Metadata-Version: 2.1
Name: chainsaddiction
Version: 0.1
Summary: Discrete time, finit state space, stationary Hidden Markov Model.
Home-page: https://gitlab.rrz.uni-hamburg.de/bal7668/chainsaddiction
Author: Michael Blaß
Author-email: mblass@posteo.net
License: BSD 3-Clause License
Project-URL: Source code, https://gitlab.rrz.uni-hamburg.de/bal7668/chainsaddiction
Project-URL: Bug Tracker, https://gitlab.rrz.uni-hamburg.de/bal7668/chainsaddiction/-/issues
Keywords: music,analysis,hmm
Platform: UNKNOWN
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Multimedia :: Sound/Audio :: Analysis
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Classifier: License :: OSI Approved :: BSD License
Requires-Dist: numpy (>='1.20')

# ChainsAddiction

ChainsAddiction is a tool for simple training discrete-time Hidden Markov
Models. It is written in `C` and features a `numpy`-based Python extension
module.

## Installation
Clone this repository, change to its root directory and issue

    pip install .

## Working with the C API

## Working with the Python interpreter
Calling Chains_addiction from `Python` is simple as pie. You just need to import
it:

    import chains_addiction as ca
    ca.hmm_poisson_fit_em(x, m, init_means, init_tpm, int_sd, max_iter=1000, tol=1e-5)

## Notes
- Currently only Poisson-distributed HMM are implemented.
- ChainsAddiction does not support Python 2. Specifically, it requires `Python >= 3.5` and `numpy >= 1.16`.

