TMVACrossValidation_BDTG_fold1.class.C

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// Class: ReadBDTG_fold1
// Automatically generated by MethodBase::MakeClass
//
 
/* configuration options =====================================================
 
#GEN -*-*-*-*-*-*-*-*-*-*-*- general info -*-*-*-*-*-*-*-*-*-*-*-
 
Method         : BDT::BDTG_fold1
TMVA Release   : 4.2.1         [262657]
ROOT Release   : 6.16/01       [397313]
Creator        : sftnight
Date           : Sun Dec 19 22:14:14 2021
Host           : Linux root-ubuntu-2004-3 5.4.0-73-generic #82-Ubuntu SMP Wed Apr 14 17:39:42 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux
Dir            : /home/sftnight/build/workspace/root-makedoc-v616/rootspi/rdoc/src/v6-16-00-patches/documentation/doxygen
Training events: 999
Analysis type  : [Classification]
 
 
#OPT -*-*-*-*-*-*-*-*-*-*-*-*- options -*-*-*-*-*-*-*-*-*-*-*-*-
 
# Set by User:
V: "False" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
H: "False" [Print method-specific help message]
NTrees: "100" [Number of trees in the forest]
MaxDepth: "2" [Max depth of the decision tree allowed]
MinNodeSize: "2.5%" [Minimum percentage of training events required in a leaf node (default: Classification: 5%, Regression: 0.2%)]
nCuts: "20" [Number of grid points in variable range used in finding optimal cut in node splitting]
BoostType: "Grad" [Boosting type for the trees in the forest (note: AdaCost is still experimental)]
Shrinkage: "1.000000e-01" [Learning rate for BoostType=Grad algorithm]
NegWeightTreatment: "pray" [How to treat events with negative weights in the BDT training (particular the boosting) : IgnoreInTraining;  Boost With inverse boostweight; Pair events with negative and positive weights in training sample and *annihilate* them (experimental!)]
# Default:
VerbosityLevel: "Default" [Verbosity level]
VarTransform: "None" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
AdaBoostR2Loss: "quadratic" [Type of Loss function in AdaBoostR2]
UseBaggedBoost: "False" [Use only a random subsample of all events for growing the trees in each boost iteration.]
AdaBoostBeta: "5.000000e-01" [Learning rate  for AdaBoost algorithm]
UseRandomisedTrees: "False" [Determine at each node splitting the cut variable only as the best out of a random subset of variables (like in RandomForests)]
UseNvars: "2" [Size of the subset of variables used with RandomisedTree option]
UsePoissonNvars: "True" [Interpret "UseNvars" not as fixed number but as mean of a Poisson distribution in each split with RandomisedTree option]
BaggedSampleFraction: "6.000000e-01" [Relative size of bagged event sample to original size of the data sample (used whenever bagging is used (i.e. UseBaggedBoost, Bagging,)]
UseYesNoLeaf: "True" [Use Sig or Bkg categories, or the purity=S/(S+B) as classification of the leaf node -> Real-AdaBoost]
Css: "1.000000e+00" [AdaCost: cost of true signal selected signal]
Cts_sb: "1.000000e+00" [AdaCost: cost of true signal selected bkg]
Ctb_ss: "1.000000e+00" [AdaCost: cost of true bkg    selected signal]
Cbb: "1.000000e+00" [AdaCost: cost of true bkg    selected bkg ]
NodePurityLimit: "5.000000e-01" [In boosting/pruning, nodes with purity > NodePurityLimit are signal; background otherwise.]
SeparationType: "giniindex" [Separation criterion for node splitting]
RegressionLossFunctionBDTG: "huber" [Loss function for BDTG regression.]
HuberQuantile: "7.000000e-01" [In the Huber loss function this is the quantile that separates the core from the tails in the residuals distribution.]
DoBoostMonitor: "False" [Create control plot with ROC integral vs tree number]
UseFisherCuts: "False" [Use multivariate splits using the Fisher criterion]
MinLinCorrForFisher: "8.000000e-01" [The minimum linear correlation between two variables demanded for use in Fisher criterion in node splitting]
UseExclusiveVars: "False" [Variables already used in fisher criterion are not anymore analysed individually for node splitting]
DoPreselection: "False" [and and apply automatic pre-selection for 100% efficient signal (bkg) cuts prior to training]
SigToBkgFraction: "1.000000e+00" [Sig to Bkg ratio used in Training (similar to NodePurityLimit, which cannot be used in real adaboost]
PruneMethod: "nopruning" [Note: for BDTs use small trees (e.g.MaxDepth=3) and NoPruning:  Pruning: Method used for pruning (removal) of statistically insignificant branches ]
PruneStrength: "0.000000e+00" [Pruning strength]
PruningValFraction: "5.000000e-01" [Fraction of events to use for optimizing automatic pruning.]
SkipNormalization: "False" [Skip normalization at initialization, to keep expectation value of BDT output according to the fraction of events]
nEventsMin: "0" [deprecated: Use MinNodeSize (in % of training events) instead]
UseBaggedGrad: "False" [deprecated: Use *UseBaggedBoost* instead:  Use only a random subsample of all events for growing the trees in each iteration.]
GradBaggingFraction: "6.000000e-01" [deprecated: Use *BaggedSampleFraction* instead: Defines the fraction of events to be used in each iteration, e.g. when UseBaggedGrad=kTRUE. ]
UseNTrainEvents: "0" [deprecated: Use *BaggedSampleFraction* instead: Number of randomly picked training events used in randomised (and bagged) trees]
NNodesMax: "0" [deprecated: Use MaxDepth instead to limit the tree size]
##
 
 
#VAR -*-*-*-*-*-*-*-*-*-*-*-* variables *-*-*-*-*-*-*-*-*-*-*-*-
 
NVar 2
x                             x                             x                             x                                                               'F'    [-4.10750675201,4.09692668915]
y                             y                             y                             y                                                               'F'    [-4.85200452805,4.07606744766]
NSpec 1
eventID                       eventID                       eventID                       I                                                               'F'    [1,1000]
 
