TShapley.h

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// Copyright (C) 2013-2024 CEA/DES
//
// This program is free software: you can redistribute it and/or modify
// it under the terms of the GNU Lesser General Public License as published
// by the Free Software Foundation, either version 3 of the License, or any
// later version.
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
// GNU Lesser General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public License
// along with this program. If not, see <http://www.gnu.org/licenses/>.
 
#ifndef TSHAPLEY_H
#define TSHAPLEY_H
 
 
#include "TSensitivity.h"
#include "TGaussianSampling.h"
 
using namespace URANIE::DataServer;
using namespace URANIE::Sampler;
 
namespace URANIE
{
namespace Sensitivity
{
class TShapley: public TSensitivity
{
public:
    enum EConditionalSampling
    {
        kNorta,
        kCopulas,
        kVines,
        kUnknown
    };
    //---------------------------------------------
    TShapley();
 
    TShapley(TDataServer *tds, const char *varexpinput,
            const char *varexpoutput, Option_t * option = "");
 
    TShapley(TDataServer *tds, const char *fcn, Int_t ns,
             const char *varexpinput, const char *varexpoutput,
             Option_t* option="");
 
    TShapley(TDataServer *tds, void (*fcn)(Double_t*,Double_t*),
             Int_t ns, const char *varexpinput, const char *varexpoutput,
             Option_t* option="");
 
    TShapley(TDataServer *tds, URANIE::Launcher::TCode *fcode,
             Int_t ns, Option_t * option = "");
 
    TShapley(TDataServer *tds, URANIE::Relauncher::TRun *run,
             Int_t ns, Option_t * option = "");
 
 
    virtual ~TShapley();
    //---------------------------------------------
    const char * getIteratorNameNo()
    {
        return _sIteratorNameNo.Data();
    }
    const char * getIteratorNameNi()
    {
        return _sIteratorNameNi.Data();
    }
    Int_t getNv() { return _nV;}
    void setNv(Int_t nv);
 
    Int_t getNm() { return _nM;}
    void setNm(Int_t nm);
 
    Int_t getNo() { return _nO;}
    void setNo(Int_t no);
 
    Int_t getNi() { return _nI;}
    void setNi(Int_t ni);
 
 
    void setSamplerMethod(EConditionalSampling nMethod);
 
    EConditionalSampling getSamplerMethod() {
        return _nCondSampler;
    }
 
    TMatrixD getMatrixNormalVariance() {
        return _matNormalCovariance;
    }
 
 
 
    //---------------------------------------------
    virtual void generateSample(Option_t * option = "");
 
 
    //---------------------------------------------
 
    void evaluateIndexes(Option_t * option = "");
 
 
 
    void printLog(Option_t *option = "");
 
    void printSummary(Option_t * option = "");
 
 
private:
    // methods
    void transfertDataTDS(TDataServer *tds);
    void splitMatrixVarCoVar(TMatrixD matVarCovar, TVectorD vecOfUnit,
                             TMatrixD &matVarCovar0, TMatrixD &matVarCovar1, TMatrixD &matVarCovar01,
                             TMatrixD matCorr, TMatrixD &matCorrtmp);
    void computeNormalCorrelationMatrix(TMatrixD matVarCovariance);
 
    TDataServer *generateConditionnalSampleByNortaOriginal(TDataServer *tds0, TDataServer *tds1, TMatrixD matVarCovar0, TMatrixD matVarCovar1, TMatrixD matVarCovar01);
    void generateConditionnalSampleByNorta(TGaussianSampling *tsanormal, TDataServer *tdsres, TList *listOfLeaves, TMatrixD matVarCovar, TVectorD &vecOf01);
 
 
    // Attributes
    TString _sIteratorNameNo;        
    TString _sIteratorNameNi;        
    Int_t _nV;                   
    Int_t _nO;                   
    Int_t _nI;                   
    Int_t _nM;                            
    TMatrixD _matNormalCovariance;        
    EConditionalSampling _nCondSampler;   
protected:
    void init(Option_t* option);
public:
    ClassDef(TShapley, ID_SENSITIVITY)
};
} // End Of namespace Sensitivity
} // End Of namespace URANIE
 
#endif
// End Of File