private void btnKernel_Click(object sender, EventArgs e)
{
double[,] sourceMatrix;
double[,] inputs;
int[] labels;
GetData(out sourceMatrix, out inputs, out labels);
//_svm.SimpleSMO(inputs, labels);
// Perform classification
SequentialMinimalOptimization smo;
var kernel = new Gaussian(1.2236000);
// Creates the Support Vector Machine using the selected kernel
var svm = new KernelSupportVectorMachine(kernel, 2);
//SupportVectorMachine svm = new SupportVectorMachine(2);
// Creates a new instance of the SMO Learning Algorithm
smo = new SequentialMinimalOptimization(svm, inputs.ToArray(), labels);
// Set learning parameters
smo.Complexity = 1.0;
smo.Tolerance = 0.001;
bool converged = true;
try
{
// Run
double error = smo.Run();
}
catch
{
converged = false;
}
//var a = sourceMatrix.Submatrix(0, sourceMatrix.GetLength(0) - 1, 0, 2);
//CreateScatterplot(graphInput, a, svm);
var ranges = Matrix.Range(sourceMatrix);
double[][] map = Matrix.CartesianProduct(
Matrix.Interval(ranges[0], 0.05),
Matrix.Interval(ranges[1], 0.05));
var result = map.Apply(svm.Compute).Apply(Math.Sign);
var graph2 = map.ToMatrix().InsertColumn(result.ToDouble());
CreateDecisionBoundaryplot(graphInput, graph2, sourceMatrix);
}
private void btnSimpleSMO_Click(object sender, EventArgs e)
{
double[,] sourceMatrix;
double[,] inputs;
int[] labels;
GetData(out sourceMatrix, out inputs, out labels);
//_svm.SimpleSMO(inputs, labels);
// Perform classification
SequentialMinimalOptimization smo;
// Creates the Support Vector Machine using the selected kernel
//svm = new KernelSupportVectorMachine(kernel, 2);
SupportVectorMachine svm = new SupportVectorMachine(2);
// Creates a new instance of the SMO Learning Algorithm
smo = new SequentialMinimalOptimization(svm, inputs.ToArray(), labels);
// Set learning parameters
smo.Complexity =1.0;
smo.Tolerance = 0.001;
bool converged = true;
try
{
// Run
double error = smo.Run();
}
catch
{
converged = false;
}
var a = sourceMatrix.Submatrix(0, sourceMatrix.GetLength(0) - 1, 0, 2);
CreateScatterplot(graphInput, a, svm);
}
