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.workinprogress.featlearn; 031 032import java.io.IOException; 033import java.util.List; 034 035import org.openimaj.image.DisplayUtilities; 036import org.openimaj.image.MBFImage; 037import org.openimaj.image.annotation.evaluation.datasets.CIFAR10Dataset; 038import org.openimaj.image.colour.RGBColour; 039import org.openimaj.math.matrix.algorithm.whitening.ZCAWhitening; 040import org.openimaj.math.statistics.normalisation.PerExampleMeanCenter; 041import org.openimaj.ml.clustering.kmeans.SphericalKMeans; 042import org.openimaj.ml.clustering.kmeans.SphericalKMeansResult; 043 044public class Test2 { 045 public static void main(String[] args) throws IOException { 046 System.out.println("start"); 047 final RandomPatchSampler<MBFImage> sampler = new RandomPatchSampler<MBFImage>( 048 CIFAR10Dataset.getTrainingImages(CIFAR10Dataset.MBFIMAGE_READER), 049 8, 8, 400000); 050 final List<MBFImage> patches = sampler.getPatches(); 051 System.out.println("stop"); 052 053 final double[][] data = new double[patches.size()][]; 054 for (int i = 0; i < data.length; i++) 055 data[i] = patches.get(i).getDoublePixelVector(); 056 057 // final PCAWhitening whitening = new PCAWhitening(); 058 final ZCAWhitening whitening = new ZCAWhitening(0.1, new PerExampleMeanCenter()); 059 whitening.train(data); 060 final double[][] wd = whitening.whiten(data); 061 062 final SphericalKMeans skm = new SphericalKMeans(1600, 10); 063 final SphericalKMeansResult res = skm.cluster(wd); 064 final MBFImage tmp = new MBFImage(40 * (8 + 1) + 1, 40 * (8 + 1) + 1); 065 tmp.fill(RGBColour.WHITE); 066 for (int i = 0; i < 40; i++) { 067 for (int j = 0; j < 40; j++) { 068 final MBFImage patch = new MBFImage(res.centroids[i * 40 + j], 8, 8, 3, false); 069 tmp.drawImage(patch, i * (8 + 1) + 1, j * (8 + 1) + 1); 070 } 071 } 072 tmp.subtractInplace(-1.5f); 073 tmp.divideInplace(3f); 074 DisplayUtilities.display(tmp); 075 } 076}