Revision ff95af43b109480f3ebdf6101910197a0bae75e5 authored by Sergio Diaz on 18 March 2019, 15:03:17 UTC, committed by Sergio Diaz on 18 March 2019, 15:03:17 UTC
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
from gpflow.util import default_float
@pytest.fixture
def mu1(): return np.array([1.0, 1.3])
@pytest.fixture
def mu2(): return np.array([-2.0, 0.3])
@pytest.fixture
def var1(): return np.array([3.0, 3.5])
@pytest.fixture
def var2(): return np.array([4.0, 4.2])
def cast(x):
return x
# return tf.cast(np.asarray(x), dtype=default_float())
@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):
quad = quadrature.ndiagquad(
[lambda *X: tf.exp(X[0])], 25,
[cast(mu)], [cast(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([1.0, 1.3])])
@pytest.mark.parametrize('var2', [np.array([3.0, 3.5])])
def test_diagquad_2d(mu1, var1, mu2, var2):
alpha = 2.5
quad = quadrature.ndiagquad(
lambda *X: tf.exp(X[0] + alpha * X[1]),
35, # using logspace=True we can reduce this, see test_diagquad_logspace
[cast(mu1), cast(mu2)], [cast(var1), cast(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([1.0, 1.3])])
@pytest.mark.parametrize('var2', [np.array([3.0, 3.5])])
def test_diagquad_logspace(mu1, var1, mu2, var2):
alpha = 2.5
quad = quadrature.ndiagquad(
lambda *X: (X[0] + alpha * X[1]),
25,
[cast(mu1), cast(mu2)], [cast(var1), cast(var2)],
logspace=True)
expected = mu1 + var1 / 2 + alpha * mu2 + alpha ** 2 * var2 / 2
assert_allclose(quad, expected)
def test_diagquad_with_kwarg(mu2, var2):
alpha = np.array([2.5, -1.3])
quad = quadrature.ndiagquad(
lambda X, Y: tf.exp(X * Y), 25,
cast(mu2), cast(var2), Y=alpha)
expected = np.exp(alpha * mu2 + alpha ** 2 * var2 / 2)
assert_allclose(quad, expected)
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