monte carlo simulation with multi-processing
Monte Hall problem is a famous problem in probability based on a game show. It has generated much controversy due to the non-intuitive nature of the solution. The objective of this mini project is to write a simulation of the game that empirically demonstrates the accepted solution is indeed the correct solution.
This project contains the following files:
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player.py- contains Player class which defines the switch strategy and has functions to choose a door and whether or not to switch. -
game.py- contains Game class which has a playGame function to play the game. -
main.py- play the game in a loop of 10,000,000 times from main and approximate probability of winning with your chosen strategy (stay or switch). -
timer.py- contains Timer class to record time.
In the main.py, I also make Monte Hall simluation achieve multi-processing. I create and initialize five processes first, and execute all five processes, giving each process 1/5 of the total simulations (2,000,000 each). Then combine the five returned results lists and take the average, to get the overall result. Next, I try to decrease/increase the number of processes to determine the optimal runtime. The following are my tested results:
num_processes = 1: 93.6119999886 s
num_processes = 5: 12.996999979 s
num_processes = 10: 11.8120000362 s
num_processes = 15: 10.9649999142 s
num_processes = 17: 11.1519999504 s
num_processes = 20: 11.9139997959 s
num_processes = 25: 11.5650000572 s
Based on my simulation test, the optimal running time number of processes is 15.