// 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 11:01:16 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/job411
Training events: 57130


#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'    [47.1748542786,746.708984375]
LepAPt                        LepAPt                            'F'    [20.0000362396,155.865875244]
LepBPt                        LepBPt                            'F'    [10.0002250671,70.5846633911]
MetSigLeptonsJets             MetSigLeptonsJets                 'F'    [1.13770258427,19.6634273529]
MetSpec                       MetSpec                           'F'    [15.0022125244,289.142150879]
SumEtLeptonsJets              SumEtLeptonsJets                  'F'    [30.1877574921,490.248291016]
VSumJetLeptonsPt              VSumJetLeptonsPt                  'F'    [0.172416463494,301.426879883]
addEt                         addEt                             'F'    [47.1748542786,378.260345459]
dPhiLepSumMet                 dPhiLepSumMet                     'F'    [0.0448210090399,3.14158248901]
dPhiLeptons                   dPhiLeptons                       'F'    [4.53725397165e-06,1.11007142067]
dRLeptons                     dRLeptons                         'F'    [0.200007483363,1.15372478962]
lep1_E                        lep1_E                            'F'    [20.0413379669,232.717926025]
lep2_E                        lep2_E                            'F'    [10.0094184875,117.131721497]


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

#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] = 47.1748542785645;
      fVmax[0] = 746.708984375;
      fVmin[1] = 20.000036239624;
      fVmax[1] = 155.865875244141;
      fVmin[2] = 10.0002250671387;
      fVmax[2] = 70.5846633911133;
      fVmin[3] = 1.13770258426666;
      fVmax[3] = 19.6634273529053;
      fVmin[4] = 15.0022125244141;
      fVmax[4] = 289.142150878906;
      fVmin[5] = 30.1877574920654;
      fVmax[5] = 490.248291015625;
      fVmin[6] = 0.172416463494301;
      fVmax[6] = 301.426879882812;
      fVmin[7] = 47.1748542785645;
      fVmax[7] = 378.260345458984;
      fVmin[8] = 0.0448210090398788;
      fVmax[8] = 3.14158248901367;
      fVmin[9] = 4.53725397164817e-06;
      fVmax[9] = 1.11007142066956;
      fVmin[10] = 0.200007483363152;
      fVmax[10] = 1.15372478961945;
      fVmin[11] = 20.0413379669189;
      fVmax[11] = 232.717926025391;
      fVmin[12] = 10.0094184875488;
      fVmax[12] = 117.131721496582;

      // 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] = -0.309161361505785;
   fWeightMatrix0to1[1][0] = 2.48553199227676;
   fWeightMatrix0to1[2][0] = 0.835761889138511;
   fWeightMatrix0to1[3][0] = 2.05417487787591;
   fWeightMatrix0to1[4][0] = -1.46783219714868;
   fWeightMatrix0to1[5][0] = -1.39760734884692;
