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

@@ -74,9 +74,10 @@ def triangle_loss(interval, scale, data, neighbors):
     x_left, x_right = interval
     xs = [x_left, x_right]
     if x_left in neighbors:
-        xs.insert(0, neighbors[x_left][1])
+        xs.insert(0, neighbors[x_left][0])
     if x_right in neighbors:
-        xs.append(neighbors[x_right][0])
+        xs.append(neighbors[x_right][1])
+    xs = [x for x in xs if x is not None]
 
     if len(xs) <= 2:
         return (x_right - x_left) / scale[0]