n for line in f_in:\n counter.step()\n add_cpd_to_synonyms(line, source, syn_met_dict, met_db_dict)\n \n counter.stop()", "def build_dict(fname):\n\t\n\twith open(fname) as file:\n\n\t\tword_count_dict = {}\n\n\t\tfor line in file:\n\t\t\tline = line.rstrip()\n\t\t\tline =line.split(' ')\n\t\t\tfor word in line:\n\t\t\t\tword = word.strip('\"!.,?_;():')\n\t\t\t\tword = word.lower()\n\t\t\t\tword_count_dict[word] = word_count_dict.get(word, 0) + 1\n\t\t#return word_count_dict\n\n\t\tfor each in word_count_dict:\n\t\t\tcount = word_count_dict[each]\n\t\t\tprint(each, count)\n\n\t\treturn", "def generate_input(file_path):\r\n\tlabel_matches = dict()\r\n\tfile_lines = []\r\n\twith open(file_path) as f:\r\n\t\tfor line in f:\r\n\t\t\tfile_lines = file_lines + [line.lower().split()]\r\n\t\tword_tuples = zip(file_lines[0::3], file_lines[1::3], file_lines[2::3])\r\n\t\tfor (words, part_of_speech, word_type) in word_tuples:\r\n\t\t\ttype_tuples = zip(words, word_type)\r\n\t\t\tfor word_and_tag in type_tuples:\r\n\t\t\t\tlabel_matches.update({word_and_tag : (label_matches.get(word_and_tag, 0) + 1)})\r\n\treturn label_matches", "def common_words_safe(filename, min_chars):\n wordPattern = re.compile('[a-zA-Z]{' + str(min_chars) + ',}')\n occurance = dict()\n try:\n with open(filename, 'r') as f:\n contents = f.read()\n except IOError as e:\n print \"IOError {0}: {1}\".format(e.errno, e.strerror)\n return\n words = wordPattern.finditer(contents)\n for wordMatch in words:\n word = wordMatch.group(0).lower()\n if word in occurance:\n occurance[word] += 1\n else:\n occurance[word] = 1\n return sorted(occurance.items(), key=lambda item:item[1], reverse=True)" ]

[ "0.70385915", "0.6925145", "0.6899971", "0.6768178", "0.6651934", "0.6420912", "0.6312901", "0.6296871", "0.6231026", "0.6214866", "0.62069726", "0.6178269", "0.616501", "0.615068", "0.61372495", "0.6134684", "0.612722", "0.61159056", "0.6110232", "0.6060298", "0.6052872", "0.6024224", "0.60239846", "0.59918666", "0.5982138", "0.5981788", "0.5977596", "0.5972763", "0.59598434", "0.59594", "0.5954592", "0.5954314", "0.5950341", "0.5945586", "0.5939405", "0.592284", "0.59227294", "0.5921953", "0.5901857", "0.58915174", "0.58891827", "0.588435", "0.58632594", "0.5839867", "0.5836892", "0.58325803", "0.5831235", "0.5831054", "0.58259505", "0.58204377", "0.58100015", "0.5801213", "0.5798646", "0.57879025", "0.5783743", "0.5783632", "0.5777174", "0.5773674", "0.576629", "0.5762148", "0.5750121", "0.57435894", "0.5742887", "0.5738712", "0.573655", "0.57355934", "0.573217", "0.57258767", "0.572451", "0.5724229", "0.572188", "0.57089233", "0.5708431", "0.5706453", "0.5705184", "0.5672568", "0.5665531", "0.5659035", "0.56569177", "0.56409246", "0.5640634", "0.563733", "0.5634866", "0.563245", "0.56282216", "0.56178486", "0.5599717", "0.55991083", "0.55925417", "0.5591636", "0.55891705", "0.558677", "0.55867386", "0.5585658", "0.5582212", "0.557763", "0.55756855", "0.5572795", "0.5568265", "0.5555953" ]

