Thanks, Chandan and David. That’s helpful. Sorry for the newbie question!
Regards, Dave -- http://about.me/david_wood > On Mar 5, 2015, at 17:17, cogmission <[email protected]> wrote: > > David, > > The CLAClassifier is configured to compute the probability of all the buckets > at the specified steps. I'm not sure how one goes about configuring this from > the OPF or NetworkAPI point of view. The TemporalMemory (new sequence memory) > always predicts the next step (at t + 1). You can pull out the prediction for > the step in question by querying the CLAClassifier or if the step == 1, you > can simply use the results of the TemporalMemory's last cycle to get the > predicted column SDR for the next cycle. > > David > > On Thu, Mar 5, 2015 at 4:09 PM, David Wood <[email protected] > <mailto:[email protected]>> wrote: > Hi Fergal, > > I’m a bit confused by your answer. Is there no way using NuPIC to estimate > multi-step predictions? > > Regards, > Dave > -- > http://about.me/david_wood <http://about.me/david_wood> > > > >> On Mar 5, 2015, at 15:49, Fergal Byrne <[email protected] >> <mailto:[email protected]>> wrote: >> >> Hi Michael, >> >> The region itself always just predicts one step ahead. You can connect a >> region with code (most of it in OPF) which will remember what happens N >> steps ahead of a timestep, but this is just a histogram record (associating >> a cell's activation with an input field value) of what is likely to come up >> after N steps. This is what is used if you specify multi-step predictions. >> >> Ignore the multi-step stuff in the White Paper. It's wrong, and has been >> abandoned. CLA on its own just does a single timestep prediction, and this >> is what also happens in neocortex. >> >> Regards, >> >> Fergal Byrne >> >> >> On Thu, Mar 5, 2015 at 12:38 AM, cogmission <[email protected] >> <mailto:[email protected]>> wrote: >> Oh the Prediction code is in CLAClassifier and the Anomaly code does the >> running total of the meta qualities... >> >> On Wed, Mar 4, 2015 at 6:36 PM, cogmission <[email protected] >> <mailto:[email protected]>> wrote: >> Hi Michael, >> >> Afaik, the "Anomaly" class is what you are looking for, just that it tracks >> the moving average of accuracy or maybe the inverse (anomaly). You could in >> any case have a look at that code to see if it either does what you are >> looking for or can be "adapted" to do more of what you're looking for. >> >> Also afaik, the steps will "overwrite" when that point in the cycle is >> reached again (so every 500 steps a new prediction quality is estimated - if >> 500-steps is one of the step configurations). >> >> Correct me if I'm wrong someone? >> >> David >> >> On Wed, Mar 4, 2015 at 6:21 PM, Michael Roy Ames via nupic >> <[email protected] <mailto:[email protected]>> wrote: >> >> >> ---------- Forwarded message ---------- >> From: Michael Roy Ames <[email protected] >> <mailto:[email protected]>> >> To: NuPIC Mailing List <[email protected] >> <mailto:[email protected]>> >> Cc: >> Date: Wed, 04 Mar 2015 16:08:38 -0800 >> Subject: Prediction. Several steps. Future or past. >> NuPIC list: >> >> "Predictions in an HTM region can be for several time steps into the future" >> - according to the HTM White paper. >> >> Question 1: Is there a NuPIC code that does prediction for the next n time >> steps? >> >> Question 2: Is there NuPIC code that keeps activation history such that one >> could access the last 15 or 20 sets of active cells? >> >> I'm interested in making NuPIC learn and recognize temporal sequences of >> data, and want to limit the amount of additional code I have to write to get >> this done. So, I'd rather use existing NuPIC functionality that works >> instead of writing algorithm that might duplicate something already in >> place. The sequences may be long (500 steps) or short (20 steps). The >> one-step predictions I've found in NuPIC examples need extra code to be >> written to 'remember' the predictions and how many predictions in-a-row have >> been correct, each additional successful prediction lending greater >> confidence to the data recognition. >> >> Question 3: Is there code that does this already (successful prediction >> tracking), or will I have to write it? >> >> MRA >> >> >> >> >> >> >> >> -- >> We find it hard to hear what another is saying because of how loudly "who >> one is", speaks... >> >> >> >> -- >> We find it hard to hear what another is saying because of how loudly "who >> one is", speaks... >> >> >> >> -- >> >> Fergal Byrne, Brenter IT >> >> http://inbits.com <http://inbits.com/> - Better Living through Thoughtful >> Technology >> http://ie.linkedin.com/in/fergbyrne/ <http://ie.linkedin.com/in/fergbyrne/> >> - https://github.com/fergalbyrne <https://github.com/fergalbyrne> >> >> Founder of Clortex: HTM in Clojure - >> https://github.com/nupic-community/clortex >> <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 >> <https://leanpub.com/realsmartmachines> >> >> Speaking on Clortex and HTM/CLA at euroClojure Krakow, June 2014: >> http://euroclojure.com/2014/ <http://euroclojure.com/2014/> >> and at LambdaJam Chicago, July 2014: http://www.lambdajam.com >> <http://www.lambdajam.com/> >> >> e:[email protected] <mailto:[email protected]> t:+353 83 >> 4214179 <tel:%2B353%2083%204214179> >> Join the quest for Machine Intelligence at http://numenta.org >> <http://numenta.org/> >> Formerly of Adnet [email protected] <mailto:[email protected]> >> http://www.adnet.ie <http://www.adnet.ie/> > > > > > -- > We find it hard to hear what another is saying because of how loudly "who one > is", speaks...
