Extreme Learning Machine implementation in Python Version 1.0
This is an implementation of the Extreme Learning Machine in python, based on the scikit-learn machine learning library.
Distance and dot product based hidden layers are provided via the RBFRandomHiddenLayer and SimpleRandomHiddenLayer classes respectively.
The SimpleRandomHiddenLayer provides the following activation functions:
tanh, sine, tribas, sigmoid, hardlim
The RBFRandomHiddenLayer provides the following activation functions:
gaussian, multiquadric and polyharmonic spline ('poly_spline')
In addition, each random hidden layer class can take a callable user provided transfer function. See the docstrings and the example ipython notebook for details.
There's a little demo in plot_elm_comparison.py (based on scikit-learn's plot_classifier_comparison).
Requires that scikit-learn be installed, along with its usual prerequisites, and ipython to use elm_notebook.py (though it can be tweaked to run without it).
This is a work in progress, it may be restructured as time goes by.
- David C Lambert March, 2013 [dcl -at- panix -dot- com]