doc: More efficient Monty Hall simulation
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1 changed files with 8 additions and 8 deletions
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@ -95,12 +95,11 @@ use std::rand::distributions::{IndependentSample, Range};
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struct SimulationResult {
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win: bool,
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switch: bool
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switch: bool,
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}
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// Run a single simulation of the Monty Hall problem.
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fn simulate<R: Rng>(rng: &mut R) -> SimulationResult {
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let random_door = Range::new(0u, 3);
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fn simulate<R: Rng>(random_door: &Range<uint>, rng: &mut R) -> SimulationResult {
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let car = random_door.ind_sample(rng);
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// This is our initial choice
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@ -121,32 +120,33 @@ fn simulate<R: Rng>(rng: &mut R) -> SimulationResult {
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// Returns the door the game host opens given our choice and knowledge of
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// where the car is. The game host will never open the door with the car.
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fn game_host_open<R: Rng>(car: uint, choice: uint, rng: &mut R) -> uint {
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let choices = free_doors(vec![car, choice]);
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let choices = free_doors(&[car, choice]);
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rand::sample(rng, choices.move_iter(), 1)[0]
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}
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// Returns the door we switch to, given our current choice and
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// the open door. There will only be one valid door.
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fn switch_door(choice: uint, open: uint) -> uint {
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free_doors(vec![choice, open])[0]
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free_doors(&[choice, open])[0]
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}
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fn free_doors(blocked: Vec<uint>) -> Vec<uint> {
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fn free_doors(blocked: &[uint]) -> Vec<uint> {
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range(0u, 3).filter(|x| !blocked.contains(x)).collect()
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}
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fn main() {
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// The estimation will be more accuraty with more simulations
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// The estimation will be more accurate with more simulations
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let num_simulations = 10000u;
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let mut rng = rand::task_rng();
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let random_door = Range::new(0u, 3);
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let (mut switch_wins, mut switch_losses) = (0u, 0u);
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let (mut keep_wins, mut keep_losses) = (0u, 0u);
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println!("Running {} simulations...", num_simulations);
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for _ in range(0, num_simulations) {
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let result = simulate(&mut rng);
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let result = simulate(&random_door, &mut rng);
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match (result.win, result.switch) {
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(true, true) => switch_wins += 1,
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