From 78cd74eb86aa605d1e5066d61fa7d2cefb4d3a7f Mon Sep 17 00:00:00 2001
From: Bas Nijholt <basnijholt@gmail.com>
Date: Wed, 19 Sep 2018 14:51:19 +0200
Subject: [PATCH] fix that the 'AverageLearner' only returns new points, and
introduce 'learner.pending_points'
---
adaptive/learner/average_learner.py | 42 ++++++++++++++++++++---------
1 file changed, 29 insertions(+), 13 deletions(-)
diff --git a/adaptive/learner/average_learner.py b/adaptive/learner/average_learner.py
index 0b0a9e4f..f9163071 100644
--- a/adaptive/learner/average_learner.py
+++ b/adaptive/learner/average_learner.py
@@ -1,6 +1,7 @@
# -*- coding: utf-8 -*-
import itertools
from math import sqrt
+import warnings
import numpy as np
@@ -14,12 +15,19 @@ class AverageLearner(BaseLearner):
The learned function must depend on an integer input variable that
represents the source of randomness.
- Parameters:
- -----------
+ Parameters
+ ----------
atol : float
Desired absolute tolerance
rtol : float
Desired relative tolerance
+
+ Attributes
+ ----------
+ data : dict
+ Sampled points and values.
+ pending_points : set
+ Points that still have to be evaluated.
"""
def __init__(self, function, atol=None, rtol=None):
@@ -31,6 +39,7 @@ class AverageLearner(BaseLearner):
rtol = np.inf
self.data = {}
+ self.pending_points = set()
self.function = function
self.atol = atol
self.rtol = rtol
@@ -40,28 +49,35 @@ class AverageLearner(BaseLearner):
@property
def n_requested(self):
- return len(self.data)
+ return len(self.data) + len(self.pending_points)
def ask(self, n, add_data=True):
points = list(range(self.n_requested, self.n_requested + n))
+
+ if any(True for p in points if p in self.data or p in self.pending_points):
+ # This means some of the points `< self.n_requested` do not exist.
+ points = list(set(range(self.n_requested + n))
+ - set(self.data)
+ - set(self.pending_points))[:n]
+
loss_improvements = [self.loss_improvement(n) / n] * n
if add_data:
self.tell_many(points, itertools.repeat(None))
return points, loss_improvements
def tell(self, n, value):
- value_is_new = not (n in self.data and value == self.data[n])
- if not value_is_new:
- value_old = self.data[n]
- self.data[n] = value
- if value is not None:
+ if n in self.data:
+ warnings.warn(f"n={n} already exists in `learner.data` with value {self.data[n]}.")
+ return
+
+ if value is None:
+ self.pending_points.add(n)
+ else:
+ self.data[n] = value
+ self.pending_points.discard(n)
self.sum_f += value
self.sum_f_sq += value**2
- if value_is_new:
- self.npoints += 1
- else:
- self.sum_f -= value_old
- self.sum_f_sq -= value_old**2
+ self.npoints += 1
@property
def mean(self):
--
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