1 // This file is part of Eigen, a lightweight C++ template library 2 // for linear algebra. 3 // 4 // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud (at) inria.fr> 5 // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1 (at) gmail.com> 6 // 7 // This Source Code Form is subject to the terms of the Mozilla 8 // Public License v. 2.0. If a copy of the MPL was not distributed 9 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 10 11 // discard stack allocation as that too bypasses malloc 12 #define EIGEN_STACK_ALLOCATION_LIMIT 0 13 // heap allocation will raise an assert if enabled at runtime 14 #define EIGEN_RUNTIME_NO_MALLOC 15 16 #include "main.h" 17 #include <Eigen/Cholesky> 18 #include <Eigen/Eigenvalues> 19 #include <Eigen/LU> 20 #include <Eigen/QR> 21 #include <Eigen/SVD> 22 23 template<typename MatrixType> void nomalloc(const MatrixType& m) 24 { 25 /* this test check no dynamic memory allocation are issued with fixed-size matrices 26 */ 27 typedef typename MatrixType::Index Index; 28 typedef typename MatrixType::Scalar Scalar; 29 30 Index rows = m.rows(); 31 Index cols = m.cols(); 32 33 MatrixType m1 = MatrixType::Random(rows, cols), 34 m2 = MatrixType::Random(rows, cols), 35 m3(rows, cols); 36 37 Scalar s1 = internal::random<Scalar>(); 38 39 Index r = internal::random<Index>(0, rows-1), 40 c = internal::random<Index>(0, cols-1); 41 42 VERIFY_IS_APPROX((m1+m2)*s1, s1*m1+s1*m2); 43 VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c))); 44 VERIFY_IS_APPROX(m1.cwiseProduct(m1.block(0,0,rows,cols)), (m1.array()*m1.array()).matrix()); 45 VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2)); 46 47 m2.col(0).noalias() = m1 * m1.col(0); 48 m2.col(0).noalias() -= m1.adjoint() * m1.col(0); 49 m2.col(0).noalias() -= m1 * m1.row(0).adjoint(); 50 m2.col(0).noalias() -= m1.adjoint() * m1.row(0).adjoint(); 51 52 m2.row(0).noalias() = m1.row(0) * m1; 53 m2.row(0).noalias() -= m1.row(0) * m1.adjoint(); 54 m2.row(0).noalias() -= m1.col(0).adjoint() * m1; 55 m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint(); 56 VERIFY_IS_APPROX(m2,m2); 57 58 m2.col(0).noalias() = m1.template triangularView<Upper>() * m1.col(0); 59 m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.col(0); 60 m2.col(0).noalias() -= m1.template triangularView<Upper>() * m1.row(0).adjoint(); 61 m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.row(0).adjoint(); 62 63 m2.row(0).noalias() = m1.row(0) * m1.template triangularView<Upper>(); 64 m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template triangularView<Upper>(); 65 m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template triangularView<Upper>(); 66 m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template triangularView<Upper>(); 67 VERIFY_IS_APPROX(m2,m2); 68 69 m2.col(0).noalias() = m1.template selfadjointView<Upper>() * m1.col(0); 70 m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.col(0); 71 m2.col(0).noalias() -= m1.template selfadjointView<Upper>() * m1.row(0).adjoint(); 72 m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.row(0).adjoint(); 73 74 m2.row(0).noalias() = m1.row(0) * m1.template selfadjointView<Upper>(); 75 m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template selfadjointView<Upper>(); 76 m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template selfadjointView<Upper>(); 77 m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template selfadjointView<Upper>(); 78 VERIFY_IS_APPROX(m2,m2); 79 80 m2.template selfadjointView<Lower>().rankUpdate(m1.col(0),-1); 81 m2.template selfadjointView<Upper>().rankUpdate(m1.row(0),-1); 82 m2.template selfadjointView<Lower>().rankUpdate(m1.col(0), m1.col(0)); // rank-2 83 84 // The following fancy matrix-matrix products are not safe yet regarding static allocation 85 m2.template selfadjointView<Lower>().rankUpdate(m1); 86 m2 += m2.template triangularView<Upper>() * m1; 87 m2.template triangularView<Upper>() = m2 * m2; 88 m1 += m1.template selfadjointView<Lower>() * m2; 89 VERIFY_IS_APPROX(m2,m2); 90 } 91 92 template<typename Scalar> 93 void ctms_decompositions() 94 { 95 const int maxSize = 16; 96 const int size = 12; 97 98 typedef Eigen::Matrix<Scalar, 99 Eigen::Dynamic, Eigen::Dynamic, 100 0, 101 maxSize, maxSize> Matrix; 102 103 typedef Eigen::Matrix<Scalar, 104 Eigen::Dynamic, 1, 105 0, 106 maxSize, 1> Vector; 107 108 typedef Eigen::Matrix<std::complex<Scalar>, 109 Eigen::Dynamic, Eigen::Dynamic, 110 0, 111 maxSize, maxSize> ComplexMatrix; 112 113 const Matrix A(Matrix::Random(size, size)), B(Matrix::Random(size, size)); 114 Matrix X(size,size); 115 const ComplexMatrix complexA(ComplexMatrix::Random(size, size)); 116 const Matrix saA = A.adjoint() * A; 117 const Vector b(Vector::Random(size)); 118 Vector x(size); 119 120 // Cholesky module 121 Eigen::LLT<Matrix> LLT; LLT.compute(A); 122 X = LLT.solve(B); 123 x = LLT.solve(b); 124 Eigen::LDLT<Matrix> LDLT; LDLT.compute(A); 125 X = LDLT.solve(B); 126 x = LDLT.solve(b); 127 128 // Eigenvalues module 129 Eigen::HessenbergDecomposition<ComplexMatrix> hessDecomp; hessDecomp.compute(complexA); 130 Eigen::ComplexSchur<ComplexMatrix> cSchur(size); cSchur.compute(complexA); 131 Eigen::ComplexEigenSolver<ComplexMatrix> cEigSolver; cEigSolver.compute(complexA); 132 Eigen::EigenSolver<Matrix> eigSolver; eigSolver.compute(A); 133 Eigen::SelfAdjointEigenSolver<Matrix> saEigSolver(size); saEigSolver.compute(saA); 134 Eigen::Tridiagonalization<Matrix> tridiag; tridiag.compute(saA); 135 136 // LU module 137 Eigen::PartialPivLU<Matrix> ppLU; ppLU.compute(A); 138 X = ppLU.solve(B); 139 x = ppLU.solve(b); 140 Eigen::FullPivLU<Matrix> fpLU; fpLU.compute(A); 141 X = fpLU.solve(B); 142 x = fpLU.solve(b); 143 144 // QR module 145 Eigen::HouseholderQR<Matrix> hQR; hQR.compute(A); 146 X = hQR.solve(B); 147 x = hQR.solve(b); 148 Eigen::ColPivHouseholderQR<Matrix> cpQR; cpQR.compute(A); 149 X = cpQR.solve(B); 150 x = cpQR.solve(b); 151 Eigen::FullPivHouseholderQR<Matrix> fpQR; fpQR.compute(A); 152 // FIXME X = fpQR.solve(B); 153 x = fpQR.solve(b); 154 155 // SVD module 156 Eigen::JacobiSVD<Matrix> jSVD; jSVD.compute(A, ComputeFullU | ComputeFullV); 157 } 158 159 void test_zerosized() { 160 // default constructors: 161 Eigen::MatrixXd A; 162 Eigen::VectorXd v; 163 // explicit zero-sized: 164 Eigen::ArrayXXd A0(0,0); 165 Eigen::ArrayXd v0(0); 166 167 // assigning empty objects to each other: 168 A=A0; 169 v=v0; 170 } 171 172 template<typename MatrixType> void test_reference(const MatrixType& m) { 173 typedef typename MatrixType::Scalar Scalar; 174 enum { Flag = MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor}; 175 enum { TransposeFlag = !MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor}; 176 typename MatrixType::Index rows = m.rows(), cols=m.cols(); 177 typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Flag > MatrixX; 178 typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, TransposeFlag> MatrixXT; 179 // Dynamic reference: 180 typedef Eigen::Ref<const MatrixX > Ref; 181 typedef Eigen::Ref<const MatrixXT > RefT; 182 183 Ref r1(m); 184 Ref r2(m.block(rows/3, cols/4, rows/2, cols/2)); 185 RefT r3(m.transpose()); 186 RefT r4(m.topLeftCorner(rows/2, cols/2).transpose()); 187 188 VERIFY_RAISES_ASSERT(RefT r5(m)); 189 VERIFY_RAISES_ASSERT(Ref r6(m.transpose())); 190 VERIFY_RAISES_ASSERT(Ref r7(Scalar(2) * m)); 191 192 // Copy constructors shall also never malloc 193 Ref r8 = r1; 194 RefT r9 = r3; 195 196 // Initializing from a compatible Ref shall also never malloc 197 Eigen::Ref<const MatrixX, Unaligned, Stride<Dynamic, Dynamic> > r10=r8, r11=m; 198 199 // Initializing from an incompatible Ref will malloc: 200 typedef Eigen::Ref<const MatrixX, Aligned> RefAligned; 201 VERIFY_RAISES_ASSERT(RefAligned r12=r10); 202 VERIFY_RAISES_ASSERT(Ref r13=r10); // r10 has more dynamic strides 203 204 } 205 206 void test_nomalloc() 207 { 208 // create some dynamic objects 209 Eigen::MatrixXd M1 = MatrixXd::Random(3,3); 210 Ref<const MatrixXd> R1 = 2.0*M1; // Ref requires temporary 211 212 // from here on prohibit malloc: 213 Eigen::internal::set_is_malloc_allowed(false); 214 215 // check that our operator new is indeed called: 216 VERIFY_RAISES_ASSERT(MatrixXd dummy(MatrixXd::Random(3,3))); 217 CALL_SUBTEST_1(nomalloc(Matrix<float, 1, 1>()) ); 218 CALL_SUBTEST_2(nomalloc(Matrix4d()) ); 219 CALL_SUBTEST_3(nomalloc(Matrix<float,32,32>()) ); 220 221 // Check decomposition modules with dynamic matrices that have a known compile-time max size (ctms) 222 CALL_SUBTEST_4(ctms_decompositions<float>()); 223 224 CALL_SUBTEST_5(test_zerosized()); 225 226 CALL_SUBTEST_6(test_reference(Matrix<float,32,32>())); 227 CALL_SUBTEST_7(test_reference(R1)); 228 CALL_SUBTEST_8(Ref<MatrixXd> R2 = M1.topRows<2>(); test_reference(R2)); 229 } 230
