pytspl.hogde_gp.exact_gp

Module for the ExactGPModel class.

Classes

ExactGPModel

The base class for any Gaussian process latent function to be used in conjunction

Module Contents

class pytspl.hogde_gp.exact_gp.ExactGPModel(train_x: torch.tensor, train_y: torch.tensor, likelihood: gpytorch.likelihoods, kernel: gpytorch.kernels.Kernel, mean_function=None)

Bases: gpytorch.models.ExactGP

The base class for any Gaussian process latent function to be used in conjunction with exact inference.

Parameters:

• train_inputs (torch.Tensor) – (size n x d) The training features \(\mathbf X\).

• train_targets (torch.Tensor) – (size n) The training targets \(\mathbf y\).

• likelihood (GaussianLikelihood) – The Gaussian likelihood that defines the observational distribution. Since we’re using exact inference, the likelihood must be Gaussian.

The forward() function should describe how to compute the prior latent distribution on a given input. Typically, this will involve a mean and kernel function. The result must be a MultivariateNormal.

Calling this model will return the posterior of the latent Gaussian process when conditioned on the training data. The output will be a MultivariateNormal.

Example:

>>> class MyGP(gpytorch.models.ExactGP):
>>>     def __init__(self, train_x, train_y, likelihood):
>>>         super().__init__(train_x, train_y, likelihood)
>>>         self.mean_module = gpytorch.means.ZeroMean()
>>>         self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())
>>>
>>>     def forward(self, x):
>>>         mean = self.mean_module(x)
>>>         covar = self.covar_module(x)
>>>         return gpytorch.distributions.MultivariateNormal(mean, covar)
>>>
>>> # train_x = ...; train_y = ...
>>> likelihood = gpytorch.likelihoods.GaussianLikelihood()
>>> model = MyGP(train_x, train_y, likelihood)
>>>
>>> # test_x = ...;
>>> model(test_x)  # Returns the GP latent function at test_x
>>> likelihood(model(test_x))  # Returns the (approximate) predictive posterior distribution at test_x

covar_module

forward(x: torch.tensor)

Forward pass for the model.

Args:

x (torch.tensor): The input data.