Thanks, I will follow through the tutorial you posted and then try again. I will post the swarm description then.
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) If I set the predicted field as "tilt" will it refrain from trying to predict the temperature also? I want to avoid it trying to predict temperature. Basically I want to shut down some of the prediction combinations in favour of the useful ones. I could imagine doing this manually (but don't know how to do it) but the best would be if the HTM can figure out the useful combinations itself. I think you are saying swarming does this? I will see if it works. John. On Mon, Sep 29, 2014 at 6:24 PM, Subutai Ahmad <[email protected]> wrote: > Hi John, > > I don't recall if you were swarming over the data. If so, did you set tilt > as the predicted field in the swarm? If so, it should always use past > values of tilt to predict future values. Additionally, if temperature > provides incremental information over and above past values of tilt, it > should also include that in the model. Maybe you could post your swarm > parameters here and someone could help you debug. > > Thanks, > > --Subutai > > On Mon, Sep 29, 2014 at 10:04 AM, John Blackburn < > [email protected]> wrote: > >> Subatai, >> >> I think the problem is, we are feeding temperature and tilt and NuPIC is >> saying "temperature and tilt are strongly correlated, so I'll forget the >> tilt and just try to predict temperature". The trouble is temperature is a >> weather effect and fundamentally unpredictable. It's hotter in the day and >> during the summer but the precise details are unpredictable. That is, the >> temperature data is not nearly as cyclic as the Hotgym example etc. >> >> We need some way of telling Nupic "you don't need to predict temperature, >> you need to predict tilt GIVEN the temperature". You might argue this is >> the sort of task a traditional stateless perceptron network would do >> better, but the best result so far has been achieved with an echo state >> network (ESN). The ESN had 10 temperature inputs, 8 tilt outputs and was >> trained to give the right outputs (supervised learning). Between the input >> and outputs is a "reservoir" of randomly connected recurrent neurons which >> maintains a short term memory. Thus the ESN has a sense of time and >> sequence like the HTM. But it also has the concept of "cause and effect" >> which I think HTM lacks (or please correct me). >> >> Analogously, Pavlov's dogs were able to predict that food would come >> after a bell rang. The ringing of the bell was unpredictable but the food >> was predictable given the ringing of the bell. This is analogous to the >> bridge with bell = temperature, food = tilt. Can HTM "reason" like the dogs >> did? >> >> On Fri, Sep 26, 2014 at 9:35 PM, Subutai Ahmad <[email protected]> >> wrote: >> >>> >>> >if you have a problem with big stationary correlational structure in >>> the inputs-- >>> >you should transform it out so NuPIC can work on the non >>> obvious features >>> >>> Agreed. In some sense this is what pooling does. The spatial pooler >>> transforms spatially correlated (but very different) patterns into a >>> consistent SDR pattern. The temporal pooler transforms temporally >>> predictable (but potentially totally different) SDR patterns into a single >>> coherent SDR representation. The encoders also can help greatly with this. >>> >>> --Subutai >>> >>> On Fri, Sep 26, 2014 at 12:01 AM, Archie, Kevin <[email protected]> >>> wrote: >>> >>>> Subutai, >>>> >>>> Thanks for the link, interesting reading. I'm amused by the problem >>>> of it being (1) a problem NuPIC isn't really well suited to and (2) the >>>> first thing lots of people are going to try anyway. >>>> >>>> Thinking about statistics, it seems to me that if you have a problem >>>> with big stationary correlational structure in the inputs--either temporal, >>>> as the sine wave example, or spatial, as John's bridge data--you should >>>> transform it out so NuPIC can work on the nonobvious features. Thinking >>>> about biology, by the time you get to cortex a lot of the stationary >>>> structure has been filtered out -- think about the processing in the >>>> retina. It's possible that NuPIC is good enough to solve lots of >>>> interesting problems even without preprocessing, but I suspect that some >>>> care to the input representation could greatly help with performance (in >>>> cycles, at least by reducing the input dimensionality) and performance (in >>>> error rate, by getting rid of lots of chaff). >>>> >>>> Or I could be all wet. It happens. >>>> >>>> - k >>>> >>>> On Sep 25, 2014, at 1:53 PM, Subutai Ahmad wrote: >>>> >>>> Hi Kevin, >>>> >>>> I did some simple experiments with swarming and correlated inputs >>>> [1]. One thing to note is that temporal correlation / sequence structure is >>>> also very important. That is independent from spatial correlation. >>>> >>>> --Subutai >>>> >>>> [1] >>>> https://github.com/subutai/nupic.subutai/tree/master/swarm_examples >>>> >>>> On Thu, Sep 25, 2014 at 11:33 AM, Archie, Kevin <[email protected]> >>>> wrote: >>>> >>>>> John, >>>>> >>>>> I think this is an example of an important general case. On both >>>>> statistical and biological grounds I suspect you should decorrelate your >>>>> inputs before producing SDRs from them. I haven't tried this myself (have >>>>> hardly done anything with NuPIC) but I'm wondering if anyone has done >>>>> substantial preprocessing to compensate for the statistics of the inputs. >>>>> Even better would be if anyone has done a comparison of feeding NuPIC >>>>> decorrelated vs. direct sensor inputs--or a theoretical argument that >>>>> decorrelating the inputs is unnecessary or unwise. >>>>> >>>>> - Kevin >>>>> >>>>> ------------------------------ >>>>> *From:* nupic [[email protected]] on behalf of John >>>>> Blackburn [[email protected]] >>>>> *Sent:* Tuesday, September 23, 2014 7:27 AM >>>>> *To:* Archie, Kevin >>>>> *Subject:* Re: Which NuPIC tutorial do you want to see next? >>>>> >>>>> Hi Matthew, >>>>> >>>>> Rather self serving, but I would love to see a tutorial related to >>>>> the "bridge" simulation I've been trying to do or similar. I have not got >>>>> NuPIC to work yet despite some effort. Basically the difference with >>>>> Hotgym >>>>> is we have 18 sensors, 10 temperature and 8 tilt (ie strain) and we want >>>>> to >>>>> make predictions on all taking account of cross-correlations. So a >>>>> tutorial >>>>> with multiple correlated time series would be great! >>>>> >>>>> At NPL we monitored a bridge every 5 minutes for 3 years recording >>>>> all 18 sensors so I think this data would be a great showcase for a >>>>> real-world NuPIC example. We also perturbed the bridge at known times >>>>> (cutting supports, adding weights etc) so we know when the anomalies >>>>> should >>>>> appear. >>>>> >>>>> John. >>>>> >>>>> On Mon, Sep 22, 2014 at 4:57 PM, Matthew Taylor <[email protected]> >>>>> wrote: >>>>> >>>>>> I have more tutorials planned, but I'd like some help deciding which >>>>>> to do first. Please answer this 1-question poll: >>>>>> >>>>>> >>>>>> https://docs.google.com/forms/d/1GBYWg_-LIaYmOz9EJ5LbFo6N2ot1xv9AA22gaNdENs0/viewform?usp=send_form >>>>>> >>>>>> Thanks, >>>>>> --------- >>>>>> Matt Taylor >>>>>> OS Community Flag-Bearer >>>>>> Numenta >>>>>> >>>>>> >>>>> >>>>> ------------------------------ >>>>> >>>>> The material in this message is private and may contain Protected >>>>> Healthcare Information (PHI). If you are not the intended recipient, be >>>>> advised that any unauthorized use, disclosure, copying or the taking of >>>>> any >>>>> action in reliance on the contents of this information is strictly >>>>> prohibited. If you have received this email in error, please immediately >>>>> notify the sender via telephone or return mail. >>>>> >>>> >>>> >>>> >>> >> >
