HMM with Poisson-distributed latent variables.
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.
Installation
Prerequisites
The installation of ChainsAddiction requires to following tools to be installed on your system:
- Python >= 3.9
- pip, setuptools
- 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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for chainsaddiction-0.2.4-cp311-cp311-macosx_11_0_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 33089aaa31c5d26965548c7fd4230a05ff43910e2d0b2530e24404e3be9d76b6 |
|
MD5 | 5694a240ad4b9234a66d7254721272c8 |
|
BLAKE2b-256 | 20d4e79821f9b6696b22a5b517a570bc0b0fd5a6854380d070e85931df79080b |