weka.datagenerators.classifiers.classification
Class BayesNet

java.lang.Object
  weka.datagenerators.DataGenerator
      weka.datagenerators.ClassificationGenerator
          weka.datagenerators.classifiers.classification.BayesNet

All Implemented Interfaces:

Serializable, OptionHandler, Randomizable, RevisionHandler

public class BayesNet
extends ClassificationGenerator

Generates random instances based on a Bayes network.

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)

 -n <num>
  The number of examples to generate (default 100)

 -A <num>
  The number of arcs to use. (default 20)

 -C <num>
  The cardinality of the attributes and the class. (default 2)

Version:

$Revision: 8034 $

Author:

FracPete (fracpete at waikato dot ac dot nz)

See Also:

BayesNetGenerator, Serialized Form

Constructor Summary

BayesNet()
initializes the generator

Method Summary

String	cardinalityTipText() Returns the tip text for this property
Instances	defineDataFormat() Initializes the format for the dataset produced.
Instance	generateExample() Generates one example of the dataset.
Instances	generateExamples() Generates all examples of the dataset.
String	generateFinished() Generates a comment string that documentats the data generator.
String	generateStart() Generates a comment string that documentates the data generator.
int	getCardinality() Gets the cardinality of the attributes (incl class attribute)
int	getNumArcs() Gets the number of arcs for the bayesian net
int	getNumAttributes() Gets the number of attributes that should be produced.
int	getNumExamples() Gets the number of examples, given by option.
String[]	getOptions() Gets the current settings of the datagenerator.
String	getRevision() Returns the revision string.
boolean	getSingleModeFlag() Return if single mode is set for the given data generator mode depends on option setting and or generator type.
String	globalInfo() Returns a string describing this data generator.
Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(String[] args) Main method for executing this class.
String	numArcsTipText() Returns the tip text for this property
String	numAttributesTipText() Returns the tip text for this property
void	setCardinality(int value) Sets the cardinality of the attributes (incl class attribute)
void	setNumArcs(int value) Sets the number of arcs for the bayesian net
void	setNumAttributes(int numAttributes) Sets the number of attributes the dataset should have.
void	setNumExamples(int numExamples) Sets the number of examples, given by option.
void	setOptions(String[] options) Parses a list of options for this object.

Methods inherited from class weka.datagenerators.ClassificationGenerator

numExamplesTipText

Methods inherited from class weka.datagenerators.DataGenerator

debugTipText, defaultOutput, formatTipText, getDatasetFormat, getDebug, getNumExamplesAct, getOutput, getRandom, getRelationName, getSeed, makeData, outputTipText, randomTipText, relationNameTipText, seedTipText, setDatasetFormat, setDebug, setOutput, setRandom, setRelationName, setSeed

Methods inherited from class java.lang.Object

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

Constructor Detail

BayesNet

public BayesNet()

initializes the generator

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 ClassificationGenerator

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)

 -n <num>
  The number of examples to generate (default 100)

 -A <num>
  The number of arcs to use. (default 20)

 -C <num>
  The cardinality of the attributes and the class. (default 2)

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class ClassificationGenerator

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 ClassificationGenerator

Returns:

an array of strings suitable for passing to setOptions

See Also:

DataGenerator.removeBlacklist(String[])

setNumAttributes

public void setNumAttributes(int numAttributes)

Sets the number of attributes the dataset should have.

Parameters:

numAttributes - the new number of attributes

getNumAttributes

public int getNumAttributes()

Gets the number of attributes that should be produced.

Returns:

the number of attributes that should be produced

numAttributesTipText

public String numAttributesTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setCardinality

public void setCardinality(int value)

Sets the cardinality of the attributes (incl class attribute)

Parameters:

value - the cardinality

getCardinality

public int getCardinality()

Gets the cardinality of the attributes (incl class attribute)

Returns:

the cardinality of the attributes

cardinalityTipText

public String cardinalityTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setNumArcs

public void setNumArcs(int value)

Sets the number of arcs for the bayesian net

Parameters:

value - the number of arcs

getNumArcs

public int getNumArcs()

Gets the number of arcs for the bayesian net

Returns:

the number of arcs

numArcsTipText

public String numArcsTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setNumExamples

public void setNumExamples(int numExamples)

Sets the number of examples, given by option.

Overrides:

setNumExamples in class ClassificationGenerator

Parameters:

numExamples - the new number of examples

getNumExamples

public int getNumExamples()

Gets the number of examples, given by option.

Overrides:

getNumExamples in class ClassificationGenerator

Returns:

the number of examples, given by option

getSingleModeFlag

public boolean getSingleModeFlag()
                          throws Exception

Return if single mode is set for the given data generator mode depends on option setting and or generator type.

Specified by:

getSingleModeFlag in class DataGenerator

Returns:

single mode flag

Throws:

Exception - if mode is not set yet

defineDataFormat

public Instances defineDataFormat()
                           throws Exception

Initializes the format for the dataset produced. Must be called before the generateExample or generateExamples methods are used. Re-initializes the random number generator with the given seed.

Overrides:

defineDataFormat in class DataGenerator

Returns:

the format for the dataset

Throws:

Exception - if the generating of the format failed

See Also:

DataGenerator.getSeed()

generateExample

public Instance generateExample()
                         throws Exception

Generates one example of the dataset.

Specified by:

generateExample in class DataGenerator

Returns:

the generated example

Throws:

Exception - if the format of the dataset is not yet defined

Exception - if the generator only works with generateExamples which means in non single mode

generateExamples

public Instances generateExamples()
                           throws Exception

Generates all examples of the dataset. Re-initializes the random number generator with the given seed, before generating instances.

Specified by:

generateExamples in class DataGenerator

Returns:

the generated dataset

Throws:

Exception - if the format of the dataset is not yet defined

Exception - if the generator only works with generateExample, which means in single mode

See Also:

DataGenerator.getSeed()

generateStart

public String generateStart()

Generates a comment string that documentates the data generator. By default this string is added at the beginning of the produced output as ARFF file type, next after the options.

Specified by:

generateStart in class DataGenerator

Returns:

string contains info about the generated rules

generateFinished

public String generateFinished()
                        throws Exception

Generates a comment string that documentats the data generator. By default this string is added at the end of theproduces output as ARFF file type.

Specified by:

generateFinished in class DataGenerator

Returns:

string contains info about the generated rules

Throws:

Exception - if the generating of the documentaion fails

getRevision

public String getRevision()

Returns the revision string.

Returns:

the revision

main

public static void main(String[] args)

Main method for executing this class.

Parameters:

args - should contain arguments for the data producer: