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// Copyright (C) 2019 EDF
// All Rights Reserved
// This code is published under the GNU Lesser General Public License (GNU LGPL)
#ifdef USE_MPI
#include <functional>
#include <memory>
#include <boost/mpi.hpp>
#ifdef _OPENMP
#include <omp.h>
#include "StOpt/core/utils/OpenmpException.h"
#endif
#include <Eigen/Dense>
#include "geners/BinaryFileArchive.hh"
#include "geners/Record.hh"
#include "geners/vectorIO.hh"
#include "StOpt/core/parallelism/ParallelComputeGridSplitting.h"
#include "StOpt/core/grids/GridIterator.h"
#include "StOpt/core/utils/eigenGeners.h"
#include "StOpt/regression/ContinuationCuts.h"
#include "StOpt/regression/ContinuationCutsGeners.h"
#include "StOpt/dp/TransitionStepRegressionDPCutDist.h"
#include "StOpt/core/parallelism/GridReach.h"
using namespace StOpt;
using namespace Eigen;
using namespace std;
TransitionStepRegressionDPCutDist::TransitionStepRegressionDPCutDist(const shared_ptr<FullGrid> &p_pGridCurrent,
const shared_ptr<FullGrid> &p_pGridPrevious,
const shared_ptr<OptimizerDPCutBase > &p_pOptimize,
const boost::mpi::communicator &p_world): TransitionStepBaseDist(p_pGridCurrent, p_pGridPrevious, p_pOptimize, p_world) {}
vector< shared_ptr< ArrayXXd > > TransitionStepRegressionDPCutDist::oneStep(const vector< shared_ptr< ArrayXXd > > &p_phiIn,
const shared_ptr< BaseRegression> &p_condExp) const
{
// number of regimes at current time
int nbRegimes = m_pOptimize->getNbRegime();
vector< shared_ptr< ArrayXXd > > phiOut(nbRegimes);
// only if the processor is working
vector < shared_ptr< ArrayXXd > > phiInExtended(p_phiIn.size());
// Organize the data splitting : spread the incoming values on an extended grid
for (size_t iReg = 0; iReg < p_phiIn.size() ; ++iReg)
{
// utilitary
ArrayXXd emptyArray;
if (p_phiIn[iReg])
{
phiInExtended[iReg] = make_shared< ArrayXXd >(m_paral->runOneStep(*p_phiIn[iReg])) ;
}
else
phiInExtended[iReg] = make_shared< ArrayXXd >(m_paral->runOneStep(emptyArray)) ;
}
if (m_gridCurrentProc->getNbPoints() > 0)
{
// allocate for solution
for (int iReg = 0; iReg < nbRegimes; ++iReg)
phiOut[iReg] = make_shared< ArrayXXd >(p_condExp->getNbSimul() * (m_pGridCurrent->getDimension() + 1), m_gridCurrentProc->getNbPoints());
// create continuation values on extended grid
vector< ContinuationCuts > contVal(p_phiIn.size());
for (size_t iReg = 0; iReg < p_phiIn.size(); ++iReg)
contVal[iReg] = ContinuationCuts(m_gridExtendPreviousStep, p_condExp, *phiInExtended[iReg]);
// number of thread
#ifdef _OPENMP
int nbThreads = omp_get_max_threads();
#else
int nbThreads = 1;
#endif
// create iterator on current grid treated for processor
int iThread = 0 ;
#ifdef _OPENMP
OpenmpException excep; // deal with exception in openmp
#pragma omp parallel for private(iThread)
#endif
for (iThread = 0; iThread < nbThreads; ++iThread)
{
#ifdef _OPENMP
excep.run([&]
{
#endif
shared_ptr< GridIterator > iterGridPoint = m_gridCurrentProc->getGridIterator();
// account fo threads
iterGridPoint->jumpToAndInc(0, 1, iThread);
// iterates on points of the grid
while (iterGridPoint->isValid())
{
ArrayXd pointCoord = iterGridPoint->getCoordinate();
// optimize the current point and the set of regimes -> get back cuts per simulation and stock point
ArrayXXd solution = static_pointer_cast<OptimizerDPCutBase>(m_pOptimize)->stepOptimize(m_gridExtendPreviousStep, pointCoord, contVal);
// copie solution
for (int iReg = 0; iReg < nbRegimes; ++iReg)
(*phiOut[iReg]).col(iterGridPoint->getCount()) = solution.col(iReg);