{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }

[ "def write_concordance(self, filename):\n out = ''\n values = [x for x in self.concordance_table.hash_table if x is not None]\n values.sort(key=lambda x: x[0])\n for v in values:\n out += f'{v[0]}: {\" \".join(str(x) for x in sorted(set(v[1])))}\\n' \n with open(filename, 'w') as f:\n f.write(out.rstrip())", "def write_cando_file(self, file_name):\n cando_writer = CandoWriter(self.dna_structure)\n cando_writer.write(file_name)", "def _write_conductances(self, cond_file_name):\n cond_file_path = os.path.join(OM_STORAGE_DIR, cond_file_name)\n\n #TODO: Check that the file doesn't already exist.\n LOG.info(\"Writing head conductance file: %s\" % cond_file_path)\n file_handle = file(cond_file_path, \"a\")\n\n file_handle.write(\"# Properties Description 1.0 (Conductivities)\\n\\n\")\n file_handle.write(\"Air %4.2f\\n\" % self.conductances[\"air\"])\n file_handle.write(\"Scalp %4.2f\\n\" % self.conductances[\"skin\"])\n file_handle.write(\"Brain %4.2f\\n\" % self.conductances[\"brain\"])\n file_handle.write(\"Skull %4.2f\\n\" % self.conductances[\"skull\"])\n\n file_handle.close()\n LOG.info(\"%s written successfully.\" % cond_file_path)\n\n return cond_file_path", "def write_output(self):\n with open(self.filename, 'a', newline='', encoding='utf-8') as \\\n csv_file:\n csv_writer = csv.writer(csv_file)\n if os.stat(self.filename).st_size == 0:\n # if the csv file needs a headers\n csv_writer.writerow(Configurations.header)\n for quote in self.quotes_objects:\n csv_writer.writerow(quote.info)", "def write_conll(conll_file, sents):\n with codecs.open(conll_file, mode = 'w', errors = 'ignore', encoding = 'utf-8') as ofile:\n for sent in sents:\n if sent:\n for element in sent:\n word = element[0]\n tag = element[1]\n ofile.write(str(tag) + '\\t' + str(word) + '\\n')\n ofile.write('\\n')", "def write_CA_atoms():\n \n import os\n choice = input('Enter the name of the file: ')\n filepath = os.path.join('/home/njesh/python-mini-project-JaneNjeri/Data', choice)\n ca_list = []\n with open(filepath, 'r') as pdb:\n for line in pdb:\n if line[:4] == 'ATOM' and line[12:16] == \" CA \":\n line_split = line.split()[6:9]\n ca_list.append(line_split)\n choice1 = input('Enter name of the outfile: ')\n filepath1 = os.path.join('/home/njesh/python-mini-project-JaneNjeri/Results', choice1)\n with open(filepath1, 'w') as outfile:\n for i in ca_list:\n outfile.writelines(i)\n print('Done!')\n print(i)", "def write_output():\n f = open(OUTPUT_FILE, 'w')\n for case_index, words in get_output():\n f.write('Case #%d: %s\\n' % (case_index, ' '.join(words)))\n f.close()", "def file_output(matches: list, output_file_name: str = 'matches.txt'):\n with open(\"test/Matches/\" + output_file_name, 'w') as f:\n for match in matches:\n for event in match.events:\n f.write(\"%s\\n\" % event.payload)\n f.write(\"\\n\")", "def write_output_file(filename, actions, log):\n f = open(filename, 'w')\n\n for i in range(len(actions)):\n f.write(str(actions[i]))\n if i < len(actions) - 1:\n f.write(',')\n f.write('\\n')\n\n for k in log.keys():\n f.write(str(k) + ' = ' + str(log.get(k)))\n f.write('\\n')\n\n f.close()", "def write_output_file(filename, actions):\n f = open(filename, 'w')\n for i in range(len(actions)):\n f.write(str(actions[i]))\n if i < len(actions) - 1:\n f.write(',')\n f.write('\\n')\n f.close()", "def write_output_file(filename, actions):\n f = open(filename, 'w')\n for i in range(len(actions)):\n f.write(str(actions[i]))\n if i < len(actions) - 1:\n f.write(',')\n f.write('\\n')\n f.close()", "def write_output_file(filename, actions):\n f = open(filename, 'w')\n for i in range(len(actions)):\n f.write(str(actions[i]))\n if i < len(actions) - 1:\n f.write(',')\n f.write('\\n')\n f.close()", "def write_corpus_to_file(output_file, corpus): \n \n file = open(output_file, 'w')\n for line in corpus: \n file.write(line)\n print ('Corpus has been writted in file')\n file.close()", "def write_file(self, filename):\n\n with open(filename, 'w', newline = '') as csvfile:\n langwriter = csv.writer(csvfile, delimiter=' ',\n quotechar='|', quoting=csv.QUOTE_MINIMAL)\n for key in self.features:\n value = self.features[key]\n l = []\n for val in value:\n l.append(str(val))\n langwriter.writerow([l])\n return", "def write_cn_cards(bc_file, bc_class):\n cn = bc_class.constituent_properties\n bc_file.write('! Constituent Properties\\n')\n if not cn.general_constituents.empty:\n # bc_file.write(cn.general_constituents.to_csv(sep=' ', index=False, header=False).replace('\\r\\n', '\\n'))\n for index, row in bc_class.constituent_properties.general_constituents.iterrows():\n bc_file.write(\n 'CN CON {} {}\\n'.format(row['ID'].astype('int'), row['CONC']))\n if not cn.sand.empty:\n # bc_file.write(cn.sand.to_csv(sep=' ', index=False, header=False).replace('\\r\\n', '\\n'))\n for index, row in bc_class.constituent_properties.sand.iterrows():\n bc_file.write(\n 'CN SND {} {} {} {} {}\\n'.format(row['ID'].astype('int'), *row[['C_0', 'C_1', 'C_2', 'C_3']].values))\n if not cn.clay.empty:\n # bc_file.write(cn.clay.to_csv(sep=' ', index=False, header=False).replace('\\r\\n', '\\n'))\n for index, row in bc_class.constituent_properties.clay.iterrows():\n bc_file.write(\n 'CN CLA {} {} {} {} {}\\n'.format(row['ID'].astype('int'), *row[['C_0', 'C_1', 'C_2', 'C_3']].values))\n if cn.salinity:\n bc_file.write('CN SAL {} {}\\n'.format(cn.salinity_id, cn.reference_concentration))\n if cn.temperature:\n bc_file.write('CN TMP {} {}\\n'.format(cn.temperature_id, cn.reference_temperature))\n if cn.vorticity:\n bc_file.write('CN VOR {} {} {} {}\\n'.format(cn.vorticity_id, cn.vorticity_normalization,\n cn.vorticity_as_term, cn.vorticity_ds_term))\n\n bc_file.write('\\n') # blank line at the end of the Constituent Properties", "def write_to_file_ann(self) -> None:\n with open(self.output_file_path, mode='w', newline='') as csv_file:\n tweet = ['id', 'created_time', 'text']\n writer = csv.DictWriter(csv_file, fieldnames=tweet)\n writer.writeheader()\n for tweet in self.unique_tweets:\n try:\n writer.writerow(tweet)\n except:\n pass\n print(\"Tweets written to a file\")", "def write_conformers(self, filename): # ccids):\n cnt = 0\n for confId in range(self.nconf): #ccids:\n w = Chem.SDWriter('%s_c%03d.sdf'%(filename,cnt+1))\n w.write(self.mol, confId=confId)\n w.flush()\n w.close()\n cnt += 1", "def result_file(accession_list):\n with open(\"../accessions_list.txt\", 'w') as file:\n file.write(accession_list)", "def writeCC(self, fileName, allSCC):\n f = open(fileName,'w')\n\n for compNumber in range(0,len(allSCC)):\n f.write(\"Component number %s: \" % (compNumber))\n f.write(\"%s\\n\" % (str(allSCC[compNumber])))\n f.close()", "def write_output(arr, filename):\n print('Started writing the output..')