def plotStats(logbook: tools.Logbook):
# plot statistics:
minFitnessValues, meanFitnessValues = logbook.select("min", "avg")
plt.figure(1)
sns.set_style("whitegrid")
plt.plot(minFitnessValues, color='red')
plt.plot(meanFitnessValues, color='green')
plt.xlabel('Generation')
plt.ylabel('Min / Average Fitness')
plt.title('Min and Average fitness over Generations')
def plot_evolution(evolution: Logbook):
args = ['gen', 'max', 'mean', 'surface', 'count']
ev_it, ev_max, ev_mean, ev_surface, ev_count = evolution.select(*args)
ev_online = np.array(ev_mean).cumsum() / np.arange(1, len(ev_mean) + 1)
ev_offline = np.array(ev_max).cumsum() / np.arange(1, len(ev_max) + 1)
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharex='all', figsize=(12, 4))
# zbieznosc
ax1.plot(ev_it, ev_max, label='max')
ax1.plot(ev_it, ev_mean, label='mean')
ax1.plot(ev_it, ev_offline, label='offline')
ax1.plot(ev_it, ev_online, label='online')
ax1.set_title('Zbieżność algorytmu')
ax1.set_xlabel('Iteracja')
ax1.set_ylabel('Przystosowanie')
ax1.grid()
ax1.legend()
# surface
ax2.plot(ev_it, np.array(ev_surface))
ax2.set_title('Suma powierchni najlepszego osobnika')
ax2.set_xlabel('Iteracja')
ax2.set_ylabel('Powierzchnia')
ax2.grid()
# count
ax3.plot(ev_it, ev_count)
ax3.set_title('Liczba skrzyń najlepszego osobnika')
ax3.set_xlabel('Iteracja')
ax3.set_ylabel('Liczba skrzyń')
ax3.grid()
plt.tight_layout()
return fig