// 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           : Tue Jan 10 17:29:18 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/run21068/job400
Training events: 1260


#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'    [60.671245575,377.676025391]
LepAPt                        LepAPt                            'F'    [20.0074310303,126.850402832]
LepBPt                        LepBPt                            'F'    [10.0047864914,54.2998809814]
MetSigLeptonsJets             MetSigLeptonsJets                 'F'    [0.0460740737617,12.663476944]
MetSpec                       MetSpec                           'F'    [0.000646304921247,155.952484131]
SumEtLeptonsJets              SumEtLeptonsJets                  'F'    [30.990530014,226.939117432]
VSumJetLeptonsPt              VSumJetLeptonsPt                  'F'    [0.726633906364,156.612548828]
addEt                         addEt                             'F'    [33.6726493835,237.179931641]
dPhiLepSumMet                 dPhiLepSumMet                     'F'    [0.0043980749324,3.14124560356]
dPhiLeptons                   dPhiLeptons                       'F'    [0.000980377197266,1.03099870682]
dRLeptons                     dRLeptons                         'F'    [0.200389400125,1.03571224213]
lep1_E                        lep1_E                            'F'    [20.6455631256,303.092254639]
lep2_E                        lep2_E                            'F'    [10.4138088226,131.389846802]


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

#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] = 60.6712455749512;
      fVmax[0] = 377.676025390625;
      fVmin[1] = 20.0074310302734;
      fVmax[1] = 126.850402832031;
      fVmin[2] = 10.004786491394;
      fVmax[2] = 54.2998809814453;
      fVmin[3] = 0.0460740737617016;
      fVmax[3] = 12.6634769439697;
      fVmin[4] = 0.000646304921247065;
      fVmax[4] = 155.952484130859;
      fVmin[5] = 30.9905300140381;
      fVmax[5] = 226.939117431641;
      fVmin[6] = 0.726633906364441;
      fVmax[6] = 156.612548828125;
      fVmin[7] = 33.6726493835449;
      fVmax[7] = 237.179931640625;
      fVmin[8] = 0.00439807493239641;
      fVmax[8] = 3.1412456035614;
      fVmin[9] = 0.000980377197265625;
      fVmax[9] = 1.03099870681763;
      fVmin[10] = 0.20038940012455;
      fVmax[10] = 1.03571224212646;
      fVmin[11] = 20.6455631256104;
      fVmax[11] = 303.092254638672;
      fVmin[12] = 10.4138088226318;
      fVmax[12] = 131.389846801758;

      // 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] = -0.286522366294792;
   fWeightMatrix0to1[1][0] = 1.88725540204571;
   fWeightMatrix0to1[2][0] = 1.03458813914293;
   fWeightMatrix0to1[3][0] = 1.75575092053297;
   fWeightMatrix0to1[4][0] = -1.52277940135079;
   fWeightMatrix0to1[5][0] = -1.73979170249005;
   fWeightMatrix0to1[6][0] = -1.09826026932613;
   fWeightMatrix0to1[7][0] = 2.63166964278042;
   fWeightMatrix0to1[8][0] = -1.21109954996247;
   fWeightMatrix0to1[9][0] = -0.704233819793322;
   fWeightMatrix0to1[10][0] = -1.64666351356841;
   fWeightMatrix0to1[11][0] = -0.573133919792165;
   fWeightMatrix0to1[12][0] = -1.61643756405462;
   fWeightMatrix0to1[13][0] = -0.115768814845877;
   fWeightMatrix0to1[0][1] = -0.613608908065789;
   fWeightMatrix0to1[1][1] = 0.526927012703438;
   fWeightMatrix0to1[2][1] = -0.400349841832598;
   fWeightMatrix0to1[3][1] = 1.72751933178592;
   fWeightMatrix0to1[4][1] = 0.254015679101072;
   fWeightMatrix0to1[5][1] = 1.3704863725679;
   fWeightMatrix0to1[6][1] = -0.526811735330286;
   fWeightMatrix0to1[7][1] = -0.695790289841607;
   fWeightMatrix0to1[8][1] = 0.406405689009652;
   fWeightMatrix0to1[9][1] = 0.411937772666198;
   fWeightMatrix0to1[10][1] = -0.799326757634946;
   fWeightMatrix0to1[11][1] = -0.439440582822004;
   fWeightMatrix0to1[12][1] = 1.49840539217291;
   fWeightMatrix0to1[13][1] = -1.41342051209975;
   fWeightMatrix0to1[0][2] = -1.92899290634003;
   fWeightMatrix0to1[1][2] = 0.240393970633132;
   fWeightMatrix0to1[2][2] = 0.749139054202634;
   fWeightMatrix0to1[3][2] = 1.64438951238075;
   fWeightMatrix0to1[4][2] = 0.011206174728277;
   fWeightMatrix0to1[5][2] = -0.558468364401088;
   fWeightMatrix0to1[6][2] = -0.345246690045118;
   fWeightMatrix0to1[7][2] = 0.493465756074663;
   fWeightMatrix0to1[8][2] = -1.43812202258306;
   fWeightMatrix0to1[9][2] = 2.16541736305644;
   fWeightMatrix0to1[10][2] = -1.14366484576979;
   fWeightMatrix0to1[11][2] = 0.887065256978124;
   fWeightMatrix0to1[12][2] = 0.599584593494266;
   fWeightMatrix0to1[13][2] = 2.02057006733763;
   fWeightMatrix0to1[0][3] = 2.07366961249606;
   fWeightMatrix0to1[1][3] = 1.43324929165644;
   fWeightMatrix0to1[2][3] = -0.607969072076741;
   fWeightMatrix0to1[3][3] = -1.6489886513003;
   fWeightMatrix0to1[4][3] = 0.667225918695308;
   fWeightMatrix0to1[5][3] = 0.0240166983352316;
   fWeightMatrix0to1[6][3] = 1.5609329760527;
   fWeightMatrix0to1[7][3] = 1.77965052595208;
   fWeightMatrix0to1[8][3] = 1.67960328186658;
   fWeightMatrix0to1[9][3] = 1.66796714891155;
   fWeightMatrix0to1[10][3] = -2.09064133510349;
   fWeightMatrix0to1[11][3] = 1.31198509004378;
   fWeightMatrix0to1[12][3] = -2.33553561756648;
   fWeightMatrix0to1[13][3] = -1.10093740020357;
   fWeightMatrix0to1[0][4] = -1.18912894270454;
   fWeightMatrix0to1[1][4] = -1.59622544544874;
   fWeightMatrix0to1[2][4] = 1.65937677816578;
   fWeightMatrix0to1[3][4] = 0.492791272182739;
   fWeightMatrix0to1[4][4] = -1.34838477236691;
   fWeightMatrix0to1[5][4] = 0.2835852255936;
   fWeightMatrix0to1[6][4] = -0.396953108113276;
   fWeightMatrix0to1[7][4] = 0.926697831664822;
   fWeightMatrix0to1[8][4] = 1.96474029008502;
   fWeightMatrix0to1[9][4] = 0.169299452419955;
   fWeightMatrix0to1[10][4] = -0.014343403014478;
   fWeightMatrix0to1[11][4] = -1.29357229589032;
   fWeightMatrix0to1[12][4] = 0.520348498873308;
   fWeightMatrix0to1[13][4] = -0.85778223935176;
   fWeightMatrix0to1[0][5] = -0.790649697704057;
   fWeightMatrix0to1[1][5] = -1.62061455786194;
   fWeightMatrix0to1[2][5] = 0.855469218414659;
   fWeightMatrix0to1[3][5] = 1.19438326790571;
   fWeightMatrix0to1[4][5] = 1.86899764389085;
   fWeightMatrix0to1[5][5] = -0.660630583381346;
   fWeightMatrix0to1[6][5] = -2.50682741688847;
   fWeightMatrix0to1[7][5] = -1.12288459336878;
   fWeightMatrix0to1[8][5] = 1.16846689203211;
   fWeightMatrix0to1[9][5] = 0.724079930039047;
   fWeightMatrix0to1[10][5] = 2.07947234503915;
   fWeightMatrix0to1[11][5] = 0.468670398453757;
   fWeightMatrix0to1[12][5] = 0.453810944875461;
   fWeightMatrix0to1[13][5] = -0.156067922922021;
   fWeightMatrix0to1[0][6] = -0.730634664604929;
   fWeightMatrix0to1[1][6] = -0.253432549089898;
   fWeightMatrix0to1[2][6] = 1.10591512485936;
   fWeightMatrix0to1[3][6] = -1.0770943616184;
   fWeightMatrix0to1[4][6] = -1.28204379398945;
   fWeightMatrix0to1[5][6] = -1.88740928083461;
   fWeightMatrix0to1[6][6] = -1.09707870566463;
   fWeightMatrix0to1[7][6] = 0.393153435335319;
   fWeightMatrix0to1[8][6] = 1.87322191345686;
   fWeightMatrix0to1[9][6] = -1.28500510122909;
   fWeightMatrix0to1[10][6] = -0.706369860392116;
   fWeightMatrix0to1[11][6] = 1.52074921383537;
   fWeightMatrix0to1[12][6] = -1.58607325569131;
   fWeightMatrix0to1[13][6] = -1.2550221085557;
   fWeightMatrix0to1[0][7] = -1.3167250740687;
   fWeightMatrix0to1[1][7] = 0.0619706616198957;
   fWeightMatrix0to1[2][7] = -1.79570046090188;
   fWeightMatrix0to1[3][7] = -1.55032468208097;
   fWeightMatrix0to1[4][7] = 1.057270731562;
   fWeightMatrix0to1[5][7] = 1.38105569936535;
   fWeightMatrix0to1[6][7] = -2.21433561820731;
   fWeightMatrix0to1[7][7] = 2.58130674852154;
   fWeightMatrix0to1[8][7] = -0.74832120201518;
   fWeightMatrix0to1[9][7] = -0.385172859468216;
   fWeightMatrix0to1[10][7] = -0.175804479472569;
   fWeightMatrix0to1[11][7] = 0.220752343255371;
   fWeightMatrix0to1[12][7] = -3.03494704614933;
   fWeightMatrix0to1[13][7] = 1.01735389592462;
   fWeightMatrix0to1[0][8] = 0.38411314544751;
   fWeightMatrix0to1[1][8] = 0.604905671696411;
   fWeightMatrix0to1[2][8] = -1.54503645056559;
   fWeightMatrix0to1[3][8] = 1.84388630919833;
   fWeightMatrix0to1[4][8] = 0.109295026809923;
   fWeightMatrix0to1[5][8] = -0.986037863263279;
   fWeightMatrix0to1[6][8] = -0.870285069626333;
   fWeightMatrix0to1[7][8] = -0.607099042211552;
   fWeightMatrix0to1[8][8] = -1.40015174511671;
   fWeightMatrix0to1[9][8] = -0.0411784289785462;
   fWeightMatrix0to1[10][8] = -0.45276147877852;
   fWeightMatrix0to1[11][8] = 1.56719057690558;
   fWeightMatrix0to1[12][8] = -0.49784884005105;
   fWeightMatrix0to1[13][8] = 0.339560016215593;
   fWeightMatrix0to1[0][9] = -0.422080779186209;
   fWeightMatrix0to1[1][9] = 0.127739007074652;
   fWeightMatrix0to1[2][9] = -1.86930213101446;
   fWeightMatrix0to1[3][9] = 0.978850194284441;
   fWeightMatrix0to1[4][9] = 0.0866763827205046;
   fWeightMatrix0to1[5][9] = -1.97916039176449;
   fWeightMatrix0to1[6][9] = 1.03335346075438;
   fWeightMatrix0to1[7][9] = -1.20271874695533;
   fWeightMatrix0to1[8][9] = -0.0335454549367237;
   fWeightMatrix0to1[9][9] = 1.31716551360159;
   fWeightMatrix0to1[10][9] = 1.84578670876948;
   fWeightMatrix0to1[11][9] = -1.79595386496075;
   fWeightMatrix0to1[12][9] = 0.0868021654102777;
   fWeightMatrix0to1[13][9] = 1.50962912107103;
   fWeightMatrix0to1[0][10] = 1.51625426265888;
   fWeightMatrix0to1[1][10] = 0.416945755538514;
   fWeightMatrix0to1[2][10] = -1.34070688205791;
   fWeightMatrix0to1[3][10] = -0.26703483927344;
   fWeightMatrix0to1[4][10] = -2.02995477677108;
   fWeightMatrix0to1[5][10] = 1.36965819718447;
   fWeightMatrix0to1[6][10] = 0.565557550165653;
   fWeightMatrix0to1[7][10] = -0.381716186693492;
   fWeightMatrix0to1[8][10] = -0.776326101371904;
   fWeightMatrix0to1[9][10] = 0.667408788588328;
   fWeightMatrix0to1[10][10] = -1.29906306764684;
   fWeightMatrix0to1[11][10] = 0.747579022646064;
   fWeightMatrix0to1[12][10] = 0.288488535811337;
   fWeightMatrix0to1[13][10] = -0.404748222012029;
   fWeightMatrix0to1[0][11] = -0.708888181668677;
   fWeightMatrix0to1[1][11] = 0.431640120736054;
   fWeightMatrix0to1[2][11] = 1.09777415404333;
   fWeightMatrix0to1[3][11] = 0.60378718285837;
   fWeightMatrix0to1[4][11] = 0.595574283036833;
   fWeightMatrix0to1[5][11] = -1.40141909600458;
   fWeightMatrix0to1[6][11] = 0.650139537915781;
   fWeightMatrix0to1[7][11] = 0.646339982619605;
   fWeightMatrix0to1[8][11] = -0.20625352453703;
   fWeightMatrix0to1[9][11] = 0.723088760237859;
   fWeightMatrix0to1[10][11] = 1.56768123064046;
   fWeightMatrix0to1[11][11] = -0.970889689485199;
   fWeightMatrix0to1[12][11] = -0.401729659526059;
   fWeightMatrix0to1[13][11] = 0.124600417098888;
   fWeightMatrix0to1[0][12] = -0.993739414109334;
   fWeightMatrix0to1[1][12] = -0.0921472866060661;
   fWeightMatrix0to1[2][12] = 1.64907825038987;
   fWeightMatrix0to1[3][12] = 1.11801657612531;
   fWeightMatrix0to1[4][12] = -0.373854373709091;
   fWeightMatrix0to1[5][12] = 1.26650162677298;
   fWeightMatrix0to1[6][12] = 1.68797769957359;
   fWeightMatrix0to1[7][12] = -1.47913189270086;
   fWeightMatrix0to1[8][12] = 0.165797179010993;
   fWeightMatrix0to1[9][12] = 0.291229503248794;
   fWeightMatrix0to1[10][12] = 0.756435519750508;
   fWeightMatrix0to1[11][12] = -2.0063728048294;
   fWeightMatrix0to1[12][12] = -1.42883859780576;
   fWeightMatrix0to1[13][12] = 1.76999540964676;
   fWeightMatrix0to1[0][13] = 1.89171947260492;
   fWeightMatrix0to1[1][13] = -1.75205221023273;
   fWeightMatrix0to1[2][13] = -0.241699778819702;
   fWeightMatrix0to1[3][13] = -0.358472997708387;
   fWeightMatrix0to1[4][13] = 0.245164678029388;
   fWeightMatrix0to1[5][13] = 1.61359344678653;
   fWeightMatrix0to1[6][13] = -1.9259779321472;
   fWeightMatrix0to1[7][13] = 0.254868660770318;
   fWeightMatrix0to1[8][13] = -0.730802288590237;
   fWeightMatrix0to1[9][13] = 0.808077151063988;
   fWeightMatrix0to1[10][13] = 1.28671186237147;
   fWeightMatrix0to1[11][13] = 1.29973107673049;
   fWeightMatrix0to1[12][13] = -1.51757565793991;
   fWeightMatrix0to1[13][13] = 0.498085940867854;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = -2.52486775467747;
   fWeightMatrix1to2[1][0] = 0.426023079236248;
   fWeightMatrix1to2[2][0] = 0.375835745452262;
   fWeightMatrix1to2[3][0] = -1.77456216890997;
   fWeightMatrix1to2[4][0] = -0.661518822127812;
   fWeightMatrix1to2[5][0] = -0.473444227443232;
   fWeightMatrix1to2[6][0] = -0.358497747643072;
   fWeightMatrix1to2[7][0] = -1.93294102532768;
   fWeightMatrix1to2[8][0] = 1.01382069632889;
   fWeightMatrix1to2[9][0] = 0.640479898739759;
   fWeightMatrix1to2[10][0] = -0.583640836132518;
   fWeightMatrix1to2[11][0] = 0.0615677396979849;
   fWeightMatrix1to2[12][0] = 1.56936575274021;
   fWeightMatrix1to2[0][1] = 1.02210227276727;
   fWeightMatrix1to2[1][1] = 0.314417290274737;
   fWeightMatrix1to2[2][1] = -0.176777152181447;
   fWeightMatrix1to2[3][1] = -2.00384278379447;
   fWeightMatrix1to2[4][1] = -0.921217799261972;
   fWeightMatrix1to2[5][1] = 1.63542906241445;
   fWeightMatrix1to2[6][1] = 1.31312600130518;
   fWeightMatrix1to2[7][1] = 0.754665346624678;
   fWeightMatrix1to2[8][1] = 0.133575905676936;
   fWeightMatrix1to2[9][1] = 1.96819125948413;
   fWeightMatrix1to2[10][1] = -1.79196715017889;
   fWeightMatrix1to2[11][1] = -1.30545686783361;
   fWeightMatrix1to2[12][1] = -0.123463815692537;
   fWeightMatrix1to2[0][2] = -1.45984963228105;
   fWeightMatrix1to2[1][2] = 0.874845593518741;
   fWeightMatrix1to2[2][2] = 1.73699896571388;
   fWeightMatrix1to2[3][2] = -1.30621508154441;
   fWeightMatrix1to2[4][2] = 0.79846908421209;
   fWeightMatrix1to2[5][2] = -0.216151012204619;
   fWeightMatrix1to2[6][2] = -1.72377899999605;
   fWeightMatrix1to2[7][2] = -1.126964779818;
   fWeightMatrix1to2[8][2] = 0.973542744716368;
   fWeightMatrix1to2[9][2] = 0.0111356947820115;
   fWeightMatrix1to2[10][2] = 0.894454678606861;
   fWeightMatrix1to2[11][2] = -1.27016648004324;
   fWeightMatrix1to2[12][2] = 1.68178117093184;
