HMM with Poisson-distributed latent variables.

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  • License: BSD License (Copyright 2019 Michael Blaß michael.blass@uni-hamburg.de)
  • Author: Michael Blaß
  • Tags hmm , poisson , hidden-markov model
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Project description

ChainsAddiction

ChainsAddiction is an easy to use tool for time series analysis using discrete-time Hidden Markov Models. It is written in C as a numpy-based Python extension module.

This is the fork maintained by Rolf Bader (canonical repository on Codeberg). It is part of the apollon / chainsaddiction / comsar stack; see the comsar repository for the full manual.

Installation

Install from PyPI (recommended, no compiler needed)

Pre-compiled wheels are provided for Windows, macOS and Linux (CPython 3.9–3.13):

python3 -m pip install bader-chainsaddiction

The import name is unchanged — you still write import chainsaddiction.

Install from source

Building from source requires

  • Python >= 3.9
  • pip, setuptools, NumPy
  • a C compiler (MSVC Build Tools on Windows, Xcode Command Line Tools on macOS, gcc/clang on Linux)

Then:

git clone https://codeberg.org/rbader/chainsaddiction path/to/ca
cd path/to/ca
python3 -m pip install .

Notes

Currently only Poisson-distributed HMM are implemented.

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  • License: BSD License (Copyright 2019 Michael Blaß michael.blass@uni-hamburg.de)
  • Author: Michael Blaß
  • Tags hmm , poisson , hidden-markov model
Classifiers

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