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							228 lines
						
					
					
						
							8.7 KiB
						
					
					
				
								// 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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								// discard stack allocation as that too bypasses malloc
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								#define STORMEIGEN_STACK_ALLOCATION_LIMIT 0
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								// heap allocation will raise an assert if enabled at runtime
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								#define STORMEIGEN_RUNTIME_NO_MALLOC
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								#include "main.h"
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								#include <StormEigen/Cholesky>
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								#include <StormEigen/Eigenvalues>
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								#include <StormEigen/LU>
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								#include <StormEigen/QR>
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								#include <StormEigen/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 StormEigen::Matrix<Scalar,
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								                        StormEigen::Dynamic, StormEigen::Dynamic,
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								                        0,
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								                        maxSize, maxSize> Matrix;
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								  typedef StormEigen::Matrix<Scalar,
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								                        StormEigen::Dynamic, 1,
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								                        0,
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								                        maxSize, 1> Vector;
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								  typedef StormEigen::Matrix<std::complex<Scalar>,
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								                        StormEigen::Dynamic, StormEigen::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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								  StormEigen::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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								  StormEigen::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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								  StormEigen::HessenbergDecomposition<ComplexMatrix> hessDecomp;        hessDecomp.compute(complexA);
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								  StormEigen::ComplexSchur<ComplexMatrix>            cSchur(size);      cSchur.compute(complexA);
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								  StormEigen::ComplexEigenSolver<ComplexMatrix>      cEigSolver;        cEigSolver.compute(complexA);
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								  StormEigen::EigenSolver<Matrix>                    eigSolver;         eigSolver.compute(A);
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								  StormEigen::SelfAdjointEigenSolver<Matrix>         saEigSolver(size); saEigSolver.compute(saA);
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								  StormEigen::Tridiagonalization<Matrix>             tridiag;           tridiag.compute(saA);
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								  // LU module
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								  StormEigen::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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								  StormEigen::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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								  StormEigen::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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								  StormEigen::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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								  StormEigen::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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								  StormEigen::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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								  StormEigen::MatrixXd A;
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								  StormEigen::VectorXd v;
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								  // explicit zero-sized:
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								  StormEigen::ArrayXXd A0(0,0);
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								  StormEigen::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 ? StormEigen::RowMajor : StormEigen::ColMajor};
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								  enum { TransposeFlag = !MatrixType::IsRowMajor ? StormEigen::RowMajor : StormEigen::ColMajor};
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								  typename MatrixType::Index rows = m.rows(), cols=m.cols();
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								  typedef StormEigen::Matrix<Scalar, StormEigen::Dynamic, Eigen::Dynamic, Flag         > MatrixX;
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								  typedef StormEigen::Matrix<Scalar, StormEigen::Dynamic, Eigen::Dynamic, TransposeFlag> MatrixXT;
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								  // Dynamic reference:
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								  typedef StormEigen::Ref<const MatrixX  > Ref;
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								  typedef StormEigen::Ref<const MatrixXT > 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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								  // Copy constructors shall also never malloc
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								  Ref r8 = r1;
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								  RefT r9 = r3;
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								  // Initializing from a compatible Ref shall also never malloc
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								  StormEigen::Ref<const MatrixX, Unaligned, Stride<Dynamic, Dynamic> > r10=r8, r11=m;
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								  // Initializing from an incompatible Ref will malloc:
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								  typedef StormEigen::Ref<const MatrixX, Aligned> RefAligned;
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								  VERIFY_RAISES_ASSERT(RefAligned r12=r10);
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								  VERIFY_RAISES_ASSERT(Ref r13=r10); // r10 has more dynamic strides
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								}
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								void test_nomalloc()
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								{
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								  // create some dynamic objects
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								  StormEigen::MatrixXd M1 = MatrixXd::Random(3,3);
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								  Ref<const MatrixXd> R1 = 2.0*M1; // Ref requires temporary
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								  // from here on prohibit malloc:
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								  StormEigen::internal::set_is_malloc_allowed(false);
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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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								  CALL_SUBTEST_7(test_reference(R1));
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								  CALL_SUBTEST_8(Ref<MatrixXd> R2 = M1.topRows<2>(); test_reference(R2));
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								}
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