This repository contains code accompanying the paper “Any-dimensional equivariant neural networks”. It includes simple implementations of the computational recipe defined in the paper and scripts to run all the numerical experiments in the paper.

Warning: the instructions assume that your current working directory is the base of this repository.

For the following instructions, you will need Python 3.9 and pip installed. Consider creating a virtual environment for this project. For example, by running:

$ python -m venv .venv
$ source .venv/bin/activate

Then, install the requirements:

$ pip install -e .[EXPTS]

All the scripts to run the numerical examples are in the experiments folders. Here is a table with all the scripts.

The results will be saved to a new folder within the results directory.

Warning: running any of this experiments might take a while.

After running any of the scripts above, you will have a new folder within the directory results/<name_of_experiment>. Modify the last few lines of the script experiments/generate_figures.py to include said folder. Run

$ python generate_figures.py

this will generate images like the ones in the paper. If you don’t modify the path in this script, it will simply generate the figures in the paper.