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java.lang.Objectweka.datagenerators.DataGenerator
weka.datagenerators.ClassificationGenerator
weka.datagenerators.classifiers.classification.BayesNet
public class BayesNet
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)
BayesNetGenerator
,
Serialized FormConstructor Summary | |
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BayesNet()
initializes the generator |
Method Summary | |
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java.lang.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. |
java.lang.String |
generateFinished()
Generates a comment string that documentats the data generator. |
java.lang.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. |
java.lang.String[] |
getOptions()
Gets the current settings of the datagenerator. |
boolean |
getSingleModeFlag()
Return if single mode is set for the given data generator mode depends on option setting and or generator type. |
java.lang.String |
globalInfo()
Returns a string describing this data generator. |
java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options. |
static void |
main(java.lang.String[] args)
Main method for executing this class. |
java.lang.String |
numArcsTipText()
Returns the tip text for this property |
java.lang.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(java.lang.String[] options)
Parses a list of options for this object. |
Methods inherited from class weka.datagenerators.ClassificationGenerator |
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numExamplesTipText |
Methods inherited from class weka.datagenerators.DataGenerator |
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debugTipText, defaultOutput, formatTipText, getDatasetFormat, getDebug, getOutput, getRandom, getRelationName, getSeed, makeData, outputTipText, randomTipText, relationNameTipText, seedTipText, setDatasetFormat, setDebug, setOutput, setRandom, setRelationName, setSeed |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public BayesNet()
Method Detail |
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public java.lang.String globalInfo()
public java.util.Enumeration 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)
-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 strings
java.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 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 failedDataGenerator.getSeed()
public Instance generateExample() throws java.lang.Exception
generateExample
in class DataGenerator
java.lang.Exception
- if the format of the dataset is not yet defined
java.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 defined
java.lang.Exception
- if the generator only works with generateExample,
which means in single modeDataGenerator.getSeed()
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 static void main(java.lang.String[] args)
args
- should contain arguments for the data producer:
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