public InputNeuron createNewInput() //Methode, die ein neues Inputneuron zur liste inputNeurons hinzufügt und das Neue InputNeuron zurueckgibt
{
InputNeuron iN = new InputNeuron();
inputNeurons.Add(iN);
return(iN);
}
public InputLayer(int size, Network n) : base(size, n)
{
InputNeuron[] InputNeurons = new InputNeuron[size];
for (int i = 0; i < size; i++)
{
InputNeurons[i] = new InputNeuron(this);
InputNeurons[i].name = "inputNeuron " + i;
}
this.neurons = InputNeurons;
}
private void CreateInputLayer()
{
var inputNeurons = new Neuron[Topology.InputCount];
for (int i = 0; i < Topology.InputCount; i++)
{
var neuron = new InputNeuron(1, NeuronType.Input);
inputNeurons[i] = neuron;
}
var inputLayer = new Layer(inputNeurons, NeuronType.Input);
Layers[0] = inputLayer;
}
private void CreateNeurons(ushort[] layerSizes, ITransferFunction[] transferFunctions)
{
if (layerSizes.Length != transferFunctions.Length)
{
throw new ArgumentException("Cannot make a network with these parameters.");
}
if (transferFunctions[0] != null)
{
throw new ArgumentException("Input layer transfer function must be null.");
}
Neurons = new Neuron[layerSizes.Length][];
for (byte l = 0; l < layerSizes.Length; l++)
{
// creates neurons in the input layer
if (l == 0)
{
Neurons[l] = new InputNeuron[layerSizes[l]];
for (ushort i = 0; i < layerSizes[l]; i++)
{
Neurons[l][i] = new InputNeuron(transferFunctions[l]);
}
}
// creates neurons in the hidden layer(s)
else if (l < layerSizes.Length - 1)
{
Neurons[l] = new HiddenNeuron[layerSizes[l]];
for (ushort i = 0; i < layerSizes[l]; i++)
{
Neurons[l][i] = new HiddenNeuron(transferFunctions[l]);
}
}
// creates neurons in the output layer
else
{
Neurons[l] = new OutputNeuron[layerSizes[l]];
for (ushort i = 0; i < layerSizes[l]; i++)
{
Neurons[l][i] = new OutputNeuron(transferFunctions[l]);
}
}
}
}
public String GetName()
{
if (OutputNeuron != null)
{
if (InputNeuron != null)
{
return(String.Format("Source: {0}, Target: {1}", InputNeuron.GetName(), OutputNeuron.GetName()));
}
else
{
return(String.Format("Input layer: {0}, Target: {1}", InputIndex, OutputNeuron.GetName())); //input
}
}
else
{
return("");
}
}
public static void Main(string[] args)
{
// InputNeuron iN = new InputNeuron();
// Console.WriteLine("Der wert aus iN ist: \n"+ iN.getValue());
//----------------SINGLE PERCEPTRON TEST----------------
NeuralNetwork NN = new NeuralNetwork();
InputNeuron i1 = NN.createNewInput();
InputNeuron i2 = NN.createNewInput();
InputNeuron i3 = NN.createNewInput();
InputNeuron i4 = NN.createNewInput();
NN.createNewHidden(3);
WorkingNeuron o1 = NN.createNewOutput();
NN.createFullMesh(new float[]{10,0,0,0,
0,0,0,0,
0,0,0,0,
10,0,0}); //gebraucht sind: #Input*#Hidden + #hidden*#Output gewichte
i1.setValue(1);
i2.setValue(2);
i3.setValue(3);
i4.setValue(4);
Console.Write("Das Output Neuron o1 gibt folgenden Wert aus:\n"+o1.getValue()+"\n\n");
//
//------------ENDE PERCEPTRON TEST--------------------
Console.Write("Press any key to continue . . . ");
Console.ReadKey(true);
}
