Added the option to force the last weight to 1 to the assignment learning

tools.

dlib/svm/structural_assignment_trainer.h

@@ -185,6 +185,19 @@ namespace dlib
             return loss_per_missed_association;
         }
 
+        bool forces_last_weight_to_1 (
+        ) const
+        {
+            return last_weight_1;
+        }
+
+        void force_last_weight_to_1 (
+            bool should_last_weight_be_1
+        )
+        {
+            last_weight_1 = should_last_weight_be_1;
+        }
+
         const assignment_function<feature_extractor> train (  
             const std::vector<sample_type>& samples,
             const std::vector<label_type>& labels
@@ -230,7 +243,10 @@ namespace dlib
             // Take the min here because we want to prevent the user from accidentally
             // forcing the bias term to be non-negative.
             const unsigned long num_nonneg = std::min(fe.num_features(),num_nonnegative_weights(fe));
-            solver(prob, weights, num_nonneg);
+            if (last_weight_1)
+                solver(prob, weights, num_nonneg, fe.num_features()-1);
+            else
+                solver(prob, weights, num_nonneg);
 
             const double bias = weights(weights.size()-1);
             return assignment_function<feature_extractor>(colm(weights,0,weights.size()-1), bias,fe,force_assignment);
@@ -249,6 +265,7 @@ namespace dlib
         unsigned long max_cache_size;
         double loss_per_false_association;
         double loss_per_missed_association;
+        bool last_weight_1;
 
         void set_defaults ()
         {
@@ -260,6 +277,7 @@ namespace dlib
             max_cache_size = 5;
             loss_per_false_association = 1;
             loss_per_missed_association = 1;
+            last_weight_1 = false;
         }
 
         feature_extractor fe;

dlib/svm/structural_assignment_trainer_abstract.h

@@ -54,6 +54,7 @@ namespace dlib
                 - #forces_assignment() == false
                 - #get_loss_per_false_association() == 1
                 - #get_loss_per_missed_association() == 1
+                - #forces_last_weight_to_1() == false
         !*/
 
         explicit structural_assignment_trainer (
@@ -70,6 +71,7 @@ namespace dlib
                 - #forces_assignment() == false
                 - #get_loss_per_false_association() == 1
                 - #get_loss_per_missed_association() == 1
+                - #forces_last_weight_to_1() == false
         !*/
 
         const feature_extractor& get_feature_extractor (
@@ -244,6 +246,26 @@ namespace dlib
                   assignment_functions generated by this object.  
         !*/
 
+        bool forces_last_weight_to_1 (
+        ) const;
+        /*!
+            ensures
+                - returns true if this trainer has the constraint that the last weight in
+                  the learned parameter vector must be 1.  This is the weight corresponding
+                  to the feature in the training vectors with the highest dimension.  
+                - Forcing the last weight to 1 also disables the bias and therefore the
+                  get_bias() field of the learned assignment_function will be 0 when
+                  forces_last_weight_to_1() == true.
+        !*/
+
+        void force_last_weight_to_1 (
+            bool should_last_weight_be_1
+        );
+        /*!
+            ensures
+                - #forces_last_weight_to_1() == should_last_weight_be_1
+        !*/
+
         const assignment_function<feature_extractor> train (  
             const std::vector<sample_type>& samples,
             const std::vector<label_type>& labels
@@ -262,6 +284,9 @@ namespace dlib
                       new_sample.first match up with the elements of new_sample.second.
                     - F.forces_assignment() == forces_assignment()
                     - F.get_feature_extractor() == get_feature_extractor()
+                    - if (forces_last_weight_to_1()) then
+                        - F.get_bias() == 0
+                        - F.get_weights()(F.get_weights().size()-1) == 1
         !*/
     };