| Name | chainsaddiction JSON |
| Version |
0.2.4
JSON |
| download |
| home_page | |
| Summary | HMM with Poisson-distributed latent variables. |
| upload_time | 2023-07-03 19:29:56 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | |
| license | Copyright 2019 Michael Blaß michael.blass@uni-hamburg.de Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 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 specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
hmm
poisson
hidden-markov model
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# 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.
Raw data
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