// 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 10:03:43 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/job404
Training events: 48964


#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'    [47.4487342834,803.330627441]
LepAPt                        LepAPt                            'F'    [20.0000915527,158.247711182]
LepBPt                        LepBPt                            'F'    [10.000123024,71.4863357544]
MetSigLeptonsJets             MetSigLeptonsJets                 'F'    [0.978936254978,16.7354717255]
MetSpec                       MetSpec                           'F'    [15.0048427582,229.921310425]
SumEtLeptonsJets              SumEtLeptonsJets                  'F'    [30.1199874878,498.085754395]
VSumJetLeptonsPt              VSumJetLeptonsPt                  'F'    [1.28708541393,323.660003662]
addEt                         addEt                             'F'    [47.4487342834,409.19140625]
dPhiLepSumMet                 dPhiLepSumMet                     'F'    [0.0808083936572,3.14158177376]
dPhiLeptons                   dPhiLeptons                       'F'    [1.1922662452e-05,1.11774706841]
dRLeptons                     dRLeptons                         'F'    [0.200001657009,1.15372478962]
lep1_E                        lep1_E                            'F'    [20.0080432892,211.499725342]
lep2_E                        lep2_E                            'F'    [10.0116767883,106.10823822]


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

#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] = 47.4487342834473;
      fVmax[0] = 803.330627441406;
      fVmin[1] = 20.0000915527344;
      fVmax[1] = 158.247711181641;
      fVmin[2] = 10.0001230239868;
      fVmax[2] = 71.4863357543945;
      fVmin[3] = 0.97893625497818;
      fVmax[3] = 16.7354717254639;
      fVmin[4] = 15.0048427581787;
      fVmax[4] = 229.921310424805;
      fVmin[5] = 30.119987487793;
      fVmax[5] = 498.085754394531;
      fVmin[6] = 1.2870854139328;
      fVmax[6] = 323.660003662109;
      fVmin[7] = 47.4487342834473;
      fVmax[7] = 409.19140625;
      fVmin[8] = 0.0808083936572075;
      fVmax[8] = 3.14158177375793;
      fVmin[9] = 1.19226624519797e-05;
      fVmax[9] = 1.11774706840515;
      fVmin[10] = 0.200001657009125;
      fVmax[10] = 1.15372478961945;
      fVmin[11] = 20.0080432891846;
      fVmax[11] = 211.499725341797;
      fVmin[12] = 10.0116767883301;
      fVmax[12] = 106.108238220215;

      // 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] = -0.384518168453924;
   fWeightMatrix0to1[1][0] = 2.05017750073871;
   fWeightMatrix0to1[2][0] = 0.895286446998211;
   fWeightMatrix0to1[3][0] = 1.3358842354018;
   fWeightMatrix0to1[4][0] = -0.931453085614545;
   fWeightMatrix0to1[5][0] = -0.839074849224794;
