Changeset 1006


Ignore:
Timestamp:
Jan 24, 2008, 7:08:00 PM (15 years ago)
Author:
Peter
Message:

fixing doxygen problems

File:
1 edited

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Added
Removed

  • trunk/yat/statistics/PearsonCorrelation.h

    r1000 r1006  
    3737  class VectorAbstract;
    3838}
    39 namespace statistics { 
     39namespace statistics {
    4040
    41   ///
    42   /// @brief Class for calculating Pearson correlation.
    43   ///   
    44  
     41  ///
     42  /// @brief Class for calculating Pearson correlation.
     43  ///
    4544  class PearsonCorrelation
    46   {
    47   public:
    48     ///
    49     /// @brief The default constructor.
    50     ///
    51     PearsonCorrelation(void);
    52 
    53     ///
    54     /// @brief The destructor.
    55     ///
    56     virtual ~PearsonCorrelation(void);
    57          
    58    
    59     ///
    60     /// \f$ \frac{\vert \sum_i(x_i-\bar{x})(y_i-\bar{y})\vert
    61     /// }{\sqrt{\sum_i (x_i-\bar{x})^2\sum_i (x_i-\bar{x})^2}} \f$.
    62     /// @return Pearson correlation, if absolute=true absolute value
    63     /// of Pearson is used.
     45  {
     46  public:
    6447    ///
     48    /// @brief The default constructor.
     49    ///
     50    PearsonCorrelation(void);
     51   
     52    ///
     53    /// @brief The destructor.
     54    ///
     55    virtual ~PearsonCorrelation(void);
     56   
     57   
     58    /**
     59       \f$ \frac{\vert \sum_i(x_i-\bar{x})(y_i-\bar{y})\vert
     60       }{\sqrt{\sum_i (x_i-\bar{x})^2\sum_i (x_i-\bar{x})^2}} \f$.
     61       @return Pearson correlation, if absolute=true absolute value
     62       of Pearson is used.
     63    */
    6564    double score(const classifier::Target& target,
    6665                 const utility::vector& value);
    67 
    68     ///
    69     /// \f$ \frac{\vert \sum_iw^2_i(x_i-\bar{x})(y_i-\bar{y})\vert }
    70     /// {\sqrt{\sum_iw^2_i(x_i-\bar{x})^2\sum_iw^2_i(y_i-\bar{y})^2}}
    71     /// \f$, where \f$ m_x = \frac{\sum w_ix_i}{\sum w_i} \f$ and \f$
    72     /// m_x = \frac{\sum w_ix_i}{\sum w_i} \f$. This expression is
    73     /// chosen to get a correlation equal to unity when \a x and \a y
    74     /// are equal. @return absolute value of weighted version of
    75     /// Pearson correlation.
    76     ///
     66   
     67    /**
     68      \f$ \frac{\vert \sum_iw^2_i(x_i-\bar{x})(y_i-\bar{y})\vert }
     69      {\sqrt{\sum_iw^2_i(x_i-\bar{x})^2\sum_iw^2_i(y_i-\bar{y})^2}}
     70      \f$, where \f$ m_x = \frac{\sum w_ix_i}{\sum w_i} \f$ and \f$
     71      m_x = \frac{\sum w_ix_i}{\sum w_i} \f$. This expression is
     72      chosen to get a correlation equal to unity when \a x and \a y
     73      are equal. @return absolute value of weighted version of
     74      Pearson correlation.
     75    */
    7776    double score(const classifier::Target& target,
    7877                 const classifier::DataLookupWeighted1D& value);
    79 
    80     ///
    81     /// \f$ \frac{\vert \sum_iw^2_i(x_i-\bar{x})(y_i-\bar{y})\vert }
    82     /// {\sqrt{\sum_iw^2_i(x_i-\bar{x})^2\sum_iw^2_i(y_i-\bar{y})^2}}
    83     /// \f$, where \f$ m_x = \frac{\sum w_ix_i}{\sum w_i} \f$ and \f$
    84     /// m_x = \frac{\sum w_ix_i}{\sum w_i} \f$. This expression is
    85     /// chosen to get a correlation equal to unity when \a x and \a y
    86     /// are equal. @return absolute value of weighted version of
    87     /// Pearson correlation.
    88     ///
     78   
     79    /**
     80      \f$ \frac{\vert \sum_iw^2_i(x_i-\bar{x})(y_i-\bar{y})\vert }
     81      {\sqrt{\sum_iw^2_i(x_i-\bar{x})^2\sum_iw^2_i(y_i-\bar{y})^2}}
     82      \f$, where \f$ m_x = \frac{\sum w_ix_i}{\sum w_i} \f$ and \f$
     83      m_x = \frac{\sum w_ix_i}{\sum w_i} \f$. This expression is
     84      chosen to get a correlation equal to unity when \a x and \a y
     85      are equal. @return absolute value of weighted version of
     86      Pearson correlation.
     87    */
    8988    double score(const classifier::Target& target,
    9089                 const utility::vector& value,
    9190                 const utility::vector& weight);
    92 
    93     ///
    94     /// The p-value is the probability of getting a correlation as
    95     /// large (or larger) as the observed value by random chance, when the true
    96     /// correlation is zero (and the data is Gaussian).
    97     ///
    98     /// @Note This function can only be used together with the
    99     /// unweighted score.
    100     ///
    101     /// @return one-sided p-value
    102     ///
    103     double p_value_one_sided() const;
    104 
    105   private:
     91   
     92    /**
     93      The p-value is the probability of getting a correlation as
     94      large (or larger) as the observed value by random chance, when the true
     95      correlation is zero (and the data is Gaussian).
     96     
     97      @Note This function can only be used together with the
     98      unweighted score.
     99       
     100      @return one-sided p-value
     101    */
     102    double p_value_one_sided() const;
     103   
     104  private:
    106105    double r_;
    107106    int nof_samples_;
    108 
    109     //    void centralize(utility::vector&, const utility::vector&);
     107   
    110108  };
    111 
     109 
    112110}}} // of namespace statistics, yat, and theplu
    113111
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