Hello NuPIC, I would like to introduce a project I've been working on called menorah:
https://github.com/nupic-community/menorah Menorah makes it really easy to run data directly from River View into NuPIC. With it, you can swarm over RV data, then turn around and create a model from the best model params from the swarm, immediately passing the live River View data into the model. It will also work with multiple streams of data, so you can specify many data feeds to be passed into your model. Menorah will pull all data from River View, format it into the proper CSV format required by NuPIC, and run a swarm for a specified predicted field. Because you specify a working directory for each menorah instance, all the NuPIC artifacts from your swarm will be stored there to be easily re-used later to run your models. Have a look at a quick example from the README: ======================================== from datetime import datetime from menorah import Menorah sources = [ ["chicago-311", "Tree Debris", "aggregate=1 day"], ["chicago-beach-weather", "Foster Weather Station", "humidity"], ["chicago-beach-weather", "Foster Weather Station", "interval_rain"], ["chicago-beach-water-quality", "Osterman Beach", "wave_height"], ] menorah = Menorah( sources, "work/example5-multifield-aggregated", since=datetime(2015, 5, 20) ) menorah.swarm(swarmParams={"swarmSize":"large"}) menorah.runModel(plot=True) ======================================== These few lines of code will load 4 River View streams into memory and swarm over all that data with the "Tree Debris" as the predicted field. Immediately after the swarm, the "runModel()" call will reset the data streams and feed all the same data into a new model created using the best model params returned by the swarm. This could be really useful for some of you having trouble getting your data into the right format. If you can write a River View parser for your data, you can make NuPIC predict it! Hope this is helpful, --------- Matt Taylor OS Community Flag-Bearer Numenta
