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// -*- C++ -*-
/**
* @brief The test file of class CompoundDistribution for standard methods
*
* 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
{
// Test basic functionnalities
//checkClassWithClassName<TestObject>();
// Create a collection of distribution
{
Collection< Distribution > coll2;
coll2.add(Dirac(1));
coll2.add(Dirac(2));
coll2.add(Bernoulli(0.7));
coll2.add(Uniform(3.0, 4.0));
JointDistribution d2(coll2);
Collection< Distribution > coll1;
coll1.add(Uniform());
coll1.add(Uniform());
JointDistribution d1(coll1);
CompoundDistribution distribution(d1, d2);
UnsignedInteger dim = distribution.getDimension();
fullprint << "distribution=" << distribution << std::endl;
fullprint << "Parameters " << distribution.getParametersCollection() << std::endl;
fullprint << "Mean " << distribution.getMean() << std::endl;
CovarianceMatrix cov(distribution.getCovariance());
CovarianceMatrix covRef(2);
covRef(0, 0) = 0.0833333;
covRef(1, 1) = 0.751111;
assert_almost_equal(cov, covRef);
// Is this distribution an elliptical distribution?
fullprint << "Elliptical distribution= " << (distribution.isElliptical() ? "true" : "false") << std::endl;
// Has this distribution an elliptical copula?
fullprint << "Elliptical copula= " << (distribution.hasEllipticalCopula() ? "true" : "false") << std::endl;
// Has this distribution an independent copula?
fullprint << "Independent copula= " << (distribution.hasIndependentCopula() ? "true" : "false") << std::endl;
// Test for realization of distribution
Point oneRealization = distribution.getRealization();
fullprint << "oneRealization=" << oneRealization << std::endl;
// Test for sampling
UnsignedInteger size = 10;
Sample oneSample = distribution.getSample( size );
fullprint << "oneSample=" << oneSample << std::endl;
// Test for sampling
size = 10000;
Sample anotherSample = distribution.getSample( size );
fullprint << "anotherSample mean=" << anotherSample.computeMean() << std::endl;
fullprint << "anotherSample covariance=" << anotherSample.computeCovariance() << std::endl;
// Define a point
Point zero(dim, 0.0);
// Show PDF and CDF of zero point
Scalar zeroPDF = distribution.computePDF( zero );
Scalar zeroCDF = distribution.computeCDF( zero );
fullprint << "Zero point= " << zero
<< " pdf=" << zeroPDF
<< " cdf=" << zeroCDF
<< std::endl;
// Get 95% quantile
Point quantile = distribution.computeQuantile( 0.95 );
fullprint << "Quantile=" << quantile << std::endl;
fullprint << "CDF(quantile)=" << distribution.computeCDF(quantile) << std::endl;
// fullprint << "entropy=" << distribution.computeEntropy() << std::endl;
// fullprint << "entropy (MC)=" << -distribution.computeLogPDF(distribution.getSample(1000000)).computeMean()[0] << std::endl;
}
// 1D tests
Normal conditionedDistribution;
Collection< Distribution > conditioningDistributionCollection;
// First conditioning distribution: continuous/continuous
{
Collection< Distribution > atoms;
atoms.add( Uniform( 0.0, 1.0) );
atoms.add( Uniform( 1.0, 2.0) );
conditioningDistributionCollection.add(JointDistribution(atoms));
}
// Second conditioning distribution: discrete/continuous
{
Collection< Distribution > atoms;
atoms.add( Binomial(3, 0.5) );
atoms.add( Uniform( 1.0, 2.0) );
conditioningDistributionCollection.add(JointDistribution(atoms));
}
// Third conditioning distribution: dirac/continuous
{
Collection< Distribution > atoms;
atoms.add( Dirac(0.0) );
atoms.add( Uniform( 1.0, 2.0) );
conditioningDistributionCollection.add(JointDistribution(atoms));
}
for (UnsignedInteger i = 0; i < conditioningDistributionCollection.getSize(); ++i)
{
fullprint << "conditioning distribution=" << conditioningDistributionCollection[i].__str__() << std::endl;
CompoundDistribution distribution(conditionedDistribution, conditioningDistributionCollection[i]);
UnsignedInteger dim = distribution.getDimension();
fullprint << "Distribution " << distribution << std::endl;
std::cout << "Distribution " << distribution << std::endl;
fullprint << "Parameters " << distribution.getParametersCollection() << std::endl;
fullprint << "Mean " << distribution.getMean() << std::endl;
fullprint << "Covariance " << distribution.getCovariance() << std::endl;
// Is this distribution an elliptical distribution?
fullprint << "Elliptical distribution= " << (distribution.isElliptical() ? "true" : "false") << std::endl;
// Has this distribution an elliptical copula?
fullprint << "Elliptical copula= " << (distribution.hasEllipticalCopula() ? "true" : "false") << std::endl;
// Has this distribution an independent copula?
fullprint << "Independent copula= " << (distribution.hasIndependentCopula() ? "true" : "false") << std::endl;
// Test for realization of distribution
Point oneRealization = distribution.getRealization();
fullprint << "oneRealization=" << oneRealization << std::endl;
// Test for sampling
UnsignedInteger size = 10;
Sample oneSample = distribution.getSample( size );
fullprint << "oneSample=" << oneSample << std::endl;
// Test for sampling
size = 10000;
Sample anotherSample = distribution.getSample( size );
fullprint << "anotherSample mean=" << anotherSample.computeMean() << std::endl;
fullprint << "anotherSample covariance=" << anotherSample.computeCovariance() << std::endl;
// Define a point
Point zero(dim, 0.0);
// Show PDF and CDF of zero point
Scalar zeroPDF = distribution.computePDF( zero );
Scalar zeroCDF = distribution.computeCDF( zero );
fullprint << "Zero point= " << zero
<< " pdf=" << zeroPDF
<< " cdf=" << zeroCDF
<< std::endl;
// Get 95% quantile
Point quantile = distribution.computeQuantile( 0.95 );
fullprint << "Quantile=" << quantile << std::endl;
fullprint << "CDF(quantile)=" << distribution.computeCDF(quantile) << std::endl;
//fullprint << "entropy=" << distribution.computeEntropy() << std::endl;
// Extract the marginals
for (UnsignedInteger j = 0; j < dim; ++j)
{
Distribution margin(distribution.getMarginal(j));
fullprint << "margin=" << margin << std::endl;
fullprint << "margin PDF=" << margin.computePDF(Point(1)) << std::endl;
fullprint << "margin CDF=" << margin.computeCDF(Point(1)) << std::endl;
fullprint << "margin quantile=" << margin.computeQuantile(0.95) << std::endl;
fullprint << "margin realization=" << margin.getRealization() << std::endl;
}
}
}
catch (TestFailed & ex)
{
std::cerr << ex << std::endl;
return ExitCode::Error;
}
return ExitCode::Success;
}
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