def cora():
G = nx.barabasi_albert_graph(1000, 3)
# G = nx.watts_strogatz_graph(1000, 5, 0.3)
# G = nx.random_regular_graph(4, 1000)
p = Population(G)
e = Evolution()
g = game.PDG(2)
e.set_population(p).set_game(g)
# p.strategy = np.ones(len(p), np.int)
f, axs = plt.subplots(3, 4)
axs = axs.reshape(12)
for r in range(11):
if r > 5:
r_ = 10 - r
p.strategy = np.zeros(len(p), dtype=np.int)
s = 1
else:
r_ = r
p.strategy = np.ones(len(p), dtype=np.int)
s = 0
n = int(round(len(p) / 10.0 * r_))
selected_list = np.random.choice(range(len(p)), n)
p.strategy[selected_list] = s
# print p.strategy
g.strategy = p.strategy
g.play()
p.show_degree(axs[r])
plt.show()
def repeat_b():
# 博弈收益参数不同，合作率曲线
e = Evolution(has_mut=False)
G = nx.random_regular_graph(4, 1000)
p = Population(G)
b = 5
for i in range(b):
g = game.PDG(i * 2 + 2)
e.set_population(p).set_game(g).set_rule(u)
print('Control Variable b: %d' % (i * 2 + 2))
e.evolve(100000, restart=True, quiet=True, autostop=False)
e.show(fmt[i], label="b=%d" % (i * 2 + 2))
plt.legend(loc='lower right')
plt.show()
def repeat2d():
e = Evolution()
bs = np.linspace(1, 10, 3)
# fig, axes = plt.subplots()
colors = 'brgcmykwa'
symbs = '.ox+*sdph-'
for i in range(1, 10):
i = 4
G = nx.random_regular_graph(i + 1, 1000)
p = Population(G)
a = [0] * len(bs)
for j, b in enumerate(bs):
g = game.PDG(b)
e.set_population(p).set_game(g).set_rule(u)
e.evolve(10000)
a[j] = e.cooperate[-1]
plt.plot(bs, a, colors[j] + symbs[j], label='b=%f' % b)
break
plt.show()
def repeat_start_pc():
# 初始Pc不同的合作变化曲线
# G = nx.watts_strogatz_graph(1000, 4, 0.2)
G = nx.barabasi_albert_graph(1000, 3)
p = Population(G)
g = game.PDG(b=10)
u = rule.DeathBirth()
e = Evolution(has_mut=False)
e.set_population(p).set_game(g).set_rule(u)
for i in range(5):
pc = (2 * i + 1) / 10.0
p.init_strategies(g, [pc, 1 - pc])
print('Initial P(C) is %.2f' % pc)
e.evolve(100000, restart=True, quiet=True, autostop=False)
e.show(fmt[i], label=r'start $P_C$=%.2f' % pc)
plt.legend(loc='lower right')
plt.title(r'Evolution under Different Start $P_C$')
plt.xlabel('Number of generations')
plt.ylabel(r'Fraction of cooperations, $\rho_c$')
plt.show()
def repeat_ss_rewire():
# 策略固定，连接倾向进行演化
from network import LatticeWithLongTie
p = LatticeWithLongTie(30)
g = game.PDG(b=8)
# u = rule.DeathBirth()
u = rule.Fermi(0.1)
a = adapter.Preference(3)
e = StaticStrategyEvolution()
e.set_game(g).set_rule(u).set_adapter(a)
e.set_population(p)
e.bind_process()
p.init_longtie()
# p.degree_distribution(loglog=False)
p_copy1 = p.copy()
plt.figure()
for i in range(6):
i = 5
pc = i / 5.0
# 复制演化前的状态
p_copy = p.copy()
e.set_population(p)
a.dynamic = p.dynamics
a.bind(p)
# if i > 0:
# p.is_equal(p_copy1)
# break
p.init_strategies(g, [pc, 1 - pc])
# p.check_cache()
# 重置网络连接，重置节点的连接策略
p = p_copy
print('static P(C) is %.2f' % pc)
e.evolve(100, restart=True, quiet=True, autostop=False)
e.show(fmt[i], label=r'static $P_C$=%.2f ' % pc)
break
plt.legend(loc='upper left')
plt.title('Static Strategy Evolution')
plt.xlabel('Number of generations')
plt.ylabel('Rewire Strategies')
plt.ylim(0, len(p))
plt.show()
def repeat_ll_rewire():
# 策略固定，连接倾向进行演化
from network import LatticeWithLongTie
# G = nx.random_regular_graph(5, 1000)
p = LatticeWithLongTie(30)
g = game.PDG(b=8)
u = rule.DeathBirth()
# u = rule.Fermi(.1)
a = adapter.Preference(3)
e = CoEvolution()
e.set_game(g).set_rule(u).set_adapter(a)
e.set_population(p)
e.bind_process()
p.degree_distribution()
print('start evolving')
e.evolve(10000, restart=True, quiet=True, autostop=False)
plt.figure()
e.show(fmt[0], label='LLN Co-Evolution')
plt.legend(loc='upper left')
plt.title('LLN Co-Evolution')
plt.xlabel('Number of generations')
plt.ylabel('Rewire Strategies')
plt.show()
p.degree_distribution()
def evolve(self, turns, **kwargs):
self.rewire = None
self.prefer = np.zeros((turns, self.adapter.category), dtype=np.int)
super(self.__class__, self).evolve(turns, **kwargs)
def show(self, fmt, label, wait=False, *args, **kwargs):
super(self.__class__, self).show(True)
f = plt.figure(2)
color = 'brgcmykw'
# symb = '.ox+*sdph'
label = ['random', 'popularity', 'knn', 'pop*sim', 'similarity']
for i in xrange(self.adapter.category):
plt.plot(self.prefer[:, i], color[i], label=label[i])
plt.title('CoEvolutionary Game')
plt.xlabel('Step')
plt.ylabel('Strategies')
plt.legend()
# plt.show()
f.show()
if __name__ == '__main__':
G = nx.random_regular_graph(5, 10)
pp = Population(G)
gg = game.PDG()
rr = rule.BirthDeath()
e = Evolution()
e.set_population(pp).set_game(gg).set_rule(rr)
e.evolve(10000)
e.show()
# G = nx.star_graph(10)
# G = nx.watts_strogatz_graph(1000, 5, 0.2)
G = nx.barabasi_albert_graph(1000, 3)
# G = nx.powerlaw_cluster_graph(1000, 10, 0.2)
# G = nx.convert_node_labels_to_integers(nx.davis_southern_women_graph())
# G = ["/../../DataSet/ASU/Douban-dataset/data/edges.csv", ',']
# G = {"path":"/../wechat/barabasi_albert_graph(5000,100)_adj.txt", "fmt":"adj"}
# G = "/../wechat/facebook.txt"
# 网络结构
p = Population(G)
# print nx.info(p)
# p.degree_distribution()
# 博弈类型
g = game.PDG(b=5)
# g = game.PGG(3)
# g = game.PGG2(3)
# 学习策略
# u = rule.BirthDeath()
u = rule.DeathBirth()
# u = rule.Fermi()
# u = rule.HeteroFermi(g.delta)
# 连接策略
a = adapter.Preference(3)
# 绘图参数
colors = 'bgrcmykw'
markers = '.,ov^v<>1234sp*hH+xDd|-'