// 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 10:12:57 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/job405
Training events: 48406


#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'    [47.0173530579,675.482849121]
LepAPt                        LepAPt                            'F'    [20.0003395081,158.143112183]
LepBPt                        LepBPt                            'F'    [10.000120163,74.1576004028]
MetSigLeptonsJets             MetSigLeptonsJets                 'F'    [1.07960414886,19.0481109619]
MetSpec                       MetSpec                           'F'    [15.0053844452,236.562576294]
SumEtLeptonsJets              SumEtLeptonsJets                  'F'    [30.1877574921,477.637084961]
VSumJetLeptonsPt              VSumJetLeptonsPt                  'F'    [0.331718683243,277.168395996]
addEt                         addEt                             'F'    [47.0173530579,338.202270508]
dPhiLepSumMet                 dPhiLepSumMet                     'F'    [0.00253169541247,3.14158678055]
dPhiLeptons                   dPhiLeptons                       'F'    [1.71661376953e-05,1.11657130718]
dRLeptons                     dRLeptons                         'F'    [0.200001657009,1.13453125954]
lep1_E                        lep1_E                            'F'    [20.0277633667,232.066116333]
lep2_E                        lep2_E                            'F'    [10.0093269348,120.71686554]


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

#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] = 47.0173530578613;
      fVmax[0] = 675.482849121094;
      fVmin[1] = 20.0003395080566;
      fVmax[1] = 158.143112182617;
      fVmin[2] = 10.0001201629639;
      fVmax[2] = 74.157600402832;
      fVmin[3] = 1.07960414886475;
      fVmax[3] = 19.0481109619141;
      fVmin[4] = 15.0053844451904;
      fVmax[4] = 236.562576293945;
      fVmin[5] = 30.1877574920654;
      fVmax[5] = 477.637084960938;
      fVmin[6] = 0.331718683242798;
      fVmax[6] = 277.168395996094;
      fVmin[7] = 47.0173530578613;
      fVmax[7] = 338.202270507812;
      fVmin[8] = 0.00253169541247189;
      fVmax[8] = 3.1415867805481;
      fVmin[9] = 1.71661376953125e-05;
      fVmax[9] = 1.11657130718231;
      fVmin[10] = 0.200001657009125;
      fVmax[10] = 1.13453125953674;
      fVmin[11] = 20.0277633666992;
      fVmax[11] = 232.066116333008;
      fVmin[12] = 10.0093269348145;
      fVmax[12] = 120.716865539551;

      // 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] = -0.223326174325915;
   fWeightMatrix0to1[1][0] = 2.28742854078502;
   fWeightMatrix0to1[2][0] = 0.833220095717885;
   fWeightMatrix0to1[3][0] = 1.80632287597199;
   fWeightMatrix0to1[4][0] = -1.84609251024766;
   fWeightMatrix0to1[5][0] = -1.51147364852346;
