def Ai(self, k, r, X, i):
T = SuitabilityGraph()
while k > 0:
TBEST = SuitabilityGraph()
for kprime in range(1, k + 1):
for v in self.__nodes:
if i > 1:
Tprime = self.Ai(kprime, v, X, i - 1)
p = self.__paths[tuple(sorted([r, v]))]
Tprime.append_path(p, self.__graph)
else:
dists = {}
for t in self.__terminals:
dists[t] = self.__dist[tuple(sorted([v, t]))]
ord_term = sorted(dists.iteritems(),
key=operator.itemgetter(1))
Tprime = SuitabilityGraph()
for j in range(kprime):
p = self.__paths[tuple(sorted([v,
ord_term[j][0]]))]
Tprime.append_path(p, self.__graph)
if self.d(TBEST) > self.d(Tprime):
TBEST = Tprime
T.append_graph(TBEST)
k -= len(set(TBEST.keys()).intersection(X))
X = set(X).difference(TBEST.keys())
return T
# pois = [359, 834, 520, 378, 755, 616, 1, 435]
# pois = [359, 834]
# terminals = [123, 456, 463, 897, 506, 639, 343, 232, 564, 766, 138, 469, 800]
suitability_graph = SuitabilityGraph()
suitability_graph.append_graph(node_weighted)
suitability_graph.extend_suitable_regions(seed, generator)
suitability_graph.extend_suitable_regions(seed, generator)
suitability_graph.extend_suitable_regions(seed, generator)
suitability_graph.extend_suitable_regions(seed, generator)
hotspots = suitability_graph.get_suitable_nodes(generator)
terminals = np.random.choice(a=m * n, size=60, replace=False)
while set(suitability_graph.keys()).intersection(terminals) != set(
terminals) or set(hotspots).intersection(terminals) != set():
terminals = np.random.choice(a=m * n, size=60, replace=False)
pois = terminals[:15]
terminals = terminals[15:]
regions = suitability_graph.get_suitable_regions(generator)
hb = HotspotBased(suitability_graph, terminals, pois)
start_time = time.clock()
forest, cost, _, _, _, _, _ = hb.steiner_forest(clone_hotspots=True,
get_lsv=False)
elapsed_time = time.clock() - start_time
ngh = NetworkXGraphHelper(suitability_graph)
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,
pois,
hot_spots=hot_spots)
start_time = time.clock()
forest, cost, gr, avg_dr, num_trees, avg_oc = hb.steiner_forest(
get_lsv=False, max_wd=2.5, awareness=awareness)
elapsed_time = time.clock() - start_time
