#ifdef WITH_TESTS

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

#include <random>


TEST(KullbackLeibler, univariateGaussEQ) {
    //if the distributions are equal, kld is 0
    Distribution::Normal<float> norm1(0,1);
    Distribution::Normal<float> norm2(0,1);

    ASSERT_EQ(0.0f, Divergence::KullbackLeibler<float>::getUnivariateGauss(norm1, norm2));
    ASSERT_EQ(0.0f, Divergence::KullbackLeibler<float>::getUnivariateGaussSymmetric(norm1, norm2));
}

TEST(KullbackLeibler, univariateGaussGEmu) {
    //bigger mu means greater kld
    Distribution::Normal<float> norm1(0,1);
    Distribution::Normal<float> norm2(0,1);
    Distribution::Normal<float> norm3(0,1);
    Distribution::Normal<float> norm4(1,1);

    ASSERT_GE(Divergence::KullbackLeibler<float>::getUnivariateGauss(norm3, norm4), Divergence::KullbackLeibler<float>::getUnivariateGauss(norm1, norm2));
    ASSERT_GE(Divergence::KullbackLeibler<float>::getUnivariateGaussSymmetric(norm3, norm4), Divergence::KullbackLeibler<float>::getUnivariateGaussSymmetric(norm1, norm2));
}

TEST(KullbackLeibler, univariateGaussGEsigma) {
    //bigger sigma means greater kld
    Distribution::Normal<float> norm1(0,1);
    Distribution::Normal<float> norm2(0,1);
    Distribution::Normal<float> norm5(0,1);
    Distribution::Normal<float> norm6(0,3);

    ASSERT_GE(Divergence::KullbackLeibler<float>::getUnivariateGauss(norm5, norm6), Divergence::KullbackLeibler<float>::getUnivariateGauss(norm1, norm2));
    ASSERT_GE(Divergence::KullbackLeibler<float>::getUnivariateGaussSymmetric(norm5, norm6), Divergence::KullbackLeibler<float>::getUnivariateGaussSymmetric(norm1, norm2));
}

TEST(KullbackLeibler, univariateGaussRAND) {

    for(int i = 0; i < 20; i++){
        auto randMu1 = rand() % 100;
        auto randMu2 = rand() % 100 + 100;

        auto randMu3 = rand() % 100;
        auto randMu4 = rand() % 100 + 200;

        Distribution::Normal<float> norm7(randMu1,1);
        Distribution::Normal<float> norm8(randMu2,1);

        Distribution::Normal<float> norm9(randMu3,1);
        Distribution::Normal<float> norm10(randMu4,1);

        ASSERT_GE(Divergence::KullbackLeibler<float>::getUnivariateGauss(norm9, norm10), Divergence::KullbackLeibler<float>::getUnivariateGauss(norm8, norm7));
        ASSERT_GE(Divergence::KullbackLeibler<float>::getUnivariateGaussSymmetric(norm9, norm10), Divergence::KullbackLeibler<float>::getUnivariateGaussSymmetric(norm8, norm7));
    }

}

TEST(KullbackLeibler, multivariateGaussEQ) {

    //eq
    Eigen::VectorXd mu1(2);
    mu1[0] = 1.0;
    mu1[1] = 1.0;

    Eigen::VectorXd mu2(2);
    mu2[0] = 1.0;
    mu2[1] = 1.0;

    Eigen::MatrixXd cov1(2,2);
    cov1(0,0) = 1.0;
    cov1(0,1) = 0.0;
    cov1(1,0) = 0.0;
    cov1(1,1) = 1.0;

    Eigen::MatrixXd cov2(2,2);
    cov2(0,0) = 1.0;
    cov2(0,1) = 0.0;
    cov2(1,0) = 0.0;
    cov2(1,1) = 1.0;

    Distribution::NormalDistributionN norm1(mu1, cov1);
    Distribution::NormalDistributionN norm2(mu2, cov2);

    ASSERT_EQ(0.0f, Divergence::KullbackLeibler<float>::getMultivariateGauss(norm1, norm2));
    ASSERT_EQ(0.0f, Divergence::KullbackLeibler<float>::getMultivariateGaussSymmetric(norm1, norm2));


}

TEST(KullbackLeibler, multivariateGaussGeMu) {

    //ge mu
    Eigen::VectorXd mu1(2);
    mu1[0] = 1.0;
    mu1[1] = 1.0;

    Eigen::VectorXd mu2(2);
    mu2[0] = 1.0;
    mu2[1] = 1.0;

