SupportVectorMachine Class

[SerializableAttribute]
public class SupportVectorMachine : SupportVectorMachine<Linear>, 
	IBinaryClassifier<double[]>, IMulticlassClassifier<double[]>, IMultilabelClassifier<double[]>, 
	IMultilabelClassifier<double[], int[]>, IClassifier<double[], int[]>, 
	IClassifier, ITransform<double[], int[]>, ICovariantTransform<double[], int[]>, 
	ITransform, IMultilabelClassifier<double[], bool[]>, IClassifier<double[], bool[]>, 
	ITransform<double[], bool[]>, ICovariantTransform<double[], bool[]>, 
	IMultilabelClassifier<double[], double[]>, IClassifier<double[], double[]>, 
	ITransform<double[], double[]>, ICovariantTransform<double[], double[]>, 
	IMulticlassClassifier<double[], int>, IClassifier<double[], int>, 
	ITransform<double[], int>, ICovariantTransform<double[], int>, 
	IMulticlassClassifier<double[], double>, IClassifier<double[], double>, 
	ITransform<double[], double>, ICovariantTransform<double[], double>, 
	IClassifier<double[], bool>, ITransform<double[], bool>, 
	ICovariantTransform<double[], bool>, ISupportVectorMachine<double[]>, 
	IBinaryLikelihoodClassifier<double[]>, IBinaryScoreClassifier<double[]>, IMulticlassOutScoreClassifier<double[], bool>, 
	IMulticlassScoreClassifierBase<double[], bool>, IMultilabelOutScoreClassifier<double[], bool>, 
	IMultilabelScoreClassifierBase<double[], bool>, IMulticlassScoreClassifier<double[]>, 
	IMulticlassScoreClassifier<double[], int>, IMulticlassOutScoreClassifier<double[], int>, 
	IMulticlassScoreClassifierBase<double[], int>, IMultilabelOutScoreClassifier<double[], int>, 
	IMultilabelScoreClassifierBase<double[], int>, IMulticlassRefScoreClassifier<double[], int[]>, 
	IMultilabelRefScoreClassifier<double[], int[]>, IMultilabelScoreClassifierBase<double[], int[]>, 
	IMulticlassScoreClassifier<double[], double>, IMulticlassOutScoreClassifier<double[], double>, 
	IMulticlassScoreClassifierBase<double[], double>, IMultilabelOutScoreClassifier<double[], double>, 
	IMultilabelScoreClassifierBase<double[], double>, IMulticlassRefScoreClassifier<double[], double[]>, 
	IMultilabelRefScoreClassifier<double[], double[]>, IMultilabelScoreClassifierBase<double[], double[]>, 
	IMulticlassRefScoreClassifier<double[], bool[]>, IMultilabelRefScoreClassifier<double[], bool[]>, 
	IMultilabelScoreClassifierBase<double[], bool[]>, IMultilabelScoreClassifier<double[]>, 
	IMultilabelScoreClassifier<double[], int>, IMultilabelScoreClassifier<double[], double>, 
	IMulticlassOutLikelihoodClassifier<double[], bool>, IMultilabelOutLikelihoodClassifier<double[], bool>, 
	IMultilabelLikelihoodClassifierBase<double[], bool>, IMulticlassLikelihoodClassifierBase<double[], bool>, 
	IMulticlassLikelihoodClassifier<double[]>, IMulticlassLikelihoodClassifier<double[], int>, 
	IMulticlassOutLikelihoodClassifier<double[], int>, IMultilabelOutLikelihoodClassifier<double[], int>, 
	IMultilabelLikelihoodClassifierBase<double[], int>, IMulticlassLikelihoodClassifierBase<double[], int>, 
	IMulticlassRefLikelihoodClassifier<double[], int[]>, IMultilabelRefLikelihoodClassifier<double[], int[]>, 
	IMultilabelLikelihoodClassifierBase<double[], int[]>, IMulticlassLikelihoodClassifier<double[], double>, 
	IMulticlassOutLikelihoodClassifier<double[], double>, IMultilabelOutLikelihoodClassifier<double[], double>, 
	IMultilabelLikelihoodClassifierBase<double[], double>, IMulticlassLikelihoodClassifierBase<double[], double>, 
	IMulticlassRefLikelihoodClassifier<double[], double[]>, IMultilabelRefLikelihoodClassifier<double[], double[]>, 
	IMultilabelLikelihoodClassifierBase<double[], double[]>, IMulticlassRefLikelihoodClassifier<double[], bool[]>, 
	IMultilabelRefLikelihoodClassifier<double[], bool[]>, IMultilabelLikelihoodClassifierBase<double[], bool[]>, 
	IMultilabelLikelihoodClassifier<double[]>, IMultilabelLikelihoodClassifier<double[], int>, 
	IMultilabelLikelihoodClassifier<double[], double>

