Source code for gpflow.probability_distributions

# 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.


# Eventually, it would be nice to not have to have our own classes for
# probability distributions. The TensorFlow "distributions" framework would
# be a good replacement.
from abc import ABC, abstractmethod

from check_shapes import ErrorContext, Shape, check_shapes, register_get_shape

from .base import TensorType


[docs] class ProbabilityDistribution(ABC): """ This is the base class for a probability distributions, over which we take the expectations in the expectations framework. """ @property @abstractmethod def shape(self) -> Shape: """ Return the shape of this distribution. Shape should be some variation of ``[N, D]``, where: * ``N`` is the number of data points. * ``D`` is the number of input dimensions. """
[docs] @register_get_shape(ProbabilityDistribution) def get_probability_distribution_shape( shaped: ProbabilityDistribution, context: ErrorContext ) -> Shape: return shaped.shape
[docs] class Gaussian(ProbabilityDistribution): @check_shapes( "mu: [N, D]", "cov: [N, D, D]", ) def __init__(self, mu: TensorType, cov: TensorType): self.mu = mu self.cov = cov @property def shape(self) -> Shape: return self.mu.shape # type: ignore[no-any-return]
[docs] class DiagonalGaussian(ProbabilityDistribution): @check_shapes( "mu: [N, D]", "cov: [N, D]", ) def __init__(self, mu: TensorType, cov: TensorType): self.mu = mu self.cov = cov @property def shape(self) -> Shape: return self.mu.shape # type: ignore[no-any-return]
[docs] class MarkovGaussian(ProbabilityDistribution): """ Gaussian distribution with Markov structure. Only covariances and covariances between t and t+1 need to be parameterised. We use the solution proposed by Carl Rasmussen, i.e. to represent Var[x_t] = cov[x_t, :, :] * cov[x_t, :, :].T Cov[x_t, x_{t+1}] = cov[t, :, :] * cov[t+1, :, :] """ @check_shapes( "mu: [N_plus_1, D]", "cov: [2, N_plus_1, D, D]", ) def __init__(self, mu: TensorType, cov: TensorType): self.mu = mu self.cov = cov @property def shape(self) -> Shape: shape = self.mu.shape if shape is None: return shape N_plus_1, D = shape N = N_plus_1 - 1 return N, D