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 * Interface describing a multivariate gaussian distribution
036 * 
037 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
038 * 
039 */
040public interface MultivariateGaussian extends MultivariateDistribution {
041
042        /**
043         * Get the covariance
044         * 
045         * @return the covariance
046         */
047        public abstract Matrix getCovariance();
048
049        /**
050         * Get the mean
051         * 
052         * @return the mean
053         */
054        public abstract Matrix getMean();
055
056        /**
057         * Get the dimensionality
058         * 
059         * @return number of dimensions
060         */
061        public abstract int numDims();
062
063        /**
064         * Get a covariance value from the covariance matrix.
065         * <p>
066         * This method is provided for efficiency as not all implementations will
067         * store the full matrix, and it would be wasteful to create it each time a
068         * value is needed.
069         * 
070         * @param row
071         *            the row of the matrix value to get
072         * @param column
073         *            the column of the matrix value to get
074         * @return the covariance at the given row and column
075         */
076        public abstract double getCovariance(int row, int column);
077}