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eigen/test/sparse_basic.cpp
Antonio Sanchez 543e34ab9d Re-implement move assignments. 
The original swap approach leads to potential undefined behavior (reading
uninitialized memory) and results in unnecessary copying of data for static
storage.

Here we pass down the move assignment to the underlying storage.  Static
storage does a one-way copy, dynamic storage does a swap.

Modified the tests to no longer read from the moved-from matrix/tensor,
since that can lead to UB. Added a test to ensure we do not access
uninitialized memory in a move.

Fixes: #2119
2021-03-10 16:55:20 +00:00

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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;
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);
// 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
{
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 (EIGEN_COMP_CXXVER >= 11)
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;
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 * 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_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)));
CALL_SUBTEST_7( bug1105<0>() );
}
#endif
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