def genetic_algorithm(pop, points, generation_number):
fitness_array = pop_fitness(pop, points)
fitness_array_cumlative = cumulative_sum(fitness_array)
summation = fitness_array_cumlative[len(fitness_array_cumlative) - 1]
# Proof that the value converges
# print("%f && %f " % (min(fitness_array), max(fitness_array)))
next_pop = []
for _ in range(pop_size):
r1, r2 = random.uniform(0, summation), random.uniform(0, summation)
idx1 = bisect.bisect_left(fitness_array_cumlative, r1)
idx2 = bisect.bisect_left(fitness_array_cumlative, r2)
Offspring1, Offspring2 = cross_over(pop[idx1], pop[idx2])
mutate(Offspring1, generation_number)
mutate(Offspring2, generation_number)
next_pop.append(Offspring1)
next_pop.append(Offspring2)
# Merge the new and old populations and take the best of both
pop = pop + next_pop
pop.sort(key=lambda x: ff.fitness(x, points), reverse=True)
next_gen = pop[:pop_size // 2] + create_population(len(pop[0]) - 1, pop_size // 2)
best_chromosome = next_gen[0]
best_val_old_pop = ff.fitness(best_chromosome, points)
return next_gen, best_val_old_pop, best_chromosome
def test_plot_rotation(self):
c = Camera(0.48271098732948303, 1920, 1080, [5, 0, 0], [math.pi / 2, 0, math.pi])
genome = np.array([5, 0, 0, math.pi / 2, 0, math.pi])
t = Person('Max Mustermann', np.array([0, 0, 0]), 1,
np.array([0.7, -0.2, 0]), np.array([0.7, 0.2, 0]))
lt = Person('Agnes Angeschaute', np.array([2, 0, 0]), 1,
np.array([1.3, -0.2, 0]), np.array([1.3, 0.2, 0]))
persons = [t, lt]
#PositionProcess.optimizeAllShots(t, t, c, [t], [], genome, [2])
snapshot = SceneSnapshot(t, lt, c, persons, [], [], [2])
optimizer = PositionProcess.CameraOptimizer(snapshot, 2)
#optimizer.fitness(genome)
winkel = arange(-math.pi, math.pi, 0.01)
s = arange(-math.pi, math.pi, 0.01)
startposition = np.array([5, 0, 0])
#startposition.reshape(-1, 1)
for i in range(0, len(winkel)):
p = startposition.dot(FitnessFunction.rotate(np.array([0, 0, 0, math.pi / 2, winkel[i]])))
s[i] = FitnessFunction.fitness(np.array([p[0],p[1],p[2],math.pi/2,winkel[i]+0.5*math.pi]), optimizer)
#s[i] = optimizer.fitness(np.array([5,0,0,math.pi/2,winkel[i]+math.pi/2]))
#s[i] = optimizer.getPersonQuality(np.array([p[0],p[1],p[2], math.pi / 2, winkel[i] + math.pi/2]), t, 1.2,
# personFitnessByImage, occultationWeight)
#s[i] = optimizer.getPersonQuality(np.array([5,0,0, math.pi / 2, winkel[i] + math.pi/2]), t, 1.2,
# personFitnessByImage, occultationWeight)
#s[i] = PositionProcess.getImageAngles(np.array([p[0],p[1],p[2], math.pi / 2, winkel[i] + math.pi*0.5]), t)[0]
#s[i] = PositionProcess.getImageAngles(np.array([5,0,0,winkel[i]+math.pi / 2, math.pi/2]), t)[0]
ax = subplot(111)
ax.plot(winkel, s)
ax.grid(True)
ticklines = ax.get_xticklines()
ticklines.extend(ax.get_yticklines())
gridlines = ax.get_xgridlines()
gridlines.extend(ax.get_ygridlines())
ticklabels = ax.get_xticklabels()
ticklabels.extend(ax.get_yticklabels())
for line in ticklines:
line.set_linewidth(3)
for line in gridlines:
line.set_linestyle('-')
for label in ticklabels:
label.set_color('r')
label.set_fontsize('medium')
show()
def pop_fitness(pop, points):
return [ff.fitness(i, points) for i in pop]