DecisionTree.cxx

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// @(#)root/tmva $Id$
// Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss, Eckhard von Toerne, Jan Therhaag
 
/**********************************************************************************
 * Project: TMVA - a Root-integrated toolkit for multivariate data analysis       *
 * Package: TMVA                                                                  *
 * Class  : TMVA::DecisionTree                                                    *
 *                                             *
 *                                                                                *
 * Description:                                                                   *
 *      Implementation of a Decision Tree                                         *
 *                                                                                *
 * Authors (alphabetical):                                                        *
 *      Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland              *
 *      Helge Voss      <Helge.Voss@cern.ch>     - MPI-K Heidelberg, Germany      *
 *      Kai Voss        <Kai.Voss@cern.ch>       - U. of Victoria, Canada         *
 *      Eckhard v. Toerne  <evt@uni-bonn.de>          - U of Bonn, Germany        *
 *      Jan Therhaag          <Jan.Therhaag@cern.ch>   - U of Bonn, Germany       *
 *                                                                                *
 * Copyright (c) 2005-2011:                                                       *
 *      CERN, Switzerland                                                         *
 *      U. of Victoria, Canada                                                    *
 *      MPI-K Heidelberg, Germany                                                 *
 *      U. of Bonn, Germany                                                       *
 *                                                                                *
 * Redistribution and use in source and binary forms, with or without             *
 * modification, are permitted according to the terms listed in LICENSE           *
 * (http://mva.sourceforge.net/license.txt)                                       *
 *                                                                                *
 **********************************************************************************/
 
/*! \class TMVA::DecisionTree
\ingroup TMVA
 
Implementation of a Decision Tree
 
In a decision tree successive decision nodes are used to categorize the
events out of the sample as either signal or background. Each node
uses only a single discriminating variable to decide if the event is
signal-like ("goes right") or background-like ("goes left"). This
forms a tree like structure with "baskets" at the end (leave nodes),
and an event is classified as either signal or background according to
whether the basket where it ends up has been classified signal or
background during the training. Training of a decision tree is the
process to define the "cut criteria" for each node. The training
starts with the root node. Here one takes the full training event
sample and selects the variable and corresponding cut value that gives
the best separation between signal and background at this stage. Using
this cut criterion, the sample is then divided into two subsamples, a
signal-like (right) and a background-like (left) sample. Two new nodes
are then created for each of the two sub-samples and they are
constructed using the same mechanism as described for the root
node. The devision is stopped once a certain node has reached either a
minimum number of events, or a minimum or maximum signal purity. These
leave nodes are then called "signal" or "background" if they contain
more signal respective background events from the training sample.
 
*/
 
#include <iostream>
#include <algorithm>
#include <vector>
#include <limits>
#include <cassert>
 
#include "TRandom3.h"
#include "TMath.h"
#include "TMatrix.h"
 
#include "TMVA/MsgLogger.h"
#include "TMVA/DecisionTree.h"
#include "TMVA/DecisionTreeNode.h"
#include "TMVA/BinarySearchTree.h"
 
#include "TMVA/Tools.h"
#include "TMVA/Config.h"
 
#include "TMVA/GiniIndex.h"
#include "TMVA/CrossEntropy.h"
#include "TMVA/MisClassificationError.h"
#include "TMVA/SdivSqrtSplusB.h"
#include "TMVA/Event.h"
#include "TMVA/BDTEventWrapper.h"
#include "TMVA/IPruneTool.h"
#include "TMVA/CostComplexityPruneTool.h"
#include "TMVA/ExpectedErrorPruneTool.h"
 
const Int_t TMVA::DecisionTree::fgRandomSeed = 0; // set nonzero for debugging and zero for random seeds
 
using std::vector;
 
ClassImp(TMVA::DecisionTree);
 
bool almost_equal_float(float x, float y, int ulp=4){
   // the machine epsilon has to be scaled to the magnitude of the values used
   // and multiplied by the desired precision in ULPs (units in the last place)
   return std::abs(x-y) < std::numeric_limits<float>::epsilon() * std::abs(x+y) * ulp
      // unless the result is subnormal
      || std::abs(x-y) < std::numeric_limits<float>::min();
}
 
bool almost_equal_double(double x, double y, int ulp=4){
   // the machine epsilon has to be scaled to the magnitude of the values used
   // and multiplied by the desired precision in ULPs (units in the last place)
   return std::abs(x-y) < std::numeric_limits<double>::epsilon() * std::abs(x+y) * ulp
      // unless the result is subnormal
      || std::abs(x-y) < std::numeric_limits<double>::min();
}
 
////////////////////////////////////////////////////////////////////////////////
/// default constructor using the GiniIndex as separation criterion,
/// no restrictions on minium number of events in a leave note or the
/// separation gain in the node splitting
 
TMVA::DecisionTree::DecisionTree():
   BinaryTree(),
   fNvars          (0),
   fNCuts          (-1),
   fUseFisherCuts  (kFALSE),
   fMinLinCorrForFisher (1),
   fUseExclusiveVars (kTRUE),
   fSepType        (NULL),
   fRegType        (NULL),
   fMinSize        (0),
   fMinNodeSize    (1),
   fMinSepGain (0),
   fUseSearchTree(kFALSE),
   fPruneStrength(0),
   fPruneMethod    (kNoPruning),
   fNNodesBeforePruning(0),
   fNodePurityLimit(0.5),
   fRandomisedTree (kFALSE),
   fUseNvars       (0),
   fUsePoissonNvars(kFALSE),
   fMyTrandom (NULL),
   fMaxDepth       (999999),
   fSigClass       (0),
   fTreeID         (0),
   fAnalysisType   (Types::kClassification),
   fDataSetInfo    (NULL)
 
{}
 
////////////////////////////////////////////////////////////////////////////////
/// constructor specifying the separation type, the min number of
/// events in a no that is still subjected to further splitting, the
/// number of bins in the grid used in applying the cut for the node
/// splitting.
 
TMVA::DecisionTree::DecisionTree( TMVA::SeparationBase *sepType, Float_t minSize, Int_t nCuts, DataSetInfo* dataInfo, UInt_t cls,
                                  Bool_t randomisedTree, Int_t useNvars, Bool_t usePoissonNvars,
                                  UInt_t nMaxDepth, Int_t iSeed, Float_t purityLimit, Int_t treeID):
   BinaryTree(),
   fNvars          (0),
   fNCuts          (nCuts),
   fUseFisherCuts  (kFALSE),
   fMinLinCorrForFisher (1),
   fUseExclusiveVars (kTRUE),
   fSepType        (sepType),
   fRegType        (NULL),
   fMinSize        (0),
   fMinNodeSize    (minSize),
   fMinSepGain     (0),
   fUseSearchTree  (kFALSE),
   fPruneStrength  (0),
   fPruneMethod    (kNoPruning),
   fNNodesBeforePruning(0),
   fNodePurityLimit(purityLimit),
   fRandomisedTree (randomisedTree),
   fUseNvars       (useNvars),
   fUsePoissonNvars(usePoissonNvars),
   fMyTrandom      (new TRandom3(iSeed)),
   fMaxDepth       (nMaxDepth),
   fSigClass       (cls),
   fTreeID         (treeID),
   fAnalysisType   (Types::kClassification),
   fDataSetInfo    (dataInfo)
{
   if (sepType == NULL) { // it is interpreted as a regression tree, where
                          // currently the separation type (simple least square)
                          // cannot be chosen freely)
      fAnalysisType = Types::kRegression;
      fRegType = new RegressionVariance();
      if ( nCuts <=0 ) {
         fNCuts = 200;
         Log() << kWARNING << " You had chosen the training mode using optimal cuts, not\n"
               << " based on a grid of " << fNCuts << " by setting the option NCuts < 0\n"
               << " as this doesn't exist yet, I set it to " << fNCuts << " and use the grid"
               << Endl;
      }
   }else{
      fAnalysisType = Types::kClassification;
   }
}
 
////////////////////////////////////////////////////////////////////////////////
/// copy constructor that creates a true copy, i.e. a completely independent tree
/// the node copy will recursively copy all the nodes
 
TMVA::DecisionTree::DecisionTree( const DecisionTree &d ):
   BinaryTree(),
   fNvars      (d.fNvars),
   fNCuts      (d.fNCuts),
   fUseFisherCuts  (d.fUseFisherCuts),
   fMinLinCorrForFisher (d.fMinLinCorrForFisher),
   fUseExclusiveVars (d.fUseExclusiveVars),
   fSepType    (d.fSepType),
   fRegType    (d.fRegType),
   fMinSize    (d.fMinSize),
   fMinNodeSize(d.fMinNodeSize),
   fMinSepGain (d.fMinSepGain),
   fUseSearchTree  (d.fUseSearchTree),
   fPruneStrength  (d.fPruneStrength),
   fPruneMethod    (d.fPruneMethod),
   fNodePurityLimit(d.fNodePurityLimit),
   fRandomisedTree (d.fRandomisedTree),
   fUseNvars       (d.fUseNvars),
   fUsePoissonNvars(d.fUsePoissonNvars),
   fMyTrandom      (new TRandom3(fgRandomSeed)),  // well, that means it's not an identical copy. But I only ever intend to really copy trees that are "outgrown" already.
   fMaxDepth   (d.fMaxDepth),
   fSigClass   (d.fSigClass),
   fTreeID     (d.fTreeID),
   fAnalysisType(d.fAnalysisType),
   fDataSetInfo    (d.fDataSetInfo)
{
   this->SetRoot( new TMVA::DecisionTreeNode ( *((DecisionTreeNode*)(d.GetRoot())) ) );
   this->SetParentTreeInNodes();
   fNNodes = d.fNNodes;
 
}
 
 
////////////////////////////////////////////////////////////////////////////////
/// destructor
 
TMVA::DecisionTree::~DecisionTree()
{
   // destruction of the tree nodes done in the "base class" BinaryTree
 
   if (fMyTrandom) delete fMyTrandom;
   if (fRegType) delete fRegType;
}
 
////////////////////////////////////////////////////////////////////////////////
/// descend a tree to find all its leaf nodes, fill max depth reached in the
/// tree at the same time.
 
void TMVA::DecisionTree::SetParentTreeInNodes( Node *n )
{
   if (n == NULL) { //default, start at the tree top, then descend recursively
      n = this->GetRoot();
      if (n == NULL) {
         Log() << kFATAL << "SetParentTreeNodes: started with undefined ROOT node" <<Endl;
         return ;
      }
   }
 
   if ((this->GetLeftDaughter(n) == NULL) && (this->GetRightDaughter(n) != NULL) ) {
      Log() << kFATAL << " Node with only one daughter?? Something went wrong" << Endl;
      return;
   }  else if ((this->GetLeftDaughter(n) != NULL) && (this->GetRightDaughter(n) == NULL) ) {
      Log() << kFATAL << " Node with only one daughter?? Something went wrong" << Endl;
      return;
   }
   else {
      if (this->GetLeftDaughter(n) != NULL) {
         this->SetParentTreeInNodes( this->GetLeftDaughter(n) );
      }
      if (this->GetRightDaughter(n) != NULL) {
         this->SetParentTreeInNodes( this->GetRightDaughter(n) );
      }
   }
   n->SetParentTree(this);
   if (n->GetDepth() > this->GetTotalTreeDepth()) this->SetTotalTreeDepth(n->GetDepth());
   return;
}
 
////////////////////////////////////////////////////////////////////////////////
/// re-create a new tree (decision tree or search tree) from XML
 
TMVA::DecisionTree* TMVA::DecisionTree::CreateFromXML(void* node, UInt_t tmva_Version_Code ) {
   std::string type("");
   gTools().ReadAttr(node,"type", type);
   DecisionTree* dt = new DecisionTree();
 
   dt->ReadXML( node, tmva_Version_Code );
   return dt;
}
 
// #### Multithreaded DecisionTree::BuildTree
#ifdef R__USE_IMT
//====================================================================================
// Had to define this struct to enable parallelization.
// Using BuildNodeInfo, each thread can return the information needed.
// After each thread returns we can then merge the results, hence the + operator.
//====================================================================================
struct BuildNodeInfo{
 
   BuildNodeInfo(Int_t fNvars, const TMVA::Event* evt){
 
      nvars = fNvars;
      xmin = std::vector<Float_t>(nvars);
      xmax = std::vector<Float_t>(nvars);
 
      // #### the initial min and max for each feature
      for (Int_t ivar=0; ivar<fNvars; ivar++) {
         const Double_t val = evt->GetValueFast(ivar);
         xmin[ivar]=val;
         xmax[ivar]=val;
      }
   };
 
   BuildNodeInfo(Int_t fNvars, std::vector<Float_t>& inxmin, std::vector<Float_t>& inxmax){
 
      nvars = fNvars;
      xmin = std::vector<Float_t>(nvars);
      xmax = std::vector<Float_t>(nvars);
 
      // #### the initial min and max for each feature
      for (Int_t ivar=0; ivar<fNvars; ivar++) {
         xmin[ivar]=inxmin[ivar];
         xmax[ivar]=inxmax[ivar];
      }
   };
   BuildNodeInfo(){};
 
   Int_t nvars = 0;
   Double_t s = 0;
   Double_t suw = 0;
   Double_t sub = 0;
   Double_t b = 0;
   Double_t buw = 0;
   Double_t bub = 0;
   Double_t target = 0;
   Double_t target2 = 0;
   std::vector<Float_t> xmin;
   std::vector<Float_t> xmax;
 
   // #### Define the addition operator for BuildNodeInfo
   // #### Make sure both BuildNodeInfos have the same nvars if we add them
   BuildNodeInfo operator+(const BuildNodeInfo& other)
   {
       BuildNodeInfo ret(nvars, xmin, xmax);
       if(nvars != other.nvars)
       {
          std::cout << "!!! ERROR BuildNodeInfo1+BuildNodeInfo2 failure. Nvars1 != Nvars2." << std::endl;
          return ret;
       }
       ret.s = s + other.s;
       ret.suw = suw + other.suw;
       ret.sub = sub + other.sub;
       ret.b = b + other.b;
       ret.buw = buw + other.buw;
       ret.bub = bub + other.bub;
       ret.target = target + other.target;
       ret.target2 = target2 + other.target2;
 
       // xmin is the min of both, xmax is the max of both
       for(Int_t i=0; i<nvars; i++)
       {
           ret.xmin[i]=xmin[i]<other.xmin[i]?xmin[i]:other.xmin[i];
           ret.xmax[i]=xmax[i]>other.xmax[i]?xmax[i]:other.xmax[i];
       }
       return ret;
   };
 
};
//===========================================================================
// Done with BuildNodeInfo declaration
//===========================================================================
 
 
////////////////////////////////////////////////////////////////////////////////
/// building the decision tree by recursively calling the splitting of
/// one (root-) node into two daughter nodes (returns the number of nodes)
 
UInt_t TMVA::DecisionTree::BuildTree( const std::vector<const TMVA::Event*> & eventSample,
                                      TMVA::DecisionTreeNode *node)
{
   if (node==NULL) {
      //start with the root node
      node = new TMVA::DecisionTreeNode();
      fNNodes = 1;
      this->SetRoot(node);
      // have to use "s" for start as "r" for "root" would be the same as "r" for "right"
      this->GetRoot()->SetPos('s');
      this->GetRoot()->SetDepth(0);
      this->GetRoot()->SetParentTree(this);
      fMinSize = fMinNodeSize/100. * eventSample.size();
      if (GetTreeID()==0){
         Log() << kDEBUG << "\tThe minimal node size MinNodeSize=" << fMinNodeSize << " fMinNodeSize="<<fMinNodeSize<< "% is translated to an actual number of events = "<< fMinSize<< " for the training sample size of " << eventSample.size() << Endl;
         Log() << kDEBUG << "\tNote: This number will be taken as absolute minimum in the node, " << Endl;
         Log() << kDEBUG << "      \tin terms of 'weighted events' and unweighted ones !! " << Endl;
      }
   }
 
