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Add no_assignment_operator to a few classes that must not be assigned, and fix a couple of warnings.

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This commit is contained in:
Gael Guennebaud
2013-07-10 23:48:26 +02:00
parent 71cccf0ed8
commit 6d1f5dbaae
13 changed files with 75 additions and 69 deletions
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1
test/cholesky.cpp
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@@ -328,4 +328,5 @@ void test_cholesky()
CALL_SUBTEST_9( LDLT<MatrixXf>(10) );
TEST_SET_BUT_UNUSED_VARIABLE(s)
TEST_SET_BUT_UNUSED_VARIABLE(nb_temporaries)
}

8
test/geo_eulerangles.cpp
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@@ -41,7 +41,7 @@ template<typename Scalar> void check_all_var(const Matrix<Scalar,3,1>& ea)
VERIFY_EULER(2,1,2, Z,Y,Z);
}
template<typename Scalar> void eulerangles(void)
template<typename Scalar> void eulerangles()
{
typedef Matrix<Scalar,3,3> Matrix3;
typedef Matrix<Scalar,3,1> Vector3;
@@ -60,13 +60,13 @@ template<typename Scalar> void eulerangles(void)
ea = m.eulerAngles(0,1,0);
check_all_var(ea);
ea = (Array3::Random() + Array3(1,1,0))*M_PI*Array3(0.5,0.5,1);
ea = (Array3::Random() + Array3(1,1,0))*Scalar(M_PI)*Array3(0.5,0.5,1);
check_all_var(ea);
ea[2] = ea[0] = internal::random<Scalar>(0,M_PI);
ea[2] = ea[0] = internal::random<Scalar>(0,Scalar(M_PI));
check_all_var(ea);
ea[0] = ea[1] = internal::random<Scalar>(0,M_PI);
ea[0] = ea[1] = internal::random<Scalar>(0,Scalar(M_PI));
check_all_var(ea);
ea[1] = 0;

4
test/mapstride.cpp
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@@ -116,7 +116,7 @@ template<int Alignment,typename MatrixType> void map_class_matrix(const MatrixTy
void test_mapstride()
{
for(int i = 0; i < g_repeat; i++) {
EIGEN_UNUSED int maxn = 30;
int maxn = 30;
CALL_SUBTEST_1( map_class_vector<Aligned>(Matrix<float, 1, 1>()) );
CALL_SUBTEST_1( map_class_vector<Unaligned>(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( map_class_vector<Aligned>(Vector4d()) );
@@ -142,5 +142,7 @@ void test_mapstride()
CALL_SUBTEST_5( map_class_matrix<Unaligned>(MatrixXi(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) );
CALL_SUBTEST_6( map_class_matrix<Aligned>(MatrixXcd(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) );
CALL_SUBTEST_6( map_class_matrix<Unaligned>(MatrixXcd(internal::random<int>(1,maxn),internal::random<int>(1,maxn))) );
TEST_SET_BUT_UNUSED_VARIABLE(maxn);
}
}

2
test/redux.cpp
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@@ -22,7 +22,7 @@ template<typename MatrixType> void matrixRedux(const MatrixType& m)
// The entries of m1 are uniformly distributed in [0,1], so m1.prod() is very small. This may lead to test
// failures if we underflow into denormals. Thus, we scale so that entires are close to 1.
MatrixType m1_for_prod = MatrixType::Ones(rows, cols) + Scalar(0.2) * m1;
MatrixType m1_for_prod = MatrixType::Ones(rows, cols) + RealScalar(0.2) * m1;
VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows, cols).sum(), Scalar(1));
VERIFY_IS_APPROX(MatrixType::Ones(rows, cols).sum(), Scalar(float(rows*cols))); // the float() here to shut up excessive MSVC warning about int->complex conversion being lossy

2
test/sizeof.cpp
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@@ -13,7 +13,7 @@ template<typename MatrixType> void verifySizeOf(const MatrixType&)
{
typedef typename MatrixType::Scalar Scalar;
if (MatrixType::RowsAtCompileTime!=Dynamic && MatrixType::ColsAtCompileTime!=Dynamic)
VERIFY(sizeof(MatrixType)==sizeof(Scalar)*size_t(MatrixType::SizeAtCompileTime));
VERIFY(sizeof(MatrixType)==sizeof(Scalar)*std::ptrdiff_t(MatrixType::SizeAtCompileTime));
else
VERIFY(sizeof(MatrixType)==sizeof(Scalar*) + 2 * sizeof(typename MatrixType::Index));
}

16
test/sparse.h
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@@ -58,8 +58,8 @@ initSparse(double density,
Matrix<Scalar,Dynamic,Dynamic,Opt1>& refMat,
SparseMatrix<Scalar,Opt2,Index>& sparseMat,
int flags = 0,
std::vector<Vector2i>* zeroCoords = 0,
std::vector<Vector2i>* nonzeroCoords = 0)
std::vector<Matrix<Index,2,1> >* zeroCoords = 0,
std::vector<Matrix<Index,2,1> >* nonzeroCoords = 0)
{
enum { IsRowMajor = SparseMatrix<Scalar,Opt2,Index>::IsRowMajor };
sparseMat.setZero();
@@ -93,11 +93,11 @@ initSparse(double density,
//sparseMat.insertBackByOuterInner(j,i) = v;
sparseMat.insertByOuterInner(j,i) = v;
if (nonzeroCoords)
nonzeroCoords->push_back(Vector2i(ai,aj));
nonzeroCoords->push_back(Matrix<Index,2,1> (ai,aj));
}
else if (zeroCoords)
{
zeroCoords->push_back(Vector2i(ai,aj));
zeroCoords->push_back(Matrix<Index,2,1> (ai,aj));
}
refMat(ai,aj) = v;
}
@@ -110,8 +110,8 @@ initSparse(double density,
Matrix<Scalar,Dynamic,Dynamic, Opt1>& refMat,
DynamicSparseMatrix<Scalar, Opt2, Index>& sparseMat,
int flags = 0,
std::vector<Vector2i>* zeroCoords = 0,
std::vector<Vector2i>* nonzeroCoords = 0)
std::vector<Matrix<Index,2,1> >* zeroCoords = 0,
std::vector<Matrix<Index,2,1> >* nonzeroCoords = 0)
{
enum { IsRowMajor = DynamicSparseMatrix<Scalar,Opt2,Index>::IsRowMajor };
sparseMat.setZero();
@@ -142,11 +142,11 @@ initSparse(double density,
{
sparseMat.insertBackByOuterInner(j,i) = v;
if (nonzeroCoords)
nonzeroCoords->push_back(Vector2i(ai,aj));
nonzeroCoords->push_back(Matrix<Index,2,1> (ai,aj));
}
else if (zeroCoords)
{
zeroCoords->push_back(Vector2i(ai,aj));
zeroCoords->push_back(Matrix<Index,2,1> (ai,aj));
}
refMat(ai,aj) = v;
}

