// 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 10:42:30 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/job414
Training events: 39628


#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'    [48.899105072,787.769287109]
LepAPt                        LepAPt                            'F'    [20.0004863739,162.429443359]
LepBPt                        LepBPt                            'F'    [10.0008592606,71.1104202271]
MetSigLeptonsJets             MetSigLeptonsJets                 'F'    [1.06155312061,21.7385215759]
MetSpec                       MetSpec                           'F'    [15.0050735474,274.839141846]
SumEtLeptonsJets              SumEtLeptonsJets                  'F'    [30.1484737396,469.726501465]
VSumJetLeptonsPt              VSumJetLeptonsPt                  'F'    [1.09349226952,329.840057373]
addEt                         addEt                             'F'    [48.899105072,391.964660645]
dPhiLepSumMet                 dPhiLepSumMet                     'F'    [0.005448944401,3.14159226418]
dPhiLeptons                   dPhiLeptons                       'F'    [1.13248825073e-05,1.10251176357]
dRLeptons                     dRLeptons                         'F'    [0.200022801757,1.11529541016]
lep1_E                        lep1_E                            'F'    [20.0075798035,206.098724365]
lep2_E                        lep2_E                            'F'    [10.0028533936,107.376251221]


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

#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] = 48.8991050720215;
      fVmax[0] = 787.769287109375;
      fVmin[1] = 20.0004863739014;
      fVmax[1] = 162.429443359375;
      fVmin[2] = 10.0008592605591;
      fVmax[2] = 71.1104202270508;
      fVmin[3] = 1.0615531206131;
      fVmax[3] = 21.7385215759277;
      fVmin[4] = 15.0050735473633;
      fVmax[4] = 274.839141845703;
      fVmin[5] = 30.148473739624;
      fVmax[5] = 469.726501464844;
      fVmin[6] = 1.09349226951599;
      fVmax[6] = 329.840057373047;
      fVmin[7] = 48.8991050720215;
      fVmax[7] = 391.964660644531;
      fVmin[8] = 0.00544894440099597;
      fVmax[8] = 3.14159226417542;
      fVmin[9] = 1.13248825073242e-05;
      fVmax[9] = 1.10251176357269;
      fVmin[10] = 0.200022801756859;
      fVmax[10] = 1.11529541015625;
      fVmin[11] = 20.0075798034668;
      fVmax[11] = 206.098724365234;
      fVmin[12] = 10.0028533935547;
      fVmax[12] = 107.376251220703;

      // 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] = -0.152439552186457;
   fWeightMatrix0to1[1][0] = 2.18079479256545;
   fWeightMatrix0to1[2][0] = 0.791031296210526;
   fWeightMatrix0to1[3][0] = 1.46557475747692;
   fWeightMatrix0to1[4][0] = -1.56184225756949;
   fWeightMatrix0to1[5][0] = -1.36514829178838;
