/usr/include/root/TMVA/GiniIndex.h is in libroot-tmva-dev 5.34.30-0ubuntu8.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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// Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss
/**********************************************************************************
* Project: TMVA - a Root-integrated toolkit for multivariate data analysis *
* Package: TMVA *
* Class : GiniIndex *
* Web : http://tmva.sourceforge.net *
* *
* 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 defniniton: *
* Gini(Sample M) = 1 - (c(1)/N)^2 - (c(2)/N)^2 .... - (c(k)/N)^2 *
* Where: M is a smaple 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 defniniton: //
// Gini(Sample M) = 1 - (c(1)/N)^2 - (c(2)/N)^2 .... - (c(k)/N)^2 //
// Where: M is a smaple 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 //
//////////////////////////////////////////////////////////////////////////
#ifndef ROOT_TMVA_SeparationBase
#include "TMVA/SeparationBase.h"
#endif
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
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