// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr> // // This Source Code Form is subject to the terms of the Mozilla // Public License v. 2.0. If a copy of the MPL was not distributed // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. #if defined (_MSC_VER) && (_MSC_VER==1800)// This unit test takes forever to compile in Release mode with MSVC 2013, // multiple hours. So let's switch off optimization for this one. #pragma optimize("" ,off)#endif static long int nb_temporaries;inline void on_temporary_creation() {// here's a great place to set a breakpoint when debugging failures in this test! #define EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN { on_temporary_creation(); }#include "sparse.h" #define VERIFY_EVALUATION_COUNT(XPR,N) {\if (nb_temporaries!=N) std::cerr << "nb_temporaries == " << nb_temporaries << "\n" ; \#XPR ) && nb_temporaries==N ); \template <typename SparseMatrixType> void sparse_product()typedef typename SparseMatrixType::StorageIndex StorageIndex;const Index rows = internal::random<Index>(1,n);const Index cols = internal::random<Index>(1,n);const Index depth = internal::random<Index>(1,n);typedef typename SparseMatrixType::Scalar Scalar;enum { Flags = SparseMatrixType::Flags };double density = (std::max)(8./(rows*cols), 0.2);typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;typedef Matrix<Scalar,Dynamic,1> DenseVector;typedef Matrix<Scalar,1,Dynamic> RowDenseVector;typedef SparseVector<Scalar,0,StorageIndex> ColSpVector;typedef SparseVector<Scalar,RowMajor,StorageIndex> RowSpVector;// test matrix-matrix product // DenseVector dv1 = DenseVector::Random(rows); // int c = internal::random<int>(0,depth-1); // sparse * sparse *refMat3t.transpose());/2));).leftCols(cols/2));refMat3);.transpose()*refMat3t.transpose());spose());#ifndef EIGEN_SPARSE_PRODUCT_IGNORE_TEMPORARY_COUNT// make sure the right product implementation is called: if ((!SparseMatrixType::IsRowMajor) && m2.rows()<=m3.cols())// 2 for transposing and get a sorted result. #endif // and that pruning is effective: // dense ?= sparse * sparse )*refMat3t.transpose());e()*refMat3t.transpose());e()*refMat3t.transpose());// test aliasing // sparse * dense matrix ));3);pose()*refMat3t.transpose());spose()*(refMat3+refMat5)*0.5);// sparse * dense vector (0));2*refMat3t.transpose().col(0));2t.transpose()*refMat3.col(0));col(0)=refMat2t.transpose()*refMat3t.transpose().col(0));// dense * sparse ));3);pose()*refMat3t.transpose());// sparse * dense and dense * sparse outer product .col(c1).transpose());ol(c)*dm5.col(c1).transpose());5.col(c1).transpose());2.col(c).transpose());1)*refMat2.col(c).transpose());t2.col(c).transpose());w(r1).transpose()*refMat2.col(c).transpose());ow(r1).transpose()*refMat2.col(c).transpose());2.row(r).transpose()*dm5.col(c1).transpose());t4=refMat2.row(r).transpose()*dm5.col(c1).transpose());t2.row(r).transpose()*dm5.col(c1).transpose());ow(r));ose()*refMat2.row(r));pose()*refMat2.row(r));// sparse matrix * sparse vector // test matrix - diagonal product // also check with a SparseWrapper: Diagonal());ranspose());M2*v1.asDiagonal());// evaluate to a dense matrix to check the .row() and .col() iterator functions // test self-adjoint and triangular-view products do {/*ForceNonZeroDiag|*/MakeUpperTriangular); while (refUp.isZero());// TODO be able to address the diagonal.... for (int k=0; k<mS.outerSize(); ++k)for (typename SparseMatrixType::InnerIterator it(mS,k); it; ++it)if (it.index() == k)// sparse selfadjointView with dense matrices template selfadjointView<Upper>()*b, refX=refS*b);template selfadjointView<Lower>()*b, refX=refS*b);template selfadjointView<Upper|Lower>()*b, refX=refS*b);template selfadjointView<Upper>(), refX=b*refS);template selfadjointView<Lower>(), refX=b*refS);template selfadjointView<Upper|Lower>(), refX=b*refS);template selfadjointView<Upper>()*b, refX+=refS*b);template