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20 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 nobody
|
f45a17ba41 | ||
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Douglas Gregor
|
43c1f8a033 | ||
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Douglas Gregor
|
4063919a57 | ||
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Douglas Gregor
|
9a074a7e51 | ||
|
Douglas Gregor
|
f027c1c895 | ||
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Rene Rivera
|
2e7f8349c5 | ||
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Rene Rivera
|
e1e2b225b1 | ||
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Douglas Gregor
|
97f5379094 | ||
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Douglas Gregor
|
41e0867907 | ||
|
Douglas Gregor
|
e62876d483 | ||
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Douglas Gregor
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70d4967318 | ||
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Douglas Gregor
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6777fb6f8f | ||
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Douglas Gregor
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7a615168e2 | ||
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Douglas Gregor
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85f275a7e0 | ||
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Douglas Gregor
|
9d25102143 | ||
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Jonathan Turkanis
|
e341b7a672 | ||
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Jonathan Turkanis
|
0452c06499 | ||
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Jonathan Turkanis
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8f1cebb377 | ||
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Jonathan Turkanis
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47da7dd548 | ||
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nobody
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2c69bbae8a |
@@ -20,6 +20,7 @@ extension bgl
|
||||
../../src/python/digraph.cpp
|
||||
# Graph I/O
|
||||
../../src/python/graphviz.cpp
|
||||
../../src/python/adjlist.cpp
|
||||
# Graph algorithms
|
||||
# Core Algorithm Patterns
|
||||
../../src/python/breadth_first_search.cpp
|
||||
@@ -47,7 +48,7 @@ extension bgl
|
||||
# Sparse Matrix Ordering
|
||||
../../src/python/cuthill_mckee_ordering.cpp
|
||||
../../src/python/king_ordering.cpp
|
||||
# ../../src/python/minimum_degree_ordering.cpp
|
||||
../../src/python/minimum_degree_ordering.cpp
|
||||
../../src/python/circle_layout.cpp
|
||||
../../src/python/fruchterman_reingold.cpp
|
||||
../../src/python/kamada_kawai_spring_layout.cpp
|
||||
|
||||
@@ -156,6 +156,7 @@ when there is a negative cycle in the graph.
|
||||
<li><a href="./bellman_visitor.html"><tt>bellman_visitor</tt></a>
|
||||
</ul>
|
||||
|
||||
<a name="python"></a>
|
||||
<h3>Python</h3>
|
||||
|
||||
To implement a model of the <tt>BellmanFordVisitor</tt> concept in Python,
|
||||
|
||||
@@ -174,6 +174,7 @@ all the out-edges of <tt>u</tt> have been examined.
|
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<li><a href="./dfs_visitor.html"><tt>dfs_visitor</tt></a>
|
||||
</ul>
|
||||
|
||||
<a name="python"></a>
|
||||
<h3>Python</h3>
|
||||
|
||||
To implement a model of the <tt>DFSVisitor</tt> concept in Python,
|
||||
|
||||
@@ -184,6 +184,7 @@ search tree and all of the adjacent vertices have been discovered
|
||||
<li><a href="./dijkstra_visitor.html"><tt>dijkstra_visitor</tt></a>
|
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</ul>
|
||||
|
||||
<a name="python"></a>
|
||||
<h3>Python</h3>
|
||||
|
||||
To implement a model of the <tt>DijkstraVisitor</tt> concept in Python,
|
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|
||||
@@ -35,7 +35,7 @@ namespace boost {
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template <class Graph, class VertexDescriptor, class OutEdgeIter>
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class adjacency_iterator_generator {
|
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public:
|
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typedef <a href="../../utility/iterator_adaptors.htm#iterator_adaptor">iterator_adaptor</a><...> type;
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typedef <a href="../../iterator/doc/iterator_adaptor.html">iterator_adaptor</a><...> type;
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};
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}
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</pre>
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@@ -380,6 +380,11 @@ Software--Practice & Experience, 21 (11), pp. 1129-1164, 1991.
|
||||
<em>An automatic reordering scheme for simultaneous equations derived from network analysis.</em><br>
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||||
Int. J. Numer. Methods Engrg. 2, pp. 523-533, 1970.
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|
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<p></p><dt><a name="palmer2000">63</a>
|
||||
<dd>C. Palmer and J. Steffan<br>
|
||||
<em>Generating Network Topologies That Obey Power Laws</em><br>
|
||||
Proceedings of GLOBECOM. November, 2000.
|
||||
|
||||
</dl>
|
||||
|
||||
<br>
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|
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@@ -242,7 +242,7 @@ UTIL: <tt>buffer(Buffer& Q)</tt>
|
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<b>Default:</b> <tt>boost::queue</tt><br>
|
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|
||||
<b>Python</b>: The buffer must derive from the <a
|
||||
href="./Buffer.html#python">Buffer</a> type for the graph.
|
||||
href="./Buffer.html">Buffer</a> type for the graph.
|
||||
|
||||
</blockquote>
|
||||
|
||||
|
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@@ -113,7 +113,7 @@ UTIL: <tt>buffer(Buffer& Q)</tt>
|
||||
<b>Default:</b> <tt>boost::queue</tt><br>
|
||||
|
||||
<b>Python</b>: The buffer must derive from the <a
|
||||
href="./Buffer.html#python">Buffer</a> type for the graph.
|
||||
href="./Buffer.html">Buffer</a> type for the graph.
|
||||
</blockquote>
|
||||
|
||||
|
||||
|
||||
+2
-2
@@ -26,9 +26,9 @@
|
||||
<ul>
|
||||
|
||||
<li>Dynamic graph algorithms such as described in <a
|
||||
href="http://citeseer.nj.nec.com/eppstein99dynamic.html">Dynamic Graph
|
||||
href="http://citeseer.ist.psu.edu/eppstein99dynamic.html">Dynamic Graph
|
||||
Algorithms</a> and <a
|
||||
href="http://citeseer.nj.nec.com/alberts98software.html">A Software
|
||||
href="http://citeseer.ist.psu.edu/alberts98software.html">A Software
|
||||
Library of Dynamic Graph Algorithms</a>.
