/// <summary>
/// Функция, очищающая все токены из соединений процедуры
/// </summary>
public void Flush()
{
foreach (var resource in Inputs.Concat(Outputs))
{
resource.Tokens.Clear();
}
}
public void Update(List <double> inputs)
{
for (int i = 0; i < inputs.Count; i++)
{
Inputs[i].Value = inputs[i];
}
ProcessLayer(Inputs.Concat(Memory).ToList(), HiddenLayers[0], HiddenFunc);
for (int i = 1; i < HiddenLayers.Count; i++)
{
ProcessLayer(HiddenLayers[i - 1], HiddenLayers[i], HiddenFunc);
}
ProcessLayer(HiddenLayers[HiddenLayers.Count - 1], Outputs.Concat(Memory).ToList(), HiddenFunc);
}
private void GenerateRand(int inputs, List <int> hiddenLayers, int outputs, int memory)
{
for (int i = 0; i < inputs; i++)
{
Inputs.Add(new Neuron());
}
for (int i = 0; i < memory; i++)
{
Memory.Add(new Neuron(hiddenLayers[hiddenLayers.Count - 1], 0, NeuronType.MEMORY));
}
for (int i = 0; i < hiddenLayers.Count; i++)
{
for (int j = 0; j < hiddenLayers[i]; j++)
{
var value = GetRandomWeight();
var prevLayer = i == 0 ? Inputs.Concat(Memory).ToList() : HiddenLayers[i - 1];
HiddenLayers[i].Add(new Neuron(prevLayer.Count, value, NeuronType.HIDDEN));
}
}
for (int i = 0; i < outputs; i++)
{
Outputs.Add(new Neuron(hiddenLayers[hiddenLayers.Count - 1], 0, NeuronType.OUTPUT));
}
}
public IEnumerable <Connection> Connections()
{
Debug.Assert(useable);
return(Inputs.Concat(Outputs));
}
