240721 knn 计算用户1和用户2之间的评分-相关度

knn通过计算电影相关度，计算用户1和用户2的评分

import json
 import numpy as np
  
 # 计算欧式距离分数
 def euclidean_score(dataset, user1, user2):
     if user1 not in dataset:
         raise TypeError('User ' + user1 + ' not present in the dataset')    if user2 not in dataset:
         raise TypeError('User ' + user2 + ' not present in the dataset')    # 提取用户1和用户2的评论过的电影
     rated_by_both = {}     for item in dataset[user1]:
         if item in dataset[user2]:
             rated_by_both[item] = 1    # 如果没有评分，得分为0
     if len(rated_by_both) == 0:
         return 0    squared_differences = [] 
    for item in dataset[user1]:
         if item in dataset[user2]:
             squared_differences.append(np.square(dataset[user1][item] - dataset[user2][item]))
         
     return 1 / (1 + np.sqrt(np.sum(squared_differences))) 
 # 计算得分
 if __name__=='__main__':
     data_file = 'movie_ratings.json'    with open(data_file, 'r') as f:
         data = json.loads(f.read())    user1 = 'John Carson'
     user2 = 'Michelle Peterson'    print "\nEuclidean score:"
     print euclidean_score(data, user1, user2)