// 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 10:53:47 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/job412
Training events: 51330


#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'    [48.6484184265,844.079467773]
LepAPt                        LepAPt                            'F'    [20.0002613068,215.671981812]
LepBPt                        LepBPt                            'F'    [10.000038147,74.0718307495]
MetSigLeptonsJets             MetSigLeptonsJets                 'F'    [1.14757633209,20.3265991211]
MetSpec                       MetSpec                           'F'    [15.0046319962,320.548706055]
SumEtLeptonsJets              SumEtLeptonsJets                  'F'    [30.1895332336,527.566101074]
VSumJetLeptonsPt              VSumJetLeptonsPt                  'F'    [2.83339142799,373.748504639]
addEt                         addEt                             'F'    [48.6484184265,438.620880127]
dPhiLepSumMet                 dPhiLepSumMet                     'F'    [0.0173770282418,3.1415913105]
dPhiLeptons                   dPhiLeptons                       'F'    [4.88758087158e-06,1.12033128738]
dRLeptons                     dRLeptons                         'F'    [0.200001657009,1.13469779491]
lep1_E                        lep1_E                            'F'    [20.0293254852,232.066116333]
lep2_E                        lep2_E                            'F'    [10.025138855,130.657516479]


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

#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] = 844.079467773438;
      fVmin[1] = 20.0002613067627;
      fVmax[1] = 215.671981811523;
      fVmin[2] = 10.0000381469727;
      fVmax[2] = 74.0718307495117;
      fVmin[3] = 1.14757633209229;
      fVmax[3] = 20.3265991210938;
      fVmin[4] = 15.0046319961548;
      fVmax[4] = 320.548706054688;
      fVmin[5] = 30.1895332336426;
      fVmax[5] = 527.566101074219;
      fVmin[6] = 2.83339142799377;
      fVmax[6] = 373.748504638672;
      fVmin[7] = 48.6484184265137;
      fVmax[7] = 438.620880126953;
      fVmin[8] = 0.0173770282417536;
      fVmax[8] = 3.1415913105011;
      fVmin[9] = 4.88758087158203e-06;
      fVmax[9] = 1.12033128738403;
      fVmin[10] = 0.200001657009125;
      fVmax[10] = 1.13469779491425;
      fVmin[11] = 20.0293254852295;
      fVmax[11] = 232.066116333008;
      fVmin[12] = 10.0251388549805;
      fVmax[12] = 130.657516479492;

      // 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] = -0.338403365498049;
   fWeightMatrix0to1[1][0] = 2.07910566172066;
   fWeightMatrix0to1[2][0] = 0.78304596292102;
   fWeightMatrix0to1[3][0] = 1.76188941792176;
   fWeightMatrix0to1[4][0] = -0.539600390761776;
   fWeightMatrix0to1[5][0] = -1.40975446067076;
