def test_random(size: int,
dists: np.ndarray,
costs: np.ndarray,
reruns_number: int = 3) -> Tuple[float, float, int]:
results = []
times = []
for i in range(reruns_number):
print("Random algorithm run: " + str(i + 1))
random_results = []
s = time.time()
for _ in range(100000):
random_results.append(
objective(dists, costs, np.random.permutation(size)))
e = time.time()
times.append(e - s)
results.append(min(random_results))
return sum(results) / len(results), sum(times) / len(times), min(results)
from QAP.utils import load_solution, load_example
from QAP.objective import objective
from QAP.solvers.genetic import genetic_solver
from QAP.solvers.selection_mechanisms import BestFit
from QAP.solvers.mutation_mechanisms import ShiftMutation, SwapMutation, UniformMutationScheduler
import numpy as np
if __name__ == '__main__':
n, dists, costs = load_example('data/qapdata/kra30a.dat', dist_first=True)
_, opt, permutation = load_solution('data/qapsoln/kra30a.sln')
assert opt == objective(dists, costs,
permutation), "something wrong with objective"
# TODO: hiperparameter tuning with WandB?
mutation_mutations = (SwapMutation(mutation_prob=0.3), ShiftMutation())
res = genetic_solver(
n,
dists,
costs,
objective,
max_iterations=1000,
population_size=100,
verbose=True,
print_every=100,
selection_size=100,
crossover_count=50,
mutation_mechanism=UniformMutationScheduler,
# mutation_prob=0.1,
mutation_mutations=mutation_mutations,
)
from QAP.utils import load_solution, load_example
from QAP.objective import objective, naive_objective
import numpy as np
if __name__ == '__main__':
n, dists, costs = load_example('data/qapdata/kra30a.dat', dist_first=True)
_, opt, permutation = load_solution('data/qapsoln/kra30a.sln')
assert opt == objective(dists, costs,
permutation), "something wrong with objective"
np.random.seed(1234)
diffs = []
for _ in range(10000):
diffs.append(objective(dists, costs, np.random.permutation(n)) - opt)
print(
f'mean difference for random permutations is {np.mean(diffs)}, min {np.min(diffs)}, max {np.max(diffs)}\n'
f'optimal is {opt}')
'lipa60b.sln',
'lipa70b.sln',
'lipa80b.sln',
'lipa90b.sln',
]
results = []
for problem, solution in zip(problems, solutions):
size, dists, costs = load_example(os.path.join(problems_folder,
problem),
dist_first=True)
_, opt, permutation = load_solution(
os.path.join(solutions_folder, solution))
if not opt == objective(dists, costs, permutation):
size, dists, costs = load_example(os.path.join(
problems_folder, problem),
dist_first=False)
_, opt, permutation = load_solution(
os.path.join(solutions_folder, solution))
if not opt == objective(dists, costs, permutation):
print(problem + " could not be read!")
continue
print("Processing problem: " + problem)
bees_result, bees_time, bees_best = test_bees(size, dists, costs,
reruns_number)
genetic_result, genetic_time, genetic_best = test_genetic(