// 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 10:48:54 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/job413
Training events: 45948


#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'    [46.6659545898,824.779174805]
LepAPt                        LepAPt                            'F'    [20.0003662109,155.865875244]
LepBPt                        LepBPt                            'F'    [10.0010261536,72.9998397827]
MetSigLeptonsJets             MetSigLeptonsJets                 'F'    [1.00477731228,19.6708087921]
MetSpec                       MetSpec                           'F'    [15.0119848251,262.380706787]
SumEtLeptonsJets              SumEtLeptonsJets                  'F'    [30.1484737396,533.700561523]
VSumJetLeptonsPt              VSumJetLeptonsPt                  'F'    [0.678276479244,374.426086426]
addEt                         addEt                             'F'    [46.6659545898,422.574371338]
dPhiLepSumMet                 dPhiLepSumMet                     'F'    [0.0517990663648,3.14159059525]
dPhiLeptons                   dPhiLeptons                       'F'    [1.81440645974e-05,1.13430726528]
dRLeptons                     dRLeptons                         'F'    [0.200005456805,1.13770997524]
lep1_E                        lep1_E                            'F'    [20.0262546539,232.717926025]
lep2_E                        lep2_E                            'F'    [10.0074262619,136.060577393]


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

#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] = 46.6659545898438;
      fVmax[0] = 824.779174804688;
      fVmin[1] = 20.0003662109375;
      fVmax[1] = 155.865875244141;
      fVmin[2] = 10.0010261535645;
      fVmax[2] = 72.9998397827148;
      fVmin[3] = 1.00477731227875;
      fVmax[3] = 19.6708087921143;
      fVmin[4] = 15.0119848251343;
      fVmax[4] = 262.380706787109;
      fVmin[5] = 30.148473739624;
      fVmax[5] = 533.700561523438;
      fVmin[6] = 0.678276479244232;
      fVmax[6] = 374.426086425781;
      fVmin[7] = 46.6659545898438;
      fVmax[7] = 422.574371337891;
      fVmin[8] = 0.0517990663647652;
      fVmax[8] = 3.14159059524536;
      fVmin[9] = 1.81440645974362e-05;
      fVmax[9] = 1.13430726528168;
      fVmin[10] = 0.200005456805229;
      fVmax[10] = 1.13770997524261;
      fVmin[11] = 20.0262546539307;
      fVmax[11] = 232.717926025391;
      fVmin[12] = 10.0074262619019;
      fVmax[12] = 136.060577392578;

      // 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] = -0.3836848012946;
   fWeightMatrix0to1[1][0] = 2.2518691892693;
   fWeightMatrix0to1[2][0] = 0.93660689612196;
   fWeightMatrix0to1[3][0] = 2.64924379309184;
   fWeightMatrix0to1[4][0] = -0.995133592509868;
   fWeightMatrix0to1[5][0] = -0.342309891831039;
   fWeightMatrix0to1[6][0] = -1.26209870371135;
   fWeightMatrix0to1[7][0] = 2.71409122566991;
   fWeightMatrix0to1[8][0] = -1.57239199720498;
   fWeightMatrix0to1[9][0] = -0.423658306675235;
   fWeightMatrix0to1[10][0] = -1.03551008610244;
   fWeightMatrix0to1[11][0] = -0.570731331540889;
   fWeightMatrix0to1[12][0] = -0.99331718193085;
   fWeightMatrix0to1[13][0] = 0.0702628859954197;
   fWeightMatrix0to1[0][1] = -0.542629559948844;
   fWeightMatrix0to1[1][1] = 0.805812138472497;
   fWeightMatrix0to1[2][1] = 0.56300823260029;
   fWeightMatrix0to1[3][1] = 5.15748787447817;
   fWeightMatrix0to1[4][1] = 2.9830416263208;
   fWeightMatrix0to1[5][1] = 2.36002187421364;
   fWeightMatrix0to1[6][1] = -0.982712911975555;
   fWeightMatrix0to1[7][1] = 0.501933579335956;
   fWeightMatrix0to1[8][1] = 5.05551452590287;
   fWeightMatrix0to1[9][1] = 1.23026397573671;
   fWeightMatrix0to1[10][1] = -1.25712817799482;
   fWeightMatrix0to1[11][1] = -0.877466116381388;
   fWeightMatrix0to1[12][1] = 0.275576934585473;
   fWeightMatrix0to1[13][1] = -1.76147575870885;
   fWeightMatrix0to1[0][2] = -1.9314464540686;
   fWeightMatrix0to1[1][2] = 0.587035805684592;
   fWeightMatrix0to1[2][2] = 0.64418195568299;
   fWeightMatrix0to1[3][2] = 4.34057380416733;
   fWeightMatrix0to1[4][2] = 1.60649825652992;
   fWeightMatrix0to1[5][2] = 0.818268738821063;
   fWeightMatrix0to1[6][2] = -0.157404210315196;
   fWeightMatrix0to1[7][2] = 2.92861544557032;
   fWeightMatrix0to1[8][2] = -3.69659651750831;
   fWeightMatrix0to1[9][2] = 2.65842508251287;
   fWeightMatrix0to1[10][2] = -1.73680252648058;
   fWeightMatrix0to1[11][2] = 1.21236900781672;
   fWeightMatrix0to1[12][2] = 2.48741748249942;
   fWeightMatrix0to1[13][2] = 0.865383896891655;
   fWeightMatrix0to1[0][3] = 1.94688325886493;
   fWeightMatrix0to1[1][3] = 1.26047137208844;
   fWeightMatrix0to1[2][3] = -0.416290885095006;
   fWeightMatrix0to1[3][3] = -2.99864282306345;
   fWeightMatrix0to1[4][3] = 0.863121515248786;
   fWeightMatrix0to1[5][3] = -0.677582416184844;
   fWeightMatrix0to1[6][3] = 0.977171906416694;
   fWeightMatrix0to1[7][3] = 2.06410560839693;
   fWeightMatrix0to1[8][3] = 0.622949502375384;
   fWeightMatrix0to1[9][3] = 1.72818512840489;
   fWeightMatrix0to1[10][3] = -2.09458386490266;
   fWeightMatrix0to1[11][3] = 0.897301440174855;
   fWeightMatrix0to1[12][3] = -1.33654185196555;
   fWeightMatrix0to1[13][3] = -0.174568588734221;
   fWeightMatrix0to1[0][4] = -1.27157572188327;
   fWeightMatrix0to1[1][4] = -1.51597206410306;
   fWeightMatrix0to1[2][4] = 1.8709166389021;
   fWeightMatrix0to1[3][4] = 1.25120913904614;
   fWeightMatrix0to1[4][4] = -0.419602250505652;
   fWeightMatrix0to1[5][4] = 1.24446126000877;
   fWeightMatrix0to1[6][4] = -0.874672065827326;
   fWeightMatrix0to1[7][4] = 3.0078305470042;
   fWeightMatrix0to1[8][4] = 1.19971923717939;
   fWeightMatrix0to1[9][4] = 0.466477944579443;
   fWeightMatrix0to1[10][4] = 0.249524893171852;
   fWeightMatrix0to1[11][4] = -1.57520485141695;
   fWeightMatrix0to1[12][4] = 1.23809171551973;
   fWeightMatrix0to1[13][4] = -0.729968604152009;
   fWeightMatrix0to1[0][5] = -0.831397897388997;
   fWeightMatrix0to1[1][5] = -1.1634943698842;
   fWeightMatrix0to1[2][5] = 0.698881797830915;
   fWeightMatrix0to1[3][5] = 1.89140901958867;
   fWeightMatrix0to1[4][5] = 1.98400505632496;
   fWeightMatrix0to1[5][5] = 0.746308310462067;
   fWeightMatrix0to1[6][5] = -2.3239175481227;
   fWeightMatrix0to1[7][5] = -2.13361030835674;
   fWeightMatrix0to1[8][5] = 0.963502896341681;
   fWeightMatrix0to1[9][5] = 1.02671814380412;
   fWeightMatrix0to1[10][5] = 2.82079435319331;
   fWeightMatrix0to1[11][5] = 0.656561555219648;
   fWeightMatrix0to1[12][5] = 1.11288912325307;
   fWeightMatrix0to1[13][5] = -0.157150141434878;
   fWeightMatrix0to1[0][6] = -0.900714700702311;
   fWeightMatrix0to1[1][6] = 0.0128702810536989;
   fWeightMatrix0to1[2][6] = 1.39868722409408;
   fWeightMatrix0to1[3][6] = -0.58848907636556;
   fWeightMatrix0to1[4][6] = -0.649865383224078;
   fWeightMatrix0to1[5][6] = -0.871560241245066;
   fWeightMatrix0to1[6][6] = -0.714904149893681;
   fWeightMatrix0to1[7][6] = 0.786758824967519;
   fWeightMatrix0to1[8][6] = 1.61727240594602;
   fWeightMatrix0to1[9][6] = -1.20897840548498;
   fWeightMatrix0to1[10][6] = -0.467418756938591;
   fWeightMatrix0to1[11][6] = 1.426174830283;
   fWeightMatrix0to1[12][6] = -0.277803165396177;
   fWeightMatrix0to1[13][6] = -1.05407250186075;
   fWeightMatrix0to1[0][7] = -1.39966800855146;
   fWeightMatrix0to1[1][7] = 0.417354270046944;
   fWeightMatrix0to1[2][7] = -1.48691500549691;
   fWeightMatrix0to1[3][7] = 2.89012797588907;
   fWeightMatrix0to1[4][7] = 4.63168989360781;
   fWeightMatrix0to1[5][7] = 4.03790226860258;
   fWeightMatrix0to1[6][7] = -1.80484659794067;
   fWeightMatrix0to1[7][7] = 7.83314481407647;
   fWeightMatrix0to1[8][7] = 0.323105433030038;
   fWeightMatrix0to1[9][7] = 0.667567381610711;
   fWeightMatrix0to1[10][7] = -0.393987358438729;
   fWeightMatrix0to1[11][7] = -0.467249241775829;
   fWeightMatrix0to1[12][7] = -1.69789820591542;
   fWeightMatrix0to1[13][7] = -0.752200261396362;
   fWeightMatrix0to1[0][8] = 0.483135875438848;
   fWeightMatrix0to1[1][8] = 0.0232899535897208;
   fWeightMatrix0to1[2][8] = -1.02161875102144;
   fWeightMatrix0to1[3][8] = 3.13661225825974;
   fWeightMatrix0to1[4][8] = 0.140912144197025;
   fWeightMatrix0to1[5][8] = -3.67366680255426;
   fWeightMatrix0to1[6][8] = 0.567673889304915;
   fWeightMatrix0to1[7][8] = -2.52497083764368;
   fWeightMatrix0to1[8][8] = -0.596657834322117;
