The RandomSetter is a wrapper object allowing to set/update a sparse matrix with random access. More...

#include <RandomSetter.h>

Classes
struct	ScalarWrapper
Public Member Functions
Index	nonZeros () const
Scalar &	operator() (Index row, Index col)
	RandomSetter (SparseMatrixType &target)
	~RandomSetter ()
Protected Attributes
HashMapType *	m_hashmaps
unsigned char	m_keyBitsOffset
Index	m_outerPackets
SparseMatrixType *	mp_target
Private Types
enum	{ SwapStorage = 1 - MapTraits<ScalarWrapper>::IsSorted, TargetRowMajor = (SparseMatrixType::Flags & RowMajorBit) ? 1 : 0, SetterRowMajor = SwapStorage ? 1-TargetRowMajor : TargetRowMajor }
typedef MapTraits < ScalarWrapper >::Type	HashMapType
typedef SparseMatrixType::Index	Index
typedef MapTraits < ScalarWrapper >::KeyType	KeyType
typedef SparseMatrixType::Scalar	Scalar
Static Private Attributes
static const int	OuterPacketMask = (1 << OuterPacketBits) - 1

Detailed Description

template<typename SparseMatrixType, template< typename T > class MapTraits = StdMapTraits, int OuterPacketBits = 6>
class Eigen::RandomSetter< SparseMatrixType, MapTraits, OuterPacketBits >

The RandomSetter is a wrapper object allowing to set/update a sparse matrix with random access.

Parameters:

SparseMatrixType	the type of the sparse matrix we are updating
MapTraits	a traits class representing the map implementation used for the temporary sparse storage. Its default value depends on the system.
OuterPacketBits	defines the number of rows (or columns) manage by a single map object as a power of two exponent.

This class temporarily represents a sparse matrix object using a generic map implementation allowing for efficient random access. The conversion from the compressed representation to a hash_map object is performed in the RandomSetter constructor, while the sparse matrix is updated back at destruction time. This strategy suggest the use of nested blocks as in this example:

 SparseMatrix<double> m(rows,cols);
 {
   RandomSetter<SparseMatrix<double> > w(m);
   // don't use m but w instead with read/write random access to the coefficients:
   for(;;)
     w(rand(),rand()) = rand;
 }
 // when w is deleted, the data are copied back to m
 // and m is ready to use.

Since hash_map objects are not fully sorted, representing a full matrix as a single hash_map would involve a big and costly sort to update the compressed matrix back. To overcome this issue, a RandomSetter use multiple hash_map, each representing 2^OuterPacketBits columns or rows according to the storage order. To reach optimal performance, this value should be adjusted according to the average number of nonzeros per rows/columns.

The possible values for the template parameter MapTraits are:

StdMapTraits: corresponds to std::map. (does not perform very well)
GnuHashMapTraits: corresponds to __gnu_cxx::hash_map (available only with GCC)
GoogleDenseHashMapTraits: corresponds to google::dense_hash_map (best efficiency, reasonable memory consumption)
GoogleSparseHashMapTraits: corresponds to google::sparse_hash_map (best memory consumption, relatively good performance)

The default map implementation depends on the availability, and the preferred order is: GoogleSparseHashMapTraits, GnuHashMapTraits, and finally StdMapTraits.

For performance and memory consumption reasons it is highly recommended to use one of the Google's hash_map implementation. To enable the support for them, you have two options:

#include <google/dense_hash_map> yourself before Eigen/Sparse header
define EIGEN_GOOGLEHASH_SUPPORT In the later case the inclusion of <google/dense_hash_map> is made for you.

See also:: http://code.google.com/p/google-sparsehash/

Definition at line 154 of file RandomSetter.h.