Hi Dionysis, The Geospatial encoder is designed to produce similar SDRs for similar inputs. Adding a bit of noise to an input should still produce a similar SDR, so the sequence learning algorithm that the anomaly detection is based on should still be able to recognize the inputs. See the geospatial demo [1] for an example of this (no two routes are exactly the same, but it still learns them fine).
[1] https://github.com/numenta/nupic.geospatial <https://github.com/numenta/nupic.geospatial> - Chetan > On Dec 16, 2015, at 1:35 AM, Dionysis Manousakas <[email protected]> wrote: > > Hi NuPIC, > > I'm trying to test the NuPIC Geospatial encoder on noisy data. > So, I traversing a whole period of sinusoidal trajectory with no noise > (you can think of angle as longitude and sinus as latitude) > and then repeat on noisy versions of the signal. The anomaly detector > tends to entirely flag the noisy trajectories as anomalies. Is this the > result to be expected or should the predictions be robust to noise? > > Best regards, > Dionysis