|
||||
|
||||
<li>Polish up code/docs for pending items and champion the formal
|
||||
|
||||
@@ -83,7 +83,7 @@ The goal of the Cuthill-Mckee (and reverse Cuthill-Mckee) ordering
|
||||
algorithm[<A
|
||||
HREF="bibliography.html#george81:__sparse_pos_def">14</A>, <A
|
||||
HREF="bibliography.html#cuthill69:reducing_bandwith">43</A>, <a
|
||||
href="bibliography.html#liu75:rcm">44</a>, <a
|
||||
href="bibliography.html#liu75:anal_cm_rcm">44</a>, <a
|
||||
href="bibliography.html#george71:fem">45</a> ] is to reduce the <a
|
||||
href="./bandwidth.html">bandwidth</a> of a graph by reordering the
|
||||
indices assigned to each vertex. The Cuthill-Mckee ordering algorithm
|
||||
|
||||
@@ -54,7 +54,7 @@ than either the Dijkstra or Bellman-Ford algorithm.
|
||||
Use breadth-first search instead of this algorithm
|
||||
when all edge weights are equal to one. For the definition of the
|
||||
shortest-path problem see Section <A
|
||||
HREF="graph_theory_review.html#sec:shortest-path-algorithms">Shortest-Paths
|
||||
HREF="graph_theory_review.html#sec:shortest-paths-algorithms">Shortest-Paths
|
||||
Algorithms</A> for some background to the shortest-path problem.
|
||||
</P>
|
||||
|
||||
|
||||
@@ -27,12 +27,12 @@
|
||||
|
||||
<P>
|
||||
<PRE>
|
||||
template <class <a href="./IncidenceGraph.html">IncidenceGraph</a>, class <a href="./DFSVisitor.html">DFSVisitor</a>, class <a href="#ColorMap">ColorMap</a>>
|
||||
template <class <a href="./IncidenceGraph.html">IncidenceGraph</a>, class <a href="./DFSVisitor.html">DFSVisitor</a>, class ColorMap>
|
||||
void depth_first_visit(IncidenceGraph& g,
|
||||
typename graph_traits<IncidenceGraph>::vertex_descriptor s,
|
||||
DFSVisitor& vis, ColorMap color)
|
||||
|
||||
template <class <a href="./IncidenceGraph.html">IncidenceGraph</a>, class <a href="./DFSVisitor.html">DFSVisitor</a>, class <a href="#ColorMap">ColorMap</a>,
|
||||
template <class <a href="./IncidenceGraph.html">IncidenceGraph</a>, class <a href="./DFSVisitor.html">DFSVisitor</a>, class ColorMap,
|
||||
class TerminatorFunc>
|
||||
void depth_first_visit(IncidenceGraph& g,
|
||||
typename graph_traits<IncidenceGraph>::vertex_descriptor s,
|
||||
|
||||
@@ -56,7 +56,7 @@ algorithm for the case when some edge weights are negative. Use
|
||||
breadth-first search instead of Dijkstra's algorithm when all edge
|
||||
weights are equal to one. For the definition of the shortest-path
|
||||
problem see Section <A
|
||||
HREF="graph_theory_review.html#sec:shortest-path-algorithms">Shortest-Paths
|
||||
HREF="graph_theory_review.html#sec:shortest-paths-algorithms">Shortest-Paths
|
||||
Algorithms</A> for some background to the shortest-path problem.
|
||||
</P>
|
||||
|
||||
|
||||
@@ -29,7 +29,7 @@ the reader has some previous acquaintance with graph algorithms, this
|
||||
chapter should be enough to get started. If the reader has no
|
||||
previous background in graph algorithms we suggest a more thorough
|
||||
introduction such as <a
|
||||
href="http://www.toc.lcs.mit.edu/~clr/">Introduction to Algorithms</a>
|
||||
href="http://toc.lcs.mit.edu/~clr/">Introduction to Algorithms</a>
|
||||
by Cormen, Leiserson, and Rivest.
|
||||
|
||||
<P>
|
||||
|
||||
@@ -80,6 +80,12 @@ September 27, 2000.
|
||||
<h2>Changes by version</h2>
|
||||
<a name="by-version">
|
||||
<ul>
|
||||
<li>Version 1.33.1<br><b>Bug Fixes</b>
|
||||
<ul>
|
||||
<li><a href="fruchterman_reingold.html"><TT>fruchterman_reingold_force_directed_layout</TT></A>: Fixed enumeration of grid-force pairs, which caused some odd graph formations along grid lines.</li>
|
||||
<li><a href="king_ordering.html"><tt>king_ordering</tt></a> and <a
|
||||
href="cuthill_mckee_ordering.html"><tt>cuthill_mckee_ordering</tt></a>: Fixed bug that caused failures with the multi-component version of these algorithms.</li>
|
||||
</ul></li>
|
||||
<li>Version 1.33.0<br><b>New algorithms and components</b>
|
||||
<ul>
|
||||
<li><a href="python.html">Experimental Python bindings</a>, from Doug Gregor and Indiana University.</li>
|
||||
|
||||
@@ -35,7 +35,7 @@ the situation where edges are both added and removed from the graph,
|
||||
hence it is called <b><i>incremental</i></b><a
|
||||
href="bibliography.html#eppstein97:dynamic_graph">[42]</a> (and not
|
||||
fully dynamic). The disjoint-sets class is described in Section <A
|
||||
HREF="../../disjoint_sets/disjoint_sets.html">Disjoint Sets</A>.
|
||||
HREF="../disjoint_sets/disjoint_sets.html">Disjoint Sets</A>.
