// Class: ReadH6AONN5MEMLP
// Automatically generated by MethodBase::MakeClass
//

/* configuration options =====================================================

#GEN -*-*-*-*-*-*-*-*-*-*-*- general info -*-*-*-*-*-*-*-*-*-*-*-

Method         : MLP::H6AONN5MEMLP
TMVA Release   : 3.8.6         [198662]
ROOT Release   : 5.10/00       [330240]
Creator        : thompson
Date           : Tue Jan 22 16:08:49 2008
Host           : Linux patlx3.fnal.gov 2.4.21-37.ELsmp #1 SMP Wed Sep 28 12:13:44 CDT 2005 i686 i686 i386 GNU/Linux
Dir            : /data/cdf04/thompson/hww_1fb/hwwcdf6.1.4v3_43/Hww/TMVAAna
Training events: 10764


#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]
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 11
LRHWW                         LRHWW                             'F'    [0,0.999995052814]
LRWW                          LRWW                              'F'    [2.17662395269e-15,1.0015938282]
LRWg                          LRWg                              'F'    [0,0.999949336052]
LRWj                          LRWj                              'F'    [-0.00159388501197,1]
LRZZ                          LRZZ                              'F'    [0,0.995219707489]
Met                           Met                               'F'    [15.1351919174,275.269592285]
MetDelPhi                     MetDelPhi                         'F'    [0.126148730516,3.13938593864]
MetSpec                       MetSpec                           'F'    [15.0015153885,147.88381958]
dPhiLeptons                   dPhiLeptons                       'F'    [0.000421762466431,3.13967418671]
dRLeptons                     dRLeptons                         'F'    [0.407130092382,4.52171230316]
dimass                        dimass                            'F'    [16.0061912537,453.994476318]


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

#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( 11 ),
        fIsNormalised( true )
   {      
      // the training input variables
      const char* inputVars[] = { "LRHWW", "LRWW", "LRWg", "LRWj", "LRZZ", "Met", "MetDelPhi", "MetSpec", "dPhiLeptons", "dRLeptons", "dimass" };

      // 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] = 0;
      fVmax[0] = 0.999995052814484;
      fVmin[1] = 2.17662395269116e-15;
      fVmax[1] = 1.00159382820129;
      fVmin[2] = 0;
      fVmax[2] = 0.999949336051941;
      fVmin[3] = -0.001593885011971;
      fVmax[3] = 1;
      fVmin[4] = 0;
      fVmax[4] = 0.995219707489014;
      fVmin[5] = 15.1351919174194;
      fVmax[5] = 275.269592285156;
      fVmin[6] = 0.126148730516434;
      fVmax[6] = 3.13938593864441;
      fVmin[7] = 15.0015153884888;
      fVmax[7] = 147.883819580078;
      fVmin[8] = 0.000421762466430664;
      fVmax[8] = 3.13967418670654;
      fVmin[9] = 0.407130092382431;
      fVmax[9] = 4.52171230316162;
      fVmin[10] = 16.0061912536621;
      fVmax[10] = 453.994476318359;

      // 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';

      // 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[11];
   double fVmax[11];
   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[11];

   // 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[13][12];   // weight matrix from layer 0 to 1
   double fWeightMatrix1to2[12][13];   // weight matrix from layer 1 to 2
   double fWeightMatrix2to3[1][12];   // weight matrix from layer 2 to 3

   double * fWeights[4];
};

