root / tmp / org.txm.analec.rcp / src / JamaPlus / SingularValueDecomposition.java0 @ 1979
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package JamaPlus; |
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import JamaPlus.util.*; |
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/** Singular Value Decomposition. |
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<P> |
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For an m-by-n matrix A with m >= n, the singular value decomposition is |
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an m-by-n orthogonal matrix U, an n-by-n diagonal matrix S, and |
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an n-by-n orthogonal matrix V so that A = U*S*V'. |
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<P> |
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The singular values, sigma[k] = S[k][k], are ordered so that |
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sigma[0] >= sigma[1] >= ... >= sigma[n-1]. |
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<P> |
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The singular value decompostion always exists, so the constructor will |
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never fail. The matrix condition number and the effective numerical |
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rank can be computed from this decomposition. |
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*/ |
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public class SingularValueDecomposition implements java.io.Serializable {
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/* ------------------------ |
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Class variables |
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* ------------------------ */ |
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/** Arrays for internal storage of U and V. |
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@serial internal storage of U. |
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@serial internal storage of V. |
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*/ |
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private double[][] U, V; |
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/** Array for internal storage of singular values. |
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@serial internal storage of singular values. |
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*/ |
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private double[] s; |
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/** Row and column dimensions. |
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@serial row dimension. |
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@serial column dimension. |
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*/ |
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private int m, n; |
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/* ------------------------ |
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Constructor |
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* ------------------------ */ |
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/** Construct the singular value decomposition |
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@param A Rectangular matrix |
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@return Structure to access U, S and V. |
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*/ |
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public SingularValueDecomposition (Matrix Arg) {
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// Derived from LINPACK code. |
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// Initialize. |
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double[][] A = Arg.getArrayCopy(); |
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m = Arg.getRowDimension(); |
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n = Arg.getColumnDimension(); |
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/* Apparently the failing cases are only a proper subset of (m<n), |
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so let's not throw error. Correct fix to come later? |
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if (m<n) {
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throw new IllegalArgumentException("Jama SVD only works for m >= n"); }
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*/ |
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int nu = Math.min(m,n); |
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s = new double [Math.min(m+1,n)]; |
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U = new double [m][nu]; |
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V = new double [n][n]; |
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double[] e = new double [n]; |
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double[] work = new double [m]; |
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boolean wantu = true; |
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boolean wantv = true; |
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// Reduce A to bidiagonal form, storing the diagonal elements |
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// in s and the super-diagonal elements in e. |
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int nct = Math.min(m-1,n); |
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int nrt = Math.max(0,Math.min(n-2,m)); |
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for (int k = 0; k < Math.max(nct,nrt); k++) {
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if (k < nct) {
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// Compute the transformation for the k-th column and |
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// place the k-th diagonal in s[k]. |
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// Compute 2-norm of k-th column without under/overflow. |
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s[k] = 0; |
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for (int i = k; i < m; i++) {
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s[k] = Math.hypot(s[k],A[i][k]); |
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} |
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if (s[k] != 0.0) {
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if (A[k][k] < 0.0) {
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s[k] = -s[k]; |
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} |
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for (int i = k; i < m; i++) {
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A[i][k] /= s[k]; |
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} |
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A[k][k] += 1.0; |
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} |
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s[k] = -s[k]; |
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} |
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for (int j = k+1; j < n; j++) {
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if ((k < nct) & (s[k] != 0.0)) {
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// Apply the transformation. |
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double t = 0; |
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for (int i = k; i < m; i++) {
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t += A[i][k]*A[i][j]; |
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} |
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t = -t/A[k][k]; |
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for (int i = k; i < m; i++) {
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A[i][j] += t*A[i][k]; |
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} |
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} |
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// Place the k-th row of A into e for the |
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// subsequent calculation of the row transformation. |
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e[j] = A[k][j]; |
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} |
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if (wantu & (k < nct)) {
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// Place the transformation in U for subsequent back |
