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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:
 *
 *   * 	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.
 *
 *   *	Neither the name of the University of Southampton nor the names of its
 * 	contributors may be used to endorse or promote products derived from this
 * 	software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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package org.openimaj.lsh.functions;

import org.openimaj.citation.annotation.Reference;
import org.openimaj.citation.annotation.ReferenceType;
import org.openimaj.feature.LongFVComparison;
import org.openimaj.util.array.SparseLongArray;

import cern.jet.random.Uniform;
import cern.jet.random.engine.MersenneTwister;

/**
 * A hash function factory that produces hash functions that approximate L1
 * (city-block) distance in closed spaces using random axis-aligned hyperplanes.
 * 
 * The hash function hashes the input vector into a binary value (i.e. 0 or 1). 
 * It works by choosing a random dimension and a random threshold along that 
 * dimension (between a given minimum and maximum which define the closed space).
 * Input vectors whose element at the chosen dimension is greater than or equal
 * to the threshold generate a 1; values less than the threshold generate a 0. 
 * 
 * @author Jonathon Hare ([email protected])
 */
 @Reference(
	type = ReferenceType.Inproceedings,
	author = { "Lv, Qin", "Charikar, Moses", "Li, Kai" },
	title = "Image similarity search with compact data structures",
	year = "2004",
	booktitle = "Proceedings of the thirteenth ACM international conference on Information and knowledge management",
	pages = { "208", "", "217" },
	url = "http://doi.acm.org/10.1145/1031171.1031213",
	publisher = "ACM",
	series = "CIKM '04"
)
public class LongHyperplaneL1Factory extends LongHashFunctionFactory {
	private class Function extends LongHashFunction {
		int dimension;
		double shift;

		Function(int ndims, MersenneTwister rng) {
			super(rng);

            Uniform uniform = new Uniform(rng);

			// choose a random dimension
			dimension = uniform.nextIntFromTo(0, ndims - 1);

			// random shift
			shift = uniform.nextDoubleFromTo(min, max);
		}

		@Override
		public int computeHashCode(long[] point) {
			return (point[dimension] - shift) >= 0 ? 1 : 0;
		}

		@Override
		public int computeHashCode(SparseLongArray array) {
			return (array.get(dimension) - shift) >= 0 ? 1 : 0;
		}
	}

	long min = 0;
	long max = 1;

	/**
	 * Construct with the given arguments.
	 * 
	 * @param ndims
	 *            The number of dimensions
	 * @param rng
	 *            A random number generator
	 * @param min
	 *            The minimum bound of the space
	 * @param max
	 *            The maximum bound of the space
	 */
	public LongHyperplaneL1Factory(int ndims, MersenneTwister rng, long min, long max) {
		super(ndims, rng);

		this.min = min;
		this.max = max;
	}

	@Override
	public Function create() {
		return new Function(ndims, rng);
	}

	@Override
	protected LongFVComparison fvDistanceFunction() {
		return LongFVComparison.CITY_BLOCK;
	}
}