def main():
argc = len(sys.argv)
if argc < 2:
sys.stderr.write("Error: bad parameters\n")
print_help()
sys.exit(1)
csv_file = sys.argv[1]
csv_fd = open(csv_file, "r")
csv_file = os.path.basename(csv_file)
normal_msgs = con_par.get_messages(csv_fd)
parser = con_par.IEC104Parser(normal_msgs)
parser.parse_conversations()
lines = parser.get_all_conversations(abstraction)
index = int(len(lines)*TRAINING)
training, testing = lines[:index], lines[index:]
tree = fpt.FPT()
for line in training:
tree.add_string(line)
alpha = 0.05
t0 = int(math.log(index, 2))
aut = alergia.alergia(tree, alpha, t0)
aut.rename_states()
fa = aut.normalize()
store_filename = os.path.splitext(os.path.basename(csv_file))[0]
store_filename = "{0}a{1}t{2}".format(store_filename, alpha, t0)
fa_fd = open("{0}.fa".format(store_filename), "w")
fa_fd.write(fa.to_fa_format(True))
fa_fd.close()
legend = "File: {0}, alpha: {1}, t0: {2}".format(csv_file, alpha, t0)
dot_fd = open("{0}.dot".format(store_filename), "w")
dot_fd.write(fa.to_dot(aggregate=False, legend=legend))
dot_fd.close()
miss = 0
for line in testing:
prob = fa.string_prob_deterministic(line)
if prob is None:
miss += 1
print("File: {0}".format(csv_file))
print("alpha: {0}, t0: {1}".format(alpha, t0))
print("States {0}".format(len(aut.get_states())))
print("Testing: {0}/{1} (missclassified/all)".format(miss, len(testing)))
if len(testing) > 0:
print("Accuracy: {0}".format((len(testing)-miss)/float(len(testing))))
csv_fd.close()
def learn_proc_pa(training):
tree = fpt.FPT()
tree.add_string_list(training)
alpha = 0.05
if len(training) > 0:
t0 = int(math.log(len(training), 2))
else:
t0 = 1
aut = alergia.alergia(tree, alpha, t0)
aut.rename_states()
return aut.normalize()
def learn_proc_pta(training):
tree = fpt.FPT()
tree.add_string_list(training)
aut = tree
aut.rename_states()
return aut.normalize()
def create_fpt(ln, ren_dict):
tree = fpt.FPT()
lines = con_par.rename_values(ln, ren_dict)
tree.add_string_list(ln)
return tree