 
============================================================================ */
 
#include <array>
#include <vector>
#include <cmath>
#include <string>
#include <iostream>
 
#define NN new BDTG_fold1Node
   
#ifndef BDTG_fold1Node__def
#define BDTG_fold1Node__def
   
class BDTG_fold1Node {
   
public:
   
   // constructor of an essentially "empty" node floating in space
   BDTG_fold1Node ( BDTG_fold1Node* left,BDTG_fold1Node* right,
                          int selector, double cutValue, bool cutType, 
                          int nodeType, double purity, double response ) :
   fLeft         ( left         ),
   fRight        ( right        ),
   fSelector     ( selector     ),
   fCutValue     ( cutValue     ),
   fCutType      ( cutType      ),
   fNodeType     ( nodeType     ),
   fPurity       ( purity       ),
   fResponse     ( response     ){
   }
 
   virtual ~BDTG_fold1Node();
 
   // test event if it descends the tree at this node to the right
   virtual bool GoesRight( const std::vector<double>& inputValues ) const;
   BDTG_fold1Node* GetRight( void )  {return fRight; };
 
   // test event if it descends the tree at this node to the left 
   virtual bool GoesLeft ( const std::vector<double>& inputValues ) const;
   BDTG_fold1Node* GetLeft( void ) { return fLeft; };   
 
   // return  S/(S+B) (purity) at this node (from  training)
 
   double GetPurity( void ) const { return fPurity; } 
   // return the node type
   int    GetNodeType( void ) const { return fNodeType; }
   double GetResponse(void) const {return fResponse;}
 
private:
 
   BDTG_fold1Node*   fLeft;     // pointer to the left daughter node
   BDTG_fold1Node*   fRight;    // pointer to the right daughter node
   int                     fSelector; // index of variable used in node selection (decision tree)   
   double                  fCutValue; // cut value applied on this node to discriminate bkg against sig
   bool                    fCutType;  // true: if event variable > cutValue ==> signal , false otherwise
   int                     fNodeType; // Type of node: -1 == Bkg-leaf, 1 == Signal-leaf, 0 = internal 
   double                  fPurity;   // Purity of node from training
   double                  fResponse; // Regression response value of node
}; 
   
//_______________________________________________________________________
   BDTG_fold1Node::~BDTG_fold1Node()
{
   if (fLeft  != NULL) delete fLeft;
   if (fRight != NULL) delete fRight;
}; 
   
//_______________________________________________________________________
bool BDTG_fold1Node::GoesRight( const std::vector<double>& inputValues ) const
{
   // test event if it descends the tree at this node to the right
   bool result;
     result = (inputValues[fSelector] > fCutValue );
   if (fCutType == true) return result; //the cuts are selecting Signal ;
   else return !result;
}
   
//_______________________________________________________________________
bool BDTG_fold1Node::GoesLeft( const std::vector<double>& inputValues ) const
{
   // test event if it descends the tree at this node to the left
   if (!this->GoesRight(inputValues)) return true;
   else return false;
}
   
#endif
   
#ifndef IClassifierReader__def
#define IClassifierReader__def
 
class IClassifierReader {
 
 public:
 
   // constructor
   IClassifierReader() : fStatusIsClean( true ) {}
   virtual ~IClassifierReader() {}
 
   // return classifier response
   virtual double GetMvaValue( const std::vector<double>& inputValues ) const = 0;
 
   // returns classifier status
   bool IsStatusClean() const { return fStatusIsClean; }
 
 protected:
 
   bool fStatusIsClean;
};
 
#endif
 
class ReadBDTG_fold1 : public IClassifierReader {
 
 public:
 
   // constructor
   ReadBDTG_fold1( std::vector<std::string>& theInputVars )
      : IClassifierReader(),
        fClassName( "ReadBDTG_fold1" ),
        fNvars( 2 )
   {
      // the training input variables
      const char* inputVars[] = { "x", "y" };
 
      // 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;
      fVmin[1] = 0;
      fVmax[1] = 0;
 
      // initialize input variable types
      fType[0] = 'F';
      fType[1] = 'F';
 
      // initialize constants
      Initialize();
 
   }
 
   // destructor
   virtual ~ReadBDTG_fold1() {
      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 override;
 
 private:
 
   // method-specific destructor
   void Clear();
 
   // common member variables
   const char* fClassName;
 
   const size_t fNvars;
   size_t GetNvar()           const { return fNvars; }
   char   GetType( int ivar ) const { return fType[ivar]; }
 
   // normalisation of input variables
   double fVmin[2];
   double fVmax[2];
   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[2];
 
   // initialize internal variables
   void Initialize();
   double GetMvaValue__( const std::vector<double>& inputValues ) const;
 
   // private members (method specific)
   std::vector<BDTG_fold1Node*> fForest;       // i.e. root nodes of decision trees
   std::vector<double>                fBoostWeights; // the weights applied in the individual boosts
};
 
double ReadBDTG_fold1::GetMvaValue__( const std::vector<double>& inputValues ) const
{
   double myMVA = 0;
   for (unsigned int itree=0; itree<fForest.size(); itree++){
      BDTG_fold1Node *current = fForest[itree];
      while (current->GetNodeType() == 0) { //intermediate node
         if (current->GoesRight(inputValues)) current=(BDTG_fold1Node*)current->GetRight();
         else current=(BDTG_fold1Node*)current->GetLeft();
      }
      myMVA += current->GetResponse();
   }
   return 2.0/(1.0+exp(-2.0*myMVA))-1.0;
};
 
