def load_language_models(self):
for x in listdir( STAT_DIR ):
if x[-4:] == ".dat":
modelfile = file(os.path.join(STAT_DIR,x))
language = x[0:-4]
self.languages.append( language )
new_model = Trigrams()
for line in modelfile:
tokens = split(line)
if len(tokens) == 2:
trigram = lower( unicode( tokens[0],'utf-8') )
probability = float(tokens[1])
new_model.add_trigram(trigram,probability)
self.models.append(new_model)
modelfile.close()
def __init__(self):
"""Lid constructor
The constructor loads automatically all language models in the
current directory.
The language models are stored in files that are made up as follows:
LANGUAGE_NAME followd by .dat.
"""
self.trigrams = Trigrams()
self.languages = [] # list of loaded language models
self.models = [] # list with the trigram models
self.load_language_models()
class Lid:
"""The basic class for Language Identification Library
"""
def __init__(self):
"""Lid constructor
The constructor loads automatically all language models in the
current directory.
The language models are stored in files that are made up as follows:
LANGUAGE_NAME followd by .dat.
"""
self.trigrams = Trigrams()
self.languages = [] # list of loaded language models
self.models = [] # list with the trigram models
self.load_language_models()
def load_language_models(self):
for x in listdir( STAT_DIR ):
if x[-4:] == ".dat":
modelfile = file(os.path.join(STAT_DIR,x))
language = x[0:-4]
self.languages.append( language )
new_model = Trigrams()
for line in modelfile:
tokens = split(line)
if len(tokens) == 2:
trigram = lower( unicode( tokens[0],'utf-8') )
probability = float(tokens[1])
new_model.add_trigram(trigram,probability)
self.models.append(new_model)
modelfile.close()
def checkText(self, text):
"""Check which language a text is."""
self.trigrams.create_trigrams(text)
self.trigrams.calculate_probabilities()
result = self.count_deviation()
language, confidence = find_best_language(self.languages, result)
answer = {'confidence':confidence}
stat = {}
for x, lang in enumerate(self.languages):
stat[lang] = result[x]
answer['stat'] = stat
return language, answer
#
# if self.is_results_equal(result):
# return '?', res
def count_result_in_percents(results, num):
new_result = [ float(result) / float(num) for result in results ]
return new_result
def count_deviation(self):
result = []
for x in range(len(self.languages)):
result.append(0)
for x in self.trigrams.trigrams.keys():
for i in range(len(self.models)):
model = self.models[i]
if model.trigrams.has_key(x):
value = model.trigrams[x] - self.trigrams.trigrams[x]
result[i] += abs( value )
else:
# otherwise set the resulting value to 1 = max. deviation
result[i] += 1
return result
def is_results_equal(self,results):
for x in range(len(results)-1):
if results[x] != results[x+1]:
return False
return True
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = "Alex Turkin <*****@*****.**>"
import sys, os.path, glob
from Trigrams import Trigrams
from lid import clean_punctuation_from_text
sys.path.insert(1,'../')
import chardet
if __name__ == "__main__":
myTrigrams = Trigrams()
if len(sys.argv) > 1:
for x in sys.argv[1:]:
for y in glob.glob(os.path.normcase(x)):
try:
f = open(y)
string = f.read()
encoding = chardet.detect(string)['encoding']
unistring = unicode(string, encoding)#.decode(encoding)
cleaned = clean_punctuation_from_text(unistring)
myTrigrams.add_trigrams_from_text(cleaned)
except IOError:
pass
myTrigrams.eliminate_frequences(2)
myTrigrams.calculate_probabilities()
