def main():
args = parse_arguments()
# Start measuring execution time
begin_time = timeit.default_timer()
# Read graph
input_file = open(args.input, 'r')
graph = networkx.Graph()
number_of_vertices, number_of_edges = [int(x) for x in input_file.readline().split()]
if args.naive:
graph.add_nodes_from(range(1, number_of_vertices + 1), colour=utils.Colours.red)
else:
graph.add_nodes_from(range(1, number_of_vertices + 1), colour=utils.Colours.grey)
for i in range(number_of_edges):
vertex1, vertex2 = [int(x) for x in input_file.readline().split()]
graph.add_edge(vertex1, vertex2)
# Find solution
if args.naive:
graph = naive.naive(graph)
elif args.greedy:
graph = greedy.greedy(graph, args.image_path)
elif args.simul:
graph = simultaneous_greedy.simultaneous_greedy(graph, args.image_path)
else:
greedy_solution = greedy.greedy(graph.copy(), args.image_path)
simul_solution = simultaneous_greedy.simultaneous_greedy(graph, args.image_path)
if utils.get_colours_use(greedy_solution)[2] > utils.get_colours_use(simul_solution)[2]:
graph = greedy_solution
else:
graph = simul_solution
# Print results
output_file = open(args.output, 'w')
# Check whether a solution was found
if graph:
output_file.write(' '.join(map(str, utils.get_colours_use(graph))[:3]) + '\n')
for node in graph.nodes(data=True):
output_file.write(str(node[0]) + ' ' + node[1]['colour'].name + '\n')
else:
output_file.write('Cannot find colouring')
# Cleanup
input_file.close()
output_file.close()
# Stop measuring execution time
execution_time = timeit.default_timer() - begin_time
if args.time:
print(execution_time)
def naive(graph):
"""
Solves problem using naive algorithm. Returns None if solution does not exist.
:return: networkx.Graph
"""
# Check if solution exist
for node in graph.nodes():
if graph.degree(node) == 0:
return None
best_coloured_graph = None
grey_colour_uses = -1
while not is_final_condition(graph):
if utils.get_colours_use(graph)[2] > grey_colour_uses and utils.is_properly_coloured(graph):
best_coloured_graph = graph.copy()
grey_colour_uses = utils.get_colours_use(graph)[2]
next_colouring(graph)
return best_coloured_graph