public static (VectorND[] inputs, VectorND[] outputs) GetSquareSheets(double inputSize, double outputSize, int inputCountXY, int outputCountXY)
{
return
(
inputs : Math2D.GetCells_WithinSquare(inputSize, inputCountXY).
Select(o => new VectorND(o.center.X, o.center.Y, -1d)).
ToArray(),
outputs : Math2D.GetCells_WithinSquare(outputSize, outputCountXY).
Select(o => new VectorND(o.center.X, o.center.Y, 1d)).
ToArray()
);
}
public TrackedItemHarness(double mapSize, double visionSize, double outputSize, int inputSizeXY, int outputSizeXY, double delayBetweenInstances)
{
MapSize = mapSize;
VisionSize = visionSize;
OutputSize = outputSize;
InputSizeXY = inputSizeXY;
InputCellCenters = Math2D.GetCells_WithinSquare(visionSize, InputSizeXY).
Select(o => o.center).
ToArray();
OutputSizeXY = outputSizeXY;
OutputCellCenters = Math2D.GetCells_WithinSquare(outputSize, OutputSizeXY).
Select(o => o.center).
ToArray();
DelayBetweenInstances = delayBetweenInstances;
}
// This is built from these two sources:
//http://www.nashcoding.com/2010/10/29/tutorial-%E2%80%93-evolving-neural-networks-with-sharpneat-2-part-3/
//SharpNeat.Domains.BoxesVisualDiscrimination.BoxesVisualDiscriminationExperiment.CreateGenomeDecoder()
//TODO: Instead of hardcoding input and output as 2D squares, the constructor should take enums for common shapes { Line, Square, Cube, CirclePerimiter, CircleArea, SphereShell, SphereFilled }
public IGenomeDecoder <NeatGenome, IBlackBox> CreateGenomeDecoder(int inputSizeXY, int outputSizeXY)
{
int numInputs = inputSizeXY * inputSizeXY;
int numOutputs = outputSizeXY * outputSizeXY;
SubstrateNodeSet inputLayer = new SubstrateNodeSet(numInputs);
SubstrateNodeSet outputLayer = new SubstrateNodeSet(numOutputs);
// Each node in each layer needs a unique ID
// Node IDs start at 1. (bias node is always zero)
// The input nodes use ID range { 1, inputSize^2 } and
// the output nodes are the next outputSize^2.
uint inputId = 1;
uint outputId = Convert.ToUInt32(numInputs + 1);
var inputCells = Math2D.GetCells_WithinSquare(_inputSizeWorld, inputSizeXY);
var outputCells = Math2D.GetCells_WithinSquare(_outputSizeWorld, outputSizeXY);
for (int y = 0; y < inputSizeXY; y++)
{
for (int x = 0; x < inputSizeXY; x++)
{
inputId++;
outputId++;
Point inputPoint = inputCells[(y * inputSizeXY) + x].center;
Point outputPoint = outputCells[(y * outputSizeXY) + x].center;
inputLayer.NodeList.Add(new SubstrateNode(inputId, new double[] { inputPoint.X, inputPoint.Y, -1d }));
outputLayer.NodeList.Add(new SubstrateNode(outputId, new double[] { outputPoint.X, outputPoint.Y, 1d }));
}
}
List <SubstrateNodeSet> nodeSetList = new List <SubstrateNodeSet>();
nodeSetList.Add(inputLayer);
nodeSetList.Add(outputLayer);
//TODO: The two samples that I copied from have the same number of inputs and outputs. This mapping may be too simplistic when the counts are different
// Define a connection mapping from the input layer to the output layer.
List <NodeSetMapping> nodeSetMappingList = new List <NodeSetMapping>();
nodeSetMappingList.Add(NodeSetMapping.Create(0, 1, (double?)null));
// Construct the substrate using a steepened sigmoid as the phenome's
// activation function. All weights under 0.2 will not generate
// connections in the final phenome.
Substrate substrate = new Substrate(nodeSetList, DefaultActivationFunctionLibrary.CreateLibraryCppn(), 0, 0.2, 5, nodeSetMappingList);
// Create genome decoder. Decodes to a neural network packaged with
// an activation scheme that defines a fixed number of activations per evaluation.
IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder = new HyperNeatDecoder(substrate, _activationSchemeCppn, _activationScheme, false);
return(genomeDecoder);
}