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

@@ -57,10 +57,10 @@ def default_loss(interval, scale, function_values):
     return loss
 
 
-def loss_of_multi_interval(xs, ys):
+def _loss_of_multi_interval(xs, ys):
     pts = list(zip(xs, ys))
-    vols = [volume(pts[i:i+3]) for i in range(len(pts)-2)]
-    return np.average(vols)
+    N = len(pts)-2
+    return sum(volume(pts[i:i+3]) for i in range(N)) / N
 
 
 def triangle_loss(interval, neighbours, scale, function_values):
@@ -79,7 +79,7 @@ def triangle_loss(interval, neighbours, scale, function_values):
     if len(xs) <= 2:
         return dx
     else:
-        return loss_of_multi_interval(xs, ys)
+        return _loss_of_multi_interval(xs, ys)
 
 
 def get_curvature_loss(area_factor=1, euclid_factor=0.02, horizontal_factor=0.02):
@@ -307,7 +307,7 @@ class Learner1D(BaseLearner):
     @staticmethod
     def _find_neighbors(x, neighbors):
         if x in neighbors:
-            return neighbors[x]
+            return neighbors
         if x is None:
             return None, None
         pos = neighbors.bisect_left(x)