<SerializableAttribute>
Public Class SupportVectorMachine
	Inherits SupportVectorMachine(Of Linear)
	Implements IBinaryClassifier(Of Double()), IMulticlassClassifier(Of Double()), 
	IMultilabelClassifier(Of Double()), IMultilabelClassifier(Of Double(), Integer()), 
	IClassifier(Of Double(), Integer()), IClassifier, 
	ITransform(Of Double(), Integer()), ICovariantTransform(Of Double(), Integer()), 
	ITransform, IMultilabelClassifier(Of Double(), Boolean()), IClassifier(Of Double(), Boolean()), 
	ITransform(Of Double(), Boolean()), ICovariantTransform(Of Double(), Boolean()), 
	IMultilabelClassifier(Of Double(), Double()), IClassifier(Of Double(), Double()), 
	ITransform(Of Double(), Double()), ICovariantTransform(Of Double(), Double()), 
	IMulticlassClassifier(Of Double(), Integer), IClassifier(Of Double(), Integer), 
	ITransform(Of Double(), Integer), ICovariantTransform(Of Double(), Integer), 
	IMulticlassClassifier(Of Double(), Double), IClassifier(Of Double(), Double), 
	ITransform(Of Double(), Double), ICovariantTransform(Of Double(), Double), 
	IClassifier(Of Double(), Boolean), ITransform(Of Double(), Boolean), 
	ICovariantTransform(Of Double(), Boolean), ISupportVectorMachine(Of Double()), 
	IBinaryLikelihoodClassifier(Of Double()), IBinaryScoreClassifier(Of Double()), 
	IMulticlassOutScoreClassifier(Of Double(), Boolean), IMulticlassScoreClassifierBase(Of Double(), Boolean), 
	IMultilabelOutScoreClassifier(Of Double(), Boolean), IMultilabelScoreClassifierBase(Of Double(), Boolean), 
	IMulticlassScoreClassifier(Of Double()), IMulticlassScoreClassifier(Of Double(), Integer), 
	IMulticlassOutScoreClassifier(Of Double(), Integer), IMulticlassScoreClassifierBase(Of Double(), Integer), 
	IMultilabelOutScoreClassifier(Of Double(), Integer), IMultilabelScoreClassifierBase(Of Double(), Integer), 
	IMulticlassRefScoreClassifier(Of Double(), Integer()), IMultilabelRefScoreClassifier(Of Double(), Integer()), 
	IMultilabelScoreClassifierBase(Of Double(), Integer()), IMulticlassScoreClassifier(Of Double(), Double), 
	IMulticlassOutScoreClassifier(Of Double(), Double), IMulticlassScoreClassifierBase(Of Double(), Double), 
	IMultilabelOutScoreClassifier(Of Double(), Double), IMultilabelScoreClassifierBase(Of Double(), Double), 
	IMulticlassRefScoreClassifier(Of Double(), Double()), IMultilabelRefScoreClassifier(Of Double(), Double()), 
	IMultilabelScoreClassifierBase(Of Double(), Double()), IMulticlassRefScoreClassifier(Of Double(), Boolean()), 
	IMultilabelRefScoreClassifier(Of Double(), Boolean()), IMultilabelScoreClassifierBase(Of Double(), Boolean()), 
	IMultilabelScoreClassifier(Of Double()), IMultilabelScoreClassifier(Of Double(), Integer), 
	IMultilabelScoreClassifier(Of Double(), Double), IMulticlassOutLikelihoodClassifier(Of Double(), Boolean), 
	IMultilabelOutLikelihoodClassifier(Of Double(), Boolean), IMultilabelLikelihoodClassifierBase(Of Double(), Boolean), 
	IMulticlassLikelihoodClassifierBase(Of Double(), Boolean), IMulticlassLikelihoodClassifier(Of Double()), 
	IMulticlassLikelihoodClassifier(Of Double(), Integer), IMulticlassOutLikelihoodClassifier(Of Double(), Integer), 
	IMultilabelOutLikelihoodClassifier(Of Double(), Integer), IMultilabelLikelihoodClassifierBase(Of Double(), Integer), 
	IMulticlassLikelihoodClassifierBase(Of Double(), Integer), IMulticlassRefLikelihoodClassifier(Of Double(), Integer()), 
	IMultilabelRefLikelihoodClassifier(Of Double(), Integer()), IMultilabelLikelihoodClassifierBase(Of Double(), Integer()), 
	IMulticlassLikelihoodClassifier(Of Double(), Double), IMulticlassOutLikelihoodClassifier(Of Double(), Double), 
	IMultilabelOutLikelihoodClassifier(Of Double(), Double), IMultilabelLikelihoodClassifierBase(Of Double(), Double), 
	IMulticlassLikelihoodClassifierBase(Of Double(), Double), IMulticlassRefLikelihoodClassifier(Of Double(), Double()), 
	IMultilabelRefLikelihoodClassifier(Of Double(), Double()), IMultilabelLikelihoodClassifierBase(Of Double(), Double()), 
	IMulticlassRefLikelihoodClassifier(Of Double(), Boolean()), IMultilabelRefLikelihoodClassifier(Of Double(), Boolean()), 
	IMultilabelLikelihoodClassifierBase(Of Double(), Boolean()), IMultilabelLikelihoodClassifier(Of Double()), 
	IMultilabelLikelihoodClassifier(Of Double(), Integer), IMultilabelLikelihoodClassifier(Of Double(), Double)

// As an example, we will try to learn a linear machine  that can 
// replicate the "exclusive-or" logical function. However, since we
// will be using a linear SVM, we will not be able to solve this 
// problem perfectly as the XOR is a non-linear classification problem:
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()
{
    UseComplexityHeuristic = true,
    UseKernelEstimation = false
};

// And then we can obtain a trained SVM by calling its Learn method
SupportVectorMachine 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:

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);