FloatKNNAssigner

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*/
/**
 * Copyright (c) 2011, The University of Southampton and the individual contributors.
 * All rights reserved.
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 * Redistribution and use in source and binary forms, with or without modification,
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package org.openimaj.ml.clustering.assignment.soft;

import org.openimaj.feature.FloatFVComparison;
import org.openimaj.knn.FloatNearestNeighbours;
import org.openimaj.knn.FloatNearestNeighboursExact;
import org.openimaj.knn.FloatNearestNeighboursProvider;
import org.openimaj.knn.approximate.FloatNearestNeighboursKDTree;
import org.openimaj.ml.clustering.assignment.SoftAssigner;
import org.openimaj.ml.clustering.CentroidsProvider;
import org.openimaj.util.pair.IndependentPair;

/**
 * A {@link SoftAssigner} that picks a fixed number of nearest neighbours.
 * Weights returned are actually the distances to the centroids.
 *
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 *
 */
public class FloatKNNAssigner implements SoftAssigner<float[], float[]> {
	protected FloatNearestNeighbours nn;
	protected int numNeighbours;

	/**
	 * Construct the assigner using the given cluster data. The assigner
	 * is backed by either a {@link FloatNearestNeighboursExact} or
	 * {@link FloatNearestNeighboursKDTree}, depending on whether the exact
	 * parameter is true or false. If the parameter is true, then the
	 * resultant {@link FloatNearestNeighboursExact} will use Euclidean
	 * distance.
	 *
	 * @param provider the cluster data provider
	 * @param exact if true, then use exact mode; false implies approximate mode.
	 * @param numNeighbours the number of nearest neighbours to select.
	 */
	public FloatKNNAssigner(CentroidsProvider<float[]> provider, boolean exact, int numNeighbours) {
		this.numNeighbours = numNeighbours;

		if (exact) {
			nn = new FloatNearestNeighboursExact(provider.getCentroids());
		} else {
			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. The assigner
	 * is backed by either a {@link FloatNearestNeighboursExact} or
	 * {@link FloatNearestNeighboursKDTree}, depending on whether the exact
	 * parameter is true or false. If the parameter is true, then the
	 * resultant {@link FloatNearestNeighboursExact} will use Euclidean
	 * distance.
	 *
	 * @param data the cluster data
	 * @param exact if true, then use exact mode; false implies approximate mode.
	 * @param numNeighbours the number of nearest neighbours to select.
	 */
	public FloatKNNAssigner(float[][] data, boolean exact, int numNeighbours) {
		this.numNeighbours = numNeighbours;

		if (exact) {
			nn = new FloatNearestNeighboursExact(data);
		} else {
			nn = new FloatNearestNeighboursKDTree(data, FloatNearestNeighboursKDTree.DEFAULT_NTREES, FloatNearestNeighboursKDTree.DEFAULT_NCHECKS);
		}
	}

	/**
	 * Construct the assigner using the given cluster data and
	 * distance function. The assigner will operate in exact mode,
	 * using a {@link FloatNearestNeighboursExact}.
	 *
	 * @param provider the cluster data provider
	 * @param comparison the distance function
	 * @param numNeighbours the number of nearest neighbours to select.
	 */
	public FloatKNNAssigner(CentroidsProvider<float[]> provider, FloatFVComparison comparison, int numNeighbours) {
		this.numNeighbours = numNeighbours;

		nn = new FloatNearestNeighboursExact(provider.getCentroids(), comparison);
	}

	/**
	 * Construct the assigner using the given cluster data and
	 * distance function. The assigner will operate in exact mode,
	 * using a {@link FloatNearestNeighboursExact}.
	 *
	 * @param data the cluster data
	 * @param comparison the distance function
	 * @param numNeighbours the number of nearest neighbours to select.
	 */
	public FloatKNNAssigner(float[][] data, FloatFVComparison comparison, int numNeighbours) {
		this.numNeighbours = numNeighbours;

		nn = new FloatNearestNeighboursExact(data, comparison);
	}

	@Override
	public int[][] assign(float[][] data) {
		int [][] indices = new int [data.length][numNeighbours];
		float [][] distances = new float [data.length][numNeighbours];

		nn.searchKNN(data, numNeighbours, indices, distances);

		return indices;
	}

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

	@Override
	public void assignWeighted(float[][] data, int[][] assignments, float[][] weights) {
		nn.searchKNN(data, numNeighbours, assignments, weights);
	}

	@Override
	public IndependentPair<int[], float[]> assignWeighted(float[] data) {
		int [][] indices = new int [data.length][numNeighbours];
		float [][] distances = new float [data.length][numNeighbours];

		nn.searchKNN(new float[][] { data }, numNeighbours, indices, distances);

		return new IndependentPair<int[], float[]>(indices[0], distances[0]);
	}

	@Override
	public int numDimensions() {
	    return nn.numDimensions();
	}

	@Override
	public int size() {
	    return nn.size();
	}
}