for i in range(generations):
for dataset in datasets:
for key in sum_dataset.keys():
sum_dataset[key][i] += dataset[key][i]
avg_func = lambda x: float(x) / n
avg_dataset = {
'generation_number': generations,
'simulation': simulation,
'average_fitnesses':
list(map(avg_func, sum_dataset['average_fitnesses'])),
'sigmas': list(map(avg_func, sum_dataset['sigmas'])),
'best_fitnesses': list(map(avg_func, sum_dataset['best_fitnesses']))
}
return avg_dataset
if __name__ == "__main__":
S = ONE_MAX
N = 10
G = 100
avg_dataset = average_n_runs(S, N, G)
plot_simulation_results(
avg_dataset,
title="Averages {} runs of {}".format(N, S['problem'].NAME),
savefig="../report/img/{}.png".format(datetime.now()))
for key in sum_dataset.keys():
sum_dataset[key][i] += dataset[key][i]
avg_func = lambda x: float(x) / n
avg_dataset = {
'generation_number': generations,
'simulation': simulation,
'average_fitnesses': list(map(avg_func, sum_dataset['average_fitnesses'])),
'sigmas': list(map(avg_func, sum_dataset['sigmas'])),
'best_fitnesses': list(map(avg_func, sum_dataset['best_fitnesses']))
}
return avg_dataset
if __name__ == "__main__":
S = ONE_MAX
N = 10
G = 100
avg_dataset = average_n_runs(S, N, G)
plot_simulation_results(
avg_dataset,
title="Averages {} runs of {}".format(
N,
S['problem'].NAME
),
savefig="../report/img/{}.png".format(datetime.now())
)
'adult_selection_method': full_generational_replacement,
'mate_selection_method': ranked,
'crossover_method': splice,
'crossover_rate': 0.25,
'mutation_method': mutate_string_genome,
'mutation_rate': 0.01,
'stop': {
'fitness': 1.0,
'generation': None
},
'plot_sigmas': False
}
SIMULATIONS = [
ONE_MAX,
LOLZ,
LOCALLY_SURPRISING,
GLOBALLY_SURPRISING
]
if __name__ == "__main__":
for simulation in SIMULATIONS:
results = run_simulation(simulation)
plot_simulation_results(results)
import datetime
from evo_alg import plot_simulation_results
from experiments.average_n_runs import average_n_runs
from problems.simulations import LOLZ
simulation = LOLZ
r = average_n_runs(simulation, 100, 100)
plot_simulation_results(
r,
# title="Averages {} runs of {}".format(
# N,
# S['problem'].NAME
# ),
savefig="../report/img/{}.png".format(datetime.datetime.now())
)