VisitorBase Class Reference

#include <vigra/random_forest/rf_visitors.hxx>

Inheritance diagram for VisitorBase:

Public Member Functions
double	return_val ()
template<class Tree, class Split, class Region, class Feature_t, class Label_t>
void	visit_after_split (Tree &tree, Split &split, Region &parent, Region &leftChild, Region &rightChild, Feature_t &features, Label_t &labels)
template<class RF, class PR, class SM, class ST>
void	visit_after_tree (RF &rf, PR &pr, SM &sm, ST &st, int index)
template<class RF, class PR>
void	visit_at_beginning (RF const &rf, PR const &pr)
template<class RF, class PR>
void	visit_at_end (RF const &rf, PR const &pr)
template<class TR, class IntT, class TopT, class Feat>
void	visit_external_node (TR &tr, IntT index, TopT node_t, Feat &features)
template<class TR, class IntT, class TopT, class Feat>
void	visit_internal_node (TR &, IntT, TopT, Feat &)

Detailed Description

Base Class from which all Visitors derive. Can be used as a template to create new Visitors.

Member Function Documentation

visit_after_split()

template<class Tree, class Split, class Region, class Feature_t, class Label_t>

void visit_after_split	(	Tree &	tree,
		Split &	split,
		Region &	parent,
		Region &	leftChild,
		Region &	rightChild,
		Feature_t &	features,
		Label_t &	labels )

do something after the the Split has decided how to process the Region (Stack entry)

Parameters

tree	reference to the tree that is currently being learned
split	reference to the split object
parent	current stack entry which was used to decide the split
leftChild	left stack entry that will be pushed
rightChild	right stack entry that will be pushed.
features	features matrix
labels	label matrix

See also

RF_Traits::StackEntry_t

visit_after_tree()

template<class RF, class PR, class SM, class ST>

void visit_after_tree	(	RF &	rf,
		PR &	pr,
		SM &	sm,
		ST &	st,
		int	index )

do something after each tree has been learned

Parameters

rf	reference to the random forest object that called this visitor
pr	reference to the preprocessor that processed the input
sm	reference to the sampler object
st	reference to the first stack entry
index	index of current tree

visit_at_end()

template<class RF, class PR>

void visit_at_end	(	RF const &	rf,
		PR const &	pr )

do something after all trees have been learned

Parameters

rf	reference to the random forest object that called this visitor
pr	reference to the preprocessor that processed the input

visit_at_beginning()

template<class RF, class PR>

void visit_at_beginning	(	RF const &	rf,
		PR const &	pr )

do something before learning starts

Parameters

rf	reference to the random forest object that called this visitor
pr	reference to the Processor class used.

visit_external_node()

template<class TR, class IntT, class TopT, class Feat>

void visit_external_node	(	TR &	tr,
		IntT	index,
		TopT	node_t,
		Feat &	features )

do some thing while traversing tree after it has been learned (external nodes)

Parameters

tr	reference to the tree object that called this visitor
index	index in the topology_ array we currently are at
node_t	type of node we have (will be e_.... - )
features	feature matrix

See also

NodeTags;

you can create the node by using a switch on node_tag and using the corresponding Node objects. Or - if you do not care about the type use the NodeBase class.

visit_internal_node()

template<class TR, class IntT, class TopT, class Feat>

void visit_internal_node	(	TR &	,
		IntT	,
		TopT	,
		Feat &	)

do something when visiting a internal node after it has been learned

See also

visit_external_node

return_val()

double return_val

(

)

return a double value. The value of the first visitor encountered that has a return value is returned with the RandomForest::learn() method - or -1.0 if no return value visitor existed. This functionality basically only exists so that the OOB - visitor can return the oob error rate like in the old version of the random forest.

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The documentation for this class was generated from the following file:

• vigra/random_forest/rf_visitors.hxx