DecisionTreeNode Node for the Decision Tree
| static TMVA::MsgLogger& | Log() |
| virtual void | ReadAttributes(void* node, UInt_t tmva_Version_Code = 262657) |
| virtual void | ReadContent(stringstream& s) |
| virtual Bool_t | ReadDataRecord(istream& is, UInt_t tmva_Version_Code = 262657) |
| static bool | fgIsTraining | static variable to flag training phase in which we need fTrainInfo |
| static UInt_t | fgTmva_Version_Code | set only when read from weightfile |
| Bool_t | fCutType | true: if event variable > cutValue ==> signal , false otherwise |
| Float_t | fCutValue | cut value appplied on this node to discriminate bkg against sig |
| UInt_t | TMVA::Node::fDepth | depth of the node within the tree (seen from root node) |
| vector<Double_t> | fFisherCoeff | the fisher coeff (offset at the last element) |
| Bool_t | fIsTerminalNode | ! flag to set node as terminal (i.e., without deleting its descendants) |
| TMVA::Node* | TMVA::Node::fLeft | pointers to the two "daughter" nodes |
| Int_t | fNodeType | Type of node: -1 == Bkg-leaf, 1 == Signal-leaf, 0 = internal |
| TMVA::Node* | TMVA::Node::fParent | the previous (parent) node |
| TMVA::BinaryTree* | TMVA::Node::fParentTree | pointer to the parent tree to which the Node belongs |
| char | TMVA::Node::fPos | position, i.e. it is a left (l) or right (r) daughter |
| Float_t | fPurity | the node purity |
| Float_t | fRMS | response RMS of the regression node |
| Float_t | fResponse | response value in case of regression |
| TMVA::Node* | TMVA::Node::fRight | pointers to the two "daughter" nodes |
| Short_t | fSelector | index of variable used in node selection (decision tree) |
| TMVA::DTNodeTrainingInfo* | fTrainInfo |
| Inheritance Chart: | |||||||||
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copy constructor
test event if it decends the tree at this node to the right
test event if it decends the tree at this node to the left
set index of variable used for discrimination at this node
{ fSelector = i; }return index of variable used for discrimination at this node
{ return fSelector; }set true: if event variable > cutValue ==> signal , false otherwise
{ fCutType = t; }set node type: 1 signal node, -1 bkg leave, 0 intermediate Node
{ fNodeType = t;}return node type: 1 signal node, -1 bkg leave, 0 intermediate Node
{ return fNodeType; }set the sum of the signal weights in the node
{ fTrainInfo->fNSigEvents = s; }set the sum of the backgr weights in the node
{ fTrainInfo->fNBkgEvents = b; }set the number of events that entered the node (during training)
{ fTrainInfo->fNEvents =nev ; }set the sum of the unweighted signal events in the node
{ fTrainInfo->fNSigEvents_unweighted = s; }set the sum of the unweighted backgr events in the node
{ fTrainInfo->fNBkgEvents_unweighted = b; }set the number of unweighted events that entered the node (during training)
{ fTrainInfo->fNEvents_unweighted =nev ; }set the sum of the unboosted signal events in the node
{ fTrainInfo->fNSigEvents_unboosted = s; }set the sum of the unboosted backgr events in the node
{ fTrainInfo->fNBkgEvents_unboosted = b; }set the number of unboosted events that entered the node (during training)
{ fTrainInfo->fNEvents_unboosted =nev ; }increment the sum of the signal weights in the node
{ fTrainInfo->fNSigEvents += s; }increment the sum of the backgr weights in the node
{ fTrainInfo->fNBkgEvents += b; }increment the number of events that entered the node (during training)
{ fTrainInfo->fNEvents +=nev ; }increment the sum of the signal weights in the node
{ fTrainInfo->fNSigEvents_unweighted += 1; }increment the sum of the backgr weights in the node
{ fTrainInfo->fNBkgEvents_unweighted += 1; }increment the number of events that entered the node (during training)
{ fTrainInfo->fNEvents_unweighted +=1 ; }return the sum of the signal weights in the node
{ return fTrainInfo->fNSigEvents; }return the sum of the backgr weights in the node
{ return fTrainInfo->fNBkgEvents; }return the number of events that entered the node (during training)
{ return fTrainInfo->fNEvents; }return the sum of unweighted signal weights in the node
{ return fTrainInfo->fNSigEvents_unweighted; }return the sum of unweighted backgr weights in the node
{ return fTrainInfo->fNBkgEvents_unweighted; }return the number of unweighted events that entered the node (during training)
{ return fTrainInfo->fNEvents_unweighted; }return the sum of unboosted signal weights in the node
{ return fTrainInfo->fNSigEvents_unboosted; }return the sum of unboosted backgr weights in the node
{ return fTrainInfo->fNBkgEvents_unboosted; }return the number of unboosted events that entered the node (during training)
{ return fTrainInfo->fNEvents_unboosted; } set the choosen index, measure of "purity" (separation between S and B) AT this node
{ fTrainInfo->fSeparationIndex =sep ; }return the separation index AT this node
{ return fTrainInfo->fSeparationIndex; }set the separation, or information gained BY this nodes selection
{ fTrainInfo->fSeparationGain =sep ; }return the gain in separation obtained by this nodes selection
{ return fTrainInfo->fSeparationGain; }recursively print the node and its daughters (--> print the 'tree')
recursively clear the nodes content (S/N etc, but not the cut criteria)
get pointers to children, mother in the tree return pointer to the left/right daughter or parent node
{ return dynamic_cast<DecisionTreeNode*>(fLeft); }set pointer to the left/right daughter and parent node
{ fLeft = dynamic_cast<DecisionTreeNode*>(l);}the node resubstitution estimate, R(t), for Cost Complexity pruning
{ fTrainInfo->fNodeR = r; }the resubstitution estimate, R(T_t), of the tree rooted at this node
{ fTrainInfo->fSubTreeR = r; } R(t) - R(T_t)
the critical point alpha = -------------
|~T_t| - 1
{ fTrainInfo->fAlpha = alpha; }the minimum alpha in the tree rooted at this node
{ fTrainInfo->fG = g; }number of terminal nodes in the subtree rooted here
{ fTrainInfo->fNTerminal = n; }number of background/signal events from the pruning validation sample
{ fTrainInfo->fNB = b; }