public Neuron(int index, Layer initLayer, Neuron.ActivationType actType = ActivationType.SIGMOID, double bias = 0)
{
Index = index;
Connections = new List <Connection>();
SelfLayer = initLayer;
ActType = actType;
Bias = bias;
}
public Layer(int neuronCount, Neuron.ActivationType neuronType, LearningType learnMethod)
{
Neurons = new Neuron[neuronCount];
NeuronType = neuronType;
LearningMethod = learnMethod;
InitializeNeurons();
}
public NeuronLayer(int neuronCount, int inputs, Neuron.ActivationType activationType)
{
neurons = new Neuron[neuronCount];
for (int i = 0; i < neuronCount; i++)
{
neurons [i] = new Neuron(inputs, activationType);
}
output = new double[neuronCount];
}
public Layer(int index, int numNeurons, Neuron.ActivationType actTye = Neuron.ActivationType.SIGMOID, IEnumerable <double> initBias = null)
{
Index = index;
NumNeurons = numNeurons;
ActType = actTye;
Neurons = new Neuron[NumNeurons];
if (initBias != null)
{
for (int i = 0; i < NumNeurons; i++)
{
Neurons[i] = new Neuron(i, this, ActType, initBias.ElementAt(i));
}
}
else
{
for (int i = 0; i < NumNeurons; i++)
{
Neurons[i] = new Neuron(i, this, ActType);
}
}
}
private Neuron.ActivationType _activationType; // ActivationType
public NeuralNetwork(Genome genome, Neuron.ActivationType activationType = Neuron.ActivationType.SIGMOID, bool bias = true, int timeOut = 1000)
{
_activationType = activationType;
_bias = bias;
_timeOut = timeOut;
_inputIds = new List <int>();
_outputIds = new List <int>();
_neurons = new Dictionary <int, Neuron>();
_unprocessedNeurons = new List <Neuron>();
Genome = genome;
Fitness = 0f;
// Fill Neurons, Input & Outputs Ids w/ Nodes in Genome
foreach (int nodeId in genome.Nodes.Keys)
{
NodeGene node = genome.Nodes[nodeId];
Neuron neuron = null;
// Input & Output Node
if (node.Type == NodeGene.TYPE.INPUT)
{
neuron = new Neuron(Neuron.NeuronType.INPUT, _activationType, false); // No bias on Input Node
neuron.AddInputConnection();
_inputIds.Add(nodeId);
}
else if (node.Type == NodeGene.TYPE.OUTPUT)
{
neuron = new Neuron(Neuron.NeuronType.OUTPUT, _activationType, _bias);
_outputIds.Add(nodeId);
}
else
{
neuron = new Neuron(Neuron.NeuronType.HIDDEN, _activationType, _bias);
}
// Add Neuron to the Network
_neurons.Add(nodeId, neuron);
}
_inputIds.Sort();
_outputIds.Sort();
// Add Genome Connections to Neurons
foreach (int connectionId in genome.Connections.Keys)
{
ConnectionGene connection = genome.Connections[connectionId];
// Ignore Disabled Nodes
if (!connection.IsEnable)
{
continue;
}
// Add Output Connection to the Neuron of the emitting Node
Neuron emittingNeuron = _neurons[connection.InNode];
emittingNeuron.AddOutputConnection(connection.OutNode, connection.Weight);
// Add Input Connection to the Neuron of the receiving Node
Neuron receivingNeuron = _neurons[connection.OutNode];
receivingNeuron.AddInputConnection();
}
}
public ArtificialNeuralNetwork(int inputCount, int outputCount, int hiddenLayers, int neuronsPerLayer, double alpha, Neuron.ActivationType firstLayerActivation, Neuron.ActivationType hiddenLayerActivation, Neuron.ActivationType outputLayerActivation)
{
m_inputCount = inputCount;
m_alpha = alpha;
if (hiddenLayers < 0)
{
hiddenLayers = 0;
}
layers = new NeuronLayer[hiddenLayers + 1];
if (hiddenLayers > 0)
{
layers [0] = new NeuronLayer(neuronsPerLayer, inputCount, firstLayerActivation);
for (int i = 1; i < hiddenLayers; i++)
{
layers [i] = new NeuronLayer(neuronsPerLayer, neuronsPerLayer, hiddenLayerActivation);
}
layers [hiddenLayers] = new NeuronLayer(outputCount, neuronsPerLayer, outputLayerActivation);
}
else
{
layers [0] = new NeuronLayer(outputCount, inputCount, outputLayerActivation);
}
}
/// <summary>
/// Starts the neural network with full controls of its setup.
/// </summary>
/// <param name="inputAmmount">Input ammount.</param>
/// <param name="outputAmmount">Output ammount.</param>
/// <param name="hiddenLayers">Hidden layers.</param>
/// <param name="neuronsPerLayer">Neurons per layer.</param>
/// <param name="alpha">Alpha.</param>
/// <param name="inputActivationMethod">Input activation method.</param>
/// <param name="hiddenLayerActivationMethod">Hidden layer activation method.</param>
/// <param name="outputActivationMethod">Output activation method.</param>
public void StartNeuralNetwork(int inputAmmount, int outputAmmount, int hiddenLayerAmmount, int neuronsInHiddenLayer, double errorAlpha, Neuron.ActivationType inputActivationMethod, Neuron.ActivationType hiddenActivationMethod, Neuron.ActivationType outputActivationMethod)
{
if (neuralNetwork != null)
{
return;
}
neuralNetwork = new ArtificialNeuralNetwork(inputAmmount, outputAmmount, hiddenLayerAmmount, neuronsInHiddenLayer, errorAlpha, inputActivationMethod, hiddenActivationMethod, outputActivationMethod);
}
