boykov_kolmogorov_max_flow_test.cpp 20 KB

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  1. // Copyright (c) 2006, Stephan Diederich
  2. //
  3. // This code may be used under either of the following two licences:
  4. //
  5. // Permission is hereby granted, free of charge, to any person
  6. // obtaining a copy of this software and associated documentation
  7. // files (the "Software"), to deal in the Software without
  8. // restriction, including without limitation the rights to use,
  9. // copy, modify, merge, publish, distribute, sublicense, and/or
  10. // sell copies of the Software, and to permit persons to whom the
  11. // Software is furnished to do so, subject to the following
  12. // conditions:
  13. //
  14. // The above copyright notice and this permission notice shall be
  15. // included in all copies or substantial portions of the Software.
  16. //
  17. // THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
  18. // EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
  19. // OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
  20. // NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
  21. // HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
  22. // WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
  23. // FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
  24. // OTHER DEALINGS IN THE SOFTWARE. OF SUCH DAMAGE.
  25. //
  26. // Or:
  27. //
  28. // Distributed under the Boost Software License, Version 1.0.
  29. // (See accompanying file LICENSE_1_0.txt or copy at
  30. // http://www.boost.org/LICENSE_1_0.txt)
  31. #include <vector>
  32. #include <iterator>
  33. #include <iostream>
  34. #include <algorithm>
  35. #include <fstream>
  36. #include <boost/test/minimal.hpp>
  37. #include <boost/graph/boykov_kolmogorov_max_flow.hpp>
  38. #include <boost/graph/adjacency_list.hpp>
  39. #include <boost/graph/adjacency_matrix.hpp>
  40. #include <boost/graph/random.hpp>
  41. #include <boost/property_map/property_map.hpp>
  42. #include <boost/random/linear_congruential.hpp>
  43. #include <boost/lexical_cast.hpp>
  44. using namespace boost;
  45. template <typename Graph, typename CapacityMap, typename ReverseEdgeMap>
  46. std::pair< typename graph_traits<Graph>::vertex_descriptor,typename graph_traits<Graph>::vertex_descriptor>
  47. fill_random_max_flow_graph(Graph& g, CapacityMap cap, ReverseEdgeMap rev, typename graph_traits<Graph>::vertices_size_type n_verts,
  48. typename graph_traits<Graph>::edges_size_type n_edges, std::size_t seed)
  49. {
  50. typedef typename graph_traits<Graph>::edge_descriptor edge_descriptor;
  51. typedef typename graph_traits<Graph>::vertex_descriptor vertex_descriptor;
  52. const int cap_low = 1;
  53. const int cap_high = 1000;
  54. //init random numer generator
  55. minstd_rand gen(seed);
  56. //generate graph
  57. generate_random_graph(g, n_verts, n_edges, gen);
  58. //init an uniform distribution int generator
  59. typedef variate_generator<minstd_rand, uniform_int<int> > tIntGen;
  60. tIntGen int_gen(gen, uniform_int<int>(cap_low, cap_high));
  61. //randomize edge-capacities
  62. //randomize_property<edge_capacity, Graph, tIntGen> (g,int_gen); //we cannot use this, as we have no idea how properties are stored, right?
  63. typename graph_traits<Graph>::edge_iterator ei, e_end;
  64. for(boost::tie(ei,e_end) = edges(g); ei != e_end; ++ei)
  65. cap[*ei] = int_gen();
  66. //get source and sink node
  67. vertex_descriptor s = random_vertex(g, gen);
  68. vertex_descriptor t = graph_traits<Graph>::null_vertex();
  69. while(t == graph_traits<Graph>::null_vertex() || t == s)
  70. t = random_vertex(g, gen);
  71. //add reverse edges (ugly... how to do better?!)
