User:Skilgannon/KDTree

/*
** KDTree.java by Julian Kent
** Licenced under the  Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License
** See full licencing details here: http://creativecommons.org/licenses/by-nc-sa/3.0/
** For additional usage rights please contact jkflying@gmail.com
**
** Example usage is given in the main method, as well as benchmarking code against Rednaxela's Gen2 Tree
*/


package jk.mega;
import java.util.ArrayDeque;
import java.util.ArrayList;
import java.util.Arrays;
//import ags.utils.*;

public class KDTree<T>{

//use a big bucketSize so that we have less node bounds (for more cache hits) and better splits
   private static final int  _bucketSize = 64;

   private final int _dimensions;
   private int _nodes;   
   private Node root;
   
   //prevent GC from having to collect _bucketSize*dimensions*8 bytes each time a leaf splits
   private double[] mem_recycle;
   
   //the starting values for bounding boxes, for easy access
   private final double[] bounds_template;

   //one big self-expanding array to keep all the node bounding boxes so that they stay in cache
   // node bounds available at:
   //low:  2 * _dimensions * node.index
   //high: 2 * _dimensions * node.index + _dimensions
   private ContiguousDoubleArrayList nodeMinMaxBounds;
/*
   public static void main(String[] args){
      int dims = 12;
      int size = 20000;
      int testsize = 200;
      int k = 40;
      int iterations = 3;
      System.out.println(
         "Config:\n"
         + "No JIT Warmup\n"
         + "Tested on random data.\n" 
         + "Training and testing points shared across iterations.\n"
         + "Searches interleaved.");
      System.out.println("Num points:     " + size);
      System.out.println("Num searches:   " + testsize);
      System.out.println("Dimensions:     " + dims);
      System.out.println("Num Neighbours: " + k);
      System.out.println();
      ArrayList<double[]> locs = new ArrayList<double[]>(size);
      for(int i = 0; i < size; i++){
         double[] loc = new double[dims];
         for(int j = 0; j < dims; j++)
            loc[j] = Math.random();
         locs.add(loc);
      }
      ArrayList<double[]> testlocs = new ArrayList<double[]>(testsize);
      for(int i = 0; i < testsize; i++){
         double[] loc = new double[dims];
         for(int j = 0; j < dims; j++)
            loc[j] = Math.random();
         testlocs.add(loc);
      }
      for(int r = 0; r < iterations; r++){
         long t1 = System.nanoTime();
         KDTree<double[]> t = new KDTree<double[]>(dims);// This tree
         for(int i = 0; i < size; i++){
            t.addPoint(locs.get(i),locs.get(i));
         }
         long t2 = System.nanoTime();
         KdTree<double[]> rt = new KdTree.Euclidean<double[]>(dims,null); //Rednaxela Gen2
         for(int i = 0; i < size; i++){
            rt.addPoint(locs.get(i),locs.get(i));
         }
         long t3 = System.nanoTime();
      
         long jtn = 0;
         long rtn = 0;
         long mjtn = 0;
         long mrtn = 0;
      
         double dist1 = 0, dist2 = 0;
         for(int i = 0; i < testsize; i++){
            long t4 = System.nanoTime();
            dist1 += t.nearestNeighbours(testlocs.get(i),k).iterator().next().distance;
            long t5 = System.nanoTime();
            dist2 += rt.nearestNeighbor(testlocs.get(i),k,true).iterator().next().distance;
            long t6 = System.nanoTime();
            long t7 = System.nanoTime();
            jtn += t5 - t4 - (t7 - t6);
            rtn += t6 - t5 - (t7 - t6); 
            mjtn = Math.max(mjtn,t5 - t4 - (t7 - t6));
            mrtn = Math.max(mrtn,t6 - t5 - (t7 - t6));
         }
      
         System.out.println("Accuracy: " + (Math.abs(dist1-dist2) < 1e-10?"100%":"BROKEN!!!"));
         if(Math.abs(dist1-dist2) > 1e-10){
            System.out.println("dist1: " + dist1 + "    dist2: " + dist2);
         }
         long jts = t2 - t1;
         long rts = t3 - t2;
         System.out.println("Iteration:      " + (r+1) + "/" + iterations);
      
         System.out.println("This tree add avg:  " + jts/size + " ns");
         System.out.println("Reds tree add avg:  " + rts/size + " ns");
      
         System.out.println("This tree knn avg:  " + jtn/testsize + " ns");
         System.out.println("Reds tree knn avg:  " + rtn/testsize + " ns");
         System.out.println("This tree knn max:  " + mjtn + " ns");
         System.out.println("Reds tree knn max:  " + mrtn + " ns");
         System.out.println();
      }
   }
   */

   public KDTree(int dimensions){
      _dimensions = dimensions;
   
      nodeMinMaxBounds = new ContiguousDoubleArrayList(2 * dimensions);
      mem_recycle = new double[_bucketSize*dimensions];
   
      bounds_template = new double[2*_dimensions];
      Arrays.fill(bounds_template,0,_dimensions,Double.POSITIVE_INFINITY);
      Arrays.fill(bounds_template,_dimensions,2*_dimensions,Double.NEGATIVE_INFINITY);
   
   //and.... start!
      root = new Node();
   }
   public int nodes(){
      return _nodes;
   }
   public int addPoint(double[] location, T payload){
   
      Node addNode = root;
   //Do a Depth First Search to find the node where it should be stored
      while(addNode.pointLocations == null){
         addNode.expandBounds(location);
         if(location[addNode.splitDim] < addNode.splitVal)
            addNode = addNode.less;
         else
            addNode = addNode.more;
      }
      addNode.expandBounds(location);
   
      int nodeSize = addNode.add(location,payload);
   
      if(nodeSize % _bucketSize == 0)
      //try splitting again once every time the node passes a _bucketSize multiple
         addNode.split();
   
      return root.entries;
   }

   public SearchResult<T> nearestNeighbour(double[] searchLocation){
   
      Node searchNode = root;
      ArrayDeque<Node> stack = new ArrayDeque<Node>(50);
   
   //Do a Depth First Search to find the Node this location would be stored
      while(searchNode.pointLocations == null){
      
         if(searchLocation[searchNode.splitDim] < searchNode.splitVal){
            stack.push(searchNode.more);   
            searchNode = searchNode.less;
         }
         else{
            stack.push(searchNode.less);  
            searchNode = searchNode.more;
         }
      }
      double minDist = Double.POSITIVE_INFINITY;
      T minValue = null;
   
      double[] array = searchNode.pointLocations.array;
   
   //Find the closest point in this Node and use as a solution
      for(int j = searchNode.entries; j-- > 0;){
      
         double distance = searchNode.pointDist(searchLocation,j);
         if(distance < minDist){
            minDist = distance;
            minValue = searchNode.pointPayloads.get(j);
         }
      }
   
   //backtrace stack
      while(stack.size() > 0){
         searchNode = stack.pop();
         if(searchNode.pointRectDist(searchLocation) < minDist){
            if(searchNode.pointLocations == null){
               if(searchLocation[searchNode.splitDim] < searchNode.splitVal){
                  stack.push(searchNode.more);
                  stack.push(searchNode.less);
               }
               else{
                  stack.push(searchNode.less);
                  stack.push(searchNode.more);
               }
            }
            else{
               array = searchNode.pointLocations.array;
               for(int j = searchNode.entries; j-- > 0;){
                  double distance = searchNode.pointDist(searchLocation,j);
                  if(distance < minDist){
                     minDist = distance;
                     minValue = searchNode.pointPayloads.get(j);
                  }
               }
            }
         }
      }
   
      return new SearchResult(minDist,minValue);  
   }
   public ArrayList<SearchResult<double[]>> nearestNeighbours(double[] searchLocation, int K){
   
      Node searchNode = root;
      ArrayDeque<Node> stack = new ArrayDeque<Node>(50);
   
   //Do a Depth First Search to find the Node this location would be stored
      while(searchNode.pointLocations == null){
      
         if(searchLocation[searchNode.splitDim] < searchNode.splitVal){
            stack.push(searchNode.more);   
            searchNode = searchNode.less;
         }
         else{
            stack.push(searchNode.less);  
            searchNode = searchNode.more;
         }
      }
      PrioQueue<T> results = new PrioQueue<T>(K);
   
      ArrayList<T> payloads = searchNode.pointPayloads;
      double[] temp = new double[_dimensions];
   //Find the closest point in this Node and use as a solution
      for(int j = searchNode.entries; j-- > 0;){
      
         double distance = searchNode.pointDist(searchLocation,j); 
         results.offer(payloads.get(j),-distance);
      }
   
   
   //backtrace stack
      while(stack.size() > 0){
         searchNode = stack.pop();
         if( searchNode.pointRectDist(searchLocation) < -results.peekPrio()){
            if(searchNode.pointLocations == null){
               if(searchLocation[searchNode.splitDim] < searchNode.splitVal){
                  stack.push(searchNode.more);
                  stack.push(searchNode.less);//less will be popped first
               }
               else{
                  stack.push(searchNode.less);
                  stack.push(searchNode.more);
               }
            }
            else{
               payloads = searchNode.pointPayloads;
               for(int j = searchNode.entries; j-- > 0;){
                  double distance = searchNode.pointDist(searchLocation,j);
                  results.offer(payloads.get(j),-distance);
               }
               
            }
         }
      }
   
      ArrayList<SearchResult<double[]>> returnResults = new ArrayList<SearchResult<double[]>>(results.elements.size());
   
   //for(int i =0, j = results.elements.size(); i<j;i++){//Forward (closest first)
      for(int i = results.elements.size(); i-- > 0;){//Reverse (Like Rednaxela Gen2)
         PrioQueue<T>.Element e = results.elements.get(i);
         SearchResult s = new SearchResult(-e.priority,e.contents);
         returnResults.add(s);
      }
      return returnResults;
   }

//NB! This Priority Queue keeps things with the HIGHEST priority. 
//If you want lowest priority items kept, negate your values
   private static class PrioQueue<S>{
      ArrayList<Element> elements;
      private double minPrio;
      PrioQueue(int size){
         elements = new ArrayList<Element>(size);
         while(size-->0){
            elements.add(new Element(null,Double.NEGATIVE_INFINITY));
         }
         minPrio = Double.NEGATIVE_INFINITY;
      }
       //uses O(log(n)) comparisons and one big shift of size O(N)
       //and is MUCH simpler than a heap --> faster JIT
      boolean offer(S value,double priority){
         
         //is this point worthy of joining the exulted ranks?
         if(priority > minPrio){
         
         //recycle object to avoid garbage collector stalls
            Element replace = elements.remove(elements.size() - 1);
            replace.update(value,priority);
            add(replace);
         
            return true;
         }
         return false;
      }
      void add(Element e){
      
         //find the right place with a binary search
         int index = searchFor(e.priority);
         //and re-insert updated value (ArrayList automatically shifts other elements up)
         elements.add(index,e);
            
         minPrio = elements.get(elements.size() - 1).priority;
      }
      int searchFor(double priority){
         int i = elements.size()-1;
         int j = 0;   
         while(i>=j){
            int index = (i+j)>>1;
            if(elements.get(index).priority < priority)
               i = index-1;
            else
               j = index+1;
         }
         return j;
      }
      double peekPrio(){
         return minPrio;
      }
      /* //Methods for using it as a priority stack - leave them out for now
      void push(S value, double priority){
         Element insert = new Element(value,priority);
         add(insert);
      }
      S pop(){
         Element remove = elements.remove(elements.size() - 1);
         if(elements.size() == 0)
            minPrio = Double.NEGATIVE_INFINITY;
         else
            minPrio = elements.get(elements.size() - 1).priority;
         return remove.contents;
      }
      int size(){
         return elements.size();
      }
      void trim(double newMinPrio){
         if(newMinPrio > minPrio){
            int index = searchFor(newMinPrio);
            int size = elements.size();
            elements.subList(index,elements.size()).clear();
            if(elements.size() == 0)
               minPrio = Double.NEGATIVE_INFINITY;
            else
               minPrio = elements.get(elements.size() - 1).priority;
         }
      }
     //   */
      class Element{
         S contents;
         double priority;
         
         Element(S con, double prio){
            contents = con;
            priority = prio;
         }
         void update(S con, double prio){
            contents = con;
            priority = prio;
         }
      }
   }


   public static class SearchResult<S>{
      double distance;
      S payload;
      SearchResult(double dist, S load){
         distance = dist;
         payload = load;
      }
   }

   private class Node {
   
   //for accessing bounding box data 
   // - if trees weren't so unbalanced might be better to use an implicit heap?
      int index;
      
   //keep track of size of subtree
      int entries;
   
   //leaf
      ContiguousDoubleArrayList pointLocations ;
      ArrayList<T> pointPayloads = new ArrayList<T>(_bucketSize);
      
   //stem
      Node less, more;
      int splitDim;
      double splitVal;
   
      private Node(){
         this(new double[_bucketSize*_dimensions]);
      }
      private Node(double[] pointMemory){
         pointLocations = new ContiguousDoubleArrayList(pointMemory);
         index = _nodes++;
         nodeMinMaxBounds.add(bounds_template);
      }
      private final double pointRectDist(double[] location){
         int minOffset = 2*index*_dimensions;
         int maxOffset = minOffset+_dimensions;
         double distance=0;
         double[] array = nodeMinMaxBounds.array;
         for(int i = _dimensions; i-- > 0; ){
         
            double lowDist = array[i+minOffset] - location[i];
            if(lowDist > 0)
               distance += sqr(lowDist);
            else{
               double highDist = location[i] - array[i+maxOffset];
               if(highDist > 0)
                  distance += sqr(highDist);
            }
         }
         return distance;
      }
      private final double pointDist(double[] location, int index){
         double distance = 0;
         int offset = index*_dimensions;
         for(int i = _dimensions; i-- > 0;)
            distance += sqr(pointLocations.array[offset+i] - location[i]);
         return distance;
      }
   
      private void expandBounds(double[] location){
         entries++;
         int offset = index*2*_dimensions;
         for(int i = 0; i < _dimensions;i++){
            nodeMinMaxBounds.array[offset+i] = Math.min(nodeMinMaxBounds.array[offset+i],location[i]);
            nodeMinMaxBounds.array[offset+_dimensions+i] = Math.max(nodeMinMaxBounds.array[offset+_dimensions+i],location[i]);
         }
      }
   
      private int add(double[] location, T load){
         pointLocations.add(location);
         pointPayloads.add(load);
         return entries;
      }
      private void split(){
         double diff = 0;
         int offset = index*2*_dimensions;
         for(int i = 0; i < _dimensions; i++){
            double min = nodeMinMaxBounds.array[offset+i];
            double max = nodeMinMaxBounds.array[offset+_dimensions+i];
            if(max - min > diff){
               diff = max - min;
               splitVal = 0.5*(max + min);
               splitDim = i;
            }
         }
      
      
         less = new Node(mem_recycle);//recycle that memory!
         more = new Node();
         
         //reduce garbage by factor of _bucketSize by recycling this array
         double[] pointLocation = new double[_dimensions];
         for(int i = 0; i < entries; i++){
            System.arraycopy(pointLocations.array,i*_dimensions,pointLocation,0,_dimensions);
            T load = pointPayloads.get(i);
         
            if(pointLocation[splitDim] < splitVal){
               less.expandBounds(pointLocation);
               less.add(pointLocation,load);
            }
            else{
               more.expandBounds(pointLocation);   
               more.add(pointLocation,load);
            }
         }
         if(less.entries*more.entries == 0){
         //one of them was 0, so the split was worthless. throw it away.
            less = null;
            more = null;
         }
         else{
         
         //we won't be needing that now, so keep it for the next split to reduce garbage
            mem_recycle = pointLocations.array;
         
            pointLocations = null;
         
            pointPayloads.clear();
            pointPayloads = null;
         }
      }
   
   }


   private static class ContiguousDoubleArrayList{
      double[] array;
      int size;
      ContiguousDoubleArrayList(){
         this(300);
      }
      ContiguousDoubleArrayList(int size){
         this(new double[size]);
      }
      ContiguousDoubleArrayList(double[] data){
         array = data;
      }
      ContiguousDoubleArrayList add(double[] da){
         if(size + da.length >= array.length){
            array = Arrays.copyOf(array,(array.length+da.length)*2);
         }
         System.arraycopy(da,0,array,size,da.length);
         size += da.length;
         return this;
      }
   }

   private static final double sqr(double d){
      return d*d;}

}