David (Wood), don't worry, this particular topic still confuses lots of people ;)
David (cogmission), you configure it using the MultiStepPrediction setting, giving a list of numbers indicating how far ahead to predict (= how far behind to record). On Thu, Mar 5, 2015 at 11:07 PM, David Wood <[email protected]> wrote: > 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]> 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 >> >> >> >> On Mar 5, 2015, at 15:49, Fergal Byrne <[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]> >> 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]> >>> 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]> wrote: >>>> >>>>> >>>>> >>>>> ---------- Forwarded message ---------- >>>>> From: Michael Roy Ames <[email protected]> >>>>> To: NuPIC Mailing List <[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 - 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 >> >> >> > > > -- > *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 - 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