void ReadBDTG_fold1::Initialize()
{
  // itree = 0
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0692927,-0.0861415) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.5269,0.00538008) , 
1, 0.298978, 1, 0, 0.157724,-0.342276) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.24553,-0.050894) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.927859,0.0855717) , 
1, -0.880057, 1, 0, 0.853389,0.353389) , 
0, 0.0100296, 1, 0, 0.5,6.79025e-17)    );
  // itree = 1
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0512509,-0.0817606) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.461006,-0.00600807) , 
1, 0.00659994, 1, 0, 0.157724,-0.308134) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.46593,-0.00880564) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.966284,0.0853423) , 
1, -0.208424, 1, 0, 0.853389,0.318141) , 
0, 0.0100296, 1, 0, 0.5,9.31133e-07)    );
  // itree = 2
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0876446,-0.0692193) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.609155,0.0218939) , 
1, 0.591356, 1, 0, 0.157724,-0.277596) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.24553,-0.0450914) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.927859,0.0716971) , 
1, -0.880057, 1, 0, 0.853389,0.286642) , 
0, 0.0100296, 1, 0, 0.5,1.6223e-05)    );
  // itree = 3
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0512509,-0.0702804) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.461006,-0.00324379) , 
1, 0.00659994, 1, 0, 0.157724,-0.25018) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.46593,-0.00952982) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.966284,0.07363) , 
1, -0.208424, 1, 0, 0.853389,0.258368) , 
0, 0.0100296, 1, 0, 0.5,3.19008e-05)    );
  // itree = 4
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0876446,-0.0599302) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.609155,0.0200355) , 
1, 0.591356, 1, 0, 0.157724,-0.225597) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.24553,-0.0400301) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.927859,0.0623964) , 
1, -0.880057, 1, 0, 0.853389,0.233041) , 
0, 0.0100296, 1, 0, 0.5,5.86548e-05)    );
  // itree = 5
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0821188,-0.0601944) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.73043,0.0330029) , 
0, 0.474451, 1, 0, 0.192909,-0.1953) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.282165,-0.0376942) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.947999,0.0661453) , 
0, -0.733043, 1, 0, 0.855107,0.225993) , 
1, 0.127393, 1, 0, 0.5,7.26571e-05)    );
  // itree = 6
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.124262,-0.0514545) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.915088,0.0663889) , 
0, 1.12121, 1, 0, 0.192909,-0.176171) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.282165,-0.034143) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.947999,0.0623525) , 
0, -0.733043, 1, 0, 0.855107,0.203917) , 
1, 0.127393, 1, 0, 0.5,9.30533e-05)    );
  // itree = 7
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.071642,-0.0549545) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.609564,0.0135076) , 
1, 0.0635093, 1, 0, 0.231573,-0.139616) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.430612,-0.00506252) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.953677,0.0586231) , 
1, -0.759956, 1, 0, 0.910081,0.213728) , 
0, 0.381566, 1, 0, 0.5,0.000171526)    );
  // itree = 8
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0306792,-0.0646138) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.219902,-0.0251687) , 
0, -1.15847, 1, 0, 0.113435,-0.191097) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.337187,-0.0176015) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.947902,0.0574977) , 
1, -0.208424, 1, 0, 0.770052,0.133773) , 
0, -0.361506, 1, 0, 0.5,0.00016085)    );
  // itree = 9
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0306792,-0.0616551) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.219902,-0.0230613) , 
0, -1.15847, 1, 0, 0.113435,-0.172416) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.25076,-0.0276904) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.904862,0.0492518) , 
1, -0.54424, 1, 0, 0.770052,0.120794) , 
0, -0.361506, 1, 0, 0.5,0.00020326)    );
  // itree = 10
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.124262,-0.0426251) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.915088,0.0596788) , 
0, 1.12121, 1, 0, 0.192909,-0.123954) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.282165,-0.02363) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.947999,0.0528106) , 
0, -0.733043, 1, 0, 0.855107,0.143739) , 
1, 0.127393, 1, 0, 0.5,0.000187199)    );
  // itree = 11
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.164486,-0.0356659) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.893413,0.0548137) , 
1, 1.38355, 1, 0, 0.231573,-0.0984087) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.834397,0.028901) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.985081,0.0611526) , 
0, 1.2668, 1, 0, 0.910081,0.150965) , 
0, 0.381566, 1, 0, 0.5,0.000246893)    );
  // itree = 12
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.124262,-0.0384821) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.915088,0.05245) , 
0, 1.12121, 1, 0, 0.192909,-0.105162) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.199242,-0.0358601) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.918243,0.045859) , 
0, -1.10458, 1, 0, 0.855107,0.122255) , 
1, 0.127393, 1, 0, 0.5,0.000301739)    );
  // itree = 13
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0306792,-0.0549406) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.219902,-0.0166691) , 
0, -1.15847, 1, 0, 0.113435,-0.124496) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.160784,-0.0330175) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.877571,0.0395819) , 
1, -0.880057, 1, 0, 0.770052,0.0875037) , 
0, -0.361506, 1, 0, 0.5,0.000313139)    );
  // itree = 14
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.136235,-0.0343998) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.947261,0.0588315) , 
0, 1.44459, 1, 0, 0.192909,-0.0894723) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.199242,-0.0287611) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.918243,0.042086) , 
0, -1.10458, 1, 0, 0.855107,0.103998) , 
1, 0.127393, 1, 0, 0.5,0.000248575)    );
  // itree = 15
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0748904,-0.0400534) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.568894,0.0130192) , 
1, 0.920452, 1, 0, 0.113435,-0.105741) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.160784,-0.0291505) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.877571,0.0362348) , 
1, -0.880057, 1, 0, 0.770052,0.0743247) , 
0, -0.361506, 1, 0, 0.5,0.000267787)    );
  // itree = 16
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.1854,-0.0275542) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.972142,0.0701165) , 
