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]> 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]> 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]> > 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 >> > > >
