# Copyright 2022 The thomaspinder Contributors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
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# ==============================================================================
import beartype.typing as tp
from jax import vmap
from jaxtyping import Float
import lineax as lx
from gpjax.kernels.computations import AbstractKernelComputation
from gpjax.typing import Array
Kernel = tp.TypeVar("Kernel", bound="gpjax.kernels.base.AbstractKernel")
[docs]
class DiagonalKernelComputation(AbstractKernelComputation):
r"""Diagonal kernel computation class. Operations with the kernel assume
a diagonal Gram matrix.
"""
[docs]
def gram(self, kernel: Kernel, x: Float[Array, "N D"]) -> lx.AbstractLinearOperator:
return lx.TaggedLinearOperator(
lx.DiagonalLinearOperator(vmap(lambda x: kernel(x, x))(x)),
lx.positive_semidefinite_tag,
)
def _cross_covariance(
self, kernel: Kernel, x: Float[Array, "N D"], y: Float[Array, "M D"]
) -> Float[Array, "N M"]:
# TODO: This is currently a dense implementation.
# We should implement a sparse LinearOperator for non-square cross-covariance matrices.
cross_cov = vmap(lambda x: vmap(lambda y: kernel(x, y))(y))(x)
return cross_cov