| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr> |
| // Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@gmail.com> |
| // Copyright (C) 2013 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@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/. |
| |
| #include "sparse.h" |
| |
| template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& ref) |
| { |
| typedef typename SparseMatrixType::Index Index; |
| typedef Matrix<Index,2,1> Vector2; |
| |
| const Index rows = ref.rows(); |
| const Index cols = ref.cols(); |
| typedef typename SparseMatrixType::Scalar Scalar; |
| enum { Flags = SparseMatrixType::Flags }; |
| |
| double density = (std::max)(8./(rows*cols), 0.01); |
| typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; |
| typedef Matrix<Scalar,Dynamic,1> DenseVector; |
| typedef Matrix<Scalar,1,Dynamic> RowDenseVector; |
| Scalar eps = 1e-6; |
| |
| Scalar s1 = internal::random<Scalar>(); |
| { |
| SparseMatrixType m(rows, cols); |
| DenseMatrix refMat = DenseMatrix::Zero(rows, cols); |
| DenseVector vec1 = DenseVector::Random(rows); |
| |
| std::vector<Vector2> zeroCoords; |
| std::vector<Vector2> nonzeroCoords; |
| initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords); |
| |
| if (zeroCoords.size()==0 || nonzeroCoords.size()==0) |
| return; |
| |
| // test coeff and coeffRef |
| for (int i=0; i<(int)zeroCoords.size(); ++i) |
| { |
| VERIFY_IS_MUCH_SMALLER_THAN( m.coeff(zeroCoords[i].x(),zeroCoords[i].y()), eps ); |
| if(internal::is_same<SparseMatrixType,SparseMatrix<Scalar,Flags> >::value) |
| VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[0].x(),zeroCoords[0].y()) = 5 ); |
| } |
| VERIFY_IS_APPROX(m, refMat); |
| |
| m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5); |
| refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5); |
| |
| VERIFY_IS_APPROX(m, refMat); |
| |
| // test InnerIterators and Block expressions |
| for (int t=0; t<10; ++t) |
| { |
| int j = internal::random<int>(0,cols-1); |
| int i = internal::random<int>(0,rows-1); |
| int w = internal::random<int>(1,cols-j-1); |
| int h = internal::random<int>(1,rows-i-1); |
| |
| VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w)); |
| for(int c=0; c<w; c++) |
| { |
| VERIFY_IS_APPROX(m.block(i,j,h,w).col(c), refMat.block(i,j,h,w).col(c)); |
| for(int r=0; r<h; r++) |
| { |
| VERIFY_IS_APPROX(m.block(i,j,h,w).col(c).coeff(r), refMat.block(i,j,h,w).col(c).coeff(r)); |
| VERIFY_IS_APPROX(m.block(i,j,h,w).coeff(r,c), refMat.block(i,j,h,w).coeff(r,c)); |
| } |
| } |
| for(int r=0; r<h; r++) |
| { |
| VERIFY_IS_APPROX(m.block(i,j,h,w).row(r), refMat.block(i,j,h,w).row(r)); |
| for(int c=0; c<w; c++) |
| { |
| VERIFY_IS_APPROX(m.block(i,j,h,w).row(r).coeff(c), refMat.block(i,j,h,w).row(r).coeff(c)); |
| VERIFY_IS_APPROX(m.block(i,j,h,w).coeff(r,c), refMat.block(i,j,h,w).coeff(r,c)); |
| } |
| } |
| |
| VERIFY_IS_APPROX(m.middleCols(j,w), refMat.middleCols(j,w)); |
| VERIFY_IS_APPROX(m.middleRows(i,h), refMat.middleRows(i,h)); |
| for(int r=0; r<h; r++) |
| { |
| VERIFY_IS_APPROX(m.middleCols(j,w).row(r), refMat.middleCols(j,w).row(r)); |
