1D: swap xs <--> points as variable names to be more consistent

adaptive/learner.py

@@ -326,19 +326,19 @@ class Learner1D(BaseLearner):
             return []
 
         # If the bounds have not been chosen yet, we choose them first.
-        xs = []
+        points = []
         for bound in self.bounds:
             if bound not in self.data and bound not in self.data_interp:
-                xs.append(bound)
+                points.append(bound)
         # Ensure we return exactly 'n' points.
-        if xs:
+        if points:
             loss_improvements = [float('inf')] * n
             if n <= 2:
-                return xs[:n], loss_improvements
+                return points[:n], loss_improvements
             else:
                 return np.linspace(*self.bounds, n), loss_improvements
 
-        def points(x, n):
+        def xs(x, n):
             if n == 1:
                 return []
             else:
@@ -355,8 +355,8 @@ class Learner1D(BaseLearner):
             quality, x, n = quals[0]
             heapq.heapreplace(quals, (quality * n / (n + 1), x, n + 1))
 
-        xs = list(itertools.chain.from_iterable(points(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)
@@ -365,7 +365,7 @@ class Learner1D(BaseLearner):
         if add_data:
             self.add_data(points, itertools.repeat(None))
 
-        return xs, loss_improvements
+        return points, loss_improvements
 
     def interpolate(self, extra_points=None):
         xs = list(self.data.keys())
@@ -472,7 +472,7 @@ class BalancingLearner(BaseLearner):
 
 
 def _max_disagreement_location_in_simplex(points, values, grad, transform):
-    """Find the point of maximum disagreement between linear and quadratic model
+    """Find the point of maximum disagreement between linear and quadratic model.
 
     Parameters
     ----------