   fWeightMatrix0to1[9][8] = -0.676587153948539;
   fWeightMatrix0to1[10][8] = -2.37830212511208;
   fWeightMatrix0to1[11][8] = 1.22663712563053;
   fWeightMatrix0to1[12][8] = 0.36806933117025;
   fWeightMatrix0to1[13][8] = -0.315445794557825;
   fWeightMatrix0to1[0][9] = -0.502990961614226;
   fWeightMatrix0to1[1][9] = 0.41736292074345;
   fWeightMatrix0to1[2][9] = -1.70259483713529;
   fWeightMatrix0to1[3][9] = -0.968776739955264;
   fWeightMatrix0to1[4][9] = 1.41108105992982;
   fWeightMatrix0to1[5][9] = -0.839475453613016;
   fWeightMatrix0to1[6][9] = 0.487112153925665;
   fWeightMatrix0to1[7][9] = 0.136440631689655;
   fWeightMatrix0to1[8][9] = -0.572376059521909;
   fWeightMatrix0to1[9][9] = 0.652474132385849;
   fWeightMatrix0to1[10][9] = 0.86177472481568;
   fWeightMatrix0to1[11][9] = -1.42822157858131;
   fWeightMatrix0to1[12][9] = 1.87862405050971;
   fWeightMatrix0to1[13][9] = 0.462682266614886;
   fWeightMatrix0to1[0][10] = 1.46902183529058;
   fWeightMatrix0to1[1][10] = 0.0554098003598859;
   fWeightMatrix0to1[2][10] = -1.26705354012425;
   fWeightMatrix0to1[3][10] = 0.953664297611743;
   fWeightMatrix0to1[4][10] = -0.140526915542139;
   fWeightMatrix0to1[5][10] = -0.111021900369911;
   fWeightMatrix0to1[6][10] = 1.10879177646593;
   fWeightMatrix0to1[7][10] = 0.353723260091193;
   fWeightMatrix0to1[8][10] = 0.900630261387243;
   fWeightMatrix0to1[9][10] = -0.392545279662278;
   fWeightMatrix0to1[10][10] = -1.91295447437846;
   fWeightMatrix0to1[11][10] = 1.15076593064952;
   fWeightMatrix0to1[12][10] = 1.3482971355912;
   fWeightMatrix0to1[13][10] = -0.641177467376843;
   fWeightMatrix0to1[0][11] = -0.489184600864143;
   fWeightMatrix0to1[1][11] = 0.0530015430130339;
   fWeightMatrix0to1[2][11] = 2.66253335962637;
   fWeightMatrix0to1[3][11] = -0.229767670446227;
   fWeightMatrix0to1[4][11] = -0.614841875458809;
   fWeightMatrix0to1[5][11] = -2.66370998085788;
   fWeightMatrix0to1[6][11] = 1.05392338303568;
   fWeightMatrix0to1[7][11] = -0.621092226106726;
   fWeightMatrix0to1[8][11] = 1.79925129303994;
   fWeightMatrix0to1[9][11] = 0.465956403875616;
   fWeightMatrix0to1[10][11] = 1.51440862877363;
   fWeightMatrix0to1[11][11] = -0.0595956818664869;
   fWeightMatrix0to1[12][11] = -2.42040658386099;
   fWeightMatrix0to1[13][11] = 1.6557668168565;
   fWeightMatrix0to1[0][12] = -0.929448359966939;
   fWeightMatrix0to1[1][12] = -0.362828158459499;
   fWeightMatrix0to1[2][12] = 2.24158076213355;
   fWeightMatrix0to1[3][12] = -0.953056378908008;
   fWeightMatrix0to1[4][12] = -1.90324095672154;
   fWeightMatrix0to1[5][12] = 0.0337483284446154;
   fWeightMatrix0to1[6][12] = 1.52083969336672;
   fWeightMatrix0to1[7][12] = -0.378447638297506;
   fWeightMatrix0to1[8][12] = -1.94388984737953;
   fWeightMatrix0to1[9][12] = -0.502364330372355;
