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
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.
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Source Distribution
Built Distributions
Hashes for chainsaddiction-0.2.5.dev0.tar.gz
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Hashes for chainsaddiction-0.2.5.dev0-cp311-cp311-win_amd64.whl
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Hashes for chainsaddiction-0.2.5.dev0-cp311-cp311-macosx_10_9_universal2.whl
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Hashes for chainsaddiction-0.2.5.dev0-cp310-cp310-win_amd64.whl
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Hashes for chainsaddiction-0.2.5.dev0-cp310-cp310-macosx_11_0_x86_64.whl
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Hashes for chainsaddiction-0.2.5.dev0-cp39-cp39-win_amd64.whl
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Hashes for chainsaddiction-0.2.5.dev0-cp39-cp39-macosx_11_0_x86_64.whl
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