Source code for gpflow.covariances.kuus

# Copyright 2017-2020 The GPflow Contributors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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import tensorflow as tf
from check_shapes import check_shapes

from ..config import default_float
from ..inducing_variables import InducingPatches, InducingPoints, Multiscale
from ..kernels import Convolutional, Kernel, SquaredExponential
from .dispatch import Kuu


[docs] @Kuu.register(InducingPoints, Kernel) @check_shapes( "inducing_variable: [M, D, 1]", "return: [M, M]", ) def Kuu_kernel_inducingpoints( inducing_variable: InducingPoints, kernel: Kernel, *, jitter: float = 0.0 ) -> tf.Tensor: Kzz = kernel(inducing_variable.Z) Kzz += jitter * tf.eye(inducing_variable.num_inducing, dtype=Kzz.dtype) return Kzz
[docs] @Kuu.register(Multiscale, SquaredExponential) @check_shapes( "inducing_variable: [M, D, 1]", "return: [M, M]", ) def Kuu_sqexp_multiscale( inducing_variable: Multiscale, kernel: SquaredExponential, *, jitter: float = 0.0 ) -> tf.Tensor: Zmu, Zlen = kernel.slice(inducing_variable.Z, inducing_variable.scales) idlengthscales2 = tf.square(kernel.lengthscales + Zlen) sc = tf.sqrt( idlengthscales2[None, ...] + idlengthscales2[:, None, ...] - kernel.lengthscales ** 2 ) d = inducing_variable._cust_square_dist(Zmu, Zmu, sc) Kzz = kernel.variance * tf.exp(-d / 2) * tf.reduce_prod(kernel.lengthscales / sc, 2) Kzz += jitter * tf.eye(inducing_variable.num_inducing, dtype=Kzz.dtype) return Kzz
[docs] @Kuu.register(InducingPatches, Convolutional) @check_shapes( "inducing_variable: [M, D, 1]", "return: [M, M]", ) def Kuu_conv_patch( inducing_variable: InducingPatches, kernel: Convolutional, jitter: float = 0.0 ) -> tf.Tensor: return kernel.base_kernel.K(inducing_variable.Z) + jitter * tf.eye( inducing_variable.num_inducing, dtype=default_float() )