//                                               -*- C++ -*-
/**
 *  @file  t_PosteriorDistribution_std.cxx
 *  @brief The test file of class PosteriorDistribution for standard methods
 *
 *  Copyright (C) 2005-2014 Airbus-EDF-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 schueller
 *  @date   2012-02-17 19:35:43 +0100 (ven. 17 févr. 2012)
 */
#include "OT.hxx"
#include "OTtestcode.hxx"

using namespace OT;
using namespace OT::Test;

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

  try
  {

    // Test basic functionnalities
    //checkClassWithClassName<TestObject>();

    UnsignedInteger observationsSize(5);
    // Create a collection of distribution
    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(ComposedDistribution(atoms));
    }
    // Second conditioning distribution: discrete/continuous
    {
      Collection< Distribution > atoms;
      atoms.add( Binomial(3, 0.5) );
      atoms.add( Uniform( 1.0, 2.0) );
      conditioningDistributionCollection.add(ComposedDistribution(atoms));
    }
    // Third conditioning distribution: dirac/continuous
    {
      Collection< Distribution > atoms;
      atoms.add( Dirac(0.0) );
      atoms.add( Uniform( 1.0, 2.0) );
      conditioningDistributionCollection.add(ComposedDistribution(atoms));
    }
    for (UnsignedInteger i = 0; i < conditioningDistributionCollection.getSize(); ++i)
    {
      fullprint << "conditioning distribution=" << conditioningDistributionCollection[i].__str__() << std::endl;
      Distribution observationsDistribution(conditionedDistribution);
      observationsDistribution.setParametersCollection(conditioningDistributionCollection[i].getMean());
      NumericalSample observations(observationsDistribution.getSample(observationsSize));
      PosteriorDistribution distribution(ConditionalDistribution(conditionedDistribution, conditioningDistributionCollection[i]), observations);
      UnsignedInteger dim(distribution.getDimension());
      fullprint << "Distribution " << distribution << std::endl;
      std::cout << "Distribution " << distribution << std::endl;
      fullprint << "range=" << distribution.getRange() << std::endl;
      NumericalPoint mean(distribution.getMean());
      fullprint << "Mean " << mean << 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
      NumericalPoint oneRealization = distribution.getRealization();
      fullprint << "oneRealization=" << oneRealization << std::endl;

      // Test for sampling
      UnsignedInteger size = 10;
      NumericalSample oneSample = distribution.getSample( size );
      fullprint << "oneSample=" << oneSample << std::endl;

      // Test for sampling
      size = 10000;
      NumericalSample anotherSample = distribution.getSample( size );
      fullprint << "anotherSample mean=" << anotherSample.computeMean() << std::endl;
      fullprint << "anotherSample covariance=" << anotherSample.computeCovariance() << std::endl;

      // Define a point
      NumericalPoint zero(dim, 0.0);

      // Show PDF and CDF of zero point
      NumericalScalar zeroPDF = distribution.computePDF( zero );
      NumericalScalar zeroCDF = distribution.computeCDF( zero );
      fullprint << "Zero point= " << zero
                << " pdf=" << zeroPDF
                << " cdf=" << zeroCDF
                << std::endl;
      // Get 95% quantile
      NumericalPoint quantile = distribution.computeQuantile( 0.95 );
      fullprint << "Quantile=" << quantile << std::endl;
      fullprint << "CDF(quantile)=" << distribution.computeCDF(quantile) << 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(NumericalPoint(1)) << std::endl;
        fullprint << "margin CDF=" << margin.computeCDF(NumericalPoint(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;
}