\n f = open(filename, 'w')\n for a in arr:\n f.write(str(a) + '\\n')\n f.close()\n print('Done!, Open the file to see the approved loans.')", "def write_crf_input(out_file, sentences, poss, lemmas, concepts):\n\n print '\\n\\tWrite out data in crf compliant format'\n f = open(out_file, 'w+')\n for position_i in range(len(sentences)):\n for position_j in range(len(sentences[position_i])):\n f.write(\n sentences[ position_i ][ position_j ] + '\\t' +\n poss[ position_i ][ position_j ] + '\\t' +\n lemmas[ position_i ][ position_j ] + '\\t' +\n concepts[ position_i ][ position_j ]\n + '\\n'\n )\n f.write('\\n')\n f.close()\n print '\\t--done'", "def write_output_file(ad_models):\n\n with open('output-data-utf8.csv', 'w', newline='', encoding='UTF-8') as output_file:\n csv_writer = csv.writer(output_file, delimiter=',')\n for ad in ad_models:\n csv_writer.writerow((ad.date.strftime('%Y/%m/%d'), ad.country_code, ad.impression, ad.clicks))", "def writeChronListToFile(self):\n ## write header\n for header_line in self.outData['header']:\n self.outFile.write(header_line + '\\n')\n ##loop through each msg list\n for msg_list in self.outData_temp:\n ## create line\n msg_line = reconstructLine(msg_list)\n ## write to file\n self.outFile.write(msg_line + '\\n')", "def write_dialogue_to_file(utterances, dialogue_index, filename):\n with open(filename, 'a') as file:\n for sentence_index in range(len(utterances[dialogue_index][0])):\n file.write('{0} {1}\\n'.format(utterances[dialogue_index][0][sentence_index],\n utterances[dialogue_index][1][sentence_index]))", "def write(self, filename):\n pass", "def write(self, filename):\n pass", "def write_to_file(info, mode='w', file=\"output4.txt\"):\n with open(file, mode, encoding='utf-8') as f:\n for line in info:\n f.write(' '.join(map(str, line)) + '\\n')", "def export(self, fname):\n f = open(fname, 'w')\n for ue in self.ue_list:\n line_components = list()\n line_components.append(ue.expression)\n line_components.append(ue.meaning)\n print >>f, '\\t'.join(line_components).encode('utf-8')", "def write_conll(cls, filename, writer, document_id, sentences):\n with open(filename, 'w') as fd:\n writer.write(fd, document_id, sentences)", "def write_output(basis, filename):\n\n logging.info('Writing output to {}'.format(filename))\n\n basis.to_csv(filename)", "def writeAlltoFile(self):\n with open(self._fname, 'w') as f:\n for elem in self.getAll():\n line = self._writeGratoLine(elem)\n f.write(line + \"\\n\")\n f.close()", "def writeOut(self):\n # import time\n self.outHeader = self.srcHeader\n for line in self.outHeader:\n self.outFile.write(line + '\\n')\n # now = time.asctime(time.localtime(time.time()))\n # self.outFile.write('%% -- %s -- Written to new alog' % now)\n for time_s in sorted(self.outData):\n for sens in self.outData[time_s]:\n for meas in self.outData[time_s][sens]:\n valu = self.outData[time_s][sens][meas]\n msg_list = [str(time_s), meas, sens, str(valu)]\n line_string = reconstructLine(msg_list)\n self.outFile.write(line_string + '\\n')", "def writeToFile(self):\n self.dto.writeToCsv()\n print(\"File written.\")", "def writeCentrality(filename, data):\n\n filePath = os.path.join(CONFIG['CENTRALITIES_PATH'], filename)\n f = open(filePath, \"w\")\n\n data = {k: v for k, v in sorted(\n data.items(), key=lambda x: x[1], reverse=True)}\n\n for k, v in data.items():\n text = f\"{k:<4}\\t{v:.6f}\\n\"\n f.write(text)", "def write(self, taxonomy, output_file):\n\n fout = open(output_file, 'w')\n for genome_id, taxa in taxonomy.items():\n fout.write(genome_id + '\\t' + ';'.join(taxa) + '\\n')\n fout.close()", "def write(filename):\n print(uc.write(filename))", "def write_file(self):\n if self._write_file == None:\n return\n\n try:\n out = file(self._write_file, \"w\")\n except IOError, e:\n print e\n sys.exit(1)\n out.writelines(\"A cases\") \n out.close()", "def write_output(self, output_file):\n\t\t# Create csv file header.\n\t\theader = ['Cohort', 'Customers',]\n\t\tfor start, end in self.day_ranges:\n\t\t\tday_range_str = '{}-{} days'.format(start, end)\n\t\t\theader.append(day_range_str)\n\n\t\twith open(output_file, 'wb') as fh:\n\t\t\twriter = csv.writer(fh)\n\t\t\twriter.writerow(header)\n\t\t\tfor cohort, cohort_value in self.output.items():\n\t\t\t\twriter.writerow(\n\t\t\t\t\tself.build_row(cohort, cohort_value)\n\t\t\t\t)", "def write_cif_file(self, file_name):\n cif_writer = CifWriter(self.dna_structure)\n cif_writer.write(file_name, self.infile, self.informat )", "def writeOutputToFile(self, expanded_acronyms, file_path):\n output_file = open(file_path, \"w\")\n if expanded_acronyms:\n for acronym in sorted(expanded_acronyms.keys()):\n output_file.write(\n acronym + \",\" + str(self._getExpansion(expanded_acronyms[acronym])) + \"\\n\")\n else:\n output_file.close(string_error_no_results_to_show)\n output_file.close()", "def write_output(word_dict):\n # create an empty output.txt file\n output = open('output.txt', 'w')\n\n for i in words_dict: \n output.write(i + \" : \" + str(words_dict[i]) + \"\\n\")", "def writexyz(self,fname):\n xyzfile = open(fname + \".xyz\",\"a+\")\n xyzfile.write(str(self.natoms) + \"\\n\\n\")\n for a in self.atoms:\n \tcxyz = a.xyz - np.array(self.pbc_correction(a.xyz))\n\t\t\txyzfile.write(str(a.type) + \"\\t\" + str(cxyz[0]) + \"\\t\" + str(cxyz[1]) + \"\\t\" + str(cxyz[2]) + \"\\n\")\n xyzfile.close()", "def writeIntrons(self, filenameout):\n printed = {}\n with open(filenameout, \"w\") as out:\n for features in self.feature_dictionary:\n intro = self.feature_dictionary[features].getIntrons()\n print(self.feature_dictionary[features].introns)\n for introns in intro:\n if \"-\".join([str(i) for i in introns]) not in printed:\n out.write(self.feature_dictionary[features].chromosome_name + \"\\t\"\n + str(introns[0]) + \"\\t\" + str(introns[1]) + \"\\t\" + self.feature_dictionary[features].strand + \"\\n\")\n printed[\"-\".join([str(i) for i in introns])] = 0", "def write_collected(self, names_file, kb_file, cat_file):\n with open(names_file, 'w') as fp:\n for kb_id, name in self.collected_names.items():\n fp.write('\\t'.join(['name', kb_id, name]) + '\\n')\n with open(kb_file, 'w') as fp:\n for kb_id, tail_set in self.collected_edges.items():\n for (rel, tail_id) in tail_set:\n fp.write('\\t'.join([rel, kb_id, tail_id]) + '\\n')\n with open(cat_file, 'w') as fp:\n for c, ms in self.collected_cat_mems.items():\n fp.write(c + '\\t' + self.kb[c].name + '\\t')\n fp.write('|'.join(ms) + '\\n')", "def write_data_file(output_file: str, companies: list):\n with open(output_file, \"w\") as f:\n # s = \"\\n\".join(companies)\n for i in range(len(companies)):\n for k in range(10):\n for j in range(len(companies[i].data[k])):\n s = f\"{i},{companies[i].data[k][j][0].