def test_cov_shape(random_process: randprocs.RandomProcess, args0: np.ndarray):
"""Test whether the covariance of the random process has the correct shape."""
n = args0.shape[0]
if random_process.output_dim == 1:
assert (random_process.cov(args0).shape == (n, n)
or random_process.cov(args0).ndim < 2)
else:
assert random_process.cov(args0).shape == (
n,
n,
random_process.output_dim,
random_process.output_dim,
)
def test_rp_mean_cov_evaluated_matches_rv_mean_cov(
random_process: randprocs.RandomProcess,
random_state: np.random.RandomState):
"""Check whether the evaluated mean and covariance function of a random process is
equivalent to the mean and covariance of the evaluated random process as a random
variable."""
x = random_state.normal(size=(10, random_process.input_dim))
np.testing.assert_allclose(
random_process(x).mean,
random_process.mean(x),
err_msg=f"Mean of evaluated {repr(random_process)} does not match the "
f"random process mean function evaluated.",
)
np.testing.assert_allclose(
random_process(x).cov,
random_process.cov(x),
err_msg=
f"Covariance of evaluated {repr(random_process)} does not match the "
f"random process mean function evaluated.",
)
