fix tests�that fail for learner: if no data is provided it should sample

uniformly

Author: Jorn

adaptive/learner/learner1D.py

@@ -286,8 +286,10 @@ def xs(x, n):
                     step = (x[1] - x[0]) / n
                     return [x[0] + step * i for i in range(1, n)]
 
+            qual = lambda loss, x_range: -loss if loss != np.inf else -(x_range[1] - x_range[0]) / self._scale[0]
+
             # Calculate how many points belong to each interval.
-            quals = [(-loss, x_range, 1) for (x_range, loss) in
+            quals = [(qual(loss, x_range), x_range, 1) for (x_range, loss) in
                      self.losses_combined.items()]
 
             heapq.heapify(quals)