BenchSparseUtil.h

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#include <Eigen/Sparse>
#include <bench/BenchTimer.h>
#include <set>
 
using namespace std;
using namespace Eigen;
using namespace Eigen;
 
#ifndef SIZE
#define SIZE 1024
#endif
 
#ifndef DENSITY
#define DENSITY 0.01
#endif
 
#ifndef SCALAR
#define SCALAR double
#endif
 
typedef SCALAR Scalar;
typedef Matrix<Scalar, Dynamic, Dynamic> DenseMatrix;
typedef Matrix<Scalar, Dynamic, 1> DenseVector;
typedef SparseMatrix<Scalar> EigenSparseMatrix;
 
void fillMatrix(float density, int rows, int cols, EigenSparseMatrix& dst) {
  dst.reserve(double(rows) * cols * density);
  for (int j = 0; j < cols; j++) {
    for (int i = 0; i < rows; i++) {
      Scalar v = (internal::random<float>(0, 1) < density) ? internal::random<Scalar>() : 0;
      if (v != 0) dst.insert(i, j) = v;
    }
  }
  dst.finalize();
}
 
void fillMatrix2(int nnzPerCol, int rows, int cols, EigenSparseMatrix& dst) {
  //   std::cout << "alloc " << nnzPerCol*cols << "\n";
  dst.reserve(nnzPerCol * cols);
  for (int j = 0; j < cols; j++) {
    std::set<int> aux;
    for (int i = 0; i < nnzPerCol; i++) {
      int k = internal::random<int>(0, rows - 1);
      while (aux.find(k) != aux.end()) k = internal::random<int>(0, rows - 1);
      aux.insert(k);
 
      dst.insert(k, j) = internal::random<Scalar>();
    }
  }
  dst.finalize();
}
 
void eiToDense(const EigenSparseMatrix& src, DenseMatrix& dst) {
  dst.setZero();
  for (int j = 0; j < src.cols(); ++j)
    for (EigenSparseMatrix::InnerIterator it(src.derived(), j); it; ++it) dst(it.index(), j) = it.value();
}
 
#ifndef NOGMM
#include "gmm/gmm.h"
typedef gmm::csc_matrix<Scalar> GmmSparse;
typedef gmm::col_matrix<gmm::wsvector<Scalar> > GmmDynSparse;
void eiToGmm(const EigenSparseMatrix& src, GmmSparse& dst) {
  GmmDynSparse tmp(src.rows(), src.cols());
  for (int j = 0; j < src.cols(); ++j)
    for (EigenSparseMatrix::InnerIterator it(src.derived(), j); it; ++it) tmp(it.index(), j) = it.value();
  gmm::copy(tmp, dst);
}
#endif
 
#ifndef NOMTL
#include <boost/numeric/mtl/mtl.hpp>
typedef mtl::compressed2D<Scalar, mtl::matrix::parameters<mtl::tag::col_major> > MtlSparse;
typedef mtl::compressed2D<Scalar, mtl::matrix::parameters<mtl::tag::row_major> > MtlSparseRowMajor;
void eiToMtl(const EigenSparseMatrix& src, MtlSparse& dst) {
  mtl::matrix::inserter<MtlSparse> ins(dst);
  for (int j = 0; j < src.cols(); ++j)
    for (EigenSparseMatrix::InnerIterator it(src.derived(), j); it; ++it) ins[it.index()][j] = it.value();
}
#endif
 
#ifdef CSPARSE
extern "C" {
#include "cs.h"
}
void eiToCSparse(const EigenSparseMatrix& src, cs*& dst) {
  cs* aux = cs_spalloc(0, 0, 1, 1, 1);
  for (int j = 0; j < src.cols(); ++j)
    for (EigenSparseMatrix::InnerIterator it(src.derived(), j); it; ++it)
      if (!cs_entry(aux, it.index(), j, it.value())) {
        std::cout << "cs_entry error\n";
        exit(2);
      }
  dst = cs_compress(aux);
  //    cs_spfree(aux);
}
#endif  // CSPARSE
 
#ifndef NOUBLAS
#include <boost/numeric/ublas/vector.hpp>
#include <boost/numeric/ublas/matrix.hpp>
#include <boost/numeric/ublas/io.hpp>
#include <boost/numeric/ublas/triangular.hpp>
#include <boost/numeric/ublas/vector_sparse.hpp>
#include <boost/numeric/ublas/matrix_sparse.hpp>
#include <boost/numeric/ublas/vector_of_vector.hpp>
#include <boost/numeric/ublas/operation.hpp>
 
typedef boost::numeric::ublas::compressed_matrix<Scalar, boost::numeric::ublas::column_major> UBlasSparse;
 
void eiToUblas(const EigenSparseMatrix& src, UBlasSparse& dst) {
  dst.resize(src.rows(), src.cols(), false);
  for (int j = 0; j < src.cols(); ++j)
    for (EigenSparseMatrix::InnerIterator it(src.derived(), j); it; ++it) dst(it.index(), j) = it.value();
}
 
template <typename EigenType, typename UblasType>
void eiToUblasVec(const EigenType& src, UblasType& dst) {
  dst.resize(src.size());
  for (int j = 0; j < src.size(); ++j) dst[j] = src.coeff(j);
}
#endif
 
#ifdef OSKI
extern "C" {
#include <oski/oski.h>
}
#endif