Winnow

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.functions.Winnow

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.classifiers.UpdateableClassifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler

public class Winnow
extends weka.classifiers.AbstractClassifier
implements weka.classifiers.UpdateableClassifier, weka.core.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:

 @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: 8108 $
Author:: J. Lindgren (jtlindgr at cs.helsinki.fi)
See Also:: Serialized Form

Field Summary
- Fields inherited from class weka.classifiers.AbstractClassifier
  BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

Winnow()

Constructors
Constructor and Description
`Winnow()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`java.lang.String`	`alphaTipText()` Returns the tip text for this property
`java.lang.String`	`balancedTipText()` Returns the tip text for this property
`java.lang.String`	`betaTipText()` Returns the tip text for this property
`void`	`buildClassifier(weka.core.Instances insts)` Builds the classifier
`double`	`classifyInstance(weka.core.Instance inst)` Outputs the prediction for the given instance.
`java.lang.String`	`defaultWeightTipText()` Returns the tip text for this property
`double`	`getAlpha()` Get the value of Alpha.
`boolean`	`getBalanced()` Get the value of Balanced.
`double`	`getBeta()` Get the value of Beta.
`weka.core.Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`double`	`getDefaultWeight()` Get the value of defaultWeight.
`int`	`getNumIterations()` Get the value of numIterations.
`java.lang.String[]`	`getOptions()` Gets the current settings of the classifier.
`java.lang.String`	`getRevision()` Returns the revision string.
`int`	`getSeed()` Get the value of Seed.
`weka.core.TechnicalInformation`	`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.
`double`	`getThreshold()` Get the value of Threshold.
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`java.util.Enumeration`	`listOptions()` Returns an enumeration describing the available options
`static void`	`main(java.lang.String[] argv)` Main method.
`java.lang.String`	`numIterationsTipText()` Returns the tip text for this property
`java.lang.String`	`seedTipText()` Returns the tip text for this property
`void`	`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(java.lang.String[] options)` Parses a given list of options. Valid options are:
`void`	`setSeed(int v)` Set the value of Seed.
`void`	`setThreshold(double t)` Set the value of Threshold.
`java.lang.String`	`thresholdTipText()` Returns the tip text for this property
`java.lang.String`	`toString()` Returns textual description of the classifier.
`void`	`updateClassifier(weka.core.Instance instance)` Updates the classifier with a new learning example

Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, debugTipText, distributionForInstance, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler
makeCopy

- Constructor Detail
  - Winnow
```
public Winnow()
```
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing classifier
    
    Returns:
    
    a description suitable for displaying in the explorer/experimenter gui
  - getTechnicalInformation
```
public weka.core.TechnicalInformation 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 interface weka.core.TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - listOptions
```
public java.util.Enumeration listOptions()
```
    Returns an enumeration describing the available options
    
    Specified by:
    
    listOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    listOptions in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    an enumeration of all the available options
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    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 interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    java.lang.Exception - if an option is not supported
  - getOptions
```
public java.lang.String[] getOptions()
```
    Gets the current settings of the classifier.
    
    Specified by:
    
    getOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    getOptions in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - getCapabilities
```
public weka.core.Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface weka.classifiers.Classifier
    
    Specified by:
    
    getCapabilities in interface weka.core.CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
  - buildClassifier
```
public void buildClassifier(weka.core.Instances insts)
                     throws java.lang.Exception
```
    Builds the classifier
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.Classifier
    
    Parameters:
    
    insts - the data to train the classifier with
    
    Throws:
    
    java.lang.Exception - if something goes wrong during building
  - updateClassifier
```
public void updateClassifier(weka.core.Instance instance)
                      throws java.lang.Exception
```
    Updates the classifier with a new learning example
    
    Specified by:
    
    updateClassifier in interface weka.classifiers.UpdateableClassifier
    
    Parameters:
    
    instance - the instance to update the classifier with
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - classifyInstance
```
public double classifyInstance(weka.core.Instance inst)
                        throws java.lang.Exception
```
    Outputs the prediction for the given instance.
    
    Specified by:
    
    classifyInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    classifyInstance in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    inst - the instance for which prediction is to be computed
    
    Returns:
    
    the prediction
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - toString
```
public java.lang.String toString()
```
    Returns textual description of the classifier.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    textual description of the classifier
  - balancedTipText
```
public java.lang.String balancedTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getBalanced
```
public boolean getBalanced()
```
    Get the value of Balanced.
    
    Returns:
    
    Value of Balanced.
  - setBalanced
```
public void setBalanced(boolean b)
```
    Set the value of Balanced.
    
    Parameters:
    
    b - Value to assign to Balanced.
  - alphaTipText
```
public java.lang.String alphaTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getAlpha
```
public double getAlpha()
```
    Get the value of Alpha.
    
    Returns:
    
    Value of Alpha.
  - setAlpha
```
public void setAlpha(double a)
```
    Set the value of Alpha.
    
    Parameters:
    
    a - Value to assign to Alpha.
  - betaTipText
```
public java.lang.String betaTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getBeta
```
public double getBeta()
```
    Get the value of Beta.
    
    Returns:
    
    Value of Beta.
  - setBeta
```
public void setBeta(double b)
```
    Set the value of Beta.
    
    Parameters:
    
    b - Value to assign to Beta.
  - thresholdTipText
```
public java.lang.String thresholdTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getThreshold
```
public double getThreshold()
```
    Get the value of Threshold.
    
    Returns:
    
    Value of Threshold.
  - setThreshold
```
public void setThreshold(double t)
```
    Set the value of Threshold.
    
    Parameters:
    
    t - Value to assign to Threshold.
  - defaultWeightTipText
```
public java.lang.String defaultWeightTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getDefaultWeight
```
public double getDefaultWeight()
```
    Get the value of defaultWeight.
    
    Returns:
    
    Value of defaultWeight.
  - setDefaultWeight
```
public void setDefaultWeight(double w)
```
    Set the value of defaultWeight.
    
    Parameters:
    
    w - Value to assign to defaultWeight.
  - numIterationsTipText
```
public java.lang.String numIterationsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getNumIterations
```
public int getNumIterations()
```
    Get the value of numIterations.
    
    Returns:
    
    Value of numIterations.
  - setNumIterations
```
public void setNumIterations(int v)
```
    Set the value of numIterations.
    
    Parameters:
    
    v - Value to assign to numIterations.
  - seedTipText
```
public java.lang.String seedTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getSeed
```
public int getSeed()
```
    Get the value of Seed.
    
    Returns:
    
    Value of Seed.
  - setSeed
```
public void setSeed(int v)
```
    Set the value of Seed.
    
    Parameters:
    
    v - Value to assign to Seed.
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface weka.core.RevisionHandler
    
    Overrides:
    
    getRevision in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method.
    
    Parameters:
    
    argv - the commandline options

Class Winnow

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Constructor Detail

Winnow

Method Detail

globalInfo

getTechnicalInformation

listOptions

setOptions

getOptions

getCapabilities

buildClassifier

updateClassifier

classifyInstance

toString

balancedTipText

getBalanced

setBalanced

alphaTipText

getAlpha

setAlpha

betaTipText

getBeta

setBeta

thresholdTipText

getThreshold

setThreshold

defaultWeightTipText

getDefaultWeight

setDefaultWeight

numIterationsTipText

getNumIterations

setNumIterations

seedTipText

getSeed

setSeed

getRevision

main