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

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

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

Method         : MLP::Kaon_Upstream_TMVA
TMVA Release   : 4.2.0         [262656]
ROOT Release   : 6.02/01       [393729]
Creator        : jonesc
Date           : Mon Feb 15 02:01:07 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/MC12/TrainMixture/TrainPhysTks-EvalPhysTks-ReweightRICH2/GhostAccFrac1.0/ProtonAccFrac1.0/KaonAccFrac1.0/TMVA-Run2-NoTkLikCDVelodEdx/MLP/Norm/ScaleF1.4/BP/NCycles750/CE/tanh/CVTest15/CVImp1e-16/NotUseRegulator/Kaon/Upstream
Training events: 2700000
Analysis type  : [Classification]


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

# Set by User:
NCycles: "750" [Number of training cycles]
HiddenLayers: "25" [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'    [147.869995117,4760267.5]
TrackPt                       TrackPt                       TrackPt                       TrackPt                                                         'F'    [0.360198408365,978077.5]
TrackChi2PerDof               TrackChi2PerDof               TrackChi2PerDof               TrackChi2PerDof                                                 'F'    [0.0029748051893,2.99983906746]
TrackNumDof                   TrackNumDof                   TrackNumDof                   TrackNumDof                                                     'I'    [4,28]
TrackGhostProbability         TrackGhostProbability         TrackGhostProbability         TrackGhostProbability                                           'F'    [0.0391823016107,1]
TrackFitVeloChi2              TrackFitVeloChi2              TrackFitVeloChi2              TrackFitVeloChi2                                                'F'    [8.64220680796e-07,53.9077644348]
TrackFitVeloNDoF              TrackFitVeloNDoF              TrackFitVeloNDoF              TrackFitVeloNDoF                                                'I'    [1,24]
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'    [-999,118.707099915]
RichDLLmu                     RichDLLmu                     RichDLLmu                     RichDLLmu                                                       'F'    [-999,81.0063018799]
RichDLLk                      RichDLLk                      RichDLLk                      RichDLLk                                                        'F'    [-999,82.1451034546]
RichDLLp                      RichDLLp                      RichDLLp                      RichDLLp                                                        'F'    [-999,92.269203186]
RichDLLbt                     RichDLLbt                     RichDLLbt                     RichDLLbt                                                       'F'    [-999,50.5774993896]
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 ReadKaon_Upstream_TMVA : public IClassifierReader {

 public:

   // constructor
   ReadKaon_Upstream_TMVA( std::vector<std::string>& theInputVars ) 
      : IClassifierReader(),
        fClassName( "ReadKaon_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] = 0.99999988079071;
      fVmin[1] = -1;
      fVmax[1] = 1;
      fVmin[2] = -1;
      fVmax[2] = 1;
      fVmin[3] = -1;
      fVmax[3] = 1;
      fVmin[4] = -1;
      fVmax[4] = 1;
      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 ~ReadKaon_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[26][19];   // weight matrix from layer 0 to 1
   double fWeightMatrix1to2[1][26];   // weight matrix from layer 1 to 2

   double * fWeights[3];
};

inline void ReadKaon_Upstream_TMVA::Initialize()
{
   // build network structure
   fLayers = 3;
   fLayerSize[0] = 19; fWeights[0] = new double[19]; 
   fLayerSize[1] = 26; fWeights[1] = new double[26]; 
   fLayerSize[2] = 1; fWeights[2] = new double[1]; 
   // weight matrix from layer 0 to 1
   fWeightMatrix0to1[0][0] = 1.25480459446989;
   fWeightMatrix0to1[1][0] = 10.7100725798596;
   fWeightMatrix0to1[2][0] = -11.0277842454204;
   fWeightMatrix0to1[3][0] = 8.18519139627966;
   fWeightMatrix0to1[4][0] = -0.670336983709817;
   fWeightMatrix0to1[5][0] = 17.1928012121468;
   fWeightMatrix0to1[6][0] = -2.27206928221522;
   fWeightMatrix0to1[7][0] = 10.3965658013097;
   fWeightMatrix0to1[8][0] = -17.494235012864;
   fWeightMatrix0to1[9][0] = -1.26346712437826;
   fWeightMatrix0to1[10][0] = -0.505553151971339;
   fWeightMatrix0to1[11][0] = -3.42329470197226;
   fWeightMatrix0to1[12][0] = 0.95446493551437;
   fWeightMatrix0to1[13][0] = 1.04114760470416;
   fWeightMatrix0to1[14][0] = -0.766542083040833;
   fWeightMatrix0to1[15][0] = 0.142376717960495;
   fWeightMatrix0to1[16][0] = 0.0281797225680946;
   fWeightMatrix0to1[17][0] = -0.82017522782521;
   fWeightMatrix0to1[18][0] = -3.28042330266046;
   fWeightMatrix0to1[19][0] = -3.46679690445265;
   fWeightMatrix0to1[20][0] = 1.85419003783051;
   fWeightMatrix0to1[21][0] = 16.5207997212218;
   fWeightMatrix0to1[22][0] = -1.55795702633003;
   fWeightMatrix0to1[23][0] = 2.07349610539378;
   fWeightMatrix0to1[24][0] = -1.21859262704253;
   fWeightMatrix0to1[0][1] = 0.42149572981878;
   fWeightMatrix0to1[1][1] = 11.2566024446007;
   fWeightMatrix0to1[2][1] = -2.15398828554002;
   fWeightMatrix0to1[3][1] = -5.90606644309857;
   fWeightMatrix0to1[4][1] = 1.41028154657464;
   fWeightMatrix0to1[5][1] = 2.39931016253193;
   fWeightMatrix0to1[6][1] = -0.225569649008159;
   fWeightMatrix0to1[7][1] = -0.802582336974965;
   fWeightMatrix0to1[8][1] = -15.7010893977046;
   fWeightMatrix0to1[9][1] = -0.239292505521648;
   fWeightMatrix0to1[10][1] = -0.0628781253882567;
   fWeightMatrix0to1[11][1] = -7.69183956368822;
   fWeightMatrix0to1[12][1] = -0.736075641304794;
   fWeightMatrix0to1[13][1] = -1.63558813212552;
   fWeightMatrix0to1[14][1] = 1.88876546588236;
   fWeightMatrix0to1[15][1] = 0.81012864908205;
   fWeightMatrix0to1[16][1] = 0.333539235510176;
   fWeightMatrix0to1[17][1] = 0.930507117423379;
   fWeightMatrix0to1[18][1] = -0.615048657834392;
   fWeightMatrix0to1[19][1] = -8.16634943441721;
   fWeightMatrix0to1[20][1] = -1.00431690166636;
   fWeightMatrix0to1[21][1] = 1.2997912628667;
   fWeightMatrix0to1[22][1] = -2.06289613753592;
   fWeightMatrix0to1[23][1] = 6.34030565900158;
   fWeightMatrix0to1[24][1] = 1.12036767159246;
   fWeightMatrix0to1[0][2] = -0.0385615316587421;
   fWeightMatrix0to1[1][2] = 0.380329941689998;
   fWeightMatrix0to1[2][2] = -0.576381285651478;
   fWeightMatrix0to1[3][2] = 0.0166551886499879;
   fWeightMatrix0to1[4][2] = -0.062957259264541;
   fWeightMatrix0to1[5][2] = 0.018840603702359;
   fWeightMatrix0to1[6][2] = 0.444370681669321;
   fWeightMatrix0to1[7][2] = -0.180538390823012;
   fWeightMatrix0to1[8][2] = 0.704991615111504;
   fWeightMatrix0to1[9][2] = -0.124348835586272;
   fWeightMatrix0to1[10][2] = 0.19378625626717;
   fWeightMatrix0to1[11][2] = 0.265582315568076;
   fWeightMatrix0to1[12][2] = 1.4529739225276;
   fWeightMatrix0to1[13][2] = 0.481642398185574;
   fWeightMatrix0to1[14][2] = 1.29069643328927;
   fWeightMatrix0to1[15][2] = -0.0770889871343465;
   fWeightMatrix0to1[16][2] = -0.162158509068162;
   fWeightMatrix0to1[17][2] = -0.00398939581835955;
   fWeightMatrix0to1[18][2] = 0.359775003563956;
   fWeightMatrix0to1[19][2] = -0.0191223377803733;
   fWeightMatrix0to1[20][2] = 1.47165384098115;
   fWeightMatrix0to1[21][2] = -0.443173654085399;
   fWeightMatrix0to1[22][2] = 1.23632712812794;
   fWeightMatrix0to1[23][2] = -0.71160812747646;
   fWeightMatrix0to1[24][2] = 0.617628966140918;
   fWeightMatrix0to1[0][3] = -0.952394805247321;
   fWeightMatrix0to1[1][3] = -1.10547510685659;
   fWeightMatrix0to1[2][3] = -6.94425984115198;
   fWeightMatrix0to1[3][3] = 2.10863981738688;
   fWeightMatrix0to1[4][3] = 0.961046171458595;
   fWeightMatrix0to1[5][3] = 2.71777873865286;
   fWeightMatrix0to1[6][3] = -0.689560658437377;
