RandomSetter< SparseMatrixType, MapTraits, OuterPacketBits > Class Template Reference

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

List of all members.

Public Member Functions

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

Protected Attributes

HashMapType * m_hashmaps
unsigned char m_keyBitsOffset
int m_outerPackets
SparseMatrixType * mp_target

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:

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:

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

Constructor & Destructor Documentation

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 (  )  [inline]

Destructor updating back the sparse matrix target


Member Function Documentation

int 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.
Scalar& operator() ( int  row,
int  col 
) [inline]
Returns:
a reference to the coefficient at given coordinates row, col

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

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