eback_LikelihoodD.class.C

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

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

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

Method         : Likelihood::LikelihoodD
TMVA Release   : 4.2.0         [262656]
ROOT Release   : 5.34/38       [336422]
Creator        : vassalli
Date           : Wed Jan 23 17:59:26 2019
Host           : Linux cvmfswrite02.sdcc.bnl.gov 3.10.0-693.11.6.el7.x86_64 #1 SMP Wed Jan 3 18:09:42 CST 2018 x86_64 x86_64 x86_64 GNU/Linux
Dir            : /direct/phenix+u/vassalli/sphenix/single/Training
Training events: 2540
Analysis type  : [Classification]


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

# Set by User:
V: "False" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
VarTransform: "Decorrelate" [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]
TransformOutput: "False" [Transform likelihood output by inverse sigmoid function]
# Default:
VerbosityLevel: "Default" [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)]
##


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

NVar 7
track_deta                    track_deta                    track_deta                    track_deta                                                      'F'    [4.76837158203e-07,1.92375802994]
track_dlayer                  track_dlayer                  track_dlayer                  track_dlayer                                                    'I'    [0,14]
track_layer                   track_layer                   track_layer                   track_layer                                                     'I'    [0,23]
track_pT                      track_pT                      track_pT                      track_pT                                                        'F'    [0.238087445498,34.1584281921]
vtx_radius                    vtx_radius                    vtx_radius                    vtx_radius                                                      'F'    [5.07211098011e-06,20.9999389648]
vtxTrack_dist                 vtxTrack_dist                 vtxTrack_dist                 vtxTrack_dist                                                   'F'    [0.0258899498731,8.61121940613]
cluster_prob                  cluster_prob                  cluster_prob                  cluster_prob                                                    'F'    [0,0.999874174595]
NSpec 1
vtx_chi2                      vtx_chi2                      vtx_chi2                      F                                                               'F'    [0,3.33078734987e-36]


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

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

#include <math.h>
#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 ReadLikelihoodD : public IClassifierReader {

 public:

   // constructor
   ReadLikelihoodD( std::vector<std::string>& theInputVars ) 
      : IClassifierReader(),
        fClassName( "ReadLikelihoodD" ),
        fNvars( 7 ),
        fIsNormalised( false )
   {      
      // the training input variables
      const char* inputVars[] = { "track_deta", "track_dlayer", "track_layer", "track_pT", "vtx_radius", "vtxTrack_dist", "cluster_prob" };

      // 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] = -1.67565214633942;
      fVmax[0] = 4.55014133453369;
      fVmin[1] = -2.19893741607666;
      fVmax[1] = 6.74663019180298;
      fVmin[2] = -1.85658717155457;
      fVmax[2] = 7.90125560760498;
      fVmin[3] = -0.673184037208557;
      fVmax[3] = 13.0733852386475;
      fVmin[4] = -1.07693314552307;
      fVmax[4] = 3.79157757759094;
      fVmin[5] = -0.87162059545517;
      fVmax[5] = 4.52489709854126;
      fVmin[6] = -0.33494171500206;
      fVmax[6] = 3.79388189315796;

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

      // initialize constants
      Initialize();

      // initialize transformation
      InitTransform();
   }

   // destructor
   virtual ~ReadLikelihoodD() {
      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;

 private:

   // method-specific destructor
   void Clear();

