devtools/eigen
1
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2026-04-10 11:34:33 +08:00
Code Issues Packages Projects Releases Wiki Activity

Split sparse_basic unit test

Browse Source
This commit is contained in:
Gael Guennebaud
2015-03-19 15:11:05 +01:00
parent f329d0908a
commit d7698c18b7
3 changed files with 259 additions and 200 deletions
Download Patch File Download Diff File Expand all files Collapse all files

204
test/sparse_basic.cpp
View File

@@ -30,7 +30,6 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
double density = (std::max)(8./(rows*cols), 0.01);
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
typedef Matrix<Scalar,1,Dynamic> RowDenseVector;
Scalar eps = 1e-6;
Scalar s1 = internal::random<Scalar>();
@@ -59,77 +58,6 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
VERIFY_IS_APPROX(m, refMat);
// test InnerIterators and Block expressions
for (int t=0; t<10; ++t)
{
Index j = internal::random<Index>(0,cols-2);
Index i = internal::random<Index>(0,rows-2);
Index w = internal::random<Index>(1,cols-j);
Index h = internal::random<Index>(1,rows-i);
VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w));
for(Index c=0; c<w; c++)
{
VERIFY_IS_APPROX(m.block(i,j,h,w).col(c), refMat.block(i,j,h,w).col(c));
for(Index r=0; r<h; r++)
{
VERIFY_IS_APPROX(m.block(i,j,h,w).col(c).coeff(r), refMat.block(i,j,h,w).col(c).coeff(r));
VERIFY_IS_APPROX(m.block(i,j,h,w).coeff(r,c), refMat.block(i,j,h,w).coeff(r,c));
}
}
for(Index r=0; r<h; r++)
{
VERIFY_IS_APPROX(m.block(i,j,h,w).row(r), refMat.block(i,j,h,w).row(r));
for(Index c=0; c<w; c++)
{
VERIFY_IS_APPROX(m.block(i,j,h,w).row(r).coeff(c), refMat.block(i,j,h,w).row(r).coeff(c));
VERIFY_IS_APPROX(m.block(i,j,h,w).coeff(r,c), refMat.block(i,j,h,w).coeff(r,c));
}
}
VERIFY_IS_APPROX(m.middleCols(j,w), refMat.middleCols(j,w));
VERIFY_IS_APPROX(m.middleRows(i,h), refMat.middleRows(i,h));
for(Index r=0; r<h; r++)
{
VERIFY_IS_APPROX(m.middleCols(j,w).row(r), refMat.middleCols(j,w).row(r));
VERIFY_IS_APPROX(m.middleRows(i,h).row(r), refMat.middleRows(i,h).row(r));
for(Index c=0; c<w; c++)
{
VERIFY_IS_APPROX(m.col(c).coeff(r), refMat.col(c).coeff(r));
VERIFY_IS_APPROX(m.row(r).coeff(c), refMat.row(r).coeff(c));
VERIFY_IS_APPROX(m.middleCols(j,w).coeff(r,c), refMat.middleCols(j,w).coeff(r,c));
VERIFY_IS_APPROX(m.middleRows(i,h).coeff(r,c), refMat.middleRows(i,h).coeff(r,c));
if(m.middleCols(j,w).coeff(r,c) != Scalar(0))
{
VERIFY_IS_APPROX(m.middleCols(j,w).coeffRef(r,c), refMat.middleCols(j,w).coeff(r,c));
}
if(m.middleRows(i,h).coeff(r,c) != Scalar(0))
{
VERIFY_IS_APPROX(m.middleRows(i,h).coeff(r,c), refMat.middleRows(i,h).coeff(r,c));
}
}
}
for(Index c=0; c<w; c++)
{
VERIFY_IS_APPROX(m.middleCols(j,w).col(c), refMat.middleCols(j,w).col(c));
VERIFY_IS_APPROX(m.middleRows(i,h).col(c), refMat.middleRows(i,h).col(c));
}
}
for(Index c=0; c<cols; c++)
{
VERIFY_IS_APPROX(m.col(c) + m.col(c), (m + m).col(c));
VERIFY_IS_APPROX(m.col(c) + m.col(c), refMat.col(c) + refMat.col(c));
}
for(Index r=0; r<rows; r++)
{
VERIFY_IS_APPROX(m.row(r) + m.row(r), (m + m).row(r));
VERIFY_IS_APPROX(m.row(r) + m.row(r), refMat.row(r) + refMat.row(r));
}
// test assertion
VERIFY_RAISES_ASSERT( m.coeffRef(-1,1) = 0 );
VERIFY_RAISES_ASSERT( m.coeffRef(0,m.cols()) = 0 );
@@ -214,82 +142,6 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
VERIFY_IS_APPROX(m2,m1);
}
// test innerVector()
{
DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
SparseMatrixType m2(rows, cols);
initSparse<Scalar>(density, refMat2, m2);
Index j0 = internal::random<Index>(0,outer-1);
Index j1 = internal::random<Index>(0,outer-1);
if(SparseMatrixType::IsRowMajor)
VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.row(j0));
else
VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.col(j0));
if(SparseMatrixType::IsRowMajor)
VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.row(j0)+refMat2.row(j1));
else
VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.col(j0)+refMat2.col(j1));
SparseMatrixType m3(rows,cols);
m3.reserve(VectorXi::Constant(outer,int(inner/2)));
for(Index j=0; j<outer; ++j)
for(Index k=0; k<(std::min)(j,inner); ++k)
m3.insertByOuterInner(j,k) = k+1;
for(Index j=0; j<(std::min)(outer, inner); ++j)
{
VERIFY(j==numext::real(m3.innerVector(j).nonZeros()));
if(j>0)
VERIFY(j==numext::real(m3.innerVector(j).lastCoeff()));
}
m3.makeCompressed();
for(Index j=0; j<(std::min)(outer, inner); ++j)
{
VERIFY(j==numext::real(m3.innerVector(j).nonZeros()));
if(j>0)
VERIFY(j==numext::real(m3.innerVector(j).lastCoeff()));
}
VERIFY(m3.innerVector(j0).nonZeros() == m3.transpose().innerVector(j0).nonZeros());
// m2.innerVector(j0) = 2*m2.innerVector(j1);
// refMat2.col(j0) = 2*refMat2.col(j1);
// VERIFY_IS_APPROX(m2, refMat2);
}
// test innerVectors()
{
DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
SparseMatrixType m2(rows, cols);
initSparse<Scalar>(density, refMat2, m2);
if(internal::random<float>(0,1)>0.5) m2.makeCompressed();
Index j0 = internal::random<Index>(0,outer-2);
Index j1 = internal::random<Index>(0,outer-2);
Index n0 = internal::random<Index>(1,outer-(std::max)(j0,j1));
if(SparseMatrixType::IsRowMajor)
VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(j0,0,n0,cols));
else
VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(0,j0,rows,n0));
if(SparseMatrixType::IsRowMajor)
VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0),
refMat2.middleRows(j0,n0)+refMat2.middleRows(j1,n0));
else
VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0),
refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0));
VERIFY_IS_APPROX(m2, refMat2);
VERIFY(m2.innerVectors(j0,n0).nonZeros() == m2.transpose().innerVectors(j0,n0).nonZeros());
m2.innerVectors(j0,n0) = m2.innerVectors(j0,n0) + m2.innerVectors(j1,n0);
if(SparseMatrixType::IsRowMajor)
refMat2.middleRows(j0,n0) = (refMat2.middleRows(j0,n0) + refMat2.middleRows(j1,n0)).eval();
else
refMat2.middleCols(j0,n0) = (refMat2.middleCols(j0,n0) + refMat2.middleCols(j1,n0)).eval();
VERIFY_IS_APPROX(m2, refMat2);
}
// test basic computations
{
DenseMatrix refM1 = DenseMatrix::Zero(rows, cols);
@@ -360,54 +212,6 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
VERIFY(m2.isApprox(m3));
}
// test generic blocks
{
DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
SparseMatrixType m2(rows, cols);
initSparse<Scalar>(density, refMat2, m2);
Index j0 = internal::random<Index>(0,outer-2);
Index j1 = internal::random<Index>(0,outer-2);
Index n0 = internal::random<Index>(1,outer-(std::max)(j0,j1));
if(SparseMatrixType::IsRowMajor)
VERIFY_IS_APPROX(m2.block(j0,0,n0,cols), refMat2.block(j0,0,n0,cols));
else
VERIFY_IS_APPROX(m2.block(0,j0,rows,n0), refMat2.block(0,j0,rows,n0));
if(SparseMatrixType::IsRowMajor)
VERIFY_IS_APPROX(m2.block(j0,0,n0,cols)+m2.block(j1,0,n0,cols),
refMat2.block(j0,0,n0,cols)+refMat2.block(j1,0,n0,cols));
else
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));
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;
VERIFY_IS_APPROX(m2,refMat2);
} else {
m2.innerVector(i) = m2.innerVector(i) * s1;
refMat2.col(i) = refMat2.col(i) * s1;
VERIFY_IS_APPROX(m2,refMat2);
}
Index r0 = internal::random<Index>(0,rows-2);
Index c0 = internal::random<Index>(0,cols-2);
Index r1 = internal::random<Index>(1,rows-r0);
Index c1 = internal::random<Index>(1,cols-c0);
VERIFY_IS_APPROX(DenseVector(m2.col(c0)), refMat2.col(c0));
VERIFY_IS_APPROX(m2.col(c0), refMat2.col(c0));
VERIFY_IS_APPROX(RowDenseVector(m2.row(r0)), refMat2.row(r0));
VERIFY_IS_APPROX(m2.row(r0), refMat2.row(r0));
VERIFY_IS_APPROX(m2.block(r0,c0,r1,c1), refMat2.block(r0,c0,r1,c1));
VERIFY_IS_APPROX((2*m2).block(r0,c0,r1,c1), (2*refMat2).block(r0,c0,r1,c1));
}
// test prune
{
SparseMatrixType m2(rows, cols);
@@ -646,8 +450,8 @@ void test_sparse_basic()
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_1(( sparse_basic(SparseMatrix<double,ColMajor,long int>(r, c)) ));
CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,long int>(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);
@@ -655,8 +459,8 @@ void test_sparse_basic()
r = c; // check square matrices in 25% of tries
}
CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,ColMajor,short int>(short(r), short(c))) ));
CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,short int>(short(r), short(c))) ));
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):