#ifndef KLB_H
#define KLB_H

#include <chrono>

#include <Indoor/math/divergence/KullbackLeibler.h>
#include <Indoor/grid/factory/v2/GridFactory.h>

#include <Indoor/floorplan/v2/Floorplan.h>
#include <Indoor/floorplan/v2/FloorplanReader.h>

#include <Indoor/grid/factory/v2/GridFactory.h>
#include <Indoor/grid/factory/v2/Importance.h>

#include <Indoor/geo/Heading.h>
#include <Indoor/geo/Point2.h>
#include <Indoor/sensors/offline/FileReader.h>

#include <KLib/math/statistics/Statistics.h>

#include <Indoor/sensors/imu/TurnDetection.h>
#include <Indoor/sensors/imu/StepDetection.h>
#include <Indoor/sensors/imu/MotionDetection.h>
#include <Indoor/sensors/pressure/RelativePressure.h>
#include <Indoor/sensors/radio/WiFiGridEstimator.h>
#include <Indoor/sensors/beacon/model/BeaconModelLogDistCeiling.h>

#include <Indoor/math/MovingAVG.h>
#include <Indoor/math/FixedFrequencyInterpolator.h>
#include <Indoor/math/divergence/KullbackLeibler.h>
#include <Indoor/math/divergence/JensenShannon.h>
#include <Indoor/data/Timestamp.h>

#include <KLib/math/filter/particles/Particle.h>
#include <KLib/math/filter/particles/ParticleFilterMixing.h>
#include <KLib/math/filter/particles/ParticleFilterInitializer.h>

#include <KLib/math/filter/particles/estimation/ParticleFilterEstimationWeightedAverage.h>
#include <KLib/math/filter/particles/estimation/ParticleFilterEstimationRegionalWeightedAverage.h>
#include <KLib/math/filter/particles/estimation/ParticleFilterEstimationOrderedWeightedAverage.h>
//#include <KLib/math/filter/particles/estimation/ParticleFilterEstimationKernelDensity.h>

#include <KLib/math/filter/particles/resampling/ParticleFilterResamplingSimple.h>
#include <KLib/math/filter/particles/resampling/ParticleFilterResamplingPercent.h>
#include <KLib/math/filter/particles/resampling/ParticleFilterResamplingDivergence.h>

#include <KLib/math/filter/merging/MarkovTransitionProbability.h>
#include <KLib/math/filter/merging/mixing/MixingSamplerDivergency.h>
#include <KLib/math/filter/merging/estimation/JointEstimationPosteriorOnly.h>

#include "Structs.h"

#include "../Plotti.h"
#include "Logic.h"
#include "../Settings.h"

double __KLD = 0.0;

//todo function return the transition prob matrix for markov chain!
//getKernelDensityProbability should work fine for first shot! nevertheless we need to do 2 kernel density estimations for both filters :( :( :(


struct ModeProbabilityTransition : public K::MarkovTransitionProbability<MyState, MyControl, MyObs>{

    Grid<MyNode>& grid;
    const double lambda;

    ModeProbabilityTransition(Grid<MyNode>& grid, double lambda) : grid(grid), lambda(lambda) {;}

    virtual Eigen::MatrixXd update(std::vector<K::ParticleFilterMixing<MyState, MyControl, MyObs>>& modes) override {

        std::vector<double> probsWifiV;
        std::vector<double> probsParticleV;

        // mode[0] -> Posterior & mode[1] -> Wifi ---- i know what im doing :)
        for(MyNode node : grid.getNodes()){
            double probParzenPosterior = calcKernelDensity(node, modes[0].getParticles());
            probsParticleV.push_back(probParzenPosterior);

            double probParzenWifi = calcKernelDensity(node, modes[1].getParticles());
            probsWifiV.push_back(probParzenWifi);
        }

        // make vectors
        Eigen::Map<Eigen::VectorXd> probsWifi(&probsWifiV[0], probsWifiV.size());
        Eigen::Map<Eigen::VectorXd> probsParticle(&probsParticleV[0], probsParticleV.size());

