Eigen::RandomSetter< SparseMatrixType, MapTraits, OuterPacketBits > Class Template Reference

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
SparseMatrixTypethe type of the sparse matrix we are updating
MapTraitsa traits class representing the map implementation used for the temporary sparse storage. Its default value depends on the system.
OuterPacketBitsdefines 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/

Public Member Functions

Index nonZeros () const
 
Scalar & operator() (Index row, Index col)
 
 RandomSetter (SparseMatrixType &target)
 
 ~RandomSetter ()
 

Constructor & Destructor Documentation

◆ RandomSetter()

template<typename SparseMatrixType , template< typename T > class MapTraits = StdMapTraits, int OuterPacketBits = 6>
Eigen::RandomSetter< SparseMatrixType, MapTraits, OuterPacketBits >::RandomSetter ( SparseMatrixType &  target)
inline

Constructs a random setter object from the sparse matrix target

Note that the initial value of target are imported. If you want to re-set a sparse matrix from scratch, then you must set it to zero first using the setZero() function.

◆ ~RandomSetter()

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

Destructor updating back the sparse matrix target

Member Function Documentation

◆ nonZeros()

template<typename SparseMatrixType , template< typename T > class MapTraits = StdMapTraits, int OuterPacketBits = 6>
Index Eigen::RandomSetter< SparseMatrixType, MapTraits, OuterPacketBits >::nonZeros ( ) const
inline
Returns
the number of non zero coefficients
Note
According to the underlying map/hash_map implementation, this function might be quite expensive.

◆ operator()()

template<typename SparseMatrixType , template< typename T > class MapTraits = StdMapTraits, int OuterPacketBits = 6>
Scalar& Eigen::RandomSetter< SparseMatrixType, MapTraits, OuterPacketBits >::operator() ( Index  row,
Index  col 
)
inline
Returns
a reference to the coefficient at given coordinates row, col

The documentation for this class was generated from the following file: