fern_trainer.h

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#pragma once
 
#include <pcl/common/common.h>
#include <pcl/ml/feature_handler.h>
#include <pcl/ml/ferns/fern.h>
#include <pcl/ml/stats_estimator.h>
 
#include <vector>
 
namespace pcl {
 
/** Trainer for a Fern. */
template <class FeatureType,
          class DataSet,
          class LabelType,
          class ExampleIndex,
          class NodeType>
class PCL_EXPORTS FernTrainer {
 
public:
  /** Constructor. */
  FernTrainer();
 
  /** Destructor. */
  virtual ~FernTrainer();
 
  /** Sets the feature handler used to create and evaluate features.
   *
   * \param[in] feature_handler the feature handler
   */
  inline void
  setFeatureHandler(
      pcl::FeatureHandler<FeatureType, DataSet, ExampleIndex>& feature_handler)
  {
    feature_handler_ = &feature_handler;
  }
 
  /** Sets the object for estimating the statistics for tree nodes.
   *
   * \param[in] stats_estimator the statistics estimator
   */
  inline void
  setStatsEstimator(
      pcl::StatsEstimator<LabelType, NodeType, DataSet, ExampleIndex>& stats_estimator)
  {
    stats_estimator_ = &stats_estimator;
  }
 
  /** Sets the maximum depth of the learned tree.
   *
   * \param[in] fern_depth maximum depth of the learned tree
   */
  inline void
  setFernDepth(const std::size_t fern_depth)
  {
    fern_depth_ = fern_depth;
  }
 
  /** Sets the number of features used to find optimal decision features.
   *
   * \param[in] num_of_features the number of features
   */
  inline void
  setNumOfFeatures(const std::size_t num_of_features)
  {
    num_of_features_ = num_of_features;
  }
 
  /** Sets the number of thresholds tested for finding the optimal decision
   *  threshold on the feature responses.
   *
   * \param[in] num_of_threshold the number of thresholds
   */
  inline void
  setNumOfThresholds(const std::size_t num_of_threshold)
  {
    num_of_thresholds_ = num_of_threshold;
  }
 
  /** Sets the input data set used for training.
   *
   * \param[in] data_set the data set used for training
   */
  inline void
  setTrainingDataSet(DataSet& data_set)
  {
    data_set_ = data_set;
  }
 
  /** Example indices that specify the data used for training.
   *
   * \param[in] examples the examples
   */
  inline void
  setExamples(std::vector<ExampleIndex>& examples)
  {
    examples_ = examples;
  }
 
  /** Sets the label data corresponding to the example data.
   *
   * \param[in] label_data the label data
   */
  inline void
  setLabelData(std::vector<LabelType>& label_data)
  {
    label_data_ = label_data;
  }
 
  /** Trains a decision tree using the set training data and settings.
   *
   * \param[out] fern destination for the trained tree
   */
  void
  train(Fern<FeatureType, NodeType>& fern);
 
protected:
  /** Creates uniformely distrebuted thresholds over the range of the supplied
   *  values.
   *
   * \param[in] num_of_thresholds the number of thresholds to create
   * \param[in] values the values for estimating the expected value range
   * \param[out] thresholds the resulting thresholds
   */
  static void
  createThresholdsUniform(const std::size_t num_of_thresholds,
                          std::vector<float>& values,
                          std::vector<float>& thresholds);
 
private:
  /** Desired depth of the learned fern. */
  std::size_t fern_depth_;
  /** Number of features used to find optimal decision features. */
  std::size_t num_of_features_;
  /** Number of thresholds. */
  std::size_t num_of_thresholds_;
 
  /** FeatureHandler instance, responsible for creating and evaluating features. */
  pcl::FeatureHandler<FeatureType, DataSet, ExampleIndex>* feature_handler_;
  /** StatsEstimator instance, responsible for gathering stats about a node. */
  pcl::StatsEstimator<LabelType, NodeType, DataSet, ExampleIndex>* stats_estimator_;
 
  /** The training data set. */
  DataSet data_set_;
  /** The label data. */
  std::vector<LabelType> label_data_;
  /** The example data. */
  std::vector<ExampleIndex> examples_;
};
 
} // namespace pcl
 
#include <pcl/ml/impl/ferns/fern_trainer.hpp>