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;
031
032import Jama.Matrix;
033
034/**
035 * Implementation of a basic {@link MultivariateGaussian} with a full covariance
036 * matrix.
037 * 
038 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
039 */
040public class FullMultivariateGaussian extends AbstractMultivariateGaussian {
041        /**
042         * The covariance matrix
043         */
044        public Matrix covar;
045
046        /**
047         * Construct the Gaussian with the provided center and covariance
048         * 
049         * @param mean
050         *            centre of the Gaussian
051         * @param covar
052         *            covariance of the Gaussian
053         */
054        public FullMultivariateGaussian(Matrix mean, Matrix covar) {
055                this.mean = mean;
056                this.covar = covar;
057        }
058
059        /**
060         * Construct the Gaussian with the zero mean and unit variance
061         * 
062         * @param ndims
063         *            number of dimensions
064         */
065        public FullMultivariateGaussian(int ndims) {
066                this.mean = new Matrix(1, ndims);
067                this.covar = Matrix.identity(ndims, ndims);
068        }
069
070        @Override
071        public Matrix getCovariance() {
072                return covar;
073        }
074
075        @Override
076        public double getCovariance(int row, int column) {
077                return this.covar.get(row, column);
078        }
079}