Changeset 8690 for trunk/anuga_core/source/anuga/utilities/test_cg_solve.py
 Timestamp:
 Feb 13, 2013, 3:26:15 PM (10 years ago)
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trunk/anuga_core/source/anuga/utilities/test_cg_solve.py
r7849 r8690 203 203 assert num.allclose(x,xe) 204 204 205 206 207 def test_sparse_solve_using_c_ext(self): 208 """Solve Small Sparse Matrix""" 209 210 A = [[2.0, 1.0, 0.0, 0.0 ], 211 [1.0, 2.0, 1.0, 0.0], 212 [0.0, 1.0, 2.0, 1.0], 213 [0.0,0.0, 1.0, 2.0]] 214 215 A = Sparse_CSR(Sparse(A)) 216 217 xe = [0.0, 1.0, 2.0, 3.0] 218 b = A*xe 219 x = [0.0, 0.0, 0.0, 0.0] 220 221 x = conjugate_gradient(A,b,x,use_c_cg=True) 222 223 assert num.allclose(x,xe) 224 225 def test_max_iter_using_c_ext(self): 226 """Test max iteration Small Sparse Matrix""" 227 228 A = [[2.0, 1.0, 0.0, 0.0 ], 229 [1.0, 2.0, 1.0, 0.0], 230 [0.0, 1.0, 2.0, 1.0], 231 [0.0,0.0, 1.0, 2.0]] 232 233 A = Sparse_CSR(Sparse(A)) 234 235 xe = [0.0, 1.0, 2.0, 3.0] 236 b = A*xe 237 x = [0.0, 0.0, 0.0, 0.0] 238 239 try: 240 x = conjugate_gradient(A,b,x,imax=2,use_c_cg=True) 241 except ConvergenceError: 242 pass 243 else: 244 msg = 'Should have raised exception' 245 raise TestError, msg 246 247 248 def test_solve_large_using_c_ext(self): 249 """Standard 1d laplacian """ 250 251 n = 50 252 A = Sparse(n,n) 253 254 for i in num.arange(0,n): 255 A[i,i] = 1.0 256 if i > 0 : 257 A[i,i1] = 0.5 258 if i < n1 : 259 A[i,i+1] = 0.5 260 261 xe = num.ones( (n,), num.float) 262 263 b = A*xe 264 265 A = Sparse_CSR(A) 266 267 x = conjugate_gradient(A,b,b,tol=1.0e5,use_c_cg=True) 268 269 assert num.allclose(x,xe) 270 271 def test_solve_large_2d_using_c_ext(self): 272 """Standard 2d laplacian""" 273 274 n = 20 275 m = 10 276 277 A = Sparse(m*n, m*n) 278 279 for i in num.arange(0,n): 280 for j in num.arange(0,m): 281 I = j+m*i 282 A[I,I] = 4.0 283 if i > 0 : 284 A[I,Im] = 1.0 285 if i < n1 : 286 A[I,I+m] = 1.0 287 if j > 0 : 288 A[I,I1] = 1.0 289 if j < m1 : 290 A[I,I+1] = 1.0 291 292 xe = num.ones( (n*m,), num.float) 293 A = Sparse_CSR(A) 294 b = A*xe 295 x = conjugate_gradient(A,b,b,iprint=1,use_c_cg=True) 296 297 assert num.allclose(x,xe) 298 299 def test_solve_large_2d_csr_matrix_using_c_ext(self): 300 """Standard 2d laplacian with csr format 301 """ 302 303 n = 100 304 m = 100 305 306 A = Sparse(m*n, m*n) 307 308 for i in num.arange(0,n): 309 for j in num.arange(0,m): 310 I = j+m*i 311 A[I,I] = 4.0 312 if i > 0 : 313 A[I,Im] = 1.0 314 if i < n1 : 315 A[I,I+m] = 1.0 316 if j > 0 : 317 A[I,I1] = 1.0 318 if j < m1 : 319 A[I,I+1] = 1.0 320 321 xe = num.ones( (n*m,), num.float) 