  72. std::list<edge_descriptor> edges_copy;
  73. boost::tie(ei, e_end) = edges(g);
  74. std::copy(ei, e_end, std::back_insert_iterator< std::list<edge_descriptor> >(edges_copy));
  75. while(!edges_copy.empty()){
  76. edge_descriptor old_edge = edges_copy.front();
  77. edges_copy.pop_front();
  78. vertex_descriptor source_vertex = target(old_edge, g);
  79. vertex_descriptor target_vertex = source(old_edge, g);
  80. bool inserted;
  81. edge_descriptor new_edge;
  82. boost::tie(new_edge,inserted) = add_edge(source_vertex, target_vertex, g);
  83. assert(inserted);
  84. rev[old_edge] = new_edge;
  85. rev[new_edge] = old_edge ;
  86. cap[new_edge] = 0;
  87. }
  88. return std::make_pair(s,t);
  89. }
  90. long test_adjacency_list_vecS(int n_verts, int n_edges, std::size_t seed){
  91. typedef adjacency_list_traits<vecS, vecS, directedS> tVectorTraits;
  92. typedef adjacency_list<vecS, vecS, directedS,
  93. property<vertex_index_t, long,
  94. property<vertex_predecessor_t, tVectorTraits::edge_descriptor,
  95. property<vertex_color_t, boost::default_color_type,
  96. property<vertex_distance_t, long> > > >,
  97. property<edge_capacity_t, long,
  98. property<edge_residual_capacity_t, long,
  99. property<edge_reverse_t, tVectorTraits::edge_descriptor > > > > tVectorGraph;
  100. tVectorGraph g;
  101. graph_traits<tVectorGraph>::vertex_descriptor src,sink;
  102. boost::tie(src,sink) = fill_random_max_flow_graph(g, get(edge_capacity,g), get(edge_reverse, g), n_verts, n_edges, seed);
  103. return boykov_kolmogorov_max_flow(g, get(edge_capacity, g),
  104. get(edge_residual_capacity, g),
  105. get(edge_reverse, g),
  106. get(vertex_predecessor, g),
  107. get(vertex_color, g),
  108. get(vertex_distance, g),
  109. get(vertex_index, g),
  110. src, sink);
  111. }
  112. long test_adjacency_list_listS(int n_verts, int n_edges, std::size_t seed){
  113. typedef adjacency_list_traits<listS, listS, directedS> tListTraits;
  114. typedef adjacency_list<listS, listS, directedS,
  115. property<vertex_index_t, long,
  116. property<vertex_predecessor_t, tListTraits::edge_descriptor,
  117. property<vertex_color_t, boost::default_color_type,
  118. property<vertex_distance_t, long> > > >,
  119. property<edge_capacity_t, long,
  120. property<edge_residual_capacity_t, long,
  121. property<edge_reverse_t, tListTraits::edge_descriptor > > > > tListGraph;
  122. tListGraph g;
  123. graph_traits<tListGraph>::vertex_descriptor src,sink;
  124. boost::tie(src,sink) = fill_random_max_flow_graph(g, get(edge_capacity,g), get(edge_reverse, g), n_verts, n_edges, seed);
  125. //initialize vertex indices
  126. graph_traits<tListGraph>::vertex_iterator vi,v_end;
  127. graph_traits<tListGraph>::vertices_size_type index = 0;
  128. for(boost::tie(vi, v_end) = vertices(g); vi != v_end; ++vi){
  129. put(vertex_index, g, *vi, index++);
  130. }
  131. return boykov_kolmogorov_max_flow(g, get(edge_capacity, g),
  132. get(edge_residual_capacity, g),
  133. get(edge_reverse, g),
  134. get(vertex_predecessor, g),
  135. get(vertex_color, g),
  136. get(vertex_distance, g),
  137. get(vertex_index, g),
  138. src, sink);
  139. }
  140. template<typename EdgeDescriptor>
  141. struct Node{
  142. boost::default_color_type vertex_color;
  143. long vertex_distance;
  144. EdgeDescriptor vertex_predecessor;
  145. };
  146. template<typename EdgeDescriptor>
  147. struct Link{
  148. long edge_capacity;
  149. long edge_residual_capacity;
  150. EdgeDescriptor edge_reverse;
  151. };
  152. long test_bundled_properties(int n_verts, int n_edges, std::size_t seed){
  153. typedef adjacency_list_traits<vecS, vecS, directedS> tTraits;
  154. typedef Node<tTraits::edge_descriptor> tVertex;
  155. typedef Link<tTraits::edge_descriptor> tEdge;
  156. typedef adjacency_list<vecS, vecS, directedS, tVertex, tEdge> tBundleGraph;
  157. tBundleGraph g;
  158. graph_traits<tBundleGraph>::vertex_descriptor src,sink;
