def process_graph(graph_name):
# load graph data
with open(f"data/{graph_name}_neighbors", "rb") as f:
neighbors = pickle.load(f)
with open(f"data/{graph_name}_edges", "rb") as f:
edges_list = pickle.load(f)
# clean folder
dir_name = f"images/{graph_name}"
for filename in os.listdir(dir_name):
if "initial" not in filename and "ter" in filename:
path_to_file = os.path.join(dir_name, filename)
os.remove(path_to_file)
# print(neighbors)
# size of the graph (number of nodes)
nodes = neighbors.keys()
n_nodes = len(nodes)
"""
sort the nodes by degree
aka the number of neighbors
"""
sorted_nodes = sorted(neighbors,
key=lambda node: len(neighbors[node]),
reverse=True)
"""
greedy algorithm
"""
print('\n======')
print('greedy algorithm')
print('======')
selected_nodes = list()
dominated_nodes = list()
step = 0
def get_next_node(dominated_nodes, nodes):
"""
function used to select a new node in the algorithm
"""
"""
EDIT THIS FUNCTION
"""
# built the reduced graph
# consisting in the original graph deprived from dominated nodes
reduced_graph = [node for node in nodes if node not in dominated_nodes]
# build a new dictionary containing adjecency relations in that graph
# we need to edit the dictionary of neighbors first.
neighbors_reduced = neighbors.copy()
"""
ADD LINES HERE
"""
neighbors_in_reduced_graph = {
node: neighbors_reduced[node]
for node in reduced_graph
}
# sort that dictionary as before
sorted_nodes_reduced = sorted(
neighbors_in_reduced_graph,
key=lambda node: len(neighbors_in_reduced_graph[node]),
reverse=True)
return sorted_nodes_reduced[0]
while len(dominated_nodes) < n_nodes:
selected_node = get_next_node(dominated_nodes, nodes)
print(selected_nodes)
# update graph
selected_nodes.append(selected_node)
print(f"\nadd {selected_node} to the dominating set")
# update the list of dominated nodes
dominated_nodes.append(selected_node)
print(f"add {selected_node} to the list of dominated nodes")
# print('neighbors : ')
for neighbor in neighbors[selected_node]:
if neighbor not in dominated_nodes:
# update the list of not dominated nodes
dominated_nodes.append(neighbor)
print(f"add {neighbor} to the list of dominated nodes")
# see how many more nodes we have to dominate
print(f"still have to dominate {n_nodes-len(dominated_nodes)} nodes")
step += 1
plot_subset(step,
sorted_nodes,
edges_list,
dominated_nodes,
selected_nodes,
graph_name,
method="ter")
def process_graph(graph_name):
# load graph data
with open(f"data/{graph_name}_neighbors", "rb") as f:
neighbors = pickle.load(f)
with open(f"data/{graph_name}_edges", "rb") as f:
edges_list = pickle.load(f)
# clean folder
dir_name = f"images/{graph_name}"
for filename in os.listdir(dir_name):
if "initial" not in filename and "bis" in filename:
path_to_file = os.path.join(dir_name, filename)
os.remove(path_to_file)
# print(neighbors)
# size of the graph (number of nodes)
nodes = neighbors.keys()
n_nodes = len(neighbors)
"""
sort the nodes by degree
aka the number of neighbors
"""
sorted_nodes = list(nodes)
# sorted does not modify the original sequence
# it returns a list
# print(type(sorted_nodes))
print('=====')
print('sorted dictionary of neighbors by degree of the node')
print('=====')
for node in sorted_nodes:
print(f"node {node}")
print(f"neighbors {neighbors[node]}")
"""
greedy algorithm
"""
print('\n======')
print('greedy algorithm')
print('======')
selected_nodes = list()
dominated_nodes = list()
step = 0
for node in sorted_nodes:
step += 1
# stop if the set is dominating
if len(dominated_nodes) < n_nodes:
# update our selected subset
# selected_nodes.append(node)
# print(f"\nadd {node} to the dominating set")
# update the list of dominated nodes
# if node not in dominated_nodes:
# dominated_nodes.append(node)
# print(f"add {node} to the list of dominated nodes")
# print('neighbors : ')
# for neighbor in neighbors[node]:
# if neighbor not in dominated_nodes:
# update the list of not dominated nodes
# dominated_nodes.append(neighbor)
# print(f"add {neighbor} to the list of dominated nodes")
# see how many more nodes we have to dominate
print(
f"still have to dominate {n_nodes-len(dominated_nodes)} nodes")
plot_subset(step,
sorted_nodes,
edges_list,
dominated_nodes,
selected_nodes,
graph_name,
method="bis")