   fWeightMatrix0to1[6][0] = -0.898753048288148;
   fWeightMatrix0to1[7][0] = 2.52122653826607;
   fWeightMatrix0to1[8][0] = -1.39650433354355;
   fWeightMatrix0to1[9][0] = -0.03812125676929;
   fWeightMatrix0to1[10][0] = -2.37533822190362;
   fWeightMatrix0to1[11][0] = -0.65508947641749;
   fWeightMatrix0to1[12][0] = -1.27486979378665;
   fWeightMatrix0to1[13][0] = -0.0361411856923238;
   fWeightMatrix0to1[0][1] = -0.555053515383;
   fWeightMatrix0to1[1][1] = 1.55547297461573;
   fWeightMatrix0to1[2][1] = -0.584571566768937;
   fWeightMatrix0to1[3][1] = 1.4046041495815;
   fWeightMatrix0to1[4][1] = 3.29731078210795;
   fWeightMatrix0to1[5][1] = 1.82679742742509;
   fWeightMatrix0to1[6][1] = -3.04554355092002;
   fWeightMatrix0to1[7][1] = 1.33645468034814;
   fWeightMatrix0to1[8][1] = 0.681375901221537;
   fWeightMatrix0to1[9][1] = 2.3857557576139;
   fWeightMatrix0to1[10][1] = -0.320913360034079;
   fWeightMatrix0to1[11][1] = -0.694227453014752;
   fWeightMatrix0to1[12][1] = 1.21856831849332;
   fWeightMatrix0to1[13][1] = -3.30216593988806;
   fWeightMatrix0to1[0][2] = -1.94740121302606;
   fWeightMatrix0to1[1][2] = 0.105874257715461;
   fWeightMatrix0to1[2][2] = -0.149804859489555;
   fWeightMatrix0to1[3][2] = 1.54486530410947;
   fWeightMatrix0to1[4][2] = 1.58943181510289;
   fWeightMatrix0to1[5][2] = -0.728244382622538;
   fWeightMatrix0to1[6][2] = 0.675108208785691;
   fWeightMatrix0to1[7][2] = 0.0985397872526952;
   fWeightMatrix0to1[8][2] = -1.45034518459992;
   fWeightMatrix0to1[9][2] = 3.07694138370407;
   fWeightMatrix0to1[10][2] = -3.95272246779967;
   fWeightMatrix0to1[11][2] = 0.68891312705334;
   fWeightMatrix0to1[12][2] = 1.16054843490798;
   fWeightMatrix0to1[13][2] = 1.59129658486319;
   fWeightMatrix0to1[0][3] = 1.88990536905723;
   fWeightMatrix0to1[1][3] = 1.2652369930041;
   fWeightMatrix0to1[2][3] = -1.33731210850303;
   fWeightMatrix0to1[3][3] = -1.64135201197841;
   fWeightMatrix0to1[4][3] = 0.787864457326191;
   fWeightMatrix0to1[5][3] = 0.333188116923088;
   fWeightMatrix0to1[6][3] = 4.22593474849644;
   fWeightMatrix0to1[7][3] = 1.45930998093117;
   fWeightMatrix0to1[8][3] = 1.49747098987676;
   fWeightMatrix0to1[9][3] = 0.551478070828729;
   fWeightMatrix0to1[10][3] = -0.503857666937226;
   fWeightMatrix0to1[11][3] = 0.951321438169782;
   fWeightMatrix0to1[12][3] = -2.05580775697346;
   fWeightMatrix0to1[13][3] = -1.70823178058446;
   fWeightMatrix0to1[0][4] = -1.31301490581122;
   fWeightMatrix0to1[1][4] = -1.597035936628;
   fWeightMatrix0to1[2][4] = 1.1739257850134;
   fWeightMatrix0to1[3][4] = 0.652093665769929;
   fWeightMatrix0to1[4][4] = -0.782591355203157;
   fWeightMatrix0to1[5][4] = 0.592994857065257;
   fWeightMatrix0to1[6][4] = 0.367802200286337;
   fWeightMatrix0to1[7][4] = 1.19520585798053;
