A python module for conceptor computation is implemented based on section 4 of the technique report and this github repository .
The module is consisted of the following files:
set up the reservoir network, drive the reservoir with dynamic patterns, train output weights to read the original pattern signals, train internal weight to reconstruct the original reservoir dynamics, compute the correlation matrices from reservoir states, compute conceptor matrices from the correlation matrices.
apply logic operations on conceptors. in particular, AND, OR, NOT, PHI functions from the original MATLAB implementation.
useful utility functions that will be repeatedly used within the module, for example, randomly initialise the weights in a reservoir network.
An IPython notebook script was also written to test the above-mentioned module, the results match with those in the technique report and can be viewed here: http://nbviewer.ipython.org/github/littleowen/Conceptor/blob/master/ConceptorTest.ipynb