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213 lines
7.9 KiB
C++
213 lines
7.9 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
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// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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// this hack is needed to make this file compiles with -pedantic (gcc)
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#ifdef __GNUC__
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#define throw(X)
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#endif
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#ifdef __INTEL_COMPILER
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// disable "warning #76: argument to macro is empty" produced by the above hack
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#pragma warning disable 76
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#endif
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// discard stack allocation as that too bypasses malloc
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#define EIGEN_STACK_ALLOCATION_LIMIT 0
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// any heap allocation will raise an assert
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#define EIGEN_NO_MALLOC
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#include "main.h"
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#include <Eigen/Cholesky>
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#include <Eigen/Eigenvalues>
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#include <Eigen/LU>
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#include <Eigen/QR>
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#include <Eigen/SVD>
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template<typename MatrixType> void nomalloc(const MatrixType& m)
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{
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/* this test check no dynamic memory allocation are issued with fixed-size matrices
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*/
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typedef typename MatrixType::Index Index;
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typedef typename MatrixType::Scalar Scalar;
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Index rows = m.rows();
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Index cols = m.cols();
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MatrixType m1 = MatrixType::Random(rows, cols),
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m2 = MatrixType::Random(rows, cols),
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m3(rows, cols);
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Scalar s1 = internal::random<Scalar>();
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Index r = internal::random<Index>(0, rows-1),
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c = internal::random<Index>(0, cols-1);
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VERIFY_IS_APPROX((m1+m2)*s1, s1*m1+s1*m2);
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VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c)));
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VERIFY_IS_APPROX(m1.cwiseProduct(m1.block(0,0,rows,cols)), (m1.array()*m1.array()).matrix());
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VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2));
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m2.col(0).noalias() = m1 * m1.col(0);
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m2.col(0).noalias() -= m1.adjoint() * m1.col(0);
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m2.col(0).noalias() -= m1 * m1.row(0).adjoint();
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m2.col(0).noalias() -= m1.adjoint() * m1.row(0).adjoint();
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m2.row(0).noalias() = m1.row(0) * m1;
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m2.row(0).noalias() -= m1.row(0) * m1.adjoint();
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m2.row(0).noalias() -= m1.col(0).adjoint() * m1;
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m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint();
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VERIFY_IS_APPROX(m2,m2);
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m2.col(0).noalias() = m1.template triangularView<Upper>() * m1.col(0);
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m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.col(0);
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m2.col(0).noalias() -= m1.template triangularView<Upper>() * m1.row(0).adjoint();
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m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.row(0).adjoint();
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m2.row(0).noalias() = m1.row(0) * m1.template triangularView<Upper>();
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m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template triangularView<Upper>();
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m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template triangularView<Upper>();
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m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template triangularView<Upper>();
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VERIFY_IS_APPROX(m2,m2);
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m2.col(0).noalias() = m1.template selfadjointView<Upper>() * m1.col(0);
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m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.col(0);
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m2.col(0).noalias() -= m1.template selfadjointView<Upper>() * m1.row(0).adjoint();
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m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.row(0).adjoint();
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m2.row(0).noalias() = m1.row(0) * m1.template selfadjointView<Upper>();
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m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template selfadjointView<Upper>();
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m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template selfadjointView<Upper>();
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m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template selfadjointView<Upper>();
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VERIFY_IS_APPROX(m2,m2);
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m2.template selfadjointView<Lower>().rankUpdate(m1.col(0),-1);
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m2.template selfadjointView<Lower>().rankUpdate(m1.row(0),-1);
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// The following fancy matrix-matrix products are not safe yet regarding static allocation
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// m1 += m1.template triangularView<Upper>() * m2.col(;
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// m1.template selfadjointView<Lower>().rankUpdate(m2);
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// m1 += m1.template triangularView<Upper>() * m2;
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// m1 += m1.template selfadjointView<Lower>() * m2;
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// VERIFY_IS_APPROX(m1,m1);
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}
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template<typename Scalar>
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void ctms_decompositions()
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{
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const int maxSize = 16;
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const int size = 12;
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typedef Eigen::Matrix<Scalar,
