Source code for gpflow.covariances.multioutput.kufs

# 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.

from typing import Callable, Union

import tensorflow as tf

from ...base import TensorType
from ...inducing_variables import (
    FallbackSeparateIndependentInducingVariables,
    FallbackSharedIndependentInducingVariables,
    InducingPoints,
    SeparateIndependentInducingVariables,
    SharedIndependentInducingVariables,
)
from ...kernels import (
    LinearCoregionalization,
    MultioutputKernel,
    SeparateIndependent,
    SharedIndependent,
)
from ..dispatch import Kuf


[docs]@Kuf.register(InducingPoints, MultioutputKernel, object) def Kuf_generic( inducing_variable: InducingPoints, kernel: MultioutputKernel, Xnew: TensorType ) -> tf.Tensor: return kernel(inducing_variable.Z, Xnew, full_cov=True, full_output_cov=True) # [M, P, N, P]
[docs]@Kuf.register(SharedIndependentInducingVariables, SharedIndependent, object) def Kuf_shared_shared( inducing_variable: SharedIndependentInducingVariables, kernel: SharedIndependent, Xnew: tf.Tensor, ) -> tf.Tensor: return Kuf(inducing_variable.inducing_variable, kernel.kernel, Xnew) # [M, N]
[docs]@Kuf.register(SeparateIndependentInducingVariables, SharedIndependent, object) def Kuf_separate_shared( inducing_variable: SeparateIndependentInducingVariables, kernel: SharedIndependent, Xnew: TensorType, ) -> tf.Tensor: return tf.stack( [Kuf(f, kernel.kernel, Xnew) for f in inducing_variable.inducing_variable_list], axis=0 ) # [L, M, N]
[docs]@Kuf.register(SharedIndependentInducingVariables, SeparateIndependent, object) def Kuf_shared_separate( inducing_variable: SharedIndependentInducingVariables, kernel: SeparateIndependent, Xnew: TensorType, ) -> tf.Tensor: return tf.stack( [Kuf(inducing_variable.inducing_variable, k, Xnew) for k in kernel.kernels], axis=0 ) # [L, M, N]
[docs]@Kuf.register(SeparateIndependentInducingVariables, SeparateIndependent, object) def Kuf_separate_separate( inducing_variable: SeparateIndependentInducingVariables, kernel: SeparateIndependent, Xnew: TensorType, ) -> tf.Tensor: Kufs = [ Kuf(f, k, Xnew) for f, k in zip(inducing_variable.inducing_variable_list, kernel.kernels) ] return tf.stack(Kufs, axis=0) # [L, M, N]
def _fallback_Kuf( kuf_impl: Callable[ [ Union[SeparateIndependentInducingVariables, SharedIndependentInducingVariables], LinearCoregionalization, TensorType, ], tf.Tensor, ], inducing_variable: Union[ SeparateIndependentInducingVariables, SharedIndependentInducingVariables ], kernel: LinearCoregionalization, Xnew: TensorType, ) -> tf.Tensor: K = tf.transpose(kuf_impl(inducing_variable, kernel, Xnew), [1, 0, 2]) # [M, L, N] return K[:, :, :, None] * tf.transpose(kernel.W)[None, :, None, :] # [M, L, N, P]
[docs]@Kuf.register( FallbackSeparateIndependentInducingVariables, LinearCoregionalization, object, ) def Kuf_fallback_separate_linear_coregionalization( inducing_variable: FallbackSeparateIndependentInducingVariables, kernel: LinearCoregionalization, Xnew: TensorType, ) -> tf.Tensor: kuf_impl = Kuf.dispatch(SeparateIndependentInducingVariables, SeparateIndependent, object) return _fallback_Kuf(kuf_impl, inducing_variable, kernel, Xnew)
[docs]@Kuf.register( FallbackSharedIndependentInducingVariables, LinearCoregionalization, object, ) def Kuf_fallback_shared_linear_coregionalization( inducing_variable: FallbackSharedIndependentInducingVariables, kernel: LinearCoregionalization, Xnew: TensorType, ) -> tf.Tensor: kuf_impl = Kuf.dispatch(SharedIndependentInducingVariables, SeparateIndependent, object) return _fallback_Kuf(kuf_impl, inducing_variable, kernel, Xnew)
[docs]@Kuf.register(SharedIndependentInducingVariables, LinearCoregionalization, object) def Kuf_shared_linear_coregionalization( inducing_variable: SharedIndependentInducingVariables, kernel: SeparateIndependent, Xnew: TensorType, ) -> tf.Tensor: return tf.stack( [Kuf(inducing_variable.inducing_variable, k, Xnew) for k in kernel.kernels], axis=0 ) # [L, M, N]
[docs]@Kuf.register(SeparateIndependentInducingVariables, LinearCoregionalization, object) def Kuf_separate_linear_coregionalization( inducing_variable: SeparateIndependentInducingVariables, kernel: LinearCoregionalization, Xnew: TensorType, ) -> tf.Tensor: return tf.stack( [Kuf(f, k, Xnew) for f, k in zip(inducing_variable.inducing_variable_list, kernel.kernels)], axis=0, ) # [L, M, N]