public NeuralNetworkIndividual(
Random random,
int inputNodes,
int outputNodes,
int maxLayers,
int maxInnerNodes)
{
this.random = random;
this.inputNodesEnabled = new int[inputNodes];
for (int i = 0; i < inputNodes; i++)
{
if (random.NextDouble() > 0.5)
{
this.inputNodesEnabled[i] = 1;
this.inputNodes++;
}
}
this.maxInnerNodes = maxInnerNodes;
var innerLayers = random.Next(maxLayers);
layers = new int[innerLayers + 2];
layers[0] = this.inputNodes;
for (int i = 1; i < innerLayers + 1; i++)
{
layers[i] = random.Next(maxInnerNodes);
}
layers[innerLayers + 1] = outputNodes;
Network = new MLPNeuralNetwork(layers);
}
public NeuralNetworkIndividual(
Random random,
int inputNodes,
int maxInnerNodes,
int[] layers)
{
this.random = random;
this.inputNodes = inputNodes;
this.maxInnerNodes = maxInnerNodes;
this.layers = layers;
Network = new MLPNeuralNetwork(layers);
}
public static MLPNeuralNetwork GetTrainedNetwork(
List <float[]> inputData,
List <float[]> expectedOutputData,
Action <int, int> reportProgress)
{
if (!inputData.Any() || !expectedOutputData.Any())
{
throw new Exception("Input data and expected output data must contain values.");
}
if (inputData.Count() != expectedOutputData.Count())
{
throw new Exception("Input data and expected output data are not the same length.");
}
var inputNodes = inputData.First().Length;
var outputNodes = expectedOutputData.First().Length;
var innerLayers = 2;
var hiddenLayerDepth = 16;
// Inner Layer + Input + Output = 3
var layers = new int[innerLayers + 2];
layers[0] = inputNodes;
for (int i = 1; i < innerLayers + 1; i++)
{
layers[i] = hiddenLayerDepth;
}
layers[innerLayers + 1] = outputNodes;
// Create the NN.
var network = new MLPNeuralNetwork(layers);
// Train the network with our known good data.
network.Train(inputData, expectedOutputData, reportProgress);
return(network);
}
