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File indexing completed on 2025-01-30 10:22:52
0001 // @(#)root/tmva $Id$ 0002 // Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss 0003 0004 /********************************************************************************** 0005 * Project: TMVA - a Root-integrated toolkit for multivariate data analysis * 0006 * Package: TMVA * 0007 * Class : GiniIndex * 0008 * * 0009 * * 0010 * Description: Implementation of the GiniIndex as separation criterion * 0011 * Large Gini Indices (maximum 0.5) mean , that the sample is well * 0012 * mixed (same amount of signal and bkg) * 0013 * bkg. Small Indices mean, well separated. * 0014 * general definition: * 0015 * Gini(Sample M) = 1 - (c(1)/N)^2 - (c(2)/N)^2 .... - (c(k)/N)^2 * 0016 * Where: M is a sample of whatever N elements (events) * 0017 * that belong to K different classes * 0018 * c(k) is the number of elements that belong to class k * 0019 * for just Signal and Background classes this boils down to: * 0020 * Gini(Sample) = 2s*b/(s+b)^2 * 0021 * * 0022 * * 0023 * Authors (alphabetical): * 0024 * Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland * 0025 * Helge Voss <Helge.Voss@cern.ch> - MPI-K Heidelberg, Germany * 0026 * Kai Voss <Kai.Voss@cern.ch> - U. of Victoria, Canada * 0027 * * 0028 * Copyright (c) 2005: * 0029 * CERN, Switzerland * 0030 * U. of Victoria, Canada * 0031 * Heidelberg U., Germany * 0032 * * 0033 * Redistribution and use in source and binary forms, with or without * 0034 * modification, are permitted according to the terms listed in LICENSE * 0035 * (http://ttmva.sourceforge.net/LICENSE) * 0036 **********************************************************************************/ 0037 0038 #ifndef ROOT_TMVA_GiniIndex 0039 #define ROOT_TMVA_GiniIndex 0040 0041 ////////////////////////////////////////////////////////////////////////// 0042 // // 0043 // GiniIndex // 0044 // // 0045 // Implementation of the GiniIndex as separation criterion // 0046 // // 0047 // Large Gini Indices (maximum 0.5) mean , that the sample is well // 0048 // mixed (same amount of signal and bkg) // 0049 // bkg. Small Indices mean, well separated. // 0050 // general definition: // 0051 // Gini(Sample M) = 1 - (c(1)/N)^2 - (c(2)/N)^2 .... - (c(k)/N)^2 // 0052 // Where: M is a sample of whatever N elements (events) // 0053 // that belong to K different classes // 0054 // c(k) is the number of elements that belong to class k // 0055 // for just Signal and Background classes this boils down to: // 0056 // Gini(Sample) = 2s*b/(s+b)^2 // 0057 ////////////////////////////////////////////////////////////////////////// 0058 0059 #include "TMVA/SeparationBase.h" 0060 0061 namespace TMVA { 0062 0063 class GiniIndex : public SeparationBase { 0064 0065 public: 0066 0067 // construtor for the GiniIndex 0068 GiniIndex() { fName="Gini"; } 0069 0070 // copy constructor 0071 GiniIndex( const GiniIndex& g): SeparationBase(g) {} 0072 0073 //destructor 0074 virtual ~GiniIndex(){} 0075 0076 // Return the separation index (a measure for "purity" of the sample") 0077 virtual Double_t GetSeparationIndex( const Double_t s, const Double_t b ); 0078 0079 protected: 0080 0081 ClassDef(GiniIndex,0); // Implementation of the GiniIndex as separation criterion 0082 }; 0083 0084 } // namespace TMVA 0085 0086 #endif 0087
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