   fWeightMatrix0to1[6][0] = -0.686346076983892;
   fWeightMatrix0to1[7][0] = 1.33239088696603;
   fWeightMatrix0to1[8][0] = -2.54073753852519;
   fWeightMatrix0to1[9][0] = -0.845136919250332;
   fWeightMatrix0to1[10][0] = -1.98472226943448;
   fWeightMatrix0to1[11][0] = -0.017207396162552;
   fWeightMatrix0to1[12][0] = -1.18020029684166;
   fWeightMatrix0to1[13][0] = -0.11022856902894;
   fWeightMatrix0to1[0][1] = -0.775342262759908;
   fWeightMatrix0to1[1][1] = 0.906826705328663;
   fWeightMatrix0to1[2][1] = 0.728557333243376;
   fWeightMatrix0to1[3][1] = 1.93638458062724;
   fWeightMatrix0to1[4][1] = 2.95982872905594;
   fWeightMatrix0to1[5][1] = 1.20166372516331;
   fWeightMatrix0to1[6][1] = -3.54374305828604;
   fWeightMatrix0to1[7][1] = 0.124043232608476;
   fWeightMatrix0to1[8][1] = 0.381225044994388;
   fWeightMatrix0to1[9][1] = 2.27369438441505;
   fWeightMatrix0to1[10][1] = -2.74093294343059;
   fWeightMatrix0to1[11][1] = 1.15232560844561;
   fWeightMatrix0to1[12][1] = -0.966087089381129;
   fWeightMatrix0to1[13][1] = 1.69453632091956;
   fWeightMatrix0to1[0][2] = -1.62293560810194;
   fWeightMatrix0to1[1][2] = 0.634293538082589;
   fWeightMatrix0to1[2][2] = 0.562791566733037;
   fWeightMatrix0to1[3][2] = 1.83596635093652;
   fWeightMatrix0to1[4][2] = -1.5435982960731;
   fWeightMatrix0to1[5][2] = 0.528933318802388;
   fWeightMatrix0to1[6][2] = -1.3098621735784;
   fWeightMatrix0to1[7][2] = 0.619184396868645;
   fWeightMatrix0to1[8][2] = -4.5012046418963;
   fWeightMatrix0to1[9][2] = 1.67250469466984;
   fWeightMatrix0to1[10][2] = -2.5358723145123;
   fWeightMatrix0to1[11][2] = 1.23678771206758;
   fWeightMatrix0to1[12][2] = 2.25055879552469;
   fWeightMatrix0to1[13][2] = 2.70091995905981;
   fWeightMatrix0to1[0][3] = 2.19625911901698;
   fWeightMatrix0to1[1][3] = 0.904600927478355;
   fWeightMatrix0to1[2][3] = -0.352726607640935;
   fWeightMatrix0to1[3][3] = -1.18005045663468;
   fWeightMatrix0to1[4][3] = 1.5067038061643;
   fWeightMatrix0to1[5][3] = 0.994639229841828;
   fWeightMatrix0to1[6][3] = 2.36889101987191;
   fWeightMatrix0to1[7][3] = 0.680409454030087;
   fWeightMatrix0to1[8][3] = 0.509148120179484;
   fWeightMatrix0to1[9][3] = 2.37749435130477;
   fWeightMatrix0to1[10][3] = -1.99900240911522;
   fWeightMatrix0to1[11][3] = 0.960752938988177;
   fWeightMatrix0to1[12][3] = -3.46300618861891;
   fWeightMatrix0to1[13][3] = 0.118541034172024;
   fWeightMatrix0to1[0][4] = -1.04788546758042;
   fWeightMatrix0to1[1][4] = -1.90978518360148;
   fWeightMatrix0to1[2][4] = 1.89822806122031;
   fWeightMatrix0to1[3][4] = 1.18977427637362;
   fWeightMatrix0to1[4][4] = -0.698700480655489;
   fWeightMatrix0to1[5][4] = 0.849701813650169;
   fWeightMatrix0to1[6][4] = 0.542358676797014;
   fWeightMatrix0to1[7][4] = -0.276257699421633;
   fWeightMatrix0to1[8][4] = 1.1390468649414;
   fWeightMatrix0to1[9][4] = 0.0591113575808294;
   fWeightMatrix0to1[10][4] = -0.279422988940961;
   fWeightMatrix0to1[11][4] = -0.694026199593186;
   fWeightMatrix0to1[12][4] = 0.550988263242605;
   fWeightMatrix0to1[13][4] = -0.67915111272365;
   fWeightMatrix0to1[0][5] = -0.820501414593758;
   fWeightMatrix0to1[1][5] = -0.868261129807098;
   fWeightMatrix0to1[2][5] = 0.570521966755753;
   fWeightMatrix0to1[3][5] = 1.38841318551197;
   fWeightMatrix0to1[4][5] = 1.59507112612399;
   fWeightMatrix0to1[5][5] = -0.602594617884464;
   fWeightMatrix0to1[6][5] = -0.583250015365405;
   fWeightMatrix0to1[7][5] = -2.92037556221747;
   fWeightMatrix0to1[8][5] = 0.485353869659855;
   fWeightMatrix0to1[9][5] = -0.104127408574508;
   fWeightMatrix0to1[10][5] = 1.88264675011805;
   fWeightMatrix0to1[11][5] = 0.87378809957432;
   fWeightMatrix0to1[12][5] = 2.07052376724717;
   fWeightMatrix0to1[13][5] = -1.425395241166;
   fWeightMatrix0to1[0][6] = -0.684712889427731;
   fWeightMatrix0to1[1][6] = 0.0356836675723958;
   fWeightMatrix0to1[2][6] = 1.35832121751207;
   fWeightMatrix0to1[3][6] = -0.707989334681623;
   fWeightMatrix0to1[4][6] = -0.147804483312567;
   fWeightMatrix0to1[5][6] = -0.808025008506791;
   fWeightMatrix0to1[6][6] = -3.24342314146727;
   fWeightMatrix0to1[7][6] = -0.191364564172522;
   fWeightMatrix0to1[8][6] = 0.171409491090982;
   fWeightMatrix0to1[9][6] = 0.264283791275431;
   fWeightMatrix0to1[10][6] = -1.58227388717709;
   fWeightMatrix0to1[11][6] = 3.18901642976096;
   fWeightMatrix0to1[12][6] = -2.21546924630623;
   fWeightMatrix0to1[13][6] = 0.66153181754326;
   fWeightMatrix0to1[0][7] = -1.37949520065137;
   fWeightMatrix0to1[1][7] = 0.845083516626663;
   fWeightMatrix0to1[2][7] = -1.51011420688732;
   fWeightMatrix0to1[3][7] = -1.36840526344005;
   fWeightMatrix0to1[4][7] = 2.58503876853472;
   fWeightMatrix0to1[5][7] = 2.41764658315305;
   fWeightMatrix0to1[6][7] = -7.930990152166;
   fWeightMatrix0to1[7][7] = 4.18786719916771;
   fWeightMatrix0to1[8][7] = -4.24760248296697;
   fWeightMatrix0to1[9][7] = 2.99014853071759;
   fWeightMatrix0to1[10][7] = -2.2922251930991;
   fWeightMatrix0to1[11][7] = 1.79478996497728;
   fWeightMatrix0to1[12][7] = -5.78710155654568;
   fWeightMatrix0to1[13][7] = 5.39290781360714;
   fWeightMatrix0to1[0][8] = 0.0946668910191321;
   fWeightMatrix0to1[1][8] = -0.409662499048427;
   fWeightMatrix0to1[2][8] = -0.861120351212414;
   fWeightMatrix0to1[3][8] = 1.39239805531812;
   fWeightMatrix0to1[4][8] = 0.646740759890656;
   fWeightMatrix0to1[5][8] = -2.29350010863407;
   fWeightMatrix0to1[6][8] = 2.34806472480626;
   fWeightMatrix0to1[7][8] = 0.21217059748623;
   fWeightMatrix0to1[8][8] = -1.52608075919895;
