def benchmark_as_pandas_2025_items(benchmark):
dim = 45
archive = GridArchive((dim, dim), [(-1, 1), (-1, 1)])
archive.initialize(10)
for x in np.linspace(-1, 1, dim):
for y in np.linspace(-1, 1, dim):
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()) == dim * dim
benchmark(archive.as_pandas)
plt.ylabel(bc_names[opt.bcs[1]]) #objective 1
'''
plt.tick_params(
axis='y', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
left=False, # ticks along the bottom edge are off
right=False, # ticks along the top edge are off
labelleftFalse) # labels along the bottom edge are off
'''
figname = '/map_' + str(i)
plt.savefig(logdir + figname)
plt.close()
#save the archive
df = archive.as_pandas(include_solutions=True)
df.to_pickle(logdir + "/archive.zip")
else:
##############################################################################################
#cma/pycma implementation
n_features = 100 #number of input features for the noise vector generator. other tolerance options available.
#get the size of the noise map that TOAD-GAN will need
vec_size = 0
n_pad = int(1 * opt.num_layer)
for noise_map in noise_maps:
nzx = int(round((noise_map.shape[-2] - n_pad * 2) * opt.scale_v))
nzy = int(round((noise_map.shape[-1] - n_pad * 2) * opt.scale_h))
vec_size += 12 * nzx * nzy * opt.num_samples
