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Diffstat (limited to 'eigen/test/sparse_basic.cpp')
| -rw-r--r-- | eigen/test/sparse_basic.cpp | 689 |
1 files changed, 0 insertions, 689 deletions
diff --git a/eigen/test/sparse_basic.cpp b/eigen/test/sparse_basic.cpp deleted file mode 100644 index d0ef722..0000000 --- a/eigen/test/sparse_basic.cpp +++ /dev/null @@ -1,689 +0,0 @@ -// 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/. - -static long g_realloc_count = 0; -#define EIGEN_SPARSE_COMPRESSED_STORAGE_REALLOCATE_PLUGIN g_realloc_count++; - -#include "sparse.h" - -template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& ref) -{ - typedef typename SparseMatrixType::StorageIndex StorageIndex; - typedef Matrix<StorageIndex,2,1> Vector2; - - const Index rows = ref.rows(); - const Index cols = ref.cols(); - //const Index inner = ref.innerSize(); - //const Index outer = ref.outerSize(); - - typedef typename SparseMatrixType::Scalar Scalar; - typedef typename SparseMatrixType::RealScalar RealScalar; - 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; - 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); - - // test coeff and coeffRef - for (std::size_t i=0; i<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[i].x(),zeroCoords[i].y()) = 5 ); - } - VERIFY_IS_APPROX(m, refMat); - - if(!nonzeroCoords.empty()) { - 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 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); - bool call_reserve = internal::random<int>()%2; - Index nnz = internal::random<int>(1,int(rows)/2); - if(call_reserve) - { - if(internal::random<int>()%2) - m2.reserve(VectorXi::Constant(m2.outerSize(), int(nnz))); - else - m2.reserve(m2.outerSize() * nnz); - } - g_realloc_count = 0; - for (Index j=0; j<cols; ++j) - { - for (Index k=0; k<nnz; ++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>(); - } - } - - if(call_reserve && !SparseMatrixType::IsRowMajor) - { - VERIFY(g_realloc_count==0); - } - - 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) ? int(m2.innerSize()) : std::max<int>(1,int(m2.innerSize())/8))); - m2.reserve(r); - for (Index 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 basic computations - { - DenseMatrix refM1 = DenseMatrix::Zero(rows, cols); - DenseMatrix refM2 = DenseMatrix::Zero(rows, cols); - DenseMatrix refM3 = DenseMatrix::Zero(rows, cols); - DenseMatrix refM4 = DenseMatrix::Zero(rows, cols); - SparseMatrixType m1(rows, cols); - SparseMatrixType m2(rows, cols); - SparseMatrixType m3(rows, cols); - SparseMatrixType m4(rows, cols); - initSparse<Scalar>(density, refM1, m1); - initSparse<Scalar>(density, refM2, m2); - initSparse<Scalar>(density, refM3, m3); - initSparse<Scalar>(density, refM4, m4); - - if(internal::random<bool>()) - m1.makeCompressed(); - - Index m1_nnz = m1.nonZeros(); - - VERIFY_IS_APPROX(m1*s1, refM1*s1); - 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(m4=m1/s1, refM1/s1); - VERIFY_IS_EQUAL(m4.nonZeros(), m1_nnz); - - 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.col(0)), refM1.col(0).dot(refM2.col(0))); - - DenseVector rv = DenseVector::Random(m1.cols()); - DenseVector cv = DenseVector::Random(m1.rows()); - Index r = internal::random<Index>(0,m1.rows()-2); - Index c = internal::random<Index>(0,m1.cols()-1); - VERIFY_IS_APPROX(( m1.template block<1,Dynamic>(r,0,1,m1.cols()).dot(rv)) , refM1.row(r).dot(rv)); - VERIFY_IS_APPROX(m1.row(r).dot(rv), refM1.row(r).dot(rv)); - VERIFY_IS_APPROX(m1.col(c).dot(cv), refM1.col(c).dot(cv)); - - 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)); - // dense cwise* sparse - VERIFY_IS_APPROX(refM4.cwiseProduct(m3), refM4.cwiseProduct(refM3)); -// VERIFY_IS_APPROX(m3.cwise()/refM4, refM3.cwise()/refM4); - - VERIFY_IS_APPROX(refM4 + m3, refM4 + refM3); - VERIFY_IS_APPROX(m3 + refM4, refM3 + refM4); - VERIFY_IS_APPROX(refM4 - m3, refM4 - refM3); - VERIFY_IS_APPROX(m3 - refM4, refM3 - refM4); - VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + RealScalar(0.5)*m3).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3); - VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + m3*RealScalar(0.5)).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3); - VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + m3.cwiseProduct(m3)).eval(), RealScalar(0.5)*refM4 + refM3.cwiseProduct(refM3)); - - VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + RealScalar(0.5)*m3).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3); - VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + m3*RealScalar(0.5)).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3); - VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + (m3+m3)).eval(), RealScalar(0.5)*refM4 + (refM3+refM3)); - VERIFY_IS_APPROX(((refM3+m3)+RealScalar(0.5)*m3).eval(), RealScalar(0.5)*refM3 + (refM3+refM3)); - VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + (refM3+m3)).eval(), RealScalar(0.5)*refM4 + (refM3+refM3)); - VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + (m3+refM3)).eval(), RealScalar(0.5)*refM4 + (refM3+refM3)); - - - VERIFY_IS_APPROX(m1.sum(), refM1.sum()); - - m4 = m1; refM4 = m4; - - VERIFY_IS_APPROX(m1*=s1, refM1*=s1); - VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz); - VERIFY_IS_APPROX(m1/=s1, refM1/=s1); - VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz); - - VERIFY_IS_APPROX(m1+=m2, refM1+=refM2); - VERIFY_IS_APPROX(m1-=m2, refM1-=refM2); - - if (rows>=2 && cols>=2) - { - VERIFY_RAISES_ASSERT( m1 += m1.innerVector(0) ); - VERIFY_RAISES_ASSERT( m1 -= m1.innerVector(0) ); - VERIFY_RAISES_ASSERT( refM1 -= m1.innerVector(0) ); - VERIFY_RAISES_ASSERT( refM1 += m1.innerVector(0) ); - } - m1 = m4; refM1 = refM4; - - // test aliasing - VERIFY_IS_APPROX((m1 = -m1), (refM1 = -refM1)); - VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz); - m1 = m4; refM1 = refM4; - VERIFY_IS_APPROX((m1 = m1.transpose()), (refM1 = refM1.transpose().eval())); - VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz); - m1 = m4; refM1 = refM4; - VERIFY_IS_APPROX((m1 = -m1.transpose()), (refM1 = -refM1.transpose().eval())); - VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz); - m1 = m4; refM1 = refM4; - VERIFY_IS_APPROX((m1 += -m1), (refM1 += -refM1)); - VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz); - m1 = m4; refM1 = refM4; - - if(m1.isCompressed()) - { - VERIFY_IS_APPROX(m1.coeffs().sum(), m1.sum()); - m1.coeffs() += s1; - for(Index j = 0; j<m1.outerSize(); ++j) - for(typename SparseMatrixType::InnerIterator it(m1,j); it; ++it) - refM1(it.row(), it.col()) += s1; - VERIFY_IS_APPROX(m1, refM1); - } - - // and/or - { - typedef SparseMatrix<bool, SparseMatrixType::Options, typename SparseMatrixType::StorageIndex> SpBool; - SpBool mb1 = m1.real().template cast<bool>(); - SpBool mb2 = m2.real().template cast<bool>(); - VERIFY_IS_EQUAL(mb1.template cast<int>().sum(), refM1.real().template cast<bool>().count()); - VERIFY_IS_EQUAL((mb1 && mb2).template cast<int>().sum(), (refM1.real().template cast<bool>() && refM2.real().template cast<bool>()).count()); - VERIFY_IS_EQUAL((mb1 || mb2).template cast<int>().sum(), (refM1.real().template cast<bool>() || refM2.real().template cast<bool>()).count()); - SpBool mb3 = mb1 && mb2; - if(mb1.coeffs().all() && mb2.coeffs().all()) - { - VERIFY_IS_EQUAL(mb3.nonZeros(), (refM1.real().template cast<bool>() && refM2.real().template cast<bool>()).count()); - } - } - } - - // test reverse iterators - { - DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols); - SparseMatrixType m2(rows, cols); - initSparse<Scalar>(density, refMat2, m2); - std::vector<Scalar> ref_value(m2.innerSize()); - std::vector<Index> ref_index(m2.innerSize()); - if(internal::random<bool>()) - m2.makeCompressed(); - for(Index j = 0; j<m2.outerSize(); ++j) - { - Index count_forward = 0; - - for(typename SparseMatrixType::InnerIterator it(m2,j); it; ++it) - { - ref_value[ref_value.size()-1-count_forward] = it.value(); - ref_index[ref_index.size()-1-count_forward] = it.index(); - count_forward++; - } - Index count_reverse = 0; - for(typename SparseMatrixType::ReverseInnerIterator it(m2,j); it; --it) - { - VERIFY_IS_APPROX( std::abs(ref_value[ref_value.size()-count_forward+count_reverse])+1, std::abs(it.value())+1); - VERIFY_IS_EQUAL( ref_index[ref_index.size()-count_forward+count_reverse] , it.index()); - count_reverse++; - } - VERIFY_IS_EQUAL(count_forward, count_reverse); - } - } - - // test transpose - { - DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols); - SparseMatrixType m2(rows, cols); - 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()); - - // check isApprox handles opposite storage order - typename Transpose<SparseMatrixType>::PlainObject m3(m2); - VERIFY(m2.isApprox(m3)); - } - - // test prune - { - SparseMatrixType m2(rows, cols); - DenseMatrix refM2(rows, cols); - refM2.setZero(); - int countFalseNonZero = 0; - int countTrueNonZero = 0; - m2.reserve(VectorXi::Constant(m2.outerSize(), int(m2.innerSize()))); - for (Index j=0; j<m2.cols(); ++j) - { - for (Index i=0; i<m2.rows(); ++i) - { - float x = internal::random<float>(0,1); - if (x<0.1f) - { - // do nothing - } - else if (x<0.5f) - { - countFalseNonZero++; - m2.insert(i,j) = Scalar(0); - } - else - { - countTrueNonZero++; - m2.insert(i,j) = Scalar(1); - refM2(i,j) = Scalar(1); - } - } - } - if(internal::random<bool>()) - m2.makeCompressed(); - VERIFY(countFalseNonZero+countTrueNonZero == m2.nonZeros()); - if(countTrueNonZero>0) - VERIFY_IS_APPROX(m2, refM2); - m2.prune(Scalar(1)); - VERIFY(countTrueNonZero==m2.nonZeros()); - VERIFY_IS_APPROX(m2, refM2); - } - - // test setFromTriplets - { - typedef Triplet<Scalar,StorageIndex> TripletType; - std::vector<TripletType> triplets; - Index ntriplets = rows*cols; - triplets.reserve(ntriplets); - DenseMatrix refMat_sum = DenseMatrix::Zero(rows,cols); - DenseMatrix