/*
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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.DoubleFVComparison;
import org.openimaj.util.array.SparseDoubleArray;

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

/**
 * A hash function factory for producing hash functions that approximate the
 * Hamming distance.
 * 
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 * 
 */
 @Reference(
        type = ReferenceType.Inproceedings,
        author = { "Indyk, Piotr", "Motwani, Rajeev" },
        title = "Approximate nearest neighbors: towards removing the curse of dimensionality",
        year = "1998",
        booktitle = "Proceedings of the thirtieth annual ACM symposium on Theory of computing",
        pages = { "604", "", "613" },
        url = "http://doi.acm.org/10.1145/276698.276876",
        publisher = "ACM",
        series = "STOC '98"
)
public class DoubleHammingFactory extends DoubleHashFunctionFactory {
        private class Function extends DoubleHashFunction {
                private int ham;

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

                        Uniform uniform = new Uniform(rng);
                        
                        if (options.bitsPerDim == 0)
                                ham = (int) uniform.nextIntFromTo(0, ndims - 1);
                        else
                                ham = (int) uniform.nextIntFromTo(0, (ndims * options.bitsPerDim) - 1);
                }

                @Override
                public int computeHashCode(double[] point) {
                        // which hash function
                        if (bitsPerDim == 0) {
                                return point[ham] == 0 ? 0 : 1;
                        } else {
                                // compact binary data
                                final int m = ham % bitsPerDim;
                                final int d = ham / bitsPerDim;
                                return (int) (HammingHelper.convert(point[d]) >>> m & 1L);
                        }
                }
                
                @Override
                public int computeHashCode(SparseDoubleArray array) {
                        // which hash function
                        if (bitsPerDim == 0) {
                                return array.get(ham) == 0 ? 0 : 1;
                        } else {
                                // compact binary data
                                final int m = ham % bitsPerDim;
                                final int d = ham / bitsPerDim;
                                return (int) (HammingHelper.convert(array.get(d)) >>> m & 1L);
                        }                       
                }
        }

        int bitsPerDim;

        /**
         * Construct a new factory using the given parameters.
         * 
         * @param ndims
         *            The number of dimensions (i.e. length of the vector being
         *            hashed)
         * @param rng
         *            A random number generator
         * @param bitsPerDim
         *            The number of bits per dimension. If the data is packed, then
         *            this will be greater than zero, and internally a single bit
         *            will be sampled for computing the hash. If zero, then it is
         *            assumed that every element of the vector being hashed is
         *            either a zero or one.
         */
        public DoubleHammingFactory(int ndims, MersenneTwister rng, int bitsPerDim) {
                super(ndims, rng);

                this.bitsPerDim = bitsPerDim;
        }

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

        @Override
        public DoubleFVComparison fvDistanceFunction() {
                if (bitsPerDim == 0)
                        return DoubleFVComparison.HAMMING;
                else
                        return DoubleFVComparison.PACKED_HAMMING;
        }
}