Source code for gpytorch.kernels.linear_kernel

#!/usr/bin/env python3

import warnings

import torch

from ..constraints import Positive
from ..lazy import MatmulLazyTensor, RootLazyTensor
from .kernel import Kernel

[docs]class LinearKernel(Kernel): r""" Computes a covariance matrix based on the Linear kernel between inputs :math:`\mathbf{x_1}` and :math:`\mathbf{x_2}`: .. math:: \begin{equation*} k_\text{Linear}(\mathbf{x_1}, \mathbf{x_2}) = v\mathbf{x_1}^\top \mathbf{x_2}. \end{equation*} where * :math:`v` is a :attr:`variance` parameter. .. note:: To implement this efficiently, we use a :obj:`gpytorch.lazy.RootLazyTensor` during training and a :class:`gpytorch.lazy.MatmulLazyTensor` during test. These lazy tensors represent matrices of the form :math:`K = XX^{\top}` and :math:`K = XZ^{\top}`. This makes inference efficient because a matrix-vector product :math:`Kv` can be computed as :math:`Kv=X(X^{\top}v)`, where the base multiply :math:`Xv` takes only :math:`O(nd)` time and space. Args: :attr:`variance_prior` (:class:`gpytorch.priors.Prior`): Prior over the variance parameter (default `None`). :attr:`variance_constraint` (Constraint, optional): Constraint to place on variance parameter. Default: `Positive`. :attr:`active_dims` (list): List of data dimensions to operate on. `len(active_dims)` should equal `num_dimensions`. """ def __init__(self, num_dimensions=None, offset_prior=None, variance_prior=None, variance_constraint=None, **kwargs): super(LinearKernel, self).__init__(**kwargs) if variance_constraint is None: variance_constraint = Positive() if num_dimensions is not None: # Remove after 1.0 warnings.warn("The `num_dimensions` argument is deprecated and no longer used.", DeprecationWarning) self.register_parameter(name="offset", parameter=torch.nn.Parameter(torch.zeros(1, 1, num_dimensions))) if offset_prior is not None: # Remove after 1.0 warnings.warn("The `offset_prior` argument is deprecated and no longer used.", DeprecationWarning) self.register_parameter(name="raw_variance", parameter=torch.nn.Parameter(torch.zeros(*self.batch_shape, 1, 1))) if variance_prior is not None: self.register_prior("variance_prior", variance_prior, lambda m: m.variance, lambda m, v: m._set_variance(v)) self.register_constraint("raw_variance", variance_constraint) @property def variance(self): return self.raw_variance_constraint.transform(self.raw_variance) @variance.setter def variance(self, value): self._set_variance(value) def _set_variance(self, value): if not torch.is_tensor(value): value = torch.as_tensor(value).to(self.raw_variance) self.initialize(raw_variance=self.raw_variance_constraint.inverse_transform(value)) def forward(self, x1, x2, diag=False, last_dim_is_batch=False, **params): x1_ = x1 * self.variance.sqrt() if last_dim_is_batch: x1_ = x1_.transpose(-1, -2).unsqueeze(-1) if x1.size() == x2.size() and torch.equal(x1, x2): # Use RootLazyTensor when x1 == x2 for efficiency when composing # with other kernels prod = RootLazyTensor(x1_) else: x2_ = x2 * self.variance.sqrt() if last_dim_is_batch: x2_ = x2_.transpose(-1, -2).unsqueeze(-1) prod = MatmulLazyTensor(x1_, x2_.transpose(-2, -1)) if diag: return prod.diag() else: return prod