source: pacpussensors/trunk/Vislab/lib3dv-1.2.0/lib3dv/eigen/test/nomalloc.cpp

Last change on this file was 136, checked in by ldecherf, 8 years ago

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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
33template<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
101template<typename Scalar>
102void 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
168void 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
181template<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
200void 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}
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