63
test/sparse_basic.cpp
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@@ -14,7 +14,8 @@
template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& ref)
{
typedef typename SparseMatrixType::Index Index;
typedef Matrix<Index,2,1> Vector2;
const Index rows = ref.rows();
const Index cols = ref.cols();
typedef typename SparseMatrixType::Scalar Scalar;
@@ -31,8 +32,8 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
DenseMatrix refMat = DenseMatrix::Zero(rows, cols);
DenseVector vec1 = DenseVector::Random(rows);
std::vector<Vector2i> zeroCoords;
std::vector<Vector2i> nonzeroCoords;
std::vector<Vector2> zeroCoords;
std::vector<Vector2> nonzeroCoords;
initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords);
if (zeroCoords.size()==0 || nonzeroCoords.size()==0)
@@ -104,11 +105,11 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
SparseMatrixType m2(rows,cols);
if(internal::random<int>()%2)
m2.reserve(VectorXi::Constant(m2.outerSize(), 2));
for (int j=0; j<cols; ++j)
for (Index j=0; j<cols; ++j)
{
for (int k=0; k<rows/2; ++k)
for (Index k=0; k<rows/2; ++k)
{
int i = internal::random<int>(0,rows-1);
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>();
}
@@ -126,8 +127,8 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
m2.reserve(VectorXi::Constant(m2.outerSize(), 2));
for (int k=0; k<rows*cols; ++k)
{
int i = internal::random<int>(0,rows-1);
int j = internal::random<int>(0,cols-1);
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
@@ -150,8 +151,8 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
m2.reserve(r);
for (int k=0; k<rows*cols; ++k)
{
int i = internal::random<int>(0,rows-1);
int j = internal::random<int>(0,cols-1);
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)
@@ -167,8 +168,8 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
SparseMatrixType m2(rows, rows);
initSparse<Scalar>(density, refMat2, m2);
int j0 = internal::random<int>(0,rows-1);
int j1 = internal::random<int>(0,rows-1);
Index j0 = internal::random<Index>(0,rows-1);
Index j1 = internal::random<Index>(0,rows-1);
if(SparseMatrixType::IsRowMajor)
VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.row(j0));
else
@@ -181,17 +182,17 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
SparseMatrixType m3(rows,rows);
m3.reserve(VectorXi::Constant(rows,rows/2));
for(int j=0; j<rows; ++j)
for(int k=0; k<j; ++k)
for(Index j=0; j<rows; ++j)
for(Index k=0; k<j; ++k)
m3.insertByOuterInner(j,k) = k+1;
for(int j=0; j<rows; ++j)
for(Index j=0; j<rows; ++j)
{
VERIFY(j==numext::real(m3.innerVector(j).nonZeros()));
if(j>0)
VERIFY(j==numext::real(m3.innerVector(j).lastCoeff()));
}
m3.makeCompressed();
for(int j=0; j<rows; ++j)
for(Index j=0; j<rows; ++j)
{
VERIFY(j==numext::real(m3.innerVector(j).nonZeros()));
if(j>0)
@@ -210,9 +211,9 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
initSparse<Scalar>(density, refMat2, m2);
if(internal::random<float>(0,1)>0.5) m2.makeCompressed();
int j0 = internal::random<int>(0,rows-2);
int j1 = internal::random<int>(0,rows-2);
int n0 = internal::random<int>(1,rows-(std::max)(j0,j1));
Index j0 = internal::random<Index>(0,rows-2);
Index j1 = internal::random<Index>(0,rows-2);
Index n0 = internal::random<Index>(1,rows-(std::max)(j0,j1));
if(SparseMatrixType::IsRowMajor)
VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(j0,0,n0,cols));
else
@@ -300,9 +301,9 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
SparseMatrixType m2(rows, rows);
initSparse<Scalar>(density, refMat2, m2);
int j0 = internal::random<int>(0,rows-2);
int j1 = internal::random<int>(0,rows-2);
int n0 = internal::random<int>(1,rows-(std::max)(j0,j1));
Index j0 = internal::random<Index>(0,rows-2);
Index j1 = internal::random<Index>(0,rows-2);
Index n0 = internal::random<Index>(1,rows-(std::max)(j0,j1));
if(SparseMatrixType::IsRowMajor)
VERIFY_IS_APPROX(m2.block(j0,0,n0,cols), refMat2.block(j0,0,n0,cols));
else
@@ -315,7 +316,7 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
VERIFY_IS_APPROX(m2.block(0,j0,rows,n0)+m2.block(0,j1,rows,n0),
refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0));
int i = internal::random<int>(0,m2.outerSize()-1);
Index i = internal::random<Index>(0,m2.outerSize()-1);
if(SparseMatrixType::IsRowMajor) {
m2.innerVector(i) = m2.innerVector(i) * s1;
refMat2.row(i) = refMat2.row(i) * s1;
@@ -334,10 +335,10 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
refM2.setZero();
int countFalseNonZero = 0;
int countTrueNonZero = 0;
for (int j=0; j<m2.outerSize(); ++j)
for (Index j=0; j<m2.outerSize(); ++j)
{
m2.startVec(j);
for (int i=0; i<m2.innerSize(); ++i)
for (Index i=0; i<m2.innerSize(); ++i)
{
float x = internal::random<float>(0,1);
if (x<0.1)
@@ -378,8 +379,8 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
refMat.setZero();
for(int i=0;i<ntriplets;++i)
{
int r = internal::random<int>(0,rows-1);
int c = internal::random<int>(0,cols-1);
Index r = internal::random<Index>(0,rows-1);
Index c = internal::random<Index>(0,cols-1);
Scalar v = internal::random<Scalar>();
triplets.push_back(TripletType(r,c,v));
refMat(r,c) += v;
@@ -456,8 +457,8 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
inc.push_back(std::pair<int,int>(0,3));
for(size_t i = 0; i< inc.size(); i++) {
int incRows = inc[i].first;
int incCols = inc[i].second;
Index incRows = inc[i].first;
Index incCols = inc[i].second;
SparseMatrixType m1(rows, cols);
DenseMatrix refMat1 = DenseMatrix::Zero(rows, cols);
initSparse<Scalar>(density, refMat1, m1);
@@ -502,7 +503,7 @@ void test_sparse_basic()
CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,ColMajor,long int>(s, s)) ));
CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,long int>(s, s)) ));
CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,ColMajor,short int>(s, s)) ));
CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,short int>(s, s)) ));
CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,ColMajor,short int>(short(s), short(s))) ));
CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,short int>(short(s), short(s))) ));
}
}