Removed unnecessary restrictions on the rbf_network_trainer

object.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402398

dlib/svm/rbf_network.h

@@ -108,14 +108,13 @@ namespace dlib
             typedef typename decision_function<kernel_type>::scalar_vector_type scalar_vector_type;
 
             // make sure requires clause is not broken
-            DLIB_ASSERT(is_binary_classification_problem(x,y) == true,
+            DLIB_ASSERT(x.nr() > 1 && x.nr() == y.nr() && x.nc() == 1 && y.nc() == 1,
                 "\tdecision_function rbf_network_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))? "true":"false")
                 );
 
             // first run all the sampes through a kcentroid object to find the rbf centers

dlib/svm/rbf_network_abstract.h

@@ -27,8 +27,7 @@ namespace dlib
                 - get_tolerance() == 0.01
 
             WHAT THIS OBJECT REPRESENTS
-                This object implements a trainer for radial basis function network for 
-                solving binary classification problems.
+                This object implements a trainer for an radial basis function network.
 
                 The implementation of this algorithm follows the normal RBF training 
                 process.  For more details see the code or the Wikipedia article
@@ -94,7 +93,13 @@ namespace dlib
         ) const
         /*!
             requires
-                - is_binary_classification_problem(x,y) == true
+                - in_sample_vector_type == a matrix or something convertable to a matrix
+                  via vector_to_matrix()
+                - in_scalar_vector_type == a matrix or something convertable to a matrix
+                  via vector_to_matrix()
+                - x.nr() > 1
+                - x.nr() == y.nr() && x.nc() == 1 && y.nc() == 1 
+                  (i.e. x and y are both column vectors of the same length)
             ensures
                 - trains a RBF network given the training samples in x and 
                   labels in y.