Source code for gpytorch.kernels.additive_structure_kernel

#!/usr/bin/env python3

from typing import Optional, Tuple

from .kernel import Kernel

[docs]class AdditiveStructureKernel(Kernel): r""" A Kernel decorator for kernels with additive structure. If a kernel decomposes additively, then this module will be much more computationally efficient. A kernel function `k` decomposes additively if it can be written as .. math:: \begin{equation*} k(\mathbf{x_1}, \mathbf{x_2}) = k'(x_1^{(1)}, x_2^{(1)}) + \ldots + k'(x_1^{(d)}, x_2^{(d)}) \end{equation*} for some kernel :math:`k'` that operates on a subset of dimensions. Given a `b x n x d` input, `AdditiveStructureKernel` computes `d` one-dimensional kernels (using the supplied base_kernel), and then adds the component kernels together. Unlike :class:`~gpytorch.kernels.AdditiveKernel`, `AdditiveStructureKernel` computes each of the additive terms in batch, making it very fast. Args: base_kernel (Kernel): The kernel to approximate with KISS-GP num_dims (int): The dimension of the input data. active_dims (tuple of ints, optional): Passed down to the `base_kernel`. """ @property def is_stationary(self) -> bool: """ Kernel is stationary if the base kernel is stationary. """ return self.base_kernel.is_stationary def __init__( self, base_kernel: Kernel, num_dims: int, active_dims: Optional[Tuple[int, ...]] = None, ): super(AdditiveStructureKernel, self).__init__(active_dims=active_dims) self.base_kernel = base_kernel self.num_dims = num_dims def forward(self, x1, x2, diag=False, last_dim_is_batch=False, **params): if last_dim_is_batch: raise RuntimeError("AdditiveStructureKernel does not accept the last_dim_is_batch argument.") res = self.base_kernel(x1, x2, diag=diag, last_dim_is_batch=True, **params) res = res.sum(-2 if diag else -3) return res def prediction_strategy(self, train_inputs, train_prior_dist, train_labels, likelihood): return self.base_kernel.prediction_strategy(train_inputs, train_prior_dist, train_labels, likelihood) def num_outputs_per_input(self, x1, x2): return self.base_kernel.num_outputs_per_input(x1, x2)