// @(#)root/tmva $Id$ // Author: Marcin Wolter, Andrzej Zemla /********************************************************************************** * Project: TMVA - a Root-integrated toolkit for multivariate data analysis * * Package: TMVA * * Class : MethodSVM * * Web : http://tmva.sourceforge.net * * * * Description: * * Support Vector Machine * * * * Authors (alphabetical): * * Marcin Wolter <Marcin.Wolter@cern.ch> - IFJ PAN, Krakow, Poland * * Andrzej Zemla <azemla@cern.ch> - IFJ PAN, Krakow, Poland * * (IFJ PAN: Henryk Niewodniczanski Inst. Nucl. Physics, Krakow, Poland) * * * * Introduction of regression by: * * Krzysztof Danielowski <danielow@cern.ch> - IFJ PAN & AGH, Krakow, Poland * * Kamil Kraszewski <kalq@cern.ch> - IFJ PAN & UJ, Krakow, Poland * * Maciej Kruk <mkruk@cern.ch> - IFJ PAN & AGH, Krakow, Poland * * * * Copyright (c) 2005: * * CERN, Switzerland * * MPI-K Heidelberg, Germany * * PAN, Krakow, Poland * * * * 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_MethodSVM #define ROOT_TMVA_MethodSVM ////////////////////////////////////////////////////////////////////////// // // // MethodSVM // // // // SMO Platt's SVM classifier with Keerthi & Shavade improvements // // // ////////////////////////////////////////////////////////////////////////// #ifndef ROOT_TMVA_MethodBase #include "TMVA/MethodBase.h" #endif #ifndef ROOT_TMVA_TMatrixD #ifndef ROOT_TMatrixDfwd #include "TMatrixDfwd.h" #endif #endif #ifndef ROOT_TMVA_TVectorD #ifndef ROOT_TVectorD #include "TVectorD.h" #endif #endif namespace TMVA { class SVWorkingSet; class SVEvent; class SVKernelFunction; class MethodSVM : public MethodBase { public: MethodSVM( const TString& jobName, const TString& methodTitle, DataSetInfo& theData, const TString& theOption = "", TDirectory* theTargetDir = 0 ); MethodSVM( DataSetInfo& theData, const TString& theWeightFile, TDirectory* theTargetDir = NULL ); virtual ~MethodSVM( 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 WriteWeightsToStream( TFile& fout ) const; void AddWeightsXMLTo ( void* parent ) const; // read weights from file void ReadWeightsFromStream( std::istream& istr ); void ReadWeightsFromStream( TFile& fFin ); void ReadWeightsFromXML ( void* wghtnode ); // calculate the MVA value Double_t GetMvaValue( Double_t* err = 0, Double_t* errUpper = 0 ); const std::vector<Float_t>& GetRegressionValues(); 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 DeclareCompatibilityOptions(); void ProcessOptions(); Float_t fCost; // cost value Float_t fTolerance; // tolerance parameter UInt_t fMaxIter; // max number of iteration UShort_t fNSubSets; // nr of subsets, default 1 Float_t fBparm; // free plane coefficient Float_t fGamma; // RBF Kernel parameter SVWorkingSet* fWgSet; // svm working set std::vector<TMVA::SVEvent*>* fInputData; // vector of training data in SVM format std::vector<TMVA::SVEvent*>* fSupportVectors; // contains support vectors SVKernelFunction* fSVKernelFunction; // kernel function TVectorD* fMinVars; // for normalization //is it still needed?? TVectorD* fMaxVars; // for normalization //is it still needed?? // for backward compatibility TString fTheKernel; // kernel name Float_t fDoubleSigmaSquared; // for RBF Kernel Int_t fOrder; // for Polynomial Kernel ( polynomial order ) Float_t fTheta; // for Sigmoidal Kernel Float_t fKappa; // for Sigmoidal Kernel ClassDef(MethodSVM,0) // Support Vector Machine }; } // namespace TMVA #endif // MethodSVM_H