Difference between revisions of "User:Chase-san/Kd-Tree"

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m (removed pointless references)
m (→‎KDTreeF: (no major change, just some minor tweaks, i++ to ++i, removing the enhanced for loop))
 
(2 intermediate revisions by the same user not shown)
Line 6: Line 6:
 
Oh yeah, am I the only one that has a Range function?
 
Oh yeah, am I the only one that has a Range function?
  
=== KDTree ===
+
=== KDTreeF ===
 
<syntaxhighlight>
 
<syntaxhighlight>
 
package org.csdgn.util;
 
package org.csdgn.util;
  
 +
import java.util.ArrayList;
 
import java.util.Arrays;
 
import java.util.Arrays;
 +
import java.util.List;
  
 
/**
 
/**
  * This is a KD Bucket Tree, for fast sorting and searching of K dimensional data.
+
  * This is a KD Bucket Tree, for fast sorting and searching of K dimensional
 +
* data.
 +
*
 
  * @author Chase
 
  * @author Chase
  *
+
  *  
 
  */
 
  */
public class KDTree {
+
public class KDTree<T> {
/**
+
protected static final int defaultBucketSize = 48;
* Item, for moving data around.
+
 
* @author Chase
 
*/
 
public static class Item {
 
public double[] pnt;
 
public Object obj;
 
public double distance;
 
private Item(double[] p, Object o) {
 
pnt = p; obj = o;
 
}
 
}
 
 
private final int dimensions;
 
private final int dimensions;
private final int bucket_size;
+
private final int bucketSize;
 
private NodeKD root;
 
private NodeKD root;
+
 
 
/**
 
/**
 
* Constructor with value for dimensions.
 
* Constructor with value for dimensions.
* @param dimensions - Number of dimensions
+
*
 +
* @param dimensions
 +
*            - Number of dimensions
 
*/
 
*/
 
public KDTree(int dimensions) {
 
public KDTree(int dimensions) {
 
this.dimensions = dimensions;
 
this.dimensions = dimensions;
this.bucket_size = 64;
+
this.bucketSize = defaultBucketSize;
 
this.root = new NodeKD();
 
this.root = new NodeKD();
 
}
 
}
+
 
 
/**
 
/**
 
* Constructor with value for dimensions and bucket size.
 
* Constructor with value for dimensions and bucket size.
* @param dimensions - Number of dimensions
+
*
* @param bucket - Size of the buckets.
+
* @param dimensions
 +
*            - Number of dimensions
 +
* @param bucket
 +
*            - Size of the buckets.
 
*/
 
*/
 
public KDTree(int dimensions, int bucket) {
 
public KDTree(int dimensions, int bucket) {
 
this.dimensions = dimensions;
 
this.dimensions = dimensions;
this.bucket_size = bucket;
+
this.bucketSize = bucket;
 
this.root = new NodeKD();
 
this.root = new NodeKD();
 
}
 
}
+
 
 
/**
 
/**
 
* Add a key and its associated value to the tree.
 
* Add a key and its associated value to the tree.
* @param key - Key to add
+
*
* @param val - object to add
+
* @param key
 +
*            - Key to add
 +
* @param val
 +
*            - object to add
 
*/
 
*/
public void add(double[] key, Object val) {
+
public void add(double[] key, T val) {
root.add(new Item(key,val));
+
root.addPoint(key, val);
 
}
 
}
+
 
 
/**
 
/**
* Returns all PointKD within a certain range defined by an upper and lower PointKD.
+
* Returns all PointKD within a certain range defined by an upper and lower
* @param low - lower bounds of area
+
* PointKD.
* @param high - upper bounds of area
+
*
 +
* @param low
 +
*            - lower bounds of area
 +
* @param high
 +
*            - upper bounds of area
 
* @return - All PointKD between low and high.
 
* @return - All PointKD between low and high.
 
*/
 
*/
public Item[] getRange(double[] low, double[] high) {
+
@SuppressWarnings("unchecked")
return root.range(high, low);
+
public List<T> getRange(double[] low, double[] high) {
 +
Object[] objs = root.range(high, low);
 +
ArrayList<T> range = new ArrayList<T>(objs.length);
 +
for(int i=0; i<objs.length; ++i) {
 +
range.add((T)objs[i]);
 +
}
 +
return range;
 
}
 
}
+
 
 
/**
 
/**
 
* Gets the N nearest neighbors to the given key.
 
* Gets the N nearest neighbors to the given key.
* @param key - Key
 
* @param num - Number of results
 
* @return Array of Item Objects, distances within the items
 
* are the square of the actual distance between them and the key
 
*/
 
public Item[] getNearestNeighbor(double[] key, int num) {
 
ShiftArray arr = new ShiftArray(num);
 
root.nearestn(arr, key);
 
return arr.getArray();
 
}
 
/**
 
* Compares arrays of double and returns the euclidean distance
 
* between them.
 
 
*  
 
*  
* @param a - The first set of numbers
+
* @param key
* @param b - The second set of numbers
+
*            - Key
* @return The distance squared between <b>a</b> and <b>b</b>.
+
* @param num
 +
*            - Number of results
 +
* @return Array of Item Objects, distances within the items are the square
 +
*        of the actual distance between them and the key
 
*/
 
*/
public static final double distance(double[] a, double[] b) {
+
public ResultHeap<T> getNearestNeighbors(double[] key, int num) {
double total = 0;
+
ResultHeap<T> heap = new ResultHeap<T>(num);
for (int i = 0; i < a.length; ++i)
+
root.nearest(heap, key);
total += (b[i] - a[i]) * (b[i] - a[i]);
+
return heap;
return Math.sqrt(total);
 
 
}
 
}
/**
+
 
* Compares arrays of double and returns the squared euclidean distance
+
 
* between them.
+
// Internal tree node
*
 
* @param a - The first set of numbers
 
* @param b - The second set of numbers
 
* @return The distance squared between <b>a</b> and <b>b</b>.
 
*/
 
public static final double distanceSq(double[] a, double[] b) {
 
double total = 0;
 
for (int i = 0; i < a.length; ++i)
 
total += (b[i] - a[i]) * (b[i] - a[i]);
 
return total;
 
}
 
 
//Internal tree node
 
 
private class NodeKD {
 
private class NodeKD {
 
private NodeKD left, right;
 
private NodeKD left, right;
private double[] upper, lower;
+
private double[] maxBounds, minBounds;
private Item[] bucket;
+
private Object[] bucketValues;
private int current, dim;
+
private double[][] bucketKeys;
 +
private boolean isLeaf;
 +
private int current, sliceDimension;
 
private double slice;
 
private double slice;
+
 
//note: we always start as a bucket
 
 
private NodeKD() {
 
private NodeKD() {
upper = lower = null;
+
bucketValues = new Object[bucketSize];
 +
bucketKeys = new double[bucketSize][];
 +
 
 
left = right = null;
 
left = right = null;
bucket = new Item[bucket_size];
+
maxBounds = minBounds = null;
 +
 +
isLeaf = true;
 +
 
current = 0;
 
current = 0;
dim = 0;
 
 
}
 
}
//when we create non-root nodes within this class
+
 
//we use this one here
+
// what it says on the tin
private NodeKD(NodeKD node) {
+
private void addPoint(double[] key, Object val) {
dim = node.dim + 1;
+
if(isLeaf) {
bucket = new Item[bucket_size];
+
addLeafPoint(key,val);
if(dim + 1 > dimensions) dim = 0;
 
left = right = null;
 
upper = lower = null;
 
current = 0;
 
}
 
//what it says on the tin
 
private void add(Item m) {
 
if(bucket == null) {
 
//Branch
 
if(m.pnt[dim] > slice)
 
right.add(m);
 
else left.add(m);
 
 
} else {
 
} else {
//Bucket
+
extendBounds(key);
if(current+1>bucket_size) {
+
if (key[sliceDimension] > slice) {
split(m);
+
right.addPoint(key, val);
return;
+
} else {
 +
left.addPoint(key, val);
 
}
 
}
bucket[current++] = m;
 
 
}
 
}
expand(m.pnt);
 
