Class KDTree

  • All Implemented Interfaces:
    java.io.Serializable, AdditionalMeasureProducer, OptionHandler, RevisionHandler, TechnicalInformationHandler

    public class KDTree
    extends NearestNeighbourSearch
    implements TechnicalInformationHandler
    Class implementing the KDTree search algorithm for nearest neighbour search.
    The connection to dataset is only a reference. For the tree structure the indexes are stored in an array.
    Building the tree:
    If a node has <maximal-inst-number> (option -L) instances no further splitting is done. Also if the split would leave one side empty, the branch is not split any further even if the instances in the resulting node are more than <maximal-inst-number> instances.
    **PLEASE NOTE:** The algorithm can not handle missing values, so it is advisable to run ReplaceMissingValues filter if there are any missing values in the dataset.

    For more information see:

    Jerome H. Friedman, Jon Luis Bentley, Raphael Ari Finkel (1977). An Algorithm for Finding Best Matches in Logarithmic Expected Time. ACM Transactions on Mathematics Software. 3(3):209-226.

    Andrew Moore (1991). A tutorial on kd-trees.

    BibTeX:

     @article{Friedman1977,
        author = {Jerome H. Friedman and Jon Luis Bentley and Raphael Ari Finkel},
        journal = {ACM Transactions on Mathematics Software},
        month = {September},
        number = {3},
        pages = {209-226},
        title = {An Algorithm for Finding Best Matches in Logarithmic Expected Time},
        volume = {3},
        year = {1977}
     }
     
     @techreport{Moore1991,
        author = {Andrew Moore},
        booktitle = {University of Cambridge Computer Laboratory Technical Report No. 209},
        howpublished = {Extract from PhD Thesis},
        title = {A tutorial on kd-trees},
        year = {1991},
        HTTP = {Available from http://www.autonlab.org/autonweb/14665.html}
     }
     

    Valid options are:

     -S <classname and options>
      Node splitting method to use.
      (default: weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide)
     -W <value>
      Set minimal width of a box
      (default: 1.0E-2).
     -L
      Maximal number of instances in a leaf
      (default: 40).
     -N
      Normalizing will be done
      (Select dimension for split, with normalising to universe).
    Version:
    $Revision: 1.3 $
    Author:
    Gabi Schmidberger (gabi[at-the-rate]cs[dot]waikato[dot]ac[dot]nz), Malcolm Ware (mfw4[at-the-rate]cs[dot]waikato[dot]ac[dot]nz), Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]nz)
    See Also:
    Serialized Form
    • Field Detail

      • MIN

        public static final int MIN
        The index of MIN value in attributes' range array.
        See Also:
        Constant Field Values
      • MAX

        public static final int MAX
        The index of MAX value in attributes' range array.
        See Also:
        Constant Field Values
      • WIDTH

        public static final int WIDTH
        The index of WIDTH (MAX-MIN) value in attributes' range array.
        See Also:
        Constant Field Values
    • Constructor Detail

      • KDTree

        public KDTree()
        Creates a new instance of KDTree.
      • KDTree

        public KDTree​(Instances insts)
        Creates a new instance of KDTree. It also builds the tree on supplied set of Instances.
        Parameters:
        insts - The instances/points on which the BallTree should be built on.
    • Method Detail

      • 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
      • kNearestNeighbours

        public Instances kNearestNeighbours​(Instance target,
                                            int k)
                                     throws java.lang.Exception
        Returns the k nearest neighbours of the supplied instance. >k neighbours are returned if there are more than one neighbours at the kth boundary.
        Specified by:
        kNearestNeighbours in class NearestNeighbourSearch
        Parameters:
        target - The instance to find the nearest neighbours for.
        k - The number of neighbours to find.
        Returns:
        The k nearest neighbours (or >k if more there are than one neighbours at the kth boundary).
        Throws:
        java.lang.Exception - if the nearest neighbour could not be found.
      • nearestNeighbour

        public Instance nearestNeighbour​(Instance target)
                                  throws java.lang.Exception
        Returns the nearest neighbour of the supplied target instance.
        Specified by:
        nearestNeighbour in class NearestNeighbourSearch
        Parameters:
        target - The instance to find the nearest neighbour for.
        Returns:
        The nearest neighbour from among the previously supplied training instances.
        Throws:
        java.lang.Exception - if the neighbours could not be found.
      • getDistances

        public double[] getDistances()
                              throws java.lang.Exception
        Returns the distances to the kNearest or 1 nearest neighbour currently found with either the kNearestNeighbours or the nearestNeighbour method.
        Specified by:
        getDistances in class NearestNeighbourSearch
        Returns:
        array containing the distances of the nearestNeighbours. The length and ordering of the array is the same as that of the instances returned by nearestNeighbour functions.
        Throws:
        java.lang.Exception - if called before calling kNearestNeighbours or nearestNeighbours.
      • setInstances

        public void setInstances​(Instances instances)
                          throws java.lang.Exception
        Builds the KDTree on the given set of instances.
        Overrides:
        setInstances in class NearestNeighbourSearch
        Parameters:
        instances - The insts on which the KDTree is to be built.
        Throws:
        java.lang.Exception - If some error occurs while building the KDTree
      • update

        public void update​(Instance instance)
                    throws java.lang.Exception
        Adds one instance to the KDTree. This updates the KDTree structure to take into account the newly added training instance.
        Specified by:
        update in class NearestNeighbourSearch
        Parameters:
        instance - the instance to be added. Usually the newly added instance in the training set.
        Throws:
        java.lang.Exception - If the instance cannot be added.
      • addInstanceInfo

