def test_no_5_some_batch_data(self):
agents = get_SmartAgents()
actual_phase = 0
orange_phase_duration = 2
phase_duration = orange_phase_duration + 1
den = [0, 0, 10, 8]
den_pred = [2, 2, 2, 78]
state_to_predict = LearningState(
actual_phase=actual_phase,
starting_actual_phase=actual_phase,
phase_duration=phase_duration,
global_densities=den_pred + [0, 0],
densities=den,
orange_phase_duration=orange_phase_duration)
for i in range(150):
# utrzymanie fazy - gorszy wybór
action_0 = [0]
env = single_simulate(agents,
actual_phase,
phase_duration,
den,
orange_phase_duration=orange_phase_duration,
actions=action_0,
u=env_settings.u_all_2)
for x in range(40):
simulate_from_env(env, action_0)
action_1 = [1]
# zmiana fazy - lepszy wybór
# print('stan bazowy', env.x[env.t])
# print(env.global_memories[-1])
simulate_from_env(env, action_1)
# print('stan', env.x[env.t])
# print('stan po', env.x[env.t])
simulate_from_env(env, [orange])
# print('stan', env.x[env.t])
simulate_from_env(env, [orange])
# print('stan', env.x[env.t])
for x in range(20):
simulate_from_env(env, action_1)
# print('stan', env.x[env.t])
memories = env.global_memories
memories += env.global_memories
save_batches(agents)
batches = get_batches()
train(agents=agents)
env.global_memories = []
for agent in env.agents:
agent.memories = []
prediction = agents[0].model.predict(
state_to_predict.to_learn_array())
# zbiega to 10,20, cokolwiek
print('prediction :)', prediction)
exportData = ExportData(learningMethod='DQN',
learningEpochs=0,
nets=memories,
netName='net14',
densityName='test_fixed_no_4')
exportData.saveToJson()
a = 23
def assign_local_state(self, densities):
global_densities = []
for road in sections_of_roads:
for sec in road:
global_densities.append(densities[sec])
local_state = LearningState(
global_densities=global_densities,
actual_phase=self.actual_phase,
phase_duration=self.phase_duration,
densities=densities,
starting_actual_phase=self.actual_phase,
yellow_phase_duration=self.yellow_phase_duration)
local_state.phase_absences = [x for x in self.phase_absences]
self.local_state = local_state
def get_radom_learning_state():
pre_cross_densities = (random.randint(0, 5), random.randint(0, 5), random.randint(0, 5))
global_aggregated_densities = ()
for i in range(12):
global_aggregated_densities = global_aggregated_densities + (random.randint(0, 10),)
phase_index: int = random.randint(0, 4)
phase_duration: int = random.randint(0, 15)
ls = LearningState(pre_cross_densities, global_aggregated_densities, phase_index, phase_duration)
return ls
def centroid(self):
if len(self.states) == 0:
return self.random_centroid
all_arr_states = [state.to_array() for state in self.states]
arr_state = []
for i in range(len(all_arr_states[0])):
mean_of_i_coordinate = np.mean([x[i] for x in all_arr_states])
arr_state.append(mean_of_i_coordinate)
return LearningState.from_array(arr_state)
def single_simulate(agents, actual_phase, phase_duration, den, orange_phase_duration=2):
densities: List[int] = den + [0, 0] # dodajemy 2 ostatnie nieistotne
global_densities: List[int] = densities
env = Env(agents)
state = LearningState(orange_phase_duration=orange_phase_duration,
actual_phase=actual_phase,
starting_actual_phase=actual_phase,
phase_duration=phase_duration,
global_densities=global_densities,
densities=densities)
env.assign_state(state)
env.step([1])
def assign_local_state(self, densities):
global_densities = []
for road in sections_of_roads:
for sec in road:
global_densities.append(densities[sec])
local_state = LearningState(global_densities=global_densities,
actual_phase=self.actual_phase,
phase_duration=self.phase_duration,
densities=densities,
starting_actual_phase=self.actual_phase,
orange_phase_duration=self.orange_phase_duration)
self.local_state = local_state
def assign_local_state(self, densities):
pre_cross_densities = ()
for sec in self.local_phase_sections:
pre_cross_densities = pre_cross_densities + (densities[sec], )
global_aggregated_densities = ()
for road in sections_of_roads:
global_aggregated_densities = global_aggregated_densities + (round(
np.sum([densities[section] for section in road])), )
