def test_1():
sensitive_path = 'sensitive/sensitive.txt'
dfa = DFA()
dfa.load_sensitive(sensitive_path)
dfa = DFA()
#dfa.load_from_yaml('automata/mydata.yaml')
dfa.load_from_pickle('automata/mydata.json')
dfa.accepts('我爱北京天安门\n')
dfa.accepts('我喜欢做假证\n')
dfa.accepts('我喜欢做嗳\n')
with open(sensitive_path, 'r', encoding='UTF-8') as f:
lines = f.readlines()
result = []
lines = [i for i in lines if i != '\n']
for i in lines:
i = '我喜欢' + i
print(i)
dfa.accepts(i)
start_time = time.time()
for i in lines:
i = '我喜欢' + i
result.append(dfa.predict(i))
during_time = time.time() - start_time
print(r'耗时:', during_time)
print(r'平均z耗时:', during_time / len(lines))
result = np.array(result)
print(r'正确个数:', len(result[result == True]))
print(r'错误个数:', len(result[result == False]))
print(r'正确率:', len(result[result == True]) / len(result))
def test_2():
sensitive_path = 'sensitive/sensitive.txt'
dfa = DFA()
dfa.load_from_pickle('automata/mydata.json')
dfa.accepts('我爱北京天安门\n')
dfa.accepts('我喜欢做假证\n')
dfa.accepts('我喜欢做嗳\n')
with open(sensitive_path, 'r', encoding='UTF-8') as f:
lines = f.readlines()
result = []
lines = [i for i in lines if i != '\n']
#for i in lines:
#print(i, '*70')
#i = ('我喜欢' + i)*70
#print(dfa.get_sensitive_words(i))
start_time = time.time()
for i in lines:
i = ('我喜欢' + i) * 2
dfa.get_sensitive_words(i)
result.append(True)
during_time = time.time() - start_time
print(r'耗时:', during_time)
print(r'平均z耗时:', during_time / len(lines))
start_time = time.time()
print(i)
i = i * 200
a = dfa.get_sensitive_words(i)
during_time = time.time() - start_time
print(a)
print(r'耗时:', during_time)
start_time = time.time()
for i in lines:
i = ('我喜欢' + i) * 4
dfa.get_sensitive_words(i)
result.append(True)
during_time = time.time() - start_time
print(r'耗时:', during_time)
print(r'平均z耗时:', during_time / len(lines))
start_time = time.time()
for i in lines:
i = ('我喜欢' + i) * 8
dfa.get_sensitive_words(i)
result.append(True)
during_time = time.time() - start_time
print(r'耗时:', during_time)
print(r'平均z耗时:', during_time / len(lines))
start_time = time.time()
for i in lines:
i = ('我喜欢' + i) * 16
dfa.get_sensitive_words(i)
result.append(True)
during_time = time.time() - start_time
print(r'耗时:', during_time)
print(r'平均z耗时:', during_time / len(lines))
result = np.array(result)
print(r'正确个数:', len(result[result == True]))
print(r'错误个数:', len(result[result == False]))
print(r'正确率:', len(result[result == True]) / len(result))
from dfa import DFA
m = DFA()
m.load_from_yaml("automata/example.yaml")
#accepted! =)
m.accepts("b")
m.accepts("ab")
m.accepts("aab")
#rejected, =(
m.accepts("ba")
m.accepts("aba")
m.accepts("bbba")
