Ignore:
Timestamp:
Aug 28, 2006, 3:03:54 PM (16 years ago)
Author:
Markus Ringnér
Message:

Fixed comments so they pass without some of the complaits from doxygen. Have not looked at the actual contents of comments

Location:
trunk/c++_tools/statistics
Files:
4 edited

Legend:

Unmodified
Added
Removed

  • trunk/c++_tools/statistics/Averager.h

    r582 r597  
    6565
    6666    ///
    67     /// Rescales the object, \f$ \forall x_i \rightarrow a*x_i\f$, \f$
    68     /// \forall x_i^2 \rightarrow a^2*x_i^2 \f$
     67    /// Rescales the object, \f$ \forall x_i \rightarrow a*x_i \f$,
     68    /// \f$ \forall x_i^2 \rightarrow a^2*x_i^2 \f$
    6969    ///
    7070    inline void rescale(double a) { x_*=a; xx_*=a*a; }
    … …  
    103103
    104104    ///
    105     /// @return \f$ \sum_i (x_i-m)^2\f$
     105    /// @return \f$ \sum_i (x_i-m)^2 \f$
    106106    ///
    107107    inline double sum_xx_centered(void) const { return xx_-x_*x_/n_; }
    … …  
    117117    ///
    118118    /// The variance is calculated as \f$ \frac{1}{N}\sum_i
    119     /// (x_i-m)^2\f$, where \f$m\f$ is the mean.
     119    /// (x_i-m)^2 \f$, where \f$ m \f$ is the mean.
    120120    ///
    121121    /// @return estimation of variance
    … …  
    127127    /// The variance is calculated using the \f$ (n-1) \f$ correction,
    128128    /// which means it is the best unbiased estimator of the variance
    129     /// \f$ \frac{1}{N-1}\sum_i (x_i-m)^2\f$, where \f$m\f$ is the
     129    /// \f$ \frac{1}{N-1}\sum_i (x_i-m)^2 \f$, where \f$ m \f$ is the
    130130    /// mean.
    131131    ///

  • trunk/c++_tools/statistics/AveragerPair.h

    r593 r597  
    5959
    6060    ///
    61     /// \f$\frac{\sum_i (x_i-m_x)(y_i-m_y)}{\sum_i
    62     /// (x_i-m_x)^2+\sum_i (y_i-m_y)^2 + n(m_x-m_y)^2}\f$
     61    /// \f$ \frac{\sum_i (x_i-m_x)(y_i-m_y)}{\sum_i
     62    /// (x_i-m_x)^2+\sum_i (y_i-m_y)^2 + n(m_x-m_y)^2} \f$
    6363    ///
    6464    /// In case of a zero denominator - zero is returned.
    … …  
    7373 
    7474    ///
    75     /// \f$\frac{\sum_i (x_i-m_x)(y_i-m_y)}{\sqrt{\sum_i
    76     /// (x_i-m_x)^2\sum_i (y_i-m_y)^2}}\f$
     75    /// \f$ \frac{\sum_i (x_i-m_x)(y_i-m_y)}{\sqrt{\sum_i
     76    /// (x_i-m_x)^2\sum_i (y_i-m_y)^2}} \f$
    7777    ///
    7878    /// @return Pearson correlation coefficient.
    … …  
    8484    ///
    8585    /// Calculating covariance using
    86     /// \f$ \frac{1}{N}\sum_i (x_i-m_x)(y_i-m_y)\f$,
    87     /// where \f$m\f$ is the mean.
     86    /// \f$ \frac{1}{N}\sum_i (x_i-m_x)(y_i-m_y) \f$,
     87    /// where \f$ m \f$ is the mean.
    8888    ///
    8989    /// @return The covariance.
    … …  
    120120
    121121    ///
    122     /// @return \f$ \sum_i (x_i-m_x)(y_i-m_y)\f$
     122    /// @return \f$ \sum_i (x_i-m_x)(y_i-m_y) \f$
    123123    ///
    124124    inline double sum_xy_centered(void) const {return xy_-x_.sum_x()*y_.mean();}

  • trunk/c++_tools/statistics/Pearson.h

    r475 r597  
    3434    ///
    3535    /// \f$ \frac{\vert \sum_i(x_i-\bar{x})(y_i-\bar{y})\vert
    36     /// }{\sqrt{\sum_i (x_i-\bar{x})^2\sum_i (x_i-\bar{x})^2}}\f$.
     36    /// }{\sqrt{\sum_i (x_i-\bar{x})^2\sum_i (x_i-\bar{x})^2}} \f$.
    3737    /// @return Pearson correlation, if absolute=true absolute value
    3838    /// of Pearson is used.
    … …  
    4343    ///
    4444    /// \f$ \frac{\vert \sum_iw^2_i(x_i-\bar{x})(y_i-\bar{y})\vert }
    45     /// {\sqrt{\sum_iw^2_i(x_i-\bar{x})^2\sum_iw^2_i(y_i-\bar{y})^2}}\f$,
    46     /// where \f$m_x = \frac{\sum w_ix_i}{\sum w_i}\f$ and \f$m_x =
    47     /// \frac{\sum w_ix_i}{\sum w_i}\f$. This expression is chosen to
    48     /// get a correlation equal to unity when \a x and \a y are
    49     /// equal. @return absolute value of weighted version of Pearson
    50     /// correlation.
     45    /// {\sqrt{\sum_iw^2_i(x_i-\bar{x})^2\sum_iw^2_i(y_i-\bar{y})^2}}
     46    /// \f$, where \f$ m_x = \frac{\sum w_ix_i}{\sum w_i} \f$ and \f$
     47    /// m_x = \frac{\sum w_ix_i}{\sum w_i} \f$. This expression is
     48    /// chosen to get a correlation equal to unity when \a x and \a y
     49    /// are equal. @return absolute value of weighted version of
     50    /// Pearson correlation.
    5151    ///
    5252    double score(const classifier::Target& target,

  • trunk/c++_tools/statistics/tScore.h

    r589 r597  
    3838    /// mean, \f$ n \f$ is the number of data points and \f$ s^2 =
    3939    /// \frac{ \sum_i (x_i-m_x)^2 + \sum_i (y_i-m_y)^2 }{ n_x + n_y -
    40     /// 2 }
     40    /// 2 } \f$
    4141    ///
    4242    /// @return t-score if absolute=true absolute value of t-score
    … …  
    4949    /// Calculates the weighted t-score, i.e. the ratio between
    5050    /// difference in mean and standard deviation of this
    51     /// difference. \f$ t = \frac{ m_x - m_y } {
    52     /// \frac{s2}{n_x}+\frac{s2}{n_y} \f$ where \f$ m \f$ is the
     51    /// difference. \f$ t = \frac{ m_x - m_y }{
     52    /// \frac{s2}{n_x}+\frac{s2}{n_y}} \f$ where \f$ m \f$ is the
    5353    /// weighted mean, n is the weighted version of number of data
    5454    /// points and \f$ s2 \f$ is an estimation of the variance \f$ s^2
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