From a8866d4acfe8fa9a7ea69785ef341552aadb9974 Mon Sep 17 00:00:00 2001
From: Bas Nijholt <basnijholt@gmail.com>
Date: Thu, 7 Sep 2017 10:49:15 +0200
Subject: [PATCH] remove _choose_points, closes #19
---
adaptive/learner.py | 35 ++++++++++-------------------------
1 file changed, 10 insertions(+), 25 deletions(-)
diff --git a/adaptive/learner.py b/adaptive/learner.py
index 8ff79804..ff9af043 100644
--- a/adaptive/learner.py
+++ b/adaptive/learner.py
@@ -72,6 +72,7 @@ class BaseLearner(metaclass=abc.ABCMeta):
(possibly by interpolation).
"""
+ @abc.abstractmethod
def choose_points(self, n, add_data=True):
"""Choose the next 'n' points to evaluate.
@@ -85,22 +86,7 @@ class BaseLearner(metaclass=abc.ABCMeta):
values. Set this to False if you do not
want to modify the state of the learner.
"""
- points, loss_improvements = self._choose_points(n)
- if add_data:
- self.add_data(points, itertools.repeat(None))
- return points, loss_improvements
-
- @abc.abstractmethod
- def _choose_points(self, n):
- """Choose the next 'n' points to evaluate.
-
- Should be overridden by subclasses.
-
- Parameters
- ----------
- n : int
- The number of points to choose.
- """
+ pass
def __getstate__(self):
return copy(self.__dict__)
@@ -139,9 +125,11 @@ class AverageLearner(BaseLearner):
self.sum_f = 0
self.sum_f_sq = 0
- def _choose_points(self, n=10):
+ def choose_points(self, n=10, add_data=True):
points = list(range(self.n_requested, self.n_requested + n))
loss_improvements = [None] * n
+ if add_data:
+ self.add_data(points, itertools.repeat(None))
return points, loss_improvements
def add_point(self, n, value):
@@ -324,7 +312,7 @@ class Learner1D(BaseLearner):
self._oldscale = self._scale
- def _choose_points(self, n=10):
+ def choose_points(self, n=10, add_data=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
@@ -373,7 +361,10 @@ class Learner1D(BaseLearner):
itertools.repeat(-quality, n)
for quality, x, n in quals))
- return (xs, loss_improvements)
+ if add_data:
+ self.add_data(points, itertools.repeat(None))
+
+ return xs, loss_improvements
def interpolate(self, extra_points=None):
xs = list(self.data.keys())
@@ -462,9 +453,6 @@ class BalancingLearner(BaseLearner):
else:
return self._choose_and_add_points(n)
- def _choose_points(self, n):
- pass
-
def add_point(self, x, y):
index, x = x
self.learners[index].add_point(x, y)
@@ -793,9 +781,6 @@ class Learner2D(BaseLearner):
else:
dev[jsimplex] = 0
- def _choose_points(self, n):
- pass
-
def _choose_and_add_points(self, n):
if n <= len(self._stack):
points = self._stack[:n]
--
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