// 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 10:33:58 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/job416
Training events: 29926


#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'    [49.7499961853,711.081665039]
LepAPt                        LepAPt                            'F'    [20.0021438599,189.268966675]
LepBPt                        LepBPt                            'F'    [10.0008592606,74.385925293]
MetSigLeptonsJets             MetSigLeptonsJets                 'F'    [0.951345682144,19.6063156128]
MetSpec                       MetSpec                           'F'    [15.0248270035,290.76965332]
SumEtLeptonsJets              SumEtLeptonsJets                  'F'    [30.2123203278,440.607177734]
VSumJetLeptonsPt              VSumJetLeptonsPt                  'F'    [0.888904690742,300.372680664]
addEt                         addEt                             'F'    [48.2450904846,354.966430664]
dPhiLepSumMet                 dPhiLepSumMet                     'F'    [0.160927712917,3.14158320427]
dPhiLeptons                   dPhiLeptons                       'F'    [2.93254852295e-05,1.04818320274]
dRLeptons                     dRLeptons                         'F'    [0.200063899159,1.09498274326]
lep1_E                        lep1_E                            'F'    [20.0025482178,202.459274292]
lep2_E                        lep2_E                            'F'    [10.0034532547,115.518630981]


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

#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] = 49.7499961853027;
      fVmax[0] = 711.081665039062;
      fVmin[1] = 20.0021438598633;
      fVmax[1] = 189.268966674805;
      fVmin[2] = 10.0008592605591;
      fVmax[2] = 74.3859252929688;
      fVmin[3] = 0.951345682144165;
      fVmax[3] = 19.606315612793;
      fVmin[4] = 15.024827003479;
      fVmax[4] = 290.769653320312;
      fVmin[5] = 30.2123203277588;
      fVmax[5] = 440.607177734375;
      fVmin[6] = 0.888904690742493;
      fVmax[6] = 300.372680664062;
      fVmin[7] = 48.2450904846191;
      fVmax[7] = 354.966430664062;
      fVmin[8] = 0.160927712917328;
      fVmax[8] = 3.14158320426941;
      fVmin[9] = 2.93254852294922e-05;
      fVmax[9] = 1.04818320274353;
      fVmin[10] = 0.200063899159431;
      fVmax[10] = 1.09498274326324;
      fVmin[11] = 20.0025482177734;
      fVmax[11] = 202.459274291992;
      fVmin[12] = 10.0034532546997;
      fVmax[12] = 115.518630981445;

      // 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] = -0.243924447852646;
   fWeightMatrix0to1[1][0] = 2.28160033328484;
   fWeightMatrix0to1[2][0] = 1.01637933246159;
   fWeightMatrix0to1[3][0] = 1.86663133595625;
   fWeightMatrix0to1[4][0] = -1.84386849604761;
   fWeightMatrix0to1[5][0] = -1.63185973695249;
   fWeightMatrix0to1[6][0] = -1.0312156629348;
   fWeightMatrix0to1[7][0] = 2.01869762840658;
   fWeightMatrix0to1[8][0] = -1.28681844774521;
   fWeightMatrix0to1[9][0] = -0.339566031246458;
   fWeightMatrix0to1[10][0] = -1.18151571657004;
   fWeightMatrix0to1[11][0] = -0.189498095614139;
   fWeightMatrix0to1[12][0] = -1.67876661284167;
   fWeightMatrix0to1[13][0] = -0.778149457835293;
   fWeightMatrix0to1[0][1] = -0.587833675200515;
   fWeightMatrix0to1[1][1] = 0.783734155327491;
   fWeightMatrix0to1[2][1] = -0.293301399940047;
   fWeightMatrix0to1[3][1] = 1.86357838240848;
   fWeightMatrix0to1[4][1] = -0.127250531928449;
   fWeightMatrix0to1[5][1] = 1.32611872475849;
   fWeightMatrix0to1[6][1] = -0.636394728047283;
   fWeightMatrix0to1[7][1] = -0.457810124236711;
   fWeightMatrix0to1[8][1] = 0.845744353165209;
   fWeightMatrix0to1[9][1] = 0.702185100510655;
   fWeightMatrix0to1[10][1] = -0.0970896881149856;
   fWeightMatrix0to1[11][1] = -0.031935125190339;
   fWeightMatrix0to1[12][1] = 1.82611213913231;
   fWeightMatrix0to1[13][1] = -1.46177357622925;
   fWeightMatrix0to1[0][2] = -1.84985580092606;
   fWeightMatrix0to1[1][2] = 0.788932511388185;
   fWeightMatrix0to1[2][2] = 0.61997014608447;
   fWeightMatrix0to1[3][2] = 1.69860264473055;
   fWeightMatrix0to1[4][2] = -0.18509073659769;
   fWeightMatrix0to1[5][2] = -0.386007651877983;
   fWeightMatrix0to1[6][2] = -3.00517858323584;
   fWeightMatrix0to1[7][2] = 0.659546706808658;
   fWeightMatrix0to1[8][2] = -1.45177620019804;
   fWeightMatrix0to1[9][2] = 1.93043119298598;
   fWeightMatrix0to1[10][2] = -1.63308616275503;
   fWeightMatrix0to1[11][2] = 1.26960630138034;
   fWeightMatrix0to1[12][2] = -0.420854485948467;
   fWeightMatrix0to1[13][2] = 0.860805039827536;
   fWeightMatrix0to1[0][3] = 1.93481123425534;
   fWeightMatrix0to1[1][3] = 1.40073557239993;
   fWeightMatrix0to1[2][3] = -0.690828625433421;
   fWeightMatrix0to1[3][3] = -1.53607724729392;
   fWeightMatrix0to1[4][3] = 1.00077265815864;
   fWeightMatrix0to1[5][3] = 0.400766507578748;
   fWeightMatrix0to1[6][3] = 4.68624211677187;
   fWeightMatrix0to1[7][3] = 2.18947477995142;
   fWeightMatrix0to1[8][3] = 1.65289886411961;
   fWeightMatrix0to1[9][3] = 2.2987486279253;
   fWeightMatrix0to1[10][3] = -1.21063984648083;
   fWeightMatrix0to1[11][3] = 1.64268602769114;
   fWeightMatrix0to1[12][3] = -3.53865539842002;
   fWeightMatrix0to1[13][3] = -1.13238146696387;
   fWeightMatrix0to1[0][4] = -1.25067109770457;
   fWeightMatrix0to1[1][4] = -1.61847640530405;
   fWeightMatrix0to1[2][4] = 1.64555337310755;
   fWeightMatrix0to1[3][4] = 0.886695350592254;
   fWeightMatrix0to1[4][4] = -1.44120894334861;
   fWeightMatrix0to1[5][4] = 0.578349014691624;
   fWeightMatrix0to1[6][4] = 1.05075608888394;
   fWeightMatrix0to1[7][4] = 0.699408688914702;
   fWeightMatrix0to1[8][4] = 1.96071379896492;
   fWeightMatrix0to1[9][4] = 0.313238572899438;
   fWeightMatrix0to1[10][4] = 0.760247475558394;
   fWeightMatrix0to1[11][4] = -0.839697217693266;
   fWeightMatrix0to1[12][4] = 0.338587867715091;
   fWeightMatrix0to1[13][4] = -1.26779208122167;
   fWeightMatrix0to1[0][5] = -0.650195281953911;
   fWeightMatrix0to1[1][5] = -1.23351561641018;
   fWeightMatrix0to1[2][5] = 0.887355423094652;
   fWeightMatrix0to1[3][5] = 1.26087219017021;
   fWeightMatrix0to1[4][5] = 1.57739476619659;
   fWeightMatrix0to1[5][5] = -0.569385952421056;
   fWeightMatrix0to1[6][5] = -2.51076679125427;
   fWeightMatrix0to1[7][5] = -2.47084319933539;
   fWeightMatrix0to1[8][5] = 1.11899083517664;
   fWeightMatrix0to1[9][5] = 0.928704814628416;
   fWeightMatrix0to1[10][5] = 2.27643055430444;
   fWeightMatrix0to1[11][5] = 0.77776978707168;
   fWeightMatrix0to1[12][5] = 1.16298946412707;
   fWeightMatrix0to1[13][5] = -1.13056245489102;
   fWeightMatrix0to1[0][6] = -0.815183930899835;
   fWeightMatrix0to1[1][6] = 0.112675476702737;
   fWeightMatrix0to1[2][6] = 1.27353157731099;
   fWeightMatrix0to1[3][6] = -0.967615126170451;
   fWeightMatrix0to1[4][6] = -1.54783318233055;
   fWeightMatrix0to1[5][6] = -1.76695198361787;
   fWeightMatrix0to1[6][6] = -1.47224362785832;
   fWeightMatrix0to1[7][6] = 0.892794240292222;
   fWeightMatrix0to1[8][6] = 1.76618338788923;
   fWeightMatrix0to1[9][6] = -0.727707796967364;
   fWeightMatrix0to1[10][6] = -0.0302257632133804;
   fWeightMatrix0to1[11][6] = 2.01099000584848;
   fWeightMatrix0to1[12][6] = -1.60453459774906;
   fWeightMatrix0to1[13][6] = -1.83008847714402;
   fWeightMatrix0to1[0][7] = -1.40092711306166;
   fWeightMatrix0to1[1][7] = 1.60517153543799;
   fWeightMatrix0to1[2][7] = -1.92585214896351;
   fWeightMatrix0to1[3][7] = -1.70721706385866;
   fWeightMatrix0to1[4][7] = 0.0660609404330079;
   fWeightMatrix0to1[5][7] = 1.08635567244665;
   fWeightMatrix0to1[6][7] = -6.32817807576524;
   fWeightMatrix0to1[7][7] = 6.59734172199718;
   fWeightMatrix0to1[8][7] = -0.865110905993968;
   fWeightMatrix0to1[9][7] = 1.58449112227415;
   fWeightMatrix0to1[10][7] = 1.01193127591297;
   fWeightMatrix0to1[11][7] = 0.575800592207619;
   fWeightMatrix0to1[12][7] = -6.81704730545695;
   fWeightMatrix0to1[13][7] = 0.449451055925819;
   fWeightMatrix0to1[0][8] = 0.205341670092366;
   fWeightMatrix0to1[1][8] = 0.523703585242847;
   fWeightMatrix0to1[2][8] = -1.69725195683309;
   fWeightMatrix0to1[3][8] = 1.43603157213776;
   fWeightMatrix0to1[4][8] = 0.443308332514654;
   fWeightMatrix0to1[5][8] = -1.22819892549451;
   fWeightMatrix0to1[6][8] = 1.41288937127963;
   fWeightMatrix0to1[7][8] = -0.0179676585706473;
   fWeightMatrix0to1[8][8] = -1.58303167239377;
   fWeightMatrix0to1[9][8] = 0.357915652883983;
   fWeightMatrix0to1[10][8] = -0.275392307888235;
   fWeightMatrix0to1[11][8] = 1.83416548227555;
   fWeightMatrix0to1[12][8] = 0.947887726122778;
   fWeightMatrix0to1[13][8] = 0.348595286622099;
   fWeightMatrix0to1[0][9] = -0.320308704769567;
   fWeightMatrix0to1[1][9] = 0.275726510447433;
   fWeightMatrix0to1[2][9] = -1.7366295401651;
   fWeightMatrix0to1[3][9] = 1.29633681282869;
   fWeightMatrix0to1[4][9] = 0.130399808677321;
   fWeightMatrix0to1[5][9] = -1.67670971263231;
   fWeightMatrix0to1[6][9] = 0.231369012837127;
   fWeightMatrix0to1[7][9] = -0.839827122724099;
   fWeightMatrix0to1[8][9] = 0.00833698749713488;
   fWeightMatrix0to1[9][9] = 0.335568357140095;
   fWeightMatrix0to1[10][9] = 1.65031021548865;
   fWeightMatrix0to1[11][9] = -1.43307526644773;
   fWeightMatrix0to1[12][9] = -0.867797539579012;
   fWeightMatrix0to1[13][9] = 1.39366480976003;
   fWeightMatrix0to1[0][10] = 1.63278311930413;
   fWeightMatrix0to1[1][10] = -0.318566354952273;
   fWeightMatrix0to1[2][10] = -1.29033997138273;
   fWeightMatrix0to1[3][10] = 0.118006228985594;
   fWeightMatrix0to1[4][10] = -0.906539106928623;
   fWeightMatrix0to1[5][10] = 1.47523125074097;
   fWeightMatrix0to1[6][10] = -1.24301030402138;
   fWeightMatrix0to1[7][10] = 0.801721424608507;
   fWeightMatrix0to1[8][10] = -0.433466153604142;
   fWeightMatrix0to1[9][10] = -0.4542866427595;
   fWeightMatrix0to1[10][10] = -0.729338037186283;
   fWeightMatrix0to1[11][10] = 1.01718698193432;
   fWeightMatrix0to1[12][10] = 0.909515570114262;
   fWeightMatrix0to1[13][10] = -0.594131525274601;
   fWeightMatrix0to1[0][11] = -0.58306217730421;
   fWeightMatrix0to1[1][11] = 0.229220706105523;
   fWeightMatrix0to1[2][11] = 1.12883816728362;
   fWeightMatrix0to1[3][11] = 0.765838304310492;
   fWeightMatrix0to1[4][11] = 1.2493947838481;
   fWeightMatrix0to1[5][11] = -1.48160944349388;
   fWeightMatrix0to1[6][11] = 1.87719709659965;
   fWeightMatrix0to1[7][11] = 0.0486152508520241;
   fWeightMatrix0to1[8][11] = 0.591148860924962;
   fWeightMatrix0to1[9][11] = 0.910871896600218;
   fWeightMatrix0to1[10][11] = 1.82524614931785;
   fWeightMatrix0to1[11][11] = -0.102731662469376;
   fWeightMatrix0to1[12][11] = 1.2340319140195;
   fWeightMatrix0to1[13][11] = 0.542704761875138;
   fWeightMatrix0to1[0][12] = -0.887979578946781;
   fWeightMatrix0to1[1][12] = -0.243493411742818;
   fWeightMatrix0to1[2][12] = 1.61307007555572;
   fWeightMatrix0to1[3][12] = 1.2527388807377;
   fWeightMatrix0to1[4][12] = 0.1551613672947;
   fWeightMatrix0to1[5][12] = 1.71998979599093;
   fWeightMatrix0to1[6][12] = 1.10792365303984;
   fWeightMatrix0to1[7][12] = -2.11888518646672;
   fWeightMatrix0to1[8][12] = 0.642391295692462;
   fWeightMatrix0to1[9][12] = -0.100738785967977;
   fWeightMatrix0to1[10][12] = 0.221845115025422;
   fWeightMatrix0to1[11][12] = -1.20563280833172;
   fWeightMatrix0to1[12][12] = -2.02354388442231;
   fWeightMatrix0to1[13][12] = 0.668990634909273;
   fWeightMatrix0to1[0][13] = 1.76670107505098;
   fWeightMatrix0to1[1][13] = -0.137640626627834;
   fWeightMatrix0to1[2][13] = -0.469696571163241;
   fWeightMatrix0to1[3][13] = -0.988454870363874;
   fWeightMatrix0to1[4][13] = -0.604214217483157;
   fWeightMatrix0to1[5][13] = 0.941542816079766;
   fWeightMatrix0to1[6][13] = -8.15944811317272;
   fWeightMatrix0to1[7][13] = 5.8724472373575;
   fWeightMatrix0to1[8][13] = -1.21497154621965;
   fWeightMatrix0to1[9][13] = 2.84277764011448;
   fWeightMatrix0to1[10][13] = 1.60881142582793;
   fWeightMatrix0to1[11][13] = 0.739410623057269;
   fWeightMatrix0to1[12][13] = -6.02984445782473;
   fWeightMatrix0to1[13][13] = 0.349385237380432;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = -3.10627560780416;
   fWeightMatrix1to2[1][0] = -0.166634458729502;
   fWeightMatrix1to2[2][0] = -0.091276766315153;
   fWeightMatrix1to2[3][0] = -1.63796678480323;
   fWeightMatrix1to2[4][0] = -0.694367071618321;
   fWeightMatrix1to2[5][0] = -0.655419392327571;
   fWeightMatrix1to2[6][0] = -0.533320224263592;
   fWeightMatrix1to2[7][0] = -2.30786340318138;
   fWeightMatrix1to2[8][0] = 0.732310172823305;
   fWeightMatrix1to2[9][0] = 1.40610795669379;
   fWeightMatrix1to2[10][0] = -1.25380291464578;
   fWeightMatrix1to2[11][0] = -0.325900182141926;
   fWeightMatrix1to2[12][0] = -0.0253156996022293;
   fWeightMatrix1to2[0][1] = 1.19454903038668;
   fWeightMatrix1to2[1][1] = 0.186629342529534;
   fWeightMatrix1to2[2][1] = -0.194302300073229;
   fWeightMatrix1to2[3][1] = -2.07187842377253;
   fWeightMatrix1to2[4][1] = -0.922841009464415;
   fWeightMatrix1to2[5][1] = 1.37355352085783;
   fWeightMatrix1to2[6][1] = 1.11986070177941;
   fWeightMatrix1to2[7][1] = 0.659440937689285;
   fWeightMatrix1to2[8][1] = 0.0894878542488058;
   fWeightMatrix1to2[9][1] = 2.79763373618991;
   fWeightMatrix1to2[10][1] = -1.80367892953002;
   fWeightMatrix1to2[11][1] = -1.2902710607335;
   fWeightMatrix1to2[12][1] = -0.315053811273184;
   fWeightMatrix1to2[0][2] = -1.50896798240165;
   fWeightMatrix1to2[1][2] = 0.841857779778997;
   fWeightMatrix1to2[2][2] = 1.67459916415689;
   fWeightMatrix1to2[3][2] = -1.28241122788787;
   fWeightMatrix1to2[4][2] = 0.785753602443922;
   fWeightMatrix1to2[5][2] = -0.096091970906831;
   fWeightMatrix1to2[6][2] = -1.78644906460329;
   fWeightMatrix1to2[7][2] = -1.11193891651445;
   fWeightMatrix1to2[8][2] = 0.963295272696027;
   fWeightMatrix1to2[9][2] = -0.106045689873081;
   fWeightMatrix1to2[10][2] = 0.876803024080962;
   fWeightMatrix1to2[11][2] = -1.15105368250964;
   fWeightMatrix1to2[12][2] = 1.62042667766698;