inline void ReadH6AONN5MEMLP::Initialize()
{
   // build network structure
   fLayers = 4;
   fLayerSize[0] = 12; fWeights[0] = new double[12]; 
   fLayerSize[1] = 13; fWeights[1] = new double[13]; 
   fLayerSize[2] = 12; fWeights[2] = new double[12]; 
   fLayerSize[3] = 1; fWeights[3] = new double[1]; 
   // weight matrix from layer 0 to 1
   fWeightMatrix0to1[0][0] = 1.68947161687445;
   fWeightMatrix0to1[1][0] = 2.46811847137345;
   fWeightMatrix0to1[2][0] = -0.423152830379053;
   fWeightMatrix0to1[3][0] = 1.6996389781137;
   fWeightMatrix0to1[4][0] = 0.446585746389936;
   fWeightMatrix0to1[5][0] = 2.81125094115633;
   fWeightMatrix0to1[6][0] = 1.41282349430458;
   fWeightMatrix0to1[7][0] = -1.05771622015871;
   fWeightMatrix0to1[8][0] = -0.1470176789744;
   fWeightMatrix0to1[9][0] = 1.41765000357;
   fWeightMatrix0to1[10][0] = 1.22595288764892;
   fWeightMatrix0to1[11][0] = 1.01872438902945;
   fWeightMatrix0to1[0][1] = 1.69492039319763;
   fWeightMatrix0to1[1][1] = 1.38960340589374;
   fWeightMatrix0to1[2][1] = 1.32069998834214;
   fWeightMatrix0to1[3][1] = -2.01801991441964;
   fWeightMatrix0to1[4][1] = 1.09930204618278;
   fWeightMatrix0to1[5][1] = 0.0577161940140724;
   fWeightMatrix0to1[6][1] = -1.45102093812467;
   fWeightMatrix0to1[7][1] = 1.07987931450352;
   fWeightMatrix0to1[8][1] = 1.99076231886993;
   fWeightMatrix0to1[9][1] = 1.87492660465731;
   fWeightMatrix0to1[10][1] = 0.515121015332939;
   fWeightMatrix0to1[11][1] = -0.171338996869393;
   fWeightMatrix0to1[0][2] = 0.367772800094802;
   fWeightMatrix0to1[1][2] = 1.06070577029185;
   fWeightMatrix0to1[2][2] = -0.415939877334002;
   fWeightMatrix0to1[3][2] = -1.43367176358581;
   fWeightMatrix0to1[4][2] = 2.04739248232911;
   fWeightMatrix0to1[5][2] = 0.0278830496318481;
   fWeightMatrix0to1[6][2] = -0.0745996992554088;
   fWeightMatrix0to1[7][2] = -0.537697174962606;
   fWeightMatrix0to1[8][2] = -0.602874366043938;
   fWeightMatrix0to1[9][2] = -0.41454113231626;
   fWeightMatrix0to1[10][2] = -1.31916752653096;
   fWeightMatrix0to1[11][2] = 0.512091501683748;
   fWeightMatrix0to1[0][3] = 0.493118744661838;
   fWeightMatrix0to1[1][3] = 0.939094760469839;
   fWeightMatrix0to1[2][3] = 1.23234569220017;
   fWeightMatrix0to1[3][3] = 1.63459132910022;
   fWeightMatrix0to1[4][3] = -1.2341952382589;
   fWeightMatrix0to1[5][3] = 0.0207828084134658;
   fWeightMatrix0to1[6][3] = -0.716466572014773;
   fWeightMatrix0to1[7][3] = -1.60339636511081;
   fWeightMatrix0to1[8][3] = -0.827529150522545;
   fWeightMatrix0to1[9][3] = -1.53694140177274;
   fWeightMatrix0to1[10][3] = -0.305312331539596;
   fWeightMatrix0to1[11][3] = 1.01367913176179;
   fWeightMatrix0to1[0][4] = 0.251421688771617;
   fWeightMatrix0to1[1][4] = -0.607429091848256;
   fWeightMatrix0to1[2][4] = 1.14115692728095;
   fWeightMatrix0to1[3][4] = -1.31513393851102;
   fWeightMatrix0to1[4][4] = -0.0408543796026979;
   fWeightMatrix0to1[5][4] = -0.484164568344092;
   fWeightMatrix0to1[6][4] = -0.332152196275794;
