DecisionForestTrainer() | pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | |
setDecisionTreeDataProvider(boost::shared_ptr< pcl::DecisionTreeTrainerDataProvider< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > > &dtdp) | pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
setExamples(std::vector< ExampleIndex > &examples) | pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
setFeatureHandler(pcl::FeatureHandler< FeatureType, DataSet, ExampleIndex > &feature_handler) | pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
setLabelData(std::vector< LabelType > &label_data) | pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
setMaxTreeDepth(const size_t max_tree_depth) | pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
setMinExamplesForSplit(size_t n) | pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
setNumberOfTreesToTrain(const size_t num_of_trees) | pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
setNumOfFeatures(const size_t num_of_features) | pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
setNumOfThresholds(const size_t num_of_threshold) | pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
setRandomFeaturesAtSplitNode(bool b) | pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
setStatsEstimator(pcl::StatsEstimator< LabelType, NodeType, DataSet, ExampleIndex > &stats_estimator) | pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
setThresholds(std::vector< float > &thres) | pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
setTrainingDataSet(DataSet &data_set) | pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | inline |
train(DecisionForest< NodeType > &forest) | pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | |
~DecisionForestTrainer() | pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > | virtual |