def generate_and_pickle_models(device_name,
pi_prior,
a_prior,
mean_prior,
cov_prior,
key_for_model_name,
table_num,
length='D',
sample_rate='15T',
limit=0):
schema = 'curated'
device_type_orig = get_type_from_dataset(device_name, schema, table_num,
limit)
print 'Device Type Generated.'
device_type = resample_and_split(device_type_orig, length, sample_rate)
print 'Device Type Resampled.'
device_models = fhmm.generate_HMMs_from_type(device_type, pi_prior,
a_prior, mean_prior,
cov_prior, key_for_model_name)
print 'Device Model Completed.'
with open(
str(device_name) + '_' + str(schema) + '_' + str(table_num) + '_' +
str(sample_rate) + '.pkl', 'w') as f:
pickle.dump(device_models, f)
return device_type, device_models
def generate_and_pickle_models(device_name,
pi_prior,
a_prior,
mean_prior,
cov_prior,
key_for_model_name,
table_num,
length='D',
sample_rate='15T',
limit=0):
schema = 'shared'
device_type_orig = get_type_from_dataset(device_name, schema, table_num,
limit)
print 'Device Type Generated.'
device_type = resample_and_split(device_type_orig, length, sample_rate)
print 'Device Type Resampled.'
device_models = fhmm.generate_HMMs_from_type(device_type, pi_prior,
a_prior, mean_prior,
cov_prior, key_for_model_name)
print 'Device Model Completed.'
for l, key in enumerate(device_models):
if device_models[key]._means_[1] < .1:
device_models.pop(key, None)
i = i + 1
print "Deleted " + str(i) + " of " + str(
l + 1) + " models due to low on-states."
with open(
str(device_name) + '_' + str(schema) + '_' + str(table_num) + '_' +
str(sample_rate) + '.pkl', 'w') as f:
pickle.dump(device_models, f)
return device_type, device_models
def generate_and_pickle_models(device_name,pi_prior,a_prior,mean_prior,cov_prior,
key_for_model_name,table_num,length='D',sample_rate='15T',limit=0):
device_type_orig=get_type_from_dataset(device_name,table_num,limit)
print 'Device Type Generated.'
device_type=resample_and_split(device_type_orig,length,sample_rate)
print 'Device Type Resampled.'
device_models=fhmm.generate_HMMs_from_type(device_type,pi_prior,a_prior,mean_prior,cov_prior,
key_for_model_name)
print 'Device Model Completed.'
with open(str(device_name)+'_'+str(table_num)+'_' + str(sample_rate)+'.pkl','w') as f:
pickle.dump(device_models,f)
return device_type,device_models
def generate_and_pickle_models(device_name,pi_prior,a_prior,mean_prior,cov_prior,
key_for_model_name,table_num,length='D',sample_rate='15T',limit=0):
schema='shared'
device_type_orig=get_type_from_dataset(device_name,schema,table_num,limit)
print 'Device Type Generated.'
device_type=resample_and_split(device_type_orig,length,sample_rate)
print 'Device Type Resampled.'
device_models=fhmm.generate_HMMs_from_type(device_type,pi_prior,a_prior,mean_prior,cov_prior,
key_for_model_name)
print 'Device Model Completed.'
for l,key in enumerate(device_models):
if device_models[key]._means_[1]<.1:
device_models.pop(key,None)
i=i+1
print "Deleted " + str(i) + " of "+str(l+1) +" models due to low on-states."
with open(str(device_name)+'_'+str(schema)+'_'+str(table_num)+'_' + str(sample_rate)+'.pkl','w') as f:
pickle.dump(device_models,f)
return device_type,device_models
def generate_and_pickle_models(
device_name,
pi_prior,
a_prior,
mean_prior,
cov_prior,
key_for_model_name,
table_num,
length="D",
sample_rate="15T",
limit=0,
):
device_type_orig = get_type_from_dataset(device_name, table_num, limit)
print "Device Type Generated."
device_type = resample_and_split(device_type_orig, length, sample_rate)
print "Device Type Resampled."
device_models = fhmm.generate_HMMs_from_type(
device_type, pi_prior, a_prior, mean_prior, cov_prior, key_for_model_name
)
print "Device Model Completed."
with open(str(device_name) + "_" + str(table_num) + "_" + str(sample_rate) + ".pkl", "w") as f:
pickle.dump(device_models, f)
return device_type, device_models
