https://github.com/GPflow/GPflow
Tip revision: 97d2d56a002f1c9c1d91c90e6bf75e17e3b66dd7 authored by Artem Artemev on 14 November 2019, 14:55:16 UTC
Change regexp
Change regexp
Tip revision: 97d2d56
mo_kuus.py
from typing import Union
import tensorflow as tf
from ..inducing_variables import (InducingPoints, FallbackSharedIndependentInducingVariables,
FallbackSeparateIndependentInducingVariables, SharedIndependentInducingVariables)
from ..kernels import (MultioutputKernel, SeparateIndependent, LinearCoregionalization,
SharedIndependent, IndependentLatent)
from .dispatch import Kuu
@Kuu.register(InducingPoints, MultioutputKernel)
def _Kuu(inducing_variable: InducingPoints, kernel: MultioutputKernel, *, jitter=0.0):
Kmm = kernel(inducing_variable.Z, full=True, full_output_cov=True) # [M, P, M, P]
M = Kmm.shape[0] * Kmm.shape[1]
jittermat = jitter * tf.reshape(tf.eye(M, dtype=Kmm.dtype), Kmm.shape)
return Kmm + jittermat
@Kuu.register(FallbackSharedIndependentInducingVariables, SharedIndependent)
def _Kuu(inducing_variable: FallbackSharedIndependentInducingVariables,
kernel: SharedIndependent,
*,
jitter=0.0):
Kmm = Kuu(inducing_variable.inducing_variable_shared, kernel.kernel) # [M, M]
jittermat = tf.eye(len(inducing_variable), dtype=Kmm.dtype) * jitter
return Kmm + jittermat
@Kuu.register(FallbackSharedIndependentInducingVariables, (SeparateIndependent, IndependentLatent))
def _Kuu(inducing_variable: FallbackSharedIndependentInducingVariables,
kernel: Union[SeparateIndependent, IndependentLatent],
*,
jitter=0.0):
Kmm = tf.stack([Kuu(inducing_variable.inducing_variable_shared, k) for k in kernel.kernels],
axis=0) # [L, M, M]
jittermat = tf.eye(len(inducing_variable), dtype=Kmm.dtype)[None, :, :] * jitter
return Kmm + jittermat
@Kuu.register(FallbackSeparateIndependentInducingVariables, SharedIndependent)
def _Kuu(inducing_variable: FallbackSeparateIndependentInducingVariables,
kernel: SharedIndependent,
*,
jitter=0.0):
Kmm = tf.stack([Kuu(f, kernel.kernel) for f in inducing_variable.inducing_variable_list],
axis=0) # [L, M, M]
jittermat = tf.eye(len(inducing_variable), dtype=Kmm.dtype)[None, :, :] * jitter
return Kmm + jittermat
@Kuu.register(FallbackSeparateIndependentInducingVariables,
(SeparateIndependent, LinearCoregionalization))
def _Kuu(inducing_variable: FallbackSeparateIndependentInducingVariables,
kernel: Union[SeparateIndependent, LinearCoregionalization],
*,
jitter=0.0):
Kmms = [Kuu(f, k) for f, k in zip(inducing_variable.inducing_variable_list, kernel.kernels)]
Kmm = tf.stack(Kmms, axis=0) # [L, M, M]
jittermat = tf.eye(len(inducing_variable), dtype=Kmm.dtype)[None, :, :] * jitter
return Kmm + jittermat