余弦相似性计算及python代码实现过程解析

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'))