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)
# particle_settings['initial_inertia'] = 0.00019464380975982382 # Brennan & Clark 1996
graph = False
if graph:
from matplotlib import pyplot as pl
from matplotlib import animation
from mpl_toolkits.mplot3d import Axes3D
for i, experiment in enumerate(parameter_estimation.ROHDE_EXPERIMENTS):
fig = pl.figure()
ax = fig.add_subplot(111, projection="3d")
# pl.axis([-0.05, 1.05,] * dimensions)
swarm = Swarm(dimensions, group_size, no_groups, **particle_settings)
# plot = pl.scatter(*zip(*[particle.position for particle in swarm.step(experiment.game)]), alpha=0.2)
plot = pl.scatter(*zip(*[particle.position for particle in swarm.step(experiment.game)]))
anim = animation.FuncAnimation(
fig, update_plot, frames=xrange(iterations), fargs=(swarm, plot, experiment.game)
)
pl.show()
else:
# new_experiments = [
# Experiment( # free ambiguous form
# Experiment.Settings(
# reference_costs=(-60., -120., -280.),
# ambiguous_reference_cost=-0.,
# success_points=85.,
# ),
