def test_ff(self):
model = self.init_model()
x = np.array([[1, 2], [0, 1]])
expect = np.array([[0.50255597219646853, 0.50255597219646853],\
[0.50255297542884314, 0.50255297542884314]])
output = model.ff(x)
self.assertTrue(is_matrix_equals(active.active(output), expect), True)
##sparse
model = self.init_model()
x = sp.csr_matrix([[1, 2], [0, 1]])
output = model.ff(x)
self.assertTrue(is_matrix_equals(active.active(output), expect), True)
def test_ff(self):
model = self.init_model();
x = np.array([[1,2],[0,1]]);
expect = np.array([[0.50255597219646853, 0.50255597219646853],\
[0.50255297542884314, 0.50255297542884314]]);
output = model.ff(x);
self.assertTrue(is_matrix_equals(active.active(output), expect), True);
##sparse
model = self.init_model();
x = sp.csr_matrix([[1,2],[0,1]]);
output = model.ff(x);
self.assertTrue(is_matrix_equals(active.active(output), expect), True);
def interface():
"""Interface with input error handling."""
answer = str(
raw_input("Sedentary lifestyle, little to no exercise?(Y or N):> "))
if answer.upper() == "Y":
return sedentary()
elif answer.upper() == "N":
answer = str(
raw_input(
"Moderately active job or exercise 2 to 3 times per week?(Y or N):> "
))
if answer.upper() == "Y":
return moderate()
elif answer.upper() == "N":
answer = str(
raw_input(
"Active job and exercise 2 to 3 times per week?(Y or N):> "
))
if answer.upper() == "Y":
return active()
elif answer.upper() == "N":
return veryactive()
else:
print "Please enter Y for yes or N for no."
interface()
else:
print "Please enter Y for yes or N for no."
interface()
else:
print "Please enter Y for yes or N for no."
interface()
def weights_matrix(n, iterations, X_training, Y_training, center='ac',
sample=1, M=None):
testing = 3
matrix_of_weights = []
for i in range(n):
weights = active.active(X_training, Y_training, iterations, center=center,
sample = sample, testing=testing, M=M)[2]
matrix_of_weights.append(weights)
return matrix_of_weights
def process_input():
# choose scan method
print("\nChoose your scan method:")
choise = input("p : passive scan\na : active scan\nf : full (active) network scan"
"\n\nMake your choice: ")
if choise == "p":
logging.info("Passive detection selected")
ip = get_ip()
passive.passive(ip)
elif choise == "a":
logging.info("Active detection selected")
ip = get_ip()
active.active(ip)
elif choise == "f":
logging.info("Full (active) network scan selected")
ip = get_ip()
fullNetworkScan.scan_network(ip)
else:
logging.info("Help function is showing")
help.help_function()
def test_active(self):
a = np.array([[1,2],[0,0]]);
# test sgmoid
standard = np.array([[0.7310585786300049,0.8807970779778823],[0.5,0.5]]);
a_sgmoid = active.active(a);
self.assertEqual(is_matrix_equals(a_sgmoid, standard), True);
# test linear
a_linear = active.active(a, 'linear');
self.assertEqual(is_matrix_equals(a_linear,a), True);
# test tanh
a = np.array([[0.1,-0.2],[0,10]]);
standard = np.array([[0.099667994624955902, -0.197375320224904],\
[0 , 0.99999999587769262]]);
a_tanh = active.active(a, "tanh");
self.assertTrue(is_matrix_equals(a_tanh, standard));
# test rel
standard = np.array([[0.1, 0],[0,10]]);
a_rel = active.active(a, "rel");
self.assertTrue(is_matrix_equals(a_rel, standard));
with self.assertRaises(Exception):
active.active(a,"unknown active_type")
def test_active(self):
a = np.array([[1.0,2],[0,0],[-1,-2]]);
# test sgmoid
standard = np.array([[0.7310585786300049,0.8807970779778823],[0.5,0.5], [0.2689414213699951, 0.11920292202211755]]);
a_sgmoid = active.active(a);
self.assertEqual(is_matrix_equals(a_sgmoid, standard), True);
# test linear
a_linear = active.active(a, 'linear');
self.assertEqual(is_matrix_equals(a_linear,a), True);
# test tanh
a = np.array([[0.1,-0.2],[0,10]]);
standard = np.array([[0.099667994624955902, -0.197375320224904],\
[0 , 0.99999999587769262]]);
a_tanh = active.active(a, "tanh");
self.assertTrue(is_matrix_equals(a_tanh, standard));
# test rel
standard = np.array([[0.1, 0],[0,10]]);
a_rel = active.active(a, "relu");
self.assertTrue(is_matrix_equals(a_rel, standard));
with self.assertRaises(Exception):
active.active(a,"unknown active_type")
def weights_matrix(n,
iterations,
X_training,
Y_training,
center='ac',
sample=1,
M=None):
testing = 3
matrix_of_weights = []
for i in range(n):
weights = active.active(X_training,
Y_training,
iterations,
center=center,
sample=sample,
testing=testing,
M=M)[2]
matrix_of_weights.append(weights)
return matrix_of_weights
else:
await warn_admins(guild)
return
@client.event
async def on_guild_leave(guild):
if (str(guild.id) in db['guilds'].keys()):
db['guilds'].pop(str(guild.id))
@client.event
async def on_message(message):
if message.author == client.user:
return
await rank_msg(message)
await stat_msg(message)
await cmd_msg(message)
await help_msg(message)
await stop_msg(client, message)
await ghot_msg(message)
await lite_msg(message)
await info_msg(message)
#admin commands
await auto_msg(message)
await reset_msg(client, message)
active()
client.run(os.environ['BOT_TOKEN'])
def run(self):
active.active(config.botcfg['token'])
