41 #include <pcl/ml/dt/decision_tree.h>
42 #include <pcl/ml/dt/decision_tree_data_provider.h>
43 #include <pcl/ml/feature_handler.h>
44 #include <pcl/ml/stats_estimator.h>
51 template <
class FeatureType,
73 feature_handler_ = &feature_handler;
84 stats_estimator_ = &stats_estimator;
94 max_tree_depth_ = max_tree_depth;
104 num_of_features_ = num_of_features;
115 num_of_thresholds_ = num_of_threshold;
125 data_set_ = data_set;
135 examples_ = examples;
145 label_data_ = label_data;
155 min_examples_for_split_ = n;
179 NodeType>::Ptr& dtdp)
181 decision_tree_trainer_data_provider_ = dtdp;
191 random_features_at_split_node_ = b;
212 trainDecisionTreeNode(std::vector<FeatureType>& features,
213 std::vector<ExampleIndex>& examples,
214 std::vector<LabelType>& label_data,
215 std::size_t max_depth,
226 createThresholdsUniform(
const std::size_t num_of_thresholds,
227 std::vector<float>& values,
228 std::vector<float>& thresholds);
232 std::size_t max_tree_depth_;
234 std::size_t num_of_features_;
236 std::size_t num_of_thresholds_;
246 std::vector<LabelType> label_data_;
248 std::vector<ExampleIndex> examples_;
251 std::size_t min_examples_for_split_;
253 std::vector<float> thresholds_;
261 decision_tree_trainer_data_provider_;
264 bool random_features_at_split_node_;
269 #include <pcl/ml/impl/dt/decision_tree_trainer.hpp>