iterGridPoint->nextInc(nbThreads);
}
#ifdef _OPENMP
});
#endif
}
#ifdef _OPENMP
excep.rethrow();
#endif
}
return phiOut;
}
void TransitionStepRegressionDPCutDist::dumpContinuationCutsValues(shared_ptr<gs::BinaryFileArchive> p_ar, const string &p_name, const int &p_iStep,
const vector< shared_ptr< ArrayXXd > > &p_phiInPrev,
const shared_ptr<BaseRegression> &p_condExp,
const bool &p_bOneFile) const
{
string stepString = boost::lexical_cast<string>(p_iStep) ;
ArrayXi initialDimensionPrev = m_pGridPrevious->getDimensions();
ArrayXi initialDimension = m_pGridCurrent->getDimensions();
if (!p_bOneFile)
{
Array< array<int, 2 >, Dynamic, 1 > gridLocalPrev = m_paral->getPreviousCalculationGrid();
shared_ptr<FullGrid> gridPrevious = m_pGridPrevious->getSubGrid(gridLocalPrev);
Array< array<int, 2 >, Dynamic, 1 > gridLocal = m_paral->getCurrentCalculationGrid();
shared_ptr<FullGrid> gridCurrent = m_pGridCurrent->getSubGrid(gridLocal);
// dump caracteristics of the splitting
// organize the hypercube splitting for parallel
vector<int> vecPrev(initialDimensionPrev.data(), initialDimensionPrev.data() + initialDimensionPrev.size());
*p_ar << gs::Record(vecPrev, "initialSizeOfMeshPrev", stepString.c_str()) ;
vector<int> vecCurrent(initialDimension.data(), initialDimension.data() + initialDimension.size());
*p_ar << gs::Record(vecCurrent, "initialSizeOfMesh", stepString.c_str()) ;
// store regressor
*p_ar << gs::Record(dynamic_cast<const BaseRegression &>(*p_condExp), "regressor", stepString.c_str()) ;
int nbCuts = m_pGridCurrent->getDimension() + 1;
vector< ArrayXXd > regressedValues(nbCuts * p_phiInPrev.size());
if (m_world.rank() < m_paral->getNbProcessorUsedPrev())
{
// regresse the values
for (size_t iReg = 0; iReg < p_phiInPrev.size(); ++iReg)
{
for (int ic = 0; ic < nbCuts; ++ic)
{
// size ( nbStock, (nb simul * nbCuts)
ArrayXXd transposeCont = p_phiInPrev[iReg]->block(ic * p_condExp->getNbSimul(), 0, p_condExp->getNbSimul(), p_phiInPrev[iReg]->cols()). transpose();
regressedValues[ic + nbCuts * iReg] = p_condExp->getCoordBasisFunctionMultiple(transposeCont).transpose();
}
}
// for cut zero add stock components
shared_ptr<GridIterator> iterRegGrid = gridPrevious->getGridIterator();
while (iterRegGrid->isValid())
{
// coordinates
ArrayXd pointCoordReg = iterRegGrid->getCoordinate();
// point number
int ipoint = iterRegGrid->getCount();
for (size_t iReg = 0; iReg < p_phiInPrev.size(); ++iReg)
{
for (int id = 0 ; id < pointCoordReg.size(); ++id)
regressedValues[nbCuts * iReg].col(ipoint) -= regressedValues[id + 1 + nbCuts * iReg].col(ipoint) * pointCoordReg(id);
}
iterRegGrid->next();
}
}
*p_ar << gs::Record(regressedValues, (p_name + "Values").c_str(), stepString.c_str()) ;
}
else
{
// utilitary
Array< array<int, 2 >, Dynamic, 1 > gridOnProc0Prev(initialDimensionPrev.size());
for (int id = 0; id < initialDimensionPrev.size(); ++id)
{
gridOnProc0Prev(id)[0] = 0 ;
gridOnProc0Prev(id)[1] = initialDimensionPrev(id) ;
}
ArrayXi splittingRatioPrev = paraOptimalSplitting(initialDimensionPrev, m_pOptimize->getDimensionToSplit(), m_world);
ParallelComputeGridSplitting paralObjectPrev(initialDimensionPrev, splittingRatioPrev, m_world);
vector< ContinuationCuts> contVal(p_phiInPrev.size());
for (size_t iReg = 0; iReg < p_phiInPrev.size(); ++iReg)
{
ArrayXXd reconstructedArray ;
if (m_world.rank() < m_paral->getNbProcessorUsedPrev())
reconstructedArray = paralObjectPrev.reconstruct(*p_phiInPrev[iReg], gridOnProc0Prev);
if (m_world.rank() == 0)
contVal[iReg] = ContinuationCuts(m_pGridPrevious, p_condExp, reconstructedArray);
