// 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 10:04:21 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/job403
Training events: 49290


#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'    [46.4561080933,776.884460449]
LepAPt                        LepAPt                            'F'    [20.0001068115,174.934341431]
LepBPt                        LepBPt                            'F'    [10.0002098083,71.4863357544]
MetSigLeptonsJets             MetSigLeptonsJets                 'F'    [0.976250171661,16.9346866608]
MetSpec                       MetSpec                           'F'    [15.0218133926,218.598831177]
SumEtLeptonsJets              SumEtLeptonsJets                  'F'    [30.113155365,520.010314941]
VSumJetLeptonsPt              VSumJetLeptonsPt                  'F'    [2.80723452568,299.029327393]
addEt                         addEt                             'F'    [46.4561080933,388.558624268]
dPhiLepSumMet                 dPhiLepSumMet                     'F'    [0.0435492135584,3.14158964157]
dPhiLeptons                   dPhiLeptons                       'F'    [1.1922662452e-05,1.11657130718]
dRLeptons                     dRLeptons                         'F'    [0.200001657009,1.15372478962]
lep1_E                        lep1_E                            'F'    [20.0262546539,248.743759155]
lep2_E                        lep2_E                            'F'    [10.0065498352,114.225990295]


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

#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] = 46.4561080932617;
      fVmax[0] = 776.884460449219;
      fVmin[1] = 20.0001068115234;
      fVmax[1] = 174.934341430664;
      fVmin[2] = 10.0002098083496;
      fVmax[2] = 71.4863357543945;
      fVmin[3] = 0.976250171661377;
      fVmax[3] = 16.9346866607666;
      fVmin[4] = 15.0218133926392;
      fVmax[4] = 218.598831176758;
      fVmin[5] = 30.1131553649902;
      fVmax[5] = 520.010314941406;
      fVmin[6] = 2.80723452568054;
      fVmax[6] = 299.029327392578;
      fVmin[7] = 46.4561080932617;
      fVmax[7] = 388.558624267578;
      fVmin[8] = 0.0435492135584354;
      fVmax[8] = 3.14158964157104;
      fVmin[9] = 1.19226624519797e-05;
      fVmax[9] = 1.11657130718231;
      fVmin[10] = 0.200001657009125;
      fVmax[10] = 1.15372478961945;
      fVmin[11] = 20.0262546539307;
      fVmax[11] = 248.743759155273;
      fVmin[12] = 10.0065498352051;
      fVmax[12] = 114.22599029541;

      // 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] = -0.369124769736657;
   fWeightMatrix0to1[1][0] = 2.11273049002197;
   fWeightMatrix0to1[2][0] = 0.0519990498394871;
   fWeightMatrix0to1[3][0] = 1.77342034881389;
   fWeightMatrix0to1[4][0] = -2.37697583910147;
   fWeightMatrix0to1[5][0] = -1.06178109263406;
   fWeightMatrix0to1[6][0] = 0.0642534669437345;
   fWeightMatrix0to1[7][0] = 1.29649363363074;
   fWeightMatrix0to1[8][0] = -1.52365823456812;
   fWeightMatrix0to1[9][0] = -0.529252229245596;
   fWeightMatrix0to1[10][0] = -0.951091602622116;
   fWeightMatrix0to1[11][0] = -0.257985179504682;
   fWeightMatrix0to1[12][0] = -1.46961722865426;
   fWeightMatrix0to1[13][0] = -0.820685661039019;
   fWeightMatrix0to1[0][1] = -0.566636010196901;
   fWeightMatrix0to1[1][1] = 1.44439111424839;
   fWeightMatrix0to1[2][1] = -0.557029275685777;
   fWeightMatrix0to1[3][1] = 1.84075526080494;
   fWeightMatrix0to1[4][1] = -0.195703740000762;
   fWeightMatrix0to1[5][1] = 1.43907628514872;
   fWeightMatrix0to1[6][1] = -3.3850954224814;
   fWeightMatrix0to1[7][1] = 3.43230620021318;
   fWeightMatrix0to1[8][1] = 1.73198130038814;
   fWeightMatrix0to1[9][1] = 2.20557403592191;
   fWeightMatrix0to1[10][1] = -4.3551685301018;
   fWeightMatrix0to1[11][1] = 0.796674691836282;
   fWeightMatrix0to1[12][1] = 1.05525836430286;
   fWeightMatrix0to1[13][1] = -1.21378327619649;
   fWeightMatrix0to1[0][2] = -1.88803193193427;
   fWeightMatrix0to1[1][2] = 1.07761578093068;
   fWeightMatrix0to1[2][2] = -0.311453018341957;
   fWeightMatrix0to1[3][2] = 1.28980523699198;
   fWeightMatrix0to1[4][2] = -0.420493872975624;
   fWeightMatrix0to1[5][2] = -0.553380356357214;
   fWeightMatrix0to1[6][2] = -4.3529851660895;
   fWeightMatrix0to1[7][2] = 2.98338468036319;
   fWeightMatrix0to1[8][2] = -2.10216208076194;
   fWeightMatrix0to1[9][2] = 3.9775304588028;
   fWeightMatrix0to1[10][2] = -2.02662441748187;
   fWeightMatrix0to1[11][2] = 1.77059355086353;
   fWeightMatrix0to1[12][2] = 0.615179617743778;
   fWeightMatrix0to1[13][2] = 0.454402219682577;
   fWeightMatrix0to1[0][3] = 1.87499999674399;
   fWeightMatrix0to1[1][3] = 1.37179918406355;
   fWeightMatrix0to1[2][3] = -0.660455817822156;
   fWeightMatrix0to1[3][3] = -1.45323574452187;
   fWeightMatrix0to1[4][3] = 0.5687759777665;
   fWeightMatrix0to1[5][3] = 0.164208474907549;
   fWeightMatrix0to1[6][3] = 6.01712212905597;
   fWeightMatrix0to1[7][3] = 1.17735756260684;
   fWeightMatrix0to1[8][3] = 1.55202331796003;
   fWeightMatrix0to1[9][3] = 1.88112322188792;
   fWeightMatrix0to1[10][3] = -0.279020427365459;
   fWeightMatrix0to1[11][3] = 1.36100720531134;
   fWeightMatrix0to1[12][3] = -1.81275722551071;
   fWeightMatrix0to1[13][3] = -0.871552060542297;
   fWeightMatrix0to1[0][4] = -1.32601201941471;
   fWeightMatrix0to1[1][4] = -1.27312713425256;
   fWeightMatrix0to1[2][4] = 1.36970633619396;
   fWeightMatrix0to1[3][4] = 0.843132087147121;
   fWeightMatrix0to1[4][4] = -1.87928110516742;
   fWeightMatrix0to1[5][4] = 0.182914918867212;
   fWeightMatrix0to1[6][4] = 1.63239847158164;
   fWeightMatrix0to1[7][4] = 2.90392003260975;
