Tutorial `~adaptive.AverageLearner`
 -----------------------------------
 
 .. note::
    Because this documentation consists of static html, the ``live_plot``
    and ``live_info`` widget is not live. Download the notebook
    in order to see the real behaviour.
 
 .. seealso::
     The complete source code of this tutorial can be found in

     :jupyter-download:notebook:`tutorial.AverageLearner`

 .. jupyter-execute::

     :hide-code:
 
     import adaptive
     adaptive.notebook_extension()
 
 The next type of learner averages a function until the uncertainty in
 the average meets some condition.
 
 This is useful for sampling a random variable. The function passed to
 the learner must formally take a single parameter, which should be used
 like a “seed” for the (pseudo-) random variable (although in the current
 implementation the seed parameter can be ignored by the function).
 

 .. jupyter-execute::

 
     def g(n):
         import random
         from time import sleep
         sleep(random.random() / 1000)
         # Properly save and restore the RNG state
         state = random.getstate()
         random.seed(n)
         val = random.gauss(0.5, 1)
         random.setstate(state)
         return val
 

 .. jupyter-execute::

 
     learner = adaptive.AverageLearner(g, atol=None, rtol=0.01)

     # `loss < 1` means that we reached the `rtol` or `atol`
     runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 1)

 .. jupyter-execute::

     :hide-code:
 
     await runner.task  # This is not needed in a notebook environment!
 

 .. jupyter-execute::

 
     runner.live_info()
 

 .. jupyter-execute::

 
     runner.live_plot(update_interval=0.1)