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Split sparse_basic unit test
This commit is contained in:
Gael Guennebaud
parent
f329d0908a
commit
d7698c18b7
@ -227,6 +227,7 @@ ei_add_test(stdvector_overload)
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ei_add_test(stdlist)
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ei_add_test(stddeque)
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ei_add_test(sparse_basic)
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ei_add_test(sparse_block)
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ei_add_test(sparse_vector)
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ei_add_test(sparse_product)
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ei_add_test(sparse_ref)
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@ -30,7 +30,6 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
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double density = (std::max)(8./(rows*cols), 0.01);
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typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
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typedef Matrix<Scalar,Dynamic,1> DenseVector;
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typedef Matrix<Scalar,1,Dynamic> RowDenseVector;
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Scalar eps = 1e-6;
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Scalar s1 = internal::random<Scalar>();
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@ -59,77 +58,6 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
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VERIFY_IS_APPROX(m, refMat);
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// test InnerIterators and Block expressions
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for (int t=0; t<10; ++t)
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{
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Index j = internal::random<Index>(0,cols-2);
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Index i = internal::random<Index>(0,rows-2);
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Index w = internal::random<Index>(1,cols-j);
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Index h = internal::random<Index>(1,rows-i);
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VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w));
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for(Index c=0; c<w; c++)
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{
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VERIFY_IS_APPROX(m.block(i,j,h,w).col(c), refMat.block(i,j,h,w).col(c));
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for(Index r=0; r<h; r++)
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{
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VERIFY_IS_APPROX(m.block(i,j,h,w).col(c).coeff(r), refMat.block(i,j,h,w).col(c).coeff(r));
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VERIFY_IS_APPROX(m.block(i,j,h,w).coeff(r,c), refMat.block(i,j,h,w).coeff(r,c));
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}
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}
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for(Index r=0; r<h; r++)
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{
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VERIFY_IS_APPROX(m.block(i,j,h,w).row(r), refMat.block(i,j,h,w).row(r));
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for(Index c=0; c<w; c++)
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{
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VERIFY_IS_APPROX(m.block(i,j,h,w).row(r).coeff(c), refMat.block(i,j,h,w).row(r).coeff(c));
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VERIFY_IS_APPROX(m.block(i,j,h,w).coeff(r,c), refMat.block(i,j,h,w).coeff(r,c));
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}
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}
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VERIFY_IS_APPROX(m.middleCols(j,w), refMat.middleCols(j,w));
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VERIFY_IS_APPROX(m.middleRows(i,h), refMat.middleRows(i,h));
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for(Index r=0; r<h; r++)
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{
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VERIFY_IS_APPROX(m.middleCols(j,w).row(r), refMat.middleCols(j,w).row(r));
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VERIFY_IS_APPROX(m.middleRows(i,h).row(r), refMat.middleRows(i,h).row(r));
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for(Index c=0; c<w; c++)
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{
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VERIFY_IS_APPROX(m.col(c).coeff(r), refMat.col(c).coeff(r));
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VERIFY_IS_APPROX(m.row(r).coeff(c), refMat.row(r).coeff(c));
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VERIFY_IS_APPROX(m.middleCols(j,w).coeff(r,c), refMat.middleCols(j,w).coeff(r,c));
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VERIFY_IS_APPROX(m.middleRows(i,h).coeff(r,c), refMat.middleRows(i,h).coeff(r,c));
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if(m.middleCols(j,w).coeff(r,c) != Scalar(0))
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{
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VERIFY_IS_APPROX(m.middleCols(j,w).coeffRef(r,c), refMat.middleCols(j,w).coeff(r,c));
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}
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if(m.middleRows(i,h).coeff(r,c) != Scalar(0))
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{
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VERIFY_IS_APPROX(m.middleRows(i,h).coeff(r,c), refMat.middleRows(i,h).coeff(r,c));
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}
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}
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}
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for(Index c=0; c<w; c++)
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{
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VERIFY_IS_APPROX(m.middleCols(j,w).col(c), refMat.middleCols(j,w).col(c));
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VERIFY_IS_APPROX(m.middleRows(i,h).col(c), refMat.middleRows(i,h).col(c));
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}
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}
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for(Index c=0; c<cols; c++)
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{
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VERIFY_IS_APPROX(m.col(c) + m.col(c), (m + m).col(c));
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VERIFY_IS_APPROX(m.col(c) + m.col(c), refMat.col(c) + refMat.col(c));
