// 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 10:52:20 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/job409
Training events: 52820


#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'    [48.6484184265,888.225952148]
LepAPt                        LepAPt                            'F'    [20.0000362396,186.522079468]
LepBPt                        LepBPt                            'F'    [10.0008134842,74.1102600098]
MetSigLeptonsJets             MetSigLeptonsJets                 'F'    [1.28498280048,23.5595493317]
MetSpec                       MetSpec                           'F'    [15.0020647049,207.062454224]
SumEtLeptonsJets              SumEtLeptonsJets                  'F'    [30.2123203278,532.093322754]
VSumJetLeptonsPt              VSumJetLeptonsPt                  'F'    [1.91322863102,348.466033936]
addEt                         addEt                             'F'    [48.6484184265,450.732757568]
dPhiLepSumMet                 dPhiLepSumMet                     'F'    [0.00602381536737,3.14159226418]
dPhiLeptons                   dPhiLeptons                       'F'    [5.72204589844e-06,1.13010489941]
dRLeptons                     dRLeptons                         'F'    [0.200046181679,1.15372478962]
lep1_E                        lep1_E                            'F'    [20.0376224518,203.165985107]
lep2_E                        lep2_E                            'F'    [10.0093269348,139.444885254]


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

#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] = 48.6484184265137;
      fVmax[0] = 888.225952148438;
      fVmin[1] = 20.000036239624;
      fVmax[1] = 186.522079467773;
      fVmin[2] = 10.0008134841919;
      fVmax[2] = 74.1102600097656;
      fVmin[3] = 1.2849828004837;
      fVmax[3] = 23.559549331665;
      fVmin[4] = 15.002064704895;
      fVmax[4] = 207.062454223633;
      fVmin[5] = 30.2123203277588;
      fVmax[5] = 532.093322753906;
      fVmin[6] = 1.91322863101959;
      fVmax[6] = 348.466033935547;
      fVmin[7] = 48.6484184265137;
      fVmax[7] = 450.732757568359;
      fVmin[8] = 0.00602381536737084;
      fVmax[8] = 3.14159226417542;
      fVmin[9] = 5.7220458984375e-06;
      fVmax[9] = 1.13010489940643;
      fVmin[10] = 0.200046181678772;
      fVmax[10] = 1.15372478961945;
      fVmin[11] = 20.0376224517822;
      fVmax[11] = 203.165985107422;
      fVmin[12] = 10.0093269348145;
      fVmax[12] = 139.444885253906;

      // 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] = -0.318587829187436;
   fWeightMatrix0to1[1][0] = 2.24608171861358;
   fWeightMatrix0to1[2][0] = 0.801365715311199;
   fWeightMatrix0to1[3][0] = 2.14056766931916;
   fWeightMatrix0to1[4][0] = -2.41649202388;
   fWeightMatrix0to1[5][0] = -1.21236763034993;
   fWeightMatrix0to1[6][0] = -1.35605402549028;
   fWeightMatrix0to1[7][0] = 1.0963172468029;
   fWeightMatrix0to1[8][0] = -1.76994363694538;
   fWeightMatrix0to1[9][0] = -0.845219570368531;
   fWeightMatrix0to1[10][0] = -0.911185710210365;
   fWeightMatrix0to1[11][0] = -0.193222627129629;
   fWeightMatrix0to1[12][0] = -0.332730078375281;
   fWeightMatrix0to1[13][0] = -0.552787834720972;
   fWeightMatrix0to1[0][1] = -0.663074607209087;
   fWeightMatrix0to1[1][1] = 0.885151449015055;
   fWeightMatrix0to1[2][1] = -0.579015587958013;
   fWeightMatrix0to1[3][1] = 3.65392000822174;
   fWeightMatrix0to1[4][1] = -0.369922613254455;
   fWeightMatrix0to1[5][1] = 3.10026525690683;
   fWeightMatrix0to1[6][1] = -1.22863979591801;
   fWeightMatrix0to1[7][1] = 3.99470620842503;
   fWeightMatrix0to1[8][1] = 0.613476934061556;
   fWeightMatrix0to1[9][1] = 0.817300427647039;
   fWeightMatrix0to1[10][1] = 0.700489223138961;
   fWeightMatrix0to1[11][1] = 0.0466911191823546;
   fWeightMatrix0to1[12][1] = 3.73694891910802;
   fWeightMatrix0to1[13][1] = -1.97656817626367;
   fWeightMatrix0to1[0][2] = -1.64608165272065;
   fWeightMatrix0to1[1][2] = 0.665236157665833;
   fWeightMatrix0to1[2][2] = 1.2111990808854;
   fWeightMatrix0to1[3][2] = 2.20064885664614;
   fWeightMatrix0to1[4][2] = -0.522864820753782;
   fWeightMatrix0to1[5][2] = 1.1258137198711;
   fWeightMatrix0to1[6][2] = -0.849541338162737;
   fWeightMatrix0to1[7][2] = 3.31726855727733;
   fWeightMatrix0to1[8][2] = -1.9768915230344;
   fWeightMatrix0to1[9][2] = 4.39187955631914;
   fWeightMatrix0to1[10][2] = -3.41859827093214;
   fWeightMatrix0to1[11][2] = 0.497356193489005;
   fWeightMatrix0to1[12][2] = 0.13781340809089;
   fWeightMatrix0to1[13][2] = -0.949607985825751;
   fWeightMatrix0to1[0][3] = 1.85088115881687;
   fWeightMatrix0to1[1][3] = 1.32628743592119;
   fWeightMatrix0to1[2][3] = -1.84618500320577;
   fWeightMatrix0to1[3][3] = -3.22266329804571;
   fWeightMatrix0to1[4][3] = 0.495146309910934;
   fWeightMatrix0to1[5][3] = -1.54319414415649;
   fWeightMatrix0to1[6][3] = 1.85382947657235;
   fWeightMatrix0to1[7][3] = -0.471064206014345;
   fWeightMatrix0to1[8][3] = 1.83277153608121;
   fWeightMatrix0to1[9][3] = 0.619118821802352;
   fWeightMatrix0to1[10][3] = -0.923300759288939;
   fWeightMatrix0to1[11][3] = 2.57220222413991;
   fWeightMatrix0to1[12][3] = -1.69329200886949;
   fWeightMatrix0to1[13][3] = 1.1521713877954;
   fWeightMatrix0to1[0][4] = -1.16251195489149;
   fWeightMatrix0to1[1][4] = -1.50239674601359;
   fWeightMatrix0to1[2][4] = 1.37004423156711;
   fWeightMatrix0to1[3][4] = -0.396868264410573;
   fWeightMatrix0to1[4][4] = -1.7424736362304;
   fWeightMatrix0to1[5][4] = 0.34316230400857;
   fWeightMatrix0to1[6][4] = -0.603660809324365;
   fWeightMatrix0to1[7][4] = 5.91336738101187;
   fWeightMatrix0to1[8][4] = 1.93364891993094;
   fWeightMatrix0to1[9][4] = -0.0891837993852124;
   fWeightMatrix0to1[10][4] = 0.474052139079397;
   fWeightMatrix0to1[11][4] = -0.592136051160592;
   fWeightMatrix0to1[12][4] = 1.50193853802965;
   fWeightMatrix0to1[13][4] = -0.999528642080342;
   fWeightMatrix0to1[0][5] = -0.709935948492873;
   fWeightMatrix0to1[1][5] = -1.15793412483071;
   fWeightMatrix0to1[2][5] = 0.99374357783625;
   fWeightMatrix0to1[3][5] = 2.09675991989141;
   fWeightMatrix0to1[4][5] = 0.911469532813814;
   fWeightMatrix0to1[5][5] = 0.318333531577822;
   fWeightMatrix0to1[6][5] = -2.60964545903953;
   fWeightMatrix0to1[7][5] = -2.46856958148899;
   fWeightMatrix0to1[8][5] = 0.531927806710962;
   fWeightMatrix0to1[9][5] = 0.979057999362726;
   fWeightMatrix0to1[10][5] = 2.6349120077548;
   fWeightMatrix0to1[11][5] = 0.611196443975042;
   fWeightMatrix0to1[12][5] = 2.01098838422679;
   fWeightMatrix0to1[13][5] = -1.39514635667974;
   fWeightMatrix0to1[0][6] = -0.881323151033509;
   fWeightMatrix0to1[1][6] = 0.0411818721606623;
   fWeightMatrix0to1[2][6] = 0.716119099050555;
   fWeightMatrix0to1[3][6] = -1.56819896322444;
   fWeightMatrix0to1[4][6] = -1.91535455553707;
   fWeightMatrix0to1[5][6] = -1.28296716765534;
   fWeightMatrix0to1[6][6] = -0.96445880007364;
   fWeightMatrix0to1[7][6] = 2.30463580695632;
   fWeightMatrix0to1[8][6] = 1.49819162043761;
   fWeightMatrix0to1[9][6] = -1.77523281530309;
   fWeightMatrix0to1[10][6] = -0.671042384396747;
   fWeightMatrix0to1[11][6] = 1.53714667306957;
   fWeightMatrix0to1[12][6] = -0.208949631098885;
   fWeightMatrix0to1[13][6] = -1.75484870867227;
   fWeightMatrix0to1[0][7] = -1.36377578146893;
   fWeightMatrix0to1[1][7] = 0.43188281998914;
   fWeightMatrix0to1[2][7] = -2.5008475882089;
   fWeightMatrix0to1[3][7] = 0.169478543006714;
   fWeightMatrix0to1[4][7] = 0.452643356581506;
   fWeightMatrix0to1[5][7] = 3.77243240999095;
   fWeightMatrix0to1[6][7] = -3.03783676079652;
   fWeightMatrix0to1[7][7] = 9.72441811710481;
   fWeightMatrix0to1[8][7] = -0.750669631787249;
   fWeightMatrix0to1[9][7] = 1.0622260673994;
   fWeightMatrix0to1[10][7] = 0.160593158740319;
   fWeightMatrix0to1[11][7] = 0.377334937927205;
   fWeightMatrix0to1[12][7] = -0.608847629767673;
   fWeightMatrix0to1[13][7] = -1.37296981923402;
   fWeightMatrix0to1[0][8] = 0.325452267491157;
   fWeightMatrix0to1[1][8] = 0.253068919003431;
   fWeightMatrix0to1[2][8] = -2.10047359541101;
   fWeightMatrix0to1[3][8] = 3.41283801680661;
   fWeightMatrix0to1[4][8] = 0.686424443108555;
   fWeightMatrix0to1[5][8] = -3.14815879950033;
   fWeightMatrix0to1[6][8] = 1.39313448059811;
   fWeightMatrix0to1[7][8] = 1.04079257207933;
   fWeightMatrix0to1[8][8] = -3.58756384389903;
   fWeightMatrix0to1[9][8] = -0.30394660321237;
