Thanks Fergal :) Actually, i am testing the application using artificially generated data based on the distribution factor.
On Fri, Aug 9, 2013 at 9:01 PM, Fergal Byrne <[email protected]>wrote: > > Hi Ramesh, > > It depends on your data (everything always depends on your data!). > > If, for example, you are trying to learn and predict stock prices for 80 > stocks, then you have a couple of choices: > > 1. Keep them all separate, encode each separately, and feed a 10k bit > array into the CLA > > 2. Group them by industry sector (or some other grouping), combine the > group of 128-bit arrays for the CLA. > > 3. Add them all together and feed the single 128-bit value into the CLA. > > What you'll get in each case: > > 1. Predictions of all your stocks, informed (hopefully) by any (possibly > hidden) correlative relationship among stocks. > > 2. Same as 1 but with likely better performance (less noise, more > correlation). > > 3. Predictions of the "index" of all your stocks (like the S&P 500) > > When you're deciding how much "detail" to feed the CLA, you will > conversely be deciding how much noise you're feeding it. The CLA is > supposed to learn from whatever predictive information is to be found > embedded in the data, it'll do this (hopefully) if that information is > there, somewhere. Your job is to oversee the diet of data and discover the > best recipe for successful prediction. > > One way to picture how the CLA learns is to regard it as building a > structure of causal flow in space (ie across the input array, across the > region) and in time (from one pattern in a sequence to the next), in > response to the analogous flows of the data. It does this by making > synaptic connections in the SP (for the data) and with previously active > cells (for sequence memory and prediction). > > These connections are constantly adjusting to try and better match the > experienced flows of the data. The plan is for noise (or spurious, > non-structural changes in data) to cancel itself out over time, while > information should monotonically (in toto) improve the structure. > > So, if you think your 80-stock-wide 10k bit array is a high-information > diet, feed it into NuPIC and see if it can give you a) stable SDR's out of > the SP, and b) any kind of predictive capacity! Make sure to donate 10% of > your earnings to Jeff's favourite charity.. > > I am already doing it through contributing 10% of my time :) > Regards, > > Fergal Byrne > > > > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > >
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