// 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 10:27:01 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/job418
Training events: 23126


#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'    [50.1963043213,950.957641602]
LepAPt                        LepAPt                            'F'    [20.001373291,127.397720337]
LepBPt                        LepBPt                            'F'    [10.0010261536,71.4863357544]
MetSigLeptonsJets             MetSigLeptonsJets                 'F'    [1.19181919098,23.3634910583]
MetSpec                       MetSpec                           'F'    [15.0101337433,210.330810547]
SumEtLeptonsJets              SumEtLeptonsJets                  'F'    [30.2201824188,513.559753418]
VSumJetLeptonsPt              VSumJetLeptonsPt                  'F'    [1.44759953022,434.137237549]
addEt                         addEt                             'F'    [48.2450904846,476.475494385]
dPhiLepSumMet                 dPhiLepSumMet                     'F'    [0.000472510961117,3.14157700539]
dPhiLeptons                   dPhiLeptons                       'F'    [3.32593917847e-05,1.06520390511]
dRLeptons                     dRLeptons                         'F'    [0.200003907084,1.13498270512]
lep1_E                        lep1_E                            'F'    [20.0262546539,208.21144104]
lep2_E                        lep2_E                            'F'    [10.0028533936,105.04108429]


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

#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] = 50.1963043212891;
      fVmax[0] = 950.957641601562;
      fVmin[1] = 20.0013732910156;
      fVmax[1] = 127.397720336914;
      fVmin[2] = 10.0010261535645;
      fVmax[2] = 71.4863357543945;
      fVmin[3] = 1.191819190979;
      fVmax[3] = 23.3634910583496;
      fVmin[4] = 15.0101337432861;
      fVmax[4] = 210.330810546875;
      fVmin[5] = 30.2201824188232;
      fVmax[5] = 513.559753417969;
      fVmin[6] = 1.44759953022003;
      fVmax[6] = 434.137237548828;
      fVmin[7] = 48.2450904846191;
      fVmax[7] = 476.475494384766;
      fVmin[8] = 0.000472510961117223;
      fVmax[8] = 3.14157700538635;
      fVmin[9] = 3.3259391784668e-05;
      fVmax[9] = 1.06520390510559;
      fVmin[10] = 0.200003907084465;
      fVmax[10] = 1.13498270511627;
      fVmin[11] = 20.0262546539307;
      fVmax[11] = 208.211441040039;
      fVmin[12] = 10.0028533935547;
      fVmax[12] = 105.041084289551;

      // 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] = -0.107499542646937;
   fWeightMatrix0to1[1][0] = 2.15511816332034;
   fWeightMatrix0to1[2][0] = 0.993734115140769;
   fWeightMatrix0to1[3][0] = 1.70050077764577;
   fWeightMatrix0to1[4][0] = -1.94356642506852;
   fWeightMatrix0to1[5][0] = -1.45177046944385;
