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Welcome to BCN's website.

This package contains an implementation of Boosted Configuration (neural) Networks (BCNs). How do BCNs work? By creating ensembles (boosting in a supervised way) of single-layered feedforward (neural) Networks. If you're familiar with scikit-learn, then using BCN will be straightforward (you can use fit, predict, cross_val_score, GridSearchCV, etc.).

It's worth mentioning that the Python package is built on top of the R package, thanks to rpy2.

BCN’s source code is available on GitHub.

Looking for a specific function? You can also use the search function available in the navigation bar.

Installing

  • 1st method: by using pip at the command line for the stable version
pip install BCN
  • 2nd method: from Github, for the development version
pip install git+https://github.com/Techtonique/bcn_python.git

or 

git clone https://github.com/Techtonique/bcn_python.git
cd BCN
make install

Quickstart

Examples of use:

Documentation

The documentation for each model can be found (work in progress) here: