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

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

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

Method         : MLP::Ghost_Upstream_TMVA
TMVA Release   : 4.2.0         [262656]
ROOT Release   : 6.02/01       [393729]
Creator        : jonesc
Date           : Sun Feb  7 16:35:07 2016
Host           : Linux lcgapp-slc6-physical1.cern.ch 2.6.32-431.29.2.el6.x86_64 #1 SMP Wed Sep 10 11:13:12 CEST 2014 x86_64 x86_64 x86_64 GNU/Linux
Dir            : /var/pcfst/r03/lhcb/jonesc/ANNPID/results/MC2015Sim09Dev03/TrainMixture/TrainPhysTks-EvalPhysTks-NoReweight/GhostAccFrac1.0/ProtonAccFrac1.0/KaonAccFrac1.0/TMVA-Run2-NoTkLikCDVelodEdx/MLP/Norm/ScaleF1.3/BP/NCycles750/CE/tanh/CVTest15/CVImp1e-16/NotUseRegulator/Ghost/Upstream
Training events: 2319076
Analysis type  : [Classification]


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

# Set by User:
NCycles: "750" [Number of training cycles]
HiddenLayers: "23" [Specification of hidden layer architecture]
NeuronType: "tanh" [Neuron activation function type]
EstimatorType: "CE" [MSE (Mean Square Estimator) for Gaussian Likelihood or CE(Cross-Entropy) for Bernoulli Likelihood]
V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
VarTransform: "Norm" [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: "True" [Print method-specific help message]
TrainingMethod: "BP" [Train with Back-Propagation (BP), BFGS Algorithm (BFGS), or Genetic Algorithm (GA - slower and worse)]
EpochMonitoring: "True" [Provide epoch-wise monitoring plots according to TestRate (caution: causes big ROOT output file!)]
ConvergenceImprove: "1.000000e-16" [Minimum improvement which counts as improvement (<0 means automatic convergence check is turned off)]
ConvergenceTests: "15" [Number of steps (without improvement) required for convergence (<0 means automatic convergence check is turned off)]
UseRegulator: "False" [Use regulator to avoid over-training]
# Default:
RandomSeed: "1" [Random seed for initial synapse weights (0 means unique seed for each run; default value '1')]
NeuronInputType: "sum" [Neuron input function type]
VerbosityLevel: "Verbose" [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)]
LearningRate: "2.000000e-02" [ANN learning rate parameter]
DecayRate: "1.000000e-02" [Decay rate for learning parameter]
TestRate: "10" [Test for overtraining performed at each #th epochs]
Sampling: "1.000000e+00" [Only 'Sampling' (randomly selected) events are trained each epoch]
SamplingEpoch: "1.000000e+00" [Sampling is used for the first 'SamplingEpoch' epochs, afterwards, all events are taken for training]
SamplingImportance: "1.000000e+00" [ The sampling weights of events in epochs which successful (worse estimator than before) are multiplied with SamplingImportance, else they are divided.]
SamplingTraining: "True" [The training sample is sampled]
SamplingTesting: "False" [The testing sample is sampled]
ResetStep: "50" [How often BFGS should reset history]
Tau: "3.000000e+00" [LineSearch "size step"]
BPMode: "sequential" [Back-propagation learning mode: sequential or batch]
BatchSize: "-1" [Batch size: number of events/batch, only set if in Batch Mode, -1 for BatchSize=number_of_events]
UpdateLimit: "10000" [Maximum times of regulator update]
CalculateErrors: "False" [Calculates inverse Hessian matrix at the end of the training to be able to calculate the uncertainties of an MVA value]
WeightRange: "1.000000e+00" [Take the events for the estimator calculations from small deviations from the desired value to large deviations only over the weight range]
##


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

NVar 18
TrackP                        TrackP                        TrackP                        TrackP                                                          'F'    [115.480003357,4998705.5]
TrackPt                       TrackPt                       TrackPt                       TrackPt                                                         'F'    [12.8830137253,867623.6875]
TrackChi2PerDof               TrackChi2PerDof               TrackChi2PerDof               TrackChi2PerDof                                                 'F'    [0.00195923517458,3.99922418594]
TrackNumDof                   TrackNumDof                   TrackNumDof                   TrackNumDof                                                     'I'    [4,27]
TrackGhostProbability         TrackGhostProbability         TrackGhostProbability         TrackGhostProbability                                           'F'    [0.00192928232718,0.399999499321]
TrackFitVeloChi2              TrackFitVeloChi2              TrackFitVeloChi2              TrackFitVeloChi2                                                'F'    [7.34402965463e-06,67.3927001953]
TrackFitVeloNDoF              TrackFitVeloNDoF              TrackFitVeloNDoF              TrackFitVeloNDoF                                                'I'    [1,23]
RichUsedR1Gas                 RichUsedR1Gas                 RichUsedR1Gas                 RichUsedR1Gas                                                   'I'    [0,1]
RichAbovePiThres              RichAbovePiThres              RichAbovePiThres              RichAbovePiThres                                                'I'    [0,1]
RichAboveKaThres              RichAboveKaThres              RichAboveKaThres              RichAboveKaThres                                                'I'    [0,1]
RichAbovePrThres              RichAbovePrThres              RichAbovePrThres              RichAbovePrThres                                                'I'    [0,1]
RichDLLe                      RichDLLe                      RichDLLe                      RichDLLe                                                        'F'    [-999,168.813705444]
RichDLLmu                     RichDLLmu                     RichDLLmu                     RichDLLmu                                                       'F'    [-999,114.397399902]
RichDLLk                      RichDLLk                      RichDLLk                      RichDLLk                                                        'F'    [-999,135.562103271]
RichDLLp                      RichDLLp                      RichDLLp                      RichDLLp                                                        'F'    [-999,123.221702576]
RichDLLbt                     RichDLLbt                     RichDLLbt                     RichDLLbt                                                       'F'    [-999,51.957901001]
InAccBrem                     InAccBrem                     InAccBrem                     InAccBrem                                                       'I'    [0,1]
BremPIDe                      BremPIDe                      BremPIDe                      BremPIDe                                                        'F'    [-999,4.79148387909]
NSpec 0


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

#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 double GetMvaValue( const std::vector<double>& inputValues ) const = 0;

   // returns classifier status
   bool IsStatusClean() const { return fStatusIsClean; }

 protected:

   bool fStatusIsClean;
};

#endif

class ReadGhost_Upstream_TMVA : public IClassifierReader {

 public:

