# | Line 29 | Line 29 | |
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29 | * @date 10/11/2004 | |
30 | * @version 1.0 | |
31 | */ | |
32 | < | #ifndef MATH_SQUAREMATRIX_HPP |
32 | > | #ifndef MATH_SQUAREMATRIX_HPP |
33 | #define MATH_SQUAREMATRIX_HPP | |
34 | ||
35 | #include "math/RectMatrix.hpp" | |
# | Line 80 | Line 80 | namespace oopse { | |
80 | ||
81 | /** | |
82 | * Retunrs the inversion of this matrix. | |
83 | < | * @todo |
83 | > | * @todo need implementation |
84 | */ | |
85 | SquareMatrix<Real, Dim> inverse() { | |
86 | SquareMatrix<Real, Dim> result; | |
# | Line 90 | Line 90 | namespace oopse { | |
90 | ||
91 | /** | |
92 | * Returns the determinant of this matrix. | |
93 | < | * @todo |
93 | > | * @todo need implementation |
94 | */ | |
95 | < | double determinant() const { |
96 | < | double det; |
95 | > | Real determinant() const { |
96 | > | Real det; |
97 | return det; | |
98 | } | |
99 | ||
100 | /** Returns the trace of this matrix. */ | |
101 | < | double trace() const { |
102 | < | double tmp = 0; |
101 | > | Real trace() const { |
102 | > | Real tmp = 0; |
103 | ||
104 | for (unsigned int i = 0; i < Dim ; i++) | |
105 | tmp += data_[i][i]; | |
# | Line 148 | Line 148 | namespace oopse { | |
148 | return true; | |
149 | } | |
150 | ||
151 | < | /** @todo need implement */ |
151 | > | /** @todo need implementation */ |
152 | void diagonalize() { | |
153 | //jacobi(m, eigenValues, ortMat); | |
154 | } | |
155 | ||
156 | /** | |
157 | – | * Finds the eigenvalues and eigenvectors of a symmetric matrix |
158 | – | * @param eigenvals a reference to a vector3 where the |
159 | – | * eigenvalues will be stored. The eigenvalues are ordered so |
160 | – | * that eigenvals[0] <= eigenvals[1] <= eigenvals[2]. |
161 | – | * @return an orthogonal matrix whose ith column is an |
162 | – | * eigenvector for the eigenvalue eigenvals[i] |
163 | – | */ |
164 | – | SquareMatrix<Real, Dim> findEigenvectors(Vector<Real, Dim>& eigenValues) { |
165 | – | SquareMatrix<Real, Dim> ortMat; |
166 | – | |
167 | – | if ( !isSymmetric()){ |
168 | – | throw(); |
169 | – | } |
170 | – | |
171 | – | SquareMatrix<Real, Dim> m(*this); |
172 | – | jacobi(m, eigenValues, ortMat); |
173 | – | |
174 | – | return ortMat; |
175 | – | } |
176 | – | /** |
157 | * Jacobi iteration routines for computing eigenvalues/eigenvectors of | |
158 | * real symmetric matrix | |
159 | * | |
160 | * @return true if success, otherwise return false | |
161 | < | * @param a source matrix |
162 | < | * @param w output eigenvalues |
163 | < | * @param v output eigenvectors |
161 | > | * @param a symmetric matrix whose eigenvectors are to be computed. On return, the matrix is |
162 | > | * overwritten |
163 | > | * @param w will contain the eigenvalues of the matrix On return of this function |
164 | > | * @param v the columns of this matrix will contain the eigenvectors. The eigenvectors are |
165 | > | * normalized and mutually orthogonal. |
166 | */ | |
167 | < | bool jacobi(const SquareMatrix<Real, Dim>& a, Vector<Real, Dim>& w, |
167 | > | |
168 | > | static int jacobi(SquareMatrix<Real, Dim>& a, Vector<Real, Dim>& d, |
169 | SquareMatrix<Real, Dim>& v); | |
170 | };//end SquareMatrix | |
171 | ||
172 | ||
173 | < | #define ROT(a,i,j,k,l) g=a(i, j);h=a(k, l);a(i, j)=g-s*(h+g*tau);a(k, l)=h+s*(g-h*tau) |
191 | < | #define MAX_ROTATIONS 60 |