   fWeightMatrix0to1[9][8] = -1.19635338726695;
   fWeightMatrix0to1[10][8] = -0.746980219639147;
   fWeightMatrix0to1[11][8] = 1.40646381950096;
   fWeightMatrix0to1[12][8] = 0.522729956825433;
   fWeightMatrix0to1[13][8] = 0.945792903341784;
   fWeightMatrix0to1[0][9] = -0.282528391326737;
   fWeightMatrix0to1[1][9] = -0.0863881107280049;
   fWeightMatrix0to1[2][9] = -1.80151130096654;
   fWeightMatrix0to1[3][9] = 1.25415895740263;
   fWeightMatrix0to1[4][9] = 1.47935302536687;
   fWeightMatrix0to1[5][9] = -0.788821806300031;
   fWeightMatrix0to1[6][9] = -0.118679164372;
   fWeightMatrix0to1[7][9] = -0.455377157438534;
   fWeightMatrix0to1[8][9] = -0.551433720696695;
   fWeightMatrix0to1[9][9] = 0.367402292009387;
   fWeightMatrix0to1[10][9] = 0.329958257337021;
   fWeightMatrix0to1[11][9] = -1.46700109640803;
   fWeightMatrix0to1[12][9] = 0.537323342340867;
   fWeightMatrix0to1[13][9] = 0.496322832797924;
   fWeightMatrix0to1[0][10] = 1.7014143457314;
   fWeightMatrix0to1[1][10] = -0.0603159161962117;
   fWeightMatrix0to1[2][10] = -1.26031964262262;
   fWeightMatrix0to1[3][10] = 0.0679421906958131;
   fWeightMatrix0to1[4][10] = 0.222769829553307;
   fWeightMatrix0to1[5][10] = 1.46126234284848;
   fWeightMatrix0to1[6][10] = -1.03187949635709;
   fWeightMatrix0to1[7][10] = -0.123607497492478;
   fWeightMatrix0to1[8][10] = -0.847796499322317;
   fWeightMatrix0to1[9][10] = -0.357102272658919;
   fWeightMatrix0to1[10][10] = -1.67602597086571;
   fWeightMatrix0to1[11][10] = 0.69230488035862;
   fWeightMatrix0to1[12][10] = 0.63529331463283;
   fWeightMatrix0to1[13][10] = -0.0600653984085459;
   fWeightMatrix0to1[0][11] = -0.58455117817324;
   fWeightMatrix0to1[1][11] = -0.397377153379667;
   fWeightMatrix0to1[2][11] = 2.657480196421;
   fWeightMatrix0to1[3][11] = 0.858839746270873;
   fWeightMatrix0to1[4][11] = 2.27637632901263;
   fWeightMatrix0to1[5][11] = -1.92160351204622;
   fWeightMatrix0to1[6][11] = 2.8428437862363;
   fWeightMatrix0to1[7][11] = -0.601649563201199;
   fWeightMatrix0to1[8][11] = 1.92753481930754;
   fWeightMatrix0to1[9][11] = 0.719370878096387;
   fWeightMatrix0to1[10][11] = 0.738689890105682;
   fWeightMatrix0to1[11][11] = -0.00613928307115115;
   fWeightMatrix0to1[12][11] = -0.873663400463527;
   fWeightMatrix0to1[13][11] = 0.0957398502022389;
   fWeightMatrix0to1[0][12] = -0.671294691127897;
   fWeightMatrix0to1[1][12] = -0.718300802377503;
   fWeightMatrix0to1[2][12] = 1.99777796505676;
   fWeightMatrix0to1[3][12] = 1.27325620479606;
   fWeightMatrix0to1[4][12] = -1.40288723679041;
   fWeightMatrix0to1[5][12] = 1.67527386053675;
   fWeightMatrix0to1[6][12] = 1.37824303715659;
   fWeightMatrix0to1[7][12] = -2.24788598770452;
   fWeightMatrix0to1[8][12] = -1.32412235515901;
   fWeightMatrix0to1[9][12] = -0.00372501473352094;
