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// Copyright (C) 2017 EDF
// All Rights Reserved
// This code is published under the GNU Lesser General Public License (GNU LGPL)
#include <Eigen/SVD>
#include <Eigen/Cholesky>
#include "StOpt/regression/LocalGridKernelRegression.h"
#include "StOpt/regression/gridKernelConstruction.h"
#include "StOpt/regression/gridKernelRegression.h"
#include <iostream>
using namespace std ;
using namespace Eigen ;
namespace StOpt
{
LocalGridKernelRegression::LocalGridKernelRegression(const bool &p_bZeroDate,
const ArrayXXd &p_particles,
const double &p_coeffBandWidth,
const double &p_coefNbGridPoint,
const bool &p_bLinear):
BaseRegression(p_bZeroDate, p_particles, true),
m_iSort(p_particles.rows(), p_particles.cols()), m_xG(p_particles.rows(), p_particles.cols()), m_coeffBandWidth(p_coeffBandWidth), m_coefNbGridPoint(p_coefNbGridPoint), m_bLinear(p_bLinear)
{
if (!p_bZeroDate)
createGrid();
}
void LocalGridKernelRegression::updateSimulations(const bool &p_bZeroDate, const ArrayXXd &p_particles)
{
BaseRegression::updateSimulationsBase(p_bZeroDate, p_particles);
if (!p_bZeroDate)
{
m_iSort.resize(p_particles.rows(), p_particles.cols());
m_xG.resize(p_particles.rows(), p_particles.cols());
createGrid();
}
}
void LocalGridKernelRegression::createGrid()
{
ArrayXi aN, aK;
int dEff;
preprocessData(m_particles, m_sing, m_coeffBandWidth, m_coefNbGridPoint, aN, aK, dEff);
// sort
ArrayXXd sX(m_particles.rows(), m_particles.cols());
for (int id = 0 ; id < m_particles.rows(); ++id)
{
vector<pair<double, int> > toSort(m_particles.cols());
for (int is = 0; is < m_particles.cols(); ++is)
toSort[is] = make_pair(m_particles(id, is), is);
sort(toSort.begin(), toSort.end());
for (int is = 0; is < m_particles.cols(); ++is)
{
m_iSort(id, is) = toSort[is].second;
sX(id, is) = toSort[is].first;
}
}
adaptiveBandwithNd(sX, m_iSort, aN, aK, m_h, m_z, m_zl, m_zr, m_g, m_xG);
}
ArrayXd LocalGridKernelRegression::getCoordBasisFunction(const ArrayXd &p_fToRegress) const
{
if (!BaseRegression::m_bZeroDate)
return gridKernelRegressedValuesOnGrid(m_particles, p_fToRegress, m_z, m_h, m_g, m_xG, m_zl, m_zr, m_bLinear) ;
else
{
ArrayXd retAverage(1);
retAverage(0) = p_fToRegress.mean();
return retAverage;
}
}
ArrayXd LocalGridKernelRegression::getCoordBasisFunctionStable(const ArrayXd &p_fToRegress) const
{
if (!BaseRegression::m_bZeroDate)
return gridKernelRegressedValuesOnGridStable(m_particles, p_fToRegress, m_z, m_h, m_g, m_xG, m_zl, m_zr, m_bLinear) ;
else
{
ArrayXd retAverage(1);
retAverage(0) = p_fToRegress.mean();
return retAverage;
}
}
ArrayXXd LocalGridKernelRegression::getCoordBasisFunctionMultiple(const ArrayXXd &p_fToRegress) const
{
if (!BaseRegression::m_bZeroDate)
{
vector< unique_ptr< ArrayXd> > regressedLocVec;
for (int ireg = 0; ireg < p_fToRegress.rows(); ++ireg)
{
regressedLocVec.push_back(make_unique<ArrayXd>(gridKernelRegressedValuesOnGrid(m_particles, p_fToRegress.row(ireg).transpose(), m_z, m_h, m_g, m_xG, m_zl, m_zr, m_bLinear)));
}
ArrayXXd regressed(p_fToRegress.rows(), regressedLocVec[0]->size());
for (int ireg = 0; ireg < p_fToRegress.rows(); ++ireg)
regressed.row(ireg) = regressedLocVec[ireg]->transpose();
return regressed;
}
else
{
ArrayXXd retAverage(p_fToRegress.rows(), 1);
for (int nsm = 0; nsm < p_fToRegress.rows(); ++nsm)