   fWeightMatrix0to1[8][4] = 1.798839139901;
   fWeightMatrix0to1[9][4] = 0.000441842412862734;
   fWeightMatrix0to1[10][4] = 0.275271597789073;
   fWeightMatrix0to1[11][4] = -1.61259895815905;
   fWeightMatrix0to1[12][4] = 0.578075110176336;
   fWeightMatrix0to1[13][4] = -1.11831394823995;
   fWeightMatrix0to1[0][5] = -0.749281684413026;
   fWeightMatrix0to1[1][5] = -1.43222679731621;
   fWeightMatrix0to1[2][5] = 0.833078790481909;
   fWeightMatrix0to1[3][5] = 1.17966539832112;
   fWeightMatrix0to1[4][5] = 2.61909751629;
   fWeightMatrix0to1[5][5] = -0.348601478989483;
   fWeightMatrix0to1[6][5] = -2.33950931332049;
   fWeightMatrix0to1[7][5] = -1.65056779216391;
   fWeightMatrix0to1[8][5] = 1.04817824389887;
   fWeightMatrix0to1[9][5] = 1.53785422217877;
   fWeightMatrix0to1[10][5] = 1.23018074768507;
   fWeightMatrix0to1[11][5] = 0.474452490624599;
   fWeightMatrix0to1[12][5] = 1.02443990441119;
   fWeightMatrix0to1[13][5] = -0.08309761708454;
   fWeightMatrix0to1[0][6] = -0.884216919488876;
   fWeightMatrix0to1[1][6] = 0.143456866326622;
   fWeightMatrix0to1[2][6] = 0.784406658023227;
   fWeightMatrix0to1[3][6] = -1.23503619272047;
   fWeightMatrix0to1[4][6] = 0.673655082890951;
   fWeightMatrix0to1[5][6] = -1.34875277892985;
   fWeightMatrix0to1[6][6] = -1.56721765478665;
   fWeightMatrix0to1[7][6] = 1.2070314876572;
   fWeightMatrix0to1[8][6] = 1.58778028392039;
   fWeightMatrix0to1[9][6] = 0.0382470761531128;
   fWeightMatrix0to1[10][6] = -1.58406069117111;
   fWeightMatrix0to1[11][6] = 1.20766333162022;
   fWeightMatrix0to1[12][6] = -0.910131857275196;
   fWeightMatrix0to1[13][6] = -2.12032019411481;
   fWeightMatrix0to1[0][7] = -1.43893040129426;
   fWeightMatrix0to1[1][7] = 1.40304038838806;
   fWeightMatrix0to1[2][7] = -2.47677227232601;
   fWeightMatrix0to1[3][7] = -1.81099509014634;
   fWeightMatrix0to1[4][7] = 8.25639972338;
   fWeightMatrix0to1[5][7] = 1.97153915079736;
   fWeightMatrix0to1[6][7] = -4.27778181106719;
   fWeightMatrix0to1[7][7] = 8.02364099632658;
   fWeightMatrix0to1[8][7] = -0.985726126054839;
   fWeightMatrix0to1[9][7] = 4.37972658880336;
   fWeightMatrix0to1[10][7] = -4.57493203548672;
   fWeightMatrix0to1[11][7] = -0.194446125572749;
   fWeightMatrix0to1[12][7] = -1.97405997843153;
   fWeightMatrix0to1[13][7] = -0.71709876981318;
   fWeightMatrix0to1[0][8] = 0.393763629505511;
   fWeightMatrix0to1[1][8] = 0.650332460070772;
   fWeightMatrix0to1[2][8] = -2.00789846425941;
   fWeightMatrix0to1[3][8] = 1.47682070940326;
   fWeightMatrix0to1[4][8] = 0.233960339463266;
   fWeightMatrix0to1[5][8] = -1.16276639120451;
   fWeightMatrix0to1[6][8] = 1.3784644345111;
   fWeightMatrix0to1[7][8] = -1.33887607402634;
   fWeightMatrix0to1[8][8] = -1.73388001581639;
   fWeightMatrix0to1[9][8] = -0.655319507901851;
   fWeightMatrix0to1[10][8] = 2.04465505696908;
   fWeightMatrix0to1[11][8] = 1.57822625564663;
   fWeightMatrix0to1[12][8] = 0.32854181906725;
   fWeightMatrix0to1[13][8] = -1.88729176646399;
   fWeightMatrix0to1[0][9] = -0.389828913846259;
   fWeightMatrix0to1[1][9] = -0.0406710107962281;
   fWeightMatrix0to1[2][9] = -2.94164439724713;
   fWeightMatrix0to1[3][9] = 1.7599605768434;
   fWeightMatrix0to1[4][9] = 0.875339729265815;
   fWeightMatrix0to1[5][9] = -0.98981147053609;
   fWeightMatrix0to1[6][9] = -0.0277439515208419;
   fWeightMatrix0to1[7][9] = -0.281675595131277;
   fWeightMatrix0to1[8][9] = 0.0500335574885874;
   fWeightMatrix0to1[9][9] = -0.197416127400758;
   fWeightMatrix0to1[10][9] = 1.68929557453881;
   fWeightMatrix0to1[11][9] = -1.74296421916659;
   fWeightMatrix0to1[12][9] = 0.386167709041426;
   fWeightMatrix0to1[13][9] = 2.07827661880476;
   fWeightMatrix0to1[0][10] = 1.56227454385569;
   fWeightMatrix0to1[1][10] = 0.280593101129399;
   fWeightMatrix0to1[2][10] = -3.57507072001248;
   fWeightMatrix0to1[3][10] = 0.03331849976759;
   fWeightMatrix0to1[4][10] = -0.687550899865712;
   fWeightMatrix0to1[5][10] = 1.10321247233779;
   fWeightMatrix0to1[6][10] = -0.252316446661973;
   fWeightMatrix0to1[7][10] = 0.0110176564844894;
   fWeightMatrix0to1[8][10] = -0.410154223566456;
   fWeightMatrix0to1[9][10] = -0.274877515284328;
   fWeightMatrix0to1[10][10] = -0.374721912503827;
   fWeightMatrix0to1[11][10] = 0.546638936051339;
   fWeightMatrix0to1[12][10] = 1.72120857532127;
   fWeightMatrix0to1[13][10] = -0.277437389737019;
   fWeightMatrix0to1[0][11] = -0.577597286554482;
   fWeightMatrix0to1[1][11] = 0.573459932686167;
   fWeightMatrix0to1[2][11] = 1.48674751299562;
   fWeightMatrix0to1[3][11] = -0.58594314767047;
   fWeightMatrix0to1[4][11] = -1.00023804679322;
   fWeightMatrix0to1[5][11] = -1.6006918034109;
   fWeightMatrix0to1[6][11] = 1.9691547419709;
   fWeightMatrix0to1[7][11] = -0.2696254940745;
   fWeightMatrix0to1[8][11] = 0.443650361454661;
   fWeightMatrix0to1[9][11] = -0.746704655000889;
   fWeightMatrix0to1[10][11] = 3.99841341934633;
   fWeightMatrix0to1[11][11] = -0.178224600357526;
   fWeightMatrix0to1[12][11] = -1.01047934555901;
   fWeightMatrix0to1[13][11] = -1.13196259193207;
   fWeightMatrix0to1[0][12] = -0.995005803094937;
   fWeightMatrix0to1[1][12] = -0.359025807090611;
   fWeightMatrix0to1[2][12] = 1.16436417571609;
   fWeightMatrix0to1[3][12] = 0.136703769324678;
   fWeightMatrix0to1[4][12] = -0.142215475596043;
   fWeightMatrix0to1[5][12] = 0.663066588827567;
   fWeightMatrix0to1[6][12] = 1.77431611034043;
   fWeightMatrix0to1[7][12] = -0.413999393996409;
   fWeightMatrix0to1[8][12] = 0.650881250737758;
   fWeightMatrix0to1[9][12] = -0.0366420376668817;
   fWeightMatrix0to1[10][12] = -2.30192642042299;
