Hi Jacob, The HMMs were removed from scikit-learn because they were out of scope. I don't believe that we are going to re-add any HMMs in scikit-learn.
If you want, you can take ownership of the HMMlearn package that I created to host the former HMM code of scikit-learn: https://github.com/hmmlearn/hmmlearn/tree/master/hmmlearn You could add your HMM code there. This package is currently orphan. I will not be doing any code review or any support of any kind with this package. Best, Gaël On Mon, Apr 28, 2014 at 12:26:55AM -0700, Jacob Schreiber wrote: > Hello all > I saw that HMMs will be removed in version 0.17. As a lover of HMMs and > sklearn, in an attempt to save them, a friend and myself have been working on > a > cython-optimized HMM package which we think may be appropriate for sklearn. > The repo is here: https://github.com/jmschrei/yahmm > To summarize, it implements forward, backward, forward-backward, viterbi, > baum-welch training, and viterbi training. It allows for silent states, > normalizes out-edges to sum to a probability of 1., and will try to simplify > your graph structure. Forward, backward, and viterbi are implemented in O(n*m) > time instead of O(m^2), where n is the average number of edges per state and m > is the number of states. > A model is can contain states with different distribution types, instead of > being limited to each character-generating state being of the same > distribution > type. Currently many distributions and kernel densities are implemented, but > the user can make their own arbitrary distribution on the fly and have it work > with this package. > The downsides are that it's hard to use its most expressive features in the > current sklearn framework of defining a classifier in one line (using > Model.from_matrix() ). Instead, it may take several lines to define the > underlying model in our package. > I'd love to hear feedback from others on the idea of merging this with > sklearn. > ------------------------------------------------------------------------------ > "Accelerate Dev Cycles with Automated Cross-Browser Testing - For FREE > Instantly run your Selenium tests across 300+ browser/OS combos. Get > unparalleled scalability from the best Selenium testing platform available. > Simple to use. Nothing to install. Get started now for free." > http://p.sf.net/sfu/SauceLabs > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- Gael Varoquaux Researcher, INRIA Parietal Laboratoire de Neuro-Imagerie Assistee par Ordinateur NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France Phone: ++ 33-1-69-08-79-68 http://gael-varoquaux.info http://twitter.com/GaelVaroquaux ------------------------------------------------------------------------------ "Accelerate Dev Cycles with Automated Cross-Browser Testing - For FREE Instantly run your Selenium tests across 300+ browser/OS combos. Get unparalleled scalability from the best Selenium testing platform available. Simple to use. Nothing to install. Get started now for free." http://p.sf.net/sfu/SauceLabs _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
