edu.umd.cloud9.example.clustering

Class ExpectationMaximization

java.lang.Object

edu.umd.cloud9.example.clustering.ExpectationMaximization

public class ExpectationMaximization
extends Object

Constructor Summary

Constructors

Constructor and Description
ExpectationMaximization()

Method Summary

Methods

Modifier and Type	Method and Description
static UnivariateGaussianMixtureModel	initialize(Point[] points, double[] means) Initializes the mixture model with points that are closet the given means.
static UnivariateGaussianMixtureModel	initialize(Point[] points, int n) Initializes the mixture model with random points.
static UnivariateGaussianMixtureModel	run(Point[] points, UnivariateGaussianMixtureModel m) Performs the Expectation-Maximization algorithm.
static Integer[]	sampleNUniquePoints(int n, int length)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

ExpectationMaximization

public ExpectationMaximization()

Method Detail

initialize

public static UnivariateGaussianMixtureModel initialize(Point[] points,
                                        double[] means)

Initializes the mixture model with points that are closet the given means.

initialize

public static UnivariateGaussianMixtureModel initialize(Point[] points,
                                        int n)

Initializes the mixture model with random points.

run

public static UnivariateGaussianMixtureModel run(Point[] points,
                                 UnivariateGaussianMixtureModel m)

Performs the Expectation-Maximization algorithm. The parameters estimated corresponds to univariate Gaussian distributions.

Parameters:

points - point set

m - initial mixture model

Returns:

learned mixture model

sampleNUniquePoints

public static final Integer[] sampleNUniquePoints(int n,
                            int length)