[scikit-learn] pomegranate v0.6.0 release

Jacob Schreiber jmschreiber91 at gmail.com
Sat Sep 10 14:20:33 EDT 2016


Hello everyone!

I just released pomegranate v0.6.0, which focuses on probabilistic
modelling for python. It currently implements basic distributions, naive
bayes, markov chains, general mixture models, hidden Markov models, and
Bayesian networks in a fast and extremely flexible manner. I have a more in
depth reddit post on it.
<https://www.reddit.com/r/Python/comments/52424a/pomegranate_v060_released_probabilistic_modelling/>

The gist is that since the last version, I've added model stacking
(mixtures of HMMs, Naive Bayes of Bayesian Networks or mixture models....),
native parallelization through joblib, extended the out-of-core API to
include all models and model stacks, and significantly increased the speed
using a BLAS backend.

I recently gave a talk at PyData Chicago about pomegranate and released an in
depth notebook tutorial
<https://github.com/jmschrei/pomegranate/blob/master/tutorials/PyData_2016_Chicago_Tutorial.ipynb>
covering all the cool new features which you should check out.

In addition, I just wrote a new tutorial on how to utilize parallelization
<https://github.com/jmschrei/pomegranate/blob/master/tutorials/Tutorial_7_Parallelization.ipynb>
to train a Gaussian mixture model, a GMM-HMM, and a mixture of GMM-HMMs (a
GMM-HMM-GMM if you will) without having to think too much about the
underlying algorithms.

I'd love for you all to check it out and let me know if you have any
feedback or want to chat about it.

Thanks!
Jacob
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