I've always been fascinated by elementary cellular automata [1]. Some rules produce interesting pseudo-random patterns with repeating features. I think it would be interesting to see if NuPIC can decipher these features from the randomly generated output of the automaton and predict the continuation of partially-developed features. I also wonder what the anomaly scores would say after NuPIC has seen several thousand rows of data.
I've put together a *very* simple program [2] to generate the output of Rule 30 [3], but I did it in JavaScript out of habit. I really need it implemented in Python to get decent integration with NuPIC. To feed cellular automaton data into NuPIC, I assume I'll need to choose some number of adjacent columns within the automatons' output (maybe 10 fields?). Each field would be simply binary, and I've got some code in place now that can extract the columns and print them to the console [4]. Is anyone else interested in this crackpot idea? I have no idea what any applications might be, I'm just fiddling around. Let me know if you're interested and we can discuss. [1] http://mathworld.wolfram.com/ElementaryCellularAutomaton.html [2] https://github.com/rhyolight/cellular-automata-engine [3] http://en.wikipedia.org/wiki/Rule_30 [4] http://youtu.be/TT2-aXrmJ6k Regards, --------- Matt Taylor OS Community Flag-Bearer Numenta
