aback_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 18:05:51 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: 2744
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-07,1.88951945305]
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'    [0.209833949804,34.1584281921]
vtx_radius                    vtx_radius                    vtx_radius                    vtx_radius                                                      'F'    [1.0621815818e-05,20.9999389648]
vtxTrack_dist                 vtxTrack_dist                 vtxTrack_dist                 vtxTrack_dist                                                   'F'    [0.0258899498731,15.3640584946]
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] = -1.45197129249573;
      fVmax[0] = 4.48454904556274;
      fVmin[1] = -2.32254314422607;
      fVmax[1] = 6.36729860305786;
      fVmin[2] = -2.78910398483276;
      fVmax[2] = 7.91067552566528;
      fVmin[3] = -0.619907855987549;
      fVmax[3] = 13.2841386795044;
      fVmin[4] = -0.831712603569031;
      fVmax[4] = 3.78498125076294;
      fVmin[5] = -0.7972412109375;
      fVmax[5] = 8.36910629272461;
      fVmin[6] = -0.323140859603882;
      fVmax[6] = 3.93525624275208;

      // 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] = -2.73200607299805;
   fHistMax[0] = 50.5020332336426;
   fNbin[1] = 10000;
   fHistMin[1] = -6;
   fHistMax[1] = 9;
   fNbin[2] = 10000;
   fHistMin[2] = -4;
   fHistMax[2] = 18;
   fNbin[3] = 10000;
   fHistMin[3] = -0.235233217477798;
   fHistMax[3] = 30.7738914489746;
   fNbin[4] = 10000;
   fHistMin[4] = -1.09674382209778;
   fHistMax[4] = 5.61845827102661;
   fNbin[5] = 10000;
   fHistMin[5] = -5.55076122283936;
   fHistMax[5] = 10.7182149887085;
   fNbin[6] = 10000;
   fHistMin[6] = -0.474508792161942;
   fHistMax[6] = 5.82351875305176;
   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] = 
{ 
   { 0.0859082341194153, 0.0858827978372574, 0.0858573764562607, 0.0858319476246834, 0.0858065262436867, 0.08578110486269, 0.0857556983828545, 0.0857302770018578, 0.0857048705220222, 0.0856794640421867, 0.0856540501117706, 0.0856286510825157, 0.0856032595038414, 0.0855778604745865, 0.0855524763464928, 0.0855270847678185, 0.0855017006397247, 0.085476316511631, 0.0854509398341179, 0.0854255631566048, 0.0854001939296722, 0.0853748247027397, 0.0853494629263878, 0.0853240936994553, 0.0852987319231033, 0.085273377597332, 0.0852480307221413, 0.0852226689457893, 0.0851973295211792, 0.0851719751954079, 0.0851466357707977, 0.0851213037967682, 0.0850959643721581, 0.0850706323981285, 0.085045300424099, 0.0850199833512306, 0.0849946513772011, 0.0849693343043327, 0.0849440172314644, 0.0849187076091766, 0.0848933979868889, 0.0848680809140205, 0.0848427787423134, 0.0848174765706062, 0.0847921818494797, 0.0847668871283531, 0.0847415998578072, 0.0847163051366806, 0.0846910178661346, 0.0846657380461693, 0.0846404582262039, 0.0846151858568192, 0.0845899134874344, 0.084564633667469, 0.0845393761992455, 0.0845141187310219, 0.0844888538122177, 0.0844635888934135, 