// 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 10:17:36 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/job406
Training events: 48966


#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'    [49.6229476929,809.992675781]
LepAPt                        LepAPt                            'F'    [20.0012817383,166.771118164]
LepBPt                        LepBPt                            'F'    [10.0008592606,71.5196914673]
MetSigLeptonsJets             MetSigLeptonsJets                 'F'    [0.845276415348,15.6614294052]
MetSpec                       MetSpec                           'F'    [15.0038528442,236.562576294]
SumEtLeptonsJets              SumEtLeptonsJets                  'F'    [30.1877574921,477.770996094]
VSumJetLeptonsPt              VSumJetLeptonsPt                  'F'    [3.10723280907,327.821258545]
addEt                         addEt                             'F'    [48.5138130188,408.254943848]
dPhiLepSumMet                 dPhiLepSumMet                     'F'    [0.0498042888939,3.14158654213]
dPhiLeptons                   dPhiLeptons                       'F'    [1.71661376953e-05,1.11657130718]
dRLeptons                     dRLeptons                         'F'    [0.200001657009,1.13453125954]
lep1_E                        lep1_E                            'F'    [20.0063362122,232.066116333]
lep2_E                        lep2_E                            'F'    [10.0073814392,118.581718445]


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

#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] = 49.6229476928711;
      fVmax[0] = 809.99267578125;
      fVmin[1] = 20.0012817382812;
      fVmax[1] = 166.771118164062;
      fVmin[2] = 10.0008592605591;
      fVmax[2] = 71.5196914672852;
      fVmin[3] = 0.845276415348053;
      fVmax[3] = 15.6614294052124;
      fVmin[4] = 15.0038528442383;
      fVmax[4] = 236.562576293945;
      fVmin[5] = 30.1877574920654;
      fVmax[5] = 477.77099609375;
      fVmin[6] = 3.10723280906677;
      fVmax[6] = 327.821258544922;
      fVmin[7] = 48.5138130187988;
      fVmax[7] = 408.254943847656;
      fVmin[8] = 0.0498042888939381;
      fVmax[8] = 3.14158654212952;
      fVmin[9] = 1.71661376953125e-05;
      fVmax[9] = 1.11657130718231;
      fVmin[10] = 0.200001657009125;
      fVmax[10] = 1.13453125953674;
      fVmin[11] = 20.0063362121582;
      fVmax[11] = 232.066116333008;
      fVmin[12] = 10.007381439209;
      fVmax[12] = 118.581718444824;

      // 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] = -0.203177906208747;
   fWeightMatrix0to1[1][0] = 1.84388501002089;
   fWeightMatrix0to1[2][0] = 0.322074826319119;
   fWeightMatrix0to1[3][0] = 1.57699478066967;
   fWeightMatrix0to1[4][0] = -2.18667498610914;
   fWeightMatrix0to1[5][0] = -1.34703560282802;