   fWeightMatrix0to1[10][12] = -0.289250052039508;
   fWeightMatrix0to1[11][12] = -1.76115833305924;
   fWeightMatrix0to1[12][12] = -1.11533777935601;
   fWeightMatrix0to1[13][12] = 0.602353949441422;
   fWeightMatrix0to1[0][13] = 1.64998011577964;
   fWeightMatrix0to1[1][13] = -0.810227462935508;
   fWeightMatrix0to1[2][13] = -0.776701186147301;
   fWeightMatrix0to1[3][13] = -1.03977132892019;
   fWeightMatrix0to1[4][13] = 0.864476308026809;
   fWeightMatrix0to1[5][13] = 2.26266130604972;
   fWeightMatrix0to1[6][13] = -11.5449180468263;
   fWeightMatrix0to1[7][13] = 5.72566655783713;
   fWeightMatrix0to1[8][13] = -4.01493891788782;
   fWeightMatrix0to1[9][13] = 6.24379734607784;
   fWeightMatrix0to1[10][13] = -0.696499901198751;
   fWeightMatrix0to1[11][13] = 2.2040228710248;
   fWeightMatrix0to1[12][13] = -5.52220411813621;
   fWeightMatrix0to1[13][13] = 5.79387474232045;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = -3.18696089840553;
   fWeightMatrix1to2[1][0] = -0.369887570713712;
   fWeightMatrix1to2[2][0] = -0.911649512669999;
   fWeightMatrix1to2[3][0] = -1.7950719599122;
   fWeightMatrix1to2[4][0] = -0.851978677778648;
   fWeightMatrix1to2[5][0] = -0.831676536731427;
   fWeightMatrix1to2[6][0] = -0.484149292643708;
   fWeightMatrix1to2[7][0] = -1.69951685792005;
   fWeightMatrix1to2[8][0] = 0.791810329083156;
   fWeightMatrix1to2[9][0] = 0.244674493954214;
   fWeightMatrix1to2[10][0] = -1.03645328985927;
   fWeightMatrix1to2[11][0] = 0.37904443062145;
   fWeightMatrix1to2[12][0] = 0.135812906733499;
   fWeightMatrix1to2[0][1] = 1.05729354628446;
   fWeightMatrix1to2[1][1] = -0.242737195408096;
   fWeightMatrix1to2[2][1] = -1.18578808050246;
   fWeightMatrix1to2[3][1] = -2.13282315387718;
   fWeightMatrix1to2[4][1] = -0.930034529719974;
   fWeightMatrix1to2[5][1] = 1.76843858835004;
   fWeightMatrix1to2[6][1] = 1.25686836862564;
   fWeightMatrix1to2[7][1] = 0.765905061915702;
   fWeightMatrix1to2[8][1] = 0.179436782078653;
   fWeightMatrix1to2[9][1] = 1.92299921612696;
   fWeightMatrix1to2[10][1] = -1.82810085142056;
   fWeightMatrix1to2[11][1] = -1.32793239258155;
   fWeightMatrix1to2[12][1] = -0.77447772629489;
   fWeightMatrix1to2[0][2] = -1.38643895835751;
   fWeightMatrix1to2[1][2] = 0.637824697448444;
   fWeightMatrix1to2[2][2] = 1.96296319718652;
   fWeightMatrix1to2[3][2] = -1.33676295956759;
   fWeightMatrix1to2[4][2] = 0.75695860825756;
   fWeightMatrix1to2[5][2] = -0.215045728269517;
   fWeightMatrix1to2[6][2] = -2.12987307830118;
   fWeightMatrix1to2[7][2] = -1.1621376247143;
   fWeightMatrix1to2[8][2] = 0.94241036059317;
   fWeightMatrix1to2[9][2] = -0.221628726223253;
   fWeightMatrix1to2[10][2] = 0.620482778713582;
   fWeightMatrix1to2[11][2] = -1.12598754116855;
   fWeightMatrix1to2[12][2] = 1.93805172473742;
