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Diffstat (limited to 'rand/examples')
| -rw-r--r-- | rand/examples/monte-carlo.rs | 48 | ||||
| -rw-r--r-- | rand/examples/monty-hall.rs | 112 |
2 files changed, 0 insertions, 160 deletions
diff --git a/rand/examples/monte-carlo.rs b/rand/examples/monte-carlo.rs deleted file mode 100644 index 39c779f..0000000 --- a/rand/examples/monte-carlo.rs +++ /dev/null @@ -1,48 +0,0 @@ -// Copyright 2018 Developers of the Rand project. -// Copyright 2013-2018 The Rust Project Developers. -// -// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or -// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license -// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! # Monte Carlo estimation of π -//! -//! Imagine that we have a square with sides of length 2 and a unit circle -//! (radius = 1), both centered at the origin. The areas are: -//! -//! ```text -//! area of circle = πr² = π * r * r = π -//! area of square = 2² = 4 -//! ``` -//! -//! The circle is entirely within the square, so if we sample many points -//! randomly from the square, roughly π / 4 of them should be inside the circle. -//! -//! We can use the above fact to estimate the value of π: pick many points in -//! the square at random, calculate the fraction that fall within the circle, -//! and multiply this fraction by 4. - -#![cfg(feature = "std")] - -use rand::distributions::{Distribution, Uniform}; - -fn main() { - let range = Uniform::new(-1.0f64, 1.0); - let mut rng = rand::thread_rng(); - - let total = 1_000_000; - let mut in_circle = 0; - - for _ in 0..total { - let a = range.sample(&mut rng); - let b = range.sample(&mut rng); - if a*a + b*b <= 1.0 { - in_circle += 1; - } - } - - // prints something close to 3.14159... - println!("π is approximately {}", 4. * (in_circle as f64) / (total as f64)); -} diff --git a/rand/examples/monty-hall.rs b/rand/examples/monty-hall.rs deleted file mode 100644 index 9fe5839..0000000 --- a/rand/examples/monty-hall.rs +++ /dev/null @@ -1,112 +0,0 @@ -// Copyright 2018 Developers of the Rand project. -// Copyright 2013-2018 The Rust Project Developers. -// -// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or -// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license -// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! ## Monty Hall Problem -//! -//! This is a simulation of the [Monty Hall Problem][]: -//! -//! > Suppose you're on a game show, and you're given the choice of three doors: -//! > Behind one door is a car; behind the others, goats. You pick a door, say -//! > No. 1, and the host, who knows what's behind the doors, opens another -//! > door, say No. 3, which has a goat. He then says to you, "Do you want to -//! > pick door No. 2?" Is it to your advantage to switch your choice? -//! -//! The rather unintuitive answer is that you will have a 2/3 chance of winning -//! if you switch and a 1/3 chance of winning if you don't, so it's better to -//! switch. -//! -//! This program will simulate the game show and with large enough simulation -//! steps it will indeed confirm that it is better to switch. -//! -//! [Monty Hall Problem]: https://en.wikipedia.org/wiki/Monty_Hall_problem - -#![cfg(feature = "std")] - -use rand::distributions::{Distribution, Uniform}; -use rand::Rng; - -struct SimulationResult { - win: bool, - switch: bool, -} - -// Run a single simulation of the Monty Hall problem. -fn simulate<R: Rng>(random_door: &Uniform<u32>, rng: &mut R) -> SimulationResult { - let car = random_door.sample(rng); - - // This is our initial choice - let mut choice = random_door.sample(rng); - - // The game host opens a door - let open = game_host_open(car, choice, rng); - - // Shall we switch? - let switch = rng.gen(); - if switch { - choice = switch_door(choice, open); - } - - SimulationResult { win: choice == car, switch } -} - -// Returns the door the game host opens given our choice and knowledge of -// where the car is. The game host will never open the door with the car. -fn game_host_open<R: Rng>(car: u32, choice: u32, rng: &mut R) -> u32 { - use rand::seq::SliceRandom; - *free_doors(&[car, choice]).choose(rng).unwrap() -} - -// Returns the door we switch to, given our current choice and -// the open door. There will only be one valid door. -fn switch_door(choice: u32, open: u32) -> u32 { - free_doors(&[choice, open])[0] -} - -fn free_doors(blocked: &[u32]) -> Vec<u32> { - (0..3).filter(|x| !blocked.contains(x)).collect() -} - -fn main() { - // The estimation will be more accurate with more simulations - let num_simulations = 10000; - - let mut rng = rand::thread_rng(); - let random_door = Uniform::new(0u32, 3); - - let (mut switch_wins, mut switch_losses) = (0, 0); - let (mut keep_wins, mut keep_losses) = (0, 0); - - println!("Running {} simulations...", num_simulations); - for _ in 0..num_simulations { - let result = simulate(&random_door, &mut rng); - - match (result.win, result.switch) { - (true, true) => switch_wins += 1, - (true, false) => keep_wins += 1, - (false, true) => switch_losses += 1, - (false, false) => keep_losses += 1, - } - } - - let total_switches = switch_wins + switch_losses; - let total_keeps = keep_wins + keep_losses; - - println!("Switched door {} times with {} wins and {} losses", - total_switches, switch_wins, switch_losses); - - println!("Kept our choice {} times with {} wins and {} losses", - total_keeps, keep_wins, keep_losses); - - // With a large number of simulations, the values should converge to - // 0.667 and 0.333 respectively. - println!("Estimated chance to win if we switch: {}", - switch_wins as f32 / total_switches as f32); - println!("Estimated chance to win if we don't: {}", - keep_wins as f32 / total_keeps as f32); -} |