   fWeightMatrix0to1[7][3] = 0.660767786317076;
   fWeightMatrix0to1[8][3] = -2.32040096892916;
   fWeightMatrix0to1[9][3] = -0.0498527945715056;
   fWeightMatrix0to1[10][3] = 0.334835106963525;
   fWeightMatrix0to1[11][3] = 0.974312891567311;
   fWeightMatrix0to1[12][3] = -30.2697222573564;
   fWeightMatrix0to1[13][3] = -6.12310790733785;
   fWeightMatrix0to1[14][3] = -2.74041386484134;
   fWeightMatrix0to1[15][3] = -0.249916732974146;
   fWeightMatrix0to1[16][3] = -8.66142482183618;
   fWeightMatrix0to1[17][3] = -0.207080100792944;
   fWeightMatrix0to1[18][3] = -0.986046865027043;
   fWeightMatrix0to1[19][3] = 1.58182272481681;
   fWeightMatrix0to1[20][3] = -1.90382131637961;
   fWeightMatrix0to1[21][3] = -5.63606830589143;
   fWeightMatrix0to1[22][3] = -7.72655172612899;
   fWeightMatrix0to1[23][3] = -0.713347557128591;
   fWeightMatrix0to1[24][3] = -1.36564380236022;
   fWeightMatrix0to1[0][4] = -0.963276374362287;
   fWeightMatrix0to1[1][4] = 0.636346672556409;
   fWeightMatrix0to1[2][4] = -0.339557170062067;
   fWeightMatrix0to1[3][4] = -0.138189961931125;
   fWeightMatrix0to1[4][4] = -0.0476106075936888;
   fWeightMatrix0to1[5][4] = 0.250138689240893;
   fWeightMatrix0to1[6][4] = -0.167641462644583;
   fWeightMatrix0to1[7][4] = 0.506955661222504;
   fWeightMatrix0to1[8][4] = 1.06859651781716;
   fWeightMatrix0to1[9][4] = 0.00247261816341928;
   fWeightMatrix0to1[10][4] = -0.562185687708185;
   fWeightMatrix0to1[11][4] = -0.300255179983443;
   fWeightMatrix0to1[12][4] = -1.61944153559825;
   fWeightMatrix0to1[13][4] = -0.0852619197601767;
   fWeightMatrix0to1[14][4] = -0.613903172134713;
   fWeightMatrix0to1[15][4] = -0.101822656537701;
   fWeightMatrix0to1[16][4] = 0.133927887952408;
   fWeightMatrix0to1[17][4] = -0.41903300702195;
   fWeightMatrix0to1[18][4] = 0.455890773529226;
   fWeightMatrix0to1[19][4] = -0.512198847148108;
   fWeightMatrix0to1[20][4] = 0.666656743110945;
   fWeightMatrix0to1[21][4] = -0.290919649917238;
   fWeightMatrix0to1[22][4] = -0.214072933670211;
   fWeightMatrix0to1[23][4] = -0.10827663970868;
   fWeightMatrix0to1[24][4] = 0.201302528796782;
   fWeightMatrix0to1[0][5] = 1.25653258852105;
   fWeightMatrix0to1[1][5] = -1.09922012739869;
   fWeightMatrix0to1[2][5] = -1.87428716474861;
   fWeightMatrix0to1[3][5] = 0.0193716185234204;
   fWeightMatrix0to1[4][5] = 0.472403705194757;
   fWeightMatrix0to1[5][5] = -1.03916018803578;
   fWeightMatrix0to1[6][5] = 0.120161053034436;
   fWeightMatrix0to1[7][5] = 12.1288211360095;
   fWeightMatrix0to1[8][5] = -1.70920464814842;
   fWeightMatrix0to1[9][5] = -0.115792009289809;
   fWeightMatrix0to1[10][5] = -0.156466078159838;
   fWeightMatrix0to1[11][5] = -1.74566015801074;
   fWeightMatrix0to1[12][5] = -0.543532879390099;
   fWeightMatrix0to1[13][5] = 0.188338934941552;
   fWeightMatrix0to1[14][5] = -0.166555367299673;
   fWeightMatrix0to1[15][5] = 0.609817007677533;
   fWeightMatrix0to1[16][5] = 0.944677884827965;
   fWeightMatrix0to1[17][5] = -1.34411467661659;
   fWeightMatrix0to1[18][5] = -0.604226671125374;
   fWeightMatrix0to1[19][5] = 0.575549960719954;
   fWeightMatrix0to1[20][5] = -3.49977945307178;
   fWeightMatrix0to1[21][5] = 1.4388340428557;
   fWeightMatrix0to1[22][5] = -2.08037424773261;
   fWeightMatrix0to1[23][5] = 1.15740014562474;
   fWeightMatrix0to1[24][5] = -0.166268767635489;
   fWeightMatrix0to1[0][6] = 0.280025413820765;
   fWeightMatrix0to1[1][6] = 1.51306226905765;
   fWeightMatrix0to1[2][6] = 9.50849233758092;
   fWeightMatrix0to1[3][6] = -2.99718598536211;
   fWeightMatrix0to1[4][6] = -1.1590089438761;
   fWeightMatrix0to1[5][6] = -0.16436746589597;
   fWeightMatrix0to1[6][6] = -0.141860326121526;
   fWeightMatrix0to1[7][6] = -1.83922737972034;
   fWeightMatrix0to1[8][6] = 2.30976470940618;
   fWeightMatrix0to1[9][6] = 0.151391032875707;
   fWeightMatrix0to1[10][6] = 1.82311455811199;
   fWeightMatrix0to1[11][6] = -1.13558720849703;
   fWeightMatrix0to1[12][6] = 27.5980136014969;