   fWeightMatrix0to1[11][12] = -1.45986135335768;
   fWeightMatrix0to1[12][12] = -1.79325177638698;
   fWeightMatrix0to1[13][12] = 1.03525442056687;
   fWeightMatrix0to1[0][13] = 1.83899806276839;
   fWeightMatrix0to1[1][13] = -0.15061254044082;
   fWeightMatrix0to1[2][13] = -0.246959300254253;
   fWeightMatrix0to1[3][13] = -0.493221170254604;
   fWeightMatrix0to1[4][13] = 10.3383318607212;
   fWeightMatrix0to1[5][13] = 1.8070978015278;
   fWeightMatrix0to1[6][13] = -5.34333654448878;
   fWeightMatrix0to1[7][13] = 9.94148103222321;
   fWeightMatrix0to1[8][13] = -1.02625160629228;
   fWeightMatrix0to1[9][13] = 8.20720098424674;
   fWeightMatrix0to1[10][13] = -7.53547788309784;
   fWeightMatrix0to1[11][13] = 1.20430379758868;
   fWeightMatrix0to1[12][13] = 0.406668447002372;
   fWeightMatrix0to1[13][13] = -0.57306776256962;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = -3.21427789716905;
   fWeightMatrix1to2[1][0] = -0.110556940087381;
   fWeightMatrix1to2[2][0] = 0.174065398565828;
   fWeightMatrix1to2[3][0] = -1.80364757157959;
   fWeightMatrix1to2[4][0] = -0.702103820359516;
   fWeightMatrix1to2[5][0] = -0.446406749191095;
   fWeightMatrix1to2[6][0] = 0.99708373288054;
   fWeightMatrix1to2[7][0] = -1.68185142486757;
   fWeightMatrix1to2[8][0] = 0.545995966834925;
   fWeightMatrix1to2[9][0] = 0.053638298897463;
   fWeightMatrix1to2[10][0] = -1.00646639732235;
   fWeightMatrix1to2[11][0] = -0.549860934204775;
   fWeightMatrix1to2[12][0] = 0.00896600355518385;
   fWeightMatrix1to2[0][1] = 1.18047085513416;
   fWeightMatrix1to2[1][1] = -0.0673093535704948;
   fWeightMatrix1to2[2][1] = -0.213591695359003;
   fWeightMatrix1to2[3][1] = -2.09020988126294;
   fWeightMatrix1to2[4][1] = -0.92480447005071;
   fWeightMatrix1to2[5][1] = 1.7533110039;
   fWeightMatrix1to2[6][1] = 2.04657169933816;
   fWeightMatrix1to2[7][1] = 1.23533084339573;
   fWeightMatrix1to2[8][1] = 0.0815508394492264;
   fWeightMatrix1to2[9][1] = 1.97268383234185;
   fWeightMatrix1to2[10][1] = -1.76598357215422;
   fWeightMatrix1to2[11][1] = -1.35867618977819;
   fWeightMatrix1to2[12][1] = -0.368259602075818;
   fWeightMatrix1to2[0][2] = -1.41804403080436;
   fWeightMatrix1to2[1][2] = 0.731275874598875;
   fWeightMatrix1to2[2][2] = 1.53464803886894;
   fWeightMatrix1to2[3][2] = -0.856963302676735;
   fWeightMatrix1to2[4][2] = 0.774604711999014;
   fWeightMatrix1to2[5][2] = -0.0186102810816668;
   fWeightMatrix1to2[6][2] = -3.43379659676234;
   fWeightMatrix1to2[7][2] = -1.26251627658996;
   fWeightMatrix1to2[8][2] = 0.924949377214744;
   fWeightMatrix1to2[9][2] = -0.207249140124453;
   fWeightMatrix1to2[10][2] = 0.993833912618642;
   fWeightMatrix1to2[11][2] = -1.14415334838871;
   fWeightMatrix1to2[12][2] = 1.5526954253442;
