Function test_lowrank_LazyTensor

test/test_utils.py:599–638

(nx)

def test_lowrank_LazyTensor(nx):
    p = 5
    n1 = 100
    n2 = 200

    shape = (n1, n2)

    rng = np.random.RandomState(42)
    X1 = rng.randn(n1, p)
    X2 = rng.randn(n2, p)
    diag_d = rng.rand(p)

    X1, X2, diag_d = nx.from_numpy(X1, X2, diag_d)

    T0 = nx.dot(X1, X2.T)

    T = ot.utils.get_lowrank_lazytensor(X1, X2)

    np.testing.assert_allclose(nx.to_numpy(T[:]), nx.to_numpy(T0))

    assert T.Q is X1
    assert T.R is X2

    # get the full tensor (not lazy)
    assert T[:].shape == shape

    # get one component
    assert T[1, 1] == nx.dot(X1[1], X2[1].T)

    # get one row
    assert T[1].shape == (n2,)

    # get one column with slices
    assert T[::10, 5].shape == (10,)

    T0 = nx.dot(X1 * diag_d[None, :], X2.T)

    T = ot.utils.get_lowrank_lazytensor(X1, X2, diag_d, nx=nx)

    np.testing.assert_allclose(nx.to_numpy(T[:]), nx.to_numpy(T0))


def test_labels_to_mask_helper(nx):