****由于最近需要做大规模的文本相似度的计算，所以用到了simhash+汉明距离来快速计算文本的相似度。**
**simhash的原理如下图:其中的weight采用的是jieba的tf-idf的结果。****
**附上python3的源代码:**
import math
import jieba
import jieba.analyse
class SimHash(object):
def __init__(self):
pass
def getBinStr(self, source):
if source == "":
return 0
else:
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 getWeight(self, source):
# fake weight with keyword
return ord(source)
def unwrap_weight(self, arr):
ret = ""
for item in arr:
tmp = 0
if int(item) > 0:
tmp = 1
ret += str(tmp)
return ret
def simHash(self, rawstr):
seg = jieba.cut(rawstr)
keywords = jieba.analyse.extract_tags("|".join(seg), topK=100, withWeight=True)
ret = []
for keyword, weight in keywords:
binstr = self.getBinStr(keyword)
keylist = []
for c in binstr:
weight = math.ceil(weight)
if c == "1":
keylist.append(int(weight))
else:
keylist.append(-int(weight))
ret.append(keylist)
# 对列表进行"降维"
rows = len(ret)
cols = len(ret[0])
result = []
for i in range(cols):
tmp = 0
for j in range(rows):
tmp += int(ret[j][i])
if tmp > 0:
tmp = "1"
elif tmp <= 0:
tmp = "0"
result.append(tmp)
return "".join(result)
def getDistince(self, hashstr1, hashstr2):
length = 0
for index, char in enumerate(hashstr1):
if char == hashstr2[index]:
continue
else:
length += 1
return length
if name == "__main__":
simhash = SimHash()
s1 = u'I am very happy'
s2 = u'I am very happu'
hash1 = simhash.simHash(s1)
hash2 = simhash.simHash(s2)
distince = simhash.getDistince(hash1, hash2)
value = 5
print("海明距离：", distince, "判定距离：", value, "是否相似：", distince<=value)