0.0844383388757706, 0.0844130888581276, 0.0843878388404846, 0.0843625888228416, 0.0843373462557793, 0.0843121111392975, 0.0842868760228157, 0.0842616409063339, 0.0842364206910133, 0.0842111930251122, 0.084185965359211, 0.0841607451438904, 0.0841355174779892, 0.0841103196144104, 0.0840851068496704, 0.0840598940849304, 0.0840346962213516, 0.0840094909071922, 0.0839842930436134, 0.0839590951800346, 0.0839339047670364, 0.0839087218046188, 0.08388352394104, 0.0838583409786224, 0.0838331654667854, 0.0838079825043678, 0.0837828144431114, 0.083757646381855, 0.0837324783205986, 0.0837073177099228, 0.0836821496486664, 0.0836569890379906, 0.0836318358778954, 0.0836066827178001, 0.0835815370082855, 0.0835563912987709, 0.0835312530398369, 0.0835061147809029, 0.0834809690713882, 0.0834558457136154, 0.0834307074546814, 0.0834055840969086, 0.0833804607391357, 0.0833553448319435, 0.0833302214741707, 0.0833051055669785, 0.0832799971103668, 0.0832548886537552, 0.0832297801971436, 0.0832046791911125, 0.0831795781850815, 0.083154484629631, 0.0831293910741806, 0.0831043049693108, 0.0830792188644409, 0.0830541327595711, 0.0830290466547012, 0.0830039754509926, 0.0829789042472839, 0.0829538330435753, 0.0829287618398666, 0.0829037055373192, 0.0828786343336105, 0.0828535854816437, 0.0828285217285156, 0.0828034728765488, 0.0827784165740013, 0.0827533751726151, 0.0827283263206482, 0.0827032923698425, 0.0826782584190369, 0.0826532170176506, 0.0826281830668449, 0.0826031640172005, 0.082578144967556, 0.0825531184673309, 0.0825280994176865, 0.0825030878186226, 0.0824780687689781, 0.0824530646204948, 0.0824280604720116, 0.0824030488729477, 0.082378052175045, 0.0823530554771423, 0.0823280662298203, 0.0823030695319176, 0.0822780802845955, 0.0822531059384346, 0.0822281241416931, 0.0822031423449516, 0.0821781679987907, 0.0821532011032104, 0.0821282342076302, 0.0821032673120499, 0.0820783078670502, 0.0820533409714699, 0.0820283889770508, 0.0820034295320511, 0.0819784849882126, 0.0819535404443741, 0.0819285959005356, 0.0819036588072777, 0.0818787217140198, 0.0818537846207619, 0.0818288549780846, 0.0818039253354073, 0.0817789956927299, 0.0817540735006332, 0.0817291587591171, 0.081704244017601, 0.0816793292760849, 0.0816544219851494, 0.0816295146942139, 0.0816046148538589, 0.0815797075629234, 0.0815548151731491, 0.0815299227833748, 0.0815050303936005, 0.0814801454544067, 0.0814552530646324, 0.0814303755760193, 0.0814054980874062, 0.081380620598793, 0.0813557431101799, 0.081330880522728, 0.081306017935276, 0.0812811553478241, 0.0812563002109528, 0.0812314301729202, 0.0812065750360489, 0.0811817273497581, 0.081156887114048, 0.0811320394277573, 0.0811071991920471, 0.081082358956337, 0.0810575261712074, 0.0810327008366585, 0.0810078606009483, 0.08098304271698, 0.080958217382431, 0.0809333994984627, 0.0809085890650749, 0.0808837786316872, 0.0808589607477188, 0.0808341577649117, 0.0808093473315239, 0.0807845443487167, 0.0807597562670708, 0.0807349607348442, 0.0807101726531982, 0.0806853771209717, 0.0806605964899063, 0.0806358158588409, 