retAverage.row(nsm).setConstant(p_fToRegress.row(nsm).mean());
return retAverage;
}
}
ArrayXd LocalGridKernelRegression::getAllSimulations(const ArrayXd &p_fToRegress) const
{
if (!BaseRegression::m_bZeroDate)
{
return gridKernelRegressedValues(m_particles, p_fToRegress, m_z, m_h, m_g, m_xG, m_zl, m_zr, m_iSort, m_bLinear) ;
}
else
{
return ArrayXd::Constant(p_fToRegress.size(), p_fToRegress.mean());
}
}
ArrayXXd LocalGridKernelRegression::getAllSimulationsMultiple(const ArrayXXd &p_fToRegress) const
{
if (!BaseRegression::m_bZeroDate)
{
ArrayXXd regressed(p_fToRegress.rows(), p_fToRegress.cols());
for (int ireg = 0; ireg < p_fToRegress.rows(); ++ireg)
{
ArrayXd regressedLoc = gridKernelRegressedValues(m_particles, p_fToRegress.row(ireg).transpose(), m_z, m_h, m_g, m_xG, m_zl, m_zr, m_iSort, m_bLinear) ;
regressed.row(ireg) = regressedLoc.transpose();
}
return regressed;
}
else
{
ArrayXXd ret(p_fToRegress.rows(), p_fToRegress.cols());
for (int ism = 0; ism < p_fToRegress.rows(); ++ism)
ret.row(ism).setConstant(p_fToRegress.row(ism).mean());
return ret;
}
}
ArrayXd LocalGridKernelRegression::reconstruction(const ArrayXd &p_basisCoefficients) const
{
if (!BaseRegression::m_bZeroDate)
return fromGridValuesGetRegAllSim(m_particles, p_basisCoefficients, m_z, m_iSort);
else
{
return ArrayXd::Constant(m_particles.cols(), p_basisCoefficients(0));
}
}
ArrayXXd LocalGridKernelRegression::reconstructionMultiple(const ArrayXXd &p_basisCoefficients) const
{
if (!BaseRegression::m_bZeroDate)
{
ArrayXXd regressed(p_basisCoefficients.rows(), m_particles.cols());
for (int ireg = 0; ireg < p_basisCoefficients.rows(); ++ireg)
{
ArrayXd regressedLoc = fromGridValuesGetRegAllSim(m_particles, p_basisCoefficients.row(ireg).transpose(), m_z, m_iSort) ;
regressed.row(ireg) = regressedLoc.transpose();
}
return regressed;
}
else
{
ArrayXXd retValue(p_basisCoefficients.rows(), m_particles.cols());
for (int nsm = 0; nsm < p_basisCoefficients.rows(); ++nsm)
retValue.row(nsm).setConstant(p_basisCoefficients(nsm, 0));
return retValue ;
}
}
double LocalGridKernelRegression::reconstructionASim(const int &p_isim, const ArrayXd &p_basisCoefficients) const
{
double ret ;
if (!BaseRegression::m_bZeroDate)
{
ret = fromGridValuesGetRegASim(m_particles.col(p_isim), p_basisCoefficients, m_z);
}
else
{
ret = p_basisCoefficients(0);
}
return ret ;
}
double LocalGridKernelRegression::getValue(const ArrayXd &p_coordinates,
const ArrayXd &p_coordBasisFunction) const
{
double ret ;
if (!BaseRegression::m_bZeroDate)
{
VectorXd x = p_coordinates.matrix();
x = ((x.array() - m_meanX) / m_etypX).matrix();
x = m_svdMatrix * x;
ret = fromGridValuesGetRegASim(x.array(), p_coordBasisFunction, m_z);
}
else
ret = p_coordBasisFunction(0);
return ret ;
}
double LocalGridKernelRegression::getAValue(const ArrayXd &p_coordinates, const ArrayXd &p_ptOfStock,
const vector< shared_ptr<InterpolatorSpectral> > &p_interpFuncBasis) const
{
if (!BaseRegression::m_bZeroDate)
{
VectorXd x = p_coordinates.matrix();
x = ((x.array() - m_meanX) / m_etypX).matrix();
x = m_svdMatrix * x;
return fromGridValuesGetRegASimOnBasis(x.array(), m_z, p_ptOfStock, p_interpFuncBasis);
}
else
{
return p_interpFuncBasis[0]->apply(p_ptOfStock);
}
}
}
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