// 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 10:29:47 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/job417
Training events: 26280


#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'    [49.2191009521,850.621887207]
LepAPt                        LepAPt                            'F'    [20.0000362396,162.429443359]
LepBPt                        LepBPt                            'F'    [10.0018510818,64.8868713379]
MetSigLeptonsJets             MetSigLeptonsJets                 'F'    [1.16568470001,17.5202083588]
MetSpec                       MetSpec                           'F'    [15.0119848251,252.419906616]
SumEtLeptonsJets              SumEtLeptonsJets                  'F'    [30.1229667664,533.675720215]
VSumJetLeptonsPt              VSumJetLeptonsPt                  'F'    [5.16211557388,344.837463379]
addEt                         addEt                             'F'    [49.2191009521,422.705413818]
dPhiLepSumMet                 dPhiLepSumMet                     'F'    [0.054340723902,3.14157605171]
dPhiLeptons                   dPhiLeptons                       'F'    [3.08752059937e-05,1.13010489941]
dRLeptons                     dRLeptons                         'F'    [0.200001657009,1.13414525986]
lep1_E                        lep1_E                            'F'    [20.0598621368,220.396377563]
lep2_E                        lep2_E                            'F'    [10.0297393799,100.190933228]


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

#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] = 49.2191009521484;
      fVmax[0] = 850.621887207031;
      fVmin[1] = 20.000036239624;
      fVmax[1] = 162.429443359375;
      fVmin[2] = 10.0018510818481;
      fVmax[2] = 64.8868713378906;
      fVmin[3] = 1.16568470001221;
      fVmax[3] = 17.5202083587646;
      fVmin[4] = 15.0119848251343;
      fVmax[4] = 252.419906616211;
      fVmin[5] = 30.1229667663574;
      fVmax[5] = 533.675720214844;
      fVmin[6] = 5.16211557388306;
      fVmax[6] = 344.837463378906;
      fVmin[7] = 49.2191009521484;
      fVmax[7] = 422.705413818359;
      fVmin[8] = 0.0543407239019871;
      fVmax[8] = 3.14157605171204;
      fVmin[9] = 3.08752059936523e-05;
      fVmax[9] = 1.13010489940643;
      fVmin[10] = 0.200001657009125;
      fVmax[10] = 1.13414525985718;
      fVmin[11] = 20.0598621368408;
      fVmax[11] = 220.396377563477;
      fVmin[12] = 10.0297393798828;
      fVmax[12] = 100.190933227539;

      // 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] = -0.27788759412375;
   fWeightMatrix0to1[1][0] = 2.06851442741713;
   fWeightMatrix0to1[2][0] = 1.16400509285645;
   fWeightMatrix0to1[3][0] = 1.65541881883671;
   fWeightMatrix0to1[4][0] = -1.54002953120279;
   fWeightMatrix0to1[5][0] = -1.13480136272062;
   fWeightMatrix0to1[6][0] = -0.864667631264863;
   fWeightMatrix0to1[7][0] = 1.07539308842296;
   fWeightMatrix0to1[8][0] = -1.71774097877057;
   fWeightMatrix0to1[9][0] = -0.154078535503003;
   fWeightMatrix0to1[10][0] = -1.0816237804885;
   fWeightMatrix0to1[11][0] = -0.199757229141263;
   fWeightMatrix0to1[12][0] = -0.540404327854663;
   fWeightMatrix0to1[13][0] = -0.225214115171391;
   fWeightMatrix0to1[0][1] = -0.565757237337637;
   fWeightMatrix0to1[1][1] = 1.10351456142242;
   fWeightMatrix0to1[2][1] = -0.132805576412873;
   fWeightMatrix0to1[3][1] = 2.67614348463749;
   fWeightMatrix0to1[4][1] = 0.53841883209347;
   fWeightMatrix0to1[5][1] = 1.95322493802253;
   fWeightMatrix0to1[6][1] = -1.67034220646605;
   fWeightMatrix0to1[7][1] = 2.15047067579573;
   fWeightMatrix0to1[8][1] = 0.611395234997826;
   fWeightMatrix0to1[9][1] = 0.889493616868136;
   fWeightMatrix0to1[10][1] = -1.55371343840759;
   fWeightMatrix0to1[11][1] = -0.454853125164405;
   fWeightMatrix0to1[12][1] = 1.94203601397419;
   fWeightMatrix0to1[13][1] = -1.1288620438561;
   fWeightMatrix0to1[0][2] = -1.90034981643979;
   fWeightMatrix0to1[1][2] = 0.42155411364907;
   fWeightMatrix0to1[2][2] = 0.9452923468138;
   fWeightMatrix0to1[3][2] = 2.20807699384876;
   fWeightMatrix0to1[4][2] = 0.331639329764352;
   fWeightMatrix0to1[5][2] = 2.77230497572307;
   fWeightMatrix0to1[6][2] = -0.871923806009563;
   fWeightMatrix0to1[7][2] = 4.38264840537621;
   fWeightMatrix0to1[8][2] = -1.84376001817985;
   fWeightMatrix0to1[9][2] = 2.38682089034733;
   fWeightMatrix0to1[10][2] = -4.52618151098667;
   fWeightMatrix0to1[11][2] = 1.50561076492281;
   fWeightMatrix0to1[12][2] = 0.660578721830998;
   fWeightMatrix0to1[13][2] = 1.67299857769122;
   fWeightMatrix0to1[0][3] = 1.96135359173406;
   fWeightMatrix0to1[1][3] = 1.12816685182941;
   fWeightMatrix0to1[2][3] = -0.441675159921396;
   fWeightMatrix0to1[3][3] = -1.0374327014586;
   fWeightMatrix0to1[4][3] = 1.42051049290089;
   fWeightMatrix0to1[5][3] = 0.544778678627522;
   fWeightMatrix0to1[6][3] = 2.04419385097686;
   fWeightMatrix0to1[7][3] = 0.614326731927812;
   fWeightMatrix0to1[8][3] = 1.26177970134436;
   fWeightMatrix0to1[9][3] = 1.58695636163129;
   fWeightMatrix0to1[10][3] = -0.83933763708416;
   fWeightMatrix0to1[11][3] = 0.398962999276005;
   fWeightMatrix0to1[12][3] = -1.52030201454386;
   fWeightMatrix0to1[13][3] = -0.143571324240249;
   fWeightMatrix0to1[0][4] = -1.25142900098117;
   fWeightMatrix0to1[1][4] = -1.66526735297449;
   fWeightMatrix0to1[2][4] = 1.80753114448334;
   fWeightMatrix0to1[3][4] = 0.913338511815218;
   fWeightMatrix0to1[4][4] = -0.900073021884232;
   fWeightMatrix0to1[5][4] = 1.10885174042453;
   fWeightMatrix0to1[6][4] = -0.467870907094025;
   fWeightMatrix0to1[7][4] = 1.7503521643272;
   fWeightMatrix0to1[8][4] = 1.66520897104546;
   fWeightMatrix0to1[9][4] = 0.349408098814426;
   fWeightMatrix0to1[10][4] = 0.399303094911451;
   fWeightMatrix0to1[11][4] = -1.70256048147209;
   fWeightMatrix0to1[12][4] = 1.42258942407801;
   fWeightMatrix0to1[13][4] = -0.336696402436948;
   fWeightMatrix0to1[0][5] = -0.698971141764014;
   fWeightMatrix0to1[1][5] = -1.37014065054836;
   fWeightMatrix0to1[2][5] = 1.00512233878001;
   fWeightMatrix0to1[3][5] = 0.87800266491681;
   fWeightMatrix0to1[4][5] = 1.75852298050488;
   fWeightMatrix0to1[5][5] = -0.281411481540648;
   fWeightMatrix0to1[6][5] = -1.83954511402221;
   fWeightMatrix0to1[7][5] = -3.16028443701925;
   fWeightMatrix0to1[8][5] = 0.682392648405269;
   fWeightMatrix0to1[9][5] = 1.44594262704642;
   fWeightMatrix0to1[10][5] = 2.57960011552325;
   fWeightMatrix0to1[11][5] = 1.10857324680525;
   fWeightMatrix0to1[12][5] = 1.67924621415834;
   fWeightMatrix0to1[13][5] = -0.703167034809635;
   fWeightMatrix0to1[0][6] = -0.819201420294396;
   fWeightMatrix0to1[1][6] = 0.097675705665198;
   fWeightMatrix0to1[2][6] = 1.4291472254362;
   fWeightMatrix0to1[3][6] = -0.718847094309017;
   fWeightMatrix0to1[4][6] = -1.15935893867888;
   fWeightMatrix0to1[5][6] = 0.0714491066186137;
   fWeightMatrix0to1[6][6] = -1.61368036043478;
   fWeightMatrix0to1[7][6] = 2.44115038738559;
   fWeightMatrix0to1[8][6] = 1.64300361260715;
   fWeightMatrix0to1[9][6] = -0.844201512870239;
   fWeightMatrix0to1[10][6] = -1.35776714610075;
   fWeightMatrix0to1[11][6] = 1.2306146542129;
   fWeightMatrix0to1[12][6] = -0.0773331050362361;
   fWeightMatrix0to1[13][6] = -1.19155768191787;
   fWeightMatrix0to1[0][7] = -1.40354113544088;
   fWeightMatrix0to1[1][7] = 0.581610320685711;
   fWeightMatrix0to1[2][7] = -1.59149266074568;
   fWeightMatrix0to1[3][7] = -0.415911440972635;
   fWeightMatrix0to1[4][7] = 1.36298983887063;
   fWeightMatrix0to1[5][7] = 4.04659002532859;
   fWeightMatrix0to1[6][7] = -3.57061806203721;
   fWeightMatrix0to1[7][7] = 7.13276045012594;
   fWeightMatrix0to1[8][7] = -0.900089814249177;
   fWeightMatrix0to1[9][7] = -0.132473204701088;
   fWeightMatrix0to1[10][7] = -1.74483661183365;
   fWeightMatrix0to1[11][7] = -0.387417092934503;
   fWeightMatrix0to1[12][7] = -1.32331129384247;
   fWeightMatrix0to1[13][7] = 0.731386649336561;
   fWeightMatrix0to1[0][8] = 0.273086936940645;
   fWeightMatrix0to1[1][8] = 0.38249432214788;
   fWeightMatrix0to1[2][8] = -1.70789245657758;
   fWeightMatrix0to1[3][8] = 2.75443173807246;
   fWeightMatrix0to1[4][8] = 0.0522720823564103;
   fWeightMatrix0to1[5][8] = -2.4716816551514;
   fWeightMatrix0to1[6][8] = 2.47251673766045;
   fWeightMatrix0to1[7][8] = -0.0936985513105139;
   fWeightMatrix0to1[8][8] = -1.81581032292784;
   fWeightMatrix0to1[9][8] = -0.819743894008365;
