Class MedianOfWidestDimension
- java.lang.Object
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- moa.classifiers.lazy.neighboursearch.kdtrees.KDTreeNodeSplitter
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- moa.classifiers.lazy.neighboursearch.kdtrees.MedianOfWidestDimension
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- All Implemented Interfaces:
Serializable
public class MedianOfWidestDimension extends KDTreeNodeSplitter
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: 8034 $
- Author:
- Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]nz)
- See Also:
- Serialized Form
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Field Summary
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Fields inherited from class moa.classifiers.lazy.neighboursearch.kdtrees.KDTreeNodeSplitter
m_EuclideanDistance, m_Instances, m_InstList, m_NormalizeNodeWidth, MAX, MIN, WIDTH
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Constructor Summary
Constructors Constructor Description MedianOfWidestDimension()
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description String
globalInfo()
Returns a string describing this nearest neighbour search algorithm.protected int
partition(int attIdx, int[] index, int l, int r)
Partitions the instances around a pivot.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 moa.classifiers.lazy.neighboursearch.kdtrees.KDTreeNodeSplitter
correctlyInitialized, getOptions, listOptions, setEuclideanDistanceFunction, setInstanceList, setInstances, setNodeWidthNormalization, setOptions, widestDim
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Method Detail
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globalInfo
public String globalInfo()
Returns a string describing this nearest neighbour search algorithm.- Returns:
- a description of the algorithm for displaying in the explorer/experimenter gui
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splitNode
public void splitNode(KDTreeNode node, int numNodesCreated, double[][] nodeRanges, double[][] universe) throws 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 classKDTreeNodeSplitter
- 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:
Exception
- If there is some problem in splitting the given node.
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partition
protected int partition(int attIdx, int[] index, int l, int r)
Partitions the instances around a pivot. Used by quicksort and kthSmallestValue.- Parameters:
attIdx
- The attribution/dimension based on which the instances should be partitioned.index
- The master index array containing indices of the instances.l
- The begining index of the portion of master index array that should be partitioned.r
- The end index of the portion of master index array that should be partitioned.- Returns:
- the index of the middle element
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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
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