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| VisitorBase |

`#include <vigra/random_forest/rf_visitors.hxx>`

Inheritance diagram for VisitorBase:

List of all members.

## 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 &tr, IntT index, TopT node_t, Feat &features)

## Detailed Description

Base Class from which all Visitors derive

## Member Function Documentation

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.

Reimplemented in StopVisiting, and OOB_Visitor.

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

Reimplemented in OnlineLearnVisitor, and VariableImportanceVisitor.

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

Reimplemented in OnlineLearnVisitor, OOB_Visitor, and VariableImportanceVisitor.

template<class RF, class PR>
template<class RF, class PR>
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template<class RF, class PR>
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.

template<class RF, class PR>
template<class RF, class PR>
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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

template<class TR, class IntT, class TopT, class Feat>
template<class TR, class IntT, class TopT, class Feat>
template<class TR, class IntT, class TopT, class Feat>
template<class TR, class IntT, class TopT, class Feat>
template<class TR, class IntT, class TopT, class Feat>
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template<class TR, class IntT, class TopT, class Feat>
template<class TR, class IntT, class TopT, class Feat>
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_.... - )
  | weight | Node weight of current node.

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.

template<class TR, class IntT, class TopT, class Feat>
template<class TR, class IntT, class TopT, class Feat>
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template<class TR, class IntT, class TopT, class Feat>
template<class TR, class IntT, class TopT, class Feat>
template<class TR, class IntT, class TopT, class Feat>
void visit_internal_node | ( | TR & | tr,
|  | IntT | index,
|  | TopT | node_t,
|  | Feat & | features |
| ) |  |  |

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

See also:
  visit_external_node

Reimplemented in OnlineLearnVisitor.

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

- vigra/random_forest/rf_visitors.hxx

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany | html generated using doxygen and Python vigra 1.7.1 (28 Sep 2010)