322 323 # Convert to csr format 324 #print 'start covert' 325 A = Sparse_CSR(A) 326 #print 'finish covert' 327 b = A*xe 328 x = conjugate_gradient(A,b,b,iprint=20,use_c_cg=True) 329 330 assert num.allclose(x,xe) 331 332 333 def test_solve_large_2d_with_default_guess_using_c_ext(self): 334 """Standard 2d laplacian using default first guess""" 335 336 n = 20 337 m = 10 338 339 A = Sparse(m*n, m*n) 340 341 for i in num.arange(0,n): 342 for j in num.arange(0,m): 343 I = j+m*i 344 A[I,I] = 4.0 345 if i > 0 : 346 A[I,Im] = 1.0 347 if i < n1 : 348 A[I,I+m] = 1.0 349 if j > 0 : 350 A[I,I1] = 1.0 351 if j < m1 : 352 A[I,I+1] = 1.0 353 354 xe = num.ones( (n*m,), num.float) 355 A = Sparse_CSR(A) 356 b = A*xe 357 x = conjugate_gradient(A,b,use_c_cg=True) 358 359 assert num.allclose(x,xe) 360 361 362 def test_sparse_solve_matrix_using_c_ext(self): 363 """Solve Small Sparse Matrix""" 364 365 A = [[2.0, 1.0, 0.0, 0.0 ], 366 [1.0, 2.0, 1.0, 0.0], 367 [0.0, 1.0, 2.0, 1.0], 368 [0.0,0.0, 1.0, 2.0]] 369 370 A = Sparse_CSR(Sparse(A)) 371 372 xe = [[0.0, 0.0],[1.0, 1.0],[2.0 ,2.0],[3.0, 3.0]] 373 b = A*xe 374 x = [[0.0, 0.0],[0.0, 0.0],[0.0 ,0.0],[0.0, 0.0]] 375 x = conjugate_gradient(A,b,x,iprint=0,use_c_cg=True) 376 377 378 assert num.allclose(x,xe) 379 380 381 382 def test_sparse_solve_using_c_ext_with_jacobi(self): 383 """Solve Small Sparse Matrix""" 384 385 A = [[2.0, 1.0, 0.0, 0.0 ], 386 [1.0, 2.0, 1.0, 0.0], 387 [0.0, 1.0, 2.0, 1.0], 388 [0.0,0.0, 1.0, 2.0]] 389 390 A = Sparse_CSR(Sparse(A)) 391 392 xe = [0.0, 1.0, 2.0, 3.0] 393 b = A*xe 394 x = [0.0, 0.0, 0.0, 0.0] 395 x = conjugate_gradient(A,b,x,use_c_cg=True,precon='Jacobi') 396 397 assert num.allclose(x,xe) 398 399 def test_max_iter_using_c_ext_with_jacobi(self): 400 """Test max iteration Small Sparse Matrix""" 401 402 A = [[2.0, 1.0, 0.0, 0.0 ], 403 [1.0, 2.0, 1.0, 0.0], 404 [0.0, 1.0, 2.0, 1.0], 405 [0.0,0.0, 1.0, 2.0]] 406 407 A = Sparse_CSR(Sparse(A)) 408 409 xe = [0.0, 1.0, 2.0, 3.0] 410 b = A*xe 411 x = [0.0, 0.0, 0.0, 0.0] 412 413 try: 414 x = conjugate_gradient(A,b,x,imax=2,use_c_cg=True, precon='Jacobi') 415 except ConvergenceError: 416 pass 417 else: 418 msg = 'Should have raised exception' 419 raise TestError, msg 420 421 422 def test_solve_large_using_c_ext_with_jacobi(self): 423 """Standard 1d laplacian """ 424 425 n = 50 426 A = Sparse(n,n) 427 428 for i in num.arange(0,n): 429 A[i,i] = 1.0 430 if i > 0 : 431 A[i,i1] = 0.5 432 if i < n1 : 433 A[i,i+1] = 0.5 434 435 xe = num.ones( (n,), num.float) 436 437 b = A*xe 438 439 A = Sparse_CSR(A) 440 441 x = conjugate_gradient(A,b,b,tol=1.0e5,use_c_cg=True, precon='Jacobi') 442 443 assert num.allclose(x,xe) 444 445 def test_solve_large_2d_using_c_ext_with_jacobi(self): 446 """Standard 2d laplacian""" 447 448 n = 20 449 m = 10 450 451 A = Sparse(m*n, m*n) 452 453 for i in num.arange(0,n): 454 for j in num.arange(0,m): 455 I = j+m*i 456 A[I,I] = 4.0 457 if i > 0 : 458 A[I,Im] = 1.0 459 if i < n1 : 460 A[I,I+m] = 1.0 461 if j > 0 : 462 A[I,I1] = 1.0 463 if j < m1 : 464 A[I,I+1] = 1.0 465 466 xe = num.ones( (n*m,), num.float) 467 A = Sparse_CSR(A) 468 b = A*xe 469 x = conjugate_gradient(A,b,b,iprint=1,use_c_cg=True, precon='Jacobi') 470 471 assert num.allclose(x,xe) 472 473 def test_solve_large_2d_csr_matrix_using_c_ext_with_jacobi(self): 474 """Standard 2d laplacian with csr format 475 """ 476 477 n = 100 478 m = 100 479 480 A = Sparse(m*n, m*n) 481 482 for i in num.arange(0,n): 483 for j in num.arange(0,m): 484 I = j+m*i 485 A[I,I] = 4.0 486 if i > 0 : 487 A[I,Im] = 1.0 488 if i < n1 : 489 A[I,I+m] = 1.0 490 if j > 0 : 491 A[I,I1] = 1.0 492 if j < m1 : 493 A[I,I+1] = 1.0 494 495 xe = num.ones( (n*m,), num.float) 496 497 # Convert to csr format 498 #print 'start covert' 499 A = Sparse_CSR(A) 500 #print 'finish covert' 501 b = A*xe 502 x = conjugate_gradient(A,b,b,iprint=20,use_c_cg=True, precon='Jacobi') 503 504 assert num.allclose(x,xe) 505 506 507 def test_solve_large_2d_with_default_guess_using_c_ext_with_jacobi(self): 508 """Standard 2d laplacian using default first guess""" 509 510 n = 20 511 m = 10 512 513 A = Sparse(m*n, m*n) 514 515 for i in num.arange(0,n): 516 for j in num.arange(0,m): 517 I = j+m*i 518 A[I,I] = 4.0 519 if i > 0 : 520 A[I,Im] = 1.0 521 if i < n1 : 522 A[I,I+m] = 1.0 523 if j > 0 : 524 A[I,I1] = 1.0 525 if j < m1 : 526 A[I,I+1] = 1.0 527 528 xe = num.ones( (n*m,), num.float) 529 A = Sparse_CSR(A) 530 b = A*xe 531 x = conjugate_gradient(A,b,use_c_cg=True, precon='Jacobi') 532 533 assert num.allclose(x,xe) 534 535 536 def test_sparse_solve_matrix_using_c_ext_with_jacobi(self): 537 """Solve Small Sparse Matrix""" 538 539 A = [[2.0, 1.0, 0.0, 0.0 ], 540 [1.0, 2.0, 1.0, 0.0], 541 [0.0, 1.0, 2.0, 1.0], 542 [0.0,0.0, 1.0, 2.0]] 543 544 A = Sparse_CSR(Sparse(A)) 545 546 xe = [[0.0, 0.0],[1.0, 1.0],[2.0 ,2.0],[3.0, 3.0]] 547 b = A*xe 548 x = [[0.0, 0.0],[0.0, 0.0],[0.0 ,0.0],[0.0, 0.0]] 549 x = conjugate_gradient(A,b,x,iprint=0,use_c_cg=True, precon='Jacobi') 550 551 552 assert num.allclose(x,xe) 553 205 554 ################################################################################ 206 555
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