// 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 10:37:09 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/job415
Training events: 34306


#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'    [49.2191009521,771.463745117]
LepAPt                        LepAPt                            'F'    [20.0000362396,162.429443359]
LepBPt                        LepBPt                            'F'    [10.0003643036,65.7519302368]
MetSigLeptonsJets             MetSigLeptonsJets                 'F'    [1.13847851753,21.1559963226]
MetSpec                       MetSpec                           'F'    [15.0119848251,261.761749268]
SumEtLeptonsJets              SumEtLeptonsJets                  'F'    [30.1199874878,517.957397461]
VSumJetLeptonsPt              VSumJetLeptonsPt                  'F'    [3.44767546654,304.726287842]
addEt                         addEt                             'F'    [49.2191009521,385.879455566]
dPhiLepSumMet                 dPhiLepSumMet                     'F'    [0.00602381536737,3.14158987999]
dPhiLeptons                   dPhiLeptons                       'F'    [2.03847885132e-05,1.13010489941]
dRLeptons                     dRLeptons                         'F'    [0.200001657009,1.13414525986]
lep1_E                        lep1_E                            'F'    [20.0563106537,202.024719238]
lep2_E                        lep2_E                            'F'    [10.0202493668,113.683082581]


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

#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] = 771.463745117188;
      fVmin[1] = 20.000036239624;
      fVmax[1] = 162.429443359375;
      fVmin[2] = 10.0003643035889;
      fVmax[2] = 65.7519302368164;
      fVmin[3] = 1.13847851753235;
      fVmax[3] = 21.1559963226318;
      fVmin[4] = 15.0119848251343;
      fVmax[4] = 261.761749267578;
      fVmin[5] = 30.119987487793;
      fVmax[5] = 517.957397460938;
      fVmin[6] = 3.44767546653748;
      fVmax[6] = 304.726287841797;
      fVmin[7] = 49.2191009521484;
      fVmax[7] = 385.879455566406;
      fVmin[8] = 0.00602381536737084;
      fVmax[8] = 3.14158987998962;
      fVmin[9] = 2.03847885131836e-05;
      fVmax[9] = 1.13010489940643;
      fVmin[10] = 0.200001657009125;
      fVmax[10] = 1.13414525985718;
      fVmin[11] = 20.0563106536865;
      fVmax[11] = 202.024719238281;
      fVmin[12] = 10.0202493667603;
      fVmax[12] = 113.683082580566;

      // 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] = -0.120089457682876;
   fWeightMatrix0to1[1][0] = 2.15254946858806;
   fWeightMatrix0to1[2][0] = 0.85053807904538;
   fWeightMatrix0to1[3][0] = 2.04040171146855;
   fWeightMatrix0to1[4][0] = -1.33285481197514;
   fWeightMatrix0to1[5][0] = -1.28946791245544;
