Source code for gpytorch.variational.delta_variational_distribution

#!/usr/bin/env python3

import torch

from ..distributions import Delta, Distribution, MultivariateNormal
from ._variational_distribution import _VariationalDistribution


class DeltaVariationalDistribution(_VariationalDistribution):
    """
    This :obj:`~gpytorch.variational._VariationalDistribution` object replaces a variational distribution
    with a single particle. It is equivalent to doing MAP inference.

    :param int num_inducing_points: Size of the variational distribution. This implies that the variational mean
        should be this size, and the variational covariance matrix should have this many rows and columns.
    :param batch_shape: Specifies an optional batch size
        for the variational parameters. This is useful for example when doing additive variational inference.
    :type batch_shape: :obj:`torch.Size`, optional
    :param float mean_init_std: (Default: 1e-3) Standard deviation of gaussian noise to add to the mean initialization.
    """

    def __init__(
        self,
        num_inducing_points: int,
        batch_shape: torch.Size = torch.Size([]),
        mean_init_std: float = 1e-3,
        **kwargs,
    ):
        super().__init__(num_inducing_points=num_inducing_points, batch_shape=batch_shape, mean_init_std=mean_init_std)
        mean_init = torch.zeros(num_inducing_points)
        mean_init = mean_init.repeat(*batch_shape, 1)
        self.register_parameter(name="variational_mean", parameter=torch.nn.Parameter(mean_init))

    def forward(self) -> Distribution:
        return Delta(self.variational_mean)

    def initialize_variational_distribution(self, prior_dist: MultivariateNormal) -> None:
        self.variational_mean.data.copy_(prior_dist.mean)
        self.variational_mean.data.add_(torch.randn_like(prior_dist.mean), alpha=self.mean_init_std)