 
}
 
}
//nearest neighbor thing
+
private void nearestn(ShiftArray arr, double[] data) {
+
private void addLeafPoint(double[] key, Object val) {
if(bucket == null) {
+
extendBounds(key);
//Branch
+
if (current + 1 > bucketSize) {
if(data[dim] > slice) {
+
splitLeaf();
right.nearestn(arr, data);
+
addPoint(key, val);
if(left.current != 0) {
+
return;
if(KDTree.distanceSq(left.nearestRect(data),data)
+
}
< arr.getLongest()) {
+
bucketKeys[current] = key;
left.nearestn(arr, data);
+
bucketValues[current] = val;
}
+
++current;
 +
}
 +
 +
/**
 +
* Find the nearest neighbor recursively.
 +
*/
 +
@SuppressWarnings("unchecked")
 +
private void nearest(ResultHeap<T> heap, double[] data) {
 +
if(current == 0)
 +
return;
 +
if(isLeaf) {
 +
//IS LEAF
 +
for (int i = 0; i < current; ++i) {
 +
double dist = pointDistSq(bucketKeys[i], data);
 +
heap.offer(dist, (T) bucketValues[i]);
 +
}
 +
} else {
 +
//IS BRANCH
 +
if (data[sliceDimension] > slice) {
 +
right.nearest(heap, data);
 +
if(left.current == 0)
 +
return;
 +
if (!heap.isFull() || regionDistSq(data,left.minBounds,left.maxBounds) < heap.getMaxKey()) {
 +
left.nearest(heap, data);
 
}
 
}
 
 
} else {
 
} else {
left.nearestn(arr, data);
+
left.nearest(heap, data);
if(right.current != 0) {
+
if (right.current == 0)
if(KDTree.distanceSq(right.nearestRect(data),data)  
+
return;
< arr.getLongest()) {
+
if (!heap.isFull() || regionDistSq(data,right.minBounds,right.maxBounds) < heap.getMaxKey()) {
right.nearestn(arr, data);
+
right.nearest(heap, data);
}
 
 
}
 
}
}
 
} else {
 
//Bucket
 
for(int i = 0; i < current; i++) {
 
bucket[i].distance = KDTree.distanceSq(bucket[i].pnt, data);
 
arr.add(bucket[i]);
 
 
}
 
}
 
}
 
}
 
}
 
}
//gets all items from within a range
+
 
private Item[] range(double[] upper, double[] lower) {
+
// gets all items from within a range
//TODO: clean this up a bit
+
private Object[] range(double[] upper, double[] lower) {
if(bucket == null) {
+
if (bucketValues == null) {
//Branch
+
// Branch
Item[] tmp = new Item[0];
+
Object[] tmp = new Object[0];
if (intersects(upper,lower,left.upper,left.lower)) {
+
if (intersects(upper, lower, left.maxBounds, left.minBounds)) {
Item[] tmpl = left.range(upper,lower);
+
Object[] tmpl = left.range(upper, lower);
if(0 == tmp.length)
+
if (0 == tmp.length) tmp = tmpl;
tmp = tmpl;
 
 
}
 
}
if (intersects(upper,lower,right.upper,right.lower)) {
+
if (intersects(upper, lower, right.maxBounds, right.minBounds)) {
Item[] tmpr = right.range(upper,lower);
+
Object[] tmpr = right.range(upper, lower);
 
if (0 == tmp.length)
 
if (0 == tmp.length)
 
tmp = tmpr;
 
tmp = tmpr;
 
else if (0 < tmpr.length) {
 
else if (0 < tmpr.length) {
Item[] tmp2 = new Item[tmp.length + tmpr.length];
+
Object[] tmp2 = new Object[tmp.length + tmpr.length];
 
System.arraycopy(tmp, 0, tmp2, 0, tmp.length);
 
System.arraycopy(tmp, 0, tmp2, 0, tmp.length);
 
System.arraycopy(tmpr, 0, tmp2, tmp.length, tmpr.length);
 
System.arraycopy(tmpr, 0, tmp2, tmp.length, tmpr.length);
Line 212: Line 206:
 
return tmp;
 
return tmp;
 
}
 
}
//Bucket
+
// Leaf
Item[] tmp = new Item[current];
+
Object[] tmp = new Object[current];
 
int n = 0;
 
int n = 0;
for (int i = 0; i < current; i++) {
+
for (int i = 0; i < current; ++i) {
if (contains(upper, lower, bucket[i].pnt)) {
+
if (contains(upper, lower, bucketKeys[i])) {
tmp[n++] = bucket[i];
+
tmp[n++] = bucketValues[i];
 
}
 
}
 
}
 
}
Item[] tmp2 = new Item[n];
+
Object[] tmp2 = new Object[n];
 