   // input variable transformation

   double fDecTF_1[3][7][7];
   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
   const bool fIsNormalised;
   bool IsNormalised() const { return fIsNormalised; }
   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 ReadLikelihoodD::Initialize() 
{
   fEpsilon = 2.2250738585072e-305;
   fNbin[0] = 10000;
   fHistMin[0] = -2.24490928649902;
   fHistMax[0] = 51.5726737976074;
   fNbin[1] = 10000;
   fHistMin[1] = -14;
   fHistMax[1] = 8;
   fNbin[2] = 10000;
   fHistMin[2] = -3;
   fHistMax[2] = 26;
   fNbin[3] = 10000;
   fHistMin[3] = -0.19918355345726;
   fHistMax[3] = 26.3295745849609;
   fNbin[4] = 10000;
   fHistMin[4] = -1.00897419452667;
   fHistMax[4] = 5.1497483253479;
   fNbin[5] = 10000;
   fHistMin[5] = -5.66342306137085;
   fHistMax[5] = 7.29997730255127;
   fNbin[6] = 10000;
   fHistMin[6] = -0.384636282920837;
   fHistMax[6] = 4.55501317977905;
   fHasDiscretPDF[0] = false; 
   fHasDiscretPDF[1] = false; 
   fHasDiscretPDF[2] = false; 
   fHasDiscretPDF[3] = false; 
   fHasDiscretPDF[4] = false; 
   fHasDiscretPDF[5] = false; 
   fHasDiscretPDF[6] = false; 
}

inline double ReadLikelihoodD::GetMvaValue__( const std::vector<double>& inputValues ) const
{
   double ps(1), pb(1);
   std::vector<double> inputValuesSig = inputValues;
   std::vector<double> inputValuesBgd = inputValues;
   Transform(inputValuesSig,0);
   Transform(inputValuesBgd,1);
   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 ReadLikelihoodD: 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 ReadLikelihoodD::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 (false) {
      // 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 ReadLikelihoodD::Clear() 
{
   // nothing to clear
}

// signal map
float ReadLikelihoodD::fRefS[][10000] = 
{ 
   { 0.0531570240855217, 0.0531480796635151, 0.0531391426920891, 0.0531301982700825, 0.0531212612986565, 0.0531123280525208, 0.0531033910810947, 0.0530944541096687, 0.0530855171382427, 0.0530765727162361, 0.0530676357448101, 0.0530586987733841, 0.0530497692525387, 0.0530408322811127, 0.0530318953096867, 0.0530229620635509, 0.0530140250921249, 0.0530050955712795, 0.0529961660504341, 0.0529872290790081, 0.0529782958328724, 0.052969366312027, 0.0529604367911816, 0.0529515035450459, 0.0529425740242004, 0.052933644503355, 0.0529247112572193, 0.0529157891869545, 0.0529068559408188, 0.0528979264199734, 0.052888996899128, 0.0528800711035728, 0.0528711453080177, 0.0528622232377529, 0.0528532937169075, 0.0528443679213524, 0.0528354421257973, 0.0528265200555325, 0.0528175942599773, 0.0528086684644222, 0.0527997501194477, 0.0527908280491829, 0.0527819097042084, 0.0527729839086533, 0.0527640581130981, 0.0527551397681236, 0.0527462214231491, 0.0527372993528843, 0.0527283810079098, 0.0527194626629353, 0.0527105443179607, 0.0527016259729862, 0.0526927076280117, 0.0526837892830372, 0.0526748709380627, 0.0526659525930882, 0.0526570379734039, 0.0526481196284294, 0.0526392087340355, 0.052630290389061, 0.0526213794946671, 0.0526124648749828, 0.0526035539805889, 0.0525946356356144, 0.0525857247412205, 0.0525768101215363, 0.0525679066777229, 0.0525589920580387, 0.0525500811636448, 0.0525411702692509, 0.0525322556495667, 0.0525233522057533, 0.0525144375860691, 0.0525055341422558, 0.0524966269731522, 0.0524877198040485, 0.0524788089096546, 0.052469901740551, 0.0524609908461571, 0.052452091127634, 0.0524431839585304, 0.0524342767894268, 0.0524253733456135, 0.0524164736270905, 0.0524075664579868, 0.0523986592888832, 0.0523897595703602, 0.0523808561265469, 0.0523719564080238, 0.0523630566895008, 0.0523541569709778, 0.0523452498018742, 0.0523363575339317, 0.0523274578154087, 0.0523185506463051, 0.0523096583783627, 0.0523007586598396, 0.0522918589413166, 0.0522829592227936, 0.0522740632295609, 0.0522651709616184, 0.0522562712430954, 0.052247378975153, 0.0522384829819202, 0.0522295832633972, 0.0522206909954548, 0.0522117987275124, 0.0522029027342796, 0.0521940104663372, 0.0521851219236851, 0.0521762296557426, 0.0521673336625099, 0.0521584413945675, 0.0521495528519154, 0.0521406605839729, 0.0521317720413208, 0.0521228797733784, 0.0521139912307262, 0.0521051064133644, 0.0520962178707123, 0.0520873293280602, 0.0520784445106983, 0.0520695559680462, 0.0520606674253941, 0.0520517863333225, 0.0520429015159607, 0.0520340129733086, 0.052025131881237, 0.0520162433385849, 0.0520073622465134, 0.0519984737038612, 0.0519895926117897, 0.0519807115197182, 0.0519718304276466, 0.0519629493355751, 0.0519540682435036, 0.051945187151432, 0.0519363060593605, 0.051927424967289, 0.0519185476005077, 0.0519096702337265, 0.0519007928669453, 0.0518919117748737, 0.0518830306828022, 0.0518741570413113, 0.0518652833998203, 0.0518564060330391, 0.0518475286662579, 0.0518386550247669, 