TMVACrossValidation_BDTG_fold2.class.C

Go to the documentation of this file.

// Class: ReadBDTG_fold2
// Automatically generated by MethodBase::MakeClass
//
 
/* configuration options =====================================================
 
#GEN -*-*-*-*-*-*-*-*-*-*-*- general info -*-*-*-*-*-*-*-*-*-*-*-
 
Method         : BDT::BDTG_fold2
TMVA Release   : 4.2.1         [262657]
ROOT Release   : 6.16/01       [397313]
Creator        : sftnight
Date           : Sun Dec 19 22:14:15 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_fold2Node
   
#ifndef BDTG_fold2Node__def
#define BDTG_fold2Node__def
   
class BDTG_fold2Node {
   
public:
   
   // constructor of an essentially "empty" node floating in space
   BDTG_fold2Node ( BDTG_fold2Node* left,BDTG_fold2Node* 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_fold2Node();
 
   // test event if it descends the tree at this node to the right
   virtual bool GoesRight( const std::vector<double>& inputValues ) const;
   BDTG_fold2Node* 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_fold2Node* 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_fold2Node*   fLeft;     // pointer to the left daughter node
   BDTG_fold2Node*   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_fold2Node::~BDTG_fold2Node()
{
   if (fLeft  != NULL) delete fLeft;
   if (fRight != NULL) delete fRight;
}; 
   
//_______________________________________________________________________
bool BDTG_fold2Node::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_fold2Node::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_fold2 : public IClassifierReader {
 
 public:
 
   // constructor
   ReadBDTG_fold2( std::vector<std::string>& theInputVars )
      : IClassifierReader(),
        fClassName( "ReadBDTG_fold2" ),
        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_fold2() {
      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_fold2Node*> fForest;       // i.e. root nodes of decision trees
   std::vector<double>                fBoostWeights; // the weights applied in the individual boosts
};
 
double ReadBDTG_fold2::GetMvaValue__( const std::vector<double>& inputValues ) const
{
   double myMVA = 0;
   for (unsigned int itree=0; itree<fForest.size(); itree++){
      BDTG_fold2Node *current = fForest[itree];
      while (current->GetNodeType() == 0) { //intermediate node
         if (current->GoesRight(inputValues)) current=(BDTG_fold2Node*)current->GetRight();
         else current=(BDTG_fold2Node*)current->GetLeft();
      }
      myMVA += current->GetResponse();
   }
   return 2.0/(1.0+exp(-2.0*myMVA))-1.0;
};
 
