Resolve "(Learner1D) add possibility to use the direct neighbors in the loss"

Closes #119 (closed)

Currently works for one R^1 \to R^1

Still have to make it work for R^1 \to R^N

Also performance is actually quite good. As in: the learner slows down about 1.5 times. Going (on my laptop) from 3 seconds per 1000 points to 4.5 seconds per 1000 points.

Which, I believe, will be more than compensated by the fact that the chosen points are generally better

adaptive/learner/learner1D.py

@@ -66,18 +66,16 @@ def _loss_of_multi_interval(xs, ys):
 def triangle_loss(interval, neighbours, scale, function_values):
     x_left, x_right = interval
     neighbour_left, neighbour_right = neighbours
-    x_scale, y_scale = scale
-    dx = (x_right - x_left) / x_scale
     xs = [neighbour_left, x_left, x_right, neighbour_right]
     # The neighbours could be None if we are at the boundary, in that case we
     # have to filter this out
     xs = [x for x in xs if x is not None]
-    y_scale = y_scale or 1
+    y_scale = scale[1] or 1
     ys = [function_values[x] / y_scale for x in xs]
-    xs = [x / x_scale for x in xs]
+    xs = [x / scale[0] for x in xs]
 
     if len(xs) <= 2:
-        return dx
+        return (x_right - x_left) / scale[0]
     else:
         return _loss_of_multi_interval(xs, ys)