1, 1.71355, 1, 0, 0.231573,-0.0662441) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.863104,0.0261717) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.993089,0.0596013) , 
0, 1.62056, 1, 0, 0.910081,0.101749) , 
0, 0.381566, 1, 0, 0.5,0.000216427)    );
  // itree = 17
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.101193,-0.0355834) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.960988,0.063115) , 
0, 1.31332, 1, 0, 0.148921,-0.0851763) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.122167,-0.0335145) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.888204,0.0361749) , 
0, -1.10458, 1, 0, 0.794633,0.0719755) , 
1, -0.208424, 1, 0, 0.5,0.000268514)    );
  // itree = 18
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0514019,-0.0417201) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.651097,0.0179669) , 
0, 0.723396, 1, 0, 0.102367,-0.0932886) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.08809,-0.0365048) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.839361,0.0300969) , 
0, -1.10458, 1, 0, 0.730119,0.0543734) , 
1, -0.54424, 1, 0, 0.5,0.000243951)    );
  // itree = 19
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.114844,-0.0306776) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.813505,0.0226849) , 
0, 0.494817, 1, 0, 0.257748,-0.0517066) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.235422,-0.0151562) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.94673,0.0407423) , 
0, -1.10458, 1, 0, 0.898128,0.0853891) , 
1, 0.463209, 1, 0, 0.5,0.0001559)    );
  // itree = 20
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0264933,-0.0459925) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.413706,0.00114721) , 
0, 0.428432, 1, 0, 0.0618969,-0.093446) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.146074,-0.0222608) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.831826,0.0270818) , 
0, -0.733043, 1, 0, 0.683263,0.0393249) , 
1, -0.880057, 1, 0, 0.5,0.000166036)    );
  // itree = 21
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.196451,-0.0227953) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.979691,0.0568176) , 
0, 1.47005, 1, 0, 0.257748,-0.044291) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.848897,0.0193132) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.992387,0.0593641) , 
1, 1.75889, 1, 0, 0.898128,0.073026) , 
1, 0.463209, 1, 0, 0.5,8.93742e-05)    );
  // itree = 22
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0306792,-0.0447121) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.219902,-0.00801468) , 
0, -1.15847, 1, 0, 0.113435,-0.0636001) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.337187,-0.00801121) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.947902,0.0366088) , 
1, -0.208424, 1, 0, 0.770052,0.0446759) , 
0, -0.361506, 1, 0, 0.5,0.000144383)    );
  // itree = 23
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0196023,-0.0500102) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.108883,-0.0196881) , 
1, -1.52396, 1, 0, 0.0618969,-0.0758161) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0808608,-0.0287749) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.798094,0.0211114) , 
0, -1.10458, 1, 0, 0.683263,0.0319075) , 
1, -0.880057, 1, 0, 0.5,0.000135981)    );
  // itree = 24
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.196451,-0.0201156) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.979691,0.0554438) , 
0, 1.47005, 1, 0, 0.257748,-0.0356899) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.848897,0.0159854) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.992387,0.0582284) , 
1, 1.75889, 1, 0, 0.898128,0.0588288) , 
1, 0.463209, 1, 0, 0.5,6.60033e-05)    );
  // itree = 25
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0612809,-0.0341574) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.250468,-0.00113239) , 
0, -0.84011, 1, 0, 0.113435,-0.0520961) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.25076,-0.0114328) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.904862,0.0272303) , 
1, -0.54424, 1, 0, 0.770052,0.0365916) , 
0, -0.361506, 1, 0, 0.5,0.000116301)    );
  // itree = 26
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.1854,-0.0189236) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.972142,0.0626115) , 
1, 1.71355, 1, 0, 0.231573,-0.0326299) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.863104,0.0146943) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.993089,0.0537174) , 
0, 1.62056, 1, 0, 0.910081,0.0500926) , 
0, 0.381566, 1, 0, 0.5,9.62482e-05)    );
  // itree = 27
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0332592,-0.0439146) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.108691,-0.0130004) , 
1, -1.3963, 1, 0, 0.0618969,-0.0596946) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0808608,-0.0237647) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.798094,0.0178185) , 
0, -1.10458, 1, 0, 0.683263,0.0251637) , 
1, -0.880057, 1, 0, 0.5,0.000135955)    );
  // itree = 28
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.151169,-0.019193) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0635833) , 
0, 1.76797, 1, 0, 0.192909,-0.0334464) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.798073,0.00959204) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.992728,0.0561852) , 
1, 1.72785, 1, 0, 0.855107,0.0388487) , 
1, 0.127393, 1, 0, 0.5,8.0117e-05)    );
  // itree = 29
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0612809,-0.0307013) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.250468,0.000446177) , 
0, -0.84011, 1, 0, 0.113435,-0.0404531) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.337187,-0.00531389) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.947902,0.0292844) , 
1, -0.208424, 1, 0, 0.770052,0.0284522) , 
0, -0.361506, 1, 0, 0.5,0.000113031)    );
  // itree = 30
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0201936,-0.0405389) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.300901,0.00250843) , 
0, 0.133467, 1, 0, 0.0618969,-0.0485745) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0808608,-0.0200662) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.798094,0.0151747) , 
0, -1.10458, 1, 0, 0.683263,0.0204726) , 
1, -0.880057, 1, 0, 0.5,0.000108154)    );
  // itree = 31
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.196451,-0.0154266) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.979691,0.0514829) , 
0, 1.47005, 1, 0, 0.257748,-0.0230514) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.848897,0.0109195) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.992387,0.0556421) , 
1, 1.75889, 1, 0, 0.898128,0.0380198) , 
1, 0.463209, 1, 0, 0.5,5.14854e-05)    );
  // itree = 32
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.1854,-0.0149044) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.972142,0.0569293) , 
1, 1.71355, 1, 0, 0.231573,-0.0225556) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.863104,0.0103199) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.993089,0.0512144) , 
0, 1.62056, 1, 0, 0.910081,0.0346828) , 
