/external/ceres-solver/internal/ceres/ |
H A D | evaluator_test_utils.h | 44 const double jacobian[200]; member in struct:ceres::internal::ExpectedEvaluation
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H A D | block_evaluate_preparer.cc | 49 // Point the jacobian blocks directly into the block sparse matrix. 52 SparseMatrix* jacobian, 54 // If the overall jacobian is not available, use the scratch space. 55 if (jacobian == NULL) { 58 jacobian, 64 down_cast<BlockSparseMatrix*>(jacobian)->mutable_values(); 50 Prepare(const ResidualBlock* residual_block, int residual_block_index, SparseMatrix* jacobian, double** jacobians) argument
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H A D | corrector.cc | 46 // and the jacobian are scaled by the squareroot of the derivative 115 double* jacobian) { 117 DCHECK(jacobian != NULL); 133 r_transpose_j += jacobian[r * num_cols + c] * residuals[r]; 137 jacobian[r * num_cols + c] = sqrt_rho1_ * 138 (jacobian[r * num_cols + c] - 112 CorrectJacobian(int num_rows, int num_cols, double* residuals, double* jacobian) argument
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H A D | corrector_test.cc | 60 double jacobian = 10.0; local 74 // The jacobian in this case will be 75 // sqrt(kRho[1]) * (1 - kAlpha) * jacobian. 76 const double kExpectedJacobian = sqrt(kRho[1]) * (1 - kAlpha) * jacobian; 79 c.CorrectJacobian(1.0, 1.0, &residuals, &jacobian); 83 ASSERT_NEAR(kExpectedJacobian, jacobian, 1e-6); 88 double jacobian = 10.0; local 102 // The jacobian in this case will be 103 // sqrt(kRho[1]) * jacobian. 104 const double kExpectedJacobian = sqrt(kRho[1]) * jacobian; 116 double jacobian = 10.0; local 147 double jacobian[2 * 3]; local 215 double jacobian[2 * 3]; local [all...] |
H A D | normal_prior_test.cc | 70 double * jacobian = new double[num_rows * num_cols]; local 74 prior.Evaluate(&x, residuals.data(), &jacobian); 82 MatrixRef J(jacobian, num_rows, num_cols); 87 delete []jacobian;
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H A D | evaluator.h | 80 // The residual, gradients and jacobian pointers can be NULL, in 98 CRSMatrix* jacobian); 101 // of the objective function. The jacobian has dimensions 113 // the jacobian for use with CHOLMOD, where as BlockOptimizationProblem 114 // creates a BlockSparseMatrix representation of the jacobian for use in the 131 // residuals, and jacobian in the corresponding arguments. Both residuals and 132 // jacobian are optional; to avoid computing them, pass NULL. 135 // values array of the jacobian is modified. 144 SparseMatrix* jacobian) = 0; 153 SparseMatrix* jacobian) { 149 Evaluate(const double* state, double* cost, double* residuals, double* gradient, SparseMatrix* jacobian) argument [all...] |
H A D | levenberg_marquardt_strategy.cc | 67 SparseMatrix* jacobian, 70 CHECK_NOTNULL(jacobian); 74 const int num_parameters = jacobian->num_cols(); 80 jacobian->SquaredColumnNorm(diagonal_.data()); 104 // Then x can be found as x = -y, but the inputs jacobian and residuals 107 linear_solver_->Solve(jacobian, residuals, solve_options, step); 122 jacobian, 65 ComputeStep( const TrustRegionStrategy::PerSolveOptions& per_solve_options, SparseMatrix* jacobian, const double* residuals, double* step) argument
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H A D | autodiff_local_parameterization_test.cc | 65 double jacobian[9]; local 66 parameterization.ComputeJacobian(x, jacobian); 70 EXPECT_EQ(jacobian[k], (i == j) ? 1.0 : 0.0); 117 double jacobian[12]; local 120 parameterization.ComputeJacobian(x, jacobian); 135 EXPECT_TRUE(IsFinite(jacobian[i])); 136 EXPECT_NEAR(jacobian[i], jacobian_ref[i], kTolerance) 139 << "\n Actual \n" << ConstMatrixRef(jacobian, 4, 3);
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H A D | dense_jacobian_writer.h | 31 // A jacobian writer that writes to dense Eigen matrices. 58 // them over to the larger jacobian later. 72 SparseMatrix* jacobian) { 74 if (jacobian != NULL) { 75 dense_jacobian = down_cast<DenseSparseMatrix*>(jacobian); 82 // Now copy the jacobians for each parameter into the dense jacobian matrix. 69 Write(int residual_id, int residual_offset, double **jacobians, SparseMatrix* jacobian) argument
