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

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

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

Method         : Likelihood::Likelihood
TMVA Release   : 4.2.1         [262657]
ROOT Release   : 6.41/01       [403713]
Creator        : root
Date           : Tue May 19 20:08:01 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:
V: "False" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
H: "True" [Print method-specific help message]
TransformOutput: "True" [Transform likelihood output by inverse sigmoid function]
# 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)]
##


#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>

#include <cmath>
#include <cstdlib>
#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 ReadLikelihood : public IClassifierReader {

 public:

   // constructor
   ReadLikelihood( std::vector<std::string>& theInputVars )
      : IClassifierReader(),
        fClassName( "ReadLikelihood" ),
        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] = 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 ~ReadLikelihood() {
      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();

   // 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)
   double       fEpsilon;
   static float fRefS[][10000]; // signal reference vector [nvars][max_nbins]
   static float fRefB[][10000]; // backgr reference vector [nvars][max_nbins]

// if a variable has its PDF encoded as a spline0 --> treat it like an Integer valued one
   bool    fHasDiscretPDF[4]; 
   int    fNbin[4]; // number of bins (discrete variables may have less bins)
   double    fHistMin[4]; 
   double    fHistMax[4]; 
   double TransformLikelihoodOutput( double, double ) const;
};

inline void ReadLikelihood::Initialize() 
{
   fEpsilon = 2.2250738585072e-305;
   fNbin[0] = 10000;
   fHistMin[0] = -9.23118686676025;
   fHistMax[0] = 7.07192516326904;
   fNbin[1] = 10000;
   fHistMin[1] = -3.70671987533569;
   fHistMax[1] = 4.02912044525146;
   fNbin[2] = 10000;
   fHistMin[2] = -5.15695810317993;
   fHistMax[2] = 4.15070819854736;
   fNbin[3] = 10000;
   fHistMin[3] = -6.31600189208984;
   fHistMax[3] = 4.52105665206909;
   fHasDiscretPDF[0] = false; 
   fHasDiscretPDF[1] = false; 
   fHasDiscretPDF[2] = false; 
   fHasDiscretPDF[3] = false; 
}

inline double ReadLikelihood::GetMvaValue__( const std::vector<double>& inputValues ) const
{
   double ps(1), pb(1);
   std::vector<double> inputValuesSig = inputValues;
   std::vector<double> inputValuesBgd = inputValues;
   for (size_t ivar = 0; ivar < GetNvar(); ivar++) {

      // dummy at present... will be used for variable transforms
      double x[2] = { inputValuesSig[ivar], inputValuesBgd[ivar] };

      for (int itype=0; itype < 2; itype++) {

         // interpolate linearly between adjacent bins
         // this is not useful for discrete variables (or forced Spline0)
         int bin = int((x[itype] - fHistMin[ivar])/(fHistMax[ivar] - fHistMin[ivar])*fNbin[ivar]) + 0;

         // since the test data sample is in general different from the training sample
         // it can happen that the min/max of the training sample are trespassed --> correct this
         if      (bin < 0) {
            bin = 0;
            x[itype] = fHistMin[ivar];
         }
         else if (bin >= fNbin[ivar]) {
            bin = fNbin[ivar]-1;
            x[itype] = fHistMax[ivar];
         }

         // find corresponding histogram from cached indices
         float ref = (itype == 0) ? fRefS[ivar][bin] : fRefB[ivar][bin];

         // sanity check
         if (ref < 0) {
            std::cout << "Fatal error in ReadLikelihood: bin entry < 0 ==> abort" << std::endl;
            std::exit(1);
         }

         double p = ref;

         if (GetType(ivar) != 'I' && !fHasDiscretPDF[ivar]) {
            float bincenter = (bin + 0.5)/fNbin[ivar]*(fHistMax[ivar] - fHistMin[ivar]) + fHistMin[ivar];
            int nextbin = bin;
            if ((x[itype] > bincenter && bin != fNbin[ivar]-1) || bin == 0) 
               nextbin++;
            else
               nextbin--;  

            double refnext      = (itype == 0) ? fRefS[ivar][nextbin] : fRefB[ivar][nextbin];
            float nextbincenter = (nextbin + 0.5)/fNbin[ivar]*(fHistMax[ivar] - fHistMin[ivar]) + fHistMin[ivar];

            double dx = bincenter - nextbincenter;
            double dy = ref - refnext;
            p += (x[itype] - bincenter) * dy/dx;
         }

         if (p < fEpsilon) p = fEpsilon; // avoid zero response

         if (itype == 0) ps *= p;
         else            pb *= p;
      }            
   }     

