/** * Construct with the given number of principal components. * * @param numComponents * the number of PCs */ public EigenImages(int numComponents) { this.numComponents = numComponents; pca = new FeatureVectorPCA(new ThinSvdPrincipalComponentAnalysis(numComponents)); }
/** * Construct with the given number of principal components. * * @param numComponents * the number of PCs */ public EigenImages(int numComponents) { this.numComponents = numComponents; pca = new FeatureVectorPCA(new ThinSvdPrincipalComponentAnalysis(numComponents)); }
/** * @param args * @throws IOException */ public static void main(String[] args) throws IOException { final File indir = new File("/Volumes/Raid/face_databases/lfw-centre-affine-pdsift/"); final Matrix samples = sample(indir, 100000); final ThinSvdPrincipalComponentAnalysis pca = new ThinSvdPrincipalComponentAnalysis(64); System.out.println("Performing PCA"); pca.learnBasis(samples); IOUtils.writeToFile(pca, new File("/Volumes/Raid/face_databases/lfw-centre-affine-pdsift-pca64.bin")); // final ThinSvdPrincipalComponentAnalysis pca = // IOUtils.readFromFile(new File( // "/Volumes/Raid/face_databases/lfw-centre-affine-pdsift-pca64.bin")); processFiles(pca, indir, new File("/Volumes/Raid/face_databases/lfw-centre-affine-pdsift-pca64" + "/")); } }
/** * @param args * @throws IOException */ public static void main(String[] args) throws IOException { final File indir = new File("/Volumes/Raid/face_databases/lfw-centre-affine-pdsift/"); final Matrix samples = sample(indir, 100000); final ThinSvdPrincipalComponentAnalysis pca = new ThinSvdPrincipalComponentAnalysis(64); System.out.println("Performing PCA"); pca.learnBasis(samples); IOUtils.writeToFile(pca, new File("/Volumes/Raid/face_databases/lfw-centre-affine-pdsift-pca64.bin")); // final ThinSvdPrincipalComponentAnalysis pca = // IOUtils.readFromFile(new File( // "/Volumes/Raid/face_databases/lfw-centre-affine-pdsift-pca64.bin")); processFiles(pca, indir, new File("/Volumes/Raid/face_databases/lfw-centre-affine-pdsift-pca64" + "/")); } }
final ThinSvdPrincipalComponentAnalysis pca = new ThinSvdPrincipalComponentAnalysis(64); pca.learnBasis(sample128);
@Override public void train(List<? extends IndependentPair<?, FImage>> data) { width = data.get(0).secondObject().width; height = data.get(0).secondObject().height; final Map<Object, List<double[]>> mapData = new HashMap<Object, List<double[]>>(); final List<double[]> listData = new ArrayList<double[]>(); for (final IndependentPair<?, FImage> item : data) { List<double[]> fvs = mapData.get(item.firstObject()); if (fvs == null) mapData.put(item.firstObject(), fvs = new ArrayList<double[]>()); final double[] fv = FImage2DoubleFV.INSTANCE.extractFeature(item.getSecondObject()).values; fvs.add(fv); listData.add(fv); } final PrincipalComponentAnalysis pca = new ThinSvdPrincipalComponentAnalysis(numComponents); pca.learnBasis(listData); final List<double[][]> ldaData = new ArrayList<double[][]>(mapData.size()); for (final Entry<?, List<double[]>> e : mapData.entrySet()) { final List<double[]> vecs = e.getValue(); final double[][] classData = new double[vecs.size()][]; for (int i = 0; i < classData.length; i++) { classData[i] = pca.project(vecs.get(i)); } ldaData.add(classData); } final LinearDiscriminantAnalysis lda = new LinearDiscriminantAnalysis(numComponents); lda.learnBasis(ldaData); basis = pca.getBasis().times(lda.getBasis()); mean = pca.getMean(); }
@Override public void train(List<? extends IndependentPair<?, FImage>> data) { width = data.get(0).secondObject().width; height = data.get(0).secondObject().height; final Map<Object, List<double[]>> mapData = new HashMap<Object, List<double[]>>(); final List<double[]> listData = new ArrayList<double[]>(); for (final IndependentPair<?, FImage> item : data) { List<double[]> fvs = mapData.get(item.firstObject()); if (fvs == null) mapData.put(item.firstObject(), fvs = new ArrayList<double[]>()); final double[] fv = FImage2DoubleFV.INSTANCE.extractFeature(item.getSecondObject()).values; fvs.add(fv); listData.add(fv); } final PrincipalComponentAnalysis pca = new ThinSvdPrincipalComponentAnalysis(numComponents); pca.learnBasis(listData); final List<double[][]> ldaData = new ArrayList<double[][]>(mapData.size()); for (final Entry<?, List<double[]>> e : mapData.entrySet()) { final List<double[]> vecs = e.getValue(); final double[][] classData = new double[vecs.size()][]; for (int i = 0; i < classData.length; i++) { classData[i] = pca.project(vecs.get(i)); } ldaData.add(classData); } final LinearDiscriminantAnalysis lda = new LinearDiscriminantAnalysis(numComponents); lda.learnBasis(ldaData); basis = pca.getBasis().times(lda.getBasis()); mean = pca.getMean(); }
final ThinSvdPrincipalComponentAnalysis pca = new ThinSvdPrincipalComponentAnalysis(64); pca.learnBasis(sample128); final double[][] sample64 = pca.project(new Matrix(sample128)).getArray();
final ThinSvdPrincipalComponentAnalysis pca = new ThinSvdPrincipalComponentAnalysis(64); pca.learnBasis(sample128); final double[][] sample64 = pca.project(new Matrix(sample128)).getArray();
final FeatureVectorPCA pca = new FeatureVectorPCA(new ThinSvdPrincipalComponentAnalysis(numPcaDims)); pca.learnBasis(vlads);
final ThinSvdPrincipalComponentAnalysis pca = new ThinSvdPrincipalComponentAnalysis(ndims); pca.learnBasis(data);