GiniIndex.cxx

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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  : TMVA::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           *
 * (see tmva/doc/LICENSE)                                          *
 **********************************************************************************/
 
/*! \class TMVA::GiniIndex
\ingroup TMVA
 
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:
 
\f[
Gini(Sample M) = 1 - (\frac{c(1)}{N})^2 - (\frac{c(2)}{N})^2 .... - (\frac{c(k)}{N})^2
\f]
 
Where:
 
\f$ M \f$ is a sample of whatever \f$ N \f$ elements (events) that belong
to \f$ K \f$ different classes.
 
\f$ c(k) \f$ is the number of elements that belong to class \f$ k \f$ for just
Signal and Background classes this boils down to:
 
\f[
Gini(Sample) = \frac{2sb}{(s+b)^2}
\f]
*/
 
#include "TMVA/GiniIndex.h"
 
#include "Rtypes.h"
 
ClassImp(TMVA::GiniIndex);
 
////////////////////////////////////////////////////////////////////////////////
/// what we use here is 2*Gini.. as for the later use the factor
/// 2 is irrelevant and hence I'd like to save this calculation
 
Double_t TMVA::GiniIndex::GetSeparationIndex( const Double_t s, const Double_t b )
{
   if (s+b <= 0)      return 0;
   if (s<=0 || b <=0) return 0;
   //   else               return s*b/(s+b)/(s+b);
   else               return 2*s*b/(s+b)/(s+b);
}