// 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 09:50:36 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/job400
Training events: 46092


#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'    [48.2055778503,708.240539551]
LepAPt                        LepAPt                            'F'    [20.0001125336,183.946380615]
LepBPt                        LepBPt                            'F'    [10.0002183914,70.5175170898]
MetSigLeptonsJets             MetSigLeptonsJets                 'F'    [1.13823473454,17.7251911163]
MetSpec                       MetSpec                           'F'    [15.0000524521,214.544067383]
SumEtLeptonsJets              SumEtLeptonsJets                  'F'    [30.1484737396,511.516204834]
VSumJetLeptonsPt              VSumJetLeptonsPt                  'F'    [1.29604113102,257.11505127]
addEt                         addEt                             'F'    [48.1082038879,354.9012146]
dPhiLepSumMet                 dPhiLepSumMet                     'F'    [0.000599204504397,3.14159059525]
dPhiLeptons                   dPhiLeptons                       'F'    [3.91006469727e-05,1.14321351051]
dRLeptons                     dRLeptons                         'F'    [0.200031235814,1.14349746704]
lep1_E                        lep1_E                            'F'    [20.0144271851,232.717926025]
lep2_E                        lep2_E                            'F'    [10.0051202774,122.221923828]


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

#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] = 48.2055778503418;
      fVmax[0] = 708.240539550781;
      fVmin[1] = 20.0001125335693;
      fVmax[1] = 183.946380615234;
      fVmin[2] = 10.0002183914185;
      fVmax[2] = 70.5175170898438;
      fVmin[3] = 1.13823473453522;
      fVmax[3] = 17.725191116333;
      fVmin[4] = 15.0000524520874;
      fVmax[4] = 214.544067382812;
      fVmin[5] = 30.148473739624;
      fVmax[5] = 511.516204833984;
      fVmin[6] = 1.29604113101959;
      fVmax[6] = 257.115051269531;
      fVmin[7] = 48.1082038879395;
      fVmax[7] = 354.901214599609;
      fVmin[8] = 0.000599204504396766;
      fVmax[8] = 3.14159059524536;
      fVmin[9] = 3.91006469726562e-05;
      fVmax[9] = 1.14321351051331;
      fVmin[10] = 0.200031235814095;
      fVmax[10] = 1.14349746704102;
      fVmin[11] = 20.0144271850586;
      fVmax[11] = 232.717926025391;
      fVmin[12] = 10.0051202774048;
      fVmax[12] = 122.221923828125;

      // 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] = -0.393668996168818;
   fWeightMatrix0to1[1][0] = 2.31538211746849;
   fWeightMatrix0to1[2][0] = -0.362663893693422;
   fWeightMatrix0to1[3][0] = 1.61619886866628;
   fWeightMatrix0to1[4][0] = -1.20194788642762;
   fWeightMatrix0to1[5][0] = -0.956009956386112;
