//                                               -*- C++ -*-
/**
 *  @file  t_TrendFactory_std.cxx
 *  @brief The test file of class TrendFactory for standard methods
 *
 *  Copyright (C) 2005-2013 EDF-EADS-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
 *  along with this library.  If not, see <http://www.gnu.org/licenses/>.
 *
 *  @author haddad
 *  @date   2011-12-15 16:16:52 +0100 (jeu. 15 déc. 2011)
 */
#include "OT.hxx"
#include "OTtestcode.hxx"

using namespace OT;
using namespace OT::Test;

int main(int argc, char *argv[])
{
  TESTPREAMBLE;
  OStream fullprint(std::cout);

  try
  {

    Description inVar(1);
    inVar[0] = "t";
    Description outVar(1);
    outVar[0] = "y";
    Description formula(1);

    Collection<NumericalMathFunction> functions(3);

    formula[0] = "1";
    functions[0] = NumericalMathFunction(inVar, outVar, formula);

    formula[0] = "cos(2 * t)";
    functions[1] = NumericalMathFunction(inVar, outVar, formula);

    formula[0] = "sin(2 * t)";
    functions[2] = NumericalMathFunction(inVar, outVar, formula);


    // We build the weights
    NumericalSample coefficients(0, 2);
    NumericalPoint 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
    NumericalMathFunction myFunction(functions, coefficients);


    // Fourth : we build a time series for estimation
    // it issued from a white noise
    const UnsignedLong dimension(2);

    // Fix the realization as a Normal
    Normal noiseDistribution(NumericalPoint(dimension, 0.), NumericalPoint(dimension, 1.), CorrelationMatrix(dimension));

    // TimeGrid parameters
    const UnsignedLong N(1000);
    const NumericalScalar timeStart(0.);
    const NumericalScalar 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);
    TimeSeries myTimeSeries(myTransformFunction(realization));
    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;
}
