| // 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/. |
| |
| #ifndef EIGEN_SPARSE_TEST_INCLUDED_FROM_SPARSE_EXTRA |
| static long g_realloc_count = 0; |
| #define EIGEN_SPARSE_COMPRESSED_STORAGE_REALLOCATE_PLUGIN g_realloc_count++; |
| |
| static long g_dense_op_sparse_count = 0; |
| #define EIGEN_SPARSE_ASSIGNMENT_FROM_DENSE_OP_SPARSE_PLUGIN g_dense_op_sparse_count++; |
| #define EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_ADD_DENSE_PLUGIN g_dense_op_sparse_count += 10; |
| #define EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_SUB_DENSE_PLUGIN g_dense_op_sparse_count += 20; |
| #endif |
| |
| #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; |
| typedef Matrix<Scalar, Dynamic, Dynamic, SparseMatrixType::IsRowMajor ? RowMajor : ColMajor> CompatibleDenseMatrix; |
| Scalar eps = Scalar(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)) { |
| Scalar v = internal::random<Scalar>(); |
| if (v == Scalar(0)) v = Scalar(1); |
| m1(i, j) = v; |
| m2.insert(i, j) = v; |
| } |
| } |
| } |
| |
| if (call_reserve && !SparseMatrixType::IsRowMajor) { |
| VERIFY(g_realloc_count == 0); |
| } |
| |
| 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)) { |
| Scalar v = internal::random<Scalar>(); |
| if (v == Scalar(0)) v = Scalar(1); |
| m1(i, j) = v; |
| m2.insert(i, j) = v; |
| } else { |
| Scalar v = internal::random<Scalar>(); |
| if (v == Scalar(0)) v = Scalar(1); |
| m1(i, j) = v; |
| m2.coeffRef(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)) { |
| Scalar v = internal::random<Scalar>(); |
| if (v == Scalar(0)) v = Scalar(1); |
| m1(i, j) = v; |
| m2.insert(i, j) = v; |
| } |
| if (mode == 3) m2.reserve(r); |
| } |
| if (internal::random<int>() % 2) m2.makeCompressed(); |
| VERIFY_IS_APPROX(m2, m1); |
| } |
| |
| // test removeOuterVectors / insertEmptyOuterVectors |
| { |
| for (int mode = 0; mode < 4; mode++) { |
| CompatibleDenseMatrix m1(rows, cols); |
| m1.setZero(); |
| SparseMatrixType m2(rows, cols); |
| Vector<Index, Dynamic> reserveSizes(outer); |
| for (Index j = 0; j < outer; j++) reserveSizes(j) = internal::random<Index>(1, inner - 1); |
| m2.reserve(reserveSizes); |
| for (Index j = 0; j < outer; j++) { |
| Index i = internal::random<Index>(0, inner - 1); |
| Scalar val = internal::random<Scalar>(); |
| m1.coeffRefByOuterInner(j, i) = val; |
| m2.insertByOuterInner(j, i) = val; |
| } |
| if (mode % 2 == 0) m2.makeCompressed(); |
| |
| if (mode < 2) { |
| Index num = internal::random<Index>(0, outer - 1); |
| Index start = internal::random<Index>(0, outer - num); |
| |
| Index newRows = SparseMatrixType::IsRowMajor ? rows - num : rows; |
| Index newCols = SparseMatrixType::IsRowMajor ? cols : cols - num; |
| |
| CompatibleDenseMatrix m3(newRows, newCols); |
| m3.setConstant(Scalar(NumTraits<RealScalar>::quiet_NaN())); |
| |
| if (SparseMatrixType::IsRowMajor) { |
| m3.topRows(start) = m1.topRows(start); |
| m3.bottomRows(newRows - start) = m1.bottomRows(newRows - start); |
| } else { |
| m3.leftCols(start) = m1.leftCols(start); |
| m3.rightCols(newCols - start) = m1.rightCols(newCols - start); |
| } |
| |
| SparseMatrixType m4 = m2; |
| m4.removeOuterVectors(start, num); |
| |
| VERIFY_IS_CWISE_EQUAL(m3, m4.toDense()); |
| } else { |
| Index num = internal::random<Index>(0, outer - 1); |
| Index start = internal::random<Index>(0, outer - 1); |
| |
| Index newRows = SparseMatrixType::IsRowMajor ? rows + num : rows; |
| Index newCols = SparseMatrixType::IsRowMajor ? cols : cols + num; |
| |
| CompatibleDenseMatrix m3(newRows, newCols); |
| m3.setConstant(Scalar(NumTraits<RealScalar>::quiet_NaN())); |
| |
| if (SparseMatrixType::IsRowMajor) { |
| m3.topRows(start) = m1.topRows(start); |
| m3.middleRows(start, num).setZero(); |
| m3.bottomRows(rows - start) = m1.bottomRows(rows - start); |
| } else { |
| m3.leftCols(start) = m1.leftCols(start); |
| m3.middleCols(start, num).setZero(); |
| m3.rightCols(cols - start) = m1.rightCols(cols - start); |
| } |
| |
| SparseMatrixType m4 = m2; |
| m4.insertEmptyOuterVectors(start, num); |
| |
| VERIFY_IS_CWISE_EQUAL(m3, m4.toDense()); |
| } |
| } |
| } |
| |
| // test sort |
| if (inner > 1) { |
| bool StorageOrdersMatch = int(DenseMatrix::IsRowMajor) == int(SparseMatrixType::IsRowMajor); |
| DenseMatrix m1(rows, cols); |
| m1.setZero(); |
| SparseMatrixType m2(rows, cols); |
| // generate random inner indices with no repeats |
| Vector<Index, Dynamic> innerIndices(inner); |
| innerIndices.setLinSpaced(inner, 0, inner - 1); |
| std::random_device rd; |
| std::mt19937 g(rd()); |
| for (Index j = 0; j < outer; j++) { |
| std::shuffle(innerIndices.begin(), innerIndices.end(), g); |
| Index nzj = internal::random<Index>(2, inner / 2); |
| for (Index k = 0; k < nzj; k++) { |
| Index i = innerIndices[k]; |
| Scalar val = internal::random<Scalar>(); |
| m1.coeffRefByOuterInner(StorageOrdersMatch ? j : i, StorageOrdersMatch ? i : j) = val; |
| m2.insertByOuterInner(j, i) = val; |
| } |
| } |
| |
| VERIFY_IS_APPROX(m2, m1); |
| // sort wrt greater |
| m2.template sortInnerIndices<std::greater<>>(); |
| // verify that all inner vectors are not sorted wrt less |
| VERIFY_IS_EQUAL(m2.template innerIndicesAreSorted<std::less<>>(), 0); |
| // verify that all inner vectors are sorted wrt greater |
| VERIFY_IS_EQUAL(m2.template innerIndicesAreSorted<std::greater<>>(), m2.outerSize()); |
| // verify that sort does not change evaluation |
| VERIFY_IS_APPROX(m2, m1); |
| // sort wrt less |
| m2.template sortInnerIndices<std::less<>>(); |
| // verify that all inner vectors are sorted wrt less |
| VERIFY_IS_EQUAL(m2.template innerIndicesAreSorted<std::less<>>(), m2.outerSize()); |
| // verify that all inner vectors are not sorted wrt greater |
| VERIFY_IS_EQUAL(m2.template innerIndicesAreSorted<std::greater<>>(), 0); |
| // verify that sort does not change evaluation |
| VERIFY_IS_APPROX(m2, m1); |
| |
| m2.makeCompressed(); |
| // sort wrt greater |
| m2.template sortInnerIndices<std::greater<>>(); |
| // verify that all inner vectors are not sorted wrt less |
| VERIFY_IS_EQUAL(m2.template innerIndicesAreSorted<std::less<>>(), 0); |
| // verify that all inner vectors are sorted wrt greater |
| VERIFY_IS_EQUAL(m2.template innerIndicesAreSorted<std::greater<>>(), m2.outerSize()); |
| // verify that sort does not change evaluation |
| VERIFY_IS_APPROX(m2, m1); |
| // sort wrt less |
| m2.template sortInnerIndices<std::less<>>(); |
| // verify that all inner vectors are sorted wrt less |
| VERIFY_IS_EQUAL(m2.template innerIndicesAreSorted<std::less<>>(), m2.outerSize()); |
| // verify that all inner vectors are not sorted wrt greater |