   fWeightMatrix0to1[6][0] = -0.753372623549554;
   fWeightMatrix0to1[7][0] = 1.38003580491104;
   fWeightMatrix0to1[8][0] = -1.68939484264793;
   fWeightMatrix0to1[9][0] = -0.779029979686507;
   fWeightMatrix0to1[10][0] = -0.969720087194179;
   fWeightMatrix0to1[11][0] = 0.10591926569969;
   fWeightMatrix0to1[12][0] = -1.64760113436515;
   fWeightMatrix0to1[13][0] = 0.0816835943247058;
   fWeightMatrix0to1[0][1] = -0.596876440303206;
   fWeightMatrix0to1[1][1] = 0.93731988892423;
   fWeightMatrix0to1[2][1] = -4.08209589341596;
   fWeightMatrix0to1[3][1] = 0.980677038666716;
   fWeightMatrix0to1[4][1] = 0.473194113403086;
   fWeightMatrix0to1[5][1] = 1.97890764275888;
   fWeightMatrix0to1[6][1] = -1.48320298951325;
   fWeightMatrix0to1[7][1] = 5.29244478441589;
   fWeightMatrix0to1[8][1] = 0.649318290835994;
   fWeightMatrix0to1[9][1] = 0.387088963566608;
   fWeightMatrix0to1[10][1] = -1.48849877155543;
   fWeightMatrix0to1[11][1] = 2.21506043992563;
   fWeightMatrix0to1[12][1] = 0.653452216714729;
   fWeightMatrix0to1[13][1] = -0.255709179174727;
   fWeightMatrix0to1[0][2] = -1.98082125553081;
   fWeightMatrix0to1[1][2] = 0.402759904419471;
   fWeightMatrix0to1[2][2] = -1.13423559879746;
   fWeightMatrix0to1[3][2] = 0.983911028052074;
   fWeightMatrix0to1[4][2] = 0.423838798755388;
   fWeightMatrix0to1[5][2] = 1.29559279216357;
   fWeightMatrix0to1[6][2] = -0.675577181609799;
   fWeightMatrix0to1[7][2] = 2.80463821947088;
   fWeightMatrix0to1[8][2] = -1.3562665557437;
   fWeightMatrix0to1[9][2] = 2.22518902049681;
   fWeightMatrix0to1[10][2] = -2.91540814874624;
   fWeightMatrix0to1[11][2] = 4.43748099871435;
   fWeightMatrix0to1[12][2] = 0.641863871937198;
   fWeightMatrix0to1[13][2] = 2.02675030603532;
   fWeightMatrix0to1[0][3] = 1.98326497561394;
   fWeightMatrix0to1[1][3] = 1.62504221052196;
   fWeightMatrix0to1[2][3] = 2.18826499909822;
   fWeightMatrix0to1[3][3] = -2.23644705265848;
   fWeightMatrix0to1[4][3] = 0.530383826611452;
   fWeightMatrix0to1[5][3] = -0.279883076332335;
   fWeightMatrix0to1[6][3] = 4.2179313265465;
   fWeightMatrix0to1[7][3] = 0.589250706229577;
   fWeightMatrix0to1[8][3] = 1.23423708613142;
   fWeightMatrix0to1[9][3] = 2.09367219389432;
   fWeightMatrix0to1[10][3] = -1.8114825438494;
   fWeightMatrix0to1[11][3] = 2.24076184869705;
   fWeightMatrix0to1[12][3] = -1.90385321853856;
   fWeightMatrix0to1[13][3] = -0.959700351404197;
   fWeightMatrix0to1[0][4] = -1.21045259738659;
   fWeightMatrix0to1[1][4] = -1.3040727723372;
   fWeightMatrix0to1[2][4] = 1.38660474516187;
   fWeightMatrix0to1[3][4] = -0.233632197710521;
   fWeightMatrix0to1[4][4] = -0.58578518586368;
   fWeightMatrix0to1[5][4] = 0.471220518572243;
   fWeightMatrix0to1[6][4] = 0.766531683768821;
   fWeightMatrix0to1[7][4] = 1.2860388419788;
