Ignore:
Timestamp:
Sep 5, 2007, 2:50:49 PM (14 years ago)
Author:
Markus Ringnér
Message:

The SNR score is now ok again and renamed SNRScore. Fixes #235"

File:
1 moved

Legend:

Unmodified
Added
Removed
  • trunk/yat/statistics/SNRScore.h

    r855 r856  
    3131
    3232namespace theplu {
    33 namespace yat {
    34   namespace utility {
    35     class vector;
    36   }
    37   namespace classifier {
    38     class DataLookWeighted1D;
    39   }
    40 namespace statistics { 
     33  namespace yat {
     34    namespace utility {
     35      class vector;
     36    }
     37    namespace classifier {
     38      class DataLookWeighted1D;
     39    }
     40    namespace statistics { 
    4141
    42   /**
    43      @brief Class for score based on signal-to-noise ratio (SNR). 
    44      
    45      Also
    46      sometimes referred to as Golub score. The score is the ratio
    47      between difference in mean and the sum of standard deviations
    48      for two groups:  \f$ \frac{ m_x-m_y}{ s_x + s_y} \f$ where \f$
    49      s \f$ is standard deviation.
    50   */
    51   class SNR : public Score
    52   {
    53  
    54   public:
    55     ///
    56     /// @brief Default Constructor.
    57     ///
    58     SNR(bool absolute=true);
    59 
    60     /**
    61        SNR is defined as \f$ \frac{m_x-m_y}{s_x+s_y} \f$ where \f$ m
    62        \f$ and \f$ s \f$ are mean and standard deviation,
    63        respectively. @see Averager
    64 
    65        @return SNR score. If absolute=true absolute value of SNR is
    66        returned
    67     */
    68     double score(const classifier::Target& target,
    69                  const utility::vector& value);
    70 
    71     /**
    72        SNR is defined as \f$ \frac{m_x-m_y}{s_x+s_y} \f$ where \f$ m
    73        \f$ and \f$ s \f$ are weighted versions of mean and standard
    74        deviation, respectively. @see AveragerWeighted
    75 
    76        @return SNR score. If absolute=true absolute value of SNR is
    77        returned
    78     */
    79     double score(const classifier::Target& target,
    80                  const classifier::DataLookupWeighted1D& value);         
    81 
    82     /**
    83        SNR is defined as \f$ \frac{m_x-m_y}{s_x+s_y} \f$ where \f$ m
    84        \f$ and \f$ s \f$ are weighted versions of mean and standard
    85        deviation, respectively. @see AveragerWeighted
    86 
    87        @return SNR score. If absolute=true absolute value of SNR is
    88        returned
    89     */
    90     double score(const classifier::Target& target,
    91                  const utility::vector& value,
    92                  const utility::vector& weight);         
    93   private:
    94     double score_;
    95   };
    96 
    97 }}} // of namespace statistics, yat, and theplu
     42      /**
     43         @brief Class for score based on signal-to-noise ratio (SNRScore). 
     44         
     45         Also
     46         sometimes referred to as Golub score. The score is the ratio
     47         between difference in mean and the sum of standard deviations
     48         for two groups:  \f$ \frac{ m_x-m_y}{ s_x + s_y} \f$ where \f$
     49         s \f$ is standard deviation.
     50      */
     51      class SNRScore : public Score
     52      {
     53       
     54      public:
     55        ///
     56        /// @brief Default Constructor.
     57        ///
     58        SNRScore(bool absolute=true);
     59       
     60        ///
     61        /// @brief The destructor.
     62        ///
     63        virtual ~SNRScore(void);
     64       
     65        /**
     66           SNRScore is defined as \f$ \frac{m_x-m_y}{s_x+s_y} \f$ where \f$ m
     67           \f$ and \f$ s \f$ are mean and standard deviation,
     68           respectively. @see Averager
     69           
     70           @return SNRScore score. If absolute=true absolute value of SNRScore is
     71           returned
     72        */
     73        double score(const classifier::Target& target,
     74                     const utility::vector& value) const;
     75       
     76        /**
     77           SNRScore is defined as \f$ \frac{m_x-m_y}{s_x+s_y} \f$ where \f$ m
     78           \f$ and \f$ s \f$ are weighted versions of mean and standard
     79           deviation, respectively. @see AveragerWeighted
     80           
     81           @return SNRScore score. If absolute=true absolute value of SNRScore is
     82           returned
     83        */
     84        double score(const classifier::Target& target,
     85                     const classifier::DataLookupWeighted1D& value) const;         
     86       
     87        /**
     88           SNRScore is defined as \f$ \frac{m_x-m_y}{s_x+s_y} \f$ where \f$ m
     89           \f$ and \f$ s \f$ are weighted versions of mean and standard
     90           deviation, respectively. @see AveragerWeighted
     91           
     92           @return SNRScore score. If absolute=true absolute value of SNRScore is
     93           returned
     94        */
     95        double score(const classifier::Target& target,
     96                     const utility::vector& value,
     97                     const utility::vector& weight) const;         
     98       
     99      };
     100     
     101     
     102    }}} // of namespace statistics, yat, and theplu
    98103
    99104#endif
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