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H A D | dogleg_strategy_test.cc | 77 Matrix jacobian = sqrtD * basis; local 78 jacobian_.reset(new DenseSparseMatrix(jacobian)); 82 residual_ = -jacobian * minimum; 105 Matrix jacobian = Ddiag.asDiagonal(); local 106 jacobian_.reset(new DenseSparseMatrix(jacobian)); 110 residual_ = -jacobian * minimum;
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H A D | residual_block_utils_test.cc | 62 double jacobian; local 63 double* jacobians[] = { &jacobian };
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H A D | block_jacobian_writer.cc | 47 // per-parameter jacobian goes where in the overall program jacobian. 63 // are there. This will determine where the F blocks start in the jacobian 64 // matrix. Also compute the number of jacobian blocks. 135 // Create evaluate prepareres that point directly into the final jacobian. This 206 BlockSparseMatrix* jacobian = new BlockSparseMatrix(bs); local 207 CHECK_NOTNULL(jacobian); 208 return jacobian;
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H A D | compressed_row_jacobian_writer.cc | 50 // Count the number of jacobian nonzeros. 64 // Allocate storage for the jacobian with some extra space at the end. 65 // Allocate more space than needed to store the jacobian so that when the LM 68 CompressedRowSparseMatrix* jacobian = local 76 int* rows = jacobian->mutable_rows(); 77 int* cols = jacobian->mutable_cols(); 112 // parameter vector. This code mirrors that in Write(), where jacobian 121 // This is the position in the values array of the jacobian where this 122 // row of the jacobian block should go. 140 vector<int>& col_blocks = *(jacobian 159 CompressedRowSparseMatrix* jacobian = local [all...] |
H A D | local_parameterization_test.cc | 55 double jacobian[9]; local 56 parameterization.ComputeJacobian(x, jacobian); 60 EXPECT_EQ(jacobian[k], (i == j) ? 1.0 : 0.0); 106 double jacobian[4 * 3]; local 107 parameterization.ComputeJacobian(x, jacobian); 113 EXPECT_EQ(jacobian[jacobian_cursor], delta_cursor == k ? 1.0 : 0.0); 118 EXPECT_EQ(jacobian[jacobian_cursor], 0.0); 184 // Autodiff jacobian at delta_x = 0. 188 double jacobian[12]; local 189 param.ComputeJacobian(x, jacobian); [all...] |
H A D | dogleg_strategy.cc | 79 SparseMatrix* jacobian, 82 CHECK_NOTNULL(jacobian); 86 const int n = jacobian->num_cols(); 121 jacobian->SquaredColumnNorm(diagonal_.data()); 127 ComputeGradient(jacobian, residuals); 128 ComputeCauchyPoint(jacobian); 131 ComputeGaussNewtonStep(per_solve_options, jacobian, residuals); 148 if (!ComputeSubspaceModel(jacobian)) { 168 SparseMatrix* jacobian, 171 jacobian 77 ComputeStep( const TrustRegionStrategy::PerSolveOptions& per_solve_options, SparseMatrix* jacobian, const double* residuals, double* step) argument 167 ComputeGradient( SparseMatrix* jacobian, const double* residuals) argument 177 ComputeCauchyPoint(SparseMatrix* jacobian) argument 510 ComputeGaussNewtonStep( const PerSolveOptions& per_solve_options, SparseMatrix* jacobian, const double* residuals) argument 637 ComputeSubspaceModel(SparseMatrix* jacobian) argument [all...] |
H A D | dynamic_autodiff_cost_function_test.cc | 119 // Prepare the jacobian. 123 vector<double*> jacobian; local 124 jacobian.push_back(jacobian_vect[0].data()); 125 jacobian.push_back(jacobian_vect[1].data()); 127 // Test jacobian computation. 130 jacobian.data())); 186 // Prepare the jacobian. 190 vector<double*> jacobian; local 191 jacobian.push_back(NULL); 192 jacobian 240 vector<double*> jacobian; local 443 vector<double*> jacobian; local 473 vector<double*> jacobian; local 495 vector<double*> jacobian; local 687 vector<double*> jacobian; local 713 vector<double*> jacobian; local 744 vector<double*> jacobian; local [all...] |
H A D | evaluator_test.cc | 69 // evaluator into the "local" jacobian. In the tests, the "subset 71 // from these jacobians. Put values in the jacobian that make this 81 MatrixRef jacobian(jacobians[k], 85 jacobian.col(j).setConstant(kFactor * (j + 1)); 130 scoped_ptr<SparseMatrix> jacobian(evaluator->CreateJacobian()); 134 ASSERT_EQ(expected_num_rows, jacobian->num_rows()); 135 ASSERT_EQ(expected_num_cols, jacobian->num_cols()); 144 expected_jacobian != NULL ? jacobian.get() : NULL)); 148 jacobian->ToDenseMatrix(&actual_jacobian); 172 (i & 4) ? expected.jacobian 551 double* jacobian = jacobians[0]; local [all...] |