   fWeightMatrix0to1[8][4] = 1.65949595872327;
   fWeightMatrix0to1[9][4] = 0.479888851823384;
   fWeightMatrix0to1[10][4] = -0.0252396075779889;
   fWeightMatrix0to1[11][4] = 0.553508148260977;
   fWeightMatrix0to1[12][4] = 0.450980645358948;
   fWeightMatrix0to1[13][4] = -0.407925874673347;
   fWeightMatrix0to1[0][5] = -0.864118700702786;
   fWeightMatrix0to1[1][5] = -1.23294330807031;
   fWeightMatrix0to1[2][5] = 0.00216811884499258;
   fWeightMatrix0to1[3][5] = 1.43108230520134;
   fWeightMatrix0to1[4][5] = 1.98262097118475;
   fWeightMatrix0to1[5][5] = -0.00670009175167116;
   fWeightMatrix0to1[6][5] = -1.88266734067005;
   fWeightMatrix0to1[7][5] = -4.72013353197976;
   fWeightMatrix0to1[8][5] = 0.742139492110559;
   fWeightMatrix0to1[9][5] = 0.549721828278226;
   fWeightMatrix0to1[10][5] = 3.03735434875986;
   fWeightMatrix0to1[11][5] = -0.033644549591912;
   fWeightMatrix0to1[12][5] = 0.435402683867225;
   fWeightMatrix0to1[13][5] = -0.368250718925979;
   fWeightMatrix0to1[0][6] = -0.84013885747519;
   fWeightMatrix0to1[1][6] = 0.238089137143131;
   fWeightMatrix0to1[2][6] = -0.876416475654751;
   fWeightMatrix0to1[3][6] = -1.89116960857065;
   fWeightMatrix0to1[4][6] = -0.212208226438224;
   fWeightMatrix0to1[5][6] = -1.24955078987415;
   fWeightMatrix0to1[6][6] = -0.181599925282504;
   fWeightMatrix0to1[7][6] = 1.68513063798522;
   fWeightMatrix0to1[8][6] = 1.32902695587081;
   fWeightMatrix0to1[9][6] = -1.02600026935776;
   fWeightMatrix0to1[10][6] = -0.0421643508266767;
   fWeightMatrix0to1[11][6] = 1.95187014668484;
   fWeightMatrix0to1[12][6] = -0.964501448272923;
   fWeightMatrix0to1[13][6] = -1.03497306065334;
   fWeightMatrix0to1[0][7] = -1.36762822209908;
   fWeightMatrix0to1[1][7] = 0.711820360274937;
   fWeightMatrix0to1[2][7] = -7.74217650508782;
   fWeightMatrix0to1[3][7] = -3.6166065491817;
   fWeightMatrix0to1[4][7] = 1.90661971476466;
   fWeightMatrix0to1[5][7] = 3.09737556908895;
   fWeightMatrix0to1[6][7] = -3.35597137735707;
   fWeightMatrix0to1[7][7] = 13.9860869778397;
   fWeightMatrix0to1[8][7] = -0.998065421583785;
   fWeightMatrix0to1[9][7] = -0.0317114147129067;
   fWeightMatrix0to1[10][7] = -1.73163926876021;
   fWeightMatrix0to1[11][7] = 6.60537952753263;
   fWeightMatrix0to1[12][7] = -1.97254197902727;
   fWeightMatrix0to1[13][7] = 1.63768230643227;
   fWeightMatrix0to1[0][8] = 0.515449458977777;
   fWeightMatrix0to1[1][8] = 0.448507999183764;
   fWeightMatrix0to1[2][8] = 3.57712772025954;
   fWeightMatrix0to1[3][8] = 2.35557072997175;
   fWeightMatrix0to1[4][8] = -0.789014097014929;
   fWeightMatrix0to1[5][8] = -3.10538545387907;
   fWeightMatrix0to1[6][8] = 2.62008217984983;
   fWeightMatrix0to1[7][8] = -0.787158094007552;
   fWeightMatrix0to1[8][8] = -1.84057807404318;
   fWeightMatrix0to1[9][8] = 0.674103810154384;
   fWeightMatrix0to1[10][8] = -1.50669059959657;
   fWeightMatrix0to1[11][8] = 1.93392810724457;
   fWeightMatrix0to1[12][8] = 1.22299631701278;
   fWeightMatrix0to1[13][8] = -0.476908179885553;
   fWeightMatrix0to1[0][9] = -0.49748535558122;
   fWeightMatrix0to1[1][9] = 0.155501124550373;
   fWeightMatrix0to1[2][9] = -0.137608401750506;
   fWeightMatrix0to1[3][9] = 0.702128717859933;
   fWeightMatrix0to1[4][9] = 0.0352437769436797;
   fWeightMatrix0to1[5][9] = -0.444300322772822;
   fWeightMatrix0to1[6][9] = 0.260355456331578;
   fWeightMatrix0to1[7][9] = 1.23943568601778;
   fWeightMatrix0to1[8][9] = 0.26523437079606;
   fWeightMatrix0to1[9][9] = 1.30443458368064;
   fWeightMatrix0to1[10][9] = 3.03949701153651;
   fWeightMatrix0to1[11][9] = -0.058609555123427;
   fWeightMatrix0to1[12][9] = -0.0487944829615738;
   fWeightMatrix0to1[13][9] = 0.814192490783975;
   fWeightMatrix0to1[0][10] = 1.46462567885475;
   fWeightMatrix0to1[1][10] = -0.236005641851429;
   fWeightMatrix0to1[2][10] = -1.63711331657551;
   fWeightMatrix0to1[3][10] = -0.21960581059329;
   fWeightMatrix0to1[4][10] = -3.19942295812903;
   fWeightMatrix0to1[5][10] = 0.572697677729271;
   fWeightMatrix0to1[6][10] = 0.338231794949744;
   fWeightMatrix0to1[7][10] = -0.580094195943779;
   fWeightMatrix0to1[8][10] = 0.471787273300134;
   fWeightMatrix0to1[9][10] = -0.0905411667047881;
   fWeightMatrix0to1[10][10] = -1.89167603024686;
   fWeightMatrix0to1[11][10] = 0.359296657104059;
   fWeightMatrix0to1[12][10] = 0.942275996449024;
   fWeightMatrix0to1[13][10] = -0.789968013768123;
   fWeightMatrix0to1[0][11] = -0.50731932953712;
   fWeightMatrix0to1[1][11] = 0.206057769776147;
   fWeightMatrix0to1[2][11] = 3.13408767771697;
   fWeightMatrix0to1[3][11] = 0.452700235277093;
   fWeightMatrix0to1[4][11] = 1.11203581452044;
   fWeightMatrix0to1[5][11] = -3.84981610633321;
   fWeightMatrix0to1[6][11] = 3.04762344314724;
   fWeightMatrix0to1[7][11] = 2.31908257700307;
   fWeightMatrix0to1[8][11] = 1.02854973490036;
   fWeightMatrix0to1[9][11] = 0.8294616270969;
   fWeightMatrix0to1[10][11] = 0.747325945180224;
   fWeightMatrix0to1[11][11] = -0.654746324221459;
   fWeightMatrix0to1[12][11] = -2.29028636594385;
   fWeightMatrix0to1[13][11] = 1.52840270271519;
   fWeightMatrix0to1[0][12] = -0.952637453229601;
   fWeightMatrix0to1[1][12] = -0.612292860411442;
   fWeightMatrix0to1[2][12] = 6.25893257879699;
   fWeightMatrix0to1[3][12] = 0.605225565345359;
   fWeightMatrix0to1[4][12] = 0.257599984298838;
   fWeightMatrix0to1[5][12] = 0.564708318375407;
   fWeightMatrix0to1[6][12] = 4.2341077730442;
   fWeightMatrix0to1[7][12] = -1.87142021981351;
   fWeightMatrix0to1[8][12] = 1.35481965314107;
   fWeightMatrix0to1[9][12] = 0.379010830724756;