   fWeightMatrix1to2[0][3] = -2.29840082536955;
   fWeightMatrix1to2[1][3] = 0.158496469707481;
   fWeightMatrix1to2[2][3] = 0.151581445318085;
   fWeightMatrix1to2[3][3] = -1.51804351931961;
   fWeightMatrix1to2[4][3] = -1.30992502163534;
   fWeightMatrix1to2[5][3] = -1.43773227995318;
   fWeightMatrix1to2[6][3] = 0.0185495092678514;
   fWeightMatrix1to2[7][3] = -1.84452023723537;
   fWeightMatrix1to2[8][3] = -1.87093690789642;
   fWeightMatrix1to2[9][3] = -1.7968221857936;
   fWeightMatrix1to2[10][3] = 0.263344677156823;
   fWeightMatrix1to2[11][3] = -1.07624034133366;
   fWeightMatrix1to2[12][3] = -1.68327222038983;
   fWeightMatrix1to2[0][4] = 2.11222762293957;
   fWeightMatrix1to2[1][4] = -3.43504925127164;
   fWeightMatrix1to2[2][4] = 0.127363976282828;
   fWeightMatrix1to2[3][4] = -1.70204021311871;
   fWeightMatrix1to2[4][4] = 0.210777198675759;
   fWeightMatrix1to2[5][4] = 1.28204619455921;
   fWeightMatrix1to2[6][4] = 6.50305260932372;
   fWeightMatrix1to2[7][4] = -0.448529371447873;
   fWeightMatrix1to2[8][4] = -2.01845260564232;
   fWeightMatrix1to2[9][4] = -1.6553645688732;
   fWeightMatrix1to2[10][4] = -1.70699894514297;
   fWeightMatrix1to2[11][4] = -1.82097610621577;
   fWeightMatrix1to2[12][4] = -0.968481023365921;
   fWeightMatrix1to2[0][5] = -0.392971505059039;
   fWeightMatrix1to2[1][5] = 0.735092162372647;
   fWeightMatrix1to2[2][5] = -0.631785706852974;
   fWeightMatrix1to2[3][5] = -0.0543763915445966;
   fWeightMatrix1to2[4][5] = -0.535706990601696;
   fWeightMatrix1to2[5][5] = -2.81164314842623;
   fWeightMatrix1to2[6][5] = 0.998573232569831;
   fWeightMatrix1to2[7][5] = 1.35262432660238;
   fWeightMatrix1to2[8][5] = -1.70755452963812;
   fWeightMatrix1to2[9][5] = -1.67401513322986;
   fWeightMatrix1to2[10][5] = 0.38138031467397;
   fWeightMatrix1to2[11][5] = 0.850369967433899;
   fWeightMatrix1to2[12][5] = -0.796082485018584;
   fWeightMatrix1to2[0][6] = -1.77911321722687;
   fWeightMatrix1to2[1][6] = 2.08580351252655;
   fWeightMatrix1to2[2][6] = 1.23321193521644;
   fWeightMatrix1to2[3][6] = 2.09345611298425;
   fWeightMatrix1to2[4][6] = 0.985982051923456;
   fWeightMatrix1to2[5][6] = -1.10034094304775;
   fWeightMatrix1to2[6][6] = -3.34434607343995;
   fWeightMatrix1to2[7][6] = -0.186586548142927;
   fWeightMatrix1to2[8][6] = -1.39093803245298;
   fWeightMatrix1to2[9][6] = -1.28235819429055;
   fWeightMatrix1to2[10][6] = -2.35342556334108;
   fWeightMatrix1to2[11][6] = -1.24240975406078;
   fWeightMatrix1to2[12][6] = -2.33874983669797;
   fWeightMatrix1to2[0][7] = 2.50183116702127;
   fWeightMatrix1to2[1][7] = -3.78609007402418;
   fWeightMatrix1to2[2][7] = 1.5152504001153;
   fWeightMatrix1to2[3][7] = -2.26429813113965;
   fWeightMatrix1to2[4][7] = 1.1243428432032;
   fWeightMatrix1to2[5][7] = -1.21169749307051;
