Classification: NPL Management Ltd - Public Thank you Matt. Those links are very useful!
I am working with John Blackburn at the National Physical Laboratory on some data we have from sensors on a bridge. We were wondering if we can get NuPIC to learn with the data for the first 6 months and then get it just to continue to just predict or show anomalies for the remainder of the data? Essentially, is it possible to turn learning off at some point during a run? Regards, Cavan From: nupic [mailto:[email protected]] On Behalf Of Matthew Taylor Sent: 18 August 2014 17:00 To: NuPIC general mailing list. Subject: Re: [nupic-discuss] Swarming [NC] Hi Cavan, Our documentation on model parameters is not great, so I created this ticket to track progress on filling it in: https://github.com/numenta/nupic/issues/1214. You can find the docs we do have here: https://github.com/numenta/nupic/wiki/Models#modelparams-schema Also, have you seen these wiki pages about swarms? - https://github.com/numenta/nupic/wiki/Running-Swarms - https://github.com/numenta/nupic/wiki/Swarming-Algorithm Regards, --------- Matt Taylor OS Community Flag-Bearer Numenta On Fri, Aug 8, 2014 at 7:40 AM, Cavan Day-Lewis < [email protected]> wrote: Classification: NPL Management Ltd - Commercial Hi, I was wondering if someone could answer some questions I have about swarming? In the "rec_centre_hourly_model_params.py" file (using the hotgym tutorial as an example) outputted after a swarm what do the parameters it produces mean? What are they used for? And why are they important? Also, without looking into the algorithms, what sort of calculations is the swarm making? I understand It goes through the input data and goes through different models and an optimization process (PSO) where it analyses which model best suits the dataset. I also understand that the swarm finishes when the error score no longer decreases, but how do you generate the error score? What are the technical differences between the different swarm sizes? Again, I know a bit about it but an explanation would be great. In particular, I would be very grateful if someone could explain what the following parameters within the model_params.py file represent: 'clParams': { 'alpha': '_classifierInput': {'n': 'spParams':{ 'synPermInactiveDec': 'tpParams': { 'activationThreshold': 'tpParams': { 'minThreshold': 'tpParams': { 'pamLength': I have just been watching the following video and found it very helpful but I still have some questions. https://www.youtube.com/watch?v=xYPKjKQ4YZ0 Apologies if these questions have already been asked before, link me to a feed if so. Thanks in advance, Cavan Mr Cavan Day-Lewis Vacation Student National Physical Laboratory Hampton Rd | Teddington | Middlesex | UK | TW11 0LW t: 020 8943 XXXX e: [email protected] w: www.npl.co.uk ________________________________ If you have received this message in error, please notify us and remove it from your system. NPL Management Ltd cannot guarantee that the e-mail or any attachments are free from viruses. NPL Management Ltd is a company registered in England and Wales, number: 2937881 Registered office: Serco House | 16 Bartley Wood Business Park | Hook, Hampshire | UK | RG27 9UY ________________________________ <http://www.npl.co.uk/> STAY CONNECTED <http://www.twitter.com/npl> <http://www.facebook.com/npldigital> <http://www.youtube.com/npldigital> <http://itunes.apple.com/us/podcast/national-physical-laboratory/id29507 6289> <https://ktn.innovateuk.org/web/measurement-network> _______________________________________________ nupic mailing list [email protected] http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org
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