def trainEventRandomly(
event_type,
source_tag,
target_tag,
obs_len,
obs_count,
algo_type="GMMHMM"
):
logger.info('[trainEventRandomly] event_type=%s, source_tag=%s, target_tag=%s'
% (event_type, source_tag, target_tag))
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"]
train_obs_len = obs_len
train_obs_count = obs_count
d = Dataset(event_type=getEventList(), motion_type=motion_set, sound_type=sound_set,
location_type=location_set, event_prob_map=getEventProbMap())
logger.debug('[trainEventRandomly] Dataset: %s' % (d))
d.randomObservations(event_type, train_obs_len, train_obs_count) # 这里是产生假序列的部分
observations = d.obs
logger.debug('[trainEventRandomly] obs: %s' % (observations))
description = '[source_tag=%s]Random train algo_type=%s for eventType=%s, random train obs_len=%s, obs_count=%s' % (
source_tag, algo_type, event_type, train_obs_len, train_obs_count)
TRAINER = ALGOMAP[algo_type]
my_trainer = TRAINER(model_param)
my_trainer.fit(d.getDataset())
return setModel(algo_type, target_tag, event_type, my_trainer.params_, status_sets,
datetime.datetime.now(), description, json.dumps(observations))
def trainEventRandomly(event_type,
source_tag,
target_tag,
obs_len,
obs_count,
algo_type="GMMHMM"):
logger.info(
'[trainEventRandomly] event_type=%s, source_tag=%s, target_tag=%s' %
(event_type, source_tag, target_tag))
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"]
train_obs_len = obs_len
train_obs_count = obs_count
d = Dataset(event_type=getEventList(),
motion_type=motion_set,
sound_type=sound_set,
location_type=location_set,
event_prob_map=getEventProbMap())
logger.debug('[trainEventRandomly] Dataset: %s' % (d))
d.randomObservations(event_type, train_obs_len,
train_obs_count) # 这里是产生假序列的部分
observations = d.obs
logger.debug('[trainEventRandomly] obs: %s' % (observations))
description = '[source_tag=%s]Random train algo_type=%s for eventType=%s, random train obs_len=%s, obs_count=%s' % (
source_tag, algo_type, event_type, train_obs_len, train_obs_count)
TRAINER = ALGOMAP[algo_type]
my_trainer = TRAINER(model_param)
my_trainer.fit(d.getDataset())
return setModel(algo_type, target_tag, event_type, my_trainer.params_,
status_sets, datetime.datetime.now(), description,
json.dumps(observations))
