6dfa3d3e9a6a668ca6380134da7db7b63fad7f6a,tensorly/decomposition/_nn_cp.py,,non_negative_parafac_hals,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,294

Before Change

                factors[mode] = tl.transpose(
                    hals_nnls_exact(tl.transpose(mttkrp), pseude_inverse, tl.transpose(factors[mode]),
                                     maxiter=5000)[0])
            factors_norm = tl.sum(tl.sum(pseude_inverse * tl.dot(tl.conj(tl.transpose(factors[mode])), factors[mode])))
            rec_error = norm_tensor ** 2 + factors_norm - 2 * tl.dot(tl.tensor_to_vec(factors[mode]),
                                                                     tl.tensor_to_vec(mttkrp))
            rec_error = rec_error ** (1 / 2) / norm_tensor

        if tol:
            rec_errors.append(rec_error)

After Change

        if tol:
            factors_norm = cp_norm((weights, factors))
            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:
                if verbose:

Instances