Function test_diags

tests/gpflow/test_kullback_leiblers.py:135–147

The covariance of q(x) can be Cholesky matrices or diagonal matrices. Here we make sure the behaviours overlap.

(white: bool)

@pytest.mark.parametrize("white", [True, False])
def test_diags(white: bool) -> None:
    """
    The covariance of q(x) can be Cholesky matrices or diagonal matrices.
    Here we make sure the behaviours overlap.
    """
    # the chols are diagonal matrices, with the same entries as the diag representation.
    chol_from_diag = tf.stack(
        [tf.linalg.diag(Datum.sqrt_diag[:, i]) for i in range(Datum.N)]  # [N, M, M]
    )
    kl_diag = gauss_kl(Datum.mu, Datum.sqrt_diag, Datum.K if white else None)
    kl_dense = gauss_kl(Datum.mu, chol_from_diag, Datum.K if white else None)

    np.testing.assert_allclose(kl_diag, kl_dense)


@pytest.mark.parametrize("diag", [True, False])