n,
edge_weighted=True,
node_weighted=True,
node_weight_generator=generator,
seed=seed)
terminals = [288, 315, 231, 312, 111, 609, 645, 434, 654, 469, 186]
# terminals = [36, 78, 28, 11]
suitability_graph = SuitabilityGraph()
suitability_graph.append_graph(node_weighted)
suitability_graph.extend_suitable_regions(seed, generator)
suitability_graph.extend_suitable_regions(seed, generator)
regions = suitability_graph.get_suitable_regions(generator)
start_time = time.clock()
dr = DreyfusIMRV2(suitability_graph,
terminals,
contract_graph=True,
within_convex_hull=False)
steiner_tree = dr.steiner_tree(consider_terminals=False)
elapsed_time = time.clock() - start_time
cost, node_cost = steiner_tree.compute_total_weights(terminals)
ngh = NetworkXGraphHelper(suitability_graph)
ngh.draw_graph(
nodes_2=terminals,
subgraphs_1=[r for _, (r, _, _, _, _, _) in regions.iteritems()],
class ClusterBased:
def __init__(self, graph, terminals):
# Check whether graph is node-weighted.
if not graph.is_node_weighted():
raise (
ValueError,
"Cluster-based algorithm only works with node-weighted graphs."
)
# Extract POI from the terminals list.
if len(terminals) > 0:
self.__poi = terminals[0]
else:
return
#
generator = SuitableNodeWeightGenerator()
# Set object variables.
self.__graph = SuitabilityGraph()
self.__graph.append_graph(graph)
self.__terminals = terminals
#
# self.__regions = self.__graph.get_suitable_regions(generator, excluded_nodes=terminals,
# get_border_internal_nodes=True, get_centroid_medoid=True,
# get_characteristic_nodes=True)
self.__regions = self.__graph.get_suitable_regions(
generator,
excluded_nodes=terminals,
get_border_internal_nodes=True,
get_centroid_medoid=True)
#
self.__nodes = []
for id_r in self.__regions:
#
# characteristic_nodes = self.__regions[id_r][6]
# self.__nodes.extend(characteristic_nodes)
border_nodes = self.__regions[id_r][1]
self.__nodes.extend(border_nodes)
#
# if len(characteristic_nodes) == 0:
# medoid = self.__regions[id_r][4]
# # print(medoid)
# self.__nodes.append(medoid)
for t in terminals:
self.__nodes.append(t)
#
self.__dist, self.__paths = self.__graph.get_dist_paths(
origins=self.__nodes, destinations=self.__nodes)
'''
'''
def steiner_tree(self, consider_terminals=False):
dr = DreyfusIMR(graph=self.__graph,
terminals=self.__terminals,
contract_graph=False,
nodes=self.__nodes,
dist_paths=(self.__dist, self.__paths))
if consider_terminals:
steiner_tree = dr.steiner_tree(consider_terminals=True)
else:
steiner_tree = dr.steiner_tree(consider_terminals=False)
# self.__refine_steiner_tree(steiner_tree)
return steiner_tree
'''
'''
def __refine_steiner_tree(self, steiner_tree):
clusters = []
for id_r in self.__regions:
region = self.__regions[id_r][0]
for n in steiner_tree:
if n in region:
clusters.append(id_r)
break
print clusters
