Switched this code to use the oca object's ability to force a weight to 1

instead of rolling its own implementation.

dlib/svm/svm_rank_trainer.h

@@ -37,15 +37,13 @@ namespace dlib
             const std::vector<ranking_pair<sample_type> >& samples_,
             const bool be_verbose_,
             const scalar_type eps_,
-            const unsigned long max_iter,
-            const bool last_weight_1_
+            const unsigned long max_iter
         ) :
             samples(samples_),
             C(C_),
             be_verbose(be_verbose_),
             eps(eps_),
-            max_iterations(max_iter),
-            last_weight_1(last_weight_1_)
+            max_iterations(max_iter)
         {
         }
 
@@ -113,8 +111,6 @@ namespace dlib
             // rank flips.  So a risk of 0.1 would mean that rank flips happen < 10% of the
             // time.
 
-            if(last_weight_1)
-                w(w.size()-1) = 1;
 
             std::vector<double> rel_scores;
             std::vector<double> nonrel_scores;
@@ -163,12 +159,6 @@ namespace dlib
 
             risk *= scale;
             subgradient = scale*subgradient;
-
-            if(last_weight_1)
-            {
-                w(w.size()-1) = 0;
-                subgradient(w.size()-1) = 0;
-            }
         }
 
     private:
@@ -183,7 +173,6 @@ namespace dlib
         const bool be_verbose;
         const scalar_type eps;
         const unsigned long max_iterations;
-        const bool last_weight_1;
     };
 
 // ----------------------------------------------------------------------------------------
@@ -198,12 +187,11 @@ namespace dlib
         const std::vector<ranking_pair<sample_type> >& samples,
         const bool be_verbose,
         const scalar_type eps,
-        const unsigned long max_iterations,
-        const bool last_weight_1
+        const unsigned long max_iterations
     )
     {
         return oca_problem_ranking_svm<matrix_type, sample_type>(
-            C, samples, be_verbose, eps, max_iterations, last_weight_1);
+            C, samples, be_verbose, eps, max_iterations);
     }
 
 // ----------------------------------------------------------------------------------------
@@ -385,12 +373,17 @@ namespace dlib
                 num_nonnegative = num_dims;
             }
 
-            solver( make_oca_problem_ranking_svm<w_type>(C, samples, verbose, eps, max_iterations, last_weight_1), 
+            unsigned long force_weight_1_idx = std::numeric_limits<unsigned long>::max(); 
+            if (last_weight_1)
+            {
+                force_weight_1_idx = num_dims-1;
+            }
+
+            solver( make_oca_problem_ranking_svm<w_type>(C, samples, verbose, eps, max_iterations), 
                     w, 
-                    num_nonnegative);
+                    num_nonnegative,
+                    force_weight_1_idx);
 
-            if(last_weight_1)
-                w(w.size()-1) = 1;
 
             // put the solution into a decision function and then return it
             decision_function<kernel_type> df;