org.ddogleg.clustering.gmm

Class ExpectationMaximizationGmm_F64

java.lang.Object

org.ddogleg.clustering.gmm.ExpectationMaximizationGmm_F64

All Implemented Interfaces:

ComputeClusters<double[]>

public class ExpectationMaximizationGmm_F64
extends java.lang.Object
implements ComputeClusters<double[]>

Standard expectation maximization based approach to fitting mixture-of-Gaussian models to a set of data. A locally optimal maximum likelihood estimate is found. The full covariance is found. Some other variants will estimate just diagonal elements or a single covariance, but that isn't yet supported.

Converged if, , where D is the sum of point from cluster distance at iteration 'i', and tol is the convergence tolerance threshold.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	ExpectationMaximizationGmm_F64.PointInfo

Constructor Summary

Constructors

Constructor and Description
ExpectationMaximizationGmm_F64(int maxIterations, double convergeTol, InitializeGmm_F64 selectInitial) Configures EM parameters

Method Summary

Modifier and Type	Method and Description
AssignCluster<double[]>	getAssignment() Returns a class which is used to assign a point to a cluster.
double	getDistanceMeasure() Returns the sum of all the distances between each point in the set.
void	init(int pointDimension, long randomSeed) Must be called first to initializes internal data structures.
void	process(java.util.List<double[]> points, int numCluster) Computes a set of clusters which segment the points into numCluster sets.
void	setVerbose(boolean verbose) If set to true then information about status will be printed to standard out.

Methods inherited from class java.lang.Object

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

Constructor Detail

ExpectationMaximizationGmm_F64

public ExpectationMaximizationGmm_F64(int maxIterations,
                                      double convergeTol,
                                      InitializeGmm_F64 selectInitial)

Configures EM parameters

Parameters:

maxIterations - Maximum number of iterations

convergeTol - If the relative change in score is less or equal than this amount it has converged

selectInitial - Used to select initial seeds for the clusters

Method Detail

init

public void init(int pointDimension,
                 long randomSeed)

Description copied from interface: ComputeClusters

Must be called first to initializes internal data structures. Only needs to be called once.

Specified by:

init in interface ComputeClusters<double[]>

Parameters:

pointDimension - Number of degrees of freedom in each point.

randomSeed - Seed for any random number generators used internally.

process

public void process(java.util.List<double[]> points,
                    int numCluster)

Description copied from interface: ComputeClusters

Computes a set of clusters which segment the points into numCluster sets.

Specified by:

process in interface ComputeClusters<double[]>

Parameters:

points - Set of points which are to be clustered. Not modified.

numCluster - Number of clusters it will use to split the points.

getAssignment

public AssignCluster<double[]> getAssignment()

Description copied from interface: ComputeClusters

Returns a class which is used to assign a point to a cluster. Only invoked after ComputeClusters.process(java.util.List<D>, int) has been called.

WARNING: The returned data structure is recycled each time compute clusters is called. Create a copy if you wish to avoid having it modified.

Specified by:

getAssignment in interface ComputeClusters<double[]>

Returns:

Instance of AssignCluster.

getDistanceMeasure

public double getDistanceMeasure()

Description copied from interface: ComputeClusters

Returns the sum of all the distances between each point in the set. Can be used to evaluate the quality of fit for all the clusters. Can only be used to compare when the same number of clusters is uesd.

NOTE: The specific distance measure is not specified and is application specific.

Specified by:

getDistanceMeasure in interface ComputeClusters<double[]>

Returns:

sum of distance between each point and their respective clusters.

setVerbose

public void setVerbose(boolean verbose)

Description copied from interface: ComputeClusters

If set to true then information about status will be printed to standard out. By default verbose is off

Specified by:

setVerbose in interface ComputeClusters<double[]>

Parameters:

verbose - true for versbose mode. False for quite mode.