   fWeightMatrix0to1[10][8] = -1.0714410067985;
   fWeightMatrix0to1[11][8] = 2.04577639204671;
   fWeightMatrix0to1[12][8] = -0.305105792988646;
   fWeightMatrix0to1[13][8] = 1.32074026843218;
   fWeightMatrix0to1[0][9] = -0.566132010886408;
   fWeightMatrix0to1[1][9] = 0.0422001059183611;
   fWeightMatrix0to1[2][9] = -2.63475499173797;
   fWeightMatrix0to1[3][9] = 0.190399887637359;
   fWeightMatrix0to1[4][9] = -0.113722142418874;
   fWeightMatrix0to1[5][9] = -1.10820010727696;
   fWeightMatrix0to1[6][9] = -1.23678806936781;
   fWeightMatrix0to1[7][9] = -0.491303188701959;
   fWeightMatrix0to1[8][9] = -0.166443635325742;
   fWeightMatrix0to1[9][9] = 1.49153003436028;
   fWeightMatrix0to1[10][9] = 0.952785448860988;
   fWeightMatrix0to1[11][9] = -0.459315077484854;
   fWeightMatrix0to1[12][9] = -1.17153280821594;
   fWeightMatrix0to1[13][9] = 1.31345123643575;
   fWeightMatrix0to1[0][10] = 1.29160504362927;
   fWeightMatrix0to1[1][10] = 0.047014091103529;
   fWeightMatrix0to1[2][10] = -2.82424363591553;
   fWeightMatrix0to1[3][10] = 0.727275661772358;
   fWeightMatrix0to1[4][10] = -1.86044597021555;
   fWeightMatrix0to1[5][10] = 0.388106291628933;
   fWeightMatrix0to1[6][10] = 0.0373258619001816;
   fWeightMatrix0to1[7][10] = 0.0434477553757131;
   fWeightMatrix0to1[8][10] = -1.52530327737371;
   fWeightMatrix0to1[9][10] = -0.228976891325984;
   fWeightMatrix0to1[10][10] = -0.667330780082385;
   fWeightMatrix0to1[11][10] = 1.67401407895771;
   fWeightMatrix0to1[12][10] = 0.283776925421255;
   fWeightMatrix0to1[13][10] = -0.737754078074476;
   fWeightMatrix0to1[0][11] = -0.660865195714469;
   fWeightMatrix0to1[1][11] = 0.363967978420443;
   fWeightMatrix0to1[2][11] = 0.0290781914251291;
   fWeightMatrix0to1[3][11] = 1.0183702974136;
   fWeightMatrix0to1[4][11] = 0.270632865277266;
   fWeightMatrix0to1[5][11] = -1.9726918002536;
   fWeightMatrix0to1[6][11] = 4.07835069436509;
   fWeightMatrix0to1[7][11] = -0.0953256229753874;
   fWeightMatrix0to1[8][11] = -0.706270794458552;
   fWeightMatrix0to1[9][11] = -0.558022410514442;
   fWeightMatrix0to1[10][11] = 2.79236478073397;
   fWeightMatrix0to1[11][11] = -0.498526406060181;
   fWeightMatrix0to1[12][11] = -0.0393552325477118;
   fWeightMatrix0to1[13][11] = 1.58559598571047;
   fWeightMatrix0to1[0][12] = -0.906603246260435;
   fWeightMatrix0to1[1][12] = -0.302131386008793;
   fWeightMatrix0to1[2][12] = 1.15990682477148;
   fWeightMatrix0to1[3][12] = 0.539952692790772;
   fWeightMatrix0to1[4][12] = -0.747704500457795;
   fWeightMatrix0to1[5][12] = 0.705627356445346;
   fWeightMatrix0to1[6][12] = 3.48331780560342;
   fWeightMatrix0to1[7][12] = -0.209726289024601;
   fWeightMatrix0to1[8][12] = -0.270073771590601;
   fWeightMatrix0to1[9][12] = 0.871450629672878;
