bpmf.py 文件源码

def _update_user_features(self):
        # Gibbs sampling for user features
        for user_id in xrange(self.n_user):
            indices = self.ratings_csr_[user_id, :].indices
            features = self.item_features_[indices, :]
            rating = self.ratings_csr_[user_id, :].data - self.mean_rating_
            rating = np.reshape(rating, (rating.shape[0], 1))

            covar = inv(
                self.alpha_user + self.beta * np.dot(features.T, features))
            lam = cholesky(covar)
            # aplha * sum(V_j * R_ij) + LAMBDA_U * mu_u
            temp = (self.beta * np.dot(features.T, rating) +
                    np.dot(self.alpha_user, self.mu_user))
            # mu_i_star
            mean = np.dot(covar, temp)
            temp_feature = mean + np.dot(
                lam, self.rand_state.randn(self.n_feature, 1))
            self.user_features_[user_id, :] = temp_feature.ravel()