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>
52template <
class FeatureType,
73 num_of_trees_to_train_ = num_of_trees;
84 decision_tree_trainer_.setFeatureHandler(feature_handler);
95 decision_tree_trainer_.setStatsEstimator(stats_estimator);
105 decision_tree_trainer_.setMaxTreeDepth(max_tree_depth);
115 decision_tree_trainer_.setNumOfFeatures(num_of_features);
126 decision_tree_trainer_.setNumOfThresholds(num_of_threshold);
136 decision_tree_trainer_.setTrainingDataSet(data_set);
146 decision_tree_trainer_.setExamples(examples);
156 decision_tree_trainer_.setLabelData(label_data);
166 decision_tree_trainer_.setMinExamplesForSplit(n);
176 decision_tree_trainer_.setThresholds(thres);
190 NodeType>::Ptr& dtdp)
192 decision_tree_trainer_.setDecisionTreeDataProvider(dtdp);
202 decision_tree_trainer_.setRandomFeaturesAtSplitNode(b);
214 std::size_t num_of_trees_to_train_{1};
218 decision_tree_trainer_;
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.
Class interface for gathering statistics for decision tree learning.
Define standard C methods and C++ classes that are common to all methods.