# Source code for gpytorch.kernels.rq_kernel

from __future__ import absolute_import, division, print_function, unicode_literals

from typing import Optional

import torch

from ..constraints import Interval, Positive
from .kernel import Kernel

[docs]class RQKernel(Kernel):
r"""
Computes a covariance matrix based on the rational quadratic kernel
between inputs :math:\mathbf{x_1} and :math:\mathbf{x_2}:

.. math::

\begin{equation*}
k_{\text{RQ}}(\mathbf{x_1}, \mathbf{x_2}) =  \left(1 + \frac{1}{2\alpha}
(\mathbf{x_1} - \mathbf{x_2})^\top \Theta^{-2} (\mathbf{x_1} - \mathbf{x_2}) \right)^{-\alpha}
\end{equation*}

where :math:\Theta is a lengthscale parameter, and :math:\alpha is the
See :class:gpytorch.kernels.Kernel for descriptions of the lengthscale options.

.. note::

This kernel does not have an outputscale parameter. To add a scaling parameter,
decorate this kernel with a :class:gpytorch.kernels.ScaleKernel.

:param ard_num_dims: Set this if you want a separate lengthscale for each input
dimension. It should be d if x1 is a n x d matrix. (Default: None.)
:param batch_shape: Set this if you want a separate lengthscale for each batch of input
data. It should be :math:B_1 \times \ldots \times B_k if :math:\mathbf x1 is
a :math:B_1 \times \ldots \times B_k \times N \times D tensor.
:param active_dims: Set this if you want to compute the covariance of only
a few input dimensions. The ints corresponds to the indices of the
dimensions. (Default: None.)
:param lengthscale_prior: Set this if you want to apply a prior to the
lengthscale parameter. (Default: None)
:param lengthscale_constraint: Set this if you want to apply a constraint
to the lengthscale parameter. (Default: Positive.)
:param alpha_constraint:
Set this if you want to apply a constraint to the alpha parameter. (Default: Positive.)
:param eps: The minimum value that the lengthscale can take (prevents
divide by zero errors). (Default: 1e-6.)

:ivar torch.Tensor lengthscale: The lengthscale parameter. Size/shape of parameter depends on the
ard_num_dims and batch_shape arguments.
:ivar torch.Tensor alpha: The rational quadratic relative weighting parameter. Size/shape of parameter depends
on the batch_shape argument
"""

has_lengthscale = True

def __init__(self, alpha_constraint: Optional[Interval] = None, **kwargs):
super(RQKernel, self).__init__(**kwargs)
self.register_parameter(name="raw_alpha", parameter=torch.nn.Parameter(torch.zeros(*self.batch_shape, 1)))
if alpha_constraint is None:
alpha_constraint = Positive()

self.register_constraint("raw_alpha", alpha_constraint)

def forward(self, x1, x2, diag=False, **params):
def postprocess_rq(dist_mat):
alpha = self.alpha
for _ in range(1, len(dist_mat.shape) - len(self.batch_shape)):
alpha = alpha.unsqueeze(-1)
return (1 + dist_mat.div(2 * alpha)).pow(-alpha)

x1_ = x1.div(self.lengthscale)
x2_ = x2.div(self.lengthscale)
return postprocess_rq(
self.covar_dist(x1_, x2_, square_dist=True, diag=diag, **params),
)

@property
def alpha(self):
return self.raw_alpha_constraint.transform(self.raw_alpha)

@alpha.setter
def alpha(self, value):
if not torch.is_tensor(value):
value = torch.as_tensor(value).to(self.raw_lengthscale)
self.initialize(raw_alpha=self.raw_alpha_constraint.inverse_transform(value))