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// Copyright (C) 2021 EDF
// All Rights Reserved
// This code is published under the GNU Lesser General Public License (GNU LGPL)
#ifndef LOCALDISCRLASTDIMREGRESSION_H
#define LOCALDISCRLASTDIMREGRESSION_H
#include <vector>
#include <memory>
#include <array>
#include <Eigen/Dense>
#include "StOpt/regression/BaseRegression.h"
#include "StOpt/regression/LocalAdaptCellRegression.h"
#include "StOpt/regression/meshCalculationLocalRegression.h"
#include "StOpt/core/grids/InterpolatorSpectral.h"
/** \file LocalDiscrLastDimRegression.h
* \brief Base class for local regressions with adapted mesh size and using a random value with discrete values in last dimension
* See for the linear version the article "Monte-Carlo valorisation of American options: facts and new algorithms to improve existing methods"
* by Bouchard, Warin in "Numerical methods in finance", Springer,2012
* \author Xavier Warin
*/
namespace StOpt
{
/**
* \defgroup Local Piecewise regression
* \brief It implements local local regression
*@{
*/
/// \class LocalDiscrLastDimRegression LocalDiscrLastDimRegression.h
/// To be used in Monte Carlo methods regression on each cell which is constructed such that each cell has
/// roughly the same number of particles
class LocalDiscrLastDimRegression : public LocalAdaptCellRegression
{
protected:
std::vector< std::shared_ptr <Eigen::ArrayXd> > m_mesh1D ; ///< Mesh representation per dimension
/// \brief To a particle affect to cell number
/// \param p_oneParticle One point
/// \return cell number
int particleToMesh(const Eigen::ArrayXd &p_oneParticle) const;
public :
/// \brief Default ructor
LocalDiscrLastDimRegression() {}
/// \brief Constructor
/// \param p_nbMesh discretization in each direction
/// \param p_bRotationAndRecale do we use SVD
LocalDiscrLastDimRegression(const Eigen::ArrayXi &p_nbMesh, const bool &p_bRotationAndRecale);
/// \brief Constructor for object constructed at each time step
/// \param p_bZeroDate first date is 0?
/// \param p_particles particles used for the meshes.
/// First dimension : dimension of the problem,
/// second dimension : the number of particles
/// \param p_nbMesh discretization in each direction
/// \param p_bRotationAndRecale do we use SVD
LocalDiscrLastDimRegression(const bool &p_bZeroDate,
const Eigen::ArrayXXd &p_particles,
const Eigen::ArrayXi &p_nbMesh,
const bool &p_bRotationAndRecale);
/// \brief Second constructor , only to be used in simulation
LocalDiscrLastDimRegression(const bool &p_bZeroDate,
const Eigen::ArrayXi &p_nbMesh,
const Eigen::Array< std::array< double, 2>, Eigen::Dynamic, Eigen::Dynamic > &p_mesh,
const std::vector< std::shared_ptr< Eigen::ArrayXd > > &p_mesh1D,
const Eigen::ArrayXd &p_meanX,
const Eigen::ArrayXd &p_etypX,
const Eigen::MatrixXd &p_svdMatrix,
const bool &p_bRotationAndRecale) ;
/// \brief Copy constructor
/// \param p_object object to copy
LocalDiscrLastDimRegression(const LocalDiscrLastDimRegression &p_object);
inline const std::vector< std::shared_ptr< Eigen::ArrayXd > > &getMesh1D() const
{
return m_mesh1D;
}
/// \brief Get some local accessors with copy (useful for python)
///@{
inline std::vector< std::shared_ptr< Eigen::ArrayXd > > getMesh1DCopy() const
{
return m_mesh1D;
}
///@}
};
/**@}*/
}
#endif /*LOCALDISCRLASTDIMREGRESSION_H*/
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