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

@@ -226,7 +226,6 @@ class Learner1D(BaseLearner):
     def _get_loss_in_interval(self, x_left, x_right):
         assert x_left is not None and x_right is not None
 
-
         if x_right - x_left < self._dx_eps:
             return 0
 
@@ -430,7 +429,7 @@ class Learner1D(BaseLearner):
         self.losses = {}
         for x_left, x_right in intervals:
             self.losses[x_left, x_right] = (
-                self.loss_per_interval((x_left, x_right), self._scale, self.data)
+                self._get_loss_in_interval(x_left, x_right)
                 if x_right - x_left >= self._dx_eps else 0)
 
         # List with "real" intervals that have interpolated intervals inside