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173 lines
7.0 KiB
C++
173 lines
7.0 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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// Eigen is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 3 of the License, or (at your option) any later version.
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//
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// Alternatively, you can redistribute it and/or
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// modify it under the terms of the GNU General Public License as
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// published by the Free Software Foundation; either version 2 of
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// the License, or (at your option) any later version.
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//
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// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
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// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License and a copy of the GNU General Public License along with
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// Eigen. If not, see <http://www.gnu.org/licenses/>.
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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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// 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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typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
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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));
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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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// Cholesky module
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Eigen::LLT<Matrix> LLT; LLT.compute(A);
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Eigen::LDLT<Matrix> LDLT; LDLT.compute(A);
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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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Eigen::FullPivLU<Matrix> fpLU; fpLU.compute(A);
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// QR module
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Eigen::HouseholderQR<Matrix> hQR; hQR.compute(A);
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Eigen::ColPivHouseholderQR<Matrix> cpQR; cpQR.compute(A);
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Eigen::FullPivHouseholderQR<Matrix> fpQR; fpQR.compute(A);
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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_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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}
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