public Network(int inputs, params int[] layerSizes)
{
var layers = new List<Neuron>();
layers.Add(new Neuron(inputs, false));
foreach (var size in layerSizes.Take(layerSizes.Length -1))
{
var n = new Neuron(size);
Neuron.Connect(layers.Last(), n);
layers.Add(n);
}
var ln = new Neuron(layerSizes.Last(), false);
Neuron.Connect(layers.Last(), ln);
layers.Add(ln);
foreach (var layer in layers)
{
layer.RandomizeWeights();
}
Layers = layers.ToArray();
}
public Weight(double initValue, Neuron connectedNeuron)
{
Value = initValue;
ConnectedNeuron = connectedNeuron;
}
public static void Disconnect(Neuron from, Neuron to)
{
from.OutLinks = from.OutLinks.Where(l => l.To != to).ToArray();
to.InLinks = to.InLinks.Where(l => l.From != from).ToArray();
}
public static void Connect(Neuron from, Neuron to)
{
var l = new NeuronLink() { From = from, To = to, Weights = new float[from.Size, to.Size] };
from.OutLinks = from.OutLinks.Union(new[] { l }).ToArray();
to.InLinks = to.InLinks.Union(new[] { l }).ToArray();
}
public NeuronConnection(Neuron From, Neuron To, VariableArgumentValue value)
{
this.From = From;
this.To = To;
this.value = value;
}
public void LinkTo(Neuron TargetNeuron, VariableArgumentValue Value)
{
var connection = new NeuronConnection(this, TargetNeuron, Value);
Out.Add(connection);
TargetNeuron.In.Add(connection);
}