   fWeightMatrix0to1[8][4] = 2.3672694488504;
   fWeightMatrix0to1[9][4] = 0.892799938306604;
   fWeightMatrix0to1[10][4] = -1.08531791808797;
   fWeightMatrix0to1[11][4] = -0.923367972532844;
   fWeightMatrix0to1[12][4] = 0.504950324949527;
   fWeightMatrix0to1[13][4] = -0.922687338655;
   fWeightMatrix0to1[0][5] = -0.777445697910653;
   fWeightMatrix0to1[1][5] = -1.37979725614983;
   fWeightMatrix0to1[2][5] = -0.0949861601943723;
   fWeightMatrix0to1[3][5] = 1.12664315994727;
   fWeightMatrix0to1[4][5] = 0.935271598664855;
   fWeightMatrix0to1[5][5] = 0.166047278044636;
   fWeightMatrix0to1[6][5] = -1.1973663355251;
   fWeightMatrix0to1[7][5] = -3.70290007726989;
   fWeightMatrix0to1[8][5] = 0.665235021330902;
   fWeightMatrix0to1[9][5] = 0.626117397119349;
   fWeightMatrix0to1[10][5] = 3.15085613330479;
   fWeightMatrix0to1[11][5] = 0.745873472250576;
   fWeightMatrix0to1[12][5] = 0.714171199470985;
   fWeightMatrix0to1[13][5] = -1.28002122186739;
   fWeightMatrix0to1[0][6] = -0.937229245532783;
   fWeightMatrix0to1[1][6] = 0.242143563151242;
   fWeightMatrix0to1[2][6] = 1.26516141837096;
   fWeightMatrix0to1[3][6] = -1.04572897487917;
   fWeightMatrix0to1[4][6] = -1.8367626615107;
   fWeightMatrix0to1[5][6] = -2.04221784998238;
   fWeightMatrix0to1[6][6] = -2.12802651227451;
   fWeightMatrix0to1[7][6] = 2.44081382030586;
   fWeightMatrix0to1[8][6] = 2.324637817279;
   fWeightMatrix0to1[9][6] = -0.509477781292046;
   fWeightMatrix0to1[10][6] = -2.52757534218735;
   fWeightMatrix0to1[11][6] = 2.11572534083939;
   fWeightMatrix0to1[12][6] = -0.982201042938349;
   fWeightMatrix0to1[13][6] = -1.70529188021802;
   fWeightMatrix0to1[0][7] = -1.37165776983369;
   fWeightMatrix0to1[1][7] = 1.12174924004398;
   fWeightMatrix0to1[2][7] = -3.14390417141309;
   fWeightMatrix0to1[3][7] = -1.68324870362782;
   fWeightMatrix0to1[4][7] = 0.539923772883308;
   fWeightMatrix0to1[5][7] = 1.57981840010138;
   fWeightMatrix0to1[6][7] = -5.40448039556291;
   fWeightMatrix0to1[7][7] = 10.0795345775745;
   fWeightMatrix0to1[8][7] = 0.294877781436345;
   fWeightMatrix0to1[9][7] = 3.26009490852935;
   fWeightMatrix0to1[10][7] = -3.96332853282777;
   fWeightMatrix0to1[11][7] = 1.28935010238217;
   fWeightMatrix0to1[12][7] = -1.8740960325453;
   fWeightMatrix0to1[13][7] = 0.333067033626807;
   fWeightMatrix0to1[0][8] = 0.435360580909271;
   fWeightMatrix0to1[1][8] = 0.218005384561077;
   fWeightMatrix0to1[2][8] = -0.187600102108533;
   fWeightMatrix0to1[3][8] = 1.89917221704236;
   fWeightMatrix0to1[4][8] = 0.541805793617915;
   fWeightMatrix0to1[5][8] = -1.30326059873569;
   fWeightMatrix0to1[6][8] = 4.61165403942266;
   fWeightMatrix0to1[7][8] = -1.63264360665831;
   fWeightMatrix0to1[8][8] = -1.22379715557464;
   fWeightMatrix0to1[9][8] = -1.10901142214242;
