def benchmark_function(graphe, nbr_it, func, *func_args, **func_kwargs):
best_sol = None
best_score = float("inf")
dist_mat = get_distance_matrix(graphe)
prob = GraphProblem(dist_mat)
if hasattr(graphe, "name"):
prob.name = graphe.name
for i in range(nbr_it):
print(func.__name__, "#", i)
sol = func(prob, *func_args, **func_kwargs)
if sol.get_max_col() < best_score:
best_sol = sol
best_score = best_sol.get_max_col()
search_step_trace.clear_all()
tracefname = "{0}.qst".format(func.__name__)
with open(tracefname, 'a') as f:
for name, data in sol.record.items():
print(prob.name, ", ", sol.get_max_col(), ", ",
search_step_trace.csv_format().format(name, data),
file=f, sep="")
print("", file=f)
solfname = "{0}_{1}.pcol".format(prob.name, best_sol.get_max_col())
with open(solfname, 'a') as f:
print(best_sol, file=f)
print("", file=f)
return best_sol
def benchmark_function(graphe, func, *func_args, **func_kwargs):
dist_mat = get_distance_matrix(graphe)
prob = GraphProblem(dist_mat)
if hasattr(graphe, "name"):
prob.name = graphe.name
if bu.search_step_trace.enabled:
bu.search_step_trace.env_name = func.__name__
sol = func(prob, *func_args, **func_kwargs)
return prob, sol