Symmetric Learning is a torch-based machine learning library tailored to optimization problems featuring symmetry priors. It provides equivariant neural network modules, models, and utilities for leveraging group symmetries in data.
Package Structure
- Neural Networks (nn): Equivariant neural network layers including linear, convolutional, normalization, and attention modules.
- Models (models): Ready-to-use equivarint architectures such as equivariant MLPs, Transformers, and CNN encoders.
- Linear Algebra (linalg): Linear algebra utilities for symmetric vector spaces, including equivariant least squares solutions, projections to invariant subspaces, and more.
- Symmetry-aware Statistics (stats): Mean, variance, and covariance for symmetric random variables.
- Representation Theory (representation_theory): Representation theory utils, enabling de isotypic decomposition of group representations, intuitive management of the degrees of freedom of equivariant linear maps, orthogonal projections to the space of equivariant linear maps, and more.
Installation
pip install symm-learning
# or
git clone https://github.com/Danfoa/symmetric_learning
cd symmetric_learning
pip install -e .
Documentation
Documentation is published per branch:
main(stable): https://danfoa.github.io/symmetric_learning/devel: https://danfoa.github.io/symmetric_learning/devel/
Local Development
Install the dev extras and serve the docs with live-reload:
pip install -e ".[dev]"
sphinx-autobuild docs docs/_build/html
Then open http://127.0.0.1:8000 — the page rebuilds and refreshes automatically as you edit files under docs/.
Citation
If you use symm-learning in research, please cite:
@software{ordonez_apraez_symmetric_learning,
author = {Ordonez Apraez, Daniel Felipe},
title = {Symmetric Learning},
year = {2026},
url = {https://github.com/Danfoa/symmetric_learning}
}
License
This project is released under the MIT License. See LICENSE.
1 maintainer
Release files for symm-learning 0.8.1
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| symm_learning-0.8.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 233.9 kB
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