// Class: ReadSVM
// Automatically generated by MethodBase::MakeClass
//

/* configuration options =====================================================

#GEN -*-*-*-*-*-*-*-*-*-*-*- general info -*-*-*-*-*-*-*-*-*-*-*-

Method         : SVM::SVM
TMVA Release   : 4.2.1         [262657]
ROOT Release   : 6.41/01       [403713]
Creator        : root
Date           : Tue May 19 20:20:45 2026
Host           : Linux d4f37374721b 4.18.0-553.117.1.el8_10.x86_64 #1 SMP Sun Apr 5 23:14:32 EDT 2026 x86_64 GNU/Linux
Dir            : /github/home/master/notebooks
Training events: 2000
Analysis type  : [Classification]


#OPT -*-*-*-*-*-*-*-*-*-*-*-*- options -*-*-*-*-*-*-*-*-*-*-*-*-

# Set by User:
VarTransform: "Norm" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
Gamma: "2.500000e-01" [RBF kernel parameter: Gamma (size of the Kernel)]
Tol: "1.000000e-03" [Tolerance parameter]
# Default:
V: "False" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
VerbosityLevel: "Default" [Verbosity level]
H: "False" [Print method-specific help message]
CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
Kernel: "RBF" [Pick which kernel ( RBF or MultiGauss )]
Order: "3" [Polynomial Kernel parameter: polynomial order]
Theta: "1.000000e+00" [Polynomial Kernel parameter: polynomial theta]
GammaList: "" [MultiGauss parameters]
Tune: "All" [Tune Parameters]
KernelList: "None" [Sum or product of kernels]
Loss: "hinge" [Loss function]
C: "1.000000e+00" [Cost parameter]
MaxIter: "1000" [Maximum number of training loops]
##


#VAR -*-*-*-*-*-*-*-*-*-*-*-* variables *-*-*-*-*-*-*-*-*-*-*-*-

NVar 4
var1+var2                     myvar1                        myvar1                        myvar1                                                          'F'    [-9.23118686676,7.07192516327]
var1-var2                     myvar2                        myvar2                        Expression 2                                                    'F'    [-3.70671987534,4.02912044525]
var3                          var3                          var3                          Variable 3                    units                             'F'    [-5.15695810318,4.15070819855]
var4                          var4                          var4                          Variable 4                    units                             'F'    [-6.31600189209,4.52105665207]
NSpec 2
var1*2                        spec1                         spec1                         Spectator 1                   units                             'F'    [-9.63254642487,9.05203056335]
var1*3                        spec2                         spec2                         Spectator 2                   units                             'F'    [-14.4488201141,13.578045845]


============================================================================ */

#include <array>
#include <vector>
#include <cmath>
#include <string>
#include <iostream>

#ifndef IClassifierReader__def
#define IClassifierReader__def

class IClassifierReader {

 public:

   // constructor
   IClassifierReader() : fStatusIsClean( true ) {}
   virtual ~IClassifierReader() {}

   // return classifier response
   virtual double GetMvaValue( const std::vector<double>& inputValues ) const = 0;

   // returns classifier status
   bool IsStatusClean() const { return fStatusIsClean; }

 protected:

   bool fStatusIsClean;
};

#endif

class ReadSVM : public IClassifierReader {

 public:

   // constructor
   ReadSVM( std::vector<std::string>& theInputVars )
      : IClassifierReader(),
        fClassName( "ReadSVM" ),
        fNvars( 4 )
   {
      // the training input variables
      const char* inputVars[] = { "var1+var2", "var1-var2", "var3", "var4" };

      // sanity checks
      if (theInputVars.size() <= 0) {
         std::cout << "Problem in class \"" << fClassName << "\": empty input vector" << std::endl;
         fStatusIsClean = false;
      }

      if (theInputVars.size() != fNvars) {
         std::cout << "Problem in class \"" << fClassName << "\": mismatch in number of input values: "
                   << theInputVars.size() << " != " << fNvars << std::endl;
         fStatusIsClean = false;
      }

