Made the max iteration limit user settable.

dlib/statistics/vector_normalizer_frobmetric.h

@@ -153,14 +153,15 @@ namespace dlib
             custom_stop_strategy(
                 double C_,
                 double eps_,
-                bool be_verbose_
+                bool be_verbose_,
+                unsigned long max_iter_
             ) 
             {
                 _C = C_;
 
                 _cur_iter = 0;
                 _gradient_thresh = eps_;
-                _max_iter = 1000;
+                _max_iter = max_iter_;
                 _verbose = be_verbose_;
             }
 
@@ -217,6 +218,7 @@ namespace dlib
             verbose = false;
             eps = 0.1;
             C = 1;
+            max_iter = 5000;
         }
 
         void be_verbose(
@@ -259,6 +261,19 @@ namespace dlib
             C = C_;
         }
 
+        void set_max_iterations (
+            unsigned long max_iterations
+        )
+        {
+            max_iter = max_iterations;
+        }
+
+        unsigned long get_max_iterations (
+        ) const
+        {
+            return max_iter;
+        }
+
         double get_c (
         ) const
         {
@@ -388,7 +403,7 @@ namespace dlib
             // Now run the main part of the algorithm
             matrix<double,0,0,mem_manager_type> Aminus;
             find_max_box_constrained(lbfgs_search_strategy(10),
-                                     custom_stop_strategy(C, eps, verbose),
+                                     custom_stop_strategy(C, eps, verbose, max_iter),
                                      objective(data, Aminus),
                                      derivative(num_triples, data, Aminus),
                                      u, 0, C/num_triples);
@@ -476,6 +491,7 @@ namespace dlib
         bool verbose;
         double eps;
         double C;
+        unsigned long max_iter;
 
         // This is just a temporary variable that doesn't contribute to the
         // state of this object.
@@ -500,6 +516,7 @@ namespace dlib
         serialize(item.verbose, out);
         serialize(item.eps, out);
         serialize(item.C, out);
+        serialize(item.max_iter, out);
     }
 
 // ----------------------------------------------------------------------------------------
@@ -522,6 +539,7 @@ namespace dlib
         deserialize(item.verbose, in);
         deserialize(item.eps, in);
         deserialize(item.C, in);
+        deserialize(item.max_iter, in);
     }
 
 // ----------------------------------------------------------------------------------------

dlib/statistics/vector_normalizer_frobmetric_abstract.h

@@ -66,6 +66,7 @@ namespace dlib
                 - means().size() == 0
                 - get_epsilon() == 0.1
                 - get_c() == 1
+                - get_max_iterations() == 5000
                 - This object is not verbose
 
             WHAT THIS OBJECT REPRESENTS
@@ -159,6 +160,25 @@ namespace dlib
                   smaller values of C may encourage better generalization. 
         !*/
        
+        void set_max_iterations (
+            unsigned long max_iterations
+        );
+        /*!
+            ensures
+                - #get_max_iterations() == max_iterations
+        !*/
+
+        unsigned long get_max_iterations (
+        ) const;
+        /*!
+            ensures
+                - The train() routine uses an iterative numerical solver to find the best
+                  distance metric.  This function returns the maximum allowable number of
+                  iterations it will use before terminating.  Note that typically the
+                  solver terminates prior to the max iteration count limit due to the error
+                  dropping below get_epsilon().
+        !*/
+
         void train (
             const std::vector<frobmetric_training_sample<matrix_type> >& samples
         );