ROOT   6.10/09 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 criteria used 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 criteria 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 *
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 criteria 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 criteria which is optimized for when trying //
56 // to increase the information in the system (making the best selection //
57 // //
58 //////////////////////////////////////////////////////////////////////////
59
60 #include "Rtypes.h"
61
62 #include "TString.h"
63
64 namespace TMVA {
65
67
68  public:
69
70  //default constructor
71  RegressionVariance(){fName = "Variance for Regression";}
72
73  //copy constructor
75
76  // destructor
77  virtual ~RegressionVariance(){}
78
79  // Return the gain in separation of the original sample is split in two sub-samples
80  // (N * Index_parent) - (N_left * Index_left) - (N_right * Index_right)
81  Double_t GetSeparationGain( const Double_t nLeft, const Double_t targetLeft, const Double_t target2Left,
82  const Double_t nTot, const Double_t targetTot, const Double_t target2Tot );
83
84  // Return the separation index (a measure for "purity" of the sample")
85  virtual Double_t GetSeparationIndex( const Double_t n, const Double_t target, const Double_t target2 );
86
87  // Return the name of the concrete Index implementation
88  TString GetName() { return fName; }
89
90  protected:
91
92  TString fName; // name of the concrete Separation Index implementation
93
94  ClassDef(RegressionVariance,0); // Interface to different separation criteria used in training algorithms
95  };
96
97
98 } // namespace TMVA
99
100 #endif
Calculate the "SeparationGain" for Regression analysis separation criteria used in various training a...
Basic string class.
Definition: TString.h:129
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:297
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)
Separation Gain: the measure of how the quality of separation of the sample increases by splitting th...
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