def test_multivariate_gaussian(hybridize: bool) -> None:
num_samples = 2000
dim = 2
mu = np.arange(0, dim) / float(dim)
L_diag = np.ones((dim, ))
L_low = 0.1 * np.ones((dim, dim)) * np.tri(dim, k=-1)
L = np.diag(L_diag) + L_low
Sigma = L.dot(L.transpose())
distr = MultivariateGaussian(mu=mx.nd.array(mu), L=mx.nd.array(L))
samples = distr.sample(num_samples)
mu_hat, L_hat = maximum_likelihood_estimate_sgd(
MultivariateGaussianOutput(dim=dim),
samples,
init_biases=
None, # todo we would need to rework biases a bit to use it in the multivariate case
hybridize=hybridize,
learning_rate=PositiveFloat(0.01),
num_epochs=PositiveInt(10),
)
distr = MultivariateGaussian(mu=mx.nd.array([mu_hat]),
L=mx.nd.array([L_hat]))
Sigma_hat = distr.variance[0].asnumpy()
assert np.allclose(
mu_hat, mu, atol=0.1,
rtol=0.1), f"mu did not match: mu = {mu}, mu_hat = {mu_hat}"
assert np.allclose(
Sigma_hat, Sigma, atol=0.1, rtol=0.1
), f"Sigma did not match: sigma = {Sigma}, sigma_hat = {Sigma_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)
LowrankMultivariateGaussianOutput(dim=target_dim, rank=2),
10,
estimator,
False,
False,
),
(
LowrankMultivariateGaussianOutput(dim=target_dim, rank=2),
10,
estimator,
True,
False,
),
(None, 10, estimator, True, True),
(
MultivariateGaussianOutput(dim=target_dim),
10,
estimator,
False,
True,
),
(
MultivariateGaussianOutput(dim=target_dim),
10,
estimator,
True,
True,
),
],
)
def test_deepvar(
mx.nd.random.gamma(shape=(3, 4, 5, 6)),
[None, mx.nd.ones(shape=(3, 4, 5))],
[None, mx.nd.ones(shape=(3, 4, 5))],
(3, 4, 5),
(),
),
(
BetaOutput(),
mx.nd.random.gamma(shape=(3, 4, 5, 6)),
[None, mx.nd.ones(shape=(3, 4, 5))],
[None, mx.nd.ones(shape=(3, 4, 5))],
(3, 4, 5),
(),
),
(
MultivariateGaussianOutput(dim=5),
mx.nd.random.normal(shape=(3, 4, 10)),
[None, mx.nd.ones(shape=(3, 4, 5))],
[None, mx.nd.ones(shape=(3, 4, 5))],
(3, 4),
(5, ),
),
(
LowrankMultivariateGaussianOutput(dim=5, rank=4),
mx.nd.random.normal(shape=(3, 4, 10)),
[None, mx.nd.ones(shape=(3, 4, 5))],
[None, mx.nd.ones(shape=(3, 4, 5))],
(3, 4),
(5, ),
),
(
[
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(),
],
)
def test_distribution_output_serde(distr_output: DistributionOutput):
distr_output_copy = decode(encode(distr_output))