   fWeightMatrix0to1[6][0] = 0.212688979023933;
   fWeightMatrix0to1[7][0] = 1.30365119229911;
   fWeightMatrix0to1[8][0] = -0.85953302064419;
   fWeightMatrix0to1[9][0] = -0.631330893121013;
   fWeightMatrix0to1[10][0] = -1.44872260541735;
   fWeightMatrix0to1[11][0] = -0.046126828310631;
   fWeightMatrix0to1[12][0] = -0.6345956224984;
   fWeightMatrix0to1[13][0] = 0.0789310980344921;
   fWeightMatrix0to1[0][1] = -0.590209410875582;
   fWeightMatrix0to1[1][1] = 0.905108183646924;
   fWeightMatrix0to1[2][1] = 0.767523413061242;
   fWeightMatrix0to1[3][1] = -0.406893026164488;
   fWeightMatrix0to1[4][1] = 0.220471736620598;
   fWeightMatrix0to1[5][1] = 1.31616051359166;
   fWeightMatrix0to1[6][1] = -3.1749908642317;
   fWeightMatrix0to1[7][1] = -1.41829312111282;
   fWeightMatrix0to1[8][1] = 1.14240601288346;
   fWeightMatrix0to1[9][1] = 3.46466266270761;
   fWeightMatrix0to1[10][1] = -1.64558999117655;
   fWeightMatrix0to1[11][1] = -0.741869920270923;
   fWeightMatrix0to1[12][1] = -2.0809412675778;
   fWeightMatrix0to1[13][1] = -4.1801621288158;
   fWeightMatrix0to1[0][2] = -1.84901434875357;
   fWeightMatrix0to1[1][2] = 0.663723829877462;
   fWeightMatrix0to1[2][2] = 1.09883218407162;
   fWeightMatrix0to1[3][2] = -2.2758453904506;
   fWeightMatrix0to1[4][2] = -0.953772798514075;
   fWeightMatrix0to1[5][2] = 0.63123221113543;
   fWeightMatrix0to1[6][2] = -4.20164561437447;
   fWeightMatrix0to1[7][2] = -0.395989255843365;
   fWeightMatrix0to1[8][2] = -1.60401305388288;
   fWeightMatrix0to1[9][2] = 2.84747731186386;
   fWeightMatrix0to1[10][2] = 1.13237907579509;
   fWeightMatrix0to1[11][2] = 1.87664207299766;
   fWeightMatrix0to1[12][2] = -0.99764553675846;
   fWeightMatrix0to1[13][2] = -3.43947102539275;
   fWeightMatrix0to1[0][3] = 1.92309772457875;
   fWeightMatrix0to1[1][3] = 1.2056389568564;
   fWeightMatrix0to1[2][3] = -0.0751739260086241;
   fWeightMatrix0to1[3][3] = -1.79006344352199;
   fWeightMatrix0to1[4][3] = 1.07531579826215;
   fWeightMatrix0to1[5][3] = -0.414106706336321;
   fWeightMatrix0to1[6][3] = 4.30443326533421;
   fWeightMatrix0to1[7][3] = 1.37936971077964;
   fWeightMatrix0to1[8][3] = 2.38311930390652;
   fWeightMatrix0to1[9][3] = 1.25140467592866;
   fWeightMatrix0to1[10][3] = -1.82099099182075;
   fWeightMatrix0to1[11][3] = 1.53787833699531;
   fWeightMatrix0to1[12][3] = -3.41075443165993;
   fWeightMatrix0to1[13][3] = -1.59133486849284;
   fWeightMatrix0to1[0][4] = -1.29458858421469;
   fWeightMatrix0to1[1][4] = -1.60194620724724;
   fWeightMatrix0to1[2][4] = 1.87447775396859;
   fWeightMatrix0to1[3][4] = 0.0902258495219737;
   fWeightMatrix0to1[4][4] = -0.0464668839548279;
   fWeightMatrix0to1[5][4] = 1.16398671946133;
   fWeightMatrix0to1[6][4] = 1.4046541758254;
   fWeightMatrix0to1[7][4] = 0.192305344086452;
