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public static GenerateClass ( int label, double x, double y, int count ) : IEnumerable |
||
| label | int | |
| x | double | |
| y | double | |
| count | int | |
| return | IEnumerable |
|
public static void Run()
{
Console.WriteLine("OneClassDemo");
var trainData = DemoHelper.GenerateClass(0, 0.5, 0.5, 100);
var parameters = new SvmParameter
{
SvmType = SvmType.ONE_CLASS,
KernelType = KernelType.Rbf,
Gamma = 0.5,
Nu = 0.5,
CacheSize = 128,
Eps = 1e-3,
Shrinking = true,
Probability = false
};
var problem = new SvmProblem
{
Y = trainData.Select(p => 1.0).ToArray(),
X = trainData.Select(p => p.ToSvmNodes()).ToArray()
};
parameters.Check(problem);
var model = Svm.Train(problem, parameters);
var x = new Point(0.9, 0.9).ToSvmNodes();
var resx = model.Predict(x);
Console.WriteLine(resx);
var y = new Point(0.5, 0.5).ToSvmNodes();
var resy = model.Predict(y);
Console.WriteLine(resy);
var z = new Point(0.45, 0.45).ToSvmNodes();
var resz = model.Predict(z);
Console.WriteLine(resz);
}
public static void Run()
{
Console.WriteLine("CSVMDemo");
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 parameters = new SvmParameter
{
SvmType = SvmType.C_SVC,
KernelType = KernelType.Rbf,
Gamma = 0.5,
CacheSize = 128,
C = 1,
Eps = 1e-3,
Shrinking = true,
Probability = false
};
var problem = new SvmProblem
{
Y = trainData.Select(p => (double)p.Label).ToArray(),
X = trainData.Select(p => p.ToSvmNodes()).ToArray()
};
parameters.Check(problem);
var model = Svm.Train(problem, parameters);
var x = new Point(0.9, 0.9).ToSvmNodes();
var resx = model.Predict(x);
Console.WriteLine(resx);
var y = new Point(0.1, 0.1).ToSvmNodes();
var resy = model.Predict(y);
Console.WriteLine(resy);
}
