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

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[[Category:Code Snippets|Kd-Tree]]
 
[[Category:Code Snippets|Kd-Tree]]
 
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.
 
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 [http://en.wikipedia.org/wiki/Zlib_License ZLIB License].
  
 
Oh yeah, am I the only one that has a Range function?
 
Oh yeah, am I the only one that has a Range function?

Revision as of 12:47, 6 July 2010

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?

KDTreeC

package org.csdgn.util.kd2;

import java.util.Arrays;

/**
 * This is a KD Bucket Tree, for fast sorting and searching of K dimensional data.
 * @author Chase
 *
 */
public class KDTreeC {
	/**
	 * Item, for moving data around.
	 * @author Chase
	 */
	public 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 bucket_size;
	private NodeKD root;
	
	/**
	 * Constructor with value for dimensions.
	 * @param dimensions - Number of dimensions
	 */
	public KDTreeC(int dimensions) {
		this.dimensions = dimensions;
		this.bucket_size = 64;
		this.root = new NodeKD(this);
	}
	
	/**
	 * Constructor with value for dimensions and bucket size.
	 * @param dimensions - Number of dimensions
	 * @param bucket - Size of the buckets.
	 */
	public KDTreeC(int dimensions, int bucket) {
		this.dimensions = dimensions;
		this.bucket_size = bucket;
		this.root = new NodeKD(this);
	}
	
	/**
	 * 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, Object val) {
		root.add(new Item(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.
	 */
	public Item[] getRange(double[] low, double[] high) {
		return root.range(high, low);
	}
	
	/**
	 * 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 b - The second set of numbers
	 * @return The distance squared between <b>a</b> and <b>b</b>.
	 */
	public static final double distance(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 Math.sqrt(total);
	}
	/**
	 * Compares arrays of double and returns the squared euclidean distance
	 * between them.
	 * 
	 * @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 KDTreeC owner;
		private NodeKD left, right;
		private double[] upper, lower;
		private Item[] bucket;
		private int current, dim;
		private double slice;
		
		//note: we always start as a bucket
		private NodeKD(KDTreeC own) {
			owner = own;
			upper = lower = null;
			left = right = null;
			bucket = new Item[own.bucket_size];
			current = 0;
			dim = 0;
		}
		//when we create non-root nodes within this class
		//we use this one here
		private NodeKD(NodeKD node) {
			owner = node.owner;
			dim = node.dim + 1;
			bucket = new Item[owner.bucket_size];
			if(dim + 1 > owner.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 {
				//Bucket
				if(current+1>owner.bucket_size) {
					split(m);
					return;
				}
				bucket[current++] = m;
			}
			expand(m.pnt);
		}
		//nearest neighbor thing
		private void nearestn(ShiftArray arr, double[] data) {
			if(bucket == null) {
				//Branch
				if(data[dim] > slice) {
					right.nearestn(arr, data);
					if(left.current != 0) {
						if(KDTreeC.distanceSq(left.nearestRect(data),data)
								< arr.getLongest()) {
							left.nearestn(arr, data);
						}
					}
							
				} else {
					left.nearestn(arr, data);
					if(right.current != 0) {
						if(KDTreeC.distanceSq(right.nearestRect(data),data) 
								< arr.getLongest()) {
							right.nearestn(arr, data);
						}
					}
				}
			} else {
				//Bucket
				for(int i = 0; i < current; i++) {
					bucket[i].distance = KDTreeC.distanceSq(bucket[i].pnt, data);
					arr.add(bucket[i]);
				}
			}
		}
		//gets all items from within a range
		private Item[] range(double[] upper, double[] lower) {
			//TODO: clean this up a bit
			if(bucket == null) {
				//Branch
				Item[] tmp = new Item[0];
				if (intersects(upper,lower,left.upper,left.lower)) {
					Item[] tmpl = left.range(upper,lower);
					if(0 == tmp.length)
						tmp = tmpl;
				}
				if (intersects(upper,lower,right.upper,right.lower)) {
					Item[] tmpr = right.range(upper,lower);
					if (0 == tmp.length)
						tmp = tmpr;
					else if (0 < tmpr.length) {
						Item[] tmp2 = new Item[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;
			}
			//Bucket
			Item[] tmp = new Item[current];
			int n = 0;
			for (int i = 0; i < current; i++) {
				if (contains(upper, lower, bucket[i].pnt)) {
					tmp[n++] = bucket[i];
				}
			}
			Item[] tmp2 = new Item[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;
		}
		//splits a bucket into a branch
		private void split(Item m) {
			//split based on our bound data
			slice = (upper[dim]+lower[dim])/2.0;
			left = new NodeKD(this);
			right = new NodeKD(this);
			for(int i=0; i<current; ++i) {
				if(bucket[i].pnt[dim] > slice)
					right.add(bucket[i]);
				else left.add(bucket[i]);
			}
			bucket = null;
			add(m);
		}
		//gets nearest point to data within this hyper rectangle
		private double[] nearestRect(double[] data) {
			double[] nearest = data.clone();
			for(int i = 0; i < data.length; ++i) {
				if(nearest[i] > upper[i]) nearest[i] = upper[i];
				if(nearest[i] < lower[i]) nearest[i] = lower[i];
			}
			return nearest;
		}
		//expands this hyper rectangle
		private void expand(double[] data) {
			if(upper == null) {
				upper = Arrays.copyOf(data, owner.dimensions);
				lower = Arrays.copyOf(data, owner.dimensions);
				return;
			}
			for(int i=0; i<data.length; ++i) {
				if(upper[i] < data[i]) upper[i] = data[i];
				if(lower[i] > data[i]) lower[i] = data[i];
			}
		}
	}
	//A simple shift array that sifts data up
	//as we add new ones to lower in the array.
	private class ShiftArray {
		private Item[] items;
		private final int max;
		private int current;
		private ShiftArray(int maximum) {
			max = maximum;
			current = 0;
			items = new Item[max];
		}
		private void add(Item m) {
			int i;
			for(i=current;i>0&&items[i-1].distance >  m.distance; --i);
			if(i >= max) return;
			if(current < max) ++current;
			System.arraycopy(items, i, items, i+1, current-(i+1));
			items[i] = m;
		}
		private double getLongest() {
			if (current < max) return Double.POSITIVE_INFINITY;
			return items[max-1].distance;
		}
		private Item[] getArray() {
			return items.clone();
		}
	}
}