System.arraycopy(tmp, 0, tmp2, 0, n);
 
System.arraycopy(tmp, 0, tmp2, 0, n);
 
return tmp2;
 
return tmp2;
 
}
 
}
+
 
//These are helper functions from here down
+
// These are helper functions from here down
//check if this hyper rectangle contains a give hyper-point
+
// check if this hyper rectangle contains a give hyper-point
 
public boolean contains(double[] upper, double[] lower, double[] point) {
 
public boolean contains(double[] upper, double[] lower, double[] point) {
if(current == 0) return false;
+
if (current == 0) return false;
for(int i=0; i<point.length; ++i) {
+
for (int i = 0; i < point.length; ++i) {
if(point[i] > upper[i] ||
+
if (point[i] > upper[i] || point[i] < lower[i]) return false;
point[i] < lower[i])  
 
return false;
 
 
}
 
}
 
return true;
 
return true;
 
}
 
}
//checks if two hyper-rectangles intersect
+
 
public boolean intersects(double[] up0, double[] low0,
+
// checks if two hyper-rectangles intersect
double[] up1, double[] low1) {
+
public boolean intersects(double[] up0, double[] low0, double[] up1, double[] low1) {
for(int i=0; i<up0.length; ++i) {
+
for (int i = 0; i < up0.length; ++i) {
if(up1[i] < low0[i] || low1[i] > up0[i]) return false;
+
if (up1[i] < low0[i] || low1[i] > up0[i]) return false;
 
}
 
}
 
return true;
 
return true;
 
}
 
}
//splits a bucket into a branch
+
 
private void split(Item m) {
+
private void splitLeaf() {
//split based on our bound data
+
double bestRange = 0;
slice = (upper[dim]+lower[dim])/2.0;
+
for(int i=0;i<dimensions;++i) {
left = new NodeKD(this);
+
double range = maxBounds[i] - minBounds[i];
right = new NodeKD(this);
+
if(range > bestRange) {
for(int i=0; i<current; ++i) {
+
sliceDimension = i;
if(bucket[i].pnt[dim] > slice)
+
bestRange = range;
right.add(bucket[i]);
+
}
else left.add(bucket[i]);
 
 
}
 
}
bucket = null;
+
add(m);
+
left = new NodeKD();
}
+
right = new NodeKD();
//gets nearest point to data within this hyper rectangle
+
private double[] nearestRect(double[] data) {
+
slice = (maxBounds[sliceDimension] + minBounds[sliceDimension]) * 0.5;
double[] nearest = data.clone();
+
for(int i = 0; i < data.length; ++i) {
+
for (int i = 0; i < current; ++i) {
if(nearest[i] > upper[i]) nearest[i] = upper[i];
+
if (bucketKeys[i][sliceDimension] > slice) {
if(nearest[i] < lower[i]) nearest[i] = lower[i];
+
right.addLeafPoint(bucketKeys[i], bucketValues[i]);
 +
} else {
 +
left.addLeafPoint(bucketKeys[i], bucketValues[i]);
 +
}
 
}
 
}
return nearest;
+
bucketKeys = null;
 +
bucketValues = null;
 +
isLeaf = false;
 
}
 
}
//expands this hyper rectangle
+
 
private void expand(double[] data) {
+
// expands this hyper rectangle
if(upper == null) {
+
private void extendBounds(double[] key) {
upper = Arrays.copyOf(data, dimensions);
+
if (maxBounds == null) {
lower = Arrays.copyOf(data, dimensions);
+
maxBounds = Arrays.copyOf(key, dimensions);
 +
minBounds = Arrays.copyOf(key, dimensions);
 
return;
 
return;
 
}
 
}
for(int i=0; i<data.length; ++i) {
+
for (int i = 0; i < key.length; ++i) {
if(upper[i] < data[i]) upper[i] = data[i];
+
if (maxBounds[i] < key[i]) maxBounds[i] = key[i];
if(lower[i] > data[i]) lower[i] = data[i];
+
if (minBounds[i] > key[i]) minBounds[i] = key[i];
 