0.051829781383276, 0.0518209040164948, 0.0518120341002941, 0.0518031567335129, 0.0517942868173122, 0.0517854131758213, 0.0517765432596207, 0.0517676696181297, 0.0517587997019291, 0.0517499260604382, 0.0517410561442375, 0.0517321899533272, 0.0517233163118362, 0.0517144463956356, 0.0517055839300156, 0.0516967102885246, 0.051687840372324, 0.0516789741814137, 0.0516701079905033, 0.0516612455248833, 0.0516523756086826, 0.0516435094177723, 0.0516346469521523, 0.0516257844865322, 0.0516169145703316, 0.0516080521047115, 0.0515991896390915, 0.0515903271734715, 0.0515814647078514, 0.0515726022422314, 0.0515637435019016, 0.0515548810362816, 0.0515460222959518, 0.0515371598303318, 0.0515283010900021, 0.0515194423496723, 0.0515105836093426, 0.0515017248690128, 0.0514928661286831, 0.0514840073883533, 0.0514751523733139, 0.0514662936329842, 0.051457442343235, 0.0514485836029053, 0.0514397285878658, 0.0514308735728264, 0.0514220185577869, 0.0514131672680378, 0.0514043085277081, 0.0513954572379589, 0.0513866059482098, 0.0513777546584606, 0.0513689033687115, 0.051360048353672, 0.0513512007892132, 0.051342349499464, 0.0513334982097149, 0.0513246469199657, 0.0513158030807972, 0.051306951791048, 0.0512981042265892, 0.0512892529368401, 0.0512804090976715, 0.0512715578079224, 0.0512627139687538, 0.0512538701295853, 0.0512450225651264, 0.0512361787259579, 0.0512273348867893, 0.0512184873223305, 0.0512096434831619, 0.0512007996439934, 0.0511919595301151, 0.0511831194162369, 0.0511742755770683, 0.0511654354631901, 0.0511565953493118, 0.051147747784853, 0.0511389076709747, 0.0511300750076771, 0.0511212311685085, 0.0511123947799206, 0.0511035546660423, 0.0510947182774544, 0.0510858818888664, 0.0510770455002785, 0.0510682091116905, 0.0510593727231026, 0.0510505363345146, 0.0510416999459267, 0.0510328598320484, 0.0510240308940411, 0.0510151982307434, 0.0510063581168652, 0.0509975291788578, 0.0509886965155602, 0.0509798638522625, 0.0509710349142551, 0.0509622022509575, 0.0509533733129501, 0.0509445406496525, 0.0509357079863548, 0.0509268827736378, 0.0509180538356304, 0.0509092248976231, 0.0509003922343254, 0.0508915707468987, 0.0508827418088913, 0.0508739128708839, 0.0508650839328766, 0.0508562624454498, 0.0508474335074425, 0.0508386120200157, 0.0508297830820084, 0.0508209615945816, 0.0508121363818645, 0.0508033111691475, 0.0507944896817207, 0.0507856644690037, 0.0507768467068672, 0.0507680252194405, 0.0507592037320137, 0.0507503822445869, 0.0507415607571602, 0.0507327429950237, 0.0507239252328873, 0.0507151037454605, 0.0507062859833241, 0.0506974644958973, 0.0506886541843414, 0.0506798326969147, 0.0506710186600685, 0.0506621971726418, 0.0506533868610859, 0.0506445690989494, 0.0506357550621033, 0.0506269410252571, 0.0506181269884109, 0.0506093092262745, 0.0506004989147186, 0.0505916886031628, 0.0505828708410263, 0.0505740605294704, 0.0505652502179146, 0.050556443631649, 0.0505476333200932, 0.0505388230085373, 0.0505300126969814, 0.0505212023854256, 0.0505123920738697, 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}; 

