125 lines
4.7 KiB
C++
125 lines
4.7 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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//
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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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#ifndef EIGEN_TESTSPARSE_H
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#define EIGEN_TESTSPARSE_H
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#define EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET
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#include "main.h"
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#ifdef min
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#undef min
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#endif
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#ifdef max
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#undef max
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#endif
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#include <unordered_map>
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#define EIGEN_UNORDERED_MAP_SUPPORT
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#include <Eigen/Cholesky>
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#include <Eigen/LU>
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#include <Eigen/Sparse>
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enum { ForceNonZeroDiag = 1, MakeLowerTriangular = 2, MakeUpperTriangular = 4, ForceRealDiag = 8 };
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/* Initializes both a sparse and dense matrix with same random values,
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* and a ratio of \a density non zero entries.
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* \param flags is a union of ForceNonZeroDiag, MakeLowerTriangular and MakeUpperTriangular
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* allowing to control the shape of the matrix.
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* \param zeroCoords and nonzeroCoords allows to get the coordinate lists of the non zero,
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* and zero coefficients respectively.
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*/
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template <typename Scalar, int Opt1, int Opt2, typename StorageIndex>
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void initSparse(double density, Matrix<Scalar, Dynamic, Dynamic, Opt1>& refMat,
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SparseMatrix<Scalar, Opt2, StorageIndex>& sparseMat, int flags = 0,
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std::vector<Matrix<StorageIndex, 2, 1> >* zeroCoords = 0,
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std::vector<Matrix<StorageIndex, 2, 1> >* nonzeroCoords = 0) {
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enum { IsRowMajor = SparseMatrix<Scalar, Opt2, StorageIndex>::IsRowMajor };
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sparseMat.setZero();
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// sparseMat.reserve(int(refMat.rows()*refMat.cols()*density));
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int nnz = static_cast<int>((1.5 * density) * static_cast<double>(IsRowMajor ? refMat.cols() : refMat.rows()));
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sparseMat.reserve(VectorXi::Constant(IsRowMajor ? refMat.rows() : refMat.cols(), nnz));
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Index insert_count = 0;
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for (Index j = 0; j < sparseMat.outerSize(); j++) {
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// sparseMat.startVec(j);
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for (Index i = 0; i < sparseMat.innerSize(); i++) {
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Index ai(i), aj(j);
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if (IsRowMajor) std::swap(ai, aj);
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Scalar v = (internal::random<double>(0, 1) < density) ? internal::random<Scalar>() : Scalar(0);
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if ((flags & ForceNonZeroDiag) && (i == j)) {
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// FIXME: the following is too conservative
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v = internal::random<Scalar>() * Scalar(3.);
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v = v * v;
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if (numext::real(v) > 0)
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v += Scalar(5);
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else
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v -= Scalar(5);
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}
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if ((flags & MakeLowerTriangular) && aj > ai)
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v = Scalar(0);
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else if ((flags & MakeUpperTriangular) && aj < ai)
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v = Scalar(0);
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if ((flags & ForceRealDiag) && (i == j)) v = numext::real(v);
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if (!numext::is_exactly_zero(v)) {
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// sparseMat.insertBackByOuterInner(j,i) = v;
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sparseMat.insertByOuterInner(j, i) = v;
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++insert_count;
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if (nonzeroCoords) nonzeroCoords->push_back(Matrix<StorageIndex, 2, 1>(ai, aj));
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} else if (zeroCoords) {
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zeroCoords->push_back(Matrix<StorageIndex, 2, 1>(ai, aj));
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}
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refMat(ai, aj) = v;
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// make sure we only insert as many as the sparse matrix supports
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if (insert_count == NumTraits<StorageIndex>::highest()) return;
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}
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}
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// sparseMat.finalize();
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}
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template <typename Scalar, int Options, typename Index>
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void initSparse(double density, Matrix<Scalar, Dynamic, 1>& refVec, SparseVector<Scalar, Options, Index>& sparseVec,
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std::vector<int>* zeroCoords = 0, std::vector<int>* nonzeroCoords = 0) {
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sparseVec.reserve(int(refVec.size() * density));
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sparseVec.setZero();
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for (int i = 0; i < refVec.size(); i++) {
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Scalar v = (internal::random<double>(0, 1) < density) ? internal::random<Scalar>() : Scalar(0);
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if (!numext::is_exactly_zero(v)) {
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sparseVec.insertBack(i) = v;
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if (nonzeroCoords) nonzeroCoords->push_back(i);
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} else if (zeroCoords)
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zeroCoords->push_back(i);
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refVec[i] = v;
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}
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}
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template <typename Scalar, int Options, typename Index>
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void initSparse(double density, Matrix<Scalar, 1, Dynamic>& refVec, SparseVector<Scalar, Options, Index>& sparseVec,
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std::vector<int>* zeroCoords = 0, std::vector<int>* nonzeroCoords = 0) {
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sparseVec.reserve(int(refVec.size() * density));
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sparseVec.setZero();
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for (int i = 0; i < refVec.size(); i++) {
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Scalar v = (internal::random<double>(0, 1) < density) ? internal::random<Scalar>() : Scalar(0);
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if (v != Scalar(0)) {
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sparseVec.insertBack(i) = v;
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if (nonzeroCoords) nonzeroCoords->push_back(i);
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} else if (zeroCoords)
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zeroCoords->push_back(i);
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refVec[i] = v;
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}
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}
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#endif // EIGEN_TESTSPARSE_H
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