   // constructor
   ReadGhost_Upstream_TMVA( std::vector<std::string>& theInputVars ) 
      : IClassifierReader(),
        fClassName( "ReadGhost_Upstream_TMVA" ),
        fNvars( 18 ),
        fIsNormalised( false )
   {      
      // the training input variables
      const char* inputVars[] = { "TrackP", "TrackPt", "TrackChi2PerDof", "TrackNumDof", "TrackGhostProbability", "TrackFitVeloChi2", "TrackFitVeloNDoF", "RichUsedR1Gas", "RichAbovePiThres", "RichAboveKaThres", "RichAbovePrThres", "RichDLLe", "RichDLLmu", "RichDLLk", "RichDLLp", "RichDLLbt", "InAccBrem", "BremPIDe" };

      // 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;
      fVmax[0] = 1;
      fVmin[1] = -1;
      fVmax[1] = 0.99999988079071;
      fVmin[2] = -1;
      fVmax[2] = 1;
      fVmin[3] = -1;
      fVmax[3] = 1;
      fVmin[4] = -1;
      fVmax[4] = 0.99999988079071;
      fVmin[5] = -1;
      fVmax[5] = 1;
      fVmin[6] = -1;
      fVmax[6] = 1;
      fVmin[7] = -1;
      fVmax[7] = 1;
      fVmin[8] = -1;
      fVmax[8] = 1;
      fVmin[9] = -1;
      fVmax[9] = 1;
      fVmin[10] = -1;
      fVmax[10] = 1;
      fVmin[11] = -1;
      fVmax[11] = 1;
      fVmin[12] = -1;
      fVmax[12] = 1;
      fVmin[13] = -1;
      fVmax[13] = 1;
      fVmin[14] = -1;
      fVmax[14] = 1;
      fVmin[15] = -1;
      fVmax[15] = 1;
      fVmin[16] = -1;
      fVmax[16] = 1;
      fVmin[17] = -1;
      fVmax[17] = 1;

      // initialize input variable types
      fType[0] = 'F';
      fType[1] = 'F';
      fType[2] = 'F';
      fType[3] = 'I';
      fType[4] = 'F';
      fType[5] = 'F';
      fType[6] = 'I';
      fType[7] = 'I';
      fType[8] = 'I';
      fType[9] = 'I';
      fType[10] = 'I';
      fType[11] = 'F';
      fType[12] = 'F';
      fType[13] = 'F';
      fType[14] = 'F';
      fType[15] = 'F';
      fType[16] = 'I';
      fType[17] = 'F';

      // initialize constants
      Initialize();

      // initialize transformation
      InitTransform();
   }

   // destructor
   virtual ~ReadGhost_Upstream_TMVA() {
      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 fMin_1[3][18];
   double fMax_1[3][18];
   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[18];
   double fVmax[18];
   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[18];

   // initialize internal variables
   void Initialize();
   double GetMvaValue__( const std::vector<double>& inputValues ) const;

   // private members (method specific)

   double ActivationFnc(double x) const;
   double OutputActivationFnc(double x) const;

   int fLayers;
   int fLayerSize[3];
   double fWeightMatrix0to1[24][19];   // weight matrix from layer 0 to 1
   double fWeightMatrix1to2[1][24];   // weight matrix from layer 1 to 2

   double * fWeights[3];
};