|
||||
|
||||
<P>
|
||||
The following five operations are the primary functions that you will
|
||||
|
||||
@@ -37,7 +37,7 @@ namespace boost {
|
||||
template <class Graph, class VertexDescriptor, class InEdgeIter>
|
||||
class inv_adjacency_iterator_generator {
|
||||
public:
|
||||
typedef <a href="../../utility/iterator_adaptors.htm#iterator_adaptor">iterator_adaptor</a><...> type;
|
||||
typedef <a href="../../iterator/doc/iterator_adaptor.html">iterator_adaptor</a><...> type;
|
||||
};
|
||||
}
|
||||
</pre>
|
||||
|
||||
@@ -95,7 +95,7 @@ IN: <tt>const Graph& g</tt>
|
||||
IN: <tt>OutputIterator spanning_tree_edges</tt>
|
||||
<blockquote>
|
||||
The edges of the minimum spanning tree are output to this <a
|
||||
href="http:www.sgi.com/tech/stl/OutputIterator.html">Output
|
||||
href="http://www.sgi.com/tech/stl/OutputIterator.html">Output
|
||||
Iterator</a>.<br>
|
||||
|
||||
<b>Python</b>: This parameter is not used in Python. Instead, a
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
<html><head><meta http-equiv="Content-Type" content="text/html; charset=ISO-8859-1"><title>Struct template layout_tolerance</title></head><body bgcolor="white" text="black" link="#0000FF" vlink="#840084" alink="#0000FF"><table cellpadding="2" width="100%"><td valign="top"><img src="../../../boost.png" alt="boost.png (6897 bytes)" width="277" height="86"></td><td align="center"><a href="../../../index.htm">Home</a></td><td align="center"><a href="../../libraries.htm">Libraries</a></td><td align="center"><a href="../../../people/people.htm">People</a></td><td align="center"><a href="../../../more/faq.htm">FAQ</a></td><td align="center"><a href="../../../more/index.htm">More</a></td></table><hr><div class="refentry" lang="en"><a name="struct.boost.layout_tolerance"></a><div class="titlepage"><div></div><div></div></div><div class="refnamediv"><h2><span class="refentrytitle">Struct template layout_tolerance</span></h2><p>boost::layout_tolerance — Determines when to terminate layout of a particular graph based on a given relative tolerance. </p></div><h2 xmlns:rev="http://www.cs.rpi.edu/~gregod/boost/tools/doc/revision" class="refsynopsisdiv-title">Synopsis</h2><div xmlns:rev="http://www.cs.rpi.edu/~gregod/boost/tools/doc/revision" class="refsynopsisdiv"><pre class="synopsis"><span class="bold"><b>template</b></span><<span class="bold"><b>typename</b></span> T = double>
|
||||
<span class="bold"><b>struct</b></span> layout_tolerance {
|
||||
<span class="emphasis"><em>// <a href="layout_tolerance.html#struct.boost.layout_toleranceconstruct-copy-destruct">construct/copy/destruct</a></em></span>
|
||||
<span class="emphasis"><em>// <a href="layout_tolerance.html#layout_toleranceconstruct-copy-destruct">construct/copy/destruct</a></em></span>
|
||||
<a href="layout_tolerance.html#id103752-bb">layout_tolerance</a>(<span class="bold"><b>const</b></span> T & = T(0.001));
|
||||
|
||||
<span class="emphasis"><em>// <a href="layout_tolerance.html#id103692-bb">public member functions</a></em></span>
|
||||
|
||||
@@ -20,6 +20,7 @@
|
||||
<BR Clear>
|
||||
|
||||
<H1><A NAME="sec:mmd">
|
||||
<img src="figs/python.gif" alt="(Python)"/>
|
||||
<TT>minimum_degree_ordering</TT>
|
||||
</H1>
|
||||
|
||||
|
||||
@@ -57,7 +57,8 @@ public:
|
||||
</PRE>
|
||||
|
||||
<p> This class template implements a generator for scale-free graphs
|
||||
using the Power Law Out Degree (PLOD) algorithm, suitable for
|
||||
using the Power Law Out Degree (PLOD) algorithm
|
||||
[<a href="bibliography.html#palmer2000">63</a>], suitable for
|
||||
initializing an <a
|
||||
href="adjacency_list.html"><tt>adjacency_list</tt></a> or other graph
|
||||
structure with iterator-based initialization. A scale-free graph
|
||||
|
||||
@@ -26,8 +26,8 @@
|
||||
<li><a href="http://www.ddj.com/articles/2000/0009/0009toc.htm">Dr. Dobb's Sept. 2000 Article</a></a></li>
|
||||
<li><a href="http://www.acm.org/pubs/citations/proceedings/oops/320384/p399-siek/">OOPSLA'99 GGCL Paper</a></li>
|
||||
<li>Lie-Quan Lee's Master's Thesis about GGCL<a href="http://www.lsc.nd.edu/downloads/research/ggcl/papers/thesis.ps">(ps)</a> <a href="http://www.lsc.nd.edu/downloads/research/ggcl/papers/thesis.pdf">(pdf)</a></li>
|
||||
<li>Dietmar Kühl's Master's Thesis: <a href="ftp://ftp.informatik.uni-konstanz.de/pub/algo/personal/kuehl/da.ps.gz">Design Pattern for the Implementation of Graph Algorithms</a></li>
|
||||
<li><a href="http://www.acl.lanl.gov/iscope99/">ISCOPE'99</a> Sparse Matrix Ordering <a href="./iscope99.pdf">(pdf)</a></li>
|
||||
<li><a href="http://www.dietmar-kuehl.de/generic-graph-algorithms.pdf">Dietmar Kühl's Master's Thesis: Design Pattern for the Implementation of Graph Algorithms</a></li>
|
||||
<li>ISCOPE'99 Sparse Matrix Ordering <a href="./iscope99.pdf">(pdf)</a></li>
|
||||
<li><a href="http://www.oonumerics.org/tmpw00/">C++ Template Workshop 2000</a>, Concept Checking</li>
|
||||
</ul>
|
||||
|
||||
|
||||
@@ -68,10 +68,10 @@ For version 1:
|
||||
See <A
|
||||
href="http://www.boost.org/libs/graph/example/cuthill_mckee_ordering.cpp"><TT>example/sloan_ordering.cpp</TT></A>.
|
||||
<H3>See Also</H3>
|
||||
<p><http://www.boost.org/libs/graph/doc/sloan_start_end_vertices.html><a href="http://www.boost.org/libs/graph/doc/sloan_start_end_vertices.html">sloan_start_end_vertices</a></http:>,
|
||||
<p><a href="http://www.boost.org/libs/graph/doc/sloan_start_end_vertices.htm">sloan_start_end_vertices</a>,
|
||||
<A
|
||||
href="http://www.boost.org/libs/graph/doc/bandwidth.html">bandwidth</A>, <a href="http://www.boost.org/libs/graph/doc/profile.html">profile</a>,
|
||||
<a href="http://www.boost.org/libs/graph/doc/wavefront.html">wavefront</a> and
|
||||
href="http://www.boost.org/libs/graph/doc/bandwidth.html">bandwidth</A>, <a href="http://www.boost.org/libs/graph/doc/profile.htm">profile</a>,
|
||||
<a href="http://www.boost.org/libs/graph/doc/wavefront.htm">wavefront</a> and
|
||||
<TT>degree_property_map</TT> in <TT>boost/graph/properties.hpp</TT>. </p>
|
||||
<p>[1] S. W. Sloan, <i>An algorithm for profile and wavefront reduction of sparse
|
||||
matrices</i>, Int. j. numer. methods eng., <b>23</b>, 239 - 251 (1986)</p>
|
||||
|
||||
+29
-29
@@ -70,10 +70,10 @@
|
||||
<li><img src="figs/python_ico.gif" alt="(Python)"/><a href="python.html">Python bindings</a></li>
|
||||
<li><a href="./visitor_concepts.html">Visitor Concepts</a>
|
||||
<OL>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><a href="./BFSVisitor.html">BFS Visitor</a>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><a href="./DFSVisitor.html">DFS Visitor</a>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><a href="./DFSVisitor.html"><a href="./DijkstraVisitor.html">Dijkstra Visitor</a>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><a href="./BellmanFordVisitor.html">Bellman Ford Visitor</a>
|
||||
<LI><a href="./BFSVisitor.html">BFS Visitor</a>
|
||||
<LI><a href="./DFSVisitor.html">DFS Visitor</a>
|
||||
<LI><a href="./DFSVisitor.html"><a href="./DijkstraVisitor.html">Dijkstra Visitor</a>
|
||||
<LI><a href="./BellmanFordVisitor.html">Bellman Ford Visitor</a>
|
||||
<LI><a href="AStarVisitor.html">A* Visitor</a></LI>
|
||||
<LI><a href="./EventVisitor.html">Event Visitor</a>
|
||||
</OL>
|
||||
@@ -127,39 +127,39 @@
|
||||
<LI><a href="./bgl_named_params.html"><tt>bgl_named_params</tt></a>
|
||||
<LI>Core Algorithm Patterns
|
||||
<OL>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><A href="./breadth_first_search.html"><tt>breadth_first_search</tt></A>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><A href="./breadth_first_search.html"><A href="./breadth_first_visit.html"><tt>breadth_first_visit</tt></A>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><A
|
||||
<LI><A href="./breadth_first_search.html"><tt>breadth_first_search</tt></A>
|
||||
<LI><A href="./breadth_first_search.html"><A href="./breadth_first_visit.html"><tt>breadth_first_visit</tt></A>
|
||||
<LI><A
|
||||
href="./depth_first_search.html"><tt>depth_first_search</tt></A>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><A href="./depth_first_visit.html"><tt>depth_first_visit</tt></A>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><A
|
||||
<LI><A href="./depth_first_visit.html"><tt>depth_first_visit</tt></A>
|
||||
<LI><A
|
||||
href="./undirected_dfs.html"><tt>undirected_dfs</tt></A>
|
||||
</OL>
|
||||
<LI>Graph Algorithms
|
||||
<OL>
|
||||
<LI>Shortest Paths Algorithms
|
||||
<OL>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><A href="./dijkstra_shortest_paths.html"><tt>dijkstra_shortest_paths</tt></A>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><A href="./bellman_ford_shortest.html"><tt>bellman_ford_shortest_paths</tt></A>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><A href="./dag_shortest_paths.html"><tt>dag_shortest_paths</tt></A>
|
||||
<LI><A href="./dijkstra_shortest_paths.html"><tt>dijkstra_shortest_paths</tt></A>
|
||||
<LI><A href="./bellman_ford_shortest.html"><tt>bellman_ford_shortest_paths</tt></A>
|
||||
<LI><A href="./dag_shortest_paths.html"><tt>dag_shortest_paths</tt></A>
|
||||
<LI><A
|
||||
href="./johnson_all_pairs_shortest.html"><tt>johnson_all_pairs_shortest_paths</tt></A>
|
||||
<li><a href="floyd_warshall_shortest.html"><tt>floyd_warshall_all_pairs_shortest_paths</tt></a></li>
|
||||
</OL>
|
||||
<LI>Minimum Spanning Tree Algorithms
|
||||
<OL>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><A
|
||||
<LI><A
|
||||
href="./kruskal_min_spanning_tree.html"><tt>kruskal_minimum_spanning_tree</tt></A>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><A
|
||||
<LI><A
|
||||
href="./prim_minimum_spanning_tree.html"><tt>prim_minimum_spanning_tree</tt></A>
|
||||
</OL>
|
||||
<LI>Connected Components Algorithms
|
||||
<OL>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><A href="./connected_components.html"><tt>connected_components</tt></A>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><A href="./strong_components.html"><tt>strong_components</tt></A>
|
||||
<LI><A href="./connected_components.html"><tt>connected_components</tt></A>
|
||||
<LI><A href="./strong_components.html"><tt>strong_components</tt></A>
|
||||
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><a href="biconnected_components.html"><tt>biconnected_components</tt></a>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><a href="biconnected_components.html#sec:articulation_points"><tt>articulation_points</tt></a>
|
||||
<LI><a href="biconnected_components.html"><tt>biconnected_components</tt></a>
|
||||
<LI><a href="biconnected_components.html#sec:articulation_points"><tt>articulation_points</tt></a>
|
||||
