# Source code for pyro.contrib.gp.likelihoods.gaussian

# Copyright (c) 2017-2019 Uber Technologies, Inc.

import torch
from torch.distributions import constraints

import pyro
import pyro.distributions as dist
from pyro.contrib.gp.likelihoods.likelihood import Likelihood
from pyro.nn.module import PyroParam

[docs]class Gaussian(Likelihood):
"""
Implementation of Gaussian likelihood, which is used for regression problems.

Gaussian likelihood uses :class:~pyro.distributions.Normal distribution.

:param torch.Tensor variance: A variance parameter, which plays the role of
noise in regression problems.
"""

def __init__(self, variance=None):
super().__init__()

variance = torch.tensor(1.0) if variance is None else variance
self.variance = PyroParam(variance, constraints.positive)

[docs]    def forward(self, f_loc, f_var, y=None):
r"""
Samples :math:y given :math:f_{loc}, :math:f_{var} according to

.. math:: y \sim \mathbb{Normal}(f_{loc}, f_{var} + \epsilon),

where :math:\epsilon is the variance parameter of this likelihood.

:param torch.Tensor f_loc: Mean of latent function output.
:param torch.Tensor f_var: Variance of latent function output.
:param torch.Tensor y: Training output tensor.
:returns: a tensor sampled from likelihood
:rtype: torch.Tensor
"""
y_var = f_var + self.variance

y_dist = dist.Normal(f_loc, y_var.sqrt())
if y is not None:
y_dist = y_dist.expand_by(y.shape[: -f_loc.dim()]).to_event(y.dim())
return pyro.sample(self._pyro_get_fullname("y"), y_dist, obs=y)