def test_model_with_transformed_distribution():
x_prior = dist.HalfNormal(2)
y_prior = dist.LogNormal(scale=3.) # transformed distribution
def model():
numpyro.sample('x', x_prior)
numpyro.sample('y', y_prior)
params = {'x': jnp.array(-5.), 'y': jnp.array(7.)}
model = handlers.seed(model, random.PRNGKey(0))
inv_transforms = {
'x': biject_to(x_prior.support),
'y': biject_to(y_prior.support)
}
expected_samples = partial(transform_fn, inv_transforms)(params)
expected_potential_energy = (-x_prior.log_prob(expected_samples['x']) -
y_prior.log_prob(expected_samples['y']) -
inv_transforms['x'].log_abs_det_jacobian(
params['x'], expected_samples['x']) -
inv_transforms['y'].log_abs_det_jacobian(
params['y'], expected_samples['y']))
reparam_model = handlers.reparam(model, {'y': TransformReparam()})
base_params = {'x': params['x'], 'y_base': params['y']}
actual_samples = constrain_fn(handlers.seed(reparam_model,
random.PRNGKey(0)), (), {},
base_params,
return_deterministic=True)
actual_potential_energy = potential_energy(reparam_model, (), {},
base_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)
def unpack_single_latent(latent):
unpacked_samples = self._unpack_latent(latent)
if self._has_transformed_dist:
# first, substitute to `param` statements in model
model = handlers.substitute(self.model, params)
return constrain_fn(model, self._inv_transforms, model_args,
model_kwargs, unpacked_samples)
else:
return transform_fn(self._inv_transforms, unpacked_samples)
