def sort_card(by, reverse=True): ma = MA() login = ma.login(config.loginId, config.password) ma.masterdata_card() for card in ma.cards.values(): card.hp_power = card.hp + card.power card.cp = card.hp_power / card.cost card.lvmax_hp_power = card.lvmax_hp + card.lvmax_power card.lvmax_cp = card.lvmax_hp_power / card.cost return sorted(ma.cards.values(), key=lambda x: getattr(x, by), reverse=reverse)
def all_in_one(invitation_id, name_prefix=None): _hash = hashlib.md5(invitation_id).hexdigest() password = _hash[:10] if not name_prefix: name_prefix = _hash[-5:] mobile = tasoo.get_tel() submit_smscode = smsVerify(mobile) sms = None time.sleep(10) for _ in range(10): try: sms = tasoo.get_sms(mobile) except AssertionError: time.sleep(2) continue if not sms: raise Exception("can't get sms") submit_smscode(sms) ma = MA() #ma.check_inspection() #ma.notification_post_devicetoken(mobile, config.password) ma.regist(mobile, password, invitation_id) ma.save_character(name_prefix+sms[:4]) ma.tutorial_next(7030) ma.tutorial_next(8000) return mobile, password, name_prefix+sms[:4]
def _get_ma_60(self, stock_code, date_obj): ma = MA().calculate(stock_code, date_obj, 60) return ma
import sys from ma import MA login_id = sys.argv[1] password = sys.argv[2] #regist = sys.argv[3] ma = MA() ma.login(login_id, password) #ma.check_inspection() #ma.notification_post_devicetoken(login_id, password) #try: #ma.regist(login_id, password, regist) #except: #pass ma.save_character(login_id) ma.tutorial_next(7030) ma.tutorial_next(8000)
(571, 444, 0, lambda: time.sleep(2)), #go (560, 514, 0, None), # go ] for i, (x, y, color, text) in enumerate(steps): print 'step %d' % i if color: while color_like(get_color(x, y), color) > 10: time.sleep(1) click(x, y) if text: if callable(text): text = text() if text: time.sleep(1) send(text) time.sleep(1) # over while get_color(619, 601) == 0: click(619, 601) time.sleep(1) from ma import MA print tel, code ma = MA() ma.login(tel, PASSWORD) ma.save_character(tel) ma.tutorial_next(7030) ma.tutorial_next(8000) os.system(r'"C:\Program Files\BlueStacks\HD-Quit.exe"')
# starting training loop def ppo(env, brain_name, agents): scores = [] for i in range(4000): agents.learn(env, brain_name) current_max_reward = agents.act(env, brain_name) mean_100 = np.mean(np.array(scores[-100:])) scores.append(current_max_reward) if mean_100 > 0.5: agents.save() print( 'Environment solved in {} episodes. Last mean 100: {}'.format( i + 1, mean_100)) break print( 'Episode: {} Score of the current episode: {} Last 100 average: {}' .format(i + 1, current_max_reward, mean_100)) return scores # instantiating multi-agent class agents = MA(config) # starting training all_scores = ppo(env, brain_name, agents) # visualizing rewards plt.plot(np.arange(1, len(all_scores) + 1), all_scores) plt.ylabel('Rewards') plt.xlabel('Episode #') plt.show()
import sys from ma import MA login_id = sys.argv[1] password = sys.argv[2] #regist = sys.argv[3] ma = MA() #ma.login(login_id, password) ma.check_inspection() ma.notification_post_devicetoken(login_id, password) ma.regist(login_id, password, regist) ma.save_character(login_id) ma.tutorial_next(7030) ma.tutorial_next(8000)
else: threshold_fitness = params["threshold_fitness"] optimizer_options = { "max_evaluations": params["max_generations"] * n_individuals + 1, "seed": params["optimizer_seed"] + i, # undefined in the paper "step_size": 1.0, "initial_guess": initial_guess, "threshold_fitness": threshold_fitness, "len_fitness_data": 0, "save_step_size_data": True } results_file = os.path.join( data_dir, "{:s}-Dim-{:d}-Trial-{:d}.pickle".format( params["benchmark_function"], params["ndim_problem"], i + 1)) if not (params["is_plot"]): solver = MA(problem, optimizer_options) results = solver.optimize(eval(params["benchmark_function"])) with open(results_file, "wb") as results_handle: pickle.dump(results, results_handle, protocol=pickle.HIGHEST_PROTOCOL) else: with open(results_file, 'rb') as results_handle: results = pickle.load(results_handle) if len(results["fitness_data"]) < min_evaluations: min_evaluations = len(results["fitness_data"]) if len(results["step_size_data"]) < min_step_size_data: min_step_size_data = len(results["step_size_data"]) if params["is_plot"]: fitness_data = np.zeros((min_evaluations, 2))