Hi,
On Sun, Apr 27, 2014 at 1:33 AM, Ritchie Lee <[email protected]> wrote: > Hi friends of NuPIC, > > Here is another example that I ran in my quest to understand how CLA > works. This is a 1D example where the signal is a discrete step from 1 to > 2 with small random Gaussian noise added throughout. Learning is on for the > first 5000 and off for the remainder. i.e., CLA training does not see the > anomaly. Here are the plots: 1. signal in black, predicted in blue. 2. > residual = abs(predicted-signal) 3. anomaly score. > > I specified specifically that CLA should use a ScalarEncoder with min=0.8 > and max=2.2. > just a check, did you use "enough bits" in `w` for good results and `resolution`, right? > Now the question is why does the anomaly score not react to the anomaly > at all? I expect it to spike at the step and in addition to that stay > excited throughout the unseen portion. > Great! this would confirm my theory in the previous mail to you. (#3) is a bug! maybe we can return last-seen as predicted (active cells), but we must then set predictive cells to all 1s (anomaly) -- Marek Otahal :o)
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