adaptive/learner/learnerND.py

@@ -222,6 +222,7 @@ class LearnerND(BaseLearner):
 
         try:
             self._tri = Triangulation(self.points)
+            self.update_losses(set(), self._tri.simplices)
             return self._tri
         except ValueError:
             # A ValueError is raised if we do not have enough points or
@@ -229,8 +230,6 @@ class LearnerND(BaseLearner):
             # a valid triangulation
             return None
 
-        # XXX: also compute losses of initial simplex
-
     @property
     def values(self):
         return np.array(list(self.data.values()), dtype=float)
@@ -359,8 +358,8 @@ class LearnerND(BaseLearner):
         # Could not find a simplex, this code should never be reached
         assert self.tri is not None
         raise AssertionError(
-            "Could not find a simplex to. Yet there should always be a simplex "
-            "available if LearnerND.tri() is not None"
+            "Could not find a simplex to subdivide. Yet there should always be" 
+            "a simplex available if LearnerND.tri() is not None."
         )
 
     def _ask_best_point(self):

adaptive/tests/test_learners.py

@@ -353,8 +353,7 @@ def test_learner_performance_is_invariant_under_scaling(learner_type, f, learner
     assert abs(learner.loss() - control.loss()) / learner.loss() < 1e-11
 
 
-# XXX: The LearnerND shouldn't fail, see https://gitlab.kwant-project.org/qt/adaptive/issues/105
-@run_with(Learner1D, Learner2D, xfail(LearnerND), AverageLearner)
+@run_with(Learner1D, Learner2D, LearnerND, AverageLearner)
 def test_balancing_learner(learner_type, f, learner_kwargs):
     """Test if the BalancingLearner works with the different types of learners."""
     learners = [learner_type(generate_random_parametrization(f), **learner_kwargs)