   fWeightMatrix0to1[6][0] = -0.539742512468084;
   fWeightMatrix0to1[7][0] = 0.86629919660827;
   fWeightMatrix0to1[8][0] = -2.11455006364045;
   fWeightMatrix0to1[9][0] = -0.404475511905677;
   fWeightMatrix0to1[10][0] = -0.139706888502763;
   fWeightMatrix0to1[11][0] = -0.428347544932016;
   fWeightMatrix0to1[12][0] = -1.51944668520097;
   fWeightMatrix0to1[13][0] = 0.0585856278416443;
   fWeightMatrix0to1[0][1] = -0.593702376244674;
   fWeightMatrix0to1[1][1] = 1.08908746223699;
   fWeightMatrix0to1[2][1] = 0.302282132788436;
   fWeightMatrix0to1[3][1] = 1.82436435954997;
   fWeightMatrix0to1[4][1] = -0.0234334370776433;
   fWeightMatrix0to1[5][1] = 0.349045751791691;
   fWeightMatrix0to1[6][1] = -3.66642586778695;
   fWeightMatrix0to1[7][1] = 3.02153971756079;
   fWeightMatrix0to1[8][1] = 2.53985819829585;
   fWeightMatrix0to1[9][1] = 0.853098029224559;
   fWeightMatrix0to1[10][1] = -4.24830584125262;
   fWeightMatrix0to1[11][1] = 2.12977281812647;
   fWeightMatrix0to1[12][1] = 1.06917876378969;
   fWeightMatrix0to1[13][1] = 1.90253527929283;
   fWeightMatrix0to1[0][2] = -1.90191664069259;
   fWeightMatrix0to1[1][2] = 0.321516825198459;
   fWeightMatrix0to1[2][2] = -1.03784878873604;
   fWeightMatrix0to1[3][2] = 0.747543225217878;
   fWeightMatrix0to1[4][2] = -0.31113185905483;
   fWeightMatrix0to1[5][2] = -0.444457166629867;
   fWeightMatrix0to1[6][2] = -2.27019386365812;
   fWeightMatrix0to1[7][2] = 4.10549793760209;
   fWeightMatrix0to1[8][2] = -1.7917942911025;
   fWeightMatrix0to1[9][2] = 2.33493543488891;
   fWeightMatrix0to1[10][2] = -3.82052692421358;
   fWeightMatrix0to1[11][2] = 1.07625529518556;
   fWeightMatrix0to1[12][2] = 0.888142922914126;
   fWeightMatrix0to1[13][2] = 3.63891196473307;
   fWeightMatrix0to1[0][3] = 1.77046230297367;
   fWeightMatrix0to1[1][3] = 1.38746721717755;
   fWeightMatrix0to1[2][3] = -1.82066335473268;
   fWeightMatrix0to1[3][3] = -1.07769586334469;
   fWeightMatrix0to1[4][3] = 0.930913111947392;
   fWeightMatrix0to1[5][3] = -0.417947437389516;
   fWeightMatrix0to1[6][3] = 4.86458225845737;
   fWeightMatrix0to1[7][3] = 2.11117629220589;
   fWeightMatrix0to1[8][3] = 2.02483308557757;
   fWeightMatrix0to1[9][3] = 1.54996731557251;
   fWeightMatrix0to1[10][3] = -1.32949048234347;
   fWeightMatrix0to1[11][3] = 1.62940826490085;
   fWeightMatrix0to1[12][3] = -2.05468322810343;
   fWeightMatrix0to1[13][3] = -0.0289238619393601;
   fWeightMatrix0to1[0][4] = -1.34835768175866;
   fWeightMatrix0to1[1][4] = -1.69935186583859;
   fWeightMatrix0to1[2][4] = 0.848948558435706;
   fWeightMatrix0to1[3][4] = 0.900189723960755;
   fWeightMatrix0to1[4][4] = -1.61857924961153;
   fWeightMatrix0to1[5][4] = 0.0887573706424754;
   fWeightMatrix0to1[6][4] = 0.625089901169597;
   fWeightMatrix0to1[7][4] = 1.66645217012656;
   fWeightMatrix0to1[8][4] = 1.85307263598454;
   fWeightMatrix0to1[9][4] = 0.274597132236994;
   fWeightMatrix0to1[10][4] = -0.265043241184939;
   fWeightMatrix0to1[11][4] = -0.567857219480576;
   fWeightMatrix0to1[12][4] = 0.365013976329711;
   fWeightMatrix0to1[13][4] = -0.43909205725507;
   fWeightMatrix0to1[0][5] = -0.568000077527474;
   fWeightMatrix0to1[1][5] = -1.62158402935751;
   fWeightMatrix0to1[2][5] = 0.375879981149772;
   fWeightMatrix0to1[3][5] = 0.889754979101955;
   fWeightMatrix0to1[4][5] = 1.14387852294981;
   fWeightMatrix0to1[5][5] = -0.129242575917782;
   fWeightMatrix0to1[6][5] = -2.85796522476118;
   fWeightMatrix0to1[7][5] = -2.26411084325549;
   fWeightMatrix0to1[8][5] = 0.207793534918254;
   fWeightMatrix0to1[9][5] = 1.13822499450006;
   fWeightMatrix0to1[10][5] = 3.22758339925506;
   fWeightMatrix0to1[11][5] = 0.544381667582144;
   fWeightMatrix0to1[12][5] = 0.788331116024712;
   fWeightMatrix0to1[13][5] = 0.162440435195559;
   fWeightMatrix0to1[0][6] = -0.834620417308735;
   fWeightMatrix0to1[1][6] = -0.235768977034238;
   fWeightMatrix0to1[2][6] = 0.619448399339037;
   fWeightMatrix0to1[3][6] = -1.26196168339277;
   fWeightMatrix0to1[4][6] = -1.70945822200446;
   fWeightMatrix0to1[5][6] = -1.64627306430156;
   fWeightMatrix0to1[6][6] = -0.454636060652929;
   fWeightMatrix0to1[7][6] = 1.66504220447474;
   fWeightMatrix0to1[8][6] = 1.77438716105022;
   fWeightMatrix0to1[9][6] = -1.01079475844487;
   fWeightMatrix0to1[10][6] = -2.05331610949724;
   fWeightMatrix0to1[11][6] = 2.09480482119975;
   fWeightMatrix0to1[12][6] = -1.16198794013643;
   fWeightMatrix0to1[13][6] = -1.43087603131507;
   fWeightMatrix0to1[0][7] = -1.50896968859071;
   fWeightMatrix0to1[1][7] = 0.525281917839478;
   fWeightMatrix0to1[2][7] = -2.64989170133295;
   fWeightMatrix0to1[3][7] = -1.6776162568275;
   fWeightMatrix0to1[4][7] = 0.76669785316041;
   fWeightMatrix0to1[5][7] = 0.814234126296017;
   fWeightMatrix0to1[6][7] = -5.12393993276397;
   fWeightMatrix0to1[7][7] = 8.37094730564979;
   fWeightMatrix0to1[8][7] = 0.309281558772361;
   fWeightMatrix0to1[9][7] = -0.185081082240875;
   fWeightMatrix0to1[10][7] = -3.95169652942091;
   fWeightMatrix0to1[11][7] = 1.84502127790151;
   fWeightMatrix0to1[12][7] = -2.08607131196013;
   fWeightMatrix0to1[13][7] = 3.95551690681406;
   fWeightMatrix0to1[0][8] = -0.0689272292982465;
   fWeightMatrix0to1[1][8] = 0.78607065459673;
   fWeightMatrix0to1[2][8] = -0.622391118910082;
   fWeightMatrix0to1[3][8] = 2.07212996865936;
   fWeightMatrix0to1[4][8] = 0.478948152184712;
   fWeightMatrix0to1[5][8] = -1.84694259828172;
   fWeightMatrix0to1[6][8] = 4.32076009912195;
   fWeightMatrix0to1[7][8] = -1.60088765522275;
   fWeightMatrix0to1[8][8] = 0.00491647050527874;
