/// <summary>
/// Add layer to the neural network.
/// Layer will automatically be added as the output layer to the last layer in the neural network.
/// </summary>
public void AddLayer(NeuralLayer newLayer)
{
if (_layers.Any())
{
var lastLayer = _layers.Last();
newLayer.ConnectLayers(lastLayer);
}
_layers.Add(newLayer);
_neuronErrors.Add(_layers.Count - 1, new double[newLayer.Neurons.Count]);
}
public NeuralLayer CreateNeuralLayer(int numberOfNeurons, IActivationFunction activationFunction, IInputFunction inputFunction)
{
var layer = new NeuralLayer();
for (int i = 0; i < numberOfNeurons; i++)
{
var neuron = new Neuron.Neuron(activationFunction, inputFunction);
layer.Neurons.Add(neuron);
}
return(layer);
}
/// <summary>
/// Flattens the inputLayer list (using SelectMany), generates a new layer with a lambda function
/// and connects the neurons (each neuron has n output neurons) of the new layer to the existing layer
/// using input and output neuron GUIDs - the inter neuron connection data is held in the
/// connecting synapse.
/// </summary>
public void ConnectLayers(NeuralLayer inputLayer)
{
var combos = Neurons.SelectMany(neuron => inputLayer.Neurons, (neuron, input) => new { neuron, input });
combos.ToList().ForEach(x => x.neuron.AddInputNeuron(x.input));
}
