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/*
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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.knn.pq;

import org.openimaj.knn.FloatNearestNeighboursExact;
import org.openimaj.knn.FloatNearestNeighboursProvider;
import org.openimaj.ml.clustering.kmeans.FloatKMeans;

/**
 * Utility methods for easily creating a {@link ByteProductQuantiser} using
 * (Exact) K-Means.
 * 
 * @author Jonathon Hare ([email protected])
 * 
 */
public final class FloatProductQuantiserUtilities {
    private FloatProductQuantiserUtilities() {
    }

	/**
	 * Learn a {@link FloatProductQuantiser} by applying exact K-Means to
	 * sub-vectors extracted from the given data. The length of the subvectors
	 * is determined by dividing the vector length by the requested number of
	 * assigners. The number of clusters per vector subset is constant, and must
	 * be less than 256.
	 * 
	 * @param data
	 *            the data to train the {@link FloatProductQuantiser} on.
	 * @param numAssigners
	 *            the number of sub-quantisers to learn
	 * @param K
	 *            the number of centroids per sub-quantiser
	 * @param nIter
	 *            the maximum number of iterations for each k-means clustering	 
	 * 
	 * @return a trained {@link FloatProductQuantiser}.
	 */
	public static FloatProductQuantiser train(float[][] data, int numAssigners, int K, int nIter) {
		if (K > 256 || K <= 0)
			throw new IllegalArgumentException("0 <= K < 256");

		final int subDim = data[0].length / numAssigners;
		final float[][] tmp = new float[data.length][subDim];

		final FloatNearestNeighboursExact[] assigners = new FloatNearestNeighboursExact[numAssigners];
		final FloatKMeans kmeans = FloatKMeans.createExact(K, 100);

		for (int i = 0; i < numAssigners; i++) {
			// copy data
			for (int j = 0; j < data.length; j++) {
				System.arraycopy(data[j], i * subDim, tmp[j], 0, subDim);
			}

			// kmeans
			final FloatNearestNeighboursProvider centroids = (FloatNearestNeighboursProvider) kmeans.cluster(tmp);

			assigners[i] = (FloatNearestNeighboursExact)centroids.getNearestNeighbours();
		}

		return new FloatProductQuantiser(assigners);
	}

	/**
	 * Learn a {@link FloatProductQuantiser} by applying exact K-Means to
	 * sub-vectors extracted from the given data. The length of the subvectors
	 * is determined by dividing the vector length by the requested number of
	 * assigners. The number of clusters per vector subset is constant, and set
	 * at 256.
	 * 
	 * @param data
	 *            the data to train the {@link FloatProductQuantiser} on.
	 * @param numAssigners
	 *            the number of sub-quantisers to learn
	 * @param nIter
	 *            the maximum number of iterations for each k-means clustering
	 *
	 * @return a trained {@link FloatProductQuantiser}.
	 */
	public static FloatProductQuantiser train(float[][] data, int numAssigners, int nIter) {
		return train(data, numAssigners, 256, nIter);
	}
}