Hidden Markov Models in Python, with scikit-learn like API
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README.rst

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hmmlearn is a set of algorithm for learning and inference of Hidden Markov Models.

Historically, this code was present in scikit-learn, but unmaintained. It has been orphaned and separated as a different package.

The learning algorithms in this package are unsupervised. For supervised learning of HMMs and similar models, see seqlearn.

Getting the latest code

To get the latest code using git, simply type:

$ git clone git://github.com/hmmlearn/hmmlearn.git

Installing

Make sure you have all the dependencies:

$ pip install scikit-learn Cython

and then install hmmlearn by running:

$ python setup.py install

in the source code directory.

Running the test suite

To run the test suite, you need nosetests and the coverage modules. Run the test suite using:

$ python setup.py build_ext --inplace && nosetests

from the root of the project.

Building the docs

To build the docs you need to have the following packages installed:

$ pip install Pillow matplotlib Sphinx numpydoc

Run the command:

$ cd doc
$ make html

The docs are built in the _build/html directory.

Making a source tarball

To create a source tarball, eg for packaging or distributing, run the following command:

$ python setup.py sdist

The tarball will be created in the dist directory.

Making a release and uploading it to PyPI

This command is only run by project manager, to make a release, and upload in to PyPI:

$ python setup.py sdist bdist_egg register upload