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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2023 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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// modification, are permitted provided that the following conditions are met:
//
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// this list of conditions and the following disclaimer in the documentation
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//
// Author: keir@google.com (Keir Mierle)
//
// TODO(keir): Implement a generic "compare sparse matrix implementations" test
// suite that can compare all the implementations. Then this file would shrink
// in size.
#include "ceres/dense_sparse_matrix.h"
#include <memory>
#include "ceres/casts.h"
#include "ceres/internal/eigen.h"
#include "ceres/linear_least_squares_problems.h"
#include "ceres/triplet_sparse_matrix.h"
#include "glog/logging.h"
#include "gtest/gtest.h"
namespace ceres::internal {
static void CompareMatrices(const SparseMatrix* a, const SparseMatrix* b) {
EXPECT_EQ(a->num_rows(), b->num_rows());
EXPECT_EQ(a->num_cols(), b->num_cols());
int num_rows = a->num_rows();
int num_cols = a->num_cols();
for (int i = 0; i < num_cols; ++i) {
Vector x = Vector::Zero(num_cols);
x(i) = 1.0;
Vector y_a = Vector::Zero(num_rows);
Vector y_b = Vector::Zero(num_rows);
a->RightMultiplyAndAccumulate(x.data(), y_a.data());
b->RightMultiplyAndAccumulate(x.data(), y_b.data());
EXPECT_EQ((y_a - y_b).norm(), 0);
}
}
class DenseSparseMatrixTest : public ::testing::Test {
protected:
void SetUp() final {
std::unique_ptr<LinearLeastSquaresProblem> problem =
CreateLinearLeastSquaresProblemFromId(1);
CHECK(problem != nullptr);
tsm.reset(down_cast<TripletSparseMatrix*>(problem->A.release()));
dsm = std::make_unique<DenseSparseMatrix>(*tsm);
num_rows = tsm->num_rows();
num_cols = tsm->num_cols();
}
int num_rows;
int num_cols;
std::unique_ptr<TripletSparseMatrix> tsm;
std::unique_ptr<DenseSparseMatrix> dsm;
};
TEST_F(DenseSparseMatrixTest, RightMultiplyAndAccumulate) {
CompareMatrices(tsm.get(), dsm.get());
// Try with a not entirely zero vector to verify column interactions, which
// could be masked by a subtle bug when using the elementary vectors.
Vector a(num_cols);
for (int i = 0; i < num_cols; i++) {
a(i) = i;
}
Vector b1 = Vector::Zero(num_rows);
Vector b2 = Vector::Zero(num_rows);
tsm->RightMultiplyAndAccumulate(a.data(), b1.data());
dsm->RightMultiplyAndAccumulate(a.data(), b2.data());
EXPECT_EQ((b1 - b2).norm(), 0);
}
TEST_F(DenseSparseMatrixTest, LeftMultiplyAndAccumulate) {
for (int i = 0; i < num_rows; ++i) {
Vector a = Vector::Zero(num_rows);
a(i) = 1.0;
Vector b1 = Vector::Zero(num_cols);
Vector b2 = Vector::Zero(num_cols);
tsm->LeftMultiplyAndAccumulate(a.data(), b1.data());
dsm->LeftMultiplyAndAccumulate(a.data(), b2.data());
EXPECT_EQ((b1 - b2).norm(), 0);
}
// Try with a not entirely zero vector to verify column interactions, which
// could be masked by a subtle bug when using the elementary vectors.
Vector a(num_rows);
for (int i = 0; i < num_rows; i++) {
a(i) = i;
}
Vector b1 = Vector::Zero(num_cols);
Vector b2 = Vector::Zero(num_cols);
tsm->LeftMultiplyAndAccumulate(a.data(), b1.data());
dsm->LeftMultiplyAndAccumulate(a.data(), b2.data());
EXPECT_EQ((b1 - b2).norm(), 0);
}
TEST_F(DenseSparseMatrixTest, ColumnNorm) {
Vector b1 = Vector::Zero(num_cols);
Vector b2 = Vector::Zero(num_cols);
tsm->SquaredColumnNorm(b1.data());
dsm->SquaredColumnNorm(b2.data());
EXPECT_EQ((b1 - b2).norm(), 0);
}
TEST_F(DenseSparseMatrixTest, Scale) {
Vector scale(num_cols);
for (int i = 0; i < num_cols; ++i) {
scale(i) = i + 1;
}
tsm->ScaleColumns(scale.data());
dsm->ScaleColumns(scale.data());
CompareMatrices(tsm.get(), dsm.get());
}
TEST_F(DenseSparseMatrixTest, ToDenseMatrix) {
Matrix tsm_dense;
Matrix dsm_dense;
tsm->ToDenseMatrix(&tsm_dense);
dsm->ToDenseMatrix(&dsm_dense);
EXPECT_EQ((tsm_dense - dsm_dense).norm(), 0.0);
}
} // namespace ceres::internal