void ReadBDTG_fold2::Initialize()
{
  // itree = 0
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0936283,-0.0812743) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.686736,0.0373472) , 
0, 0.428049, 1, 0, 0.201814,-0.298186) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.245152,-0.0509697) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.944189,0.0888378) , 
0, -0.748436, 1, 0, 0.892014,0.392014) , 
1, 0.249751, 1, 0, 0.5,-1.57809e-17)    );
  // itree = 1
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0936283,-0.0736491) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.686736,0.0336611) , 
0, 0.428049, 1, 0, 0.201814,-0.268439) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.245152,-0.0459964) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.944189,0.0806101) , 
0, -0.748436, 1, 0, 0.892014,0.352919) , 
1, 0.249751, 1, 0, 0.5,5.31501e-06)    );
  // itree = 2
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0936283,-0.0674993) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.686736,0.0304111) , 
0, 0.428049, 1, 0, 0.201814,-0.241821) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.245152,-0.0416929) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.944189,0.074142) , 
0, -0.748436, 1, 0, 0.892014,0.317963) , 
1, 0.249751, 1, 0, 0.5,2.16575e-05)    );
  // itree = 3
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0992651,-0.0648198) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.86363,0.0667711) , 
1, 1.12673, 1, 0, 0.147878,-0.268824) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.293426,-0.0369316) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.911054,0.066999) , 
1, -0.653781, 1, 0, 0.809451,0.236328) , 
0, -0.130818, 1, 0, 0.5,4.31602e-05)    );
  // itree = 4
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0468466,-0.0689443) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.574295,0.0144298) , 
1, 0.676501, 1, 0, 0.0887808,-0.287639) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.236929,-0.0402085) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.89226,0.0598113) , 
1, -0.653781, 1, 0, 0.762419,0.18365) , 
0, -0.492335, 1, 0, 0.5,5.73432e-05)    );
  // itree = 5
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.104838,-0.0567656) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.819715,0.0538986) , 
0, 1.12583, 1, 0, 0.155451,-0.219474) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.155963,-0.0463317) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.915029,0.0592413) , 
0, -0.751536, 1, 0, 0.813927,0.199987) , 
1, -0.175395, 1, 0, 0.5,1.00642e-05)    );
  // itree = 6
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0468466,-0.0621558) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.574295,0.0143566) , 
1, 0.676501, 1, 0, 0.0887808,-0.238704) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.236929,-0.0336557) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.89226,0.0527925) , 
1, -0.653781, 1, 0, 0.762419,0.152383) , 
0, -0.492335, 1, 0, 0.5,3.34374e-05)    );
  // itree = 7
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.156607,-0.0429434) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.967952,0.078077) , 
0, 1.47472, 1, 0, 0.201814,-0.151694) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.245152,-0.0304392) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.944189,0.0561324) , 
0, -0.748436, 1, 0, 0.892014,0.199388) , 
1, 0.249751, 1, 0, 0.5,-1.66222e-05)    );
  // itree = 8
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0992651,-0.0503258) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.86363,0.0574897) , 
1, 1.12673, 1, 0, 0.147878,-0.171896) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.293426,-0.0221118) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.911054,0.0504906) , 
1, -0.653781, 1, 0, 0.809451,0.151239) , 
0, -0.130818, 1, 0, 0.5,9.2639e-05)    );
  // itree = 9
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0468466,-0.0547591) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.574295,0.0119827) , 
1, 0.676501, 1, 0, 0.0887808,-0.18338) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.165056,-0.0416798) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.857489,0.0422511) , 
1, -1.01761, 1, 0, 0.762419,0.117184) , 
0, -0.492335, 1, 0, 0.5,9.78073e-05)    );
  // itree = 10
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.104838,-0.0447304) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.819715,0.0453633) , 
0, 1.12583, 1, 0, 0.155451,-0.141899) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0996805,-0.0491157) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.888664,0.0448558) , 
0, -1.0541, 1, 0, 0.813927,0.129295) , 
1, -0.175395, 1, 0, 0.5,3.90192e-06)    );
  // itree = 11
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0380742,-0.0550033) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.651255,0.0274469) , 
0, 0.776938, 1, 0, 0.0955529,-0.161454) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0309557,-0.0595487) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.820136,0.0363451) , 
0, -1.36785, 1, 0, 0.739872,0.0957536) , 
1, -0.600542, 1, 0, 0.5,-1.40112e-06)    );
  // itree = 12
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0992651,-0.0430548) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.86363,0.0522451) , 
1, 1.12673, 1, 0, 0.147878,-0.121993) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.293426,-0.0152091) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.911054,0.0414938) , 
1, -0.653781, 1, 0, 0.809451,0.106956) , 
0, -0.130818, 1, 0, 0.5,-0.000134924)    );
  // itree = 13
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.124872,-0.0382875) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.77463,0.0235549) , 
0, 0.428049, 1, 0, 0.282347,-0.0741218) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.877512,0.0328594) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.984053,0.0591965) , 
1, 1.32457, 1, 0, 0.93882,0.149095) , 
1, 0.674897, 1, 0, 0.5,-0.000114402)    );
  // itree = 14
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0380742,-0.0502976) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.651255,0.0235404) , 