0.0806110352277756, 0.0805862545967102, 0.0805614814162254, 0.0805367156863213, 0.0805119425058365, 0.0804871916770935, 0.0804624259471893, 0.0804376751184464, 0.0804129168391228, 0.0803881660103798, 0.0803634151816368, 0.0803386718034744, 0.0803139358758926, 0.0802891850471497, 0.0802644565701485, 0.0802397280931473, 0.0802149921655655, 0.0801902711391449, 0.0801655426621437, 0.0801408290863037, 0.0801161006093025, 0.0800913944840431, 0.0800666809082031, 0.0800419747829437, 0.0800172761082649, 0.0799925774335861, 0.0799678713083267, 0.0799431800842285, 0.0799184963107109, 0.0798938050866127, 0.0798691138625145, 0.0798444375395775, 0.0798197537660599, 0.0797950774431229, 0.0797704011201859, 0.0797457322478294, 0.079721063375473, 0.0796964019536972, 0.0796717405319214, 0.0796470865607262, 0.0796224325895309, 0.0795977786183357, 0.0795731320977211, 0.0795484855771065, 0.0795238390564919, 0.0794992074370384, 0.0794745683670044, 0.079449936747551, 0.0794253051280975, 0.0794006735086441, 0.0793760567903519, 0.079351432621479, 0.0793268233537674, 0.0793022066354752, 0.0792775899171829, 0.0792529881000519, 0.0792283862829208, 0.0792037770152092, 0.0791791826486588, 0.0791545808315277, 0.0791299939155579, 0.079105406999588, 0.0790808126330376, 0.0790562331676483, 0.0790316537022591, 0.0790070742368698, 0.0789825022220612, 0.0789579376578331, 0.0789333656430244, 0.0789087936282158, 0.0788842365145683, 0.0788596794009209, 0.078835129737854, 0.0788105800747871, 0.0787860304117203, 0.0787614807486534, 0.0787369385361671, 0.0787124037742615, 0.0786878690123558, 0.0786633342504501, 0.0786387994885445, 0.0786142721772194, 0.0785897523164749, 0.0785652250051498, 0.078540712594986, 0.0785161927342415, 0.0784916803240776, 0.0784671679139137, 0.078442670404911, 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}; 

// background map
float ReadLikelihoodD::fRefB[][10000] = 
{ 
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0.00079332269, 0.00079660869, 0.00079994096, 0.00080324232, 0.00080657454, 0.00080989138, 0.0008132236, 0.00081655587, 0.00081990357, 0.00082323578, 0.00082658348, 0.00082993117, 0.00083330972, 0.00083664194, 0.00084002054, 0.00084336812, 0.00084676215, 0.00085010985, 0.00085347297, 0.00085688231, 0.00086029171, 0.00086365483, 0.00086707959, 0.00087045814, 0.00087385211, 0.00087727694, 0.00088067091, 0.00088409573, 0.00088752049, 0.00089094532, 0.00089437014, 0.00089781039, 0.00090123515, 0.00090469088, 0.00090813113, 0.0009115868, 0.00091502705, 0.00091851351, 0.00092195376, 0.00092544034, 0.00092889596, 0.00093238248, 0.00093585358, 0.00093935552, 0.0009428112, 0.00094632857, 0.00094979967, 0.00095333252, 0.00095680356, 0.00096033636, 0.00096382288, 0.00096737116, 0.00097087305, 0.00097442133, 0.00097792328, 0.0009814715, 0.00098498887, 0.00098852173, 0.00099208532, 0.00099561806, 0.00099919725, 0.0010027608, 0.0010063092, 0.0010098573, 0.0010134518, 0.0010170001, 0.0010206101, 0.0010241737, 0.0010277836, 0.0010313472, 0.0010349726, 0.0010385517, 0.0010421771, 0.0010457562, 