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}
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for(Index r=0; r<rows; r++)
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{
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VERIFY_IS_APPROX(m.row(r) + m.row(r), (m + m).row(r));
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VERIFY_IS_APPROX(m.row(r) + m.row(r), refMat.row(r) + refMat.row(r));
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}
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// test assertion
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VERIFY_RAISES_ASSERT( m.coeffRef(-1,1) = 0 );
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VERIFY_RAISES_ASSERT( m.coeffRef(0,m.cols()) = 0 );
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@ -214,82 +142,6 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
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VERIFY_IS_APPROX(m2,m1);
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}
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// test innerVector()
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{
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DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
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SparseMatrixType m2(rows, cols);
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initSparse<Scalar>(density, refMat2, m2);
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Index j0 = internal::random<Index>(0,outer-1);
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Index j1 = internal::random<Index>(0,outer-1);
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if(SparseMatrixType::IsRowMajor)
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VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.row(j0));
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else
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VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.col(j0));
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if(SparseMatrixType::IsRowMajor)
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VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.row(j0)+refMat2.row(j1));
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else
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VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.col(j0)+refMat2.col(j1));
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SparseMatrixType m3(rows,cols);
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m3.reserve(VectorXi::Constant(outer,int(inner/2)));
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for(Index j=0; j<outer; ++j)
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for(Index k=0; k<(std::min)(j,inner); ++k)
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m3.insertByOuterInner(j,k) = k+1;
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for(Index j=0; j<(std::min)(outer, inner); ++j)
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{
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VERIFY(j==numext::real(m3.innerVector(j).nonZeros()));
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if(j>0)
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VERIFY(j==numext::real(m3.innerVector(j).lastCoeff()));
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}
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m3.makeCompressed();
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for(Index j=0; j<(std::min)(outer, inner); ++j)
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{
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VERIFY(j==numext::real(m3.innerVector(j).nonZeros()));
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if(j>0)
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VERIFY(j==numext::real(m3.innerVector(j).lastCoeff()));
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}
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VERIFY(m3.innerVector(j0).nonZeros() == m3.transpose().innerVector(j0).nonZeros());
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// m2.innerVector(j0) = 2*m2.innerVector(j1);
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// refMat2.col(j0) = 2*refMat2.col(j1);
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// VERIFY_IS_APPROX(m2, refMat2);
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}
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// test innerVectors()
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{
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DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
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SparseMatrixType m2(rows, cols);
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initSparse<Scalar>(density, refMat2, m2);
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if(internal::random<float>(0,1)>0.5) m2.makeCompressed();
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Index j0 = internal::random<Index>(0,outer-2);
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Index j1 = internal::random<Index>(0,outer-2);
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Index n0 = internal::random<Index>(1,outer-(std::max)(j0,j1));
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if(SparseMatrixType::IsRowMajor)
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VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(j0,0,n0,cols));
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else
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VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(0,j0,rows,n0));
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if(SparseMatrixType::IsRowMajor)
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VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0),
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refMat2.middleRows(j0,n0)+refMat2.middleRows(j1,n0));
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else
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VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0),
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refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0));
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VERIFY_IS_APPROX(m2, refMat2);
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VERIFY(m2.innerVectors(j0,n0).nonZeros() == m2.transpose().innerVectors(j0,n0).nonZeros());
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m2.innerVectors(j0,n0) = m2.innerVectors(j0,n0) + m2.innerVectors(j1,n0);
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if(SparseMatrixType::IsRowMajor)
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refMat2.middleRows(j0,n0) = (refMat2.middleRows(j0,n0) + refMat2.middleRows(j1,n0)).eval();
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else
