hmms 0.2.3

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

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

Intro

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 .

• Sources of the project: Pypi, Github,

Requirements

• 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