   fWeightMatrix1to2[0][3] = -1.58614349978699;
   fWeightMatrix1to2[1][3] = 0.468770822964648;
   fWeightMatrix1to2[2][3] = 0.00309368827053539;
   fWeightMatrix1to2[3][3] = -1.62042255185056;
   fWeightMatrix1to2[4][3] = -1.31949995868265;
   fWeightMatrix1to2[5][3] = -1.8608336986332;
   fWeightMatrix1to2[6][3] = -0.471193151300359;
   fWeightMatrix1to2[7][3] = -1.86001192751781;
   fWeightMatrix1to2[8][3] = -1.60881413684478;
   fWeightMatrix1to2[9][3] = -1.70860403423016;
   fWeightMatrix1to2[10][3] = 0.0695797387470703;
   fWeightMatrix1to2[11][3] = -0.923214260359788;
   fWeightMatrix1to2[12][3] = -1.78594352072837;
   fWeightMatrix1to2[0][4] = 0.647821450532571;
   fWeightMatrix1to2[1][4] = -1.3197714769479;
   fWeightMatrix1to2[2][4] = -2.59111409130698;
   fWeightMatrix1to2[3][4] = -1.21572116302095;
   fWeightMatrix1to2[4][4] = 0.158363561476658;
   fWeightMatrix1to2[5][4] = 0.675473520025215;
   fWeightMatrix1to2[6][4] = 0.608938741555354;
   fWeightMatrix1to2[7][4] = -0.154305603168138;
   fWeightMatrix1to2[8][4] = -1.88492394349779;
   fWeightMatrix1to2[9][4] = -1.74672428723559;
   fWeightMatrix1to2[10][4] = -1.65302805747448;
   fWeightMatrix1to2[11][4] = -1.71776573467235;
   fWeightMatrix1to2[12][4] = 1.76886078075307;
   fWeightMatrix1to2[0][5] = -0.700638781004677;
   fWeightMatrix1to2[1][5] = 0.24904280183921;
   fWeightMatrix1to2[2][5] = -2.22910236660073;
   fWeightMatrix1to2[3][5] = -0.344515797201557;
   fWeightMatrix1to2[4][5] = -0.596812821828582;
   fWeightMatrix1to2[5][5] = -2.57165544842643;
   fWeightMatrix1to2[6][5] = 0.605485036498606;
   fWeightMatrix1to2[7][5] = 0.932819663162974;
   fWeightMatrix1to2[8][5] = -1.41380507654865;
   fWeightMatrix1to2[9][5] = -1.6112111153967;
   fWeightMatrix1to2[10][5] = 0.104562647470508;
   fWeightMatrix1to2[11][5] = 0.742395920609784;
   fWeightMatrix1to2[12][5] = -2.21703508997693;
   fWeightMatrix1to2[0][6] = -1.58749297769034;
   fWeightMatrix1to2[1][6] = 2.26661948393361;
   fWeightMatrix1to2[2][6] = 6.11559842992363;
   fWeightMatrix1to2[3][6] = 1.83669645989812;
   fWeightMatrix1to2[4][6] = 0.733885132791049;
   fWeightMatrix1to2[5][6] = -1.42537052564487;
   fWeightMatrix1to2[6][6] = -0.488130296240788;
   fWeightMatrix1to2[7][6] = -0.743331096229699;
   fWeightMatrix1to2[8][6] = -2.2317090786445;
   fWeightMatrix1to2[9][6] = -1.71603658392837;
   fWeightMatrix1to2[10][6] = -2.34048326639356;
   fWeightMatrix1to2[11][6] = -1.52216059741979;
   fWeightMatrix1to2[12][6] = -2.70231697746021;
   fWeightMatrix1to2[0][7] = 1.19611109419301;
   fWeightMatrix1to2[1][7] = -4.90558729031982;
   fWeightMatrix1to2[2][7] = 0.824688423499875;
   fWeightMatrix1to2[3][7] = -2.00126380196519;
   fWeightMatrix1to2[4][7] = 0.966954370777087;
   fWeightMatrix1to2[5][7] = -1.50186195049638;
