10a6518986bad8bd0eed0f2287922b0d6612e033,tensorly/decomposition/_nn_cp.py,,non_negative_parafac_hals,#,294

Before Change

            rec_error = tl.sqrt(tl.abs(norm_tensor**2 + factors_norm**2 - 2*iprod)) / norm_tensor
            rec_errors.append(rec_error)

            if iteration > 1:
                if verbose:
                    print("reconstruction error={}, variation={}.".format(
                        rec_errors[-1], rec_errors[-2] - rec_errors[-1]))

                if tol and abs(rec_errors[-2] - rec_errors[-1]) < tol:
                    if verbose:
                        print("converged in {} iterations.".format(iteration))
                    break

    cp_tensor = CPTensor((weights, factors))
    if return_errors:
        return cp_tensor, rec_errors
    else:

After Change

            iprod = tl.sum(tl.sum(mttkrp*factor, axis=0)*weights)
            rec_error = tl.sqrt(tl.abs(norm_tensor**2 + factors_norm**2 - 2*iprod)) / norm_tensor
            rec_errors.append(rec_error)
            if iteration >= 1:
                rec_error_decrease = rec_errors[-2] - rec_errors[-1]

                if verbose:
                    print("iteration {}, reconstruction error: {}, decrease = {}".format(iteration, rec_error, rec_error_decrease))

                if cvg_criterion == "abs_rec_error":
                    stop_flag = abs(rec_error_decrease) < tol
                elif cvg_criterion == "rec_error":
                    stop_flag = rec_error_decrease < tol
                else:
                    raise TypeError("Unknown convergence criterion")

                if stop_flag:
                    if verbose:
                        print("PARAFAC converged after {} iterations".format(iteration))
                    break
            else:
                if verbose:
                    print("reconstruction error={}".format(rec_errors[-1]))

    cp_tensor = CPTensor((weights, factors))
    if return_errors:
        return cp_tensor, rec_errors
    else:

Instances