add missing files
diff --git a/unsupported/Eigen/src/SparseExtra/CMakeLists.txt b/unsupported/Eigen/src/SparseExtra/CMakeLists.txt new file mode 100644 index 0000000..7ea32ca --- /dev/null +++ b/unsupported/Eigen/src/SparseExtra/CMakeLists.txt
@@ -0,0 +1,6 @@ +FILE(GLOB Eigen_SparseExtra_SRCS "*.h") + +INSTALL(FILES + ${Eigen_SparseExtra_SRCS} + DESTINATION ${INCLUDE_INSTALL_DIR}/unsupported/Eigen/src/SparseExtra COMPONENT Devel + )
diff --git a/unsupported/test/sparse_extra.cpp b/unsupported/test/sparse_extra.cpp new file mode 100644 index 0000000..fa6dc50 --- /dev/null +++ b/unsupported/test/sparse_extra.cpp
@@ -0,0 +1,153 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2010 Gael Guennebaud <g.gael@free.fr> +// +// Eigen is free software; you can redistribute it and/or +// modify it under the terms of the GNU Lesser General Public +// License as published by the Free Software Foundation; either +// version 3 of the License, or (at your option) any later version. +// +// Alternatively, you can redistribute it and/or +// modify it under the terms of the GNU General Public License as +// published by the Free Software Foundation; either version 2 of +// the License, or (at your option) any later version. +// +// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY +// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS +// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the +// GNU General Public License for more details. +// +// You should have received a copy of the GNU Lesser General Public +// License and a copy of the GNU General Public License along with +// Eigen. If not, see <http://www.gnu.org/licenses/>. + +#include "sparse.h" +#include <Eigen/SparseExtra> + +template<typename SetterType,typename DenseType, typename Scalar, int Options> +bool test_random_setter(SparseMatrix<Scalar,Options>& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords) +{ + typedef SparseMatrix<Scalar,Options> SparseType; + { + sm.setZero(); + SetterType w(sm); + std::vector<Vector2i> remaining = nonzeroCoords; + while(!remaining.empty()) + { + int i = ei_random<int>(0,static_cast<int>(remaining.size())-1); + w(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y()); + remaining[i] = remaining.back(); + remaining.pop_back(); + } + } + return sm.isApprox(ref); +} + +template<typename SetterType,typename DenseType, typename T> +bool test_random_setter(DynamicSparseMatrix<T>& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords) +{ + sm.setZero(); + std::vector<Vector2i> remaining = nonzeroCoords; + while(!remaining.empty()) + { + int i = ei_random<int>(0,static_cast<int>(remaining.size())-1); + sm.coeffRef(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y()); + remaining[i] = remaining.back(); + remaining.pop_back(); + } + return sm.isApprox(ref); +} + +template<typename SparseMatrixType> void sparse_extra(const SparseMatrixType& ref) +{ + const int rows = ref.rows(); + const int 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; + Scalar eps = 1e-6; + + SparseMatrixType m(rows, cols); + DenseMatrix refMat = DenseMatrix::Zero(rows, cols); + DenseVector vec1 = DenseVector::Random(rows); + + std::vector<Vector2i> zeroCoords; + std::vector<Vector2i> 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(ei_is_same_type<SparseMatrixType,SparseMatrix<Scalar,Flags> >::ret) + 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); + + // random setter +// { +// m.setZero(); +// VERIFY_IS_NOT_APPROX(m, refMat); +// SparseSetter<SparseMatrixType, RandomAccessPattern> w(m); +// std::vector<Vector2i> remaining = nonzeroCoords; +// while(!remaining.empty()) +// { +// int i = ei_random<int>(0,remaining.size()-1); +// w->coeffRef(remaining[i].x(),remaining[i].y()) = refMat.coeff(remaining[i].x(),remaining[i].y()); +// remaining[i] = remaining.back(); +// remaining.pop_back(); +// } +// } +// VERIFY_IS_APPROX(m, refMat); + + VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, StdMapTraits> >(m,refMat,nonzeroCoords) )); + #ifdef EIGEN_UNORDERED_MAP_SUPPORT + VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, StdUnorderedMapTraits> >(m,refMat,nonzeroCoords) )); + #endif + #ifdef _DENSE_HASH_MAP_H_ + VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, GoogleDenseHashMapTraits> >(m,refMat,nonzeroCoords) )); + #endif + #ifdef _SPARSE_HASH_MAP_H_ + VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, GoogleSparseHashMapTraits> >(m,refMat,nonzeroCoords) )); + #endif + + + // test RandomSetter + /*{ + SparseMatrixType m1(rows,cols), m2(rows,cols); + DenseMatrix refM1 = DenseMatrix::Zero(rows, rows); + initSparse<Scalar>(density, refM1, m1); + { + Eigen::RandomSetter<SparseMatrixType > setter(m2); + for (int j=0; j<m1.outerSize(); ++j) + for (typename SparseMatrixType::InnerIterator i(m1,j); i; ++i) + setter(i.index(), j) = i.value(); + } + VERIFY_IS_APPROX(m1, m2); + }*/ + + +} + +void test_sparse_extra() +{ + for(int i = 0; i < g_repeat; i++) { + CALL_SUBTEST_1( sparse_extra(SparseMatrix<double>(8, 8)) ); + CALL_SUBTEST_2( sparse_extra(SparseMatrix<std::complex<double> >(16, 16)) ); + CALL_SUBTEST_1( sparse_extra(SparseMatrix<double>(33, 33)) ); + + CALL_SUBTEST_3( sparse_extra(DynamicSparseMatrix<double>(8, 8)) ); + } +}
diff --git a/unsupported/test/sparse_ldlt.cpp b/unsupported/test/sparse_ldlt.cpp new file mode 100644 index 0000000..8671807 --- /dev/null +++ b/unsupported/test/sparse_ldlt.cpp
@@ -0,0 +1,68 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2010 Gael Guennebaud <g.gael@free.fr> +// +// Eigen is free software; you can redistribute it and/or +// modify it under the terms of the GNU Lesser General Public +// License as published by the Free Software Foundation; either +// version 3 of the License, or (at your option) any later version. +// +// Alternatively, you can redistribute it and/or +// modify it under the terms of the GNU General Public License as +// published by the Free Software Foundation; either version 2 of +// the License, or (at your option) any later version. +// +// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY +// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS +// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the +// GNU General Public License for more details. +// +// You should have received a copy of the GNU Lesser General Public +// License and a copy of the GNU General Public License along with +// Eigen. If not, see <http://www.gnu.org/licenses/>. + +#include "sparse.h" + +#ifdef EIGEN_TAUCS_SUPPORT +#include <Eigen/TaucsSupport> +#endif + +template<typename Scalar> void sparse_ldlt(int rows, int cols) +{ + double density = std::max(8./(rows*cols), 0.01); + typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; + typedef Matrix<Scalar,Dynamic,1> DenseVector; + + SparseMatrix<Scalar> m2(rows, cols); + DenseMatrix refMat2(rows, cols); + + DenseVector b = DenseVector::Random(cols); + DenseVector refX(cols), x(cols); + + initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeUpperTriangular, 0, 0); + for(int i=0; i<rows; ++i) + m2.coeffRef(i,i) = refMat2(i,i) = ei_abs(ei_real(refMat2(i,i))); + + refX = refMat2.template selfadjointView<Upper>().ldlt().solve(b); + typedef SparseMatrix<Scalar,Upper|SelfAdjoint> SparseSelfAdjointMatrix; + x = b; + SparseLDLT<SparseSelfAdjointMatrix> ldlt(m2); + if (ldlt.succeeded()) + ldlt.solveInPlace(x); + else + std::cerr << "warning LDLT failed\n"; + + VERIFY_IS_APPROX(refMat2.template selfadjointView<Upper>() * x, b); + VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LDLT: default"); +} + +void test_sparse_ldlt() +{ + for(int i = 0; i < g_repeat; i++) { + CALL_SUBTEST_1(sparse_ldlt<double>(8, 8) ); + int s = ei_random<int>(1,300); + CALL_SUBTEST_2(sparse_ldlt<std::complex<double> >(s,s) ); + CALL_SUBTEST_1(sparse_ldlt<double>(s,s) ); + } +}
