// 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 10:43:55 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/job407
Training events: 49528


#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'    [47.6823577881,732.116455078]
LepAPt                        LepAPt                            'F'    [20.0003395081,161.648132324]
LepBPt                        LepBPt                            'F'    [10.0010261536,69.9040908813]
MetSigLeptonsJets             MetSigLeptonsJets                 'F'    [1.16472387314,19.2874412537]
MetSpec                       MetSpec                           'F'    [15.0119848251,241.141921997]
SumEtLeptonsJets              SumEtLeptonsJets                  'F'    [30.1484737396,493.95300293]
VSumJetLeptonsPt              VSumJetLeptonsPt                  'F'    [0.678276479244,293.303070068]
addEt                         addEt                             'F'    [47.6823577881,370.165649414]
dPhiLepSumMet                 dPhiLepSumMet                     'F'    [0.00143814424518,3.14159059525]
dPhiLeptons                   dPhiLeptons                       'F'    [2.52577920037e-05,1.1460981369]
dRLeptons                     dRLeptons                         'F'    [0.200028494,1.17128431797]
lep1_E                        lep1_E                            'F'    [20.0069885254,232.717926025]
lep2_E                        lep2_E                            'F'    [10.0076971054,122.221923828]


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

#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] = 47.6823577880859;
      fVmax[0] = 732.116455078125;
      fVmin[1] = 20.0003395080566;
      fVmax[1] = 161.648132324219;
      fVmin[2] = 10.0010261535645;
      fVmax[2] = 69.9040908813477;
      fVmin[3] = 1.16472387313843;
      fVmax[3] = 19.2874412536621;
      fVmin[4] = 15.0119848251343;
      fVmax[4] = 241.14192199707;
      fVmin[5] = 30.148473739624;
      fVmax[5] = 493.953002929688;
      fVmin[6] = 0.678276479244232;
      fVmax[6] = 293.303070068359;
      fVmin[7] = 47.6823577880859;
      fVmax[7] = 370.165649414062;
      fVmin[8] = 0.00143814424518496;
      fVmax[8] = 3.14159059524536;
      fVmin[9] = 2.52577920036856e-05;
      fVmax[9] = 1.14609813690186;
      fVmin[10] = 0.200028494000435;
      fVmax[10] = 1.17128431797028;
      fVmin[11] = 20.0069885253906;
      fVmax[11] = 232.717926025391;
      fVmin[12] = 10.0076971054077;
      fVmax[12] = 122.221923828125;

      // 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] = -0.418123075795464;
   fWeightMatrix0to1[1][0] = 1.8755796140874;
   fWeightMatrix0to1[2][0] = 1.00791977944453;
   fWeightMatrix0to1[3][0] = 2.62156792932643;
   fWeightMatrix0to1[4][0] = -0.773112333263616;
   fWeightMatrix0to1[5][0] = -0.4446315338965;
   fWeightMatrix0to1[6][0] = -1.52346996167579;
   fWeightMatrix0to1[7][0] = 2.70038692591917;
   fWeightMatrix0to1[8][0] = -1.66869566800072;
   fWeightMatrix0to1[9][0] = -0.835639642141709;
   fWeightMatrix0to1[10][0] = -1.46359244671537;
   fWeightMatrix0to1[11][0] = 0.281260270843264;
   fWeightMatrix0to1[12][0] = -0.367573951514058;
   fWeightMatrix0to1[13][0] = -0.684270752521892;
   fWeightMatrix0to1[0][1] = -0.39065417186827;
   fWeightMatrix0to1[1][1] = 1.08987085575721;
   fWeightMatrix0to1[2][1] = 0.646563323992563;
   fWeightMatrix0to1[3][1] = 4.90878188192663;
   fWeightMatrix0to1[4][1] = 1.60768255356222;
   fWeightMatrix0to1[5][1] = 4.24106061005085;
   fWeightMatrix0to1[6][1] = -1.0433733734668;
   fWeightMatrix0to1[7][1] = -1.72638646091517;
   fWeightMatrix0to1[8][1] = 0.350721094093152;
   fWeightMatrix0to1[9][1] = -0.58661352168654;
   fWeightMatrix0to1[10][1] = 1.23490769893328;
   fWeightMatrix0to1[11][1] = -0.255164214326158;
   fWeightMatrix0to1[12][1] = 1.2506957767729;
   fWeightMatrix0to1[13][1] = -3.34684052167006;
   fWeightMatrix0to1[0][2] = -2.02843607795687;
   fWeightMatrix0to1[1][2] = -0.0801882043509578;
   fWeightMatrix0to1[2][2] = 0.291485222085862;
   fWeightMatrix0to1[3][2] = 3.79987658831311;
   fWeightMatrix0to1[4][2] = 2.12568649481925;
   fWeightMatrix0to1[5][2] = 2.83910045601914;
   fWeightMatrix0to1[6][2] = -0.605731950128436;
   fWeightMatrix0to1[7][2] = 3.98727652284368;
   fWeightMatrix0to1[8][2] = -1.5359658106282;
   fWeightMatrix0to1[9][2] = 3.14759169246806;
   fWeightMatrix0to1[10][2] = -2.64679755021727;
   fWeightMatrix0to1[11][2] = 2.09709246589833;
   fWeightMatrix0to1[12][2] = 1.51610735851625;
   fWeightMatrix0to1[13][2] = -1.44368473062834;
   fWeightMatrix0to1[0][3] = 2.02098635087868;
   fWeightMatrix0to1[1][3] = 1.32314370225116;
   fWeightMatrix0to1[2][3] = -0.293269575969205;
   fWeightMatrix0to1[3][3] = -2.03398026094402;
   fWeightMatrix0to1[4][3] = 1.13269404546648;
   fWeightMatrix0to1[5][3] = -0.965441516240803;
   fWeightMatrix0to1[6][3] = 1.0909023957206;
   fWeightMatrix0to1[7][3] = 4.17936300979112;
   fWeightMatrix0to1[8][3] = 1.83822106163656;
   fWeightMatrix0to1[9][3] = 0.676834891553997;
   fWeightMatrix0to1[10][3] = -0.210691771166779;
   fWeightMatrix0to1[11][3] = 1.00623069000433;
   fWeightMatrix0to1[12][3] = -3.73743727227431;
   fWeightMatrix0to1[13][3] = -0.640125160864426;
   fWeightMatrix0to1[0][4] = -1.1747517100665;
   fWeightMatrix0to1[1][4] = -1.55546170352791;
   fWeightMatrix0to1[2][4] = 2.07864257046572;
   fWeightMatrix0to1[3][4] = 0.667460567347831;
   fWeightMatrix0to1[4][4] = -0.835084939389933;
   fWeightMatrix0to1[5][4] = 0.55485252795825;
   fWeightMatrix0to1[6][4] = -0.970475735087562;
   fWeightMatrix0to1[7][4] = 2.85395944241129;
