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這篇文章主要介紹python如何實(shí)現(xiàn)Simhash算法,文中介紹的非常詳細(xì),具有一定的參考價(jià)值,感興趣的小伙伴們一定要看完!
simhash包含分詞、hash、加權(quán)、合并、降維五大步驟
simhash代碼如下:
import jieba import jieba.analyse import numpy as np class SimHash(object): def simHash(self, content): seg = jieba.cut(content) # jieba.analyse.set_stop_words('stopword.txt') # jieba基于TF-IDF提取關(guān)鍵詞 keyWords = jieba.analyse.extract_tags("|".join(seg), topK=10, withWeight=True) keyList = [] for feature, weight in keyWords: # print('feature:' + feature) print('weight: {}'.format(weight)) # weight = math.ceil(weight) weight = int(weight) binstr = self.string_hash(feature) print('feature: %s , string_hash %s' % (feature, binstr)) temp = [] for c in binstr: if (c == '1'): temp.append(weight) else: temp.append(-weight) keyList.append(temp) listSum = np.sum(np.array(keyList), axis=0) if (keyList == []): return '00' simhash = '' for i in listSum: if (i > 0): simhash = simhash + '1' else: simhash = simhash + '0' return simhash def string_hash(self, source): if source == "": return 0 else: temp = source[0] temp1 = ord(temp) x = ord(source[0]) << 7 m = 1000003 mask = 2 ** 128 - 1 for c in source: x = ((x * m) ^ ord(c)) & mask x ^= len(source) if x == -1: x = -2 x = bin(x).replace('0b', '').zfill(64)[-64:] return str(x) def getDistance(self, hashstr1, hashstr2): ''' 計(jì)算兩個(gè)simhash的漢明距離 ''' length = 0 for index, char in enumerate(hashstr1): if char == hashstr2[index]: continue else: length += 1 return length
分詞是將文本文檔進(jìn)行分割成不同的詞組,比如詞1為:今天星期四,詞2為:今天星期五
得出分詞結(jié)果為【今天,星期四】【今天,星期五】
hash是將分詞結(jié)果取hash值
星期四hash為:0010001100100000101001101010000000101111011010010001100011011110
今天hash為:0010001111010100010011110001110010100011110111111011001011110101
星期五hash為:0010001100100000101001101010000000101111011010010000000010010001
降維是將合并的結(jié)果進(jìn)行降維,如果值大于0,則置為1小于0 則置為0,因此得到的結(jié)果為:
一般simhash采用海明距離來(lái)進(jìn)行計(jì)算相似度,海明距離計(jì)算如下:
對(duì)于A,B兩個(gè)n維二進(jìn)制數(shù)
二者的海明距離為:
其中:
舉例:
1000與1111的海明距離為3
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