diff --git a/unsupported/test/sparse_llt.cpp b/unsupported/test/sparse_llt.cpp new file mode 100644 index 0000000..97360f1 --- /dev/null +++ b/unsupported/test/sparse_llt.cpp
@@ -0,0 +1,91 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2010 Gael Guennebaud <g.gael@free.fr> +// +// Eigen is free software; you can redistribute it and/or +// modify it under the terms of the GNU Lesser General Public +// License as published by the Free Software Foundation; either +// version 3 of the License, or (at your option) any later version. +// +// Alternatively, you can redistribute it and/or +// modify it under the terms of the GNU General Public License as +// published by the Free Software Foundation; either version 2 of +// the License, or (at your option) any later version. +// +// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY +// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS +// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the +// GNU General Public License for more details. +// +// You should have received a copy of the GNU Lesser General Public +// License and a copy of the GNU General Public License along with +// Eigen. If not, see <http://www.gnu.org/licenses/>. + +#include "sparse.h" + +#ifdef EIGEN_CHOLMOD_SUPPORT +#include <Eigen/CholmodSupport> +#endif + +#ifdef EIGEN_TAUCS_SUPPORT +#include <Eigen/TaucsSupport> +#endif + +template<typename Scalar> void sparse_llt(int rows, int cols) +{ + double density = std::max(8./(rows*cols), 0.01); + typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; + typedef Matrix<Scalar,Dynamic,1> DenseVector; + + // TODO fix the issue with complex (see SparseLLT::solveInPlace) + SparseMatrix<Scalar> m2(rows, cols); + DenseMatrix refMat2(rows, cols); + + DenseVector b = DenseVector::Random(cols); + DenseVector refX(cols), x(cols); + + initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeLowerTriangular, 0, 0); + for(int i=0; i<rows; ++i) + m2.coeffRef(i,i) = refMat2(i,i) = ei_abs(ei_real(refMat2(i,i))); + + refX = refMat2.template selfadjointView<Lower>().llt().solve(b); + if (!NumTraits<Scalar>::IsComplex) + { + x = b; + SparseLLT<SparseMatrix<Scalar> > (m2).solveInPlace(x); + VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LLT: default"); + } + #ifdef EIGEN_CHOLMOD_SUPPORT + x = b; + SparseLLT<SparseMatrix<Scalar> ,Cholmod>(m2).solveInPlace(x); + VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LLT: cholmod"); + #endif + + #ifdef EIGEN_TAUCS_SUPPORT + // TODO fix TAUCS with complexes + if (!NumTraits<Scalar>::IsComplex) + { + x = b; +// SparseLLT<SparseMatrix<Scalar> ,Taucs>(m2,IncompleteFactorization).solveInPlace(x); +// VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LLT: taucs (IncompleteFactorization)"); + + x = b; + SparseLLT<SparseMatrix<Scalar> ,Taucs>(m2,SupernodalMultifrontal).solveInPlace(x); + VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LLT: taucs (SupernodalMultifrontal)"); + x = b; + SparseLLT<SparseMatrix<Scalar> ,Taucs>(m2,SupernodalLeftLooking).solveInPlace(x); + VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LLT: taucs (SupernodalLeftLooking)"); + } + #endif +} + +void test_sparse_llt() +{ + for(int i = 0; i < g_repeat; i++) { + CALL_SUBTEST_1(sparse_llt<double>(8, 8) ); + int s = ei_random<int>(1,300); + CALL_SUBTEST_2(sparse_llt<std::complex<double> >(s,s) ); + CALL_SUBTEST_1(sparse_llt<double>(s,s) ); + } +}
