Raymond | dee0849 | 2015-04-02 10:43:13 -0700 | [diff] [blame] | 1 | /* |
| 2 | * Licensed to the Apache Software Foundation (ASF) under one or more |
| 3 | * contributor license agreements. See the NOTICE file distributed with |
| 4 | * this work for additional information regarding copyright ownership. |
| 5 | * The ASF licenses this file to You under the Apache License, Version 2.0 |
| 6 | * (the "License"); you may not use this file except in compliance with |
| 7 | * the License. You may obtain a copy of the License at |
| 8 | * |
| 9 | * http://www.apache.org/licenses/LICENSE-2.0 |
| 10 | * |
| 11 | * Unless required by applicable law or agreed to in writing, software |
| 12 | * distributed under the License is distributed on an "AS IS" BASIS, |
| 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 14 | * See the License for the specific language governing permissions and |
| 15 | * limitations under the License. |
| 16 | */ |
| 17 | |
| 18 | package org.apache.commons.math.linear; |
| 19 | |
| 20 | import org.apache.commons.math.MathRuntimeException; |
| 21 | import org.apache.commons.math.MaxIterationsExceededException; |
| 22 | import org.apache.commons.math.exception.util.LocalizedFormats; |
| 23 | import org.apache.commons.math.util.MathUtils; |
| 24 | import org.apache.commons.math.util.FastMath; |
| 25 | |
| 26 | /** |
| 27 | * Calculates the eigen decomposition of a real <strong>symmetric</strong> |
| 28 | * matrix. |
| 29 | * <p> |
| 30 | * The eigen decomposition of matrix A is a set of two matrices: V and D such |
| 31 | * that A = V D V<sup>T</sup>. A, V and D are all m × m matrices. |
| 32 | * </p> |
| 33 | * <p> |
| 34 | * As of 2.0, this class supports only <strong>symmetric</strong> matrices, and |
| 35 | * hence computes only real realEigenvalues. This implies the D matrix returned |
| 36 | * by {@link #getD()} is always diagonal and the imaginary values returned |
| 37 | * {@link #getImagEigenvalue(int)} and {@link #getImagEigenvalues()} are always |
| 38 | * null. |
| 39 | * </p> |
| 40 | * <p> |
| 41 | * When called with a {@link RealMatrix} argument, this implementation only uses |
| 42 | * the upper part of the matrix, the part below the diagonal is not accessed at |
| 43 | * all. |
| 44 | * </p> |
| 45 | * <p> |
| 46 | * This implementation is based on the paper by A. Drubrulle, R.S. Martin and |
| 47 | * J.H. Wilkinson 'The Implicit QL Algorithm' in Wilksinson and Reinsch (1971) |
| 48 | * Handbook for automatic computation, vol. 2, Linear algebra, Springer-Verlag, |
| 49 | * New-York |
| 50 | * </p> |
| 51 | * @version $Revision: 1002040 $ $Date: 2010-09-28 09:18:31 +0200 (mar. 28 sept. 2010) $ |
| 52 | * @since 2.0 |
| 53 | */ |
| 54 | public class EigenDecompositionImpl implements EigenDecomposition { |
| 55 | |
| 56 | /** Maximum number of iterations accepted in the implicit QL transformation */ |
| 57 | private byte maxIter = 30; |
| 58 | |
| 59 | /** Main diagonal of the tridiagonal matrix. */ |
| 60 | private double[] main; |
| 61 | |
| 62 | /** Secondary diagonal of the tridiagonal matrix. */ |
| 63 | private double[] secondary; |
| 64 | |
| 65 | /** |
| 66 | * Transformer to tridiagonal (may be null if matrix is already |
