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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// 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 {
namespace 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->RightMultiply(x.data(), y_a.data());
b->RightMultiply(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, RightMultiply) {
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->RightMultiply(a.data(), b1.data());
dsm->RightMultiply(a.data(), b2.data());
EXPECT_EQ((b1 - b2).norm(), 0);
}
TEST_F(DenseSparseMatrixTest, LeftMultiply) {
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->LeftMultiply(a.data(), b1.data());
dsm->LeftMultiply(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->LeftMultiply(a.data(), b1.data());
dsm->LeftMultiply(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 internal
} // namespace ceres
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