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] > <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 >