inline void ReadGhost_Upstream_TMVA::Initialize()
{
   // build network structure
   fLayers = 3;
   fLayerSize[0] = 19; fWeights[0] = new double[19]; 
   fLayerSize[1] = 24; fWeights[1] = new double[24]; 
   fLayerSize[2] = 1; fWeights[2] = new double[1]; 
   // weight matrix from layer 0 to 1
   fWeightMatrix0to1[0][0] = -28.8235276402351;
   fWeightMatrix0to1[1][0] = 18.1179601357501;
   fWeightMatrix0to1[2][0] = -8.52689528193455;
   fWeightMatrix0to1[3][0] = 0.238715718622262;
   fWeightMatrix0to1[4][0] = -3.12686766022984;
   fWeightMatrix0to1[5][0] = -0.813444862724644;
   fWeightMatrix0to1[6][0] = -5.04933747643488;
   fWeightMatrix0to1[7][0] = 0.796968446946299;
   fWeightMatrix0to1[8][0] = -5.51034510487075;
   fWeightMatrix0to1[9][0] = -1.92915465680577;
   fWeightMatrix0to1[10][0] = -6.61850432176457;
   fWeightMatrix0to1[11][0] = -0.8539362574005;
   fWeightMatrix0to1[12][0] = -4.90610011250877;
   fWeightMatrix0to1[13][0] = -6.77920702538568;
   fWeightMatrix0to1[14][0] = 4.46463044297702;
   fWeightMatrix0to1[15][0] = 6.08200736375721;
   fWeightMatrix0to1[16][0] = -4.80329455998095;
   fWeightMatrix0to1[17][0] = 9.34707444920875;
   fWeightMatrix0to1[18][0] = 101.576062903747;
   fWeightMatrix0to1[19][0] = 9.31863555438497;
   fWeightMatrix0to1[20][0] = -2.95172538155447;
   fWeightMatrix0to1[21][0] = -3.78305101101327;
   fWeightMatrix0to1[22][0] = 3.72496528958454;
   fWeightMatrix0to1[0][1] = -9.33322490779389;
   fWeightMatrix0to1[1][1] = -2.11551706812704;
   fWeightMatrix0to1[2][1] = 9.89698954995123;
   fWeightMatrix0to1[3][1] = 10.6792520708773;
   fWeightMatrix0to1[4][1] = -3.71912605530765;
   fWeightMatrix0to1[5][1] = 4.52560112218402;
   fWeightMatrix0to1[6][1] = 7.40666380066313;
   fWeightMatrix0to1[7][1] = -2.43776417781483;
   fWeightMatrix0to1[8][1] = 5.77549675975998;
   fWeightMatrix0to1[9][1] = -0.633390758204077;
   fWeightMatrix0to1[10][1] = -0.543855950502001;
   fWeightMatrix0to1[11][1] = 1.44416662448065;
   fWeightMatrix0to1[12][1] = -5.82228851309656;
   fWeightMatrix0to1[13][1] = -11.2001331799996;
   fWeightMatrix0to1[14][1] = 4.11186540442602;
   fWeightMatrix0to1[15][1] = 2.81873843276544;
   fWeightMatrix0to1[16][1] = 4.50734300653147;
   fWeightMatrix0to1[17][1] = -4.82462552310321;
   fWeightMatrix0to1[18][1] = 9.68127417541106;
   fWeightMatrix0to1[19][1] = -4.96774306711291;
   fWeightMatrix0to1[20][1] = 5.16047495600305;
   fWeightMatrix0to1[21][1] = -2.66819617365737;
   fWeightMatrix0to1[22][1] = 1.90960733543184;
   fWeightMatrix0to1[0][2] = 1.20841004108954;
   fWeightMatrix0to1[1][2] = 1.2856139347032;
   fWeightMatrix0to1[2][2] = -0.218244886232357;
   fWeightMatrix0to1[3][2] = 0.532657033198121;
   fWeightMatrix0to1[4][2] = 0.407016111749752;
   fWeightMatrix0to1[5][2] = 1.67413601571311;
   fWeightMatrix0to1[6][2] = -0.855892969692506;
   fWeightMatrix0to1[7][2] = 0.0891684262598578;
   fWeightMatrix0to1[8][2] = -3.56815561553495;
   fWeightMatrix0to1[9][2] = -0.548797672492775;
   fWeightMatrix0to1[10][2] = -1.85681105228998;
   fWeightMatrix0to1[11][2] = 1.05718151700927;
   fWeightMatrix0to1[12][2] = 0.297650418208365;
   fWeightMatrix0to1[13][2] = 2.42897897404609;
   fWeightMatrix0to1[14][2] = -1.9223252267845;
   fWeightMatrix0to1[15][2] = -1.12538269574368;
   fWeightMatrix0to1[16][2] = 0.211785301282892;
   fWeightMatrix0to1[17][2] = 2.37104496928907;
   fWeightMatrix0to1[18][2] = 0.234439838498426;
   fWeightMatrix0to1[19][2] = -0.530028023204687;
   fWeightMatrix0to1[20][2] = 0.72164198385016;
   fWeightMatrix0to1[21][2] = 1.17691065839371;
   fWeightMatrix0to1[22][2] = -0.543502132571592;
   fWeightMatrix0to1[0][3] = -2.04311026469124;
   fWeightMatrix0to1[1][3] = -1.20669204090619;
   fWeightMatrix0to1[2][3] = 2.84599560502273;
   fWeightMatrix0to1[3][3] = -0.0428768838023468;
   fWeightMatrix0to1[4][3] = 0.764981446141033;
   fWeightMatrix0to1[5][3] = 13.254480649856;
   fWeightMatrix0to1[6][3] = 12.7179297886987;
   fWeightMatrix0to1[7][3] = -0.738027494997737;
   fWeightMatrix0to1[8][3] = 9.65195879775912;
   fWeightMatrix0to1[9][3] = 0.729439816081347;
   fWeightMatrix0to1[10][3] = 3.54698380408994;
   fWeightMatrix0to1[11][3] = 0.722389335868264;
   fWeightMatrix0to1[12][3] = 3.8116227888659;
   fWeightMatrix0to1[13][3] = 1.29519723982304;
   fWeightMatrix0to1[14][3] = -8.00536286376089;
   fWeightMatrix0to1[15][3] = 0.382319441007033;
   fWeightMatrix0to1[16][3] = -0.620489936725906;
   fWeightMatrix0to1[17][3] = -0.594032430143821;
   fWeightMatrix0to1[18][3] = -1.24639686530961;
   fWeightMatrix0to1[19][3] = -8.71099523835395;
   fWeightMatrix0to1[20][3] = -21.8260598087776;
   fWeightMatrix0to1[21][3] = -0.905990470026797;
   fWeightMatrix0to1[22][3] = 2.84720242525331;
   fWeightMatrix0to1[0][4] = -0.128451061458668;
   fWeightMatrix0to1[1][4] = -0.738513088569237;
   fWeightMatrix0to1[2][4] = -0.0825866260035262;
   fWeightMatrix0to1[3][4] = -0.0712505734471451;
   fWeightMatrix0to1[4][4] = 0.15701222819364;
   fWeightMatrix0to1[5][4] = -0.532063492778823;
   fWeightMatrix0to1[6][4] = -0.0249874117382801;
   fWeightMatrix0to1[7][4] = 0.297619824736495;
   fWeightMatrix0to1[8][4] = 0.44218670466297;
   fWeightMatrix0to1[9][4] = -0.0484410163962655;
   fWeightMatrix0to1[10][4] = 0.165145172176373;
   fWeightMatrix0to1[11][4] = 0.124990780150127;
   fWeightMatrix0to1[12][4] = 0.0251106329175592;
   fWeightMatrix0to1[13][4] = 23.2911053864737;
   fWeightMatrix0to1[14][4] = 0.0870123706241847;
   fWeightMatrix0to1[15][4] = -1.74559222576903;
   fWeightMatrix0to1[16][4] = -0.519289869475094;
   fWeightMatrix0to1[17][4] = 0.0399217597959512;
   fWeightMatrix0to1[18][4] = 0.00051066486788519;
   fWeightMatrix0to1[19][4] = -0.594375807368349;
   fWeightMatrix0to1[20][4] = -0.33967527746065;
   fWeightMatrix0to1[21][4] = 4.97537434246333;
   fWeightMatrix0to1[22][4] = 0.293044991670871;
   fWeightMatrix0to1[0][5] = -2.95172967604919;
   fWeightMatrix0to1[1][5] = -3.5263451558428;
   fWeightMatrix0to1[2][5] = -0.104988454456669;
   fWeightMatrix0to1[3][5] = -1.15061253439687;
   fWeightMatrix0to1[4][5] = -0.0715594411502014;
   fWeightMatrix0to1[5][5] = -2.86708033419629;
   fWeightMatrix0to1[6][5] = 1.10447656169782;
   fWeightMatrix0to1[7][5] = 0.621292714879421;
   fWeightMatrix0to1[8][5] = 6.41817954524736;
   fWeightMatrix0to1[9][5] = 0.667761368656389;
   fWeightMatrix0to1[10][5] = 1.36775387527847;
   fWeightMatrix0to1[11][5] = 0.112038172340811;
   fWeightMatrix0to1[12][5] = -0.203563838168373;
   fWeightMatrix0to1[13][5] = -2.60184011197919;
   fWeightMatrix0to1[14][5] = 9.2051230344433;
   fWeightMatrix0to1[15][5] = 0.0524808339472907;
   fWeightMatrix0to1[16][5] = -2.35027451855934;
   fWeightMatrix0to1[17][5] = -2.76921662688559;
   fWeightMatrix0to1[18][5] = -0.314862249743789;
   fWeightMatrix0to1[19][5] = 0.759622312404639;
   fWeightMatrix0to1[20][5] = -0.871870784581863;
   fWeightMatrix0to1[21][5] = -0.695669442739959;
   fWeightMatrix0to1[22][5] = 0.739377812201173;
   fWeightMatrix0to1[0][6] = 2.32670542713004;
   fWeightMatrix0to1[1][6] = 5.10393412568441;
   fWeightMatrix0to1[2][6] = -4.08088896094672;
   fWeightMatrix0to1[3][6] = 5.30677559774394;
   fWeightMatrix0to1[4][6] = 0.609124263112554;
   fWeightMatrix0to1[5][6] = -10.1662198753156;
   fWeightMatrix0to1[6][6] = -11.7488485439802;
   fWeightMatrix0to1[7][6] = 2.3288600406762;
   fWeightMatrix0to1[8][6] = -10.3405923035609;
   fWeightMatrix0to1[9][6] = -1.12570702167963;
   fWeightMatrix0to1[10][6] = -2.86135469420225;
   fWeightMatrix0to1[11][6] = -1.21981110702533;
   fWeightMatrix0to1[12][6] = -2.78748116745333;
   fWeightMatrix0to1[13][6] = -0.942261773333106;
   fWeightMatrix0to1[14][6] = 9.01263496577765;
   fWeightMatrix0to1[15][6] = -0.676648040028556;
   fWeightMatrix0to1[16][6] = 2.37933866028368;
   fWeightMatrix0to1[17][6] = 3.25946275744495;
   fWeightMatrix0to1[18][6] = 0.722995029541239;
   fWeightMatrix0to1[19][6] = 7.22806885534227;
   fWeightMatrix0to1[20][6] = 19.6726737149188;
   fWeightMatrix0to1[21][6] = 1.27123516722087;
   fWeightMatrix0to1[22][6] = -1.76577157566855;
   fWeightMatrix0to1[0][7] = -4.53250763399869;
   fWeightMatrix0to1[1][7] = 1.07731097933625;
   fWeightMatrix0to1[2][7] = -1.28312699053419;
   fWeightMatrix0to1[3][7] = 8.53559666002778;
   fWeightMatrix0to1[4][7] = 0.768046995834967;
   fWeightMatrix0to1[5][7] = 2.63776039846738;
   fWeightMatrix0to1[6][7] = 10.8351630754303;
   fWeightMatrix0to1[7][7] = -0.040149495759664;
   fWeightMatrix0to1[8][7] = 1.90953191788529;
   fWeightMatrix0to1[9][7] = 0.482345306969967;
   fWeightMatrix0to1[10][7] = 8.47775986139964;
   fWeightMatrix0to1[11][7] = -0.0715229067551338;
   fWeightMatrix0to1[12][7] = 5.5365199107072;
   fWeightMatrix0to1[13][7] = -7.6536861774725;
   fWeightMatrix0to1[14][7] = -1.53569330206581;
   fWeightMatrix0to1[15][7] = 2.8228874196151;
   fWeightMatrix0to1[16][7] = 1.22168370157107;
   fWeightMatrix0to1[17][7] = 1.27473210206898;
   fWeightMatrix0to1[18][7] = -12.6049978136943;
   fWeightMatrix0to1[19][7] = -12.5801619094304;
   fWeightMatrix0to1[20][7] = 4.59069092307855;
   fWeightMatrix0to1[21][7] = -5.97889830222176;
   fWeightMatrix0to1[22][7] = -2.15706226635161;
   fWeightMatrix0to1[0][8] = 0.244266503521318;
   fWeightMatrix0to1[1][8] = 0.374002155986517;
   fWeightMatrix0to1[2][8] = -0.246207984824095;
   fWeightMatrix0to1[3][8] = -0.00926102605044792;
   fWeightMatrix0to1[4][8] = -2.3803440928526;
   fWeightMatrix0to1[5][8] = -0.0464569821242879;
   fWeightMatrix0to1[6][8] = -0.233543365534403;
   fWeightMatrix0to1[7][8] = 1.22612963268979;
   fWeightMatrix0to1[8][8] = 0.374761940191732;
   fWeightMatrix0to1[9][8] = 0.180197358176372;
   fWeightMatrix0to1[10][8] = -2.3081891159118;
   fWeightMatrix0to1[11][8] = 0.0930530281694091;
   fWeightMatrix0to1[12][8] = 0.135218178596279;
   fWeightMatrix0to1[13][8] = -0.128773111767146;
   fWeightMatrix0to1[14][8] = -4.96440261003749;
   fWeightMatrix0to1[15][8] = 0.211308722001751;
   fWeightMatrix0to1[16][8] = 0.05350927229231;
   fWeightMatrix0to1[17][8] = 1.60071738648069;
   fWeightMatrix0to1[18][8] = -10.7980550376298;
   fWeightMatrix0to1[19][8] = 0.458147645910678;
   fWeightMatrix0to1[20][8] = 0.394231385200735;
   fWeightMatrix0to1[21][8] = -0.226366366428004;
   fWeightMatrix0to1[22][8] = -1.88224720460216;
   fWeightMatrix0to1[0][9] = 0.995393267216435;
   fWeightMatrix0to1[1][9] = 3.3742016012253;
   fWeightMatrix0to1[2][9] = 5.59735965781804;
   fWeightMatrix0to1[3][9] = 0.127566854051597;
   fWeightMatrix0to1[4][9] = 1.85134147789409;
   fWeightMatrix0to1[5][9] = 0.277730824627698;
   fWeightMatrix0to1[6][9] = -0.0436891106587404;
   fWeightMatrix0to1[7][9] = 0.567222106941223;
   fWeightMatrix0to1[8][9] = -0.155307567494789;
   fWeightMatrix0to1[9][9] = 0.470899858894981;
   fWeightMatrix0to1[10][9] = -1.47476157860231;
   fWeightMatrix0to1[11][9] = -1.31077140513991;
   fWeightMatrix0to1[12][9] = -5.7905387452349;
   fWeightMatrix0to1[13][9] = -0.252081869581582;
   fWeightMatrix0to1[14][9] = -2.03305648842447;
   fWeightMatrix0to1[15][9] = 0.184675585330675;
   fWeightMatrix0to1[16][9] = -0.460066516552684;
   fWeightMatrix0to1[17][9] = 5.55965852806746;
   fWeightMatrix0to1[18][9] = -12.1406757784777;
   fWeightMatrix0to1[19][9] = 3.43873867882442;
   fWeightMatrix0to1[20][9] = -0.0602437808379762;
   fWeightMatrix0to1[21][9] = -0.258257212550598;
   fWeightMatrix0to1[22][9] = -0.840476742981897;
