Changeset 748 for trunk/yat/statistics

Timestamp:

Feb 11, 2007, 6:48:22 PM (17 years ago)

Author:

Peter

Message:

documentation for SNR improved. fixes #123

File:

• trunk/yat/statistics/SNR.h (modified) (2 diffs)

trunk/yat/statistics/SNR.h

@@ -45 +45 @@
      sometimes referred to as Golub score. The score is the ratio
      between difference in mean and the sum of standard deviations
-     for two groups.  \f$ \frac{ \frac{1}{n_x}\sum x_i -
-     \frac{1}{n_y}\sum y_i }{ \sigma_x + \sigma_y} \f$ where \f$
-     \sigma \f$ is standard deviation.
+     for two groups:  \f$ \frac{ m_x-m_y}{ s_x + s_y} \f$ where \f$
+     s \f$ is standard deviation.
   */
   class SNR : public Score
@@ -58 +57 @@
     SNR(bool absolute=true);
 
-    ///
-    /// Calculates the value of SNR score. @return SNR score if
-    /// absolute=true absolute value of t-score is returned
-    ///
+    /**
+       SNR is defined as \f$ \frac{m_x-m_y}{s_x+s_y} \f$ where \f$ m
+       \f$ and \f$ s \f$ are mean and standard deviation,
+       respectively. @see Averager
+
+       @return SNR score. If absolute=true absolute value of SNR is
+       returned
+    */
     double score(const classifier::Target& target, 
                  const utility::vector& value); 
 
-    ///
-    /// Weighted version of SNR @return t-score if absolute=true
-    /// absolute value of t-score is returned.
-    ///
+    /**
+       SNR is defined as \f$ \frac{m_x-m_y}{s_x+s_y} \f$ where \f$ m
+       \f$ and \f$ s \f$ are weighted versions of mean and standard
+       deviation, respectively. @see AveragerWeighted
+
+       @return SNR score. If absolute=true absolute value of SNR is
+       returned
+    */
     double score(const classifier::Target& target, 
                  const classifier::DataLookupWeighted1D& value);          
 
-    ///
-    /// Weighted version of SNR @return t-score if absolute=true
-    /// absolute value of t-score is returned.
-    ///
+    /**
+       SNR is defined as \f$ \frac{m_x-m_y}{s_x+s_y} \f$ where \f$ m
+       \f$ and \f$ s \f$ are weighted versions of mean and standard
+       deviation, respectively. @see AveragerWeighted
+
+       @return SNR score. If absolute=true absolute value of SNR is
+       returned
+    */
     double score(const classifier::Target& target, 
                  const utility::vector& value,