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// multiplication. |
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for (int i = k; i < m; i++) {
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U[i][k] = A[i][k]; |
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} |
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} |
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if (k < nrt) {
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// Compute the k-th row transformation and place the |
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// k-th super-diagonal in e[k]. |
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// Compute 2-norm without under/overflow. |
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e[k] = 0; |
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for (int i = k+1; i < n; i++) {
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e[k] = Math.hypot(e[k],e[i]); |
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} |
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if (e[k] != 0.0) {
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if (e[k+1] < 0.0) {
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e[k] = -e[k]; |
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} |
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for (int i = k+1; i < n; i++) {
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e[i] /= e[k]; |
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} |
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e[k+1] += 1.0; |
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} |
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e[k] = -e[k]; |
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if ((k+1 < m) & (e[k] != 0.0)) {
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// Apply the transformation. |
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for (int i = k+1; i < m; i++) {
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work[i] = 0.0; |
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} |
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for (int j = k+1; j < n; j++) {
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for (int i = k+1; i < m; i++) {
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work[i] += e[j]*A[i][j]; |
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} |
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} |
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for (int j = k+1; j < n; j++) {
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double t = -e[j]/e[k+1]; |
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for (int i = k+1; i < m; i++) {
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A[i][j] += t*work[i]; |
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} |
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} |
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} |
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if (wantv) {
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// Place the transformation in V for subsequent |
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// back multiplication. |
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for (int i = k+1; i < n; i++) {
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V[i][k] = e[i]; |
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} |
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} |
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} |
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} |
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// Set up the final bidiagonal matrix or order p. |
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int p = Math.min(n,m+1); |
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if (nct < n) {
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s[nct] = A[nct][nct]; |
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} |
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if (m < p) {
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s[p-1] = 0.0; |
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} |
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if (nrt+1 < p) {
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e[nrt] = A[nrt][p-1]; |
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} |
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e[p-1] = 0.0; |
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// If required, generate U. |
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if (wantu) {
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for (int j = nct; j < nu; j++) {
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for (int i = 0; i < m; i++) {
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U[i][j] = 0.0; |
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} |
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U[j][j] = 1.0; |
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} |
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for (int k = nct-1; k >= 0; k--) {
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if (s[k] != 0.0) {
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for (int j = k+1; j < nu; j++) {
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double t = 0; |
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for (int i = k; i < m; i++) {
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t += U[i][k]*U[i][j]; |
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} |
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t = -t/U[k][k]; |
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for (int i = k; i < m; i++) {
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U[i][j] += t*U[i][k]; |
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} |
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} |
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for (int i = k; i < m; i++ ) {
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U[i][k] = -U[i][k]; |
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} |
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U[k][k] = 1.0 + U[k][k]; |
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for (int i = 0; i < k-1; i++) {
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U[i][k] = 0.0; |
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} |
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} else {
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for (int i = 0; i < m; i++) {
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U[i][k] = 0.0; |
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} |
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U[k][k] = 1.0; |
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} |
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} |
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} |
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// If required, generate V. |
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if (wantv) {
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for (int k = n-1; k >= 0; k--) {
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if ((k < nrt) & (e[k] != 0.0)) {
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for (int j = k+1; j < nu; j++) {
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double t = 0; |
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for (int i = k+1; i < n; i++) {
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t += V[i][k]*V[i][j]; |
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} |
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t = -t/V[k+1][k]; |
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for (int i = k+1; i < n; i++) {
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V[i][j] += t*V[i][k]; |
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} |
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} |
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} |
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for (int i = 0; i < n; i++) {
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V[i][k] = 0.0; |
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} |
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V[k][k] = 1.0; |
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} |
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} |
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// Main iteration loop for the singular values. |
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int pp = p-1; |
