WIP: AverageLearner2D and AverageLearner1D

This merge request implements a Learner2D that can learn averages on the points, the AverageLearner2D.

When chosing points the learner can either

• add more values at an existing points
• add more triangles

The learner compares the loss of potential new triangles with the standard error of an existing point.

The relative importance of both can be adjusted by a hyperparameter learner.weight. From the doc-string:

When weight > 1 adding more points to existing points will be prioritized (making the standard error of a point more imporant,) otherwise adding new triangles will be prioritized (making the loss of a triangle more important.)

All tests that pass for the Learner2D currently pass for the AvererageLearner2D too.

Run with:

import adaptive
adaptive.notebook_extension()

def ring(xy_seed):
    import numpy as np
    import random
    (x, y), seed = xy_seed
    random.seed(seed)
    noise =  0.5 * (random.random() - 1)
    a = 0.2
    return x + np.exp(-(x**2 + y**2 - 0.75**2)**2/a**4) + noise

learner = adaptive.AverageLearner2D(ring, bounds=[(-1, 1), (-1, 1)], weight=.1)
runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 0.01, log=True)
runner.live_info()

which results in:

>>> print(learner.mean_values_per_point()) 
5.2737642585551328

and