/usr/include/root/TMVA/RegressionVariance.h is in libroot-tmva-dev 5.34.14-1build1.
This file is owned by root:root, with mode 0o644.
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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 : RegressionVariance *
* Web : http://tmva.sourceforge.net *
* *
* Description: Calculate the separation critiera useded in regression *
* *
* There are two things: the Separation Index, and the Separation Gain *
* Separation Index: *
* Measure of the "Variance" of a sample. *
* *
* Separation Gain: *
* the measure of how the quality of separation of the sample increases *
* by splitting the sample e.g. into a "left-node" and a "right-node" *
* (N * Index_parent) - (N_left * Index_left) - (N_right * Index_right) *
* this is then the quality crition which is optimized for when trying *
* to increase the information in the system (making the best selection *
* *
* *
* Authors (alphabetical): *
* Helge Voss <Helge.Voss@cern.ch> - MPI-K Heidelberg, Germany *
* *
* 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://tmva.sourceforge.net/LICENSE) *
**********************************************************************************/
#ifndef ROOT_TMVA_RegressionVariance
#define ROOT_TMVA_RegressionVariance
//////////////////////////////////////////////////////////////////////////
// //
// RegressionVariance //
// //
// Calculate the "SeparationGain" for Regression analysis //
// separation critiera used in various training algorithms //
// //
// There are two things: the Separation Index, and the Separation Gain //
// Separation Index: //
// Measure of the "Variance" of a sample. //
// //
// Separation Gain: //
// the measure of how the quality of separation of the sample increases //
// by splitting the sample e.g. into a "left-node" and a "right-node" //
// (N * Index_parent) - (N_left * Index_left) - (N_right * Index_right) //
// this is then the quality crition which is optimized for when trying //
// to increase the information in the system (making the best selection //
// //
//////////////////////////////////////////////////////////////////////////
#ifndef ROOT_Rtypes
#include "Rtypes.h"
#endif
#ifndef ROOT_TString
#include "TString.h"
#endif
namespace TMVA {
class RegressionVariance {
public:
//default constructor
RegressionVariance(){fName = "Variance for Regression";}
//copy constructor
RegressionVariance( const RegressionVariance& s ): fName ( s.fName ) {}
// destructor
virtual ~RegressionVariance(){}
// Return the gain in separation of the original sample is splitted in two sub-samples
// (N * Index_parent) - (N_left * Index_left) - (N_right * Index_right)
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 );
// Return the separation index (a measure for "purity" of the sample")
virtual Double_t GetSeparationIndex( const Double_t &n, const Double_t &target, const Double_t &target2 );
// Return the name of the concrete Index implementation
TString GetName() { return fName; }
protected:
TString fName; // name of the concrete Separation Index impementation
ClassDef(RegressionVariance,0) // Interface to different separation critiera used in training algorithms
};
} // namespace TMVA
#endif
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