<LI><a href="./incremental_components.html">Incremental Connected Components</a>
|
||||
<OL>
|
||||
<LI><A href="./incremental_components.html#sec:initialize-incremental-components"><tt>initialize_incremental_components</tt></A>
|
||||
@@ -178,35 +178,35 @@
|
||||
|
||||
<li>Sparse Matrix Ordering Algorithms
|
||||
<ol>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><A
|
||||
<LI><A
|
||||
href="./cuthill_mckee_ordering.html"><tt>cuthill_mckee_ordering</tt></a>
|
||||
<li><img src="figs/python_ico.gif" alt="(Python)"/><a href="king_ordering.html"><tt>king_ordering</tt></a></li>
|
||||
<li><a href="king_ordering.html"><tt>king_ordering</tt></a></li>
|
||||
<LI><a href="./minimum_degree_ordering.html"><tt>minimum_degree_ordering</tt></a>
|
||||
</ol>
|
||||
</li>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><A href="./topological_sort.html"><tt>topological_sort</tt></A>
|
||||
<li><img src="figs/python_ico.gif" alt="(Python)"/><a href="./transitive_closure.html"><tt>transitive_closure</tt></a>
|
||||
<LI><A href="./topological_sort.html"><tt>topological_sort</tt></A>
|
||||
<li><a href="./transitive_closure.html"><tt>transitive_closure</tt></a>
|
||||
<LI><A href="./copy_graph.html"><tt>copy_graph</tt></A>
|
||||
<LI><A href="./transpose_graph.html"><tt>transpose_graph</tt></A>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><A href="./isomorphism.html"><tt>isomorphism</tt></A>
|
||||
<LI><A href="./isomorphism.html"><tt>isomorphism</tt></A>
|
||||
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><A
|
||||
<LI><A
|
||||
href="sequential_vertex_coloring.html"><tt>sequential_vertex_coloring</tt></A>
|
||||
<li><a href="./sloan_ordering.htm"><tt>sloan_ordering</tt></a></li>
|
||||
<LI><A href="./wavefront.htm"><tt>ith_wavefront</tt>, <tt>max_wavefront</tt>, <tt>aver_wavefront</tt>, and <tt>rms_wavefront</tt></A></LI>
|
||||
<LI><img src="figs/python_ico.gif" alt="(Python)"/><A href="betweenness_centrality.html"><tt>brandes_betweenness_centrality</tt></A></LI>
|
||||
<LI><A href="betweenness_centrality.html"><tt>brandes_betweenness_centrality</tt></A></LI>
|
||||
<li>Layout algorithms
|
||||
<ol>
|
||||
<li><a href="random_layout.html"><tt>random_graph_layout</tt></a></li>
|
||||
<li><img src="figs/python_ico.gif" alt="(Python)"/><a href="circle_layout.html"><tt>circle_layout</tt></a></li>
|
||||
<li><img src="figs/python_ico.gif" alt="(Python)"/><a href="kamada_kawai_spring_layout.html"><tt>kamada_kawai_spring_layout</tt></a></li>
|
||||
<li><img src="figs/python_ico.gif" alt="(Python)"/><a href="fruchterman_reingold.html"><tt>fruchterman_reingold_force_directed_layout</tt></a></li>
|
||||
<li><a href="circle_layout.html"><tt>circle_layout</tt></a></li>
|
||||
<li><a href="kamada_kawai_spring_layout.html"><tt>kamada_kawai_spring_layout</tt></a></li>
|
||||
<li><a href="fruchterman_reingold.html"><tt>fruchterman_reingold_force_directed_layout</tt></a></li>
|
||||
<li><a href="gursoy_atun_layout.html"><tt>gursoy_atun_layout</tt></a></li>
|
||||
</ol>
|
||||
</li>
|
||||
<li>Clustering algorithms
|
||||
<ol>
|
||||
<li><img src="figs/python_ico.gif" alt="(Python)"/><a href="bc_clustering.html"><tt>betweenness_centrality_clustering</tt></a></li>
|
||||
<li><a href="bc_clustering.html"><tt>betweenness_centrality_clustering</tt></a></li>
|
||||
</ol>
|
||||
</li>
|
||||
<li><a href="astar_search.html"><tt>astar_search</tt></a></li>
|
||||
|
||||
@@ -59,7 +59,7 @@ IN: <tt>VertexListGraph& g</tt>
|
||||
OUT: <tt>OutputIterator result</tt>
|
||||
<blockquote>
|
||||
The vertex descriptors of the graph will be output to the
|
||||
<TT>result</TT> output iterator in their topological order. The
|
||||
<TT>result</TT> output iterator in <b>reverse</b> topological order. The
|
||||
iterator type must model <a
|
||||
href="http://www.sgi.com/tech/stl/OutputIterator.html">Output
|
||||
Iterator</a>.<br>
|
||||
|
||||
@@ -386,7 +386,7 @@ namespace boost {
|
||||
template <class EdgeIterator>
|
||||
inline adjacency_list(EdgeIterator first, EdgeIterator last,
|
||||
vertices_size_type n,
|
||||
edges_size_type m = 0,
|
||||
edges_size_type = 0,
|
||||
const GraphProperty& p = GraphProperty())
|
||||
: Base(n, first, last), m_property(p) { }
|
||||
|
||||
@@ -394,7 +394,7 @@ namespace boost {
|
||||
inline adjacency_list(EdgeIterator first, EdgeIterator last,
|
||||
EdgePropertyIterator ep_iter,
|
||||
vertices_size_type n,
|
||||
edges_size_type m = 0,
|
||||
edges_size_type = 0,
|
||||
const GraphProperty& p = GraphProperty())
|
||||
: Base(n, first, last, ep_iter), m_property(p) { }
|
||||
#endif
|
||||
|
||||
@@ -164,7 +164,7 @@ namespace boost {
|
||||
|
||||
// Find one vertex from each connected component
|
||||
BGL_FORALL_VERTICES_T(v, G, Graph) {
|
||||
if (get(color, v) != Color::white()) {
|
||||
if (get(color, v) == Color::white()) {
|
||||
depth_first_visit(G, v, dfs_visitor<>(), color);
|
||||
vertex_queue.push_back(v);
|
||||
}
|
||||
|
||||
@@ -13,6 +13,7 @@
|
||||
|
||||
#include <map> // for vertex_map in copy_impl
|
||||
#include <boost/config.hpp>
|
||||
#include <boost/detail/workaround.hpp>
|
||||
#include <boost/operators.hpp>
|
||||
#include <boost/property_map.hpp>
|
||||
#include <boost/pending/integer_range.hpp>
|
||||
@@ -2669,12 +2670,16 @@ namespace boost {
|
||||
#if !defined(BOOST_NO_HASH) && !defined(BOOST_NO_TEMPLATE_PARTIAL_SPECIALIZATION)
|
||||
namespace BOOST_STD_EXTENSION_NAMESPACE {
|
||||
|
||||
#if BOOST_WORKAROUND( _STLPORT_VERSION, >= 0x500 )
|
||||
// STLport 5 already defines a hash<void*> specialization.
