Revision e60bc961b69056a4925f129d0a2042471f999042 authored by Rene Brun on 19 September 2006, 14:37:13 UTC, committed by Rene Brun on 19 September 2006, 14:37:13 UTC
some changes in the class TGeoPNEntry (needed for alignment purposes): 1. Constructor of TGeoPNEntry performs a check upon definition of a symbolic link to a physical node. The check is done redundantly also upon creation of a physical node starting from a symbolic link to prevent mis-usage of these objects in-between geometries. 2. Allows storage of an additional user-defined TGeoHMatrix. The matrix should be created by the user but once TGeoPNEntry::SetMatrix() is called becomes owned by TGeoManager. git-svn-id: http://root.cern.ch/svn/root/trunk@16298 27541ba8-7e3a-0410-8455-c3a389f83636
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SeparationBase.h
// @(#)root/tmva $Id: SeparationBase.h,v 1.6 2006/05/23 09:53:10 stelzer Exp $
// Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss
/**********************************************************************************
* Project: TMVA - a Root-integrated toolkit for multivariate data analysis *
* Package: TMVA *
* Class : SeparationBase *
* *
* Description: An interface to different separation critiera useded in various *
* training algorithms, as there are: *
* Gini-Index, Cross Entropy, Misclassification Error, e.t.c. *
* *
* There are two things: the Separation Index, and the Separation Gain *
* Separation Index: *
* Measure of the "purity" of a sample. If all elements (events) in the *
* sample belong to the same class (e.g. signal or backgr), than the *
* separation index is 0 (meaning 100% purity (or 0% purity as it is *
* symmetric. The index becomes maximal, for perfectly mixed samples *
* eg. purity=50% , N_signal = N_bkg *
* *
* 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): *
* Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland *
* Xavier Prudent <prudent@lapp.in2p3.fr> - LAPP, France *
* Helge Voss <Helge.Voss@cern.ch> - MPI-KP Heidelberg, Germany *
* Kai Voss <Kai.Voss@cern.ch> - U. of Victoria, Canada *
* *
* Copyright (c) 2005: *
* CERN, Switzerland, *
* U. of Victoria, Canada, *
* Heidelberg U., Germany, *
* LAPP, Annecy, France *
* *
* Redistribution and use in source and binary forms, with or without *
* modification, are permitted according to the terms listed in LICENSE *
* (http://mva.sourceforge.net/license.txt) *
* *
* File and Version Information: *
* $Id: SeparationBase.h,v 1.6 2006/05/23 09:53:10 stelzer Exp $
**********************************************************************************/
#ifndef ROOT_TMVA_SeparationBase
#define ROOT_TMVA_SeparationBase
//////////////////////////////////////////////////////////////////////////
// //
// SeparationBase //
// //
// An interface to calculate the "SeparationGain" for different //
// separation critiera used in various training algorithms //
// //
// There are two things: the Separation Index, and the Separation Gain //
// Separation Index: //
// Measure of the "purity" of a sample. If all elements (events) in the //
// sample belong to the same class (e.g. signal or backgr), than the //
// separation index is 0 (meaning 100% purity (or 0% purity as it is //
// symmetric. The index becomes maximal, for perfectly mixed samples //
// eg. purity=50% , N_signal = N_bkg //
// //
// 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 //
// //
//////////////////////////////////////////////////////////////////////////
#include "Rtypes.h"
#include "TString.h"
namespace TMVA {
class SeparationBase {
public:
//default constructor
SeparationBase(){}
// destructor
virtual ~SeparationBase(){}
// 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& nSelS, const Double_t& nSelB,
const Double_t& nTotS, const Double_t& nTotB );
// Return the separation index (a measure for "purity" of the sample")
virtual Double_t GetSeparationIndex( const Double_t &s, const Double_t &b ) = 0;
// Return the name of the concrete Index implementation
TString GetName(){return fName;}
protected:
TString fName; // name of the concrete Separation Index impementation
ClassDef(SeparationBase,0) // Interface to different separation critiera used in training algorithms
};
} // namespace TMVA
#endif
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