   UInt_t nevents = eventSample.size();
 
   if (nevents > 0 ) {
      if (fNvars==0) fNvars = eventSample[0]->GetNVariables(); // should have been set before, but ... well..
      fVariableImportance.resize(fNvars);
   }
   else Log() << kFATAL << ":<BuildTree> eventsample Size == 0 " << Endl;
 
   // sum up the totals
   // sig and bkg for classification
   // err and err2 for regression
 
   // #### Set up prerequisite info for multithreading
   UInt_t nPartitions = TMVA::Config::Instance().GetThreadExecutor().GetPoolSize();
   auto seeds = ROOT::TSeqU(nPartitions);
 
   // #### need a lambda function to pass to TThreadExecutor::MapReduce (multi-threading)
   auto f = [this, &eventSample, &nPartitions](UInt_t partition = 0){
 
      Int_t start = 1.0*partition/nPartitions*eventSample.size();
      Int_t end   = (partition+1.0)/nPartitions*eventSample.size();
 
      BuildNodeInfo nodeInfof(fNvars, eventSample[0]);
 
      for(Int_t iev=start; iev<end; iev++){
         const TMVA::Event* evt = eventSample[iev];
         const Double_t weight = evt->GetWeight();
         const Double_t orgWeight = evt->GetOriginalWeight(); // unboosted!
         if (evt->GetClass() == fSigClass) {
            nodeInfof.s += weight;
            nodeInfof.suw += 1;
            nodeInfof.sub += orgWeight; 
         }
         else {
            nodeInfof.b += weight;
            nodeInfof.buw += 1;
            nodeInfof.bub += orgWeight;
         }
         if ( DoRegression() ) {
            const Double_t tgt = evt->GetTarget(0);
            nodeInfof.target +=weight*tgt;
            nodeInfof.target2+=weight*tgt*tgt;
         }
 
         // save the min and max for each feature
         for (UInt_t ivar=0; ivar<fNvars; ivar++) {
            const Double_t val = evt->GetValueFast(ivar);
            if (iev==start){
               nodeInfof.xmin[ivar]=val;
               nodeInfof.xmax[ivar]=val;
            }
            if (val < nodeInfof.xmin[ivar]) nodeInfof.xmin[ivar]=val;
            if (val > nodeInfof.xmax[ivar]) nodeInfof.xmax[ivar]=val;
         }
      }
      return nodeInfof;
   };
 
   // #### Need an initial struct to pass to std::accumulate
   BuildNodeInfo nodeInfoInit(fNvars, eventSample[0]);
 
   // #### Run the threads in parallel then merge the results
   auto redfunc = [nodeInfoInit](std::vector<BuildNodeInfo> v) -> BuildNodeInfo { return std::accumulate(v.begin(), v.end(), nodeInfoInit); };
   BuildNodeInfo nodeInfo = TMVA::Config::Instance().GetThreadExecutor().MapReduce(f, seeds, redfunc);
   //NodeInfo nodeInfo(fNvars);
 
   if (nodeInfo.s+nodeInfo.b < 0) {
      Log() << kWARNING << " One of the Decision Tree nodes has negative total number of signal or background events. "
            << "(Nsig="<<nodeInfo.s<<" Nbkg="<<nodeInfo.b<<" Probaby you use a Monte Carlo with negative weights. That should in principle "
            << "be fine as long as on average you end up with something positive. For this you have to make sure that the "
            << "minimal number of (unweighted) events demanded for a tree node (currently you use: MinNodeSize="<<fMinNodeSize
            << "% of training events, you can set this via the BDT option string when booking the classifier) is large enough "
            << "to allow for reasonable averaging!!!" << Endl
            << " If this does not help.. maybe you want to try the option: NoNegWeightsInTraining which ignores events "
            << "with negative weight in the training." << Endl;
      double nBkg=0.;
      for (UInt_t i=0; i<eventSample.size(); i++) {
         if (eventSample[i]->GetClass() != fSigClass) {
            nBkg += eventSample[i]->GetWeight();
            Log() << kDEBUG << "Event "<< i<< " has (original) weight: " <<  eventSample[i]->GetWeight()/eventSample[i]->GetBoostWeight()
                  << " boostWeight: " << eventSample[i]->GetBoostWeight() << Endl;
         }
      }
      Log() << kDEBUG << " that gives in total: " << nBkg<<Endl;
   }
 
   node->SetNSigEvents(nodeInfo.s);
   node->SetNBkgEvents(nodeInfo.b);
   node->SetNSigEvents_unweighted(nodeInfo.suw);
   node->SetNBkgEvents_unweighted(nodeInfo.buw);
   node->SetNSigEvents_unboosted(nodeInfo.sub);
   node->SetNBkgEvents_unboosted(nodeInfo.bub);
   node->SetPurity();
   if (node == this->GetRoot()) {
      node->SetNEvents(nodeInfo.s+nodeInfo.b);
      node->SetNEvents_unweighted(nodeInfo.suw+nodeInfo.buw);
      node->SetNEvents_unboosted(nodeInfo.sub+nodeInfo.bub);
   }
 
   // save the min and max for each feature
   for (UInt_t ivar=0; ivar<fNvars; ivar++) {
      node->SetSampleMin(ivar,nodeInfo.xmin[ivar]);
      node->SetSampleMax(ivar,nodeInfo.xmax[ivar]);
   }
 
   // I now demand the minimum number of events for both daughter nodes. Hence if the number
   // of events in the parent node is not at least two times as big, I don't even need to try
   // splitting
 
   // ask here for actual "events" independent of their weight.. OR the weighted events
   // to exceed the min requested number of events per daughter node
   // (NOTE: make sure that at the eventSample at the ROOT node has sum_of_weights == sample.size() !
   //   if ((eventSample.size() >= 2*fMinSize ||s+b >= 2*fMinSize) && node->GetDepth() < fMaxDepth
   // std::cout << "------------------------------------------------------------------"<<std::endl;
   // std::cout << "------------------------------------------------------------------"<<std::endl;
   // std::cout << " eveSampleSize = "<< eventSample.size() << " s+b="<<s+b << std::endl;
   if ((eventSample.size() >= 2*fMinSize  && nodeInfo.s+nodeInfo.b >= 2*fMinSize) && node->GetDepth() < fMaxDepth
       && ( ( nodeInfo.s!=0 && nodeInfo.b !=0 && !DoRegression()) || ( (nodeInfo.s+nodeInfo.b)!=0 && DoRegression()) ) ) {
 
      // Train the node and figure out the separation gain and split points
      Double_t separationGain;
      if (fNCuts > 0){
         separationGain = this->TrainNodeFast(eventSample, node);
      }
      else {
         separationGain = this->TrainNodeFull(eventSample, node);
      }
 
      // The node has been trained and there is nothing to be gained by splitting
      if (separationGain < std::numeric_limits<double>::epsilon()) { // we could not gain anything, e.g. all events are in one bin,
         // no cut can actually do anything to improve the node
         // hence, naturally, the current node is a leaf node
         if (DoRegression()) {
            node->SetSeparationIndex(fRegType->GetSeparationIndex(nodeInfo.s+nodeInfo.b,nodeInfo.target,nodeInfo.target2));
            node->SetResponse(nodeInfo.target/(nodeInfo.s+nodeInfo.b));
            if( almost_equal_double(nodeInfo.target2/(nodeInfo.s+nodeInfo.b),nodeInfo.target/(nodeInfo.s+nodeInfo.b)*nodeInfo.target/(nodeInfo.s+nodeInfo.b)) ){
               node->SetRMS(0);
            }else{
               node->SetRMS(TMath::Sqrt(nodeInfo.target2/(nodeInfo.s+nodeInfo.b) - nodeInfo.target/(nodeInfo.s+nodeInfo.b)*nodeInfo.target/(nodeInfo.s+nodeInfo.b)));
            }
         }
         else {
            node->SetSeparationIndex(fSepType->GetSeparationIndex(nodeInfo.s,nodeInfo.b));
            if (node->GetPurity() > fNodePurityLimit) node->SetNodeType(1);
            else node->SetNodeType(-1);
         }
         if (node->GetDepth() > this->GetTotalTreeDepth()) this->SetTotalTreeDepth(node->GetDepth());
      }
      else {
         // #### Couldn't parallelize this part (filtering events from mother node to daughter nodes)
         // #### ... would need to avoid the push_back or use some threadsafe mutex locked version...
         std::vector<const TMVA::Event*> leftSample; leftSample.reserve(nevents);
         std::vector<const TMVA::Event*> rightSample; rightSample.reserve(nevents);
 
         Double_t nRight=0, nLeft=0;
         Double_t nRightUnBoosted=0, nLeftUnBoosted=0;
 
         for (UInt_t ie=0; ie< nevents ; ie++) {
            if (node->GoesRight(*eventSample[ie])) {
               rightSample.push_back(eventSample[ie]);
               nRight += eventSample[ie]->GetWeight();
               nRightUnBoosted += eventSample[ie]->GetOriginalWeight();
            }
            else {
               leftSample.push_back(eventSample[ie]);
               nLeft += eventSample[ie]->GetWeight();
               nLeftUnBoosted += eventSample[ie]->GetOriginalWeight();
            }
         }
         // sanity check
         if (leftSample.empty() || rightSample.empty()) {
 
            Log() << kERROR << "<TrainNode> all events went to the same branch" << Endl
                  << "---                       Hence new node == old node ... check" << Endl
                  << "---                         left:" << leftSample.size()
                  << " right:" << rightSample.size() << Endl
                  << " while the separation is thought to be " << separationGain
                  << "\n when cutting on variable " << node->GetSelector()
                  << " at value " << node->GetCutValue()
                  << kFATAL << "--- this should never happen, please write a bug report to Helge.Voss@cern.ch" << Endl;
         }
 
         // continue building daughter nodes for the left and the right eventsample
         TMVA::DecisionTreeNode *rightNode = new TMVA::DecisionTreeNode(node,'r');
         fNNodes++;
         rightNode->SetNEvents(nRight);
         rightNode->SetNEvents_unboosted(nRightUnBoosted);
         rightNode->SetNEvents_unweighted(rightSample.size());
 
         TMVA::DecisionTreeNode *leftNode = new TMVA::DecisionTreeNode(node,'l');
 
         fNNodes++;
         leftNode->SetNEvents(nLeft);
         leftNode->SetNEvents_unboosted(nLeftUnBoosted);
         leftNode->SetNEvents_unweighted(leftSample.size());
 
         node->SetNodeType(0);
         node->SetLeft(leftNode);
         node->SetRight(rightNode);
 
         this->BuildTree(rightSample, rightNode);
         this->BuildTree(leftSample,  leftNode );
 
      }
   }
   else{ // it is a leaf node
      if (DoRegression()) {
         node->SetSeparationIndex(fRegType->GetSeparationIndex(nodeInfo.s+nodeInfo.b,nodeInfo.target,nodeInfo.target2));
         node->SetResponse(nodeInfo.target/(nodeInfo.s+nodeInfo.b));
         if( almost_equal_double(nodeInfo.target2/(nodeInfo.s+nodeInfo.b), nodeInfo.target/(nodeInfo.s+nodeInfo.b)*nodeInfo.target/(nodeInfo.s+nodeInfo.b)) ) {
            node->SetRMS(0);
         }else{
            node->SetRMS(TMath::Sqrt(nodeInfo.target2/(nodeInfo.s+nodeInfo.b) - nodeInfo.target/(nodeInfo.s+nodeInfo.b)*nodeInfo.target/(nodeInfo.s+nodeInfo.b)));
         }
      }
      else {
         node->SetSeparationIndex(fSepType->GetSeparationIndex(nodeInfo.s,nodeInfo.b));
         if   (node->GetPurity() > fNodePurityLimit) node->SetNodeType(1);
         else node->SetNodeType(-1);
         // loop through the event sample ending up in this node and check for events with negative weight
         // those "cannot" be boosted normally. Hence, if there is one of those
         // is misclassified, find randomly as many events with positive weights in this
         // node as needed to get the same absolute number of weight, and mark them as
         // "not to be boosted" in order to make up for not boosting the negative weight event
      }
 
 
      if (node->GetDepth() > this->GetTotalTreeDepth()) this->SetTotalTreeDepth(node->GetDepth());
   }
 
   //   if (IsRootNode) this->CleanTree();
   return fNNodes;
}
 
// Standard DecisionTree::BuildTree (multithreading is not enabled)
#else
 
UInt_t TMVA::DecisionTree::BuildTree( const std::vector<const TMVA::Event*> & eventSample,
                                      TMVA::DecisionTreeNode *node)
{
   if (node==NULL) {
      //start with the root node
      node = new TMVA::DecisionTreeNode();
      fNNodes = 1;
      this->SetRoot(node);
      // have to use "s" for start as "r" for "root" would be the same as "r" for "right"
      this->GetRoot()->SetPos('s');
      this->GetRoot()->SetDepth(0);
      this->GetRoot()->SetParentTree(this);
      fMinSize = fMinNodeSize/100. * eventSample.size();
      if (GetTreeID()==0){
         Log() << kDEBUG << "\tThe minimal node size MinNodeSize=" << fMinNodeSize << " fMinNodeSize="<<fMinNodeSize<< "% is translated to an actual number of events = "<< fMinSize<< " for the training sample size of " << eventSample.size() << Endl;
         Log() << kDEBUG << "\tNote: This number will be taken as absolute minimum in the node, " << Endl;
         Log() << kDEBUG << "      \tin terms of 'weighted events' and unweighted ones !! " << Endl;
      }
   }
 
   UInt_t nevents = eventSample.size();
 
   if (nevents > 0 ) {
      if (fNvars==0) fNvars = eventSample[0]->GetNVariables(); // should have been set before, but ... well..
      fVariableImportance.resize(fNvars);
   }
   else Log() << kFATAL << ":<BuildTree> eventsample Size == 0 " << Endl;
 
   Double_t s=0, b=0;
   Double_t suw=0, buw=0;
   Double_t sub=0, bub=0; // unboosted!
   Double_t target=0, target2=0;
   Float_t *xmin = new Float_t[fNvars];
   Float_t *xmax = new Float_t[fNvars];
 
   // initializing xmin and xmax for each variable
   for (UInt_t ivar=0; ivar<fNvars; ivar++) {
      xmin[ivar]=xmax[ivar]=0;
   }
   // sum up the totals
   // sig and bkg for classification
   // err and err2 for regression
   for (UInt_t iev=0; iev<eventSample.size(); iev++) {
      const TMVA::Event* evt = eventSample[iev];
      const Double_t weight = evt->GetWeight();
      const Double_t orgWeight = evt->GetOriginalWeight(); // unboosted!
      if (evt->GetClass() == fSigClass) {
         s += weight;
         suw += 1;
         sub += orgWeight;
      }
      else {
         b += weight;
         buw += 1;
         bub += orgWeight;
      }
      if ( DoRegression() ) {
         const Double_t tgt = evt->GetTarget(0);
         target +=weight*tgt;
         target2+=weight*tgt*tgt;
      }
 
      // save the min and max for each feature
      for (UInt_t ivar=0; ivar<fNvars; ivar++) {
         const Double_t val = evt->GetValueFast(ivar);
         if (iev==0) xmin[ivar]=xmax[ivar]=val;
         if (val < xmin[ivar]) xmin[ivar]=val;
         if (val > xmax[ivar]) xmax[ivar]=val;
      }
   }
 
 
   if (s+b < 0) {
      Log() << kWARNING << " One of the Decision Tree nodes has negative total number of signal or background events. "
            << "(Nsig="<<s<<" Nbkg="<<b<<" Probaby you use a Monte Carlo with negative weights. That should in principle "
            << "be fine as long as on average you end up with something positive. For this you have to make sure that the "
            << "minimul number of (unweighted) events demanded for a tree node (currently you use: MinNodeSize="<<fMinNodeSize
            << "% of training events, you can set this via the BDT option string when booking the classifier) is large enough "
            << "to allow for reasonable averaging!!!" << Endl
            << " If this does not help.. maybe you want to try the option: NoNegWeightsInTraining which ignores events "
            << "with negative weight in the training." << Endl;
      double nBkg=0.;
      for (UInt_t i=0; i<eventSample.size(); i++) {
         if (eventSample[i]->GetClass() != fSigClass) {
            nBkg += eventSample[i]->GetWeight();
            Log() << kDEBUG << "Event "<< i<< " has (original) weight: " <<  eventSample[i]->GetWeight()/eventSample[i]->GetBoostWeight()
                  << " boostWeight: " << eventSample[i]->GetBoostWeight() << Endl;
         }
      }
      Log() << kDEBUG << " that gives in total: " << nBkg<<Endl;
   }
 
   node->SetNSigEvents(s);
   node->SetNBkgEvents(b);
   node->SetNSigEvents_unweighted(suw);
   node->SetNBkgEvents_unweighted(buw);
   node->SetNSigEvents_unboosted(sub);
   node->SetNBkgEvents_unboosted(bub);
   node->SetPurity();
   if (node == this->GetRoot()) {
      node->SetNEvents(s+b);
      node->SetNEvents_unweighted(suw+buw);
      node->SetNEvents_unboosted(sub+bub);
   }
 
   // save the min and max for each feature
   for (UInt_t ivar=0; ivar<fNvars; ivar++) {
      node->SetSampleMin(ivar,xmin[ivar]);
      node->SetSampleMax(ivar,xmax[ivar]);
 
   }
   delete[] xmin;
   delete[] xmax;
 
   // I now demand the minimum number of events for both daughter nodes. Hence if the number
   // of events in the parent node is not at least two times as big, I don't even need to try
   // splitting
 
   // ask here for actuall "events" independent of their weight.. OR the weighted events
   // to execeed the min requested number of events per dauther node
   // (NOTE: make sure that at the eventSample at the ROOT node has sum_of_weights == sample.size() !
   //   if ((eventSample.size() >= 2*fMinSize ||s+b >= 2*fMinSize) && node->GetDepth() < fMaxDepth 
   // std::cout << "------------------------------------------------------------------"<<std::endl;
   // std::cout << "------------------------------------------------------------------"<<std::endl;
   // std::cout << " eveSampleSize = "<< eventSample.size() << " s+b="<<s+b << std::endl;
   if ((eventSample.size() >= 2*fMinSize  && s+b >= 2*fMinSize) && node->GetDepth() < fMaxDepth
       && ( ( s!=0 && b !=0 && !DoRegression()) || ( (s+b)!=0 && DoRegression()) ) ) {
      Double_t separationGain;
      if (fNCuts > 0){
         separationGain = this->TrainNodeFast(eventSample, node);
      } else {
         separationGain = this->TrainNodeFull(eventSample, node);
      }
      if (separationGain < std::numeric_limits<double>::epsilon()) { // we could not gain anything, e.g. all events are in one bin,
         /// if (separationGain < 0.00000001) { // we could not gain anything, e.g. all events are in one bin,
         // no cut can actually do anything to improve the node
         // hence, naturally, the current node is a leaf node
         if (DoRegression()) {
            node->SetSeparationIndex(fRegType->GetSeparationIndex(s+b,target,target2));
            node->SetResponse(target/(s+b));
            if( almost_equal_double(target2/(s+b),target/(s+b)*target/(s+b)) ){
               node->SetRMS(0);
            }else{
               node->SetRMS(TMath::Sqrt(target2/(s+b) - target/(s+b)*target/(s+b)));
            }
         }
         else {
            node->SetSeparationIndex(fSepType->GetSeparationIndex(s,b));
 
            if (node->GetPurity() > fNodePurityLimit) node->SetNodeType(1);
            else node->SetNodeType(-1);
         }
         if (node->GetDepth() > this->GetTotalTreeDepth()) this->SetTotalTreeDepth(node->GetDepth());
 
      } else {
 
         std::vector<const TMVA::Event*> leftSample; leftSample.reserve(nevents);
         std::vector<const TMVA::Event*> rightSample; rightSample.reserve(nevents);
 
         Double_t nRight=0, nLeft=0;
         Double_t nRightUnBoosted=0, nLeftUnBoosted=0;
 
         for (UInt_t ie=0; ie< nevents ; ie++) {
            if (node->GoesRight(*eventSample[ie])) {
               rightSample.push_back(eventSample[ie]);
               nRight += eventSample[ie]->GetWeight();
               nRightUnBoosted += eventSample[ie]->GetOriginalWeight();
            }
            else {
               leftSample.push_back(eventSample[ie]);
               nLeft += eventSample[ie]->GetWeight();
               nLeftUnBoosted += eventSample[ie]->GetOriginalWeight();
            }
         }
 
         // sanity check
         if (leftSample.empty() || rightSample.empty()) {
 
            Log() << kERROR << "<TrainNode> all events went to the same branch" << Endl
                  << "---                       Hence new node == old node ... check" << Endl
                  << "---                         left:" << leftSample.size()
                  << " right:" << rightSample.size() << Endl
                  << " while the separation is thought to be " << separationGain
                  << "\n when cutting on variable " << node->GetSelector()
                  << " at value " << node->GetCutValue()
                  << kFATAL << "--- this should never happen, please write a bug report to Helge.Voss@cern.ch" << Endl;
         }
 
         // continue building daughter nodes for the left and the right eventsample
         TMVA::DecisionTreeNode *rightNode = new TMVA::DecisionTreeNode(node,'r');
         fNNodes++;
         rightNode->SetNEvents(nRight);
         rightNode->SetNEvents_unboosted(nRightUnBoosted);
         rightNode->SetNEvents_unweighted(rightSample.size());
 
         TMVA::DecisionTreeNode *leftNode = new TMVA::DecisionTreeNode(node,'l');
 
         fNNodes++;
         leftNode->SetNEvents(nLeft);
         leftNode->SetNEvents_unboosted(nLeftUnBoosted);
         leftNode->SetNEvents_unweighted(leftSample.size());
 
         node->SetNodeType(0);
         node->SetLeft(leftNode);
         node->SetRight(rightNode);
 
         this->BuildTree(rightSample, rightNode);
         this->BuildTree(leftSample,  leftNode );
 
      }
   }
   else{ // it is a leaf node
      if (DoRegression()) {
         node->SetSeparationIndex(fRegType->GetSeparationIndex(s+b,target,target2));
         node->SetResponse(target/(s+b));
         if( almost_equal_double(target2/(s+b), target/(s+b)*target/(s+b)) ) {
            node->SetRMS(0);
         }else{
            node->SetRMS(TMath::Sqrt(target2/(s+b) - target/(s+b)*target/(s+b)));
         }
      }
      else {
         node->SetSeparationIndex(fSepType->GetSeparationIndex(s,b));
         if   (node->GetPurity() > fNodePurityLimit) node->SetNodeType(1);
         else node->SetNodeType(-1);
         // loop through the event sample ending up in this node and check for events with negative weight
         // those "cannot" be boosted normally. Hence, if there is one of those
         // is misclassified, find randomly as many events with positive weights in this
         // node as needed to get the same absolute number of weight, and mark them as 
         // "not to be boosted" in order to make up for not boosting the negative weight event
      }
 
 
      if (node->GetDepth() > this->GetTotalTreeDepth()) this->SetTotalTreeDepth(node->GetDepth());
   }
 
   //   if (IsRootNode) this->CleanTree();
   return fNNodes;
}
 
#endif
 
////////////////////////////////////////////////////////////////////////////////
/// fill the existing the decision tree structure by filling event
/// in from the top node and see where they happen to end up
 
void TMVA::DecisionTree::FillTree( const std::vector<TMVA::Event*> & eventSample )
{
   for (UInt_t i=0; i<eventSample.size(); i++) {
      this->FillEvent(*(eventSample[i]),NULL);
   }
}
 
////////////////////////////////////////////////////////////////////////////////
/// fill the existing the decision tree structure by filling event
/// in from the top node and see where they happen to end up
 
void TMVA::DecisionTree::FillEvent( const TMVA::Event & event,
                                    TMVA::DecisionTreeNode *node )
{
   if (node == NULL) { // that's the start, take the Root node
      node = this->GetRoot();
   }
 
   node->IncrementNEvents( event.GetWeight() );
   node->IncrementNEvents_unweighted( );
 
   if (event.GetClass() == fSigClass) {
      node->IncrementNSigEvents( event.GetWeight() );
      node->IncrementNSigEvents_unweighted( );
   }
   else {
      node->IncrementNBkgEvents( event.GetWeight() );
      node->IncrementNBkgEvents_unweighted( );
   }
   node->SetSeparationIndex(fSepType->GetSeparationIndex(node->GetNSigEvents(),
                                                         node->GetNBkgEvents()));
 
   if (node->GetNodeType() == 0) { //intermediate node --> go down
      if (node->GoesRight(event))
         this->FillEvent(event, node->GetRight());
      else
         this->FillEvent(event, node->GetLeft());
   }
}
 
////////////////////////////////////////////////////////////////////////////////
/// clear the tree nodes (their S/N, Nevents etc), just keep the structure of the tree
 
void TMVA::DecisionTree::ClearTree()
{
   if (this->GetRoot()!=NULL) this->GetRoot()->ClearNodeAndAllDaughters();
 
}
 
////////////////////////////////////////////////////////////////////////////////
/// remove those last splits that result in two leaf nodes that
/// are both of the type (i.e. both signal or both background)
/// this of course is only a reasonable thing to do when you use
/// "YesOrNo" leafs, while it might loose s.th. if you use the
/// purity information in the nodes.
/// --> hence I don't call it automatically in the tree building
 
UInt_t TMVA::DecisionTree::CleanTree( DecisionTreeNode *node )
{
   if (node==NULL) {
      node = this->GetRoot();
   }
 
   DecisionTreeNode *l = node->GetLeft();
   DecisionTreeNode *r = node->GetRight();
 
   if (node->GetNodeType() == 0) {
      this->CleanTree(l);
      this->CleanTree(r);
      if (l->GetNodeType() * r->GetNodeType() > 0) {
 
         this->PruneNode(node);
      }
   }
   // update the number of nodes after the cleaning
   return this->CountNodes();
 
}
 
////////////////////////////////////////////////////////////////////////////////
/// prune (get rid of internal nodes) the Decision tree to avoid overtraining
/// several different pruning methods can be applied as selected by the
/// variable "fPruneMethod".
 
Double_t TMVA::DecisionTree::PruneTree( const EventConstList* validationSample )
{
   IPruneTool* tool(NULL);
   PruningInfo* info(NULL);
 
   if( fPruneMethod == kNoPruning ) return 0.0;
 
   if      (fPruneMethod == kExpectedErrorPruning)
      //      tool = new ExpectedErrorPruneTool(logfile);
      tool = new ExpectedErrorPruneTool();
   else if (fPruneMethod == kCostComplexityPruning)
      {
         tool = new CostComplexityPruneTool();
      }
   else {
      Log() << kFATAL << "Selected pruning method not yet implemented "
            << Endl;
   }
 
   if(!tool) return 0.0;
 
   tool->SetPruneStrength(GetPruneStrength());
   if(tool->IsAutomatic()) {
      if(validationSample == NULL){
         Log() << kFATAL << "Cannot automate the pruning algorithm without an "
               << "independent validation sample!" << Endl;
      }else if(validationSample->size() == 0) {
         Log() << kFATAL << "Cannot automate the pruning algorithm with "
               << "independent validation sample of ZERO events!" << Endl;
      }
   }
 
   info = tool->CalculatePruningInfo(this,validationSample);
   Double_t pruneStrength=0;
   if(!info) {
      Log() << kFATAL << "Error pruning tree! Check prune.log for more information."
            << Endl;
   } else {
      pruneStrength = info->PruneStrength;
 
      //   Log() << kDEBUG << "Optimal prune strength (alpha): " << pruneStrength
      //           << " has quality index " << info->QualityIndex << Endl;
 
 
      for (UInt_t i = 0; i < info->PruneSequence.size(); ++i) {
 
         PruneNode(info->PruneSequence[i]);
      }
      // update the number of nodes after the pruning
      this->CountNodes();
   }
 
   delete tool;
   delete info;
 
   return pruneStrength;
};
 
 
////////////////////////////////////////////////////////////////////////////////
/// run the validation sample through the (pruned) tree and fill in the nodes
/// the variables NSValidation and NBValidadtion (i.e. how many of the Signal
/// and Background events from the validation sample. This is then later used
/// when asking for the "tree quality" ..
 
void TMVA::DecisionTree::ApplyValidationSample( const EventConstList* validationSample ) const
{
   GetRoot()->ResetValidationData();
   for (UInt_t ievt=0; ievt < validationSample->size(); ievt++) {
      CheckEventWithPrunedTree((*validationSample)[ievt]);
   }
}
 
////////////////////////////////////////////////////////////////////////////////
/// return the misclassification rate of a pruned tree
/// a "pruned tree" may have set the variable "IsTerminal" to "arbitrary" at
/// any node, hence this tree quality testing will stop there, hence test
/// the pruned tree (while the full tree is still in place for normal/later use)
 
Double_t TMVA::DecisionTree::TestPrunedTreeQuality( const DecisionTreeNode* n, Int_t mode ) const
{
   if (n == NULL) { // default, start at the tree top, then descend recursively
      n = this->GetRoot();
      if (n == NULL) {
         Log() << kFATAL << "TestPrunedTreeQuality: started with undefined ROOT node" <<Endl;
         return 0;
      }
   }
 
   if( n->GetLeft() != NULL && n->GetRight() != NULL && !n->IsTerminal() ) {
      return (TestPrunedTreeQuality( n->GetLeft(), mode ) +
              TestPrunedTreeQuality( n->GetRight(), mode ));
   }
   else { // terminal leaf (in a pruned subtree of T_max at least)
      if (DoRegression()) {
         Double_t sumw = n->GetNSValidation() + n->GetNBValidation();
         return n->GetSumTarget2() - 2*n->GetSumTarget()*n->GetResponse() + sumw*n->GetResponse()*n->GetResponse();
      }
      else {
         if (mode == 0) {
            if (n->GetPurity() > this->GetNodePurityLimit()) // this is a signal leaf, according to the training
               return n->GetNBValidation();
            else
               return n->GetNSValidation();
         }
         else if ( mode == 1 ) {
            // calculate the weighted error using the pruning validation sample
            return (n->GetPurity() * n->GetNBValidation() + (1.0 - n->GetPurity()) * n->GetNSValidation());
         }
         else {
            throw std::string("Unknown ValidationQualityMode");
         }
      }
   }
}
 
////////////////////////////////////////////////////////////////////////////////
/// pass a single validation event through a pruned decision tree
/// on the way down the tree, fill in all the "intermediate" information
/// that would normally be there from training.
 
void TMVA::DecisionTree::CheckEventWithPrunedTree( const Event* e ) const
{
   DecisionTreeNode* current =  this->GetRoot();
   if (current == NULL) {
      Log() << kFATAL << "CheckEventWithPrunedTree: started with undefined ROOT node" <<Endl;
   }
 
   while(current != NULL) {
      if(e->GetClass() == fSigClass)
         current->SetNSValidation(current->GetNSValidation() + e->GetWeight());
      else
         current->SetNBValidation(current->GetNBValidation() + e->GetWeight());
 
      if (e->GetNTargets() > 0) {
         current->AddToSumTarget(e->GetWeight()*e->GetTarget(0));
         current->AddToSumTarget2(e->GetWeight()*e->GetTarget(0)*e->GetTarget(0));
      }
 
      if (current->GetRight() == NULL || current->GetLeft() == NULL) {
         current = NULL;
      }
      else {
         if (current->GoesRight(*e))
            current = (TMVA::DecisionTreeNode*)current->GetRight();
         else
            current = (TMVA::DecisionTreeNode*)current->GetLeft();
      }
   }
}
 
////////////////////////////////////////////////////////////////////////////////
/// calculate the normalization factor for a pruning validation sample
 
Double_t TMVA::DecisionTree::GetSumWeights( const EventConstList* validationSample ) const
{
   Double_t sumWeights = 0.0;
   for( EventConstList::const_iterator it = validationSample->begin();
        it != validationSample->end(); ++it ) {
      sumWeights += (*it)->GetWeight();
   }
   return sumWeights;
}
 
////////////////////////////////////////////////////////////////////////////////
/// return the number of terminal nodes in the sub-tree below Node n
 
UInt_t TMVA::DecisionTree::CountLeafNodes( TMVA::Node *n )
{
   if (n == NULL) { // default, start at the tree top, then descend recursively
      n =  this->GetRoot();
      if (n == NULL) {
         Log() << kFATAL << "CountLeafNodes: started with undefined ROOT node" <<Endl;
         return 0;
      }
   }
 
   UInt_t countLeafs=0;
 
   if ((this->GetLeftDaughter(n) == NULL) && (this->GetRightDaughter(n) == NULL) ) {
      countLeafs += 1;
   }
   else {
      if (this->GetLeftDaughter(n) != NULL) {
         countLeafs += this->CountLeafNodes( this->GetLeftDaughter(n) );
      }
      if (this->GetRightDaughter(n) != NULL) {
         countLeafs += this->CountLeafNodes( this->GetRightDaughter(n) );
      }
   }
   return countLeafs;
}
 
////////////////////////////////////////////////////////////////////////////////
/// descend a tree to find all its leaf nodes
 
void TMVA::DecisionTree::DescendTree( Node* n )
{
   if (n == NULL) { // default, start at the tree top, then descend recursively
      n =  this->GetRoot();
      if (n == NULL) {
         Log() << kFATAL << "DescendTree: started with undefined ROOT node" <<Endl;
         return ;
      }
   }
 
   if ((this->GetLeftDaughter(n) == NULL) && (this->GetRightDaughter(n) == NULL) ) {
      // do nothing
   }
   else if ((this->GetLeftDaughter(n) == NULL) && (this->GetRightDaughter(n) != NULL) ) {
      Log() << kFATAL << " Node with only one daughter?? Something went wrong" << Endl;
      return;
   }
   else if ((this->GetLeftDaughter(n) != NULL) && (this->GetRightDaughter(n) == NULL) ) {
      Log() << kFATAL << " Node with only one daughter?? Something went wrong" << Endl;
      return;
   }
   else {
      if (this->GetLeftDaughter(n) != NULL) {
         this->DescendTree( this->GetLeftDaughter(n) );
      }
      if (this->GetRightDaughter(n) != NULL) {
         this->DescendTree( this->GetRightDaughter(n) );
      }
   }
}
 
////////////////////////////////////////////////////////////////////////////////
/// prune away the subtree below the node
 
void TMVA::DecisionTree::PruneNode( DecisionTreeNode* node )
{
   DecisionTreeNode *l = node->GetLeft();
   DecisionTreeNode *r = node->GetRight();
 
   node->SetRight(NULL);
   node->SetLeft(NULL);
   node->SetSelector(-1);
   node->SetSeparationGain(-1);
   if (node->GetPurity() > fNodePurityLimit) node->SetNodeType(1);
   else node->SetNodeType(-1);
   this->DeleteNode(l);
   this->DeleteNode(r);
   // update the stored number of nodes in the Tree
   this->CountNodes();
 
}
 
////////////////////////////////////////////////////////////////////////////////
/// prune a node temporarily (without actually deleting its descendants
/// which allows testing the pruned tree quality for many different
/// pruning stages without "touching" the tree.
 
void TMVA::DecisionTree::PruneNodeInPlace( DecisionTreeNode* node ) {
   if(node == NULL) return;
   node->SetNTerminal(1);
   node->SetSubTreeR( node->GetNodeR() );
   node->SetAlpha( std::numeric_limits<double>::infinity( ) );
   node->SetAlphaMinSubtree( std::numeric_limits<double>::infinity( ) );
   node->SetTerminal(kTRUE); // set the node to be terminal without deleting its descendants FIXME not needed
}
 
////////////////////////////////////////////////////////////////////////////////
/// retrieve node from the tree. Its position (up to a maximal tree depth of 64)
/// is coded as a sequence of left-right moves starting from the root, coded as
/// 0-1 bit patterns stored in the "long-integer"  (i.e. 0:left ; 1:right
 
TMVA::Node* TMVA::DecisionTree::GetNode( ULong_t sequence, UInt_t depth )
{
   Node* current = this->GetRoot();
 
   for (UInt_t i =0;  i < depth; i++) {
      ULong_t tmp = 1 << i;
      if ( tmp & sequence) current = this->GetRightDaughter(current);
      else current = this->GetLeftDaughter(current);
   }
 
   return current;
}
 
////////////////////////////////////////////////////////////////////////////////
///
 
void TMVA::DecisionTree::GetRandomisedVariables(Bool_t *useVariable, UInt_t *mapVariable, UInt_t &useNvars){
   for (UInt_t ivar=0; ivar<fNvars; ivar++) useVariable[ivar]=kFALSE;
   if (fUseNvars==0) { // no number specified ... choose s.th. which hopefully works well
      // watch out, should never happen as it is initialised automatically in MethodBDT already!!!
      fUseNvars        =  UInt_t(TMath::Sqrt(fNvars)+0.6);
   }
   if (fUsePoissonNvars) useNvars=TMath::Min(fNvars,TMath::Max(UInt_t(1),(UInt_t) fMyTrandom->Poisson(fUseNvars)));
   else useNvars = fUseNvars;
 
   UInt_t nSelectedVars = 0;
   while (nSelectedVars < useNvars) {
      Double_t bla = fMyTrandom->Rndm()*fNvars;
      useVariable[Int_t (bla)] = kTRUE;
      nSelectedVars = 0;
      for (UInt_t ivar=0; ivar < fNvars; ivar++) {
         if (useVariable[ivar] == kTRUE) {
            mapVariable[nSelectedVars] = ivar;
            nSelectedVars++;
         }
      }
   }
   if (nSelectedVars != useNvars) { std::cout << "Bug in TrainNode - GetRandisedVariables()... sorry" << std::endl; std::exit(1);}
}
 
// Multithreaded version of DecisionTree::TrainNodeFast
#ifdef R__USE_IMT
//====================================================================================
// Had to define this struct to enable parallelization in TrainNodeFast.
// Using TrainNodeInfo, each thread can return the information needed.
// After each thread returns we can then merge the results, hence the + operator.
//====================================================================================
struct TrainNodeInfo{
 
   TrainNodeInfo(Int_t cNvars_, UInt_t* nBins_){
 
      cNvars = cNvars_;
      nBins = nBins_;
 
      nSelS            = std::vector< std::vector<Double_t> >(cNvars);
      nSelB            = std::vector< std::vector<Double_t> >(cNvars);
      nSelS_unWeighted = std::vector< std::vector<Double_t> >(cNvars);
      nSelB_unWeighted = std::vector< std::vector<Double_t> >(cNvars);
      target           = std::vector< std::vector<Double_t> >(cNvars);
      target2          = std::vector< std::vector<Double_t> >(cNvars);
 
      for(Int_t ivar=0; ivar<cNvars; ivar++){
          nSelS[ivar]            = std::vector<Double_t>(nBins[ivar], 0);
          nSelB[ivar]            = std::vector<Double_t>(nBins[ivar], 0);
          nSelS_unWeighted[ivar] = std::vector<Double_t>(nBins[ivar], 0);
          nSelB_unWeighted[ivar] = std::vector<Double_t>(nBins[ivar], 0);
          target[ivar]           = std::vector<Double_t>(nBins[ivar], 0);
          target2[ivar]          = std::vector<Double_t>(nBins[ivar], 0);
      }
   };
 
   TrainNodeInfo(){};
 
   // #### malloc problem if I define this and try to destruct xmin and xmax...
   // #### Maybe someone better at C++ can figure out why and fix this if it's a 
   // ### serious memory problem
   //~TrainNodeInfo(){
   //   delete [] xmin;
   //   delete [] xmax;
   //};
 
   Int_t cNvars = 0;
   UInt_t*   nBins;
 
   Double_t nTotS = 0;
   Double_t nTotS_unWeighted = 0;
   Double_t nTotB = 0;
   Double_t nTotB_unWeighted = 0;
 
   std::vector< std::vector<Double_t> > nSelS;
   std::vector< std::vector<Double_t> > nSelB;
   std::vector< std::vector<Double_t> > nSelS_unWeighted;
   std::vector< std::vector<Double_t> > nSelB_unWeighted;
   std::vector< std::vector<Double_t> > target;
   std::vector< std::vector<Double_t> > target2;
 
   // Define the addition operator for TrainNodeInfo
   // Make sure both TrainNodeInfos have the same nvars if we add them
   TrainNodeInfo operator+(const TrainNodeInfo& other)
   {           
       TrainNodeInfo ret(cNvars, nBins);
        
       // check that the two are compatible to add
       if(cNvars != other.cNvars) 
       {
          std::cout << "!!! ERROR TrainNodeInfo1+TrainNodeInfo2 failure. cNvars1 != cNvars2." << std::endl;
          return ret;
       }
 
       // add the signal, background, and target sums
       for (Int_t ivar=0; ivar<cNvars; ivar++) {
          for (UInt_t ibin=0; ibin<nBins[ivar]; ibin++) {
             ret.nSelS[ivar][ibin] = nSelS[ivar][ibin] + other.nSelS[ivar][ibin];
             ret.nSelB[ivar][ibin] = nSelB[ivar][ibin] + other.nSelB[ivar][ibin];
             ret.nSelS_unWeighted[ivar][ibin] = nSelS_unWeighted[ivar][ibin] + other.nSelS_unWeighted[ivar][ibin];
             ret.nSelB_unWeighted[ivar][ibin] = nSelB_unWeighted[ivar][ibin] + other.nSelB_unWeighted[ivar][ibin];
             ret.target[ivar][ibin] = target[ivar][ibin] + other.target[ivar][ibin];
             ret.target2[ivar][ibin] = target2[ivar][ibin] + other.target2[ivar][ibin];
          }
       }
 
       ret.nTotS = nTotS + other.nTotS;
       ret.nTotS_unWeighted = nTotS_unWeighted + other.nTotS_unWeighted;
       ret.nTotB = nTotB + other.nTotB;
       ret.nTotB_unWeighted = nTotB_unWeighted + other.nTotB_unWeighted;
 
       return ret;
   };
 
};
//===========================================================================
// Done with TrainNodeInfo declaration
//===========================================================================
 
////////////////////////////////////////////////////////////////////////////////
/// Decide how to split a node using one of the variables that gives
/// the best separation of signal/background. In order to do this, for each
/// variable a scan of the different cut values in a grid (grid = fNCuts) is
/// performed and the resulting separation gains are compared.
/// in addition to the individual variables, one can also ask for a fisher
/// discriminant being built out of (some) of the variables and used as a
/// possible multivariate split.
 
Double_t TMVA::DecisionTree::TrainNodeFast( const EventConstList & eventSample,
                                            TMVA::DecisionTreeNode *node )
{
   // #### OK let's comment this one to see how to parallelize it
   Double_t  separationGainTotal = -1;
   Double_t *separationGain    = new Double_t[fNvars+1];
   Int_t    *cutIndex          = new Int_t[fNvars+1];  //-1;
 
   // #### initialize the sep gain and cut index values
   for (UInt_t ivar=0; ivar <= fNvars; ivar++) {
      separationGain[ivar]=-1;
      cutIndex[ivar]=-1;
   }
   // ### set up some other variables
   Int_t     mxVar = -1;
   Bool_t    cutType = kTRUE;
   UInt_t nevents = eventSample.size();
 
 
   // the +1 comes from the fact that I treat later on the Fisher output as an
   // additional possible variable.
   Bool_t *useVariable = new Bool_t[fNvars+1];   // for performance reasons instead of std::vector<Bool_t> useVariable(fNvars);
   UInt_t *mapVariable = new UInt_t[fNvars+1];    // map the subset of variables used in randomised trees to the original variable number (used in the Event() )
 
   std::vector<Double_t> fisherCoeff;
 
   // #### set up a map to the subset of variables using two arrays
   if (fRandomisedTree) { // choose for each node splitting a random subset of variables to choose from
      UInt_t tmp=fUseNvars;
      GetRandomisedVariables(useVariable,mapVariable,tmp);
   }
   else {
      for (UInt_t ivar=0; ivar < fNvars; ivar++) {
         useVariable[ivar] = kTRUE;
         mapVariable[ivar] = ivar;
      }
   }
   // #### last variable entry is the fisher variable
   useVariable[fNvars] = kFALSE; //by default fisher is not used..
 
   // #### Begin Fisher calculation
   Bool_t fisherOK = kFALSE; // flag to show that the fisher discriminant could be calculated correctly or not;
   if (fUseFisherCuts) {
      useVariable[fNvars] = kTRUE; // that's were I store the "fisher MVA"
 
      //use for the Fisher discriminant ONLY those variables that show
      //some reasonable linear correlation in either Signal or Background
      Bool_t *useVarInFisher = new Bool_t[fNvars];   // for performance reasons instead of std::vector<Bool_t> useVariable(fNvars);
      UInt_t *mapVarInFisher = new UInt_t[fNvars];   // map the subset of variables used in randomised trees to the original variable number (used in the Event() )
      for (UInt_t ivar=0; ivar < fNvars; ivar++) {
         useVarInFisher[ivar] = kFALSE;
         mapVarInFisher[ivar] = ivar;
      }
 
      std::vector<TMatrixDSym*>* covMatrices;
      covMatrices = gTools().CalcCovarianceMatrices( eventSample, 2 ); // currently for 2 classes only
      if (!covMatrices){
         Log() << kWARNING << " in TrainNodeFast, the covariance Matrices needed for the Fisher-Cuts returned error --> revert to just normal cuts for this node" << Endl;
         fisherOK = kFALSE;
      }else{
         TMatrixD *ss = new TMatrixD(*(covMatrices->at(0)));
         TMatrixD *bb = new TMatrixD(*(covMatrices->at(1)));
         const TMatrixD *s = gTools().GetCorrelationMatrix(ss);
         const TMatrixD *b = gTools().GetCorrelationMatrix(bb);
 
         for (UInt_t ivar=0; ivar < fNvars; ivar++) {
            for (UInt_t jvar=ivar+1; jvar < fNvars; jvar++) {
               if (  ( TMath::Abs( (*s)(ivar, jvar)) > fMinLinCorrForFisher) ||
                     ( TMath::Abs( (*b)(ivar, jvar)) > fMinLinCorrForFisher) ){
                  useVarInFisher[ivar] = kTRUE;
                  useVarInFisher[jvar] = kTRUE;
               }
            }
         }
         // now as you know which variables you want to use, count and map them:
         // such that you can use an array/matrix filled only with THOSE variables
         // that you used
         UInt_t nFisherVars = 0;
         for (UInt_t ivar=0; ivar < fNvars; ivar++) {
            //now .. pick those variables that are used in the FIsher and are also
            //  part of the "allowed" variables in case of Randomized Trees)
            if (useVarInFisher[ivar] && useVariable[ivar]) {
               mapVarInFisher[nFisherVars++]=ivar;
               // now exclude the variables used in the Fisher cuts, and don't
               // use them anymore in the individual variable scan
               if (fUseExclusiveVars) useVariable[ivar] = kFALSE;
            }
         }
 
 
         fisherCoeff = this->GetFisherCoefficients(eventSample, nFisherVars, mapVarInFisher);
         fisherOK = kTRUE;
      }
      delete [] useVarInFisher;
      delete [] mapVarInFisher;
 
   }
   // #### End Fisher calculation
 
 
   UInt_t cNvars = fNvars;
   if (fUseFisherCuts && fisherOK) cNvars++;  // use the Fisher output simple as additional variable
 
   // #### set up the binning info arrays
   // #### each var has its own binning since some may be integers 
   UInt_t*   nBins = new UInt_t [cNvars];
   Double_t* binWidth = new Double_t [cNvars];
   Double_t* invBinWidth = new Double_t [cNvars];
   Double_t** cutValues = new Double_t* [cNvars];
 
   // #### set up the xmin and xmax arrays
   // #### each var has its own range
   Double_t *xmin = new Double_t[cNvars]; 
   Double_t *xmax = new Double_t[cNvars];
 