   fWeightMatrix0to1[10][12] = 0.530078725877214;
   fWeightMatrix0to1[11][12] = -0.932796721567991;
   fWeightMatrix0to1[12][12] = -1.08061962490929;
   fWeightMatrix0to1[13][12] = 2.55607947396179;
   fWeightMatrix0to1[0][13] = 1.95998603805449;
   fWeightMatrix0to1[1][13] = -1.69975402494822;
   fWeightMatrix0to1[2][13] = -0.542193456241121;
   fWeightMatrix0to1[3][13] = 7.09446749440447;
   fWeightMatrix0to1[4][13] = 5.55352943162853;
   fWeightMatrix0to1[5][13] = 5.44120216870956;
   fWeightMatrix0to1[6][13] = 0.00628551832701973;
   fWeightMatrix0to1[7][13] = 12.4894719059759;
   fWeightMatrix0to1[8][13] = 0.340933696287454;
   fWeightMatrix0to1[9][13] = 2.26618934129791;
   fWeightMatrix0to1[10][13] = 0.387001877070671;
   fWeightMatrix0to1[11][13] = 0.150313856736602;
   fWeightMatrix0to1[12][13] = 0.0623762569914268;
   fWeightMatrix0to1[13][13] = -4.56329161373943;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = -2.9760850664808;
   fWeightMatrix1to2[1][0] = 0.339144134832334;
   fWeightMatrix1to2[2][0] = -1.05833434104562;
   fWeightMatrix1to2[3][0] = -0.504151688523718;
   fWeightMatrix1to2[4][0] = -0.830951611568158;
   fWeightMatrix1to2[5][0] = -0.742487599306499;
   fWeightMatrix1to2[6][0] = -0.676104283917202;
   fWeightMatrix1to2[7][0] = -2.49653547664643;
   fWeightMatrix1to2[8][0] = -0.277326661455326;
   fWeightMatrix1to2[9][0] = 0.0018253972091458;
   fWeightMatrix1to2[10][0] = -1.29286451124148;
   fWeightMatrix1to2[11][0] = -0.174680358261179;
   fWeightMatrix1to2[12][0] = 0.0403667438646007;
   fWeightMatrix1to2[0][1] = 0.935352065159797;
   fWeightMatrix1to2[1][1] = 0.542075239639722;
   fWeightMatrix1to2[2][1] = -0.291344460563205;
   fWeightMatrix1to2[3][1] = -2.21734735688997;
   fWeightMatrix1to2[4][1] = -0.916791989825558;
   fWeightMatrix1to2[5][1] = 1.53986599589911;
   fWeightMatrix1to2[6][1] = 1.28051266814212;
   fWeightMatrix1to2[7][1] = 0.740687610556697;
   fWeightMatrix1to2[8][1] = -0.297750317732971;
   fWeightMatrix1to2[9][1] = 1.9283047674653;
   fWeightMatrix1to2[10][1] = -1.65198081516393;
   fWeightMatrix1to2[11][1] = -1.34742974872081;
   fWeightMatrix1to2[12][1] = -0.147749141231006;
   fWeightMatrix1to2[0][2] = -1.61721934139388;
   fWeightMatrix1to2[1][2] = 0.885342893958349;
   fWeightMatrix1to2[2][2] = 2.29180934921692;
   fWeightMatrix1to2[3][2] = -1.41999943964575;
   fWeightMatrix1to2[4][2] = 0.778807913769942;
   fWeightMatrix1to2[5][2] = -0.394759435107158;
   fWeightMatrix1to2[6][2] = -1.73631323847209;
   fWeightMatrix1to2[7][2] = -1.09047835620223;
   fWeightMatrix1to2[8][2] = 0.506116006848949;
   fWeightMatrix1to2[9][2] = 0.040883997800122;
   fWeightMatrix1to2[10][2] = 1.1381113858844;
   fWeightMatrix1to2[11][2] = -1.22217094770606;
   fWeightMatrix1to2[12][2] = 1.85167931242822;
