simhash算法:海量千万级的数据去重

simhash算法:海量千万级的数据去重

simhash算法及原理参考:

简单易懂讲解simhash算法 hash 哈希:https://blog.csdn.net/le_le_name/article/details/51615931

simhash算法及原理简介:https://blog.csdn.net/lengye7/article/details/79789206

使用SimHash进行海量文本去重:https://www.cnblogs.com/maybe2030/p/5203186.html#_label3

 

 

python实现:

python使用simhash实现文本相似性对比(全代码展示):https://blog.csdn.net/weixin_43750200/article/details/84789361

simhash的py实现:https://blog.csdn.net/gzt940726/article/details/80460419

 

python库simhash使用

详情请查看:https://leons.im/posts/a-python-implementation-of-simhash-algorithm/

(1) 查看simhash值

>>> from simhash import Simhash
>>> print '%x' % Simhash(u'I am very happy'.split()).value
9f8fd7efdb1ded7f

Simhash()接收一个token序列,或者叫特征序列。

 

(2)计算两个simhash值距离

>>> hash1 = Simhash(u'I am very happy'.split())
>>> hash2 = Simhash(u'I am very sad'.split())
>>> print hash1.distance(hash2)



(3)建立索引

simhash被用来去重。如果两两分别计算simhash值,数据量较大的情况下肯定hold不住。有专门的数据结构,参考:http://www.cnblogs.com/maybe2030/p/5203186.html#_label4

from simhash import Simhash, SimhashIndex
# 建立索引
data = {
u'1': u'How are you I Am fine . blar blar blar blar blar Thanks .'.lower().split(),
u'2': u'How are you i am fine .'.lower().split(),
u'3': u'This is simhash test .'.lower().split(),
}
objs = [(id, Simhash(sent)) for id, sent in data.items()]
index = SimhashIndex(objs, k=10) # k是容忍度;k越大,检索出的相似文本就越多
# 检索
s1 = Simhash(u'How are you . blar blar blar blar blar Thanks'.lower().split())
print index.get_near_dups(s1)
# 增加新索引
index.add(u'4', s1)

 



 

posted @ 2019-07-08 12:39  -零  阅读(10778)  评论(1编辑  收藏  举报