Source code for gpflow.conditionals.multioutput.sample_conditionals

# 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 (
    SeparateIndependentInducingVariables,
    SharedIndependentInducingVariables,
)
from ...kernels import LinearCoregionalization, SeparateIndependent
from ..dispatch import conditional, sample_conditional
from ..util import mix_latent_gp, sample_mvn


[docs]@sample_conditional.register( object, SharedIndependentInducingVariables, LinearCoregionalization, object ) def _sample_conditional( Xnew: tf.Tensor, inducing_variable: SharedIndependentInducingVariables, kernel: LinearCoregionalization, 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 conditinoal 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 efficent one. :return: [N, P] (full_output_cov = False) or [N, P, P] (full_output_cov = True) """ if full_cov: raise NotImplementedError("full_cov not yet implemented") if full_output_cov: raise NotImplementedError("full_output_cov not yet implemented") ind_conditional = conditional.dispatch( object, SeparateIndependentInducingVariables, SeparateIndependent, object ) g_mu, g_var = ind_conditional( Xnew, inducing_variable, kernel, f, white=white, q_sqrt=q_sqrt ) # [..., N, L], [..., N, L] g_sample = sample_mvn(g_mu, g_var, full_cov, num_samples=num_samples) # [..., (S), N, L] f_mu, f_var = mix_latent_gp(kernel.W, g_mu, g_var, full_cov, full_output_cov) f_sample = tf.tensordot(g_sample, kernel.W, [[-1], [-1]]) # [..., N, P] return f_sample, f_mu, f_var