def main():
import networkx as nx
from complex_systems.pgg_diffusion import PGG_diffusion
from complex_systems.quasi_unit_disk_graph import gen_quasi_unit_disk_weight
from complex_systems.quasi_unit_disk_graph import remove_edges_from_graph
import pylab as plt
import numpy as np
nb_node = 400
xmax = 1000
ymax = 1000
coop_ratio = 0.5
synergy = 2
nb_round = 100
nb_game_per_round = 1
nb_def = []
nb_coop = []
inner_radius = 45
outer_radius = 75
alpha = 0.3
nb_seeder = 40
# Generate a Random Graph
p = dict((i,(np.random.uniform(low=0.0, high=xmax),np.random.uniform(low=0.0, high=ymax))) for i in range(nb_node))
G = nx.random_geometric_graph(nb_node,outer_radius,pos=p)
G = gen_quasi_unit_disk_weight(G=G, outer_radius=outer_radius, inner_radius=inner_radius, alpha=alpha)
G = remove_edges_from_graph(G)
# initialize the game
PGG = PGG_diffusion(G=G, synergy=synergy,nb_simulation_step=nb_game_per_round, nb_seeder=nb_seeder, buffer_size = 100, cooperator_ratio=coop_ratio, noise_var=0.0)
for i in range(nb_round):
PGG.run_game()
nb_def.append(PGG.defector_counter())
nb_coop.append(PGG.cooperator_counter())
plt.figure()
plt.plot(nb_def,'b-*')
plt.plot(nb_coop,'r-*')
plt.figure()
nx.draw_networkx(G, node_size=20, pos=p, with_labels = False)
time_stamps_dist = PGG.get_distribution_time_stamps()
x = []
y = []
for key,val in time_stamps_dist.iteritems():
x.append(key)
y.append(val)
plt.figure()
plt.bar(x,y)
plt.show()
def main():
import networkx as nx
from complex_systems.pgg_diffusion import PGG_diffusion
from complex_systems.quasi_unit_disk_graph import gen_quasi_unit_disk_weight
from complex_systems.quasi_unit_disk_graph import remove_edges_from_graph
import pylab as plt
import numpy as np
nb_node = 400
xmax = 1000
ymax = 1000
coop_ratio = 0.5
synergy = 2
nb_round = 100
nb_game_per_round = 1
nb_def = []
nb_coop = []
inner_radius = 45
outer_radius = 75
alpha = 0.3
nb_seeder = 40
# Generate a Random Graph
p = dict((i, (np.random.uniform(low=0.0, high=xmax),
np.random.uniform(low=0.0, high=ymax)))
for i in range(nb_node))
G = nx.random_geometric_graph(nb_node, outer_radius, pos=p)
G = gen_quasi_unit_disk_weight(G=G,
outer_radius=outer_radius,
inner_radius=inner_radius,
alpha=alpha)
G = remove_edges_from_graph(G)
# initialize the game
PGG = PGG_diffusion(G=G,
synergy=synergy,
nb_simulation_step=nb_game_per_round,
nb_seeder=nb_seeder,
buffer_size=100,
cooperator_ratio=coop_ratio,
noise_var=0.0)
for i in range(nb_round):
PGG.run_game()
nb_def.append(PGG.defector_counter())
nb_coop.append(PGG.cooperator_counter())
plt.figure()
plt.plot(nb_def, 'b-*')
plt.plot(nb_coop, 'r-*')
plt.figure()
nx.draw_networkx(G, node_size=20, pos=p, with_labels=False)
time_stamps_dist = PGG.get_distribution_time_stamps()
x = []
y = []
for key, val in time_stamps_dist.iteritems():
x.append(key)
y.append(val)
plt.figure()
plt.bar(x, y)
plt.show()
def main(parameters):
import networkx as nx
from complex_systems.dygraph import DyGraph
from complex_systems.pgg_diffusion import PGG_diffusion
from complex_systems.quasi_unit_disk_graph import gen_quasi_unit_disk_weight