   fWeightMatrix0to1[8][4] = 1.81778737011704;
   fWeightMatrix0to1[9][4] = -0.566984373146208;
   fWeightMatrix0to1[10][4] = 1.23938839899039;
   fWeightMatrix0to1[11][4] = -1.11074601634399;
   fWeightMatrix0to1[12][4] = -0.0975991333412447;
   fWeightMatrix0to1[13][4] = -1.95240928874957;
   fWeightMatrix0to1[0][5] = -0.901485725028981;
   fWeightMatrix0to1[1][5] = -1.58758939124143;
   fWeightMatrix0to1[2][5] = 0.77974914741068;
   fWeightMatrix0to1[3][5] = 1.28727113717381;
   fWeightMatrix0to1[4][5] = 2.13437533556138;
   fWeightMatrix0to1[5][5] = -0.0694473051347165;
   fWeightMatrix0to1[6][5] = -2.72918979399024;
   fWeightMatrix0to1[7][5] = -2.91598269932164;
   fWeightMatrix0to1[8][5] = 0.481477019016386;
   fWeightMatrix0to1[9][5] = 0.693473978219376;
   fWeightMatrix0to1[10][5] = 1.59637040873684;
   fWeightMatrix0to1[11][5] = 1.51646471496187;
   fWeightMatrix0to1[12][5] = 2.31730941109122;
   fWeightMatrix0to1[13][5] = -0.22967842693216;
   fWeightMatrix0to1[0][6] = -0.864684871005284;
   fWeightMatrix0to1[1][6] = -0.257629940021649;
   fWeightMatrix0to1[2][6] = 1.41326503318161;
   fWeightMatrix0to1[3][6] = -0.937864532668702;
   fWeightMatrix0to1[4][6] = -0.53510761418473;
   fWeightMatrix0to1[5][6] = -0.371267136796981;
   fWeightMatrix0to1[6][6] = -0.819273598786;
   fWeightMatrix0to1[7][6] = 0.912688921482141;
   fWeightMatrix0to1[8][6] = 1.68104379424418;
   fWeightMatrix0to1[9][6] = -1.45677505154291;
   fWeightMatrix0to1[10][6] = -0.26746047056463;
   fWeightMatrix0to1[11][6] = 2.04779893167072;
   fWeightMatrix0to1[12][6] = -0.560575359121817;
   fWeightMatrix0to1[13][6] = -2.93496102408767;
   fWeightMatrix0to1[0][7] = -1.30867606521108;
   fWeightMatrix0to1[1][7] = 0.2392684082829;
   fWeightMatrix0to1[2][7] = -1.48285201000155;
   fWeightMatrix0to1[3][7] = 3.82642526903192;
   fWeightMatrix0to1[4][7] = 4.14833495424181;
   fWeightMatrix0to1[5][7] = 6.85861663066686;
   fWeightMatrix0to1[6][7] = -1.64905762305794;
   fWeightMatrix0to1[7][7] = 8.39722295062915;
   fWeightMatrix0to1[8][7] = -0.497596570026448;
   fWeightMatrix0to1[9][7] = -0.543088433197203;
   fWeightMatrix0to1[10][7] = 1.78978033023384;
   fWeightMatrix0to1[11][7] = 0.548503331042828;
   fWeightMatrix0to1[12][7] = -1.88894243568991;
   fWeightMatrix0to1[13][7] = -4.10440911873156;
   fWeightMatrix0to1[0][8] = 0.626073623718308;
   fWeightMatrix0to1[1][8] = 1.04692454998667;
   fWeightMatrix0to1[2][8] = -1.08961111415588;
   fWeightMatrix0to1[3][8] = 3.79697598806754;
   fWeightMatrix0to1[4][8] = -0.0198176151254347;
   fWeightMatrix0to1[5][8] = -1.68701273952441;
   fWeightMatrix0to1[6][8] = 1.46002397840752;
   fWeightMatrix0to1[7][8] = -2.78754459889617;
   fWeightMatrix0to1[8][8] = -1.41676413882751;
   fWeightMatrix0to1[9][8] = 0.714201825754954;
   fWeightMatrix0to1[10][8] = 0.503846659460847;
   fWeightMatrix0to1[11][8] = 0.274206777589196;
   fWeightMatrix0to1[12][8] = -3.2578469413699;
   fWeightMatrix0to1[13][8] = -0.912372343335098;
   fWeightMatrix0to1[0][9] = -0.561037788095847;
   fWeightMatrix0to1[1][9] = 0.239262602733667;
   fWeightMatrix0to1[2][9] = -1.78751197197444;
   fWeightMatrix0to1[3][9] = -1.02883467320616;
   fWeightMatrix0to1[4][9] = 0.92550641538645;
   fWeightMatrix0to1[5][9] = 0.11604496614711;
   fWeightMatrix0to1[6][9] = -0.0455118477339268;
   fWeightMatrix0to1[7][9] = 0.421671289263613;
   fWeightMatrix0to1[8][9] = 0.436280994938697;
   fWeightMatrix0to1[9][9] = 2.70845566272358;
   fWeightMatrix0to1[10][9] = 0.108389628379018;
   fWeightMatrix0to1[11][9] = -1.91884678672945;
   fWeightMatrix0to1[12][9] = -0.924710623071421;
   fWeightMatrix0to1[13][9] = -0.263134016753535;
   fWeightMatrix0to1[0][10] = 1.49008619259588;
   fWeightMatrix0to1[1][10] = 0.600863165765859;
   fWeightMatrix0to1[2][10] = -1.1038867510429;
   fWeightMatrix0to1[3][10] = 0.107982890333015;
   fWeightMatrix0to1[4][10] = -0.21761367910201;
   fWeightMatrix0to1[5][10] = 1.54719669066481;
   fWeightMatrix0to1[6][10] = 0.522426444580529;
   fWeightMatrix0to1[7][10] = 0.497894271579836;
   fWeightMatrix0to1[8][10] = 0.0936365220122254;
   fWeightMatrix0to1[9][10] = 1.32645235495919;
   fWeightMatrix0to1[10][10] = -0.281005011430433;
   fWeightMatrix0to1[11][10] = 0.000200593689938705;
   fWeightMatrix0to1[12][10] = -0.131140462989321;
   fWeightMatrix0to1[13][10] = -0.198412229913122;
   fWeightMatrix0to1[0][11] = -0.20695989376211;
   fWeightMatrix0to1[1][11] = 0.190327291476647;
   fWeightMatrix0to1[2][11] = 2.50955807653624;
   fWeightMatrix0to1[3][11] = 0.550445328280083;
   fWeightMatrix0to1[4][11] = 0.262750531076024;
   fWeightMatrix0to1[5][11] = -4.61677646683115;
   fWeightMatrix0to1[6][11] = 0.894788912919222;
   fWeightMatrix0to1[7][11] = -2.31671280232046;
   fWeightMatrix0to1[8][11] = -0.223406689418309;
   fWeightMatrix0to1[9][11] = -1.14883743638968;
   fWeightMatrix0to1[10][11] = 3.50624034696198;
   fWeightMatrix0to1[11][11] = -0.376786694900849;
   fWeightMatrix0to1[12][11] = -1.3384165995452;
   fWeightMatrix0to1[13][11] = 1.29295232123321;
   fWeightMatrix0to1[0][12] = -0.839845486431886;
   fWeightMatrix0to1[1][12] = -1.15812930204474;
   fWeightMatrix0to1[2][12] = 1.99405278053335;
   fWeightMatrix0to1[3][12] = -0.835016096291749;
   fWeightMatrix0to1[4][12] = -0.860919545442909;
   fWeightMatrix0to1[5][12] = -1.71104250074613;
   fWeightMatrix0to1[6][12] = 1.02772192487685;
   fWeightMatrix0to1[7][12] = 0.243116903806791;
   fWeightMatrix0to1[8][12] = 0.531227814018569;
   fWeightMatrix0to1[9][12] = -0.46697552312891;
   fWeightMatrix0to1[10][12] = 0.228574845135253;
   fWeightMatrix0to1[11][12] = -0.661315254492352;
   fWeightMatrix0to1[12][12] = -1.33212543530646;
   fWeightMatrix0to1[13][12] = 1.74246181309502;
   fWeightMatrix0to1[0][13] = 1.94343370974186;
   fWeightMatrix0to1[1][13] = -1.69259350094562;
   fWeightMatrix0to1[2][13] = -0.762841651909115;
   fWeightMatrix0to1[3][13] = 6.01508228152125;
   fWeightMatrix0to1[4][13] = 3.16981491057278;
   fWeightMatrix0to1[5][13] = 6.85265165351458;
   fWeightMatrix0to1[6][13] = 0.859533200468065;
   fWeightMatrix0to1[7][13] = 10.5004923081298;
   fWeightMatrix0to1[8][13] = 0.329664338479198;
   fWeightMatrix0to1[9][13] = 1.44257663880819;
   fWeightMatrix0to1[10][13] = 2.12661474264176;
   fWeightMatrix0to1[11][13] = 0.511995299286146;
   fWeightMatrix0to1[12][13] = 0.217486341300104;
   fWeightMatrix0to1[13][13] = -6.03348941958589;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = -3.1712678705469;
   fWeightMatrix1to2[1][0] = 0.114576313685598;
   fWeightMatrix1to2[2][0] = -1.13863731203305;
   fWeightMatrix1to2[3][0] = -2.11706089557985;
   fWeightMatrix1to2[4][0] = -0.830359484999573;
   fWeightMatrix1to2[5][0] = -1.56850056614734;
   fWeightMatrix1to2[6][0] = -0.97565313859575;
   fWeightMatrix1to2[7][0] = -2.25185765195552;
   fWeightMatrix1to2[8][0] = -0.367576735657429;
   fWeightMatrix1to2[9][0] = -0.470288074734951;
   fWeightMatrix1to2[10][0] = -1.57586380112308;
   fWeightMatrix1to2[11][0] = -0.383672060183092;
   fWeightMatrix1to2[12][0] = 0.775989847220865;
   fWeightMatrix1to2[0][1] = 0.958618281630641;
   fWeightMatrix1to2[1][1] = 0.914340224756175;
   fWeightMatrix1to2[2][1] = -0.180865066957393;
   fWeightMatrix1to2[3][1] = -1.82190866474809;
   fWeightMatrix1to2[4][1] = -0.957400065149547;
   fWeightMatrix1to2[5][1] = 1.71165103422111;
   fWeightMatrix1to2[6][1] = 1.83677145821215;
   fWeightMatrix1to2[7][1] = 0.66818249797524;
   fWeightMatrix1to2[8][1] = -0.0670644051668203;
   fWeightMatrix1to2[9][1] = 1.86582650607092;
   fWeightMatrix1to2[10][1] = -1.89233513051529;
   fWeightMatrix1to2[11][1] = -1.11259364891269;
   fWeightMatrix1to2[12][1] = -0.172207938530172;
   fWeightMatrix1to2[0][2] = -1.64050233977474;
   fWeightMatrix1to2[1][2] = 1.55192784226016;
   fWeightMatrix1to2[2][2] = 1.41036781049325;
   fWeightMatrix1to2[3][2] = -1.29720575717717;
   fWeightMatrix1to2[4][2] = 0.762297854005719;
   fWeightMatrix1to2[5][2] = -0.215429819984903;
   fWeightMatrix1to2[6][2] = -1.91983206653024;
   fWeightMatrix1to2[7][2] = -1.12622631175431;
   fWeightMatrix1to2[8][2] = 0.664758213851082;
   fWeightMatrix1to2[9][2] = -0.0661254834765783;
   fWeightMatrix1to2[10][2] = 1.07310750411638;
   fWeightMatrix1to2[11][2] = -1.16706118353775;
   fWeightMatrix1to2[12][2] = 1.52932412962488;
   fWeightMatrix1to2[0][3] = -2.10450889685125;
   fWeightMatrix1to2[1][3] = 1.7114106192873;
   fWeightMatrix1to2[2][3] = -0.604252455956109;
   fWeightMatrix1to2[3][3] = -1.75476936863407;
   fWeightMatrix1to2[4][3] = -1.33308310525681;
   fWeightMatrix1to2[5][3] = -1.81867757157561;
   fWeightMatrix1to2[6][3] = -1.27808764015174;
   fWeightMatrix1to2[7][3] = -1.25888615670174;
   fWeightMatrix1to2[8][3] = -1.88123282044016;
   fWeightMatrix1to2[9][3] = -1.90404227306972;
   fWeightMatrix1to2[10][3] = 0.291956077561966;
   fWeightMatrix1to2[11][3] = -1.29634047194899;
   fWeightMatrix1to2[12][3] = -5.31664026659496;
   fWeightMatrix1to2[0][4] = 0.198347018701291;
   fWeightMatrix1to2[1][4] = -2.37448411997077;
   fWeightMatrix1to2[2][4] = -1.0872093934513;
   fWeightMatrix1to2[3][4] = -1.62526647305641;
   fWeightMatrix1to2[4][4] = 0.200201483944103;
   fWeightMatrix1to2[5][4] = 1.08209440544885;
   fWeightMatrix1to2[6][4] = 0.0768681421901049;
   fWeightMatrix1to2[7][4] = -0.639999418282978;
   fWeightMatrix1to2[8][4] = -1.66958476330583;
   fWeightMatrix1to2[9][4] = -1.87514743435648;
   fWeightMatrix1to2[10][4] = -1.21488721489791;
   fWeightMatrix1to2[11][4] = -1.93306811249657;
   fWeightMatrix1to2[12][4] = -3.39160228261367;
   fWeightMatrix1to2[0][5] = -0.919075456687871;
   fWeightMatrix1to2[1][5] = 1.53347343295137;
   fWeightMatrix1to2[2][5] = -1.74462418476774;
   fWeightMatrix1to2[3][5] = -0.334484863565061;
   fWeightMatrix1to2[4][5] = -0.64006692734162;
   fWeightMatrix1to2[5][5] = -2.84230157709465;
   fWeightMatrix1to2[6][5] = 0.994765168168513;
   fWeightMatrix1to2[7][5] = 1.26805380107169;
   fWeightMatrix1to2[8][5] = -0.984089962286545;
   fWeightMatrix1to2[9][5] = -1.65941511919518;
   fWeightMatrix1to2[10][5] = 0.486408582900145;
   fWeightMatrix1to2[11][5] = 1.0461246660119;
   fWeightMatrix1to2[12][5] = -3.7726617879778;
   fWeightMatrix1to2[0][6] = -1.48467735229289;
   fWeightMatrix1to2[1][6] = -0.59156837383161;
   fWeightMatrix1to2[2][6] = 0.147007827930607;
   fWeightMatrix1to2[3][6] = 0.488705986766293;
   fWeightMatrix1to2[4][6] = 0.863766828885747;
   fWeightMatrix1to2[5][6] = -1.98368624653267;
   fWeightMatrix1to2[6][6] = -0.874216984243514;
   fWeightMatrix1to2[7][6] = -0.51355175054713;
   fWeightMatrix1to2[8][6] = -2.50079800567621;
   fWeightMatrix1to2[9][6] = -1.67364688585139;
   fWeightMatrix1to2[10][6] = -2.77804678261606;
   fWeightMatrix1to2[11][6] = -1.68319550048101;
   fWeightMatrix1to2[12][6] = -2.13154450167044;
   fWeightMatrix1to2[0][7] = 0.868906914947014;
   fWeightMatrix1to2[1][7] = -0.497189314810019;
   fWeightMatrix1to2[2][7] = 0.504801629830879;
   fWeightMatrix1to2[3][7] = -2.54732446733228;
   fWeightMatrix1to2[4][7] = 1.10191273460395;
   fWeightMatrix1to2[5][7] = -1.6494530401613;
   fWeightMatrix1to2[6][7] = -1.80885214685501;
   fWeightMatrix1to2[7][7] = 1.29190488113297;
   fWeightMatrix1to2[8][7] = -3.31264987603999;
   fWeightMatrix1to2[9][7] = -0.138115956408271;
   fWeightMatrix1to2[10][7] = 0.451833070585806;
   fWeightMatrix1to2[11][7] = -2.61548513537898;
   fWeightMatrix1to2[12][7] = -7.19062675570454;
   fWeightMatrix1to2[0][8] = -1.78265445690645;
   fWeightMatrix1to2[1][8] = -0.49433960912729;
   fWeightMatrix1to2[2][8] = 1.52778220511142;
   fWeightMatrix1to2[3][8] = -0.725466316801466;
   fWeightMatrix1to2[4][8] = 0.204261789425447;
   fWeightMatrix1to2[5][8] = -1.02633231282999;
   fWeightMatrix1to2[6][8] = -1.45313658246732;
   fWeightMatrix1to2[7][8] = -1.96376260793754;
   fWeightMatrix1to2[8][8] = -1.0035258554681;
   fWeightMatrix1to2[9][8] = -1.49081313874246;
   fWeightMatrix1to2[10][8] = 0.912085509013168;
   fWeightMatrix1to2[11][8] = -1.50233892687477;
   fWeightMatrix1to2[12][8] = -1.66840170462931;
   fWeightMatrix1to2[0][9] = 1.49562689416173;
   fWeightMatrix1to2[1][9] = -2.41172606896436;
   fWeightMatrix1to2[2][9] = -1.52430606629837;
   fWeightMatrix1to2[3][9] = 1.60023741122279;
   fWeightMatrix1to2[4][9] = -1.67969456478094;
   fWeightMatrix1to2[5][9] = 0.802765070090396;
   fWeightMatrix1to2[6][9] = 2.01209214370068;
   fWeightMatrix1to2[7][9] = -0.497144325234352;
   fWeightMatrix1to2[8][9] = -3.16035477332096;
   fWeightMatrix1to2[9][9] = -0.465013487109336;
   fWeightMatrix1to2[10][9] = -0.993873377086358;
   fWeightMatrix1to2[11][9] = 0.85381643536699;
   fWeightMatrix1to2[12][9] = -2.32847198820167;
   fWeightMatrix1to2[0][10] = 0.34225919419571;
   fWeightMatrix1to2[1][10] = 0.866123074526545;
   fWeightMatrix1to2[2][10] = -2.10661861127376;
   fWeightMatrix1to2[3][10] = 0.161378612695991;
   fWeightMatrix1to2[4][10] = -1.9504719571122;
   fWeightMatrix1to2[5][10] = -0.175847507085341;
   fWeightMatrix1to2[6][10] = -3.08149179426874;
   fWeightMatrix1to2[7][10] = -1.88278917235939;
   fWeightMatrix1to2[8][10] = -0.0100365302483326;
   fWeightMatrix1to2[9][10] = 0.410708973895931;
   fWeightMatrix1to2[10][10] = -0.071601219198901;
   fWeightMatrix1to2[11][10] = 0.411685023896657;
   fWeightMatrix1to2[12][10] = -0.72181930741021;
   fWeightMatrix1to2[0][11] = -1.45179961565191;
   fWeightMatrix1to2[1][11] = -1.47205776059378;
   fWeightMatrix1to2[2][11] = -1.45919731218784;
   fWeightMatrix1to2[3][11] = 1.39011620103071;
   fWeightMatrix1to2[4][11] = -1.69064593930023;
   fWeightMatrix1to2[5][11] = -0.968731062504349;
   fWeightMatrix1to2[6][11] = -2.79025093387424;
   fWeightMatrix1to2[7][11] = -0.595640434262227;
   fWeightMatrix1to2[8][11] = -0.431594636074083;
   fWeightMatrix1to2[9][11] = -0.987380036168963;
   fWeightMatrix1to2[10][11] = -1.73045006802283;
