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 > >
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