}
 
}
 
}
 
}
 
}
 
}
//A simple shift array that sifts data up
+
//as we add new ones to lower in the array.
+
/* I may have borrowed these from an early version of Red's tree. I however forget. */
private class ShiftArray {
+
private static final double pointDistSq(double[] p1, double[] p2) {
private Item[] items;
+
        double d = 0;
private final int max;
+
        double q = 0;
private int current;
+
        for (int i = 0; i < p1.length; ++i) {
private ShiftArray(int maximum) {
+
            d += (q=(p1[i] - p2[i]))*q;
max = maximum;
+
        }
current = 0;
+
        return d;
items = new Item[max];
+
    }
}
+
 
private void add(Item m) {
+
    private static final double regionDistSq(double[] point, double[] min, double[] max) {
int i;
+
        double d = 0;
for(i=current;i>0&&items[i-1].distance > m.distance; --i);
+
        double q = 0;
if(i >= max) return;
+
        for (int i = 0; i < point.length; ++i) {
if(current < max) ++current;
+
            if (point[i] > max[i]) {
System.arraycopy(items, i, items, i+1, current-(i+1));
+
            d += (q = (point[i] - max[i]))*q;
items[i] = m;
+
            } else if (point[i] < min[i]) {
}
+
                d += (q = (point[i] - min[i]))*q;
private double getLongest() {
+
            }
if (current < max) return Double.POSITIVE_INFINITY;
+
        }
return items[max-1].distance;
+
        return d;
}
+
    }
private Item[] getArray() {
+
}
return items.clone();
+
</syntaxhighlight>
}
+
 
 +
=== ResultHeap ===
 +
<syntaxhighlight>
 +
package org.csdgn.util;
 +
 
 +
/**
 +
* @author Chase
 +
*
 +
* @param <T>
 +
*/
 +
public class ResultHeap<T> {
 +
private Object[] data;
 +
private double[] keys;
 +
private int capacity;
 +
private int size;
 +
 
 +
protected ResultHeap(int capacity) {
 +
this.data = new Object[capacity];
 +
this.keys = new double[capacity];
 +
this.capacity = capacity;
 +
this.size = 0;
 +
}
 +
 
 +
protected void offer(double key, T value) {
 +
int i = size;
 +
for (; i > 0 && keys[i - 1] > key; --i);
 +
if (i >= capacity) return;
 +
if (size < capacity) ++size;
 +
int j = i + 1;
 +
System.arraycopy(keys, i, keys, j, size - j);
 +
keys[i] = key;
 +
System.arraycopy(data, i, data, j, size - j);
 +
data[i] = value;
 +
}
 +
 
 +
public double getMaxKey() {
 +
return keys[size - 1];
 +
}
 +
 +
@SuppressWarnings("unchecked")
 +
public T removeMax() {
 +
if(isEmpty()) return null;
 +
return (T)data[--size];
 +
}
 +
 
 +
public boolean isEmpty() {
 +
return size == 0;
 +
}
 +
 
 +
public boolean isFull() {
 +
return size == capacity;
 +
}
 +
 
 +
public int size() {
 +
return size;
 +
}
 +
 
 +
public int capacity() {
 +
return capacity;
 
}
 
}
 
}
 
}
 
</syntaxhighlight>
 
</syntaxhighlight>

Latest revision as of 22:00, 7 November 2012

Everyone and their brother has one of these now, me and Simonton started it, but I was to inexperienced to get anything written, I took an hour or two to rewrite it today, because I am no longer completely terrible at these things. So here is mine if you care to see it.

This and all my other code in which I display on the robowiki falls under the ZLIB License.

Oh yeah, am I the only one that has a Range function?

KDTreeF

package org.csdgn.util;

import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;

/**
 * This is a KD Bucket Tree, for fast sorting and searching of K dimensional
 * data.
 * 
 * @author Chase
 * 
 */
public class KDTree<T> {
	protected static final int defaultBucketSize = 48;

	private final int dimensions;
	private final int bucketSize;
	private NodeKD root;

	/**
	 * Constructor with value for dimensions.
	 * 
	 * @param dimensions
	 *            - Number of dimensions
	 */
	public KDTree(int dimensions) {
		this.dimensions = dimensions;
		this.bucketSize = defaultBucketSize;
		this.root = new NodeKD();
	}

	/**
	 * Constructor with value for dimensions and bucket size.
	 * 
	 * @param dimensions
	 *            - Number of dimensions
	 * @param bucket
	 *            - Size of the buckets.
	 */
	public KDTree(int dimensions, int bucket) {
		this.dimensions = dimensions;
		this.bucketSize = bucket;
		this.root = new NodeKD();
	}

