source: trunk/yat/statistics/SNR.h @ 748

#ifndef _theplu_yat_statistics_snr
#define _theplu_yat_statistics_snr

// $Id: SNR.h 748 2007-02-11 17:48:22Z peter $

/*
  Copyright (C) The authors contributing to this file.

  This file is part of the yat library, http://lev.thep.lu.se/trac/yat

  The yat library is free software; you can redistribute it and/or
  modify it under the terms of the GNU General Public License as
  published by the Free Software Foundation; either version 2 of the
  License, or (at your option) any later version.

  The yat library is distributed in the hope that it will be useful,
  but WITHOUT ANY WARRANTY; without even the implied warranty of
  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
  General Public License for more details.

  You should have received a copy of the GNU General Public License
  along with this program; if not, write to the Free Software
  Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
  02111-1307, USA.
*/

#include "Score.h"

#include <gsl/gsl_cdf.h>

namespace theplu {
namespace yat {
  namespace utility {
    class vector;
  }
  namespace classifier {
    class DataLookWeighted1D;
  }
namespace statistics {  

  /**
     @brief Class for score based on signal-to-noise ratio (SNR).  
     
     Also
     sometimes referred to as Golub score. The score is the ratio
     between difference in mean and the sum of standard deviations
     for two groups:  \f$ \frac{ m_x-m_y}{ s_x + s_y} \f$ where \f$
     s \f$ is standard deviation.
  */
  class SNR : public Score
  {
  
  public:
    ///
    /// @brief Default Constructor.
    ///
    SNR(bool absolute=true);

    /**
       SNR is defined as \f$ \frac{m_x-m_y}{s_x+s_y} \f$ where \f$ m
       \f$ and \f$ s \f$ are mean and standard deviation,
       respectively. @see Averager

       @return SNR score. If absolute=true absolute value of SNR is
       returned
    */
    double score(const classifier::Target& target, 
                 const utility::vector& value); 

    /**
       SNR is defined as \f$ \frac{m_x-m_y}{s_x+s_y} \f$ where \f$ m
       \f$ and \f$ s \f$ are weighted versions of mean and standard
       deviation, respectively. @see AveragerWeighted

       @return SNR score. If absolute=true absolute value of SNR is
       returned
    */
    double score(const classifier::Target& target, 
                 const classifier::DataLookupWeighted1D& value);          

    /**
       SNR is defined as \f$ \frac{m_x-m_y}{s_x+s_y} \f$ where \f$ m
       \f$ and \f$ s \f$ are weighted versions of mean and standard
       deviation, respectively. @see AveragerWeighted

       @return SNR score. If absolute=true absolute value of SNR is
       returned
    */
    double score(const classifier::Target& target, 
                 const utility::vector& value, 
                 const utility::vector& weight);          
  private:
    double score_;
  };

}}} // of namespace statistics, yat, and theplu

#endif