List of usage examples for org.apache.commons.math3.linear DefaultRealMatrixChangingVisitor DefaultRealMatrixChangingVisitor
DefaultRealMatrixChangingVisitor
From source file:fp.overlapr.algorithmen.StressMajorization.java
/** * Fhrt die Stress-Majorization durch. siehe: Gansner, Koren, North: Graph * Drawing by Stress Majorization, 2004//from w w w . jav a 2s . c o m * * @param graph * Graph, dessen Knoten-Positionen neu berechnet werden sollen * @param d * Matrix, die die idealen Distanzen (d_ij) zwischen den Knoten * enthlt * @return Matrix, die die neuen x- und y-Werte der einzelnen Knoten enthlt */ public static double[][] doStressMajorization(Graph graph, double[][] d) { int iter = 0; // X holen Array2DRowRealMatrix X = new Array2DRowRealMatrix(graph.getKnotenAnzahl(), 2); for (int i = 0; i < graph.getKnotenAnzahl(); i++) { X.setEntry(i, 0, graph.getKnoten().get(i).getX()); X.setEntry(i, 1, graph.getKnoten().get(i).getY()); } // D holen Array2DRowRealMatrix D = new Array2DRowRealMatrix(d); // W berechnen Array2DRowRealMatrix W = new Array2DRowRealMatrix(D.getRowDimension(), D.getColumnDimension()); W.walkInRowOrder(new DefaultRealMatrixChangingVisitor() { @Override public double visit(int row, int column, double value) { if (D.getEntry(row, column) == 0) { return 0.0; } else { return 1.0 / (D.getEntry(row, column) * D.getEntry(row, column)); } } }); // LW berechnen Array2DRowRealMatrix LW = new Array2DRowRealMatrix(D.getRowDimension(), D.getColumnDimension()); LW.walkInRowOrder(new DefaultRealMatrixChangingVisitor() { @Override public double visit(int row, int column, double value) { if (row != column) { return (-1) * W.getEntry(row, column); } else { return value; } } }); LW.walkInRowOrder(new DefaultRealMatrixChangingVisitor() { @Override public double visit(int row, int column, double value) { if (row == column) { double sum = 0; for (int k = 0; k < LW.getColumnDimension(); k++) { if (k != row) { sum = sum + W.getEntry(row, k); } } return sum; } else { return value; } } }); double[][] x = null; while (iter < ITER) { iter++; // LX berechnen Array2DRowRealMatrix LX = new Array2DRowRealMatrix(D.getRowDimension(), D.getColumnDimension()); LX.walkInRowOrder(new DefaultRealMatrixChangingVisitor() { @Override public double visit(int row, int column, double value) { if (row != column) { // norm 2 double term1 = FastMath.pow((X.getEntry(row, 0) - X.getEntry(column, 0)), 2); double term2 = FastMath.pow((X.getEntry(row, 1) - X.getEntry(column, 1)), 2); double abst = Math.sqrt(term1 + term2); return (-1) * W.getEntry(row, column) * D.getEntry(row, column) * inv(abst); } else { return value; } } }); LX.walkInRowOrder(new DefaultRealMatrixChangingVisitor() { @Override public double visit(int row, int column, double value) { if (row == column) { double sum = 0; for (int k = 0; k < LX.getColumnDimension(); k++) { if (k != row) { sum = sum + LX.getEntry(row, k); } } return (-1) * sum; } else { return value; } } }); /* * Lineare Gleichungssysteme lsen */ // x-Werte holen ArrayRealVector xWerte = new ArrayRealVector(X.getColumn(0)); // y-Werte holen ArrayRealVector yWerte = new ArrayRealVector(X.getColumn(1)); // b_x berechnen ArrayRealVector b_x = (ArrayRealVector) LX.operate(xWerte); // b_y berechnen ArrayRealVector b_y = (ArrayRealVector) LX.operate(yWerte); /* * CG-Verfahren anwenden */ // neue x-Werte berechnen mittels PCG // xWerte = conjugateGradientsMethod(LW, b_x, xWerte); // neue y-Werte berechnen mittels PCG // yWerte = conjugateGradientsMethod(LW, b_y, yWerte); ConjugateGradient cg = new ConjugateGradient(Integer.MAX_VALUE, TOL, false); xWerte = (ArrayRealVector) cg.solve(LW, JacobiPreconditioner.create(LW), b_x); yWerte = (ArrayRealVector) cg.solve(LW, JacobiPreconditioner.create(LW), b_y); /* * neue Positiones-Werte zurckgeben */ x = new double[X.getRowDimension()][2]; for (int i = 0; i < x.length; i++) { x[i][0] = xWerte.getEntry(i); x[i][1] = yWerte.getEntry(i); X.setEntry(i, 0, xWerte.getEntry(i)); X.setEntry(i, 1, yWerte.getEntry(i)); } } return x; }
From source file:com.itemanalysis.psychometrics.factoranalysis.QuartiminCriteria.java
public void computeValues(RealMatrix L) { int k = L.getColumnDimension(); int p = L.getRowDimension(); RealMatrix I = new IdentityMatrix(p); RealMatrix L2 = MatrixUtils.multiplyElements(L, L); RealMatrix N = new Array2DRowRealMatrix(k, k); N.walkInRowOrder(new DefaultRealMatrixChangingVisitor() { @Override/* w ww. j a va 2 s . c o m*/ public double visit(int row, int column, double value) { if (row == column) return 0.0; return 1.0; } }); RealMatrix X = L2.multiply(N); double sum = MatrixUtils.sumMatrix(MatrixUtils.multiplyElements(L2, X)); functionValue = sum / 4.0; gradient = MatrixUtils.multiplyElements(L, X); }
From source file:com.itemanalysis.psychometrics.factoranalysis.GPArotationTest.java
public void genericTest() { //add test here double[][] x = { { 2, 3 }, { 4, 5 } }; RealMatrix X = new Array2DRowRealMatrix(x); X.walkInRowOrder(new DefaultRealMatrixChangingVisitor() { @Override//from ww w.ja va2 s . c o m public double visit(int row, int column, double value) { return value * value; } }); printMatrix(X, "PP"); }
From source file:com.itemanalysis.psychometrics.factoranalysis.ObliminCriteria.java
public void computeValues(RealMatrix L) { final int k = L.getColumnDimension(); final int p = L.getRowDimension(); RealMatrix I = new IdentityMatrix(p); RealMatrix L2 = MatrixUtils.multiplyElements(L, L); RealMatrix N = new Array2DRowRealMatrix(k, k); N.walkInRowOrder(new DefaultRealMatrixChangingVisitor() { @Override/* ww w.j a va2 s. c om*/ public double visit(int row, int column, double value) { if (row == column) return 0.0; return 1.0; } }); RealMatrix C = new Array2DRowRealMatrix(p, p); C.walkInRowOrder(new DefaultRealMatrixChangingVisitor() { @Override public double visit(int row, int column, double value) { return gam / (double) p; } }); RealMatrix X = I.subtract(C).multiply(L2).multiply(N); double sum = MatrixUtils.sumMatrix(MatrixUtils.multiplyElements(L2, X)); functionValue = sum / 4.0; gradient = MatrixUtils.multiplyElements(L, X); }
From source file:com.itemanalysis.psychometrics.factoranalysis.GeominCriteria.java
