Source code for gpflow.conditionals.sample_conditionals

# Copyright 2017-2020 The GPflow Contributors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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from typing import Optional, Tuple

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