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}