Tutorial 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.

See also

The complete source code of this tutorial can be found in tutorial.AverageLearner.ipynb

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).

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

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)

await runner.task  # This is not needed in a notebook environment!

runner.live_info()

runner.live_plot(update_interval=0.1)

Make live