Howdy everyone! I am pleased to announce the release of pomegranate v0.8.0, for fast and flexible probabilistic modeling in Python. The core set of models in pomegranate include Bayesian networks, hidden Markov models, mixtures, and Bayes classifiers, among others. While no new models have been added in this release, this update adds many more features, including extending out-of-core learning, minibatch learning, semi-supervised learning, GPU support, and more built-in parallelization support.
pomegranate is pip installable using `pip install pomegranate`. Wheels have been built for Windows, bypassing the need for a C++ compiler. Please see the full announcement here <https://www.reddit.com/r/MachineLearning/comments/75ax1f/p_pomegranate_v080_released_probabilistic/>, check out the GitHub here <https://github.com/jmschrei/pomegranate>, or read the documentation here <https://pomegranate.readthedocs.io/en/latest/>. I'd love to answer any questions or hear any comments! Jacob
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
