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

docs/source/tutorial/tutorial.Learner1D.rst

@@ -149,11 +149,12 @@ curvature. To do this, you need to tell the learner to look at the curvature
 by specifying ``loss_per_interval``.
 
 .. jupyter-execute::
+
     from adaptive.learner.learner1D import (get_curvature_loss,
                                             uniform_loss,
                                             default_loss)
-    curvature_loss = curvature_loss()
-    learner = adaptive.Learner1D(f, bounds=(-1, 1), loss_per_interval=curvature_loss
+    curvature_loss = get_curvature_loss()
+    learner = adaptive.Learner1D(f, bounds=(-1, 1), loss_per_interval=curvature_loss)
     runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 0.01)
 
 .. jupyter-execute::
@@ -181,13 +182,14 @@ including nearest neighboring intervals in this plot: We will look at 100 points
     learner_2 = adaptive.Learner1D(ff, (-1, 1), loss_per_interval=curvature_loss)
 
     npoints_goal = lambda l: l.npoints >= 100
-    adaptive.BlockingRunner(learner_h, goal=npoints_goal)
-    adaptive.BlockingRunner(learner_1, goal=npoints_goal)
-    adaptive.BlockingRunner(learner_2, goal=npoints_goal)
+    # adaptive.runner.simple is a non parallel blocking runner.
+    adaptive.runner.simple(learner_h, goal=npoints_goal)
+    adaptive.runner.simple(learner_1, goal=npoints_goal)
+    adaptive.runner.simple(learner_2, goal=npoints_goal)
 
     (learner_h.plot().relabel('homogeneous')
      + learner_1.plot().relabel('euclidean loss')
-     + learner_2.plot().relabel('curvature loss'))
+     + learner_2.plot().relabel('curvature loss')).cols(2)
 
 More info about using custom loss functions can be found
 in :ref:`Custom adaptive logic for 1D and 2D`.