173 | > | /*========================================================================= |
174 | ||
175 | < | template<typename Real, int Dim> |
176 | < | bool SquareMatrix<Real, Dim>::jacobi(const SquareMatrix<Real, Dim>& a, Vector<Real, Dim>& w, |
195 | < | SquareMatrix<Real, Dim>& v) { |
196 | < | const int N = Dim; |
197 | < | int i, j, k, iq, ip; |
198 | < | double tresh, theta, tau, t, sm, s, h, g, c; |
199 | < | double tmp; |
200 | < | Vector<Real, Dim> b, z; |
175 | > | Program: Visualization Toolkit |
176 | > | Module: $RCSfile: SquareMatrix.hpp,v $ |
177 | ||
178 | < | // initialize |
179 | < | for (ip=0; ip<N; ip++) { |
180 | < | for (iq=0; iq<N; iq++) |
205 | < | v(ip, iq) = 0.0; |
206 | < | v(ip, ip) = 1.0; |
207 | < | } |
208 | < | |
209 | < | for (ip=0; ip<N; ip++) { |
210 | < | b(ip) = w(ip) = a(ip, ip); |
211 | < | z(ip) = 0.0; |
212 | < | } |
178 | > | Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen |
179 | > | All rights reserved. |
180 | > | See Copyright.txt or http://www.kitware.com/Copyright.htm for details. |
181 | ||
182 | < | // begin rotation sequence |
183 | < | for (i=0; i<MAX_ROTATIONS; i++) { |
184 | < | sm = 0.0; |
217 | < | for (ip=0; ip<2; ip++) { |
218 | < | for (iq=ip+1; iq<N; iq++) |
219 | < | sm += fabs(a(ip, iq)); |
220 | < | } |
221 | < | |
222 | < | if (sm == 0.0) |
223 | < | break; |
182 | > | This software is distributed WITHOUT ANY WARRANTY; without even |
183 | > | the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR |
184 | > | PURPOSE. See the above copyright notice for more information. |
185 | ||
186 | < | if (i < 4) |
226 | < | tresh = 0.2*sm/(9); |
227 | < | else |
228 | < | tresh = 0.0; |
186 | > | =========================================================================*/ |
187 | ||
188 | < | for (ip=0; ip<2; ip++) { |
189 | < | for (iq=ip+1; iq<N; iq++) { |
232 | < | g = 100.0*fabs(a(ip, iq)); |
233 | < | if (i > 4 && (fabs(w(ip))+g) == fabs(w(ip)) |
234 | < | && (fabs(w(iq))+g) == fabs(w(iq))) { |
235 | < | a(ip, iq) = 0.0; |
236 | < | } else if (fabs(a(ip, iq)) > tresh) { |
237 | < | h = w(iq) - w(ip); |
238 | < | if ( (fabs(h)+g) == fabs(h)) { |
239 | < | t = (a(ip, iq)) / h; |
240 | < | } else { |
241 | < | theta = 0.5*h / (a(ip, iq)); |
242 | < | t = 1.0 / (fabs(theta)+sqrt(1.0+theta*theta)); |
188 | > | #define VTK_ROTATE(a,i,j,k,l) g=a(i, j);h=a(k, l);a(i, j)=g-s*(h+g*tau);\ |
189 | > | a(k, l)=h+s*(g-h*tau) |
190 | ||
191 | < | if (theta < 0.0) |
245 | < | t = -t; |
246 | < | } |
191 | > | #define VTK_MAX_ROTATIONS 20 |
192 | ||
193 | < | c = 1.0 / sqrt(1+t*t); |
194 | < | s = t*c; |
195 | < | tau = s/(1.0+c); |
196 | < | h = t*a(ip, iq); |
197 | < | z(ip) -= h; |
198 | < | z(iq) += h; |
199 | < | w(ip) -= h; |
200 | < | w(iq) += h; |
201 | < | a(ip, iq)=0.0; |
202 | < | |
203 | < | for (j=0;j<ip-1;j++) |
204 | < | ROT(a,j,ip,j,iq); |
193 | > | // Jacobi iteration for the solution of eigenvectors/eigenvalues of a nxn |
194 | > | // real symmetric matrix. Square nxn matrix a; size of matrix in n; |
195 | > | // output eigenvalues in w; and output eigenvectors in v. Resulting |
196 | > | // eigenvalues/vectors are sorted in decreasing order; eigenvectors are |
197 | > | // normalized. |
198 | > | template<typename Real, int Dim> |
199 | > | int SquareMatrix<Real, Dim>::jacobi(SquareMatrix<Real, Dim>& a, Vector<Real, Dim>& w, |
