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// -*- C++ -*-
/**
* @brief The test file of FunctionalChaosAlgoritm 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
{
PlatformInfo::SetNumericalPrecision(3);
// Problem parameters
UnsignedInteger dimension = 3;
Scalar a = 7.0;
Scalar b = 0.1;
// Create the Ishigami function
Description inputVariables(dimension);
inputVariables[0] = "xi1";
inputVariables[1] = "xi2";
inputVariables[2] = "xi3";
Description formula(1);
formula[0] = (OSS() << "sin(xi1) + (" << a << ") * (sin(xi2)) ^ 2 + (" << b << ") * xi3^4 * sin(xi1)");
SymbolicFunction model(inputVariables, formula);
// Create the input distribution
Collection<Distribution> marginals(dimension);
marginals[0] = Uniform(-M_PI, M_PI);
marginals[1] = Uniform(-M_PI, M_PI);
marginals[2] = Uniform(-M_PI, M_PI);
JointDistribution distribution(marginals);
// Fix sampling size
const UnsignedInteger samplingSize = 100;
// Get input & output sample
LHSExperiment lhs(distribution, samplingSize);
Sample inputSample = lhs.generate();
Sample outputSample = model(inputSample);
// Validation of results on independent samples
const UnsignedInteger validationSize = 10;
Sample inputValidation = distribution.getSample(validationSize);
Sample outputValidation = model(inputValidation);
// 1) SPC algorithm
// Create the orthogonal basis
Collection<OrthogonalUniVariatePolynomialFamily> polynomialCollection(dimension, LegendreFactory());
LinearEnumerateFunction enumerateFunction(dimension);
OrthogonalProductPolynomialFactory productBasis(polynomialCollection, enumerateFunction);
// Create the adaptive strategy
UnsignedInteger degree = 8;
UnsignedInteger basisSize = enumerateFunction.getStrataCumulatedCardinal(degree);
AdaptiveStrategy adaptiveStrategy = FixedStrategy(productBasis, basisSize);
// Select the fitting algorithm
FittingAlgorithm fittingAlgorithm = KFold();
// LSMSF
LeastSquaresMetaModelSelectionFactory leastSquaresFactory(LARS(), fittingAlgorithm);
// Projection strategy
LeastSquaresStrategy projectionStrategy(inputSample, outputSample, leastSquaresFactory);
FunctionalChaosAlgorithm algo(inputSample, outputSample, distribution, adaptiveStrategy, projectionStrategy);
// Reinitialize the RandomGenerator to see the effect of the sampling method only
RandomGenerator::SetSeed(0);
algo.run();
// Get the results
FunctionalChaosResult result = algo.getResult();
// MetaModelValidation - SPC
Function metamodel(result.getMetaModel());
Sample metamodelPredictions(metamodel(inputValidation));
MetaModelValidation metaModelValidationSPC(outputValidation, metamodelPredictions);
fullprint << "Sparse chaos scoring" << std::endl;
fullprint << "R2 = " << std::setprecision(PlatformInfo::GetNumericalPrecision()) << std::fixed << metaModelValidationSPC.computeR2Score() << std::endl;
fullprint << "Residual sample = " << metaModelValidationSPC.getResidualSample() << std::endl;
// 2) Kriging algorithm
// KrigingAlgorithm
Basis basis(QuadraticBasisFactory(dimension).build());
// model computed
Point scale(3);
scale[0] = 3.52;
scale[1] = 2.15;
scale[2] = 2.99;
Point amplitude(1, 11.41);
CovarianceModel covarianceModel = GeneralizedExponential(scale, amplitude, 2.0);
KrigingAlgorithm algo2(inputSample, outputSample, covarianceModel, basis);
algo2.setOptimizeParameters(false);
algo2.run();
KrigingResult result2 = algo2.getResult();
// MetaModelValidation - KG
Function metamodel2(result2.getMetaModel());
Sample metamodelPredictions2(metamodel2(inputValidation));
MetaModelValidation metaModelValidationKG(outputValidation, metamodelPredictions2);
fullprint << "Kriging scoring" << std::endl;
fullprint << "R2 = " << std::setprecision(PlatformInfo::GetNumericalPrecision()) << std::fixed << metaModelValidationKG.computeR2Score() << std::endl;
PlatformInfo::SetNumericalPrecision(2);
fullprint << "Residual sample = " << metaModelValidationKG.getResidualSample() << std::endl;
}
catch (TestFailed & ex)
{
std::cerr << ex << std::endl;
return ExitCode::Error;
}
return ExitCode::Success;
}
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