Package weka.classifiers.functions
Class Winnow
java.lang.Object
weka.classifiers.Classifier
weka.classifiers.functions.Winnow
- All Implemented Interfaces:
Serializable
,Cloneable
,UpdateableClassifier
,CapabilitiesHandler
,OptionHandler
,RevisionHandler
,TechnicalInformationHandler
Implements Winnow and Balanced Winnow algorithms by Littlestone.
For more information, see
N. Littlestone (1988). Learning quickly when irrelevant attributes are abound: A new linear threshold algorithm. Machine Learning. 2:285-318.
N. Littlestone (1989). Mistake bounds and logarithmic linear-threshold learning algorithms. University of California, Santa Cruz.
Does classification for problems with nominal attributes (which it converts into binary attributes). BibTeX:
For more information, see
N. Littlestone (1988). Learning quickly when irrelevant attributes are abound: A new linear threshold algorithm. Machine Learning. 2:285-318.
N. Littlestone (1989). Mistake bounds and logarithmic linear-threshold learning algorithms. University of California, Santa Cruz.
Does classification for problems with nominal attributes (which it converts into binary attributes). BibTeX:
@article{Littlestone1988, author = {N. Littlestone}, journal = {Machine Learning}, pages = {285-318}, title = {Learning quickly when irrelevant attributes are abound: A new linear threshold algorithm}, volume = {2}, year = {1988} } @techreport{Littlestone1989, address = {University of California, Santa Cruz}, author = {N. Littlestone}, institution = {University of California}, note = {Technical Report UCSC-CRL-89-11}, title = {Mistake bounds and logarithmic linear-threshold learning algorithms}, year = {1989} }Valid options are:
-L Use the baLanced version (default false)
-I <int> The number of iterations to be performed. (default 1)
-A <double> Promotion coefficient alpha. (default 2.0)
-B <double> Demotion coefficient beta. (default 0.5)
-H <double> Prediction threshold. (default -1.0 == number of attributes)
-W <double> Starting weights. (default 2.0)
-S <int> Default random seed. (default 1)
- Version:
- $Revision: 5523 $
- Author:
- J. Lindgren (jtlindgr at cs.helsinki.fi)
- See Also:
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionReturns the tip text for this propertyReturns the tip text for this propertyReturns the tip text for this propertyvoid
buildClassifier
(Instances insts) Builds the classifierdouble
classifyInstance
(Instance inst) Outputs the prediction for the given instance.Returns the tip text for this propertydouble
getAlpha()
Get the value of Alpha.boolean
Get the value of Balanced.double
getBeta()
Get the value of Beta.Returns default capabilities of the classifier.double
Get the value of defaultWeight.int
Get the value of numIterations.String[]
Gets the current settings of the classifier.Returns the revision string.int
getSeed()
Get the value of Seed.Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.double
Get the value of Threshold.Returns a string describing classifierReturns an enumeration describing the available optionsstatic void
Main method.Returns the tip text for this propertyReturns the tip text for this propertyvoid
setAlpha
(double a) Set the value of Alpha.void
setBalanced
(boolean b) Set the value of Balanced.void
setBeta
(double b) Set the value of Beta.void
setDefaultWeight
(double w) Set the value of defaultWeight.void
setNumIterations
(int v) Set the value of numIterations.void
setOptions
(String[] options) Parses a given list of options.void
setSeed
(int v) Set the value of Seed.void
setThreshold
(double t) Set the value of Threshold.Returns the tip text for this propertytoString()
Returns textual description of the classifier.void
updateClassifier
(Instance instance) Updates the classifier with a new learning exampleMethods inherited from class weka.classifiers.Classifier
debugTipText, distributionForInstance, forName, getDebug, makeCopies, makeCopy, setDebug
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Constructor Details
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Winnow
public Winnow()
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Method Details
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globalInfo
Returns a string describing classifier- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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getTechnicalInformation
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.- Specified by:
getTechnicalInformation
in interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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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. Valid options are:-L Use the baLanced version (default false)
-I <int> The number of iterations to be performed. (default 1)
-A <double> Promotion coefficient alpha. (default 2.0)
-B <double> Demotion coefficient beta. (default 0.5)
-H <double> Prediction threshold. (default -1.0 == number of attributes)
-W <double> Starting weights. (default 2.0)
-S <int> Default random seed. (default 1)
- 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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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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buildClassifier
Builds the classifier- Specified by:
buildClassifier
in classClassifier
- Parameters:
insts
- the data to train the classifier with- Throws:
Exception
- if something goes wrong during building
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updateClassifier
Updates the classifier with a new learning example- Specified by:
updateClassifier
in interfaceUpdateableClassifier
- Parameters:
instance
- the instance to update the classifier with- Throws:
Exception
- if something goes wrong
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classifyInstance
Outputs the prediction for the given instance.- Overrides:
classifyInstance
in classClassifier
- Parameters:
inst
- the instance for which prediction is to be computed- Returns:
- the prediction
- Throws:
Exception
- if something goes wrong
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toString
Returns textual description of the classifier. -
balancedTipText
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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getBalanced
public boolean getBalanced()Get the value of Balanced.- Returns:
- Value of Balanced.
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setBalanced
public void setBalanced(boolean b) Set the value of Balanced.- Parameters:
b
- Value to assign to Balanced.
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alphaTipText
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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getAlpha
public double getAlpha()Get the value of Alpha.- Returns:
- Value of Alpha.
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setAlpha
public void setAlpha(double a) Set the value of Alpha.- Parameters:
a
- Value to assign to Alpha.
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betaTipText
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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getBeta
public double getBeta()Get the value of Beta.- Returns:
- Value of Beta.
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setBeta
public void setBeta(double b) Set the value of Beta.- Parameters:
b
- Value to assign to Beta.
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thresholdTipText
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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getThreshold
public double getThreshold()Get the value of Threshold.- Returns:
- Value of Threshold.
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setThreshold
public void setThreshold(double t) Set the value of Threshold.- Parameters:
t
- Value to assign to Threshold.
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defaultWeightTipText
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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getDefaultWeight
public double getDefaultWeight()Get the value of defaultWeight.- Returns:
- Value of defaultWeight.
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setDefaultWeight
public void setDefaultWeight(double w) Set the value of defaultWeight.- Parameters:
w
- Value to assign to defaultWeight.
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numIterationsTipText
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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getNumIterations
public int getNumIterations()Get the value of numIterations.- Returns:
- Value of numIterations.
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setNumIterations
public void setNumIterations(int v) Set the value of numIterations.- Parameters:
v
- Value to assign to numIterations.
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seedTipText
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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getSeed
public int getSeed()Get the value of Seed.- Returns:
- Value of Seed.
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setSeed
public void setSeed(int v) Set the value of Seed.- Parameters:
v
- Value to assign to Seed.
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getRevision
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
Main method.- Parameters:
argv
- the commandline options
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