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 trainRandomRnnRBM():
#event_type = "dining_in_restaurant"
train_obs_len = 10 #todo need more than 10 or less than 10 length seqs
train_obs_count = 10
event_list = getEventList()
sound_set = get_sound_set()
motion_set = get_motion_set()
location_one_set = get_location_one_set()
location_two_set = get_location_two_set()
#the original location type is location_one_type
d = Dataset(event_type=getEventList(),
motion_type=motion_set,
sound_type=sound_set,
location_type=location_two_set,
location_one_type=location_one_set,
event_prob_map=getEventProbMap())
#logger.debug('[trainEventRandomly] Dataset: %s' % (d))
print "dataset", d
event_dict = {}
for event_type in event_list: #
d.randomObservations(event_type, train_obs_len,
train_obs_count) # 这里是产生假序列的部分
observation_list = d.obs
binary_sequences = d.convert_binary_sequence(observation_list)
event_dict.update({event_type: binary_sequences})
# (self,
# event_params_dict=None, # key is the eventtype ,and the value is the params of RnnRbm,
# #如果eventtype下有参数，则fit时，是从之前训练的基础上开始训练，如果是空。则是用buildrbmrnn的
# #default参数来初始化并开始巡礼那
# weights_dict=None,
# seq_len = 0
# ):
senz_len = len(sound_set) + len(motion_set) + len(location_one_set) + len(
location_two_set)
# def cold_start_params_dict(event_list):
# temp_dict = {}
# for event in event_list: temp_dict.update({event:None})
# return temp_dict
cmptor = Comparator(senz_len=senz_len, event_list=event_list)
params_dict = cmptor.fit(event_dict)
ids = save_rnnrbm_params(params_dict=params_dict,
base_set_dict=dict(motion=motion_set,
location_two=location_two_set,
location_one=location_one_set,
sound=sound_set),
tag="random_v0")
print "The ids saved in leancloud are", ids
print "observations event dict start"
for i in event_dict:
print i
print "observation event dict end"
return ids
def trainRandomRnnRBM():
#event_type = "dining_in_restaurant"
train_obs_len = 10 #todo need more than 10 or less than 10 length seqs
train_obs_count = 10
event_list = getEventList()
sound_set = get_sound_set()
motion_set = get_motion_set()
location_one_set = get_location_one_set()
location_two_set = get_location_two_set()
#the original location type is location_one_type
d = Dataset(event_type=getEventList(), motion_type=motion_set, sound_type=sound_set,
location_type=location_two_set, location_one_type=location_one_set, event_prob_map=getEventProbMap())
#logger.debug('[trainEventRandomly] Dataset: %s' % (d))
print "dataset",d
event_dict = {}
for event_type in event_list: #
d.randomObservations(event_type, train_obs_len, train_obs_count) # 这里是产生假序列的部分
observation_list = d.obs
binary_sequences = d.convert_binary_sequence(observation_list)
event_dict.update({event_type: binary_sequences})
# (self,
# event_params_dict=None, # key is the eventtype ,and the value is the params of RnnRbm,
# #如果eventtype下有参数，则fit时，是从之前训练的基础上开始训练，如果是空。则是用buildrbmrnn的
# #default参数来初始化并开始巡礼那
# weights_dict=None,
# seq_len = 0
# ):
senz_len = len(sound_set) + len(motion_set) + len(location_one_set) + len(location_two_set)
# def cold_start_params_dict(event_list):
# temp_dict = {}
# for event in event_list: temp_dict.update({event:None})
# return temp_dict
cmptor = Comparator(senz_len=senz_len,event_list=event_list)
params_dict = cmptor.fit(event_dict)
ids = save_rnnrbm_params(params_dict=params_dict,
base_set_dict=dict(
motion=motion_set,
location_two=location_two_set,
location_one=location_one_set,
sound=sound_set
),
tag="random_v0")
print "The ids saved in leancloud are",ids
print "observations event dict start"
for i in event_dict:
print i
print "observation event dict end"
return ids
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))