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calDist.py
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75 lines (71 loc) · 2.35 KB
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#!/usr/bin/env python
import jieba
class NGram(object):
def __init__(self, n):
# n is the order of n-gram language model
self.n = n
self.unigram = {}
self.bigram = {}
# scan a sentence, extract the ngram and update their
# frequence.
#
# @param sentence list{str}
# @return none
def scan(self, sentence):
# file your code here
for line in sentence:
self.ngram(line)
#unigram
if self.n == 1:
try:
fip = open("test_file/data.uni","w",encoding='utf-8')
except:
print >> sys.stderr ,"failed to open data.uni"
for i in self.unigram:
fip.write("%s %d\n" % (i,self.unigram[i]))
if self.n == 2:
try:
fip = open("test_file/data.bi","w",encoding='utf-8')
except:
print >> sys.stderr ,"failed to open data.bi"
for i in self.bigram:
fip.write("%s %d\n" % (i,self.bigram[i]))
# caluclate the ngram of the words
#
# @param words list{str}
# @return none
def ngram(self, words):
# unigram
if self.n == 1:
for word in words:
if word not in self.unigram:
self.unigram[word] = 1
else:
self.unigram[word] = self.unigram[word] + 1
# bigram
if self.n == 2:
num = 0
stri = ''
for i in words:
num = num + 1
if num == 2:
stri = stri + " "
stri = stri + i
if num == 2:
if stri not in self.bigram:
self.bigram[stri] = 1
else:
self.bigram[stri] = self.bigram[stri] + 1
num = 0
stri = ''
if __name__=="__main__":
import sys
fip = open('test_file/baiduqaclean2.txt',"r",encoding='utf-8')
sentence = []
for line in fip:
if len(line.strip())!=0:
sentence.append(jieba.cut(line.strip()))
uni = NGram(1)
bi = NGram(2)
uni.scan(sentence)
bi.scan(sentence)