|
||||
#else
|
||||
template <>
|
||||
struct hash< void* > // Need this when vertex_descriptor=void*
|
||||
{
|
||||
std::size_t
|
||||
operator()(void* v) const { return (std::size_t)v; }
|
||||
};
|
||||
#endif
|
||||
|
||||
template <typename V>
|
||||
struct hash< boost::detail::stored_edge<V> >
|
||||
|
||||
@@ -201,12 +201,12 @@ namespace boost {
|
||||
template<typename Descriptor>
|
||||
typename graph::detail::bundled_result<Graph, Descriptor>::type&
|
||||
operator[](Descriptor x)
|
||||
{ return this->m_g[local_to_global(x)]; }
|
||||
{ return const_cast<Graph&>(this->m_g)[x]; }
|
||||
|
||||
template<typename Descriptor>
|
||||
typename graph::detail::bundled_result<Graph, Descriptor>::type const&
|
||||
operator[](Descriptor x) const
|
||||
{ return this->m_g[local_to_global(x)]; }
|
||||
{ return this->m_g[x]; }
|
||||
#endif // BOOST_GRAPH_NO_BUNDLED_PROPERTIES
|
||||
|
||||
//private:
|
||||
|
||||
@@ -131,41 +131,21 @@ struct grid_force_pairs
|
||||
apply_force(*v, *u);
|
||||
}
|
||||
|
||||
// Repulse vertices in the bucket to the right
|
||||
if (column < columns - 1) {
|
||||
bucket_t& r_bucket = buckets[row * columns + column + 1];
|
||||
for (v = r_bucket.begin(); v != r_bucket.end(); ++v) {
|
||||
apply_force(*u, *v);
|
||||
apply_force(*v, *u);
|
||||
}
|
||||
}
|
||||
|
||||
// Repulse vertices in bucket below
|
||||
if (row < rows - 1) {
|
||||
bucket_t& b_bucket = buckets[(row + 1) * columns + column];
|
||||
for (v = b_bucket.begin(); v != b_bucket.end(); ++v) {
|
||||
apply_force(*u, *v);
|
||||
apply_force(*v, *u);
|
||||
}
|
||||
}
|
||||
|
||||
// Repulse vertices in bucket below and to the right
|
||||
if (column < columns - 1 && row < rows - 1) {
|
||||
bucket_t& br_bucket = buckets[(row + 1) * columns + column + 1];
|
||||
for (v = br_bucket.begin(); v != br_bucket.end(); ++v) {
|
||||
apply_force(*u, *v);
|
||||
apply_force(*v, *u);
|
||||
}
|
||||
}
|
||||
|
||||
// Repulse vertices in bucket above and to the right
|
||||
if (column < columns - 1 && row > 0) {
|
||||
bucket_t& ur_bucket = buckets[(row - 1) * columns + column + 1];
|
||||
for (v = ur_bucket.begin(); v != ur_bucket.end(); ++v) {
|
||||
apply_force(*u, *v);
|
||||
apply_force(*v, *u);
|
||||
}
|
||||
}
|
||||
std::size_t adj_start_row = row == 0? 0 : row - 1;
|
||||
std::size_t adj_end_row = row == rows - 1? row : row + 1;
|
||||
std::size_t adj_start_column = column == 0? 0 : column - 1;
|
||||
std::size_t adj_end_column = column == columns - 1? column : column + 1;
|
||||
for (std::size_t other_row = adj_start_row; other_row <= adj_end_row;
|
||||
++other_row)
|
||||
for (std::size_t other_column = adj_start_column;
|
||||
other_column <= adj_end_column; ++other_column)
|
||||
if (other_row != row || other_column != column) {
|
||||
// Repulse vertices in this bucket
|
||||
bucket_t& other_bucket
|
||||
= buckets[other_row * columns + other_column];
|
||||
for (v = other_bucket.begin(); v != other_bucket.end(); ++v)
|
||||
apply_force(*u, *v);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -294,7 +294,7 @@ namespace boost {
|
||||
|
||||
// Find one vertex from each connected component
|
||||
BGL_FORALL_VERTICES_T(v, G, Graph) {
|
||||
if (get(color, v) != Color::white()) {
|
||||
if (get(color, v) == Color::white()) {
|
||||
depth_first_visit(G, v, dfs_visitor<>(), color);
|
||||
vertex_queue.push_back(v);
|
||||
}
|
||||
|
||||
@@ -11,7 +11,6 @@
|
||||
|
||||
#include <functional>
|
||||
#include <boost/property_map.hpp>
|
||||
#include <cmath>
|
||||
#include <boost/optional.hpp>
|
||||
#include <vector>
|
||||
|
||||
@@ -87,23 +86,38 @@ private:
|
||||
group** children;
|
||||
};
|
||||
|
||||
size_type log2(size_type n)
|
||||
{
|
||||
size_type leading_zeroes = 0;
|
||||
do {
|
||||
size_type next = n << 1;
|
||||
if (n == (next >> 1)) {
|
||||
++leading_zeroes;
|
||||
n = next;
|
||||
} else {
|
||||
break;
|
||||
}
|
||||
} while (true);
|
||||
return sizeof(size_type) * CHAR_BIT - leading_zeroes - 1;
|
||||
}
|
||||
|
||||
public:
|
||||
relaxed_heap(size_type n, const Compare& compare = Compare(),
|
||||
const ID& id = ID())
|
||||
: compare(compare), id(id), root(smallest_key), groups(n),
|
||||
smallest_value(0)
|
||||
{
|
||||
#ifndef BOOST_NO_STDC_NAMESPACE
|
||||
using std::log;
|
||||
#endif // BOOST_NO_STDC_NAMESPACE
|
||||
if (n == 0) {
|
||||
root.children = new group*[1];
|
||||
return;
|
||||
}
|
||||
|
||||
if (n == 0) return;
|
||||
log_n = log2(n);
|
||||
|
||||
log_n = static_cast<size_type>(log((double)n) / log(2.0));
|
||||
if (log_n == 0) log_n = 1;
|
||||
size_type g = n / log_n;
|
||||
if (n % log_n > 0) ++g;
|
||||
size_type log_g = static_cast<size_type>(log((double)g) / log(2.0));
|
||||
size_type log_g = log2(g);
|
||||
size_type r = log_g;
|
||||
|
||||
// Reserve an appropriate amount of space for data structures, so
|
||||
@@ -120,7 +134,7 @@ public:
|
||||
root.children[r] = &index_to_group[idx];
|
||||
idx = build_tree(root, idx, r, log_g + 1);
|
||||
if (idx != g)
|
||||
