Squashed 'third_party/eigen/' content from commit 61d72f6
Change-Id: Iccc90fa0b55ab44037f018046d2fcffd90d9d025
git-subtree-dir: third_party/eigen
git-subtree-split: 61d72f6383cfa842868c53e30e087b0258177257
diff --git a/test/sparse_basic.cpp b/test/sparse_basic.cpp
new file mode 100644
index 0000000..ce41d71
--- /dev/null
+++ b/test/sparse_basic.cpp
@@ -0,0 +1,550 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@gmail.com>
+// Copyright (C) 2013 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@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 SparseMatrixType> void sparse_basic(const SparseMatrixType& ref)
+{
+ typedef typename SparseMatrixType::Index Index;
+ typedef Matrix<Index,2,1> Vector2;
+
+ const Index rows = ref.rows();
+ const Index cols = ref.cols();
+ typedef typename SparseMatrixType::Scalar Scalar;
+ enum { Flags = SparseMatrixType::Flags };
+
+ double density = (std::max)(8./(rows*cols), 0.01);
+ typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
+ typedef Matrix<Scalar,Dynamic,1> DenseVector;
+ typedef Matrix<Scalar,1,Dynamic> RowDenseVector;
+ Scalar eps = 1e-6;
+
+ Scalar s1 = internal::random<Scalar>();
+ {
+ SparseMatrixType m(rows, cols);
+ DenseMatrix refMat = DenseMatrix::Zero(rows, cols);
+ DenseVector vec1 = DenseVector::Random(rows);
+
+ std::vector<Vector2> zeroCoords;
+ std::vector<Vector2> nonzeroCoords;
+ initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords);
+
+ if (zeroCoords.size()==0 || nonzeroCoords.size()==0)
+ return;
+
+ // test coeff and coeffRef
+ for (int i=0; i<(int)zeroCoords.size(); ++i)
+ {
+ VERIFY_IS_MUCH_SMALLER_THAN( m.coeff(zeroCoords[i].x(),zeroCoords[i].y()), eps );
+ if(internal::is_same<SparseMatrixType,SparseMatrix<Scalar,Flags> >::value)
+ VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[0].x(),zeroCoords[0].y()) = 5 );
+ }
+ VERIFY_IS_APPROX(m, refMat);
+
+ m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
+ refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
+
+ VERIFY_IS_APPROX(m, refMat);
+
+ // test InnerIterators and Block expressions
+ for (int t=0; t<10; ++t)
+ {
+ int j = internal::random<int>(0,cols-1);
+ int i = internal::random<int>(0,rows-1);
+ int w = internal::random<int>(1,cols-j-1);
+ int h = internal::random<int>(1,rows-i-1);
+
+ VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w));
+ for(int 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(int 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(int 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(int 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(int 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(int 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(int 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(int 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(int 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 assertion
+ VERIFY_RAISES_ASSERT( m.coeffRef(-1,1) = 0 );
+ VERIFY_RAISES_ASSERT( m.coeffRef(0,m.cols()) = 0 );
+ }
+
+ // test insert (inner random)
+ {
+ DenseMatrix m1(rows,cols);
+ m1.setZero();
+ SparseMatrixType m2(rows,cols);
+ if(internal::random<int>()%2)
+ m2.reserve(VectorXi::Constant(m2.outerSize(), 2));
+ for (Index j=0; j<cols; ++j)
+ {
+ for (Index k=0; k<rows/2; ++k)
+ {