   fWeightMatrix0to1[6][0] = -0.434384388537805;
   fWeightMatrix0to1[7][0] = 1.79764536075083;
   fWeightMatrix0to1[8][0] = -1.54499140286548;
   fWeightMatrix0to1[9][0] = -0.867949716497269;
   fWeightMatrix0to1[10][0] = -1.14285343910601;
   fWeightMatrix0to1[11][0] = -0.147458000735048;
   fWeightMatrix0to1[12][0] = -1.524937602283;
   fWeightMatrix0to1[13][0] = -1.05962628148263;
   fWeightMatrix0to1[0][1] = -0.421231563026552;
   fWeightMatrix0to1[1][1] = 0.745915450725967;
   fWeightMatrix0to1[2][1] = -0.522997381089249;
   fWeightMatrix0to1[3][1] = 0.91034841757329;
   fWeightMatrix0to1[4][1] = 2.56389724284252;
   fWeightMatrix0to1[5][1] = 1.30912852419215;
   fWeightMatrix0to1[6][1] = -2.29715685206532;
   fWeightMatrix0to1[7][1] = -0.0161364893516969;
   fWeightMatrix0to1[8][1] = 0.638030931636167;
   fWeightMatrix0to1[9][1] = 2.29306287773082;
   fWeightMatrix0to1[10][1] = -1.26799364033589;
   fWeightMatrix0to1[11][1] = 0.244624150884799;
   fWeightMatrix0to1[12][1] = 1.14462411076747;
   fWeightMatrix0to1[13][1] = -1.95297734183742;
   fWeightMatrix0to1[0][2] = -1.72338750288555;
   fWeightMatrix0to1[1][2] = 0.396278330103262;
   fWeightMatrix0to1[2][2] = 0.565420035200165;
   fWeightMatrix0to1[3][2] = -0.565301323053809;
   fWeightMatrix0to1[4][2] = 0.959195660460204;
   fWeightMatrix0to1[5][2] = -1.02410097160426;
   fWeightMatrix0to1[6][2] = -2.40359641517643;
   fWeightMatrix0to1[7][2] = 0.404877500469581;
   fWeightMatrix0to1[8][2] = -1.83697766479691;
   fWeightMatrix0to1[9][2] = 4.14628852145469;
   fWeightMatrix0to1[10][2] = -1.97280115688359;
   fWeightMatrix0to1[11][2] = 1.31834245759115;
   fWeightMatrix0to1[12][2] = 0.875252057090126;
   fWeightMatrix0to1[13][2] = -0.212961560873203;
   fWeightMatrix0to1[0][3] = 2.02710240093432;
   fWeightMatrix0to1[1][3] = 1.36548018703085;
   fWeightMatrix0to1[2][3] = -0.83209618862926;
   fWeightMatrix0to1[3][3] = -2.6604658796241;
   fWeightMatrix0to1[4][3] = 0.329036264748641;
   fWeightMatrix0to1[5][3] = 0.60402380993694;
   fWeightMatrix0to1[6][3] = 3.68731375103314;
   fWeightMatrix0to1[7][3] = 1.71852218405228;
   fWeightMatrix0to1[8][3] = 1.32203025789325;
   fWeightMatrix0to1[9][3] = 1.2538734331792;
   fWeightMatrix0to1[10][3] = -1.23642671192143;
   fWeightMatrix0to1[11][3] = 1.12482065180133;
   fWeightMatrix0to1[12][3] = -2.00011525042135;
   fWeightMatrix0to1[13][3] = -1.39485196752492;
   fWeightMatrix0to1[0][4] = -1.08399405480056;
   fWeightMatrix0to1[1][4] = -1.63835045794743;
   fWeightMatrix0to1[2][4] = 1.46289364821765;
   fWeightMatrix0to1[3][4] = -0.391122460996191;
   fWeightMatrix0to1[4][4] = -0.856835274306764;
   fWeightMatrix0to1[5][4] = 1.05525779503417;
   fWeightMatrix0to1[6][4] = -0.348228631032701;
   fWeightMatrix0to1[7][4] = 1.75980960535051;
   fWeightMatrix0to1[8][4] = 1.71576631625087;
   fWeightMatrix0to1[9][4] = 0.531182438950925;
   fWeightMatrix0to1[10][4] = 0.468047729462358;
   fWeightMatrix0to1[11][4] = -1.04085380708566;
   fWeightMatrix0to1[12][4] = 0.540648654304222;
   fWeightMatrix0to1[13][4] = -1.58861116605653;
   fWeightMatrix0to1[0][5] = -0.577279953026339;
   fWeightMatrix0to1[1][5] = -1.23110990289471;
   fWeightMatrix0to1[2][5] = 0.642618992655834;
   fWeightMatrix0to1[3][5] = 1.11814375788264;
   fWeightMatrix0to1[4][5] = 1.89188116012266;
   fWeightMatrix0to1[5][5] = -0.392109057805278;
   fWeightMatrix0to1[6][5] = -1.89647150539494;
   fWeightMatrix0to1[7][5] = -2.27986339467037;
   fWeightMatrix0to1[8][5] = 0.888120654554429;
   fWeightMatrix0to1[9][5] = 0.655929797546082;
   fWeightMatrix0to1[10][5] = 2.42084478887018;
   fWeightMatrix0to1[11][5] = 0.981563334828013;
   fWeightMatrix0to1[12][5] = 0.706525580422921;
   fWeightMatrix0to1[13][5] = -1.39840378768272;
   fWeightMatrix0to1[0][6] = -0.692437992502043;
   fWeightMatrix0to1[1][6] = -0.121219267424442;
   fWeightMatrix0to1[2][6] = 1.17035128664446;
   fWeightMatrix0to1[3][6] = -2.89733847201603;
   fWeightMatrix0to1[4][6] = 0.605638166003461;
   fWeightMatrix0to1[5][6] = -1.60850818210668;
   fWeightMatrix0to1[6][6] = -2.60835446491966;
   fWeightMatrix0to1[7][6] = 1.53355766201092;
   fWeightMatrix0to1[8][6] = 1.67370035568222;
   fWeightMatrix0to1[9][6] = 0.891145118085795;
   fWeightMatrix0to1[10][6] = -0.890094471764891;
   fWeightMatrix0to1[11][6] = 2.23163352236111;
   fWeightMatrix0to1[12][6] = -1.03917433107152;
   fWeightMatrix0to1[13][6] = -2.10102996541098;
   fWeightMatrix0to1[0][7] = -1.25128925355959;
   fWeightMatrix0to1[1][7] = 0.248473936383797;
   fWeightMatrix0to1[2][7] = -2.05000239437913;
   fWeightMatrix0to1[3][7] = -5.32196449879343;
   fWeightMatrix0to1[4][7] = 4.26972461291065;
   fWeightMatrix0to1[5][7] = 1.42301795908168;
   fWeightMatrix0to1[6][7] = -5.93073333958047;
   fWeightMatrix0to1[7][7] = 4.40938288346109;
   fWeightMatrix0to1[8][7] = -0.93543531015328;
   fWeightMatrix0to1[9][7] = 3.98740619140761;
   fWeightMatrix0to1[10][7] = -0.669908055838929;
   fWeightMatrix0to1[11][7] = 0.909766126197712;
   fWeightMatrix0to1[12][7] = -1.88135509937643;
   fWeightMatrix0to1[13][7] = -0.802353838741631;
   fWeightMatrix0to1[0][8] = 0.159728006170076;
   fWeightMatrix0to1[1][8] = 0.167516927227825;
   fWeightMatrix0to1[2][8] = -1.48656495325727;
   fWeightMatrix0to1[3][8] = 3.48367558649619;
   fWeightMatrix0to1[4][8] = 1.32057903311509;
   fWeightMatrix0to1[5][8] = -1.09712328279773;
   fWeightMatrix0to1[6][8] = 2.04961770691039;
   fWeightMatrix0to1[7][8] = -1.19799681530899;
   fWeightMatrix0to1[8][8] = -1.59898367027062;
   fWeightMatrix0to1[9][8] = 1.99675326566539;
   fWeightMatrix0to1[10][8] = -1.07360209806507;
   fWeightMatrix0to1[11][8] = 1.62873424330093;
   fWeightMatrix0to1[12][8] = 0.964687191828404;
   fWeightMatrix0to1[13][8] = 0.20813776594297;
   fWeightMatrix0to1[0][9] = -0.354327582631219;
   fWeightMatrix0to1[1][9] = 0.451534923571479;
   fWeightMatrix0to1[2][9] = -2.07668588058219;
   fWeightMatrix0to1[3][9] = 0.726115411129646;
   fWeightMatrix0to1[4][9] = 1.12689499268904;
   fWeightMatrix0to1[5][9] = -1.51254034799266;
   fWeightMatrix0to1[6][9] = -0.825534259541042;
   fWeightMatrix0to1[7][9] = 0.308197951206449;
   fWeightMatrix0to1[8][9] = -0.235588186345832;
   fWeightMatrix0to1[9][9] = -0.207943773419894;
   fWeightMatrix0to1[10][9] = 1.9574536094828;
   fWeightMatrix0to1[11][9] = -2.5088919246989;
   fWeightMatrix0to1[12][9] = -0.183803755005573;
   fWeightMatrix0to1[13][9] = 0.870204698651245;
   fWeightMatrix0to1[0][10] = 1.56882504140597;
   fWeightMatrix0to1[1][10] = 0.335660933550778;
   fWeightMatrix0to1[2][10] = -1.58525478085447;
   fWeightMatrix0to1[3][10] = -0.553090948832041;
   fWeightMatrix0to1[4][10] = -0.562892309286924;
   fWeightMatrix0to1[5][10] = 1.73007518172328;
   fWeightMatrix0to1[6][10] = -0.969222308900282;
   fWeightMatrix0to1[7][10] = -1.49348247158269;
   fWeightMatrix0to1[8][10] = -0.622736058603812;
   fWeightMatrix0to1[9][10] = 0.299510384338769;
   fWeightMatrix0to1[10][10] = -1.36645004246045;
   fWeightMatrix0to1[11][10] = 0.40303900849332;
   fWeightMatrix0to1[12][10] = 1.26156043865059;
   fWeightMatrix0to1[13][10] = -0.791872999488422;
   fWeightMatrix0to1[0][11] = -0.448008349028369;
   fWeightMatrix0to1[1][11] = 0.3111201468093;
   fWeightMatrix0to1[2][11] = 0.83753519022392;
   fWeightMatrix0to1[3][11] = 0.829649441819839;
   fWeightMatrix0to1[4][11] = 1.56577790446854;
   fWeightMatrix0to1[5][11] = -1.18818821807556;
   fWeightMatrix0to1[6][11] = 3.4363298509452;
   fWeightMatrix0to1[7][11] = 0.724436934831439;
   fWeightMatrix0to1[8][11] = 0.444014908573085;
   fWeightMatrix0to1[9][11] = 0.815193168694489;
   fWeightMatrix0to1[10][11] = 1.57202496972573;
   fWeightMatrix0to1[11][11] = -0.359442887830592;
   fWeightMatrix0to1[12][11] = -0.984448254863713;
   fWeightMatrix0to1[13][11] = 0.424308080751598;
   fWeightMatrix0to1[0][12] = -0.774305338621929;
   fWeightMatrix0to1[1][12] = -0.494628660504796;
   fWeightMatrix0to1[2][12] = 1.7267196273359;
   fWeightMatrix0to1[3][12] = 0.0920769090076305;
   fWeightMatrix0to1[4][12] = -0.10860034877007;
   fWeightMatrix0to1[5][12] = 1.75027080091799;
   fWeightMatrix0to1[6][12] = 1.81331418049058;
   fWeightMatrix0to1[7][12] = -1.38345415484846;
   fWeightMatrix0to1[8][12] = 0.37048172728514;
   fWeightMatrix0to1[9][12] = 1.81046071247136;
   fWeightMatrix0to1[10][12] = 0.161899747511968;