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refMat2.middleCols(j0,n0) = (refMat2.middleCols(j0,n0) + refMat2.middleCols(j1,n0)).eval();
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VERIFY_IS_APPROX(m2, refMat2);
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}
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// test basic computations
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{
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DenseMatrix refM1 = DenseMatrix::Zero(rows, cols);
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@ -360,54 +212,6 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
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VERIFY(m2.isApprox(m3));
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}
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// test generic blocks
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{
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DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
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SparseMatrixType m2(rows, cols);
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initSparse<Scalar>(density, refMat2, m2);
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Index j0 = internal::random<Index>(0,outer-2);
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Index j1 = internal::random<Index>(0,outer-2);
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Index n0 = internal::random<Index>(1,outer-(std::max)(j0,j1));
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if(SparseMatrixType::IsRowMajor)
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VERIFY_IS_APPROX(m2.block(j0,0,n0,cols), refMat2.block(j0,0,n0,cols));
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else
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VERIFY_IS_APPROX(m2.block(0,j0,rows,n0), refMat2.block(0,j0,rows,n0));
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if(SparseMatrixType::IsRowMajor)
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VERIFY_IS_APPROX(m2.block(j0,0,n0,cols)+m2.block(j1,0,n0,cols),
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refMat2.block(j0,0,n0,cols)+refMat2.block(j1,0,n0,cols));
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else
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VERIFY_IS_APPROX(m2.block(0,j0,rows,n0)+m2.block(0,j1,rows,n0),
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refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0));
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Index i = internal::random<Index>(0,m2.outerSize()-1);
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if(SparseMatrixType::IsRowMajor) {
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m2.innerVector(i) = m2.innerVector(i) * s1;
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refMat2.row(i) = refMat2.row(i) * s1;
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VERIFY_IS_APPROX(m2,refMat2);
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} else {
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m2.innerVector(i) = m2.innerVector(i) * s1;
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refMat2.col(i) = refMat2.col(i) * s1;
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VERIFY_IS_APPROX(m2,refMat2);
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}
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Index r0 = internal::random<Index>(0,rows-2);
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Index c0 = internal::random<Index>(0,cols-2);
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Index r1 = internal::random<Index>(1,rows-r0);
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Index c1 = internal::random<Index>(1,cols-c0);
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VERIFY_IS_APPROX(DenseVector(m2.col(c0)), refMat2.col(c0));
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VERIFY_IS_APPROX(m2.col(c0), refMat2.col(c0));
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VERIFY_IS_APPROX(RowDenseVector(m2.row(r0)), refMat2.row(r0));
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VERIFY_IS_APPROX(m2.row(r0), refMat2.row(r0));
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VERIFY_IS_APPROX(m2.block(r0,c0,r1,c1), refMat2.block(r0,c0,r1,c1));
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VERIFY_IS_APPROX((2*m2).block(r0,c0,r1,c1), (2*refMat2).block(r0,c0,r1,c1));
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}
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// test prune
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{
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SparseMatrixType m2(rows, cols);
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@ -646,8 +450,8 @@ void test_sparse_basic()
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CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, ColMajor>(r, c)) ));
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CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, RowMajor>(r, c)) ));
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CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(r, c)) ));
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CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,ColMajor,long int>(r, c)) ));
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CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,long int>(r, c)) ));
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CALL_SUBTEST_5(( sparse_basic(SparseMatrix<double,ColMajor,long int>(r, c)) ));
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CALL_SUBTEST_5(( sparse_basic(SparseMatrix<double,RowMajor,long int>(r, c)) ));
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r = Eigen::internal::random<int>(1,100);
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c = Eigen::internal::random<int>(1,100);
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@ -655,8 +459,8 @@ void test_sparse_basic()
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r = c; // check square matrices in 25% of tries
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}
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CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,ColMajor,short int>(short(r), short(c))) ));
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CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,short int>(short(r), short(c))) ));
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CALL_SUBTEST_6(( sparse_basic(SparseMatrix<double,ColMajor,short int>(short(r), short(c))) ));
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CALL_SUBTEST_6(( sparse_basic(SparseMatrix<double,RowMajor,short int>(short(r), short(c))) ));
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}
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// Regression test for bug 900: (manually insert higher values here, if you have enough RAM):
