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DDogleg Numerics is a high performance Java library for non-linear optimization, robust model fitting, polynomial root finding, sorting, and more.

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/*
 * Copyright (c) 2012-2017, Peter Abeles. All Rights Reserved.
 *
 * This file is part of DDogleg (http://ddogleg.org).
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.ddogleg.nn.wrap;

import org.ddogleg.nn.NearestNeighbor;
import org.ddogleg.nn.NnData;
import org.ddogleg.nn.alg.*;
import org.ddogleg.struct.FastQueue;

import java.util.List;

/**
 * Wrapper around {@link KdTree} for {@link NearestNeighbor}
 *
 * @author Peter Abeles
 */
public class KdTreeNearestNeighbor implements NearestNeighbor {

	// tree being searched
	KdTree tree;
	// creates a tree from data
	KdTreeConstructor constructor;
	// searches the tree for the nearest neighbor
	KdTreeSearch1 search;
	// searches the tree for the N nearest neighbors
	KdTreeSearchN searchN;
	// Used internally during tree construction
	AxisSplitter splitter;

	// storage for multiple results
	FastQueue found = new FastQueue(KdTreeResult.class,true);

	// used to recycle memory
	KdTreeMemory memory = new KdTreeMemory();

	public KdTreeNearestNeighbor(KdTreeSearch1 search, KdTreeSearchN searchN, AxisSplitter splitter) {
		this.search = search;
		this.searchN = searchN;
		this.splitter = splitter;
	}

	public KdTreeNearestNeighbor() {
		this( new KdTreeSearch1Standard(), new KdTreeSearchNStandard(), new AxisSplitterMedian());
	}

	@Override
	public void init( int N ) {
		constructor = new KdTreeConstructor(memory,N,splitter);
	}

	@Override
	public void setPoints(List points, List data) {
		if( tree != null )
			memory.recycleGraph(tree);
		tree = constructor.construct(points,data);
		search.setTree(tree);
		searchN.setTree(tree);
	}

	@Override
	public boolean findNearest( double[] point , double maxDistance , NnData result ) {
		if( maxDistance < 0 )
			search.setMaxDistance(Double.MAX_VALUE);
		else
			search.setMaxDistance(maxDistance);
		KdTree.Node found = search.findNeighbor(point);
		if( found == null )
			return false;

		result.point = found.point;
		result.data = (D)found.data;
		result.distance = search.getDistance();

		return true;
	}

	@Override
	public void findNearest(double[] point, double maxDistance, int numNeighbors, FastQueue> results) {
		results.reset();

		if( maxDistance <= 0 )
			searchN.setMaxDistance(Double.MAX_VALUE);
		else
			searchN.setMaxDistance(maxDistance);

		found.reset();
		searchN.findNeighbor(point, numNeighbors, found);

		for( int i = 0; i < found.size; i++ ) {
			KdTreeResult k = found.get(i);
			NnData r = results.grow();

			r.point = k.node.point;
			r.data = (D)k.node.data;
			r.distance = k.distance;
		}
	}
}