__str__()},{companies[i].data[k][j][1]}\\n\"\n f.write(s)", "def WriteOutput(self, rows, fileName, access='wb'):\n \n outputFile = open(fileName, access)\n try: \n outputFile.write(self.GetBanner())\n csv.writer(outputFile, dialect='excel-tab').writerows(rows)\n print 'Wrote secondary output to: %s' %(fileName) \n except IOError:\n print 'Error writing output to: %s' %(fileName) \n finally:\n outputFile.close()", "def write_cadnano_file(self, file_name):\n cadnano_writer = CadnanoWriter(self.dna_structure)\n cadnano_writer.write(file_name)", "def export_file(self):\n if self.args.keyfilter:\n self.filter_keys()\n if self.args.datafilter:\n self.filter_values()\n json.dump(self.outputdata, self.outfile, indent=self.args.indent)\n self.outfile.write('\\n')", "def write_output(self, failed_genes):\r\n file_prefix = self.file_name.strip('sambamba_output.txt')\r\n fieldnames = ['GeneSymbol;Accession', 'percentage30']\r\n with open (f'../results/{file_prefix}.coverage_output.csv', 'w', newline = '') as output:\r\n csvwriter = csv.DictWriter(output, fieldnames=fieldnames)\r\n csvwriter.writeheader()\r\n csvwriter.writerows(failed_genes)", "def writecontactstocsv(self , contact_entries):\n rx = re.compile('\\W+')\n allcontacts = []\n for entry in contact_entries:\n if entry.name is not None and len(entry.phone_number) > 0 and len(entry.group_membership_info) > 0:\n\n # Clean up characters in contact name; replace all non-alphanumerics with spaces\n fullname = entry.name.full_name.text\n fullname = rx.sub(' ', fullname).strip()\n for rawPhoneNumber in entry.phone_number:\n # Remove non-numeric characters from the phone number\n phone_number = re.sub(\"[^0-9]\", \"\", rawPhoneNumber.text)\n # Save contact for later insert\n allcontacts.append((fullname, phone_number))\n\n allcontacts = tuple(set(allcontacts))\n\n csvfilename = \"Downloads/ContactExport\"+time.strftime(\"%Y%m%d-%H%M%S\")+\".csv\"\n csvfile = open(csvfilename, \"w\")\n for csvFullName, csvPhoneNumber in allcontacts:\n line = \"\\\"%s\\\",%s\\n\" % (csvFullName, csvPhoneNumber)\n csvfile.write(line)\n\n csvfile.close()", "def build_catalog(filename):\n\n write_to_file(filename)", "def write_output(self):", "def write(self, args):\n\t\tnewcsvfile = self.filename[:len(self.filename)-4] + \"NEW.csv\" #clever naming MIGHT NEED TO CHANGE THIS LATER/OVERWRITE OLD FILE?\n\t\twith open(newcsvfile, 'wb') as f:\n\t\t\twriter = csv.writer(f)\n\t\t\twriter.writerows(self.all_likes)", "def write(cls, vas):\n with open(Y, 'w') as f_i:\n for items in vas:\n f_i.write('%s ' % items)\n print(\"File written successfully. Check out \\\"output.txt\\\" file\")\n f_i.close()", "def write_output(label1, label2, label3, submission_file):\n with open(submission_file, 'w') as f:\n f.write('Id,Bound'+ '\\n')\n for index, lab in enumerate(label1):\n f.write(str(index) + ',' + str(int(lab)) + '\\n')\n for index, lab in enumerate(label2):\n f.write(str(len(label1) + index) + ',' + str(int(lab)) + '\\n')\n for index, lab in enumerate(label3):\n f.write(str(len(label1) + len(label2) + index) + ',' + str(int(lab)))\n if index < len(label3) - 1:\n f.write('\\n')", "def write(self, outputFile):\n \n try: \n f = open(outputFile + '.py', 'w')\n for trail in self.trails: \n f.write(\"[\")\n for index in trail:\n f.write(\"({0}, {1}), \".format(*index)) \n f.write(\"]\\n\")\n \n except IOError, e:\n msg = \"Exception encountered when attempting \" + \\\n \"to write data to file: {0}.\" + \\\n \"\\n\\t -- Exception was: {1}\" + \\\n \"\\n\\t For help use --help\".format(outputFile, e)\n raise Usage(e)", "def write_to_file(self, filename: str) -> None:", "def write_to_file(self, results):\n with open(self.outputFilename, \"w\") as csvFile:\n csvWriter = csv.writer(csvFile, delimiter=',') \n title_row = ('asset_id', 'component_id', 'latitude', 'longitude', 'installation_date', 'commissioning_date', 'street_name', 'cabinet_id', 'nominal_wattage', 'current_time', 'current_LogValue', 'current_IsLogValueOff') \n csvWriter.writerow(title_row)\n for record in results:\n csvWriter.writerow(record)", "def write(self, outfile):\n outfile.write(\n '\\t'.join(\n [\n str(i) for i in [\n self.chrom, self.start, self.end, self.name,\n self.count, self.fold_change, self.log10p\n ]\n ]\n )\n )\n outfile.write('\\n')", "def write_to_txt(self):\r\n file = open(self.output_path, 'w')\r\n for question_id in self.question_ids:\r\n file.write(self.questions[question_id].question_string+str(self.questions[question_id].answer)+'\\n')\r\n file.close()", "def to_cheetah_file(self, filename):\n translate.write_cheetah(self, filename)\n return", "def writetofile(invertedindex, filename):\n file = open(filename + '.txt', 'w', encoding='utf-8')\n for word in invertedindex.keys():\n file.write(word)\n file.write(' : ')\n for docid in invertedindex[word][0]:\n file.write(str(docid) + ' ')\n file.write('\\n')", "def write_to_file(self, filename):\n self.octree.write(str.encode(filename))\n print(\"Save octomap to \"+filename)", "def write_gct_file(output_file, class_names, class_counts, expression_matrix):\n total_genes = len(expression_matrix)\n first_key = list(expression_matrix.keys())[0]\n total_samples = len(expression_matrix[first_key])\n\n headers = ['NAME', 'DESCRIPTION']\n\n for c_name, c_count in zip(class_names, class_counts):\n for i in range(c_count):\n headers.append('{}_{}'.format(c_name, i + 1))\n\n with open(output_file, 'w') as f:\n f.write('#1.2\\n')\n f.write('{} {}\\n'.format(total_genes, total_samples))\n f.write('\\t'.join(headers))\n f.write('\\n')\n\n for g_name, values in expression_matrix.items():\n f.write(g_name)\n f.write('\\tna\\t')\n f.write('\\t'.join(\n ['{0:.2f}'.format(v) for v in values]\n ))\n f.write('\\n')", "def corpusWriter(self):\n with open('corpus.txt', 'w') as file:\n for quote in self.quotes:\n file.write(quote + '\\n')", "def create_chceckfile(artist_list):\n with open(\"Udemy_Course/Object_Oriented_Programing_and_Classes/OOP_Song_Class/checkfile.txt\", \"w\") as checkfile:\n for new_artist in artist_list:\n for new_album in new_artist.albums:\n for new_song in new_album.tracks:\n print(\"{0.name}\\t{1.name}\\t{1.year}\\t{2.title}\".format\n (new_artist, new_album, new_song), file=checkfile)", "def write(read_file):\n princesses = filter_by_status(read(read_file))\n princesses = sort_by_status(princesses)\n princesses = sort_by_place(princesses)\n\n file = open(\"princesses_to_save.txt\", \"w\")\n for word in header:\n for something in word:\n file.write(\"{:20}\".format(something))\n file.write(\"\\n\")\n for i in range(len(princesses)):\n str1 = princesses[i]\n for word in str1:\n file.write(\"{:20}\".format(word))\n if i != len(princesses) - 1:\n file.write(\"\\n\")", "def write_csv(self, outputfile):\n d = csv.writer(outputfile, quoting=csv.QUOTE_ALL)\n for row in self.translations.iteritems():\n d.writerow(row)", "def write_candidates_file(self, min_count, stops, tags, filename):\n filename = os.path.join(filename)\n candidates = self.candidates(min_count, stops, tags)\n with open(filename, \"w\", encoding=\"utf-8\") as file:\n for wordi, wordj in candidates:\n file.write(\"{} {}\\n\".format(wordi, wordj))\n print(\"Success: Candidates written to '{}'\".format(filename))", "def handle(self, *args, **options):\n self.stdout.write('exporting corpus to text file')\n basetext = '\\n'.join([x.text_str for x in BaseText.objects.all() if x.check_age()])\n with open(os.path.join(BASE_DIR, 'corpus.txt'), 'w') as f:\n f.write(basetext)", "def write_to(self, filepath):\n output = self._generate_output()\n with open(filepath, 'wb') as out:\n out.write(output.encode('utf-8'))\n out.write(b'<!