0, 0.381566, 1, 0, 0.5,8.87255e-05)    );
  // itree = 33
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0100108,-0.0458457) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.315943,0.00297507) , 
0, 0.133467, 1, 0, 0.0481884,-0.0491461) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0668222,-0.0218786) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.759177,0.0130659) , 
0, -1.10458, 1, 0, 0.636111,0.0149594) , 
1, -1.21587, 1, 0, 0.5,0.0001182)    );
  // itree = 34
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.196451,-0.0132225) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.979691,0.0477581) , 
0, 1.47005, 1, 0, 0.257748,-0.0189885) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.848897,0.00936055) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.992387,0.0534979) , 
1, 1.75889, 1, 0, 0.898128,0.0313618) , 
1, 0.463209, 1, 0, 0.5,5.87266e-05)    );
  // itree = 35
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0238544,-0.0423186) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.115536,-0.0049332) , 
1, -1.44262, 1, 0, 0.0481884,-0.0432053) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0668222,-0.0203857) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.759177,0.0118117) , 
0, -1.10458, 1, 0, 0.636111,0.0131309) , 
1, -1.21587, 1, 0, 0.5,8.84084e-05)    );
  // itree = 36
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0259012,-0.0365186) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.192961,-0.00367061) , 
0, -1.31765, 1, 0, 0.113435,-0.0264001) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.337187,-0.0047779) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.947902,0.0235312) , 
1, -0.208424, 1, 0, 0.770052,0.0185223) , 
0, -0.361506, 1, 0, 0.5,4.67371e-05)    );
  // itree = 37
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.34003,-0.00883739) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.988534,0.0530977) , 
1, 1.7417, 1, 0, 0.406501,-0.00926962) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.968545,0.0450784) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0522048) , 
1, 0.0699768, 1, 0, 0.993793,0.049193) , 
0, 1.49617, 1, 0, 0.5,3.78137e-05)    );
  // itree = 38
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0100108,-0.0433266) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.315943,0.00531623) , 
0, 0.133467, 1, 0, 0.0481884,-0.0368541) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0193558,-0.0415341) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.68124,0.0086303) , 
0, -1.84765, 1, 0, 0.636111,0.01121) , 
1, -1.21587, 1, 0, 0.5,8.25684e-05)    );
  // itree = 39
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.34003,-0.00798994) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.988534,0.0514131) , 
1, 1.7417, 1, 0, 0.406501,-0.00824381) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.968545,0.042862) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0519528) , 
1, 0.0699768, 1, 0, 0.993793,0.043738) , 
0, 1.49617, 1, 0, 0.5,3.18515e-05)    );
  // itree = 40
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0542543) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0357707,-0.0298416) , 
1, -2.03574, 1, 0, 0.0203808,-0.0438781) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0140548,-0.0422116) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.637007,0.00709279) , 
0, -1.84765, 1, 0, 0.584166,0.00778439) , 
1, -1.55169, 1, 0, 0.5,7.18082e-05)    );
  // itree = 41
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.34003,-0.007222) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.988534,0.0497424) , 
1, 1.7417, 1, 0, 0.406501,-0.0073585) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.968545,0.0408715) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0517358) , 
1, 0.0699768, 1, 0, 0.993793,0.039052) , 
0, 1.49617, 1, 0, 0.5,3.01894e-05)    );
  // itree = 42
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0332592,-0.0355737) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.108691,-0.00287643) , 
1, -1.3963, 1, 0, 0.0618969,-0.0254549) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0227948,-0.0365633) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.727748,0.00796038) , 
0, -1.84765, 1, 0, 0.683263,0.010741) , 
1, -0.880057, 1, 0, 0.5,6.55475e-05)    );
  // itree = 43
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0612809,-0.0221375) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.250468,0.00373123) , 
0, -0.84011, 1, 0, 0.113435,-0.0187954) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.638184,0.000694127) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.991018,0.0416931) , 
1, 1.13484, 1, 0, 0.770052,0.0131919) , 
0, -0.361506, 1, 0, 0.5,3.62548e-05)    );
  // itree = 44
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.34003,-0.00642178) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.988534,0.0483183) , 
1, 1.7417, 1, 0, 0.406501,-0.0063979) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.968545,0.0396108) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0514738) , 
1, 0.0699768, 1, 0, 0.993793,0.0340787) , 
0, 1.49617, 1, 0, 0.5,4.61095e-05)    );
  // itree = 45
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.053534) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0357707,-0.0254051) , 
1, -2.03574, 1, 0, 0.0203808,-0.0343177) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0420189,-0.0227572) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.673791,0.00648409) , 
0, -1.47611, 1, 0, 0.584166,0.00610969) , 
1, -1.55169, 1, 0, 0.5,7.438e-05)    );
  // itree = 46
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.34003,-0.0058167) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.988534,0.0469358) , 
1, 1.7417, 1, 0, 0.406501,-0.00571362) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.968545,0.0376693) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0513128) , 
1, 0.0699768, 1, 0, 0.993793,0.0304534) , 
0, 1.49617, 1, 0, 0.5,4.42788e-05)    );
  // itree = 47
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0332592,-0.0327533) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.108691,-0.0018162) , 
1, -1.3963, 1, 0, 0.0618969,-0.0200842) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0227948,-0.0326446) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.727748,0.00650798) , 
0, -1.84765, 1, 0, 0.683263,0.00849941) , 
1, -0.880057, 1, 0, 0.5,6.91006e-05)    );
  // itree = 48
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0612809,-0.0195563) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.250468,0.0039042) , 
0, -0.84011, 1, 0, 0.113435,-0.0147859) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.337187,-0.00339674) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.947902,0.016485) , 
1, -0.208424, 1, 0, 0.770052,0.0104064) , 
0, -0.361506, 1, 0, 0.5,4.53786e-05)    );
  // itree = 49
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.34003,-0.00529139) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.988534,0.0455027) , 
1, 1.7417, 1, 0, 0.406501,-0.00507238) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.968545,0.0366012) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0511318) , 
1, 0.0699768, 1, 0, 0.993793,0.0270426) , 
0, 1.49617, 1, 0, 0.5,4.04322e-05)    );
  // itree = 50
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0529452) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0357707,-0.0209652) , 
1, -2.03574, 1, 0, 0.0203808,-0.0268481) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0140548,-0.0361318) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.637007,0.00472039) , 
0, -1.84765, 1, 0, 0.584166,0.00478428) , 
1, -1.55169, 1, 0, 0.5,6.19611e-05)    );
  // itree = 51
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.34003,-0.00478085) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.988534,0.0437021) , 
1, 1.7417, 1, 0, 0.406501,-0.00454889) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.968545,0.034744) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0510124) , 
1, 0.0699768, 1, 0, 0.993793,0.0242461) , 
0, 1.49617, 1, 0, 0.5,3.5364e-05)    );
  // itree = 52
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0612809,-0.0176996) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.250468,0.00413602) , 
0, -0.84011, 1, 0, 0.113435,-0.012154) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.638184,9.74188e-05) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.991018,0.0378849) , 
1, 1.13484, 1, 0, 0.770052,0.00858324) , 
0, -0.361506, 1, 0, 0.5,5.44861e-05)    );
  // itree = 53
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0201936,-0.0263759) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.300901,0.00482438) , 
0, 0.133467, 1, 0, 0.0618969,-0.0152548) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.226753,-0.013905) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.707669,0.00537903) , 
1, -0.661827, 1, 0, 0.683263,0.00646418) , 
1, -0.880057, 1, 0, 0.5,5.84833e-05)    );
  // itree = 54
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0526527) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0357707,-0.018129) , 
1, -2.03574, 1, 0, 0.0203808,-0.022346) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0526476) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.613377,0.00370004) , 
0, -2.21919, 1, 0, 0.584166,0.00396911) , 
1, -1.55169, 1, 0, 0.5,4.05885e-05)    );
  // itree = 55
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.34003,-0.00424252) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.988534,0.0418554) , 
1, 1.7417, 1, 0, 0.406501,-0.00394834) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.968545,0.0327286) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0508666) , 
1, 0.0699768, 1, 0, 0.993793,0.0209674) , 
0, 1.49617, 1, 0, 0.5,1.83206e-05)    );
  // itree = 56
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.051371) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0877761,-0.0032725) , 
0, -0.245483, 1, 0, 0.0203808,-0.0199946) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.223707,-0.00360422) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.813687,0.00760754) , 
1, -0.111059, 1, 0, 0.584166,0.00355068) , 
1, -1.55169, 1, 0, 0.5,3.56591e-05)    );
  // itree = 57
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.34003,-0.00382097) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.988534,0.0398454) , 
1, 1.7417, 1, 0, 0.406501,-0.00354557) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.972916,0.00241867) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0537305) , 
0, 1.74471, 1, 0, 0.993793,0.0188487) , 
0, 1.49617, 1, 0, 0.5,1.96599e-05)    );
  // itree = 58
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0748904,-0.00458587) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.568894,-0.0178517) , 
1, 0.920452, 1, 0, 0.113435,-0.00945565) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.638184,-9.97732e-05) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.991018,0.0353102) , 
1, 1.13484, 1, 0, 0.770052,0.00666782) , 
0, -0.361506, 1, 0, 0.5,3.65978e-05)    );
  // itree = 59
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0100108,-0.0344563) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.315943,0.0122126) , 
0, 0.133467, 1, 0, 0.0481884,-0.0135334) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.229704,-0.00581957) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.769458,0.00657987) , 
1, -0.316574, 1, 0, 0.636111,0.0041359) , 
1, -1.21587, 1, 0, 0.5,4.52415e-05)    );
  // itree = 60
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.34003,-0.00337969) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.988534,0.0381036) , 
1, 1.7417, 1, 0, 0.406501,-0.00307361) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.972916,0.000398199) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0533968) , 
0, 1.74471, 1, 0, 0.993793,0.016409) , 
0, 1.49617, 1, 0, 0.5,2.80809e-05)    );
  // itree = 61
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0516402) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.189145,0.0200757) , 
1, 0.254025, 1, 0, 0.0305227,-0.0155498) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.237747,-0.00298232) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.873043,0.00788964) , 
0, 0.117664, 1, 0, 0.593538,0.00314823) , 
0, -1.47611, 1, 0, 0.5,4.17727e-05)    );
  // itree = 62
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0524377) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0357707,-0.0127287) , 
1, -2.03574, 1, 0, 0.0203808,-0.0159747) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0524482) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.613377,0.00287777) , 
0, -2.21919, 1, 0, 0.584166,0.00282692) , 
1, -1.55169, 1, 0, 0.5,2.0071e-05)    );
  // itree = 63
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.34003,-0.00303466) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.988534,0.0359018) , 
1, 1.7417, 1, 0, 0.406501,-0.00273393) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.972916,-0.000375297) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0530381) , 
0, 1.74471, 1, 0, 0.993793,0.0144644) , 
0, 1.49617, 1, 0, 0.5,4.10024e-06)    );
  // itree = 64
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0510482) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0877761,-0.000104367) , 
0, -0.245483, 1, 0, 0.0203808,-0.0142646) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.052203) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.613377,0.00258991) , 
0, -2.21919, 1, 0, 0.584166,0.00252463) , 
1, -1.55169, 1, 0, 0.5,1.82093e-05)    );
  // itree = 65
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.023419,-0.0198531) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.539378,0.000685171) , 
1, -1.24421, 1, 0, 0.406501,-0.00245986) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.972916,-0.000538262) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0527539) , 
0, 1.74471, 1, 0, 0.993793,0.0129986) , 
0, 1.49617, 1, 0, 0.5,1.17427e-06)    );
  // itree = 66
