def test_laplace(mu_b: Tuple[float, float], hybridize: bool) -> None:
"""
Test to check that maximizing the likelihood recovers the parameters
"""
# test instance
mu, b = mu_b
# generate samples
mus = mx.nd.zeros((NUM_SAMPLES, )) + mu
bs = mx.nd.zeros((NUM_SAMPLES, )) + b
laplace_distr = Laplace(mu=mus, b=bs)
samples = laplace_distr.sample()
init_biases = [
mu - START_TOL_MULTIPLE * TOL * mu,
inv_softplus(b + START_TOL_MULTIPLE * TOL * b),
]
mu_hat, b_hat = maximum_likelihood_estimate_sgd(LaplaceOutput(),
samples,
hybridize=hybridize,
init_biases=init_biases)
assert (np.abs(mu_hat - mu) <
TOL * mu), f"mu did not match: mu = {mu}, mu_hat = {mu_hat}"
assert (np.abs(b_hat - b) <
TOL * b), f"b did not match: b = {b}, b_hat = {b_hat}"
)
< 0.05
)
# can only calculated cdf for gaussians currently
if isinstance(distr1, Gaussian) and isinstance(distr2, Gaussian):
emp_cdf, edges = empirical_cdf(samples_mix.asnumpy())
calc_cdf = mixture.cdf(mx.nd.array(edges)).asnumpy()
assert np.allclose(calc_cdf[1:, :], emp_cdf, atol=1e-2)
@pytest.mark.parametrize(
"distribution_outputs",
[
((GaussianOutput(), GaussianOutput()),),
((GaussianOutput(), StudentTOutput(), LaplaceOutput()),),
((MultivariateGaussianOutput(3), MultivariateGaussianOutput(3)),),
],
)
@pytest.mark.parametrize("serialize_fn", serialize_fn_list)
def test_mixture_output(distribution_outputs, serialize_fn) -> None:
mdo = MixtureDistributionOutput(*distribution_outputs)
args_proj = mdo.get_args_proj()
args_proj.initialize()
input = mx.nd.ones(shape=(512, 30))
distr_args = args_proj(input)
d = mdo.distribution(distr_args)
d = serialize_fn(d)
mx.nd.random.gamma(shape=(3, 4, 5)),
[None],
[None],
(3, 4),
(5, ),
),
(
DirichletMultinomialOutput(dim=5, n_trials=10),
mx.nd.random.gamma(shape=(3, 4, 5)),
[None],
[None],
(3, 4),
(5, ),
),
(
LaplaceOutput(),
mx.nd.random.normal(shape=(3, 4, 5, 6)),
[None, mx.nd.ones(shape=(3, 4, 5))],
[None, mx.nd.ones(shape=(3, 4, 5))],
(3, 4, 5),
(),
),
(
NegativeBinomialOutput(),
mx.nd.random.normal(shape=(3, 4, 5, 6)),
[None],
[None, mx.nd.ones(shape=(3, 4, 5))],
(3, 4, 5),
(),
),
(