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/*
* File: KDTree.java
* Authors: Kevin R. Dixon
* Company: Sandia National Laboratories
* Project: Cognitive Foundry
*
* Copyright Jul 28, 2009, Sandia Corporation.
* Under the terms of Contract DE-AC04-94AL85000, there is a non-exclusive
* license for use of this work by or on behalf of the U.S. Government.
* Export of this program may require a license from the United States
* Government. See CopyrightHistory.txt for complete details.
*
*/
package gov.sandia.cognition.math.geometry;
import gov.sandia.cognition.annotation.PublicationReference;
import gov.sandia.cognition.annotation.PublicationReferences;
import gov.sandia.cognition.annotation.PublicationType;
import gov.sandia.cognition.collection.CollectionUtil;
import gov.sandia.cognition.math.Metric;
import gov.sandia.cognition.math.matrix.Vectorizable;
import gov.sandia.cognition.math.matrix.VectorizableIndexComparator;
import gov.sandia.cognition.util.AbstractCloneableSerializable;
import gov.sandia.cognition.util.CloneableSerializable;
import gov.sandia.cognition.util.ObjectUtil;
import gov.sandia.cognition.util.Pair;
import java.util.AbstractCollection;
import java.util.ArrayList;
import java.util.Collection;
import java.util.Comparator;
import java.util.Iterator;
import java.util.PriorityQueue;
/**
* Implementation of a kd-tree. Every node in a KDTree has a k-dimensional
* Vectorizable point and an associated value as a generic "DataType." The At
* each depth in the KDTree, the KDTree partitions the Vectorizables into two
* sets along a particular dimension according to the Vectorizable stored in the
* node value. The dimension used to partition at a particular depth is (depth %
* k). Vectorizables with values less than or equal to the node-value-dimension
* are placed into the left subtree, and those greater than the
* node-value-dimension are stored into the right subtree. This makes
* average-case nearest-neighbor lookup into a balanced KDTree with "N" points
* of O(log(N)), rather than the typical "N" time for linear search.
* Construction of a balanced KDTree with N points takes average-case O(N
* log(N)).
*
* @param Type of Vectorizable, the first values
* @param Type of data in the Pair, the second values
* @param Type of Pair to use in the KDTree.
* @author Kevin R. Dixon
* @since 3.0
*/
@PublicationReferences(
references =
{
@PublicationReference(
author = "Andrew W. Moore",
title = "An intoductory tutorial on kd-trees",
type = PublicationType.TechnicalReport,
publication
= "University of Cambridge Computer Laboratory Technical Report No. 209",
year = 1991,
url
= "http://www.autonlab.org/autonweb/14665.html?branch=1&language=2"
),
@PublicationReference(
author = "Wikipedia",
title = "kd-tree",
type = PublicationType.WebPage,
year = 2009,
url = "http://en.wikipedia.org/wiki/Kd-tree"
)
}
)
public class KDTree>
extends AbstractCollection
implements CloneableSerializable
{
/**
* Number of elements in this subtree.
*/
protected int num;
/**
* VectorType,DataType value for this node of the subtree.
*/
protected PairType value;
/**
* Parent of this node of the subtree.
*/
protected KDTree parent;
/**
* Left child of this subtree
*/
protected KDTree leftChild;
/**
* Right child of this subtree.
*/
protected KDTree rightChild;
/**
* Comparator of this node to determine less than, greater than, or
* equality.
*/
protected PairFirstVectorizableIndexComparator comparator;
/**
* Default constructor
*/
public KDTree()
{
this(null, null, null);
this.num = 0;
}
/**
* Creates a balanced KDTree from the given points.
*
* @param points Points to load into the KDTree.
*/
public KDTree(
Collection points)
{
this(CollectionUtil.asArrayList(points),
new PairFirstVectorizableIndexComparator(0),
CollectionUtil.getFirst(points).getFirst().convertToVector().getDimensionality(),
null);
}
/**
* Creates a KDTree subtree for recursion purposes.
*
* @param value Value of the head of the subtree.
* @param comparator Comparator to use for the Vectorizables.
* @param parent Parent node of this subtree.
*/
protected KDTree(
PairType value,
PairFirstVectorizableIndexComparator comparator,
KDTree parent)
{
this.num = 1;
this.value = value;
this.comparator = comparator;
this.parent = parent;
this.leftChild = null;
this.rightChild = null;
}
/**
* Creates a balanced KDTree subtree for recursion purposes from the given
* ArrayList of points. This is an O(n log n) operation for "n" points
* because we use a clever linear-time kth selection algorithm in
* CollectionUtil.findKthLargest().