-- handrolled for excellence -->\\n')", "def write(self, file):\n\n # Initialize output buffer\n out = ''\n\n # Print specification\n for key, value in self.specification.items():\n out += f'{key} : {value}\\n'\n\n # Print the tour\n if self.tour:\n out += 'TOUR_SECTION\\n'\n for s in self.tour:\n out += str(s) + '\\n'\n out += '-1\\n'\n\n # Append EOF\n out += 'EOF\\n'\n\n # Write to file\n with open(file, 'w') as f:\n f.write(out)", "def output(self, filename):\n with open(filename, 'w') as f:\n op = {}\n layer_res = []\n alphas_res = []\n for layer in self._layers:\n weights = []\n alphas = []\n for neuron in layer._neurons:\n weights.append(neuron._weights)\n alphas.append(neuron._alpha)\n layer_res.append(weights)\n alphas_res.append(alphas)\n op['layers'] = layer_res\n op['alphas'] = alphas_res\n json.dump(op, f, indent='\\t')", "def _write_ce_collector_attributes_file(self, attributes_file):\n attributes_file_contents = (\n \"# Do not edit - file generated by osg-configure\\n\"\n + self.ce_attributes_str + \"\\n\"\n )\n return utilities.atomic_write(attributes_file, attributes_file_contents)", "def write_domains_to_file_by_category(self, file_name, row):\n with open(\"output/\"+file_name+\".csv\", \"a+\") as f:\n f.write(row[self.domain_label] + \",\" + row[self.category_label] + \"\\n\")", "def write_to_files():\n\t# Create output files\n\toutput = [None, \\\n\t\t open(\"priority-1.txt\", \"w\"), \\\n\t\t open(\"priority-2.txt\", \"w\"), \\\n\t\t open(\"priority-3.txt\", \"w\"), \\\n\t\t open(\"priority-4.txt\", \"w\"), \\\n\t\t open(\"priority-5.txt\", \"w\"), ]\n\n\t# Loop over all fields and write them to the correct file\n\tfor field in sorted(reportlog.keys()):\n\t\tpriority = reportlog[field]['priority']\n\t\tlabel = reportlog[field]['label']\n\n\t\toutput[priority].write(\"intphas_%s\\t%s\\n\" % (field, label))\n\t\toutput[priority].flush()\n\n\t# Close files\n\tfor i in [1,2,3,4,5]:\n\t\toutput[i].close()", "def write(self, filename):\n f = open(filename, 'w')\n f.write(str(self.m) + \"\\n\")\n f.write(str(self.n) + \"\\n\")\n for i in self.values:\n for j in i:\n f.write(str(j)+\"\\n\")\n f.closed", "def write_analysis_details(self, csvfile):\n #filepath, total words, line count, most common word\n f = open(csvfile, 'w')\n most_common = self.most_common()\n f.write('filepath,total words,line count,most common word\\n')\n f.write(f'{self.filepath},{self.word_count()},{self.sentence_count()},{self.most_common()[0]}')\n f.close()", "def generate_csv(self, output_file):\n try: # We are going to \"try\" something\n csv_file = open(output_file, 'w+') # open \"output_file\" as a writable file and return a handle called \"csv_file\"\n except OSError as err: # If something goes wrong with the open, we catch the exception\n fatal(\"{0}\".format(err), -1) # exit with something other than 0 so the shell knows something went wrong\n \n writer = csv.writer(csv_file) # create a CSV writing object that's pointing at our open file handle\n\n writer.writerow([\"Question\",\"Answers\"]) # Let's write the top row\n for k in self.questions.keys(): # Let's walk down the directory by key\n # write the \"key\" (which is the question) and then let's take the list of answers and create a comma delmited list.\n # this is likely totally wrong since you could have an answer in it that also has a comma...\n writer.writerow([k, \",\".join(self.questions[k].answers)]) # insert a key (which is the question) and then let's take the array of \n\n csv_file.close() # close the csv_file file handle", "def __export_file(self, filename, output):\n outfile = open(filename, \"w\")\n outfile.write(output)\n outfile.close\n print(\"Output written to file: \" + filename + \"\\n\")", "def write_exact_graph_to_file(self, output_file):\n print(\"Writing output file.\")\n with open(output_file, 'w') as f:\n f.write(\"# graph number = 0 name = interval_graph\\n\")\n f.write(str(len(self.vertices)) + \"\\n\")\n for node in self.vertices:\n for arc in self.out_arcs_lists[node]:\n s = self.arc_info[arc]['start']\n t = self.arc_info[arc]['destin']\n w = self.arc_info[arc]['weight']\n f.write(\"{} {} {}\\n\".format(s, t, w))", "def whriteInOuput(finalOutput):\n\n os.chdir(\"D:/IIHT/Python/Project/NRPT all companies scrapper/caches\")\n #open text file, return an object of type io.TextIOWrapper\n with open(\"Companies Website.txt\", \"w\") as writ:\n #write each line in the object op, return an object of type int\n writ.write('\\n'.join(finalOutput) + \"\\n\")", "def WriteFile(self, filename) :\n\n # open file for writing:\n f = open(filename, 'w')\n\n ## loop over key/value pairs:\n #for k,v in self.iteritems():\n # # add line; at least the specified number of characters \n # # is used for the key:\n # f.write( '%-20s:%s\\n' % (k,v) )\n ##endfor\n\n # write processed input:\n f.writelines(self.outfile)\n \n # close file:\n f.close()", "def output(owners, filename):\n\n out = open(filename, 'wb')\n writer = csv.writer(out)\n writer.writerow([\n 'Property Address',\n 'License Type',\n 'House',\n 'Street',\n 'License / Folio number',\n 'Civic address',\n 'Business name 1',\n 'Business name 2',\n 'Mail address 1',\n 'Mail address 2',\n 'Total Assess',\n 'Included Assess',\n 'Ann Chg',\n 'Unit'\n ])\n\n for owner in owners:\n owner.output_to(writer)", "def write_po(self, outputfile):\n raise NotImplementedError(\n \"Writing to this file format is not yet implemented\")", "def write(self, filename=None):\n if filename == None:\n filename = self.ofilename\n\n ofile = open(filename, 'w')\n\n ofile.write('# Susceptibility: %E d(susc): %E Coercivity: %E d(coer): %E\\n' % (self.susceptibility_mean, self.susceptibility_std, self.coercivity_mean, self.coercivity_std) )\n ofile.write('# H[] M[] Mfit[]\\n')\n\n #for i in range(len(self.h)):\n # ofile.write(\" %12.10f %12.10f %12.10f\\n\" % ( self.h[i], self.m[i], self.m_fit[i] ) )\n\n ofile.close()", "def write_output(output,fasta,CDR1_pos,CDR2_pos):\n # fasta file is the igblast input file\n with open(output, 'w') as f:\n header = \"\\t\".join(['Name', 'CDRL1_kabat_AA', 'CDRL2_kabat_AA'])\n f.write(header + '\\n')\n for record in SeqIO.parse(fasta, \"fasta\"):\n ID = str(record.id)\n seq = str(record.seq)\n CDR1_aa=''\n CDR2_aa = ''\n CDR1_index = CDR1_pos[ID]\n CDR2_index = CDR2_pos[ID]\n if CDR1_index != []:\n CDR1_start, CDR1_end = fix_aa_pos((int(CDR1_index[0]) - 1), int(CDR1_index[1]))\n CDR1_nuc = seq[CDR1_start:CDR1_end]\n CDR1_aa = translation(CDR1_nuc)\n if CDR2_index != []:\n CDR2_start, CDR2_end = fix_aa_pos((int(CDR2_index[0]) - 1), int(CDR2_index[1]))\n CDR2_nuc = seq[CDR2_start:CDR2_end]\n CDR2_aa = translation(CDR2_nuc)\n f.write(\"\\t\".join([ID, CDR1_aa, CDR2_aa]) + '\\n')", "def storePatternsInFile(self, outFile):\n self.oFile = outFile\n writer = open(self.oFile, 'w+')\n for x, y in self.finalPatterns.items():\n patternsAndSupport = str(x) + \":\" + str(y)\n writer.write(\"%s \\n\" % patternsAndSupport)", "def _write_interactions_to_file(self, results, writer):\n for index_pair, interactions in results.items():\n repetition = 0\n for interaction, results in interactions:\n\n if results is not None:\n (\n scores,\n score_diffs,\n turns,\n score_per_turns,\n score_diffs_per_turns,\n initial_cooperation,\n cooperations,\n state_distribution,\n state_to_action_distributions,\n winner_index,\n ) = results\n for index, player_index in enumerate(index_pair):\n opponent_index = index_pair[index - 1]\n row = [\n self.num_interactions,\n player_index,\n opponent_index,\n repetition,\n str(self.players[player_index]),\n str(self.players[opponent_index]),\n ]\n history = actions_to_str([i[index] for i in interaction])\n row.append(history)\n\n if results is not None:\n row.append(scores[index])\n row.append(score_diffs[index])\n row.append(turns)\n row.append(score_per_turns[index])\n row.append(score_diffs_per_turns[index])\n row.append(int(winner_index is index))\n row.append(initial_cooperation[index])\n row.append(cooperations[index])\n\n states = [(C, C), (C, D), (D, C), (D, D)]\n if index == 1:\n states = [s[::-1] for s in states]\n for state in states:\n row.append(state_distribution[state])\n for state in states:\n row.append(\n state_to_action_distributions[index][(state, C)]\n )\n row.append(\n state_to_action_distributions[index][(state, D)]\n )\n\n row.append(\n int(cooperations[index] >= cooperations[index - 1])\n )\n\n writer.writerow(row)\n repetition += 1\n self.num_interactions += 1", "def write(name, keyword, domain, citation, author, description, species, version, contact, license, values, output):\n write_namespace(\n name, keyword, domain, author, citation, values,\n namespace_description=description,\n namespace_species=species,\n namespace_version=version,\n author_contact=contact,\n author_copyright=license,\n file=output,\n )", "def write_index_to_file(output_file, items): \n \n file = open(output_file, 'w')\n for item in items: \n str0 = str(item[0])\n str1 = ' '.join(str(x) for x in item[1])\n file.write( str0 + ' ' + str1 + '\\n') \n # file.write(item)\n print ('An inverted index has been writted in file')\n file.close()", "def write_result(file_name, name, entries, extra_includes, src_file_names):\r\n\r\n with open(file_name, 'w', newline='\\n') as f:\r\n f.write('// Generated by %s\\n' % os.path.basename(__file__))\r\n f.write('// Based on %s: %s\\n' %\r\n ((\"this file\" if len(src_file_names) < 2 else\r\n \"these files\"), \", \".join(src_file_names)))\r\n methods = entries[0]\r\n if len(methods) != 0:\r\n f.write(to_PyMethodDef(name, methods, extra_includes))\r\n f.write('\\n')\r\n\r\n properties = entries[1]\r\n if len(properties) != 0:\r\n f.write('\\n')\r\n f.write(to_PyGetSetDef(name, properties))", "def write_to_file(self):\n\t\twith self.driver.session() as session:\n\t\t\ttry:\n\t\t\t\tfile_name = None\n\t\t\t\tdonor_file_name = None\n\t\t\t\tfull_file_name = None\n\t\t\t\tcomplete_file_name = None\n\t\t\tcyph = \"MATCH (d:Donor) RETURN d.email\"\n\t\t\tresult = session.run(cyph) \n\t\t\t\n\t\t\tfor donor in result:\n\t\t\t\tdonor_file_name = donor['email']\n\t\t\t\tfull_file_name = \"{}.txt\".format(donor_file_name)\n\t\t\t\tcomplete_file_name = os.path.join(os.getcwd(), full_file_name)\n\t\t\t\tletter = self.letter_to_file(donor_full_name,total_donations)\n\t\t\t\twith open(complete_file_name, 'w+') as f:\n\t\t\t\t\tf.write(letter)\n\t\t\t\n\t\t\texcept Exception as e:\n\t\t\t\tprint(\"Error occurred. See below\")\n\t\t\t\tprint(e)\n\t\t\t\tprint('\\n')\n\n\tdef letter_to_file(self, donor):\n\t\tstr_letter = \"Dear {},\\n\\tThank you for your kind donation(s).\\n\\tIt will be put to very good use.\\n\\t\\tSincerely,\\n\\t\\t\\t-The Team\".format(donor)\n\t\treturn str_letter\n\n\tdef show_donors(self):\n\t\t\"\"\"\n\t\tLists the donor names\n\t\t\"\"\"\n\t\twith self.driver.session() as session:\n\t\t\tstr_build = \"\"\n\t\t\ttry:\n\t\t\t\tcyph = \"\"\"\n\t\t\t\tMATCH (d:Donor)\n\t\t\t\tRETURN d.full_name as full_name, d.email as email\n\t\t\t\t\"\"\"\n\t\t\t\tresult = session.run(cyph)\n\t\t\t\tfor record in result:\n\t\t\t\t\tstr_build += record['full_name'] + ' -- ' + record['email'] + '\\n'\n\t\t\texcept Exception as e:\n\t\t\t\tprint(\"Error occurred. See below.\")\n\t\t\t\tprint(e)\n\t\treturn str_build", "def write(self, fname):\n pass", "def compDataWriter(sentences, predictions, output_file):\n assert len(sentences) == len(predictions), \"Missing predictions for sentences!\"\n lines = list()\n for k in range(len(sentences)):\n assert len(sentences) == len(predictions), \"Missing tag predictions for words!\"\n sentence = sentences[k]\n tags = predictions[k]\n line_list = [sentence[i]+TAGCHAR+tags[i] for i in range(len(sentence))]\n line = WHITESPACE.join(line_list)\n lines.append(line)\n assert len(lines) == len(sentences), \"Missing tagged sentence!\"\n with open(output_file, 'w') as file:\n file.write(\"\\n\".join(lines))", "def write_annotations(self, output_file):\n logging.info(self._header)\n np.savetxt(output_file, self._zeroes, header=\" \".join(self._header),fmt='%i',comments='')", "def write_to_file(content, filename):\n if not os.path.isfile(filename): # Checking if file already exists, don't append data if it does.\n for j in range(len(content)): # For each dialog in dialogues array.\n with open(filename, 'a') as file: # Open a text file in append mode and write data into it.\n for k in range(len(content[j][0])):\n file.write('{0} {1}\\n'.format(str(content[j][0][k]).lower().split(\"(\")[0],\n str(content[j][1][k])).lower())", "def write(self, outputfile):\n outfile = open(outputfile, 'w')\n if (outputfile.lower().endswith('.po')):\n self.write_po(outfile)\n elif (outputfile.lower().endswith('.json')):\n self.write_json(outfile)\n elif (outputfile.lower().endswith('.xml')):\n self.write_properties(outfile)\n outfile.close()", "def write_file_simple(self,filename):\n\n output = open(filename,\"w\")\n # write header\n output.write(\"# %1s %3s %22s %6s %22s\\n\"%(\"l\",\"n\",\"nu_theo (muHz)\",\"unused\",\"Inertia\"))\n for i in range(self.modes.shape[0]):\n output.write(\" %1d %3d %22.15e 0.0 %22.15e\\n\"%( \\\n self.modes[\"l\"][i], \\\n self.modes[\"n\"][i], \\\n self.modes[\"freq\"][i]*self.glb[ifreq_ref], \\\n self.modes[\"inertia\"][i]))\n output.close()", "def writeCADFile(self, filename):\n valid_filetypes = [\"brep\", \"bstl\", \"egads\", \"egg\", \"iges\", \"igs\", \"sens\", \"step\", \"stl\", \"stp\", \"tess\", \"grid\"]\n file_extension = filename.split(\".\")[-1]\n if file_extension.lower() not in valid_filetypes:\n raise OSError(\n \"CAD filename \"\n + filename\n + \" must have a valid exension. \"\n + \"Consult the EngineeringSketchPad docs for the DUMP function\"\n )\n if self.comm.rank == 0:\n modelCopy = self.espModel.Copy()\n n_branches, _, _ = modelCopy.Info()\n modelCopy.NewBrch(\n n_branches, modelCopy.GetCode(\"dump\"), \"<none>\", 0, filename, \"0\", \"0\", \"0\", \"\", \"\", \"\", \"\", \"\"\n )\n modelCopy.Build(0, 0)" ]

