Package weka.core.neighboursearch
Class KDTree
- java.lang.Object
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- weka.core.neighboursearch.NearestNeighbourSearch
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- weka.core.neighboursearch.KDTree
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- 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
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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
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Field Detail
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MIN
public static final int MIN
The index of MIN value in attributes' range array.- See Also:
- Constant Field Values
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MAX
public static final int MAX
The index of MAX value in attributes' range array.- See Also:
- Constant Field Values
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WIDTH
public static final int WIDTH
The index of WIDTH (MAX-MIN) value in attributes' range array.- See Also:
- Constant Field Values
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Constructor Detail
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KDTree
public KDTree()
Creates a new instance of KDTree.
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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.
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Method Detail
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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 interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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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 classNearestNeighbourSearch
- 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.
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nearestNeighbour
public Instance nearestNeighbour(Instance target) throws java.lang.Exception
Returns the nearest neighbour of the supplied target instance.- Specified by:
nearestNeighbour
in classNearestNeighbourSearch
- 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.
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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 classNearestNeighbourSearch
- 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.
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setInstances
public void setInstances(Instances instances) throws java.lang.Exception
Builds the KDTree on the given set of instances.- Overrides:
setInstances
in classNearestNeighbourSearch
- Parameters:
instances
- The insts on which the KDTree is to be built.- Throws:
java.lang.Exception
- If some error occurs while building the KDTree
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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 classNearestNeighbourSearch
- 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.
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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 classNearestNeighbourSearch
- Parameters:
instance
- the new instance. Usually this is the test instance supplied to update the range of attributes in the distance function.
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measureTreeSize
public double measureTreeSize()
Returns the size of the tree.- Returns:
- the size of the tree
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measureNumLeaves
public double measureNumLeaves()
Returns the number of leaves.- Returns:
- the number of leaves
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measureMaxDepth
public double measureMaxDepth()
Returns the depth of the tree.- Returns:
- The depth of the tree
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enumerateMeasures
public java.util.Enumeration enumerateMeasures()
Returns an enumeration of the additional measure names.- Specified by:
enumerateMeasures
in interfaceAdditionalMeasureProducer
- Overrides:
enumerateMeasures
in classNearestNeighbourSearch
- Returns:
- an enumeration of the measure names
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getMeasure
public double getMeasure(java.lang.String additionalMeasureName)
Returns the value of the named measure.- Specified by:
getMeasure
in interfaceAdditionalMeasureProducer
- Overrides:
getMeasure
in classNearestNeighbourSearch
- 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.
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setMeasurePerformance
public void setMeasurePerformance(boolean measurePerformance)
Sets whether to calculate the performance statistics or not.- Overrides:
setMeasurePerformance
in classNearestNeighbourSearch
- Parameters:
measurePerformance
- Should be true if performance statistics are to be measured.
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centerInstances
public void centerInstances(Instances centers, int[] assignments, double pc) throws java.lang.Exception
Assigns instances to centers using KDTree.- Parameters:
centers
- the current centersassignments
- the centerindex for each instancepc
- the threshold value for pruning.- Throws:
java.lang.Exception
- If there is some problem assigning instances to centers.
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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 centerscentList
- the list of centers to work withassignments
- index list of last assignments- Throws:
java.lang.Exception
- If there is error assigning the instances.
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minBoxRelWidthTipText
public java.lang.String minBoxRelWidthTipText()
Tip text for this property.- Returns:
- the tip text for this property
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setMinBoxRelWidth
public void setMinBoxRelWidth(double i)
Sets the minimum relative box width.- Parameters:
i
- the minimum relative box width
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getMinBoxRelWidth
public double getMinBoxRelWidth()
Gets the minimum relative box width.- Returns:
- the minimum relative box width
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maxInstInLeafTipText
public java.lang.String maxInstInLeafTipText()
Tip text for this property.- Returns:
- the tip text for this property
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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
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getMaxInstInLeaf
public int getMaxInstInLeaf()
Get the maximum number of instances in a leaf.- Returns:
- the maximum number of instances in a leaf
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normalizeNodeWidthTipText
public java.lang.String normalizeNodeWidthTipText()
Tip text for this property.- Returns:
- the tip text for this property
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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.
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getNormalizeNodeWidth
public boolean getNormalizeNodeWidth()
Gets the normalize flag.- Returns:
- True if normalizing
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getDistanceFunction
public DistanceFunction getDistanceFunction()
returns the distance function currently in use.- Overrides:
getDistanceFunction
in classNearestNeighbourSearch
- Returns:
- the distance function
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setDistanceFunction
public void setDistanceFunction(DistanceFunction df) throws java.lang.Exception
sets the distance function to use for nearest neighbour search.- Overrides:
setDistanceFunction
in classNearestNeighbourSearch
- Parameters:
df
- the distance function to use- Throws:
java.lang.Exception
- if not EuclideanDistance
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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
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getNodeSplitter
public KDTreeNodeSplitter getNodeSplitter()
Returns the splitting method currently in use to split the nodes of the KDTree.- Returns:
- The KDTreeNodeSplitter currently in use.
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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.
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globalInfo
public java.lang.String globalInfo()
Returns a string describing this nearest neighbour search algorithm.- Overrides:
globalInfo
in classNearestNeighbourSearch
- Returns:
- a description of the algorithm for displaying in the explorer/experimenter gui
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listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options.- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classNearestNeighbourSearch
- Returns:
- an enumeration of all the available options.
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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 interfaceOptionHandler
- Overrides:
setOptions
in classNearestNeighbourSearch
- Parameters:
options
- the list of options as an array of strings- Throws:
java.lang.Exception
- if an option is not supported
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getOptions
public java.lang.String[] getOptions()
Gets the current settings of KDtree.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classNearestNeighbourSearch
- Returns:
- an array of strings suitable for passing to setOptions
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Returns:
- the revision
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