@@ -19,6 +19,8 @@ class AverageLearner(BaseLearner):

         Desired absolute tolerance.

     rtol : float

         Desired relative tolerance.

+    min_npoints : int

+        Minimum number of points required to estimate the standard deviation.

     Attributes

     ----------

@@ -30,7 +32,9 @@ class AverageLearner(BaseLearner):

         Number of evaluated points.

     """

-    def __init__(self, function, atol=None, rtol=None):

+    def __init__(self, function, atol=None, rtol=None, min_npoints=2):

+        if min_npoints < 2:

+            raise ValueError("`min_npoints` should be at least 2.")

         if atol is None and rtol is None:

             raise Exception("At least one of `atol` and `rtol` should be set.")

         if atol is None:

@@ -44,6 +48,7 @@ class AverageLearner(BaseLearner):

         self.atol = atol

         self.rtol = rtol

         self.npoints = 0

+        self.min_npoints = min_npoints

         self.sum_f = 0

         self.sum_f_sq = 0

@@ -92,7 +97,7 @@ class AverageLearner(BaseLearner):

         """The corrected sample standard deviation of the values

         in `data`."""

         n = self.npoints

-        if n < 2:

+        if n < self.min_npoints:

             return np.inf

         numerator = self.sum_f_sq - n * self.mean ** 2

         if numerator < 0:

@@ -106,7 +111,7 @@ class AverageLearner(BaseLearner):

             n = self.npoints if real else self.n_requested

         else:

             n = n

-        if n < 2:

+        if n < self.min_npoints:

             return np.inf

         standard_error = self.std / sqrt(n)

         return max(

@@ -4,6 +4,7 @@ import flaky

 import numpy as np

 from adaptive.learner import AverageLearner

+from adaptive.runner import simple

 def test_only_returns_new_points():

@@ -46,3 +47,20 @@ def test_avg_std_and_npoints():

             assert learner.npoints == len(learner.data)

             assert abs(learner.sum_f - values.sum()) < 1e-13

             assert abs(learner.std - std) < 1e-13

+

+

+def test_min_npoints():

+    def f(npoints_similar: int):

+        def _f(seed):

+            if seed < npoints_similar:

+                return 0.1 + 1e-8 * random.random()

+            return random.random()

+

+        return _f

+

+    for npoints_similar in range(1, 5):

+        learner = AverageLearner(

+            f(npoints_similar), atol=0.01, rtol=0.01, min_npoints=npoints_similar + 1

+        )

+        simple(learner, lambda l: l.loss() < 1)

+        assert learner.npoints > npoints_similar