   fWeightMatrix0to1[7][4] = 0.977672462646503;
   fWeightMatrix0to1[8][4] = 0.0258241271912341;
   fWeightMatrix0to1[9][4] = 0.677407977029712;
   fWeightMatrix0to1[10][4] = -0.0863185979077634;
   fWeightMatrix0to1[11][4] = -0.0669044089066977;
   fWeightMatrix0to1[0][5] = -0.894786887116269;
   fWeightMatrix0to1[1][5] = -0.639302455819293;
   fWeightMatrix0to1[2][5] = -2.04460780260642;
   fWeightMatrix0to1[3][5] = 0.430452227569155;
   fWeightMatrix0to1[4][5] = 1.17353111611932;
   fWeightMatrix0to1[5][5] = -1.7354892973108;
   fWeightMatrix0to1[6][5] = 1.49001559106989;
   fWeightMatrix0to1[7][5] = -0.0982667284913572;
   fWeightMatrix0to1[8][5] = -0.187589697843569;
   fWeightMatrix0to1[9][5] = 0.216471725831547;
   fWeightMatrix0to1[10][5] = -0.166248770446373;
   fWeightMatrix0to1[11][5] = -0.966267509743991;
   fWeightMatrix0to1[0][6] = -1.14296519346094;
   fWeightMatrix0to1[1][6] = -0.100639965260505;
   fWeightMatrix0to1[2][6] = 1.14100397323501;
   fWeightMatrix0to1[3][6] = -0.681796545232712;
   fWeightMatrix0to1[4][6] = 0.0622589454017567;
   fWeightMatrix0to1[5][6] = -0.466778592619129;
   fWeightMatrix0to1[6][6] = -1.22068592983984;
   fWeightMatrix0to1[7][6] = 1.65404128284595;
   fWeightMatrix0to1[8][6] = 0.36568922436881;
   fWeightMatrix0to1[9][6] = 1.60840462916675;
   fWeightMatrix0to1[10][6] = 1.43284256756872;
   fWeightMatrix0to1[11][6] = 0.670170525851989;
   fWeightMatrix0to1[0][7] = -0.532948904735527;
   fWeightMatrix0to1[1][7] = 0.278413730030205;
   fWeightMatrix0to1[2][7] = 1.85759424048165;
   fWeightMatrix0to1[3][7] = -0.828322963396193;
   fWeightMatrix0to1[4][7] = 1.58338488155531;
   fWeightMatrix0to1[5][7] = -0.676549466363819;
   fWeightMatrix0to1[6][7] = -0.370525217295628;
   fWeightMatrix0to1[7][7] = 0.18111983914872;
   fWeightMatrix0to1[8][7] = 0.00401875111016684;
   fWeightMatrix0to1[9][7] = 1.46085918945001;
   fWeightMatrix0to1[10][7] = -1.47611118547378;
   fWeightMatrix0to1[11][7] = 2.24030698018001;
   fWeightMatrix0to1[0][8] = -0.300457172118767;
   fWeightMatrix0to1[1][8] = -0.630262739338355;
   fWeightMatrix0to1[2][8] = 1.62869958851522;
   fWeightMatrix0to1[3][8] = -1.86833005835286;
   fWeightMatrix0to1[4][8] = -0.0597147926926143;
   fWeightMatrix0to1[5][8] = 0.89021857816515;
   fWeightMatrix0to1[6][8] = -0.573692765380444;
   fWeightMatrix0to1[7][8] = -0.0593194966288885;
   fWeightMatrix0to1[8][8] = 1.74575792662837;
   fWeightMatrix0to1[9][8] = -0.0115206220043303;
   fWeightMatrix0to1[10][8] = 1.3760769350417;
   fWeightMatrix0to1[11][8] = -1.04589448457288;
   fWeightMatrix0to1[0][9] = -0.183484061639826;
   fWeightMatrix0to1[1][9] = -1.66890673007129;
   fWeightMatrix0to1[2][9] = 1.74456096663868;
   fWeightMatrix0to1[3][9] = -1.1319316980566;
   fWeightMatrix0to1[4][9] = -0.123858744191721;
   fWeightMatrix0to1[5][9] = 0.64557115292348;
   fWeightMatrix0to1[6][9] = -1.46057855180332;
   fWeightMatrix0to1[7][9] = 1.21035337925966;
   fWeightMatrix0to1[8][9] = -1.30209058900664;
