matrix_factorization.py 文件源码

def update_row_param(self, phi_csr, mu0, c, v, r_prev, u_prev, phi_r, phi_u, num_process):

        nrow = self.y_csr.shape[0]

        # Update 'c' and 'v' block-wise in parallel.
        if num_process == 1:
            r, u = self.update_row_param_blockwise(self.y_csr, phi_csr, mu0, c, v, r_prev, u_prev, phi_r, phi_u)
        else:
            n_block = num_process
            block_ind = np.linspace(0, nrow, 1 + n_block, dtype=int)
            ru = joblib.Parallel(n_jobs=num_process)(
                joblib.delayed(self.update_row_param_blockwise)(
                    self.y_csr[block_ind[m]:block_ind[m + 1], :],
                    phi_csr[block_ind[m]:block_ind[m + 1], :],
                    mu0, c, v,
                    r_prev[block_ind[m]:block_ind[m + 1]],
                    u_prev[block_ind[m]:block_ind[m + 1]],
                    phi_r[block_ind[m]:block_ind[m + 1]],
                    phi_u)
                for m in range(n_block))
            r = np.concatenate([ru_i[0] for ru_i in ru])
            u = np.vstack([ru_i[1] for ru_i in ru])

        return r, u