/// <summary>
/// Constructs a new instance of the <see cref="NeuralNetwork"/> class.
/// </summary>
/// <param name="nodeGenes">The list of node genes.</param>
/// <param name="edgeGenes">The list of edge genes.</param>
internal NeuralNetwork(IList <NeuralChromosome.NodeGene> nodeGenes, IList <NeuralChromosome.EdgeGene> edgeGenes, ActivationFunction activationFunction)
{
_activation = activationFunction;
_activationFunction = ActivationFunctions.Get(activationFunction);
var neuronMap = new Dictionary <uint, Neuron>();
foreach (var gene in nodeGenes)
{
var neuron = new Neuron(gene.ID);
_neurons.Add(neuron);
neuronMap[gene.ID] = neuron;
if (gene.Type == NeuralChromosome.NodeGene.NodeType.Input)
{
_inputNeurons.Add(neuron);
}
else if (gene.Type == NeuralChromosome.NodeGene.NodeType.Output)
{
_outputNeurons.Add(neuron);
}
}
foreach (var gene in edgeGenes)
{
if (gene.Enabled)
{
var edge = new Connection(neuronMap[gene.Input.ID], neuronMap[gene.Output.ID], gene.Weight);
_connections.Add(edge);
}
}
}
/// <summary>
/// Constructs a new instance of the <see cref="NeuralNetwork"/> class.
/// </summary>
/// <param name="adjacencyMatrix">The adjacency matrix representation of the network.</param>
/// <param name="inputNeurons">The input neuron ids.</param>
/// <param name="outputNeurons">The output neuron ids.</param>
/// <param name="activationFunction">The activation function.</param>
public NeuralNetwork(double[,] adjacencyMatrix, IList <uint> inputNeurons, IList <uint> outputNeurons,
ActivationFunction activationFunction = ActivationFunction.Sigmoid) //Temp! need to fix chromosome metadata.
{
_adjacencyMatrix = adjacencyMatrix;
_activation = activationFunction;
_activationFunction = ActivationFunctions.Get(activationFunction);
var n = adjacencyMatrix.GetLength(0);
var m = adjacencyMatrix.GetLength(1);
if (n == 0 || m == 0)
{
throw new ArgumentException("Error! Adjacency matrix cannot contain a 0 dimension.");
}
if (n != m)
{
throw new ArgumentException("Error! Adjacency matrix must be a square matrix.");
}
if (inputNeurons.Any(index => index >= n))
{
throw new ArgumentException("Error! Input neuron index out of bounds.");
}
if (outputNeurons.Any(index => index >= n))
{
throw new ArgumentException("Error! Ouput neuron index out of bounds.");
}
if (inputNeurons.Intersect(outputNeurons).Count() != 0)
{
throw new ArgumentException("Error! Input neurons cannot also be output neuron.");
}
for (var i = 0; i < n; i++)
{
_neurons.Add(new Neuron((uint)i));
}
for (var i = 0; i < n; i++)
{
for (var j = 0; j < n; j++)
{
if (adjacencyMatrix[i, j] != 0)
{
_connections.Add(new Connection(_neurons[i], _neurons[j], adjacencyMatrix[i, j]));
}
}
}
foreach (var index in inputNeurons)
{
_inputNeurons.Add(_neurons[(int)index]);
}
foreach (var index in outputNeurons)
{
_outputNeurons.Add(_neurons[(int)index]);
}
}
