Changed the example program to suggest a logarithmic grid search

rather than one that takes linear steps.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%404010

examples/svm_ex.cpp

@@ -106,9 +106,9 @@ int main()
     // should look at the model_selection_ex.cpp program for examples of more sophisticated 
     // strategies for determining good parameter choices.
     cout << "doing cross validation" << endl;
-    for (double gamma = 0.00001; gamma <= 1; gamma += 0.1)
+    for (double gamma = 0.00001; gamma <= 1; gamma *= 5)
     {
-        for (double nu = 0.00001; nu < max_nu; nu += 0.1)
+        for (double nu = 0.00001; nu < max_nu; nu *= 5)
         {
             // tell the trainer the parameters we want to use
             trainer.set_kernel(kernel_type(gamma));
@@ -125,13 +125,13 @@ int main()
 
 
     // From looking at the output of the above loop it turns out that a good value for 
-    // nu and gamma for this problem is 0.1 for both.  So that is what we will use.
+    // nu and gamma for this problem is 0.15625 for both.  So that is what we will use.
 
     // Now we train on the full set of data and obtain the resulting decision function.  We use the
-    // value of 0.1 for nu and gamma.  The decision function will return values >= 0 for samples it predicts
+    // value of 0.15625 for nu and gamma.  The decision function will return values >= 0 for samples it predicts
     // are in the +1 class and numbers < 0 for samples it predicts to be in the -1 class.
-    trainer.set_kernel(kernel_type(0.1));
-    trainer.set_nu(0.1);
+    trainer.set_kernel(kernel_type(0.15625));
+    trainer.set_nu(0.15625);
     typedef decision_function<kernel_type> dec_funct_type;
     typedef normalized_function<dec_funct_type> funct_type;
 
@@ -219,7 +219,7 @@ int main()
 
 
 
-    // Lastly, note that the decision functions we trained above involved well over 100 
+    // Lastly, note that the decision functions we trained above involved well over 200 
     // basis vectors.  Support vector machines in general tend to find decision functions
     // that involve a lot of basis vectors.  This is significant because the more 
     // basis vectors in a decision function, the longer it takes to classify new examples.