类 SymmetricalUncertAttributeEval
java.lang.Object
weka.attributeSelection.ASEvaluation
weka.attributeSelection.SymmetricalUncertAttributeEval
public class SymmetricalUncertAttributeEval
extends ASEvaluation
implements AttributeEvaluator, OptionHandler
SymmetricalUncertAttributeEval :
Evaluates the worth of an attribute by measuring the symmetrical uncertainty with respect to the class.
SymmU(Class, Attribute) = 2 * (H(Class) - H(Class | Attribute)) / H(Class) + H(Attribute).
Valid options are:
Evaluates the worth of an attribute by measuring the symmetrical uncertainty with respect to the class.
SymmU(Class, Attribute) = 2 * (H(Class) - H(Class | Attribute)) / H(Class) + H(Attribute).
Valid options are:
-M treat missing values as a seperate value.
- 版本:
- $Revision: 5511 $
- 作者:
- Mark Hall (mhall@cs.waikato.ac.nz)
- 另请参阅:
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构造器概要
构造器 -
方法概要
修饰符和类型方法说明void
buildEvaluator
(Instances data) Initializes a symmetrical uncertainty attribute evaluator.double
evaluateAttribute
(int attribute) evaluates an individual attribute by measuring the symmetrical uncertainty between it and the class.Returns the capabilities of this evaluator.boolean
get whether missing values are being distributed or notString[]
Gets the current settings of WrapperSubsetEval.Returns the revision string.Returns a string describing this attribute evaluatorReturns an enumeration describing the available options.static void
Main method for testing this class.Returns the tip text for this propertyvoid
setMissingMerge
(boolean b) distribute the counts for missing values across observed valuesvoid
setOptions
(String[] options) Parses a given list of options.toString()
Return a description of the evaluator从类继承的方法 weka.attributeSelection.ASEvaluation
clean, forName, makeCopies, postProcess
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构造器详细资料
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SymmetricalUncertAttributeEval
public SymmetricalUncertAttributeEval()Constructor
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方法详细资料
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globalInfo
Returns a string describing this attribute evaluator- 返回:
- a description of the evaluator suitable for displaying in the explorer/experimenter gui
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listOptions
Returns an enumeration describing the available options.- 指定者:
listOptions
在接口中OptionHandler
- 返回:
- an enumeration of all the available options.
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setOptions
Parses a given list of options. Valid options are:-M treat missing values as a seperate value.
- 指定者:
setOptions
在接口中OptionHandler
- 参数:
options
- the list of options as an array of strings- 抛出:
Exception
- if an option is not supported
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missingMergeTipText
Returns the tip text for this property- 返回:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setMissingMerge
public void setMissingMerge(boolean b) distribute the counts for missing values across observed values- 参数:
b
- true=distribute missing values.
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getMissingMerge
public boolean getMissingMerge()get whether missing values are being distributed or not- 返回:
- true if missing values are being distributed.
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getOptions
Gets the current settings of WrapperSubsetEval.- 指定者:
getOptions
在接口中OptionHandler
- 返回:
- an array of strings suitable for passing to setOptions()
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getCapabilities
Returns the capabilities of this evaluator.- 指定者:
getCapabilities
在接口中CapabilitiesHandler
- 覆盖:
getCapabilities
在类中ASEvaluation
- 返回:
- the capabilities of this evaluator
- 另请参阅:
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buildEvaluator
Initializes a symmetrical uncertainty attribute evaluator. Discretizes all attributes that are numeric.- 指定者:
buildEvaluator
在类中ASEvaluation
- 参数:
data
- set of instances serving as training data- 抛出:
Exception
- if the evaluator has not been generated successfully
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evaluateAttribute
evaluates an individual attribute by measuring the symmetrical uncertainty between it and the class.- 指定者:
evaluateAttribute
在接口中AttributeEvaluator
- 参数:
attribute
- the index of the attribute to be evaluated- 返回:
- the uncertainty
- 抛出:
Exception
- if the attribute could not be evaluated
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toString
Return a description of the evaluator -
getRevision
Returns the revision string.- 指定者:
getRevision
在接口中RevisionHandler
- 覆盖:
getRevision
在类中ASEvaluation
- 返回:
- the revision
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main
Main method for testing this class.- 参数:
argv
- should contain the following arguments: -t training file
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