.toString())) {
k = new DoubleGaussL2(Double.parseDouble(kernelParamTextField
.getText()));
} else if ("TriangleL2".equalsIgnoreCase(kernelBox
.getSelectedItem().toString())) {
k = new DoubleTriangleL2(
Double.parseDouble(kernelParamTextField.getText()));
} else if ("Polynomial".equalsIgnoreCase(kernelBox
.getSelectedItem().toString())) {
k = new DoublePolynomial(Integer.parseInt(kernelParamTextField
.getText()));
} else if ("HPlolynomial".equalsIgnoreCase(kernelBox
.getSelectedItem().toString())) {
k = new DoubleHPolynomial(Integer.parseInt(kernelParamTextField
.getText()));
}
LaSVM<double[]> svm = new LaSVM<double[]>(k);
svm.setC(Double.parseDouble(regularizationField.getText()));
svm.train(localTrain);
// info
classnameLabel.setText(svm.getClass().getSimpleName());
double[] alphas = svm.getAlphas();
int sv = 0;
for (int s = 0; s < alphas.length; s++) {
if (alphas[s] != 0) {
sv++;
}
}
svLabel.setText("" + sv);
validate();
// save current classifier
model.classifier = svm;
} else if ("smo".equalsIgnoreCase(classifierBox.getSelectedItem()
.toString())) {
Kernel<double[]> k = new DoubleLinear();
if ("GaussianL2".equalsIgnoreCase(kernelBox.getSelectedItem()
.toString())) {
k = new DoubleGaussL2(Double.parseDouble(kernelParamTextField
.getText()));
} else if ("TriangleL2".equalsIgnoreCase(kernelBox
.getSelectedItem().toString())) {
k = new DoubleTriangleL2(
Double.parseDouble(kernelParamTextField.getText()));
} else if ("Polynomial".equalsIgnoreCase(kernelBox
.getSelectedItem().toString())) {
k = new DoublePolynomial(Integer.parseInt(kernelParamTextField
.getText()));