//                                               -*- C++ -*-
/**
 *  @file  t_KernelMixture_std.cxx
 *  @brief The test file of class KernelMixture 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 schueller
 *  @date   2012-07-16 15:59:45 +0200 (Mon, 16 Jul 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
    {

      UnsignedLong dimension(3);
      NumericalPoint meanPoint(dimension, 1.0);
      meanPoint[0] = 0.5;
      meanPoint[1] = -0.5;
      NumericalPoint sigma(dimension, 1.0);
      sigma[0] = 2.0;
      sigma[1] = 3.0;

      NumericalSample sample(0, dimension);
      // Create a collection of distribution
      Mixture::DistributionCollection aCollection;

      aCollection.add( Normal(meanPoint, sigma, IdentityMatrix(dimension)) );
      sample.add(meanPoint);
      meanPoint += NumericalPoint(dimension, 1.0);
      aCollection.add( Normal(meanPoint, sigma, IdentityMatrix(dimension)) );
      sample.add(meanPoint);
      meanPoint += NumericalPoint(dimension, 1.0);
      aCollection.add( Normal(meanPoint, sigma, IdentityMatrix(dimension)) );
      sample.add(meanPoint);

      // Instanciate one distribution object
      Normal kernel;
      KernelMixture distribution(kernel, sigma, sample);
      fullprint << "Distribution " << distribution << std::endl;
      std::cout << "Distribution " << distribution << std::endl;

      Mixture distributionRef(aCollection);

      // 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
      NumericalPoint oneRealization = distribution.getRealization();
      fullprint << "oneRealization=" << oneRealization << std::endl;

      // Test for sampling
      UnsignedLong size = 10000;
      NumericalSample 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;

      // Define a point
      NumericalPoint point( dimension, 1.0 );
      fullprint << "Point= " << point << std::endl;

      // Show PDF and CDF of point
      NumericalScalar eps(1e-5);
      NumericalPoint DDF = distribution.computeDDF( point );
      fullprint << "ddf     =" << DDF << std::endl;
      fullprint << "ddf (ref)=" << distributionRef.computeDDF( point ) << std::endl;
      NumericalPoint ddfFD(distribution.ContinuousDistribution::computeDDF(point));
      fullprint << "ddf (FD)=" << ddfFD << std::endl;
      NumericalScalar LPDF = distribution.computeLogPDF( point );
      fullprint << "log pdf=" << LPDF << std::endl;
      NumericalScalar PDF = distribution.computePDF( point );
      fullprint << "pdf     =" << PDF << std::endl;
      fullprint << "pdf (ref)=" << distributionRef.computePDF( point ) << std::endl;
      if (dimension == 1)
        {
          fullprint << "pdf (FD)=" << (distribution.computeCDF( point + NumericalPoint(1, eps) ) - distribution.computeCDF( point  + NumericalPoint(1, -eps) )) / (2.0 * eps) << std::endl;
        }
      NumericalScalar CDF = distribution.computeCDF( point );
      fullprint << "cdf=" << CDF << std::endl;
      NumericalScalar CCDF = distribution.computeComplementaryCDF( point );
      fullprint << "ccdf=" << CCDF << std::endl;
      fullprint << "cdf (ref)=" << distributionRef.computeCDF( point ) << std::endl;
      NumericalComplex CF = distribution.computeCharacteristicFunction( point[0] );
      fullprint << "characteristic function=" << CF << std::endl;
      NumericalComplex LCF = distribution.computeLogCharacteristicFunction( point[0] );
      fullprint << "log characteristic function=" << LCF << std::endl;
      NumericalPoint quantile = distribution.computeQuantile( 0.95 );
      fullprint << "quantile=" << quantile << std::endl;
      fullprint << "quantile (ref)=" << distributionRef.computeQuantile( 0.95 ) << std::endl;
      fullprint << "cdf(quantile)=" << distribution.computeCDF(quantile) << std::endl;
      NumericalPoint mean = distribution.getMean();
      fullprint << "mean=" << mean << std::endl;
      fullprint << "mean (ref)=" << distributionRef.getMean() << std::endl;
      CovarianceMatrix covariance = distribution.getCovariance();
      fullprint << "covariance=" << covariance << std::endl;
      fullprint << "covariance (ref)=" << distributionRef.getCovariance() << std::endl;
      //    KernelMixture::NumericalPointCollection parameters = distribution.getParametersCollection();
      //    fullprint << "parameters=" << parameters << std::endl;
    }
  catch (TestFailed & ex)
    {
      std::cerr << ex << std::endl;
      return ExitCode::Error;
    }


  return ExitCode::Success;
}