   fWeightMatrix0to1[10][12] = 0.73714874800514;
   fWeightMatrix0to1[11][12] = -1.13428647896616;
   fWeightMatrix0to1[12][12] = -2.90336321051282;
   fWeightMatrix0to1[13][12] = 1.68645750041536;
   fWeightMatrix0to1[0][13] = 1.96949067515663;
   fWeightMatrix0to1[1][13] = -1.64455517452379;
   fWeightMatrix0to1[2][13] = -6.54904774167373;
   fWeightMatrix0to1[3][13] = -2.80713557562759;
   fWeightMatrix0to1[4][13] = 0.846648732164488;
   fWeightMatrix0to1[5][13] = 2.46327829094171;
   fWeightMatrix0to1[6][13] = -4.87502454963484;
   fWeightMatrix0to1[7][13] = 16.5813300492426;
   fWeightMatrix0to1[8][13] = -0.536550737929164;
   fWeightMatrix0to1[9][13] = 1.09863911897978;
   fWeightMatrix0to1[10][13] = -1.32488351889897;
   fWeightMatrix0to1[11][13] = 7.87269270888994;
   fWeightMatrix0to1[12][13] = 1.42359882387545;
   fWeightMatrix0to1[13][13] = 0.00177264159860801;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = -3.30144304511489;
   fWeightMatrix1to2[1][0] = -0.794773772595184;
   fWeightMatrix1to2[2][0] = -0.891992644402911;
   fWeightMatrix1to2[3][0] = -1.50247248795266;
   fWeightMatrix1to2[4][0] = -0.738063948733524;
   fWeightMatrix1to2[5][0] = -1.35372484846167;
   fWeightMatrix1to2[6][0] = -0.82097179362675;
   fWeightMatrix1to2[7][0] = -2.46219366748594;
   fWeightMatrix1to2[8][0] = 0.180987940882679;
   fWeightMatrix1to2[9][0] = -0.491100802062979;
   fWeightMatrix1to2[10][0] = -1.39024516966096;
   fWeightMatrix1to2[11][0] = -0.164624496855477;
   fWeightMatrix1to2[12][0] = 0.534076551087223;
   fWeightMatrix1to2[0][1] = 1.10839862225373;
   fWeightMatrix1to2[1][1] = -0.0462447853293143;
   fWeightMatrix1to2[2][1] = -0.861855453463894;
   fWeightMatrix1to2[3][1] = -1.98268945544444;
   fWeightMatrix1to2[4][1] = -0.906717171056723;
   fWeightMatrix1to2[5][1] = 1.64457976467896;
   fWeightMatrix1to2[6][1] = 1.37335159970298;
   fWeightMatrix1to2[7][1] = 0.819999246497921;
   fWeightMatrix1to2[8][1] = 0.172120353288658;
   fWeightMatrix1to2[9][1] = 1.93719859062324;
   fWeightMatrix1to2[10][1] = -1.70274718764078;
   fWeightMatrix1to2[11][1] = -1.30252808586892;
   fWeightMatrix1to2[12][1] = -0.322939031620834;
   fWeightMatrix1to2[0][2] = -1.65530654136442;
   fWeightMatrix1to2[1][2] = 2.29679758488098;
   fWeightMatrix1to2[2][2] = 0.915175841940668;
   fWeightMatrix1to2[3][2] = -1.05856727761521;
   fWeightMatrix1to2[4][2] = 0.104454810025184;
   fWeightMatrix1to2[5][2] = -0.663161128895012;
   fWeightMatrix1to2[6][2] = -2.16361592873279;
   fWeightMatrix1to2[7][2] = -1.38381797068121;
   fWeightMatrix1to2[8][2] = -0.187060834900688;
   fWeightMatrix1to2[9][2] = -0.424336646219595;
   fWeightMatrix1to2[10][2] = -0.0602290778725912;
   fWeightMatrix1to2[11][2] = -1.27209325063146;
   fWeightMatrix1to2[12][2] = 3.62027620504772;