0.0010493969, 0.0010529915, 0.0010566323, 0.0010602422, 0.001063883, 0.001067493, 0.0010711491, 0.0010747744, 0.0010784462, 0.0010820716, 0.0010857432, 0.0010893994, 0.0010930711, 0.0010967118, 0.0011003988, 0.0011040706, 0.0011077576, 0.0011114293, 0.0011151009, 0.0011188189, 0.0011225214, 0.001126193, 0.0011298954, 0.0011336135, 0.001137316, 0.0011410493, 0.0011447518, 0.0011485006, 0.0011522032, 0.0011559519, 0.0011596698, 0.0011634341, 0.0011671673, 0.0011709315, 0.0011746649, 0.0011784446, 0.0011821934, 0.0011859729, 0.0011897219, 0.0011935168, 0.0011972811, 0.0012010761, 0.0012048557, 0.0012086662, 0.0012124458, 0.0012162564, 0.0012200514, 0.0012238619, 0.0012276723, 0.0012314983, 0.0012353088, 0.0012391193, 0.0012429607, 0.001246802, 0.001250628, 0.0012544538, 0.0012583261, 0.0012621519, 0.0012660242, 0.0012698502, 0.0012737378, 0.0012775945, 0.0012814821, 0.0012853389, 0.0012892265, 0.0012930988, 0.0012970017, 0.0013008894, 0.0013047925, 0.0013086647, 0.0013125985, 0.0013164862, 0.0013204201, 0.0013243231, 0.001328257, 0.0013321601, 0.0013361095, 0.0013400279, 0.0013439773, 0.0013479112, 0.0013518605, 0.0013557945, 0.0013597591, 0.0013637085, 0.0013676579, 0.001371638, 0.0013756182, 0.0013795829, 0.0013835786, 0.0013875434, 0.0013915082, 0.0013955192, 0.0013995147, 0.0014035103, 0.0014075059, 0.0014115324, 0.001415528, 0.0014195545, 0.0014235657, 0.0014276075, 0.0014316186, 0.0014356604, 0.0014396869, 0.0014437442, 0.0014477862, 0.0014518434, 0.0014558854, 0.0014599427, 0.0014640001, 0.0014680728, 0.0014721456, 0.0014762182, 0.001480291, 0.0014843947, 0.0014884673, 0.001492571, 0.0014966436, 0.0015007628, 0.001504851, 0.0015089699, 0.0015130736, 0.0015172081, 0.0015213116, 0.0015254307, 0.0015295652, 0.0015336842, 0.0015378341, 0.0015419686, 0.0015461184, 0.0015502529, 0.0015544182, 0.0015585681   }, 
}; 

   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] = 0.0890580131339;
   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] = 0.0890580131339;
   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] = 2.19278226644;
   fDecTF_1[1][0][1] = 0.0413815748456;
   fDecTF_1[1][0][2] = -0.0477622521399;
   fDecTF_1[1][0][3] = 0.102692518896;
   fDecTF_1[1][0][4] = -0.0682285307994;
   fDecTF_1[1][0][5] = -0.0875508545291;
   fDecTF_1[1][0][6] = -0.0629171162134;
   fDecTF_1[1][1][0] = 0.0413815748456;
   fDecTF_1[1][1][1] = 0.743615318449;
   fDecTF_1[1][1][2] = -0.36519850913;
   fDecTF_1[1][1][3] = 0.0134320028221;
   fDecTF_1[1][1][4] = -0.000759531302815;
   fDecTF_1[1][1][5] = -0.049562275452;
   fDecTF_1[1][1][6] = -0.0116566436082;
   fDecTF_1[1][2][0] = -0.0477622521399;
   fDecTF_1[1][2][1] = -0.36519850913;
   fDecTF_1[1][2][2] = 0.783990539403;
   fDecTF_1[1][2][3] = 0.0403619315347;
   fDecTF_1[1][2][4] = 0.0514386684618;
   fDecTF_1[1][2][5] = -0.324443579358;
   fDecTF_1[1][2][6] = 0.0369738097883;
   fDecTF_1[1][3][0] = 0.102692518896;
   fDecTF_1[1][3][1] = 0.0134320028221;
   fDecTF_1[1][3][2] = 0.0403619315347;
   fDecTF_1[1][3][3] = 0.879679617177;
