ROOT  6.06/09
Reference Guide
RegressionVariance.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 : RegressionVariance *
8  * Web : http://tmva.sourceforge.net *
9  * *
10  * Description: Calculate the separation critiera useded in regression *
11  * *
12  * There are two things: the Separation Index, and the Separation Gain *
13  * Separation Index: *
14  * Measure of the "Variance" of a sample. *
15  * *
16  * Separation Gain: *
17  * the measure of how the quality of separation of the sample increases *
18  * by splitting the sample e.g. into a "left-node" and a "right-node" *
19  * (N * Index_parent) - (N_left * Index_left) - (N_right * Index_right) *
20  * this is then the quality crition which is optimized for when trying *
21  * to increase the information in the system (making the best selection *
22  * *
23  * *
24  * Authors (alphabetical): *
25  * Helge Voss <Helge.Voss@cern.ch> - MPI-K Heidelberg, Germany *
26  * *
27  * Copyright (c) 2005: *
28  * CERN, Switzerland *
29  * U. of Victoria, Canada *
30  * Heidelberg U., Germany *
31  * *
32  * Redistribution and use in source and binary forms, with or without *
33  * modification, are permitted according to the terms listed in LICENSE *
34  * (http://tmva.sourceforge.net/LICENSE) *
35  **********************************************************************************/
36 
37 #ifndef ROOT_TMVA_RegressionVariance
38 #define ROOT_TMVA_RegressionVariance
39 
40 //////////////////////////////////////////////////////////////////////////
41 // //
42 // RegressionVariance //
43 // //
44 // Calculate the "SeparationGain" for Regression analysis //
45 // separation critiera used in various training algorithms //
46 // //
47 // There are two things: the Separation Index, and the Separation Gain //
48 // Separation Index: //
49 // Measure of the "Variance" of a sample. //
50 // //
51 // Separation Gain: //
52 // the measure of how the quality of separation of the sample increases //
53 // by splitting the sample e.g. into a "left-node" and a "right-node" //
54 // (N * Index_parent) - (N_left * Index_left) - (N_right * Index_right) //
55 // this is then the quality crition which is optimized for when trying //
56 // to increase the information in the system (making the best selection //
57 // //
58 //////////////////////////////////////////////////////////////////////////
59 
60 #ifndef ROOT_Rtypes
61 #include "Rtypes.h"
62 #endif
63 
64 #ifndef ROOT_TString
65 #include "TString.h"
66 #endif
67 
68 namespace TMVA {
69 
71 
72  public:
73 
74  //default constructor
75  RegressionVariance(){fName = "Variance for Regression";}
76 
77  //copy constructor
79 
80  // destructor
81  virtual ~RegressionVariance(){}
82 
83  // Return the gain in separation of the original sample is splitted in two sub-samples
84  // (N * Index_parent) - (N_left * Index_left) - (N_right * Index_right)
85  Double_t GetSeparationGain( const Double_t &nLeft, const Double_t &targetLeft, const Double_t &target2Left,
86  const Double_t &nTot, const Double_t &targetTot, const Double_t &target2Tot );
87 
88  // Return the separation index (a measure for "purity" of the sample")
89  virtual Double_t GetSeparationIndex( const Double_t &n, const Double_t &target, const Double_t &target2 );
90 
91  // Return the name of the concrete Index implementation
92  TString GetName() { return fName; }
93 
94  protected:
95 
96  TString fName; // name of the concrete Separation Index impementation
97 
98  ClassDef(RegressionVariance,0) // Interface to different separation critiera used in training algorithms
99  };
100 
101 
102 } // namespace TMVA
103 
104 #endif
Double_t GetSeparationGain(const Double_t &nLeft, const Double_t &targetLeft, const Double_t &target2Left, const Double_t &nTot, const Double_t &targetTot, const Double_t &target2Tot)
Basic string class.
Definition: TString.h:137
virtual Double_t GetSeparationIndex(const Double_t &n, const Double_t &target, const Double_t &target2)
Separation Index: a simple Variance.
#define ClassDef(name, id)
Definition: Rtypes.h:254
double Double_t
Definition: RtypesCore.h:55
Abstract ClassifierFactory template that handles arbitrary types.
RegressionVariance(const RegressionVariance &s)
const Int_t n
Definition: legend1.C:16