Now accumulating absolute flocking errors per neuron -- for our measure of 'dispersion.'  Will use this for "stop flocking", tuning, etc.  Also, will probably want to re-institute momentum during gradient descent for flocking.  Also, may want to consider ying-yanging outputError-FlockingError.  I.E. this would be a back and forth between sopBP and Flocking to reach the same Output Error but with dispersion minimized.  Specifically, on one epoch just sopBP would set a new OutputError point.  Then one epoch of flocking would improve dispersion but also would "hurt/loss" OutputError.  So sopBP would be appropriately tuned and applied to attempt to make-up for the outputError 'loss'.  More sopBP iterations may be required, and then more flocking... until the same initial outputError set-point is reached, but with much less dispersion.
Merge remote-tracking branch 'origin/normalizeFirst' into normalizeFirst

Conflicts:
	lib/core/Trainers.rb
Have gotten deltaAbsFlockingError working as a first draft.   Now examining convergence data.