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gongmingming gavinljj angel1288 baconhhh guangzhan wenyaxinluoyang hevan1sky zu515 ripingit recomendation hell-to-heaven qq1134093272 taweilin ywadmin jiangsy163 helloworld19930113 zichangzhang01 pengjinfu nightqiuhua amberyu2218 rainbowf xuzetao tt1back

return S1,S2,entropy

return S1,S2,minEntropy

def DecimalScaling(self):
        arr_ = list()
        j = self.x_max // 10 if self.x_max % 10 == 0 else self.x_max // 10 + 1
        for x in self.arr:
            arr_.append(round(x / math.pow(10, j), 4))
        print("经过Decimal Scaling标准化后的数据为:\n{}".format(arr_))

    movies = dict()
    for (sim_user,sim) in newNU[:self.k]:
        for movieID in self.train[sim_user].keys():
            movies.setdefault(movieID,0)  #书中少了这句代码
            movies[movieID] += sim * self.train[sim_user][movieID]
    newMovies = sorted(movies.items(), key = lambda  k:k[1], reverse=True)
    return newMovies