public SigmoidNeuron(int numberOfInputs, SigmoidFunction function, double minInitialWeight, double maxInitialWeight, bool biased) : base(numberOfInputs)
{
this.function = function;
this.SetInitialWeights(minInitialWeight, maxInitialWeight);
this.biased = biased;
if (this.biased) biasedWeight = rand.NextDouble() * (maxInitialWeight - minInitialWeight) + minInitialWeight;
}
public SigmoidLayer(int numberOfNeurons, int numberOfInputs, SigmoidFunction function, double minInitialWeight, double maxInitialWeight, bool biased)
: base(numberOfNeurons, numberOfInputs)
{
for (int i = 0; i < numberOfNeurons; i++)
{
neurons[i] = new SigmoidNeuron(numberOfInputs, function, minInitialWeight, maxInitialWeight, biased);
}
}
public PerceptronNetwork(SigmoidFunction function, int numberOfInputs, double minInitialWeight, double maxInitialWeight, bool biased, params int[] numberOfNeurons)
: base(numberOfInputs, numberOfNeurons.Length)
{
layers[0] = new InputLayer(numberOfInputs);
for (int i = 1; i < numberOfLayers; i++)
{
layers[i] = new SigmoidLayer(numberOfNeurons[i], numberOfNeurons[i - 1], function, minInitialWeight, maxInitialWeight, biased);
}
}
private void trainNeuronBtn_Click(object sender, EventArgs e)
{
if (perceptronNetwork != null) perceptronNetwork = null;
SigmoidFunction sigmoidFunction = new SigmoidFunction();
int[] numberOfNeurons = {numberOfInputs, int.Parse(hiddenNeuronsTB.Text), numberOfInputs};
bool biased = biasedChkBox.Checked;
perceptronNetwork = new PerceptronNetwork(sigmoidFunction, numberOfInputs, -0.5, 0.5, biased, numberOfNeurons);
perceptronTraining = new PerceptronTraining(ref perceptronNetwork);
int epochs = int.Parse(epochsTB.Text);
double trainingStep = double.Parse(trainingStepTB.Text);
perceptronTraining.TrainingStep = trainingStep;
perceptronTraining.RunTraining(ref trainingSet, epochs);
trained = true;
computeBtn.Enabled = trained;
updateTrainingOutputList();
}
