https://github.com/GPflow/GPflow
Tip revision: 4f1faf11ce53037efcdd3ed60be52646c3206222 authored by Sergio Diaz on 10 September 2019, 15:11:24 UTC
SGPR and FITC updated
SGPR and FITC updated
Tip revision: 4f1faf1
kuus.py
import tensorflow as tf
from ..inducing_variables import InducingPoints, Multiscale
from ..kernels import Kernel, SquaredExponential
from .dispatch import Kuu
@Kuu.register(InducingPoints, Kernel)
def _Kuu(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(inducing_variable: Multiscale, kernel: SquaredExponential, *, jitter=0.0):
Zmu, Zlen = kernel.slice(inducing_variable.Z, inducing_variable.scales)
idlengthscale2 = tf.square(kernel.lengthscale + Zlen)
sc = tf.sqrt(idlengthscale2[None, ...] + idlengthscale2[:, None, ...] -
kernel.lengthscale**2)
d = inducing_variable._cust_square_dist(Zmu, Zmu, sc)
Kzz = kernel.variance * tf.exp(-d / 2) * tf.reduce_prod(
kernel.lengthscale / sc, 2)
Kzz += jitter * tf.eye(len(inducing_variable), dtype=Kzz.dtype)
return Kzz