SubspaceCluster (weka-stable 3.6.8 API)

java.lang.Object
- weka.datagenerators.DataGenerator
- - weka.datagenerators.ClusterGenerator
  - - weka.datagenerators.clusterers.SubspaceCluster

All Implemented Interfaces:: Serializable, OptionHandler, Randomizable, RevisionHandler

public class SubspaceCluster
extends ClusterGenerator

A data generator that produces data points in hyperrectangular subspace clusters.

Valid options are:

 -h
  Prints this help.

 -o <file>
  The name of the output file, otherwise the generated data is
  printed to stdout.

 -r <name>
  The name of the relation.

 -d
  Whether to print debug informations.

 -S
  The seed for random function (default 1)

 -a <num>
  The number of attributes (default 1).

 -c
  Class Flag, if set, the cluster is listed in extra attribute.

 -b <range>
  The indices for boolean attributes.

 -m <range>
  The indices for nominal attributes.

 -P <num>
  The noise rate in percent (default 0.0).
  Can be between 0% and 30%. (Remark: The original 
  algorithm only allows noise up to 10%.)

 -C <cluster-definition>
  A cluster definition of class 'SubspaceClusterDefinition'
  (definition needs to be quoted to be recognized as 
  a single argument).

 
 Options specific to weka.datagenerators.clusterers.SubspaceClusterDefinition:

 -A <range>
  Generates randomly distributed instances in the cluster.

 -U <range>
  Generates uniformly distributed instances in the cluster.

 -G <range>
  Generates gaussian distributed instances in the cluster.

 -D <num>,<num>
  The attribute min/max (-A and -U) or mean/stddev (-G) for
  the cluster.

 -N <num>..<num>
  The range of number of instances per cluster (default 1..50).

 -I
  Uses integer instead of continuous values (default continuous).

Version:: $Revision: 1.5 $
Author:: Gabi Schmidberger (gabi@cs.waikato.ac.nz), FracPete (fracpete at waikato dot ac dot nz)
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`CONTINUOUS` cluster subtype: continuous
`static int`	`GAUSSIAN` cluster type: gaussian
`static int`	`INTEGER` cluster subtype: integer
`protected ClusterDefinition[]`	`m_Clusters` cluster list
`protected double[]`	`m_globalMaxValue` store global max values
`protected double[]`	`m_globalMinValue` store global min values
`protected double`	`m_NoiseRate` noise rate in percent (option P, between 0 and 30)
`protected int[]`	`m_numValues` if nominal, store number of values
`static Tag[]`	`TAGS_CLUSTERSUBTYPE` the tags for the cluster types
`static Tag[]`	`TAGS_CLUSTERTYPE` the tags for the cluster types
`static int`	`TOTAL_UNIFORM` cluster type: total uniform
`static int`	`UNIFORM_RANDOM` cluster type: uniform/random

Fields inherited from class weka.datagenerators.ClusterGenerator
m_booleanCols, m_ClassFlag, m_nominalCols, m_NumAttributes

Fields inherited from class weka.datagenerators.DataGenerator
m_CreatingRelationName, m_DatasetFormat, m_Debug, m_DefaultOutput, m_NumExamplesAct, m_OptionBlacklist, m_Output, m_Random, m_RelationName, m_Seed

Constructor Summary

Constructors
Constructor and Description

SubspaceCluster()
initializes the generator, sets the number of clusters to 0, since user has to specify them explicitly

Constructors
Constructor and Description
`SubspaceCluster()` initializes the generator, sets the number of clusters to 0, since user has to specify them explicitly