   fWeightMatrix0to1[9][8] = -0.468014068920578;
   fWeightMatrix0to1[10][8] = -0.826421992955256;
   fWeightMatrix0to1[11][8] = 2.38197326785595;
   fWeightMatrix0to1[12][8] = 0.565764607529987;
   fWeightMatrix0to1[13][8] = -0.172884205284089;
   fWeightMatrix0to1[0][9] = -0.106783851733235;
   fWeightMatrix0to1[1][9] = 0.396011220424497;
   fWeightMatrix0to1[2][9] = -2.51360706400806;
   fWeightMatrix0to1[3][9] = 1.82222630108434;
   fWeightMatrix0to1[4][9] = -0.219536892060542;
   fWeightMatrix0to1[5][9] = -2.38667832449236;
   fWeightMatrix0to1[6][9] = 1.64881881209543;
   fWeightMatrix0to1[7][9] = -0.284023625207631;
   fWeightMatrix0to1[8][9] = 1.31818452379464;
   fWeightMatrix0to1[9][9] = 1.30443765128045;
   fWeightMatrix0to1[10][9] = 1.14871460924864;
   fWeightMatrix0to1[11][9] = -1.8501003662415;
   fWeightMatrix0to1[12][9] = -0.117517808799764;
   fWeightMatrix0to1[13][9] = 2.93296305659172;
   fWeightMatrix0to1[0][10] = 1.85538720963201;
   fWeightMatrix0to1[1][10] = 0.396951202624672;
   fWeightMatrix0to1[2][10] = -1.8241123523093;
   fWeightMatrix0to1[3][10] = 0.94604911073423;
   fWeightMatrix0to1[4][10] = -1.93292557084631;
   fWeightMatrix0to1[5][10] = 1.31596740911864;
   fWeightMatrix0to1[6][10] = -0.674481115668832;
   fWeightMatrix0to1[7][10] = -0.0819309201857582;
   fWeightMatrix0to1[8][10] = -0.0666575396770562;
   fWeightMatrix0to1[9][10] = 0.281530192570115;
   fWeightMatrix0to1[10][10] = -1.13126649690572;
   fWeightMatrix0to1[11][10] = 1.67475056288123;
   fWeightMatrix0to1[12][10] = 1.43302036394478;
   fWeightMatrix0to1[13][10] = -1.36918877981967;
   fWeightMatrix0to1[0][11] = -0.596806321936018;
   fWeightMatrix0to1[1][11] = 0.141837953316867;
   fWeightMatrix0to1[2][11] = 2.87009346201148;
   fWeightMatrix0to1[3][11] = -0.442537985493634;
   fWeightMatrix0to1[4][11] = 0.402230606465421;
   fWeightMatrix0to1[5][11] = -1.84308359837707;
   fWeightMatrix0to1[6][11] = 4.11586824432155;
   fWeightMatrix0to1[7][11] = 0.443712075114197;
   fWeightMatrix0to1[8][11] = -0.240371874694708;
   fWeightMatrix0to1[9][11] = 1.37436511633911;
   fWeightMatrix0to1[10][11] = -0.115484811608527;
   fWeightMatrix0to1[11][11] = 0.267240998228103;
   fWeightMatrix0to1[12][11] = -1.00236468361665;
   fWeightMatrix0to1[13][11] = -0.997547796045987;
   fWeightMatrix0to1[0][12] = -0.877921060471469;
   fWeightMatrix0to1[1][12] = -0.91536423529617;
   fWeightMatrix0to1[2][12] = 2.02482426619871;
   fWeightMatrix0to1[3][12] = 0.00352997717780687;
   fWeightMatrix0to1[4][12] = -0.588647400197913;
   fWeightMatrix0to1[5][12] = 1.59056769308958;
   fWeightMatrix0to1[6][12] = 1.61894932779227;
   fWeightMatrix0to1[7][12] = -1.03645387357549;
   fWeightMatrix0to1[8][12] = 0.178404697550253;
   fWeightMatrix0to1[9][12] = 0.6705938226286;