   fWeightMatrix0to1[10][8] = -3.18505274210428;
   fWeightMatrix0to1[11][8] = 1.53374264598472;
   fWeightMatrix0to1[12][8] = 0.687366564457636;
   fWeightMatrix0to1[13][8] = 0.625215928068296;
   fWeightMatrix0to1[0][9] = -0.610605739631899;
   fWeightMatrix0to1[1][9] = 1.33739972330002;
   fWeightMatrix0to1[2][9] = -3.05538794393567;
   fWeightMatrix0to1[3][9] = 1.23964471517711;
   fWeightMatrix0to1[4][9] = -0.223307931862887;
   fWeightMatrix0to1[5][9] = -2.81702259568312;
   fWeightMatrix0to1[6][9] = 0.534918669922984;
   fWeightMatrix0to1[7][9] = -0.0498785134785078;
   fWeightMatrix0to1[8][9] = -0.292657894692225;
   fWeightMatrix0to1[9][9] = -0.223739967612964;
   fWeightMatrix0to1[10][9] = 0.843820516191448;
   fWeightMatrix0to1[11][9] = -1.0349860197782;
   fWeightMatrix0to1[12][9] = -0.153767618001765;
   fWeightMatrix0to1[13][9] = 0.92578323623622;
   fWeightMatrix0to1[0][10] = 1.30363039862737;
   fWeightMatrix0to1[1][10] = -0.792490542692807;
   fWeightMatrix0to1[2][10] = -2.11607101761458;
   fWeightMatrix0to1[3][10] = 0.178141952258475;
   fWeightMatrix0to1[4][10] = -1.95227096872658;
   fWeightMatrix0to1[5][10] = 1.33040901422609;
   fWeightMatrix0to1[6][10] = -1.31443527259528;
   fWeightMatrix0to1[7][10] = -0.591170932757081;
   fWeightMatrix0to1[8][10] = -1.16929657306257;
   fWeightMatrix0to1[9][10] = 0.504119871679195;
   fWeightMatrix0to1[10][10] = -0.609820716409745;
   fWeightMatrix0to1[11][10] = 0.827441387091758;
   fWeightMatrix0to1[12][10] = 0.883029924690135;
   fWeightMatrix0to1[13][10] = -1.345412880679;
   fWeightMatrix0to1[0][11] = -0.594857994996274;
   fWeightMatrix0to1[1][11] = -1.19392503469729;
   fWeightMatrix0to1[2][11] = 1.50316715718521;
   fWeightMatrix0to1[3][11] = 1.02561574188902;
   fWeightMatrix0to1[4][11] = 0.324672203169931;
   fWeightMatrix0to1[5][11] = -1.44196726137382;
   fWeightMatrix0to1[6][11] = 2.33716042550349;
   fWeightMatrix0to1[7][11] = 0.398447451960753;
   fWeightMatrix0to1[8][11] = 0.511184857602448;
   fWeightMatrix0to1[9][11] = -1.01834002310868;
   fWeightMatrix0to1[10][11] = 0.334254030051117;
   fWeightMatrix0to1[11][11] = 0.689561098727758;
   fWeightMatrix0to1[12][11] = -1.16026974952991;
   fWeightMatrix0to1[13][11] = 0.607462111459831;
   fWeightMatrix0to1[0][12] = -0.970595905883447;
   fWeightMatrix0to1[1][12] = -1.5217358663896;
   fWeightMatrix0to1[2][12] = 2.42951274077098;
   fWeightMatrix0to1[3][12] = 1.202996309923;
   fWeightMatrix0to1[4][12] = -0.705873269940821;
   fWeightMatrix0to1[5][12] = 0.817676381742316;
   fWeightMatrix0to1[6][12] = 2.72202064024513;
   fWeightMatrix0to1[7][12] = -0.568212191764599;
   fWeightMatrix0to1[8][12] = -0.235579923137185;
   fWeightMatrix0to1[9][12] = -1.9231986135033;
   fWeightMatrix0to1[10][12] = -0.000192703686318633;
   fWeightMatrix0to1[11][12] = -0.82566697986956;
   fWeightMatrix0to1[12][12] = -2.23537045157435;
   fWeightMatrix0to1[13][12] = 0.567933101921277;
   fWeightMatrix0to1[0][13] = 2.11885393166827;
   fWeightMatrix0to1[1][13] = -0.799707262219314;
   fWeightMatrix0to1[2][13] = -1.42774586076452;
   fWeightMatrix0to1[3][13] = -0.872131093580711;
   fWeightMatrix0to1[4][13] = 0.994298235402145;
   fWeightMatrix0to1[5][13] = 1.13010682435351;
   fWeightMatrix0to1[6][13] = -8.78540101467732;
   fWeightMatrix0to1[7][13] = 13.9005581875467;
   fWeightMatrix0to1[8][13] = 0.915610427679238;
   fWeightMatrix0to1[9][13] = 6.34343851678622;
   fWeightMatrix0to1[10][13] = -4.60779704787834;
   fWeightMatrix0to1[11][13] = 1.64154968357513;
   fWeightMatrix0to1[12][13] = 1.14436653908183;
   fWeightMatrix0to1[13][13] = 0.157588766452518;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = -2.71014413217372;
   fWeightMatrix1to2[1][0] = -0.119758803218322;
   fWeightMatrix1to2[2][0] = 0.0185533895187079;
   fWeightMatrix1to2[3][0] = -1.53917009554774;
   fWeightMatrix1to2[4][0] = -0.627547323783328;
   fWeightMatrix1to2[5][0] = -0.838415520432288;
   fWeightMatrix1to2[6][0] = -0.612987557987505;
   fWeightMatrix1to2[7][0] = -1.78326938610522;
   fWeightMatrix1to2[8][0] = 1.0102804561036;
   fWeightMatrix1to2[9][0] = 0.0360835228545731;
   fWeightMatrix1to2[10][0] = -1.48018851621905;
   fWeightMatrix1to2[11][0] = 0.000762679053727007;
   fWeightMatrix1to2[12][0] = 0.223628903545043;
   fWeightMatrix1to2[0][1] = 1.07790640948373;
   fWeightMatrix1to2[1][1] = -0.776914831888875;
   fWeightMatrix1to2[2][1] = -1.03195112204461;
   fWeightMatrix1to2[3][1] = -2.3368197115081;
   fWeightMatrix1to2[4][1] = -0.878030928867498;
   fWeightMatrix1to2[5][1] = 1.72768593691283;
   fWeightMatrix1to2[6][1] = 1.28862692006763;
   fWeightMatrix1to2[7][1] = 0.812769710706401;
   fWeightMatrix1to2[8][1] = -0.178244552717227;
   fWeightMatrix1to2[9][1] = 2.44265511497138;
   fWeightMatrix1to2[10][1] = -1.69342853046398;
   fWeightMatrix1to2[11][1] = -1.21490915747777;
   fWeightMatrix1to2[12][1] = -0.504184132564489;
   fWeightMatrix1to2[0][2] = -1.75711716498401;
   fWeightMatrix1to2[1][2] = 1.80838556235053;
   fWeightMatrix1to2[2][2] = -0.205102021945251;
   fWeightMatrix1to2[3][2] = -0.8885048724634;
   fWeightMatrix1to2[4][2] = 0.865707810916806;
   fWeightMatrix1to2[5][2] = -0.200600159666531;
   fWeightMatrix1to2[6][2] = -1.89182123483099;
   fWeightMatrix1to2[7][2] = -1.0884307175672;
   fWeightMatrix1to2[8][2] = 1.69986038865912;
   fWeightMatrix1to2[9][2] = 0.17087096607417;
   fWeightMatrix1to2[10][2] = 0.461024057081928;
   fWeightMatrix1to2[11][2] = -1.2434749278323;
   fWeightMatrix1to2[12][2] = 2.25499052564802;
