Nick,

I don't have an answer to your particular problem, but I want to point out a 
resource that may help.

It's new and experimental, but Cerebro 2 will let you see the state of your 
model at each point in time, including the connections in the SP.
https://github.com/chetan51/nupic.cerebro2

Perhaps you can use it to see more clearly what the SP is doing. Let me know 
how it goes!

- Chetan

On May 12, 2014 at 6:42:19 AM, Nicholas Mitri ([email protected]) wrote:

Hey all,  

So I’ve been running clustering tests on the SP and comparing it against other 
algorithms.  

I’m passing the data points through the SP for several passes while restricting 
the number of columns to that of the desired cluster number.  
The results, unfortunately, seem to be very inconsistent. Using the RAND index 
to evaluate the outcome relative to the true labels of the data points, I’m 
getting results varying between 56% and 95% on one of Wine database for 
example.  

Where is this inconsistency coming from? I‘ve set the potential pool to span 
the entire input with potentialPCT set to 1. I thought that would reduce the 
effect of the random permanence initialization step. It evidently hasn’t.  

Any tips are appreciated!  

best,  
Nick  
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