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Matrix generation cleanup

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1. Convert a CompressedRowSparseMatrix constructor which
takes a TripletSparseMatrix as input into a factory method
which allows the input to be transposed.

2. Move the random matrix creation routine for CompressedRowSparseMatrix
from being a standalone function to a static method.

3. Add a corresponding random matrix generation static method to
TripletSparseMatrix.

4. Add a new constructor to TripletSparseMatrix, which takes as input
the row, col and values arrays.

Change-Id: Iec7b184646818f432a5e6822bea3b2f3128a82aa
This commit is contained in:
Sameer Agarwal
2017-05-01 17:24:22 -07:00
parent d72e19d985
commit 086ff01aca
8 changed files with 371 additions and 130 deletions
Download Patch File Download Diff File Expand all files Collapse all files
internal/ceres/compressed_row_sparse_matrix_test.cc
+75 -21
View File
@@ -70,7 +70,7 @@ void CompareMatrices(const SparseMatrix* a, const SparseMatrix* b) {
}
class CompressedRowSparseMatrixTest : public ::testing::Test {
protected :
protected:
virtual void SetUp() {
scoped_ptr<LinearLeastSquaresProblem> problem(
CreateLinearLeastSquaresProblemFromId(1));
@@ -78,7 +78,7 @@ class CompressedRowSparseMatrixTest : public ::testing::Test {
CHECK_NOTNULL(problem.get());
tsm.reset(down_cast<TripletSparseMatrix*>(problem->A.release()));
crsm.reset(new CompressedRowSparseMatrix(*tsm));
crsm.reset(CompressedRowSparseMatrix::FromTripletSparseMatrix(*tsm));
num_rows = tsm->num_rows();
num_cols = tsm->num_cols();
@@ -93,9 +93,11 @@ class CompressedRowSparseMatrixTest : public ::testing::Test {
// With all blocks of size 1, crsb_rows and crsb_cols are equivalent to
// rows and cols.
std::copy(crsm->rows(), crsm->rows() + crsm->num_rows() + 1,
std::copy(crsm->rows(),
crsm->rows() + crsm->num_rows() + 1,
std::back_inserter(*crsm->mutable_crsb_rows()));
std::copy(crsm->cols(), crsm->cols() + crsm->num_nonzeros(),
std::copy(crsm->cols(),
crsm->cols() + crsm->num_nonzeros(),
std::back_inserter(*crsm->mutable_crsb_cols()));
}
@@ -172,9 +174,10 @@ TEST_F(CompressedRowSparseMatrixTest, AppendRows) {
tsm_appendage.Resize(i, num_cols);
tsm->AppendRows(tsm_appendage);
CompressedRowSparseMatrix crsm_appendage(tsm_appendage);
crsm->AppendRows(crsm_appendage);
scoped_ptr<CompressedRowSparseMatrix> crsm_appendage(
CompressedRowSparseMatrix::FromTripletSparseMatrix(tsm_appendage));
crsm->AppendRows(*crsm_appendage);
CompareMatrices(tsm.get(), crsm.get());
}
}
@@ -219,9 +222,9 @@ TEST_F(CompressedRowSparseMatrixTest, AppendAndDeleteBlockDiagonalMatrix) {
EXPECT_EQ(expected_col_blocks, crsm->col_blocks());
EXPECT_EQ(crsm->crsb_cols().size(),
pre_crsb_cols.size() + row_and_column_blocks.size());
pre_crsb_cols.size() + row_and_column_blocks.size());
EXPECT_EQ(crsm->crsb_rows().size(),
pre_crsb_rows.size() + row_and_column_blocks.size());
pre_crsb_rows.size() + row_and_column_blocks.size());
for (int i = 0; i < row_and_column_blocks.size(); ++i) {
EXPECT_EQ(crsm->crsb_rows()[i + pre_crsb_rows.size()],
pre_crsb_rows.back() + i + 1);
@@ -234,7 +237,6 @@ TEST_F(CompressedRowSparseMatrixTest, AppendAndDeleteBlockDiagonalMatrix) {
EXPECT_EQ(crsm->crsb_rows(), pre_crsb_rows);
EXPECT_EQ(crsm->crsb_cols(), pre_crsb_cols);
}
TEST_F(CompressedRowSparseMatrixTest, ToDenseMatrix) {
@@ -278,8 +280,8 @@ TEST(CompressedRowSparseMatrix, CreateBlockDiagonalMatrix) {
}
scoped_ptr<CompressedRowSparseMatrix> matrix(
CompressedRowSparseMatrix::CreateBlockDiagonalMatrix(
diagonal.data(), blocks));
CompressedRowSparseMatrix::CreateBlockDiagonalMatrix(diagonal.data(),
blocks));
EXPECT_EQ(matrix->num_rows(), 5);
EXPECT_EQ(matrix->num_cols(), 5);
@@ -351,7 +353,6 @@ TEST(CompressedRowSparseMatrix, Transpose) {
cols[5] = 2;
cols[6] = 5;
rows[2] = 7;
cols[7] = 0;
cols[8] = 1;
@@ -394,21 +395,19 @@ TEST(CompressedRowSparseMatrix, Transpose) {
TEST(CompressedRowSparseMatrix, ComputeOuterProduct) {
// "Randomly generated seed."
SetRandomState(29823);
int kMaxNumRowBlocks = 10;
int kMaxNumColBlocks = 10;
int kNumTrials = 10;
const int kMaxNumRowBlocks = 10;
const int kMaxNumColBlocks = 10;
const int kNumTrials = 10;
// Create a random matrix, compute its outer product using Eigen and
// ComputeOuterProduct. Convert both matrices to dense matrices and
// compare their upper triangular parts.
for (int num_row_blocks = 1;
num_row_blocks < kMaxNumRowBlocks;
for (int num_row_blocks = 1; num_row_blocks < kMaxNumRowBlocks;
++num_row_blocks) {
for (int num_col_blocks = 1;
num_col_blocks < kMaxNumColBlocks;
for (int num_col_blocks = 1; num_col_blocks < kMaxNumColBlocks;
++num_col_blocks) {
for (int trial = 0; trial < kNumTrials; ++trial) {
RandomMatrixOptions options;
CompressedRowSparseMatrix::RandomMatrixOptions options;
options.num_row_blocks = num_row_blocks;
options.num_col_blocks = num_col_blocks;
options.min_row_block_size = 1;
@@ -426,7 +425,7 @@ TEST(CompressedRowSparseMatrix, ComputeOuterProduct) {
VLOG(2) << "block density: " << options.block_density;
scoped_ptr<CompressedRowSparseMatrix> random_matrix(
CreateRandomCompressedRowSparseMatrix(options));
CompressedRowSparseMatrix::CreateRandomMatrix(options));
Eigen::MappedSparseMatrix<double, Eigen::RowMajor> mapped_random_matrix(
random_matrix->num_rows(),
@@ -480,5 +479,60 @@ TEST(CompressedRowSparseMatrix, ComputeOuterProduct) {
}
}
TEST(CompressedRowSparseMatrix, FromTripletSparseMatrix) {
TripletSparseMatrix::RandomMatrixOptions options;
options.num_rows = 5;
options.num_cols = 7;
options.density = 0.5;
const int kNumTrials = 10;
for (int i = 0; i < kNumTrials; ++i) {
scoped_ptr<TripletSparseMatrix> tsm(
TripletSparseMatrix::CreateRandomMatrix(options));
scoped_ptr<CompressedRowSparseMatrix> crsm(
CompressedRowSparseMatrix::FromTripletSparseMatrix(*tsm));
Matrix expected;
tsm->ToDenseMatrix(&expected);
Matrix actual;
crsm->ToDenseMatrix(&actual);
EXPECT_NEAR((expected - actual).norm() / actual.norm(),
0.0,
std::numeric_limits<double>::epsilon())
<< "\nexpected: \n"
<< expected << "\nactual: \n"
<< actual;
}
}
TEST(CompressedRowSparseMatrix, FromTripletSparseMatrixTransposed) {
TripletSparseMatrix::RandomMatrixOptions options;
options.num_rows = 5;
options.num_cols = 7;
options.density = 0.5;
const int kNumTrials = 10;
for (int i = 0; i < kNumTrials; ++i) {
scoped_ptr<TripletSparseMatrix> tsm(
TripletSparseMatrix::CreateRandomMatrix(options));
scoped_ptr<CompressedRowSparseMatrix> crsm(
CompressedRowSparseMatrix::FromTripletSparseMatrixTransposed(*tsm));
Matrix tmp;
tsm->ToDenseMatrix(&tmp);
Matrix expected = tmp.transpose();
Matrix actual;
crsm->ToDenseMatrix(&actual);
EXPECT_NEAR((expected - actual).norm() / actual.norm(),
0.0,
std::numeric_limits<double>::epsilon())
<< "\nexpected: \n"
<< expected << "\nactual: \n"
<< actual;
}
}
// TODO(sameeragarwal) Add tests for the random matrix creation methods.
} // namespace internal
} // namespace ceres