Now integrating Contextual Learning and Self-Org "modules"

First looks appear good.  Except that the code is a bit "clumsy."  But problem refactoring as well as code refactoring should help a whole lot.

Just updated example rotation code and applied it to current selfOrgContext project.

continuing to rework 2 hidden layer Project2 (SelfOrg and Context).

Considering "serious refactoring" of links, neurons, and training....

Started using "strategy pattern" for plugging (possibly cascaded) learning modules within neurons.  May also want to consider doing this for layers also.

Continuing to make more progress on implementing Learning Strategy daisy-chained objects... for a  variety of learning methods: bp self-org...

Will try to better abstract/generisize function names across learning strategies.

Making major refactoring on Trainer and Learning Strategies that attach to Neurons.

OK, now have simple bp demo working with strategy neurons.

Made major strides on refractoring the trainer, by using learning strategy and very simple, clear-cut methods in the training class.

Wow, looking good.  Now have simple normalization and self-org strategies working.  More testing...  Then combining with context strategy.

Just finished coding, but not testing, the context adapter.  Had to deal with different arguments at initialization for the adapter than that for the learning strategies.  Lookin good so far.

Still more progress...

Proj1NeuronSelfOrgAndContext appears to be working now.  Also got context adapter to perform similar function to "drop-out" related to neuron's output. -- which, now, simply 'drops' to its 'default' output of ioFunction(netInput = 0.0).

Now working on project 2 that has 2 hidden layers and an output layer.  The hidden layers will be self-orged, and then the output layer will be supervised 'learned.'

Seemed have all 4 layers working: normalization, self-org on hiddenLayer1, then on hiddenLayer2, then supervised backprop on the output layer.