   fWeightMatrix1to2[0][3] = -2.0645116367488;
   fWeightMatrix1to2[1][3] = -0.424384074079561;
   fWeightMatrix1to2[2][3] = 1.2215445139714;
   fWeightMatrix1to2[3][3] = -5.5577652023201;
   fWeightMatrix1to2[4][3] = -1.44473391081602;
   fWeightMatrix1to2[5][3] = -1.92991851813272;
   fWeightMatrix1to2[6][3] = 0.78585830194846;
   fWeightMatrix1to2[7][3] = -1.81160704725847;
   fWeightMatrix1to2[8][3] = -4.14640246270479;
   fWeightMatrix1to2[9][3] = -1.91261074022906;
   fWeightMatrix1to2[10][3] = 0.388910581304943;
   fWeightMatrix1to2[11][3] = -1.32922593797994;
   fWeightMatrix1to2[12][3] = -2.03549033393455;
   fWeightMatrix1to2[0][4] = 0.480497530422449;
   fWeightMatrix1to2[1][4] = -0.718897030683717;
   fWeightMatrix1to2[2][4] = 0.407401020931573;
   fWeightMatrix1to2[3][4] = -3.87560975177925;
   fWeightMatrix1to2[4][4] = 0.239300295256163;
   fWeightMatrix1to2[5][4] = 1.97208070687697;
   fWeightMatrix1to2[6][4] = 1.0658474758468;
   fWeightMatrix1to2[7][4] = -0.929312552159101;
   fWeightMatrix1to2[8][4] = -3.30560968222224;
   fWeightMatrix1to2[9][4] = -1.79595061839718;
   fWeightMatrix1to2[10][4] = -1.23121604517771;
   fWeightMatrix1to2[11][4] = -1.67582976726099;
   fWeightMatrix1to2[12][4] = 0.198669522924729;
   fWeightMatrix1to2[0][5] = -0.654402008776087;
   fWeightMatrix1to2[1][5] = 2.11036186046025;
   fWeightMatrix1to2[2][5] = -0.852818766329667;
   fWeightMatrix1to2[3][5] = -2.73417626034429;
   fWeightMatrix1to2[4][5] = -0.513201660879847;
   fWeightMatrix1to2[5][5] = -2.96861887542505;
   fWeightMatrix1to2[6][5] = 0.794284941266973;
   fWeightMatrix1to2[7][5] = 0.994467862287293;
   fWeightMatrix1to2[8][5] = -2.99094556605721;
   fWeightMatrix1to2[9][5] = -1.72276720370541;
   fWeightMatrix1to2[10][5] = 0.566427210794386;
   fWeightMatrix1to2[11][5] = 1.27081638956144;
   fWeightMatrix1to2[12][5] = -0.100257109049515;
   fWeightMatrix1to2[0][6] = -1.32911049802021;
   fWeightMatrix1to2[1][6] = -0.969573435884539;
   fWeightMatrix1to2[2][6] = 0.990173974687072;
   fWeightMatrix1to2[3][6] = 2.24566305072055;
   fWeightMatrix1to2[4][6] = 0.850072838230916;
   fWeightMatrix1to2[5][6] = -0.940932778216237;
   fWeightMatrix1to2[6][6] = -0.35901375076325;
   fWeightMatrix1to2[7][6] = -0.795451816019074;
   fWeightMatrix1to2[8][6] = -2.43745190857089;
   fWeightMatrix1to2[9][6] = -1.20276537043989;
   fWeightMatrix1to2[10][6] = -2.52217390630005;
   fWeightMatrix1to2[11][6] = -1.42529548928399;
   fWeightMatrix1to2[12][6] = -2.9578345859044;
   fWeightMatrix1to2[0][7] = 1.16788251515732;
   fWeightMatrix1to2[1][7] = -2.46431088332665;
   fWeightMatrix1to2[2][7] = 0.89386293143963;
   fWeightMatrix1to2[3][7] = -6.86115565607146;
   fWeightMatrix1to2[4][7] = 1.48823479299305;
   fWeightMatrix1to2[5][7] = -1.56166336301417;