   fWeightMatrix0to1[11][12] = -1.50460457133028;
   fWeightMatrix0to1[12][12] = -1.99897129156981;
   fWeightMatrix0to1[13][12] = 0.435809996538409;
   fWeightMatrix0to1[0][13] = 1.66149465233425;
   fWeightMatrix0to1[1][13] = -1.91889855383697;
   fWeightMatrix0to1[2][13] = -0.198394740329347;
   fWeightMatrix0to1[3][13] = -5.66618108104114;
   fWeightMatrix0to1[4][13] = 4.81185328394036;
   fWeightMatrix0to1[5][13] = 0.952940763031861;
   fWeightMatrix0to1[6][13] = -9.43763957567097;
   fWeightMatrix0to1[7][13] = 4.33057790544463;
   fWeightMatrix0to1[8][13] = -0.500528500476801;
   fWeightMatrix0to1[9][13] = 8.05470402822451;
   fWeightMatrix0to1[10][13] = -0.362464033111804;
   fWeightMatrix0to1[11][13] = 1.51942640567297;
   fWeightMatrix0to1[12][13] = 1.00703699373798;
   fWeightMatrix0to1[13][13] = -0.69772231190871;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = -3.12787285409358;
   fWeightMatrix1to2[1][0] = 0.1367107200382;
   fWeightMatrix1to2[2][0] = -0.0326713996690597;
   fWeightMatrix1to2[3][0] = -1.64430288181875;
   fWeightMatrix1to2[4][0] = -0.691756049548955;
   fWeightMatrix1to2[5][0] = -0.462788758038252;
   fWeightMatrix1to2[6][0] = -0.664400022898371;
   fWeightMatrix1to2[7][0] = -1.43051674251916;
   fWeightMatrix1to2[8][0] = 0.606740541716986;
   fWeightMatrix1to2[9][0] = 0.148005897541379;
   fWeightMatrix1to2[10][0] = -1.21328924284122;
   fWeightMatrix1to2[11][0] = -0.436006352044658;
   fWeightMatrix1to2[12][0] = -0.122495989882759;
   fWeightMatrix1to2[0][1] = 1.19422170027039;
   fWeightMatrix1to2[1][1] = -0.0266831781922975;
   fWeightMatrix1to2[2][1] = -0.220965648227146;
   fWeightMatrix1to2[3][1] = -1.9163441848335;
   fWeightMatrix1to2[4][1] = -0.923571586218113;
   fWeightMatrix1to2[5][1] = 1.79720893978085;
   fWeightMatrix1to2[6][1] = 1.30808449081963;
   fWeightMatrix1to2[7][1] = 0.779862150879022;
   fWeightMatrix1to2[8][1] = 0.161328989209591;
   fWeightMatrix1to2[9][1] = 1.91373704356273;
   fWeightMatrix1to2[10][1] = -1.6521741697889;
   fWeightMatrix1to2[11][1] = -1.26403636348076;
   fWeightMatrix1to2[12][1] = -0.188676042653716;
   fWeightMatrix1to2[0][2] = -1.22352661883923;
   fWeightMatrix1to2[1][2] = 1.06894700859972;
   fWeightMatrix1to2[2][2] = 1.67341943194195;
   fWeightMatrix1to2[3][2] = -1.23400657685263;
   fWeightMatrix1to2[4][2] = 0.789710107157542;
   fWeightMatrix1to2[5][2] = -0.34611154089812;
   fWeightMatrix1to2[6][2] = -1.77959037518899;
   fWeightMatrix1to2[7][2] = -1.35194219176202;
   fWeightMatrix1to2[8][2] = 1.09924799991188;
   fWeightMatrix1to2[9][2] = -0.137200808010733;
   fWeightMatrix1to2[10][2] = 0.901787476483927;
   fWeightMatrix1to2[11][2] = -1.14430371327042;
   fWeightMatrix1to2[12][2] = 1.67246455333558;
   fWeightMatrix1to2[0][3] = -1.75587395997867;
