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range loss simulation + auto select normal distribution or student

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Sam Hadow
2025-05-09 10:06:07 +02:00
parent 68c9eb9fa9
commit 5a601c9155
1 changed files with 22 additions and 8 deletions
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src/simulation.py
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@@ -3,7 +3,7 @@ import os
import time import time
from heapq import heappush, heappop from heapq import heappush, heappop
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from scipy.stats import t from scipy.stats import t, norm
import numpy as np import numpy as np
from multiprocessing import Pool from multiprocessing import Pool
@@ -182,8 +182,13 @@ def simulation_wrapper1(simulation_time, num_runs, min_runs, confidence_level, l
std_dev = np.std(run_results, ddof=1) std_dev = np.std(run_results, ddof=1)
# confidence interval # confidence interval
t_value = t.ppf((1 + confidence_level)/2, n-1) if n>= 30:
ci = t_value * std_dev / np.sqrt(n) z_value = norm.ppf((1 + confidence_level)/2)
ci = z_value * std_dev / np.sqrt(n)
else:
t_value = t.ppf((1 + confidence_level)/2, n-1)
ci = t_value * std_dev / np.sqrt(n)
# loss rate # loss rate
mean_loss = np.mean(loss_rates) mean_loss = np.mean(loss_rates)
@@ -245,7 +250,11 @@ def simulation_wrapper2(simulation_time, num_runs, min_runs, confidence_level, l
f"{first_lambda if first_lambda is not None else 'never'}; " f"{first_lambda if first_lambda is not None else 'never'}; "
f" last λ with data: {last_lambda}") f" last λ with data: {last_lambda}")
lambda_vals2.extend([last_lambda + i/1000 for i in range(-100,51)]) if first_lambda is not None:
range_vals = int((last_lambda - first_lambda + 0.5)*1000)
lambda_vals2.extend(first_lambda + i/1000 for i in range(0, range_vals) )
else:
lambda_vals2.extend([last_lambda + i/1000 for i in range(-100,51)])
@@ -271,8 +280,12 @@ def simulation_wrapper2(simulation_time, num_runs, min_runs, confidence_level, l
std_dev = np.std(loss_rates, ddof=1) std_dev = np.std(loss_rates, ddof=1)
# confidence interval # confidence interval
t_value = t.ppf((1 + confidence_level)/2, n-1) if n>= 30:
ci = t_value * std_dev / np.sqrt(n) z_value = norm.ppf((1 + confidence_level)/2)
ci = z_value * std_dev / np.sqrt(n)
else:
t_value = t.ppf((1 + confidence_level)/2, n-1)
ci = t_value * std_dev / np.sqrt(n)
# store results # store results
lambda_points.append(lambda_val) lambda_points.append(lambda_val)
@@ -297,7 +310,7 @@ def simulation_wrapper2(simulation_time, num_runs, min_runs, confidence_level, l
sorted_C = sorted(mean_at_lambda1.items(), key=lambda item: item[1][0]) sorted_C = sorted(mean_at_lambda1.items(), key=lambda item: item[1][0])
(best_C, (mean1, lower1, upper1)), (_, (_, lower2, _)) = sorted_C[:2] (best_C, (mean1, lower1, upper1)), (_, (_, lower2, _)) = sorted_C[:2]
if upper1 < lower2: if upper1 < lower2:
print(f"Optimal C at λ=1 is {best_C} with Mean RT = {mean1:.2f} (non-overlapping CIs)") print(f"\nOptimal C at λ=1 is {best_C} with Mean RT = {mean1:.2f} (non-overlapping CIs)")
else: else:
print("Confidence intervals overlap between the two best C.") print("Confidence intervals overlap between the two best C.")
else: else:
@@ -305,10 +318,11 @@ def simulation_wrapper2(simulation_time, num_runs, min_runs, confidence_level, l
#plot curves #plot curves
if len(lambda_vals) > 1: if len(lambda_vals) > 1:
ax_loss.set_xlim(left=2) ax_loss.set_xlim(left=1)
ax_loss.set_xlabel('Arrival Rate (λ)') ax_loss.set_xlabel('Arrival Rate (λ)')
ax_loss.set_ylabel('Mean Loss Rate') 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.set_title(f'Mean Loss Rate vs Arrival Rate ({num_runs} runs, {int(confidence_level*100)}% CI)')
ax_loss.axhline(y=0.05, color='red', linestyle='--', linewidth=1, label='5% Loss Threshold')
ax_loss.legend() ax_loss.legend()
ax_loss.grid(True) ax_loss.grid(True)