#!/usr/bin/env python3
from typing import Any, Optional
import torch
from torch import Tensor
from torch.distributions import Laplace
from ..constraints import Interval, Positive
from ..distributions import base_distributions
from ..priors import Prior
from .likelihood import _OneDimensionalLikelihood
[docs]class LaplaceLikelihood(_OneDimensionalLikelihood):
r"""
A Laplace likelihood/noise model for GP regression.
It has one learnable parameter: :math:`\sigma` - the noise
:param batch_shape: The batch shape of the learned noise parameter (default: []).
:param noise_prior: Prior for noise parameter :math:`\sigma`.
:param noise_constraint: Constraint for noise parameter :math:`\sigma`.
:var torch.Tensor noise: :math:`\sigma` parameter (noise)
"""
def __init__(
self,
batch_shape: torch.Size = torch.Size([]),
noise_prior: Optional[Prior] = None,
noise_constraint: Optional[Interval] = None,
) -> None:
super().__init__()
if noise_constraint is None:
noise_constraint = Positive()
self.raw_noise = torch.nn.Parameter(torch.zeros(*batch_shape, 1))
if noise_prior is not None:
self.register_prior("noise_prior", noise_prior, lambda m: m.noise, lambda m, v: m._set_noise(v))
self.register_constraint("raw_noise", noise_constraint)
@property
def noise(self) -> Tensor:
return self.raw_noise_constraint.transform(self.raw_noise)
@noise.setter
def noise(self, value: Tensor) -> None:
self._set_noise(value)
def _set_noise(self, value: Tensor) -> None:
if not torch.is_tensor(value):
value = torch.as_tensor(value).to(self.raw_noise)
self.initialize(raw_noise=self.raw_noise_constraint.inverse_transform(value))
def forward(self, function_samples: Tensor, *args: Any, **kwargs: Any) -> Laplace:
return base_distributions.Laplace(loc=function_samples, scale=self.noise.sqrt())