001/**
002 * Copyright (c) 2011, The University of Southampton and the individual contributors.
003 * All rights reserved.
004 *
005 * Redistribution and use in source and binary forms, with or without modification,
006 * are permitted provided that the following conditions are met:
007 *
008 *   *  Redistributions of source code must retain the above copyright notice,
009 *      this list of conditions and the following disclaimer.
010 *
011 *   *  Redistributions in binary form must reproduce the above copyright notice,
012 *      this list of conditions and the following disclaimer in the documentation
013 *      and/or other materials provided with the distribution.
014 *
015 *   *  Neither the name of the University of Southampton nor the names of its
016 *      contributors may be used to endorse or promote products derived from this
017 *      software without specific prior written permission.
018 *
019 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
020 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
021 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
022 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
023 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
024 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
025 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
026 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
027 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
028 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
029 */
030package org.openimaj.math.statistics.distribution.kernel;
031
032import java.util.Random;
033
034/**
035 * Standard univariate (1-d) kernel (window) implementations
036 * 
037 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
038 * 
039 */
040public enum StandardUnivariateKernels implements UnivariateKernel {
041        /**
042         * Univariate Gaussian kernel
043         * 
044         * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
045         */
046        Gaussian {
047                @Override
048                public double sample(Random rng) {
049                        return rng.nextGaussian();
050                }
051
052                @Override
053                public double getCutOff() {
054                        // 99.7% of all the data lies within 3 s.d. of the mean
055                        return 3;
056                }
057
058                @Override
059                public double evaluate(double value) {
060                        return Math.exp(-(value * value) / 2) / Math.sqrt(2 * Math.PI);
061                }
062        },
063        /**
064         * Flat window
065         * 
066         * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
067         */
068        Flat {
069                @Override
070                public double sample(Random rng) {
071                        return rng.nextGaussian() - 0.5;
072                }
073
074                @Override
075                public double getCutOff() {
076                        return 0.5;
077                }
078
079                @Override
080                public double evaluate(double value) {
081                        if (value > 0.5)
082                                return 0;
083                        if (value < -0.5)
084                                return 0;
085                        return 1;
086                }
087
088        }
089}