   fWeightMatrix0to1[8][4] = 2.99057805646921;
   fWeightMatrix0to1[9][4] = 1.34539801262473;
   fWeightMatrix0to1[10][4] = -0.278549723000138;
   fWeightMatrix0to1[11][4] = -1.36886621793119;
   fWeightMatrix0to1[12][4] = -1.28112549510166;
   fWeightMatrix0to1[13][4] = -2.76146848227894;
   fWeightMatrix0to1[0][5] = -0.800474878767562;
   fWeightMatrix0to1[1][5] = -1.35242433072672;
   fWeightMatrix0to1[2][5] = 0.629282351246921;
   fWeightMatrix0to1[3][5] = 0.651030649164804;
   fWeightMatrix0to1[4][5] = 2.46938429109778;
   fWeightMatrix0to1[5][5] = 0.430493094312855;
   fWeightMatrix0to1[6][5] = -1.52567902213586;
   fWeightMatrix0to1[7][5] = -2.55088350278025;
   fWeightMatrix0to1[8][5] = 1.44698542267657;
   fWeightMatrix0to1[9][5] = 0.868965276871185;
   fWeightMatrix0to1[10][5] = 2.18622528905614;
   fWeightMatrix0to1[11][5] = 1.09566735170052;
   fWeightMatrix0to1[12][5] = 2.03545164738102;
   fWeightMatrix0to1[13][5] = 0.0176186121171554;
   fWeightMatrix0to1[0][6] = -0.916137434471433;
   fWeightMatrix0to1[1][6] = -0.0456389255607185;
   fWeightMatrix0to1[2][6] = 1.21650398612006;
   fWeightMatrix0to1[3][6] = -1.04162067298446;
   fWeightMatrix0to1[4][6] = -0.302842282183567;
   fWeightMatrix0to1[5][6] = -0.838900495420895;
   fWeightMatrix0to1[6][6] = -0.270738444910457;
   fWeightMatrix0to1[7][6] = -0.757034479134646;
   fWeightMatrix0to1[8][6] = 2.15372210828543;
   fWeightMatrix0to1[9][6] = -0.372927808409513;
   fWeightMatrix0to1[10][6] = -0.59860608234627;
   fWeightMatrix0to1[11][6] = 1.78474900061493;
   fWeightMatrix0to1[12][6] = -1.50296880759296;
   fWeightMatrix0to1[13][6] = -2.85561468830216;
   fWeightMatrix0to1[0][7] = -1.42212473717041;
   fWeightMatrix0to1[1][7] = 0.43669005588157;
   fWeightMatrix0to1[2][7] = -0.872706571714756;
   fWeightMatrix0to1[3][7] = -5.57963444712366;
   fWeightMatrix0to1[4][7] = 1.37313918893548;
   fWeightMatrix0to1[5][7] = 2.21699975564022;
   fWeightMatrix0to1[6][7] = -4.57977925396691;
   fWeightMatrix0to1[7][7] = 1.25773949355957;
   fWeightMatrix0to1[8][7] = 0.0305716013388548;
   fWeightMatrix0to1[9][7] = 3.35290555011612;
   fWeightMatrix0to1[10][7] = 0.365988201411556;
   fWeightMatrix0to1[11][7] = 0.299137797771323;
   fWeightMatrix0to1[12][7] = -6.30793975647747;
   fWeightMatrix0to1[13][7] = -5.21634351086644;
   fWeightMatrix0to1[0][8] = 0.421502804735491;
   fWeightMatrix0to1[1][8] = -0.123722805728233;
   fWeightMatrix0to1[2][8] = -0.906042883309314;
   fWeightMatrix0to1[3][8] = 2.39655903857478;
   fWeightMatrix0to1[4][8] = -0.63837875213711;
   fWeightMatrix0to1[5][8] = -1.76148790290032;
   fWeightMatrix0to1[6][8] = 3.62268118950737;
   fWeightMatrix0to1[7][8] = -0.368846947746275;
   fWeightMatrix0to1[8][8] = -1.58497335313057;