+ Index i = internal::random<Index>(0,rows-1);
+ if (m1.coeff(i,j)==Scalar(0))
+ m2.insert(i,j) = m1(i,j) = internal::random<Scalar>();
+ }
+ }
+ m2.finalize();
+ VERIFY_IS_APPROX(m2,m1);
+ }
+
+ // test insert (fully random)
+ {
+ DenseMatrix m1(rows,cols);
+ m1.setZero();
+ SparseMatrixType m2(rows,cols);
+ if(internal::random<int>()%2)
+ m2.reserve(VectorXi::Constant(m2.outerSize(), 2));
+ for (int k=0; k<rows*cols; ++k)
+ {
+ Index i = internal::random<Index>(0,rows-1);
+ Index j = internal::random<Index>(0,cols-1);
+ if ((m1.coeff(i,j)==Scalar(0)) && (internal::random<int>()%2))
+ m2.insert(i,j) = m1(i,j) = internal::random<Scalar>();
+ else
+ {
+ Scalar v = internal::random<Scalar>();
+ m2.coeffRef(i,j) += v;
+ m1(i,j) += v;
+ }
+ }
+ VERIFY_IS_APPROX(m2,m1);
+ }
+
+ // test insert (un-compressed)
+ for(int mode=0;mode<4;++mode)
+ {
+ DenseMatrix m1(rows,cols);
+ m1.setZero();
+ SparseMatrixType m2(rows,cols);
+ VectorXi r(VectorXi::Constant(m2.outerSize(), ((mode%2)==0) ? m2.innerSize() : std::max<int>(1,m2.innerSize()/8)));
+ m2.reserve(r);
+ for (int k=0; k<rows*cols; ++k)
+ {
+ Index i = internal::random<Index>(0,rows-1);
+ Index j = internal::random<Index>(0,cols-1);
+ if (m1.coeff(i,j)==Scalar(0))
+ m2.insert(i,j) = m1(i,j) = internal::random<Scalar>();
+ if(mode==3)
+ m2.reserve(r);
+ }
+ if(internal::random<int>()%2)
+ m2.makeCompressed();
+ VERIFY_IS_APPROX(m2,m1);
+ }
+
+ // test innerVector()
+ {
+ DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
+ SparseMatrixType m2(rows, rows);
+ initSparse<Scalar>(density, refMat2, m2);
+ Index j0 = internal::random<Index>(0,rows-1);
+ Index j1 = internal::random<Index>(0,rows-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,rows);
+ m3.reserve(VectorXi::Constant(rows,rows/2));
+ for(Index j=0; j<rows; ++j)
+ for(Index k=0; k<j; ++k)
+ m3.insertByOuterInner(j,k) = k+1;
+ for(Index j=0; j<rows; ++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<rows; ++j)
+ {
+ VERIFY(j==numext::real(m3.innerVector(j).nonZeros()));
+ if(j>0)
+ VERIFY(j==numext::real(m3.innerVector(j).lastCoeff()));
+ }
+
+ //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, rows);
+ SparseMatrixType m2(rows, rows);
+ initSparse<Scalar>(density, refMat2, m2);
+ if(internal::random<float>(0,1)>0.5) m2.makeCompressed();
+
+ Index j0 = internal::random<Index>(0,rows-2);
+ Index j1 = internal::random<Index>(0,rows-2);
+ Index n0 = internal::random<Index>(1,rows-(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);
+
+ 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 basic computations
+ {
+ DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
+ DenseMatrix refM2 = DenseMatrix::Zero(rows, rows);
+ DenseMatrix refM3 = DenseMatrix::Zero(rows, rows);
+ DenseMatrix refM4 = DenseMatrix::Zero(rows, rows);
+ SparseMatrixType m1(rows, rows);
+ SparseMatrixType m2(rows, rows);
+ SparseMatrixType m3(rows, rows);
+ SparseMatrixType m4(rows, rows);
+ initSparse<Scalar>(density, refM1, m1);
+ initSparse<Scalar>(density, refM2, m2);
+ initSparse<Scalar>(density, refM3, m3);
+ initSparse<Scalar>(density, refM4, m4);
+
+ VERIFY_IS_APPROX(m1+m2, refM1+refM2);
+ VERIFY_IS_APPROX(m1+m2+m3, refM1+refM2+refM3);
+ VERIFY_IS_APPROX(m3.cwiseProduct(m1+m2), refM3.cwiseProduct(refM1+refM2));
+ VERIFY_IS_APPROX(m1*s1-m2, refM1*s1-refM2);
+
+ VERIFY_IS_APPROX(m1*=s1, refM1*=s1);
+ VERIFY_IS_APPROX(m1/=s1, refM1/=s1);
+
+ VERIFY_IS_APPROX(m1+=m2, refM1+=refM2);
+ VERIFY_IS_APPROX(m1-=m2, refM1-=refM2);
+
+ if(SparseMatrixType::IsRowMajor)
+ VERIFY_IS_APPROX(m1.innerVector(0).dot(refM2.row(0)), refM1.row(0).dot(refM2.row(0)));
+ else
+ VERIFY_IS_APPROX(m1.innerVector(0).dot(refM2.row(0)), refM1.col(0).dot(refM2.row(0)));
+
+ VERIFY_IS_APPROX(m1.conjugate(), refM1.conjugate());
+ VERIFY_IS_APPROX(m1.real(), refM1.real());
+
+ refM4.setRandom();
+ // sparse cwise* dense
+ VERIFY_IS_APPROX(m3.cwiseProduct(refM4), refM3.cwiseProduct(refM4));
+// VERIFY_IS_APPROX(m3.cwise()/refM4, refM3.cwise()/refM4);
+
+ // test aliasing
+ VERIFY_IS_APPROX((m1 = -m1), (refM1 = -refM1));
+ VERIFY_IS_APPROX((m1 = m1.transpose()), (refM1 = refM1.transpose().eval()));
+ VERIFY_IS_APPROX((m1 = -m1.transpose()), (refM1 = -refM1.transpose().eval()));
+ VERIFY_IS_APPROX((m1 += -m1), (refM1 += -refM1));
+ }
+
+ // test transpose
+ {
+ DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
+ SparseMatrixType m2(rows, rows);
+ initSparse<Scalar>(density, refMat2, m2);
+ VERIFY_IS_APPROX(m2.transpose().eval(), refMat2.transpose().eval());
+ VERIFY_IS_APPROX(m2.transpose(), refMat2.transpose());
+
+ VERIFY_IS_APPROX(SparseMatrixType(m2.adjoint()), refMat2.adjoint());
+ }
+
+
+
+ // test generic blocks
+ {
+ DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
+ SparseMatrixType m2(rows, rows);
+ initSparse<Scalar>(density, refMat2, m2);
+ Index j0 = internal::random<Index>(0,rows-2);
+ Index j1 = internal::random<Index>(0,rows-2);
+ Index n0 = internal::random<Index>(1,rows-(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);
+ }
+
+ VERIFY_IS_APPROX(DenseVector(m2.col(j0)), refMat2.col(j0));
+ VERIFY_IS_APPROX(m2.col(j0), refMat2.col(j0));
+
+ VERIFY_IS_APPROX(RowDenseVector(m2.row(j0)), refMat2.row(j0));
+ VERIFY_IS_APPROX(m2.row(j0), refMat2.row(j0));
+
+ VERIFY_IS_APPROX(m2.block(j0,j1,n0,n0), refMat2.block(j0,j1,n0,n0));
+ VERIFY_IS_APPROX((2*m2).block(j0,j1,n0,n0), (2*refMat2).block(j0,j1,n0,n0));
+ }
+
+ // test prune
+ {
+ SparseMatrixType m2(rows, rows);
+ DenseMatrix refM2(rows, rows);
+ refM2.setZero();
+ int countFalseNonZero = 0;
+ int countTrueNonZero = 0;
+ for (Index j=0; j<m2.outerSize(); ++j)
+ {
+ m2.startVec(j);
+ for (Index i=0; i<m2.innerSize(); ++i)
+ {
+ float x = internal::random<float>(0,1);
+ if (x<0.1)
+ {
+ // do nothing
+ }
+ else if (x<0.5)
+ {
+ countFalseNonZero++;
+ m2.insertBackByOuterInner(j,i) = Scalar(0);
+ }
+ else
+ {
+ countTrueNonZero++;
+ m2.insertBackByOuterInner(j,i) = Scalar(1);
+ if(SparseMatrixType::IsRowMajor)
+ refM2(j,i) = Scalar(1);
+ else
+ refM2(i,j) = Scalar(1);
+ }
+ }
+ }
+ m2.finalize();
+ VERIFY(countFalseNonZero+countTrueNonZero == m2.nonZeros());
+ VERIFY_IS_APPROX(m2, refM2);
+ m2.prune(Scalar(1));
+ VERIFY(countTrueNonZero==m2.nonZeros());
+ VERIFY_IS_APPROX(m2, refM2);
+ }
+
+ // test setFromTriplets
+ {
+ typedef Triplet<Scalar,Index> TripletType;
+ std::vector<TripletType> triplets;
+ int ntriplets = rows*cols;
+ triplets.reserve(ntriplets);
+ DenseMatrix refMat(rows,cols);
+ refMat.setZero();
+ for(int 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));
