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