   fWeightMatrix0to1[9][8] = 0.764614019018317;
   fWeightMatrix0to1[10][8] = -1.32769682424972;
   fWeightMatrix0to1[11][8] = 0.848586270367534;
   fWeightMatrix0to1[12][8] = -0.43283748665905;
   fWeightMatrix0to1[13][8] = -0.146232994792004;
   fWeightMatrix0to1[0][9] = -0.548551389108057;
   fWeightMatrix0to1[1][9] = -0.052539268873441;
   fWeightMatrix0to1[2][9] = -1.9143977791228;
   fWeightMatrix0to1[3][9] = -1.58168740273028;
   fWeightMatrix0to1[4][9] = 0.162721739803899;
   fWeightMatrix0to1[5][9] = -0.679493136449734;
   fWeightMatrix0to1[6][9] = 1.17793476841165;
   fWeightMatrix0to1[7][9] = -2.00378929518923;
   fWeightMatrix0to1[8][9] = -0.755035488218932;
   fWeightMatrix0to1[9][9] = -0.946606426546533;
   fWeightMatrix0to1[10][9] = 1.2651022783707;
   fWeightMatrix0to1[11][9] = -0.99177694668202;
   fWeightMatrix0to1[12][9] = 0.229737591579874;
   fWeightMatrix0to1[13][9] = -0.358046015439717;
   fWeightMatrix0to1[0][10] = 1.40333398060347;
   fWeightMatrix0to1[1][10] = -0.375302813712139;
   fWeightMatrix0to1[2][10] = -2.30295472296512;
   fWeightMatrix0to1[3][10] = -2.82317487170346;
   fWeightMatrix0to1[4][10] = -1.35063804080125;
   fWeightMatrix0to1[5][10] = 2.07782713988607;
   fWeightMatrix0to1[6][10] = -0.650438270115102;
   fWeightMatrix0to1[7][10] = -1.52202100609818;
   fWeightMatrix0to1[8][10] = -1.44286965848468;
   fWeightMatrix0to1[9][10] = -0.53975858939036;
   fWeightMatrix0to1[10][10] = -2.17366175712953;
   fWeightMatrix0to1[11][10] = 1.09978338941767;
   fWeightMatrix0to1[12][10] = 1.78887256102816;
   fWeightMatrix0to1[13][10] = -0.957126388565485;
   fWeightMatrix0to1[0][11] = -0.510657492891367;
   fWeightMatrix0to1[1][11] = -0.230402219528048;
   fWeightMatrix0to1[2][11] = 1.71253440238144;
   fWeightMatrix0to1[3][11] = 2.01173947576686;
   fWeightMatrix0to1[4][11] = 1.42339056944207;
   fWeightMatrix0to1[5][11] = -1.56334146080138;
   fWeightMatrix0to1[6][11] = 1.00690834823915;
   fWeightMatrix0to1[7][11] = 0.523603664536145;
   fWeightMatrix0to1[8][11] = 1.15331656603322;
   fWeightMatrix0to1[9][11] = 1.76342219260058;
   fWeightMatrix0to1[10][11] = -0.804872275470562;
   fWeightMatrix0to1[11][11] = -0.094637269597526;
   fWeightMatrix0to1[12][11] = -0.732575472927072;
   fWeightMatrix0to1[13][11] = 2.12302361873437;
   fWeightMatrix0to1[0][12] = -0.857560680703985;
   fWeightMatrix0to1[1][12] = -0.736081290818457;
   fWeightMatrix0to1[2][12] = 1.51112430213824;
   fWeightMatrix0to1[3][12] = 2.81588943932078;
   fWeightMatrix0to1[4][12] = -0.164473595583183;
   fWeightMatrix0to1[5][12] = 2.08768592534905;
   fWeightMatrix0to1[6][12] = 2.19367888277467;
   fWeightMatrix0to1[7][12] = -1.7533426025299;
   fWeightMatrix0to1[8][12] = 0.61603546640121;
   fWeightMatrix0to1[9][12] = -0.0384169712368255;
   fWeightMatrix0to1[10][12] = 0.940664370034667;
   fWeightMatrix0to1[11][12] = -0.311046378965734;
   fWeightMatrix0to1[12][12] = -1.52999650500456;
   fWeightMatrix0to1[13][12] = 1.72555224128734;
   fWeightMatrix0to1[0][13] = 1.99719026393579;
   fWeightMatrix0to1[1][13] = -1.35339864325843;
   fWeightMatrix0to1[2][13] = 0.800217122884789;
   fWeightMatrix0to1[3][13] = -6.98173870847522;
   fWeightMatrix0to1[4][13] = 0.226890793190961;
   fWeightMatrix0to1[5][13] = 2.03827906276293;
   fWeightMatrix0to1[6][13] = -9.46362819465555;
   fWeightMatrix0to1[7][13] = 0.815411147602361;
   fWeightMatrix0to1[8][13] = -0.291621684784185;
   fWeightMatrix0to1[9][13] = 7.61735427386472;
   fWeightMatrix0to1[10][13] = 1.894098636709;
   fWeightMatrix0to1[11][13] = 0.646306641303357;
   fWeightMatrix0to1[12][13] = -7.53882014974491;
   fWeightMatrix0to1[13][13] = -11.1594061005174;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = -0.269112827657256;
   fWeightMatrix1to2[1][0] = -0.303730860843752;
   fWeightMatrix1to2[2][0] = -0.437481057575315;
   fWeightMatrix1to2[3][0] = -1.74165205613865;
   fWeightMatrix1to2[4][0] = -0.622545030538181;
   fWeightMatrix1to2[5][0] = -1.49448063805734;
   fWeightMatrix1to2[6][0] = -0.312868052439175;
   fWeightMatrix1to2[7][0] = -2.09554798800038;
   fWeightMatrix1to2[8][0] = 0.0249437157490187;
   fWeightMatrix1to2[9][0] = -0.120188089588333;
   fWeightMatrix1to2[10][0] = -1.3964461103225;
   fWeightMatrix1to2[11][0] = -0.113668005179371;
   fWeightMatrix1to2[12][0] = -0.304394701233186;
   fWeightMatrix1to2[0][1] = 1.56770272786944;
   fWeightMatrix1to2[1][1] = 0.556405944740383;
   fWeightMatrix1to2[2][1] = -0.516548548181066;
   fWeightMatrix1to2[3][1] = -2.00180575497043;
   fWeightMatrix1to2[4][1] = -0.896760589422227;
   fWeightMatrix1to2[5][1] = 1.74954879714548;
   fWeightMatrix1to2[6][1] = 0.889078941656323;
   fWeightMatrix1to2[7][1] = 0.896672689711104;
   fWeightMatrix1to2[8][1] = 0.139663696442014;
   fWeightMatrix1to2[9][1] = 2.01064449130109;
   fWeightMatrix1to2[10][1] = -1.6332984015684;
   fWeightMatrix1to2[11][1] = -1.18279886454101;
   fWeightMatrix1to2[12][1] = 0.151572420440069;
   fWeightMatrix1to2[0][2] = -1.6860320974317;
   fWeightMatrix1to2[1][2] = -0.103867249606972;
   fWeightMatrix1to2[2][2] = 2.38992008592804;
   fWeightMatrix1to2[3][2] = -1.17614479956194;
   fWeightMatrix1to2[4][2] = 0.572711232928636;
   fWeightMatrix1to2[5][2] = -0.11476734355857;
   fWeightMatrix1to2[6][2] = -2.88042039521434;
   fWeightMatrix1to2[7][2] = -1.17796563346328;
   fWeightMatrix1to2[8][2] = 0.992241627194496;
   fWeightMatrix1to2[9][2] = -0.400469712153945;
   fWeightMatrix1to2[10][2] = 0.642308687047926;
   fWeightMatrix1to2[11][2] = -1.47960217276273;
   fWeightMatrix1to2[12][2] = 1.04053983746391;
