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

Project description

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 effectivness of the computationally expensive parts is powered by Cython.

You can build two models:

  • Discrete-time Hidden Markov Model

Usually just reffered as the Hidden Markov Model.

  • Continuous-time Hidden Markov Model

The variant of the Hidden Markov Model, where the state transition can occure in the continuous time, and that allows random distribution of the observation times.

Before starting to work, it is recommended to go trough tutorial with examples, the ipython notebook, covering most of the main usecases.

For deeper understanding of the topic you can see the corresponding diploma thesis. Or read the main referenced articles: Dt-HMM, Ct-HMM .

Requirements

  • python 3.5

  • libraries: Cython, ipython, matplotlib, notebook, numpy, pandas, scipy,

  • libraries for testing environment: pytest

Download & Install

After installing Numpy and Cython, you can install the package directly from pypi.

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

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0.1

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