Ok, with this and previous commit I'm working on CONTEXT.  -- to test learning only on part of the example set.  Other parts of the network learn different subsets of the examples, determined by previous layers' neural outputs.  Then we attempt to find which neurons are also able to easily learn other examples, using the non-mon neural function for flocking.   MADE some real coding progress today, I think!!  Next need to deal with "when each layer learns."

Greatly stripped/simplified Trainers.rb

Basic BPDemo using the 'new' stripped/simplified Trainers.rb seems to be working fine!!

Got the initial version of Project7pt1 working!!

Working on Self-Org using NonMon neuron

Seem to have SelfOrgDemo working at first cut!!

Improved Training Sequencer

Now in process of incorporating SelfOrg code into project7pt1 code.

Just got "Self-Org + 1st layer of Context Learning" working, I think!  ... Wrong, I had to make a number of changes.  Now into partial refactoring.

Project7pt1 is much closer to implementing intended divide-learning, then reintegrate using a combination of context learning and self-org learning.

Continuing experiments to validate context and self-org functions.  Now need to try "contrived noise" in example set.

Still validating and refactoring a bit.  so far so good.  Just about to play with variations on basic training set.

Now learning about initial conditions relative to sel-org using current system.  setting the 3 initial weights for 1-neuron 1-input network, and the resulting partitioning hyperplane makes good sense so far.  May adjust algorithm according to what I've learned.

Normalizing, both the mean and max-range, significantly improves the self-organization.

Now in process of implementing normalization of neuron inputs via the neurons input links.

Looks like implementation, using links to normalize each of the inputs to a neuron, is working.

Finally, this version looks pretty practical.  When performing self org, (1) First normalize inputs within links, (2) then perform simple bi-clustering self-org,  and (3) then, finally, un-normalize to determine correct weights (including bias weight). -- Step (3) is important because we want to use neurons for further learning of various forms.