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eigen/test/sparse_basic.cpp

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// 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 = 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));
}
}
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, 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_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, 4) == 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
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