https://github.com/GPflow/GPflow
Revision 22edd92d06c201b1f40a656e5bbca84a5f483f2d authored by st-- on 14 February 2020, 14:49:30 UTC, committed by GitHub on 14 February 2020, 14:49:30 UTC
- clean up of the documentation - changes initialization from W=zeros() to W=0.1*ones() which makes it easier to use out of the box - no longer requires manual assigning of symmetry-breaking values to the W matrix after kernel construction - adds output_covariance() method that computes the covariance between outputs (and output_variance() for its diagonal)
1 parent fea9f0c
Tip revision: 22edd92d06c201b1f40a656e5bbca84a5f483f2d authored by st-- on 14 February 2020, 14:49:30 UTC
Improve documentation and behaviour of Coregion kernel (#1255)
Improve documentation and behaviour of Coregion kernel (#1255)
Tip revision: 22edd92
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)
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