   fWeightMatrix0to1[9][9] = -1.89606467654604;
   fWeightMatrix0to1[10][9] = -0.970749837823885;
   fWeightMatrix0to1[11][9] = 0.483657791202562;
   fWeightMatrix0to1[0][10] = -0.62965977219929;
   fWeightMatrix0to1[1][10] = 0.956483859624464;
   fWeightMatrix0to1[2][10] = 1.84448494100534;
   fWeightMatrix0to1[3][10] = -2.02560280301935;
   fWeightMatrix0to1[4][10] = 0.871100694796679;
   fWeightMatrix0to1[5][10] = -1.27451436415254;
   fWeightMatrix0to1[6][10] = -2.9701261815216;
   fWeightMatrix0to1[7][10] = 1.31689525339896;
   fWeightMatrix0to1[8][10] = -0.399895643951945;
   fWeightMatrix0to1[9][10] = -1.18425791557395;
   fWeightMatrix0to1[10][10] = -1.04903214185664;
   fWeightMatrix0to1[11][10] = -1.10322828766433;
   fWeightMatrix0to1[0][11] = -1.07156372215608;
   fWeightMatrix0to1[1][11] = 0.148537067899626;
   fWeightMatrix0to1[2][11] = -0.229060144427753;
   fWeightMatrix0to1[3][11] = 0.939342936802946;
   fWeightMatrix0to1[4][11] = 1.04673995619725;
   fWeightMatrix0to1[5][11] = 1.43588231714994;
   fWeightMatrix0to1[6][11] = -2.05800989753775;
   fWeightMatrix0to1[7][11] = -1.49010124288818;
   fWeightMatrix0to1[8][11] = 2.2036451899239;
   fWeightMatrix0to1[9][11] = -1.36770861395954;
   fWeightMatrix0to1[10][11] = 0.496093157599756;
   fWeightMatrix0to1[11][11] = -0.191259378012063;
   // weight matrix from layer 1 to 2
   fWeightMatrix1to2[0][0] = -1.62026632471849;
   fWeightMatrix1to2[1][0] = 0.372087454187249;
   fWeightMatrix1to2[2][0] = -0.836598772031547;
   fWeightMatrix1to2[3][0] = -0.814843878849933;
   fWeightMatrix1to2[4][0] = -2.21779873734479;
   fWeightMatrix1to2[5][0] = -0.21333748447208;
   fWeightMatrix1to2[6][0] = 1.58944388957467;
   fWeightMatrix1to2[7][0] = 1.65919707073936;
   fWeightMatrix1to2[8][0] = 1.61085409112487;
   fWeightMatrix1to2[9][0] = -1.94054211124858;
   fWeightMatrix1to2[10][0] = -0.377439690828793;
   fWeightMatrix1to2[0][1] = 0.720781463380943;
   fWeightMatrix1to2[1][1] = -1.5525026404778;
   fWeightMatrix1to2[2][1] = -0.944260135388997;
   fWeightMatrix1to2[3][1] = -0.0592950762342603;
   fWeightMatrix1to2[4][1] = 1.44529565767567;
   fWeightMatrix1to2[5][1] = -1.91829715865128;
   fWeightMatrix1to2[6][1] = -0.77204577753434;
   fWeightMatrix1to2[7][1] = 0.0608678820917878;
   fWeightMatrix1to2[8][1] = -1.37076557204046;
   fWeightMatrix1to2[9][1] = -2.02892542288919;
   fWeightMatrix1to2[10][1] = 1.21725204627027;
   fWeightMatrix1to2[0][2] = 0.533339541419098;
   fWeightMatrix1to2[1][2] = -0.113042335879419;
   fWeightMatrix1to2[2][2] = -0.933195949205692;
   fWeightMatrix1to2[3][2] = -0.144312905385284;
   fWeightMatrix1to2[4][2] = 1.03847673928725;
   fWeightMatrix1to2[5][2] = -1.44957876836251;
   fWeightMatrix1to2[6][2] = 1.00627288691113;
   fWeightMatrix1to2[7][2] = -2.00149571179075;
   fWeightMatrix1to2[8][2] = 0.25973768888349;
   fWeightMatrix1to2[9][2] = -0.114584347258752;
   fWeightMatrix1to2[10][2] = -0.471001528604517;
   fWeightMatrix1to2[0][3] = -1.24264878811548;
   fWeightMatrix1to2[1][3] = 1.00396420278131;
   fWeightMatrix1to2[2][3] = 0.909049690437961;
   fWeightMatrix1to2[3][3] = -2.1097969807082;
   fWeightMatrix1to2[4][3] = 0.594035806627104;
   fWeightMatrix1to2[5][3] = 0.293255648567016;
   fWeightMatrix1to2[6][3] = 1.61509860237825;
   fWeightMatrix1to2[7][3] = 0.27676385533113;
   fWeightMatrix1to2[8][3] = -1.23842027849883;
   fWeightMatrix1to2[9][3] = -1.56665766788586;
   fWeightMatrix1to2[10][3] = 0.141770253825407;
   fWeightMatrix1to2[0][4] = 0.622551358757395;
   fWeightMatrix1to2[1][4] = 0.426458335673572;
   fWeightMatrix1to2[2][4] = 0.380497935751377;
   fWeightMatrix1to2[3][4] = -0.865286756364957;
   fWeightMatrix1to2[4][4] = 0.331157451241065;
   fWeightMatrix1to2[5][4] = -0.485141225589176;
   fWeightMatrix1to2[6][4] = -1.58046599348042;
   fWeightMatrix1to2[7][4] = 0.278150472348462;
   fWeightMatrix1to2[8][4] = 0.923537394177008;
   fWeightMatrix1to2[9][4] = -0.0939593468247158;
   fWeightMatrix1to2[10][4] = 0.623266383668055;
   fWeightMatrix1to2[0][5] = -0.149745497593903;
   fWeightMatrix1to2[1][5] = 0.899193093299354;
   fWeightMatrix1to2[2][5] = -0.334595408361763;
   fWeightMatrix1to2[3][5] = -0.574162642714199;
   fWeightMatrix1to2[4][5] = -2.29416246490348;
   fWeightMatrix1to2[5][5] = -1.40408998350983;
   fWeightMatrix1to2[6][5] = -0.915244140365508;
   fWeightMatrix1to2[7][5] = 0.528315202661693;
   fWeightMatrix1to2[8][5] = -2.2805045111585;
   fWeightMatrix1to2[9][5] = 1.41733005097026;
   fWeightMatrix1to2[10][5] = -0.56525282191677;
   fWeightMatrix1to2[0][6] = -1.07095556789927;
   fWeightMatrix1to2[1][6] = 1.39905326022826;
   fWeightMatrix1to2[2][6] = -1.27665969483918;
   fWeightMatrix1to2[3][6] = -1.86225415715224;
   fWeightMatrix1to2[4][6] = 0.835652224321386;
   fWeightMatrix1to2[5][6] = -0.907710765300982;
   fWeightMatrix1to2[6][6] = -0.0572914165390077;
   fWeightMatrix1to2[7][6] = -2.34451595149624;
   fWeightMatrix1to2[8][6] = -0.892000036886155;
   fWeightMatrix1to2[9][6] = 1.88578874508975;
   fWeightMatrix1to2[10][6] = -3.7967866594485;
   fWeightMatrix1to2[0][7] = 1.48625443624101;
   fWeightMatrix1to2[1][7] = 0.645113921808004;
   fWeightMatrix1to2[2][7] = -0.364622994803198;
   fWeightMatrix1to2[3][7] = -1.10644833503668;
   fWeightMatrix1to2[4][7] = -1.37038239462621;
   fWeightMatrix1to2[5][7] = -0.867636875213182;
   fWeightMatrix1to2[6][7] = -0.89459776990971;
   fWeightMatrix1to2[7][7] = -1.62415720557937;
   fWeightMatrix1to2[8][7] = -0.0169522138991975;
   fWeightMatrix1to2[9][7] = -1.22410359695801;
   fWeightMatrix1to2[10][7] = 1.7649009444086;
   fWeightMatrix1to2[0][8] = -1.30849134993285;
   fWeightMatrix1to2[1][8] = 0.568273141941294;
   fWeightMatrix1to2[2][8] = 0.790925949863102;
   fWeightMatrix1to2[3][8] = 0.775698804790897;
   fWeightMatrix1to2[4][8] = -1.85566818945017;
   fWeightMatrix1to2[5][8] = 0.71441396023427;
   fWeightMatrix1to2[6][8] = -2.25079020103165;
   fWeightMatrix1to2[7][8] = 0.254734126110145;
