[mlpack] mlpack 3.2.0 released

[Ryan Curtin]

Hello there,

Today I tagged mlpack 3.2.0 and released it.  It didn't contain quite
everything that we discussed at the last mlpack video meeting, but it
has most of it, and as the few final PRs get merged, we can easily
release patch updates.

You can download the code at

  https://www.mlpack.org/files/mlpack-3.2.0.tar.gz

and you can find the documentation updated on the website at
https://www.mlpack.org/.  The Windows installer for 3.2.0 isn't finished
building yet, but when it is, I'll update the homepage link.

Here is a changelog:

-----

  * Fix occasionally-failing RADICAL test (#1924).

  * Fix gcc 9 OpenMP compilation issue (#1970).

  * Added support for loading and saving of images (#1903).

  * Add Multiple Pole Balancing Environment (#1901, #1951).

  * Added functionality for scaling of data (#1876); see the
    command-line binding `mlpack_preprocess_scale` or Python binding
    `preprocess_scale()`.

  * Add new parameter `maximum_depth` to decision tree and random forest
    bindings (#1916).

  * Fix prediction output of softmax regression when test set accuracy
    is calculated (#1922).

  * Pendulum environment now checks for termination. All RL environments
    now have an option to terminate after a set number of time steps (no
    limit by default) (#1941).

  * Add support for probabilistic KDE (kernel density estimation) error
    bounds when using the Gaussian kernel (#1934).

  * Fix negative distances for cover tree computation (#1979).

  * Fix cover tree building when all pairwise distances are 0 (#1986).

  * Improve KDE pruning by reclaiming not used error tolerance (#1954,
    #1984).

  * Optimizations for sparse matrix accesses in z-score normalization
    for CF (#1989).

  * Add `kmeans_max_iterations` option to GMM training binding
    `gmm_train_main`.

  * Bump minimum Armadillo version to 8.400.0 due to ensmallen
    dependency requirement (#2015).

-----

This release is the result of the hard work of everyone, and it contains
a lot of code that was written as part of GSoC this year.  Awesome work
everyone!  There is some really cool support in here and I hope that you
find it useful. :)

If you're using mlpack and you find bugs, performance issues,
regressions, or just have some feature requests (or even want to
contribute), please let us know!  You can send an email to this list,
you can post an issue on Github, or you can chat in IRC.

See http://mlpack.org/community.html for more information.

Have a great day!

Ryan

-- 
Ryan Curtin    | "Maybe the next time."
ryan at ratml.org |   - J.G. Ballard