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

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

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

Method         : PDEFoam::PDEFoam
TMVA Release   : 4.2.1         [262657]
ROOT Release   : 6.41/01       [403713]
Creator        : root
Date           : Tue May 19 20:08:54 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: "False" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
H: "False" [Print method-specific help message]
TailCut: "1.000000e-03" [Fraction of outlier events that are excluded from the foam in each dimension]
VolFrac: "6.660000e-02" [Size of sampling box, used for density calculation during foam build-up (maximum value: 1.0 is equivalent to volume of entire foam)]
nActiveCells: "500" [Maximum number of active cells to be created by the foam]
nSampl: "2000" [Number of generated MC events per cell]
nBin: "5" [Number of bins in edge histograms]
Compress: "True" [Compress foam output file]
Nmin: "100" [Number of events in cell required to split cell]
Kernel: "None" [Kernel type used]
# Default:
VerbosityLevel: "Default" [Verbosity level]
VarTransform: "None" [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)"]
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)]
SigBgSeparate: "False" [Separate foams for signal and background]
MultiTargetRegression: "False" [Do regression with multiple targets]
MaxDepth: "0" [Maximum depth of cell tree (0=unlimited)]
FillFoamWithOrigWeights: "False" [Fill foam with original or boost weights]
UseYesNoCell: "False" [Return -1 or 1 for bkg or signal like events]
DTLogic: "None" [Use decision tree algorithm to split cells]
TargetSelection: "Mean" [Target selection method]
##


#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 ReadPDEFoam : public IClassifierReader {

 public:

   // constructor
   ReadPDEFoam( std::vector<std::string>& theInputVars )
      : IClassifierReader(),
        fClassName( "ReadPDEFoam" ),
        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] = 0;
      fVmax[0] = 0;
      fVmin[1] = 0;
      fVmax[1] = 0;
      fVmin[2] = 0;
      fVmax[2] = 0;
      fVmin[3] = 0;
      fVmax[3] = 0;

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

      // initialize constants
      Initialize();

   }

   // destructor
   virtual ~ReadPDEFoam() {
      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();

   // 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> ReadPDEFoam::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 {
         retval = GetMulticlassValues__( inputValues );
   }

   return retval;
}