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

   inline double ReadLikelihoodD::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;
         retval = 0;
      }
      else {
         if (IsNormalised()) {
            // 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] ));
            }
            retval = GetMvaValue__( iV );
         }
         else {
            retval = GetMvaValue__( inputValues );
         }
      }

      return retval;
   }

//_______________________________________________________________________
inline void ReadLikelihoodD::InitTransform_1()
{
   // Decorrelation transformation, initialisation
   fDecTF_1[0][0][0] = 27.0668590723;
   fDecTF_1[0][0][1] = -0.102704506124;
   fDecTF_1[0][0][2] = 0.0890580131338;
   fDecTF_1[0][0][3] = 0.0242159404894;
   fDecTF_1[0][0][4] = -0.0106533801082;
   fDecTF_1[0][0][5] = -0.0763979023359;
   fDecTF_1[0][0][6] = -0.186346492405;
   fDecTF_1[0][1][0] = -0.102704506124;
   fDecTF_1[0][1][1] = 0.847575557299;
   fDecTF_1[0][1][2] = -0.242560178076;
   fDecTF_1[0][1][3] = 0.0182891251938;
   fDecTF_1[0][1][4] = 0.0821012099698;
   fDecTF_1[0][1][5] = -0.0538568394973;
   fDecTF_1[0][1][6] = 0.00775689740398;
   fDecTF_1[0][2][0] = 0.0890580131338;
   fDecTF_1[0][2][1] = -0.242560178076;
   fDecTF_1[0][2][2] = 0.71968404754;
   fDecTF_1[0][2][3] = -0.00969692787942;
   fDecTF_1[0][2][4] = -0.139832729084;
   fDecTF_1[0][2][5] = -0.233062934644;
   fDecTF_1[0][2][6] = -0.00166452233002;
   fDecTF_1[0][3][0] = 0.0242159404894;
   fDecTF_1[0][3][1] = 0.0182891251938;
   fDecTF_1[0][3][2] = -0.00969692787942;
   fDecTF_1[0][3][3] = 0.321930615803;
   fDecTF_1[0][3][4] = 0.012123504094;
   fDecTF_1[0][3][5] = -0.0448230797299;
   fDecTF_1[0][3][6] = 0.109011434678;
   fDecTF_1[0][4][0] = -0.0106533801082;
   fDecTF_1[0][4][1] = 0.0821012099698;
   fDecTF_1[0][4][2] = -0.139832729084;
   fDecTF_1[0][4][3] = 0.012123504094;
   fDecTF_1[0][4][4] = 0.21755207221;
   fDecTF_1[0][4][5] = -0.0144184432797;
   fDecTF_1[0][4][6] = 0.00655200854044;
   fDecTF_1[0][5][0] = -0.0763979023359;
   fDecTF_1[0][5][1] = -0.0538568394973;
   fDecTF_1[0][5][2] = -0.233062934644;
   fDecTF_1[0][5][3] = -0.0448230797299;
   fDecTF_1[0][5][4] = -0.0144184432797;
   fDecTF_1[0][5][5] = 0.641849154757;
   fDecTF_1[0][5][6] = -0.0328090952214;
   fDecTF_1[0][6][0] = -0.186346492405;
   fDecTF_1[0][6][1] = 0.00775689740398;
   fDecTF_1[0][6][2] = -0.00166452233002;
   fDecTF_1[0][6][3] = 0.109011434678;
   fDecTF_1[0][6][4] = 0.00655200854044;
   fDecTF_1[0][6][5] = -0.0328090952214;
   fDecTF_1[0][6][6] = 3.29896885811;
   fDecTF_1[1][0][0] = 2.27890466291;
   fDecTF_1[1][0][1] = 0.0407504475996;
   fDecTF_1[1][0][2] = 0.0427445300708;
   fDecTF_1[1][0][3] = 0.0523377885901;
   fDecTF_1[1][0][4] = -0.0794200191497;
   fDecTF_1[1][0][5] = -0.168651065713;
   fDecTF_1[1][0][6] = -0.0921835178936;
   fDecTF_1[1][1][0] = 0.0407504475996;
   fDecTF_1[1][1][1] = 1.24556983816;
   fDecTF_1[1][1][2] = -0.770602022959;
   fDecTF_1[1][1][3] = 0.0151977813333;
   fDecTF_1[1][1][4] = 0.00553025174872;
   fDecTF_1[1][1][5] = -0.0903949445368;
   fDecTF_1[1][1][6] = 0.0530530924549;
   fDecTF_1[1][2][0] = 0.0427445300708;
   fDecTF_1[1][2][1] = -0.770602022959;
   fDecTF_1[1][2][2] = 1.21225211463;
   fDecTF_1[1][2][3] = 0.0264988877049;
   fDecTF_1[1][2][4] = 0.0328335676692;
   fDecTF_1[1][2][5] = -0.371432233863;
   fDecTF_1[1][2][6] = 0.0134650165459;
   fDecTF_1[1][3][0] = 0.0523377885901;
   fDecTF_1[1][3][1] = 0.0151977813333;
   fDecTF_1[1][3][2] = 0.0264988877049;
   fDecTF_1[1][3][3] = 0.739857805171;
   fDecTF_1[1][3][4] = 0.0460086409285;
   fDecTF_1[1][3][5] = 0.0576497528903;
