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High-Performance implementation of an Extreme Learning Machine

Project description

High Performance toolbox for Extreme Learning Machines.

Extreme learning machines (ELM) are a particular kind of Artificial Neural Networks,
which solve classification and regression problems. Their performance is comparable
to a classical Multilayer Perceptron trained with Error Back-Propagation algorithm,
but the training time is up to 6 orders of magnitude smaller. (yes, a million times!)

ELMs are suitable for processing huge datasets and dealing with Big Data,
and this toolbox is created as their fastest and most scalable implementation.

Documentation is available here:,
it uses Numpydocs.

NEW: Parallel HP-ELM tutorial! See the documentation:

- Efficient matrix math implementation without bottlenecks
- Efficient data storage (HDF5 file format)
- Data size not limited by the available memory
- GPU accelerated computations (if you have one)
- Regularization and model selection (for in-memory models)

Main classes:
- hpelm.ELM for in-memory computations (dataset fits into RAM)
- hpelm.HPELM for out-of-memory computations (dataset on disk in HDF5 format)

Example usage::
>>> from hpelm import ELM
>>> elm = ELM(X.shape[1], T.shape[1])
>>> elm.add_neurons(20, "sigm")
>>> elm.add_neurons(10, "rbf_l2")
>>> elm.train(X, T, "LOO")
>>> Y = elm.predict(X)

If you use the toolbox, cite our open access paper "High Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications" in IEEE Access.

author={Akusok, A. and Bj\"{o}rk, K.-M. and Miche, Y. and Lendasse, A.},
journal={Access, IEEE},
title={High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications},

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