KernelDiscriminantAnalysis Class

[SerializableAttribute]
public class KernelDiscriminantAnalysis : BaseDiscriminantAnalysis, 
	ISupervisedLearning<KernelDiscriminantAnalysisPipeline, double[], int>

<SerializableAttribute>
Public Class KernelDiscriminantAnalysis
	Inherits BaseDiscriminantAnalysis
	Implements ISupervisedLearning(Of KernelDiscriminantAnalysisPipeline, Double(), Integer)

// Create some sample input data instances. This is the same
// data used in the Gutierrez-Osuna's example available on:
// http://research.cs.tamu.edu/prism/lectures/pr/pr_l10.pdf

double[][] inputs = 
{
    // Class 0
    new double[] {  4,  1 }, 
    new double[] {  2,  4 },
    new double[] {  2,  3 },
    new double[] {  3,  6 },
    new double[] {  4,  4 },

    // Class 1
    new double[] {  9, 10 },
    new double[] {  6,  8 },
    new double[] {  9,  5 },
    new double[] {  8,  7 },
    new double[] { 10,  8 }
};

int[] output = 
{
    0, 0, 0, 0, 0, // The first five are from class 0
    1, 1, 1, 1, 1  // The last five are from class 1
};


// We'll create a KDA using a Linear kernel
var kda = new KernelDiscriminantAnalysis()
{
    Kernel = new Linear() // We can choose any kernel function
};

// Compute the analysis and create a classifier
var classifier = kda.Learn(inputs, output); 

// Now we can project the data into KDA space:
double[][] projection = kda.Transform(inputs);

// Or perform classification using:
int[] results = kda.Classify(inputs);