Package weka.classifiers.mi
Class MILR
java.lang.Object
weka.classifiers.Classifier
weka.classifiers.mi.MILR
- All Implemented Interfaces:
Serializable
,Cloneable
,CapabilitiesHandler
,MultiInstanceCapabilitiesHandler
,OptionHandler
,RevisionHandler
Uses either standard or collective multi-instance assumption, but within linear regression. For the collective assumption, it offers arithmetic or geometric mean for the posteriors.
Valid options are:
-D Turn on debugging output.
-R <ridge> Set the ridge in the log-likelihood.
-A [0|1|2] Defines the type of algorithm: 0. standard MI assumption 1. collective MI assumption, arithmetic mean for posteriors 2. collective MI assumption, geometric mean for posteriors
- Version:
- $Revision: 9144 $
- Author:
- Eibe Frank (eibe@cs.waikato.ac.nz), Xin Xu (xx5@cs.waikato.ac.nz)
- See Also:
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Field Summary
FieldsModifier and TypeFieldDescriptionstatic final int
collective MI assumption, arithmetic mean for posteriorsstatic final int
standard MI assumptionstatic final int
collective MI assumption, geometric mean for posteriorsstatic final Tag[]
the types of algorithms -
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionReturns the tip text for this propertyvoid
buildClassifier
(Instances train) Builds the classifierdouble[]
Computes the distribution for a given exemplarGets the type of algorithm.Returns default capabilities of the classifier.Returns the capabilities of this multi-instance classifier for the relational data.String[]
Gets the current settings of the classifier.Returns the revision string.double
getRidge()
Gets the ridge in the log-likelihood.Returns the tip text for this propertyReturns an enumeration describing the available optionsstatic void
Main method for testing this class.Returns the tip text for this propertyvoid
setAlgorithmType
(SelectedTag newType) Sets the algorithm type.void
setOptions
(String[] options) Parses a given list of options.void
setRidge
(double ridge) Sets the ridge in the log-likelihood.toString()
Gets a string describing the classifier.Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
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Field Details
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ALGORITHMTYPE_DEFAULT
public static final int ALGORITHMTYPE_DEFAULTstandard MI assumption- See Also:
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ALGORITHMTYPE_ARITHMETIC
public static final int ALGORITHMTYPE_ARITHMETICcollective MI assumption, arithmetic mean for posteriors- See Also:
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ALGORITHMTYPE_GEOMETRIC
public static final int ALGORITHMTYPE_GEOMETRICcollective MI assumption, geometric mean for posteriors- See Also:
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TAGS_ALGORITHMTYPE
the types of algorithms
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Constructor Details
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MILR
public MILR()
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Method Details
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globalInfo
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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listOptions
Returns an enumeration describing the available options- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classClassifier
- Returns:
- an enumeration of all the available options
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setOptions
Parses a given list of options.- Specified by:
setOptions
in interfaceOptionHandler
- Overrides:
setOptions
in classClassifier
- Parameters:
options
- the list of options as an array of strings- Throws:
Exception
- if an option is not supported
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getOptions
Gets the current settings of the classifier.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classClassifier
- Returns:
- an array of strings suitable for passing to setOptions
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ridgeTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setRidge
public void setRidge(double ridge) Sets the ridge in the log-likelihood.- Parameters:
ridge
- the ridge
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getRidge
public double getRidge()Gets the ridge in the log-likelihood.- Returns:
- the ridge
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algorithmTypeTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getAlgorithmType
Gets the type of algorithm.- Returns:
- the algorithm type
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setAlgorithmType
Sets the algorithm type.- Parameters:
newType
- the new algorithm type
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getCapabilities
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classClassifier
- Returns:
- the capabilities of this classifier
- See Also:
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getMultiInstanceCapabilities
Returns the capabilities of this multi-instance classifier for the relational data.- Specified by:
getMultiInstanceCapabilities
in interfaceMultiInstanceCapabilitiesHandler
- Returns:
- the capabilities of this object
- See Also:
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buildClassifier
Builds the classifier- Specified by:
buildClassifier
in classClassifier
- Parameters:
train
- the training data to be used for generating the boosted classifier.- Throws:
Exception
- if the classifier could not be built successfully
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distributionForInstance
Computes the distribution for a given exemplar- Overrides:
distributionForInstance
in classClassifier
- Parameters:
exmp
- the exemplar for which distribution is computed- Returns:
- the distribution
- Throws:
Exception
- if the distribution can't be computed successfully
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toString
Gets a string describing the classifier. -
getRevision
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
Main method for testing this class.- Parameters:
argv
- should contain the command line arguments to the scheme (see Evaluation)
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