+ refMat(r,c) += v;
+ }
+ SparseMatrixType m(rows,cols);
+ m.setFromTriplets(triplets.begin(), triplets.end());
+ VERIFY_IS_APPROX(m, refMat);
+ }
+
+ // test triangularView
+ {
+ DenseMatrix refMat2(rows, rows), refMat3(rows, rows);
+ SparseMatrixType m2(rows, rows), m3(rows, rows);
+ initSparse<Scalar>(density, refMat2, m2);
+ refMat3 = refMat2.template triangularView<Lower>();
+ m3 = m2.template triangularView<Lower>();
+ VERIFY_IS_APPROX(m3, refMat3);
+
+ refMat3 = refMat2.template triangularView<Upper>();
+ m3 = m2.template triangularView<Upper>();
+ VERIFY_IS_APPROX(m3, refMat3);
+
+ refMat3 = refMat2.template triangularView<UnitUpper>();
+ m3 = m2.template triangularView<UnitUpper>();
+ VERIFY_IS_APPROX(m3, refMat3);
+
+ refMat3 = refMat2.template triangularView<UnitLower>();
+ m3 = m2.template triangularView<UnitLower>();
+ VERIFY_IS_APPROX(m3, refMat3);
+
+ refMat3 = refMat2.template triangularView<StrictlyUpper>();
+ m3 = m2.template triangularView<StrictlyUpper>();
+ VERIFY_IS_APPROX(m3, refMat3);
+
+ refMat3 = refMat2.template triangularView<StrictlyLower>();
+ m3 = m2.template triangularView<StrictlyLower>();
+ VERIFY_IS_APPROX(m3, refMat3);
+ }
+
+ // test selfadjointView
+ if(!SparseMatrixType::IsRowMajor)
+ {
+ DenseMatrix refMat2(rows, rows), refMat3(rows, rows);
+ SparseMatrixType m2(rows, rows), m3(rows, rows);
+ initSparse<Scalar>(density, refMat2, m2);
+ refMat3 = refMat2.template selfadjointView<Lower>();
+ m3 = m2.template selfadjointView<Lower>();
+ VERIFY_IS_APPROX(m3, refMat3);
+ }
+
+ // test sparseView
+ {
+ DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
+ SparseMatrixType m2(rows, rows);
+ initSparse<Scalar>(density, refMat2, m2);
+ VERIFY_IS_APPROX(m2.eval(), refMat2.sparseView().eval());
+ }
+
+ // test diagonal
+ {
+ DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
+ SparseMatrixType m2(rows, rows);
+ initSparse<Scalar>(density, refMat2, m2);
+ VERIFY_IS_APPROX(m2.diagonal(), refMat2.diagonal().eval());
+ }
+
+ // test conservative resize
+ {
+ std::vector< std::pair<Index,Index> > inc;
+ inc.push_back(std::pair<Index,Index>(-3,-2));
+ inc.push_back(std::pair<Index,Index>(0,0));
+ inc.push_back(std::pair<Index,Index>(3,2));
+ inc.push_back(std::pair<Index,Index>(3,0));
+ inc.push_back(std::pair<Index,Index>(0,3));
+
+ for(size_t i = 0; i< inc.size(); i++) {
+ Index incRows = inc[i].first;
+ Index 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);
+ }
+}
+
+void test_sparse_basic()
+{
+ for(int i = 0; i < g_repeat; i++) {
+ int s = Eigen::internal::random<int>(1,50);
+ EIGEN_UNUSED_VARIABLE(s);
+ CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(8, 8)) ));
+ CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, ColMajor>(s, s)) ));
+ CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, RowMajor>(s, s)) ));
+ CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(s, s)) ));
+ CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,ColMajor,long int>(s, s)) ));
+ CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,long int>(s, s)) ));
+
+ CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,ColMajor,short int>(short(s), short(s))) ));
+ CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,short int>(short(s), short(s))) ));
+ }
+}