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595 lines
21 KiB
C++
595 lines
21 KiB
C++
// 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) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
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// Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@gmail.com>
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// Copyright (C) 2013 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@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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static long g_realloc_count = 0;
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#define EIGEN_SPARSE_COMPRESSED_STORAGE_REALLOCATE_PLUGIN g_realloc_count++;
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#include "sparse.h"
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template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& ref)
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{
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typedef typename SparseMatrixType::StorageIndex StorageIndex;
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typedef Matrix<StorageIndex,2,1> Vector2;
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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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enum { Flags = SparseMatrixType::Flags };
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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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Scalar eps = 1e-6;
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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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DenseVector vec1 = DenseVector::Random(rows);
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std::vector<Vector2> zeroCoords;
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std::vector<Vector2> nonzeroCoords;
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initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords);
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// test coeff and coeffRef
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for (std::size_t i=0; i<zeroCoords.size(); ++i)
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{
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VERIFY_IS_MUCH_SMALLER_THAN( m.coeff(zeroCoords[i].x(),zeroCoords[i].y()), eps );
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if(internal::is_same<SparseMatrixType,SparseMatrix<Scalar,Flags> >::value)
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VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[i].x(),zeroCoords[i].y()) = 5 );
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}
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VERIFY_IS_APPROX(m, refMat);
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if(!nonzeroCoords.empty()) {
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m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
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refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
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}
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VERIFY_IS_APPROX(m, refMat);
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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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}
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// test insert (inner random)
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{
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DenseMatrix m1(rows,cols);
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m1.setZero();
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SparseMatrixType m2(rows,cols);
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bool call_reserve = internal::random<int>()%2;
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Index nnz = internal::random<int>(1,int(rows)/2);
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if(call_reserve)
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{
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if(internal::random<int>()%2)
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m2.reserve(VectorXi::Constant(m2.outerSize(), int(nnz)));
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else
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m2.reserve(m2.outerSize() * nnz);
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}
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g_realloc_count = 0;
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for (Index j=0; j<cols; ++j)
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{
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for (Index k=0; k<nnz; ++k)
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{
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Index i = internal::random<Index>(0,rows-1);
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if (m1.coeff(i,j)==Scalar(0))
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m2.insert(i,j) = m1(i,j) = internal::random<Scalar>();
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}
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}
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if(call_reserve && !SparseMatrixType::IsRowMajor)
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{
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VERIFY(g_realloc_count==0);
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}
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m2.finalize();
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VERIFY_IS_APPROX(m2,m1);
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}
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// test insert (fully random)
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{
