余弦相似性计算及python代码实现过程解析

import jiebaimport jieba.analysedef 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, v2def 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'))