   fWeightMatrix0to1[10][12] = -0.426277059508375;
   fWeightMatrix0to1[11][12] = -1.60902636038153;
   fWeightMatrix0to1[12][12] = -3.19538077671;
   fWeightMatrix0to1[13][12] = 1.95155199442994;
   fWeightMatrix0to1[0][13] = 2.04782206705652;
   fWeightMatrix0to1[1][13] = -1.82296342640204;
   fWeightMatrix0to1[2][13] = -0.485988133627702;
   fWeightMatrix0to1[3][13] = 2.5788335732292;
   fWeightMatrix0to1[4][13] = 1.09684173743826;
   fWeightMatrix0to1[5][13] = 5.27711948390852;
   fWeightMatrix0to1[6][13] = -2.81214687034107;
   fWeightMatrix0to1[7][13] = 15.6908786136547;
   fWeightMatrix0to1[8][13] = 0.126953777866336;
   fWeightMatrix0to1[9][13] = 4.23284510066892;
   fWeightMatrix0to1[10][13] = -0.537399539153471;
   fWeightMatrix0to1[11][13] = -0.0640349203542944;
   fWeightMatrix0to1[12][13] = -0.258931077278313;
   fWeightMatrix0to1[13][13] = -4.41759093529837;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = -3.04221774656392;
   fWeightMatrix1to2[1][0] = -0.0690466512172958;
   fWeightMatrix1to2[2][0] = -0.566573594543459;
   fWeightMatrix1to2[3][0] = -1.95178234267826;
   fWeightMatrix1to2[4][0] = -1.02540225363023;
   fWeightMatrix1to2[5][0] = -1.3834802614511;
   fWeightMatrix1to2[6][0] = -0.824647671482816;
   fWeightMatrix1to2[7][0] = -1.8482766602634;
   fWeightMatrix1to2[8][0] = 1.34549859652613;
   fWeightMatrix1to2[9][0] = -0.320692287716411;
   fWeightMatrix1to2[10][0] = -0.87828254194366;
   fWeightMatrix1to2[11][0] = -0.207845533026066;
   fWeightMatrix1to2[12][0] = -0.384057926627033;
   fWeightMatrix1to2[0][1] = 1.05858432166294;
   fWeightMatrix1to2[1][1] = 0.345864852367204;
   fWeightMatrix1to2[2][1] = -0.160427512770581;
   fWeightMatrix1to2[3][1] = -2.03387713870974;
   fWeightMatrix1to2[4][1] = -0.929721724857499;
   fWeightMatrix1to2[5][1] = 1.64197498090561;
   fWeightMatrix1to2[6][1] = 1.32661054732398;
   fWeightMatrix1to2[7][1] = 0.763114626462325;
   fWeightMatrix1to2[8][1] = -0.109853121207158;
   fWeightMatrix1to2[9][1] = 1.95498530881289;
   fWeightMatrix1to2[10][1] = -1.69394591107885;
   fWeightMatrix1to2[11][1] = -1.31144192096933;
   fWeightMatrix1to2[12][1] = -0.261977158437405;
   fWeightMatrix1to2[0][2] = -1.52321515380432;
   fWeightMatrix1to2[1][2] = 0.953794723316451;
   fWeightMatrix1to2[2][2] = 0.861236200630296;
   fWeightMatrix1to2[3][2] = -1.59465492220381;
   fWeightMatrix1to2[4][2] = 0.624123021608635;
   fWeightMatrix1to2[5][2] = -0.300194852987334;
   fWeightMatrix1to2[6][2] = -1.17646898178916;
   fWeightMatrix1to2[7][2] = -1.20976617051151;
   fWeightMatrix1to2[8][2] = 2.30743780700564;
   fWeightMatrix1to2[9][2] = -0.0178585122434179;
   fWeightMatrix1to2[10][2] = 0.478807414566625;
   fWeightMatrix1to2[11][2] = -1.15554365612749;
   fWeightMatrix1to2[12][2] = 2.04452652790247;
