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// -*- C++ -*-
/**
* @brief The test file of class Test
*
* 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);
UnsignedInteger size = 100;
UnsignedInteger dim = 10;
CorrelationMatrix R(dim);
for (UnsignedInteger i = 0; i < dim; i++)
{
for (UnsignedInteger j = 0; j < i; j++)
{
R(i, j) = (i + j + 1.0) / (2.0 * dim);
}
}
Point mean(dim, 2.0);
Point sigma(dim, 3.0);
Normal distribution(mean, sigma, R);
Sample sample(distribution.getSample(size));
Indices indices(dim - 1);
indices.fill(1);
Sample sampleX(sample.getMarginal(indices));
Sample sampleY(sample.getMarginal(0));
Indices selection(5);
for (UnsignedInteger i = 0; i < 5; i++)
{
selection[i] = i;
}
Indices selection2(1, 0);
Sample sampleX0(sampleX.getMarginal(0));
Sample sampleZ(size, 1);
for (UnsignedInteger i = 0; i < size; i++)
{
sampleZ(i, 0) = sampleY(i, 0) * sampleY(i, 0);
}
fullprint << "LinearModelFisher pvalue=" << std::setprecision(2) << LinearModelTest::LinearModelFisher(sampleY, sampleZ).getPValue() << std::endl;
fullprint << "LinearModelResidualMean pvalue=" << std::setprecision(2) << LinearModelTest::LinearModelResidualMean(sampleY, sampleZ).getPValue() << std::endl;
// Regression test between 2 samples : firstSample of dimension n and secondSample of dimension 1. If firstSample[i] is the numerical sample extracted from firstSample (ith coordinate of each point of the numerical sample), PartialRegression performs the Regression test simultaneously on all firstSample[i] and secondSample, for i in the selection. The Regression test tests ifthe regression model between two scalar numerical samples is significant. It is based on the deviation analysis of the regression. The t-test is used.
// The two tests must be equal
fullprint << "PartialRegressionX0Y=" << LinearModelTest::PartialRegression(sampleX, sampleY, selection2, 0.10) << std::endl;
fullprint << "FullRegressionX0Y=" << LinearModelTest::FullRegression(sampleX0, sampleY, 0.10) << std::endl;
fullprint << "PartialRegressionXY=" << LinearModelTest::PartialRegression(sampleX, sampleY, selection, 0.10) << std::endl;
// Regression test between 2 samples : firstSample of dimension n and secondSample of dimension 1. If firstSample[i] is the numerical sample extracted from firstSample (ith coordinate of each point of the numerical sample), FullRegression performs the Regression test simultaneously on all firstSample[i] and secondSample. The Regression test tests if the regression model between two scalar numerical samples is significant. It is based on the deviation analysis of the regression. The t-test is used.
fullprint << "FullRegressionXZ=" << LinearModelTest::FullRegression(sampleX, sampleZ, 0.10) << std::endl;
//fullprint << "FullRegressionZZ=" << LinearModelTest::FullRegression(sampleZ, sampleZ, 0.10) << std::endl;
return ExitCode::Success;
}
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