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.demos.sandbox;
031
032import org.openimaj.data.RandomData;
033import org.openimaj.math.matrix.MatrixUtils;
034import org.openimaj.math.statistics.distribution.MixtureOfGaussians;
035import org.openimaj.ml.gmm.GaussianMixtureModelEM;
036import org.openimaj.ml.gmm.GaussianMixtureModelEM.CovarianceType;
037
038public class GmmEmTesting {
039        // public static void main(String[] args) {
040        // final Random rng = new Random();
041        // final double[][] data = new double[1000][4];
042        // for (int j = 0; j < data.length / 2; j++) {
043        // for (int i = 0; i < data[0].length; i++) {
044        // data[j][i] = rng.nextGaussian() * (i + 1);
045        // }
046        // }
047        //
048        // for (int j = data.length / 2; j < data.length; j++) {
049        // for (int i = 0; i < data[0].length; i++) {
050        // data[j][i] = 10 + rng.nextGaussian() * (i + 1);
051        // }
052        // }
053        //
054        // final GaussianMixtureModelEM gmmem = new GaussianMixtureModelEM(2,
055        // CovarianceType.Spherical);
056        // final MixtureOfGaussians model = gmmem.estimate(data);
057        //
058        // System.out.println(MatrixUtils.toString(model.gaussians[0].getCovariance()));
059        // System.out.println();
060        // System.out.println(MatrixUtils.toString(model.gaussians[1].getCovariance()));
061        // }
062
063        public static void main(String[] args) {
064                final double[][] data = RandomData.getRandomDoubleArray(10000, 64, -1d, 1d);
065
066                final GaussianMixtureModelEM gmmem = new GaussianMixtureModelEM(512, CovarianceType.Diagonal);
067                final MixtureOfGaussians model = gmmem.estimate(data);
068
069                System.out.println(MatrixUtils.toString(model.gaussians[0].getCovariance()));
070                System.out.println();
071                System.out.println(MatrixUtils.toString(model.gaussians[1].getCovariance()));
072        }
073}