   fWeightMatrix1to2[0][3] = -1.73117046524679;
   fWeightMatrix1to2[1][3] = 0.593280323033836;
   fWeightMatrix1to2[2][3] = 0.0553877535739254;
   fWeightMatrix1to2[3][3] = -1.57691508077604;
   fWeightMatrix1to2[4][3] = -1.31119383389925;
   fWeightMatrix1to2[5][3] = -1.88329218657836;
   fWeightMatrix1to2[6][3] = -0.38746645244843;
   fWeightMatrix1to2[7][3] = -1.91742720995605;
   fWeightMatrix1to2[8][3] = -1.65183417513039;
   fWeightMatrix1to2[9][3] = -1.79682885481369;
   fWeightMatrix1to2[10][3] = 0.180734733289192;
   fWeightMatrix1to2[11][3] = -1.01441065454723;
   fWeightMatrix1to2[12][3] = -1.59565368115017;
   fWeightMatrix1to2[0][4] = 1.01386449409726;
   fWeightMatrix1to2[1][4] = -0.668362387061251;
   fWeightMatrix1to2[2][4] = 0.284274213673215;
   fWeightMatrix1to2[3][4] = -1.30016764477459;
   fWeightMatrix1to2[4][4] = 0.241302854985123;
   fWeightMatrix1to2[5][4] = 1.08870046713619;
   fWeightMatrix1to2[6][4] = 0.877367135910187;
   fWeightMatrix1to2[7][4] = -0.891571683543565;
   fWeightMatrix1to2[8][4] = -1.95512711136616;
   fWeightMatrix1to2[9][4] = -1.71421038717968;
   fWeightMatrix1to2[10][4] = -0.780382431510122;
   fWeightMatrix1to2[11][4] = -1.61268228758049;
   fWeightMatrix1to2[12][4] = 0.292089994945369;
   fWeightMatrix1to2[0][5] = -0.100061696793301;
   fWeightMatrix1to2[1][5] = 1.19187262741015;
   fWeightMatrix1to2[2][5] = -0.425997276673979;
   fWeightMatrix1to2[3][5] = -0.10583887633885;
   fWeightMatrix1to2[4][5] = -0.497275437924329;
   fWeightMatrix1to2[5][5] = -2.6195397688325;
   fWeightMatrix1to2[6][5] = 0.918838041436973;
   fWeightMatrix1to2[7][5] = 1.28452583596286;
   fWeightMatrix1to2[8][5] = -1.30368555888358;
   fWeightMatrix1to2[9][5] = -0.997512530783628;
   fWeightMatrix1to2[10][5] = 1.03366624262064;
   fWeightMatrix1to2[11][5] = 1.61131400991339;
   fWeightMatrix1to2[12][5] = 0.380804155597417;
   fWeightMatrix1to2[0][6] = -1.00300520575886;
   fWeightMatrix1to2[1][6] = 1.38099991864204;
   fWeightMatrix1to2[2][6] = 1.52781349626197;
   fWeightMatrix1to2[3][6] = 0.772222085497882;
   fWeightMatrix1to2[4][6] = 1.03082286821288;
   fWeightMatrix1to2[5][6] = -1.03309541729085;
   fWeightMatrix1to2[6][6] = 0.311548353829738;
   fWeightMatrix1to2[7][6] = 0.20085436267719;
   fWeightMatrix1to2[8][6] = -1.58884403977022;
   fWeightMatrix1to2[9][6] = -0.0266183067314733;
   fWeightMatrix1to2[10][6] = -1.84119492465681;
   fWeightMatrix1to2[11][6] = -1.78591411757196;
   fWeightMatrix1to2[12][6] = -1.56627538199792;
   fWeightMatrix1to2[0][7] = 1.51691534090512;
   fWeightMatrix1to2[1][7] = -2.28752518516177;
   fWeightMatrix1to2[2][7] = 1.49159380104086;
   fWeightMatrix1to2[3][7] = -1.66591822730947;
   fWeightMatrix1to2[4][7] = 1.14901639311763;
   fWeightMatrix1to2[5][7] = -1.44716064793835;
