def random_neighbor(G, i):
l = neighbors(G, i)
return random.choice(l) if len(l) > 0 else None
def main(args):
if len(args) != 1:
sys.exit("Usage: python origianl.py <graphml file>")
C=3
L=6 #损失值
select_strength=1 #选择强度
fname = args[0]
G = networkx.read_graphml(fname)
G = networkx.convert_node_labels_to_integers(G)
'''
给每个参与者初始化投资额，利用之前initial_investment.npy文件
'''
a=[]
defender = [Defender(i) for i in range(0, len(G))]
for i in range(len(G)):
defender[i].set_degree(G.degree()[i])
for i in range(len(G)):
if defender[i].get_degree()<2:
defender[i].set_investment(random.uniform(0,0.2))
elif defender[i].get_degree()<4 and defender[i].get_degree()>=2:
defender[i].set_investment(random.uniform(0.2,0.4))
elif defender[i].get_degree()<6 and defender[i].get_degree()>=4:
defender[i].set_investment(random.uniform(0.4,0.6))
elif defender[i].get_degree()<8 and defender[i].get_degree()>=6:
defender[i].set_investment(random.uniform(0.6,0.8))
elif defender[i].get_degree()>=8:
defender[i].set_investment(random.uniform(0.8,1))
a.append(defender[i].get_investment())
numpy.save('initial_investment.npy',a)
for i in range(len(G)):
sum_weight=0.0+defender[i].get_degree()
for j in neighbors(G,i):
sum_weight+=defender[j].get_degree()
defender[i].set_weight((defender[i].get_degree())/sum_weight)
#pdb.set_trace()
'''开始演化博弈'''
for i in range(300):
#计算每个参与者的风险和收益
for j in range(0,len(G)):
sum=0
for k in neighbors(G,j):
sum = sum+defender[k].get_weight()*defender[k].get_investment()
defender[j].set_risk(math.exp(-sum-defender[j].get_weight()*defender[j].get_investment()))
defender[j].set_payoff(-C*defender[j].get_investment()-L*defender[j].get_risk())
if i==0:
pp=0
for jo in range(0,len(G)):
pp=pp+defender[jo].get_payoff()
print pp
'''更新策略'''
for jj in range(0,len(G)):
jjj=random_neighbor(G, jj)
if defender[jj].get_payoff() < defender[jjj].get_payoff():
imitation_probabily=1.0/(1+math.exp((-select_strength)*(defender[jjj].get_payoff()-defender[jj].get_payoff())))
if random.random() <= imitation_probabily:
defender[jj].set_investment_update(((defender[jj].get_degree()+0.1)/defender[jjj].get_degree()+0.1)*defender[jjj].get_investment())
else:
defender[jj].set_investment_update((defender[jj]).get_investment())
else:
(defender[jj]).set_investment_update((defender[jj]).get_investment())
for jjjj in range(0,len(G)):
defender[jjjj].set_investment(defender[jjjj].get_investment_update())
'''
for j in range(0,len(G)):
print defender[j].get_investment()
'''
p=0
for j in range(0,len(G)):
p=p+defender[j].get_payoff()
print p
def main(args):
if len(args) != 1:
sys.exit("Usage: python add_mechanism.py <graphml file>")
C = 3
L = 6 #损失值
select_strength = 1 #选择强度
fname = args[0]
G = networkx.read_graphml(fname)
G = networkx.convert_node_labels_to_integers(G)
'''
给每个参与者初始化投资额，利用之前initial_investment.npy文件
'''
a = numpy.load('initial_investment.npy')
defender = [Defender(i) for i in range(0, len(G))]
for i in range(len(G)):
defender[i].set_investment(a[i])
defender[i].set_degree(G.degree()[i])
#pdb.set_trace()
for i in range(len(G)):
sum_degree = 0.0 + defender[i].get_degree()
for j in neighbors(G, i):
sum_degree += defender[j].get_degree()
defender[i].set_weight((defender[i].get_degree()) / sum_degree)
'''开始演化博弈'''
for i in range(300):
#计算每个参与者的风险和收益
for j in range(0, len(G)):
sum = 0
neighbors_num = 0
sum_weight = 0.0 + defender[j].get_weight()
for k in neighbors(G, j):
neighbors_num += 1
sum_weight += defender[k].get_weight()
sum = sum + defender[k].get_weight(
) * defender[k].get_investment()
defender[j].set_risk(
math.exp(-sum - defender[j].get_weight() *
defender[j].get_investment()))
defender[j].set_payoff(-C * defender[j].get_investment() -
L * defender[j].get_risk())
defender[j].set_ave_neighbor_invest(
(defender[j].get_weight() * defender[j].get_investment() + sum)
/ sum_weight)
if i == 0:
pp = 0
for jo in range(0, len(G)):
pp = pp + defender[jo].get_payoff()
print pp
for jj in range(len(G)):
taxsum = 0
for kk in neighbors(G, jj):
ksum = 0
for kkk in neighbors(G, kk):
if kkk != jj:
ksum = ksum + defender[kkk].get_weight(
) * defender[kkk].get_investment()
defender[kk].set_tax(
L * (defender[kk].get_risk() -
math.exp(-defender[kk].get_weight() *
defender[kk].get_investment() - ksum -
defender[jj].get_weight() *
defender[jj].get_ave_neighbor_invest())))
taxsum = taxsum + defender[kk].get_tax()
defender[jj].set_addtax(taxsum)
defender[jj].set_payoff(defender[jj].get_payoff() - taxsum)
'''更新策略'''
for jjj in range(0, len(G)):
jjjj = random_neighbor(G, jjj)
if defender[jjj].get_payoff() < defender[jjjj].get_payoff():
imitation_probabily = 1.0 / (1 + math.exp(
(-select_strength) *
(defender[jjjj].get_payoff() - defender[jjj].get_payoff()))
)
if random.random() <= imitation_probabily:
defender[jjj].set_investment_update(
((defender[jjj].get_degree() + 0.1) /
defender[jjjj].get_degree() + 0.1) *
defender[jjjj].get_investment())
else:
defender[jjj].set_investment_update(
(defender[jjj]).get_investment())
else:
(defender[jjj]).set_investment_update(
(defender[jjj]).get_investment())
for jjjjj in range(0, len(G)):
defender[jjjjj].set_investment(
defender[jjjjj].get_investment_update())
'''
for j in range(0,len(G)):
print defender[j].get_investment()
'''
p = 0
for j in range(0, len(G)):
p = p + defender[j].get_payoff() + defender[j].get_addtax()
defender[j].set_tax(0.0)
defender[j].set_addtax(0.0)
print p
'''
def neighbors_():
'''Functional equivalent to <Graph>.neighbors(<v>)'''
g = Graph([(1, 2), (2, 3)])
print(function.neighbors(g, 2)) # [1, 3]
