public NeuralNetwork(IActivationFunction activationFunc, int layersQuantity, int neuronsPerLayer, Neuron bias = null)
{
if (ReferenceEquals(bias, null))
{
InputNeurons = new NeuralLayer(neuronsPerLayer, activationFunc);
NeuralLayer prevLayer = InputNeurons;
HiddenLayers = new List<NeuralLayer>(layersQuantity);
for (int i = 0; i < layersQuantity; i++)
{
HiddenLayers.Add(new NeuralLayer(neuronsPerLayer, activationFunc));
HiddenLayers[i].ConnectToMe(prevLayer);
prevLayer = HiddenLayers[i];
}
OutputNeurons = new NeuralLayer(neuronsPerLayer, activationFunc);
OutputNeurons.ConnectToMe(prevLayer);
}
else
{
InputNeurons = new NeuralLayer(neuronsPerLayer, activationFunc);
NeuralLayer prevLayer = InputNeurons;
HiddenLayers = new List<NeuralLayer>(layersQuantity);
for (int i = 0; i < layersQuantity; i++)
{
HiddenLayers.Add(new NeuralLayer(neuronsPerLayer, activationFunc));
HiddenLayers[i].ConnectToMe(prevLayer);
HiddenLayers[i].ConnectToMe(bias);
prevLayer = HiddenLayers[i];
}
OutputNeurons = new NeuralLayer(neuronsPerLayer, activationFunc);
OutputNeurons.ConnectToMe(prevLayer);
OutputNeurons.ConnectToMe(bias);
}
}
public void ConnectToInput(Neuron input)
{
if (ReferenceEquals(input, null))
throw new ArgumentNullException(nameof(input));
In.Add(input, Helper.Rand.NextDouble);
}
public void ConnectToMe(Neuron input)
{
if (ReferenceEquals(input, null))
throw new ArgumentNullException(nameof(input));
foreach (var item in Layer)
{
item.ConnectToInput(input);
}
}
