Point Cloud Library (PCL)  1.14.0-dev
List of all members | Public Member Functions | Static Protected Member Functions
pcl::FernTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType > Class Template Reference

Trainer for a Fern. More...

#include <pcl/ml/ferns/fern_trainer.h>

Public Member Functions

 FernTrainer ()
 Constructor. More...
 
void setFeatureHandler (pcl::FeatureHandler< FeatureType, DataSet, ExampleIndex > &feature_handler)
 Sets the feature handler used to create and evaluate features. More...
 
void setStatsEstimator (pcl::StatsEstimator< LabelType, NodeType, DataSet, ExampleIndex > &stats_estimator)
 Sets the object for estimating the statistics for tree nodes. More...
 
void setFernDepth (const std::size_t fern_depth)
 Sets the maximum depth of the learned tree. More...
 
void setNumOfFeatures (const std::size_t num_of_features)
 Sets the number of features used to find optimal decision features. More...
 
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. More...
 
void setTrainingDataSet (DataSet &data_set)
 Sets the input data set used for training. More...
 
void setExamples (std::vector< ExampleIndex > &examples)
 Example indices that specify the data used for training. More...
 
void setLabelData (std::vector< LabelType > &label_data)
 Sets the label data corresponding to the example data. More...
 
void train (Fern< FeatureType, NodeType > &fern)
 Trains a decision tree using the set training data and settings. More...
 

Static Protected Member Functions

static void createThresholdsUniform (const std::size_t num_of_thresholds, std::vector< float > &values, std::vector< float > &thresholds)
 Creates uniformly distributed thresholds over the range of the supplied values. More...
 

Detailed Description

template<class FeatureType, class DataSet, class LabelType, class ExampleIndex, class NodeType>
class pcl::FernTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >

Trainer for a Fern.

Definition at line 55 of file fern_trainer.h.

Constructor & Destructor Documentation

◆ FernTrainer()

template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
pcl::FernTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::FernTrainer

Constructor.

Definition at line 47 of file fern_trainer.hpp.

Member Function Documentation

◆ createThresholdsUniform()

template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
void pcl::FernTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::createThresholdsUniform ( const std::size_t  num_of_thresholds,
std::vector< float > &  values,
std::vector< float > &  thresholds 
)
staticprotected

Creates uniformly distributed thresholds over the range of the supplied values.

Parameters
[in]num_of_thresholdsthe number of thresholds to create
[in]valuesthe values for estimating the expected value range
[out]thresholdsthe resulting thresholds

Definition at line 291 of file fern_trainer.hpp.

◆ setExamples()

template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
void pcl::FernTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::setExamples ( std::vector< ExampleIndex > &  examples)
inline

Example indices that specify the data used for training.

Parameters
[in]examplesthe examples

Definition at line 129 of file fern_trainer.h.

◆ setFeatureHandler()

template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
void pcl::FernTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::setFeatureHandler ( pcl::FeatureHandler< FeatureType, DataSet, ExampleIndex > &  feature_handler)
inline

Sets the feature handler used to create and evaluate features.

Parameters
[in]feature_handlerthe feature handler

Definition at line 66 of file fern_trainer.h.

◆ setFernDepth()

template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
void pcl::FernTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::setFernDepth ( const std::size_t  fern_depth)
inline

Sets the maximum depth of the learned tree.

Parameters
[in]fern_depthmaximum depth of the learned tree

Definition at line 88 of file fern_trainer.h.

◆ setLabelData()

template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
void pcl::FernTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::setLabelData ( std::vector< LabelType > &  label_data)
inline

Sets the label data corresponding to the example data.

Parameters
[in]label_datathe label data

Definition at line 139 of file fern_trainer.h.

◆ setNumOfFeatures()

template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
void pcl::FernTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::setNumOfFeatures ( const std::size_t  num_of_features)
inline

Sets the number of features used to find optimal decision features.

Parameters
[in]num_of_featuresthe number of features

Definition at line 98 of file fern_trainer.h.

◆ setNumOfThresholds()

template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
void pcl::FernTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::setNumOfThresholds ( const std::size_t  num_of_threshold)
inline

Sets the number of thresholds tested for finding the optimal decision threshold on the feature responses.

Parameters
[in]num_of_thresholdthe number of thresholds

Definition at line 109 of file fern_trainer.h.

◆ setStatsEstimator()

template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
void pcl::FernTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::setStatsEstimator ( pcl::StatsEstimator< LabelType, NodeType, DataSet, ExampleIndex > &  stats_estimator)
inline

Sets the object for estimating the statistics for tree nodes.

Parameters
[in]stats_estimatorthe statistics estimator

Definition at line 77 of file fern_trainer.h.

◆ setTrainingDataSet()

template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
void pcl::FernTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::setTrainingDataSet ( DataSet &  data_set)
inline

Sets the input data set used for training.

Parameters
[in]data_setthe data set used for training

Definition at line 119 of file fern_trainer.h.

◆ train()

template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
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.

Parameters
[out]ferndestination for the trained tree

Definition at line 64 of file fern_trainer.hpp.

References pcl::Fern< FeatureType, NodeType >::accessFeature(), pcl::Fern< FeatureType, NodeType >::accessThreshold(), and pcl::Fern< FeatureType, NodeType >::initialize().

The documentation for this class was generated from the following files: