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<div class="title">RandomSetter< SparseMatrixType, MapTraits, OuterPacketBits > Class Template Reference</div> </div>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><h3>template<typename SparseMatrixType, template< typename T > class MapTraits = StdMapTraits, int OuterPacketBits = 6><br/>
class Eigen::RandomSetter< SparseMatrixType, MapTraits, OuterPacketBits ></h3>
<p>The <a class="el" href="classEigen_1_1RandomSetter.html" title="The RandomSetter is a wrapper object allowing to set/update a sparse matrix with random access...">RandomSetter</a> is a wrapper object allowing to set/update a sparse matrix with random access. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">SparseMatrixType</td><td>the type of the sparse matrix we are updating </td></tr>
<tr><td class="paramname">MapTraits</td><td>a traits class representing the map implementation used for the temporary sparse storage. Its default value depends on the system. </td></tr>
<tr><td class="paramname">OuterPacketBits</td><td>defines the number of rows (or columns) manage by a single map object as a power of two exponent.</td></tr>
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<p>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 <a class="el" href="classEigen_1_1RandomSetter.html" title="The RandomSetter is a wrapper object allowing to set/update a sparse matrix with random access...">RandomSetter</a> constructor, while the sparse matrix is updated back at destruction time. This strategy suggest the use of nested blocks as in this example:</p>
<div class="fragment"><div class="line">* SparseMatrix<double> m(rows,cols);</div>
<div class="line">* {</div>
<div class="line">* RandomSetter<SparseMatrix<double> > w(m);</div>
<div class="line">* <span class="comment">// don't use m but w instead with read/write random access to the coefficients:</span></div>
<div class="line">* <span class="keywordflow">for</span>(;;)</div>
<div class="line">* w(rand(),rand()) = rand;</div>
<div class="line">* }</div>
<div class="line">* <span class="comment">// when w is deleted, the data are copied back to m</span></div>
<div class="line">* <span class="comment">// and m is ready to use.</span></div>
<div class="line">* </div>
</div><!-- fragment --><p>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 <a class="el" href="classEigen_1_1RandomSetter.html" title="The RandomSetter is a wrapper object allowing to set/update a sparse matrix with random access...">RandomSetter</a> 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.</p>
<p>The possible values for the template parameter MapTraits are:</p>
<ul>
<li><b><a class="el" href="structEigen_1_1StdMapTraits.html">StdMapTraits</a>:</b> corresponds to std::map. (does not perform very well)</li>
<li><b>GnuHashMapTraits:</b> corresponds to __gnu_cxx::hash_map (available only with GCC)</li>
<li><b>GoogleDenseHashMapTraits:</b> corresponds to google::dense_hash_map (best efficiency, reasonable memory consumption)</li>
<li><b>GoogleSparseHashMapTraits:</b> corresponds to google::sparse_hash_map (best memory consumption, relatively good performance)</li>
</ul>
<p>The default map implementation depends on the availability, and the preferred order is: GoogleSparseHashMapTraits, GnuHashMapTraits, and finally <a class="el" href="structEigen_1_1StdMapTraits.html">StdMapTraits</a>.</p>
<p>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:</p>
<ul>
<li>#include <google/dense_hash_map> yourself <b>before</b> Eigen/Sparse header</li>
<li>define EIGEN_GOOGLEHASH_SUPPORT In the later case the inclusion of <google/dense_hash_map> is made for you.</li>
</ul>
<dl class="section see"><dt>See Also</dt><dd><a href="http://code.google.com/p/google-sparsehash/">http://code.google.com/p/google-sparsehash/</a> </dd></dl>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:abe29bfb5f2d88cf2a50ffb577ec6f0a4"><td class="memItemLeft" align="right" valign="top">Index </td><td class="memItemRight" valign="bottom"><a class="el" href="classEigen_1_1RandomSetter.html#abe29bfb5f2d88cf2a50ffb577ec6f0a4">nonZeros</a> () const </td></tr>
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<tr class="memitem:ac40b13c3b1b753f4f6aa8c6885eec706"><td class="memItemLeft" align="right" valign="top">Scalar & </td><td class="memItemRight" valign="bottom"><a class="el" href="classEigen_1_1RandomSetter.html#ac40b13c3b1b753f4f6aa8c6885eec706">operator()</a> (Index row, Index col)</td></tr>
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<tr class="memitem:a96b5dce4700ec9a2875b2e5532e6ad85"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classEigen_1_1RandomSetter.html#a96b5dce4700ec9a2875b2e5532e6ad85">RandomSetter</a> (SparseMatrixType &target)</td></tr>
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<tr class="memitem:aa72ee212394d51949c7d8573d0c2fc05"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classEigen_1_1RandomSetter.html#aa72ee212394d51949c7d8573d0c2fc05">~RandomSetter</a> ()</td></tr>
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<h2 class="groupheader">Constructor & Destructor Documentation</h2>
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<td class="memname"><a class="el" href="classEigen_1_1RandomSetter.html">RandomSetter</a> </td>
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<p>Constructs a random setter object from the sparse matrix <em>target</em> </p>
<p>Note that the initial value of <em>target</em> 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. </p>
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<td class="memname">~<a class="el" href="classEigen_1_1RandomSetter.html">RandomSetter</a> </td>
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<p>Destructor updating back the sparse matrix target </p>
<p>References <a class="el" href="classEigen_1_1RandomSetter.html#abe29bfb5f2d88cf2a50ffb577ec6f0a4">RandomSetter< SparseMatrixType, MapTraits, OuterPacketBits >::nonZeros()</a>.</p>
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<h2 class="groupheader">Member Function Documentation</h2>
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<dl class="section return"><dt>Returns</dt><dd>the number of non zero coefficients</dd></dl>
<dl class="section note"><dt>Note</dt><dd>According to the underlying map/hash_map implementation, this function might be quite expensive. </dd></dl>
<p>Referenced by <a class="el" href="classEigen_1_1RandomSetter.html#aa72ee212394d51949c7d8573d0c2fc05">RandomSetter< SparseMatrixType, MapTraits, OuterPacketBits >::~RandomSetter()</a>.</p>
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<td class="memname">Scalar& operator() </td>
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<td class="paramname"><em>row</em>, </td>
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<dl class="section return"><dt>Returns</dt><dd>a reference to the coefficient at given coordinates <em>row</em>, <em>col</em> </dd></dl>
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<hr/>The documentation for this class was generated from the following file:<ul>
<li><a class="el" href="RandomSetter_8h_source.html">RandomSetter.h</a></li>
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