0, 0.776938, 1, 0, 0.0955529,-0.126503) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0309557,-0.0566831) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.820136,0.0318539) , 
0, -1.36785, 1, 0, 0.739872,0.0749305) , 
1, -0.600542, 1, 0, 0.5,-6.07396e-05)    );
  // itree = 15
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0101518,-0.0574469) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.167327,-0.022697) , 
0, -1.35713, 1, 0, 0.0887808,-0.11271) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.165056,-0.0312298) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.857489,0.0320838) , 
1, -1.01761, 1, 0, 0.762419,0.0716448) , 
0, -0.492335, 1, 0, 0.5,-0.000171423)    );
  // itree = 16
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.222954,-0.0270788) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.980644,0.0640843) , 
0, 1.47472, 1, 0, 0.282347,-0.0600141) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.877512,0.0280182) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.984053,0.0567863) , 
1, 1.32457, 1, 0, 0.93882,0.12035) , 
1, 0.674897, 1, 0, 0.5,-0.000214654)    );
  // itree = 17
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.177343,-0.028531) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.884268,0.0303524) , 
1, 0.674897, 1, 0, 0.337521,-0.0466022) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.879624,0.0262442) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.993818,0.0601584) , 
0, 1.43853, 1, 0, 0.950277,0.128834) , 
0, 0.953734, 1, 0, 0.5,-8.33689e-05)    );
  // itree = 18
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0245268,-0.0487229) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.553577,0.0116052) , 
0, 0.428049, 1, 0, 0.0955529,-0.0952481) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0309557,-0.0530016) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.820136,0.0272504) , 
0, -1.36785, 1, 0, 0.739872,0.0564941) , 
1, -0.600542, 1, 0, 0.5,2.41246e-06)    );
  // itree = 19
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0146569,-0.0502025) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.255522,-0.0102305) , 
1, -0.4292, 1, 0, 0.0887808,-0.0844035) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.165056,-0.0268202) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.857489,0.027335) , 
1, -1.01761, 1, 0, 0.762419,0.0536876) , 
0, -0.492335, 1, 0, 0.5,-0.000106393)    );
  // itree = 20
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.23083,-0.0232083) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.9446,0.0435879) , 
1, 1.10004, 1, 0, 0.337521,-0.0379348) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.879624,0.0209591) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.993818,0.0581272) , 
0, 1.43853, 1, 0, 0.950277,0.104522) , 
0, 0.953734, 1, 0, 0.5,-0.000160888)    );
  // itree = 21
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0245268,-0.0454648) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.553577,0.00988452) , 
0, 0.428049, 1, 0, 0.0955529,-0.0753284) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0309557,-0.050807) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.820136,0.0236267) , 
0, -1.36785, 1, 0, 0.739872,0.0445931) , 
1, -0.600542, 1, 0, 0.5,-5.21607e-05)    );
  // itree = 22
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.222954,-0.0208358) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.980644,0.0566819) , 
0, 1.47472, 1, 0, 0.282347,-0.0387815) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.877512,0.0186289) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.984053,0.0525907) , 
1, 1.32457, 1, 0, 0.93882,0.0777734) , 
1, 0.674897, 1, 0, 0.5,-0.000137854)    );
  // itree = 23
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0101518,-0.052867) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.167327,-0.0134048) , 
0, -1.35713, 1, 0, 0.0887808,-0.0641383) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.105624,-0.0454746) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.80531,0.0206754) , 
1, -1.74528, 1, 0, 0.762419,0.0408618) , 
0, -0.492335, 1, 0, 0.5,-4.14494e-05)    );
  // itree = 24
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.23083,-0.019509) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.9446,0.0394153) , 
1, 1.10004, 1, 0, 0.337521,-0.0290896) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.879624,0.0163806) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.993818,0.0555354) , 
0, 1.43853, 1, 0, 0.950277,0.0802463) , 
0, 0.953734, 1, 0, 0.5,-9.80029e-05)    );
  // itree = 25
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0245268,-0.0420832) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.553577,0.00762276) , 
0, 0.428049, 1, 0, 0.0955529,-0.0591484) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0309557,-0.0480028) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.820136,0.0203865) , 
0, -1.36785, 1, 0, 0.739872,0.0350597) , 
1, -0.600542, 1, 0, 0.5,-1.27712e-05)    );
  // itree = 26
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.222954,-0.0174969) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.980644,0.0526679) , 
0, 1.47472, 1, 0, 0.282347,-0.0298891) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.877512,0.0146425) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.984053,0.05053) , 
1, 1.32457, 1, 0, 0.93882,0.0600075) , 
1, 0.674897, 1, 0, 0.5,-8.39723e-05)    );
  // itree = 27
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0101518,-0.051036) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.167327,-0.0101872) , 
0, -1.35713, 1, 0, 0.0887808,-0.0492601) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.105624,-0.043594) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.80531,0.0176992) , 
1, -1.74528, 1, 0, 0.762419,0.0314215) , 
0, -0.492335, 1, 0, 0.5,-8.39961e-06)    );
  // itree = 28
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.23083,-0.0163301) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.9446,0.0355327) , 
1, 1.10004, 1, 0, 0.337521,-0.0225025) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.879624,0.012409) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.993818,0.0534134) , 