  159. boost::tie(src,sink) = fill_random_max_flow_graph(g, get(&tEdge::edge_capacity,g), get(&tEdge::edge_reverse, g), n_verts, n_edges, seed);
  160. return boykov_kolmogorov_max_flow(g, get(&tEdge::edge_capacity, g),
  161. get(&tEdge::edge_residual_capacity, g),
  162. get(&tEdge::edge_reverse, g),
  163. get(&tVertex::vertex_predecessor, g),
  164. get(&tVertex::vertex_color, g),
  165. get(&tVertex::vertex_distance, g),
  166. get(vertex_index, g),
  167. src, sink);
  168. }
  169. long test_overloads(int n_verts, int n_edges, std::size_t seed){
  170. typedef adjacency_list_traits<vecS, vecS, directedS> tTraits;
  171. typedef property <edge_capacity_t, long,
  172. property<edge_residual_capacity_t, long,
  173. property<edge_reverse_t, tTraits::edge_descriptor> > >tEdgeProperty;
  174. typedef adjacency_list<vecS, vecS, directedS, no_property, tEdgeProperty> tGraph;
  175. tGraph g;
  176. graph_traits<tGraph>::vertex_descriptor src,sink;
  177. boost::tie(src,sink) = fill_random_max_flow_graph(g, get(edge_capacity,g), get(edge_reverse, g), n_verts, n_edges, seed);
  178. std::vector<graph_traits<tGraph>::edge_descriptor> predecessor_vec(n_verts);
  179. std::vector<default_color_type> color_vec(n_verts);
  180. std::vector<graph_traits<tGraph>::vertices_size_type> distance_vec(n_verts);
  181. long flow_overload_1 =
  182. boykov_kolmogorov_max_flow(g,
  183. get(edge_capacity,g),
  184. get(edge_residual_capacity,g),
  185. get(edge_reverse,g),
  186. get(vertex_index,g),
  187. src, sink);
  188. long flow_overload_2 =
  189. boykov_kolmogorov_max_flow(g,
  190. get(edge_capacity,g),
  191. get(edge_residual_capacity,g),
  192. get(edge_reverse,g),
  193. boost::make_iterator_property_map(
  194. color_vec.begin(), get(vertex_index, g)),
  195. get(vertex_index,g),
  196. src, sink);
  197. BOOST_CHECK(flow_overload_1 == flow_overload_2);
  198. return flow_overload_1;
  199. }
  200. template<class Graph,
  201. class EdgeCapacityMap,
  202. class ResidualCapacityEdgeMap,
  203. class ReverseEdgeMap,
  204. class PredecessorMap,
  205. class ColorMap,
  206. class DistanceMap,
  207. class IndexMap>
  208. class boykov_kolmogorov_test
  209. : public detail::bk_max_flow<
  210. Graph, EdgeCapacityMap, ResidualCapacityEdgeMap, ReverseEdgeMap,
  211. PredecessorMap, ColorMap, DistanceMap, IndexMap
  212. >
  213. {
  214. typedef typename graph_traits<Graph>::edge_descriptor tEdge;
  215. typedef typename graph_traits<Graph>::vertex_descriptor tVertex;
  216. typedef typename property_traits< typename property_map<Graph, edge_capacity_t>::const_type>::value_type tEdgeVal;
  217. typedef typename graph_traits<Graph>::vertex_iterator tVertexIterator;
  218. typedef typename graph_traits<Graph>::out_edge_iterator tOutEdgeIterator;
  219. typedef typename property_traits<ColorMap>::value_type tColorValue;
  220. typedef color_traits<tColorValue> tColorTraits;
  221. typedef typename property_traits<DistanceMap>::value_type tDistanceVal;
  222. typedef typename detail::bk_max_flow<
  223. Graph, EdgeCapacityMap, ResidualCapacityEdgeMap, ReverseEdgeMap,
  224. PredecessorMap, ColorMap, DistanceMap, IndexMap
  225. > tSuper;
  226. public:
  227. boykov_kolmogorov_test(Graph& g,
  228. typename graph_traits<Graph>::vertex_descriptor src,
  229. typename graph_traits<Graph>::vertex_descriptor sink)
  230. : tSuper(g, get(edge_capacity,g), get(edge_residual_capacity,g),
  231. get(edge_reverse, g), get(vertex_predecessor, g),
  232. get(vertex_color, g), get(vertex_distance, g),
  233. get(vertex_index, g), src, sink)
  234. { }
  235. void invariant_four(tVertex v) const{
  236. //passive nodes in S or T
  237. if(v == tSuper::m_source || v == tSuper::m_sink)
  238. return;
  239. typename std::list<tVertex>::const_iterator it = find(tSuper::m_orphans.begin(), tSuper::m_orphans.end(), v);
  240. // a node is active, if its in the active_list AND (is has_a_parent, or its already in the orphans_list or its the sink, or its the source)
  241. bool is_active = (tSuper::m_in_active_list_map[v] && (tSuper::has_parent(v) || it != tSuper::m_orphans.end() ));
  242. if(this->get_tree(v) != tColorTraits::gray() && !is_active){
  243. typename graph_traits<Graph>::out_edge_iterator ei,e_end;
  244. for(boost::tie(ei, e_end) = out_edges(v, tSuper::m_g); ei != e_end; ++ei){
  245. const tVertex& other_node = target(*ei, tSuper::m_g);
  246. if(this->get_tree(other_node) != this->get_tree(v)){
  247. if(this->get_tree(v) == tColorTraits::black())
  248. BOOST_CHECK(tSuper::m_res_cap_map[*ei] == 0);
  249. else
  250. BOOST_CHECK(tSuper::m_res_cap_map[tSuper::m_rev_edge_map[*ei]] == 0);
  251. }
  252. }
  253. }
  254. }
  255. void invariant_five(const tVertex& v) const{
  256. BOOST_CHECK(this->get_tree(v) != tColorTraits::gray() || tSuper::m_time_map[v] <= tSuper::m_time);
  257. }
  258. void invariant_six(const tVertex& v) const{
  259. if(this->get_tree(v) == tColorTraits::gray() || tSuper::m_time_map[v] != tSuper::m_time)
  260. return;
  261. else{
  262. tVertex current_node = v;
  263. tDistanceVal distance = 0;
  264. tColorValue color = this->get_tree(v);
  265. tVertex terminal = (color == tColorTraits::black()) ? tSuper::m_source : tSuper::m_sink;
  266. while(current_node != terminal){
  267. BOOST_CHECK(tSuper::has_parent(current_node));
  268. tEdge e = this->get_edge_to_parent(current_node);
  269. ++distance;
  270. current_node = (color == tColorTraits::black())? source(e, tSuper::m_g) : target(e, tSuper::m_g);
  271. if(distance > tSuper::m_dist_map[v])
  272. break;
  273. }
  274. BOOST_CHECK(distance == tSuper::m_dist_map[v]);
  275. }
  276. }
  277. void invariant_seven(const tVertex& v) const{
  278. if(this->get_tree(v) == tColorTraits::gray())
  279. return;
  280. else{
  281. tColorValue color = this->get_tree(v);
  282. long time = tSuper::m_time_map[v];
  283. tVertex current_node = v;
  284. while(tSuper::has_parent(current_node)){
  285. tEdge e = this->get_edge_to_parent(current_node);
  286. current_node = (color == tColorTraits::black()) ? source(e, tSuper::m_g) : target(e, tSuper::m_g);
  287. BOOST_CHECK(tSuper::m_time_map[current_node] >= time);
  288. }
  289. }
  290. }//invariant_seven
  291. void invariant_eight(const tVertex& v) const{
  292. if(this->get_tree(v) == tColorTraits::gray())
  293. return;
  294. else{
  295. tColorValue color = this->get_tree(v);
  296. long time = tSuper::m_time_map[v];
  297. tDistanceVal distance = tSuper::m_dist_map[v];
  298. tVertex current_node = v;
  299. while(tSuper::has_parent(current_node)){
  300. tEdge e = this->get_edge_to_parent(current_node);
  301. current_node = (color == tColorTraits::black()) ? source(e, tSuper::m_g) : target(e, tSuper::m_g);
  302. if(tSuper::m_time_map[current_node] == time)
  303. BOOST_CHECK(tSuper::m_dist_map[current_node] < distance);
  304. }
  305. }
  306. }//invariant_eight
  307. void check_invariants(){
  308. tVertexIterator vi, v_end;
  309. for(boost::tie(vi, v_end) = vertices(tSuper::m_g); vi != v_end; ++vi){
  310. invariant_four(*vi);
  311. invariant_five(*vi);
  312. invariant_six(*vi);
  313. invariant_seven(*vi);
  314. invariant_eight(*vi);
  315. }
  316. }
  317. tEdgeVal test(){
  318. this->add_active_node(this->m_sink);
  319. this->augment_direct_paths();
  320. check_invariants();
  321. //start the main-loop
  322. while(true){
  323. bool path_found;
  324. tEdge connecting_edge;
  325. boost::tie(connecting_edge, path_found) = this->grow(); //find a path from source to sink
  326. if(!path_found){
  327. //we're finished, no more paths were found
  328. break;
  329. }
  330. check_invariants();
  331. this->m_time++;
  332. this->augment(connecting_edge); //augment that path
  333. check_invariants();
  334. this->adopt(); //rebuild search tree structure
  335. check_invariants();
  336. }
  337. //check if flow is the sum of outgoing edges of src
  338. tOutEdgeIterator ei, e_end;
  339. tEdgeVal src_sum = 0;
  340. for(boost::tie(ei, e_end) = out_edges(this->m_source, this->m_g); ei != e_end; ++ei){
  341. src_sum += this->m_cap_map[*ei] - this->m_res_cap_map[*ei];
  342. }
  343. BOOST_CHECK(this->m_flow == src_sum);
  344. //check if flow is the sum of ingoing edges of sink
  345. tEdgeVal sink_sum = 0;
  346. for(boost::tie(ei, e_end) = out_edges(this->m_sink, this->m_g); ei != e_end; ++ei){
  347. tEdge in_edge = this->m_rev_edge_map[*ei];
  348. sink_sum += this->m_cap_map[in_edge] - this->m_res_cap_map[in_edge];
  349. }
  350. BOOST_CHECK(this->m_flow == sink_sum);
  351. return this->m_flow;
  352. }
  353. };
  354. long test_algorithms_invariant(int n_verts, int n_edges, std::size_t seed)
  355. {
  356. typedef adjacency_list_traits<vecS, vecS, directedS> tVectorTraits;
  357. typedef adjacency_list<vecS, vecS, directedS,
  358. property<vertex_index_t, long,
  359. property<vertex_predecessor_t, tVectorTraits::edge_descriptor,
  360. property<vertex_color_t, default_color_type,
  361. property<vertex_distance_t, long> > > >,
  362. property<edge_capacity_t, long,
  363. property<edge_residual_capacity_t, long,
  364. property<edge_reverse_t, tVectorTraits::edge_descriptor > > > > tVectorGraph;
  365. tVectorGraph g;
  366. graph_traits<tVectorGraph>::vertex_descriptor src, sink;
  367. boost::tie(src,sink) = fill_random_max_flow_graph(g, get(edge_capacity,g), get(edge_reverse, g), n_verts, n_edges, seed);
  368. typedef property_map<tVectorGraph, edge_capacity_t>::type tEdgeCapMap;
  369. typedef property_map<tVectorGraph, edge_residual_capacity_t>::type tEdgeResCapMap;
  370. typedef property_map<tVectorGraph, edge_reverse_t>::type tRevEdgeMap;
  371. typedef property_map<tVectorGraph, vertex_predecessor_t>::type tVertexPredMap;
  372. typedef property_map<tVectorGraph, vertex_color_t>::type tVertexColorMap;
  373. typedef property_map<tVectorGraph, vertex_distance_t>::type tDistanceMap;
  374. typedef property_map<tVectorGraph, vertex_index_t>::type tIndexMap;
  375. typedef boykov_kolmogorov_test<
  376. tVectorGraph, tEdgeCapMap, tEdgeResCapMap, tRevEdgeMap, tVertexPredMap,
  377. tVertexColorMap, tDistanceMap, tIndexMap
  378. > tKolmo;
  379. tKolmo instance(g, src, sink);
  380. return instance.test();
  381. }
  382. int test_main(int argc, char* argv[])
  383. {
  384. int n_verts = 10;
  385. int n_edges = 500;
  386. std::size_t seed = 1;
  387. if (argc > 1) n_verts = lexical_cast<int>(argv[1]);
  388. if (argc > 2) n_edges = lexical_cast<int>(argv[2]);
  389. if (argc > 3) seed = lexical_cast<std::size_t>(argv[3]);
  390. //we need at least 2 vertices to create src and sink in random graphs
  391. //this case is also caught in boykov_kolmogorov_max_flow
  392. if (n_verts<2)
  393. n_verts = 2;
  394. // below are checks for different calls to boykov_kolmogorov_max_flow and different graph-types
  395. //checks support of vecS storage
  396. long flow_vecS = test_adjacency_list_vecS(n_verts, n_edges, seed);
  397. std::cout << "vecS flow: " << flow_vecS << std::endl;
  398. //checks support of listS storage (especially problems with vertex indices)
  399. long flow_listS = test_adjacency_list_listS(n_verts, n_edges, seed);
  400. std::cout << "listS flow: " << flow_listS << std::endl;
  401. BOOST_CHECK(flow_vecS == flow_listS);
  402. //checks bundled properties
  403. long flow_bundles = test_bundled_properties(n_verts, n_edges, seed);
  404. std::cout << "bundles flow: " << flow_bundles << std::endl;
  405. BOOST_CHECK(flow_listS == flow_bundles);
  406. //checks overloads
  407. long flow_overloads = test_overloads(n_verts, n_edges, seed);
  408. std::cout << "overloads flow: " << flow_overloads << std::endl;
  409. BOOST_CHECK(flow_bundles == flow_overloads);
  410. // excessive test version where Boykov-Kolmogorov's algorithm invariants are
  411. // checked
  412. long flow_invariants = test_algorithms_invariant(n_verts, n_edges, seed);
  413. std::cout << "invariants flow: " << flow_invariants << std::endl;
  414. BOOST_CHECK(flow_overloads == flow_invariants);
  415. return 0;
  416. }