Revision ff4bb90d1135f1b57db3e4f6e4a2173894aa1b73 authored by st-- on 01 December 2020, 12:56:56 UTC, committed by GitHub on 01 December 2020, 12:56:56 UTC
* Replace len(inducing_variable) with inducing_variable.num inducing property (#1594).

  Adds support for inducing variables with dynamically changing shape. Change usage from `len(inducing_variable)` to `inducing_variable.num_inducing` instead. Resolves #1578.

* HeteroskedasticTFPConditional should construct tensors at class-construction, not at module-import time (#1598)
2 parent s 6f7f0d8 + 60e19f8
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# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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 .base import TensorType

class ProbabilityDistribution:
    This is the base class for a probability distributions,
    over which we take the expectations in the expectations framework.

class Gaussian(ProbabilityDistribution):
    def __init__(self, mu: TensorType, cov: TensorType): = mu  # [N, D]
        self.cov = cov  # [N, D, D]

class DiagonalGaussian(ProbabilityDistribution):
    def __init__(self, mu: TensorType, cov: TensorType): = mu  # [N, D]
        self.cov = cov  # [N, D]

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
    Var[x_t] = cov[x_t, :, :] * cov[x_t, :, :].T
    Cov[x_t, x_{t+1}] = cov[t, :, :] * cov[t+1, :, :]

    def __init__(self, mu: TensorType, cov: TensorType): = mu  # N+[1, D]
        self.cov = cov  # 2 x (N+1)[, D, D]
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