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

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

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

Method         : MLP::H6AONN5MEMLP
TMVA Release   : 3.8.6         [198662]
ROOT Release   : 5.10/00       [330240]
Creator        : thompson
Date           : Tue Jan 22 16:28:56 2008
Host           : Linux patlx3.fnal.gov 2.4.21-37.ELsmp #1 SMP Wed Sep 28 12:13:44 CDT 2005 i686 i686 i386 GNU/Linux
Dir            : /data/cdf04/thompson/hww_1fb/hwwcdf6.1.4v3_43/Hww/TMVAAna
Training events: 22190


#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]
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 11
LRHWW                         LRHWW                             'F'    [0,1]
LRWW                          LRWW                              'F'    [0,1.001734972]
LRWg                          LRWg                              'F'    [0,0.999952673912]
LRWj                          LRWj                              'F'    [-0.0757067129016,1]
LRZZ                          LRZZ                              'F'    [0,1]
Met                           Met                               'F'    [15.1351919174,274.802154541]
MetDelPhi                     MetDelPhi                         'F'    [0.143834546208,3.13630962372]
MetSpec                       MetSpec                           'F'    [15.0015153885,188.142364502]
dPhiLeptons                   dPhiLeptons                       'F'    [7.39097595215e-05,3.14087033272]
dRLeptons                     dRLeptons                         'F'    [0.401803344488,4.43655967712]
dimass                        dimass                            'F'    [16.0089683533,573.807128906]


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

#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( 11 ),
        fIsNormalised( true )
   {      
      // the training input variables
      const char* inputVars[] = { "LRHWW", "LRWW", "LRWg", "LRWj", "LRZZ", "Met", "MetDelPhi", "MetSpec", "dPhiLeptons", "dRLeptons", "dimass" };

      // 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] = 0;
      fVmax[0] = 1;
      fVmin[1] = 0;
      fVmax[1] = 1.00173497200012;
      fVmin[2] = 0;
      fVmax[2] = 0.999952673912048;
      fVmin[3] = -0.0757067129015923;
      fVmax[3] = 1;
      fVmin[4] = 0;
      fVmax[4] = 1;
      fVmin[5] = 15.1351919174194;
      fVmax[5] = 274.802154541016;
      fVmin[6] = 0.143834546208382;
      fVmax[6] = 3.13630962371826;
      fVmin[7] = 15.0015153884888;
      fVmax[7] = 188.142364501953;
      fVmin[8] = 7.39097595214844e-05;
      fVmax[8] = 3.1408703327179;
      fVmin[9] = 0.401803344488144;
      fVmax[9] = 4.43655967712402;
      fVmin[10] = 16.0089683532715;
      fVmax[10] = 573.80712890625;

      // 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';

      // 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[11];
   double fVmax[11];
   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[11];

   // 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[13][12];   // weight matrix from layer 0 to 1
   double fWeightMatrix1to2[12][13];   // weight matrix from layer 1 to 2
   double fWeightMatrix2to3[1][12];   // weight matrix from layer 2 to 3

   double * fWeights[4];
};

inline void ReadH6AONN5MEMLP::Initialize()
{
   // build network structure
   fLayers = 4;
   fLayerSize[0] = 12; fWeights[0] = new double[12]; 
   fLayerSize[1] = 13; fWeights[1] = new double[13]; 
   fLayerSize[2] = 12; fWeights[2] = new double[12]; 
   fLayerSize[3] = 1; fWeights[3] = new double[1]; 
   // weight matrix from layer 0 to 1
   fWeightMatrix0to1[0][0] = 1.3655238820722;
   fWeightMatrix0to1[1][0] = 1.69674168523753;
   fWeightMatrix0to1[2][0] = 0.574523376341876;
   fWeightMatrix0to1[3][0] = 1.7584225618094;
   fWeightMatrix0to1[4][0] = 0.340262230979231;
   fWeightMatrix0to1[5][0] = 1.90168818385564;
   fWeightMatrix0to1[6][0] = 1.52921832876048;
   fWeightMatrix0to1[7][0] = -0.421399993971939;
   fWeightMatrix0to1[8][0] = 0.5377607992608;
   fWeightMatrix0to1[9][0] = 1.44905843446165;
   fWeightMatrix0to1[10][0] = 1.09605170741562;
   fWeightMatrix0to1[11][0] = 0.0780494267384143;
   fWeightMatrix0to1[0][1] = 1.34151155715329;
   fWeightMatrix0to1[1][1] = 0.79717932603909;
   fWeightMatrix0to1[2][1] = 0.960358150129187;
   fWeightMatrix0to1[3][1] = -1.79789989475666;
   fWeightMatrix0to1[4][1] = 1.1311882825473;
   fWeightMatrix0to1[5][1] = -0.166368454074899;
   fWeightMatrix0to1[6][1] = -0.998597360632153;
   fWeightMatrix0to1[7][1] = 1.70647728035585;
   fWeightMatrix0to1[8][1] = 1.8383418470337;
   fWeightMatrix0to1[9][1] = 0.230557206714046;
   fWeightMatrix0to1[10][1] = 0.32893348323043;
   fWeightMatrix0to1[11][1] = -0.312993104801475;
   fWeightMatrix0to1[0][2] = 0.138874981911961;
   fWeightMatrix0to1[1][2] = 0.383123162624494;
   fWeightMatrix0to1[2][2] = 0.404325978769622;
   fWeightMatrix0to1[3][2] = -1.84881921229344;
   fWeightMatrix0to1[4][2] = 1.87478591338588;
   fWeightMatrix0to1[5][2] = -0.0321805134880711;
   fWeightMatrix0to1[6][2] = -0.173751544028926;
   fWeightMatrix0to1[7][2] = -0.23110400676349;
   fWeightMatrix0to1[8][2] = -0.496148250576307;
   fWeightMatrix0to1[9][2] = -0.602898503717735;
   fWeightMatrix0to1[10][2] = -0.660964431729048;
   fWeightMatrix0to1[11][2] = 0.930134168061604;
   fWeightMatrix0to1[0][3] = 0.860048175098089;
   fWeightMatrix0to1[1][3] = 0.536965391710769;
   fWeightMatrix0to1[2][3] = 0.812020265523432;
   fWeightMatrix0to1[3][3] = 1.83202372036783;
   fWeightMatrix0to1[4][3] = -0.998552632759102;
   fWeightMatrix0to1[5][3] = -0.170008062752894;
   fWeightMatrix0to1[6][3] = -0.379479603847301;
   fWeightMatrix0to1[7][3] = -2.11983226740107;
   fWeightMatrix0to1[8][3] = -0.892143316678764;
   fWeightMatrix0to1[9][3] = -1.13969739444068;
   fWeightMatrix0to1[10][3] = -0.351686402437062;
   fWeightMatrix0to1[11][3] = 0.431462180819865;
   fWeightMatrix0to1[0][4] = 0.138772183614904;
   fWeightMatrix0to1[1][4] = -0.0178651948325183;
   fWeightMatrix0to1[2][4] = 0.9301751277476;
   fWeightMatrix0to1[3][4] = -1.04017518118214;
   fWeightMatrix0to1[4][4] = -0.248549277170069;
   fWeightMatrix0to1[5][4] = -1.54543663240122;
   fWeightMatrix0to1[6][4] = -0.801716373179114;
   fWeightMatrix0to1[7][4] = 0.165240972253523;
   fWeightMatrix0to1[8][4] = -0.197443849704718;
   fWeightMatrix0to1[9][4] = 0.436441964719583;
   fWeightMatrix0to1[10][4] = -0.0321947258042463;
   fWeightMatrix0to1[11][4] = -0.514197420109861;
   fWeightMatrix0to1[0][5] = -0.618402654853365;
   fWeightMatrix0to1[1][5] = -0.313965049058966;
   fWeightMatrix0to1[2][5] = -2.53094669320195;
   fWeightMatrix0to1[3][5] = 0.69650253275276;
   fWeightMatrix0to1[4][5] = 0.710023069531411;
   fWeightMatrix0to1[5][5] = -1.8388172129349;
   fWeightMatrix0to1[6][5] = 1.20220708099028;
   fWeightMatrix0to1[7][5] = -0.582620464652825;
   fWeightMatrix0to1[8][5] = -0.172734966018485;
   fWeightMatrix0to1[9][5] = 1.04341364481047;
   fWeightMatrix0to1[10][5] = 0.171946552089645;
   fWeightMatrix0to1[11][5] = -0.978900993053251;
   fWeightMatrix0to1[0][6] = -1.41998577162993;
   fWeightMatrix0to1[1][6] = -0.865324896141606;
   fWeightMatrix0to1[2][6] = 1.49049458743851;
   fWeightMatrix0to1[3][6] = -0.58205653664835;
   fWeightMatrix0to1[4][6] = 0.00999447841616979;
   fWeightMatrix0to1[5][6] = -0.359986808590419;
   fWeightMatrix0to1[6][6] = -1.97764708062606;
   fWeightMatrix0to1[7][6] = 0.91997130876799;
   fWeightMatrix0to1[8][6] = 0.712223047462948;
   fWeightMatrix0to1[9][6] = 0.0546039711004311;
   fWeightMatrix0to1[10][6] = 2.06638947791944;
   fWeightMatrix0to1[11][6] = 0.641655852162619;
   fWeightMatrix0to1[0][7] = -0.321155806937596;
   fWeightMatrix0to1[1][7] = 0.17632242379608;
   fWeightMatrix0to1[2][7] = 1.33941984576778;
   fWeightMatrix0to1[3][7] = -0.379832480999721;
   fWeightMatrix0to1[4][7] = 1.11651118101258;
   fWeightMatrix0to1[5][7] = 0.0880201627195484;
   fWeightMatrix0to1[6][7] = 0.0465777921662186;
   fWeightMatrix0to1[7][7] = -0.808235103040409;
   fWeightMatrix0to1[8][7] = -0.379403188885857;
   fWeightMatrix0to1[9][7] = 1.87839296845768;
   fWeightMatrix0to1[10][7] = -0.656797467955032;
   fWeightMatrix0to1[11][7] = 2.2240940079751;
   fWeightMatrix0to1[0][8] = -0.180720710274282;
   fWeightMatrix0to1[1][8] = 0.131797993714753;
   fWeightMatrix0to1[2][8] = 2.47891134092949;
   fWeightMatrix0to1[3][8] = -2.08195701845977;
   fWeightMatrix0to1[4][8] = 0.302827653097163;
   fWeightMatrix0to1[5][8] = 0.433480248788263;
   fWeightMatrix0to1[6][8] = -0.284398050716946;
   fWeightMatrix0to1[7][8] = -0.234147524778565;
   fWeightMatrix0to1[8][8] = 2.10391883249321;
   fWeightMatrix0to1[9][8] = 0.441851800538043;
   fWeightMatrix0to1[10][8] = -0.160553231941192;
   fWeightMatrix0to1[11][8] = -1.30970323773329;
   fWeightMatrix0to1[0][9] = 0.324526752861528;
   fWeightMatrix0to1[1][9] = -2.46570322976323;
   fWeightMatrix0to1[2][9] = 2.15582104440723;
   fWeightMatrix0to1[3][9] = -1.16921072483682;
   fWeightMatrix0to1[4][9] = 0.511947399730409;
   fWeightMatrix0to1[5][9] = 0.288408301080512;
   fWeightMatrix0to1[6][9] = -1.50267898796417;
   fWeightMatrix0to1[7][9] = 0.726628749049921;
   fWeightMatrix0to1[8][9] = -0.983841116282634;
   fWeightMatrix0to1[9][9] = -2.36786964597525;
   fWeightMatrix0to1[10][9] = -1.48674707613788;
   fWeightMatrix0to1[11][9] = -0.213231803941346;
   fWeightMatrix0to1[0][10] = -0.267096577873847;
   fWeightMatrix0to1[1][10] = 1.6917044067354;
   fWeightMatrix0to1[2][10] = 1.11822619120366;
   fWeightMatrix0to1[3][10] = -1.83482498591703;
   fWeightMatrix0to1[4][10] = 0.418076236794588;
   fWeightMatrix0to1[5][10] = -1.87822126020697;
   fWeightMatrix0to1[6][10] = -1.84051367827486;
   fWeightMatrix0to1[7][10] = 0.84904046822622;
   fWeightMatrix0to1[8][10] = -0.407534480883594;
   fWeightMatrix0to1[9][10] = -0.250307503013924;
   fWeightMatrix0to1[10][10] = -1.27168982643953;
   fWeightMatrix0to1[11][10] = -1.30294960688802;
   fWeightMatrix0to1[0][11] = -1.34525557967546;
   fWeightMatrix0to1[1][11] = -1.29616161150223;
   fWeightMatrix0to1[2][11] = 0.013108073336852;
   fWeightMatrix0to1[3][11] = 1.12257273805554;
   fWeightMatrix0to1[4][11] = 1.71793663196078;
   fWeightMatrix0to1[5][11] = 1.60764601891807;
   fWeightMatrix0to1[6][11] = -0.869352492260657;
   fWeightMatrix0to1[7][11] = -1.44811057245234;
   fWeightMatrix0to1[8][11] = 2.00173352201059;
   fWeightMatrix0to1[9][11] = -2.44564475465293;
   fWeightMatrix0to1[10][11] = 0.0281534915388234;
   fWeightMatrix0to1[11][11] = -0.348513226009141;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = -1.48568389190751;
   fWeightMatrix1to2[1][0] = 0.0865544745501835;
   fWeightMatrix1to2[2][0] = -0.877474644086871;
   fWeightMatrix1to2[3][0] = -0.898426729878528;
   fWeightMatrix1to2[4][0] = -1.95564877365745;
   fWeightMatrix1to2[5][0] = 0.78342770934655;
   fWeightMatrix1to2[6][0] = 1.86858333599301;
   fWeightMatrix1to2[7][0] = 1.35587653357704;
   fWeightMatrix1to2[8][0] = 1.53430383102352;
   fWeightMatrix1to2[9][0] = -1.96812477941741;
   fWeightMatrix1to2[10][0] = -0.265968664824526;
   fWeightMatrix1to2[0][1] = 0.851693195063922;
   fWeightMatrix1to2[1][1] = -1.56421591381463;
   fWeightMatrix1to2[2][1] = -0.899739814142478;
   fWeightMatrix1to2[3][1] = -0.0766905274442043;
   fWeightMatrix1to2[4][1] = 1.26653893575492;
   fWeightMatrix1to2[5][1] = -2.47447551863817;
   fWeightMatrix1to2[6][1] = -0.712217185837311;
   fWeightMatrix1to2[7][1] = -0.212985876698859;
   fWeightMatrix1to2[8][1] = -1.38574963444086;
   fWeightMatrix1to2[9][1] = -1.24954209962979;
   fWeightMatrix1to2[10][1] = 0.0120689399094671;
   fWeightMatrix1to2[0][2] = 0.511833531590644;
   fWeightMatrix1to2[1][2] = -0.337118890700066;
   fWeightMatrix1to2[2][2] = -1.1355073334991;
   fWeightMatrix1to2[3][2] = -0.235659888111727;
   fWeightMatrix1to2[4][2] = 1.68108514679904;
   fWeightMatrix1to2[5][2] = -0.685131612587853;
   fWeightMatrix1to2[6][2] = 0.989522139998497;
   fWeightMatrix1to2[7][2] = -2.11464913975215;
   fWeightMatrix1to2[8][2] = 0.32762501876918;
   fWeightMatrix1to2[9][2] = -0.209525451175814;
   fWeightMatrix1to2[10][2] = -0.256493758071642;
   fWeightMatrix1to2[0][3] = -1.32462517053823;