}
if (m_world.rank() == 0)
*p_ar << gs::Record(contVal, (p_name + "Values").c_str(), stepString.c_str()) ;
}
if (m_world.rank() == 0)
p_ar->flush() ; // necessary for python mapping
m_world.barrier() ; // onlyt to prevent the reading in simualtion before the end of writting
}
void TransitionStepRegressionDPCutDist::dumpBellmanCutsValues(shared_ptr<gs::BinaryFileArchive> p_ar, const string &p_name, const int &p_iStep,
const vector< shared_ptr< ArrayXXd > > &p_phiIn,
const shared_ptr<BaseRegression> &p_condExp,
const bool &p_bOneFile) const
{
string stepString = boost::lexical_cast<string>(p_iStep) ;
ArrayXi initialDimension = m_pGridCurrent->getDimensions();
if (!p_bOneFile)
{
Array< array<int, 2 >, Dynamic, 1 > gridLocal = m_paral->getCurrentCalculationGrid();
shared_ptr<FullGrid> gridCurrent = m_pGridCurrent->getSubGrid(gridLocal);
// dump caracteristics of the splitting
// organize the hypercube splitting for parallel
vector<int> vecCurrent(initialDimension.data(), initialDimension.data() + initialDimension.size());
*p_ar << gs::Record(vecCurrent, "initialSizeOfMesh", stepString.c_str()) ;
// store regressor
*p_ar << gs::Record(dynamic_cast<const BaseRegression &>(*p_condExp), "regressor", stepString.c_str()) ;
int nbCuts = m_pGridCurrent->getDimension() + 1;
vector< ArrayXXd > regressedValues(nbCuts * p_phiIn.size());
if (m_world.rank() < m_paral->getNbProcessorUsed())
{
// regresse the values
for (size_t iReg = 0; iReg < p_phiIn.size(); ++iReg)
{
for (int ic = 0; ic < nbCuts; ++ic)
{
// size ( nbStock, (nb simul * nbCuts)
ArrayXXd transposeCont = p_phiIn[iReg]->block(ic * p_condExp->getNbSimul(), 0, p_condExp->getNbSimul(), p_phiIn[iReg]->cols()). transpose();
regressedValues[ic + nbCuts * iReg] = p_condExp->getCoordBasisFunctionMultiple(transposeCont).transpose();
}
}
// for cut zero add stock components
shared_ptr<GridIterator> iterRegGrid = gridCurrent->getGridIterator();
while (iterRegGrid->isValid())
{
// coordinates
ArrayXd pointCoordReg = iterRegGrid->getCoordinate();
// point number
int ipoint = iterRegGrid->getCount();
for (size_t iReg = 0; iReg < p_phiIn.size(); ++iReg)
{
for (int id = 0 ; id < pointCoordReg.size(); ++id)
regressedValues[nbCuts * iReg].col(ipoint) -= regressedValues[id + 1 + nbCuts * iReg].col(ipoint) * pointCoordReg(id);
}
iterRegGrid->next();
}
}
*p_ar << gs::Record(regressedValues, (p_name + "Values").c_str(), stepString.c_str()) ;
}
else
{
// utilitary
Array< array<int, 2 >, Dynamic, 1 > gridOnProc0(initialDimension.size());
for (int id = 0; id < initialDimension.size(); ++id)
{
gridOnProc0(id)[0] = 0 ;
gridOnProc0(id)[1] = initialDimension(id) ;
}
ArrayXi splittingRatio = paraOptimalSplitting(initialDimension, m_pOptimize->getDimensionToSplit(), m_world);
ParallelComputeGridSplitting paralObject(initialDimension, splittingRatio, m_world);
vector< ContinuationCuts> contVal(p_phiIn.size());
for (size_t iReg = 0; iReg < p_phiIn.size(); ++iReg)
{
ArrayXXd reconstructedArray ;
if (m_world.rank() < m_paral->getNbProcessorUsed())
reconstructedArray = paralObject.reconstruct(*p_phiIn[iReg], gridOnProc0);
if (m_world.rank() == 0)
contVal[iReg] = ContinuationCuts(m_pGridCurrent, p_condExp, reconstructedArray);
}
if (m_world.rank() == 0)
*p_ar << gs::Record(contVal, (p_name + "Values").c_str(), stepString.c_str()) ;
}
if (m_world.rank() == 0)
p_ar->flush() ; // necessary for python mapping
m_world.barrier() ; // onlyt to prevent the reading in simualtion before the end of writting
}
#endif
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