   fWeightMatrix0to1[6][0] = -0.665819627335347;
   fWeightMatrix0to1[7][0] = 1.56276468605614;
   fWeightMatrix0to1[8][0] = -1.29644344610286;
   fWeightMatrix0to1[9][0] = -1.36750099050923;
   fWeightMatrix0to1[10][0] = -1.43786539579775;
   fWeightMatrix0to1[11][0] = -0.336948362612245;
   fWeightMatrix0to1[12][0] = -1.37356184444456;
   fWeightMatrix0to1[13][0] = -0.134146566788349;
   fWeightMatrix0to1[0][1] = -0.483127957081558;
   fWeightMatrix0to1[1][1] = 1.16116649014738;
   fWeightMatrix0to1[2][1] = -0.509377875319868;
   fWeightMatrix0to1[3][1] = 2.27778048847054;
   fWeightMatrix0to1[4][1] = 2.91658701281398;
   fWeightMatrix0to1[5][1] = 2.60075411792021;
   fWeightMatrix0to1[6][1] = -0.693135948299256;
   fWeightMatrix0to1[7][1] = 0.839422905718943;
   fWeightMatrix0to1[8][1] = 0.799271285420497;
   fWeightMatrix0to1[9][1] = 2.70131886333789;
   fWeightMatrix0to1[10][1] = -1.06863788948008;
   fWeightMatrix0to1[11][1] = -0.287824561724324;
   fWeightMatrix0to1[12][1] = 1.25968313734249;
   fWeightMatrix0to1[13][1] = -2.43041414450377;
   fWeightMatrix0to1[0][2] = -1.75726703866116;
   fWeightMatrix0to1[1][2] = 0.653364950461056;
   fWeightMatrix0to1[2][2] = 0.689637133143548;
   fWeightMatrix0to1[3][2] = 1.56789894180951;
   fWeightMatrix0to1[4][2] = 1.57123034266326;
   fWeightMatrix0to1[5][2] = 1.85460706149602;
   fWeightMatrix0to1[6][2] = -2.62719092573287;
   fWeightMatrix0to1[7][2] = 1.74351004917877;
   fWeightMatrix0to1[8][2] = -1.42867212659026;
   fWeightMatrix0to1[9][2] = 4.44788161085272;
   fWeightMatrix0to1[10][2] = -0.869528257983791;
   fWeightMatrix0to1[11][2] = 0.971920303484319;
   fWeightMatrix0to1[12][2] = 0.810829711467928;
   fWeightMatrix0to1[13][2] = -1.93273007709395;
   fWeightMatrix0to1[0][3] = 1.99401923558937;
   fWeightMatrix0to1[1][3] = 1.20408259146712;
   fWeightMatrix0to1[2][3] = -0.609194671988468;
   fWeightMatrix0to1[3][3] = -1.73011415586817;
   fWeightMatrix0to1[4][3] = 0.28949473127863;
   fWeightMatrix0to1[5][3] = -0.253415823297862;
   fWeightMatrix0to1[6][3] = 1.95572967853401;
   fWeightMatrix0to1[7][3] = 0.966309499381031;
   fWeightMatrix0to1[8][3] = 1.59876018177627;
   fWeightMatrix0to1[9][3] = 0.96678857688044;
   fWeightMatrix0to1[10][3] = -1.3861844879277;
   fWeightMatrix0to1[11][3] = 1.17498973716157;
   fWeightMatrix0to1[12][3] = -1.96040997584968;
   fWeightMatrix0to1[13][3] = -0.847741879162851;
   fWeightMatrix0to1[0][4] = -1.13723150376612;
   fWeightMatrix0to1[1][4] = -1.53934933655364;
   fWeightMatrix0to1[2][4] = 1.70344789019179;
   fWeightMatrix0to1[3][4] = 0.935687250121877;
   fWeightMatrix0to1[4][4] = -0.372852601802916;
   fWeightMatrix0to1[5][4] = 1.94838026127576;
   fWeightMatrix0to1[6][4] = -2.19600234265932;
   fWeightMatrix0to1[7][4] = 3.79938892972426;
   fWeightMatrix0to1[8][4] = 1.87031128487609;
   fWeightMatrix0to1[9][4] = 1.52678538865858;
   fWeightMatrix0to1[10][4] = 0.552568226585793;
   fWeightMatrix0to1[11][4] = -1.51235242998282;
   fWeightMatrix0to1[12][4] = 0.863840427388944;
   fWeightMatrix0to1[13][4] = -3.6387524393381;
   fWeightMatrix0to1[0][5] = -0.503915522765194;
   fWeightMatrix0to1[1][5] = -1.18401820907198;
   fWeightMatrix0to1[2][5] = 0.803613806562957;
   fWeightMatrix0to1[3][5] = 1.13499382737343;
   fWeightMatrix0to1[4][5] = 1.7685232364702;
   fWeightMatrix0to1[5][5] = 0.110836898113055;
   fWeightMatrix0to1[6][5] = -2.38661633399668;
   fWeightMatrix0to1[7][5] = -1.67980572566656;
   fWeightMatrix0to1[8][5] = 1.13257262045551;
   fWeightMatrix0to1[9][5] = 0.747807069517102;
   fWeightMatrix0to1[10][5] = 2.12004270382359;
   fWeightMatrix0to1[11][5] = 0.713716963777685;
   fWeightMatrix0to1[12][5] = 0.924819083427154;
   fWeightMatrix0to1[13][5] = -0.980137924186968;
   fWeightMatrix0to1[0][6] = -0.694246515216403;
   fWeightMatrix0to1[1][6] = -0.0747101919714384;
   fWeightMatrix0to1[2][6] = 1.28454466392856;
   fWeightMatrix0to1[3][6] = -0.885544077258453;
   fWeightMatrix0to1[4][6] = -1.20280803583319;
   fWeightMatrix0to1[5][6] = -1.53668722417069;
   fWeightMatrix0to1[6][6] = 0.0736029897576562;
   fWeightMatrix0to1[7][6] = -0.292824435810951;
   fWeightMatrix0to1[8][6] = 1.72733931116689;
   fWeightMatrix0to1[9][6] = -1.98207780778433;
   fWeightMatrix0to1[10][6] = -0.616924987457077;
   fWeightMatrix0to1[11][6] = 1.56733395248436;
   fWeightMatrix0to1[12][6] = -0.966802158874161;
   fWeightMatrix0to1[13][6] = -1.34311521784267;
   fWeightMatrix0to1[0][7] = -1.30644814492603;
   fWeightMatrix0to1[1][7] = 0.48785264099044;
   fWeightMatrix0to1[2][7] = -1.85447913154064;
   fWeightMatrix0to1[3][7] = -1.26735697956272;
   fWeightMatrix0to1[4][7] = 2.48224472675538;
   fWeightMatrix0to1[5][7] = 3.19160805039111;
   fWeightMatrix0to1[6][7] = -2.53715025107365;
   fWeightMatrix0to1[7][7] = 3.48394683013319;
   fWeightMatrix0to1[8][7] = -0.861118022020971;
   fWeightMatrix0to1[9][7] = 1.03107768380798;
   fWeightMatrix0to1[10][7] = 0.244520079366989;
   fWeightMatrix0to1[11][7] = 0.126583873842757;
   fWeightMatrix0to1[12][7] = -1.86749494352023;
   fWeightMatrix0to1[13][7] = -1.63731495503185;
   fWeightMatrix0to1[0][8] = 0.0478702713279933;
   fWeightMatrix0to1[1][8] = 0.381789638183922;
   fWeightMatrix0to1[2][8] = -1.35255757347603;
   fWeightMatrix0to1[3][8] = 2.46909382690686;
   fWeightMatrix0to1[4][8] = 1.16246746623273;
   fWeightMatrix0to1[5][8] = -1.14324241451009;
   fWeightMatrix0to1[6][8] = 1.31145205843814;
   fWeightMatrix0to1[7][8] = 0.0777988725258128;
   fWeightMatrix0to1[8][8] = -1.75667207332687;
   fWeightMatrix0to1[9][8] = -0.0607251347482949;
   fWeightMatrix0to1[10][8] = -0.244692685837634;
   fWeightMatrix0to1[11][8] = 3.14198301905533;
   fWeightMatrix0to1[12][8] = 0.659219640602442;
   fWeightMatrix0to1[13][8] = -1.13020357475864;
   fWeightMatrix0to1[0][9] = -0.302915897632607;
   fWeightMatrix0to1[1][9] = -0.0960425891903053;
   fWeightMatrix0to1[2][9] = -1.68254619355352;
   fWeightMatrix0to1[3][9] = 0.144003179243364;
   fWeightMatrix0to1[4][9] = 1.10770095198939;
   fWeightMatrix0to1[5][9] = -0.900881707921338;
   fWeightMatrix0to1[6][9] = -0.264347398838281;
   fWeightMatrix0to1[7][9] = -0.824267991334738;
   fWeightMatrix0to1[8][9] = 0.0295509585579907;
   fWeightMatrix0to1[9][9] = 1.35207005122482;
   fWeightMatrix0to1[10][9] = 2.05368034411641;
   fWeightMatrix0to1[11][9] = -2.39738717126541;
   fWeightMatrix0to1[12][9] = 0.40888900267283;
   fWeightMatrix0to1[13][9] = 0.236095157978623;
   fWeightMatrix0to1[0][10] = 1.62784162704322;
   fWeightMatrix0to1[1][10] = -0.157117602701044;
   fWeightMatrix0to1[2][10] = -1.45737667523636;
   fWeightMatrix0to1[3][10] = -0.186342724268856;
   fWeightMatrix0to1[4][10] = -1.55029663952395;
   fWeightMatrix0to1[5][10] = 1.55095922794929;
   fWeightMatrix0to1[6][10] = -0.975733602063508;
   fWeightMatrix0to1[7][10] = -0.731470334214361;
   fWeightMatrix0to1[8][10] = -0.400758740174086;
   fWeightMatrix0to1[9][10] = -0.769056543884727;
   fWeightMatrix0to1[10][10] = -1.11692709213566;
   fWeightMatrix0to1[11][10] = 0.914623795599494;
   fWeightMatrix0to1[12][10] = 1.44266694993522;
   fWeightMatrix0to1[13][10] = -0.28544251035051;
   fWeightMatrix0to1[0][11] = -0.507438417257472;
   fWeightMatrix0to1[1][11] = 0.445779368650434;
   fWeightMatrix0to1[2][11] = 0.916756530295992;
   fWeightMatrix0to1[3][11] = 0.749343932472637;
   fWeightMatrix0to1[4][11] = 2.52548292752001;
   fWeightMatrix0to1[5][11] = -2.1016074569145;
   fWeightMatrix0to1[6][11] = 3.99828159900511;
   fWeightMatrix0to1[7][11] = 0.999523358649717;
   fWeightMatrix0to1[8][11] = 0.503031276520208;
   fWeightMatrix0to1[9][11] = 0.0753946875278917;
   fWeightMatrix0to1[10][11] = 1.12566223985028;
   fWeightMatrix0to1[11][11] = -0.122862117810381;
   fWeightMatrix0to1[12][11] = -0.536035595013158;
   fWeightMatrix0to1[13][11] = 2.58109458106566;
   fWeightMatrix0to1[0][12] = -0.787823493501777;
   fWeightMatrix0to1[1][12] = -0.353825716917434;
   fWeightMatrix0to1[2][12] = 1.69257368174655;
   fWeightMatrix0to1[3][12] = 0.3437660756037;
   fWeightMatrix0to1[4][12] = 0.347898308282861;
   fWeightMatrix0to1[5][12] = 1.29657576152365;
   fWeightMatrix0to1[6][12] = 1.78370409317518;
   fWeightMatrix0to1[7][12] = -1.1281210954386;
   fWeightMatrix0to1[8][12] = 0.63964244607156;
   fWeightMatrix0to1[9][12] = -0.417444300649753;
