saberbud/ceres-solver
1
0
Fork 0
mirror of https://github.com/ceres-solver/ceres-solver.git synced 2026-08-30 00:50:37 +08:00
Code Issues Packages Projects Releases Wiki Activity

Clean up sparse_cholesky_test

Browse Source 
Change-Id: I210d1181d4696b237094af1fa040064f293baa6a
This commit is contained in:
Sergiu Deitsch
2022-11-13 18:38:34 +01:00
parent addcd342fd
commit d3201798ea
2 changed files with 17 additions and 18 deletions
Download Patch File Download Diff File Expand all files Collapse all files
internal/ceres/block_sparse_matrix.cc
+2 -2
View File
@@ -510,7 +510,7 @@ std::unique_ptr<BlockSparseMatrix> BlockSparseMatrix::CreateRandomMatrix(
options.min_col_block_size, options.max_col_block_size);
std::uniform_int_distribution<int> row_distribution(
options.min_row_block_size, options.max_row_block_size);
auto* bs = new CompressedRowBlockStructure();
auto bs = std::make_unique<CompressedRowBlockStructure>();
if (options.col_blocks.empty()) {
CHECK_GT(options.num_col_blocks, 0);
CHECK_GT(options.min_col_block_size, 0);
@@ -555,7 +555,7 @@ std::unique_ptr<BlockSparseMatrix> BlockSparseMatrix::CreateRandomMatrix(
}
}
auto matrix = std::make_unique<BlockSparseMatrix>(bs);
auto matrix = std::make_unique<BlockSparseMatrix>(bs.release());
double* values = matrix->mutable_values();
std::normal_distribution<double> standard_normal_distribution;
std::generate_n(
internal/ceres/sparse_cholesky_test.cc
+15 -16
View File
@@ -77,9 +77,9 @@ std::unique_ptr<BlockSparseMatrix> CreateRandomFullRankMatrix(
return random_matrix;
}
static bool ComputeExpectedSolution(const CompressedRowSparseMatrix& lhs,
const Vector& rhs,
Vector* solution) {
bool ComputeExpectedSolution(const CompressedRowSparseMatrix& lhs,
const Vector& rhs,
Vector* solution) {
Matrix eigen_lhs;
lhs.ToDenseMatrix(&eigen_lhs);
if (lhs.storage_type() ==
@@ -167,11 +167,14 @@ std::string ParamInfoToString(testing::TestParamInfo<Param> info) {
class SparseCholeskyTest : public ::testing::TestWithParam<Param> {};
TEST_P(SparseCholeskyTest, FactorAndSolve) {
const int kMinNumBlocks = 1;
const int kMaxNumBlocks = 10;
const int kNumTrials = 10;
const int kMinBlockSize = 1;
const int kMaxBlockSize = 5;
constexpr int kMinNumBlocks = 1;
constexpr int kMaxNumBlocks = 10;
constexpr int kNumTrials = 10;
constexpr int kMinBlockSize = 1;
constexpr int kMaxBlockSize = 5;
Param param = GetParam();
std::mt19937 prng;
std::uniform_real_distribution<double> distribution(0.1, 1.0);
@@ -179,7 +182,6 @@ TEST_P(SparseCholeskyTest, FactorAndSolve) {
++num_blocks) {
for (int trial = 0; trial < kNumTrials; ++trial) {
const double block_density = distribution(prng);
Param param = GetParam();
SparseCholeskySolverUnitTest(::testing::get<0>(param),
::testing::get<1>(param),
::testing::get<2>(param),
@@ -302,16 +304,13 @@ using testing::_;
using testing::Return;
TEST(RefinedSparseCholesky, StorageType) {
auto* mock_sparse_cholesky = new MockSparseCholesky;
auto* mock_iterative_refiner = new MockSparseIterativeRefiner;
EXPECT_CALL(*mock_sparse_cholesky, StorageType())
auto sparse_cholesky = std::make_unique<MockSparseCholesky>();
auto iterative_refiner = std::make_unique<MockSparseIterativeRefiner>();
EXPECT_CALL(*sparse_cholesky, StorageType())
.Times(1)
.WillRepeatedly(
Return(CompressedRowSparseMatrix::StorageType::UPPER_TRIANGULAR));
EXPECT_CALL(*mock_iterative_refiner, Refine(_, _, _, _)).Times(0);
std::unique_ptr<SparseCholesky> sparse_cholesky(mock_sparse_cholesky);
std::unique_ptr<SparseIterativeRefiner> iterative_refiner(
mock_iterative_refiner);
EXPECT_CALL(*iterative_refiner, Refine(_, _, _, _)).Times(0);
RefinedSparseCholesky refined_sparse_cholesky(std::move(sparse_cholesky),
std::move(iterative_refiner));
EXPECT_EQ(refined_sparse_cholesky.StorageType(),