Point Cloud Library (PCL) 1.12.0
|
#include <pcl/ml/ferns/fern_trainer.h>
Public Member Functions | |
FernTrainer () | |
Constructor. | |
virtual | ~FernTrainer () |
Destructor. | |
void | setFeatureHandler (pcl::FeatureHandler< FeatureType, DataSet, ExampleIndex > &feature_handler) |
Sets the feature handler used to create and evaluate features. | |
void | setStatsEstimator (pcl::StatsEstimator< LabelType, NodeType, DataSet, ExampleIndex > &stats_estimator) |
Sets the object for estimating the statistics for tree nodes. | |
void | setFernDepth (const std::size_t fern_depth) |
Sets the maximum depth of the learned tree. | |
void | setNumOfFeatures (const std::size_t num_of_features) |
Sets the number of features used to find optimal decision features. | |
void | setNumOfThresholds (const std::size_t num_of_threshold) |
Sets the number of thresholds tested for finding the optimal decision threshold on the feature responses. | |
void | setTrainingDataSet (DataSet &data_set) |
Sets the input data set used for training. | |
void | setExamples (std::vector< ExampleIndex > &examples) |
Example indices that specify the data used for training. | |
void | setLabelData (std::vector< LabelType > &label_data) |
Sets the label data corresponding to the example data. | |
void | train (Fern< FeatureType, NodeType > &fern) |
Trains a decision tree using the set training data and settings. | |
Static Protected Member Functions | |
static void | createThresholdsUniform (const std::size_t num_of_thresholds, std::vector< float > &values, std::vector< float > &thresholds) |
Creates uniformely distrebuted thresholds over the range of the supplied values. | |
Trainer for a Fern.
Definition at line 55 of file fern_trainer.h.
pcl::FernTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::FernTrainer | ( | ) |
Constructor.
Definition at line 47 of file fern_trainer.hpp.
|
virtual |
Destructor.
Definition at line 63 of file fern_trainer.hpp.
|
staticprotected |
Creates uniformely distrebuted thresholds over the range of the supplied values.
[in] | num_of_thresholds | the number of thresholds to create |
[in] | values | the values for estimating the expected value range |
[out] | thresholds | the resulting thresholds |
Definition at line 299 of file fern_trainer.hpp.
References pcl::ConstCloudIterator< PointT >::ConstCloudIterator().
|
inline |
Example indices that specify the data used for training.
[in] | examples | the examples |
Definition at line 132 of file fern_trainer.h.
|
inline |
Sets the feature handler used to create and evaluate features.
[in] | feature_handler | the feature handler |
Definition at line 69 of file fern_trainer.h.
|
inline |
Sets the maximum depth of the learned tree.
[in] | fern_depth | maximum depth of the learned tree |
Definition at line 91 of file fern_trainer.h.
|
inline |
Sets the label data corresponding to the example data.
[in] | label_data | the label data |
Definition at line 142 of file fern_trainer.h.
|
inline |
Sets the number of features used to find optimal decision features.
[in] | num_of_features | the number of features |
Definition at line 101 of file fern_trainer.h.
|
inline |
Sets the number of thresholds tested for finding the optimal decision threshold on the feature responses.
[in] | num_of_threshold | the number of thresholds |
Definition at line 112 of file fern_trainer.h.
|
inline |
Sets the object for estimating the statistics for tree nodes.
[in] | stats_estimator | the statistics estimator |
Definition at line 80 of file fern_trainer.h.
|
inline |
Sets the input data set used for training.
[in] | data_set | the data set used for training |
Definition at line 122 of file fern_trainer.h.
void pcl::FernTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::train | ( | pcl::Fern< FeatureType, NodeType > & | fern | ) |
Trains a decision tree using the set training data and settings.
[out] | fern | destination for the trained tree |
Definition at line 72 of file fern_trainer.hpp.
References pcl::ConstCloudIterator< PointT >::ConstCloudIterator(), and pcl::ConstCloudIterator< PointT >::size().