// Class: ReadH6AONN5MEMLP
// Automatically generated by MethodBase::MakeClass
//

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

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

Method         : MLP::H6AONN5MEMLP
TMVA Release   : 3.8.6         [198662]
ROOT Release   : 5.26/00       [334336]
Creator        : stdenis
Date           : Sun Jan  1 09:56:39 2012
Host           : Linux psr-lts309-32bit-kvm 2.4.21-63.ELsmp #1 SMP Wed Nov 4 04:34:43 CST 2009 i686 i686 i386 GNU/Linux
Dir            : /data/cdf04/stdenis/batch/run20572/job401
Training events: 47762


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

# Set by User:
V: "False" [Verbose mode]
NCycles: "1000" [Number of training cycles]
HiddenLayers: "N+1,N" [Specification of hidden layer architecture]
# Default:
D: "False" [use-decorrelated-variables flag (depreciated)]
Normalise: "True" [Normalise input variables]
VarTransform: "None" [Variable transformation method]
VarTransformType: "Signal" [Use signal or background events for var transform]
NbinsMVAPdf: "60" [Number of bins used to create MVA PDF]
NsmoothMVAPdf: "2" [Number of smoothing iterations for MVA PDF]
VerboseLevel: "Info" [Verbosity level]
H: "False" [Print classifier-specific help message]
CreateMVAPdfs: "False" [Create PDFs for classifier outputs]
TxtWeightFilesOnly: "True" [if True, write all weights as text files]
NeuronType: "sigmoid" [Neuron activation function type]
NeuronInputType: "sum" [Neuron input function type]
RandomSeed: "1" [Random Number Seed for TRandom3]
RandomFile: "None" [Random Number input file for TRandom3]
TrainingMethod: "BP" [Train with Back-Propagation (BP - default) or Genetic Algorithm (GA - slower and worse)]
LearningRate: "0.02" [ANN learning rate parameter]
DecayRate: "0.01" [Decay rate for learning parameter]
TestRate: "10" [Test for overtraining performed at each #th epochs]
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]
##


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

NVar 13
Ht                            Ht                                'F'    [47.7866363525,750.656311035]
LepAPt                        LepAPt                            'F'    [20.0001049042,178.378601074]
LepBPt                        LepBPt                            'F'    [10.0004081726,68.4152984619]
MetSigLeptonsJets             MetSigLeptonsJets                 'F'    [0.975870490074,19.2163314819]
MetSpec                       MetSpec                           'F'    [15.0050735474,236.562576294]
SumEtLeptonsJets              SumEtLeptonsJets                  'F'    [30.1877574921,481.37588501]
VSumJetLeptonsPt              VSumJetLeptonsPt                  'F'    [0.862621068954,308.832702637]
addEt                         addEt                             'F'    [47.7866363525,378.177581787]
dPhiLepSumMet                 dPhiLepSumMet                     'F'    [0.0177443344146,3.14158248901]
dPhiLeptons                   dPhiLeptons                       'F'    [1.71661376953e-05,1.11657130718]
dRLeptons                     dRLeptons                         'F'    [0.200001657009,1.13453125954]
lep1_E                        lep1_E                            'F'    [20.0121021271,196.200546265]
lep2_E                        lep2_E                            'F'    [10.0116767883,124.551948547]


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

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

#ifndef IClassifierReader__def
#define IClassifierReader__def

class IClassifierReader {

 public:

   // constructor
   IClassifierReader() {}
   virtual ~IClassifierReader() {}

   // return classifier response
   virtual double GetMvaValue( const std::vector<double>& inputValues ) const = 0;
};

#endif

class ReadH6AONN5MEMLP : public IClassifierReader {

 public:

   // constructor
   ReadH6AONN5MEMLP( std::vector<std::string>& theInputVars ) 
      : IClassifierReader(),
        fClassName( "ReadH6AONN5MEMLP" ),
        fStatusIsClean( true ),
        fNvars( 13 ),
        fIsNormalised( true )
   {      
      // the training input variables
      const char* inputVars[] = { "Ht", "LepAPt", "LepBPt", "MetSigLeptonsJets", "MetSpec", "SumEtLeptonsJets", "VSumJetLeptonsPt", "addEt", "dPhiLepSumMet", "dPhiLeptons", "dRLeptons", "lep1_E", "lep2_E" };