*
* @param points Points to load into the subtree.
* @param dimensionality Dimensionality of the Vectorizables.
* @param comparator Comparator to use for the Vectorizables.
* @param parent Parent node of this subtree.
*/
protected KDTree(
ArrayList points,
PairFirstVectorizableIndexComparator comparator,
int dimensionality,
KDTree parent)
{
this.parent = parent;
this.comparator = comparator;
this.num = points.size();
if (num <= 0)
{
throw new IllegalArgumentException("No points!");
}
else if (num == 1)
{
this.value = points.get(0);
}
else
{
final int medianIndex = this.num / 2;
int[] indices = CollectionUtil.findKthLargest(
medianIndex, points, comparator);
// This is the median of the axis.
this.value = points.get(indices[medianIndex]);
final int childAxis = (this.comparator.comparator.getIndex() + 1)
% dimensionality;
PairFirstVectorizableIndexComparator childComparator
= new PairFirstVectorizableIndexComparator(childAxis);
// Left child recursion
final int leftNum = medianIndex;
if (leftNum > 0)
{
ArrayList leftPoints
= new ArrayList(leftNum);
for (int i = 0; i < leftNum; i++)
{
leftPoints.add(points.get(indices[i]));
}
this.leftChild = new KDTree(
leftPoints, childComparator, dimensionality, this);
}
// Right child recursion
final int rightNum = num - medianIndex - 1;
if (rightNum > 0)
{
ArrayList rightPoints = new ArrayList(
rightNum);
for (int i = medianIndex + 1; i < this.num; i++)
{
rightPoints.add(points.get(indices[i]));
}
this.rightChild = new KDTree(
rightPoints, childComparator, dimensionality, this);
}
}
}
@Override
@SuppressWarnings("unchecked")
public KDTree clone()
{
KDTree clone;
try
{
clone = (KDTree) super.clone();
clone.leftChild = ObjectUtil.cloneSafe(this.leftChild);
clone.rightChild = ObjectUtil.cloneSafe(this.rightChild);
clone.value = ObjectUtil.cloneSmart(this.value);
clone.parent = this.parent;
clone.comparator = this.comparator;
}
catch (CloneNotSupportedException ex)
{
clone = null;
}
return clone;
}
/**
* Creates a balanced KDTree based on the given collection of Pairs. This is
* an O(n log n) operation for "n" points because we use a clever
* linear-time kth selection algorithm in CollectionUtil.findKthLargest().
*
* @param Type of Vectorizable, the first values.
* @param Type of data in the Pair, the second values.
* @param Type of Pair to use in the KDTree.
* @param points Points to load into the tree.
* @return Balanced KDTree that contains all the given points.
*/
public static >
KDTree createBalanced(
Collection points)
{
return new KDTree(points);
}
/**
* Rebalances the KDTree. Does not modify this KDTree.
*
* @return Balanced representation of this KDTree.
*/
public KDTree reblanace()
{
return createBalanced(this);
}
@Override
public boolean add(
PairType point)
{
if (this.value == null)
{
this.num = 1;
this.value = point;
this.comparator = new PairFirstVectorizableIndexComparator(0);
}
else
{
this.num++;
int comparison = this.comparator.compare(point, this.value);
if (comparison <= 0)
{
if (this.leftChild == null)
{
int dimension
= point.getFirst().convertToVector().getDimensionality();
int childAxis = (this.comparator.comparator.getIndex() + 1)
% dimension;
PairFirstVectorizableIndexComparator childComparator
= new PairFirstVectorizableIndexComparator(childAxis);
this.leftChild = new KDTree(
point, childComparator, this);
}
else
{
this.leftChild.add(point);
}
}
else if (this.rightChild == null)
{
int dimension
= point.getFirst().convertToVector().getDimensionality();
int childAxis = (this.comparator.comparator.getIndex() + 1)
% dimension;
PairFirstVectorizableIndexComparator childComparator
= new PairFirstVectorizableIndexComparator(childAxis);
this.rightChild = new KDTree(
point, childComparator, this);
}
else
{
this.rightChild.add(point);
}
}
return true;
}
@Override
public int size()
{
return this.num;
}
/**
* Iterates through the KDTree using "inorder", also known as "symmetric
* traversal", of the tree. That is, the recursion proceeds as traverse the
* left subtree, visit the node, traverse the right subtree.
*
* @return Inorder iterator of the KDTree.
*/
@PublicationReference(
author = "Wikipedia",
title = "Tree traversal",
type = PublicationType.WebPage,
year = 2009,
url = "http://en.wikipedia.org/wiki/Tree_traversal#Traversal"
)
@Override
public Iterator iterator()
{
return new InOrderKDTreeIterator(this);
}
@Override
public String toString()
{
return this.toString("Head->");
}
/**
* Recursively prints out the tree "inorder" by printing out the left
* subtree, then the node, then the right subtree.
*
* @param prefix Prefix to tack onto the recursion values.
* @return String representation of the KDTree.
*/
protected String toString(
String prefix)
{
String retval = prefix + " (" + this.value.getFirst() + " -> "
+ this.value.getSecond() + ")\n";
if (this.leftChild != null)
{
retval += this.leftChild.toString(prefix + "L");
}
if (this.rightChild != null)
{
retval += this.rightChild.toString(prefix + "R");
}
return retval;
}
/**
* Finds the "num" nearest neighbors to the given "key" stored in the
* KDTree.
*
* @param key Vector to find the nearest neighbors of.
* @param k Number of neighbors to find.
* @param metric Metric to use to evaluate the nearness of other points.
* @return Collection of nearest points to the "key" query. If "num" is
* greater than or equal to the number of points in the KDTRee, then the
* KDTree is returned.
*/
public Collection findNearest(
VectorType key,
int k,
Metric metric)
{
if (k < this.size())
{
Neighborhood neighborhood
= new Neighborhood(k);
this.findNearest(key, k, neighborhood, metric);
return neighborhood;
}
else
{
return this;
}
}
/**
* Finds the "num" nearest neighbors to the given "key" stored in the
* KDTree.
*
* @param key Vector to find the nearest neighbors of.
* @param k Number of neighbors to find.
* @param neighborhood PriorityQueue to store the current nearest neighbors.
* @param metric Metric to use to evaluate the nearness of other points.
*/
protected void findNearest(
VectorType key,
int k,
Neighborhood neighborhood,
Metric metric)
{
KDTree closer = null;
KDTree further = null;
// If we've got children, then see which child is closer
if ((this.leftChild != null) || (this.rightChild != null))
{
int comparison = this.comparator.comparator.compare(
key, this.value.getFirst());
if (comparison <= 0)
{
closer = this.leftChild;
further = this.rightChild;
}
else
{
closer = this.rightChild;
further = this.leftChild;
}
// recurse into the closer subtree if it exists.
if (closer != null)
{
closer.findNearest(key, k, neighborhood, metric);
}
}
// If there's space in the queue, then add our value.
if (!neighborhood.isFull())
{
// Compute our distance to the key
double distance = metric.evaluate(this.value.getFirst(), key);
neighborhood.add(this.value, distance);
// If there's still space, then recurse to the further tree.
if (further != null)
{
// if( !neighborhood.isFull() )
{
further.findNearest(key, k, neighborhood, metric);
// further.findNearest(key, num, neighborhood, metric);
}
}
}
// The queue is full, so we need to see if we're closer than
// the furthest neighbor.
else
{
// We need to see if it's possible that the BOTH us and the
// further subtree could contain a better point than the furthest
// neighbor so far.
double minimumDistance = this.computeMinimumDifference(key);
if (minimumDistance < neighborhood.getFurthestNeighborDistance())
{
double distance = metric.evaluate(this.value.getFirst(), key);
neighborhood.offer(this.value, distance);
if (further != null)
{
further.findNearest(key, num, neighborhood, metric);
}
}
}
}
/**
* Finds the neighbors within a given distance to the given "key" stored in
* the KDTree.
*
* @param key Vector to find the nearest neighbors of.
* @param radius Radius of desired neighborhood.
* @param metric Metric to use to evaluate the nearness of other points.
* @return Collection of points within a given distance to the "key" query.
*/
public Collection findNearestWithinRadius(
VectorType key,
double radius,
Metric metric)
{
Neighborhood neighborhood
= new Neighborhood(this.size());
this.findNearestWithinRadius(key, radius, neighborhood, metric);
return neighborhood;
}
/**
* Finds the neighbors within a given distance to the given "key" stored in
* the KDTree.
*
* @param key Vector to find the nearest neighbors of.
* @param radius Radius of desired neighborhood.
* @param neighborhood PriorityQueue to store the neighbors.
* @param metric Metric to use to evaluate the nearness of other points.