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254
test/sparse_block.cpp
Normal file
254
test/sparse_block.cpp
Normal file
@ -0,0 +1,254 @@
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#include "sparse.h"
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template<typename SparseMatrixType> void sparse_block(const SparseMatrixType& ref)
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{
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const Index rows = ref.rows();
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const Index cols = ref.cols();
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const Index inner = ref.innerSize();
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const Index outer = ref.outerSize();
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typedef typename SparseMatrixType::Scalar Scalar;
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double density = (std::max)(8./(rows*cols), 0.01);
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typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
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typedef Matrix<Scalar,Dynamic,1> DenseVector;
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typedef Matrix<Scalar,1,Dynamic> RowDenseVector;
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Scalar s1 = internal::random<Scalar>();
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{
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SparseMatrixType m(rows, cols);
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DenseMatrix refMat = DenseMatrix::Zero(rows, cols);
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initSparse<Scalar>(density, refMat, m);
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VERIFY_IS_APPROX(m, refMat);
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// test InnerIterators and Block expressions
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for (int t=0; t<10; ++t)
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{
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Index j = internal::random<Index>(0,cols-2);
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Index i = internal::random<Index>(0,rows-2);
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Index w = internal::random<Index>(1,cols-j);
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Index h = internal::random<Index>(1,rows-i);
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VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w));
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for(Index c=0; c<w; c++)
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{
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VERIFY_IS_APPROX(m.block(i,j,h,w).col(c), refMat.block(i,j,h,w).col(c));
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for(Index r=0; r<h; r++)
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{
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VERIFY_IS_APPROX(m.block(i,j,h,w).col(c).coeff(r), refMat.block(i,j,h,w).col(c).coeff(r));
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VERIFY_IS_APPROX(m.block(i,j,h,w).coeff(r,c), refMat.block(i,j,h,w).coeff(r,c));
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}
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}
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for(Index r=0; r<h; r++)
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{
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VERIFY_IS_APPROX(m.block(i,j,h,w).row(r), refMat.block(i,j,h,w).row(r));
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for(Index c=0; c<w; c++)
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{
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VERIFY_IS_APPROX(m.block(i,j,h,w).row(r).coeff(c), refMat.block(i,j,h,w).row(r).coeff(c));
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VERIFY_IS_APPROX(m.block(i,j,h,w).coeff(r,c), refMat.block(i,j,h,w).coeff(r,c));
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}
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}
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VERIFY_IS_APPROX(m.middleCols(j,w), refMat.middleCols(j,w));
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VERIFY_IS_APPROX(m.middleRows(i,h), refMat.middleRows(i,h));
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for(Index r=0; r<h; r++)
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{
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VERIFY_IS_APPROX(m.middleCols(j,w).row(r), refMat.middleCols(j,w).row(r));
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VERIFY_IS_APPROX(m.middleRows(i,h).row(r), refMat.middleRows(i,h).row(r));
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for(Index c=0; c<w; c++)
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{
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VERIFY_IS_APPROX(m.col(c).coeff(r), refMat.col(c).coeff(r));
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VERIFY_IS_APPROX(m.row(r).coeff(c), refMat.row(r).coeff(c));
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VERIFY_IS_APPROX(m.middleCols(j,w).coeff(r,c), refMat.middleCols(j,w).coeff(r,c));
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VERIFY_IS_APPROX(m.middleRows(i,h).coeff(r,c), refMat.middleRows(i,h).coeff(r,c));
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if(m.middleCols(j,w).coeff(r,c) != Scalar(0))
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{
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VERIFY_IS_APPROX(m.middleCols(j,w).coeffRef(r,c), refMat.middleCols(j,w).coeff(r,c));
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}
|
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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 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 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));
|
||||
}
|
||||
}
|
||||
|
||||
void test_sparse_block()
|
||||
{
|
||||
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_block(SparseMatrix<double>(1, 1)) ));
|
||||
CALL_SUBTEST_1(( sparse_block(SparseMatrix<double>(8, 8)) ));
|
||||
CALL_SUBTEST_1(( sparse_block(SparseMatrix<double>(r, c)) ));
|
||||
CALL_SUBTEST_2(( sparse_block(SparseMatrix<std::complex<double>, ColMajor>(r, c)) ));
|
||||
CALL_SUBTEST_2(( sparse_block(SparseMatrix<std::complex<double>, RowMajor>(r, c)) ));
|
||||
|
||||
CALL_SUBTEST_3(( sparse_block(SparseMatrix<double,ColMajor,long int>(r, c)) ));
|
||||
CALL_SUBTEST_3(( sparse_block(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_4(( sparse_block(SparseMatrix<double,ColMajor,short int>(short(r), short(c))) ));
|
||||
CALL_SUBTEST_4(( sparse_block(SparseMatrix<double,RowMajor,short int>(short(r), short(c))) ));
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user