1 // Ceres Solver - A fast non-linear least squares minimizer 2 // Copyright 2010, 2011, 2012 Google Inc. All rights reserved. 3 // http://code.google.com/p/ceres-solver/ 4 // 5 // Redistribution and use in source and binary forms, with or without 6 // modification, are permitted provided that the following conditions are met: 7 // 8 // * Redistributions of source code must retain the above copyright notice, 9 // this list of conditions and the following disclaimer. 10 // * Redistributions in binary form must reproduce the above copyright notice, 11 // this list of conditions and the following disclaimer in the documentation 12 // and/or other materials provided with the distribution. 13 // * Neither the name of Google Inc. nor the names of its contributors may be 14 // used to endorse or promote products derived from this software without 15 // specific prior written permission. 16 // 17 // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 18 // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 19 // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE 20 // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE 21 // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR 22 // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF 23 // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS 24 // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN 25 // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) 26 // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 27 // POSSIBILITY OF SUCH DAMAGE. 28 // 29 // Author: keir (at) google.com (Keir Mierle) 30 // 31 // Tests shared across evaluators. The tests try all combinations of linear 32 // solver and num_eliminate_blocks (for schur-based solvers). 33 34 #include "ceres/evaluator.h" 35 36 #include "ceres/casts.h" 37 #include "ceres/cost_function.h" 38 #include "ceres/crs_matrix.h" 39 #include "ceres/evaluator_test_utils.h" 40 #include "ceres/internal/eigen.h" 41 #include "ceres/internal/scoped_ptr.h" 42 #include "ceres/local_parameterization.h" 43 #include "ceres/problem_impl.h" 44 #include "ceres/program.h" 45 #include "ceres/sized_cost_function.h" 46 #include "ceres/sparse_matrix.h" 47 #include "ceres/types.h" 48 #include "gtest/gtest.h" 49 50 namespace ceres { 51 namespace internal { 52 53 // TODO(keir): Consider pushing this into a common test utils file. 54 template<int kFactor, int kNumResiduals, 55 int N0 = 0, int N1 = 0, int N2 = 0, bool kSucceeds = true> 56 class ParameterIgnoringCostFunction 57 : public SizedCostFunction<kNumResiduals, N0, N1, N2> { 58 typedef SizedCostFunction<kNumResiduals, N0, N1, N2> Base; 59 public: 60 virtual bool Evaluate(double const* const* parameters, 61 double* residuals, 62 double** jacobians) const { 63 for (int i = 0; i < Base::num_residuals(); ++i) { 64 residuals[i] = i + 1; 65 } 66 if (jacobians) { 67 for (int k = 0; k < Base::parameter_block_sizes().size(); ++k) { 68 // The jacobians here are full sized, but they are transformed in the 69 // evaluator into the "local" jacobian. In the tests, the "subset 70 // constant" parameterization is used, which should pick out columns 71 // from these jacobians. Put values in the jacobian that make this 72 // obvious; in particular, make the jacobians like this: 73 // 74 // 1 2 3 4 ... 75 // 1 2 3 4 ... .* kFactor 76 // 1 2 3 4 ... 77 // 78 // where the multiplication by kFactor makes it easier to distinguish 79 // between Jacobians of different residuals for the same parameter. 80 if (jacobians[k] != NULL) { 81 MatrixRef jacobian(jacobians[k], 82 Base::num_residuals(), 83 Base::parameter_block_sizes()[k]); 84 for (int j = 0; j < Base::parameter_block_sizes()[k]; ++j) { 85 jacobian.col(j).setConstant(kFactor * (j + 1)); 86 } 87 } 88 } 89 } 90 return kSucceeds; 91 } 92 }; 93 94 struct EvaluatorTest 95 : public ::testing::TestWithParam<pair<LinearSolverType, int> > { 96 Evaluator* CreateEvaluator(Program* program) { 97 // This program is straight from the ProblemImpl, and so has no index/offset 98 // yet; compute it here as required by the evalutor implementations. 99 program->SetParameterOffsetsAndIndex(); 100 101 VLOG(1) << "Creating evaluator with type: " << GetParam().first 102 << " and num_eliminate_blocks: " << GetParam().second; 103 Evaluator::Options options; 104 options.linear_solver_type = GetParam().first; 105 options.num_eliminate_blocks = GetParam().second; 106 string error; 107 return Evaluator::Create(options, program, &error); 108 } 109 110 void EvaluateAndCompare(ProblemImpl *problem, 111 int expected_num_rows, 112 int expected_num_cols, 113 double expected_cost, 114 const double* expected_residuals, 115 const double* expected_gradient, 116 const double* expected_jacobian) { 117 scoped_ptr<Evaluator> evaluator( 118 CreateEvaluator(problem->mutable_program())); 119 int num_residuals = expected_num_rows; 120 int num_parameters = expected_num_cols; 121 122 double cost = -1; 123 124 Vector residuals(num_residuals); 125 residuals.setConstant(-2000); 126 127 Vector gradient(num_parameters); 128 gradient.setConstant(-3000); 129 130 scoped_ptr<SparseMatrix> jacobian(evaluator->CreateJacobian()); 131 132 ASSERT_EQ(expected_num_rows, evaluator->NumResiduals()); 133 ASSERT_EQ(expected_num_cols, evaluator->NumEffectiveParameters()); 134 ASSERT_EQ(expected_num_rows, jacobian->num_rows()); 135 ASSERT_EQ(expected_num_cols, jacobian->num_cols()); 136 137 vector<double> state(evaluator->NumParameters()); 138 139 ASSERT_TRUE(evaluator->Evaluate( 140 &state[0], 141 &cost, 142 expected_residuals != NULL ? &residuals[0] : NULL, 143 expected_gradient != NULL ? &gradient[0] : NULL, 144 expected_jacobian != NULL ? jacobian.get() : NULL)); 145 146 Matrix actual_jacobian; 147 if (expected_jacobian != NULL) { 148 jacobian->ToDenseMatrix(&actual_jacobian); 149 } 150 151 CompareEvaluations(expected_num_rows, 152 expected_num_cols, 153 expected_cost, 154 expected_residuals, 155 expected_gradient, 156 expected_jacobian, 157 cost, 158 &residuals[0], 159 &gradient[0], 160 actual_jacobian.data()); 161 } 162 163 // Try all combinations of parameters for the evaluator. 164 void CheckAllEvaluationCombinations(const ExpectedEvaluation &expected) { 165 for (int i = 0; i < 8; ++i) { 166 EvaluateAndCompare(&problem, 167 expected.num_rows, 168 expected.num_cols, 169 expected.cost, 170 (i & 1) ? expected.residuals : NULL, 171 (i & 2) ? expected.gradient : NULL, 172 (i & 4) ? expected.jacobian : NULL); 173 } 174 } 175 176 // The values are ignored completely by the cost function. 177 double x[2]; 178 double y[3]; 179 double z[4]; 180 181 ProblemImpl problem; 182 }; 183 184 void SetSparseMatrixConstant(SparseMatrix* sparse_matrix, double value) { 185 VectorRef(sparse_matrix->mutable_values(), 186 sparse_matrix->num_nonzeros()).setConstant(value); 187 } 188 189 TEST_P(EvaluatorTest, SingleResidualProblem) { 190 problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 3, 2, 3, 4>, 191 NULL, 192 x, y, z); 193 194 ExpectedEvaluation expected = { 195 // Rows/columns 196 3, 9, 197 // Cost 198 7.0, 199 // Residuals 200 { 1.0, 2.0, 3.0 }, 201 // Gradient 202 { 6.0, 12.0, // x 203 6.0, 12.0, 18.0, // y 204 6.0, 12.0, 18.0, 24.0, // z 205 }, 206 // Jacobian 207 // x y z 208 { 1, 2, 1, 2, 3, 1, 2, 3, 4, 209 1, 2, 1, 2, 3, 1, 2, 3, 4, 210 1, 2, 1, 2, 3, 1, 2, 3, 4 211 } 212 }; 213 CheckAllEvaluationCombinations(expected); 214 } 215 216 TEST_P(EvaluatorTest, SingleResidualProblemWithPermutedParameters) { 217 // Add the parameters in explicit order to force the ordering in the program. 218 problem.AddParameterBlock(x, 2); 219 problem.AddParameterBlock(y, 3); 220 problem.AddParameterBlock(z, 4); 221 222 // Then use a cost function which is similar to the others, but swap around 223 // the ordering of the parameters to the cost function. This shouldn't affect 224 // the jacobian evaluation, but requires explicit handling in the evaluators. 225 // At one point the compressed row evaluator had a bug that went undetected 226 // for a long time, since by chance most users added parameters to the problem 227 // in the same order that they occured as parameters to a cost function. 228 problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 3, 4, 3, 2>, 229 NULL, 230 z, y, x); 231 232 ExpectedEvaluation expected = { 233 // Rows/columns 234 3, 9, 235 // Cost 236 7.0, 237 // Residuals 238 { 1.0, 2.0, 3.0 }, 239 // Gradient 240 { 6.0, 12.0, // x 241 6.0, 12.0, 18.0, // y 242 6.0, 12.0, 18.0, 24.0, // z 243 }, 244 // Jacobian 245 // x y z 246 { 1, 2, 1, 2, 3, 1, 2, 3, 4, 247 1, 2, 1, 2, 3, 1, 2, 3, 4, 248 1, 2, 1, 2, 3, 1, 2, 3, 4 249 } 250 }; 251 CheckAllEvaluationCombinations(expected); 252 } 253 254 TEST_P(EvaluatorTest, SingleResidualProblemWithNuisanceParameters) { 255 // These parameters are not used. 256 double a[2]; 257 double b[1]; 258 double c[1]; 259 double d[3]; 260 261 // Add the parameters in a mixed order so the Jacobian is "checkered" with the 262 // values from the other parameters. 263 problem.AddParameterBlock(a, 2); 264 problem.AddParameterBlock(x, 2); 265 problem.AddParameterBlock(b, 1); 266 problem.AddParameterBlock(y, 3); 267 problem.AddParameterBlock(c, 1); 268 problem.AddParameterBlock(z, 4); 269 problem.AddParameterBlock(d, 3); 270 271 problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 3, 2, 3, 4>, 272 NULL, 273 x, y, z); 274 275 ExpectedEvaluation expected = { 276 // Rows/columns 277 3, 16, 278 // Cost 279 7.0, 280 // Residuals 281 { 1.0, 2.0, 3.0 }, 282 // Gradient 283 { 0.0, 0.0, // a 284 6.0, 12.0, // x 285 0.0, // b 286 6.0, 12.0, 18.0, // y 287 0.0, // c 288 6.0, 12.0, 18.0, 24.0, // z 289 0.0, 0.0, 0.0, // d 290 }, 291 // Jacobian 292 // a x b y c z d 293 { 0, 0, 1, 2, 0, 1, 2, 3, 0, 1, 2, 3, 4, 0, 0, 0, 294 0, 0, 1, 2, 0, 1, 2, 3, 0, 1, 2, 3, 4, 0, 0, 0, 295 0, 0, 1, 2, 0, 1, 2, 3, 0, 1, 2, 3, 4, 0, 0, 0 296 } 297 }; 298 CheckAllEvaluationCombinations(expected); 299 } 300 301 TEST_P(EvaluatorTest, MultipleResidualProblem) { 302 // Add the parameters in explicit order to force the ordering in the program. 303 problem.AddParameterBlock(x, 2); 304 problem.AddParameterBlock(y, 3); 305 problem.AddParameterBlock(z, 4); 306 307 // f(x, y) in R^2 308 problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 2, 2, 3>, 309 NULL, 310 x, y); 311 312 // g(x, z) in R^3 313 problem.AddResidualBlock(new ParameterIgnoringCostFunction<2, 3, 2, 4>, 314 NULL, 315 x, z); 316 317 // h(y, z) in R^4 318 problem.AddResidualBlock(new ParameterIgnoringCostFunction<3, 4, 3, 4>, 319 NULL, 320 y, z); 321 322 ExpectedEvaluation expected = { 323 // Rows/columns 324 9, 9, 325 // Cost 326 // f g h 327 ( 1 + 4 + 1 + 4 + 9 + 1 + 4 + 9 + 16) / 2.0, 328 // Residuals 329 { 1.0, 2.0, // f 330 1.0, 2.0, 3.0, // g 331 1.0, 2.0, 3.0, 4.0 // h 332 }, 333 // Gradient 334 { 15.0, 30.0, // x 335 33.0, 66.0, 99.0, // y 336 42.0, 84.0, 126.0, 168.0 // z 337 }, 338 // Jacobian 339 // x y z 340 { /* f(x, y) */ 1, 2, 1, 2, 3, 0, 0, 0, 0, 341 1, 2, 1, 2, 3, 0, 0, 0, 0, 342 343 /* g(x, z) */ 2, 4, 0, 0, 0, 2, 4, 6, 8, 344 2, 4, 0, 0, 0, 2, 4, 6, 8, 345 2, 4, 0, 0, 0, 2, 4, 6, 8, 346 347 /* h(y, z) */ 0, 0, 3, 6, 9, 3, 6, 9, 12, 348 0, 0, 3, 6, 9, 3, 6, 9, 12, 349 0, 0, 3, 6, 9, 3, 6, 9, 12, 350 0, 0, 3, 6, 9, 3, 6, 9, 12 351 } 352 }; 353 CheckAllEvaluationCombinations(expected); 354 } 355 356 TEST_P(EvaluatorTest, MultipleResidualsWithLocalParameterizations) { 357 // Add the parameters in explicit order to force the ordering in the program. 358 problem.AddParameterBlock(x, 2); 359 360 // Fix y's first dimension. 361 vector<int> y_fixed; 362 y_fixed.push_back(0); 363 problem.AddParameterBlock(y, 3, new SubsetParameterization(3, y_fixed)); 364 365 // Fix z's second dimension. 366 vector<int> z_fixed; 367 z_fixed.push_back(1); 368 problem.AddParameterBlock(z, 4, new SubsetParameterization(4, z_fixed)); 369 370 // f(x, y) in R^2 371 problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 2, 2, 3>, 372 NULL, 373 x, y); 374 375 // g(x, z) in R^3 376 problem.AddResidualBlock(new ParameterIgnoringCostFunction<2, 3, 2, 4>, 377 NULL, 378 x, z); 379 380 // h(y, z) in R^4 381 problem.AddResidualBlock(new ParameterIgnoringCostFunction<3, 4, 3, 4>, 382 NULL, 383 y, z); 384 385 ExpectedEvaluation expected = { 386 // Rows/columns 387 9, 7, 388 // Cost 389 // f g h 390 ( 1 + 4 + 1 + 4 + 9 + 1 + 4 + 9 + 16) / 2.0, 391 // Residuals 392 { 1.0, 2.0, // f 393 1.0, 2.0, 3.0, // g 394 1.0, 2.0, 3.0, 4.0 // h 395 }, 396 // Gradient 397 { 15.0, 30.0, // x 398 66.0, 99.0, // y 399 42.0, 126.0, 168.0 // z 400 }, 401 // Jacobian 402 // x y z 403 { /* f(x, y) */ 1, 2, 2, 3, 0, 0, 0, 404 1, 2, 2, 3, 0, 0, 0, 405 406 /* g(x, z) */ 2, 4, 0, 0, 2, 6, 8, 407 2, 4, 0, 0, 2, 6, 8, 408 2, 4, 0, 0, 2, 6, 8, 409 410 /* h(y, z) */ 0, 0, 6, 9, 3, 9, 12, 411 0, 0, 6, 9, 3, 9, 12, 412 0, 0, 6, 9, 3, 9, 12, 413 0, 0, 6, 9, 3, 9, 12 414 } 415 }; 416 CheckAllEvaluationCombinations(expected); 417 } 418 419 TEST_P(EvaluatorTest, MultipleResidualProblemWithSomeConstantParameters) { 420 // The values are ignored completely by the cost function. 421 double x[2]; 422 double y[3]; 423 double z[4]; 424 425 // Add the parameters in explicit order to force the ordering in the program. 426 problem.AddParameterBlock(x, 2); 427 problem.AddParameterBlock(y, 3); 428 problem.AddParameterBlock(z, 4); 429 430 // f(x, y) in R^2 431 problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 2, 2, 3>, 432 NULL, 433 x, y); 434 435 // g(x, z) in R^3 436 problem.AddResidualBlock(new ParameterIgnoringCostFunction<2, 3, 2, 4>, 437 NULL, 438 x, z); 439 440 // h(y, z) in R^4 441 problem.AddResidualBlock(new ParameterIgnoringCostFunction<3, 4, 3, 4>, 442 NULL, 443 y, z); 444 445 // For this test, "z" is constant. 446 problem.SetParameterBlockConstant(z); 447 448 // Create the reduced program which is missing the fixed "z" variable. 449 // Normally, the preprocessing of the program that happens in solver_impl 450 // takes care of this, but we don't want to invoke the solver here. 451 Program reduced_program; 452 vector<ParameterBlock*>* parameter_blocks = 453 problem.mutable_program()->mutable_parameter_blocks(); 454 455 // "z" is the last parameter; save it for later and pop it off temporarily. 456 // Note that "z" will still get read during evaluation, so it cannot be 457 // deleted at this point. 458 ParameterBlock* parameter_block_z = parameter_blocks->back(); 459 parameter_blocks->pop_back(); 460 461 ExpectedEvaluation expected = { 462 // Rows/columns 463 9, 5, 464 // Cost 465 // f g h 466 ( 1 + 4 + 1 + 4 + 9 + 1 + 4 + 9 + 16) / 2.0, 467 // Residuals 468 { 1.0, 2.0, // f 469 1.0, 2.0, 3.0, // g 470 1.0, 2.0, 3.0, 4.0 // h 471 }, 472 // Gradient 473 { 15.0, 30.0, // x 474 33.0, 66.0, 99.0, // y 475 }, 476 // Jacobian 477 // x y 478 { /* f(x, y) */ 1, 2, 1, 2, 3, 479 1, 2, 1, 2, 3, 480 481 /* g(x, z) */ 2, 4, 0, 0, 0, 482 2, 4, 0, 0, 0, 483 2, 4, 0, 0, 0, 484 485 /* h(y, z) */ 0, 0, 3, 6, 9, 486 0, 0, 3, 6, 9, 487 0, 0, 3, 6, 9, 488 0, 0, 3, 6, 9 489 } 490 }; 491 CheckAllEvaluationCombinations(expected); 492 493 // Restore parameter block z, so it will get freed in a consistent way. 494 parameter_blocks->push_back(parameter_block_z); 495 } 496 497 TEST_P(EvaluatorTest, EvaluatorAbortsForResidualsThatFailToEvaluate) { 498 // Switch the return value to failure. 499 problem.AddResidualBlock( 500 new ParameterIgnoringCostFunction<20, 3, 2, 3, 4, false>, NULL, x, y, z); 501 502 // The values are ignored. 503 double state[9]; 504 505 scoped_ptr<Evaluator> evaluator(CreateEvaluator(problem.mutable_program())); 506 scoped_ptr<SparseMatrix> jacobian(evaluator->CreateJacobian()); 507 double cost; 508 EXPECT_FALSE(evaluator->Evaluate(state, &cost, NULL, NULL, NULL)); 509 } 510 511 // In the pairs, the first argument is the linear solver type, and the second 512 // argument is num_eliminate_blocks. Changing the num_eliminate_blocks only 513 // makes sense for the schur-based solvers. 514 // 515 // Try all values of num_eliminate_blocks that make sense given that in the 516 // tests a maximum of 4 parameter blocks are present. 517 INSTANTIATE_TEST_CASE_P( 518 LinearSolvers, 519 EvaluatorTest, 520 ::testing::Values(make_pair(DENSE_QR, 0), 521 make_pair(DENSE_SCHUR, 0), 522 make_pair(DENSE_SCHUR, 1), 523 make_pair(DENSE_SCHUR, 2), 524 make_pair(DENSE_SCHUR, 3), 525 make_pair(DENSE_SCHUR, 4), 526 make_pair(SPARSE_SCHUR, 0), 527 make_pair(SPARSE_SCHUR, 1), 528 make_pair(SPARSE_SCHUR, 2), 529 make_pair(SPARSE_SCHUR, 3), 530 make_pair(SPARSE_SCHUR, 4), 531 make_pair(ITERATIVE_SCHUR, 0), 532 make_pair(ITERATIVE_SCHUR, 1), 533 make_pair(ITERATIVE_SCHUR, 2), 534 make_pair(ITERATIVE_SCHUR, 3), 535 make_pair(ITERATIVE_SCHUR, 4), 536 make_pair(SPARSE_NORMAL_CHOLESKY, 0))); 537 538 // Simple cost function used to check if the evaluator is sensitive to 539 // state changes. 540 class ParameterSensitiveCostFunction : public SizedCostFunction<2, 2> { 541 public: 542 virtual bool Evaluate(double const* const* parameters, 543 double* residuals, 544 double** jacobians) const { 545 double x1 = parameters[0][0]; 546 double x2 = parameters[0][1]; 547 residuals[0] = x1 * x1; 548 residuals[1] = x2 * x2; 549 550 if (jacobians != NULL) { 551 double* jacobian = jacobians[0]; 552 if (jacobian != NULL) { 553 jacobian[0] = 2.0 * x1; 554 jacobian[1] = 0.0; 555 jacobian[2] = 0.0; 556 jacobian[3] = 2.0 * x2; 557 } 558 } 559 return true; 560 } 561 }; 562 563 TEST(Evaluator, EvaluatorRespectsParameterChanges) { 564 ProblemImpl problem; 565 566 double x[2]; 567 x[0] = 1.0; 568 x[1] = 1.0; 569 570 problem.AddResidualBlock(new ParameterSensitiveCostFunction(), NULL, x); 571 Program* program = problem.mutable_program(); 572 program->SetParameterOffsetsAndIndex(); 573 574 Evaluator::Options options; 575 options.linear_solver_type = DENSE_QR; 576 options.num_eliminate_blocks = 0; 577 string error; 578 scoped_ptr<Evaluator> evaluator(Evaluator::Create(options, program, &error)); 579 scoped_ptr<SparseMatrix> jacobian(evaluator->CreateJacobian()); 580 581 ASSERT_EQ(2, jacobian->num_rows()); 582 ASSERT_EQ(2, jacobian->num_cols()); 583 584 double state[2]; 585 state[0] = 2.0; 586 state[1] = 3.0; 587 588 // The original state of a residual block comes from the user's 589 // state. So the original state is 1.0, 1.0, and the only way we get 590 // the 2.0, 3.0 results in the following tests is if it respects the 591 // values in the state vector. 592 593 // Cost only; no residuals and no jacobian. 594 { 595 double cost = -1; 596 ASSERT_TRUE(evaluator->Evaluate(state, &cost, NULL, NULL, NULL)); 597 EXPECT_EQ(48.5, cost); 598 } 599 600 // Cost and residuals, no jacobian. 601 { 602 double cost = -1; 603 double residuals[2] = { -2, -2 }; 604 ASSERT_TRUE(evaluator->Evaluate(state, &cost, residuals, NULL, NULL)); 605 EXPECT_EQ(48.5, cost); 606 EXPECT_EQ(4, residuals[0]); 607 EXPECT_EQ(9, residuals[1]); 608 } 609 610 // Cost, residuals, and jacobian. 611 { 612 double cost = -1; 613 double residuals[2] = { -2, -2}; 614 SetSparseMatrixConstant(jacobian.get(), -1); 615 ASSERT_TRUE(evaluator->Evaluate(state, 616 &cost, 617 residuals, 618 NULL, 619 jacobian.get())); 620 EXPECT_EQ(48.5, cost); 621 EXPECT_EQ(4, residuals[0]); 622 EXPECT_EQ(9, residuals[1]); 623 Matrix actual_jacobian; 624 jacobian->ToDenseMatrix(&actual_jacobian); 625 626 Matrix expected_jacobian(2, 2); 627 expected_jacobian 628 << 2 * state[0], 0, 629 0, 2 * state[1]; 630 631 EXPECT_TRUE((actual_jacobian.array() == expected_jacobian.array()).all()) 632 << "Actual:\n" << actual_jacobian 633 << "\nExpected:\n" << expected_jacobian; 634 } 635 } 636 637 } // namespace internal 638 } // namespace ceres 639
