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Class weka.classifiers.lazy.IBk

java.lang.Object
    |
    +----weka.classifiers.Classifier
            |
            +----weka.classifiers.DistributionClassifier
                    |
                    +----weka.classifiers.lazy.IBk

public class IBk

extends DistributionClassifier

implements OptionHandler, UpdateableClassifier, WeightedInstancesHandler

Aha, D., and D. Kibler (1991) "Instance-based learning algorithms", Machine Learning, vol.6, pp. 37-66.

Valid options are:

-K num
Set the number of nearest neighbours to use in prediction (default 1)

-W num
Set a fixed window size for incremental train/testing. As new training instances are added, oldest instances are removed to maintain the number of training instances at this size. (default no window)

-D
Neighbours will be weighted by the inverse of their distance when voting. (default equal weighting)

-F
Neighbours will be weighted by their similarity when voting. (default equal weighting)

-X
Selects the number of neighbours to use by hold-one-out cross validation, with an upper limit given by the -K option.

-S
When k is selected by cross-validation for numeric class attributes, minimize mean-squared error. (default mean absolute error)

-N
Turns off normalization.

-E
KDTrees class and its options (can only use the same distance function as XMeans).

Version:

$Revision: 1.23 $

Author:

Gabi Schmidberger (gabi@cs.waikato.ac.nz)

Author:

Stuart Inglis (singlis@cs.waikato.ac.nz)

Author:

Len Trigg (trigg@cs.waikato.ac.nz)

Author:

Eibe Frank (eibe@cs.waikato.ac.nz)

TAGS_WEIGHTING

 

WEIGHT_INVERSE

 

WEIGHT_NONE

 

WEIGHT_SIMILARITY

 

IBk()

IB1 classifer.

IBk(int)

IBk classifier.

buildClassifier(Instances)

Generates the classifier.

distributionForInstance(Instance)

Calculates the class membership probabilities for the given test instance.

getAttributeMax(int)

Get an attributes maximum observed value

getAttributeMin(int)

Get an attributes minimum observed value

getCrossValidate()

Gets whether hold-one-out cross-validation will be used to select the best k value

getDebug()

Get the value of Debug.

getDistanceWeighting()

Gets the distance weighting method used.

getKDTree()

Gets the KDTree class.

getKNN()

Gets the number of neighbours the learner will use.

getMeanSquared()

Gets whether the mean squared error is used rather than mean absolute error when doing cross-validation.

getNoNormalization()

Gets whether normalization is turned off.

getNumTraining()

Get the number of training instances the classifier is currently using

getOptions()

Gets the current settings of IBk.

getWindowSize()

Gets the maximum number of instances allowed in the training pool.

listOptions()

Returns an enumeration describing the available options.

main(String[])

Main method for testing this class.

setCrossValidate(boolean)

Sets whether hold-one-out cross-validation will be used to select the best k value

setDebug(boolean)

Set the value of Debug.

setDistanceWeighting(SelectedTag)

Sets the distance weighting method used.

setKDTree(KDTree)

Sets the KDTree class.

setKNN(int)

Set the number of neighbours the learner is to use.

setMeanSquared(boolean)

Sets whether the mean squared error is used rather than mean absolute error when doing cross-validation.

setNoNormalization(boolean)

Set whether normalization is turned off.

setOptions(String[])

Parses a given list of options.

setWindowSize(int)

Sets the maximum number of instances allowed in the training pool.

toString()

Returns a description of this classifier.

updateClassifier(Instance)

Adds the supplied instance to the training set

public static final int WEIGHT_NONE

public static final int WEIGHT_INVERSE

public static final int WEIGHT_SIMILARITY

public static final Tag[] TAGS_WEIGHTING

public IBk(int k)

          IBk classifier. Simple instance-based learner that uses the class of the nearest k training instances for the class of the test instances.

Parameters:

k - the number of nearest neighbours to use for prediction

public IBk()

          IB1 classifer. Instance-based learner. Predicts the class of the single nearest training instance for each test instance.

public boolean getDebug()

          Get the value of Debug.

Returns:

Value of Debug.

public void setDebug(boolean newDebug)

          Set the value of Debug.

Parameters:

newDebug - Value to assign to Debug.

public void setKDTree(KDTree k)

          Sets the KDTree class.

Parameters:

k - a KDTree object with all options set

public KDTree getKDTree()

          Gets the KDTree class.

