class TMVA::DecisionTreeNode: public TMVA::Node

 constructor of an essentially "empty" node floating in space

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

 copy constructor of a node. It will result in an explicit copy of
 the node and recursively all it's daughters

 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

 return the S/(S+B) (purity) for the node
 REM: even if nodes with purity 0.01 are very PURE background nodes, they still
      get a small value of the purity.

print the node

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

 Read the data block

 clear the nodes (their S/N, Nevents etc), just keep the structure of the tree

 temporary stored node values (number of events, etc.) that originate
 not from the training but from the validation data (used in pruning)

 printout of the node (can be read in with ReadDataRecord)

 recursive printout of the node and its daughters

 return the minimum of variable ivar from the training sample
 that pass/end up in this node

 return the maximum of variable ivar from the training sample
 that pass/end up in this node

 set the minimum of variable ivar from the training sample
 that pass/end up in this node

 set the maximum of variable ivar from the training sample
 that pass/end up in this node

 read attribute from xml

 add attribute to xml

 adding attributes to tree node  (well, was used in BinarySearchTree,
 and somehow I guess someone programmed it such that we need this in
 this tree too, although we don't..)

 reading attributes from tree node  (well, was used in BinarySearchTree,
 and somehow I guess someone programmed it such that we need this in
 this tree too, although we don't..)

 set index of variable used for discrimination at this node

 set the cut value applied at this node

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

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

set the response of the node (for regression)

return the response of the node (for regression)

set the RMS of the response of the node (for regression)

return the RMS of the response of the node (for regression)

 set the sum of the signal weights in the node

 set the sum of the backgr weights in the node

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

 set the sum of the unweighted signal events in the node

 set the sum of the unweighted backgr events in the node

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

 increment the sum of the signal weights in the node

 increment the sum of the backgr weights in the node

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

 increment the sum of the signal weights in the node

 increment the sum of the backgr weights in the node

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

 return the sum of the signal weights in the node

 return the sum of the backgr weights in the node

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

 return the sum of unweighted signal weights in the node

 return the sum of unweighted backgr weights in the node

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

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

 set the separation, or information gained BY this nodes selection

 get pointers to children, mother in the tree

 the node resubstitution estimate, R(t), for Cost Complexity pruning

 the resubstitution estimate, R(T_t), of the tree rooted at this node

                             R(t) - R(T_t)
 the critical point alpha =  -------------
                              |~T_t| - 1

 the minimum alpha in the tree rooted at this node

 number of terminal nodes in the subtree rooted here

 number of background/signal events from the pruning validation sample

 flag indicates whether this node is terminal