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// Copyright (C) 2018 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/LaplacianConstKernelRegression.h"
#include "StOpt/regression/nDDominanceKernel.h"
using namespace std ;
using namespace Eigen ;
namespace StOpt
{
LaplacianConstKernelRegression::LaplacianConstKernelRegression(const bool &p_bZeroDate,
const ArrayXXd &p_particles,
const ArrayXd &p_h):
BaseRegression(p_bZeroDate, p_particles, false), m_h(p_h), m_tree(p_particles)
{
}
LaplacianConstKernelRegression::LaplacianConstKernelRegression(const ArrayXd &p_h):
BaseRegression(false), m_h(p_h)
{
}
LaplacianConstKernelRegression::LaplacianConstKernelRegression(const bool &p_bZeroDate,
const ArrayXXd &p_particles):
BaseRegression(p_bZeroDate, p_particles, false), m_tree(p_particles)
{
}
void LaplacianConstKernelRegression::updateSimulations(const bool &p_bZeroDate, const ArrayXXd &p_particles)
{
BaseRegression::updateSimulationsBase(p_bZeroDate, p_particles);
m_tree = KDTree(p_particles);
}
ArrayXXd LaplacianConstKernelRegression::regressFunction(const ArrayXXd &p_fToRegress) const
{
// dimension
int nD = m_particles.rows();
// creation of the 2^d terms
int nbSum = pow(2, nD);
vector< shared_ptr<ArrayXXd> > vecToAdd(nbSum);
// calculate exp values
Eigen::ArrayXi iCoord(nD) ;
for (int i = 0; i < nbSum; ++i)
{
int ires = i;
for (int id = nD - 1 ; id >= 0 ; --id)
{
unsigned int idec = (ires >> id) ;
iCoord(id) = -(2 * idec - 1);
ires -= (idec << id);
}
vecToAdd[i] = make_shared<ArrayXXd>(1 + p_fToRegress.rows(), m_particles.cols());
for (int is = 0; is < m_particles.cols(); ++is)
{
double ssum = 0;
for (int id = 0; id < nD; ++id)
ssum += iCoord(id) * m_particles(id, is) / m_h(id);
double expSum = exp(ssum);
for (int ifunc = 0; ifunc < p_fToRegress.rows(); ++ifunc)
(*vecToAdd[i])(ifunc, is) = expSum * p_fToRegress(ifunc, is);
(*vecToAdd[i])(p_fToRegress.rows(), is) = expSum;
}
}
vector< shared_ptr<ArrayXXd> > fDomin(nbSum);
// kernel resolution
nDDominanceKernel(m_particles, vecToAdd, fDomin);
// reconstruction of the 2^d terms
ArrayXXd reconsY(p_fToRegress.rows(), p_fToRegress.cols());
ArrayXd recons(p_fToRegress.cols());
for (int is = 0; is < m_particles.cols(); ++is)
{
for (int ifunc = 0; ifunc < p_fToRegress.rows(); ++ifunc)
reconsY(ifunc, is) = p_fToRegress(ifunc, is);
recons(is) = 1;
for (int id = 0; id < nbSum; ++id)
{
for (int ifunc = 0 ; ifunc < p_fToRegress.rows(); ++ifunc)
reconsY(ifunc, is) += (*fDomin[id])(ifunc, is) * (*vecToAdd[nbSum - 1 - id])(p_fToRegress.rows(), is);
recons(is) += (*fDomin[id])(p_fToRegress.rows(), is) * (*vecToAdd[nbSum - 1 - id])(p_fToRegress.rows(), is);
}
}
for (int ifunc = 0 ; ifunc < p_fToRegress.rows(); ++ifunc)
for (int is = 0; is < m_particles.cols(); ++is)
reconsY(ifunc, is) /= recons(is);
return reconsY;
}
ArrayXd LaplacianConstKernelRegression::getCoordBasisFunction(const ArrayXd &p_fToRegress) const
{
if (!BaseRegression::m_bZeroDate)
{
const ArrayXXd fToRegress = Map<const ArrayXXd>(p_fToRegress.data(), 1, p_fToRegress.size()) ;
ArrayXXd regressed = regressFunction(fToRegress);
ArrayXd toReturn = Map<ArrayXd>(regressed.data(), regressed.size());
return toReturn;
}
else
{
ArrayXd retAverage(1);
retAverage(0) = p_fToRegress.mean();
return retAverage;
}
}
ArrayXXd LaplacianConstKernelRegression::getCoordBasisFunctionMultiple(const ArrayXXd &p_fToRegress) const
{
if (!BaseRegression::m_bZeroDate)
{
return regressFunction(p_fToRegress);
}
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 LaplacianConstKernelRegression::getAllSimulations(const ArrayXd &p_fToRegress) const
{
if (!BaseRegression::m_bZeroDate)
{
const ArrayXXd fToRegress = Map<const ArrayXXd>(p_fToRegress.data(), 1, p_fToRegress.size()) ;
ArrayXXd regressed = regressFunction(fToRegress);
ArrayXd toReturn = Map<ArrayXd>(regressed.data(), regressed.size());
return toReturn;
}
else
{
return ArrayXd::Constant(p_fToRegress.size(), p_fToRegress.mean());
}
}
ArrayXXd LaplacianConstKernelRegression::getAllSimulationsMultiple(const ArrayXXd &p_fToRegress) const
{
if (!BaseRegression::m_bZeroDate)
{
return regressFunction(p_fToRegress);
}
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 LaplacianConstKernelRegression::reconstruction(const ArrayXd &p_basisCoefficients) const
{
if (!BaseRegression::m_bZeroDate)
return p_basisCoefficients; // basis function are here regressed values !
else
{
return ArrayXd::Constant(m_particles.cols(), p_basisCoefficients(0));
}
}
ArrayXXd LaplacianConstKernelRegression::reconstructionMultiple(const ArrayXXd &p_basisCoefficients) const
{
if (!BaseRegression::m_bZeroDate)
{
return p_basisCoefficients; // basis function are here regressed values !
}
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 LaplacianConstKernelRegression::reconstructionASim(const int &p_isim, const ArrayXd &p_basisCoefficients) const
{
double ret ;
if (!BaseRegression::m_bZeroDate)
{
ret = p_basisCoefficients(p_isim);
}
else
{
ret = p_basisCoefficients(0);
}
return ret ;
}
double LaplacianConstKernelRegression::getValue(const ArrayXd &p_coordinates,
const ArrayXd &p_coordBasisFunction) const
{
double ret ;
if (!BaseRegression::m_bZeroDate)
{
// Use KDTree to find nearest point close to a given one and regress the associated
// regressed value
return p_coordBasisFunction(m_tree.nearestIndex(p_coordinates));
}
else
ret = p_coordBasisFunction(0);
return ret ;
}
double LaplacianConstKernelRegression::getAValue(const ArrayXd &p_coordinates, const ArrayXd &p_ptOfStock,
const vector< shared_ptr<InterpolatorSpectral> > &p_interpFuncBasis) const
{
if (!BaseRegression::m_bZeroDate)
{
return p_interpFuncBasis[m_tree.nearestIndex(p_coordinates)]->apply(p_ptOfStock);
}
else
{
return p_interpFuncBasis[0]->apply(p_ptOfStock);
}
}
}
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