	/**
	 * Add a key and its associated value to the tree.
	 * 
	 * @param key
	 *            - Key to add
	 * @param val
	 *            - object to add
	 */
	public void add(double[] key, T val) {
		root.addPoint(key, val);
	}

	/**
	 * Returns all PointKD within a certain range defined by an upper and lower
	 * PointKD.
	 * 
	 * @param low
	 *            - lower bounds of area
	 * @param high
	 *            - upper bounds of area
	 * @return - All PointKD between low and high.
	 */
	@SuppressWarnings("unchecked")
	public List<T> getRange(double[] low, double[] high) {
		Object[] objs = root.range(high, low);
		ArrayList<T> range = new ArrayList<T>(objs.length);
		for(int i=0; i<objs.length; ++i) {
			range.add((T)objs[i]);
		}
		return range;
	}

	/**
	 * Gets the N nearest neighbors to the given key.
	 * 
	 * @param key
	 *            - Key
	 * @param num
	 *            - Number of results
	 * @return Array of Item Objects, distances within the items are the square
	 *         of the actual distance between them and the key
	 */
	public ResultHeap<T> getNearestNeighbors(double[] key, int num) {
		ResultHeap<T> heap = new ResultHeap<T>(num);
		root.nearest(heap, key);
		return heap;
	}


	// Internal tree node
	private class NodeKD {
		private NodeKD left, right;
		private double[] maxBounds, minBounds;
		private Object[] bucketValues;
		private double[][] bucketKeys;
		private boolean isLeaf;
		private int current, sliceDimension;
		private double slice;

		private NodeKD() {
			bucketValues = new Object[bucketSize];
			bucketKeys = new double[bucketSize][];

			left = right = null;
			maxBounds = minBounds = null;
			
			isLeaf = true;
			
			current = 0;
		}

		// what it says on the tin
		private void addPoint(double[] key, Object val) {
			if(isLeaf) {
				addLeafPoint(key,val);
			} else {
				extendBounds(key);
				if (key[sliceDimension] > slice) {
					right.addPoint(key, val);
				} else {
					left.addPoint(key, val);
				}
			}
		}
		
		private void addLeafPoint(double[] key, Object val) {
			extendBounds(key);
			if (current + 1 > bucketSize) {
				splitLeaf();
				addPoint(key, val);
				return;
			}
			bucketKeys[current] = key;
			bucketValues[current] = val;
			++current;
		}
		
		/**
		 * Find the nearest neighbor recursively.
		 */
		@SuppressWarnings("unchecked")
		private void nearest(ResultHeap<T> heap, double[] data) {
			if(current == 0)
				return;
			if(isLeaf) {
				//IS LEAF
				for (int i = 0; i < current; ++i) {
					double dist = pointDistSq(bucketKeys[i], data);
					heap.offer(dist, (T) bucketValues[i]);
				}
			} else {
				//IS BRANCH
				if (data[sliceDimension] > slice) {
					right.nearest(heap, data);
					if(left.current == 0)
						return;
					if (!heap.isFull() || regionDistSq(data,left.minBounds,left.maxBounds) < heap.getMaxKey()) {
						left.nearest(heap, data);
					}
				} else {
					left.nearest(heap, data);
					if (right.current == 0)
						return;
					if (!heap.isFull() || regionDistSq(data,right.minBounds,right.maxBounds) < heap.getMaxKey()) {
						right.nearest(heap, data);
					}
				}
			}
		}

		// gets all items from within a range
		private Object[] range(double[] upper, double[] lower) {
			if (bucketValues == null) {
				// Branch
				Object[] tmp = new Object[0];
				if (intersects(upper, lower, left.maxBounds, left.minBounds)) {
					Object[] tmpl = left.range(upper, lower);
					if (0 == tmp.length) tmp = tmpl;
				}
				if (intersects(upper, lower, right.maxBounds, right.minBounds)) {
					Object[] tmpr = right.range(upper, lower);
					if (0 == tmp.length)
						tmp = tmpr;
					else if (0 < tmpr.length) {
						Object[] tmp2 = new Object[tmp.length + tmpr.length];
						System.arraycopy(tmp, 0, tmp2, 0, tmp.length);
						System.arraycopy(tmpr, 0, tmp2, tmp.length, tmpr.length);
						tmp = tmp2;
					}
				}
				return tmp;
			}
			// Leaf
			Object[] tmp = new Object[current];
			int n = 0;
			for (int i = 0; i < current; ++i) {
				if (contains(upper, lower, bucketKeys[i])) {
					tmp[n++] = bucketValues[i];
				}
			}
			Object[] tmp2 = new Object[n];
			System.arraycopy(tmp, 0, tmp2, 0, n);
			return tmp2;
		}

