类 BayesNet
java.lang.Object
weka.datagenerators.DataGenerator
weka.datagenerators.ClassificationGenerator
weka.datagenerators.classifiers.classification.BayesNet
- 所有已实现的接口:
Serializable
,OptionHandler
,Randomizable
,RevisionHandler
Generates random instances based on a Bayes network.
Valid options are:
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)
-N <num> The number of attributes to generate. (default 10)
-C <num> The cardinality of the attributes and the class. (default 2)
- 版本:
- $Revision: 11753 $
- 作者:
- FracPete (fracpete at waikato dot ac dot nz)
- 另请参阅:
-
构造器概要
构造器 -
方法概要
修饰符和类型方法说明Returns the tip text for this propertyInitializes the format for the dataset produced.Generates one example of the dataset.Generates all examples of the dataset.Generates a comment string that documentats the data generator.Generates a comment string that documentates the data generator.int
Gets the cardinality of the attributes (incl class attribute)int
Gets the number of arcs for the bayesian netint
Gets the number of attributes that should be produced.int
Gets the number of examples, given by option.String[]
Gets the current settings of the datagenerator.Returns the revision string.int
getSeed()
Gets the random number seed.boolean
Return if single mode is set for the given data generator mode depends on option setting and or generator type.Returns a string describing this data generator.Returns an enumeration describing the available options.static void
Main method for executing this class.Returns the tip text for this propertyReturns the tip text for this propertyvoid
setCardinality
(int value) Sets the cardinality of the attributes (incl class attribute)void
setNumArcs
(int value) Sets the number of arcs for the bayesian netvoid
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.void
setSeed
(int newSeed) Sets the random number seed.从类继承的方法 weka.datagenerators.ClassificationGenerator
numExamplesTipText
从类继承的方法 weka.datagenerators.DataGenerator
debugTipText, defaultOutput, formatTipText, getDatasetFormat, getDebug, getNumExamplesAct, getOutput, getRandom, getRelationName, makeData, outputTipText, randomTipText, relationNameTipText, seedTipText, setDatasetFormat, setDebug, setOutput, setRandom, setRelationName
-
构造器详细资料
-
BayesNet
public BayesNet()initializes the generator
-
-
方法详细资料
-
globalInfo
Returns a string describing this data generator.- 返回:
- a description of the data generator suitable for displaying in the explorer/experimenter gui
-
listOptions
Returns an enumeration describing the available options.- 指定者:
listOptions
在接口中OptionHandler
- 覆盖:
listOptions
在类中ClassificationGenerator
- 返回:
- an enumeration of all the available options
-
setOptions
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)
-N <num> The number of attributes to generate. (default 10)
-C <num> The cardinality of the attributes and the class. (default 2)
- 指定者:
setOptions
在接口中OptionHandler
- 覆盖:
setOptions
在类中ClassificationGenerator
- 参数:
options
- the list of options as an array of strings- 抛出:
Exception
- if an option is not supported
-
getOptions
Gets the current settings of the datagenerator.- 指定者:
getOptions
在接口中OptionHandler
- 覆盖:
getOptions
在类中ClassificationGenerator
- 返回:
- an array of strings suitable for passing to setOptions
- 另请参阅:
-
DataGenerator.removeBlacklist(String[])
-
setNumAttributes
public void setNumAttributes(int numAttributes) Sets the number of attributes the dataset should have.- 参数:
numAttributes
- the new number of attributes
-
getNumAttributes
public int getNumAttributes()Gets the number of attributes that should be produced.- 返回:
- the number of attributes that should be produced
-
numAttributesTipText
Returns the tip text for this property- 返回:
- 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)- 参数:
value
- the cardinality
-
getCardinality
public int getCardinality()Gets the cardinality of the attributes (incl class attribute)- 返回:
- the cardinality of the attributes
-
cardinalityTipText
Returns the tip text for this property- 返回:
- 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- 参数:
value
- the number of arcs
-
getNumArcs
public int getNumArcs()Gets the number of arcs for the bayesian net- 返回:
- the number of arcs
-
numArcsTipText
Returns the tip text for this property- 返回:
- 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.- 覆盖:
setNumExamples
在类中ClassificationGenerator
- 参数:
numExamples
- the new number of examples
-
getNumExamples
public int getNumExamples()Gets the number of examples, given by option.- 覆盖:
getNumExamples
在类中ClassificationGenerator
- 返回:
- the number of examples, given by option
-
getSeed
public int getSeed()Gets the random number seed.- 指定者:
getSeed
在接口中Randomizable
- 覆盖:
getSeed
在类中DataGenerator
- 返回:
- the random number seed.
-
setSeed
public void setSeed(int newSeed) Sets the random number seed.- 指定者:
setSeed
在接口中Randomizable
- 覆盖:
setSeed
在类中DataGenerator
- 参数:
newSeed
- the new random number seed.
-
getSingleModeFlag
Return if single mode is set for the given data generator mode depends on option setting and or generator type.- 指定者:
getSingleModeFlag
在类中DataGenerator
- 返回:
- single mode flag
- 抛出:
Exception
- if mode is not set yet
-
defineDataFormat
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.- 覆盖:
defineDataFormat
在类中DataGenerator
- 返回:
- the format for the dataset
- 抛出:
Exception
- if the generating of the format failed- 另请参阅:
-
generateExample
Generates one example of the dataset.- 指定者:
generateExample
在类中DataGenerator
- 返回:
- the generated example
- 抛出:
Exception
- if the format of the dataset is not yet definedException
- if the generator only works with generateExamples which means in non single mode
-
generateExamples
Generates all examples of the dataset. Re-initializes the random number generator with the given seed, before generating instances.- 指定者:
generateExamples
在类中DataGenerator
- 返回:
- the generated dataset
- 抛出:
Exception
- if the format of the dataset is not yet definedException
- if the generator only works with generateExample, which means in single mode- 另请参阅:
-
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.- 指定者:
generateStart
在类中DataGenerator
- 返回:
- string contains info about the generated rules
-
generateFinished
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.- 指定者:
generateFinished
在类中DataGenerator
- 返回:
- string contains info about the generated rules
- 抛出:
Exception
- if the generating of the documentaion fails
-
getRevision
Returns the revision string.- 返回:
- the revision
-
main
Main method for executing this class.- 参数:
args
- should contain arguments for the data producer:
-