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.
Note: this package has currently no maintainer. Nobody will answer questions. In particular, the person who is making this code available on Github will not answer questions, fix bugs, or maintain the package in any way.
If you are interested in contributing, or fixing bugs, please open an issue on Github and we will gladly give you contributor rights.
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.
Getting the latest code
To get the latest code using git, simply type:
git clone git://github.com/hmmlearn/hmmlearn.git
As any Python packages, to install hmmlearn, simply do:
python setup.py install
in the source code directory.
HMMLearn depends on scikit-learn.
Running the test suite
To run the test suite, you need nosetests and the coverage modules. Run the test suite using:
from the root of the project.
Building the docs
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.
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. 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.
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