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// -*- C++ -*-
/**
* @brief The test file of ProbabilitySimulationAlgorithm 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;
static Bool stop(void * )
{
std::cerr << "-- stop?" << std::endl;
return false;
}
static void progress(Scalar percent, void * )
{
std::cerr << "-- progress=" << percent << "%" << std::endl;
}
int main(int, char *[])
{
TESTPREAMBLE;
OStream fullprint(std::cout);
try
{
/* We create a numerical math function */
Description input(4);
input[0] = "E";
input[1] = "F";
input[2] = "L";
input[3] = "I";
SymbolicFunction myFunction(input, Description(1, "-F*L^3/(3*E*I)"));
UnsignedInteger dim = myFunction.getInputDimension();
/* We create a normal distribution point of dimension 1 */
Point mean(dim, 0.0);
mean[0] = 50.0; // E
mean[1] = 1.0; // F
mean[2] = 10.0; // L
mean[3] = 5.0; // I
Point sigma(dim, 1.0);
IdentityMatrix R(dim);
Normal myDistribution(mean, sigma, R);
/* We create a 'usual' RandomVector from the Distribution */
RandomVector vect(myDistribution);
/* We create a composite random vector */
CompositeRandomVector output(myFunction, vect);
/* We create an Event from this RandomVector */
ThresholdEvent myEvent(output, Less(), -3.0);
Collection<WeightedExperiment> experiments(1, MonteCarloExperiment());
// qmc
experiments.add(LowDiscrepancyExperiment());
// importance sampling
mean[0] = 4.99689645939288809018e+01;
mean[1] = 1.84194175946153282375e+00;
mean[2] = 1.04454036676956398821e+01;
mean[3] = 4.66776215562709406726e+00;
Normal myImportance(mean, sigma, R);
experiments.add(ImportanceSamplingExperiment(myImportance));
// randomized qmc
LowDiscrepancyExperiment lde;
lde.setRandomize(true);
experiments.add(lde);
// randomized lhs
LHSExperiment lhse;
lhse.setAlwaysShuffle(true);
experiments.add(lhse);
for (UnsignedInteger i = 0; i < experiments.getSize(); ++ i)
{
RandomGenerator::SetSeed(0);
/* We create a Monte Carlo algorithm */
ProbabilitySimulationAlgorithm myAlgo(myEvent, experiments[i]);
myAlgo.setMaximumOuterSampling(250);
myAlgo.setBlockSize(4);
myAlgo.setMaximumCoefficientOfVariation(0.1);
myAlgo.setProgressCallback(&progress);
myAlgo.setStopCallback(&stop);
fullprint << "algo=" << myAlgo << std::endl;
/* Perform the simulation */
myAlgo.run();
/* Stream out the result */
fullprint << "algo result=" << myAlgo.getResult() << std::endl;
fullprint << "Confidence length at level 99%=" << myAlgo.getResult().getConfidenceLength(0.99) << std::endl;
fullprint << "Confidence length at level 80%=" << myAlgo.getResult().getConfidenceLength(0.8) << std::endl;
fullprint << "Probability distribution=" << myAlgo.getResult().getProbabilityDistribution() << std::endl;
/* Use the standard deviation as a stopping rule */
myAlgo = ProbabilitySimulationAlgorithm(myEvent, experiments[i]);
myAlgo.setMaximumOuterSampling(250);
myAlgo.setBlockSize(4);
myAlgo.setMaximumCoefficientOfVariation(0.0);
myAlgo.setMaximumStandardDeviation(0.1);
fullprint << "algo=" << myAlgo << std::endl;
/* Perform the simulation */
myAlgo.run();
/* Stream out the result */
fullprint << "algo result=" << myAlgo.getResult() << std::endl;
fullprint << "Probability distribution=" << myAlgo.getResult().getProbabilityDistribution() << std::endl;
}
}
catch (TestFailed & ex)
{
std::cerr << ex << std::endl;
return ExitCode::Error;
}
return ExitCode::Success;
}
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