   fWeightMatrix1to2[0][3] = -3.0604644239133;
   fWeightMatrix1to2[1][3] = 1.00109794594111;
   fWeightMatrix1to2[2][3] = 0.00473760332093702;
   fWeightMatrix1to2[3][3] = -1.52079323258921;
   fWeightMatrix1to2[4][3] = -1.28252973954045;
   fWeightMatrix1to2[5][3] = -2.06762600160037;
   fWeightMatrix1to2[6][3] = -0.40274015450527;
   fWeightMatrix1to2[7][3] = -1.44891286917384;
   fWeightMatrix1to2[8][3] = -1.53512542492403;
   fWeightMatrix1to2[9][3] = -1.35987964495015;
   fWeightMatrix1to2[10][3] = 0.316640385783149;
   fWeightMatrix1to2[11][3] = -0.491429211060966;
   fWeightMatrix1to2[12][3] = -1.80940029756989;
   fWeightMatrix1to2[0][4] = 1.74634896368214;
   fWeightMatrix1to2[1][4] = -1.97544408283607;
   fWeightMatrix1to2[2][4] = -0.35157399011528;
   fWeightMatrix1to2[3][4] = -1.67458990038529;
   fWeightMatrix1to2[4][4] = 0.171338721685468;
   fWeightMatrix1to2[5][4] = 0.667330903947874;
   fWeightMatrix1to2[6][4] = -0.238986721270938;
   fWeightMatrix1to2[7][4] = -0.385427612665709;
   fWeightMatrix1to2[8][4] = -2.86041302096779;
   fWeightMatrix1to2[9][4] = -1.24374757465134;
   fWeightMatrix1to2[10][4] = -1.40176087882028;
   fWeightMatrix1to2[11][4] = -1.46876383957493;
   fWeightMatrix1to2[12][4] = -0.646649824070511;
   fWeightMatrix1to2[0][5] = 2.06378523793428;
   fWeightMatrix1to2[1][5] = 0.51557966913576;
   fWeightMatrix1to2[2][5] = -1.1528086804759;
   fWeightMatrix1to2[3][5] = -0.219987830337705;
   fWeightMatrix1to2[4][5] = -0.623968607156926;
   fWeightMatrix1to2[5][5] = -2.79324058675188;
   fWeightMatrix1to2[6][5] = -0.507075946246815;
   fWeightMatrix1to2[7][5] = 0.797681857895551;
   fWeightMatrix1to2[8][5] = -2.12390661007057;
   fWeightMatrix1to2[9][5] = -1.61165868323013;
   fWeightMatrix1to2[10][5] = 0.108146092466755;
   fWeightMatrix1to2[11][5] = 1.05433536779896;
   fWeightMatrix1to2[12][5] = -1.08045036551558;
   fWeightMatrix1to2[0][6] = -3.17810008891168;
   fWeightMatrix1to2[1][6] = -0.736122437447294;
   fWeightMatrix1to2[2][6] = 1.52479415405427;
   fWeightMatrix1to2[3][6] = 0.480108056358458;
   fWeightMatrix1to2[4][6] = 1.05781275594428;
   fWeightMatrix1to2[5][6] = -1.62394021575524;
   fWeightMatrix1to2[6][6] = -1.02254667112255;
   fWeightMatrix1to2[7][6] = -0.0170305581558509;
   fWeightMatrix1to2[8][6] = -2.83174382917379;
   fWeightMatrix1to2[9][6] = 0.547764332069215;
   fWeightMatrix1to2[10][6] = -2.80833075121169;
   fWeightMatrix1to2[11][6] = -1.24014148776926;
   fWeightMatrix1to2[12][6] = -3.45020298204048;
   fWeightMatrix1to2[0][7] = 4.15962206123133;
   fWeightMatrix1to2[1][7] = -2.50258682856847;
   fWeightMatrix1to2[2][7] = 0.709670408090245;
   fWeightMatrix1to2[3][7] = -1.30826918528815;
   fWeightMatrix1to2[4][7] = 1.20581937212897;
   fWeightMatrix1to2[5][7] = -1.7347410190165;
