Skip to main content

Browse the archive

Revision 235853956f699c32dcc9ce4c4311724c3f90705a authored by st-- on 15 April 2020, 12:19:00 UTC, committed by GitHub on 15 April 2020, 12:19:00 UTC 
Release 2.0.1 (#1436)
 
Release notes:
- Improve structure of likelihoods subdirectory (#1416)
- Update README.md (#1401) and GPflow 2 upgrade guide (#1414)
- Improved handling of invalid values for constrained Parameters (#1408)
- Improvements on types/function annotations (#1406, #1420)
- Documentation improvements (metalearning with GPs: #1382, coregionalization notebook: #1402, MCMC notebook: #1410, intro to gpflow with tensorflow 2: #1413)
- Minor documentation fixes (#1429, #1430, #1433)
- Fix: move matplotlib import inside ImageToTensorBoard (#1399)
- Fix: tf.function compilation of ndiagquad (#1418)
- Fix: cache tensorboard file writers and re-use them (#1424)
2 parent s 47e788a + 3fc050d
Raw File
test_quadrature.py 
# Copyright 2018 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.

import numpy as np
import pytest
import tensorflow as tf
from numpy.testing import assert_allclose

import gpflow.quadrature as quadrature


@pytest.mark.parametrize("mu", [np.array([1.0, 1.3])])
@pytest.mark.parametrize("var", [np.array([3.0, 3.5])])
def test_diagquad_1d(mu, var):
    num_gauss_hermite_points = 25
    quad = quadrature.ndiagquad([lambda *X: tf.exp(X[0])], num_gauss_hermite_points, [mu], [var])
    expected = np.exp(mu + var / 2)
    assert_allclose(quad[0], expected)


@pytest.mark.parametrize("mu1", [np.array([1.0, 1.3])])
@pytest.mark.parametrize("var1", [np.array([3.0, 3.5])])
@pytest.mark.parametrize("mu2", [np.array([-2.0, 0.3])])
@pytest.mark.parametrize("var2", [np.array([4.0, 4.2])])
def test_diagquad_2d(mu1, var1, mu2, var2):
    alpha = 2.5
    # using logspace=True we can reduce this, see test_diagquad_logspace
    num_gauss_hermite_points = 35
    quad = quadrature.ndiagquad(
        lambda *X: tf.exp(X[0] + alpha * X[1]), num_gauss_hermite_points, [mu1, mu2], [var1, var2],
    )
    expected = np.exp(mu1 + var1 / 2 + alpha * mu2 + alpha ** 2 * var2 / 2)
    assert_allclose(quad, expected)


@pytest.mark.parametrize("mu1", [np.array([1.0, 1.3])])
@pytest.mark.parametrize("var1", [np.array([3.0, 3.5])])
@pytest.mark.parametrize("mu2", [np.array([-2.0, 0.3])])
@pytest.mark.parametrize("var2", [np.array([4.0, 4.2])])
def test_diagquad_logspace(mu1, var1, mu2, var2):
    alpha = 2.5
    num_gauss_hermite_points = 25
    quad = quadrature.ndiagquad(
        lambda *X: (X[0] + alpha * X[1]),
        num_gauss_hermite_points,
        [mu1, mu2],
        [var1, var2],
        logspace=True,
    )
    expected = mu1 + var1 / 2 + alpha * mu2 + alpha ** 2 * var2 / 2
    assert_allclose(quad, expected)


@pytest.mark.parametrize("mu1", [np.array([1.0, 1.3])])
@pytest.mark.parametrize("var1", [np.array([3.0, 3.5])])
def test_diagquad_with_kwarg(mu1, var1):
    alpha = np.array([2.5, -1.3])
    num_gauss_hermite_points = 25
    quad = quadrature.ndiagquad(
        lambda X, Y: tf.exp(X * Y), num_gauss_hermite_points, mu1, var1, Y=alpha
    )
    expected = np.exp(alpha * mu1 + alpha ** 2 * var1 / 2)
    assert_allclose(quad, expected)
back to top