SupportVectorMachineTKernel Class

[SerializableAttribute]
public class SupportVectorMachine<TKernel> : SupportVectorMachine<TKernel, double[]>
where TKernel : Object, IKernel<double[]>

<SerializableAttribute>
Public Class SupportVectorMachine(Of TKernel As {Object, IKernel(Of Double())})
	Inherits SupportVectorMachine(Of TKernel, Double())

// As an example, we will try to learn a decision machine 
// that can replicate the "exclusive-or" logical function:

double[][] inputs =
{
    new double[] { 0, 0 }, // the XOR function takes two booleans
    new double[] { 0, 1 }, // and computes their exclusive or: the
    new double[] { 1, 0 }, // output is true only if the two booleans
    new double[] { 1, 1 }  // are different
};

int[] xor = // this is the output of the xor function
{
    0, // 0 xor 0 = 0 (inputs are equal)
    1, // 0 xor 1 = 1 (inputs are different)
    1, // 1 xor 0 = 1 (inputs are different)
    0, // 1 xor 1 = 0 (inputs are equal)
};

// Now, we can create the sequential minimal optimization teacher
var learn = new SequentialMinimalOptimization<Gaussian>()
{
    UseComplexityHeuristic = true,
    UseKernelEstimation = true
};

// And then we can obtain a trained SVM by calling its Learn method
SupportVectorMachine<Gaussian> svm = learn.Learn(inputs, xor);

// Finally, we can obtain the decisions predicted by the machine:
bool[] prediction = svm.Decide(inputs);

// As an example, we will try to learn a decision machine 
// that can replicate the "exclusive-or" logical function:

Sparse<double>[] inputs =
{
    Sparse.FromDense(new double[] { 0, 0 }), // the XOR function takes two booleans
    Sparse.FromDense(new double[] { 0, 1 }), // and computes their exclusive or: the
    Sparse.FromDense(new double[] { 1, 0 }), // output is true only if the two booleans
    Sparse.FromDense(new double[] { 1, 1 })  // are different
};

int[] xor = // this is the output of the xor function
{
    0, // 0 xor 0 = 0 (inputs are equal)
    1, // 0 xor 1 = 1 (inputs are different)
    1, // 1 xor 0 = 1 (inputs are different)
    0, // 1 xor 1 = 0 (inputs are equal)
};

// Now, we can create the sequential minimal optimization teacher
var learn = new SequentialMinimalOptimization<Gaussian, Sparse<double>>()
{
    UseComplexityHeuristic = true,
    UseKernelEstimation = true
};

// And then we can obtain a trained SVM by calling its Learn method
var svm = learn.Learn(inputs, xor);

// Finally, we can obtain the decisions predicted by the machine:
bool[] prediction = svm.Decide(inputs);