def trainEvent(observations, event_type, source_tag, target_tag, algo_type):
'''Train model by input observations
'''
model = getModel(algo_type, source_tag, event_type)
model_param = model["modelParam"]
status_sets = model["statusSets"]
sound_set = status_sets["sound"]
motion_set = status_sets["motion"]
location_set = status_sets["location"]
d = Dataset(event_type=getEventList(),
motion_type=motion_set,
sound_type=sound_set,
location_type=location_set,
event_prob_map=getEventProbMap())
numerical_obs = d._convertObs2Dataset(observations)
logger.debug('[trainEvent] numerical_obs=%s' % (numerical_obs))
TRAINER = ALGOMAP[algo_type]
my_trainer = TRAINER(model_param)
my_trainer.fit(numerical_obs)
description = '[source_tag=%s]Train model algo_type=%s for eventType=%s' % (
source_tag, algo_type, event_type)
return setModel(algo_type, target_tag, event_type, my_trainer.params_,
status_sets, datetime.datetime.now(), description,
json.dumps(observations))
def trainEvent(observations, event_type, source_tag, target_tag, algo_type):
'''Train model by input observations
'''
model = getModel(algo_type, source_tag, event_type)
model_param = model["modelParam"]
status_sets = model["statusSets"]
sound_set = status_sets["sound"]
motion_set = status_sets["motion"]
location_set = status_sets["location"]
d = Dataset(event_type=getEventList(), motion_type=motion_set, sound_type=sound_set,
location_type=location_set, event_prob_map=getEventProbMap())
numerical_obs = d._convertObs2Dataset(observations)
logger.debug('[trainEvent] numerical_obs=%s' % (numerical_obs))
TRAINER = ALGOMAP[algo_type]
my_trainer = TRAINER(model_param)
my_trainer.fit(numerical_obs)
description = '[source_tag=%s]Train model algo_type=%s for eventType=%s' % (
source_tag, algo_type, event_type)
return setModel(algo_type, target_tag, event_type, my_trainer.params_, status_sets,
datetime.datetime.now(), description, json.dumps(observations))
