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<Title>Boost Graph Library: Gürsoy-Atun Layout</Title>
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<H1>
<TT>gursoy_atun_layout</TT>
</H1>
<P>
<h3>Synopsis</h3>
<PRE>
<em>// Non-named parameter version</em>
template<typename VertexListAndIncidenceGraph, typename Topology,
typename PositionMap, typename VertexIndexMap,
typename EdgeWeightMap>
void
gursoy_atun_layout(const VertexListAndIncidenceGraph& g,
const Topology& space,
PositionMap position,
int nsteps = num_vertices(g),
double diameter_initial = sqrt((double)num_vertices(g)),
double diameter_final = 1,
double learning_constant_initial = 0.8,
double learning_constant_final = 0.2,
VertexIndexMap vertex_index_map = get(vertex_index, g),
EdgeWeightMap weight = dummy_property_map());
<em>// Named parameter version</em>
template<typename VertexListAndIncidenceGraph, typename Topology,
typename PositionMap, typename P, typename T, typename R>
void
gursoy_atun_layout(const VertexListAndIncidenceGraph& g,
const Topology& space,
PositionMap position,
const bgl_named_params<P,T,R>& params = <em>all defaults</em>);
</PRE>
<h3>Description</h3>
<P> This algorithm [<A HREF="bibliography.html#gursoy00">60</A>]
performs layout of directed graphs, either weighted or unweighted. This
algorithm is very different from the <a
href="kamada_kawai_spring_layout.html">Kamada-Kawai</a> and <a
href="fruchterman_reingold.html">Fruchterman-Reingold</a> algorithms,
because it does not explicitly strive to layout graphs in a visually
pleasing manner. Instead, it attempts to distribute the vertices
uniformly within a <em>topology</em> (e.g., rectangle, sphere, heart shape),
keeping vertices close to their neighbors; <a href="topology.html">various
topologies</a> are provided by BGL, and users can also create their own. The
algorithm itself is
based on <a
href="http://davis.wpi.edu/~matt/courses/soms/">Self-Organizing
Maps</a>.
<p>
<a href="topology.html#square_topology"><img src="figs/ga-square.png"></a>
<a href="topology.html#heart_topology"><img src="figs/ga-heart.png"></a>
<a href="topology.html#circle_topology"><img src="figs/ga-circle.png"></a>
</p>
<h3>Where Defined</h3>
<a href="../../../boost/graph/gursoy_atun_layout.hpp"><tt>boost/graph/gursoy_atun_layout.hpp</tt></a>
<h3>Parameters</h3>
IN: <tt>const Graph& g</tt>
<blockquote>
The graph object on which the algorithm will be applied. The type
<tt>Graph</tt> must be a model of <a
href="./VertexAndEdgeListGraph.html">Vertex List Graph</a> and <a
href="IncidenceGraph.html">Incidence Graph</a>.
</blockquote>
IN: <tt>const Topology& space</tt>
<blockquote>
The topology on which the graph will be laid out. The type must
model the <a href="topology.html#topology-concept">Topology</a> concept.
</blockquote>
OUT: <tt>PositionMap position</tt>
<blockquote>
The property map that stores the position of each vertex. The type
<tt>PositionMap</tt> must be a model of <a
href="../../property_map/doc/LvaluePropertyMap.html">Lvalue Property
Map</a> such that the vertex descriptor type of <tt>Graph</tt> is
convertible to its key type. Its value type must be
<tt>Topology::point_type</tt>.
</blockquote>
IN: <tt>int nsteps</tt>
<blockquote>
The number of iterations to perform.<br>
<b>Default</b>: <tt>num_vertices(g)</tt>
</blockquote>
IN: <tt>double diameter_initial</tt>
<blockquote>
When a vertex is selected to be updated, all vertices that are
reachable from that vertex within a certain diameter (in graph
terms) will also be updated. This diameter begins at
<tt>diameter_initial</tt> in the first iteration and ends at
<tt>diameter_final</tt> in the last iteration, progressing
exponentially. Generally the diameter decreases, in a manner similar to
the cooling schedule in <a
href="fruchterman_reingold.html">Fruchterman-Reingold</a>. The
diameter should typically decrease in later iterations, so this value
should not be less than <tt>diameter_final</tt>.<br>
<b>Default</b>: <tt>sqrt((double)num_vertices(g))</tt>
</blockquote>
IN: <tt>double diameter_final</tt>
<blockquote>
The final value of the diameter.<br>
<b>Default</b>: 1.0
</blockquote>
IN: <tt>double learning_constant_initial</tt>
<blockquote>
The learning rate affects how far vertices can moved to rearrange
themselves in a given iteration. The learning rate progresses
linearly from the initial value to the final value, both of which
should be between 0 and 1. The learning rate should typically
decrease, so the initial value should not exceed the final
value.<br> <b>Default</b>: 0.8
</blockquote>
IN: <tt>double learning_constant_final</tt>
<blockquote>
The final learning rate constant.<br>
<b>Default</b>: 0.2
</blockquote>
IN: <tt>VertexIndexMap vertex_index_map</tt>
<blockquote>
This maps each vertex to an integer in the range <tt>[0,
num_vertices(g))</tt>.
