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## weka.classifiers.lazy
Class IBk

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

All Implemented Interfaces:
  java.io.Serializable, java.lang.Cloneable, UpdateableClassifier, AdditionalMeasureProducer, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

    public class IBk

extends Classifier
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: 6573 $
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

Field Summary
`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.

Constructor Summary
`IBk()` IB1 classifer.
`IBk(int k)` IBk classifier.

Method Summary
`void` | `buildClassifier(Instances instances)` Generates the classifier.
`java.lang.String` | `crossValidateTipText()` Returns the tip text for this property.
`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.
`java.util.Enumeration` | `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(java.lang.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.
`java.lang.String[]` | `getOptions()` Gets the current settings of IBk.
`java.lang.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.
`java.lang.String` | `globalInfo()` Returns a string describing classifier.
`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.
`java.lang.String` | `meanSquaredTipText()` Returns the tip text for this property.
`java.lang.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(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.
`java.lang.String` | `windowSizeTipText()` Returns the tip text for this property.

Methods inherited from class weka.classifiers.Classifier
`classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug`

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 java.lang.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 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.

  Parameters:
    `newMethod` - the distance weighting method to use

### 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.

### nearestNeighbourSearchAlgorithmTipText

    public java.lang.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 `CapabilitiesHandler`
  Overrides:
    `getCapabilities` in class `Classifier`
  Returns:
    the capabilities of this classifier
  See Also:
    `Capabilities`

### 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:

       -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 `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()

### enumerateMeasures

    public java.util.Enumeration 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(java.lang.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:
    `java.lang.IllegalArgumentException` - if the named measure is not supported

### toString

    public java.lang.String toString()

  Returns a description of this classifier.

  Overrides:
    `toString` in class `java.lang.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 java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Overrides:
    `getRevision` in class `Classifier`
  Returns:
    the revision

### main

    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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