suitability_graph.append_graph(graph)
suitability_graph.extend_suitable_regions(seed, generator)
suitability_graph.extend_suitable_regions(seed, generator)
regions = suitability_graph.get_suitable_regions(generator)
# suitability_graph.contract_suitable_regions(generator)
# paths = []
# for t in terminals:
# _, paths_t = dijkstra(suitability_graph, t, [poi], consider_node_weights=False)
# paths.append(paths_t[poi])
# _, paths_poi = suitability_graph.compute_shortest(poi, terminals)
suitability_graph.compute_dist_paths(origins=[poi], destinations=terminals)
paths = [
suitability_graph.paths[tuple(sorted([t, poi]))] for t in terminals
]
tree = SuitabilityGraph()
for p in paths:
tree.append_path(p, suitability_graph)
ngh = NetworkXGraphHelper(suitability_graph)
ngh.draw_graph(
special_nodes=[(terminals, None, None), ([poi], None, None)],
# subgraphs_1=[r for _, (r, _, _, _, _, _) in regions.iteritems()],
special_subgraphs=[(tree, None)],
print_node_labels=False)
if __name__ == '__main__':
m = 30
n = 30
seed = 0
generator = SuitableNodeWeightGenerator()
graph = GridDigraphGenerator().generate(m,
n,
node_weighted=True,
node_weight_generator=generator,
seed=seed)
suitability_graph = SuitabilityGraph()
suitability_graph.append_graph(graph)
suitability_graph.compute_dist_paths(compute_paths=False)
pois = [265, 312]
hot_spots = suitability_graph.get_suitable_nodes(generator,
excluded_nodes=pois)
nodes_left_rh = list(
set(suitability_graph.keys()).difference(pois).difference(hot_spots))
ind = np.random.choice(a=len(nodes_left_rh), size=30, replace=False)
terminals = [nodes_left_rh[i] for i in ind]
hot_spots = set(
suitability_graph.keys()).difference(terminals).difference(pois)
for awareness in np.arange(0.0, 1.1, 0.1):
hb = HotspotBased(suitability_graph,
terminals,
print("seed:", seed)
for msns in range(len(ms)):
print("nodes:", ms[msns] * ns[msns])
graph = GridDigraphGenerator().generate(
ms[msns],
ns[msns],
node_weighted=True,
node_weight_generator=generator,
seed=seed)
suitability_graph = SuitabilityGraph()
suitability_graph.append_graph(graph)
hotspots = suitability_graph.get_suitable_nodes(generator)
start_time = time.clock()
suitability_graph.compute_dist_paths(origins=hotspots,
destinations=hotspots,
compute_paths=False)
print "compute", time.clock() - start_time, "# hotspots:", len(
hotspots)
for num_seats in capacity:
# suitability_graph.extend_suitable_regions(seed, generator)
# hotspots = suitability_graph.get_suitable_nodes(generator)
# print i, "# hotspots:", len(hotspots)
for num_terminals in nums_terminals:
new_terminals = set()
for sample in range(num_samples):
graph_temp = SuitabilityGraph()
graph_temp.append_graph(suitability_graph)
graph_temp.dist = dict(suitability_graph.dist)
graph_temp.pairs_dist_paths = set(
seed = 1
m = n = 10
gh = GridDigraphGenerator()
graph = gh.generate(m,
n,
edge_weighted=False,
node_weighted=True,
node_weight_generator=generator,
seed=seed)
suitability_graph = SuitabilityGraph()
suitability_graph.append_graph(graph)
suitability_graph.extend_suitable_regions(seed, generator)
suitability_graph.extend_suitable_regions(seed, generator)
regions = suitability_graph.get_suitable_regions(generator)
suitability_graph.contract_suitable_regions(generator)
ngh = NetworkXGraphHelper(suitability_graph)
ngh.draw_graph(print_node_labels=True)
suitability_graph.compute_dist_paths(origins=[53], destinations=[65, 86])
print suitability_graph.dist[(53, 65)], suitability_graph.paths[(53, 65)]
print suitability_graph.dist[(53, 86)], suitability_graph.paths[(53, 86)]
suitability_graph.compute_dist_paths(origins=[28], destinations=[72])
print suitability_graph.dist[(28, 72)], suitability_graph.paths[(28, 72)]