GiniIndex.h

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// @(#)root/tmva $Id$
// Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss
 
/**********************************************************************************
 * Project: TMVA - a Root-integrated toolkit for multivariate data analysis       *
 * Package: TMVA                                                                  *
 * Class  : GiniIndex                                                             *
 *                                             *
 *                                                                                *
 * Description: Implementation of the GiniIndex as separation criterion           *
 *              Large Gini Indices (maximum 0.5) mean , that the sample is well   *
 *              mixed (same amount of signal and bkg)                             *
 *              bkg. Small Indices mean, well separated.                          *
 *              general definition:                                               *
 *              Gini(Sample M) = 1 - (c(1)/N)^2 - (c(2)/N)^2 .... - (c(k)/N)^2    *
 *              Where: M is a sample of whatever N elements (events)              *
 *                     that belong to K different classes                         *
 *                     c(k) is the number of elements that belong to class k      *
 *              for just Signal and Background classes this boils down to:        *
 *              Gini(Sample) = 2s*b/(s+b)^2                                       *
 *                                                                                *
 *                                                                                *
 * Authors (alphabetical):                                                        *
 *      Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland              *
 *      Helge Voss      <Helge.Voss@cern.ch>     - MPI-K Heidelberg, Germany      *
 *      Kai Voss        <Kai.Voss@cern.ch>       - U. of Victoria, Canada         *
 *                                                                                *
 * Copyright (c) 2005:                                                            *
 *      CERN, Switzerland                                                         *
 *      U. of Victoria, Canada                                                    *
 *      Heidelberg U., Germany                                                    *
 *                                                                                *
 * Redistribution and use in source and binary forms, with or without             *
 * modification, are permitted according to the terms listed in LICENSE           *
 * (http://ttmva.sourceforge.net/LICENSE)                                         *
 **********************************************************************************/
 
#ifndef ROOT_TMVA_GiniIndex
#define ROOT_TMVA_GiniIndex
 
//////////////////////////////////////////////////////////////////////////
//                                                                      //
// GiniIndex                                                            //
//                                                                      //
// Implementation of the GiniIndex as separation criterion              //
//                                                                      //
//     Large Gini Indices (maximum 0.5) mean , that the sample is well  //
//     mixed (same amount of signal and bkg)                            //
//     bkg. Small Indices mean, well separated.                         //
//     general definition:                                              //
//     Gini(Sample M) = 1 - (c(1)/N)^2 - (c(2)/N)^2 .... - (c(k)/N)^2   //
//     Where: M is a sample of whatever N elements (events)             //
//            that belong to K different classes                        //
//            c(k) is the number of elements that belong to class k     //
//     for just Signal and Background classes this boils down to:       //
//     Gini(Sample) = 2s*b/(s+b)^2                                      //
//////////////////////////////////////////////////////////////////////////
 
#include "TMVA/SeparationBase.h"
 
namespace TMVA {
 
   class GiniIndex : public SeparationBase {
 
   public:
 
      // construtor for the GiniIndex
      GiniIndex() { fName="Gini"; }
 
      // copy constructor
   GiniIndex( const GiniIndex& g): SeparationBase(g) {}
 
      //destructor
      virtual ~GiniIndex(){}
 
      // Return the separation index (a measure for "purity" of the sample")
      virtual Double_t GetSeparationIndex( const Double_t s, const Double_t b );
 
   protected:
 
      ClassDef(GiniIndex,0); // Implementation of the GiniIndex as separation criterion
   };
 
} // namespace TMVA
 
#endif