def __init__(self):
self.logger = self.__create_logger()
self.train = Train()
self.eye = Goldeneye()
self.sts = SignalTrackingSystem(queue=self.eye.result)
self.info_number = 0
self.rounds_done = 0
self.stop_signals = []
self.signals_seen = 0
def test_10(self):
myTrainInfo = Train()
myTrainInfo.assign_graph(
process_input('TrainsPythonGA/data/input.txt'))
self.assertEquals(myTrainInfo.routes_in_distance('CC', 30), 7)
def test_1(self):
myTrainInfo = Train()
myTrainInfo.assign_graph(
process_input('TrainsPythonGA/data/input.txt'))
self.assertEquals(myTrainInfo.calculate_distance('ABC'), 9)
def test_9(self):
myTrainInfo = Train()
myTrainInfo.assign_graph(
process_input('TrainsPythonGA/data/input.txt'))
self.assertEquals(myTrainInfo.calculate_shortest_route('BB'), 9)
def test_7(self):
myTrainInfo = Train()
myTrainInfo.assign_graph(
process_input('TrainsPythonGA/data/input.txt'))
self.assertEquals(myTrainInfo.calculate_strips('AC', 4, 'Y'), 3)
#load dataset from text
text_database = Getdata()
data = text_database.Getdata_from_textfile('Data\MaleFemale.txt')
#set only the output column
#remove the text values from the dataset
#Normalize data
#Required Automation
source = data[:, 1:4]
target = data[:, :1]
#Split data for training and testing
split_data = Split()
s_train, s_test, t_train, t_test = split_data.general_split(source, target)
#Choose classifer and build trained model
clf = Classifiers(log=True)
train = Train()
trained_model_nb = train.train_model(clf.navie_bayes(), s_train, t_train)
#Predict
predicted_output = trained_model_nb.predict(s_test)
print('Input:')
print(s_test)
print('Output:')
print(predicted_output)
#Check performance
nb_performance = Performance_metrix()
nb_performance.measure_performance(source, target, trained_model_nb)
print "The application will now end."
raise SystemExit
return 0
if __name__ == '__main__':
clean_screen()
presentation()
if len(sys.argv) == 1:
input_path = 'TrainsPythonGA/data/input.txt'
else:
input_path = sys.argv[1]
myTrainInfo = Train()
myTrainInfo.assign_graph(process_input(input_path))
mode = insert_mode()
if mode == '1':
print myTrainInfo.calculate_distance(insert_path())
elif mode == '2':
print myTrainInfo.calculate_strips(insert_strip(),
int(insert_max_stops()),
specify_stops())
elif mode == '3':
print myTrainInfo.calculate_shortest_route(insert_strip())
else:
print myTrainInfo.routes_in_distance(insert_strip(),
int(insert_max_dist()))