Source code for gpflow.covariances.kuus

import tensorflow as tf

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


[docs]@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
[docs]@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
[docs]@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())