IBk (weka-stable 3.8.3 API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.lazy.IBk

All Implemented Interfaces:: Serializable, Cloneable, Classifier, UpdateableClassifier, AdditionalMeasureProducer, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class IBk
extends AbstractClassifier
implements OptionHandler, UpdateableClassifier, WeightedInstancesHandler, TechnicalInformationHandler, AdditionalMeasureProducer

K-nearest neighbours classifier. Can select appropriate value of K based on cross-validation. Can also do distance weighting.

For more information, see

D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.

BibTeX:

 @article{Aha1991,
    author = {D. Aha and D. Kibler},
    journal = {Machine Learning},
    pages = {37-66},
    title = {Instance-based learning algorithms},
    volume = {6},
    year = {1991}
 }

Valid options are:

 -I
  Weight neighbours by the inverse of their distance
  (use when k > 1)

 -F
  Weight neighbours by 1 - their distance
  (use when k > 1)

 -K <number of neighbors>
  Number of nearest neighbours (k) used in classification.
  (Default = 1)

 -E
  Minimise mean squared error rather than mean absolute
  error when using -X option with numeric prediction.

 -W <window size>
  Maximum number of training instances maintained.
  Training instances are dropped FIFO. (Default = no window)

 -X
  Select the number of nearest neighbours between 1
  and the k value specified using hold-one-out evaluation
  on the training data (use when k > 1)

 -A
  The nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).

Version:: $Revision: 14827 $
Author:: Stuart Inglis ([email protected]), Len Trigg ([email protected]), Eibe Frank ([email protected])
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static Tag[]`	`TAGS_WEIGHTING` possible instance weighting methods.
`static int`	`WEIGHT_INVERSE` weight by 1/distance.
`static int`	`WEIGHT_NONE` no weighting.
`static int`	`WEIGHT_SIMILARITY` weight by 1-distance.

Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

IBk()
IB1 classifer.

IBk(int k)
IBk classifier.

Constructors
Constructor and Description
`IBk()` IB1 classifer.
`IBk(int k)` IBk classifier.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances instances)` Generates the classifier.
`String`	`crossValidateTipText()` Returns the tip text for this property.
`String`	`distanceWeightingTipText()` Returns the tip text for this property.
`double[]`	`distributionForInstance(Instance instance)` Calculates the class membership probabilities for the given test instance.
`Enumeration<String>`	`enumerateMeasures()` Returns an enumeration of the additional measure names produced by the neighbour search algorithm, plus the chosen K in case cross-validation is enabled.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`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.
`double`	`getMeasure(String additionalMeasureName)` Returns the value of the named measure from the neighbour search algorithm, plus the chosen K in case cross-validation is enabled.
`NearestNeighbourSearch`	`getNearestNeighbourSearchAlgorithm()` Returns the current nearestNeighbourSearch algorithm in use.
`int`	`getNumTraining()` Get the number of training instances the classifier is currently using.
`String[]`	`getOptions()` Gets the current settings of IBk.
`String`	`getRevision()` Returns the revision string.
`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.
`int`	`getWindowSize()` Gets the maximum number of instances allowed in the training pool.
`String`	`globalInfo()` Returns a string describing classifier.
`String`	`KNNTipText()` Returns the tip text for this property.
`Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(String[] argv)` Main method for testing this class.
`String`	`meanSquaredTipText()` Returns the tip text for this property.
`String`	`nearestNeighbourSearchAlgorithmTipText()` Returns the tip text for this property.
`Instances`	`pruneToK(Instances neighbours, double[] distances, int k)` Prunes the list to contain the k nearest neighbors.
`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`	`setNearestNeighbourSearchAlgorithm(NearestNeighbourSearch nearestNeighbourSearchAlgorithm)` Sets the nearestNeighbourSearch algorithm to be used for finding nearest neighbour(s).
`void`	`setOptions(String[] options)` Parses a given list of options.
`void`	`setWindowSize(int newWindowSize)` Sets the maximum number of instances allowed in the training pool.
`String`	`toString()` Returns a description of this classifier.
`void`	`updateClassifier(Instance instance)` Adds the supplied instance to the training set.
`String`	`windowSizeTipText()` Returns the tip text for this property.

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

- Field Detail
  - WEIGHT_NONE
```
public static final int WEIGHT_NONE
```
    no weighting.
    
    See Also:
    
    Constant Field Values
  - WEIGHT_INVERSE
```
public static final int WEIGHT_INVERSE
```
    weight by 1/distance.
    
    See Also:
    
    Constant Field Values
  - WEIGHT_SIMILARITY
```
public static final int WEIGHT_SIMILARITY
```
    weight by 1-distance.
    
