Changeset 695 for trunk/yat/regression


Ignore:
Timestamp:
Oct 25, 2006, 11:20:17 AM (15 years ago)
Author:
Peter
Message:

References #81 improved documentation

File:
1 edited

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  • trunk/yat/regression/OneDimensional.h

    r682 r695  
    5555    virtual ~OneDimensional(void) {};
    5656         
    57     ///
    58     /// This function computes the best-fit given a model (see
    59     /// specific class for details) by minimizing \f$
    60     /// \sum{(\hat{y_i}-y_i)^2} \f$, where \f$ \hat{y} \f$ is the fitted value.
    61     ///
     57    /**
     58      This function computes the best-fit given a model (see
     59      specific class for details) by minimizing \f$
     60      \sum{(\hat{y_i}-y_i)^2} \f$, where \f$ \hat{y} \f$ is the fitted value.
     61    */
    6262    virtual void fit(const utility::vector& x, const utility::vector& y)=0;
    6363   
    6464    ///
    65     /// function predicting in one point
     65    /// @return expected value in @a x accrding to the fitted model
    6666    ///
    6767    virtual double predict(const double x) const=0;
    6868
    69     ///
    70     /// @return expected prediction error for a new data point in @a x
    71     ///
     69    /**
     70       The prediction error is defined as the square root of the
     71       expected squared deviation a new data point will have from
     72       value the model provides. The expected squared deviation is
     73       defined as \f$ E(Y|x - \hat{y}(x))^2 \f$ and is typically
     74       divided into two terms \f$ E(Y|x - E(Y|x))^2 \f$ and \f$
     75       E(E(Y|x) - \hat{y}(x))^2 \f$, which is the conditional variance
     76       in \f$ x \f$ and the squared standard error (see
     77       standard_error()) of the model estimation in \f$ x \f$,
     78       respectively.
     79   
     80       @return expected prediction error for a new data point in @a x
     81    */
    7282    virtual double prediction_error(const double x) const=0;
    7383
    7484    ///
    75     /// @brief print output to @a os
     85    /// @brief print output to ostream @a os
     86    ///
     87    /// Printing estimated model to @a os in the points defined by @a
     88    /// min, @a max, and @a n. The values printed for each point is
     89    /// the x-value, the estimated y-value, and the estimated standard
     90    /// deviation of a new data poiunt will have from the y-value
     91    /// given the x-value (see prediction_error()).
     92    ///
     93    /// @param n number of points printed
     94    /// @param min smallest x-value for which the model is printed
     95    /// @param max largest x-value for which the model is printed
    7696    ///
    7797    std::ostream& print(std::ostream& os,const double min,
    7898                        double max, const u_int n) const;
    7999
    80     ///
    81     /// @return error of model value in @a x
    82     ///
     100    /**
     101       The standard error is defined as \f$ \sqrt{E(Y|x -
     102       \hat{y}(x))^2 }\f$
     103
     104       @return error of model value in @a x
     105    */
    83106    virtual double standard_error(const double x) const=0;
    84107
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