   fWeightMatrix0to1[6][0] = -1.32195252820841;
   fWeightMatrix0to1[7][0] = 2.06792075349895;
   fWeightMatrix0to1[8][0] = -1.28517640152689;
   fWeightMatrix0to1[9][0] = -0.625030619941634;
   fWeightMatrix0to1[10][0] = -1.53108931486036;
   fWeightMatrix0to1[11][0] = -0.117025636996532;
   fWeightMatrix0to1[12][0] = -0.746264649767288;
   fWeightMatrix0to1[13][0] = -0.51786798726546;
   fWeightMatrix0to1[0][1] = -0.454081581075536;
   fWeightMatrix0to1[1][1] = 1.09487992453112;
   fWeightMatrix0to1[2][1] = -0.325144358139095;
   fWeightMatrix0to1[3][1] = 2.00650285735334;
   fWeightMatrix0to1[4][1] = 1.38759062101092;
   fWeightMatrix0to1[5][1] = 1.46296700634381;
   fWeightMatrix0to1[6][1] = -1.86538080928268;
   fWeightMatrix0to1[7][1] = 0.128494419883346;
   fWeightMatrix0to1[8][1] = 0.835032924758334;
   fWeightMatrix0to1[9][1] = 0.856626906095704;
   fWeightMatrix0to1[10][1] = -1.43907521488769;
   fWeightMatrix0to1[11][1] = -0.0448397083447431;
   fWeightMatrix0to1[12][1] = 1.53046403276345;
   fWeightMatrix0to1[13][1] = -1.26953086280974;
   fWeightMatrix0to1[0][2] = -1.76110689235785;
   fWeightMatrix0to1[1][2] = 0.781427052742413;
   fWeightMatrix0to1[2][2] = 0.325678087754487;
   fWeightMatrix0to1[3][2] = 1.70054056553453;
   fWeightMatrix0to1[4][2] = 0.475408418380842;
   fWeightMatrix0to1[5][2] = 0.17675425530021;
   fWeightMatrix0to1[6][2] = -0.702353351075194;
   fWeightMatrix0to1[7][2] = 4.08451864549696;
   fWeightMatrix0to1[8][2] = -1.46093204323702;
   fWeightMatrix0to1[9][2] = 5.33822792076092;
   fWeightMatrix0to1[10][2] = -1.94755320432595;
   fWeightMatrix0to1[11][2] = 1.29727601840514;
   fWeightMatrix0to1[12][2] = 0.286119251102682;
   fWeightMatrix0to1[13][2] = -0.174601242496141;
   fWeightMatrix0to1[0][3] = 1.97416045896562;
   fWeightMatrix0to1[1][3] = 0.778170336560668;
   fWeightMatrix0to1[2][3] = -1.88407870619113;
   fWeightMatrix0to1[3][3] = -1.41135469232445;
   fWeightMatrix0to1[4][3] = 1.02710576091271;
   fWeightMatrix0to1[5][3] = 0.0415447370993914;
   fWeightMatrix0to1[6][3] = 3.08525008065004;
   fWeightMatrix0to1[7][3] = 0.387848475653052;
   fWeightMatrix0to1[8][3] = 1.65283154989757;
   fWeightMatrix0to1[9][3] = 1.03478589476361;
   fWeightMatrix0to1[10][3] = -1.00662246712403;
   fWeightMatrix0to1[11][3] = 1.80447290115223;
   fWeightMatrix0to1[12][3] = -1.55315083546717;
   fWeightMatrix0to1[13][3] = -0.225581985720041;
   fWeightMatrix0to1[0][4] = -1.1254723842463;
   fWeightMatrix0to1[1][4] = -1.91669012098154;
   fWeightMatrix0to1[2][4] = 0.998849286983267;
   fWeightMatrix0to1[3][4] = 0.991817292976486;
   fWeightMatrix0to1[4][4] = -0.915592239987471;
   fWeightMatrix0to1[5][4] = 0.381128360336175;
   fWeightMatrix0to1[6][4] = -0.123584108253021;
   fWeightMatrix0to1[7][4] = 1.49166593364946;
   fWeightMatrix0to1[8][4] = 1.95853066568528;
   fWeightMatrix0to1[9][4] = 0.325897081311802;
   fWeightMatrix0to1[10][4] = 0.447788648042333;
   fWeightMatrix0to1[11][4] = -0.811744453156149;
   fWeightMatrix0to1[12][4] = 1.19874383617374;
   fWeightMatrix0to1[13][4] = -0.787278496316424;
   fWeightMatrix0to1[0][5] = -0.526947833870353;
   fWeightMatrix0to1[1][5] = -1.31397450801548;
   fWeightMatrix0to1[2][5] = 1.05339809233393;
   fWeightMatrix0to1[3][5] = 1.4571651517703;
   fWeightMatrix0to1[4][5] = 1.58137872927988;
   fWeightMatrix0to1[5][5] = -0.164367684434395;
   fWeightMatrix0to1[6][5] = -2.10266852844652;
   fWeightMatrix0to1[7][5] = -3.15501589658453;
   fWeightMatrix0to1[8][5] = 1.12011583153039;
   fWeightMatrix0to1[9][5] = 0.20439405273331;
   fWeightMatrix0to1[10][5] = 2.1722138029929;
   fWeightMatrix0to1[11][5] = 0.96774848918592;
   fWeightMatrix0to1[12][5] = 1.58965363372294;
   fWeightMatrix0to1[13][5] = -0.731550288412139;
   fWeightMatrix0to1[0][6] = -0.71244162563691;
   fWeightMatrix0to1[1][6] = 0.0746342808834646;
   fWeightMatrix0to1[2][6] = 0.834603253354656;
   fWeightMatrix0to1[3][6] = -0.851450125452847;
   fWeightMatrix0to1[4][6] = -0.340806508419963;
   fWeightMatrix0to1[5][6] = -1.40726448048615;
   fWeightMatrix0to1[6][6] = -1.83958680214539;
   fWeightMatrix0to1[7][6] = 2.68698150551916;
   fWeightMatrix0to1[8][6] = 1.73768694462073;
   fWeightMatrix0to1[9][6] = 0.398471950612497;
   fWeightMatrix0to1[10][6] = -0.981420788241204;
   fWeightMatrix0to1[11][6] = 1.88597857970487;
   fWeightMatrix0to1[12][6] = -0.715337878554999;
   fWeightMatrix0to1[13][6] = -2.20049771801285;
   fWeightMatrix0to1[0][7] = -1.27853686857193;
   fWeightMatrix0to1[1][7] = 0.915535329822734;
   fWeightMatrix0to1[2][7] = -2.48634276675129;
   fWeightMatrix0to1[3][7] = -1.49601561737392;
   fWeightMatrix0to1[4][7] = 3.18864535029252;
   fWeightMatrix0to1[5][7] = 1.93120461410136;
   fWeightMatrix0to1[6][7] = -4.24161813510106;
   fWeightMatrix0to1[7][7] = 8.06791276628049;
   fWeightMatrix0to1[8][7] = -0.821064570448601;
   fWeightMatrix0to1[9][7] = 3.02357765991431;
   fWeightMatrix0to1[10][7] = -0.981962486319965;
   fWeightMatrix0to1[11][7] = 0.249428332355721;
   fWeightMatrix0to1[12][7] = -2.00007473614216;
   fWeightMatrix0to1[13][7] = -0.839846952114623;
   fWeightMatrix0to1[0][8] = 0.0367365289148692;
   fWeightMatrix0to1[1][8] = 0.176056391745969;
   fWeightMatrix0to1[2][8] = -1.93236182188649;
   fWeightMatrix0to1[3][8] = 1.4328184480029;
   fWeightMatrix0to1[4][8] = 0.0937352166892912;
   fWeightMatrix0to1[5][8] = -1.56315521196828;
   fWeightMatrix0to1[6][8] = 2.60638436912213;
   fWeightMatrix0to1[7][8] = -2.35698144208583;
   fWeightMatrix0to1[8][8] = -1.65774945471848;
   fWeightMatrix0to1[9][8] = 0.777233004303185;