   fWeightMatrix1to2[6][7] = -0.0827430879134946;
   fWeightMatrix1to2[7][7] = 0.851191755361947;
   fWeightMatrix1to2[8][7] = -2.20662433303642;
   fWeightMatrix1to2[9][7] = -0.298610581605254;
   fWeightMatrix1to2[10][7] = -0.34844378736171;
   fWeightMatrix1to2[11][7] = -1.22973365487719;
   fWeightMatrix1to2[12][7] = -2.64901261804381;
   fWeightMatrix1to2[0][8] = -2.03942109986193;
   fWeightMatrix1to2[1][8] = -1.08846003646049;
   fWeightMatrix1to2[2][8] = 3.43342837034425;
   fWeightMatrix1to2[3][8] = -0.55479039942869;
   fWeightMatrix1to2[4][8] = 0.175012912036924;
   fWeightMatrix1to2[5][8] = -1.54471649913829;
   fWeightMatrix1to2[6][8] = -1.27714015488403;
   fWeightMatrix1to2[7][8] = -1.18135272139094;
   fWeightMatrix1to2[8][8] = -1.30258525126445;
   fWeightMatrix1to2[9][8] = -0.616300391474697;
   fWeightMatrix1to2[10][8] = 1.49402531816219;
   fWeightMatrix1to2[11][8] = -0.383517276318282;
   fWeightMatrix1to2[12][8] = 1.4679008799597;
   fWeightMatrix1to2[0][9] = 1.52252323512279;
   fWeightMatrix1to2[1][9] = -2.24497456374568;
   fWeightMatrix1to2[2][9] = -4.86173354264001;
   fWeightMatrix1to2[3][9] = 1.0307341696195;
   fWeightMatrix1to2[4][9] = -1.80205966214555;
   fWeightMatrix1to2[5][9] = 1.19675966246274;
   fWeightMatrix1to2[6][9] = 1.05800160618957;
   fWeightMatrix1to2[7][9] = -0.871444193146884;
   fWeightMatrix1to2[8][9] = -1.19877282079251;
   fWeightMatrix1to2[9][9] = -0.468042081377394;
   fWeightMatrix1to2[10][9] = -0.972554596664381;
   fWeightMatrix1to2[11][9] = 0.26960152402363;
   fWeightMatrix1to2[12][9] = 0.961728265816232;
   fWeightMatrix1to2[0][10] = 0.085995275288342;
   fWeightMatrix1to2[1][10] = 2.40679091192554;
   fWeightMatrix1to2[2][10] = -1.02651633337214;
   fWeightMatrix1to2[3][10] = 1.25545915856414;
   fWeightMatrix1to2[4][10] = -2.11328916931556;
   fWeightMatrix1to2[5][10] = -0.480248907541068;
   fWeightMatrix1to2[6][10] = -2.62512113740406;
   fWeightMatrix1to2[7][10] = -2.16650899757148;
   fWeightMatrix1to2[8][10] = -0.623321736229176;
   fWeightMatrix1to2[9][10] = 0.272102669057359;
   fWeightMatrix1to2[10][10] = -0.0313237985626393;
   fWeightMatrix1to2[11][10] = 1.24168353581393;
   fWeightMatrix1to2[12][10] = -0.901590862154356;
   fWeightMatrix1to2[0][11] = -0.930185752348762;
   fWeightMatrix1to2[1][11] = -2.09555052014947;
   fWeightMatrix1to2[2][11] = -3.25020466989804;
   fWeightMatrix1to2[3][11] = 1.35776893371228;
   fWeightMatrix1to2[4][11] = -1.60700773638768;
   fWeightMatrix1to2[5][11] = -0.61797773906526;
   fWeightMatrix1to2[6][11] = -2.07291994112785;
   fWeightMatrix1to2[7][11] = 0.0248495436239624;
   fWeightMatrix1to2[8][11] = -0.381248554390308;
   fWeightMatrix1to2[9][11] = -0.0747903265936171;
   fWeightMatrix1to2[10][11] = -1.49681004763408;