| VERIFY_IS_APPROX(m.middleRows(i,h).row(r), refMat.middleRows(i,h).row(r)); |
| for(int c=0; c<w; c++) |
| { |
| VERIFY_IS_APPROX(m.col(c).coeff(r), refMat.col(c).coeff(r)); |
| VERIFY_IS_APPROX(m.row(r).coeff(c), refMat.row(r).coeff(c)); |
| |
| VERIFY_IS_APPROX(m.middleCols(j,w).coeff(r,c), refMat.middleCols(j,w).coeff(r,c)); |
| VERIFY_IS_APPROX(m.middleRows(i,h).coeff(r,c), refMat.middleRows(i,h).coeff(r,c)); |
| if(m.middleCols(j,w).coeff(r,c) != Scalar(0)) |
| { |
| VERIFY_IS_APPROX(m.middleCols(j,w).coeffRef(r,c), refMat.middleCols(j,w).coeff(r,c)); |
| } |
| if(m.middleRows(i,h).coeff(r,c) != Scalar(0)) |
| { |
| VERIFY_IS_APPROX(m.middleRows(i,h).coeff(r,c), refMat.middleRows(i,h).coeff(r,c)); |
| } |
| } |
| } |
| for(int c=0; c<w; c++) |
| { |
| VERIFY_IS_APPROX(m.middleCols(j,w).col(c), refMat.middleCols(j,w).col(c)); |
| VERIFY_IS_APPROX(m.middleRows(i,h).col(c), refMat.middleRows(i,h).col(c)); |
| } |
| } |
| |
| for(int c=0; c<cols; c++) |
| { |
| VERIFY_IS_APPROX(m.col(c) + m.col(c), (m + m).col(c)); |
| VERIFY_IS_APPROX(m.col(c) + m.col(c), refMat.col(c) + refMat.col(c)); |
| } |
| |
| for(int r=0; r<rows; r++) |
| { |
| VERIFY_IS_APPROX(m.row(r) + m.row(r), (m + m).row(r)); |
| VERIFY_IS_APPROX(m.row(r) + m.row(r), refMat.row(r) + refMat.row(r)); |
| } |
| |
| |
| // test assertion |
| VERIFY_RAISES_ASSERT( m.coeffRef(-1,1) = 0 ); |
| VERIFY_RAISES_ASSERT( m.coeffRef(0,m.cols()) = 0 ); |
| } |
| |
| // test insert (inner random) |
| { |
| DenseMatrix m1(rows,cols); |
| m1.setZero(); |
| SparseMatrixType m2(rows,cols); |
| if(internal::random<int>()%2) |
| m2.reserve(VectorXi::Constant(m2.outerSize(), 2)); |
| for (Index j=0; j<cols; ++j) |
| { |
| for (Index k=0; k<rows/2; ++k) |
| { |
| Index i = internal::random<Index>(0,rows-1); |
| if (m1.coeff(i,j)==Scalar(0)) |
| m2.insert(i,j) = m1(i,j) = internal::random<Scalar>(); |
| } |
| } |
| m2.finalize(); |
| VERIFY_IS_APPROX(m2,m1); |
| } |
| |
| // test insert (fully random) |
| { |
| DenseMatrix m1(rows,cols); |
| m1.setZero(); |
| SparseMatrixType m2(rows,cols); |
| if(internal::random<int>()%2) |
| m2.reserve(VectorXi::Constant(m2.outerSize(), 2)); |
| for (int k=0; k<rows*cols; ++k) |
| { |
| Index i = internal::random<Index>(0,rows-1); |
| Index j = internal::random<Index>(0,cols-1); |
| if ((m1.coeff(i,j)==Scalar(0)) && (internal::random<int>()%2)) |
| m2.insert(i,j) = m1(i,j) = internal::random<Scalar>(); |
| else |
| { |
| Scalar v = internal::random<Scalar>(); |
| m2.coeffRef(i,j) += v; |
| m1(i,j) += v; |
| } |
| } |
| VERIFY_IS_APPROX(m2,m1); |
| } |
| |
| // test insert (un-compressed) |
| for(int mode=0;mode<4;++mode) |
| { |
| DenseMatrix m1(rows,cols); |
| m1.setZero(); |
| SparseMatrixType m2(rows,cols); |
| VectorXi r(VectorXi::Constant(m2.outerSize(), ((mode%2)==0) ? m2.innerSize() : std::max<int>(1,m2.innerSize()/8))); |
| m2.reserve(r); |
| for (int k=0; k<rows*cols; ++k) |
| { |
| Index i = internal::random<Index>(0,rows-1); |
| Index j = internal::random<Index>(0,cols-1); |
| if (m1.coeff(i,j)==Scalar(0)) |
| m2.insert(i,j) = m1(i,j) = internal::random<Scalar>(); |
