I've been playing around with NuPIC for a while and am still trying to wrap my head around how to use it. Right now I'm playing with some prediction scenarios where you have a number of input fields and you're trying to predict one output.
My understaning is that if the inputs aren't related temporally, then it's a Spatial Pooling problem. If there are common patterns in the data, then it may be helpful to create hierarchies of SPs. The data I'm looking at right now probably doesn't have common patterns. It's basically a bunch of categorical data from which you're trying to predict a boolean outcome. There are about 15M rows in the training set. So my thinking is to create 1 SP where the inputDimensions is wide enough to accomodate all of the fields and columnDimensions sized so that rows get grouped together. (If there were 100k columns, then on average 150 rows would be pooled together.) In theory I could run all of the training data through the SP, then run it through again (without learning) and calculate an outcome probability for each column. Then I could run the test data through and it's probability would be the probability of the column it matches. Is that a reasonable approach or am I way out in left field? Thanks, Ryan
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