   fWeightMatrix0to1[6][0] = -0.481796107181601;
   fWeightMatrix0to1[7][0] = 1.7031708776737;
   fWeightMatrix0to1[8][0] = -1.49657189201827;
   fWeightMatrix0to1[9][0] = -0.462743191513273;
   fWeightMatrix0to1[10][0] = -1.06404105454872;
   fWeightMatrix0to1[11][0] = -0.128026434997106;
   fWeightMatrix0to1[12][0] = -0.529869767707378;
   fWeightMatrix0to1[13][0] = 0.0593283595574795;
   fWeightMatrix0to1[0][1] = -0.532826681128339;
   fWeightMatrix0to1[1][1] = 0.916484999953393;
   fWeightMatrix0to1[2][1] = -0.297206744304559;
   fWeightMatrix0to1[3][1] = 4.30412078193576;
   fWeightMatrix0to1[4][1] = -0.321834382492774;
   fWeightMatrix0to1[5][1] = 4.64309974783282;
   fWeightMatrix0to1[6][1] = -2.03468155081897;
   fWeightMatrix0to1[7][1] = 2.88488502936865;
   fWeightMatrix0to1[8][1] = 1.06855696614007;
   fWeightMatrix0to1[9][1] = 0.719841746603109;
   fWeightMatrix0to1[10][1] = -1.17006427504566;
   fWeightMatrix0to1[11][1] = 1.62713034724437;
   fWeightMatrix0to1[12][1] = 0.830487345783174;
   fWeightMatrix0to1[13][1] = -2.7814194619497;
   fWeightMatrix0to1[0][2] = -1.7141651099226;
   fWeightMatrix0to1[1][2] = 0.693248854457947;
   fWeightMatrix0to1[2][2] = -0.318529665721541;
   fWeightMatrix0to1[3][2] = 2.44992461135908;
   fWeightMatrix0to1[4][2] = -0.287152031358538;
   fWeightMatrix0to1[5][2] = 1.37076018399509;
   fWeightMatrix0to1[6][2] = -1.99288058827807;
   fWeightMatrix0to1[7][2] = 4.91081388966338;
   fWeightMatrix0to1[8][2] = -1.80849853679464;
   fWeightMatrix0to1[9][2] = 2.25994072674284;
   fWeightMatrix0to1[10][2] = -2.42953864948177;
   fWeightMatrix0to1[11][2] = 1.36597949069716;
   fWeightMatrix0to1[12][2] = 1.33744189071572;
   fWeightMatrix0to1[13][2] = 1.71906417617561;
   fWeightMatrix0to1[0][3] = 1.50896493323194;
   fWeightMatrix0to1[1][3] = 1.34242368130234;
   fWeightMatrix0to1[2][3] = -1.1679569632915;
   fWeightMatrix0to1[3][3] = -2.49023279885196;
   fWeightMatrix0to1[4][3] = 0.613594105590465;
   fWeightMatrix0to1[5][3] = -0.332620568123878;
   fWeightMatrix0to1[6][3] = 4.17025862150697;
   fWeightMatrix0to1[7][3] = 1.45779309030346;
   fWeightMatrix0to1[8][3] = 2.02628634216497;
   fWeightMatrix0to1[9][3] = 1.60695622280662;
   fWeightMatrix0to1[10][3] = -1.30414951260509;
   fWeightMatrix0to1[11][3] = 2.10178228417142;
   fWeightMatrix0to1[12][3] = -1.47561588144273;
   fWeightMatrix0to1[13][3] = 0.195305548884981;
   fWeightMatrix0to1[0][4] = -1.25419535959596;
   fWeightMatrix0to1[1][4] = -1.46716896452453;
   fWeightMatrix0to1[2][4] = 1.9279976422391;
   fWeightMatrix0to1[3][4] = 0.412557398933858;
   fWeightMatrix0to1[4][4] = -1.43775247335128;
   fWeightMatrix0to1[5][4] = 0.160707189113255;
   fWeightMatrix0to1[6][4] = 1.4359610869747;
   fWeightMatrix0to1[7][4] = 0.737017911723547;
   fWeightMatrix0to1[8][4] = 2.51728851838086;
   fWeightMatrix0to1[9][4] = 0.163923657459008;
   fWeightMatrix0to1[10][4] = 0.285214966956004;
   fWeightMatrix0to1[11][4] = 0.0888785995081846;
   fWeightMatrix0to1[12][4] = 1.57845979845458;
   fWeightMatrix0to1[13][4] = -0.560579756828133;
   fWeightMatrix0to1[0][5] = -0.358727958369389;
   fWeightMatrix0to1[1][5] = -1.04824626041763;
   fWeightMatrix0to1[2][5] = 0.771027051813969;
   fWeightMatrix0to1[3][5] = 0.82353764020369;
   fWeightMatrix0to1[4][5] = 1.71648788808357;
   fWeightMatrix0to1[5][5] = -0.980709317116097;
   fWeightMatrix0to1[6][5] = -1.28623395211929;
   fWeightMatrix0to1[7][5] = -4.74299106540909;
   fWeightMatrix0to1[8][5] = 0.512360362124204;
   fWeightMatrix0to1[9][5] = 1.15638856878163;
   fWeightMatrix0to1[10][5] = 2.73670880832258;
   fWeightMatrix0to1[11][5] = 0.655059712535751;
   fWeightMatrix0to1[12][5] = 1.82127383597559;
   fWeightMatrix0to1[13][5] = -0.324141901722854;
   fWeightMatrix0to1[0][6] = -0.955292596688066;
   fWeightMatrix0to1[1][6] = -0.00454503027228938;
   fWeightMatrix0to1[2][6] = 0.969758804593241;
   fWeightMatrix0to1[3][6] = -1.69857722144061;
   fWeightMatrix0to1[4][6] = -1.77002004116871;
   fWeightMatrix0to1[5][6] = -0.46931051501889;
   fWeightMatrix0to1[6][6] = -0.566564388145146;
   fWeightMatrix0to1[7][6] = 2.96556099641364;
   fWeightMatrix0to1[8][6] = 1.94666949951638;
   fWeightMatrix0to1[9][6] = -1.14956039683353;
   fWeightMatrix0to1[10][6] = -0.712226977886588;
   fWeightMatrix0to1[11][6] = 2.30145029774179;
   fWeightMatrix0to1[12][6] = -0.746398179445279;
   fWeightMatrix0to1[13][6] = -1.83436982480844;
   fWeightMatrix0to1[0][7] = -2.03316860069382;
   fWeightMatrix0to1[1][7] = 0.186260931145462;
   fWeightMatrix0to1[2][7] = -1.91467017648189;
   fWeightMatrix0to1[3][7] = 1.39940108633346;
   fWeightMatrix0to1[4][7] = -0.00494461463087719;
   fWeightMatrix0to1[5][7] = 5.4614409494006;
   fWeightMatrix0to1[6][7] = -4.18770743337352;
   fWeightMatrix0to1[7][7] = 12.7492223945247;
   fWeightMatrix0to1[8][7] = 0.12238664218337;
   fWeightMatrix0to1[9][7] = -0.402709707850633;
   fWeightMatrix0to1[10][7] = -0.859348679688349;
   fWeightMatrix0to1[11][7] = 1.90576873775147;
   fWeightMatrix0to1[12][7] = -2.00481099679942;
   fWeightMatrix0to1[13][7] = -0.295829810608332;
   fWeightMatrix0to1[0][8] = -0.406063208983256;
   fWeightMatrix0to1[1][8] = 0.118236432468035;
   fWeightMatrix0to1[2][8] = -0.984368500242921;
   fWeightMatrix0to1[3][8] = 1.77391934149652;
   fWeightMatrix0to1[4][8] = -0.51919779249033;
   fWeightMatrix0to1[5][8] = -1.82927614365354;
   fWeightMatrix0to1[6][8] = 3.59845220461895;
   fWeightMatrix0to1[7][8] = -1.1754207796141;
   fWeightMatrix0to1[8][8] = -0.0785764639596749;
