de.lmu.ifi.dbs.elki.algorithm

Class DependencyDerivator.Parameterizer<V extends NumberVector>

java.lang.Object

de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer

de.lmu.ifi.dbs.elki.algorithm.AbstractNumberVectorDistanceBasedAlgorithm.Parameterizer<V>

de.lmu.ifi.dbs.elki.algorithm.DependencyDerivator.Parameterizer<V>

All Implemented Interfaces:

Parameterizer

Enclosing class:

DependencyDerivator<V extends NumberVector>

public static class DependencyDerivator.Parameterizer<V extends NumberVector>
extends AbstractNumberVectorDistanceBasedAlgorithm.Parameterizer<V>

Parameterization class.

Author:

Erich Schubert

Field Summary

Fields

Modifier and Type	Field and Description
static OptionID	DEPENDENCY_DERIVATOR_RANDOM_SAMPLE_ID Flag to use random sample (use knn query around centroid, if flag is not set).
protected EigenPairFilter	filter Filter to select eigenvectors.
static OptionID	OUTPUT_ACCURACY_ID Parameter to specify the threshold for output accuracy fraction digits, must be an integer equal to or greater than 0.
protected int	outputAccuracy Output accuracy.
protected PCARunner	pca Class to compute PCA with
protected boolean	randomSample Flag to enable random sampling
static OptionID	SAMPLE_SIZE_ID Optional parameter to specify the threshold for the size of the random sample to use, must be an integer greater than 0.
protected int	sampleSize Sample size.

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractNumberVectorDistanceBasedAlgorithm.Parameterizer

distanceFunction

Constructor Summary

Constructors

Constructor and Description
Parameterizer()

Method Summary

Modifier and Type	Method and Description
protected DependencyDerivator<V>	makeInstance() Make an instance after successful configuration.
protected void	makeOptions(Parameterization config) Add all options.

Methods inherited from class de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer

configure, make

Methods inherited from class java.lang.Object

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

Field Detail

DEPENDENCY_DERIVATOR_RANDOM_SAMPLE_ID

public static final OptionID DEPENDENCY_DERIVATOR_RANDOM_SAMPLE_ID

Flag to use random sample (use knn query around centroid, if flag is not set).

OUTPUT_ACCURACY_ID

public static final OptionID OUTPUT_ACCURACY_ID

Parameter to specify the threshold for output accuracy fraction digits, must be an integer equal to or greater than 0.

SAMPLE_SIZE_ID

public static final OptionID SAMPLE_SIZE_ID

Optional parameter to specify the threshold for the size of the random sample to use, must be an integer greater than 0.

Default value: the size of the complete dataset

outputAccuracy

protected int outputAccuracy

Output accuracy.

sampleSize

protected int sampleSize

Sample size.

randomSample

protected boolean randomSample

Flag to enable random sampling

pca

protected PCARunner pca

Class to compute PCA with

filter

protected EigenPairFilter filter

Filter to select eigenvectors.

Constructor Detail

Parameterizer

public Parameterizer()

Method Detail

makeOptions

protected void makeOptions(Parameterization config)

Description copied from class: AbstractParameterizer

Add all options. ALWAYS call super.makeOptions(config), unless you have a strong reason to do otherwise!

Overrides:

makeOptions in class AbstractNumberVectorDistanceBasedAlgorithm.Parameterizer<V extends NumberVector>

Parameters:

config - Parameterization to add options to.

makeInstance

protected DependencyDerivator<V> makeInstance()

Description copied from class: AbstractParameterizer

Make an instance after successful configuration.

Specified by:

makeInstance in class AbstractParameterizer

Returns:

instance