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...*
