AverageLearner math domain error

def f(salt):
    return {0: 0.17210054184489873, 1: 0.17210054169556288}[salt]

learner = adaptive.AverageLearner(f, atol=0.01)

r = adaptive.runner.simple(learner, goal=lambda l: l.loss() < 1) 

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-52-5661bc7ff0a6> in <module>()
      4 learner = adaptive.AverageLearner(f, atol=0.01)
      5 
----> 6 r = adaptive.runner.simple(learner, goal=lambda l: l.loss() < 1)

/gscratch/home/t-banijh/adaptive/adaptive/runner.py in simple(learner, goal)
    441         learner as its sole argument, and return True if we should stop.
    442     """
--> 443     while not goal(learner):
    444         xs, _ = learner.ask(1)
    445         for x in xs:

<ipython-input-52-5661bc7ff0a6> in <lambda>(l)
      4 learner = adaptive.AverageLearner(f, atol=0.01)
      5 
----> 6 r = adaptive.runner.simple(learner, goal=lambda l: l.loss() < 1)

/gscratch/home/t-banijh/adaptive/adaptive/learner/average_learner.py in loss(self, real, n)
     81         if n < 2:
     82             return np.inf
---> 83         standard_error = self.std / sqrt(n)
     84         return max(standard_error / self.atol,
     85                    standard_error / abs(self.mean) / self.rtol)

/gscratch/home/t-banijh/adaptive/adaptive/learner/average_learner.py in std(self)
     72         if n < 2:
     73             return np.inf
---> 74         return sqrt((self.sum_f_sq - n * self.mean**2) / (n - 1))
     75 
     76     def loss(self, real=True, *, n=None):

ValueError: math domain error