Thanks for the tip Subutai! The wiki page I’m reading doesn’t go into anomaly likelihood. Would you happen to have a document similar to the one you posted about the CLA classifier that I can dig more into for the mathematical formulation? Something that reflects the work done in : https://github.com/numenta/nupic/blob/b6e5cf3c566e2d6ec60aeae24c4da4db27744138/nupic/algorithms/anomaly.py <https://github.com/numenta/nupic/blob/b6e5cf3c566e2d6ec60aeae24c4da4db27744138/nupic/algorithms/anomaly.py>
I’d also be interested in what algorithms you think would be suitable to test HTM’s temporal anomaly detection against. I’m unfamiliar in my research with any alg that’s comparable. thanks, Nick > On Oct 21, 2014, at 6:15 PM, Subutai Ahmad <[email protected]> wrote: > > Hi Nick, > > At Numenta we use the difference between predicted and active columns, plus > the anomaly likelihood calculations. We've had very good results with that > combination. As mentioned on the wiki page when we tried out confidences > (over a year ago) we didn't get good results. > > --Subutai > > On Tue, Oct 21, 2014 at 1:59 AM, Nicholas Mitri <[email protected] > <mailto:[email protected]>> wrote: > Thanks Scott. > For the temporal anomaly detector, the wiki mentions using confidence > parameters to calculate the anomaly score but the actual code uses predictive > states instead. Is the latter the final approach Nupic is going with? Or > should I be looking into reintroducing confidence based anomaly scores? > > thanks, > Nick > >> On Oct 21, 2014, at 2:21 AM, Scott Purdy <[email protected] >> <mailto:[email protected]>> wrote: >> >> The algorithms are pretty geared around temporal data. If you have purely >> spatial data like your chart then I wouldn't recommend using NuPIC. You >> could use the spatial pooler and use the average overlap of active columns >> with the input bits to approximate it if you really wanted to use NuPIC. >> >> On Sun, Oct 19, 2014 at 12:28 PM, Nicholas Mitri <[email protected] >> <mailto:[email protected]>> wrote: >> Hey all, >> >> I was just reading the anomaly page on the wiki and was curious if there’s >> an implementation of the non-temporal anomaly detection. >> I’m running an older build of nupic and I can’t seem to find an anomaly.py >> file like the one available in the current codebase. >> >> I’d like to try it out against other spatial anomaly detectors (euc, >> manhattan, mahalanobis, etc) and see what kind of boundary it creates in a >> 2D feature space. >> The image below is the result of using 1-class SVM as a novelty detector >> (from scikit-learn tutorials). I’d like to investigate what kind of >> visualization the spatial pooler and the non-temporal detector would >> produce. >> >> <figure_1.png> >> >> best, >> Nick >> > >