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DenseMatrix m1(rows,cols);
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m1.setZero();
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SparseMatrixType m2(rows,cols);
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if(internal::random<int>()%2)
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m2.reserve(VectorXi::Constant(m2.outerSize(), 2));
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for (int k=0; k<rows*cols; ++k)
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{
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Index i = internal::random<Index>(0,rows-1);
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Index j = internal::random<Index>(0,cols-1);
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if ((m1.coeff(i,j)==Scalar(0)) && (internal::random<int>()%2))
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m2.insert(i,j) = m1(i,j) = internal::random<Scalar>();
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else
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{
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Scalar v = internal::random<Scalar>();
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m2.coeffRef(i,j) += v;
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m1(i,j) += v;
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}
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}
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VERIFY_IS_APPROX(m2,m1);
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}
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// test insert (un-compressed)
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for(int mode=0;mode<4;++mode)
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{
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DenseMatrix m1(rows,cols);
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m1.setZero();
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SparseMatrixType m2(rows,cols);
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VectorXi r(VectorXi::Constant(m2.outerSize(), ((mode%2)==0) ? int(m2.innerSize()) : std::max<int>(1,int(m2.innerSize())/8)));
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m2.reserve(r);
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for (Index k=0; k<rows*cols; ++k)
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{
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Index i = internal::random<Index>(0,rows-1);
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Index j = internal::random<Index>(0,cols-1);
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if (m1.coeff(i,j)==Scalar(0))
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m2.insert(i,j) = m1(i,j) = internal::random<Scalar>();
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if(mode==3)
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m2.reserve(r);
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}
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if(internal::random<int>()%2)
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m2.makeCompressed();
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VERIFY_IS_APPROX(m2,m1);
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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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DenseMatrix refM2 = DenseMatrix::Zero(rows, cols);
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DenseMatrix refM3 = DenseMatrix::Zero(rows, cols);
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DenseMatrix refM4 = DenseMatrix::Zero(rows, cols);
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SparseMatrixType m1(rows, cols);
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SparseMatrixType m2(rows, cols);
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SparseMatrixType m3(rows, cols);
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SparseMatrixType m4(rows, cols);
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initSparse<Scalar>(density, refM1, m1);
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initSparse<Scalar>(density, refM2, m2);
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initSparse<Scalar>(density, refM3, m3);
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initSparse<Scalar>(density, refM4, m4);
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if(internal::random<bool>())
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m1.makeCompressed();
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VERIFY_IS_APPROX(m1*s1, refM1*s1);
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VERIFY_IS_APPROX(m1+m2, refM1+refM2);
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VERIFY_IS_APPROX(m1+m2+m3, refM1+refM2+refM3);
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VERIFY_IS_APPROX(m3.cwiseProduct(m1+m2), refM3.cwiseProduct(refM1+refM2));
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VERIFY_IS_APPROX(m1*s1-m2, refM1*s1-refM2);
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if(SparseMatrixType::IsRowMajor)
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VERIFY_IS_APPROX(m1.innerVector(0).dot(refM2.row(0)), refM1.row(0).dot(refM2.row(0)));
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else
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VERIFY_IS_APPROX(m1.innerVector(0).dot(refM2.col(0)), refM1.col(0).dot(refM2.col(0)));
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DenseVector rv = DenseVector::Random(m1.cols());
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DenseVector cv = DenseVector::Random(m1.rows());
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Index r = internal::random<Index>(0,m1.rows()-2);
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Index c = internal::random<Index>(0,m1.cols()-1);
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VERIFY_IS_APPROX(( m1.template block<1,Dynamic>(r,0,1,m1.cols()).dot(rv)) , refM1.row(r).dot(rv));
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VERIFY_IS_APPROX(m1.row(r).dot(rv), refM1.row(r).dot(rv));
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VERIFY_IS_APPROX(m1.col(c).dot(cv), refM1.col(c).dot(cv));
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VERIFY_IS_APPROX(m1.conjugate(), refM1.conjugate());
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VERIFY_IS_APPROX(m1.real(), refM1.real());
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refM4.setRandom();
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// sparse cwise* dense
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VERIFY_IS_APPROX(m3.cwiseProduct(refM4), refM3.cwiseProduct(refM4));
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// dense cwise* sparse
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VERIFY_IS_APPROX(refM4.cwiseProduct(m3), refM4.cwiseProduct(refM3));
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// VERIFY_IS_APPROX(m3.cwise()/refM4, refM3.cwise()/refM4);
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VERIFY_IS_APPROX(refM4 + m3, refM4 + refM3);
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VERIFY_IS_APPROX(m3 + refM4, refM3 + refM4);
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VERIFY_IS_APPROX(refM4 - m3, refM4 - refM3);
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VERIFY_IS_APPROX(m3 - refM4, refM3 - refM4);
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VERIFY_IS_APPROX(m1.sum(), refM1.sum());
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VERIFY_IS_APPROX(m1*=s1, refM1*=s1);
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VERIFY_IS_APPROX(m1/=s1, refM1/=s1);
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VERIFY_IS_APPROX(m1+=m2, refM1+=refM2);
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VERIFY_IS_APPROX(m1-=m2, refM1-=refM2);
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// test aliasing
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VERIFY_IS_APPROX((m1 = -m1), (refM1 = -refM1));
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VERIFY_IS_APPROX((m1 = m1.transpose()), (refM1 = refM1.transpose().eval()));
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VERIFY_IS_APPROX((m1 = -m1.transpose()), (refM1 = -refM1.transpose().eval()));
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VERIFY_IS_APPROX((m1 += -m1), (refM1 += -refM1));
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if(m1.isCompressed())
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{
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VERIFY_IS_APPROX(m1.coeffs().sum(), m1.sum());
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m1.coeffs() += s1;
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for(Index j = 0; j<m1.outerSize(); ++j)
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for(typename SparseMatrixType::InnerIterator it(m1,j); it; ++it)
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refM1(it.row(), it.col()) += s1;
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VERIFY_IS_APPROX(m1, refM1);
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}
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}
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// test transpose
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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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VERIFY_IS_APPROX(m2.transpose().eval(), refMat2.transpose().eval());
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VERIFY_IS_APPROX(m2.transpose(), refMat2.transpose());
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VERIFY_IS_APPROX(SparseMatrixType(m2.adjoint()), refMat2.adjoint());
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// check isApprox handles opposite storage order
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typename Transpose<SparseMatrixType>::PlainObject m3(m2);
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VERIFY(m2.isApprox(m3));
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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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DenseMatrix refM2(rows, cols);
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refM2.setZero();
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int countFalseNonZero = 0;
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int countTrueNonZero = 0;
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m2.reserve(VectorXi::Constant(m2.outerSize(), int(m2.innerSize())));
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for (Index j=0; j<m2.cols(); ++j)
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{
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for (Index i=0; i<m2.rows(); ++i)
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{
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float x = internal::random<float>(0,1);
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if (x<0.1f)
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{
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// do nothing
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}
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else if (x<0.5f)
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{
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countFalseNonZero++;
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m2.insert(i,j) = Scalar(0);
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}
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else
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{
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countTrueNonZero++;
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m2.insert(i,j) = Scalar(1);
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refM2(i,j) = Scalar(1);
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}
