java.lang.Object
- weka.core.neighboursearch.NearestNeighbourSearch
- - weka.core.neighboursearch.KDTree

All Implemented Interfaces:

java.io.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: 1.3 $

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:

Serialized Form

Field Summary

Fields
Modifier and Type	Field	Description
`static int`	`MAX`	The index of MAX value in attributes' range array.
`static int`	`MIN`	The index of MIN value in attributes' range array.
`static int`	`WIDTH`	The index of WIDTH (MAX-MIN) value in attributes' range array.

Constructor Summary

Constructors
Constructor Description

KDTree()
Creates a new instance of KDTree.

KDTree(Instances insts)
Creates a new instance of KDTree.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`void`	`addInstanceInfo(Instance instance)`	Adds one instance to KDTree loosly.
`void`	`assignSubToCenters(KDTreeNode node, Instances centers, int[] centList, int[] assignments)`	Assigns instances of this node to center.
`void`	`centerInstances(Instances centers, int[] assignments, double pc)`	Assigns instances to centers using KDTree.
`java.util.Enumeration`	`enumerateMeasures()`	Returns an enumeration of the additional measure names.
`DistanceFunction`	`getDistanceFunction()`	returns the distance function currently in use.
`double[]`	`getDistances()`	Returns the distances to the kNearest or 1 nearest neighbour currently found with either the kNearestNeighbours or the nearestNeighbour method.
`int`	`getMaxInstInLeaf()`	Get the maximum number of instances in a leaf.
`double`	`getMeasure(java.lang.String additionalMeasureName)`	Returns the value of the named measure.
`double`	`getMinBoxRelWidth()`	Gets the minimum relative box width.
`KDTreeNodeSplitter`	`getNodeSplitter()`	Returns the splitting method currently in use to split the nodes of the KDTree.
`boolean`	`getNormalizeNodeWidth()`	Gets the normalize flag.
`java.lang.String[]`	`getOptions()`	Gets the current settings of KDtree.
`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.
`Instances`	`kNearestNeighbours(Instance target, int k)`	Returns the k nearest neighbours of the supplied instance.
`java.util.Enumeration`	`listOptions()`	Returns an enumeration describing the available options.
`java.lang.String`	`maxInstInLeafTipText()`	Tip text for this property.
`double`	`measureMaxDepth()`	Returns the depth of the tree.
`double`	`measureNumLeaves()`	Returns the number of leaves.
`double`	`measureTreeSize()`	Returns the size of the tree.
`java.lang.String`	`minBoxRelWidthTipText()`	Tip text for this property.
`Instance`	`nearestNeighbour(Instance target)`	Returns the nearest neighbour of the supplied target instance.
`java.lang.String`	`nodeSplitterTipText()`	Returns the tip text for this property.
`java.lang.String`	`normalizeNodeWidthTipText()`	Tip text for this property.
`void`	`setDistanceFunction(DistanceFunction df)`	sets the distance function to use for nearest neighbour search.
`void`	`setInstances(Instances instances)`	Builds the KDTree on the given set of instances.
`void`	`setMaxInstInLeaf(int i)`	Sets the maximum number of instances in a leaf.
`void`	`setMeasurePerformance(boolean measurePerformance)`	Sets whether to calculate the performance statistics or not.
`void`	`setMinBoxRelWidth(double i)`	Sets the minimum relative box width.
`void`	`setNodeSplitter(KDTreeNodeSplitter splitter)`	Sets the splitting method to use to split the nodes of the KDTree.
`void`	`setNormalizeNodeWidth(boolean n)`	Sets the flag for normalizing the widths of a KDTree Node by the width of the dimension in the universe.
`void`	`setOptions(java.lang.String[] options)`	Parses a given list of options.
`void`	`update(Instance instance)`	Adds one instance to the KDTree.

Methods inherited from class weka.core.neighboursearch.NearestNeighbourSearch
combSort11, distanceFunctionTipText, getInstances, getMeasurePerformance, getPerformanceStats, measurePerformanceTipText, quickSort

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - MIN
```
public static final int MIN
```
    The index of MIN value in attributes' range array.
    
    See Also:
    
    Constant Field Values
  - MAX
```
public static final int MAX
```
    The index of MAX value in attributes' range array.
    
    See Also:
    
    Constant Field Values
  - WIDTH
```
public static final int WIDTH
```
    The index of WIDTH (MAX-MIN) value in attributes' range array.
    
    See Also:
    
    Constant Field Values
- Constructor Detail
  - 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 Detail
  - 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 java.lang.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:
    
    java.lang.Exception - if the nearest neighbour could not be found.
  - nearestNeighbour
```
public Instance nearestNeighbour(Instance target)
                          throws java.lang.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:
    
    java.lang.Exception - if the neighbours could not be found.
  - getDistances
```
public double[] getDistances()
                      throws java.lang.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:
    
    java.lang.Exception - if called before calling kNearestNeighbours or nearestNeighbours.
  - setInstances
```
public void setInstances(Instances instances)
                  throws java.lang.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:
    
    java.lang.Exception - If some error occurs while building the KDTree
  - update
```
public void update(Instance instance)
            throws java.lang.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:
    
    java.lang.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 java.util.Enumeration 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(java.lang.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:
    
    java.lang.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 java.lang.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:
    
    java.lang.Exception - If there is some problem assigning instances to centers.
  - assignSubToCenters
```
public void assignSubToCenters(KDTreeNode node,
                               Instances centers,
                               int[] centList,
                               int[] assignments)
                        throws java.lang.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:
    
    java.lang.Exception - If there is error assigning the instances.
  - minBoxRelWidthTipText
```
public java.lang.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 java.lang.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 java.lang.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 java.lang.Exception
```
    sets the distance function to use for nearest neighbour search.
    
    Overrides:
    
    setDistanceFunction in class NearestNeighbourSearch
    
    Parameters:
    
    df - the distance function to use
    
    Throws:
    
    java.lang.Exception - if not EuclideanDistance
  - nodeSplitterTipText
```
public java.lang.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 java.lang.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 java.util.Enumeration 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(java.lang.String[] options)
                throws java.lang.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:
    
    java.lang.Exception - if an option is not supported
  - getOptions
```
public java.lang.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 java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Returns:
    
    the revision

Constructor	Description
`KDTree()`	Creates a new instance of KDTree.
`KDTree(Instances insts)`	Creates a new instance of KDTree.

Class KDTree

Field Summary

Constructor Summary

Method Summary

Methods inherited from class weka.core.neighboursearch.NearestNeighbourSearch

Methods inherited from class java.lang.Object

Field Detail

MIN

MAX

WIDTH

Constructor Detail

KDTree

KDTree

Method Detail

getTechnicalInformation

kNearestNeighbours

nearestNeighbour

getDistances

setInstances

update

addInstanceInfo

measureTreeSize

measureNumLeaves

measureMaxDepth

enumerateMeasures

getMeasure

setMeasurePerformance

centerInstances

assignSubToCenters

minBoxRelWidthTipText

setMinBoxRelWidth

getMinBoxRelWidth

maxInstInLeafTipText

setMaxInstInLeaf

getMaxInstInLeaf

normalizeNodeWidthTipText

setNormalizeNodeWidth

getNormalizeNodeWidth

getDistanceFunction

setDistanceFunction

nodeSplitterTipText

getNodeSplitter

setNodeSplitter

globalInfo

listOptions

setOptions

getOptions

getRevision