      // 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] = 47.7866363525391;
      fVmax[0] = 750.656311035156;
      fVmin[1] = 20.0001049041748;
      fVmax[1] = 178.378601074219;
      fVmin[2] = 10.0004081726074;
      fVmax[2] = 68.4152984619141;
      fVmin[3] = 0.975870490074158;
      fVmax[3] = 19.2163314819336;
      fVmin[4] = 15.0050735473633;
      fVmax[4] = 236.562576293945;
      fVmin[5] = 30.1877574920654;
      fVmax[5] = 481.375885009766;
      fVmin[6] = 0.862621068954468;
      fVmax[6] = 308.832702636719;
      fVmin[7] = 47.7866363525391;
      fVmax[7] = 378.177581787109;
      fVmin[8] = 0.0177443344146013;
      fVmax[8] = 3.14158248901367;
      fVmin[9] = 1.71661376953125e-05;
      fVmax[9] = 1.11657130718231;
      fVmin[10] = 0.200001657009125;
      fVmax[10] = 1.13453125953674;
      fVmin[11] = 20.0121021270752;
      fVmax[11] = 196.200546264648;
      fVmin[12] = 10.0116767883301;
      fVmax[12] = 124.551948547363;

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

      // initialize constants
      Initialize();

   }

   // destructor
   virtual ~ReadH6AONN5MEMLP() {
      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
   {
      // classifier response value
      double retval = 0;

      // classifier response, sanity check first
      if (!fStatusIsClean) {
         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;
            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;
   }

 private:

   // method-specific destructor
   void Clear();

   // common member variables
   const char* fClassName;
   bool fStatusIsClean;

   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[13];
   double fVmax[13];
   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[13];

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

   // private members (method specific)

   double ActivationFnc(double x) const;

   int fLayers;
   int fLayerSize[4];
   double fWeightMatrix0to1[15][14];   // weight matrix from layer 0 to 1
   double fWeightMatrix1to2[14][15];   // weight matrix from layer 1 to 2
   double fWeightMatrix2to3[1][14];   // weight matrix from layer 2 to 3

   double * fWeights[4];
};

