Updated the interface to the structural_graph_labeling_trainer so the user

can set the per class loss to whatever they want.

dlib/svm/structural_graph_labeling_trainer.h

@@ -32,6 +32,8 @@ namespace dlib
             eps = 0.1;
             num_threads = 2;
             max_cache_size = 40;
+            loss_pos = 1.0;
+            loss_neg = 1.0;
         }
 
         void set_num_threads (
@@ -124,6 +126,42 @@ namespace dlib
             return C;
         }
 
+
+        void set_loss_on_positive_class (
+            double loss
+        )
+        {
+            // make sure requires clause is not broken
+            DLIB_ASSERT(loss >= 0,
+                    "\t structural_graph_labeling_trainer::set_loss_on_positive_class()"
+                    << "\n\t Invalid inputs were given to this function."
+                    << "\n\t loss: " << loss 
+                    << "\n\t this: " << this );
+
+            loss_pos = loss;
+        }
+
+        void set_loss_on_negative_class (
+            double loss
+        )
+        {
+            // make sure requires clause is not broken
+            DLIB_ASSERT(loss >= 0,
+                    "\t structural_graph_labeling_trainer::set_loss_on_negative_class()"
+                    << "\n\t Invalid inputs were given to this function."
+                    << "\n\t loss: " << loss 
+                    << "\n\t this: " << this );
+
+            loss_neg = loss;
+        }
+
+        double get_loss_on_negative_class (
+        ) const { return loss_neg; }
+
+        double get_loss_on_positive_class (
+        ) const { return loss_pos; }
+
+
         template <
             typename graph_type
             >
@@ -150,6 +188,8 @@ namespace dlib
             prob.set_c(C);
             prob.set_epsilon(eps);
             prob.set_max_cache_size(max_cache_size);
+            prob.set_loss_on_positive_class(loss_pos);
+            prob.set_loss_on_negative_class(loss_neg);
 
             matrix<double,0,1> w;
             solver(prob, w, prob.get_num_edge_weights());
@@ -203,6 +243,8 @@ namespace dlib
         bool verbose;
         unsigned long num_threads;
         unsigned long max_cache_size;
+        double loss_pos;
+        double loss_neg;
     };
 
 // ----------------------------------------------------------------------------------------

dlib/svm/structural_graph_labeling_trainer_abstract.h

@@ -48,6 +48,8 @@ namespace dlib
                 - #get_epsilon() == 0.1
                 - #get_num_threads() == 2
                 - #get_max_cache_size() == 40
+                - #get_loss_on_positive_class() == 1.0
+                - #get_loss_on_negative_class() == 1.0
         !*/
 
         void set_num_threads (
@@ -159,6 +161,46 @@ namespace dlib
                   better generalization. 
         !*/
 
+        void set_loss_on_positive_class (
+            double loss
+        );
+        /*!
+            requires
+                - loss >= 0
+            ensures
+                - #get_loss_on_positive_class() == loss
+        !*/
+
+        void set_loss_on_negative_class (
+            double loss
+        );
+        /*!
+            requires
+                - loss >= 0
+            ensures
+                - #get_loss_on_negative_class() == loss
+        !*/
+
+        double get_loss_on_positive_class (
+        ) const;
+        /*!
+            ensures
+                - returns the loss incurred when a graph node which is supposed to have
+                  a label of true gets misclassified.  This value controls how much we care 
+                  about correctly classifying nodes which should be labeled as true.  Larger 
+                  loss values indicate that we care more strongly than smaller values.
+        !*/
+
+        double get_loss_on_negative_class (
+        ) const;
+        /*!
+            ensures
+                - returns the loss incurred when a graph node which is supposed to have
+                  a label of false gets misclassified.  This value controls how much we care 
+                  about correctly classifying nodes which should be labeled as false.  Larger 
+                  loss values indicate that we care more strongly than smaller values.
+        !*/
+
         template <
             typename graph_type
             >