matrix_factorization.py 文件源码

def update_col_param(self, phi_csc, mu0, r, u, c_prev, v_prev, phi_c, phi_v, num_process):

        ncol = self.y_csc.shape[1]

        if num_process == 1:
            c, v = self.update_col_param_blockwise(self.y_csc, phi_csc, mu0, r, u, c_prev, v_prev, phi_c, phi_v)
        else:
            # Update 'c' and 'v' block-wise in parallel.
            n_block = num_process
            block_ind = np.linspace(0, ncol, 1 + n_block, dtype=int)
            cv = joblib.Parallel(n_jobs=num_process)(
                joblib.delayed(self.update_col_param_blockwise)(
                    self.y_csc[:, block_ind[m]:block_ind[m + 1]],
                    phi_csc[:, block_ind[m]:block_ind[m + 1]],
                    mu0, r, u,
                    c_prev[block_ind[m]:block_ind[m + 1]],
                    v_prev[block_ind[m]:block_ind[m + 1]],
                    phi_c[block_ind[m]:block_ind[m + 1]],
                    phi_v)
                for m in range(n_block))
            c = np.concatenate([cv_j[0] for cv_j in cv])
            v = np.vstack([cv_j[1] for cv_j in cv])

        return c, v