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

@@ -72,11 +72,7 @@ def _loss_of_multi_interval(xs, ys):
 
 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][0])
-    if x_right in neighbors:
-        xs.append(neighbors[x_right][1])
+    xs = [neighbors[x_left][0], x_left, x_right, neighbors[x_right][1]]
     xs = [x for x in xs if x is not None]
 
     if len(xs) <= 2:
@@ -123,12 +119,12 @@ def _get_neighbors_from_list(xs):
 
 
 def _get_intervals(x, neighbors, nn_neighbors):
-    n = nn_neighbors
-    index = neighbors.bisect_left(x)
-    points = [neighbors.iloc[i] for i in range(index - n - 1, index + 2 + n)
-              if 0 <= i < len(neighbors)]
-    intervals = zip(points, points[1:])
-    return list(intervals)
+    nn = nn_neighbors
+    i = neighbors.index(x)
+    start = max(0, i - nn - 1)
+    end = min(len(neighbors), i + nn + 2)
+    points = neighbors.keys()[start:end]
+    return list(zip(points, points[1:]))
 
 
 class Learner1D(BaseLearner):