inline void ReadH6AONN5MEMLP::Initialize()
{
   // build network structure
   fLayers = 4;
   fLayerSize[0] = 14; fWeights[0] = new double[14]; 
   fLayerSize[1] = 15; fWeights[1] = new double[15]; 
   fLayerSize[2] = 14; fWeights[2] = new double[14]; 
   fLayerSize[3] = 1; fWeights[3] = new double[1]; 
   // weight matrix from layer 0 to 1
   fWeightMatrix0to1[0][0] = -0.156096357678606;
   fWeightMatrix0to1[1][0] = 2.0834386958498;
   fWeightMatrix0to1[2][0] = 0.803441213050607;
   fWeightMatrix0to1[3][0] = 1.31757207410795;
   fWeightMatrix0to1[4][0] = -2.43898246581683;
   fWeightMatrix0to1[5][0] = -0.762640809517283;
   fWeightMatrix0to1[6][0] = -0.474921622376861;
   fWeightMatrix0to1[7][0] = 1.97987374069402;
   fWeightMatrix0to1[8][0] = -1.63456629653259;
   fWeightMatrix0to1[9][0] = -0.645641369887991;
   fWeightMatrix0to1[10][0] = -1.40555183652321;
   fWeightMatrix0to1[11][0] = -0.566092100220191;
   fWeightMatrix0to1[12][0] = -1.46998862332917;
   fWeightMatrix0to1[13][0] = -1.07327609397625;
   fWeightMatrix0to1[0][1] = -0.463763656120233;
   fWeightMatrix0to1[1][1] = 0.385813892983001;
   fWeightMatrix0to1[2][1] = -3.52272586214023;
   fWeightMatrix0to1[3][1] = -1.01018792082769;
   fWeightMatrix0to1[4][1] = 3.22299550591727;
   fWeightMatrix0to1[5][1] = 1.42989987976887;
   fWeightMatrix0to1[6][1] = -4.55658164981245;
   fWeightMatrix0to1[7][1] = 2.1877531486667;
   fWeightMatrix0to1[8][1] = 0.910174007386775;
   fWeightMatrix0to1[9][1] = 0.476721482069941;
   fWeightMatrix0to1[10][1] = -1.54963615800978;
   fWeightMatrix0to1[11][1] = 1.12801325237175;
   fWeightMatrix0to1[12][1] = 1.04672768547086;
   fWeightMatrix0to1[13][1] = -1.81235857600825;
   fWeightMatrix0to1[0][2] = -1.988174171224;
   fWeightMatrix0to1[1][2] = 0.22931841579179;
   fWeightMatrix0to1[2][2] = -2.23709417739758;
   fWeightMatrix0to1[3][2] = 0.446123248571653;
   fWeightMatrix0to1[4][2] = 1.72142124160485;
   fWeightMatrix0to1[5][2] = -0.822838713053675;
   fWeightMatrix0to1[6][2] = -1.4596772222087;
   fWeightMatrix0to1[7][2] = 3.58335408176807;
   fWeightMatrix0to1[8][2] = -2.085766575334;
   fWeightMatrix0to1[9][2] = 2.37431057993156;
   fWeightMatrix0to1[10][2] = -2.63155767485027;
   fWeightMatrix0to1[11][2] = 2.95102002149471;
   fWeightMatrix0to1[12][2] = 0.586937080064388;
   fWeightMatrix0to1[13][2] = 0.137373174318996;
   fWeightMatrix0to1[0][3] = 2.01403062965132;
   fWeightMatrix0to1[1][3] = 1.66839013362181;
   fWeightMatrix0to1[2][3] = -0.171365042846369;
   fWeightMatrix0to1[3][3] = -2.7992566234188;
   fWeightMatrix0to1[4][3] = 1.2530745655505;
   fWeightMatrix0to1[5][3] = -0.287950451715537;
   fWeightMatrix0to1[6][3] = 6.02703425690282;
   fWeightMatrix0to1[7][3] = 0.627581316544028;
   fWeightMatrix0to1[8][3] = 0.959034848637461;
   fWeightMatrix0to1[9][3] = 1.63862828889702;
   fWeightMatrix0to1[10][3] = -2.02394879422628;
   fWeightMatrix0to1[11][3] = 2.34445300263735;
   fWeightMatrix0to1[12][3] = -1.88130668139812;
   fWeightMatrix0to1[13][3] = -1.17821665302851;
   fWeightMatrix0to1[0][4] = -1.17941041501147;
   fWeightMatrix0to1[1][4] = -1.82357908044775;
   fWeightMatrix0to1[2][4] = 0.999521560977105;
   fWeightMatrix0to1[3][4] = -0.679502306544939;
   fWeightMatrix0to1[4][4] = -0.797263149356104;
   fWeightMatrix0to1[5][4] = 0.462962106996921;
   fWeightMatrix0to1[6][4] = 0.53587863853039;
   fWeightMatrix0to1[7][4] = 3.28240019454823;
   fWeightMatrix0to1[8][4] = 1.96048077616551;
