Relaxed some unnecessarily restrictive requires clauses.

dlib/svm/svm_c_linear_trainer.h

@@ -558,15 +558,25 @@ namespace dlib
         ) const
         {
             // make sure requires clause is not broken
-            DLIB_ASSERT(is_binary_classification_problem(x,y) == true,
+            DLIB_ASSERT(is_learning_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)
+                << "\n\t is_learning_problem(x,y): " << is_learning_problem(x,y)
                 );
+#ifdef ENABLE_ASSERTS
+            for (long i = 0; i < x.size(); ++i)
+            {
+                DLIB_ASSERT(y(i) == +1 || y(i) == -1,
+                    "\t decision_function svm_c_linear_trainer::train(x,y)"
+                    << "\n\t invalid inputs were given to this function"
+                    << "\n\t y("<<i<<"): " << y(i)
+                );
+            }
+#endif
 
 
             typedef matrix<scalar_type,0,1> w_type;

dlib/svm/svm_c_linear_trainer_abstract.h

@@ -256,7 +256,9 @@ namespace dlib
         ) const;
         /*!
             requires
-                - is_binary_classification_problem(x,y) == true
+                - is_learning_problem(x,y) == true
+                  (Note that it is ok for x.size() == 1)
+                - All elements of y must be equal to +1 or -1
                 - x == a matrix or something convertible to a matrix via mat().
                   Also, x should contain sample_type objects.
                 - y == a matrix or something convertible to a matrix via mat().
@@ -285,7 +287,9 @@ namespace dlib
         ) const;
         /*!
             requires
-                - is_binary_classification_problem(x,y) == true
+                - is_learning_problem(x,y) == true
+                  (Note that it is ok for x.size() == 1)
+                - All elements of y must be equal to +1 or -1
                 - x == a matrix or something convertible to a matrix via mat().
                   Also, x should contain sample_type objects.
                 - y == a matrix or something convertible to a matrix via mat().