    See Also:
    
    Constant Field Values
  - TAGS_WEIGHTING
```
public static final Tag[] TAGS_WEIGHTING
```
    possible instance weighting methods.
- 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 String globalInfo()
```
    Returns a string describing classifier.
    
    Returns:
    
    a description suitable for displaying in the explorer/experimenter gui
  - getTechnicalInformation
```
public 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 TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - KNNTipText
```
public 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 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 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.
    
    Parameters:
    
    newMethod - the distance weighting method to use
  - meanSquaredTipText
```
public 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 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.
  - nearestNeighbourSearchAlgorithmTipText
```
public String nearestNeighbourSearchAlgorithmTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getNearestNeighbourSearchAlgorithm
```
public NearestNeighbourSearch getNearestNeighbourSearchAlgorithm()
```
    Returns the current nearestNeighbourSearch algorithm in use.
    
    Returns:
    
    the NearestNeighbourSearch algorithm currently in use.
  - setNearestNeighbourSearchAlgorithm
```
public void setNearestNeighbourSearchAlgorithm(NearestNeighbourSearch nearestNeighbourSearchAlgorithm)
```
    Sets the nearestNeighbourSearch algorithm to be used for finding nearest neighbour(s).
    
    Parameters:
    
    nearestNeighbourSearchAlgorithm - - The NearestNeighbourSearch class.
  - getNumTraining
```
public int getNumTraining()
```
    Get the number of training instances the classifier is currently using.
    
    Returns:
    
    the number of training instances the classifier is currently using
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface Classifier
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - buildClassifier
```
public void buildClassifier(Instances instances)
                     throws Exception
```
    Generates the classifier.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Parameters:
    
    instances - set of instances serving as training data
    
    Throws:
    
    Exception - if the classifier has not been generated successfully
  - updateClassifier
```
public void updateClassifier(Instance instance)
                      throws Exception
```
    Adds the supplied instance to the training set.
    
    Specified by:
    
    updateClassifier in interface UpdateableClassifier
    
    Parameters:
    
    instance - the instance to add
    
    Throws:
    
    Exception - if instance could not be incorporated successfully
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws Exception
```
    Calculates the class membership probabilities for the given test instance.
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    
    instance - the instance to be classified
    
    Returns:
    
    predicted class probability distribution
    
    Throws:
    
    Exception - if an error occurred during the prediction
  - listOptions
```
public Enumeration<Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class AbstractClassifier
    
    Returns:
    
    an enumeration of all the available options.
  - setOptions
```
public void setOptions(String[] options)
                throws Exception
```
    Parses a given list of options.
    Valid options are:
```
 -I
  Weight neighbours by the inverse of their distance
  (use when k > 1)
```
```
 -F
  Weight neighbours by 1 - their distance
  (use when k > 1)
```
```
 -K <number of neighbors>
  Number of nearest neighbours (k) used in classification.
  (Default = 1)
```
```
 -E
  Minimise mean squared error rather than mean absolute
  error when using -X option with numeric prediction.
```
```
 -W <window size>
  Maximum number of training instances maintained.
  Training instances are dropped FIFO. (Default = no window)
```
```
 -X
  Select the number of nearest neighbours between 1
  and the k value specified using hold-one-out evaluation
  on the training data (use when k > 1)
```
```
 -A
  The nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).
 
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class AbstractClassifier
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    Exception - if an option is not supported
  - getOptions
```
public String[] getOptions()
```
    Gets the current settings of IBk.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class AbstractClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions()
  - enumerateMeasures
```
public Enumeration<String> enumerateMeasures()
```
    Returns an enumeration of the additional measure names produced by the neighbour search algorithm, plus the chosen K in case cross-validation is enabled.
    
    Specified by:
    
    enumerateMeasures in interface AdditionalMeasureProducer
    
    Returns:
    
    an enumeration of the measure names
  - getMeasure
```
public double getMeasure(String additionalMeasureName)
```
    Returns the value of the named measure from the neighbour search algorithm, plus the chosen K in case cross-validation is enabled.
    
    Specified by:
    
    getMeasure in interface AdditionalMeasureProducer
    
    Parameters:
    
    additionalMeasureName - the name of the measure to query for its value
    
    Returns:
    
    the value of the named measure
    
    Throws:
    
    IllegalArgumentException - if the named measure is not supported
  - toString
```
public String toString()
```
    Returns a description of this classifier.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    a description of this classifier as a string.
  - pruneToK
```
public Instances pruneToK(Instances neighbours,
                          double[] distances,
                          int k)
```
    Prunes the list to contain the k nearest neighbors. If there are multiple neighbors at the k'th distance, all will be kept.
    
    Parameters:
    
    neighbours - the neighbour instances.
    
    distances - the distances of the neighbours from target instance.
    
    k - the number of neighbors to keep.
    
    Returns:
    
    the pruned neighbours.
  - getRevision
```
public String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractClassifier
    
    Returns:
    
    the revision
  - main
```
public static void main(String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - should contain command line options (see setOptions)

Class IBk

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

Field Detail

WEIGHT_NONE

WEIGHT_INVERSE

WEIGHT_SIMILARITY

TAGS_WEIGHTING

Constructor Detail

IBk

IBk

Method Detail

globalInfo

getTechnicalInformation

KNNTipText

setKNN

getKNN

windowSizeTipText

getWindowSize

setWindowSize

distanceWeightingTipText

getDistanceWeighting

setDistanceWeighting

meanSquaredTipText

getMeanSquared

setMeanSquared

crossValidateTipText

getCrossValidate

setCrossValidate

nearestNeighbourSearchAlgorithmTipText

getNearestNeighbourSearchAlgorithm

setNearestNeighbourSearchAlgorithm

getNumTraining

getCapabilities

buildClassifier

updateClassifier

distributionForInstance

listOptions

setOptions

getOptions

enumerateMeasures

getMeasure

toString

pruneToK

getRevision

main