Documentation of 'weka.classifiers.lazy.IBk' Java class
IBk
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weka.classifiers.lazy

Class IBk

    • Field Detail

      • 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

      • buildClassifier

        public void buildClassifier(Instances instances)
                     throws java.lang.Exception
        Generates the classifier.
        Specified by:
        buildClassifier in interface 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.
        Specified by:
        distributionForInstance in interface Classifier
        Overrides:
        distributionForInstance in class AbstractClassifier
        Parameters:
        instance - the instance to be classified
        Returns:
        predicted class probability distribution
        Throws:
        java.lang.Exception - if an error occurred during the prediction

      • 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 AbstractClassifier
        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 AbstractClassifier
        Returns:
        an array of strings suitable for passing to setOptions()

      • enumerateMeasures

        public java.util.Enumeration<java.lang.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(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.

      • 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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