   fWeightMatrix0to1[9][4] = -0.394292015239088;
   fWeightMatrix0to1[10][4] = -2.05630500746119;
   fWeightMatrix0to1[11][4] = 0.939657671835564;
   fWeightMatrix0to1[12][4] = 0.410432270395812;
   fWeightMatrix0to1[13][4] = -1.45305271110088;
   fWeightMatrix0to1[0][5] = -0.609132825985038;
   fWeightMatrix0to1[1][5] = -1.40263502512609;
   fWeightMatrix0to1[2][5] = 1.05854119668451;
   fWeightMatrix0to1[3][5] = 1.3692155737858;
   fWeightMatrix0to1[4][5] = 0.293760019213864;
   fWeightMatrix0to1[5][5] = 0.697662320272819;
   fWeightMatrix0to1[6][5] = -1.83375921960821;
   fWeightMatrix0to1[7][5] = -2.76043876682619;
   fWeightMatrix0to1[8][5] = 0.792777809331565;
   fWeightMatrix0to1[9][5] = 0.922197909598335;
   fWeightMatrix0to1[10][5] = 2.74338029554817;
   fWeightMatrix0to1[11][5] = -0.330828177255766;
   fWeightMatrix0to1[12][5] = 0.71095243988924;
   fWeightMatrix0to1[13][5] = -1.48758048586963;
   fWeightMatrix0to1[0][6] = -0.686513539069062;
   fWeightMatrix0to1[1][6] = -0.324247148423973;
   fWeightMatrix0to1[2][6] = 0.609141615932214;
   fWeightMatrix0to1[3][6] = -2.28333494801789;
   fWeightMatrix0to1[4][6] = -1.40756192881448;
   fWeightMatrix0to1[5][6] = -1.25736925998305;
   fWeightMatrix0to1[6][6] = -0.781090463125631;
   fWeightMatrix0to1[7][6] = 1.78531468083025;
   fWeightMatrix0to1[8][6] = 1.77798273376716;
   fWeightMatrix0to1[9][6] = -1.39815149355333;
   fWeightMatrix0to1[10][6] = -1.73822837005058;
   fWeightMatrix0to1[11][6] = 2.77990652578708;
   fWeightMatrix0to1[12][6] = -1.03522733360965;
   fWeightMatrix0to1[13][6] = -2.0343126875928;
   fWeightMatrix0to1[0][7] = -1.1954675724244;
   fWeightMatrix0to1[1][7] = 0.209770158549048;
   fWeightMatrix0to1[2][7] = -6.29310969104288;
   fWeightMatrix0to1[3][7] = -5.99433258035166;
   fWeightMatrix0to1[4][7] = 4.94543275092723;
   fWeightMatrix0to1[5][7] = 1.20503930340679;
   fWeightMatrix0to1[6][7] = -6.97072649144295;
   fWeightMatrix0to1[7][7] = 10.3960026353672;
   fWeightMatrix0to1[8][7] = -0.959430150114058;
   fWeightMatrix0to1[9][7] = -0.238762724421221;
   fWeightMatrix0to1[10][7] = -2.47899882344187;
   fWeightMatrix0to1[11][7] = 3.90345967651414;
   fWeightMatrix0to1[12][7] = -1.72692023995289;
   fWeightMatrix0to1[13][7] = -0.134418151133854;
   fWeightMatrix0to1[0][8] = 0.337796822872319;
   fWeightMatrix0to1[1][8] = -0.0904734272798885;
   fWeightMatrix0to1[2][8] = 1.19244659943842;
   fWeightMatrix0to1[3][8] = 1.29954701597435;
   fWeightMatrix0to1[4][8] = 0.786648906330034;
   fWeightMatrix0to1[5][8] = -2.72133648378315;
   fWeightMatrix0to1[6][8] = 5.27244203495657;
   fWeightMatrix0to1[7][8] = 1.14751939429847;
   fWeightMatrix0to1[8][8] = 0.194261230686302;
   fWeightMatrix0to1[9][8] = -0.0437132359283082;
   fWeightMatrix0to1[10][8] = -2.53699708553425;
   fWeightMatrix0to1[11][8] = 2.53456568453651;
   fWeightMatrix0to1[12][8] = 1.03214869261135;
   fWeightMatrix0to1[13][8] = 0.656993897615082;
   fWeightMatrix0to1[0][9] = -0.361106349359798;
   fWeightMatrix0to1[1][9] = 0.298604639103033;
   fWeightMatrix0to1[2][9] = -1.97176033382587;
   fWeightMatrix0to1[3][9] = -0.295384116183018;
   fWeightMatrix0to1[4][9] = -0.459677890160166;
   fWeightMatrix0to1[5][9] = -2.05218036135235;
   fWeightMatrix0to1[6][9] = 1.42587715727273;
   fWeightMatrix0to1[7][9] = 1.24808284968914;
   fWeightMatrix0to1[8][9] = 2.88288282779888;
   fWeightMatrix0to1[9][9] = 0.981509035709546;
   fWeightMatrix0to1[10][9] = 0.605013813560523;
