MultivariateMixtureT Class

[SerializableAttribute]
public class MultivariateMixture<T> : MultivariateContinuousDistribution, 
	IMixture<T>, IDistribution, ICloneable, IFittableDistribution<double[], MixtureOptions>, 
	IFittable<double[], MixtureOptions>, IFittable<double[]>, 
	IFittableDistribution<double[]>, IDistribution<double[]>, ISampleableDistribution<double[]>, 
	IRandomNumberGenerator<double[]>
where T : Object, IMultivariateDistribution<double[]>

<SerializableAttribute>
Public Class MultivariateMixture(Of T As {Object, IMultivariateDistribution(Of Double())})
	Inherits MultivariateContinuousDistribution
	Implements IMixture(Of T), IDistribution, ICloneable, 
	IFittableDistribution(Of Double(), MixtureOptions), IFittable(Of Double(), MixtureOptions), 
	IFittable(Of Double()), IFittableDistribution(Of Double()), 
	IDistribution(Of Double()), ISampleableDistribution(Of Double()), 
	IRandomNumberGenerator(Of Double())

// Randomly initialize some mixture components
MultivariateNormalDistribution[] components = new MultivariateNormalDistribution[2];
components[0] = new MultivariateNormalDistribution(new double[] { 2 }, new double[,] { { 1 } });
components[1] = new MultivariateNormalDistribution(new double[] { 5 }, new double[,] { { 1 } });

// Create an initial mixture
var mixture = new MultivariateMixture<MultivariateNormalDistribution>(components);

// Now, suppose we have a weighted data
// set. Those will be the input points:

double[][] points = new double[] { 0, 3, 1, 7, 3, 5, 1, 2, -1, 2, 7, 6, 8, 6 } // (14 points)
    .ToArray();

// And those are their respective unnormalized weights:
double[] weights = { 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 3, 1, 1 }; // (14 weights)

// Let's normalize the weights so they sum up to one:
weights = weights.Divide(weights.Sum());

// Now we can fit our model to the data:
mixture.Fit(points, weights);   // done!

// Our model will be:
double mean1 = mixture.Components[0].Mean[0]; // 1.41126
double mean2 = mixture.Components[1].Mean[0]; // 6.53301

// With mixture coefficients
double pi1 = mixture.Coefficients[0]; // 0.51408489193241225
double pi2 = mixture.Coefficients[1]; // 0.48591510806758775

// If we need the GaussianMixtureModel functionality, we can
// use the estimated mixture to initialize a new model:
GaussianMixtureModel gmm = new GaussianMixtureModel(mixture);

mean1 = gmm.Gaussians[0].Mean[0]; // 1.41126 (same)
mean2 = gmm.Gaussians[1].Mean[0]; // 6.53301 (same)

p1 = gmm.Gaussians[0].Proportion; // 0.51408 (same)
p2 = gmm.Gaussians[1].Proportion; // 0.48591 (same)