   fWeightMatrix1to2[6][7] = 0.792576351505038;
   fWeightMatrix1to2[7][7] = 1.07523185232183;
   fWeightMatrix1to2[8][7] = -4.97921788771729;
   fWeightMatrix1to2[9][7] = 0.0424812443626037;
   fWeightMatrix1to2[10][7] = 0.737349053099672;
   fWeightMatrix1to2[11][7] = -2.19378859437174;
   fWeightMatrix1to2[12][7] = -1.95917843410156;
   fWeightMatrix1to2[0][8] = -1.77042107307391;
   fWeightMatrix1to2[1][8] = -0.47667477485927;
   fWeightMatrix1to2[2][8] = 4.53881847618623;
   fWeightMatrix1to2[3][8] = -1.0089498287632;
   fWeightMatrix1to2[4][8] = 0.245304297440055;
   fWeightMatrix1to2[5][8] = -0.775387546587268;
   fWeightMatrix1to2[6][8] = -1.55870688909151;
   fWeightMatrix1to2[7][8] = -1.63811188097806;
   fWeightMatrix1to2[8][8] = -1.54751408519916;
   fWeightMatrix1to2[9][8] = -1.47962280701914;
   fWeightMatrix1to2[10][8] = 1.0386584602724;
   fWeightMatrix1to2[11][8] = -1.26593248474659;
   fWeightMatrix1to2[12][8] = -1.14536809187155;
   fWeightMatrix1to2[0][9] = 1.27472143741786;
   fWeightMatrix1to2[1][9] = -0.467853543199665;
   fWeightMatrix1to2[2][9] = -1.9379322799878;
   fWeightMatrix1to2[3][9] = 0.119965796900763;
   fWeightMatrix1to2[4][9] = -1.72966854598602;
   fWeightMatrix1to2[5][9] = 0.650813889328664;
   fWeightMatrix1to2[6][9] = 0.824426684551297;
   fWeightMatrix1to2[7][9] = -0.980254015632288;
   fWeightMatrix1to2[8][9] = -3.26561248533631;
   fWeightMatrix1to2[9][9] = -0.0485083078807329;
   fWeightMatrix1to2[10][9] = -0.805072321497814;
   fWeightMatrix1to2[11][9] = 0.1505774104593;
   fWeightMatrix1to2[12][9] = 1.38934501660847;
   fWeightMatrix1to2[0][10] = 0.55602541480606;
   fWeightMatrix1to2[1][10] = 1.61585976136274;
   fWeightMatrix1to2[2][10] = -2.17259356185159;
   fWeightMatrix1to2[3][10] = 1.10450884608189;
   fWeightMatrix1to2[4][10] = -1.84502434803534;
   fWeightMatrix1to2[5][10] = -1.2536662515987;
   fWeightMatrix1to2[6][10] = -1.97667027368929;
   fWeightMatrix1to2[7][10] = -1.89406624751147;
   fWeightMatrix1to2[8][10] = -0.735790693055911;
   fWeightMatrix1to2[9][10] = 0.718635428456985;
   fWeightMatrix1to2[10][10] = -0.160386855435911;
   fWeightMatrix1to2[11][10] = 0.182454005662906;
   fWeightMatrix1to2[12][10] = -1.17443890974324;
   fWeightMatrix1to2[0][11] = -1.2308986840986;
   fWeightMatrix1to2[1][11] = -1.59091195781333;
   fWeightMatrix1to2[2][11] = -0.848352968322665;
   fWeightMatrix1to2[3][11] = 2.14598145603811;
   fWeightMatrix1to2[4][11] = -1.73547527392307;
   fWeightMatrix1to2[5][11] = -0.207759406730791;
   fWeightMatrix1to2[6][11] = -2.21552743888872;
   fWeightMatrix1to2[7][11] = -0.927837205808519;
   fWeightMatrix1to2[8][11] = -1.41416334413167;
   fWeightMatrix1to2[9][11] = -0.625823773966801;
   fWeightMatrix1to2[10][11] = -1.66641131526355;
   fWeightMatrix1to2[11][11] = 0.958307114893832;
   fWeightMatrix1to2[12][11] = 0.537798275383428;
   fWeightMatrix1to2[0][12] = 0.0515192741722576;
   fWeightMatrix1to2[1][12] = -1.7001554858362;
   fWeightMatrix1to2[2][12] = -2.06511228193365;
   fWeightMatrix1to2[3][12] = -1.33532518983091;
   fWeightMatrix1to2[4][12] = -1.94694830266452;
   fWeightMatrix1to2[5][12] = 0.675610190864074;
   fWeightMatrix1to2[6][12] = -1.54542889132883;
   fWeightMatrix1to2[7][12] = -1.58068795031017;
   fWeightMatrix1to2[8][12] = -0.181889170531258;
   fWeightMatrix1to2[9][12] = -2.46391023351835;
   fWeightMatrix1to2[10][12] = -1.90054761234032;
   fWeightMatrix1to2[11][12] = -1.95441910012662;
   fWeightMatrix1to2[12][12] = -0.803346101705706;
   fWeightMatrix1to2[0][13] = -1.79833326451869;
   fWeightMatrix1to2[1][13] = -2.37755830051686;
   fWeightMatrix1to2[2][13] = -1.49705015760769;
   fWeightMatrix1to2[3][13] = -0.812417484357534;
   fWeightMatrix1to2[4][13] = -1.64724915916464;
   fWeightMatrix1to2[5][13] = 0.600222159082656;
   fWeightMatrix1to2[6][13] = -1.65450176297879;
   fWeightMatrix1to2[7][13] = -1.37002867559512;
   fWeightMatrix1to2[8][13] = 3.05102696933462;
   fWeightMatrix1to2[9][13] = -0.0191604682327339;
   fWeightMatrix1to2[10][13] = -0.0143992465689926;
   fWeightMatrix1to2[11][13] = 0.252021038525072;
   fWeightMatrix1to2[12][13] = -0.00853711008583627;
   fWeightMatrix1to2[0][14] = 0.652634729794951;
   fWeightMatrix1to2[1][14] = -0.283551133546097;
   fWeightMatrix1to2[2][14] = -2.58494494947833;
   fWeightMatrix1to2[3][14] = 0.160350321800144;
   fWeightMatrix1to2[4][14] = -1.87489758289501;
   fWeightMatrix1to2[5][14] = -0.193326777632255;
   fWeightMatrix1to2[6][14] = 0.116899087306823;
   fWeightMatrix1to2[7][14] = -0.454717430394286;
   fWeightMatrix1to2[8][14] = -0.775709906645858;
   fWeightMatrix1to2[9][14] = 0.105973144878512;
   fWeightMatrix1to2[10][14] = 0.987720692072872;
   fWeightMatrix1to2[11][14] = -1.40215985983749;
   fWeightMatrix1to2[12][14] = 0.110516244995739;
   // weight matrix from layer 2 to 3
   fWeightMatrix2to3[0][0] = -0.660619167134384;
   fWeightMatrix2to3[0][1] = -0.877492535647532;
   fWeightMatrix2to3[0][2] = -1.25979577989908;
   fWeightMatrix2to3[0][3] = -0.785764986642388;
   fWeightMatrix2to3[0][4] = 0.811584962293852;
   fWeightMatrix2to3[0][5] = -1.31194480835043;
   fWeightMatrix2to3[0][6] = 0.254821418887782;
   fWeightMatrix2to3[0][7] = 1.46370226946802;
   fWeightMatrix2to3[0][8] = -1.60248209502115;
   fWeightMatrix2to3[0][9] = 0.921283753028341;
   fWeightMatrix2to3[0][10] = 1.28537254536381;
   fWeightMatrix2to3[0][11] = -0.833637539942783;
   fWeightMatrix2to3[0][12] = -0.850298546513987;
   fWeightMatrix2to3[0][13] = 0.938376000164091;
}

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
}