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0514619) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.189145,0.0187193) , 
1, 0.254025, 1, 0, 0.0305227,-0.0123015) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0526193) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.622298,0.00250875) , 
1, -2.22332, 1, 0, 0.593538,0.00245811) , 
0, -1.47611, 1, 0, 0.5,5.97798e-06)    );
  // itree = 67
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.34003,-0.00258466) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.988534,0.0336408) , 
1, 1.7417, 1, 0, 0.406501,-0.00222836) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.972916,-0.000737342) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.052495) , 
0, 1.74471, 1, 0, 0.993793,0.0116753) , 
0, 1.49617, 1, 0, 0.5,-1.48522e-05)    );
  // itree = 68
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0513141) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.189145,0.0162953) , 
1, 0.254025, 1, 0, 0.0305227,-0.0111805) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.237747,-0.00250802) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.873043,0.0062068) , 
0, 0.117664, 1, 0, 0.593538,0.00222593) , 
0, -1.47611, 1, 0, 0.5,-1.3907e-06)    );
  // itree = 69
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0505565) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.052565) , 
1, -1.21428, 1, 0, 0,-0.0185596) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.035854,-0.0183986) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.616933,0.00259818) , 
0, -1.31372, 1, 0, 0.529843,0.00109233) , 
0, -2.21919, 1, 0, 0.5,-1.45498e-05)    );
  // itree = 70
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0522412) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.148378,-0.00553457) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.77302,0.00494206) , 
1, -0.298018, 1, 0, 0.525988,0.00100809) , 
1, -2.22332, 1, 0, 0.5,-2.07562e-05)    );
  // itree = 71
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.370026,-0.00211129) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0548003) , 
1, 2.07347, 1, 0, 0.406501,-0.00197651) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.972916,-0.00158434) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0522703) , 
0, 1.74471, 1, 0, 0.993793,0.0102907) , 
0, 1.49617, 1, 0, 0.5,-2.35279e-05)    );
  // itree = 72
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0520267) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.148378,-0.00497643) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.77302,0.00445129) , 
1, -0.298018, 1, 0, 0.525988,0.000918396) , 
1, -2.22332, 1, 0, 0.5,-9.08324e-06)    );
  // itree = 73
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0575759,-0.0107575) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.566403,0.00091082) , 
0, -0.981502, 1, 0, 0.406501,-0.00177836) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.972916,-0.0014415) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0520948) , 
0, 1.74471, 1, 0, 0.993793,0.00931329) , 
0, 1.49617, 1, 0, 0.5,-1.25344e-05)    );
  // itree = 74
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.050477) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.052315) , 
1, -1.21428, 1, 0, 0,-0.0162602) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.035854,-0.0162908) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.616933,0.00226585) , 
0, -1.31372, 1, 0, 0.529843,0.000964281) , 
0, -2.21919, 1, 0, 0.5,-5.87212e-06)    );
  // itree = 75
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0613623,-0.00797237) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.655443,0.00151227) , 
0, -0.651401, 1, 0, 0.431858,-0.00149298) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.974343,-0.00574169) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.057115) , 
1, 2.04976, 1, 0, 0.991879,0.0106811) , 
1, 1.80648, 1, 0, 0.5,-1.16591e-05)    );
  // itree = 76
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0518744) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.148378,-0.00460426) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.77302,0.00411863) , 
1, -0.298018, 1, 0, 0.525988,0.000826718) , 
1, -2.22332, 1, 0, 0.5,-5.65199e-06)    );
  // itree = 77
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.30838,0.000339948) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.882998,-0.00927785) , 
0, 0.751845, 1, 0, 0.406501,-0.00156494) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.972916,-0.00223073) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0519285) , 
0, 1.74471, 1, 0, 0.993793,0.00820842) , 
0, 1.49617, 1, 0, 0.5,-8.99398e-06)    );
  // itree = 78
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.255303,-0.00285457) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.915821,0.00641283) , 
0, 0.705902, 1, 0, 0.431858,-0.00131569) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.974343,-0.00551088) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0565376) , 
1, 2.04976, 1, 0, 0.991879,0.0094893) , 
1, 1.80648, 1, 0, 0.5,-9.58955e-07)    );
  // itree = 79
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0504179) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0520726) , 
1, -1.21428, 1, 0, 0,-0.014372) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.035854,-0.0148838) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.616933,0.0020205) , 
0, -1.31372, 1, 0, 0.529843,0.000863085) , 
0, -2.21919, 1, 0, 0.5,4.98301e-06)    );
  // itree = 80
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0517055) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.148378,-0.00419715) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.77302,0.00375067) , 
1, -0.298018, 1, 0, 0.525988,0.000747264) , 
1, -2.22332, 1, 0, 0.5,-9.90366e-07)    );
  // itree = 81
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.255303,-0.00254744) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.915821,0.00566708) , 
0, 0.705902, 1, 0, 0.431858,-0.00118689) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.974343,-0.00505717) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0561114) , 
1, 2.04976, 1, 0, 0.991879,0.00853133) , 
1, 1.80648, 1, 0, 0.5,-4.39939e-06)    );
  // itree = 82
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0503727) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0518826) , 
1, -1.21428, 1, 0, 0,-0.0129048) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.035854,-0.0138509) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.616933,0.00183382) , 
0, -1.31372, 1, 0, 0.529843,0.000771592) , 
0, -2.21919, 1, 0, 0.5,1.27845e-06)    );
  // itree = 83
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0508029) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0877761,0.00787591) , 
0, -0.245483, 1, 0, 0.0203808,-0.00715127) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.387023,0.00238267) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.950225,-0.0064894) , 
0, 0.753102, 1, 0, 0.584166,0.00125061) , 
1, -1.55169, 1, 0, 0.5,-3.68411e-06)    );
  // itree = 84
