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refactored run-time Clazz methods; Jama -> gov.nist.jama
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‎sources/net.sf.j2s.core/test/dev/js/core/corebottom.js‎

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})();
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Clazz._coreLoaded = true;
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‎sources/net.sf.j2s.core/test/dev/js/core/corebottom2.js‎

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‎sources/net.sf.j2s.core/test/dev/js/core/coretop.js‎

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;(function() {
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if (Jmol._debugCode)return;
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‎sources/net.sf.j2s.core/test/dev/js/core/coretop2.js‎

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‎sources/net.sf.j2s.core/test/src/gov/nist/jama/ChangeLog‎

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2012-11-09 Bruce Miller
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* Version 1.0.3 Released.
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* Patched hqr2 method in Jama.EigenvalueDecomposition
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to avoid infinite loop;
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Thanks Frederic Devernay <frederic.devernay@m4x.org>
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* Updated unchecked type coding in Matrix read.
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* Added serialization ID to Jama.Matrix and Decomposition classes.
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* Cleaned up javadoc documentation to Jama.Matrix and Decomposition classes.
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2005-07-13 Bruce Miller
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* Version 1.0.2 Released.
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2005-07-12 Bruce Miller
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* Although SVD sometimes fails on cases m < n,
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the test code, apparently successfully, invokes
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SVD to compute rank on an m<n matrix. Deferring
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proposal to signal error on m < n, for now.
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Possible fix to generalize SVD to come later.
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2005-03-04 Roldan Pozo
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* Updated CholeskyDecomposition to fix problem with scaling by
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diagonals; modified TestMatrix to better test this case.
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2003-07-28 Cleve Moler
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* Updated SingularValueDecomposition to repair lack of convergience
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on rank deficient matrices.
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* Note that SVD still doesn't handle m < n
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Apparently this case can be handled by a pre-transposition,
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or more directly by a modification to the source, but this
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modified version isn't available yet.
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2000-09-11 Bruce Miller <bruce.miller@nist.gov>
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* Version 1.0.1 Released.
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2000-09-11 Bruce Miller <bruce.miller@nist.gov>
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* Jama.Matrix print methods which create a NumberFormat, now set its
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Locale to US so that the reader will recognize them even when the
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default locale is not US. Similar change to Jama.test.TestMatrix.
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(Thanks Ulrich Eberhardinger <u.eberhardinger@ipc.uni-stuttgart.de>)
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1998-08-05 The Jama Team
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* Initial Version released (1.0.0)
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‎sources/net.sf.j2s.core/test/src/gov/nist/jama/CholeskyDecomposition.java‎