   fWeightMatrix1to2[6][7] = 6.03150021650417;
   fWeightMatrix1to2[7][7] = 1.29711413803392;
   fWeightMatrix1to2[8][7] = -2.08375719556401;
   fWeightMatrix1to2[9][7] = 0.228297342101835;
   fWeightMatrix1to2[10][7] = 0.396865499296773;
   fWeightMatrix1to2[11][7] = -2.15139582687429;
   fWeightMatrix1to2[12][7] = -2.96289407438905;
   fWeightMatrix1to2[0][8] = -1.75727956022693;
   fWeightMatrix1to2[1][8] = -0.995141311269823;
   fWeightMatrix1to2[2][8] = 1.80359731058948;
   fWeightMatrix1to2[3][8] = -0.625091108441336;
   fWeightMatrix1to2[4][8] = 0.269055826812815;
   fWeightMatrix1to2[5][8] = -0.990629527465335;
   fWeightMatrix1to2[6][8] = -0.94919657852305;
   fWeightMatrix1to2[7][8] = -1.74843528293729;
   fWeightMatrix1to2[8][8] = -1.01854623201611;
   fWeightMatrix1to2[9][8] = -1.4112959632864;
   fWeightMatrix1to2[10][8] = 0.920439102352116;
   fWeightMatrix1to2[11][8] = -1.368388365342;
   fWeightMatrix1to2[12][8] = -1.15110272953275;
   fWeightMatrix1to2[0][9] = 0.870772622354631;
   fWeightMatrix1to2[1][9] = -2.72594576266483;
   fWeightMatrix1to2[2][9] = -0.334895895181529;
   fWeightMatrix1to2[3][9] = 1.12933078196308;
   fWeightMatrix1to2[4][9] = -1.79469583985449;
   fWeightMatrix1to2[5][9] = 2.06697761014314;
   fWeightMatrix1to2[6][9] = 4.96749056842609;
   fWeightMatrix1to2[7][9] = -0.516215046036291;
   fWeightMatrix1to2[8][9] = -1.51773727069505;
   fWeightMatrix1to2[9][9] = -0.156074878843945;
   fWeightMatrix1to2[10][9] = -1.64759492202562;
   fWeightMatrix1to2[11][9] = 0.293552384843288;
   fWeightMatrix1to2[12][9] = 1.10487717293153;
   fWeightMatrix1to2[0][10] = 2.26519690879883;
   fWeightMatrix1to2[1][10] = 2.14500348375208;
   fWeightMatrix1to2[2][10] = -1.84871864149082;
   fWeightMatrix1to2[3][10] = 0.764632383701558;
   fWeightMatrix1to2[4][10] = -1.93813354679681;
   fWeightMatrix1to2[5][10] = -1.07803687234296;
   fWeightMatrix1to2[6][10] = -5.31282553123184;
   fWeightMatrix1to2[7][10] = -1.73099268416184;
   fWeightMatrix1to2[8][10] = -1.08566316245877;
   fWeightMatrix1to2[9][10] = 0.486196424689029;
   fWeightMatrix1to2[10][10] = 0.504024841938334;
   fWeightMatrix1to2[11][10] = 0.705708592841757;
   fWeightMatrix1to2[12][10] = -1.42416828054133;
   fWeightMatrix1to2[0][11] = -1.0646051528052;
   fWeightMatrix1to2[1][11] = -1.56802865774478;
   fWeightMatrix1to2[2][11] = -0.214739055634728;
   fWeightMatrix1to2[3][11] = 1.47653756799228;
   fWeightMatrix1to2[4][11] = -1.57296359160514;
   fWeightMatrix1to2[5][11] = -0.51202801327645;
   fWeightMatrix1to2[6][11] = -0.435612273244908;
   fWeightMatrix1to2[7][11] = -0.183538860221221;
   fWeightMatrix1to2[8][11] = -0.893057451701866;
   fWeightMatrix1to2[9][11] = -0.527924888864393;
   fWeightMatrix1to2[10][11] = -1.47722861293729;