   fWeightMatrix1to2[0][3] = -1.70485554686013;
   fWeightMatrix1to2[1][3] = 2.08096906318497;
   fWeightMatrix1to2[2][3] = 0.220818852302584;
   fWeightMatrix1to2[3][3] = -1.44880632928097;
   fWeightMatrix1to2[4][3] = -1.31840143692739;
   fWeightMatrix1to2[5][3] = -2.10712243491194;
   fWeightMatrix1to2[6][3] = -0.620316454862815;
   fWeightMatrix1to2[7][3] = -1.86893510215876;
   fWeightMatrix1to2[8][3] = -1.7614886423705;
   fWeightMatrix1to2[9][3] = -1.91374131921907;
   fWeightMatrix1to2[10][3] = 0.574119420891097;
   fWeightMatrix1to2[11][3] = -0.818095896992939;
   fWeightMatrix1to2[12][3] = -1.82852739022145;
   fWeightMatrix1to2[0][4] = 0.463703069850637;
   fWeightMatrix1to2[1][4] = -1.86625142713935;
   fWeightMatrix1to2[2][4] = -2.04477560208186;
   fWeightMatrix1to2[3][4] = -1.23893059749783;
   fWeightMatrix1to2[4][4] = 0.274193531425756;
   fWeightMatrix1to2[5][4] = 0.138526870894344;
   fWeightMatrix1to2[6][4] = 0.741815317635203;
   fWeightMatrix1to2[7][4] = -0.765019966422366;
   fWeightMatrix1to2[8][4] = -3.24803415270133;
   fWeightMatrix1to2[9][4] = -1.7400055933031;
   fWeightMatrix1to2[10][4] = -1.87645748171315;
   fWeightMatrix1to2[11][4] = -1.90352949648566;
   fWeightMatrix1to2[12][4] = 1.77362458380791;
   fWeightMatrix1to2[0][5] = -1.09362349286045;
   fWeightMatrix1to2[1][5] = -0.426865429616894;
   fWeightMatrix1to2[2][5] = -0.309457221604289;
   fWeightMatrix1to2[3][5] = 0.0331154975240098;
   fWeightMatrix1to2[4][5] = -0.775210228162975;
   fWeightMatrix1to2[5][5] = -3.05453077594706;
   fWeightMatrix1to2[6][5] = 0.373197919291743;
   fWeightMatrix1to2[7][5] = 0.905179212314185;
   fWeightMatrix1to2[8][5] = -1.74400439056483;
   fWeightMatrix1to2[9][5] = -1.5147850969033;
   fWeightMatrix1to2[10][5] = 0.218484282910158;
   fWeightMatrix1to2[11][5] = 1.29741303094726;
   fWeightMatrix1to2[12][5] = -2.74578334388545;
   fWeightMatrix1to2[0][6] = -1.69355855835261;
   fWeightMatrix1to2[1][6] = 2.84677666494093;
   fWeightMatrix1to2[2][6] = 1.65153930657829;
   fWeightMatrix1to2[3][6] = 1.06512858331359;
   fWeightMatrix1to2[4][6] = 0.81651724319652;
   fWeightMatrix1to2[5][6] = -0.767010359361784;
   fWeightMatrix1to2[6][6] = -0.712254117199236;
   fWeightMatrix1to2[7][6] = -0.436173857665085;
   fWeightMatrix1to2[8][6] = -1.32358794678436;
   fWeightMatrix1to2[9][6] = -1.42690595899608;
   fWeightMatrix1to2[10][6] = -2.80017087308328;
   fWeightMatrix1to2[11][6] = -1.5946846266082;
   fWeightMatrix1to2[12][6] = -2.40080809924442;
   fWeightMatrix1to2[0][7] = 1.61975012577731;
   fWeightMatrix1to2[1][7] = -7.64064770829106;
   fWeightMatrix1to2[2][7] = 3.84160248363092;
   fWeightMatrix1to2[3][7] = -1.63748209879418;
   fWeightMatrix1to2[4][7] = 1.62264741499246;
   fWeightMatrix1to2[5][7] = -2.31028571201281;
   fWeightMatrix1to2[6][7] = 0.632525699691477;
   fWeightMatrix1to2[7][7] = 0.584104448128504;
   fWeightMatrix1to2[8][7] = -2.85308280101568;
   fWeightMatrix1to2[9][7] = 0.757743733675772;
   fWeightMatrix1to2[10][7] = 0.314194718308571;
   fWeightMatrix1to2[11][7] = -2.11386101246244;
   fWeightMatrix1to2[12][7] = -8.69843677822614;
   fWeightMatrix1to2[0][8] = -1.88787720465055;
   fWeightMatrix1to2[1][8] = -1.47066870275416;
   fWeightMatrix1to2[2][8] = 0.893454286553759;
   fWeightMatrix1to2[3][8] = -0.558095335399848;
   fWeightMatrix1to2[4][8] = 0.286861030262749;
   fWeightMatrix1to2[5][8] = -0.937124775206943;
   fWeightMatrix1to2[6][8] = -1.22169499913621;
   fWeightMatrix1to2[7][8] = -1.74582743108598;
   fWeightMatrix1to2[8][8] = -0.989696852287099;
   fWeightMatrix1to2[9][8] = -1.5106606267158;
   fWeightMatrix1to2[10][8] = 0.73274912504963;
   fWeightMatrix1to2[11][8] = -1.34180999304295;
   fWeightMatrix1to2[12][8] = -1.43943194183508;
   fWeightMatrix1to2[0][9] = 1.62446670931869;
   fWeightMatrix1to2[1][9] = -0.271839315178891;
   fWeightMatrix1to2[2][9] = -1.49036779661226;
   fWeightMatrix1to2[3][9] = 1.73490339828169;
   fWeightMatrix1to2[4][9] = -1.84517769258636;
   fWeightMatrix1to2[5][9] = 1.27048081158875;
   fWeightMatrix1to2[6][9] = 0.885195944243891;
   fWeightMatrix1to2[7][9] = -0.995665692796682;
   fWeightMatrix1to2[8][9] = -0.931524030407868;
   fWeightMatrix1to2[9][9] = -0.222915599560796;
   fWeightMatrix1to2[10][9] = -1.25778313222846;
   fWeightMatrix1to2[11][9] = 0.397070055803978;
   fWeightMatrix1to2[12][9] = 1.80445644215195;
   fWeightMatrix1to2[0][10] = -0.0934292859642727;
   fWeightMatrix1to2[1][10] = 0.965301429529877;
   fWeightMatrix1to2[2][10] = -2.73173628731525;
   fWeightMatrix1to2[3][10] = 0.779682928062457;
   fWeightMatrix1to2[4][10] = -1.85216033799918;
   fWeightMatrix1to2[5][10] = -0.696857794086288;
   fWeightMatrix1to2[6][10] = -2.48953005676853;
   fWeightMatrix1to2[7][10] = -1.80771506522609;
   fWeightMatrix1to2[8][10] = -2.18517477564911;
   fWeightMatrix1to2[9][10] = 0.518116495306831;
   fWeightMatrix1to2[10][10] = -0.0882766903678553;
   fWeightMatrix1to2[11][10] = 1.26077750631163;
   fWeightMatrix1to2[12][10] = 1.84794472063792;
   fWeightMatrix1to2[0][11] = -1.00135214962131;
   fWeightMatrix1to2[1][11] = -4.9940345566496;
   fWeightMatrix1to2[2][11] = 3.94318573776618;
   fWeightMatrix1to2[3][11] = 1.77217292111127;
   fWeightMatrix1to2[4][11] = -1.49501879646813;
   fWeightMatrix1to2[5][11] = -1.31258881587754;
   fWeightMatrix1to2[6][11] = -1.73787524009052;
   fWeightMatrix1to2[7][11] = -0.748070985349337;
   fWeightMatrix1to2[8][11] = -1.2245827631216;
   fWeightMatrix1to2[9][11] = -0.059297608692315;
   fWeightMatrix1to2[10][11] = -1.66546902722928;
