def fitness(particle_position):
"""
Use particle position as parameters for PSO settings, return similarity of
resultant model to Rohde et al. experimental data. Expects 6-dimensional
search space.
"""
global fit_count
# Constants:
dimensions = 3
group_size = 2
no_groups = 100
iterations, particle_settings = get_parameters(particle_position)
particle_settings['respect_boundaries'] = True
scores = []
# run psos with these params
for i, experiment in enumerate(ROHDE_EXPERIMENTS): # TODO: parallelize
print ' ', fit_count, i, '\b' * 100,
swarm = Swarm(dimensions, group_size, no_groups, **particle_settings)
for groups in swarm.step_until(experiment.game, max_iterations=iterations, return_groups=True):
pass
scores.append(experiment.difference(get_coordination_results(groups)))
fit_count += 1
return -1 * sum(scores) / float(len(scores))
dimensions = 2
group_size = 50
no_groups = 50
save = True
graph = True
swarm = Swarm(dimensions, group_size, no_groups, respect_boundaries=False, velocity_dampening=0.2)
fitness_func = lambda (x, y): -abs((y * 10) - (x * 5) ** 2)
if graph:
from matplotlib import pyplot as pl
from matplotlib import animation
fig = pl.figure()
#ax = pl.axis([0, 10,] * dimensions)
ax = pl.axis([0, 5, 0, 10])
plot = pl.scatter(*zip(*[particle.position for particle in swarm.step(fitness_func)]))
anim = animation.FuncAnimation(fig, update_plot, frames=iterations, fargs=(swarm, plot, fitness_func))
anim.save('squares.mp4', fps=10, extra_args=['-vcodec', 'libx264'])
pl.show()
else:
for i in swarm.step_until(game, max_iterations=iterations):
pass
print i
final = swarm.get_best_position_coords(fitness_func)
all_experiments = {}
# for i, experiment in enumerate(parameter_estimation.ROHDE_EXPERIMENTS):
for i, experiment in enumerate(new_experiments):
print ("repair" if particle_settings["respect_boundaries"] else "reject"), i
# print 'standard', i
swarm = Swarm(len(experiment.settings.reference_costs), group_size, no_groups, **particle_settings)
# swarm = Swarm(dimensions, group_size, no_groups, **particle_settings)
simulation_results = []
for __ in xrange(250): # 250 sims of...
swarm = Swarm(len(experiment.settings.reference_costs), group_size, no_groups, **particle_settings)
# d = len(experiment.settings.reference_costs) # brennan clark
# swarm = Swarm(d, group_size, no_groups, particle_distribution=brennan_clark(d), **particle_settings) # brennan clark
# for particle in swarm.particles:
# particle._time = 350
for j, groups in enumerate(
swarm.step_until(experiment.game, max_iterations=iterations, return_groups=True)
):
pass
# if i == 0:
# pass
# from matplotlib import pyplot as plt
# from mpl_toolkits.mplot3d import Axes3D
# import os
# os.chdir(r'I:\Users\Chase Stevens\Dropbox\Dissertation\anim3')
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
# positions = zip(*[particle.position for group in groups for particle in group])
# ax.scatter(*positions, c='r', marker='o', alpha=0.1)
# plt.savefig('{}.png'.format(str(j).zfill(5)))
no_groups,
particle_distribution=parameter_estimation.mitchell_sampling_factory(dimensions),
)
fitness_func = parameter_estimation.fitness
if graph:
from matplotlib import pyplot as pl
from matplotlib import animation
fig = pl.figure()
pl.axis([-0.05, 1.05,] * dimensions)
plot = pl.scatter(*zip(*[particle.position for particle in swarm.step(fitness_func)]), alpha=0.2)
anim = animation.FuncAnimation(fig, update_plot, frames=xrange(iterations), fargs=(swarm, plot, fitness_func))
pl.show()
else:
t = time.time()
for i, __ in enumerate(swarm.step_until(fitness_func, max_iterations=iterations)):
with open(output_location, 'w') as f:
json.dump(swarm.to_dict(), f)
print
print i, time.time() - t
t = time.time()
print
final = swarm.get_best_position_coords(fitness_func)
print final, fitness_func(final)
