Monte Calro Simulation Using python

monte_carlo.py

import random

## CONST
T = 60
DT = 0.01
LAMBDA = 2
MU = 1
N_MAX = 20

## CLASS
class Agent:
    def __init__(self, arrived_time):
        self.arrived_time = arrived_time
        self.finished_time = None

    def set_finished_time(self, finished_time):
        self.finished_time = finished_time

    def get_waiting_time(self):
        return self.finished_time - self.arrived_time

##  UTIL
def average(arr):
    return sum(arr) / len(arr)

## MAIN
not_arrived_agents = []
queue_agents = []
queue_length_list = []
finished_agents = []

# memo: agentの配列を作成
last_arrived_time = 0
while True:
    time_gap = random.expovariate(LAMBDA)
    last_arrived_time += time_gap
    if last_arrived_time > T:
        break
    not_arrived_agents.append(Agent(last_arrived_time))

# memo: 単位時間あたりのループを回す
loop = int(T / DT)
for n in range(loop):
    t = round(n * DT, 2)

    # memo: 到着を確認する
    for a in not_arrived_agents:
        if a.arrived_time <= t:
            not_arrived_agents.pop(0)
            if len(queue_agents) < N_MAX:
                queue_agents.append(a)
        else:
            break

    # memo: もし行列に誰もいなければループを抜ける
    if not queue_agents:
        continue

    # memo: 処理されたかを確認する
    processed_agent = queue_agents[0]
    if processed_agent.finished_time is None:
        processed_agent.set_finished_time(t + random.expovariate(MU))
    else:
        if processed_agent.finished_time <= t:
            finished_agents.append(processed_agent)
            queue_agents.pop(0)
    # if t % 10 == 0.0:
    #     averate_waiting_time = average(list(map(lambda a: a.get_waiting_time(), finished_agents)))
    #     # print(len(queue_agents))
    #     print(averate_waiting_time)
    queue_length_list.append(len(queue_agents))

# memo: 行列の長さの平均を出力
average_queue_length = average(queue_length_list)
print(average_queue_length)

# memo: 平均の待ち時間を出力
averate_waiting_time = average(list(map(lambda a: a.get_waiting_time(), finished_agents)))
print(averate_waiting_time)