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Eigen::Dynamic, Eigen::Dynamic,
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0,
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maxSize, maxSize> Matrix;
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typedef Eigen::Matrix<Scalar,
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Eigen::Dynamic, 1,
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0,
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maxSize, 1> Vector;
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typedef Eigen::Matrix<std::complex<Scalar>,
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Eigen::Dynamic, Eigen::Dynamic,
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0,
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maxSize, maxSize> ComplexMatrix;
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const Matrix A(Matrix::Random(size, size)), B(Matrix::Random(size, size));
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Matrix X(size,size);
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const ComplexMatrix complexA(ComplexMatrix::Random(size, size));
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const Matrix saA = A.adjoint() * A;
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const Vector b(Vector::Random(size));
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Vector x(size);
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// Cholesky module
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Eigen::LLT<Matrix> LLT; LLT.compute(A);
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X = LLT.solve(B);
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x = LLT.solve(b);
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Eigen::LDLT<Matrix> LDLT; LDLT.compute(A);
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X = LDLT.solve(B);
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x = LDLT.solve(b);
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// Eigenvalues module
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Eigen::HessenbergDecomposition<ComplexMatrix> hessDecomp; hessDecomp.compute(complexA);
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Eigen::ComplexSchur<ComplexMatrix> cSchur(size); cSchur.compute(complexA);
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Eigen::ComplexEigenSolver<ComplexMatrix> cEigSolver; cEigSolver.compute(complexA);
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Eigen::EigenSolver<Matrix> eigSolver; eigSolver.compute(A);
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Eigen::SelfAdjointEigenSolver<Matrix> saEigSolver(size); saEigSolver.compute(saA);
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Eigen::Tridiagonalization<Matrix> tridiag; tridiag.compute(saA);
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// LU module
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Eigen::PartialPivLU<Matrix> ppLU; ppLU.compute(A);
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X = ppLU.solve(B);
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x = ppLU.solve(b);
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Eigen::FullPivLU<Matrix> fpLU; fpLU.compute(A);
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X = fpLU.solve(B);
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x = fpLU.solve(b);
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// QR module
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Eigen::HouseholderQR<Matrix> hQR; hQR.compute(A);
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X = hQR.solve(B);
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x = hQR.solve(b);
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Eigen::ColPivHouseholderQR<Matrix> cpQR; cpQR.compute(A);
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X = cpQR.solve(B);
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x = cpQR.solve(b);
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Eigen::FullPivHouseholderQR<Matrix> fpQR; fpQR.compute(A);
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// FIXME X = fpQR.solve(B);
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x = fpQR.solve(b);
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// SVD module
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Eigen::JacobiSVD<Matrix> jSVD; jSVD.compute(A, ComputeFullU | ComputeFullV);
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}
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void test_zerosized() {
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// default constructors:
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Eigen::MatrixXd A;
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Eigen::VectorXd v;
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// explicit zero-sized:
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Eigen::ArrayXXd A0(0,0);
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Eigen::ArrayXd v0(0);
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// assigning empty objects to each other:
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A=A0;
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v=v0;
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}
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template<typename MatrixType> void test_reference(const MatrixType& m) {
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typedef typename MatrixType::Scalar Scalar;
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enum { Flag = MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor};
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enum { TransposeFlag = !MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor};
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typename MatrixType::Index rows = m.rows(), cols=m.cols();
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// Dynamic reference:
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typedef Eigen::Ref<const Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Flag > > Ref;
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typedef Eigen::Ref<const Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, TransposeFlag> > RefT;
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Ref r1(m);
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Ref r2(m.block(rows/3, cols/4, rows/2, cols/2));
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RefT r3(m.transpose());
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RefT r4(m.topLeftCorner(rows/2, cols/2).transpose());
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VERIFY_RAISES_ASSERT(RefT r5(m));
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VERIFY_RAISES_ASSERT(Ref r6(m.transpose()));
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VERIFY_RAISES_ASSERT(Ref r7(Scalar(2) * m));
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}
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void test_nomalloc()
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{
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// check that our operator new is indeed called:
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VERIFY_RAISES_ASSERT(MatrixXd dummy(MatrixXd::Random(3,3)));
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CALL_SUBTEST_1(nomalloc(Matrix<float, 1, 1>()) );
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CALL_SUBTEST_2(nomalloc(Matrix4d()) );
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CALL_SUBTEST_3(nomalloc(Matrix<float,32,32>()) );
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// Check decomposition modules with dynamic matrices that have a known compile-time max size (ctms)
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CALL_SUBTEST_4(ctms_decompositions<float>());
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CALL_SUBTEST_5(test_zerosized());
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CALL_SUBTEST_6(test_reference(Matrix<float,32,32>()));
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}
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