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// -*- C++ -*-
/**
* @brief The test file of class MaximumEntropyOrderStatisticsDistribution 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;
class TestObject : public MaximumEntropyOrderStatisticsDistribution
{
public:
TestObject() : MaximumEntropyOrderStatisticsDistribution() {}
virtual ~TestObject() {}
};
int main(int, char *[])
{
TESTPREAMBLE;
OStream fullprint(std::cout);
PlatformInfo::SetNumericalPrecision(4);
try
{
// Test basic functionnalities
checkClassWithClassName<TestObject>();
// Force the use of the approximation to avoid timeout
ResourceMap::SetAsBool("MaximumEntropyOrderStatisticsDistribution-UseApproximation", true);
JointDistribution::DistributionCollection coll;
coll.add(Trapezoidal(-2.0, -1.1, -1.0, 1.0));
coll.add(LogUniform(1.0, 1.2));
coll.add(Triangular(3.0, 4.5, 5.0));
coll.add(Beta(2.5, 3.5, 4.7, 5.2));
// Instantiate one distribution object
MaximumEntropyOrderStatisticsDistribution distribution(coll);
UnsignedInteger dim = distribution.getDimension();
fullprint << "Distribution " << distribution << std::endl;
// Is this distribution elliptical ?
fullprint << "Elliptical = " << (distribution.isElliptical() ? "true" : "false") << std::endl;
// Is this distribution continuous ?
fullprint << "Continuous = " << (distribution.isContinuous() ? "true" : "false") << std::endl;
// Test for realization of distribution
Point oneRealization = distribution.getRealization();
fullprint << "oneRealization=" << oneRealization << std::endl;
// Test for sampling
UnsignedInteger size = 10000;
Sample oneSample = distribution.getSample( size );
fullprint << "oneSample first=" << oneSample[0] << " last=" << oneSample[size - 1] << std::endl;
fullprint << "mean=" << oneSample.computeMean() << std::endl;
fullprint << "covariance=" << oneSample.computeCovariance() << std::endl;
fullprint << "skewness=" << oneSample.computeSkewness() << std::endl;
fullprint << "kurtosis=" << oneSample.computeKurtosis() << std::endl;
// Define a point
Point point(distribution.getDimension());
point[1] = 3.2;
point[2] = 4.2;
point[3] = 5.0;
fullprint << "Point= " << point << std::endl;
//
// // Show PDF and CDF of point
// Scalar eps(1e-5);
Point DDF = distribution.computeDDF( point );
fullprint << "ddf =" << DDF << std::endl;
Scalar LPDF = distribution.computeLogPDF( point );
fullprint << "log pdf=" << LPDF << std::endl;
Scalar PDF = distribution.computePDF( point );
fullprint << "pdf =" << PDF << std::endl;
// point[0]=1.; point[1] = 3.31; point[2] = 5.;
Scalar CDF = distribution.computeCDF( point );
fullprint << "cdf=" << CDF << std::endl;
Scalar CCDF = distribution.computeComplementaryCDF( point );
fullprint << "ccdf=" << CCDF << std::endl;
// Scalar Survival = distribution.computeSurvivalFunction( point );
// fullprint << "survival=" << Survival << std::endl;
Point quantile = distribution.computeQuantile( 0.95 );
fullprint << "quantile=" << quantile << std::endl;
fullprint << "cdf(quantile)=" << distribution.computeCDF(quantile) << std::endl;
Point quantileTail = distribution.computeQuantile( 0.95, true );
fullprint << "quantile (tail)=" << quantileTail << std::endl;
Scalar CDFTail = distribution.computeComplementaryCDF( quantileTail );
fullprint << "cdf (tail)=" << CDFTail << std::endl;
// Point PDFgr = distribution.computePDFGradient( point );
// fullprint << "pdf gradient =" << PDFgr << std::endl;
// Takes too much time
// fullprint << "entropy=" << distribution.computeEntropy() << std::endl;
// fullprint << "entropy (MC)=" << -distribution.computeLogPDF(distribution.getSample(1000000)).computeMean()[0] << std::endl;
Point mean = distribution.getMean();
fullprint << "mean=" << mean << std::endl;
Point standardDeviation = distribution.getStandardDeviation();
fullprint << "standard deviation=" << standardDeviation << std::endl;
Point skewness = distribution.getSkewness();
fullprint << "skewness=" << skewness << std::endl;
Point kurtosis = distribution.getKurtosis();
fullprint << "kurtosis=" << kurtosis << std::endl;
ResourceMap::SetAsUnsignedInteger( "GaussKronrod-MaximumSubIntervals", 20 );
ResourceMap::SetAsScalar( "GaussKronrod-MaximumError", 1.0e-4 );
CovarianceMatrix covariance = distribution.getCovariance();
fullprint << "covariance=" << covariance << std::endl;
CovarianceMatrix correlation = distribution.getCorrelation();
fullprint << "correlation=" << correlation << std::endl;
CovarianceMatrix spearman = distribution.getSpearmanCorrelation();
fullprint << "spearman=" << spearman << std::endl;
// CovarianceMatrix kendall = distribution.getKendallTau();
// fullprint << "kendall=" << kendall << std::endl;
ResourceMap::SetAsUnsignedInteger( "GaussKronrod-MaximumSubIntervals", 100 );
ResourceMap::SetAsScalar( "GaussKronrod-MaximumError", 1.0e-12 );
MaximumEntropyOrderStatisticsDistribution::PointWithDescriptionCollection parameters = distribution.getParametersCollection();
fullprint << "parameters=" << parameters << std::endl;
// fullprint << "Standard representative=" << distribution.getStandardRepresentative().__str__() << std::endl;
// Extract the marginals
for (UnsignedInteger i = 0; i < dim; ++ i)
{
Distribution margin(distribution.getMarginal(i));
fullprint << "margin=" << margin << std::endl;
fullprint << "margin PDF=" << margin.computePDF(point[i]) << std::endl;
fullprint << "margin CDF=" << margin.computeCDF(point[i]) << std::endl;
fullprint << "margin quantile=" << margin.computeQuantile(0.95) << std::endl;
fullprint << "margin realization=" << margin.getRealization() << std::endl;
fullprint << "margin range=" << margin.getRange() << std::endl;
}
// Extract a 2-D marginal
Indices indices(2, 0);
indices[1] = 1;
fullprint << "indices=" << indices << std::endl;
Distribution margins(distribution.getMarginal(indices));
fullprint << "margins=" << margins << std::endl;
// fullprint << "margins PDF=" << margins.computePDF(Point(2)) << std::endl;
// fullprint << "margins CDF=" << margins.computeCDF(Point(2)) << std::endl;
// quantile = margins.computeQuantile(0.5);
// fullprint << "margins quantile=" << quantile << std::endl;
// fullprint << "margins CDF(quantile)=" << margins.computeCDF(quantile) << std::endl;
fullprint << "margins realization=" << margins.getRealization() << std::endl;
}
catch (TestFailed & ex)
{
std::cerr << ex << std::endl;
return ExitCode::Error;
}
return ExitCode::Success;
}
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