| 67 | * tridiagonal). |
| 68 | */ |
| 69 | private TriDiagonalTransformer transformer; |
| 70 | |
| 71 | /** Real part of the realEigenvalues. */ |
| 72 | private double[] realEigenvalues; |
| 73 | |
| 74 | /** Imaginary part of the realEigenvalues. */ |
| 75 | private double[] imagEigenvalues; |
| 76 | |
| 77 | /** Eigenvectors. */ |
| 78 | private ArrayRealVector[] eigenvectors; |
| 79 | |
| 80 | /** Cached value of V. */ |
| 81 | private RealMatrix cachedV; |
| 82 | |
| 83 | /** Cached value of D. */ |
| 84 | private RealMatrix cachedD; |
| 85 | |
| 86 | /** Cached value of Vt. */ |
| 87 | private RealMatrix cachedVt; |
| 88 | |
| 89 | /** |
| 90 | * Calculates the eigen decomposition of the given symmetric matrix. |
| 91 | * @param matrix The <strong>symmetric</strong> matrix to decompose. |
| 92 | * @param splitTolerance dummy parameter, present for backward compatibility only. |
| 93 | * @exception InvalidMatrixException (wrapping a |
| 94 | * {@link org.apache.commons.math.ConvergenceException} if algorithm |
| 95 | * fails to converge |
| 96 | */ |
| 97 | public EigenDecompositionImpl(final RealMatrix matrix,final double splitTolerance) |
| 98 | throws InvalidMatrixException { |
| 99 | if (isSymmetric(matrix)) { |
| 100 | transformToTridiagonal(matrix); |
| 101 | findEigenVectors(transformer.getQ().getData()); |
| 102 | } else { |
| 103 | // as of 2.0, non-symmetric matrices (i.e. complex eigenvalues) are |
| 104 | // NOT supported |
| 105 | // see issue https://issues.apache.org/jira/browse/MATH-235 |
| 106 | throw new InvalidMatrixException( |
| 107 | LocalizedFormats.ASSYMETRIC_EIGEN_NOT_SUPPORTED); |
| 108 | } |
| 109 | } |
| 110 | |
| 111 | /** |
| 112 | * Calculates the eigen decomposition of the symmetric tridiagonal |
| 113 | * matrix. The Householder matrix is assumed to be the identity matrix. |
| 114 | * @param main Main diagonal of the symmetric triadiagonal form |
| 115 | * @param secondary Secondary of the tridiagonal form |
| 116 | * @param splitTolerance dummy parameter, present for backward compatibility only. |
| 117 | * @exception InvalidMatrixException (wrapping a |
| 118 | * {@link org.apache.commons.math.ConvergenceException} if algorithm |
| 119 | * fails to converge |
| 120 | */ |
| 121 | public EigenDecompositionImpl(final double[] main,final double[] secondary, |
| 122 | final double splitTolerance) |
| 123 | throws InvalidMatrixException { |
| 124 | this.main = main.clone(); |
| 125 | this.secondary = secondary.clone(); |
| 126 | transformer = null; |
| 127 | final int size=main.length; |
| 128 | double[][] z = new double[size][size]; |
| 129 | for (int i=0;i<size;i++) { |
| 130 | z[i][i]=1.0; |
| 131 | } |
| 132 | findEigenVectors(z); |
| 133 | } |
| 134 | |
| 135 | /** |
| 136 | * Check if a matrix is symmetric. |
| 137 | * @param matrix |
| 138 | * matrix to check |
| 139 | * @return true if matrix is symmetric |
| 140 | */ |
| 141 | private boolean isSymmetric(final RealMatrix matrix) { |
| 142 | final int rows = matrix.getRowDimension(); |
| 143 | final int columns = matrix.getColumnDimension(); |
| 144 | final double eps = 10 * rows * columns * MathUtils.EPSILON; |