*/
protected void findNearestWithinRadius(
VectorType key,
double radius,
Neighborhood neighborhood,
Metric metric)
{
KDTree closer = null;
KDTree further = null;
// If we've got children, then see which child is closer
if ((this.leftChild != null) || (this.rightChild != null))
{
int comparison = this.comparator.comparator.compare(
key, this.value.getFirst());
if (comparison <= 0)
{
closer = this.leftChild;
further = this.rightChild;
}
else
{
closer = this.rightChild;
further = this.leftChild;
}
// recurse into the closer subtree if it exists.
if (closer != null)
{
closer.findNearestWithinRadius(key, radius, neighborhood, metric);
}
}
// Compute our distance to the key
double distance = metric.evaluate(this.value.getFirst(), key);
if (distance <= radius)
{
// Add this node if the distance is less than the radius.
neighborhood.add(this.value, distance);
// Only investigate the further tree if this node was within radius.
if (further != null)
{
further.findNearestWithinRadius(key, radius, neighborhood,
metric);
}
}
}
/**
* Computes the minimum absolute difference between the given key and the
* "first" value stored in this subtree for the index given by the embedded
* comparator. That is, the minimum distance "this is done by intersecting
* the splitting hyperplane with a hypersphere around the search node [key]
* that has a radius equal to the current nearest distance. Since the
* hyperplanes are all axis-aligned this is implemented as a simple
* comparison to see whether the difference between the splitting coordinate
* and the search point is less than the distance from the search point to
* the current best."
*
* @param key Vector to compare against.
* @return Minimum absolute difference for the given index between the key
* and the first value stored in this subtree.
*/
protected double computeMinimumDifference(
VectorType key)
{
int index = this.comparator.comparator.getIndex();
double delta = key.convertToVector().getElement(index)
- this.value.getFirst().convertToVector().getElement(index);
return Math.abs(delta);
}
/**
* Comparator for Pairs that have a Vectorizable as its first parameter.
*/
protected static class PairFirstVectorizableIndexComparator
extends AbstractCloneableSerializable
implements Comparator>
{
/**
* Embedded comparator for the Vectorizable argument.
*/
public VectorizableIndexComparator comparator;
/**
* Creates a new instance of PairFirstVectorizableIndexComparator
*
* @param index Index of the Vectorizable to compare against.
*/
public PairFirstVectorizableIndexComparator(
int index)
{
this.comparator = new VectorizableIndexComparator(index);
}
public int compare(
Pair o1,
Pair o2)
{
return this.comparator.compare(o1.getFirst(), o2.getFirst());
}
}
/**
* Iterates through the KDTree using "inorder", also known as "symmetric
* traversal", of the tree. That is, the recursion proceeds as traverse the
* left subtree, visit the node, traverse the right subtree.
*
* @param Type of Vectorizable, the first values
* @param Type of data in the Pair, the second values
* @param Type of Pair to use in the KDTree.
*/
@PublicationReference(
author = "Wikipedia",
title = "Tree traversal",
type = PublicationType.WebPage,
year = 2009,
url = "http://en.wikipedia.org/wiki/Tree_traversal#Traversal"
)
protected static class InOrderKDTreeIterator>
implements Iterator
{
/**
* Value of the node
*/
public PairType nodeValue;
/**
* Iterator for the left subtree.
*/
public InOrderKDTreeIterator leftIterator;
/**
* Iterator for the right subtree.
*/
public InOrderKDTreeIterator rightIterator;
/**
* Creates a new instance of InOrderKDTreeIterator
*
* @param node Node from which to iterate.
*/
public InOrderKDTreeIterator(
KDTree node)
{
if (node.leftChild != null)
{
this.leftIterator
= new InOrderKDTreeIterator(
node.leftChild);
}
if (node.rightChild != null)
{
this.rightIterator
= new InOrderKDTreeIterator(
node.rightChild);
}
this.nodeValue = node.value;
}
public boolean hasNext()
{
return (this.nodeValue != null) || ((this.rightIterator != null)
&& (this.rightIterator.hasNext())) || ((this.leftIterator
!= null) && (this.leftIterator.hasNext()));
}
public PairType next()
{
PairType retval = null;
if ((this.leftIterator != null)
&& this.leftIterator.hasNext())
{
retval = this.leftIterator.next();
}
else
{
this.leftIterator = null;
if (this.nodeValue != null)
{
retval = this.nodeValue;
this.nodeValue = null;
}
else if (this.rightIterator != null)
{
if (this.rightIterator.hasNext())
{
retval = this.rightIterator.next();
}
else
{
this.rightIterator = null;
}
}
}
if (retval == null)
{
throw new IllegalArgumentException(
"Should not have called null since we have no values to iterate!");
}
return retval;
}
public void remove()
{
throw new UnsupportedOperationException("Not supported yet.");
}
}
/**
* A Collection of nearby pairs.
