Changed the reduced_decision_function_trainer2 object so that it performs a final

optimization of the weights before it finishes.  This ensures that we always
end up with the best weights for the support vectors we obtain after the
conjugate gradient optimization step.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403069

dlib/svm/reduced.h

@@ -552,7 +552,27 @@ namespace dlib
             // out_vectors matrices
             obj.vector_to_state(opt_starting_point);
 
-            decision_function<kernel_type> new_df(beta, 
+
+            // Do a final reoptimization of beta just to make sure it is optimal given the new
+            // set of support vectors.
+            for (long r = 0; r < K_inv.nr(); ++r)
+            {
+                for (long c = 0; c < K_inv.nc(); ++c)
+                {
+                    K_inv(r,c) = kernel(out_vectors(r), out_vectors(c));
+                }
+            }
+            K_inv = pinv(K_inv);
+            for (long r = 0; r < K.nr(); ++r)
+            {
+                for (long c = 0; c < K.nc(); ++c)
+                {
+                    K(r,c) = kernel(out_vectors(r), dec_funct.support_vectors(c));
+                }
+            }
+
+
+            decision_function<kernel_type> new_df(K_inv*K*dec_funct.alpha, 
                                                   0,
                                                   kernel, 
                                                   out_vectors);