   fWeightMatrix1to2[0][3] = -1.74461624170666;
   fWeightMatrix1to2[1][3] = 0.682620479101955;
   fWeightMatrix1to2[2][3] = 0.0572696247893098;
   fWeightMatrix1to2[3][3] = -1.55422815745142;
   fWeightMatrix1to2[4][3] = -1.31026638086208;
   fWeightMatrix1to2[5][3] = -1.8742076755879;
   fWeightMatrix1to2[6][3] = -0.3811479230925;
   fWeightMatrix1to2[7][3] = -1.88661470179286;
   fWeightMatrix1to2[8][3] = -1.62311339911851;
   fWeightMatrix1to2[9][3] = -1.72015439528033;
   fWeightMatrix1to2[10][3] = 0.143591406028686;
   fWeightMatrix1to2[11][3] = -0.983133921516612;
   fWeightMatrix1to2[12][3] = -1.60844728415827;
   fWeightMatrix1to2[0][4] = 0.190786327418679;
   fWeightMatrix1to2[1][4] = -1.55119408202062;
   fWeightMatrix1to2[2][4] = -0.343132490945559;
   fWeightMatrix1to2[3][4] = -1.28078917256178;
   fWeightMatrix1to2[4][4] = 0.218658596363254;
   fWeightMatrix1to2[5][4] = 1.02826483707203;
   fWeightMatrix1to2[6][4] = 0.765421027637003;
   fWeightMatrix1to2[7][4] = -1.11726194071087;
   fWeightMatrix1to2[8][4] = -2.08320358064464;
   fWeightMatrix1to2[9][4] = -1.067993848029;
   fWeightMatrix1to2[10][4] = -1.45348107750568;
   fWeightMatrix1to2[11][4] = -1.51872427708187;
   fWeightMatrix1to2[12][4] = -0.896637170249612;
   fWeightMatrix1to2[0][5] = -0.707972988797471;
   fWeightMatrix1to2[1][5] = 0.660715066989334;
   fWeightMatrix1to2[2][5] = -0.828741309944517;
   fWeightMatrix1to2[3][5] = 0.012593893957648;
   fWeightMatrix1to2[4][5] = -0.522603463402475;
   fWeightMatrix1to2[5][5] = -2.89451644185691;
   fWeightMatrix1to2[6][5] = 0.794692572315666;
   fWeightMatrix1to2[7][5] = 0.904883601695916;
   fWeightMatrix1to2[8][5] = -1.61543751933231;
   fWeightMatrix1to2[9][5] = -0.859260905818097;
   fWeightMatrix1to2[10][5] = 0.312184836816919;
   fWeightMatrix1to2[11][5] = 0.962899232772109;
   fWeightMatrix1to2[12][5] = -1.10418419978026;
   fWeightMatrix1to2[0][6] = -1.57342820466169;
   fWeightMatrix1to2[1][6] = -0.14278369124706;
   fWeightMatrix1to2[2][6] = 1.18695199293427;
   fWeightMatrix1to2[3][6] = 1.49740979375705;
   fWeightMatrix1to2[4][6] = 1.02781838597874;
   fWeightMatrix1to2[5][6] = -2.05015712391698;
   fWeightMatrix1to2[6][6] = 0.246626484193078;
   fWeightMatrix1to2[7][6] = -0.202761573266097;
   fWeightMatrix1to2[8][6] = -2.06044444242267;
   fWeightMatrix1to2[9][6] = -5.05750785600888;
   fWeightMatrix1to2[10][6] = -2.06525846138573;
   fWeightMatrix1to2[11][6] = -1.62385686885905;
   fWeightMatrix1to2[12][6] = -2.20500818923813;
   fWeightMatrix1to2[0][7] = 1.61242196404721;
   fWeightMatrix1to2[1][7] = -2.96233026050562;
   fWeightMatrix1to2[2][7] = 1.4216088472479;
   fWeightMatrix1to2[3][7] = -2.31448053636407;
   fWeightMatrix1to2[4][7] = 1.13147991065701;
   fWeightMatrix1to2[5][7] = -0.782424536124387;