| 145 | for (int i = 0; i < rows; ++i) { |
| 146 | for (int j = i + 1; j < columns; ++j) { |
| 147 | final double mij = matrix.getEntry(i, j); |
| 148 | final double mji = matrix.getEntry(j, i); |
| 149 | if (FastMath.abs(mij - mji) > |
| 150 | (FastMath.max(FastMath.abs(mij), FastMath.abs(mji)) * eps)) { |
| 151 | return false; |
| 152 | } |
| 153 | } |
| 154 | } |
| 155 | return true; |
| 156 | } |
| 157 | |
| 158 | /** {@inheritDoc} */ |
| 159 | public RealMatrix getV() throws InvalidMatrixException { |
| 160 | |
| 161 | if (cachedV == null) { |
| 162 | final int m = eigenvectors.length; |
| 163 | cachedV = MatrixUtils.createRealMatrix(m, m); |
| 164 | for (int k = 0; k < m; ++k) { |
| 165 | cachedV.setColumnVector(k, eigenvectors[k]); |
| 166 | } |
| 167 | } |
| 168 | // return the cached matrix |
| 169 | return cachedV; |
| 170 | |
| 171 | } |
| 172 | |
| 173 | /** {@inheritDoc} */ |
| 174 | public RealMatrix getD() throws InvalidMatrixException { |
| 175 | if (cachedD == null) { |
| 176 | // cache the matrix for subsequent calls |
| 177 | cachedD = MatrixUtils.createRealDiagonalMatrix(realEigenvalues); |
| 178 | } |
| 179 | return cachedD; |
| 180 | } |
| 181 | |
| 182 | /** {@inheritDoc} */ |
| 183 | public RealMatrix getVT() throws InvalidMatrixException { |
| 184 | |
| 185 | if (cachedVt == null) { |
| 186 | final int m = eigenvectors.length; |
| 187 | cachedVt = MatrixUtils.createRealMatrix(m, m); |
| 188 | for (int k = 0; k < m; ++k) { |
| 189 | cachedVt.setRowVector(k, eigenvectors[k]); |
| 190 | } |
| 191 | |
| 192 | } |
| 193 | |
| 194 | // return the cached matrix |
| 195 | return cachedVt; |
| 196 | } |
| 197 | |
| 198 | /** {@inheritDoc} */ |
| 199 | public double[] getRealEigenvalues() throws InvalidMatrixException { |
| 200 | return realEigenvalues.clone(); |
| 201 | } |
| 202 | |
| 203 | /** {@inheritDoc} */ |
| 204 | public double getRealEigenvalue(final int i) throws InvalidMatrixException, |
| 205 | ArrayIndexOutOfBoundsException { |
| 206 | return realEigenvalues[i]; |
| 207 | } |
| 208 | |
| 209 | /** {@inheritDoc} */ |
| 210 | public double[] getImagEigenvalues() throws InvalidMatrixException { |
| 211 | return imagEigenvalues.clone(); |
| 212 | } |
| 213 | |
| 214 | /** {@inheritDoc} */ |
| 215 | public double getImagEigenvalue(final int i) throws InvalidMatrixException, |
| 216 | ArrayIndexOutOfBoundsException { |
| 217 | return imagEigenvalues[i]; |
| 218 | } |
| 219 | |
| 220 | /** {@inheritDoc} */ |
| 221 | public RealVector getEigenvector(final int i) |
| 222 | throws InvalidMatrixException, ArrayIndexOutOfBoundsException { |
| 223 | return eigenvectors[i].copy(); |
| 224 | } |
| 225 | |
| 226 | /** |
| 227 | * Return the determinant of the matrix |
| 228 | * @return determinant of the matrix |
| 229 | */ |
| 230 | public double getDeterminant() { |
| 231 | double determinant = 1; |
| 232 | for (double lambda : realEigenvalues) { |
| 233 | determinant *= lambda; |
| 234 | } |
| 235 | return determinant; |
| 236 | } |
| 237 | |
| 238 | /** {@inheritDoc} */ |
| 239 | public DecompositionSolver getSolver() { |
| 240 | return new Solver(realEigenvalues, imagEigenvalues, eigenvectors); |
| 241 | } |
| 242 | |
| 243 | /** Specialized solver. */ |
