C++ vector_type类代码示例

  Multiply( const matrix_type & A ,
            const size_type nrow ,
            const size_type ncol ,
            const vector_type & x ,
            const vector_type & y )
  {
    CudaSparseSingleton & s = CudaSparseSingleton::singleton();
    const scalar_type alpha = 1 , beta = 0 ;

    cusparseStatus_t status =
      cusparseScsrmv( s.handle ,
                      CUSPARSE_OPERATION_NON_TRANSPOSE ,
                      nrow , ncol , A.coefficients.dimension_0() ,
                      &alpha ,
                      s.descra ,
                      A.coefficients.ptr_on_device() ,
                      A.graph.row_map.ptr_on_device() ,
                      A.graph.entries.ptr_on_device() ,
                      x.ptr_on_device() ,
                      &beta ,
                      y.ptr_on_device() );

    if ( CUSPARSE_STATUS_SUCCESS != status ) {
      throw std::runtime_error( std::string("ERROR - cusparseDcsrmv " ) );
    }
  }

bool compareRank1(const vector_type& y,
                          const vector_type& y_exp,
                          const scalar_type rel_tol,
                          const scalar_type abs_tol,
                          Teuchos::FancyOStream& out)
{
  typedef typename vector_type::size_type size_type;
  typename vector_type::HostMirror hy = Kokkos::create_mirror_view(y);
  typename vector_type::HostMirror hy_exp = Kokkos::create_mirror_view(y_exp);
  Kokkos::deep_copy(hy, y);
  Kokkos::deep_copy(hy_exp, y_exp);

  size_type num_rows = y.dimension_0();
  bool success = true;
  for (size_type i=0; i<num_rows; ++i) {
    for (size_type j=0; j<y.sacado_size(); ++j) {
      scalar_type diff = std::abs( hy(i).fastAccessCoeff(j) - hy_exp(i).fastAccessCoeff(j) );
      scalar_type tol = rel_tol*std::abs(hy_exp(i).fastAccessCoeff(j)) + abs_tol;
      bool s = diff < tol;
      out << "y_expected(" << i << ").coeff(" << j << ") - "
          << "y(" << i << ").coeff(" << j << ") = " << hy_exp(i).fastAccessCoeff(j)
          << " - " << hy(i).fastAccessCoeff(j) << " == "
          << diff << " < " << tol << " : ";
      if (s)
        out << "passed";
      else
        out << "failed";
      out << std::endl;
      success = success && s;
    }
  }
  return success;
}

  static void apply( const matrix_type & A ,
                     const vector_type & x ,
                     const vector_type & y )
  {
    CudaSparseSingleton & s = CudaSparseSingleton::singleton();
    const double alpha = 1 , beta = 0 ;
    const int n = A.graph.row_map.dimension_0() - 1 ;
    const int nz = A.graph.entries.dimension_0();

    cusparseStatus_t status =
      cusparseDcsrmv( s.handle ,
                      CUSPARSE_OPERATION_NON_TRANSPOSE ,
                      n , n , nz ,
                      &alpha ,
                      s.descra ,
                      A.values.ptr_on_device() ,
                      A.graph.row_map.ptr_on_device() ,
                      A.graph.entries.ptr_on_device() ,
                      x.ptr_on_device() ,
                      &beta ,
                      y.ptr_on_device() );

    if ( CUSPARSE_STATUS_SUCCESS != status ) {
      throw std::runtime_error( std::string("ERROR - cusparseDcsrmv " ) );
    }
  }

 void prod( sparse_matrix_type const& A,
            vector_type const& x,
            vector_type& b ) const
 {
     int ierr = 0;
     petsc_sparse_matrix_type const& _A = dynamic_cast<petsc_sparse_matrix_type const&>( A );
     petsc_vector_type const& _x = dynamic_cast<petsc_vector_type const&>( x );
     petsc_vector_type const& _b = dynamic_cast<petsc_vector_type const&>( b );
     if ( _A.mapCol().worldComm().globalSize() == x.map().worldComm().globalSize() )
     {
         //std::cout << "BackendPetsc::prod STANDART"<< std::endl;
         ierr = MatMult( _A.mat(), _x.vec(), _b.vec() );
         CHKERRABORT( _A.comm().globalComm(),ierr );
     }
     else
     {
         //std::cout << "BackendPetsc::prod with convert"<< std::endl;
         auto x_convert = petscMPI_vector_type(_A.mapColPtr());
         x_convert.duplicateFromOtherPartition(x);
         x_convert.close();
         ierr = MatMult( _A.mat(), x_convert.vec(), _b.vec() );
         CHKERRABORT( _A.comm().globalComm(),ierr );
     }
     b.close();
 }

