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