@@ -278,39 +278,36 @@ class Learner1D(BaseLearner):

             loss_improvements = [np.inf] * n

             points = np.linspace(*self.bounds, n + 1)[1:].tolist()

         else:

-            def xs(x, n):

+            def xs(x_left, x_right, n):

                 if n == 1:

+                    # This is just an optimization

                     return []

                 else:

-                    step = (x[1] - x[0]) / n

-                    return [x[0] + step * i for i in range(1, n)]

+                    step = (x_right - x_left) / n

+                    return [x_left + step * i for i in range(1, n)]

             # Calculate how many points belong to each interval.

             x_scale = self._scale[0]

             quals = []

             for ((x_left, x_right), loss) in self.losses_combined.items():

-                dx = x_right - x_left

-                if abs(dx) < self._dx_eps:

-                    # The interval is too small and should not be subdivided

-                    quality = 0

-                elif not math.isinf(loss):

-                    quality = -loss

-                else:

-                    quality = -dx / x_scale

+                quality = -loss if not math.isinf(loss) else -(x_right - x_left) / x_scale

                 quals.append((quality, (x_left, x_right), 1))

             heapq.heapify(quals)

             for point_number in range(n):

                 quality, x, n = quals[0]

+                if abs(x[1] - x[0]) / (n + 1) <= self._dx_eps:

+                    # The interval is too small and should not be subdivided

+                    quality = np.inf

                 heapq.heapreplace(quals, (quality * n / (n + 1), x, n + 1))

-            points = list(itertools.chain.from_iterable(xs(x, n)

-                          for quality, x, n in quals))

+            points = list(itertools.chain.from_iterable(

+                xs(*x, n) for quality, x, n in quals))

             loss_improvements = list(itertools.chain.from_iterable(

-                                     itertools.repeat(-quality, n-1)

+                                     itertools.repeat(-quality, n - 1)

                                      for quality, x, n in quals))

         if add_data: