def main(): import networkx as nx from complex_systems.pgg import PublicGoodGames import pylab as plt import numpy as np nb_node = 1000 edge_prob = 10.0/nb_node coop_ratio = 0.5 synergy = 8 nb_round = 100 nb_game_per_round = 10 nb_def = [] nb_coop = [] # Generate a Random Graph G = nx.fast_gnp_random_graph(nb_node, edge_prob) # initialize the game PGG = PublicGoodGames(G=G, synergy=synergy,nb_simulation_step=nb_game_per_round, cooperator_ratio=coop_ratio) for i in range(nb_round): PGG.run_game() nb_def.append(PGG.defector_counter()) nb_coop.append(PGG.cooperator_counter()) #print nb_coop, nb_def 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, with_labels = False) plt.show()
def main():
from complex_systems.dygraph import DyGraph
from complex_systems.pgg import PublicGoodGames
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 networkx as nx
import pylab as plt
outer_radius = 75
inner_radius = 40
alpha_quasi_unit_disk = 0.9
synergy = 8
coop_ratio = 0.5
noise_var = 1
nb_node = 400
time_step = 5.0
G = DyGraph(time_stop=2000.0, time_step=time_step)
G.generate_mobility_levy_walk(
alpha=0.9,
beta=0.9,
size_max=1000,
f_min=10,
f_max=1000,
s_min=5,
s_max=1000.0,
b_c=2,
radius=200.0,
nb_node=nb_node,
)
first_run = True
resultats = []
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 = PublicGoodGames(G=g,
synergy=synergy,
cooperator_ratio=coop_ratio,
noise_var=noise_var)
nb_coop = PGG.run_game(time_step)
resultats.append(nb_coop)
strategies = PGG.get_strategies()
first_run = False
else:
PGG = PublicGoodGames(G=g,
synergy=synergy,
cooperator_ratio=coop_ratio,
noise_var=noise_var)
PGG.set_strategies(strategies)
nb_coop = PGG.run_game(time_step)
strategies = PGG.get_strategies()
resultats.append(nb_coop)
plt.figure()
plt.plot(resultats, '-o')
plt.show()
def main():
from complex_systems.dygraph import DyGraph
from complex_systems.pgg import PublicGoodGames
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 networkx as nx
import pylab as plt
outer_radius = 75
inner_radius = 40
alpha_quasi_unit_disk = 0.9
synergy = 8
coop_ratio = 0.5
noise_var = 1
nb_node = 400
time_step = 5.0
G = DyGraph(time_stop=2000.0, time_step=time_step)
G.generate_mobility_levy_walk(
alpha=0.9,
beta=0.9,
size_max=1000,
f_min=10,
f_max=1000,
s_min=5,
s_max=1000.0,
b_c=2,
radius=200.0,
nb_node=nb_node,
)
first_run = True
resultats = []
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 = PublicGoodGames(G=g, synergy=synergy,
cooperator_ratio=coop_ratio,
noise_var=noise_var)
nb_coop = PGG.run_game(time_step)
resultats.append(nb_coop)
strategies = PGG.get_strategies()
first_run = False
else:
PGG = PublicGoodGames(G=g, synergy=synergy,
cooperator_ratio=coop_ratio,
noise_var=noise_var)
PGG.set_strategies(strategies)
nb_coop = PGG.run_game(time_step)
strategies = PGG.get_strategies()
resultats.append(nb_coop)
plt.figure()
plt.plot(resultats, '-o')
plt.show()
def main():
import networkx as nx
from complex_systems.pgg import PublicGoodGames
import pylab as plt
import numpy as np
nb_node = 1000
edge_prob = 10.0 / nb_node
coop_ratio = 0.5
synergy = 8
nb_round = 100
nb_game_per_round = 10
nb_def = []
nb_coop = []
# Generate a Random Graph
G = nx.fast_gnp_random_graph(nb_node, edge_prob)
# initialize the game
PGG = PublicGoodGames(G=G,
synergy=synergy,
nb_simulation_step=nb_game_per_round,
cooperator_ratio=coop_ratio)
for i in range(nb_round):
PGG.run_game()
nb_def.append(PGG.defector_counter())
nb_coop.append(PGG.cooperator_counter())
#print nb_coop, nb_def
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, with_labels=False)
plt.show()
def run_simu(parameters, synergy):
import numpy as np
import networkx as nx
from complex_systems.dygraph import DyGraph
from complex_systems.pgg import PublicGoodGames
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
number_of_node = parameters['number_of_node']
size_of_simulation_area = parameters['size_of_simulation_area']
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']
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=size_of_simulation_area,
f_min=f_min,
f_max=f_max,
s_min=s_min,
s_max=s_max,
b_c=2,
radius=outer_radius,
nb_node=number_of_node,
velocity=velocity,
)
first_run = True
resultats = []
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 = PublicGoodGames(G=g,
synergy=synergy,
cooperator_ratio=coop_ratio,
noise_var=noise_var)
nb_coop = PGG.run_game(sampling_interval)
resultats.append(nb_coop)
strategies = PGG.get_strategies()
first_run = False
else:
PGG = PublicGoodGames(G=g,
synergy=synergy,
cooperator_ratio=coop_ratio,
noise_var=noise_var)
PGG.set_strategies(strategies)
nb_coop = PGG.run_game(sampling_interval)
strategies = PGG.get_strategies()
resultats.append(nb_coop)
return (synergy, nb_coop, np.mean(G.avg_degree()))
def run_simu(parameters, synergy):
import numpy as np
import networkx as nx
from complex_systems.dygraph import DyGraph
from complex_systems.pgg import PublicGoodGames
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
number_of_node = parameters['number_of_node']
size_of_simulation_area = parameters['size_of_simulation_area']
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']
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=size_of_simulation_area,
f_min=f_min,
f_max=f_max,
s_min=s_min,
s_max=s_max,
b_c=2,
radius=outer_radius,
nb_node=number_of_node,
velocity=velocity,
)
first_run = True
resultats = []
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 = PublicGoodGames(G=g, synergy=synergy,
cooperator_ratio=coop_ratio,
noise_var=noise_var)
nb_coop = PGG.run_game(sampling_interval)
resultats.append(nb_coop)
strategies = PGG.get_strategies()
first_run = False
else:
PGG = PublicGoodGames(G=g, synergy=synergy,
cooperator_ratio=coop_ratio,
noise_var=noise_var)
PGG.set_strategies(strategies)
nb_coop = PGG.run_game(sampling_interval)
strategies = PGG.get_strategies()
resultats.append(nb_coop)
return (synergy, nb_coop, np.mean(G.avg_degree()))