      // validate input variables
      for (size_t ivar = 0; ivar < theInputVars.size(); ivar++) {
         if (theInputVars[ivar] != inputVars[ivar]) {
            std::cout << "Problem in class \"" << fClassName << "\": mismatch in input variable names" << std::endl
                      << " for variable [" << ivar << "]: " << theInputVars[ivar].c_str() << " != " << inputVars[ivar] << std::endl;
            fStatusIsClean = false;
         }
      }

      // initialize min and max vectors (for normalisation)
      fVmin[0] = -1;
      fVmax[0] = 1;
      fVmin[1] = -1;
      fVmax[1] = 1;
      fVmin[2] = -1;
      fVmax[2] = 1;
      fVmin[3] = -1;
      fVmax[3] = 0.99999988079071;

      // initialize input variable types
      fType[0] = 'F';
      fType[1] = 'F';
      fType[2] = 'F';
      fType[3] = 'F';

      // initialize constants
      Initialize();

      // initialize transformation
      InitTransform();
   }

   // destructor
   virtual ~ReadSVM() {
      Clear(); // method-specific
   }

   // the classifier response
   // "inputValues" is a vector of input values in the same order as the
   // variables given to the constructor
   double GetMvaValue( const std::vector<double>& inputValues ) const override;

 private:

   // method-specific destructor
   void Clear();

   // input variable transformation

   double fOff_1[3][4];
   double fScal_1[3][4];
   void InitTransform_1();
   void Transform_1( std::vector<double> & iv, int sigOrBgd ) const;
   void InitTransform();
   void Transform( std::vector<double> & iv, int sigOrBgd ) const;

   // common member variables
   const char* fClassName;

   const size_t fNvars;
   size_t GetNvar()           const { return fNvars; }
   char   GetType( int ivar ) const { return fType[ivar]; }

   // normalisation of input variables
   double fVmin[4];
   double fVmax[4];
   double NormVariable( double x, double xmin, double xmax ) const {
      // normalise to output range: [-1, 1]
      return 2*(x - xmin)/(xmax - xmin) - 1.0;
   }

   // type of input variable: 'F' or 'I'
   char   fType[4];

   // initialize internal variables
   void Initialize();
   double GetMvaValue__( const std::vector<double>& inputValues ) const;

   // private members (method specific)
   // not implemented for class: "ReadSVM"
   float        fBparameter;
   int          fNOfSuppVec;
   static float fAllSuppVectors[][1137];
   static float fAlphaTypeCoef[1137];

   // Kernel parameter(s) 
   float fGamma;
};

inline void ReadSVM::Initialize() 
{
   fBparameter = -0.380685865879059;
   fNOfSuppVec = 1137;
   fGamma = 0.25;
}

inline double ReadSVM::GetMvaValue__(const std::vector<double>& inputValues ) const
{
   double mvaval = 0; 
   double temp = 0; 

   for (int ievt = 0; ievt < fNOfSuppVec; ievt++ ){
      temp = 0;
      for ( unsigned int ivar = 0; ivar < GetNvar(); ivar++ ) {
         temp += (fAllSuppVectors[ivar][ievt] - inputValues[ivar])  
               * (fAllSuppVectors[ivar][ievt] - inputValues[ivar]); 
      }
      mvaval += fAlphaTypeCoef[ievt] * exp( -fGamma * temp ); 
   }
   mvaval -= fBparameter;
   return 1./(1. + exp(mvaval));
}
// Clean up
inline void ReadSVM::Clear() 
{
   // nothing to clear 
}

