While using powerful off-the-shelf Machine Learning libraries and toolboxes can save a lot of work, they can also impede understanding of the methods hidden inside them and lead to incorrect use.
Echo State Network (ESN) is a powerful supervised recurrent neural network training approach that is also easy to understand and implement. Here you will find some instructional minimalistic self-contained demo scripts in several programming languages that implement, train, and use ESNs – all in one page of code, in plain sight, with no custom subroutines where the "action" is hidden.
This ESN demo solves a classical task of learning to generate/predict a chaotic attractor. The Mackey-Glass (delay=17) time series data for the examples is available here: MackeyGlass_t17.txt, or MackeyGlass_t17.zip (needs unpacking). They are generated using Oger toolbox.
Requires Matlab (tested on version R2008b).
Source code: minimalESN.m.
Source code: minimalESN.py (remove ".txt").
Requires R programming environment (tested on version 2.15.1). It's free.
Source code: minimalESN.R.
For comparison and introduction a virtually identical program is implemented using Oger, a powerful reservoir computing toolbox.
Requires Oger (tested on version 1.1.3), and its prerequisites. They are all free.
Source code: minimalESN_Oger.py (remove ".txt").
If you find these implementations useful, please take a look at (and cite) my tutorial book chapter on applying Echo State Networks.