If anyone wants to play around with it, I've created a python project that can create all elementary cellular automaton easily.
https://github.com/rhyolight/automatatron It's truly amazing to me that only about 80 lines of python code can create the whole library of ECAs. It's a testament to the idea that a very simple ruleset can create extraordinarily complex behavior. You can currently use a handler function to get iteration output rows, but I'm going to have to add the ability to stream a subset of columns from a running automata so specific columns can be pushed into NuPIC instead of the entire output (as soon as I find time). --------- Matt Taylor OS Community Flag-Bearer Numenta On Sun, Jan 18, 2015 at 2:18 AM, Fergal Byrne <[email protected]> wrote: > Hi Matthew, > > This would be a great demo (Wolfram's CA stuff appeals to most of us nerds). > I predict that if you feed a fixed set of bits into NuPIC, the TM will learn > the rule you've picked and will be able to predict the next pattern for all > but the edge bits (which will be partly random as far as it can tell). I'd > also predict that a single-order TM (one cell per column) will be also able > to do this learning. > > These two predictions come directly from the CLA theory (Subutai can verify > this), so it could be a good integration test for new implementations > (assuming NuPIC matches my predictions, of course!). > > > Regards, > > Fergal Byrne > > On Sat, Jan 17, 2015 at 10:23 PM, Jeff Fohl <[email protected]> wrote: >> >> I used to be a bit of a cellular automata nerd. I would be interested in >> seeing what you discover. You could also possibly just feed in the values >> for the center column of rule 30 - though that has been shown to be highly >> random, so I am not sure what the utility of it would be? >> >> - Jeff >> >> On Sat, Jan 17, 2015 at 1:59 PM, Matthew Taylor <[email protected]> wrote: >>> >>> 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 >>> >> > > > > -- > > Fergal Byrne, Brenter IT > > http://inbits.com - Better Living through Thoughtful Technology > http://ie.linkedin.com/in/fergbyrne/ - https://github.com/fergalbyrne > > Founder of Clortex: HTM in Clojure - > https://github.com/nupic-community/clortex > > Author, Real Machine Intelligence with Clortex and NuPIC > Read for free or buy the book at https://leanpub.com/realsmartmachines > > Speaking on Clortex and HTM/CLA at euroClojure Krakow, June 2014: > http://euroclojure.com/2014/ > and at LambdaJam Chicago, July 2014: http://www.lambdajam.com > > e:[email protected] t:+353 83 4214179 > Join the quest for Machine Intelligence at http://numenta.org > Formerly of Adnet [email protected] http://www.adnet.ie
