ground/gcs/src/libs/eigen/test/nomalloc.cpp

   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@inria.fr>
   5 // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@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 // this hack is needed to make this file compiles with -pedantic (gcc)
  12 #ifdef __GNUC__
  13 #define throw(X)
  14 #endif
  15
  16 #ifdef __INTEL_COMPILER
  17   // disable "warning #76: argument to macro is empty" produced by the above hack
  18   #pragma warning disable 76
  19 #endif
  20
  21 // discard stack allocation as that too bypasses malloc
  22 #define EIGEN_STACK_ALLOCATION_LIMIT 0
  23 // any heap allocation will raise an assert
  24 #define EIGEN_NO_MALLOC
  25
  26 #include "main.h"
  27 #include <Eigen/Cholesky>
  28 #include <Eigen/Eigenvalues>
  29 #include <Eigen/LU>
  30 #include <Eigen/QR>
  31 #include <Eigen/SVD>
  32
  33 template<typename MatrixType> void nomalloc(const MatrixType& m)
  34 {
  35   /* this test check no dynamic memory allocation are issued with fixed-size matrices
  36   */
  37   typedef typename MatrixType::Index Index;
  38   typedef typename MatrixType::Scalar Scalar;
  39
  40   Index rows = m.rows();
  41   Index cols = m.cols();
  42
  43   MatrixType m1 = MatrixType::Random(rows, cols),
  44              m2 = MatrixType::Random(rows, cols),
  45              m3(rows, cols);
  46
  47   Scalar s1 = internal::random<Scalar>();
  48
  49   Index r = internal::random<Index>(0, rows-1),
  50         c = internal::random<Index>(0, cols-1);
  51
  52   VERIFY_IS_APPROX((m1+m2)*s1,              s1*m1+s1*m2);
  53   VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c)));
  54   VERIFY_IS_APPROX(m1.cwiseProduct(m1.block(0,0,rows,cols)), (m1.array()*m1.array()).matrix());
  55   VERIFY_IS_APPROX((m1*m1.transpose())*m2,  m1*(m1.transpose()*m2));
  56
  57   m2.col(0).noalias() = m1 * m1.col(0);
  58   m2.col(0).noalias() -= m1.adjoint() * m1.col(0);
  59   m2.col(0).noalias() -= m1 * m1.row(0).adjoint();
  60   m2.col(0).noalias() -= m1.adjoint() * m1.row(0).adjoint();
  61
  62   m2.row(0).noalias() = m1.row(0) * m1;
  63   m2.row(0).noalias() -= m1.row(0) * m1.adjoint();
  64   m2.row(0).noalias() -= m1.col(0).adjoint() * m1;
  65   m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint();
  66   VERIFY_IS_APPROX(m2,m2);
  67
  68   m2.col(0).noalias() = m1.template triangularView<Upper>() * m1.col(0);
  69   m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.col(0);
  70   m2.col(0).noalias() -= m1.template triangularView<Upper>() * m1.row(0).adjoint();
  71   m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.row(0).adjoint();
  72
  73   m2.row(0).noalias() = m1.row(0) * m1.template triangularView<Upper>();
  74   m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template triangularView<Upper>();
  75   m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template triangularView<Upper>();
  76   m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template triangularView<Upper>();
  77   VERIFY_IS_APPROX(m2,m2);
  78
  79   m2.col(0).noalias() = m1.template selfadjointView<Upper>() * m1.col(0);
  80   m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.col(0);
  81   m2.col(0).noalias() -= m1.template selfadjointView<Upper>() * m1.row(0).adjoint();
  82   m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.row(0).adjoint();
  83
  84   m2.row(0).noalias() = m1.row(0) * m1.template selfadjointView<Upper>();
  85   m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template selfadjointView<Upper>();
  86   m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template selfadjointView<Upper>();
  87   m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template selfadjointView<Upper>();
  88   VERIFY_IS_APPROX(m2,m2);
  89
  90   m2.template selfadjointView<Lower>().rankUpdate(m1.col(0),-1);
  91   m2.template selfadjointView<Lower>().rankUpdate(m1.row(0),-1);
  92
  93   // The following fancy matrix-matrix products are not safe yet regarding static allocation
  94 //   m1 += m1.template triangularView<Upper>() * m2.col(;
  95 //   m1.template selfadjointView<Lower>().rankUpdate(m2);
  96 //   m1 += m1.template triangularView<Upper>() * m2;
  97 //   m1 += m1.template selfadjointView<Lower>() * m2;
  98 //   VERIFY_IS_APPROX(m1,m1);
  99 }
 100
 101 template<typename Scalar>
 102 void ctms_decompositions()
 103 {
 104   const int maxSize = 16;
