/*
 * CondensationBackwardFilter.h
 *
 *  Created on: Jun 23, 2015
 *      Author: Toni Fetzer
 */

#ifndef BACKWARDSIMULATION_H_
#define BACKWARDSIMULATION_H_


#include <vector>
#include <memory>
#include <algorithm>

#include "BackwardFilterTransition.h"
#include "BackwardFilter.h"

#include "../Particle.h"

#include "../filtering/resampling/ParticleFilterResampling.h"
#include "../filtering/estimation/ParticleFilterEstimation.h"
#include "../filtering/ParticleFilterEvaluation.h"
#include "../filtering/ParticleFilterInitializer.h"

#include "../sampling/ParticleTrajectorieSampler.h"

#include "../../Assertions.h"

namespace SMC {

    /**
     * the main-class for the Backward Simulation Filter
     * running "backwards" in time, generates multiple backwards trajectories
     * (Realizations) by repeating the backward simulation M time.
     * it can be started at a random time T of any forward particle filter
     * [Monte Carlo smoothing for non-linear time series Godsill et al. '03]
     * @param State the (user-defined) state for each particle
     * @param numRealizations is the number of backward trajectories starting
     */
    template <typename State, typename Control, typename Observation>
    class BackwardSimulation : public BackwardFilter<State, Control, Observation>{

    private:

        /** all smoothed particles T -> 1*/
        std::vector<std::vector<Particle<State>>> backwardParticles;

        /** container for particles */
        std::vector<Particle<State>> smoothedParticles;

        /** the estimation function to use */
        std::unique_ptr<ParticleFilterEstimation<State>> estimation;

        /** the transition function to use */
        std::unique_ptr<BackwardFilterTransition<State, Control>> transition;

        /** the resampler to use */
        std::unique_ptr<ParticleFilterResampling<State>> resampler;

        /** the sampler for drawing trajectories */
        std::unique_ptr<ParticleTrajectorieSampler<State>> sampler;

        /** the percentage-of-efficient-particles-threshold for resampling */
        double nEffThresholdPercent = 0.25;

        /** number of realizations to be calculated */
        int numRealizations;

        /** is update called the first time? */
        bool firstFunctionCall;


    public:

        /** ctor */
        BackwardSimulation(int numRealizations) {
            this->numRealizations = numRealizations;
            backwardParticles.reserve(numRealizations);
            smoothedParticles.reserve(numRealizations);
            firstFunctionCall = true;
        }

        /** dtor */
        ~BackwardSimulation() {
            ;
        }

        /** reset **/
        void reset(){
            this->numRealizations = numRealizations;

            backwardParticles.clear();
            backwardParticles.reserve(numRealizations);

            smoothedParticles.clear();
            smoothedParticles.reserve(numRealizations);

            firstFunctionCall = true;
        }

        /** access to all backward / smoothed particles */
        const std::vector<std::vector<Particle<State>>>& getbackwardParticles() {
            return backwardParticles;
        }

        /** set the estimation method to use */
        void setEstimation(std::unique_ptr<ParticleFilterEstimation<State>> estimation) {
            Assert::isNotNull(estimation, "setEstimation() MUST not be called with a nullptr!");
            this->estimation = std::move(estimation);
        }

        /** set the transition method to use */
        void setTransition(std::unique_ptr<BackwardFilterTransition<State, Control>> transition) {
            Assert::isNotNull(transition, "setTransition() MUST not be called with a nullptr!");
            this->transition = std::move(transition);
        }

        /** set the resampling method to use */
        void setResampling(std::unique_ptr<ParticleFilterResampling<State>> resampler) {
            Assert::isNotNull(resampler, "setResampling() MUST not be called with a nullptr!");
            this->resampler = std::move(resampler);
        }

        /** set the sampler method to use */
        void setSampler(std::unique_ptr<ParticleTrajectorieSampler<State>> sampler){
            Assert::isNotNull(sampler, "setSampler() MUST not be called with a nullptr!");
            this->sampler = std::move(sampler);
        }


        /** set the resampling threshold as the percentage of efficient particles */
        void setNEffThreshold(const double thresholdPercent) {
            this->nEffThresholdPercent = thresholdPercent;
        }