float ReadSVM::fAlphaTypeCoef[] =
{ -1, 0.133726671338081, -1, -1, -1, -1, -1, 0.126401394605637, 0.426829218864441, -1, 2.92765927314758, 0.618688821792603, 3.64293575286865, 1.2542769908905, -1, 0.00113087764475495, -1, -1, -1, -1, -1, 0.0632801577448845, -1, -1, -1, 1.23357939720154, 0.0238649882376194, 0.00495392363518476, -1, 0.514745473861694, -1, -1, -1, 2.99418592453003, 0.00965872965753078, -1, 3.43551206588745, -1, -1, 0.0292115695774555, 0.832218527793884, 0.016337925568223, -1, -1, -1, -1, -1, 0.0413720197975636, 0.135880246758461, 3.59287738800049, 2.36955642700195, -1, 1.48482143878937, -1, 0.6246178150177, -1, -1, 0.574555456638336, 3.64002442359924, -1, 0.0194861870259047, -1, -1, 3.64247679710388, 0.125284940004349, -1, -1, -1, -1, 3.62304401397705, -1, 2.05797743797302, 0.0269990675151348, -1, -1, -1, -1, 1.89832890033722, 1.00835621356964, 0.0244970824569464, -1, -1, -1, -1, 0.0365857481956482, -1, 0.00485378131270409, -1, -1, -1, 0.700072288513184, 0.00988965667784214, -1, 1.64334309101105, -1, -1, 0.000478251400636509, -1, 2.07610583305359, -1, -1, -1, -1, -1, -1, 0.146668583154678, 0.00550595438107848, 2.98777008056641, -1, -1, -1, 0.172167345881462, -1, -1, -1, -1, -1, -1, 0.324837148189545, 1.60194933414459, -1, -1, 0.0162570867687464, -1, -1, 0.695279359817505, 0.13057816028595, -1, -1, -1, -1, 0.00196893373504281, -1, 3.5650417804718, 3.52081203460693, -1, -1, 2.13364338874817, 0.175518542528152, -1, 0.97782838344574, -1, -1, -1, -1, -1, -1, 0.210102319717407, -1, 0.164197340607643, -1, 1.54431593418121, -1, -1, 0.0922280848026276, -1, 3.32894492149353, 0.00872016418725252, -1, -1, -1, 2.14608311653137, -1, 1.17517471313477, 3.04132223129272, 0.0133807631209493, 0.459639936685562, 0.0423094928264618, 0.140617161989212, 1.05984306335449, -1, -1, -1, 2.13326168060303, -1, -1, 1.96043002605438, -1, 0.0213139336556196, -1, 0.00140451523475349, -1, -1, -1, 0.43744695186615, -1, 2.92286133766174, -1, -1, 2.47106647491455, -1, 0.00678165117278695, 0.000251671852311119, 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0.138310790061951, -1, 0.0111416336148977, 0.0203324034810066, -1, 3.12468290328979, -1, 2.61284852027893, -1, 1.46284484863281, -1, -1, 0.190915659070015, -1, -1, -1, -1, -1, 1.87952435016632, -1, -1, 0.0309107545763254, 0.00147599773481488, -1, -1, -1, -1, 0.870078086853027, 0.162341922521591, 3.61451864242554, 0.000825454539153725, 0.0222130063921213, -1, -1, 0.0103851445019245, 0.388339906930923, -1, 0.508967041969299, 1.48919546604156, 3.64561581611633, 1.08198463916779, 0.188782125711441, 0.00117135839536786, -1, 0.0172518361359835, -1, -1, 2.1191041469574, -1, -1, 3.54855942726135, 0.186171561479568, -1, 3.55158281326294, 0.839722752571106, -1, -1, 0.0147547796368599, 0.399722069501877, -1, 3.48434209823608, -1, 0.0879736095666885, -1, -1, -1, 0.599462985992432, -1, -1, 3.32646083831787, -1, 2.12292838096619, 0.0179909504950047, 2.19360160827637, 0.0155289229005575, 1.21900713443756, 3.61135244369507, -1, -1, 1.4876526594162, -1, -1, 1.09159719944, -1, -1, 0.00352430786006153, -1, -1, -1, -1, 0.0613948777318001, 0.00419183634221554, -1, 0.00952736474573612, -1, -1, -1, 0.00203606928698719, 0.119309708476067, 3.19515514373779, 2.37190818786621, 0.0762344151735306, -1, 0.1212502643466, -1, -1, -1, -1, -1, 1.29375386238098, 1.06260228157043, 3.47659373283386, -1, -1, -1, -1, -1, -1, 1.47260797023773, 0.000326689216308296, 0.00764964567497373, -1, -1, -1, 0.179102703928947, 3.20141935348511, -1, -1, -1, -1, -1, -1, -1, 0.0427689366042614, 0.0739718452095985, -1, 0.0824392959475517, -1, 0.110837310552597, 3.644690990448, -1, 0.00878998637199402, -1, -1, 2.94472670555115, -1, -1, 0.0743653252720833, 3.63589835166931, -1, -1, 1.49872517585754, -1, -1, -1, -1, -1, -1, 0.00219605001620948, -1, -1, 0.546183109283447, 3.55517363548279, 0.221180200576782, 2.88460516929626, -1, 0.0650815740227699, -1, 1.30548095703125, -1, 3.63827610015869, 3.35554099082947, 2.39460134506226, -1, 2.90945053100586, 2.35487818717957, -1, 3.28181791305542, 1.86991059780121, 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float ReadSVM::fAllSuppVectors[][1137] =
{
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};