   fWeightMatrix0to1[13][6] = 1.90271091608616;
   fWeightMatrix0to1[14][6] = 1.5237349965665;
   fWeightMatrix0to1[15][6] = -0.544904974560863;
   fWeightMatrix0to1[16][6] = 3.48244102483852;
   fWeightMatrix0to1[17][6] = 1.14962070660526;
   fWeightMatrix0to1[18][6] = 0.248926050076671;
   fWeightMatrix0to1[19][6] = 0.0624357614606827;
   fWeightMatrix0to1[20][6] = 2.80631939174258;
   fWeightMatrix0to1[21][6] = 3.39442058905842;
   fWeightMatrix0to1[22][6] = 9.4504920972522;
   fWeightMatrix0to1[23][6] = -2.64244195273943;
   fWeightMatrix0to1[24][6] = 0.152544940338045;
   fWeightMatrix0to1[0][7] = -5.36428712159312;
   fWeightMatrix0to1[1][7] = -7.47535301272875;
   fWeightMatrix0to1[2][7] = 1.09714467800184;
   fWeightMatrix0to1[3][7] = 0.637380780486285;
   fWeightMatrix0to1[4][7] = -2.73621162822423;
   fWeightMatrix0to1[5][7] = -2.69128405516261;
   fWeightMatrix0to1[6][7] = 3.11822296737366;
   fWeightMatrix0to1[7][7] = -0.149840490707689;
   fWeightMatrix0to1[8][7] = 7.94978779486013;
   fWeightMatrix0to1[9][7] = 0.28558314019467;
   fWeightMatrix0to1[10][7] = -2.65808867342678;
   fWeightMatrix0to1[11][7] = 3.57908634653717;
   fWeightMatrix0to1[12][7] = -10.8948645761229;
   fWeightMatrix0to1[13][7] = -6.31167513205056;
   fWeightMatrix0to1[14][7] = 2.89316354623803;
   fWeightMatrix0to1[15][7] = -1.76579310245149;
   fWeightMatrix0to1[16][7] = 6.22768287730364;
   fWeightMatrix0to1[17][7] = 2.86976585819081;
   fWeightMatrix0to1[18][7] = 5.9015951057193;
   fWeightMatrix0to1[19][7] = 4.44573402081804;
   fWeightMatrix0to1[20][7] = -1.51674923789095;
   fWeightMatrix0to1[21][7] = -3.81072662942715;
   fWeightMatrix0to1[22][7] = 8.83192527079689;
   fWeightMatrix0to1[23][7] = -2.36478094932652;
   fWeightMatrix0to1[24][7] = -1.22196897881656;
   fWeightMatrix0to1[0][8] = 0.707707075869521;
   fWeightMatrix0to1[1][8] = -7.82793624277424;
   fWeightMatrix0to1[2][8] = 1.16875750762687;
   fWeightMatrix0to1[3][8] = -0.651604100377312;
   fWeightMatrix0to1[4][8] = 0.0932896564018863;
   fWeightMatrix0to1[5][8] = -0.314160191479493;
   fWeightMatrix0to1[6][8] = 0.559485775231508;
   fWeightMatrix0to1[7][8] = -2.06544939376334;
   fWeightMatrix0to1[8][8] = 0.439433243194279;
   fWeightMatrix0to1[9][8] = -0.0988192057919993;
   fWeightMatrix0to1[10][8] = -0.352128808756903;
   fWeightMatrix0to1[11][8] = 3.14692410727504;
   fWeightMatrix0to1[12][8] = -0.108382944108325;
   fWeightMatrix0to1[13][8] = 0.491752594032008;
   fWeightMatrix0to1[14][8] = 5.0509632495394;
   fWeightMatrix0to1[15][8] = 2.86464057555463;
   fWeightMatrix0to1[16][8] = 2.22152380902919;
   fWeightMatrix0to1[17][8] = -0.534407859291248;
   fWeightMatrix0to1[18][8] = 0.390873255463695;
   fWeightMatrix0to1[19][8] = -0.636454861362838;
   fWeightMatrix0to1[20][8] = -0.186023936399263;
   fWeightMatrix0to1[21][8] = -1.93557309191369;
   fWeightMatrix0to1[22][8] = -0.428330922331607;
   fWeightMatrix0to1[23][8] = 3.71537963358952;
   fWeightMatrix0to1[24][8] = 1.92933514389449;
   fWeightMatrix0to1[0][9] = -0.00648099438015565;
   fWeightMatrix0to1[1][9] = -7.25403431861483;
   fWeightMatrix0to1[2][9] = 0.582111669648815;
   fWeightMatrix0to1[3][9] = -1.58266904770496;
   fWeightMatrix0to1[4][9] = 0.0957275863192744;
   fWeightMatrix0to1[5][9] = -0.0756635485227038;
   fWeightMatrix0to1[6][9] = -0.960677013951383;
   fWeightMatrix0to1[7][9] = -0.935445688211433;
   fWeightMatrix0to1[8][9] = 0.353460367723657;
   fWeightMatrix0to1[9][9] = -0.120432968575084;
   fWeightMatrix0to1[10][9] = -0.121769222587176;
   fWeightMatrix0to1[11][9] = 4.02739590728398;
   fWeightMatrix0to1[12][9] = -0.153192229249204;
   fWeightMatrix0to1[13][9] = -0.0373314295169015;
   fWeightMatrix0to1[14][9] = 0.088440353986238;
   fWeightMatrix0to1[15][9] = 0.056838031193345;
   fWeightMatrix0to1[16][9] = 0.73776825850816;
   fWeightMatrix0to1[17][9] = 0.151938482621311;
   fWeightMatrix0to1[18][9] = -0.0780637140165643;
