# | Line 78 | Line 78 | namespace oopse { | |
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78 | return m; | |
79 | } | |
80 | ||
81 | < | /** Retunrs the inversion of this matrix. */ |
81 | > | /** |
82 | > | * Retunrs the inversion of this matrix. |
83 | > | * @todo need implementation |
84 | > | */ |
85 | SquareMatrix<Real, Dim> inverse() { | |
86 | SquareMatrix<Real, Dim> result; | |
87 | ||
88 | return result; | |
89 | } | |
90 | ||
91 | < | /** Returns the determinant of this matrix. */ |
92 | < | double determinant() const { |
93 | < | double det; |
91 | > | /** |
92 | > | * Returns the determinant of this matrix. |
93 | > | * @todo need implementation |
94 | > | */ |
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 142 | Line 148 | namespace oopse { | |
148 | return true; | |
149 | } | |
150 | ||
151 | + | /** @todo need implementation */ |
152 | void diagonalize() { | |
153 | < | jacobi(m, eigenValues, ortMat); |
153 | > | //jacobi(m, eigenValues, ortMat); |
154 | } | |
155 | ||
156 | /** | |
# | Line 158 | Line 165 | namespace oopse { | |
165 | SquareMatrix<Real, Dim> ortMat; | |
166 | ||
167 | if ( !isSymmetric()){ | |
168 | < | throw(); |
168 | > | //throw(); |
169 | } | |
170 | ||
171 | SquareMatrix<Real, Dim> m(*this); | |
# | Line 175 | Line 182 | namespace oopse { | |
182 | * @param w output eigenvalues | |
183 | * @param v output eigenvectors | |
184 | */ | |
185 | < | void jacobi(const SquareMatrix<Real, Dim>& a, |
179 | < | Vector<Real, Dim>& w, |
185 | > | bool jacobi(SquareMatrix<Real, Dim>& a, Vector<Real, Dim>& w, |
186 | SquareMatrix<Real, Dim>& v); | |
187 | };//end SquareMatrix | |
188 | ||
# | Line 184 | Line 190 | namespace oopse { | |
190 | #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 | |
192 | ||
193 | < | template<Real, int Dim> |
194 | < | void SquareMatrix<Real, int Dim>::jacobi(SquareMatrix<Real, Dim>& a, |
195 | < | Vector<Real, Dim>& w, |
190 | < | SquareMatrix<Real, Dim>& v) { |
193 | > | template<typename Real, int Dim> |
194 | > | bool SquareMatrix<Real, Dim>::jacobi(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; |
198 | > | Real tresh, theta, tau, t, sm, s, h, g, c; |
199 | > | Real tmp; |
200 | Vector<Real, Dim> b, z; | |
201 | ||
202 | // initialize | |
203 | < | for (ip=0; ip<N; ip++) |
204 | < | { |
205 | < | for (iq=0; iq<N; iq++) v(ip, iq) = 0.0; |
206 | < | v(ip, ip) = 1.0; |
203 | > | for (ip=0; ip<N; ip++) { |
204 | > | for (iq=0; iq<N; iq++) |
205 | > | v(ip, iq) = 0.0; |
206 | > | v(ip, ip) = 1.0; |
207 | } | |
208 | < | for (ip=0; ip<N; ip++) |
209 | < | { |
210 | < | b(ip) = w(ip) = a(ip, ip); |
211 | < | z(ip) = 0.0; |
208 | > | |
209 | > | for (ip=0; ip<N; ip++) { |
210 | > | b(ip) = w(ip) = a(ip, ip); |
211 | > | z(ip) = 0.0; |
212 | } | |
213 | ||
214 | // begin rotation sequence | |
215 | < | for (i=0; i<MAX_ROTATIONS; i++) |
216 | < | { |
217 | < | sm = 0.0; |
218 | < | for (ip=0; ip<2; ip++) |
219 | < | { |
220 | < | for (iq=ip+1; iq<N; iq++) sm += fabs(a(ip, iq)); |
221 | < | } |
222 | < | if (sm == 0.0) break; |
215 | > | for (i=0; i<MAX_ROTATIONS; i++) { |
216 | > | 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; |
224 | ||
225 | < | if (i < 4) tresh = 0.2*sm/(9); |
226 | < | else tresh = 0.0; |
225 | > | if (i < 4) |
226 | > | tresh = 0.2*sm/(9); |
227 | > | else |
228 | > | tresh = 0.0; |
229 | ||
230 | < | for (ip=0; ip<2; ip++) |
231 | < | { |
232 | < | for (iq=ip+1; iq<N; iq++) |
233 | < | { |
234 | < | g = 100.0*fabs(a(ip, iq)); |
235 | < | if (i > 4 && (fabs(w(ip))+g) == fabs(w(ip)) |
236 | < | && (fabs(w(iq))+g) == fabs(w(iq))) |
237 | < | { |
238 | < | a(ip, iq) = 0.0; |
239 | < | } |
240 | < | else if (fabs(a(ip, iq)) > tresh) |
241 | < | { |
242 | < | h = w(iq) - w(ip); |
