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/*
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*/
/**
 * Copyright (c) 2011, The University of Southampton and the individual contributors.
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without modification,
 * are permitted provided that the following conditions are met:
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 *   * 	Redistributions of source code must retain the above copyright notice,
 * 	this list of conditions and the following disclaimer.
 *
 *   *	Redistributions in binary form must reproduce the above copyright notice,
 * 	this list of conditions and the following disclaimer in the documentation
 * 	and/or other materials provided with the distribution.
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 * 	contributors may be used to endorse or promote products derived from this
 * 	software without specific prior written permission.
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 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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package org.openimaj.ml.clustering.assignment.hard;

import org.openimaj.knn.FloatNearestNeighbours;
import org.openimaj.knn.FloatNearestNeighboursProvider;
import org.openimaj.knn.approximate.FloatNearestNeighboursKDTree;
import org.openimaj.ml.clustering.assignment.HardAssigner;
import org.openimaj.ml.clustering.CentroidsProvider;
import org.openimaj.util.pair.IntFloatPair;

/**
 * A {@link HardAssigner} that uses a {@link FloatNearestNeighboursKDTree} to
 * generate approximately correct cluster assignments.
 * 
 * @author Jonathon Hare ([email protected])
 *
 */
public class KDTreeFloatEuclideanAssigner implements HardAssigner {
	protected FloatNearestNeighboursKDTree nn;
	
	/**
	 * Construct the assigner using the given cluster data.
	 * 
	 * @param provider the cluster data provider
	 */
	public KDTreeFloatEuclideanAssigner(CentroidsProvider provider) {
		if (provider instanceof FloatNearestNeighboursProvider) {
			FloatNearestNeighbours internal = ((FloatNearestNeighboursProvider)provider).getNearestNeighbours();
			
			if (internal != null && internal instanceof FloatNearestNeighboursKDTree) {
				nn = (FloatNearestNeighboursKDTree) internal;
				return;
			}
		}
		
		nn = new FloatNearestNeighboursKDTree(provider.getCentroids(), 
				FloatNearestNeighboursKDTree.DEFAULT_NTREES, FloatNearestNeighboursKDTree.DEFAULT_NCHECKS);
	}
	
	/**
	 * Construct the assigner using the given cluster data.
	 * 
	 * @param data the cluster data
	 */
	public KDTreeFloatEuclideanAssigner(float[][] data) {
		nn = new FloatNearestNeighboursKDTree(data, FloatNearestNeighboursKDTree.DEFAULT_NTREES, FloatNearestNeighboursKDTree.DEFAULT_NCHECKS);
	}
	
	@Override
	public int[] assign(float[][] data) {
		int [] argmins = new int [data.length];
		float [] mins = new float [data.length];
		nn.searchNN(data, argmins, mins);
		return argmins;
	}

	@Override
	public int assign(float[] data) {
		return assign(new float[][] { data })[0];
	}

	@Override
	public void assignDistance(float[][] data, int[] indices, float[] distances) {
		nn.searchNN(data, indices, distances);
	}

	@Override
	public IntFloatPair assignDistance(float[] data) {
		int [] index = new int [1];
		float [] distance = new float [1];
		
		nn.searchNN(new float[][] { data }, index, distance);
		
		return new IntFloatPair(index[0], distance[0]);
	}
	
	@Override
	public int size() {
	    return nn.size();
	}
	
	@Override
	public int numDimensions() {
	    return nn.numDimensions();
	}
}