def task1():
functions = ['rastrigin', 'rosenbrock', 'eggholder', 'ackley']
topologies = ['ring', 'star']
print('task1:')
total_time = time.time()
for F, T in itertools.product(functions, topologies):
print(F, T, 'time: ', end='')
t = time.time()
pso = PSO(function=F, topology=T, seed=19520624)
pso.optimize()
pso.print_log()
print(time.time() - t)
print('Total time: ', time.time() - total_time)
def test002():
dims = (400, 300)
mins = [0, 0, 0, 3, 3, 3]
maxes = [20, 8, 12, 12, 12, 12]
root = Tk()
root.geometry(str(dims[0]) + 'x' + str(dims[1]))
# domain specific
numBoxes = 1
correct = [5., 0., 10., 3,7,7]
problem = PrismProblem(
root, dims, numBoxes,
mins*numBoxes, maxes*numBoxes,
radius=20
)
correct_img = problem.get_image(correct)
correct_img.save('../data/correct.bmp')
swarm = PSO(
zip(mins*numBoxes, maxes*numBoxes),
problem.get_likelihood_func
)
first_guess = swarm.optimize(
10, 50, correct_img,
lambda x: render_particles(root, dims, x)
)
print 'First guess: ', first_guess
print 'First score: ', np.linalg.norm(np.subtract(first_guess, correct))
metropolis = MH(
problem.get_next,
problem.get_likelihood_func,
problem.get_prior_prob,
lambda x: problem.render(problem.get_image(x), x)
)
# execution
first_guess = problem.get_random_cube()
guess = metropolis.optimize(
correct_img, first_guess, trials=160
)
im = problem.get_image(guess)
im.save('../data/guess.bmp')
print guess
print 'Score: ', np.linalg.norm(np.subtract(guess, correct))
def test003():
dims = (400, 300)
mins = [0, 0, 0, 0, 0]
maxes = [3, 7, 7, 7, 7]
root = Tk()
root.geometry(str(dims[0]) + 'x' + str(dims[1]))
# domain specific
numBoxes = 2
correct = [1., 2., 3., 6., 2.] + [3., 2., 3., 6., 2.]
problem = FurnitureProblem(
root, dims, numBoxes,
mins*numBoxes, maxes*numBoxes,
radius=12
)
correct_img = problem.get_image(correct)
correct_img.save('../data/correct.bmp')
swarm = PSO(
zip(mins*numBoxes, maxes*numBoxes),
problem.get_likelihood_func
)
first_guess = swarm.optimize(
8, 3, correct_img,
lambda x: render_particles(root, dims, x)
)
print 'First guess: ', first_guess
print 'First score: ', np.linalg.norm(
np.subtract(first_guess, correct)
)
metropolis = MH(
problem.get_next,
problem.get_likelihood_func,
problem.get_prior_prob,
lambda x: problem.render(problem.get_image(x), x)
)
guess = metropolis.optimize(
correct_img, first_guess, trials=900
)
im = problem.get_image(guess)
im.save('../data/guess.bmp')
print 'Guess: ', guess
print 'Score: ', np.linalg.norm(np.subtract(guess, correct))
def _task2(F, T, N):
a = np.zeros((10, ))
for i in range(10):
t = time.time()
pso = PSO(num_var=10,
population_size=N,
max_generation=10**9 + 7,
function=F,
topology=T,
seed=19520624 + i)
fitness = pso.optimize()[1]
pso.print_log()
del pso
gc.collect()
a[i] = fitness
print('\t', i, F, T, N, 'time: ', time.time() - t)
return (F, T, N), np.mean(a), np.std(a)
def test001():
dims = (400, 300)
mins = [0, 0, 0, 2]
maxes = [17, 15, 15, 8]
root = Tk()
root.geometry(str(dims[0]) + 'x' + str(dims[1]))
# domain specific
numBoxes = 1
correct = [0., 0., 0., 5.]
problem = CubeProblem(
root, dims, numBoxes,
mins*numBoxes, maxes*numBoxes,
radius=20
)
correct_img = problem.get_image(correct)
correct_img = Image.open('../data/test-real.bmp')
correct_img.save('../data/correct.bmp')
swarm = PSO(
zip(mins*numBoxes, maxes*numBoxes),
problem.get_likelihood_func
)
first_guess = swarm.optimize(
8, 60, correct_img,
lambda x: render_particles(root, dims, x)
)
print 'First guess: ', first_guess
print 'First score: ', np.linalg.norm(np.subtract(first_guess, correct))
metropolis = MH(
problem.get_next,
problem.get_likelihood_func,
problem.get_prior_prob,
lambda x: problem.render(problem.get_image(x), x)
)
guess = metropolis.optimize(
correct_img, first_guess, trials=200
)
im = problem.get_image(guess)
im.save('../data/guess.bmp')
print 'Guess: ', guess
print 'Score: ', np.linalg.norm(np.subtract(guess, correct))
from cost import RastriginModel, RosenbrockModel, AckleyModel
from plot import plot_states
from pso import PSO
import logging as log
import argparse
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--plot',
action='store_true',
help='plot the PSO evolution.')
parser.add_argument('--population', nargs='?', type=int, const=100)
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
log.basicConfig(level=log.INFO)
cost_model = AckleyModel
pso = PSO(cost_model,
num_particles=args.population,
max_iter=500,
min_cummulative_error=1E-2)
states = pso.optimize()
if args.plot: plot_states(states, cost_model)
