public class BayesNet extends ClassificationGenerator
-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)
-N <num> The number of attributes to generate. (default 10)
-C <num> The cardinality of the attributes and the class. (default 2)
BayesNetGenerator
,
Serialized FormConstructor and Description |
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BayesNet()
initializes the generator
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Modifier and Type | Method and Description |
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java.lang.String |
cardinalityTipText()
Returns the tip text for this property
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Instances |
defineDataFormat()
Initializes the format for the dataset produced.
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Instance |
generateExample()
Generates one example of the dataset.
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Instances |
generateExamples()
Generates all examples of the dataset.
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java.lang.String |
generateFinished()
Generates a comment string that documentats the data generator.
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java.lang.String |
generateStart()
Generates a comment string that documentates the data generator.
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int |
getCardinality()
Gets the cardinality of the attributes (incl class attribute)
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int |
getNumArcs()
Gets the number of arcs for the bayesian net
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int |
getNumAttributes()
Gets the number of attributes that should be produced.
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int |
getNumExamples()
Gets the number of examples, given by option.
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java.lang.String[] |
getOptions()
Gets the current settings of the datagenerator.
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java.lang.String |
getRevision()
Returns the revision string.
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int |
getSeed()
Gets the random number seed.
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boolean |
getSingleModeFlag()
Return if single mode is set for the given data generator mode depends on
option setting and or generator type.
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java.lang.String |
globalInfo()
Returns a string describing this data generator.
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java.util.Enumeration<Option> |
listOptions()
Returns an enumeration describing the available options.
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static void |
main(java.lang.String[] args)
Main method for executing this class.
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java.lang.String |
numArcsTipText()
Returns the tip text for this property
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java.lang.String |
numAttributesTipText()
Returns the tip text for this property
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void |
setCardinality(int value)
Sets the cardinality of the attributes (incl class attribute)
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void |
setNumArcs(int value)
Sets the number of arcs for the bayesian net
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void |
setNumAttributes(int numAttributes)
Sets the number of attributes the dataset should have.
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void |
setNumExamples(int numExamples)
Sets the number of examples, given by option.
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void |
setOptions(java.lang.String[] options)
Parses a list of options for this object.
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void |
setSeed(int newSeed)
Sets the random number seed.
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numExamplesTipText
debugTipText, defaultOutput, formatTipText, getDatasetFormat, getDebug, getNumExamplesAct, getOutput, getRandom, getRelationName, makeData, outputTipText, randomTipText, relationNameTipText, seedTipText, setDatasetFormat, setDebug, setOutput, setRandom, setRelationName
public java.lang.String globalInfo()
public java.util.Enumeration<Option> listOptions()
listOptions
in interface OptionHandler
listOptions
in class ClassificationGenerator
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-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)
-N <num> The number of attributes to generate. (default 10)
-C <num> The cardinality of the attributes and the class. (default 2)
setOptions
in interface OptionHandler
setOptions
in class ClassificationGenerator
options
- the list of options as an array of stringsjava.lang.Exception
- if an option is not supportedpublic java.lang.String[] getOptions()
getOptions
in interface OptionHandler
getOptions
in class ClassificationGenerator
DataGenerator.removeBlacklist(String[])
public void setNumAttributes(int numAttributes)
numAttributes
- the new number of attributespublic int getNumAttributes()
public java.lang.String numAttributesTipText()
public void setCardinality(int value)
value
- the cardinalitypublic int getCardinality()
public java.lang.String cardinalityTipText()
public void setNumArcs(int value)
value
- the number of arcspublic int getNumArcs()
public java.lang.String numArcsTipText()
public void setNumExamples(int numExamples)
setNumExamples
in class ClassificationGenerator
numExamples
- the new number of examplespublic int getNumExamples()
getNumExamples
in class ClassificationGenerator
public int getSeed()
getSeed
in interface Randomizable
getSeed
in class DataGenerator
public void setSeed(int newSeed)
setSeed
in interface Randomizable
setSeed
in class DataGenerator
newSeed
- the new random number seed.public boolean getSingleModeFlag() throws java.lang.Exception
getSingleModeFlag
in class DataGenerator
java.lang.Exception
- if mode is not set yetpublic Instances defineDataFormat() throws java.lang.Exception
defineDataFormat
in class DataGenerator
java.lang.Exception
- if the generating of the format failedgetSeed()
public Instance generateExample() throws java.lang.Exception
generateExample
in class DataGenerator
java.lang.Exception
- if the format of the dataset is not yet definedjava.lang.Exception
- if the generator only works with generateExamples which
means in non single modepublic Instances generateExamples() throws java.lang.Exception
generateExamples
in class DataGenerator
java.lang.Exception
- if the format of the dataset is not yet definedjava.lang.Exception
- if the generator only works with generateExample, which
means in single modegetSeed()
public java.lang.String generateStart()
generateStart
in class DataGenerator
public java.lang.String generateFinished() throws java.lang.Exception
generateFinished
in class DataGenerator
java.lang.Exception
- if the generating of the documentaion failspublic java.lang.String getRevision()
public static void main(java.lang.String[] args)
args
- should contain arguments for the data producer: