# 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, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import tensorflow as tf from ..config import default_float from ..inducing_variables import InducingPatches, InducingPoints, Multiscale from ..kernels import Convolutional, Kernel, SquaredExponential from .dispatch import Kuu @Kuu.register(InducingPoints, Kernel) def Kuu_kernel_inducingpoints(inducing_variable: InducingPoints, kernel: Kernel, *, jitter=0.0): Kzz = kernel(inducing_variable.Z) Kzz += jitter * tf.eye(len(inducing_variable), dtype=Kzz.dtype) return Kzz @Kuu.register(Multiscale, SquaredExponential) def Kuu_sqexp_multiscale(inducing_variable: Multiscale, kernel: SquaredExponential, *, jitter=0.0): 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(len(inducing_variable), dtype=Kzz.dtype) return Kzz @Kuu.register(InducingPatches, Convolutional) def Kuu_conv_patch(feat, kern, jitter=0.0): return kern.base_kernel.K(feat.Z) + jitter * tf.eye(len(feat), dtype=default_float())