| VERIFY_IS_EQUAL(m2.template innerIndicesAreSorted<std::greater<>>(), 0); |
| // verify that sort does not change evaluation |
| 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); |
| |
| // mixed sparse-dense |
| 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); |
| |
| refM3 = refM1; |
| |
| VERIFY_IS_APPROX(refM1 += m2, refM3 += refM2); |
| VERIFY_IS_APPROX(refM1 -= m2, refM3 -= refM2); |
| |
| g_dense_op_sparse_count = 0; |
| VERIFY_IS_APPROX(refM1 = m2 + refM4, refM3 = refM2 + refM4); |
| VERIFY_IS_EQUAL(g_dense_op_sparse_count, 10); |
| g_dense_op_sparse_count = 0; |
| VERIFY_IS_APPROX(refM1 += m2 + refM4, refM3 += refM2 + refM4); |
| VERIFY_IS_EQUAL(g_dense_op_sparse_count, 1); |
| g_dense_op_sparse_count = 0; |
| VERIFY_IS_APPROX(refM1 -= m2 + refM4, refM3 -= refM2 + refM4); |
| VERIFY_IS_EQUAL(g_dense_op_sparse_count, 1); |
| g_dense_op_sparse_count = 0; |
| VERIFY_IS_APPROX(refM1 = refM4 + m2, refM3 = refM2 + refM4); |
| VERIFY_IS_EQUAL(g_dense_op_sparse_count, 1); |
| g_dense_op_sparse_count = 0; |
| VERIFY_IS_APPROX(refM1 += refM4 + m2, refM3 += refM2 + refM4); |
| VERIFY_IS_EQUAL(g_dense_op_sparse_count, 1); |
| g_dense_op_sparse_count = 0; |
| VERIFY_IS_APPROX(refM1 -= refM4 + m2, refM3 -= refM2 + refM4); |
| VERIFY_IS_EQUAL(g_dense_op_sparse_count, 1); |
| |
| g_dense_op_sparse_count = 0; |
| VERIFY_IS_APPROX(refM1 = m2 - refM4, refM3 = refM2 - refM4); |
| VERIFY_IS_EQUAL(g_dense_op_sparse_count, 20); |
| g_dense_op_sparse_count = 0; |
| VERIFY_IS_APPROX(refM1 += m2 - refM4, refM3 += refM2 - refM4); |
| VERIFY_IS_EQUAL(g_dense_op_sparse_count, 1); |
| g_dense_op_sparse_count = 0; |
| VERIFY_IS_APPROX(refM1 -= m2 - refM4, refM3 -= refM2 - refM4); |
| VERIFY_IS_EQUAL(g_dense_op_sparse_count, 1); |
| g_dense_op_sparse_count = 0; |
| VERIFY_IS_APPROX(refM1 = refM4 - m2, refM3 = refM4 - refM2); |
| VERIFY_IS_EQUAL(g_dense_op_sparse_count, 1); |
| g_dense_op_sparse_count = 0; |
| VERIFY_IS_APPROX(refM1 += refM4 - m2, refM3 += refM4 - refM2); |
| VERIFY_IS_EQUAL(g_dense_op_sparse_count, 1); |
| g_dense_op_sparse_count = 0; |
| VERIFY_IS_APPROX(refM1 -= refM4 - m2, refM3 -= refM4 - refM2); |
| VERIFY_IS_EQUAL(g_dense_op_sparse_count, 1); |
| refM3 = m3; |
| |
| 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 / insertFromTriplets |
| { |
| typedef Triplet<Scalar, StorageIndex> TripletType; |
| Index ntriplets = rows * cols; |
| |
| std::vector<TripletType> triplets; |
| |
| 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; |
| } |
| |
| std::vector<TripletType> moreTriplets; |
| moreTriplets.reserve(ntriplets); |
| DenseMatrix refMat_sum_more = refMat_sum; |
| DenseMatrix refMat_prod_more = refMat_prod; |
| DenseMatrix refMat_last_more = refMat_last; |
| |
| 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>(); |
| moreTriplets.push_back(TripletType(r, c, v)); |
| refMat_sum_more(r, c) += v; |
| if (std::abs(refMat_prod_more(r, c)) == 0) |
| refMat_prod_more(r, c) = v; |
| else |
| refMat_prod_more(r, c) *= v; |
| refMat_last_more(r, c) = v; |
| } |
| |
| SparseMatrixType m(rows, cols); |
| |
| // test setFromTriplets / insertFromTriplets |
| |
| m.setFromTriplets(triplets.begin(), triplets.end()); |
| VERIFY_IS_APPROX(m, refMat_sum); |
| VERIFY_IS_EQUAL(m.innerIndicesAreSorted(), m.outerSize()); |
