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