0, 1.43853, 1, 0, 0.950277,0.062143) , 
0, 0.953734, 1, 0, 0.5,-5.7846e-05)    );
  // itree = 29
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0245268,-0.0388684) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.553577,0.00579818) , 
0, 0.428049, 1, 0, 0.0955529,-0.0470268) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0309557,-0.0453856) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.820136,0.0175695) , 
0, -1.36785, 1, 0, 0.739872,0.0279048) , 
1, -0.600542, 1, 0, 0.5,8.69394e-06)    );
  // itree = 30
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.222954,-0.0146466) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.980644,0.049085) , 
0, 1.47472, 1, 0, 0.282347,-0.0232805) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.877512,0.011131) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.984053,0.0487717) , 
1, 1.32457, 1, 0, 0.93882,0.0467885) , 
1, 0.674897, 1, 0, 0.5,-4.92052e-05)    );
  // itree = 31
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0245268,-0.0368473) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.553577,0.00517984) , 
0, 0.428049, 1, 0, 0.0955529,-0.0407978) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0309557,-0.0444054) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.820136,0.0159295) , 
0, -1.36785, 1, 0, 0.739872,0.0242122) , 
1, -0.600542, 1, 0, 0.5,9.77764e-06)    );
  // itree = 32
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.23083,-0.0135369) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.9446,0.0314276) , 
1, 1.10004, 1, 0, 0.337521,-0.0174222) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.694506,-0.00688222) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.982758,0.042401) , 
1, -0.423486, 1, 0, 0.950277,0.0481288) , 
0, 0.953734, 1, 0, 0.5,-4.06541e-05)    );
  // itree = 33
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0101518,-0.0486211) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.167327,-0.00653842) , 
0, -1.35713, 1, 0, 0.0887808,-0.0341657) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.105624,-0.0413118) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.80531,0.0142675) , 
1, -1.74528, 1, 0, 0.762419,0.0218247) , 
0, -0.492335, 1, 0, 0.5,1.33927e-05)    );
  // itree = 34
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.271765,-0.011824) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.958575,0.0356081) , 
0, 1.31525, 1, 0, 0.359047,-0.0142571) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.842735,0.0309216) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.990038,0.0429632) , 
0, -0.293774, 1, 0, 0.973474,0.047779) , 
1, 1.10004, 1, 0, 0.5,-2.57068e-05)    );
  // itree = 35
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0108287,-0.0552636) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0866747,-0.0162693) , 
1, -1.93822, 1, 0, 0.0602846,-0.0419398) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0947143,-0.0180265) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.820547,0.0140612) , 
0, -0.738649, 1, 0, 0.656315,0.0149391) , 
1, -1.02569, 1, 0, 0.5,2.19777e-05)    );
  // itree = 36
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.271765,-0.0107241) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.958575,0.0337677) , 
0, 1.31525, 1, 0, 0.359047,-0.0126042) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.842735,0.0294504) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.990038,0.041583) , 
0, -0.293774, 1, 0, 0.973474,0.042307) , 
1, 1.10004, 1, 0, 0.5,-7.2522e-06)    );
  // itree = 37
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0108287,-0.054149) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0866747,-0.0146722) , 
1, -1.93822, 1, 0, 0.0602846,-0.03703) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0219072,-0.0430327) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.744699,0.0108356) , 
0, -1.44248, 1, 0, 0.656315,0.0132079) , 
1, -1.02569, 1, 0, 0.5,3.24719e-05)    );
  // itree = 38
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.271765,-0.00977122) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.956681,0.0309941) , 
1, 1.10004, 1, 0, 0.390086,-0.00989015) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.916958,0.0109932) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.993249,0.0488669) , 
0, 1.52864, 1, 0, 0.97829,0.0430034) , 
0, 1.31525, 1, 0, 0.5,-6.28664e-06)    );
  // itree = 39
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0245268,-0.0313386) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.553577,0.00717309) , 
0, 0.428049, 1, 0, 0.0955529,-0.0256738) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0309557,-0.0401408) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.820136,0.0116001) , 
0, -1.36785, 1, 0, 0.739872,0.0152778) , 
1, -0.600542, 1, 0, 0.5,3.2026e-05)    );
  // itree = 40
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.271765,-0.00883642) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.958575,0.0295927) , 
0, 1.31525, 1, 0, 0.359047,-0.0101007) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.938515,0.0167202) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0559974) , 
1, 1.67412, 1, 0, 0.973474,0.0339174) , 
1, 1.10004, 1, 0, 0.5,-2.69784e-06)    );
  // itree = 41
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0108287,-0.0533462) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0866747,-0.0117543) , 
1, -1.93822, 1, 0, 0.0602846,-0.0300038) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0219072,-0.0406492) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.744699,0.00923322) , 
0, -1.44248, 1, 0, 0.656315,0.0107113) , 
1, -1.02569, 1, 0, 0.5,3.33515e-05)    );
  // itree = 42
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0101518,-0.0450018) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.167327,-0.00286433) , 
0, -1.35713, 1, 0, 0.0887808,-0.0210292) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.105624,-0.0328516) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.80531,0.00983105) , 
1, -1.74528, 1, 0, 0.762419,0.0134213) , 
0, -0.492335, 1, 0, 0.5,9.28305e-07)    );
  // itree = 43
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.271765,-0.00814918) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.958575,0.0279789) , 
0, 1.31525, 1, 0, 0.359047,-0.00908719) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.842735,0.0293843) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.990038,0.0372651) , 
0, -0.293774, 1, 0, 0.973474,0.0304214) , 
1, 1.10004, 1, 0, 0.5,-2.36993e-05)    );
  // itree = 44
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.302176,-0.00679545) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.984216,0.0415659) , 
0, 1.67677, 1, 0, 0.359047,-0.00817095) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.842735,0.0268718) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.990038,0.0360776) , 
0, -0.293774, 1, 0, 0.973474,0.0274613) , 
1, 1.10004, 1, 0, 0.5,3.26949e-06)    );
  // itree = 45
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0738888) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0670235,-0.016686) , 
1, -2.66667, 1, 0, 0.0602846,-0.0246975) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0219072,-0.0380009) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.744699,0.00785355) , 
0, -1.44248, 1, 0, 0.656315,0.0088175) , 
1, -1.02569, 1, 0, 0.5,2.78471e-05)    );
  // itree = 46
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0146569,-0.0343799) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.255522,0.00214469) , 
1, -0.4292, 1, 0, 0.0887808,-0.0172258) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.105624,-0.0294125) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.80531,0.00836222) , 
1, -1.74528, 1, 0, 0.762419,0.0110009) , 
0, -0.492335, 1, 0, 0.5,5.06536e-06)    );
  // itree = 47
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.313255,-0.00631923) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.978404,0.04018) , 
1, 1.52519, 1, 0, 0.390086,-0.00650517) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.916958,0.00167173) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.993249,0.0461461) , 
0, 1.52864, 1, 0, 0.97829,0.0282157) , 
0, 1.31525, 1, 0, 0.5,-1.71239e-05)    );
  // itree = 48
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0746042) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0400824,-0.0289227) , 
1, -2.58522, 1, 0, 0.0295475,-0.0367685) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0398094,-0.026976) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.697663,0.00708922) , 
0, -1.09057, 1, 0, 0.552463,0.00411324) , 
1, -1.87598, 1, 0, 0.5,1.16805e-05)    );
  // itree = 49
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.302176,-0.00577418) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.984216,0.039356) , 
0, 1.67677, 1, 0, 0.359047,-0.00669942) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.842735,0.0263594) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.990038,0.0330108) , 
0, -0.293774, 1, 0, 0.973474,0.0224641) , 
1, 1.10004, 1, 0, 0.5,-9.14869e-06)    );
  // itree = 50
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.023212,-0.0169579) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.00919382,-0.0478627) , 
0, -2.30277, 1, 0, 0.0131709,-0.0269297) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0994955,-0.0148622) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.754263,0.00733219) , 
1, -0.83435, 1, 0, 0.585944,0.0047664) , 
0, -1.57689, 1, 0, 0.5,1.04314e-05)    );
  // itree = 51
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.313255,-0.00541875) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.978404,0.0384833) , 
1, 1.52519, 1, 0, 0.390086,-0.00541421) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.916958,0.000896129) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.993249,0.0446095) , 
0, 1.52864, 1, 0, 0.97829,0.0235312) , 
0, 1.31525, 1, 0, 0.5,-5.38994e-06)    );
  // itree = 52
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0769331) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0400824,-0.026678) , 
1, -2.58522, 1, 0, 0.0295475,-0.0319311) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0398094,-0.024393) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.697663,0.0061746) , 
0, -1.09057, 1, 0, 0.552463,0.00357986) , 
1, -1.87598, 1, 0, 0.5,1.71325e-05)    );
  // itree = 53
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.302176,-0.00491989) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.984216,0.0367811) , 
0, 1.67677, 1, 0, 0.359047,-0.00563992) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.842735,0.0263942) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.990038,0.0300915) , 
0, -0.293774, 1, 0, 0.973474,0.0189421) , 
1, 1.10004, 1, 0, 0.5,-6.7657e-07)    );
  // itree = 54
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.023212,-0.0134983) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.00919382,-0.0467108) , 
0, -2.30277, 1, 0, 0.0131709,-0.0231037) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0171008,-0.0461594) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.627438,0.00439923) , 
1, -1.96752, 1, 0, 0.585944,0.00409458) , 
0, -1.57689, 1, 0, 0.5,1.35005e-05)    );
  // itree = 55
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0657587,-0.0187152) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.531669,0.0011057) , 
1, -0.884476, 1, 0, 0.359047,-0.00517487) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.842735,0.0262818) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.990038,0.0285639) , 
0, -0.293774, 1, 0, 0.973474,0.0173593) , 
1, 1.10004, 1, 0, 0.5,-5.41575e-06)    );
  // itree = 56
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.353141,-0.00414102) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0653181) , 
1, 1.95034, 1, 0, 0.390086,-0.00449906) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.916958,0.000313599) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.993249,0.0431276) , 
0, 1.52864, 1, 0, 0.97829,0.0195612) , 
0, 1.31525, 1, 0, 0.5,-3.07618e-06)    );
  // itree = 57
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.023212,-0.0119571) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.00919382,-0.0460515) , 
0, -2.30277, 1, 0, 0.0131709,-0.0209768) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.075421) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.604705,0.00352913) , 
1, -2.34525, 1, 0, 0.585944,0.00372375) , 
0, -1.57689, 1, 0, 0.5,1.74552e-05)    );
  // itree = 58
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.353141,-0.00376728) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.066058) , 
1, 1.95034, 1, 0, 0.390086,-0.0040523) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.883053,-0.00232318) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.051758) , 
1, 0.0158096, 1, 0, 0.97829,0.0176323) , 
0, 1.31525, 1, 0, 0.5,-2.36302e-07)    );
  // itree = 59
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.023212,-0.0106721) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.00919382,-0.0447395) , 
0, -2.30277, 1, 0, 0.0131709,-0.0189666) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0747312) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.604705,0.00321317) , 
1, -2.34525, 1, 0, 0.585944,0.0033749) , 
0, -1.57689, 1, 0, 0.5,2.25751e-05)    );
  // itree = 60
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.302176,-0.00385277) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.984216,0.0344783) , 
0, 1.67677, 1, 0, 0.359047,-0.00421123) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.842735,0.0270654) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.990038,0.0244298) , 
0, -0.293774, 1, 0, 0.973474,0.0141734) , 
1, 1.10004, 1, 0, 0.5,6.29892e-06)    );
  // itree = 61
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.078226) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0400824,-0.0218336) , 
1, -2.58522, 1, 0, 0.0295475,-0.0222698) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0398094,-0.0207859) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.697663,0.00467041) , 
0, -1.09057, 1, 0, 0.552463,0.00249965) , 
1, -1.87598, 1, 0, 0.5,1.45848e-05)    );
  // itree = 62
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.353141,-0.00328811) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0672053) , 
1, 1.95034, 1, 0, 0.390086,-0.00344315) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.916958,-0.00333905) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.993249,0.0415936) , 
0, 1.52864, 1, 0, 0.97829,0.0149986) , 
0, 1.31525, 1, 0, 0.5,2.92962e-06)    );
  // itree = 63
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.023212,-0.00791482) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.00919382,-0.0434086) , 
0, -2.30277, 1, 0, 0.0131709,-0.0164634) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0994955,-0.0107255) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.754263,0.00484547) , 
1, -0.83435, 1, 0, 0.585944,0.00292811) , 
0, -1.57689, 1, 0, 0.5,1.8431e-05)    );
  // itree = 64
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.325742,-0.00356559) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.921208,0.0140886) , 
1, 0.907519, 1, 0, 0.407781,-0.00294638) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.921208,-0.00989019) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0577998) , 
1, 1.65302, 1, 0, 0.987254,0.0156216) , 
1, 1.52519, 1, 0, 0.5,8.58278e-06)    );
  // itree = 65
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0787363) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0400824,-0.019542) , 
1, -2.58522, 1, 0, 0.0295475,-0.0194228) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0398094,-0.0186077) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.697663,0.00406398) , 
0, -1.09057, 1, 0, 0.552463,0.00218993) , 
1, -1.87598, 1, 0, 0.5,2.15847e-05)    );
  // itree = 66
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.313255,-0.00330031) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.978404,0.0336304) , 
1, 1.52519, 1, 0, 0.390086,-0.00306607) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.883053,-0.00354947) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0515738) , 
1, 0.0158096, 1, 0, 0.97829,0.013402) , 
0, 1.31525, 1, 0, 0.5,1.121e-05)    );
  // itree = 67
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.023212,-0.00519965) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.00919382,-0.0420294) , 
0, -2.30277, 1, 0, 0.0131709,-0.0143102) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0734512) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.604705,0.00251133) , 
1, -2.34525, 1, 0, 0.585944,0.00255641) , 
0, -1.57689, 1, 0, 0.5,2.55939e-05)    );
  // itree = 68
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.353141,-0.0027252) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0690477) , 
1, 1.95034, 1, 0, 0.390086,-0.0027868) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.916958,-0.00647071) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.993249,0.040919) , 
0, 1.52864, 1, 0, 0.97829,0.012197) , 
0, 1.31525, 1, 0, 0.5,1.31205e-05)    );
  // itree = 69
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.077152) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0400824,-0.0179452) , 
1, -2.58522, 1, 0, 0.0295475,-0.0166013) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.389108,-0.00176518) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.960564,0.0232276) , 
1, 0.96295, 1, 0, 0.552463,0.00187879) , 
1, -1.87598, 1, 0, 0.5,2.47259e-05)    );
  // itree = 70
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.023212,-0.0044479) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.00919382,-0.041562) , 
0, -2.30277, 1, 0, 0.0131709,-0.0132911) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.052327) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.620758,0.00266982) , 
0, -1.33552, 1, 0, 0.585944,0.00237346) , 
0, -1.57689, 1, 0, 0.5,2.30034e-05)    );
  // itree = 71
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.353141,-0.00247027) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0693188) , 
1, 1.95034, 1, 0, 0.390086,-0.0025165) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.916958,-0.00613606) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.993249,0.0400377) , 
0, 1.52864, 1, 0, 0.97829,0.0110004) , 
