Thanks Matt :). The fact that it predicts the last value it saw when confused explains a lot! I’ll definitely look out for Subutai’s (timely!) presentation.
best, Nick On Apr 26, 2014, at 6:48 PM, Matthew Taylor <[email protected]> wrote: > Comments inline... > > On Sat, Apr 26, 2014 at 6:30 AM, Nicholas Mitri <[email protected]> wrote: > Hello all, > > I’m a little confused by how Input/prediction pairs are formed and plotted in > some of the tutorials. I’ve attached a 1 step prediction plot for the sine > wave tutorial as well as 1 step and 5 step prediction plots for an apnea > predictor I’m working on for reference. > My understanding is that the number of steps specified for multi step > prediction dictates the delay used to associate an input with the proper > prediction. So, following the sine wave tutorial, part of the code includes a > shifter class that (I assume) queues the predictions until they’re needed. > > I'm not exactly sure how the shifter works, but you're probably right. > > Looking at the sine plot attached, I don’t understand the shifted plots i.e. > the lag in the predicted plot. > > While the prediction is usually lagging, there are times when it is not, > which means the shifting is occurring properly. The results don't look that > usual to me, because you're only a few hundred rows into the data. It takes > quite awhile for NuPIC to get better at predicting sines (it's not very good > at it anyway). > > Also, the anomaly score seems erratic. It’s 1 between samples 60 and 100 when > the 2 plots are identical, which is weird in itself since the model hasn’t > had enough time to learn the bottom portion of the sine wave. > > This doesn't seem unusual to me either. During the time when the score is a > flat 1.0, it is continuously wrong with its prediction, simply predicting the > last value it saw (that's what is usually does when it doesn't understand any > patterns). So an anomaly score of 1.0 during this period makes sense because > every value it sees next is misunderstood. > > > The same applies to the other attached plots where anomaly is max in segments > where prediction and input are in line, low in segments where they are off, > and 0 for very low input values which is misleading. > > I’d appreciate some guidance in analyzing these plots :). > > You know who knows a lot about anomaly scores? ;) Subutai Ahmad. Luckily, > he'll be giving a presentation about them [1] at the hackathon next weekend, > and I'll be streaming it live on UStream. > > [1] > http://numenta.org/hack/schedule/#anomaly_detection_using_the_cortical_learning_algorithm_subutai_ahmad > --------- > Matt Taylor > OS Community Flag-Bearer > Numenta > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org
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