def bayesian_updating_iteration(params,
until_convergence: bool = True,
sensitivity_analysis: bool = False):
start_time = dt.now()
DataGenerator.param_validation(params=params)
targets_locations = eval(params['TARGET_LOCATIONS'])
if targets_locations is None and params.getint('NUM_TARGETS'):
targets_locations = Area.generate_targets(
num_targets=params.getint('NUM_TARGETS'))
area = Area(num_cells_axis=params.getint('N'),
t_interval=params.getint('T_INTERVAL'),
pta=params.getfloat('pta'),
alpha=params.getfloat('alpha'),
targets_locations=targets_locations)
agent = Agent(current_location=eval(params['AGENT_POSITION']),
lambda_strength=params.getint('LAMBDA_SENSOR'),
p_S=Agent.get_p_S_from_initial_prior(
prior=params.getfloat('INITIAL_PRIOR_P'), area=area),
entropy_updates=[],
information_gain_updates=[],
converge_iter=200 - 1,
p_S_history=[])
"""
2 Running modes: until convergence (all the targets identified and not False Positives) or until infinity
"""
if not sensitivity_analysis:
agent.bayesian_update(area=area,
until_convergence=until_convergence,
verbose=True,
stop_after_iteration=200)
print('Bayesian Updating Done in {}'.format(dt.now() - start_time))
else:
target_prob = agent.get_prob_history(targets_locations[2])
# non_target_loc = random.choice([loc for loc in zip(range(params.getint('N')), range(params.getint('N'))) if loc not in targets_locations])
# Randomly selected non targets
list_of_loc = [(9, 10), (7, 15), (1, 16)]
agent_pos = eval(params['AGENT_POSITION'])
non_target_prob = {}
for loc in list_of_loc:
dist = np.round(
np.sqrt(
np.power(loc[0] - agent_pos[0], 2) +
np.power(loc[1] - agent_pos[1], 2)))
non_target_prob[dist] = agent.get_prob_history(loc)
ent_upd, info_gain_upd, conv_iter = agent.get_metrics()
return ent_upd, info_gain_upd, conv_iter, target_prob, non_target_prob
def _get_generated_params_for_sequence(self, random_num_targets:bool=True) -> Tuple[Any, Area, int, List[Location]]:
"""
Used for generating the parameters for the X data generation as agent location, number of targets,
location of the targets, etc.
If random_num_targets is True -> generating num of targets from 1 to N
:return: agent, area, num_of_targets, targets_location
"""
params = config["PARAMS"]
if random_num_targets:
num_targets = np.random.randint(1, params.getint('N'))
else:
num_targets = params.getint('NUM_TARGETS')
targets_locations = Area.generate_targets(num_targets=num_targets)
area = Area(num_cells_axis=params.getint('N'), t_interval=params.getint('T_INTERVAL'),
pta=params.getfloat('pta'), alpha=params.getfloat('alpha'),
targets_locations=targets_locations)
agent = Agent.Agent(current_location=eval(params['AGENT_POSITION']),
lambda_strength=params.getint('LAMBDA_SENSOR'),
p_S=Agent.Agent.get_p_S_from_initial_prior(prior=params.getfloat('INITIAL_PRIOR_P'),
area=area),
entropy_updates=[], information_gain_updates=[], converge_iter=200 - 1, p_S_history=[])
return agent, area, len(targets_locations), targets_locations