local_state = LearningState(
pre_cross_densities=pre_cross_densities,
global_aggregated_densities=global_aggregated_densities,
phase_index=self.actual_phase,
phase_duration=self.phase_duration)
self.local_state = local_state
def single_simulate(agents, actual_phase, phase_duration, den, orange_phase_duration=2, actions=[1], u=env_settings.u):
densities: List[int] = den + [0, 0] # dodajemy 2 ostatnie nieistotne - one sa juz na wyjsciu
global_densities: List[int] = densities
env = Env(agents)
env.u = u
state = LearningState(orange_phase_duration=orange_phase_duration,
actual_phase=actual_phase,
starting_actual_phase=actual_phase,
phase_duration=phase_duration,
global_densities=global_densities,
densities=densities)
env.assign_state(state)
env.assign_local_states_to_agents()
env.step(actions)
return env
def assign_local_state(self, densities):
pre_cross_densities = ()
for sec in self.local_phase_sections:
pre_cross_densities = pre_cross_densities + (densities[sec],)
global_aggregated_densities = ()
global_densities = []
for road in sections_of_roads:
for sec in road:
global_densities.append(densities[sec])
local_state = LearningState(pre_cross_densities=pre_cross_densities,
global_aggregated_densities=global_aggregated_densities,
global_densities=global_densities,
actual_phase=self.actual_phase,
phase_duration=self.phase_duration,
densities=densities,
starting_actual_phase=self.actual_phase)
self.local_state = local_state
def assign_local_state(self, densities):
pre_cross_densities = ()
for sec in self.local_phase_sections:
pre_cross_densities = pre_cross_densities + (densities[sec],)
global_aggregated_densities = ()
global_densities = []
densities_9 = []
for road in sections_of_roads:
global_aggregated_densities = global_aggregated_densities + (
round(np.sum([densities[section] for section in road])),)
for sec in road:
global_densities.append(densities[sec])
for sec in self.sections_9_indexes:
densities_9.append(densities[sec])
local_state = LearningState(pre_cross_densities=pre_cross_densities,
global_aggregated_densities=global_aggregated_densities,
global_densities=global_densities,
actual_phase=self.actual_phase,
phase_duration=self.phase_duration,
densities_9=densities_9,
starting_actual_phase=self.actual_phase)
self.local_state = local_state
def add_state(self, state: LearningState):
state.cluster_index = self.get_cluster(state).index
self.states.append(state)
self.clusters[state.cluster_index].states.append(state)
from model.LearningState import LearningState
from services.parser import to_array
S1 = []
S1.append(
LearningState(pre_cross_densities=(1, 0, 2),
global_aggregated_densities=(1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 0.0, 0.0, 0.0),
phase_index=3,
phase_duration=3))
S1.append(
LearningState(pre_cross_densities=(2, 1, 1),
global_aggregated_densities=(1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 0.0, 0.0, 0.0),
phase_index=3,
phase_duration=1))
S1.append(
LearningState(pre_cross_densities=(2, 2, 2),
global_aggregated_densities=(1.0, 1.0, 0.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 0.0, 0.0, 1.0),
phase_index=3,
phase_duration=1))
S1.append(
LearningState(pre_cross_densities=(2, 0, 2),
global_aggregated_densities=(1.0, 1.0, 0.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 0.0, 0.0, 1.0),
phase_index=0,
phase_duration=0))
S1.append(
LearningState(pre_cross_densities=(1, 0, 1),
global_aggregated_densities=(1.0, 1.0, 0.0, 1.0, 1.0, 1.0,
from model.LearningState import LearningState from services.parser import to_array S1=[] S1.append(LearningState(pre_cross_densities=(1, 0, 2), global_aggregated_densities=(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0), actual_phase=3, phase_duration=3)) S1.append(LearningState(pre_cross_densities=(2, 1, 1), global_aggregated_densities=(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0), actual_phase=3, phase_duration=1)) S1.append(LearningState(pre_cross_densities=(2, 2, 2), global_aggregated_densities=(1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0), actual_phase=3, phase_duration=1)) S1.append(LearningState(pre_cross_densities=(2, 