Package weka.classifiers.mi
Class SimpleMI
java.lang.Object
weka.classifiers.Classifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.mi.SimpleMI
- All Implemented Interfaces:
Serializable
,Cloneable
,CapabilitiesHandler
,MultiInstanceCapabilitiesHandler
,OptionHandler
,RevisionHandler
public class SimpleMI
extends SingleClassifierEnhancer
implements OptionHandler, MultiInstanceCapabilitiesHandler
Reduces MI data into mono-instance data.
Valid options are:
-M [1|2|3] The method used in transformation: 1.arithmatic average; 2.geometric centor; 3.using minimax combined features of a bag (default: 1) Method 3: Define s to be the vector of the coordinate-wise maxima and minima of X, ie., s(X)=(minx1, ..., minxm, maxx1, ...,maxxm), transform the exemplars into mono-instance which contains attributes s(X)
-D If set, classifier is run in debug mode and may output additional info to the console
-W Full name of base classifier. (default: weka.classifiers.rules.ZeroR)
Options specific to classifier weka.classifiers.rules.ZeroR:
-D If set, classifier is run in debug mode and may output additional info to the console
- Version:
- $Revision: 9144 $
- Author:
- Eibe Frank (eibe@cs.waikato.ac.nz), Xin Xu (xx5@cs.waikato.ac.nz), Lin Dong (ld21@cs.waikato.ac.nz)
- See Also:
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Field Summary
FieldsModifier and TypeFieldDescriptionstatic final Tag[]
the transformation methodsstatic final int
arithmetic averagestatic final int
geometric averagestatic final int
using minimax combined features of a bag -
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionvoid
buildClassifier
(Instances train) Builds the classifierdouble[]
distributionForInstance
(Instance newBag) Computes the distribution for a given exemplarReturns 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.Get the method used in transformation.Returns a string describing this filterReturns an enumeration describing the available options.static void
Main method for testing this class.static double[]
Get the minimal and maximal value of a certain attribute in a certain datavoid
setOptions
(String[] options) Parses a given list of options.void
setTransformMethod
(SelectedTag newMethod) Set the method used in transformation.toString()
Gets a string describing the classifier.Implements MITransform (3 type of transformation) 1.arithmatic average; 2.geometric centor; 3.merge minima and maxima attribute value togetherReturns the tip text for this propertyMethods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getClassifier, setClassifier
Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
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Field Details
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TRANSFORMMETHOD_ARITHMETIC
public static final int TRANSFORMMETHOD_ARITHMETICarithmetic average- See Also:
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TRANSFORMMETHOD_GEOMETRIC
public static final int TRANSFORMMETHOD_GEOMETRICgeometric average- See Also:
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TRANSFORMMETHOD_MINIMAX
public static final int TRANSFORMMETHOD_MINIMAXusing minimax combined features of a bag- See Also:
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TAGS_TRANSFORMMETHOD
the transformation methods
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Constructor Details
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SimpleMI
public SimpleMI()
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Method Details
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globalInfo
Returns a string describing this filter- Returns:
- a description of the filter 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 classSingleClassifierEnhancer
- Returns:
- an enumeration of all the available options.
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setOptions
Parses a given list of options. Valid options are:-M [1|2|3] The method used in transformation: 1.arithmatic average; 2.geometric centor; 3.using minimax combined features of a bag (default: 1) Method 3: Define s to be the vector of the coordinate-wise maxima and minima of X, ie., s(X)=(minx1, ..., minxm, maxx1, ...,maxxm), transform the exemplars into mono-instance which contains attributes s(X)
-D If set, classifier is run in debug mode and may output additional info to the console
-W Full name of base classifier. (default: weka.classifiers.rules.ZeroR)
Options specific to classifier weka.classifiers.rules.ZeroR:
-D If set, classifier is run in debug mode and may output additional info to the console
- Specified by:
setOptions
in interfaceOptionHandler
- Overrides:
setOptions
in classSingleClassifierEnhancer
- 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 classSingleClassifierEnhancer
- Returns:
- an array of strings suitable for passing to setOptions
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transformMethodTipText
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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setTransformMethod
Set the method used in transformation.- Parameters:
newMethod
- the index of method to use.
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getTransformMethod
Get the method used in transformation.- Returns:
- the index of method used.
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transform
Implements MITransform (3 type of transformation) 1.arithmatic average; 2.geometric centor; 3.merge minima and maxima attribute value together- Parameters:
train
- the multi-instance dataset (with relational attribute)- Returns:
- the transformed dataset with each bag contain mono-instance (without relational attribute) so that any classifier not for MI dataset can be applied on it.
- Throws:
Exception
- if the transformation fails
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minimax
Get the minimal and maximal value of a certain attribute in a certain data- Parameters:
data
- the dataattIndex
- the index of the attribute- Returns:
- the double array containing in entry 0 for min and 1 for max.
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getCapabilities
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classSingleClassifierEnhancer
- 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:
newBag
- 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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