   fWeightMatrix1to2[11][11] = 0.915194379085032;
   fWeightMatrix1to2[12][11] = 1.29908079477098;
   fWeightMatrix1to2[0][12] = 0.240007222700155;
   fWeightMatrix1to2[1][12] = 0.445117808308245;
   fWeightMatrix1to2[2][12] = 4.02712254316596;
   fWeightMatrix1to2[3][12] = -1.2260091899762;
   fWeightMatrix1to2[4][12] = -1.82148209669478;
   fWeightMatrix1to2[5][12] = 0.542074607161229;
   fWeightMatrix1to2[6][12] = -1.35557983754891;
   fWeightMatrix1to2[7][12] = -1.17720749942852;
   fWeightMatrix1to2[8][12] = 0.148946083433088;
   fWeightMatrix1to2[9][12] = -1.95654977736379;
   fWeightMatrix1to2[10][12] = -2.20917576924756;
   fWeightMatrix1to2[11][12] = -0.988652364862073;
   fWeightMatrix1to2[12][12] = -0.321602263217931;
   fWeightMatrix1to2[0][13] = -1.57064950079969;
   fWeightMatrix1to2[1][13] = -3.50230189529793;
   fWeightMatrix1to2[2][13] = -4.7296055911817;
   fWeightMatrix1to2[3][13] = -1.27970720972281;
   fWeightMatrix1to2[4][13] = -1.67133554019761;
   fWeightMatrix1to2[5][13] = 0.92906250551477;
   fWeightMatrix1to2[6][13] = -1.54670775298444;
   fWeightMatrix1to2[7][13] = -1.17639280195743;
   fWeightMatrix1to2[8][13] = 1.48699020635118;
   fWeightMatrix1to2[9][13] = -0.101612490053248;
   fWeightMatrix1to2[10][13] = 0.215911047888285;
   fWeightMatrix1to2[11][13] = 0.0205476936868831;
   fWeightMatrix1to2[12][13] = 0.375467126440925;
   fWeightMatrix1to2[0][14] = 0.529487639812294;
   fWeightMatrix1to2[1][14] = -1.025276648472;
   fWeightMatrix1to2[2][14] = -2.20758825522165;
   fWeightMatrix1to2[3][14] = -1.10963611568104;
   fWeightMatrix1to2[4][14] = -1.90322230060686;
   fWeightMatrix1to2[5][14] = -0.18423960050427;
   fWeightMatrix1to2[6][14] = 0.20932378240388;
   fWeightMatrix1to2[7][14] = 0.13596424940851;
   fWeightMatrix1to2[8][14] = 0.331529090562655;
   fWeightMatrix1to2[9][14] = 0.232834207235992;
   fWeightMatrix1to2[10][14] = 0.956734334099026;
   fWeightMatrix1to2[11][14] = -0.915554707522788;
   fWeightMatrix1to2[12][14] = -0.274641513851684;
   // weight matrix from layer 2 to 3
   fWeightMatrix2to3[0][0] = 0.542864946932207;
   fWeightMatrix2to3[0][1] = 0.948739469253447;
   fWeightMatrix2to3[0][2] = -0.562001456386443;
   fWeightMatrix2to3[0][3] = -0.968311287913995;
   fWeightMatrix2to3[0][4] = 0.711871874348933;
   fWeightMatrix2to3[0][5] = 0.630610967135141;
   fWeightMatrix2to3[0][6] = 0.0752452963771209;
   fWeightMatrix2to3[0][7] = 1.71285134451682;
   fWeightMatrix2to3[0][8] = 0.802254656707185;
   fWeightMatrix2to3[0][9] = 0.478543628224723;
   fWeightMatrix2to3[0][10] = 1.13330235313703;
   fWeightMatrix2to3[0][11] = 0.398831970760573;
   fWeightMatrix2to3[0][12] = -0.962782840286864;
   fWeightMatrix2to3[0][13] = 0.475693275967369;
}

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
}