refMat_prod = DenseMatrix::Zero(rows,cols); - DenseMatrix refMat_last = DenseMatrix::Zero(rows,cols); - - for(Index i=0;i<ntriplets;++i) - { - StorageIndex r = internal::random<StorageIndex>(0,StorageIndex(rows-1)); - StorageIndex c = internal::random<StorageIndex>(0,StorageIndex(cols-1)); - Scalar v = internal::random<Scalar>(); - triplets.push_back(TripletType(r,c,v)); - refMat_sum(r,c) += v; - if(std::abs(refMat_prod(r,c))==0) - refMat_prod(r,c) = v; - else - refMat_prod(r,c) *= v; - refMat_last(r,c) = v; - } - SparseMatrixType m(rows,cols); - m.setFromTriplets(triplets.begin(), triplets.end()); - VERIFY_IS_APPROX(m, refMat_sum); - - m.setFromTriplets(triplets.begin(), triplets.end(), std::multiplies<Scalar>()); - VERIFY_IS_APPROX(m, refMat_prod); -#if (defined(__cplusplus) && __cplusplus >= 201103L) - m.setFromTriplets(triplets.begin(), triplets.end(), [] (Scalar,Scalar b) { return b; }); - VERIFY_IS_APPROX(m, refMat_last); -#endif - } - - // test Map - { - DenseMatrix refMat2(rows, cols), refMat3(rows, cols); - SparseMatrixType m2(rows, cols), m3(rows, cols); - initSparse<Scalar>(density, refMat2, m2); - initSparse<Scalar>(density, refMat3, m3); - { - Map<SparseMatrixType> mapMat2(m2.rows(), m2.cols(), m2.nonZeros(), m2.outerIndexPtr(), m2.innerIndexPtr(), m2.valuePtr(), m2.innerNonZeroPtr()); - Map<SparseMatrixType> mapMat3(m3.rows(), m3.cols(), m3.nonZeros(), m3.outerIndexPtr(), m3.innerIndexPtr(), m3.valuePtr(), m3.innerNonZeroPtr()); - VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3); - VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3); - } - { - MappedSparseMatrix<Scalar,SparseMatrixType::Options,StorageIndex> mapMat2(m2.rows(), m2.cols(), m2.nonZeros(), m2.outerIndexPtr(), m2.innerIndexPtr(), m2.valuePtr(), m2.innerNonZeroPtr()); - MappedSparseMatrix<Scalar,SparseMatrixType::Options,StorageIndex> mapMat3(m3.rows(), m3.cols(), m3.nonZeros(), m3.outerIndexPtr(), m3.innerIndexPtr(), m3.valuePtr(), m3.innerNonZeroPtr()); - VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3); - VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3); - } - - Index i = internal::random<Index>(0,rows-1); - Index j = internal::random<Index>(0,cols-1); - m2.coeffRef(i,j) = 123; - if(internal::random<bool>()) - m2.makeCompressed(); - Map<SparseMatrixType> mapMat2(rows, cols, m2.nonZeros(), m2.outerIndexPtr(), m2.innerIndexPtr(), m2.valuePtr(), m2.innerNonZeroPtr()); - VERIFY_IS_EQUAL(m2.coeff(i,j),Scalar(123)); - VERIFY_IS_EQUAL(mapMat2.coeff(i,j),Scalar(123)); - mapMat2.coeffRef(i,j) = -123; - VERIFY_IS_EQUAL(m2.coeff(i,j),Scalar(-123)); - } - - // test triangularView - { - DenseMatrix refMat2(rows, cols), refMat3(rows, cols); - SparseMatrixType m2(rows, cols), m3(rows, cols); - 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); - - // check sparse-triangular to dense - refMat3 = m2.template triangularView<StrictlyUpper>(); - VERIFY_IS_APPROX(refMat3, DenseMatrix(refMat2.template triangularView<StrictlyUpper>())); - } - - // 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); - - refMat3 += refMat2.template selfadjointView<Lower>(); - m3 += m2.template selfadjointView<Lower>(); - VERIFY_IS_APPROX(m3, refMat3); - - refMat3 -= refMat2.template selfadjointView<Lower>(); - m3 -= m2.template