Package weka.classifiers.mi
Class MILR
- java.lang.Object
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- weka.classifiers.Classifier
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- weka.classifiers.mi.MILR
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- All Implemented Interfaces:
java.io.Serializable
,java.lang.Cloneable
,CapabilitiesHandler
,MultiInstanceCapabilitiesHandler
,OptionHandler
,RevisionHandler
public class MILR extends Classifier implements OptionHandler, MultiInstanceCapabilitiesHandler
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:
- Serialized Form
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Field Summary
Fields Modifier and Type Field Description static int
ALGORITHMTYPE_ARITHMETIC
collective MI assumption, arithmetic mean for posteriorsstatic int
ALGORITHMTYPE_DEFAULT
standard MI assumptionstatic int
ALGORITHMTYPE_GEOMETRIC
collective MI assumption, geometric mean for posteriorsstatic Tag[]
TAGS_ALGORITHMTYPE
the types of algorithms
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Constructor Summary
Constructors Constructor Description MILR()
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description java.lang.String
algorithmTypeTipText()
Returns the tip text for this propertyvoid
buildClassifier(Instances train)
Builds the classifierdouble[]
distributionForInstance(Instance exmp)
Computes the distribution for a given exemplarSelectedTag
getAlgorithmType()
Gets the type of algorithm.Capabilities
getCapabilities()
Returns default capabilities of the classifier.Capabilities
getMultiInstanceCapabilities()
Returns the capabilities of this multi-instance classifier for the relational data.java.lang.String[]
getOptions()
Gets the current settings of the classifier.java.lang.String
getRevision()
Returns the revision string.double
getRidge()
Gets the ridge in the log-likelihood.java.lang.String
globalInfo()
Returns the tip text for this propertyjava.util.Enumeration
listOptions()
Returns an enumeration describing the available optionsstatic void
main(java.lang.String[] argv)
Main method for testing this class.java.lang.String
ridgeTipText()
Returns the tip text for this propertyvoid
setAlgorithmType(SelectedTag newType)
Sets the algorithm type.void
setOptions(java.lang.String[] options)
Parses a given list of options.void
setRidge(double ridge)
Sets the ridge in the log-likelihood.java.lang.String
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 Detail
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ALGORITHMTYPE_DEFAULT
public static final int ALGORITHMTYPE_DEFAULT
standard MI assumption- See Also:
- Constant Field Values
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ALGORITHMTYPE_ARITHMETIC
public static final int ALGORITHMTYPE_ARITHMETIC
collective MI assumption, arithmetic mean for posteriors- See Also:
- Constant Field Values
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ALGORITHMTYPE_GEOMETRIC
public static final int ALGORITHMTYPE_GEOMETRIC
collective MI assumption, geometric mean for posteriors- See Also:
- Constant Field Values
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TAGS_ALGORITHMTYPE
public static final Tag[] TAGS_ALGORITHMTYPE
the types of algorithms
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Method Detail
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globalInfo
public java.lang.String 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
public java.util.Enumeration 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
public void setOptions(java.lang.String[] options) throws java.lang.Exception
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:
java.lang.Exception
- if an option is not supported
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getOptions
public java.lang.String[] 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
public java.lang.String 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
public java.lang.String 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
public SelectedTag getAlgorithmType()
Gets the type of algorithm.- Returns:
- the algorithm type
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setAlgorithmType
public void setAlgorithmType(SelectedTag newType)
Sets the algorithm type.- Parameters:
newType
- the new algorithm type
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getCapabilities
public Capabilities getCapabilities()
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classClassifier
- Returns:
- the capabilities of this classifier
- See Also:
Capabilities
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getMultiInstanceCapabilities
public Capabilities 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:
Capabilities
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buildClassifier
public void buildClassifier(Instances train) throws java.lang.Exception
Builds the classifier- Specified by:
buildClassifier
in classClassifier
- Parameters:
train
- the training data to be used for generating the boosted classifier.- Throws:
java.lang.Exception
- if the classifier could not be built successfully
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distributionForInstance
public double[] distributionForInstance(Instance exmp) throws java.lang.Exception
Computes the distribution for a given exemplar- Overrides:
distributionForInstance
in classClassifier
- Parameters:
exmp
- the exemplar for which distribution is computed- Returns:
- the distribution
- Throws:
java.lang.Exception
- if the distribution can't be computed successfully
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toString
public java.lang.String toString()
Gets a string describing the classifier.- Overrides:
toString
in classjava.lang.Object
- Returns:
- a string describing the classifer built.
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
public static void main(java.lang.String[] argv)
Main method for testing this class.- Parameters:
argv
- should contain the command line arguments to the scheme (see Evaluation)
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