11 #ifndef CONDITIONALPROBABILITYTREE_H__ 12 #define CONDITIONALPROBABILITYTREE_H__ 16 #include <shogun/lib/config.h> 46 :m_num_passes(num_passes), m_feats(NULL)
54 virtual const char*
get_name()
const {
return "VwConditionalProbabilityTree"; }
59 m_num_passes = num_passes;
82 virtual int32_t apply_multiclass_example(
VwExample* ex);
93 virtual bool train_machine(
CFeatures* data);
123 virtual bool which_subtree(bnode_t *node,
VwExample *ex)=0;
126 void compute_conditional_probabilities(
VwExample *ex);
131 float64_t accumulate_conditional_probability(bnode_t *leaf);
The node of the tree structure forming a TreeMachine The node contains pointer to its parent and poin...
int32_t get_num_passes() const
VwConditionalProbabilityTreeNodeData()
int32_t m_num_passes
number of passes for online training
virtual const char * get_name() const
Multiclass Labels for multi-class classification.
void set_features(CStreamingVwFeatures *feats)
void set_num_passes(int32_t num_passes)
This class implements streaming features for use with VW.
CBinaryTreeMachineNode< VwConditionalProbabilityTreeNodeData > bnode_t
all of classes and functions are contained in the shogun namespace
CVwConditionalProbabilityTree(int32_t num_passes=1)
The class Features is the base class of all feature objects.
virtual bool train_require_labels() const
virtual ~CVwConditionalProbabilityTree()
CStreamingVwFeatures * m_feats
online features
class TreeMachine, a base class for tree based multiclass classifiers. This class is derived from CBa...
std::map< int32_t, bnode_t * > m_leaves
class => leaf mapping