source: branches/peters_vector/lib/statistics/Score.h @ 469

// $Id: Score.h 469 2005-12-19 14:58:29Z peter $

#ifndef _theplu_statistics_score_
#define _theplu_statistics_score_

namespace theplu {
namespace classifier {
  class Target;
  class VectorAbstract;
}

namespace statistics {

  ///
  /// Abstract Base Class defining the interface for the score classes.
  ///
  class Score 
  {

  public:
    ///
    ///   Constructor
    ///    
    Score(bool absolute=true) ;
    
    ///
    ///   Destructor
    ///
    virtual ~Score(void) {};
    
    ///
    ///  Function changing mode of Score
    ///
    inline void absolute(bool absolute) {absolute_=absolute;}

    ///
    /// Targets with this label are considered to be in positive
    /// group. All others are considered to be in negative
    /// group. Default is 1.
    ///
    /// @return label for positive class
    ///
    inline int& positive_label(void) { return positive_label_; }

    ///
    /// Function calculating the score. In absolute mode, also the
    /// score using negated class labels is calculated, and the
    /// largest of the two scores are calculated. Absolute mode should
    /// be used when two-tailed test is wanted.
    ///
    /// @return statistica.
    ///
    /// @param target vector of targets (most often +1 -1)
    /// @param value vector of the values
    /// 
    virtual double 
    score(const classifier::Target& target, 
          const classifier::VectorAbstract& value) = 0; 
 
    ///
    /// Function calculating the weighted version of score. In
    /// absolute mode, also the score using negated class labels is
    /// calculated, and the largest of the two scores are
    /// calculated. Absolute mode should be used when two-tailed test
    /// is wanted.
    ///
    /// @return statistica (weighted version)
    ///
    /// @param target is +1 or -1
    /// @param value vector of the values
    /// @param weight vector of accompanied weight to the values
    /// @train_set defining which values to use (number of values used
    /// in the calculation is equal to size of \a train_set)
    ///
    virtual double 
    score(const classifier::Target& target, 
          const classifier::VectorAbstract& value,
          const classifier::VectorAbstract& weight) = 0; 

    inline bool class_one(int i) const { return i==positive_label_; }
    
  protected:
    inline bool weighted(void) const { return weighted_; }

    bool absolute_;
    int positive_label_;
    bool weighted_;

  }; // class Score

}} // of namespace statistics and namespace theplu

#endif