   fWeightMatrix0to1[10][8] = 0.489915408065292;
   fWeightMatrix0to1[11][8] = 1.74246541995189;
   fWeightMatrix0to1[12][8] = -0.497473613312359;
   fWeightMatrix0to1[13][8] = 0.772628020463739;
   fWeightMatrix0to1[0][9] = -0.387363708813406;
   fWeightMatrix0to1[1][9] = 0.121238989427788;
   fWeightMatrix0to1[2][9] = -2.92745361293875;
   fWeightMatrix0to1[3][9] = 1.31408762952435;
   fWeightMatrix0to1[4][9] = 0.987413250237348;
   fWeightMatrix0to1[5][9] = -1.83553461252991;
   fWeightMatrix0to1[6][9] = 0.281634675530099;
   fWeightMatrix0to1[7][9] = 0.0446168633519038;
   fWeightMatrix0to1[8][9] = -0.0254777889057737;
   fWeightMatrix0to1[9][9] = 0.601216655571461;
   fWeightMatrix0to1[10][9] = 1.25349250546945;
   fWeightMatrix0to1[11][9] = -1.229299252963;
   fWeightMatrix0to1[12][9] = -0.0909120896811675;
   fWeightMatrix0to1[13][9] = 1.55771267033409;
   fWeightMatrix0to1[0][10] = 1.5808444457468;
   fWeightMatrix0to1[1][10] = -0.399513055569783;
   fWeightMatrix0to1[2][10] = -2.65558236466389;
   fWeightMatrix0to1[3][10] = 0.138115517827935;
   fWeightMatrix0to1[4][10] = -1.87106682716682;
   fWeightMatrix0to1[5][10] = 1.37240706557535;
   fWeightMatrix0to1[6][10] = -1.54569076765159;
   fWeightMatrix0to1[7][10] = 0.130003234385636;
   fWeightMatrix0to1[8][10] = -0.521373468876996;
   fWeightMatrix0to1[9][10] = -0.713216430267595;
   fWeightMatrix0to1[10][10] = -1.71215472055556;
   fWeightMatrix0to1[11][10] = 1.02774258276573;
   fWeightMatrix0to1[12][10] = 1.23968848066639;
   fWeightMatrix0to1[13][10] = -0.634952408712982;
   fWeightMatrix0to1[0][11] = -0.489234132040391;
   fWeightMatrix0to1[1][11] = 0.594505335733171;
   fWeightMatrix0to1[2][11] = 1.60015904623284;
   fWeightMatrix0to1[3][11] = 0.834380774251822;
   fWeightMatrix0to1[4][11] = 1.86129122826876;
   fWeightMatrix0to1[5][11] = -1.25538505261114;
   fWeightMatrix0to1[6][11] = 2.93628227293542;
   fWeightMatrix0to1[7][11] = 0.210402135890891;
   fWeightMatrix0to1[8][11] = 0.554306513161971;
   fWeightMatrix0to1[9][11] = 0.788949024300924;
   fWeightMatrix0to1[10][11] = 1.87623727908088;
   fWeightMatrix0to1[11][11] = -0.446573448139688;
   fWeightMatrix0to1[12][11] = -0.757455959648193;
   fWeightMatrix0to1[13][11] = 1.13016734028416;
   fWeightMatrix0to1[0][12] = -0.81648801902336;
   fWeightMatrix0to1[1][12] = -0.248524499154443;
   fWeightMatrix0to1[2][12] = 2.55262443684311;
   fWeightMatrix0to1[3][12] = 1.19950022991161;
   fWeightMatrix0to1[4][12] = -0.546876163323278;
   fWeightMatrix0to1[5][12] = 2.05800246265686;
   fWeightMatrix0to1[6][12] = 3.56227014690151;
   fWeightMatrix0to1[7][12] = -1.44269138575136;
   fWeightMatrix0to1[8][12] = 0.612913825176766;
   fWeightMatrix0to1[9][12] = 0.0590235961118073;
   fWeightMatrix0to1[10][12] = 0.816411441375226;
   fWeightMatrix0to1[11][12] = -1.735796284838;
   fWeightMatrix0to1[12][12] = -2.20667355539576;
   fWeightMatrix0to1[13][12] = 1.21235102357448;
   fWeightMatrix0to1[0][13] = 1.66423010560508;
   fWeightMatrix0to1[1][13] = -0.502901424720802;
   fWeightMatrix0to1[2][13] = -0.307815183389059;
   fWeightMatrix0to1[3][13] = -1.09174734496986;
   fWeightMatrix0to1[4][13] = 2.9175257083599;
   fWeightMatrix0to1[5][13] = 1.82777998011329;
   fWeightMatrix0to1[6][13] = -5.2772623816736;
   fWeightMatrix0to1[7][13] = 11.156917685313;
   fWeightMatrix0to1[8][13] = -1.12337335943931;
   fWeightMatrix0to1[9][13] = 6.56597017962155;
   fWeightMatrix0to1[10][13] = -0.55553625941257;
   fWeightMatrix0to1[11][13] = -0.181782337230126;
   fWeightMatrix0to1[12][13] = -0.745105131115661;
   fWeightMatrix0to1[13][13] = -2.88800985775118;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = -1.32351875734635;
   fWeightMatrix1to2[1][0] = -0.389415066980367;
   fWeightMatrix1to2[2][0] = -0.0279052866801723;
   fWeightMatrix1to2[3][0] = -1.76328470918284;
   fWeightMatrix1to2[4][0] = -0.698948930071025;
   fWeightMatrix1to2[5][0] = -0.92590453439661;
   fWeightMatrix1to2[6][0] = -0.0864309675175915;
   fWeightMatrix1to2[7][0] = -2.15320933323762;
   fWeightMatrix1to2[8][0] = 0.277980533324642;
   fWeightMatrix1to2[9][0] = -0.170638872436261;
   fWeightMatrix1to2[10][0] = -1.36470676517877;
   fWeightMatrix1to2[11][0] = -0.18492106576885;
   fWeightMatrix1to2[12][0] = -0.199009571001434;
   fWeightMatrix1to2[0][1] = 1.98166083991601;
   fWeightMatrix1to2[1][1] = 0.18403089776931;
   fWeightMatrix1to2[2][1] = -0.222540905608673;
   fWeightMatrix1to2[3][1] = -2.07598382532341;
   fWeightMatrix1to2[4][1] = -0.936083855538514;
   fWeightMatrix1to2[5][1] = 1.64290854937972;
   fWeightMatrix1to2[6][1] = 1.26054528852186;
   fWeightMatrix1to2[7][1] = 0.667695816219133;
   fWeightMatrix1to2[8][1] = 0.0926563717347396;
   fWeightMatrix1to2[9][1] = 1.89605876989719;
   fWeightMatrix1to2[10][1] = -1.74254390863067;
   fWeightMatrix1to2[11][1] = -1.31072893481635;
   fWeightMatrix1to2[12][1] = -0.266260570634618;
   fWeightMatrix1to2[0][2] = -3.55814789307377;
   fWeightMatrix1to2[1][2] = 1.92183357858134;
   fWeightMatrix1to2[2][2] = 1.69921975740603;
   fWeightMatrix1to2[3][2] = -1.76571507186962;
   fWeightMatrix1to2[4][2] = 0.777687770300489;
   fWeightMatrix1to2[5][2] = -0.194185904150317;
   fWeightMatrix1to2[6][2] = -1.3644624866284;
   fWeightMatrix1to2[7][2] = -1.1353757072548;
   fWeightMatrix1to2[8][2] = 0.921077109591406;
   fWeightMatrix1to2[9][2] = -0.136698395294774;
   fWeightMatrix1to2[10][2] = 0.287390972657846;
   fWeightMatrix1to2[11][2] = -1.20876860587491;
   fWeightMatrix1to2[12][2] = 1.70218259708938;
   fWeightMatrix1to2[0][3] = -1.52903466639574;
   fWeightMatrix1to2[1][3] = 0.613479744716518;
   fWeightMatrix1to2[2][3] = 0.0553538140857676;
   fWeightMatrix1to2[3][3] = -1.59527092667943;
   fWeightMatrix1to2[4][3] = -1.31183981921151;
   fWeightMatrix1to2[5][3] = -1.90605545143766;
   fWeightMatrix1to2[6][3] = -0.299927081420826;
   fWeightMatrix1to2[7][3] = -1.89276380260405;
   fWeightMatrix1to2[8][3] = -1.67427493046678;
   fWeightMatrix1to2[9][3] = -1.77961515927349;
   fWeightMatrix1to2[10][3] = 0.162600020669063;
   fWeightMatrix1to2[11][3] = -0.993968836227962;
   fWeightMatrix1to2[12][3] = -1.60767095336697;
   fWeightMatrix1to2[0][4] = 3.29758571148636;
   fWeightMatrix1to2[1][4] = -0.773000612167975;
   fWeightMatrix1to2[2][4] = -0.229163703877986;
   fWeightMatrix1to2[3][4] = -1.76719068928246;
   fWeightMatrix1to2[4][4] = 0.186965580076465;
   fWeightMatrix1to2[5][4] = 1.05579684768933;
   fWeightMatrix1to2[6][4] = 1.01363837678243;
   fWeightMatrix1to2[7][4] = -1.21137527493931;
   fWeightMatrix1to2[8][4] = -1.94339655172803;
   fWeightMatrix1to2[9][4] = -1.6874947712557;
   fWeightMatrix1to2[10][4] = -1.90491117722346;
   fWeightMatrix1to2[11][4] = -1.85412467945659;
   fWeightMatrix1to2[12][4] = -0.294248175204646;
   fWeightMatrix1to2[0][5] = 0.984906698099038;
   fWeightMatrix1to2[1][5] = 0.540262051855287;
   fWeightMatrix1to2[2][5] = -0.843360743488751;
   fWeightMatrix1to2[3][5] = -0.446684637277053;
   fWeightMatrix1to2[4][5] = -0.546160422645752;
   fWeightMatrix1to2[5][5] = -2.9387046669209;
   fWeightMatrix1to2[6][5] = 1.18973613528536;
   fWeightMatrix1to2[7][5] = 1.03521613624312;
   fWeightMatrix1to2[8][5] = -1.90557880807511;
   fWeightMatrix1to2[9][5] = -1.72135943576455;
   fWeightMatrix1to2[10][5] = 0.308302125853757;
   fWeightMatrix1to2[11][5] = 1.37094250261764;
   fWeightMatrix1to2[12][5] = -1.05157402361559;
   fWeightMatrix1to2[0][6] = -3.64165186250943;
   fWeightMatrix1to2[1][6] = 0.247545814183872;
   fWeightMatrix1to2[2][6] = 1.11437566633484;
   fWeightMatrix1to2[3][6] = 1.80750102648245;
   fWeightMatrix1to2[4][6] = 1.02764442170314;
   fWeightMatrix1to2[5][6] = -1.35193694464637;
   fWeightMatrix1to2[6][6] = 0.199317148163864;
   fWeightMatrix1to2[7][6] = -0.379302226805741;
   fWeightMatrix1to2[8][6] = -1.89806948538088;
   fWeightMatrix1to2[9][6] = -1.40223004599961;
   fWeightMatrix1to2[10][6] = -2.41201427364585;
   fWeightMatrix1to2[11][6] = -2.14018074870054;
   fWeightMatrix1to2[12][6] = -2.60787769317572;
   fWeightMatrix1to2[0][7] = 7.14469355118306;
   fWeightMatrix1to2[1][7] = -3.04445441835204;
   fWeightMatrix1to2[2][7] = 1.41397169294455;
   fWeightMatrix1to2[3][7] = -2.25974471179241;
   fWeightMatrix1to2[4][7] = 1.09720778268172;
   fWeightMatrix1to2[5][7] = -1.34350463267559;
