SRNCA

Symbolic Regression Neural Cellular Automata

This is a research library for symbolic regression with inductive biases from cellular automata.

To set it up:

Clone the repository:

git clone https://github.com/riveSunder/SRNCA.git

Create a virtual environment:

Use your environment manager of choice to set up a virtual environment for the project. Replace these steps with those pertaining to your virtual environment manager of choice if needed.

virtualenv my_env --python=python3.8

or:

python3 -m venv my_env

Install the dependencies using the requirements text file:

pip install -r requirements.txt

install the local code:

pip install -e .

Run tests:

python -m testing.test_all

If the tests pass, you can get started with the tutorial notebooks in the notebooks folder.

To assess testing line coverage:

coverage run -m testing.test_all && coverage report

Which should yield a report similar to:

Ran 9 tests in 57.792s

OK
Name                          Stmts   Miss  Cover
-------------------------------------------------
srnca/__init__.py                 0      0   100%
srnca/nca.py                    122      0   100%
srnca/utils.py                   71      0   100%
testing/__init__.py               0      0   100%
testing/srnca/test_nca.py        71      0   100%
testing/srnca/test_utils.py      74      0   100%
testing/test_all.py               9      0   100%
-------------------------------------------------
TOTAL                           347      0   100%

See coverage.txt for the latest test_commit coverage.

See the demonstration notebook at notebooks/texture_nca.ipynb to train NCA to generate image textures.