   // the likelihood ratio (transform it ?)
   return TransformLikelihoodOutput( ps, pb );   
}

inline double ReadLikelihood::TransformLikelihoodOutput( double ps, double pb ) const
{
   // returns transformed or non-transformed output
   if (ps < fEpsilon) ps = fEpsilon;
   if (pb < fEpsilon) pb = fEpsilon;
   double r = ps/(ps + pb);
   if (r >= 1.0) r = 1. - 1.e-15;

   if (true) {
      // inverse Fermi function

      // sanity check
      if      (r <= 0.0) r = fEpsilon;
      else if (r >= 1.0) r = 1. - 1.e-15;

      double tau = 15.0;
      r = - log(1.0/r - 1.0)/tau;
   }

   return r;
}

// Clean up
inline void ReadLikelihood::Clear() 
{
   // nothing to clear
}

// signal map
float ReadLikelihood::fRefS[][10000] = 
{ 
   { 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 5.30355646333192e-06, 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}; 

// background map
float ReadLikelihood::fRefB[][10000] = 
{ 
   { 0.00018207943, 0.00018177273, 0.00018146633, 0.00018116116, 0.00018085506, 0.00018054956, 0.00018024439, 0.0001799395, 0.00017963431, 0.00017933066, 0.00017902636, 0.00017872255, 0.00017841859, 0.00017811522, 0.00017781199, 0.00017750938, 0.00017720679, 0.00017690449, 0.00017660188, 0.00017630018, 0.00017599847, 0.00017569783, 0.0001753963, 0.00017509566, 0.00017479503, 0.00017449484, 0.00017419436, 0.00017389524, 0.00017359566, 0.0001732961, 0.00017299697, 0.0001726983, 0.00017239935, 0.00017210188, 0.00017180383, 0.00017150606, 0.00017120816, 0.00017091101, 0.000170614, 0.00017031808, 0.00017002123, 0.00016972514, 0.00016942905, 0.00016913311, 0.00016883764, 0.00016854261, 0.00016824818, 0.0001679533, 0.00016765889, 0.00016736462, 0.00016707051, 0.00016677745, 0.00016648424, 0.00016619073, 0.00016589797, 0.00016560522, 0.00016531307, 0.00016502109, 0.00016472938, 0.00016443785, 0.00016414661, 0.00016385507, 0.00016356444, 0.00016327441, 0.00016298394, 0.00016269392, 0.00016240419, 0.00016211433, 0.00016182505, 0.00016153671, 0.00016124804, 0.00016095923, 0.00016067103, 0.00016038297, 0.00016009537, 0.00015980823, 0.00015952109, 0.0001592341, 0.00015894725, 0.00015866072, 0.00015837463, 0.00015808931, 0.00015780338, 0.00015751805, 0.00015723289, 0.00015694772, 0.00015666315, 0.00015637874, 0.00015609538, 0.00015581113, 0.00015552761, 0.00015524426, 0.00015496135, 0.00015467801, 0.00015439617, 0.00015411388, 0.00015383158, 0.00015354974, 0.00015326837, 0.00015298667, 0.00015270636, 0.00015242558, 0.0001521451, 0.00015186449, 0.00015158461, 0.00015130489, 0.00015102609, 0.00015074653, 0.00015046757, 0.00015018892, 0.00014991027, 0.00014963208, 0.00014935478, 0.00014907673, 0.00014879945, 0.00014852216, 0.00014824547, 0.00014796833, 0.00014769271, 0.00014741649, 0.00014714086, 0.0001468648, 0.00014658962, 0.00014631446, 0.00014603989, 0.00014576534, 0.00014549108, 0.00014521666, 0.00014494301, 0.00014466936, 0.00014439679, 0.00014412327, 0.0001438507, 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0.00010939408, 0.00010915636, 0.0001089185, 0.00010868198, 0.00010844487, 0.00010820791, 0.0001079714, 0.00010773535, 0.00010749899, 0.00010726401, 0.00010702856, 0.00010679342, 0.00010655812, 0.00010632359, 0.0001060892, 0.00010585573, 0.0001056215, 0.00010538787, 0.00010515456, 0.00010492107, 0.0001046882, 0.00010445641, 0.00010422368, 0.00010399157, 0.00010375976, 0.00010352825, 0.0001032966, 0.00010306617, 0.00010283542, 0.00010260498, 0.00010237423, 0.0001021444, 0.00010191456, 0.00010168533, 0.0001014561, 0.00010122718, 0.0001009981, 0.00010076962, 0.00010054145, 0.0001003142, 0.00010008603, 9.9858764e-05, 9.9631514e-05, 9.9404555e-05, 9.9177603e-05, 9.8951859e-05, 9.872565e-05, 9.8499455e-05, 9.8273864e-05, 9.8048586e-05, 9.7823293e-05, 9.7598153e-05, 9.7374228e-05, 9.7149852e-05, 9.6925469e-05, 9.6701711e-05, 9.6478077e-05, 9.6254771e-05, 9.6031908e-05, 9.5809191e-05, 9.5586634e-05, 9.5364383e-05, 9.5141972e-05, 9.4920324e-05, 9.469928e-05, 9.4477786e-05, 9.4256742e-05, 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}; 

inline double ReadLikelihood::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 {
         retval = GetMvaValue__( inputValues );
   }

   return retval;
}