0, 2), global_aggregated_densities=(1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0), actual_phase=0, phase_duration=0)) S1.append(LearningState(pre_cross_densities=(1, 0, 1), global_aggregated_densities=(1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0), actual_phase=3, phase_duration=2)) S1.append(LearningState(pre_cross_densities=(2, 3, 0), global_aggregated_densities=(1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 2.0, 1.0, 1.0, 1.0, 0.0, 1.0), actual_phase=0, phase_duration=0)) S1.append(LearningState(pre_cross_densities=(2, 2, 1), global_aggregated_densities=(1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 2.0, 1.0, 1.0, 1.0, 0.0, 1.0), actual_phase=2, phase_duration=1)) S1.append(LearningState(pre_cross_densities=(1, 2, 2), global_aggregated_densities=(1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 2.0, 1.0, 1.0, 1.0, 0.0, 1.0), actual_phase=0, phase_duration=0)) S1.append(LearningState(pre_cross_densities=(2, 3, 1), global_aggregated_densities=(1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 2.0, 1.0, 1.0, 1.0, 0.0, 2.0), actual_phase=1, phase_duration=1)) S1.append(LearningState(pre_cross_densities=(2, 0, 1), global_aggregated_densities=(1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 2.0, 1.0, 1.0, 1.0, 0.0, 2.0), actual_phase=0, phase_duration=0)) S1.append(LearningState(pre_cross_densities=(1, 0, 1), global_aggregated_densities=(1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 2.0, 1.0, 1.0, 1.0, 0.0, 2.0), actual_phase=1, phase_duration=1)) S1.append(LearningState(pre_cross_densities=(1, 2, 0), global_aggregated_densities=(1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0), actual_phase=0, phase_duration=0)) S1.append(LearningState(pre_cross_densities=(2, 1, 0), global_aggregated_densities=(1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0), actual_phase=1, phase_duration=1)) S1.append(LearningState(pre_cross_densities=(2, 0, 2), global_aggregated_densities=(1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0), actual_phase=0, phase_duration=0)) S1.append(LearningState(pre_cross_densities=(1, 2, 1), global_aggregated_densities=(1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0), actual_phase=2, phase_duration=1)) S1.append(LearningState(pre_cross_densities=(2, 0, 2), global_aggregated_densities=(1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0), actual_phase=0, phase_duration=0)) S1.append(LearningState(pre_cross_densities=(1, 1, 0), global_aggregated_densities=(1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0), actual_phase=2, phase_duration=1)) S1.append(LearningState(pre_cross_densities=(2, 2, 0), global_aggregated_densities=(1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0), actual_phase=0, phase_duration=0)) S2=[] S2.append(to_array(LearningState(pre_cross_densities=(2, 2, 2), global_aggregated_densities=(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0), actual_phase=0, phase_duration=0))) S2.append(to_array(LearningState(pre_cross_densities=(0, 2, 2), global_aggregated_densities=(0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0), actual_phase=0, phase_duration=0))) S2.append(to_array(LearningState(pre_cross_densities=(2, 0, 1), global_aggregated_densities=(0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0), actual_phase=0, phase_duration=0))) S2.append(to_array(LearningState(pre_cross_densities=(1, 2, 0), global_aggregated_densities=(0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0), actual_phase=2, phase_duration=1))) S2.append(to_array(LearningState(pre_cross_densities=(1, 2, 2), global_aggregated_densities=(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0), actual_phase=1, phase_duration=1))) S2.append(to_array(LearningState(pre_cross_densities=(2, 0, 1), global_aggregated_densities=(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0), actual_phase=2, phase_duration=1))) S2.append(to_array(LearningState(pre_cross_densities=(1, 2, 0), global_aggregated_densities=(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0), actual_phase=0, phase_duration=0)))