   fDecTF_1[1][3][4] = 0.0227215358809;
   fDecTF_1[1][3][5] = 0.0265111727911;
   fDecTF_1[1][3][6] = 0.149186634175;
   fDecTF_1[1][4][0] = -0.0682285307994;
   fDecTF_1[1][4][1] = -0.000759531302815;
   fDecTF_1[1][4][2] = 0.0514386684618;
   fDecTF_1[1][4][3] = 0.0227215358809;
   fDecTF_1[1][4][4] = 0.231102833977;
   fDecTF_1[1][4][5] = -0.0950737099895;
   fDecTF_1[1][4][6] = 0.0247073794289;
   fDecTF_1[1][5][0] = -0.0875508545291;
   fDecTF_1[1][5][1] = -0.049562275452;
   fDecTF_1[1][5][2] = -0.324443579358;
   fDecTF_1[1][5][3] = 0.0265111727911;
   fDecTF_1[1][5][4] = -0.0950737099895;
   fDecTF_1[1][5][5] = 0.862093234636;
   fDecTF_1[1][5][6] = -0.067506563072;
   fDecTF_1[1][6][0] = -0.0629171162134;
   fDecTF_1[1][6][1] = -0.0116566436082;
   fDecTF_1[1][6][2] = 0.0369738097883;
   fDecTF_1[1][6][3] = 0.149186634175;
   fDecTF_1[1][6][4] = 0.0247073794289;
   fDecTF_1[1][6][5] = -0.067506563072;
   fDecTF_1[1][6][6] = 2.60626167624;
   fDecTF_1[2][0][0] = 2.74532550119;
   fDecTF_1[2][0][1] = -0.12817413701;
   fDecTF_1[2][0][2] = 0.179752383808;
   fDecTF_1[2][0][3] = 0.0687285850854;
   fDecTF_1[2][0][4] = -0.0195211518683;
   fDecTF_1[2][0][5] = -0.188589816057;
   fDecTF_1[2][0][6] = -0.0384833281469;
   fDecTF_1[2][1][0] = -0.12817413701;
   fDecTF_1[2][1][1] = 0.672010745909;
   fDecTF_1[2][1][2] = -0.157218435352;
   fDecTF_1[2][1][3] = 0.0568572181471;
   fDecTF_1[2][1][4] = 0.07537362512;
   fDecTF_1[2][1][5] = -0.140632030546;
   fDecTF_1[2][1][6] = 0.0199709174269;
   fDecTF_1[2][2][0] = 0.179752383808;
   fDecTF_1[2][2][1] = -0.157218435352;
   fDecTF_1[2][2][2] = 0.478420291869;
   fDecTF_1[2][2][3] = -0.0517398292383;
   fDecTF_1[2][2][4] = -0.0958070496562;
   fDecTF_1[2][2][5] = -0.113670790663;
   fDecTF_1[2][2][6] = -0.0221255650624;
   fDecTF_1[2][3][0] = 0.0687285850854;
   fDecTF_1[2][3][1] = 0.0568572181471;
   fDecTF_1[2][3][2] = -0.0517398292383;
   fDecTF_1[2][3][3] = 0.400540971984;
   fDecTF_1[2][3][4] = -0.00707413335374;
   fDecTF_1[2][3][5] = 4.78224582782e-07;
   fDecTF_1[2][3][6] = 0.0985909249645;
   fDecTF_1[2][4][0] = -0.0195211518683;
   fDecTF_1[2][4][1] = 0.07537362512;
   fDecTF_1[2][4][2] = -0.0958070496562;
   fDecTF_1[2][4][3] = -0.00707413335374;
   fDecTF_1[2][4][4] = 0.186931328641;
   fDecTF_1[2][4][5] = -0.0201152629788;
   fDecTF_1[2][4][6] = 0.00995331602034;
   fDecTF_1[2][5][0] = -0.188589816057;
   fDecTF_1[2][5][1] = -0.140632030546;
   fDecTF_1[2][5][2] = -0.113670790663;
   fDecTF_1[2][5][3] = 4.78224582765e-07;
   fDecTF_1[2][5][4] = -0.0201152629788;
   fDecTF_1[2][5][5] = 0.62353012582;
   fDecTF_1[2][5][6] = -0.0268203762349;
   fDecTF_1[2][6][0] = -0.0384833281469;
   fDecTF_1[2][6][1] = 0.0199709174269;
   fDecTF_1[2][6][2] = -0.0221255650624;
   fDecTF_1[2][6][3] = 0.0985909249645;
   fDecTF_1[2][6][4] = 0.00995331602034;
   fDecTF_1[2][6][5] = -0.0268203762349;
   fDecTF_1[2][6][6] = 2.87117146651;
}

//_______________________________________________________________________
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 );
}