r = static_cast<size_type>(log((double)(g - idx)) / log(2.0));
|
||||
r = static_cast<size_type>(log2(g-idx));
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -46,6 +46,9 @@
|
||||
#include <sstream>
|
||||
#endif
|
||||
|
||||
using std::free;
|
||||
using std::malloc;
|
||||
|
||||
#ifndef GRAPHVIZ_DIRECTED
|
||||
#error Need to define the GRAPHVIZ_DIRECTED macro to either 0 or 1
|
||||
#endif
|
||||
|
||||
@@ -46,6 +46,9 @@
|
||||
#include <sstream>
|
||||
#endif
|
||||
|
||||
using std::free;
|
||||
using std::malloc;
|
||||
|
||||
#ifndef GRAPHVIZ_DIRECTED
|
||||
#error Need to define the GRAPHVIZ_DIRECTED macro to either 0 or 1
|
||||
#endif
|
||||
|
||||
@@ -24,6 +24,9 @@
|
||||
#include <sstream>
|
||||
#endif
|
||||
|
||||
using std::free;
|
||||
using std::malloc;
|
||||
|
||||
#ifndef GRAPHVIZ_DIRECTED
|
||||
#error Need to define the GRAPHVIZ_DIRECTED macro to either 0 or 1
|
||||
#endif
|
||||
|
||||
@@ -0,0 +1,109 @@
|
||||
// Copyright 2005 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)
|
||||
|
||||
// Authors: Douglas Gregor
|
||||
// Andrew Lumsdaine
|
||||
#include "basic_graph.hpp"
|
||||
#include <boost/python.hpp>
|
||||
#include <fstream>
|
||||
#include <string>
|
||||
#include <sstream>
|
||||
#include <boost/graph/iteration_macros.hpp>
|
||||
|
||||
namespace boost { namespace graph { namespace python {
|
||||
|
||||
template<typename DirectedS>
|
||||
void
|
||||
basic_graph<DirectedS>::read_adjlist(const std::string& filename,
|
||||
const std::string& node_id)
|
||||
{
|
||||
vector_property_map<std::string, VertexIndexMap> id_map
|
||||
= get_vertex_map<std::string>(node_id);
|
||||
|
||||
|
||||
std::ifstream file_in(filename.c_str());
|
||||
std::string line;
|
||||
std::map<std::string, vertex_descriptor> name_to_vertex;
|
||||
|
||||
while (std::getline(file_in, line)) {
|
||||
std::istringstream in(line);
|
||||
std::string src, tgt;
|
||||
if (in >> src >> tgt) {
|
||||
typename std::map<std::string, vertex_descriptor>::iterator pos;
|
||||
|
||||
// Find or create the source vertex
|
||||
vertex_descriptor src_vert;
|
||||
pos = name_to_vertex.find(src);
|
||||
if (pos == name_to_vertex.end()) {
|
||||
src_vert = add_vertex();
|
||||
name_to_vertex[src] = src_vert;
|
||||
put(id_map, src_vert, src);
|
||||
} else {
|
||||
src_vert = pos->second;
|
||||
}
|
||||
|
||||
// Find or create the target vertex
|
||||
vertex_descriptor tgt_vert;
|
||||
pos = name_to_vertex.find(tgt);
|
||||
if (pos == name_to_vertex.end()) {
|
||||
tgt_vert = add_vertex();
|
||||
name_to_vertex[tgt] = tgt_vert;
|
||||
put(id_map, tgt_vert, tgt);
|
||||
} else {
|
||||
tgt_vert = pos->second;
|
||||
}
|
||||
|
||||
// Add the edge
|
||||
add_edge(src_vert, tgt_vert);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template<typename DirectedS>
|
||||
void
|
||||
basic_graph<DirectedS>::write_adjlist(const std::string& filename,
|
||||
const std::string& node_id)
|
||||
{
|
||||
std::ofstream out(filename.c_str());
|
||||
VertexIndexMap index_map = get_vertex_index_map();
|
||||
|
||||
bool have_id_map = has_vertex_map(node_id);
|
||||
vector_property_map<std::string, VertexIndexMap> id_map;
|
||||
if (have_id_map)
|
||||
id_map = get_vertex_map<std::string>(node_id);
|
||||
|
||||
edge_iterator ei, ei_end;
|
||||
for (tie(ei, ei_end) = edges(); ei != ei_end; ++ei) {
|
||||
edge_descriptor e = *ei;
|
||||
if (have_id_map)
|
||||
out << get(id_map, source(e)) << ' '
|
||||
<< get(id_map, target(e)) << std::endl;
|
||||
else
|
||||
out << get(index_map, source(e)) << ' '
|
||||
<< get(index_map, target(e)) << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
// Explicit instantiations
|
||||
template
|
||||
void
|
||||
basic_graph<undirectedS>::read_adjlist(const std::string& filename,
|
||||
const std::string& node_id);
|
||||
template
|
||||
void
|
||||
basic_graph<undirectedS>::write_adjlist(const std::string& filename,
|
||||
const std::string& node_id);
|
||||
|
||||
template
|
||||
void
|
||||
basic_graph<bidirectionalS>::read_adjlist(const std::string& filename,
|
||||
const std::string& node_id);
|
||||
template
|
||||
void
|
||||
basic_graph<bidirectionalS>::write_adjlist(const std::string& filename,
|
||||
const std::string& node_id);
|
||||
|
||||
} } } // end namespace boost::graph::python
|
||||
@@ -127,6 +127,7 @@ basic_graph<DirectedS>::basic_graph(const std::string& filename,
|
||||
{
|
||||
switch (kind) {
|
||||
case gfk_adjlist:
|
||||
read_adjlist(filename);
|
||||
break;
|
||||
case gfk_graphviz:
|
||||
read_graphviz(filename);
|
||||
@@ -615,6 +616,9 @@ void export_basic_graph(const char* name)
|
||||
.def("get_edge_string_map", &Graph::get_edge_string_map)
|
||||
.def("get_edge_object_map", &Graph::get_edge_object_map)
|
||||
// Graph I/O
|
||||
.def("read_adjlist", &Graph::read_adjlist)
|
||||
.def("write_adjlist", &Graph::write_adjlist)
|
||||
.def("write_adjlist", &Graph::write_adjlist_def)
|
||||
.def("read_graphviz", &Graph::read_graphviz)
|
||||
