Mario, I played around with your example a bit, substituting more general model params (not created from swarming). My source code is here:
- https://github.com/rhyolight/online-anomaly-detection/tree/master/mat_src Here is a video of it plotting: - http://youtu.be/tw1dmxcI8pU Doesn't look too bad, does it? This is only after NuPIC has seen 5-6K rows of data, and it's not flagging every QRS complex as an anomaly. Keep in mind I'm using a threshold to highlight the bottom chart as red when the anomaly likelihood goes above 0.9. --------- Matt Taylor OS Community Flag-Bearer Numenta On Tue, Nov 11, 2014 at 1:04 AM, Mario Tambos <[email protected]> wrote: > Hi guys, > > Thank you for your answers and sorry for the delay. > Here's a repo I've made with the code: > > https://github.com/mtambos/online-anomaly-detection > > The code for the OPF is in src/cla. It is bassically a modified version of > hotgym. > The data is in src/experiments/ecg1_chfdbchf13 > You can run the experiment with: src/run_ecg1_chfdbchf13.py --do_cla > >> Hi Mario, >> >> I am working on EEG data and anomaly likelihood and had a look at your >> info. How do you encode the time stamp and feed it into learning anomaly >> calcs? I could not see any time stamp encoding. > > > Matt is right, the timestamp is in the first column (marked in the header as > datetime and with flag "T"), but it's not stricktly needed. > >> >> Mario, any chance we can see your code? You haven't called >> "disableLearning()" on the model instance, have you? If you can push your >> code to an online repository, I'll try to run it. > > > Nope, I haven't called disableLearning() > >> >> Have you tried using the AnomalyLiklihood instead of the anomaly score? >> See >> >> https://github.com/numenta/nupic/tree/master/examples/opf/clients/hotgym/anomaly/one_gym#anomaly-likelihood >> for an example. > > > Yes, I'm using the anomaly_likelihood instead of anomaly_score. > > Thanks again, > > Mario
