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Class weka.classifiers.IBk 

java.lang.Object
   |
   +----weka.classifiers.Classifier
           |
           +----weka.classifiers.DistributionClassifier
                   |
                   +----weka.classifiers.IBk
public class IBk
extends DistributionClassifier
implements OptionHandler, UpdateableClassifier, WeightedInstancesHandler
K-nearest neighbour classifier. For more information, see

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 neighbors 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
Neighbors will be weighted by the inverse of their distance when voting. (default equal weighting)

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

-X
Selects the number of neighbors 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)

Version:
$Revision: 1.13 $
Author:
Stuart Inglis (singlis@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)

Variable Index

o TAGS_WEIGHTING
o WEIGHT_INVERSE
o WEIGHT_NONE
o WEIGHT_SIMILARITY

Constructor Index

o IBk()
IB1 classifer.
o IBk(int)
IBk classifier.

Method Index

o buildClassifier(Instances)
Generates the classifier.
o distributionForInstance(Instance)
Calculates the class membership probabilities for the given test instance.
o getAttributeMax(int)
Get an attributes maximum observed value
o getAttributeMin(int)
Get an attributes minimum observed value
o getCrossValidate()
Gets whether hold-one-out cross-validation will be used to select the best k value
o getDebug()
Get the value of Debug.
o getDistanceWeighting()
Gets the distance weighting method used.
o getKNN()
Gets the number of neighbours the learner will use.
o getMeanSquared()
Gets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
o getNumTraining()
Get the number of training instances the classifier is currently using
o getOptions()
Gets the current settings of IBk.
o getWindowSize()
Gets the maximum number of instances allowed in the training pool.
o listOptions()
Returns an enumeration describing the available options
o main(String[])
Main method for testing this class.
o setCrossValidate(boolean)
Sets whether hold-one-out cross-validation will be used to select the best k value
o setDebug(boolean)
Set the value of Debug.
o setDistanceWeighting(SelectedTag)
Sets the distance weighting method used.
o setKNN(int)
Set the number of neighbours the learner is to use.
o setMeanSquared(boolean)
Sets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
o setOptions(String[])
Parses a given list of options.
o setWindowSize(int)
Sets the maximum number of instances allowed in the training pool.
o toString()
Returns a description of this classifier.
o updateClassifier(Instance)
Adds the supplied instance to the training set

Variables

o WEIGHT_NONE 
 public static final int WEIGHT_NONE
o WEIGHT_INVERSE 
 public static final int WEIGHT_INVERSE
o WEIGHT_SIMILARITY 
 public static final int WEIGHT_SIMILARITY
o TAGS_WEIGHTING 
 public static final Tag TAGS_WEIGHTING[]

Constructors

o IBk 
 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 neighbors to use for prediction
o IBk 
 public IBk()
IB1 classifer. Instance-based learner. Predicts the class of the single nearest training instance for each test instance.

Methods

o getDebug 
 public boolean getDebug()
Get the value of Debug.

Returns:
Value of Debug.
o setDebug 
 public void setDebug(boolean newDebug)
Set the value of Debug.

Parameters:
newDebug - Value to assign to Debug.
o setKNN 
 public void setKNN(int k)
Set the number of neighbours the learner is to use.

Parameters:
k - the number of neighbours.
o getKNN 
 public int getKNN()
Gets the number of neighbours the learner will use.

Returns:
the number of neighbours.
o getWindowSize 
 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.
o setWindowSize 
 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.
o getDistanceWeighting 
 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.
o setDistanceWeighting 
 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
o getMeanSquared 
 public boolean getMeanSquared()
Gets whether the mean squared error is used rather than mean absolute error when doing cross-validation.

Returns:
true if so.
o setMeanSquared 
 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.
o getCrossValidate 
 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.
o setCrossValidate 
 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.
o getNumTraining 
 public int getNumTraining()
Get the number of training instances the classifier is currently using

o getAttributeMin 
 public double getAttributeMin(int index)
Get an attributes minimum observed value

o getAttributeMax 
 public double getAttributeMax(int index)
Get an attributes maximum observed value

o buildClassifier 
 public void buildClassifier(Instances instances) throws Exception
Generates the classifier.

Parameters:
instances - set of instances serving as training data
Throws: Exception
if the classifier has not been generated successfully
Overrides:
buildClassifier in class Classifier
o updateClassifier 
 public void updateClassifier(Instance instance) throws Exception
Adds the supplied instance to the training set

Parameters:
instance - the instance to add
Throws: Exception
if instance could not be incorporated successfully
o distributionForInstance 
 public double[] distributionForInstance(Instance instance) throws Exception
Calculates the class membership probabilities for the given test instance.

Parameters:
instance - the instance to be classified
Returns:
predicted class probability distribution
Throws: Exception
if an error occurred during the prediction
Overrides:
distributionForInstance in class DistributionClassifier
o listOptions 
 public Enumeration listOptions()
Returns an enumeration describing the available options

Returns:
an enumeration of all the available options
o setOptions 
 public void setOptions(String options[]) throws Exception
Parses a given list of options. Valid options are:

-K num
Set the number of nearest neighbors 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
Neighbors will be weighted by the inverse of their distance when voting. (default equal weighting)

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

-X
Select the number of neighbors 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: Exception
if an option is not supported
o getOptions 
 public String[] getOptions()
Gets the current settings of IBk.

Returns:
an array of strings suitable for passing to setOptions()
o toString 
 public String toString()
Returns a description of this classifier.

Returns:
a description of this classifier as a string.
Overrides:
toString in class Object
o main 
 public static void main(String argv[])
Main method for testing this class.

Parameters:
argv - should contain command line options (see setOptions) 
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