Metadata-Version: 2.1
Name: chainsaddiction
Version: 0.2.2
Summary: HMM with Poisson-distributed latent variables.
Author-email: Michael Blaß <mblass@posteo.net>
License: Copyright 2019 Michael Blaß michael.blass@uni-hamburg.de
        
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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

# 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
### Prerequisites

The installation of ChainsAddiction requires to following tools to be installed
on your system:

- Python >= 3.7
- pip
- C compiler


### Install from PyPi

You can install chainsaddiction from PyPi with:

    python3 -m pip install chainsaddiction

Please note that ChainsAddiction is a CPython extension module. You have to
have set up a C compiler in order to install. Currently we provide wheels for
macOS. So, if you are using this OS you do not need a compiler.


### Install from source

First, 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.