| VERIFY(m.isCompressed()); |
| m.insertFromTriplets(moreTriplets.begin(), moreTriplets.end()); |
| VERIFY_IS_APPROX(m, refMat_sum_more); |
| VERIFY_IS_EQUAL(m.innerIndicesAreSorted(), m.outerSize()); |
| |
| m.setFromTriplets(triplets.begin(), triplets.end(), std::multiplies<Scalar>()); |
| VERIFY_IS_APPROX(m, refMat_prod); |
| VERIFY_IS_EQUAL(m.innerIndicesAreSorted(), m.outerSize()); |
| VERIFY(m.isCompressed()); |
| m.insertFromTriplets(moreTriplets.begin(), moreTriplets.end(), std::multiplies<Scalar>()); |
| VERIFY_IS_APPROX(m, refMat_prod_more); |
| VERIFY_IS_EQUAL(m.innerIndicesAreSorted(), m.outerSize()); |
| |
| m.setFromTriplets(triplets.begin(), triplets.end(), [](Scalar, Scalar b) { return b; }); |
| VERIFY_IS_APPROX(m, refMat_last); |
| VERIFY_IS_EQUAL(m.innerIndicesAreSorted(), m.outerSize()); |
| m.setFromTriplets(triplets.begin(), triplets.end(), [](Scalar, Scalar b) { return b; }); |
| VERIFY(m.isCompressed()); |
| m.insertFromTriplets(moreTriplets.begin(), moreTriplets.end(), [](Scalar, Scalar b) { return b; }); |
| VERIFY_IS_APPROX(m, refMat_last_more); |
| VERIFY_IS_EQUAL(m.innerIndicesAreSorted(), m.outerSize()); |
| |
| // insert into an uncompressed matrix |
| |
| VectorXi reserveSizes(m.outerSize()); |
| for (Index i = 0; i < m.outerSize(); i++) reserveSizes[i] = internal::random<int>(1, 7); |
| |
| m.setFromTriplets(triplets.begin(), triplets.end()); |
| m.reserve(reserveSizes); |
| VERIFY(!m.isCompressed()); |
| m.insertFromTriplets(moreTriplets.begin(), moreTriplets.end()); |
| VERIFY_IS_APPROX(m, refMat_sum_more); |
| VERIFY_IS_EQUAL(m.innerIndicesAreSorted(), m.outerSize()); |
| |
| m.setFromTriplets(triplets.begin(), triplets.end(), std::multiplies<Scalar>()); |
| m.reserve(reserveSizes); |
| VERIFY(!m.isCompressed()); |
| m.insertFromTriplets(moreTriplets.begin(), moreTriplets.end(), std::multiplies<Scalar>()); |
| VERIFY_IS_APPROX(m, refMat_prod_more); |
| VERIFY_IS_EQUAL(m.innerIndicesAreSorted(), m.outerSize()); |
| |
| m.setFromTriplets(triplets.begin(), triplets.end(), [](Scalar, Scalar b) { return b; }); |
| m.reserve(reserveSizes); |
| VERIFY(!m.isCompressed()); |
| m.insertFromTriplets(moreTriplets.begin(), moreTriplets.end(), [](Scalar, Scalar b) { return b; }); |
| VERIFY_IS_APPROX(m, refMat_last_more); |
| VERIFY_IS_EQUAL(m.innerIndicesAreSorted(), m.outerSize()); |
| |
| // test setFromSortedTriplets / insertFromSortedTriplets |
| |
| struct triplet_comp { |
| inline bool operator()(const TripletType& a, const TripletType& b) { |
| return SparseMatrixType::IsRowMajor ? ((a.row() != b.row()) ? (a.row() < b.row()) : (a.col() < b.col())) |
| : ((a.col() != b.col()) ? (a.col() < b.col()) : (a.row() < b.row())); |
| } |
| }; |
| |
| // stable_sort is only necessary when the reduction functor is dependent on the order of the triplets |
| // this is the case with refMat_last |
| // for most cases, std::sort is sufficient and preferred |
| |
| std::stable_sort(triplets.begin(), triplets.end(), triplet_comp()); |
| std::stable_sort(moreTriplets.begin(), moreTriplets.end(), triplet_comp()); |
| |
| m.setFromSortedTriplets(triplets.begin(), triplets.end()); |
| VERIFY_IS_APPROX(m, refMat_sum); |
| VERIFY_IS_EQUAL(m.innerIndicesAreSorted(), m.outerSize()); |
| VERIFY(m.isCompressed()); |
