/usr/include/shogun/multiclass/tree/ConditionalProbabilityTree.h is in libshogun-dev 3.2.0-7.3build4.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | /*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2012 Chiyuan Zhang
* Copyright (C) 2012 Chiyuan Zhang
*/
#ifndef CONDITIONALPROBABILITYTREE_H__
#define CONDITIONALPROBABILITYTREE_H__
#include <map>
#include <shogun/features/streaming/StreamingDenseFeatures.h>
#include <shogun/multiclass/tree/TreeMachine.h>
#include <shogun/multiclass/tree/ConditionalProbabilityTreeNodeData.h>
namespace shogun
{
/**
* Conditional Probability Tree.
*
* See reference:
*
* Alina Beygelzimer, John Langford, Yuri Lifshits, Gregory Sorkin, Alex
* Strehl. Conditional Probability Tree Estimation Analysis and Algorithms. UAI 2009.
*/
class CConditionalProbabilityTree: public CTreeMachine<ConditionalProbabilityTreeNodeData>
{
public:
/** constructor */
CConditionalProbabilityTree(int32_t num_passes=1)
:m_num_passes(num_passes), m_feats(NULL)
{
}
/** destructor */
virtual ~CConditionalProbabilityTree() { SG_UNREF(m_feats); }
/** get name */
virtual const char* get_name() const { return "ConditionalProbabilityTree"; }
/** set number of passes */
void set_num_passes(int32_t num_passes)
{
m_num_passes = num_passes;
}
/** get number of passes */
int32_t get_num_passes() const
{
return m_num_passes;
}
/** set features
* @param feats features
*/
void set_features(CStreamingDenseFeatures<float32_t> *feats)
{
SG_REF(feats);
SG_UNREF(m_feats);
m_feats = feats;
}
/** apply machine to data in means of multiclass classification problem */
virtual CMulticlassLabels* apply_multiclass(CFeatures* data=NULL);
/** apply machine one single example.
* @param ex a vector to be applied
*/
virtual int32_t apply_multiclass_example(SGVector<float32_t> ex);
/** print the tree structure for debug purpose */
void print_tree();
protected:
/** the labels will be embedded in the streaming features */
virtual bool train_require_labels() const { return false; }
/** train machine
*
* @param data training data
*
* @return whether training was successful
*/
virtual bool train_machine(CFeatures* data);
/** train on a single example (online learning)
* @param ex the example being trained
* @param label the label of this training example
*/
void train_example(SGVector<float32_t> ex, int32_t label);
/** train on a path from a node up to the root
* @param ex the instance of the training example
* @param node the leaf node
*/
void train_path(SGVector<float32_t> ex, node_t *node);
/** train a single node
* @param ex the example being trained
* @param label label
* @param node the node
*/
void train_node(SGVector<float32_t> ex, float64_t label, node_t *node);
/** predict a single node
* @param ex the example being predicted
* @param node the node
*/
float64_t predict_node(SGVector<float32_t> ex, node_t *node);
/** create a new OnlineLinear machine for a node
* @param ex the Example instance for training the new machine
*/
int32_t create_machine(SGVector<float32_t> ex);
/** decide which subtree to go, when training the tree structure.
* @param node the node being decided
* @param ex the example being decided
* @return true if should go left, false otherwise
*/
virtual bool which_subtree(node_t *node, SGVector<float32_t> ex)=0;
/** compute conditional probabilities for ex along the whole tree for predicting */
void compute_conditional_probabilities(SGVector<float32_t> ex);
/** accumulate along the path to the root the conditional probability for a
* particular leaf node.
*/
float64_t accumulate_conditional_probability(node_t *leaf);
int32_t m_num_passes; ///< number of passes for online training
std::map<int32_t, node_t*> m_leaves; ///< class => leaf mapping
CStreamingDenseFeatures<float32_t> *m_feats; ///< online features
};
} /* shogun */
#endif /* end of include guard: CONDITIONALPROBABILITYTREE_H__ */
|