   fWeightMatrix1to2[6][7] = 0.309608063862466;
   fWeightMatrix1to2[7][7] = 0.611297113870058;
   fWeightMatrix1to2[8][7] = -2.00188274484222;
   fWeightMatrix1to2[9][7] = -0.382677771349337;
   fWeightMatrix1to2[10][7] = -0.0825744997066915;
   fWeightMatrix1to2[11][7] = -2.46761443422454;
   fWeightMatrix1to2[12][7] = -1.54704289918988;
   fWeightMatrix1to2[0][8] = -1.75541704897924;
   fWeightMatrix1to2[1][8] = -0.828966478089693;
   fWeightMatrix1to2[2][8] = 1.82752266030993;
   fWeightMatrix1to2[3][8] = -0.580890324175979;
   fWeightMatrix1to2[4][8] = 0.288025868206231;
   fWeightMatrix1to2[5][8] = -0.973203843552008;
   fWeightMatrix1to2[6][8] = -1.18548651965347;
   fWeightMatrix1to2[7][8] = -1.77763712189069;
   fWeightMatrix1to2[8][8] = -0.993590809121902;
   fWeightMatrix1to2[9][8] = -1.48215403705191;
   fWeightMatrix1to2[10][8] = 0.846443651112434;
   fWeightMatrix1to2[11][8] = -1.4787078089688;
   fWeightMatrix1to2[12][8] = -1.24044556286773;
   fWeightMatrix1to2[0][9] = 1.71478779180549;
   fWeightMatrix1to2[1][9] = -1.0249716873773;
   fWeightMatrix1to2[2][9] = -0.759101428220276;
   fWeightMatrix1to2[3][9] = 1.49851994121841;
   fWeightMatrix1to2[4][9] = -1.78933514433764;
   fWeightMatrix1to2[5][9] = 1.23764756029631;
   fWeightMatrix1to2[6][9] = 0.791010793733435;
   fWeightMatrix1to2[7][9] = -1.01113523290261;
   fWeightMatrix1to2[8][9] = -1.08637529971915;
   fWeightMatrix1to2[9][9] = 0.34111539387551;
   fWeightMatrix1to2[10][9] = -1.21546403662599;
   fWeightMatrix1to2[11][9] = 0.766999516222317;
   fWeightMatrix1to2[12][9] = 1.88737133279635;
   fWeightMatrix1to2[0][10] = 1.03674014589697;
   fWeightMatrix1to2[1][10] = 0.921713823937121;
   fWeightMatrix1to2[2][10] = -1.23451398683451;
   fWeightMatrix1to2[3][10] = 0.760228158551058;
   fWeightMatrix1to2[4][10] = -1.92372826911368;
   fWeightMatrix1to2[5][10] = -0.00656916107015855;
   fWeightMatrix1to2[6][10] = -2.21151002596723;
   fWeightMatrix1to2[7][10] = -1.82257933906768;
   fWeightMatrix1to2[8][10] = -0.511939419462813;
   fWeightMatrix1to2[9][10] = 0.851120649994243;
   fWeightMatrix1to2[10][10] = 0.932621393557268;
   fWeightMatrix1to2[11][10] = 1.7176668472621;
   fWeightMatrix1to2[12][10] = 0.278373599107968;
   fWeightMatrix1to2[0][11] = -0.594202427224245;
   fWeightMatrix1to2[1][11] = -1.49272761006507;
   fWeightMatrix1to2[2][11] = 0.0559557722792408;
   fWeightMatrix1to2[3][11] = 1.51670652417111;
   fWeightMatrix1to2[4][11] = -1.5346941478812;
   fWeightMatrix1to2[5][11] = -0.383888039350927;
   fWeightMatrix1to2[6][11] = -1.70669077860042;
   fWeightMatrix1to2[7][11] = -0.52218425763458;
   fWeightMatrix1to2[8][11] = -0.634762177408585;
   fWeightMatrix1to2[9][11] = 0.373164889588931;
   fWeightMatrix1to2[10][11] = -0.898238224928875;