   fWeightMatrix1to2[1][3] = 0.0105184998115805;
   fWeightMatrix1to2[2][3] = 1.10550640173464;
   fWeightMatrix1to2[3][3] = -2.07452287860907;
   fWeightMatrix1to2[4][3] = 0.636392753924104;
   fWeightMatrix1to2[5][3] = -0.137937602301336;
   fWeightMatrix1to2[6][3] = 1.29024911635958;
   fWeightMatrix1to2[7][3] = 0.31403168898666;
   fWeightMatrix1to2[8][3] = -1.82582336868445;
   fWeightMatrix1to2[9][3] = -2.06536618800439;
   fWeightMatrix1to2[10][3] = -0.302286695851031;
   fWeightMatrix1to2[0][4] = 0.360799606172474;
   fWeightMatrix1to2[1][4] = -0.249007203734692;
   fWeightMatrix1to2[2][4] = 0.170929509802295;
   fWeightMatrix1to2[3][4] = -0.975306072262131;
   fWeightMatrix1to2[4][4] = 0.21123068406585;
   fWeightMatrix1to2[5][4] = 0.989426941448279;
   fWeightMatrix1to2[6][4] = -0.948160032953359;
   fWeightMatrix1to2[7][4] = 0.134684055242846;
   fWeightMatrix1to2[8][4] = 1.05091553995128;
   fWeightMatrix1to2[9][4] = 0.648993793989391;
   fWeightMatrix1to2[10][4] = -0.494979060374095;
   fWeightMatrix1to2[0][5] = -0.613657304185895;
   fWeightMatrix1to2[1][5] = -0.12077137134161;
   fWeightMatrix1to2[2][5] = -0.383623548692438;
   fWeightMatrix1to2[3][5] = -0.671492344977729;
   fWeightMatrix1to2[4][5] = -2.06859581624932;
   fWeightMatrix1to2[5][5] = -0.839262100706651;
   fWeightMatrix1to2[6][5] = -0.745149115611615;
   fWeightMatrix1to2[7][5] = 0.348424187907204;
   fWeightMatrix1to2[8][5] = -1.95106337880307;
   fWeightMatrix1to2[9][5] = 1.4405306284749;
   fWeightMatrix1to2[10][5] = -0.747329329705143;
   fWeightMatrix1to2[0][6] = -1.25719549440212;
   fWeightMatrix1to2[1][6] = 1.00447433855651;
   fWeightMatrix1to2[2][6] = -1.24489786966564;
   fWeightMatrix1to2[3][6] = -1.88259474250087;
   fWeightMatrix1to2[4][6] = 0.985556911799408;
   fWeightMatrix1to2[5][6] = -1.58866248230843;
   fWeightMatrix1to2[6][6] = 0.441907421941218;
   fWeightMatrix1to2[7][6] = -2.48224768821585;
   fWeightMatrix1to2[8][6] = -0.974776358435205;
   fWeightMatrix1to2[9][6] = 0.726437930424975;
   fWeightMatrix1to2[10][6] = -3.27134664001373;
   fWeightMatrix1to2[0][7] = 1.0271017921172;
   fWeightMatrix1to2[1][7] = 0.185376051038649;
   fWeightMatrix1to2[2][7] = -0.97933705913719;
   fWeightMatrix1to2[3][7] = -1.28645262772079;
   fWeightMatrix1to2[4][7] = -1.12363161020693;
   fWeightMatrix1to2[5][7] = -0.611439671727062;
   fWeightMatrix1to2[6][7] = -0.637483014412254;
   fWeightMatrix1to2[7][7] = -1.88384273826102;
   fWeightMatrix1to2[8][7] = 0.0549023605341747;
   fWeightMatrix1to2[9][7] = -1.22245632386051;
   fWeightMatrix1to2[10][7] = 1.07744539174648;
   fWeightMatrix1to2[0][8] = -1.59037795151872;
   fWeightMatrix1to2[1][8] = -0.512840531677879;
   fWeightMatrix1to2[2][8] = 0.886872356665446;
   fWeightMatrix1to2[3][8] = 0.758036086015656;
   fWeightMatrix1to2[4][8] = -1.31927003465093;
   fWeightMatrix1to2[5][8] = 1.8531962739199;
   fWeightMatrix1to2[6][8] = -2.37532792755869;
   fWeightMatrix1to2[7][8] = 0.322238960680337;