from complex_systems.quasi_unit_disk_graph import remove_edges_from_graph
import pylab as plt
import numpy as np
nb_node = 400
xmax = 1000
ymax = 1000
coop_ratio = 0.5
synergy = 9
nb_game_per_round = 1
nb_def = []
nb_coop = []
number_of_node = parameters['number_of_node']
nb_seeder = parameters['number_of_seeder']
size_of_simulation_area = parameters['size_of_simulation_area']
buffer_size = parameters['buffer_size']
outer_radius = parameters['outer_radius']
inner_radius = parameters['inner_radius']
alpha_quasi_unit_disk = parameters['alpha_quasi_unit_disk']
coop_ratio = parameters['initial_cooperator_ratio']
simulation_length = parameters['simulation_length']
sampling_interval = parameters['sampling_interval']
alpha_levy = parameters['alpha_levy']
#noise_var = parameters['noise_variance']
noise_var = 0.1
beta = parameters['beta']
f_min = parameters['f_min']
f_max = parameters['f_max']
s_min = parameters['s_min']
s_max = parameters['s_max']
velocity = parameters['velocity']
G = DyGraph(time_stop=simulation_length,
time_step=sampling_interval)
G.generate_mobility_levy_walk(
alpha=alpha_levy,
beta=beta,
size_max=xmax,
f_min=f_min,
f_max=f_max,
s_min=s_min,
s_max=s_max,
b_c=2,
radius=outer_radius,
nb_node=nb_node,
velocity=velocity,
)
first_run = True
for g in G:
g = gen_quasi_unit_disk_weight(G=g, outer_radius=outer_radius,
inner_radius=inner_radius, alpha=alpha_quasi_unit_disk)
g = remove_edges_from_graph(g)
if first_run == True:
PGG = PGG_diffusion(G=g, synergy=synergy,
cooperator_ratio=coop_ratio,
noise_var=noise_var,
buffer_size=buffer_size,
nb_seeder=int(nb_seeder))
PGG.run_game()
nb_def.append(PGG.defector_counter())
nb_coop.append(PGG.cooperator_counter())
strategies = PGG.get_strategies()
time_stamps = PGG.get_time_stamps()
states = PGG.get_states()
#sent_update = PGG.get_sent_update()
seeder = PGG.get_seeder()
#print time_stamps[0]
#print sent_update
#print strategies
#print states
first_run = False
else:
PGG = PGG_diffusion(G=g, synergy=synergy,
cooperator_ratio=coop_ratio,
buffer_size=buffer_size,
noise_var=noise_var)
PGG.set_strategies(strategies)
PGG.set_time_stamps(time_stamps)
PGG.set_nodes_states(states)
#PGG.set_sent_update(sent_update)
PGG.set_seeder(seeder)
res = PGG.run_game()
nb_def.append(PGG.defector_counter())
nb_coop.append(PGG.cooperator_counter())
strategies = PGG.get_strategies()
time_stamps = PGG.get_time_stamps()
states = PGG.get_states()
#print states
#print strategies
#print time_stamps[0]
#sent_update = PGG.get_sent_update()
#seeder = PGG.get_seeder()
#print seeder
print synergy, res, np.mean(G.avg_degree())
plt.figure()
plt.plot(nb_def,'b-*')
plt.plot(nb_coop,'r-*')
#plt.figure()
#print np.mean([val for key, val in G.degree().iteritems()])
#nx.draw_networkx(G, node_size=20, pos=p, with_labels = False)
time_stamps_dist = PGG.get_distribution_time_stamps()
x = []
y = []
for key,val in time_stamps_dist.iteritems():
x.append(key)
y.append(val)
plt.figure()
plt.bar(x,y)
plt.show()
def main(parameters):
import networkx as nx
from complex_systems.dygraph import DyGraph
from complex_systems.pgg_diffusion import PGG_diffusion
from complex_systems.quasi_unit_disk_graph import gen_quasi_unit_disk_weight
from complex_systems.quasi_unit_disk_graph import remove_edges_from_graph
import pylab as plt
import numpy as np
nb_node = 400
xmax = 1000
ymax = 1000
coop_ratio = 0.5
synergy = 9
nb_game_per_round = 1
nb_def = []
nb_coop = []
number_of_node = parameters['number_of_node']
nb_seeder = parameters['number_of_seeder']
size_of_simulation_area = parameters['size_of_simulation_area']
buffer_size = parameters['buffer_size']
outer_radius = parameters['outer_radius']
inner_radius = parameters['inner_radius']
alpha_quasi_unit_disk = parameters['alpha_quasi_unit_disk']
coop_ratio = parameters['initial_cooperator_ratio']
simulation_length = parameters['simulation_length']
sampling_interval = parameters['sampling_interval']
alpha_levy = parameters['alpha_levy']
#noise_var = parameters['noise_variance']
noise_var = 0.1
beta = parameters['beta']
f_min = parameters['f_min']
f_max = parameters['f_max']
s_min = parameters['s_min']
s_max = parameters['s_max']
velocity = parameters['velocity']
G = DyGraph(time_stop=simulation_length, time_step=sampling_interval)
G.generate_mobility_levy_walk(
alpha=alpha_levy,
beta=beta,
size_max=xmax,
f_min=f_min,
f_max=f_max,
s_min=s_min,
s_max=s_max,
b_c=2,
radius=outer_radius,
nb_node=nb_node,
velocity=velocity,
)
first_run = True
for g in G:
g = gen_quasi_unit_disk_weight(G=g,
outer_radius=outer_radius,
inner_radius=inner_radius,
alpha=alpha_quasi_unit_disk)
g = remove_edges_from_graph(g)
if first_run == True:
PGG = PGG_diffusion(G=g,
synergy=synergy,
cooperator_ratio=coop_ratio,
noise_var=noise_var,
buffer_size=buffer_size,
nb_seeder=int(nb_seeder))
PGG.run_game()
nb_def.append(PGG.defector_counter())
nb_coop.append(PGG.cooperator_counter())
strategies = PGG.get_strategies()
time_stamps = PGG.get_time_stamps()
states = PGG.get_states()
#sent_update = PGG.get_sent_update()
seeder = PGG.get_seeder()
#print time_stamps[0]
#print sent_update
#print strategies
#print states
first_run = False
else:
PGG = PGG_diffusion(G=g,
synergy=synergy,
cooperator_ratio=coop_ratio,
buffer_size=buffer_size,
noise_var=noise_var)
PGG.set_strategies(strategies)
PGG.set_time_stamps(time_stamps)
PGG.set_nodes_states(states)
#PGG.set_sent_update(sent_update)
PGG.set_seeder(seeder)
res = PGG.run_game()
nb_def.append(PGG.defector_counter())
nb_coop.append(PGG.cooperator_counter())
strategies = PGG.get_strategies()
time_stamps = PGG.get_time_stamps()
states = PGG.get_states()
#print states
#print strategies
#print time_stamps[0]
#sent_update = PGG.get_sent_update()
#seeder = PGG.get_seeder()
#print seeder
print synergy, res, np.mean(G.avg_degree())
plt.figure()
plt.plot(nb_def, 'b-*')
plt.plot(nb_coop, 'r-*')
#plt.figure()
#print np.mean([val for key, val in G.degree().iteritems()])
#nx.draw_networkx(G, node_size=20, pos=p, with_labels = False)
time_stamps_dist = PGG.get_distribution_time_stamps()
x = []
y = []
for key, val in time_stamps_dist.iteritems():
x.append(key)
y.append(val)
plt.figure()
plt.bar(x, y)
plt.show()