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GiniIndex.h
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1 // @(#)root/tmva $Id$
2 // Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss
3 
4 /**********************************************************************************
5  * Project: TMVA - a Root-integrated toolkit for multivariate data analysis *
6  * Package: TMVA *
7  * Class : GiniIndex *
8  * Web : http://tmva.sourceforge.net *
9  * *
10  * Description: Implementation of the GiniIndex as separation criterion *
11  * Large Gini Indices (maximum 0.5) mean , that the sample is well *
12  * mixed (same amount of signal and bkg) *
13  * bkg. Small Indices mean, well separated. *
14  * general defniniton: *
15  * Gini(Sample M) = 1 - (c(1)/N)^2 - (c(2)/N)^2 .... - (c(k)/N)^2 *
16  * Where: M is a smaple of whatever N elements (events) *
17  * that belong to K different classes *
18  * c(k) is the number of elements that belong to class k *
19  * for just Signal and Background classes this boils down to: *
20  * Gini(Sample) = 2s*b/(s+b)^2 *
21  * *
22  * *
23  * Authors (alphabetical): *
24  * Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland *
25  * Helge Voss <Helge.Voss@cern.ch> - MPI-K Heidelberg, Germany *
26  * Kai Voss <Kai.Voss@cern.ch> - U. of Victoria, Canada *
27  * *
28  * Copyright (c) 2005: *
29  * CERN, Switzerland *
30  * U. of Victoria, Canada *
31  * Heidelberg U., Germany *
32  * *
33  * Redistribution and use in source and binary forms, with or without *
34  * modification, are permitted according to the terms listed in LICENSE *
35  * (http://ttmva.sourceforge.net/LICENSE) *
36  **********************************************************************************/
37 
38 #ifndef ROOT_TMVA_GiniIndex
39 #define ROOT_TMVA_GiniIndex
40 
41 //////////////////////////////////////////////////////////////////////////
42 // //
43 // GiniIndex //
44 // //
45 // Implementation of the GiniIndex as separation criterion //
46 // //
47 // Large Gini Indices (maximum 0.5) mean , that the sample is well //
48 // mixed (same amount of signal and bkg) //
49 // bkg. Small Indices mean, well separated. //
50 // general definition: //
51 // Gini(Sample M) = 1 - (c(1)/N)^2 - (c(2)/N)^2 .... - (c(k)/N)^2 //
52 // Where: M is a sample of whatever N elements (events) //
53 // that belong to K different classes //
54 // c(k) is the number of elements that belong to class k //
55 // for just Signal and Background classes this boils down to: //
56 // Gini(Sample) = 2s*b/(s+b)^2 //
57 //////////////////////////////////////////////////////////////////////////
58 
59 #include "TMVA/SeparationBase.h"
60 
61 namespace TMVA {
62 
63  class GiniIndex : public SeparationBase {
64 
65  public:
66 
67  // construtor for the GiniIndex
68  GiniIndex() { fName="Gini"; }
69 
70  // copy constructor
71  GiniIndex( const GiniIndex& g): SeparationBase(g) {}
72 
73  //destructor
74  virtual ~GiniIndex(){}
75 
76  // Return the separation index (a measure for "purity" of the sample")
77  virtual Double_t GetSeparationIndex( const Double_t s, const Double_t b );
78 
79  protected:
80 
81  ClassDef(GiniIndex,0); // Implementation of the GiniIndex as separation criterion
82  };
83 
84 } // namespace TMVA
85 
86 #endif
87