#include <vigra/random_forest/rf_visitors.hxx>
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double | return_val () |
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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) |
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template<class RF , class PR , class SM , class ST > |
void | visit_after_tree (RF &rf, PR &pr, SM &sm, ST &st, int index) |
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template<class RF , class PR > |
void | visit_at_beginning (RF const &rf, PR const &pr) |
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template<class RF , class PR > |
void | visit_at_end (RF const &rf, PR const &pr) |
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template<class TR , class IntT , class TopT , class Feat > |
void | visit_external_node (TR &tr, IntT index, TopT node_t, Feat &features) |
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template<class TR , class IntT , class TopT , class Feat > |
void | visit_internal_node (TR &, IntT, TopT, Feat &) |
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Detailed Description
Base Class from which all Visitors derive. Can be used as a template to create new Visitors.
◆ visit_after_split()
void visit_after_split |
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Tree & |
tree, |
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Split & |
split, |
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Region & |
parent, |
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Region & |
leftChild, |
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Region & |
rightChild, |
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Feature_t & |
features, |
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Label_t & |
labels |
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do something after the the Split has decided how to process the Region (Stack entry)
- Parameters
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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()
void visit_after_tree |
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RF & |
rf, |
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PR & |
pr, |
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SM & |
sm, |
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ST & |
st, |
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int |
index |
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do something after each tree has been learned
- Parameters
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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()
void visit_at_end |
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RF const & |
rf, |
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PR const & |
pr |
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do something after all trees have been learned
- Parameters
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rf | reference to the random forest object that called this visitor |
pr | reference to the preprocessor that processed the input |
◆ visit_at_beginning()
void visit_at_beginning |
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RF const & |
rf, |
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PR const & |
pr |
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do something before learning starts
- Parameters
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rf | reference to the random forest object that called this visitor |
pr | reference to the Processor class used. |
◆ visit_external_node()
void visit_external_node |
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TR & |
tr, |
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IntT |
index, |
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TopT |
node_t, |
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Feat & |
features |
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do some thing while traversing tree after it has been learned (external nodes)
- Parameters
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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()
void visit_internal_node |
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TR & |
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IntT |
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TopT |
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Feat & |
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do something when visiting a internal node after it has been learned
- See also
- visit_external_node
◆ 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.
The documentation for this class was generated from the following file: