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Diffstat (limited to 'test/sparse_block.cpp')
| -rw-r--r-- | test/sparse_block.cpp | 317 |
1 files changed, 317 insertions, 0 deletions
diff --git a/test/sparse_block.cpp b/test/sparse_block.cpp new file mode 100644 index 000000000..2a0b3b617 --- /dev/null +++ b/test/sparse_block.cpp @@ -0,0 +1,317 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@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/. + +#include "sparse.h" + +template<typename T> +typename Eigen::internal::enable_if<(T::Flags&RowMajorBit)==RowMajorBit, typename T::RowXpr>::type +innervec(T& A, Index i) +{ + return A.row(i); +} + +template<typename T> +typename Eigen::internal::enable_if<(T::Flags&RowMajorBit)==0, typename T::ColXpr>::type +innervec(T& A, Index i) +{ + return A.col(i); +} + +template<typename SparseMatrixType> void sparse_block(const SparseMatrixType& ref) +{ + 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::StorageIndex StorageIndex; + + double density = (std::max)(8./(rows*cols), 0.01); + typedef Matrix<Scalar,Dynamic,Dynamic,SparseMatrixType::IsRowMajor?RowMajor:ColMajor> DenseMatrix; + typedef Matrix<Scalar,Dynamic,1> DenseVector; + typedef Matrix<Scalar,1,Dynamic> RowDenseVector; + typedef SparseVector<Scalar> SparseVectorType; + + Scalar s1 = internal::random<Scalar>(); + { + SparseMatrixType m(rows, cols); + DenseMatrix refMat = DenseMatrix::Zero(rows, cols); + initSparse<Scalar>(density, refMat, m); + + 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 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); + Index r0 = internal::random<Index>(0,rows-1); + Index c0 = internal::random<Index>(0,cols-1); + + VERIFY_IS_APPROX(m2.innerVector(j0), innervec(refMat2,j0)); + VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), innervec(refMat2,j0)+innervec(refMat2,j1)); + + m2.innerVector(j0) *= Scalar(2); + innervec(refMat2,j0) *= Scalar(2); + VERIFY_IS_APPROX(m2, refMat2); + + m2.row(r0) *= Scalar(3); + refMat2.row(r0) *= Scalar(3); + VERIFY_IS_APPROX(m2, refMat2); + + m2.col(c0) *= Scalar(4); + refMat2.col(c0) *= Scalar(4); + VERIFY_IS_APPROX(m2, refMat2); + + m2.row(r0) /= Scalar(3); + refMat2.row(r0) /= Scalar(3); + VERIFY_IS_APPROX(m2, refMat2); + + m2.col(c0) /= Scalar(4); + refMat2.col(c0) /= Scalar(4); + VERIFY_IS_APPROX(m2, refMat2); + + SparseVectorType v1; + VERIFY_IS_APPROX(v1 = m2.col(c0) * 4, refMat2.col(c0)*4); + VERIFY_IS_APPROX(v1 = m2.row(r0) * 4, refMat2.row(r0).transpose()*4); + + 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) = internal::convert_index<StorageIndex>(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)>0.5f) 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)); + + if(m2.nonZeros()>0) + { + VERIFY_IS_APPROX(m2, refMat2); + SparseMatrixType m3(rows, cols); + DenseMatrix refMat3(rows, cols); refMat3.setZero(); + Index n = internal::random<Index>(1,10); + for(Index k=0; k<n; ++k) + { + Index o1 = internal::random<Index>(0,outer-1); + Index o2 = internal::random<Index>(0,outer-1); + if(SparseMatrixType::IsRowMajor) + { + m3.innerVector(o1) = m2.row(o2); + refMat3.row(o1) = refMat2.row(o2); + } + else + { + m3.innerVector(o1) = m2.col(o2); + refMat3.col(o1) = refMat2.col(o2); + } + if(internal::random<bool>()) + m3.makeCompressed(); + } + if(m3.nonZeros()>0) + VERIFY_IS_APPROX(m3, refMat3); + } + } +} + +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))) )); + } +} |