   fWeightMatrix1to2[8][8] = -0.526957596928218;
   fWeightMatrix1to2[9][8] = -1.95243451648882;
   fWeightMatrix1to2[10][8] = 0.928884873891484;
   fWeightMatrix1to2[0][9] = -0.425378642135783;
   fWeightMatrix1to2[1][9] = 1.20448784102091;
   fWeightMatrix1to2[2][9] = 1.24984503155885;
   fWeightMatrix1to2[3][9] = 0.078640504793885;
   fWeightMatrix1to2[4][9] = 1.01586778421514;
   fWeightMatrix1to2[5][9] = -1.28728503974155;
   fWeightMatrix1to2[6][9] = -1.27757732970517;
   fWeightMatrix1to2[7][9] = -0.815975065633659;
   fWeightMatrix1to2[8][9] = -0.562986186316554;
   fWeightMatrix1to2[9][9] = -1.24612244441362;
   fWeightMatrix1to2[10][9] = -0.910625292796999;
   fWeightMatrix1to2[0][10] = 0.905851765550299;
   fWeightMatrix1to2[1][10] = -0.852211692603032;
   fWeightMatrix1to2[2][10] = -2.04891225387201;
   fWeightMatrix1to2[3][10] = 1.26918858505613;
   fWeightMatrix1to2[4][10] = -1.22893394909703;
   fWeightMatrix1to2[5][10] = -0.702831998745524;
   fWeightMatrix1to2[6][10] = -1.30774254146412;
   fWeightMatrix1to2[7][10] = 0.353463195244114;
   fWeightMatrix1to2[8][10] = -0.587015526459941;
   fWeightMatrix1to2[9][10] = -0.813047989015593;
   fWeightMatrix1to2[10][10] = -0.971124875775387;
   fWeightMatrix1to2[0][11] = 0.754543255432467;
   fWeightMatrix1to2[1][11] = -0.585133462206116;
   fWeightMatrix1to2[2][11] = 0.0410131032182505;
   fWeightMatrix1to2[3][11] = -1.27358789154762;
   fWeightMatrix1to2[4][11] = -0.727638359901824;
   fWeightMatrix1to2[5][11] = -1.28468235065272;
   fWeightMatrix1to2[6][11] = 0.852150500629808;
   fWeightMatrix1to2[7][11] = 0.914264911276255;
   fWeightMatrix1to2[8][11] = -0.376043585358862;
   fWeightMatrix1to2[9][11] = 0.656867356127227;
   fWeightMatrix1to2[10][11] = 0.290470960479303;
   fWeightMatrix1to2[0][12] = -0.182680106855064;
   fWeightMatrix1to2[1][12] = -0.264682192087415;
   fWeightMatrix1to2[2][12] = -0.679372363764981;
   fWeightMatrix1to2[3][12] = -0.450895416293371;
   fWeightMatrix1to2[4][12] = 1.64341621899653;
   fWeightMatrix1to2[5][12] = 1.22829044875902;
   fWeightMatrix1to2[6][12] = -2.05618031618682;
   fWeightMatrix1to2[7][12] = -2.02590474668683;
   fWeightMatrix1to2[8][12] = 0.141629160320324;
   fWeightMatrix1to2[9][12] = -0.317248232563357;
   fWeightMatrix1to2[10][12] = 0.719446326715711;
   // weight matrix from layer 2 to 3
   fWeightMatrix2to3[0][0] = -0.182045731064515;
   fWeightMatrix2to3[0][1] = -0.0521319959629289;
   fWeightMatrix2to3[0][2] = -0.938551651985426;
   fWeightMatrix2to3[0][3] = -0.452732730868741;
   fWeightMatrix2to3[0][4] = 0.519046062363469;
   fWeightMatrix2to3[0][5] = -0.97587865260127;
   fWeightMatrix2to3[0][6] = -1.45935159332204;
   fWeightMatrix2to3[0][7] = -0.60776438646775;
   fWeightMatrix2to3[0][8] = 0.801844125872954;
   fWeightMatrix2to3[0][9] = -0.235771319981548;
   fWeightMatrix2to3[0][10] = -0.457231031216493;
   fWeightMatrix2to3[0][11] = 1.0561226675435;
}

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
}