   fWeightMatrix1to2[11][11] = 0.608776810060031;
   fWeightMatrix1to2[12][11] = -0.0201331222796263;
   fWeightMatrix1to2[0][12] = -0.105493390824501;
   fWeightMatrix1to2[1][12] = -1.13975686294114;
   fWeightMatrix1to2[2][12] = -1.13080410040727;
   fWeightMatrix1to2[3][12] = -1.82859403842363;
   fWeightMatrix1to2[4][12] = -1.73964639944405;
   fWeightMatrix1to2[5][12] = 0.367873114729075;
   fWeightMatrix1to2[6][12] = 0.755568722065353;
   fWeightMatrix1to2[7][12] = -1.25166508837233;
   fWeightMatrix1to2[8][12] = -0.4483007543656;
   fWeightMatrix1to2[9][12] = -2.34725630081068;
   fWeightMatrix1to2[10][12] = -1.34518458507033;
   fWeightMatrix1to2[11][12] = -2.04192207193123;
   fWeightMatrix1to2[12][12] = -0.606266904206548;
   fWeightMatrix1to2[0][13] = -4.47719199061371;
   fWeightMatrix1to2[1][13] = -2.2350583119254;
   fWeightMatrix1to2[2][13] = -0.95199180437118;
   fWeightMatrix1to2[3][13] = -1.01154697129126;
   fWeightMatrix1to2[4][13] = -1.62754970181038;
   fWeightMatrix1to2[5][13] = 0.111437816093736;
   fWeightMatrix1to2[6][13] = -1.01237447342721;
   fWeightMatrix1to2[7][13] = -1.74842024151056;
   fWeightMatrix1to2[8][13] = 1.00707061901615;
   fWeightMatrix1to2[9][13] = -0.0839581144660939;
   fWeightMatrix1to2[10][13] = 0.0598447770591504;
   fWeightMatrix1to2[11][13] = -0.229975328538304;
   fWeightMatrix1to2[12][13] = 0.011093701190935;
   fWeightMatrix1to2[0][14] = 0.483190134870788;
   fWeightMatrix1to2[1][14] = -0.77579713529602;
   fWeightMatrix1to2[2][14] = -1.30969276955546;
   fWeightMatrix1to2[3][14] = -1.13821442932804;
   fWeightMatrix1to2[4][14] = -1.74703005165379;
   fWeightMatrix1to2[5][14] = 0.0301173438414762;
   fWeightMatrix1to2[6][14] = 1.84316727875247;
   fWeightMatrix1to2[7][14] = 0.355006984303203;
   fWeightMatrix1to2[8][14] = 0.0751212425714602;
   fWeightMatrix1to2[9][14] = 0.150588384896541;
   fWeightMatrix1to2[10][14] = 1.15108146589287;
   fWeightMatrix1to2[11][14] = -1.77028024067691;
   fWeightMatrix1to2[12][14] = 0.102296748591715;
   // weight matrix from layer 2 to 3
   fWeightMatrix2to3[0][0] = -1.3454901685381;
   fWeightMatrix2to3[0][1] = 0.106002208106845;
   fWeightMatrix2to3[0][2] = 0.0643198457535069;
   fWeightMatrix2to3[0][3] = -1.05553198339461;
   fWeightMatrix2to3[0][4] = 0.902206262485521;
   fWeightMatrix2to3[0][5] = 0.530595605691912;
   fWeightMatrix2to3[0][6] = 0.754878683498826;
   fWeightMatrix2to3[0][7] = 2.03438583935942;
   fWeightMatrix2to3[0][8] = -0.350975061168376;
   fWeightMatrix2to3[0][9] = 0.893601160774086;
   fWeightMatrix2to3[0][10] = 1.38367968460371;
   fWeightMatrix2to3[0][11] = -0.190234263222648;
   fWeightMatrix2to3[0][12] = -1.3248840088611;
   fWeightMatrix2to3[0][13] = 0.0488246596331313;
}

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
}