# 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 ...experimental.check_shapes import check_shapes
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)
@check_shapes(
"inducing_variable: [M, D, 1]",
"Xnew: [batch..., N, D]",
"return: [M, P, batch..., N, P]",
)
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)
[docs]@Kuf.register(SharedIndependentInducingVariables, SharedIndependent, object)
@check_shapes(
"inducing_variable: [M, D, P]",
"Xnew: [batch..., N, D]",
"return: [M, batch..., N]",
)
def Kuf_shared_shared(
inducing_variable: SharedIndependentInducingVariables,
kernel: SharedIndependent,
Xnew: tf.Tensor,
) -> tf.Tensor:
return Kuf(inducing_variable.inducing_variable, kernel.kernel, Xnew)
[docs]@Kuf.register(SeparateIndependentInducingVariables, SharedIndependent, object)
@check_shapes(
"inducing_variable: [M, D, P]",
"Xnew: [batch..., N, D]",
"return: [L, M, batch..., N]",
)
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
)
[docs]@Kuf.register(SharedIndependentInducingVariables, SeparateIndependent, object)
@check_shapes(
"inducing_variable: [M, D, P]",
"Xnew: [batch..., N, D]",
"return: [L, M, batch..., N]",
)
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
)
[docs]@Kuf.register(SeparateIndependentInducingVariables, SeparateIndependent, object)
@check_shapes(
"inducing_variable: [M, D, P]",
"Xnew: [batch..., N, D]",
"return: [L, M, batch..., N]",
)
def Kuf_separate_separate(
inducing_variable: SeparateIndependentInducingVariables,
kernel: SeparateIndependent,
Xnew: TensorType,
) -> tf.Tensor:
n_iv = len(inducing_variable.inducing_variable_list)
n_k = len(kernel.kernels)
assert (
n_iv == n_k
), f"Must have same number of inducing variables and kernels. Found {n_iv} and {n_k}."
Kufs = [
Kuf(f, k, Xnew) for f, k in zip(inducing_variable.inducing_variable_list, kernel.kernels)
]
return tf.stack(Kufs, axis=0)
@check_shapes(
"inducing_variable: [M, D, L]",
"Xnew: [batch..., N, D]",
"return: [M, L, batch..., N, P]",
)
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])
return K[:, :, :, None] * tf.transpose(kernel.W)[None, :, None, :]
[docs]@Kuf.register(
FallbackSeparateIndependentInducingVariables,
LinearCoregionalization,
object,
)
@check_shapes(
"inducing_variable: [M, D, L]",
"kernel.W: [P, L]",
"Xnew: [batch..., N, D]",
"return: [M, L, batch..., N, P]",
)
def Kuf_fallback_separate_linear_coregionalization(
inducing_variable: FallbackSeparateIndependentInducingVariables,
kernel: LinearCoregionalization,
Xnew: TensorType,
) -> tf.Tensor:
kuf_impl = Kuf.dispatch_or_raise(
SeparateIndependentInducingVariables, SeparateIndependent, object
)
return _fallback_Kuf(kuf_impl, inducing_variable, kernel, Xnew)
[docs]@Kuf.register(
FallbackSharedIndependentInducingVariables,
LinearCoregionalization,
object,
)
@check_shapes(
"inducing_variable: [M, D, P]",
"kernel.W: [P, L]",
"Xnew: [batch..., N, D]",
"return: [M, L, batch..., N, P]",
)
def Kuf_fallback_shared_linear_coregionalization(
inducing_variable: FallbackSharedIndependentInducingVariables,
kernel: LinearCoregionalization,
Xnew: TensorType,
) -> tf.Tensor:
kuf_impl = Kuf.dispatch_or_raise(
SharedIndependentInducingVariables, SeparateIndependent, object
)
return _fallback_Kuf(kuf_impl, inducing_variable, kernel, Xnew)
[docs]@Kuf.register(SharedIndependentInducingVariables, LinearCoregionalization, object)
@check_shapes(
"inducing_variable: [M, D, L]",
"kernel.W: [P, L]",
"Xnew: [batch..., N, D]",
"return: [L, M, batch..., N]",
)
def Kuf_shared_linear_coregionalization(
inducing_variable: SharedIndependentInducingVariables,
kernel: LinearCoregionalization,
Xnew: TensorType,
) -> tf.Tensor:
return tf.stack(
[Kuf(inducing_variable.inducing_variable, k, Xnew) for k in kernel.kernels], axis=0
)
[docs]@Kuf.register(SeparateIndependentInducingVariables, LinearCoregionalization, object)
@check_shapes(
"inducing_variable: [M, D, L]",
"kernel.W: [P, L]",
"Xnew: [batch..., N, D]",
"return: [L, M, batch..., N]",
)
def Kuf_separate_linear_coregionalization(
inducing_variable: SeparateIndependentInducingVariables,
kernel: LinearCoregionalization,
Xnew: TensorType,
) -> tf.Tensor:
n_iv = len(inducing_variable.inducing_variable_list)
n_k = len(kernel.kernels)
assert (
n_iv == n_k
), f"Must have same number of inducing variables and kernels. Found {n_iv} and {n_k}."
return tf.stack(
[Kuf(f, k, Xnew) for f, k in zip(inducing_variable.inducing_variable_list, kernel.kernels)],
axis=0,
)