From fdc31b7be46db4c1de123bbffb3225c31cf6d005 Mon Sep 17 00:00:00 2001
From: Bas Nijholt <basnijholt@gmail.com>
Date: Thu, 7 Sep 2017 15:02:38 +0200
Subject: [PATCH] 1D: swap xs <--> points as variable names to be more
consistent
---
adaptive/learner.py | 18 +++++++++---------
1 file changed, 9 insertions(+), 9 deletions(-)
diff --git a/adaptive/learner.py b/adaptive/learner.py
index a6fddbb8..b5982bc1 100644
--- a/adaptive/learner.py
+++ b/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
----------
--
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