updated the svm_c_linear_trainer

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403504

dlib/svm/svm_c_linear_trainer.h

@@ -41,13 +41,15 @@ namespace dlib
             const scalar_type C_neg,
             const in_sample_vector_type& samples_,
             const in_scalar_vector_type& labels_,
-            bool be_verbose_
+            const bool be_verbose_,
+            const scalar_type eps_
         ) :
             samples(samples_),
             labels(labels_),
             Cpos(C_pos),
             Cneg(C_neg),
-            be_verbose(be_verbose_)
+            be_verbose(be_verbose_),
+            eps(eps_)
         {
             dot_prods.resize(samples.size());
             is_first_call = true;
@@ -83,10 +85,7 @@ namespace dlib
                 cout << endl;
             }
 
-            if (current_error_gap/current_objective_value < 0.001)
+            if (current_error_gap/current_objective_value < eps)
-                return true;
-
-            if (num_iterations > 10000)
                 return true;
 
             return false;
@@ -369,7 +368,8 @@ namespace dlib
         const scalar_type Cpos;
         const scalar_type Cneg;
 
-        bool be_verbose;
+        const bool be_verbose;
+        const scalar_type eps;
     };
 
 
@@ -384,10 +384,11 @@ namespace dlib
         const scalar_type C_neg,
         const in_sample_vector_type& samples,
         const in_scalar_vector_type& labels,
-        bool be_verbose
+        const bool be_verbose,
+        const scalar_type eps
     )
     {
-        return oca_problem_c_svm<matrix_type, in_sample_vector_type, in_scalar_vector_type>(C_pos, C_neg, samples, labels, be_verbose);
+        return oca_problem_c_svm<matrix_type, in_sample_vector_type, in_scalar_vector_type>(C_pos, C_neg, samples, labels, be_verbose, eps);
     }
 // ----------------------------------------------------------------------------------------
 
@@ -419,10 +420,14 @@ namespace dlib
                 - #get_oca() == oca() (i.e. an instance of oca with default parameters) 
                 - #get_c_class1() == 1
                 - #get_c_class2() == 1
+                - #get_epsilon() == 0.001
+                - this object will not be verbose unless be_verbose() is called
         !*/
         {
             Cpos = 1;
             Cneg = 1;
+            verbose = false;
+            eps = 0.001;
         }
 
         explicit svm_c_linear_trainer (
@@ -439,10 +444,69 @@ namespace dlib
                 - #get_c_class2() == C
         !*/
         {
+            // make sure requires clause is not broken
+            DLIB_ASSERT(C > 0,
+                "\t svm_c_linear_trainer::svm_c_linear_trainer()"
+                << "\n\t C must be greater than 0"
+                << "\n\t C:    " << C 
+                << "\n\t this: " << this
+                );
+
             Cpos = C;
             Cneg = C;
         }
 
+        void set_epsilon (
+            scalar_type eps_
+        )
+        /*!
+            requires
+                - eps > 0
+            ensures
+                - #get_epsilon() == eps 
+        !*/
+        {
+            // make sure requires clause is not broken
+            DLIB_ASSERT(eps_ > 0,
+                "\t void svm_c_linear_trainer::set_epsilon()"
+                << "\n\t eps_ must be greater than 0"
+                << "\n\t eps_: " << eps_ 
+                << "\n\t this: " << this
+                );
+
+            eps = eps_;
+        }
+
+        const scalar_type get_epsilon (
+        ) const { return eps; }
+        /*!
+            ensures
+                - returns the error epsilon that determines when training should stop.
+                  Smaller values may result in a more accurate solution but take longer 
+                  to execute.
+        !*/
+
+        void be_verbose (
+        )
+        /*!
+            ensures
+                - This object will print status messages to standard out so that a 
+                  user can observe the progress of the algorithm.
+        !*/
+        {
+            verbose = true;
+        }
+
+        void be_quiet (
+        )
+        /*!
+            ensures
+                - this object will not print anything to standard out
+        !*/
+        {
+            verbose = false;
+        }
+
         void set_oca (
             const oca& item
         )
@@ -485,6 +549,14 @@ namespace dlib
                 - #get_c_class2() == C 
         !*/
         {
+            // make sure requires clause is not broken
+            DLIB_ASSERT(C > 0,
+                "\t void svm_c_linear_trainer::set_c()"
+                << "\n\t C must be greater than 0"
+                << "\n\t C:    " << C 
+                << "\n\t this: " << this
+                );
+
             Cpos = C;
             Cneg = C;
         }
@@ -529,6 +601,14 @@ namespace dlib
                 - #get_c_class1() == C
         !*/
         {
+            // make sure requires clause is not broken
+            DLIB_ASSERT(C > 0,
+                "\t void svm_c_linear_trainer::set_c_class1()"
+                << "\n\t C must be greater than 0"
+                << "\n\t C:    " << C 
+                << "\n\t this: " << this
+                );
+
             Cpos = C;
         }
 
@@ -542,6 +622,14 @@ namespace dlib
                 - #get_c_class2() == C
         !*/
         {
+            // make sure requires clause is not broken
+            DLIB_ASSERT(C > 0,
+                "\t void svm_c_linear_trainer::set_c_class2()"
+                << "\n\t C must be greater than 0"
+                << "\n\t C:    " << C 
+                << "\n\t this: " << this
+                );
+
             Cneg = C;
         }
 
@@ -570,12 +658,71 @@ namespace dlib
                         - F(new_x) < 0
         !*/
         {
+            scalar_type obj;
+            return do_train(vector_to_matrix(x),vector_to_matrix(y),obj);
+        }
+
+
+        template <
+            typename in_sample_vector_type,
+            typename in_scalar_vector_type
+            >
+        const decision_function<kernel_type> train (
+            const in_sample_vector_type& x,
+            const in_scalar_vector_type& y,
+            scalar_type& svm_objective
+        ) const
+        /*!
+            requires
+                - is_binary_classification_problem(x,y) == true
+                - x == a matrix or something convertible to a matrix via vector_to_matrix().
+                  Also, x should contain sample_type objects.
+                - y == a matrix or something convertible to a matrix via vector_to_matrix().
+                  Also, y should contain scalar_type objects.
+            ensures
+                - trains a C support vector classifier given the training samples in x and 
+                  labels in y.  
+                - #svm_objective == the final value of the SVM objective function
+                - returns a decision function F with the following properties:
+                    - if (new_x is a sample predicted have +1 label) then
+                        - F(new_x) >= 0
+                    - else
+                        - F(new_x) < 0
+        !*/
+        {
+            return do_train(vector_to_matrix(x),vector_to_matrix(y),svm_objective);
+        }
+
+    private:
+
+        template <
+            typename in_sample_vector_type,
+            typename in_scalar_vector_type
+            >
+        const decision_function<kernel_type> do_train (
+            const in_sample_vector_type& x,
+            const in_scalar_vector_type& y,
+            scalar_type& svm_objective
+        ) const
+        {
+            // make sure requires clause is not broken
+            DLIB_ASSERT(is_binary_classification_problem(x,y) == true,
+                "\t decision_function svm_c_linear_trainer::train(x,y)"
+                << "\n\t invalid inputs were given to this function"
+                << "\n\t x.nr(): " << x.nr() 
+                << "\n\t y.nr(): " << y.nr() 
+                << "\n\t x.nc(): " << x.nc() 
+                << "\n\t y.nc(): " << y.nc() 
+                << "\n\t is_binary_classification_problem(x,y): " << is_binary_classification_problem(x,y)
+                );
+
+
             typedef matrix<scalar_type,0,1> w_type;
             w_type w;
 
-            scalar_type obj = solver(make_oca_problem_c_svm<w_type>(Cpos, Cneg, vector_to_matrix(x), vector_to_matrix(y), true), w);
+            svm_objective = solver(
-
+                make_oca_problem_c_svm<w_type>(Cpos, Cneg, x, y, verbose, eps), 
-            std::cout << "final obj: "<< obj << std::endl;
+                w);
 
             // put the solution into a decision function and then return it
             decision_function<kernel_type> df;
@@ -587,12 +734,12 @@ namespace dlib
 
             return df;
         }
-
-    private:
         
         scalar_type Cpos;
         scalar_type Cneg;
         oca solver;
+        scalar_type eps;
+        bool verbose;
     }; 
 
 // ----------------------------------------------------------------------------------------