Jim, To be clear you have 186 training examples and 256 columns, 100% potential pool and you have 1 column on at a time, so you would expect 100% accuracy. Correct? You might want to note this up front. Something like "this should be an easy task for the spatial pooler, however certain parameter configurations were found to be problematic."
Also I know you know this but for others reading along, to do "pooling" the number of training examples needs to be at least > than number of columns and preferably >>. Otherwise each column will perfectly fit a single example. Also you need to test on a separate set of data to see how well the model generalizes. Ian On Fri, Jul 4, 2014 at 12:29 AM, Jim Bridgewater <[email protected]> wrote: > Hi everyone, > > I'm working on a NuPIC vision toolkit for the Season of NuPIC this > summer and I've created a GitHub repo for it called nupic.vision. The > URL is: > > https://github.com/baroobob/nupic.vision > > There is a demo for those who want to try it out. > > I've attached the write up of results from using this toolkit to > investigate the effects of the synapse connection threshold, > synPermConnected, the permanence increment for active synapses, > synPermActiveInc, and the permanence decrement for inactive synapses, > synPermInactiveDec, on image recognition accuracy and the amount of > training required. > > -- > Jim Bridgewater, PhD > Arizona State University > 480-227-9592 > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > >
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