// Non-named parameter version 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()); // Named parameter version 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 = all defaults);
This algorithm  performs layout of directed graphs, either weighted or unweighted. This algorithm is very different from the Kamada-Kawai and Fruchterman-Reingold 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 topology (e.g., rectangle, sphere, heart shape), keeping vertices close to their neighbors; various topologies are provided by BGL, and users can also create their own. The algorithm itself is based on Self-Organizing Maps.
The graph object on which the algorithm will be applied. The type Graph must be a model of Vertex List Graph and Incidence Graph.IN: const Topology& space
The topology on which the graph will be laid out. The type must model the Topology concept.OUT: PositionMap position
The property map that stores the position of each vertex. The type PositionMap must be a model of Lvalue Property Map such that the vertex descriptor type of Graph is convertible to its key type. Its value type must be Topology::point_type.IN: int nsteps
The number of iterations to perform.IN: double diameter_initial
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 diameter_initial in the first iteration and ends at diameter_final in the last iteration, progressing exponentially. Generally the diameter decreases, in a manner similar to the cooling schedule in Fruchterman-Reingold. The diameter should typically decrease in later iterations, so this value should not be less than diameter_final.IN: double diameter_final
The final value of the diameter.IN: double learning_constant_initial
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.IN: double learning_constant_final
The final learning rate constant.IN: VertexIndexMap vertex_index_map
This maps each vertex to an integer in the range [0, num_vertices(g)). This is only necessary when no displacement map is provided. The type VertexIndexMap must be a model of Readable Property Map. 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.IN: EdgeWeightMap weight
Default: get(vertex_index, g) Note: if you use this default, make sure your graph has an internal vertex_index property. For example, adjacenty_list with VertexList=listS does not have an internal vertex_index property.
This maps each edge to a weight. The type EdgeWeightMap must be a model of Readable Property Map. The value type of the map must be an floating-point type compatible with double. The edge descriptor type of the graph needs to be usable as the key type of the map. When this map is a dummy_property_map, the algorithm assumes the graph is unweighted.
Executes the algorithm for n iterations.IN: diameter_range(std::pair
Range specifying the parameters (diameter_initial, diameter_final).IN: learning_constant_range(std::pair
Default: std::make_pair(sqrt((double)num_vertices(g)), 1.0)
Range specifying the parameters (learning_constant_initial, learning_constant_final).IN: edge_weight(EdgeWeightMap weight)
Default: std::make_pair(0.8, 0.2)
Equivalent to the non-named weight parameter.IN: vertex_index_map(VertexIndexMap i_map)
Equivalent to the non-named vertex_index_map parameter.
Default: get(vertex_index, g) Note: if you use this default, make sure your graph has an internal vertex_index property. For example, adjacency_list with VertexList=listS does not have an internal vertex_index property.
|Copyright © 2004 Trustees of Indiana University||
Jeremiah Willcock, Indiana University ()|
Doug Gregor, Indiana University ()
Andrew Lumsdaine, Indiana University ()