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`protected boolean`	`checkCoverage()` Checks, whether all attributes are covered by cluster definitions and returns TRUE in that case.
`String`	`clusterDefinitionsTipText()` Returns the tip text for this property
`protected double`	`defaultNoiseRate()` returns the default noise rate
`protected int`	`defaultNumAttributes()` returns the default number of attributes
`Instances`	`defineDataFormat()` Initializes the format for the dataset produced.
`Instance`	`generateExample()` Generate an example of the dataset.
`Instances`	`generateExamples()` Generate all examples of the dataset.
`String`	`generateFinished()` Compiles documentation about the data generation after the generation process
`String`	`generateStart()` Compiles documentation about the data generation before the generation process
`ClusterDefinition[]`	`getClusterDefinitions()` returns the currently set clusters
`protected ClusterDefinition[]`	`getClusters()` returns the current cluster definitions, if necessary initializes them
`double`	`getNoiseRate()` Gets the percentage of noise set.
`int[]`	`getNumValues()` returns array that stores the number of values for a nominal attribute.
`String[]`	`getOptions()` Gets the current settings of the datagenerator.
`String`	`getRevision()` Returns the revision string.
`boolean`	`getSingleModeFlag()` Gets the single mode flag.
`String`	`globalInfo()` Returns a string describing this data generator.
`boolean`	`isBoolean(int index)` Returns true if attribute is boolean
`boolean`	`isNominal(int index)` Returns true if attribute is nominal
`Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(String[] args)` Main method for testing this class.
`String`	`noiseRateTipText()` Returns the tip text for this property
`String`	`numAttributesTipText()` Returns the tip text for this property
`void`	`setClusterDefinitions(ClusterDefinition[] value)` sets the clusters to use
`void`	`setNoiseRate(double newNoiseRate)` Sets the percentage of noise set.
`void`	`setNumAttributes(int numAttributes)` Sets the number of attributes the dataset should have.
`void`	`setOptions(String[] options)` Parses a list of options for this object.

Methods inherited from class weka.datagenerators.ClusterGenerator
booleanColsTipText, checkIndices, classFlagTipText, getBooleanCols, getClassFlag, getNominalCols, getNumAttributes, nominalColsTipText, setBooleanCols, setBooleanIndices, setClassFlag, setNominalCols, setNominalIndices

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - m_NoiseRate
```
protected double m_NoiseRate
```
    noise rate in percent (option P, between 0 and 30)
  - m_Clusters
```
protected ClusterDefinition[] m_Clusters
```
    cluster list
  - m_numValues
```
protected int[] m_numValues
```
    if nominal, store number of values
  - m_globalMinValue
```
protected double[] m_globalMinValue
```
    store global min values
  - m_globalMaxValue
```
protected double[] m_globalMaxValue
```
    store global max values
  - UNIFORM_RANDOM
```
public static final int UNIFORM_RANDOM
```
    cluster type: uniform/random
    
    See Also:
    
    Constant Field Values
  - TOTAL_UNIFORM
```
public static final int TOTAL_UNIFORM
```
    cluster type: total uniform
    
    See Also:
    
    Constant Field Values
  - GAUSSIAN
```
public static final int GAUSSIAN
```
    cluster type: gaussian
    
    See Also:
    
    Constant Field Values
  - TAGS_CLUSTERTYPE
```
public static final Tag[] TAGS_CLUSTERTYPE
```
    the tags for the cluster types
  - CONTINUOUS
```
public static final int CONTINUOUS
```
    cluster subtype: continuous
    
    See Also:
    
    Constant Field Values
  - INTEGER
```
public static final int INTEGER
```
    cluster subtype: integer
    
    See Also:
    
    Constant Field Values
  - TAGS_CLUSTERSUBTYPE
```
public static final Tag[] TAGS_CLUSTERSUBTYPE
```
    the tags for the cluster types
- Constructor Detail
  - SubspaceCluster
```
public SubspaceCluster()
```
    initializes the generator, sets the number of clusters to 0, since user has to specify them explicitly
- Method Detail
  - globalInfo
```
public String globalInfo()
```
    Returns a string describing this data generator.
    
