def makeDict(fileName):
words_transformed = []
words_clean = {}
words_freq = {}
n = 0
mine = codecs.open(fileName, 'r', 'utf-8')
for line in mine:
# wordsInLine = line.split()
# print len(wordsInLine)
# for word in wordsInLine:
(word, freq) = line.split(',')
# print len(wordsInLine)
# for word in wordsInLine:
freq = int(freq.rstrip('\r\n'))
word = preprocess_word(word.rstrip('\n\r' + r'\x11'))
thisWord = leven.transIn(word)
words_transformed.append(thisWord)
words_clean[thisWord] = word
words_freq[thisWord] = freq
# print "dictionary: ",len(words_transformed), " words."
# for word in words_transformed:
# dump(word)
# print
# dump(words_clean[word])
# print
# print
return (words_transformed, words_clean, words_freq)
def makeDict(fileName):
words_transformed = []
words_clean = {}
words_freq = {}
n = 0
mine = codecs.open(fileName, 'r', 'utf-8')
for line in mine:
# wordsInLine = line.split()
# print len(wordsInLine)
# for word in wordsInLine:
(word, freq) = line.split(',')
# print len(wordsInLine)
# for word in wordsInLine:
freq = int(freq.rstrip('\r\n'))
word = preprocess_word(word.rstrip('\n\r'+r'\x11'))
thisWord = leven.transIn(word)
words_transformed.append(thisWord)
words_clean[thisWord] = word
words_freq[thisWord] = freq
# print "dictionary: ",len(words_transformed), " words."
# for word in words_transformed:
# dump(word)
# print
# dump(words_clean[word])
# print
# print
return (words_transformed, words_clean, words_freq)
def getCloseWords(wordIn,
word_dicts,
rules,
max_weight,
threshold=3,
fast=True,
debug=False):
import Levenshtein
# out = difflib.get_close_matches('ἐστιν',words)
(dict_words, words_clean, words_freq) = word_dicts
# print "word in:"
# print dump(wordIn)
# wordIn = preprocess_word(wordIn)
# print "word in pp:"
# print dump(wordIn)
wordInTrans = leven.transIn(wordIn)
if (debug):
print
print "getCloseWords for", wordInTrans.encode(
'utf-8'), "(", wordIn.encode('utf-8'), ")"
dump(wordIn)
output_words = []
#dict_words_set = set(dict_words)
n = 0
# print "Now comparing to..."
if wordInTrans in dict_words:
pass
# print "short-circuting dictionary word"
# output_words.append((wordInTrans,0,0,0,'xxx','yyy'))
else:
for word in dict_words:
# print u"*****" + words_clean[n]
# print "word into comparison:"
# print dump(word)
lev_distance = Levenshtein.distance(
wordInTrans, word
) # difflib.SequenceMatcher(None, word, wordInTrans).ratio()
# print "distance: ",
# print ratio
if lev_distance <= threshold:
edits = Levenshtein.editops(wordInTrans, word)
w = weight_for_leven_edits(wordInTrans,
word,
edits,
rules,
max_weight,
debug=False)
output_words.append(
(word, lev_distance, len(edits), w, 'xxx', 'yyy'))
if (lev_distance == 0) and (fast == True):
# In the case of an exact match, cut the search short
# We might have got some close matches ahead of time, so this
# will not create a complete list
output_words = sorted(
output_words,
key=lambda word: int(words_freq[word[0]]))
return sorted(output_words, key=lambda word: int(word[3]))
n = n + 1
return sorted(output_words, key=lambda word: word[3])
def getCloseWords(wordIn, word_dicts, rules, max_weight, threshold=3, fast=True, debug=False):
import Levenshtein
# out = difflib.get_close_matches('ἐστιν',words)
(dict_words, words_clean, words_freq) = word_dicts
# print "word in:"
# print dump(wordIn)
# wordIn = preprocess_word(wordIn)
# print "word in pp:"
# print dump(wordIn)
wordInTrans = leven.transIn(wordIn)
if (debug):
print
print "getCloseWords for", wordInTrans.encode('utf-8'), "(", wordIn.encode('utf-8'),")"
dump(wordIn)
output_words = []
#dict_words_set = set(dict_words)
n = 0
# print "Now comparing to..."
if wordInTrans in dict_words:
pass
# print "short-circuting dictionary word"
# output_words.append((wordInTrans,0,0,0,'xxx','yyy'))
else:
for word in dict_words:
# print u"*****" + words_clean[n]
# print "word into comparison:"
# print dump(word)
lev_distance = Levenshtein.distance(
wordInTrans, word) # difflib.SequenceMatcher(None, word, wordInTrans).ratio()
# print "distance: ",
# print ratio
if lev_distance <= threshold:
edits = Levenshtein.editops(wordInTrans, word)
w = weight_for_leven_edits(wordInTrans, word, edits, rules, max_weight, debug=False)
output_words.append(
(word, lev_distance, len(edits), w, 'xxx', 'yyy'))
if (lev_distance == 0) and (fast == True):
# In the case of an exact match, cut the search short
# We might have got some close matches ahead of time, so this
# will not create a complete list
output_words = sorted(
output_words, key=lambda word: int(words_freq[word[0]]))
return sorted(output_words, key=lambda word: int(word[3]))
n = n + 1
return sorted(output_words, key=lambda word: word[3])