inline double ReadSVM::GetMvaValue( const std::vector<double>& inputValues ) const
{
   // classifier response value
   double retval = 0;

   // classifier response, sanity check first
   if (!IsStatusClean()) {
      std::cout << "Problem in class \"" << fClassName << "\": cannot return classifier response"
                << " because status is dirty" << std::endl;
   }
   else {
         // normalise variables
         std::vector<double> iV;
         iV.reserve(inputValues.size());
         int ivar = 0;
         for (std::vector<double>::const_iterator varIt = inputValues.begin();
              varIt != inputValues.end(); varIt++, ivar++) {
            iV.push_back(NormVariable( *varIt, fVmin[ivar], fVmax[ivar] ));
         }
         Transform( iV, -1 );
         retval = GetMvaValue__( iV );
   }

   return retval;
}

//_______________________________________________________________________
inline void ReadSVM::InitTransform_1()
{
   double fMin_1[3][4];
   double fMax_1[3][4];
   // Normalization transformation, initialisation
   fMin_1[0][0] = -5.16234207153;
   fMax_1[0][0] = 5.78214550018;
   fScal_1[0][0] = 2.0/(fMax_1[0][0]-fMin_1[0][0]);
   fOff_1[0][0] = fMin_1[0][0]*fScal_1[0][0]+1.;
   fMin_1[1][0] = -9.23118686676;
   fMax_1[1][0] = 7.07192516327;
   fScal_1[1][0] = 2.0/(fMax_1[1][0]-fMin_1[1][0]);
   fOff_1[1][0] = fMin_1[1][0]*fScal_1[1][0]+1.;
   fMin_1[2][0] = -9.23118686676;
   fMax_1[2][0] = 7.07192516327;
   fScal_1[2][0] = 2.0/(fMax_1[2][0]-fMin_1[2][0]);
   fOff_1[2][0] = fMin_1[2][0]*fScal_1[2][0]+1.;
   fMin_1[0][1] = -3.70671987534;
   fMax_1[0][1] = 4.02912044525;
   fScal_1[0][1] = 2.0/(fMax_1[0][1]-fMin_1[0][1]);
   fOff_1[0][1] = fMin_1[0][1]*fScal_1[0][1]+1.;
   fMin_1[1][1] = -3.45779681206;
   fMax_1[1][1] = 3.45797157288;
   fScal_1[1][1] = 2.0/(fMax_1[1][1]-fMin_1[1][1]);
   fOff_1[1][1] = fMin_1[1][1]*fScal_1[1][1]+1.;
   fMin_1[2][1] = -3.70671987534;
   fMax_1[2][1] = 4.02912044525;
   fScal_1[2][1] = 2.0/(fMax_1[2][1]-fMin_1[2][1]);
   fOff_1[2][1] = fMin_1[2][1]*fScal_1[2][1]+1.;
   fMin_1[0][2] = -3.18263268471;
   fMax_1[0][2] = 3.6047577858;
   fScal_1[0][2] = 2.0/(fMax_1[0][2]-fMin_1[0][2]);
   fOff_1[0][2] = fMin_1[0][2]*fScal_1[0][2]+1.;
   fMin_1[1][2] = -5.15695810318;
   fMax_1[1][2] = 4.15070819855;
