/usr/include/root/TMVA/MethodDT.h is in libroot-tmva-dev 5.34.30-0ubuntu8.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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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 : MethodDT (Boosted Decision Trees) *
* Web : http://tmva.sourceforge.net *
* *
* Description: *
* Analysis of Boosted Decision Trees *
* *
* Authors (alphabetical): *
* Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland *
* Helge Voss <Helge.Voss@cern.ch> - MPI-K Heidelberg, Germany *
* Or Cohen <orcohenor@gmail.com> - Weizmann Inst., Israel *
* *
* Copyright (c) 2005: *
* CERN, Switzerland *
* MPI-K Heidelberg, 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_MethodDT
#define ROOT_TMVA_MethodDT
//////////////////////////////////////////////////////////////////////////
// //
// MethodDT //
// //
// Analysis of Single Decision Tree //
// //
//////////////////////////////////////////////////////////////////////////
#include <vector>
#ifndef ROOT_TH1
#include "TH1.h"
#endif
#ifndef ROOT_TH2
#include "TH2.h"
#endif
#ifndef ROOT_TTree
#include "TTree.h"
#endif
#ifndef ROOT_TMVA_MethodBase
#include "TMVA/MethodBase.h"
#endif
#ifndef ROOT_TMVA_DecisionTree
#include "TMVA/DecisionTree.h"
#endif
#ifndef ROOT_TMVA_Event
#include "TMVA/Event.h"
#endif
namespace TMVA {
class MethodBoost;
class MethodDT : public MethodBase {
public:
MethodDT( const TString& jobName,
const TString& methodTitle,
DataSetInfo& theData,
const TString& theOption = "",
TDirectory* theTargetDir = 0 );
MethodDT( DataSetInfo& dsi,
const TString& theWeightFile,
TDirectory* theTargetDir = NULL );
virtual ~MethodDT( void );
virtual Bool_t HasAnalysisType( Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets );
void Train( void );
using MethodBase::ReadWeightsFromStream;
// write weights to file
void AddWeightsXMLTo( void* parent ) const;
// read weights from file
void ReadWeightsFromStream( std::istream& istr );
void ReadWeightsFromXML ( void* wghtnode );
// calculate the MVA value
Double_t GetMvaValue( Double_t* err = 0, Double_t* errUpper = 0 );
// the option handling methods
void DeclareOptions();
void ProcessOptions();
void DeclareCompatibilityOptions();
void GetHelpMessage() const;
// ranking of input variables
const Ranking* CreateRanking();
Double_t PruneTree( );
Double_t TestTreeQuality( DecisionTree *dt );
Double_t GetPruneStrength () { return fPruneStrength; }
void SetMinNodeSize(Double_t sizeInPercent);
void SetMinNodeSize(TString sizeInPercent);
Int_t GetNNodesBeforePruning(){return fTree->GetNNodesBeforePruning();}
Int_t GetNNodes(){return fTree->GetNNodes();}
private:
// Init used in the various constructors
void Init( void );
private:
std::vector<Event*> fEventSample; // the training events
DecisionTree* fTree; // the decision tree
//options for the decision Tree
SeparationBase *fSepType; // the separation used in node splitting
TString fSepTypeS; // the separation (option string) used in node splitting
Int_t fMinNodeEvents; // min number of events in node
Float_t fMinNodeSize; // min percentage of training events in node
TString fMinNodeSizeS; // string containing min percentage of training events in node
Int_t fNCuts; // grid used in cut applied in node splitting
Bool_t fUseYesNoLeaf; // use sig or bkg classification in leave nodes or sig/bkg
Double_t fNodePurityLimit; // purity limit for sig/bkg nodes
UInt_t fMaxDepth; // max depth
Double_t fErrorFraction; // ntuple var: misclassification error fraction
Double_t fPruneStrength; // a parameter to set the "amount" of pruning..needs to be adjusted
DecisionTree::EPruneMethod fPruneMethod; // method used for prunig
TString fPruneMethodS; // prune method option String
Bool_t fAutomatic; // use user given prune strength or automatically determined one using a validation sample
Bool_t fRandomisedTrees; // choose a random subset of possible cut variables at each node during training
Int_t fUseNvars; // the number of variables used in the randomised tree splitting
Bool_t fUsePoissonNvars; // fUseNvars is used as a poisson mean, and the actual value of useNvars is at each step drawn form that distribution
std::vector<Double_t> fVariableImportance; // the relative importance of the different variables
Double_t fDeltaPruneStrength; // step size in pruning, is adjusted according to experience of previous trees
// debugging flags
static const Int_t fgDebugLevel = 0; // debug level determining some printout/control plots etc.
Bool_t fPruneBeforeBoost; //aincient variable, only needed for "CompatibilityOptions"
ClassDef(MethodDT,0) // Analysis of Decision Trees
};
}
#endif
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