H A D | minimizer.h | 112 jacobian = NULL; 176 SparseMatrix* jacobian; member in struct:ceres::internal::Minimizer::Options
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H A D | problem.cc | 185 CRSMatrix* jacobian) { 190 jacobian); 181 Evaluate(const EvaluateOptions& evaluate_options, double* cost, vector<double>* residuals, vector<double>* gradient, CRSMatrix* jacobian) argument
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H A D | program_evaluator.h | 32 // and stores the result into a jacobian. The particular type of jacobian is 36 // pointers to the jacobian blocks where the cost function evaluates to. 38 // jacobian blocks in the passed sparse matrix. 55 // SparseMatrix* jacobian, 60 // // Create a jacobian that this writer can write. Same as 69 // // larger sparse jacobian. 73 // SparseMatrix* jacobian); 130 SparseMatrix* jacobian) { 132 ScopedExecutionTimer call_type_timer(gradient == NULL && jacobian 125 Evaluate(const Evaluator::EvaluateOptions& evaluate_options, const double* state, double* cost, double* residuals, double* gradient, SparseMatrix* jacobian) argument [all...] |
H A D | trust_region_minimizer.cc | 64 void TrustRegionMinimizer::EstimateScale(const SparseMatrix& jacobian, argument 66 jacobian.SquaredColumnNorm(scale); 67 for (int i = 0; i < jacobian.num_cols(); ++i) { 90 SparseMatrix* jacobian = CHECK_NOTNULL(options_.jacobian); local 130 jacobian)) { 171 EstimateScale(*jacobian, scale.data()); 172 jacobian->ScaleColumns(scale.data()); 220 jacobian, 243 jacobian [all...] |
H A D | trust_region_minimizer_test.cc | 53 // indicate which of the four variables/columns of the jacobian are 90 SparseMatrix* jacobian) { 124 if (jacobian != NULL) { 126 dense_jacobian = down_cast<DenseSparseMatrix*>(jacobian); 227 scoped_ptr<SparseMatrix> jacobian(powell_evaluator.CreateJacobian()); 234 minimizer_options.jacobian = jacobian.get(); 85 Evaluate(const Evaluator::EvaluateOptions& evaluate_options, const double* state, double* cost, double* residuals, double* gradient, SparseMatrix* jacobian) argument
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H A D | covariance_test.cc | 129 const double* jacobian) 130 : jacobian_(jacobian, jacobian + num_residuals * parameter_block_size) { 214 virtual bool ComputeJacobian(const double* x, double* jacobian) const { 215 jacobian[0] = x[0]; 216 jacobian[1] = x[1]; 239 double jacobian[] = { 1.0, 0.0, 0.0, 1.0}; local 240 problem_.AddResidualBlock(new UnaryCostFunction(2, 2, jacobian), NULL, x); 244 double jacobian[] = { 2.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 2.0 }; local 245 problem_.AddResidualBlock(new UnaryCostFunction(3, 3, jacobian), NUL 127 UnaryCostFunction(const int num_residuals, const int16 parameter_block_size, const double* jacobian) argument 249 double jacobian = 5.0; local 629 double jacobian[] = { 1.0, 0.0, 0.0, 1.0}; local 634 double jacobian[] = { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 }; local 639 double jacobian = 5.0; local [all...] |
/external/ceres-solver/include/ceres/internal/ |
H A D | numeric_diff.h | 94 double *jacobian) { 107 Map<JacobianMatrix> parameter_jacobian(jacobian, 141 // Compute this column of the jacobian in 3 steps: 190 double *jacobian) { 89 EvaluateJacobianForParameterBlock( const CostFunctor* functor, double const* residuals_at_eval_point, const double relative_step_size, double **parameters, double *jacobian) argument 185 EvaluateJacobianForParameterBlock( const CostFunctor* functor, double const* residuals_at_eval_point, const double relative_step_size, double **parameters, double *jacobian) argument
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/external/eigen/unsupported/Eigen/src/AutoDiff/ |
H A D | AutoDiffVector.h | 73 : m_values(other.values()), m_jacobian(other.jacobian()) 77 : m_values(other.values()), m_jacobian(other.jacobian()) 84 m_jacobian = other.jacobian(); 91 m_jacobian = other.jacobian(); 98 inline const JacobianType& jacobian() const { return m_jacobian; } function in class:Eigen::AutoDiffVector 99 inline JacobianType& jacobian() { return m_jacobian; } function in class:Eigen::AutoDiffVector 111 m_jacobian + other.jacobian()); 119 m_jacobian += other.jacobian(); 133 m_jacobian - other.jacobian()); 141 m_jacobian -= other.jacobian(); [all...] |