Returns:

flag if KDTrees are used

public void setKNN(int k)

          Set the number of neighbours the learner is to use.

Parameters:

k - the number of neighbours.

public int getKNN()

          Gets the number of neighbours the learner will use.

Returns:

the number of neighbours

public int getWindowSize()

          Gets the maximum number of instances allowed in the training pool. The addition of new instances above this value will result in old instances being removed. A value of 0 signifies no limit to the number of training instances.

Returns:

Value of WindowSize

public void setWindowSize(int newWindowSize)

          Sets the maximum number of instances allowed in the training pool. The addition of new instances above this value will result in old instances being removed. A value of 0 signifies no limit to the number of training instances.

Parameters:

newWindowSize - Value to assign to WindowSize.

public SelectedTag getDistanceWeighting()

          Gets the distance weighting method used. Will be one of WEIGHT_NONE, WEIGHT_INVERSE, or WEIGHT_SIMILARITY

Returns:

the distance weighting method used.

public void setDistanceWeighting(SelectedTag newMethod)

          Sets the distance weighting method used. Values other than WEIGHT_NONE, WEIGHT_INVERSE, or WEIGHT_SIMILARITY will be ignored.

Parameters:

newDistanceWeighting - the distance weighting method to use

public boolean getMeanSquared()

          Gets whether the mean squared error is used rather than mean absolute error when doing cross-validation.

Returns:

true if so.

public void setMeanSquared(boolean newMeanSquared)

          Sets whether the mean squared error is used rather than mean absolute error when doing cross-validation.

Parameters:

newMeanSquared - true if so.

public boolean getCrossValidate()

          Gets whether hold-one-out cross-validation will be used to select the best k value

Returns:

true if cross-validation will be used.

public void setCrossValidate(boolean newCrossValidate)

          Sets whether hold-one-out cross-validation will be used to select the best k value

Parameters:

newCrossValidate - true if cross-validation should be used.

public int getNumTraining()

          Get the number of training instances the classifier is currently using

public double getAttributeMin(int index) throws java.lang.Exception

          Get an attributes minimum observed value

public double getAttributeMax(int index) throws java.lang.Exception

          Get an attributes maximum observed value

public boolean getNoNormalization()

          Gets whether normalization is turned off.

Returns:

Value of DontNormalize.

public void setNoNormalization(boolean v)

          Set whether normalization is turned off.

Parameters:

v - Value to assign to DontNormalize.

public void buildClassifier(Instances instances) throws java.lang.Exception

          Generates the classifier.

Parameters:

instances - set of instances serving as training data

Throws:

java.lang.Exception - if the classifier has not been generated successfully

Overrides:

buildClassifier in class Classifier

public void updateClassifier(Instance instance) throws java.lang.Exception

          Adds the supplied instance to the training set

Parameters:

instance - the instance to add

Throws:

java.lang.Exception - if instance could not be incorporated successfully

public double[] distributionForInstance(Instance instance) throws java.lang.Exception

          Calculates the class membership probabilities for the given test instance.

Parameters:

instance - the instance to be classified

Returns:

predicted class probability distribution

Throws:

java.lang.Exception - if an error occurred during the prediction

Overrides:

distributionForInstance in class DistributionClassifier

public java.util.Enumeration listOptions()

          Returns an enumeration describing the available options.

Returns:

an enumeration of all the available options.

public void setOptions(java.lang.String options[]) throws java.lang.Exception

          Parses a given list of options. Valid options are:

-K num
Set the number of nearest neighbours to use in prediction (default 1)

-W num
Set a fixed window size for incremental train/testing. As new training instances are added, oldest instances are removed to maintain the number of training instances at this size. (default no window)

-D
Neighbours will be weighted by the inverse of their distance when voting. (default equal weighting)

-F
Neighbours will be weighted by their similarity when voting. (default equal weighting)

-X
Select the number of neighbours to use by hold-one-out cross validation, with an upper limit given by the -K option.

-S
When k is selected by cross-validation for numeric class attributes, minimize mean-squared error. (default mean absolute error)

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

public java.lang.String[] getOptions()

          Gets the current settings of IBk.

Returns:

an array of strings suitable for passing to setOptions()

public java.lang.String toString()

          Returns a description of this classifier.

Returns:

a description of this classifier as a string.

Overrides:

toString in class java.lang.Object

public static void main(java.lang.String argv[])

          Main method for testing this class.

Parameters:

argv - should contain command line options (see setOptions)

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