   fWeightMatrix1to2[11][11] = 0.797634297438196;
   fWeightMatrix1to2[12][11] = -3.92876536784029;
   fWeightMatrix1to2[0][12] = -0.324335937114326;
   fWeightMatrix1to2[1][12] = -1.90469728460596;
   fWeightMatrix1to2[2][12] = -3.07528189158928;
   fWeightMatrix1to2[3][12] = -1.65444011412318;
   fWeightMatrix1to2[4][12] = -1.70642889771284;
   fWeightMatrix1to2[5][12] = -0.55429672591064;
   fWeightMatrix1to2[6][12] = -1.22424082453751;
   fWeightMatrix1to2[7][12] = -1.50949411056611;
   fWeightMatrix1to2[8][12] = -0.836657854508148;
   fWeightMatrix1to2[9][12] = -2.66633450284678;
   fWeightMatrix1to2[10][12] = -1.48806532156586;
   fWeightMatrix1to2[11][12] = -1.66335253082389;
   fWeightMatrix1to2[12][12] = -0.251250615247164;
   fWeightMatrix1to2[0][13] = -1.6195476875716;
   fWeightMatrix1to2[1][13] = -2.42868131597235;
   fWeightMatrix1to2[2][13] = -0.748045802022971;
   fWeightMatrix1to2[3][13] = -0.905513849720732;
   fWeightMatrix1to2[4][13] = -1.69600353553704;
   fWeightMatrix1to2[5][13] = 0.695630809766367;
   fWeightMatrix1to2[6][13] = -1.68477610153552;
   fWeightMatrix1to2[7][13] = -1.01493761052038;
   fWeightMatrix1to2[8][13] = 1.36607179416625;
   fWeightMatrix1to2[9][13] = 0.190517651309713;
   fWeightMatrix1to2[10][13] = -0.0419253793561937;
   fWeightMatrix1to2[11][13] = -0.0288897098252667;
   fWeightMatrix1to2[12][13] = 0.532296866428927;
   fWeightMatrix1to2[0][14] = 0.398353762472191;
   fWeightMatrix1to2[1][14] = -1.40712466589736;
   fWeightMatrix1to2[2][14] = -2.76244830097371;
   fWeightMatrix1to2[3][14] = -0.837502211244635;
   fWeightMatrix1to2[4][14] = -1.78560908192768;
   fWeightMatrix1to2[5][14] = -0.762351103594297;
   fWeightMatrix1to2[6][14] = 0.0236995928951709;
   fWeightMatrix1to2[7][14] = -0.302821588579824;
   fWeightMatrix1to2[8][14] = -0.281195343224177;
   fWeightMatrix1to2[9][14] = -0.363919259333822;
   fWeightMatrix1to2[10][14] = 0.762511572506654;
   fWeightMatrix1to2[11][14] = -1.37008975995906;
   fWeightMatrix1to2[12][14] = 0.668430023254252;
   // weight matrix from layer 2 to 3
   fWeightMatrix2to3[0][0] = 0.87480204165932;
   fWeightMatrix2to3[0][1] = 0.0624636928736754;
   fWeightMatrix2to3[0][2] = -1.4393419834102;
   fWeightMatrix2to3[0][3] = -0.871203481230533;
   fWeightMatrix2to3[0][4] = 0.95012463875937;
   fWeightMatrix2to3[0][5] = -1.0054399758322;
   fWeightMatrix2to3[0][6] = 0.960936821249894;
   fWeightMatrix2to3[0][7] = 1.58592289764837;
   fWeightMatrix2to3[0][8] = -1.54266213013056;
   fWeightMatrix2to3[0][9] = 0.619302686304196;
   fWeightMatrix2to3[0][10] = 1.06733421905966;
   fWeightMatrix2to3[0][11] = 0.785863827355706;
   fWeightMatrix2to3[0][12] = -0.796094231112534;
   fWeightMatrix2to3[0][13] = 0.750287056272859;
}

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
}