Package weka.core.neighboursearch

Class KDTree

java.lang.Object
weka.core.neighboursearch.NearestNeighbourSearch
weka.core.neighboursearch.KDTree
All Implemented Interfaces:
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: 14830 $
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:

  • Field Details

    • MIN

      public static final int MIN
      The index of MIN value in attributes' range array.
      See Also:

    • MAX

      public static final int MAX
      The index of MAX value in attributes' range array.
      See Also:

    • WIDTH

      public static final int WIDTH
      The index of WIDTH (MAX-MIN) value in attributes' range array.
      See Also:

  • Constructor Details

    • 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 Details

    • 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 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:
      Exception - if the nearest neighbour could not be found.

    • nearestNeighbour

      public Instance nearestNeighbour(Instance target) throws 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:
      Exception - if the neighbours could not be found.

    • getDistances

      public double[] getDistances() throws 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:
      Exception - if called before calling kNearestNeighbours or nearestNeighbours.

    • setInstances

      public void setInstances(Instances instances) throws 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:
      Exception - If some error occurs while building the KDTree

    • update

      public void update(Instance instance) throws 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:
      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

    • enumerateMeasures

      public Enumeration<String> enumerateMeasures()
      Returns an enumeration of the additional measure names.
      Specified by:
      enumerateMeasures in interface AdditionalMeasureProducer
      Overrides:
      enumerateMeasures in class NearestNeighbourSearch
      Returns:
      an enumeration of the measure names

    • getMeasure

      public double getMeasure(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:
      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 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:
      Exception - If there is some problem assigning instances to centers.

    • assignSubToCenters

      public void assignSubToCenters(KDTreeNode node, Instances centers, int[] centList, int[] assignments) throws 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:
      Exception - If there is error assigning the instances.

    • minBoxRelWidthTipText

      public 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 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 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

    • getDistanceFunction

      public DistanceFunction getDistanceFunction()
      returns the distance function currently in use.
      Overrides:
      getDistanceFunction in class NearestNeighbourSearch
      Returns:
      the distance function

    • setDistanceFunction

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

    • nodeSplitterTipText

      public 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 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

    • listOptions

      public Enumeration<Option> listOptions()
      Returns an enumeration describing the available options.
      Specified by:
      listOptions in interface OptionHandler
      Overrides:
      listOptions in class NearestNeighbourSearch
      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: 

       -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:
      Exception - if an option is not supported

    • getOptions

      public String[] getOptions()
      Gets the current settings of KDtree.
      Specified by:
      getOptions in interface OptionHandler
      Overrides:
      getOptions in class NearestNeighbourSearch
      Returns:
      an array of strings suitable for passing to setOptions

    • getRevision

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