Source code for gpflow.covariances.multioutput.kuus

# Copyright 2017-2020 The GPflow 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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from typing import Union

import tensorflow as tf
from check_shapes import check_shapes

from ...inducing_variables import (
    FallbackSeparateIndependentInducingVariables,
    FallbackSharedIndependentInducingVariables,
    InducingPoints,
)
from ...kernels import (
    IndependentLatent,
    LinearCoregionalization,
    MultioutputKernel,
    SeparateIndependent,
    SharedIndependent,
)
from ..dispatch import Kuu


[docs] @Kuu.register(InducingPoints, MultioutputKernel) @check_shapes( "inducing_variable: [M, D, 1]", "return: [M, P, M, P]", ) def Kuu_generic( inducing_variable: InducingPoints, kernel: MultioutputKernel, *, jitter: float = 0.0 ) -> tf.Tensor: Kmm = kernel(inducing_variable.Z, full_cov=True, full_output_cov=True) M = tf.shape(Kmm)[0] * tf.shape(Kmm)[1] jittermat = jitter * tf.reshape(tf.eye(M, dtype=Kmm.dtype), tf.shape(Kmm)) return Kmm + jittermat
[docs] @Kuu.register(FallbackSharedIndependentInducingVariables, SharedIndependent) @check_shapes( "inducing_variable: [M, D, P]", "return: [M, M]", ) def Kuu_shared_shared( inducing_variable: FallbackSharedIndependentInducingVariables, kernel: SharedIndependent, *, jitter: float = 0.0, ) -> tf.Tensor: Kmm = Kuu(inducing_variable.inducing_variable, kernel.kernel) jittermat = tf.eye(inducing_variable.num_inducing, dtype=Kmm.dtype) * jitter return Kmm + jittermat
[docs] @Kuu.register(FallbackSharedIndependentInducingVariables, (SeparateIndependent, IndependentLatent)) @check_shapes( "inducing_variable: [M, D, P]", "return: [L, M, M]", ) def Kuu_fallback_shared( inducing_variable: FallbackSharedIndependentInducingVariables, kernel: Union[SeparateIndependent, IndependentLatent], *, jitter: float = 0.0, ) -> tf.Tensor: Kmm = tf.stack([Kuu(inducing_variable.inducing_variable, k) for k in kernel.kernels], axis=0) jittermat = tf.eye(inducing_variable.num_inducing, dtype=Kmm.dtype)[None, :, :] * jitter return Kmm + jittermat
[docs] @Kuu.register(FallbackSeparateIndependentInducingVariables, SharedIndependent) @check_shapes( "inducing_variable: [M, D, P]", "return: [L, M, M]", ) def Kuu_fallback_separate_shared( inducing_variable: FallbackSeparateIndependentInducingVariables, kernel: SharedIndependent, *, jitter: float = 0.0, ) -> tf.Tensor: Kmm = tf.stack( [Kuu(f, kernel.kernel) for f in inducing_variable.inducing_variable_list], axis=0 ) jittermat = tf.eye(inducing_variable.num_inducing, dtype=Kmm.dtype)[None, :, :] * jitter return Kmm + jittermat
[docs] @Kuu.register( FallbackSeparateIndependentInducingVariables, (SeparateIndependent, LinearCoregionalization) ) @check_shapes( "inducing_variable: [M, D, P]", "return: [L, M, M]", ) def Kuu_fallbace_separate( inducing_variable: FallbackSeparateIndependentInducingVariables, kernel: Union[SeparateIndependent, LinearCoregionalization], *, jitter: float = 0.0, ) -> tf.Tensor: n_iv = len(inducing_variable.inducing_variable_list) n_k = len(kernel.kernels) assert ( n_iv == n_k ), f"Must have same number of inducing variables and kernels. Found {n_iv} and {n_k}." Kmms = [Kuu(f, k) for f, k in zip(inducing_variable.inducing_variable_list, kernel.kernels)] Kmm = tf.stack(Kmms, axis=0) jittermat = tf.eye(inducing_variable.num_inducing, dtype=Kmm.dtype)[None, :, :] * jitter return Kmm + jittermat