   fWeightMatrix0to1[10][12] = -0.644892111502951;
   fWeightMatrix0to1[11][12] = -0.444050609221117;
   fWeightMatrix0to1[12][12] = -1.71407018358671;
   fWeightMatrix0to1[13][12] = -0.858268050598084;
   fWeightMatrix0to1[0][13] = 1.5827840679324;
   fWeightMatrix0to1[1][13] = -1.08892802635188;
   fWeightMatrix0to1[2][13] = -0.208209482981332;
   fWeightMatrix0to1[3][13] = -0.290835887454926;
   fWeightMatrix0to1[4][13] = 0.918243757375295;
   fWeightMatrix0to1[5][13] = 0.87196386146203;
   fWeightMatrix0to1[6][13] = -8.89235250628641;
   fWeightMatrix0to1[7][13] = 13.4777226148167;
   fWeightMatrix0to1[8][13] = 1.30715143514332;
   fWeightMatrix0to1[9][13] = 0.246662971461288;
   fWeightMatrix0to1[10][13] = -6.73904888104522;
   fWeightMatrix0to1[11][13] = 2.10382366972634;
   fWeightMatrix0to1[12][13] = 0.823779411721893;
   fWeightMatrix0to1[13][13] = 3.90824320684149;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = -2.86164179283439;
   fWeightMatrix1to2[1][0] = -0.491352530980201;
   fWeightMatrix1to2[2][0] = -0.187609170582158;
   fWeightMatrix1to2[3][0] = -1.79830499279324;
   fWeightMatrix1to2[4][0] = -0.412772557493691;
   fWeightMatrix1to2[5][0] = -0.810433279824456;
   fWeightMatrix1to2[6][0] = -0.552991184987898;
   fWeightMatrix1to2[7][0] = -1.99469360296982;
   fWeightMatrix1to2[8][0] = 0.865862311896509;
   fWeightMatrix1to2[9][0] = 0.178962275466169;
   fWeightMatrix1to2[10][0] = -1.48836300557368;
   fWeightMatrix1to2[11][0] = -0.243319668452136;
   fWeightMatrix1to2[12][0] = 0.0873387721570177;
   fWeightMatrix1to2[0][1] = 1.13065806639296;
   fWeightMatrix1to2[1][1] = -0.656392006584575;
   fWeightMatrix1to2[2][1] = 0.0450039682132215;
   fWeightMatrix1to2[3][1] = -2.26559854682168;
   fWeightMatrix1to2[4][1] = -0.78084811302276;
   fWeightMatrix1to2[5][1] = 2.00053574538085;
   fWeightMatrix1to2[6][1] = 1.31991458710978;
   fWeightMatrix1to2[7][1] = 0.763781677745042;
   fWeightMatrix1to2[8][1] = 0.254632985999171;
   fWeightMatrix1to2[9][1] = 1.95251020270474;
   fWeightMatrix1to2[10][1] = -1.57923119588323;
   fWeightMatrix1to2[11][1] = -1.3051028438292;
   fWeightMatrix1to2[12][1] = -0.247945152088795;
   fWeightMatrix1to2[0][2] = -1.71936694473145;
   fWeightMatrix1to2[1][2] = 2.15136761310557;
   fWeightMatrix1to2[2][2] = 2.11824829474285;
   fWeightMatrix1to2[3][2] = -0.858611914297178;
   fWeightMatrix1to2[4][2] = 0.177627233687181;
   fWeightMatrix1to2[5][2] = 0.354034479082066;
   fWeightMatrix1to2[6][2] = -1.90240087669038;
   fWeightMatrix1to2[7][2] = -1.48336768250367;
   fWeightMatrix1to2[8][2] = 0.903916853828954;
   fWeightMatrix1to2[9][2] = -0.131222004175619;
   fWeightMatrix1to2[10][2] = 0.856278535003496;
   fWeightMatrix1to2[11][2] = -1.1547856515058;
   fWeightMatrix1to2[12][2] = 1.82491626480866;