 105   const int size    = 12;
 106
 107   typedef Eigen::Matrix<Scalar,
 108                         Eigen::Dynamic, Eigen::Dynamic,
 109                         0,
 110                         maxSize, maxSize> Matrix;
 111
 112   typedef Eigen::Matrix<Scalar,
 113                         Eigen::Dynamic, 1,
 114                         0,
 115                         maxSize, 1> Vector;
 116
 117   typedef Eigen::Matrix<std::complex<Scalar>,
 118                         Eigen::Dynamic, Eigen::Dynamic,
 119                         0,
 120                         maxSize, maxSize> ComplexMatrix;
 121
 122   const Matrix A(Matrix::Random(size, size)), B(Matrix::Random(size, size));
 123   Matrix X(size,size);
 124   const ComplexMatrix complexA(ComplexMatrix::Random(size, size));
 125   const Matrix saA = A.adjoint() * A;
 126   const Vector b(Vector::Random(size));
 127   Vector x(size);
 128
 129   // Cholesky module
 130   Eigen::LLT<Matrix>  LLT;  LLT.compute(A);
 131   X = LLT.solve(B);
 132   x = LLT.solve(b);
 133   Eigen::LDLT<Matrix> LDLT; LDLT.compute(A);
 134   X = LDLT.solve(B);
 135   x = LDLT.solve(b);
 136
 137   // Eigenvalues module
 138   Eigen::HessenbergDecomposition<ComplexMatrix> hessDecomp;        hessDecomp.compute(complexA);
 139   Eigen::ComplexSchur<ComplexMatrix>            cSchur(size);      cSchur.compute(complexA);
 140   Eigen::ComplexEigenSolver<ComplexMatrix>      cEigSolver;        cEigSolver.compute(complexA);
 141   Eigen::EigenSolver<Matrix>                    eigSolver;         eigSolver.compute(A);
 142   Eigen::SelfAdjointEigenSolver<Matrix>         saEigSolver(size); saEigSolver.compute(saA);
 143   Eigen::Tridiagonalization<Matrix>             tridiag;           tridiag.compute(saA);
 144
 145   // LU module
 146   Eigen::PartialPivLU<Matrix> ppLU; ppLU.compute(A);
 147   X = ppLU.solve(B);
 148   x = ppLU.solve(b);
 149   Eigen::FullPivLU<Matrix>    fpLU; fpLU.compute(A);
 150   X = fpLU.solve(B);
 151   x = fpLU.solve(b);
 152
 153   // QR module
 154   Eigen::HouseholderQR<Matrix>        hQR;  hQR.compute(A);
 155   X = hQR.solve(B);
 156   x = hQR.solve(b);
 157   Eigen::ColPivHouseholderQR<Matrix>  cpQR; cpQR.compute(A);
 158   X = cpQR.solve(B);
 159   x = cpQR.solve(b);
 160   Eigen::FullPivHouseholderQR<Matrix> fpQR; fpQR.compute(A);
 161   // FIXME X = fpQR.solve(B);
 162   x = fpQR.solve(b);
 163
 164   // SVD module
 165   Eigen::JacobiSVD<Matrix> jSVD; jSVD.compute(A, ComputeFullU | ComputeFullV);
 166 }
 167
 168 void test_zerosized() {
 169   // default constructors:
 170   Eigen::MatrixXd A;
 171   Eigen::VectorXd v;
 172   // explicit zero-sized:
 173   Eigen::ArrayXXd A0(0,0);
 174   Eigen::ArrayXd v0(std::ptrdiff_t(0)); // FIXME ArrayXd(0) is ambiguous
 175
 176   // assigning empty objects to each other:
 177   A=A0;
 178   v=v0;
 179 }
 180
 181 template<typename MatrixType> void test_reference(const MatrixType& m) {
 182   typedef typename MatrixType::Scalar Scalar;
 183   enum { Flag          =  MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor};
 184   enum { TransposeFlag = !MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor};
 185   typename MatrixType::Index rows = m.rows(), cols=m.cols();
 186   // Dynamic reference:
 187   typedef Eigen::Ref<const Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Flag         > > Ref;
 188   typedef Eigen::Ref<const Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, TransposeFlag> > RefT;
 189
 190   Ref r1(m);
 191   Ref r2(m.block(rows/3, cols/4, rows/2, cols/2));
 192   RefT r3(m.transpose());
 193   RefT r4(m.topLeftCorner(rows/2, cols/2).transpose());
 194
 195   VERIFY_RAISES_ASSERT(RefT r5(m));
 196   VERIFY_RAISES_ASSERT(Ref r6(m.transpose()));
 197   VERIFY_RAISES_ASSERT(Ref r7(Scalar(2) * m));
 198 }
 199
 200 void test_nomalloc()
 201 {
 202   // check that our operator new is indeed called:
 203   VERIFY_RAISES_ASSERT(MatrixXd dummy(MatrixXd::Random(3,3)));
 204   CALL_SUBTEST_1(nomalloc(Matrix<float, 1, 1>()) );
 205   CALL_SUBTEST_2(nomalloc(Matrix4d()) );
 206   CALL_SUBTEST_3(nomalloc(Matrix<float,32,32>()) );
 207
 208   // Check decomposition modules with dynamic matrices that have a known compile-time max size (ctms)
 209   CALL_SUBTEST_4(ctms_decompositions<float>());
 210   CALL_SUBTEST_5(test_zerosized());
 211   CALL_SUBTEST_6(test_reference(Matrix<float,32,32>()));
 212 }