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RROMPy/tests/1_utilities/gaussian_process_fitting.py
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gaussian_process_fitting.py
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# Copyright (C) 2018-2020 by the RROMPy authors
#
# This file is part of RROMPy.
#
# RROMPy is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# RROMPy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
#
import numpy as np
from rrompy.utilities.poly_fitting.gaussian_process import (kernels,
polybases, polyfitname,
GaussianProcessInterpolator)
from rrompy.parameter import checkParameterList
def test_gaussian():
polygpname = "MONOMIAL_GAUSSIAN"
assert polygpname in polybases
fitname = polyfitname(polygpname)
assert fitname == "polyfit_gaussian"
radialGaussian = kernels["GAUSSIAN"]
xSupp = checkParameterList(np.arange(-1, 3), 1)
ySupp = np.array([0., 2., 1., 0.])
deg = None
kernelCoeffs = {"length_scale": 1., "length_scale_bounds" : (1e-5, 1e3)}
trainCoeffs = {"random_state": 0, "n_restarts_optimizer": 10}
GP = GaussianProcessInterpolator()
wellcond, msg = GP.setupByInterpolation(xSupp, ySupp, deg, polygpname,
kernelCoeffs = kernelCoeffs,
trainCoeffs = trainCoeffs)
assert wellcond
assert msg is None
assert np.allclose(GP(xSupp), ySupp, atol = 1e-5)
assert isinstance(GP.GP.kernel_,
(radialGaussian(**kernelCoeffs).__class__,))
def test_matern():
polygpname = "MONOMIAL_MATERN"
xSupp = checkParameterList([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.],
[0., 1., 1.], [1., 0., 1.], [1., 1., 0.]], 3)
ySupp = np.array([0., 2., 1., 0., -1., 0.])
deg = 1
kernelCoeffs = {"length_scale_bounds" : (1e-5, 1e3), "nu": 2.5}
trainCoeffs = {"random_state": 0, "n_restarts_optimizer": 10}
GP = GaussianProcessInterpolator()
wellcond, msg = GP.setupByInterpolation(xSupp, ySupp, deg, polygpname,
degreetype = "TOTAL",
kernelCoeffs = kernelCoeffs,
trainCoeffs = trainCoeffs)
yPred, yStd = GP(xSupp, return_std = True)
assert wellcond
assert "Fitting {} samples with degree [1, 1, 1]".format(len(xSupp)) in msg
assert np.allclose(yPred, ySupp, atol = 1e-5)
assert len(yStd) == len(xSupp)
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