selfadjointView<Lower>(); - VERIFY_IS_APPROX(m3, refMat3); - - // selfadjointView only works for square matrices: - SparseMatrixType m4(rows, rows+1); - VERIFY_RAISES_ASSERT(m4.template selfadjointView<Lower>()); - VERIFY_RAISES_ASSERT(m4.template selfadjointView<Upper>()); - } - - // 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()); - - // sparse view on expressions: - VERIFY_IS_APPROX((s1*m2).eval(), (s1*refMat2).sparseView().eval()); - VERIFY_IS_APPROX((m2+m2).eval(), (refMat2+refMat2).sparseView().eval()); - VERIFY_IS_APPROX((m2*m2).eval(), (refMat2.lazyProduct(refMat2)).sparseView().eval()); - VERIFY_IS_APPROX((m2*m2).eval(), (refMat2*refMat2).sparseView().eval()); - } - - // test diagonal - { - DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols); - SparseMatrixType m2(rows, cols); - initSparse<Scalar>(density, refMat2, m2); - VERIFY_IS_APPROX(m2.diagonal(), refMat2.diagonal().eval()); - DenseVector d = m2.diagonal(); - VERIFY_IS_APPROX(d, refMat2.diagonal().eval()); - d = m2.diagonal().array(); - VERIFY_IS_APPROX(d, refMat2.diagonal().eval()); - VERIFY_IS_APPROX(const_cast<const SparseMatrixType&>(m2).diagonal(), refMat2.diagonal().eval()); - - initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag); - m2.diagonal() += refMat2.diagonal(); - refMat2.diagonal() += refMat2.diagonal(); - VERIFY_IS_APPROX(m2, refMat2); - } - - // test diagonal to sparse - { - DenseVector d = DenseVector::Random(rows); - DenseMatrix refMat2 = d.asDiagonal(); - SparseMatrixType m2(rows, rows); - m2 = d.asDiagonal(); - VERIFY_IS_APPROX(m2, refMat2); - SparseMatrixType m3(d.asDiagonal()); - VERIFY_IS_APPROX(m3, refMat2); - refMat2 += d.asDiagonal(); - m2 += d.asDiagonal(); - VERIFY_IS_APPROX(m2, refMat2); - } - - // test conservative resize - { - std::vector< std::pair<StorageIndex,StorageIndex> > inc; - if(rows > 3 && cols > 2) - inc.push_back(std::pair<StorageIndex,StorageIndex>(-3,-2)); - inc.push_back(std::pair<StorageIndex,StorageIndex>(0,0)); - inc.push_back(std::pair<StorageIndex,StorageIndex>(3,2)); - inc.push_back(std::pair<StorageIndex,StorageIndex>(3,0)); - inc.push_back(std::pair<StorageIndex,StorageIndex>(0,3)); - - for(size_t i = 0; i< inc.size(); i++) { - StorageIndex incRows = inc[i].first; - StorageIndex 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); - for(int k=0; k<rows*rows/4; ++k) - { - Index i = internal::random<Index>(0,rows-1); - Index j = internal::random<Index>(0,rows-1); - Scalar v = internal::random<Scalar>(); - m1.coeffRef(i,j) = v; - refMat1.coeffRef(i,j) = v; - VERIFY_IS_APPROX(m1, refMat1); - if(internal::random<Index>(0,10)<2) - m1.makeCompressed(); - } - m1.setIdentity(); - refMat1.setIdentity(); - VERIFY_IS_APPROX(m1, refMat1); - } - - // test array/vector of InnerIterator - { - typedef typename SparseMatrixType::InnerIterator IteratorType; - - DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols); - SparseMatrixType m2(rows, cols); - initSparse<Scalar>(density, refMat2, m2); - IteratorType static_array[2]; - static_array[0] = IteratorType(m2,0); - static_array[1] = IteratorType(m2,m2.outerSize()-1); - VERIFY( static_array[0] || m2.innerVector(static_array[0].outer()).nonZeros() == 0 ); - VERIFY( static_array[1] || m2.innerVector(static_array[1].outer()).nonZeros() == 0 ); - if(static_array[0] && static_array[1]) - { - ++(static_array[1]); - static_array[1] = IteratorType(m2,0); - VERIFY( static_array[1] ); - VERIFY( static_array[1].index() == static_array[0].index() ); - VERIFY( static_array[1].outer() == static_array[0].outer() ); - VERIFY( static_array[1].value() == static_array[0].value() ); - } - - std::vector<IteratorType> iters(2); - iters[0] = IteratorType(m2,0); - iters[1] = IteratorType(m2,m2.outerSize()-1); - } -} - - -template<typename SparseMatrixType> -void big_sparse_triplet(Index rows, Index cols, double density) { - typedef typename SparseMatrixType::StorageIndex StorageIndex; - typedef typename SparseMatrixType::Scalar Scalar; - typedef Triplet<Scalar,Index> TripletType; - std::vector<TripletType> triplets; - double nelements = density * rows*cols; - VERIFY(nelements>=0 && nelements < NumTraits<StorageIndex>::highest()); - Index ntriplets = Index(nelements); - triplets.reserve(ntriplets); - Scalar sum = Scalar(0); - for(Index i=0;i<ntriplets;++i) - { - Index r = internal::random<Index>(0,rows-1); - Index c = internal::random<Index>(0,cols-1); - // use positive values to prevent numerical cancellation errors in sum - Scalar v = numext::abs(internal::random<Scalar>()); - triplets.push_back(TripletType(r,c,v)); - sum += v; - } - SparseMatrixType m(rows,cols); - m.setFromTriplets(triplets.begin(), triplets.end()); - VERIFY(m.nonZeros() <= ntriplets); - VERIFY_IS_APPROX(sum, m.sum()); -} - - -void test_sparse_basic() -{ - for(int i = 0; i < g_repeat; i++) { - int r = Eigen::internal::random<int>(1,200), c = Eigen::internal::random<int>(1,200); - if(Eigen::internal::random<int>(0,4) == 0) { - r = c; // check square matrices in 25% of tries - } - EIGEN_UNUSED_VARIABLE(r+c); - CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(1, 1)) )); - CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(8, 8)) )); - CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, ColMajor>(r, c)) )); - CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, RowMajor>(r, c)) )); - CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(r, c)) )); - CALL_SUBTEST_5(( sparse_basic(SparseMatrix<double,ColMajor,long int>(r, c)) )); - CALL_SUBTEST_5(( sparse_basic(SparseMatrix<double,RowMajor,long int>(r, c)) )); - - r = Eigen::internal::random<int>(1,100); - c = Eigen::internal::random<int>(1,100); - if(Eigen::internal::random<int>(0,4) == 0) { - r = c; // check square matrices in 25% of tries - } - - CALL_SUBTEST_6(( sparse_basic(SparseMatrix<double,ColMajor,short int>(short(r), short(c))) )); - CALL_SUBTEST_6(( sparse_basic(SparseMatrix<double,RowMajor,short int>(short(r), short(c))) )); - } - - // Regression test for bug 900: (manually insert higher values here, if you have enough RAM): - CALL_SUBTEST_3((big_sparse_triplet<SparseMatrix<float, RowMajor, int> >(10000, 10000, 0.125))); - CALL_SUBTEST_4((big_sparse_triplet<SparseMatrix<double, ColMajor, long int> >(10000, 10000, 0.125))); - - // Regression test for bug 1105 -#ifdef EIGEN_TEST_PART_7 - { - int n = Eigen::internal::random<int>(200,600); - SparseMatrix<std::complex<double>,0, long> mat(n, n); - std::complex<double> val; - - for(int i=0; i<n; ++i) - { - mat.coeffRef(i, i%(n/10)) = val; - VERIFY(mat.data().allocatedSize()<20*n); - } - } -#endif -} |