Trainer for decision trees.
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#include <pcl/ml/dt/decision_forest_trainer.h>
template<class FeatureType, class DataSet, class LabelType, class ExampleIndex, class NodeType>
class pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >
Trainer for decision trees.
Definition at line 61 of file decision_forest_trainer.h.
◆ DecisionForestTrainer()
template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
◆ ~DecisionForestTrainer()
template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
◆ setDecisionTreeDataProvider()
template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
Specify the data provider.
- Parameters
-
[in] | dtdp | The data provider that should implement getDatasetAndLabels(...) function |
Definition at line 175 of file decision_forest_trainer.h.
◆ setExamples()
template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
void pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::setExamples |
( |
std::vector< ExampleIndex > & |
examples | ) |
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inline |
Example indices that specify the data used for training.
- Parameters
-
[in] | examples | The examples. |
Definition at line 139 of file decision_forest_trainer.h.
◆ setFeatureHandler()
template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
Sets the feature handler used to create and evaluate features.
- Parameters
-
[in] | feature_handler | The feature handler. |
Definition at line 85 of file decision_forest_trainer.h.
◆ setLabelData()
template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
void pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::setLabelData |
( |
std::vector< LabelType > & |
label_data | ) |
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inline |
Sets the label data corresponding to the example data.
- Parameters
-
[in] | label_data | The label data. |
Definition at line 148 of file decision_forest_trainer.h.
◆ setMaxTreeDepth()
template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
void pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::setMaxTreeDepth |
( |
const size_t |
max_tree_depth | ) |
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inline |
Sets the maximum depth of the learned tree.
- Parameters
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[in] | max_tree_depth | Maximum depth of the learned tree. |
Definition at line 103 of file decision_forest_trainer.h.
◆ setMinExamplesForSplit()
template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
◆ setNumberOfTreesToTrain()
template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
void pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::setNumberOfTreesToTrain |
( |
const size_t |
num_of_trees | ) |
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inline |
Sets the number of trees to train.
- Parameters
-
[in] | num_of_trees | The number of trees. |
Definition at line 76 of file decision_forest_trainer.h.
◆ setNumOfFeatures()
template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
void pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::setNumOfFeatures |
( |
const size_t |
num_of_features | ) |
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inline |
Sets the number of features used to find optimal decision features.
- Parameters
-
[in] | num_of_features | The number of features. |
Definition at line 112 of file decision_forest_trainer.h.
◆ setNumOfThresholds()
template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
void pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::setNumOfThresholds |
( |
const size_t |
num_of_threshold | ) |
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inline |
Sets the number of thresholds tested for finding the optimal decision threshold on the feature responses.
- Parameters
-
[in] | num_of_threshold | The number of thresholds. |
Definition at line 121 of file decision_forest_trainer.h.
◆ setRandomFeaturesAtSplitNode()
template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
void pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::setRandomFeaturesAtSplitNode |
( |
bool |
b | ) |
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inline |
◆ setStatsEstimator()
template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
Sets the object for estimating the statistics for tree nodes.
- Parameters
-
[in] | stats_estimator | The statistics estimator. |
Definition at line 94 of file decision_forest_trainer.h.
◆ setThresholds()
template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
void pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::setThresholds |
( |
std::vector< float > & |
thres | ) |
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inline |
Specify the thresholds to be used when evaluating features.
- Parameters
-
[in] | thres | The threshold values. |
Definition at line 166 of file decision_forest_trainer.h.
◆ setTrainingDataSet()
template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
void pcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >::setTrainingDataSet |
( |
DataSet & |
data_set | ) |
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inline |
Sets the input data set used for training.
- Parameters
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[in] | data_set | The data set used for training. |
Definition at line 130 of file decision_forest_trainer.h.
◆ train()
template<class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType >
Trains a decision forest using the set training data and settings.
- Parameters
-
[out] | forest | Destination for the trained forest. |
Definition at line 60 of file decision_forest_trainer.hpp.
The documentation for this class was generated from the following files: