| // 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/linear_least_squares_problems.h" |
| #include "ceres/triplet_sparse_matrix.h" |
| #include "ceres/internal/eigen.h" |
| #include "glog/logging.h" |
| #include "gtest/gtest.h" |
| |
| namespace ceres { |
| namespace internal { |
| |
| 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 : |
| virtual void SetUp() { |
| std::unique_ptr<LinearLeastSquaresProblem> problem( |
| CreateLinearLeastSquaresProblemFromId(1)); |
| |
| CHECK_NOTNULL(problem.get()); |
| |
| tsm.reset(down_cast<TripletSparseMatrix*>(problem->A.release())); |
| dsm.reset(new 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 |