N元分词算法

#!/usr/bin/env python

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.split())
        #unigram
        if self.n == 1:
            try:
                fip = open("data.uni","w")
            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("data.bi","w")
            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
    try:
        fip = open(sys.argv[1],"r")
    except:
        print >> sys.stderr, "failed to open input file"
    sentence = []
    for line in fip:
        if len(line.strip())!=0:
            sentence.append(line.strip())
    uni = NGram(1)
    bi = NGram(2)
    uni.scan(sentence)
    bi.scan(sentence)