def initialise_visible_agents(self, sim, generated_data_number,
PF_add_threshold, train_mode,
type_selection_mode,
parameter_estimation_mode, polynomial_degree,
apply_adversary, type_estimation_mode,
mutation_rate, do_estimation):
agent_index = 0
self.apply_adversary = apply_adversary
param_estim = None
# if do_estimation:
for agent in sim.agents:
x, y = agent.get_position()
u_a = unknown_agent.UnknownAgent(x, y, agent.direction,
agent.index)
if not do_estimation:
u_a.agent_type = agent.agent_type
u_a.set_parameters(sim, agent.level, agent.radius, agent.angle)
else:
param_estim = parameter_estimation.ParameterEstimation(
generated_data_number, PF_add_threshold, train_mode,
apply_adversary, mutation_rate)
param_estim.estimation_initialisation()
param_estim.estimation_configuration(
type_selection_mode, parameter_estimation_mode,
polynomial_degree, type_estimation_mode)
if parameter_estimation_mode == 'MIN':
param_estim.l1_estimation.train_data.initialise_particle_data_set(
u_a, sim)
param_estim.l2_estimation.train_data.initialise_particle_data_set(
u_a, sim)
param_estim.f1_estimation.train_data.initialise_particle_data_set(
u_a, sim)
param_estim.f2_estimation.train_data.initialise_particle_data_set(
u_a, sim)
u_a.agents_parameter_estimation = param_estim
u_a.set_type_parameters(sim)
u_a.find_estimated_target(sim)
self.visible_agents.append(u_a)
agent_index += 1
if sim.enemy_agent is not None:
x, y = sim.enemy_agent.get_position()
a = unknown_agent.UnknownAgent(x, y, sim.enemy_agent.direction,
agent_index)
a.agent_type = 'w'
self.visible_agents.append(a)
return
def initialise_parameter_estimation(self, type_selection_mode,
parameter_estimation_mode,
generated_data_number,
PF_add_threshold, PF_del_threshold,
PF_weight):
param_estim = parameter_estimation.ParameterEstimation()
param_estim.estimation_initialisation()
polynomial_degree = 4
param_estim.estimation_configuration(type_selection_mode,
parameter_estimation_mode,
generated_data_number,
polynomial_degree,
PF_add_threshold,
PF_del_threshold, PF_weight)
self.estimated_parameter = param_estim
def initialise_visible_agents(self, sim, generated_data_number,
PF_add_threshold, train_mode,
type_selection_mode,
parameter_estimation_mode, polynomial_degree,
apply_adversary, type_estimation_mode,
mutation_rate):
agent_index = 0
self.apply_adversary = apply_adversary
for agent in sim.agents:
x, y = agent.get_position()
a = unknown_agent.Agent(x, y, agent.direction, agent_index)
a.agent_type = agent.agent_type
a.level = agent.level
a.radius = agent.radius
a.angle = agent.angle
self.visible_agents.append(a)
agent_index += 1
if sim.enemy_agent is not None:
x, y = sim.enemy_agent.get_position()
a = unknown_agent.Agent(x, y, sim.enemy_agent.direction,
agent_index)
a.agent_type = 'w'
self.visible_agents.append(a)
for unknown_a in self.visible_agents:
param_estim = parameter_estimation.ParameterEstimation(
generated_data_number, PF_add_threshold, train_mode,
apply_adversary, mutation_rate, unknown_a, sim)
param_estim.estimation_initialisation()
param_estim.estimation_configuration(type_selection_mode,
parameter_estimation_mode,
polynomial_degree,
type_estimation_mode)
unknown_a.agents_parameter_estimation = param_estim
return
# real_sim.draw_map_with_level()
main_sim.draw_map()
main_sim.log_map(logfile)
used_mem_before = psutil.virtual_memory().used
search_tree = None
time_step = 0
begin_time = time.time()
polynomial_degree = 4
agants_parameter_estimation = []
for i in range(len(main_sim.agents)):
param_estim = parameter_estimation.ParameterEstimation()
param_estim.estimation_initialisation()
param_estim.estimation_configuration(type_selection_mode,
parameter_estimation_mode,
generated_data_number,
polynomial_degree, PF_add_threshold,
PF_del_threshold, PF_weight)
agants_parameter_estimation.append(param_estim)
while main_sim.items_left() > 0:
# while time_step < 110:
print 'main run count: ', time_step
for i in range(len(main_sim.agents)):
def initialise_visible_agents(self, sim, generated_data_number,
PF_add_threshold, train_mode,
type_selection_mode,
parameter_estimation_mode, polynomial_degree,
apply_adversary, type_estimation_mode,
mutation_rate):
self.apply_adversary = apply_adversary
# 1. getting the map empty positions
empty_positions = []
empty_positions = [(int(x), int(y)) for x in range(sim.dim_w)
for y in range(sim.dim_h)]
empty_positions.remove(sim.main_agent.get_position())
# 2. taking the visible and invisible content
self.agent_memory = list()
self.item_memory = list()
self.init_visibility(sim)
# 3. setting memory and map
# a. visible stuff
for ag in self.visible_agents:
ag.already_seen = True
pos = ag.position
empty_positions.remove(pos)
self.agent_memory.append(ag)
for item in self.visible_items:
item.already_seen = True
pos = item.get_position()
empty_positions.remove(pos)
self.item_memory.append(item)
# b. removing the visible position
for x in range(sim.dim_w):
for y in range(sim.dim_h):
if self.see_object((x, y)):
self.already_seen_position.add((x, y))
if (x, y) in empty_positions:
empty_positions.remove((x, y))
# c. invisible stuff
for item in self.invisible_items:
item.already_seem = False
x, y = sample(empty_positions, 1)[0]
item.position = position.position(x, y)
empty_positions.remove((x, y))
self.item_memory.append(item)
for ag in self.invisible_agents:
ag.already_seen = False
pos = sample(empty_positions, 1)[0]
ag.position = pos
empty_positions.remove(pos)
self.agent_memory.append(ag)
# 4. starting the param estimation
for unknown_a in self.agent_memory:
param_estim = parameter_estimation.ParameterEstimation(
generated_data_number, PF_add_threshold, train_mode,
mutation_rate, apply_adversary, unknown_a, sim)
param_estim.estimation_initialisation()
param_estim.estimation_configuration(type_selection_mode,
parameter_estimation_mode,
polynomial_degree,
type_estimation_mode)
unknown_a.agents_parameter_estimation = param_estim