def test_mean_shape(random_process: randprocs.RandomProcess,
args0: np.ndarray):
"""Test whether the mean of the random process has the correct shape."""
if random_process.output_dim == 1:
assert random_process.mean(args0).ndim == 1
else:
assert random_process.mean(args0).shape[1] == random_process.output_dim
def test_std_shape(random_process: randprocs.RandomProcess, args0: np.ndarray):
"""Test whether the standard deviation of the random process has the correct
shape."""
if random_process.output_dim == 1:
assert random_process.std(args0).ndim == 1
else:
assert random_process.std(args0).shape[1] == random_process.output_dim
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 is None:
assert random_process.covmatrix(args0).shape == (n, n)
else:
assert random_process.covmatrix(args0).shape == (
random_process.output_dim,
random_process.output_dim,
n,
n,
)
def test_sample_paths_are_deterministic_functions(
random_process: randprocs.RandomProcess, args0: np.ndarray
):
"""When sampling paths from a random process, repeated evaluation of the sample path
at the same inputs should return the same values."""
sample_path = random_process.sample()
np.testing.assert_array_equal(sample_path(args0), sample_path(args0))
def test_rp_mean_cov_evaluated_matches_rv_mean_cov(
random_process: randprocs.RandomProcess, rng: np.random.Generator
):
"""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 = rng.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.covmatrix(x),
err_msg=f"Covariance of evaluated {repr(random_process)} does not match the "
f"random process mean function evaluated.",
)
def test_samples_are_callables(random_process: randprocs.RandomProcess,
random_state: np.random.RandomState):
"""When not specifying inputs to the sample method it should return ``size`` number
of callables."""
assert callable(random_process.sample(random_state=random_state))
def test_samples_are_callables(
random_process: randprocs.RandomProcess, rng: np.random.Generator
):
"""When not specifying inputs to the sample method it should return ``size`` number
of callables."""
assert callable(random_process.sample(rng=rng))
def test_var_shape(random_process: randprocs.RandomProcess, args0: np.ndarray):
"""Test whether the variance of the random process has the correct shape."""
if random_process.output_dim is None:
assert random_process.var(args0).ndim == 1
else:
assert random_process.var(args0).shape[1] == random_process.output_dim
def test_std_shape(random_process: randprocs.RandomProcess, args0: np.ndarray):
"""Test whether the standard deviation of the random process has the correct
shape."""
expected_shape = args0.shape[:-1] + random_process.output_shape
assert random_process.std(args0).shape == expected_shape
def test_var_shape(random_process: randprocs.RandomProcess, args0: np.ndarray):
"""Test whether the variance of the random process has the correct shape."""
expected_shape = args0.shape[:-1] + random_process.output_shape
assert random_process.var(args0).shape == expected_shape
