LearnerND scale output values before computing loss

Closes #78 (closed)

In a way similar to the Learner1D

Recompute all losses when the scale increases with a factor 1.1.

My simple measurements indicate that with the ring_of_fire as test function the learner slows down approximately 10%. But we get in return that we scale everything to equivalent sizes before computing the loss.

TODO:

• Add some test(s).

adaptive/learner/learnerND.py

@@ -276,6 +276,7 @@ class LearnerND(BaseLearner):
         if not self.inside_bounds(point):
             return
 
+        self._update_range(value)
         if tri is not None:
             simplex = self._pending_to_simplex.get(point)
             if simplex is not None and not self._simplex_exists(simplex):
@@ -283,7 +284,6 @@ class LearnerND(BaseLearner):
             to_delete, to_add = tri.add_point(
                 point, simplex, transform=self._transform)
             self.update_losses(to_delete, to_add)
-            self._update_range(value)
 
     def _simplex_exists(self, simplex):
         simplex = tuple(sorted(simplex))