235 | < | if ( (fabs(h)+g) == fabs(h)) t = (a(ip, iq)) / h; |
236 | < | else |
237 | < | { |
238 | < | theta = 0.5*h / (a(ip, iq)); |
239 | < | t = 1.0 / (fabs(theta)+sqrt(1.0+theta*theta)); |
240 | < | if (theta < 0.0) t = -t; |
241 | < | } |
242 | < | c = 1.0 / sqrt(1+t*t); |
243 | < | s = t*c; |
244 | < | tau = s/(1.0+c); |
245 | < | h = t*a(ip, iq); |
246 | < | z(ip) -= h; |
247 | < | z(iq) += h; |
248 | < | w(ip) -= h; |
249 | < | w(iq) += h; |
250 | < | a(ip, iq)=0.0; |
251 | < | for (j=0;j<ip-1;j++) |
252 | < | { |
253 | < | ROT(a,j,ip,j,iq); |
254 | < | } |
255 | < | for (j=ip+1;j<iq-1;j++) |
256 | < | { |
257 | < | ROT(a,ip,j,j,iq); |
258 | < | } |
259 | < | for (j=iq+1; j<N; j++) |
260 | < | { |
261 | < | ROT(a,ip,j,iq,j); |
262 | < | } |
263 | < | for (j=0; j<N; j++) |
264 | < | { |
265 | < | ROT(v,j,ip,j,iq); |
266 | < | } |
267 | < | } |
268 | < | } |
269 | < | } |
230 | > | for (ip=0; ip<2; ip++) { |
231 | > | 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)); |
243 | ||
244 | < | for (ip=0; ip<N; ip++) |
245 | < | { |
246 | < | b(ip) += z(ip); |
274 | < | w(ip) = b(ip); |
275 | < | z(ip) = 0.0; |
276 | < | } |
277 | < | } |
244 | > | if (theta < 0.0) |
245 | > | t = -t; |
246 | > | } |
247 | ||
248 | + | c = 1.0 / sqrt(1+t*t); |
249 | + | s = t*c; |
250 | + | tau = s/(1.0+c); |
251 | + | h = t*a(ip, iq); |
252 | + | z(ip) -= h; |
253 | + | z(iq) += h; |
254 | + | w(ip) -= h; |
255 | + | w(iq) += h; |
256 | + | a(ip, iq)=0.0; |
257 | + | |
258 | + | for (j=0;j<ip-1;j++) |
259 | + | ROT(a,j,ip,j,iq); |
260 | + | |
261 | + | for (j=ip+1;j<iq-1;j++) |
262 | + | ROT(a,ip,j,j,iq); |
263 | + | |
264 | + | for (j=iq+1; j<N; j++) |
265 | + | 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; |
277 | + | } |
278 | + | |
279 | + | } // end for (i=0; i<MAX_ROTATIONS; i++) |
280 | + | |
281 | if ( i >= MAX_ROTATIONS ) | |
282 | < | return false; |
282 | > | return false; |
283 | ||
284 | // sort eigenfunctions | |
285 | < | for (j=0; j<N; j++) |
286 | < | { |
287 | < | k = j; |
288 | < | tmp = w(k); |
289 | < | for (i=j; i<N; i++) |
290 | < | { |
291 | < | if (w(i) >= tmp) |
292 | < | { |
293 | < | k = i; |
294 | < | tmp = w(k); |
295 | < | } |
296 | < | } |
297 | < | if (k != j) |
298 | < | { |
299 | < | w(k) = w(j); |
300 | < | w(j) = tmp; |
301 | < | for (i=0; i<N; i++) |
302 | < | { |
303 | < | tmp = v(i, j); |
302 | < | v(i, j) = v(i, k); |
303 | < | v(i, k) = tmp; |
304 | < | } |
305 | < | } |
285 | > | for (j=0; j<N; j++) { |
286 | > | k = j; |
287 | > | tmp = w(k); |
288 | > | for (i=j; i<N; i++) { |
289 | > | if (w(i) >= tmp) { |
290 | > | k = i; |
291 | > | tmp = w(k); |
292 | > | } |
293 | > | } |
294 | > | |
295 | > | if (k != j) { |
296 | > | w(k) = w(j); |
297 | > | w(j) = tmp; |
298 | > | for (i=0; i<N; i++) { |
299 | > | tmp = v(i, j); |
300 | > | v(i, j) = v(i, k); |
301 | > | v(i, k) = tmp; |
302 | > | } |
303 | > | } |
304 | } | |
305 | ||
306 | // insure eigenvector consistency (i.e., Jacobi can compute | |
# | Line 311 | Line 309 | void SquareMatrix<Real, int Dim>::jacobi(SquareMatrix< | |
309 | // hyperstreamline/other stuff. We will select the most | |
310 | // positive eigenvector. | |
311 | int numPos; | |
312 | < | for (j=0; j<N; j++) |
313 | < | { |
314 | < | for (numPos=0, i=0; i<N; i++) if ( v(i, j) >= 0.0 ) numPos++; |
317 | < | if ( numPos < 2 ) for(i=0; i<N; i++) v(i, j) *= -1.0; |
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; |
315 | } | |
316 | ||
317 | return true; | |
# | Line 325 | Line 322 | void SquareMatrix<Real, int Dim>::jacobi(SquareMatrix< | |
322 | ||
323 | } | |
324 | ||
328 | – | |
329 | – | } |
325 | #endif //MATH_SQUAREMATRIX_HPP |
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