boost_1_45_0/libs/graph_parallel/doc/html/sorted_rmat_generator.html - nest-learning-thermostat/5.0/boost - Git at Google

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 <h1 class="title"><a class="reference external" href="http://www.osl.iu.edu/research/pbgl"><img align="middle" alt="Parallel BGL" class="align-middle" src="pbgl-logo.png" /></a> Sorted R-MAT generator</h1>

 <!-- Copyright (C) 2004-2009 The Trustees of Indiana University.
 Use, modification and distribution is subject to the Boost Software
 License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
 http://www.boost.org/LICENSE_1_0.txt) -->
 <pre class="literal-block">
  template&lt;typename RandomGenerator, typename Graph,
           typename EdgePredicate = keep_all_edges&gt;
  class sorted_rmat_iterator
  {
  public:
    typedef std::input_iterator_tag iterator_category;
    typedef std::pair&lt;vertices_size_type, vertices_size_type&gt; value_type;
    typedef const value_type&amp; reference;
    typedef const value_type* pointer;
    typedef void difference_type;

    sorted_rmat_iterator();
    sorted_rmat_iterator(RandomGenerator&amp; gen, vertices_size_type n,
                         edges_size_type m, double a, double b, double c,
                         double d, bool permute_vertices = true);
    // Iterator operations
    reference operator*() const;
    pointer operator-&gt;() const;
    sorted_rmat_iterator&amp; operator++();
    sorted_rmat_iterator operator++(int);
    bool operator==(const sorted_rmat_iterator&amp; other) const;
    bool operator!=(const sorted_rmat_iterator&amp; other) const;
 };
 </pre>
 <p>This class template implements a generator for R-MAT graphs <a class="citation-reference" href="#czf04" id="id1">[CZF04]</a>,
 suitable for initializing an adjacency_list or other graph structure
 with iterator-based initialization. An R-MAT graph has a scale-free
 distribution w.r.t. vertex degree and is implemented using
 Recursive-MATrix partitioning.  The output of this generator is sorted
 for use with <a class="reference external" href="http://www.boost.org/libs/graph/doc/compressed_sparse_row.html">compressed sparse row graph</a>.</p>
 <div class="section" id="where-defined">
 <h1>Where Defined</h1>
 <p>&lt;<tt class="docutils literal"><span class="pre">boost/graph/rmat_graph_generator.hpp</span></tt>&gt;</p>
 </div>
 <div class="section" id="constructors">
 <h1>Constructors</h1>
 <pre class="literal-block">
 sorted_rmat_iterator();
 </pre>
 <p>Constructs a past-the-end iterator.</p>
 <pre class="literal-block">
 sorted_rmat_iterator(RandomGenerator&amp; gen, vertices_size_type n,
                      edges_size_type m, double a, double b, double c,
                      double d, bool permute_vertices = true,
                      EdgePredicate ep = keep_all_edges());
 </pre>
 <p>Constructs an R-MAT generator iterator that creates a graph with <tt class="docutils literal"><span class="pre">n</span></tt>
 vertices and <tt class="docutils literal"><span class="pre">m</span></tt> edges.  <tt class="docutils literal"><span class="pre">a</span></tt>, <tt class="docutils literal"><span class="pre">b</span></tt>, <tt class="docutils literal"><span class="pre">c</span></tt>, and <tt class="docutils literal"><span class="pre">d</span></tt> represent
 the probability that a generated edge is placed of each of the 4
 quadrants of the partitioned adjacency matrix.  Probabilities are
 drawn from the random number generator <tt class="docutils literal"><span class="pre">gen</span></tt>.  Vertex indices are
 permuted to eliminate locality when <tt class="docutils literal"><span class="pre">permute_vertices</span></tt> is true.
 <tt class="docutils literal"><span class="pre">ep</span></tt> allows the user to specify which edges are retained, this is
 useful in the case where the user wishes to refrain from storing
 remote edges locally during generation to reduce memory consumption.</p>
 </div>
 <div class="section" id="example">
 <h1>Example</h1>
 <pre class="literal-block">
 #include &lt;boost/graph/compressed_sparse_row_graph.hpp&gt;
 #include &lt;boost/graph/rmat_graph_generator.hpp&gt;
 #include &lt;boost/random/linear_congruential.hpp&gt;

