merged changes and updated abstract file.

dlib/svm/rvm.h

@@ -147,10 +147,23 @@ namespace dlib
         typedef decision_function<kernel_type> trained_function_type;
 
         rvm_trainer (
-        ) : eps(0.001)
+        ) : eps(0.001), max_iterations(2000)
         {
         }
 
+        void set_max_iterations (
+            unsigned long max_iterations_
+        )
+        {
+            max_iterations = max_iterations_;
+        }
+
+        unsigned long get_max_iterations (
+        ) const
+        { 
+            return max_iterations;
+        }
+
         void set_epsilon (
             scalar_type eps_
         )
@@ -288,9 +301,11 @@ namespace dlib
             bool search_all_alphas = false;
             unsigned long ticker = 0;
             const unsigned long rounds_of_narrow_search = 100;
+            unsigned long iterations = 0;
 
-            while (true)
+            while (iterations != max_iterations)
             {
+                iterations++;
                 if (recompute_beta)
                 {
                     // calculate the current t_estimate. (this is the predicted t value for each sample according to the
@@ -572,6 +587,7 @@ namespace dlib
     // private member variables
         kernel_type kernel;
         scalar_type eps;
+        unsigned long max_iterations;
 
         const static scalar_type tau;
 

dlib/svm/rvm_abstract.h

@@ -50,6 +50,7 @@ namespace dlib
                 - This object is properly initialized and ready to be used
                   to train a relevance vector machine.
                 - #get_epsilon() == 0.001
+                - #get_max_iterations() == 2000
         !*/
 
         void set_epsilon (
@@ -86,6 +87,22 @@ namespace dlib
                 - returns a copy of the kernel function in use by this object
         !*/
 
+        unsigned long get_max_iterations (
+        ) const; 
+        /*!
+            ensures
+                - returns the maximum number of iterations the RVM optimizer is allowed to
+                  run before it is required to stop and return a result.
+        !*/
+
+        void set_max_iterations (
+            unsigned long max_iter
+        ); 
+        /*!
+            ensures
+                - #get_max_iterations() == max_iter
+        !*/
+
         template <
             typename in_sample_vector_type,
             typename in_scalar_vector_type

examples/rvm_ex.cpp

@@ -103,6 +103,9 @@ int main()
     // stopping epsilon bigger.  However, this might make the outputs less
     // reliable.  But sometimes it works out well.  0.001 is the default.
     trainer.set_epsilon(0.001);
+    // You can also set an explicit limit on the number of iterations used by the numeric
+    // solver.  The default is 2000.
+    trainer.set_max_iterations(2000);
 
     // Now we loop over some different gamma values to see how good they are.  Note
     // that this is a very simple way to try out a few possible parameter choices.  You