From 982d69a542c569d3e5ddd8e84f1cb6fc855fcd6a Mon Sep 17 00:00:00 2001
From: Bas Nijholt <basnijholt@gmail.com>
Date: Thu, 20 Sep 2018 12:32:59 +0200
Subject: [PATCH] 1D: create 'tell_pending' which deprecates 'tell(x, None)'
---
adaptive/learner/learner1D.py | 60 +++++++++++++++++------------------
1 file changed, 30 insertions(+), 30 deletions(-)
diff --git a/adaptive/learner/learner1D.py b/adaptive/learner/learner1D.py
index ef6e1f83..550da04e 100644
--- a/adaptive/learner/learner1D.py
+++ b/adaptive/learner/learner1D.py
@@ -188,7 +188,8 @@ class Learner1D(BaseLearner):
if (b is not None) and right_loss_is_unknown:
self.losses_combined[x, b] = float('inf')
- def find_neighbors(self, x, neighbors):
+ @staticmethod
+ def find_neighbors(x, neighbors):
if x in neighbors:
return neighbors[x]
pos = neighbors.bisect_left(x)
@@ -212,7 +213,7 @@ class Learner1D(BaseLearner):
When the function returns a vector the learners y-scale is set by
the level with the the largest peak-to-peak value.
- """
+ """
self._bbox[0][0] = min(self._bbox[0][0], x)
self._bbox[0][1] = max(self._bbox[0][1], x)
self._scale[0] = self._bbox[0][1] - self._bbox[0][0]
@@ -236,36 +237,26 @@ class Learner1D(BaseLearner):
# The point is already evaluated before
return
- real = y is not None
- if real:
- # either it is a float/int, if not, try casting to a np.array
- if not isinstance(y, (float, int)):
- y = np.asarray(y, dtype=float)
+ # either it is a float/int, if not, try casting to a np.array
+ if not isinstance(y, (float, int)):
+ y = np.asarray(y, dtype=float)
- # Add point to the real data dict
- self.data[x] = y
- # remove from set of pending points
- self.pending_points.discard(x)
+ # Add point to the real data dict
+ self.data[x] = y
- if self._vdim is None:
- try:
- self._vdim = len(np.squeeze(y))
- except TypeError:
- self._vdim = 1
- else:
- # The keys of pending_points are the unknown points
- self.pending_points.add(x)
+ # remove from set of pending points
+ self.pending_points.discard(x)
- # Update the neighbors
- self.update_neighbors(x, self.neighbors_combined)
- if real:
- self.update_neighbors(x, self.neighbors)
+ if self._vdim is None:
+ try:
+ self._vdim = len(np.squeeze(y))
+ except TypeError:
+ self._vdim = 1
- # Update the scale
+ self.update_neighbors(x, self.neighbors_combined)
+ self.update_neighbors(x, self.neighbors)
self.update_scale(x, y)
-
- # Update the losses
- self.update_losses(x, real)
+ self.update_losses(x, real=True)
# If the scale has increased enough, recompute all losses.
if self._scale[1] > self._oldscale[1] * 2:
@@ -275,7 +266,15 @@ class Learner1D(BaseLearner):
self._oldscale = deepcopy(self._scale)
- def ask(self, n, add_data=True):
+ def tell_pending(self, x):
+ if x in self.data:
+ # The point is already evaluated before
+ return
+ self.pending_points.add(x)
+ self.update_neighbors(x, self.neighbors_combined)
+ self.update_losses(x, real=False)
+
+ def ask(self, n, tell_pending=True):
"""Return n points that are expected to maximally reduce the loss."""
# Find out how to divide the n points over the intervals
# by finding positive integer n_i that minimize max(L_i / n_i) subject
@@ -326,8 +325,9 @@ class Learner1D(BaseLearner):
itertools.repeat(-quality, n - 1)
for quality, x, n in quals))
- if add_data:
- self.tell_many(points, itertools.repeat(None))
+ if tell_pending:
+ for p in points:
+ self.tell_pending(p)
return points, loss_improvements
--
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