Metadata-Version: 2.1
Name: chainsaddiction
Version: 0.2.5.dev1
Summary: HMM with Poisson-distributed latent variables.
Author-email: Michael Blaß <mblass@posteo.net>
License: Copyright 2019 Michael Blaß michael.blass@uni-hamburg.de
        
        Redistribution and use in source and binary forms, with or without
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        1. Redistributions of source code must retain the above copyright notice, this
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        2. Redistributions in binary form must reproduce the above copyright notice,
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        3. Neither the name of the copyright holder nor the names of its contributors
        may be used to endorse or promote products derived from this software without
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Project-URL: Repository, https://github.com/teagum/chainsaddiction
Project-URL: Documentation, https://chainsaddiction.rtfd.org
Keywords: hmm,poisson,hidden-markov model
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: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy

[![build](https://github.com/Teagum/chainsaddiction/actions/workflows/build.yml/badge.svg)](
https://github.com/Teagum/chainsaddiction/actions/workflows/build.yml)

# 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.


## Installation
### Install from PyPi

We currently provide wheels for macOS and Windows AMD 64, which you can install from PyPI via:

    python3 -m pip install chainsaddiction

Linux users have to build from source until we get that manylinux thing running.


### Install from source

Before attemting to build ChainsAddiction from source, make sure you have

- Python >= 3.9
- pip, setuptools
- C compiler

installed and ready to go.

Then, clone the source code by typing the following command in your terminal app.
Replace `path/to/ca` with the path to where ChainsAddiction should be cloned:

    git clone https://github.com/teagum/chainsaddiction path/to/ca

Second, change to the root directory of your freshly cloned code repository:

    cd path/to/ca

Third, instruct Python to build and install ChainsAddiction:

    python3 -m pip install .

---

## Notes
Currently only Poisson-distributed HMM are implemented.
