def simhash(words, doc_corpus):
sims = []
ws = []
# doc_corpus 中向量索引与词的对应关系处理
[ws.extend(sorted(set([w1 for w1 in w if w1 not in ws]))) for w in words]
for word, corpus in zip(words, doc_corpus):
feature = [(ws[c[0]], c[1]) for c in corpus]
sim = Simhash(word)
sim.build_by_features(feature)
sims.append(sim)
return sims
from simhash import Simhash, SimhashIndex
# simhash
str0 = 'The Apache Hadoop software library is a framework that allows for the distributed processing large data'
str1 = 'The Apache Hadoop software library is a framework that allows for the distributed processing big data'
sh0 = Simhash(str0)
sh1 = Simhash(str1)
print(sh0.distance(sh1)) # 计算海明距离
features = [('Apache', 10), ('Hadoop', 15), ('framework', 3),
('distributed', 10), ('data', 6)]
sh0.build_by_features(features)
sh1.build_by_features(features)
print(sh0.distance(sh1))
# build a simhash index
data = {
1: u'How are you? I Am fine. blar blar blar blar blar Thanks.',
2: u'How are you i am fine. blar blar blar blar blar than',
3: u'This is simhash test.',
}
objs = [(str(k), Simhash(v)) for k, v in data.items()]
index = SimhashIndex(objs, k=3)
print(index.bucket_size())
s1 = Simhash(u'How are you i am fine. blar blar blar blar blar thank')
print(index.get_near_dups(s1))
index.add('4', s1)
print(index.get_near_dups(s1))
def generate_simhash(self, tokens): #Generate a Simhash from Spacy tokens. sh = Simhash(u'', f=self.hash_size) #silly interface... sh.build_by_features(tokens) return sh
