HMMLearn: Hidden Markov Models in Python, with scikit-learn like API
HMMlearn is a set of algorithm for learning and inference of Hiden Markov Models.
Historically, this code was present in scikit-learn, but unmaintained. It has been orphaned and separated as a different package.
Continuous integration (ie running tests) is found on: https://travis-ci.org/hmmlearn/hmmlearn
The learning algorithms in this package are unsupervised. For supervised learning of HMMs and similar models, see seqlearn.
To get the latest code using git, simply type:
git clone git://github.com/hmmlearn/hmmlearn.git
As with any Python packages, to install hmmlearn, simply do:
python setup.py install
in the source code directory.
HMMLearn depends on scikit-learn.
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.
To build the docs you need to have setuptools and sphinx (>=0.5) installed. Run the command:
cd doc make html
The docs are built in the build/sphinx/html directory.
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. This command will compile the docs, and the resulting tarball can be installed with no extra dependencies than the Python standard library. You will need setuptool and sphinx.
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