TMVA::DecisionTreeNode

class TMVA::DecisionTreeNode : public TMVA::Node

    public:

                              DecisionTreeNode(TMVA::Event* e = NULL)
                              DecisionTreeNode(TMVA::Node* p)
                              DecisionTreeNode(const TMVA::DecisionTreeNode&)
                      virtual ~DecisionTreeNode()
               static TClass* Class()
                       Bool_t GetCutType() const
                     Double_t GetCutValue() const
                     Double_t GetNEvents() const
                        Int_t GetNodeType() const
                     Double_t GetSeparationGain() const
                     Double_t GetSeparationIndex() const
                     Double_t GetSoverSB() const
               virtual Bool_t GoesLeft(const TMVA::Event*) const
               virtual Bool_t GoesRight(const TMVA::Event*) const
              virtual TClass* IsA() const
      TMVA::DecisionTreeNode& operator=(const TMVA::DecisionTreeNode&)
                 virtual void PrintRec(ostream& os, Int_t depth = 0, const string pos = root) const
         virtual TMVA::NodeID ReadRec(ifstream& is, TMVA::NodeID nodeID, TMVA::Node* parent = NULL)
                         void SetCutType(Bool_t t)
                         void SetCutValue(Double_t c)
                         void SetNEvents(Double_t nev)
                         void SetNodeType(Int_t t)
                         void SetSeparationGain(Double_t sep)
                         void SetSeparationIndex(Double_t sep)
                         void SetSoverSB(Double_t ssb)
                 virtual void ShowMembers(TMemberInspector& insp, char* parent)
                 virtual void Streamer(TBuffer& b)
                         void StreamerNVirtual(TBuffer& b)

Data Members

    private:

      Double_t fCutValue         cut value appplied on this node to discriminate bkg against sig
        Bool_t fCutType          true: if event variable > cutValue ==> signal , false otherwise
      Double_t fSoverSB          S/(S+B) at this node (from  training)
      Double_t fSeparationIndex  measure of "purity" (separation between S and B) AT this node
      Double_t fSeparationGain   measure of "purity", separation, or information gained BY this nodes selection
      Double_t fNEvents          number of events in that entered the node (during training)
         Int_t fNodeType         Type of node: -1 == Bkg-leaf, 1 == Signal-leaf, 0 = internal 

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 Node for the Decision Tree

 The node specifies ONE variable out of the given set of selection variable
 that is used to split the sample which "arrives" at the node, into a left
 (background-enhanced) and a right (signal-enhanced) sample.
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 constructor of an essentially "empty" node floating in space

 constructor of a daughter node as a daughter of 'p'

 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

recursively print the node and its daughters (--> print the 'tree')

recursively read the node and its daughters (--> read the 'tree')

 set true: if event variable > cutValue ==> signal , false otherwise

 return kTRUE: Cuts select signal, kFALSE: Cuts select bkg

 set node type: 1 signal node, -1 bkg leave, 0 intermediate Node

 return node type: 1 signal node, -1 bkg leave, 0 intermediate Node

set S/(S+B) at this node (from  training)

return  S/(S+B) at this node (from  training)

 set the choosen index, measure of "purity" (separation between S and B) AT this node

 return the separation index AT this node

 set the separation, or information gained BY this nodes selection

 return the gain in separation obtained by this nodes selection

 set the number of events that entered the node (during training)

 return  the number of events that entered the node (during training)