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int iter = 0; |
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double eps = Math.pow(2.0,-52.0); |
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double tiny = Math.pow(2.0,-966.0); |
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while (p > 0) {
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int k,kase; |
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// Here is where a test for too many iterations would go. |
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// This section of the program inspects for |
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// negligible elements in the s and e arrays. On |
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// completion the variables kase and k are set as follows. |
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// kase = 1 if s(p) and e[k-1] are negligible and k<p |
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// kase = 2 if s(k) is negligible and k<p |
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// kase = 3 if e[k-1] is negligible, k<p, and |
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// s(k), ..., s(p) are not negligible (qr step). |
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// kase = 4 if e(p-1) is negligible (convergence). |
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for (k = p-2; k >= -1; k--) {
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if (k == -1) {
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break; |
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} |
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if (Math.abs(e[k]) <= |
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tiny + eps*(Math.abs(s[k]) + Math.abs(s[k+1]))) {
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e[k] = 0.0; |
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break; |
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} |
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} |
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if (k == p-2) {
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kase = 4; |
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} else {
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int ks; |
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for (ks = p-1; ks >= k; ks--) {
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if (ks == k) {
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break; |
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} |
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double t = (ks != p ? Math.abs(e[ks]) : 0.) + |
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(ks != k+1 ? Math.abs(e[ks-1]) : 0.); |
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if (Math.abs(s[ks]) <= tiny + eps*t) {
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s[ks] = 0.0; |
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break; |
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} |
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} |
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if (ks == k) {
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kase = 3; |
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} else if (ks == p-1) {
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kase = 1; |
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} else {
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kase = 2; |
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k = ks; |
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} |
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} |
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k++; |
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// Perform the task indicated by kase. |
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switch (kase) {
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// Deflate negligible s(p). |
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case 1: {
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double f = e[p-2]; |
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e[p-2] = 0.0; |
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for (int j = p-2; j >= k; j--) {
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double t = Math.hypot(s[j],f); |
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double cs = s[j]/t; |
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double sn = f/t; |
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s[j] = t; |
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if (j != k) {
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f = -sn*e[j-1]; |
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e[j-1] = cs*e[j-1]; |
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} |
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if (wantv) {
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for (int i = 0; i < n; i++) {
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t = cs*V[i][j] + sn*V[i][p-1]; |
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V[i][p-1] = -sn*V[i][j] + cs*V[i][p-1]; |
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V[i][j] = t; |
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} |
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} |
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} |
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} |
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break; |
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// Split at negligible s(k). |
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case 2: {
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double f = e[k-1]; |
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e[k-1] = 0.0; |
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for (int j = k; j < p; j++) {
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double t = Math.hypot(s[j],f); |
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double cs = s[j]/t; |
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double sn = f/t; |
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s[j] = t; |
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f = -sn*e[j]; |
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e[j] = cs*e[j]; |
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if (wantu) {
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for (int i = 0; i < m; i++) {
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t = cs*U[i][j] + sn*U[i][k-1]; |
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U[i][k-1] = -sn*U[i][j] + cs*U[i][k-1]; |
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U[i][j] = t; |
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} |
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} |
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} |
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} |
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break; |
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|
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// Perform one qr step. |
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|
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case 3: {
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// Calculate the shift. |
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|
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double scale = Math.max(Math.max(Math.max(Math.max( |
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Math.abs(s[p-1]),Math.abs(s[p-2])),Math.abs(e[p-2])), |
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Math.abs(s[k])),Math.abs(e[k])); |
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double sp = s[p-1]/scale; |
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double spm1 = s[p-2]/scale; |
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double epm1 = e[p-2]/scale; |
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double sk = s[k]/scale; |
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double ek = e[k]/scale; |
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double b = ((spm1 + sp)*(spm1 - sp) + epm1*epm1)/2.0; |
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double c = (sp*epm1)*(sp*epm1); |
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double shift = 0.0; |
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if ((b != 0.0) | (c != 0.0)) {