        /** get the used transition method */
        BackwardFilterTransition<State, Control>* getTransition() {
            return this->transition.get();
        }

        /**
         * perform update: transition -> correction -> approximation
         * gets the weighted sample set of a standard condensation
         * particle filter in REVERSED order!
         */
        State update(std::vector<Particle<State>> const& forwardParticles) {

            // sanity checks (if enabled)
            Assert::isNotNull(transition, "transition MUST not be null! call setTransition() first!");
            Assert::isNotNull(estimation, "estimation MUST not be null! call setEstimation() first!");

            //storage for single trajectories / smoothed particles
            smoothedParticles.clear();

            // Choose \tilde x_T = x^(i)_T with probability w^(i)_T
            // Therefore sample independently from the categorical distribution of weights.
            if(firstFunctionCall){

                smoothedParticles = sampler->drawTrajectorie(forwardParticles, numRealizations);

                firstFunctionCall = false;
                backwardParticles.push_back(smoothedParticles);

                const State es = estimation->estimate(smoothedParticles);
                return es;
            }

            // compute weights using the transition model
            // transitionWeigths[numRealizations][numParticles]
            std::vector<std::vector<double>> transitionWeights = transition->transition(forwardParticles, backwardParticles.back());

            //get the next trajectorie for a realisation
            for(int j = 0; j < numRealizations; ++j){

                //vector for the current smoothedWeights at time t
                std::vector<Particle<State>> smoothedWeights;
                smoothedWeights.resize(forwardParticles.size());
                smoothedWeights = forwardParticles;

                //check if all transitionWeights are zero
                double weightSumTransition = std::accumulate(transitionWeights[j].begin(), transitionWeights[j].end(), 0.0);
                Assert::isNot0(weightSumTransition, "all transition weights for smoothing are zero");

                int i = 0;
                for (auto& w : transitionWeights.at(j)) {

                    // multiply the weight of the particles at time t and normalize
                    smoothedWeights.at(i).weight = (smoothedWeights.at(i).weight * w);
                    if(smoothedWeights.at(i).weight != smoothedWeights.at(i).weight) {throw "detected NaN";}

                    // iter
                    ++i;
                }

                //get the sum of all weights
                auto lambda = [](double current, const Particle<State>& a){return current + a.weight; };
                double weightSumSmoothed = std::accumulate(smoothedWeights.begin(), smoothedWeights.end(), 0.0, lambda);

                //normalize the weights
                if(weightSumSmoothed != 0.0){
                    for (int i = 0; i < smoothedWeights.size(); ++i){
                        smoothedWeights.at(i).weight /= weightSumSmoothed;
                    }

                    //check if normalization worked
                    double normWeightSum = std::accumulate(smoothedWeights.begin(), smoothedWeights.end(), 0.0, lambda);
                    Assert::isNear(normWeightSum, 1.0, 0.001, "Smoothed weights do not sum to 1");
                }


                //draw the next trajectorie at time t for a realization and save them
                smoothedParticles.push_back(sampler->drawSingleParticle(smoothedWeights));

                //throw if weight of smoothedParticle is zero
                //in practice this is possible, if a particle is completely separated from the rest and is therefore
                //weighted zero or very very low.
                Assert::isNot0(smoothedParticles.back().weight, "smoothed particle has zero weight");
            }


            if(resampler)
            {

                //TODO - does this even make sense?
                std::cout << "Warning - Resampling is not yet implemented!" << std::endl;
//                //resampling if necessery
//                double sum = 0.0;
//                double weightSum = std::accumulate(smoothedParticles.begin().weight, smoothedParticles.end().weight, 0.0);
//                for (auto& p : smoothedParticles) {
//                    p.weight /= weightSum;
//                    sum += (p.weight * p.weight);
//                }

//                const double neff = 1.0/sum;
//                if (neff != neff) {throw "detected NaN";}

//                // if the number of efficient particles is too low, perform resampling
//               if (neff < smoothedParticles.size() * nEffThresholdPercent) { resampler->resample(smoothedParticles); }
            }

            // push_back the smoothedParticles
            backwardParticles.push_back(smoothedParticles);

            // estimate the current state
            const State est = estimation->estimate(smoothedParticles);

            // done
            return est;

        }

    };

}


#endif /* BACKWARDSIMULATION_H_ */