From b034e4378e5e9c7921828093cf15f74432d8ad2a Mon Sep 17 00:00:00 2001
From: Bas Nijholt <basnijholt@gmail.com>
Date: Wed, 19 Sep 2018 14:11:37 +0200
Subject: [PATCH] 2D: rename 'learner._interp' to 'learner.pending_points' as
in other learners
---
adaptive/learner/learner2D.py | 23 +++++++++++++----------
1 file changed, 13 insertions(+), 10 deletions(-)
diff --git a/adaptive/learner/learner2D.py b/adaptive/learner/learner2D.py
index 6a56e836..b762fc1f 100644
--- a/adaptive/learner/learner2D.py
+++ b/adaptive/learner/learner2D.py
@@ -166,6 +166,9 @@ class Learner2D(BaseLearner):
----------
data : dict
Sampled points and values.
+ pending_points : set
+ Points that still have to be evaluated and are currently
+ interpolated, see `data_combined`.
stack_size : int, default 10
The size of the new candidate points stack. Set it to 1
to recalculate the best points at each call to `ask`.
@@ -180,7 +183,7 @@ class Learner2D(BaseLearner):
-------
data_combined : dict
Sampled points and values so far including
- the unknown interpolated ones.
+ the unknown interpolated points in `pending_points`.
Notes
-----
@@ -217,7 +220,7 @@ class Learner2D(BaseLearner):
self.bounds = tuple((float(a), float(b)) for a, b in bounds)
self.data = OrderedDict()
self._stack = OrderedDict()
- self._interp = set()
+ self.pending_points = set()
self.xy_mean = np.mean(self.bounds, axis=1)
self._xy_scale = np.ptp(self.bounds, axis=1)
@@ -263,14 +266,14 @@ class Learner2D(BaseLearner):
@property
def bounds_are_done(self):
- return not any((p in self._interp or p in self._stack)
+ return not any((p in self.pending_points or p in self._stack)
for p in self._bounds_points)
def data_combined(self):
# Interpolate the unfinished points
data_combined = copy(self.data)
- if self._interp:
- points_interp = list(self._interp)
+ if self.pending_points:
+ points_interp = list(self.pending_points)
if self.bounds_are_done:
values_interp = self.ip()(self._scale(points_interp))
else:
@@ -303,17 +306,17 @@ class Learner2D(BaseLearner):
point = tuple(point)
if value is None:
- self._interp.add(point)
+ self.pending_points.add(point)
self._ip_combined = None
else:
self.data[point] = value
- self._interp.discard(point)
+ self.pending_points.discard(point)
self._ip = None
self._stack.pop(point, None)
def _fill_stack(self, stack_till=1):
- if len(self.data) + len(self._interp) < self.ndim + 1:
+ if len(self.data) + len(self.pending_points) < self.ndim + 1:
raise ValueError("too few points...")
# Interpolate
@@ -366,7 +369,7 @@ class Learner2D(BaseLearner):
self._stack = OrderedDict(zip(points[:self.stack_size],
loss_improvements))
for point in points[:n]:
- self._interp.discard(point)
+ self.pending_points.discard(point)
return points[:n], loss_improvements[:n]
@@ -379,7 +382,7 @@ class Learner2D(BaseLearner):
return self._loss
def remove_unfinished(self):
- self._interp = set()
+ self.pending_points = set()
for p in self._bounds_points:
if p not in self.data:
self._stack[p] = np.inf
--
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