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

#include <RandomSetter.h>

Public Member Functions
	RandomSetter (SparseMatrixType &target)

	~RandomSetter ()

Scalar &	operator() (Index row, Index col)

Index	nonZeros () const

Protected Attributes
HashMapType *	m_hashmaps

SparseMatrixType *	mp_target

Index	m_outerPackets

unsigned char	m_keyBitsOffset

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.

Template 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 Google's hash_map implementations. To enable the support for them, you must define EIGEN_GOOGLEHASH_SUPPORT. This will include both <google/dense_hash_map> and <google/sparse_hash_map> for you.

See also: https://github.com/sparsehash/sparsehash