余弦相似性计算及python代码实现过程解析

A：西米喜欢健身

B：超超不爱健身，喜欢打游戏

step1:分词

A：西米／喜欢／健身

B：超超／不／喜欢／健身，喜欢／打／游戏

step2:列出两个句子的并集

西米／喜欢／健身／超超／不／打／游戏

step3:计算词频向量

A：[1,1,1,0,0,0,0]

B：[0,1,1,1,1,1,1]

step4:计算余弦值

余弦值越大，证明夹角越小，两个向量越相似。

step5:python代码实现

import jieba
import jieba.analyse
def words2vec(words1=None, words2=None):
v1 = []
v2 = []
tag1 = jieba.analyse.extract_tags(words1, withWeight=True)
tag2 = jieba.analyse.extract_tags(words2, withWeight=True)
tag_dict1 = {i[0]: i[1] for i in tag1}
tag_dict2 = {i[0]: i[1] for i in tag2}
merged_tag = set(tag_dict1.keys()) | set(tag_dict2.keys())
for i in merged_tag:
 if i in tag_dict1:
  v1.append(tag_dict1[i])
 else:
  v1.append(0)
 if i in tag_dict2:
  v2.append(tag_dict2[i])
 else:
  v2.append(0)
return v1, v2
def cosine_similarity(vector1, vector2):
dot_product = 0.0
normA = 0.0
normB = 0.0
for a, b in zip(vector1, vector2):
 dot_product += a * b
 normA += a ** 2
 normB += b ** 2
if normA == 0.0 or normB == 0.0:
 return 0
else:
 return round(dot_product / ((normA**0.5)*(normB**0.5)) * 100, 2)  
def cosine(str1, str2):
vec1, vec2 = words2vec(str1, str2)
return cosine_similarity(vec1, vec2)
print(cosine('阿克苏苹果', '阿克苏苹果'))

来源：https://www.cnblogs.com/guoxueyuan/p/7779239.html