   fWeightMatrix1to2[6][7] = 0.174157202487603;
   fWeightMatrix1to2[7][7] = 0.384363028602759;
   fWeightMatrix1to2[8][7] = -2.148615188582;
   fWeightMatrix1to2[9][7] = 4.80375497315194;
   fWeightMatrix1to2[10][7] = -0.251631886546574;
   fWeightMatrix1to2[11][7] = -1.85704751646422;
   fWeightMatrix1to2[12][7] = -2.11427126537063;
   fWeightMatrix1to2[0][8] = -1.73365246778725;
   fWeightMatrix1to2[1][8] = -0.841904114129791;
   fWeightMatrix1to2[2][8] = 1.85387261670693;
   fWeightMatrix1to2[3][8] = -0.583473409913468;
   fWeightMatrix1to2[4][8] = 0.27003416856283;
   fWeightMatrix1to2[5][8] = -0.917787458502261;
   fWeightMatrix1to2[6][8] = -1.21440113238258;
   fWeightMatrix1to2[7][8] = -1.74568143348922;
   fWeightMatrix1to2[8][8] = -0.998123245540387;
   fWeightMatrix1to2[9][8] = -1.25726295988592;
   fWeightMatrix1to2[10][8] = 0.81516923106252;
   fWeightMatrix1to2[11][8] = -1.36309213808659;
   fWeightMatrix1to2[12][8] = -1.21909448964456;
   fWeightMatrix1to2[0][9] = 1.89199724946487;
   fWeightMatrix1to2[1][9] = -0.836402389587974;
   fWeightMatrix1to2[2][9] = -0.720680775657927;
   fWeightMatrix1to2[3][9] = 1.32653237919382;
   fWeightMatrix1to2[4][9] = -1.7621634171376;
   fWeightMatrix1to2[5][9] = 1.55813657809999;
   fWeightMatrix1to2[6][9] = 1.12354661906439;
   fWeightMatrix1to2[7][9] = -0.948529350574689;
   fWeightMatrix1to2[8][9] = -1.26419244230579;
   fWeightMatrix1to2[9][9] = 2.56198960371361;
   fWeightMatrix1to2[10][9] = -0.761664454689076;
   fWeightMatrix1to2[11][9] = 0.815755343252925;
   fWeightMatrix1to2[12][9] = 1.16772640105067;
   fWeightMatrix1to2[0][10] = 0.127294744655809;
   fWeightMatrix1to2[1][10] = 0.721324587599663;
   fWeightMatrix1to2[2][10] = -1.75196923194707;
   fWeightMatrix1to2[3][10] = 1.07200533199872;
   fWeightMatrix1to2[4][10] = -1.93727254308726;
   fWeightMatrix1to2[5][10] = -0.103466939947555;
   fWeightMatrix1to2[6][10] = -2.22092492891763;
   fWeightMatrix1to2[7][10] = -1.9809194896259;
   fWeightMatrix1to2[8][10] = -0.719131860065193;
   fWeightMatrix1to2[9][10] = 1.31944300965691;
   fWeightMatrix1to2[10][10] = -0.00582646484595008;
   fWeightMatrix1to2[11][10] = 0.885284705385833;
   fWeightMatrix1to2[12][10] = -0.336226110274363;
   fWeightMatrix1to2[0][11] = -1.13489190110261;
   fWeightMatrix1to2[1][11] = -1.92912527895627;
   fWeightMatrix1to2[2][11] = -0.453546689722132;
   fWeightMatrix1to2[3][11] = 1.41077860979778;
   fWeightMatrix1to2[4][11] = -1.57320947808629;
   fWeightMatrix1to2[5][11] = -0.666355153097004;
   fWeightMatrix1to2[6][11] = -2.06776092169749;
   fWeightMatrix1to2[7][11] = -0.859799564950984;
   fWeightMatrix1to2[8][11] = -0.731732626993293;
   fWeightMatrix1to2[9][11] = 1.05069261173303;
   fWeightMatrix1to2[10][11] = -1.85883792949011;