   fWeightMatrix0to1[0][10] = 20.8465413073813;
   fWeightMatrix0to1[1][10] = -0.423307742370857;
   fWeightMatrix0to1[2][10] = 0.897403203967452;
   fWeightMatrix0to1[3][10] = -6.43169903405557;
   fWeightMatrix0to1[4][10] = 1.59778895080251;
   fWeightMatrix0to1[5][10] = 0.348133557290235;
   fWeightMatrix0to1[6][10] = -8.98466659337426;
   fWeightMatrix0to1[7][10] = 0.24160859211655;
   fWeightMatrix0to1[8][10] = -0.162524336699689;
   fWeightMatrix0to1[9][10] = -0.0911168403281854;
   fWeightMatrix0to1[10][10] = -0.51518044061348;
   fWeightMatrix0to1[11][10] = -0.570505534005657;
   fWeightMatrix0to1[12][10] = -5.90034894745909;
   fWeightMatrix0to1[13][10] = -0.145587150974906;
   fWeightMatrix0to1[14][10] = -4.39640865281968;
   fWeightMatrix0to1[15][10] = 0.106873452301595;
   fWeightMatrix0to1[16][10] = 0.0234452211940486;
   fWeightMatrix0to1[17][10] = 0.0220288877032798;
   fWeightMatrix0to1[18][10] = -9.46838861226607;
   fWeightMatrix0to1[19][10] = -3.5302574051721;
   fWeightMatrix0to1[20][10] = 2.26691019931282;
   fWeightMatrix0to1[21][10] = -0.307301273883581;
   fWeightMatrix0to1[22][10] = 0.887911995711123;
   fWeightMatrix0to1[0][11] = 55.388708164381;
   fWeightMatrix0to1[1][11] = -1.10652647555923;
   fWeightMatrix0to1[2][11] = -41.0688736330646;
   fWeightMatrix0to1[3][11] = -25.140321673858;
   fWeightMatrix0to1[4][11] = -10.5209093076642;
   fWeightMatrix0to1[5][11] = 12.0190734096547;
   fWeightMatrix0to1[6][11] = -41.9998613277334;
   fWeightMatrix0to1[7][11] = 0.13300080903575;
   fWeightMatrix0to1[8][11] = 12.6930653386532;
   fWeightMatrix0to1[9][11] = 2.29109654016353;
   fWeightMatrix0to1[10][11] = -5.90143140101845;
   fWeightMatrix0to1[11][11] = -0.260756031337357;
   fWeightMatrix0to1[12][11] = -19.0582587261531;
   fWeightMatrix0to1[13][11] = 7.74046634666238;
   fWeightMatrix0to1[14][11] = 7.13734170118194;
   fWeightMatrix0to1[15][11] = 4.17519959406967;
   fWeightMatrix0to1[16][11] = 0.973993047062735;
   fWeightMatrix0to1[17][11] = -2.52658003905935;
   fWeightMatrix0to1[18][11] = -9.3291998176057;
   fWeightMatrix0to1[19][11] = 60.3797311077784;
   fWeightMatrix0to1[20][11] = -4.73338175168905;
   fWeightMatrix0to1[21][11] = 4.00190968117137;
   fWeightMatrix0to1[22][11] = 0.415775350325495;
   fWeightMatrix0to1[0][12] = 9.17483038175887;
   fWeightMatrix0to1[1][12] = 1.56929894742262;
   fWeightMatrix0to1[2][12] = -19.9541465883728;
   fWeightMatrix0to1[3][12] = -2.48800194856765;
   fWeightMatrix0to1[4][12] = -0.102119163035276;
   fWeightMatrix0to1[5][12] = -0.437613798765989;
   fWeightMatrix0to1[6][12] = 1.68828930400712;
   fWeightMatrix0to1[7][12] = -0.642375145703339;
   fWeightMatrix0to1[8][12] = -2.46598250363954;
   fWeightMatrix0to1[9][12] = -1.43665624783761;
   fWeightMatrix0to1[10][12] = -0.756884787320195;
   fWeightMatrix0to1[11][12] = 2.2353435159932;
   fWeightMatrix0to1[12][12] = -2.51656021630644;
   fWeightMatrix0to1[13][12] = 2.44165577758771;
   fWeightMatrix0to1[14][12] = -7.23098721711636;
   fWeightMatrix0to1[15][12] = 0.16758090575202;
   fWeightMatrix0to1[16][12] = 0.934502540094296;
   fWeightMatrix0to1[17][12] = -1.49507491186157;
   fWeightMatrix0to1[18][12] = -9.54773940799648;
   fWeightMatrix0to1[19][12] = -1.51884169672856;
   fWeightMatrix0to1[20][12] = -3.63241702228926;
   fWeightMatrix0to1[21][12] = 1.80773979078041;
   fWeightMatrix0to1[22][12] = -0.103674827233444;
   fWeightMatrix0to1[0][13] = -16.6031832877286;
   fWeightMatrix0to1[1][13] = -1.35911011438676;
   fWeightMatrix0to1[2][13] = 17.3622616955956;
   fWeightMatrix0to1[3][13] = 10.5837493877297;
   fWeightMatrix0to1[4][13] = -1.17047358351183;
   fWeightMatrix0to1[5][13] = -4.04917115626239;
   fWeightMatrix0to1[6][13] = 2.11848847452499;
   fWeightMatrix0to1[7][13] = -0.876880436595914;
   fWeightMatrix0to1[8][13] = -3.85975930632706;
   fWeightMatrix0to1[9][13] = -0.762494784617702;
   fWeightMatrix0to1[10][13] = -13.3683849525218;
   fWeightMatrix0to1[11][13] = 1.29245691674924;
   fWeightMatrix0to1[12][13] = -6.482116906586;
   fWeightMatrix0to1[13][13] = -2.14864705455009;
   fWeightMatrix0to1[14][13] = 1.87227828911405;
   fWeightMatrix0to1[15][13] = -2.28549515160688;
   fWeightMatrix0to1[16][13] = -0.571311582064844;
   fWeightMatrix0to1[17][13] = 0.486732347602508;
   fWeightMatrix0to1[18][13] = -12.1176985558209;
   fWeightMatrix0to1[19][13] = -5.36114439817321;
   fWeightMatrix0to1[20][13] = 2.16495089562338;
   fWeightMatrix0to1[21][13] = -0.641843522543631;
   fWeightMatrix0to1[22][13] = -0.714864844285281;
   fWeightMatrix0to1[0][14] = -17.8036646778996;
   fWeightMatrix0to1[1][14] = -2.27103662989255;
   fWeightMatrix0to1[2][14] = 18.9058585223317;
   fWeightMatrix0to1[3][14] = 9.41539947086539;
   fWeightMatrix0to1[4][14] = 0.890237459963133;
   fWeightMatrix0to1[5][14] = -3.88902253147735;
   fWeightMatrix0to1[6][14] = -1.73313797691596;
   fWeightMatrix0to1[7][14] = 1.33202995977689;
   fWeightMatrix0to1[8][14] = -5.46454032920736;
   fWeightMatrix0to1[9][14] = 0.757071087547616;
   fWeightMatrix0to1[10][14] = -12.4422758237133;
   fWeightMatrix0to1[11][14] = -1.51909807177771;
   fWeightMatrix0to1[12][14] = -5.18246107795957;
   fWeightMatrix0to1[13][14] = 0.926879398231622;
   fWeightMatrix0to1[14][14] = 0.537378342706348;
   fWeightMatrix0to1[15][14] = -1.91191173965803;
   fWeightMatrix0to1[16][14] = -0.073864942181643;
   fWeightMatrix0to1[17][14] = -3.1626913650149;
   fWeightMatrix0to1[18][14] = -11.0956670471305;
   fWeightMatrix0to1[19][14] = -7.7346366728782;
   fWeightMatrix0to1[20][14] = 2.6223372540234;
   fWeightMatrix0to1[21][14] = 0.0244764258354605;
   fWeightMatrix0to1[22][14] = 0.305118399670689;
