pyndl 1.2.3
pip install pyndl
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Naive discriminative learning implements learning and classification models based on the Rescorla-Wagner equations.
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- License: MIT License (MIT)
- Author: Konstantin Sering
- Requires: Python <3.13, >=3.9
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Project description
pyndl is an implementation of Naive Discriminative Learning in Python. It was created to analyse huge amounts of text file corpora. Especially, it allows to efficiently apply the Rescorla-Wagner learning rule to these corpora.
Installation
The easiest way to install pyndl is using pip:
pip install --user pyndlFor more information have a look at the Installation Guide.
Documentation
pyndl uses sphinx to create a documentation manual. The documentation is hosted on Read the Docs.
Getting involved
The pyndl project welcomes help in the following ways:
Making Pull Requests for code, tests or documentation.
Commenting on open issues and pull requests.
Helping to answer questions in the issue section.
Creating feature requests or adding bug reports in the issue section.
For more information on how to contribute to pyndl have a look at the development section.
Authors and Contributers
pyndl was mainly developed by Konstantin Sering, Marc Weitz, David-Elias Künstle, Elnaz Shafaei Bajestan and Lennart Schneider. For the full list of contributers have a look at Github’s Contributor summary.
Currently, it is maintained by Konstantin Sering and Marc Weitz.
Funding
pyndl was partially funded by the Humboldt grant, the ERC advanced grant (no. 742545) and by the University of Tübingen.
Acknowledgements
This package is build as a python replacement for the R ndl2 package. Some ideas on how to build the API and how to efficiently run the Rescorla Wagner iterative learning on large text corpora are inspired by the way the ndl2 package solves this problems. The ndl2 package is available on Github here.
Project details
Unverified details
These details have not been verified by PyPIProject links
Meta
- License: MIT License (MIT)
- Author: Konstantin Sering
- Requires: Python <3.13, >=3.9
Classifiers
- Development Status
- Environment
- Intended Audience
- License
- Operating System
- Programming Language
- Topic
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