| 244 | private static class Solver implements DecompositionSolver { |
| 245 | |
| 246 | /** Real part of the realEigenvalues. */ |
| 247 | private double[] realEigenvalues; |
| 248 | |
| 249 | /** Imaginary part of the realEigenvalues. */ |
| 250 | private double[] imagEigenvalues; |
| 251 | |
| 252 | /** Eigenvectors. */ |
| 253 | private final ArrayRealVector[] eigenvectors; |
| 254 | |
| 255 | /** |
| 256 | * Build a solver from decomposed matrix. |
| 257 | * @param realEigenvalues |
| 258 | * real parts of the eigenvalues |
| 259 | * @param imagEigenvalues |
| 260 | * imaginary parts of the eigenvalues |
| 261 | * @param eigenvectors |
| 262 | * eigenvectors |
| 263 | */ |
| 264 | private Solver(final double[] realEigenvalues, |
| 265 | final double[] imagEigenvalues, |
| 266 | final ArrayRealVector[] eigenvectors) { |
| 267 | this.realEigenvalues = realEigenvalues; |
| 268 | this.imagEigenvalues = imagEigenvalues; |
| 269 | this.eigenvectors = eigenvectors; |
| 270 | } |
| 271 | |
| 272 | /** |
| 273 | * Solve the linear equation A × X = B for symmetric matrices A. |
| 274 | * <p> |
| 275 | * This method only find exact linear solutions, i.e. solutions for |
| 276 | * which ||A × X - B|| is exactly 0. |
| 277 | * </p> |
| 278 | * @param b |
| 279 | * right-hand side of the equation A × X = B |
| 280 | * @return a vector X that minimizes the two norm of A × X - B |
| 281 | * @exception IllegalArgumentException |
| 282 | * if matrices dimensions don't match |
| 283 | * @exception InvalidMatrixException |
| 284 | * if decomposed matrix is singular |
| 285 | */ |
| 286 | public double[] solve(final double[] b) |
| 287 | throws IllegalArgumentException, InvalidMatrixException { |
| 288 | |
| 289 | if (!isNonSingular()) { |
| 290 | throw new SingularMatrixException(); |
| 291 | } |
| 292 | |
| 293 | final int m = realEigenvalues.length; |
| 294 | if (b.length != m) { |
| 295 | throw MathRuntimeException.createIllegalArgumentException( |
| 296 | LocalizedFormats.VECTOR_LENGTH_MISMATCH, |
| 297 | b.length, m); |
| 298 | } |
| 299 | |
| 300 | final double[] bp = new double[m]; |
| 301 | for (int i = 0; i < m; ++i) { |
| 302 | final ArrayRealVector v = eigenvectors[i]; |
| 303 | final double[] vData = v.getDataRef(); |
| 304 | final double s = v.dotProduct(b) / realEigenvalues[i]; |
| 305 | for (int j = 0; j < m; ++j) { |
| 306 | bp[j] += s * vData[j]; |
| 307 | } |
| 308 | } |
| 309 | |
| 310 | return bp; |
| 311 | |
| 312 | } |
| 313 | |
| 314 | /** |
| 315 | * Solve the linear equation A × X = B for symmetric matrices A. |
| 316 | * <p> |
| 317 | * This method only find exact linear solutions, i.e. solutions for |
| 318 | * which ||A × X - B|| is exactly 0. |
| 319 | * </p> |
| 320 | * @param b |
| 321 | * right-hand side of the equation A × X = B |
| 322 | * @return a vector X that minimizes the two norm of A × X - B |
| 323 | * @exception IllegalArgumentException |
| 324 | * if matrices dimensions don't match |
| 325 | * @exception InvalidMatrixException |
| 326 | * if decomposed matrix is singular |
| 327 | */ |
| 328 | public RealVector solve(final RealVector b) |
| 329 | throws IllegalArgumentException, InvalidMatrixException { |
