Source code for pyro.contrib.gp.kernels.static

from __future__ import absolute_import, division, print_function

import torch
from torch.distributions import constraints
from torch.nn import Parameter

from .kernel import Kernel

[docs]class Constant(Kernel):
r"""
Implementation of Constant kernel:

:math:k(x, z) = \sigma^2.
"""
def __init__(self, input_dim, variance=None, active_dims=None):
super(Constant, self).__init__(input_dim, active_dims)

variance = torch.tensor(1.) if variance is None else variance
self.variance = Parameter(variance)
self.set_constraint("variance", constraints.positive)

[docs]    def forward(self, X, Z=None, diag=False):
if diag:
return self.variance.expand(X.size(0))

if Z is None:
Z = X
return self.variance.expand(X.size(0), Z.size(0))

[docs]class WhiteNoise(Kernel):
r"""
Implementation of WhiteNoise kernel:

:math:k(x, z) = \sigma^2 \delta(x, z),

where :math:\delta is a Dirac delta function.
"""
def __init__(self, input_dim, variance=None, active_dims=None):
super(WhiteNoise, self).__init__(input_dim, active_dims)

variance = torch.tensor(1.) if variance is None else variance
self.variance = Parameter(variance)
self.set_constraint("variance", constraints.positive)

[docs]    def forward(self, X, Z=None, diag=False):
if diag:
return self.variance.expand(X.size(0))

if Z is None:
return self.variance.expand(X.size(0)).diag()
else:
return X.data.new_zeros(X.size(0), Z.size(0))