Thanks for clarification. I'll try to compare how swarming is actually helpful for the problem.
On Sat, Oct 17, 2015 at 5:33 PM, Matthew Taylor <[email protected]> wrote: > The best way to get good predictions is to swarm over data and use the > best model returned by the swarm for that predicted field. That model is > not the one that is used in river-runner. I am using a generic set of model > params that work well for most scalar input fields. These params are not > optimized for prediction, so the predictions are not good. > > --------- > Matt Taylor > OS Community Flag-Bearer > Numenta > > On Sat, Oct 17, 2015 at 8:05 AM, Marek Otahal <[email protected]> > wrote: > >> Great timing Matt! >> >> I was just planing to run a bunch of NAB & River data through HTM for >> this weekend :) I guess https://github.com/nupic-community/river-runner >> is what I'm looking for, right? >> >> PS: I don't understand the note about predictions, afaik anomaly >> detection is based on predictions, so either the models are not optimized >> at all, or are optimized for predictions (and anomalies as well). >> PPS: This leads me to "Do you have optimized params for specific streams >> of River?" If not, are you interested in some? Where to publish..? >> >> Cheers, >> >> On Sat, Oct 17, 2015 at 4:37 PM, Matthew Taylor <[email protected]> wrote: >> >>> Hello NuPIC, >>> >>> Getting data out of River View is easier than ever with >>> https://github.com/nupic-community/riverpy. I have also updated >>> https://github.com/nupic-community/river-runner to use riverpy as a >>> data client, providing another example. >>> >>> This new client is useful because I have recently made changes in River >>> View that disallow the retrieval of very large amounts of data in one >>> request. Riverpy provides a data cursor with a way to navigate through >>> large streams of data easily. >>> >>> Regards, >>> --------- >>> Matt Taylor >>> OS Community Flag-Bearer >>> Numenta >>> >> >> >> >> -- >> Marek Otahal :o) >> > > -- Marek Otahal :o)
