Tutorial Learner2D

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.Learner2D.ipynb

import adaptive
adaptive.notebook_extension()

import numpy as np
from functools import partial

Besides 1D functions, we can also learn 2D functions: \(\ f: ℝ^2 → ℝ\).

def ring(xy, wait=True):
    import numpy as np
    from time import sleep
    from random import random
    if wait:
        sleep(random()/10)
    x, y = xy
    a = 0.2
    return x + np.exp(-(x**2 + y**2 - 0.75**2)**2/a**4)

learner = adaptive.Learner2D(ring, bounds=[(-1, 1), (-1, 1)])

runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 0.01)

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

runner.live_info()

def plot(learner):
    plot = learner.plot(tri_alpha=0.2)
    return (plot.Image + plot.EdgePaths.I + plot).cols(2)

runner.live_plot(plotter=plot, update_interval=0.1)

%%opts EdgePaths (color='w')

import itertools

# Create a learner and add data on homogeneous grid, so that we can plot it
learner2 = adaptive.Learner2D(ring, bounds=learner.bounds)
n = int(learner.npoints**0.5)
xs, ys = [np.linspace(*bounds, n) for bounds in learner.bounds]
xys = list(itertools.product(xs, ys))
learner2.tell_many(xys, map(partial(ring, wait=False), xys))

(learner2.plot(n).relabel('Homogeneous grid') + learner.plot().relabel('With adaptive') +
 learner2.plot(n, tri_alpha=0.4) + learner.plot(tri_alpha=0.4)).cols(2)

Make live