   fWeightMatrix0to1[0][15] = -21.2261601712676;
   fWeightMatrix0to1[1][15] = -2.7477238258157;
   fWeightMatrix0to1[2][15] = 19.9720359007437;
   fWeightMatrix0to1[3][15] = 8.33197987342972;
   fWeightMatrix0to1[4][15] = -1.0328093904855;
   fWeightMatrix0to1[5][15] = -5.96241285024341;
   fWeightMatrix0to1[6][15] = 2.01607485403676;
   fWeightMatrix0to1[7][15] = 1.15200480862602;
   fWeightMatrix0to1[8][15] = -6.24771230179492;
   fWeightMatrix0to1[9][15] = -0.91002958045562;
   fWeightMatrix0to1[10][15] = -14.0618956952686;
   fWeightMatrix0to1[11][15] = -1.39032435815094;
   fWeightMatrix0to1[12][15] = -4.14700448312969;
   fWeightMatrix0to1[13][15] = 0.00410586588410581;
   fWeightMatrix0to1[14][15] = -0.858107884658289;
   fWeightMatrix0to1[15][15] = -3.19832454410533;
   fWeightMatrix0to1[16][15] = -2.66985556349478;
   fWeightMatrix0to1[17][15] = -2.54862581200876;
   fWeightMatrix0to1[18][15] = -12.2082498864498;
   fWeightMatrix0to1[19][15] = -6.21722683830921;
   fWeightMatrix0to1[20][15] = -0.638180688118146;
   fWeightMatrix0to1[21][15] = 1.77771411072742;
   fWeightMatrix0to1[22][15] = 0.338536047358787;
   fWeightMatrix0to1[0][16] = -4.13878513501723;
   fWeightMatrix0to1[1][16] = 6.62265239789763;
   fWeightMatrix0to1[2][16] = 1.1436249395859;
   fWeightMatrix0to1[3][16] = -1.9582912108346;
   fWeightMatrix0to1[4][16] = 0.245437113667408;
   fWeightMatrix0to1[5][16] = 2.71121703906476;
   fWeightMatrix0to1[6][16] = 1.67308334419192;
   fWeightMatrix0to1[7][16] = -2.30570527172042;
   fWeightMatrix0to1[8][16] = 2.90906432348328;
   fWeightMatrix0to1[9][16] = -0.233031450854262;
   fWeightMatrix0to1[10][16] = 8.83743829186402;
   fWeightMatrix0to1[11][16] = 0.16699429862634;
   fWeightMatrix0to1[12][16] = -0.0825629952911697;
   fWeightMatrix0to1[13][16] = -1.95383590657576;
   fWeightMatrix0to1[14][16] = 3.84122331280171;
   fWeightMatrix0to1[15][16] = 1.91232853199067;
   fWeightMatrix0to1[16][16] = 0.721513028389783;
   fWeightMatrix0to1[17][16] = -0.4999064546984;
   fWeightMatrix0to1[18][16] = -2.00730190760852;
   fWeightMatrix0to1[19][16] = -2.1563946894131;
   fWeightMatrix0to1[20][16] = 3.97498547512085;
   fWeightMatrix0to1[21][16] = -2.1895799442439;
   fWeightMatrix0to1[22][16] = 0.737601966957068;
   fWeightMatrix0to1[0][17] = 4.08321374012104;
   fWeightMatrix0to1[1][17] = 2.17412055356222;
   fWeightMatrix0to1[2][17] = -1.47022490461453;
   fWeightMatrix0to1[3][17] = -4.32616092695204;
   fWeightMatrix0to1[4][17] = 3.11257926643948;
   fWeightMatrix0to1[5][17] = -2.29511738351582;
   fWeightMatrix0to1[6][17] = -1.90658953605043;
   fWeightMatrix0to1[7][17] = -0.545660374300149;
   fWeightMatrix0to1[8][17] = 0.0531147735406193;
   fWeightMatrix0to1[9][17] = 0.769409560021437;
   fWeightMatrix0to1[10][17] = 5.92618384339273;
   fWeightMatrix0to1[11][17] = -0.436510676678523;
   fWeightMatrix0to1[12][17] = -0.167538375854154;
   fWeightMatrix0to1[13][17] = 2.10368474738084;
   fWeightMatrix0to1[14][17] = 0.678691660543915;
   fWeightMatrix0to1[15][17] = -2.10200046398851;
   fWeightMatrix0to1[16][17] = -0.4052394641543;
   fWeightMatrix0to1[17][17] = 0.479051760127;
   fWeightMatrix0to1[18][17] = -5.44485772684408;
   fWeightMatrix0to1[19][17] = 1.90371862608527;
   fWeightMatrix0to1[20][17] = -3.96820495687473;
   fWeightMatrix0to1[21][17] = 2.25102721533759;
   fWeightMatrix0to1[22][17] = -1.07836167143533;
   fWeightMatrix0to1[0][18] = -13.842024672139;
   fWeightMatrix0to1[1][18] = 9.56520898681502;
   fWeightMatrix0to1[2][18] = 5.40848129508261;
   fWeightMatrix0to1[3][18] = 2.70668212395763;
   fWeightMatrix0to1[4][18] = 3.02573024759065;
   fWeightMatrix0to1[5][18] = 4.06687496621383;
   fWeightMatrix0to1[6][18] = 9.2797246799676;
   fWeightMatrix0to1[7][18] = 3.11259306353421;
   fWeightMatrix0to1[8][18] = 3.70474201808795;
   fWeightMatrix0to1[9][18] = -3.40080147207341;
   fWeightMatrix0to1[10][18] = 5.98310374117341;
   fWeightMatrix0to1[11][18] = -1.14815366990839;
   fWeightMatrix0to1[12][18] = 2.50997725745526;
   fWeightMatrix0to1[13][18] = 5.36883642591599;
   fWeightMatrix0to1[14][18] = 1.06183753849383;
   fWeightMatrix0to1[15][18] = 8.04971634150929;
   fWeightMatrix0to1[16][18] = -1.01820538638485;
   fWeightMatrix0to1[17][18] = 2.33611568895203;
   fWeightMatrix0to1[18][18] = 4.10990780040237;
   fWeightMatrix0to1[19][18] = -11.265553136156;
   fWeightMatrix0to1[20][18] = 3.12050769839619;
   fWeightMatrix0to1[21][18] = -1.76629432404731;
   fWeightMatrix0to1[22][18] = 4.98819810608674;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = 0.783074724622431;
   fWeightMatrix1to2[0][1] = -1.81982429447114;
   fWeightMatrix1to2[0][2] = -0.846268884123519;
   fWeightMatrix1to2[0][3] = -0.67243930760346;
   fWeightMatrix1to2[0][4] = 1.74332367161372;
   fWeightMatrix1to2[0][5] = -0.575332404691166;
   fWeightMatrix1to2[0][6] = -0.619763706181825;
   fWeightMatrix1to2[0][7] = 0.936434564384421;
   fWeightMatrix1to2[0][8] = -0.610948875566717;
   fWeightMatrix1to2[0][9] = 1.11793467987501;
   fWeightMatrix1to2[0][10] = -0.975166091709296;
   fWeightMatrix1to2[0][11] = -0.69046059748438;
   fWeightMatrix1to2[0][12] = 1.65859101039029;
   fWeightMatrix1to2[0][13] = 0.841774185101912;
   fWeightMatrix1to2[0][14] = -0.337234722410429;
   fWeightMatrix1to2[0][15] = -0.497599094224417;
   fWeightMatrix1to2[0][16] = -1.07971046839963;
   fWeightMatrix1to2[0][17] = 1.02102413421805;
   fWeightMatrix1to2[0][18] = -4.8195656486813;
   fWeightMatrix1to2[0][19] = 0.651976969292217;
   fWeightMatrix1to2[0][20] = -0.707778816690049;
   fWeightMatrix1to2[0][21] = 0.687719986799629;
   fWeightMatrix1to2[0][22] = 3.31766165628267;
   fWeightMatrix1to2[0][23] = -4.80754190827471;
}