public void computeValues(RealMatrix L) { // vgQ.geomin <- function(L, delta=.01){ // k <- ncol(L) // p <- nrow(L) // L2 <- L^2 + delta // pro <- exp(rowSums(log(L2))/k) // list(Gq=(2/k)*(L/L2)*matrix(rep(pro,k),p), // f= sum(pro), // Method="Geomin") // }/*w w w. jav a 2 s . co m*/ int p = L.getRowDimension(); int k = L.getColumnDimension(); final RealMatrix L2 = MatrixUtils.multiplyElements(L, L).scalarAdd(delta); final double[] rowSums = new double[p]; L2.walkInRowOrder(new DefaultRealMatrixPreservingVisitor() { @Override public void visit(int row, int column, double value) { rowSums[row] += Math.log(value); } }); double sum = 0.0; for (int i = 0; i < p; i++) { rowSums[i] = Math.exp(rowSums[i] / (double) k); sum += rowSums[i]; } functionValue = sum; final RealMatrix M = new Array2DRowRealMatrix(p, k); M.walkInRowOrder(new DefaultRealMatrixChangingVisitor() { @Override public double visit(int row, int column, double value) { return rowSums[row]; } }); final double c = (2.0 / (double) k); gradient = L.copy(); gradient.walkInRowOrder(new DefaultRealMatrixChangingVisitor() { @Override public double visit(int row, int column, double value) { return c * (value / L2.getEntry(row, column)) * M.getEntry(row, column); } }); }
From source file:com.itemanalysis.psychometrics.factoranalysis.GPArotation.java
/** * Conducts orthogonal rotation of factor loadings. * * @param A matrix of orthogonal factor loadings * @return a matrix of rotated factor loadings. * @throws ConvergenceException//from w w w. j a v a 2 s . c o m */ private RotationResults GPForth(RealMatrix A, boolean normalize, int maxIter, double eps) throws ConvergenceException { int ncol = A.getColumnDimension(); if (normalize) { //elementwise division by normalizing weights final RealMatrix W = getNormalizingWeights(A, true); A.walkInRowOrder(new DefaultRealMatrixChangingVisitor() { @Override public double visit(int row, int column, double value) { return value / W.getEntry(row, column); } }); } RealMatrix Tmat = new IdentityMatrix(ncol); double alpha = 1; RealMatrix L = A.multiply(Tmat); gpFunction.computeValues(L); double f = gpFunction.getValue(); RealMatrix VgQ = gpFunction.getGradient(); RealMatrix G = A.transpose().multiply(VgQ); double VgQtF = gpFunction.getValue(); RealMatrix VgQt = gpFunction.getGradient(); RealMatrix Tmatt = null; int iter = 0; double s = eps + 0.5; double s2 = 0; int innnerIter = 11; while (iter < maxIter) { RealMatrix M = Tmat.transpose().multiply(G); RealMatrix S = (M.add(M.transpose())); S = S.scalarMultiply(0.5); RealMatrix Gp = G.subtract(Tmat.multiply(S)); s = Math.sqrt((Gp.transpose().multiply(Gp)).getTrace()); s2 = Math.pow(s, 2); if (s < eps) break; alpha *= 2.0; for (int j = 0; j < innnerIter; j++) { Gp = Gp.scalarMultiply(alpha); RealMatrix X = (Tmat.subtract(Gp)); SingularValueDecomposition SVD = new SingularValueDecomposition(X); Tmatt = SVD.getU().multiply(SVD.getV().transpose()); L = A.multiply(Tmatt); gpFunction.computeValues(L); VgQt = gpFunction.getGradient(); VgQtF = gpFunction.getValue(); if (VgQtF < f - 0.5 * s2 * alpha) { break; } alpha /= 2.0; } Tmat = Tmatt; f = VgQtF; G = A.transpose().multiply(VgQt); iter++; } boolean convergence = s < eps; if (!convergence) { throw new ConvergenceException(); } if (normalize) { //elementwise multiplication by normalizing weights final RealMatrix W = getNormalizingWeights(A, true); A.walkInRowOrder(new DefaultRealMatrixChangingVisitor() { @Override public double visit(int row, int column, double value) { return value * W.getEntry(row, column); } }); } RealMatrix Phi = Tmat.transpose().multiply(Tmat); RotationResults result = new RotationResults(gpFunction.getValue(), L, Phi, Tmat, rotationMethod); return result; }
From source file:com.analog.lyric.dimple.test.solvers.sumproduct.TestSampledFactors.java
/** * Generates a random covariance matrix with given dimension. *///from ww w .java2s .c o m RealMatrix randCovariance(int n) { RealMatrix A = MatrixUtils.createRealMatrix(n, n); // Randomize A.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() { @Override public double visit(int row, int column, double value) { return testRand.nextDouble(); } }); RealMatrix B = A.add(A.transpose()); // B is symmetric double minEig = Doubles.min(new EigenDecomposition(B).getRealEigenvalues()); double r = testRand.nextGaussian(); r *= r; r += Math.ulp(1.0); RealMatrix I = MatrixUtils.createRealIdentityMatrix(n); RealMatrix C = B.add(I.scalarMultiply(r - minEig)); return C; }
From source file:fp.overlapr.algorithmen.StressMajorization.java
@Deprecated private static ArrayRealVector conjugateGradientsMethod(Array2DRowRealMatrix A, ArrayRealVector b, ArrayRealVector werte) {/*w ww . j a v a2 s .co m*/ Array2DRowRealMatrix preJacobi = new Array2DRowRealMatrix(A.getRowDimension(), A.getColumnDimension()); // Predconditioner berechnen preJacobi.walkInRowOrder(new DefaultRealMatrixChangingVisitor() { @Override public double visit(int row, int column, double value) { if (row == column) { return 1 / A.getEntry(row, column); } else { return 0.0; } } }); // x_k beliebig whlen ArrayRealVector x_k = new ArrayRealVector(werte); // r_k berechnen ArrayRealVector r_k = b.subtract(A.operate(x_k)); // h_k berechnen ArrayRealVector h_k = (ArrayRealVector) preJacobi.operate(r_k); // d_k = r_k ArrayRealVector d_k = h_k; // x_k+1 und r_k+1 und d_k+1, sowie alpha und beta und z erzeugen ArrayRealVector x_k1; ArrayRealVector r_k1; ArrayRealVector d_k1; ArrayRealVector h_k1; double alpha; double beta; ArrayRealVector z; do { // Speichere Matrix-Vektor-Produkt, um es nur einmal auszurechnen z = (ArrayRealVector) A.operate(d_k); // Finde von x_k in Richtung d_k den Ort x_k1 des Minimums der // Funktion E // und aktualisere den Gradienten bzw. das Residuum alpha = r_k.dotProduct(h_k) / d_k.dotProduct(z); x_k1 = x_k.add(d_k.mapMultiply(alpha)); r_k1 = r_k.subtract(z.mapMultiply(alpha)); h_k1 = (ArrayRealVector) preJacobi.operate(r_k1); // Korrigiere die Suchrichtung d_k1 beta = r_k1.dotProduct(h_k1) / r_k.dotProduct(h_k); d_k1 = h_k1.add(d_k.mapMultiply(beta)); // Werte "eins" weitersetzen x_k = x_k1; r_k = r_k1; d_k = d_k1; h_k = h_k1; } while (r_k1.getNorm() > TOL); return x_k1; }
From source file:com.itemanalysis.psychometrics.factoranalysis.GPArotation.java
private RotationResults GPFoblq(RealMatrix A, boolean normalize, int maxIter, double eps) throws ConvergenceException { int ncol = A.getColumnDimension(); RealMatrix Tinner = null;//ww w . ja v a 2s . c o m RealMatrix TinnerInv = null; RealMatrix Tmat = new IdentityMatrix(ncol); if (normalize) { //elementwise division by normalizing weights final RealMatrix W = getNormalizingWeights(A, true); A.walkInRowOrder(new DefaultRealMatrixChangingVisitor() { @Override public double visit(int row, int column, double value) { return value / W.getEntry(row, column); } }); } RealMatrix TmatInv = new LUDecomposition(Tmat).getSolver().getInverse(); RealMatrix L = A.multiply(TmatInv.transpose()); //compute gradientAt and function value gpFunction.computeValues(L); RealMatrix