Revision 3e8bf1822003feaee9f4ca9e46cc06e4a81d041e authored by Vincent Dutordoir on 25 February 2020, 17:39:09 UTC, committed by GitHub on 25 February 2020, 17:39:09 UTC
This PR (superseding #1271) enables auto-generation of all of GPflow's documentation. Upon a new merge in `develop`, we trigger our new repo [GPflow/docs](https://github.com/GPflow/docs) to: - copy the complete content of GPflow/doc - compile the notebooks `.ipynb` from the `.pct.py` files - create the `rst` files for the api documentation required for readthedocs. #### TODO: - [ ] Allow for documentation at different tags of the source code.
1 parent 167973b
test_features.py
# Copyright 2017 Mark van der Wilk
#
# 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
from numpy.testing import assert_allclose, assert_equal
import pytest
import gpflow
from gpflow.inducing_variables import InducingPoints, Multiscale, InducingPatches
from gpflow.covariances import Kuu, Kuf
from gpflow.config import default_jitter
@pytest.mark.parametrize('N, D', [[17, 3], [10, 7]])
def test_inducing_points_inducing_variable_len(N, D):
Z = np.random.randn(N, D)
inducing_variable = InducingPoints(Z)
assert_equal(len(inducing_variable), N)
_kernel_setups = [
gpflow.kernels.SquaredExponential(variance=0.46,
lengthscale=np.random.uniform(0.5, 3., 5)),
gpflow.kernels.Periodic(base=gpflow.kernels.SquaredExponential(variance=1.8),
period=0.4)
]
@pytest.mark.parametrize('N', [10, 101])
@pytest.mark.parametrize('kernel', _kernel_setups)
def test_inducing_equivalence(N, kernel):
# Inducing inducing must be the same as the kernel evaluations
Z = np.random.randn(N, 5)
inducing_variable = InducingPoints(Z)
assert_allclose(Kuu(inducing_variable, kernel), kernel(Z))
@pytest.mark.parametrize('N, M, D', [[23, 13, 3], [10, 5, 7]])
def test_multi_scale_inducing_equivalence_inducing_points(N, M, D):
# Multiscale must be equivalent to inducing points when variance is zero
Xnew, Z = np.random.randn(N, D), np.random.randn(M, D)
rbf = gpflow.kernels.SquaredExponential(1.3441, lengthscale=np.random.uniform(0.5, 3., D))
inducing_variable_zero_lengthscale = Multiscale(Z, scales=np.zeros(Z.shape) + 1e-10)
inducing_variable_inducing_point = InducingPoints(Z)
multi_scale_Kuf = Kuf(inducing_variable_zero_lengthscale, rbf, Xnew)
inducing_point_Kuf = Kuf(inducing_variable_inducing_point, rbf, Xnew)
relative_error_Kuf = np.abs(multi_scale_Kuf - inducing_point_Kuf) / inducing_point_Kuf
assert np.max(relative_error_Kuf) < 0.1e-2 # 0.1 %
multi_scale_Kuu = Kuu(inducing_variable_zero_lengthscale, rbf)
inducing_point_Kuu = Kuu(inducing_variable_inducing_point, rbf)
relative_error_Kuu = np.abs(multi_scale_Kuu - inducing_point_Kuu) / inducing_point_Kuu
assert np.max(relative_error_Kuu) < 0.1e-2 # 0.1 %
_inducing_variables_and_kernels = [
[
2,
InducingPoints(np.random.randn(71, 2)),
gpflow.kernels.SquaredExponential(variance=1.84,
lengthscale=np.random.uniform(0.5, 3., 2))
],
[
2,
InducingPoints(np.random.randn(71, 2)),
gpflow.kernels.Matern12(variance=1.84,
lengthscale=np.random.uniform(0.5, 3., 2))
],
[
2,
Multiscale(np.random.randn(71, 2),
np.random.uniform(0.5, 3, size=(71, 2))),
gpflow.kernels.SquaredExponential(variance=1.84,
lengthscale=np.random.uniform(0.5, 3., 2))
],
[
9,
InducingPatches(np.random.randn(71, 4)),
gpflow.kernels.Convolutional(gpflow.kernels.SquaredExponential(), [3, 3], [2, 2])
]
]
@pytest.mark.parametrize('input_dim, inducing_variable, kernel', _inducing_variables_and_kernels)
def test_inducing_variables_psd_schur(input_dim, inducing_variable, kernel):
# Conditional variance must be PSD.
X = np.random.randn(5, input_dim)
Kuf_values = Kuf(inducing_variable, kernel, X)
Kuu_values = Kuu(inducing_variable, kernel, jitter=default_jitter())
Kff_values = kernel(X)
Qff_values = Kuf_values.numpy().T @ np.linalg.solve(Kuu_values, Kuf_values)
assert np.all(np.linalg.eig(Kff_values - Qff_values)[0] > 0.0)
![swh spinner](/static/img/swh-spinner.gif)
Computing file changes ...