Point Cloud Library (PCL) 1.12.0
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decision_forest_evaluator.hpp
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37
38#pragma once
39
40#include <pcl/common/common.h>
41#include <pcl/ml/dt/decision_forest.h>
42#include <pcl/ml/dt/decision_forest_evaluator.h>
43#include <pcl/ml/feature_handler.h>
44#include <pcl/ml/stats_estimator.h>
45
46#include <vector>
47
48template <class FeatureType,
49 class DataSet,
50 class LabelType,
51 class ExampleIndex,
52 class NodeType>
55: tree_evaluator_()
56{}
57
58template <class FeatureType,
59 class DataSet,
60 class LabelType,
61 class ExampleIndex,
62 class NodeType>
65{}
66
67template <class FeatureType,
68 class DataSet,
69 class LabelType,
70 class ExampleIndex,
71 class NodeType>
72void
77 stats_estimator,
78 DataSet& data_set,
79 std::vector<ExampleIndex>& examples,
80 std::vector<LabelType>& label_data)
81{
82 const std::size_t num_of_examples = examples.size();
83 label_data.resize(num_of_examples, 0);
84
85 for (std::size_t forest_index = 0; forest_index < forest.size(); ++forest_index) {
86 tree_evaluator_.evaluateAndAdd(forest[forest_index],
87 feature_handler,
88 stats_estimator,
89 data_set,
90 examples,
91 label_data);
92 }
93
94 const float inv_num_of_trees = 1.0f / static_cast<float>(forest.size());
95 for (std::size_t label_index = 0; label_index < label_data.size(); ++label_index) {
96 label_data[label_index] *= inv_num_of_trees;
97 }
98}
99
100template <class FeatureType,
101 class DataSet,
102 class LabelType,
103 class ExampleIndex,
104 class NodeType>
105void
110 stats_estimator,
111 DataSet& data_set,
112 ExampleIndex example,
113 std::vector<NodeType>& leaves)
114{
115 leaves.resize(forest.size());
116 for (std::size_t forest_index = 0; forest_index < forest.size(); ++forest_index) {
117 NodeType leave;
118 tree_evaluator_.evaluate(forest[forest_index],
119 feature_handler,
120 stats_estimator,
121 data_set,
122 example,
123 leave);
124 leaves[forest_index] = leave;
125 }
126}
void evaluate(pcl::DecisionForest< NodeType > &DecisionForestEvaluator, pcl::FeatureHandler< FeatureType, DataSet, ExampleIndex > &feature_handler, pcl::StatsEstimator< LabelType, NodeType, DataSet, ExampleIndex > &stats_estimator, DataSet &data_set, std::vector< ExampleIndex > &examples, std::vector< LabelType > &label_data)
Evaluates the specified examples using the supplied forest.
Class representing a decision forest.
Utility class interface which is used for creating and evaluating features.
Class interface for gathering statistics for decision tree learning.
Define standard C methods and C++ classes that are common to all methods.