def main(games_in_a_set=100000): for shuffle in [True, False]: game0 = monopoly.Game(cutoff=1000, trading_enabled=False, image_exporting=0, shuffle=shuffle, trip_to_start=True) trade_count = [] winners = [0, 0, 0] wins1 = [] wins2 = [] for i in range(games_in_a_set): # Play game. player1 = monopoly.Player(1, buying_threshold=500, group_ordering=random_ordering(), ) player2 = monopoly.Player(2, buying_threshold=500, group_ordering=random_ordering(), ) game0.new_players([player1, player2]) results = game0.play() # Store length. winners[results['winner']] += 1 if results['winner'] == 1: wins1.append(1) wins2.append(0) elif results['winner'] == 2: wins1.append(0) wins2.append(1) else: wins1.append(0) wins2.append(0) trade_count.append(results['trade count']) print(mean_confidence_interval(wins1)) print(mean_confidence_interval(wins2)) print(winners, shuffle, sum(trade_count) / games_in_a_set)
else: past_number_of_fami[numFlow] += 1 break time_unit += 30 count += NoFlow retry = retry + 1 print "Actual number of reties:", retry # time.sleep(3) opt_total_cost_result = list() opt_total_cost_err = list() for num in opt_total_cost_dict: result, err = mean_confidence_interval(opt_total_cost_dict[num], confidence=0.95) opt_total_cost_result.append(result) opt_total_cost_err.append(err) opt_comm_cost_result = list() opt_comm_cost_err = list() for num in opt_total_comm_cost_dict: result, err = mean_confidence_interval(opt_total_comm_cost_dict[num], confidence=0.95) opt_comm_cost_result.append(result) opt_comm_cost_err.append(err) opt_buff_cost_result = list() opt_buff_cost_err = list()
def main(): _, lower, upper = mean_confidence_interval(get_data()) print "(%.14f, %.14f)" % (lower, upper)
model = range(5) # [1.0, 2.0, 2.1, 3.0, 3.1] run_size = 360 average = np.zeros(shape=[5, 3, 5, 17]) # here we store the average of the output with indices indicating [Rho, Capacity, Model Version] low_bound = np.zeros(shape=[5, 3, 5, 17]) # here we store the lower bound of the output with indices indicating [Rho, Capacity, Model Version] up_bound = np.zeros(shape=[5, 3, 5, 17]) # here we store the upper bound of the output with indices indicating [Rho, Capacity, Model Version] for i in rho: for j in cap: for k in model: temp = results[:run_size] temp = np.asarray(temp) results = results[run_size:] # stats of reserved instances: [demand_r_mean, demand_r_low, demand_r_up] = cn.mean_confidence_interval(temp[:, 0]) [decision_r_mean, decision_r_low, decision_r_up] = cn.mean_confidence_interval(temp[:, 3]) [state_r_mean, state_r_low, state_r_up] = cn.mean_confidence_interval(temp[:, 6]) [state_lr_mean, state_lr_low, state_lr_up] = cn.mean_confidence_interval(temp[:, 7]) # stats of on-demand instances: [demand_o_mean, demand_o_low, demand_o_up] = cn.mean_confidence_interval(temp[:, 1]) [decision_o_mean, decision_o_low, decision_o_up] = cn.mean_confidence_interval(temp[:, 4]) [state_o_mean, state_o_low, state_o_up] = cn.mean_confidence_interval(temp[:, 8]) # stats of spot instances:
# pickle.dump(bast_buff_cost_err, open("bast_buff_cost_err.pickle", "wb")) # # pickle.dump(bast_mig_time_result, open("bast_mig_time_result.pickle", "wb")) # pickle.dump(bast_mig_time_err, open("bast_mig_time_err.pickle", "wb")) # # pickle.dump(bast_exec_time_result, open("bast_exec_time_result.pickle", "wb")) # pickle.dump(bast_exec_time_err, open("bast_exec_time_err.pickle", "wb")) # # pickle.dump(bast_comp_cost_result, open("bast_comp_cost_result.pickle", "wb")) # pickle.dump(bast_comp_cost_err, open("bast_comp_cost_err.pickle", "wb")) base_total_cost_result = list() base_total_cost_err = list() for num in base_total_cost_dict: result, err = mean_confidence_interval(base_total_cost_dict[num], confidence=0.95) base_total_cost_result.append(result) base_total_cost_err.append(err) base_comm_cost_result = list() base_comm_cost_err = list() for num in base_total_comm_cost_dict: result, err = mean_confidence_interval(base_total_comm_cost_dict[num], confidence=0.95) base_comm_cost_result.append(result) base_comm_cost_err.append(err) base_buff_cost_result = list() base_buff_cost_err = list()