   fDecTF_1[1][3][6] = 0.112035155524;
   fDecTF_1[1][4][0] = -0.0794200191497;
   fDecTF_1[1][4][1] = 0.00553025174872;
   fDecTF_1[1][4][2] = 0.0328335676692;
   fDecTF_1[1][4][3] = 0.0460086409285;
   fDecTF_1[1][4][4] = 0.235581388038;
   fDecTF_1[1][4][5] = -0.129140454589;
   fDecTF_1[1][4][6] = 0.000535839477814;
   fDecTF_1[1][5][0] = -0.168651065713;
   fDecTF_1[1][5][1] = -0.0903949445368;
   fDecTF_1[1][5][2] = -0.371432233863;
   fDecTF_1[1][5][3] = 0.0576497528903;
   fDecTF_1[1][5][4] = -0.129140454589;
   fDecTF_1[1][5][5] = 1.23539721205;
   fDecTF_1[1][5][6] = -0.0631777871682;
   fDecTF_1[1][6][0] = -0.0921835178936;
   fDecTF_1[1][6][1] = 0.0530530924549;
   fDecTF_1[1][6][2] = 0.0134650165459;
   fDecTF_1[1][6][3] = 0.112035155524;
   fDecTF_1[1][6][4] = 0.000535839477814;
   fDecTF_1[1][6][5] = -0.0631777871682;
   fDecTF_1[1][6][6] = 2.72633681797;
   fDecTF_1[2][0][0] = 2.94419043961;
   fDecTF_1[2][0][1] = -0.152567025531;
   fDecTF_1[2][0][2] = 0.254248783193;
   fDecTF_1[2][0][3] = 0.0481216625111;
   fDecTF_1[2][0][4] = -0.0580805029856;
   fDecTF_1[2][0][5] = -0.182243166567;
   fDecTF_1[2][0][6] = -0.0752939948126;
   fDecTF_1[2][1][0] = -0.152567025531;
   fDecTF_1[2][1][1] = 0.759025995602;
   fDecTF_1[2][1][2] = -0.177952221859;
   fDecTF_1[2][1][3] = 0.0666514158326;
   fDecTF_1[2][1][4] = 0.079405377253;
   fDecTF_1[2][1][5] = -0.15841539197;
   fDecTF_1[2][1][6] = 0.0636230796128;
   fDecTF_1[2][2][0] = 0.254248783193;
   fDecTF_1[2][2][1] = -0.177952221859;
   fDecTF_1[2][2][2] = 0.495142887452;
   fDecTF_1[2][2][3] = -0.0660491338792;
   fDecTF_1[2][2][4] = -0.114595366538;
   fDecTF_1[2][2][5] = -0.0943178320728;
   fDecTF_1[2][2][6] = -0.0389854862561;
   fDecTF_1[2][3][0] = 0.0481216625111;
   fDecTF_1[2][3][1] = 0.0666514158326;
   fDecTF_1[2][3][2] = -0.0660491338792;
   fDecTF_1[2][3][3] = 0.395389268942;
   fDecTF_1[2][3][4] = 0.00708505084189;
   fDecTF_1[2][3][5] = -0.00816884156516;
   fDecTF_1[2][3][6] = 0.0919510279928;
   fDecTF_1[2][4][0] = -0.0580805029856;
   fDecTF_1[2][4][1] = 0.079405377253;
   fDecTF_1[2][4][2] = -0.114595366538;
   fDecTF_1[2][4][3] = 0.00708505084189;
   fDecTF_1[2][4][4] = 0.198296449795;
   fDecTF_1[2][4][5] = -0.0370904264136;
   fDecTF_1[2][4][6] = -0.00290741622568;
   fDecTF_1[2][5][0] = -0.182243166567;
   fDecTF_1[2][5][1] = -0.15841539197;
   fDecTF_1[2][5][2] = -0.0943178320728;
   fDecTF_1[2][5][3] = -0.00816884156516;
   fDecTF_1[2][5][4] = -0.0370904264136;
   fDecTF_1[2][5][5] = 0.694861109546;
   fDecTF_1[2][5][6] = -0.0133066752699;
   fDecTF_1[2][6][0] = -0.0752939948126;
   fDecTF_1[2][6][1] = 0.0636230796128;
   fDecTF_1[2][6][2] = -0.0389854862561;
   fDecTF_1[2][6][3] = 0.0919510279928;
   fDecTF_1[2][6][4] = -0.00290741622568;
   fDecTF_1[2][6][5] = -0.0133066752699;
   fDecTF_1[2][6][6] = 2.94274096666;
}

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

   // 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);
      indicesGet.push_back( 4);
      indicesGet.push_back( 5);
      indicesGet.push_back( 6);
   } 
   if ( indicesPut.empty() ) { 
      indicesPut.reserve(fNvars);
      indicesPut.push_back( 0);
      indicesPut.push_back( 1);
      indicesPut.push_back( 2);
      indicesPut.push_back( 3);
      indicesPut.push_back( 4);
      indicesPut.push_back( 5);
      indicesPut.push_back( 6);
   } 

   std::vector<double> tv;
   for (int i=0; i<7;i++) {
      double v = 0;
      for (int j=0; j<7; j++)
         v += iv[indicesGet.at(j)] * fDecTF_1[cls][i][j];
      tv.push_back(v);
   }
   for (int i=0; i<7;i++) iv[indicesPut.at(i)] = tv[i];
}

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

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