Made it so you can get the optimal alpha values.

dlib/svm/svm_c_linear_dcd_trainer.h

@@ -414,6 +414,9 @@ namespace dlib
             dlib::rand rnd;
 
         public:
+
+            const std::vector<scalar_type>& get_alpha () const { return alpha; }
+
             friend void serialize(const optimizer_state& item, std::ostream& out)
             {
                 const int version = 1;

dlib/svm/svm_c_linear_dcd_trainer_abstract.h

@@ -304,10 +304,16 @@ namespace dlib
         !*/
 
         // optimizer_state is used to record the internal state of the SVM optimizer.  It
-        // can be used with the following train() routine to warm-start the optimizer.
-        // Note, that optimizer_state objects are serializable but are otherwise completely
-        // opaque to the user.
-        class optimizer_state;
+        // can be used with the following train() routine to warm-start the optimizer or
+        // access the optimal alpha values (see the Hsieh paper mentioned above).  The
+        // optimizer_state objects are serializable and allow you to get the alphas, but
+        // are otherwise completely opaque to the user.
+        class optimizer_state
+        {
+        public:
+            const std::vector<scalar_type>& get_alpha (
+            ) const; 
+        };
 
         template <
             typename in_sample_vector_type,
@@ -355,6 +361,8 @@ namespace dlib
                 - #state == the internal state of the optimizer at the solution to the SVM
                   problem.  Therefore, passing #state to a new call to train() will start
                   the optimizer from the current solution.
+                - #state.get_alpha().size() == x.size()
+                - #state.get_alpha() == the optimal alpha/dual values learned by the optimizer.
                 - returns a decision function F with the following properties:
                     - F.alpha.size() == 1
                     - F.basis_vectors.size() == 1