   fWeightMatrix1to2[6][7] = 0.0889832849199333;
   fWeightMatrix1to2[7][7] = 0.694916608581676;
   fWeightMatrix1to2[8][7] = -1.87937446520266;
   fWeightMatrix1to2[9][7] = -0.352859362418951;
   fWeightMatrix1to2[10][7] = -0.0564748506442067;
   fWeightMatrix1to2[11][7] = -1.92238057775796;
   fWeightMatrix1to2[12][7] = -3.0102055162004;
   fWeightMatrix1to2[0][8] = -2.12858780856866;
   fWeightMatrix1to2[1][8] = -1.50811435359166;
   fWeightMatrix1to2[2][8] = 2.687503354458;
   fWeightMatrix1to2[3][8] = -0.56226504088113;
   fWeightMatrix1to2[4][8] = 0.0502844028980358;
   fWeightMatrix1to2[5][8] = -0.718470541483956;
   fWeightMatrix1to2[6][8] = -1.99840394964911;
   fWeightMatrix1to2[7][8] = -1.97221903450606;
   fWeightMatrix1to2[8][8] = -1.01252183078778;
   fWeightMatrix1to2[9][8] = -1.93413283973042;
   fWeightMatrix1to2[10][8] = 0.571180208122544;
   fWeightMatrix1to2[11][8] = -1.73474029524351;
   fWeightMatrix1to2[12][8] = -1.06597238088037;
   fWeightMatrix1to2[0][9] = 5.49801188355206;
   fWeightMatrix1to2[1][9] = -0.431544863020875;
   fWeightMatrix1to2[2][9] = -0.436214954619637;
   fWeightMatrix1to2[3][9] = 2.02277296561514;
   fWeightMatrix1to2[4][9] = -1.79290966765282;
   fWeightMatrix1to2[5][9] = 1.37547914113295;
   fWeightMatrix1to2[6][9] = 2.5653312818085;
   fWeightMatrix1to2[7][9] = -0.834067080128595;
   fWeightMatrix1to2[8][9] = -1.25965815755966;
   fWeightMatrix1to2[9][9] = -0.788916109085427;
   fWeightMatrix1to2[10][9] = -1.62870584653442;
   fWeightMatrix1to2[11][9] = 0.413556050560529;
   fWeightMatrix1to2[12][9] = 1.86552580610711;
   fWeightMatrix1to2[0][10] = 2.44017287090161;
   fWeightMatrix1to2[1][10] = 0.0881852589898708;
   fWeightMatrix1to2[2][10] = -2.59952722345126;
   fWeightMatrix1to2[3][10] = 0.694958354204109;
   fWeightMatrix1to2[4][10] = -1.74623144101404;
   fWeightMatrix1to2[5][10] = -1.25878446780839;
   fWeightMatrix1to2[6][10] = -1.72326757776544;
   fWeightMatrix1to2[7][10] = -1.55217615990605;
   fWeightMatrix1to2[8][10] = -1.49667124530103;
   fWeightMatrix1to2[9][10] = 0.611498306037643;
   fWeightMatrix1to2[10][10] = -0.35717989401563;
   fWeightMatrix1to2[11][10] = 1.0984740261888;
   fWeightMatrix1to2[12][10] = -1.51127671882008;
   fWeightMatrix1to2[0][11] = 1.15624955590535;
   fWeightMatrix1to2[1][11] = -1.950968670765;
   fWeightMatrix1to2[2][11] = -1.07483359147246;
   fWeightMatrix1to2[3][11] = 1.57437131049451;
   fWeightMatrix1to2[4][11] = -1.59358933620428;
   fWeightMatrix1to2[5][11] = -0.860393149568731;
   fWeightMatrix1to2[6][11] = -1.82470937741621;
   fWeightMatrix1to2[7][11] = -0.752370520065714;
   fWeightMatrix1to2[8][11] = -1.26569478773758;
   fWeightMatrix1to2[9][11] = -0.482428508527012;
   fWeightMatrix1to2[10][11] = -1.60470992589843;
