| createThresholdsUniform(const std::size_t num_of_thresholds, std::vector< float > &values, std::vector< float > &thresholds) | pcl::DecisionTreeTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | protectedstatic |
| DecisionTreeTrainer() | pcl::DecisionTreeTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | |
| setDecisionTreeDataProvider(typename pcl::DecisionTreeTrainerDataProvider< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::Ptr &dtdp) | pcl::DecisionTreeTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
| setExamples(std::vector< ExampleIndex > &examples) | pcl::DecisionTreeTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
| setFeatureHandler(pcl::FeatureHandler< FeatureType, DataSet, ExampleIndex > &feature_handler) | pcl::DecisionTreeTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
| setLabelData(std::vector< LabelType > &label_data) | pcl::DecisionTreeTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
| setMaxTreeDepth(const std::size_t max_tree_depth) | pcl::DecisionTreeTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
| setMinExamplesForSplit(std::size_t n) | pcl::DecisionTreeTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
| setNumOfFeatures(const std::size_t num_of_features) | pcl::DecisionTreeTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
| setNumOfThresholds(const std::size_t num_of_threshold) | pcl::DecisionTreeTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
| setRandomFeaturesAtSplitNode(bool b) | pcl::DecisionTreeTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
| setStatsEstimator(pcl::StatsEstimator< LabelType, NodeType, DataSet, ExampleIndex > &stats_estimator) | pcl::DecisionTreeTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
| setThresholds(std::vector< float > &thres) | pcl::DecisionTreeTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
| setTrainingDataSet(DataSet &data_set) | pcl::DecisionTreeTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
| train(DecisionTree< NodeType > &tree) | pcl::DecisionTreeTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | |
| trainDecisionTreeNode(std::vector< FeatureType > &features, std::vector< ExampleIndex > &examples, std::vector< LabelType > &label_data, std::size_t max_depth, NodeType &node) | pcl::DecisionTreeTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | protected |
| ~DecisionTreeTrainer() | pcl::DecisionTreeTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | virtual |