IndependentComponentAnalysis Class

[SerializableAttribute]
public class IndependentComponentAnalysis : MultipleTransformBase<double[], double>, 
	IMultivariateAnalysis, IAnalysis, IParallel, ISupportsCancellation, IUnsupervisedLearning<MultivariateLinearRegression, double[], double[]>

<SerializableAttribute>
Public Class IndependentComponentAnalysis
	Inherits MultipleTransformBase(Of Double(), Double)
	Implements IMultivariateAnalysis, IAnalysis, IParallel, ISupportsCancellation, 
	IUnsupervisedLearning(Of MultivariateLinearRegression, Double(), Double())

// Let's create a random dataset containing
// 5000 samples of two dimensional samples.
// 
double[][] source = Jagged.Random(5000, 2);

// Now, we will mix the samples the dimensions of the samples.
// A small amount of the second column will be applied to the
// first, and vice-versa. 
// 
double[][] mix =
{
    new double[] {  0.25, 0.25 },
    new double[] { -0.25, 0.75 },    
};

// mix the source data
double[][] input = source.Dot(mix);

// Now, we can use ICA to identify any linear mixing between the variables, such
// as the matrix multiplication we did above. After it has identified it, we will
// be able to revert the process, retrieving our original samples again

// Create a new Independent Component Analysis
var ica = new IndependentComponentAnalysis()
{
    Algorithm = IndependentComponentAlgorithm.Parallel,
    Contrast = new Logcosh()
};

// Learn the demixing transformation from the data
MultivariateLinearRegression demix = ica.Learn(input);

// Now, we can retrieve the mixing and demixing matrices that were 
// used to alter the data. Note that the analysis was able to detect
// this information automatically:

double[][] mixingMatrix = ica.MixingMatrix; // same as the 'mix' matrix
double[][] revertMatrix = ica.DemixingMatrix; // inverse of the 'mix' matrix

// We can use the regression to recover the separate sources
double[][] result = demix.Transform(input);