Hidden Markov Models in Python with scikit-learn like API
hmmlearn is a set of algorithms for unsupervised learning and inference of Hidden Markov Models. For supervised learning learning of HMMs and similar models see seqlearn.
Getting the latest code
To get the latest code using git, simply type:
$ git clone https://github.com/hmmlearn/hmmlearn.git
Make sure you have all the dependencies:
$ pip install numpy scipy scikit-learn
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 pytest. Run the test suite using:
$ python setup.py build_ext --inplace $ py.test --doctest-modules hmmlearn
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 sphinx-gallery sphinx_rtd_theme 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
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