// 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 20:22:59 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: 14000
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 7
m_jj                          m_jj                          m_jj                          m_jj                                                            'F'    [0.146605730057,13.0981254578]
m_jjj                         m_jjj                         m_jjj                         m_jjj                                                           'F'    [0.34201452136,7.39196777344]
m_lv                          m_lv                          m_lv                          m_lv                                                            'F'    [0.26678776741,3.68225979805]
m_jlv                         m_jlv                         m_jlv                         m_jlv                                                           'F'    [0.384410560131,6.58312129974]
m_bb                          m_bb                          m_bb                          m_bb                                                            'F'    [0.0934824943542,7.85979413986]
m_wbb                         m_wbb                         m_wbb                         m_wbb                                                           'F'    [0.385025799274,4.54251241684]
m_wwbb                        m_wwbb                        m_wwbb                        m_wwbb                                                          'F'    [0.432279825211,4.07281732559]
NSpec 0


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

#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( 7 )
   {
      // the training input variables
      const char* inputVars[] = { "m_jj", "m_jjj", "m_lv", "m_jlv", "m_bb", "m_wbb", "m_wwbb" };

      // 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;
      fVmin[4] = 0;
      fVmax[4] = 0;
      fVmin[5] = 0;
      fVmax[5] = 0;
      fVmin[6] = 0;
      fVmax[6] = 0;

      // initialize input variable types
      fType[0] = 'F';
      fType[1] = 'F';
      fType[2] = 'F';
      fType[3] = 'F';
      fType[4] = 'F';
      fType[5] = 'F';
      fType[6] = '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[7];
   double fVmax[7];
   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[7];

   // 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[7]; 
   int    fNbin[7]; // number of bins (discrete variables may have less bins)
   double    fHistMin[7]; 
   double    fHistMax[7]; 
   double TransformLikelihoodOutput( double, double ) const;
};