   fWeightMatrix0to1[10][8] = -0.0321177279668725;
   fWeightMatrix0to1[11][8] = 1.79032818374508;
   fWeightMatrix0to1[12][8] = -0.75745155326321;
   fWeightMatrix0to1[13][8] = -0.273116074131065;
   fWeightMatrix0to1[0][9] = -0.327573375908992;
   fWeightMatrix0to1[1][9] = 0.343251019208656;
   fWeightMatrix0to1[2][9] = -1.44292175763319;
   fWeightMatrix0to1[3][9] = 0.594901570525796;
   fWeightMatrix0to1[4][9] = -0.0226086136281507;
   fWeightMatrix0to1[5][9] = -0.411765502548769;
   fWeightMatrix0to1[6][9] = 0.585367083789852;
   fWeightMatrix0to1[7][9] = 0.926490061740278;
   fWeightMatrix0to1[8][9] = 0.660760671687608;
   fWeightMatrix0to1[9][9] = 1.51107557968835;
   fWeightMatrix0to1[10][9] = 1.5107641108155;
   fWeightMatrix0to1[11][9] = -2.79373078316481;
   fWeightMatrix0to1[12][9] = -0.270432347136698;
   fWeightMatrix0to1[13][9] = 1.58210880147331;
   fWeightMatrix0to1[0][10] = 1.63201914140062;
   fWeightMatrix0to1[1][10] = -0.255495391138869;
   fWeightMatrix0to1[2][10] = -1.19331416858073;
   fWeightMatrix0to1[3][10] = 0.0980240772178642;
   fWeightMatrix0to1[4][10] = -1.997250040769;
   fWeightMatrix0to1[5][10] = 2.09276529332874;
   fWeightMatrix0to1[6][10] = -1.03334846648976;
   fWeightMatrix0to1[7][10] = -1.1371102702938;
   fWeightMatrix0to1[8][10] = -0.21196994233679;
   fWeightMatrix0to1[9][10] = 0.516935808968038;
   fWeightMatrix0to1[10][10] = -0.175838868250948;
   fWeightMatrix0to1[11][10] = -0.0569998102799626;
   fWeightMatrix0to1[12][10] = 1.76520597124947;
   fWeightMatrix0to1[13][10] = -0.12630653793859;
   fWeightMatrix0to1[0][11] = -0.577972407919066;
   fWeightMatrix0to1[1][11] = 0.682758606180819;
   fWeightMatrix0to1[2][11] = 1.15835211744495;
   fWeightMatrix0to1[3][11] = 0.615186395528901;
   fWeightMatrix0to1[4][11] = 1.14083570029934;
   fWeightMatrix0to1[5][11] = -3.03634253397125;
   fWeightMatrix0to1[6][11] = 2.7674323294427;
   fWeightMatrix0to1[7][11] = 1.6362147802053;
   fWeightMatrix0to1[8][11] = 0.226726136420128;
   fWeightMatrix0to1[9][11] = 1.37127723640069;
   fWeightMatrix0to1[10][11] = 1.7741728320557;
   fWeightMatrix0to1[11][11] = -0.463908001565868;
   fWeightMatrix0to1[12][11] = -0.342586173566552;
   fWeightMatrix0to1[13][11] = 0.810987145723433;
   fWeightMatrix0to1[0][12] = -0.947443556346031;
   fWeightMatrix0to1[1][12] = -0.358036172725117;
   fWeightMatrix0to1[2][12] = 1.49049291748967;
   fWeightMatrix0to1[3][12] = -0.194784223578033;
   fWeightMatrix0to1[4][12] = -0.0465803816699066;
   fWeightMatrix0to1[5][12] = 0.31736728718563;
   fWeightMatrix0to1[6][12] = 3.42288236815238;
   fWeightMatrix0to1[7][12] = -1.12012435976072;
   fWeightMatrix0to1[8][12] = 0.14294718931531;
   fWeightMatrix0to1[9][12] = 0.713597278087468;
   fWeightMatrix0to1[10][12] = 0.25882369145482;
   fWeightMatrix0to1[11][12] = -1.00469724859612;
   fWeightMatrix0to1[12][12] = -1.7494258968914;
   fWeightMatrix0to1[13][12] = 1.43211463910688;
   fWeightMatrix0to1[0][13] = 1.78129812106171;
   fWeightMatrix0to1[1][13] = -1.15821553659715;
   fWeightMatrix0to1[2][13] = -0.421040263620839;
   fWeightMatrix0to1[3][13] = 1.35468727883687;
   fWeightMatrix0to1[4][13] = 0.265845362107413;
   fWeightMatrix0to1[5][13] = 5.04423372117627;
   fWeightMatrix0to1[6][13] = -4.87099678618584;
   fWeightMatrix0to1[7][13] = 11.6011970383281;
   fWeightMatrix0to1[8][13] = 0.0910406619629793;
   fWeightMatrix0to1[9][13] = -0.106295494486396;
   fWeightMatrix0to1[10][13] = -2.74860947159645;
   fWeightMatrix0to1[11][13] = -0.531609830274449;
   fWeightMatrix0to1[12][13] = -0.407834511871752;
   fWeightMatrix0to1[13][13] = -0.712473258051706;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = -2.92804404749358;
   fWeightMatrix1to2[1][0] = -0.236374310404925;
   fWeightMatrix1to2[2][0] = -0.364761490844343;
   fWeightMatrix1to2[3][0] = -1.83246951519543;
   fWeightMatrix1to2[4][0] = -0.971777502868807;
   fWeightMatrix1to2[5][0] = -1.18709402913202;
   fWeightMatrix1to2[6][0] = -0.422081930394634;
   fWeightMatrix1to2[7][0] = -2.50997481144091;
   fWeightMatrix1to2[8][0] = 0.43351387853797;
   fWeightMatrix1to2[9][0] = -0.324337094175057;
   fWeightMatrix1to2[10][0] = -0.960974258317824;
   fWeightMatrix1to2[11][0] = -0.203722988610531;
   fWeightMatrix1to2[12][0] = -0.145391549355634;
   fWeightMatrix1to2[0][1] = 1.10807089990687;
   fWeightMatrix1to2[1][1] = 0.34980288009676;
   fWeightMatrix1to2[2][1] = -0.148672797137792;
   fWeightMatrix1to2[3][1] = -2.44133976949562;
   fWeightMatrix1to2[4][1] = -0.907828011557765;
   fWeightMatrix1to2[5][1] = 1.50654360683885;
   fWeightMatrix1to2[6][1] = 1.42885861477475;
   fWeightMatrix1to2[7][1] = 0.764031904170815;
   fWeightMatrix1to2[8][1] = 0.092883883054202;
   fWeightMatrix1to2[9][1] = 1.96787666355831;
   fWeightMatrix1to2[10][1] = -1.59311869467441;
   fWeightMatrix1to2[11][1] = -1.31335704749004;
   fWeightMatrix1to2[12][1] = -0.221584952043482;
   fWeightMatrix1to2[0][2] = -1.4868185219561;
   fWeightMatrix1to2[1][2] = 0.859065877732048;
   fWeightMatrix1to2[2][2] = 1.66389226572444;
   fWeightMatrix1to2[3][2] = -1.1880158216674;
   fWeightMatrix1to2[4][2] = 0.787199747351992;
   fWeightMatrix1to2[5][2] = -0.158941500454308;
   fWeightMatrix1to2[6][2] = -1.71502894465121;
   fWeightMatrix1to2[7][2] = -1.13453702215312;
   fWeightMatrix1to2[8][2] = 0.936996845322039;
   fWeightMatrix1to2[9][2] = -0.0853137020769664;
   fWeightMatrix1to2[10][2] = 0.920664769898754;
   fWeightMatrix1to2[11][2] = -1.18623322197322;
   fWeightMatrix1to2[12][2] = 1.70700768812624;