   fWeightMatrix1to2[0][3] = -1.68777929635584;
   fWeightMatrix1to2[1][3] = -0.164528169067619;
   fWeightMatrix1to2[2][3] = 0.19909326832523;
   fWeightMatrix1to2[3][3] = -1.88926013012331;
   fWeightMatrix1to2[4][3] = -1.22428604001335;
   fWeightMatrix1to2[5][3] = -2.1178714291233;
   fWeightMatrix1to2[6][3] = 0.556126529016562;
   fWeightMatrix1to2[7][3] = -1.80308155885222;
   fWeightMatrix1to2[8][3] = -4.31845182828314;
   fWeightMatrix1to2[9][3] = -1.55041864186227;
   fWeightMatrix1to2[10][3] = 0.574117882420677;
   fWeightMatrix1to2[11][3] = -1.02367505383532;
   fWeightMatrix1to2[12][3] = -2.16783151116509;
   fWeightMatrix1to2[0][4] = 0.262357220130376;
   fWeightMatrix1to2[1][4] = -1.8385743158013;
   fWeightMatrix1to2[2][4] = -1.0131523955637;
   fWeightMatrix1to2[3][4] = -1.54376098772157;
   fWeightMatrix1to2[4][4] = -0.121139623043545;
   fWeightMatrix1to2[5][4] = 0.214435853001023;
   fWeightMatrix1to2[6][4] = 0.153194407340387;
   fWeightMatrix1to2[7][4] = -0.816171964892507;
   fWeightMatrix1to2[8][4] = -1.56213355166625;
   fWeightMatrix1to2[9][4] = -2.26932091515546;
   fWeightMatrix1to2[10][4] = -1.3642289082282;
   fWeightMatrix1to2[11][4] = -1.73315900974921;
   fWeightMatrix1to2[12][4] = -1.61791654365105;
   fWeightMatrix1to2[0][5] = -0.443538917930014;
   fWeightMatrix1to2[1][5] = 2.48397829683834;
   fWeightMatrix1to2[2][5] = -0.657787201241627;
   fWeightMatrix1to2[3][5] = -0.120906108935621;
   fWeightMatrix1to2[4][5] = -0.498688812470758;
   fWeightMatrix1to2[5][5] = -3.00345217161517;
   fWeightMatrix1to2[6][5] = 0.704612985963917;
   fWeightMatrix1to2[7][5] = 1.30435386434813;
   fWeightMatrix1to2[8][5] = -3.66628270760692;
   fWeightMatrix1to2[9][5] = -1.3841552448053;
   fWeightMatrix1to2[10][5] = 0.239581899158921;
   fWeightMatrix1to2[11][5] = 1.18447028632621;
   fWeightMatrix1to2[12][5] = 0.0707456844924219;
   fWeightMatrix1to2[0][6] = -1.61895440106382;
   fWeightMatrix1to2[1][6] = 0.34586602560198;
   fWeightMatrix1to2[2][6] = 0.972614206531274;
   fWeightMatrix1to2[3][6] = 1.69012545563115;
   fWeightMatrix1to2[4][6] = 0.65297211724043;
   fWeightMatrix1to2[5][6] = -0.88135948856485;
   fWeightMatrix1to2[6][6] = -0.175260408560798;
   fWeightMatrix1to2[7][6] = -0.329174076666259;
   fWeightMatrix1to2[8][6] = 3.76241546319364;
   fWeightMatrix1to2[9][6] = -1.74996443002634;
   fWeightMatrix1to2[10][6] = -1.87978656257326;
   fWeightMatrix1to2[11][6] = -1.52327484810712;
   fWeightMatrix1to2[12][6] = -1.37402986344577;
   fWeightMatrix1to2[0][7] = 1.60831673416134;
   fWeightMatrix1to2[1][7] = -3.84533395738249;
   fWeightMatrix1to2[2][7] = 1.54790912922056;
   fWeightMatrix1to2[3][7] = -3.23910313215837;
   fWeightMatrix1to2[4][7] = 1.05553319412942;
   fWeightMatrix1to2[5][7] = -1.72768665724334;