200 | > | SquareMatrix<Real, Dim>& v) { |
201 | > | const int n = Dim; |
202 | > | int i, j, k, iq, ip, numPos; |
203 | > | Real tresh, theta, tau, t, sm, s, h, g, c, tmp; |
204 | > | Real bspace[4], zspace[4]; |
205 | > | Real *b = bspace; |
206 | > | Real *z = zspace; |
207 | ||
208 | < | for (j=ip+1;j<iq-1;j++) |
209 | < | ROT(a,ip,j,j,iq); |
210 | < | |
211 | < | for (j=iq+1; j<N; j++) |
212 | < | ROT(a,ip,j,iq,j); |
266 | < | |
267 | < | for (j=0; j<N; j++) |
268 | < | ROT(v,j,ip,j,iq); |
269 | < | } |
270 | < | } |
271 | < | }//for (ip=0; ip<2; ip++) |
272 | < | |
273 | < | for (ip=0; ip<N; ip++) { |
274 | < | b(ip) += z(ip); |
275 | < | w(ip) = b(ip); |
276 | < | z(ip) = 0.0; |
208 | > | // only allocate memory if the matrix is large |
209 | > | if (n > 4) |
210 | > | { |
211 | > | b = new Real[n]; |
212 | > | z = new Real[n]; |
213 | } | |
278 | – | |
279 | – | } // end for (i=0; i<MAX_ROTATIONS; i++) |
214 | ||
215 | < | if ( i >= MAX_ROTATIONS ) |
216 | < | return false; |
217 | < | |
218 | < | // sort eigenfunctions |
219 | < | for (j=0; j<N; j++) { |
220 | < | k = j; |
221 | < | tmp = w(k); |
222 | < | for (i=j; i<N; i++) { |
223 | < | if (w(i) >= tmp) { |
224 | < | k = i; |
225 | < | tmp = w(k); |
215 | > | // initialize |
216 | > | for (ip=0; ip<n; ip++) |
217 | > | { |
218 | > | for (iq=0; iq<n; iq++) |
219 | > | { |
220 | > | v(ip, iq) = 0.0; |
221 | > | } |
222 | > | v(ip, ip) = 1.0; |
223 | > | } |
224 | > | for (ip=0; ip<n; ip++) |
225 | > | { |
226 | > | b[ip] = w[ip] = a(ip, ip); |
227 | > | z[ip] = 0.0; |
228 | > | } |
229 | > | |
230 | > | // begin rotation sequence |
231 | > | for (i=0; i<VTK_MAX_ROTATIONS; i++) |
232 | > | { |
233 | > | sm = 0.0; |
234 | > | for (ip=0; ip<n-1; ip++) |
235 | > | { |
236 | > | for (iq=ip+1; iq<n; iq++) |
237 | > | { |
238 | > | sm += fabs(a(ip, iq)); |
239 | } | |
240 | + | } |
241 | + | if (sm == 0.0) |
242 | + | { |
243 | + | break; |
244 | + | } |
245 | + | |
246 | + | if (i < 3) // first 3 sweeps |
247 | + | { |
248 | + | tresh = 0.2*sm/(n*n); |
249 | + | } |
250 | + | else |
251 | + | { |
252 | + | tresh = 0.0; |
253 | + | } |
254 | + | |
255 | + | for (ip=0; ip<n-1; ip++) |
256 | + | { |
257 | + | for (iq=ip+1; iq<n; iq++) |
258 | + | { |
259 | + | g = 100.0*fabs(a(ip, iq)); |
260 | + | |
261 | + | // after 4 sweeps |
262 | + | if (i > 3 && (fabs(w[ip])+g) == fabs(w[ip]) |
263 | + | && (fabs(w[iq])+g) == fabs(w[iq])) |
264 | + | { |
265 | + | a(ip, iq) = 0.0; |
266 | + | } |
267 | + | else if (fabs(a(ip, iq)) > tresh) |
268 | + | { |
269 | + | h = w[iq] - w[ip]; |
270 | + | if ( (fabs(h)+g) == fabs(h)) |
271 | + | { |
272 | + | t = (a(ip, iq)) / h; |
273 | + | } |
274 | + | else |
275 | + | { |
276 | + | theta = 0.5*h / (a(ip, iq)); |
277 | + | t = 1.0 / (fabs(theta)+sqrt(1.0+theta*theta)); |
278 | + | if (theta < 0.0) |
279 | + | { |
280 | + | t = -t; |
281 | + | } |
282 | + | } |
283 | + | c = 1.0 / sqrt(1+t*t); |
284 | + | s = t*c; |
285 | + | tau = s/(1.0+c); |
286 | + | h = t*a(ip, iq); |
287 | + | z[ip] -= h; |
288 | + | z[iq] += h; |
289 | + | w[ip] -= h; |
290 | + | w[iq] += h; |
291 | + | a(ip, iq)=0.0; |
292 | + | |
293 | + | // ip already shifted left by 1 unit |
294 | + | for (j = 0;j <= ip-1;j++) |
295 | + | { |
296 | + | VTK_ROTATE(a,j,ip,j,iq); |
297 | + | } |
298 | + | // ip and iq already shifted left by 1 unit |
