def test_logit_normal_likelihood(mu: float, sigma: float, hybridize: bool): """ 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 = LogitNormal(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( LogitNormalOutput(), samples, init_biases=init_biases, hybridize=hybridize, learning_rate=PositiveFloat(0.001), num_epochs=PositiveInt(5), ) 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}"
@pytest.mark.parametrize( "distr_output", [ BetaOutput(), CategoricalOutput(num_cats=3), DeterministicOutput(value=42.0), DirichletMultinomialOutput(dim=3, n_trials=5), DirichletOutput(dim=4), EmpiricalDistributionOutput(num_samples=10, distr_output=GaussianOutput()), GammaOutput(), GaussianOutput(), GenParetoOutput(), LaplaceOutput(), LogitNormalOutput(), LoglogisticOutput(), LowrankMultivariateGaussianOutput(dim=5, rank=2), MultivariateGaussianOutput(dim=4), NegativeBinomialOutput(), OneInflatedBetaOutput(), PiecewiseLinearOutput(num_pieces=10), PoissonOutput(), StudentTOutput(), UniformOutput(), WeibullOutput(), ZeroAndOneInflatedBetaOutput(), ZeroInflatedBetaOutput(), ZeroInflatedNegativeBinomialOutput(), ZeroInflatedPoissonOutput(), ],