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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GiniIndex.h
// @(#)root/tmva $Id: GiniIndex.h,v 1.4 2006/05/22 08:04:39 andreas.hoecker Exp $
// Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss
/**********************************************************************************
* Project: TMVA - a Root-integrated toolkit for multivariate data analysis *
* Package: TMVA *
* Class : GiniIndex *
* *
* Description: Implementation of the GiniIndex as separation criterion *
* Large Gini Indices (maximum 0.5) mean , that the sample is well *
* mixed (same amount of signal and bkg) *
* bkg. Small Indices mean, well separated. *
* general defniniton: *
* Gini(Sample M) = 1 - (c(1)/N)^2 - (c(2)/N)^2 .... - (c(k)/N)^2 *
* Where: M is a smaple of whatever N elements (events) *
* that belong to K different classes *
* c(k) is the number of elements that belong to class k *
* for just Signal and Background classes this boils down to: *
* Gini(Sample) = 2s*b/(s+b)^2 *
* *
* *
* 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://tmva.sourceforge.net/license.txt) *
* *
**********************************************************************************/
#ifndef ROOT_TMVA_GiniIndex
#define ROOT_TMVA_GiniIndex
//////////////////////////////////////////////////////////////////////////
// //
// GiniIndex //
// //
// Implementation of the GiniIndex as separation criterion //
// //
// Large Gini Indices (maximum 0.5) mean , that the sample is well //
// mixed (same amount of signal and bkg) //
// bkg. Small Indices mean, well separated. //
// general defniniton: //
// Gini(Sample M) = 1 - (c(1)/N)^2 - (c(2)/N)^2 .... - (c(k)/N)^2 //
// Where: M is a smaple of whatever N elements (events) //
// that belong to K different classes //
// c(k) is the number of elements that belong to class k //
// for just Signal and Background classes this boils down to: //
// Gini(Sample) = 2s*b/(s+b)^2 //
//////////////////////////////////////////////////////////////////////////
#ifndef ROOT_TMVA_SeparationBase
#include "TMVA/SeparationBase.h"
#endif
namespace TMVA {
class GiniIndex : public SeparationBase {
public:
// construtor for the GiniIndex
GiniIndex() { fName="Gini"; }
//destructor
virtual ~GiniIndex(){}
protected:
// Return the separation index (a measure for "purity" of the sample")
virtual Double_t GetSeparationIndex( const Double_t &s, const Double_t &b );
ClassDef(GiniIndex,0) //Implementation of the GiniIndex as separation criterion
};
} // namespace TMVA
#endif
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