NICE_VisLearning/classifier/fpclassifier/randomforest/SCInformationGain.h

/**
 * @file SCInformationGain.h
 * @brief the information gain splitting criterion
 * @author Sven Sickert
 * @date 01/12/2017

*/
#ifndef SCInformationGainINCLUDE
#define SCInformationGainINCLUDE

#include "SplittingCriterion.h"

namespace OBJREC {

class SCInformationGain : public SplittingCriterion
{
  protected:

    double entropy_left,
           entropy_right,
           count_left,
           count_right;

    bool   use_shannon_entropy;

  public:

    /* default constructor */
    SCInformationGain();

    /* simple constructor */
    SCInformationGain( int _min_examples );

    /** config constructor */
    SCInformationGain( const NICE::Config *conf );

    /** copy constructor */
    SCInformationGain( const SCInformationGain &obj );

    /* simple destructor */
    virtual ~SCInformationGain();

    /* cloning function */
    virtual SplittingCriterion* clone();

    /**
     * @brief evaluate the split and return if split is possible
     * @param values unsorted list of feature values of a certain dimension
     * @param threshold threshold for current feature dimension
     * @param distribution_left class distribution for left child node after splitting
     * @param distribution_right class distribution for right child node after splitting
     * @param maxClassNo maximum class number
     * @return possible split or not
     */
    virtual bool evaluateSplit(
        const FeatureValuesUnsorted & values,
        double threshold,
        double* distribution_left,
        double* distribution_right,
        int maxClassNo );

    /**
     * @brief compute purity based on given split
     * @return purity value
     */
    double computePurity() const;

};
    
} // namespace


#endif