9d25f96af2268c5772128496aa3d8a28795e3490,caserec/recommenders/rating_prediction/matrixfactorization.py,MatrixFactorization,_create_factors,#MatrixFactorization#,84

Before Change

            self.map_users_index.update({u: user})

    def _create_factors(self):
        self.p = self.init_mean * np.random.randn(len(self.users), self.factors) + self.init_stdev ** 2
        self.q = self.init_mean * np.random.randn(len(self.items), self.factors) + self.init_stdev ** 2
        self.bi = self.init_mean * np.random.randn(len(self.items), 1) + self.init_stdev ** 2
        self.bu = self.init_mean * np.random.randn(len(self.users), 1) + self.init_stdev ** 2

    def _predict(self, u, i, cond=True):
        if self.baseline:

After Change

        self.train_set["list_feedback"] = list_feedback

    def _create_factors(self):
        self.p = np.random.normal(self.init_mean, self.init_stdev, (len(self.users), self.factors))
        self.q = np.random.normal(self.init_mean, self.init_stdev, (len(self.items), self.factors))

        if self.baseline:
            self.bu = np.zeros(len(self.users), np.double)
            self.bi = np.zeros(len(self.items), np.double)

Instances