// @(#)root/tmva $Id$ // Author: Abhishek Narain /********************************************************************************** * Project: TMVA - a Root-integrated toolkit for multivariate data analysis * * Package: TMVA * * Class : MethodBayesClassifier * * Web : http://tmva.sourceforge.net * * * * Description: * * Bayesian Classifier * * * * Authors (alphabetical): * * Abhishek Narain, - University of Houston * * * * Copyright (c) 2005-2006: * * University of Houston, * * CERN, Switzerland * * U. of Victoria, Canada * * MPI-K Heidelberg, 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) * **********************************************************************************/ #ifndef ROOT_TMVA_MethodBayesClassifier #define ROOT_TMVA_MethodBayesClassifier ////////////////////////////////////////////////////////////////////////// // // // MethodBayesClassifier // // // // Description... // // // ////////////////////////////////////////////////////////////////////////// #ifndef ROOT_TMVA_MethodBase #include "TMVA/MethodBase.h" #endif #ifndef ROOT_TMVA_Types #include "TMVA/Types.h" #endif namespace TMVA { class MethodBayesClassifier : public MethodBase { public: MethodBayesClassifier( const TString& jobName, const TString& methodTitle, DataSetInfo& theData, const TString& theOption = "", TDirectory* theTargetDir = 0 ); MethodBayesClassifier( DataSetInfo& theData, const TString& theWeightFile, TDirectory* theTargetDir = NULL ); virtual ~MethodBayesClassifier( void ); virtual Bool_t HasAnalysisType( Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets ); // training method 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 ); void Init( void ); // ranking of input variables const Ranking* CreateRanking() { return 0; } protected: // make ROOT-independent C++ class for classifier response (classifier-specific implementation) void MakeClassSpecific( std::ostream&, const TString& ) const; // get help message text void GetHelpMessage() const; private: // the option handling methods void DeclareOptions(); void ProcessOptions(); ClassDef(MethodBayesClassifier,0) // Friedman's BayesClassifier method }; } // namespace TMVA #endif // MethodBayesClassifier_H