bpmf.py 文件源码

def _udpate_item_features(self):
        # Gibbs sampling for item features
        for item_id in xrange(self.n_item):
            indices = self.ratings_csc_[:, item_id].indices
            features = self.user_features_[indices, :]
            rating = self.ratings_csc_[:, item_id].data - self.mean_rating_
            rating = np.reshape(rating, (rating.shape[0], 1))

            covar = inv(self.alpha_item +
                        self.beta * np.dot(features.T, features))
            lam = cholesky(covar)

            temp = (self.beta * np.dot(features.T, rating) +
                    np.dot(self.alpha_item, self.mu_item))

            mean = np.dot(covar, temp)
            temp_feature = mean + np.dot(
                lam, self.rand_state.randn(self.n_feature, 1))
            self.item_features_[item_id, :] = temp_feature.ravel()