refactor code
This commit is contained in:
@@ -174,12 +174,12 @@ def simulation_wrapper1(simulation_time, num_runs, min_runs, confidence_level, l
|
||||
run_results = [res[0] for res in successful_results]
|
||||
loss_rates = [res[1] for res in successful_results]
|
||||
|
||||
n = len(run_results)
|
||||
# reject if not enough successful run
|
||||
if len(run_results) >= min_runs:
|
||||
if n >= min_runs:
|
||||
# statistics
|
||||
mean_rt = np.mean(run_results)
|
||||
std_dev = np.std(run_results, ddof=1)
|
||||
n = len(run_results)
|
||||
|
||||
# confidence interval
|
||||
t_value = t.ppf((1 + confidence_level)/2, n-1)
|
||||
@@ -196,8 +196,8 @@ def simulation_wrapper1(simulation_time, num_runs, min_runs, confidence_level, l
|
||||
losses.append(mean_loss)
|
||||
|
||||
print(f"C={c}, λ={lambda_val:.2f}, Mean RT={mean_rt:.2f} ± {ci:.2f}, Mean Loss Rate={mean_loss:.2%}")
|
||||
elif len(run_results) > 0:
|
||||
print(f"λ={lambda_val:.2f} skipped - only {len(run_results)} successful run(s)")
|
||||
elif n > 0:
|
||||
print(f"λ={lambda_val:.2f} skipped - only {n} successful run(s)")
|
||||
continue
|
||||
else:
|
||||
print(f"Stopped at λ={lambda_val:.2f} - no successful run")
|
||||
@@ -213,6 +213,85 @@ def simulation_wrapper1(simulation_time, num_runs, min_runs, confidence_level, l
|
||||
|
||||
loss_data[c] = (np.array(lambda_points), np.array(losses))
|
||||
|
||||
# plot curves
|
||||
if len(lambda_vals)>1:
|
||||
ax_rt.set_xlim(left=0)
|
||||
ax_rt.set_xlabel('Arrival Rate (λ)')
|
||||
ax_rt.set_ylabel('Mean Response Time')
|
||||
ax_rt.set_title(f'Mean Response Time vs Arrival Rate ({num_runs} runs, {int(confidence_level*100)}% CI)')
|
||||
ax_rt.legend()
|
||||
ax_rt.grid(True)
|
||||
|
||||
return loss_data, mean_at_lambda1
|
||||
|
||||
def simulation_wrapper2(simulation_time, num_runs, min_runs, confidence_level, lambda_vals, max_loss_value=0.1):
|
||||
fig, (ax_rt, ax_loss) = plt.subplots(2,1, figsize=(12,10), gridspec_kw={'height_ratios': [4, 3], 'hspace': 0.4}, sharex=False)
|
||||
loss_data, mean_at_lambda1 = simulation_wrapper1(simulation_time, num_runs, min_runs, confidence_level, lambda_vals, ax_rt)
|
||||
|
||||
if len(lambda_vals) > 1:
|
||||
for c, (lams, losses) in loss_data.items():
|
||||
lambda_vals2 = []
|
||||
if len(lams)==0:
|
||||
print(f"C={c}: no data at all.")
|
||||
continue
|
||||
pos_idx = np.where(losses > 0)[0]
|
||||
if pos_idx.size > 0:
|
||||
first_lambda = lams[pos_idx[0]]
|
||||
else:
|
||||
first_lambda = None
|
||||
last_lambda = lams[-1]
|
||||
|
||||
print(f"C={c:>1} first λ with loss > 0: "
|
||||
f"{first_lambda if first_lambda is not None else 'never'}; "
|
||||
f" last λ with data: {last_lambda}")
|
||||
|
||||
lambda_vals2.extend([last_lambda + i/1000 for i in range(-100,51)])
|
||||
|
||||
|
||||
|
||||
lambda_points = []
|
||||
losses = []
|
||||
ci_lower = []
|
||||
ci_upper = []
|
||||
print(f"\nProcessing C={c}")
|
||||
|
||||
with Pool() as pool:
|
||||
for lambda_val in lambda_vals2:
|
||||
args_list = [(c, lambda_val, simulation_time, max_loss_value) for _ in range(num_runs)]
|
||||
results = pool.map(run_single_simulation, args_list)
|
||||
|
||||
successful_results = [res for res in results if res is not None]
|
||||
loss_rates = [res[1] for res in successful_results]
|
||||
|
||||
n = len(loss_rates)
|
||||
|
||||
if n >= min_runs:
|
||||
# statistics
|
||||
mean_loss = np.mean(loss_rates)
|
||||
std_dev = np.std(loss_rates, ddof=1)
|
||||
|
||||
# confidence interval
|
||||
t_value = t.ppf((1 + confidence_level)/2, n-1)
|
||||
ci = t_value * std_dev / np.sqrt(n)
|
||||
|
||||
# store results
|
||||
lambda_points.append(lambda_val)
|
||||
losses.append(mean_loss)
|
||||
ci_lower.append(mean_loss - ci)
|
||||
ci_upper.append(mean_loss + ci)
|
||||
|
||||
print(f"C={c}, λ={lambda_val:.4f}, Mean Loss Rate={mean_loss:.2%} ± {ci:.2f}")
|
||||
elif n > 0:
|
||||
print(f"λ={lambda_val:.4f} skipped - only {n} successful run(s)")
|
||||
continue
|
||||
else:
|
||||
print(f"Stopped at λ={lambda_val:.4f} - no successful run")
|
||||
break
|
||||
|
||||
ax_loss.plot(lambda_points, losses, label=f'C={c}')
|
||||
ax_loss.fill_between(lambda_points, ci_lower, ci_upper, alpha=0.2)
|
||||
|
||||
|
||||
# determine optimal C for lamba = 1
|
||||
if mean_at_lambda1:
|
||||
sorted_C = sorted(mean_at_lambda1.items(), key=lambda item: item[1][0])
|
||||
@@ -224,91 +303,15 @@ def simulation_wrapper1(simulation_time, num_runs, min_runs, confidence_level, l
|
||||
else:
|
||||
print("\nNo valid λ=1 data for any C.")
