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// -*- C++ -*-
/**
* @brief The test file of class TrendFactory 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
{
Description inVar(1);
inVar[0] = "t";
Description outVar(1);
outVar[0] = "y";
Description formula(1);
Collection<Function> functions(3);
formula[0] = "1";
functions[0] = SymbolicFunction(inVar, formula);
formula[0] = "cos(2 * t)";
functions[1] = SymbolicFunction(inVar, formula);
formula[0] = "sin(2 * t)";
functions[2] = SymbolicFunction(inVar, formula);
// We build the weights
Sample coefficients(0, 2);
Point p(2);
p[0] = 1.5;
p[1] = 2.5;
coefficients.add(p);
p[0] = -0.5;
p[1] = 0.5;
coefficients.add(p);
p[0] = 1.;
p[1] = 1.;
coefficients.add(p);
// Third, build the function
DualLinearCombinationFunction myFunction(functions, coefficients);
// Fourth : we build a time series for estimation
// it issued from a white noise
const UnsignedInteger dimension = 2;
// Fix the realization as a Normal
Normal noiseDistribution(Point(dimension, 0.), Point(dimension, 1.), CorrelationMatrix(dimension));
// TimeGrid parameters
const UnsignedInteger N = 1000;
const Scalar timeStart = 0.;
const Scalar timeStep = 0.1;
RegularGrid timeGrid(timeStart, timeStep, N);
// White noise
const WhiteNoise myWhiteNoise(noiseDistribution, timeGrid);
TimeSeries realization(myWhiteNoise.getRealization());
fullprint << "White noise realization = " << realization << std::endl;
// We make a trend transform to the time series
// We get a time series which contains values of time
TrendTransform myTransformFunction(myFunction, timeGrid);
TimeSeries myTimeSeries(timeGrid, myTransformFunction(realization.getValues()));
fullprint << "myTimeSeries = " << myTimeSeries << std::endl;
// We wants to get the coefficients using a factory
// Build a factory using default constructor
TrendFactory myDefaultFactory;
fullprint << "myDefaultFactory = " << myDefaultFactory << std::endl;
TrendTransform myEstimateTrend(myDefaultFactory.build(myTimeSeries, functions));
fullprint << "myEstimateTrend = " << myEstimateTrend << std::endl;
// We fix a new fitting algorithm
myDefaultFactory.setFittingAlgorithm(KFold());
fullprint << "myDefaultFactory = " << myDefaultFactory << std::endl;
TrendTransform myNewEstimateTrend(myDefaultFactory.build(myTimeSeries, functions));
fullprint << "myNewEstimateTrend = " << myNewEstimateTrend << std::endl;
}
catch (TestFailed & ex)
{
std::cerr << ex << std::endl;
return ExitCode::Error;
}
return ExitCode::Success;
}
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