 typedef boost::compressed_sparse_row_graph&lt;&gt; Graph;
 typedef boost::sorted_rmat_iterator&lt;boost::minstd_rand, Graph&gt;
   RMATGen;

 int main()
 {
   boost::minstd_rand gen;
   // Create graph with 100 nodes and 400 edges
   Graph g(RMATGen(gen, 100, 400, 0.57, 0.19, 0.19, 0.05),
           RMATGen(), 100);
   return 0;
 }
 </pre>
 </div>
 <div class="section" id="bibliography">
 <h1>Bibliography</h1>
 <table class="docutils citation" frame="void" id="czf04" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
 <tr><td class="label"><a class="fn-backref" href="#id1">[CZF04]</a></td><td>D Chakrabarti, Y Zhan, and C Faloutsos.  R-MAT: A Recursive
 Model for Graph Mining. In Proceedings of 4th International Conference
 on Data Mining, pages 442--446, 2004.</td></tr>
 </tbody>
 </table>
 <hr class="docutils" />
 <p>Copyright (C) 2009 The Trustees of Indiana University.</p>
 <p>Authors: Nick Edmonds and Andrew Lumsdaine</p>
 </div>
 </div>
 <div class="footer">
 <hr class="footer" />
 Generated on: 2009-05-31 00:21 UTC.
 Generated by <a class="reference external" href="http://docutils.sourceforge.net/">Docutils</a> from <a class="reference external" href="http://docutils.sourceforge.net/rst.html">reStructuredText</a> source.

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	<head>
	<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
	<meta name="generator" content="Docutils 0.6: http://docutils.sourceforge.net/" />
	<title>Parallel BGL Sorted R-MAT generator</title>
	<link rel="stylesheet" href="../../../../rst.css" type="text/css" />
	</head>
	<body>
	<div class="document" id="logo-sorted-r-mat-generator">
	<h1 class="title"><a class="reference external" href="http://www.osl.iu.edu/research/pbgl"><img align="middle" alt="Parallel BGL" class="align-middle" src="pbgl-logo.png" /></a> Sorted R-MAT generator</h1>

	<!-- Copyright (C) 2004-2009 The Trustees of Indiana University.
	Use, modification and distribution is subject to the Boost Software
	License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
	http://www.boost.org/LICENSE_1_0.txt) -->
	<pre class="literal-block">
	template<typename RandomGenerator, typename Graph,
	typename EdgePredicate = keep_all_edges>
	class sorted_rmat_iterator
	{
	public:
	typedef std::input_iterator_tag iterator_category;
	typedef std::pair<vertices_size_type, vertices_size_type> value_type;
	typedef const value_type& reference;
	typedef const value_type* pointer;
	typedef void difference_type;