selfadjointView<Lower>()*b, refX-=refS*b);template selfadjointView<Upper|Lower>()*b, refX+=refS*b);// sparse selfadjointView with sparse matrices template selfadjointView<Lower>()*mS,template selfadjointView<Lower>()*refS);template selfadjointView<Lower>(),template selfadjointView<Lower>());// sparse triangularView with dense matrices template triangularView<Upper>()*b, refX=refA.template triangularView<Upper>()*b);template triangularView<Lower>()*b, refX=refA.template triangularView<Lower>()*b);template triangularView<Upper>(), refX=b*refA.template triangularView<Upper>());template triangularView<Lower>(), refX=b*refA.template triangularView<Lower>());// sparse triangularView with sparse matrices template triangularView<Lower>()*mS, refX = refA.template triangularView<Lower>()*refS);template triangularView<Lower>(), refX = refS * refA.template triangularView<Lower>());template triangularView<Upper>()*mS, refX = refA.template triangularView<Upper>()*refS);template triangularView<Upper>(), refX = refS * refA.template triangularView<Upper>());// New test for Bug in SparseTimeDenseProduct template <typename SparseMatrixType, typename DenseMatrixType> void sparse_product_regression_test()// This code does not compile with afflicted versions of the bug // This code produces a segfault with afflicted versions of another SparseTimeDenseProduct // bug template <typename Scalar>void bug_942()typedef Matrix<Scalar, Dynamic, 1> Vector;typedef SparseMatrix<Scalar, ColMajor> ColSpMat;typedef SparseMatrix<Scalar, RowMajor> RowSpMat;double res = 2;template <typename Real>void test_mixing_types()typedef std::complex<Real> Cplx;typedef SparseMatrix<Real> SpMatReal;typedef SparseMatrix<Cplx> SpMatCplx;typedef SparseMatrix<Cplx,RowMajor> SpRowMatCplx;typedef Matrix<Real,Dynamic,Dynamic> DenseMatReal;typedef Matrix<Cplx,Dynamic,Dynamic> DenseMatCplx;double density = (std::max)(8./(n*n), 0.2);template cast<Cplx>() * dC1 );template cast<Cplx>() );template cast<Cplx>().transpose() * dC1 );template cast<Cplx>() );template cast<Cplx>() * dC1.transpose() );template cast<Cplx>().transpose() );template cast<Cplx>().transpose() * dC1.transpose() );template cast<Cplx>().transpose() );template cast<Cplx>() * dC1 );template cast<Cplx>() );template cast<Cplx>().transpose() * dC1 );template cast<Cplx>() );template cast<Cplx>() * dC1.transpose() );template cast<Cplx>().transpose() );template cast<Cplx>().transpose() * dC1.transpose() );template cast<Cplx>().transpose() );template cast<Cplx>() * dC1 );template cast<Cplx>() );template cast<Cplx>().transpose() * dC1 );template cast<Cplx>() );template cast<Cplx>() * dC1.transpose() );template cast<Cplx>().transpose() );template cast<Cplx>().transpose() * dC1.transpose() );e() * dR1.template cast<Cplx>().transpose() );template cast<Cplx>() * dC1 );template cast<Cplx>() );template cast<Cplx>().transpose() * dC1 );template cast<Cplx>() );template cast<Cplx>() * dC1.transpose() );template cast<Cplx>().transpose() );template cast<Cplx>().transpose() * dC1.transpose() );e() * dR1.template cast<Cplx>().transpose() );template cast<Cplx>() * dC1 );template cast<Cplx>() );template cast<Cplx>().transpose() * dC1 );template cast<Cplx>() );template cast<Cplx>() * dC1.transpose() );template cast<Cplx>().transpose() );template cast<Cplx>().transpose() * dC1.transpose() );template cast<Cplx>().transpose() );template cast<Cplx>() * sC1 );template cast<Cplx>() * dC1 );template cast<Cplx>() );template cast<Cplx>() );template cast<Cplx>().row(0) * sC1 );template cast<Cplx>() * dC1.col(0) );template cast<Cplx>() );template cast<Cplx>().col(0) );for (int i = 0; i < g_repeat; i++) {double ,ColMajor> >()) );double ,RowMajor> >()) );double >()) );double >, ColMajor > >()) );double >, RowMajor > >()) );float ,ColMajor,long int > >()) );double ,RowMajor>, Matrix<double float >()) );quality 94%
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