   fWeightMatrix0to1[10][12] = 0.872054271531697;
   fWeightMatrix0to1[11][12] = -1.01560162164888;
   fWeightMatrix0to1[12][12] = -1.89828647782925;
   fWeightMatrix0to1[13][12] = 0.361230290619605;
   fWeightMatrix0to1[0][13] = 1.5663968221888;
   fWeightMatrix0to1[1][13] = -1.41749424907269;
   fWeightMatrix0to1[2][13] = -0.413363529024557;
   fWeightMatrix0to1[3][13] = 0.325051287437716;
   fWeightMatrix0to1[4][13] = 3.41226576207286;
   fWeightMatrix0to1[5][13] = 5.40777966261254;
   fWeightMatrix0to1[6][13] = -4.77203629645541;
   fWeightMatrix0to1[7][13] = 5.54460119303664;
   fWeightMatrix0to1[8][13] = -1.14598091579308;
   fWeightMatrix0to1[9][13] = 5.65406467481118;
   fWeightMatrix0to1[10][13] = 1.56444102630861;
   fWeightMatrix0to1[11][13] = 0.407106082565903;
   fWeightMatrix0to1[12][13] = 0.855804298453703;
   fWeightMatrix0to1[13][13] = -6.14579900539814;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = -1.90137592189204;
   fWeightMatrix1to2[1][0] = 0.111702227665584;
   fWeightMatrix1to2[2][0] = 0.359851208450754;
   fWeightMatrix1to2[3][0] = -1.71516476824851;
   fWeightMatrix1to2[4][0] = -0.703653285479839;
   fWeightMatrix1to2[5][0] = -0.881401978471904;
   fWeightMatrix1to2[6][0] = -0.221625795140342;
   fWeightMatrix1to2[7][0] = -1.84548347278257;
   fWeightMatrix1to2[8][0] = 0.603198238011516;
   fWeightMatrix1to2[9][0] = -0.0248861419560847;
   fWeightMatrix1to2[10][0] = -1.08186934549478;
   fWeightMatrix1to2[11][0] = -0.3646127134211;
   fWeightMatrix1to2[12][0] = -0.187204004980309;
   fWeightMatrix1to2[0][1] = 1.38635316373237;
   fWeightMatrix1to2[1][1] = 0.451372314114454;
   fWeightMatrix1to2[2][1] = -0.188997131181193;
   fWeightMatrix1to2[3][1] = -2.04517861712453;
   fWeightMatrix1to2[4][1] = -0.924140192600136;
   fWeightMatrix1to2[5][1] = 1.62184629963294;
   fWeightMatrix1to2[6][1] = 1.33706868177073;
   fWeightMatrix1to2[7][1] = 0.7796793043946;
   fWeightMatrix1to2[8][1] = 0.0780164639238076;
   fWeightMatrix1to2[9][1] = 1.93909643375072;
   fWeightMatrix1to2[10][1] = -1.71432683179219;
   fWeightMatrix1to2[11][1] = -1.30419279471409;
   fWeightMatrix1to2[12][1] = -0.256736449739028;
   fWeightMatrix1to2[0][2] = -1.3326601699853;
   fWeightMatrix1to2[1][2] = 0.861159063945738;
   fWeightMatrix1to2[2][2] = 1.67799001980145;
   fWeightMatrix1to2[3][2] = -1.30949425453467;
   fWeightMatrix1to2[4][2] = 0.786135200380954;
   fWeightMatrix1to2[5][2] = -0.156097713627394;
   fWeightMatrix1to2[6][2] = -1.85455281121594;
   fWeightMatrix1to2[7][2] = -1.14231629685176;
   fWeightMatrix1to2[8][2] = 0.876748916634797;
   fWeightMatrix1to2[9][2] = 0.0332319940607209;
   fWeightMatrix1to2[10][2] = 0.918911067216671;
   fWeightMatrix1to2[11][2] = -1.1600344903218;
   fWeightMatrix1to2[12][2] = 1.53743853354256;
