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 // This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2015 Gael Guennebaud // // 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 Eigen::internal::enable_if<(T::Flags&RowMajorBit)==RowMajorBit, typename T::RowXpr>::type innervec(T& A, Index i) { return A.row(i); } template typename Eigen::internal::enable_if<(T::Flags&RowMajorBit)==0, typename T::ColXpr>::type innervec(T& A, Index i) { return A.col(i); } template 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 DenseMatrix; typedef Matrix DenseVector; typedef Matrix RowDenseVector; typedef SparseVector SparseVectorType; Scalar s1 = internal::random(); { SparseMatrixType m(rows, cols); DenseMatrix refMat = DenseMatrix::Zero(rows, cols); initSparse(density, refMat, m); VERIFY_IS_APPROX(m, refMat); // test InnerIterators and Block expressions for (int t=0; t<10; ++t) { Index j = internal::random(0,cols-2); Index i = internal::random(0,rows-2); Index w = internal::random(1,cols-j); Index h = internal::random(1,rows-i); VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w)); for(Index c=0; c(density, refMat2, m2); Index j0 = internal::random(0,outer-1); Index j1 = internal::random(0,outer-1); Index r0 = internal::random(0,rows-1); Index c0 = internal::random(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(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(density, refMat2, m2); if(internal::random(0,1)>0.5f) m2.makeCompressed(); Index j0 = internal::random(0,outer-2); Index j1 = internal::random(0,outer-2); Index n0 = internal::random(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(density, refMat2, m2); Index j0 = internal::random(0,outer-2); Index j1 = internal::random(0,outer-2); Index n0 = internal::random(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(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(0,rows-2); Index c0 = internal::random(0,cols-2); Index r1 = internal::random(1,rows-r0); Index c1 = internal::random(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(1,10); for(Index k=0; k(0,outer-1); Index o2 = internal::random(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()) 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(1,200), c = Eigen::internal::random(1,200); if(Eigen::internal::random(0,4) == 0) { r = c; // check square matrices in 25% of tries } EIGEN_UNUSED_VARIABLE(r+c); CALL_SUBTEST_1(( sparse_block(SparseMatrix(1, 1)) )); CALL_SUBTEST_1(( sparse_block(SparseMatrix(8, 8)) )); CALL_SUBTEST_1(( sparse_block(SparseMatrix(r, c)) )); CALL_SUBTEST_2(( sparse_block(SparseMatrix, ColMajor>(r, c)) )); CALL_SUBTEST_2(( sparse_block(SparseMatrix, RowMajor>(r, c)) )); CALL_SUBTEST_3(( sparse_block(SparseMatrix(r, c)) )); CALL_SUBTEST_3(( sparse_block(SparseMatrix(r, c)) )); r = Eigen::internal::random(1,100); c = Eigen::internal::random(1,100); if(Eigen::internal::random(0,4) == 0) { r = c; // check square matrices in 25% of tries } CALL_SUBTEST_4(( sparse_block(SparseMatrix(short(r), short(c))) )); CALL_SUBTEST_4(( sparse_block(SparseMatrix(short(r), short(c))) )); } }