| if(mode==3) |
| m2.reserve(r); |
| } |
| if(internal::random<int>()%2) |
| m2.makeCompressed(); |
| VERIFY_IS_APPROX(m2,m1); |
| } |
| |
| // test innerVector() |
| { |
| DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows); |
| SparseMatrixType m2(rows, rows); |
| initSparse<Scalar>(density, refMat2, m2); |
| Index j0 = internal::random<Index>(0,rows-1); |
| Index j1 = internal::random<Index>(0,rows-1); |
| if(SparseMatrixType::IsRowMajor) |
| VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.row(j0)); |
| else |
| VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.col(j0)); |
| |
| if(SparseMatrixType::IsRowMajor) |
| VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.row(j0)+refMat2.row(j1)); |
| else |
| VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.col(j0)+refMat2.col(j1)); |
| |
| SparseMatrixType m3(rows,rows); |
| m3.reserve(VectorXi::Constant(rows,rows/2)); |
| for(Index j=0; j<rows; ++j) |
| for(Index k=0; k<j; ++k) |
| m3.insertByOuterInner(j,k) = k+1; |
| for(Index j=0; j<rows; ++j) |
| { |
| VERIFY(j==numext::real(m3.innerVector(j).nonZeros())); |
| if(j>0) |
| VERIFY(j==numext::real(m3.innerVector(j).lastCoeff())); |
| } |
| m3.makeCompressed(); |
| for(Index j=0; j<rows; ++j) |
| { |
| VERIFY(j==numext::real(m3.innerVector(j).nonZeros())); |
| if(j>0) |
| VERIFY(j==numext::real(m3.innerVector(j).lastCoeff())); |
| } |
| |
| //m2.innerVector(j0) = 2*m2.innerVector(j1); |
| //refMat2.col(j0) = 2*refMat2.col(j1); |
| //VERIFY_IS_APPROX(m2, refMat2); |
| } |
| |
| // test innerVectors() |
| { |
| DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows); |
| SparseMatrixType m2(rows, rows); |
| initSparse<Scalar>(density, refMat2, m2); |
| if(internal::random<float>(0,1)>0.5) m2.makeCompressed(); |
| |
| Index j0 = internal::random<Index>(0,rows-2); |
| Index j1 = internal::random<Index>(0,rows-2); |
| Index n0 = internal::random<Index>(1,rows-(std::max)(j0,j1)); |
| if(SparseMatrixType::IsRowMajor) |
| VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(j0,0,n0,cols)); |
| else |
| VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(0,j0,rows,n0)); |
| if(SparseMatrixType::IsRowMajor) |
| VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0), |
| refMat2.middleRows(j0,n0)+refMat2.middleRows(j1,n0)); |
| else |
| VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0), |
| refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0)); |
| |
| VERIFY_IS_APPROX(m2, refMat2); |
| |
| m2.innerVectors(j0,n0) = m2.innerVectors(j0,n0) + m2.innerVectors(j1,n0); |
| if(SparseMatrixType::IsRowMajor) |
| refMat2.middleRows(j0,n0) = (refMat2.middleRows(j0,n0) + refMat2.middleRows(j1,n0)).eval(); |
| else |
| refMat2.middleCols(j0,n0) = (refMat2.middleCols(j0,n0) + refMat2.middleCols(j1,n0)).eval(); |
| |
| VERIFY_IS_APPROX(m2, refMat2); |
| |
| } |
| |
| // test basic computations |
| { |
| DenseMatrix refM1 = DenseMatrix::Zero(rows, rows); |
| DenseMatrix refM2 = DenseMatrix::Zero(rows, rows); |
| DenseMatrix refM3 = DenseMatrix::Zero(rows, rows); |
| DenseMatrix refM4 = DenseMatrix::Zero(rows, rows); |
