Thanks, Subutai. Yes, this does help! One question: traditional neural nets can be thought of as a function where you have input and output and you train it (supervised learning) to give the right output for given input. These NNs are not sequence based. I understand HTM is more advanced than this, but can it behave like a function as well? Eg, if tilt(t) = F[temp(t)] can HTM find this function? (current value from current value) or can it only discover:
tilt(t) = F[tilt(t-1), tilt(t-2),..., temp(t-1), temp(t-2)..] ? (ie current value from past values) On Tue, Sep 30, 2014 at 7:28 PM, Subutai Ahmad <[email protected]> wrote: > On Tue, Sep 30, 2014 at 3:47 AM, John Blackburn < > [email protected]> wrote: > >> What I want it to do is primarily predict tilt(t+1) from temperature(t), >> I do not want it to predict temperature(t+1) from temperature(t) as this >> will not work well with the data. (unlike Hotgym, the temperature cannot be >> predicted from previous temperatures) >> > > John, > > I'm not sure if this is a terminology thing, but I want to clarify the > above statement to make sure we're on the same page. If tilt is the > predicted field, NuPIC will always try to predict tilt(t+1) from tilt(t), > tilt(t-1), title(t-2), ..., etc. It will only include temperature(t) if it > helps. Note that if temperature(t) is completely correlated with tilt(t), > adding temperature(t) will not help, because the information is already > available in tilt(t). > > So, the swarm does not look for correlations between fields. It only looks > to see which fields add value over and above the past values of the > predicted field. > > My apologies if this was already obvious to you! This use of past values > is a very big difference from traditional batch analytics, so just wanted > to be clear. > > --Subutai > >