   fWeightMatrix1to2[11][11] = 0.806907035357177;
   fWeightMatrix1to2[12][11] = 0.45256779163616;
   fWeightMatrix1to2[0][12] = -0.0640997743377356;
   fWeightMatrix1to2[1][12] = -2.63163834201899;
   fWeightMatrix1to2[2][12] = -1.97629126506793;
   fWeightMatrix1to2[3][12] = -2.07163674873097;
   fWeightMatrix1to2[4][12] = -1.85209187621147;
   fWeightMatrix1to2[5][12] = -0.862493962584309;
   fWeightMatrix1to2[6][12] = -2.07189359727223;
   fWeightMatrix1to2[7][12] = -1.47906778356574;
   fWeightMatrix1to2[8][12] = 0.0888324828141507;
   fWeightMatrix1to2[9][12] = -2.57637493266489;
   fWeightMatrix1to2[10][12] = -1.70057641959682;
   fWeightMatrix1to2[11][12] = -1.95277816729843;
   fWeightMatrix1to2[12][12] = -0.167324855389737;
   fWeightMatrix1to2[0][13] = -1.79298256635746;
   fWeightMatrix1to2[1][13] = -4.86312644148848;
   fWeightMatrix1to2[2][13] = -1.06461438148376;
   fWeightMatrix1to2[3][13] = -0.76477239425994;
   fWeightMatrix1to2[4][13] = -1.65938430486798;
   fWeightMatrix1to2[5][13] = 0.0762547397314349;
   fWeightMatrix1to2[6][13] = -1.58462350848791;
   fWeightMatrix1to2[7][13] = -1.51951114604221;
   fWeightMatrix1to2[8][13] = 1.02720786320335;
   fWeightMatrix1to2[9][13] = -0.286392385257223;
   fWeightMatrix1to2[10][13] = -0.226185585742172;
   fWeightMatrix1to2[11][13] = 0.478870677040953;
   fWeightMatrix1to2[12][13] = 5.33442219293639;
   fWeightMatrix1to2[0][14] = 0.406823400064893;
   fWeightMatrix1to2[1][14] = -0.914476838406368;
   fWeightMatrix1to2[2][14] = -2.59255622068521;
   fWeightMatrix1to2[3][14] = -1.43228427719021;
   fWeightMatrix1to2[4][14] = -1.87852917739054;
   fWeightMatrix1to2[5][14] = -0.97940681900849;
   fWeightMatrix1to2[6][14] = -0.260844122550086;
   fWeightMatrix1to2[7][14] = -0.203487440377025;
   fWeightMatrix1to2[8][14] = -0.843066178560982;
   fWeightMatrix1to2[9][14] = -0.422474157563874;
   fWeightMatrix1to2[10][14] = 0.663468399067945;
   fWeightMatrix1to2[11][14] = -1.61018914667488;
   fWeightMatrix1to2[12][14] = 0.868261077248381;
   // weight matrix from layer 2 to 3
   fWeightMatrix2to3[0][0] = -0.74563129435538;
   fWeightMatrix2to3[0][1] = -1.75921214093588;
   fWeightMatrix2to3[0][2] = -1.81484555033329;
   fWeightMatrix2to3[0][3] = -1.10535586117124;
   fWeightMatrix2to3[0][4] = 0.753073281302953;
   fWeightMatrix2to3[0][5] = -0.735086246090777;
   fWeightMatrix2to3[0][6] = -1.6279008514763;
   fWeightMatrix2to3[0][7] = 1.63033877771303;
   fWeightMatrix2to3[0][8] = -4.68109732941961;
   fWeightMatrix2to3[0][9] = 0.106967456747065;
   fWeightMatrix2to3[0][10] = 0.962969460850749;
   fWeightMatrix2to3[0][11] = -1.13258196606573;
   fWeightMatrix2to3[0][12] = -0.843126608201746;
   fWeightMatrix2to3[0][13] = 0.929364200887679;
}

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
}