【发布时间】:2018-04-08 13:56:06
【问题描述】:
我正在使用 Boost BGL C++,我需要 Graph 来执行从源顶点到目标顶点的 BFS 并返回所有唯一路径。
现在,我想到了一种使用过滤图来获取包含从源到目标的路径的图的子集的方法,但我意识到它基本上不是过滤,因为过滤后的图包含访问但不是部分的顶点从源到目标的路径。有什么方法可以获取这些信息或其他方法更好吗?
参考代码:
boost::filtered_graph<DirectedGraph, boost::keep_all, std::function<bool(VertexDescr)>> Graph::getUniquePathsFromSource(VertexDescr source, VertexDescr target, DirectedGraph const & g)
{
std::vector<double> distances(num_vertices(g));
std::vector<boost::default_color_type> colormap(num_vertices(g));
// Run BFS and record all distances from the source node
breadth_first_search(g, source,
visitor(make_bfs_visitor(boost::record_distances(distances.data(), boost::on_tree_edge())))
.color_map(colormap.data())
);
for (auto vd : boost::make_iterator_range(vertices(g)))
if (colormap.at(vd) == boost::default_color_type{})
distances.at(vd) = -1;
distances[source] = -2;
boost::filtered_graph<DirectedGraph, boost::keep_all, std::function<bool(VertexDescr)>>
fg(g, {}, [&](VertexDescr vd) { return distances[vd] != -1; });
// Print edge list
std::cout << "filtered out-edges:" << std::endl;
std::cout << "Source Vertex: " << source << std::endl;
auto ei = boost::edges(fg);
typedef boost::property_map<DirectedGraph, boost::edge_weight_t>::type WeightMap;
WeightMap weights = get(boost::edge_weight, fg);
for (auto it = ei.first; it != ei.second; ++it)
{
if (source != boost::target(*it, g)) {
std::cout << "Edge Probability " << *it << ": " << get(weights, *it) << std::endl;
}
}
return fg;
}
输入(顶点1,顶点2,权重):
0 1 0.001
0 2 0.1
0 3 0.001
1 5 0.001
2 3 0.001
3 4 0.1
1 482 0.1
482 635 0.001
4 705 0.1
705 5 0.1
1 1491 0.01
1 1727 0.01
1 1765 0.01
输出(源 = 0,目标 = 5):
Source Vertex: 0
Edge Probability (0,1): 0.001
Edge Probability (0,2): 0.1
Edge Probability (0,3): 0.001
Edge Probability (1,5): 0.001
Edge Probability (1,482): 0.1
Edge Probability (1,1491): 0.01
Edge Probability (1,1727): 0.01
Edge Probability (1,1765): 0.01
Edge Probability (2,3): 0.001
Edge Probability (3,4): 0.1
Edge Probability (4,705): 0.1
Edge Probability (482,635): 0.001
Edge Probability (705,5): 0.1
预期输出:
0->1->5
0->3->4->705->5
0->2->3->4->705->5
【问题讨论】: