void TranslateInstance(Frame frame)
{
// Create a new Linear kernel
IKernel kernel = new Linear();
// Create a new Multi-class Support Vector Machine with one input,
// using the linear kernel and for four disjoint classes.
var machine = new MulticlassSupportVectorMachine(5, kernel, numOfClasses);
// Create the Multi-class learning algorithm for the machine
var teacher = new MulticlassSupportVectorLearning(machine, inputs, outputs);
// Configure the learning algorithm to use SMO to train the
// underlying SVMs in each of the binary class subproblems.
teacher.Algorithm = (svm, classInputs, classOutputs, i, j) =>
new SequentialMinimalOptimization(svm, classInputs, classOutputs);
// Run the learning algorithm
double error = teacher.Run(); // output should be 0
double[] distances = new double[5];
distances = LeapEventListener.getDistances(frame);
int decision = machine.Compute(distances); //svm AI
output.Text = output.Text + Char2SvmClass.class2svm(decision);
}
private void TranslateBtn_Click(object sender, EventArgs e)
{
double[] distances = new double[5];
distances = LeapEventListener.getDistances(currentFrame);
if (LeapEventListener.isZeros(distances) == true)
{
MessageBox.Show("The frame is null.\n Try reconnecting the Leap device", "Application Error",
MessageBoxButtons.OKCancel, MessageBoxIcon.Warning);
}
else
{
// Create a new Linear kernel
IKernel kernel = new Linear();
// Create a new Multi-class Support Vector Machine with one input,
// using the linear kernel and for four disjoint classes.
var machine = new MulticlassSupportVectorMachine(5, kernel, numOfClasses);
// Create the Multi-class learning algorithm for the machine
var teacher = new MulticlassSupportVectorLearning(machine, inputs, outputs);
// Configure the learning algorithm to use SMO to train the
// underlying SVMs in each of the binary class subproblems.
teacher.Algorithm = (svm, classInputs, classOutputs, i, j) =>
new SequentialMinimalOptimization(svm, classInputs, classOutputs);
// Run the learning algorithm
double error = teacher.Run(); // output should be 0
int decision = machine.Compute(distances); //svm AI
output.Text = output.Text + Char2SvmClass.class2svm(decision);
}
}