   fWeightMatrix1to2[11][11] = 1.27224488175385;
   fWeightMatrix1to2[12][11] = 1.60847303175671;
   fWeightMatrix1to2[0][12] = 0.392735755904236;
   fWeightMatrix1to2[1][12] = 0.246609683863294;
   fWeightMatrix1to2[2][12] = -0.729964687509518;
   fWeightMatrix1to2[3][12] = -1.51260874264665;
   fWeightMatrix1to2[4][12] = -1.73644016582005;
   fWeightMatrix1to2[5][12] = 0.60093337461827;
   fWeightMatrix1to2[6][12] = -1.13430767004406;
   fWeightMatrix1to2[7][12] = -1.11589859463008;
   fWeightMatrix1to2[8][12] = 0.206925835894317;
   fWeightMatrix1to2[9][12] = -2.34898478621622;
   fWeightMatrix1to2[10][12] = -0.740053475803545;
   fWeightMatrix1to2[11][12] = -1.12974612690341;
   fWeightMatrix1to2[12][12] = 1.02583074207755;
   fWeightMatrix1to2[0][13] = -1.4666883203598;
   fWeightMatrix1to2[1][13] = -2.67206430583463;
   fWeightMatrix1to2[2][13] = -0.715950838239593;
   fWeightMatrix1to2[3][13] = -1.25528439907061;
   fWeightMatrix1to2[4][13] = -1.60922983439281;
   fWeightMatrix1to2[5][13] = 1.01122439663145;
   fWeightMatrix1to2[6][13] = -1.55168282767227;
   fWeightMatrix1to2[7][13] = -1.18064021210667;
   fWeightMatrix1to2[8][13] = 1.60622871538847;
   fWeightMatrix1to2[9][13] = 0.657952732331324;
   fWeightMatrix1to2[10][13] = 0.0833754831915906;
   fWeightMatrix1to2[11][13] = 0.0936652611602441;
   fWeightMatrix1to2[12][13] = 1.33604312773054;
   fWeightMatrix1to2[0][14] = 1.18882464979629;
   fWeightMatrix1to2[1][14] = -0.180217297472534;
   fWeightMatrix1to2[2][14] = -1.02101941836127;
   fWeightMatrix1to2[3][14] = -1.10308990768765;
   fWeightMatrix1to2[4][14] = -1.70761412064404;
   fWeightMatrix1to2[5][14] = 0.121948828218104;
   fWeightMatrix1to2[6][14] = 0.457992657476338;
   fWeightMatrix1to2[7][14] = 0.0521548684432605;
   fWeightMatrix1to2[8][14] = 0.542216776269295;
   fWeightMatrix1to2[9][14] = 0.673263174976122;
   fWeightMatrix1to2[10][14] = 1.59228419160967;
   fWeightMatrix1to2[11][14] = -1.13175892643266;
   fWeightMatrix1to2[12][14] = 1.66539018913589;
   // weight matrix from layer 2 to 3
   fWeightMatrix2to3[0][0] = 0.544940564843099;
   fWeightMatrix2to3[0][1] = -1.19748404385017;
   fWeightMatrix2to3[0][2] = 0.16074340814641;
   fWeightMatrix2to3[0][3] = -0.0625621939431144;
   fWeightMatrix2to3[0][4] = 0.946037474986381;
   fWeightMatrix2to3[0][5] = 0.114450836808419;
   fWeightMatrix2to3[0][6] = 0.570636937911124;
   fWeightMatrix2to3[0][7] = 1.75624472565978;
   fWeightMatrix2to3[0][8] = -0.214016970807011;
   fWeightMatrix2to3[0][9] = 0.71865079744677;
   fWeightMatrix2to3[0][10] = 0.871355048984397;
   fWeightMatrix2to3[0][11] = 1.47036574482029;
   fWeightMatrix2to3[0][12] = -1.62485065280112;
   fWeightMatrix2to3[0][13] = 1.22611938685994;
}

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
}