        // get kld
        double kld = Divergence::KullbackLeibler<double>::getGeneralFromSamples(probsParticle, probsWifi, Divergence::LOGMODE::NATURALIS);

        // debugging global variable
        __KLD = kld;

        //exp. distribution
        double expKld = std::exp(-lambda * kld);

        //create the matrix
        Eigen::MatrixXd m(2,2);
        m << 1-expKld, expKld, 0, 1;

        return m;

    }

    double calcKernelDensity(const MyNode node, const std::vector<K::Particle<MyState>> particles){

        int size = particles.size();
        double prob = 0;

        #pragma omp parallel for reduction(+:prob) num_threads(6)
        for(int i = 0; i < size; ++i){
            double distance = particles[i].state.position.getDistanceInCM(node);
            prob += Distribution::Normal<double>::getProbability(0, 100, distance) * particles[i].weight;
        }

        return prob;
    }
};


struct ModeProbabilityTransitionNormal : public K::MarkovTransitionProbability<MyState, MyControl, MyObs>{

    const double lambda;

    //this is a hack! it is possible that the sigma of z is getting 0 and therefore the rank decreases to 2 and
    //no inverse matrix is possible
    Distribution::Uniform<float> uniRand = Distribution::Uniform<float>(-0.1, 0.1);

    /** ctor */
    ModeProbabilityTransitionNormal(double lambda) : lambda(lambda) {;}

    virtual Eigen::MatrixXd update(std::vector<K::ParticleFilterMixing<MyState, MyControl, MyObs>>& modes) override {

        Assert::equal(modes[0].getParticles().size(), modes[1].getParticles().size(), "Particle.size() differs!");

        // create eigen matrix for posterior and wifi
        Eigen::MatrixXd mParticle(modes[0].getParticles().size(), 3);
        Eigen::MatrixXd mWifi(modes[1].getParticles().size(), 3);

        #pragma omp parallel for num_threads(6)
        for(int i = 0; i < modes[0].getParticles().size(); ++i){
            mParticle(i,0) = (modes[0].getParticles()[i].state.position.x_cm / 100.0) + uniRand.draw();
            mParticle(i,1) = (modes[0].getParticles()[i].state.position.y_cm / 100.0) + uniRand.draw();
            mParticle(i,2) = (modes[0].getParticles()[i].state.position.z_cm / 100.0) + uniRand.draw();

            mWifi(i,0) = (modes[1].getParticles()[i].state.position.x_cm / 100.0) + uniRand.draw();
            mWifi(i,1) = (modes[1].getParticles()[i].state.position.y_cm / 100.0) + uniRand.draw();
            mWifi(i,2) = (modes[1].getParticles()[i].state.position.z_cm / 100.0) + uniRand.draw();
        }

        // create normal distributions
        Eigen::VectorXd meanParticle(3);
        Point3 estParticle = modes[0].getEstimation().position.inMeter();
        meanParticle << estParticle.x, estParticle.y, estParticle.z;
        Distribution::NormalDistributionN normParticle = Distribution::NormalDistributionN::getNormalNFromSamplesAndMean(mParticle, meanParticle);

        Eigen::VectorXd meanWifi(3);
        Point3 estWifi = modes[1].getEstimation().position.inMeter();
        meanWifi << estWifi.x, estWifi.y, estWifi.z;
        Distribution::NormalDistributionN normWifi = Distribution::NormalDistributionN::getNormalNFromSamplesAndMean(mWifi, meanWifi);

        // get kld
        double kld = Divergence::KullbackLeibler<double>::getMultivariateGauss(normParticle, normWifi);

        if(kld > 20){
            std::cout << "STTTTTOOOOOOP" << std::endl;
        }


        // debugging global variable
        __KLD = kld;

        //exp. distribution
        double expKld = std::exp(-lambda * kld);

        Assert::isTrue(expKld < 1.0, "exp. distribution greater 1!");

        //create the matrix
        Eigen::MatrixXd m(2,2);
        m << expKld, 1- expKld, 0, 1;

        return m;

    }
};


#endif // KLB_H