weka.core.neighboursearch.kdtrees
Class MedianOfWidestDimension
java.lang.Object
weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter
weka.core.neighboursearch.kdtrees.MedianOfWidestDimension
- All Implemented Interfaces:
- java.io.Serializable, OptionHandler, TechnicalInformationHandler
public class MedianOfWidestDimension
- extends KDTreeNodeSplitter
- implements TechnicalInformationHandler
The class that splits a KDTree node based on the median value of a dimension in which the node's points have the widest spread.
For more information see also:
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.
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}
}
- Version:
- $Revision: 1.1 $
- Author:
- Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]nz)
- See Also:
- Serialized Form
Method Summary |
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. |
int |
select(int attIdx,
int[] indices,
int left,
int right,
int k)
Implements computation of the kth-smallest element according
to Manber's "Introduction to Algorithms". |
void |
splitNode(KDTreeNode node,
int numNodesCreated,
double[][] nodeRanges,
double[][] universe)
Splits a node into two based on the median value of the dimension
in which the points have the widest spread. |
Methods inherited from class java.lang.Object |
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
MedianOfWidestDimension
public MedianOfWidestDimension()
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 median 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.
select
public int select(int attIdx,
int[] indices,
int left,
int right,
int k)
- Implements computation of the kth-smallest element according
to Manber's "Introduction to Algorithms".
- Parameters:
attIdx
- The dimension/attribute of the instances in
which to find the kth-smallest element.indices
- The master index array containing indices of
the instances.left
- The begining index of the portion of the master
index array in which to find the kth-smallest element.right
- The end index of the portion of the master index
array in which to find the kth-smallest element.k
- The value of k
- Returns:
- The index of the kth-smallest element