unsigned_type checksum(vector_type& input)
{
    unsigned_type sum = 0;
    for (vector_type::const_iterator i = input.begin(); i != input.end(); ++i)
        sum += (unsigned_type)((*i).m_key);
    return sum;
}

    /*!
     * \brief Sum a value into existing value at the global row index. The
     * global index must exist on process.
     */
    static void sumIntoGlobalValue( vector_type& vector,
    				    global_ordinal_type global_row,
    				    const scalar_type& value )
    {
	MCLS_REQUIRE( vector.getMap()->isNodeGlobalElement( global_row ) );
	vector.sumIntoGlobalValue( global_row, value );
    }

    /*!
     * \brief Sum a value into existing value at the local row index. The
     * local index must exist on process.
     */
    static void sumIntoLocalValue( vector_type& vector,
    				   local_ordinal_type local_row,
    				   const scalar_type& value )
    {
	MCLS_REQUIRE( vector.getMap()->isNodeLocalElement( local_row ) );
	vector.sumIntoLocalValue( local_row, value );
    }

	public: vector_type simulate(vector_type const& u, vector_type const& e, real_type na_value = real_type(0)) const
	{
		namespace ublas = ::boost::numeric::ublas;
		namespace ublasx = ::boost::numeric::ublasx;

		size_type n_obs = u.size();
		size_type n_e = e.size();

		DCS_ASSERT(
			n_obs == n_e,
			throw ::std::logic_error("Size of Input Data and Noise Data does not match.")
		);

		size_type n_a = ublasx::size(a_); // # of output channels
		size_type n_b = ublasx::size(b_); // # of input channels
		size_type k_min = 0;
		size_type k_max = n_obs - ::std::min(static_cast<size_type>(d_*ts_), n_obs);

		vector_type y(n_obs, na_value);

//		for (size_type k = 0; k < k_min; ++k)
//		{
//			y(k) = e_var_*e(k);
//		}
		for (size_type k = k_min; k < k_max; ++k)
		{
			y(k) = 0;

			if (n_a > 0 && k > 0)
			{
				size_type nn_a = ::std::min(n_a, k);

				y(k) -=	ublas::inner_prod(
						//a_,
						ublas::subrange(a_, 0, nn_a),
						//::dcs::math::la::subslice(y, k-1, -1, n_a)
						ublas::subslice(y, k-1, -1, nn_a)
					);
			}

			if (n_b > 0)
			{
				size_type nn_b = ::std::min(n_b, k+1);

				y(k) += ublas::inner_prod(
						//b_,
						ublas::subrange(b_, 0, nn_b),
						//::dcs::math::la::subslice(u, k, -1, n_b)
						ublas::subslice(u, k, -1, nn_b)
					);
			}

			y(k) += e_var_*e(k);
		}

		return y;
	}

 typename vector_type::const_iterator find_id(const TId id) const noexcept {
     const element_type element {
         id,
         osmium::index::empty_value<TValue>()
     };
     return std::lower_bound(m_vector.begin(), m_vector.end(), element, [](const element_type& a, const element_type& b) {
         return a.first < b.first;
     });
 }

matrix_type Reflector(const vector_type& x)
{
    using namespace boost::numeric::ublas;

    matrix_type F(x.size(), x.size());

    Reflector<matrix_type, vector_type>(x, F);

    return F;
}

    void setBounds( vector_type const& __lb, vector_type const& __up )
    {
        GST_SMART_ASSERT( __lb.size() == __up.size() )( __lb )( __up )( "inconsistent bounds definition" );
        M_lb = __lb;
        M_ub = __up;
        M_lb_ub = __up - __lb;

        GST_SMART_ASSERT( *std::min_element( M_lb_ub.begin(), M_lb_ub.end() ) >= 0 )
        ( M_lb )( M_ub )( "lower and upper bounds are not properly defined" );
    }

    decorated_tuple(cow_pointer_type d, const vector_type& v)
        : super(tuple_impl_info::statically_typed)
        , m_decorated(std::move(d)), m_mapping(v) {
#       ifdef CPPA_DEBUG
        const cow_pointer_type& ptr = m_decorated; // prevent detaching
#       endif
        CPPA_REQUIRE(ptr->size() >= sizeof...(ElementTypes));
        CPPA_REQUIRE(v.size() == sizeof...(ElementTypes));
        CPPA_REQUIRE(*(std::max_element(v.begin(), v.end())) < ptr->size());
    }

/**
 * @test Test MaRC::Vector magnitude (norm) calculation.
 */
bool test_vector_magnitude()
{
    using vector_type = MaRC::Vector<int, 3>;
    vector_type const v{ 3, 4, 5 };

    double const mag =
        std::sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);

    return MaRC::almost_equal(v.magnitude(), mag, ulps);
}

// ===================================================
// Methods
// ===================================================
Int
SolverAztecOO::solve ( vector_type& solution, const vector_type& rhs )
{
    M_solver.SetLHS ( &solution.epetraVector() );
    // The Solver from Aztecoo takes a non const (because of rescaling?)
    // We should be careful if you use scaling
    Epetra_FEVector* rhsVectorPtr ( const_cast<Epetra_FEVector*> (&rhs.epetraVector() ) );
    M_solver.SetRHS ( rhsVectorPtr );

    Int  maxiter (M_maxIter);
    Real mytol  (M_tolerance);
    Int status;

    if ( isPreconditionerSet() && M_preconditioner->preconditionerType().compare ("AztecOO") )
    {
        M_solver.SetPrecOperator (M_preconditioner->preconditioner() );
    }

    status = M_solver.Iterate (maxiter, mytol);

#ifdef HAVE_LIFEV_DEBUG
    M_displayer->comm()->Barrier();
    M_displayer->leaderPrint ( "  o-  Number of iterations = ", M_solver.NumIters() );
    M_displayer->leaderPrint ( "  o-  Norm of the true residual = ", M_solver.TrueResidual() );
    M_displayer->leaderPrint ( "  o-  Norm of the true ratio    = ",  M_solver.ScaledResidual() );
#endif

    /* try to solve again (reason may be:
      -2 "Aztec status AZ_breakdown: numerical breakdown"
      -3 "Aztec status AZ_loss: loss of precision"
      -4 "Aztec status AZ_ill_cond: GMRES hessenberg ill-conditioned"
    */
    if ( status <= -2 )
    {
        maxiter     = M_maxIter;
        mytol       = M_tolerance;
        Int oldIter = M_solver.NumIters();
        status      = M_solver.Iterate (maxiter, mytol);

#ifdef HAVE_LIFEV_DEBUG
        M_displayer->comm()->Barrier();
        M_displayer->leaderPrint ( "  o-  Second run: number of iterations = ", M_solver.NumIters() );
        M_displayer->leaderPrint ( "  o-  Norm of the true residual = ",  M_solver.TrueResidual() );
        M_displayer->leaderPrint ( "  o-  Norm of the true ratio    = ",  M_solver.ScaledResidual() );
#endif
        return ( M_solver.NumIters() + oldIter );
    }

    return ( M_solver.NumIters() );
}

Real
SolverAztecOO::computeResidual ( vector_type& solution, vector_type& rhs )
{
    vector_type Ax ( solution.map() );
    vector_type res ( rhs );

    M_solver.GetUserMatrix()->Apply ( solution.epetraVector(), Ax.epetraVector() );

    res.epetraVector().Update ( 1, Ax.epetraVector(), -1 );

    Real residual;

    res.norm2 ( &residual );

    return residual;
}