source: trunk/src/tScore.h @ 112

// $Id: tScore.h 112 2004-07-07 10:23:44Z peter $

#ifndef _theplu_cpptools_t_score_
#define _theplu_cpptools_t_score_ 

// C++ tools include
/////////////////////
#include "Score.h"
#include "vector.h"
#include <gsl/gsl_cdf.h>

// Standard C++ includes
////////////////////////
//#include <utility>
//#include <vector>

namespace theplu {
namespace cpptools {  
  ///
  /// Class for Fisher's t-test.
  ///   
  
  class tScore : public Score
  {
  
  public:
    ///
    /// Default Constructor.
    ///
    tScore();

    ///
    /// Constructor taking a value vector and a target vector (+1 or -1).
    ///
    tScore(const gslapi::vector&, const gslapi::vector&,
           const std::vector<size_t>& = std::vector<size_t>());
         
    ///
    /// Destructor 
    ///
    virtual ~tScore(void) {};
         
    
    ///
    /// Calculates the t-score, i.e. the ratio between difference in
    /// mean and standard deviation of this difference.
    /// @return \f$ \frac{\frac{1}{n_x}\sum x_i - \frac{1}{n_y}\sum y_i}
    /// {\frac{\sum x_i^2 + \sum y_i^2}{n_x-1+n_y-1}} \f$
    ///
    double score();
       
    ///
    /// Calculates the t-score, i.e. the ratio between difference in
    /// mean and standard deviation of this difference.
    /// @return \f$ \frac{\frac{1}{n_x}\sum x_i - \frac{1}{n_y}\sum y_i}
    /// {\frac{\sum x_i^2 + \sum y_i^2}{n_x-1+n_y-1}} \f$
    ///
    double score(const gslapi::vector&, const gslapi::vector&,  
                 const std::vector<size_t>& = std::vector<size_t>());

    ///
    ///Calculates the p-value, i.e. the probability of observing a t-score
    ///equally or larger if the null hypothesis is true. If P is near zero,
    ///this casts doubt on this hypothesis. The null hypothesis is ...
    /// @return the one-sided p-value
    ///
    double p_value();
         
  private:
    gslapi::vector value_;
    gslapi::vector target_;
    std::vector<size_t> train_set_;
        
  };

}} // of namespace cpptools and namespace theplu

#endif