|  | // 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 { | 
|  |  | 
|  | 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.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 |