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python-Levenshtein ʹÓÃ
ʹÓà pip install python-Levenshtein Ö¸Áî°²×° Levenshtein
# -*- coding: utf-8 -*- import difflib # import jieba import Levenshtein str1 = "ÎҵĹÇ÷ÀÑ©°× Ò²³¤²»³öÇàïý" str2 = "Ñ©µÄÈÕ×Ó ÎÒÖ»Ï뵽ѩÖÐÈ¥si" # 1. difflib seq = difflib.SequenceMatcher(None, str1,str2) ratio = seq.ratio() print 'difflib similarity1: ', ratio # difflib È¥µôÁбíÖв»ÐèÒª±È½ÏµÄ×Ö·û seq = difflib.SequenceMatcher(lambda x: x in ' ÎÒµÄÑ©', str1,str2) ratio = seq.ratio() print 'difflib similarity2: ', ratio # 2. hamming¾àÀ룬str1ºÍstr2³¤¶È±ØÐëÒ»Ö£¬ÃèÊöÁ½¸öµÈ³¤×Ö´®Ö®¼ä¶ÔӦλÖÃÉϲ»Í¬×Ö·ûµÄ¸öÊý # sim = Levenshtein.hamming(str1, str2) # print 'hamming similarity: ', sim # 3. ±à¼¾àÀ룬ÃèÊöÓÉÒ»¸ö×Ö´®×ª»¯³ÉÁíÒ»¸ö×Ö´®×îÉٵIJÙ×÷´ÎÊý£¬ÔÚÆäÖеIJÙ×÷°üÀ¨ ²åÈ롢ɾ³ý¡¢Ìæ»» sim = Levenshtein.distance(str1, str2) print 'Levenshtein similarity: ', sim # 4.¼ÆËãÀ³ÎÄ˹̹±È sim = Levenshtein.ratio(str1, str2) print 'Levenshtein.ratio similarity: ', sim # 5.¼ÆËãjaro¾àÀë sim = Levenshtein.jaro(str1, str2 ) print 'Levenshtein.jaro similarity: ', sim # 6. Jaro¨CWinkler¾àÀë sim = Levenshtein.jaro_winkler(str1 , str2 ) print 'Levenshtein.jaro_winkler similarity: ', sim
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difflib similarity1: 0.246575342466
difflib similarity2: 0.0821917808219
Levenshtein similarity: 33
Levenshtein.ratio similarity: 0.27397260274
Levenshtein.jaro similarity: 0.490208958959
Levenshtein.jaro_winkler similarity: 0.490208958959
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