Changeset 695 for trunk/yat/regression/OneDimensional.h

Timestamp:

Oct 25, 2006, 11:20:17 AM (16 years ago)

Author:

Peter

Message:

References #81 improved documentation

File:

• trunk/yat/regression/OneDimensional.h (modified) (1 diff)

trunk/yat/regression/OneDimensional.h

@@ -55 +55 @@
     virtual ~OneDimensional(void) {};
          
-    ///
-    /// This function computes the best-fit given a model (see
-    /// specific class for details) by minimizing \f$
-    /// \sum{(\hat{y_i}-y_i)^2} \f$, where \f$ \hat{y} \f$ is the fitted value.
-    ///
+    /**
+       This function computes the best-fit given a model (see
+       specific class for details) by minimizing \f$
+       \sum{(\hat{y_i}-y_i)^2} \f$, where \f$ \hat{y} \f$ is the fitted value.
+    */
     virtual void fit(const utility::vector& x, const utility::vector& y)=0; 
     
     ///
-    /// function predicting in one point
+    /// @return expected value in @a x accrding to the fitted model
     ///
     virtual double predict(const double x) const=0;
 
-    ///
-    /// @return expected prediction error for a new data point in @a x
-    ///
+    /**
+       The prediction error is defined as the square root of the
+       expected squared deviation a new data point will have from
+       value the model provides. The expected squared deviation is
+       defined as \f$ E(Y|x - \hat{y}(x))^2 \f$ and is typically
+       divided into two terms \f$ E(Y|x - E(Y|x))^2 \f$ and \f$
+       E(E(Y|x) - \hat{y}(x))^2 \f$, which is the conditional variance
+       in \f$ x \f$ and the squared standard error (see
+       standard_error()) of the model estimation in \f$ x \f$,
+       respectively.
+    
+       @return expected prediction error for a new data point in @a x
+    */
     virtual double prediction_error(const double x) const=0;
 
     ///
-    /// @brief print output to @a os
+    /// @brief print output to ostream @a os
+    ///
+    /// Printing estimated model to @a os in the points defined by @a
+    /// min, @a max, and @a n. The values printed for each point is
+    /// the x-value, the estimated y-value, and the estimated standard
+    /// deviation of a new data poiunt will have from the y-value
+    /// given the x-value (see prediction_error()).
+    /// 
+    /// @param n number of points printed
+    /// @param min smallest x-value for which the model is printed
+    /// @param max largest x-value for which the model is printed
     ///
     std::ostream& print(std::ostream& os,const double min, 
                         double max, const u_int n) const;
 
-    ///
-    /// @return error of model value in @a x
-    ///
+    /**
+       The standard error is defined as \f$ \sqrt{E(Y|x -
+       \hat{y}(x))^2 }\f$
+
+       @return error of model value in @a x
+    */
     virtual double standard_error(const double x) const=0;