# 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 Optional
import tensorflow as tf
from ..base import SamplesMeanAndVariance
from ..inducing_variables import InducingVariables
from ..kernels import Kernel
from .dispatch import conditional, sample_conditional
from .util import sample_mvn
[docs]@sample_conditional.register(object, object, Kernel, object)
@sample_conditional.register(object, InducingVariables, Kernel, object)
def _sample_conditional(
Xnew: tf.Tensor,
inducing_variable: InducingVariables,
kernel: Kernel,
f: tf.Tensor,
*,
full_cov: bool = False,
full_output_cov: bool = False,
q_sqrt: Optional[tf.Tensor] = None,
white: bool = False,
num_samples: Optional[int] = None,
) -> SamplesMeanAndVariance:
"""
`sample_conditional` will return a sample from the conditional distribution.
In most cases this means calculating the conditional mean m and variance v and then
returning m + sqrt(v) * eps, with eps ~ N(0, 1).
However, for some combinations of Mok and Mof more efficient sampling routines exists.
The dispatcher will make sure that we use the most efficient one.
:return: samples, mean, cov
samples has shape [num_samples, N, P] or [N, P] if num_samples is None
mean and cov as for conditional()
"""
if full_cov and full_output_cov:
msg = "The combination of both `full_cov` and `full_output_cov` is not permitted."
raise NotImplementedError(msg)
mean, cov = conditional(
Xnew,
inducing_variable,
kernel,
f,
q_sqrt=q_sqrt,
white=white,
full_cov=full_cov,
full_output_cov=full_output_cov,
)
if full_cov:
# mean: [..., N, P]
# cov: [..., P, N, N]
mean_for_sample = tf.linalg.adjoint(mean) # [..., P, N]
samples = sample_mvn(
mean_for_sample, cov, full_cov=True, num_samples=num_samples
) # [..., (S), P, N]
samples = tf.linalg.adjoint(samples) # [..., (S), N, P]
else:
# mean: [..., N, P]
# cov: [..., N, P] or [..., N, P, P]
samples = sample_mvn(
mean, cov, full_cov=full_output_cov, num_samples=num_samples
) # [..., (S), N, P]
return samples, mean, cov