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

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)
	Constructs a random setter object from the sparse matrix target. More...
	~RandomSetter ()
	Destructor updating back the sparse matrix target.
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.

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/

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.

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

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

• vendor/eigen/unsupported/Eigen/src/SparseExtra/RandomSetter.h