This is a minor change. All it does is slightly change the stopping condition

used by the svm_c_linear_trainer so that the epsilon value is not implicitly
scaled by the C values given by the user.  This should make this object
slightly easier to use.

dlib/svm/svm_c_linear_trainer.h

@@ -48,8 +48,9 @@ namespace dlib
         ) :
             samples(samples_),
             labels(labels_),
-            Cpos(C_pos),
-            Cneg(C_neg),
+            C(std::min(C_pos,C_neg)),
+            Cpos(C_pos/C),
+            Cneg(C_neg/C),
             be_verbose(be_verbose_),
             eps(eps_),
             max_iterations(max_iter)
@@ -61,7 +62,7 @@ namespace dlib
         virtual scalar_type get_c (
         ) const 
         {
-            return 1;
+            return C;
         }
 
         virtual long get_num_dimensions (
@@ -292,6 +293,7 @@ namespace dlib
 
         const in_sample_vector_type& samples;
         const in_scalar_vector_type& labels;
+        const scalar_type C;
         const scalar_type Cpos;
         const scalar_type Cneg;
 
@@ -349,7 +351,7 @@ namespace dlib
             Cpos = 1;
             Cneg = 1;
             verbose = false;
-            eps = 0.001;
+            eps = 0.0001;
             max_iterations = 10000;
             learn_nonnegative_weights = false;
             last_weight_1 = false;
@@ -370,7 +372,7 @@ namespace dlib
             Cpos = C;
             Cneg = C;
             verbose = false;
-            eps = 0.001;
+            eps = 0.0001;
             max_iterations = 10000;
             learn_nonnegative_weights = false;
             last_weight_1 = false;

dlib/svm/svm_c_linear_trainer_abstract.h

@@ -49,7 +49,7 @@ 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
+                - #get_epsilon() == 0.0001
                 - this object will not be verbose unless be_verbose() is called
                 - #get_max_iterations() == 10000
                 - #learns_nonnegative_weights() == false
@@ -68,7 +68,7 @@ namespace dlib
                 - #get_oca() == oca() (i.e. an instance of oca with default parameters) 
                 - #get_c_class1() == C
                 - #get_c_class2() == C
-                - #get_epsilon() == 0.001
+                - #get_epsilon() == 0.0001
                 - this object will not be verbose unless be_verbose() is called
                 - #get_max_iterations() == 10000
                 - #learns_nonnegative_weights() == false

dlib/test/svm_c_linear.cpp

@@ -110,7 +110,7 @@ namespace
         svm_c_linear_trainer<sparse_linear_kernel<sparse_sample_type> > trainer;
         trainer.set_c(1e4);
         //trainer.be_verbose();
-        trainer.set_epsilon(1e-8);
+        trainer.set_epsilon(1e-11);
 
 
         double obj;
@@ -182,13 +182,13 @@ namespace
         svm_c_linear_trainer<linear_kernel<sample_type> > trainer;
         trainer.set_c(1e4);
         //trainer.be_verbose();
-        trainer.set_epsilon(1e-8);
+        trainer.set_epsilon(1e-11);
 
 
         double obj;
         decision_function<linear_kernel<sample_type> > df = trainer.train(samples, labels, obj);
         dlog << LDEBUG << "obj: "<< obj;
-        DLIB_TEST_MSG(abs(obj - 0.72222222222) < 1e-8, obj);
+        DLIB_TEST_MSG(abs(obj - 0.72222222222) < 1e-8, abs(obj - 0.72222222222));
         // There shouldn't be any margin violations since this dataset is so trivial.  So that means the objective
         // should be exactly the squared norm of the decision plane (times 0.5).
         DLIB_TEST_MSG(abs(length_squared(df.basis_vectors(0))*0.5 + df.b*df.b*0.5 - 0.72222222222) < 1e-8,