# Copyright Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0
import torch
import pyro
import pyro.distributions as dist
from pyro.ops.tensor_utils import safe_normalize
from .reparam import Reparam
[docs]class ProjectedNormalReparam(Reparam):
"""
Reparametrizer for :class:`~pyro.distributions.ProjectedNormal` latent
variables.
This reparameterization works only for latent variables, not likelihoods.
"""
[docs] def apply(self, msg):
name = msg["name"]
fn = msg["fn"]
value = msg["value"]
is_observed = msg["is_observed"]
if is_observed:
raise NotImplementedError(
"ProjectedNormalReparam does not support observe statements"
)
fn, event_dim = self._unwrap(fn)
assert isinstance(fn, dist.ProjectedNormal)
# Differentiably invert transform.
value_normal = None
if value is not None:
# We use an arbitrary injection, which works only for initialization.
value_normal = value - fn.concentration
# Draw parameter-free noise.
new_fn = dist.Normal(torch.zeros_like(fn.concentration), 1).to_event(1)
x = pyro.sample(
"{}_normal".format(name),
self._wrap(new_fn, event_dim),
obs=value_normal,
infer={"is_observed": is_observed},
)
# Differentiably transform.
if value is None:
value = safe_normalize(x + fn.concentration)
# Simulate a pyro.deterministic() site.
new_fn = dist.Delta(value, event_dim=event_dim).mask(False)
return {"fn": new_fn, "value": value, "is_observed": True}