   fWeightMatrix0to1[11][9] = -3.3498615981695;
   fWeightMatrix0to1[12][9] = -0.467953719644752;
   fWeightMatrix0to1[13][9] = 0.9148098791188;
   fWeightMatrix0to1[0][10] = 1.56755674968768;
   fWeightMatrix0to1[1][10] = 0.237208233880035;
   fWeightMatrix0to1[2][10] = -1.71510960617312;
   fWeightMatrix0to1[3][10] = 0.0816700722755253;
   fWeightMatrix0to1[4][10] = -1.29435193032792;
   fWeightMatrix0to1[5][10] = 1.63327072150168;
   fWeightMatrix0to1[6][10] = -0.516183319268487;
   fWeightMatrix0to1[7][10] = -0.495335861564547;
   fWeightMatrix0to1[8][10] = 1.24080519381031;
   fWeightMatrix0to1[9][10] = -0.289872296725706;
   fWeightMatrix0to1[10][10] = -2.10242572768703;
   fWeightMatrix0to1[11][10] = 0.933963211979597;
   fWeightMatrix0to1[12][10] = 0.891505751070679;
   fWeightMatrix0to1[13][10] = -1.15438872200313;
   fWeightMatrix0to1[0][11] = -0.474537282775288;
   fWeightMatrix0to1[1][11] = 0.198386172400518;
   fWeightMatrix0to1[2][11] = 5.06109013469787;
   fWeightMatrix0to1[3][11] = -0.509601552752664;
   fWeightMatrix0to1[4][11] = 3.03625442754538;
   fWeightMatrix0to1[5][11] = -1.17978490230961;
   fWeightMatrix0to1[6][11] = 1.54256289762902;
   fWeightMatrix0to1[7][11] = 0.691033468462516;
   fWeightMatrix0to1[8][11] = -0.393315163671071;
   fWeightMatrix0to1[9][11] = 0.91778311796865;
   fWeightMatrix0to1[10][11] = 1.52488006935509;
   fWeightMatrix0to1[11][11] = 0.26479149985471;
   fWeightMatrix0to1[12][11] = -1.06775765585808;
   fWeightMatrix0to1[13][11] = 0.289846519726335;
   fWeightMatrix0to1[0][12] = -1.0251980960259;
   fWeightMatrix0to1[1][12] = -0.708152943551233;
   fWeightMatrix0to1[2][12] = 2.47746163423798;
   fWeightMatrix0to1[3][12] = 1.49307306503808;
   fWeightMatrix0to1[4][12] = 0.748140930313071;
   fWeightMatrix0to1[5][12] = 1.63238655666899;
   fWeightMatrix0to1[6][12] = 3.53264522708034;
   fWeightMatrix0to1[7][12] = -1.23748797734508;
   fWeightMatrix0to1[8][12] = -0.177268227382992;
   fWeightMatrix0to1[9][12] = 0.136250645238383;
   fWeightMatrix0to1[10][12] = -0.019952677227407;
   fWeightMatrix0to1[11][12] = -0.0351975244947098;
   fWeightMatrix0to1[12][12] = -2.32220863616807;
   fWeightMatrix0to1[13][12] = 0.275566062522372;
   fWeightMatrix0to1[0][13] = 1.86932569180983;
   fWeightMatrix0to1[1][13] = -1.56886595902614;
   fWeightMatrix0to1[2][13] = -4.45007186643984;
   fWeightMatrix0to1[3][13] = -4.40653073751435;
   fWeightMatrix0to1[4][13] = 4.88358289816929;
   fWeightMatrix0to1[5][13] = 0.615348969969078;
   fWeightMatrix0to1[6][13] = -10.0431790090058;
   fWeightMatrix0to1[7][13] = 12.6532900365085;
   fWeightMatrix0to1[8][13] = -0.573711539807433;
   fWeightMatrix0to1[9][13] = 1.0143913148901;
   fWeightMatrix0to1[10][13] = -1.79387355747273;
   fWeightMatrix0to1[11][13] = 4.89355967860478;
   fWeightMatrix0to1[12][13] = 1.40926693790008;
   fWeightMatrix0to1[13][13] = 0.355453904657791;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = -2.77060497893135;
   fWeightMatrix1to2[1][0] = -0.179898512585445;
   fWeightMatrix1to2[2][0] = -0.09358441706331;
   fWeightMatrix1to2[3][0] = -1.32406939580072;
   fWeightMatrix1to2[4][0] = -0.682406180240691;
   fWeightMatrix1to2[5][0] = -0.62020450971043;
   fWeightMatrix1to2[6][0] = -0.309119052547966;
   fWeightMatrix1to2[7][0] = -1.47920882764997;
   fWeightMatrix1to2[8][0] = 0.467880332249137;
   fWeightMatrix1to2[9][0] = 0.17534602308184;
   fWeightMatrix1to2[10][0] = -1.49681176077928;
   fWeightMatrix1to2[11][0] = -0.0475754044346001;
   fWeightMatrix1to2[12][0] = -0.03371027845584;
   fWeightMatrix1to2[0][1] = 1.05679009156028;
   fWeightMatrix1to2[1][1] = -0.0826777403520957;
   fWeightMatrix1to2[2][1] = -0.577386926318947;
   fWeightMatrix1to2[3][1] = -1.9345875779609;
   fWeightMatrix1to2[4][1] = -0.922195304299487;
   fWeightMatrix1to2[5][1] = 1.98760816417254;
   fWeightMatrix1to2[6][1] = 1.22907876123275;
   fWeightMatrix1to2[7][1] = 0.731519121073805;
   fWeightMatrix1to2[8][1] = 0.183412655927488;
   fWeightMatrix1to2[9][1] = 1.89311130985596;
   fWeightMatrix1to2[10][1] = -1.65088930241217;
   fWeightMatrix1to2[11][1] = -1.25347731454622;
   fWeightMatrix1to2[12][1] = -0.201536470380368;
   fWeightMatrix1to2[0][2] = -1.79307031327384;
   fWeightMatrix1to2[1][2] = 4.58064430882363;
   fWeightMatrix1to2[2][2] = -0.907801690947097;
   fWeightMatrix1to2[3][2] = -0.612706711378931;
   fWeightMatrix1to2[4][2] = 0.795325640888794;
   fWeightMatrix1to2[5][2] = -0.815047575398103;
   fWeightMatrix1to2[6][2] = -1.76257290149239;
   fWeightMatrix1to2[7][2] = -1.05810731165622;
   fWeightMatrix1to2[8][2] = 0.557769022730671;
   fWeightMatrix1to2[9][2] = -0.421326154228449;
   fWeightMatrix1to2[10][2] = -0.264287190197006;
   fWeightMatrix1to2[11][2] = -1.23692102418376;
   fWeightMatrix1to2[12][2] = 1.23407719323942;
   fWeightMatrix1to2[0][3] = -1.69106292413047;
   fWeightMatrix1to2[1][3] = 3.06758725235441;
   fWeightMatrix1to2[2][3] = -1.13935278767004;
   fWeightMatrix1to2[3][3] = -0.712551274860987;
   fWeightMatrix1to2[4][3] = -1.34395781221754;
   fWeightMatrix1to2[5][3] = -2.84343825645803;
   fWeightMatrix1to2[6][3] = -0.63135507903021;
   fWeightMatrix1to2[7][3] = -2.18508929850707;
   fWeightMatrix1to2[8][3] = -2.00988600697289;
   fWeightMatrix1to2[9][3] = -1.62896547686663;
   fWeightMatrix1to2[10][3] = 0.428151628147463;
   fWeightMatrix1to2[11][3] = -0.845931805741951;
   fWeightMatrix1to2[12][3] = -2.25501609089008;
   fWeightMatrix1to2[0][4] = 0.567390440311759;
   fWeightMatrix1to2[1][4] = -4.98717203764196;
   fWeightMatrix1to2[2][4] = 2.65291080196095;
   fWeightMatrix1to2[3][4] = -1.35485528336128;
   fWeightMatrix1to2[4][4] = 0.230953160602219;
   fWeightMatrix1to2[5][4] = 1.13606276953176;
   fWeightMatrix1to2[6][4] = 1.25396543330499;
   fWeightMatrix1to2[7][4] = -0.563940750683826;
   fWeightMatrix1to2[8][4] = -2.11098602627854;
   fWeightMatrix1to2[9][4] = -1.65167503266712;
   fWeightMatrix1to2[10][4] = -1.37322271799261;
   fWeightMatrix1to2[11][4] = -1.63722197949654;
   fWeightMatrix1to2[12][4] = -0.683561453994447;
   fWeightMatrix1to2[0][5] = -0.407503115742045;
   fWeightMatrix1to2[1][5] = -0.280782777758262;
   fWeightMatrix1to2[2][5] = -1.76433256621874;
   fWeightMatrix1to2[3][5] = 0.28189664415218;
   fWeightMatrix1to2[4][5] = -0.513211919652415;
   fWeightMatrix1to2[5][5] = -2.89196262568544;
   fWeightMatrix1to2[6][5] = 0.846740556533746;
   fWeightMatrix1to2[7][5] = 1.32325831089395;
   fWeightMatrix1to2[8][5] = -1.78660087757399;
   fWeightMatrix1to2[9][5] = -1.83988637424236;
   fWeightMatrix1to2[10][5] = -0.0699646788920481;
   fWeightMatrix1to2[11][5] = 1.13294345436979;
   fWeightMatrix1to2[12][5] = -1.02930078101321;
   fWeightMatrix1to2[0][6] = -1.48005882411319;
   fWeightMatrix1to2[1][6] = 3.36383840556955;
   fWeightMatrix1to2[2][6] = 0.12554310331439;
   fWeightMatrix1to2[3][6] = 1.90874989432128;
   fWeightMatrix1to2[4][6] = 0.962856568111337;
