source: trunk/yat/statistics/SNRScore.h

#ifndef _theplu_yat_statistics_snr
#define _theplu_yat_statistics_snr

// $Id: SNRScore.h 2992 2013-03-03 05:03:44Z peter $

/*
  Copyright (C) 2006, 2007 Jari Häkkinen, Peter Johansson, Markus Ringnér
  Copyright (C) 2008 Jari Häkkinen, Peter Johansson

  This file is part of the yat library, http://dev.thep.lu.se/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 3 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 yat. If not, see <http://www.gnu.org/licenses/>.
*/

#include "Score.h"

#include <gsl/gsl_cdf.h>

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

      /**
         @brief Class for score based on signal-to-noise ratio (SNRScore).  
         
         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 SNRScore : public Score
      {
        
      public:
        ///
        /// @brief Default Constructor.
        ///
        SNRScore(bool absolute=true);
        
        ///
        /// @brief The destructor.
        ///
        virtual ~SNRScore(void);
        
        /**
           SNRScore 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 SNRScore score. If absolute=true absolute value of SNRScore is
           returned
        */
        double score(const classifier::Target& target, 
                     const utility::VectorBase& value) const; 
        
        /**
           SNRScore 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 SNRScore score. If absolute=true absolute value of SNRScore is
           returned
        */
        double score(const classifier::Target& target,
                     const classifier::DataLookupWeighted1D& value) const;          
        
        /**
           SNRScore 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 SNRScore score. If absolute=true absolute value of SNRScore is
           returned
        */
        double score(const classifier::Target& target, 
                     const utility::VectorBase& value, 
                     const utility::VectorBase& weight) const;          
        
      };
      
      
    }}} // of namespace statistics, yat, and theplu

#endif