001/*
002        AUTOMATICALLY GENERATED BY jTemp FROM
003        /Users/jsh2/Work/openimaj/target/checkout/machine-learning/nearest-neighbour/src/main/jtemp/org/openimaj/lsh/functions/#T#GaussianFactory.jtemp
004*/
005/**
006 * Copyright (c) 2011, The University of Southampton and the individual contributors.
007 * All rights reserved.
008 *
009 * Redistribution and use in source and binary forms, with or without modification,
010 * are permitted provided that the following conditions are met:
011 *
012 *   *  Redistributions of source code must retain the above copyright notice,
013 *      this list of conditions and the following disclaimer.
014 *
015 *   *  Redistributions in binary form must reproduce the above copyright notice,
016 *      this list of conditions and the following disclaimer in the documentation
017 *      and/or other materials provided with the distribution.
018 *
019 *   *  Neither the name of the University of Southampton nor the names of its
020 *      contributors may be used to endorse or promote products derived from this
021 *      software without specific prior written permission.
022 *
023 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
024 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
025 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
026 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
027 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
028 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
029 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
030 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
031 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
032 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
033 */
034package org.openimaj.lsh.functions;
035
036import org.openimaj.feature.DoubleFVComparison;
037
038import cern.jet.random.Normal;
039import cern.jet.random.Uniform;
040import cern.jet.random.engine.MersenneTwister;
041
042/**
043 * A hash function factory for producing hash functions using Gaussian
044 * distributions to approximate the Euclidean distance.
045 * 
046 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
047 */
048public class DoubleGaussianFactory extends DoublePStableFactory {
049        private class Function extends PStableFunction {
050                Function(int ndims, MersenneTwister rng) {
051                        super(rng);
052
053                        final Uniform uniform = new Uniform(0, w, rng);
054                        final Normal normal = new Normal(0, 1, rng);
055
056                        b = (float) uniform.nextDouble();
057
058                        // random direction
059                        r = new double[ndims];
060                        for (int i = 0; i < ndims; i++) {
061                                r[i] = normal.nextDouble();
062                        }
063                }
064        }
065
066        /**
067         * Construct with the given parameters.
068         * 
069         * @param ndims
070         *            number of dimensions of the data
071         * @param rng
072         *            the random number generator
073         * @param w
074         *            the width parameter
075         */
076        public DoubleGaussianFactory(int ndims, MersenneTwister rng, double w) {
077                super(ndims, rng, w);
078        }
079
080        @Override
081        public Function create() {
082                return new Function(ndims, rng);
083        }
084
085        @Override
086        protected DoubleFVComparison fvDistanceFunction() {
087                return DoubleFVComparison.EUCLIDEAN;
088        }
089}