   fWeightMatrix1to2[0][3] = -1.82982488667869;
   fWeightMatrix1to2[1][3] = 0.440566630567059;
   fWeightMatrix1to2[2][3] = -0.145646011729336;
   fWeightMatrix1to2[3][3] = -1.54936122303578;
   fWeightMatrix1to2[4][3] = -1.31965057104211;
   fWeightMatrix1to2[5][3] = -1.92928991628604;
   fWeightMatrix1to2[6][3] = -0.368305519090621;
   fWeightMatrix1to2[7][3] = -1.844172948214;
   fWeightMatrix1to2[8][3] = -1.63001748464154;
   fWeightMatrix1to2[9][3] = -1.6597716338823;
   fWeightMatrix1to2[10][3] = 0.147541052746128;
   fWeightMatrix1to2[11][3] = -0.97755516612706;
   fWeightMatrix1to2[12][3] = -1.68758126766978;
   fWeightMatrix1to2[0][4] = 0.303404204828851;
   fWeightMatrix1to2[1][4] = -1.51774340872587;
   fWeightMatrix1to2[2][4] = -0.778092773392149;
   fWeightMatrix1to2[3][4] = -1.05417512019494;
   fWeightMatrix1to2[4][4] = 0.276473458331677;
   fWeightMatrix1to2[5][4] = 0.727135840664374;
   fWeightMatrix1to2[6][4] = 0.58609105772606;
   fWeightMatrix1to2[7][4] = -0.616107098482405;
   fWeightMatrix1to2[8][4] = -1.88625227311917;
   fWeightMatrix1to2[9][4] = -1.6034488389263;
   fWeightMatrix1to2[10][4] = -1.8958963609085;
   fWeightMatrix1to2[11][4] = -1.55126957347157;
   fWeightMatrix1to2[12][4] = -1.128571725105;
   fWeightMatrix1to2[0][5] = -0.51050701406626;
   fWeightMatrix1to2[1][5] = -0.0853352338326277;
   fWeightMatrix1to2[2][5] = -0.212121236011941;
   fWeightMatrix1to2[3][5] = 0.561797404325436;
   fWeightMatrix1to2[4][5] = -0.498673512941615;
   fWeightMatrix1to2[5][5] = -3.07445581275204;
   fWeightMatrix1to2[6][5] = 0.665697932532015;
   fWeightMatrix1to2[7][5] = 1.59118249304854;
   fWeightMatrix1to2[8][5] = -1.97629541770493;
   fWeightMatrix1to2[9][5] = -0.894769202180158;
   fWeightMatrix1to2[10][5] = 0.093898754072112;
   fWeightMatrix1to2[11][5] = 1.35389023002799;
   fWeightMatrix1to2[12][5] = -1.16817843248734;
   fWeightMatrix1to2[0][6] = -2.58166894972109;
   fWeightMatrix1to2[1][6] = 4.23514303225027;
   fWeightMatrix1to2[2][6] = 0.694723415098111;
   fWeightMatrix1to2[3][6] = 3.3070919876309;
   fWeightMatrix1to2[4][6] = 1.02855872115475;
   fWeightMatrix1to2[5][6] = -1.4239017329597;
   fWeightMatrix1to2[6][6] = -0.784916042204884;
   fWeightMatrix1to2[7][6] = -0.508850620988409;
   fWeightMatrix1to2[8][6] = -2.13529577967007;
   fWeightMatrix1to2[9][6] = -1.37108413630222;
   fWeightMatrix1to2[10][6] = -2.46720973974399;
   fWeightMatrix1to2[11][6] = -1.9726742661977;
   fWeightMatrix1to2[12][6] = -2.19015725586756;
   fWeightMatrix1to2[0][7] = 3.06511444377435;
   fWeightMatrix1to2[1][7] = -10.4139194229766;
   fWeightMatrix1to2[2][7] = 5.38490224630045;
   fWeightMatrix1to2[3][7] = -3.05874740897295;
   fWeightMatrix1to2[4][7] = 1.28002695959347;
   fWeightMatrix1to2[5][7] = -0.992495852991972;