VgQ = gpFunction.getGradient(); RealMatrix VgQt = VgQ; double f = gpFunction.getValue(); double ft = f; RealMatrix G = ((L.transpose().multiply(VgQ).multiply(TmatInv)).transpose()).scalarMultiply(-1.0); int iter = 0; double alpha = 1.0; double s = eps + 0.5; double s2 = Math.pow(s, 2); int innerMaxIter = 10; int innerCount = 0; IdentityMatrix I = new IdentityMatrix(G.getRowDimension()); RealMatrix V1 = MatrixUtils.getVector(ncol, 1.0); while (iter < maxIter) { RealMatrix M = MatrixUtils.multiplyElements(Tmat, G); RealMatrix diagP = new DiagonalMatrix(V1.multiply(M).getRow(0)); RealMatrix Gp = G.subtract(Tmat.multiply(diagP)); s = Math.sqrt(Gp.transpose().multiply(Gp).getTrace()); s2 = Math.pow(s, 2); if (s < eps) { break; } alpha = 2.0 * alpha; innerCount = 0; for (int i = 0; i < innerMaxIter; i++) { RealMatrix X = Tmat.subtract(Gp.scalarMultiply(alpha)); RealMatrix X2 = MatrixUtils.multiplyElements(X, X); RealMatrix V = V1.multiply(X2); V.walkInRowOrder(new DefaultRealMatrixChangingVisitor() { @Override public double visit(int row, int column, double value) { return 1.0 / Math.sqrt(value); } }); //compute new value of T, its inverse, and the rotated loadings RealMatrix diagV = new DiagonalMatrix(V.getRow(0)); Tinner = X.multiply(diagV); TinnerInv = new LUDecomposition(Tinner).getSolver().getInverse(); L = A.multiply(TinnerInv.transpose()); //compute new values of the gradientAt and the rotation criteria gpFunction.computeValues(L); VgQt = gpFunction.getGradient(); ft = gpFunction.getValue(); innerCount++; if (ft < f - 0.5 * s2 * alpha) { break; } alpha = alpha / 2.0; } // System.out.println(iter + " " + f + " " + s + " " + Math.log10(s) + " " + alpha + " " + innerCount); Tmat = Tinner; f = ft; G = (L.transpose().multiply(VgQt).multiply(TinnerInv)).transpose().scalarMultiply(-1.0); iter++; } boolean convergence = s < eps; if (!convergence) { throw new ConvergenceException(); } if (normalize) { //elementwise multiplication by normalizing weights final RealMatrix W = getNormalizingWeights(A, true); A.walkInRowOrder(new DefaultRealMatrixChangingVisitor() { @Override public double visit(int row, int column, double value) { return value * W.getEntry(row, column); } }); } RealMatrix Phi = Tmat.transpose().multiply(Tmat); RotationResults result = new RotationResults(gpFunction.getValue(), L, Phi, Tmat, rotationMethod); return result; }
From source file:com.itemanalysis.psychometrics.factoranalysis.GPArotation.java
private RealMatrix getNormalizingWeights(RealMatrix A, boolean normalize) { int nrow = A.getRowDimension(); int ncol = A.getColumnDimension(); final double[] w = new double[nrow]; RealMatrix W = new Array2DRowRealMatrix(nrow, ncol); if (!normalize) { W.walkInRowOrder(new DefaultRealMatrixChangingVisitor() { @Override//from w ww. j a va 2 s . c om public double visit(int row, int column, double value) { return 1.0; } }); return W; } //compute row sum of squared loadings A.walkInRowOrder(new DefaultRealMatrixPreservingVisitor() { @Override public void visit(int row, int column, double value) { w[row] += value * value; } }); //compute normalizing weights for the matrix W.walkInRowOrder(new DefaultRealMatrixChangingVisitor() { @Override public double visit(int row, int column, double value) { return Math.sqrt(w[row]); } }); return W; }