Much learned: see Evernotes for this date and previous few days for more info.  Just about to refactor Context using prepend of module for context.  We'll see how it works

Did Refactoring and it was even simpler and cleaner than I had anticipated!!  Yeah Ruby

Minor deletions of debugging puts..

Did a lot with docker: rubyrunner and redisrunner containers were developed for extreme portability and potential parallelism using distributed containers.

Cleaning up; got rid of MultiPhaseTrainingSequencer; other simplifications.

Continuing clean-up.  Added args for selfOrg and supervised # of epochs.  Did some simple experiments.  All appears in working order.

A lot of refactoring and cleaning up again.  Modularized selfOrg, selfOrgWithContext, forwardPropWithContext, etc.  Now need to make sure that learning DOES occur across contexts in Proj4...

Working Fine, and then I doing "Much refactoring."  Need to test and debug tomorrow!!

Debugged a lot.  got it working, but need to clean-up significantly for future sanity

Still working on organizing arrays and arrays of arrays.

"Return Point" here.  I'm about to create 2 new classes: Layer and LayerArray

In midst of adding and incorporating Layer and LayerArray

Appear to have those classes working ok.  Now on to the fun stuff...

Got most exceptions added to Layer and LayerArray...

more of the same.  about done with the refactoring and error-checking and abstractions in LayerArray and Layer!!

more of the same.  All 3 mains are working.

still more reorganization and clean-up and refactoring

Finished first-cut of Experiment Runner sans said class.

Got some initial results that are somewhat encouraging, perhaps.  notes: check using sigmoid on output and other layers.  numbers of epochs for proj4 vs proj3

More results. storing in Proj3vsProj4Results.txt

Just confirmed that vertical and/or horizontal shifts of the 4 class dataset appear to produce the expected network weights, and very, very nearly the same MSE.

Now running 4 diff simulations: 2 with 30 degree rotations, with and without integration of training examples across 2nd layer of network.