private void trainButton_Click(object sender, EventArgs e)
{
if (IsDataComplete() == false)
{
return;
}
if (activationFunctionCmbx.SelectedItem.ToString() == "Sigmoid")
{
TransferFunction.slope = Double.Parse(valATxt.Text);
}
PrinicpleComponentAnalysis PCA = null;
if (pcaCheckBox.Checked == true)
{
int num_Components = Int32.Parse(numPCTxtBox.Text);
double pcaAlpha = Double.Parse(pcaAlphaTxtBox.Text);
TransferFunction transferFunction = new TransferFunction(TransferFunction.Type.Linear);
PCA = new PrinicpleComponentAnalysis(pcaAlpha, transferFunction, num_Components);
}
Train(PCA);
EvaluateClassifier(PCA);
output = new OutputForm(this);
output.Show();
}
public PrinicpleComponentAnalysis(double alpha, TransferFunction transferFunction, int num_Components)
{
this.alpha = alpha;
this.num_Components = num_Components;
this.transferFunction = transferFunction;
this.layers = new Layer[2];
layers[0] = new Layer(30 * 30, 0);
layers[1] = new Layer(num_Components, 30 * 30);
}
private void activationFunctionCmbx_SelectedIndexChanged(object sender, EventArgs e)
{
if (activationFunctionCmbx.SelectedItem.ToString() == "Tanh")
{
valALbl.Hide();
valATxt.Hide();
mainTransferFunction = new TransferFunction(TransferFunction.Type.Hyperbolic);
}
else
{
valALbl.Show();
valATxt.Show();
mainTransferFunction = new TransferFunction(TransferFunction.Type.Sigmoid);
}
}
public MultiLayerPerceptron(int[] layers, double alpha, TransferFunction transferFunction)
{
this.alpha = alpha;
this.transferFunction = transferFunction;
this.layers = new Layer[layers.Length];
for (int i = 0; i < layers.Length; i++)
{
if (i == 0)
{
this.layers[i] = new Layer(layers[i], 0);
}
else
{
this.layers[i] = new Layer(layers[i], layers[i - 1]);
}
}
}
public OCR(int[] layers, int num_Classes, double alpha, double threshold, TransferFunction transferFunction)
{
this.threshold = threshold;
this.num_Classes = num_Classes;
MLP = new MultiLayerPerceptron(layers, alpha, transferFunction);
}