[ "0.7794726", "0.66742295", "0.64932483", "0.64526165", "0.6379942", "0.63655496", "0.63634735", "0.62910575", "0.6240714", "0.6233921", "0.6233921", "0.6233921", "0.61785156", "0.61412483", "0.61257005", "0.610843", "0.6082861", "0.60720426", "0.6064205", "0.60603034", "0.59847915", "0.5953382", "0.5949586", "0.59256744", "0.59232116", "0.59232116", "0.5918855", "0.5918259", "0.591524", "0.59104925", "0.5906709", "0.59009737", "0.58934045", "0.58857846", "0.58828205", "0.5871059", "0.58642375", "0.58520657", "0.5847941", "0.58440024", "0.5836401", "0.5835469", "0.58210695", "0.580742", "0.57888246", "0.5781007", "0.5777897", "0.5768409", "0.5767276", "0.5766805", "0.57632726", "0.5750538", "0.57470477", "0.5733779", "0.573351", "0.572666", "0.5726504", "0.57206035", "0.5718034", "0.57089895", "0.5704777", "0.5703387", "0.56983274", "0.56963384", "0.56868637", "0.5682352", "0.56822103", "0.56810313", "0.5677822", "0.5676366", "0.5673577", "0.56727034", "0.5670391", "0.5669521", "0.5668411", "0.566751", "0.5666728", "0.5647113", "0.56437045", "0.5642565", "0.5636676", "0.56290835", "0.5627325", "0.5618395", "0.5613953", "0.56119215", "0.5611875", "0.5611426", "0.560923", "0.5607486", "0.5606954", "0.560589", "0.560346", "0.5602905", "0.5598933", "0.55940706", "0.5592159", "0.55870605", "0.557992", "0.557991" ]

