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// Copyright (C) 2016 EDF
// All Rights Reserved
// This code is published under the GNU Lesser General Public License (GNU LGPL)
#ifndef LOCALSAMESIZECONSTREGRESSION_H
#define LOCALSAMESIZECONSTREGRESSION_H
#include <vector>
#include <memory>
#include <array>
#include <Eigen/Dense>
#include "StOpt/regression/LocalSameSizeRegression.h"
#include "StOpt/core/grids/InterpolatorSpectral.h"
/** \file LocalSameSizeConstRegression.h
* \brief Compute conditional expectations by using constant local regressions with fixed size meshes.
* \author Xavier Warin
*/
namespace StOpt
{
/**
* \defgroup LocalConstSameSize Piecewise constant regressions with constant mesh size
* \brief It implements local constant functions for performing local regressions with constant meshes
*@{
*/
/// \class LocalSameSizeConstRegression LocalSameSizeConstRegression.h
/// Constant regression on each cell
class LocalSameSizeConstRegression : public LocalSameSizeRegression
{
protected :
Eigen::ArrayXd m_matReg ; ///< Regression diagonal matrix (rank the number of meshes ).
public :
/// \brief Default constructor
LocalSameSizeConstRegression() {}
/// \brief Constructor
/// \param p_lowValues in each dimension minimal value of the grid
/// \param p_step in each dimension the step size
/// \param p_nbStep in each dimension the number of steps
LocalSameSizeConstRegression(const Eigen::ArrayXd &p_lowValues, const Eigen::ArrayXd &p_step, const Eigen::ArrayXi &p_nbStep) : LocalSameSizeRegression(p_lowValues, p_step, p_nbStep) {} ;
/// \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_lowValues in each dimension minimal value of the grid
/// \param p_step in each dimension the step size
/// \param p_nbStep in each dimension the number of steps
LocalSameSizeConstRegression(const bool &p_bZeroDate,
const Eigen::ArrayXXd &p_particles,
const Eigen::ArrayXd &p_lowValues,
const Eigen::ArrayXd &p_step,
const Eigen::ArrayXi &p_nbStep);
/// \brief Constructor only used for serialization for simulation part
/// \param p_bZeroDate first date is 0?
/// \param p_lowValues in each dimension minimal value of the grid
/// \param p_step in each dimension the step size
/// \param p_nbStep in each dimension the number of steps
LocalSameSizeConstRegression(const bool &p_bZeroDate,
const Eigen::ArrayXd &p_lowValues,
const Eigen::ArrayXd &p_step,
const Eigen::ArrayXi &p_nbStep):
LocalSameSizeRegression(p_bZeroDate, p_lowValues, p_step, p_nbStep) {}
/// \brief Copy constructor
/// \param p_objet object to copy
LocalSameSizeConstRegression(const LocalSameSizeConstRegression &p_objet);
/// \brief update the particles used in regression and construct the matrices
/// \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
void updateSimulations(const bool &p_bZeroDate, const Eigen::ArrayXXd &p_particles);
/// \brief Get some local accessors
///@{
inline const Eigen::ArrayXd &getMatReg() const
{
return m_matReg;
}
///@}
/// \brief conditional expectation basis function coefficient calculation
/// \param p_fToRegress function to regress associated to each simulation used in optimization
/// \return regression coordinates on the basis (size : number of meshes multiplied by the dimension plus one)
/// @{
Eigen::ArrayXd getCoordBasisFunction(const Eigen::ArrayXd &p_fToRegress) const;
Eigen::ArrayXXd getCoordBasisFunctionMultiple(const Eigen::ArrayXXd &p_fToRegress) const ;
///@}
/// \brief conditional expectation calculation
/// \param p_fToRegress simulations to regress used in optimization
/// \return regressed value function
/// @{
Eigen::ArrayXd getAllSimulations(const Eigen::ArrayXd &p_fToRegress) const ;
Eigen::ArrayXXd getAllSimulationsMultiple(const Eigen::ArrayXXd &p_fToRegress) const;
///@}
/// \brief Use basis functions to reconstruct the solution
/// \param p_basisCoefficients basis coefficients
///@{
Eigen::ArrayXd reconstruction(const Eigen::ArrayXd &p_basisCoefficients) const;
Eigen::ArrayXXd reconstructionMultiple(const Eigen::ArrayXXd &p_basisCoefficients) const;
/// @}
/// \brief use basis function to reconstruct a given simulation
/// \param p_isim simulation number
/// \param p_basisCoefficients basis coefficients to reconstruct a given conditional expectation
double reconstructionASim(const int &p_isim, const Eigen::ArrayXd &p_basisCoefficients) const ;
/// \brief conditional expectation reconstruction
/// \param p_coordinates coordinates to interpolate (uncertainty sample)
/// \param p_coordBasisFunction regression coordinates on the basis (size: number of meshes multiplied by the dimension plus one)
/// \return regressed value function reconstructed for each simulation
double getValue(const Eigen::ArrayXd &p_coordinates,
const Eigen::ArrayXd &p_coordBasisFunction) const;
/// \brief permits to reconstruct a function with basis functions coefficients values given on a grid
/// \param p_coordinates coordinates (uncertainty sample)
/// \param p_ptOfStock grid point
/// \param p_interpFuncBasis spectral interpolator to interpolate the basis functions coefficients used in regression on the grid (given for each basis function)
double getAValue(const Eigen::ArrayXd &p_coordinates, const Eigen::ArrayXd &p_ptOfStock,
const std::vector< std::shared_ptr<InterpolatorSpectral> > &p_interpFuncBasis) const;
/// \brief get the number of basis functions
inline int getNumberOfFunction() const
{
if (m_bZeroDate)
return 1;
else
return m_nbMeshTotal ;
}
/// \brief Clone the regressor
std::shared_ptr<BaseRegression> clone() const
{
return std::static_pointer_cast<BaseRegression>(std::make_shared<LocalSameSizeConstRegression>(*this));
}
};
/**@}*/
}
#endif /*LOCALSAMESIZECONSTREGRESSION_H*/
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