   fScal_1[1][2] = 2.0/(fMax_1[1][2]-fMin_1[1][2]);
   fOff_1[1][2] = fMin_1[1][2]*fScal_1[1][2]+1.;
   fMin_1[2][2] = -5.15695810318;
   fMax_1[2][2] = 4.15070819855;
   fScal_1[2][2] = 2.0/(fMax_1[2][2]-fMin_1[2][2]);
   fOff_1[2][2] = fMin_1[2][2]*fScal_1[2][2]+1.;
   fMin_1[0][3] = -2.94924092293;
   fMax_1[0][3] = 3.84948801994;
   fScal_1[0][3] = 2.0/(fMax_1[0][3]-fMin_1[0][3]);
   fOff_1[0][3] = fMin_1[0][3]*fScal_1[0][3]+1.;
   fMin_1[1][3] = -6.31600189209;
   fMax_1[1][3] = 4.52105665207;
   fScal_1[1][3] = 2.0/(fMax_1[1][3]-fMin_1[1][3]);
   fOff_1[1][3] = fMin_1[1][3]*fScal_1[1][3]+1.;
   fMin_1[2][3] = -6.31600189209;
   fMax_1[2][3] = 4.52105665207;
   fScal_1[2][3] = 2.0/(fMax_1[2][3]-fMin_1[2][3]);
   fOff_1[2][3] = fMin_1[2][3]*fScal_1[2][3]+1.;
}

//_______________________________________________________________________
inline void ReadSVM::Transform_1( std::vector<double>& iv, int cls) const
{
   // Normalization transformation
   if (cls < 0 || cls > 2) {
   if (2 > 1 ) cls = 2;
      else cls = 2;
   }
   const int nVar = 4;

   // get indices of used variables

   // define the indices of the variables which are transformed by this transformation
   static std::vector<int> indicesGet;
   static std::vector<int> indicesPut;

   if ( indicesGet.empty() ) {
      indicesGet.reserve(fNvars);
      indicesGet.push_back( 0);
      indicesGet.push_back( 1);
      indicesGet.push_back( 2);
      indicesGet.push_back( 3);
   }
   if ( indicesPut.empty() ) {
      indicesPut.reserve(fNvars);
      indicesPut.push_back( 0);
      indicesPut.push_back( 1);
      indicesPut.push_back( 2);
      indicesPut.push_back( 3);
   }

   static std::vector<double> dv;
   dv.resize(nVar);
   for (int ivar=0; ivar<nVar; ivar++) dv[ivar] = iv[indicesGet.at(ivar)];
   for (int ivar=0;ivar<4;ivar++) {
      double offset = fOff_1[cls][ivar];
      double scale  = fScal_1[cls][ivar];
      iv[indicesPut.at(ivar)] = scale*dv[ivar]-offset;
   }
}

//_______________________________________________________________________
inline void ReadSVM::InitTransform()
{
   InitTransform_1();
}

//_______________________________________________________________________
inline void ReadSVM::Transform( std::vector<double>& iv, int sigOrBgd ) const
{
   Transform_1( iv, sigOrBgd );
}