def test_loglogistic_likelihood(mu: float, sigma: float,
hybridize: bool) -> None:
"""
Test to check that maximizing the likelihood recovers the parameters
"""
# generate samples
mus = mx.nd.zeros((NUM_SAMPLES, )) + mu
sigmas = mx.nd.zeros((NUM_SAMPLES, )) + sigma
distr = Loglogistic(mus, sigmas)
samples = distr.sample()
init_biases = [
mu - START_TOL_MULTIPLE * TOL * mu,
inv_softplus(sigma - START_TOL_MULTIPLE * TOL * sigma),
]
mu_hat, sigma_hat = maximum_likelihood_estimate_sgd(
LoglogisticOutput(),
samples,
init_biases=init_biases,
hybridize=hybridize,
learning_rate=PositiveFloat(0.05),
num_epochs=PositiveInt(10),
)
print("mu:", mu_hat, "sigma:", sigma_hat)
assert (np.abs(mu_hat - mu) <
TOL * mu), f"mu did not match: mu = {mu}, mu_hat = {mu_hat}"
assert (np.abs(sigma_hat - sigma) < TOL * sigma
), f"sigma did not match: sigma = {sigma}, sigma_hat = {sigma_hat}"
low=mx.nd.zeros(shape=(3, 4, 5)),
high=mx.nd.ones(shape=(3, 4, 5)),
),
[
BoxCoxTransform(
lambda_1=mx.nd.ones(shape=(3, 4, 5)),
lambda_2=mx.nd.zeros(shape=(3, 4, 5)),
)
],
),
(3, 4, 5),
(),
),
(
Loglogistic(
mx.nd.zeros(shape=(3, 4, 5)),
mx.nd.ones(shape=(3, 4, 5)),
),
(3, 4, 5),
(),
),
(
Weibull(
mx.nd.ones(shape=(3, 4, 5)),
mx.nd.ones(shape=(3, 4, 5)),
),
(3, 4, 5),
(),
),
],
)
def test_distribution_shapes(