Source code for gpytorch.likelihoods.laplace_likelihood

#!/usr/bin/env python3

import torch

from ..constraints import Positive
from ..distributions import base_distributions
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: []). :type batch_shape: torch.Size, optional :param noise_prior: Prior for noise parameter :math:`\sigma`. :type noise_prior: ~gpytorch.priors.Prior, optional :param noise_constraint: Constraint for noise parameter :math:`\sigma`. :type noise_constraint: ~gpytorch.constraints.Interval, optional :var torch.Tensor noise: :math:`\sigma` parameter (noise) """ def __init__(self, batch_shape=torch.Size([]), noise_prior=None, noise_constraint=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): return self.raw_noise_constraint.transform(self.raw_noise) @noise.setter def noise(self, value): self._set_noise(value) def _set_noise(self, value): 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, **kwargs): return base_distributions.Laplace(loc=function_samples, scale=self.noise.sqrt())