Added some comments and cleaned up code slightly.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403420

examples/kcentroid_ex.cpp

@@ -94,8 +94,9 @@ int main()
     m(0) = -1.5; m(1) = sinc(m(0))+0.9; cout << "   " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
     m(0) = -0.5; m(1) = sinc(m(0))+1;   cout << "   " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
 
+    // And finally print out the mean and standard deviation of points that are actually from sinc().  
     cout << "\nmean: " << rs.mean() << endl;
-    cout << "standard deviation: " << sqrt(rs.variance()) << endl;
+    cout << "standard deviation: " << rs.stddev() << endl;
 
     // The output is as follows:
     /*

examples/rank_features_ex.cpp

@@ -100,7 +100,9 @@ int main()
     // The radial_basis_kernel has a parameter called gamma that we need to set.  Generally,
     // you should try the same gamma that you are using for training.  But if you don't
     // have a particular gamma in mind then you can use the following function to
-    // find a reasonable default gamma for your data.
+    // find a reasonable default gamma for your data.  Another reasonable way to pick a gamma
+    // is often to use 1.0/compute_mean_squared_distance(samples).  This second way has the
+    // bonus of being quite fast.  
     const double gamma = verbose_find_gamma_with_big_centroid_gap(samples, labels);
 
     // Next we declare an instance of the kcentroid object.  It is used by rank_features()