https://github.com/GPflow/GPflow
Tip revision: ddeedc6dd96060885edd207bd0772579438e9482 authored by Mark van der Wilk on 10 January 2018, 16:34:42 UTC
Added independent but distinct kernels for multi-output support in `conditional()`.
Added independent but distinct kernels for multi-output support in `conditional()`.
Tip revision: ddeedc6
expectations_quadrature.py
# Copyright 2017 the GPflow authors.
#
# 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 __future__ import print_function
import types
import warnings
import numpy as np
import functools
import tensorflow as tf
from multipledispatch import dispatch
from functools import partial
from . import kernels, mean_functions
from .probability_distributions import Gaussian, DiagonalGaussian, MarkovGaussian
from .features import InducingFeature
from .quadrature import mvnquad
# By default multipledispatch uses a global namespace in multipledispatch.core.global_namespace.
# We define our own GPflow namespace to avoid any conflict which may arise.
gpflow_md_namespace = dict()
dispatch = partial(dispatch, namespace=gpflow_md_namespace)
def quadrature_fallback(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
try:
return func(*args, **kwargs)
except NotImplementedError as e:
print(str(e))
return _quadrature_expectation(*args)
return wrapper
# Generic case, quadrature method
def get_eval_func(obj, feature, slice=np.s_[...]):
if feature is not None:
# kernel + feature combination
if not isinstance(feature, InducingFeature) or not isinstance(obj, kernels.Kernel):
raise TypeError("If `feature` is supplied, `obj` must be a kernel.")
return lambda x: tf.transpose(feature.Kuf(obj, x))[slice]
elif isinstance(obj, mean_functions.MeanFunction):
return lambda x: obj(x)[slice]
elif isinstance(obj, kernels.Kernel):
return lambda x: obj.Kdiag(x)
else:
raise NotImplementedError()
@dispatch(Gaussian, object, (InducingFeature, type(None)), object, (InducingFeature, type(None)))
def _expectation(p, obj1, feature1, obj2, feature2, H=40):
warnings.warn("Quadrature is used to calculate the expectation. This means that "
"an analytical implementations is not available for the given combination.")
if obj2 is None:
eval_func = lambda x: get_eval_func(obj1, feature1)(x)
elif obj1 is None:
eval_func = lambda x: get_eval_func(obj2, feature2)(x)
else:
eval_func = lambda x: (get_eval_func(obj1, feature1, np.s_[:, :, None])(x) *
get_eval_func(obj2, feature2, np.s_[:, None, :])(x))
return mvnquad(eval_func, p.mu, p.cov, H)
@dispatch(MarkovGaussian, object, (InducingFeature, type(None)), object, (InducingFeature, type(None)))
def _expectation(p, obj1, feature1, obj2, feature2, H=40):
warnings.warn("Quadrature is used to calculate the expectation. This means that "
"an analytical implementations is not available for the given combination.")
if obj2 is None:
eval_func = lambda x: get_eval_func(obj1, feature1)(tf.split(x, 2, 1)[0])
elif obj1 is None:
eval_func = lambda x: get_eval_func(obj2, feature2)(tf.split(x, 2, 1)[1])
else:
eval_func = lambda x: (get_eval_func(obj1, feature1, np.s_[:, :, None])(tf.split(x, 2, 1)[0]) *
get_eval_func(obj2, feature2, np.s_[:, None, :])(tf.split(x, 2, 1)[1]))
return mvnquad(eval_func, p.mu, p.cov, H)
def quadrature_expectation(p, obj1, obj2=None):
if isinstance(obj1, tuple):
feat1, obj1 = obj1
else:
feat1 = None
if isinstance(obj2, tuple):
feat2, obj2 = obj2
else:
feat2 = None
return _quadrature_expectation(p, obj1, feat1, obj2, feat2)
@dispatch(Gaussian, object, (InducingFeature, type(None)), object, (InducingFeature, type(None)))
def _quadrature_expectation(p, obj1, feat1, obj2, feat2):
gauss_quadrature_impl = _expectation.dispatch(Gaussian, object, type(None), object, type(None))
return gauss_quadrature_impl(p, obj1, feat1, obj2, feat2)
@dispatch(DiagonalGaussian, object, (InducingFeature, type(None)), object, (InducingFeature, type(None)))
def _quadrature_expectation(p, obj1, feat1, obj2, feat2):
p_gauss = Gaussian(p.mu, tf.matrix_diag(p.var))
gauss_quadrature_impl = _expectation.dispatch(Gaussian, object, type(None), object, type(None))
return gauss_quadrature_impl(p_gauss, obj1, feat1, obj2, feat2)
@dispatch(MarkovGaussian, object, (InducingFeature, type(None)), object, (InducingFeature, type(None)))
def _quadrature_expectation(p, obj1, feat1, obj2, feat2):
mu = tf.concat((p.mu[:-1, :], p.mu[1:, :]), 1) # Nx2D
fXcovt = tf.concat((p.cov[0, :-1, :, :], p.cov[1, :-1, :, :]), 2) # NxDx2D
fXcovb = tf.concat((tf.transpose(p.cov[1, :-1, :, :], (0, 2, 1)), p.cov[0, 1:, :, :]), 2)
cov = tf.concat((fXcovt, fXcovb), 1) # Nx2Dx2D
p_gauss = MarkovGaussian(mu, cov)
gauss_quadrature_impl = _expectation.dispatch(MarkovGaussian, object, type(None), object, type(None))
return gauss_quadrature_impl(p_gauss, obj1, feat1, obj2, feat2)