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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@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/.
#ifndef EIGEN_TESTSPARSE_H
#define EIGEN_TESTSPARSE_H
#define EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET
#include "main.h"
#if EIGEN_GNUC_AT_LEAST(4,0) && !defined __ICC && !defined(__clang__)
#ifdef min
#undef min
#endif
#ifdef max
#undef max
#endif
#include <tr1/unordered_map>
#define EIGEN_UNORDERED_MAP_SUPPORT
namespace std {
using std::tr1::unordered_map;
}
#endif
#ifdef EIGEN_GOOGLEHASH_SUPPORT
#include <google/sparse_hash_map>
#endif
#include <Eigen/Cholesky>
#include <Eigen/LU>
#include <Eigen/Sparse>
enum {
ForceNonZeroDiag = 1,
MakeLowerTriangular = 2,
MakeUpperTriangular = 4,
ForceRealDiag = 8
};
/* Initializes both a sparse and dense matrix with same random values,
* and a ratio of \a density non zero entries.
* \param flags is a union of ForceNonZeroDiag, MakeLowerTriangular and MakeUpperTriangular
* allowing to control the shape of the matrix.
* \param zeroCoords and nonzeroCoords allows to get the coordinate lists of the non zero,
* and zero coefficients respectively.
*/
template<typename Scalar,int Opt1,int Opt2,typename StorageIndex> void
initSparse(double density,
Matrix<Scalar,Dynamic,Dynamic,Opt1>& refMat,
SparseMatrix<Scalar,Opt2,StorageIndex>& sparseMat,
int flags = 0,
std::vector<Matrix<StorageIndex,2,1> >* zeroCoords = 0,
std::vector<Matrix<StorageIndex,2,1> >* nonzeroCoords = 0)
{
enum { IsRowMajor = SparseMatrix<Scalar,Opt2,StorageIndex>::IsRowMajor };
sparseMat.setZero();
//sparseMat.reserve(int(refMat.rows()*refMat.cols()*density));
sparseMat.reserve(VectorXi::Constant(IsRowMajor ? refMat.rows() : refMat.cols(), int((1.5*density)*(IsRowMajor?refMat.cols():refMat.rows()))));
for(Index j=0; j<sparseMat.outerSize(); j++)
{
//sparseMat.startVec(j);
for(Index i=0; i<sparseMat.innerSize(); i++)
{
Index ai(i), aj(j);
if(IsRowMajor)
std::swap(ai,aj);
Scalar v = (internal::random<double>(0,1) < density) ? internal::random<Scalar>() : Scalar(0);
if ((flags&ForceNonZeroDiag) && (i==j))
{
// FIXME: the following is too conservative
v = internal::random<Scalar>()*Scalar(3.);
v = v*v;
if(numext::real(v)>0) v += Scalar(5);
else v -= Scalar(5);
}
if ((flags & MakeLowerTriangular) && aj>ai)
v = Scalar(0);
else if ((flags & MakeUpperTriangular) && aj<ai)
v = Scalar(0);
if ((flags&ForceRealDiag) && (i==j))
v = numext::real(v);
if (v!=Scalar(0))
{
//sparseMat.insertBackByOuterInner(j,i) = v;
sparseMat.insertByOuterInner(j,i) = v;
if (nonzeroCoords)
nonzeroCoords->push_back(Matrix<StorageIndex,2,1> (ai,aj));
}
else if (zeroCoords)
{
zeroCoords->push_back(Matrix<StorageIndex,2,1> (ai,aj));
}
refMat(ai,aj) = v;
}
}
//sparseMat.finalize();
}
template<typename Scalar,int Opt1,int Opt2,typename Index> void
initSparse(double density,
Matrix<Scalar,Dynamic,Dynamic, Opt1>& refMat,
DynamicSparseMatrix<Scalar, Opt2, Index>& sparseMat,
int flags = 0,
std::vector<Matrix<Index,2,1> >* zeroCoords = 0,
std::vector<Matrix<Index,2,1> >* nonzeroCoords = 0)
{
enum { IsRowMajor = DynamicSparseMatrix<Scalar,Opt2,Index>::IsRowMajor };
sparseMat.setZero();
sparseMat.reserve(int(refMat.rows()*refMat.cols()*density));
for(int j=0; j<sparseMat.outerSize(); j++)
{
sparseMat.startVec(j); // not needed for DynamicSparseMatrix
for(int i=0; i<sparseMat.innerSize(); i++)
{
int ai(i), aj(j);
if(IsRowMajor)
std::swap(ai,aj);
Scalar v = (internal::random<double>(0,1) < density) ? internal::random<Scalar>() : Scalar(0);
if ((flags&ForceNonZeroDiag) && (i==j))
{
v = internal::random<Scalar>()*Scalar(3.);
v = v*v + Scalar(5.);
}
if ((flags & MakeLowerTriangular) && aj>ai)
v = Scalar(0);
else if ((flags & MakeUpperTriangular) && aj<ai)
v = Scalar(0);
if ((flags&ForceRealDiag) && (i==j))
v = numext::real(v);
if (v!=Scalar(0))
{
sparseMat.insertBackByOuterInner(j,i) = v;
if (nonzeroCoords)
nonzeroCoords->push_back(Matrix<Index,2,1> (ai,aj));
}
else if (zeroCoords)
{
zeroCoords->push_back(Matrix<Index,2,1> (ai,aj));
}
refMat(ai,aj) = v;
}
}
sparseMat.finalize();
}
template<typename Scalar,int Options,typename Index> void
initSparse(double density,
Matrix<Scalar,Dynamic,1>& refVec,
SparseVector<Scalar,Options,Index>& sparseVec,
std::vector<int>* zeroCoords = 0,
std::vector<int>* nonzeroCoords = 0)
{
sparseVec.reserve(int(refVec.size()*density));
sparseVec.setZero();
for(int i=0; i<refVec.size(); i++)
{
Scalar v = (internal::random<double>(0,1) < density) ? internal::random<Scalar>() : Scalar(0);
if (v!=Scalar(0))
{
sparseVec.insertBack(i) = v;
if (nonzeroCoords)
nonzeroCoords->push_back(i);
}
else if (zeroCoords)
zeroCoords->push_back(i);
refVec[i] = v;
}
}
template<typename Scalar,int Options,typename Index> void
initSparse(double density,
Matrix<Scalar,1,Dynamic>& refVec,
SparseVector<Scalar,Options,Index>& sparseVec,
std::vector<int>* zeroCoords = 0,
std::vector<int>* nonzeroCoords = 0)
{
sparseVec.reserve(int(refVec.size()*density));
sparseVec.setZero();
for(int i=0; i<refVec.size(); i++)
{
Scalar v = (internal::random<double>(0,1) < density) ? internal::random<Scalar>() : Scalar(0);
if (v!=Scalar(0))
{
sparseVec.insertBack(i) = v;
if (nonzeroCoords)
nonzeroCoords->push_back(i);
}
else if (zeroCoords)
zeroCoords->push_back(i);
refVec[i] = v;
}
}
#include <unsupported/Eigen/SparseExtra>
#endif // EIGEN_TESTSPARSE_H
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