   fWeightMatrix1to2[0][3] = 0.795003159227937;
   fWeightMatrix1to2[1][3] = 0.115309742408345;
   fWeightMatrix1to2[2][3] = -0.166909099210979;
   fWeightMatrix1to2[3][3] = -1.67778334108965;
   fWeightMatrix1to2[4][3] = -1.31004452127575;
   fWeightMatrix1to2[5][3] = -2.1995680336239;
   fWeightMatrix1to2[6][3] = -0.464930832622081;
   fWeightMatrix1to2[7][3] = -1.85600889205106;
   fWeightMatrix1to2[8][3] = -2.45087110315287;
   fWeightMatrix1to2[9][3] = -1.93607050600148;
   fWeightMatrix1to2[10][3] = 0.126732380180304;
   fWeightMatrix1to2[11][3] = -1.09801960156692;
   fWeightMatrix1to2[12][3] = -2.20650201216542;
   fWeightMatrix1to2[0][4] = 4.5050217059935;
   fWeightMatrix1to2[1][4] = -1.20375887420063;
   fWeightMatrix1to2[2][4] = -0.326051119742761;
   fWeightMatrix1to2[3][4] = -2.13866162586018;
   fWeightMatrix1to2[4][4] = 0.211225265058646;
   fWeightMatrix1to2[5][4] = 0.857092187250771;
   fWeightMatrix1to2[6][4] = 0.73544129204813;
   fWeightMatrix1to2[7][4] = -0.828642661142552;
   fWeightMatrix1to2[8][4] = -2.31642086392485;
   fWeightMatrix1to2[9][4] = -1.70743556717549;
   fWeightMatrix1to2[10][4] = -1.40253024697189;
   fWeightMatrix1to2[11][4] = -1.74729659937703;
   fWeightMatrix1to2[12][4] = -0.538456052126306;
   fWeightMatrix1to2[0][5] = 3.5167328890068;
   fWeightMatrix1to2[1][5] = 1.3213704525402;
   fWeightMatrix1to2[2][5] = -0.738312809104612;
   fWeightMatrix1to2[3][5] = 0.343082683440775;
   fWeightMatrix1to2[4][5] = -0.532796158069822;
   fWeightMatrix1to2[5][5] = -3.0404305070202;
   fWeightMatrix1to2[6][5] = 0.738856868596543;
   fWeightMatrix1to2[7][5] = 1.26649083278503;
   fWeightMatrix1to2[8][5] = -1.29691149823823;
   fWeightMatrix1to2[9][5] = -1.47975494886058;
   fWeightMatrix1to2[10][5] = 0.486538766196163;
   fWeightMatrix1to2[11][5] = 1.21018625174943;
   fWeightMatrix1to2[12][5] = -0.4965353144411;
   fWeightMatrix1to2[0][6] = -3.68760368848035;
   fWeightMatrix1to2[1][6] = -0.676057847884836;
   fWeightMatrix1to2[2][6] = 1.5753234809501;
   fWeightMatrix1to2[3][6] = 1.54729684610421;
   fWeightMatrix1to2[4][6] = 0.980227192217137;
   fWeightMatrix1to2[5][6] = -1.08242402828208;
   fWeightMatrix1to2[6][6] = 0.0494600055045176;
   fWeightMatrix1to2[7][6] = -0.398367346800374;
   fWeightMatrix1to2[8][6] = -2.02515031208299;
   fWeightMatrix1to2[9][6] = -1.46867912378948;
   fWeightMatrix1to2[10][6] = -2.20425035245061;
   fWeightMatrix1to2[11][6] = -1.7314469004275;
   fWeightMatrix1to2[12][6] = -2.38419726367276;
   fWeightMatrix1to2[0][7] = 4.29590030930756;
   fWeightMatrix1to2[1][7] = -2.32036430292168;
   fWeightMatrix1to2[2][7] = 1.52231579846855;
   fWeightMatrix1to2[3][7] = -2.26592565295882;
   fWeightMatrix1to2[4][7] = 1.12440955395031;
   fWeightMatrix1to2[5][7] = -1.38315399579246;
