TRegression.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/>.
// $Id$
// $Author$
// $Date$
// $Revision$
// $State$
 
#ifndef TREGRESSION_H
#define TREGRESSION_H
#include "TSensitivity.h"
 
namespace URANIE
{
namespace Sensitivity
{
class TRegression: public TSensitivity
{
 
    // Attributes
public:
    enum EMethod
    {
      kSRC, kSRRC, kPCC, kPRCC, kUnknown
    };
private:
    int _nMethod;               
 
    Double_t _dr2;                  
    Double_t _dr2A;                 
    Double_t _dSumOfSquareIndexes; 
 
    TMatrixD _mmatCorr;            
    TMatrixD _mmatCorrRank;        
    URANIE::DataServer::UMessageLogger *_fLogger;   
    TString _sCompleteMethod;
    URANIE::DataServer::TDataServer *_fake;
    // Operations
public:
    //---------------------------------------------
 
    TRegression();
    /*
     \param tds (URANIE::DataServer::TDataServer *) the dataserver
     \param varexpinput(const char*)[""] The list of input attributes to pass to the function separated by the caracter ":"
     \param varexpoutput (const char *)[""]  The list of output attributes separated by the caracter ":"
     \param 
     */
    TRegression(URANIE::DataServer::TDataServer *tds, const char *varexpinput,
            const char *varexpoutput, Option_t * option = "src");
    virtual ~TRegression();
 
    //---------------------------------------------
 
    void setMethod(EMethod nmethod)
    {
      _nMethod |=  (0x1 << nmethod);
    }
    void clearMethod()
    {
      _nMethod = 0;
    }
    int getMethod()
    {
        return _nMethod;
    }
 
 
    //---------------------------------------------
    void generateSample(Option_t * option = "")
    {
    TString soption = option;
    soption.ToLower();
    }
    ;
 
    //---------------------------------------------
    void evaluateIndexes(Option_t * option = "");
 
    void preTreatment();
 
    void getUncertaintyFromRhoTransformation(double &rho, double &CImin, double &CImax);
 
 
    //---------------------------------------------
 
    Double_t getR2()
    {
      (*_fLogger) << Endl <<  " TRegression::getR2() :: this method returns the latest R2 computed, disregarding the algorithm used and if a vector has been analysed." << Endl;
      (*_fLogger) << "   ==>  The proper results are now stored in the result tuple with flag __R2__ and __R2A__ " << Endl << Endl;
      return _dr2;
    }
    Double_t getR2A()
    {
      (*_fLogger) << Endl <<  " TRegression::getR2A() :: this method returns the latest R2A computed, disregarding the algorithm used and if a vector has been analysed." << Endl;
      (*_fLogger) << "   ==>  The proper results are now stored in the result tuple with flag __R2__ and __R2A__ " << Endl << Endl;
        return _dr2A;
    }
 
    Double_t getSumOfSquareIndexes()
    {
        return _dSumOfSquareIndexes;
    }
 
    //---------------------------------------------
    virtual void printLog(Option_t *option = "");
 
    ClassDef(URANIE::Sensitivity::TRegression, ID_SENSITIVITY)
    //Classe de
};
 
} // Fin du namespace Sensitivity
} // Fin du namespace URANIE
#endif