def predictByRnnRbm(seq=None):
seq = [[
{
"location": "economy_hotel",
"motion": "unknown",
"location_one": "hotel",
"sound": "unknown",
#"tenMinScale":80,
#"timestamp":1438580199333
},
{
"location": "economy_hotel",
"motion": "unknown",
"location_one": "hotel",
"sound": "unknown",
#"tenMinScale":80,
#"timestamp":1438580199333
},
{
"location": "economy_hotel",
"motion": "unknown",
"location_one": "hotel",
"sound": "unknown",
#"tenMinScale":80,
#"timestamp":1438580199333
},
{
"location": "economy_hotel",
"motion": "unknown",
"location_one": "hotel",
"sound": "unknown",
#"tenMinScale":80,
#"timestamp":1438580199333
}
]]
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())
binary_sequences = d.convert_binary_sequence(seq)
event_params_dict = get_all_rnnrbm_params(tag=None, event_list=event_list)
cmptor = Comparator(event_params_dict=event_params_dict)
label = cmptor.predict(binary_sequences[0])
print label
return label
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 predictByRnnRbm(seq=None):
seq = [[
{
"location":"economy_hotel",
"motion":"unknown",
"location_one": "hotel",
"sound":"unknown",
#"tenMinScale":80,
#"timestamp":1438580199333
},
{
"location":"economy_hotel",
"motion":"unknown",
"location_one": "hotel",
"sound":"unknown",
#"tenMinScale":80,
#"timestamp":1438580199333
},
{
"location":"economy_hotel",
"motion":"unknown",
"location_one": "hotel",
"sound":"unknown",
#"tenMinScale":80,
#"timestamp":1438580199333
},
{
"location":"economy_hotel",
"motion":"unknown",
"location_one": "hotel",
"sound":"unknown",
#"tenMinScale":80,
#"timestamp":1438580199333
}
]]
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())
binary_sequences = d.convert_binary_sequence(seq)
event_params_dict = get_all_rnnrbm_params(tag=None,event_list=event_list)
cmptor = Comparator(event_params_dict=event_params_dict)
label = cmptor.predict(binary_sequences[0])
print label
return label
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