	sorted_rmat_iterator();
	sorted_rmat_iterator(RandomGenerator& gen, vertices_size_type n,
	edges_size_type m, double a, double b, double c,
	double d, bool permute_vertices = true);
	// Iterator operations
	reference operator*() const;
	pointer operator->() const;
	sorted_rmat_iterator& operator++();
	sorted_rmat_iterator operator++(int);
	bool operator==(const sorted_rmat_iterator& other) const;
	bool operator!=(const sorted_rmat_iterator& other) const;
	};
	</pre>
	<p>This class template implements a generator for R-MAT graphs <a class="citation-reference" href="#czf04" id="id1">[CZF04]</a>,
	suitable for initializing an adjacency_list or other graph structure
	with iterator-based initialization. An R-MAT graph has a scale-free
	distribution w.r.t. vertex degree and is implemented using
	Recursive-MATrix partitioning. The output of this generator is sorted
	for use with <a class="reference external" href="http://www.boost.org/libs/graph/doc/compressed_sparse_row.html">compressed sparse row graph</a>.</p>
	<div class="section" id="where-defined">
	<h1>Where Defined</h1>
	<p><<tt class="docutils literal"><span class="pre">boost/graph/rmat_graph_generator.hpp</span></tt>></p>
	</div>
	<div class="section" id="constructors">
	<h1>Constructors</h1>
	<pre class="literal-block">
	sorted_rmat_iterator();
	</pre>
	<p>Constructs a past-the-end iterator.</p>
	<pre class="literal-block">
	sorted_rmat_iterator(RandomGenerator& gen, vertices_size_type n,
	edges_size_type m, double a, double b, double c,
	double d, bool permute_vertices = true,
	EdgePredicate ep = keep_all_edges());
	</pre>
	<p>Constructs an R-MAT generator iterator that creates a graph with <tt class="docutils literal"><span class="pre">n</span></tt>
	vertices and <tt class="docutils literal"><span class="pre">m</span></tt> edges. <tt class="docutils literal"><span class="pre">a</span></tt>, <tt class="docutils literal"><span class="pre">b</span></tt>, <tt class="docutils literal"><span class="pre">c</span></tt>, and <tt class="docutils literal"><span class="pre">d</span></tt> represent
	the probability that a generated edge is placed of each of the 4
	quadrants of the partitioned adjacency matrix. Probabilities are
	drawn from the random number generator <tt class="docutils literal"><span class="pre">gen</span></tt>. Vertex indices are
	permuted to eliminate locality when <tt class="docutils literal"><span class="pre">permute_vertices</span></tt> is true.
	<tt class="docutils literal"><span class="pre">ep</span></tt> allows the user to specify which edges are retained, this is
	useful in the case where the user wishes to refrain from storing
	remote edges locally during generation to reduce memory consumption.</p>
	</div>
	<div class="section" id="example">
	<h1>Example</h1>
	<pre class="literal-block">
	#include <boost/graph/compressed_sparse_row_graph.hpp>
	#include <boost/graph/rmat_graph_generator.hpp>
	#include <boost/random/linear_congruential.hpp>

	typedef boost::compressed_sparse_row_graph<> Graph;
	typedef boost::sorted_rmat_iterator<boost::minstd_rand, Graph>
	RMATGen;

	int main()
	{
	boost::minstd_rand gen;
	// Create graph with 100 nodes and 400 edges
	Graph g(RMATGen(gen, 100, 400, 0.57, 0.19, 0.19, 0.05),
	RMATGen(), 100);
	return 0;
	}
	</pre>
	</div>
	<div class="section" id="bibliography">
	<h1>Bibliography</h1>
	<table class="docutils citation" frame="void" id="czf04" rules="none">
	<colgroup><col class="label" /><col /></colgroup>
	<tbody valign="top">
	<tr><td class="label"><a class="fn-backref" href="#id1">[CZF04]</a></td><td>D Chakrabarti, Y Zhan, and C Faloutsos. R-MAT: A Recursive
	Model for Graph Mining. In Proceedings of 4th International Conference
	on Data Mining, pages 442--446, 2004.</td></tr>
	</tbody>
	</table>
	<hr class="docutils" />
	<p>Copyright (C) 2009 The Trustees of Indiana University.</p>
	<p>Authors: Nick Edmonds and Andrew Lumsdaine</p>
	</div>
	</div>
	<div class="footer">
	<hr class="footer" />
	Generated on: 2009-05-31 00:21 UTC.
	Generated by <a class="reference external" href="http://docutils.sourceforge.net/">Docutils</a> from <a class="reference external" href="http://docutils.sourceforge.net/rst.html">reStructuredText</a> source.

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