org.openimaj.util.tree

Class DoubleKDTree.ApproximateBBFMedianSplit

java.lang.Object

org.openimaj.util.tree.DoubleKDTree.ApproximateBBFMedianSplit

All Implemented Interfaces:

DoubleKDTree.SplitChooser

Enclosing class:

DoubleKDTree

public static class DoubleKDTree.ApproximateBBFMedianSplit
extends Object
implements DoubleKDTree.SplitChooser

Approximate best-bin-first median splitting. Best bin is chosen from the dimension with the largest range.

Author:

Jonathon Hare (jsh2@ecs.soton.ac.uk)

Constructor Summary

Constructors

Constructor and Description
ApproximateBBFMedianSplit() Construct with the default maximum number of items per bucket
ApproximateBBFMedianSplit(int maxBucketSize) Construct with the given maximum number of items per bucket

Method Summary

Modifier and Type	Method and Description
IntDoublePair	chooseSplit(double[][] pnts, IntArrayView inds, int depth, double[] minBounds, double[] maxBounds) Choose the dimension and discriminant on which to split the data.

Methods inherited from class java.lang.Object

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

Constructor Detail

ApproximateBBFMedianSplit

public ApproximateBBFMedianSplit()

Construct with the default maximum number of items per bucket

ApproximateBBFMedianSplit

public ApproximateBBFMedianSplit(int maxBucketSize)

Construct with the given maximum number of items per bucket

Parameters:

maxBucketSize - maximum number of items per bucket

Method Detail

chooseSplit

public IntDoublePair chooseSplit(double[][] pnts,
                                 IntArrayView inds,
                                 int depth,
                                 double[] minBounds,
                                 double[] maxBounds)

Description copied from interface: DoubleKDTree.SplitChooser

Choose the dimension and discriminant on which to split the data.

Specified by:

chooseSplit in interface DoubleKDTree.SplitChooser

Parameters:

pnts - the raw data

inds - the indices of the data under consideration

depth - the depth of the current data in the tree

minBounds - the minimum bounds

maxBounds - the maximum bounds

Returns:

the dimension and discriminant, or null iff this is a leaf (containing all the points in inds).