324 lines
12 KiB
C++
324 lines
12 KiB
C++
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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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#include "AnnoyingScalar.h"
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template<typename T>
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typename Eigen::internal::enable_if<(T::Flags&RowMajorBit)==RowMajorBit, typename T::RowXpr>::type
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innervec(T& A, Index i)
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{
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return A.row(i);
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}
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template<typename T>
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typename Eigen::internal::enable_if<(T::Flags&RowMajorBit)==0, typename T::ColXpr>::type
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innervec(T& A, Index i)
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{
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return A.col(i);
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}
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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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typedef typename SparseMatrixType::RealScalar RealScalar;
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typedef typename SparseMatrixType::StorageIndex StorageIndex;
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double density = (std::max)(8./(rows*cols), 0.01);
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typedef Matrix<Scalar,Dynamic,Dynamic,SparseMatrixType::IsRowMajor?RowMajor:ColMajor> 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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typedef SparseVector<Scalar> SparseVectorType;
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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))
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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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}
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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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Index r0 = internal::random<Index>(0,rows-1);
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Index c0 = internal::random<Index>(0,cols-1);
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VERIFY_IS_APPROX(m2.innerVector(j0), innervec(refMat2,j0));
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VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), innervec(refMat2,j0)+innervec(refMat2,j1));
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m2.innerVector(j0) *= Scalar(2);
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innervec(refMat2,j0) *= Scalar(2);
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VERIFY_IS_APPROX(m2, refMat2);
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m2.row(r0) *= Scalar(3);
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refMat2.row(r0) *= Scalar(3);
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VERIFY_IS_APPROX(m2, refMat2);
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m2.col(c0) *= Scalar(4);
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refMat2.col(c0) *= Scalar(4);
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VERIFY_IS_APPROX(m2, refMat2);
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m2.row(r0) /= Scalar(3);
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refMat2.row(r0) /= Scalar(3);
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VERIFY_IS_APPROX(m2, refMat2);
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m2.col(c0) /= Scalar(4);
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refMat2.col(c0) /= Scalar(4);
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VERIFY_IS_APPROX(m2, refMat2);
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SparseVectorType v1;
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VERIFY_IS_APPROX(v1 = m2.col(c0) * 4, refMat2.col(c0)*4);
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VERIFY_IS_APPROX(v1 = m2.row(r0) * 4, refMat2.row(r0).transpose()*4);
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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) = internal::convert_index<StorageIndex>(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(RealScalar(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(RealScalar(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)>0.5f) 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 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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if(m2.nonZeros()>0)
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{
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VERIFY_IS_APPROX(m2, refMat2);
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SparseMatrixType m3(rows, cols);
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DenseMatrix refMat3(rows, cols); refMat3.setZero();
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Index n = internal::random<Index>(1,10);
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for(Index k=0; k<n; ++k)
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{
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Index o1 = internal::random<Index>(0,outer-1);
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Index o2 = internal::random<Index>(0,outer-1);
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if(SparseMatrixType::IsRowMajor)
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{
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m3.innerVector(o1) = m2.row(o2);
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refMat3.row(o1) = refMat2.row(o2);
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}
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else
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{
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m3.innerVector(o1) = m2.col(o2);
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refMat3.col(o1) = refMat2.col(o2);
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}
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if(internal::random<bool>())
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m3.makeCompressed();
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}
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if(m3.nonZeros()>0)
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VERIFY_IS_APPROX(m3, refMat3);
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}
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}
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}
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EIGEN_DECLARE_TEST(sparse_block)
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{
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||
|
|
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))) ));
|
||
|
|
#ifndef EIGEN_TEST_ANNOYING_SCALAR_DONT_THROW
|
||
|
|
AnnoyingScalar::dont_throw = true;
|
||
|
|
#endif
|
||
|
|
CALL_SUBTEST_5(( sparse_block(SparseMatrix<AnnoyingScalar>(r,c)) ));
|
||
|
|
}
|
||
|
|
}
|