   fWeightMatrix1to2[6][7] = 1.68967540424523;
   fWeightMatrix1to2[7][7] = 1.65572605814363;
   fWeightMatrix1to2[8][7] = -8.14567094785881;
   fWeightMatrix1to2[9][7] = 0.658597089941259;
   fWeightMatrix1to2[10][7] = -0.592855532926148;
   fWeightMatrix1to2[11][7] = -1.8432207481652;
   fWeightMatrix1to2[12][7] = -3.45757580488504;
   fWeightMatrix1to2[0][8] = -1.76896144680834;
   fWeightMatrix1to2[1][8] = -2.16209341546042;
   fWeightMatrix1to2[2][8] = 1.49133055867585;
   fWeightMatrix1to2[3][8] = -0.833402617224421;
   fWeightMatrix1to2[4][8] = 0.269409661099169;
   fWeightMatrix1to2[5][8] = -1.11716317199837;
   fWeightMatrix1to2[6][8] = -1.3816975743005;
   fWeightMatrix1to2[7][8] = -1.49712823838257;
   fWeightMatrix1to2[8][8] = -2.07359735201312;
   fWeightMatrix1to2[9][8] = -1.41026565441212;
   fWeightMatrix1to2[10][8] = 0.904484218123293;
   fWeightMatrix1to2[11][8] = -1.41373128449721;
   fWeightMatrix1to2[12][8] = -1.87274352922208;
   fWeightMatrix1to2[0][9] = 2.1449291079807;
   fWeightMatrix1to2[1][9] = -0.728697703785453;
   fWeightMatrix1to2[2][9] = 0.439715283770402;
   fWeightMatrix1to2[3][9] = 1.12088887391886;
   fWeightMatrix1to2[4][9] = -1.69544472084388;
   fWeightMatrix1to2[5][9] = 1.13595709155518;
   fWeightMatrix1to2[6][9] = 1.52299819294674;
   fWeightMatrix1to2[7][9] = -1.05147922762897;
   fWeightMatrix1to2[8][9] = -4.03050733152446;
   fWeightMatrix1to2[9][9] = 0.230041639335681;
   fWeightMatrix1to2[10][9] = -1.06020043568762;
   fWeightMatrix1to2[11][9] = 0.599344181707703;
   fWeightMatrix1to2[12][9] = 2.35008298249491;
   fWeightMatrix1to2[0][10] = 0.43310071538202;
   fWeightMatrix1to2[1][10] = -0.17124802863014;
   fWeightMatrix1to2[2][10] = -1.96201218624857;
   fWeightMatrix1to2[3][10] = 0.755121366985355;
   fWeightMatrix1to2[4][10] = -1.99618741678358;
   fWeightMatrix1to2[5][10] = -0.829919369371453;
   fWeightMatrix1to2[6][10] = -2.43588920490717;
   fWeightMatrix1to2[7][10] = -1.99787449474378;
   fWeightMatrix1to2[8][10] = 1.02377092080923;
   fWeightMatrix1to2[9][10] = 0.255856971837253;
   fWeightMatrix1to2[10][10] = 0.674773485568675;
   fWeightMatrix1to2[11][10] = 0.959677362896679;
   fWeightMatrix1to2[12][10] = -1.74147030391163;
   fWeightMatrix1to2[0][11] = -1.232143478456;
   fWeightMatrix1to2[1][11] = -1.86272561912482;
   fWeightMatrix1to2[2][11] = -0.547664927662138;
   fWeightMatrix1to2[3][11] = 0.96687311951584;
   fWeightMatrix1to2[4][11] = -1.73938946263497;
   fWeightMatrix1to2[5][11] = -1.06435870811396;
   fWeightMatrix1to2[6][11] = -1.71251867176765;
   fWeightMatrix1to2[7][11] = 0.0729962954941336;
   fWeightMatrix1to2[8][11] = -0.463956812101544;
   fWeightMatrix1to2[9][11] = -0.679961037668981;
   fWeightMatrix1to2[10][11] = -1.82885191220471;
