#ifdef WITH_TESTS
#ifdef WITH_EIGEN

#include "../../Tests.h"
#include "../../../math/divergence/KullbackLeibler.h"
#include "../../../math/Distributions.h"

#include <random>

#include <KLib/misc/gnuplot/Gnuplot.h>
#include <KLib/misc/gnuplot/GnuplotPlot.h>
#include <KLib/misc/gnuplot/GnuplotPlotElementLines.h>
#include <KLib/misc/gnuplot/GnuplotPlotElementPoints.h>


TEST(NormalN, multivariateGaussSampleData) {

    Eigen::VectorXd mu(2);
    mu[0] = 0.0;
    mu[1] = 0.0;

    Eigen::MatrixXd cov(2,2);
    cov(0,0) = 1.0;
    cov(0,1) = 0.0;
    cov(1,0) = 0.0;
    cov(1,1) = 1.0;

    Distribution::NormalDistributionN normTest(mu, cov);

    // draw
    K::Gnuplot gp;
    K::GnuplotPlot plot;
    K::GnuplotPlotElementPoints pParticles;

    gp << "set terminal qt size 1200,1200 \n";
    gp << "set xrange [-5:5] \n set yrange [-5:5] \n";

    // gen
    std::vector<Eigen::VectorXd> vals;
    for(int i = 0; i < 100000; ++i){
        Eigen::VectorXd vec = normTest.draw();
        vals.push_back(vec);

        K::GnuplotPoint2 pos(vec[0], vec[1]);
        pParticles.add(pos);
    }

    plot.add(&pParticles);

    gp.draw(plot);
    gp.flush();

    sleep(5);

    //create matrix out of the vector
    Eigen::MatrixXd m(vals.size(), vals[0].rows());

    for(int i = 0; i < vals.size(); ++i){
        m(i,0) = vals[i][0];
        m(i,1) = vals[i][1];
    }

    Distribution::NormalDistributionN norm =  Distribution::NormalDistributionN::getNormalNFromSamples(m);

    //get distance between original and approximated mu and sigma
    Eigen::MatrixXd diffM = cov - norm.getSigma();
    double distM = diffM.norm();

    Eigen::VectorXd diffV = mu - norm.getMu();
    double distV = diffV.norm();

    ASSERT_NEAR(0.0, distM, 0.01);
    ASSERT_NEAR(0.0, distV, 0.01);

}

#endif
#endif