//                                               -*- C++ -*-
/**
 *  @brief The test file of class CumulativeDistributionNetwork for standard methods
 *
 *  Copyright 2005-2017 Airbus-EDF-IMACS-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/>.
 *
 */
#include "openturns/OT.hxx"
#include "openturns/OTtestcode.hxx"

using namespace OT;
using namespace OT::Test;

class TestObject : public CumulativeDistributionNetwork
{
public:
  TestObject() : CumulativeDistributionNetwork() {}
  virtual ~TestObject() {}
};


int main(int argc, char *argv[])
{
  TESTPREAMBLE;
  OStream fullprint(std::cout);
  setRandomGenerator();
  try
  {
    // Test basic functionnalities
    checkClassWithClassName<TestObject>();

    // Instanciate one distribution object
    BipartiteGraph graph(0);
    Indices first(0);
    first.add(0);
    first.add(1);
    graph.add(first);
    Indices second(0);
    second.add(0);
    second.add(1);
    graph.add(second);
    CumulativeDistributionNetwork distribution(CumulativeDistributionNetwork::DistributionCollection(2, Normal(2)), graph);
    fullprint << "Distribution " << distribution << std::endl;
    std::cout << "Distribution " << distribution << std::endl;

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

    // Test for sampling
    UnsignedInteger size = 10000;
    Sample 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
    Point point( distribution.getDimension(), 1.0 );
    fullprint << "Point= " << point << std::endl;
    UnsignedInteger oldPrecision = PlatformInfo::GetNumericalPrecision();
    PlatformInfo::SetNumericalPrecision(5);
    // Show PDF and CDF of point
    Scalar LPDF = distribution.computeLogPDF( point );
    fullprint << std::setprecision(5) << "log pdf=" << LPDF << std::endl;
    Scalar PDF = distribution.computePDF( point );
    fullprint << std::setprecision(5) << "pdf     =" << PDF << std::endl;
    fullprint << std::setprecision(5) << "pdf (FD)=" << distribution.DistributionImplementation::computePDF(point) << std::endl;
    Scalar CDF = distribution.computeCDF( point );
    fullprint << std::setprecision(5) << "cdf=" << CDF << std::endl;
    Scalar CCDF = distribution.computeComplementaryCDF( point );
    fullprint << std::setprecision(5) << "ccdf=" << CCDF << std::endl;
    Scalar Survival = distribution.computeSurvivalFunction( point );
    fullprint << std::setprecision(5) << "survival=" << Survival << std::endl;
    Point InverseSurvival = distribution.computeInverseSurvivalFunction(0.95);
    fullprint << "Inverse survival=" << InverseSurvival << std::endl;
    fullprint << "Survival(inverse survival)=" << distribution.computeSurvivalFunction(InverseSurvival) << std::endl;
    Point quantile = distribution.computeQuantile( 0.95 );
    fullprint << "quantile=" << quantile << std::endl;
    fullprint << "cdf(quantile)=" << distribution.computeCDF(quantile) << std::endl;
    // Confidence regions
    if (distribution.getDimension() <= 2)
    {
      ResourceMap::SetAsUnsignedInteger("Distribution-MinimumVolumeLevelSetSamplingSize", 1000 );
      Scalar threshold;
      fullprint << "Minimum volume interval=" << distribution.computeMinimumVolumeIntervalWithMarginalProbability(0.95, threshold) << std::endl;
      fullprint << "threshold=" << threshold << std::endl;
      Scalar beta;
      LevelSet levelSet(distribution.computeMinimumVolumeLevelSetWithThreshold(0.95, beta));
      fullprint << "Minimum volume level set=" << levelSet << std::endl;
      fullprint << "beta=" << beta << std::endl;
      fullprint << "Bilateral confidence interval=" << distribution.computeBilateralConfidenceIntervalWithMarginalProbability(0.95, beta) << std::endl;
      fullprint << "beta=" << beta << std::endl;
      fullprint << "Unilateral confidence interval (lower tail)=" << distribution.computeUnilateralConfidenceIntervalWithMarginalProbability(0.95, false, beta) << std::endl;
      fullprint << "beta=" << beta << std::endl;
      fullprint << "Unilateral confidence interval (upper tail)=" << distribution.computeUnilateralConfidenceIntervalWithMarginalProbability(0.95, true, beta) << std::endl;
      fullprint << "beta=" << beta << std::endl;
    }
    Point mean = distribution.getMean();
    fullprint << "mean=" << mean << std::endl;
    Point standardDeviation = distribution.getStandardDeviation();
    fullprint << "standard deviation=" << standardDeviation << std::endl;
    Point skewness = distribution.getSkewness();
    fullprint << "skewness=" << skewness << std::endl;
    Point kurtosis = distribution.getKurtosis();
    fullprint << "kurtosis=" << kurtosis << std::endl;
    CovarianceMatrix covariance = distribution.getCovariance();
    fullprint << "covariance=" << covariance << std::endl;
    CovarianceMatrix correlation = distribution.getCorrelation();
    fullprint << "correlation=" << correlation << std::endl;
    CovarianceMatrix spearman = distribution.getSpearmanCorrelation();
    fullprint << "spearman=" << spearman << std::endl;
    CovarianceMatrix kendall = distribution.getKendallTau();
    fullprint << "kendall=" << kendall << std::endl;
    PlatformInfo::SetNumericalPrecision(oldPrecision);
  }
  catch (TestFailed & ex)
  {
    std::cerr << ex << std::endl;
    return ExitCode::Error;
  }


  return ExitCode::Success;
}