|
||||
|
||||
#plot curves
|
||||
if len(lambda_vals) > 1:
|
||||
ax_loss.set_xlim(left=2)
|
||||
ax_loss.set_xlabel('Arrival Rate (λ)')
|
||||
ax_loss.set_ylabel('Mean Loss Rate')
|
||||
ax_loss.set_title(f'Mean Loss Rate vs Arrival Rate ({num_runs} runs, {int(confidence_level*100)}% CI)')
|
||||
ax_loss.legend()
|
||||
ax_loss.grid(True)
|
||||
|
||||
# plot curves
|
||||
if len(lambda_vals)>1:
|
||||
ax_rt.set_xlim(left=0)
|
||||
ax_rt.set_xlabel('Arrival Rate (λ)')
|
||||
ax_rt.set_ylabel('Mean Response Time')
|
||||
ax_rt.set_title(f'Mean Response Time vs Arrival Rate ({num_runs} runs, {int(confidence_level*100)}% CI)')
|
||||
ax_rt.legend()
|
||||
ax_rt.grid(True)
|
||||
|
||||
return loss_data
|
||||
|
||||
def simulation_wrapper2(simulation_time, num_runs, min_runs, confidence_level, lambda_vals, max_loss_value=0.1):
|
||||
fig, (ax_rt, ax_loss) = plt.subplots(2,1, figsize=(12,10), gridspec_kw={'height_ratios': [4, 3], 'hspace': 0.4}, sharex=False)
|
||||
loss_data = simulation_wrapper1(simulation_time, num_runs, min_runs, confidence_level, lambda_vals, ax_rt)
|
||||
|
||||
for c, (lams, losses) in loss_data.items():
|
||||
lambda_vals2 = []
|
||||
if len(lams)==0:
|
||||
print(f"C={c}: no data at all.")
|
||||
continue
|
||||
pos_idx = np.where(losses > 0)[0]
|
||||
if pos_idx.size > 0:
|
||||
first_lambda = lams[pos_idx[0]]
|
||||
else:
|
||||
first_lambda = None
|
||||
last_lambda = lams[-1]
|
||||
|
||||
print(f"C={c:>1} first λ with loss > 0: "
|
||||
f"{first_lambda if first_lambda is not None else 'never'}; "
|
||||
f" last λ with data: {last_lambda}")
|
||||
|
||||
lambda_vals2.extend([last_lambda + i/1000 for i in range(-100,51)])
|
||||
|
||||
|
||||
|
||||
lambda_points = []
|
||||
losses = []
|
||||
ci_lower = []
|
||||
ci_upper = []
|
||||
print(f"\nProcessing C={c}")
|
||||
|
||||
with Pool() as pool:
|
||||
for lambda_val in lambda_vals2:
|
||||
args_list = [(c, lambda_val, simulation_time, max_loss_value) for _ in range(num_runs)]
|
||||
results = pool.map(run_single_simulation, args_list)
|
||||
|
||||
successful_results = [res for res in results if res is not None]
|
||||
loss_rates = [res[1] for res in successful_results]
|
||||
|
||||
n = len(loss_rates)
|
||||
|
||||
if n >= min_runs:
|
||||
# statistics
|
||||
mean_loss = np.mean(loss_rates)
|
||||
std_dev = np.std(loss_rates, ddof=1)
|
||||
|
||||
# confidence interval
|
||||
t_value = t.ppf((1 + confidence_level)/2, n-1)
|
||||
ci = t_value * std_dev / np.sqrt(n)
|
||||
|
||||
# store results
|
||||
lambda_points.append(lambda_val)
|
||||
losses.append(mean_loss)
|
||||
ci_lower.append(mean_loss - ci)
|
||||
ci_upper.append(mean_loss + ci)
|
||||
|
||||
print(f"C={c}, λ={lambda_val:.4f}, Mean Loss Rate={mean_loss:.2%} ± {ci:.2f}")
|
||||
elif n > 0:
|
||||
print(f"λ={lambda_val:.4f} skipped - only {n} successful run(s)")
|
||||
continue
|
||||
else:
|
||||
print(f"Stopped at λ={lambda_val:.4f} - no successful run")
|
||||
break
|
||||
|
||||
ax_loss.plot(lambda_points, losses, label=f'C={c}')
|
||||
ax_loss.fill_between(lambda_points, ci_lower, ci_upper, alpha=0.2)
|
||||
|
||||
ax_loss.set_xlim(left=2)
|
||||
ax_loss.set_xlabel('Arrival Rate (λ)')
|
||||
ax_loss.set_ylabel('Mean Loss Rate')
|
||||
ax_loss.set_title(f'Mean Loss Rate vs Arrival Rate ({num_runs} runs, {int(confidence_level*100)}% CI)')
|
||||
ax_loss.legend()
|
||||
ax_loss.grid(True)
|
||||
|
||||
plt.show()
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user