This abstracts the attribute 'nn_neighbors' away and makes it easier
for the user, because one can now just set the 'loss_per_interval'
and the 'nn_neighbors' will be set be default.

Because data is now in the 'BaseLearner'

This makes the code work with any number of neighbors. Now the
new triangle loss even works with nn_neigbors=0. I also added
that we pass the neighbors in all loss_per_interval functions.

Resolve "(LearnerND) allow any convex hull as domain"

Closes #114

See merge request !127

We were always dividing the total function execution time by
the number of cores, however sometimes the user sets 'ntasks'
so instead we should divide by '_get_max_tasks()'.

Also the future start_time wasn't measured in the case where
one uses a SequentialExecutor because the Future that is
returned already contains the result.

Previously it reported 100% which is not a good estimate either.

Because after closing the notebook and then opening it, the javascript
is gone. Without this change, one could not load it again.

save execution time on futures inside runners

See merge request !130

Previously we were wrapping the learner function and doing the
timing there. This makes things more complicated in the case that
we are learning an 'async def' function.

Test that runners run at all (!), and that an async function can be
used with the AsyncExecutor.

Make BaseRunner an abstract base class

Closes #107

See merge request !111

Because it breaks the examples in the docs

exponentially decay message frequency in live_info

See merge request !108

Closes #118

The code is posed on https://github.com/python-adaptive/adaptive/issues/1

See https://stackoverflow.com/a/24946631/3447047

See http://help.zenodo.org/#versioning for more information.

and remove the code to find the complete author list from git  because
our CI job will ensure that all the contributors will be included
in that file.