def main(argv):
"""
When the model has built, then load data real-time to predict the state at the moment.
:param argv:
argv[0]: client ID
argv[1]: connect_port_name
:return:
"""
_ID = argv[0]
_PORT_NAME = argv[1]
model = Model.read_from_file(_ID)
p_model = PresentationModel.apply(model)
print('>> Start to receive data...')
# open serial port
ser = serial.Serial(_PORT_NAME, 9600)
for _ in range(20):
ser.readline()
while True:
try:
# retrieve the line
line = ser.readline().decode()
data = [float(val) for val in line.split(',')]
# no missing column in the data
if len(data) == 3:
# calculate mean gap
p_model.add(data)
# is "gap" in K-envelope?
state = p_model.predict()
print("OK" if state == 0 else "warning !!!")
# put result into the target file
fp = open(p_model.TARGET_FILE, 'w')
fp.write(str(state))
fp.close()
except KeyboardInterrupt:
print('>> exiting !')
break
except IOError:
continue
def main(argv):
"""
When the model has built, then load data real-time to predict the state at the moment.
:param argv:
argv[0]: client_ID
argv[1]: connect_port_name
:return:
"""
_ID = argv[0]
_PORT_NAME = argv[1]
model = Model.read_from_file(_ID)
p_model = PresentationModel.apply(model)
print('>> Start to receive data...')
# open serial port
ser = serial.Serial(_PORT_NAME, 9600)
for _ in range(20):
ser.readline()
while True:
try:
line = ser.readline()
data = [float(val) for val in line.decode().split(',')]
if len(data) == 3:
p_model.add_to_buffer(data)
prediction = p_model.predict()
p_model.add_to_pool(prediction)
print(p_model.mean_buffer)
print('%f => res:%d' % (p_model.now_mean, prediction))
fp = open(p_model.TARGET_FILE, 'w')
fp.write(str(p_model.take_result()))
fp.close()
except KeyboardInterrupt:
break
# close serial
ser.flush()
ser.close()
def main(argv):
"""
When the model has built, then load data real-time to predict the state at the moment.
:param argv:
argv[0]: client_ID
argv[1]: file_name (with .csv)
:return:
"""
_ID = argv[0]
_FILE_NAME = argv[1]
model = Model.read_from_file(_ID)
p_model = PresentationModel.apply(model)
print('>> Start to receive data...')
fp = open(_FILE_NAME, 'r')
for line in fp:
try:
data = [float(val) for val in line.split(',')[1:]]
if len(data) == 3:
p_model.add_to_buffer(data)
prediction = p_model.predict()
p_model.add_to_pool(prediction)
print(p_model.mean_buffer)
print('%f => res:%d' % (p_model.now_mean, prediction))
fp = open(p_model.TARGET_FILE, 'w')
fp.write(str(p_model.take_result()))
fp.close()
except KeyboardInterrupt:
break
# close serial
fp.close()