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additive_structure_kernel.py
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additive_structure_kernel.py
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#!/usr/bin/env python3
from .kernel import Kernel
from ..lazy import LazyTensor, NonLazyTensor
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:
:attr:`base_kernel` (Kernel):
The kernel to approximate with KISS-GP
:attr:`num_dims` (int):
The dimension of the input data.
:attr:`active_dims` (tuple of ints, optional):
Passed down to the `base_kernel`.
"""
def __init__(self, base_kernel, num_dims, active_dims=None):
super(AdditiveStructureKernel, self).__init__(active_dims=active_dims)
self.base_kernel = base_kernel
self.num_dims = num_dims
def forward(self, x1, x2, batch_dims=None, **params):
if batch_dims == (0, 2):
raise RuntimeError("AdditiveStructureKernel does not accept the batch_dims argument.")
res = self.base_kernel(x1, x2, batch_dims=(0, 2), **params)
evaluate = False
if not isinstance(res, LazyTensor):
evaluate = True
res = NonLazyTensor(res)
res = res.sum_batch(sum_batch_size=x1.size(-1))
if evaluate:
res = res.evaluate()
return res
def size(self, x1, x2):
return self.base_kernel.size(x1, x2)