Simhash的算法简单的来说就是，从海量文本中快速搜索和已知simhash相差小于k位的simhash集合，这里每个文本都可以用一个simhash值来代表，一个simhash有64bit，相似的文本，64bit也相似，论文中k的经验值为3。该方法的缺点如优点一样明显，主要有两点，对于短文本，k值很敏感；另一个是由于算法是以空间换时间，系统内存吃不消。

复制代码 代码如下:

#!/usr/bin/python

# coding=utf-8

class simhash:

#构造函数

def __init__(self, tokens='', hashbits=128):

self.hashbits = hashbits

self.hash = self.simhash(tokens);

#toString函数

def __str__(self):

return str(self.hash)

#生成simhash值

def simhash(self, tokens):

v = [0] * self.hashbits

for t in [self._string_hash(x) for x in tokens]: #t为token的普通hash值

for i in range(self.hashbits):

bitmask = 1 << i

if t & bitmask :

v[i] += 1 #查看当前bit位是否为1,是的话将该位+1

else:

v[i] -= 1 #否则的话,该位-1

fingerprint = 0

for i in range(self.hashbits):

if v[i] >= 0:

fingerprint += 1 << i

return fingerprint #整个文档的fingerprint为最终各个位>=0的和

#求海明距离

def hamming_distance(self, other):

x = (self.hash ^ other.hash) & ((1 << self.hashbits) - 1)

tot = 0;

while x :

tot += 1

x &= x - 1

return tot

#求相似度

def similarity (self, other):

a = float(self.hash)

b = float(other.hash)

if a > b : return b / a

else: return a / b

#针对source生成hash值 (一个可变长度版本的Python的内置散列)

def _string_hash(self, source):

if source == "":

return 0

else:

x = ord(source[0]) << 7

m = 1000003

mask = 2 ** self.hashbits - 1

for c in source:

x = ((x * m) ^ ord(c)) & mask

x ^= len(source)

if x == -1:

x = -2

return x

if __name__ == '__main__':

s = 'This is a test string for testing'

hash1 = simhash(s.split())

s = 'This is a test string for testing also'

hash2 = simhash(s.split())

s = 'nai nai ge xiong cao'

hash3 = simhash(s.split())

print(hash1.hamming_distance(hash2) , " " , hash1.similarity(hash2))

print(hash1.hamming_distance(hash3) , " " , hash1.similarity(hash3))