        public void addInstanceInfo​(Instance instance)
        Adds one instance to KDTree loosly. It only changes the ranges in EuclideanDistance, and does not affect the structure of the KDTree.
        Overrides:
        addInstanceInfo in class NearestNeighbourSearch
        Parameters:
        instance - the new instance. Usually this is the test instance supplied to update the range of attributes in the distance function.
      • measureTreeSize

        public double measureTreeSize()
        Returns the size of the tree.
        Returns:
        the size of the tree
      • measureNumLeaves

        public double measureNumLeaves()
        Returns the number of leaves.
        Returns:
        the number of leaves
      • measureMaxDepth

        public double measureMaxDepth()
        Returns the depth of the tree.
        Returns:
        The depth of the tree
      • getMeasure

        public double getMeasure​(java.lang.String additionalMeasureName)
        Returns the value of the named measure.
        Specified by:
        getMeasure in interface AdditionalMeasureProducer
        Overrides:
        getMeasure in class NearestNeighbourSearch
        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.
      • setMeasurePerformance

        public void setMeasurePerformance​(boolean measurePerformance)
        Sets whether to calculate the performance statistics or not.
        Overrides:
        setMeasurePerformance in class NearestNeighbourSearch
        Parameters:
        measurePerformance - Should be true if performance statistics are to be measured.
      • centerInstances

        public void centerInstances​(Instances centers,
                                    int[] assignments,
                                    double pc)
                             throws java.lang.Exception
        Assigns instances to centers using KDTree.
        Parameters:
        centers - the current centers
        assignments - the centerindex for each instance
        pc - the threshold value for pruning.
        Throws:
        java.lang.Exception - If there is some problem assigning instances to centers.
      • assignSubToCenters

        public void assignSubToCenters​(KDTreeNode node,
                                       Instances centers,
                                       int[] centList,
                                       int[] assignments)
                                throws java.lang.Exception
        Assigns instances of this node to center. Center to be assign to is decided by the distance function.
        Parameters:
        node - The KDTreeNode whose instances are to be assigned.
        centers - all the input centers
        centList - the list of centers to work with
        assignments - index list of last assignments
        Throws:
        java.lang.Exception - If there is error assigning the instances.
      • minBoxRelWidthTipText

        public java.lang.String minBoxRelWidthTipText()
        Tip text for this property.
        Returns:
        the tip text for this property
      • setMinBoxRelWidth

        public void setMinBoxRelWidth​(double i)
        Sets the minimum relative box width.
        Parameters:
        i - the minimum relative box width
      • getMinBoxRelWidth

        public double getMinBoxRelWidth()
        Gets the minimum relative box width.
        Returns:
        the minimum relative box width
      • maxInstInLeafTipText

        public java.lang.String maxInstInLeafTipText()
        Tip text for this property.
        Returns:
        the tip text for this property
      • setMaxInstInLeaf

        public void setMaxInstInLeaf​(int i)
        Sets the maximum number of instances in a leaf.
        Parameters:
        i - the maximum number of instances in a leaf
      • getMaxInstInLeaf

        public int getMaxInstInLeaf()
        Get the maximum number of instances in a leaf.
        Returns:
        the maximum number of instances in a leaf
      • normalizeNodeWidthTipText

        public java.lang.String normalizeNodeWidthTipText()
        Tip text for this property.
        Returns:
        the tip text for this property
      • setNormalizeNodeWidth

        public void setNormalizeNodeWidth​(boolean n)
        Sets the flag for normalizing the widths of a KDTree Node by the width of the dimension in the universe.
        Parameters:
        n - true to use normalizing.
      • getNormalizeNodeWidth

        public boolean getNormalizeNodeWidth()
        Gets the normalize flag.
        Returns:
        True if normalizing
      • setDistanceFunction

        public void setDistanceFunction​(DistanceFunction df)
                                 throws java.lang.Exception
        sets the distance function to use for nearest neighbour search.
        Overrides:
        setDistanceFunction in class NearestNeighbourSearch
        Parameters:
        df - the distance function to use
        Throws:
        java.lang.Exception - if not EuclideanDistance
      • nodeSplitterTipText

        public java.lang.String nodeSplitterTipText()
        Returns the tip text for this property.
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui
      • getNodeSplitter

        public KDTreeNodeSplitter getNodeSplitter()
        Returns the splitting method currently in use to split the nodes of the KDTree.
        Returns:
        The KDTreeNodeSplitter currently in use.
      • setNodeSplitter

        public void setNodeSplitter​(KDTreeNodeSplitter splitter)
        Sets the splitting method to use to split the nodes of the KDTree.
        Parameters:
        splitter - The KDTreeNodeSplitter to use.
      • globalInfo

        public java.lang.String globalInfo()
        Returns a string describing this nearest neighbour search algorithm.
        Overrides:
        globalInfo in class NearestNeighbourSearch
        Returns:
        a description of the algorithm for displaying in the explorer/experimenter gui
      • setOptions

        public void setOptions​(java.lang.String[] options)
                        throws java.lang.Exception
        Parses a given list of options.

        Valid options are:

         -S <classname and options>
          Node splitting method to use.
          (default: weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide)
         -W <value>
          Set minimal width of a box
          (default: 1.0E-2).
         -L
          Maximal number of instances in a leaf
          (default: 40).
         -N
          Normalizing will be done
          (Select dimension for split, with normalising to universe).
        Specified by:
        setOptions in interface OptionHandler
        Overrides:
        setOptions in class NearestNeighbourSearch
        Parameters:
        options - the list of options as an array of strings
        Throws:
        java.lang.Exception - if an option is not supported
      • getRevision

        public java.lang.String getRevision()
        Returns the revision string.
        Specified by:
        getRevision in interface RevisionHandler
        Returns:
        the revision