weka.core.neighboursearch.kdtrees

Class KDTreeNodeSplitter

java.lang.Object

weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter

All Implemented Interfaces:

Serializable, OptionHandler, RevisionHandler

Direct Known Subclasses:

KMeansInpiredMethod, MedianOfWidestDimension, MidPointOfWidestDimension, SlidingMidPointOfWidestSide

public abstract class KDTreeNodeSplitter
extends Object
implements Serializable, OptionHandler, RevisionHandler

Class that splits up a KDTreeNode.

Version:

$Revision: 10203 $

Author:

Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]nz)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static int	MAX Index of max value in an array of attributes' range.
static int	MIN Index of min value in an array of attributes' range.
static int	WIDTH Index of width value (max-min) in an array of attributes' range.

Constructor Summary

Constructors

Constructor and Description
KDTreeNodeSplitter() default constructor.
KDTreeNodeSplitter(int[] instList, Instances insts, EuclideanDistance e) Creates a new instance of KDTreeNodeSplitter.

Method Summary

Methods

Modifier and Type	Method and Description
String[]	getOptions() Gets the current settings of the object.
String	getRevision() Returns the revision string.
Enumeration<Option>	listOptions() Returns an enumeration describing the available options.
void	setEuclideanDistanceFunction(EuclideanDistance func) Sets the EuclideanDistance object to use for splitting nodes.
void	setInstanceList(int[] instList) Sets the master index array containing indices of the training instances.
void	setInstances(Instances inst) Sets the training instances on which the tree is (or is to be) built.
void	setNodeWidthNormalization(boolean normalize) Sets whether if a nodes region is normalized or not.
void	setOptions(String[] options) Parses a given list of options.
abstract void	splitNode(KDTreeNode node, int numNodesCreated, double[][] nodeRanges, double[][] universe) Splits a node into two.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

MIN

public static final int MIN

Index of min value in an array of attributes' range.

See Also:

Constant Field Values

MAX

public static final int MAX

Index of max value in an array of attributes' range.

See Also:

Constant Field Values

WIDTH

public static final int WIDTH

Index of width value (max-min) in an array of attributes' range.

See Also:

Constant Field Values

Constructor Detail

KDTreeNodeSplitter

public KDTreeNodeSplitter()

default constructor.

KDTreeNodeSplitter

public KDTreeNodeSplitter(int[] instList,
                  Instances insts,
                  EuclideanDistance e)

Creates a new instance of KDTreeNodeSplitter.

Parameters:

instList - Reference of the master index array.

insts - The set of training instances on which the tree is built.

e - The EuclideanDistance object that is used in tree contruction.

Method Detail

listOptions

public Enumeration<Option> listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Returns:

an enumeration of all the available options.

setOptions

public void setOptions(String[] options)
                throws Exception

Parses a given list of options.

Specified by:

setOptions in interface OptionHandler

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 the object.

Specified by:

getOptions in interface OptionHandler

Returns:

an array of strings suitable for passing to setOptions

splitNode

public abstract void splitNode(KDTreeNode node,
             int numNodesCreated,
             double[][] nodeRanges,
             double[][] universe)
                        throws Exception

Splits a node into two. After splitting two new nodes are created and correctly initialised. And, node.left and node.right are set appropriately.

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.

setInstances

public void setInstances(Instances inst)

Sets the training instances on which the tree is (or is to be) built.

Parameters:

inst - The training instances.

setInstanceList

public void setInstanceList(int[] instList)

Sets the master index array containing indices of the training instances. This array will be rearranged as the tree is built, so that each node is assigned a portion in this array which contain the instances insides the node's region.

Parameters:

instList - The master index array.

setEuclideanDistanceFunction

public void setEuclideanDistanceFunction(EuclideanDistance func)

Sets the EuclideanDistance object to use for splitting nodes.

Parameters:

func - The EuclideanDistance object.

setNodeWidthNormalization

public void setNodeWidthNormalization(boolean normalize)

Sets whether if a nodes region is normalized or not. If set to true then, when selecting the widest attribute/dimension for splitting, the width of each attribute/dimension, of the points inside the node's region, is divided by the width of that attribute/dimension for the whole point-space. Thus, each attribute/dimension of that node is normalized.

Parameters:

normalize - Should be true if normalization is required.

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Returns:

the revision