| 330 | |
| 331 | if (!isNonSingular()) { |
| 332 | throw new SingularMatrixException(); |
| 333 | } |
| 334 | |
| 335 | final int m = realEigenvalues.length; |
| 336 | if (b.getDimension() != m) { |
| 337 | throw MathRuntimeException.createIllegalArgumentException( |
| 338 | LocalizedFormats.VECTOR_LENGTH_MISMATCH, b |
| 339 | .getDimension(), m); |
| 340 | } |
| 341 | |
| 342 | final double[] bp = new double[m]; |
| 343 | for (int i = 0; i < m; ++i) { |
| 344 | final ArrayRealVector v = eigenvectors[i]; |
| 345 | final double[] vData = v.getDataRef(); |
| 346 | final double s = v.dotProduct(b) / realEigenvalues[i]; |
| 347 | for (int j = 0; j < m; ++j) { |
| 348 | bp[j] += s * vData[j]; |
| 349 | } |
| 350 | } |
| 351 | |
| 352 | return new ArrayRealVector(bp, false); |
| 353 | |
| 354 | } |
| 355 | |
| 356 | /** |
| 357 | * Solve the linear equation A × X = B for symmetric matrices A. |
| 358 | * <p> |
| 359 | * This method only find exact linear solutions, i.e. solutions for |
| 360 | * which ||A × X - B|| is exactly 0. |
| 361 | * </p> |
| 362 | * @param b |
| 363 | * right-hand side of the equation A × X = B |
| 364 | * @return a matrix X that minimizes the two norm of A × X - B |
| 365 | * @exception IllegalArgumentException |
| 366 | * if matrices dimensions don't match |
| 367 | * @exception InvalidMatrixException |
| 368 | * if decomposed matrix is singular |
| 369 | */ |
| 370 | public RealMatrix solve(final RealMatrix b) |
| 371 | throws IllegalArgumentException, InvalidMatrixException { |
| 372 | |
| 373 | if (!isNonSingular()) { |
| 374 | throw new SingularMatrixException(); |
| 375 | } |
| 376 | |
| 377 | final int m = realEigenvalues.length; |
| 378 | if (b.getRowDimension() != m) { |
| 379 | throw MathRuntimeException |
| 380 | .createIllegalArgumentException( |
| 381 | LocalizedFormats.DIMENSIONS_MISMATCH_2x2, |
| 382 | b.getRowDimension(), b.getColumnDimension(), m, |
| 383 | "n"); |
| 384 | } |
| 385 | |
| 386 | final int nColB = b.getColumnDimension(); |
| 387 | final double[][] bp = new double[m][nColB]; |
| 388 | for (int k = 0; k < nColB; ++k) { |
| 389 | for (int i = 0; i < m; ++i) { |
| 390 | final ArrayRealVector v = eigenvectors[i]; |
| 391 | final double[] vData = v.getDataRef(); |
| 392 | double s = 0; |
| 393 | for (int j = 0; j < m; ++j) { |
| 394 | s += v.getEntry(j) * b.getEntry(j, k); |
| 395 | } |
| 396 | s /= realEigenvalues[i]; |
| 397 | for (int j = 0; j < m; ++j) { |
| 398 | bp[j][k] += s * vData[j]; |
| 399 | } |
| 400 | } |
| 401 | } |
| 402 | |
| 403 | return MatrixUtils.createRealMatrix(bp); |
| 404 | |
| 405 | } |
| 406 | |
| 407 | /** |
| 408 | * Check if the decomposed matrix is non-singular. |
| 409 | * @return true if the decomposed matrix is non-singular |
| 410 | */ |
| 411 | public boolean isNonSingular() { |
| 412 | for (int i = 0; i < realEigenvalues.length; ++i) { |
| 413 | if ((realEigenvalues[i] == 0) && (imagEigenvalues[i] == 0)) { |
| 414 | return false; |
| 415 | } |
| 416 | } |
| 417 | return true; |
| 418 | } |
| 419 | |
| 420 | /** |
| 421 | * Get the inverse of the decomposed matrix. |
| 422 | * @return inverse matrix |
| 423 | * @throws InvalidMatrixException |
| 424 | * if decomposed matrix is singular |
| 425 | */ |
| 426 | public RealMatrix getInverse() throws InvalidMatrixException { |
| 427 | |
| 428 | if (!isNonSingular()) { |
| 429 | throw new SingularMatrixException(); |
| 430 | } |
| 431 | |
| 432 | final int m = realEigenvalues.length; |
| 433 | final double[][] invData = new double[m][m]; |
| 434 | |
| 435 | for (int i = 0; i < m; ++i) { |
| 436 | final double[] invI = invData[i]; |
| 437 | for (int j = 0; j < m; ++j) { |
| 438 | double invIJ = 0; |
| 439 | for (int k = 0; k < m; ++k) { |
| 440 | final double[] vK = eigenvectors[k].getDataRef(); |
| 441 | invIJ += vK[i] * vK[j] / realEigenvalues[k]; |
| 442 | } |
| 443 | invI[j] = invIJ; |
| 444 | } |
| 445 | } |
| 446 | return MatrixUtils.createRealMatrix(invData); |
| 447 | |
| 448 | } |
| 449 | |
| 450 | } |
| 451 | |
| 452 | /** |
| 453 | * Transform matrix to tridiagonal. |
| 454 | * @param matrix |
| 455 | * matrix to transform |
| 456 | */ |
| 457 | private void transformToTridiagonal(final RealMatrix matrix) { |
| 458 | |
| 459 | // transform the matrix to tridiagonal |
| 460 | transformer = new TriDiagonalTransformer(matrix); |
| 461 | main = transformer.getMainDiagonalRef(); |
| 462 | secondary = transformer.getSecondaryDiagonalRef(); |
| 463 | |
| 464 | } |
| 465 | |
| 466 | /** |
| 467 | * Find eigenvalues and eigenvectors (Dubrulle et al., 1971) |
| 468 | * @param householderMatrix Householder matrix of the transformation |
| 469 | * to tri-diagonal form. |
| 470 | */ |
| 471 | private void findEigenVectors(double[][] householderMatrix) { |
| 472 | |
| 473 | double[][]z = householderMatrix.clone(); |
| 474 | final int n = main.length; |
| 475 | realEigenvalues = new double[n]; |
| 476 | imagEigenvalues = new double[n]; |
| 477 | double[] e = new double[n]; |
| 478 | for (int i = 0; i < n - 1; i++) { |
| 479 | realEigenvalues[i] = main[i]; |
| 480 | e[i] = secondary[i]; |
| 481 | } |
| 482 | realEigenvalues[n - 1] = main[n - 1]; |
| 483 | e[n - 1] = 0.0; |
| 484 | |
| 485 | // Determine the largest main and secondary value in absolute term. |
| 486 | double maxAbsoluteValue=0.0; |
| 487 | for (int i = 0; i < n; i++) { |
| 488 | if (FastMath.abs(realEigenvalues[i])>maxAbsoluteValue) { |
| 489 | maxAbsoluteValue=FastMath.abs(realEigenvalues[i]); |
| 490 | } |
| 491 | if (FastMath.abs(e[i])>maxAbsoluteValue) { |
| 492 | maxAbsoluteValue=FastMath.abs(e[i]); |
| 493 | } |
| 494 | } |
| 495 | // Make null any main and secondary value too small to be significant |
| 496 | if (maxAbsoluteValue!=0.0) { |
| 497 | for (int i=0; i < n; i++) { |
| 498 | if (FastMath.abs(realEigenvalues[i])<=MathUtils.EPSILON*maxAbsoluteValue) { |
| 499 | realEigenvalues[i]=0.0; |
| 500 | } |
| 501 | if (FastMath.abs(e[i])<=MathUtils.EPSILON*maxAbsoluteValue) { |
| 502 | e[i]=0.0; |
| 503 | } |
| 504 | } |
| 505 | } |
| 506 | |
| 507 | for (int j = 0; j < n; j++) { |
| 508 | int its = 0; |
| 509 | int m; |
| 510 | do { |
| 511 | for (m = j; m < n - 1; m++) { |
| 512 | double delta = FastMath.abs(realEigenvalues[m]) + FastMath.abs(realEigenvalues[m + 1]); |
| 513 | if (FastMath.abs(e[m]) + delta == delta) { |
| 514 | break; |
| 515 | } |
| 516 | } |
| 517 | if (m != j) { |
| 518 | if (its == maxIter) |
| 519 | throw new InvalidMatrixException( |
| 520 | new MaxIterationsExceededException(maxIter)); |
| 521 | its++; |
| 522 | double q = (realEigenvalues[j + 1] - realEigenvalues[j]) / (2 * e[j]); |
| 523 | double t = FastMath.sqrt(1 + q * q); |
| 524 | if (q < 0.0) { |
| 525 | q = realEigenvalues[m] - realEigenvalues[j] + e[j] / (q - t); |
| 526 | } else { |
| 527 | q = realEigenvalues[m] - realEigenvalues[j] + e[j] / (q + t); |
| 528 | } |
| 529 | double u = 0.0; |
| 530 | double s = 1.0; |
| 531 | double c = 1.0; |
| 532 | int i; |
| 533 | for (i = m - 1; i >= j; i--) { |
| 534 | double p = s * e[i]; |
| 535 | double h = c * e[i]; |
| 536 | if (FastMath.abs(p) >= FastMath.abs(q)) { |
| 537 | c = q / p; |
| 538 | t = FastMath.sqrt(c * c + 1.0); |
| 539 | e[i + 1] = p * t; |
| 540 | s = 1.0 / t; |
| 541 | c = c * s; |
| 542 | } else { |
| 543 | s = p / q; |
| 544 | t = FastMath.sqrt(s * s + 1.0); |
| 545 | e[i + 1] = q * t; |
| 546 | c = 1.0 / t; |
| 547 | s = s * c; |
| 548 | } |
| 549 | if (e[i + 1] == 0.0) { |
| 550 | realEigenvalues[i + 1] -= u; |
| 551 | e[m] = 0.0; |
| 552 | break; |
| 553 | } |
| 554 | q = realEigenvalues[i + 1] - u; |
| 555 | t = (realEigenvalues[i] - q) * s + 2.0 * c * h; |
| 556 | u = s * t; |
| 557 | realEigenvalues[i + 1] = q + u; |
| 558 | q = c * t - h; |
| 559 | for (int ia = 0; ia < n; ia++) { |
| 560 | p = z[ia][i + 1]; |
| 561 | z[ia][i + 1] = s * z[ia][i] + c * p; |
| 562 | z[ia][i] = c * z[ia][i] - s * p; |
| 563 | } |
| 564 | } |
| 565 | if (t == 0.0 && i >= j) |
| 566 | continue; |
| 567 | realEigenvalues[j] -= u; |
| 568 | e[j] = q; |
| 569 | e[m] = 0.0; |
| 570 | } |
| 571 | } while (m != j); |
| 572 | } |
| 573 | |
| 574 | //Sort the eigen values (and vectors) in increase order |
| 575 | for (int i = 0; i < n; i++) { |
| 576 | int k = i; |
| 577 | double p = realEigenvalues[i]; |
| 578 | for (int j = i + 1; j < n; j++) { |
| 579 | if (realEigenvalues[j] > p) { |
| 580 | k = j; |
| 581 | p = realEigenvalues[j]; |
| 582 | } |
| 583 | } |
| 584 | if (k != i) { |
| 585 | realEigenvalues[k] = realEigenvalues[i]; |
| 586 | realEigenvalues[i] = p; |
| 587 | for (int j = 0; j < n; j++) { |
| 588 | p = z[j][i]; |
| 589 | z[j][i] = z[j][k]; |
| 590 | z[j][k] = p; |
| 591 | } |
| 592 | } |
| 593 | } |
| 594 | |
| 595 | // Determine the largest eigen value in absolute term. |
| 596 | maxAbsoluteValue=0.0; |
| 597 | for (int i = 0; i < n; i++) { |
| 598 | if (FastMath.abs(realEigenvalues[i])>maxAbsoluteValue) { |
| 599 | maxAbsoluteValue=FastMath.abs(realEigenvalues[i]); |
| 600 | } |
| 601 | } |
| 602 | // Make null any eigen value too small to be significant |
| 603 | if (maxAbsoluteValue!=0.0) { |
| 604 | for (int i=0; i < n; i++) { |
| 605 | if (FastMath.abs(realEigenvalues[i])<MathUtils.EPSILON*maxAbsoluteValue) { |
| 606 | realEigenvalues[i]=0.0; |
| 607 | } |
| 608 | } |
| 609 | } |
| 610 | eigenvectors = new ArrayRealVector[n]; |
| 611 | double[] tmp = new double[n]; |
| 612 | for (int i = 0; i < n; i++) { |
| 613 | for (int j = 0; j < n; j++) { |
| 614 | tmp[j] = z[j][i]; |
| 615 | } |
| 616 | eigenvectors[i] = new ArrayRealVector(tmp); |
| 617 | } |
| 618 | } |
| 619 | } |