Revision

**8e64ed039275cf1d3b66856277023c9548317bd8**authored by Jesper Nielsen on**11 April 2022, 09:22:07 UTC**, committed by GitHub on**11 April 2022, 09:22:07 UTC****1 parent**3f74b5c

expectations.py

```
# Copyright 2018 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, Tuple, Union, cast
import tensorflow as tf
from ..base import TensorType
from ..inducing_variables import InducingVariables
from ..kernels import Kernel
from ..mean_functions import MeanFunction
from ..probability_distributions import (
DiagonalGaussian,
Gaussian,
MarkovGaussian,
ProbabilityDistribution,
)
from . import dispatch
ProbabilityDistributionLike = Union[ProbabilityDistribution, Tuple[TensorType, TensorType]]
"""
Either a prabability distribution, or a tuple of mean, covariance that is turned into an
appropriate Gaussian distribution, depending on the shape of the covariance.
"""
ExpectationObject = Union[Kernel, MeanFunction, None]
PackedExpectationObject = Union[ExpectationObject, Tuple[Kernel, InducingVariables]]
def expectation(
p: ProbabilityDistributionLike,
obj1: PackedExpectationObject,
obj2: PackedExpectationObject = None,
nghp: Optional[int] = None,
) -> tf.Tensor:
"""
Compute the expectation <obj1(x) obj2(x)>_p(x)
Uses multiple-dispatch to select an analytical implementation,
if one is available. If not, it falls back to quadrature.
:type p: (mu, cov) tuple or a `ProbabilityDistribution` object
:type obj1: kernel, mean function, (kernel, inducing_variable), or None
:type obj2: kernel, mean function, (kernel, inducing_variable), or None
:param int nghp: passed to `_quadrature_expectation` to set the number
of Gauss-Hermite points used: `num_gauss_hermite_points`
:return: a 1-D, 2-D, or 3-D tensor containing the expectation
Allowed combinations
- Psi statistics:
>>> eKdiag = expectation(p, kernel) (N) # Psi0
>>> eKxz = expectation(p, (kernel, inducing_variable)) (NxM) # Psi1
>>> exKxz = expectation(p, identity_mean, (kernel, inducing_variable)) (NxDxM)
>>> eKzxKxz = expectation(p, (kernel, inducing_variable), (kernel, inducing_variable)) (NxMxM) # Psi2
- kernels and mean functions:
>>> eKzxMx = expectation(p, (kernel, inducing_variable), mean) (NxMxQ)
>>> eMxKxz = expectation(p, mean, (kernel, inducing_variable)) (NxQxM)
- only mean functions:
>>> eMx = expectation(p, mean) (NxQ)
>>> eM1x_M2x = expectation(p, mean1, mean2) (NxQ1xQ2)
.. note:: mean(x) is 1xQ (row vector)
- different kernels. This occurs, for instance, when we are calculating Psi2 for Sum kernels:
>>> eK1zxK2xz = expectation(p, (kern1, inducing_variable), (kern2, inducing_variable)) (NxMxM)
"""
p, obj1, feat1, obj2, feat2 = _init_expectation(p, obj1, obj2)
try:
return dispatch.expectation(p, obj1, feat1, obj2, feat2, nghp=nghp)
except NotImplementedError as error:
return dispatch.quadrature_expectation(p, obj1, feat1, obj2, feat2, nghp=nghp)
def quadrature_expectation(
p: ProbabilityDistributionLike,
obj1: PackedExpectationObject,
obj2: PackedExpectationObject = None,
nghp: Optional[int] = None,
) -> tf.Tensor:
"""
Compute the expectation <obj1(x) obj2(x)>_p(x)
Uses Gauss-Hermite quadrature for approximate integration.
:type p: (mu, cov) tuple or a `ProbabilityDistribution` object
:type obj1: kernel, mean function, (kernel, inducing_variable), or None
:type obj2: kernel, mean function, (kernel, inducing_variable), or None
:param int num_gauss_hermite_points: passed to `_quadrature_expectation` to set
the number of Gauss-Hermite points used
:return: a 1-D, 2-D, or 3-D tensor containing the expectation
"""
print(f"2. p={p}, obj1={obj1}, obj2={obj2}")
p, obj1, feat1, obj2, feat2 = _init_expectation(p, obj1, obj2)
return dispatch.quadrature_expectation(p, obj1, feat1, obj2, feat2, nghp=nghp)
def _init_expectation(
p: ProbabilityDistributionLike, obj1: PackedExpectationObject, obj2: PackedExpectationObject
) -> Tuple[
ProbabilityDistribution,
ExpectationObject,
Optional[InducingVariables],
ExpectationObject,
Optional[InducingVariables],
]:
if isinstance(p, tuple):
mu, cov = p
classes = [DiagonalGaussian, Gaussian, MarkovGaussian]
p = classes[cov.ndim - 2](*p)
obj1, feat1 = obj1 if isinstance(obj1, tuple) else (obj1, None)
obj2, feat2 = obj2 if isinstance(obj2, tuple) else (obj2, None)
return (
# type-ignore instead of cast, because it dependes on versions whether a cast is necessary.
p, # type: ignore
cast(ExpectationObject, obj1),
cast(Optional[InducingVariables], feat1),
cast(ExpectationObject, obj2),
cast(Optional[InducingVariables], feat2),
)
```

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