Source code for gpflow.covariances.kufs

# 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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import tensorflow as tf

from ..base import TensorLike
from ..inducing_variables import InducingPatches, InducingPoints, Multiscale
from ..kernels import Convolutional, Kernel, SquaredExponential
from .dispatch import Kuf


[docs]@Kuf.register(InducingPoints, Kernel, TensorLike) def Kuf_kernel_inducingpoints(inducing_variable: InducingPoints, kernel: Kernel, Xnew): return kernel(inducing_variable.Z, Xnew)
[docs]@Kuf.register(Multiscale, SquaredExponential, TensorLike) def Kuf_sqexp_multiscale(inducing_variable: Multiscale, kernel: SquaredExponential, Xnew): Xnew, _ = kernel.slice(Xnew, None) Zmu, Zlen = kernel.slice(inducing_variable.Z, inducing_variable.scales) idlengthscales = kernel.lengthscales + Zlen d = inducing_variable._cust_square_dist(Xnew, Zmu, idlengthscales) lengthscales = tf.reduce_prod(kernel.lengthscales / idlengthscales, 1) lengthscales = tf.reshape(lengthscales, (1, -1)) return tf.transpose(kernel.variance * tf.exp(-0.5 * d) * lengthscales)
[docs]@Kuf.register(InducingPatches, Convolutional, object) def Kuf_conv_patch(inducing_variable, kernel, Xnew): Xp = kernel.get_patches(Xnew) # [N, num_patches, patch_len] bigKzx = kernel.base_kernel.K( inducing_variable.Z, Xp ) # [M, N, P] -- thanks to broadcasting of kernels Kzx = tf.reduce_sum(bigKzx * kernel.weights if hasattr(kernel, "weights") else bigKzx, [2]) return Kzx / kernel.num_patches