1 // Ceres Solver - A fast non-linear least squares minimizer 2 // Copyright 2010, 2011, 2012 Google Inc. All rights reserved. 3 // http://code.google.com/p/ceres-solver/ 4 // 5 // Redistribution and use in source and binary forms, with or without 6 // modification, are permitted provided that the following conditions are met: 7 // 8 // * Redistributions of source code must retain the above copyright notice, 9 // this list of conditions and the following disclaimer. 10 // * Redistributions in binary form must reproduce the above copyright notice, 11 // this list of conditions and the following disclaimer in the documentation 12 // and/or other materials provided with the distribution. 13 // * Neither the name of Google Inc. nor the names of its contributors may be 14 // used to endorse or promote products derived from this software without 15 // specific prior written permission. 16 // 17 // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 18 // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 19 // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE 20 // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE 21 // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR 22 // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF 23 // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS 24 // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN 25 // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) 26 // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 27 // POSSIBILITY OF SUCH DAMAGE. 28 // 29 // Author: sameeragarwal (at) google.com (Sameer Agarwal) 30 31 #include "ceres/block_sparse_matrix.h" 32 33 #include <string> 34 #include "ceres/casts.h" 35 #include "ceres/internal/eigen.h" 36 #include "ceres/internal/scoped_ptr.h" 37 #include "ceres/linear_least_squares_problems.h" 38 #include "ceres/triplet_sparse_matrix.h" 39 #include "glog/logging.h" 40 #include "gtest/gtest.h" 41 42 namespace ceres { 43 namespace internal { 44 45 class BlockSparseMatrixTest : public ::testing::Test { 46 protected : 47 virtual void SetUp() { 48 scoped_ptr<LinearLeastSquaresProblem> problem( 49 CreateLinearLeastSquaresProblemFromId(2)); 50 CHECK_NOTNULL(problem.get()); 51 A_.reset(down_cast<BlockSparseMatrix*>(problem->A.release())); 52 53 problem.reset(CreateLinearLeastSquaresProblemFromId(1)); 54 CHECK_NOTNULL(problem.get()); 55 B_.reset(down_cast<TripletSparseMatrix*>(problem->A.release())); 56 57 CHECK_EQ(A_->num_rows(), B_->num_rows()); 58 CHECK_EQ(A_->num_cols(), B_->num_cols()); 59 CHECK_EQ(A_->num_nonzeros(), B_->num_nonzeros()); 60 } 61 62 scoped_ptr<BlockSparseMatrix> A_; 63 scoped_ptr<TripletSparseMatrix> B_; 64 }; 65 66 TEST_F(BlockSparseMatrixTest, SetZeroTest) { 67 A_->SetZero(); 68 EXPECT_EQ(13, A_->num_nonzeros()); 69 } 70 71 TEST_F(BlockSparseMatrixTest, RightMultiplyTest) { 72 Vector y_a = Vector::Zero(A_->num_rows()); 73 Vector y_b = Vector::Zero(A_->num_rows()); 74 for (int i = 0; i < A_->num_cols(); ++i) { 75 Vector x = Vector::Zero(A_->num_cols()); 76 x[i] = 1.0; 77 A_->RightMultiply(x.data(), y_a.data()); 78 B_->RightMultiply(x.data(), y_b.data()); 79 EXPECT_LT((y_a - y_b).norm(), 1e-12); 80 } 81 } 82 83 TEST_F(BlockSparseMatrixTest, LeftMultiplyTest) { 84 Vector y_a = Vector::Zero(A_->num_cols()); 85 Vector y_b = Vector::Zero(A_->num_cols()); 86 for (int i = 0; i < A_->num_rows(); ++i) { 87 Vector x = Vector::Zero(A_->num_rows()); 88 x[i] = 1.0; 89 A_->LeftMultiply(x.data(), y_a.data()); 90 B_->LeftMultiply(x.data(), y_b.data()); 91 EXPECT_LT((y_a - y_b).norm(), 1e-12); 92 } 93 } 94 95 TEST_F(BlockSparseMatrixTest, SquaredColumnNormTest) { 96 Vector y_a = Vector::Zero(A_->num_cols()); 97 Vector y_b = Vector::Zero(A_->num_cols()); 98 A_->SquaredColumnNorm(y_a.data()); 99 B_->SquaredColumnNorm(y_b.data()); 100 EXPECT_LT((y_a - y_b).norm(), 1e-12); 101 } 102 103 TEST_F(BlockSparseMatrixTest, ToDenseMatrixTest) { 104 Matrix m_a; 105 Matrix m_b; 106 A_->ToDenseMatrix(&m_a); 107 B_->ToDenseMatrix(&m_b); 108 EXPECT_LT((m_a - m_b).norm(), 1e-12); 109 } 110 111 } // namespace internal 112 } // namespace ceres 113