		// These are helper functions from here down
		// check if this hyper rectangle contains a give hyper-point
		public boolean contains(double[] upper, double[] lower, double[] point) {
			if (current == 0) return false;
			for (int i = 0; i < point.length; ++i) {
				if (point[i] > upper[i] || point[i] < lower[i]) return false;
			}
			return true;
		}

		// checks if two hyper-rectangles intersect
		public boolean intersects(double[] up0, double[] low0, double[] up1, double[] low1) {
			for (int i = 0; i < up0.length; ++i) {
				if (up1[i] < low0[i] || low1[i] > up0[i]) return false;
			}
			return true;
		}

		private void splitLeaf() {
			double bestRange = 0;
			for(int i=0;i<dimensions;++i) {
				double range = maxBounds[i] - minBounds[i];
				if(range > bestRange) {
					sliceDimension = i;
					bestRange = range;
				}
			}
			
			left = new NodeKD();
			right = new NodeKD();
			
			slice = (maxBounds[sliceDimension] + minBounds[sliceDimension]) * 0.5;
			
			for (int i = 0; i < current; ++i) {
				if (bucketKeys[i][sliceDimension] > slice) {
					right.addLeafPoint(bucketKeys[i], bucketValues[i]);
				} else {
					left.addLeafPoint(bucketKeys[i], bucketValues[i]);
				}
			}
			bucketKeys = null;
			bucketValues = null;
			isLeaf = false;
		}

		// expands this hyper rectangle
		private void extendBounds(double[] key) {
			if (maxBounds == null) {
				maxBounds = Arrays.copyOf(key, dimensions);
				minBounds = Arrays.copyOf(key, dimensions);
				return;
			}
			for (int i = 0; i < key.length; ++i) {
				if (maxBounds[i] < key[i]) maxBounds[i] = key[i];
				if (minBounds[i] > key[i]) minBounds[i] = key[i];
			}
		}
	}
	
	/* I may have borrowed these from an early version of Red's tree. I however forget. */
	private static final double pointDistSq(double[] p1, double[] p2) {
        double d = 0;
        double q = 0;
        for (int i = 0; i < p1.length; ++i) {
            d += (q=(p1[i] - p2[i]))*q;
        }
        return d;
    }

    private static final double regionDistSq(double[] point, double[] min, double[] max) {
        double d = 0;
        double q = 0;
        for (int i = 0; i < point.length; ++i) {
            if (point[i] > max[i]) {
            	d += (q = (point[i] - max[i]))*q;
            } else if (point[i] < min[i]) {
                d += (q = (point[i] - min[i]))*q;
            }
        }
        return d;
    }
}

ResultHeap

package org.csdgn.util;

/**
 * @author Chase
 * 
 * @param <T>
 */
public class ResultHeap<T> {
	private Object[] data;
	private double[] keys;
	private int capacity;
	private int size;

	protected ResultHeap(int capacity) {
		this.data = new Object[capacity];
		this.keys = new double[capacity];
		this.capacity = capacity;
		this.size = 0;
	}

	protected void offer(double key, T value) {
		int i = size;
		for (; i > 0 && keys[i - 1] > key; --i);
		if (i >= capacity) return;
		if (size < capacity) ++size;
		int j = i + 1;
		System.arraycopy(keys, i, keys, j, size - j);
		keys[i] = key;
		System.arraycopy(data, i, data, j, size - j);
		data[i] = value;
	}

	public double getMaxKey() {
		return keys[size - 1];
	}
	
	@SuppressWarnings("unchecked")
	public T removeMax() {
		if(isEmpty()) return null;
		return (T)data[--size];
	}

	public boolean isEmpty() {
		return size == 0;
	}

	public boolean isFull() {
		return size == capacity;
	}

	public int size() {
		return size;
	}

	public int capacity() {
		return capacity;
	}
}