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.. Regards, Fergal Byrne
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