inline double ReadGhost_Upstream_TMVA::GetMvaValue__( const std::vector<double>& inputValues ) const
{
   if (inputValues.size() != (unsigned int)fLayerSize[0]-1) {
      std::cout << "Input vector needs to be of size " << fLayerSize[0]-1 << std::endl;
      return 0;
   }

   for (int l=0; l<fLayers; l++)
      for (int i=0; i<fLayerSize[l]; i++) fWeights[l][i]=0;

   for (int l=0; l<fLayers-1; l++)
      fWeights[l][fLayerSize[l]-1]=1;

   for (int i=0; i<fLayerSize[0]-1; i++)
      fWeights[0][i]=inputValues[i];

   // layer 0 to 1
   for (int o=0; o<fLayerSize[1]-1; o++) {
      for (int i=0; i<fLayerSize[0]; i++) {
         double inputVal = fWeightMatrix0to1[o][i] * fWeights[0][i];
         fWeights[1][o] += inputVal;
      }
      fWeights[1][o] = ActivationFnc(fWeights[1][o]);
   }
   // layer 1 to 2
   for (int o=0; o<fLayerSize[2]; o++) {
      for (int i=0; i<fLayerSize[1]; i++) {
         double inputVal = fWeightMatrix1to2[o][i] * fWeights[1][i];
         fWeights[2][o] += inputVal;
      }
      fWeights[2][o] = OutputActivationFnc(fWeights[2][o]);
   }

   return fWeights[2][0];
}

double ReadGhost_Upstream_TMVA::ActivationFnc(double x) const {
   // hyperbolic tan
   return tanh(x);
}
double ReadGhost_Upstream_TMVA::OutputActivationFnc(double x) const {
   // sigmoid
   return 1.0/(1.0+exp(-x));
}
   
// Clean up
inline void ReadGhost_Upstream_TMVA::Clear() 
{
   // clean up the arrays
   for (int lIdx = 0; lIdx < 3; lIdx++) {
      delete[] fWeights[lIdx];
   }
}
   inline double ReadGhost_Upstream_TMVA::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] ));
            }
            Transform( iV, -1 );
            retval = GetMvaValue__( iV );
         }
         else {
            std::vector<double> iV;
            int ivar = 0;
            for (std::vector<double>::const_iterator varIt = inputValues.begin();
                 varIt != inputValues.end(); varIt++, ivar++) {
               iV.push_back(*varIt);
            }
            Transform( iV, -1 );
            retval = GetMvaValue__( iV );
         }
      }