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}
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}
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if(internal::random<bool>())
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m2.makeCompressed();
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VERIFY(countFalseNonZero+countTrueNonZero == m2.nonZeros());
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if(countTrueNonZero>0)
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VERIFY_IS_APPROX(m2, refM2);
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m2.prune(Scalar(1));
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VERIFY(countTrueNonZero==m2.nonZeros());
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VERIFY_IS_APPROX(m2, refM2);
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}
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// test setFromTriplets
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{
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typedef Triplet<Scalar,StorageIndex> TripletType;
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std::vector<TripletType> triplets;
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Index ntriplets = rows*cols;
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triplets.reserve(ntriplets);
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DenseMatrix refMat_sum = DenseMatrix::Zero(rows,cols);
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DenseMatrix refMat_prod = DenseMatrix::Zero(rows,cols);
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DenseMatrix refMat_last = DenseMatrix::Zero(rows,cols);
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for(Index i=0;i<ntriplets;++i)
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{
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StorageIndex r = internal::random<StorageIndex>(0,StorageIndex(rows-1));
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StorageIndex c = internal::random<StorageIndex>(0,StorageIndex(cols-1));
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Scalar v = internal::random<Scalar>();
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triplets.push_back(TripletType(r,c,v));
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refMat_sum(r,c) += v;
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if(std::abs(refMat_prod(r,c))==0)
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refMat_prod(r,c) = v;
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else
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refMat_prod(r,c) *= v;
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refMat_last(r,c) = v;
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}
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SparseMatrixType m(rows,cols);
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m.setFromTriplets(triplets.begin(), triplets.end());
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VERIFY_IS_APPROX(m, refMat_sum);
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m.setFromTriplets(triplets.begin(), triplets.end(), std::multiplies<Scalar>());
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VERIFY_IS_APPROX(m, refMat_prod);
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#if (defined(__cplusplus) && __cplusplus >= 201103L)
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m.setFromTriplets(triplets.begin(), triplets.end(), [] (Scalar,Scalar b) { return b; });
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VERIFY_IS_APPROX(m, refMat_last);
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#endif
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}
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// test Map
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{
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DenseMatrix refMat2(rows, cols), refMat3(rows, cols);
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SparseMatrixType m2(rows, cols), m3(rows, cols);
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initSparse<Scalar>(density, refMat2, m2);
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initSparse<Scalar>(density, refMat3, m3);
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{
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Map<SparseMatrixType> mapMat2(m2.rows(), m2.cols(), m2.nonZeros(), m2.outerIndexPtr(), m2.innerIndexPtr(), m2.valuePtr(), m2.innerNonZeroPtr());
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Map<SparseMatrixType> mapMat3(m3.rows(), m3.cols(), m3.nonZeros(), m3.outerIndexPtr(), m3.innerIndexPtr(), m3.valuePtr(), m3.innerNonZeroPtr());
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VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3);
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VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3);
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}
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{
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MappedSparseMatrix<Scalar,SparseMatrixType::Options,StorageIndex> mapMat2(m2.rows(), m2.cols(), m2.nonZeros(), m2.outerIndexPtr(), m2.innerIndexPtr(), m2.valuePtr(), m2.innerNonZeroPtr());
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MappedSparseMatrix<Scalar,SparseMatrixType::Options,StorageIndex> mapMat3(m3.rows(), m3.cols(), m3.nonZeros(), m3.outerIndexPtr(), m3.innerIndexPtr(), m3.valuePtr(), m3.innerNonZeroPtr());
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VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3);
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VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3);
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}
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Index i = internal::random<Index>(0,rows-1);
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Index j = internal::random<Index>(0,cols-1);
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m2.coeffRef(i,j) = 123;
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if(internal::random<bool>())
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m2.makeCompressed();
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Map<SparseMatrixType> mapMat2(rows, cols, m2.nonZeros(), m2.outerIndexPtr(), m2.innerIndexPtr(), m2.valuePtr(), m2.innerNonZeroPtr());
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VERIFY_IS_EQUAL(m2.coeff(i,j),Scalar(123));
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VERIFY_IS_EQUAL(mapMat2.coeff(i,j),Scalar(123));
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mapMat2.coeffRef(i,j) = -123;
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VERIFY_IS_EQUAL(m2.coeff(i,j),Scalar(-123));
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}
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// test triangularView
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{
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DenseMatrix refMat2(rows, cols), refMat3(rows, cols);
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SparseMatrixType m2(rows, cols), m3(rows, cols);
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initSparse<Scalar>(density, refMat2, m2);
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refMat3 = refMat2.template triangularView<Lower>();
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m3 = m2.template triangularView<Lower>();
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VERIFY_IS_APPROX(m3, refMat3);
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refMat3 = refMat2.template triangularView<Upper>();
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m3 = m2.template triangularView<Upper>();
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VERIFY_IS_APPROX(m3, refMat3);
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{
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refMat3 = refMat2.template triangularView<UnitUpper>();
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m3 = m2.template triangularView<UnitUpper>();
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VERIFY_IS_APPROX(m3, refMat3);
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refMat3 = refMat2.template triangularView<UnitLower>();
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m3 = m2.template triangularView<UnitLower>();
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VERIFY_IS_APPROX(m3, refMat3);
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}
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refMat3 = refMat2.template triangularView<StrictlyUpper>();
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m3 = m2.template triangularView<StrictlyUpper>();
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VERIFY_IS_APPROX(m3, refMat3);
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refMat3 = refMat2.template triangularView<StrictlyLower>();
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m3 = m2.template triangularView<StrictlyLower>();
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VERIFY_IS_APPROX(m3, refMat3);
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// check sparse-traingular to dense
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refMat3 = m2.template triangularView<StrictlyUpper>();
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VERIFY_IS_APPROX(refMat3, DenseMatrix(refMat2.template triangularView<StrictlyUpper>()));
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}
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// test selfadjointView
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if(!SparseMatrixType::IsRowMajor)
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{
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DenseMatrix refMat2(rows, rows), refMat3(rows, rows);
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SparseMatrixType m2(rows, rows), m3(rows, rows);
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initSparse<Scalar>(density, refMat2, m2);
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refMat3 = refMat2.template selfadjointView<Lower>();
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m3 = m2.template selfadjointView<Lower>();
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VERIFY_IS_APPROX(m3, refMat3);
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// selfadjointView only works for square matrices:
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SparseMatrixType m4(rows, rows+1);
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VERIFY_RAISES_ASSERT(m4.template selfadjointView<Lower>());
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VERIFY_RAISES_ASSERT(m4.template selfadjointView<Upper>());
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}
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// test sparseView
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{
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DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
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SparseMatrixType m2(rows, rows);
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initSparse<Scalar>(density, refMat2, m2);
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VERIFY_IS_APPROX(m2.eval(), refMat2.sparseView().eval());
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// sparse view on expressions:
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VERIFY_IS_APPROX((s1*m2).eval(), (s1*refMat2).sparseView().eval());
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VERIFY_IS_APPROX((m2+m2).eval(), (refMat2+refMat2).sparseView().eval());
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VERIFY_IS_APPROX((m2*m2).eval(), (refMat2.lazyProduct(refMat2)).sparseView().eval());
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VERIFY_IS_APPROX((m2*m2).eval(), (refMat2*refMat2).sparseView().eval());