    Returns:
    
    a description of the data generator suitable for displaying in the explorer/experimenter gui
  - listOptions
```
public Enumeration listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class ClusterGenerator
    
    Returns:
    
    an enumeration of all the available options
  - setOptions
```
public void setOptions(String[] options)
                throws Exception
```
    Parses a list of options for this object.
    Valid options are:
```
 -h
  Prints this help.
```
```
 -o <file>
  The name of the output file, otherwise the generated data is
  printed to stdout.
```
```
 -r <name>
  The name of the relation.
```
```
 -d
  Whether to print debug informations.
```
```
 -S
  The seed for random function (default 1)
```
```
 -a <num>
  The number of attributes (default 1).
```
```
 -c
  Class Flag, if set, the cluster is listed in extra attribute.
```
```
 -b <range>
  The indices for boolean attributes.
```
```
 -m <range>
  The indices for nominal attributes.
```
```
 -P <num>
  The noise rate in percent (default 0.0).
  Can be between 0% and 30%. (Remark: The original 
  algorithm only allows noise up to 10%.)
```
```
 -C <cluster-definition>
  A cluster definition of class 'SubspaceClusterDefinition'
  (definition needs to be quoted to be recognized as 
  a single argument).
```
```
 
 Options specific to weka.datagenerators.clusterers.SubspaceClusterDefinition:
 
```
```
 -A <range>
  Generates randomly distributed instances in the cluster.
```
```
 -U <range>
  Generates uniformly distributed instances in the cluster.
```
```
 -G <range>
  Generates gaussian distributed instances in the cluster.
```
```
 -D <num>,<num>
  The attribute min/max (-A and -U) or mean/stddev (-G) for
  the cluster.
```
```
 -N <num>..<num>
  The range of number of instances per cluster (default 1..50).
```
```
 -I
  Uses integer instead of continuous values (default continuous).
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class ClusterGenerator
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    Exception - if an option is not supported
  - getOptions
```
public String[] getOptions()
```
    Gets the current settings of the datagenerator.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class ClusterGenerator
    
    Returns:
    
    an array of strings suitable for passing to setOptions
    
    See Also:
    
    DataGenerator.removeBlacklist(String[])
  - getClusters
```
protected ClusterDefinition[] getClusters()
```
    returns the current cluster definitions, if necessary initializes them
    
    Returns:
    
    the current cluster definitions
  - defaultNumAttributes
```
protected int defaultNumAttributes()
```
    returns the default number of attributes
    
    Overrides:
    
    defaultNumAttributes in class ClusterGenerator
    
    Returns:
    
    the default number of attributes
  - setNumAttributes
```
public void setNumAttributes(int numAttributes)
```
    Sets the number of attributes the dataset should have.
    
    Overrides:
    
    setNumAttributes in class ClusterGenerator
    
    Parameters:
    
    numAttributes - the new number of attributes
  - numAttributesTipText
```
public String numAttributesTipText()
```
    Returns the tip text for this property
    
    Overrides:
    
    numAttributesTipText in class ClusterGenerator
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - defaultNoiseRate
```
protected double defaultNoiseRate()
```
    returns the default noise rate
    
    Returns:
    
    the default noise rate
  - getNoiseRate
```
public double getNoiseRate()
```
    Gets the percentage of noise set.
    
    Returns:
    
    the percentage of noise set
  - setNoiseRate
```
public void setNoiseRate(double newNoiseRate)
```
    Sets the percentage of noise set.
    
    Parameters:
    
    newNoiseRate - new percentage of noise
  - noiseRateTipText
```
public String noiseRateTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getClusterDefinitions
```
public ClusterDefinition[] getClusterDefinitions()
```
    returns the currently set clusters
    
    Returns:
    
    the currently set clusters
  - setClusterDefinitions
```
public void setClusterDefinitions(ClusterDefinition[] value)
                           throws Exception
```
    sets the clusters to use
    
    Parameters:
    
    value - the clusters do use
    
    Throws:
    
    Exception - if clusters are not the correct class
  - clusterDefinitionsTipText
```
public String clusterDefinitionsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - checkCoverage
```
protected boolean checkCoverage()
```
    Checks, whether all attributes are covered by cluster definitions and returns TRUE in that case.
    
    Returns:
    
    whether all attributes are covered
  - getSingleModeFlag
```
public boolean getSingleModeFlag()
```
    Gets the single mode flag.
    
    Specified by:
    
    getSingleModeFlag in class DataGenerator
    
    Returns:
    
    true if methode generateExample can be used.
  - defineDataFormat
```
public Instances defineDataFormat()
                           throws Exception
```
    Initializes the format for the dataset produced.
    
    Overrides:
    
    defineDataFormat in class DataGenerator
    
    Returns:
    
    the output data format
    
    Throws:
    
    Exception - data format could not be defined
    
    See Also:
    
    DataGenerator.defaultRelationName()
  - isBoolean
```
public boolean isBoolean(int index)
```
    Returns true if attribute is boolean
    
    Parameters:
    
    index - of the attribute
    
    Returns:
    
    true if the attribute is boolean
  - isNominal
```
public boolean isNominal(int index)
```
    Returns true if attribute is nominal
    
    Parameters:
    
    index - of the attribute
    
    Returns:
    
    true if the attribute is nominal
  - getNumValues
```
public int[] getNumValues()
```
    returns array that stores the number of values for a nominal attribute.
    
    Returns:
    
    the array that stores the number of values for a nominal attribute
  - generateExample
```
public Instance generateExample()
                         throws Exception
```
    Generate an example of the dataset.
    
    Specified by:
    
    generateExample in class DataGenerator
    
    Returns:
    
    the instance generated
    
    Throws:
    
    Exception - if format not defined or generating
    examples one by one is not possible, because voting is chosen
  - generateExamples
```
public Instances generateExamples()
                           throws Exception
```
    Generate all examples of the dataset.
    
    Specified by:
    
    generateExamples in class DataGenerator
    
    Returns:
    
    the instance generated
    
    Throws:
    
    Exception - if format not defined
  - generateFinished
```
public String generateFinished()
                        throws Exception
```
    Compiles documentation about the data generation after the generation process
    
    Specified by:
    
    generateFinished in class DataGenerator
    
    Returns:
    
    string with additional information about generated dataset
    
    Throws:
    
    Exception - no input structure has been defined
  - generateStart
```
public String generateStart()
```
    Compiles documentation about the data generation before the generation process
    
    Specified by:
    
    generateStart in class DataGenerator
    
    Returns:
    
    string with additional information
  - getRevision
```
public String getRevision()
```
    Returns the revision string.
    
    Returns:
    
    the revision
  - main
```
public static void main(String[] args)
```
    Main method for testing this class.
    
    Parameters:
    
    args - should contain arguments for the data producer:

Class SubspaceCluster

Field Summary

Fields inherited from class weka.datagenerators.ClusterGenerator

Fields inherited from class weka.datagenerators.DataGenerator

Constructor Summary

Method Summary

Methods inherited from class weka.datagenerators.ClusterGenerator

Methods inherited from class weka.datagenerators.DataGenerator

Methods inherited from class java.lang.Object

Field Detail

m_NoiseRate

m_Clusters

m_numValues

m_globalMinValue

m_globalMaxValue

UNIFORM_RANDOM

TOTAL_UNIFORM

GAUSSIAN

TAGS_CLUSTERTYPE

CONTINUOUS

INTEGER

TAGS_CLUSTERSUBTYPE

Constructor Detail

SubspaceCluster

Method Detail

globalInfo

listOptions

setOptions

getOptions

getClusters

defaultNumAttributes

setNumAttributes

numAttributesTipText

defaultNoiseRate

getNoiseRate

setNoiseRate

noiseRateTipText

getClusterDefinitions

setClusterDefinitions

clusterDefinitionsTipText

checkCoverage

getSingleModeFlag

defineDataFormat

isBoolean

isNominal

getNumValues

generateExample

generateExamples

generateFinished

generateStart

getRevision

main