0, 1.31525, 1, 0, 0.5,9.30912e-06)    );
  // itree = 72
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.023212,-0.0038014) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.00919382,-0.0403993) , 
0, -2.30277, 1, 0, 0.0131709,-0.0121319) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0520999) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.620758,0.0024322) , 
0, -1.33552, 1, 0, 0.585944,0.00216494) , 
0, -1.57689, 1, 0, 0.5,1.97119e-05)    );
  // itree = 73
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0776128) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0400824,-0.0167874) , 
1, -2.58522, 1, 0, 0.0295475,-0.015119) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.389108,-0.00162306) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.960564,0.0222624) , 
1, 0.96295, 1, 0, 0.552463,0.00169435) , 
1, -1.87598, 1, 0, 0.5,7.51052e-06)    );
  // itree = 74
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.17635,0.0018681) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.729067,-0.00508671) , 
0, 0.0160863, 1, 0, 0.390086,-0.00227871) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.883053,-0.00557045) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0514852) , 
1, 0.0158096, 1, 0, 0.97829,0.00995511) , 
0, 1.31525, 1, 0, 0.5,7.33684e-06)    );
  // itree = 75
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.023212,-0.00364233) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.00919382,-0.0393532) , 
0, -2.30277, 1, 0, 0.0131709,-0.0112757) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0519309) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.620758,0.00225015) , 
0, -1.33552, 1, 0, 0.585944,0.00201082) , 
0, -1.57689, 1, 0, 0.5,1.71884e-05)    );
  // itree = 76
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.346789,-0.00232335) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.987333,0.0326253) , 
0, 1.67677, 1, 0, 0.407781,-0.00200056) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.921208,-0.0118319) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0588436) , 
1, 1.65302, 1, 0, 0.987254,0.0106093) , 
1, 1.52519, 1, 0, 0.5,6.21222e-06)    );
  // itree = 77
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0770074) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0400824,-0.0155353) , 
1, -2.58522, 1, 0, 0.0295475,-0.0137065) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.389108,-0.00131344) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.960564,0.0202304) , 
1, 0.96295, 1, 0, 0.552463,0.0015475) , 
1, -1.87598, 1, 0, 0.5,1.71029e-05)    );
  // itree = 78
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.023212,-0.00312338) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.00919382,-0.0386951) , 
0, -2.30277, 1, 0, 0.0131709,-0.010472) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0994955,-0.00710483) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.754263,0.00316089) , 
1, -0.83435, 1, 0, 0.585944,0.00186684) , 
0, -1.57689, 1, 0, 0.5,1.54104e-05)    );
  // itree = 79
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0759021,-0.00945005) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.700922,0.00225479) , 
0, -0.533323, 1, 0, 0.432597,-0.00151887) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0553066) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.987929,0.0211886) , 
0, 1.94105, 1, 0, 0.991589,0.0111569) , 
0, 1.67677, 1, 0, 0.5,9.56483e-06)    );
  // itree = 80
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0221563,-0.00650413) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0354625,-0.0354346) , 
1, -2.30187, 1, 0, 0.0295475,-0.012298) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.389108,-0.00125419) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.960564,0.0193217) , 
1, 0.96295, 1, 0, 0.552463,0.00138409) , 
1, -1.87598, 1, 0, 0.5,1.13978e-05)    );
  // itree = 81
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.00818923,-0.0349399) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0336266,0.00422174) , 
0, -1.70119, 1, 0, 0.0131709,-0.00931846) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0712858) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.604705,0.00176554) , 
1, -2.34525, 1, 0, 0.585944,0.00165759) , 
0, -1.57689, 1, 0, 0.5,1.06414e-05)    );
  // itree = 82
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.179373,0.00245144) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.77394,-0.0058394) , 
1, -0.00187962, 1, 0, 0.407781,-0.00168209) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.921208,-0.0113732) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0592246) , 
1, 1.65302, 1, 0, 0.987254,0.00890644) , 
1, 1.52519, 1, 0, 0.5,2.99714e-06)    );
  // itree = 83
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0759021,-0.00849998) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.700922,0.00193772) , 
0, -0.533323, 1, 0, 0.432597,-0.00140132) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0551846) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.987929,0.0209279) , 
0, 1.94105, 1, 0, 0.991589,0.0103077) , 
0, 1.67677, 1, 0, 0.5,1.05518e-05)    );
  // itree = 84
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0225968,-0.0156894) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.45322,0.00848542) , 
1, -0.600542, 1, 0, 0.215566,0.00338565) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.515241,-0.0540889) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.893327,0.00533119) , 
0, 0.390491, 1, 0, 0.85425,-0.00418919) , 
0, 0.230699, 1, 0, 0.5,1.22427e-05)    );
  // itree = 85
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0221563,-0.00180774) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0354625,-0.0347472) , 
1, -2.30187, 1, 0, 0.0295475,-0.0104567) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.44735,-0.000791292) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.994136,0.0360064) , 
0, 1.37285, 1, 0, 0.552463,0.00117714) , 
1, -1.87598, 1, 0, 0.5,9.94702e-06)    );
  // itree = 86
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0976818,-0.00456647) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.533528,0.011022) , 
