A universal machine learning architecture; hierarchical growing competitive neural network that MaxEnt approximates the PDF of high-dimensional distributions induced by data point clouds in terms of the natural scale-space clustering structure, recasts data points in terms of fuzzy membership vectors over the clustering structure, and learns fuzzy predicates in a semi-supervised fashion for classification/accepts fuzzy predicates to constrain generation of virtual data points/accepts fuzzy predicates to constrain querying of real data points.
This project uses Python3.8
It's recommended to use virtualenv.
Run python -m pip install virtualenv.
Then cd to repo directory and run python -m virtualenv .venv to create local folder called '.venv' to store virtual environment.
Then run source .venv/bin/activate to activate virtualenv.
Run deactivate or source deactivate at any time to exit virtualenv.
After activating virtualenv, run python -m pip install -r "requirements.txt".
After installation, run command mkdocs serve.
Tests managed using the Pytest framework.
Run tests using command pytest -v.
Run command pytest --cov=proteus --cov-report=html to generate coverage report.
Coverage report can be viewed in documentation site.