   fWeightMatrix0to1[19][9] = 0.071512461495089;
   fWeightMatrix0to1[20][9] = -0.0647597116857163;
   fWeightMatrix0to1[21][9] = -2.7684298746583;
   fWeightMatrix0to1[22][9] = 0.136850357915182;
   fWeightMatrix0to1[23][9] = 3.72824620297126;
   fWeightMatrix0to1[24][9] = 0.0745939492094331;
   fWeightMatrix0to1[0][10] = -0.0336501791145184;
   fWeightMatrix0to1[1][10] = -0.688392502040021;
   fWeightMatrix0to1[2][10] = 1.60660784828608;
   fWeightMatrix0to1[3][10] = 1.98866380055387;
   fWeightMatrix0to1[4][10] = 0.122697428874051;
   fWeightMatrix0to1[5][10] = -1.46676141614813;
   fWeightMatrix0to1[6][10] = 0.582853658231401;
   fWeightMatrix0to1[7][10] = -2.96871347387726;
   fWeightMatrix0to1[8][10] = -0.487659275074383;
   fWeightMatrix0to1[9][10] = -0.0444869537378362;
   fWeightMatrix0to1[10][10] = 1.51559798160365;
   fWeightMatrix0to1[11][10] = 0.125176221226811;
   fWeightMatrix0to1[12][10] = 0.144100550342591;
   fWeightMatrix0to1[13][10] = -4.66809383001523;
   fWeightMatrix0to1[14][10] = -0.0741013749226683;
   fWeightMatrix0to1[15][10] = 0.0657705786888373;
   fWeightMatrix0to1[16][10] = -3.38258727877981;
   fWeightMatrix0to1[17][10] = -0.326577170076596;
   fWeightMatrix0to1[18][10] = -0.59986865858336;
   fWeightMatrix0to1[19][10] = -3.01317671384738;
   fWeightMatrix0to1[20][10] = -0.0114983221124538;
   fWeightMatrix0to1[21][10] = -0.883815345135935;
   fWeightMatrix0to1[22][10] = 0.907501142963569;
   fWeightMatrix0to1[23][10] = -1.06392517431262;
   fWeightMatrix0to1[24][10] = -0.253209002630914;
   fWeightMatrix0to1[0][11] = -2.87984836696296;
   fWeightMatrix0to1[1][11] = 21.2474014706971;
   fWeightMatrix0to1[2][11] = 2.21663065508981;
   fWeightMatrix0to1[3][11] = 10.1015310733684;
   fWeightMatrix0to1[4][11] = -1.55136643862519;
   fWeightMatrix0to1[5][11] = -0.824732732572365;
   fWeightMatrix0to1[6][11] = 1.85155153290369;
   fWeightMatrix0to1[7][11] = -3.37275199931524;
   fWeightMatrix0to1[8][11] = -18.2067202598591;
   fWeightMatrix0to1[9][11] = -1.19440801678138;
   fWeightMatrix0to1[10][11] = -3.20727186884922;
   fWeightMatrix0to1[11][11] = -8.07590625236617;
   fWeightMatrix0to1[12][11] = 35.4357924007357;
   fWeightMatrix0to1[13][11] = 17.0417456013563;
   fWeightMatrix0to1[14][11] = 4.2344562203392;
   fWeightMatrix0to1[15][11] = -2.39603085079612;
   fWeightMatrix0to1[16][11] = 0.203960993917928;
   fWeightMatrix0to1[17][11] = -0.207702405491453;
   fWeightMatrix0to1[18][11] = 1.84902763280214;
   fWeightMatrix0to1[19][11] = 8.56011691095631;
   fWeightMatrix0to1[20][11] = -1.85275646478308;
   fWeightMatrix0to1[21][11] = -1.37499792964914;
   fWeightMatrix0to1[22][11] = 3.84940176628942;
   fWeightMatrix0to1[23][11] = -1.67286088034229;
   fWeightMatrix0to1[24][11] = -1.39382854323732;
   fWeightMatrix0to1[0][12] = -6.15605704166037;
   fWeightMatrix0to1[1][12] = 17.4939274179265;
   fWeightMatrix0to1[2][12] = 2.91505334382608;
   fWeightMatrix0to1[3][12] = -4.30405615218916;
   fWeightMatrix0to1[4][12] = -1.77449034273597;
   fWeightMatrix0to1[5][12] = -1.98097057855051;
   fWeightMatrix0to1[6][12] = 1.19666824787997;
   fWeightMatrix0to1[7][12] = -3.64497857141691;
   fWeightMatrix0to1[8][12] = 9.86799748908815;
   fWeightMatrix0to1[9][12] = -0.806395424756998;
   fWeightMatrix0to1[10][12] = -0.842862385252969;
   fWeightMatrix0to1[11][12] = 10.3754969524564;
   fWeightMatrix0to1[12][12] = -4.70627044593262;
   fWeightMatrix0to1[13][12] = 0.797312561664656;
   fWeightMatrix0to1[14][12] = 4.07039898033164;
   fWeightMatrix0to1[15][12] = -1.43379105534526;
   fWeightMatrix0to1[16][12] = 7.42282149837037;
   fWeightMatrix0to1[17][12] = -0.984463141938066;
   fWeightMatrix0to1[18][12] = 16.6038921528832;
   fWeightMatrix0to1[19][12] = 12.8092405329543;
   fWeightMatrix0to1[20][12] = -1.08632771790469;
   fWeightMatrix0to1[21][12] = -2.47464156182426;
   fWeightMatrix0to1[22][12] = 11.8431758468911;
   fWeightMatrix0to1[23][12] = -3.75136658969224;
   fWeightMatrix0to1[24][12] = 0.856408096851963;
   fWeightMatrix0to1[0][13] = 6.82045490989094;
   fWeightMatrix0to1[1][13] = 32.9010414922993;
   fWeightMatrix0to1[2][13] = 2.45018446929188;
   fWeightMatrix0to1[3][13] = -13.0340689118643;
   fWeightMatrix0to1[4][13] = 3.14834717765546;
   fWeightMatrix0to1[5][13] = -3.00331698777886;
   fWeightMatrix0to1[6][13] = -3.06460892244949;
   fWeightMatrix0to1[7][13] = -3.62340469935347;
   fWeightMatrix0to1[8][13] = -36.7687144696679;
   fWeightMatrix0to1[9][13] = -1.0751036213184;
   fWeightMatrix0to1[10][13] = 3.62724536977536;
   fWeightMatrix0to1[11][13] = -13.789285673646;
   fWeightMatrix0to1[12][13] = -10.0775444326712;
   fWeightMatrix0to1[13][13] = -2.38492050527555;
   fWeightMatrix0to1[14][13] = -6.70399618166137;
   fWeightMatrix0to1[15][13] = 0.315873429131673;
   fWeightMatrix0to1[16][13] = -6.60063977553605;
   fWeightMatrix0to1[17][13] = 1.60274118641573;
   fWeightMatrix0to1[18][13] = -14.7187581043476;
   fWeightMatrix0to1[19][13] = -11.5423070372315;
   fWeightMatrix0to1[20][13] = -1.05836402855522;
   fWeightMatrix0to1[21][13] = -2.12977648476622;
   fWeightMatrix0to1[22][13] = -11.7795860123707;
   fWeightMatrix0to1[23][13] = 11.8910822492426;
   fWeightMatrix0to1[24][13] = -1.00896422645343;
   fWeightMatrix0to1[0][14] = 3.01329125268506;
   fWeightMatrix0to1[1][14] = 7.49182096620569;
   fWeightMatrix0to1[2][14] = 1.49736368331518;
   fWeightMatrix0to1[3][14] = 5.54907559541499;
   fWeightMatrix0to1[4][14] = 0.451013952377349;
   fWeightMatrix0to1[5][14] = -2.53956110762937;
   fWeightMatrix0to1[6][14] = -4.91449718384159;
   fWeightMatrix0to1[7][14] = -0.846947447709732;
   fWeightMatrix0to1[8][14] = -2.60429269274358;
   fWeightMatrix0to1[9][14] = -1.64864633852537;
   fWeightMatrix0to1[10][14] = 1.72519339836675;
   fWeightMatrix0to1[11][14] = -9.67546011479788;
   fWeightMatrix0to1[12][14] = -3.02953308596027;
   fWeightMatrix0to1[13][14] = -6.84548769267997;
   fWeightMatrix0to1[14][14] = -3.02357856114901;
   fWeightMatrix0to1[15][14] = -0.69711737955117;
   fWeightMatrix0to1[16][14] = -3.23302651043604;
   fWeightMatrix0to1[17][14] = -0.98401621840667;
   fWeightMatrix0to1[18][14] = -12.7329802601715;
   fWeightMatrix0to1[19][14] = -16.1424712392517;
   fWeightMatrix0to1[20][14] = 3.51999045080142;
   fWeightMatrix0to1[21][14] = -3.06204989540321;
   fWeightMatrix0to1[22][14] = -5.94387480189466;
   fWeightMatrix0to1[23][14] = 0.617027484964122;
   fWeightMatrix0to1[24][14] = 1.05077506967057;
   fWeightMatrix0to1[0][15] = 3.85239853700965;
   fWeightMatrix0to1[1][15] = 5.33053444376438;
   fWeightMatrix0to1[2][15] = 2.2435399786178;
   fWeightMatrix0to1[3][15] = -0.268961297510674;
   fWeightMatrix0to1[4][15] = 2.53862994491935;
   fWeightMatrix0to1[5][15] = -2.04786982952402;
   fWeightMatrix0to1[6][15] = -4.12710460312943;
   fWeightMatrix0to1[7][15] = -3.6434299601636;
   fWeightMatrix0to1[8][15] = -14.0710838823168;
   fWeightMatrix0to1[9][15] = 0.226762927666045;
   fWeightMatrix0to1[10][15] = 1.57134624808438;
   fWeightMatrix0to1[11][15] = -11.9938883499032;
   fWeightMatrix0to1[12][15] = -3.94893996563645;
   fWeightMatrix0to1[13][15] = -3.94432144329225;
   fWeightMatrix0to1[14][15] = -6.75325561100578;
   fWeightMatrix0to1[15][15] = 2.6970060474449;
   fWeightMatrix0to1[16][15] = -3.90103589060699;
   fWeightMatrix0to1[17][15] = -2.0281878824321;
   fWeightMatrix0to1[18][15] = -16.8600833575163;
   fWeightMatrix0to1[19][15] = -17.989049403599;
   fWeightMatrix0to1[20][15] = 2.16712847299044;
   fWeightMatrix0to1[21][15] = -1.27172188640456;
   fWeightMatrix0to1[22][15] = -8.27687787527368;
   fWeightMatrix0to1[23][15] = 2.87757048360274;
   fWeightMatrix0to1[24][15] = -0.18056170296196;
   fWeightMatrix0to1[0][16] = 1.63834711980395;
   fWeightMatrix0to1[1][16] = -6.04133476347382;
   fWeightMatrix0to1[2][16] = 0.403931353797245;
