Source code for gpytorch.kernels.cosine_kernel

#!/usr/bin/env python3

import math
from typing import Optional

import torch

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

[docs]class CosineKernel(Kernel): r""" Computes a covariance matrix based on the cosine kernel between inputs :math:`\mathbf{x_1}` and :math:`\mathbf{x_2}`: .. math:: \begin{equation*} k_{\text{Cosine}}(\mathbf{x_1}, \mathbf{x_2}) = \cos \left( \pi \Vert \mathbf{x_1} - \mathbf{x_2} \Vert_2 / p \right) \end{equation*} where :math:`p` is the period length parameter. Args: batch_shape (torch.Size, optional): Set this if you want a separate lengthscale for each batch of input data. It should be `b` if x1 is a `b x n x d` tensor. Default: `torch.Size([])` active_dims (tuple of ints, optional): 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`. period_length_prior (Prior, optional): Set this if you want to apply a prior to the period length parameter. Default: `None` period_length_constraint (Constraint, optional): Set this if you want to apply a constraint to the period length parameter. Default: `Positive`. eps (float): The minimum value that the lengthscale/period length can take (prevents divide by zero errors). Default: `1e-6`. Attributes: period_length (Tensor): The period length parameter. Size = `*batch_shape x 1 x 1`. Example: >>> x = torch.randn(10, 5) >>> # Non-batch: Simple option >>> covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.CosineKernel()) >>> >>> batch_x = torch.randn(2, 10, 5) >>> # Batch: Simple option >>> covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.CosineKernel()) >>> # Batch: different lengthscale for each batch >>> covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.CosineKernel(batch_shape=torch.Size([2]))) >>> covar = covar_module(x) # Output: LazyVariable of size (2 x 10 x 10) """ is_stationary = True def __init__( self, period_length_prior: Optional[Prior] = None, period_length_constraint: Optional[Interval] = None, **kwargs, ): super(CosineKernel, self).__init__(**kwargs) self.register_parameter( name="raw_period_length", parameter=torch.nn.Parameter(torch.zeros(*self.batch_shape, 1, 1)) ) if period_length_constraint is None: period_length_constraint = Positive() if period_length_prior is not None: if not isinstance(period_length_prior, Prior): raise TypeError("Expected gpytorch.priors.Prior but got " + type(period_length_prior).__name__) self.register_prior( "period_length_prior", period_length_prior, lambda m: m.period_length, lambda m, v: m._set_period_length(v), ) self.register_constraint("raw_period_length", period_length_constraint) @property def period_length(self): return self.raw_period_length_constraint.transform(self.raw_period_length) @period_length.setter def period_length(self, value): return self._set_period_length(value) def _set_period_length(self, value): if not torch.is_tensor(value): value = torch.as_tensor(value).to(self.raw_period_length) self.initialize(raw_period_length=self.raw_period_length_constraint.inverse_transform(value)) def forward(self, x1, x2, **params): x1_ = x1.div(self.period_length) x2_ = x2.div(self.period_length) diff = self.covar_dist(x1_, x2_, **params) res = torch.cos(diff.mul(math.pi)) return res