|
|
}
|
|
|
|
// test diagonal
|
|
{
|
|
DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
|
|
SparseMatrixType m2(rows, cols);
|
|
initSparse<Scalar>(density, refMat2, m2);
|
|
VERIFY_IS_APPROX(m2.diagonal(), refMat2.diagonal().eval());
|
|
VERIFY_IS_APPROX(const_cast<const SparseMatrixType&>(m2).diagonal(), refMat2.diagonal().eval());
|
|
|
|
initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag);
|
|
m2.diagonal() += refMat2.diagonal();
|
|
refMat2.diagonal() += refMat2.diagonal();
|
|
VERIFY_IS_APPROX(m2, refMat2);
|
|
}
|
|
|
|
// test diagonal to sparse
|
|
{
|
|
DenseVector d = DenseVector::Random(rows);
|
|
DenseMatrix refMat2 = d.asDiagonal();
|
|
SparseMatrixType m2(rows, rows);
|
|
m2 = d.asDiagonal();
|
|
VERIFY_IS_APPROX(m2, refMat2);
|
|
SparseMatrixType m3(d.asDiagonal());
|
|
VERIFY_IS_APPROX(m3, refMat2);
|
|
refMat2 += d.asDiagonal();
|
|
m2 += d.asDiagonal();
|
|
VERIFY_IS_APPROX(m2, refMat2);
|
|
}
|
|
|
|
// test conservative resize
|
|
{
|
|
std::vector< std::pair<StorageIndex,StorageIndex> > inc;
|
|
if(rows > 3 && cols > 2)
|
|
inc.push_back(std::pair<StorageIndex,StorageIndex>(-3,-2));
|
|
inc.push_back(std::pair<StorageIndex,StorageIndex>(0,0));
|
|
inc.push_back(std::pair<StorageIndex,StorageIndex>(3,2));
|
|
inc.push_back(std::pair<StorageIndex,StorageIndex>(3,0));
|
|
inc.push_back(std::pair<StorageIndex,StorageIndex>(0,3));
|
|
|
|
for(size_t i = 0; i< inc.size(); i++) {
|
|
StorageIndex incRows = inc[i].first;
|
|
StorageIndex incCols = inc[i].second;
|
|
SparseMatrixType m1(rows, cols);
|
|
DenseMatrix refMat1 = DenseMatrix::Zero(rows, cols);
|
|
initSparse<Scalar>(density, refMat1, m1);
|
|
|
|
m1.conservativeResize(rows+incRows, cols+incCols);
|
|
refMat1.conservativeResize(rows+incRows, cols+incCols);
|
|
if (incRows > 0) refMat1.bottomRows(incRows).setZero();
|
|
if (incCols > 0) refMat1.rightCols(incCols).setZero();
|
|
|
|
VERIFY_IS_APPROX(m1, refMat1);
|
|
|
|
// Insert new values
|
|
if (incRows > 0)
|
|
m1.insert(m1.rows()-1, 0) = refMat1(refMat1.rows()-1, 0) = 1;
|
|
if (incCols > 0)
|
|
m1.insert(0, m1.cols()-1) = refMat1(0, refMat1.cols()-1) = 1;
|
|
|
|
VERIFY_IS_APPROX(m1, refMat1);
|
|
|
|
|
|
}
|
|
}
|
|
|
|
// test Identity matrix
|
|
{
|
|
DenseMatrix refMat1 = DenseMatrix::Identity(rows, rows);
|
|
SparseMatrixType m1(rows, rows);
|
|
m1.setIdentity();
|
|
VERIFY_IS_APPROX(m1, refMat1);
|
|
for(int k=0; k<rows*rows/4; ++k)
|
|
{
|
|
Index i = internal::random<Index>(0,rows-1);
|
|
Index j = internal::random<Index>(0,rows-1);
|
|
Scalar v = internal::random<Scalar>();
|
|
m1.coeffRef(i,j) = v;
|
|
refMat1.coeffRef(i,j) = v;
|
|
VERIFY_IS_APPROX(m1, refMat1);
|
|
if(internal::random<Index>(0,10)<2)
|
|
m1.makeCompressed();
|
|
}
|
|
m1.setIdentity();
|
|
refMat1.setIdentity();
|
|
VERIFY_IS_APPROX(m1, refMat1);
|
|
}
|
|
|
|
// test array/vector of InnerIterator
|
|
{
|
|
typedef typename SparseMatrixType::InnerIterator IteratorType;
|
|
|
|
DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
|
|
SparseMatrixType m2(rows, cols);
|
|
initSparse<Scalar>(density, refMat2, m2);
|
|
IteratorType static_array[2];
|
|
static_array[0] = IteratorType(m2,0);
|
|
static_array[1] = IteratorType(m2,m2.outerSize()-1);
|
|
VERIFY( static_array[0] || m2.innerVector(static_array[0].outer()).nonZeros() == 0 );
|
|
VERIFY( static_array[1] || m2.innerVector(static_array[1].outer()).nonZeros() == 0 );
|
|
if(static_array[0] && static_array[1])
|
|
{
|
|
++(static_array[1]);
|
|
static_array[1] = IteratorType(m2,0);
|
|
VERIFY( static_array[1] );
|
|
VERIFY( static_array[1].index() == static_array[0].index() );
|
|
VERIFY( static_array[1].outer() == static_array[0].outer() );
|
|
VERIFY( static_array[1].value() == static_array[0].value() );
|
|
}
|
|
|
|
std::vector<IteratorType> iters(2);
|
|
iters[0] = IteratorType(m2,0);
|
|
iters[1] = IteratorType(m2,m2.outerSize()-1);
|
|
}
|
|
}
|
|
|
|
|
|
template<typename SparseMatrixType>
|
|
void big_sparse_triplet(Index rows, Index cols, double density) {
|
|
typedef typename SparseMatrixType::StorageIndex StorageIndex;
|
|
typedef typename SparseMatrixType::Scalar Scalar;
|
|
typedef Triplet<Scalar,Index> TripletType;
|
|
std::vector<TripletType> triplets;
|
|
double nelements = density * rows*cols;
|
|
VERIFY(nelements>=0 && nelements < NumTraits<StorageIndex>::highest());
|
|
Index ntriplets = Index(nelements);
|
|
triplets.reserve(ntriplets);
|
|
Scalar sum = Scalar(0);
|
|
for(Index i=0;i<ntriplets;++i)
|
|
{
|
|
Index r = internal::random<Index>(0,rows-1);
|
|
Index c = internal::random<Index>(0,cols-1);
|
|
Scalar v = internal::random<Scalar>();
|
|
triplets.push_back(TripletType(r,c,v));
|
|
sum += v;
|
|
}
|
|
SparseMatrixType m(rows,cols);
|
|
m.setFromTriplets(triplets.begin(), triplets.end());
|
|
VERIFY(m.nonZeros() <= ntriplets);
|
|
VERIFY_IS_APPROX(sum, m.sum());
|
|
}
|
|
|
|
|
|
void test_sparse_basic()
|
|
{
|
|
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_basic(SparseMatrix<double>(1, 1)) ));
|
|
CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(8, 8)) ));
|
|
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_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);
|
|
if(Eigen::internal::random<int>(0,4) == 0) {
|
|
r = c; // check square matrices in 25% of tries
|
|
}
|
|
|
|
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):
|
|
CALL_SUBTEST_3((big_sparse_triplet<SparseMatrix<float, RowMajor, int> >(10000, 10000, 0.125)));
|
|
CALL_SUBTEST_4((big_sparse_triplet<SparseMatrix<double, ColMajor, long int> >(10000, 10000, 0.125)));
|
|
|
|
// Regression test for bug 1105
|
|
#ifdef EIGEN_TEST_PART_7
|
|
{
|
|
int n = Eigen::internal::random<int>(200,600);
|
|
SparseMatrix<std::complex<double>,0, long> mat(n, n);
|
|
std::complex<double> val;
|
|
|
|
for(int i=0; i<n; ++i)
|
|
{
|
|
mat.coeffRef(i, i%(n/10)) = val;
|
|
VERIFY(mat.data().allocatedSize()<20*n);
|
|
}
|
|
}
|
|
#endif
|
|
}
|