The type <tt>VertexIndexMap</tt> must be a model of <a
href="../../property_map/doc/ReadablePropertyMap.html">Readable Property
Map</a>. The value type of the map must be an integer type. The
vertex descriptor type of the graph needs to be usable as the key
type of the map.<br>
<b>Default:</b> <tt>get(vertex_index, g)</tt>
Note: if you use this default, make sure your graph has
an internal <tt>vertex_index</tt> property. For example,
<tt>adjacenty_list</tt> with <tt>VertexList=listS</tt> does
not have an internal <tt>vertex_index</tt> property.
</blockquote>
IN: <tt>EdgeWeightMap weight</tt>
<blockquote>
This maps each edge to an weight.
num_vertices(g))</tt>. This is only necessary when no
displacement map is provided.
The type <tt>EdgeWeightMap</tt> must be a model of <a
href="../../property_map/doc/ReadablePropertyMap.html">Readable Property
Map</a>. The value type of the map must be an floating-point type
compatible with <tt>double</tt>. The edge descriptor type of the
graph needs to be usable as the key type of the map. When this map
is a <tt>dummy_property_map</tt>, the algorithm assumes the graph is
unweighted.<br>
<b>Default:</b> <tt>dummy_property_map()</tt>
</blockquote>
<h3>Named Parameters</h3>
IN: <tt>iterations(int n)</tt>
<blockquote>
Executes the algorithm for <em>n</em> iterations.<br>
<b>Default:</b> <tt>num_vertices(g)</tt>
</blockquote>
IN: <tt>diameter_range(std::pair<T, T> range)</tt>
<blockquote>
Range specifying the parameters (<tt>diameter_initial</tt>, <tt>diameter_final</tt>). <br>
<b>Default:</b> <tt>std::make_pair(sqrt((double)num_vertices(g)), 1.0)</tt>
</blockquote>
IN: <tt>learning_constant_range(std::pair<T, T> range)</tt>
<blockquote>
Range specifying the parameters (<tt>learning_constant_initial</tt>, <tt>learning_constant_final</tt>). <br>
<b>Default:</b> <tt>std::make_pair(0.8, 0.2)</tt>
</blockquote>
IN: <tt>edge_weight(EdgeWeightMap weight)</tt>
<blockquote>
Equivalent to the non-named <tt>weight</tt> parameter.<br>
<b>Default:</b> <tt>dummy_property_map()</tt>
</blockquote>
IN: <tt>vertex_index_map(VertexIndexMap i_map)</tt>
<blockquote>
Equivalent to the non-named <tt>vertex_index_map</tt> parameter.<br>
<b>Default:</b> <tt>get(vertex_index, g)</tt>
Note: if you use this default, make sure your graph has
an internal <tt>vertex_index</tt> property. For example,
<tt>adjacenty_list</tt> with <tt>VertexList=listS</tt> does
not have an internal <tt>vertex_index</tt> property.
</blockquote>
<br>
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<TABLE>
<TR valign=top>
<TD nowrap>Copyright © 2004 Trustees of Indiana University</TD><TD>
Jeremiah Willcock, Indiana University (<script language="Javascript">address("osl.iu.edu", "jewillco")</script>)<br>
<A HREF="http://www.boost.org/people/doug_gregor.html">Doug Gregor</A>, Indiana University (<script language="Javascript">address("cs.indiana.edu", "dgregor")</script>)<br>
<A HREF="http://www.osl.iu.edu/~lums">Andrew Lumsdaine</A>,
Indiana University (<script language="Javascript">address("osl.iu.edu", "lums")</script>)
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