   // construct and intialize binning/cuts
   for (UInt_t ivar=0; ivar<cNvars; ivar++) {
      // ncuts means that we need n+1 bins for each variable
      nBins[ivar] = fNCuts+1;
      if (ivar < fNvars) {
         if (fDataSetInfo->GetVariableInfo(ivar).GetVarType() == 'I') {
            nBins[ivar] = node->GetSampleMax(ivar) - node->GetSampleMin(ivar) + 1;
         }
      }
 
      cutValues[ivar] = new Double_t [nBins[ivar]];
   }
 
   // init the range and cutvalues for each var now that we know the binning
   for (UInt_t ivar=0; ivar < cNvars; ivar++) {
      if (ivar < fNvars){
         xmin[ivar]=node->GetSampleMin(ivar);
         xmax[ivar]=node->GetSampleMax(ivar);
         if (almost_equal_float(xmax[ivar], xmin[ivar])) {
            // std::cout << " variable " << ivar << " has no proper range in (xmax[ivar]-xmin[ivar] = " << xmax[ivar]-xmin[ivar] << std::endl;
            // std::cout << " will set useVariable[ivar]=false"<<std::endl;
            useVariable[ivar]=kFALSE;
         }
 
      } else { // the fisher variable
         xmin[ivar]=999;
         xmax[ivar]=-999;
         // too bad, for the moment I don't know how to do this without looping
         // once to get the "min max" and then AGAIN to fill the histogram
         for (UInt_t iev=0; iev<nevents; iev++) {
            // returns the Fisher value (no fixed range)
            Double_t result = fisherCoeff[fNvars]; // the fisher constant offset
            for (UInt_t jvar=0; jvar<fNvars; jvar++)
               result += fisherCoeff[jvar]*(eventSample[iev])->GetValueFast(jvar);
            if (result > xmax[ivar]) xmax[ivar]=result;
            if (result < xmin[ivar]) xmin[ivar]=result;
         }
      }
      // this loop could take a long time if nbins is large
      for (UInt_t ibin=0; ibin<nBins[ivar]; ibin++) {
         cutValues[ivar][ibin]=0;
      }
   }
 
   // ====================================================================
   // ====================================================================
   // Parallelized Version
   // ====================================================================
   // ====================================================================
 
   // #### Figure out the cut values, loops through vars then through cuts
   // #### if ncuts is on the order of the amount of training data/10 - ish then we get some gains from parallelizing this
   // fill the cut values for the scan:
   auto varSeeds = ROOT::TSeqU(cNvars);
   auto fvarInitCuts = [this, &useVariable, &cutValues, &invBinWidth, &binWidth, &nBins, &xmin, &xmax](UInt_t ivar = 0){
 
      if ( useVariable[ivar] ) {
 
         //set the grid for the cut scan on the variables like this:
         //
         //  |       |        |         |         |   ...      |        |
         // xmin                                                       xmax
         //
         // cut      0        1         2         3   ...     fNCuts-1 (counting from zero)
         // bin  0       1         2         3       .....      nBins-1=fNCuts (counting from zero)
         // --> nBins = fNCuts+1
         // (NOTE, the cuts at xmin or xmax would just give the whole sample and
         //  hence can be safely omitted
 
         binWidth[ivar] = ( xmax[ivar] - xmin[ivar] ) / Double_t(nBins[ivar]);
         invBinWidth[ivar] = 1./binWidth[ivar];
         if (ivar < fNvars) {
            if (fDataSetInfo->GetVariableInfo(ivar).GetVarType() == 'I') { invBinWidth[ivar] = 1; binWidth[ivar] = 1; }
         }
 
         // std::cout << "ivar="<<ivar
         //           <<" min="<<xmin[ivar]
         //           << " max="<<xmax[ivar]
         //           << " width=" << istepSize
         //           << " nBins["<<ivar<<"]="<<nBins[ivar]<<std::endl;
         for (UInt_t icut=0; icut<nBins[ivar]-1; icut++) {
            cutValues[ivar][icut]=xmin[ivar]+(Double_t(icut+1))*binWidth[ivar];
            //            std::cout << " cutValues["<<ivar<<"]["<<icut<<"]=" <<  cutValues[ivar][icut] << std::endl;
         }
      }
      return 0;
   };
   TMVA::Config::Instance().GetThreadExecutor().Map(fvarInitCuts, varSeeds);
  
   // #### Loop through the events to get the total sig and background
   // #### Then loop through the vars to get the counts in each bin in each var
   // #### So we have a loop through the events and a loop through the vars, but no loop through the cuts this is a calculation
 
   TrainNodeInfo nodeInfo(cNvars, nBins);
   UInt_t nPartitions = TMVA::Config::Instance().GetThreadExecutor().GetPoolSize();
 
   // #### When nbins is low compared to ndata this version of parallelization is faster, so use it 
   // #### Parallelize by chunking the data into the same number of sections as we have processors
   if(eventSample.size() >= cNvars*fNCuts*nPartitions*2)
   {
      auto seeds = ROOT::TSeqU(nPartitions);
 
      // need a lambda function to pass to TThreadExecutor::MapReduce
      auto f = [this, &eventSample, &fisherCoeff, &useVariable, &invBinWidth,
                &nBins, &xmin, &cNvars, &nPartitions](UInt_t partition = 0){
 
         UInt_t start = 1.0*partition/nPartitions*eventSample.size();
         UInt_t end   = (partition+1.0)/nPartitions*eventSample.size();
 
         TrainNodeInfo nodeInfof(cNvars, nBins);
 
         for(UInt_t iev=start; iev<end; iev++) {
 
            Double_t eventWeight =  eventSample[iev]->GetWeight();
            if (eventSample[iev]->GetClass() == fSigClass) {
               nodeInfof.nTotS+=eventWeight;
               nodeInfof.nTotS_unWeighted++;    }
            else {
               nodeInfof.nTotB+=eventWeight;
               nodeInfof.nTotB_unWeighted++;
            }
 
            // #### Count the number in each bin
            Int_t iBin=-1;
            for (UInt_t ivar=0; ivar < cNvars; ivar++) {
               // now scan trough the cuts for each varable and find which one gives
               // the best separationGain at the current stage.
               if ( useVariable[ivar] ) {
                  Double_t eventData;
                  if (ivar < fNvars) eventData = eventSample[iev]->GetValueFast(ivar);
                  else { // the fisher variable
                     eventData = fisherCoeff[fNvars];
                     for (UInt_t jvar=0; jvar<fNvars; jvar++)
                        eventData += fisherCoeff[jvar]*(eventSample[iev])->GetValueFast(jvar);
 
                  }
                  // #### figure out which bin it belongs in ...
                  // "maximum" is nbins-1 (the "-1" because we start counting from 0 !!
                  iBin = TMath::Min(Int_t(nBins[ivar]-1),TMath::Max(0,int (invBinWidth[ivar]*(eventData-xmin[ivar]) ) ));
                  if (eventSample[iev]->GetClass() == fSigClass) {
                     nodeInfof.nSelS[ivar][iBin]+=eventWeight;
                     nodeInfof.nSelS_unWeighted[ivar][iBin]++;
                  }
                  else {
                     nodeInfof.nSelB[ivar][iBin]+=eventWeight;
                     nodeInfof.nSelB_unWeighted[ivar][iBin]++;
                  }
                  if (DoRegression()) {
                     nodeInfof.target[ivar][iBin] +=eventWeight*eventSample[iev]->GetTarget(0);
                     nodeInfof.target2[ivar][iBin]+=eventWeight*eventSample[iev]->GetTarget(0)*eventSample[iev]->GetTarget(0);
                  }
               }
            }
         }
         return nodeInfof;
      };
 
      // #### Need an intial struct to pass to std::accumulate
      TrainNodeInfo nodeInfoInit(cNvars, nBins);
 
      // #### Run the threads in parallel then merge the results
      auto redfunc = [nodeInfoInit](std::vector<TrainNodeInfo> v) -> TrainNodeInfo { return std::accumulate(v.begin(), v.end(), nodeInfoInit); };
      nodeInfo = TMVA::Config::Instance().GetThreadExecutor().MapReduce(f, seeds, redfunc);
   }
 
 
   // #### When nbins is close to the order of the data this version of parallelization is faster
   // #### Parallelize by vectorizing the variable loop
   else {
 
      auto fvarFillNodeInfo = [this, &nodeInfo, &eventSample, &fisherCoeff, &useVariable, &invBinWidth, &nBins, &xmin](UInt_t ivar = 0){
 
         for(UInt_t iev=0; iev<eventSample.size(); iev++) {
 
            Int_t iBin=-1;
            Double_t eventWeight =  eventSample[iev]->GetWeight(); 
 
            // Only count the net signal and background once
            if(ivar==0){
                if (eventSample[iev]->GetClass() == fSigClass) {
                   nodeInfo.nTotS+=eventWeight;
                   nodeInfo.nTotS_unWeighted++;    }
                else {
                   nodeInfo.nTotB+=eventWeight;
                   nodeInfo.nTotB_unWeighted++;
                }
            }
         
            // Figure out which bin the event belongs in and increment the bin in each histogram vector appropriately
            if ( useVariable[ivar] ) {
               Double_t eventData;
               if (ivar < fNvars) eventData = eventSample[iev]->GetValueFast(ivar); 
               else { // the fisher variable
                  eventData = fisherCoeff[fNvars];
                  for (UInt_t jvar=0; jvar<fNvars; jvar++)
                     eventData += fisherCoeff[jvar]*(eventSample[iev])->GetValueFast(jvar);
                  
               }
               // #### figure out which bin it belongs in ...
               // "maximum" is nbins-1 (the "-1" because we start counting from 0 !!
               iBin = TMath::Min(Int_t(nBins[ivar]-1),TMath::Max(0,int (invBinWidth[ivar]*(eventData-xmin[ivar]) ) ));
               if (eventSample[iev]->GetClass() == fSigClass) {
                  nodeInfo.nSelS[ivar][iBin]+=eventWeight;
                  nodeInfo.nSelS_unWeighted[ivar][iBin]++;
               } 
               else {
                  nodeInfo.nSelB[ivar][iBin]+=eventWeight;
                  nodeInfo.nSelB_unWeighted[ivar][iBin]++;
               }
               if (DoRegression()) {
                  nodeInfo.target[ivar][iBin] +=eventWeight*eventSample[iev]->GetTarget(0);
                  nodeInfo.target2[ivar][iBin]+=eventWeight*eventSample[iev]->GetTarget(0)*eventSample[iev]->GetTarget(0);
               }
            }
         }
         return 0;
      };
 
      TMVA::Config::Instance().GetThreadExecutor().Map(fvarFillNodeInfo, varSeeds);
   }
 
   // now turn each "histogram" into a cumulative distribution
   // #### loops through the vars and then the bins, if the bins are on the order of the training data this is worth parallelizing
   // #### doesn't hurt otherwise, pretty unnoticeable 
   auto fvarCumulative = [&nodeInfo, &useVariable, &nBins, this, &eventSample](UInt_t ivar = 0){
      if (useVariable[ivar]) {
         for (UInt_t ibin=1; ibin < nBins[ivar]; ibin++) {
            nodeInfo.nSelS[ivar][ibin]+=nodeInfo.nSelS[ivar][ibin-1];
            nodeInfo.nSelS_unWeighted[ivar][ibin]+=nodeInfo.nSelS_unWeighted[ivar][ibin-1];
            nodeInfo.nSelB[ivar][ibin]+=nodeInfo.nSelB[ivar][ibin-1];
            nodeInfo.nSelB_unWeighted[ivar][ibin]+=nodeInfo.nSelB_unWeighted[ivar][ibin-1];
            if (DoRegression()) {
               nodeInfo.target[ivar][ibin] +=nodeInfo.target[ivar][ibin-1] ;
               nodeInfo.target2[ivar][ibin]+=nodeInfo.target2[ivar][ibin-1];
            }
         }
         if (nodeInfo.nSelS_unWeighted[ivar][nBins[ivar]-1] +nodeInfo.nSelB_unWeighted[ivar][nBins[ivar]-1] != eventSample.size()) {
            Log() << kFATAL << "Helge, you have a bug ....nodeInfo.nSelS_unw..+nodeInfo.nSelB_unw..= "
                  << nodeInfo.nSelS_unWeighted[ivar][nBins[ivar]-1] +nodeInfo.nSelB_unWeighted[ivar][nBins[ivar]-1] 
                  << " while eventsample size = " << eventSample.size()
                  << Endl;
         }
         double lastBins=nodeInfo.nSelS[ivar][nBins[ivar]-1] +nodeInfo.nSelB[ivar][nBins[ivar]-1];
         double totalSum=nodeInfo.nTotS+nodeInfo.nTotB;
         if (TMath::Abs(lastBins-totalSum)/totalSum>0.01) {
            Log() << kFATAL << "Helge, you have another bug ....nodeInfo.nSelS+nodeInfo.nSelB= "
                  << lastBins
                  << " while total number of events = " << totalSum
                  << Endl;
         }
      }
      return 0;
   };
   TMVA::Config::Instance().GetThreadExecutor().Map(fvarCumulative, varSeeds);
 
   // #### Again, if bins is on the order of the training data or with an order or so, then this is worth parallelizing
   // now select the optimal cuts for each varable and find which one gives
   // the best separationGain at the current stage
   auto fvarMaxSep = [&nodeInfo, &useVariable, this, &separationGain, &cutIndex, &nBins] (UInt_t ivar = 0){
      if (useVariable[ivar]) {
         Double_t sepTmp;
         for (UInt_t iBin=0; iBin<nBins[ivar]-1; iBin++) { // the last bin contains "all events" -->skip
            // the separationGain is defined as the various indices (Gini, CorssEntropy, e.t.c)
            // calculated by the "SamplePurities" from the branches that would go to the
            // left or the right from this node if "these" cuts were used in the Node:
            // hereby: nodeInfo.nSelS and nodeInfo.nSelB would go to the right branch
            //        (nodeInfo.nTotS - nodeInfo.nSelS) + (nodeInfo.nTotB - nodeInfo.nSelB)  would go to the left branch;
 
            // only allow splits where both daughter nodes match the specified minimum number
            // for this use the "unweighted" events, as you are interested in statistically
            // significant splits, which is determined by the actual number of entries
            // for a node, rather than the sum of event weights.
 
