Point Cloud Library (PCL)  1.12.1-dev
decision_forest_trainer.h
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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_tree.h>
43 #include <pcl/ml/dt/decision_tree_trainer.h>
44 #include <pcl/ml/feature_handler.h>
45 #include <pcl/ml/stats_estimator.h>
46 
47 #include <vector>
48 
49 namespace pcl {
50 
51 /** Trainer for decision trees. */
52 template <class FeatureType,
53  class DataSet,
54  class LabelType,
55  class ExampleIndex,
56  class NodeType>
58 
59 public:
60  /** Constructor. */
62 
63  /** Destructor. */
65 
66  /** Sets the number of trees to train.
67  *
68  * \param[in] num_of_trees the number of trees
69  */
70  inline void
71  setNumberOfTreesToTrain(const std::size_t num_of_trees)
72  {
73  num_of_trees_to_train_ = num_of_trees;
74  }
75 
76  /** Sets the feature handler used to create and evaluate features.
77  *
78  * \param[in] feature_handler the feature handler
79  */
80  inline void
83  {
84  decision_tree_trainer_.setFeatureHandler(feature_handler);
85  }
86 
87  /** Sets the object for estimating the statistics for tree nodes.
88  *
89  * \param[in] stats_estimator the statistics estimator
90  */
91  inline void
94  {
95  decision_tree_trainer_.setStatsEstimator(stats_estimator);
96  }
97 
98  /** Sets the maximum depth of the learned tree.
99  *
100  * \param[in] max_tree_depth maximum depth of the learned tree
101  */
102  inline void
103  setMaxTreeDepth(const std::size_t max_tree_depth)
104  {
105  decision_tree_trainer_.setMaxTreeDepth(max_tree_depth);
106  }
107 
108  /** Sets the number of features used to find optimal decision features.
109  *
110  * \param[in] num_of_features the number of features
111  */
112  inline void
113  setNumOfFeatures(const std::size_t num_of_features)
114  {
115  decision_tree_trainer_.setNumOfFeatures(num_of_features);
116  }
117 
118  /** Sets the number of thresholds tested for finding the optimal decision threshold on
119  * the feature responses.
120  *
121  * \param[in] num_of_threshold the number of thresholds
122  */
123  inline void
124  setNumOfThresholds(const std::size_t num_of_threshold)
125  {
126  decision_tree_trainer_.setNumOfThresholds(num_of_threshold);
127  }
128 
129  /** Sets the input data set used for training.
130  *
131  * \param[in] data_set the data set used for training
132  */
133  inline void
134  setTrainingDataSet(DataSet& data_set)
135  {
136  decision_tree_trainer_.setTrainingDataSet(data_set);
137  }
138 
139  /** Example indices that specify the data used for training.
140  *
141  * \param[in] examples the examples
142  */
143  inline void
144  setExamples(std::vector<ExampleIndex>& examples)
145  {
146  decision_tree_trainer_.setExamples(examples);
147  }
148 
149  /** Sets the label data corresponding to the example data.
150  *
151  * \param[in] label_data the label data
152  */
153  inline void
154  setLabelData(std::vector<LabelType>& label_data)
155  {
156  decision_tree_trainer_.setLabelData(label_data);
157  }
158 
159  /** Sets the minimum number of examples to continue growing a tree.
160  *
161  * \param[in] n number of examples
162  */
163  inline void
164  setMinExamplesForSplit(std::size_t n)
165  {
166  decision_tree_trainer_.setMinExamplesForSplit(n);
167  }
168 
169  /** Specify the thresholds to be used when evaluating features.
170  *
171  * \param[in] thres the threshold values
172  */
173  void
174  setThresholds(std::vector<float>& thres)
175  {
176  decision_tree_trainer_.setThresholds(thres);
177  }
178 
179  /** Specify the data provider.
180  *
181  * \param[in] dtdp the data provider that should implement getDatasetAndLabels()
182  * function
183  */
184  void
186  typename pcl::DecisionTreeTrainerDataProvider<FeatureType,
187  DataSet,
188  LabelType,
189  ExampleIndex,
190  NodeType>::Ptr& dtdp)
191  {
192  decision_tree_trainer_.setDecisionTreeDataProvider(dtdp);
193  }
194 
195  /** Specify if the features are randomly generated at each split node.
196  *
197  * \param[in] b do it or not
198  */
199  void
201  {
202  decision_tree_trainer_.setRandomFeaturesAtSplitNode(b);
203  }
204 
205  /** Trains a decision forest using the set training data and settings.
206  *
207  * \param[out] forest destination for the trained forest
208  */
209  void
210  train(DecisionForest<NodeType>& forest);
211 
212 private:
213  /** The number of trees to train. */
214  std::size_t num_of_trees_to_train_;
215 
216  /** The trainer for the decision trees of the forest. */
218  decision_tree_trainer_;
219 };
220 
221 } // namespace pcl
222 
223 #include <pcl/ml/impl/dt/decision_forest_trainer.hpp>
Class representing a decision forest.
Trainer for decision trees.
void setMinExamplesForSplit(std::size_t n)
Sets the minimum number of examples to continue growing a tree.
void setStatsEstimator(pcl::StatsEstimator< LabelType, NodeType, DataSet, ExampleIndex > &stats_estimator)
Sets the object for estimating the statistics for tree nodes.
void setNumberOfTreesToTrain(const std::size_t num_of_trees)
Sets the number of trees to train.
void setTrainingDataSet(DataSet &data_set)
Sets the input data set used for training.
void setThresholds(std::vector< float > &thres)
Specify the thresholds to be used when evaluating features.
virtual ~DecisionForestTrainer()
Destructor.
void setNumOfFeatures(const std::size_t num_of_features)
Sets the number of features used to find optimal decision features.
void setRandomFeaturesAtSplitNode(bool b)
Specify if the features are randomly generated at each split node.
void setExamples(std::vector< ExampleIndex > &examples)
Example indices that specify the data used for training.
void setMaxTreeDepth(const std::size_t max_tree_depth)
Sets the maximum depth of the learned tree.
void setDecisionTreeDataProvider(typename pcl::DecisionTreeTrainerDataProvider< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::Ptr &dtdp)
Specify the data provider.
void setNumOfThresholds(const std::size_t num_of_threshold)
Sets the number of thresholds tested for finding the optimal decision threshold on the feature respon...
void setLabelData(std::vector< LabelType > &label_data)
Sets the label data corresponding to the example data.
void setFeatureHandler(pcl::FeatureHandler< FeatureType, DataSet, ExampleIndex > &feature_handler)
Sets the feature handler used to create and evaluate features.
Trainer for decision trees.
Utility class interface which is used for creating and evaluating features.
Define standard C methods and C++ classes that are common to all methods.
#define PCL_EXPORTS
Definition: pcl_macros.h:323