@Override public void learnBasis(double[][] data) { inner.learnBasis(data); this.basis = inner.getBasis(); this.eigenvalues = inner.getEigenValues(); this.mean = inner.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(); }
@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 Matrix xbar = new Matrix(new double[][] { pc.getMean() }); double[] newModel = (y.minus(xbar)).times(pc.getBasis()).getArray()[0];