            Double_t sl = nodeInfo.nSelS_unWeighted[ivar][iBin];
            Double_t bl = nodeInfo.nSelB_unWeighted[ivar][iBin];
            Double_t s  = nodeInfo.nTotS_unWeighted;
            Double_t b  = nodeInfo.nTotB_unWeighted;
            Double_t slW = nodeInfo.nSelS[ivar][iBin];
            Double_t blW = nodeInfo.nSelB[ivar][iBin];
            Double_t sW  = nodeInfo.nTotS;
            Double_t bW  = nodeInfo.nTotB;
            Double_t sr = s-sl;
            Double_t br = b-bl;
            Double_t srW = sW-slW;
            Double_t brW = bW-blW;
            //            std::cout << "sl="<<sl << " bl="<<bl<<" fMinSize="<<fMinSize << "sr="<<sr << " br="<<br  <<std::endl;
            if ( ((sl+bl)>=fMinSize && (sr+br)>=fMinSize)
                 && ((slW+blW)>=fMinSize && (srW+brW)>=fMinSize)
                 ) {
 
               if (DoRegression()) {
                  sepTmp = fRegType->GetSeparationGain(nodeInfo.nSelS[ivar][iBin]+nodeInfo.nSelB[ivar][iBin], 
                                                       nodeInfo.target[ivar][iBin],nodeInfo.target2[ivar][iBin],
                                                       nodeInfo.nTotS+nodeInfo.nTotB,
                                                       nodeInfo.target[ivar][nBins[ivar]-1],nodeInfo.target2[ivar][nBins[ivar]-1]);
               } else {
                  sepTmp = fSepType->GetSeparationGain(nodeInfo.nSelS[ivar][iBin], nodeInfo.nSelB[ivar][iBin], nodeInfo.nTotS, nodeInfo.nTotB);
               }
               if (separationGain[ivar] < sepTmp) {
                  separationGain[ivar] = sepTmp;
                  cutIndex[ivar]       = iBin;
               }
            }
         }
      }
      return 0;
   };
   TMVA::Config::Instance().GetThreadExecutor().Map(fvarMaxSep, varSeeds);
 
   // you found the best separation cut for each variable, now compare the variables
   for (UInt_t ivar=0; ivar < cNvars; ivar++) {
      if (useVariable[ivar] ) {
         if (separationGainTotal < separationGain[ivar]) {
            separationGainTotal = separationGain[ivar];
            mxVar = ivar;
         }
      }
   }
 
   if (mxVar >= 0) {
      if (DoRegression()) {
         node->SetSeparationIndex(fRegType->GetSeparationIndex(nodeInfo.nTotS+nodeInfo.nTotB,nodeInfo.target[0][nBins[mxVar]-1],nodeInfo.target2[0][nBins[mxVar]-1]));
         node->SetResponse(nodeInfo.target[0][nBins[mxVar]-1]/(nodeInfo.nTotS+nodeInfo.nTotB));
         if ( almost_equal_double(nodeInfo.target2[0][nBins[mxVar]-1]/(nodeInfo.nTotS+nodeInfo.nTotB),  nodeInfo.target[0][nBins[mxVar]-1]/(nodeInfo.nTotS+nodeInfo.nTotB)*nodeInfo.target[0][nBins[mxVar]-1]/(nodeInfo.nTotS+nodeInfo.nTotB))) {
            node->SetRMS(0);
            
         }else{ 
            node->SetRMS(TMath::Sqrt(nodeInfo.target2[0][nBins[mxVar]-1]/(nodeInfo.nTotS+nodeInfo.nTotB) - nodeInfo.target[0][nBins[mxVar]-1]/(nodeInfo.nTotS+nodeInfo.nTotB)*nodeInfo.target[0][nBins[mxVar]-1]/(nodeInfo.nTotS+nodeInfo.nTotB)));
         }
      }
      else {
         node->SetSeparationIndex(fSepType->GetSeparationIndex(nodeInfo.nTotS,nodeInfo.nTotB));
         if (mxVar >=0){ 
            if (nodeInfo.nSelS[mxVar][cutIndex[mxVar]]/nodeInfo.nTotS > nodeInfo.nSelB[mxVar][cutIndex[mxVar]]/nodeInfo.nTotB) cutType=kTRUE;
            else cutType=kFALSE;
         }
      }
      node->SetSelector((UInt_t)mxVar);
      node->SetCutValue(cutValues[mxVar][cutIndex[mxVar]]);
      node->SetCutType(cutType);
      node->SetSeparationGain(separationGainTotal);
      if (mxVar < (Int_t) fNvars){ // the fisher cut is actually not used in this node, hence don't need to store fisher components
         node->SetNFisherCoeff(0);
         fVariableImportance[mxVar] += separationGainTotal*separationGainTotal * (nodeInfo.nTotS+nodeInfo.nTotB) * (nodeInfo.nTotS+nodeInfo.nTotB) ;
         //for (UInt_t ivar=0; ivar<fNvars; ivar++) fVariableImportance[ivar] += separationGain[ivar]*separationGain[ivar] * (nodeInfo.nTotS+nodeInfo.nTotB) * (nodeInfo.nTotS+nodeInfo.nTotB) ;
      }else{
         // allocate Fisher coefficients (use fNvars, and set the non-used ones to zero. Might
         // be even less storage space on average than storing also the mapping used otherwise
         // can always be changed relatively easy
         node->SetNFisherCoeff(fNvars+1);
         for (UInt_t ivar=0; ivar<=fNvars; ivar++) {
            node->SetFisherCoeff(ivar,fisherCoeff[ivar]);
            // take 'fisher coeff. weighted estimate as variable importance, "Don't fill the offset coefficient though :)
            if (ivar<fNvars){
               fVariableImportance[ivar] += fisherCoeff[ivar]*fisherCoeff[ivar]*separationGainTotal*separationGainTotal * (nodeInfo.nTotS+nodeInfo.nTotB) * (nodeInfo.nTotS+nodeInfo.nTotB) ;
            }
         }
      }
   }
   else {
      separationGainTotal = 0;
   }
 
   // #### Now in TrainNodeInfo, but I got a malloc segfault when I tried to destruct arrays there.
   // #### So, I changed these from dynamic arrays to std::vector to fix this memory problem
   // #### so no need to destruct them anymore. I didn't see any performance drop as a result.
   for (UInt_t i=0; i<cNvars; i++) {
   //   delete [] nodeInfo.nSelS[i];
   //   delete [] nodeInfo.nSelB[i];
   //   delete [] nodeInfo.nSelS_unWeighted[i];
   //   delete [] nodeInfo.nSelB_unWeighted[i];
   //   delete [] nodeInfo.target[i];
   //   delete [] nodeInfo.target2[i];
     delete [] cutValues[i];
   }
   //delete [] nodeInfo.nSelS;
   //delete [] nodeInfo.nSelB;
   //delete [] nodeInfo.nSelS_unWeighted;
   //delete [] nodeInfo.nSelB_unWeighted;
   //delete [] nodeInfo.target;
   //delete [] nodeInfo.target2;
 
   // #### left these as dynamic arrays as they were before
   // #### since I didn't need to mess with them for parallelization
   delete [] cutValues;
 
   delete [] xmin;
   delete [] xmax;
 
   delete [] useVariable;
   delete [] mapVariable;
 
   delete [] separationGain;
   delete [] cutIndex;
 
   delete [] nBins;
   delete [] binWidth;
   delete [] invBinWidth;
 
   return separationGainTotal;
}
 
// Standard version of DecisionTree::TrainNodeFast (multithreading is not enabled)
#else
Double_t TMVA::DecisionTree::TrainNodeFast( const EventConstList & eventSample,
                                            TMVA::DecisionTreeNode *node )
{
// #### OK let's comment this one to see how to parallelize it
   Double_t  separationGainTotal = -1, sepTmp;
   Double_t *separationGain    = new Double_t[fNvars+1];
   Int_t    *cutIndex          = new Int_t[fNvars+1];  //-1;
 
   // #### initialize the sep gain and cut index values
   for (UInt_t ivar=0; ivar <= fNvars; ivar++) {
      separationGain[ivar]=-1;
      cutIndex[ivar]=-1;
   }
   // ### set up some other variables
   Int_t     mxVar = -1;
   Bool_t    cutType = kTRUE;
   Double_t  nTotS, nTotB;
   Int_t     nTotS_unWeighted, nTotB_unWeighted;
   UInt_t nevents = eventSample.size();
 
 
   // the +1 comes from the fact that I treat later on the Fisher output as an 
   // additional possible variable.
   Bool_t *useVariable = new Bool_t[fNvars+1];   // for performance reasons instead of std::vector<Bool_t> useVariable(fNvars);
   UInt_t *mapVariable = new UInt_t[fNvars+1];    // map the subset of variables used in randomised trees to the original variable number (used in the Event() ) 
 
   std::vector<Double_t> fisherCoeff;
 
   // #### set up a map to the subset of variables using two arrays
   if (fRandomisedTree) { // choose for each node splitting a random subset of variables to choose from
      UInt_t tmp=fUseNvars;
      GetRandomisedVariables(useVariable,mapVariable,tmp);
   }
   else {
      for (UInt_t ivar=0; ivar < fNvars; ivar++) {
         useVariable[ivar] = kTRUE;
         mapVariable[ivar] = ivar;
      }
   }
   // #### last variable entry is the fisher variable
   useVariable[fNvars] = kFALSE; //by default fisher is not used..
 
   // #### Begin Fisher calculation
   Bool_t fisherOK = kFALSE; // flag to show that the fisher discriminant could be calculated correctly or not;
   if (fUseFisherCuts) {
      useVariable[fNvars] = kTRUE; // that's were I store the "fisher MVA"
 
      //use for the Fisher discriminant ONLY those variables that show
      //some reasonable linear correlation in either Signal or Background
      Bool_t *useVarInFisher = new Bool_t[fNvars];   // for performance reasons instead of std::vector<Bool_t> useVariable(fNvars);
      UInt_t *mapVarInFisher = new UInt_t[fNvars];   // map the subset of variables used in randomised trees to the original variable number (used in the Event() ) 
      for (UInt_t ivar=0; ivar < fNvars; ivar++) {
         useVarInFisher[ivar] = kFALSE;
         mapVarInFisher[ivar] = ivar;
      }
 
      std::vector<TMatrixDSym*>* covMatrices;
      covMatrices = gTools().CalcCovarianceMatrices( eventSample, 2 ); // currently for 2 classes only
      if (!covMatrices){
         Log() << kWARNING << " in TrainNodeFast, the covariance Matrices needed for the Fisher-Cuts returned error --> revert to just normal cuts for this node" << Endl;
         fisherOK = kFALSE;
      }else{
         TMatrixD *ss = new TMatrixD(*(covMatrices->at(0)));
         TMatrixD *bb = new TMatrixD(*(covMatrices->at(1)));
         const TMatrixD *s = gTools().GetCorrelationMatrix(ss);
         const TMatrixD *b = gTools().GetCorrelationMatrix(bb);
 
         for (UInt_t ivar=0; ivar < fNvars; ivar++) {
            for (UInt_t jvar=ivar+1; jvar < fNvars; jvar++) {
               if (  ( TMath::Abs( (*s)(ivar, jvar)) > fMinLinCorrForFisher) ||
                     ( TMath::Abs( (*b)(ivar, jvar)) > fMinLinCorrForFisher) ){
                  useVarInFisher[ivar] = kTRUE;
                  useVarInFisher[jvar] = kTRUE;
               }
            }
         }
         // now as you know which variables you want to use, count and map them:
         // such that you can use an array/matrix filled only with THOSE variables
         // that you used
         UInt_t nFisherVars = 0;
         for (UInt_t ivar=0; ivar < fNvars; ivar++) {
            //now .. pick those variables that are used in the FIsher and are also
            //  part of the "allowed" variables in case of Randomized Trees)
            if (useVarInFisher[ivar] && useVariable[ivar]) {
               mapVarInFisher[nFisherVars++]=ivar;
               // now exclud the variables used in the Fisher cuts, and don't 
               // use them anymore in the individual variable scan
               if (fUseExclusiveVars) useVariable[ivar] = kFALSE;
            }
         }
 
 
         fisherCoeff = this->GetFisherCoefficients(eventSample, nFisherVars, mapVarInFisher);
         fisherOK = kTRUE;
      }
      delete [] useVarInFisher;
      delete [] mapVarInFisher;
 
   }
   // #### End Fisher calculation
 
 
   UInt_t cNvars = fNvars;
   if (fUseFisherCuts && fisherOK) cNvars++;  // use the Fisher output simple as additional variable
 
   // #### OK now what's going on...
   // #### looks like we are setting up histograms
   UInt_t*   nBins = new UInt_t [cNvars];
   Double_t* binWidth = new Double_t [cNvars];
   Double_t* invBinWidth = new Double_t [cNvars];
 
   Double_t** nSelS = new Double_t* [cNvars];
   Double_t** nSelB = new Double_t* [cNvars];
   Double_t** nSelS_unWeighted = new Double_t* [cNvars];
   Double_t** nSelB_unWeighted = new Double_t* [cNvars];
   Double_t** target = new Double_t* [cNvars];
   Double_t** target2 = new Double_t* [cNvars];
   Double_t** cutValues = new Double_t* [cNvars];
 
   // #### looping through the variables...
   for (UInt_t ivar=0; ivar<cNvars; ivar++) {
      // #### ncuts means that we need n+1 bins for each variable
      nBins[ivar] = fNCuts+1;
      if (ivar < fNvars) {
         if (fDataSetInfo->GetVariableInfo(ivar).GetVarType() == 'I') {
            nBins[ivar] = node->GetSampleMax(ivar) - node->GetSampleMin(ivar) + 1;
         }
      }
 
      // #### make some new arrays for each ith var, size=nbins for each array
      // #### integer features get the same number of bins as values, set later
      nSelS[ivar] = new Double_t [nBins[ivar]];
      nSelB[ivar] = new Double_t [nBins[ivar]];
      nSelS_unWeighted[ivar] = new Double_t [nBins[ivar]];
      nSelB_unWeighted[ivar] = new Double_t [nBins[ivar]];
      target[ivar] = new Double_t [nBins[ivar]];
      target2[ivar] = new Double_t [nBins[ivar]];
      cutValues[ivar] = new Double_t [nBins[ivar]];
 
   }
 
   // #### xmin and xmax for earch variable
   Double_t *xmin = new Double_t[cNvars];
   Double_t *xmax = new Double_t[cNvars];
 
   // #### ok loop through each variable to initialize all the values
   for (UInt_t ivar=0; ivar < cNvars; ivar++) {
      if (ivar < fNvars){
         xmin[ivar]=node->GetSampleMin(ivar);
         xmax[ivar]=node->GetSampleMax(ivar);
         if (almost_equal_float(xmax[ivar], xmin[ivar])) {
            // std::cout << " variable " << ivar << " has no proper range in (xmax[ivar]-xmin[ivar] = " << xmax[ivar]-xmin[ivar] << std::endl;
            // std::cout << " will set useVariable[ivar]=false"<<std::endl;
            useVariable[ivar]=kFALSE;
         }
 
      } else { // the fisher variable
         xmin[ivar]=999;
         xmax[ivar]=-999;
         // too bad, for the moment I don't know how to do this without looping
         // once to get the "min max" and then AGAIN to fill the histogram
         for (UInt_t iev=0; iev<nevents; iev++) {
            // returns the Fisher value (no fixed range)
            Double_t result = fisherCoeff[fNvars]; // the fisher constant offset
            for (UInt_t jvar=0; jvar<fNvars; jvar++)
               result += fisherCoeff[jvar]*(eventSample[iev])->GetValueFast(jvar);
            if (result > xmax[ivar]) xmax[ivar]=result;
            if (result < xmin[ivar]) xmin[ivar]=result;
         }
      }
      for (UInt_t ibin=0; ibin<nBins[ivar]; ibin++) {
         nSelS[ivar][ibin]=0;
         nSelB[ivar][ibin]=0;
         nSelS_unWeighted[ivar][ibin]=0;
         nSelB_unWeighted[ivar][ibin]=0;
         target[ivar][ibin]=0;
         target2[ivar][ibin]=0;
         cutValues[ivar][ibin]=0;
      }
   }
 
   // #### Nothing to parallelize here really, no loop through events
   // #### only figures out the bin edge values for the "histogram" arrays
   // fill the cut values for the scan:
   for (UInt_t ivar=0; ivar < cNvars; ivar++) {
 
      if ( useVariable[ivar] ) {
 
         //set the grid for the cut scan on the variables like this:
         // 
         //  |       |        |         |         |   ...      |        |  
         // xmin                                                       xmax
         //
         // cut      0        1         2         3   ...     fNCuts-1 (counting from zero)
         // bin  0       1         2         3       .....      nBins-1=fNCuts (counting from zero)
         // --> nBins = fNCuts+1
         // (NOTE, the cuts at xmin or xmax would just give the whole sample and
         //  hence can be safely omitted
 
         binWidth[ivar] = ( xmax[ivar] - xmin[ivar] ) / Double_t(nBins[ivar]);
         invBinWidth[ivar] = 1./binWidth[ivar];
         if (ivar < fNvars) {
            if (fDataSetInfo->GetVariableInfo(ivar).GetVarType() == 'I') { invBinWidth[ivar] = 1; binWidth[ivar] = 1; }
         }
 
         // std::cout << "ivar="<<ivar
         //           <<" min="<<xmin[ivar]  
         //           << " max="<<xmax[ivar] 
         //           << " widht=" << istepSize 
         //           << " nBins["<<ivar<<"]="<<nBins[ivar]<<std::endl;
         for (UInt_t icut=0; icut<nBins[ivar]-1; icut++) {
            cutValues[ivar][icut]=xmin[ivar]+(Double_t(icut+1))*binWidth[ivar];
            //            std::cout << " cutValues["<<ivar<<"]["<<icut<<"]=" <<  cutValues[ivar][icut] << std::endl;
         }
      }
   }
 