| SparseMatrixType m1(rows, rows); |
| SparseMatrixType m2(rows, rows); |
| SparseMatrixType m3(rows, rows); |
| SparseMatrixType m4(rows, rows); |
| initSparse<Scalar>(density, refM1, m1); |
| initSparse<Scalar>(density, refM2, m2); |
| initSparse<Scalar>(density, refM3, m3); |
| initSparse<Scalar>(density, refM4, m4); |
| |
| VERIFY_IS_APPROX(m1+m2, refM1+refM2); |
| VERIFY_IS_APPROX(m1+m2+m3, refM1+refM2+refM3); |
| VERIFY_IS_APPROX(m3.cwiseProduct(m1+m2), refM3.cwiseProduct(refM1+refM2)); |
| VERIFY_IS_APPROX(m1*s1-m2, refM1*s1-refM2); |
| |
| VERIFY_IS_APPROX(m1*=s1, refM1*=s1); |
| VERIFY_IS_APPROX(m1/=s1, refM1/=s1); |
| |
| VERIFY_IS_APPROX(m1+=m2, refM1+=refM2); |
| VERIFY_IS_APPROX(m1-=m2, refM1-=refM2); |
| |
| if(SparseMatrixType::IsRowMajor) |
| VERIFY_IS_APPROX(m1.innerVector(0).dot(refM2.row(0)), refM1.row(0).dot(refM2.row(0))); |
| else |
| VERIFY_IS_APPROX(m1.innerVector(0).dot(refM2.row(0)), refM1.col(0).dot(refM2.row(0))); |
| |
| VERIFY_IS_APPROX(m1.conjugate(), refM1.conjugate()); |
| VERIFY_IS_APPROX(m1.real(), refM1.real()); |
| |
| refM4.setRandom(); |
| // sparse cwise* dense |
| VERIFY_IS_APPROX(m3.cwiseProduct(refM4), refM3.cwiseProduct(refM4)); |
| // VERIFY_IS_APPROX(m3.cwise()/refM4, refM3.cwise()/refM4); |
| |
| // test aliasing |
| VERIFY_IS_APPROX((m1 = -m1), (refM1 = -refM1)); |
| VERIFY_IS_APPROX((m1 = m1.transpose()), (refM1 = refM1.transpose().eval())); |
| VERIFY_IS_APPROX((m1 = -m1.transpose()), (refM1 = -refM1.transpose().eval())); |
| VERIFY_IS_APPROX((m1 += -m1), (refM1 += -refM1)); |
| } |
| |
| // test transpose |
| { |
| DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows); |
| SparseMatrixType m2(rows, rows); |
| initSparse<Scalar>(density, refMat2, m2); |
| VERIFY_IS_APPROX(m2.transpose().eval(), refMat2.transpose().eval()); |
| VERIFY_IS_APPROX(m2.transpose(), refMat2.transpose()); |
| |
| VERIFY_IS_APPROX(SparseMatrixType(m2.adjoint()), refMat2.adjoint()); |
| } |
| |
| |
| |
| // test generic blocks |
| { |
| DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows); |
| SparseMatrixType m2(rows, rows); |
| initSparse<Scalar>(density, refMat2, m2); |
| Index j0 = internal::random<Index>(0,rows-2); |
| Index j1 = internal::random<Index>(0,rows-2); |
| Index n0 = internal::random<Index>(1,rows-(std::max)(j0,j1)); |
| if(SparseMatrixType::IsRowMajor) |
| VERIFY_IS_APPROX(m2.block(j0,0,n0,cols), refMat2.block(j0,0,n0,cols)); |
| else |
| VERIFY_IS_APPROX(m2.block(0,j0,rows,n0), refMat2.block(0,j0,rows,n0)); |
| |
| if(SparseMatrixType::IsRowMajor) |
| VERIFY_IS_APPROX(m2.block(j0,0,n0,cols)+m2.block(j1,0,n0,cols), |
| refMat2.block(j0,0,n0,cols)+refMat2.block(j1,0,n0,cols)); |
| else |
| VERIFY_IS_APPROX(m2.block(0,j0,rows,n0)+m2.block(0,j1,rows,n0), |
| refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0)); |
| |
| Index i = internal::random<Index>(0,m2.outerSize()-1); |
| if(SparseMatrixType::IsRowMajor) { |
| m2.innerVector(i) = m2.innerVector(i) * s1; |
| refMat2.row(i) = refMat2.row(i) * s1; |
| VERIFY_IS_APPROX(m2,refMat2); |
| } else { |
| m2.innerVector(i) = m2.innerVector(i) * s1; |
| refMat2.col(i) = refMat2.col(i) * s1; |
| VERIFY_IS_APPROX(m2,refMat2); |
| } |
| |
| VERIFY_IS_APPROX(DenseVector(m2.col(j0)), refMat2.col(j0)); |
| VERIFY_IS_APPROX(m2.col(j0), refMat2.col(j0)); |
| |
| VERIFY_IS_APPROX(RowDenseVector(m2.row(j0)), refMat2.row(j0)); |
| VERIFY_IS_APPROX(m2.row(j0), refMat2.row(j0)); |
| |
| VERIFY_IS_APPROX(m2.block(j0,j1,n0,n0), refMat2.block(j0,j1,n0,n0)); |
| VERIFY_IS_APPROX((2*m2).block(j0,j1,n0,n0), (2*refMat2).block(j0,j1,n0,n0)); |
| } |
| |
| // test prune |
| { |
| SparseMatrixType m2(rows, rows); |
| DenseMatrix refM2(rows, rows); |
| refM2.setZero(); |
| int countFalseNonZero = 0; |
| int countTrueNonZero = 0; |
| for (Index j=0; j<m2.outerSize(); ++j) |
| { |
| m2.startVec(j); |
| for (Index i=0; i<m2.innerSize(); ++i) |
| { |
| float x = internal::random<float>(0,1); |
| if (x<0.1) |
| { |
| // do nothing |
| } |
| else if (x<0.5) |
| { |
| countFalseNonZero++; |
| m2.insertBackByOuterInner(j,i) = Scalar(0); |
| } |
| else |
| { |
| countTrueNonZero++; |
| m2.insertBackByOuterInner(j,i) = Scalar(1); |
| if(SparseMatrixType::IsRowMajor) |
| refM2(j,i) = Scalar(1); |
| else |
| refM2(i,j) = Scalar(1); |
| } |
| } |
| } |
| m2.finalize(); |
| VERIFY(countFalseNonZero+countTrueNonZero == m2.nonZeros()); |
| VERIFY_IS_APPROX(m2, refM2); |
| m2.prune(Scalar(1)); |
| VERIFY(countTrueNonZero==m2.nonZeros()); |
| VERIFY_IS_APPROX(m2, refM2); |
| } |
| |
| // test setFromTriplets |
| { |
| typedef Triplet<Scalar,Index> TripletType; |
| std::vector<TripletType> triplets; |
| int ntriplets = rows*cols; |
| triplets.reserve(ntriplets); |
| DenseMatrix refMat(rows,cols); |
| refMat.setZero(); |
| for(int i=0;i<ntriplets;++i) |
| { |
| Index r = internal::random<Index>(0,rows-1); |
| Index c = internal::random<Index>(0,cols-1); |
| Scalar v = internal::random<Scalar>(); |
| triplets.push_back(TripletType(r,c,v)); |
| refMat(r,c) += v; |
| } |
| SparseMatrixType m(rows,cols); |
| m.setFromTriplets(triplets.begin(), triplets.end()); |
| VERIFY_IS_APPROX(m, refMat); |
| } |
| |
| // test triangularView |
| { |
| DenseMatrix refMat2(rows, rows), refMat3(rows, rows); |
| SparseMatrixType m2(rows, rows), m3(rows, rows); |
| initSparse<Scalar>(density, refMat2, m2); |
| refMat3 = refMat2.template triangularView<Lower>(); |
| m3 = m2.template triangularView<Lower>(); |
| VERIFY_IS_APPROX(m3, refMat3); |
| |
| refMat3 = refMat2.template triangularView<Upper>(); |
| m3 = m2.template triangularView<Upper>(); |
| VERIFY_IS_APPROX(m3, refMat3); |
| |
| refMat3 = refMat2.template triangularView<UnitUpper>(); |
| m3 = m2.template triangularView<UnitUpper>(); |
| VERIFY_IS_APPROX(m3, refMat3); |
| |
| refMat3 = refMat2.template triangularView<UnitLower>(); |
| m3 = m2.template triangularView<UnitLower>(); |
| VERIFY_IS_APPROX(m3, refMat3); |
| |
| refMat3 = refMat2.template triangularView<StrictlyUpper>(); |
| m3 = m2.template triangularView<StrictlyUpper>(); |
| VERIFY_IS_APPROX(m3, refMat3); |
| |
| refMat3 = refMat2.template triangularView<StrictlyLower>(); |
| m3 = m2.template triangularView<StrictlyLower>(); |
| VERIFY_IS_APPROX(m3, refMat3); |
| } |
| |
| // test selfadjointView |
| if(!SparseMatrixType::IsRowMajor) |
| { |
| DenseMatrix refMat2(rows, rows), refMat3(rows, rows); |
| SparseMatrixType m2(rows, rows), m3(rows, rows); |
| initSparse<Scalar>(density, refMat2, m2); |
| refMat3 = refMat2.template selfadjointView<Lower>(); |
| m3 = m2.template selfadjointView<Lower>(); |
| VERIFY_IS_APPROX(m3, refMat3); |
| } |
| |
| // test sparseView |
| { |
| DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows); |
| SparseMatrixType m2(rows, rows); |
| initSparse<Scalar>(density, refMat2, m2); |
| VERIFY_IS_APPROX(m2.eval(), refMat2.sparseView().eval()); |
| } |
| |
| // test diagonal |
| { |
| DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows); |
| SparseMatrixType m2(rows, rows); |
| initSparse<Scalar>(density, refMat2, m2); |
| VERIFY_IS_APPROX(m2.diagonal(), refMat2.diagonal().eval()); |
| } |
| |
| // test conservative resize |
| { |
| std::vector< std::pair<Index,Index> > inc; |
| inc.push_back(std::pair<Index,Index>(-3,-2)); |
| inc.push_back(std::pair<Index,Index>(0,0)); |
| inc.push_back(std::pair<Index,Index>(3,2)); |
| inc.push_back(std::pair<Index,Index>(3,0)); |
| inc.push_back(std::pair<Index,Index>(0,3)); |
| |
| for(size_t i = 0; i< inc.size(); i++) { |
| Index incRows = inc[i].first; |
| Index incCols = inc[i].second; |
| SparseMatrixType m1(rows, cols); |
| DenseMatrix refMat1 = DenseMatrix::Zero(rows, cols); |
| initSparse<Scalar>(density, refMat1, m1); |
| |
| m1.conservativeResize(rows+incRows, cols+incCols); |
| refMat1.conservativeResize(rows+incRows, cols+incCols); |
| if (incRows > 0) refMat1.bottomRows(incRows).setZero(); |
| if (incCols > 0) refMat1.rightCols(incCols).setZero(); |
| |
| VERIFY_IS_APPROX(m1, refMat1); |
| |
| // Insert new values |
| if (incRows > 0) |
| m1.insert(m1.rows()-1, 0) = refMat1(refMat1.rows()-1, 0) = 1; |
| if (incCols > 0) |
| m1.insert(0, m1.cols()-1) = refMat1(0, refMat1.cols()-1) = 1; |
| |
| VERIFY_IS_APPROX(m1, refMat1); |
| |
| |
| } |
| } |
| |
| // test Identity matrix |
| { |
| DenseMatrix refMat1 = DenseMatrix::Identity(rows, rows); |
| SparseMatrixType m1(rows, rows); |
| m1.setIdentity(); |
| VERIFY_IS_APPROX(m1, refMat1); |
| } |
| } |
| |
| void test_sparse_basic() |
| { |
| for(int i = 0; i < g_repeat; i++) { |
| int s = Eigen::internal::random<int>(1,50); |
| EIGEN_UNUSED_VARIABLE(s); |
| CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(8, 8)) )); |
| CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, ColMajor>(s, s)) )); |
| CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, RowMajor>(s, s)) )); |
| CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(s, s)) )); |
| CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,ColMajor,long int>(s, s)) )); |
| CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,long int>(s, s)) )); |
| |
| CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,ColMajor,short int>(short(s), short(s))) )); |
| CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,short int>(short(s), short(s))) )); |
| } |
| } |
