Ah, of course. This is the one step in the process that I am having trouble replicating (I'm trying to create a variation of semantic folding for use as a "universal encoder"). I can replicate everything up to the step where I've generated a 1D word/context vector, but in the whitepaper it then states:
the contexts represent vectors that can be used to create a two - dimensional map in such a way that similar context - vectors are placed closer to each, using topological (local) inhibition mechanisms and/or by using competitive Hebbian learning principles.
I'm wondering if anyone has a little more detail on how this is done. I suppose topology isn't necessarily needed in my case, but it would help fill the remaining gap in my understanding of the process.