| m.insertFromSortedTriplets(moreTriplets.begin(), moreTriplets.end()); |
| VERIFY_IS_APPROX(m, refMat_sum_more); |
| VERIFY_IS_EQUAL(m.innerIndicesAreSorted(), m.outerSize()); |
| |
| m.setFromSortedTriplets(triplets.begin(), triplets.end(), std::multiplies<Scalar>()); |
| VERIFY_IS_APPROX(m, refMat_prod); |
| VERIFY_IS_EQUAL(m.innerIndicesAreSorted(), m.outerSize()); |
| VERIFY(m.isCompressed()); |
| m.insertFromSortedTriplets(moreTriplets.begin(), moreTriplets.end(), std::multiplies<Scalar>()); |
| VERIFY_IS_APPROX(m, refMat_prod_more); |
| VERIFY_IS_EQUAL(m.innerIndicesAreSorted(), m.outerSize()); |
| |
| m.setFromSortedTriplets(triplets.begin(), triplets.end(), [](Scalar, Scalar b) { return b; }); |
| VERIFY_IS_APPROX(m, refMat_last); |
| VERIFY_IS_EQUAL(m.innerIndicesAreSorted(), m.outerSize()); |
| VERIFY(m.isCompressed()); |
| m.insertFromSortedTriplets(moreTriplets.begin(), moreTriplets.end(), [](Scalar, Scalar b) { return b; }); |
| VERIFY_IS_APPROX(m, refMat_last_more); |
| VERIFY_IS_EQUAL(m.innerIndicesAreSorted(), m.outerSize()); |
| |
| // insert into an uncompressed matrix |
| |
| m.setFromSortedTriplets(triplets.begin(), triplets.end()); |
| m.reserve(reserveSizes); |
| VERIFY(!m.isCompressed()); |
| m.insertFromSortedTriplets(moreTriplets.begin(), moreTriplets.end()); |
| VERIFY_IS_APPROX(m, refMat_sum_more); |
| VERIFY_IS_EQUAL(m.innerIndicesAreSorted(), m.outerSize()); |
| |
| m.setFromSortedTriplets(triplets.begin(), triplets.end(), std::multiplies<Scalar>()); |
| m.reserve(reserveSizes); |
| VERIFY(!m.isCompressed()); |
| m.insertFromSortedTriplets(moreTriplets.begin(), moreTriplets.end(), std::multiplies<Scalar>()); |
| VERIFY_IS_APPROX(m, refMat_prod_more); |
| VERIFY_IS_EQUAL(m.innerIndicesAreSorted(), m.outerSize()); |
| |
| m.setFromSortedTriplets(triplets.begin(), triplets.end(), [](Scalar, Scalar b) { return b; }); |
| m.reserve(reserveSizes); |
| VERIFY(!m.isCompressed()); |
| m.insertFromSortedTriplets(moreTriplets.begin(), moreTriplets.end(), [](Scalar, Scalar b) { return b; }); |
| VERIFY_IS_APPROX(m, refMat_last_more); |
| VERIFY_IS_EQUAL(m.innerIndicesAreSorted(), m.outerSize()); |
| } |
| |
| // 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); |
| } |
| |
| 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>())); |
| |
| // check sparse triangular view iteration-based evaluation |
| m2.setZero(); |
| VERIFY_IS_CWISE_EQUAL(m2.template triangularView<UnitLower>().toDense(), DenseMatrix::Identity(rows, cols)); |
| VERIFY_IS_CWISE_EQUAL(m2.template triangularView<UnitUpper>().toDense(), DenseMatrix::Identity(rows, cols)); |
| } |
| |
| // 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; |
| 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); |
| m2.setZero(); |
| m2 += d.asDiagonal(); |
| refMat2.setZero(); |
| refMat2 += d.asDiagonal(); |
| VERIFY_IS_APPROX(m2, refMat2); |
| m2.setZero(); |
| m2 -= d.asDiagonal(); |
| refMat2.setZero(); |
| refMat2 -= d.asDiagonal(); |
| VERIFY_IS_APPROX(m2, refMat2); |
| |
| initSparse<Scalar>(density, refMat2, m2); |
| m2.makeCompressed(); |
| m2 += d.asDiagonal(); |
| refMat2 += d.asDiagonal(); |
| VERIFY_IS_APPROX(m2, refMat2); |
| |
| initSparse<Scalar>(density, refMat2, m2); |
| m2.makeCompressed(); |
| VectorXi res(rows); |