   fWeightMatrix1to2[0][3] = -1.6937752259364;
   fWeightMatrix1to2[1][3] = -0.797925945786209;
   fWeightMatrix1to2[2][3] = 0.0661056007267253;
   fWeightMatrix1to2[3][3] = -2.09811368899183;
   fWeightMatrix1to2[4][3] = -1.55453163809122;
   fWeightMatrix1to2[5][3] = -1.47038622711851;
   fWeightMatrix1to2[6][3] = -0.361798340306594;
   fWeightMatrix1to2[7][3] = -1.86224781358731;
   fWeightMatrix1to2[8][3] = -1.53944423307712;
   fWeightMatrix1to2[9][3] = -1.75049883623813;
   fWeightMatrix1to2[10][3] = 0.4925422351533;
   fWeightMatrix1to2[11][3] = -1.00002668826062;
   fWeightMatrix1to2[12][3] = -1.76735588954727;
   fWeightMatrix1to2[0][4] = 0.532559720800312;
   fWeightMatrix1to2[1][4] = -1.96519559629824;
   fWeightMatrix1to2[2][4] = -0.684183803558042;
   fWeightMatrix1to2[3][4] = -1.15150460205263;
   fWeightMatrix1to2[4][4] = 0.521119002143306;
   fWeightMatrix1to2[5][4] = 0.976218787269438;
   fWeightMatrix1to2[6][4] = 0.727331434320359;
   fWeightMatrix1to2[7][4] = -0.928944574794568;
   fWeightMatrix1to2[8][4] = -2.12407233902435;
   fWeightMatrix1to2[9][4] = -1.6394830342446;
   fWeightMatrix1to2[10][4] = -1.69869700720239;
   fWeightMatrix1to2[11][4] = -1.76477460583102;
   fWeightMatrix1to2[12][4] = -1.19582530918123;
   fWeightMatrix1to2[0][5] = -0.555001889306932;
   fWeightMatrix1to2[1][5] = 0.632707362512397;
   fWeightMatrix1to2[2][5] = -1.42056701508519;
   fWeightMatrix1to2[3][5] = -0.34180055726525;
   fWeightMatrix1to2[4][5] = -0.193752238622828;
   fWeightMatrix1to2[5][5] = -2.78094543681339;
   fWeightMatrix1to2[6][5] = 0.646847501072024;
   fWeightMatrix1to2[7][5] = 1.11081357971076;
   fWeightMatrix1to2[8][5] = -1.46594365850914;
   fWeightMatrix1to2[9][5] = -1.5317582047184;
   fWeightMatrix1to2[10][5] = 0.020305833479775;
   fWeightMatrix1to2[11][5] = 1.11474958644736;
   fWeightMatrix1to2[12][5] = -1.14420384698045;
   fWeightMatrix1to2[0][6] = -1.08008152088617;
   fWeightMatrix1to2[1][6] = 4.2061137734203;
   fWeightMatrix1to2[2][6] = -0.771424760727387;
   fWeightMatrix1to2[3][6] = 2.90427556041643;
   fWeightMatrix1to2[4][6] = 1.22998491494454;
   fWeightMatrix1to2[5][6] = -1.7521993776923;
   fWeightMatrix1to2[6][6] = -0.502723448474948;
   fWeightMatrix1to2[7][6] = -0.188030063018189;
   fWeightMatrix1to2[8][6] = -2.05441786527418;
   fWeightMatrix1to2[9][6] = -1.08571930713643;
   fWeightMatrix1to2[10][6] = -1.98160774776308;
   fWeightMatrix1to2[11][6] = -1.60041964061871;
   fWeightMatrix1to2[12][6] = -2.33652952165306;
   fWeightMatrix1to2[0][7] = 1.42677939965168;
   fWeightMatrix1to2[1][7] = -8.624973940611;
   fWeightMatrix1to2[2][7] = 2.82811503336499;
   fWeightMatrix1to2[3][7] = -3.1366183127591;
   fWeightMatrix1to2[4][7] = 2.36234824084843;
   fWeightMatrix1to2[5][7] = -0.569727352475973;
   fWeightMatrix1to2[6][7] = 0.538679013317931;
   fWeightMatrix1to2[7][7] = 0.879910247543549;
   fWeightMatrix1to2[8][7] = -1.68256866557665;
   fWeightMatrix1to2[9][7] = 0.34133464259841;
   fWeightMatrix1to2[10][7] = 0.180335697773841;
   fWeightMatrix1to2[11][7] = -1.90104515347049;
   fWeightMatrix1to2[12][7] = -2.10784388514164;
   fWeightMatrix1to2[0][8] = -1.57935933979664;
   fWeightMatrix1to2[1][8] = -2.21735693859522;
   fWeightMatrix1to2[2][8] = 3.08071350815683;
   fWeightMatrix1to2[3][8] = -0.822326263566615;
   fWeightMatrix1to2[4][8] = 0.708632086798517;
   fWeightMatrix1to2[5][8] = -0.510578920803126;
   fWeightMatrix1to2[6][8] = -1.02786652384974;
   fWeightMatrix1to2[7][8] = -1.54040012547414;
   fWeightMatrix1to2[8][8] = -1.02583494870823;
   fWeightMatrix1to2[9][8] = -1.21764333695298;
   fWeightMatrix1to2[10][8] = 1.03377292119192;
   fWeightMatrix1to2[11][8] = -1.35211820503034;
   fWeightMatrix1to2[12][8] = -1.30513871805314;
   fWeightMatrix1to2[0][9] = 1.69984271946664;
   fWeightMatrix1to2[1][9] = -0.283234695707734;
   fWeightMatrix1to2[2][9] = -0.754855139117217;
   fWeightMatrix1to2[3][9] = 1.40769847656112;
   fWeightMatrix1to2[4][9] = -1.81613037880803;
   fWeightMatrix1to2[5][9] = 1.41531078775494;
   fWeightMatrix1to2[6][9] = 0.790442104136667;
   fWeightMatrix1to2[7][9] = -0.994739885430082;
   fWeightMatrix1to2[8][9] = -1.05760955802803;
   fWeightMatrix1to2[9][9] = -0.352846425051511;
   fWeightMatrix1to2[10][9] = -0.796226110294562;
   fWeightMatrix1to2[11][9] = 0.507421724020599;
   fWeightMatrix1to2[12][9] = 1.84944230256971;
   fWeightMatrix1to2[0][10] = 0.388101516731085;
   fWeightMatrix1to2[1][10] = 5.21294098670272;
   fWeightMatrix1to2[2][10] = -4.43130257502301;
   fWeightMatrix1to2[3][10] = 1.4574856520349;
   fWeightMatrix1to2[4][10] = -1.90033544203373;
   fWeightMatrix1to2[5][10] = -0.998441322053016;
   fWeightMatrix1to2[6][10] = -1.97936411552881;
   fWeightMatrix1to2[7][10] = -1.38897400562975;
   fWeightMatrix1to2[8][10] = -0.926953419164378;
   fWeightMatrix1to2[9][10] = 0.269631465305083;
   fWeightMatrix1to2[10][10] = -0.818763297044196;
   fWeightMatrix1to2[11][10] = 0.892368462051386;
   fWeightMatrix1to2[12][10] = -0.574714253948279;
   fWeightMatrix1to2[0][11] = -0.934537017180423;
   fWeightMatrix1to2[1][11] = -2.63689880957696;
   fWeightMatrix1to2[2][11] = 1.62385549778643;
   fWeightMatrix1to2[3][11] = 2.27101605528969;
   fWeightMatrix1to2[4][11] = -1.55499434766832;
   fWeightMatrix1to2[5][11] = 0.446053356162741;
   fWeightMatrix1to2[6][11] = -2.18337964967131;
   fWeightMatrix1to2[7][11] = -0.966372578447254;
   fWeightMatrix1to2[8][11] = -0.357437924884725;
   fWeightMatrix1to2[9][11] = 0.000623404817882182;
   fWeightMatrix1to2[10][11] = -1.21984879688067;
