from numpyro import optim
from numpyro.contrib.autoguide import (AutoContinuousELBO, AutoDiagonalNormal,
AutoIAFNormal, AutoBNAFNormal,
AutoLaplaceApproximation,
AutoLowRankMultivariateNormal,
AutoMultivariateNormal)
from numpyro.contrib.nn.auto_reg_nn import AutoregressiveNN
import numpyro.distributions as dist
from numpyro.distributions import constraints, transforms
from numpyro.distributions.flows import InverseAutoregressiveTransform
from numpyro.handlers import substitute
from numpyro.infer import SVI
from numpyro.infer.util import init_to_median
from numpyro.util import fori_loop
init_strategy = init_to_median(num_samples=2)
@pytest.mark.parametrize('auto_class', [
AutoDiagonalNormal,
AutoIAFNormal,
AutoBNAFNormal,
AutoMultivariateNormal,
AutoLaplaceApproximation,
AutoLowRankMultivariateNormal,
])
def test_beta_bernoulli(auto_class):
data = np.array([[1.0] * 8 + [0.0] * 2, [1.0] * 4 + [0.0] * 6]).T
def model(data):
f = numpyro.sample('beta', dist.Beta(np.ones(2), np.ones(2)))
inv_transforms['y'].log_abs_det_jacobian(params['y'], expected_samples['y'])
)
base_inv_transforms = {'x': biject_to(x_prior.support), 'y': biject_to(y_prior.base_dist.support)}
actual_samples = constrain_fn(
handlers.seed(model, random.PRNGKey(0)), (), {}, base_inv_transforms, params)
actual_potential_energy = potential_energy(model, (), {}, base_inv_transforms, params)
assert_allclose(expected_samples['x'], actual_samples['x'])
assert_allclose(expected_samples['y'], actual_samples['y'])
assert_allclose(actual_potential_energy, expected_potential_energy)
@pytest.mark.parametrize('init_strategy', [
init_to_feasible(),
init_to_median(num_samples=2),
init_to_prior(),
init_to_uniform(),
])
def test_initialize_model_change_point(init_strategy):
def model(data):
alpha = 1 / np.mean(data)
lambda1 = numpyro.sample('lambda1', dist.Exponential(alpha))
lambda2 = numpyro.sample('lambda2', dist.Exponential(alpha))
tau = numpyro.sample('tau', dist.Uniform(0, 1))
lambda12 = np.where(np.arange(len(data)) < tau * len(data), lambda1, lambda2)
numpyro.sample('obs', dist.Poisson(lambda12), obs=data)
count_data = np.array([
13, 24, 8, 24, 7, 35, 14, 11, 15, 11, 22, 22, 11, 57,
11, 19, 29, 6, 19, 12, 22, 12, 18, 72, 32, 9, 7, 13,