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Evolutionary Learning


Explicit Components

Loop

Octopus


Supervised Learning

Utilities

View

Network Learning (oSL)

Supervised example training of a Network by multi-core resilient propagation algorithm, using the Encog library by Jeff Heaton

Inputs

Name	ID	Description	Type
Input	Input	Input Values [x lists (x = number of training samples) of y numbers (y = dimensionality of sample input); Input and Output can be one or many dimensional, and the number of dimensions can be different]	Number
Output	Output	Output Values [x lists (x = number of training samples) of z numbers (z = dimensionality of sample output); Input and Output can be one or many dimensional, and the number of dimensions can be different]	Number
Layers	Layers	Hidden Layer Count	Integer
Nodes	Nodes	Node Count per Hidden Layer, 0 = Maximum of Input and Output Dimensionality	Integer
Seed	Seed	Random Seed	Integer
SeedNet	SeedNet	Seed Network to continue training on it; a seed model overrides the internal model which is saved between updates of the component if 'reset' is false.	Network oSL
On	On	Switch Off to not calculate anything.	Boolean
Settings	Settings	Settings for the Learning process	RPROP Learning Settings

Outputs

Name	ID	Description	Type
Network	Network	Trained Network	Network oSL
Err	Err	Error rates during the history of Learning Steps	Number
Hist	Hist	History of Networks during Learning	Network oSL
Iter	Iter	Iteration Count	Integer
Time	Time	Total time spent on learning in milliseconds	Number