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// -*- C++ -*-
/**
* @brief The test file of GeneralLinearModelAlgorithm class
*
* Copyright 2005-2025 Airbus-EDF-IMACS-ONERA-Phimeca
*
* This library 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.
*
* This library 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 this library. If not, see <http://www.gnu.org/licenses/>.
*
*/
#include "openturns/OT.hxx"
#include "openturns/OTtestcode.hxx"
using namespace OT;
using namespace OT::Test;
int main(int, char *[])
{
TESTPREAMBLE;
OStream fullprint(std::cout);
try
{
// Set Numerical precision to 3
PlatformInfo::SetNumericalPrecision(3);
std::cout << "================" << std::endl;
std::cout << "Test using NLOpt" << std::endl;
std::cout << "================" << std::endl;
// Calibration of default optimizer
ResourceMap::SetAsScalar("GeneralLinearModelAlgorithm-DefaultOptimizationLowerBound", 1.0e-5);
ResourceMap::SetAsScalar("GeneralLinearModelAlgorithm-DefaultOptimizationUpperBound", 100);
// Data & estimation
const UnsignedInteger inputDimension = 1;
Sample X = Normal(0, 1).getSample(100);
X = X.sortAccordingToAComponent(0);
SquaredExponential covarianceModel(1);
Description inDescription(1);
inDescription[0] = "x";
Description formula(1);
formula[0] = "x - 0.6 * cos(x/3)";
SymbolicFunction model(inDescription, formula);
const Sample Y = model(X);
const Basis basis = QuadraticBasisFactory(inputDimension).build();
GeneralLinearModelAlgorithm algo(X, Y, covarianceModel, basis, true);
NLopt solver("LN_NELDERMEAD");
algo.setOptimizationAlgorithm(solver);
algo.run();
// perform an evaluation
GeneralLinearModelResult result = algo.getResult();
const Function metaModel = result.getMetaModel();
const CovarianceModel conditionalCovariance = result.getCovarianceModel();
const Sample residual = metaModel(X) - Y;
assert_almost_equal(residual.computeCentralMoment(2), Point(1, 1.06e-05), 1e-5, 1e-5);
const Point parameter = {0.619144, 0.000937};
assert_almost_equal(conditionalCovariance.getParameter(), parameter, 1e-2, 1e-2);
std::cout << "Test Ok" << std::endl;
}
catch (TestFailed & ex)
{
std::cerr << ex << std::endl;
return ExitCode::Error;
}
return ExitCode::Success;
}
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