| // Copyright 2018 The Rust Project Developers. See the COPYRIGHT |
| // file at the top-level directory of this distribution and at |
| // https://rust-lang.org/COPYRIGHT. |
| // |
| // 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. |
| //! The Bernoulli distribution. |
| |
| use Rng; |
| use distributions::Distribution; |
| |
| /// The Bernoulli distribution. |
| /// |
| /// This is a special case of the Binomial distribution where `n = 1`. |
| /// |
| /// # Example |
| /// |
| /// ```rust |
| /// use rand::distributions::{Bernoulli, Distribution}; |
| /// |
| /// let d = Bernoulli::new(0.3); |
| /// let v = d.sample(&mut rand::thread_rng()); |
| /// println!("{} is from a Bernoulli distribution", v); |
| /// ``` |
| /// |
| /// # Precision |
| /// |
| /// This `Bernoulli` distribution uses 64 bits from the RNG (a `u64`), |
| /// so only probabilities that are multiples of 2<sup>-64</sup> can be |
| /// represented. |
| #[derive(Clone, Copy, Debug)] |
| pub struct Bernoulli { |
| /// Probability of success, relative to the maximal integer. |
| p_int: u64, |
| } |
| |
| impl Bernoulli { |
| /// Construct a new `Bernoulli` with the given probability of success `p`. |
| /// |
| /// # Panics |
| /// |
| /// If `p < 0` or `p > 1`. |
| /// |
| /// # Precision |
| /// |
| /// For `p = 1.0`, the resulting distribution will always generate true. |
| /// For `p = 0.0`, the resulting distribution will always generate false. |
| /// |
| /// This method is accurate for any input `p` in the range `[0, 1]` which is |
| /// a multiple of 2<sup>-64</sup>. (Note that not all multiples of |
| /// 2<sup>-64</sup> in `[0, 1]` can be represented as a `f64`.) |
| #[inline] |
| pub fn new(p: f64) -> Bernoulli { |
| assert!((p >= 0.0) & (p <= 1.0), "Bernoulli::new not called with 0 <= p <= 0"); |
| // Technically, this should be 2^64 or `u64::MAX + 1` because we compare |
| // using `<` when sampling. However, `u64::MAX` rounds to an `f64` |
| // larger than `u64::MAX` anyway. |
| const MAX_P_INT: f64 = ::core::u64::MAX as f64; |
| let p_int = if p < 1.0 { |
| (p * MAX_P_INT) as u64 |
| } else { |
| // Avoid overflow: `MAX_P_INT` cannot be represented as u64. |
| ::core::u64::MAX |
| }; |
| Bernoulli { p_int } |
| } |
| } |
| |
| impl Distribution<bool> for Bernoulli { |
| #[inline] |
| fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> bool { |
| // Make sure to always return true for p = 1.0. |
| if self.p_int == ::core::u64::MAX { |
| return true; |
| } |
| let r: u64 = rng.gen(); |
| r < self.p_int |
| } |
| } |
| |
| #[cfg(test)] |
| mod test { |
| use Rng; |
| use distributions::Distribution; |
| use super::Bernoulli; |
| |
| #[test] |
| fn test_trivial() { |
| let mut r = ::test::rng(1); |
| let always_false = Bernoulli::new(0.0); |
| let always_true = Bernoulli::new(1.0); |
| for _ in 0..5 { |
| assert_eq!(r.sample::<bool, _>(&always_false), false); |
| assert_eq!(r.sample::<bool, _>(&always_true), true); |
| assert_eq!(Distribution::<bool>::sample(&always_false, &mut r), false); |
| assert_eq!(Distribution::<bool>::sample(&always_true, &mut r), true); |
| } |
| } |
| |
| #[test] |
| fn test_average() { |
| const P: f64 = 0.3; |
| let d = Bernoulli::new(P); |
| const N: u32 = 10_000_000; |
| |
| let mut sum: u32 = 0; |
| let mut rng = ::test::rng(2); |
| for _ in 0..N { |
| if d.sample(&mut rng) { |
| sum += 1; |
| } |
| } |
| let avg = (sum as f64) / (N as f64); |
| |
| assert!((avg - P).abs() < 1e-3); |
| } |
| } |