   fWeightMatrix1to2[11][11] = 1.01910009336439;
   fWeightMatrix1to2[12][11] = 0.222427829440432;
   fWeightMatrix1to2[0][12] = -4.38631647581322;
   fWeightMatrix1to2[1][12] = -0.852791737617273;
   fWeightMatrix1to2[2][12] = -1.70120039463888;
   fWeightMatrix1to2[3][12] = -2.32751129055311;
   fWeightMatrix1to2[4][12] = -1.76477624842108;
   fWeightMatrix1to2[5][12] = -0.577555055734616;
   fWeightMatrix1to2[6][12] = -3.57003009188193;
   fWeightMatrix1to2[7][12] = -1.54996339621223;
   fWeightMatrix1to2[8][12] = -0.887365268394173;
   fWeightMatrix1to2[9][12] = -2.82466813161805;
   fWeightMatrix1to2[10][12] = -1.26549270970736;
   fWeightMatrix1to2[11][12] = -1.4729044642718;
   fWeightMatrix1to2[12][12] = -0.498679388090051;
   fWeightMatrix1to2[0][13] = -5.61550655149999;
   fWeightMatrix1to2[1][13] = -2.3867990655003;
   fWeightMatrix1to2[2][13] = -1.43318125492643;
   fWeightMatrix1to2[3][13] = -1.68681685049964;
   fWeightMatrix1to2[4][13] = -1.57692771556456;
   fWeightMatrix1to2[5][13] = 0.103659460853635;
   fWeightMatrix1to2[6][13] = -4.62116504506919;
   fWeightMatrix1to2[7][13] = -1.06973885106692;
   fWeightMatrix1to2[8][13] = 0.596108424170737;
   fWeightMatrix1to2[9][13] = 0.226540116510001;
   fWeightMatrix1to2[10][13] = 0.714786659933145;
   fWeightMatrix1to2[11][13] = -0.00488901972592253;
   fWeightMatrix1to2[12][13] = 1.28721752852172;
   fWeightMatrix1to2[0][14] = 3.29242011610731;
   fWeightMatrix1to2[1][14] = -1.19394210774177;
   fWeightMatrix1to2[2][14] = -2.25572158403812;
   fWeightMatrix1to2[3][14] = -1.06301782061666;
   fWeightMatrix1to2[4][14] = -1.67127603829715;
   fWeightMatrix1to2[5][14] = -0.838819315430104;
   fWeightMatrix1to2[6][14] = 0.28345217810911;
   fWeightMatrix1to2[7][14] = -0.067786532415075;
   fWeightMatrix1to2[8][14] = -0.432375858755326;
   fWeightMatrix1to2[9][14] = -0.0981388969024984;
   fWeightMatrix1to2[10][14] = 0.780603883622621;
   fWeightMatrix1to2[11][14] = -1.40483496795638;
   fWeightMatrix1to2[12][14] = -0.256624697024381;
   // weight matrix from layer 2 to 3
   fWeightMatrix2to3[0][0] = 0.736582114887075;
   fWeightMatrix2to3[0][1] = 7.89216552664885;
   fWeightMatrix2to3[0][2] = -1.48035063133148;
   fWeightMatrix2to3[0][3] = -0.930505294808271;
   fWeightMatrix2to3[0][4] = 0.998437815544713;
   fWeightMatrix2to3[0][5] = 9.88319189896665;
   fWeightMatrix2to3[0][6] = -1.63216282276711;
   fWeightMatrix2to3[0][7] = 1.67369236323486;
   fWeightMatrix2to3[0][8] = 3.52338949870743;
   fWeightMatrix2to3[0][9] = 1.09122283296275;
   fWeightMatrix2to3[0][10] = 1.06926280828436;
   fWeightMatrix2to3[0][11] = 0.779363972364402;
   fWeightMatrix2to3[0][12] = 6.48816292393782;
   fWeightMatrix2to3[0][13] = -0.0828216241489724;
}

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
}