   fWeightMatrix1to2[6][7] = 0.221968559778405;
   fWeightMatrix1to2[7][7] = 1.45417604254372;
   fWeightMatrix1to2[8][7] = -2.77307314423384;
   fWeightMatrix1to2[9][7] = 1.48620432324994;
   fWeightMatrix1to2[10][7] = 0.0823267232611311;
   fWeightMatrix1to2[11][7] = -1.6662414738285;
   fWeightMatrix1to2[12][7] = -2.61964560014655;
   fWeightMatrix1to2[0][8] = -1.58893655466838;
   fWeightMatrix1to2[1][8] = -2.10713049657431;
   fWeightMatrix1to2[2][8] = -0.121867356274563;
   fWeightMatrix1to2[3][8] = -0.27017482274208;
   fWeightMatrix1to2[4][8] = 0.309070355991774;
   fWeightMatrix1to2[5][8] = -0.877913888519733;
   fWeightMatrix1to2[6][8] = -1.31589045259484;
   fWeightMatrix1to2[7][8] = -1.65209413916559;
   fWeightMatrix1to2[8][8] = -1.11302495436151;
   fWeightMatrix1to2[9][8] = -1.24344220197486;
   fWeightMatrix1to2[10][8] = 0.662168379296217;
   fWeightMatrix1to2[11][8] = -1.4004679185503;
   fWeightMatrix1to2[12][8] = -1.47148705830062;
   fWeightMatrix1to2[0][9] = 2.94462248839313;
   fWeightMatrix1to2[1][9] = -4.69740287071641;
   fWeightMatrix1to2[2][9] = 0.490941287116793;
   fWeightMatrix1to2[3][9] = 1.37353637443985;
   fWeightMatrix1to2[4][9] = -1.76095952029931;
   fWeightMatrix1to2[5][9] = 1.41780089741459;
   fWeightMatrix1to2[6][9] = 1.07033824492547;
   fWeightMatrix1to2[7][9] = -0.713723914007221;
   fWeightMatrix1to2[8][9] = -1.49110046108204;
   fWeightMatrix1to2[9][9] = 0.833156269087126;
   fWeightMatrix1to2[10][9] = -0.777848531820109;
   fWeightMatrix1to2[11][9] = 0.8046201190073;
   fWeightMatrix1to2[12][9] = 0.528578897793083;
   fWeightMatrix1to2[0][10] = -0.00207990068928734;
   fWeightMatrix1to2[1][10] = 3.54629072407105;
   fWeightMatrix1to2[2][10] = -5.49657841798172;
   fWeightMatrix1to2[3][10] = -0.00785507659855783;
   fWeightMatrix1to2[4][10] = -1.8070382293954;
   fWeightMatrix1to2[5][10] = -0.646118216964103;
   fWeightMatrix1to2[6][10] = -1.95247277673052;
   fWeightMatrix1to2[7][10] = -1.18342432223607;
   fWeightMatrix1to2[8][10] = -1.79094825676775;
   fWeightMatrix1to2[9][10] = -0.0147760115145612;
   fWeightMatrix1to2[10][10] = -0.419703552217428;
   fWeightMatrix1to2[11][10] = 1.04165859614851;
   fWeightMatrix1to2[12][10] = -1.18034175760637;
   fWeightMatrix1to2[0][11] = -1.10929916522203;
   fWeightMatrix1to2[1][11] = -2.21605441222958;
   fWeightMatrix1to2[2][11] = -1.55304437710985;
   fWeightMatrix1to2[3][11] = 2.36096469508608;
   fWeightMatrix1to2[4][11] = -1.6110418371369;
   fWeightMatrix1to2[5][11] = -0.532629334432296;
   fWeightMatrix1to2[6][11] = -1.98488047644671;
   fWeightMatrix1to2[7][11] = -0.474199693590428;
   fWeightMatrix1to2[8][11] = -0.236489377394198;
   fWeightMatrix1to2[9][11] = 1.117759111188;
   fWeightMatrix1to2[10][11] = -1.492709398252;
   fWeightMatrix1to2[11][11] = 0.976667742989692;
   fWeightMatrix1to2[12][11] = 0.438200145309904;
   fWeightMatrix1to2[0][12] = 0.119489128335542;
   fWeightMatrix1to2[1][12] = -0.175394803744928;
   fWeightMatrix1to2[2][12] = -1.98917967879819;
   fWeightMatrix1to2[3][12] = -1.28692058904756;
   fWeightMatrix1to2[4][12] = -1.63071048084185;
   fWeightMatrix1to2[5][12] = -0.0585823175497676;
   fWeightMatrix1to2[6][12] = -1.01467583166438;
   fWeightMatrix1to2[7][12] = -0.845491970125863;
   fWeightMatrix1to2[8][12] = 0.161630994739513;
   fWeightMatrix1to2[9][12] = -1.76049418376472;
   fWeightMatrix1to2[10][12] = -1.73876324391173;
   fWeightMatrix1to2[11][12] = -1.52818441780636;
   fWeightMatrix1to2[12][12] = -0.379002425425732;
   fWeightMatrix1to2[0][13] = -2.0186497172941;
   fWeightMatrix1to2[1][13] = -1.4774693215807;
   fWeightMatrix1to2[2][13] = -0.484748796573586;
   fWeightMatrix1to2[3][13] = -0.615992875392997;
   fWeightMatrix1to2[4][13] = -1.54303122954972;
   fWeightMatrix1to2[5][13] = 0.276487118034678;
   fWeightMatrix1to2[6][13] = -1.87879592273842;
   fWeightMatrix1to2[7][13] = -0.955390391776685;
   fWeightMatrix1to2[8][13] = 1.65724150458683;
   fWeightMatrix1to2[9][13] = 0.00442329156457383;
   fWeightMatrix1to2[10][13] = -0.296393878497413;
   fWeightMatrix1to2[11][13] = 0.126512244855067;
   fWeightMatrix1to2[12][13] = -0.0371553119757794;
   fWeightMatrix1to2[0][14] = 0.724977237623978;
   fWeightMatrix1to2[1][14] = -0.746362167366195;
   fWeightMatrix1to2[2][14] = -2.03630333606265;
   fWeightMatrix1to2[3][14] = -0.864070410927774;
   fWeightMatrix1to2[4][14] = -1.66967412789414;
   fWeightMatrix1to2[5][14] = -0.231681267155942;
   fWeightMatrix1to2[6][14] = 0.217376458378444;
   fWeightMatrix1to2[7][14] = 0.412724184571141;
   fWeightMatrix1to2[8][14] = 0.545023026234367;
   fWeightMatrix1to2[9][14] = 0.564651844200317;
   fWeightMatrix1to2[10][14] = 0.723886839752841;
   fWeightMatrix1to2[11][14] = -1.20796426434828;
   fWeightMatrix1to2[12][14] = 0.209204302742073;
   // weight matrix from layer 2 to 3
   fWeightMatrix2to3[0][0] = -0.571386393868319;
   fWeightMatrix2to3[0][1] = -0.662701536792165;
   fWeightMatrix2to3[0][2] = -2.17484286367959;
   fWeightMatrix2to3[0][3] = -1.54622208833026;
   fWeightMatrix2to3[0][4] = 0.985150037630431;
   fWeightMatrix2to3[0][5] = -0.524118991414609;
   fWeightMatrix2to3[0][6] = 0.378097968239391;
   fWeightMatrix2to3[0][7] = 1.97978050259678;
   fWeightMatrix2to3[0][8] = -1.65958346877119;
   fWeightMatrix2to3[0][9] = 1.07782396851115;
   fWeightMatrix2to3[0][10] = 0.799457786296207;
   fWeightMatrix2to3[0][11] = 0.842567106164807;
   fWeightMatrix2to3[0][12] = -1.04681914153326;
   fWeightMatrix2to3[0][13] = 0.894715893839749;
}

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
}