def setup():
archive = CVTArchive(1000, [(-1, 1), (-1, 1)],
samples=20_000,
use_kd_tree=use_kd_tree)
archive.initialize(solutions.shape[1])
# Let numba compile.
archive.add(solutions[0], objective_values[0], behavior_values[0])
return (archive,), {}
def benchmark_get_10k_random_elites(use_kd_tree, benchmark, benchmark_data_10k):
n, solutions, objective_values, behavior_values = benchmark_data_10k
archive = CVTArchive(1000, [(-1, 1), (-1, 1)],
samples=20_000,
use_kd_tree=use_kd_tree)
archive.initialize(solutions.shape[1])
for i in range(n):
archive.add(solutions[i], objective_values[i], behavior_values[i])
@benchmark
def get_elites():
for i in range(n):
sol, obj, beh = archive.get_random_elite()
def benchmark_as_pandas_2000_items(benchmark):
bins = 2000
archive = CVTArchive(bins, [(-1, 1), (-1, 1)],
use_kd_tree=True,
samples=50_000)
archive.initialize(10)
for x, y in archive.centroids:
sol = np.random.random(10)
sol[0] = x
sol[1] = y
archive.add(sol, 1.0, np.array([x, y]))
# Archive should be full.
assert len(archive.as_pandas()) == bins
benchmark(archive.as_pandas)