// Copyright (C) 2023 EDF
// All Rights Reserved
// This code is published under the GNU Lesser General Public License (GNU LGPL)
#ifndef SIMULATEMULTISTAGEREGRESSION_H
#define SIMULATEMULTISTAGEREGRESSION_H
#include <Eigen/Dense>
#include <functional>
#include <memory>
#include <boost/lexical_cast.hpp>
#ifdef USE_MPI
#include <boost/mpi.hpp>
#endif
#include "geners/BinaryFileArchive.hh"
#include "StOpt/core/utils/StateWithStocks.h"
#include "StOpt/core/grids/SpaceGrid.h"
#include "StOpt/regression/BaseRegression.h"
#include "StOpt/dp/SimulateStepMultiStageRegression.h"
#include "StOpt/dp/OptimizerMultiStageDPBase.h"
#include "StOpt/dp/SimulatorMultiStageDPBase.h"


/** \file SimulateMultiStageRegression.h
 *  \brief Defines a simple program showing how to use simulations when at each transition step in time
 *         a deterministic optimization  of nPeriod is achieved.
 *        A simple grid  is used
 *  \author Xavier Warin
 */

/// \brief Simulate the optimal strategy , version using continuation values when a  deterministic optimization  of nPeriod is achieved
///        on each transisiotn step
/// \param p_grid                   grid used for  deterministic state (stocks for example)
/// \param p_optimize               optimizer defining the optimization between two time steps
/// \param p_funcFinalValue         function defining the final value
/// \param p_pointStock             initial point stock
/// \param p_initialRegime          regime at initial date
/// \param p_fileToDump             name of the file used to dump continuation values in optimization
/// \param p_world                  MPI communicator
double SimulateMultiStageRegression(const std::shared_ptr<StOpt::SpaceGrid> &p_grid,
                                    const std::shared_ptr<StOpt::OptimizerMultiStageDPBase > &p_optimize,
                                    const std::function<double(const int &, const Eigen::ArrayXd &, const Eigen::ArrayXd &)>   &p_funcFinalValue,
                                    const Eigen::ArrayXd &p_pointStock,
                                    const int &p_initialRegime,
                                    const std::string   &p_fileToDump
#ifdef USE_MPI
                                    , const boost::mpi::communicator &p_world
#endif
                                   )
{
    // from the optimizer get back the simulator
    std::shared_ptr< StOpt::SimulatorMultiStageDPBase> simulator = p_optimize->getSimulator();
    int nbStep = simulator->getNbStep();
    std::vector< StOpt::StateWithStocks> states;
    states.reserve(simulator->getNbSimul());
    for (int is = 0; is < simulator->getNbSimul(); ++is)
        states.push_back(StOpt::StateWithStocks(p_initialRegime, p_pointStock, Eigen::ArrayXd::Zero(simulator->getDimension())));
    std::shared_ptr<gs::BinaryFileArchive> ar =  std::make_shared<gs::BinaryFileArchive>(p_fileToDump.c_str(), "r");
    // name for continuation object in archive
    std::string nameAr = "Continuation";
    // Name for deterministic continuation in archive
    std::string nameArContValDet =  "ContinuationDet";
    // cost function
    Eigen::ArrayXXd costFunction = Eigen::ArrayXXd::Zero(p_optimize->getSimuFuncSize(), simulator->getNbSimul());
    // iterate on time steps
    for (int istep = 0; istep < nbStep; ++istep)
    {
        // Number of transition achieved for the current time step
        int  nbPeriodsOfCurrentStep = simulator->getNbPeriodsInTransition();
        std::vector<Eigen::ArrayXXd> costFunctionPeriod(nbPeriodsOfCurrentStep);
        for (int iPeriod = 0; iPeriod < nbPeriodsOfCurrentStep; ++iPeriod)
            costFunctionPeriod[iPeriod] = Eigen::ArrayXXd::Zero(p_optimize->getSimuFuncSize(), simulator->getNbSimul());
        costFunctionPeriod[0] = costFunction;
        std::string toStorBellDet = nameArContValDet + boost::lexical_cast<std::string>(nbStep - 1 - istep);
        StOpt::SimulateStepMultiStageRegression(ar, nbStep - 1 - istep, nameAr, toStorBellDet, p_grid, p_optimize
#ifdef USE_MPI
                                                , p_world
#endif
                                               ).oneStep(states, costFunctionPeriod);
        // new stochastic state
        Eigen::ArrayXXd particles =  simulator->stepForwardAndGetParticles();
        for (int is = 0; is < simulator->getNbSimul(); ++is)
            states[is].setStochasticRealization(particles.col(is));
        // store current cost function
        costFunction = costFunctionPeriod[nbPeriodsOfCurrentStep - 1];
    }
    // final : accept to exercise if not already done entirely (here suppose one function to follow)
    for (int is = 0; is < simulator->getNbSimul(); ++is)
        costFunction(0, is) += p_funcFinalValue(states[is].getRegime(), states[is].getPtStock(), states[is].getStochasticRealization()) * simulator->getActu();
    // average gain/cost
    return costFunction.mean();
}
#endif /* SIMULATEMULTISTAGEREGRESSION_H */