   fWeightMatrix1to2[0][3] = -1.36337746166923;
   fWeightMatrix1to2[1][3] = 0.441917681239279;
   fWeightMatrix1to2[2][3] = 0.0608031574266282;
   fWeightMatrix1to2[3][3] = -2.70883418720362;
   fWeightMatrix1to2[4][3] = -1.26203010729245;
   fWeightMatrix1to2[5][3] = -2.17684359848637;
   fWeightMatrix1to2[6][3] = 0.135226612327203;
   fWeightMatrix1to2[7][3] = -1.86911105128734;
   fWeightMatrix1to2[8][3] = -1.73772573128386;
   fWeightMatrix1to2[9][3] = -1.75585403950354;
   fWeightMatrix1to2[10][3] = 0.474476674695516;
   fWeightMatrix1to2[11][3] = -1.01473839828486;
   fWeightMatrix1to2[12][3] = -1.81386045577336;
   fWeightMatrix1to2[0][4] = 0.884083573727223;
   fWeightMatrix1to2[1][4] = -1.55023334963667;
   fWeightMatrix1to2[2][4] = -0.586016390189219;
   fWeightMatrix1to2[3][4] = 0.206946574149013;
   fWeightMatrix1to2[4][4] = 0.144253097167291;
   fWeightMatrix1to2[5][4] = 1.5175383342404;
   fWeightMatrix1to2[6][4] = 1.21447880704042;
   fWeightMatrix1to2[7][4] = -1.21096793982619;
   fWeightMatrix1to2[8][4] = -2.04080720203983;
   fWeightMatrix1to2[9][4] = -2.20130432985485;
   fWeightMatrix1to2[10][4] = -1.44450451513293;
   fWeightMatrix1to2[11][4] = -2.0077368302499;
   fWeightMatrix1to2[12][4] = -0.661200071632214;
   fWeightMatrix1to2[0][5] = -0.590894639747337;
   fWeightMatrix1to2[1][5] = 1.00345824018125;
   fWeightMatrix1to2[2][5] = -0.708155831339981;
   fWeightMatrix1to2[3][5] = -3.88439720100192;
   fWeightMatrix1to2[4][5] = -0.61761456755408;
   fWeightMatrix1to2[5][5] = -3.20415474512468;
   fWeightMatrix1to2[6][5] = 0.685647100218492;
   fWeightMatrix1to2[7][5] = 0.89083170262466;
   fWeightMatrix1to2[8][5] = -1.45064375839084;
   fWeightMatrix1to2[9][5] = -1.45795835006626;
   fWeightMatrix1to2[10][5] = 1.13465702969244;
   fWeightMatrix1to2[11][5] = 1.16269234896236;
   fWeightMatrix1to2[12][5] = -0.319108060716613;
   fWeightMatrix1to2[0][6] = -1.11287073916899;
   fWeightMatrix1to2[1][6] = -0.890997686713517;
   fWeightMatrix1to2[2][6] = 0.790197065373091;
   fWeightMatrix1to2[3][6] = 3.54890247797312;
   fWeightMatrix1to2[4][6] = 0.697845273825226;
   fWeightMatrix1to2[5][6] = -0.905001303444611;
   fWeightMatrix1to2[6][6] = -0.532660068812292;
   fWeightMatrix1to2[7][6] = -0.758205592843213;
   fWeightMatrix1to2[8][6] = -1.82351824055827;
   fWeightMatrix1to2[9][6] = -1.71194973139813;
   fWeightMatrix1to2[10][6] = -3.04817833070521;
   fWeightMatrix1to2[11][6] = -1.63197430511409;
   fWeightMatrix1to2[12][6] = -2.68773951074219;
   fWeightMatrix1to2[0][7] = 1.82434322338214;
   fWeightMatrix1to2[1][7] = -2.45521391739646;
   fWeightMatrix1to2[2][7] = 1.32086004537677;
   fWeightMatrix1to2[3][7] = -7.61165967843263;
   fWeightMatrix1to2[4][7] = 0.989217393799674;
   fWeightMatrix1to2[5][7] = -2.0003130827827;
   fWeightMatrix1to2[6][7] = 0.682634153811315;
   fWeightMatrix1to2[7][7] = 0.272154693134497;
   fWeightMatrix1to2[8][7] = -2.33628259269072;
   fWeightMatrix1to2[9][7] = 0.0378079622216236;
   fWeightMatrix1to2[10][7] = 1.0000055440371;
   fWeightMatrix1to2[11][7] = -2.05253961532986;
   fWeightMatrix1to2[12][7] = -2.43450354616031;
   fWeightMatrix1to2[0][8] = -1.73733279344608;
   fWeightMatrix1to2[1][8] = -0.89520450512858;
   fWeightMatrix1to2[2][8] = 1.78704749974873;
   fWeightMatrix1to2[3][8] = -1.74895509580211;
   fWeightMatrix1to2[4][8] = 0.316783826861684;
   fWeightMatrix1to2[5][8] = -1.13100533606827;
   fWeightMatrix1to2[6][8] = -1.08767678764262;
   fWeightMatrix1to2[7][8] = -1.72722440593941;
   fWeightMatrix1to2[8][8] = -1.07892985675957;
   fWeightMatrix1to2[9][8] = -1.44200664517859;
   fWeightMatrix1to2[10][8] = 0.802233085856469;
   fWeightMatrix1to2[11][8] = -1.28628454800698;
   fWeightMatrix1to2[12][8] = -1.26962847953934;
   fWeightMatrix1to2[0][9] = 1.57921972290466;
   fWeightMatrix1to2[1][9] = -0.0514471494247006;
   fWeightMatrix1to2[2][9] = -0.493092308849196;
   fWeightMatrix1to2[3][9] = 1.4037234566834;
   fWeightMatrix1to2[4][9] = -1.79458408158199;
   fWeightMatrix1to2[5][9] = 1.14836665312587;
   fWeightMatrix1to2[6][9] = 1.01442811784496;
   fWeightMatrix1to2[7][9] = -0.995662696394587;
   fWeightMatrix1to2[8][9] = -1.15126062690944;
   fWeightMatrix1to2[9][9] = -0.142225385161192;
   fWeightMatrix1to2[10][9] = -0.820451091196149;
   fWeightMatrix1to2[11][9] = 0.486771287618096;
   fWeightMatrix1to2[12][9] = 1.85735589266022;
   fWeightMatrix1to2[0][10] = 0.484684081842917;
   fWeightMatrix1to2[1][10] = 0.810448264807002;
   fWeightMatrix1to2[2][10] = -2.00937062663011;
   fWeightMatrix1to2[3][10] = 3.43359978981522;
   fWeightMatrix1to2[4][10] = -1.95347510914723;
   fWeightMatrix1to2[5][10] = -0.521327932627732;
   fWeightMatrix1to2[6][10] = -1.95994872835744;
   fWeightMatrix1to2[7][10] = -1.83176181287732;
   fWeightMatrix1to2[8][10] = -0.745715801480469;
   fWeightMatrix1to2[9][10] = 0.171874989162752;
   fWeightMatrix1to2[10][10] = -0.273936518867073;
   fWeightMatrix1to2[11][10] = 0.884399089005478;
   fWeightMatrix1to2[12][10] = -0.739920565759247;
   fWeightMatrix1to2[0][11] = -1.00084514303694;
   fWeightMatrix1to2[1][11] = -2.09569445808595;
   fWeightMatrix1to2[2][11] = -0.724882902279395;
   fWeightMatrix1to2[3][11] = 2.11850682718183;
   fWeightMatrix1to2[4][11] = -1.87045471348315;
   fWeightMatrix1to2[5][11] = -0.975211816665716;
   fWeightMatrix1to2[6][11] = -1.98514385113776;
   fWeightMatrix1to2[7][11] = -1.0376631604327;
   fWeightMatrix1to2[8][11] = -1.3823515493906;
   fWeightMatrix1to2[9][11] = -0.893506174772181;
   fWeightMatrix1to2[10][11] = -1.49215072147283;