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shift = Math.sqrt(b*b + c); |
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if (b < 0.0) {
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shift = -shift; |
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} |
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shift = c/(b + shift); |
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} |
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double f = (sk + sp)*(sk - sp) + shift; |
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double g = sk*ek; |
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// Chase zeros. |
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|
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for (int j = k; j < p-1; j++) {
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double t = Math.hypot(f,g); |
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double cs = f/t; |
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double sn = g/t; |
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if (j != k) {
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e[j-1] = t; |
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} |
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f = cs*s[j] + sn*e[j]; |
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e[j] = cs*e[j] - sn*s[j]; |
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g = sn*s[j+1]; |
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s[j+1] = cs*s[j+1]; |
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if (wantv) {
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for (int i = 0; i < n; i++) {
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t = cs*V[i][j] + sn*V[i][j+1]; |
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V[i][j+1] = -sn*V[i][j] + cs*V[i][j+1]; |
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V[i][j] = t; |
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} |
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} |
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t = Math.hypot(f,g); |
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cs = f/t; |
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sn = g/t; |
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s[j] = t; |
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f = cs*e[j] + sn*s[j+1]; |
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s[j+1] = -sn*e[j] + cs*s[j+1]; |
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g = sn*e[j+1]; |
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e[j+1] = cs*e[j+1]; |
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if (wantu && (j < m-1)) {
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for (int i = 0; i < m; i++) {
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t = cs*U[i][j] + sn*U[i][j+1]; |
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U[i][j+1] = -sn*U[i][j] + cs*U[i][j+1]; |
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U[i][j] = t; |
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} |
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} |
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} |
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e[p-2] = f; |
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iter = iter + 1; |
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} |
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break; |
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|
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// Convergence. |
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|
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case 4: {
|
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|
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// Make the singular values positive. |
| 433 |
|
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if (s[k] <= 0.0) {
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s[k] = (s[k] < 0.0 ? -s[k] : 0.0); |
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if (wantv) {
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for (int i = 0; i <= pp; i++) {
|
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V[i][k] = -V[i][k]; |
| 439 |
} |
| 440 |
} |
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} |
| 442 |
|
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// Order the singular values. |
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|
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while (k < pp) {
|
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if (s[k] >= s[k+1]) {
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break; |
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} |
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double t = s[k]; |
| 450 |
s[k] = s[k+1]; |
| 451 |
s[k+1] = t; |
| 452 |
if (wantv && (k < n-1)) {
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for (int i = 0; i < n; i++) {
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t = V[i][k+1]; V[i][k+1] = V[i][k]; V[i][k] = t; |
| 455 |
} |
| 456 |
} |
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if (wantu && (k < m-1)) {
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for (int i = 0; i < m; i++) {
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t = U[i][k+1]; U[i][k+1] = U[i][k]; U[i][k] = t; |
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} |
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} |
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k++; |
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} |
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iter = 0; |
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p--; |
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} |
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break; |
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} |
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} |
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} |
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|
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/* ------------------------ |
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Public Methods |
| 474 |
* ------------------------ */ |
| 475 |
|
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/** Return the left singular vectors |
| 477 |
@return U |
| 478 |
*/ |
| 479 |
|
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public Matrix getU () {
|
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return new Matrix(U,m,Math.min(m+1,n)); |
| 482 |
} |
| 483 |
|
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/** Return the right singular vectors |
| 485 |
@return V |
| 486 |
*/ |
| 487 |
|
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public Matrix getV () {
|
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return new Matrix(V,n,n); |
| 490 |
} |
| 491 |
|
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/** Return the one-dimensional array of singular values |
| 493 |
@return diagonal of S. |
| 494 |
*/ |
| 495 |
|
| 496 |
public double[] getSingularValues () {
|
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return s; |
| 498 |
} |
| 499 |
|
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/** Return the diagonal matrix of singular values |
| 501 |
@return S |
| 502 |
*/ |
| 503 |
|
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public Matrix getS () {
|
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Matrix X = new Matrix(n,n); |
| 506 |
double[][] S = X.getArray(); |
| 507 |
for (int i = 0; i < n; i++) {
|
| 508 |
for (int j = 0; j < n; j++) {
|
| 509 |
S[i][j] = 0.0; |
| 510 |
} |
| 511 |
S[i][i] = this.s[i]; |
| 512 |
} |
| 513 |
return X; |
| 514 |
} |
| 515 |
|
| 516 |
/** Two norm |
| 517 |
@return max(S) |
| 518 |
*/ |
| 519 |
|
| 520 |
public double norm2 () {
|
| 521 |
return s[0]; |
| 522 |
} |
| 523 |
|
| 524 |
/** Two norm condition number |
| 525 |
@return max(S)/min(S) |
| 526 |
*/ |
| 527 |
|
| 528 |
public double cond () {
|
| 529 |
return s[0]/s[Math.min(m,n)-1]; |
| 530 |
} |
| 531 |
|
| 532 |
/** Effective numerical matrix rank |
| 533 |
@return Number of nonnegligible singular values. |
| 534 |
*/ |
| 535 |
|
| 536 |
public int rank () {
|
| 537 |
double eps = Math.pow(2.0,-52.0); |
| 538 |
double tol = Math.max(m,n)*s[0]*eps; |
| 539 |
int r = 0; |
| 540 |
for (int i = 0; i < s.length; i++) {
|
| 541 |
if (s[i] > tol) {
|
| 542 |
r++; |
| 543 |
} |
| 544 |
} |
| 545 |
return r; |
| 546 |
} |
| 547 |
} |