    Eigen::VectorXd mu3(2);
    mu3[0] = 1.0;
    mu3[1] = 1.0;

    Eigen::VectorXd mu4(2);
    mu4[0] = 1.0;
    mu4[1] = 3.0;

    Eigen::MatrixXd cov1(2,2);
    cov1(0,0) = 1.0;
    cov1(0,1) = 0.0;
    cov1(1,0) = 0.0;
    cov1(1,1) = 1.0;

    Eigen::MatrixXd cov2(2,2);
    cov2(0,0) = 1.0;
    cov2(0,1) = 0.0;
    cov2(1,0) = 0.0;
    cov2(1,1) = 1.0;

    Eigen::MatrixXd cov3(2,2);
    cov3(0,0) = 1.0;
    cov3(0,1) = 0.0;
    cov3(1,0) = 0.0;
    cov3(1,1) = 1.0;

    Eigen::MatrixXd cov4(2,2);
    cov4(0,0) = 1.0;
    cov4(0,1) = 0.0;
    cov4(1,0) = 0.0;
    cov4(1,1) = 1.0;

    Distribution::NormalDistributionN norm1(mu1, cov1);
    Distribution::NormalDistributionN norm2(mu2, cov2);
    Distribution::NormalDistributionN norm3(mu3, cov3);
    Distribution::NormalDistributionN norm4(mu4, cov4);

    double kld12 = Divergence::KullbackLeibler<float>::getMultivariateGauss(norm1, norm2);
    double kld34 = Divergence::KullbackLeibler<float>::getMultivariateGauss(norm3, norm4);
    std::cout << kld34 << " > " << kld12 << std::endl;

    double kld12sym = Divergence::KullbackLeibler<float>::getMultivariateGaussSymmetric(norm1, norm2);
    double kld34sym = Divergence::KullbackLeibler<float>::getMultivariateGaussSymmetric(norm3, norm4);
    std::cout << kld34sym << " > " << kld12sym << std::endl;

    ASSERT_GE(kld34, kld12);
    ASSERT_GE(kld34sym, kld12sym);
}

TEST(KullbackLeibler, multivariateGaussGeCov) {

    //ge cov
    Eigen::VectorXd mu1(2);
    mu1[0] = 1.0;
    mu1[1] = 1.0;

    Eigen::VectorXd mu2(2);
    mu2[0] = 1.0;
    mu2[1] = 1.0;

    Eigen::VectorXd mu3(2);
    mu3[0] = 1.0;
    mu3[1] = 1.0;

    Eigen::VectorXd mu4(2);
    mu4[0] = 1.0;
    mu4[1] = 1.0;

    Eigen::MatrixXd cov1(2,2);
    cov1(0,0) = 1.0;
    cov1(0,1) = 0.0;
    cov1(1,0) = 0.0;
    cov1(1,1) = 1.0;

    Eigen::MatrixXd cov2(2,2);
    cov2(0,0) = 1.0;
    cov2(0,1) = 0.0;
    cov2(1,0) = 0.0;
    cov2(1,1) = 1.0;

    Eigen::MatrixXd cov3(2,2);
    cov3(0,0) = 1.0;
    cov3(0,1) = 0.0;
    cov3(1,0) = 0.0;
    cov3(1,1) = 1.0;

    Eigen::MatrixXd cov4(2,2);
    cov4(0,0) = 3.0;
    cov4(0,1) = 0.0;
    cov4(1,0) = 0.0;
    cov4(1,1) = 1.0;

    Distribution::NormalDistributionN norm1(mu1, cov1);
    Distribution::NormalDistributionN norm2(mu2, cov2);
    Distribution::NormalDistributionN norm3(mu3, cov3);
    Distribution::NormalDistributionN norm4(mu4, cov4);

    double kld12 = Divergence::KullbackLeibler<float>::getMultivariateGauss(norm1, norm2);
    double kld34 = Divergence::KullbackLeibler<float>::getMultivariateGauss(norm3, norm4);
    std::cout << kld34 << " >" << kld12 << std::endl;

    double kld12sym = Divergence::KullbackLeibler<float>::getMultivariateGaussSymmetric(norm1, norm2);
    double kld34sym = Divergence::KullbackLeibler<float>::getMultivariateGaussSymmetric(norm3, norm4);
    std::cout << kld34sym << " > " << kld12sym << std::endl;

    ASSERT_GE(kld34, kld12);
    ASSERT_GE(kld34sym, kld12sym);
}

#endif