   fWeightMatrix1to2[11][11] = 0.842665787836321;
   fWeightMatrix1to2[12][11] = -0.111788450201664;
   fWeightMatrix1to2[0][12] = -0.258723200079076;
   fWeightMatrix1to2[1][12] = 0.355567608272885;
   fWeightMatrix1to2[2][12] = -2.1515480220778;
   fWeightMatrix1to2[3][12] = -1.69253832238098;
   fWeightMatrix1to2[4][12] = -1.86447146125158;
   fWeightMatrix1to2[5][12] = -0.524560543636224;
   fWeightMatrix1to2[6][12] = -1.6242560342252;
   fWeightMatrix1to2[7][12] = -1.43156644723988;
   fWeightMatrix1to2[8][12] = -1.44350327842647;
   fWeightMatrix1to2[9][12] = -2.47084336858066;
   fWeightMatrix1to2[10][12] = -1.0765165769583;
   fWeightMatrix1to2[11][12] = -1.76552603824534;
   fWeightMatrix1to2[12][12] = -0.487219560082829;
   fWeightMatrix1to2[0][13] = -1.81831505272241;
   fWeightMatrix1to2[1][13] = -3.03064848362648;
   fWeightMatrix1to2[2][13] = -0.65325999288921;
   fWeightMatrix1to2[3][13] = -1.34674218307031;
   fWeightMatrix1to2[4][13] = -1.73592733772486;
   fWeightMatrix1to2[5][13] = 0.0272675420551247;
   fWeightMatrix1to2[6][13] = -1.73952473590335;
   fWeightMatrix1to2[7][13] = -1.5704719104159;
   fWeightMatrix1to2[8][13] = 2.90892476731973;
   fWeightMatrix1to2[9][13] = -0.0412472373515762;
   fWeightMatrix1to2[10][13] = 0.810869787263324;
   fWeightMatrix1to2[11][13] = 0.0438834617900117;
   fWeightMatrix1to2[12][13] = -0.0937021348756732;
   fWeightMatrix1to2[0][14] = 0.600202513081836;
   fWeightMatrix1to2[1][14] = -0.704428959658678;
   fWeightMatrix1to2[2][14] = -2.16344782611475;
   fWeightMatrix1to2[3][14] = -1.29616048462656;
   fWeightMatrix1to2[4][14] = -2.07172469960806;
   fWeightMatrix1to2[5][14] = -0.79860092174591;
   fWeightMatrix1to2[6][14] = -0.212638271441413;
   fWeightMatrix1to2[7][14] = 0.141992237618711;
   fWeightMatrix1to2[8][14] = 0.78423920431216;
   fWeightMatrix1to2[9][14] = -0.242137106587152;
   fWeightMatrix1to2[10][14] = 1.09468474062975;
   fWeightMatrix1to2[11][14] = -1.42579847450069;
   fWeightMatrix1to2[12][14] = -0.275127662241509;
   // weight matrix from layer 2 to 3
   fWeightMatrix2to3[0][0] = -0.349993070223721;
   fWeightMatrix2to3[0][1] = -1.50222021411281;
   fWeightMatrix2to3[0][2] = 0.687939974573129;
   fWeightMatrix2to3[0][3] = -0.999553942841023;
   fWeightMatrix2to3[0][4] = 0.476013395733573;
   fWeightMatrix2to3[0][5] = -0.624023725348389;
   fWeightMatrix2to3[0][6] = 0.373422192407752;
   fWeightMatrix2to3[0][7] = 1.85865845146971;
   fWeightMatrix2to3[0][8] = -0.625166706722248;
   fWeightMatrix2to3[0][9] = 0.629207708406663;
   fWeightMatrix2to3[0][10] = 0.858239815901647;
   fWeightMatrix2to3[0][11] = -0.403988800109799;
   fWeightMatrix2to3[0][12] = -0.814082965056632;
   fWeightMatrix2to3[0][13] = 0.578893774600044;
}

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
}