weka.core.neighboursearch.kdtrees
Class MidPointOfWidestDimension

java.lang.Object
  extended by weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
      extended by weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension
All Implemented Interfaces:
java.io.Serializable, OptionHandler, RevisionHandler, TechnicalInformationHandler

public class MidPointOfWidestDimension
extends KDTreeNodeSplitter
implements TechnicalInformationHandler

The class that splits a KDTree node based on the midpoint value of a dimension in which the node's points have the widest spread.

For more information see also:

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

BibTeX:

 @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 = {http://www.autonlab.org/autonweb/14665.html}
 }
 

Version:
$Revision: 1.2 $
Author:
Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]nz)
See Also:
Serialized Form

Field Summary
 
Fields inherited from class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
MAX, MIN, WIDTH
 
Constructor Summary
MidPointOfWidestDimension()
           
 
Method Summary
 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.
 java.lang.String globalInfo()
          Returns a string describing this nearest neighbour search algorithm.
 void splitNode(KDTreeNode node, int numNodesCreated, double[][] nodeRanges, double[][] universe)
          Splits a node into two based on the midpoint value of the dimension in which the points have the widest spread.
 
Methods inherited from class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
getOptions, listOptions, setEuclideanDistanceFunction, setInstanceList, setInstances, setNodeWidthNormalization, setOptions
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Constructor Detail

MidPointOfWidestDimension

public MidPointOfWidestDimension()
Method Detail

globalInfo

public java.lang.String globalInfo()
Returns a string describing this nearest neighbour search algorithm.

Returns:
a description of the algorithm 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

splitNode

public void splitNode(KDTreeNode node,
                      int numNodesCreated,
                      double[][] nodeRanges,
                      double[][] universe)
               throws java.lang.Exception
Splits a node into two based on the midpoint value of the dimension in which the points have the widest spread. After splitting two new nodes are created and correctly initialised. And, node.left and node.right are set appropriately.

Specified by:
splitNode in class KDTreeNodeSplitter
Parameters:
node - The node to split.
numNodesCreated - The number of nodes that so far have been created for the tree, so that the newly created nodes are assigned correct/meaningful node numbers/ids.
nodeRanges - The attributes' range for the points inside the node that is to be split.
universe - The attributes' range for the whole point-space.
Throws:
java.lang.Exception - If there is some problem in splitting the given node.

getRevision

public java.lang.String getRevision()
Returns the revision string.

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