   fWeightMatrix1to2[6][7] = -0.0828665769232915;
   fWeightMatrix1to2[7][7] = 0.975710715160979;
   fWeightMatrix1to2[8][7] = -2.39813265982675;
   fWeightMatrix1to2[9][7] = 0.114890087937389;
   fWeightMatrix1to2[10][7] = -0.115980391124092;
   fWeightMatrix1to2[11][7] = -2.20102349156071;
   fWeightMatrix1to2[12][7] = -2.76395675820439;
   fWeightMatrix1to2[0][8] = -1.52296669585513;
   fWeightMatrix1to2[1][8] = -0.83673150886561;
   fWeightMatrix1to2[2][8] = 1.84901839383738;
   fWeightMatrix1to2[3][8] = -0.628082906912671;
   fWeightMatrix1to2[4][8] = 0.270378640842148;
   fWeightMatrix1to2[5][8] = -0.918513914739556;
   fWeightMatrix1to2[6][8] = -1.28025416062014;
   fWeightMatrix1to2[7][8] = -1.76541726070047;
   fWeightMatrix1to2[8][8] = -1.04849625093539;
   fWeightMatrix1to2[9][8] = -1.42417766659857;
   fWeightMatrix1to2[10][8] = 0.803294435631897;
   fWeightMatrix1to2[11][8] = -1.34754645293448;
   fWeightMatrix1to2[12][8] = -1.25699198471266;
   fWeightMatrix1to2[0][9] = 4.96729260778584;
   fWeightMatrix1to2[1][9] = -0.124572124583328;
   fWeightMatrix1to2[2][9] = -0.951716487362472;
   fWeightMatrix1to2[3][9] = 2.13793735774988;
   fWeightMatrix1to2[4][9] = -1.7975071673958;
   fWeightMatrix1to2[5][9] = 0.867158282604903;
   fWeightMatrix1to2[6][9] = 0.70679523036574;
   fWeightMatrix1to2[7][9] = -0.740942800839077;
   fWeightMatrix1to2[8][9] = -1.99237268526953;
   fWeightMatrix1to2[9][9] = -0.244332619597355;
   fWeightMatrix1to2[10][9] = -1.33179238015619;
   fWeightMatrix1to2[11][9] = 0.76896366747498;
   fWeightMatrix1to2[12][9] = 1.9578529790866;
   fWeightMatrix1to2[0][10] = 0.820099358452761;
   fWeightMatrix1to2[1][10] = 0.683233679392632;
   fWeightMatrix1to2[2][10] = -1.63745872941119;
   fWeightMatrix1to2[3][10] = 0.915382730282799;
   fWeightMatrix1to2[4][10] = -1.93325017884947;
   fWeightMatrix1to2[5][10] = -0.506849661166192;
   fWeightMatrix1to2[6][10] = -2.24033648743769;
   fWeightMatrix1to2[7][10] = -1.65688112966872;
   fWeightMatrix1to2[8][10] = -0.868592600212646;
   fWeightMatrix1to2[9][10] = 0.346169815978516;
   fWeightMatrix1to2[10][10] = -0.215867444779777;
   fWeightMatrix1to2[11][10] = 0.873204284419449;
   fWeightMatrix1to2[12][10] = -1.25120297397439;
   fWeightMatrix1to2[0][11] = -1.66042721534665;
   fWeightMatrix1to2[1][11] = -1.65313138700288;
   fWeightMatrix1to2[2][11] = 0.0233703268454236;
   fWeightMatrix1to2[3][11] = 1.40038560503042;
   fWeightMatrix1to2[4][11] = -1.5706679993803;
   fWeightMatrix1to2[5][11] = -0.702609382799724;
   fWeightMatrix1to2[6][11] = -1.63756385201199;
   fWeightMatrix1to2[7][11] = -0.25120682941356;
   fWeightMatrix1to2[8][11] = -0.752893424713007;
   fWeightMatrix1to2[9][11] = -0.637346002149919;
   fWeightMatrix1to2[10][11] = -1.73522075526524;
