Monty Hall Problem Simulation

monty_hall.py

import numpy as np


def monty_hall():
    doors = np.array([1, 0, 0])
    np.random.shuffle(doors)
    initial_choice = np.random.randint(len(doors))
    goat_door = 0
    for i in range(len(doors)):
        if i == initial_choice:
            continue
        elif doors[i] == 0:
            goat_door = i
            break
    switch_choice = 3-(initial_choice+goat_door)
    return doors[initial_choice]==1, doors[switch_choice]==1
    
    
def monte_carlo_simulation(n=10000):
    no_switch_results = []
    switch_results = []
    for _ in range(n):
        no_switch, switch = monty_hall()
        no_switch_results.append(no_switch)
        switch_results.append(switch)
    return sum(no_switch_results)/n, sum(switch_results)/n


if __name__ == "__main__":
    switch, no_switch = monte_carlo_simulation()
    print("Probability of winning with switching: {}".format(switch))
    print("Probability of winning with no switching: {}".format(no_switch))