Skip to main content

Efficient discrete and continuous-time hidden Markov model library able to handle hundreds of hidden states

Project description

UPDATE 2023/Feb/27 Direct Pypi installation is now fixed.


HMMs is the Hidden Markov Models library for Python. It is easy to use general purpose library implementing all the important submethods needed for the training, examining and experimenting with the data models.

The computationally expensive parts are powered by Cython to ensure high speed.

The library supports the building of two models:

Discrete-time Hidden Markov Model
Usually simply referred to as the Hidden Markov Model.
Continuous-time Hidden Markov Model
The variant of the Hidden Markov Model where the state transition as well as observations occurs in the continuous time.

Before starting work, you may check out the tutorial with examples. the ipython notebook, covering most of the common use-cases.

For the deeper understanding of the topic refer to the corresponding diploma thesis. Or read some of the main referenced articles: Dt-HMM, Ct-HMM .


  • python 3.5
  • libraries: Cython, ipython, matplotlib, notebook, numpy, pandas, scipy,
  • libraries for testing environment: pytest

Download & Install

The Numpy and Cython must be installed before installing the library package from pypi.

(env)$ python -m pip install numpy cython
(env)$ python -m pip install hmms

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

hmms-0.2.3.tar.gz (524.8 kB view hashes)

Uploaded source

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