source: trunk/yat/statistics/SNRScore.h

#ifndef _theplu_yat_statistics_snr
#define _theplu_yat_statistics_snr

// $Id: SNRScore.h 4207 2022-08-26 04:36:28Z peter $

/*
  Copyright (C) 2006, 2007 Jari Häkkinen, Peter Johansson, Markus Ringnér
  Copyright (C) 2008 Jari Häkkinen, Peter Johansson
  Copyright (C) 2022 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