   fWeightMatrix1to2[11][11] = 0.74457028502025;
   fWeightMatrix1to2[12][11] = 0.249119535730737;
   fWeightMatrix1to2[0][12] = 0.577953343043415;
   fWeightMatrix1to2[1][12] = -1.08185527832529;
   fWeightMatrix1to2[2][12] = -0.992871574030252;
   fWeightMatrix1to2[3][12] = -1.71981037305956;
   fWeightMatrix1to2[4][12] = -1.7429331736726;
   fWeightMatrix1to2[5][12] = -0.0664025462728371;
   fWeightMatrix1to2[6][12] = -0.748900354066165;
   fWeightMatrix1to2[7][12] = -1.00046110245023;
   fWeightMatrix1to2[8][12] = -0.339871009158598;
   fWeightMatrix1to2[9][12] = -2.47384697335482;
   fWeightMatrix1to2[10][12] = -1.49494741520652;
   fWeightMatrix1to2[11][12] = -1.84572191950557;
   fWeightMatrix1to2[12][12] = -0.685823784425245;
   fWeightMatrix1to2[0][13] = -4.47290102301002;
   fWeightMatrix1to2[1][13] = -2.14844113772656;
   fWeightMatrix1to2[2][13] = -0.510859299292962;
   fWeightMatrix1to2[3][13] = -0.939115570806873;
   fWeightMatrix1to2[4][13] = -1.6268600139809;
   fWeightMatrix1to2[5][13] = 0.498390561413771;
   fWeightMatrix1to2[6][13] = -1.44701259748806;
   fWeightMatrix1to2[7][13] = -1.28198273591593;
   fWeightMatrix1to2[8][13] = 1.64757957526507;
   fWeightMatrix1to2[9][13] = -0.225160244090755;
   fWeightMatrix1to2[10][13] = 0.347387367077968;
   fWeightMatrix1to2[11][13] = -0.0945569280172272;
   fWeightMatrix1to2[12][13] = 0.714945071138509;
   fWeightMatrix1to2[0][14] = 1.91973476879886;
   fWeightMatrix1to2[1][14] = -0.513341395074803;
   fWeightMatrix1to2[2][14] = -1.04036736895177;
   fWeightMatrix1to2[3][14] = -1.07080920870108;
   fWeightMatrix1to2[4][14] = -1.74699069550532;
   fWeightMatrix1to2[5][14] = -0.292402151143841;
   fWeightMatrix1to2[6][14] = 0.432532255438713;
   fWeightMatrix1to2[7][14] = 0.169653722703992;
   fWeightMatrix1to2[8][14] = 0.119092927660091;
   fWeightMatrix1to2[9][14] = 0.0886225279569349;
   fWeightMatrix1to2[10][14] = 1.0493149105878;
   fWeightMatrix1to2[11][14] = -1.58112647271433;
   fWeightMatrix1to2[12][14] = -0.123873008307847;
   // weight matrix from layer 2 to 3
   fWeightMatrix2to3[0][0] = 0.766812939202826;
   fWeightMatrix2to3[0][1] = -0.316134910255038;
   fWeightMatrix2to3[0][2] = -0.878253090378915;
   fWeightMatrix2to3[0][3] = -1.38366547375587;
   fWeightMatrix2to3[0][4] = 0.902280508351926;
   fWeightMatrix2to3[0][5] = -0.380034864847104;
   fWeightMatrix2to3[0][6] = 0.404126347400581;
   fWeightMatrix2to3[0][7] = 1.82955481261194;
   fWeightMatrix2to3[0][8] = 0.867458675933583;
   fWeightMatrix2to3[0][9] = 0.931344783635016;
   fWeightMatrix2to3[0][10] = 1.12923091921339;
   fWeightMatrix2to3[0][11] = -0.712534134880202;
   fWeightMatrix2to3[0][12] = -0.922179729327231;
   fWeightMatrix2to3[0][13] = 0.206085985143468;
}

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
}