| for (Index i = 0; i < rows; ++i) res(i) = internal::random<int>(0, 3); |
| m2.reserve(res); |
| m2 -= d.asDiagonal(); |
| refMat2 -= 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)); |
| inc.push_back(std::pair<StorageIndex, StorageIndex>(0, -1)); |
| inc.push_back(std::pair<StorageIndex, StorageIndex>(-1, 0)); |
| inc.push_back(std::pair<StorageIndex, StorageIndex>(-1, -1)); |
| |
| 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); |
| |
| SparseMatrixType m2 = m1; |
| m2.makeCompressed(); |
| |
| m1.conservativeResize(rows + incRows, cols + incCols); |
| m2.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); |
| VERIFY_IS_APPROX(m2, 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); |
| } |
| |
| // test reserve with empty rows/columns |
| { |
| SparseMatrixType m1(0, cols); |
| m1.reserve(ArrayXi::Constant(m1.outerSize(), 1)); |
| SparseMatrixType m2(rows, 0); |
| m2.reserve(ArrayXi::Constant(m2.outerSize(), 1)); |
| } |
| |
| // test move |
| { |
| using TransposedType = SparseMatrix<Scalar, SparseMatrixType::IsRowMajor ? ColMajor : RowMajor, |
| typename SparseMatrixType::StorageIndex>; |
| DenseMatrix refMat1 = DenseMatrix::Random(rows, cols); |
| SparseMatrixType m1(rows, cols); |
| initSparse<Scalar>(density, refMat1, m1); |
| // test move ctor |
| SparseMatrixType m2(std::move(m1)); |
| VERIFY_IS_APPROX(m2, refMat1); |
| // test move assignment |
| m1 = std::move(m2); |
| VERIFY_IS_APPROX(m1, refMat1); |
| // test move ctor (SparseMatrixBase) |
| TransposedType m3(std::move(m1.transpose())); |
| VERIFY_IS_APPROX(m3, refMat1.transpose()); |
| // test move assignment (SparseMatrixBase) |
| m2 = std::move(m3.transpose()); |
| VERIFY_IS_APPROX(m2, refMat1); |
| } |
| } |
| |
| 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 * static_cast<double>(rows * cols); |
| VERIFY(nelements >= 0 && nelements < static_cast<double>(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()); |
| } |
| |
| template <int> |
| void bug1105() { |
| // Regression test for bug 1105 |
| 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); |
| } |
| } |
| |
| #ifndef EIGEN_SPARSE_TEST_INCLUDED_FROM_SPARSE_EXTRA |
| |
| EIGEN_DECLARE_TEST(sparse_basic) { |
| g_dense_op_sparse_count = 0; // Suppresses compiler warning. |
| 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, 3) == 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_2((sparse_basic(SparseMatrix<float, RowMajor>(r, c)))); |
| CALL_SUBTEST_2((sparse_basic(SparseMatrix<float, ColMajor>(r, c)))); |
| CALL_SUBTEST_3((sparse_basic(SparseMatrix<double, ColMajor>(r, c)))); |
| CALL_SUBTEST_3((sparse_basic(SparseMatrix<double, RowMajor>(r, c)))); |
| CALL_SUBTEST_4((sparse_basic(SparseMatrix<double, ColMajor, long int>(r, c)))); |
| CALL_SUBTEST_4((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, 3) == 0) { |
| r = c; // check square matrices in 25% of tries |
| } |
| |
| CALL_SUBTEST_5((sparse_basic(SparseMatrix<double, ColMajor, short int>(short(r), short(c))))); |
| CALL_SUBTEST_5((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_5((big_sparse_triplet<SparseMatrix<float, RowMajor, int>>(10000, 10000, 0.125))); |
| CALL_SUBTEST_5((big_sparse_triplet<SparseMatrix<double, ColMajor, long int>>(10000, 10000, 0.125))); |
| |
| CALL_SUBTEST_5(bug1105<0>()); |
| } |
| #endif |