   fWeightMatrix1to2[8][8] = -0.557552881558574;
   fWeightMatrix1to2[9][8] = -2.16774220681125;
   fWeightMatrix1to2[10][8] = 1.81875553226921;
   fWeightMatrix1to2[0][9] = 0.0379375156500776;
   fWeightMatrix1to2[1][9] = 1.29207718991557;
   fWeightMatrix1to2[2][9] = 1.51013575289106;
   fWeightMatrix1to2[3][9] = 0.127270250880133;
   fWeightMatrix1to2[4][9] = 1.02316284297644;
   fWeightMatrix1to2[5][9] = -1.21724487367421;
   fWeightMatrix1to2[6][9] = -1.28360236575166;
   fWeightMatrix1to2[7][9] = -0.517124505658746;
   fWeightMatrix1to2[8][9] = -0.437396175358191;
   fWeightMatrix1to2[9][9] = -1.63495764911954;
   fWeightMatrix1to2[10][9] = -0.978373772342783;
   fWeightMatrix1to2[0][10] = 1.14155609591925;
   fWeightMatrix1to2[1][10] = 0.0473711332507376;
   fWeightMatrix1to2[2][10] = -1.95716839975317;
   fWeightMatrix1to2[3][10] = 1.29040199776563;
   fWeightMatrix1to2[4][10] = -1.67495038263125;
   fWeightMatrix1to2[5][10] = -0.65756745927251;
   fWeightMatrix1to2[6][10] = -1.37114607512359;
   fWeightMatrix1to2[7][10] = 0.652014382395629;
   fWeightMatrix1to2[8][10] = -0.343047405770572;
   fWeightMatrix1to2[9][10] = -0.355088448420129;
   fWeightMatrix1to2[10][10] = -0.834137137529395;
   fWeightMatrix1to2[0][11] = 0.649399238483675;
   fWeightMatrix1to2[1][11] = -0.0855145384110076;
   fWeightMatrix1to2[2][11] = 0.142060698010815;
   fWeightMatrix1to2[3][11] = -1.417204885596;
   fWeightMatrix1to2[4][11] = -0.632779109846491;
   fWeightMatrix1to2[5][11] = -0.775869902340652;
   fWeightMatrix1to2[6][11] = 0.918950899508775;
   fWeightMatrix1to2[7][11] = 1.10274849810322;
   fWeightMatrix1to2[8][11] = 0.0296925498296716;
   fWeightMatrix1to2[9][11] = 1.1344697013168;
   fWeightMatrix1to2[10][11] = 0.115485684443586;
   fWeightMatrix1to2[0][12] = 0.101773151799514;
   fWeightMatrix1to2[1][12] = 0.809255382495267;
   fWeightMatrix1to2[2][12] = -0.724195105554074;
   fWeightMatrix1to2[3][12] = -0.442465836579917;
   fWeightMatrix1to2[4][12] = 1.26938576802467;
   fWeightMatrix1to2[5][12] = 0.649919349863825;
   fWeightMatrix1to2[6][12] = -1.78474438512697;
   fWeightMatrix1to2[7][12] = -2.01041989901341;
   fWeightMatrix1to2[8][12] = 0.197815567571606;
   fWeightMatrix1to2[9][12] = -0.245331404816149;
   fWeightMatrix1to2[10][12] = 1.78803124451618;
   // weight matrix from layer 2 to 3
   fWeightMatrix2to3[0][0] = -0.253301425445233;
   fWeightMatrix2to3[0][1] = 0.466607184542433;
   fWeightMatrix2to3[0][2] = -0.250370048128024;
   fWeightMatrix2to3[0][3] = -0.361160904792614;
   fWeightMatrix2to3[0][4] = -0.765270572710058;
   fWeightMatrix2to3[0][5] = -1.45423561895882;
   fWeightMatrix2to3[0][6] = -1.79892366433004;
   fWeightMatrix2to3[0][7] = 0.234447070363376;
   fWeightMatrix2to3[0][8] = 0.859657012334496;
   fWeightMatrix2to3[0][9] = 1.33940526199404;
   fWeightMatrix2to3[0][10] = -0.995900695494867;
   fWeightMatrix2to3[0][11] = 0.795970024288521;
}

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
}