weka.classifiers.lazy
Class IBk

java.lang.Object
  weka.classifiers.Classifier
      weka.classifiers.lazy.IBk

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, java.io.Serializable, UpdateableClassifier, WeightedInstancesHandler

public class IBk

extends Classifier

implements OptionHandler, UpdateableClassifier, WeightedInstancesHandler

K-nearest neighbours 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)

-I
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.

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

-N
Turns off normalization.

Version:

$Revision: 1.29 $

Author:

Stuart Inglis (singlis@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Nested Class Summary

private class	IBk.NeighborList
private class	IBk.NeighborNode

Field Summary

protected int	m_ClassType The class attribute type
protected boolean	m_CrossValidate Whether to select k by cross validation
protected int	m_DistanceWeighting Whether the neighbours should be distance-weighted
protected boolean	m_DontNormalize True if normalization is turned off
protected int	m_kNN The number of neighbours to use for classification (currently)
protected int	m_kNNUpper The value of kNN provided by the user.
protected boolean	m_kNNValid Whether the value of k selected by cross validation has been invalidated by a change in the training instances
protected double[]	m_Max The maximum values for numeric attributes.
protected boolean	m_MeanSquared Whether to minimise mean squared error rather than mean absolute error when cross-validating on numeric prediction tasks
protected double[]	m_Min The minimum values for numeric attributes.
protected double	m_NumAttributesUsed The number of attributes the contribute to a prediction
protected int	m_NumClasses The number of class values (or 1 if predicting numeric)
protected Instances	m_Train The training instances used for classification.
protected int	m_WindowSize The maximum number of training instances allowed.
static Tag[]	TAGS_WEIGHTING
static int	WEIGHT_INVERSE
static int	WEIGHT_NONE
static int	WEIGHT_SIMILARITY

Fields inherited from class weka.classifiers.Classifier

m_Debug

Constructor Summary

IBk()
IB1 classifer.

IBk(int k)
IBk classifier.

Method Summary

void	buildClassifier(Instances instances) Generates the classifier.
protected void	crossValidate() Select the best value for k by hold-one-out cross-validation.
java.lang.String	crossValidateTipText() Returns the tip text for this property
protected double	difference(int index, double val1, double val2) Computes the difference between two given attribute values.
protected double	distance(Instance first, Instance second) Calculates the distance between two instances
java.lang.String	distanceWeightingTipText() Returns the tip text for this property
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
protected IBk.NeighborList	findNeighbors(Instance instance) Build the list of nearest k neighbors to the given test instance.
double	getAttributeMax(int index) Get an attributes maximum observed value
double	getAttributeMin(int index) Get an attributes minimum observed value
boolean	getCrossValidate() Gets whether hold-one-out cross-validation will be used to select the best k value
SelectedTag	getDistanceWeighting() Gets the distance weighting method used.
int	getKNN() Gets the number of neighbours the learner will use.
boolean	getMeanSquared() Gets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
boolean	getNoNormalization() Gets whether normalization is turned off.
int	getNumTraining() Get the number of training instances the classifier is currently using
java.lang.String[]	getOptions() Gets the current settings of IBk.
int	getWindowSize() Gets the maximum number of instances allowed in the training pool.
java.lang.String	globalInfo() Returns a string describing classifier
protected void	init() Initialise scheme variables.
java.lang.String	KNNTipText() Returns the tip text for this property
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Main method for testing this class.
protected double[]	makeDistribution(IBk.NeighborList neighborlist) Turn the list of nearest neighbors into a probability distribution
java.lang.String	meanSquaredTipText() Returns the tip text for this property
java.lang.String	noNormalizationTipText() Returns the tip text for this property
protected double	norm(double x, int i) Normalizes a given value of a numeric attribute.
void	setCrossValidate(boolean newCrossValidate) Sets whether hold-one-out cross-validation will be used to select the best k value
void	setDistanceWeighting(SelectedTag newMethod) Sets the distance weighting method used.
void	setKNN(int k) Set the number of neighbours the learner is to use.
void	setMeanSquared(boolean newMeanSquared) Sets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
void	setNoNormalization(boolean v) Set whether normalization is turned off.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setWindowSize(int newWindowSize) Sets the maximum number of instances allowed in the training pool.
java.lang.String	toString() Returns a description of this classifier.
void	updateClassifier(Instance instance) Adds the supplied instance to the training set
protected void	updateMinMax(Instance instance) Updates the minimum and maximum values for all the attributes based on a new instance.
java.lang.String	windowSizeTipText() Returns the tip text for this property

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, setDebug

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

m_Train

protected Instances m_Train

The training instances used for classification.

m_NumClasses

protected int m_NumClasses

The number of class values (or 1 if predicting numeric)

m_ClassType

protected int m_ClassType

The class attribute type

m_Min

protected double[] m_Min

The minimum values for numeric attributes.

m_Max

protected double[] m_Max

The maximum values for numeric attributes.

m_kNN

protected int m_kNN

The number of neighbours to use for classification (currently)

m_kNNUpper

protected int m_kNNUpper

The value of kNN provided by the user. This may differ from m_kNN if cross-validation is being used

m_kNNValid

protected boolean m_kNNValid

Whether the value of k selected by cross validation has been invalidated by a change in the training instances

m_WindowSize

protected int m_WindowSize

The maximum number of training instances allowed. When this limit is reached, old training instances are removed, so the training data is "windowed". Set to 0 for unlimited numbers of instances.

m_DistanceWeighting

protected int m_DistanceWeighting

Whether the neighbours should be distance-weighted

m_CrossValidate

protected boolean m_CrossValidate

Whether to select k by cross validation

m_MeanSquared

protected boolean m_MeanSquared

Whether to minimise mean squared error rather than mean absolute error when cross-validating on numeric prediction tasks

m_DontNormalize

protected boolean m_DontNormalize

True if normalization is turned off

WEIGHT_NONE

public static final int WEIGHT_NONE

See Also:

Constant Field Values

WEIGHT_INVERSE

public static final int WEIGHT_INVERSE

See Also:

Constant Field Values

WEIGHT_SIMILARITY

public static final int WEIGHT_SIMILARITY

See Also:

Constant Field Values

TAGS_WEIGHTING

public static final Tag[] TAGS_WEIGHTING

m_NumAttributesUsed

protected double m_NumAttributesUsed

The number of attributes the contribute to a prediction

Constructor Detail

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

IBk

public IBk()

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

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

KNNTipText

public java.lang.String KNNTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setKNN

public void setKNN(int k)

Set the number of neighbours the learner is to use.

Parameters:

k - the number of neighbours.

getKNN

public int getKNN()

Gets the number of neighbours the learner will use.

Returns:

the number of neighbours.

windowSizeTipText

public java.lang.String windowSizeTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

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.

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.

distanceWeightingTipText

public java.lang.String distanceWeightingTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

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.

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.

meanSquaredTipText

public java.lang.String meanSquaredTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

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.

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.

crossValidateTipText

public java.lang.String crossValidateTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

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.

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.

getNumTraining

public int getNumTraining()

Get the number of training instances the classifier is currently using

getAttributeMin

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

Get an attributes minimum observed value

Throws:

java.lang.Exception

getAttributeMax

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

Get an attributes maximum observed value

Throws:

java.lang.Exception

noNormalizationTipText

public java.lang.String noNormalizationTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getNoNormalization

public boolean getNoNormalization()

Gets whether normalization is turned off.

Returns:

Value of DontNormalize.

setNoNormalization

public void setNoNormalization(boolean v)

Set whether normalization is turned off.

Parameters:

v - Value to assign to DontNormalize.

buildClassifier

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

Generates the classifier.

Specified by:

buildClassifier in class Classifier

Parameters:

instances - set of instances serving as training data

Throws:

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

updateClassifier

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

Adds the supplied instance to the training set

Specified by:

updateClassifier in interface UpdateableClassifier

Parameters:

instance - the instance to add

Throws:

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

distributionForInstance

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

Calculates the class membership probabilities for the given test instance.

Overrides:

distributionForInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

predicted class probability distribution

Throws:

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

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class Classifier

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:

-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)

-I
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.

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

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class Classifier

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 IBk.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class Classifier

Returns:

an array of strings suitable for passing to setOptions()

toString

public java.lang.String toString()

Returns a description of this classifier.

Returns:

a description of this classifier as a string.

init

protected void init()

Initialise scheme variables.

distance

protected double distance(Instance first,
                          Instance second)

Calculates the distance between two instances

Returns:

the distance between the two given instances, between 0 and 1

difference

protected double difference(int index,
                            double val1,
                            double val2)

Computes the difference between two given attribute values.

norm

protected double norm(double x,
                      int i)

Normalizes a given value of a numeric attribute.

Parameters:

x - the value to be normalized

i - the attribute's index

updateMinMax

protected void updateMinMax(Instance instance)

Updates the minimum and maximum values for all the attributes based on a new instance.

Parameters:

instance - the new instance

findNeighbors

protected IBk.NeighborList findNeighbors(Instance instance)

Build the list of nearest k neighbors to the given test instance.

Parameters:

instance - the instance to search for neighbours of

Returns:

a list of neighbors

makeDistribution

protected double[] makeDistribution(IBk.NeighborList neighborlist)
                             throws java.lang.Exception

Turn the list of nearest neighbors into a probability distribution

Parameters:

neighborlist - the list of nearest neighboring instances

Returns:

the probability distribution

Throws:

java.lang.Exception

crossValidate

protected void crossValidate()

Select the best value for k by hold-one-out cross-validation. If the class attribute is nominal, classification error is minimised. If the class attribute is numeric, mean absolute error is minimised

main

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

Main method for testing this class.

Parameters:

argv - should contain command line options (see setOptions)