Conceptually I think it makes a lot of sense that nupic learns faster for specific inputs, if you group inputs individually.
When many sequences are entered at once, the SP can better learn how to represent each sequence as it is entered and the TM's lookup table will learn a very strong association. It's like in life, it's easier to learn one thing at a time than everything at once. On Wed, Dec 2, 2015 at 9:24 AM, Sebastián Narváez <[email protected]> wrote: > I have a training data with various sequences, which I pass trough a nupic > structure (made of SPs, TMs and a CLAClassifier on top). Some of the > sequences are alike (there are only one or two elements that change between > them). I've noticed that if I put the alike sequences together, one after > the other, nupic will learn the differences better than if I put them > appart. I reset() all the TMs when a sequence ends, so my guess is that it > has to do with the classifier. Anyone knows why could this happen? > Note that I have not done any serious tests about this. I could, but it > would take some time. >