diff --git a/unsupported/test/sparse_lu.cpp b/unsupported/test/sparse_lu.cpp new file mode 100644 index 0000000..bdf79ca --- /dev/null +++ b/unsupported/test/sparse_lu.cpp
@@ -0,0 +1,115 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2010 Gael Guennebaud <g.gael@free.fr> +// +// Eigen is free software; you can redistribute it and/or +// modify it under the terms of the GNU Lesser General Public +// License as published by the Free Software Foundation; either +// version 3 of the License, or (at your option) any later version. +// +// Alternatively, you can redistribute it and/or +// modify it under the terms of the GNU General Public License as +// published by the Free Software Foundation; either version 2 of +// the License, or (at your option) any later version. +// +// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY +// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS +// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the +// GNU General Public License for more details. +// +// You should have received a copy of the GNU Lesser General Public +// License and a copy of the GNU General Public License along with +// Eigen. If not, see <http://www.gnu.org/licenses/>. + +#include "sparse.h" + +#ifdef EIGEN_UMFPACK_SUPPORT +#include <Eigen/UmfPackSupport> +#endif + +#ifdef EIGEN_SUPERLU_SUPPORT +#include <Eigen/SuperLUSupport> +#endif + +template<typename Scalar> void sparse_lu(int rows, int cols) +{ + double density = std::max(8./(rows*cols), 0.01); + typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; + typedef Matrix<Scalar,Dynamic,1> DenseVector; + + DenseVector vec1 = DenseVector::Random(rows); + + std::vector<Vector2i> zeroCoords; + std::vector<Vector2i> nonzeroCoords; + + static int count = 0; + SparseMatrix<Scalar> m2(rows, cols); + DenseMatrix refMat2(rows, cols); + + DenseVector b = DenseVector::Random(cols); + DenseVector refX(cols), x(cols); + + initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag, &zeroCoords, &nonzeroCoords); + + FullPivLU<DenseMatrix> refLu(refMat2); + refX = refLu.solve(b); + #if defined(EIGEN_SUPERLU_SUPPORT) || defined(EIGEN_UMFPACK_SUPPORT) + Scalar refDet = refLu.determinant(); + #endif + x.setZero(); + // // SparseLU<SparseMatrix<Scalar> > (m2).solve(b,&x); + // // VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LU: default"); + #ifdef EIGEN_SUPERLU_SUPPORT + { + x.setZero(); + SparseLU<SparseMatrix<Scalar>,SuperLU> slu(m2); + if (slu.succeeded()) + { + if (slu.solve(b,&x)) { + VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LU: SuperLU"); + } + // std::cerr << refDet << " == " << slu.determinant() << "\n"; + if (slu.solve(b, &x, SvTranspose)) { + VERIFY(b.isApprox(m2.transpose() * x, test_precision<Scalar>())); + } + + if (slu.solve(b, &x, SvAdjoint)) { + VERIFY(b.isApprox(m2.adjoint() * x, test_precision<Scalar>())); + } + + if (count==0) { + VERIFY_IS_APPROX(refDet,slu.determinant()); // FIXME det is not very stable for complex + } + } + } + #endif + #ifdef EIGEN_UMFPACK_SUPPORT + { + // check solve + x.setZero(); + SparseLU<SparseMatrix<Scalar>,UmfPack> slu(m2); + if (slu.succeeded()) { + if (slu.solve(b,&x)) { + if (count==0) { + VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LU: umfpack"); // FIXME solve is not very stable for complex + } + } + VERIFY_IS_APPROX(refDet,slu.determinant()); + // TODO check the extracted data + //std::cerr << slu.matrixL() << "\n"; + } + } + #endif + count++; +} + +void test_sparse_lu() +{ + for(int i = 0; i < g_repeat; i++) { + CALL_SUBTEST_1(sparse_lu<double>(8, 8) ); + int s = ei_random<int>(1,300); + CALL_SUBTEST_2(sparse_lu<std::complex<double> >(s,s) ); + CALL_SUBTEST_1(sparse_lu<double>(s,s) ); + } +}