   fWeightMatrix1to2[11][11] = 0.868676207122149;
   fWeightMatrix1to2[12][11] = 0.399877607925335;
   fWeightMatrix1to2[0][12] = -0.155851781489356;
   fWeightMatrix1to2[1][12] = -1.08526220153358;
   fWeightMatrix1to2[2][12] = -1.2334601821111;
   fWeightMatrix1to2[3][12] = -1.59699520026343;
   fWeightMatrix1to2[4][12] = -1.42402199139361;
   fWeightMatrix1to2[5][12] = -0.339612382703504;
   fWeightMatrix1to2[6][12] = -1.07580244160081;
   fWeightMatrix1to2[7][12] = -1.09297887819816;
   fWeightMatrix1to2[8][12] = -0.0850541737641012;
   fWeightMatrix1to2[9][12] = -2.34676964830531;
   fWeightMatrix1to2[10][12] = -1.99182760868784;
   fWeightMatrix1to2[11][12] = -1.76407737896583;
   fWeightMatrix1to2[12][12] = -0.457643223642512;
   fWeightMatrix1to2[0][13] = -1.89233741813076;
   fWeightMatrix1to2[1][13] = -4.72930026459622;
   fWeightMatrix1to2[2][13] = -1.01326897439575;
   fWeightMatrix1to2[3][13] = -1.42780971853242;
   fWeightMatrix1to2[4][13] = -1.76016837440637;
   fWeightMatrix1to2[5][13] = 1.67232789881741;
   fWeightMatrix1to2[6][13] = -1.91328903078377;
   fWeightMatrix1to2[7][13] = -1.41713055268429;
   fWeightMatrix1to2[8][13] = 1.67207119220577;
   fWeightMatrix1to2[9][13] = 0.502023625141437;
   fWeightMatrix1to2[10][13] = 0.779799264091447;
   fWeightMatrix1to2[11][13] = 0.0727176731501533;
   fWeightMatrix1to2[12][13] = 0.443909101862277;
   fWeightMatrix1to2[0][14] = 0.774078814451386;
   fWeightMatrix1to2[1][14] = -1.15061694747777;
   fWeightMatrix1to2[2][14] = -1.90684864723944;
   fWeightMatrix1to2[3][14] = -1.13540690862158;
   fWeightMatrix1to2[4][14] = -1.43462788100014;
   fWeightMatrix1to2[5][14] = -0.027510637294321;
   fWeightMatrix1to2[6][14] = 0.267150595760439;
   fWeightMatrix1to2[7][14] = 0.0606249917266861;
   fWeightMatrix1to2[8][14] = 0.424113341924041;
   fWeightMatrix1to2[9][14] = 0.344661259190421;
   fWeightMatrix1to2[10][14] = 0.848064241928466;
   fWeightMatrix1to2[11][14] = -1.46260546604145;
   fWeightMatrix1to2[12][14] = 0.130187708134657;
   // weight matrix from layer 2 to 3
   fWeightMatrix2to3[0][0] = 0.619766552366718;
   fWeightMatrix2to3[0][1] = -0.747515257309341;
   fWeightMatrix2to3[0][2] = -0.998042085985617;
   fWeightMatrix2to3[0][3] = -0.812454062989043;
   fWeightMatrix2to3[0][4] = 1.34063887717605;
   fWeightMatrix2to3[0][5] = 1.13225530474582;
   fWeightMatrix2to3[0][6] = 0.934502893863892;
   fWeightMatrix2to3[0][7] = 1.78900913647695;
   fWeightMatrix2to3[0][8] = 0.364327495362465;
   fWeightMatrix2to3[0][9] = 1.07793666258533;
   fWeightMatrix2to3[0][10] = 0.890890189180118;
   fWeightMatrix2to3[0][11] = 0.161095034261692;
   fWeightMatrix2to3[0][12] = -0.40855332485787;
   fWeightMatrix2to3[0][13] = 0.679117345953067;
}

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
}