   fWeightMatrix0to1[9][8] = -0.459217933495395;
   fWeightMatrix0to1[10][8] = -1.60695178241064;
   fWeightMatrix0to1[11][8] = 3.09850351991718;
   fWeightMatrix0to1[12][8] = -0.475867994913432;
   fWeightMatrix0to1[13][8] = -0.0805078724625748;
   fWeightMatrix0to1[0][9] = 0.775766575323362;
   fWeightMatrix0to1[1][9] = 0.286777944606686;
   fWeightMatrix0to1[2][9] = -1.783561102736;
   fWeightMatrix0to1[3][9] = 1.79332868585475;
   fWeightMatrix0to1[4][9] = 0.319768155372617;
   fWeightMatrix0to1[5][9] = -0.468826619911866;
   fWeightMatrix0to1[6][9] = 0.48186548884604;
   fWeightMatrix0to1[7][9] = -0.317302364680625;
   fWeightMatrix0to1[8][9] = 1.90230583343687;
   fWeightMatrix0to1[9][9] = 1.418895157565;
   fWeightMatrix0to1[10][9] = 2.35935230533014;
   fWeightMatrix0to1[11][9] = -1.09362658202221;
   fWeightMatrix0to1[12][9] = 0.0452939445364956;
   fWeightMatrix0to1[13][9] = 1.51509557777551;
   fWeightMatrix0to1[0][10] = 2.29390709655856;
   fWeightMatrix0to1[1][10] = 0.431503161438267;
   fWeightMatrix0to1[2][10] = -3.15698664721011;
   fWeightMatrix0to1[3][10] = -1.21407429600356;
   fWeightMatrix0to1[4][10] = -1.02234555393757;
   fWeightMatrix0to1[5][10] = 0.796907353676281;
   fWeightMatrix0to1[6][10] = 0.119695592196465;
   fWeightMatrix0to1[7][10] = 0.952205028180473;
   fWeightMatrix0to1[8][10] = 0.660966554365786;
   fWeightMatrix0to1[9][10] = 0.346273926610391;
   fWeightMatrix0to1[10][10] = -0.424804214564287;
   fWeightMatrix0to1[11][10] = 1.38905351910167;
   fWeightMatrix0to1[12][10] = 1.92352036665979;
   fWeightMatrix0to1[13][10] = -0.86960926888419;
   fWeightMatrix0to1[0][11] = -0.301387088325699;
   fWeightMatrix0to1[1][11] = 0.612561321448001;
   fWeightMatrix0to1[2][11] = 1.79231549549969;
   fWeightMatrix0to1[3][11] = -0.46383544403113;
   fWeightMatrix0to1[4][11] = 0.66699636455172;
   fWeightMatrix0to1[5][11] = -2.45346714987544;
   fWeightMatrix0to1[6][11] = 2.40402537109385;
   fWeightMatrix0to1[7][11] = -1.50774609546635;
   fWeightMatrix0to1[8][11] = -0.140490009319912;
   fWeightMatrix0to1[9][11] = 1.4882625343316;
   fWeightMatrix0to1[10][11] = 1.51792449542162;
   fWeightMatrix0to1[11][11] = 0.518779939725334;
   fWeightMatrix0to1[12][11] = -1.66431203432361;
   fWeightMatrix0to1[13][11] = -0.317644111204933;
   fWeightMatrix0to1[0][12] = -0.363115841143166;
   fWeightMatrix0to1[1][12] = -0.0092856087531325;
   fWeightMatrix0to1[2][12] = 1.62681951936202;
   fWeightMatrix0to1[3][12] = -1.34211384210446;
   fWeightMatrix0to1[4][12] = -0.213720598982189;
   fWeightMatrix0to1[5][12] = 1.63434737087976;
   fWeightMatrix0to1[6][12] = 1.93558553778036;
   fWeightMatrix0to1[7][12] = -1.41812917124546;
   fWeightMatrix0to1[8][12] = -0.408963730618401;
   fWeightMatrix0to1[9][12] = 0.837144723583469;
   fWeightMatrix0to1[10][12] = -0.0322423890626342;
   fWeightMatrix0to1[11][12] = -0.520499886796804;
   fWeightMatrix0to1[12][12] = -1.98315502013263;
   fWeightMatrix0to1[13][12] = 0.625644102276537;
   fWeightMatrix0to1[0][13] = 0.89649361822784;
   fWeightMatrix0to1[1][13] = -2.39025987095516;
   fWeightMatrix0to1[2][13] = 0.801187720137079;
   fWeightMatrix0to1[3][13] = 2.70367850615473;
   fWeightMatrix0to1[4][13] = -0.492227530093298;
   fWeightMatrix0to1[5][13] = 4.91221538492955;
   fWeightMatrix0to1[6][13] = -4.85374413566624;
   fWeightMatrix0to1[7][13] = 13.6367956372583;
   fWeightMatrix0to1[8][13] = 0.269790083403416;
   fWeightMatrix0to1[9][13] = 0.196975991130768;
   fWeightMatrix0to1[10][13] = -0.241468850898994;
   fWeightMatrix0to1[11][13] = 1.20082859864448;
   fWeightMatrix0to1[12][13] = -0.390815436876752;
   fWeightMatrix0to1[13][13] = -1.73639551778498;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = -2.79767935010148;
   fWeightMatrix1to2[1][0] = 0.198598152613599;
   fWeightMatrix1to2[2][0] = -0.839310567331374;
   fWeightMatrix1to2[3][0] = -2.41655129126527;
   fWeightMatrix1to2[4][0] = -0.785337957915004;
   fWeightMatrix1to2[5][0] = -0.284102330591968;
   fWeightMatrix1to2[6][0] = -0.930218495563177;
   fWeightMatrix1to2[7][0] = -2.05118978594129;
   fWeightMatrix1to2[8][0] = 0.116610354071821;
   fWeightMatrix1to2[9][0] = 0.0784551718737897;
   fWeightMatrix1to2[10][0] = -2.04627519279091;
   fWeightMatrix1to2[11][0] = -0.220395412327783;
   fWeightMatrix1to2[12][0] = -0.38449003554811;
   fWeightMatrix1to2[0][1] = 1.04147068562696;
   fWeightMatrix1to2[1][1] = 0.278071718233304;
   fWeightMatrix1to2[2][1] = -0.223988222871651;
   fWeightMatrix1to2[3][1] = -2.19931836504218;
   fWeightMatrix1to2[4][1] = -0.922986679196998;
   fWeightMatrix1to2[5][1] = 1.14258539510047;
   fWeightMatrix1to2[6][1] = 1.28458844300667;
   fWeightMatrix1to2[7][1] = 0.750776700205342;
   fWeightMatrix1to2[8][1] = 0.0338100966595737;
   fWeightMatrix1to2[9][1] = 2.00701879290086;
   fWeightMatrix1to2[10][1] = -1.49939084954803;
   fWeightMatrix1to2[11][1] = -1.29905179862033;
   fWeightMatrix1to2[12][1] = -0.383186747680667;
   fWeightMatrix1to2[0][2] = -2.48241765895656;
   fWeightMatrix1to2[1][2] = 0.0861499439814454;
   fWeightMatrix1to2[2][2] = 1.3490021171966;
   fWeightMatrix1to2[3][2] = -1.27572962611768;
   fWeightMatrix1to2[4][2] = 1.19493656979659;
   fWeightMatrix1to2[5][2] = 2.59131178646392;
   fWeightMatrix1to2[6][2] = -1.85171205094105;
   fWeightMatrix1to2[7][2] = -1.22902157171892;
   fWeightMatrix1to2[8][2] = 0.62214794801425;
   fWeightMatrix1to2[9][2] = -0.63598839894873;
   fWeightMatrix1to2[10][2] = 0.338033257927143;
   fWeightMatrix1to2[11][2] = -1.16277852163207;
   fWeightMatrix1to2[12][2] = 1.36848168859001;
   fWeightMatrix1to2[0][3] = -1.23971788643401;
   fWeightMatrix1to2[1][3] = 0.485490336535873;
   fWeightMatrix1to2[2][3] = 1.94942951868379;
   fWeightMatrix1to2[3][3] = -2.04467269250976;
   fWeightMatrix1to2[4][3] = -2.04771883924968;
   fWeightMatrix1to2[5][3] = -3.2549151368402;
   fWeightMatrix1to2[6][3] = -0.472456036810413;
   fWeightMatrix1to2[7][3] = -2.08856764780248;
   fWeightMatrix1to2[8][3] = -1.71786484163265;
   fWeightMatrix1to2[9][3] = -1.3964768864128;
   fWeightMatrix1to2[10][3] = 1.9739364256655;
   fWeightMatrix1to2[11][3] = -0.981230764062923;
   fWeightMatrix1to2[12][3] = -1.79436811132568;
   fWeightMatrix1to2[0][4] = 1.14054105189115;
   fWeightMatrix1to2[1][4] = -1.50120540878702;
   fWeightMatrix1to2[2][4] = -1.25691789900225;
   fWeightMatrix1to2[3][4] = -1.50097658904816;
   fWeightMatrix1to2[4][4] = 0.442696217021724;
   fWeightMatrix1to2[5][4] = 1.16229127269743;
   fWeightMatrix1to2[6][4] = 0.37135069646294;
   fWeightMatrix1to2[7][4] = -0.858753146152104;
   fWeightMatrix1to2[8][4] = -2.8624644562344;
   fWeightMatrix1to2[9][4] = -1.57620078378941;
   fWeightMatrix1to2[10][4] = -1.77743426879767;
   fWeightMatrix1to2[11][4] = -1.74946835849515;
   fWeightMatrix1to2[12][4] = -1.58699796542755;
   fWeightMatrix1to2[0][5] = -0.444329648565698;
   fWeightMatrix1to2[1][5] = 0.805976383514058;
   fWeightMatrix1to2[2][5] = 2.6695305976287;
   fWeightMatrix1to2[3][5] = -0.635117630108887;
   fWeightMatrix1to2[4][5] = -0.662602913486518;
   fWeightMatrix1to2[5][5] = -5.22387152349261;
   fWeightMatrix1to2[6][5] = 0.556562902286743;
   fWeightMatrix1to2[7][5] = 0.71619170006161;
   fWeightMatrix1to2[8][5] = -1.47918145580142;
   fWeightMatrix1to2[9][5] = -1.28794407777567;
   fWeightMatrix1to2[10][5] = 0.477090445361026;
   fWeightMatrix1to2[11][5] = 1.15730191743743;
   fWeightMatrix1to2[12][5] = -0.226284006805237;
   fWeightMatrix1to2[0][6] = -0.854591398614513;
   fWeightMatrix1to2[1][6] = 0.109640686705328;
   fWeightMatrix1to2[2][6] = -0.285818950543093;
   fWeightMatrix1to2[3][6] = 1.88018678836037;
   fWeightMatrix1to2[4][6] = 1.10494400089551;
   fWeightMatrix1to2[5][6] = 3.02294965017787;
   fWeightMatrix1to2[6][6] = -0.568082987814947;
   fWeightMatrix1to2[7][6] = -0.275852165685124;
   fWeightMatrix1to2[8][6] = -1.54279905467922;
   fWeightMatrix1to2[9][6] = -1.41059649713974;
   fWeightMatrix1to2[10][6] = -2.53991025113167;
   fWeightMatrix1to2[11][6] = -1.61585562186407;
   fWeightMatrix1to2[12][6] = -1.69156892307247;
   fWeightMatrix1to2[0][7] = 2.15681148841411;
   fWeightMatrix1to2[1][7] = -3.56158287507854;
   fWeightMatrix1to2[2][7] = 2.01684920617466;
   fWeightMatrix1to2[3][7] = -3.30345727295307;
   fWeightMatrix1to2[4][7] = 1.15811898075532;
   fWeightMatrix1to2[5][7] = -8.31236619817708;
