simple_net(important_vars,
important_features,
'bpsk\\lstm_simple_net.gv',
var_list=var_list,
mode_list=mode_list)
# BPSK
if 'mt' in system:
data_path = parentdir + '\\tank_systems\\data\\train\\'
mana = mt_data_manager()
mana.load_data(data_path)
mana.add_noise(20)
var_list = mana.cfg.variables
mode_list = ['normal'] + mana.cfg.faults
inputs, _ = mana.random_h_batch(batch=test_batch,
step_num=64,
prop=0.2,
sample_rate=1.0)
# CNN
if 'cnn' in network:
ann = 'mt_cnn_distill_(8, 16, 32, 64).cnn'
ann = parentdir + '\\ann_diagnoser\\mt\\train\\20db\\{}\\'.format(
args.index) + ann
important_vars = heat_map_feature_input(
ann,
inputs,
figname=
'mt\\importance_heat_map_between_varialbe_feature_of_CNN',
isCNN=True)
important_features = heat_map_fault_feature(
ann,
inputs,
two_fault=0)
labels = torch.sum(labels * torch.Tensor([1, 2, 3, 4, 5, 6]), 1).long()
labels = labels.detach().numpy()
if args.model is None:
inputs = inputs.detach().numpy()
numpy2arff(inputs, labels, args.output, var_list, mode_list)
elif 'encoder' in args.model:
encoder2arff(inputs, labels, args.model, args.output, mode_list)
else:
feature2arff(inputs, labels, args.model, args.output, mode_list)
if args.system == 'mt':
step_len = 64
data_path = parentdir + '\\tank_systems\\data\\{}\\'.format(
args.purpose)
mana = mt_data_manager()
mana.load_data(data_path)
mana.add_noise(snr)
var_list = mana.cfg.variables[0:11]
mode_list = ['normal'] + mana.cfg.faults
inputs, labels = mana.random_h_batch(batch=batch,
step_num=64,
prop=0.2,
sample_rate=1.0)
if args.model is None:
numpy2arff(inputs, labels, args.output, var_list, mode_list)
elif 'encoder' in args.model:
encoder2arff(inputs, labels, args.model, args.output, mode_list)
else:
feature2arff(inputs, labels, args.model, args.output, mode_list)