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DecisionTree | ![]() |
#include <vigra/random_forest/rf_decisionTree.hxx>
Public Types | |
| typedef Int32 | TreeInt |
Public Member Functions | |
| template<class T> | |
| DecisionTree (ProblemSpec< T > ext_param) | |
| Create tree with parameters. | |
| template<class U, class C> | |
| TreeInt | getToLeaf (MultiArrayView< 2, U, C > const &features) const |
| template<class U, class C, class Visitor_t> | |
| TreeInt | getToLeaf (MultiArrayView< 2, U, C > const &features, Visitor_t &visitor) const |
| bool | isLeafNode (TreeInt in) const |
| template<class U, class C, class U2, class C2, class StackEntry_t, class Stop_t, class Split_t, class Visitor_t, class Random_t> | |
| void | learn (MultiArrayView< 2, U, C > const &features, MultiArrayView< 2, U2, C2 > const &labels, StackEntry_t const &stack_entry, Split_t split, Stop_t stop, Visitor_t &visitor, Random_t &randint) |
| void | reset (unsigned int classCount=0) |
| template<class Visitor_t> | |
| void | traverse_mem_order (Visitor_t visitor) const |
This class is actually meant to be used in conjunction with the Random Forest Classifier
RandomForest decisionTree(RF_Traits::Options_t()
.features_per_node(RF_ALL)
.tree_count(1) );
| TreeInt getToLeaf | ( | MultiArrayView< 2, U, C > const & | features | ) | const |
same thing as above, without any visitors
| TreeInt getToLeaf | ( | MultiArrayView< 2, U, C > const & | features, | |
| Visitor_t & | visitor | |||
| ) | const |
data driven traversal from root to leaf
traverse through tree with data given in features. Use Visitors to collect statistics along the way.
| bool isLeafNode | ( | TreeInt | in | ) | const |
is a node a Leaf Node?
| template<class U, class C, class U2, class C2, class StackEntry_t, class Stop_t, class Split_t, class Visitor_t, class Random_t> | ||||
| void learn | ( | MultiArrayView< 2, U, C > const & | features, | |
| MultiArrayView< 2, U2, C2 > const & | labels, | |||
| StackEntry_t const & | stack_entry, | |||
| Split_t | split, | |||
| Stop_t | stop, | |||
| Visitor_t & | visitor, | |||
| Random_t & | randint | |||
| ) | ||||
learn a Tree
| StackEntry_t | The Stackentry containing Node/StackEntry_t Information used during learing. Each Split functor has a Stack entry associated with it (Split_t::StackEntry_t) |
| void reset | ( | unsigned int | classCount = 0 |
) |
clears all memory used.
| template<class Visitor_t> | |||||
| void traverse_mem_order | ( | Visitor_t | visitor | ) | const |
traverse tree to get statistics
Tree is traversed in order the Nodes are in memory (i.e. if no relearning//pruning scheme is utilized this will be pre order)
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© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) |
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