# 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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# ==============================================================================
import beartype.typing as tp
from jax import vmap
from jaxtyping import Float
from gpjax.kernels.computations.base import AbstractKernelComputation
from gpjax.typing import Array
K = tp.TypeVar("K", bound="gpjax.kernels.base.AbstractKernel")
[docs]
class DenseKernelComputation(AbstractKernelComputation):
r"""Dense kernel computation class. Operations with the kernel assume
a dense gram matrix structure.
"""
def _cross_covariance(
self, kernel: K, x: Float[Array, "N D"], y: Float[Array, "M D"]
) -> Float[Array, "N M"]:
cross_cov = vmap(lambda x: vmap(lambda y: kernel(x, y))(y))(x)
return cross_cov