matrix_factorization.py 文件源码

def update_per_row(self, y_i, phi_i, J, mu0, c, v, r_prev_i, u_prev_i, phi_r_i, phi_u):
        # Params:
        #   J - column indices

        nnz_i = len(J)
        residual_i = y_i - mu0 - c[J]
        prior_Phi = np.diag(np.concatenate(([phi_r_i], phi_u)))
        v_T = np.hstack((np.ones((nnz_i, 1)), v[J, :]))
        post_Phi_i = prior_Phi + \
                     np.dot(v_T.T,
                            np.tile(phi_i[:, np.newaxis], (1, 1 + self.num_factor)) * v_T)  # Weighted sum of v_j * v_j.T
        post_mean_i = np.squeeze(np.dot(phi_i * residual_i, v_T))
        C, lower = scipy.linalg.cho_factor(post_Phi_i)
        post_mean_i = scipy.linalg.cho_solve((C, lower), post_mean_i)
        # Generate Gaussian, recycling the Cholesky factorization from the posterior mean computation.
        ru_i = math.sqrt(1 - self.relaxation ** 2) * scipy.linalg.solve_triangular(C, np.random.randn(len(post_mean_i)),
                                                                                   lower=lower)
        ru_i += post_mean_i + self.relaxation * (post_mean_i - np.concatenate(([r_prev_i], u_prev_i)))
        r_i = ru_i[0]
        u_i = ru_i[1:]

        return r_i, u_i