   fWeightMatrix1to2[1][3] = 4.38854732247668;
   fWeightMatrix1to2[2][3] = 0.925558178785458;
   fWeightMatrix1to2[3][3] = -1.6662526162909;
   fWeightMatrix1to2[4][3] = -1.31350863202838;
   fWeightMatrix1to2[5][3] = -1.57072282859189;
   fWeightMatrix1to2[6][3] = -0.76348616516508;
   fWeightMatrix1to2[7][3] = -1.86227033517865;
   fWeightMatrix1to2[8][3] = -1.79873077457926;
   fWeightMatrix1to2[9][3] = -1.89343146101035;
   fWeightMatrix1to2[10][3] = -0.154470020263966;
   fWeightMatrix1to2[11][3] = -1.22222556613966;
   fWeightMatrix1to2[12][3] = -2.73581927908241;
   fWeightMatrix1to2[0][4] = 0.949611281449322;
   fWeightMatrix1to2[1][4] = -4.91239685126414;
   fWeightMatrix1to2[2][4] = -0.431005803479942;
   fWeightMatrix1to2[3][4] = -1.24088981635954;
   fWeightMatrix1to2[4][4] = 0.214821747887121;
   fWeightMatrix1to2[5][4] = 0.943070175328502;
   fWeightMatrix1to2[6][4] = 0.941076540884255;
   fWeightMatrix1to2[7][4] = -0.918896849012684;
   fWeightMatrix1to2[8][4] = -2.0147193437971;
   fWeightMatrix1to2[9][4] = -1.54982157665389;
   fWeightMatrix1to2[10][4] = -0.596714323350211;
   fWeightMatrix1to2[11][4] = -1.49971924535204;
   fWeightMatrix1to2[12][4] = -0.376538862277041;
   fWeightMatrix1to2[0][5] = -0.68814479442689;
   fWeightMatrix1to2[1][5] = 0.919127097168252;
   fWeightMatrix1to2[2][5] = -0.767052343127794;
   fWeightMatrix1to2[3][5] = 0.0321185368632067;
   fWeightMatrix1to2[4][5] = -0.529497414276476;
   fWeightMatrix1to2[5][5] = -2.85926308405785;
   fWeightMatrix1to2[6][5] = 0.518828309351065;
   fWeightMatrix1to2[7][5] = 1.47649989481706;
   fWeightMatrix1to2[8][5] = -1.63755408839567;
   fWeightMatrix1to2[9][5] = -1.75976182227058;
   fWeightMatrix1to2[10][5] = 0.214857648710998;
   fWeightMatrix1to2[11][5] = 0.937949429038982;
   fWeightMatrix1to2[12][5] = -1.18521404355327;
   fWeightMatrix1to2[0][6] = -2.35131556481023;
   fWeightMatrix1to2[1][6] = 5.07061517652297;
   fWeightMatrix1to2[2][6] = 1.82659706133386;
   fWeightMatrix1to2[3][6] = 0.72261603971234;
   fWeightMatrix1to2[4][6] = 0.986682328864341;
   fWeightMatrix1to2[5][6] = -2.03375623449036;
   fWeightMatrix1to2[6][6] = -0.580755174172081;
   fWeightMatrix1to2[7][6] = 0.281998580127071;
   fWeightMatrix1to2[8][6] = -2.24157497853121;
   fWeightMatrix1to2[9][6] = -0.840628396791638;
   fWeightMatrix1to2[10][6] = -2.9116851261697;
   fWeightMatrix1to2[11][6] = -1.98541193271157;
   fWeightMatrix1to2[12][6] = -2.2107033110766;
   fWeightMatrix1to2[0][7] = 1.87992490718644;
   fWeightMatrix1to2[1][7] = -4.83523912340383;
   fWeightMatrix1to2[2][7] = 0.649028372767886;
   fWeightMatrix1to2[3][7] = -1.41238957022888;
   fWeightMatrix1to2[4][7] = 1.13252984214362;
   fWeightMatrix1to2[5][7] = -1.24457953465269;
   fWeightMatrix1to2[6][7] = 0.343359058458807;
   fWeightMatrix1to2[7][7] = 1.00345039727837;
   fWeightMatrix1to2[8][7] = -1.87948606945966;
   fWeightMatrix1to2[9][7] = -0.000682289145044944;
   fWeightMatrix1to2[10][7] = 0.764562179398619;
   fWeightMatrix1to2[11][7] = -1.76869269901281;
   fWeightMatrix1to2[12][7] = -2.10421280649458;
   fWeightMatrix1to2[0][8] = -1.66066303411733;
   fWeightMatrix1to2[1][8] = -1.40466592133981;
   fWeightMatrix1to2[2][8] = 1.63899913340403;
   fWeightMatrix1to2[3][8] = -0.545977042185538;
   fWeightMatrix1to2[4][8] = 0.275081750948815;
   fWeightMatrix1to2[5][8] = -1.25225647561285;
   fWeightMatrix1to2[6][8] = -1.17410723145719;
   fWeightMatrix1to2[7][8] = -1.8300735819148;
   fWeightMatrix1to2[8][8] = -1.00199890547339;
   fWeightMatrix1to2[9][8] = -1.44075311664262;
   fWeightMatrix1to2[10][8] = 0.871801699858399;
   fWeightMatrix1to2[11][8] = -1.35657542840781;
   fWeightMatrix1to2[12][8] = -1.23911862455263;
   fWeightMatrix1to2[0][9] = 3.03293211946455;
   fWeightMatrix1to2[1][9] = -5.61165441299675;
   fWeightMatrix1to2[2][9] = -1.38687002287624;
   fWeightMatrix1to2[3][9] = 1.76779883318484;
   fWeightMatrix1to2[4][9] = -1.79221837632117;
   fWeightMatrix1to2[5][9] = 2.12916015069966;
   fWeightMatrix1to2[6][9] = 0.884329180532474;
   fWeightMatrix1to2[7][9] = -0.693170532218105;
   fWeightMatrix1to2[8][9] = -1.25597211579543;
   fWeightMatrix1to2[9][9] = -0.0408636561765163;
   fWeightMatrix1to2[10][9] = 0.343899423093176;
   fWeightMatrix1to2[11][9] = 1.12906033733577;
   fWeightMatrix1to2[12][9] = 0.874579809305211;
   fWeightMatrix1to2[0][10] = -0.0383860468490221;
   fWeightMatrix1to2[1][10] = 1.42826062446321;
   fWeightMatrix1to2[2][10] = -1.79531741106538;
   fWeightMatrix1to2[3][10] = 0.756776546438759;
   fWeightMatrix1to2[4][10] = -1.92675831110257;
   fWeightMatrix1to2[5][10] = -0.397738562954895;
   fWeightMatrix1to2[6][10] = -2.17651228495509;
   fWeightMatrix1to2[7][10] = -1.68809565810555;
   fWeightMatrix1to2[8][10] = -1.02485142803126;
   fWeightMatrix1to2[9][10] = 0.555136242242051;
   fWeightMatrix1to2[10][10] = -0.652813836086494;
   fWeightMatrix1to2[11][10] = 0.618791709321964;
   fWeightMatrix1to2[12][10] = -1.08707674646477;
   fWeightMatrix1to2[0][11] = -1.11685561258707;
   fWeightMatrix1to2[1][11] = -1.54865725661029;
   fWeightMatrix1to2[2][11] = -0.0775181091216244;
   fWeightMatrix1to2[3][11] = 1.69894932274186;
   fWeightMatrix1to2[4][11] = -1.56072491581661;
   fWeightMatrix1to2[5][11] = -0.668566383126374;
   fWeightMatrix1to2[6][11] = -2.13144438536211;
   fWeightMatrix1to2[7][11] = -0.250918736714533;
   fWeightMatrix1to2[8][11] = -0.519042721400405;
   fWeightMatrix1to2[9][11] = -0.48598193698788;
   fWeightMatrix1to2[10][11] = -1.76747173669327;
   fWeightMatrix1to2[11][11] = 0.676449902156844;
   fWeightMatrix1to2[12][11] = 0.0159448970948474;
   fWeightMatrix1to2[0][12] = -0.625697564245071;
   fWeightMatrix1to2[1][12] = -0.667765839682345;
   fWeightMatrix1to2[2][12] = -1.29048659884632;
   fWeightMatrix1to2[3][12] = -1.80454065984463;
   fWeightMatrix1to2[4][12] = -1.73627945951494;
   fWeightMatrix1to2[5][12] = 0.0266291632543861;
   fWeightMatrix1to2[6][12] = -1.17574330367879;
   fWeightMatrix1to2[7][12] = -0.728235365602283;
   fWeightMatrix1to2[8][12] = -0.481284681936726;
   fWeightMatrix1to2[9][12] = -2.17417920174992;
   fWeightMatrix1to2[10][12] = -1.74462562045105;
   fWeightMatrix1to2[11][12] = -1.9674743343258;
   fWeightMatrix1to2[12][12] = -0.688154947579063;
   fWeightMatrix1to2[0][13] = -2.71283421360704;
   fWeightMatrix1to2[1][13] = -2.42148425030618;
   fWeightMatrix1to2[2][13] = -0.881820164673091;
   fWeightMatrix1to2[3][13] = -1.11908626120378;
   fWeightMatrix1to2[4][13] = -1.62012919785461;
   fWeightMatrix1to2[5][13] = 0.561715969863393;
   fWeightMatrix1to2[6][13] = -1.94354502784573;
   fWeightMatrix1to2[7][13] = -0.709873179299158;
   fWeightMatrix1to2[8][13] = 0.889248183674207;
   fWeightMatrix1to2[9][13] = 0.260897699518087;
   fWeightMatrix1to2[10][13] = -0.272194456505819;
   fWeightMatrix1to2[11][13] = -0.238000658240805;
   fWeightMatrix1to2[12][13] = -0.360964816532286;
   fWeightMatrix1to2[0][14] = 0.640478970292694;
   fWeightMatrix1to2[1][14] = -0.640800174044537;
   fWeightMatrix1to2[2][14] = -1.47642218612492;
   fWeightMatrix1to2[3][14] = -0.966609436303077;
   fWeightMatrix1to2[4][14] = -1.73501602068977;
   fWeightMatrix1to2[5][14] = -0.175302917823886;
   fWeightMatrix1to2[6][14] = 0.175026800469408;
   fWeightMatrix1to2[7][14] = 0.392290644934597;
   fWeightMatrix1to2[8][14] = 0.151615083289458;
   fWeightMatrix1to2[9][14] = 0.241362941352202;
   fWeightMatrix1to2[10][14] = 0.916812297990016;
   fWeightMatrix1to2[11][14] = -1.64646031777023;
   fWeightMatrix1to2[12][14] = -0.0304071589248944;
   // weight matrix from layer 2 to 3
   fWeightMatrix2to3[0][0] = -0.23076796572028;
   fWeightMatrix2to3[0][1] = -0.756424535604865;
   fWeightMatrix2to3[0][2] = -0.668155150359988;
   fWeightMatrix2to3[0][3] = 0.430613958947046;
   fWeightMatrix2to3[0][4] = 0.915495079083844;
   fWeightMatrix2to3[0][5] = 0.498551942159678;
   fWeightMatrix2to3[0][6] = -0.322401366188262;
   fWeightMatrix2to3[0][7] = 1.9343581635561;
   fWeightMatrix2to3[0][8] = -0.431822255300582;
   fWeightMatrix2to3[0][9] = 1.01893650925179;
   fWeightMatrix2to3[0][10] = 0.387043925160743;
   fWeightMatrix2to3[0][11] = 0.427990952559679;
   fWeightMatrix2to3[0][12] = -0.262190225993011;
   fWeightMatrix2to3[0][13] = 0.765485847903963;
}

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
}