      return retval;
   }

//_______________________________________________________________________
inline void ReadGhost_Upstream_TMVA::InitTransform_1()
{
   // Normalization transformation, initialisation
   fMin_1[0][0] = 115.480003357;
   fMax_1[0][0] = 4998705.5;
   fMin_1[1][0] = 125.38999939;
   fMax_1[1][0] = 4954328;
   fMin_1[2][0] = 115.480003357;
   fMax_1[2][0] = 4998705.5;
   fMin_1[0][1] = 16.465473175;
   fMax_1[0][1] = 867623.6875;
   fMin_1[1][1] = 12.8830137253;
   fMax_1[1][1] = 724337;
   fMin_1[2][1] = 12.8830137253;
   fMax_1[2][1] = 867623.6875;
   fMin_1[0][2] = 0.00195923517458;
   fMax_1[0][2] = 3.99805927277;
   fMin_1[1][2] = 0.00263477140106;
   fMax_1[1][2] = 3.99922418594;
   fMin_1[2][2] = 0.00195923517458;
   fMax_1[2][2] = 3.99922418594;
   fMin_1[0][3] = 4;
   fMax_1[0][3] = 27;
   fMin_1[1][3] = 4;
   fMax_1[1][3] = 27;
   fMin_1[2][3] = 4;
   fMax_1[2][3] = 27;
   fMin_1[0][4] = 0.00192928232718;
   fMax_1[0][4] = 0.399999499321;
   fMin_1[1][4] = 0.0022268560715;
   fMax_1[1][4] = 0.399999022484;
   fMin_1[2][4] = 0.00192928232718;
   fMax_1[2][4] = 0.399999499321;
   fMin_1[0][5] = 7.34402965463e-06;
   fMax_1[0][5] = 67.3927001953;
   fMin_1[1][5] = 7.90732883615e-05;
   fMax_1[1][5] = 63.098651886;
   fMin_1[2][5] = 7.34402965463e-06;
   fMax_1[2][5] = 67.3927001953;
   fMin_1[0][6] = 1;
   fMax_1[0][6] = 23;
   fMin_1[1][6] = 1;
   fMax_1[1][6] = 23;
   fMin_1[2][6] = 1;
   fMax_1[2][6] = 23;
   fMin_1[0][7] = 0;
   fMax_1[0][7] = 1;
   fMin_1[1][7] = 0;
   fMax_1[1][7] = 1;
   fMin_1[2][7] = 0;
   fMax_1[2][7] = 1;
   fMin_1[0][8] = 0;
   fMax_1[0][8] = 1;
   fMin_1[1][8] = 0;
   fMax_1[1][8] = 1;
   fMin_1[2][8] = 0;
   fMax_1[2][8] = 1;
   fMin_1[0][9] = 0;
   fMax_1[0][9] = 1;
   fMin_1[1][9] = 0;
   fMax_1[1][9] = 1;
   fMin_1[2][9] = 0;
   fMax_1[2][9] = 1;
   fMin_1[0][10] = 0;
   fMax_1[0][10] = 1;
   fMin_1[1][10] = 0;
   fMax_1[1][10] = 1;
   fMin_1[2][10] = 0;
   fMax_1[2][10] = 1;
   fMin_1[0][11] = -999;
   fMax_1[0][11] = 168.813705444;
   fMin_1[1][11] = -999;
   fMax_1[1][11] = 137.75289917;
   fMin_1[2][11] = -999;
   fMax_1[2][11] = 168.813705444;
   fMin_1[0][12] = -999;
   fMax_1[0][12] = 114.397399902;
   fMin_1[1][12] = -999;
   fMax_1[1][12] = 90.4578018188;
   fMin_1[2][12] = -999;
   fMax_1[2][12] = 114.397399902;
   fMin_1[0][13] = -999;
   fMax_1[0][13] = 135.562103271;
   fMin_1[1][13] = -999;
   fMax_1[1][13] = 87.2134017944;
   fMin_1[2][13] = -999;
   fMax_1[2][13] = 135.562103271;
   fMin_1[0][14] = -999;
   fMax_1[0][14] = 123.221702576;
   fMin_1[1][14] = -999;
   fMax_1[1][14] = 94.3625030518;
   fMin_1[2][14] = -999;
   fMax_1[2][14] = 123.221702576;
   fMin_1[0][15] = -999;
   fMax_1[0][15] = 51.1268997192;
   fMin_1[1][15] = -999;
   fMax_1[1][15] = 51.957901001;
   fMin_1[2][15] = -999;
   fMax_1[2][15] = 51.957901001;
   fMin_1[0][16] = 0;
   fMax_1[0][16] = 1;
   fMin_1[1][16] = 0;
   fMax_1[1][16] = 1;
   fMin_1[2][16] = 0;
   fMax_1[2][16] = 1;
   fMin_1[0][17] = -999;
   fMax_1[0][17] = 4.79148387909;
   fMin_1[1][17] = -999;
   fMax_1[1][17] = 4.79148387909;
   fMin_1[2][17] = -999;
   fMax_1[2][17] = 4.79148387909;
}

//_______________________________________________________________________
inline void ReadGhost_Upstream_TMVA::Transform_1( std::vector<double>& iv, int cls) const
{
   // Normalization transformation
   if (cls < 0 || cls > 2) {
   if (2 > 1 ) cls = 2;
      else cls = 2;
   }
   const int nVar = 18;

   // get indices of used variables

   // 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);
      indicesGet.push_back( 7);
      indicesGet.push_back( 8);
      indicesGet.push_back( 9);
      indicesGet.push_back( 10);
      indicesGet.push_back( 11);
      indicesGet.push_back( 12);
      indicesGet.push_back( 13);
      indicesGet.push_back( 14);
      indicesGet.push_back( 15);
      indicesGet.push_back( 16);
      indicesGet.push_back( 17);
   } 
   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);
      indicesPut.push_back( 7);
      indicesPut.push_back( 8);
      indicesPut.push_back( 9);
      indicesPut.push_back( 10);
      indicesPut.push_back( 11);
      indicesPut.push_back( 12);
      indicesPut.push_back( 13);
      indicesPut.push_back( 14);
      indicesPut.push_back( 15);
      indicesPut.push_back( 16);
      indicesPut.push_back( 17);
   } 

   static std::vector<double> dv;
   dv.resize(nVar);
   for (int ivar=0; ivar<nVar; ivar++) dv[ivar] = iv[indicesGet.at(ivar)];
   for (int ivar=0;ivar<18;ivar++) {
      double offset = fMin_1[cls][ivar];
      double scale  = 1.0/(fMax_1[cls][ivar]-fMin_1[cls][ivar]);
      iv[indicesPut.at(ivar)] = (dv[ivar]-offset)*scale * 2 - 1;
   }
}

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

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