public void Activate()
{
var netInput = Properties.NetInput;
var activation = ActivationFunction.Activation(netInput);
Properties.ActivationValue = activation;
}
public Vector FeedForward(Vector inputVector)
{
lastInput = inputVector;
Matrix inducedField
= weightMatrix.Times(inputVector)
.Plus(biasVector);
Vector inducedFieldVector = new Vector(NumberOfNeurons());
for (int i = 0; i < NumberOfNeurons(); ++i)
{
inducedFieldVector.SetValue(i, inducedField.Get(i, 0));
}
lastInducedField = inducedFieldVector.CopyVector();
Vector resultVector = new Vector(NumberOfNeurons());
for (int i = 0; i < NumberOfNeurons(); ++i)
{
resultVector.SetValue(i,
activationFunction.Activation(inducedFieldVector.GetValue(i)));
}
// set the result as the last activation value
lastActivationValues = resultVector.CopyVector();
return(resultVector);
}
public override float getValue() //hier halte ich mich eher an Brotcrunsher als an Bene, siehe das fehlende float value attribut
{
float sum = 0;
foreach (Connection c in connections)
{
sum += c.getValue();
}
//Console.WriteLine("[WORKING NEURON] In die Akt'Fkt eingegeben wird:\n"+sum); //fuer debugging!
//Console.WriteLine("von ihr ausgegeben wird:\n"+activationFunction.Activation(sum));
return(activationFunction.Activation(sum));
}
public void CalculateOutput()
{
// Start with the bias.
float output = Bias;
// Add each linked neuron's output multiplied by its associated weight.
foreach (uint previousLayerNeuronID in NeuronLayer.PreviousWeightLayer.GetPreviousLayerConnections(this))
{
output += previousNeuronLayer[previousLayerNeuronID].Output * NeuronLayer.PreviousWeightLayer.GetWeightBetweenNeurons(previousNeuronLayer[previousLayerNeuronID], this);
}
// Apply the activation and save the output.
Output = activationFunction.Activation(output);
}
public double Activation(double x)
{
return(_activationFunction.Activation(x));
}
