Thanks, Matt. The warnings I was referring to were from the API overview page <https://github.com/numenta/nupic/wiki/NuPIC-API---A-bird's-eye-view> on the wiki.
I'll do some more digging, and possibly follow up with more questions in subsequent threads. Beau On Fri, Jun 27, 2014 at 8:37 AM, Matthew Taylor <[email protected]> wrote: > You could use the OPF for this. What warnings about the state of the code > are you referring to? > > If you want to make predictions about many fields at the same time, you'll > need to create a model for each field you want to predict. Remember that > swarming is not a part of running NuPIC, it is just a step you might want > to run before you create a NuPIC model so you can find the best model > parameters for your data. You don't need to swarm, and in fact the swarming > logic is not a part of the OPF. > > It is hard to find the right model parameters for your data without > running a swarm. Without it, you end up manually tuning the model params > starting from a best guess in your head. The swarm will provide a best > guess that is more likely to be successful, and then you can manually tune > the model params from there. > > My suggestion is to create one data set with all the sensor data you want > in columns, each row marked with a timestamp, aggregated and normalized so > that each row has a data value for each column (this is not necessary, as > far as I know, a value within a row might be empty if there is no data). > Then for each field you want to predict, run a swarm that defines that > predicted field, saving the model params off to the side to use later. Then > when you have model params for each field, instantiate CLA models through > the OPF for each model param, and go through your data and pass each row > into each model. Each model will be tuned to predict a specific field in > the data, so you'll have your predictions for each field. > > > --------- > Matt Taylor > OS Community Flag-Bearer > Numenta > > > On Thu, Jun 26, 2014 at 2:51 PM, Beau Cronin <[email protected]> > wrote: > >> Hi nupic-land! >> >> I will be gathering data over a long period of time from multiple sensors >> mounted on a common platform - GPS, sound level, light level, humidity, >> temperature, accelerometer - as well as gathering data from public APIs, >> including weather and transit conditions. So, my dataset will consist of >> multiple measurement streams, measured at different temporal resolutions >> (maybe 100-1000 Hz for accelerometer, 0.1 Hz for GPS, once a minute for >> transit, etc.). Each stream will have timestamps that allow me to keep them >> in register, so I'll know how they line up. >> >> I want to build a system that learns the joint structure of these >> streams, as well as making predictions about their values in the future. I >> expect this structure to be rich and hierarchical. >> >> My question is: which level of the NUPIC API should I use to build this? >> I suspect I'll want a non-trivial region topology to capture phenomena at >> different levels and time scales, which makes me think I should at least >> use the Network level. But that's in C++, if I understand correctly, and >> I'd much rather use python. This would suggest the OPF, but the warnings >> about the current state of that code scare me - and I'd like to be close >> enough to the action that I can dig in and understand what's going on, >> rather than, say, use swarming to treat the system as more of a black box. >> >> Any suggestions appreciated. >> >> Best, >> >> Beau >> >> _______________________________________________ >> nupic mailing list >> [email protected] >> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org >> >> > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > >
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