   fWeightMatrix1to2[6][7] = 0.195197943434322;
   fWeightMatrix1to2[7][7] = 0.632530681630284;
   fWeightMatrix1to2[8][7] = -2.76712560649348;
   fWeightMatrix1to2[9][7] = 0.648501396065684;
   fWeightMatrix1to2[10][7] = -0.060606662157841;
   fWeightMatrix1to2[11][7] = -1.86377225007063;
   fWeightMatrix1to2[12][7] = -3.93784731543368;
   fWeightMatrix1to2[0][8] = -1.95491018155464;
   fWeightMatrix1to2[1][8] = -0.63416944378585;
   fWeightMatrix1to2[2][8] = 2.13046366308703;
   fWeightMatrix1to2[3][8] = -0.672364971687449;
   fWeightMatrix1to2[4][8] = -0.0991555618838931;
   fWeightMatrix1to2[5][8] = -1.18814120018577;
   fWeightMatrix1to2[6][8] = -1.30725700488671;
   fWeightMatrix1to2[7][8] = -1.88788393660455;
   fWeightMatrix1to2[8][8] = -0.805606427086462;
   fWeightMatrix1to2[9][8] = -1.70152217463897;
   fWeightMatrix1to2[10][8] = 1.30962372166818;
   fWeightMatrix1to2[11][8] = -1.35473479740853;
   fWeightMatrix1to2[12][8] = -1.15582083899341;
   fWeightMatrix1to2[0][9] = 1.58584314910853;
   fWeightMatrix1to2[1][9] = 0.01710691260552;
   fWeightMatrix1to2[2][9] = -0.595522215088183;
   fWeightMatrix1to2[3][9] = 1.49566248521914;
   fWeightMatrix1to2[4][9] = -1.84536638913948;
   fWeightMatrix1to2[5][9] = 1.21065474444273;
   fWeightMatrix1to2[6][9] = 0.964273143234081;
   fWeightMatrix1to2[7][9] = -1.00691279125702;
   fWeightMatrix1to2[8][9] = -1.10278803121112;
   fWeightMatrix1to2[9][9] = -0.123269817672764;
   fWeightMatrix1to2[10][9] = -1.05377740543055;
   fWeightMatrix1to2[11][9] = 0.518465921484499;
   fWeightMatrix1to2[12][9] = 1.87071835411799;
   fWeightMatrix1to2[0][10] = 0.855910531500272;
   fWeightMatrix1to2[1][10] = 1.13145396589903;
   fWeightMatrix1to2[2][10] = -2.46628217161682;
   fWeightMatrix1to2[3][10] = 1.54716558102936;
   fWeightMatrix1to2[4][10] = -2.13420576537523;
   fWeightMatrix1to2[5][10] = -0.467183699488057;
   fWeightMatrix1to2[6][10] = -2.28699331305911;
   fWeightMatrix1to2[7][10] = -1.80677482083574;
   fWeightMatrix1to2[8][10] = -0.943344278107537;
   fWeightMatrix1to2[9][10] = 0.472781925909283;
   fWeightMatrix1to2[10][10] = 0.322606965661809;
   fWeightMatrix1to2[11][10] = 0.92233713255338;
   fWeightMatrix1to2[12][10] = -0.632220664797621;
   fWeightMatrix1to2[0][11] = -0.720861499355094;
   fWeightMatrix1to2[1][11] = -1.26072019007322;
   fWeightMatrix1to2[2][11] = 1.48702184458091;
   fWeightMatrix1to2[3][11] = 1.36925581482643;
   fWeightMatrix1to2[4][11] = -1.93521128813243;
   fWeightMatrix1to2[5][11] = -1.51348470777351;
   fWeightMatrix1to2[6][11] = -2.12038694139962;
   fWeightMatrix1to2[7][11] = -0.820175396924206;
   fWeightMatrix1to2[8][11] = -0.536233482947093;
   fWeightMatrix1to2[9][11] = -0.0453083191614878;
   fWeightMatrix1to2[10][11] = 0.346182270931998;
