Simplified requires clause of train() function.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%404022

dlib/svm/rvm.h

@@ -693,7 +693,7 @@ namespace dlib
         {
 
             // make sure requires clause is not broken
-            DLIB_ASSERT(x.nr() > 1 && x.nr() == t.nr() && x.nc() == 1 && t.nc() == 1,
+            DLIB_ASSERT(is_learning_problem(x,t) && x.size() > 0,
                 "\tdecision_function rvm_regression_trainer::train(x,t)"
                 << "\n\t invalid inputs were given to this function"
                 << "\n\t x.nr(): " << x.nr() 

dlib/svm/rvm_abstract.h

@@ -223,9 +223,8 @@ namespace dlib
                   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.
-                - 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)
+                - is_learning_problem(x,y) == true
+                - x.size() > 0
             ensures
                 - trains a RVM given the training samples in x and 
                   labels in y and returns the resulting decision_function.