   // #### Loop through the events to get the total sig and background
   nTotS=0; nTotB=0;
   nTotS_unWeighted=0; nTotB_unWeighted=0;
   for (UInt_t iev=0; iev<nevents; iev++) {
 
      Double_t eventWeight =  eventSample[iev]->GetWeight();
      if (eventSample[iev]->GetClass() == fSigClass) {
         nTotS+=eventWeight;
         nTotS_unWeighted++;    }
      else {
         nTotB+=eventWeight;
         nTotB_unWeighted++;
      }
 
      // #### Count the number in each bin (fill array "histograms")
      Int_t iBin=-1;
      for (UInt_t ivar=0; ivar < cNvars; ivar++) {
         // now scan trough the cuts for each varable and find which one gives
         // the best separationGain at the current stage.
         if ( useVariable[ivar] ) {
            Double_t eventData;
            if (ivar < fNvars) eventData = eventSample[iev]->GetValueFast(ivar);
            else { // the fisher variable
               eventData = fisherCoeff[fNvars];
               for (UInt_t jvar=0; jvar<fNvars; jvar++)
                  eventData += fisherCoeff[jvar]*(eventSample[iev])->GetValueFast(jvar);
 
            }
            // #### figure out which bin the event belongs in ...
            // "maximum" is nbins-1 (the "-1" because we start counting from 0 !!
            iBin = TMath::Min(Int_t(nBins[ivar]-1),TMath::Max(0,int (invBinWidth[ivar]*(eventData-xmin[ivar]) ) ));
            if (eventSample[iev]->GetClass() == fSigClass) {
               nSelS[ivar][iBin]+=eventWeight;
               nSelS_unWeighted[ivar][iBin]++;
            }
            else {
               nSelB[ivar][iBin]+=eventWeight;
               nSelB_unWeighted[ivar][iBin]++;
            }
            if (DoRegression()) {
               target[ivar][iBin] +=eventWeight*eventSample[iev]->GetTarget(0);
               target2[ivar][iBin]+=eventWeight*eventSample[iev]->GetTarget(0)*eventSample[iev]->GetTarget(0);
            }
         }
      }
   }
 
   // now turn the "histogram" into a cumulative distribution
   for (UInt_t ivar=0; ivar < cNvars; ivar++) {
      if (useVariable[ivar]) {
         for (UInt_t ibin=1; ibin < nBins[ivar]; ibin++) {
            nSelS[ivar][ibin]+=nSelS[ivar][ibin-1];
            nSelS_unWeighted[ivar][ibin]+=nSelS_unWeighted[ivar][ibin-1];
            nSelB[ivar][ibin]+=nSelB[ivar][ibin-1];
            nSelB_unWeighted[ivar][ibin]+=nSelB_unWeighted[ivar][ibin-1];
            if (DoRegression()) {
               target[ivar][ibin] +=target[ivar][ibin-1] ;
               target2[ivar][ibin]+=target2[ivar][ibin-1];
            }
         }
         if (nSelS_unWeighted[ivar][nBins[ivar]-1] +nSelB_unWeighted[ivar][nBins[ivar]-1] != eventSample.size()) {
            Log() << kFATAL << "Helge, you have a bug ....nSelS_unw..+nSelB_unw..= "
                  << nSelS_unWeighted[ivar][nBins[ivar]-1] +nSelB_unWeighted[ivar][nBins[ivar]-1]
                  << " while eventsample size = " << eventSample.size()
                  << Endl;
         }
         double lastBins=nSelS[ivar][nBins[ivar]-1] +nSelB[ivar][nBins[ivar]-1];
         double totalSum=nTotS+nTotB;
         if (TMath::Abs(lastBins-totalSum)/totalSum>0.01) {
            Log() << kFATAL << "Helge, you have another bug ....nSelS+nSelB= "
                  << lastBins
                  << " while total number of events = " << totalSum
                  << Endl;
         }
      }
   }
   // #### Loops over vars and bins, but not events, not worth parallelizing unless nbins is on the order of ndata/10 ish ...
   // now select the optimal cuts for each varable and find which one gives
   // the best separationGain at the current stage
   for (UInt_t ivar=0; ivar < cNvars; ivar++) {
      if (useVariable[ivar]) {
         for (UInt_t iBin=0; iBin<nBins[ivar]-1; iBin++) { // the last bin contains "all events" -->skip
            // the separationGain is defined as the various indices (Gini, CorssEntropy, e.t.c)
            // calculated by the "SamplePurities" fom the branches that would go to the
            // left or the right from this node if "these" cuts were used in the Node:
            // hereby: nSelS and nSelB would go to the right branch
            //        (nTotS - nSelS) + (nTotB - nSelB)  would go to the left branch;
 
            // only allow splits where both daughter nodes match the specified miniumum number
            // for this use the "unweighted" events, as you are interested in statistically 
            // significant splits, which is determined by the actual number of entries
            // for a node, rather than the sum of event weights.
 
            Double_t sl = nSelS_unWeighted[ivar][iBin];
            Double_t bl = nSelB_unWeighted[ivar][iBin];
            Double_t s  = nTotS_unWeighted;
            Double_t b  = nTotB_unWeighted;
            Double_t slW = nSelS[ivar][iBin];
            Double_t blW = nSelB[ivar][iBin];
            Double_t sW  = nTotS;
            Double_t bW  = nTotB;
            Double_t sr = s-sl;
            Double_t br = b-bl;
            Double_t srW = sW-slW;
            Double_t brW = bW-blW;
            //            std::cout << "sl="<<sl << " bl="<<bl<<" fMinSize="<<fMinSize << "sr="<<sr << " br="<<br  <<std::endl;
            if ( ((sl+bl)>=fMinSize && (sr+br)>=fMinSize)
                 && ((slW+blW)>=fMinSize && (srW+brW)>=fMinSize)
                 ) {
 
               if (DoRegression()) {
                  sepTmp = fRegType->GetSeparationGain(nSelS[ivar][iBin]+nSelB[ivar][iBin],
                                                       target[ivar][iBin],target2[ivar][iBin],
                                                       nTotS+nTotB,
                                                       target[ivar][nBins[ivar]-1],target2[ivar][nBins[ivar]-1]);
               } else {
                  sepTmp = fSepType->GetSeparationGain(nSelS[ivar][iBin], nSelB[ivar][iBin], nTotS, nTotB);
               }
               if (separationGain[ivar] < sepTmp) {
                  separationGain[ivar] = sepTmp;
                  cutIndex[ivar]       = iBin;
               }
            }
         }
      }
   }
 
   //now you have found the best separation cut for each variable, now compare the variables
   for (UInt_t ivar=0; ivar < cNvars; ivar++) {
      if (useVariable[ivar] ) {
         if (separationGainTotal < separationGain[ivar]) {
            separationGainTotal = separationGain[ivar];
            mxVar = ivar;
         }
      }
   }
 
   if (mxVar >= 0) {
      if (DoRegression()) {
         node->SetSeparationIndex(fRegType->GetSeparationIndex(nTotS+nTotB,target[0][nBins[mxVar]-1],target2[0][nBins[mxVar]-1]));
         node->SetResponse(target[0][nBins[mxVar]-1]/(nTotS+nTotB));
         if ( almost_equal_double(target2[0][nBins[mxVar]-1]/(nTotS+nTotB),  target[0][nBins[mxVar]-1]/(nTotS+nTotB)*target[0][nBins[mxVar]-1]/(nTotS+nTotB))) {
            node->SetRMS(0);
         }else{
            node->SetRMS(TMath::Sqrt(target2[0][nBins[mxVar]-1]/(nTotS+nTotB) - target[0][nBins[mxVar]-1]/(nTotS+nTotB)*target[0][nBins[mxVar]-1]/(nTotS+nTotB)));
         }
      }
      else {
         node->SetSeparationIndex(fSepType->GetSeparationIndex(nTotS,nTotB));
         if (mxVar >=0){
            if (nSelS[mxVar][cutIndex[mxVar]]/nTotS > nSelB[mxVar][cutIndex[mxVar]]/nTotB) cutType=kTRUE;
            else cutType=kFALSE;
         }
      }
      node->SetSelector((UInt_t)mxVar);
      node->SetCutValue(cutValues[mxVar][cutIndex[mxVar]]);
      node->SetCutType(cutType);
      node->SetSeparationGain(separationGainTotal);
      if (mxVar < (Int_t) fNvars){ // the fisher cut is actually not used in this node, hence don't need to store fisher components
         node->SetNFisherCoeff(0);
         fVariableImportance[mxVar] += separationGainTotal*separationGainTotal * (nTotS+nTotB) * (nTotS+nTotB) ;
         //for (UInt_t ivar=0; ivar<fNvars; ivar++) fVariableImportance[ivar] += separationGain[ivar]*separationGain[ivar] * (nTotS+nTotB) * (nTotS+nTotB) ;
      }else{
         // allocate Fisher coefficients (use fNvars, and set the non-used ones to zero. Might
         // be even less storage space on average than storing also the mapping used otherwise
         // can always be changed relatively easy
         node->SetNFisherCoeff(fNvars+1);
         for (UInt_t ivar=0; ivar<=fNvars; ivar++) {
            node->SetFisherCoeff(ivar,fisherCoeff[ivar]);
            // take 'fisher coeff. weighted estimate as variable importance, "Don't fill the offset coefficient though :) 
            if (ivar<fNvars){
               fVariableImportance[ivar] += fisherCoeff[ivar]*fisherCoeff[ivar]*separationGainTotal*separationGainTotal * (nTotS+nTotB) * (nTotS+nTotB) ;
            }
         }
      }
   }
   else {
      separationGainTotal = 0;
   }
   // if (mxVar > -1) {
   //   std::cout << "------------------------------------------------------------------"<<std::endl;
   //   std::cout << "cutting on Var: " << mxVar << " with cutIndex " << cutIndex[mxVar] << " being: " << cutValues[mxVar][cutIndex[mxVar]] << std::endl;
   //   std::cout << " nSelS = " << nSelS_unWeighted[mxVar][cutIndex[mxVar]] << " nSelB = " << nSelB_unWeighted[mxVar][cutIndex[mxVar]] << " (right) sum:= " << nSelS_unWeighted[mxVar][cutIndex[mxVar]] + nSelB_unWeighted[mxVar][cutIndex[mxVar]] << std::endl;
   //   std::cout << " nSelS = " << nTotS_unWeighted - nSelS_unWeighted[mxVar][cutIndex[mxVar]] << " nSelB = " << nTotB_unWeighted-nSelB_unWeighted[mxVar][cutIndex[mxVar]] << " (left) sum:= " << nTotS_unWeighted + nTotB_unWeighted - nSelS_unWeighted[mxVar][cutIndex[mxVar]] - nSelB_unWeighted[mxVar][cutIndex[mxVar]] << std::endl;
   //   std::cout << " nSelS = " << nSelS[mxVar][cutIndex[mxVar]] << " nSelB = " << nSelB[mxVar][cutIndex[mxVar]] << std::endl;
   //   std::cout << " s/s+b " << nSelS_unWeighted[mxVar][cutIndex[mxVar]]/( nSelS_unWeighted[mxVar][cutIndex[mxVar]] + nSelB_unWeighted[mxVar][cutIndex[mxVar]]) 
   //             << " s/s+b " << (nTotS - nSelS_unWeighted[mxVar][cutIndex[mxVar]])/( nTotS-nSelS_unWeighted[mxVar][cutIndex[mxVar]] + nTotB-nSelB_unWeighted[mxVar][cutIndex[mxVar]]) << std::endl;
   //   std::cout << " nTotS = " << nTotS << " nTotB = " << nTotB << std::endl;
   //   std::cout << " separationGainTotal " << separationGainTotal << std::endl;
   // } else {
   //   std::cout << "------------------------------------------------------------------"<<std::endl;
   //   std::cout << " obviously didn't find new mxVar " << mxVar << std::endl;
   // }
   for (UInt_t i=0; i<cNvars; i++) {
      delete [] nSelS[i];
      delete [] nSelB[i];
      delete [] nSelS_unWeighted[i];
      delete [] nSelB_unWeighted[i];
      delete [] target[i];
      delete [] target2[i];
      delete [] cutValues[i];
   }
   delete [] nSelS;
   delete [] nSelB;
   delete [] nSelS_unWeighted;
   delete [] nSelB_unWeighted;
   delete [] target;
   delete [] target2;
   delete [] cutValues;
 
   delete [] xmin;
   delete [] xmax;
 
   delete [] useVariable;
   delete [] mapVariable;
 
   delete [] separationGain;
   delete [] cutIndex;
 
   delete [] nBins;
   delete [] binWidth;
   delete [] invBinWidth;
 
   return separationGainTotal;
 
}
#endif
 
 
////////////////////////////////////////////////////////////////////////////////
/// calculate the fisher coefficients for the event sample and the variables used
 
std::vector<Double_t>  TMVA::DecisionTree::GetFisherCoefficients(const EventConstList &eventSample, UInt_t nFisherVars, UInt_t *mapVarInFisher){
   std::vector<Double_t> fisherCoeff(fNvars+1);
 
   // initialization of global matrices and vectors
   // average value of each variables for S, B, S+B
   TMatrixD* meanMatx = new TMatrixD( nFisherVars, 3 );
 
   // the covariance 'within class' and 'between class' matrices
   TMatrixD* betw = new TMatrixD( nFisherVars, nFisherVars );
   TMatrixD* with = new TMatrixD( nFisherVars, nFisherVars );
   TMatrixD* cov  = new TMatrixD( nFisherVars, nFisherVars );
 
   //
   // compute mean values of variables in each sample, and the overall means
   //
 
   // initialize internal sum-of-weights variables
   Double_t sumOfWeightsS = 0;
   Double_t sumOfWeightsB = 0;
 
 
   // init vectors
   Double_t* sumS = new Double_t[nFisherVars];
   Double_t* sumB = new Double_t[nFisherVars];
   for (UInt_t ivar=0; ivar<nFisherVars; ivar++) { sumS[ivar] = sumB[ivar] = 0; }
 
   UInt_t nevents = eventSample.size();
   // compute sample means
   for (UInt_t ievt=0; ievt<nevents; ievt++) {
 
      // read the Training Event into "event"
      const Event * ev = eventSample[ievt];
 
      // sum of weights
      Double_t weight = ev->GetWeight();
      if (ev->GetClass() == fSigClass) sumOfWeightsS += weight;
      else                             sumOfWeightsB += weight;
 
      Double_t* sum = ev->GetClass() == fSigClass ? sumS : sumB;
      for (UInt_t ivar=0; ivar<nFisherVars; ivar++) {
         sum[ivar] += ev->GetValueFast( mapVarInFisher[ivar] )*weight;
      }
   }
   for (UInt_t ivar=0; ivar<nFisherVars; ivar++) {
      (*meanMatx)( ivar, 2 ) = sumS[ivar];
      (*meanMatx)( ivar, 0 ) = sumS[ivar]/sumOfWeightsS;
 
      (*meanMatx)( ivar, 2 ) += sumB[ivar];
      (*meanMatx)( ivar, 1 ) = sumB[ivar]/sumOfWeightsB;
 
      // signal + background
      (*meanMatx)( ivar, 2 ) /= (sumOfWeightsS + sumOfWeightsB);
   }
 
   delete [] sumS;
 
   delete [] sumB;
 
   // the matrix of covariance 'within class' reflects the dispersion of the
   // events relative to the center of gravity of their own class
 
   // assert required
 
   assert( sumOfWeightsS > 0 && sumOfWeightsB > 0 );
 
   // product matrices (x-<x>)(y-<y>) where x;y are variables
 
   const Int_t nFisherVars2 = nFisherVars*nFisherVars;
   Double_t *sum2Sig  = new Double_t[nFisherVars2];
   Double_t *sum2Bgd  = new Double_t[nFisherVars2];
   Double_t *xval    = new Double_t[nFisherVars2];
   memset(sum2Sig,0,nFisherVars2*sizeof(Double_t));
   memset(sum2Bgd,0,nFisherVars2*sizeof(Double_t));
 