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.30838,0.000571336) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.882998,-0.00957769) , 
0, 0.751845, 1, 0, 0.406501,-0.00133633) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.972916,-0.00413871) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.05176) , 
0, 1.74471, 1, 0, 0.993793,0.00699754) , 
0, 1.49617, 1, 0, 0.5,-9.548e-06)    );
  // itree = 85
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.255303,-0.00243755) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.915821,0.0055873) , 
0, 0.705902, 1, 0, 0.431858,-0.00107566) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.974343,-0.00417028) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0557067) , 
1, 2.04976, 1, 0, 0.991879,0.00774694) , 
1, 1.80648, 1, 0, 0.5,-2.14576e-06)    );
  // itree = 86
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0516165) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.238741,-0.00241888) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.864195,0.00515088) , 
1, 0.242493, 1, 0, 0.525988,0.00064933) , 
1, -2.22332, 1, 0, 0.5,2.83834e-06)    );
  // itree = 87
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0503276) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0517261) , 
1, -1.21428, 1, 0, 0,-0.0115352) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.035854,-0.0129157) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.616933,0.00166356) , 
0, -1.31372, 1, 0, 0.529843,0.000688462) , 
0, -2.21919, 1, 0, 0.5,-2.57983e-08)    );
  // itree = 88
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0794958,-0.00597196) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.521991,0.00438936) , 
1, -0.656273, 1, 0, 0.308422,0.00186372) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.385457,-0.0372634) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.975627,0.0122942) , 
0, -0.632722, 1, 0, 0.921008,-0.00410937) , 
1, 0.799026, 1, 0, 0.5,-4.28231e-06)    );
  // itree = 89
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.338226,0.000476929) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.898941,-0.0100668) , 
1, 1.03785, 1, 0, 0.431858,-0.00101934) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.974343,-0.0024812) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0558399) , 
1, 2.04976, 1, 0, 0.991879,0.00733124) , 
1, 1.80648, 1, 0, 0.5,-3.25856e-06)    );
  // itree = 90
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.30838,0.000772914) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.882998,-0.0098425) , 
0, 0.751845, 1, 0, 0.406501,-0.00113322) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.972916,-0.00538604) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.051632) , 
0, 1.74471, 1, 0, 0.993793,0.00599687) , 
0, 1.49617, 1, 0, 0.5,1.91388e-06)    );
  // itree = 91
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0514456) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.238741,-0.00230852) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.864195,0.00490484) , 
1, 0.242493, 1, 0, 0.525988,0.000585238) , 
1, -2.22332, 1, 0, 0.5,7.5664e-06)    );
  // itree = 92
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0794958,-0.00517208) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.521991,0.00393215) , 
1, -0.656273, 1, 0, 0.308422,0.00178192) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.385457,-0.0347278) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.975627,0.0112918) , 
0, -0.632722, 1, 0, 0.921008,-0.00390156) , 
1, 0.799026, 1, 0, 0.5,4.49425e-06)    );
  // itree = 93
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.255303,-0.00216376) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.915821,0.00494683) , 
0, 0.705902, 1, 0, 0.431858,-0.00094863) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.974343,-0.00114677) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0558042) , 
1, 2.04976, 1, 0, 0.991879,0.00688421) , 
1, 1.80648, 1, 0, 0.5,4.4527e-06)    );
  // itree = 94
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0794958,-0.00465372) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.521991,0.00362682) , 
1, -0.656273, 1, 0, 0.308422,0.00170637) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.385457,-0.0320901) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.975627,0.0101876) , 
0, -0.632722, 1, 0, 0.921008,-0.00372431) , 
1, 0.799026, 1, 0, 0.5,7.99953e-06)    );
  // itree = 95
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.376979,0.000214668) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.912219,-0.014672) , 
1, 1.29365, 1, 0, 0.431858,-0.000906124) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.974343,0.000413815) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0557551) , 
1, 2.04976, 1, 0, 0.991879,0.00660152) , 
1, 1.80648, 1, 0, 0.5,7.38981e-06)    );
  // itree = 96
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0502858) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0513847) , 
1, -1.21428, 1, 0, 0,-0.0100267) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.035854,-0.0103672) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.616933,0.00137054) , 
0, -1.31372, 1, 0, 0.529843,0.000610142) , 
0, -2.21919, 1, 0, 0.5,1.10335e-05)    );
  // itree = 97
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.355813,0.000538599) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.940859,-0.0129206) , 
0, 1.06667, 1, 0, 0.442938,-0.000754572) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0520879) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0502135) , 
1, 0.251817, 1, 0, 1,0.0066753) , 
0, 1.86771, 1, 0, 0.5,6.50018e-06)    );
  // itree = 98
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0513003) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.238741,-0.00219957) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.864195,0.00468698) , 
1, 0.242493, 1, 0, 0.525988,0.000522151) , 
1, -2.22332, 1, 0, 0.5,1.02302e-05)    );
  // itree = 99
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.175157,-0.000814569) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.872161,0.00869963) , 
0, 0.705902, 1, 0, 0.308422,0.00164731) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.385457,-0.0299949) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.975627,0.00915726) , 
0, -0.632722, 1, 0, 0.921008,-0.00359726) , 
1, 0.799026, 1, 0, 0.5,7.14627e-06)    );
   return;
};
 
// Clean up
inline void ReadBDTG_fold1::Clear() 
{
   for (unsigned int itree=0; itree<fForest.size(); itree++) { 
      delete fForest[itree]; 
   }
}
   inline double ReadBDTG_fold1::GetMvaValue( const std::vector<double>& inputValues ) const
   {
      // classifier response value
      double retval = 0;
 
      // classifier response, sanity check first
      if (!IsStatusClean()) {
         std::cout << "Problem in class \"" << fClassName << "\": cannot return classifier response"
                   << " because status is dirty" << std::endl;
         retval = 0;
      }
      else {
            retval = GetMvaValue__( inputValues );
      }
 
      return retval;
   }