Skip to main content

Hidden Markov Models in Python with scikit-learn like API

Project description


GitHub PyPI
Read the Docs Build CodeCov

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.

Note: This package is under limited-maintenance mode.


The required dependencies to use hmmlearn are

  • Python >= 3.6

  • NumPy >= 1.10

  • scikit-learn >= 0.16

You also need Matplotlib >= 1.1.1 to run the examples and pytest >= 2.6.0 to run the tests.


Requires a C compiler and Python headers.

To install from PyPI:

pip install --upgrade --user hmmlearn

To install from the repo:

pip install --user git+

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

hmmlearn-0.3.0.tar.gz (77.9 kB view hashes)

Uploaded source

Built Distributions

hmmlearn-0.3.0-cp311-cp311-win_amd64.whl (123.6 kB view hashes)

Uploaded cp311

hmmlearn-0.3.0-cp310-cp310-win_amd64.whl (123.8 kB view hashes)

Uploaded cp310

hmmlearn-0.3.0-cp39-cp39-win_amd64.whl (123.8 kB view hashes)

Uploaded cp39

hmmlearn-0.3.0-cp38-cp38-win_amd64.whl (123.6 kB view hashes)

Uploaded cp38

hmmlearn-0.3.0-cp37-cp37m-win_amd64.whl (124.1 kB view hashes)

Uploaded cp37

hmmlearn-0.3.0-cp36-cp36m-win_amd64.whl (124.6 kB view hashes)

Uploaded cp36

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page