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:chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions

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Blocked by #238 Currently we use the "ad-hoc" package layout where the "adaptive" package is directly in the repository root. Such a layout can cause confusion when it comes to...

Blocked
refactor
priority: low

the docstring says: ``` plotter : function A function that takes the learner as a argument and returns a holoviews object. By default ``learner.plot()`` will be called. ``` But not...

enhancement
help wanted
Runner

Hi, This project is really cool! I was wondering is there any paper, technical report on your implementation? If yes, is it possible to share?

Documentation

> I'm plotting a 2D Brillouin zone with C3 symmetry. Is there a way to add a callback so that after each point is calculated, I can "tell" the learner...

enhancement
API
Runner

If people are using the async runner then they have to jump through some hoops (`runner.task.print_stack()`) to find out what went wrong. It would be good if we could provide...

enhancement
Runner

The keys in `data` for the `SequenceLearner` are not just the points in the domain: they are tuples that also contain some index into the sequence of values. This does...

bug

## ([original issue on GitLab](https://gitlab.kwant-project.org/qt/adaptive/issues/117)) _opened by Bas Nijholt ([@basnijholt](https://gitlab.kwant-project.org/basnijholt)) at 2018-10-20T12:51:05.772Z_ We should add the following "real world usage" code to the tutorial as an "Advanced example" once we...

LearnerND
Blocked

## ([original issue on GitLab](https://gitlab.kwant-project.org/qt/adaptive/issues/37)) _opened by Rafal Skolasinski ([@r-j-skolasinski](https://gitlab.kwant-project.org/r-j-skolasinski)) at 2017-12-08T13:13:46.873Z_ typical (problematic) behaviour in such simulations can be mimic with ```python def levels(x): return np.array([x**2 % 1.5, (x**2...

Learner1D

The following code fails to finish the calculation ```python import adaptive adaptive.notebook_extension() def dummy(x): return x sequence = range(20) learners = [adaptive.SequenceLearner(dummy, sequence)] bl = adaptive.BalancingLearner(learners) runner = adaptive.Runner(bl, lambda...

The following code causes the 2D learner to break, whereas the 1D learner works fine. ```python import adaptive import numpy as np adaptive.notebook_extension() def f(x): return np.array([[x, x**2], [-x, 1/(1+x**2)]])...

Learner2D