// @(#)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
 MethodSVM.h:1
 MethodSVM.h:2
 MethodSVM.h:3
 MethodSVM.h:4
 MethodSVM.h:5
 MethodSVM.h:6
 MethodSVM.h:7
 MethodSVM.h:8
 MethodSVM.h:9
 MethodSVM.h:10
 MethodSVM.h:11
 MethodSVM.h:12
 MethodSVM.h:13
 MethodSVM.h:14
 MethodSVM.h:15
 MethodSVM.h:16
 MethodSVM.h:17
 MethodSVM.h:18
 MethodSVM.h:19
 MethodSVM.h:20
 MethodSVM.h:21
 MethodSVM.h:22
 MethodSVM.h:23
 MethodSVM.h:24
 MethodSVM.h:25
 MethodSVM.h:26
 MethodSVM.h:27
 MethodSVM.h:28
 MethodSVM.h:29
 MethodSVM.h:30
 MethodSVM.h:31
 MethodSVM.h:32
 MethodSVM.h:33
 MethodSVM.h:34
 MethodSVM.h:35
 MethodSVM.h:36
 MethodSVM.h:37
 MethodSVM.h:38
 MethodSVM.h:39
 MethodSVM.h:40
 MethodSVM.h:41
 MethodSVM.h:42
 MethodSVM.h:43
 MethodSVM.h:44
 MethodSVM.h:45
 MethodSVM.h:46
 MethodSVM.h:47
 MethodSVM.h:48
 MethodSVM.h:49
 MethodSVM.h:50
 MethodSVM.h:51
 MethodSVM.h:52
 MethodSVM.h:53
 MethodSVM.h:54
 MethodSVM.h:55
 MethodSVM.h:56
 MethodSVM.h:57
 MethodSVM.h:58
 MethodSVM.h:59
 MethodSVM.h:60
 MethodSVM.h:61
 MethodSVM.h:62
 MethodSVM.h:63
 MethodSVM.h:64
 MethodSVM.h:65
 MethodSVM.h:66
 MethodSVM.h:67
 MethodSVM.h:68
 MethodSVM.h:69
 MethodSVM.h:70
 MethodSVM.h:71
 MethodSVM.h:72
 MethodSVM.h:73
 MethodSVM.h:74
 MethodSVM.h:75
 MethodSVM.h:76
 MethodSVM.h:77
 MethodSVM.h:78
 MethodSVM.h:79
 MethodSVM.h:80
 MethodSVM.h:81
 MethodSVM.h:82
 MethodSVM.h:83
 MethodSVM.h:84
 MethodSVM.h:85
 MethodSVM.h:86
 MethodSVM.h:87
 MethodSVM.h:88
 MethodSVM.h:89
 MethodSVM.h:90
 MethodSVM.h:91
 MethodSVM.h:92
 MethodSVM.h:93
 MethodSVM.h:94
 MethodSVM.h:95
 MethodSVM.h:96
 MethodSVM.h:97
 MethodSVM.h:98
 MethodSVM.h:99
 MethodSVM.h:100
 MethodSVM.h:101
 MethodSVM.h:102
 MethodSVM.h:103
 MethodSVM.h:104
 MethodSVM.h:105
 MethodSVM.h:106
 MethodSVM.h:107
 MethodSVM.h:108
 MethodSVM.h:109
 MethodSVM.h:110
 MethodSVM.h:111
 MethodSVM.h:112
 MethodSVM.h:113
 MethodSVM.h:114
 MethodSVM.h:115
 MethodSVM.h:116
 MethodSVM.h:117
 MethodSVM.h:118
 MethodSVM.h:119
 MethodSVM.h:120
 MethodSVM.h:121
 MethodSVM.h:122
 MethodSVM.h:123
 MethodSVM.h:124
 MethodSVM.h:125
 MethodSVM.h:126
 MethodSVM.h:127
 MethodSVM.h:128
 MethodSVM.h:129
 MethodSVM.h:130
 MethodSVM.h:131
 MethodSVM.h:132
 MethodSVM.h:133
 MethodSVM.h:134
 MethodSVM.h:135
 MethodSVM.h:136
 MethodSVM.h:137
 MethodSVM.h:138
 MethodSVM.h:139
 MethodSVM.h:140
 MethodSVM.h:141