*
* @param Type of Vectorizable.
* @param Type of output data.
* @param Type of Pair.
*/
protected static class Neighborhood>
extends AbstractCollection
{
/**
* Maximum number of Neighbors in the Neighborhood.
*/
private int k;
/**
* PriorityQueue to store the neighbors.
*/
PriorityQueue> priorityQueue;
/**
* Creates a new Neighborhood.
*
* @param k Maximum number of Neighbors in the Neighborhood.
*/
public Neighborhood(
int k)
{
this.priorityQueue
= new PriorityQueue>(k);
this.k = k;
}
/**
* Returns true if the Neighborhood is full.
*
* @return True if the Neighborhood is full, false if not full.
*/
public boolean isFull()
{
return this.size() >= this.k;
}
/**
* Returns the distance of the furthest Neighbor.
*
* @return Distance of the furthest Neighbor.
*/
public double getFurthestNeighborDistance()
{
return this.priorityQueue.peek().distance;
}
/**
* Adds the neighbor to the priority queue.
*
* @param value Value to add.
* @param distance Distance to associate with the neighbor to the queue.
*/
public void add(
PairType value,
double distance)
{
while (this.isFull())
{
this.priorityQueue.remove();
}
this.priorityQueue.add(
new Neighbor(value, distance));
}
/**
* Offers the neighbor if there is space or it's closer than the
* furthest neighbor.
*
* @param value Value of the neighbor.
* @param distance Distance to the key value.
* @return True if added, false if not added.
*/
public boolean offer(
PairType value,
double distance)
{
// If we're full, then see if we're closer than the furthest
// neighbor.
if (this.isFull())
{
if (distance < this.getFurthestNeighborDistance())
{
this.priorityQueue.remove();
}
}
// If we find there is space, then add the new neighbor.
if (!this.isFull())
{
this.add(value, distance);
return true;
}
else
{
return false;
}
}
@Override
public Iterator iterator()
{
return new NeighborhoodIterator();
}
@Override
public int size()
{
return this.priorityQueue.size();
}
/**
* Holds neighbor information used during the evaluate method and is put
* into a priority queue.
*
* @param Type of Vectorizable, the first values
* @param Type of data in the Pair, the second values
* @param Type of Pair to use in the KDTree.
*/
protected class Neighbor>
extends AbstractCloneableSerializable
implements Comparable>
{
// Note: This class does not follow the get/set pattern in order to
// make it as fast as possible, because it is used within the evaluate
// method. Also, its a private internal class, so no one else should
// use it.
/**
* Pair to store.
*/
PairType pair;
/**
* Distance associated with this value.
*/
private double distance;
/**
* Creates a new neighbor.
*
* @param value The value associated with the neighbor.
* @param distance Distance associated with this value.
*/
public Neighbor(
final PairType value,
final double distance)
{
this.pair = value;
this.distance = distance;
}
public int compareTo(
final Neighbor other)
{
// We reverse the comparison so that the item at the head of the
// priority queue is the furthest neighbor
return -Double.compare(this.distance, other.distance);
}
@Override
@SuppressWarnings("unchecked")
public boolean equals(
Object obj)
{
if (obj == null)
{
return false;
}
else if (obj instanceof Neighbor)
{
return ((Neighbor) obj).pair.getFirst().convertToVector().equals(
this.pair.getFirst().convertToVector());
}
else
{
return false;
}
}
@Override
public int hashCode()
{
return this.pair.getFirst().hashCode();
}
}
/**
* Iterator for the Neighborhood.
*/
protected class NeighborhoodIterator
implements Iterator
{
/**
* PriorityQueue iterator.
*/
Iterator> priorityQueueIterator;
/**
* Default constructor.
*/
public NeighborhoodIterator()
{
this.priorityQueueIterator = priorityQueue.iterator();
}
public boolean hasNext()
{
return this.priorityQueueIterator.hasNext();
}
public PairType next()
{
Neighbor next
= this.priorityQueueIterator.next();
return next.pair;
}
public void remove()
{
throw new UnsupportedOperationException("Not supported yet.");
}
}
}
}