299 | + | for (j = ip+1;j <= iq-1;j++) |
300 | + | { |
301 | + | VTK_ROTATE(a,ip,j,j,iq); |
302 | + | } |
303 | + | // iq already shifted left by 1 unit |
304 | + | for (j=iq+1; j<n; j++) |
305 | + | { |
306 | + | VTK_ROTATE(a,ip,j,iq,j); |
307 | + | } |
308 | + | for (j=0; j<n; j++) |
309 | + | { |
310 | + | VTK_ROTATE(v,j,ip,j,iq); |
311 | + | } |
312 | + | } |
313 | + | } |
314 | + | } |
315 | + | |
316 | + | for (ip=0; ip<n; ip++) |
317 | + | { |
318 | + | b[ip] += z[ip]; |
319 | + | w[ip] = b[ip]; |
320 | + | z[ip] = 0.0; |
321 | + | } |
322 | } | |
323 | < | |
324 | < | if (k != j) { |
325 | < | w(k) = w(j); |
326 | < | w(j) = tmp; |
327 | < | for (i=0; i<N; i++) { |
328 | < | tmp = v(i, j); |
329 | < | v(i, j) = v(i, k); |
330 | < | v(i, k) = tmp; |
323 | > | |
324 | > | //// this is NEVER called |
325 | > | if ( i >= VTK_MAX_ROTATIONS ) |
326 | > | { |
327 | > | std::cout << "vtkMath::Jacobi: Error extracting eigenfunctions" << std::endl; |
328 | > | return 0; |
329 | > | } |
330 | > | |
331 | > | // sort eigenfunctions these changes do not affect accuracy |
332 | > | for (j=0; j<n-1; j++) // boundary incorrect |
333 | > | { |
334 | > | k = j; |
335 | > | tmp = w[k]; |
336 | > | for (i=j+1; i<n; i++) // boundary incorrect, shifted already |
337 | > | { |
338 | > | if (w[i] >= tmp) // why exchage if same? |
339 | > | { |
340 | > | k = i; |
341 | > | tmp = w[k]; |
342 | } | |
343 | + | } |
344 | + | if (k != j) |
345 | + | { |
346 | + | w[k] = w[j]; |
347 | + | w[j] = tmp; |
348 | + | for (i=0; i<n; i++) |
349 | + | { |
350 | + | tmp = v(i, j); |
351 | + | v(i, j) = v(i, k); |
352 | + | v(i, k) = tmp; |
353 | + | } |
354 | + | } |
355 | } | |
356 | < | } |
356 | > | // insure eigenvector consistency (i.e., Jacobi can compute vectors that |
357 | > | // are negative of one another (.707,.707,0) and (-.707,-.707,0). This can |
358 | > | // reek havoc in hyperstreamline/other stuff. We will select the most |
359 | > | // positive eigenvector. |
360 | > | int ceil_half_n = (n >> 1) + (n & 1); |
361 | > | for (j=0; j<n; j++) |
362 | > | { |
363 | > | for (numPos=0, i=0; i<n; i++) |
364 | > | { |
365 | > | if ( v(i, j) >= 0.0 ) |
366 | > | { |
367 | > | numPos++; |
368 | > | } |
369 | > | } |
370 | > | // if ( numPos < ceil(double(n)/double(2.0)) ) |
371 | > | if ( numPos < ceil_half_n) |
372 | > | { |
373 | > | for(i=0; i<n; i++) |
374 | > | { |
375 | > | v(i, j) *= -1.0; |
376 | > | } |
377 | > | } |
378 | > | } |
379 | ||
380 | < | // insure eigenvector consistency (i.e., Jacobi can compute |
381 | < | // vectors that are negative of one another (.707,.707,0) and |
382 | < | // (-.707,-.707,0). This can reek havoc in |
383 | < | // hyperstreamline/other stuff. We will select the most |
384 | < | // positive eigenvector. |
385 | < | int numPos; |
312 | < | for (j=0; j<N; j++) { |
313 | < | for (numPos=0, i=0; i<N; i++) if ( v(i, j) >= 0.0 ) numPos++; |
314 | < | if ( numPos < 2 ) for(i=0; i<N; i++) v(i, j) *= -1.0; |
380 | > | if (n > 4) |
381 | > | { |
382 | > | delete [] b; |
383 | > | delete [] z; |
384 | > | } |
385 | > | return 1; |
386 | } | |
387 | ||
317 | – | return true; |
318 | – | } |
388 | ||
320 | – | #undef ROT |
321 | – | #undef MAX_ROTATIONS |
322 | – | |
389 | } | |
324 | – | |
390 | #endif //MATH_SQUAREMATRIX_HPP | |
391 | + |
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