   fWeightMatrix1to2[11][11] = 0.903462197366655;
   fWeightMatrix1to2[12][11] = 0.116452460482027;
   fWeightMatrix1to2[0][12] = -0.42299413031173;
   fWeightMatrix1to2[1][12] = -0.502722851506614;
   fWeightMatrix1to2[2][12] = -1.24608880732198;
   fWeightMatrix1to2[3][12] = -0.898950142217729;
   fWeightMatrix1to2[4][12] = -1.77046872279162;
   fWeightMatrix1to2[5][12] = -0.184556727841078;
   fWeightMatrix1to2[6][12] = -1.27920263937844;
   fWeightMatrix1to2[7][12] = -1.17435040890578;
   fWeightMatrix1to2[8][12] = 0.189738572066856;
   fWeightMatrix1to2[9][12] = -6.24134213873258;
   fWeightMatrix1to2[10][12] = -0.614742658213285;
   fWeightMatrix1to2[11][12] = -1.95915467351771;
   fWeightMatrix1to2[12][12] = 0.293332841646203;
   fWeightMatrix1to2[0][13] = -1.94570282952709;
   fWeightMatrix1to2[1][13] = -2.48768598272321;
   fWeightMatrix1to2[2][13] = -0.92945854721881;
   fWeightMatrix1to2[3][13] = -0.939979070649916;
   fWeightMatrix1to2[4][13] = -1.59807748799076;
   fWeightMatrix1to2[5][13] = 0.591013772166534;
   fWeightMatrix1to2[6][13] = -1.56518117050441;
   fWeightMatrix1to2[7][13] = -1.35318864963869;
   fWeightMatrix1to2[8][13] = 1.15744990077861;
   fWeightMatrix1to2[9][13] = 1.01655787507361;
   fWeightMatrix1to2[10][13] = 0.193802793037939;
   fWeightMatrix1to2[11][13] = 0.00358679203257185;
   fWeightMatrix1to2[12][13] = -0.310328333735411;
   fWeightMatrix1to2[0][14] = 0.594961286344004;
   fWeightMatrix1to2[1][14] = -0.779396964712319;
   fWeightMatrix1to2[2][14] = -1.5173939198773;
   fWeightMatrix1to2[3][14] = -0.959920219230902;
   fWeightMatrix1to2[4][14] = -1.73780773519672;
   fWeightMatrix1to2[5][14] = -0.141827945899992;
   fWeightMatrix1to2[6][14] = 0.241729601378008;
   fWeightMatrix1to2[7][14] = -0.270691889494659;
   fWeightMatrix1to2[8][14] = 0.26221802501706;
   fWeightMatrix1to2[9][14] = 1.46471594940908;
   fWeightMatrix1to2[10][14] = 0.876480000974422;
   fWeightMatrix1to2[11][14] = -1.55782248681099;
   fWeightMatrix1to2[12][14] = 0.0481827861142911;
   // weight matrix from layer 2 to 3
   fWeightMatrix2to3[0][0] = -0.436908905307327;
   fWeightMatrix2to3[0][1] = 0.611619716038239;
   fWeightMatrix2to3[0][2] = 0.32057647353563;
   fWeightMatrix2to3[0][3] = -0.804572091341853;
   fWeightMatrix2to3[0][4] = 0.913064282581505;
   fWeightMatrix2to3[0][5] = 0.498736961012823;
   fWeightMatrix2to3[0][6] = 0.645987605937985;
   fWeightMatrix2to3[0][7] = 1.57548381875926;
   fWeightMatrix2to3[0][8] = 0.631722616002673;
   fWeightMatrix2to3[0][9] = 0.824869930169458;
   fWeightMatrix2to3[0][10] = 0.137241420906595;
   fWeightMatrix2to3[0][11] = 0.510796874242758;
   fWeightMatrix2to3[0][12] = 0.404013473837409;
   fWeightMatrix2to3[0][13] = -0.0709615679918301;
}

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
}