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'])