![]() So I’ll go straight to the second option… NumPy and Board Games I’ll show this code in an appendix, but it’s not the version I want to focus on in this article. Keep a tally of wins and losses, and you can work out the winning percentage for each scenario. For each of these configurations, run another for loop which repeats 10 million times to simulate a large number of attacks. You can loop through each of the six attack configurations shown in the table above. One way of proceeding would be to use nested for loops. Using a for loop to simulate repeated attacks Let’s run each scenario 10 million times to get a reasonable estimate of the winning probabilities. There are six attack configurations: Attacker attacks with To get a reasonable estimate of the probability of winning in each attack scenario, you’d need to run many attacks for each attack configuration and work out the winning percentages. (number of attacker wins / total number of bouts) * 100 You can simulate several attacks and keep a tally of how many bouts the attacker won and how many the defender won.
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