test_base.py 文件源码

def test_tucker_tensor():
    """test for random.tucker_tensor"""
    shape = (10, 11, 12)
    rank = 4

    tensor = tucker_tensor(shape, rank, full=True)
    for i in range(T.ndim(tensor)):
        T.assert_equal(matrix_rank(T.to_numpy(unfold(tensor, i))), rank)

    core, factors = tucker_tensor(shape, rank, full=False)
    for i, factor in enumerate(factors):
        T.assert_equal(factor.shape, (shape[i], rank),
                err_msg=('{}-th factor has shape {}, expected {}'.format(
                     i, factor.shape, (shape[i], rank))))

    shape = (10, 11, 12)
    rank = (6, 4, 5)
    tensor = tucker_tensor(shape, rank, full=True)
    for i in range(T.ndim(tensor)):
        T.assert_equal(matrix_rank(T.to_numpy(unfold(tensor, i))),  min(shape[i], rank[i]))

    core, factors = tucker_tensor(shape, rank, full=False)
    for i, factor in enumerate(factors):
        T.assert_equal(factor.shape, (shape[i], rank[i]),
                err_msg=('{}-th factor has shape {}, expected {}.'.format(
                     i, factor.shape, (shape[i], rank[i]))))
    T.assert_equal(core.shape, rank, err_msg='core has shape {}, expected {}.'.format(
                                     core.shape, rank))
    for factor in factors:
        T.assert_array_almost_equal(T.dot(T.transpose(factor), factor), T.tensor(np.eye(factor.shape[1])))
    tensor = tucker_to_tensor(core, factors)
    reconstructed = multi_mode_dot(tensor, factors, transpose=True)
    T.assert_array_almost_equal(core, reconstructed)

    with T.assert_raises(ValueError):
        tucker_tensor((3, 4, 5), (3, 6, 3))