inline void ReadLikelihood::Initialize() 
{
   fEpsilon = 2.2250738585072e-305;
   fNbin[0] = 10000;
   fHistMin[0] = 0.146605730056763;
   fHistMax[0] = 13.0981254577637;
   fNbin[1] = 10000;
   fHistMin[1] = 0.342014521360397;
   fHistMax[1] = 7.3919677734375;
   fNbin[2] = 10000;
   fHistMin[2] = 0.266787767410278;
   fHistMax[2] = 3.68225979804993;
   fNbin[3] = 10000;
   fHistMin[3] = 0.384410560131073;
   fHistMax[3] = 6.58312129974365;
   fNbin[4] = 10000;
   fHistMin[4] = 0.093482494354248;
   fHistMax[4] = 7.85979413986206;
   fNbin[5] = 10000;
   fHistMin[5] = 0.385025799274445;
   fHistMax[5] = 4.5425124168396;
   fNbin[6] = 10000;
   fHistMin[6] = 0.432279825210571;
   fHistMax[6] = 4.07281732559204;
   fHasDiscretPDF[0] = false; 
   fHasDiscretPDF[1] = false; 
   fHasDiscretPDF[2] = false; 
   fHasDiscretPDF[3] = false; 
   fHasDiscretPDF[4] = false; 
   fHasDiscretPDF[5] = false; 
   fHasDiscretPDF[6] = 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] = 
{ 
   { 0.0310875363647938, 0.0305065345019102, 0.0299386363476515, 0.0293838232755661, 0.0288421176373959, 0.0283135194331408, 0.0277980249375105, 0.0272956006228924, 0.0268062986433506, 0.026330066844821, 0.0258669760078192, 0.025416973978281, 0.0249800402671099, 0.0245562288910151, 0.0241455063223839, 0.023747906088829, 0.0233633760362864, 0.0229919515550137, 0.0226336326450109, 0.0222884006798267, 0.021956292912364, 0.0216372516006231, 0.0213313195854425, 0.0210384912788868, 0.020758768543601, 0.0204921159893274, 0.0202386043965816, 0.0199981611222029, 0.0197708234190941, 0.0195565745234489, 0.0193554479628801, 0.0191674083471298, 0.018992442637682, 0.0188306123018265, 0.0186818726360798, 0.0185462199151516, 0.0184236895292997, 0.01831422932446, 0.0182178933173418, 0.0181346256285906, 0.0180644802749157, 0.0180074069648981, 0.0179634392261505, 0.0179325919598341, 0.0179148521274328, 0.0179101806133986, 0.0179186332970858, 0.0179401896893978, 0.0179747845977545, 0.018022533506155, 0.0180833730846643, 0.0181572809815407, 0.0182443298399448, 0.0183444656431675, 0.0184577070176601, 0.0185840018093586, 0.0187234524637461, 0.0188759416341782, 0.0190415680408478, 0.0192203018814325, 0.019412137567997, 0.0196170303970575, 0.0198350604623556, 0.0200661793351173, 0.0203103851526976, 0.0205676984041929, 0.0208381321281195, 0.0211216025054455, 0.0214182287454605, 0.0217279456555843, 0.0220507644116879, 0.0223866384476423, 0.0227356497198343, 0.0230977330356836, 0.0234729405492544, 0.0238612685352564, 0.0242626518011093, 0.0246771387755871, 0.0251047480851412, 0.0255454462021589, 0.02599922940135, 0.0264661200344563, 0.0269460808485746, 0.0274391826242208, 0.0279453881084919, 0.0284646656364202, 0.0289970636367798, 0.0295425318181515, 0.0301011241972446, 0.03067278675735, 0.0312575735151768, 0.0318554453551769, 0.0324664041399956, 0.033090490847826, 0.0337276756763458, 0.0343779362738132, 0.035041332244873, 0.035717785358429, 0.0364073701202869, 0.037110012024641, 0.0378257744014263, 0.0385546535253525, 0.0392965972423553, 0.04005166888237, 0.0408198423683643, 0.0416010841727257, 0.0423954389989376, 0.0431496761739254, 0.0438245423138142, 0.044516634196043, 0.0452260449528694, 0.0459527559578419, 0.0466967076063156, 0.0474579371511936, 0.0482364743947983, 0.0490322820842266, 0.0498453862965107, 0.050675705075264, 0.0515233725309372, 0.0523882582783699, 0.0532704666256905, 0.0541699603199959, 0.0550866946578026, 0.0560207068920135, 0.0569720156490803, 0.0579406246542931, 0.0589265041053295, 0.0599296130239964, 0.060950018465519, 0.0619877353310585, 0.0630427449941635, 0.064115010201931, 0.0652045533061028, 0.0663113594055176, 0.0674354434013367, 0.0685768276453018, 0.0697354972362518, 0.0709113627672195, 0.0721045807003975, 0.0733150690793991, 0.0745428651571274, 0.0757879018783569, 0.0770502537488937, 0.0783298760652542, 0.0796267166733742, 0.0809409022331238, 0.0822723358869553, 0.0836210176348686, 0.0849870145320892, 0.0863702893257141, 0.0877708569169044, 0.0891886726021767, 0.0906238108873367, 0.0920762121677399, 0.0935458466410637, 0.0950328037142754, 0.0965369939804077, 0.0980585142970085, 0.0995972752571106, 0.101153329014778, 0.102726690471172, 0.104317307472229, 0.105925187468529, 0.107550419867039, 0.109192855656147, 0.11085256934166, 0.112529583275318, 0.114223875105381, 0.115935437381268, 0.117664329707623, 0.119410365819931, 0.12117376178503, 0.122954495251179, 0.124752439558506, 0.126567676663399, 0.128400221467018, 0.1302500218153, 0.132117077708244, 0.134001463651657, 0.136035099625587, 0.138100430369377, 0.140187621116638, 0.14229653775692, 0.1444271504879, 0.146579429507256, 0.148753449320793, 0.150949195027351, 0.153166681528091, 0.155405834317207, 0.157666742801666, 0.159949332475662, 0.162253513932228, 0.164579495787621, 0.166927233338356, 0.169296696782112, 0.171687841415405, 0.174100667238235, 0.176535248756409, 0.178991556167603, 0.181469574570656, 0.183969303965569, 0.186490684747696, 0.189033776521683, 0.191598638892174, 0.194185152649879, 0.196793466806412, 0.199423477053642, 0.202075138688087, 0.20474861562252, 0.207443654537201, 0.210160508751869, 0.212898999452591, 0.215659290552139, 0.218441292643547, 0.221244931221008, 0.22407041490078, 0.22691747546196, 0.229786336421967, 0.232676893472672, 0.235589027404785, 0.238523095846176, 0.24147866666317, 0.244456067681313, 0.247455179691315, 0.250476032495499, 0.253518521785736, 0.256582766771317, 0.25966876745224, 0.262776345014572, 0.26590570807457, 0.269056767225266, 0.272229790687561, 0.275424212217331, 0.278640389442444, 0.2818783223629, 0.285138010978699, 0.288419365882874, 0.291722416877747, 0.295047253370285, 0.298393785953522, 0.301761895418167, 0.305151849985123, 0.308563560247421, 0.311996877193451, 0.315452009439468, 0.318928748369217, 0.322427242994308, 0.325947463512421, 0.329489350318909, 0.33305299282074, 0.33674031496048, 0.34054833650589, 0.344372779130936, 0.348213762044907, 0.352071136236191, 0.355944901704788, 0.359835207462311, 0.363741844892502, 0.367664992809296, 0.37160462141037, 0.375560581684113, 0.379533112049103, 0.383522033691406, 0.387527406215668, 0.391549229621887, 0.395587623119354, 0.399642258882523, 0.403713464736938, 0.407801002264023, 0.41190505027771, 0.416025608778, 0.420162498950958, 0.424315899610519, 0.428485751152039, 0.432672113180161, 0.436874836683273, 0.441094040870667, 0.445329695940018, 0.449581742286682, 0.453850269317627, 0.458135217428207, 0.462436616420746, 0.466754496097565, 0.471088767051697, 0.475439578294754, 0.47980672121048, 0.484190434217453, 0.488590449094772, 0.493007004261017, 0.497439980506897, 0.501889407634735, 0.506355345249176, 0.51083767414093, 0.515336632728577, 0.519851803779602, 0.524383544921875, 0.528931617736816, 0.533496141433716, 0.538077116012573, 0.542674541473389, 0.547288537025452, 0.551918804645538, 0.556565642356873, 0.561228930950165, 0.565908551216125, 0.570604681968689, 0.57531726360321, 0.580046236515045, 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}; 

// background map
float ReadLikelihood::fRefB[][10000] = 
{ 
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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;
}