   fWeightMatrix0to1[3][16] = -1.30215526884238;
   fWeightMatrix0to1[4][16] = 0.824612099744493;
   fWeightMatrix0to1[5][16] = -4.50201551916465;
   fWeightMatrix0to1[6][16] = 0.42683384538797;
   fWeightMatrix0to1[7][16] = 2.7155836080296;
   fWeightMatrix0to1[8][16] = -1.41090398600929;
   fWeightMatrix0to1[9][16] = -1.41651213604482;
   fWeightMatrix0to1[10][16] = -0.567853462030891;
   fWeightMatrix0to1[11][16] = -2.14781876525397;
   fWeightMatrix0to1[12][16] = -3.79337706283681;
   fWeightMatrix0to1[13][16] = -2.68924788518753;
   fWeightMatrix0to1[14][16] = -0.0764417516658599;
   fWeightMatrix0to1[15][16] = 0.513475692577212;
   fWeightMatrix0to1[16][16] = -1.01387464379603;
   fWeightMatrix0to1[17][16] = 1.44802308548421;
   fWeightMatrix0to1[18][16] = 6.69543150784965;
   fWeightMatrix0to1[19][16] = -1.04210586260299;
   fWeightMatrix0to1[20][16] = -1.3919333728703;
   fWeightMatrix0to1[21][16] = 0.214182676841768;
   fWeightMatrix0to1[22][16] = -0.83932588813646;
   fWeightMatrix0to1[23][16] = -1.78452438593033;
   fWeightMatrix0to1[24][16] = -0.631523671579223;
   fWeightMatrix0to1[0][17] = -1.55660019220529;
   fWeightMatrix0to1[1][17] = -9.61375257288939;
   fWeightMatrix0to1[2][17] = -1.14625751819799;
   fWeightMatrix0to1[3][17] = 0.983962178076946;
   fWeightMatrix0to1[4][17] = -0.668478724699799;
   fWeightMatrix0to1[5][17] = -2.53991681232463;
   fWeightMatrix0to1[6][17] = -2.00247608314293;
   fWeightMatrix0to1[7][17] = -2.72690855016503;
   fWeightMatrix0to1[8][17] = 2.13967450058901;
   fWeightMatrix0to1[9][17] = 1.28772362710635;
   fWeightMatrix0to1[10][17] = 1.28172952770492;
   fWeightMatrix0to1[11][17] = 1.77056244270804;
   fWeightMatrix0to1[12][17] = 4.06901647599434;
   fWeightMatrix0to1[13][17] = -1.19007777134337;
   fWeightMatrix0to1[14][17] = 0.082311483490485;
   fWeightMatrix0to1[15][17] = -0.977554754683156;
   fWeightMatrix0to1[16][17] = -2.37328576216248;
   fWeightMatrix0to1[17][17] = -0.590586993034211;
   fWeightMatrix0to1[18][17] = 8.2556627636186;
   fWeightMatrix0to1[19][17] = 0.80962035678876;
   fWeightMatrix0to1[20][17] = 1.41119965507224;
   fWeightMatrix0to1[21][17] = 0.544531839468049;
   fWeightMatrix0to1[22][17] = 0.528124417495004;
   fWeightMatrix0to1[23][17] = -1.59072650570875;
   fWeightMatrix0to1[24][17] = 0.0542730606108371;
   fWeightMatrix0to1[0][18] = 1.68288012574957;
   fWeightMatrix0to1[1][18] = -8.71078334082705;
   fWeightMatrix0to1[2][18] = 0.884403051527405;
   fWeightMatrix0to1[3][18] = 3.63659553241934;
   fWeightMatrix0to1[4][18] = 0.684581899805954;
   fWeightMatrix0to1[5][18] = -0.829958577415508;
   fWeightMatrix0to1[6][18] = 3.47173836335563;
   fWeightMatrix0to1[7][18] = -0.0820236284301736;
   fWeightMatrix0to1[8][18] = 11.3150876313957;
   fWeightMatrix0to1[9][18] = 2.34838297904382;
   fWeightMatrix0to1[10][18] = -0.487872012618121;
   fWeightMatrix0to1[11][18] = 3.40802500283974;
   fWeightMatrix0to1[12][18] = -0.0278197724065399;
   fWeightMatrix0to1[13][18] = -1.88675830570522;
   fWeightMatrix0to1[14][18] = 0.343760601726463;
   fWeightMatrix0to1[15][18] = 0.82235360506575;
   fWeightMatrix0to1[16][18] = -0.514835553940444;
   fWeightMatrix0to1[17][18] = -0.311648892719085;
   fWeightMatrix0to1[18][18] = -2.05060593742992;
   fWeightMatrix0to1[19][18] = 5.95122794241222;
   fWeightMatrix0to1[20][18] = -0.988535759537143;
   fWeightMatrix0to1[21][18] = -0.954462722075327;
   fWeightMatrix0to1[22][18] = -2.77148434981759;
   fWeightMatrix0to1[23][18] = -1.01546028035418;
   fWeightMatrix0to1[24][18] = 0.425241663527639;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = -0.971207168683549;
   fWeightMatrix1to2[0][1] = 1.5107203285022;
   fWeightMatrix1to2[0][2] = -2.24311167495383;
   fWeightMatrix1to2[0][3] = -1.88614397925744;
   fWeightMatrix1to2[0][4] = -0.209347704628478;
   fWeightMatrix1to2[0][5] = 2.9948041436548;
   fWeightMatrix1to2[0][6] = 1.12186252025536;
   fWeightMatrix1to2[0][7] = 1.32994246195144;
   fWeightMatrix1to2[0][8] = -0.837270012884602;
   fWeightMatrix1to2[0][9] = 0.250366001868662;
   fWeightMatrix1to2[0][10] = -1.02722729329734;
   fWeightMatrix1to2[0][11] = -1.51760157561213;
   fWeightMatrix1to2[0][12] = -0.780179947763032;
   fWeightMatrix1to2[0][13] = -1.42437492896631;
   fWeightMatrix1to2[0][14] = 0.903018881074819;
   fWeightMatrix1to2[0][15] = -0.0340980796407367;
   fWeightMatrix1to2[0][16] = -0.62034012542852;
   fWeightMatrix1to2[0][17] = 1.12755994133418;
   fWeightMatrix1to2[0][18] = -1.3576909781856;
   fWeightMatrix1to2[0][19] = -0.813550132058971;
   fWeightMatrix1to2[0][20] = -0.85399773612259;
   fWeightMatrix1to2[0][21] = 2.34232572939302;
   fWeightMatrix1to2[0][22] = -0.55144028935027;
   fWeightMatrix1to2[0][23] = 0.944959406850441;
   fWeightMatrix1to2[0][24] = -0.813567122489695;
   fWeightMatrix1to2[0][25] = -0.221404747213582;
}

inline double ReadKaon_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 ReadKaon_Upstream_TMVA::ActivationFnc(double x) const {
   // hyperbolic tan
   return tanh(x);
}
double ReadKaon_Upstream_TMVA::OutputActivationFnc(double x) const {
   // sigmoid
   return 1.0/(1.0+exp(-x));
}
   
// Clean up
inline void ReadKaon_Upstream_TMVA::Clear() 
{
   // clean up the arrays
   for (int lIdx = 0; lIdx < 3; lIdx++) {
      delete[] fWeights[lIdx];
   }
}
   inline double ReadKaon_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 ReadKaon_Upstream_TMVA::InitTransform_1()
{
   // Normalization transformation, initialisation
   fMin_1[0][0] = 147.869995117;
   fMax_1[0][0] = 4760267.5;
   fMin_1[1][0] = 275.899993896;
   fMax_1[1][0] = 4386670.5;
   fMin_1[2][0] = 147.869995117;
   fMax_1[2][0] = 4760267.5;
   fMin_1[0][1] = 0.360198408365;
   fMax_1[0][1] = 978077.5;
   fMin_1[1][1] = 18.0699863434;
   fMax_1[1][1] = 597943.1875;
   fMin_1[2][1] = 0.360198408365;
   fMax_1[2][1] = 978077.5;
   fMin_1[0][2] = 0.0029748051893;
   fMax_1[0][2] = 2.99983906746;
   fMin_1[1][2] = 0.0110797118396;
   fMax_1[1][2] = 2.99882555008;
   fMin_1[2][2] = 0.0029748051893;
   fMax_1[2][2] = 2.99983906746;
   fMin_1[0][3] = 4;
   fMax_1[0][3] = 28;
   fMin_1[1][3] = 4;
   fMax_1[1][3] = 26;
   fMin_1[2][3] = 4;
   fMax_1[2][3] = 28;
   fMin_1[0][4] = 0.0402400195599;
   fMax_1[0][4] = 1;
   fMin_1[1][4] = 0.0391823016107;
   fMax_1[1][4] = 1;
   fMin_1[2][4] = 0.0391823016107;
   fMax_1[2][4] = 1;
   fMin_1[0][5] = 8.64220680796e-07;
   fMax_1[0][5] = 53.9077644348;
   fMin_1[1][5] = 0.000101416291727;
   fMax_1[1][5] = 49.5305862427;
   fMin_1[2][5] = 8.64220680796e-07;
   fMax_1[2][5] = 53.9077644348;
   fMin_1[0][6] = 1;
   fMax_1[0][6] = 24;
   fMin_1[1][6] = 1;
   fMax_1[1][6] = 22;
   fMin_1[2][6] = 1;
   fMax_1[2][6] = 24;
   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] = 118.707099915;
   fMin_1[1][11] = -999;
   fMax_1[1][11] = 78.7629013062;
   fMin_1[2][11] = -999;
   fMax_1[2][11] = 118.707099915;
   fMin_1[0][12] = -999;
   fMax_1[0][12] = 81.0063018799;
   fMin_1[1][12] = -999;
   fMax_1[1][12] = 47.2327003479;
   fMin_1[2][12] = -999;
   fMax_1[2][12] = 81.0063018799;
   fMin_1[0][13] = -999;
   fMax_1[0][13] = 79.2463989258;
   fMin_1[1][13] = -999;
   fMax_1[1][13] = 82.1451034546;
   fMin_1[2][13] = -999;
   fMax_1[2][13] = 82.1451034546;
   fMin_1[0][14] = -999;
   fMax_1[0][14] = 92.269203186;
   fMin_1[1][14] = -999;
   fMax_1[1][14] = 72.6592025757;
   fMin_1[2][14] = -999;
   fMax_1[2][14] = 92.269203186;
   fMin_1[0][15] = -999;
   fMax_1[0][15] = 50.5774993896;
   fMin_1[1][15] = -999;
   fMax_1[1][15] = 48.0363998413;
   fMin_1[2][15] = -999;
   fMax_1[2][15] = 50.5774993896;
   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 ReadKaon_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 ReadKaon_Upstream_TMVA::InitTransform()
{
   InitTransform_1();
}

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