def act_dyjs(env, socl_net, agent_no, agent, record, T, Tfi):
assert isinstance(socl_net, SoclNet)
global_arg = arg.init_global_arg()
# 根据对自己影响的大小选择强化对象
out_power = []
agent.policy_now = 'dyjs_noaim' # 添加当前行动记录
for x in range(global_arg["Nagent"]):
if x != agent_no:
out_power.append(socl_net.power[x][agent_no]['weight'])
elif x == agent_no:
out_power.append(0)
if max(out_power) > socl_net.arg['power_thld']: # 对自己影响力大于一个阈值的人才会考虑定义角色
to_power = random_choice(norm_softmax(out_power))
for aim in range(global_arg['Nagent']):
if aim != to_power:
delta = env.arg['ACT']['dyjs']['delta_power'](
socl_net.power[to_power][aim]['weight'])
socl_net.power_delta(to_power, aim, delta)
leadership_bill.leader_bill.add_record(
record_type='talking',
meeting="dyjs",
talking_type="dyjs",
speaker=Sign(agent_no, T + Tfi, 'act_dyjs'),
listener=Sign(aim, T + Tfi, 'act_dyjs'))
agent.policy_now = 'dyjs' # 添加当前行动记录
logging.debug(agent.policy_now)
return socl_net, agent
def __init__(self):
# 环境初始化
self.global_arg = arg.init_global_arg()
env_arg = arg.init_env_arg(self.global_arg)
# 增加nk的一个读入操作
self.main_env = Env(env_arg)
for model_type in ['st', 'ed']:
if all_config['checkpoint']['env'][model_type]['enable']:
self.main_env.nkmodel_load(all_config['checkpoint']['env']['path'], model_type)
self.main_env.nkmodel_save(all_config["nkmodel_path"][model_type], model_type)
# 个体初始化
self.agents = []
csv_head_agent = ['agent_no'] + ['st_state'] + ['st_value'] + ['insight'] + ['xplr'] + ['xplt'] + ['enable']
moniter.AppendToCsv(csv_head_agent, all_config['agent_csv_path'])
for i in range(self.global_arg["Nagent"]):
# 个体随机初始位置
start_st_label = [randint(0, self.main_env.P - 1) for j in range(self.main_env.N)]
state_start = State(start_st_label)
self.agents.append(Agent(arg.init_agent_arg(self.global_arg,
self.main_env.arg),
self.main_env))
self.agents[i].state_now = deepcopy(state_start)
self.agents[i].agent_id = i
# 去除了一开始给一个全局area，改为添加一个包含起点的点area
start_area = Area(self.agents[i].state_now, [False] * self.main_env.N, 0)
start_area.info = get_area_sample_distr(env=self.main_env, area=start_area, state=self.agents[i].state_now,
T_stmp=0, sample_num=1, dfs_r=1)
start_area.sign = Sign(i, 0, 'start')
self.agents[i].renew_m_info(start_area, 0)
self.a_plan = None
logging.info("state:%s, st_value:%s,insight:%.5s ,xplr:%.5s, xplt:%.5s, enable:%.5s" % (
str(self.agents[i].state_now),
self.main_env.getValue(self.agents[i].state_now, 0),
self.agents[i].agent_arg['a']['insight'],
self.agents[i].agent_arg['a']['xplr'],
self.agents[i].agent_arg['a']['xplt'],
self.agents[i].agent_arg['a']['enable']))
# 记录agent信息
csv_info_agent = ['agent%d' % i] \
+ [self.agents[i].state_now] \
+ [self.main_env.getValue(self.agents[i].state_now, 0)] \
+ [self.agents[i].agent_arg['a']['insight']] \
+ [self.agents[i].agent_arg['a']['xplr']] \
+ [self.agents[i].agent_arg['a']['xplt']] \
+ [self.agents[i].agent_arg['a']['enable']]
moniter.AppendToCsv(csv_info_agent, all_config['agent_csv_path'])
# 社会网络初始化
soclnet_arg = arg.init_soclnet_arg(self.global_arg, env_arg)
self.socl_net = SoclNet(soclnet_arg)
self.socl_net.new_flat_init() # 修改初始化方法
# self.socl_net.flat_init()
if all_config['checkpoint']['socl_network']['enable']:
self.socl_net.power_load(all_config['checkpoint']['socl_network']['power'])
self.socl_net.relat_load(all_config['checkpoint']['socl_network']['relat'])
self.record = Record()
self.metric = metrics.register_all_metrics(metrics.Metrics())
def __init__(self):
self.arg = arg.init_global_arg()
self.T = self.arg['T']
self.Ts = self.arg['Ts']
self.Nagent = self.arg['Nagent']
self.agents = [[]] * self.Nagent
self.env = []
self.socl_net = []
def act_whlj(env, socl_net, agent_no, agent, record, T, Tfi):
assert isinstance(socl_net, SoclNet)
global_arg = arg.init_global_arg()
to_select = [x for x in range(global_arg['Nagent'])]
del to_select[agent_no]
to_whlj = sample(to_select, env.arg['ACT']['whlj']['k'])
for aim in to_whlj:
delta = env.arg['ACT']['whlj']['delta_relate'](
socl_net.relat[aim][agent_no]['weight'])
socl_net.relat_delta(aim, agent_no, delta)
leadership_bill.leader_bill.add_record(
record_type='talking',
meeting="whlj",
talking_type="whlj",
speaker=Sign(agent_no, T + Tfi, 'act_whlj'),
listener=Sign(aim, T + Tfi, 'act_whlj'))
agent.policy_now = 'whlj' # 添加当前行动记录
logging.debug(agent.policy_now)
return socl_net, agent
if (state is None):
state = area.center
# logging.debug("start")
state_values = get_area_sample_value(env, area, sample_num, state, dfs_r)
all_value = sorted(state_values)
return {
"max": all_value[-1],
"min": all_value[0],
"avg": sum(all_value) / len(all_value),
"mid": all_value[len(all_value) // 2],
"p0.16": all_value[int(round((len(all_value) - 1) * 0.16))],
"p0.84": all_value[int(round((len(all_value) - 1) * 0.84))],
'T_stmp': T_stmp
}
if (__name__ == "__main__"):
import numpy as np
all_config.load()
moniter.LogInit()
logging.info("Start")
global_arg = arg.init_global_arg()
env_arg = arg.init_env_arg(global_arg)
N = env_arg['N']
k = env_arg['K']
P = env_arg['P']
T = env_arg['T']
env = Env(env_arg)
print(env.getModelPeakDistri())
def __init__(self):
self.global_arg = arg.init_global_arg()
self.main_env = Env(arg.init_env_arg(self.global_arg))
self.agents = []