Package elki.data.synthetic.bymodel

Class GeneratorMain

java.lang.Object

elki.data.synthetic.bymodel.GeneratorMain

public class GeneratorMain
extends java.lang.Object

Generate a data set according to a given model.

Key idea of this generator is to re-generate points if they are more likely to belong to a different cluster than the one they were generated for. The benefit is that we should end up with a data set that follows closely the model that we specified.

The drawbacks are that on one hand, specifications might be unsatisfiable. For this a retry count is kept and an AbortException is thrown when the maximum number of retries is exceeded.

On the other hand, the model might not be exactly as specified. When the generator reports an "Density correction factor estimation" that differs from 1.0 this is an indication that the result is not exact.

On the third hand, rejecting points introduces effects where one generator can influence others, so random generator results will not be stable with respect to the addition of new dimensions and similar if there are any rejects involved. So this generator is not entirely optimal for generating data sets for scalability tests on the number of dimensions, although if clusters overlap little enough (so that no rejects happen) the results should be as expected.

Since:

0.2

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
private class	GeneratorMain.AssignLabelsByDensity	Reassign objects in certain labels; but also always test against the model.
private class	GeneratorMain.AssignLabelsByDistance	Reassign objects in certain labels; but also always test against the model.
private class	GeneratorMain.TestModel	Reject objects with a higher density in another generator.

Field Summary

Fields

Modifier and Type	Field	Description
protected java.util.ArrayList<GeneratorInterface>	generators	List of clusters to generate.
private static Logging	LOG	Class logger.
protected java.util.regex.Pattern	relabelClusters	Pattern, which clusters (e.g., "Noise") to relabel by the second best cluster.
protected boolean	relabelDistance	Relabel objects by distance.
protected boolean	testAgainstModel	Controls whether points are tested against the model during generation.

Constructor Summary

Constructors

Constructor	Description
GeneratorMain()

Method Summary

Modifier and Type	Method	Description
void	addCluster(GeneratorInterface c)	Add a cluster to the cluster list.
MultipleObjectsBundle	generate()	Main loop to generate data set.
java.util.List<GeneratorInterface>	getGenerators()	Access the generators.
private void	initLabelsAndModels(java.util.ArrayList<GeneratorInterface> generators, ClassLabel[] labels, Model[] models, java.util.regex.Pattern reassign)	Initialize cluster labels and models.
boolean	isTestAgainstModel()	Return value of the testAgainstModel flag.
void	setReassignByDistance(boolean bydistance)	Relabel objects by distance, instead of by density.
void	setReassignPattern(java.util.regex.Pattern reassign)	Set the reassignment pattern.
void	setTestAgainstModel(boolean testAgainstModel)	Set the value of the testAgainstModel flag.

Methods inherited from class java.lang.Object

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

Field Detail

LOG

private static final Logging LOG

Class logger.

generators

protected java.util.ArrayList<GeneratorInterface> generators

List of clusters to generate.

testAgainstModel

protected boolean testAgainstModel

Controls whether points are tested against the model during generation.

relabelClusters

protected java.util.regex.Pattern relabelClusters

Pattern, which clusters (e.g., "Noise") to relabel by the second best cluster.

relabelDistance

protected boolean relabelDistance

Relabel objects by distance.

Constructor Detail

GeneratorMain

public GeneratorMain()

Method Detail

addCluster

public void addCluster(GeneratorInterface c)

Add a cluster to the cluster list.

Parameters:

c - cluster to add

generate

public MultipleObjectsBundle generate()

Main loop to generate data set.

Returns:

Generated data set

initLabelsAndModels

private void initLabelsAndModels(java.util.ArrayList<GeneratorInterface> generators,
                                 ClassLabel[] labels,
                                 Model[] models,
                                 java.util.regex.Pattern reassign)

Initialize cluster labels and models.

Clusters that are set to "reassign" will have their labels set to null, or if there is only one possible reassignment, to this target label.

Parameters:

generators - Cluster generators

labels - Labels (output)

models - Models (output)

reassign - Pattern for clusters to reassign.

isTestAgainstModel

public boolean isTestAgainstModel()

Return value of the testAgainstModel flag.

Returns:

value of testAgainstModel

setTestAgainstModel

public void setTestAgainstModel(boolean testAgainstModel)

Set the value of the testAgainstModel flag.

Parameters:

testAgainstModel - New value

getGenerators

public java.util.List<GeneratorInterface> getGenerators()

Access the generators.

Returns:

generators

setReassignPattern

public void setReassignPattern(java.util.regex.Pattern reassign)

Set the reassignment pattern.

Parameters:

reassign - Reassignment pattern.

setReassignByDistance

public void setReassignByDistance(boolean bydistance)

Relabel objects by distance, instead of by density.

Parameters:

bydistance - Boolean when to use distances.