   // 'within class' covariance
   for (UInt_t ievt=0; ievt<nevents; ievt++) {
 
      // read the Training Event into "event"
      //      const Event* ev = eventSample[ievt];
      const Event* ev = eventSample.at(ievt);
 
      Double_t weight = ev->GetWeight(); // may ignore events with negative weights
 
      for (UInt_t x=0; x<nFisherVars; x++) {
         xval[x] = ev->GetValueFast( mapVarInFisher[x] );
      }
      Int_t k=0;
      for (UInt_t x=0; x<nFisherVars; x++) {
         for (UInt_t y=0; y<nFisherVars; y++) {
            if ( ev->GetClass() == fSigClass ) sum2Sig[k] += ( (xval[x] - (*meanMatx)(x, 0))*(xval[y] - (*meanMatx)(y, 0)) )*weight;
            else                               sum2Bgd[k] += ( (xval[x] - (*meanMatx)(x, 1))*(xval[y] - (*meanMatx)(y, 1)) )*weight;
            k++;
         }
      }
   }
   Int_t k=0;
   for (UInt_t x=0; x<nFisherVars; x++) {
      for (UInt_t y=0; y<nFisherVars; y++) {
         (*with)(x, y) = sum2Sig[k]/sumOfWeightsS + sum2Bgd[k]/sumOfWeightsB;
         k++;
      }
   }
 
   delete [] sum2Sig;
   delete [] sum2Bgd;
   delete [] xval;
 
 
   // the matrix of covariance 'between class' reflects the dispersion of the
   // events of a class relative to the global center of gravity of all the class
   // hence the separation between classes
 
 
   Double_t prodSig, prodBgd;
 
   for (UInt_t x=0; x<nFisherVars; x++) {
      for (UInt_t y=0; y<nFisherVars; y++) {
 
         prodSig = ( ((*meanMatx)(x, 0) - (*meanMatx)(x, 2))*
                     ((*meanMatx)(y, 0) - (*meanMatx)(y, 2)) );
         prodBgd = ( ((*meanMatx)(x, 1) - (*meanMatx)(x, 2))*
                     ((*meanMatx)(y, 1) - (*meanMatx)(y, 2)) );
 
         (*betw)(x, y) = (sumOfWeightsS*prodSig + sumOfWeightsB*prodBgd) / (sumOfWeightsS + sumOfWeightsB);
      }
   }
 
 
 
   // compute full covariance matrix from sum of within and between matrices
   for (UInt_t x=0; x<nFisherVars; x++)
      for (UInt_t y=0; y<nFisherVars; y++)
         (*cov)(x, y) = (*with)(x, y) + (*betw)(x, y);
 
   // Fisher = Sum { [coeff]*[variables] }
   //
   // let Xs be the array of the mean values of variables for signal evts
   // let Xb be the array of the mean values of variables for backgd evts
   // let InvWith be the inverse matrix of the 'within class' correlation matrix
   //
   // then the array of Fisher coefficients is
   // [coeff] =TMath::Sqrt(fNsig*fNbgd)/fNevt*transpose{Xs-Xb}*InvWith
   TMatrixD* theMat = with; // Fishers original
   //   TMatrixD* theMat = cov; // Mahalanobis
 
   TMatrixD invCov( *theMat );
   if ( TMath::Abs(invCov.Determinant()) < 10E-24 ) {
      Log() << kWARNING << "FisherCoeff matrix is almost singular with determinant="
            << TMath::Abs(invCov.Determinant())
            << " did you use the variables that are linear combinations or highly correlated?"
            << Endl;
   }
   if ( TMath::Abs(invCov.Determinant()) < 10E-120 ) {
      Log() << kFATAL << "FisherCoeff matrix is singular with determinant="
            << TMath::Abs(invCov.Determinant())
            << " did you use the variables that are linear combinations?"
            << Endl;
   }
 
   invCov.Invert();
 
   // apply rescaling factor
   Double_t xfact = TMath::Sqrt( sumOfWeightsS*sumOfWeightsB ) / (sumOfWeightsS + sumOfWeightsB);
 
   // compute difference of mean values
   std::vector<Double_t> diffMeans( nFisherVars );
 
   for (UInt_t ivar=0; ivar<=fNvars; ivar++) fisherCoeff[ivar] = 0;
   for (UInt_t ivar=0; ivar<nFisherVars; ivar++) {
      for (UInt_t jvar=0; jvar<nFisherVars; jvar++) {
         Double_t d = (*meanMatx)(jvar, 0) - (*meanMatx)(jvar, 1);
         fisherCoeff[mapVarInFisher[ivar]] += invCov(ivar, jvar)*d;
      }
 
      // rescale
      fisherCoeff[mapVarInFisher[ivar]] *= xfact;
   }
 
   // offset correction
   Double_t f0 = 0.0;
   for (UInt_t ivar=0; ivar<nFisherVars; ivar++){
      f0 += fisherCoeff[mapVarInFisher[ivar]]*((*meanMatx)(ivar, 0) + (*meanMatx)(ivar, 1));
   }
   f0 /= -2.0;
 
   fisherCoeff[fNvars] = f0;  //as we start counting variables from "zero", I store the fisher offset at the END
 
   return fisherCoeff;
}
 
////////////////////////////////////////////////////////////////////////////////
/// train a node by finding the single optimal cut for a single variable
/// that best separates signal and background (maximizes the separation gain)
 
Double_t TMVA::DecisionTree::TrainNodeFull( const EventConstList & eventSample,
                                            TMVA::DecisionTreeNode *node )
{
   Double_t nTotS = 0.0, nTotB = 0.0;
   Int_t nTotS_unWeighted = 0, nTotB_unWeighted = 0;
 
   std::vector<TMVA::BDTEventWrapper> bdtEventSample;
 
   // List of optimal cuts, separation gains, and cut types (removed background or signal) - one for each variable
   // each spot in parallel no problem
   std::vector<Double_t> lCutValue( fNvars, 0.0 );
   std::vector<Double_t> lSepGain( fNvars, -1.0e6 );
   std::vector<Char_t> lCutType( fNvars ); // <----- bool is stored (for performance reasons, no std::vector<bool>  has been taken)
   lCutType.assign( fNvars, Char_t(kFALSE) );
 
   // Initialize (un)weighted counters for signal & background
   // Construct a list of event wrappers that point to the original data
   for( std::vector<const TMVA::Event*>::const_iterator it = eventSample.begin(); it != eventSample.end(); ++it ) {
      if((*it)->GetClass() == fSigClass) { // signal or background event
         nTotS += (*it)->GetWeight();
         ++nTotS_unWeighted;
      }
      else {
         nTotB += (*it)->GetWeight();
         ++nTotB_unWeighted;
      }
      bdtEventSample.push_back(TMVA::BDTEventWrapper(*it));
   }
 
   std::vector<Char_t> useVariable(fNvars); // <----- bool is stored (for performance reasons, no std::vector<bool>  has been taken)
   useVariable.assign( fNvars, Char_t(kTRUE) );
 
   for (UInt_t ivar=0; ivar < fNvars; ivar++) useVariable[ivar]=Char_t(kFALSE);
   if (fRandomisedTree) { // choose for each node splitting a random subset of variables to choose from
      if (fUseNvars ==0 ) { // no number specified ... choose s.th. which hopefully works well
         // watch out, should never happen as it is initialised automatically in MethodBDT already!!!
         fUseNvars        =  UInt_t(TMath::Sqrt(fNvars)+0.6);
      }
      Int_t nSelectedVars = 0;
      while (nSelectedVars < fUseNvars) {
         Double_t bla = fMyTrandom->Rndm()*fNvars;
         useVariable[Int_t (bla)] = Char_t(kTRUE);
         nSelectedVars = 0;
         for (UInt_t ivar=0; ivar < fNvars; ivar++) {
            if(useVariable[ivar] == Char_t(kTRUE)) nSelectedVars++;
         }
      }
   }
   else {
      for (UInt_t ivar=0; ivar < fNvars; ivar++) useVariable[ivar] = Char_t(kTRUE);
   }
   for( UInt_t ivar = 0; ivar < fNvars; ivar++ ) { // loop over all discriminating variables
      if(!useVariable[ivar]) continue; // only optimze with selected variables
 
      TMVA::BDTEventWrapper::SetVarIndex(ivar); // select the variable to sort by
 
      std::sort( bdtEventSample.begin(),bdtEventSample.end() ); // sort the event data 
 
 
      Double_t bkgWeightCtr = 0.0, sigWeightCtr = 0.0;
 
      std::vector<TMVA::BDTEventWrapper>::iterator it = bdtEventSample.begin(), it_end = bdtEventSample.end();
      for( ; it != it_end; ++it ) {
         if((**it)->GetClass() == fSigClass ) // specify signal or background event
            sigWeightCtr += (**it)->GetWeight();
         else
            bkgWeightCtr += (**it)->GetWeight();
         // Store the accumulated signal (background) weights
         it->SetCumulativeWeight(false,bkgWeightCtr);
         it->SetCumulativeWeight(true,sigWeightCtr);
      }
 
      const Double_t fPMin = 1.0e-6;
      Bool_t cutType = kFALSE;
      Long64_t index = 0;
      Double_t separationGain = -1.0, sepTmp = 0.0, cutValue = 0.0, dVal = 0.0, norm = 0.0;
 
      // Locate the optimal cut for this (ivar-th) variable
      for( it = bdtEventSample.begin(); it != it_end; ++it ) {
         if( index == 0 ) { ++index; continue; }
         if( *(*it) == NULL ) {
            Log() << kFATAL << "In TrainNodeFull(): have a null event! Where index="
                  << index << ", and parent node=" << node->GetParent() << Endl;
            break;
         }
         dVal = bdtEventSample[index].GetVal() - bdtEventSample[index-1].GetVal();
         norm = TMath::Abs(bdtEventSample[index].GetVal() + bdtEventSample[index-1].GetVal());
         // Only allow splits where both daughter nodes have the specified minimum number of events
         // Splits are only sensible when the data are ordered (eg. don't split inside a sequence of 0's)
         if( index >= fMinSize && (nTotS_unWeighted + nTotB_unWeighted) - index >= fMinSize && TMath::Abs(dVal/(0.5*norm + 1)) > fPMin ) {
 
            sepTmp = fSepType->GetSeparationGain( it->GetCumulativeWeight(true), it->GetCumulativeWeight(false), sigWeightCtr, bkgWeightCtr );
            if( sepTmp > separationGain ) {
               separationGain = sepTmp;
               cutValue = it->GetVal() - 0.5*dVal;
               Double_t nSelS = it->GetCumulativeWeight(true);
               Double_t nSelB = it->GetCumulativeWeight(false);
               // Indicate whether this cut is improving the node purity by removing background (enhancing signal)
               // or by removing signal (enhancing background)
               if( nSelS/sigWeightCtr > nSelB/bkgWeightCtr ) cutType = kTRUE;
               else cutType = kFALSE;
            }
         }
         ++index;
      }
      lCutType[ivar] = Char_t(cutType);
      lCutValue[ivar] = cutValue;
      lSepGain[ivar] = separationGain;
   }
   Double_t separationGain = -1.0;
   Int_t iVarIndex = -1;
   for( UInt_t ivar = 0; ivar < fNvars; ivar++ ) {
      if( lSepGain[ivar] > separationGain ) {
         iVarIndex = ivar;
         separationGain = lSepGain[ivar];
      }
   }
 
   // #### storing the best values into the node
   if(iVarIndex >= 0) {
      node->SetSelector(iVarIndex);
      node->SetCutValue(lCutValue[iVarIndex]);
      node->SetSeparationGain(lSepGain[iVarIndex]);
      node->SetCutType(lCutType[iVarIndex]);
      fVariableImportance[iVarIndex] += separationGain*separationGain * (nTotS+nTotB) * (nTotS+nTotB);
   }
   else {
      separationGain = 0.0;
   }
 
   return separationGain;
}
 
////////////////////////////////////////////////////////////////////////////////
/// get the pointer to the leaf node where a particular event ends up in...
/// (used in gradient boosting)
 
TMVA::DecisionTreeNode* TMVA::DecisionTree::GetEventNode(const TMVA::Event & e) const
{
   TMVA::DecisionTreeNode *current = (TMVA::DecisionTreeNode*)this->GetRoot();
   while(current->GetNodeType() == 0) { // intermediate node in a tree
      current = (current->GoesRight(e)) ?
         (TMVA::DecisionTreeNode*)current->GetRight() :
         (TMVA::DecisionTreeNode*)current->GetLeft();
   }
   return current;
}
 
////////////////////////////////////////////////////////////////////////////////
/// the event e is put into the decision tree (starting at the root node)
/// and the output is NodeType (signal) or (background) of the final node (basket)
/// in which the given events ends up. I.e. the result of the classification if
/// the event for this decision tree.
 
Double_t TMVA::DecisionTree::CheckEvent( const TMVA::Event * e, Bool_t UseYesNoLeaf ) const
{
   TMVA::DecisionTreeNode *current = this->GetRoot();
   if (!current){
      Log() << kFATAL << "CheckEvent: started with undefined ROOT node" <<Endl;
      return 0; //keeps coverity happy that doesn't know that kFATAL causes an exit
   }
 
   while (current->GetNodeType() == 0) { // intermediate node in a (pruned) tree
      current = (current->GoesRight(*e)) ?
         current->GetRight() :
         current->GetLeft();
      if (!current) {
         Log() << kFATAL << "DT::CheckEvent: inconsistent tree structure" <<Endl;
      }
 
   }
 
   if (DoRegression()) {
      // Note: This path is also taken for MethodBDT with analysis type
      // kClassification and kMulticlass when using GradBoost.
      // See TMVA::MethodBDT::InitGradBoost
      return current->GetResponse();
   } else {
      if (UseYesNoLeaf) return Double_t ( current->GetNodeType() );
      else              return current->GetPurity();
   }
}
 
////////////////////////////////////////////////////////////////////////////////
/// calculates the purity S/(S+B) of a given event sample
 
Double_t  TMVA::DecisionTree::SamplePurity( std::vector<TMVA::Event*> eventSample )
{
   Double_t sumsig=0, sumbkg=0, sumtot=0;
   for (UInt_t ievt=0; ievt<eventSample.size(); ievt++) {
      if (eventSample[ievt]->GetClass() != fSigClass) sumbkg+=eventSample[ievt]->GetWeight();
      else sumsig+=eventSample[ievt]->GetWeight();
      sumtot+=eventSample[ievt]->GetWeight();
   }
   // sanity check
   if (sumtot!= (sumsig+sumbkg)){
      Log() << kFATAL << "<SamplePurity> sumtot != sumsig+sumbkg"
            << sumtot << " " << sumsig << " " << sumbkg << Endl;
   }
   if (sumtot>0) return sumsig/(sumsig + sumbkg);
   else return -1;
}
 
////////////////////////////////////////////////////////////////////////////////
/// Return the relative variable importance, normalized to all
/// variables together having the importance 1. The importance in
/// evaluated as the total separation-gain that this variable had in
/// the decision trees (weighted by the number of events)
 
vector< Double_t >  TMVA::DecisionTree::GetVariableImportance()
{
   std::vector<Double_t> relativeImportance(fNvars);
   Double_t  sum=0;
   for (UInt_t i=0; i< fNvars; i++) {
      sum += fVariableImportance[i];
      relativeImportance[i] = fVariableImportance[i];
   }
 
   for (UInt_t i=0; i< fNvars; i++) {
      if (sum > std::numeric_limits<double>::epsilon())
         relativeImportance[i] /= sum;
      else
         relativeImportance[i] = 0;
   }
   return relativeImportance;
}
 
////////////////////////////////////////////////////////////////////////////////
/// returns the relative importance of variable ivar
 
Double_t  TMVA::DecisionTree::GetVariableImportance( UInt_t ivar )
{
   std::vector<Double_t> relativeImportance = this->GetVariableImportance();
   if (ivar < fNvars) return relativeImportance[ivar];
   else {
      Log() << kFATAL << "<GetVariableImportance>" << Endl
            << "---                     ivar = " << ivar << " is out of range " << Endl;
   }
 
   return -1;
}