def initializeSoundexDict(freqDict):
soundexScore = Soundex().soundex
freqDictSoundex = {}
for c in freqDict:
freqDictSoundex[c] = soundexScore(c.capitalize())
return freqDictSoundex
def soundex(word):
word = word.capitalize()
sound_candidates = []
soundexScore = Soundex().soundex
wordScore = soundexScore(word)
for c in freqDict:
if (wordScore == freqDictSoundex[c]):
sound_candidates.append(c)
if len(sound_candidates) == 0:
sound_candidates.append(word)
words = []
for i in range(min(len(sound_candidates), candidates_nb)):
words.append(max(sound_candidates, key=P))
sound_candidates.remove(words[i])
return words
def has_player_name_fuzzy(current, edu):
"if the EDU has a word that sounds like a player name"
tokens = edu.tokens
soundex = lambda w: Soundex().soundex(w)
return has_one_of_words(current.players, tokens, norm=soundex)
phonemes2 = np.array([ipa[c] for c in pad2])
features = {'word 1 encoding': phonemes1, 'word 2 encoding': phonemes2}
return features
#%% Open some things
if __name__ == '__main__':
TRAIN_PATH = '../data/cognet_train.csv'
TEST_PATH = '../data/cognet_test.csv'
DEV_PATH = '../data/cognet_dev.csv'
DATA_PATH = '../data/extracted_features.npy'
SUPPORTED_LANGS_PATH = '../data/cognet_supported_langs.tsv'
IPA_ENCODING_PATH = '../data/ipa_encodings.pickle'
v = DictVectorizer(sparse=False)
soundex = Soundex()
epitran_dict = create_epitran_dict()
with open(IPA_ENCODING_PATH, 'rb') as f:
ipa = pickle.load(f)
ipa = defaultdict(lambda: np.array([0.] * 24), ipa)
#%% FEATURE EXTRACTION
if __name__ == '__main__':
print('Reading training data...')
train_data = pd.read_csv(TRAIN_PATH)
print('Extracting features...')
x_train = v.fit_transform([
extract_features(str(lang1), str(word1), str(lang2), str(word2))\
for lang1, word1, lang2, word2 in\
zip(train_data['lang 1'], train_data['translit 1'], train_data['lang 2'], train_data['translit 2'])
])
# -*- coding: utf-8 -*- """ Created on Sun Jun 18 00:04:21 2017 @author: Milton """ from foneticaBR import foneticaBR from buscabr import buscaBR from metaphoneBR import metaphoneBR # Soundex - Instalar no Python as bibliotecas soundex e silpa_common from soundex import Soundex chaveRoberto = foneticaBR() chavebr = buscaBR() chavemeta = metaphoneBR() chavesoundex = Soundex() texto = 'JOSSEPH' print(chaveRoberto.chavefonetica(texto)) print(chavebr.chaveBR(texto, False)) print(chavebr.chaveBR(texto, True)) print(chavemeta.chaveMetaphoneBR(texto)) print(chavesoundex.soundex(texto))
def sndx(s):
return Soundex().soundex(stemmer(s, args.stem))
from soundex import Soundex
from collections import defaultdict
from wordfreq import word_frequency
soundex = Soundex().soundex
sound_words = defaultdict(set)
with open('eff_short_wordlist_1.txt','r') as fh:
for line in fh:
word = line.split()[1]
sound = soundex(word)
if len(sound) > 1: # and sound not in ('i245', 't651'):
sound_words[sound].add(word)
for word_set in sound_words.values():
if len(word_set) > 1:
word_list = [ (word_frequency(word, 'en'), word) for word in word_set ]
word_list.sort()
print(word_list[-1][-1])
else:
print(list(word_set)[0])
'ben': SCHEMES[BENGALI],
'guj': SCHEMES[GUJARATI],
'hin': SCHEMES[DEVANAGARI],
'kan': SCHEMES[KANNADA],
'mal': SCHEMES[MALAYALAM],
'mar': SCHEMES[DEVANAGARI],
"ori": SCHEMES[ORIYA],
'pun': SCHEMES[GURMUKHI],
'tam': SCHEMES[TAMIL],
'tel': SCHEMES[TELUGU],
'urd': SCHEMES[DEVANAGARI]
}
if lang in scheme_dict:
src_scheme = scheme_dict[lang]
scheme_map = SchemeMap(src_scheme, SCHEMES[HK])
instance = Soundex()
findings = {}
for i, line in enumerate(inp_bible):
curr_line_id = 23146 + i
if line == "" or line == "\n":
continue
for index, name in enumerate(names_reference):
lids = ast.literal_eval(name[lid_col])
for col in col_references:
romans = name[col]
if romans == "":
continue
print(".", end="")
