See below... On Sat, Feb 13, 2016 at 6:59 PM, Wakan Tanka <[email protected]> wrote: > > Thank you very much Matt, informative as always ;) > > One more questions: > > > 2) Swarming is optimized only for prediction. It may not be the best > > method to find model params for anomalies. We have identified a set of > > model params that are decent for most one-dimensional scalar input > > anomaly detection, and we generally reuse those in all our anomaly models. > > 1. Isn't anomaly just prediction where NuPIC missed. Why is such difference > between anomaly and prediction during swarming? >
Swarming is only for prediction. The process specifically uses prediction accuracy as a goal when permuting over model params. It completely ignores anomalies. It is impossible to rate how well anomaly detection is doing without labeled input data that calls out where the actual anomalies are. Swarming cannot do that. > 2. Is it possible to somehow display and being able to read something useful > from the internal state of model object? Probably not unless you know an awful lot about the theory, and even then, our engineers only seem to need to look into the cellular state when they are debugging something unexpected. > 3. Regarding inferenceType: is there any type which is combination of > TemporalMultiStep and TemporalAnomaly or is there any reason why NuPIC cannot > output multiple steps and also anomalies at the same time? If you use TemporalAnomaly, you'll still get predictions out. So if you want both you can have both, it just increases the processing time. Regards, --------- Matt Taylor OS Community Flag-Bearer Numenta
