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

@@ -12,6 +12,7 @@ from .learnerND import volume
 from ..notebook_integration import ensure_holoviews
 from ..utils import cache_latest
 
+
 def uniform_loss(interval, scale, function_values):
     """Loss function that samples the domain uniformly.