   fWeightMatrix1to2[11][11] = 1.04238321929712;
   fWeightMatrix1to2[12][11] = -0.269684649891064;
   fWeightMatrix1to2[0][12] = -0.143988474316619;
   fWeightMatrix1to2[1][12] = -0.481227440661623;
   fWeightMatrix1to2[2][12] = -1.19700892162344;
   fWeightMatrix1to2[3][12] = -1.55847781894402;
   fWeightMatrix1to2[4][12] = -1.74424893514536;
   fWeightMatrix1to2[5][12] = 0.0561769059889694;
   fWeightMatrix1to2[6][12] = -0.911937968346426;
   fWeightMatrix1to2[7][12] = -1.11744360335239;
   fWeightMatrix1to2[8][12] = 0.189959365707129;
   fWeightMatrix1to2[9][12] = -2.21981257909933;
   fWeightMatrix1to2[10][12] = -0.68227901421009;
   fWeightMatrix1to2[11][12] = -1.93428312793158;
   fWeightMatrix1to2[12][12] = 0.439765750354457;
   fWeightMatrix1to2[0][13] = -1.6120834463365;
   fWeightMatrix1to2[1][13] = -2.19483521933578;
   fWeightMatrix1to2[2][13] = -0.534544523433113;
   fWeightMatrix1to2[3][13] = -1.15131573857447;
   fWeightMatrix1to2[4][13] = -1.64613721695555;
   fWeightMatrix1to2[5][13] = 0.751459668332294;
   fWeightMatrix1to2[6][13] = -1.21653942064809;
   fWeightMatrix1to2[7][13] = -1.19682106116584;
   fWeightMatrix1to2[8][13] = 1.66844975252422;
   fWeightMatrix1to2[9][13] = 0.132460936608373;
   fWeightMatrix1to2[10][13] = 0.66998730956645;
   fWeightMatrix1to2[11][13] = -0.0356297069945564;
   fWeightMatrix1to2[12][13] = 0.841772184175837;
   fWeightMatrix1to2[0][14] = 0.767111046658646;
   fWeightMatrix1to2[1][14] = -0.844951305544016;
   fWeightMatrix1to2[2][14] = -1.79570079885625;
   fWeightMatrix1to2[3][14] = -1.16700437810679;
   fWeightMatrix1to2[4][14] = -2.01349948642154;
   fWeightMatrix1to2[5][14] = -0.596191798378007;
   fWeightMatrix1to2[6][14] = 0.429085363084945;
   fWeightMatrix1to2[7][14] = -0.464779659898447;
   fWeightMatrix1to2[8][14] = -0.0354694218579089;
   fWeightMatrix1to2[9][14] = -0.237033198321974;
   fWeightMatrix1to2[10][14] = 1.21059803460927;
   fWeightMatrix1to2[11][14] = -1.42349119235359;
   fWeightMatrix1to2[12][14] = -0.0451333984189299;
   // weight matrix from layer 2 to 3
   fWeightMatrix2to3[0][0] = 0.50633428608185;
   fWeightMatrix2to3[0][1] = -0.292277882221236;
   fWeightMatrix2to3[0][2] = 0.11588782411623;
   fWeightMatrix2to3[0][3] = -0.827764817358601;
   fWeightMatrix2to3[0][4] = 0.529774318013775;
   fWeightMatrix2to3[0][5] = -0.744043648790333;
   fWeightMatrix2to3[0][6] = 0.334432659091631;
   fWeightMatrix2to3[0][7] = 1.43888018731392;
   fWeightMatrix2to3[0][8] = -0.582566375131637;
   fWeightMatrix2to3[0][9] = 0.6490121498316;
   fWeightMatrix2to3[0][10] = -0.00010004010477239;
   fWeightMatrix2to3[0][11] = 1.01411095417848;
   fWeightMatrix2to3[0][12] = -0.601399311690876;
   fWeightMatrix2to3[0][13] = 0.745986741149893;
}

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
}