.def("write_graphviz", &Graph::write_graphviz)
|
||||
.def("write_graphviz", &Graph::write_graphviz_def)
|
||||
|
||||
@@ -307,6 +307,15 @@ class basic_graph
|
||||
bool has_edge_map(const std::string& name) const;
|
||||
|
||||
// Graph I/O
|
||||
void read_adjlist(const std::string& filename,
|
||||
const std::string& node_id = std::string("node_id"));
|
||||
|
||||
void write_adjlist(const std::string& filename,
|
||||
const std::string& node_id = std::string("node_id"));
|
||||
|
||||
void write_adjlist_def(const std::string& filename)
|
||||
{ write_adjlist(filename); }
|
||||
|
||||
void read_graphviz(const std::string& filename,
|
||||
const std::string& node_id = std::string("node_id"));
|
||||
|
||||
|
||||
@@ -0,0 +1,97 @@
|
||||
// Copyright 2005 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)
|
||||
|
||||
// Authors: Ben Martin
|
||||
// Douglas Gregor
|
||||
// Andrew Lumsdaine
|
||||
#include <boost/graph/minimum_degree_ordering.hpp>
|
||||
//#include "graph.hpp"
|
||||
#include "digraph.hpp"
|
||||
#include <boost/python.hpp>
|
||||
#include <vector.h>
|
||||
#include <iostream.h>
|
||||
|
||||
namespace boost { namespace graph { namespace python {
|
||||
|
||||
template<typename GraphT>
|
||||
boost::python::list
|
||||
minimum_degree_ordering
|
||||
(GraphT& g,
|
||||
const vector_property_map<int, typename GraphT::VertexIndexMap>* outdegree,
|
||||
const vector_property_map<int, typename GraphT::VertexIndexMap>* supernode_size,
|
||||
const int delta,
|
||||
const typename GraphT::VertexIndexMap *id)
|
||||
{
|
||||
typedef std::vector<int> InversePermutationMap;
|
||||
typedef std::vector<int> PermutationMap;
|
||||
typedef vector_property_map<int, typename GraphT::VertexIndexMap> OutDegreeMap;
|
||||
typedef vector_property_map<int, typename GraphT::VertexIndexMap> SuperNodeSizeMap;
|
||||
typedef typename GraphT::VertexIndexMap VertexIndexMap;
|
||||
|
||||
int n = num_vertices(g);
|
||||
|
||||
OutDegreeMap out_degree_map = outdegree ? *outdegree : g. template get_vertex_map<int>("outdegree");
|
||||
InversePermutationMap inv_perm_map(n, 0);
|
||||
PermutationMap perm_map(n, 0);
|
||||
SuperNodeSizeMap sns_map =
|
||||
supernode_size ? *supernode_size : g. template get_vertex_map<int>("supernode_size");
|
||||
VertexIndexMap vertex_index_map = id ? *id : g.get_vertex_index_map();
|
||||
|
||||
typedef typename GraphT::vertex_iterator VertexIterator;
|
||||
VertexIterator v, vs, ve;
|
||||
using std::pair;
|
||||
std::pair<VertexIterator, VertexIterator> p;
|
||||
p = vertices(g);
|
||||
vs = p.first;
|
||||
ve = p.second;
|
||||
for (v = vs; v != ve; ++v)
|
||||
sns_map[*v] = 1;
|
||||
|
||||
boost::minimum_degree_ordering<GraphT, OutDegreeMap,
|
||||
InversePermutationMap, PermutationMap,
|
||||
SuperNodeSizeMap, VertexIndexMap>
|
||||
(g,
|
||||
out_degree_map,
|
||||
inv_perm_map,
|
||||
perm_map,
|
||||
sns_map,
|
||||
delta,
|
||||
vertex_index_map);
|
||||
|
||||
// Build the new ordering
|
||||
typedef typename graph_traits<GraphT>::vertex_descriptor vertex_descriptor;
|
||||
std::vector<vertex_descriptor> new_ordering(num_vertices(g));
|
||||
vs = p.first;
|
||||
ve = p.second;
|
||||
for (v = vs; v != ve; ++v)
|
||||
new_ordering[perm_map[get(vertex_index_map, *v)]] = *v;
|
||||
|
||||
// Build resulting list for Python
|
||||
typedef typename graph_traits<GraphT>::vertices_size_type vertices_size_type;
|
||||
boost::python::list result;
|
||||
typedef typename std::vector<vertex_descriptor>::iterator iterator;
|
||||
for (iterator i = new_ordering.begin(); i != new_ordering.end(); ++i)
|
||||
result.append(*i);
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
void export_minimum_degree_ordering()
|
||||
{
|
||||
using boost::python::arg;
|
||||
using boost::python::def;
|
||||
|
||||
def("minimum_degree_ordering",
|
||||
&minimum_degree_ordering<Digraph>,
|
||||
(arg("graph"),
|
||||
arg("out_degree") = (vector_property_map<int, Digraph::VertexIndexMap>*)0,
|
||||
arg("supernode_size") =
|
||||
(vector_property_map<int, Digraph::VertexIndexMap>*)0,
|
||||
arg("delta") = 0,
|
||||
arg("id") = (Digraph::VertexIndexMap*)0));
|
||||
}
|
||||
|
||||
} } } // end namespace boost::graph::python
|
||||
@@ -37,7 +37,7 @@ extern void export_incremental_components();
|
||||
extern void export_topological_sort();
|
||||
extern void export_cuthill_mckee_ordering();
|
||||
extern void export_king_ordering();
|
||||
//extern void export_minimum_degree_ordering();
|
||||
extern void export_minimum_degree_ordering();
|
||||
extern void export_sequential_vertex_coloring();
|
||||
extern void export_betweenness_centrality();
|
||||
extern void export_page_rank();
|
||||
@@ -121,7 +121,7 @@ BOOST_PYTHON_MODULE(bgl)
|
||||
// Sparse Matrix Ordering
|
||||
export_cuthill_mckee_ordering();
|
||||
export_king_ordering();
|
||||
// export_minimum_degree_ordering();
|
||||
export_minimum_degree_ordering();
|
||||
|
||||
// Other algorithms
|
||||
export_topological_sort();
|
||||
|
||||
Reference in New Issue
Block a user