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

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

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

Method         : DL::DL_CPU
TMVA Release   : 4.2.1         [262657]
ROOT Release   : 6.41/01       [403713]
Creator        : root
Date           : Tue May 19 20:08:55 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: 4000
Analysis type  : [Classification]


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

# Set by User:
V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
VarTransform: "N" [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)"]
H: "False" [Print method-specific help message]
Layout: "TANH|100,TANH|50,TANH|10,LINEAR" [Layout of the network.]
ErrorStrategy: "CROSSENTROPY" [Loss function: Mean squared error (regression) or cross entropy (binary classification).]
WeightInitialization: "XAVIERUNIFORM" [Weight initialization strategy]
Architecture: "CPU" [Which architecture to perform the training on.]
TrainingStrategy: "Optimizer=ADAM,LearningRate=1e-3,TestRepetitions=1,ConvergenceSteps=10,BatchSize=100,MaxEpochs=20" [Defines the training strategies.]
# Default:
VerbosityLevel: "Verbose" [Verbosity level]
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)]
InputLayout: "0|0|0" [The Layout of the input]
BatchLayout: "0|0|0" [The Layout of the batch]
RandomSeed: "0" [Random seed used for weight initialization and batch shuffling]
ValidationSize: "20%" [Part of the training data to use for validation. Specify as 0.2 or 20% to use a fifth of the data set as validation set. Specify as 100 to use exactly 100 events. (Default: 20%)]
##


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

NVar 4
var1                          var1                          var1                          var1                                                            'F'    [-4.05916023254,3.26447582245]
var2                          var2                          var2                          Variable 2                                                      'F'    [-3.68905711174,3.78774046898]
var3                          var3                          var3                          Variable 3                    units                             'F'    [-3.6296145916,3.91998791695]
var4                          var4                          var4                          Variable 4                    units                             'F'    [-4.84856987,4.3625254631]
NSpec 0


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

#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 std::vector<double> GetMulticlassValues( const std::vector<double>& inputValues ) const = 0;

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

 protected:

   bool fStatusIsClean;
};

#endif

class ReadDL_CPU : public IClassifierReader {

 public:

   // constructor
   ReadDL_CPU( std::vector<std::string>& theInputVars )
      : IClassifierReader(),
        fClassName( "ReadDL_CPU" ),
        fNvars( 4 )
   {
      // the training input variables
      const char* inputVars[] = { "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] = 1;

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

      // initialize constants
      Initialize();

      // initialize transformation
      InitTransform();
   }

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

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

 private:

   // method-specific destructor
   void Clear();

   // input variable transformation

   double fOff_1[5][4];
   double fScal_1[5][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();
   std::vector<double> GetMulticlassValues__( const std::vector<double>& inputValues ) const;

   // private members (method specific)
inline std::vector<double> ReadDL_CPU::GetMulticlassValues( const std::vector<double>& inputValues ) const
{
   // classifier response value
   std::vector<double> retval;

   // classifier response, sanity check first
   if (!IsStatusClean()) {
      std::cout << "Problem in class \"" << fClassName << "\": cannot return classifier response"
                << " because status is dirty" << std::endl;
   }
   else {
         std::vector<double> iV(inputValues);
         Transform( iV, -1 );
         retval = GetMulticlassValues__( iV );
   }

   return retval;
}

//_______________________________________________________________________
inline void ReadDL_CPU::InitTransform_1()
{
   double fMin_1[5][4];
   double fMax_1[5][4];
   // Normalization transformation, initialisation
   fMin_1[0][0] = -3.12874746323;
   fMax_1[0][0] = 3.26447582245;
   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] = -4.05916023254;
   fMax_1[1][0] = 2.87491154671;
   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] = -3.18505716324;
   fMax_1[2][0] = 2.87491154671;
   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[3][0] = -1.16817617416;
   fMax_1[3][0] = 1.72694301605;
   fScal_1[3][0] = 2.0/(fMax_1[3][0]-fMin_1[3][0]);
   fOff_1[3][0] = fMin_1[3][0]*fScal_1[3][0]+1.;
   fMin_1[4][0] = -4.05916023254;
   fMax_1[4][0] = 3.26447582245;
   fScal_1[4][0] = 2.0/(fMax_1[4][0]-fMin_1[4][0]);
   fOff_1[4][0] = fMin_1[4][0]*fScal_1[4][0]+1.;
   fMin_1[0][1] = -3.08905720711;
   fMax_1[0][1] = 3.1251244545;
   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] = -3.68905711174;
   fMax_1[1][1] = 3.18774032593;
   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] = -2.65968227386;
   fMax_1[2][1] = 3.78774046898;
   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[3][1] = -1.5123976469;
   fMax_1[3][1] = 1.68332743645;
   fScal_1[3][1] = 2.0/(fMax_1[3][1]-fMin_1[3][1]);
   fOff_1[3][1] = fMin_1[3][1]*fScal_1[3][1]+1.;
   fMin_1[4][1] = -3.68905711174;
   fMax_1[4][1] = 3.78774046898;
   fScal_1[4][1] = 2.0/(fMax_1[4][1]-fMin_1[4][1]);
   fOff_1[4][1] = fMin_1[4][1]*fScal_1[4][1]+1.;
   fMin_1[0][2] = -3.57268309593;
   fMax_1[0][2] = 3.91515159607;
   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] = -3.6296145916;
   fMax_1[1][2] = 3.56402516365;
   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] = -3.57268309593;
   fMax_1[2][2] = 3.91998791695;
   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[3][2] = -1.88815784454;
   fMax_1[3][2] = 1.89106762409;
   fScal_1[3][2] = 2.0/(fMax_1[3][2]-fMin_1[3][2]);
   fOff_1[3][2] = fMin_1[3][2]*fScal_1[3][2]+1.;
   fMin_1[4][2] = -3.6296145916;
   fMax_1[4][2] = 3.91998791695;
   fScal_1[4][2] = 2.0/(fMax_1[4][2]-fMin_1[4][2]);
   fOff_1[4][2] = fMin_1[4][2]*fScal_1[4][2]+1.;
   fMin_1[0][3] = -3.29704141617;
   fMax_1[0][3] = 4.3625254631;
   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] = -4.84856987;
   fMax_1[1][3] = 3.54116511345;
   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] = -4.79704141617;
   fMax_1[2][3] = 2.8625254631;
   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.;
   fMin_1[3][3] = -1.89776241779;
   fMax_1[3][3] = 1.89909875393;
   fScal_1[3][3] = 2.0/(fMax_1[3][3]-fMin_1[3][3]);
   fOff_1[3][3] = fMin_1[3][3]*fScal_1[3][3]+1.;
   fMin_1[4][3] = -4.84856987;
   fMax_1[4][3] = 4.3625254631;
   fScal_1[4][3] = 2.0/(fMax_1[4][3]-fMin_1[4][3]);
   fOff_1[4][3] = fMin_1[4][3]*fScal_1[4][3]+1.;
}

//_______________________________________________________________________
inline void ReadDL_CPU::Transform_1( std::vector<double>& iv, int cls) const
{
   // Normalization transformation
   if (cls < 0 || cls > 4) {
   if (4 > 1 ) cls = 4;
      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 ReadDL_CPU::InitTransform()
{
   InitTransform_1();
}

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