def test_sample_positive_constant_distribution(self):
"""Returns a constant positive sample distribution."""
constant_dist = Distribution(dist_type="constant", constant=1)
sample = constant_dist.sample_positive()
expected = constant_dist.constant*ones(1)
assert sample == expected
def test_sample_positivie_numpy(self):
"""Returns a numpy positive sample distribution."""
numpy_dist = Distribution(
dist_type="numpy",
dist_name="normal",
loc=-10, scale=0.2
)
sample = numpy_dist.sample_positive()
expected = abs(
random.default_rng(
seed=numpy_dist.seed
).normal(-10, 0.2)
)
assert sample == expected
def test_sample_positive_weights_distribution_data(
self,
fixture_weights_distribution
):
"""Returns a weights sample distribution from a data array."""
weights_dist = Distribution(
dist_type="weights",
data=pytest.data_2D
)
sample = weights_dist.sample_positive(5)
expected_sample = abs(
random.default_rng(int(time())).choice(
a=pytest.data_2D[:, 0], p=pytest.data_2D[:, 1], size=5
)
)
assert all(sample == expected_sample)
def test_sample_positive_empirical_distribution_file(
self,
fixture_empirical_distribution
):
"""Returns an empirical sample distribution from a file."""
empirical_dist = Distribution(
dist_type="empirical",
filename=pytest.filename,
kernel="gaussian",
bandwidth=0.1
)
sample = empirical_dist.sample_positive(5)
expected_sample = abs(
KernelDensity(
kernel="gaussian",
bandwidth=0.1
).fit(pytest.data_file.reshape(-1, 1)).sample(
n_samples=5, random_state=int(time())
).flatten()
)
assert all(sample == expected_sample)
