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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);
ResourceMap::Set("GeneralLinearModelAlgorithm-LinearAlgebra", "HMAT");
try
{
// Set Numerical precision to 3
PlatformInfo::SetNumericalPrecision(3);
std::cout << "========================" << std::endl;
std::cout << "Test standard using HMat" << std::endl;
std::cout << "=======================" << std::endl;
UnsignedInteger sampleSize = 6;
UnsignedInteger inputDimension = 1;
// Create the function to estimate
Description input(inputDimension);
input[0] = "x0";
Description formulas(1);
formulas[0] = "x0";
SymbolicFunction model(input, formulas);
Sample X(sampleSize, inputDimension);
Sample X2(sampleSize, inputDimension);
for ( UnsignedInteger i = 0; i < sampleSize; ++ i )
{
X(i, 0) = 3.0 + i;
X2(i, 0) = 2.5 + i;
}
X(0, 0) = 1.0;
X(1, 0) = 3.0;
X2(0, 0) = 2.0;
X2(1, 0) = 4.0;
Sample Y = model(X);
for ( UnsignedInteger i = 0; i < sampleSize; ++ i )
{
Y(i, 0) += 0.01 * DistFunc::rNormal();
}
// Add a small noise to data
model(X2);
Basis basis = LinearBasisFactory(inputDimension).build();
DiracCovarianceModel covarianceModel(inputDimension);
GeneralLinearModelAlgorithm algo(X, Y, covarianceModel, basis);
algo.run();
// perform an evaluation
GeneralLinearModelResult result = algo.getResult();
Function metaModel = result.getMetaModel();
result.getCovarianceModel();
const Sample residual = metaModel(X) - Y;
assert_almost_equal(residual.computeCentralMoment(2), Point(1, 0.00013144), 1e-5, 1e-5);
std::cout << "Test Ok" << std::endl;
}
catch (TestFailed & ex)
{
std::cerr << ex << std::endl;
return ExitCode::Error;
}
return ExitCode::Success;
}
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