   fWeightMatrix1to2[11][11] = 0.889436893276093;
   fWeightMatrix1to2[12][11] = 0.666511420169445;
   fWeightMatrix1to2[0][12] = -1.10677062794328;
   fWeightMatrix1to2[1][12] = -0.700618109472243;
   fWeightMatrix1to2[2][12] = -2.5526725323695;
   fWeightMatrix1to2[3][12] = -2.14132221321128;
   fWeightMatrix1to2[4][12] = -2.10113153859107;
   fWeightMatrix1to2[5][12] = -1.0916530116138;
   fWeightMatrix1to2[6][12] = -1.39043858206376;
   fWeightMatrix1to2[7][12] = -1.16265687614228;
   fWeightMatrix1to2[8][12] = -0.724570276812454;
   fWeightMatrix1to2[9][12] = -2.34397627946324;
   fWeightMatrix1to2[10][12] = -1.97811826041045;
   fWeightMatrix1to2[11][12] = -1.74537316084703;
   fWeightMatrix1to2[12][12] = -0.922798337901103;
   fWeightMatrix1to2[0][13] = -0.90103983136439;
   fWeightMatrix1to2[1][13] = -2.60153121877786;
   fWeightMatrix1to2[2][13] = -2.84570589364801;
   fWeightMatrix1to2[3][13] = -1.07633818113715;
   fWeightMatrix1to2[4][13] = -1.89060079679535;
   fWeightMatrix1to2[5][13] = 0.264873646588341;
   fWeightMatrix1to2[6][13] = -1.85098403392461;
   fWeightMatrix1to2[7][13] = -0.886133738033973;
   fWeightMatrix1to2[8][13] = 0.661463414459084;
   fWeightMatrix1to2[9][13] = 0.338891762777818;
   fWeightMatrix1to2[10][13] = 0.302107573081142;
   fWeightMatrix1to2[11][13] = -0.0263971813983717;
   fWeightMatrix1to2[12][13] = -0.643602745241412;
   fWeightMatrix1to2[0][14] = 0.753287367033028;
   fWeightMatrix1to2[1][14] = -0.448045737621768;
   fWeightMatrix1to2[2][14] = -2.59510861983014;
   fWeightMatrix1to2[3][14] = -1.74886343946604;
   fWeightMatrix1to2[4][14] = -1.82009886542976;
   fWeightMatrix1to2[5][14] = 0.381409912290923;
   fWeightMatrix1to2[6][14] = -0.0991199218890959;
   fWeightMatrix1to2[7][14] = 0.012338486268858;
   fWeightMatrix1to2[8][14] = -0.345719407395779;
   fWeightMatrix1to2[9][14] = 0.191553077066982;
   fWeightMatrix1to2[10][14] = 0.755546881547668;
   fWeightMatrix1to2[11][14] = -1.4380032782942;
   fWeightMatrix1to2[12][14] = -0.281632629286099;
   // weight matrix from layer 2 to 3
   fWeightMatrix2to3[0][0] = 0.921052653395056;
   fWeightMatrix2to3[0][1] = -1.17370919896776;
   fWeightMatrix2to3[0][2] = -1.10464882299766;
   fWeightMatrix2to3[0][3] = 0.804606692005168;
   fWeightMatrix2to3[0][4] = 1.32260032337324;
   fWeightMatrix2to3[0][5] = -0.64693067038242;
   fWeightMatrix2to3[0][6] = 0.32983002158932;
   fWeightMatrix2to3[0][7] = 1.75810438947916;
   fWeightMatrix2to3[0][8] = -0.524154561668716;
   fWeightMatrix2to3[0][9] = 1.02009119708774;
   fWeightMatrix2to3[0][10] = 1.39281626228497;
   fWeightMatrix2to3[0][11] = 0.143413085190052;
   fWeightMatrix2to3[0][12] = -0.693368895060533;
   fWeightMatrix2to3[0][13] = 0.558378698933857;
}

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
}