Added a comment about picking a reasonable gamma.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403744

examples/rvm_regression_ex.cpp

@@ -28,18 +28,6 @@ int main()
     // Here we declare that our samples will be 1 dimensional column vectors.  
     typedef matrix<double,1,1> sample_type;
 
-    // Now we are making a typedef for the kind of kernel we want to use.  I picked the
-    // radial basis kernel because it only has one parameter and generally gives good
-    // results without much fiddling.
-    typedef radial_basis_kernel<sample_type> kernel_type;
-
-    // Here we declare an instance of the rvm_regression_trainer object.  This is the
-    // object that we will later use to do the training.
-    rvm_regression_trainer<kernel_type> trainer;
-    // Here we set the kernel we want to use for training.  The 0.05 is the gamma 
-    // parameter to the radial_basis_kernel.
-    trainer.set_kernel(kernel_type(0.05));
-
     // Now sample some points from the sinc() function
     sample_type m;
     std::vector<sample_type> samples;
@@ -51,6 +39,28 @@ int main()
         labels.push_back(sinc(x));
     }
 
+    // Now we are making a typedef for the kind of kernel we want to use.  I picked the
+    // radial basis kernel because it only has one parameter and generally gives good
+    // results without much fiddling.
+    typedef radial_basis_kernel<sample_type> kernel_type;
+
+    // Here we declare an instance of the rvm_regression_trainer object.  This is the
+    // object that we will later use to do the training.
+    rvm_regression_trainer<kernel_type> trainer;
+
+    // Here we set the kernel we want to use for training.   The radial_basis_kernel 
+    // has a parameter called gamma that we need to determine.  As a rule of thumb, a good 
+    // gamma to try is 1.0/(mean squared distance between your sample points).  So 
+    // below we are using a similar value.   Note also that using an inappropriately large
+    // gamma will cause the RVM training algorithm to run extremely slowly.  What
+    // "large" means is relative to how spread out your data is.  So it is important
+    // to use a rule like this as a starting point for determining the gamma value
+    // if you want to use the RVM.  It is also probably a good idea to normalize your
+    // samples as shown in the rvm_ex.cpp example program.
+    const double gamma = 2.0/compute_mean_squared_distance(samples);
+    cout << "using gamma of " << gamma << endl;
+    trainer.set_kernel(kernel_type(gamma));
+
     // now train a function based on our sample points
     decision_function<kernel_type> test = trainer.train(samples, labels);
 
@@ -62,6 +72,7 @@ int main()
     m(0) = 5.0; cout << sinc(m(0)) << "   " << test(m) << endl;
 
     // The output is as follows:
+    //using gamma of 0.05
     //0.239389   0.240989
     //0.998334   0.999538
     //-0.189201   -0.188453