public static void Run()
{
Console.WriteLine("NuSVMDemo");
var class1 = DemoHelper.GenerateClass(0, 0.1, 0.1, 50);
var class2 = DemoHelper.GenerateClass(1, 0.8, 0.8, 50);
var trainData = class1.Concat(class2);
var trainer = SVM.Create(new NuSupportVectorClassification(0.1), new RbfKernel(0.5));
var model = trainer.Train(trainData.Select(p => Tuple.Create(p.ToArray(), p.Label)));
var x = new Point(0.9, 0.9).ToArray();
var resx = model.Predict(x);
Console.WriteLine(resx);
var y = new Point(0.0, 0.0).ToArray();
var resy = model.Predict(y);
Console.WriteLine(resy);
}
public static void Run()
{
Console.WriteLine("EpsSVRDemo");
var rnd = new Random();
var trainData = DemoHelper.Range(-10.0, 10.01, 0.1).Select(val => new { X = val, Y = DemoHelper.Sinc(val) + (rnd.NextDouble() - 0.5) / 4 });
var trainer = SVM.Create(new EpsilonSupportVectorRegression(1.0, 0.1), new RbfKernel(0.5));
var model = trainer.Train(trainData.Select(p => Tuple.Create(p.X.ToArray(), p.Y)));
foreach (var item in DemoHelper.Range(-1.0, 1.01, 0.1))
{
var x = item.ToArray();
var yPred = model.Predict(x);
var yReal = DemoHelper.Sinc(item);
Console.WriteLine("x: {0}", item);
Console.WriteLine("y_real: {0}", yReal);
Console.WriteLine("y_pred: {0}", yPred);
Console.WriteLine();
}
}
public static void Run()
{
Console.WriteLine("OneClassDemo");
var trainData = DemoHelper.GenerateClass(0, 0.5, 0.5, 100);
var trainer = SVM.Create(new OneClass(0.5), new RbfKernel(0.5));
var model = trainer.Train(trainData.Select(p => p.ToArray()));
var x = new Point(0.9, 0.9).ToArray();
var resx = model.Predict(x);
Console.WriteLine(resx);
var y = new Point(0.5, 0.5).ToArray();
var resy = model.Predict(y);
Console.WriteLine(resy);
var z = new Point(0.45, 0.45).ToArray();
var resz = model.Predict(z);
Console.WriteLine(resz);
}