   fWeightMatrix1to2[5][6] = -0.152865624267587;
   fWeightMatrix1to2[6][6] = -0.479648976677944;
   fWeightMatrix1to2[7][6] = 0.315183306588064;
   fWeightMatrix1to2[8][6] = -2.21125883607005;
   fWeightMatrix1to2[9][6] = -0.943930684185703;
   fWeightMatrix1to2[10][6] = -2.90952595656879;
   fWeightMatrix1to2[11][6] = -1.67627432691509;
   fWeightMatrix1to2[12][6] = -1.81540571219882;
   fWeightMatrix1to2[0][7] = 1.68018128526514;
   fWeightMatrix1to2[1][7] = -9.38375342997956;
   fWeightMatrix1to2[2][7] = 5.30993081506868;
   fWeightMatrix1to2[3][7] = -2.0088844739977;
   fWeightMatrix1to2[4][7] = 1.15041933230823;
   fWeightMatrix1to2[5][7] = -0.77078737882739;
   fWeightMatrix1to2[6][7] = 0.423959975559127;
   fWeightMatrix1to2[7][7] = 1.17665610783343;
   fWeightMatrix1to2[8][7] = -1.99381113434275;
   fWeightMatrix1to2[9][7] = -0.130326238698539;
   fWeightMatrix1to2[10][7] = 0.0622985405894562;
   fWeightMatrix1to2[11][7] = -1.92740297841067;
   fWeightMatrix1to2[12][7] = -2.5551482947094;
   fWeightMatrix1to2[0][8] = -1.74801395257835;
   fWeightMatrix1to2[1][8] = -1.4404133729459;
   fWeightMatrix1to2[2][8] = 2.36053891980715;
   fWeightMatrix1to2[3][8] = -0.75220291304117;
   fWeightMatrix1to2[4][8] = 0.268317878150487;
   fWeightMatrix1to2[5][8] = -0.679632651337308;
   fWeightMatrix1to2[6][8] = -1.17363709776841;
   fWeightMatrix1to2[7][8] = -1.44197242949359;
   fWeightMatrix1to2[8][8] = -1.03990112196349;
   fWeightMatrix1to2[9][8] = -1.25915975882779;
   fWeightMatrix1to2[10][8] = 0.646363440353336;
   fWeightMatrix1to2[11][8] = -1.33983084098294;
   fWeightMatrix1to2[12][8] = -1.23632650408226;
   fWeightMatrix1to2[0][9] = 1.65514367379513;
   fWeightMatrix1to2[1][9] = -0.218058216266414;
   fWeightMatrix1to2[2][9] = -0.858068703054999;
   fWeightMatrix1to2[3][9] = 1.77720954759727;
   fWeightMatrix1to2[4][9] = -1.79897025947967;
   fWeightMatrix1to2[5][9] = 1.8567712098992;
   fWeightMatrix1to2[6][9] = 0.808920440764202;
   fWeightMatrix1to2[7][9] = -1.02577403270271;
   fWeightMatrix1to2[8][9] = -0.992941868049912;
   fWeightMatrix1to2[9][9] = -0.360506139928729;
   fWeightMatrix1to2[10][9] = -1.04928938483684;
   fWeightMatrix1to2[11][9] = 0.500735600764524;
   fWeightMatrix1to2[12][9] = 1.83198633824961;
   fWeightMatrix1to2[0][10] = 0.440451265520073;
   fWeightMatrix1to2[1][10] = 0.732045203889547;
   fWeightMatrix1to2[2][10] = -4.61844623020807;
   fWeightMatrix1to2[3][10] = 0.542108950005348;
   fWeightMatrix1to2[4][10] = -1.92118772794875;
   fWeightMatrix1to2[5][10] = -1.79963923497118;
   fWeightMatrix1to2[6][10] = -2.12058040605178;
   fWeightMatrix1to2[7][10] = -1.85424878434778;
   fWeightMatrix1to2[8][10] = -1.0262516628918;
   fWeightMatrix1to2[9][10] = 0.506655102574136;
   fWeightMatrix1to2[10][10] = -0.473514371562282;
   fWeightMatrix1to2[11][10] = 1.04875441973753;
   fWeightMatrix1to2[12][10] = -0.841882907144659;
   fWeightMatrix1to2[0][11] = -0.954096436190935;
   fWeightMatrix1to2[1][11] = -4.13130214552897;
   fWeightMatrix1to2[2][11] = 1.56720203193989;
   fWeightMatrix1to2[3][11] = 2.319413555764;
   fWeightMatrix1to2[4][11] = -1.54263727814243;
   fWeightMatrix1to2[5][11] = 0.320805824762046;
   fWeightMatrix1to2[6][11] = -1.64306991167809;
   fWeightMatrix1to2[7][11] = 0.262939325252119;
   fWeightMatrix1to2[8][11] = -0.615362474445361;
   fWeightMatrix1to2[9][11] = -0.156873807622878;
   fWeightMatrix1to2[10][11] = -1.86252437653259;
   fWeightMatrix1to2[11][11] = 1.03253463938658;
   fWeightMatrix1to2[12][11] = 0.402101689683563;
   fWeightMatrix1to2[0][12] = -0.0170132299850423;
   fWeightMatrix1to2[1][12] = -1.06283289586489;
   fWeightMatrix1to2[2][12] = -1.39540647949185;
   fWeightMatrix1to2[3][12] = -1.41760352217876;
   fWeightMatrix1to2[4][12] = -1.71649863154372;
   fWeightMatrix1to2[5][12] = 0.196881996589887;
   fWeightMatrix1to2[6][12] = -0.827003555250737;
   fWeightMatrix1to2[7][12] = -0.553264628973757;
   fWeightMatrix1to2[8][12] = -0.556150782188443;
   fWeightMatrix1to2[9][12] = -2.10181329527626;
   fWeightMatrix1to2[10][12] = -1.67280659492896;
   fWeightMatrix1to2[11][12] = -1.55549485544233;
   fWeightMatrix1to2[12][12] = -0.612802508662605;
   fWeightMatrix1to2[0][13] = -1.90275889966112;
   fWeightMatrix1to2[1][13] = -1.78849545943202;
   fWeightMatrix1to2[2][13] = -0.461167129037299;
   fWeightMatrix1to2[3][13] = -0.635145923572381;
   fWeightMatrix1to2[4][13] = -1.60476713451827;
   fWeightMatrix1to2[5][13] = 0.670143183412271;
   fWeightMatrix1to2[6][13] = -1.61898964295536;
   fWeightMatrix1to2[7][13] = -0.823155703550498;
   fWeightMatrix1to2[8][13] = 0.901602887951372;
   fWeightMatrix1to2[9][13] = 0.121700049955183;
   fWeightMatrix1to2[10][13] = -0.342469218016467;
   fWeightMatrix1to2[11][13] = 0.0693521830255072;
   fWeightMatrix1to2[12][13] = -0.124548114053519;
   fWeightMatrix1to2[0][14] = 0.890655557969695;
   fWeightMatrix1to2[1][14] = -0.817510842287;
   fWeightMatrix1to2[2][14] = -1.95211298674488;
   fWeightMatrix1to2[3][14] = -0.654805048956011;
   fWeightMatrix1to2[4][14] = -1.7289138396915;
   fWeightMatrix1to2[5][14] = 0.148055263364597;
   fWeightMatrix1to2[6][14] = 0.478676188460305;
   fWeightMatrix1to2[7][14] = 0.481848392589416;
   fWeightMatrix1to2[8][14] = 0.0110325494005804;
   fWeightMatrix1to2[9][14] = 0.218648559277742;
   fWeightMatrix1to2[10][14] = 0.664284576778962;
   fWeightMatrix1to2[11][14] = -1.26099676890029;
   fWeightMatrix1to2[12][14] = 0.0658710002344749;
   // weight matrix from layer 2 to 3
   fWeightMatrix2to3[0][0] = -0.249823485182569;
   fWeightMatrix2to3[0][1] = -0.792592032173999;
   fWeightMatrix2to3[0][2] = -1.33250087137519;
   fWeightMatrix2to3[0][3] = -1.14742917214832;
   fWeightMatrix2to3[0][4] = 0.922048092345129;
   fWeightMatrix2to3[0][5] = 1.40844937402654;
   fWeightMatrix2to3[0][6] = 0.229363331263492;
   fWeightMatrix2to3[0][7] = 1.93391005542539;
   fWeightMatrix2to3[0][8] = 0.483793210368219;
   fWeightMatrix2to3[0][9] = 1.04286612175288;
   fWeightMatrix2to3[0][10] = 0.385130557501929;
   fWeightMatrix2to3[0][11] = 0.780073489871519;
   fWeightMatrix2to3[0][12] = -0.0453593494497526;
   fWeightMatrix2to3[0][13] = 0.824531322475022;
}

inline double ReadH6AONN5MEMLP::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]-1; o++) {
      for (int i=0; i<fLayerSize[1]; i++) {
         double inputVal = fWeightMatrix1to2[o][i] * fWeights[1][i];
         fWeights[2][o] += inputVal;
      }
      fWeights[2][o] = ActivationFnc(fWeights[2][o]);
   }
   // layer 2 to 3
   for (int o=0; o<fLayerSize[3]; o++) {
      for (int i=0; i<fLayerSize[2]; i++) {
         double inputVal = fWeightMatrix2to3[o][i] * fWeights[2][i];
         fWeights[3][o] += inputVal;
      }
   }

   return fWeights[3][0];
}

double ReadH6AONN5MEMLP::ActivationFnc(double x) const {
   // sigmoid
   return 1.0/(1.0+exp(-x));
}
   
// Clean up
inline void ReadH6AONN5MEMLP::Clear() 
{
   // nothing to clear
}