   fWeightMatrix1to2[6][7] = 0.306155893729448;
   fWeightMatrix1to2[7][7] = 0.313905271999056;
   fWeightMatrix1to2[8][7] = -2.28375561210726;
   fWeightMatrix1to2[9][7] = 0.113411925508277;
   fWeightMatrix1to2[10][7] = -0.639755091847384;
   fWeightMatrix1to2[11][7] = -1.89129842875254;
   fWeightMatrix1to2[12][7] = -2.10016625374403;
   fWeightMatrix1to2[0][8] = -1.54194712944822;
   fWeightMatrix1to2[1][8] = -0.890620190867397;
   fWeightMatrix1to2[2][8] = 1.8421183298272;
   fWeightMatrix1to2[3][8] = -0.616572997835539;
   fWeightMatrix1to2[4][8] = 0.262742186320173;
   fWeightMatrix1to2[5][8] = -0.916482589627196;
   fWeightMatrix1to2[6][8] = -1.27471843423841;
   fWeightMatrix1to2[7][8] = -1.74605530561382;
   fWeightMatrix1to2[8][8] = -1.00960468177627;
   fWeightMatrix1to2[9][8] = -1.49776951119883;
   fWeightMatrix1to2[10][8] = 0.822579800539793;
   fWeightMatrix1to2[11][8] = -1.35650788033116;
   fWeightMatrix1to2[12][8] = -1.20406526324805;
   fWeightMatrix1to2[0][9] = 5.65119903468524;
   fWeightMatrix1to2[1][9] = -0.402022307692751;
   fWeightMatrix1to2[2][9] = -0.775943007899748;
   fWeightMatrix1to2[3][9] = 1.67635813195242;
   fWeightMatrix1to2[4][9] = -1.80337412343162;
   fWeightMatrix1to2[5][9] = 1.23452731269863;
   fWeightMatrix1to2[6][9] = 1.45498373663645;
   fWeightMatrix1to2[7][9] = -1.20351748226346;
   fWeightMatrix1to2[8][9] = -1.4104253147767;
   fWeightMatrix1to2[9][9] = 0.120429661350414;
   fWeightMatrix1to2[10][9] = -1.29968860453695;
   fWeightMatrix1to2[11][9] = 0.374623649373524;
   fWeightMatrix1to2[12][9] = 1.08631590612262;
   fWeightMatrix1to2[0][10] = -0.962903181175043;
   fWeightMatrix1to2[1][10] = 1.45451843723817;
   fWeightMatrix1to2[2][10] = -1.56035444898137;
   fWeightMatrix1to2[3][10] = 0.520934197385885;
   fWeightMatrix1to2[4][10] = -1.90849740524801;
   fWeightMatrix1to2[5][10] = -0.460694339777134;
   fWeightMatrix1to2[6][10] = -1.48109051864727;
   fWeightMatrix1to2[7][10] = -1.85486815970781;
   fWeightMatrix1to2[8][10] = -0.869886799749887;
   fWeightMatrix1to2[9][10] = 0.257772826180588;
   fWeightMatrix1to2[10][10] = -0.535851278874098;
   fWeightMatrix1to2[11][10] = 0.775108125239724;
   fWeightMatrix1to2[12][10] = -0.702412685676834;
   fWeightMatrix1to2[0][11] = 0.332221333645022;
   fWeightMatrix1to2[1][11] = -2.21927001734634;
   fWeightMatrix1to2[2][11] = -0.389755551447533;
   fWeightMatrix1to2[3][11] = 1.90832133468411;
   fWeightMatrix1to2[4][11] = -1.58510855392017;
   fWeightMatrix1to2[5][11] = -0.739761496532713;
   fWeightMatrix1to2[6][11] = -1.90103272796042;
   fWeightMatrix1to2[7][11] = -0.811459322768201;
   fWeightMatrix1to2[8][11] = -0.951299986336027;
   fWeightMatrix1to2[9][11] = -0.584334970244031;
   fWeightMatrix1to2[10][11] = -1.71099446737897;
   fWeightMatrix1to2[11][11] = 0.906955463745593;
   fWeightMatrix1to2[12][11] = 0.0257092262893646;
   fWeightMatrix1to2[0][12] = -0.880077371734364;
   fWeightMatrix1to2[1][12] = -0.993536012270824;
   fWeightMatrix1to2[2][12] = -1.3249210975518;
   fWeightMatrix1to2[3][12] = -1.16084552200211;
   fWeightMatrix1to2[4][12] = -1.76208022178563;
   fWeightMatrix1to2[5][12] = -0.0688644259227586;
   fWeightMatrix1to2[6][12] = -1.27287490299383;
   fWeightMatrix1to2[7][12] = -1.25312923538786;
   fWeightMatrix1to2[8][12] = -0.466456009642484;
   fWeightMatrix1to2[9][12] = -2.81017339833382;
   fWeightMatrix1to2[10][12] = -1.48397238335673;
   fWeightMatrix1to2[11][12] = -1.68528603731963;
   fWeightMatrix1to2[12][12] = -0.421230842263889;
   fWeightMatrix1to2[0][13] = -2.25480305677116;
   fWeightMatrix1to2[1][13] = -1.82048130660791;
   fWeightMatrix1to2[2][13] = -0.854525101758473;
   fWeightMatrix1to2[3][13] = -0.913885422132197;
   fWeightMatrix1to2[4][13] = -1.59308142165023;
   fWeightMatrix1to2[5][13] = 0.448475544561064;
   fWeightMatrix1to2[6][13] = -0.876652965796557;
   fWeightMatrix1to2[7][13] = -1.09110094864729;
   fWeightMatrix1to2[8][13] = 0.945006869878002;
   fWeightMatrix1to2[9][13] = -0.0384177862244189;
   fWeightMatrix1to2[10][13] = 0.073691131405275;
   fWeightMatrix1to2[11][13] = -0.182090882095331;
   fWeightMatrix1to2[12][13] = -0.127913483675346;
   fWeightMatrix1to2[0][14] = 2.46048576831961;
   fWeightMatrix1to2[1][14] = -0.994786540450627;
   fWeightMatrix1to2[2][14] = -1.4935636456615;
   fWeightMatrix1to2[3][14] = -1.11092498457854;
   fWeightMatrix1to2[4][14] = -1.75281041093402;
   fWeightMatrix1to2[5][14] = -0.359324883694181;
   fWeightMatrix1to2[6][14] = 0.674529419103109;
   fWeightMatrix1to2[7][14] = -0.173748297095458;
   fWeightMatrix1to2[8][14] = -0.192932379342536;
   fWeightMatrix1to2[9][14] = -0.161461930261323;
   fWeightMatrix1to2[10][14] = 0.714410253396257;
   fWeightMatrix1to2[11][14] = -1.40973281790295;
   fWeightMatrix1to2[12][14] = -0.119788664664893;
   // weight matrix from layer 2 to 3
   fWeightMatrix2to3[0][0] = 0.664448682024908;
   fWeightMatrix2to3[0][1] = -0.703807451138189;
   fWeightMatrix2to3[0][2] = 0.257103701152336;
   fWeightMatrix2to3[0][3] = -1.2576916754568;
   fWeightMatrix2to3[0][4] = 0.895798904775559;
   fWeightMatrix2to3[0][5] = -0.13236229016189;
   fWeightMatrix2to3[0][6] = 0.245548503219875;
   fWeightMatrix2to3[0][7] = 1.63775883706169;
   fWeightMatrix2to3[0][8] = 0.179644101235615;
   fWeightMatrix2to3[0][9] = 0.640432743595119;
   fWeightMatrix2to3[0][10] = 0.696128103997344;
   fWeightMatrix2to3[0][11] = 0.904326030341091;
   fWeightMatrix2to3[0][12] = -0.305669430022453;
   fWeightMatrix2to3[0][13] = 0.106429083784534;
}

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
}