{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }

["def make_k_matrix(self):\r\n K = self.uv_vol + self.Epsilon * self.guv_vol + \\\r\n (...TRUNCATED)

["0.6495986","0.6089255","0.6045119","0.59890914","0.5949488","0.59035623","0.5859298","0.58462423",(...TRUNCATED)

{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }

["def updateC(A, U, B):\n \n m_dim = A.shape[1] \n q_dim = B.shape[0]\n \n C_tenso(...TRUNCATED)

["0.6325033","0.6273725","0.6251581","0.62479377","0.6177961","0.6087597","0.6022537","0.60215706","(...TRUNCATED)

{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }

["def covar(fx,cx):\n \n fx = np.array(fx)\n cx = np.array(cx)\n \n shape_fx = fx.sha(...TRUNCATED)

["0.650418","0.650212","0.6441079","0.6313763","0.6310517","0.62949276","0.62782884","0.62631303","0(...TRUNCATED)

{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }

["def matrix_vector_prod(m,u):\n each_product = []\n for v in m:\n each_product.append((...TRUNCATED)

["0.7003199","0.6513981","0.64759356","0.6454179","0.6377554","0.6326698","0.6245358","0.620894","0.(...TRUNCATED)

{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }

["def _tridisolve(d, e, b, overwrite_b=True):\n\t\tN = len(b)\n\t\t# work vectors\n\t\tdw = d.copy()(...TRUNCATED)

["0.63466734","0.61827254","0.61033237","0.6093494","0.60769826","0.5885008","0.58844715","0.5877297(...TRUNCATED)

{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }

["def fast_logdet(matrix):\n sign, ld = np.linalg.slogdet(matrix)\n if not sign > 0:\n (...TRUNCATED)

["0.7205463","0.69225436","0.6803772","0.6577487","0.65662503","0.6258033","0.6235449","0.6192166","(...TRUNCATED)

{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }

["def bd_toeplitz_inverse_multiplication_prep(*arrs):\n \n t = []\n for c in arrs: # loop o(...TRUNCATED)

["0.65743506","0.63173485","0.60780877","0.60345995","0.5920918","0.5710167","0.5684219","0.56176597(...TRUNCATED)