0, -0.389714, 1, 0, 0.215566,0.00322681) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.515241,-0.0480241) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.893327,0.004615) , 
0, 0.390491, 1, 0, 0.85425,-0.00398976) , 
0, 0.230699, 1, 0, 0.5,1.29556e-05)    );
  // itree = 87
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.179373,0.00266148) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.77394,-0.00575543) , 
1, -0.00187962, 1, 0, 0.407781,-0.00147471) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.921208,-0.0104868) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0591844) , 
1, 1.65302, 1, 0, 0.987254,0.00787804) , 
1, 1.52519, 1, 0, 0.5,1.37117e-05)    );
  // itree = 88
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.023212,0.0040454) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.00919382,-0.0369192) , 
0, -2.30277, 1, 0, 0.0131709,-0.00811105) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0517429) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.620758,0.00179284) , 
0, -1.33552, 1, 0, 0.585944,0.0014546) , 
0, -1.57689, 1, 0, 0.5,1.92766e-05)    );
  // itree = 89
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0225968,-0.0144491) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.45322,0.00769059) , 
1, -0.600542, 1, 0, 0.215566,0.00309757) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.515241,-0.0429353) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.893327,0.00396992) , 
0, 0.390491, 1, 0, 0.85425,-0.00383545) , 
0, 0.230699, 1, 0, 0.5,9.99536e-06)    );
  // itree = 90
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.179373,0.00267824) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.77394,-0.00558957) , 
1, -0.00187962, 1, 0, 0.407781,-0.00137494) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.921208,-0.00812337) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0593745) , 
1, 1.65302, 1, 0, 0.987254,0.00731152) , 
1, 1.52519, 1, 0, 0.5,7.45138e-06)    );
  // itree = 91
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.075422) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0400824,-0.00955586) , 
1, -2.58522, 1, 0, 0.0295475,-0.00948807) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.389108,-0.00105209) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.960564,0.016958) , 
1, 0.96295, 1, 0, 0.552463,0.00107251) , 
1, -1.87598, 1, 0, 0.5,1.29893e-05)    );
  // itree = 92
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.023212,0.00384374) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.00919382,-0.0366668) , 
0, -2.30277, 1, 0, 0.0131709,-0.00766578) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.230025,0.00435105) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.871054,-0.00277793) , 
1, -0.0789012, 1, 0, 0.585944,0.00136667) , 
0, -1.57689, 1, 0, 0.5,1.13533e-05)    );
  // itree = 93
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0225968,-0.0135565) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.45322,0.00705272) , 
1, -0.600542, 1, 0, 0.215566,0.00283187) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.515241,-0.0383455) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.893327,0.00355175) , 
0, 0.390491, 1, 0, 0.85425,-0.00351073) , 
0, 0.230699, 1, 0, 0.5,7.23697e-06)    );
  // itree = 94
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.17635,0.00289129) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.768244,-0.00433672) , 
0, 0.0165516, 1, 0, 0.432597,-0.00113156) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0550153) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.987929,0.0173523) , 
0, 1.94105, 1, 0, 0.991589,0.00829448) , 
0, 1.67677, 1, 0, 0.5,5.02275e-06)    );
  // itree = 95
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0221563,0.00176747) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0354625,-0.034009) , 
1, -2.30187, 1, 0, 0.0295475,-0.00885035) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.0887808,0.015142) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.610558,-0.000380091) , 
1, -1.30114, 1, 0, 0.552463,0.00099589) , 
1, -1.87598, 1, 0, 0.5,8.0409e-06)    );
  // itree = 96
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.023212,0.00380015) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.00919382,-0.0350814) , 
0, -2.30277, 1, 0, 0.0131709,-0.00699713) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.230025,0.00415575) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.871054,-0.00272572) , 
1, -0.0789012, 1, 0, 0.585944,0.00124024) , 
0, -1.57689, 1, 0, 0.5,4.22741e-06)    );
  // itree = 97
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.219085,0.00166516) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.831636,-0.00644365) , 
1, 0.301253, 1, 0, 0.407781,-0.00128025) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.921208,-0.00592606) , 
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0600104) , 
1, 1.65302, 1, 0, 0.987254,0.00677188) , 
1, 1.52519, 1, 0, 0.5,1.19133e-06)    );
  // itree = 98
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.17635,0.00270532) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.768244,-0.00398821) , 
0, 0.0165516, 1, 0, 0.432597,-0.00101969) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 1,0.0548514) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.987929,0.015597) , 
0, 1.94105, 1, 0, 0.991589,0.00749515) , 
0, 1.67677, 1, 0, 0.5,7.02545e-06)    );
  // itree = 99
  fBoostWeights.push_back(1);
  fForest.push_back( 
NN(
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0,-0.0753262) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.0400824,-0.00828405) , 
1, -2.58522, 1, 0, 0.0295475,-0.00848986) , 
NN(
NN(
0, 
0, 
-1, 0, 1, -99, 0.389108,-0.00101761) , 
NN(
0, 
0, 
-1, 0, 1, -99, 0.960564,0.0163399) , 
1, 0.96295, 1, 0, 0.552463,0.000957303) , 
1, -1.87598, 1, 0, 0.5,9.49259e-06)    );
   return;
};
 
// Clean up
inline void ReadBDTG_fold2::Clear() 
{
   for (unsigned int itree=0; itree<fForest.size(); itree++) { 
      delete fForest[itree]; 
   }
}
   inline double ReadBDTG_fold2::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;
   }