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package gov.nist.jama;
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/** Cholesky Decomposition.
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<P>
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For a symmetric, positive definite matrix A, the Cholesky decomposition
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is an lower triangular matrix L so that A = L*L'.
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<P>
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If the matrix is not symmetric or positive definite, the constructor
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returns a partial decomposition and sets an internal flag that may
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be queried by the isSPD() method.
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*/
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public class CholeskyDecomposition implements java.io.Serializable {
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/* ------------------------
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Class variables
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* ------------------------ */
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/** Array for internal storage of decomposition.
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@serial internal array storage.
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*/
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private double[][] L;
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/** Row and column dimension (square matrix).
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@serial matrix dimension.
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*/
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private int n;
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/** Symmetric and positive definite flag.
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@serial is symmetric and positive definite flag.
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*/
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private boolean isspd;
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/* ------------------------
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Constructor
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* ------------------------ */
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/** Cholesky algorithm for symmetric and positive definite matrix.
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Structure to access L and isspd flag.
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@param Arg Square, symmetric matrix.
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*/
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public CholeskyDecomposition (Matrix Arg) {
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// Initialize.
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double[][] A = Arg.getArray();
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n = Arg.getRowDimension();
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L = new double[n][n];
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isspd = (Arg.getColumnDimension() == n);
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// Main loop.
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for (int j = 0; j < n; j++) {
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double[] Lrowj = L[j];
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double d = 0.0;
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for (int k = 0; k < j; k++) {
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double[] Lrowk = L[k];
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double s = 0.0;
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for (int i = 0; i < k; i++) {
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s += Lrowk[i]*Lrowj[i];
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}
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Lrowj[k] = s = (A[j][k] - s)/L[k][k];
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d = d + s*s;
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isspd = isspd & (A[k][j] == A[j][k]);
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}
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d = A[j][j] - d;
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isspd = isspd & (d > 0.0);
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L[j][j] = Math.sqrt(Math.max(d,0.0));
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for (int k = j+1; k < n; k++) {
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L[j][k] = 0.0;
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}
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}
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}
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/* ------------------------
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Temporary, experimental code.
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* ------------------------ *\
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\** Right Triangular Cholesky Decomposition.
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<P>
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For a symmetric, positive definite matrix A, the Right Cholesky
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decomposition is an upper triangular matrix R so that A = R'*R.
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This constructor computes R with the Fortran inspired column oriented
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algorithm used in LINPACK and MATLAB. In Java, we suspect a row oriented,
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lower triangular decomposition is faster. We have temporarily included
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this constructor here until timing experiments confirm this suspicion.
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*\
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\** Array for internal storage of right triangular decomposition. **\
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private transient double[][] R;
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\** Cholesky algorithm for symmetric and positive definite matrix.
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@param A Square, symmetric matrix.
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@param rightflag Actual value ignored.
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@return Structure to access R and isspd flag.
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*\
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public CholeskyDecomposition (Matrix Arg, int rightflag) {
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// Initialize.
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double[][] A = Arg.getArray();
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n = Arg.getColumnDimension();
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R = new double[n][n];
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isspd = (Arg.getColumnDimension() == n);
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// Main loop.
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for (int j = 0; j < n; j++) {
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double d = 0.0;
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for (int k = 0; k < j; k++) {
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double s = A[k][j];
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for (int i = 0; i < k; i++) {
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s = s - R[i][k]*R[i][j];
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}
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R[k][j] = s = s/R[k][k];
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d = d + s*s;
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isspd = isspd & (A[k][j] == A[j][k]);
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}
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d = A[j][j] - d;
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isspd = isspd & (d > 0.0);
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R[j][j] = Math.sqrt(Math.max(d,0.0));
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for (int k = j+1; k < n; k++) {
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R[k][j] = 0.0;
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}
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}
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}
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\** Return upper triangular factor.
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@return R
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*\
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public Matrix getR () {
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return new Matrix(R,n,n);
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}
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\* ------------------------
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End of temporary code.
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* ------------------------ */
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/* ------------------------
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Public Methods
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* ------------------------ */
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/** Is the matrix symmetric and positive definite?
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@return true if A is symmetric and positive definite.
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*/
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public boolean isSPD () {
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return isspd;
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}
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/** Return triangular factor.
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@return L
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*/
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public Matrix getL () {
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return new Matrix(L,n,n);
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}
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/** Solve A*X = B
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@param B A Matrix with as many rows as A and any number of columns.
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@return X so that L*L'*X = B
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@exception IllegalArgumentException Matrix row dimensions must agree.
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@exception RuntimeException Matrix is not symmetric positive definite.
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*/
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public Matrix solve (Matrix B) {
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if (B.getRowDimension() != n) {
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throw new IllegalArgumentException("Matrix row dimensions must agree.");
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}
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if (!isspd) {
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throw new RuntimeException("Matrix is not symmetric positive definite.");
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}
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// Copy right hand side.
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double[][] X = B.getArrayCopy();
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int nx = B.getColumnDimension();
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// Solve L*Y = B;
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for (int k = 0; k < n; k++) {
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for (int j = 0; j < nx; j++) {
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for (int i = 0; i < k ; i++) {
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X[k][j] -= X[i][j]*L[k][i];
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}
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X[k][j] /= L[k][k];
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}
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}
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// Solve L'*X = Y;
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for (int k = n-1; k >= 0; k--) {
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for (int j = 0; j < nx; j++) {
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for (int i = k+1; i < n ; i++) {
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X[k][j] -= X[i][j]*L[i][k];
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}
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X[k][j] /= L[k][k];
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}
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}
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return new Matrix(X,n,nx);
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}
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private static final long serialVersionUID = 1;
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}
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